diff options
Diffstat (limited to 'inference-engine/thirdparty/clDNN/kernel_selector')
212 files changed, 20523 insertions, 14423 deletions
diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/CMakeLists.txt b/inference-engine/thirdparty/clDNN/kernel_selector/CMakeLists.txt index ceb1a3388..f8f68371c 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/CMakeLists.txt +++ b/inference-engine/thirdparty/clDNN/kernel_selector/CMakeLists.txt @@ -68,6 +68,14 @@ file(GLOB __CLDNN_Sources__core_common "${__CLDNN_Directory__core_common}/*.cpp" ) +set(__CLDNN_Directory__core_cache "${__CLDNN_Directory__core}/cache") +set(__CLDNN_Label__core_cache "${__CLDNN_Label__core}\\cache") +file(GLOB __CLDNN_Sources__core_cache + "${__CLDNN_Directory__core_cache}/*.h" + "${__CLDNN_Directory__core_cache}/*.hpp" + "${__CLDNN_Directory__core_cache}/*.cpp" + ) + set(__CLDNN_Directory__actual_kernels "${__CLDNN_Directory__core}/actual_kernels") set(__CLDNN_Label__actual_kernels "${__CLDNN_Label__core}\\actual_kernels") file(GLOB_RECURSE __CLDNN_Sources__actual_kernels @@ -104,6 +112,7 @@ set(__CLDNN_AllSources ${__CLDNN_Sources__core} ${__CLDNN_Sources__common} ${__CLDNN_Sources__core_common} + ${__CLDNN_Sources__core_cache} ${__CLDNN_Sources__actual_kernels} ${__CLDNN_Sources__cl_kernels} ${__CLDNN_Sources__cg_cache} @@ -117,6 +126,7 @@ source_group("${__CLDNN_Label__main}" FILES ${__CLDNN_Sources__main} source_group("${__CLDNN_Label__core}" FILES ${__CLDNN_Sources__core}) source_group("${__CLDNN_Label__common}" FILES ${__CLDNN_Sources__common}) source_group("${__CLDNN_Label__core_common}" FILES ${__CLDNN_Sources__core_common}) +source_group("${__CLDNN_Label__core_cache}" FILES ${__CLDNN_Sources__core_cache}) #source_group("${__CLDNN_Label__actual_kernels}" FILES ${__CLDNN_Sources__actual_kernels}) source_group("${__CLDNN_Label__cl_kernels}" FILES ${__CLDNN_Sources__cl_kernels}) source_group("${__CLDNN_Label__cg_cache}" FILES ${__CLDNN_Sources__cg_cache}) @@ -127,6 +137,7 @@ include_directories( "${__CLDNN_Directory__main}" "${__CLDNN_Directory__core}" "${__CLDNN_Directory__core}/common" + "${__CLDNN_Directory__core}/cache" "${__CLDNN_Directory__actual_kernels}" "${__CLDNN_Directory__common}" ) diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/common/common_tools.h b/inference-engine/thirdparty/clDNN/kernel_selector/common/common_tools.h index 6afad0d49..0f23cbeb4 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/common/common_tools.h +++ b/inference-engine/thirdparty/clDNN/kernel_selector/common/common_tools.h @@ -38,6 +38,8 @@ namespace kernel_selector case Datatype::INT32: case Datatype::UINT32: return 4; + case Datatype::INT64: + return 8; default: return 0; } diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/common/common_types.h b/inference-engine/thirdparty/clDNN/kernel_selector/common/common_types.h index 6763bb7f3..46a8877b0 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/common/common_types.h +++ b/inference-engine/thirdparty/clDNN/kernel_selector/common/common_types.h @@ -1,5 +1,4 @@ -/* -// Copyright (c) 2016 Intel Corporation +// Copyright (c) 2016-2018 Intel Corporation // // Licensed under the Apache License, Version 2.0 (the "License"); // you may not use this file except in compliance with the License. @@ -12,11 +11,12 @@ // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. -*/ #pragma once + #include <cstddef> -#include <stdint.h> +#include <cstdint> + namespace kernel_selector { @@ -57,7 +57,13 @@ namespace kernel_selector LSTM_GEMM, LSTM_ELT, EMBED, - SOFT_MAX_LOSS_GRAD + SOFT_MAX_LOSS_GRAD, + BORDER, + TILE, + SELECT, + BROADCAST, + GEMM, + INDEX_SELECT }; //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// @@ -72,6 +78,7 @@ namespace kernel_selector UINT16, INT32, UINT32, + INT64, F16, F32, }; @@ -105,6 +112,15 @@ namespace kernel_selector ELU, RELU_GRAD, RELU_NEGATIVE_SLOPE_GRAD, + SIN, + ASIN, + SINH, + COS, + ACOS, + COSH, + LOG, + LOG2, + EXP, NONE, NONE_GRAD }; @@ -116,7 +132,8 @@ namespace kernel_selector { MAX, AVG, - MAX_WITH_ARGMAX + MAX_WITH_ARGMAX, + BILINEAR }; //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// @@ -298,6 +315,17 @@ namespace kernel_selector }; //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// + // TileAxis + //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// + enum class TileAxis + { + X, + Y, + FEATURE, + BATCH, + }; + + //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// // SampleType //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// enum class SampleType @@ -307,6 +335,27 @@ namespace kernel_selector }; //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// + // BorderType + //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// + enum class BorderType + { + ZERO, + MIRROR, + MIRROR_101, + }; + + //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// + // IndexSelectAxis + //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// + enum class IndexSelectAxis + { + BATCH, + FEATURE, + X, + Y + }; + + //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// // NonLinearParams //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// struct NonLinearParams @@ -315,9 +364,7 @@ namespace kernel_selector float n = 0.f; NonLinearParams() = default; - NonLinearParams(const NonLinearParams&) = default; - NonLinearParams& operator=(const NonLinearParams&) = default; - NonLinearParams(float m, float n) : m(m), n(n) {} + NonLinearParams(const float m, const float n) : m(m), n(n) {} }; //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// @@ -329,25 +376,36 @@ namespace kernel_selector T y = 0; Size() = default; - Size(const Size&) = default; - Size& operator=(const Size&) = default; Size(T x, T y) : x(x), y(y) {} }; //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// + // DimTensor + //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// + template <typename T = std::uint32_t> + struct DimTensor { + T b = 0; + T f = 0; + T y = 0; + T x = 0; + + DimTensor() = default; + DimTensor(T b, T f, T y, T x) : b(b), f(f), y(y), x(x) {} + }; + + //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// // AutoTunerMode //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// enum class TuningMode { TUNING_DISABLED, // Tuning is disabled. TUNING_USE_CACHE, // Tuning using the cached data (no on-line tuning for non-existing data). - TUNING_TUNE_AND_CACHE // Tuning using the cached data if exist, tune and update cache otherwise. + TUNING_TUNE_AND_CACHE // Tuning using the cached data if exist, tune and update cache otherwise.attention_params }; //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// - // typedefs + // Aliases: //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// - typedef Size<uint32_t> uSize; - typedef Size<size_t> stSize; - + using uSize = Size<std::uint32_t>; + using stSize = Size<std::size_t>; } diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/common/tensor_type.cpp b/inference-engine/thirdparty/clDNN/kernel_selector/common/tensor_type.cpp index 740707a18..555ca1ea5 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/common/tensor_type.cpp +++ b/inference-engine/thirdparty/clDNN/kernel_selector/common/tensor_type.cpp @@ -24,33 +24,39 @@ namespace kernel_selector { std::array<std::array<int, 5>, DataLayout::DataLayoutCount> DataTensor::dataChannelArray { { - { -1,-1, 0,-1, 1 }, // DataLayout::bf - { -1,-1, 1,-1, 0 }, // DataLayout::fb + // explaination: + // 0, 1, 2, 3, 4 means the ordering starts from X, then Y, then F, thenR, then B + // -1 means it's not used + //X, Y, F, R, B + {-1,-1, 0,-1, 1 }, // DataLayout::bf + {-1,-1, 1,-1, 0 }, // DataLayout::fb { 0, 1, 2,-1, 3 }, // DataLayout::bfyx { 2, 3, 1,-1, 0 }, // DataLayout::yxfb { 1, 2, 0,-1, 3 }, // DataLayout::byxf { 1, 2, 3,-1, 0 }, // DataLayout::fyxb - { -1,-1, 0,-1, 1 }, // DataLayout::bs_f_bsv8__af8 - { -1,-1, 0,-1, 1 }, // DataLayout::bs_f_bsv16__af8 + {-1,-1, 0,-1, 1 }, // DataLayout::bs_f_bsv8__af8 + {-1,-1, 0,-1, 1 }, // DataLayout::bs_f_bsv16__af8 { 0, 1, 2,-1, 3 }, // DataLayout::bf8_xy16 { 0, 1, 2, 3, 4 }, // DataLayout::brfyx { 2, 1, 0,-1, 3 }, // DataLayout::winograd_2x3_s1_data { 1, 2, 0,-1, 3 }, // DataLayout::byxf_af32 + { 0, 1, 3,-1, 2 }, // DataLayout::fs_bs_yx_bsv4_fsv32 } }; std::array<std::array<int, 4>, WeightsLayout::WeightsLayoutCount> WeightsTensor::weightsChannelArray { { - { -1,-1, 0, 1 }, // WeightsLayout::oi - { -1,-1, 1, 0 }, // WeightsLayout::io + //X, Y, I, O + {-1,-1, 0, 1 }, // WeightsLayout::oi + {-1,-1, 1, 0 }, // WeightsLayout::io { 0, 1, 2, 3 }, // WeightsLayout::oiyx { 1, 2, 0, 3 }, // WeightsLayout::oyxi { 1, 2, 3, 0 }, // WeightsLayout::iyxo { 2, 3, 1, 0 }, // WeightsLayout::yxio { 0, 1, 2, 3 }, // WeightsLayout::os_iyx_osv16 { 0, 1, 2, 3 }, // WeightsLayout::os_iyx_osv16_rotate_180 - { -1,-1, 0, 1 }, // WeightsLayout::os_i_osv8__ai8 - { -1,-1, 0, 1 }, // WeightsLayout::os_i_osv16__ai8 - { -1,-1, 0, 1 }, // WeightsLayout::os_i_osv16 + {-1,-1, 0, 1 }, // WeightsLayout::os_i_osv8__ai8 + {-1,-1, 0, 1 }, // WeightsLayout::os_i_osv16__ai8 + {-1,-1, 0, 1 }, // WeightsLayout::os_i_osv16 { 1, 2, 3, 0 }, // WeightsLayout::i_yxs_os_yxsv2_osv16 { 1, 2, 3, 0 }, // WeightsLayout::iy_xs_os_xsv2_osv16__ao32 { 1, 2, 3, 0 }, // WeightsLayout::iy_xs_os_xsv2_osv8__ao32 @@ -61,7 +67,8 @@ namespace kernel_selector { 0, 1, 2, 3 }, // WeightsLayout::winograd_6x3_s1_fused_weights { 0, 1, 2, 3 }, // WeightsLayout::image_2d_weights_winograd_6x3_s1_fbxyb { 0, 1, 2, 3 }, // WeightsLayout::image_2d_weights_winograd_6x3_s1_xfbyb - { 0, 1, 2, 3}, // WeightsLayout::os_is_yx_isa8_osv8_isv4 + { 0, 1, 2, 3 }, // WeightsLayout::os_is_yx_isa8_osv8_isv4 + { 1, 2, 0, 3 }, // WeightsLayout::is_o_yx_isv32 } }; NDims DataTensor::GetSimpleDims(const std::vector<size_t>& d, DataLayout l) @@ -91,6 +98,11 @@ namespace kernel_selector assert(newDims.size() == 4); newDims[0] = RoundUp(newDims[0], 32); break; + case fs_bs_yx_bsv4_fsv32: + assert(newDims.size() == 4); + newDims[3] = RoundUp(newDims[3], 32); + newDims[2] = RoundUp(newDims[2], 4); + break; default: break; } @@ -105,7 +117,7 @@ namespace kernel_selector pitch *= newDims[i]; } - if (l == byxf_af32) + if (l == byxf_af32 || l == fs_bs_yx_bsv4_fsv32) { ret[0].pitch = 1; ret[1].pitch = ret[0].pitch * newDims[0]; @@ -204,7 +216,8 @@ namespace kernel_selector // TODO: [FUTURE] Use C++17 [[fallthrough]] instead of code duplication to get portable warning avoidance. if ((x.pitch == f.pitch && y.pitch == x.v*x.pitch) || // YX - no Features (val/pitch) - (y.v == 1 && x.v == 1 && x.pitch == f.pitch && y.pitch == f.pitch)) // Feature only + (y.v == 1 && x.v == 1 && x.pitch == f.pitch && y.pitch == f.pitch) || // Feature only + (f.v * f.pitch == x.pitch && f.v * f.pitch == y.pitch && y.v == 1 && x.v == 1)) // Feature only { l = targetLayout; break; @@ -213,7 +226,8 @@ namespace kernel_selector case Tensor::byxf: if ((x.pitch == f.pitch && y.pitch == x.v*x.pitch) || // YX - no Features (val/pitch) - (y.v == 1 && x.v == 1 && x.pitch == f.pitch && y.pitch == f.pitch)) // Feature only + (y.v == 1 && x.v == 1 && x.pitch == f.pitch && y.pitch == f.pitch) || // Feature only + (f.v * f.pitch == x.pitch && f.v * f.pitch == y.pitch && y.v == 1 && x.v == 1)) // Feature only { l = targetLayout; break; @@ -280,6 +294,10 @@ namespace kernel_selector newDims[3] = RoundUp(newDims[3], 8); newDims[2] = RoundUp(newDims[2], 32); break; + case is_o_yx_isv32: + assert(newDims.size() == 4); + newDims[0] = RoundUp(newDims[0], 32); + break; default: break; } diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/common/tensor_type.h b/inference-engine/thirdparty/clDNN/kernel_selector/common/tensor_type.h index 1f4fad12d..8331ab0a2 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/common/tensor_type.h +++ b/inference-engine/thirdparty/clDNN/kernel_selector/common/tensor_type.h @@ -48,6 +48,7 @@ namespace kernel_selector brfyx, // 4D+batch winograd_2x3_s1_data, //winograd convolution input, F(2,3) -- filter 3x3 with stride 1 byxf_af32, // for MMAD convolution + fs_bs_yx_bsv4_fsv32, // for batched MMAD DataLayoutCount // NMBER OF ELEMENTS IN ENUM }; @@ -78,6 +79,7 @@ namespace kernel_selector image_2d_weights_winograd_6x3_s1_fbxyb, // image 2d winograd convolution weights for fused kernel, F(2, 3) --filter 3x3 with stride 1 image_2d_weights_winograd_6x3_s1_xfbyb, // image 2d winograd convolution weights for fused kernel, F(2, 3) --filter 3x3 with stride 1 os_is_yx_isa8_osv8_isv4, // for MMAD convolution + is_o_yx_isv32, // for MMAD 1x1 convolutions WeightsLayoutCount // NMBER OF ELEMENTS IN ENUM }; diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/activation/activation_kernel_base.cpp b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/activation/activation_kernel_base.cpp index 7feeb60d6..358b66d1c 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/activation/activation_kernel_base.cpp +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/activation/activation_kernel_base.cpp @@ -96,7 +96,7 @@ namespace kernel_selector auto jit = CreateJit(kernelName, cldnn_jit, entry_point); auto& kernel = kd.kernels[0]; - FillCLKernelData(kernel, runInfo, kernelName, jit, entry_point); + FillCLKernelData(kernel, runInfo, params.engineInfo, kernelName, jit, entry_point); if (newParams.gradient) kernel.arguments.push_back({ ArgumentDescriptor::Types::INPUT, 1 }); diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/arg_max_min/arg_max_min_kernel_axis.cpp b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/arg_max_min/arg_max_min_kernel_axis.cpp index a69ce07c6..894101bff 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/arg_max_min/arg_max_min_kernel_axis.cpp +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/arg_max_min/arg_max_min_kernel_axis.cpp @@ -75,7 +75,7 @@ namespace kernel_selector auto jit = CreateJit(kernelName, cldnn_jit, entry_point); auto& kernel = kd.kernels[0]; - FillCLKernelData(kernel, runInfo, kernelName, jit, entry_point); + FillCLKernelData(kernel, runInfo, params.engineInfo, kernelName, jit, entry_point); kd.estimatedTime = FORCE_PRIORITY_9; diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/arg_max_min/arg_max_min_kernel_base.cpp b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/arg_max_min/arg_max_min_kernel_base.cpp index 455b249cc..a51824c52 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/arg_max_min/arg_max_min_kernel_base.cpp +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/arg_max_min/arg_max_min_kernel_base.cpp @@ -78,7 +78,7 @@ namespace kernel_selector auto jit = CreateJit(kernelName, cldnn_jit, entry_point); auto& kernel = kd.kernels[0]; - FillCLKernelData(kernel, runInfo, kernelName, jit, entry_point); + FillCLKernelData(kernel, runInfo, params.engineInfo, kernelName, jit, entry_point); kd.estimatedTime = estimatedTime; diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/arg_max_min/arg_max_min_kernel_opt.cpp b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/arg_max_min/arg_max_min_kernel_opt.cpp index 70af969b3..8e086dae6 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/arg_max_min/arg_max_min_kernel_opt.cpp +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/arg_max_min/arg_max_min_kernel_opt.cpp @@ -78,7 +78,7 @@ namespace kernel_selector runInfo.lws1 = 1; runInfo.lws2 = 1; - FillCLKernelData(kernel, runInfo, kernelName, jit, entryPoint); + FillCLKernelData(kernel, runInfo, params.engineInfo, kernelName, jit, entryPoint); size = (size / 128 + 1) * topK; } diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/average_unpooling/average_unpooling_kernel_base.cpp b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/average_unpooling/average_unpooling_kernel_base.cpp index 46e8e85d8..c94e624bd 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/average_unpooling/average_unpooling_kernel_base.cpp +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/average_unpooling/average_unpooling_kernel_base.cpp @@ -95,7 +95,7 @@ namespace kernel_selector auto jit = CreateJit(kernelName, cldnn_jit, entry_point); auto& kernel = kd.kernels[0]; - FillCLKernelData(kernel, runInfo, kernelName, jit, entry_point); + FillCLKernelData(kernel, runInfo, params.engineInfo, kernelName, jit, entry_point); kd.estimatedTime = estimatedTime; diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/batch_norm/batch_norm_kernel_base.cpp b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/batch_norm/batch_norm_kernel_base.cpp index 811d4412f..ebf881f50 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/batch_norm/batch_norm_kernel_base.cpp +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/batch_norm/batch_norm_kernel_base.cpp @@ -80,7 +80,7 @@ namespace kernel_selector auto& kernel = kd.kernels[0]; int inputs_num = 1 + orgParams.batchNormParams.with_inv_var; - FillCLKernelData(kernel, runInfo, kernelName, jit, entry_point, "", false, false, inputs_num); + FillCLKernelData(kernel, runInfo, params.engineInfo, kernelName, jit, entry_point, "", false, false, inputs_num); kd.estimatedTime = estimatedTime; diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/batch_norm_grad/batch_norm_grad_kernel_base.cpp b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/batch_norm_grad/batch_norm_grad_kernel_base.cpp index c5b15712a..25d9115dd 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/batch_norm_grad/batch_norm_grad_kernel_base.cpp +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/batch_norm_grad/batch_norm_grad_kernel_base.cpp @@ -70,7 +70,7 @@ namespace kernel_selector auto jit = CreateJit(kernelName, cldnn_jit, entry_point); auto& kernel = kd.kernels[0]; - FillCLKernelData(kernel, runInfo, kernelName, jit, entry_point, "", false, false, 3); + FillCLKernelData(kernel, runInfo, params.engineInfo, kernelName, jit, entry_point, "", false, false, 3); kd.estimatedTime = estimatedTime; diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/border/border_kernel_base.cpp b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/border/border_kernel_base.cpp new file mode 100644 index 000000000..3346c4b0a --- /dev/null +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/border/border_kernel_base.cpp @@ -0,0 +1,78 @@ +// Copyright (c) 2018 Intel Corporation +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + + +#include "border_kernel_base.h" + +#include "kernel_selector_utils.h" + + +namespace kernel_selector +{ + JitConstants BorderKernelBase::GetJitConstants(const border_params& params) const + { + JitConstants jit = MakeBaseParamsJitConstants(params); + + jit.AddConstants({ + MakeJitConstant("LT_SIZES", params.lt_sizes), + MakeJitConstant("RB_SIZES", params.rb_sizes), + MakeJitConstant(toString(params.b_type), "") + }); + + return jit; + } + + BorderKernelBase::DispatchData BorderKernelBase::SetDefault(const border_params& params) const + { + const auto& output = params.output; + + DispatchData kd; + + kd.fp16UnitUsed = params.inputs[0].GetDType() == Datatype::F16; + + std::vector<size_t> global{output.X().v, output.Y().v, output.Batch().v * output.Feature().v}; + const auto& local = GetOptimalLocalWorkGroupSizes(global); + + kd.gws0 = global[0]; + kd.gws1 = global[1]; + kd.gws2 = global[2]; + + kd.lws0 = local[0]; + kd.lws1 = local[1]; + kd.lws2 = local[2]; + + return kd; + } + + KernelsData BorderKernelBase::GetCommonKernelsData(const Params& params, const optional_params& options, float estimated_time) const + { + assert(params.GetType() == KernelType::BORDER); + + const auto& prim_params = static_cast<const border_params&>(params); // NOLINT(cppcoreguidelines-pro-type-static-cast-downcast) + + auto run_info = SetDefault(prim_params); + KernelData k_data = KernelData::Default<border_params>(params); + + auto cldnn_jit = GetJitConstants(prim_params); + auto entry_point = GetEntryPoint(kernelName, prim_params.layerID, options); + auto jit = CreateJit(kernelName, cldnn_jit, entry_point); + + auto& kernel = k_data.kernels[0]; + FillCLKernelData(kernel, run_info, params.engineInfo, kernelName, jit, entry_point); + + k_data.estimatedTime = estimated_time; + + return {k_data}; + } +} diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/border/border_kernel_base.h b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/border/border_kernel_base.h new file mode 100644 index 000000000..43c10c715 --- /dev/null +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/border/border_kernel_base.h @@ -0,0 +1,72 @@ +// Copyright (c) 2018 Intel Corporation +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#pragma once + +#include "common_kernel_base.h" +#include "kernel_selector_params.h" + + +namespace kernel_selector +{ + //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// + // border_params + //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// + struct border_params : public base_params + { + DimTensor<> lt_sizes; + DimTensor<> rb_sizes; + BorderType b_type = BorderType::ZERO; + + + border_params() + : base_params(KernelType::BORDER) + { + } + + ParamsKey GetParamsKey() const override + { + ParamsKey k = base_params::GetParamsKey(); + // k.EnableBorderType(b_type); + return k; + } + }; + + //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// + // border_optional_params + //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// + struct border_optional_params : optional_params + { + border_optional_params() + : optional_params(KernelType::BORDER) + { + } + }; + + //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// + // BorderKernelBase + //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// + class BorderKernelBase : public common_kernel_base + { + public: + using common_kernel_base::common_kernel_base; + + using DispatchData = CommonDispatchData; + + protected: + JitConstants GetJitConstants(const border_params& params) const; + DispatchData SetDefault(const border_params& params) const; + KernelsData GetCommonKernelsData(const Params& params, const optional_params&, float estimated_time) const; + }; +} diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/border/border_kernel_ref.cpp b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/border/border_kernel_ref.cpp new file mode 100644 index 000000000..9029d7afc --- /dev/null +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/border/border_kernel_ref.cpp @@ -0,0 +1,51 @@ +// Copyright (c) 2018 Intel Corporation +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#include "border_kernel_ref.h" + + +namespace kernel_selector +{ + ParamsKey BorderKernelRef::GetSupportedKey() const + { + ParamsKey k; + + k.EnableInputDataType(Datatype::F16); + k.EnableInputDataType(Datatype::F32); + k.EnableInputDataType(Datatype::INT8); + k.EnableInputDataType(Datatype::UINT8); + + k.EnableOutputDataType(Datatype::F32); + k.EnableOutputDataType(Datatype::F16); + k.EnableOutputDataType(Datatype::INT8); + k.EnableOutputDataType(Datatype::UINT8); + + k.EnableInputLayout(DataLayout::bfyx); + k.EnableInputLayout(DataLayout::yxfb); + k.EnableInputLayout(DataLayout::byxf); + + k.EnableOutputLayout(DataLayout::bfyx); + k.EnableOutputLayout(DataLayout::yxfb); + k.EnableOutputLayout(DataLayout::byxf); + + k.EnableBatching(); + + return k; + } + + KernelsData BorderKernelRef::GetKernelsData(const Params& params, const optional_params& options) const + { + return GetCommonKernelsData(params, options, FORCE_PRIORITY_9); + } +} diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/border/border_kernel_ref.h b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/border/border_kernel_ref.h new file mode 100644 index 000000000..0862ed144 --- /dev/null +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/border/border_kernel_ref.h @@ -0,0 +1,30 @@ +// Copyright (c) 2018 Intel Corporation +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#pragma once + +#include "border_kernel_base.h" + + +namespace kernel_selector +{ + class BorderKernelRef : public BorderKernelBase + { + public: + BorderKernelRef() : BorderKernelBase("border_gpu_ref") {} + + KernelsData GetKernelsData(const Params& params, const optional_params& options) const override; + ParamsKey GetSupportedKey() const override; + }; +} diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/border/border_kernel_selector.cpp b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/border/border_kernel_selector.cpp new file mode 100644 index 000000000..42e352c6a --- /dev/null +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/border/border_kernel_selector.cpp @@ -0,0 +1,30 @@ +// Copyright (c) 2018 Intel Corporation +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + + +#include "border_kernel_selector.h" +#include "border_kernel_ref.h" + +namespace kernel_selector +{ + border_kernel_selector::border_kernel_selector() + { + Attach<BorderKernelRef>(); + } + + KernelsData border_kernel_selector::GetBestKernels(const Params& params, const optional_params& options) const + { + return GetNaiveBestKernel(params, options, KernelType::BORDER); + } +} diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/border/border_kernel_selector.h b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/border/border_kernel_selector.h new file mode 100644 index 000000000..515a9a497 --- /dev/null +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/border/border_kernel_selector.h @@ -0,0 +1,34 @@ +// Copyright (c) 2018 Intel Corporation +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#pragma once + +#include "kernel_selector.h" + + +namespace kernel_selector +{ + class border_kernel_selector : public kernel_selector_base + { + public: + static border_kernel_selector &Instance() { + static border_kernel_selector instance; + return instance; + } + + border_kernel_selector(); + + KernelsData GetBestKernels(const Params& params, const optional_params& options) const override; + }; +} diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/broadcast/broadcast_kernel_base.cpp b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/broadcast/broadcast_kernel_base.cpp new file mode 100644 index 000000000..3d3b2f4d3 --- /dev/null +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/broadcast/broadcast_kernel_base.cpp @@ -0,0 +1,71 @@ +// Copyright (c) 2018 Intel Corporation +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + + +#include "broadcast_kernel_base.h" + +#include "kernel_selector_utils.h" + + +namespace kernel_selector +{ + JitConstants BroadcastKernelBase::GetJitConstants(const broadcast_params& params) + { + JitConstants jit = MakeBaseParamsJitConstants(params); + return jit; + } + + BroadcastKernelBase::DispatchData BroadcastKernelBase::SetDefault(const broadcast_params& params) + { + const auto& output = params.output; + + DispatchData kd; + + kd.fp16UnitUsed = params.inputs[0].GetDType() == Datatype::F16; + + std::vector<size_t> global{output.X().v, output.Y().v, output.Batch().v * output.Feature().v}; + const auto& local = GetOptimalLocalWorkGroupSizes(global); + + kd.gws0 = global[0]; + kd.gws1 = global[1]; + kd.gws2 = global[2]; + + kd.lws0 = local[0]; + kd.lws1 = local[1]; + kd.lws2 = local[2]; + + return kd; + } + + KernelsData BroadcastKernelBase::GetCommonKernelsData(const Params& params, const optional_params& options, float estimated_time) const + { + assert(params.GetType() == KernelType::BROADCAST); + + const auto& prim_params = static_cast<const broadcast_params&>(params); // NOLINT(cppcoreguidelines-pro-type-static-cast-downcast) + + auto run_info = SetDefault(prim_params); + KernelData k_data = KernelData::Default<broadcast_params>(params); + + auto cldnn_jit = GetJitConstants(prim_params); + auto entry_point = GetEntryPoint(kernelName, prim_params.layerID, options); + auto jit = CreateJit(kernelName, cldnn_jit, entry_point); + + auto& kernel = k_data.kernels[0]; + FillCLKernelData(kernel, run_info, params.engineInfo, kernelName, jit, entry_point); + + k_data.estimatedTime = estimated_time; + + return {k_data}; + } +} diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/broadcast/broadcast_kernel_base.h b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/broadcast/broadcast_kernel_base.h new file mode 100644 index 000000000..cf4865e80 --- /dev/null +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/broadcast/broadcast_kernel_base.h @@ -0,0 +1,60 @@ +// Copyright (c) 2018 Intel Corporation +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#pragma once + +#include "common_kernel_base.h" +#include "kernel_selector_params.h" + + +namespace kernel_selector +{ + //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// + // broadcast_params + //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// + struct broadcast_params : public base_params + { + broadcast_params() + : base_params(KernelType::BROADCAST) + { + } + }; + + //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// + // broadcast_optional_params + //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// + struct broadcast_optional_params : optional_params + { + broadcast_optional_params() + : optional_params(KernelType::BROADCAST) + { + } + }; + + //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// + // BroadcastKernelBase + //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// + class BroadcastKernelBase : public common_kernel_base + { + public: + using common_kernel_base::common_kernel_base; + + using DispatchData = CommonDispatchData; + + protected: + static JitConstants GetJitConstants(const broadcast_params& params); + static DispatchData SetDefault(const broadcast_params& params); + KernelsData GetCommonKernelsData(const Params& params, const optional_params&, float estimated_time) const; + }; +} diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/broadcast/broadcast_kernel_ref.cpp b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/broadcast/broadcast_kernel_ref.cpp new file mode 100644 index 000000000..0be42a5e2 --- /dev/null +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/broadcast/broadcast_kernel_ref.cpp @@ -0,0 +1,51 @@ +// Copyright (c) 2018 Intel Corporation +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#include "broadcast_kernel_ref.h" + + +namespace kernel_selector +{ + ParamsKey BroadcastKernelRef::GetSupportedKey() const + { + ParamsKey k; + + k.EnableInputDataType(Datatype::F16); + k.EnableInputDataType(Datatype::F32); + k.EnableInputDataType(Datatype::INT8); + k.EnableInputDataType(Datatype::UINT8); + + k.EnableOutputDataType(Datatype::F32); + k.EnableOutputDataType(Datatype::F16); + k.EnableOutputDataType(Datatype::INT8); + k.EnableOutputDataType(Datatype::UINT8); + + k.EnableInputLayout(DataLayout::bfyx); + k.EnableInputLayout(DataLayout::yxfb); + k.EnableInputLayout(DataLayout::byxf); + + k.EnableOutputLayout(DataLayout::bfyx); + k.EnableOutputLayout(DataLayout::yxfb); + k.EnableOutputLayout(DataLayout::byxf); + + k.EnableBatching(); + + return k; + } + + KernelsData BroadcastKernelRef::GetKernelsData(const Params& params, const optional_params& options) const + { + return GetCommonKernelsData(params, options, FORCE_PRIORITY_9); + } +} diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/broadcast/broadcast_kernel_ref.h b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/broadcast/broadcast_kernel_ref.h new file mode 100644 index 000000000..ccca397ab --- /dev/null +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/broadcast/broadcast_kernel_ref.h @@ -0,0 +1,30 @@ +// Copyright (c) 2018 Intel Corporation +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#pragma once + +#include "broadcast_kernel_base.h" + + +namespace kernel_selector +{ + class BroadcastKernelRef : public BroadcastKernelBase + { + public: + BroadcastKernelRef() : BroadcastKernelBase("broadcast_gpu_ref") {} + + KernelsData GetKernelsData(const Params& params, const optional_params& options) const override; + ParamsKey GetSupportedKey() const override; + }; +} diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/broadcast/broadcast_kernel_selector.cpp b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/broadcast/broadcast_kernel_selector.cpp new file mode 100644 index 000000000..02ae904d2 --- /dev/null +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/broadcast/broadcast_kernel_selector.cpp @@ -0,0 +1,30 @@ +// Copyright (c) 2018 Intel Corporation +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + + +#include "broadcast_kernel_selector.h" +#include "broadcast_kernel_ref.h" + +namespace kernel_selector +{ + broadcast_kernel_selector::broadcast_kernel_selector() + { + Attach<BroadcastKernelRef>(); + } + + KernelsData broadcast_kernel_selector::GetBestKernels(const Params& params, const optional_params& options) const + { + return GetNaiveBestKernel(params, options, KernelType::BROADCAST); + } +} diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/broadcast/broadcast_kernel_selector.h b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/broadcast/broadcast_kernel_selector.h new file mode 100644 index 000000000..ec7f4da55 --- /dev/null +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/broadcast/broadcast_kernel_selector.h @@ -0,0 +1,34 @@ +// Copyright (c) 2018 Intel Corporation +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#pragma once + +#include "kernel_selector.h" + + +namespace kernel_selector +{ + class broadcast_kernel_selector : public kernel_selector_base + { + public: + static broadcast_kernel_selector &Instance() { + static broadcast_kernel_selector instance; + return instance; + } + + broadcast_kernel_selector(); + + KernelsData GetBestKernels(const Params& params, const optional_params& options) const override; + }; +} diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/concatenation/concatenation_kernel_base.cpp b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/concatenation/concatenation_kernel_base.cpp index b81cfe879..f9df941ad 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/concatenation/concatenation_kernel_base.cpp +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/concatenation/concatenation_kernel_base.cpp @@ -71,9 +71,18 @@ namespace kernel_selector // Determine global work sizes. if (params.inputs[0].GetLayout() != params.output.GetLayout()) { - kd.gws0 = dims.size() < 2 ? 1 : dims[2].v; - kd.gws1 = dims.size() < 3 ? 1 : dims[1].v; - kd.gws2 = dims.size() < 4 ? 1 : dims[0].v; + if (params.inputs[0].GetLayout() == kernel_selector::Tensor::DataLayout::yxfb) + { + kd.gws0 = dims.size() < 2 ? 1 : dims[3].v; + kd.gws1 = dims.size() < 3 ? 1 : dims[1].v; + kd.gws2 = dims.size() < 4 ? 1 : dims[0].v; + } + else + { + kd.gws0 = dims.size() < 2 ? 1 : dims[2].v; + kd.gws1 = dims.size() < 3 ? 1 : dims[1].v; + kd.gws2 = dims.size() < 4 ? 1 : dims[0].v; + } } else { @@ -124,7 +133,7 @@ namespace kernel_selector kernel.workGroups.global = { runInfo.gws0, runInfo.gws1, runInfo.gws2 }; kernel.workGroups.local = { runInfo.lws0, runInfo.lws1, runInfo.lws2 }; - kernel.kernelString = GetKernelString(kernelName, jit, entryPoint); + kernel.kernelString = GetKernelString(kernelName, jit, entryPoint, params.engineInfo); kernel.arguments.push_back({ ArgumentDescriptor::Types::INPUT, (uint32_t)i }); kernel.arguments.push_back({ ArgumentDescriptor::Types::OUTPUT, 0 }); diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/concatenation/concatenation_kernel_ref.cpp b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/concatenation/concatenation_kernel_ref.cpp index 5520f427a..c5c6ae53a 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/concatenation/concatenation_kernel_ref.cpp +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/concatenation/concatenation_kernel_ref.cpp @@ -26,9 +26,13 @@ namespace kernel_selector k.EnableInputDataType(Datatype::F16); k.EnableInputDataType(Datatype::F32); k.EnableInputDataType(Datatype::INT8); + k.EnableInputDataType(Datatype::INT32); + k.EnableInputDataType(Datatype::INT64); k.EnableOutputDataType(Datatype::F16); k.EnableOutputDataType(Datatype::F32); k.EnableOutputDataType(Datatype::INT8); + k.EnableOutputDataType(Datatype::INT32); + k.EnableOutputDataType(Datatype::INT64); k.EnableAllInputLayout(); k.EnableAllOutputLayout(); k.EnableTensorOffset(); @@ -60,10 +64,20 @@ namespace kernel_selector //case for input == bfyx, output == yxfb and input == yxfb, output == bfyx if (input_format != output_format) { - dim_index[0] = 3; - dim_index[1] = 2; - dim_index[2] = 0; - dim_index[3] = 1; + if (input_format == kernel_selector::Tensor::DataLayout::yxfb) + { + dim_index[0] = 2; + dim_index[1] = 3; + dim_index[2] = 1; + dim_index[3] = 0; + } + else + { + dim_index[0] = 3; + dim_index[1] = 2; + dim_index[2] = 0; + dim_index[3] = 1; + } } cldnnJit.AddConstant(MakeJitConstant("INPUT_DIM_0", dim_index[0])); diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/convolution/convolution_kernel_base.cpp b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/convolution/convolution_kernel_base.cpp index 878a02048..86bfe937c 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/convolution/convolution_kernel_base.cpp +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/convolution/convolution_kernel_base.cpp @@ -241,7 +241,7 @@ namespace kernel_selector auto jit = CreateJit(finalKernelName, cldnnJit, entryPoint); auto& kernel = kd.kernels[0]; - FillCLKernelData(kernel, runInfo, finalKernelName, jit, entryPoint, exeMode, true, !newParams.bias.empty(), 1, newParams.int8_quantization, newParams.output_calibration); + FillCLKernelData(kernel, runInfo, params.engineInfo, finalKernelName, jit, entryPoint, exeMode, true, !newParams.bias.empty(), 1, newParams.int8_quantization, newParams.output_calibration); kernel.arguments.push_back({ ArgumentDescriptor::Types::SPLIT, 0 }); kd.estimatedTime = runInfo.effiency; diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/convolution/convolution_kernel_bfyx_3x3_dw_opt.cpp b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/convolution/convolution_kernel_bfyx_3x3_dw_opt.cpp index ad56556bc..b92df30b7 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/convolution/convolution_kernel_bfyx_3x3_dw_opt.cpp +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/convolution/convolution_kernel_bfyx_3x3_dw_opt.cpp @@ -1,5 +1,5 @@ /* -// Copyright (c) 2017 Intel Corporation +// Copyright (c) 2017-2018 Intel Corporation // // Licensed under the Apache License, Version 2.0 (the "License"); // you may not use this file except in compliance with the License. @@ -22,7 +22,7 @@ namespace kernel_selector ConvolutionKernel_bfyx_3x3_dw_opt::ConvolutionKernel_bfyx_3x3_dw_opt() : ConvolutionKernelBase("convolution_gpu_bfyx_3x3_dw_opt") { // Generate the dispatch options to the auto-tuner. - std::vector<size_t> tileXDimSizes = { 1,2,4,5,6,8,10,12,14,16 }; + std::vector<size_t> tileXDimSizes = { 1,2,4,5,6,8,10,12,14 }; std::vector<size_t> tileYDimSizes = { 1,2,3,4,5,6,7 }; std::vector<std::string> executionModes = { /*AGE_BASED ,*/ ROUND_ROBIN }; @@ -141,6 +141,18 @@ namespace kernel_selector KernelsData ConvolutionKernel_bfyx_3x3_dw_opt::GetTunedKernelsDataByIndex(const Params& params, const optional_params& options, const int autoTuneIndex) const { + constexpr int simdSize = 16; + + KernelData kd = KernelData::Default<convolution_params>(params); + convolution_params& convParams = *static_cast<convolution_params*>(kd.params.get()); + DispatchData runInfo = SetDefault(convParams, autoTuneIndex); + + if (static_cast<int>(static_cast<int>(runInfo.gws0 - 1) / simdSize) * runInfo.cldnnStyle.blockWidth + simdSize > convParams.inputs[0].Y().pitch) + { + // Internal Error - requested tile size is not supported for y pitch + return{}; + } + return GetCommonKernelsData(params, options, GetAutoTuneOptions(params, autoTuneIndex).exeMode, autoTuneIndex); } diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/convolution/convolution_kernel_bfyx_depthwise_weights_lwg.cpp b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/convolution/convolution_kernel_bfyx_depthwise_weights_lwg.cpp new file mode 100644 index 000000000..f6841db94 --- /dev/null +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/convolution/convolution_kernel_bfyx_depthwise_weights_lwg.cpp @@ -0,0 +1,100 @@ +/* +// Copyright (c) 2018 Intel Corporation +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +*/ + +#include "convolution_kernel_bfyx_depthwise_weights_lwg.h" +#include "kernel_selector_utils.h" + +namespace kernel_selector +{ + ParamsKey ConvolutionKernel_bfyx_depthwise_weights_lwg::GetSupportedKey() const + { + ParamsKey k; + k.EnableInputDataType(Datatype::F32); + k.EnableInputDataType(Datatype::F16); + k.EnableInputWeightsType(WeightsType::F16); + k.EnableInputWeightsType(WeightsType::F32); + k.EnableOutputDataType(Datatype::F32); + k.EnableOutputDataType(Datatype::F16); + k.EnableInputLayout(DataLayout::bfyx); + k.EnableOutputLayout(DataLayout::bfyx); + k.EnableTensorOffset(); + k.EnableTensorPitches(); + k.EnableBiasPerFeature(); + k.EnableNonBiasTerm(); + k.EnableBatching(); + k.EnableSplitSupport(); + k.EnableSubGroup(); + k.EnableSubGroupShort(); + k.EnableDepthwiseSeparableOpt(); + return k; + } + + bool ConvolutionKernel_bfyx_depthwise_weights_lwg::Validate(const Params& p, const optional_params& o) const + { + if (!ConvolutionKernelBase::Validate(p, o) || + !CovolutionCheckInput(p, o)) + { + return false; + } + + const convolution_params& cp = static_cast<const convolution_params&>(p); + if (!cp.depthwiseSeparableOpt) + return false; + + if ((cp.filterSize.x > 4) || + (cp.filterSize.y > 4) || + (cp.inputs[0].Feature().v != cp.split)) + { + return false; + } + + return true; + } + + ConvolutionKernelBase::DispatchData ConvolutionKernel_bfyx_depthwise_weights_lwg::SetDefault(const convolution_params& params, int) const + { + DispatchData runInfo = Parent::SetDefault(params); + const auto& out = params.output; + + std::vector<size_t> global = { out.X().v * out.Y().v, out.Feature().v, out.Batch().v }; + + runInfo.gws0 = Align(global[0], 16); + runInfo.gws1 = global[1]; + runInfo.gws2 = global[2]; + runInfo.lws0 = 16; + runInfo.lws1 = 1; + runInfo.lws2 = 1; + + runInfo.effiency = FORCE_PRIORITY_6; + + return runInfo; + } + + JitConstants ConvolutionKernel_bfyx_depthwise_weights_lwg::GetJitConstants(const convolution_params& params, const DispatchData& kd) const + { + auto mem_consts = ConvolutionKernelBase::GetJitConstants(params, kd); + + if(params.padding.x != 0 || params.padding.y != 0) + mem_consts.AddConstant(MakeJitConstant("BOUNDARY_CHECK", 1)); + + return mem_consts; + } + + KernelsData ConvolutionKernel_bfyx_depthwise_weights_lwg::GetKernelsData(const Params& params, const optional_params& options) const + { + return GetCommonKernelsData(params, options); + } +}
\ No newline at end of file diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/convolution/convolution_kernel_bfyx_depthwise_weights_lwg.h b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/convolution/convolution_kernel_bfyx_depthwise_weights_lwg.h new file mode 100644 index 000000000..b578f8fd5 --- /dev/null +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/convolution/convolution_kernel_bfyx_depthwise_weights_lwg.h @@ -0,0 +1,39 @@ +/* +// Copyright (c) 2018 Intel Corporation +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +*/ + +#pragma once + +#include "convolution_kernel_base.h" + +namespace kernel_selector +{ + class ConvolutionKernel_bfyx_depthwise_weights_lwg : public ConvolutionKernelBase + { + public: + using Parent = ConvolutionKernelBase; + ConvolutionKernel_bfyx_depthwise_weights_lwg() : ConvolutionKernelBase("convolution_gpu_bfyx_depthwise_weights_lwg") {} + virtual ~ConvolutionKernel_bfyx_depthwise_weights_lwg() {} + + virtual KernelsData GetKernelsData(const Params& params, const optional_params& options) const override; + virtual ParamsKey GetSupportedKey() const override; + + protected: + bool Validate(const Params&, const optional_params&) const override; + std::vector<WeightsLayout> GetSupportedWeightLayouts(const convolution_params&) const override { return{ WeightsLayout::oiyx }; } + JitConstants GetJitConstants(const convolution_params& params, const DispatchData& kd) const override; + DispatchData SetDefault(const convolution_params& params, int autoTuneIndex = -1) const override; + }; +}
\ No newline at end of file diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/convolution/convolution_kernel_mmad_batched.cpp b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/convolution/convolution_kernel_mmad_batched.cpp new file mode 100644 index 000000000..ce73392ac --- /dev/null +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/convolution/convolution_kernel_mmad_batched.cpp @@ -0,0 +1,97 @@ +/* +// Copyright (c) 2018 Intel Corporation +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +*/ + +#include "convolution_kernel_mmad_batched.h" +#include "kernel_selector_utils.h" + +namespace kernel_selector { + + ParamsKey ConvolutionKernel_mmad_batched::GetSupportedKey() const + { + ParamsKey k; + k.EnableInputDataType(Datatype::INT8); + k.EnableOutputDataType(Datatype::INT8); + k.EnableInputWeightsType(WeightsType::INT8); + k.EnableInputLayout(DataLayout::fs_bs_yx_bsv4_fsv32); + k.EnableOutputLayout(DataLayout::fs_bs_yx_bsv4_fsv32); + k.EnableTensorOffset(); + k.EnableTensorPitches(); + k.EnableDilation(); + k.EnableBiasPerFeature(); + k.EnableNonBiasTerm(); + k.EnableBatching(); + k.EnableSplitSupport(); + k.EnableInt8Quantization(); + k.EnableOutputCalibration(); + k.DisableTuning(); + return k; + } + + ConvolutionKernelBase::DispatchData ConvolutionKernel_mmad_batched::SetDefault(const convolution_params& arg, int) const + { + DispatchData runInfo = ConvolutionKernelBase::SetDefault(arg); + + constexpr size_t sub_group_size = 8; + + const auto of_maps = arg.output.Feature().v; + const size_t of_threads_per_batch = RoundUp(of_maps, sub_group_size); + + runInfo.effiency = FORCE_PRIORITY_3; + + runInfo.gws0 = arg.output.X().v; + runInfo.gws1 = arg.output.Y().v; + runInfo.gws2 = of_threads_per_batch * ((arg.output.Batch().v+3) / 4); + + runInfo.lws0 = 1; + runInfo.lws1 = 1; + runInfo.lws2 = sub_group_size; + + return runInfo; + } + + JitConstants ConvolutionKernel_mmad_batched::GetJitConstants(const convolution_params& params, const DispatchData& runInfo) const + { + auto jit = Parent::GetJitConstants(params, runInfo); + + jit.AddConstant(MakeJitConstant("SUB_GROUP_SIZE", runInfo.lws2)); + + // pitch for special block format used in this kernel + const size_t ifm_32_aligned = Align(params.weights.IFM().v, 32); + const size_t filter_ofm_block_pitch = (ifm_32_aligned / 32) * params.weights.X().v * params.weights.Y().v * 4 * 8 * 8; + jit.AddConstant(MakeJitConstant("FILTER_OFM_BLOCK_PITCH", filter_ofm_block_pitch)); + + const size_t in_x_pitch = 32 * 4; + const size_t in_y_pitch = 32 * 4 * params.inputs[0].X().LogicalDimPadded(); + const size_t in_b_block_pitch = in_y_pitch * params.inputs[0].Y().LogicalDimPadded(); + const size_t in_f_block_pitch = in_b_block_pitch * ((params.inputs[0].Batch().v + 3) / 4); + const size_t in_offset = in_x_pitch * params.inputs[0].X().pad.before + in_y_pitch * params.inputs[0].Y().pad.before; + + jit.AddConstant(MakeJitConstant("IN_X_PITCH", in_x_pitch)); + jit.AddConstant(MakeJitConstant("IN_Y_PITCH", in_y_pitch)); + jit.AddConstant(MakeJitConstant("IN_B_BLOCK_PITCH", in_b_block_pitch)); + jit.AddConstant(MakeJitConstant("IN_F_BLOCK_PITCH", in_f_block_pitch)); + jit.AddConstant(MakeJitConstant("IN_OFFSET", in_offset)); + return jit; + } + + KernelsData ConvolutionKernel_mmad_batched::GetKernelsData(const Params& params, const optional_params& options) const + { + KernelsData kd = GetCommonKernelsData(params, options); + if(!kd.empty()) + kd[0].estimatedTime = FORCE_PRIORITY_3; + return kd; + } +}
\ No newline at end of file diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/convolution/convolution_kernel_mmad_batched.h b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/convolution/convolution_kernel_mmad_batched.h new file mode 100644 index 000000000..8a3dda451 --- /dev/null +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/convolution/convolution_kernel_mmad_batched.h @@ -0,0 +1,43 @@ +/* +// Copyright (c) 2018 Intel Corporation +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +*/ + +#pragma once + +#include "convolution_kernel_base.h" + +namespace kernel_selector { + + class ConvolutionKernel_mmad_batched : public ConvolutionKernelBase + { + public: + using Parent = ConvolutionKernelBase; + ConvolutionKernel_mmad_batched() : ConvolutionKernelBase("convolution_gpu_mmad_batched") {} + virtual ~ConvolutionKernel_mmad_batched() {} + + virtual KernelsData GetKernelsData(const Params& params, const optional_params& options) const override; + virtual ParamsKey GetSupportedKey() const override; + + protected: + JitConstants GetJitConstants(const convolution_params& params, const DispatchData& kd) const override; + DispatchData SetDefault(const convolution_params& arg, int autoTuneIndex = -1) const override; + virtual std::vector<WeightsLayout> GetSupportedWeightLayouts(const convolution_params&) const override + { + return{ + WeightsLayout::os_is_yx_isa8_osv8_isv4, + }; + } + }; +}
\ No newline at end of file diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/convolution/convolution_kernel_selector.cpp b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/convolution/convolution_kernel_selector.cpp index 21d9c92ff..aa5850593 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/convolution/convolution_kernel_selector.cpp +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/convolution/convolution_kernel_selector.cpp @@ -1,5 +1,5 @@ /* -// Copyright (c) 2016 Intel Corporation +// Copyright (c) 2016-2018 Intel Corporation // // Licensed under the Apache License, Version 2.0 (the "License"); // you may not use this file except in compliance with the License. @@ -35,6 +35,8 @@ #include "convolution_kernel_MMAD_blocks.h" #include "convolution_kernel_1x1_gemm_MMAD.h" #include "convolution_kernel_byxf_af32_depthwise.h" +#include "convolution_kernel_mmad_batched.h" +#include "convolution_kernel_bfyx_depthwise_weights_lwg.h" #include <iostream> @@ -61,6 +63,8 @@ namespace kernel_selector Attach<ConvolutionKernel_MMAD_blocks>(); Attach<ConvolutionKernel_1x1_gemm_MMAD>(); Attach<ConvolutionKernel_byxf_af32_depthiwise>(); + Attach<ConvolutionKernel_mmad_batched>(); + Attach<ConvolutionKernel_bfyx_depthwise_weights_lwg>(); //Attach<ConvolutionKernel_Tutorial>(); //In order to use this implementation for tutorial purposes please uncomment this line } diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/convolution/convolution_kernel_selector.h b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/convolution/convolution_kernel_selector.h index 9275a5fcb..6b4c756d0 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/convolution/convolution_kernel_selector.h +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/convolution/convolution_kernel_selector.h @@ -17,7 +17,6 @@ #pragma once #include "kernel_selector.h" -#include "kernel_runner_interface.h" namespace kernel_selector { diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/convolution_grad_weights/convolution_grad_weights_kernel_1x1.cpp b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/convolution_grad_weights/convolution_grad_weights_kernel_1x1.cpp index b6065edc6..13f8ba40b 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/convolution_grad_weights/convolution_grad_weights_kernel_1x1.cpp +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/convolution_grad_weights/convolution_grad_weights_kernel_1x1.cpp @@ -22,11 +22,8 @@ namespace kernel_selector ParamsKey ConvolutionGradWeightsKernel1x1::GetSupportedKey() const { ParamsKey k; - k.EnableInputDataType(Datatype::F16); k.EnableInputDataType(Datatype::F32); - k.EnableInputWeightsType(WeightsType::F16); k.EnableInputWeightsType(WeightsType::F32); - k.EnableOutputDataType(Datatype::F16); k.EnableOutputDataType(Datatype::F32); k.EnableInputLayout(DataLayout::bfyx); k.EnableOutputLayout(DataLayout::yxfb); diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/convolution_grad_weights/convolution_grad_weights_kernel_3x3.cpp b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/convolution_grad_weights/convolution_grad_weights_kernel_3x3.cpp new file mode 100644 index 000000000..c892f6d2d --- /dev/null +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/convolution_grad_weights/convolution_grad_weights_kernel_3x3.cpp @@ -0,0 +1,75 @@ +/* +// Copyright (c) 2018 Intel Corporation +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +*/ + +#include "convolution_grad_weights_kernel_3x3.h" + +namespace kernel_selector +{ + + ParamsKey ConvolutionGradWeightsKernel3x3::GetSupportedKey() const + { + ParamsKey k; + k.EnableInputDataType(Datatype::F32); + k.EnableInputWeightsType(WeightsType::F32); + k.EnableOutputDataType(Datatype::F32); + k.EnableInputLayout(DataLayout::bfyx); + k.EnableOutputLayout(DataLayout::yxfb); + k.EnableOutputLayout(DataLayout::bfyx); + k.EnableOutputLayout(DataLayout::byxf); + k.EnableTensorOffset(); + k.EnableTensorPitches(); + k.EnableBiasPerFeature(); + k.EnableNonBiasTerm(); + k.EnableMomentum(); + k.EnableBatching(); + k.EnableSplitSupport(); + k.EnableGradient(); + k.DisableTuning(); + return k; + } + + bool ConvolutionGradWeightsKernel3x3::Validate(const Params& p, const optional_params&) const + { + const auto& params = static_cast<const convolution_grad_weights_params&>(p); + + if (params.stride.x != 1 || params.stride.y != 1) + return false; + if (params.filterSize.x != 3 || params.filterSize.y != 3) + return false; + return true; + } + + ConvolutionGradWeightsKernelBase::DispatchData ConvolutionGradWeightsKernel3x3::SetDefault(const convolution_grad_weights_params& params) const + { + auto input_features = params.weights.IFM().v; + auto output_features = params.weights.OFM().v; + + DispatchData kd; + + kd.gws0 = Align(output_features, 16); + kd.gws1 = input_features; + kd.gws2 = 1; + kd.lws0 = std::min(std::max(kd.gws0, static_cast<size_t>(1)), static_cast<size_t>(32)); + while (kd.gws0 % kd.lws0 != 0) + { + kd.lws0 -= 16; + } + kd.lws1 = 1; + kd.lws2 = 1; + kd.effiency = FORCE_PRIORITY_8; + return kd; + } +} diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/convolution_grad_weights/convolution_grad_weights_kernel_3x3.h b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/convolution_grad_weights/convolution_grad_weights_kernel_3x3.h new file mode 100644 index 000000000..39fcb7e96 --- /dev/null +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/convolution_grad_weights/convolution_grad_weights_kernel_3x3.h @@ -0,0 +1,33 @@ +/* +// Copyright (c) 2018 Intel Corporation +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +*/ + +#pragma once + +#include "convolution_grad_weights_kernel_base.h" + +namespace kernel_selector { + + class ConvolutionGradWeightsKernel3x3 : public ConvolutionGradWeightsKernelBase + { + public: + ConvolutionGradWeightsKernel3x3() : ConvolutionGradWeightsKernelBase("convolution_grad_weights_3x3") {} + virtual ~ConvolutionGradWeightsKernel3x3() {} + + virtual DispatchData SetDefault(const convolution_grad_weights_params& params) const override; + virtual bool Validate(const Params& p, const optional_params& o) const override; + virtual ParamsKey GetSupportedKey() const override; + }; +}
\ No newline at end of file diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/convolution_grad_weights/convolution_grad_weights_kernel_7x7.cpp b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/convolution_grad_weights/convolution_grad_weights_kernel_7x7.cpp new file mode 100644 index 000000000..8bd5000d6 --- /dev/null +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/convolution_grad_weights/convolution_grad_weights_kernel_7x7.cpp @@ -0,0 +1,73 @@ +/* +// Copyright (c) 2018 Intel Corporation +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +*/ + +#include "convolution_grad_weights_kernel_7x7.h" + +namespace kernel_selector +{ + + ParamsKey ConvolutionGradWeightsKernel7x7::GetSupportedKey() const + { + ParamsKey k; + k.EnableInputDataType(Datatype::F32); + k.EnableInputWeightsType(WeightsType::F32); + k.EnableOutputDataType(Datatype::F32); + k.EnableInputLayout(DataLayout::bfyx); + k.EnableOutputLayout(DataLayout::yxfb); + k.EnableOutputLayout(DataLayout::bfyx); + k.EnableOutputLayout(DataLayout::byxf); + k.EnableTensorOffset(); + k.EnableTensorPitches(); + k.EnableBiasPerFeature(); + k.EnableNonBiasTerm(); + k.EnableMomentum(); + k.EnableBatching(); + k.EnableSplitSupport(); + k.EnableGradient(); + k.DisableTuning(); + return k; + } + + bool ConvolutionGradWeightsKernel7x7::Validate(const Params& p, const optional_params&) const + { + const auto& params = static_cast<const convolution_grad_weights_params&>(p); + + if (params.filterSize.x != 7 || params.filterSize.y != 7) + return false; + return true; + } + + ConvolutionGradWeightsKernelBase::DispatchData ConvolutionGradWeightsKernel7x7::SetDefault(const convolution_grad_weights_params& params) const + { + auto input_features = params.weights.IFM().v; + auto output_features = params.weights.OFM().v; + + DispatchData kd; + + kd.gws0 = 8; + kd.gws1 = Align(output_features, 16); + kd.gws2 = input_features; + kd.lws0 = 1; + kd.lws1 = std::min(std::max(kd.gws1, static_cast<size_t>(1)), static_cast<size_t>(32)); + while (kd.gws1 % kd.lws1 != 0) + { + kd.lws1 -= 16; + } + kd.lws2 = 1; + kd.effiency = FORCE_PRIORITY_8; + return kd; + } +} diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/convolution_grad_weights/convolution_grad_weights_kernel_7x7.h b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/convolution_grad_weights/convolution_grad_weights_kernel_7x7.h new file mode 100644 index 000000000..286caf5c0 --- /dev/null +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/convolution_grad_weights/convolution_grad_weights_kernel_7x7.h @@ -0,0 +1,33 @@ +/* +// Copyright (c) 2018 Intel Corporation +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +*/ + +#pragma once + +#include "convolution_grad_weights_kernel_base.h" + +namespace kernel_selector { + + class ConvolutionGradWeightsKernel7x7 : public ConvolutionGradWeightsKernelBase + { + public: + ConvolutionGradWeightsKernel7x7() : ConvolutionGradWeightsKernelBase("convolution_grad_weights_7x7") {} + virtual ~ConvolutionGradWeightsKernel7x7() {} + + virtual DispatchData SetDefault(const convolution_grad_weights_params& params) const override; + virtual bool Validate(const Params& p, const optional_params& o) const override; + virtual ParamsKey GetSupportedKey() const override; + }; +}
\ No newline at end of file diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/convolution_grad_weights/convolution_grad_weights_kernel_base.cpp b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/convolution_grad_weights/convolution_grad_weights_kernel_base.cpp index 2998075b7..1e2cd30a8 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/convolution_grad_weights/convolution_grad_weights_kernel_base.cpp +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/convolution_grad_weights/convolution_grad_weights_kernel_base.cpp @@ -124,7 +124,7 @@ namespace kernel_selector auto jit = CreateJit(kernelName, cldnn_jit, entry_point); auto& kernel = kd.kernels[0]; - FillCLKernelData(kernel, runInfo, kernelName, jit, entry_point, ROUND_ROBIN, true, !orgParams.bias.empty()); + FillCLKernelData(kernel, runInfo, params.engineInfo, kernelName, jit, entry_point, ROUND_ROBIN, true, !orgParams.bias.empty()); if (newParams.use_momentum) { kernel.arguments.push_back({ ArgumentDescriptor::Types::PREV_WEIGHTS_GRADIENT, 0 }); diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/convolution_grad_weights/convolution_grad_weights_kernel_ref.cpp b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/convolution_grad_weights/convolution_grad_weights_kernel_ref.cpp index 7ca7c9a39..3c29a7616 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/convolution_grad_weights/convolution_grad_weights_kernel_ref.cpp +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/convolution_grad_weights/convolution_grad_weights_kernel_ref.cpp @@ -24,7 +24,6 @@ namespace kernel_selector ParamsKey k; k.EnableInputDataType(Datatype::F16); k.EnableInputDataType(Datatype::F32); - k.EnableInputWeightsType(WeightsType::F16); k.EnableInputWeightsType(WeightsType::F32); k.EnableOutputDataType(Datatype::F16); k.EnableOutputDataType(Datatype::F32); diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/convolution_grad_weights/convolution_grad_weights_kernel_selector.cpp b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/convolution_grad_weights/convolution_grad_weights_kernel_selector.cpp index 16b842e9e..fb045a273 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/convolution_grad_weights/convolution_grad_weights_kernel_selector.cpp +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/convolution_grad_weights/convolution_grad_weights_kernel_selector.cpp @@ -18,6 +18,8 @@ #include "convolution_grad_weights_kernel_ref.h" #include "convolution_grad_weights_kernel_1x1.h" #include "convolution_grad_weights_kernel_yxfb.h" +#include "convolution_grad_weights_kernel_3x3.h" +#include "convolution_grad_weights_kernel_7x7.h" namespace kernel_selector { @@ -26,6 +28,8 @@ namespace kernel_selector Attach<ConvolutionGradWeightsKernelRef>(); Attach<ConvolutionGradWeightsKernel1x1>(); Attach<ConvolutionGradWeightsKernel_yxfb>(); + Attach<ConvolutionGradWeightsKernel3x3>(); + Attach<ConvolutionGradWeightsKernel7x7>(); } KernelsData convolution_grad_weights_kernel_selector::GetBestKernels(const Params& params, const optional_params& options) const diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/convolution_grad_weights/convolution_grad_weights_kernel_yxfb.cpp b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/convolution_grad_weights/convolution_grad_weights_kernel_yxfb.cpp index e6fb6d026..f53d51b39 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/convolution_grad_weights/convolution_grad_weights_kernel_yxfb.cpp +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/convolution_grad_weights/convolution_grad_weights_kernel_yxfb.cpp @@ -24,7 +24,6 @@ namespace kernel_selector ParamsKey k; k.EnableInputDataType(Datatype::F32); k.EnableInputWeightsType(WeightsType::F32); - k.EnableOutputDataType(Datatype::F16); k.EnableOutputDataType(Datatype::F32); k.EnableInputLayout(DataLayout::yxfb); k.EnableOutputLayout(DataLayout::yxfb); @@ -64,11 +63,11 @@ namespace kernel_selector DispatchData kd; - kd.gws0 = 32; + kd.gws0 = 16; kd.gws1 = input_features * output_features; kd.gws2 = x * y; - kd.lws0 = 32; + kd.lws0 = 16; kd.lws1 = 1; kd.lws2 = 1; kd.effiency = FORCE_PRIORITY_7; diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/deconvolution/deconvolution_kernel_base.cpp b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/deconvolution/deconvolution_kernel_base.cpp index 5369d7ac0..cbc0bd780 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/deconvolution/deconvolution_kernel_base.cpp +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/deconvolution/deconvolution_kernel_base.cpp @@ -57,6 +57,7 @@ namespace kernel_selector MakeJitConstant("FILTER_ARRAY_NUM", dp.split), MakeJitConstant("INPUT0_OFFSET_WITH_PADDING", input_offset_with_padding), MakeJitConstant("DEPTHWISE_SEPARABLE_OPT", dp.depthwiseSeparableOpt), + MakeJitConstant("FUSED_ELTWISE", dp.fused_eltwise) }); return jit; @@ -114,13 +115,15 @@ namespace kernel_selector return{}; } - auto cldnn_jit = GetJitConstants(orgParams); - auto entry_point = GetEntryPoint(kernelName, orgParams.layerID, options); + auto cldnn_jit = GetJitConstants(newParams); + auto entry_point = GetEntryPoint(kernelName, newParams.layerID, options); auto jit = CreateJit(kernelName, cldnn_jit, entry_point); auto& kernel = kd.kernels[0]; - FillCLKernelData(kernel, runInfo, kernelName, jit, entry_point, ROUND_ROBIN, true, !orgParams.bias.empty()); + FillCLKernelData(kernel, runInfo, params.engineInfo, kernelName, jit, entry_point, ROUND_ROBIN, true, !newParams.bias.empty()); kernel.arguments.push_back({ ArgumentDescriptor::Types::SPLIT, 0 }); + if (orgParams.fused_eltwise) + kernel.arguments.push_back({ ArgumentDescriptor::Types::INPUT, 1 }); kd.estimatedTime = runInfo.effiency; diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/deconvolution/deconvolution_kernel_base.h b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/deconvolution/deconvolution_kernel_base.h index ac5baec0f..206614a70 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/deconvolution/deconvolution_kernel_base.h +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/deconvolution/deconvolution_kernel_base.h @@ -34,6 +34,7 @@ namespace kernel_selector uSize padding; uint32_t split = 1; bool depthwiseSeparableOpt = false; + bool fused_eltwise = false; virtual std::string to_string() const override; diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/eltwise/eltwise_kernel_base.cpp b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/eltwise/eltwise_kernel_base.cpp index 613fbb4f8..5feac0ca5 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/eltwise/eltwise_kernel_base.cpp +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/eltwise/eltwise_kernel_base.cpp @@ -41,6 +41,22 @@ namespace kernel_selector } } + ParamsKey eltwise_params::GetParamsKey() const + { + ParamsKey k = base_params::GetParamsKey(); + if (int8_quantization) + { + k.EnableInt8Quantization(); + } + + if (output_calibration) + { + k.EnableOutputCalibration(); + } + + return k; + } + bool EltwiseKernelBase::Validate(const Params& p, const optional_params& o) const { if (p.GetType() != KernelType::ELTWISE || @@ -56,7 +72,7 @@ namespace kernel_selector return false; } - auto& operations = params.eltwiseParams.operations; + auto& operations = params.operations; if (operations.size() == 0) { @@ -91,24 +107,24 @@ namespace kernel_selector JitConstants jit = MakeBaseParamsJitConstants(params); jit.AddConstants({ - MakeJitConstant("ELTWISE_LAYOUT_BASED", params.eltwiseParams.layoutBased), - MakeJitConstant("QUANTIZATION_TERM", params.eltwiseParams.int8_quantization), + MakeJitConstant("ELTWISE_LAYOUT_BASED", params.layoutBased), + MakeJitConstant("QUANTIZATION_TERM", params.int8_quantization), }); - if (params.eltwiseParams.int8_quantization) + if (params.int8_quantization) { - if (params.eltwiseParams.output_calibration) + if (params.output_calibration) { - jit.AddConstant(MakeJitConstant("CALIBRATION_TERM", params.eltwiseParams.output_calibration)); + jit.AddConstant(MakeJitConstant("CALIBRATION_TERM", params.output_calibration)); jit.AddConstant(MakeJitConstant("O_QF", params.output_calibration_factors[0])); } else - jit.AddConstants({ MakeJitConstant("O_QF", params.eltwiseParams.output_quantization_factor) }); + jit.AddConstants({ MakeJitConstant("O_QF", params.output_quantization_factor) }); } std::string inputs_decls, vload_decls; - auto& updateInputs = params.eltwiseParams.updateInputIds; + auto& updateInputs = params.updateInputIds; for (size_t i = 0; i < params.inputs.size(); i++) { @@ -143,8 +159,8 @@ namespace kernel_selector std::string do_eltwise; - auto& operations = params.eltwiseParams.operations; - auto& coefficients = params.eltwiseParams.coefficients; + auto& operations = params.operations; + auto& coefficients = params.coefficients; for (size_t op_num = 0; op_num < operations.size(); op_num++) { @@ -187,7 +203,7 @@ namespace kernel_selector cast_type = "(MAKE_VECTOR_TYPE(UNIT_TYPE, 8))"; op = "const MAKE_VECTOR_TYPE(UNIT_TYPE, 8) tmp" + op_num_str + " = "; } - else if(params.eltwiseParams.int8_quantization) + else if(params.int8_quantization) { cast_type = "(int)"; op = "const int tmp" + op_num_str + " = "; @@ -251,7 +267,7 @@ namespace kernel_selector jit.AddConstant(MakeJitConstant("DO_ELTWISE", do_eltwise)); - if (params.eltwiseParams.layoutBased || params.eltwiseParams.int8_quantization) + if (params.layoutBased || params.int8_quantization) { jit.Merge(GetTensorFriendlyWorkGroupsJit(params.inputs[0])); } @@ -264,32 +280,27 @@ namespace kernel_selector return GetJitConstantsCommon(params, false); } - KernelsData EltwiseKernelBase::GetCommonKernelsData(const Params& params, const optional_params& options) const + EltwiseKernelBase::DispatchData EltwiseKernelBase::SetDefault(const eltwise_params& params) const { - if (!Validate(params, options)) - { - return{}; - } - - KernelData kd = KernelData::Default<eltwise_params>(params); - eltwise_params& newParams = *static_cast<eltwise_params*>(kd.params.get()); + DispatchData kd; - auto entry_point = GetEntryPoint(kernelName, newParams.layerID, options); - auto cldnn_jit = GetJitConstants(newParams); - std::string jit = CreateJit(kernelName, cldnn_jit, entry_point); - - const auto& out = newParams.output; - auto& kernel = kd.kernels[0]; - if (newParams.eltwiseParams.layoutBased || newParams.eltwiseParams.int8_quantization) + if (params.layoutBased || params.int8_quantization) { - kernel.workGroups.global = GetTensorFriendlyWorkGroups(newParams.inputs[0]); + auto global = GetTensorFriendlyWorkGroups(params.inputs[0]); + kd.gws0 = global[0]; + kd.gws1 = global[1]; + kd.gws2 = global[2]; } - else if (CheckInputsOutputNoPitchSameDims(newParams)) + else if (CheckInputsOutputNoPitchSameDims(params)) { - kernel.workGroups.global = { newParams.inputs[0].LogicalSize(), 1, 1 }; + kd.gws0 = params.inputs[0].LogicalSize(); + kd.gws1 = 1; + kd.gws2 = 1; } else { + const auto& out = params.output; + std::vector<size_t> gws; for (const auto& o : out.GetDims()) { @@ -301,11 +312,42 @@ namespace kernel_selector gws.push_back(1U); } - kernel.workGroups.global = { gws[0], gws[1], gws[2] * gws[3] }; + kd.gws0 = gws[0]; + kd.gws1 = gws[1]; + kd.gws2 = gws[2] * gws[3]; } - kernel.workGroups.local = GetOptimalLocalWorkGroupSizes(kernel.workGroups.global); - kernel.kernelString = GetKernelString(kernelName, jit, entry_point, ROUND_ROBIN); - kernel.arguments = GetArgsDesc((uint32_t)newParams.inputs.size(), false, false, newParams.eltwiseParams.int8_quantization, newParams.eltwiseParams.output_calibration); + + auto local = GetOptimalLocalWorkGroupSizes( { kd.gws0, kd.gws1, kd.gws2 } ); + kd.lws0 = local[0]; + kd.lws1 = local[1]; + kd.lws2 = local[2]; + + return kd; + } + + KernelsData EltwiseKernelBase::GetCommonKernelsData(const Params& params, const optional_params& options) const + { + if (!Validate(params, options)) + { + return{}; + } + + KernelData kd = KernelData::Default<eltwise_params>(params); + eltwise_params& newParams = *static_cast<eltwise_params*>(kd.params.get()); + + auto entry_point = GetEntryPoint(kernelName, newParams.layerID, options); + auto cldnn_jit = GetJitConstants(newParams); + std::string jit = CreateJit(kernelName, cldnn_jit, entry_point); + + DispatchData runInfo = SetDefault(newParams); + + auto& kernel = kd.kernels[0]; + + kernel.workGroups.global = { runInfo.gws0, runInfo.gws1, runInfo.gws2 }; + kernel.workGroups.local = { runInfo.lws0, runInfo.lws1, runInfo.lws2 }; + + kernel.kernelString = GetKernelString(kernelName, jit, entry_point, params.engineInfo, ROUND_ROBIN); + kernel.arguments = GetArgsDesc((uint32_t)newParams.inputs.size(), false, false, newParams.int8_quantization, newParams.output_calibration); kd.estimatedTime = DONT_USE_IF_HAVE_SOMETHING_ELSE; diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/eltwise/eltwise_kernel_base.h b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/eltwise/eltwise_kernel_base.h index 9d7127822..161140849 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/eltwise/eltwise_kernel_base.h +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/eltwise/eltwise_kernel_base.h @@ -25,7 +25,7 @@ namespace kernel_selector //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// struct eltwise_params : public base_params { - eltwise_params() : base_params(KernelType::ELTWISE), eltwiseParams() {} + eltwise_params() : base_params(KernelType::ELTWISE) {} struct InputType { @@ -87,35 +87,17 @@ namespace kernel_selector uint32_t tmpId; }; - struct DedicatedParams - { - std::vector<eltwise_params::Node> operations; - std::vector<float> coefficients; - std::vector<UpdateInputData> updateInputIds; - bool layoutBased = false; - bool int8_quantization = false; - bool output_calibration = false; - float output_quantization_factor = 1.0f; - }; - - DedicatedParams eltwiseParams; + std::vector<eltwise_params::Node> operations; + std::vector<float> coefficients; + std::vector<UpdateInputData> updateInputIds; + + bool layoutBased = false; + bool int8_quantization = false; + bool output_calibration = false; + float output_quantization_factor = 1.0f; + MultiDataTensor output_calibration_factors; - - virtual ParamsKey GetParamsKey() const - { - ParamsKey k = base_params::GetParamsKey(); - if (eltwiseParams.int8_quantization) - { - k.EnableInt8Quantization(); - } - - if (eltwiseParams.output_calibration) - { - k.EnableOutputCalibration(); - } - - return k; - } + virtual ParamsKey GetParamsKey() const; }; //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// @@ -141,6 +123,7 @@ namespace kernel_selector protected: virtual bool Validate(const Params& p, const optional_params& o) const override; virtual JitConstants GetJitConstants(const eltwise_params& params) const; + virtual DispatchData SetDefault(const eltwise_params& params) const; KernelsData GetCommonKernelsData(const Params& params, const optional_params& options) const; }; } diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/eltwise/eltwise_kernel_fs_bs_yx_bsv4_fsv32.cpp b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/eltwise/eltwise_kernel_fs_bs_yx_bsv4_fsv32.cpp new file mode 100644 index 000000000..571a013ce --- /dev/null +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/eltwise/eltwise_kernel_fs_bs_yx_bsv4_fsv32.cpp @@ -0,0 +1,222 @@ +/* +// Copyright (c) 2018 Intel Corporation +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +*/ + +#include "eltwise_kernel_fs_bs_yx_bsv4_fsv32.h" +#include "kernel_selector_utils.h" + +namespace kernel_selector { + + ParamsKey EltwiseKernel_fs_bs_yx_bsv4_fsv32::GetSupportedKey() const + { + ParamsKey k; + k.EnableInputDataType(Datatype::INT8); + k.EnableOutputDataType(Datatype::INT8); + k.EnableInputLayout(DataLayout::fs_bs_yx_bsv4_fsv32); + k.EnableOutputLayout(DataLayout::fs_bs_yx_bsv4_fsv32); + k.EnableTensorOffset(); + k.EnableTensorPitches(); + k.EnableBatching(); + k.EnableInt8Quantization(); + k.EnableOutputCalibration(); + return k; + } + + EltwiseKernelBase::DispatchData EltwiseKernel_fs_bs_yx_bsv4_fsv32::SetDefault(const eltwise_params& params) const + { + DispatchData kd; + + kd.gws0 = params.output.X().v; + kd.gws1 = params.output.Y().v; + // we process 4 batches and 4 features per workitem + kd.gws2 = (params.output.Batch().v / 4) * (params.output.Feature().v / 4); + kd.lws0 = 1; + kd.lws1 = 1; + kd.lws2 = 8; + + return kd; + } + + JitConstants EltwiseKernel_fs_bs_yx_bsv4_fsv32::GetJitConstants(const eltwise_params& params) const + { + JitConstants jit = MakeBaseParamsJitConstants(params); + + const size_t in_x_pitch = 32 * 4; + const size_t in_y_pitch = 32 * 4 * params.inputs[0].X().LogicalDimPadded(); + const size_t in_b_block_pitch = in_y_pitch * params.inputs[0].Y().LogicalDimPadded(); + const size_t in_f_block_pitch = in_b_block_pitch * ((params.inputs[0].Batch().v + 3) / 4); + const size_t in_offset = in_x_pitch * params.inputs[0].X().pad.before + in_y_pitch * params.inputs[0].Y().pad.before; + + jit.AddConstant(MakeJitConstant("IN_X_PITCH", in_x_pitch)); + jit.AddConstant(MakeJitConstant("IN_Y_PITCH", in_y_pitch)); + jit.AddConstant(MakeJitConstant("IN_B_BLOCK_PITCH", in_b_block_pitch)); + jit.AddConstant(MakeJitConstant("IN_F_BLOCK_PITCH", in_f_block_pitch)); + jit.AddConstant(MakeJitConstant("IN_OFFSET", in_offset)); + + /////////////// + jit.AddConstants({ + MakeJitConstant("ELTWISE_LAYOUT_BASED", params.layoutBased), + MakeJitConstant("QUANTIZATION_TERM", params.int8_quantization), + }); + + if (params.int8_quantization) + { + if (params.output_calibration) + { + jit.AddConstant(MakeJitConstant("CALIBRATION_TERM", params.output_calibration)); + jit.AddConstant(MakeJitConstant("O_QF", params.output_calibration_factors[0])); + + } + else + jit.AddConstants({ MakeJitConstant("O_QF", params.output_quantization_factor) }); + } + + std::string inputs_decls; + auto& updateInputs = params.updateInputIds; + + for (size_t i = 0; i < params.inputs.size(); i++) + { + //const should be added only to inputs which will not be updated + std::string const_str = "const"; + for (size_t update_input_idx = 0; update_input_idx < updateInputs.size(); update_input_idx++) + { + if (updateInputs[update_input_idx].inputId == i) + { + const_str = ""; + break; + } + } + + inputs_decls += const_str + " __global " + toCLType(params.inputs[i].GetDType()) + "* input" + std::to_string(i) + ", "; + } + + jit.AddConstant(MakeJitConstant("INPUTS_DECLS", inputs_decls)); + jit.AddConstant(MakeJitConstant("ELTWISE_NO_PITCH_SAME_DIMS", CheckInputsOutputNoPitchSameDims(params))); + + std::string do_eltwise; + + auto& operations = params.operations; + auto& coefficients = params.coefficients; + + for (size_t op_num = 0; op_num < operations.size(); op_num++) + { + const std::string op_num_str = std::to_string(op_num); + const auto& ew = operations[op_num]; + + for (size_t input_idx = 0; input_idx < ew.inputs.size(); input_idx++) + { + const auto& input = ew.inputs[input_idx]; + const std::string name = "INPUT_" + op_num_str + "_" + std::to_string(input_idx); + switch (input.mode) + { + case EltwiseInputMode::SCALAR: + jit.AddConstant(MakeJitConstant(name, input.scalar)); + break; + case EltwiseInputMode::INPUT_BUFFER: + jit.AddConstant(MakeJitConstant(name, "GET_INPUT(input" + std::to_string(input.index) + ", INPUT" + std::to_string(input.index) + ")")); + break; + case EltwiseInputMode::OUTPUT_BUFFER: + jit.AddConstant(MakeJitConstant(name, "output[GET_INDEX(OUTPUT, )]")); + break; + case EltwiseInputMode::UNORDERED_ACCESS_INPUT_BUFFER: + jit.AddConstant(MakeJitConstant(name, "input" + std::to_string(input.index) + "[(size_t)tmp" + std::to_string(input.tmpIndex) + "]")); + break; + case EltwiseInputMode::INTERMEDIATE_RESULTS_INDEX: + jit.AddConstant(MakeJitConstant(name, "tmp" + std::to_string(input.tmpIndex))); + break; + default: + break; + } + } + std::string input0_str, input1_str, cast_type, op; + + if (params.int8_quantization) + { + cast_type = "(int16)"; + op = "const int16 tmp" + op_num_str + " = "; + } + else + { + cast_type = "(UNIT_TYPE)"; + op = "const UNIT_TYPE tmp" + op_num_str + " = "; + } + + input0_str = cast_type + "INPUT_" + op_num_str + "_0"; + input1_str = cast_type + "INPUT_" + op_num_str + "_1"; + + if (ew.mode == EltwiseMode::ADD) + { + std::vector<std::string> coeff_strings(ew.inputs.size(), ""); + for (size_t input_idx = 0; input_idx < ew.inputs.size(); input_idx++) + { + const auto& input = ew.inputs[input_idx]; + if (input.mode == EltwiseInputMode::INPUT_BUFFER && input.index < coefficients.size()) + { + const float c = coefficients[input.index]; + if (c != 1.0f) + coeff_strings[input_idx] = cast_type + "(" + std::to_string(c) + ")*"; + } + } + + input0_str = coeff_strings[0] + input0_str; + input1_str = coeff_strings[1] + input1_str; + } + + + switch (ew.mode) + { + case EltwiseMode::ADD: op += input0_str + " + " + input1_str; break; + case EltwiseMode::SUB: op += input0_str + " - " + input1_str; break; + case EltwiseMode::MUL: op += input0_str + " * " + input1_str; break; + case EltwiseMode::DIV: op += input0_str + " / " + input1_str; break; + case EltwiseMode::MODULU: op += cast_type + "fmod(" + input0_str + ", " + input1_str + ")"; break; + case EltwiseMode::MIN: op += cast_type + "fmin(" + input0_str + ", " + input1_str + ")"; break; + case EltwiseMode::MAX: op += cast_type + "fmax(" + input0_str + ", " + input1_str + ")"; break; + case EltwiseMode::POW: op += cast_type + "pow(" + input0_str + ", " + input1_str + ")"; break; + case EltwiseMode::SQRT: op += cast_type + "sqrt(" + input0_str + ")"; break; + case EltwiseMode::RSQRT: op += cast_type + "1/sqrt(" + input0_str + ")"; break; + case EltwiseMode::ASSIGN: op += input0_str; break; + default: + break; + } + + std::string opname = "OPERATION" + op_num_str; + jit.AddConstant(MakeJitConstant(opname, op)); + do_eltwise += "\\\n\t" + opname + ";"; + } + + for (size_t update_input_idx = 0; update_input_idx < updateInputs.size(); update_input_idx++) + do_eltwise += "\\\n\tinput" + std::to_string(updateInputs[update_input_idx].inputId) + + "[GET_INDEX(INPUT, " + std::to_string(updateInputs[update_input_idx].inputId) + + ")] = tmp" + std::to_string(updateInputs[update_input_idx].tmpId) + ";"; + + do_eltwise += "\\\n\tres = tmp" + std::to_string(operations.size() - 1) + ";"; + + jit.AddConstant(MakeJitConstant("DO_ELTWISE", do_eltwise)); + + if (params.layoutBased || params.int8_quantization) + { + jit.Merge(GetTensorFriendlyWorkGroupsJit(params.inputs[0])); + } + + /////////////// + return jit; + } + + KernelsData EltwiseKernel_fs_bs_yx_bsv4_fsv32::GetKernelsData(const Params& params, const optional_params& options) const + { + return GetCommonKernelsData(params, options); + } +}
\ No newline at end of file diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/eltwise/eltwise_kernel_fs_bs_yx_bsv4_fsv32.h b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/eltwise/eltwise_kernel_fs_bs_yx_bsv4_fsv32.h new file mode 100644 index 000000000..b1fb3e950 --- /dev/null +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/eltwise/eltwise_kernel_fs_bs_yx_bsv4_fsv32.h @@ -0,0 +1,35 @@ +/* +// Copyright (c) 2018 Intel Corporation +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +*/ + +#pragma once + +#include "eltwise_kernel_base.h" + +namespace kernel_selector +{ + class EltwiseKernel_fs_bs_yx_bsv4_fsv32 : public EltwiseKernelBase + { + public: + EltwiseKernel_fs_bs_yx_bsv4_fsv32() : EltwiseKernelBase("eltwise_fs_bs_yx_bsv4_fsv32") {} + virtual ~EltwiseKernel_fs_bs_yx_bsv4_fsv32() {} + + virtual KernelsData GetKernelsData(const Params& params, const optional_params& options) const override; + virtual ParamsKey GetSupportedKey() const override; + protected: + JitConstants GetJitConstants(const eltwise_params& params) const override; + virtual DispatchData SetDefault(const eltwise_params& params) const override; + }; +}
\ No newline at end of file diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/eltwise/eltwise_kernel_ref.cpp b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/eltwise/eltwise_kernel_ref.cpp index 3840f463e..3a7776575 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/eltwise/eltwise_kernel_ref.cpp +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/eltwise/eltwise_kernel_ref.cpp @@ -22,8 +22,16 @@ namespace kernel_selector { ParamsKey EltwiseKernelRef::GetSupportedKey() const { ParamsKey k; - k.EnableAllInputDataType(); - k.EnableAllOutputDataType(); + k.EnableInputDataType(Datatype::F16); + k.EnableInputDataType(Datatype::F32); + k.EnableInputDataType(Datatype::INT8); + k.EnableInputDataType(Datatype::INT32); + k.EnableInputDataType(Datatype::INT64); + k.EnableOutputDataType(Datatype::F16); + k.EnableOutputDataType(Datatype::F32); + k.EnableOutputDataType(Datatype::INT8); + k.EnableOutputDataType(Datatype::INT32); + k.EnableOutputDataType(Datatype::INT64); k.EnableDifferentTypes(); k.EnableAllInputLayout(); k.EnableAllOutputLayout(); @@ -35,6 +43,25 @@ namespace kernel_selector { return k; } + bool EltwiseKernelRef::Validate(const Params& p, const optional_params& o) const + { + if (!EltwiseKernelBase::Validate(p, o)) + { + return false; + } + + const eltwise_params& params = static_cast<const eltwise_params&>(p); + for (size_t i = 0; i < params.inputs.size(); i++) + { + if (params.inputs[i].GetLayout() == DataLayout::fs_bs_yx_bsv4_fsv32) + return false; + } + if (params.output.GetLayout() == DataLayout::fs_bs_yx_bsv4_fsv32) + return false; + + return true; + } + KernelsData EltwiseKernelRef::GetKernelsData(const Params& params, const optional_params& options) const { return GetCommonKernelsData(params, options); diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/eltwise/eltwise_kernel_ref.h b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/eltwise/eltwise_kernel_ref.h index 2249dc8c9..c2ccf054d 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/eltwise/eltwise_kernel_ref.h +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/eltwise/eltwise_kernel_ref.h @@ -28,5 +28,8 @@ namespace kernel_selector virtual KernelsData GetKernelsData(const Params& params, const optional_params& options) const override; virtual ParamsKey GetSupportedKey() const override; + protected: + bool Validate(const Params& p, const optional_params& o) const override; + }; }
\ No newline at end of file diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/eltwise/eltwise_kernel_selector.cpp b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/eltwise/eltwise_kernel_selector.cpp index d71deddfb..cf7565216 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/eltwise/eltwise_kernel_selector.cpp +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/eltwise/eltwise_kernel_selector.cpp @@ -17,13 +17,15 @@ #include "eltwise_kernel_selector.h" #include "eltwise_kernel_ref.h" #include "eltwise_kernel_vload8.h" - +#include "eltwise_kernel_fs_bs_yx_bsv4_fsv32.h" + namespace kernel_selector { eltwise_kernel_selector::eltwise_kernel_selector() { Attach<EltwiseKernelRef>(); Attach<EltwiseKernel_vload8>(); + Attach<EltwiseKernel_fs_bs_yx_bsv4_fsv32>(); } KernelsData eltwise_kernel_selector::GetBestKernels(const Params& params, const optional_params& options) const diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/eltwise/eltwise_kernel_vload8.cpp b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/eltwise/eltwise_kernel_vload8.cpp index 934bc44cd..5ceb75084 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/eltwise/eltwise_kernel_vload8.cpp +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/eltwise/eltwise_kernel_vload8.cpp @@ -45,6 +45,15 @@ namespace kernel_selector { } const auto& ewParams = static_cast<const eltwise_params&>(params); + + for (size_t i = 0; i < ewParams.inputs.size(); i++) + { + if (ewParams.inputs[i].GetLayout() == DataLayout::fs_bs_yx_bsv4_fsv32) + return false; + } + if (ewParams.output.GetLayout() == DataLayout::fs_bs_yx_bsv4_fsv32) + return false; + const auto& output = ewParams.output; const auto count = output.PhysicalSize(); @@ -62,16 +71,16 @@ namespace kernel_selector { //TODO: add support to this implementation when user requests input values updates bool bCheckUpdateInput = true; - if (!ewParams.eltwiseParams.updateInputIds.empty()) + if (!ewParams.updateInputIds.empty()) bCheckUpdateInput = false; //TODO: add support for reading from output buffer and using its values in computation bool bCheckUseOutput = true; - for (size_t op = 0; op < ewParams.eltwiseParams.operations.size(); op++) + for (size_t op = 0; op < ewParams.operations.size(); op++) { - for (size_t input_idx = 0; input_idx < ewParams.eltwiseParams.operations[op].inputs.size(); input_idx++) + for (size_t input_idx = 0; input_idx < ewParams.operations[op].inputs.size(); input_idx++) { - if (ewParams.eltwiseParams.operations[op].inputs[input_idx].mode == EltwiseInputMode::OUTPUT_BUFFER) + if (ewParams.operations[op].inputs[input_idx].mode == EltwiseInputMode::OUTPUT_BUFFER) { bCheckUseOutput = false; break; @@ -114,7 +123,7 @@ namespace kernel_selector { auto& kernel = kd.kernels[0]; kernel.workGroups.global = { std::max(newParams.inputs[0].LogicalSize()/8, (size_t)1), 1, 1 }; kernel.workGroups.local = GetOptimalLocalWorkGroupSizes(kernel.workGroups.global); - kernel.kernelString = GetKernelString(kernelName, jit, entry_point, ROUND_ROBIN); + kernel.kernelString = GetKernelString(kernelName, jit, entry_point, params.engineInfo, ROUND_ROBIN); kernel.arguments = GetArgsDesc((uint32_t)newParams.inputs.size(), false, false); kd.estimatedTime = FORCE_PRIORITY_8; diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/embed/embed_kernel_ref.cpp b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/embed/embed_kernel_ref.cpp index d4c9b126c..f126daa94 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/embed/embed_kernel_ref.cpp +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/embed/embed_kernel_ref.cpp @@ -103,7 +103,7 @@ namespace kernel_selector auto& kernel = kd.kernels[0]; - FillCLKernelData(kernel, runInfo, kernelName, jit, entry_point, ROUND_ROBIN, true, !newParams.bias.empty()); + FillCLKernelData(kernel, runInfo, params.engineInfo, kernelName, jit, entry_point, ROUND_ROBIN, true, !newParams.bias.empty()); kd.estimatedTime = runInfo.effiency; diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/fully_connected/fully_connected_kernel_base.cpp b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/fully_connected/fully_connected_kernel_base.cpp index 6702ebc69..20e6e8dca 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/fully_connected/fully_connected_kernel_base.cpp +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/fully_connected/fully_connected_kernel_base.cpp @@ -49,7 +49,7 @@ namespace kernel_selector std::unique_ptr<FullyConnectedKernelBase::DispatchData> FullyConnectedKernelBase::SetDefault(const fully_connected_params& params) const { - std::unique_ptr<DispatchData> dispatchData = std::make_unique<DispatchData>(); + std::unique_ptr<DispatchData> dispatchData = std::unique_ptr<DispatchData>(new DispatchData()); dispatchData->fp16UnitUsed = params.inputs[0].GetDType() == Datatype::F16; // Determine global work sizes. @@ -122,10 +122,10 @@ namespace kernel_selector std::string jit = CreateJit(kernelName, cldnn_jit, entry_point); auto& kernel = kd.kernels[0]; - FillCLKernelData(kernel, *runInfo.get(), kernelName, jit, entry_point, ROUND_ROBIN, true, !orgParams.bias.empty(), 1, newParams.int8_quantization, newParams.output_calibration); + FillCLKernelData(kernel, *runInfo.get(), params.engineInfo, kernelName, jit, entry_point, ROUND_ROBIN, true, !orgParams.bias.empty(), 1, newParams.int8_quantization, newParams.output_calibration); kd.estimatedTime = estimated_time; kd.autoTuneIndex = -1; return{ kd }; } -}
\ No newline at end of file +} diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/fully_connected/fully_connected_kernel_bs_f_bsv16_b1.cpp b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/fully_connected/fully_connected_kernel_bs_f_bsv16_b1.cpp index 0f836133b..b98b528a8 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/fully_connected/fully_connected_kernel_bs_f_bsv16_b1.cpp +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/fully_connected/fully_connected_kernel_bs_f_bsv16_b1.cpp @@ -60,7 +60,7 @@ namespace kernel_selector std::unique_ptr<FullyConnected_bs_f_bsv16_b1::FullyConnectedKernelBase::DispatchData> FullyConnected_bs_f_bsv16_b1::SetDefault(const fully_connected_params& arg) const { - auto run_info = std::make_unique<DispatchData>(*FullyConnectedKernelBase::SetDefault(arg).get()); + auto run_info = std::unique_ptr<DispatchData>(new DispatchData(*FullyConnectedKernelBase::SetDefault(arg))); // Properties of chunk and unit. const char* chunk_type = "uint"; @@ -100,4 +100,4 @@ namespace kernel_selector { return GetCommonKernelsData(params, optParams, DataLayout::bf, {WeightsLayout::os_i_osv16}, FORCE_PRIORITY_5); } -}
\ No newline at end of file +} diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/fully_connected/fully_connected_kernel_fb_io_block.cpp b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/fully_connected/fully_connected_kernel_fb_io_block.cpp index 1ceb3ebc9..b32c8a54e 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/fully_connected/fully_connected_kernel_fb_io_block.cpp +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/fully_connected/fully_connected_kernel_fb_io_block.cpp @@ -37,7 +37,7 @@ namespace kernel_selector std::unique_ptr<FullyConnected_fb_io_block::FullyConnectedKernelBase::DispatchData> FullyConnected_fb_io_block::SetDefault(const fully_connected_params& arg) const { - auto kd = std::make_unique<DispatchData>(*FullyConnectedKernelBase::SetDefault(arg).get()); + auto kd = std::unique_ptr<DispatchData>(new DispatchData(*FullyConnectedKernelBase::SetDefault(arg))); const auto& output = arg.output; auto batch_size = output.Batch().v; @@ -146,4 +146,4 @@ namespace kernel_selector // return GetCommonKernelsData(params, optParams, DataLayout::fb, WeightsLayout::io, estimated_time); return GetCommonKernelsData(params, optParams, DataLayout::yxfb, { WeightsLayout::yxio }, estimated_time); } -}
\ No newline at end of file +} diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/fully_connected/fully_connected_kernel_mmad_batched.cpp b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/fully_connected/fully_connected_kernel_mmad_batched.cpp new file mode 100644 index 000000000..46e4dea8d --- /dev/null +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/fully_connected/fully_connected_kernel_mmad_batched.cpp @@ -0,0 +1,117 @@ +/* +// Copyright (c) 2016 Intel Corporation +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +*/ + +#include "fully_connected_kernel_mmad_batched.h" +#include "kernel_selector_utils.h" + +namespace kernel_selector +{ + ParamsKey FullyConnected_mmad_batched::GetSupportedKey() const + { + ParamsKey k; + k.EnableInputDataType(Datatype::INT8); + k.EnableOutputDataType(Datatype::INT8); + k.EnableInputWeightsType(WeightsType::INT8); + k.EnableInputLayout(DataLayout::fs_bs_yx_bsv4_fsv32); + k.EnableOutputLayout(DataLayout::fs_bs_yx_bsv4_fsv32); + k.EnableOutputLayout(DataLayout::bf); + k.EnableBiasPerOutput(); + k.EnableBiasPerFeature(); + k.EnableNonBiasTerm(); + k.EnableTensorOffset(); + k.EnableTensorPitches(); + k.EnableBatching(); + k.EnableInt8Quantization(); + k.EnableOutputCalibration(); + return k; + } + + bool FullyConnected_mmad_batched::Validate(const Params& p, const optional_params& o) const + { + if (!FullyConnectedKernelBase::Validate(p, o)) + { + return false; + } + + const auto& params = static_cast<const fully_connected_params&>(p); + + // we do not support padded input + if (params.inputs[0].X().pad.Total() != 0 || params.inputs[0].Y().pad.Total() != 0) + return false; + + size_t batch = params.inputs[0].Batch().v; + // batch must be a multiple of 8 + if (batch % 8 != 0) + { + return false; + } + + return true; + } + + JitConstants FullyConnected_mmad_batched::GetJitConstants(const fully_connected_params& params, const DispatchData& runInfo) const + { + auto jit = Parent::GetJitConstants(params, runInfo); + + jit.AddConstant(MakeJitConstant("SUB_GROUP_SIZE", runInfo.lws1)); + + // pitch for special block format used in this kernel + const size_t ifm_32_aligned = Align(params.weights.IFM().v, 32); + const size_t filter_ofm_block_pitch = (ifm_32_aligned / 32) * params.weights.X().v * params.weights.Y().v * 4 * 8 * 8; + jit.AddConstant(MakeJitConstant("FILTER_OFM_BLOCK_PITCH", filter_ofm_block_pitch)); + + const size_t in_x_pitch = 32 * 4; + const size_t in_y_pitch = 32 * 4 * params.inputs[0].X().LogicalDimPadded(); + const size_t in_b_block_pitch = in_y_pitch * params.inputs[0].Y().LogicalDimPadded(); + const size_t in_f_block_pitch = in_b_block_pitch * ((params.inputs[0].Batch().v + 3) / 4); + const size_t in_offset = in_x_pitch * params.inputs[0].X().pad.before + in_y_pitch * params.inputs[0].Y().pad.before; + + jit.AddConstant(MakeJitConstant("IN_X_PITCH", in_x_pitch)); + jit.AddConstant(MakeJitConstant("IN_Y_PITCH", in_y_pitch)); + jit.AddConstant(MakeJitConstant("IN_B_BLOCK_PITCH", in_b_block_pitch)); + jit.AddConstant(MakeJitConstant("IN_F_BLOCK_PITCH", in_f_block_pitch)); + jit.AddConstant(MakeJitConstant("IN_OFFSET", in_offset)); + + return jit; + } + + std::unique_ptr<FullyConnected_mmad_batched::Parent::DispatchData> FullyConnected_mmad_batched::SetDefault(const fully_connected_params& params) const + { + auto runInfo = Parent::SetDefault(params); + + constexpr size_t sub_group_size = 8; + + const auto of_maps = params.output.Feature().v; + const size_t of_threads_per_batch = RoundUp(of_maps, sub_group_size); + + runInfo->gws0 = params.output.Batch().v / 8; // we process 8 batches in a single WG + runInfo->gws1 = of_threads_per_batch; + runInfo->gws2 = 1; + + runInfo->lws0 = 1; + runInfo->lws1 = sub_group_size; + runInfo->lws2 = 1; + + runInfo->effiency = FORCE_PRIORITY_1; + return std::move(runInfo); + } + + KernelsData FullyConnected_mmad_batched::GetKernelsData(const Params& params, const optional_params& options) const + { + return GetCommonKernelsData(params, options, DataLayout::fs_bs_yx_bsv4_fsv32, + { WeightsLayout::os_is_yx_isa8_osv8_isv4 }, FORCE_PRIORITY_1); + } +}
\ No newline at end of file diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/fully_connected/fully_connected_kernel_mmad_batched.h b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/fully_connected/fully_connected_kernel_mmad_batched.h new file mode 100644 index 000000000..61af89f19 --- /dev/null +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/fully_connected/fully_connected_kernel_mmad_batched.h @@ -0,0 +1,38 @@ +/* +// Copyright (c) 2016 Intel Corporation +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +*/ + +#pragma once + +#include "fully_connected_kernel_base.h" + +namespace kernel_selector { + + class FullyConnected_mmad_batched : public FullyConnectedKernelBase + { + public: + using Parent = FullyConnectedKernelBase; + + FullyConnected_mmad_batched() : Parent("fully_connected_gpu_mmad_batched") {} + + KernelsData GetKernelsData(const Params& params, const optional_params& options) const override; + ParamsKey GetSupportedKey() const override; + + protected: + bool Validate(const Params& p, const optional_params& o) const override; + JitConstants GetJitConstants(const fully_connected_params& params, const DispatchData& kd) const override; + std::unique_ptr<DispatchData> SetDefault(const fully_connected_params& params) const override; + }; +}
\ No newline at end of file diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/fully_connected/fully_connected_kernel_selector.cpp b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/fully_connected/fully_connected_kernel_selector.cpp index d7c1a1a85..529e1ca33 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/fully_connected/fully_connected_kernel_selector.cpp +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/fully_connected/fully_connected_kernel_selector.cpp @@ -30,6 +30,7 @@ #include "fully_connected_kernel_bf_io_input_spatial.h" #include "fully_connected_kernel_image_tutorial.h" #include "fully_connected_kernel_MMAD.h" +#include "fully_connected_kernel_mmad_batched.h" namespace kernel_selector { @@ -49,6 +50,7 @@ namespace kernel_selector { Attach<FullyConnected_fb_io_b8_f8>(); Attach<FullyConnected_bf_io_input_spatial>(); Attach<FullyConnectedKernelMMAD>(); + Attach<FullyConnected_mmad_batched>(); } KernelsData fully_connected_kernel_selector::GetBestKernels(const Params& params, const optional_params& options) const diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/fully_connected_grad_input/fully_connected_grad_input_kernel_base.cpp b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/fully_connected_grad_input/fully_connected_grad_input_kernel_base.cpp index 94d0b4751..e40848af7 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/fully_connected_grad_input/fully_connected_grad_input_kernel_base.cpp +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/fully_connected_grad_input/fully_connected_grad_input_kernel_base.cpp @@ -80,7 +80,7 @@ namespace kernel_selector auto jit = CreateJit(kernelName, cldnn_jit, entry_point); auto& kernel = kd.kernels[0]; - FillCLKernelData(kernel, runInfo, kernelName, jit, entry_point, ROUND_ROBIN, true, !orgParams.bias.empty()); + FillCLKernelData(kernel, runInfo, params.engineInfo, kernelName, jit, entry_point, ROUND_ROBIN, true, !orgParams.bias.empty()); kernel.arguments.push_back({ ArgumentDescriptor::Types::INPUT, 1 }); kd.estimatedTime = runInfo.effiency; diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/fully_connected_grad_weights/fully_connected_grad_weights_kernel_base.cpp b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/fully_connected_grad_weights/fully_connected_grad_weights_kernel_base.cpp index 55d8bdf88..67328ac99 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/fully_connected_grad_weights/fully_connected_grad_weights_kernel_base.cpp +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/fully_connected_grad_weights/fully_connected_grad_weights_kernel_base.cpp @@ -82,7 +82,7 @@ namespace kernel_selector auto jit = CreateJit(kernelName, cldnn_jit, entry_point); auto& kernel = kd.kernels[0]; - FillCLKernelData(kernel, runInfo, kernelName, jit, entry_point, ROUND_ROBIN, true, !orgParams.bias.empty()); + FillCLKernelData(kernel, runInfo, params.engineInfo, kernelName, jit, entry_point, ROUND_ROBIN, true, !orgParams.bias.empty()); if (orgParams.use_momentum) { kernel.arguments.push_back({ ArgumentDescriptor::Types::PREV_WEIGHTS_GRADIENT, 0 }); diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/fully_connected_grad_weights/fully_connected_grad_weights_kernel_ref.cpp b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/fully_connected_grad_weights/fully_connected_grad_weights_kernel_ref.cpp index 9ec0f8e17..bb8380457 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/fully_connected_grad_weights/fully_connected_grad_weights_kernel_ref.cpp +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/fully_connected_grad_weights/fully_connected_grad_weights_kernel_ref.cpp @@ -24,7 +24,6 @@ namespace kernel_selector ParamsKey k; k.EnableInputDataType(Datatype::F16); k.EnableInputDataType(Datatype::F32); - k.EnableInputWeightsType(WeightsType::F16); k.EnableInputWeightsType(WeightsType::F32); k.EnableOutputDataType(Datatype::F16); k.EnableOutputDataType(Datatype::F32); diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/gemm/gemm_kernel_base.cpp b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/gemm/gemm_kernel_base.cpp new file mode 100644 index 000000000..12af8a1c5 --- /dev/null +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/gemm/gemm_kernel_base.cpp @@ -0,0 +1,98 @@ +/* +// Copyright (c) 2018 Intel Corporation +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +*/ + +#include "gemm_kernel_base.h" + +#include "kernel_selector_utils.h" + + +namespace kernel_selector +{ + JitConstants GemmKernelBase::GetJitConstants(const gemm_params& params) const + { + JitConstants jit = MakeBaseParamsJitConstants(params); + + jit.AddConstants({ + MakeJitConstant("X1", params.inputs[0].X().v), + MakeJitConstant("Y1", params.inputs[0].Y().v), + MakeJitConstant("X2", params.inputs[1].X().v), + MakeJitConstant("Y2", params.inputs[1].Y().v), + MakeJitConstant("ALPHA", params.alpha), + MakeJitConstant("BETA", params.beta), + MakeJitConstant("TRANSPOSE_INPUT1", params.transpose_input1), + MakeJitConstant("TRANSPOSE_INPUT2", params.transpose_input2), + }); + + if (params.inputs.size() > 2) + { + jit.AddConstants({MakeJitConstant("OUT_BIAS_TERM", true),}); + } + else + jit.AddConstants({ MakeJitConstant("OUT_BIAS_TERM", false)}); + + return jit; + } + + GemmKernelBase::DispatchData GemmKernelBase::SetDefault(const gemm_params& params) const + { + const auto& output = params.output; + + DispatchData kd; + + kd.fp16UnitUsed = params.inputs[0].GetDType() == Datatype::F16; + std::vector<size_t> global{ params.inputs[0].Y().v, params.inputs[1].X().v, output.Batch().v }; + + if (params.transpose_input1 && params.transpose_input2) + global ={ params.inputs[0].X().v, params.inputs[1].Y().v, output.Batch().v }; + else if(params.transpose_input1) + global = { params.inputs[0].X().v, params.inputs[1].X().v, output.Batch().v }; + else if (params.transpose_input2) + global = { params.inputs[0].Y().v, params.inputs[1].Y().v, output.Batch().v }; + + const auto& local = GetOptimalLocalWorkGroupSizes(global); + + kd.gws0 = global[0]; + kd.gws1 = global[1]; + kd.gws2 = global[2]; + + kd.lws0 = local[0]; + kd.lws1 = local[1]; + kd.lws2 = local[2]; + + return kd; + } + + KernelsData GemmKernelBase::GetCommonKernelsData(const Params& params, const optional_params& options, float estimated_time) const + { + assert(params.GetType() == KernelType::GEMM); + + const auto& prim_params = static_cast<const gemm_params&>(params); + + auto run_info = SetDefault(prim_params); + KernelData k_data = KernelData::Default<gemm_params>(params); + + auto cldnn_jit = GetJitConstants(prim_params); + auto entry_point = GetEntryPoint(kernelName, prim_params.layerID, options); + auto jit = CreateJit(kernelName, cldnn_jit, entry_point); + + auto& kernel = k_data.kernels[0]; + FillCLKernelData(kernel, run_info, params.engineInfo, kernelName, jit, entry_point, ROUND_ROBIN, false, false, (uint32_t)prim_params.inputs.size()); + + k_data.estimatedTime = estimated_time; + + return { k_data }; + } +}
\ No newline at end of file diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/gemm/gemm_kernel_base.h b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/gemm/gemm_kernel_base.h new file mode 100644 index 000000000..643a0bec7 --- /dev/null +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/gemm/gemm_kernel_base.h @@ -0,0 +1,69 @@ +// Copyright (c) 2018 Intel Corporation +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#pragma once + +#include "common_kernel_base.h" +#include "kernel_selector_params.h" + + +namespace kernel_selector +{ + //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// + // gemm_params + //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// + struct gemm_params : public base_params + { + gemm_params() : + base_params(KernelType::GEMM), + alpha(1.0f), + beta(0.0f), + transpose_input1(false), + transpose_input2(false) + {} + + float alpha; + float beta; + bool transpose_input1; + bool transpose_input2; + + }; + + //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// + // gemm_optional_params + //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// + struct gemm_optional_params : optional_params + { + gemm_optional_params() + : optional_params(KernelType::GEMM) + { + } + }; + + //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// + // BorderKernelBase + //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// + class GemmKernelBase : public common_kernel_base + { + public: + using common_kernel_base::common_kernel_base; + + using DispatchData = CommonDispatchData; + + protected: + JitConstants GetJitConstants(const gemm_params& params) const; + DispatchData SetDefault(const gemm_params& params) const; + KernelsData GetCommonKernelsData(const Params& params, const optional_params&, float estimated_time) const; + }; +} diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/gemm/gemm_kernel_ref.cpp b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/gemm/gemm_kernel_ref.cpp new file mode 100644 index 000000000..585d9d90d --- /dev/null +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/gemm/gemm_kernel_ref.cpp @@ -0,0 +1,41 @@ +/* +// Copyright (c) 2018 Intel Corporation +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +*/ + +#include "gemm_kernel_ref.h" + +namespace kernel_selector +{ + ParamsKey GemmKernelRef::GetSupportedKey() const + { + ParamsKey k; + + k.EnableInputDataType(Datatype::F16); + k.EnableInputDataType(Datatype::F32); + k.EnableOutputDataType(Datatype::F32); + k.EnableOutputDataType(Datatype::F16); + k.EnableInputLayout(DataLayout::bfyx); + k.EnableOutputLayout(DataLayout::bfyx); + + k.EnableBatching(); + + return k; + } + + KernelsData GemmKernelRef::GetKernelsData(const Params& params, const optional_params& options) const + { + return GetCommonKernelsData(params, options, FORCE_PRIORITY_9); + } +}
\ No newline at end of file diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/gemm/gemm_kernel_ref.h b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/gemm/gemm_kernel_ref.h new file mode 100644 index 000000000..89727597d --- /dev/null +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/gemm/gemm_kernel_ref.h @@ -0,0 +1,30 @@ +// Copyright (c) 2018 Intel Corporation +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#pragma once + +#include "gemm_kernel_base.h" + + +namespace kernel_selector +{ + class GemmKernelRef : public GemmKernelBase + { + public: + GemmKernelRef() : GemmKernelBase("gemm_ref") {} + + KernelsData GetKernelsData(const Params& params, const optional_params& options) const override; + ParamsKey GetSupportedKey() const override; + }; +} diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/gemm/gemm_kernel_selector.cpp b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/gemm/gemm_kernel_selector.cpp new file mode 100644 index 000000000..a31f3cb9f --- /dev/null +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/gemm/gemm_kernel_selector.cpp @@ -0,0 +1,31 @@ +/* +// Copyright (c) 2018 Intel Corporation +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +*/ + +#include "gemm_kernel_selector.h" +#include "gemm_kernel_ref.h" + +namespace kernel_selector +{ + gemm_kernel_selector::gemm_kernel_selector() + { + Attach<GemmKernelRef>(); + } + + KernelsData gemm_kernel_selector::GetBestKernels(const Params& params, const optional_params& options) const + { + return GetNaiveBestKernel(params, options, KernelType::GEMM); + } +}
\ No newline at end of file diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/gemm/gemm_kernel_selector.h b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/gemm/gemm_kernel_selector.h new file mode 100644 index 000000000..7a7896afd --- /dev/null +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/gemm/gemm_kernel_selector.h @@ -0,0 +1,35 @@ +/* +// Copyright (c) 2018 Intel Corporation +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +*/ + +#pragma once + +#include "kernel_selector.h" + +namespace kernel_selector +{ + class gemm_kernel_selector : public kernel_selector_base + { + public: + static gemm_kernel_selector &Instance() { + static gemm_kernel_selector instance; + return instance; + } + + gemm_kernel_selector(); + + KernelsData GetBestKernels(const Params& params, const optional_params& options) const override; + }; +}
\ No newline at end of file diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/index_select/index_select_kernel_base.cpp b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/index_select/index_select_kernel_base.cpp new file mode 100644 index 000000000..c0dc0851c --- /dev/null +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/index_select/index_select_kernel_base.cpp @@ -0,0 +1,86 @@ +// Copyright (c) 2018 Intel Corporation +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + + +#include "index_select_kernel_base.h" + +#include "kernel_selector_utils.h" + + +namespace kernel_selector +{ + JitConstants IndexSelectKernelBase::GetJitConstants(const index_select_params& params) + { + JitConstants jit = MakeBaseParamsJitConstants(params); + + jit.AddConstant(MakeJitConstant(toString(params.axis), "")); + + return jit; + } + + IndexSelectKernelBase::DispatchData IndexSelectKernelBase::SetDefault(const index_select_params& params) + { + const auto& output = params.output; + const auto& indices = params.inputs.at(1); + DispatchData kd; + + kd.fp16UnitUsed = params.inputs[0].GetDType() == Datatype::F16; + + std::vector<size_t> global; + if (params.axis == IndexSelectAxis::BATCH) + { + global = { 1, indices.X().v, output.Feature().v }; + } + else if (params.axis == IndexSelectAxis::X || params.axis == IndexSelectAxis::Y) + { + global = { output.Batch().v, indices.X().v, output.Feature().v }; + } + else if(params.axis == IndexSelectAxis::FEATURE) + { + global = { output.Batch().v, indices.X().v, output.Y().v }; + } + const auto& local = GetOptimalLocalWorkGroupSizes(global); + + kd.gws0 = global[0]; + kd.gws1 = global[1]; + kd.gws2 = global[2]; + + kd.lws0 = local[0]; + kd.lws1 = local[1]; + kd.lws2 = local[2]; + + return kd; + } + + KernelsData IndexSelectKernelBase::GetCommonKernelsData(const Params& params, const optional_params& options, float estimated_time) const + { + assert(params.GetType() == KernelType::INDEX_SELECT); + + const auto& prim_params = static_cast<const index_select_params&>(params); // NOLINT(cppcoreguidelines-pro-type-static-cast-downcast) + + auto run_info = SetDefault(prim_params); + KernelData k_data = KernelData::Default<index_select_params>(params); + + auto cldnn_jit = GetJitConstants(prim_params); + auto entry_point = GetEntryPoint(kernelName, prim_params.layerID, options); + auto jit = CreateJit(kernelName, cldnn_jit, entry_point); + + auto& kernel = k_data.kernels[0]; + FillCLKernelData(kernel, run_info, params.engineInfo, kernelName, jit, entry_point, ROUND_ROBIN, false, false, (uint32_t)prim_params.inputs.size()); + + k_data.estimatedTime = estimated_time; + + return {k_data}; + } +} diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/index_select/index_select_kernel_base.h b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/index_select/index_select_kernel_base.h new file mode 100644 index 000000000..c7abe43bc --- /dev/null +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/index_select/index_select_kernel_base.h @@ -0,0 +1,61 @@ +// Copyright (c) 2018 Intel Corporation +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#pragma once + +#include "common_kernel_base.h" +#include "kernel_selector_params.h" + + +namespace kernel_selector +{ + //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// + // index_select_params + //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// + struct index_select_params : public base_params + { + index_select_params() + : base_params(KernelType::INDEX_SELECT) + {} + + IndexSelectAxis axis = IndexSelectAxis::BATCH; + }; + + //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// + // index_select_optional_params + //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// + struct index_select_optional_params : optional_params + { + index_select_optional_params() + : optional_params(KernelType::INDEX_SELECT) + {} + }; + + //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// + // IndexSelectKernelBase + //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// + class IndexSelectKernelBase : public common_kernel_base + { + public: + using common_kernel_base::common_kernel_base; + virtual ~IndexSelectKernelBase() {} + + using DispatchData = CommonDispatchData; + + protected: + static JitConstants GetJitConstants(const index_select_params& params); + static DispatchData SetDefault(const index_select_params& params); + KernelsData GetCommonKernelsData(const Params& params, const optional_params&, float estimated_time) const; + }; +} diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/index_select/index_select_kernel_ref.cpp b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/index_select/index_select_kernel_ref.cpp new file mode 100644 index 000000000..b5ab92dc1 --- /dev/null +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/index_select/index_select_kernel_ref.cpp @@ -0,0 +1,58 @@ +// Copyright (c) 2018 Intel Corporation +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#include "index_select_kernel_ref.h" + + +namespace kernel_selector +{ + ParamsKey IndexSelectKernelRef::GetSupportedKey() const + { + ParamsKey k; + + k.EnableInputDataType(Datatype::F16); + k.EnableInputDataType(Datatype::F32); + k.EnableInputDataType(Datatype::INT8); + k.EnableInputDataType(Datatype::UINT8); + k.EnableInputDataType(Datatype::INT32); + + k.EnableOutputDataType(Datatype::F32); + k.EnableOutputDataType(Datatype::F16); + k.EnableOutputDataType(Datatype::INT8); + k.EnableOutputDataType(Datatype::UINT8); + k.EnableOutputDataType(Datatype::INT32); + + k.EnableInputLayout(DataLayout::bfyx); + k.EnableInputLayout(DataLayout::yxfb); + + k.EnableOutputLayout(DataLayout::bfyx); + k.EnableOutputLayout(DataLayout::yxfb); + + k.EnableBatching(); + + k.EnableIndexSelectAxis(IndexSelectAxis::BATCH); + k.EnableIndexSelectAxis(IndexSelectAxis::FEATURE); + k.EnableIndexSelectAxis(IndexSelectAxis::Y); + k.EnableIndexSelectAxis(IndexSelectAxis::X); + + k.EnableDifferentTypes(); + + return k; + } + + KernelsData IndexSelectKernelRef::GetKernelsData(const Params& params, const optional_params& options) const + { + return GetCommonKernelsData(params, options, FORCE_PRIORITY_9); + } +} diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/index_select/index_select_kernel_ref.h b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/index_select/index_select_kernel_ref.h new file mode 100644 index 000000000..3dd16198f --- /dev/null +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/index_select/index_select_kernel_ref.h @@ -0,0 +1,30 @@ +// Copyright (c) 2018 Intel Corporation +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#pragma once + +#include "index_select_kernel_base.h" + + +namespace kernel_selector +{ + class IndexSelectKernelRef : public IndexSelectKernelBase + { + public: + IndexSelectKernelRef() : IndexSelectKernelBase("index_select_gpu_ref") {} + + KernelsData GetKernelsData(const Params& params, const optional_params& options) const override; + ParamsKey GetSupportedKey() const override; + }; +} diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/index_select/index_select_kernel_selector.cpp b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/index_select/index_select_kernel_selector.cpp new file mode 100644 index 000000000..3d1693046 --- /dev/null +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/index_select/index_select_kernel_selector.cpp @@ -0,0 +1,30 @@ +// Copyright (c) 2018 Intel Corporation +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + + +#include "index_select_kernel_selector.h" +#include "index_select_kernel_ref.h" + +namespace kernel_selector +{ + index_select_kernel_selector::index_select_kernel_selector() + { + Attach<IndexSelectKernelRef>(); + } + + KernelsData index_select_kernel_selector::GetBestKernels(const Params& params, const optional_params& options) const + { + return GetNaiveBestKernel(params, options, KernelType::INDEX_SELECT); + } +} diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/index_select/index_select_kernel_selector.h b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/index_select/index_select_kernel_selector.h new file mode 100644 index 000000000..21363f9f5 --- /dev/null +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/index_select/index_select_kernel_selector.h @@ -0,0 +1,34 @@ +// Copyright (c) 2018 Intel Corporation +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#pragma once + +#include "kernel_selector.h" + + +namespace kernel_selector +{ + class index_select_kernel_selector : public kernel_selector_base + { + public: + static index_select_kernel_selector &Instance() { + static index_select_kernel_selector instance; + return instance; + } + + index_select_kernel_selector(); + + KernelsData GetBestKernels(const Params& params, const optional_params& options) const override; + }; +} diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/lookup_table/lookup_table_kernel_axis.cpp b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/lookup_table/lookup_table_kernel_axis.cpp index 8176f002b..af6737941 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/lookup_table/lookup_table_kernel_axis.cpp +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/lookup_table/lookup_table_kernel_axis.cpp @@ -86,7 +86,7 @@ namespace kernel_selector auto jit = CreateJit(kernelName, cldnn_jit, entry_point); auto& kernel = kd.kernels[0]; - FillCLKernelData(kernel, runInfo, kernelName, jit, entry_point, "", false, false, 2); + FillCLKernelData(kernel, runInfo, params.engineInfo, kernelName, jit, entry_point, "", false, false, 2); kd.estimatedTime = FORCE_PRIORITY_9; diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/lookup_table/lookup_table_kernel_base.cpp b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/lookup_table/lookup_table_kernel_base.cpp index de31047d1..bb3f20f7f 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/lookup_table/lookup_table_kernel_base.cpp +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/lookup_table/lookup_table_kernel_base.cpp @@ -81,7 +81,7 @@ namespace kernel_selector auto jit = CreateJit(kernelName, cldnn_jit, entry_point); auto& kernel = kd.kernels[0]; - FillCLKernelData(kernel, runInfo, kernelName, jit, entry_point, "", false, false, 2); + FillCLKernelData(kernel, runInfo, params.engineInfo, kernelName, jit, entry_point, "", false, false, 2); kd.estimatedTime = estimatedTime; diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/lrn/lrn_kernel_base.cpp b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/lrn/lrn_kernel_base.cpp index 4907d9992..9165ea692 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/lrn/lrn_kernel_base.cpp +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/lrn/lrn_kernel_base.cpp @@ -103,7 +103,7 @@ namespace kernel_selector auto jit = CreateJit(kernelName, cldnnJit, entryPoint); auto& kernel = kd.kernels[0]; - FillCLKernelData(kernel, runInfo, kernelName, jit, entryPoint); + FillCLKernelData(kernel, runInfo, params.engineInfo, kernelName, jit, entryPoint); kd.estimatedTime = estimatedTime; diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/lstm/lstm_elt_kernel_base.cpp b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/lstm/lstm_elt_kernel_base.cpp index a74b21e27..6170abd46 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/lstm/lstm_elt_kernel_base.cpp +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/lstm/lstm_elt_kernel_base.cpp @@ -77,7 +77,7 @@ namespace kernel_selector auto jit = CreateJit(kernelName, cldnnJit, entryPoint); kernel.workGroups.global = { out.X().v, out.Batch().v, 1 }; - kernel.kernelString = GetKernelString(kernelName, jit, entryPoint); + kernel.kernelString = GetKernelString(kernelName, jit, entryPoint, params.engineInfo); kernel.arguments.push_back({ ArgumentDescriptor::Types::INPUT, 0 }); kernel.arguments.push_back({ ArgumentDescriptor::Types::OUTPUT, 0 }); if (orgParams.has_cell) { diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/lstm/lstm_elt_kernel_ref.cpp b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/lstm/lstm_elt_kernel_ref.cpp index 6afb8504c..a068f9ae4 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/lstm/lstm_elt_kernel_ref.cpp +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/lstm/lstm_elt_kernel_ref.cpp @@ -22,8 +22,10 @@ namespace kernel_selector { ParamsKey LSTMEltKernelRef::GetSupportedKey() const { ParamsKey k; - k.EnableAllInputDataType(); - k.EnableAllOutputDataType(); + k.EnableInputDataType(Datatype::F16); + k.EnableInputDataType(Datatype::F32); + k.EnableOutputDataType(Datatype::F16); + k.EnableOutputDataType(Datatype::F32); k.EnableDifferentTypes(); k.EnableAllInputLayout(); k.EnableAllOutputLayout(); diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/lstm/lstm_gemm_kernel_base.cpp b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/lstm/lstm_gemm_kernel_base.cpp index 6d2c9bcf0..703008546 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/lstm/lstm_gemm_kernel_base.cpp +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/lstm/lstm_gemm_kernel_base.cpp @@ -35,6 +35,7 @@ namespace kernel_selector } jit.AddConstants({ MakeJitConstant("WEIGHTS", weights)}); + jit.AddConstants({ MakeJitConstant("DIRECTION", params.direction)}); return jit; } @@ -64,7 +65,7 @@ namespace kernel_selector auto jit = CreateJit(kernelName, cldnnJit, entryPoint); kernel.workGroups.global = { out.X().v, out.Batch().v, 1 }; - kernel.kernelString = GetKernelString(kernelName, jit, entryPoint); + kernel.kernelString = GetKernelString(kernelName, jit, entryPoint, params.engineInfo); kernel.arguments.push_back({ ArgumentDescriptor::Types::INPUT, 0 }); kernel.arguments.push_back({ ArgumentDescriptor::Types::OUTPUT, 0 }); kernel.arguments.push_back({ ArgumentDescriptor::Types::WEIGHTS, 0 }); diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/lstm/lstm_gemm_kernel_base.h b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/lstm/lstm_gemm_kernel_base.h index 6fd517586..e766120e0 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/lstm/lstm_gemm_kernel_base.h +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/lstm/lstm_gemm_kernel_base.h @@ -34,6 +34,7 @@ namespace kernel_selector DataTensor hidden; bool hasBias = false; bool hasHidden = false; + uint32_t direction = 0; void SetBias(const DataTensor& v) { bias = v; diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/lstm/lstm_gemm_kernel_ref.cpp b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/lstm/lstm_gemm_kernel_ref.cpp index 167afcb5f..6484dd951 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/lstm/lstm_gemm_kernel_ref.cpp +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/lstm/lstm_gemm_kernel_ref.cpp @@ -22,8 +22,10 @@ namespace kernel_selector { ParamsKey LSTMGemmKernelRef::GetSupportedKey() const { ParamsKey k; - k.EnableAllInputDataType(); - k.EnableAllOutputDataType(); + k.EnableInputDataType(Datatype::F16); + k.EnableInputDataType(Datatype::F32); + k.EnableOutputDataType(Datatype::F16); + k.EnableOutputDataType(Datatype::F32); k.EnableDifferentTypes(); k.EnableAllInputLayout(); k.EnableAllOutputLayout(); diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/max_unpooling/max_unpooling_kernel_base.cpp b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/max_unpooling/max_unpooling_kernel_base.cpp index faea3ea6e..d6e036f40 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/max_unpooling/max_unpooling_kernel_base.cpp +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/max_unpooling/max_unpooling_kernel_base.cpp @@ -91,7 +91,7 @@ namespace kernel_selector auto jit = CreateJit(kernelName, cldnn_jit, entry_point); auto& kernel = kd.kernels[0]; - FillCLKernelData(kernel, runInfo, kernelName, jit, entry_point); + FillCLKernelData(kernel, runInfo, params.engineInfo, kernelName, jit, entry_point); kernel.arguments.push_back({ ArgumentDescriptor::Types::INPUT, 1 }); kd.estimatedTime = estimatedTime; diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/mvn/mvn_kernel_base.cpp b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/mvn/mvn_kernel_base.cpp index 23de0ff1a..4775a41b7 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/mvn/mvn_kernel_base.cpp +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/mvn/mvn_kernel_base.cpp @@ -82,7 +82,7 @@ namespace kernel_selector auto jit = CreateJit(finalKernelName, cldnn_jit, entry_point); auto& kernel = kd.kernels[0]; - FillCLKernelData(kernel, runInfo, finalKernelName, jit, entry_point); + FillCLKernelData(kernel, runInfo, params.engineInfo, finalKernelName, jit, entry_point); kd.estimatedTime = estimated_time; diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/normalize/normalize_kernel_base.cpp b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/normalize/normalize_kernel_base.cpp index a50849125..b4e4c04c7 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/normalize/normalize_kernel_base.cpp +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/normalize/normalize_kernel_base.cpp @@ -82,7 +82,7 @@ namespace kernel_selector auto jit = CreateJit(kernelName, cldnn_jit, entry_point); auto& kernel = kd.kernels[0]; - FillCLKernelData(kernel, runInfo, kernelName, jit, entry_point); + FillCLKernelData(kernel, runInfo, params.engineInfo, kernelName, jit, entry_point); kernel.arguments.push_back({ ArgumentDescriptor::Types::SCALE_TABLE, 0 }); kd.estimatedTime = estimated_time; diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/permute/permute_kernel_ref.cpp b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/permute/permute_kernel_ref.cpp index 057c4e655..ca6977952 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/permute/permute_kernel_ref.cpp +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/permute/permute_kernel_ref.cpp @@ -24,8 +24,14 @@ namespace kernel_selector ParamsKey k; k.EnableInputDataType(Datatype::F16); k.EnableInputDataType(Datatype::F32); + k.EnableInputDataType(Datatype::INT8); + k.EnableInputDataType(Datatype::INT32); + k.EnableInputDataType(Datatype::INT64); k.EnableOutputDataType(Datatype::F16); k.EnableOutputDataType(Datatype::F32); + k.EnableOutputDataType(Datatype::INT8); + k.EnableOutputDataType(Datatype::INT32); + k.EnableOutputDataType(Datatype::INT64); k.EnableAllInputLayout(); k.EnableAllOutputLayout(); k.EnableTensorOffset(); @@ -72,7 +78,7 @@ namespace kernel_selector kernel.workGroups.global = { gws[0], gws[1], gws[2] * gws[3] }; kernel.workGroups.local = GetOptimalLocalWorkGroupSizes(kernel.workGroups.global); - kernel.kernelString = GetKernelString(kernelName, jit, entry_point, ROUND_ROBIN); + kernel.kernelString = GetKernelString(kernelName, jit, entry_point, params.engineInfo, ROUND_ROBIN); kernel.arguments = GetArgsDesc(1, false, false); kd.estimatedTime = DONT_USE_IF_HAVE_SOMETHING_ELSE; diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/pooling/pooling_kernel_base.cpp b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/pooling/pooling_kernel_base.cpp index 13290c42d..9e5a9ad50 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/pooling/pooling_kernel_base.cpp +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/pooling/pooling_kernel_base.cpp @@ -129,7 +129,7 @@ namespace kernel_selector auto jit = CreateJit(kernelName, cldnn_jit, entry_point); auto& kernel = kd.kernels[0]; - FillCLKernelData(kernel, runInfo, kernelName, jit, entry_point); + FillCLKernelData(kernel, runInfo, params.engineInfo, kernelName, jit, entry_point); if(orgParams.poolType == PoolType::MAX_WITH_ARGMAX) kernel.arguments.push_back({ ArgumentDescriptor::Types::INPUT, 1 }); diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/pooling/pooling_kernel_gpu_fs_bs_yx_bsv4_fsv32.cpp b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/pooling/pooling_kernel_gpu_fs_bs_yx_bsv4_fsv32.cpp new file mode 100644 index 000000000..5157b4d5e --- /dev/null +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/pooling/pooling_kernel_gpu_fs_bs_yx_bsv4_fsv32.cpp @@ -0,0 +1,83 @@ +/* +// Copyright (c) 2018 Intel Corporation +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +*/ + +#include "pooling_kernel_gpu_fs_bs_yx_bsv4_fsv32.h" + +namespace kernel_selector +{ + ParamsKey PoolingKerneGPU_fs_bs_yx_bsv4_fsv32::GetSupportedKey() const + { + ParamsKey k; + k.EnableInputDataType(Datatype::INT8); + k.EnableOutputDataType(Datatype::INT8); + k.EnableInputLayout(DataLayout::fs_bs_yx_bsv4_fsv32); + k.EnableOutputLayout(DataLayout::fs_bs_yx_bsv4_fsv32); + k.EnableTensorOffset(); + k.EnableTensorPitches(); + k.EnableBatching(); + k.EnablePoolType(PoolType::MAX); + k.EnablePoolType(PoolType::AVG); + k.EnablePoolRemainder(PoolRemainder::FLOOR); + k.EnablePoolRemainder(PoolRemainder::CEIL); + k.EnablePoolKernelDividerMode(KernelDividerMode::FIXED); + k.EnablePoolKernelDividerMode(KernelDividerMode::DYNAMIC); + k.EnablePoolKernelDividerMode(KernelDividerMode::DYNAMIC_WITH_PADDING); + k.EnableDifferentTypes(); + return k; + } + + PoolingKernelBase::DispatchData PoolingKerneGPU_fs_bs_yx_bsv4_fsv32::SetDefault(const pooling_params& params) const + { + constexpr int simdSize = 8; + + DispatchData runInfo = PoolingKernelBase::SetDefault(params); + + runInfo.gws0 = params.output.X().v; + runInfo.gws1 = params.output.Y().v; + // we got fs_bs_yx_bsv4_fsv32 format, we process 4 batches and 4 features per workitem + runInfo.gws2 = (RoundUp(params.output.Feature().v, 32) * RoundUp(params.output.Batch().v, 4)) / (4*4); + + runInfo.lws0 = 1; + runInfo.lws1 = 1; + runInfo.lws2 = simdSize; + + return runInfo; + } + + JitConstants PoolingKerneGPU_fs_bs_yx_bsv4_fsv32::GetJitConstants(const pooling_params& params, DispatchData kd) const + { + auto jit = PoolingKernelBase::GetJitConstants(params, kd); + + const size_t in_x_pitch = 32 * 4; + const size_t in_y_pitch = 32 * 4 * params.inputs[0].X().LogicalDimPadded(); + const size_t in_b_block_pitch = in_y_pitch * params.inputs[0].Y().LogicalDimPadded(); + const size_t in_f_block_pitch = in_b_block_pitch * ((params.inputs[0].Batch().v + 3) / 4); + const size_t in_offset = in_x_pitch * params.inputs[0].X().pad.before + in_y_pitch * params.inputs[0].Y().pad.before; + + jit.AddConstant(MakeJitConstant("IN_X_PITCH", in_x_pitch)); + jit.AddConstant(MakeJitConstant("IN_Y_PITCH", in_y_pitch)); + jit.AddConstant(MakeJitConstant("IN_B_BLOCK_PITCH", in_b_block_pitch)); + jit.AddConstant(MakeJitConstant("IN_F_BLOCK_PITCH", in_f_block_pitch)); + jit.AddConstant(MakeJitConstant("IN_OFFSET", in_offset)); + + return jit; + } + + KernelsData PoolingKerneGPU_fs_bs_yx_bsv4_fsv32::GetKernelsData(const Params& params, const optional_params& options) const + { + return GetCommonKernelsData(params, options, FORCE_PRIORITY_1); + } +}
\ No newline at end of file diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/pooling/pooling_kernel_gpu_fs_bs_yx_bsv4_fsv32.h b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/pooling/pooling_kernel_gpu_fs_bs_yx_bsv4_fsv32.h new file mode 100644 index 000000000..efb5c67cd --- /dev/null +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/pooling/pooling_kernel_gpu_fs_bs_yx_bsv4_fsv32.h @@ -0,0 +1,36 @@ +/* +// Copyright (c) 2018 Intel Corporation +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +*/ + +#pragma once + +#include "pooling_kernel_base.h" + +namespace kernel_selector +{ + class PoolingKerneGPU_fs_bs_yx_bsv4_fsv32 : public PoolingKernelBase + { + public: + PoolingKerneGPU_fs_bs_yx_bsv4_fsv32() : PoolingKernelBase("pooling_gpu_fs_bs_yx_bsv4_fsv32") {} + virtual ~PoolingKerneGPU_fs_bs_yx_bsv4_fsv32() {} + + virtual KernelsData GetKernelsData(const Params& params, const optional_params& options) const override; + virtual ParamsKey GetSupportedKey() const override; + DispatchData SetDefault(const pooling_params& params) const override; + protected: + JitConstants GetJitConstants(const pooling_params& params, DispatchData kd) const override; + + }; +}
\ No newline at end of file diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/pooling/pooling_kernel_selector.cpp b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/pooling/pooling_kernel_selector.cpp index 5a7d83b47..91ec4d2dc 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/pooling/pooling_kernel_selector.cpp +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/pooling/pooling_kernel_selector.cpp @@ -22,6 +22,7 @@ #include "pooling_kernel_gpu_byxf_padding_opt.h" #include "pooling_kernel_gpu_byxf_af32.h" #include "pooling_kernel_gpu_int8_ref.h" +#include "pooling_kernel_gpu_fs_bs_yx_bsv4_fsv32.h" namespace kernel_selector { @@ -34,6 +35,7 @@ namespace kernel_selector { Attach<PoolingKernelGPUByxfPaddingOpt>(); Attach<PoolingKernelGPUInt8Ref>(); Attach<PoolingKerneGPU_byxf_af32>(); + Attach<PoolingKerneGPU_fs_bs_yx_bsv4_fsv32>(); } KernelsData pooling_kernel_selector::GetBestKernels(const Params& params, const optional_params& options) const diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/region_yolo/region_yolo_kernel_ref.cpp b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/region_yolo/region_yolo_kernel_ref.cpp index 62420b510..6e5577a84 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/region_yolo/region_yolo_kernel_ref.cpp +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/region_yolo/region_yolo_kernel_ref.cpp @@ -92,7 +92,7 @@ namespace kernel_selector auto jit = CreateJit(kernelName, cldnn_jit, entry_point); auto& kernel = kd.kernels[0]; - FillCLKernelData(kernel, runInfo, kernelName, jit, entry_point); + FillCLKernelData(kernel, runInfo, params.engineInfo, kernelName, jit, entry_point); kd.estimatedTime = FORCE_PRIORITY_9; diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/reorder/reorder_kernel.cpp b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/reorder/reorder_kernel.cpp index 87ee70660..ba6f7ce1a 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/reorder/reorder_kernel.cpp +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/reorder/reorder_kernel.cpp @@ -24,11 +24,15 @@ namespace kernel_selector ParamsKey k; k.EnableInputDataType(Datatype::UINT8); k.EnableInputDataType(Datatype::INT8); + k.EnableInputDataType(Datatype::INT32); + k.EnableInputDataType(Datatype::INT64); k.EnableInputDataType(Datatype::F16); k.EnableInputDataType(Datatype::F32); k.EnableOutputDataType(Datatype::F16); k.EnableOutputDataType(Datatype::F32); k.EnableOutputDataType(Datatype::INT8); + k.EnableOutputDataType(Datatype::INT32); + k.EnableOutputDataType(Datatype::INT64); k.EnableOutputDataType(Datatype::UINT8); k.EnableDifferentTypes(); k.EnableAllInputLayout(); diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/reorder/reorder_kernel_base.cpp b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/reorder/reorder_kernel_base.cpp index d050de529..867a3c8b9 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/reorder/reorder_kernel_base.cpp +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/reorder/reorder_kernel_base.cpp @@ -69,6 +69,14 @@ namespace kernel_selector MakeJitConstant("OUTPUT", output), }; + if (fp16Supported) + { + jit.Merge(MakeUnitTypeJitConstants(Datatype::F16)); + } + else + { + jit.Merge(MakeUnitTypeJitConstants(Datatype::F32)); + } return jit; } @@ -185,7 +193,7 @@ namespace kernel_selector auto& kernel = kd.kernels[0]; - FillCLKernelData(kernel, runInfo, kernelName, jit, entry_point); + FillCLKernelData(kernel, runInfo, params.engineInfo, kernelName, jit, entry_point); kernel.arguments = GetArgsDesc(1, false, false); @@ -215,7 +223,7 @@ namespace kernel_selector auto& kernel = kd.kernels[0]; - FillCLKernelData(kernel, runInfo, kernelName, jit, entry_point); + FillCLKernelData(kernel, runInfo, params.engineInfo, kernelName, jit, entry_point); kernel.arguments = GetArgsDesc(1, false, false); if (newParams.mode == MeanSubtractMode::IN_BUFFER) diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/reorg_yolo/reorg_yolo_kernel_ref.cpp b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/reorg_yolo/reorg_yolo_kernel_ref.cpp index 9ae8d10d9..0462e4a8f 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/reorg_yolo/reorg_yolo_kernel_ref.cpp +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/reorg_yolo/reorg_yolo_kernel_ref.cpp @@ -87,7 +87,7 @@ namespace kernel_selector auto jit = CreateJit(kernelName, cldnn_jit, entry_point); auto& kernel = kd.kernels[0]; - FillCLKernelData(kernel, runInfo, kernelName, jit, entry_point); + FillCLKernelData(kernel, runInfo, params.engineInfo, kernelName, jit, entry_point); kd.estimatedTime = FORCE_PRIORITY_9; diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/reshape/reshape_kernel_ref.cpp b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/reshape/reshape_kernel_ref.cpp index 2984e2e8f..9c9c760ee 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/reshape/reshape_kernel_ref.cpp +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/reshape/reshape_kernel_ref.cpp @@ -24,8 +24,14 @@ namespace kernel_selector ParamsKey k; k.EnableInputDataType(Datatype::F16); k.EnableInputDataType(Datatype::F32); + k.EnableInputDataType(Datatype::INT8); + k.EnableInputDataType(Datatype::INT32); + k.EnableInputDataType(Datatype::INT64); k.EnableOutputDataType(Datatype::F16); k.EnableOutputDataType(Datatype::F32); + k.EnableOutputDataType(Datatype::INT8); + k.EnableOutputDataType(Datatype::INT32); + k.EnableOutputDataType(Datatype::INT64); k.EnableAllInputLayout(); k.EnableAllOutputLayout(); k.EnableTensorOffset(); @@ -60,7 +66,7 @@ namespace kernel_selector kernel.workGroups.global = { gws[0], gws[1], gws[2] * gws[3] }; kernel.workGroups.local = GetOptimalLocalWorkGroupSizes(kernel.workGroups.global); - kernel.kernelString = GetKernelString(kernelName, jit, entry_point, ROUND_ROBIN); + kernel.kernelString = GetKernelString(kernelName, jit, entry_point, params.engineInfo, ROUND_ROBIN); kernel.arguments = GetArgsDesc(1, false, false); kd.estimatedTime = DONT_USE_IF_HAVE_SOMETHING_ELSE; diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/roi_pooling/roi_pooling_kernel_ref.cpp b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/roi_pooling/roi_pooling_kernel_ref.cpp index 0683beb3e..64dde2c32 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/roi_pooling/roi_pooling_kernel_ref.cpp +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/roi_pooling/roi_pooling_kernel_ref.cpp @@ -1,5 +1,5 @@ /* -// Copyright (c) 2016 Intel Corporation +// Copyright (c) 2016-2018 Intel Corporation // // Licensed under the Apache License, Version 2.0 (the "License"); // you may not use this file except in compliance with the License. @@ -27,6 +27,9 @@ namespace kernel_selector { k.EnableOutputDataType(Datatype::F32); k.EnableInputLayout(DataLayout::bfyx); k.EnableOutputLayout(DataLayout::brfyx); + k.EnablePoolType(PoolType::MAX); + k.EnablePoolType(PoolType::AVG); + k.EnablePoolType(PoolType::BILINEAR); k.EnableTensorOffset(); k.EnableTensorPitches(); k.EnableBatching(); @@ -69,7 +72,6 @@ namespace kernel_selector { }); jit.AddConstants({ - MakeJitConstant("MAX_POOL", rp.mode == PoolType::MAX), MakeJitConstant("USE_OLD_SCALE_AND_ROUNDING", rp.groupSize == 0) }); @@ -94,11 +96,11 @@ namespace kernel_selector { auto jit = CreateJit(kernelName, cldnn_jit, entry_point); auto& kernel = kd.kernels[0]; - FillCLKernelData(kernel, runInfo, kernelName, jit, entry_point); + FillCLKernelData(kernel, runInfo, params.engineInfo, kernelName, jit, entry_point); kernel.arguments.push_back({ ArgumentDescriptor::Types::INPUT, 1 }); kd.estimatedTime = FORCE_PRIORITY_9; return{ kd }; } -}
\ No newline at end of file +} diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/scale_grad_weights/scale_grad_weights_kernel_base.cpp b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/scale_grad_weights/scale_grad_weights_kernel_base.cpp index 471b61e6d..61edddabf 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/scale_grad_weights/scale_grad_weights_kernel_base.cpp +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/scale_grad_weights/scale_grad_weights_kernel_base.cpp @@ -57,7 +57,7 @@ namespace kernel_selector auto jit = CreateJit(kernelName, cldnn_jit, entry_point); auto& kernel = kd.kernels[0]; - FillCLKernelData(kernel, runInfo, kernelName, jit, entry_point, ROUND_ROBIN, true, !orgParams.bias.empty(), 2); + FillCLKernelData(kernel, runInfo, params.engineInfo, kernelName, jit, entry_point, ROUND_ROBIN, true, !orgParams.bias.empty(), 2); if (orgParams.use_momentum) { diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/scale_grad_weights/scale_grad_weights_kernel_ref.cpp b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/scale_grad_weights/scale_grad_weights_kernel_ref.cpp index 4b9190f18..51b1122d4 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/scale_grad_weights/scale_grad_weights_kernel_ref.cpp +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/scale_grad_weights/scale_grad_weights_kernel_ref.cpp @@ -24,7 +24,6 @@ namespace kernel_selector ParamsKey k; k.EnableInputDataType(Datatype::F16); k.EnableInputDataType(Datatype::F32); - k.EnableInputWeightsType(WeightsType::F16); k.EnableInputWeightsType(WeightsType::F32); k.EnableOutputDataType(Datatype::F16); k.EnableOutputDataType(Datatype::F32); diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/select/select_kernel_base.cpp b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/select/select_kernel_base.cpp new file mode 100644 index 000000000..09b3a0151 --- /dev/null +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/select/select_kernel_base.cpp @@ -0,0 +1,177 @@ +/* +// Copyright (c) 2018 Intel Corporation +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +*/ + +#include "select_kernel_base.h" +#include "kernel_selector_utils.h" + +namespace kernel_selector +{ + + bool SelectKernelBase::Validate(const Params& p, const optional_params& o) const + { + if (p.GetType() != KernelType::SELECT || + o.GetType() != KernelType::SELECT) + { + return false; + } + + const select_params& params = static_cast<const select_params&>(p); + + if (params.inputs[0].GetDType() != params.inputs[1].GetDType()) + { + return false; + } + + if (params.inputs.size() != 3) + { + return false; + } + + return true; + } + + JitConstants SelectKernelBase::GetJitConstantsCommon(const select_params& params) const + { + JitConstants jit = MakeBaseParamsJitConstants(params); + + std::string inputs_decls; + + for (size_t i = 0; i < params.inputs.size(); i++) + { + std::string const_str = "const"; + + inputs_decls += const_str + " __global " + toCLType(params.inputs[i].GetDType()) + "* input" + std::to_string(i) + ", "; + } + + jit.AddConstant(MakeJitConstant("INPUTS_DECLS", inputs_decls)); + + std::string destType, absType; + + // i8, i8, i8 + // i8, i8, u8 + // u8, u8, i8 + // u8, u8, u8 + if ((params.inputs[2].GetDType() == Datatype::INT8 + || params.inputs[2].GetDType() == Datatype::UINT8) + && (params.inputs[0].GetDType() == Datatype::INT8 + || params.inputs[0].GetDType() == Datatype::UINT8)) + { + jit.AddConstant(MakeJitConstant("MASK", "INPUT_2")); + } + else + { + // x, x, f32 + // x, x, f16 + if (params.inputs[2].GetDType() == Datatype::F32 + || params.inputs[2].GetDType() == Datatype::F16) + { + absType = "fabs"; + } + // f32, f32, i8 + // f32, f32, u8 + // f16, f16, i8 + // f16, f16, u8 + else + { + absType = "abs"; + } + + // f32, f32, x + if (params.inputs[0].GetDType() == Datatype::F32) { + destType = "int"; + } + // f16, f16, x + else if (params.inputs[0].GetDType() == Datatype::F16) { + destType = "short"; + } + // i8, i8, f32 + // i8, i8, f16 + // u8, u8, f32 + // u8, u8, f16 + else + { + destType = "char"; + } + + jit.AddConstant(MakeJitConstant("MASK", "convert_" + destType + "_rtp(" + absType + "(INPUT_2))")); + } + + return jit; + } + + JitConstants SelectKernelBase::GetJitConstants(const select_params& params) const + { + return GetJitConstantsCommon(params); + } + + SelectKernelBase::DispatchData SelectKernelBase::SetDefault(const select_params& params) const + { + DispatchData kd; + + const auto& out = params.output; + + std::vector<size_t> gws; + for (const auto& o : out.GetDims()) + { + gws.push_back(o.v); + } + + for (size_t i = gws.size(); i < 4; i++) + { + gws.push_back(1U); + } + + kd.gws0 = gws[0]; + kd.gws1 = gws[1]; + kd.gws2 = gws[2] * gws[3]; + + auto local = GetOptimalLocalWorkGroupSizes( { kd.gws0, kd.gws1, kd.gws2 } ); + kd.lws0 = local[0]; + kd.lws1 = local[1]; + kd.lws2 = local[2]; + + return kd; + } + + KernelsData SelectKernelBase::GetCommonKernelsData(const Params& params, const optional_params& options) const + { + if (!Validate(params, options)) + { + return{}; + } + + KernelData kd = KernelData::Default<select_params>(params); + select_params& newParams = *static_cast<select_params*>(kd.params.get()); + + auto entry_point = GetEntryPoint(kernelName, newParams.layerID, options); + auto cldnn_jit = GetJitConstants(newParams); + std::string jit = CreateJit(kernelName, cldnn_jit, entry_point); + + DispatchData runInfo = SetDefault(newParams); + + auto& kernel = kd.kernels[0]; + + kernel.workGroups.global = { runInfo.gws0, runInfo.gws1, runInfo.gws2 }; + kernel.workGroups.local = { runInfo.lws0, runInfo.lws1, runInfo.lws2 }; + + kernel.kernelString = GetKernelString(kernelName, jit, entry_point, params.engineInfo, ROUND_ROBIN); + kernel.arguments = GetArgsDesc((uint32_t)newParams.inputs.size(), false, false); + + kd.estimatedTime = DONT_USE_IF_HAVE_SOMETHING_ELSE; + + return{ kd }; + } +} diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/select/select_kernel_base.h b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/select/select_kernel_base.h new file mode 100644 index 000000000..c1d48d991 --- /dev/null +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/select/select_kernel_base.h @@ -0,0 +1,62 @@ +/* +// Copyright (c) 2018 Intel Corporation +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +*/ + +#pragma once + +#include "common_kernel_base.h" + +namespace kernel_selector +{ + //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// + // select_params + //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// + struct select_params : public base_params + { + select_params() : base_params(KernelType::SELECT) {} + + virtual ParamsKey GetParamsKey() const + { + return base_params::GetParamsKey(); + } + }; + + //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// + // select_optional_params + //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// + struct select_optional_params : optional_params + { + select_optional_params() : optional_params(KernelType::SELECT) {} + }; + + //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// + // SelectKernelBase + //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// + class SelectKernelBase : public common_kernel_base + { + public: + using common_kernel_base::common_kernel_base; + virtual ~SelectKernelBase() {} + + using DispatchData = CommonDispatchData; + JitConstants GetJitConstantsCommon(const select_params& params) const; + + protected: + virtual bool Validate(const Params& p, const optional_params& o) const override; + virtual JitConstants GetJitConstants(const select_params& params) const; + virtual DispatchData SetDefault(const select_params& params) const; + KernelsData GetCommonKernelsData(const Params& params, const optional_params& options) const; + }; +} diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/select/select_kernel_ref.cpp b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/select/select_kernel_ref.cpp new file mode 100644 index 000000000..f7f776c15 --- /dev/null +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/select/select_kernel_ref.cpp @@ -0,0 +1,64 @@ +/* +// Copyright (c) 2018 Intel Corporation +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +*/ + +#include "select_kernel_ref.h" +#include "kernel_selector_utils.h" + +namespace kernel_selector { + + ParamsKey SelectKernelRef::GetSupportedKey() const + { + ParamsKey k; + + k.EnableInputDataType(Datatype::F16); + k.EnableInputDataType(Datatype::F32); + k.EnableInputDataType(Datatype::INT8); + k.EnableInputDataType(Datatype::UINT8); + + k.EnableOutputDataType(Datatype::F32); + k.EnableOutputDataType(Datatype::F16); + k.EnableOutputDataType(Datatype::INT8); + k.EnableOutputDataType(Datatype::UINT8); + + k.EnableInputLayout(DataLayout::bfyx); + k.EnableInputLayout(DataLayout::yxfb); + k.EnableInputLayout(DataLayout::byxf); + + k.EnableOutputLayout(DataLayout::bfyx); + k.EnableOutputLayout(DataLayout::yxfb); + k.EnableOutputLayout(DataLayout::byxf); + + k.EnableBatching(); + k.EnableDifferentTypes(); + + return k; + } + + bool SelectKernelRef::Validate(const Params& p, const optional_params& o) const + { + if (!SelectKernelBase::Validate(p, o)) + { + return false; + } + + return true; + } + + KernelsData SelectKernelRef::GetKernelsData(const Params& params, const optional_params& options) const + { + return GetCommonKernelsData(params, options); + } +}
\ No newline at end of file diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/select/select_kernel_ref.h b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/select/select_kernel_ref.h new file mode 100644 index 000000000..a72c0e90a --- /dev/null +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/select/select_kernel_ref.h @@ -0,0 +1,35 @@ +/* +// Copyright (c) 2018 Intel Corporation +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +*/ + +#pragma once + +#include "select_kernel_base.h" + +namespace kernel_selector +{ + class SelectKernelRef : public SelectKernelBase + { + public: + SelectKernelRef() : SelectKernelBase("select_gpu_ref") {} + virtual ~SelectKernelRef() {} + + virtual KernelsData GetKernelsData(const Params& params, const optional_params& options) const override; + virtual ParamsKey GetSupportedKey() const override; + protected: + bool Validate(const Params& p, const optional_params& o) const override; + + }; +}
\ No newline at end of file diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/select/select_kernel_selector.cpp b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/select/select_kernel_selector.cpp new file mode 100644 index 000000000..ec1218166 --- /dev/null +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/select/select_kernel_selector.cpp @@ -0,0 +1,31 @@ +/* +// Copyright (c) 2018 Intel Corporation +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +*/ + +#include "select_kernel_selector.h" +#include "select_kernel_ref.h" + +namespace kernel_selector +{ + select_kernel_selector::select_kernel_selector() + { + Attach<SelectKernelRef>(); + } + + KernelsData select_kernel_selector::GetBestKernels(const Params& params, const optional_params& options) const + { + return GetNaiveBestKernel(params, options, KernelType::SELECT); + } +}
\ No newline at end of file diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/select/select_kernel_selector.h b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/select/select_kernel_selector.h new file mode 100644 index 000000000..b3de11649 --- /dev/null +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/select/select_kernel_selector.h @@ -0,0 +1,35 @@ +/* +// Copyright (c) 2018 Intel Corporation +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +*/ + +#pragma once + +#include "kernel_selector.h" + +namespace kernel_selector +{ + class select_kernel_selector : public kernel_selector_base + { + public: + static select_kernel_selector &Instance() { + static select_kernel_selector instance_; + return instance_; + } + + select_kernel_selector(); + + KernelsData GetBestKernels(const Params& params, const optional_params& options) const override; + }; +}
\ No newline at end of file diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/softmax/softmax_kernel_base.cpp b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/softmax/softmax_kernel_base.cpp index 4f02da734..4d2c36d39 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/softmax/softmax_kernel_base.cpp +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/softmax/softmax_kernel_base.cpp @@ -88,7 +88,7 @@ namespace kernel_selector auto jit = CreateJit(kernelName, cldnn_jit, entry_point); auto& kernel = kd.kernels[0]; - FillCLKernelData(kernel, runInfo, kernelName, jit, entry_point); + FillCLKernelData(kernel, runInfo, params.engineInfo, kernelName, jit, entry_point); kd.estimatedTime = runInfo.effiency; diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/softmax_loss_grad/softmax_loss_grad_kernel_base.cpp b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/softmax_loss_grad/softmax_loss_grad_kernel_base.cpp index 0a4473714..da816abac 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/softmax_loss_grad/softmax_loss_grad_kernel_base.cpp +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/softmax_loss_grad/softmax_loss_grad_kernel_base.cpp @@ -74,7 +74,7 @@ namespace kernel_selector auto jit = CreateJit(kernelName, cldnn_jit, entry_point); auto& kernel = kd.kernels[0]; - FillCLKernelData(kernel, runInfo, kernelName, jit, entry_point); + FillCLKernelData(kernel, runInfo, params.engineInfo, kernelName, jit, entry_point); kernel.arguments.push_back({ ArgumentDescriptor::Types::INPUT, 1 }); kd.estimatedTime = runInfo.effiency; diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/tile/tile_kernel_ref.cpp b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/tile/tile_kernel_ref.cpp new file mode 100644 index 000000000..37c206d3b --- /dev/null +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/tile/tile_kernel_ref.cpp @@ -0,0 +1,153 @@ +/* +// Copyright (c) 2018 Intel Corporation +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +*/ + +#include "tile_kernel_ref.h" +#include "kernel_selector_utils.h" + +namespace kernel_selector +{ + static int32_t GetTileChannelIndex(const tile_params& params) + { + Tensor::DataChannelName name = Tensor::DataChannelName::X; + switch (params.axis) + { + case TileAxis::X: name = Tensor::DataChannelName::X; break; + case TileAxis::Y: name = Tensor::DataChannelName::Y; break; + case TileAxis::FEATURE: name = Tensor::DataChannelName::FEATURE; break; + case TileAxis::BATCH: name = Tensor::DataChannelName::BATCH; break; + default: break; + } + + return DataTensor::Channelndex(params.output.GetLayout(), name); + } + + ParamsKey TileKernelRef::GetSupportedKey() const + { + ParamsKey k; + k.EnableInputDataType(Datatype::F16); + k.EnableInputDataType(Datatype::F32); + k.EnableOutputDataType(Datatype::F16); + k.EnableOutputDataType(Datatype::F32); + k.EnableInputLayout(DataLayout::bfyx); + k.EnableOutputLayout(DataLayout::bfyx); + k.EnableTensorOffset(); + k.EnableTensorPitches(); + k.EnableBatching(); + return k; + } + + CommonDispatchData TileKernelRef::SetDefault(const tile_params& params, const optional_params&) const + { + CommonDispatchData runInfo; + + auto in = params.inputs[0]; + + size_t inner_size = 1; + size_t outer_size = 1; + + const int32_t axis = GetTileChannelIndex(params); + + for (int32_t i = 0; i <= axis; i++) + { + inner_size *= in.GetDims()[i].v; + } + + for (int32_t i = axis+1; i < static_cast<int32_t>(in.GetDims().size()); i++) + { + outer_size *= in.GetDims()[i].v; + } + + if (inner_size > 1) + { + runInfo.gws0 = outer_size; + runInfo.gws1 = inner_size; + runInfo.gws2 = 1; + + runInfo.lws0 = 1; + runInfo.lws1 = 1; + runInfo.lws2 = 1; + } + else + { + runInfo.gws0 = Align(outer_size, 16); + runInfo.gws1 = 1; + runInfo.gws2 = 1; + + runInfo.lws0 = 16; + runInfo.lws1 = 1; + runInfo.lws2 = 1; + } + + runInfo.fp16UnitUsed = params.inputs[0].GetDType() == Datatype::F16; + + return runInfo; + } + + JitConstants TileKernelRef::GetJitConstants(const tile_params& params) const + { + JitConstants jit = MakeBaseParamsJitConstants(params); + + auto in = params.inputs[0]; + auto out = params.output; + + size_t inner_size = 1; + size_t outer_size = 1; + size_t axis_pitch = 1; + + const int32_t axis = GetTileChannelIndex(params); + + for (int32_t i = 0; i <= axis; i++) + { + inner_size *= in.GetDims()[i].v; + axis_pitch *= in.GetDims()[i].LogicalDimPadded(); + } + for (int32_t i = axis+1; i < static_cast<int32_t>(in.GetDims().size()); i++) + { + outer_size *= in.GetDims()[i].v; + } + + jit.AddConstant(MakeJitConstant("TILES", params.tiles)); + jit.AddConstant(MakeJitConstant("AXIS_PITCH", axis_pitch)); + jit.AddConstant(MakeJitConstant("OUTER_SIZE", outer_size)); + if (inner_size == 1) + { + jit.AddConstant(MakeJitConstant("OUTPUT_SIZE", out.LogicalSize())); + jit.AddConstant(MakeJitConstant("DENSE", 1)); + } + return jit; + } + + KernelsData TileKernelRef::GetKernelsData(const Params& params, const optional_params& options) const + { + assert(params.GetType() == KernelType::TILE); + + KernelData kd = KernelData::Default<tile_params>(params); + tile_params& newParams = *static_cast<tile_params*>(kd.params.get()); + + auto runInfo = SetDefault(newParams, options); + auto entry_point = GetEntryPoint(kernelName, newParams.layerID, options); + auto cldnn_jit = GetJitConstants(newParams); + std::string jit = CreateJit(kernelName, cldnn_jit, entry_point); + + auto& kernel = kd.kernels[0]; + + FillCLKernelData(kernel, runInfo, params.engineInfo, kernelName, jit, entry_point); + + kd.estimatedTime = DONT_USE_IF_HAVE_SOMETHING_ELSE; + + return{ kd }; + } +} diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/tile/tile_kernel_ref.h b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/tile/tile_kernel_ref.h new file mode 100644 index 000000000..967dab817 --- /dev/null +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/tile/tile_kernel_ref.h @@ -0,0 +1,58 @@ +/* +// Copyright (c) 2018 Intel Corporation +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +*/ + +#pragma once + +#include "common_kernel_base.h" + +namespace kernel_selector +{ + //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// + // tile_params + //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// + struct tile_params : public base_params + { + tile_params() : base_params(KernelType::TILE) {} + + TileAxis axis; + int tiles; + + virtual ParamsKey GetParamsKey() const + { + return base_params::GetParamsKey(); + } + }; + + //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// + // tile_optional_params + //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// + struct tile_optional_params : optional_params + { + tile_optional_params() : optional_params(KernelType::TILE) {} + }; + + class TileKernelRef : public common_kernel_base + { + public: + TileKernelRef() : common_kernel_base("tile_ref") {} + virtual ~TileKernelRef() {} + + virtual JitConstants GetJitConstants(const tile_params& params) const; + virtual CommonDispatchData SetDefault(const tile_params& params, const optional_params&) const; + virtual KernelsData GetKernelsData(const Params& params, const optional_params& options) const override; + virtual ParamsKey GetSupportedKey() const override; + }; +} diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/tile/tile_kernel_selector.cpp b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/tile/tile_kernel_selector.cpp new file mode 100644 index 000000000..c0ca49de7 --- /dev/null +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/tile/tile_kernel_selector.cpp @@ -0,0 +1,31 @@ +/* +// Copyright (c) 2018 Intel Corporation +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +*/ + +#include "tile_kernel_selector.h" +#include "tile_kernel_ref.h" + +namespace kernel_selector { + + tile_kernel_selector::tile_kernel_selector() + { + Attach<TileKernelRef>(); + } + + KernelsData tile_kernel_selector::GetBestKernels(const Params& params, const optional_params& options) const + { + return GetNaiveBestKernel(params, options, KernelType::TILE); + } +} diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/tile/tile_kernel_selector.h b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/tile/tile_kernel_selector.h new file mode 100644 index 000000000..c0b10fa10 --- /dev/null +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/tile/tile_kernel_selector.h @@ -0,0 +1,37 @@ +/* +// Copyright (c) 2018 Intel Corporation +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +*/ + +#pragma once + +#include "kernel_selector.h" + +namespace kernel_selector +{ + class tile_kernel_selector : public kernel_selector_base + { + public: + static tile_kernel_selector &Instance() { + static tile_kernel_selector instance_; + return instance_; + } + + tile_kernel_selector(); + + virtual ~tile_kernel_selector() {} + + virtual KernelsData GetBestKernels(const Params& params, const optional_params& options) const override; + }; +} diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/upsampling/upsampling_kernel_base.cpp b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/upsampling/upsampling_kernel_base.cpp index ea0d89515..889daf8bc 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/upsampling/upsampling_kernel_base.cpp +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/upsampling/upsampling_kernel_base.cpp @@ -76,7 +76,7 @@ namespace kernel_selector kernel.workGroups.global = { out.X().v, out.Y().v, out.Feature().v * out.Batch().v }; kernel.workGroups.local = GetOptimalLocalWorkGroupSizes(kernel.workGroups.global); - kernel.kernelString = GetKernelString(kernelName, jit, entry_point, ROUND_ROBIN); + kernel.kernelString = GetKernelString(kernelName, jit, entry_point, params.engineInfo, ROUND_ROBIN); kernel.arguments = GetArgsDesc((uint32_t)newParams.inputs.size(), false, false); kd.estimatedTime = DONT_USE_IF_HAVE_SOMETHING_ELSE; diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/upsampling/upsampling_kernel_ref.cpp b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/upsampling/upsampling_kernel_ref.cpp index 46a927483..9037ebc0a 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/upsampling/upsampling_kernel_ref.cpp +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/upsampling/upsampling_kernel_ref.cpp @@ -15,15 +15,16 @@ */ #include "upsampling_kernel_ref.h" -#include "kernel_selector_utils.h" namespace kernel_selector { ParamsKey UpSamplingKernelRef::GetSupportedKey() const { ParamsKey k; - k.EnableAllInputDataType(); - k.EnableAllOutputDataType(); + k.EnableInputDataType(Datatype::F16); + k.EnableInputDataType(Datatype::F32); + k.EnableOutputDataType(Datatype::F16); + k.EnableOutputDataType(Datatype::F32); k.EnableDifferentTypes(); k.EnableAllInputLayout(); k.EnableAllOutputLayout(); diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/auto_tuner.h b/inference-engine/thirdparty/clDNN/kernel_selector/core/auto_tuner.h index 387a9584b..864e1ce11 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/auto_tuner.h +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/auto_tuner.h @@ -18,6 +18,7 @@ #include <atomic> #include <mutex> +#include <map> #include "kernel_selector_common.h" namespace kernel_selector @@ -48,7 +49,7 @@ namespace kernel_selector This device ID is also written in the cache file that is generated in the on-line mode. 2. Find the hash of the node you want to change. This hash can be obtained by: - std::string hash = std::to_string(std::hash<std::string>{}(params.to_string())); + std::string hash = std::to_string(create_hash(params.to_string())); 3. Change the kernel name and/or config index. For example: { "17001023283013862129", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 203) } diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/auto_tuner_offline.cpp b/inference-engine/thirdparty/clDNN/kernel_selector/core/auto_tuner_offline.cpp index 4bac23f41..062138a79 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/auto_tuner_offline.cpp +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/auto_tuner_offline.cpp @@ -40,12588 +40,4 @@ namespace kernel_selector } return instance; } - - // SKL GT4e - void tuning_cache_193B(tuning_data& td) - { - td.td.insert({ - { "4583484812233029888", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "7560832358324865221", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "7382044526960590018", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "12372261924257291610", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 167) }, - { "1547771611689525848", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 326) }, - { "3134973665622945888", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 326) }, - { "18260030211719729324", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 246) }, - { "7416143717989012766", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 95) }, - { "12028963907131702705", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 325) }, - { "2464531851392092325", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 247) }, - { "8181308759455478086", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 323) }, - { "546062289721803579", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 323) }, - { "4889405384318695802", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 325) }, - { "12841232643395100314", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 324) }, - { "14108361259911144680", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 334) }, - { "2726453304845436156", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 46) }, - { "2607416795507802412", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 206) }, - { "2175404966338020579", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 325) }, - { "14666883719480623074", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 201) }, - { "3752993663604843837", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 127) }, - { "5274735654559844733", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 326) }, - { "8174421295799601683", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 124) }, - { "1967655354607438665", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 206) }, - { "15762542971370422224", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 327) }, - { "8183203099539372914", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 201) }, - { "4075343423548891274", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 63) }, - { "13264497096898621015", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 326) }, - { "679058537775669048", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 326) }, - { "3375634256357960999", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 326) }, - { "2844616672368585285", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 327) }, - { "14235558866846276172", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 124) }, - { "18066867692765966577", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 325) }, - { "9861424412782371874", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 207) }, - { "607078314875528651", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 202) }, - { "6234885984223387670", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 63) }, - { "7223570329858821704", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 326) }, - { "17234843749633035510", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 82) }, - { "11516168882438876247", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 320) }, - { "11312664612825940140", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 325) }, - { "14846039494240217143", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 326) }, - { "3390376200501119384", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 135) }, - { "1113077760071340574", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 327) }, - { "4614875083188849196", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 40) }, - { "10859023312681572942", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 86) }, - { "5588692131556725717", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 243) }, - { "7653946972043115920", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 85) }, - { "9773458066743315157", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 127) }, - { "10491513939202460216", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "8140122945471321201", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 42) }, - { "15079423575410353790", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 240) }, - { "12844146569641472927", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 243) }, - { "13443914015380511668", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 80) }, - { "13404457916017756196", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 127) }, - { "6402415801415013013", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "2040762223425679479", std::make_tuple("fully_connected_gpu_bf_io_gemm", -1) }, - { "8595156989254845134", std::make_tuple("convolution_gpu_bfyx_1x1_hgemm_buf_16x1", -1) }, - { "14493123117003003092", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, - { "7391309333582046386", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 325) }, - { "9935182178960843140", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 327) }, - { "15422142509105297183", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 111) }, - { "14849987788569183527", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 327) }, - { "14923132847727661051", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 326) }, - { "13271555597925466454", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 325) }, - { "6865593216823998846", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 325) }, - { "12385437755245281331", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 325) }, - { "4530047829451377456", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 325) }, - { "127643210248119703", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 325) }, - { "2599051617462913767", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 356) }, - { "3024020696533545102", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 327) }, - { "16205377892664082400", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 326) }, - { "15135655146332608939", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 135) }, - { "10848724554175904486", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 206) }, - { "15558120704022404428", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 204) }, - { "14120354125904513152", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 325) }, - { "8475075092501403968", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 326) }, - { "15892943371777404347", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 134) }, - { "15078418657871922661", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 207) }, - { "3502053626453342387", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 135) }, - { "1230316443026403527", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 326) }, - { "12846418701225147646", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 135) }, - { "17386994561779281406", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 325) }, - { "7367684259946371231", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 326) }, - { "5451072983028714092", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 204) }, - { "11758623888547009364", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 327) }, - { "15958650715061024845", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 325) }, - { "13899144453581769028", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 325) }, - { "10884229860266073967", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 323) }, - { "6629431845229592220", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 204) }, - { "13199442294147992119", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 327) }, - { "5032929712205664246", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 326) }, - { "14871333176552512036", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 326) }, - { "17890435688048047959", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 325) }, - { "11353661571093800805", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 326) }, - { "3101908018947919238", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 327) }, - { "7495240482209084478", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 94) }, - { "6964506613327100469", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 94) }, - { "3670645005971806718", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 84) }, - { "69949758775887534", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 241) }, - { "13654393413005772278", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 243) }, - { "11101512074369779300", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 80) }, - { "2273811004985590823", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "7457154125218067377", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "7709677514862642399", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "8010619564572573208", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "16479793487852125428", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "3147355028342035061", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "12672939642957531547", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "12627961914394914920", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "16210688853876861607", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "1899485873740458557", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 284) }, - { "669151029135558505", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 286) }, - { "8912067280071688393", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 286) }, - { "7714589858275971005", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 292) }, - { "9794413496918699979", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 294) }, - { "18350040136091421971", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 284) }, - { "16931304566154830346", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 294) }, - { "12816950084297042217", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 284) }, - { "2359632276970855181", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 325) }, - { "16592641501972654496", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 323) }, - { "7754054384598160936", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 292) }, - { "18195884921517044108", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 325) }, - { "16256130331524359070", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 292) }, - { "13497279823712860029", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 282) }, - { "6095972148204769193", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 326) }, - { "4149964766407000732", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 326) }, - { "10262104071809780712", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 356) }, - { "10707129891337660055", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 180) }, - { "3585075254981736756", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 292) }, - { "3181067565488724209", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 174) }, - { "8636008354706344794", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 246) }, - { "4966150965920189853", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 247) }, - { "5569253153294942795", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 240) }, - { "11521347729886549503", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 174) }, - { "12399471154320580621", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 182) }, - { "14851218369956754103", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 240) }, - { "14859848826604327499", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 252) }, - { "14783159891899899660", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 181) }, - { "2369671961317151564", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 247) }, - { "1691004331056506231", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 247) }, - { "4465288557833228023", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 174) }, - { "13538111995551348621", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 182) }, - { "1886751914747841929", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 252) }, - { "8445964247944285746", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 182) }, - { "803205084059316676", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 325) }, - { "12654698468722759675", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 240) }, - { "13484605287576302088", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 180) }, - { "2469399061693302590", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 252) }, - { "14782181149367028912", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 252) }, - { "1448238652280623323", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 182) }, - { "2076478920663115306", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 284) }, - { "7369834759425644726", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 95) }, - { "11702633755046828968", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 252) }, - { "2944972038827287015", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 206) }, - { "8261441437673092886", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 206) }, - { "16694312773479519523", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 207) }, - { "5486494868955566721", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 201) }, - { "8246009573416434030", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 205) }, - { "9457894602447879547", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "4598302923247277427", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 204) }, - { "3116224788980631217", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 201) }, - { "17103527368951412486", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 204) }, - { "10150428063205056209", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "16984028253790680977", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 202) }, - { "17857105233471273424", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 202) }, - { "2715609009808401074", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 205) }, - { "232807837985324954", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 201) }, - { "13933912937625580405", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, - { "11254635684957519432", std::make_tuple("convolution_gpu_bfyx_1x1_hgemm_buf_16x1", -1) }, - { "6729077823331194042", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, - { "15961933828477762733", std::make_tuple("convolution_gpu_bfyx_1x1_hgemm_buf_16x1", -1) }, - { "827225131390571924", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 213) }, - { "10429613013253088132", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "14629385997654952321", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, - { "7546586420552408243", std::make_tuple("convolution_gpu_bfyx_1x1_hgemm_buf_16x1", -1) }, - { "11897687507601277182", std::make_tuple("convolution_gpu_bfyx_1x1_hgemm_buf_16x1", -1) }, - { "13975409361394567866", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 320) }, - { "6458124573210430792", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 360) }, - { "2385616965635993249", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, - { "14462438074931673266", std::make_tuple("convolution_gpu_bfyx_1x1_hgemm_buf_16x1", -1) }, - { "9338654554616107568", std::make_tuple("convolution_gpu_bfyx_1x1_hgemm_buf_16x1", -1) }, - { "15476402794704488137", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 204) }, - { "13654816209891478730", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 228) }, - { "1680424228660495363", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "12929981792125924963", std::make_tuple("convolution_gpu_bfyx_1x1_hgemm_buf_16x1", -1) }, - { "4698507050987130777", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 242) }, - { "1094144958579794349", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "12358908585763044267", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 327) }, - { "9793373151408615612", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "90849151510482266", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "17277787450259342076", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 327) }, - { "2367877811435050998", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 326) }, - { "433942345363552443", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "14575816691130255191", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "3675622521877371819", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 247) }, - { "7601637686045360430", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 254) }, - { "9818496628902493298", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 327) }, - { "377651990943545344", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 327) }, - { "16357661916741979192", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 335) }, - { "5417669424921804056", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "854020380490533945", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 325) }, - { "11984602132438314210", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 325) }, - { "13100228219613095795", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 334) }, - { "9808704199834907703", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "13071373212254908241", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 202) }, - { "18392748682101174561", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "11928475964162658765", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 326) }, - { "11937547211842355800", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 367) }, - { "4637568849323640167", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "16812695025037565299", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 261) }, - { "10487883723723512839", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 325) }, - { "15315014737515653325", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 327) }, - { "1579905786032546689", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 135) }, - { "4243996335899627971", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, - { "2545885699369058867", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, - { "16926950874716567095", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, - { "6355395905401306995", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "2096779676054335057", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "4217179485243909459", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "17101789600628162503", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 358) }, - { "6139574161497189424", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 213) }, - { "16559140502701231107", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 207) }, - { "11459784003592366395", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 207) }, - { "7869916853707978306", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 84) }, - //{ "3889519976910355277", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, - //{ "12081835728078383819", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, - //{ "14923692894655929923", std::make_tuple("fully_connected_gpu_bf_io_gemm", -1) }, - { "1841901358010744236", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "10888203577545955226", std::make_tuple("convolution_gpu_bfyx_1x1_hgemm_buf_16x1", -1) }, - { "9799890897264103013", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, - { "1827296932806936575", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 325) }, - { "13010820430079828498", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 323) }, - { "6245781545617904772", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 325) }, - { "8714031312599034571", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "12922099252166105096", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 222) }, - { "3042887030242700493", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "1419879016567682338", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 325) }, - { "12870587285162108523", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 323) }, - { "6103433181190121715", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 323) }, - { "3469599265931338557", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "14812617666668076833", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "14854734265631496499", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 323) }, - { "7637441820772916248", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 335) }, - { "14695781272831602408", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 207) }, - { "15696910741835640150", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "17790593820165047954", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "1433224983833208570", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 182) }, - { "16185194021453870096", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 174) }, - { "10310918050196558188", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 246) }, - { "14885938077915823034", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 242) }, - { "14442357887993453368", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 254) }, - { "4719130523147011420", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 246) }, - { "8870222084473246330", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 175) }, - { "9350596936816632825", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 246) }, - { "6183248276225219542", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 173) }, - { "3757195189216622027", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 253) }, - { "8318857994507665384", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 175) }, - { "12864512857659000129", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 282) }, - { "6615646900347529347", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 245) }, - { "5208923086986567490", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 246) }, - { "9390793435913144215", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 174) }, - { "11997629302296435180", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 174) }, - { "4858270366437120918", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 175) }, - { "15975176007724247667", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 244) }, - { "16837473534895641370", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 244) }, - { "16214153687871223428", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 183) }, - { "8858112708913743577", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 183) }, - { "8844677471730173649", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 183) }, - { "9043742986995534354", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 253) }, - { "15621341038256548867", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 244) }, - { "18268980125375728709", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 253) }, - { "9757167087033785227", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "6513982093384445397", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 135) }, - { "9273893819042428704", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 142) }, - { "8270840662337272430", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 135) }, - { "15021512490648380369", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 123) }, - { "6578908625437515675", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 47) }, - { "13762042713029963144", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 125) }, - { "668798769117277023", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 126) }, - { "10917498758625273194", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 47) }, - { "14335423820860953927", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 135) }, - { "13978750151855895830", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 82) }, - { "8965747921518186477", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 7) }, - { "4428101657497677982", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 84) }, - { "8151272056391095510", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "17656341100957270390", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 4) }, - { "10295330953350618042", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 7) }, - { "15901675909820977223", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 7) }, - { "6026876733674266377", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 7) }, - { "381149736509958403", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 7) }, - { "7962991673727743706", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 7) }, - { "10064251191248475177", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "16663239694378513014", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 335) }, - { "4690831975451405214", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, - { "16132498413588349821", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, - { "10609644803793651808", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, - { "16520784657717262379", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, - { "2271187702055786721", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, - { "1872921634399989626", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, - { "12535576637355537200", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, - { "7771729980527620398", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, - { "14849108908297747749", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, - { "14043770215999952932", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "15277856047844308598", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "8048617952947915835", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "11446745541571732900", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, - { "17422822627612865758", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, - { "13954144830230671601", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 327) }, - { "11198908896401597838", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 254) }, - { "5582896843095691256", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 109) }, - { "8133587696326295326", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 109) }, - { "2007192658799516915", std::make_tuple("fully_connected_gpu_bf_io_gemm", -1) }, - { "9492402787848610840", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, - { "10515519878978734341", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, - { "1375156980278317418", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "13455881643467418059", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "12788968383428254917", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 255) }, - { "12304975739476881266", std::make_tuple("fully_connected_gpu_bf_io_gemm", -1) }, - { "3390014193205017427", std::make_tuple("fully_connected_gpu_bf_io_gemm", -1) }, - { "13587202155230938291", std::make_tuple("fully_connected_gpu_bf_io_gemm", -1) }, - { "14462744723628661203", std::make_tuple("fully_connected_gpu_bf_io_gemm", -1) }, - { "16109721499545711936", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 155) }, - { "10090923790949378407", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "16672038432561840773", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "2973773544904290726", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "17193614571243427089", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "2862029728492027826", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "11066930104187448422", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "2755147389712995637", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "10662798624911535617", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 345) }, - { "2172121470071868949", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "3579916582911190192", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "5495063314176654751", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "3771003491521695667", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 359) }, - { "15514370342945522276", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 359) }, - { "17285639145064557279", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 365) }, - { "12642574441854544900", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 356) }, - { "5471430682416582179", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 359) }, - { "8561154029325525444", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 340) }, - { "8939900194037985459", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "2129726780118554358", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 352) }, - { "15463465056816958579", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 353) }, - { "9268536904925062469", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 367) }, - { "45977313646881991", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 367) }, - { "7092246390386193774", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 340) }, - { "14801984300948838261", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "2797723586312707948", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 354) }, - { "8451212914744825089", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "5131348852069018593", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 207) }, - { "13619081494170885939", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 357) }, - { "8818679285688095197", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 206) }, - { "4608292692528881356", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "9729987752669765456", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 286) }, - 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{ "16159032667792855758", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "3374037004378790060", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 247) }, - { "6765409971512438438", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 320) }, - { "10098892297878373639", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "981877665302032867", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "7472350511000146655", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 241) }, - { "15513971895394346930", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 102) }, - { "6169721205327431190", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 324) }, - { "4519054607159036572", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 246) }, - { "12750124851833311828", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 341) }, - { "4333851142313192116", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "6041620003527819661", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 241) }, - { "15091361629922645798", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 246) }, - { "9348121965341418899", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 331) }, - { "393951904144235223", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "3220084080191614421", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 127) }, - { "11610588256244825741", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "2802357220980817497", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 324) }, - { "8972812517118478580", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 353) }, - { "4207115359813621211", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 79) }, - { "16582237002610438015", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 99) }, - { "772342953072606219", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "8546247990965609013", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "7971830510840138313", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 206) }, - { "3570484486449791727", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, - { "11461079340079820563", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, - { "12663860560275361463", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, - { "8132521728369930959", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "16108573960501496757", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "11086699387784339943", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 119) }, - { "4013707396889204359", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 206) }, - { "11850332373794932468", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 101) }, - { "14763982961176216679", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 93) }, - { "8207349115037232863", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 206) }, - { "3273748387141431306", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 84) }, - //{ "580936360000782237", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, - //{ "10682918518101379579", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, - //{ "13178480813522103091", std::make_tuple("fully_connected_gpu_bf_io_gemm", -1) }, - { "14472322679644532468", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 111) }, - { "8378137527264154204", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "10180255575636684134", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 142) }, - { "18242121098885244699", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 322) }, - { "6178572652675599622", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 320) }, - { "6558074021146321216", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 243) }, - { "11038938372264857379", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 253) }, - { "17137800360536507200", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 138) }, - { "14016185289182597841", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 253) }, - { "8970519484272874266", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 320) }, - { "3289746379259038515", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 320) }, - { "18389174979070260315", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 320) }, - { "17666483005735191253", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 254) }, - { "10845781902676865789", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 253) }, - { "13646634862315619979", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "5072154928583891344", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 335) }, - { "7757331094141318304", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 206) }, - { "16779678846332091086", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "12223166874490429642", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 254) }, - { "13316017702896072758", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 174) }, - { "10390896207372295988", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 135) }, - { "8386498395042623384", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 241) }, - { "17923632501885139982", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 243) }, - { "13398326377839777956", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 246) }, - { "13520557646924372128", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 245) }, - { "5996787039089786722", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 175) }, - { "2626376166907387273", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 135) }, - { "6147643392694904814", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 175) }, - { "4311921348668650791", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 245) }, - { "8046109476498335792", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 174) }, - { "14190077682825257613", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 246) }, - { "5032302126047788183", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 245) }, - { "15256375572125522238", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 243) }, - { "5083173538217738703", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 175) }, - { "17269467004855120308", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 175) }, - { "2901056469731554922", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 175) }, - { "9747637051217505111", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 245) }, - { "7175860674618956918", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 241) }, - { "9530922411870814200", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 181) }, - { "11015319643831560673", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 181) }, - { "5182740559503076121", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 181) }, - { "7567277014404457462", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 243) }, - { "15973842639221447367", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 247) }, - { "15951492056203075273", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 169) }, - { "5283253936050062275", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 181) }, - { "17650690912303447913", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 135) }, - { "9614936270604202220", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 142) }, - { "13998661469619523378", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 135) }, - { "10584034255622783869", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 120) }, - { "2973436171295280783", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 41) }, - { "1908809004094565452", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 62) }, - { "1623383628456201603", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 127) }, - { "5766507688771440170", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 22) }, - { "16626226341188424071", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 62) }, - { "14619055893081624406", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 81) }, - { "17222005830854879661", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 7) }, - { "14224121742920800990", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 84) }, - { "5859124386313585730", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "9596156698919548146", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 7) }, - { "10482582307328548806", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 7) }, - { "407189201971322683", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 7) }, - { "16626507949474235269", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 6) }, - { "879005904827468163", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 7) }, - { "8460847842045253466", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 7) }, - { "2729099061601852493", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "8233922303282945338", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 238) }, - { "7402006230339617617", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "8420763628389536977", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "8325767678959979628", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "7673672840505587739", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "14166169053627992481", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "120923426036313670", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 330) }, - { "7348084298010357768", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 329) }, - { "14653065651448352526", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 330) }, - { "11008522061447263744", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 327) }, - { "305505245310584136", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 327) }, - { "6472139251351862598", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, - { "10586018593856542117", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, - { "16706121580364790904", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "5495776091407365966", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "16430562172386510259", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "5673972310424776040", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "8797843396807284399", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 331) }, - { "1698321314111848001", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 327) }, - { "5762290464889692462", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 215) }, - { "3218248162832023196", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 215) }, - { "12988961529988078346", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 214) }, - { "4232250144427804891", std::make_tuple("fully_connected_gpu_bf_io_gemm", -1) }, - { "11683680166617045816", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, - { "6252429564537528709", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, - { "11717348577195224554", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "9275303306340702111", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 227) }, - { "12245096462203481681", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 246) }, - { "18439435691655740074", std::make_tuple("fully_connected_gpu_bf_io_gemm", -1) }, - { "15272426400992401555", std::make_tuple("fully_connected_gpu_bf_io_gemm", -1) }, - { "9136831791301215059", std::make_tuple("fully_connected_gpu_bf_io_gemm", -1) }, - { "18238669114790278675", std::make_tuple("fully_connected_gpu_bf_io_gemm", -1) }, - { "15376246520426368532", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 110) }, - { "16469788155263456039", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "9360494451263553093", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "7897877428349481398", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "9354818521586974021", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "5853553261686771766", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "6351572488552853754", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "11372638316835753193", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "6170074103544756465", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 182) }, - { "4544242784357021697", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "14225108809796795520", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "178353385245384751", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 224) }, - { "2002574142025049539", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 356) }, - { "11630971824787392820", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 213) }, - { "11542493210215136239", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 217) }, - { "9595803435783166868", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "17610828776103321939", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 211) }, - { "18312069177632970412", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 237) }, - { "7577483892218843723", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 212) }, - { "4355933224673863178", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "15124932296735391043", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 284) }, - { "16888042302987189589", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 360) }, - { "1584639932403433303", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 362) }, - { "15516194807992507442", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 237) }, - { "6614374536332038989", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 212) }, - { "5097818987523855112", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "6623182990939010641", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "17001023283013862129", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 203) }, - { "7935150275452094595", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 203) }, - { "2326323992207208685", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 202) }, - { "5072735784865711772", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 353) }, - { "4683841893192741312", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 206) }, - { "10341773151035665956", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 212) }, - 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{ "14910223536998380801", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 47) }, - { "18277685132620834972", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 254) }, - { "13090887980792573261", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 126) }, - { "3067806959725855130", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 141) }, - { "17791773192152464021", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 63) }, - { "13603318842632052764", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 7) }, - { "12072881177966014126", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "14413047954443174304", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 259) }, - { "9118663018352672834", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 335) }, - { "3558391988878894288", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 263) }, - { "10047727261970275928", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 262) }, - { "11527382293059267033", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 313) }, - { "8445575388700666150", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 313) }, - { "4560479630843098090", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "5737652209336034549", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 222) }, - { "1159910924058542880", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 345) }, - { "7203172595229334691", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 345) }, - { "8153567933591966877", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 356) }, - { "6069305481947437228", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, - { "2216113049194620088", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 212) }, - { "10395837712676962495", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 360) }, - { "5041111302824362529", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 164) }, - { "12542825714985999760", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "4599539412023802059", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 303) }, - { "4570119951370893062", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 263) }, - { "16897917745917378359", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 263) }, - { "6947523163603267191", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 142) }, - { "2322126126611987721", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 141) }, - { "6518845972912144959", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 142) }, - { "9741774854327055438", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 254) }, - { "7079854103926842364", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 253) }, - { "5035895518536085765", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 254) }, - { "4307157272240924516", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 271) }, - { "142486914279119363", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 207) }, - { "1532263118203058517", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "13529694429433303321", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 271) }, - { "2820916926593580316", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 246) }, - { "11140657515428786448", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 257) }, - { "9028970753877215614", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 126) }, - { "2324120381399737261", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 246) }, - { "14311837335039030749", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 257) }, - { "6105963806494867023", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 134) }, - { "14746731245428677019", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 127) }, - { "9213886570531053949", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 41) }, - { "385046297070779752", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 255) }, - { "15882699446713091149", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 127) }, - { "475043738497218394", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 142) }, - { "6351347283201596793", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 62) }, - { "16290626406346691996", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 14) }, - { "14038308632095412386", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "15928183143089896780", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 271) }, - { "14071202918199194502", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 341) }, - { "12383676694875725364", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 271) }, - { "1830378766184274696", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 231) }, - { "13133105923394042921", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 159) }, - { "12458275468325297142", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 231) }, - { "1819720745131968914", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "5140267554909755437", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 231) }, - { "6021498606197929608", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 271) }, - { "5832188635321350875", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 231) }, - { "1411786954276574458", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 280) }, - { "18193548056717743576", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 110) }, - { "7989522965312833517", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 110) }, - }); - } - - //KBLGT2 - void tuning_cache_5912(tuning_data& td) - { - td.td.insert({ - { "4583484812233029888", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "7560832358324865221", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "7382044526960590018", std::make_tuple("convolution_gpu_bfyx_direct_10_12_16", -1) }, - { "12372261924257291610", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 246) }, - { "1547771611689525848", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "3134973665622945888", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "18260030211719729324", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 327) }, - { "7416143717989012766", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 324) }, - { "12028963907131702705", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 345) }, - { "2464531851392092325", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 327) }, - { "8181308759455478086", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 322) }, - { "546062289721803579", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "4889405384318695802", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 367) }, - { "12841232643395100314", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 330) }, - { "14108361259911144680", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 345) }, - { "2726453304845436156", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 86) }, - { "2607416795507802412", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 325) }, - { "2175404966338020579", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 326) }, - { "14666883719480623074", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 320) }, - { "3752993663604843837", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 135) }, - { "5274735654559844733", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 335) }, - { "8174421295799601683", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 202) }, - { "1967655354607438665", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 325) }, - { "15762542971370422224", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 323) }, - { "8183203099539372914", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 323) }, - { "4075343423548891274", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 135) }, - { "13264497096898621015", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 333) }, - { "679058537775669048", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 327) }, - { "3375634256357960999", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 325) }, - { "2844616672368585285", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 334) }, - { "14235558866846276172", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 201) }, - { "18066867692765966577", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 325) }, - { "9861424412782371874", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 320) }, - { "607078314875528651", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 323) }, - { "6234885984223387670", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 133) }, - { "7223570329858821704", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 334) }, - { "17234843749633035510", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 202) }, - { "11516168882438876247", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 320) }, - { "11312664612825940140", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 367) }, - { "14846039494240217143", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 325) }, - { "3390376200501119384", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 323) }, - { "1113077760071340574", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 334) }, - { "4614875083188849196", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 82) }, - { "10859023312681572942", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 242) }, - { "5588692131556725717", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 240) }, - { "7653946972043115920", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 243) }, - { "9773458066743315157", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "10491513939202460216", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "8140122945471321201", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 82) }, - { "15079423575410353790", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 241) }, - { "12844146569641472927", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 241) }, - { "13443914015380511668", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 240) }, - { "13404457916017756196", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "6402415801415013013", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "2040762223425679479", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, - { "8595156989254845134", std::make_tuple("convolution_gpu_bfyx_1x1_hgemm_buf_16x1", -1) }, - { "14493123117003003092", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, - { "7391309333582046386", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 367) }, - { "9935182178960843140", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 333) }, - { "15422142509105297183", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 335) }, - { "14849987788569183527", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 333) }, - { "14923132847727661051", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 367) }, - { "13271555597925466454", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 367) }, - { "6865593216823998846", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 360) }, - { "12385437755245281331", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 333) }, - { "4530047829451377456", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 333) }, - { "127643210248119703", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 365) }, - { "2599051617462913767", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 322) }, - { "3024020696533545102", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 334) }, - { "16205377892664082400", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 324) }, - { "15135655146332608939", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 327) }, - { "10848724554175904486", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 326) }, - { "15558120704022404428", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 323) }, - { "14120354125904513152", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 325) }, - { "8475075092501403968", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 322) }, - { "15892943371777404347", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 326) }, - { "15078418657871922661", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 327) }, - { "3502053626453342387", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 322) }, - { "1230316443026403527", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 325) }, - { "12846418701225147646", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 326) }, - { "17386994561779281406", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 326) }, - { "7367684259946371231", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 325) }, - { "5451072983028714092", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 326) }, - { "11758623888547009364", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 334) }, - { "15958650715061024845", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 334) }, - { "13899144453581769028", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 334) }, - { "10884229860266073967", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 323) }, - { "6629431845229592220", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 325) }, - { "13199442294147992119", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 335) }, - { "5032929712205664246", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 335) }, - { "14871333176552512036", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 335) }, - { "17890435688048047959", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 325) }, - { "11353661571093800805", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 326) }, - { "3101908018947919238", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 334) }, - { "7495240482209084478", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 95) }, - { "6964506613327100469", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 101) }, - { "3670645005971806718", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 240) }, - { "69949758775887534", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 242) }, - { "13654393413005772278", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 240) }, - { "11101512074369779300", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 243) }, - { "2273811004985590823", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "7457154125218067377", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "7709677514862642399", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "8010619564572573208", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "16479793487852125428", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "3147355028342035061", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "12672939642957531547", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 263) }, - { "12627961914394914920", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "16210688853876861607", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 294) }, - { "1899485873740458557", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 293) }, - { "669151029135558505", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 303) }, - { "8912067280071688393", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "7714589858275971005", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 196) }, - { "9794413496918699979", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 305) }, - { "18350040136091421971", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 287) }, - { "16931304566154830346", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 305) }, - { "12816950084297042217", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 303) }, - { "2359632276970855181", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 303) }, - { "16592641501972654496", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 303) }, - { "7754054384598160936", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 196) }, - { "18195884921517044108", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 301) }, - { "16256130331524359070", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 305) }, - { "13497279823712860029", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 293) }, - { "6095972148204769193", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 301) }, - { "4149964766407000732", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 302) }, - { "10262104071809780712", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 301) }, - { "10707129891337660055", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 174) }, - { "3585075254981736756", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 306) }, - { "3181067565488724209", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 172) }, - { "8636008354706344794", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 248) }, - { "4966150965920189853", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 248) }, - { "5569253153294942795", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 256) }, - { "11521347729886549503", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 181) }, - { "12399471154320580621", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 180) }, - { "14851218369956754103", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 259) }, - { "14859848826604327499", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 263) }, - { "14783159891899899660", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 261) }, - { "2369671961317151564", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 256) }, - { "1691004331056506231", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 256) }, - { "4465288557833228023", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 263) }, - { "13538111995551348621", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 263) }, - { "1886751914747841929", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 263) }, - { "8445964247944285746", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 261) }, - { "803205084059316676", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 256) }, - { "12654698468722759675", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 259) }, - { "13484605287576302088", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 262) }, - { "2469399061693302590", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 263) }, - { "14782181149367028912", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 263) }, - { "1448238652280623323", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 261) }, - { "2076478920663115306", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 256) }, - { "7369834759425644726", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 110) }, - { "11702633755046828968", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 263) }, - { "2944972038827287015", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 95) }, - { "8261441437673092886", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 214) }, - { "16694312773479519523", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 214) }, - { "5486494868955566721", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 204) }, - { "8246009573416434030", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 214) }, - { "9457894602447879547", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 209) }, - { "4598302923247277427", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 214) }, - { "3116224788980631217", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 205) }, - { "17103527368951412486", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 214) }, - { "10150428063205056209", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 209) }, - { "16984028253790680977", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 215) }, - { "17857105233471273424", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 214) }, - { "2715609009808401074", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 202) }, - { "232807837985324954", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 201) }, - { "13933912937625580405", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, - { "11254635684957519432", std::make_tuple("convolution_gpu_bfyx_1x1_hgemm_buf_16x1", -1) }, - { "6729077823331194042", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, - { "15961933828477762733", std::make_tuple("convolution_gpu_bfyx_1x1_hgemm_buf_16x1", -1) }, - { "827225131390571924", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "10429613013253088132", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "14629385997654952321", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, - { "7546586420552408243", std::make_tuple("convolution_gpu_bfyx_1x1_hgemm_buf_16x1", -1) }, - { "11897687507601277182", std::make_tuple("convolution_gpu_bfyx_1x1_hgemm_buf_16x1", -1) }, - { "13975409361394567866", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 222) }, - { "6458124573210430792", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 333) }, - { "2385616965635993249", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, - { "14462438074931673266", std::make_tuple("convolution_gpu_bfyx_1x1_hgemm_buf_16x1", -1) }, - { "9338654554616107568", std::make_tuple("convolution_gpu_bfyx_1x1_hgemm_buf_16x1", -1) }, - { "15476402794704488137", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 209) }, - { "13654816209891478730", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 227) }, - { "1680424228660495363", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 109) }, - { "12929981792125924963", std::make_tuple("convolution_gpu_bfyx_1x1_hgemm_buf_16x1", -1) }, - { "4698507050987130777", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 209) }, - { "1094144958579794349", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "12358908585763044267", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 287) }, - { "9793373151408615612", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "90849151510482266", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "17277787450259342076", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 367) }, - { "2367877811435050998", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 327) }, - { "433942345363552443", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "14575816691130255191", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "3675622521877371819", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 325) }, - { "7601637686045360430", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 337) }, - { "9818496628902493298", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "377651990943545344", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 325) }, - { "16357661916741979192", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 345) }, - { "5417669424921804056", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "854020380490533945", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 323) }, - { "11984602132438314210", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 360) }, - { "13100228219613095795", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 268) }, - { "9808704199834907703", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "13071373212254908241", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 323) }, - { "18392748682101174561", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 334) }, - { "11928475964162658765", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "11937547211842355800", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 372) }, - { "4637568849323640167", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "16812695025037565299", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 341) }, - { "10487883723723512839", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 334) }, - { "15315014737515653325", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "1579905786032546689", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "4243996335899627971", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, - { "2545885699369058867", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, - { "16926950874716567095", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, - { "6355395905401306995", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "2096779676054335057", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "4217179485243909459", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "17101789600628162503", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 223) }, - { "6139574161497189424", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 222) }, - { "16559140502701231107", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 214) }, - { "11459784003592366395", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 214) }, - { "7869916853707978306", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 87) }, - //{ "3889519976910355277", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, - //{ "12081835728078383819", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, - //{ "14923692894655929923", std::make_tuple("fully_connected_gpu_bf_io_gemm", -1) }, - { "1841901358010744236", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "10888203577545955226", std::make_tuple("convolution_gpu_bfyx_1x1_hgemm_buf_16x1", -1) }, - { "9799890897264103013", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, - { "1827296932806936575", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 325) }, - { "13010820430079828498", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 325) }, - { "6245781545617904772", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 255) }, - { "8714031312599034571", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "12922099252166105096", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 268) }, - { "3042887030242700493", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 262) }, - { "1419879016567682338", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 326) }, - { "12870587285162108523", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 327) }, - { "6103433181190121715", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 325) }, - { "3469599265931338557", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "14812617666668076833", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 260) }, - { "14854734265631496499", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 325) }, - { "7637441820772916248", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 326) }, - { "14695781272831602408", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 215) }, - { "15696910741835640150", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 259) }, - { "17790593820165047954", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "1433224983833208570", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 199) }, - { "16185194021453870096", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 172) }, - { "10310918050196558188", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 248) }, - { "14885938077915823034", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 245) }, - { "14442357887993453368", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 263) }, - { "4719130523147011420", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 248) }, - { "8870222084473246330", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "9350596936816632825", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 181) }, - { "6183248276225219542", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "3757195189216622027", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 256) }, - { "8318857994507665384", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "12864512857659000129", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 248) }, - { "6615646900347529347", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 256) }, - { "5208923086986567490", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 248) }, - { "9390793435913144215", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 262) }, - { "11997629302296435180", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 262) }, - { "4858270366437120918", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 262) }, - { "15975176007724247667", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 256) }, - { "16837473534895641370", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 248) }, - { "16214153687871223428", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 260) }, - { "8858112708913743577", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 260) }, - { "8844677471730173649", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 262) }, - { "9043742986995534354", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 263) }, - { "15621341038256548867", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 248) }, - { "18268980125375728709", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "9757167087033785227", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 269) }, - { "6513982093384445397", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 247) }, - 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{ "11757187678986741715", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 175) }, - { "13038212285326297688", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 142) }, - { "13919423909034348565", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 175) }, - { "12925256096286953030", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 258) }, - { "6275163484075546689", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 258) }, - { "239651884801599911", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 262) }, - { "9500211224156027451", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 183) }, - { "10902538092301362853", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 182) }, - { "8454943813981348115", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 261) }, - { "11722951613064434115", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 193) }, - { "13547342611064538960", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 174) }, - { "15171119202712914112", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 260) }, - { "5451487099025245427", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 260) }, - { "1814940262511664251", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 199) }, - { "16341609351317463829", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 195) }, - { "14343280871046671393", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 193) }, - { "15586404971308258630", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 172) }, - { "15891211707425019144", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 262) }, - { "15351688973597240327", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 261) }, - { "1844016761754156672", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 174) }, - { "17925606428283439978", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 193) }, - { "15050158761219834868", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 173) }, - { "17448180555072943363", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 174) }, - { "11422222075976800614", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 262) }, - { "14284377769814732906", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 95) }, - { "667777413731244716", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 173) }, - { "11624071786842686451", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 95) }, - { "3874974512053082278", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 215) }, - { "5471037497181745651", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 210) }, - { "6371386660654628561", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 204) }, - { "5331835606773958814", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 211) }, - { "16163821504542698475", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 211) }, - { "5697543838890997891", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 110) }, - { "6217438921274668801", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 203) }, - { "2633095809604510774", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 213) }, - { "11218297661079136641", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 211) }, - { "3374196543196230185", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 213) }, - { "17797320202829145544", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 210) }, - { "17198778757516749818", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 203) }, - { "6440981718484677922", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 203) }, - { "1616603916015535857", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, - { "7643715911083095268", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "137903092932521503", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 203) }, - { "13203019690952060789", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 324) }, - { "3918152537861570517", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 246) }, - { "8782903242853500098", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "18312668164562040079", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 84) }, - { "15160703466234996170", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 135) }, - { "2751241748685218213", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 123) }, - { "12622728760401804660", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 135) }, - { "1290624457831957354", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 123) }, - { "10924946887162830574", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 135) }, - { "10789202693606479024", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 203) }, - { "3718558874911694616", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 326) }, - { "12835389389575311182", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 84) }, - { "17406431092101974143", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 207) }, - { "1400409391266374603", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 84) }, - { "12154660333025778322", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 206) }, - { "4800587664660105589", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, - { "16474284418841532356", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "18117355153710110681", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "8449591498895477846", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "2962899568083589487", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "3491333679577961640", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "13945298510228460890", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 271) }, - { "4282198629458668761", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "779525528509830615", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "15002237905129290671", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 332) }, - { "16833026567865627676", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 335) }, - { "16991060247581867302", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "6902644989079870993", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 335) }, - { "15088285782819494786", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 330) }, - { "3379661203936923589", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 203) }, - { "10049571207493913006", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 224) }, - { "15691689005236690951", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 214) }, - { "11275109735493317886", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 224) }, - { "12220860296984467101", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 208) }, - { "44210723233569665", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 110) }, - { "14014987361364503383", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 328) }, - { "18189351665719757712", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "16159032667792855758", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "3374037004378790060", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 332) }, - { "6765409971512438438", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "10098892297878373639", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "981877665302032867", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "7472350511000146655", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 247) }, - { "15513971895394346930", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 341) }, - { "6169721205327431190", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "4519054607159036572", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 356) }, - { "12750124851833311828", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "4333851142313192116", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "6041620003527819661", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 356) }, - { "15091361629922645798", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 331) }, - { "9348121965341418899", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 102) }, - { "393951904144235223", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "3220084080191614421", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 203) }, - { "11610588256244825741", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 331) }, - { "2802357220980817497", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "8972812517118478580", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 354) }, - { "4207115359813621211", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 79) }, - { "16582237002610438015", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 103) }, - { "772342953072606219", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "8546247990965609013", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "7971830510840138313", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "3570484486449791727", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, - { "11461079340079820563", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, - { "12663860560275361463", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, - { "8132521728369930959", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "16108573960501496757", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "11086699387784339943", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "4013707396889204359", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 231) }, - { "11850332373794932468", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 231) }, - { "14763982961176216679", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 102) }, - { "8207349115037232863", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 206) }, - { "3273748387141431306", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 85) }, - //{ "580936360000782237", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, - //{ "10682918518101379579", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, - //{ "13178480813522103091", std::make_tuple("fully_connected_gpu_bf_io_gemm", -1) }, - { "14472322679644532468", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 142) }, - { "8378137527264154204", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "10180255575636684134", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 156) }, - { "18242121098885244699", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 241) }, - { "6178572652675599622", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 336) }, - { "6558074021146321216", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 332) }, - { "11038938372264857379", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "17137800360536507200", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 270) }, - { "14016185289182597841", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "8970519484272874266", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 247) }, - { "3289746379259038515", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 246) }, - { "18389174979070260315", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 241) }, - { "17666483005735191253", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 326) }, - { "10845781902676865789", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "13646634862315619979", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 336) }, - { "5072154928583891344", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "7757331094141318304", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 212) }, - { "16779678846332091086", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 327) }, - { "12223166874490429642", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "13316017702896072758", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 262) }, - { "10390896207372295988", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 175) }, - { "8386498395042623384", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 166) }, - { "17923632501885139982", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 241) }, - { "13398326377839777956", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 262) }, - { "13520557646924372128", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 295) }, - { "5996787039089786722", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "2626376166907387273", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 171) }, - { "6147643392694904814", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "4311921348668650791", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 261) }, - { "8046109476498335792", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "14190077682825257613", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 295) }, - { "5032302126047788183", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 262) }, - { "15256375572125522238", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 203) }, - { "5083173538217738703", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 195) }, - { "17269467004855120308", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 195) }, - { "2901056469731554922", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 196) }, - { "9747637051217505111", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 262) }, - { "7175860674618956918", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 291) }, - 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{ "16524474021378494125", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "4407550747921719377", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "7259905085241841240", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 331) }, - { "6666210546769702280", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "7814543122045448412", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "13554702187867408038", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "2547880010597993852", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 182) }, - { "9061076702890952738", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 182) }, - { "15460429275475874158", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 256) }, - { "7724185199575851246", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 242) }, - { "8533091468352267196", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 253) }, - { "15025120359649460106", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 248) }, - { "2613575328969629284", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "7463954007838579697", std::make_tuple("convolution_gpu_bfyx_direct_10_12_16", -1) }, - { "9151324495773628566", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "651020886445062493", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 256) }, - { "8237821273547216740", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "2875927974837744359", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 248) }, - { "3674322065648064195", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 256) }, - { "137871170540938640", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 256) }, - { "3066826388383295007", std::make_tuple("convolution_gpu_bfyx_direct_10_12_16", -1) }, - { "17483221428915982776", std::make_tuple("convolution_gpu_bfyx_direct_10_12_16", -1) }, - { "3403906310423395442", std::make_tuple("convolution_gpu_bfyx_direct_10_12_16", -1) }, - { "3888283018836731569", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 256) }, - { "13928684419408478520", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 256) }, - { "11520548550630007970", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "4922714504620931501", std::make_tuple("convolution_gpu_bfyx_direct_10_12_16", -1) }, - { "15683804450763499599", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "12686604223669447758", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 256) }, - { "10746289671948325353", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 248) }, - { "2487976264999747775", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 248) }, - { "6163010595188500945", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "10404333823880552577", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 262) }, - { "16662409111036688294", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 262) }, - { "5400706842524705774", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 257) }, - { "17423097433955762667", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 256) }, - { "18131954418490925431", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 274) }, - { "16549854027697846882", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 260) }, - { "10340073416712988987", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 155) }, - { "4633923265089466898", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 273) }, - { "6808980404170272597", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 260) }, - { "10592783998150232858", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 158) }, - { "1594829714229111215", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 156) }, - { "17361714725103230834", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 157) }, - { "15732140959902969012", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 102) }, - { "5796974850751105634", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 98) }, - { "10588059104387338398", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 63) }, - { "11738780323979052397", std::make_tuple("convolution_gpu_bfyx_direct_10_12_16", -1) }, - { "16342972196376030503", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 5) }, - { "10406201782146034797", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 7) }, - { "17342758321852264926", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 7) }, - { "15951978466742016539", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "14100026884590707572", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 335) }, - { "8368507377481570353", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 335) }, - { "2230145893895952661", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 346) }, - { "6320236287540885300", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 346) }, - { "3152975593520548377", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 346) }, - { "11756338894156892549", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 345) }, - { "9310466098863912618", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 346) }, - { "18376135355406021646", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 345) }, - { "16780457022162749898", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "17140702790441856730", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "2578325663193624576", std::make_tuple("convolution_gpu_yxfb_yxio_b16", -1) }, - { "8784358107340738205", std::make_tuple("convolution_gpu_yxfb_yxio_b16", -1) }, - { "2955459120402821540", std::make_tuple("convolution_gpu_yxfb_yxio_b16", -1) }, - { "2840794055129352139", std::make_tuple("convolution_gpu_yxfb_yxio_b16", -1) }, - { "7104266560248570112", std::make_tuple("convolution_gpu_yxfb_yxio_b16", -1) }, - { "11113125355390956764", std::make_tuple("convolution_gpu_yxfb_yxio_b16", -1) }, - { "9127827617126714860", std::make_tuple("fully_connected_gpu_fb_io_b8_f8_vload", -1) }, - { "2268275392299271167", std::make_tuple("fully_connected_gpu_fb_io_block_fp16", -1) }, - { "10615831454139478379", std::make_tuple("fully_connected_gpu_bs_f_bsv8_af8_vload", -1) }, - { "8205640825965213946", std::make_tuple("convolution_gpu_yxfb_yxio_b16", -1) }, - { "14337168375989245254", std::make_tuple("convolution_gpu_yxfb_yxio_b16", -1) }, - { "11664399629496237233", std::make_tuple("convolution_gpu_yxfb_yxio_b16", -1) }, - { "15750539817895707253", std::make_tuple("convolution_gpu_yxfb_yxio_b16", -1) }, - { "921209976738626097", std::make_tuple("convolution_gpu_yxfb_yxio_b16", -1) }, - { "8590416145336196354", std::make_tuple("convolution_gpu_yxfb_yxio_b16", -1) }, - { "10463632805036507382", std::make_tuple("convolution_gpu_yxfb_yxio_b16", -1) }, - { "13637537549252005181", std::make_tuple("convolution_gpu_yxfb_yxio_b16", -1) }, - { "7581949584623524395", std::make_tuple("convolution_gpu_yxfb_yxio_b16", -1) }, - { "9814647153117279415", std::make_tuple("convolution_gpu_yxfb_yxio_b16", -1) }, - { "3444250649099578792", std::make_tuple("convolution_gpu_yxfb_yxio_b16", -1) }, - { "4039483032571506874", std::make_tuple("fully_connected_gpu_fb_io_b8_f8_vload", -1) }, - { "14309249337788077160", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "6254493271976962295", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 262) }, - { "12387660887222981357", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, - { "7723131901316908741", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, - { "13963554827358438190", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, - { "5001552360784483833", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, - { "14201142257504107783", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, - { "1066668660701816536", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, - { "4664196755018349672", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, - { "1103228955716492167", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 215) }, - { "8618835732380720921", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "8391292909068775212", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 255) }, - { "9488974186647231896", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 248) }, - { "9069334144391048686", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 246) }, - { "12493863403516600413", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 259) }, - { "13375084585444085517", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 248) }, - { "18040173797801558071", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 254) }, - { "15329174116169594863", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 247) }, - { "14910223536998380801", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 127) }, - { "18277685132620834972", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 275) }, - { "13090887980792573261", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 135) }, - { "3067806959725855130", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 159) }, - { "17791773192152464021", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 141) }, - { "13603318842632052764", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 46) }, - { "12072881177966014126", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "14413047954443174304", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 262) }, - { "9118663018352672834", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 260) }, - { "3558391988878894288", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 260) }, - { "10047727261970275928", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 313) }, - { "11527382293059267033", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 313) }, - { "8445575388700666150", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 313) }, - { "4560479630843098090", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "5737652209336034549", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 273) }, - { "1159910924058542880", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 273) }, - { "7203172595229334691", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 273) }, - { "8153567933591966877", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 362) }, - { "6069305481947437228", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, - { "2216113049194620088", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 366) }, - { "10395837712676962495", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 362) }, - { "5041111302824362529", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 202) }, - { "12542825714985999760", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "4599539412023802059", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 303) }, - { "4570119951370893062", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 199) }, - { "16897917745917378359", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 197) }, - { "6947523163603267191", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 156) }, - { "2322126126611987721", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 159) }, - { "6518845972912144959", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 158) }, - { "9741774854327055438", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "7079854103926842364", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "5035895518536085765", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "4307157272240924516", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 271) }, - { "142486914279119363", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 212) }, - { "1532263118203058517", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "13529694429433303321", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 271) }, - { "2820916926593580316", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 116) }, - { "11140657515428786448", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "9028970753877215614", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 102) }, - { "2324120381399737261", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 154) }, - { "14311837335039030749", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 264) }, - { "6105963806494867023", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 249) }, - { "14746731245428677019", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 245) }, - { "9213886570531053949", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 167) }, - { "385046297070779752", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 155) }, - { "15882699446713091149", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 125) }, - { "475043738497218394", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 159) }, - { "6351347283201596793", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 103) }, - { "16290626406346691996", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 32) }, - { "14038308632095412386", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "15928183143089896780", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 343) }, - { "14071202918199194502", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 341) }, - { "12383676694875725364", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 342) }, - { "1830378766184274696", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 239) }, - { "13133105923394042921", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 238) }, - { "12458275468325297142", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 239) }, - { "1819720745131968914", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "5140267554909755437", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 230) }, - { "6021498606197929608", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 271) }, - { "5832188635321350875", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 231) }, - { "1411786954276574458", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 366) }, - { "18193548056717743576", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "7989522965312833517", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "2024521518658822242", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 345) }, - { "9692442768327912638", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 333) }, - { "8031184163763974840", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 333) }, - { "13429621587271813054", std::make_tuple("fully_connected_gpu_fb_io_block_fp16", -1) }, - { "17938009873557420461", std::make_tuple("fully_connected_gpu_bs_f_bsv8_af8_vload", -1) }, - { "14123865825367908500", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 367) }, - }); - } - - //SKL GT2 - void tuning_cache_1912(tuning_data& td) - { - td.td.insert({ - { "4583484812233029888", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "7560832358324865221", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "7382044526960590018", std::make_tuple("convolution_gpu_bfyx_direct_10_12_16", -1) }, - { "12372261924257291610", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 247) }, - { "1547771611689525848", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "3134973665622945888", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "18260030211719729324", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 327) }, - { "7416143717989012766", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 110) }, - { "12028963907131702705", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 345) }, - { "2464531851392092325", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 326) }, - { "8181308759455478086", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 322) }, - { "546062289721803579", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "4889405384318695802", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 333) }, - { "12841232643395100314", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 331) }, - { "14108361259911144680", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 347) }, - { "2726453304845436156", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 86) }, - { "2607416795507802412", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 326) }, - { "2175404966338020579", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 321) }, - { "14666883719480623074", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 320) }, - { "3752993663604843837", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 135) }, - { "5274735654559844733", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 335) }, - { "8174421295799601683", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 202) }, - { "1967655354607438665", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 327) }, - { "15762542971370422224", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 323) }, - { "8183203099539372914", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 323) }, - { "4075343423548891274", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 135) }, - { "13264497096898621015", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 333) }, - { "679058537775669048", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 326) }, - { "3375634256357960999", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 326) }, - { "2844616672368585285", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 335) }, - { "14235558866846276172", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 202) }, - { "18066867692765966577", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 327) }, - { "9861424412782371874", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 324) }, - { "607078314875528651", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 323) }, - { "6234885984223387670", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 133) }, - { "7223570329858821704", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 334) }, - { "17234843749633035510", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 202) }, - { "11516168882438876247", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 323) }, - { "11312664612825940140", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 367) }, - { "14846039494240217143", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 320) }, - { "3390376200501119384", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 323) }, - { "1113077760071340574", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 334) }, - { "4614875083188849196", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 82) }, - { "10859023312681572942", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 240) }, - { "5588692131556725717", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 241) }, - { "7653946972043115920", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 243) }, - { "9773458066743315157", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "10491513939202460216", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "8140122945471321201", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 82) }, - { "15079423575410353790", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 241) }, - { "12844146569641472927", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 241) }, - { "13443914015380511668", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 243) }, - { "13404457916017756196", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "6402415801415013013", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "2040762223425679479", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, - { "8595156989254845134", std::make_tuple("convolution_gpu_bfyx_1x1_hgemm_buf_16x1", -1) }, - { "14493123117003003092", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, - { "7391309333582046386", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "9935182178960843140", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 333) }, - { "15422142509105297183", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 335) }, - { "14849987788569183527", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 333) }, - { "14923132847727661051", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 367) }, - { "13271555597925466454", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 334) }, - { "6865593216823998846", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 334) }, - { "12385437755245281331", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 333) }, - { "4530047829451377456", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 333) }, - { "127643210248119703", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 365) }, - { "2599051617462913767", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 322) }, - { "3024020696533545102", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 333) }, - { "16205377892664082400", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 324) }, - { "15135655146332608939", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 327) }, - { "10848724554175904486", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 326) }, - { "15558120704022404428", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 324) }, - { "14120354125904513152", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 367) }, - { "8475075092501403968", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 325) }, - { "15892943371777404347", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 327) }, - { "15078418657871922661", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 327) }, - { "3502053626453342387", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 327) }, - { "1230316443026403527", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 325) }, - { "12846418701225147646", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 327) }, - { "17386994561779281406", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 326) }, - { "7367684259946371231", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 324) }, - { "5451072983028714092", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 326) }, - { "11758623888547009364", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 334) }, - { "15958650715061024845", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 334) }, - { "13899144453581769028", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 334) }, - { "10884229860266073967", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 323) }, - { "6629431845229592220", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 325) }, - { "13199442294147992119", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 335) }, - { "5032929712205664246", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 335) }, - { "14871333176552512036", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 335) }, - { "17890435688048047959", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 323) }, - { "11353661571093800805", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 323) }, - { "3101908018947919238", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 334) }, - { "7495240482209084478", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 95) }, - { "6964506613327100469", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 101) }, - { "3670645005971806718", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 243) }, - { "69949758775887534", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 242) }, - { "13654393413005772278", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 240) }, - { "11101512074369779300", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 243) }, - { "2273811004985590823", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "7457154125218067377", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "7709677514862642399", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "8010619564572573208", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "16479793487852125428", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "3147355028342035061", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "12672939642957531547", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 262) }, - { "12627961914394914920", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "16210688853876861607", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 295) }, - { "1899485873740458557", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 293) }, - { "669151029135558505", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 301) }, - { "8912067280071688393", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "7714589858275971005", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 199) }, - { "9794413496918699979", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 305) }, - { "18350040136091421971", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 285) }, - { "16931304566154830346", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 306) }, - { "12816950084297042217", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 281) }, - { "2359632276970855181", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 302) }, - { "16592641501972654496", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 301) }, - { "7754054384598160936", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 199) }, - { "18195884921517044108", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 303) }, - { "16256130331524359070", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 305) }, - { "13497279823712860029", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 293) }, - { "6095972148204769193", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 302) }, - { "4149964766407000732", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 302) }, - { "10262104071809780712", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 301) }, - { "10707129891337660055", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 172) }, - { "3585075254981736756", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 305) }, - { "3181067565488724209", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 172) }, - { "8636008354706344794", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 248) }, - { "4966150965920189853", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 248) }, - { "5569253153294942795", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 259) }, - { "11521347729886549503", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 180) }, - { "12399471154320580621", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 262) }, - { "14851218369956754103", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 259) }, - { "14859848826604327499", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 263) }, - { "14783159891899899660", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 262) }, - { "2369671961317151564", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 256) }, - { "1691004331056506231", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 256) }, - { "4465288557833228023", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 263) }, - { "13538111995551348621", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 262) }, - { "1886751914747841929", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 263) }, - { "8445964247944285746", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 263) }, - { "803205084059316676", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 259) }, - { "12654698468722759675", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 259) }, - { "13484605287576302088", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 262) }, - { "2469399061693302590", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 262) }, - { "14782181149367028912", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 263) }, - { "1448238652280623323", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 261) }, - { "2076478920663115306", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 259) }, - { "7369834759425644726", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 111) }, - { "11702633755046828968", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 262) }, - { "2944972038827287015", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 95) }, - { "8261441437673092886", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 214) }, - { "16694312773479519523", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 214) }, - { "5486494868955566721", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 204) }, - { "8246009573416434030", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 214) }, - { "9457894602447879547", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 209) }, - { "4598302923247277427", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 214) }, - { "3116224788980631217", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 205) }, - { "17103527368951412486", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 214) }, - { "10150428063205056209", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 209) }, - { "16984028253790680977", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 214) }, - { "17857105233471273424", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 213) }, - { "2715609009808401074", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 202) }, - { "232807837985324954", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 201) }, - { "13933912937625580405", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, - { "11254635684957519432", std::make_tuple("convolution_gpu_bfyx_1x1_hgemm_buf_16x1", -1) }, - { "6729077823331194042", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, - { "15961933828477762733", std::make_tuple("convolution_gpu_bfyx_1x1_hgemm_buf_16x1", -1) }, - { "827225131390571924", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "10429613013253088132", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "14629385997654952321", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, - { "7546586420552408243", std::make_tuple("convolution_gpu_bfyx_1x1_hgemm_buf_16x1", -1) }, - { "11897687507601277182", std::make_tuple("convolution_gpu_bfyx_1x1_hgemm_buf_16x1", -1) }, - { "13975409361394567866", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 221) }, - { "6458124573210430792", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 334) }, - { "2385616965635993249", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, - { "14462438074931673266", std::make_tuple("convolution_gpu_bfyx_1x1_hgemm_buf_16x1", -1) }, - { "9338654554616107568", std::make_tuple("convolution_gpu_bfyx_1x1_hgemm_buf_16x1", -1) }, - { "15476402794704488137", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 211) }, - { "13654816209891478730", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 228) }, - { "1680424228660495363", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 220) }, - { "12929981792125924963", std::make_tuple("convolution_gpu_bfyx_1x1_hgemm_buf_16x1", -1) }, - { "4698507050987130777", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 209) }, - { "1094144958579794349", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "12358908585763044267", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 367) }, - { "9793373151408615612", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "90849151510482266", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "17277787450259342076", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 367) }, - { "2367877811435050998", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 325) }, - { "433942345363552443", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "14575816691130255191", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "3675622521877371819", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 326) }, - { "7601637686045360430", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 338) }, - { "9818496628902493298", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "377651990943545344", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 323) }, - { "16357661916741979192", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 345) }, - { "5417669424921804056", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "854020380490533945", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 323) }, - { "11984602132438314210", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 367) }, - { "13100228219613095795", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 341) }, - { "9808704199834907703", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "13071373212254908241", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 323) }, - { "18392748682101174561", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 334) }, - { "11928475964162658765", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "11937547211842355800", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 367) }, - { "4637568849323640167", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "16812695025037565299", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 340) }, - { "10487883723723512839", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 334) }, - { "15315014737515653325", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "1579905786032546689", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "4243996335899627971", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, - { "2545885699369058867", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, - { "16926950874716567095", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, - { "6355395905401306995", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "2096779676054335057", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "4217179485243909459", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "17101789600628162503", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 223) }, - { "6139574161497189424", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 224) }, - { "16559140502701231107", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 214) }, - { "11459784003592366395", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 214) }, - { "7869916853707978306", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 87) }, - //{ "3889519976910355277", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, - //{ "12081835728078383819", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, - //{ "14923692894655929923", std::make_tuple("fully_connected_gpu_bf_io_gemm", -1) }, - { "1841901358010744236", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "10888203577545955226", std::make_tuple("convolution_gpu_bfyx_1x1_hgemm_buf_16x1", -1) }, - { "9799890897264103013", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, - { "1827296932806936575", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 326) }, - { "13010820430079828498", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 325) }, - { "6245781545617904772", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 255) }, - { "8714031312599034571", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "12922099252166105096", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 266) }, - { "3042887030242700493", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 263) }, - { "1419879016567682338", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 320) }, - { "12870587285162108523", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 325) }, - { "6103433181190121715", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 322) }, - { "3469599265931338557", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "14812617666668076833", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 262) }, - { "14854734265631496499", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 322) }, - { "7637441820772916248", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 325) }, - { "14695781272831602408", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 215) }, - { "15696910741835640150", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 262) }, - { "17790593820165047954", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "1433224983833208570", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 199) }, - { "16185194021453870096", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 182) }, - { "10310918050196558188", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 251) }, - { "14885938077915823034", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 245) }, - { "14442357887993453368", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 263) }, - { "4719130523147011420", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 248) }, - { "8870222084473246330", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "9350596936816632825", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 182) }, - { "6183248276225219542", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "3757195189216622027", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 263) }, - { "8318857994507665384", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "12864512857659000129", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 248) }, - { "6615646900347529347", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 256) }, - { "5208923086986567490", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 251) }, - { "9390793435913144215", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 262) }, - { "11997629302296435180", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 262) }, - { "4858270366437120918", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 262) }, - { "15975176007724247667", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 256) }, - { "16837473534895641370", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 251) }, - { "16214153687871223428", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 260) }, - { "8858112708913743577", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 260) }, - { "8844677471730173649", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 260) }, - { "9043742986995534354", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 263) }, - { "15621341038256548867", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 248) }, - { "18268980125375728709", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "9757167087033785227", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "6513982093384445397", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 142) }, - { "9273893819042428704", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 259) }, - { "8270840662337272430", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 142) }, - { "15021512490648380369", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 243) }, - { "6578908625437515675", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 84) }, - { "13762042713029963144", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 134) }, - { "668798769117277023", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 135) }, - { "10917498758625273194", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 127) }, - { "14335423820860953927", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 134) }, - { "13978750151855895830", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 121) }, - { "8965747921518186477", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 43) }, - { "4428101657497677982", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 127) }, - { "8151272056391095510", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 63) }, - { "17656341100957270390", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 124) }, - { "10295330953350618042", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 7) }, - { "15901675909820977223", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "6026876733674266377", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 7) }, - { "381149736509958403", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 7) }, - { "7962991673727743706", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 7) }, - { "10064251191248475177", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "16663239694378513014", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 335) }, - { "4690831975451405214", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, - { "16132498413588349821", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, - { "10609644803793651808", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, - { "16520784657717262379", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, - { "2271187702055786721", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, - { "1872921634399989626", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, - { "12535576637355537200", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, - { "7771729980527620398", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, - { "14849108908297747749", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, - { "14043770215999952932", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "15277856047844308598", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "8048617952947915835", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "11446745541571732900", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, - { "17422822627612865758", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, - { "13954144830230671601", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 339) }, - { "11198908896401597838", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 228) }, - { "5582896843095691256", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 228) }, - { "8133587696326295326", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 228) }, - { "2007192658799516915", std::make_tuple("fully_connected_gpu_bf_io_gemm", -1) }, - { "9492402787848610840", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, - { "10515519878978734341", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, - { "1375156980278317418", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "13455881643467418059", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "12788968383428254917", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 257) }, - { "12304975739476881266", std::make_tuple("fully_connected_gpu_bf_io_gemm", -1) }, - { "3390014193205017427", std::make_tuple("fully_connected_gpu_bf_io_gemm", -1) }, - { "13587202155230938291", std::make_tuple("fully_connected_gpu_bf_io_gemm", -1) }, - { "14462744723628661203", std::make_tuple("fully_connected_gpu_bf_io_gemm", -1) }, - { "16109721499545711936", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 159) }, - { "10090923790949378407", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "16672038432561840773", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "2973773544904290726", std::make_tuple("convolution_gpu_bfyx_direct_10_12_16", -1) }, - { "17193614571243427089", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "2862029728492027826", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "11066930104187448422", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "2755147389712995637", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "10662798624911535617", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 294) }, - { "2172121470071868949", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "3579916582911190192", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "5495063314176654751", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "3771003491521695667", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 366) }, - { "15514370342945522276", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 221) }, - { "17285639145064557279", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 366) }, - { "12642574441854544900", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "5471430682416582179", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 234) }, - { "8561154029325525444", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "8939900194037985459", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 234) }, - { "2129726780118554358", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 352) }, - { "15463465056816958579", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 360) }, - { "9268536904925062469", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 354) }, - { "45977313646881991", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 361) }, - { "7092246390386193774", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "14801984300948838261", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 234) }, - 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{ "10049571207493913006", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 206) }, - { "15691689005236690951", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 213) }, - { "11275109735493317886", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 207) }, - { "12220860296984467101", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 208) }, - { "44210723233569665", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 110) }, - { "14014987361364503383", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 329) }, - { "18189351665719757712", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "16159032667792855758", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "3374037004378790060", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 329) }, - { "6765409971512438438", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "10098892297878373639", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "981877665302032867", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "7472350511000146655", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 247) }, - { "15513971895394346930", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 341) }, - { "6169721205327431190", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "4519054607159036572", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 322) }, - { "12750124851833311828", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "4333851142313192116", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "6041620003527819661", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "15091361629922645798", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 331) }, - { "9348121965341418899", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 102) }, - { "393951904144235223", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "3220084080191614421", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 202) }, - { "11610588256244825741", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 331) }, - { "2802357220980817497", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "8972812517118478580", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 354) }, - { "4207115359813621211", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 79) }, - { "16582237002610438015", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 103) }, - { "772342953072606219", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "8546247990965609013", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "7971830510840138313", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "3570484486449791727", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, - { "11461079340079820563", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, - { "12663860560275361463", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, - { "8132521728369930959", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "16108573960501496757", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "11086699387784339943", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "4013707396889204359", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 231) }, - { "11850332373794932468", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 231) }, - { "14763982961176216679", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 101) }, - { "8207349115037232863", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 206) }, - { "3273748387141431306", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 85) }, - //{ "580936360000782237", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, - //{ "10682918518101379579", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, - //{ "13178480813522103091", std::make_tuple("fully_connected_gpu_bf_io_gemm", -1) }, - { "14472322679644532468", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 118) }, - { "8378137527264154204", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "10180255575636684134", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "18242121098885244699", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "6178572652675599622", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 336) }, - { "6558074021146321216", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 332) }, - { "11038938372264857379", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "17137800360536507200", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 271) }, - { "14016185289182597841", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "8970519484272874266", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 246) }, - { "3289746379259038515", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 322) }, - { "18389174979070260315", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 247) }, - { "17666483005735191253", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "10845781902676865789", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "13646634862315619979", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 336) }, - { "5072154928583891344", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "7757331094141318304", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 211) }, - { "16779678846332091086", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 327) }, - { "12223166874490429642", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "13316017702896072758", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 262) }, - { "10390896207372295988", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 175) }, - { "8386498395042623384", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 167) }, - { "17923632501885139982", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 241) }, - { "13398326377839777956", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 257) }, - { "13520557646924372128", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 295) }, - { "5996787039089786722", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "2626376166907387273", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 171) }, - { "6147643392694904814", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "4311921348668650791", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 262) }, - { "8046109476498335792", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "14190077682825257613", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 294) }, - { "5032302126047788183", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 261) }, - { "15256375572125522238", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 203) }, - { "5083173538217738703", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 195) }, - { "17269467004855120308", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 195) }, - { "2901056469731554922", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 199) }, - { "9747637051217505111", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 262) }, - { "7175860674618956918", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 291) }, - { "9530922411870814200", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 175) }, - { "11015319643831560673", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 175) }, - { "5182740559503076121", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 175) }, - { "7567277014404457462", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 262) }, - { "15973842639221447367", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 294) }, - { "15951492056203075273", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "5283253936050062275", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 178) }, - { "17650690912303447913", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 142) }, - { "9614936270604202220", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 263) }, - { "13998661469619523378", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 141) }, - { "10584034255622783869", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 245) }, - { "2973436171295280783", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 126) }, - { "1908809004094565452", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 247) }, - { "1623383628456201603", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 125) }, - { "5766507688771440170", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 126) }, - { "16626226341188424071", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 246) }, - { "14619055893081624406", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 121) }, - { "17222005830854879661", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 41) }, - { "14224121742920800990", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 167) }, - { "5859124386313585730", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 62) }, - { "9596156698919548146", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 84) }, - { "10482582307328548806", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 1) }, - { "407189201971322683", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 22) }, - { "16626507949474235269", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 7) }, - { "879005904827468163", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 7) }, - { "8460847842045253466", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 7) }, - { "2729099061601852493", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "8233922303282945338", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 238) }, - { "7402006230339617617", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 231) }, - { "8420763628389536977", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 231) }, - { "8325767678959979628", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "7673672840505587739", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "14166169053627992481", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 331) }, - { "120923426036313670", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 271) }, - { "7348084298010357768", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 271) }, - { "14653065651448352526", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 271) }, - { "11008522061447263744", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 343) }, - { "305505245310584136", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 343) }, - { "6472139251351862598", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, - { "10586018593856542117", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, - { "16706121580364790904", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "5495776091407365966", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "16430562172386510259", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "5673972310424776040", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "8797843396807284399", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 271) }, - { "1698321314111848001", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 342) }, - { "5762290464889692462", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "3218248162832023196", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 207) }, - { "12988961529988078346", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "4232250144427804891", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, - { "11683680166617045816", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, - { "6252429564537528709", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, - { "11717348577195224554", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "9275303306340702111", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "12245096462203481681", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 263) }, - { "18439435691655740074", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, - { "15272426400992401555", std::make_tuple("fully_connected_gpu_bf_io_gemm", -1) }, - { "9136831791301215059", std::make_tuple("fully_connected_gpu_bs_f_bsv16_b1", -1) }, - { "18238669114790278675", std::make_tuple("fully_connected_gpu_bf_io_gemm", -1) }, - { "15376246520426368532", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 149) }, - { "16469788155263456039", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "9360494451263553093", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "7897877428349481398", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "9354818521586974021", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "5853553261686771766", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "6351572488552853754", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "11372638316835753193", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "6170074103544756465", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 327) }, - { "4544242784357021697", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "14225108809796795520", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "178353385245384751", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "2002574142025049539", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 364) }, - { "11630971824787392820", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 219) }, - { "11542493210215136239", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "9595803435783166868", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "17610828776103321939", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 238) }, - { "18312069177632970412", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "7577483892218843723", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 238) }, - { "4355933224673863178", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "15124932296735391043", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 365) }, - { "16888042302987189589", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "1584639932403433303", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "15516194807992507442", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "6614374536332038989", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 238) }, - { "5097818987523855112", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - 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{ "18040173797801558071", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 255) }, - { "15329174116169594863", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 247) }, - { "14910223536998380801", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 84) }, - { "18277685132620834972", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 275) }, - { "13090887980792573261", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 135) }, - { "3067806959725855130", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 158) }, - { "17791773192152464021", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 141) }, - { "13603318842632052764", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 46) }, - { "12072881177966014126", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "14413047954443174304", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 260) }, - { "9118663018352672834", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 260) }, - { "3558391988878894288", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 260) }, - { "10047727261970275928", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 313) }, - { "11527382293059267033", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 314) }, - { "8445575388700666150", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 313) }, - { "4560479630843098090", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "5737652209336034549", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 273) }, - { "1159910924058542880", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 273) }, - { "7203172595229334691", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 273) }, - { "8153567933591966877", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 362) }, - { "6069305481947437228", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, - { "2216113049194620088", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 366) }, - { "10395837712676962495", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 362) }, - { "5041111302824362529", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 202) }, - { "12542825714985999760", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "4599539412023802059", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 303) }, - { "4570119951370893062", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 199) }, - { "16897917745917378359", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 198) }, - { "6947523163603267191", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 156) }, - { "2322126126611987721", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 159) }, - { "6518845972912144959", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 156) }, - { "9741774854327055438", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "7079854103926842364", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "5035895518536085765", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "4307157272240924516", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 271) }, - { "142486914279119363", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 213) }, - { "1532263118203058517", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "13529694429433303321", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 271) }, - { "2820916926593580316", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 116) }, - { "11140657515428786448", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "9028970753877215614", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 102) }, - { "2324120381399737261", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 154) }, - { "14311837335039030749", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 264) }, - { "6105963806494867023", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 249) }, - { "14746731245428677019", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 242) }, - { "9213886570531053949", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 167) }, - { "385046297070779752", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 154) }, - { "15882699446713091149", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 125) }, - { "475043738497218394", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 159) }, - { "6351347283201596793", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 103) }, - { "16290626406346691996", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 32) }, - { "14038308632095412386", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "15928183143089896780", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 343) }, - { "14071202918199194502", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 341) }, - { "12383676694875725364", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 342) }, - { "1830378766184274696", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 239) }, - { "13133105923394042921", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 238) }, - { "12458275468325297142", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 239) }, - { "1819720745131968914", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "5140267554909755437", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 231) }, - { "6021498606197929608", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 271) }, - { "5832188635321350875", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 231) }, - { "1411786954276574458", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 366) }, - { "18193548056717743576", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "7989522965312833517", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - }); - } - - //KBL GT3e - void tuning_cache_5927(tuning_data& td) - { - td.td.insert({ - { "4583484812233029888", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "7560832358324865221", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "7382044526960590018", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "12372261924257291610", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 206) }, - { "1547771611689525848", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 325) }, - { "3134973665622945888", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 327) }, - { "18260030211719729324", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 325) }, - { "7416143717989012766", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 246) }, - { "12028963907131702705", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 335) }, - { "2464531851392092325", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 326) }, - { "8181308759455478086", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 334) }, - { "546062289721803579", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 333) }, - { "4889405384318695802", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 326) }, - { "12841232643395100314", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 107) }, - { "14108361259911144680", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 333) }, - { "2726453304845436156", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 86) }, - { "2607416795507802412", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 326) }, - { "2175404966338020579", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 326) }, - { "14666883719480623074", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 204) }, - { "3752993663604843837", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 63) }, - { "5274735654559844733", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 325) }, - { "8174421295799601683", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 124) }, - { "1967655354607438665", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 326) }, - { "15762542971370422224", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 327) }, - { "8183203099539372914", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 202) }, - { "4075343423548891274", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 207) }, - { "13264497096898621015", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 327) }, - { "679058537775669048", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 327) }, - { "3375634256357960999", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 327) }, - { "2844616672368585285", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 327) }, - { "14235558866846276172", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 124) }, - { "18066867692765966577", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 325) }, - { "9861424412782371874", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 325) }, - { "607078314875528651", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 201) }, - { "6234885984223387670", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 207) }, - { "7223570329858821704", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 334) }, - { "17234843749633035510", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 124) }, - { "11516168882438876247", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 325) }, - { "11312664612825940140", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 326) }, - { "14846039494240217143", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 325) }, - { "3390376200501119384", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 135) }, - { "1113077760071340574", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 334) }, - { "4614875083188849196", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 40) }, - { "10859023312681572942", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 240) }, - { "5588692131556725717", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 243) }, - { "7653946972043115920", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 80) }, - { "9773458066743315157", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 127) }, - { "10491513939202460216", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "8140122945471321201", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 82) }, - { "15079423575410353790", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 240) }, - { "12844146569641472927", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 246) }, - { "13443914015380511668", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 80) }, - { "13404457916017756196", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 127) }, - { "6402415801415013013", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 111) }, - { "2040762223425679479", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, - { "8595156989254845134", std::make_tuple("convolution_gpu_bfyx_1x1_hgemm_buf_16x1", -1) }, - { "14493123117003003092", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, - { "7391309333582046386", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 325) }, - { "9935182178960843140", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 335) }, - { "15422142509105297183", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 327) }, - { "14849987788569183527", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 335) }, - { "14923132847727661051", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 325) }, - { "13271555597925466454", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 323) }, - { "6865593216823998846", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 323) }, - { "12385437755245281331", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 335) }, - { "4530047829451377456", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 335) }, - { "127643210248119703", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 326) }, - { "2599051617462913767", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 333) }, - { "3024020696533545102", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 335) }, - { "16205377892664082400", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 326) }, - { "15135655146332608939", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 135) }, - { "10848724554175904486", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 325) }, - { "15558120704022404428", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 204) }, - { "14120354125904513152", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 323) }, - { "8475075092501403968", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 326) }, - { "15892943371777404347", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 327) }, - { "15078418657871922661", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 135) }, - { "3502053626453342387", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 327) }, - { "1230316443026403527", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 326) }, - { "12846418701225147646", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 326) }, - { "17386994561779281406", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 325) }, - { "7367684259946371231", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 326) }, - { "5451072983028714092", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 326) }, - { "11758623888547009364", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 327) }, - { "15958650715061024845", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 326) }, - { "13899144453581769028", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 326) }, - { "10884229860266073967", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 326) }, - { "6629431845229592220", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 325) }, - { "13199442294147992119", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 327) }, - { "5032929712205664246", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 326) }, - { "14871333176552512036", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 326) }, - { "17890435688048047959", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 325) }, - { "11353661571093800805", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 323) }, - { "3101908018947919238", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 327) }, - { "7495240482209084478", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 94) }, - { "6964506613327100469", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 247) }, - { "3670645005971806718", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 241) }, - { "69949758775887534", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 241) }, - { "13654393413005772278", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 242) }, - { "11101512074369779300", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 80) }, - { "2273811004985590823", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "7457154125218067377", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "7709677514862642399", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "8010619564572573208", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "16479793487852125428", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "3147355028342035061", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "12672939642957531547", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "12627961914394914920", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "16210688853876861607", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 305) }, - { "1899485873740458557", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 287) }, - { "669151029135558505", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 290) }, - { "8912067280071688393", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 290) }, - { "7714589858275971005", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 143) }, - { "9794413496918699979", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 199) }, - { "18350040136091421971", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 284) }, - { "16931304566154830346", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 294) }, - { "12816950084297042217", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 287) }, - { "2359632276970855181", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 282) }, - { "16592641501972654496", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 293) }, - { "7754054384598160936", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 180) }, - { "18195884921517044108", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 289) }, - { "16256130331524359070", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 291) }, - { "13497279823712860029", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 282) }, - { "6095972148204769193", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 293) }, - { "4149964766407000732", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 293) }, - { "10262104071809780712", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 293) }, - { "10707129891337660055", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 198) }, - { "3585075254981736756", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 199) }, - { "3181067565488724209", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 175) }, - { "8636008354706344794", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 247) }, - { "4966150965920189853", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 242) }, - { "5569253153294942795", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 248) }, - { "11521347729886549503", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 183) }, - { "12399471154320580621", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 179) }, - { "14851218369956754103", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 248) }, - { "14859848826604327499", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 182) }, - { "14783159891899899660", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 175) }, - { "2369671961317151564", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 248) }, - { "1691004331056506231", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 248) }, - { "4465288557833228023", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 182) }, - { "13538111995551348621", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 181) }, - { "1886751914747841929", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 263) }, - { "8445964247944285746", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 175) }, - { "803205084059316676", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 248) }, - { "12654698468722759675", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 248) }, - { "13484605287576302088", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 180) }, - { "2469399061693302590", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 182) }, - { "14782181149367028912", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 179) }, - { "1448238652280623323", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 183) }, - { "2076478920663115306", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 248) }, - { "7369834759425644726", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 95) }, - { "11702633755046828968", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 180) }, - { "2944972038827287015", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 95) }, - { "8261441437673092886", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 205) }, - { "16694312773479519523", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 206) }, - { "5486494868955566721", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 201) }, - { "8246009573416434030", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 246) }, - { "9457894602447879547", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "4598302923247277427", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 214) }, - { "3116224788980631217", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 201) }, - { "17103527368951412486", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 246) }, - { "10150428063205056209", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 203) }, - { "16984028253790680977", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 205) }, - { "17857105233471273424", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 202) }, - { "2715609009808401074", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 205) }, - { "232807837985324954", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 205) }, - { "13933912937625580405", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, - { "11254635684957519432", std::make_tuple("convolution_gpu_bfyx_1x1_hgemm_buf_16x1", -1) }, - { "6729077823331194042", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, - { "15961933828477762733", std::make_tuple("convolution_gpu_bfyx_1x1_hgemm_buf_16x1", -1) }, - { "827225131390571924", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "10429613013253088132", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "14629385997654952321", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, - { "7546586420552408243", std::make_tuple("convolution_gpu_bfyx_1x1_hgemm_buf_16x1", -1) }, - { "11897687507601277182", std::make_tuple("convolution_gpu_bfyx_1x1_hgemm_buf_16x1", -1) }, - { "13975409361394567866", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 320) }, - { "6458124573210430792", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 320) }, - { "2385616965635993249", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, - { "14462438074931673266", std::make_tuple("convolution_gpu_bfyx_1x1_hgemm_buf_16x1", -1) }, - { "9338654554616107568", std::make_tuple("convolution_gpu_bfyx_1x1_hgemm_buf_16x1", -1) }, - { "15476402794704488137", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 242) }, - { "13654816209891478730", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 227) }, - { "1680424228660495363", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "12929981792125924963", std::make_tuple("convolution_gpu_bfyx_1x1_hgemm_buf_16x1", -1) }, - { "4698507050987130777", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 243) }, - { "1094144958579794349", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "12358908585763044267", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 327) }, - { "9793373151408615612", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "90849151510482266", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "17277787450259342076", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 325) }, - { "2367877811435050998", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 323) }, - { "433942345363552443", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "14575816691130255191", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "3675622521877371819", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 247) }, - { "7601637686045360430", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 335) }, - { "9818496628902493298", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "377651990943545344", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 325) }, - { "16357661916741979192", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 262) }, - { "5417669424921804056", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "854020380490533945", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 325) }, - { "11984602132438314210", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 326) }, - { "13100228219613095795", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 338) }, - { "9808704199834907703", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "13071373212254908241", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 202) }, - { "18392748682101174561", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "11928475964162658765", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 326) }, - { "11937547211842355800", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 368) }, - { "4637568849323640167", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "16812695025037565299", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 333) }, - { "10487883723723512839", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "15315014737515653325", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 325) }, - { "1579905786032546689", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "4243996335899627971", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, - { "2545885699369058867", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, - { "16926950874716567095", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, - { "6355395905401306995", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "2096779676054335057", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "4217179485243909459", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 195) }, - { "17101789600628162503", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 214) }, - { "6139574161497189424", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 214) }, - { "16559140502701231107", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 205) }, - { "11459784003592366395", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 207) }, - { "7869916853707978306", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 84) }, - //{ "3889519976910355277", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, - //{ "12081835728078383819", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, - //{ "14923692894655929923", std::make_tuple("fully_connected_gpu_bf_io_gemm", -1) }, - { "1841901358010744236", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "10888203577545955226", std::make_tuple("convolution_gpu_bfyx_1x1_hgemm_buf_16x1", -1) }, - { "9799890897264103013", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, - { "1827296932806936575", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 333) }, - { "13010820430079828498", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 335) }, - { "6245781545617904772", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 327) }, - { "8714031312599034571", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 262) }, - { "12922099252166105096", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "3042887030242700493", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 262) }, - { "1419879016567682338", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 252) }, - { "12870587285162108523", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 252) }, - { "6103433181190121715", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 252) }, - { "3469599265931338557", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 263) }, - { "14812617666668076833", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 262) }, - { "14854734265631496499", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 253) }, - { "7637441820772916248", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 325) }, - { "14695781272831602408", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 247) }, - { "15696910741835640150", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 257) }, - { "17790593820165047954", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 262) }, - { "1433224983833208570", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 180) }, - { "16185194021453870096", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 173) }, - { "10310918050196558188", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 244) }, - { "14885938077915823034", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 247) }, - { "14442357887993453368", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 248) }, - { "4719130523147011420", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 244) }, - { "8870222084473246330", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 182) }, - { "9350596936816632825", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 173) }, - { "6183248276225219542", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 183) }, - { "3757195189216622027", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 254) }, - { "8318857994507665384", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 183) }, - { "12864512857659000129", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 244) }, - { "6615646900347529347", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 248) }, - { "5208923086986567490", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 244) }, - { "9390793435913144215", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 181) }, - { "11997629302296435180", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 182) }, - { "4858270366437120918", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "15975176007724247667", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 253) }, - { "16837473534895641370", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 244) }, - { "16214153687871223428", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "8858112708913743577", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 262) }, - { "8844677471730173649", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "9043742986995534354", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 248) }, - { "15621341038256548867", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 244) }, - { "18268980125375728709", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 253) }, - 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{ "14215445060938730397", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 362) }, - { "12720976113342879024", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 362) }, - { "14766625154638709852", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 329) }, - { "11757187678986741715", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 175) }, - { "13038212285326297688", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 156) }, - { "13919423909034348565", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 166) }, - { "12925256096286953030", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 245) }, - { "6275163484075546689", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 240) }, - { "239651884801599911", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 249) }, - { "9500211224156027451", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 183) }, - { "10902538092301362853", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 182) }, - { "8454943813981348115", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 249) }, - { "11722951613064434115", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 181) }, - { "13547342611064538960", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 175) }, - { "15171119202712914112", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 169) }, - { "5451487099025245427", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 169) }, - { "1814940262511664251", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 174) }, - { "16341609351317463829", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 182) }, - { "14343280871046671393", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 181) }, - { "15586404971308258630", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 174) }, - { "15891211707425019144", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 329) }, - { "15351688973597240327", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 329) }, - { "1844016761754156672", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 174) }, - { "17925606428283439978", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 180) }, - { "15050158761219834868", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 181) }, - { "17448180555072943363", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 171) }, - { "11422222075976800614", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 249) }, - { "14284377769814732906", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 207) }, - { "667777413731244716", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 181) }, - { "11624071786842686451", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 95) }, - { "3874974512053082278", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 202) }, - { "5471037497181745651", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 203) }, - { "6371386660654628561", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 203) }, - { "5331835606773958814", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 201) }, - { "16163821504542698475", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 214) }, - { "5697543838890997891", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 215) }, - { "6217438921274668801", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 203) }, - { "2633095809604510774", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 201) }, - { "11218297661079136641", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 214) }, - { "3374196543196230185", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 202) }, - { "17797320202829145544", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 200) }, - { "17198778757516749818", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 203) }, - { "6440981718484677922", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 203) }, - { "1616603916015535857", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, - { "7643715911083095268", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "137903092932521503", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 167) }, - { "13203019690952060789", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 94) }, - { "3918152537861570517", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 243) }, - { "8782903242853500098", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 326) }, - { "18312668164562040079", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 84) }, - { "15160703466234996170", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 62) }, - { "2751241748685218213", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 123) }, - { "12622728760401804660", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 206) }, - { "1290624457831957354", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 124) }, - { "10924946887162830574", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 63) }, - { "10789202693606479024", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 124) }, - { "3718558874911694616", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 135) }, - { "12835389389575311182", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 41) }, - { "17406431092101974143", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 86) }, - { "1400409391266374603", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 84) }, - { "12154660333025778322", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 86) }, - { "4800587664660105589", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, - { "16474284418841532356", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "18117355153710110681", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 343) }, - { "8449591498895477846", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "2962899568083589487", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 218) }, - { "3491333679577961640", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 226) }, - { "13945298510228460890", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 188) }, - { "4282198629458668761", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "779525528509830615", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 335) }, - { "15002237905129290671", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 215) }, - { "16833026567865627676", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 222) }, - { "16991060247581867302", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 324) }, - { "6902644989079870993", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 353) }, - { "15088285782819494786", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 353) }, - { "3379661203936923589", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 203) }, - { "10049571207493913006", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 224) }, - { "15691689005236690951", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 213) }, - { "11275109735493317886", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 224) }, - { "12220860296984467101", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 206) }, - { "44210723233569665", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "14014987361364503383", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 284) }, - { "18189351665719757712", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "16159032667792855758", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "3374037004378790060", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 284) }, - { "6765409971512438438", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 332) }, - { "10098892297878373639", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "981877665302032867", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "7472350511000146655", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 246) }, - { "15513971895394346930", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 341) }, - { "6169721205327431190", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "4519054607159036572", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 240) }, - { "12750124851833311828", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 331) }, - { "4333851142313192116", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "6041620003527819661", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 240) }, - { "15091361629922645798", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 247) }, - { "9348121965341418899", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 342) }, - { "393951904144235223", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "3220084080191614421", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 202) }, - { "11610588256244825741", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "2802357220980817497", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 324) }, - { "8972812517118478580", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 353) }, - { "4207115359813621211", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 207) }, - { "16582237002610438015", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 337) }, - { "772342953072606219", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "8546247990965609013", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "7971830510840138313", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "3570484486449791727", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, - { "11461079340079820563", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, - { "12663860560275361463", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, - { "8132521728369930959", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "16108573960501496757", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "11086699387784339943", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 231) }, - { "4013707396889204359", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 211) }, - { "11850332373794932468", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 212) }, - { "14763982961176216679", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 206) }, - { "8207349115037232863", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 206) }, - { "3273748387141431306", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 84) }, - //{ "580936360000782237", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, - //{ "10682918518101379579", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, - //{ "13178480813522103091", std::make_tuple("fully_connected_gpu_bf_io_gemm", -1) }, - { "14472322679644532468", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "8378137527264154204", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "10180255575636684134", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "18242121098885244699", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 329) }, - { "6178572652675599622", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 329) }, - { "6558074021146321216", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 243) }, - { "11038938372264857379", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 263) }, - { "17137800360536507200", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 253) }, - { "14016185289182597841", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 263) }, - { "8970519484272874266", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 364) }, - { "3289746379259038515", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 364) }, - { "18389174979070260315", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 364) }, - { "17666483005735191253", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 262) }, - { "10845781902676865789", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 263) }, - { "13646634862315619979", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 362) }, - { "5072154928583891344", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "7757331094141318304", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 206) }, - { "16779678846332091086", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 236) }, - { "12223166874490429642", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 263) }, - { "13316017702896072758", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 175) }, - { "10390896207372295988", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 166) }, - { "8386498395042623384", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 242) }, - { "17923632501885139982", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 163) }, - { "13398326377839777956", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 291) }, - { "13520557646924372128", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 243) }, - { "5996787039089786722", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 181) }, - { "2626376166907387273", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 175) }, - { "6147643392694904814", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 181) }, - { "4311921348668650791", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 291) }, - { "8046109476498335792", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 181) }, - { "14190077682825257613", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 241) }, - { "5032302126047788183", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 169) }, - { "15256375572125522238", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 245) }, - { "5083173538217738703", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 175) }, - { "17269467004855120308", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 171) }, - 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{ "17423097433955762667", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 242) }, - { "18131954418490925431", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 262) }, - { "16549854027697846882", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 260) }, - { "10340073416712988987", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 155) }, - { "4633923265089466898", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 262) }, - { "6808980404170272597", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 260) }, - { "10592783998150232858", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 159) }, - { "1594829714229111215", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 143) }, - { "17361714725103230834", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 158) }, - { "15732140959902969012", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 102) }, - { "5796974850751105634", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 99) }, - { "10588059104387338398", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "11738780323979052397", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 142) }, - { "16342972196376030503", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 3) }, - { "10406201782146034797", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 7) }, - { "17342758321852264926", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 42) }, - { "15951978466742016539", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "14100026884590707572", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "8368507377481570353", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "2230145893895952661", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 345) }, - { "6320236287540885300", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 345) }, - { "3152975593520548377", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 345) }, - { "11756338894156892549", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 345) }, - { "9310466098863912618", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 345) }, - { "18376135355406021646", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 345) }, - { "2024521518658822242", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 345) }, - { "9692442768327912638", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 335) }, - { "8031184163763974840", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 335) }, - { "13429621587271813054", std::make_tuple("fully_connected_gpu_bs_f_bsv8_af8_vload", -1) }, - { "17938009873557420461", std::make_tuple("fully_connected_gpu_bs_f_bsv8_af8_vload", -1) }, - { "16780457022162749898", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "17140702790441856730", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "2578325663193624576", std::make_tuple("convolution_gpu_yxfb_yxio_b16", -1) }, - { "8784358107340738205", std::make_tuple("convolution_gpu_yxfb_yxio_b16", -1) }, - { "2955459120402821540", std::make_tuple("convolution_gpu_yxfb_yxio_b16", -1) }, - { "2840794055129352139", std::make_tuple("convolution_gpu_yxfb_yxio_b16", -1) }, - { "7104266560248570112", std::make_tuple("convolution_gpu_yxfb_yxio_b16", -1) }, - { "11113125355390956764", std::make_tuple("convolution_gpu_yxfb_yxio_b16", -1) }, - { "9127827617126714860", std::make_tuple("fully_connected_gpu_fb_io_b8_f8_vload", -1) }, - { "2268275392299271167", std::make_tuple("fully_connected_gpu_fb_io_block_fp16", -1) }, - { "10615831454139478379", std::make_tuple("fully_connected_gpu_bs_f_bsv8_af8_vload", -1) }, - { "8205640825965213946", std::make_tuple("convolution_gpu_yxfb_yxio_b16", -1) }, - { "14337168375989245254", std::make_tuple("convolution_gpu_yxfb_yxio_b16", -1) }, - { "11664399629496237233", std::make_tuple("convolution_gpu_yxfb_yxio_b16", -1) }, - { "15750539817895707253", std::make_tuple("convolution_gpu_yxfb_yxio_b16", -1) }, - { "921209976738626097", std::make_tuple("convolution_gpu_yxfb_yxio_b16", -1) }, - { "8590416145336196354", std::make_tuple("convolution_gpu_yxfb_yxio_b16", -1) }, - { "10463632805036507382", std::make_tuple("convolution_gpu_yxfb_yxio_b16", -1) }, - { "13637537549252005181", std::make_tuple("convolution_gpu_yxfb_yxio_b16", -1) }, - { "7581949584623524395", std::make_tuple("convolution_gpu_yxfb_yxio_b16", -1) }, - { "9814647153117279415", std::make_tuple("convolution_gpu_yxfb_yxio_b16", -1) }, - { "3444250649099578792", std::make_tuple("convolution_gpu_yxfb_yxio_b16", -1) }, - { "4039483032571506874", std::make_tuple("fully_connected_gpu_fb_io_b8_f8_vload", -1) }, - { "14309249337788077160", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "6254493271976962295", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "12387660887222981357", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, - { "7723131901316908741", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, - { "13963554827358438190", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, - { "5001552360784483833", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, - { "14201142257504107783", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, - { "1066668660701816536", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, - { "4664196755018349672", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, - { "1103228955716492167", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 246) }, - { "8618835732380720921", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 273) }, - { "8391292909068775212", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 255) }, - { "9488974186647231896", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 256) }, - { "9069334144391048686", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 207) }, - { "12493863403516600413", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 278) }, - { "13375084585444085517", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 251) }, - { "18040173797801558071", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 252) }, - { "15329174116169594863", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 241) }, - { "14910223536998380801", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 84) }, - { "18277685132620834972", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 155) }, - { "13090887980792573261", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 126) }, - { "3067806959725855130", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 158) }, - { "17791773192152464021", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 172) }, - { "13603318842632052764", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 7) }, - { "12072881177966014126", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "14413047954443174304", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "9118663018352672834", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "3558391988878894288", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 345) }, - { "10047727261970275928", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "11527382293059267033", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 345) }, - { "8445575388700666150", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 345) }, - { "4560479630843098090", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "5737652209336034549", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 347) }, - { "1159910924058542880", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 273) }, - { "7203172595229334691", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 273) }, - { "8153567933591966877", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 353) }, - { "6069305481947437228", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, - { "2216113049194620088", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 227) }, - { "10395837712676962495", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 360) }, - { "5041111302824362529", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 201) }, - { "12542825714985999760", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "4599539412023802059", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "4570119951370893062", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "16897917745917378359", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "6947523163603267191", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "2322126126611987721", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "6518845972912144959", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "9741774854327055438", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "7079854103926842364", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "5035895518536085765", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "4307157272240924516", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 271) }, - { "142486914279119363", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 206) }, - { "1532263118203058517", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "13529694429433303321", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 271) }, - { "2820916926593580316", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 250) }, - { "11140657515428786448", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 257) }, - { "9028970753877215614", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 206) }, - { "2324120381399737261", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "14311837335039030749", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 288) }, - { "6105963806494867023", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 134) }, - { "14746731245428677019", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 242) }, - { "9213886570531053949", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 54) }, - { "385046297070779752", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 154) }, - { "15882699446713091149", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 127) }, - { "475043738497218394", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 159) }, - { "6351347283201596793", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 93) }, - { "16290626406346691996", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 62) }, - { "14038308632095412386", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "15928183143089896780", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "14071202918199194502", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "12383676694875725364", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "1830378766184274696", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "13133105923394042921", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "12458275468325297142", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "1819720745131968914", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "5140267554909755437", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 225) }, - { "6021498606197929608", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 238) }, - { "5832188635321350875", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 238) }, - { "1411786954276574458", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "18193548056717743576", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "7989522965312833517", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "14123865825367908500", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 231) }, - }); - } - - //APL 10W - void tuning_cache_5A84(tuning_data& td) - { - td.td.insert({ - { "4583484812233029888", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "7560832358324865221", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "7382044526960590018", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "12372261924257291610", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 355) }, - { "1547771611689525848", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "3134973665622945888", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "18260030211719729324", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 325) }, - { "7416143717989012766", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "12028963907131702705", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "2464531851392092325", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 331) }, - { "8181308759455478086", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "546062289721803579", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "4889405384318695802", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "12841232643395100314", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "14108361259911144680", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "2726453304845436156", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 203) }, - { "2607416795507802412", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 327) }, - { "2175404966338020579", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 328) }, - { "14666883719480623074", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 327) }, - { "3752993663604843837", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 56) }, - { "5274735654559844733", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 331) }, - { "8174421295799601683", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 247) }, - { "1967655354607438665", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 327) }, - { "15762542971370422224", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 330) }, - { "8183203099539372914", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 327) }, - { "4075343423548891274", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 132) }, - { "13264497096898621015", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 362) }, - { "679058537775669048", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 330) }, - { "3375634256357960999", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 330) }, - { "2844616672368585285", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 336) }, - { "14235558866846276172", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 247) }, - { "18066867692765966577", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 330) }, - { "9861424412782371874", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 325) }, - { "607078314875528651", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 325) }, - { "6234885984223387670", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 132) }, - { "7223570329858821704", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 336) }, - { "17234843749633035510", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 205) }, - { "11516168882438876247", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 328) }, - { "11312664612825940140", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 327) }, - { "14846039494240217143", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 328) }, - { "3390376200501119384", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 115) }, - { "1113077760071340574", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 336) }, - { "4614875083188849196", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 87) }, - { "10859023312681572942", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 247) }, - 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{ "3181067565488724209", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 194) }, - { "8636008354706344794", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 256) }, - { "4966150965920189853", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 256) }, - { "5569253153294942795", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 294) }, - { "11521347729886549503", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 175) }, - { "12399471154320580621", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 260) }, - { "14851218369956754103", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 293) }, - { "14859848826604327499", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 180) }, - { "14783159891899899660", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 175) }, - { "2369671961317151564", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 251) }, - { "1691004331056506231", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 251) }, - { "4465288557833228023", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "13538111995551348621", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 180) }, - { "1886751914747841929", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 180) }, - { "8445964247944285746", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 175) }, - { "803205084059316676", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 293) }, - { "12654698468722759675", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 294) }, - { "13484605287576302088", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 253) }, - { "2469399061693302590", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 179) }, - { "14782181149367028912", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 179) }, - { "1448238652280623323", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 174) }, - { "2076478920663115306", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 293) }, - { "7369834759425644726", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 214) }, - { "11702633755046828968", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 179) }, - { "2944972038827287015", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 214) }, - { "8261441437673092886", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 211) }, - { "16694312773479519523", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 217) }, - { "5486494868955566721", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 211) }, - { "8246009573416434030", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 215) }, - { "9457894602447879547", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 221) }, - { "4598302923247277427", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 217) }, - { "3116224788980631217", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "17103527368951412486", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 214) }, - { "10150428063205056209", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 255) }, - { "16984028253790680977", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 211) }, - { "17857105233471273424", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 217) }, - { "2715609009808401074", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 251) }, - { "232807837985324954", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "13933912937625580405", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, - { "11254635684957519432", std::make_tuple("convolution_gpu_bfyx_1x1_hgemm_buf_16x1", -1) }, - { "6729077823331194042", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, - { "15961933828477762733", std::make_tuple("convolution_gpu_bfyx_1x1_hgemm_buf_16x1", -1) }, - { "827225131390571924", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "10429613013253088132", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "14629385997654952321", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, - { "7546586420552408243", std::make_tuple("convolution_gpu_bfyx_1x1_hgemm_buf_16x1", -1) }, - { "11897687507601277182", std::make_tuple("convolution_gpu_bfyx_1x1_hgemm_buf_16x1", -1) }, - { "13975409361394567866", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "6458124573210430792", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 332) }, - { "2385616965635993249", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, - { "14462438074931673266", std::make_tuple("convolution_gpu_bfyx_1x1_hgemm_buf_16x1", -1) }, - { "9338654554616107568", std::make_tuple("convolution_gpu_bfyx_1x1_hgemm_buf_16x1", -1) }, - { "15476402794704488137", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 217) }, - { "13654816209891478730", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 230) }, - { "1680424228660495363", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 225) }, - { "12929981792125924963", std::make_tuple("convolution_gpu_bfyx_1x1_hgemm_buf_16x1", -1) }, - { "4698507050987130777", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 356) }, - { "1094144958579794349", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "12358908585763044267", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 303) }, - { "9793373151408615612", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "90849151510482266", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "17277787450259342076", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 303) }, - { "2367877811435050998", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 325) }, - { "433942345363552443", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "14575816691130255191", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "3675622521877371819", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 325) }, - { "7601637686045360430", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "9818496628902493298", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "377651990943545344", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 331) }, - { "16357661916741979192", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "5417669424921804056", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "854020380490533945", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 328) }, - { "11984602132438314210", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 355) }, - { "13100228219613095795", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "9808704199834907703", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "13071373212254908241", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 325) }, - { "18392748682101174561", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "11928475964162658765", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "11937547211842355800", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 231) }, - { "4637568849323640167", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "16812695025037565299", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "10487883723723512839", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "15315014737515653325", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 331) }, - { "1579905786032546689", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 327) }, - { "4243996335899627971", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, - { "2545885699369058867", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, - { "16926950874716567095", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, - { "6355395905401306995", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "2096779676054335057", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "4217179485243909459", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "17101789600628162503", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 224) }, - { "6139574161497189424", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 224) }, - { "16559140502701231107", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 213) }, - { "11459784003592366395", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 212) }, - { "7869916853707978306", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 94) }, - //{ "3889519976910355277", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, - //{ "12081835728078383819", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, - //{ "14923692894655929923", std::make_tuple("fully_connected_gpu_bf_io_gemm", -1) }, - { "1841901358010744236", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "10888203577545955226", std::make_tuple("convolution_gpu_bfyx_1x1_hgemm_buf_16x1", -1) }, - { "9799890897264103013", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, - { "1827296932806936575", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 256) }, - { "13010820430079828498", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 256) }, - { "6245781545617904772", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 256) }, - { "8714031312599034571", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "12922099252166105096", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "3042887030242700493", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "1419879016567682338", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 251) }, - { "12870587285162108523", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 251) }, - { "6103433181190121715", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 251) }, - { "3469599265931338557", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "14812617666668076833", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "14854734265631496499", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 364) }, - { "7637441820772916248", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 364) }, - { "14695781272831602408", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 217) }, - { "15696910741835640150", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 256) }, - { "17790593820165047954", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "1433224983833208570", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "16185194021453870096", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 194) }, - { "10310918050196558188", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 293) }, - { "14885938077915823034", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 294) }, - { "14442357887993453368", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 256) }, - { "4719130523147011420", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 256) }, - { "8870222084473246330", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 192) }, - { "9350596936816632825", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 182) }, - { "6183248276225219542", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 256) }, - { "3757195189216622027", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 293) }, - { "8318857994507665384", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 192) }, - { "12864512857659000129", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 256) }, - { "6615646900347529347", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 249) }, - { "5208923086986567490", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 293) }, - { "9390793435913144215", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "11997629302296435180", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "4858270366437120918", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 265) }, - { "15975176007724247667", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 249) }, - { "16837473534895641370", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 293) }, - { "16214153687871223428", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 180) }, - { "8858112708913743577", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 180) }, - { "8844677471730173649", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 180) }, - { "9043742986995534354", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 250) }, - { "15621341038256548867", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 256) }, - { "18268980125375728709", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 256) }, - { "9757167087033785227", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 256) }, - { "6513982093384445397", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 250) }, - { "9273893819042428704", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 260) }, - { "8270840662337272430", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 141) }, - { "15021512490648380369", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 247) }, - { "6578908625437515675", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "13762042713029963144", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 325) }, - { "668798769117277023", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 172) }, - { "10917498758625273194", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "14335423820860953927", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 326) }, - { "13978750151855895830", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 247) }, - { "8965747921518186477", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 62) }, - { "4428101657497677982", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 245) }, - { "8151272056391095510", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 62) }, - { "17656341100957270390", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 44) }, - { "10295330953350618042", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 7) }, - { "15901675909820977223", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 207) }, - { "6026876733674266377", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 6) }, - { "381149736509958403", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 4) }, - { "7962991673727743706", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 6) }, - { "10064251191248475177", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "16663239694378513014", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "4690831975451405214", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, - { "16132498413588349821", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, - { "10609644803793651808", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, - { "16520784657717262379", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, - { "2271187702055786721", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, - { "1872921634399989626", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, - { "12535576637355537200", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, - { "7771729980527620398", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, - { "14849108908297747749", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, - { "14043770215999952932", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "15277856047844308598", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "8048617952947915835", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "11446745541571732900", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, - { "17422822627612865758", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, - { "13954144830230671601", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 217) }, - { "11198908896401597838", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 218) }, - { "5582896843095691256", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 218) }, - { "8133587696326295326", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 218) }, - { "2007192658799516915", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, - { "9492402787848610840", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, - { "10515519878978734341", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, - { "1375156980278317418", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "13455881643467418059", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "12788968383428254917", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 216) }, - { "12304975739476881266", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, - { "3390014193205017427", std::make_tuple("fully_connected_gpu_bf_io_gemm", -1) }, - { "13587202155230938291", std::make_tuple("fully_connected_gpu_bf_io_gemm", -1) }, - { "14462744723628661203", std::make_tuple("fully_connected_gpu_bf_io_gemm", -1) }, - { "16109721499545711936", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 106) }, - { "10090923790949378407", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "16672038432561840773", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "2973773544904290726", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "17193614571243427089", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "2862029728492027826", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "11066930104187448422", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "2755147389712995637", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "10662798624911535617", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 224) }, - { "2172121470071868949", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "3579916582911190192", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 357) }, - { "5495063314176654751", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "3771003491521695667", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 365) }, - { "15514370342945522276", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 361) }, - { "17285639145064557279", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 361) }, - { "12642574441854544900", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "5471430682416582179", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 254) }, - { "8561154029325525444", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "8939900194037985459", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 224) }, - { "2129726780118554358", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 331) }, - { "15463465056816958579", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "9268536904925062469", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 356) }, - { "45977313646881991", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 326) }, - { "7092246390386193774", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "14801984300948838261", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 224) }, - { "2797723586312707948", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 364) }, - { "8451212914744825089", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 361) }, - { "5131348852069018593", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 355) }, - { "13619081494170885939", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 361) }, - { "8818679285688095197", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 356) }, - { "4608292692528881356", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 358) }, - { "9729987752669765456", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "1782966703272153440", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "15641674846325113216", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 217) }, - { "18140951659547259039", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 359) }, - { "3416294810798281053", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 355) }, - { "12066560812164094695", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 355) }, - { "9332596500956923556", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 328) }, - { "15067550526427941795", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "15428062440621131394", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 216) }, - { "8873614802459592665", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 364) }, - { "3367130693014583254", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 330) }, - { "14135594471530769414", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 330) }, - { "14971707650115908544", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "3036512701943687724", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 331) }, - { "5334291640387922287", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 331) }, - { "16245760498096322525", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 302) }, - { "9928406318940388716", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 328) }, - { "3369894612786523432", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "590505356692040012", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 351) }, - { "13240472672791632740", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 224) }, - { "7128145024365641089", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 224) }, - { "11497327844388026594", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 224) }, - { "7301757962797024939", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 224) }, - { "18121689595247452649", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 224) }, - { "875296362957469305", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 357) }, - { "14912119584313592912", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 331) }, - { "12494969618927201911", std::make_tuple("fully_connected_gpu_fb_io_ref", -1) }, - { "4640611487944119712", std::make_tuple("fully_connected_gpu_fb_io_ref", -1) }, - { "1692411934657235774", std::make_tuple("fully_connected_gpu_fb_io_ref", -1) }, - { "1673006919995519093", std::make_tuple("fully_connected_gpu_fb_io_ref", -1) }, - { "10601835610089648700", std::make_tuple("fully_connected_gpu_fb_io_ref", -1) }, - { "13262672660175739705", std::make_tuple("fully_connected_gpu_fb_io_ref", -1) }, - { "7639015398436550592", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "13867172651521406104", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 355) }, - { "14587150810299279663", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 364) }, - { "17271409929705935575", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "16744813357455687598", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "17215047912921813592", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 364) }, - { "4093195092417588676", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "17895953872149392740", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 293) }, - { "5918874715861160937", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 328) }, - 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{ "14187063304165334647", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "11593893535334124231", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "9218293603091125898", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 187) }, - { "3614865264081581688", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 333) }, - { "8860682105104682547", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 326) }, - { "3775781894241463386", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 353) }, - { "8857354069987696352", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 354) }, - { "5611508857136313396", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 333) }, - { "10872828113308792940", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 364) }, - { "3726594456692340607", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 156) }, - { "7541331569935741737", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 326) }, - { "5639394073086652531", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 332) }, - { "11158391063762007051", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 333) }, - { "6319861294308997034", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 158) }, - { "6893801771793379570", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 364) }, - { "9015970699147699643", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 364) }, - { "9252735579930779632", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 353) }, - { "16237353798629485972", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 367) }, - { "10916127635689513485", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 334) }, - { "14631094106016920364", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 328) }, - { "5050075828787158563", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 364) }, - { "2277573429750402800", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 353) }, - { "7462044209068160751", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 333) }, - { "8879836520351993142", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 333) }, - { "560198731460537880", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 333) }, - { "6414187394150266523", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 333) }, - { "15317838148382459105", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 364) }, - { "11219109605495282242", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 326) }, - { "10404790565578782014", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 200) }, - { "4657890394631454901", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 111) }, - { "4256171754976506222", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 201) }, - { "12658039760507507230", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "9883901352719605734", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 211) }, - { "5115148310176289236", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 204) }, - { "4892280615322354003", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 207) }, - { "15491567059821267605", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 110) }, - { "4716932801711295063", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 206) }, - { "15559962129967760292", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 205) }, - { "7866546777503165080", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "12994023006726461909", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "1869893771689012539", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 250) }, - { "5635187738652974532", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "12849693339574251399", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 303) }, - { "13233683642200681957", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 303) }, - { "4310121962651039089", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 333) }, - { "7620758476872568593", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 333) }, - { "10344702612951473525", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 187) }, - { "1668590302432600271", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 303) }, - { "8750610033922701675", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 334) }, - { "6913992575736424382", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 302) }, - { "17945230226911262869", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 297) }, - { "7356559449640788577", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 289) }, - { "18349175655630268884", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 331) }, - { "16817085704588915904", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 185) }, - { "9503107262691437536", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 331) }, - { "15595806193584438610", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 303) }, - { "14283867094396458105", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 297) }, - { "14215445060938730397", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 335) }, - { "12720976113342879024", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 333) }, - { "14766625154638709852", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 334) }, - { "11757187678986741715", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 182) }, - { "13038212285326297688", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 303) }, - { "13919423909034348565", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 254) }, - { "12925256096286953030", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 257) }, - { "6275163484075546689", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 257) }, - { "239651884801599911", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 249) }, - { "9500211224156027451", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 175) }, - { "10902538092301362853", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 174) }, - { "8454943813981348115", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 249) }, - { "11722951613064434115", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 183) }, - { "13547342611064538960", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 179) }, - { "15171119202712914112", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "5451487099025245427", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "1814940262511664251", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 179) }, - { "16341609351317463829", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 178) }, - { "14343280871046671393", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 183) }, - { "15586404971308258630", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 179) }, - { "15891211707425019144", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 249) }, - { "15351688973597240327", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 252) }, - { "1844016761754156672", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 179) }, - { "17925606428283439978", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 183) }, - { "15050158761219834868", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 183) }, - { "17448180555072943363", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 179) }, - { "11422222075976800614", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 249) }, - { "14284377769814732906", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 109) }, - { "667777413731244716", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 183) }, - { "11624071786842686451", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 214) }, - { "3874974512053082278", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 213) }, - { "5471037497181745651", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 218) }, - { "6371386660654628561", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 210) }, - { "5331835606773958814", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 215) }, - { "16163821504542698475", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 212) }, - { "5697543838890997891", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 151) }, - { "6217438921274668801", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 353) }, - { "2633095809604510774", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 215) }, - { "11218297661079136641", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 212) }, - { "3374196543196230185", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 214) }, - { "17797320202829145544", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 217) }, - { "17198778757516749818", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 209) }, - { "6440981718484677922", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 359) }, - { "1616603916015535857", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, - { "7643715911083095268", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "137903092932521503", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 326) }, - { "13203019690952060789", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 335) }, - { "3918152537861570517", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 331) }, - { "8782903242853500098", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "18312668164562040079", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 80) }, - { "15160703466234996170", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 109) }, - { "2751241748685218213", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 322) }, - { "12622728760401804660", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 113) }, - { "1290624457831957354", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 324) }, - { "10924946887162830574", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 133) }, - { "10789202693606479024", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 322) }, - { "3718558874911694616", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 335) }, - { "12835389389575311182", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 202) }, - { "17406431092101974143", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 206) }, - { "1400409391266374603", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 202) }, - { "12154660333025778322", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 206) }, - { "4800587664660105589", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, - { "16474284418841532356", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "18117355153710110681", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "8449591498895477846", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "2962899568083589487", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "3491333679577961640", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "13945298510228460890", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "4282198629458668761", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "779525528509830615", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "15002237905129290671", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "16833026567865627676", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 335) }, - { "16991060247581867302", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 326) }, - { "6902644989079870993", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 329) }, - { "15088285782819494786", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "3379661203936923589", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 221) }, - { "10049571207493913006", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 224) }, - { "15691689005236690951", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 230) }, - { "11275109735493317886", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 211) }, - { "12220860296984467101", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 220) }, - { "44210723233569665", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 107) }, - { "14014987361364503383", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 303) }, - { "18189351665719757712", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "16159032667792855758", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "3374037004378790060", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 302) }, - { "6765409971512438438", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "10098892297878373639", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 151) }, - { "981877665302032867", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "7472350511000146655", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 323) }, - { "15513971895394346930", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 325) }, - { "6169721205327431190", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "4519054607159036572", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 361) }, - { "12750124851833311828", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 103) }, - { "4333851142313192116", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "6041620003527819661", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 361) }, - { "15091361629922645798", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 353) }, - { "9348121965341418899", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 327) }, - { "393951904144235223", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "3220084080191614421", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 323) }, - { "11610588256244825741", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "2802357220980817497", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "8972812517118478580", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 358) }, - { "4207115359813621211", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 75) }, - { "16582237002610438015", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 323) }, - { "772342953072606219", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "8546247990965609013", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 334) }, - { "7971830510840138313", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "3570484486449791727", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, - { "11461079340079820563", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, - { "12663860560275361463", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, - { "8132521728369930959", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "16108573960501496757", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "11086699387784339943", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "4013707396889204359", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 230) }, - { "11850332373794932468", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 230) }, - { "14763982961176216679", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 110) }, - { "8207349115037232863", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 213) }, - { "3273748387141431306", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 95) }, - //{ "580936360000782237", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, - //{ "10682918518101379579", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, - //{ "13178480813522103091", std::make_tuple("fully_connected_gpu_bf_io_gemm", -1) }, - { "14472322679644532468", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 141) }, - { "8378137527264154204", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "10180255575636684134", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 158) }, - { "18242121098885244699", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "6178572652675599622", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "6558074021146321216", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 259) }, - { "11038938372264857379", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 119) }, - { "17137800360536507200", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 148) }, - { "14016185289182597841", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 261) }, - { "8970519484272874266", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "3289746379259038515", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "18389174979070260315", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "17666483005735191253", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 142) }, - { "10845781902676865789", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 262) }, - { "13646634862315619979", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 227) }, - { "5072154928583891344", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 330) }, - { "7757331094141318304", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 263) }, - { "16779678846332091086", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 262) }, - { "12223166874490429642", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 263) }, - { "13316017702896072758", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 178) }, - { "10390896207372295988", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 181) }, - { "8386498395042623384", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 252) }, - { "17923632501885139982", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 254) }, - { "13398326377839777956", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 257) }, - { "13520557646924372128", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 257) }, - { "5996787039089786722", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 172) }, - { "2626376166907387273", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 180) }, - { "6147643392694904814", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 173) }, - { "4311921348668650791", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 251) }, - { "8046109476498335792", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 173) }, - { "14190077682825257613", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 257) }, - { "5032302126047788183", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "15256375572125522238", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 251) }, - { "5083173538217738703", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 178) }, - { "17269467004855120308", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 177) }, - { "2901056469731554922", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 177) }, - { "9747637051217505111", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "7175860674618956918", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 252) }, - { "9530922411870814200", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 180) }, - { "11015319643831560673", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 180) }, - { "5182740559503076121", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 180) }, - { "7567277014404457462", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 252) }, - { "15973842639221447367", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 257) }, - { "15951492056203075273", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 257) }, - { "5283253936050062275", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 173) }, - { "17650690912303447913", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 143) }, - { "9614936270604202220", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 262) }, - { "13998661469619523378", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 153) }, - { "10584034255622783869", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 241) }, - { "2973436171295280783", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 54) }, - { "1908809004094565452", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 245) }, - { "1623383628456201603", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 166) }, - { "5766507688771440170", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 54) }, - { "16626226341188424071", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 246) }, - { "14619055893081624406", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 121) }, - { "17222005830854879661", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 87) }, - { "14224121742920800990", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 207) }, - { "5859124386313585730", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 95) }, - { "9596156698919548146", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 82) }, - { "2729099061601852493", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "8233922303282945338", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "7402006230339617617", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 231) }, - { "8420763628389536977", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 229) }, - { "8325767678959979628", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 230) }, - { "7673672840505587739", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 231) }, - { "14166169053627992481", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 230) }, - { "120923426036313670", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 110) }, - { "7348084298010357768", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 110) }, - { "14653065651448352526", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 107) }, - { "11008522061447263744", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 324) }, - { "305505245310584136", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 323) }, - { "6472139251351862598", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, - { "10586018593856542117", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, - { "16706121580364790904", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "5495776091407365966", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "16430562172386510259", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "5673972310424776040", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "8797843396807284399", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 107) }, - { "1698321314111848001", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 321) }, - { "5762290464889692462", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 206) }, - { "3218248162832023196", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 107) }, - { "12988961529988078346", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 214) }, - { "4232250144427804891", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, - { "11683680166617045816", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, - { "6252429564537528709", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, - { "11717348577195224554", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "9275303306340702111", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "12245096462203481681", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 140) }, - { "18439435691655740074", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, - { "15272426400992401555", std::make_tuple("fully_connected_gpu_bf_io_gemm", -1) }, - { "9136831791301215059", std::make_tuple("fully_connected_gpu_bfyx_ref", -1) }, - { "18238669114790278675", std::make_tuple("fully_connected_gpu_bf_io_gemm", -1) }, - { "15376246520426368532", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 146) }, - { "16469788155263456039", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "9360494451263553093", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "7897877428349481398", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "9354818521586974021", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "5853553261686771766", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "6351572488552853754", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "11372638316835753193", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "6170074103544756465", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 150) }, - { "4544242784357021697", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "14225108809796795520", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "178353385245384751", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "2002574142025049539", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 362) }, - { "11630971824787392820", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 360) }, - { "11542493210215136239", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "9595803435783166868", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "17610828776103321939", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 231) }, - { "18312069177632970412", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 116) }, - { "7577483892218843723", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 231) }, - { "4355933224673863178", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "15124932296735391043", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 331) }, - { "16888042302987189589", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 362) }, - { "1584639932403433303", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 362) }, - { "15516194807992507442", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 172) }, - { "6614374536332038989", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 231) }, - { "5097818987523855112", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "6623182990939010641", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 362) }, - { "17001023283013862129", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 356) }, - { "7935150275452094595", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 362) }, - { "2326323992207208685", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "5072735784865711772", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "4683841893192741312", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 221) }, - { "10341773151035665956", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "14109534738984061372", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 303) }, - { "16706244336960642883", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 362) }, - { "1967030672241059921", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "1482100699000420627", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 361) }, - { "13632911653636980024", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 361) }, - { "6198830126915940359", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 366) }, - { "12125006289181390694", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 223) }, - { "14152716242882609401", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "16732621354152092286", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "17921489101554455214", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 361) }, - { "7384108582424003436", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "13139625572508441980", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 330) }, - { "16491532291908469567", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 360) }, - { "17788367809717898285", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 367) }, - { "1509728225855233852", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 365) }, - { "3816705689596666600", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "14157505468412850916", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 109) }, - { "17366807170224886960", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 231) }, - { "14548509699664316785", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 231) }, - { "10404702662303016402", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 230) }, - { "16436357970364549479", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 231) }, - { "4858167644379876157", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 231) }, - { "11718418772370938734", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 160) }, - { "989564341557094953", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 205) }, - { "8133676065307881979", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, - { "14555883089089918919", std::make_tuple("fully_connected_gpu_bf_io_gemm", -1) }, - { "14026570177552137240", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "11686670048744589243", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "6678796313875454849", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "641417817126876622", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "9622546530872848323", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "9194788897910888066", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "522181557896569275", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "3332334993503432420", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "16131448347558322280", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 322) }, - { "13852065717057446998", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "4342360467977736802", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "16336482874764861478", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 231) }, - { "6075691042233712335", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "7570346182940928159", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 110) }, - { "12971822824884826169", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "3033264172690274208", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "17301887391757619741", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 329) }, - { "15790005937034794347", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 329) }, - { "15464327246951632247", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "5659168916726488798", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 325) }, - { "8079376692609682448", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "15160738482264643601", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "13953639482255428227", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "12293786134765875615", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 231) }, - { "18214412375127043522", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "10670103699537731664", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "10147266284710177932", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "11443268857010762276", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 230) }, - { "4659943649635556150", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "11726298758004767743", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "13896680298436380632", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - 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{ "16662409111036688294", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 262) }, - { "5400706842524705774", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 261) }, - { "17423097433955762667", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 251) }, - { "18131954418490925431", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 258) }, - { "16549854027697846882", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 256) }, - { "10340073416712988987", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 159) }, - { "4633923265089466898", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 258) }, - { "6808980404170272597", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 256) }, - { "10592783998150232858", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 247) }, - { "1594829714229111215", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 156) }, - { "17361714725103230834", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 152) }, - { "15732140959902969012", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "5796974850751105634", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 84) }, - { "10588059104387338398", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 183) }, - { "11738780323979052397", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 182) }, - { "16342972196376030503", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 206) }, - { "10406201782146034797", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 4) }, - { "17342758321852264926", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 206) }, - { "15951978466742016539", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "14100026884590707572", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "8368507377481570353", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "16780457022162749898", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "17140702790441856730", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "2578325663193624576", std::make_tuple("convolution_gpu_yxfb_yxio_b16", -1) }, - { "8784358107340738205", std::make_tuple("convolution_gpu_yxfb_yxio_b16", -1) }, - { "2955459120402821540", std::make_tuple("convolution_gpu_yxfb_yxio_b16", -1) }, - { "2840794055129352139", std::make_tuple("convolution_gpu_yxfb_yxio_b16", -1) }, - { "7104266560248570112", std::make_tuple("convolution_gpu_yxfb_yxio_b16", -1) }, - { "11113125355390956764", std::make_tuple("convolution_gpu_yxfb_yxio_b16", -1) }, - { "9127827617126714860", std::make_tuple("fully_connected_gpu_fb_io_b8_f8_vload", -1) }, - { "2268275392299271167", std::make_tuple("fully_connected_gpu_fb_io_block_fp16", -1) }, - { "10615831454139478379", std::make_tuple("fully_connected_gpu_bs_f_bsv8_af8_vload", -1) }, - { "8205640825965213946", std::make_tuple("convolution_gpu_yxfb_yxio_b16", -1) }, - { "14337168375989245254", std::make_tuple("convolution_gpu_yxfb_yxio_b16", -1) }, - { "11664399629496237233", std::make_tuple("convolution_gpu_yxfb_yxio_b16", -1) }, - { "15750539817895707253", std::make_tuple("convolution_gpu_yxfb_yxio_b16", -1) }, - { "921209976738626097", std::make_tuple("convolution_gpu_yxfb_yxio_b16", -1) }, - { "8590416145336196354", std::make_tuple("convolution_gpu_yxfb_yxio_b16", -1) }, - { "10463632805036507382", std::make_tuple("convolution_gpu_yxfb_yxio_b16", -1) }, - { "13637537549252005181", std::make_tuple("convolution_gpu_yxfb_yxio_b16", -1) }, - { "7581949584623524395", std::make_tuple("convolution_gpu_yxfb_yxio_b16", -1) }, - { "9814647153117279415", std::make_tuple("convolution_gpu_yxfb_yxio_b16", -1) }, - { "3444250649099578792", std::make_tuple("convolution_gpu_yxfb_yxio_b16", -1) }, - { "4039483032571506874", std::make_tuple("fully_connected_gpu_bs_f_bsv8_af8_vload", -1) }, - { "14309249337788077160", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "6254493271976962295", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "12387660887222981357", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, - { "7723131901316908741", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, - { "13963554827358438190", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, - { "5001552360784483833", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, - { "14201142257504107783", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, - { "1066668660701816536", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, - { "4664196755018349672", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, - { "1103228955716492167", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 217) }, - { "8618835732380720921", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 251) }, - { "8391292909068775212", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 254) }, - { "9488974186647231896", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 251) }, - { "9069334144391048686", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 99) }, - { "12493863403516600413", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 256) }, - { "13375084585444085517", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 251) }, - { "18040173797801558071", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 135) }, - { "15329174116169594863", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 247) }, - { "14910223536998380801", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 246) }, - { "18277685132620834972", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 251) }, - { "13090887980792573261", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 172) }, - { "3067806959725855130", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 153) }, - { "17791773192152464021", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 143) }, - { "13603318842632052764", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 242) }, - { "12072881177966014126", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "14413047954443174304", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 192) }, - { "9118663018352672834", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 224) }, - { "3558391988878894288", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 329) }, - { "10047727261970275928", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 224) }, - { "11527382293059267033", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 225) }, - { "8445575388700666150", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 225) }, - { "4560479630843098090", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "12542825714985999760", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "4599539412023802059", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 303) }, - { "4570119951370893062", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 197) }, - { "16897917745917378359", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 197) }, - { "6947523163603267191", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 157) }, - { "2322126126611987721", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 158) }, - { "6518845972912144959", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 158) }, - { "9741774854327055438", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 231) }, - { "7079854103926842364", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 231) }, - { "5035895518536085765", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 231) }, - { "4307157272240924516", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 151) }, - { "142486914279119363", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 142) }, - { "1532263118203058517", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 212) }, - { "13529694429433303321", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 271) }, - { "2820916926593580316", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 255) }, - { "11140657515428786448", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "9028970753877215614", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 103) }, - { "14038308632095412386", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "15928183143089896780", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 270) }, - { "14071202918199194502", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 270) }, - { "12383676694875725364", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 271) }, - }); - td.td.insert({ - // style sample - { "108008098283423515", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 87) }, - { "4060303280041214217", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "6537702661107952067", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 228) }, - { "14207620784078112608", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 246) }, - { "15507553344802827573", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 348) }, - { "11202969539314825145", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 364) }, - { "17875115440447156357", std::make_tuple("convolution_gpu_bfyx_direct_10_12_16", -1) }, - { "5043345769472136917", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 363) }, - { "16920049042115931889", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 110) }, - { "18396735425525918800", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 321) }, - { "2188753401875518777", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 325) }, - { "861151538204493788", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 5) }, - { "6577112081591081699", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 325) }, - { "6662263400328602558", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 361) }, - { "12062286938998602641", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "3532486493536194182", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 335) }, - { "14486900605080248966", std::make_tuple("convolution_gpu_bfyx_direct_10_12_16", -1) }, - { "3986970741207127928", std::make_tuple("convolution_gpu_bfyx_direct_10_12_16", -1) }, - { "12055000818441091810", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 334) }, - { "6473775431261965926", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 334) }, - { "13358640031183139493", std::make_tuple("convolution_gpu_bfyx_direct_10_12_16", -1) }, - { "6917849789850282518", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "339005357927126796", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 356) }, - { "3341093105217903149", std::make_tuple("convolution_gpu_bfyx_direct_10_12_16", -1) }, - { "4002803423257090980", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 364) }, - { "491985190756430188", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 365) }, - { "7294200033269380787", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 365) }, - { "4133961720345934549", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 6) }, - { "15578894483600472601", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 333) }, - { "5153485325286526589", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 327) }, - { "11666701706717643100", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "4425021395842654484", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 340) }, - { "16007037430422291336", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 367) }, - { "3766679421476531097", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 214) }, - { "12016934279017055632", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 340) }, - { "6099288410648891214", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "14944495584618629508", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "9930151769697976322", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 326) }, - { "2751149427305557313", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "8308207826619932628", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "3823293373281864380", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 87) }, - { "10416260780913304720", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 360) }, - { "2847588473935575710", std::make_tuple("convolution_gpu_bfyx_direct_10_12_16", -1) }, - { "6114169197348303753", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "12362870423757408187", std::make_tuple("convolution_gpu_bfyx_direct_10_12_16", -1) }, - { "15330333360513835100", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 361) }, - { "4337663535143862248", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "12324726684926692530", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "11152914598877675570", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "17706702842712421674", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 334) }, - { "8596083086448639289", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 361) }, - { "3826763780015674157", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 335) }, - { "13022765751713107854", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "12054929554615967645", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 126) }, - { "8483866344820602196", std::make_tuple("convolution_gpu_bfyx_direct_10_12_16", -1) }, - { "17257458463329928325", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 329) }, - { "17396276238049115844", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "12139918033335162307", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 214) }, - { "17613450189830338239", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 20) }, - { "466805001581651681", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 327) }, - { "12375983338952375600", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 126) }, - { "2835926422026106846", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "7562282591986948344", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, - { "10222410309423438801", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 325) }, - { "9667626193041507177", std::make_tuple("convolution_gpu_bfyx_direct_10_12_16", -1) }, - { "5918842657011667447", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 327) }, - { "10197866743342998409", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 364) }, - { "10481938393331020691", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "18128936267842454401", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 340) }, - { "8968418225456926192", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 335) }, - { "13004007524122679918", std::make_tuple("convolution_gpu_bfyx_direct_10_12_16", -1) }, - { "3587239831348133052", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 360) }, - { "13594576107143259571", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 326) }, - { "3622666399417827014", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 6) }, - { "12576876344393380361", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "13160857254841009807", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 214) }, - { "5445489344860863060", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "1213577713645615257", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 361) }, - { "17692282381799629643", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "6196533506278647179", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "8978870911977287031", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 157) }, - { "6336679824344178824", std::make_tuple("convolution_gpu_bfyx_direct_10_12_16", -1) }, - { "6223842516539111057", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 367) }, - { "1046547531196124397", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "58154090876617650", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "12346479378618214663", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 340) }, - { "2502439462576713842", std::make_tuple("convolution_gpu_bfyx_direct_10_12_16", -1) }, - { "4182038693129989035", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "12115518620344827362", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 231) }, - { "604454303639822310", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 360) }, - { "5453339018427413517", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "9083797214718240599", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 167) }, - { "5886784323972875305", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 206) }, - { "1077955953397294307", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 126) }, - { "1705252754140106824", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 46) }, - { "14801234233433168563", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 367) }, - { "6099288410648891214", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "9667626193041507177", std::make_tuple("convolution_gpu_bfyx_direct_10_12_16", -1) }, - { "8308207826619932628", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "14486900605080248966", std::make_tuple("convolution_gpu_bfyx_direct_10_12_16", -1) }, - { "12362870423757408187", std::make_tuple("convolution_gpu_bfyx_direct_10_12_16", -1) }, - { "15507553344802827573", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 348) }, - { "18128936267842454401", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 340) }, - { "7491177930963608610", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 349) }, - { "74789225791237471", std::make_tuple("convolution_gpu_bfyx_direct_10_12_16", -1) }, - { "13384934269447336301", std::make_tuple("convolution_gpu_bfyx_direct_10_12_16", -1) }, - { "13121630338540122290", std::make_tuple("convolution_gpu_bfyx_direct_10_12_16", -1) }, - { "15469602039104029406", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "7160031288662381100", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, - { "7329115981778571341", std::make_tuple("convolution_gpu_bfyx_direct_10_12_16", -1) }, - }); - } }
\ No newline at end of file diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/auto_tuner_offline.h b/inference-engine/thirdparty/clDNN/kernel_selector/core/auto_tuner_offline.h index db293cc47..b7008d688 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/auto_tuner_offline.h +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/auto_tuner_offline.h @@ -25,12 +25,20 @@ namespace kernel_selector { // SKL GT4e void tuning_cache_193B(tuning_data&); - //KBLGT2 - void tuning_cache_5912(tuning_data&); + void tuning_cache_193B_B1_B16(tuning_data&); + void tuning_cache_193B_B8(tuning_data&); + void tuning_cache_193B_B32_B64(tuning_data&); //SKL GT2 void tuning_cache_1912(tuning_data&); + void tuning_cache_1912_B1_B16(tuning_data&); + void tuning_cache_1912_B8(tuning_data&); + void tuning_cache_1912_B32_B64(tuning_data&); //KBL GT3e void tuning_cache_5927(tuning_data&); + void tuning_cache_5927_B1(tuning_data&); + //ICL GT2 + void tuning_cache_8A52(tuning_data&); + void tuning_cache_8A52_B1_B16(tuning_data&); //APL 10W void tuning_cache_5A84(tuning_data&); // Device ID for APL E3930. @@ -50,9 +58,9 @@ namespace kernel_selector const std::map<std::string, void(*)(tuning_data&)> sku_cache_fillers { { "0x193B" , tuning_cache_193B }, - { "0x5912" , tuning_cache_5912 }, { "0x1912" , tuning_cache_1912 }, { "0x5927" , tuning_cache_5927 }, + { "0x8A52" , tuning_cache_8A52 }, { "0x5A84" , tuning_cache_5A84 }, { "0x5A85" , tuning_cache_5A84 }, { "0x3184" , tuning_cache_5A84 }, diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/cache/cache_APL.cpp b/inference-engine/thirdparty/clDNN/kernel_selector/core/cache/cache_APL.cpp new file mode 100644 index 000000000..20699192f --- /dev/null +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/cache/cache_APL.cpp @@ -0,0 +1,2572 @@ +/* +// Copyright (c) 2018 Intel Corporation +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +*/ + +#include "auto_tuner.h" +#include "auto_tuner_offline.h" +namespace kernel_selector +{ + //APL 10W + void tuning_cache_5A84(tuning_data& td) + { + td.td.insert({ + { "4583484812233029888", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "7560832358324865221", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "7382044526960590018", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "12372261924257291610", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 355) }, + { "1547771611689525848", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "3134973665622945888", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "18260030211719729324", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 325) }, + { "7416143717989012766", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "12028963907131702705", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "2464531851392092325", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 331) }, + { "8181308759455478086", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "546062289721803579", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "4889405384318695802", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "12841232643395100314", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "14108361259911144680", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "2726453304845436156", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 203) }, + { "2607416795507802412", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 327) }, + { "2175404966338020579", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 328) }, + { "14666883719480623074", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 327) }, + { "3752993663604843837", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 56) }, + { "5274735654559844733", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 331) }, + { "8174421295799601683", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 247) }, + { "1967655354607438665", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 327) }, + { "15762542971370422224", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 330) }, + { "8183203099539372914", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 327) }, + { "4075343423548891274", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 132) }, + { "13264497096898621015", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 362) }, + { "679058537775669048", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 330) }, + { "3375634256357960999", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 330) }, + { "2844616672368585285", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 336) }, + { "14235558866846276172", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 247) }, + { "18066867692765966577", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 330) }, + { "9861424412782371874", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 325) }, + { "607078314875528651", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 325) }, + { "6234885984223387670", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 132) }, + { "7223570329858821704", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 336) }, + { "17234843749633035510", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 205) }, + { "11516168882438876247", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 328) }, + { "11312664612825940140", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 327) }, + { "14846039494240217143", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 328) }, + { "3390376200501119384", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 115) }, + { "1113077760071340574", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 336) }, + { "4614875083188849196", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 87) }, + { "10859023312681572942", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 247) }, + { "5588692131556725717", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 287) }, + { "7653946972043115920", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 247) }, + { "9773458066743315157", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, + { "10491513939202460216", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 208) }, + { "8140122945471321201", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 86) }, + { "15079423575410353790", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 356) }, + { "12844146569641472927", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 287) }, + { "13443914015380511668", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 247) }, + { "13404457916017756196", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 69) }, + { "6402415801415013013", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 208) }, + { "2040762223425679479", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, + { "8595156989254845134", std::make_tuple("convolution_gpu_bfyx_1x1_hgemm_buf_16x1", -1) }, + { "14493123117003003092", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, + { "7391309333582046386", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "9935182178960843140", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "15422142509105297183", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "14849987788569183527", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "14923132847727661051", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "13271555597925466454", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "6865593216823998846", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "12385437755245281331", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "4530047829451377456", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "127643210248119703", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "2599051617462913767", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "3024020696533545102", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "16205377892664082400", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 333) }, + { "15135655146332608939", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 327) }, + { "10848724554175904486", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 327) }, + { "15558120704022404428", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 326) }, + { "14120354125904513152", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 330) }, + { "8475075092501403968", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 330) }, + { "15892943371777404347", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 331) }, + { "15078418657871922661", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 355) }, + { "3502053626453342387", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, + { "1230316443026403527", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 328) }, + { "12846418701225147646", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 331) }, + { "17386994561779281406", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 330) }, + { "7367684259946371231", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 328) }, + { "5451072983028714092", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 327) }, + { "11758623888547009364", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, + { "15958650715061024845", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, + { "13899144453581769028", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 331) }, + { "10884229860266073967", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 330) }, + { "6629431845229592220", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 326) }, + { "13199442294147992119", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 331) }, + { "5032929712205664246", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, + { "14871333176552512036", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 362) }, + { "17890435688048047959", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 328) }, + { "11353661571093800805", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 328) }, + { "3101908018947919238", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 336) }, + { "7495240482209084478", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 210) }, + { "6964506613327100469", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 103) }, + { "3670645005971806718", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 247) }, + { "69949758775887534", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 356) }, + { "13654393413005772278", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 287) }, + { "11101512074369779300", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 247) }, + { "2273811004985590823", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, + { "7457154125218067377", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 208) }, + { "7709677514862642399", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 246) }, + { "8010619564572573208", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, + { "16479793487852125428", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "3147355028342035061", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "12672939642957531547", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "12627961914394914920", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "16210688853876861607", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 224) }, + { "1899485873740458557", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 302) }, + { "669151029135558505", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 328) }, + { "8912067280071688393", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 328) }, + { "7714589858275971005", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "9794413496918699979", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "18350040136091421971", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 294) }, + { "16931304566154830346", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 290) }, + { "12816950084297042217", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 302) }, + { "2359632276970855181", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 331) }, + { "16592641501972654496", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 331) }, + { "7754054384598160936", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "18195884921517044108", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 331) }, + { "16256130331524359070", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 301) }, + { "13497279823712860029", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 302) }, + { "6095972148204769193", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 328) }, + { "4149964766407000732", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 328) }, + { "10262104071809780712", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 328) }, + { "10707129891337660055", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 178) }, + { "3585075254981736756", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "3181067565488724209", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 194) }, + { "8636008354706344794", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 256) }, + { "4966150965920189853", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 256) }, + { "5569253153294942795", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 294) }, + { "11521347729886549503", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 175) }, + { "12399471154320580621", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 260) }, + { "14851218369956754103", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 293) }, + { "14859848826604327499", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 180) }, + { "14783159891899899660", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 175) }, + { "2369671961317151564", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 251) }, + { "1691004331056506231", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 251) }, + { "4465288557833228023", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "13538111995551348621", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 180) }, + { "1886751914747841929", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 180) }, + { "8445964247944285746", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 175) }, + { "803205084059316676", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 293) }, + { "12654698468722759675", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 294) }, + { "13484605287576302088", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 253) }, + { "2469399061693302590", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 179) }, + { "14782181149367028912", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 179) }, + { "1448238652280623323", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 174) }, + { "2076478920663115306", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 293) }, + { "7369834759425644726", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 214) }, + { "11702633755046828968", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 179) }, + { "2944972038827287015", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 214) }, + { "8261441437673092886", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 211) }, + { "16694312773479519523", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 217) }, + { "5486494868955566721", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 211) }, + { "8246009573416434030", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 215) }, + { "9457894602447879547", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 221) }, + { "4598302923247277427", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 217) }, + { "3116224788980631217", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, + { "17103527368951412486", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 214) }, + { "10150428063205056209", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 255) }, + { "16984028253790680977", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 211) }, + { "17857105233471273424", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 217) }, + { "2715609009808401074", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 251) }, + { "232807837985324954", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, + { "13933912937625580405", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, + { "11254635684957519432", std::make_tuple("convolution_gpu_bfyx_1x1_hgemm_buf_16x1", -1) }, + { "6729077823331194042", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, + { "15961933828477762733", std::make_tuple("convolution_gpu_bfyx_1x1_hgemm_buf_16x1", -1) }, + { "827225131390571924", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "10429613013253088132", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "14629385997654952321", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, + { "7546586420552408243", std::make_tuple("convolution_gpu_bfyx_1x1_hgemm_buf_16x1", -1) }, + { "11897687507601277182", std::make_tuple("convolution_gpu_bfyx_1x1_hgemm_buf_16x1", -1) }, + { "13975409361394567866", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "6458124573210430792", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 332) }, + { "2385616965635993249", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, + { "14462438074931673266", std::make_tuple("convolution_gpu_bfyx_1x1_hgemm_buf_16x1", -1) }, + { "9338654554616107568", std::make_tuple("convolution_gpu_bfyx_1x1_hgemm_buf_16x1", -1) }, + { "15476402794704488137", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 217) }, + { "13654816209891478730", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 230) }, + { "1680424228660495363", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 225) }, + { "12929981792125924963", std::make_tuple("convolution_gpu_bfyx_1x1_hgemm_buf_16x1", -1) }, + { "4698507050987130777", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 356) }, + { "1094144958579794349", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "12358908585763044267", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 303) }, + { "9793373151408615612", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "90849151510482266", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "17277787450259342076", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 303) }, + { "2367877811435050998", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 325) }, + { "433942345363552443", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "14575816691130255191", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "3675622521877371819", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 325) }, + { "7601637686045360430", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "9818496628902493298", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "377651990943545344", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 331) }, + { "16357661916741979192", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "5417669424921804056", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "854020380490533945", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 328) }, + { "11984602132438314210", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 355) }, + { "13100228219613095795", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "9808704199834907703", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "13071373212254908241", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 325) }, + { "18392748682101174561", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "11928475964162658765", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "11937547211842355800", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 231) }, + { "4637568849323640167", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "16812695025037565299", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "10487883723723512839", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, + { "15315014737515653325", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 331) }, + { "1579905786032546689", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 327) }, + { "4243996335899627971", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, + { "2545885699369058867", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, + { "16926950874716567095", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, + { "6355395905401306995", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "2096779676054335057", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "4217179485243909459", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "17101789600628162503", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 224) }, + { "6139574161497189424", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 224) }, + { "16559140502701231107", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 213) }, + { "11459784003592366395", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 212) }, + { "7869916853707978306", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 94) }, + //{ "3889519976910355277", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, + //{ "12081835728078383819", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, + //{ "14923692894655929923", std::make_tuple("fully_connected_gpu_bf_io_gemm", -1) }, + { "1841901358010744236", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "10888203577545955226", std::make_tuple("convolution_gpu_bfyx_1x1_hgemm_buf_16x1", -1) }, + { "9799890897264103013", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, + { "1827296932806936575", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 256) }, + { "13010820430079828498", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 256) }, + { "6245781545617904772", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 256) }, + { "8714031312599034571", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "12922099252166105096", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "3042887030242700493", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "1419879016567682338", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 251) }, + { "12870587285162108523", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 251) }, + { "6103433181190121715", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 251) }, + { "3469599265931338557", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "14812617666668076833", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "14854734265631496499", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 364) }, + { "7637441820772916248", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 364) }, + { "14695781272831602408", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 217) }, + { "15696910741835640150", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 256) }, + { "17790593820165047954", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "1433224983833208570", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "16185194021453870096", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 194) }, + { "10310918050196558188", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 293) }, + { "14885938077915823034", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 294) }, + { "14442357887993453368", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 256) }, + { "4719130523147011420", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 256) }, + { "8870222084473246330", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 192) }, + { "9350596936816632825", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 182) }, + { "6183248276225219542", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 256) }, + { "3757195189216622027", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 293) }, + { "8318857994507665384", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 192) }, + { "12864512857659000129", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 256) }, + { "6615646900347529347", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 249) }, + { "5208923086986567490", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 293) }, + { "9390793435913144215", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "11997629302296435180", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "4858270366437120918", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 265) }, + { "15975176007724247667", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 249) }, + { "16837473534895641370", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 293) }, + { "16214153687871223428", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 180) }, + { "8858112708913743577", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 180) }, + { "8844677471730173649", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 180) }, + { "9043742986995534354", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 250) }, + { "15621341038256548867", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 256) }, + { "18268980125375728709", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 256) }, + { "9757167087033785227", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 256) }, + { "6513982093384445397", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 250) }, + { "9273893819042428704", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 260) }, + { "8270840662337272430", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 141) }, + { "15021512490648380369", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 247) }, + { "6578908625437515675", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, + { "13762042713029963144", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 325) }, + { "668798769117277023", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 172) }, + { "10917498758625273194", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, + { "14335423820860953927", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 326) }, + { "13978750151855895830", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 247) }, + { "8965747921518186477", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 62) }, + { "4428101657497677982", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 245) }, + { "8151272056391095510", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 62) }, + { "17656341100957270390", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 44) }, + { "10295330953350618042", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 7) }, + { "15901675909820977223", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 207) }, + { "6026876733674266377", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 6) }, + { "381149736509958403", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 4) }, + { "7962991673727743706", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 6) }, + { "10064251191248475177", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "16663239694378513014", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "4690831975451405214", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, + { "16132498413588349821", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, + { "10609644803793651808", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, + { "16520784657717262379", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, + { "2271187702055786721", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, + { "1872921634399989626", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, + { "12535576637355537200", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, + { "7771729980527620398", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, + { "14849108908297747749", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, + { "14043770215999952932", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "15277856047844308598", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "8048617952947915835", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "11446745541571732900", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, + { "17422822627612865758", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, + { "13954144830230671601", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 217) }, + { "11198908896401597838", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 218) }, + { "5582896843095691256", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 218) }, + { "8133587696326295326", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 218) }, + { "2007192658799516915", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, + { "9492402787848610840", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, + { "10515519878978734341", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, + { "1375156980278317418", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "13455881643467418059", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "12788968383428254917", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 216) }, + { "12304975739476881266", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, + { "3390014193205017427", std::make_tuple("fully_connected_gpu_bf_io_gemm", -1) }, + { "13587202155230938291", std::make_tuple("fully_connected_gpu_bf_io_gemm", -1) }, + { "14462744723628661203", std::make_tuple("fully_connected_gpu_bf_io_gemm", -1) }, + { "16109721499545711936", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 106) }, + { "10090923790949378407", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "16672038432561840773", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "2973773544904290726", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "17193614571243427089", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "2862029728492027826", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "11066930104187448422", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "2755147389712995637", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "10662798624911535617", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 224) }, + { "2172121470071868949", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "3579916582911190192", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 357) }, + { "5495063314176654751", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "3771003491521695667", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 365) }, + { "15514370342945522276", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 361) }, + { "17285639145064557279", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 361) }, + { "12642574441854544900", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "5471430682416582179", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 254) }, + { "8561154029325525444", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "8939900194037985459", 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std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "17153828952517174005", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 290) }, + { "9864812885638557249", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 328) }, + { "8410695282651246173", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 178) }, + { "3011957000022205132", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 290) }, + { "18202466898415347292", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 175) }, + { "11433166800587133728", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 251) }, + { "4499160027703324879", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 251) }, + { "10225878843410985743", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 251) }, + { "10961131057009777878", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 262) }, + { "17123897723015586893", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "3938875063592179645", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 261) }, + { "7589320923145169660", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 186) }, + { "13907115679251591389", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "5262155845067632954", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 251) }, + { "12323619994816664201", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 251) }, + { "13711624246076632711", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "13879644216615040961", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 213) }, + { "13418213186769741623", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 190) }, + { "9850414237385072276", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 215) }, + { "13762987373425432087", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 211) }, + { "15387492794262813616", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 211) }, + { "9455446170928387706", 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std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, + { "4003433148846544263", std::make_tuple("fully_connected_gpu_bf_io_gemm", -1) }, + { "4361250474585164062", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 328) }, + { "10523363119855336043", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 328) }, + { "1718324808394833635", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "8430284238380067998", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 290) }, + { "3950448771871155887", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 363) }, + { "9033877528655370244", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 364) }, + { "8878071105867359307", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 297) }, + { "7511984934520363336", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "860443413504997114", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "4957638663977636791", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "14793503588688729262", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 335) }, + { "9303039486341715392", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 178) }, + { "8923406201866512905", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 290) }, + { "14629889085799380442", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 178) }, + { "4811310048537439646", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 261) }, + { "8622985922687454592", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 251) }, + { "8611046137980763541", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "1879796404388368873", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 179) }, + { "6928835003016610382", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 256) }, + { "8603207107304593583", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 251) }, + { "6477198553362516437", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 251) }, + { "6377441002585730862", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 256) }, + { "15761554874575656075", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 209) }, + { "118354408955419547", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 213) }, + { "13601202334102031245", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 214) }, + { "11716196499333250570", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 356) }, + { "16984923535088627888", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, + { "364996668506826202", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 215) }, + { "6412527114952548517", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 209) }, + { "4892959859293355837", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "16127331840410137228", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 225) }, + { "13335944978055152562", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "10715829903767495958", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "6065404265303390338", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, + { "3897655522585667381", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, + { "1860663592951633878", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, + { "5698748062275134041", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, + { "15945452307780131237", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, + { "6764685582382238740", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, + { "4737109912659941670", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, + { "9371952894576491521", std::make_tuple("convolution_gpu_bfyx_1x1_hgemm_buf_16x1", -1) }, + { "12696412964119109465", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 328) }, + { "17825874529822806486", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "8220763890959777277", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "17731591992960147987", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "8950668477702067729", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "14991602704357959545", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 328) }, + { "16383540667048742064", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "16820082917500285799", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "6820284286806022849", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "17285815901490707654", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, + { "994182747184593564", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, + { "6642767323474835034", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, + { "3215659303601163167", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 338) }, + { "54975980454651672", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 337) }, + { "11529876081402974396", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 327) }, + { "3644282167178264526", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "360872770877634346", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "16720108310653948550", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, + { "14353390922580547467", std::make_tuple("convolution_gpu_bfyx_1x1_hgemm_buf_16x1", -1) }, + { "9868561386826862471", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, + { "17465517455679097501", std::make_tuple("convolution_gpu_bfyx_1x1_hgemm_buf_16x1", -1) }, + { "5570311824197099845", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, + { "7524311370696987092", std::make_tuple("convolution_gpu_bfyx_1x1_hgemm_buf_16x1", -1) }, + { "14070988879848388270", std::make_tuple("convolution_gpu_bfyx_1x1_hgemm_buf_16x1", -1) }, + { "8296551195150971668", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, + { "14352796912241296357", std::make_tuple("convolution_gpu_bfyx_1x1_hgemm_buf_16x1", -1) }, + { "9840495023131952174", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, + { "4720851194954041037", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 337) }, + { "17009318615658405230", std::make_tuple("convolution_gpu_bfyx_1x1_hgemm_buf_16x1", -1) }, + }); + td.td.insert({ + { "9421643783312790618", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, + { "10693348571961406417", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "2940027113687311893", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "6090625728451718945", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, + { "5643908654122573882", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { 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std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, + { "16474284418841532356", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "18117355153710110681", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "8449591498895477846", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "2962899568083589487", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "3491333679577961640", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "13945298510228460890", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "4282198629458668761", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "779525528509830615", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "15002237905129290671", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "16833026567865627676", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 335) }, + { "16991060247581867302", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 326) }, + { "6902644989079870993", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 329) }, + { "15088285782819494786", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "3379661203936923589", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 221) }, + { "10049571207493913006", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 224) }, + { "15691689005236690951", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 230) }, + { "11275109735493317886", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 211) }, + { "12220860296984467101", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 220) }, + { "44210723233569665", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 107) }, + { "14014987361364503383", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 303) }, + { "18189351665719757712", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "16159032667792855758", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "3374037004378790060", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 302) }, + { "6765409971512438438", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "10098892297878373639", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 151) }, + { "981877665302032867", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "7472350511000146655", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 323) }, + { "15513971895394346930", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 325) }, + { "6169721205327431190", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "4519054607159036572", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 361) }, + { "12750124851833311828", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 103) }, + { "4333851142313192116", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "6041620003527819661", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 361) }, + { "15091361629922645798", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 353) }, + { "9348121965341418899", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 327) }, + { "393951904144235223", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "3220084080191614421", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 323) }, + { "11610588256244825741", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, + { "2802357220980817497", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "8972812517118478580", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 358) }, + { "4207115359813621211", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 75) }, + { "16582237002610438015", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 323) }, + { "772342953072606219", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, + { "8546247990965609013", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 334) }, + { "7971830510840138313", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, + { "3570484486449791727", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, + { "11461079340079820563", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, + { "12663860560275361463", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, + { "8132521728369930959", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "16108573960501496757", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "11086699387784339943", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "4013707396889204359", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 230) }, + { "11850332373794932468", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 230) }, + { "14763982961176216679", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 110) }, + { "8207349115037232863", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 213) }, + { "3273748387141431306", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 95) }, + //{ "580936360000782237", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, + //{ "10682918518101379579", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, + //{ "13178480813522103091", std::make_tuple("fully_connected_gpu_bf_io_gemm", -1) }, + { "14472322679644532468", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 141) }, + { "8378137527264154204", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "10180255575636684134", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 158) }, + { "18242121098885244699", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "6178572652675599622", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "6558074021146321216", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 259) }, + { "11038938372264857379", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 119) }, + { "17137800360536507200", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 148) }, + { "14016185289182597841", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 261) }, + { "8970519484272874266", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "3289746379259038515", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "18389174979070260315", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "17666483005735191253", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 142) }, + { "10845781902676865789", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 262) }, + { "13646634862315619979", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 227) }, + { "5072154928583891344", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 330) }, + { "7757331094141318304", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 263) }, + { "16779678846332091086", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 262) }, + { "12223166874490429642", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 263) }, + { "13316017702896072758", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 178) }, + { "10390896207372295988", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 181) }, + { "8386498395042623384", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 252) }, + { "17923632501885139982", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 254) }, + { "13398326377839777956", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 257) }, + { "13520557646924372128", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 257) }, + { "5996787039089786722", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 172) }, + { "2626376166907387273", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 180) }, + { "6147643392694904814", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 173) }, + { "4311921348668650791", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 251) }, + { "8046109476498335792", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 173) }, + { "14190077682825257613", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 257) }, + { "5032302126047788183", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "15256375572125522238", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 251) }, + { "5083173538217738703", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 178) }, + { "17269467004855120308", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 177) }, + { "2901056469731554922", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 177) }, + { "9747637051217505111", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "7175860674618956918", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 252) }, + { "9530922411870814200", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 180) }, + { "11015319643831560673", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 180) }, + { "5182740559503076121", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 180) }, + { "7567277014404457462", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 252) }, + { "15973842639221447367", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 257) }, + { "15951492056203075273", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 257) }, + { "5283253936050062275", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 173) }, + { "17650690912303447913", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 143) }, + { "9614936270604202220", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 262) }, + { "13998661469619523378", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 153) }, + { "10584034255622783869", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 241) }, + { "2973436171295280783", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 54) }, + { "1908809004094565452", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 245) }, + { "1623383628456201603", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 166) }, + { "5766507688771440170", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 54) }, + { "16626226341188424071", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 246) }, + { "14619055893081624406", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 121) }, + { "17222005830854879661", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 87) }, + { "14224121742920800990", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 207) }, + { "5859124386313585730", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 95) }, + { "9596156698919548146", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 82) }, + { "2729099061601852493", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "8233922303282945338", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "7402006230339617617", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 231) }, + { "8420763628389536977", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 229) }, + { "8325767678959979628", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 230) }, + { "7673672840505587739", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 231) }, + { "14166169053627992481", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 230) }, + { "120923426036313670", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 110) }, + { "7348084298010357768", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 110) }, + { "14653065651448352526", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 107) }, + { "11008522061447263744", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 324) }, + { "305505245310584136", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 323) }, + { "6472139251351862598", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, + { "10586018593856542117", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, + { "16706121580364790904", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "5495776091407365966", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "16430562172386510259", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "5673972310424776040", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "8797843396807284399", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 107) }, + { "1698321314111848001", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 321) }, + { "5762290464889692462", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 206) }, + { "3218248162832023196", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 107) }, + { "12988961529988078346", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 214) }, + { "4232250144427804891", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, + { "11683680166617045816", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, + { "6252429564537528709", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, + { "11717348577195224554", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "9275303306340702111", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "12245096462203481681", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 140) }, + { "18439435691655740074", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, + { "15272426400992401555", std::make_tuple("fully_connected_gpu_bf_io_gemm", -1) }, + { "9136831791301215059", std::make_tuple("fully_connected_gpu_bfyx_ref", -1) }, + { "18238669114790278675", std::make_tuple("fully_connected_gpu_bf_io_gemm", -1) }, + { "15376246520426368532", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 146) }, + { "16469788155263456039", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "9360494451263553093", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "7897877428349481398", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "9354818521586974021", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "5853553261686771766", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "6351572488552853754", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "11372638316835753193", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "6170074103544756465", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 150) }, + { "4544242784357021697", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "14225108809796795520", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "178353385245384751", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "2002574142025049539", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 362) }, + { "11630971824787392820", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 360) }, + { "11542493210215136239", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "9595803435783166868", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "17610828776103321939", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 231) }, + { "18312069177632970412", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 116) }, + { "7577483892218843723", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 231) }, + { "4355933224673863178", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "15124932296735391043", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 331) }, + { "16888042302987189589", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 362) }, + { "1584639932403433303", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 362) }, + { "15516194807992507442", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 172) }, + { "6614374536332038989", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 231) }, + { "5097818987523855112", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "6623182990939010641", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 362) }, + { "17001023283013862129", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 356) }, + { "7935150275452094595", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 362) }, + { "2326323992207208685", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "5072735784865711772", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "4683841893192741312", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 221) }, + { "10341773151035665956", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "14109534738984061372", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 303) }, + { "16706244336960642883", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 362) }, + { "1967030672241059921", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, + { "1482100699000420627", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 361) }, + { "13632911653636980024", 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-1) }, + { "11396985422513105543", std::make_tuple("convolution_gpu_yxfb_yxio_b16", -1) }, + { "12114476173765693172", std::make_tuple("convolution_gpu_yxfb_yxio_b16", -1) }, + { "17041468169694105561", std::make_tuple("convolution_gpu_yxfb_yxio_b16", -1) }, + { "16498300259966485293", std::make_tuple("convolution_gpu_yxfb_yxio_b16", -1) }, + { "1173136780324694038", std::make_tuple("convolution_gpu_yxfb_yxio_b16", -1) }, + { "15786764202107923723", std::make_tuple("convolution_gpu_yxfb_yxio_b16", -1) }, + { "8913526950888110377", std::make_tuple("convolution_gpu_yxfb_yxio_b16", -1) }, + { "15988378956341507229", std::make_tuple("convolution_gpu_yxfb_yxio_b16", -1) }, + { "10993107955805947401", std::make_tuple("convolution_gpu_yxfb_yxio_b16", -1) }, + { "6214677989814002369", std::make_tuple("convolution_gpu_yxfb_yxio_b16", -1) }, + { "10884202393733523875", std::make_tuple("convolution_gpu_yxfb_yxio_b16", -1) }, + { "4424960026145600447", std::make_tuple("convolution_gpu_yxfb_yxio_b16", -1) }, + { "9763754389347695094", std::make_tuple("convolution_gpu_yxfb_yxio_b16", -1) }, + { "105055722864217258", std::make_tuple("convolution_gpu_yxfb_yxio_b16", -1) }, + { "9775648000771985077", std::make_tuple("convolution_gpu_yxfb_yxio_b16", -1) }, + { "15967893151722576439", std::make_tuple("convolution_gpu_yxfb_yxio_b16", -1) }, + { "5774841809066688068", std::make_tuple("fully_connected_gpu_fb_io_block_fp16", -1) }, + { "13402919586406297042", std::make_tuple("convolution_gpu_yxfb_yxio_b16", -1) }, + { "11335142595937152387", std::make_tuple("convolution_gpu_yxfb_yxio_b16", -1) }, + { "2689568881580764024", std::make_tuple("convolution_gpu_yxfb_yxio_b16", -1) }, + { "6571325912136856822", std::make_tuple("convolution_gpu_yxfb_yxio_b16", -1) }, + { "18122652705874970766", std::make_tuple("convolution_gpu_yxfb_yxio_b16", -1) }, + { "2000008755333069005", std::make_tuple("convolution_gpu_yxfb_yxio_b16", -1) }, + { "10361998183258703575", std::make_tuple("convolution_gpu_yxfb_yxio_b16", -1) }, + { "12348644068948200883", std::make_tuple("convolution_gpu_yxfb_yxio_b16", -1) }, + { "736422312606696687", std::make_tuple("convolution_gpu_yxfb_yxio_b16", -1) }, + { "16240864447025932692", std::make_tuple("convolution_gpu_yxfb_yxio_b16", -1) }, + { "8589562027950762944", std::make_tuple("convolution_gpu_yxfb_yxio_b16", -1) }, + { "9162564861963233717", std::make_tuple("convolution_gpu_yxfb_yxio_b16", -1) }, + { "3167738956362101592", std::make_tuple("convolution_gpu_yxfb_yxio_b16", -1) }, + { "7260746128189749064", std::make_tuple("convolution_gpu_yxfb_yxio_b16", -1) }, + { "15308960063718398523", std::make_tuple("convolution_gpu_yxfb_yxio_b16", -1) }, + { "10129304668926912275", std::make_tuple("convolution_gpu_yxfb_yxio_b16", -1) }, + { "4853130422682926168", std::make_tuple("convolution_gpu_yxfb_yxio_b16", -1) }, + { "14453982453535955244", std::make_tuple("convolution_gpu_yxfb_yxio_b16", -1) }, + { "16608982023596566351", std::make_tuple("convolution_gpu_yxfb_yxio_b16", -1) }, + { "2470663389603706356", std::make_tuple("convolution_gpu_yxfb_yxio_b16", -1) }, + { "4240407752719875080", std::make_tuple("convolution_gpu_yxfb_yxio_b16", -1) }, + { "4846563120992975368", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 231) }, + { "3706088306568590662", std::make_tuple("convolution_gpu_yxfb_yxio_b16", -1) }, + { "14866563628584464675", std::make_tuple("convolution_gpu_yxfb_yxio_b16", -1) }, + { "14721943524627076027", std::make_tuple("convolution_gpu_yxfb_yxio_b16", -1) }, + { "9323941828298277387", std::make_tuple("convolution_gpu_yxfb_yxio_b16", -1) }, + { "14109366965145192619", std::make_tuple("convolution_gpu_yxfb_yxio_b16", -1) }, + { "4923997413838231159", std::make_tuple("fully_connected_gpu_fb_io_block_fp16", -1) }, + { "181017193671999192", std::make_tuple("fully_connected_gpu_fb_io_block_fp16", -1) }, + { "10757412618207229106", std::make_tuple("fully_connected_gpu_fb_io_block_fp16", -1) }, + { "6395263375773555188", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "5979046470758784946", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "3927359449523162508", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "18232387132890063687", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 328) }, + { "6709212639543074230", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 328) }, + { "10086813986911195558", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 326) }, + { "3109992766790372487", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "794530296606789816", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "1249133049911188319", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "2006024870459798086", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 331) }, + { "11914297820344167381", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 331) }, + { "13079795735173763117", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 331) }, + { "6241224766048532539", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "16524474021378494125", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "4407550747921719377", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 364) }, + { "7259905085241841240", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 364) }, + { "6666210546769702280", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, + { "7814543122045448412", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, + { "13554702187867408038", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "2547880010597993852", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "9061076702890952738", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "15460429275475874158", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 256) }, + { "7724185199575851246", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 256) }, + { "8533091468352267196", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 256) }, + { "15025120359649460106", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 256) }, + { "2613575328969629284", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "7463954007838579697", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "9151324495773628566", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "651020886445062493", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 256) }, + { "8237821273547216740", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "2875927974837744359", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 256) }, + { "3674322065648064195", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 256) }, + { "137871170540938640", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 256) }, + { "3066826388383295007", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 256) }, + { "17483221428915982776", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 256) }, + { "3403906310423395442", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 256) }, + { "3888283018836731569", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 256) }, + { "13928684419408478520", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 256) }, + { "11520548550630007970", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 256) }, + { "4922714504620931501", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 256) }, + { "15683804450763499599", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 256) }, + { "12686604223669447758", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 256) }, + { "10746289671948325353", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 256) }, + { "2487976264999747775", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 256) }, + { "6163010595188500945", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 256) }, + { "10404333823880552577", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 261) }, + { "16662409111036688294", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 262) }, + { "5400706842524705774", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 261) }, + { "17423097433955762667", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 251) }, + { "18131954418490925431", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 258) }, + { "16549854027697846882", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 256) }, + { "10340073416712988987", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 159) }, + { "4633923265089466898", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 258) }, + { "6808980404170272597", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 256) }, + { "10592783998150232858", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 247) }, + { "1594829714229111215", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 156) }, + { "17361714725103230834", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 152) }, + { "15732140959902969012", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "5796974850751105634", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 84) }, + { "10588059104387338398", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 183) }, + { "11738780323979052397", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 182) }, + { "16342972196376030503", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 206) }, + { "10406201782146034797", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 4) }, + { "17342758321852264926", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 206) }, + { "15951978466742016539", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "14100026884590707572", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "8368507377481570353", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "16780457022162749898", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "17140702790441856730", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "2578325663193624576", std::make_tuple("convolution_gpu_yxfb_yxio_b16", -1) }, + { "8784358107340738205", std::make_tuple("convolution_gpu_yxfb_yxio_b16", -1) }, + { "2955459120402821540", std::make_tuple("convolution_gpu_yxfb_yxio_b16", -1) }, + { "2840794055129352139", std::make_tuple("convolution_gpu_yxfb_yxio_b16", -1) }, + { "7104266560248570112", std::make_tuple("convolution_gpu_yxfb_yxio_b16", -1) }, + { "11113125355390956764", std::make_tuple("convolution_gpu_yxfb_yxio_b16", -1) }, + { "9127827617126714860", std::make_tuple("fully_connected_gpu_fb_io_b8_f8_vload", -1) }, + { "2268275392299271167", std::make_tuple("fully_connected_gpu_fb_io_block_fp16", -1) }, + { "10615831454139478379", std::make_tuple("fully_connected_gpu_bs_f_bsv8_af8_vload", -1) }, + { "8205640825965213946", std::make_tuple("convolution_gpu_yxfb_yxio_b16", -1) }, + { "14337168375989245254", std::make_tuple("convolution_gpu_yxfb_yxio_b16", -1) }, + { "11664399629496237233", std::make_tuple("convolution_gpu_yxfb_yxio_b16", -1) }, + { "15750539817895707253", std::make_tuple("convolution_gpu_yxfb_yxio_b16", -1) }, + { "921209976738626097", std::make_tuple("convolution_gpu_yxfb_yxio_b16", -1) }, + { "8590416145336196354", std::make_tuple("convolution_gpu_yxfb_yxio_b16", -1) }, + { "10463632805036507382", std::make_tuple("convolution_gpu_yxfb_yxio_b16", -1) }, + { "13637537549252005181", std::make_tuple("convolution_gpu_yxfb_yxio_b16", -1) }, + { "7581949584623524395", std::make_tuple("convolution_gpu_yxfb_yxio_b16", -1) }, + { "9814647153117279415", std::make_tuple("convolution_gpu_yxfb_yxio_b16", -1) }, + { "3444250649099578792", std::make_tuple("convolution_gpu_yxfb_yxio_b16", -1) }, + { "4039483032571506874", std::make_tuple("fully_connected_gpu_bs_f_bsv8_af8_vload", -1) }, + { "14309249337788077160", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "6254493271976962295", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "12387660887222981357", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, + { "7723131901316908741", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, + { "13963554827358438190", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, + { "5001552360784483833", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, + { "14201142257504107783", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, + { "1066668660701816536", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, + { "4664196755018349672", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, + { "1103228955716492167", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 217) }, + { "8618835732380720921", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 251) }, + { "8391292909068775212", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 254) }, + { "9488974186647231896", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 251) }, + { "9069334144391048686", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 99) }, + { "12493863403516600413", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 256) }, + { "13375084585444085517", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 251) }, + { "18040173797801558071", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 135) }, + { "15329174116169594863", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 247) }, + { "14910223536998380801", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 246) }, + { "18277685132620834972", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 251) }, + { "13090887980792573261", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 172) }, + { "3067806959725855130", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 153) }, + { "17791773192152464021", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 143) }, + { "13603318842632052764", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 242) }, + { "12072881177966014126", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "14413047954443174304", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 192) }, + { "9118663018352672834", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 224) }, + { "3558391988878894288", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 329) }, + { "10047727261970275928", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 224) }, + { "11527382293059267033", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 225) }, + { "8445575388700666150", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 225) }, + { "4560479630843098090", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "12542825714985999760", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "4599539412023802059", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 303) }, + { "4570119951370893062", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 197) }, + { "16897917745917378359", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 197) }, + { "6947523163603267191", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 157) }, + { "2322126126611987721", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 158) }, + { "6518845972912144959", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 158) }, + { "9741774854327055438", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 231) }, + { "7079854103926842364", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 231) }, + { "5035895518536085765", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 231) }, + { "4307157272240924516", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 151) }, + { "142486914279119363", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 142) }, + { "1532263118203058517", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 212) }, + { "13529694429433303321", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 271) }, + { "2820916926593580316", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 255) }, + { "11140657515428786448", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "9028970753877215614", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 103) }, + { "14038308632095412386", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "15928183143089896780", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 270) }, + { "14071202918199194502", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 270) }, + { "12383676694875725364", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 271) }, + }); + td.td.insert({ + // style sample + { "108008098283423515", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 87) }, + { "4060303280041214217", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "6537702661107952067", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 228) }, + { "14207620784078112608", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 246) }, + { "15507553344802827573", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 348) }, + { "11202969539314825145", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 364) }, + { "17875115440447156357", std::make_tuple("convolution_gpu_bfyx_direct_10_12_16", -1) }, + { "5043345769472136917", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 363) }, + { "16920049042115931889", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 110) }, + { "18396735425525918800", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 321) }, + { "2188753401875518777", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 325) }, + { "861151538204493788", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 5) }, + { "6577112081591081699", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 325) }, + { "6662263400328602558", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 361) }, + { "12062286938998602641", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "3532486493536194182", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 335) }, + { "14486900605080248966", std::make_tuple("convolution_gpu_bfyx_direct_10_12_16", -1) }, + { "3986970741207127928", std::make_tuple("convolution_gpu_bfyx_direct_10_12_16", -1) }, + { "12055000818441091810", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 334) }, + { "6473775431261965926", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 334) }, + { "13358640031183139493", std::make_tuple("convolution_gpu_bfyx_direct_10_12_16", -1) }, + { "6917849789850282518", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "339005357927126796", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 356) }, + { "3341093105217903149", std::make_tuple("convolution_gpu_bfyx_direct_10_12_16", -1) }, + { "4002803423257090980", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 364) }, + { "491985190756430188", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 365) }, + { "7294200033269380787", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 365) }, + { "4133961720345934549", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 6) }, + { "15578894483600472601", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 333) }, + { "5153485325286526589", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 327) }, + { "11666701706717643100", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "4425021395842654484", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 340) }, + { "16007037430422291336", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 367) }, + { "3766679421476531097", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 214) }, + { "12016934279017055632", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 340) }, + { "6099288410648891214", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "14944495584618629508", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "9930151769697976322", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 326) }, + { "2751149427305557313", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "8308207826619932628", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "3823293373281864380", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 87) }, + { "10416260780913304720", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 360) }, + { "2847588473935575710", std::make_tuple("convolution_gpu_bfyx_direct_10_12_16", -1) }, + { "6114169197348303753", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "12362870423757408187", std::make_tuple("convolution_gpu_bfyx_direct_10_12_16", -1) }, + { "15330333360513835100", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 361) }, + { "4337663535143862248", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "12324726684926692530", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "11152914598877675570", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "17706702842712421674", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 334) }, + { "8596083086448639289", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 361) }, + { "3826763780015674157", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 335) }, + { "13022765751713107854", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "12054929554615967645", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 126) }, + { "8483866344820602196", std::make_tuple("convolution_gpu_bfyx_direct_10_12_16", -1) }, + { "17257458463329928325", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 329) }, + { "17396276238049115844", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "12139918033335162307", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 214) }, + { "17613450189830338239", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 20) }, + { "466805001581651681", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 327) }, + { "12375983338952375600", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 126) }, + { "2835926422026106846", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "7562282591986948344", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, + { "10222410309423438801", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 325) }, + { "9667626193041507177", std::make_tuple("convolution_gpu_bfyx_direct_10_12_16", -1) }, + { "5918842657011667447", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 327) }, + { "10197866743342998409", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 364) }, + { "10481938393331020691", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "18128936267842454401", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 340) }, + { "8968418225456926192", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 335) }, + { "13004007524122679918", std::make_tuple("convolution_gpu_bfyx_direct_10_12_16", -1) }, + { "3587239831348133052", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 360) }, + { "13594576107143259571", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 326) }, + { "3622666399417827014", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 6) }, + { "12576876344393380361", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "13160857254841009807", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 214) }, + { "5445489344860863060", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "1213577713645615257", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 361) }, + { "17692282381799629643", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "6196533506278647179", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "8978870911977287031", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 157) }, + { "6336679824344178824", std::make_tuple("convolution_gpu_bfyx_direct_10_12_16", -1) }, + { "6223842516539111057", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 367) }, + { "1046547531196124397", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "58154090876617650", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "12346479378618214663", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 340) }, + { "2502439462576713842", std::make_tuple("convolution_gpu_bfyx_direct_10_12_16", -1) }, + { "4182038693129989035", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "12115518620344827362", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 231) }, + { "604454303639822310", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 360) }, + { "5453339018427413517", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "9083797214718240599", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 167) }, + { "5886784323972875305", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 206) }, + { "1077955953397294307", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 126) }, + { "1705252754140106824", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 46) }, + { "14801234233433168563", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 367) }, + { "6099288410648891214", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "9667626193041507177", std::make_tuple("convolution_gpu_bfyx_direct_10_12_16", -1) }, + { "8308207826619932628", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "14486900605080248966", std::make_tuple("convolution_gpu_bfyx_direct_10_12_16", -1) }, + { "12362870423757408187", std::make_tuple("convolution_gpu_bfyx_direct_10_12_16", -1) }, + { "15507553344802827573", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 348) }, + { "18128936267842454401", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 340) }, + { "7491177930963608610", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 349) }, + { "74789225791237471", std::make_tuple("convolution_gpu_bfyx_direct_10_12_16", -1) }, + { "13384934269447336301", std::make_tuple("convolution_gpu_bfyx_direct_10_12_16", -1) }, + { "13121630338540122290", std::make_tuple("convolution_gpu_bfyx_direct_10_12_16", -1) }, + { "15469602039104029406", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "7160031288662381100", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "7329115981778571341", std::make_tuple("convolution_gpu_bfyx_direct_10_12_16", -1) }, + }); + } +}
\ No newline at end of file diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/cache/cache_GT3.cpp b/inference-engine/thirdparty/clDNN/kernel_selector/core/cache/cache_GT3.cpp new file mode 100644 index 000000000..4bfd3d1aa --- /dev/null +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/cache/cache_GT3.cpp @@ -0,0 +1,26 @@ +/* +// Copyright (c) 2018 Intel Corporation +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +*/ + +#include "auto_tuner.h" +#include "auto_tuner_offline.h" +namespace kernel_selector +{ + // KBL GT3e + void tuning_cache_5927(tuning_data& td) + { + tuning_cache_5927_B1(td); + } +}
\ No newline at end of file diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/cache/cache_GT3_B1.cpp b/inference-engine/thirdparty/clDNN/kernel_selector/core/cache/cache_GT3_B1.cpp new file mode 100644 index 000000000..980aac5b5 --- /dev/null +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/cache/cache_GT3_B1.cpp @@ -0,0 +1,1937 @@ +/* +// Copyright (c) 2018 Intel Corporation +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +*/ + +#include "auto_tuner.h" +#include "auto_tuner_offline.h" +namespace kernel_selector +{ + // KBL GT3e + void tuning_cache_5927_B1(tuning_data& td) + { + td.td.insert({ + { "1375156980278317418", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "13455881643467418059", std::make_tuple("convolution_gpu_bfyx_direct_10_12_16", -1) }, + { "12788968383428254917", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, + { "13131740479277027362", std::make_tuple("fully_connected_gpu_bf_io_gemm", -1) }, + { "3390014193205017427", std::make_tuple("fully_connected_gpu_bf_io_gemm", -1) }, + { "1270467775674221667", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, + { "14462744723628661203", std::make_tuple("fully_connected_gpu_bf_io_gemm", -1) }, + { "8203171222962341018", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "9795194069954915563", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "13369603621524676979", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, + { "13575423234109624706", std::make_tuple("fully_connected_gpu_yxfb_ref", -1) }, + { "10721885719016335538", std::make_tuple("fully_connected_gpu_fb_oi_ref", -1) }, + { "14567947256029724271", std::make_tuple("fully_connected_gpu_bs_f_bsv16_af8_vload", -1) }, + { "10749263296616139689", std::make_tuple("fully_connected_gpu_fb_oi_ref", -1) }, + { "11717348577195224554", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "9275303306340702111", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "12245096462203481681", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 138) }, + { "4999505377862312410", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, + { "15272426400992401555", std::make_tuple("fully_connected_gpu_bf_io_gemm", -1) }, + { "9325097933807426691", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, + { "18238669114790278675", std::make_tuple("fully_connected_gpu_bf_io_gemm", -1) }, + { "6664482192233202590", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 267) }, + { "7454366978268164047", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 231) }, + { "16135569134646688251", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 249) }, + { "10572945270796129630", std::make_tuple("fully_connected_gpu_yxfb_ref", -1) }, + { "17495198214524203238", std::make_tuple("fully_connected_gpu_fb_oi_ref", -1) }, + { "5221108094913859739", std::make_tuple("fully_connected_gpu_fb_io_ref", -1) }, + { "1092633914190498221", std::make_tuple("fully_connected_gpu_fb_io_ref", -1) }, + { "2738256633362038820", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "16689586259416414782", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, + { "1525652349412826502", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, + { "17683302016987200208", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 219) }, + { "5615525527388396983", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 339) }, + { "3992735701291817771", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "13208739898218342989", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, + { "9536348721941264933", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, + { "12803521018213865796", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 345) }, + { "8854783036772473804", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 149) }, + { "6766480740724769248", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 225) }, + { "768423629375648579", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 215) }, + { "4044100281521441011", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 210) }, + { "873240542570331563", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, + { "12875236165672036211", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, + { "12008819728839685704", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 252) }, + { "2486645741683554648", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 157) }, + { "368578589584714524", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 159) }, + { "301201776306602054", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 206) }, + { "13152181652632422771", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 246) }, + { "10311747599696543062", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, + { "11258322449556590366", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, + { "14095734330183410835", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 248) }, + { "14910223536998380801", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 82) }, + { "3352689317181436056", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 126) }, + { "15832740972576959202", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 285) }, + { "14732184525012592889", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 131) }, + { "8421045774757048067", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 3) }, + { "941232110069825628", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 40) }, + { "8975333906619899020", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "14800592533315327674", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 350) }, + { "11816277809167487786", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 350) }, + { "957781751038897330", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "10498289589469975939", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "12970943403831707924", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 350) }, + { "1300292367195167745", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 350) }, + { "3399837016486623477", 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std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 329) }, + { "9979259596137305973", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 321) }, + { "17225578855755054959", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 323) }, + { "6471563320494376693", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 327) }, + { "8146945902795164796", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 323) }, + { "18372284940315010254", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 321) }, + { "2194607895573544953", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 325) }, + { "1332624116953483870", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 322) }, + { "158222105675022402", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 320) }, + { "6830387121684699972", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 331) }, + { "11077503608116183709", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 321) }, + { "17847109385592002207", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 320) }, + { "13384754476437374504", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 326) }, + { "11462462742322068863", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 323) }, + { "4265693151382066296", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 326) }, + { "11070620435959083971", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 321) }, + { "6982733543386888622", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 320) }, + { "3563614453014995411", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 126) }, + { "3498490999014554104", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 321) }, + { "15595549493819416194", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 111) }, + { "14532844474906286088", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 205) }, + { "9562291747339451180", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 40) }, + { "6772239376357727149", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, + { "10690972785852373520", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 166) }, + { "4488336106517889531", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 81) }, + { "10058614204420018541", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 3) }, + { "13865227850818392065", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 87) }, + { "14100870590396726248", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 40) }, + { "10848277915422577656", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 43) }, + { "8121179472578287280", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 5) }, + { "2502125887857336825", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 40) }, + { "13192808619929896995", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 4) }, + { "5115661026367632863", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 5) }, + { "12812685418923919055", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 5) }, + { "6293403765897901528", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "17596685300497748803", std::make_tuple("convolution_gpu_bfyx_3x3_dw_opt", 69) }, + { "2150326211917340956", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "530491406341772040", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "15197248015210313435", std::make_tuple("convolution_gpu_bfyx_3x3_dw_opt", 69) }, + { "2816353973187452604", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, + { "16383540667048742064", std::make_tuple("convolution_gpu_bfyx_direct_10_12_16", -1) }, + { "16820082917500285799", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "6820284286806022849", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "17285815901490707654", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, + { "994182747184593564", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, + { "6642767323474835034", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 333) }, + { "3215659303601163167", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 350) }, + { "54975980454651672", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 350) }, + { "11529876081402974396", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 321) }, + { "14026570177552137240", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 338) }, + { "11686670048744589243", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 253) }, + { "6678796313875454849", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "641417817126876622", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 325) }, + { "9622546530872848323", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 341) }, + { "9194788897910888066", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 328) }, + { "522181557896569275", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 341) }, + { "3332334993503432420", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, + { "16131448347558322280", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 325) }, + { "6585223640997887253", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 303) }, + { "6205240287062600210", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 238) }, + { "17522452942286240233", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "6571438978296387721", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 238) }, + { "15511138074959300404", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 236) }, + { "11107930597263802755", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 270) }, + { "10320711719466983961", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 238) }, + { "16884228931101540030", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 271) }, + { "8253823502854784432", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 271) }, + { "6025872155179042054", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 271) }, + { "10173283505468233128", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 328) }, + { "16094174852600023296", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 328) }, + { "11077876432364512822", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, + { "10586018593856542117", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, + { "3109104171383198425", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, + { "18136135457402651842", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, + { "11834683513280095384", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, + { "4806571630436601566", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, + { "14849108908297747749", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, + { "3644282167178264526", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "360872770877634346", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "16720108310653948550", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, + { "14353390922580547467", std::make_tuple("convolution_gpu_bfyx_1x1_hgemm_buf_16x1", -1) }, + { "9868561386826862471", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, + { "17465517455679097501", std::make_tuple("convolution_gpu_bfyx_1x1_hgemm_buf_16x1", -1) }, + { "5570311824197099845", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, + { "7524311370696987092", std::make_tuple("convolution_gpu_bfyx_1x1_hgemm_buf_16x1", -1) }, + { "14070988879848388270", std::make_tuple("convolution_gpu_bfyx_1x1_hgemm_buf_16x1", -1) }, + { "8296551195150971668", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, + { "14352796912241296357", std::make_tuple("convolution_gpu_bfyx_1x1_hgemm_buf_16x1", -1) }, + { "9840495023131952174", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, + { "4720851194954041037", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 347) }, + { "13852065717057446998", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, + { "4342360467977736802", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 230) }, + { "16336482874764861478", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "6075691042233712335", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "7570346182940928159", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, + { "12971822824884826169", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "3033264172690274208", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 323) }, + { "17301887391757619741", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 253) }, + { "15790005937034794347", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 327) }, + { "15464327246951632247", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 341) }, + { "5659168916726488798", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 329) }, + { "8079376692609682448", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, + { "15160738482264643601", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, + { "17900257435531434807", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "16789135236017252073", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "13224814158106791463", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "5078905972285278557", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "4196367396954155354", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "7009873605945341897", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "7199295899520406795", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, + { "16833854122884184025", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 336) }, + { "14599780481362761532", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, + { "2572395498687401679", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 336) }, + { "11810221946429451169", std::make_tuple("convolution_gpu_bfyx_1x1_hgemm_buf_16x1", -1) }, + { "18084635102736402756", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 320) }, + { "59739211822469868", std::make_tuple("convolution_gpu_bfyx_1x1_hgemm_buf_16x1", -1) }, + { "5240181393417899912", std::make_tuple("convolution_gpu_bfyx_1x1_hgemm_buf_16x1", -1) }, + { "15962137123591591534", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 345) }, + { "10989937450490049763", std::make_tuple("convolution_gpu_bfyx_1x1_hgemm_buf_16x1", -1) }, + { "9798585825695496550", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "9220830217525628783", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 261) }, + { "2235210915304938149", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 187) }, + { "3930314908786112883", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 284) }, + { "1334070221835422461", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "6681818065741882453", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 334) }, + { "6980201892073961793", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 322) }, + { "11530101016435264783", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 325) }, + { "4801117903303888658", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 327) }, + { "5782934278345953016", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 322) }, + { "13951717514084457087", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 251) }, + { "2721793280965260548", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 331) }, + { "8124736388338424498", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 255) }, + { "12223993560805441284", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "9860570706348640782", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "14043770215999952932", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "15277856047844308598", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "8048617952947915835", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "11446745541571732900", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, + { "17422822627612865758", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, + { "13954144830230671601", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 333) }, + { "11198908896401597838", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 228) }, + { "5582896843095691256", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 225) }, + { "8133587696326295326", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 228) }, + { "2007192658799516915", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, + { "9492402787848610840", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, + { "10515519878978734341", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, + { "16706121580364790904", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 297) }, + { "5495776091407365966", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 324) }, + { "16430562172386510259", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "5673972310424776040", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "8797843396807284399", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 341) }, + { "1698321314111848001", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 328) }, + { "5762290464889692462", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 222) }, + { "3218248162832023196", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 231) }, + { "12988961529988078346", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 220) }, + { "4232250144427804891", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, + { "11683680166617045816", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, + { "6252429564537528709", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, + + }); + } +}
\ No newline at end of file diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/cache/cache_ICL.cpp b/inference-engine/thirdparty/clDNN/kernel_selector/core/cache/cache_ICL.cpp new file mode 100644 index 000000000..a5e90ad19 --- /dev/null +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/cache/cache_ICL.cpp @@ -0,0 +1,26 @@ +/* +// Copyright (c) 2018 Intel Corporation +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +*/ + +#include "auto_tuner.h" +#include "auto_tuner_offline.h" +namespace kernel_selector +{ + // ICL_GT2 + void tuning_cache_8A52(tuning_data& td) + { + tuning_cache_8A52_B1_B16(td); + } +}
\ No newline at end of file diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/cache/cache_ICL_B1_B16.cpp b/inference-engine/thirdparty/clDNN/kernel_selector/core/cache/cache_ICL_B1_B16.cpp new file mode 100644 index 000000000..fbf1719ca --- /dev/null +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/cache/cache_ICL_B1_B16.cpp @@ -0,0 +1,1823 @@ +/* +// Copyright (c) 2018 Intel Corporation +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +*/ + +#include "auto_tuner.h" +#include "auto_tuner_offline.h" +namespace kernel_selector +{ + // KBL GT3e + void tuning_cache_8A52_B1_B16(tuning_data& td) + { + td.td.insert({ + { "1375156980278317418", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "13455881643467418059", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "12788968383428254917", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, + { "13131740479277027362", std::make_tuple("fully_connected_gpu_bf_io_gemm", -1) }, + { "3390014193205017427", std::make_tuple("fully_connected_gpu_bf_io_gemm", -1) }, + { "1270467775674221667", std::make_tuple("fully_connected_gpu_bf_io_gemm", -1) }, + { "14462744723628661203", std::make_tuple("fully_connected_gpu_bf_io_gemm", -1) }, + { "11717348577195224554", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "9275303306340702111", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 259) }, + { "12245096462203481681", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 252) }, + { "4999505377862312410", std::make_tuple("fully_connected_gpu_bf_io_gemm", -1) }, + { "15272426400992401555", std::make_tuple("fully_connected_gpu_bf_io_gemm", -1) }, + { "9325097933807426691", std::make_tuple("fully_connected_gpu_bs_f_bsv16_b1", -1) }, + { "18238669114790278675", std::make_tuple("fully_connected_gpu_bf_io_gemm", -1) }, + { "2738256633362038820", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "16689586259416414782", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, + { "1525652349412826502", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, + { "17683302016987200208", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 312) }, + { "5615525527388396983", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 343) }, + { "3992735701291817771", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "13208739898218342989", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, + { "9536348721941264933", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, + { "12803521018213865796", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 277) }, + { "8854783036772473804", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 266) }, + { "6766480740724769248", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "768423629375648579", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 323) }, + { "4044100281521441011", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 208) }, + { "873240542570331563", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, + { "12875236165672036211", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, + { "12008819728839685704", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 253) }, + { "2486645741683554648", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 248) }, + { "368578589584714524", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 159) }, + { "301201776306602054", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 246) }, + { "13152181652632422771", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 244) }, + { "10311747599696543062", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, + { "11258322449556590366", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, + { "14095734330183410835", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 249) }, + { "14910223536998380801", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 87) }, + { "3352689317181436056", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 127) }, + { "15832740972576959202", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 287) }, + { "14732184525012592889", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 132) }, + { "8421045774757048067", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 40) }, + { "941232110069825628", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 40) }, + { "5326247361632903583", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "6214194654733781771", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 263) }, + { "10025839973092358719", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 330) }, + { "16711955423531846725", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, + { "2915165824085219545", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 143) }, + { "17108987360340581555", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, + { "11972097635078477347", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, + { "16926950874716567095", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, + { "12643423612381102003", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 108) }, + { "18423051691107460439", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 327) }, + { "15381833359831622179", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, + { "12040626513219974957", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, + { "10647227605517025377", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, + { "8127570953237266335", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, + { "12876112384009608387", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, + { "12663860560275361463", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, + { "3217246278485567748", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 219) }, + { "15713964605078748923", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "12293786134765875615", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 326) }, + { "16043683538361975370", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "10670103699537731664", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 322) }, + { "17854578307286932628", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "11443268857010762276", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 326) }, + { "4479117540570599742", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "11726298758004767743", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 322) }, + { "2968031010495399536", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "3797957937905580811", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 327) }, + { "1474271081523145413", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "8526484907799590618", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 324) }, + { "13723543003759101485", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "11728824117049687850", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "13268525255152984893", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 320) }, + { "14397348576352573007", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "8616686489737649890", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 325) }, + { "13176385389367548697", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "14990645740260870030", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 324) }, + { "7472330881076141262", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 329) }, + { "10892456883214928095", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 320) }, + { "9522661528867955338", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 329) }, + { "17856816245251319111", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 321) }, + { "14872992823083730615", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 330) }, + { "3106591708459602370", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 324) }, + { "11609821372586026178", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 331) }, + { "7678457226823073886", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 325) }, + { "10118395047539851751", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 330) }, + { "5389189982064081933", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 321) }, + { "1742897526168249500", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 329) }, + { "15331103261044247142", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 320) }, + { "6644418194983229139", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 330) }, + { "12478309735214802531", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 321) }, + { "18012549942299450620", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 329) }, + { "11873734271080160669", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 320) }, + { "10424278617647597641", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 329) }, + { "9553032671453999824", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 327) }, + { "3860603464276263676", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "1207026216972160297", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 326) }, + { "9519623751582710696", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 120) }, + { "10328182165125764988", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 321) }, + { "2231648183489019418", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 120) }, + { "17599383258252980421", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 321) }, + { "16208488491972128275", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 122) }, + { "13379165253894817165", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 326) }, + { "2566302789609970663", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 122) }, + { "1478419046264331178", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 323) }, + { "3087801652564627458", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 124) }, + { "16103943009195163681", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 326) }, + { "14230385851791760020", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 120) }, + { "15293727142789007900", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 323) }, + { "13973179950424276578", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 121) }, + { "713121569924250372", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 320) }, + { "7947870656736319919", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 121) }, + { "1663285216972929652", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 323) }, + { "14767888121198814523", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 122) }, + { "2124033349728954551", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 325) }, + { "8762901342272872498", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 121) }, + { "17006133396401462698", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 324) }, + { "10783981060353445280", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 121) }, + { "15110359240685619357", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 325) }, + { "7875272450497189442", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 121) }, + { "3281207855459771997", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 321) }, + { "11932770338770247767", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 124) }, + { "15860915170591763391", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 324) }, + { "11716771904412649891", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 121) }, + { "1095495157025479260", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 320) }, + { "8402692278765063674", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 122) }, + { "509781001842353609", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 324) }, + { "3255465741612432300", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 124) }, + { "13439896617880328331", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 324) }, + { "7134654288295280046", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 121) }, + { "6769243149577568817", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 322) }, + { "3480732841490521799", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 121) }, + { "18269685060032395235", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 323) }, + { "15649927926091502215", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 120) }, + { "69439315851965666", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 321) }, + { "156456996459945842", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 122) }, + { "3012566432840424198", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 326) }, + { "16431165572426232677", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 124) }, + { "6324565723045697080", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 327) }, + { "5390559917122707732", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 121) }, + { "5469227748156438008", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 322) }, + { "17163158934005653629", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 122) }, + { "2307310127637739872", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 326) }, + { "1999979442136861875", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 121) }, + { "2527189070714658176", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 334) }, + { "8329846097322076175", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 205) }, + { "16783619135298589974", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 40) }, + { "12214162812589030126", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 205) }, + { "9216608098626790565", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 20) }, + { "5179760459095053114", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 200) }, + { "2452226948562393335", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 21) }, + { "4499586349553581439", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 205) }, + { "12668149981216388765", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 40) }, + { "2287356884312581209", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 200) }, + { "11115684531624462986", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 21) }, + { "6483208845600234755", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 200) }, + { "3752171257634205726", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 21) }, + { "1774158624592967937", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 200) }, + { "16881283637687482989", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 21) }, + { "14749947225382670869", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 205) }, + { "7351733901977025859", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 21) }, + { "435888248913413834", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 200) }, + { "13713406612642090169", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 20) }, + { "16582132711225619740", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 200) }, + { "10436819182310112786", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 20) }, + { "14546281065004619074", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 205) }, + { "12558716383635737426", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 21) }, + { "12609361477548272638", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 200) }, + { "8107447526839063293", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 40) }, + { "10995907213890714701", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 205) }, + { "4871907623235871050", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 21) }, + { "7394217382008802567", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 205) }, + { "3880189981766119529", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 20) }, + { "3759057398165607194", 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std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "11686670048744589243", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "6678796313875454849", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, + { "641417817126876622", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, + { "9622546530872848323", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, + { "9194788897910888066", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, + { "522181557896569275", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 362) }, + { "3332334993503432420", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 208) }, + { "16131448347558322280", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 325) }, + { "6585223640997887253", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "6205240287062600210", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 302) }, + { "17522452942286240233", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 221) }, + { "6571438978296387721", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 208) }, + { "15511138074959300404", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, + { "11107930597263802755", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 209) }, + { "10320711719466983961", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 209) }, + { "16884228931101540030", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 254) }, + { "8253823502854784432", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 252) }, + { "6025872155179042054", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 250) }, + { "10173283505468233128", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, + { "16094174852600023296", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, + { "11077876432364512822", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, + { "10586018593856542117", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, + { "3109104171383198425", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, + { "18136135457402651842", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, + { "11834683513280095384", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, + { "4806571630436601566", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, + { "14849108908297747749", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, + { "3644282167178264526", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "360872770877634346", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "16720108310653948550", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, + { "14353390922580547467", std::make_tuple("convolution_gpu_bfyx_1x1_hgemm_buf_16x1", -1) }, + { "9868561386826862471", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, + { "17465517455679097501", std::make_tuple("convolution_gpu_bfyx_1x1_hgemm_buf_16x1", -1) }, + { "5570311824197099845", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, + { "7524311370696987092", std::make_tuple("convolution_gpu_bfyx_1x1_hgemm_buf_16x1", -1) }, + { "14070988879848388270", std::make_tuple("convolution_gpu_bfyx_1x1_hgemm_buf_16x1", -1) }, + { "8296551195150971668", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, + { "14352796912241296357", std::make_tuple("convolution_gpu_bfyx_1x1_hgemm_buf_16x1", -1) }, + { "9840495023131952174", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, + { "4720851194954041037", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 351) }, + { "13852065717057446998", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "4342360467977736802", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 256) }, + { "16336482874764861478", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 159) }, + { "6075691042233712335", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "7570346182940928159", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 176) }, + { "12971822824884826169", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "3033264172690274208", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 262) }, + { "17301887391757619741", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 331) }, + { "15790005937034794347", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 326) }, + { "15464327246951632247", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 323) }, + { "5659168916726488798", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 330) }, + { "8079376692609682448", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 208) }, + { "15160738482264643601", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 321) }, + { "17900257435531434807", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "16789135236017252073", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "13224814158106791463", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "5078905972285278557", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "4196367396954155354", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "7009873605945341897", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "7199295899520406795", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 349) }, + { "16833854122884184025", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 330) }, + { "14599780481362761532", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 349) }, + { "2572395498687401679", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 370) }, + { "11810221946429451169", std::make_tuple("convolution_gpu_bfyx_1x1_hgemm_buf_16x1", -1) }, + { "18084635102736402756", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 332) }, + { "59739211822469868", std::make_tuple("convolution_gpu_bfyx_1x1_hgemm_buf_16x1", -1) }, + { "5240181393417899912", std::make_tuple("convolution_gpu_bfyx_1x1_hgemm_buf_16x1", -1) }, + { "15962137123591591534", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 349) }, + { "10989937450490049763", std::make_tuple("convolution_gpu_bfyx_1x1_hgemm_buf_16x1", -1) }, + { "9798585825695496550", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "9220830217525628783", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "2235210915304938149", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 157) }, + { "3930314908786112883", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "1334070221835422461", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 157) }, + { "6681818065741882453", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 150) }, + { "6980201892073961793", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 262) }, + { "11530101016435264783", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 366) }, + { "4801117903303888658", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 324) }, + { "5782934278345953016", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 324) }, + { "13951717514084457087", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 330) }, + { "2721793280965260548", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 330) }, + { "8124736388338424498", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 332) }, + { "12223993560805441284", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "9860570706348640782", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "14043770215999952932", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "15277856047844308598", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "8048617952947915835", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "11446745541571732900", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, + { "17422822627612865758", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, + { "13954144830230671601", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 334) }, + { "11198908896401597838", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 213) }, + { "5582896843095691256", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 209) }, + { "8133587696326295326", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 209) }, + { "2007192658799516915", std::make_tuple("fully_connected_gpu_bf_io_gemm", -1) }, + { "9492402787848610840", std::make_tuple("fully_connected_gpu_bs_f_bsv16_b1", -1) }, + { "10515519878978734341", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, + { "16706121580364790904", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "5495776091407365966", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "16430562172386510259", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 218) }, + { "5673972310424776040", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 335) }, + { "8797843396807284399", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 334) }, + { "1698321314111848001", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 326) }, + { "5762290464889692462", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 209) }, + { "3218248162832023196", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 211) }, + { "12988961529988078346", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 208) }, + { "4232250144427804891", std::make_tuple("fully_connected_gpu_bf_io_gemm", -1) }, + { "11683680166617045816", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, + { "6252429564537528709", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, + + }); + } +}
\ No newline at end of file diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/cache/cache_SKL_GT2.cpp b/inference-engine/thirdparty/clDNN/kernel_selector/core/cache/cache_SKL_GT2.cpp new file mode 100644 index 000000000..4ebd2fcb9 --- /dev/null +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/cache/cache_SKL_GT2.cpp @@ -0,0 +1,28 @@ +/* +// Copyright (c) 2018 Intel Corporation +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +*/ + +#include "auto_tuner.h" +#include "auto_tuner_offline.h" +namespace kernel_selector +{ + //SKL GT2 + void tuning_cache_1912(tuning_data& td) + { + tuning_cache_1912_B1_B16(td); + tuning_cache_1912_B8(td); + tuning_cache_1912_B32_B64(td); + } +}
\ No newline at end of file diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/cache/cache_SKL_GT2_B1_B16.cpp b/inference-engine/thirdparty/clDNN/kernel_selector/core/cache/cache_SKL_GT2_B1_B16.cpp new file mode 100644 index 000000000..db3f82754 --- /dev/null +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/cache/cache_SKL_GT2_B1_B16.cpp @@ -0,0 +1,3478 @@ +/* +// Copyright (c) 2018 Intel Corporation +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +*/ + +#include "auto_tuner.h" +#include "auto_tuner_offline.h" +namespace kernel_selector +{ + //SKL GT2 + void tuning_cache_1912_B1_B16(tuning_data& td) + { + td.td.insert({ + { "1375156980278317418", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "13455881643467418059", std::make_tuple("convolution_gpu_bfyx_direct_10_12_16", -1) }, + { "12788968383428254917", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 257) }, + { "13131740479277027362", std::make_tuple("fully_connected_gpu_bf_io_gemm", -1) }, + { "3390014193205017427", std::make_tuple("fully_connected_gpu_bf_io_gemm", -1) }, + { "1270467775674221667", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, + { "14462744723628661203", std::make_tuple("fully_connected_gpu_bf_io_gemm", -1) }, + { "8203171222962341018", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "9795194069954915563", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 349) }, + { "13369603621524676979", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, + { "13575423234109624706", std::make_tuple("fully_connected_gpu_bs_f_bsv16_af8_vload", -1) }, + { "10721885719016335538", std::make_tuple("fully_connected_gpu_fb_oi_ref", -1) }, + { "14567947256029724271", std::make_tuple("fully_connected_gpu_bs_f_bsv16_af8_vload", -1) }, + { "10749263296616139689", std::make_tuple("fully_connected_gpu_fb_oi_ref", -1) }, + { "11717348577195224554", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "9275303306340702111", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "12245096462203481681", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 263) }, + { "4999505377862312410", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, + { "15272426400992401555", std::make_tuple("fully_connected_gpu_bf_io_gemm", -1) }, + { "9325097933807426691", std::make_tuple("fully_connected_gpu_bs_f_bsv16_b1", -1) }, + { "18238669114790278675", std::make_tuple("fully_connected_gpu_bf_io_gemm", -1) }, + { "6664482192233202590", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "7454366978268164047", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 231) }, + { "16135569134646688251", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 252) }, + { "10572945270796129630", std::make_tuple("fully_connected_gpu_bs_f_bsv8_af8_vload", -1) }, + { "17495198214524203238", std::make_tuple("fully_connected_gpu_fb_oi_ref", -1) }, + { "5221108094913859739", std::make_tuple("fully_connected_gpu_bs_f_bsv8_af8_vload", -1) }, + { "1092633914190498221", std::make_tuple("fully_connected_gpu_fb_io_ref", -1) }, + { "2738256633362038820", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "16689586259416414782", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, + { "1525652349412826502", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, + { "17683302016987200208", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 373) }, + { "5615525527388396983", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "3992735701291817771", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "13208739898218342989", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, + { "9536348721941264933", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, + { "12803521018213865796", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, + { "8854783036772473804", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 264) }, + { "6766480740724769248", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 208) }, + { "768423629375648579", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 215) }, + { "4044100281521441011", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, + { "873240542570331563", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, + { "12875236165672036211", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, + { "12008819728839685704", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 254) }, + { "2486645741683554648", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 252) }, + { "368578589584714524", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 157) }, + { "301201776306602054", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 99) }, + { "13152181652632422771", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 245) }, + { "10311747599696543062", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, + { "11258322449556590366", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, + { "14095734330183410835", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 255) }, + { "14910223536998380801", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 85) }, + { "3352689317181436056", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 127) }, + { "15832740972576959202", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 254) }, + { "14732184525012592889", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 136) }, + { "8421045774757048067", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 40) }, + { "941232110069825628", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 84) }, + { "8975333906619899020", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "14800592533315327674", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 319) }, + { "11816277809167487786", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 319) }, + { "957781751038897330", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "10498289589469975939", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "12970943403831707924", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, + { "1300292367195167745", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, + { "3399837016486623477", 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std::make_tuple("fully_connected_gpu_yxfb_ref", -1) }, + { "10437599469161149176", std::make_tuple("fully_connected_gpu_yxfb_ref", -1) }, + { "4490223883171428014", std::make_tuple("fully_connected_gpu_yxfb_ref", -1) }, + { "2529786184394804665", std::make_tuple("fully_connected_gpu_yxfb_ref", -1) }, + { "6995235840871804844", std::make_tuple("fully_connected_gpu_fb_io_ref", -1) }, + { "1671208365782918441", std::make_tuple("fully_connected_gpu_yxfb_ref", -1) }, + { "13104509059416300615", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 266) }, + { "10090923790949378407", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "3429844423226609965", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "706049518431331645", std::make_tuple("convolution_gpu_bfyx_direct_10_12_16", -1) }, + { "17193614571243427089", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "3621424752591567930", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { 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std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "5622089373755094139", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 373) }, + { "2129726780118554358", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 352) }, + { "4160656836528944651", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 367) }, + { "11052732052072367261", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 352) }, + { "18432787283148809023", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 352) }, + { "16172528828198474326", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "16327433707667075261", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 370) }, + { "2797723586312707948", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 373) }, + { "8451212914744825089", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 368) }, + { "7025975403069487257", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 208) }, + { "8913950860101596091", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 361) }, + { "15308578014507211237", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 355) }, + { "13132804928635689780", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 375) }, + { "4465781406991476376", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, + { "16266491618150971928", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 373) }, + { "181006047500375768", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 218) }, + { "18140951659547259039", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 352) }, + { "272730229972987861", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 358) }, + { "14898892437285105327", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 369) }, + { "17252449599613270108", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 352) }, + { "13436376034548670107", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 373) }, + { "13787436604877398090", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 219) }, + { "8873614802459592665", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 358) }, + { "13663893159182636270", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 352) }, + { "1361159591875955678", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 352) }, + { "5912303851874077576", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "16245760498096322525", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 373) }, + { "9928406318940388716", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 366) }, + { "3036512701943687724", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 359) }, + { "5334291640387922287", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 365) }, + { "3002986032379998259", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 157) }, + { "16469788155263456039", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "8709632541892447149", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "9524303276541517389", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "9354818521586974021", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "16781127329510211966", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "6351572488552853754", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "907036267078333137", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "11855070245618904113", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 322) }, + { "4544242784357021697", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "18218631037214746168", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "178353385245384751", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "17658152048177750315", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 362) }, + { "11636129433022017868", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 217) }, + { "2622434279674583815", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "14335074487552883436", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "11175955260573469979", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 132) }, + { "2732519635571994212", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "13893789954946953427", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 238) }, + { "4355933224673863178", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "18037918102910297531", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 367) }, + { "16071723603031305677", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "1697248235682953135", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "7843498978148810586", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "6767159196241633301", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 238) }, + { "5097818987523855112", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "6623182990939010641", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "6711878663358611849", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 214) }, + { "8671491767142900139", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 223) }, + { "12164298124869114517", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 359) }, + { "17089801601582809764", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "75742659105146536", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 355) }, + { "4652136280940317116", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 200) }, + { "9751582946441607796", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 365) }, + { "16706244336960642883", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "12581879452540858313", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, + { "17443356777503458523", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 223) }, + { "939718260623752240", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 354) }, + { "14131851237755716991", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 200) }, + { "7474639594232203854", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 365) }, + { "14152716242882609401", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "7998930863626763670", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "10323345824599612614", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "30229601562833524", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "17788367809717898285", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 352) }, + { "1509728225855233852", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "13139625572508441980", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 356) }, + { "16491532291908469567", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 363) }, + { "6355395905401306995", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "2096779676054335057", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "4217179485243909459", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 313) }, + { "17101789600628162503", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 370) }, + { "6139574161497189424", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 370) }, + { "16559140502701231107", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 210) }, + { "11459784003592366395", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 214) }, + { "7869916853707978306", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 56) }, + //{ "3889519976910355277", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, + //{ "12081835728078383819", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, + //{ "14923692894655929923", std::make_tuple("fully_connected_gpu_bf_io_gemm", -1) }, + { "12794369485239257709", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "13338594271376045657", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 373) }, + { "677249604491773387", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 373) }, + { "2668729552208169959", std::make_tuple("convolution_gpu_bfyx_direct_10_12_16", -1) }, + { "13011676362747785816", std::make_tuple("convolution_gpu_bfyx_direct_10_12_16", -1) }, + { "4678607855896512523", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "4356817283284529593", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "1885075753696445410", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "17806712457019493207", std::make_tuple("fully_connected_gpu_bs_f_bsv16_af8_vload", -1) }, + { "11862259122805366807", std::make_tuple("fully_connected_gpu_bs_f_bsv16_af8_vload", -1) }, + { "15201438563802430490", std::make_tuple("fully_connected_gpu_fb_oi_ref", -1) }, + { "8132521728369930959", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "16108573960501496757", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "11086699387784339943", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "4013707396889204359", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 231) }, + { "11850332373794932468", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 231) }, + { "14763982961176216679", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 110) }, + { "8207349115037232863", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 110) }, + { "3273748387141431306", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 87) }, + { "580936360000782237", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, + { "10682918518101379579", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, + { "13178480813522103091", std::make_tuple("fully_connected_gpu_bf_io_gemm", -1) }, + { "17109520309574369561", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "13754408679115174221", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 231) }, + { "16717713360264747483", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 231) }, + { "1045854873741563331", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "16767392067294252396", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "6114241186364821679", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 231) }, + { "11241838709529552265", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 231) }, + { "15192230303376521834", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 90) }, + { "5374969798377773063", std::make_tuple("fully_connected_gpu_bs_f_bsv8_af8_vload", -1) }, + { "592245952014430043", std::make_tuple("fully_connected_gpu_bs_f_bsv8_af8_vload", -1) }, + { "3114869763557037270", std::make_tuple("fully_connected_gpu_fb_oi_ref", -1) }, + { "11254635684957519432", std::make_tuple("convolution_gpu_bfyx_1x1_hgemm_buf_16x1", -1) }, + { "16816222375242496370", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, + { "12809199739984715013", std::make_tuple("convolution_gpu_bfyx_1x1_hgemm_buf_16x1", -1) }, + { "5040730152867713388", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 345) }, + { "10429613013253088132", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "15451919862187018297", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, + { "7546586420552408243", std::make_tuple("convolution_gpu_bfyx_1x1_hgemm_buf_16x1", -1) }, + { "14487682847898298214", std::make_tuple("convolution_gpu_bfyx_1x1_hgemm_buf_16x1", -1) }, + { "3106710091841093202", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 328) }, + { "6458124573210430792", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 343) }, + { "9182897385081081193", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, + { "14462438074931673266", std::make_tuple("convolution_gpu_bfyx_1x1_hgemm_buf_16x1", -1) }, + { "18133334552107213128", std::make_tuple("convolution_gpu_bfyx_1x1_hgemm_buf_16x1", -1) }, + { "38736266675995457", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 209) }, + { "13654816209891478730", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 231) }, + { "6263019986730305851", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 218) }, + { "12929981792125924963", std::make_tuple("convolution_gpu_bfyx_1x1_hgemm_buf_16x1", -1) }, + { "3138374672801504481", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 226) }, + { "4465701487417893814", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "12977678792503377525", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 351) }, + { "10879218241103462088", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "2221145174704245189", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "4635570915184713874", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "16075006181495932250", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 349) }, + { "3863816884636503247", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "5440983284868981549", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 333) }, + { "15428591250165788477", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 328) }, + { "16567638487719493784", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 347) }, + { "18059267466971880386", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 333) }, + { "10808909442136736629", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 345) }, + { "5682190700442712936", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 333) }, + { "712165731154577189", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 226) }, + { "7469127846325904854", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 226) }, + { "5926747396493954633", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 229) }, + { "3477539135137665170", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 229) }, + { "16235115911229280717", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 224) }, + { "17009318615658405230", std::make_tuple("convolution_gpu_bfyx_1x1_hgemm_buf_16x1", -1) }, + { "9421643783312790618", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, + { "2294026590516781945", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 347) }, + { "2940027113687311893", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "6090625728451718945", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, + { "5643908654122573882", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 349) }, + { "9065894438656900887", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 328) }, + { "11185156002426041243", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 334) }, + { "14670068483447729857", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, + { "4623542918584461522", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 332) }, + { "1143214652021653634", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 224) }, + { "1434535531617424039", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 230) }, + { "17025268985366223779", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 223) }, + { "11507538232733291666", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 220) }, + { "6149673627320838019", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, + { "16243196137456624852", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 350) }, + { "8059328623525062913", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "3662747857062156477", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 351) }, + { "314054598858070952", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 347) }, + { "14122213471825630433", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "14985236276429954162", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 351) }, + { "3265415000818832667", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 351) }, + { "856877003890134554", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 328) }, + { "14805540705424073865", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "3788462090984291082", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 332) }, + { "2715447739580688669", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 335) }, + { "7171904645566467208", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "10308431308942416781", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 224) }, + { "8712136292276123857", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 224) }, + { "8700574100180128776", 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std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 297) }, + { "1604661321386793876", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, + { "8990561333549136048", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 230) }, + { "12278364834477923930", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "3122997634505472500", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 341) }, + { "15669490019428002270", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 342) }, + { "116291934148608396", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 342) }, + { "14729854278671832528", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 239) }, + { "10591379189397010097", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 237) }, + { "11929531534620071758", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 239) }, + { "1819720745131968914", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "10607904718265020949", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 231) }, + { "913496537924971856", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 271) }, + { "916389941321470163", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 231) }, + { "1411786954276574458", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 364) }, + { "2730604806511016352", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 107) }, + { "5843679089588930933", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 109) }, + { "7304346312452588844", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 367) }, + { "2423754482456771339", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 352) }, + { "3653156933813711765", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 205) }, + { "5219399418946822456", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "14217181622713951411", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 303) }, + { "13025323039227543550", 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std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 270) }, + { "10548792624072794724", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 116) }, + { "4239415134522959352", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "9028970753877215614", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 127) }, + { "2324120381399737261", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 245) }, + { "10267260789603562117", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 255) }, + { "9988801796928462423", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 249) }, + { "12516911293946682547", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 246) }, + { "9213886570531053949", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 125) }, + { "385046297070779752", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 154) }, + { "12541834857357563605", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 132) }, + { "475043738497218394", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 158) }, + { "6351347283201596793", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 96) }, + { "16290626406346691996", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 86) }, + { "4569338575782832784", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "7575675354187625951", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 315) }, + { "5795073619189010837", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, + { "15123868617509445149", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, + { "5601435819039968726", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, + { "14104238386345631681", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, + { "17377293745073971167", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, + { "12134712464763856064", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, + { "5524215233998361104", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, + { "1103228955716492167", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 214) }, + { "8618835732380720921", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, + { "15908673392788376468", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 252) }, + { "8482147530539941792", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 254) }, + { "9069334144391048686", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 101) }, + { "12493863403516600413", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 252) }, + { "16692569816843207989", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 277) }, + { "3438116423688595487", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 255) }, + { "15602863681196390535", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 240) }, + { "18277685132620834972", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 276) }, + { "16541722316343690197", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 128) }, + { "3067806959725855130", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 159) }, + { "17791773192152464021", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 102) }, + { "13603318842632052764", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 14) }, + { "13131740479277027362", std::make_tuple("fully_connected_gpu_bf_io_gemm", -1) }, + { "15334195300678132907", std::make_tuple("fully_connected_gpu_bf_io_gemm", -1) }, + { "2038505773698938555", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, + { "12090536142661253835", std::make_tuple("fully_connected_gpu_bf_io_gemm", -1) }, + { "4999505377862312410", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, + { "3934290309368153435", std::make_tuple("fully_connected_gpu_bf_io_gemm", -1) }, + { "5951936376654416075", std::make_tuple("fully_connected_gpu_bs_f_bsv16_b1", -1) }, + { "13204120207726209723", std::make_tuple("fully_connected_gpu_bf_io_gemm", -1) }, + { "17108987360340581555", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, + { "4795705973706796563", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, + { "4084106758501882407", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, + { "8127570953237266335", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, + { "7500192998744460131", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, + { "2379484884827231127", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, + { "13933912937625580405", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, + { "2040762223425679479", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, + { "4800587664660105589", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, + { "1616603916015535857", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, + { 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std::make_tuple("fully_connected_gpu_fb_oi_ref", -1) }, + { "6580334406272192111", std::make_tuple("fully_connected_gpu_yxfb_ref", -1) }, + { "17790026124881397912", std::make_tuple("fully_connected_gpu_yxfb_ref", -1) }, + { "13314092088416047551", std::make_tuple("fully_connected_gpu_yxfb_ref", -1) }, + { "8479958930889587809", std::make_tuple("fully_connected_gpu_yxfb_ref", -1) }, + { "3750338655074082587", std::make_tuple("fully_connected_gpu_fb_io_ref", -1) }, + { "16683485007140805060", std::make_tuple("fully_connected_gpu_yxfb_ref", -1) }, + { "3889519976910355277", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, + { "12081835728078383819", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, + { "14923692894655929923", std::make_tuple("fully_connected_gpu_bf_io_gemm", -1) }, + { "580936360000782237", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, + { "10682918518101379579", 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std::make_tuple("fully_connected_gpu_bs_f_bsv8_af8_vload", -1) }, + { "17951403431757222177", std::make_tuple("fully_connected_gpu_bs_f_bsv16_af8_vload", -1) }, + { "1074748462756364699", std::make_tuple("fully_connected_gpu_fb_oi_ref", -1) }, + { "11955992313739654625", std::make_tuple("fully_connected_gpu_bs_f_bsv8_af8_vload", -1) }, + { "11939914680143672459", std::make_tuple("fully_connected_gpu_fb_oi_ref", -1) }, + { "17806712457019493207", std::make_tuple("fully_connected_gpu_bs_f_bsv16_af8_vload", -1) }, + { "11862259122805366807", std::make_tuple("fully_connected_gpu_bs_f_bsv16_af8_vload", -1) }, + { "15201438563802430490", std::make_tuple("fully_connected_gpu_fb_oi_ref", -1) }, + { "5374969798377773063", std::make_tuple("fully_connected_gpu_bs_f_bsv8_af8_vload", -1) }, + { "592245952014430043", std::make_tuple("fully_connected_gpu_bs_f_bsv8_af8_vload", -1) }, + { "3114869763557037270", std::make_tuple("fully_connected_gpu_fb_oi_ref", -1) }, + { "17147293671640396193", std::make_tuple("fully_connected_gpu_bs_f_bsv16_af8_vload", -1) }, + { "6911215749850066204", std::make_tuple("fully_connected_gpu_bs_f_bsv8_af8_vload", -1) }, + { "2814805887448339818", std::make_tuple("fully_connected_gpu_bs_f_bsv8_af8_vload", -1) }, + { "1120455113299469776", std::make_tuple("fully_connected_gpu_bs_f_bsv16_af8_vload", -1) }, + { "8002233052700666718", std::make_tuple("fully_connected_gpu_bs_f_bsv16_af8_vload", -1) }, + { "16436006771518788093", std::make_tuple("fully_connected_gpu_bs_f_bsv16_af8_vload", -1) }, + { "11083993858285515074", std::make_tuple("fully_connected_gpu_bs_f_bsv16_af8_vload", -1) }, + { "4133424990380177132", std::make_tuple("fully_connected_gpu_bs_f_bsv16_af8_vload", -1) }, + { "1044978617045366709", std::make_tuple("fully_connected_gpu_bs_f_bsv8_af8_vload", -1) }, + { "952318454591754214", std::make_tuple("fully_connected_gpu_bs_f_bsv8_af8_vload", -1) }, + { "5762878778443755104", std::make_tuple("fully_connected_gpu_bs_f_bsv8_af8_vload", -1) }, + }); + } +}
\ No newline at end of file diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/cache/cache_SKL_GT2_B32_B64.cpp b/inference-engine/thirdparty/clDNN/kernel_selector/core/cache/cache_SKL_GT2_B32_B64.cpp new file mode 100644 index 000000000..08b19536b --- /dev/null +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/cache/cache_SKL_GT2_B32_B64.cpp @@ -0,0 +1,29 @@ +/* +// Copyright (c) 2018 Intel Corporation +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +*/ + +#include "auto_tuner.h" +#include "auto_tuner_offline.h" +namespace kernel_selector +{ + //SKL GT2 + void tuning_cache_1912_B32_B64(tuning_data& td) + { + td.td.insert({ + + { "9500850790449116723", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 16) }, + }); + } +}
\ No newline at end of file diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/cache/cache_SKL_GT2_B8.cpp b/inference-engine/thirdparty/clDNN/kernel_selector/core/cache/cache_SKL_GT2_B8.cpp new file mode 100644 index 000000000..f15d59e32 --- /dev/null +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/cache/cache_SKL_GT2_B8.cpp @@ -0,0 +1,29 @@ +/* +// Copyright (c) 2018 Intel Corporation +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +*/ + +#include "auto_tuner.h" +#include "auto_tuner_offline.h" +namespace kernel_selector +{ + //SKL GT2 + void tuning_cache_1912_B8(tuning_data& td) + { + td.td.insert({ + + { "9832505855130134649", std::make_tuple("convolution_gpu_yxfb_yxio_b16", 0) }, + }); + } +}
\ No newline at end of file diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/cache/cache_SKL_GT4e.cpp b/inference-engine/thirdparty/clDNN/kernel_selector/core/cache/cache_SKL_GT4e.cpp new file mode 100644 index 000000000..bb2c47b7c --- /dev/null +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/cache/cache_SKL_GT4e.cpp @@ -0,0 +1,28 @@ +/* +// Copyright (c) 2018 Intel Corporation +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +*/ + +#include "auto_tuner.h" +#include "auto_tuner_offline.h" +namespace kernel_selector +{ + // SKL GT4e + void tuning_cache_193B(tuning_data& td) + { + tuning_cache_193B_B1_B16(td); + tuning_cache_193B_B8(td); + tuning_cache_193B_B32_B64(td); + } +}
\ No newline at end of file diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/cache/cache_SKL_GT4e_B1_B16.cpp b/inference-engine/thirdparty/clDNN/kernel_selector/core/cache/cache_SKL_GT4e_B1_B16.cpp new file mode 100644 index 000000000..a020fdf5b --- /dev/null +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/cache/cache_SKL_GT4e_B1_B16.cpp @@ -0,0 +1,3710 @@ +/* +// Copyright (c) 2018 Intel Corporation +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +*/ + +#include "auto_tuner.h" +#include "auto_tuner_offline.h" +namespace kernel_selector +{ + // SKL GT4e + void tuning_cache_193B_B1_B16(tuning_data& td) + { + td.td.insert({ + { "11207257238719531888", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 325) }, + { "12348135936862667024", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 202) }, + { "1540041682425757361", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 320) }, + { "6949539207944972855", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "4197617702037834389", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 326) }, + { "13898284586432291433", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "3715177305271762194", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 205) }, + { "481328129206881674", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 325) }, + { "10001963042016663554", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 325) }, + { "15471470494305051299", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 323) }, + { "14716719350966652036", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 333) }, + { "13404888565084206853", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 200) }, + { "1249137685908951501", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 320) }, + { "4840004190985490064", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "5120466856097219243", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "4701832665603867798", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 243) }, + { "6181272224000872375", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 325) }, + { "2030309697153345387", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 335) }, + { "11207257238719531888", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 325) }, + { "12348135936862667024", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 202) }, + { "1540041682425757361", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 320) }, + { "6949539207944972855", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "4197617702037834389", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 326) }, + { "13898284586432291433", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "3715177305271762194", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 205) }, + { "481328129206881674", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 325) }, + { "10001963042016663554", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 325) }, + { "15471470494305051299", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 323) }, + { "14716719350966652036", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 333) }, + { "13404888565084206853", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 200) }, + { "1249137685908951501", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 320) }, + { "4840004190985490064", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "5120466856097219243", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "4701832665603867798", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 243) }, + { "6181272224000872375", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 325) }, + { "2030309697153345387", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 335) }, + { "10728212277329722684", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "877436308867220589", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "18375125668176498051", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "14907097142953816744", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "2525260242689556544", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 375) }, + { "13328449155966085543", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 373) }, + { "11856266545854830143", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "15993427814066246646", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "2100891581797371600", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 275) }, + { "12242618640422208652", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "6133592828563353516", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 304) }, + { "18232278892738147217", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 333) }, + { "11992625045241269569", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 298) }, + { "12601126285773042005", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 297) }, + { "5079055505117153635", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 195) }, + { "7457899998356343871", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 291) }, + { "6343888265369366589", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 296) }, + { "10791067159964399241", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 309) }, + { "11327097771110264965", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 183) }, + { "5245308722062496788", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 314) }, + { "10792503079194374004", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 178) }, + { "4818231379191523896", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 288) }, + { "2198278382394812839", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 258) }, + { "3800011935243649447", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 298) }, + { "9631545863582097486", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 255) }, + { "7777333052643961206", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 251) }, + { "1779941298820543013", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 179) }, + { "3621930417735246405", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 263) }, + { "14435120971846098308", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 188) }, + { "2893564501191050837", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 181) }, + { "8108843303778211282", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 256) }, + { "3682813162987778705", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 257) }, + { "15494543914974994991", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 179) }, + { "7565221050911842393", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 93) }, + { "5629670679897666607", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 257) }, + { "11754316727756881612", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 96) }, + { "10990741293315393791", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 208) }, + { "17024388383581997032", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 208) }, + { "10302338806536775954", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 204) }, + { "7915318733663535312", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 205) }, + { "13702692566238948173", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 208) }, + { "2909728331855309274", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 207) }, + { "13071545223094862275", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 206) }, + { "9631481972809246378", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 211) }, + { "13540002981450186147", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 203) }, + { "7076937538747704750", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 203) }, + { "10290107543739998181", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, + { "6767245864232675168", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "9287404618748313247", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "8728178019712933221", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 294) }, + { "18251360413872841969", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 295) }, + { "18271689282126907793", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 294) }, + { "954796765467489259", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 325) }, + { "13597240991532942069", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 178) }, + { "4135003545872878882", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 296) }, + { "11883485911218628865", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 293) }, + { "2242915551775617989", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 291) }, + { "10556089809203693400", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 290) }, + { "3727142736386026852", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 333) }, + { "1622880009460832832", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 182) }, + { "4437258459981739942", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 296) }, + { "14691372262153587653", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 295) }, + { "12181607120522804433", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 334) }, + { "3159681096461848644", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 295) }, + { "6729785110495533200", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 295) }, + { "15322019609805777935", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 183) }, + { "7024495439434892956", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 295) }, + { "10416622008071151225", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 173) }, + { "5796500397424307442", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 249) }, + { "15702382940521972117", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 249) }, + { "6093575518270471235", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 258) }, + { "5805383505505929391", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 258) }, + { "1801731858063091191", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 175) }, + { "1559798212423183813", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 176) }, + { "5594180958505308003", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 249) }, + { "4766071144928072260", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 177) }, + { "8650948093564284852", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 248) }, + { "3883845471211207871", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 248) }, + { "4366168099274266975", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 171) }, + { "578703329577922869", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 253) }, + { "16863960779539003201", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 249) }, + { "15450609897480659306", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 178) }, + { "8203550467004532364", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 257) }, + { "7431849514656037251", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 257) }, + { "14484890926084856480", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 179) }, + { "4424217045094988504", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 249) }, + { "7994179151788368291", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 180) }, + { "15192024816519005250", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 258) }, + { "4747159205186229582", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 103) }, + { "5485971317082563152", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 249) }, + { "18128162750557822655", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 91) }, + { "12421707187947291166", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 208) }, + { "792684262493086891", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 208) }, + { "941626985322260281", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 207) }, + { "11868551452004726281", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 200) }, + { "14352303529756685990", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 206) }, + { "10702234389482091891", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 208) }, + { "3895088069642140043", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 200) }, + { "5334566325056222430", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 205) }, + { "8306337702797456793", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 208) }, + { "15720507574336564201", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 240) }, + { "3277243911383750280", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 208) }, + { "18150429561058646714", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 240) }, + { "11169292427557543138", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 203) }, + { "13933912937625580405", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, + { "1375156980278317418", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "13455881643467418059", 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std::make_tuple("fully_connected_gpu_bs_f_bsv16_af8_vload", -1) }, + { "10749263296616139689", std::make_tuple("fully_connected_gpu_fb_io_ref", -1) }, + { "11717348577195224554", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "9275303306340702111", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "12245096462203481681", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 246) }, + { "4999505377862312410", std::make_tuple("fully_connected_gpu_bf_io_gemm", -1) }, + { "15272426400992401555", std::make_tuple("fully_connected_gpu_bf_io_gemm", -1) }, + { "9325097933807426691", std::make_tuple("fully_connected_gpu_bs_f_bsv16_b1", -1) }, + { "18238669114790278675", std::make_tuple("fully_connected_gpu_bf_io_gemm", -1) }, + { "6664482192233202590", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "7454366978268164047", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 231) }, + { "16135569134646688251", 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}, + { "18253784177599134876", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 189) }, + { "12319073009094248232", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 223) }, + { "9954050478761346921", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 223) }, + { "4640696923527766618", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 358) }, + { "1436052878894538927", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 358) }, + { "16011429608661242565", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 358) }, + { "4381329435655511217", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 353) }, + { "13972357557211413688", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 353) }, + { "13104509059416300615", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 158) }, + { "10090923790949378407", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "3429844423226609965", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "706049518431331645", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "17193614571243427089", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "3621424752591567930", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "11066930104187448422", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "209732971447020989", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "16044646335477470657", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 346) }, + { "2172121470071868949", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "3392693938352572136", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "5495063314176654751", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "14553856088069405595", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 213) }, + { "4967444801764057340", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 358) }, + { "12160764253455777655", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 366) }, + { "17723621158215826108", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 338) }, + { "2171768477223405739", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, + { "12672995204641007004", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 232) }, + { "5622089373755094139", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, + { "2129726780118554358", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 338) }, + { "4160656836528944651", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, + { "11052732052072367261", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 360) }, + { "18432787283148809023", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 360) }, + { "16172528828198474326", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 232) }, + { "16327433707667075261", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, + { "2797723586312707948", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 354) }, + { "8451212914744825089", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "7025975403069487257", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 201) }, + { "8913950860101596091", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 357) }, + { "15308578014507211237", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 207) }, + { "13132804928635689780", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 356) }, + { "4465781406991476376", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 287) }, + { "16266491618150971928", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 222) }, + { "181006047500375768", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 286) }, + { "18140951659547259039", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 362) }, + { "272730229972987861", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 202) }, + { "14898892437285105327", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 354) }, + { "17252449599613270108", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 282) }, + { "13436376034548670107", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 219) }, + { "13787436604877398090", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 286) }, + { "8873614802459592665", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 365) }, + { "13663893159182636270", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 338) }, + { "1361159591875955678", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 338) }, + { "5912303851874077576", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 338) }, + { "16245760498096322525", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 362) }, + { "9928406318940388716", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 354) }, + { "3036512701943687724", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 327) }, + { "5334291640387922287", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 356) }, + { "3002986032379998259", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 149) }, + { "16469788155263456039", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "8709632541892447149", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "9524303276541517389", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "9354818521586974021", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "16781127329510211966", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "6351572488552853754", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "907036267078333137", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "11855070245618904113", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 182) }, + { "4544242784357021697", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "18218631037214746168", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "178353385245384751", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "17658152048177750315", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 212) }, + { "11636129433022017868", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 212) }, + { "2622434279674583815", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "14335074487552883436", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "11175955260573469979", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 215) }, + { "2732519635571994212", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 238) }, + { "13893789954946953427", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 215) }, + { "4355933224673863178", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "18037918102910297531", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 284) }, + { "16071723603031305677", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 362) }, + { "1697248235682953135", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 362) }, + { "7843498978148810586", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 237) }, + { "6767159196241633301", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 215) }, + { "5097818987523855112", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "6623182990939010641", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "6711878663358611849", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 203) }, + { "8671491767142900139", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 203) }, + { "12164298124869114517", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 203) }, + { "17089801601582809764", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 353) }, + { "75742659105146536", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 206) }, + { "4652136280940317116", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 215) }, + { "9751582946441607796", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 206) }, + { "16706244336960642883", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 366) }, + { "12581879452540858313", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 203) }, + { "17443356777503458523", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 282) }, + { "939718260623752240", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 280) }, + { "14131851237755716991", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 215) }, + { "7474639594232203854", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 206) }, + { "14152716242882609401", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 360) }, + { "7998930863626763670", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 336) }, + { "10323345824599612614", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 336) }, + { "30229601562833524", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "17788367809717898285", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 366) }, + { "1509728225855233852", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "13139625572508441980", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 243) }, + { "16491532291908469567", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 353) }, + { "6355395905401306995", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "2096779676054335057", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "4217179485243909459", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", -1) }, + { "17101789600628162503", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 213) }, + { "6139574161497189424", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 213) }, + { "16559140502701231107", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 207) }, + { "11459784003592366395", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 207) }, + { "7869916853707978306", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 84) }, + //{ "3889519976910355277", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, + //{ "12081835728078383819", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, + //{ "14923692894655929923", std::make_tuple("fully_connected_gpu_bf_io_gemm", -1) }, + { "12794369485239257709", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "13338594271376045657", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "677249604491773387", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "2668729552208169959", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "13011676362747785816", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "4678607855896512523", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 213) }, + { "4356817283284529593", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "1885075753696445410", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 110) }, + { "17806712457019493207", std::make_tuple("fully_connected_gpu_bs_f_bsv16_af8_vload", -1) }, + { "11862259122805366807", std::make_tuple("fully_connected_gpu_bs_f_bsv16_af8_vload", -1) }, + { "15201438563802430490", std::make_tuple("fully_connected_gpu_fb_oi_ref", -1) }, + { "8132521728369930959", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "16108573960501496757", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "11086699387784339943", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 119) }, + { "4013707396889204359", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 214) }, + { "11850332373794932468", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 215) }, + { "14763982961176216679", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 93) }, + { "8207349115037232863", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 93) }, + { "3273748387141431306", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 84) }, + { "580936360000782237", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, + { "10682918518101379579", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, + { "13178480813522103091", std::make_tuple("fully_connected_gpu_bf_io_gemm", -1) }, + { "17109520309574369561", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "13754408679115174221", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 231) }, + { "16717713360264747483", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "1045854873741563331", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "16767392067294252396", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "6114241186364821679", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 214) }, + { "11241838709529552265", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 212) }, + { "15192230303376521834", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 103) }, + { "5374969798377773063", std::make_tuple("fully_connected_gpu_bs_f_bsv8_af8_vload", -1) }, + { "592245952014430043", std::make_tuple("fully_connected_gpu_bs_f_bsv8_af8_vload", -1) }, + { "3114869763557037270", std::make_tuple("fully_connected_gpu_fb_oi_ref", -1) }, + { "11254635684957519432", std::make_tuple("convolution_gpu_bfyx_1x1_hgemm_buf_16x1", -1) }, + { "16816222375242496370", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, + { "12809199739984715013", std::make_tuple("convolution_gpu_bfyx_1x1_hgemm_buf_16x1", -1) }, + { "5040730152867713388", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 213) }, + { "10429613013253088132", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "15451919862187018297", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, + { "7546586420552408243", std::make_tuple("convolution_gpu_bfyx_1x1_hgemm_buf_16x1", -1) }, + { "14487682847898298214", std::make_tuple("convolution_gpu_bfyx_1x1_hgemm_buf_16x1", -1) }, + { "3106710091841093202", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 214) }, + { "6458124573210430792", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 360) }, + { "9182897385081081193", std::make_tuple("convolution_gpu_winograd_6x3_s1_fused", -1) }, + { "14462438074931673266", std::make_tuple("convolution_gpu_bfyx_1x1_hgemm_buf_16x1", -1) }, + { "18133334552107213128", std::make_tuple("convolution_gpu_bfyx_1x1_hgemm_buf_16x1", -1) }, + { "38736266675995457", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 204) }, + { "13654816209891478730", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 222) }, + { "6263019986730305851", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 110) }, + { "12929981792125924963", std::make_tuple("convolution_gpu_bfyx_1x1_hgemm_buf_16x1", -1) }, + { "3138374672801504481", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 243) }, + { "4465701487417893814", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "12977678792503377525", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 335) }, + { "10879218241103462088", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "2221145174704245189", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "4635570915184713874", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "16075006181495932250", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "3863816884636503247", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "5440983284868981549", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "15428591250165788477", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 360) }, + { "16567638487719493784", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 334) }, + { "18059267466971880386", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 328) }, + { "10808909442136736629", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 363) }, + { "5682190700442712936", 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std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 327) }, + { "15595549493819416194", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 99) }, + { "14532844474906286088", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 205) }, + { "9562291747339451180", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 40) }, + { "6772239376357727149", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 126) }, + { "10690972785852373520", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 49) }, + { "4488336106517889531", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 82) }, + { "10058614204420018541", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 5) }, + { "13865227850818392065", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 40) }, + { "14100870590396726248", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 40) }, + { "10848277915422577656", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 47) }, + { "8121179472578287280", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 5) }, + { "2502125887857336825", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 5) }, + { "13192808619929896995", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 4) }, + { "5115661026367632863", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 5) }, + { "12812685418923919055", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 5) }, + { "13131740479277027362", std::make_tuple("fully_connected_gpu_bf_io_gemm", -1) }, + { "15334195300678132907", std::make_tuple("fully_connected_gpu_bf_io_gemm", -1) }, + { "2038505773698938555", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, + { "12090536142661253835", std::make_tuple("fully_connected_gpu_bf_io_gemm", -1) }, + { "4999505377862312410", std::make_tuple("fully_connected_gpu_bf_io_gemm", -1) }, + { "3934290309368153435", std::make_tuple("fully_connected_gpu_bf_io_gemm", -1) }, + { "5951936376654416075", std::make_tuple("fully_connected_gpu_bs_f_bsv16_b1", -1) }, + { "13204120207726209723", std::make_tuple("fully_connected_gpu_bf_io_gemm", -1) }, + { "17108987360340581555", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, + { "4795705973706796563", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, + { "4084106758501882407", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, + { "8127570953237266335", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, + { "7500192998744460131", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, + { "2379484884827231127", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, + { "13933912937625580405", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, + { "2040762223425679479", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, + { "4800587664660105589", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, + { "1616603916015535857", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, + { "10290107543739998181", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, + { "5927467766675317093", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, + { "3349519148124496343", std::make_tuple("fully_connected_gpu_bf_io_gemm", -1) }, + { "13477416097954638887", std::make_tuple("fully_connected_gpu_bf_io_gemm", -1) }, + { "6942049339361951275", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, + { "5303170164698694791", std::make_tuple("fully_connected_gpu_bf_io_gemm", -1) }, + { "12494969618927201911", std::make_tuple("fully_connected_gpu_yxfb_ref", -1) }, + { "7875724726741958520", std::make_tuple("fully_connected_gpu_fb_io_ref", -1) }, + { "13835908664998757647", std::make_tuple("fully_connected_gpu_fb_oi_ref", -1) }, + { "6407471972820516685", std::make_tuple("fully_connected_gpu_fb_io_ref", -1) }, + { "5385316497510064491", std::make_tuple("fully_connected_gpu_fb_oi_ref", -1) }, + { "17377315194963069204", std::make_tuple("fully_connected_gpu_fb_oi_ref", -1) }, + { "3889519976910355277", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, + { "12081835728078383819", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, + { "14923692894655929923", std::make_tuple("fully_connected_gpu_bf_io_gemm", -1) }, + { "580936360000782237", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, + { "10682918518101379579", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, + { "13178480813522103091", std::make_tuple("fully_connected_gpu_bf_io_gemm", -1) }, + { "6149673627320838019", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, + { "11077876432364512822", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, + { "6062246008880097669", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, + { "4806571630436601566", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, + { "2458592904274981909", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, + { "2007192658799516915", std::make_tuple("fully_connected_gpu_bf_io_gemm", -1) }, + { "1051506168926530904", std::make_tuple("fully_connected_gpu_bs_f_bsv16_b1", -1) }, + { "4163359403543480821", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, + { "4232250144427804891", std::make_tuple("fully_connected_gpu_bf_io_gemm", -1) }, + { "5415319660821122528", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, + { "3286629188347536485", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, + { "13575423234109624706", std::make_tuple("fully_connected_gpu_yxfb_ref", -1) }, + { "1841155673858789206", std::make_tuple("fully_connected_gpu_fb_io_ref", -1) }, + { "6708349666663292171", std::make_tuple("fully_connected_gpu_bs_f_bsv16_af8_vload", -1) }, + { "5083163738120585821", std::make_tuple("fully_connected_gpu_fb_io_ref", -1) }, + { "10572945270796129630", std::make_tuple("fully_connected_gpu_fb_oi_ref", -1) }, + { "4436244774193918646", std::make_tuple("fully_connected_gpu_fb_oi_ref", -1) }, + { "12985942652866621579", std::make_tuple("fully_connected_gpu_fb_io_ref", -1) }, + { "775538461106687677", std::make_tuple("fully_connected_gpu_fb_io_ref", -1) }, + { "9533360488591027707", std::make_tuple("fully_connected_gpu_bs_f_bsv16_af8_vload", -1) }, + { "8913823292181409151", std::make_tuple("fully_connected_gpu_bs_f_bsv16_af8_vload", -1) }, + { "11583985978586657985", std::make_tuple("fully_connected_gpu_bs_f_bsv16_af8_vload", -1) }, + { "1485662490111767875", std::make_tuple("fully_connected_gpu_bs_f_bsv8_af8_vload", -1) }, + { "11872464450773754851", std::make_tuple("fully_connected_gpu_bs_f_bsv8_af8_vload", -1) }, + { "5364060938737428149", std::make_tuple("fully_connected_gpu_bs_f_bsv8_af8_vload", -1) }, + { "2613462626256090659", std::make_tuple("fully_connected_gpu_bs_f_bsv16_af8_vload", -1) }, + { "14668725050395069435", std::make_tuple("fully_connected_gpu_bs_f_bsv16_af8_vload", -1) }, + { "17381516856910544374", std::make_tuple("fully_connected_gpu_bs_f_bsv8_af8_vload", -1) }, + { "6450532136308941035", std::make_tuple("fully_connected_gpu_bs_f_bsv8_af8_vload", -1) }, + { "2321767794934000238", std::make_tuple("fully_connected_gpu_bs_f_bsv8_af8_vload", -1) }, + { "7995820969034996638", std::make_tuple("fully_connected_gpu_bs_f_bsv8_af8_vload", -1) }, + { "17951403431757222177", std::make_tuple("fully_connected_gpu_bs_f_bsv16_af8_vload", -1) }, + { "1074748462756364699", std::make_tuple("fully_connected_gpu_fb_oi_ref", -1) }, + { "11955992313739654625", std::make_tuple("fully_connected_gpu_bs_f_bsv8_af8_vload", -1) }, + { "11939914680143672459", std::make_tuple("fully_connected_gpu_fb_io_ref", -1) }, + { "17806712457019493207", std::make_tuple("fully_connected_gpu_bs_f_bsv16_af8_vload", -1) }, + { "11862259122805366807", std::make_tuple("fully_connected_gpu_bs_f_bsv16_af8_vload", -1) }, + { "15201438563802430490", std::make_tuple("fully_connected_gpu_fb_oi_ref", -1) }, + { "5374969798377773063", std::make_tuple("fully_connected_gpu_bs_f_bsv8_af8_vload", -1) }, + { "592245952014430043", std::make_tuple("fully_connected_gpu_bs_f_bsv8_af8_vload", -1) }, + { "3114869763557037270", std::make_tuple("fully_connected_gpu_fb_oi_ref", -1) }, + { "17147293671640396193", std::make_tuple("fully_connected_gpu_bs_f_bsv8_af8_vload", -1) }, + { "6911215749850066204", std::make_tuple("fully_connected_gpu_bs_f_bsv8_af8_vload", -1) }, + { "2814805887448339818", std::make_tuple("fully_connected_gpu_bs_f_bsv8_af8_vload", -1) }, + { "1120455113299469776", std::make_tuple("fully_connected_gpu_bs_f_bsv16_af8_vload", -1) }, + { "8002233052700666718", std::make_tuple("fully_connected_gpu_bs_f_bsv16_af8_vload", -1) }, + { "16436006771518788093", std::make_tuple("fully_connected_gpu_bs_f_bsv16_af8_vload", -1) }, + { "11083993858285515074", std::make_tuple("fully_connected_gpu_bs_f_bsv16_af8_vload", -1) }, + { "4133424990380177132", std::make_tuple("fully_connected_gpu_bs_f_bsv16_af8_vload", -1) }, + { "1044978617045366709", std::make_tuple("fully_connected_gpu_fb_io_b8_f8_vload", -1) }, + { "952318454591754214", std::make_tuple("fully_connected_gpu_bs_f_bsv8_af8_vload", -1) }, + { "5762878778443755104", std::make_tuple("fully_connected_gpu_bs_f_bsv8_af8_vload", -1) }, + }); + } +}
\ No newline at end of file diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/cache/cache_SKL_GT4e_B32_B64.cpp b/inference-engine/thirdparty/clDNN/kernel_selector/core/cache/cache_SKL_GT4e_B32_B64.cpp new file mode 100644 index 000000000..c41fd5c7e --- /dev/null +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/cache/cache_SKL_GT4e_B32_B64.cpp @@ -0,0 +1,29 @@ +/* +// Copyright (c) 2018 Intel Corporation +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +*/ + +#include "auto_tuner.h" +#include "auto_tuner_offline.h" +namespace kernel_selector +{ + // SKL GT4e + void tuning_cache_193B_B32_B64(tuning_data& td) + { + td.td.insert({ + + { "10794662801660960189", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 749) }, + }); + } +}
\ No newline at end of file diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/cache/cache_SKL_GT4e_B8.cpp b/inference-engine/thirdparty/clDNN/kernel_selector/core/cache/cache_SKL_GT4e_B8.cpp new file mode 100644 index 000000000..d82ede47d --- /dev/null +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/cache/cache_SKL_GT4e_B8.cpp @@ -0,0 +1,169 @@ +/* +// Copyright (c) 2018 Intel Corporation +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +*/ + +#include "auto_tuner.h" +#include "auto_tuner_offline.h" +namespace kernel_selector +{ + // SKL GT4e + void tuning_cache_193B_B8(tuning_data& td) + { + td.td.insert({ + { "11207257238719531888", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 325) }, + /* { "12348135936862667024", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 202) }, + { "1540041682425757361", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 320) }, + { "6949539207944972855", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "4197617702037834389", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 326) }, + { "13898284586432291433", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "3715177305271762194", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 205) }, + { "481328129206881674", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 325) }, + { "10001963042016663554", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 325) }, + { "15471470494305051299", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 323) }, + { "14716719350966652036", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 333) }, + { "13404888565084206853", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 200) }, + { "1249137685908951501", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 320) }, + { "4840004190985490064", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "5120466856097219243", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "4701832665603867798", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 243) }, + { "6181272224000872375", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 325) }, + { "2030309697153345387", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 335) }, + { "11207257238719531888", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 325) }, + { "12348135936862667024", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 202) }, + { "1540041682425757361", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 320) }, + { "6949539207944972855", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "4197617702037834389", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 326) }, + { "13898284586432291433", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "3715177305271762194", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 205) }, + { "481328129206881674", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 325) }, + { "10001963042016663554", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 325) }, + { "15471470494305051299", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 323) }, + { "14716719350966652036", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 333) }, + { "13404888565084206853", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 200) }, + { "1249137685908951501", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 320) }, + { "4840004190985490064", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "5120466856097219243", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "4701832665603867798", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 243) }, + { "6181272224000872375", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 325) }, + { "2030309697153345387", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 335) }, + { "10728212277329722684", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "877436308867220589", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "18375125668176498051", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "14907097142953816744", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "2525260242689556544", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 375) }, + { "13328449155966085543", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 373) }, + { "11856266545854830143", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "15993427814066246646", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "2100891581797371600", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 275) }, + { "12242618640422208652", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "6133592828563353516", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 304) }, + { "18232278892738147217", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 333) }, + { "11992625045241269569", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 298) }, + { "12601126285773042005", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 297) }, + { "5079055505117153635", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 195) }, + { "7457899998356343871", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 291) }, + { "6343888265369366589", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 296) }, + { "10791067159964399241", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 309) }, + { "11327097771110264965", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 183) }, + { "5245308722062496788", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 314) }, + { "10792503079194374004", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 178) }, + { "4818231379191523896", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 288) }, + { "2198278382394812839", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 258) }, + { "3800011935243649447", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 298) }, + { "9631545863582097486", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 255) }, + { "7777333052643961206", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 251) }, + { "1779941298820543013", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 179) }, + { "3621930417735246405", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 263) }, + { "14435120971846098308", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 188) }, + { "2893564501191050837", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 181) }, + { "8108843303778211282", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 256) }, + { "3682813162987778705", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 257) }, + { "15494543914974994991", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 179) }, + { "7565221050911842393", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 93) }, + { "5629670679897666607", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 257) }, + { "11754316727756881612", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 96) }, + { "10990741293315393791", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 208) }, + { "17024388383581997032", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 208) }, + { "10302338806536775954", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 204) }, + { "7915318733663535312", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 205) }, + { "13702692566238948173", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 208) }, + { "2909728331855309274", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 207) }, + { "13071545223094862275", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 206) }, + { "9631481972809246378", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 211) }, + { "13540002981450186147", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 203) }, + { "7076937538747704750", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 203) }, + { "10290107543739998181", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) }, + { "6767245864232675168", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "9287404618748313247", std::make_tuple("convolution_gpu_bfyx_gemm_like", -1) }, + { "8728178019712933221", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 294) }, + { "18251360413872841969", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 295) }, + { "18271689282126907793", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 294) }, + { "954796765467489259", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 325) }, + { "13597240991532942069", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 178) }, + { "4135003545872878882", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 296) }, + { "11883485911218628865", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 293) }, + { "2242915551775617989", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 291) }, + { "10556089809203693400", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 290) }, + { "3727142736386026852", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 333) }, + { "1622880009460832832", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 182) }, + { "4437258459981739942", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 296) }, + { "14691372262153587653", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 295) }, + { "12181607120522804433", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 334) }, + { "3159681096461848644", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 295) }, + { "6729785110495533200", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 295) }, + { "15322019609805777935", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 183) }, + { "7024495439434892956", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 295) }, + { "10416622008071151225", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 173) }, + { "5796500397424307442", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 249) }, + { "15702382940521972117", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 249) }, + { "6093575518270471235", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 258) }, + { "5805383505505929391", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 258) }, + { "1801731858063091191", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 175) }, + { "1559798212423183813", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 176) }, + { "5594180958505308003", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 249) }, + { "4766071144928072260", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 177) }, + { "8650948093564284852", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 248) }, + { "3883845471211207871", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 248) }, + { "4366168099274266975", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 171) }, + { "578703329577922869", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 253) }, + { "16863960779539003201", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 249) }, + { "15450609897480659306", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 178) }, + { "8203550467004532364", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 257) }, + { "7431849514656037251", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 257) }, + { "14484890926084856480", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 179) }, + { "4424217045094988504", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 249) }, + { "7994179151788368291", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 180) }, + { "15192024816519005250", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 258) }, + { "4747159205186229582", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 103) }, + { "5485971317082563152", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 249) }, + { "18128162750557822655", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 91) }, + { "12421707187947291166", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 208) }, + { "792684262493086891", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 208) }, + { "941626985322260281", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 207) }, + { "11868551452004726281", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 200) }, + { "14352303529756685990", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 206) }, + { "10702234389482091891", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 208) }, + { "3895088069642140043", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 200) }, + { "5334566325056222430", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 205) }, + { "8306337702797456793", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 208) }, + { "15720507574336564201", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 240) }, + { "3277243911383750280", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 208) }, + { "18150429561058646714", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 240) }, + { "11169292427557543138", std::make_tuple("convolution_gpu_bfyx_os_iyx_osv16", 203) }, + { "13933912937625580405", std::make_tuple("fully_connected_gpu_bf_io_input_spatial", -1) },*/ + + }); + } +}
\ No newline at end of file diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/activation_opt.cl b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/activation_opt.cl index 91dda4716..e53743a65 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/activation_opt.cl +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/activation_opt.cl @@ -14,7 +14,8 @@ // limitations under the License. */ -#include "include/include_all.cl" +#include "include/common.cl" +#include "include/data_types.cl" KERNEL(activation)( #if GRADIENT diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/activation_ref.cl b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/activation_ref.cl index 0fbe6948c..79e0495e3 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/activation_ref.cl +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/activation_ref.cl @@ -14,7 +14,8 @@ // limitations under the License. */ -#include "include/include_all.cl" +#include "include/common.cl" +#include "include/data_types.cl" // TODO: move it from layout based to memory based KERNEL(activation)( diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/arg_max_min_axis.cl b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/arg_max_min_axis.cl index d57833c48..9a7691b1a 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/arg_max_min_axis.cl +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/arg_max_min_axis.cl @@ -13,7 +13,8 @@ // limitations under the License. -#include "include/include_all.cl" +#include "include/common.cl" +#include "include/data_types.cl" #define GLOBAL_SIZE 128 #define LOCAL_SIZE GLOBAL_SIZE diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/arg_max_min_gpu_ref.cl b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/arg_max_min_gpu_ref.cl index f14ccfee0..3ad4ac6d7 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/arg_max_min_gpu_ref.cl +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/arg_max_min_gpu_ref.cl @@ -13,7 +13,8 @@ // limitations under the License. -#include "include/include_all.cl" +#include "include/common.cl" +#include "include/data_types.cl" #define GLOBAL_SIZE 128 #define LOCAL_SIZE GLOBAL_SIZE diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/arg_max_min_opt.cl b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/arg_max_min_opt.cl index 795f0258d..5d1ea0dec 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/arg_max_min_opt.cl +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/arg_max_min_opt.cl @@ -12,7 +12,8 @@ // See the License for the specific language governing permissions and // limitations under the License. -#include "include/include_all.cl" +#include "include/common.cl" +#include "include/data_types.cl" #ifndef SG_SIZE #define SG_SIZE 16 diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/border_gpu_ref.cl b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/border_gpu_ref.cl new file mode 100644 index 000000000..8f00411b1 --- /dev/null +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/border_gpu_ref.cl @@ -0,0 +1,91 @@ +// Copyright (c) 2018 Intel Corporation +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#include "include/include_all.cl" + + +KERNEL(border_gpu_ref)( + const __global UNIT_TYPE* input, + __global UNIT_TYPE* output) +{ + // [CONSTEXPR] + // Border sizes (left-top set and right-bottom set): + const uint blt_sx = LT_SIZES_SIZE_X; + const uint blt_sy = LT_SIZES_SIZE_Y; + const uint blt_sf = LT_SIZES_FEATURE_NUM; + const uint blt_sb = LT_SIZES_BATCH_NUM; + + const uint brb_sx = RB_SIZES_SIZE_X; + const uint brb_sy = RB_SIZES_SIZE_Y; + const uint brb_sf = RB_SIZES_FEATURE_NUM; + const uint brb_sb = RB_SIZES_BATCH_NUM; + + // Input sizes: + const uint in_sx = INPUT0_SIZE_X; + const uint in_sy = INPUT0_SIZE_Y; + const uint in_sf = INPUT0_FEATURE_NUM; + const uint in_sb = INPUT0_BATCH_NUM; + + // Input limits (exclusive; tested on output position): + const uint in_lx = in_sx + blt_sx; + const uint in_ly = in_sy + blt_sy; + const uint in_lf = in_sf + blt_sf; + const uint in_lb = in_sb + blt_sb; + + + const uint out_x = (uint) get_global_id(0); + const uint out_y = (uint) get_global_id(1); + const uint out_fb = (uint) get_global_id(2); + + const uint out_f = out_fb % OUTPUT_FEATURE_NUM; + const uint out_b = out_fb / OUTPUT_FEATURE_NUM; + +#ifdef BORDER_TYPE_ZERO + UNIT_TYPE in_val = UNIT_VAL_ZERO; + if (out_x >= blt_sx & out_x < in_lx & + out_y >= blt_sy & out_y < in_ly & + out_f >= blt_sf & out_f < in_lf & + out_b >= blt_sb & out_b < in_lb) + { + const uint in_x = out_x - blt_sx; + const uint in_y = out_y - blt_sy; + const uint in_f = out_f - blt_sf; + const uint in_b = out_b - blt_sb; + + const uint in_pos = GET_DATA_INDEX(INPUT0, in_b, in_f, in_y, in_x); + in_val = input[in_pos]; + } +#elif defined BORDER_TYPE_MIRROR + const uint in_x = (out_x >= blt_sx & out_x < in_lx) ? out_x - blt_sx : (out_x < blt_sx ? blt_sx - 1 - out_x : in_sx + in_lx - 1 - out_x); + const uint in_y = (out_y >= blt_sy & out_y < in_ly) ? out_y - blt_sy : (out_y < blt_sy ? blt_sy - 1 - out_y : in_sy + in_ly - 1 - out_y); + const uint in_f = (out_f >= blt_sf & out_f < in_lf) ? out_f - blt_sf : (out_f < blt_sf ? blt_sf - 1 - out_f : in_sf + in_lf - 1 - out_f); + const uint in_b = (out_b >= blt_sb & out_b < in_lb) ? out_b - blt_sb : (out_b < blt_sb ? blt_sb - 1 - out_b : in_sb + in_lb - 1 - out_b); + + const uint in_pos = GET_DATA_INDEX(INPUT0, in_b, in_f, in_y, in_x); + UNIT_TYPE in_val = input[in_pos]; +#elif defined BORDER_TYPE_MIRROR_101 + const uint in_x = (out_x >= blt_sx & out_x < in_lx) ? out_x - blt_sx : (out_x < blt_sx ? blt_sx - out_x : in_sx + in_lx - 2 - out_x); + const uint in_y = (out_y >= blt_sy & out_y < in_ly) ? out_y - blt_sy : (out_y < blt_sy ? blt_sy - out_y : in_sy + in_ly - 2 - out_y); + const uint in_f = (out_f >= blt_sf & out_f < in_lf) ? out_f - blt_sf : (out_f < blt_sf ? blt_sf - out_f : in_sf + in_lf - 2 - out_f); + const uint in_b = (out_b >= blt_sb & out_b < in_lb) ? out_b - blt_sb : (out_b < blt_sb ? blt_sb - out_b : in_sb + in_lb - 2 - out_b); + + const uint in_pos = GET_DATA_INDEX(INPUT0, in_b, in_f, in_y, in_x); + UNIT_TYPE in_val = input[in_pos]; +#else + #error Unsupported border type. +#endif + + const uint out_pos = GET_DATA_INDEX(OUTPUT, out_b, out_f, out_y, out_x); + output[out_pos] = in_val; +}
\ No newline at end of file diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/broadcast_gpu_ref.cl b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/broadcast_gpu_ref.cl new file mode 100644 index 000000000..286608ff5 --- /dev/null +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/broadcast_gpu_ref.cl @@ -0,0 +1,48 @@ +// Copyright (c) 2018 Intel Corporation +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#include "include/include_all.cl" + + +KERNEL(broadcast_gpu_ref)( + const __global UNIT_TYPE* input, + __global UNIT_TYPE* output) +{ + // [CONSTEXPR] + // Input sizes: + const uint in_sx = INPUT0_SIZE_X; + const uint in_sy = INPUT0_SIZE_Y; + const uint in_sf = INPUT0_FEATURE_NUM; + const uint in_sb = INPUT0_BATCH_NUM; + + + const uint out_x = (uint) get_global_id(0); + const uint out_y = (uint) get_global_id(1); + const uint out_fb = (uint) get_global_id(2); + + const uint out_f = out_fb % OUTPUT_FEATURE_NUM; + const uint out_b = out_fb / OUTPUT_FEATURE_NUM; + + + const uint in_x = out_x % in_sx; + const uint in_y = out_y % in_sy; + const uint in_f = out_f % in_sf; + const uint in_b = out_b % in_sb; + + const uint in_pos = GET_DATA_INDEX(INPUT0, in_b, in_f, in_y, in_x); + const uint out_pos = GET_DATA_INDEX(OUTPUT, out_b, out_f, out_y, out_x); + + + output[out_pos] = input[in_pos]; +}
\ No newline at end of file diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/convolution_gpu_1x1_gemm_mmad.cl b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/convolution_gpu_1x1_gemm_mmad.cl index 6c2c8ac33..2823e704a 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/convolution_gpu_1x1_gemm_mmad.cl +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/convolution_gpu_1x1_gemm_mmad.cl @@ -13,7 +13,7 @@ // limitations under the License. #include "include/common.cl" -#include "include/activation_functions.cl" + #include "include/data_types.cl" #include "include/fetch.cl" #include "include/mmad.cl" @@ -65,7 +65,6 @@ KERNEL(convolution_1x1_gemm_MMAD)( tileC[i] = 0; } - __attribute__((opencl_unroll_hint(1))) for (uint k = 0; k < FILTER_IFM_MMAD_NUM; ++k) { // load A tile ( input ) diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/convolution_gpu_bfyx_depthwise_weights_lwg.cl b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/convolution_gpu_bfyx_depthwise_weights_lwg.cl new file mode 100644 index 000000000..f21b03d27 --- /dev/null +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/convolution_gpu_bfyx_depthwise_weights_lwg.cl @@ -0,0 +1,106 @@ +// Copyright (c) 2018 Intel Corporation +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#include "include/include_all.cl" + +#if FP16_UNIT_USED + #define ALIGNED_BLOCK_READ(ptr, byte_offset) as_half(intel_sub_group_block_read_us8((const __global ushort*)(ptr) + (byte_offset))) + #define ALIGNED_BLOCK_WRITE(ptr, byte_offset, val) intel_sub_group_block_write_us((__global ushort*)(ptr) + (byte_offset), as_ushort8(val)) +#else + #define ALIGNED_BLOCK_READ(ptr, byte_offset) as_float(intel_sub_group_block_read((const __global uint*)(ptr) + (byte_offset))) + #define ALIGNED_BLOCK_WRITE(ptr, byte_offset, val) intel_sub_group_block_write((__global uint*)(ptr) + (byte_offset), as_uint8(val)) +#endif + +__attribute__((intel_reqd_sub_group_size(16))) +__attribute__((reqd_work_group_size(16, 1, 1))) +KERNEL(convolution_depthwise_weights_lwg)( + __global INPUT0_TYPE* input, + __global OUTPUT_TYPE* output, + __global FILTER_TYPE* weights, +#if BIAS_TERM + __global BIAS_TYPE* biases, +#endif + uint split_idx) +{ + const uint yx = get_global_id(0); + const uint x = yx % OUTPUT_SIZE_X; + const uint y = yx / OUTPUT_SIZE_X; + const uint f = get_global_id(1); + const uint b = get_global_id(2); + + UNIT_TYPE dotProd = UNIT_VAL_ZERO; + + const int input_x = x * STRIDE_SIZE_X - PADDING_SIZE_X; + const int input_y = y * STRIDE_SIZE_Y - PADDING_SIZE_Y; + + const uint in_split_offset = (f / FILTER_OFM_NUM) * INPUT0_FEATURE_PITCH * FILTER_IFM_NUM; + + const uint filter_offset = f*FILTER_OFM_PITCH; + const uint input_offset = b*INPUT0_BATCH_PITCH + INPUT0_OFFSET + in_split_offset; + +#if FILTER_SIZE_Y * FILTER_SIZE_X % 16 == 0 && !FP16_UNIT_USED + UNIT_TYPE w = ALIGNED_BLOCK_READ(weights, filter_offset); +#else + const uint lid = get_local_id(0); + UNIT_TYPE w = UNIT_VAL_ZERO; + if(lid < FILTER_SIZE_X * FILTER_SIZE_Y) + w = weights[filter_offset + lid]; +#endif + + __attribute__((opencl_unroll_hint(FILTER_SIZE_Y))) + for (uint j = 0; j < FILTER_SIZE_Y ; ++j) + { + const int input_offset_y = input_y + j * DILATION_SIZE_Y; +#if BOUNDARY_CHECK + const bool zero_y = input_offset_y >= INPUT0_SIZE_Y || input_offset_y < 0; + + if(!zero_y) + { +#endif + __attribute__((opencl_unroll_hint(FILTER_SIZE_X))) + for (uint i = 0; i < FILTER_SIZE_X ; ++i) + { + const int input_offset_x = input_x + i * DILATION_SIZE_X; +#if BOUNDARY_CHECK + const bool zero_x = input_offset_x >= INPUT0_SIZE_X || input_offset_x < 0; + + if(!zero_x) + { +#endif + dotProd = mad(input[input_offset + (uint)input_offset_x*INPUT0_X_PITCH + (uint)input_offset_y*INPUT0_Y_PITCH], + intel_sub_group_shuffle( w, j*FILTER_Y_PITCH + i*FILTER_X_PITCH), dotProd); + } + } +#if BOUNDARY_CHECK + } + } +#endif + + if(yx >= OUTPUT_SIZE_X * OUTPUT_SIZE_Y) + return; + +#if BIAS_TERM +#if BIAS_PER_OUTPUT + const uint bias_index = GET_DATA_INDEX(BIAS, b, f, y, x); +#elif BIAS_PER_OFM + const uint bias_index = f; +#endif + dotProd += (UNIT_TYPE)biases[bias_index]; +#endif + + const uint out_split_offset = split_idx * OUTPUT_FEATURE_PITCH * OUTPUT_FEATURE_NUM; + const uint dst_index = GET_DATA_INDEX(OUTPUT, b, f, y, x) + out_split_offset; + output[dst_index] = ACTIVATION(dotProd, NL_M, NL_N); + +} diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/convolution_gpu_bfyx_gemm_like_fp32.cl b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/convolution_gpu_bfyx_gemm_like_fp32.cl index da85a7264..0366f8fc3 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/convolution_gpu_bfyx_gemm_like_fp32.cl +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/convolution_gpu_bfyx_gemm_like_fp32.cl @@ -1,5 +1,5 @@ /* -// Copyright (c) 2016 Intel Corporation +// Copyright (c) 2018 Intel Corporation // // Licensed under the Apache License, Version 2.0 (the "License"); // you may not use this file except in compliance with the License. @@ -84,7 +84,6 @@ KERNEL(convolution_f32)( _result.s6 = mad( _rowA, sub_group_broadcast( colB, 6 ), _result.s6 ); \ _result.s7 = mad( _rowA, sub_group_broadcast( colB, 7 ), _result.s7 ); \ } - typedef CAT( float, FILTER_SIZE_X ) float_t; // Walk DOWN src0 (patch 0, 1, 2, ...) and DOWN src1. // Inner loop loads and FMADs one row (FILTER_SIZE_X) of each input patch diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/convolution_gpu_bfyx_os_iyx_osv16.cl b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/convolution_gpu_bfyx_os_iyx_osv16.cl index d74cb37f8..e70ca2e5c 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/convolution_gpu_bfyx_os_iyx_osv16.cl +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/convolution_gpu_bfyx_os_iyx_osv16.cl @@ -12,8 +12,9 @@ // See the License for the specific language governing permissions and // limitations under the License. +#include "include/common.cl" +#include "include/data_types.cl" -#include "include/include_all.cl" // --------------------------------------------------------------------------------------------------------------------- // Just-in-time macro definitions: diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/convolution_gpu_byxf_af32_depthwise.cl b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/convolution_gpu_byxf_af32_depthwise.cl index b6f65179f..393c63dd7 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/convolution_gpu_byxf_af32_depthwise.cl +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/convolution_gpu_byxf_af32_depthwise.cl @@ -13,7 +13,7 @@ // limitations under the License. #include "include/common.cl" -#include "include/activation_functions.cl" + #include "include/data_types.cl" #include "include/fetch.cl" #include "include/mmad.cl" diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/convolution_gpu_mmad.cl b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/convolution_gpu_mmad.cl index 78d2474c1..f9cbd2e22 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/convolution_gpu_mmad.cl +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/convolution_gpu_mmad.cl @@ -13,7 +13,7 @@ // limitations under the License. #include "include/common.cl" -#include "include/activation_functions.cl" + #include "include/data_types.cl" #include "include/fetch.cl" #include "include/mmad.cl" diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/convolution_gpu_mmad_batched.cl b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/convolution_gpu_mmad_batched.cl new file mode 100644 index 000000000..f1df74aad --- /dev/null +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/convolution_gpu_mmad_batched.cl @@ -0,0 +1,116 @@ +// Copyright (c) 2018 Intel Corporation +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#include "include/common.cl" + +#include "include/data_types.cl" +#include "include/fetch.cl" +#include "include/mmad.cl" + +#define FILTER_IFM_MMAD_NUM ((FILTER_IFM_NUM + 31) / 32) +#define FILTER_OFM_MMAD_NUM ((FILTER_OFM_NUM + 7) / 8) +#define FILTER_IFM_ALIGNED (FILTER_IFM_MMAD_NUM * 32) +#define FILTER_OFM_ALIGNED (FILTER_OFM_MMAD_NUM * 8) +// input data is in blocks 4batch x 32 features +// each SIMD process 4 batches and 8 output features + +__attribute__((intel_reqd_sub_group_size(SUB_GROUP_SIZE))) +KERNEL(convolution_mmad_batched)( + __global INPUT0_TYPE* input, + __global OUTPUT_TYPE* output, + __global FILTER_TYPE* weights, +#if BIAS_TERM + __global BIAS_TYPE* biases, +#endif +#if QUANTIZATION_TERM + const __global float* quantizations, +#endif +#if CALIBRATION_TERM + const __global float* calibrations, +#endif + uint split_idx) +{ + const uint x = get_global_id(0); + const uint y = get_global_id(1); + + const uint f = get_global_id(2) % FILTER_OFM_ALIGNED; + const uint b_block = get_global_id(2) / FILTER_OFM_ALIGNED; + const uint f_block = f / 32; + + int4 dotProd = 0; + + const int input_x = x * STRIDE_SIZE_X - PADDING_SIZE_X; + const int input_y = y * STRIDE_SIZE_Y - PADDING_SIZE_Y; + + const uint filter_offset = (get_group_id(2) % FILTER_OFM_MMAD_NUM) * FILTER_OFM_BLOCK_PITCH; + const uint input_offset = IN_OFFSET + IN_B_BLOCK_PITCH * b_block; + + for (uint k = 0; k < FILTER_IFM_MMAD_NUM; ++k) + { + for (uint j = 0; j < FILTER_SIZE_Y ; ++j) + { + const int input_offset_y = input_y + j * DILATION_SIZE_Y; + const bool zero_y = input_offset_y >= INPUT0_SIZE_Y || input_offset_y < 0; + + if(!zero_y) + { + for (uint i = 0; i < FILTER_SIZE_X ; ++i) + { + const int input_offset_x = input_x + i * DILATION_SIZE_X; + const bool zero_x = input_offset_x >= INPUT0_SIZE_X || input_offset_x < 0; + + if(!zero_x) + { + uint input_idx = input_offset + input_offset_y * IN_Y_PITCH + input_offset_x * IN_X_PITCH + k * IN_F_BLOCK_PITCH; + uint filter_idx = filter_offset + k*FILTER_Y_PITCH * FILTER_SIZE_Y + j*FILTER_Y_PITCH + i*FILTER_X_PITCH; + + int4 input_data = as_int4(intel_sub_group_block_read4((const __global uint*)(input + input_idx))); + int8 weights_data = as_int8(intel_sub_group_block_read8((const __global uint*)(weights + filter_idx))); + + dotProd = MMAD_4x8(input_data, weights_data, dotProd); + } + } + } + } + } + +for(uint b = 0; b < 4; b++) +{ + +#if BIAS_TERM + const uint bias_index = f; +#if QUANTIZATION_TERM +#if CALIBRATION_TERM + dotProd[b] = (UNIT_TYPE)round(((float)dotProd[b] * quantizations[f] * I_QF + biases[bias_index]) * calibrations[f]); +#else // CALIBRATION_TERM + dotProd[b] = (UNIT_TYPE)round(((float)dotProd[b] * quantizations[f] * I_QF + biases[bias_index]) * O_QF); +#endif // CALIBRATION_TERM +#else // QUANTIZATION_TERM + dotProd[b] += (UNIT_TYPE)biases[bias_index]; +#endif // QUANTIZATION_TERM +#endif // BIAS_TERM + + const uint dst_index = GET_DATA_FS_BS_YX_BSV4_FSV32_INDEX(OUTPUT, b_block*4 + b, f, y, x); +#if QUANTIZATION_TERM + output[dst_index] = ACTIVATION(convert_char(dotProd[b]), NL_M, NL_N); +#else + output[dst_index] = ACTIVATION(dotProd[b], NL_M, NL_N); +#endif +} +} + +#undef FILTER_IFM_MMAD_NUM +#undef FILTER_OFM_MMAD_NUM +#undef FILTER_IFM_ALIGNED +#undef FILTER_OFM_ALIGNED
\ No newline at end of file diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/convolution_gpu_mmad_blocks.cl b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/convolution_gpu_mmad_blocks.cl index 00cb2e133..1f65c168f 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/convolution_gpu_mmad_blocks.cl +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/convolution_gpu_mmad_blocks.cl @@ -13,7 +13,7 @@ // limitations under the License. #include "include/common.cl" -#include "include/activation_functions.cl" + #include "include/data_types.cl" #include "include/fetch.cl" #include "include/mmad.cl" diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/convolution_gpu_winograd_2x3_s1_fused.cl b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/convolution_gpu_winograd_2x3_s1_fused.cl index 9779d8812..1623a9559 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/convolution_gpu_winograd_2x3_s1_fused.cl +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/convolution_gpu_winograd_2x3_s1_fused.cl @@ -19,7 +19,7 @@ // -------------------------------------------------------------------------------------------------------------------------------- #include "include/data_types.cl" -#include "include/activation_functions.cl" + #define DOT4i0( _result, _A, _B, i) \ { \ diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/convolution_gpu_winograd_6x3_s1_fused.cl b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/convolution_gpu_winograd_6x3_s1_fused.cl index eee7219ac..2dca091f3 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/convolution_gpu_winograd_6x3_s1_fused.cl +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/convolution_gpu_winograd_6x3_s1_fused.cl @@ -18,8 +18,9 @@ // Output matrix dimensions: M x N // -------------------------------------------------------------------------------------------------------------------------------- +#include "include/common.cl" #include "include/data_types.cl" -#include "include/activation_functions.cl" + #define DOT8i_0( _result, _A, _B, i) \ { \ @@ -66,9 +67,9 @@ _result = mad(_A.s7, sub_group_broadcast( _B.s7, i), _result); \ } -#define UNIT_TYPE_2 CAT(UNIT_TYPE, 2) -#define UNIT_TYPE_4 CAT(UNIT_TYPE, 4) -#define UNIT_TYPE_8 CAT(UNIT_TYPE, 8) +#define UNIT_TYPE_2 MAKE_VECTOR_TYPE(UNIT_TYPE, 2) +#define UNIT_TYPE_4 MAKE_VECTOR_TYPE(UNIT_TYPE, 4) +#define UNIT_TYPE_8 MAKE_VECTOR_TYPE(UNIT_TYPE, 8) __attribute__((reqd_work_group_size(16, 1, 8))) diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/convolution_grad_weights_1x1.cl b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/convolution_grad_weights_1x1.cl index b1a9709f6..0066ddd61 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/convolution_grad_weights_1x1.cl +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/convolution_grad_weights_1x1.cl @@ -85,7 +85,7 @@ KERNEL(convolution_grad_weights_gpu_1x1)( #if BIAS_TERM result = fma(input[input_idx], grad, result); #else - if(!grad_zero_x && !grad_zero_y) + if(!grad_zero) { uint input_grad_idx = grad_split_offset + b*INPUT0_BATCH_PITCH + ofm*INPUT0_FEATURE_PITCH + j*INPUT0_X_PITCH*16 + local_id*INPUT0_X_PITCH + i*INPUT0_Y_PITCH; result = fma(input[input_idx], input_grad[input_grad_idx], result); @@ -124,7 +124,7 @@ KERNEL(convolution_grad_weights_gpu_1x1)( if(ifm == 0) { #if MOMENTUM - UNIT_TYPE update_gradient_b = lr * (prev_grad_b[ofm] * MOMENTUM_FACTOR + grad_b); + UNIT_TYPE update_gradient_b = lr * grad_b + prev_grad_b[ofm] * MOMENTUM_FACTOR; bias[ofm] -= update_gradient_b; prev_grad_b[ofm] = update_gradient_b; #else diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/convolution_grad_weights_3x3.cl b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/convolution_grad_weights_3x3.cl new file mode 100644 index 000000000..df6a45957 --- /dev/null +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/convolution_grad_weights_3x3.cl @@ -0,0 +1,182 @@ +// Copyright (c) 2018 Intel Corporation +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#include "include/include_all.cl" + +KERNEL(convolution_grad_weights_gpu_3x3)( + const __global UNIT_TYPE* input_grad, + __global UNIT_TYPE* output, + __global UNIT_TYPE* filter, +#if BIAS_TERM + __global UNIT_TYPE* bias, +#endif +#if MOMENTUM + __global UNIT_TYPE* prev_grad_w, +#if BIAS_TERM + __global UNIT_TYPE* prev_grad_b, +#endif +#endif + const __global UNIT_TYPE* input, + uint split_idx, + float lr) +{ + const uint ofm = get_global_id(0); + const uint ifm = get_global_id(1); + + if (ofm >= INPUT0_FEATURE_NUM || ifm >= INPUT1_FEATURE_NUM) + return; + + const int in_x = -PADDING_SIZE_X; + const int in_y = -PADDING_SIZE_Y; + + ACCUMULATOR_TYPE grad_w[9] = {}; +#if BIAS_TERM + ACCUMULATOR_TYPE grad_b = 0; +#endif + + uint weights_idx = ofm * FILTER_OFM_PITCH + ifm * FILTER_IFM_PITCH; + + for(int b = 0; b < INPUT0_BATCH_NUM; b++) + { + const uint grad_split_offset = split_idx * INPUT0_FEATURE_PITCH * FILTER_OFM_NUM; + const uint in_split_offset = split_idx * INPUT1_FEATURE_PITCH * FILTER_IFM_NUM; + + for (uint i = 0; i < INPUT0_SIZE_Y; i++) + { + + for (uint j = 0; j < INPUT0_SIZE_X; j+=2) + { + float2 grad; + if (j + 1 >= INPUT0_SIZE_X) + { + uint input_grad_idx = grad_split_offset + b*INPUT0_BATCH_PITCH + ofm*INPUT0_FEATURE_PITCH + j*INPUT0_X_PITCH + i*INPUT0_Y_PITCH; + grad.s0 = input_grad[input_grad_idx]; + grad.s1 = 0; + } + else + { + uint input_grad_idx = grad_split_offset + b*INPUT0_BATCH_PITCH + ofm*INPUT0_FEATURE_PITCH + j*INPUT0_X_PITCH + i*INPUT0_Y_PITCH; + grad = vload2(0, &input_grad[input_grad_idx]); + } + for (uint y = 0; y < 3; y++) + { + const int input_offset_y = in_y + y + i; + const bool zero_y = input_offset_y >= INPUT1_SIZE_Y || input_offset_y < 0; + const int input_offset_x = in_x + j; + const bool zero_x = input_offset_x < 0 || input_offset_x + 3 >= INPUT1_SIZE_X; + uint input_idx = in_split_offset + b*INPUT1_BATCH_PITCH + ifm*INPUT1_FEATURE_PITCH + (uint)input_offset_x*INPUT1_X_PITCH + (uint)input_offset_y*INPUT1_Y_PITCH; + union v4 { + float s[4]; + float4 v; + }; + union v4 inp; + if (zero_y) + continue; + if (zero_x) + { + for (uint k = 0; k < 4; k++) + { + if (input_offset_x + k >= INPUT1_SIZE_X || input_offset_x + k < 0) + inp.s[k] = 0; + else + inp.s[k] = input[input_idx + k]; + } + } + else + { + inp.v = vload4(0, &input[input_idx]); + } + for (uint x = 0; x < 3; x++) + { + grad_w[y * 3 + x] = mad(inp.s[x] * lr, grad.s0, grad_w[y * 3 + x]); + grad_w[y * 3 + x] = mad(inp.s[x + 1] * lr, grad.s1, grad_w[y * 3 + x]); + } + } +#if BIAS_TERM + grad_b += grad.s0; + grad_b += grad.s1; +#endif + } + } + } + + union { + float s[8]; + float8 v; + } uweights_0_7; + float uweights8; + +#if MOMENTUM + float dwa[9]; + uweights_0_7.v = vload8(0, &prev_grad_w[weights_idx]); + dwa[0 * 3 + 0] = uweights_0_7.v.s0; + dwa[0 * 3 + 1] = uweights_0_7.v.s1; + dwa[0 * 3 + 2] = uweights_0_7.v.s2; + dwa[1 * 3 + 0] = uweights_0_7.v.s3; + dwa[1 * 3 + 1] = uweights_0_7.v.s4; + dwa[1 * 3 + 2] = uweights_0_7.v.s5; + dwa[2 * 3 + 0] = uweights_0_7.v.s6; + dwa[2 * 3 + 1] = uweights_0_7.v.s7; + dwa[2 * 3 + 2] = prev_grad_w[weights_idx + 8]; +#endif + + uweights_0_7.v = vload8(0, &filter[weights_idx]); + uweights8 = filter[weights_idx + 8]; + +#if MOMENTUM + float8 newDelta_0_7 = (float8)( + grad_w[0 * 3 + 0] + (MOMENTUM_FACTOR * dwa[0 * 3 + 0]), + grad_w[0 * 3 + 1] + (MOMENTUM_FACTOR * dwa[0 * 3 + 1]), + grad_w[0 * 3 + 2] + (MOMENTUM_FACTOR * dwa[0 * 3 + 2]), + grad_w[1 * 3 + 0] + (MOMENTUM_FACTOR * dwa[1 * 3 + 0]), + grad_w[1 * 3 + 1] + (MOMENTUM_FACTOR * dwa[1 * 3 + 1]), + grad_w[1 * 3 + 2] + (MOMENTUM_FACTOR * dwa[1 * 3 + 2]), + grad_w[2 * 3 + 0] + (MOMENTUM_FACTOR * dwa[2 * 3 + 0]), + grad_w[2 * 3 + 1] + (MOMENTUM_FACTOR * dwa[2 * 3 + 1])); + float newDelta8 = grad_w[2 * 3 + 2] + (MOMENTUM_FACTOR * dwa[2 * 3 + 2]); +#else + float8 newDelta_0_7 = (float8)( + grad_w[0 * 3 + 0], + grad_w[0 * 3 + 1], + grad_w[0 * 3 + 2], + grad_w[1 * 3 + 0], + grad_w[1 * 3 + 1], + grad_w[1 * 3 + 2], + grad_w[2 * 3 + 0], + grad_w[2 * 3 + 1]); + float newDelta8 = grad_w[2 * 3 + 2]; +#endif + uweights8 -= newDelta8; + uweights_0_7.v -= newDelta_0_7; + + vstore8(uweights_0_7.v, 0, &filter[weights_idx]); + filter[weights_idx + 8] = uweights8; +#if MOMENTUM + vstore8(newDelta_0_7, 0, &prev_grad_w[weights_idx]); + prev_grad_w[weights_idx + 8] = newDelta8; +#endif + +#if BIAS_TERM + if(ifm == 0) + { +#if MOMENTUM + UNIT_TYPE update_gradient_b = lr * grad_b + prev_grad_b[ofm] * MOMENTUM_FACTOR; + bias[ofm] -= update_gradient_b; + prev_grad_b[ofm] = update_gradient_b; +#else + bias[ofm] -= lr * grad_b; +#endif + } +#endif +} diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/convolution_grad_weights_7x7.cl b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/convolution_grad_weights_7x7.cl new file mode 100644 index 000000000..e5d9fde84 --- /dev/null +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/convolution_grad_weights_7x7.cl @@ -0,0 +1,105 @@ +// Copyright (c) 2018 Intel Corporation +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#include "include/include_all.cl" + +KERNEL(convolution_grad_weights_gpu_7x7)( + const __global UNIT_TYPE* input_grad, + __global UNIT_TYPE* output, + __global UNIT_TYPE* filter, +#if BIAS_TERM + __global UNIT_TYPE* bias, +#endif +#if MOMENTUM + __global UNIT_TYPE* prev_grad_w, +#if BIAS_TERM + __global UNIT_TYPE* prev_grad_b, +#endif +#endif + const __global UNIT_TYPE* input, + uint split_idx, + float lr) +{ + const uint x_filter = get_global_id(0); + const uint ofm = get_global_id(1); + const uint ifm = get_global_id(2); + + if (x_filter >= 7 || ofm >= INPUT0_FEATURE_NUM || ifm >= INPUT1_FEATURE_NUM) + return; + + const int in_x = -PADDING_SIZE_X; + const int in_y = -PADDING_SIZE_Y; + + ACCUMULATOR_TYPE grad_w[7] = { 0, 0, 0, 0, 0, 0, 0 }; +#if BIAS_TERM + ACCUMULATOR_TYPE grad_b = UNIT_VAL_ZERO; +#endif + + uint weights_idx = ofm * FILTER_OFM_PITCH + ifm * FILTER_IFM_PITCH; + + for(int b = 0; b < INPUT0_BATCH_NUM; b++) + { + const uint grad_split_offset = split_idx * INPUT0_FEATURE_PITCH * FILTER_OFM_NUM; + const uint in_split_offset = split_idx * INPUT1_FEATURE_PITCH * FILTER_IFM_NUM; + + for(int i = 0; i < INPUT0_SIZE_Y; i++) + { + for(int j = 0; j < INPUT0_SIZE_X; j++) + { + float grad; + uint input_grad_idx = grad_split_offset + b*INPUT0_BATCH_PITCH + ofm*INPUT0_FEATURE_PITCH + j*INPUT0_X_PITCH + i*INPUT0_Y_PITCH; + grad = input_grad[input_grad_idx]; + for(uint y_filter = 0; y_filter < 7; y_filter++) + { + const int input_offset_y = in_y + y_filter + i * STRIDE_SIZE_Y; + const bool zero_y = input_offset_y >= INPUT1_SIZE_Y || input_offset_y < 0; + const int input_offset_x = in_x + x_filter + j * STRIDE_SIZE_X; + const bool zero_x = input_offset_x < 0 || input_offset_x >= INPUT1_SIZE_X; + uint input_idx = in_split_offset + b*INPUT1_BATCH_PITCH + ifm*INPUT1_FEATURE_PITCH + (uint)input_offset_x*INPUT1_X_PITCH + (uint)input_offset_y*INPUT1_Y_PITCH; + if(!zero_x && !zero_y) + { + const float delta_f = input[input_idx] * lr * grad; + grad_w[y_filter] += delta_f; + } + } +#if BIAS_TERM + grad_b += grad; +#endif + } + } + } + for(uint y_filter = 0; y_filter < 7; y_filter++) + { + uint address = weights_idx + 48 - (7 * (6 - y_filter) + (6 - x_filter)); +#if MOMENTUM + float dw = prev_grad_w[address]; + const float delta_f_m = MOMENTUM_FACTOR * dw; + grad_w[y_filter] += delta_f_m; + prev_grad_w[address] = grad_w[y_filter]; +#endif + filter[address] -= grad_w[y_filter]; + } +#if BIAS_TERM + if(ifm == 0 && x_filter == 0) + { +#if MOMENTUM + UNIT_TYPE update_gradient_b = lr * grad_b + prev_grad_b[ofm] * MOMENTUM_FACTOR; + bias[ofm] -= update_gradient_b; + prev_grad_b[ofm] = update_gradient_b; +#else + bias[ofm] -= lr * grad_b; +#endif + } +#endif +}
\ No newline at end of file diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/convolution_grad_weights_ref.cl b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/convolution_grad_weights_ref.cl index 2eb8f610a..cba96cbc3 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/convolution_grad_weights_ref.cl +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/convolution_grad_weights_ref.cl @@ -16,14 +16,14 @@ KERNEL(convolution_grad_weights_gpu_ref)( const __global UNIT_TYPE* input_grad, __global UNIT_TYPE* output, - __global UNIT_TYPE* filter, + __global float* filter, #if BIAS_TERM - __global UNIT_TYPE* bias, + __global float* bias, #endif #if MOMENTUM - __global UNIT_TYPE* prev_grad_w, + __global float* prev_grad_w, #if BIAS_TERM - __global UNIT_TYPE* prev_grad_b, + __global float* prev_grad_b, #endif #endif const __global UNIT_TYPE* input, @@ -49,10 +49,10 @@ KERNEL(convolution_grad_weights_gpu_ref)( for(int b = 0; b < INPUT0_BATCH_NUM; b++) { - UNIT_TYPE result = UNIT_VAL_ZERO; + ACCUMULATOR_TYPE result = ACCUMULATOR_TYPE_ZERO; #if BIAS_TERM - UNIT_TYPE result_bias = UNIT_VAL_ZERO; + ACCUMULATOR_TYPE result_bias = ACCUMULATOR_TYPE_ZERO; #endif const uint grad_split_offset = split_idx * INPUT0_FEATURE_PITCH * FILTER_OFM_NUM; @@ -68,16 +68,16 @@ KERNEL(convolution_grad_weights_gpu_ref)( const bool zero_x = input_offset_x >= INPUT1_SIZE_X || input_offset_x < 0; #if BIAS_TERM uint input_grad_idx = grad_split_offset + b*INPUT0_BATCH_PITCH + ofm*INPUT0_FEATURE_PITCH + j*INPUT0_X_PITCH + i*INPUT0_Y_PITCH; - UNIT_TYPE grad = input_grad[input_grad_idx]; + ACCUMULATOR_TYPE grad = TO_ACCUMULATOR_TYPE(input_grad[input_grad_idx]); #endif if(!zero_x && !zero_y) { uint input_idx = in_split_offset + b*INPUT1_BATCH_PITCH + ifm*INPUT1_FEATURE_PITCH + (uint)input_offset_x*INPUT1_X_PITCH + (uint)input_offset_y*INPUT1_Y_PITCH; #if BIAS_TERM - result = fma(input[input_idx], grad, result); + result = fma(TO_ACCUMULATOR_TYPE(input[input_idx]), grad, result); #else uint input_grad_idx = grad_split_offset + b*INPUT0_BATCH_PITCH + ofm*INPUT0_FEATURE_PITCH + j*INPUT0_X_PITCH + i*INPUT0_Y_PITCH; - result = fma(input[input_idx], input_grad[input_grad_idx], result); + result = fma(TO_ACCUMULATOR_TYPE(input[input_idx]), TO_ACCUMULATOR_TYPE(input_grad[input_grad_idx]), result); #endif } #if BIAS_TERM @@ -94,7 +94,7 @@ KERNEL(convolution_grad_weights_gpu_ref)( } #if MOMENTUM - UNIT_TYPE update_gradient_w = lr * (grad_w + DECAY_RATE * filter[weights_idx]) + prev_grad_w[weights_idx] * MOMENTUM_FACTOR; + float update_gradient_w = lr * (grad_w + DECAY_RATE * filter[weights_idx]) + prev_grad_w[weights_idx] * MOMENTUM_FACTOR; filter[weights_idx] -= update_gradient_w; prev_grad_w[weights_idx] = update_gradient_w; #else @@ -105,7 +105,7 @@ KERNEL(convolution_grad_weights_gpu_ref)( if(ifm == 0 && id_x == 0 && id_y == 0) { #if MOMENTUM - UNIT_TYPE update_gradient_b = lr * grad_b + prev_grad_b[ofm] * MOMENTUM_FACTOR; + float update_gradient_b = lr * grad_b + prev_grad_b[ofm] * MOMENTUM_FACTOR; bias[ofm] -= update_gradient_b; prev_grad_b[ofm] = update_gradient_b; #else diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/convolution_grad_weights_yxfb.cl b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/convolution_grad_weights_yxfb.cl index be35a8fb4..1d6ffeafd 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/convolution_grad_weights_yxfb.cl +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/convolution_grad_weights_yxfb.cl @@ -57,20 +57,20 @@ KERNEL(convolution_grad_weights_gpu_ref)( for(int y = 0; y < INPUT0_SIZE_Y; y++) { const int input_offset_y = in_y + y * STRIDE_SIZE_Y; - const bool zero_y = input_offset_y >= INPUT1_SIZE_Y || input_offset_y < 0; + const bool zero_y = input_offset_y >= INPUT1_SIZE_Y || input_offset_y < 0; for (uint x = 0; x < INPUT0_SIZE_X; x++) { const int input_offset_x = in_x + x * STRIDE_SIZE_X; - const bool zero_x = input_offset_x >= INPUT1_SIZE_X || input_offset_x < 0; + const bool zero_x = input_offset_x >= INPUT1_SIZE_X || input_offset_x < 0; for (uint b = 0; b < INPUT0_BATCH_NUM / 16; b++) { #if BIAS_TERM uint input_grad_idx = grad_split_offset + b*16*INPUT0_BATCH_PITCH + ofm*INPUT0_FEATURE_PITCH + x*INPUT0_X_PITCH + y*INPUT0_Y_PITCH; UNIT_TYPE grad = as_float(intel_sub_group_block_read((const __global uint*)(input_grad + input_grad_idx))); - grad_b += grad; + grad_b += grad; #endif - if(!zero_x && !zero_y) - { + if(!zero_x && !zero_y) + { uint input_idx = in_split_offset + b*16*INPUT1_BATCH_PITCH + ifm*INPUT1_FEATURE_PITCH + (uint)input_offset_x*INPUT1_X_PITCH + (uint)input_offset_y*INPUT1_Y_PITCH; #if BIAS_TERM grad_w = fma(as_float(intel_sub_group_block_read((const __global uint*)(input + input_idx))), grad, grad_w); @@ -78,8 +78,8 @@ KERNEL(convolution_grad_weights_gpu_ref)( uint input_grad_idx = grad_split_offset + b*16*INPUT0_BATCH_PITCH + ofm*INPUT0_FEATURE_PITCH + x*INPUT0_X_PITCH + y*INPUT0_Y_PITCH; grad_w = fma(as_float(intel_sub_group_block_read((const __global uint*)(input + input_idx))), as_float(intel_sub_group_block_read((const __global uint*)(input_grad + input_grad_idx))), grad_w); #endif - } - } + } + } } } diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/deconvolution_gpu_bfyx_opt.cl b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/deconvolution_gpu_bfyx_opt.cl index 659742ecf..2b2e0c962 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/deconvolution_gpu_bfyx_opt.cl +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/deconvolution_gpu_bfyx_opt.cl @@ -24,7 +24,11 @@ KERNEL(deconvolution_gpu_bfyx_opt)( #if BIAS_TERM const __global UNIT_TYPE* bias, #endif - uint split_idx) + uint split_idx +#if FUSED_ELTWISE + , const __global UNIT_TYPE* fuse_input +#endif + ) { UNIT_TYPE result = UNIT_VAL_ZERO; @@ -104,8 +108,17 @@ KERNEL(deconvolution_gpu_bfyx_opt)( #endif const uint out_split_offset = split_idx * OUTPUT_FEATURE_PITCH * FILTER_OFM_NUM; const uint dst_index = OUTPUT_OFFSET + out_split_offset + batch_offset*OUTPUT_BATCH_PITCH + ofm_offset*OUTPUT_FEATURE_PITCH + id_y*OUTPUT_Y_PITCH + id_x*OUTPUT_X_PITCH; +#if FUSED_ELTWISE + const uint fused_index = INPUT1_OFFSET + split_idx * INPUT1_FEATURE_PITCH * FILTER_OFM_NUM + batch_offset*INPUT1_BATCH_PITCH + ofm_offset*INPUT1_FEATURE_PITCH + id_y*INPUT1_Y_PITCH + id_x*INPUT1_X_PITCH; +#if !GRADIENT + output[dst_index] = ACTIVATION(result + fuse_input[fused_index], NL_M, NL_N); +#else + output[dst_index] = result + fuse_input[fused_index]; +#endif +#else output[dst_index] = ACTIVATION(result, NL_M, NL_N); +#endif } #undef ACTIVATION -#undef WORK_GROUP_GROUP_SIZE
\ No newline at end of file +#undef WORK_GROUP_GROUP_SIZE diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/deconvolution_gpu_ref.cl b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/deconvolution_gpu_ref.cl index 6fafd4afd..d2a369b22 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/deconvolution_gpu_ref.cl +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/deconvolution_gpu_ref.cl @@ -21,7 +21,11 @@ KERNEL(deconvolution_gpu_yxfb_ref)( #if BIAS_TERM const __global UNIT_TYPE* bias, #endif - uint split_idx) + uint split_idx +#if FUSED_ELTWISE + , const __global UNIT_TYPE* fuse_input +#endif + ) { UNIT_TYPE result = UNIT_VAL_ZERO; @@ -95,7 +99,17 @@ KERNEL(deconvolution_gpu_yxfb_ref)( #endif const uint out_split_offset = split_idx * OUTPUT_FEATURE_PITCH * FILTER_OFM_NUM; const uint dst_index = OUTPUT_OFFSET + out_split_offset + batch_offset*OUTPUT_BATCH_PITCH + ofm_offset*OUTPUT_FEATURE_PITCH + out_y*OUTPUT_Y_PITCH + out_x*OUTPUT_X_PITCH; +#if FUSED_ELTWISE + const uint fused_index = INPUT1_OFFSET + split_idx * INPUT1_FEATURE_PITCH * FILTER_OFM_NUM + batch_offset*INPUT1_BATCH_PITCH + ofm_offset*INPUT1_FEATURE_PITCH + out_y*INPUT1_Y_PITCH + out_x*INPUT1_X_PITCH; +#if !GRADIENT + output[dst_index] = ACTIVATION(result + fuse_input[fused_index], NL_M, NL_N); +#else + output[dst_index] = result + fuse_input[fused_index]; +#endif + +#else output[dst_index] = ACTIVATION(result, NL_M, NL_N); +#endif } #undef ACTIVATION diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/eltwise_fs_bs_yx_bsv4_fsv32.cl b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/eltwise_fs_bs_yx_bsv4_fsv32.cl new file mode 100644 index 000000000..fe5e4a8a4 --- /dev/null +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/eltwise_fs_bs_yx_bsv4_fsv32.cl @@ -0,0 +1,78 @@ +/* +// Copyright (c) 2018 Intel Corporation +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +*/ + +#include "include/include_all.cl" + +#define GET_INDEX(src) \ + GET_DATA_FS_BS_YX_BSV4_FSV32_INDEX(src, d4, d3, d2, d1) + +int16 FUNC(get_int16)(const __global UNIT_TYPE* src, uint idx) +{ + int4 int_data = as_int4(intel_sub_group_block_read4((const __global uint*)(src + idx))); + int16 to_return; + for(uint b = 0; b < 4; b++) + { + for(uint f = 0; f < 4; f++) + { + to_return[b * 4 + f] = as_char4(int_data[b])[f]; + } + } + return to_return; +} +#define GET_INPUT(A, B) FUNC_CALL(get_int16)(A, GET_INDEX(B)) + +__attribute__((intel_reqd_sub_group_size(8))) +KERNEL(eltwise_fs_bs_yx_bsv4_fsv32)( + INPUTS_DECLS + __global UNIT_TYPE* output +#if CALIBRATION_TERM + , const __global float* calibrations +#endif + ) +{ + const uint of_32_aligned = ((OUTPUT_FEATURE_NUM + 31) / 32) * 32; + const uint d1 = get_global_id(0); // X + const uint d2 = get_global_id(1); // Y + const uint d3 = (get_global_id(2) * 4) % of_32_aligned; // Feature + const uint d4 = 4 * ((get_global_id(2) * 4) / of_32_aligned); // Batch + + int16 res; + + DO_ELTWISE; + + int4 char_result; + for(uint b = 0; b < 4; b++) + { + char4 char_res; + for(uint f = 0; f < 4; f++) + { + int res_tmp = res[b * 4 + f]; + #if CALIBRATION_TERM + res_tmp = (int)round(((float)res_tmp) * calibrations[d3+f]); + #else // CALIBRATION_TERM + res_tmp = (int)round(((float)res_tmp) * O_QF); + #endif // CALIBRATION_TERM + char_res[f] = ACTIVATION(convert_char(res_tmp), NL_M, NL_N); + } + // pack 4 chars into int + char_result[b] = as_int(char_res); + } + + uint output_offset = GET_DATA_FS_BS_YX_BSV4_FSV32_INDEX(OUTPUT, d4, d3, d2, d1); + intel_sub_group_block_write4((__global uint*)(output + output_offset), as_uint4(char_result)); +} + +#undef GET_INDEX diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/embed_ref.cl b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/embed_ref.cl index b23f08a70..c51a61e3a 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/embed_ref.cl +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/embed_ref.cl @@ -13,7 +13,8 @@ // limitations under the License. -#include "include/include_all.cl" +#include "include/common.cl" +#include "include/data_types.cl" KERNEL(embed_ref)(const __global UNIT_TYPE* input0, __global UNIT_TYPE* output, __global UNIT_TYPE* weights, __global UNIT_TYPE* biases) { diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/fully_connected_gpu_MMAD.cl b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/fully_connected_gpu_MMAD.cl index 65a8149ac..dde840da7 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/fully_connected_gpu_MMAD.cl +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/fully_connected_gpu_MMAD.cl @@ -14,7 +14,7 @@ #include "include/common.cl" -#include "include/activation_functions.cl" + #include "include/data_types.cl" #include "include/fetch.cl" #include "include/mmad.cl" diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/fully_connected_gpu_bf_io_input_spatial.cl b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/fully_connected_gpu_bf_io_input_spatial.cl index e0d827da1..c01814b9a 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/fully_connected_gpu_bf_io_input_spatial.cl +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/fully_connected_gpu_bf_io_input_spatial.cl @@ -26,6 +26,7 @@ // - RELU - [0/1] Indicates that ReLU activation function should be used on output. // - NEGATIVE_SLOPE - [float] Factor for negative output values (required when ReLU is specified). +#define ACC_TYPE float __attribute__((intel_reqd_sub_group_size(16))) KERNEL (fully_connected_gpu_bf_io_input_spatial)( @@ -42,7 +43,7 @@ KERNEL (fully_connected_gpu_bf_io_input_spatial)( const uint batch_id = get_global_id(1); const uint outXIdx = batch_id * FILTER_OFM_NUM + x; - UNIT_TYPE result = UNIT_VAL_ZERO; + ACC_TYPE result = UNIT_VAL_ZERO; uint input_idx = batch_id * INPUT0_ELEMENTS_COUNT + get_sub_group_local_id(); input_idx = MULTIPLY_OFFSET(UNIT_TYPE, input_idx); @@ -66,7 +67,7 @@ KERNEL (fully_connected_gpu_bf_io_input_spatial)( s_w_idx += MULTIPLY_OFFSET(UNIT_TYPE, FILTER_OFM_NUM * 16); } input_idx -= MULTIPLY_OFFSET(UNIT_TYPE, get_sub_group_local_id()); - weight_idx += MULTIPLY_OFFSET(UNIT_TYPE, input_slices * FILTER_OFM_NUM); + weight_idx += MULTIPLY_OFFSET(UNIT_TYPE, input_slices * FILTER_OFM_NUM); for (uint i = 0; i < INPUT0_ELEMENTS_COUNT % 16; i++) { UNIT_TYPE _in = *OFFSET_GLOBAL_PTR(UNIT_TYPE, input, input_idx); @@ -81,7 +82,7 @@ KERNEL (fully_connected_gpu_bf_io_input_spatial)( #endif if(x < FILTER_OFM_NUM) { - output[x] = ACTIVATION(result, NL_M, NL_N); + output[x] = ACTIVATION((UNIT_TYPE)(result), NL_M, NL_N); } } diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/fully_connected_gpu_mmad_batched.cl b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/fully_connected_gpu_mmad_batched.cl new file mode 100644 index 000000000..fe4496ab1 --- /dev/null +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/fully_connected_gpu_mmad_batched.cl @@ -0,0 +1,115 @@ +// Copyright (c) 2016-2017 Intel Corporation +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#include "include/common.cl" + +#include "include/data_types.cl" +#include "include/fetch.cl" +#include "include/mmad.cl" + +#define FILTER_IFM_MMAD_NUM ((FILTER_IFM_NUM + 31) / 32) +#define FILTER_OFM_MMAD_NUM ((FILTER_OFM_NUM + 7) / 8) +#define FILTER_IFM_ALIGNED (FILTER_IFM_MMAD_NUM * 32) +#define FILTER_OFM_ALIGNED (FILTER_OFM_MMAD_NUM * 8) + +__attribute__((intel_reqd_sub_group_size(8))) +KERNEL(fully_connected_kernel_mmad_batched)( + const __global INPUT0_TYPE* input, + __global OUTPUT_TYPE* output, + const __global FILTER_TYPE* weights +#if BIAS_TERM + , const __global BIAS_TYPE* biases +#endif +#if QUANTIZATION_TERM + ,const __global float* quantizations +#endif +#if CALIBRATION_TERM + ,const __global float* calibrations +#endif + ) +{ + const uint sg_channel = get_sub_group_local_id(); + + const uint batch_id = (uint)get_group_id(0) * 8; + const uint b_block = batch_id / 4; + const uint f = get_global_id(1) % FILTER_OFM_ALIGNED; + + uint in_addr = IN_OFFSET + b_block * IN_B_BLOCK_PITCH; + + const uint filter_offset = (get_group_id(1) % FILTER_OFM_MMAD_NUM) * FILTER_OFM_BLOCK_PITCH; + uint filter_idx = filter_offset; + + int8 tileA; + int8 tileB; + int8 tileC = 0; + + for(uint z = 0; z < FILTER_IFM_MMAD_NUM; z++ ) + { + for (uint k = 0; k < FILTER_SIZE_X * FILTER_SIZE_Y; ++k) + { + // load A tile ( input ) + // load 8 batches 4 channels per WI, so we'll have 8x32 block + + tileA.lo = as_int4(intel_sub_group_block_read4((const __global uint*)(input + in_addr))); + tileA.hi = as_int4(intel_sub_group_block_read4((const __global uint*)(input + in_addr + IN_B_BLOCK_PITCH))); + + // load B tile ( weights ) + tileB = as_int8(intel_sub_group_block_read8((const __global uint*)(weights + filter_idx))); + + // compute C tile ( output ) + tileC = MMAD_8x8(tileA, tileB, tileC); // here we output 8 batches per workitem, and each workitem gets different output feature + + in_addr += 32 * 4; // 4 batches * 4 features per channel * 8 SIMD channels + filter_idx += 32*8; // 32 features per channel * 8 output features per SIMD channel + } + in_addr += IN_F_BLOCK_PITCH; + in_addr -= (FILTER_SIZE_X * FILTER_SIZE_Y * 32 * 4); + } + +#if BIAS_TERM +#if BIAS_PER_OUTPUT + const uint bias_index = GET_DATA_INDEX(BIAS, batch_id, f, y, x); +#elif BIAS_PER_OFM + const uint bias_index = f; +#endif + for(uint i = 0; i < 8; i++) + { +#if CALIBRATION_TERM + tileC[i] = (UNIT_TYPE)round(((float)tileC[i] * quantizations[f] * I_QF + biases[bias_index]) * calibrations[f]); +#else // CALIBRATION_TERM + tileC[i] = (UNIT_TYPE)round(((float)tileC[i] * quantizations[f] * I_QF + biases[bias_index]) * O_QF); +#endif // CALIBRATION_TERM + } +#endif // BIAS_TERM + + // save to output + if(f < FILTER_OFM_NUM) + { + for(uint i = 0; i < 8; i++) + { + const uint curr_b = batch_id + i; +#if defined OUTPUT_LAYOUT_FS_BS_YX_BSV4_FSV32 + const uint dst_index = GET_DATA_FS_BS_YX_BSV4_FSV32_INDEX(OUTPUT, curr_b, f, 0, 0); +#else + const uint dst_index = GET_DATA_INDEX(OUTPUT, curr_b, f, 0, 0); +#endif + output[dst_index] = ACTIVATION(convert_char(tileC[i]), NL_M, NL_N); + } + } +} + +#undef FILTER_IFM_MMAD_NUM +#undef FILTER_OFM_MMAD_NUM +#undef FILTER_IFM_ALIGNED +#undef FILTER_OFM_ALIGNED
\ No newline at end of file diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/fully_connected_grad_weights_gpu_ref.cl b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/fully_connected_grad_weights_gpu_ref.cl index 2e0f43d29..7b911d4f1 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/fully_connected_grad_weights_gpu_ref.cl +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/fully_connected_grad_weights_gpu_ref.cl @@ -18,14 +18,14 @@ KERNEL(fully_connected_grad_weights_gpu_ref)( const __global INPUT0_TYPE* input_grad, __global OUTPUT_TYPE* output, - __global FILTER_TYPE* weights, + __global float* weights, #if BIAS_TERM - __global UNIT_TYPE* bias, + __global float* bias, #endif #if MOMENTUM - __global UNIT_TYPE* prev_grad_w, + __global float* prev_grad_w, #if BIAS_TERM - __global UNIT_TYPE* prev_grad_b, + __global float* prev_grad_b, #endif #endif const __global INPUT1_TYPE* input, @@ -48,15 +48,15 @@ KERNEL(fully_connected_grad_weights_gpu_ref)( { const uint input_grad_idx = GET_DATA_INDEX(INPUT0, b, 0, 0, ofm); const uint input_idx = GET_DATA_INDEX(INPUT1, b, ifm, id_y, id_x); - UNIT_TYPE grad = input_grad[input_grad_idx]; - grad_w += input[input_idx] * grad; + ACCUMULATOR_TYPE grad = TO_ACCUMULATOR_TYPE(input_grad[input_grad_idx]); + grad_w += TO_ACCUMULATOR_TYPE(input[input_idx] * grad); #if BIAS_TERM - grad_b += grad; + grad_b += TO_ACCUMULATOR_TYPE(grad); #endif } #if MOMENTUM - UNIT_TYPE update_gradient_w = lr * (grad_w + DECAY_RATE * weights[filter_idx]) + prev_grad_w[filter_idx] * MOMENTUM_FACTOR; + float update_gradient_w = lr * (grad_w + DECAY_RATE * weights[filter_idx]) + prev_grad_w[filter_idx] * MOMENTUM_FACTOR; weights[filter_idx] -= update_gradient_w; prev_grad_w[filter_idx] = update_gradient_w; #else @@ -67,7 +67,7 @@ KERNEL(fully_connected_grad_weights_gpu_ref)( if(ifm == 0 && id_x == 0 && id_y == 0) { #if MOMENTUM - UNIT_TYPE update_gradient_b = lr * grad_b + prev_grad_b[ofm] * MOMENTUM_FACTOR; + float update_gradient_b = lr * grad_b + prev_grad_b[ofm] * MOMENTUM_FACTOR; bias[ofm] -= update_gradient_b; prev_grad_b[ofm] = update_gradient_b; #else diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/gemm_ref.cl b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/gemm_ref.cl new file mode 100644 index 000000000..26656ab58 --- /dev/null +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/gemm_ref.cl @@ -0,0 +1,68 @@ +// Copyright (c) 2018 Intel Corporation +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + + +#include "include/common.cl" +#include "include/data_types.cl" + +KERNEL(gemm_ref) + (const __global UNIT_TYPE* input0, + const __global UNIT_TYPE* input1, +#if OUT_BIAS_TERM + const __global UNIT_TYPE* input2, +#endif + __global UNIT_TYPE* output) +{ + const uint x = (uint)get_global_id(0); + const uint y = (uint)get_global_id(1); + const uint b = (uint)get_global_id(2); + uint in0_idx=0; + uint in1_idx=0; + float value = 0; + +#if TRANSPOSE_INPUT1 +for (uint i = 0; i < Y1; ++i) + { + in0_idx = i * X1 + x + b * X1 * Y1; +#else + for (uint i = 0; i < X1; ++i) + { + in0_idx = x * X1 + i + b * X1 * Y1; +#endif + +#if TRANSPOSE_INPUT2 + in1_idx = y * X2 + i + b * X2 * Y2; +#else + in1_idx = i * X2 + y + b * X2 * Y2; +#endif + + value = fma(input0[in0_idx], input1[in1_idx], value); + } +#if TRANSPOSE_INPUT1 && TRANSPOSE_INPUT2 + uint out_idx = y * X1 + x + b * X1 * Y2; +#elif TRANSPOSE_INPUT1 + uint out_idx = x * X2 + y + b * X1 * Y1; +#elif TRANSPOSE_INPUT2 + uint out_idx = x * Y2 + y + b * X2 * Y2; +#else + uint out_idx = x * X2 + y + b * X2 * Y1; +#endif + + float beta_out = 0; +#if OUT_BIAS_TERM + beta_out = BETA * input2[out_idx]; +#endif + output[out_idx] = fma(ALPHA, value, beta_out); +} +
\ No newline at end of file diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/include/activation_functions.cl b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/include/activation_functions.cl deleted file mode 100644 index a7d776459..000000000 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/include/activation_functions.cl +++ /dev/null @@ -1,67 +0,0 @@ -/* -// Copyright (c) 2016 Intel Corporation -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// http://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. -*/ -#include "common.cl" - -// TODO: use native_exp and use cast for APL -#define ACTIVATION_LOGISTIC(input) (UNIT_VAL_ONE/(UNIT_VAL_ONE + exp(-input))) -#define ACTIVATION_HYPERBOLIC_TAN(input) (tanh(input)) -#define ACTIVATION_RELU(input) (UNIT_MAX_FUNC(UNIT_VAL_ZERO, input)) -#define ACTIVATION_RELU_NEGATIVE_SLOPE(input, slope) isinf(TO_UNIT_TYPE(slope)) ? ((input >= UNIT_VAL_ZERO) ? \ - input : -TO_UNIT_TYPE(slope)) : \ - (UNIT_MAX_FUNC(input, UNIT_VAL_ZERO) + TO_UNIT_TYPE(slope) * UNIT_MIN_FUNC(input, UNIT_VAL_ZERO)) -#define ACTIVATION_ELU(input, alpha) (UNIT_MAX_FUNC(input, UNIT_VAL_ZERO) + \ - TO_UNIT_TYPE(alpha) * (exp(UNIT_MIN_FUNC(input, UNIT_VAL_ZERO)) - UNIT_VAL_ONE)); -#define ACTIVATION_CLAMP(input, m, n) (UNIT_MAX_FUNC(TO_UNIT_TYPE(m), UNIT_MIN_FUNC(TO_UNIT_TYPE(n), input))) -#define ACTIVATION_SOFTRELU(input) (log(UNIT_VAL_ONE + exp(input))) -#define ACTIVATION_ABS(input) (fabs(input)) -#define ACTIVATION_LINEAR(input, m, n) (m*input + n) -#define ACTIVATION_SQUARE(input) (input*input) -#define ACTIVATION_SQRT(input) (sqrt(input)) - -#define ACTIVATION_RELU_GRAD(input_grad, input) (input_grad * (input > UNIT_VAL_ZERO ? TO_UNIT_TYPE(1) : TO_UNIT_TYPE(0))) -#define ACTIVATION_RELU_NEGATIVE_SLOPE_GRAD(input_grad, input, slope) (input_grad * ((input > UNIT_VAL_ZERO ? TO_UNIT_TYPE(1) : TO_UNIT_TYPE(0)) + TO_UNIT_TYPE(slope) * (input <= 0 ? TO_UNIT_TYPE(1) : TO_UNIT_TYPE(0)))) - -#if defined ACTIVATION_FUNCTION_LOGISTIC - #define ACTIVATION(input, m, n) ACTIVATION_LOGISTIC(input) -#elif defined ACTIVATION_FUNCTION_HYPERBOLIC_TAN - #define ACTIVATION(input, m, n) ACTIVATION_HYPERBOLIC_TAN(input) -#elif defined ACTIVATION_FUNCTION_RELU - #define ACTIVATION(input, m, n) ACTIVATION_RELU(input) -#elif defined ACTIVATION_FUNCTION_RELU_NEGATIVE_SLOPE - #define ACTIVATION(input, m, n) ACTIVATION_RELU_NEGATIVE_SLOPE(input, m) -#elif defined ACTIVATION_FUNCTION_CLAMP - #define ACTIVATION(input, m, n) ACTIVATION_CLAMP(input, m, n) -#elif defined ACTIVATION_FUNCTION_SOFTRELU - #define ACTIVATION(input, m, n) ACTIVATION_SOFTRELU(input) -#elif defined ACTIVATION_FUNCTION_ABS - #define ACTIVATION(input, m, n) ACTIVATION_ABS(input) -#elif defined ACTIVATION_FUNCTION_LINEAR - #define ACTIVATION(input, m, n) ACTIVATION_LINEAR(input, m, n) -#elif defined ACTIVATION_FUNCTION_SQUARE - #define ACTIVATION(input, m, n) ACTIVATION_SQUARE(input) -#elif defined ACTIVATION_FUNCTION_SQRT - #define ACTIVATION(input, m, n) ACTIVATION_SQRT(input) -#elif defined ACTIVATION_FUNCTION_ELU - #define ACTIVATION(input, m, n) ACTIVATION_ELU(input, m) -#elif defined ACTIVATION_FUNCTION_RELU_GRAD - #define ACTIVATION(input_grad, input, m, n) ACTIVATION_RELU_GRAD(input_grad, input) -#elif defined ACTIVATION_FUNCTION_RELU_NEGATIVE_SLOPE_GRAD - #define ACTIVATION(input_grad, input, m, n) ACTIVATION_RELU_NEGATIVE_SLOPE_GRAD(input_grad, input, m) -#elif defined ACTIVATION_FUNCTION_NONE_GRAD - #define ACTIVATION(input_grad, input, m, n) input_grad -#else - #define ACTIVATION(input, m, n) input -#endif diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/include/data_types.cl b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/include/data_types.cl index 61cc5e297..99492168d 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/include/data_types.cl +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/include/data_types.cl @@ -21,47 +21,8 @@ // TODO: currently we calculate on float32 because it's lot of "add" operation and it stuck on the value "8192.0f" #if !defined(ACCUMULATOR_TYPE) #define ACCUMULATOR_TYPE float -#endif - - -#if FP16_UNIT_USED - #ifndef UNIT_TYPE - #define UNIT_TYPE half - #endif - - #define UNIT_VAL_MAX HALF_MAX - #define UNIT_VAL_MIN -UNIT_VAL_MAX - #define UNIT_VAL_ONE 1.0h - #define UNIT_VAL_ZERO 0.0h - #define TO_UNIT_TYPE(v) convert_half(v) - #define UNIT_MAX_FUNC fmax - #define UNIT_MIN_FUNC fmin - -#elif INT8_UNIT_USED - #ifndef UNIT_TYPE - #define UNIT_TYPE char - #endif - - #define UNIT_VAL_MAX CHAR_MAX - #define UNIT_VAL_MIN -UNIT_VAL_MAX - #define UNIT_VAL_ONE (char) 1 - #define UNIT_VAL_ZERO (char) 0 - #define TO_UNIT_TYPE(v) convert_char(v) - #define UNIT_MAX_FUNC max - #define UNIT_MIN_FUNC min - -#else - #ifndef UNIT_TYPE - #define UNIT_TYPE float - #endif - - #define UNIT_VAL_MAX FLT_MAX - #define UNIT_VAL_MIN -UNIT_VAL_MAX - #define UNIT_VAL_ONE 1.0f - #define UNIT_VAL_ZERO 0.0f - #define TO_UNIT_TYPE(v) (float)(v) - #define UNIT_MAX_FUNC fmax - #define UNIT_MIN_FUNC fmin + #define TO_ACCUMULATOR_TYPE(v) (float)(v) + #define ACCUMULATOR_TYPE_ZERO 0.0f #endif // Creates vector type. diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/include/fetch.cl b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/include/fetch.cl index 42ff9f24a..582e9f525 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/include/fetch.cl +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/include/fetch.cl @@ -79,6 +79,45 @@ inline uint FUNC(get_byxf_af32_index)(uint b, uint f, uint y, uint x, uint y_pit CAT(prefix, _FEATURE_NUM), \ CAT(prefix, _OFFSET)) +inline uint FUNC(get_fs_bs_yx_bsv4_fsv32_index)(uint b, uint f, uint y, uint x, + uint x_pad_before, uint x_size, uint x_pad_after, + uint y_pad_before, uint y_size, uint y_pad_after, + uint size_f, uint size_b) +{ + const uint f_32_aligned = ((size_f + 31)/32) * 32; + const uint b_4_aligned = ((size_b + 3)/4) * 4; + const uint fsv_idx = f % 32; + const uint bsv_idx = b % 4; + const uint fs_idx = f / 32; + const uint bs_idx = b / 4; + + const uint x_pitch = 32 * 4; + const uint y_pitch = 32 * 4 * (x_pad_before + x_size + x_pad_after); + const uint bs_pitch = y_pitch * (y_pad_before + y_size + y_pad_after); + const uint fs_pitch = bs_pitch * (b_4_aligned / 4); + uint offset = x_pitch * x_pad_before + y_pitch * y_pad_before; + + size_t idx = offset + fsv_idx + bsv_idx * 32; + idx += 32*4 * x; + idx += y * y_pitch; + idx += bs_idx * bs_pitch; + idx += fs_idx * fs_pitch; + + return idx; +} + +#define GET_DATA_FS_BS_YX_BSV4_FSV32_INDEX(prefix, b, f, y, x)\ + FUNC_CALL(get_fs_bs_yx_bsv4_fsv32_index)( \ + b, f, y, x, \ + CAT(prefix, _PAD_BEFORE_SIZE_X), \ + CAT(prefix, _SIZE_X), \ + CAT(prefix, _PAD_AFTER_SIZE_X), \ + CAT(prefix, _PAD_BEFORE_SIZE_Y), \ + CAT(prefix, _SIZE_Y), \ + CAT(prefix, _PAD_AFTER_SIZE_Y), \ + CAT(prefix, _FEATURE_NUM), \ + CAT(prefix, _BATCH_NUM)) + #define GET_FILTER_INDEX(prefix, o, i, y, x) \ CAT(prefix, _OFFSET) + \ (x)*CAT(prefix, _X_PITCH) + \ @@ -210,6 +249,23 @@ inline uint FUNC(get_os_is_yx_isa8_osv8_isv4_index)(uint o, uint i, uint y, uint CAT(prefix, _OFM_NUM), \ CAT(prefix, _OFFSET)) + +inline uint FUNC(get_is_o_yx_isv32_index)(uint o, uint i, uint y, uint x, uint i_size, uint o_size, uint x_size, uint y_size) +{ + const uint i_aligned_to_32 = ((i_size + 31) / 32) * 32; + const uint i_val = i % 32; + const uint i_slice = i / 32; + const size_t idx = i_val + 32* (x + x_size * (y + y_size * (o + o_size * i_slice) ) ); + return idx; +} + +#define GET_FILTER_IS_O_YX_ISV32(prefix, o, i, y, x)\ + FUNC_CALL(get_is_o_yx_isv32_index)(\ + o, i, y, x, CAT(prefix, _IFM_NUM),\ + CAT(prefix, _OFM_NUM),\ + CAT(prefix, _SIZE_X),\ + CAT(prefix, _SIZE_Y)) + #define DECLARE_SAMPLER const sampler_t imageSampler = CLK_NORMALIZED_COORDS_FALSE | CLK_ADDRESS_CLAMP | CLK_FILTER_NEAREST #if FP16_UNIT_USED diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/include/include_all.cl b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/include/include_all.cl index 6156a7675..6b030bc31 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/include/include_all.cl +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/include/include_all.cl @@ -16,7 +16,6 @@ #include "common.cl" #include "data_types.cl" -#include "activation_functions.cl" #include "sub_group.cl" #include "reshape_dims.cl" #include "fetch.cl"
\ No newline at end of file diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/include/mmad.cl b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/include/mmad.cl index 0cbcf2585..12000752e 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/include/mmad.cl +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/include/mmad.cl @@ -37,6 +37,25 @@ inline int FUNC(mmad8)(int8 A_scalars, int8 B_vectors, int acc) return acc; } +inline int4 FUNC(mmad4x8)(int4 A_vectors, int8 B_vectors, int4 acc) +{ + int4 ret; + for(uint i = 0; i < 4; i++) + { + int8 A_scalars; + A_scalars.s0 = sub_group_broadcast(A_vectors[i], 0); + A_scalars.s1 = sub_group_broadcast(A_vectors[i], 1); + A_scalars.s2 = sub_group_broadcast(A_vectors[i], 2); + A_scalars.s3 = sub_group_broadcast(A_vectors[i], 3); + A_scalars.s4 = sub_group_broadcast(A_vectors[i], 4); + A_scalars.s5 = sub_group_broadcast(A_vectors[i], 5); + A_scalars.s6 = sub_group_broadcast(A_vectors[i], 6); + A_scalars.s7 = sub_group_broadcast(A_vectors[i], 7); + ret[i] = FUNC_CALL(mmad8)(A_scalars, B_vectors, acc[i]); + } + return ret; +} + inline int8 FUNC(mmad8x8)(int8 A_vectors, int8 B_vectors, int8 acc) { int8 ret; @@ -58,4 +77,5 @@ inline int8 FUNC(mmad8x8)(int8 A_vectors, int8 B_vectors, int8 acc) #define MMAD_8(A, B, C) FUNC_CALL(mmad8)(A, B, C) +#define MMAD_4x8(A, B, C) FUNC_CALL(mmad4x8)(A, B, C) #define MMAD_8x8(A, B, C) FUNC_CALL(mmad8x8)(A, B, C) diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/index_select_gpu_ref.cl b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/index_select_gpu_ref.cl new file mode 100644 index 000000000..9862c1a86 --- /dev/null +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/index_select_gpu_ref.cl @@ -0,0 +1,60 @@ +// Copyright (c) 2018 Intel Corporation +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#include "include/include_all.cl" + + +KERNEL(index_select_gpu_ref)( + const __global UNIT_TYPE* input, + const __global int* indices, + __global UNIT_TYPE* output) +{ + // [CONSTEXPR]: + const uint input_sx = INPUT0_SIZE_X; + const uint input_sy = INPUT0_SIZE_Y; + const uint input_sf = INPUT0_FEATURE_NUM; + const uint input_sb = INPUT0_BATCH_NUM; + + const uint out_b = (uint) get_global_id(0); + const uint indices_idx = (uint) get_global_id(1); + const uint feature_idx = (uint) get_global_id(2); + const uint indices_value = indices[indices_idx]; + + // [LOGIC]: +#ifdef INDEX_SELECT_AXIS_BATCH + for(uint x = 0; x < input_sx; x++) + { + for(uint y = 0; y < input_sy; y++) + { + output[GET_DATA_INDEX(OUTPUT, indices_idx, feature_idx, y, x)] = input[GET_DATA_INDEX(INPUT0, indices_value, feature_idx, y, x)]; + } + } +#elif defined INDEX_SELECT_AXIS_FEATURE + for(uint x = 0; x < input_sx; x++) + { + output[GET_DATA_INDEX(OUTPUT, out_b, indices_idx, feature_idx, x)] = input[GET_DATA_INDEX(INPUT0, out_b, indices_value, feature_idx, x)]; + } +#elif defined INDEX_SELECT_AXIS_X + for(uint i = 0; i < input_sx; i++) + { + output[GET_DATA_INDEX(OUTPUT, out_b, feature_idx, i, indices_idx)] = input[GET_DATA_INDEX(INPUT0, out_b, feature_idx, i, indices_value)]; + } +#elif defined INDEX_SELECT_AXIS_Y + + for(uint i = 0; i < input_sx; i++) + { + output[GET_DATA_INDEX(OUTPUT, out_b, feature_idx, indices_idx, i)] = input[GET_DATA_INDEX(INPUT0, out_b, feature_idx, indices_value, i)]; + } +#endif +}
\ No newline at end of file diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/lookup_table_axis.cl b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/lookup_table_axis.cl index 5f83af9c5..99549fc9c 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/lookup_table_axis.cl +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/lookup_table_axis.cl @@ -50,7 +50,8 @@ #endif -#include "include/include_all.cl" +#include "include/common.cl" +#include "include/data_types.cl" KERNEL(lookup_table_axis)(const __global UNIT_TYPE* input0, const __global float* indices, __global UNIT_TYPE* output) { diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/lookup_table_ref.cl b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/lookup_table_ref.cl index f223aa3e3..a8e25fd20 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/lookup_table_ref.cl +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/lookup_table_ref.cl @@ -13,7 +13,8 @@ // limitations under the License. -#include "include/include_all.cl" +#include "include/common.cl" +#include "include/data_types.cl" KERNEL(lookup_table)(const __global UNIT_TYPE* input0, const __global float* indices, __global UNIT_TYPE* output) { diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/lrn_gpu_across_channel_multiple_features.cl b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/lrn_gpu_across_channel_multiple_features.cl index 64fd93844..bccf5ca47 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/lrn_gpu_across_channel_multiple_features.cl +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/lrn_gpu_across_channel_multiple_features.cl @@ -12,8 +12,9 @@ // See the License for the specific language governing permissions and // limitations under the License. +#include "include/common.cl" +#include "include/data_types.cl" -#include "include/include_all.cl" #ifdef FORCE_SIMD_16 __attribute__((intel_reqd_sub_group_size(16))) diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/lrn_gpu_across_channel_ref.cl b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/lrn_gpu_across_channel_ref.cl index d96b32982..a603248d3 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/lrn_gpu_across_channel_ref.cl +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/lrn_gpu_across_channel_ref.cl @@ -12,8 +12,9 @@ // See the License for the specific language governing permissions and // limitations under the License. +#include "include/common.cl" +#include "include/data_types.cl" -#include "include/include_all.cl" #if FP16_UNIT_USED #define UNIT_CVT_FUNC(val) convert_half(val) diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/lrn_gpu_across_channel_yxfb_b8_opt.cl b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/lrn_gpu_across_channel_yxfb_b8_opt.cl index 74ff5d4b2..e3f214921 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/lrn_gpu_across_channel_yxfb_b8_opt.cl +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/lrn_gpu_across_channel_yxfb_b8_opt.cl @@ -12,8 +12,9 @@ // See the License for the specific language governing permissions and // limitations under the License. +#include "include/common.cl" +#include "include/data_types.cl" -#include "include/include_all.cl" #if FP16_UNIT_USED #define UNIT_CVT_FUNC(val) convert_half(val) diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/lrn_gpu_within_channel.cl b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/lrn_gpu_within_channel.cl index 6fe659549..6224a944f 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/lrn_gpu_within_channel.cl +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/lrn_gpu_within_channel.cl @@ -12,8 +12,8 @@ // See the License for the specific language governing permissions and // limitations under the License. - -#include "include/include_all.cl" +#include "include/common.cl" +#include "include/data_types.cl" KERNEL (lrn_gpu_within_channel)(const __global UNIT_TYPE* input, __global UNIT_TYPE* output) diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/lrn_gpu_within_channel_opt.cl b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/lrn_gpu_within_channel_opt.cl index 6227d4361..cadc762af 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/lrn_gpu_within_channel_opt.cl +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/lrn_gpu_within_channel_opt.cl @@ -12,8 +12,8 @@ // See the License for the specific language governing permissions and // limitations under the License. - -#include "include/include_all.cl" +#include "include/common.cl" +#include "include/data_types.cl" KERNEL (lrn_gpu_within_channel_opt)(const __global UNIT_TYPE* input, __global UNIT_TYPE* output) diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/lrn_ref.cl b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/lrn_ref.cl index 69d8739b1..f785a34b6 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/lrn_ref.cl +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/lrn_ref.cl @@ -17,7 +17,7 @@ #include "include/common.cl" #include "include/fetch.cl" #include "include/data_types.cl" -#include "include/activation_functions.cl" + KERNEL(normalization)(__global const INPUT0_TYPE* input, __global OUTPUT_TYPE* output) { diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/lstm_elt_gpu_bfyx_ref.cl b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/lstm_elt_gpu_bfyx_ref.cl index d0212e9bb..f4d8f723a 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/lstm_elt_gpu_bfyx_ref.cl +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/lstm_elt_gpu_bfyx_ref.cl @@ -15,9 +15,12 @@ #include "include/include_all.cl" -// tempGEMM = [ 1, direction, batch, 4 * hidden_size ] -// cell = [ 1, direction, batch, hidden_size ] optional -// output = [ 2, direction, batch, hidden_size ] output +#define ACTIVATION_LOGISTIC(input) (UNIT_VAL_ONE/(UNIT_VAL_ONE + exp(-input))) +#define ACTIVATION_HYPERBOLIC_TAN(input) (tanh(input)) + +// tempGEMM = [ batch, direction, 1, 4 * hidden_size ] +// cell = [ batch, direction, 1, hidden_size ] optional +// output = [ batch, direction, 2, hidden_size ] output KERNEL(lstm_elt)( const __global INPUT0_TYPE* input, __global OUTPUT_TYPE* output diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/lstm_gemm_gpu_bfyx_ref.cl b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/lstm_gemm_gpu_bfyx_ref.cl index 6c0d549a9..39800750a 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/lstm_gemm_gpu_bfyx_ref.cl +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/lstm_gemm_gpu_bfyx_ref.cl @@ -15,12 +15,16 @@ #include "include/include_all.cl" -// input = [ 1, sequence, batch, input_size ] +#ifndef DIRECTION +#define DIRECTION 0 +#endif + +// input = [ batch, sequence, 1, input_size ] // weights = [ 1, direction, 4 * hidden_size, input_size ] // recurrent = [ 1, direction, 4 * hidden_size, hidden_size ] // biases = [ 1, 1, direction, 4 * hidden_size ] optional -// hidden = [ 1, direction, batch, hidden_size ] optional -// tempGEMM = [ 1, direction, batch, 4 * hidden_size ] output +// hidden = [ batch, direction, 1, hidden_size ] optional +// tempGEMM = [ batch, direction, 1, 4 * hidden_size ] output KERNEL(lstm_gemm)( const __global INPUT0_TYPE* input, __global OUTPUT_TYPE* output, @@ -40,22 +44,21 @@ KERNEL(lstm_gemm)( ACCUMULATOR_TYPE dotProd = 0; for(uint x = 0; x < INPUT0_SIZE_X; ++x ) { const uint input_idx = GET_DATA_INDEX(INPUT0, b, 0, 0, x); - const uint weights_idx = GET_DATA_INDEX(WEIGHTS, 0, 0, y, x); - + const uint weights_idx = GET_DATA_INDEX(WEIGHTS, 0, DIRECTION, y, x); dotProd += (ACCUMULATOR_TYPE)(input[input_idx] * weights[weights_idx]); - } + } #if HIDDEN_TERM for(uint x = 0; x < HIDDEN_SIZE_X; ++x ) { - const uint hidden_idx = GET_DATA_INDEX(HIDDEN, b, 0, 0, x); - const uint recurrent_idx = GET_DATA_INDEX(RECURRENT, 0, 0, y, x); + const uint recurrent_idx = GET_DATA_INDEX(RECURRENT, 0, DIRECTION, y, x); dotProd += (ACCUMULATOR_TYPE)(hidden[hidden_idx] * recurrent[recurrent_idx]); } #endif #if BIAS_TERM - dotProd += (ACCUMULATOR_TYPE)biases[y]; + const uint bias_idx = GET_DATA_INDEX(BIAS, 0, 0, DIRECTION, y); + dotProd += (ACCUMULATOR_TYPE)biases[bias_idx]; #endif const uint output_idx = GET_DATA_INDEX(OUTPUT, b, 0, 0, y); output[output_idx] = (OUTPUT_TYPE)dotProd; diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/mvn_gpu_bfyx_opt.cl b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/mvn_gpu_bfyx_opt.cl index df06a369f..bbccb2b46 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/mvn_gpu_bfyx_opt.cl +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/mvn_gpu_bfyx_opt.cl @@ -12,8 +12,9 @@ // See the License for the specific language governing permissions and // limitations under the License. +#include "include/common.cl" +#include "include/data_types.cl" -#include "include/include_all.cl" #if FP16_UNIT_USED #define UNIT_CVT_FUNC(val) convert_half(val) diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/mvn_gpu_ref_accross_channels.cl b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/mvn_gpu_ref_accross_channels.cl index 1cea6a210..4a9383de6 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/mvn_gpu_ref_accross_channels.cl +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/mvn_gpu_ref_accross_channels.cl @@ -12,8 +12,9 @@ // See the License for the specific language governing permissions and // limitations under the License. +#include "include/common.cl" +#include "include/data_types.cl" -#include "include/include_all.cl" #if FP16_UNIT_USED #define UNIT_CVT_FUNC(val) convert_half(val) diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/mvn_gpu_ref_within_channels.cl b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/mvn_gpu_ref_within_channels.cl index 4e854336b..223793ae3 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/mvn_gpu_ref_within_channels.cl +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/mvn_gpu_ref_within_channels.cl @@ -12,8 +12,9 @@ // See the License for the specific language governing permissions and // limitations under the License. +#include "include/common.cl" +#include "include/data_types.cl" -#include "include/include_all.cl" #if FP16_UNIT_USED #define UNIT_CVT_FUNC(val) convert_half(val) diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/normalize_gpu_across_spatial_ref.cl b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/normalize_gpu_across_spatial_ref.cl index b055241cc..c4328400b 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/normalize_gpu_across_spatial_ref.cl +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/normalize_gpu_across_spatial_ref.cl @@ -12,8 +12,9 @@ // See the License for the specific language governing permissions and // limitations under the License. +#include "include/common.cl" +#include "include/data_types.cl" -#include "include/include_all.cl" #if FP16_UNIT_USED #define UNIT_CVT_FUNC(val) convert_half(val) diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/normalize_gpu_within_spatial_ref.cl b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/normalize_gpu_within_spatial_ref.cl index 8e6cb77e6..c4e8bd9e4 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/normalize_gpu_within_spatial_ref.cl +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/normalize_gpu_within_spatial_ref.cl @@ -12,8 +12,9 @@ // See the License for the specific language governing permissions and // limitations under the License. +#include "include/common.cl" +#include "include/data_types.cl" -#include "include/include_all.cl" #if FP16_UNIT_USED #define UNIT_CVT_FUNC(val) convert_half(val) diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/permute_ref.cl b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/permute_ref.cl index 146e70c4c..a980555cd 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/permute_ref.cl +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/permute_ref.cl @@ -12,7 +12,9 @@ // See the License for the specific language governing permissions and // limitations under the License. -#include "include/include_all.cl" +#include "include/common.cl" +#include "include/data_types.cl" + KERNEL (permute_ref)(const __global UNIT_TYPE* input, __global UNIT_TYPE* output) { diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/pooling_gpu_fs_bs_yx_bsv4_fsv32.cl b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/pooling_gpu_fs_bs_yx_bsv4_fsv32.cl new file mode 100644 index 000000000..130cd8cca --- /dev/null +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/pooling_gpu_fs_bs_yx_bsv4_fsv32.cl @@ -0,0 +1,169 @@ +// Copyright (c) 2018 Intel Corporation +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + + +#include "include/include_all.cl" + +#if MAX_POOLING + #define INIT_VAL CHAR_MIN +#elif AVG_POOLING + #define INIT_VAL 0 +#else +#error +#endif + + +inline int FUNC(apply_pooling)(int tmp, int in) +{ +#if MAX_POOLING + return max(tmp, in); +#elif AVG_POOLING + return tmp + in; +#endif +} + +__attribute__((intel_reqd_sub_group_size(8))) +KERNEL(pooling_gpu_fs_bs_yx_bsv4_fsv32)( + const __global UNIT_TYPE* input, + __global UNIT_TYPE* output) +{ + const uint x = (uint)get_global_id(0); + const uint y = (uint)get_global_id(1); + const uint bf = (uint)get_global_id(2); + // we process 4 features per workitem that's why we need to divide it + const uint aligned32_features = ((INPUT0_FEATURE_NUM + 31) / 32) * 32; + const uint f = 4 * (bf % (aligned32_features / 4)); + const uint b_block = bf / (aligned32_features / 4); + + if (x >= OUTPUT_SIZE_X) + { + return; + } + + const int offset_x = (int)x*STRIDE_SIZE_X - PADDING_SIZE_X; + const int offset_y = (int)y*STRIDE_SIZE_Y - PADDING_SIZE_Y; + + int4 result[4]; + for(uint b = 0; b < 4; b++) + { + result[b] = INIT_VAL; + } + +#ifdef CHECK_BOUNDRY + if (offset_x + POOL_SIZE_X < 0 || offset_x >= INPUT0_SIZE_X || + offset_y + POOL_SIZE_Y < 0 || offset_y >= INPUT0_SIZE_Y) + { + return; + } + +#ifdef DYNAMIC_KERNEL_DIVIDER + uint num_elementes = 0; +#endif + + const uint batch_and_feature_offset = GET_DATA_FS_BS_YX_BSV4_FSV32_INDEX(INPUT0, b_block * 4, f, 0, 0); + for(uint j = 0; j < POOL_SIZE_Y; j++) + { + int input_offset_y = offset_y + j; + bool zero_y = input_offset_y >= INPUT0_SIZE_Y || input_offset_y < 0; + if(!zero_y) + { + for(uint i = 0; i < POOL_SIZE_X; i++) + { + int input_offset_x = offset_x + i; + bool zero = input_offset_x >= INPUT0_SIZE_X || input_offset_x < 0; + if(!zero) + { + const uint input_idx = batch_and_feature_offset + input_offset_y*IN_Y_PITCH + input_offset_x*IN_X_PITCH; + + int4 int_data = as_int4(intel_sub_group_block_read4((const __global uint*)(input + input_idx))); + for(uint b = 0; b < 4; b++) + { + char4 input_data = as_char4(int_data[b]); + result[b][0] = FUNC_CALL(apply_pooling)(result[b][0], (int)input_data[0]); + result[b][1] = FUNC_CALL(apply_pooling)(result[b][1], (int)input_data[1]); + result[b][2] = FUNC_CALL(apply_pooling)(result[b][2], (int)input_data[2]); + result[b][3] = FUNC_CALL(apply_pooling)(result[b][3], (int)input_data[3]); + + } + +#ifdef DYNAMIC_KERNEL_DIVIDER + num_elementes++; +#endif + } + } + } + } +#ifdef DYNAMIC_WITH_PADDING_KERNEL_DIVIDER + const int hend = min(offset_y + POOL_SIZE_Y, INPUT0_SIZE_Y + PADDING_SIZE_Y); + const int wend = min(offset_x + POOL_SIZE_X, INPUT0_SIZE_X + PADDING_SIZE_X); + const uint num_elementes = (hend - offset_y) * (wend - offset_x); +#endif +#else + uint input_idx = GET_DATA_FS_BS_YX_BSV4_FSV32_INDEX(INPUT0, b_block * 4, f, offset_y, offset_x); + + for(uint j = 0; j < POOL_SIZE_Y; j++) + { + for(uint i = 0; i < POOL_SIZE_X; i++) + { + int4 int_data = as_int4(intel_sub_group_block_read4((const __global uint*)(input + input_idx))); + for(uint b = 0; b < 4; b++) + { + char4 input_data = as_char4(int_data[b]); + result[b][0] = FUNC_CALL(apply_pooling)(result[b][0], (int)input_data[0]); + result[b][1] = FUNC_CALL(apply_pooling)(result[b][1], (int)input_data[1]); + result[b][2] = FUNC_CALL(apply_pooling)(result[b][2], (int)input_data[2]); + result[b][3] = FUNC_CALL(apply_pooling)(result[b][3], (int)input_data[3]); + } + + input_idx += IN_X_PITCH; + } + input_idx += (IN_Y_PITCH - POOL_SIZE_X*IN_X_PITCH); + } + +#if defined(DYNAMIC_KERNEL_DIVIDER) || defined(DYNAMIC_WITH_PADDING_KERNEL_DIVIDER) + const uint num_elementes = POOL_SIZE_X*POOL_SIZE_Y; +#endif +#endif + +#if defined AVG_POOLING + #if defined(DYNAMIC_KERNEL_DIVIDER) || defined(DYNAMIC_WITH_PADDING_KERNEL_DIVIDER) + for(uint b = 0; b < 4; b++) + { + for(uint i = 0; i < 4; i++) + { + result[b][i] = convert_int(round(((float)result[b][i] / max(num_elementes, (uint)1))); + } + } + #else + for(uint b = 0; b < 4; b++) + { + for(uint i = 0; i < 4; i++) + { + result[b][i] = convert_int(round((float)result[b][i] / (int)(POOL_SIZE_Y * POOL_SIZE_X))); + } + } + #endif +#endif + +for(uint b = 0; b < 4; b++) +{ + for(uint op = 0; op < 4; op++) + { + const uint output_pos = GET_DATA_FS_BS_YX_BSV4_FSV32_INDEX(OUTPUT, b_block*4 + b, f+op, y, x); + output[output_pos] = ACTIVATION(convert_char(result[b][op]), NL_M ,NL_N); + } +} +} + +#undef INIT_VAL
\ No newline at end of file diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/region_yolo_gpu_ref.cl b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/region_yolo_gpu_ref.cl index 41ad9738c..de62864f8 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/region_yolo_gpu_ref.cl +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/region_yolo_gpu_ref.cl @@ -12,7 +12,8 @@ // See the License for the specific language governing permissions and // limitations under the License. -#include "include/include_all.cl" +#include "include/common.cl" +#include "include/data_types.cl" #define IW INPUT0_SIZES[0] #define IH INPUT0_SIZES[1] diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/reorder_data.cl b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/reorder_data.cl index 66b77224d..591a07c0c 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/reorder_data.cl +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/reorder_data.cl @@ -15,7 +15,7 @@ #include "include/reshape_dims.cl" #include "include/fetch.cl" -#include "include/activation_functions.cl" + #include "include/data_types.cl" ///////////////////////// Input Index ///////////////////////// @@ -30,6 +30,8 @@ inline uint FUNC(get_input_index)(uint b, uint f, uint y, uint x) return GET_DATA_BF8_XY16_INDEX(INPUT0, b, f, y, x); #elif defined INPUT0_LAYOUT_BYXF_AF32 return GET_DATA_BYXF_AF32_INDEX(INPUT0, b, f, y, x); +#elif defined INPUT0_LAYOUT_FS_BS_YX_BSV4_FSV32 + return GET_DATA_FS_BS_YX_BSV4_FSV32_INDEX(INPUT0, b, f, y, x); #else #error reorder_data.cl: input format - not supported #endif @@ -48,6 +50,8 @@ inline uint FUNC(get_output_index)(uint b, uint f, uint y, uint x) return GET_DATA_BF8_XY16_INDEX(OUTPUT, b, f, y, x); #elif defined OUTPUT_LAYOUT_BYXF_AF32 return GET_DATA_BYXF_AF32_INDEX(OUTPUT, b, f, y, x); +#elif defined OUTPUT_LAYOUT_FS_BS_YX_BSV4_FSV32 + return GET_DATA_FS_BS_YX_BSV4_FSV32_INDEX(OUTPUT, b, f, y, x); #else #error reorder_data.cl: output format - not supported #endif diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/reorder_data_fast_b1.cl b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/reorder_data_fast_b1.cl index 3cacb1dae..af494888d 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/reorder_data_fast_b1.cl +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/reorder_data_fast_b1.cl @@ -16,7 +16,7 @@ #include "include/fetch.cl" #include "include/data_types.cl" #include "include/reshape_dims.cl" -#include "include/activation_functions.cl" + ///////////////////////// Input Index ///////////////////////// inline uint FUNC(get_input_index)(uint b, uint f, uint y, uint x) diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/reorder_data_to_yxfb_batched.cl b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/reorder_data_to_yxfb_batched.cl index 551afc5a6..76006fad6 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/reorder_data_to_yxfb_batched.cl +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/reorder_data_to_yxfb_batched.cl @@ -15,7 +15,7 @@ #include "include/reshape_dims.cl" #include "include/fetch.cl" -#include "include/activation_functions.cl" + #include "include/data_types.cl" ///////////////////////// Input Index ///////////////////////// diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/reorder_weights.cl b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/reorder_weights.cl index fa95844c1..33a662a12 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/reorder_weights.cl +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/reorder_weights.cl @@ -36,6 +36,8 @@ inline uint FUNC(get_input_index)(uint o, uint i, uint y, uint x) #error - not supported yet #elif defined INPUT0_LAYOUT_OS_IS_YX_ISA8_OSV8_ISV4 return GET_FILTER_OS_IS_YX_ISA8_OSV8_ISV4(INPUT0, o, i, y, x); +#elif defined INPUT0_LAYOUT_IS_O_YX_ISV32 + return GET_FILTER_IS_O_YX_ISV32(INPUT0, o, i, y, x); #else #error reorder_weights.cl: input format - not supported #endif @@ -62,6 +64,8 @@ inline uint FUNC(get_output_index)(uint o, uint i, uint y, uint x) return 0; //will not be used for images #elif defined OUTPUT_LAYOUT_OS_IS_YX_ISA8_OSV8_ISV4 return GET_FILTER_OS_IS_YX_ISA8_OSV8_ISV4(OUTPUT, o, i, y, x); +#elif defined OUTPUT_LAYOUT_IS_O_YX_ISV32 + return GET_FILTER_IS_O_YX_ISV32(OUTPUT, o, i, y, x); #else #error reorder_weights.cl: output format - not supported #endif diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/reorg_yolo_gpu_ref.cl b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/reorg_yolo_gpu_ref.cl index c4c74026d..47bdf2ae4 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/reorg_yolo_gpu_ref.cl +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/reorg_yolo_gpu_ref.cl @@ -12,7 +12,9 @@ // See the License for the specific language governing permissions and // limitations under the License. -#include "include/include_all.cl" +#include "include/common.cl" +#include "include/data_types.cl" + #if OUTPUT_LAYOUT_BFYX #define IW INPUT0_SIZES[0] #define IH INPUT0_SIZES[1] diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/reshape_ref.cl b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/reshape_ref.cl index 0767707b5..870ec87bd 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/reshape_ref.cl +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/reshape_ref.cl @@ -12,7 +12,9 @@ // See the License for the specific language governing permissions and // limitations under the License. -#include "include/include_all.cl" +#include "include/common.cl" +#include "include/data_types.cl" + KERNEL (reshape_ref)(const __global UNIT_TYPE* input, __global UNIT_TYPE* output) { diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/roi_pooling_ref.cl b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/roi_pooling_ref.cl index 57e87ce72..6df286d73 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/roi_pooling_ref.cl +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/roi_pooling_ref.cl @@ -1,4 +1,4 @@ -// Copyright (c) 2016-2017 Intel Corporation +// Copyright (c) 2016-2018 Intel Corporation // // Licensed under the Apache License, Version 2.0 (the "License"); // you may not use this file except in compliance with the License. @@ -12,8 +12,8 @@ // See the License for the specific language governing permissions and // limitations under the License. - -#include "include/include_all.cl" +#include "include/common.cl" +#include "include/data_types.cl" /**************************************************************************** @@ -77,6 +77,46 @@ KERNEL(roi_pooling_gpu) // the pooled data is limited by its dimensions. (Is this clear?) const __global UNIT_TYPE * roi_ptr = &src_rois[PITCH_ROI_R * r]; + +#if BILINEAR_POOLING + const uint output_offset = OUTPUT_OFFSET + x*OUTPUT_X_PITCH + y*OUTPUT_Y_PITCH + c*OUTPUT_FEATURE_PITCH + r*OUTPUT_ROI_PITCH; + + COORD_T roi_start_w = roi_ptr[1]; + COORD_T roi_start_h = roi_ptr[2]; + COORD_T roi_end_w = roi_ptr[3]; + COORD_T roi_end_h = roi_ptr[4]; + + COORD_T height_scale = (roi_end_h - roi_start_h) * (SRC_H - 1) / (COORD_T)(POOLED_HEIGHT - 1); + COORD_T width_scale = (roi_end_w - roi_start_w) * (SRC_W - 1) / (COORD_T)(POOLED_WIDTH - 1); + + COORD_T in_y = y*height_scale + roi_start_h*(COORD_T)(SRC_H - 1); + COORD_T in_x = x*width_scale + roi_start_w*(COORD_T)(SRC_W - 1); + + if (in_y < 0 || in_y > (COORD_T)(SRC_H - 1) || in_x < 0 || in_x > (COORD_T)(SRC_W - 1) || roi_ptr[0] == -1) { + dst_data[output_offset] = ACTIVATION((UNIT_TYPE)0, NL_M, NL_N); + return; + } + + int top_y_index = (int)(floor(in_y)); + int bottom_y_index = (int)(min(ceil(in_y), (COORD_T)SRC_H - 1)); + int left_x_index = (int)(floor(in_x)); + int right_x_index = (int)(min(ceil(in_x), (COORD_T)SRC_W - 1)); + + const __global UNIT_TYPE* data = src_data + INPUT0_OFFSET + INPUT0_FEATURE_PITCH*c; + + ACCUM_T top_left = (ACCUM_T)data[top_y_index*INPUT0_Y_PITCH + left_x_index*INPUT0_X_PITCH]; + ACCUM_T top_right = (ACCUM_T)data[top_y_index*INPUT0_Y_PITCH + right_x_index*INPUT0_X_PITCH]; + ACCUM_T bottom_left = (ACCUM_T)data[bottom_y_index*INPUT0_Y_PITCH + left_x_index*INPUT0_X_PITCH]; + ACCUM_T bottom_right = (ACCUM_T)data[bottom_y_index*INPUT0_Y_PITCH + right_x_index*INPUT0_X_PITCH]; + + ACCUM_T top = top_left + (top_right - top_left) * (in_x - left_x_index); + ACCUM_T bottom = bottom_left + (bottom_right - bottom_left) * (in_x - left_x_index); + + ACCUM_T res = top + (bottom - top) * (in_y - top_y_index); + + dst_data[output_offset] = ACTIVATION((UNIT_TYPE)res, NL_M, NL_N); +#else + #if USE_OLD_SCALE_AND_ROUNDING const int roi_x = round(roi_ptr[1] * SPATIAL_SCALE); const int roi_y = round(roi_ptr[2] * SPATIAL_SCALE); @@ -136,7 +176,7 @@ KERNEL(roi_pooling_gpu) #if 0 const COORD_T group_bin_w = (COORD_T)roi_w / DST_W; const COORD_T group_bin_h = (COORD_T)roi_h / DST_H; - + const uint group_x = CLAMP(x * group_bin_w, 0, GROUP_SIZE - 1); const uint group_y = CLAMP(y * group_bin_h, 0, GROUP_SIZE - 1); #else @@ -147,25 +187,34 @@ KERNEL(roi_pooling_gpu) const uint work_c = group_x + GROUP_SIZE * (group_y + GROUP_SIZE * c); #endif - const __global UNIT_TYPE * data = src_data + INPUT0_OFFSET + INPUT0_FEATURE_PITCH*work_c; + const __global UNIT_TYPE* data = src_data + INPUT0_OFFSET + INPUT0_FEATURE_PITCH*work_c; - ACCUM_T res = MAX_POOL && x_begin < x_after && y_begin < y_after ? UNIT_VAL_MIN : 0; +#if MAX_POOLING + ACCUM_T res = x_begin < x_after && y_begin < y_after ? UNIT_VAL_MIN : 0; +#else + ACCUM_T res = 0; +#endif for (int yy = y_begin; yy < y_after; ++yy) for (int xx = x_begin; xx < x_after; ++xx) { UNIT_TYPE val = data[xx*INPUT0_X_PITCH + yy*INPUT0_Y_PITCH]; - - res = MAX_POOL ? MAX(res, (ACCUM_T)val) : res + (ACCUM_T)val; +#if MAX_POOLING + res = MAX(res, (ACCUM_T)val); +#else + res = res + (ACCUM_T)val; +#endif } - if (!MAX_POOL) +#if (!MAX_POOLING) { //TODO(ruv): again, differs from the standard fixed size area (?) const COORD_T area = (y_after - y_begin) * (x_after - x_begin); if (area) res /= area; } +#endif const uint output_offset = OUTPUT_OFFSET + x*OUTPUT_X_PITCH + y*OUTPUT_Y_PITCH + c*OUTPUT_FEATURE_PITCH + r*OUTPUT_ROI_PITCH; dst_data[output_offset] = ACTIVATION((UNIT_TYPE)res, NL_M, NL_N); -}
\ No newline at end of file +#endif +} diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/scale_grad_weights_gpu_ref.cl b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/scale_grad_weights_gpu_ref.cl index 28b5af386..fde8e9733 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/scale_grad_weights_gpu_ref.cl +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/scale_grad_weights_gpu_ref.cl @@ -21,14 +21,14 @@ KERNEL(scale_grad_weights_gpu_ref)( const __global UNIT_TYPE* input, const __global UNIT_TYPE* input_grad, __global OUTPUT_TYPE* output, - __global UNIT_TYPE* scale, + __global float* scale, #if BIAS_TERM - __global UNIT_TYPE* bias, + __global float* bias, #endif #if MOMENTUM - __global UNIT_TYPE* prev_grad_w, + __global float* prev_grad_w, #if BIAS_TERM - __global UNIT_TYPE* prev_grad_b, + __global float* prev_grad_b, #endif #endif const float lr @@ -48,9 +48,9 @@ KERNEL(scale_grad_weights_gpu_ref)( { for (uint x = 0; x < INPUT0_SIZE_X; x++) { - UNIT_TYPE in_g = input_grad[grad_idx]; + ACCUMULATOR_TYPE in_g = TO_ACCUMULATOR_TYPE(input_grad[grad_idx]); grad_sum[local_idx] += in_g * lr; - grad_sum_in[local_idx] += in_g * input[grad_idx] * lr; + grad_sum_in[local_idx] += in_g * TO_ACCUMULATOR_TYPE(input[grad_idx]) * lr; grad_idx += INPUT0_X_PITCH; } grad_idx += INPUT0_Y_PITCH - INPUT0_SIZE_X * INPUT0_X_PITCH; @@ -71,7 +71,7 @@ KERNEL(scale_grad_weights_gpu_ref)( if (local_idx == 0) { #if MOMENTUM - UNIT_TYPE update_gradient_w = grad_sum_in[0] + prev_grad_w[f] * MOMENTUM_FACTOR + DECAY_RATE * lr * scale[f]; + ACCUMULATOR_TYPE update_gradient_w = grad_sum_in[0] + prev_grad_w[f] * MOMENTUM_FACTOR + DECAY_RATE * lr * scale[f]; scale[f] -= update_gradient_w; prev_grad_w[f] = update_gradient_w; #else @@ -80,7 +80,7 @@ KERNEL(scale_grad_weights_gpu_ref)( #if BIAS_TERM #if MOMENTUM - UNIT_TYPE update_gradient_b = prev_grad_b[f] * MOMENTUM_FACTOR + grad_sum[0]; + ACCUMULATOR_TYPE update_gradient_b = prev_grad_b[f] * MOMENTUM_FACTOR + grad_sum[0]; bias[f] -= update_gradient_b; prev_grad_b[f] = update_gradient_b; #else diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/select_gpu_ref.cl b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/select_gpu_ref.cl new file mode 100644 index 000000000..d1f0d02e6 --- /dev/null +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/select_gpu_ref.cl @@ -0,0 +1,51 @@ +/* +// Copyright (c) 2018 Intel Corporation +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +*/ + +#include "include/include_all.cl" + +#define GET_INDEX(prefix, num) \ + CAT(CAT(prefix, num), _OFFSET) + \ + (d1 % CAT(CAT(prefix, num), _SIZES)[0])*CAT(CAT(prefix, num), _PITCHES)[0] + \ + (d2 % CAT(CAT(prefix, num), _SIZES)[1])*CAT(CAT(prefix, num), _PITCHES)[1] + \ + (d3 % CAT(CAT(prefix, num), _SIZES)[2])*CAT(CAT(prefix, num), _PITCHES)[2] + \ + (d4 % CAT(CAT(prefix, num), _SIZES)[3])*CAT(CAT(prefix, num), _PITCHES)[3] + +#define INPUT_0 input0[GET_INDEX(INPUT, 0)] +#define INPUT_1 input1[GET_INDEX(INPUT, 1)] +#define INPUT_2 input2[GET_INDEX(INPUT, 2)] + +KERNEL(select)( + INPUTS_DECLS + __global OUTPUT_TYPE* output) +{ + +const uint d1 = (uint) get_global_id(0); +const uint d2 = (uint) get_global_id(1); +const uint d34 = (uint) get_global_id(2); + +const uint d3 = d34 % OUTPUT_SIZES[2]; +const uint d4 = d34 / OUTPUT_SIZES[2]; + +uint output_offset = OUTPUT_OFFSET + + d1*OUTPUT_PITCHES[0] + + d2*OUTPUT_PITCHES[1] + + d3*OUTPUT_PITCHES[2] + + d4*OUTPUT_PITCHES[3]; + +const OUTPUT_TYPE res = select(INPUT_1, INPUT_0, MASK); + +output[output_offset] = res; +} diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/softmax_gpu_bf.cl b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/softmax_gpu_bf.cl index 2f7b1ceec..2a1be2567 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/softmax_gpu_bf.cl +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/softmax_gpu_bf.cl @@ -12,8 +12,9 @@ // See the License for the specific language governing permissions and // limitations under the License. +#include "include/common.cl" +#include "include/data_types.cl" -#include "include/include_all.cl" __attribute__((reqd_work_group_size(LWS, 1, 1))) KERNEL (softmax_gpu_continoues_bfyx)(const __global UNIT_TYPE* input, __global UNIT_TYPE* output) diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/softmax_gpu_fb.cl b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/softmax_gpu_fb.cl index 2cd6878bf..b1c6cf6d8 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/softmax_gpu_fb.cl +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/softmax_gpu_fb.cl @@ -12,8 +12,9 @@ // See the License for the specific language governing permissions and // limitations under the License. +#include "include/common.cl" +#include "include/data_types.cl" -#include "include/include_all.cl" UNIT_TYPE FUNC(find_max_value)(__local UNIT_TYPE* partial_max, const int global_id, const int idx, const int batch_offset, const int data_sets_count, const __global UNIT_TYPE* input) { diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/softmax_gpu_items_class_optimized.cl b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/softmax_gpu_items_class_optimized.cl index 158b86dca..368405119 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/softmax_gpu_items_class_optimized.cl +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/softmax_gpu_items_class_optimized.cl @@ -14,10 +14,10 @@ // limitations under the License. */ +#include "include/common.cl" +#include "include/data_types.cl" -#include "include/include_all.cl" - #define DATA_PER_WORKITEM ( (INPUT0_CLASS_NUM + (WORKITEMS_PER_CLASSES - 1) ) / WORKITEMS_PER_CLASSES) #define FULL_ITERATIONS_NUM (INPUT0_CLASS_NUM / WORKITEMS_PER_CLASSES) diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/softmax_gpu_ref.cl b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/softmax_gpu_ref.cl index 0ef4dbcc7..135888025 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/softmax_gpu_ref.cl +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/softmax_gpu_ref.cl @@ -14,10 +14,10 @@ // limitations under the License. */ +#include "include/common.cl" +#include "include/data_types.cl" -#include "include/include_all.cl" - __attribute__((intel_reqd_sub_group_size(16))) KERNEL(softmax)(__global INPUT0_TYPE* input, __global OUTPUT_TYPE* output) { diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/tile_ref.cl b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/tile_ref.cl new file mode 100644 index 000000000..b0f561601 --- /dev/null +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/tile_ref.cl @@ -0,0 +1,49 @@ +// Copyright (c) 2018 Intel Corporation +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#include "include/common.cl" +#include "include/data_types.cl" + +#if DENSE +__attribute__((intel_reqd_sub_group_size(16))) +__attribute__((reqd_work_group_size(16, 1, 1))) +#endif +KERNEL (tile_ref)(const __global UNIT_TYPE* input, __global UNIT_TYPE* output) +{ +#if DENSE + + const uint id = get_global_id(0); + const uint group_id = id / 16; + const uint lid = get_local_id(0); + const uint idx = min((uint)(id), (uint)(OUTER_SIZE - 1)); + UNIT_TYPE val = input[idx]; + + for (int t = 0; t < TILES; t++) + { + UNIT_TYPE save_val = intel_sub_group_shuffle(val, (t*16 + lid)/TILES); + int offset = group_id*16*TILES + t*16 + lid; + if (offset < OUTPUT_SIZE) + output[offset] = save_val; + } +#else + const uint outer_idx = get_global_id(0); + const uint inner_idx = get_global_id(1); + if (inner_idx >= AXIS_PITCH) return; + + for (int t = 0; t < TILES; t++) + { + output[outer_idx*TILES*AXIS_PITCH + t*AXIS_PITCH + inner_idx] = input[outer_idx*AXIS_PITCH + inner_idx]; + } +#endif +} diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/upsampling_ref.cl b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/upsampling_ref.cl index b841e5e47..78342ada5 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/upsampling_ref.cl +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/upsampling_ref.cl @@ -14,7 +14,8 @@ // limitations under the License. */ -#include "include/include_all.cl" +#include "include/common.cl" +#include "include/data_types.cl" KERNEL (upsampling_gpu_ref)(__global UNIT_TYPE* input, __global UNIT_TYPE* output) { diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/common/common_kernel_base.cpp b/inference-engine/thirdparty/clDNN/kernel_selector/core/common/common_kernel_base.cpp index 3f949d82b..4a2344e49 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/common/common_kernel_base.cpp +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/common/common_kernel_base.cpp @@ -15,6 +15,7 @@ */ #include "common_kernel_base.h" +#include <iostream> #if defined __INTEL_COMPILER #pragma warning disable: 177 @@ -83,6 +84,7 @@ namespace kernel_selector } std::replace(kernelID.begin(), kernelID.end(), '.', '_'); + std::replace(kernelID.begin(), kernelID.end(), '/', '_'); kernelID += "_" + std::to_string(UniqeID()); @@ -143,7 +145,7 @@ namespace kernel_selector return args; } - std::shared_ptr<KernelString> common_kernel_base::GetKernelString(const std::string& name, const std::string& jit, const std::string& entry_point, const std::string& exe_mode) const + std::shared_ptr<KernelString> common_kernel_base::GetKernelString(const std::string& name, const std::string& jit, const std::string& entry_point, const EngineInfo& engine_info, const std::string& exe_mode) const { std::shared_ptr<KernelString> kernel_string = std::make_shared<KernelString>(); @@ -154,6 +156,10 @@ namespace kernel_selector kernel_string->str = codes[0]; kernel_string->jit = jit; kernel_string->options = exe_mode + " -cl-mad-enable"; + if (engine_info.bIMMADSupport) + kernel_string->options += " -DMMAD_SUPPORTED=1"; + if (engine_info.bIMADSupport) + kernel_string->options += " -DIMAD_SUPPORTED=1"; kernel_string->entry_point = entry_point; kernel_string->batch_compilation = true; } @@ -185,13 +191,13 @@ namespace kernel_selector } } - void common_kernel_base::FillCLKernelData(clKernelData& kernel, const CommonDispatchData& runInfo, + void common_kernel_base::FillCLKernelData(clKernelData& kernel, const CommonDispatchData& runInfo, const EngineInfo& engine_info, const std::string& kernelMapName, const std::string& jit, const std::string& entryPoint, const std::string& exeMode, bool weights, bool bias, int number_of_inputs, bool quantization, bool calibration) const { Check_RunInfoData(kernelMapName, runInfo); kernel.workGroups.global = { runInfo.gws0, runInfo.gws1, runInfo.gws2 }; kernel.workGroups.local = { runInfo.lws0, runInfo.lws1, runInfo.lws2 }; - kernel.kernelString = GetKernelString(kernelMapName, jit, entryPoint, exeMode); + kernel.kernelString = GetKernelString(kernelMapName, jit, entryPoint, engine_info, exeMode); kernel.arguments = GetArgsDesc(number_of_inputs, weights, bias, quantization, calibration); } }
\ No newline at end of file diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/common/common_kernel_base.h b/inference-engine/thirdparty/clDNN/kernel_selector/core/common/common_kernel_base.h index 324cd9d67..917b4e5ac 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/common/common_kernel_base.h +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/common/common_kernel_base.h @@ -18,8 +18,6 @@ #include "kernel_base.h" #include "jitter.h" -#include <sstream> -#include <assert.h> namespace kernel_selector { @@ -51,7 +49,7 @@ namespace kernel_selector std::string CreateJit(const std::string& template_name, const JitConstants& constants, const std::string& kernel_name) const; std::string GetEntryPoint(const std::string& templateName, const std::string& layerID, const optional_params& options) const; Arguments GetArgsDesc(uint32_t num_of_input, bool use_weights, bool use_bias, bool use_quantization = false, bool use_calibration = 0) const; - std::shared_ptr<KernelString> GetKernelString(const std::string& kernel_name, const std::string& jit, const std::string& entry_point, const std::string& exe_mode = ROUND_ROBIN) const; - void FillCLKernelData(clKernelData& kernel, const CommonDispatchData& runInfo, const std::string& kernel_map_name, const std::string& jit, const std::string& entry_point, const std::string& exe_mode = ROUND_ROBIN, + std::shared_ptr<KernelString> GetKernelString(const std::string& kernel_name, const std::string& jit, const std::string& entry_point, const EngineInfo& engine_info, const std::string& exe_mode = ROUND_ROBIN) const; + void FillCLKernelData(clKernelData& kernel, const CommonDispatchData& runInfo, const EngineInfo& engine_info, const std::string& kernel_map_name, const std::string& jit, const std::string& entry_point, const std::string& exe_mode = ROUND_ROBIN, bool weights = false, bool bias = false, int number_of_inputs = 1, bool quantization = false, bool calibration = false) const; }; }
\ No newline at end of file diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/common/jitter.cpp b/inference-engine/thirdparty/clDNN/kernel_selector/core/common/jitter.cpp new file mode 100644 index 000000000..1a426a06a --- /dev/null +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/common/jitter.cpp @@ -0,0 +1,347 @@ +/* +// Copyright (c) 2018 Intel Corporation +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +*/ + +#include "jitter.h" + +namespace kernel_selector { + + std::string toCLType(WeightsType wType) + { + switch (wType) + { + case WeightsType::INT8: return GetTypeName<int8_t>(); + case WeightsType::F16: return "half"; + case WeightsType::F32: return GetTypeName<float>(); + default: return ""; + } + } + + std::string toCLType(Datatype dType) + { + switch (dType) + { + case Datatype::INT8: return GetTypeName<int8_t>(); + case Datatype::UINT8: return GetTypeName<uint8_t>(); + case Datatype::INT16: return GetTypeName<int16_t>(); + case Datatype::UINT16: return GetTypeName<uint16_t>(); + case Datatype::INT32: return GetTypeName<int32_t>(); + case Datatype::UINT32: return GetTypeName<uint32_t>(); + case Datatype::INT64: return GetTypeName<int64_t>(); + case Datatype::F16: return "half"; + case Datatype::F32: return GetTypeName<float>(); + default: return ""; + } + } + + std::string getMeanOpString(MeanOp op) + { + switch (op) + { + case MeanOp::NONE: return "val"; + case MeanOp::DIV: return "val/mean_val"; + case MeanOp::MUL: return "val*mean_val"; + case MeanOp::SUB: return "val-mean_val"; + default: return ""; + } + } + + JitDefinitions JitConstants::GetDefinitions() const + { + JitDefinitions definitons; + definitons.reserve(_constants.size() * 6); //assuming max 6 pairs per jit_constant + + for (auto& constant : _constants) { + auto def = constant->GetDefinitions(); + definitons.insert(definitons.end(), def.begin(), def.end()); + } + return definitons; + } + + JitDefinitions DataTensorJitConstant::GetDefinitions() const + { + JitDefinitions baseDefinitions = TensorBaseTJitConstant::GetDefinitions(_tensor); + + JitDefinitions definitions{ + { _name + "_SIZE_X", toCodeString(_tensor.X().v) }, + { _name + "_SIZE_Y", toCodeString(_tensor.Y().v) }, + { _name + "_FEATURE_NUM", toCodeString(_tensor.Feature().v) }, + { _name + "_ROI_NUM", toCodeString(_tensor.ROI().v) }, + { _name + "_BATCH_NUM", toCodeString(_tensor.Batch().v) }, + { _name + "_X_PITCH", toCodeString(_tensor.X().pitch) }, + { _name + "_Y_PITCH", toCodeString(_tensor.Y().pitch) }, + { _name + "_FEATURE_PITCH", toCodeString(_tensor.Feature().pitch) }, + { _name + "_ROI_PITCH", toCodeString(_tensor.ROI().pitch) }, + { _name + "_BATCH_PITCH", toCodeString(_tensor.Batch().pitch) }, + { _name + "_PAD_BEFORE_SIZE_X", toCodeString(_tensor.X().pad.before) }, + { _name + "_PAD_BEFORE_SIZE_Y", toCodeString(_tensor.Y().pad.before) }, + { _name + "_PAD_BEFORE_FEATURE_NUM", toCodeString(_tensor.Feature().pad.before) }, + { _name + "_PAD_BEFORE_BATCH_NUM", toCodeString(_tensor.Batch().pad.before) }, + { _name + "_PAD_AFTER_SIZE_X", toCodeString(_tensor.X().pad.after) }, + { _name + "_PAD_AFTER_SIZE_Y", toCodeString(_tensor.Y().pad.after) }, + { _name + "_PAD_AFTER_FEATURE_NUM", toCodeString(_tensor.Feature().pad.after) }, + { _name + "_PAD_AFTER_BATCH_NUM", toCodeString(_tensor.Batch().pad.after) }, + }; + + definitions.insert(definitions.end(), baseDefinitions.begin(), baseDefinitions.end()); + + return definitions; + } + + JitDefinitions WeightTensorJitConstant::GetDefinitions() const + { + JitDefinitions baseDefinitions = TensorBaseTJitConstant::GetDefinitions(_tensor); + + JitDefinitions definitions{ + { _name + "_SIZE_X", toCodeString(_tensor.X().v) }, + { _name + "_SIZE_Y", toCodeString(_tensor.Y().v) }, + { _name + "_IFM_NUM", toCodeString(_tensor.IFM().v) }, + { _name + "_OFM_NUM", toCodeString(_tensor.OFM().v) }, + { _name + "_X_PITCH", toCodeString(_tensor.X().pitch) }, + { _name + "_Y_PITCH", toCodeString(_tensor.Y().pitch) }, + { _name + "_IFM_PITCH", toCodeString(_tensor.IFM().pitch) }, + { _name + "_OFM_PITCH", toCodeString(_tensor.OFM().pitch) }, + }; + + definitions.insert(definitions.end(), baseDefinitions.begin(), baseDefinitions.end()); + + return definitions; + } + + std::shared_ptr<JitConstant> MakeActivationJitConstants(ActivationFunction activation_function) + { + // TODO: use native_exp and use cast for APL + switch (activation_function) + { + case ActivationFunction::LOGISTIC: + return MakeJitConstant("ACTIVATION(input, m, n)", "(UNIT_VAL_ONE/(UNIT_VAL_ONE + exp(-input)))"); + case ActivationFunction::HYPERBOLIC_TAN: + return MakeJitConstant("ACTIVATION(input, m, n)", "(tanh(input))"); + case ActivationFunction::RELU: + return MakeJitConstant("ACTIVATION(input, m, n)", "(UNIT_MAX_FUNC(UNIT_VAL_ZERO, input))"); + case ActivationFunction::RELU_NEGATIVE_SLOPE: + return MakeJitConstant("ACTIVATION(input, slope, n)", "isinf(TO_UNIT_TYPE(slope)) ? ((input >= UNIT_VAL_ZERO) ? \ + input : -TO_UNIT_TYPE(slope)) : \ + (UNIT_MAX_FUNC(input, UNIT_VAL_ZERO) + TO_UNIT_TYPE(slope) * UNIT_MIN_FUNC(input, UNIT_VAL_ZERO))"); + case ActivationFunction::ELU: + return MakeJitConstant("ACTIVATION(input, alpha, n)", "(UNIT_MAX_FUNC(input, UNIT_VAL_ZERO) + \ + TO_UNIT_TYPE(alpha) * (exp(UNIT_MIN_FUNC(input, UNIT_VAL_ZERO)) - UNIT_VAL_ONE));"); + case ActivationFunction::CLAMP: + return MakeJitConstant("ACTIVATION(input, m, n)", "(UNIT_MAX_FUNC(TO_UNIT_TYPE(m), UNIT_MIN_FUNC(TO_UNIT_TYPE(n), input)))"); + case ActivationFunction::SOFTRELU: + return MakeJitConstant("ACTIVATION(input, m, n)", "(log(UNIT_VAL_ONE + exp(input)))"); + case ActivationFunction::ABS: + return MakeJitConstant("ACTIVATION(input, m, n)", "(fabs(input))"); + case ActivationFunction::LINEAR: + return MakeJitConstant("ACTIVATION(input, m, n)", "(m*input + n)"); + case ActivationFunction::SQUARE: + return MakeJitConstant("ACTIVATION(input, m, n)", "(input*input)"); + case ActivationFunction::SQRT: + return MakeJitConstant("ACTIVATION(input, m, n)", "(sqrt(input))"); + case ActivationFunction::SIN: + return MakeJitConstant("ACTIVATION(input, m, n)", "(sin(input))"); + case ActivationFunction::ASIN: + return MakeJitConstant("ACTIVATION(input, m, n)", "(asin(input))"); + case ActivationFunction::SINH: + return MakeJitConstant("ACTIVATION(input, m, n)", "(sinh(input))"); + case ActivationFunction::COS: + return MakeJitConstant("ACTIVATION(input, m, n)", "(cos(input))"); + case ActivationFunction::ACOS: + return MakeJitConstant("ACTIVATION(input, m, n)", "(acos(input))"); + case ActivationFunction::COSH: + return MakeJitConstant("ACTIVATION(input, m, n)", "(cosh(input))"); + case ActivationFunction::LOG: + return MakeJitConstant("ACTIVATION(input, m, n)", "(log(input))"); + case ActivationFunction::LOG2: + return MakeJitConstant("ACTIVATION(input, m, n)", "(log2(input))"); + case ActivationFunction::EXP: + return MakeJitConstant("ACTIVATION(input, m, n)", "(exp(input))"); + case ActivationFunction::RELU_GRAD: + return MakeJitConstant("ACTIVATION(input_grad, input, m, n)", "(input_grad * (input > UNIT_VAL_ZERO ? TO_UNIT_TYPE(1) : TO_UNIT_TYPE(0)))"); + case ActivationFunction::RELU_NEGATIVE_SLOPE_GRAD: + return MakeJitConstant("ACTIVATION(input_grad, input, slope, n)", "(input_grad * ((input > UNIT_VAL_ZERO ? TO_UNIT_TYPE(1) : TO_UNIT_TYPE(0)) + TO_UNIT_TYPE(slope) * (input <= 0 ? TO_UNIT_TYPE(1) : TO_UNIT_TYPE(0))))"); + case ActivationFunction::NONE_GRAD: + return MakeJitConstant("ACTIVATION(input_grad, input, m, n)", "input_grad"); + case ActivationFunction::NONE: + default: + return MakeJitConstant("ACTIVATION(input, m, n)", "input"); + } + } + + JitConstants MakeUnitTypeJitConstants(Datatype dataType) + { + std::string unit_type; + std::string unit_max_val; + std::string unit_min_val; + std::string unit_val_one; + std::string unit_val_zero; + std::string to_unit_type; + std::string unit_max_func; + std::string unit_min_func; + switch (dataType) + { + case Datatype::INT8: + unit_type = "char"; + unit_max_val = "CHAR_MAX"; + unit_min_val = "-UNIT_VAL_MAX"; + unit_val_one = "(char) 1"; + unit_val_zero = "(char) 0"; + to_unit_type = "convert_char(v)"; + unit_max_func = "max"; + unit_min_func = "min"; + break; + case Datatype::INT32: + unit_type = "int"; + unit_max_val = "INT_MAX"; + unit_min_val = "-UNIT_VAL_MAX"; + unit_val_one = "(int) 1"; + unit_val_zero = "(int) 0"; + to_unit_type = "convert_int(v)"; + unit_max_func = "max"; + unit_min_func = "min"; + break; + case Datatype::INT64: + unit_type = "long"; + unit_max_val = "LONG_MAX"; + unit_min_val = "-UNIT_VAL_MAX"; + unit_val_one = "(long) 1"; + unit_val_zero = "(long) 0"; + to_unit_type = "convert_long(v)"; + unit_max_func = "max"; + unit_min_func = "min"; + break; + case Datatype::F16: + unit_type = "half"; + unit_max_val = "HALF_MAX"; + unit_min_val = "-UNIT_VAL_MAX"; + unit_val_one = "1.0h"; + unit_val_zero = "0.0h"; + to_unit_type = "convert_half(v)"; + unit_max_func = "fmax"; + unit_min_func = "fmin"; + break; + default: + unit_type = "float"; + unit_max_val = "FLT_MAX"; + unit_min_val = "-UNIT_VAL_MAX"; + unit_val_one = "1.0f"; + unit_val_zero = "0.0f"; + to_unit_type = "(float)(v)"; + unit_max_func = "fmax"; + unit_min_func = "fmin"; + break; + } + + return JitConstants + { + MakeJitConstant("UNIT_TYPE", unit_type), + MakeJitConstant("UNIT_VAL_MAX", unit_max_val), + MakeJitConstant("UNIT_VAL_MIN", unit_min_val), + MakeJitConstant("UNIT_VAL_ONE", unit_val_one), + MakeJitConstant("UNIT_VAL_ZERO", unit_val_zero), + MakeJitConstant("TO_UNIT_TYPE(v)", to_unit_type), + MakeJitConstant("UNIT_MAX_FUNC", unit_max_func), + MakeJitConstant("UNIT_MIN_FUNC", unit_min_func), + }; + } + //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// + // MakeBaseParamsJitConstants + //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// + JitConstants MakeBaseParamsJitConstants(const base_params& params) + { + bool bFP16Used = params.output.GetDType() == Datatype::F16; + bool bInt8Used = params.output.GetDType() == Datatype::INT8; + bool bInt32Used = params.output.GetDType() == Datatype::INT32; + bool bInt64Used = params.output.GetDType() == Datatype::INT64; + for (const auto& i : params.inputs) + { + bFP16Used |= i.GetDType() == Datatype::F16; + bInt8Used |= i.GetDType() == Datatype::INT8; + bInt32Used |= i.GetDType() == Datatype::INT32; + bInt64Used |= i.GetDType() == Datatype::INT64; + } + + JitConstants jit{ + MakeJitConstant("OUTPUT", params.output), + MakeJitConstant("FP64_SUPPORTED", params.engineInfo.bFP64Support), + MakeJitConstant("FP16_SUPPORTED", params.engineInfo.bFP16Support), + MakeJitConstant("FP16_UNIT_USED", bFP16Used), + MakeJitConstant("INT8_UNIT_USED", bInt8Used), + MakeJitConstant("INT32_UNIT_USED", bInt32Used), + MakeJitConstant("INT64_UNIT_USED", bInt64Used), + MakeJitConstant("GRADIENT", params.gradient), + }; + + // for activation function + jit.AddConstants({ + MakeJitConstant("NL_M", params.activationParams.m), + MakeJitConstant("NL_N", params.activationParams.n), + MakeActivationJitConstants(params.activationFunc), + }); + + if (bInt8Used) + { + jit.Merge(MakeUnitTypeJitConstants(Datatype::INT8)); + } + else if (bFP16Used) + { + jit.Merge(MakeUnitTypeJitConstants(Datatype::F16)); + } + else if (bInt32Used) + { + jit.Merge(MakeUnitTypeJitConstants(Datatype::INT32)); + } + else if (bInt64Used) + { + jit.Merge(MakeUnitTypeJitConstants(Datatype::INT64)); + } + else + { + jit.Merge(MakeUnitTypeJitConstants(Datatype::F32)); + } + + for (size_t i = 0; i < params.inputs.size(); i++) + { + jit.AddConstant(MakeJitConstant("INPUT" + toCodeString(i), params.inputs[i])); + } + + return jit; + } + + //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// + // MakeLoopUnrollParamsJitConstants + //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// + JitConstants MakeLoopUnrollParamsJitConstants(uint32_t loopCount) + { + JitConstants jit{ + MakeJitConstant("LOOP0(VAR, STMT)", ""), + MakeJitConstant("LOOP1(VAR, STMT)", "(STMT); (VAR)++;"), + }; + + for (uint32_t i = 2; i <= loopCount + 1; i++) + { + jit.AddConstant({ + MakeJitConstant("LOOP" + toCodeString(i) + "(VAR, STMT)", "LOOP" + toCodeString(i - 1) + "(VAR, STMT); (STMT); (VAR)++;"), + }); + } + + jit.AddConstant({ + MakeJitConstant("LOOP(N, VAR, STMT)", "CAT(LOOP, N)((VAR), (STMT))"), + }); + + return jit; + } + +}
\ No newline at end of file diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/common/jitter.h b/inference-engine/thirdparty/clDNN/kernel_selector/core/common/jitter.h index 1618a02e4..3e65a0bde 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/common/jitter.h +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/common/jitter.h @@ -1,5 +1,5 @@ /* -// Copyright (c) 2016 Intel Corporation +// Copyright (c) 2016-2018 Intel Corporation // // Licensed under the Apache License, Version 2.0 (the "License"); // you may not use this file except in compliance with the License. @@ -16,13 +16,15 @@ /////////////////////////////////////////////////////////////////////////////////////////////////// #pragma once -#include "tensor_type.h" + +#include "kernel_selector_common.h" #include "kernel_selector_params.h" -#include <algorithm> -#include <iostream> +#include "tensor_type.h" + #include <sstream> #include <cmath> + namespace kernel_selector { using JitDefinitions = std::vector<std::pair<std::string, std::string>>; @@ -53,44 +55,9 @@ inline std::string GetTypeName<float>() { return "float"; } template <> inline std::string GetTypeName<double>() { return "double"; } -inline std::string toCLType(WeightsType wType) -{ - switch (wType) - { - case WeightsType::INT8: return GetTypeName<int8_t>(); - case WeightsType::F16: return "half"; - case WeightsType::F32: return GetTypeName<float>(); - default: return ""; - } -} - -inline std::string toCLType(Datatype dType) -{ - switch (dType) - { - case Datatype::INT8: return GetTypeName<int8_t>(); - case Datatype::UINT8: return GetTypeName<uint8_t>(); - case Datatype::INT16: return GetTypeName<int16_t>(); - case Datatype::UINT16: return GetTypeName<uint16_t>(); - case Datatype::INT32: return GetTypeName<int32_t>(); - case Datatype::UINT32: return GetTypeName<uint32_t>(); - case Datatype::F16: return "half"; - case Datatype::F32: return GetTypeName<float>(); - default: return ""; - } -} - -inline std::string getMeanOpString(MeanOp op) -{ - switch (op) - { - case MeanOp::NONE: return "val"; - case MeanOp::DIV: return "val/mean_val"; - case MeanOp::MUL: return "val*mean_val"; - case MeanOp::SUB: return "val-mean_val"; - default: return ""; - } -} +std::string toCLType(WeightsType wType); +std::string toCLType(Datatype dType); +std::string getMeanOpString(MeanOp op); //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// // ToCodeString functions @@ -248,35 +215,7 @@ class DataTensorJitConstant : public TensorBaseTJitConstant<Datatype, DataLayout public: DataTensorJitConstant(const std::string& name, const DataTensor& t) : TensorBaseTJitConstant(name), _tensor(t) {} - JitDefinitions GetDefinitions() const override - { - JitDefinitions baseDefinitions = TensorBaseTJitConstant::GetDefinitions(_tensor); - - JitDefinitions definitions{ - { _name + "_SIZE_X", toCodeString(_tensor.X().v) }, - { _name + "_SIZE_Y", toCodeString(_tensor.Y().v) }, - { _name + "_FEATURE_NUM", toCodeString(_tensor.Feature().v) }, - { _name + "_ROI_NUM", toCodeString(_tensor.ROI().v) }, - { _name + "_BATCH_NUM", toCodeString(_tensor.Batch().v) }, - { _name + "_X_PITCH", toCodeString(_tensor.X().pitch) }, - { _name + "_Y_PITCH", toCodeString(_tensor.Y().pitch) }, - { _name + "_FEATURE_PITCH", toCodeString(_tensor.Feature().pitch) }, - { _name + "_ROI_PITCH", toCodeString(_tensor.ROI().pitch) }, - { _name + "_BATCH_PITCH", toCodeString(_tensor.Batch().pitch) }, - { _name + "_PAD_BEFORE_SIZE_X", toCodeString(_tensor.X().pad.before) }, - { _name + "_PAD_BEFORE_SIZE_Y", toCodeString(_tensor.Y().pad.before) }, - { _name + "_PAD_BEFORE_FEATURE_NUM", toCodeString(_tensor.Feature().pad.before) }, - { _name + "_PAD_BEFORE_BATCH_NUM", toCodeString(_tensor.Batch().pad.before) }, - { _name + "_PAD_AFTER_SIZE_X", toCodeString(_tensor.X().pad.after) }, - { _name + "_PAD_AFTER_SIZE_Y", toCodeString(_tensor.Y().pad.after) }, - { _name + "_PAD_AFTER_FEATURE_NUM", toCodeString(_tensor.Feature().pad.after) }, - { _name + "_PAD_AFTER_BATCH_NUM", toCodeString(_tensor.Batch().pad.after) }, - }; - - definitions.insert(definitions.end(), baseDefinitions.begin(), baseDefinitions.end()); - - return definitions; - } + JitDefinitions GetDefinitions() const override; }; inline std::shared_ptr<JitConstant> MakeJitConstant(const std::string& name, const DataTensor& value) @@ -294,25 +233,7 @@ class WeightTensorJitConstant : public TensorBaseTJitConstant<WeightsType, Weigh public: WeightTensorJitConstant(const std::string& name, const WeightsTensor& t) : TensorBaseTJitConstant(name), _tensor(t) {} - JitDefinitions GetDefinitions() const override - { - JitDefinitions baseDefinitions = TensorBaseTJitConstant::GetDefinitions(_tensor); - - JitDefinitions definitions{ - { _name + "_SIZE_X", toCodeString(_tensor.X().v) }, - { _name + "_SIZE_Y", toCodeString(_tensor.Y().v) }, - { _name + "_IFM_NUM", toCodeString(_tensor.IFM().v) }, - { _name + "_OFM_NUM", toCodeString(_tensor.OFM().v) }, - { _name + "_X_PITCH", toCodeString(_tensor.X().pitch) }, - { _name + "_Y_PITCH", toCodeString(_tensor.Y().pitch) }, - { _name + "_IFM_PITCH", toCodeString(_tensor.IFM().pitch) }, - { _name + "_OFM_PITCH", toCodeString(_tensor.OFM().pitch) }, - }; - - definitions.insert(definitions.end(), baseDefinitions.begin(), baseDefinitions.end()); - - return definitions; - } + JitDefinitions GetDefinitions() const override; }; inline std::shared_ptr<JitConstant> MakeJitConstant(const std::string& name, const WeightsTensor& value) @@ -375,6 +296,35 @@ inline std::shared_ptr<JitConstant> MakeJitConstant(const std::string& name, con } //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// +// DimTensor +//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// +template <typename T> +class DimVectorJitConstant : public JitConstant +{ + const DimTensor<T> _dims; + +public: + DimVectorJitConstant(const std::string& name, const DimTensor<T>& size) : JitConstant(name), _dims(size) {} + + JitDefinitions GetDefinitions() const override + { + JitDefinitions definitions{ + { _name + "_BATCH_NUM", toCodeString(_dims.b) }, + { _name + "_FEATURE_NUM", toCodeString(_dims.f) }, + { _name + "_SIZE_Y", toCodeString(_dims.y) }, + { _name + "_SIZE_X", toCodeString(_dims.x) }, + }; + return definitions; + } +}; + +template <typename T> +std::shared_ptr<JitConstant> MakeJitConstant(const std::string& name, const DimTensor<T>& value) +{ + return std::make_shared<DimVectorJitConstant<T>>(name, value); +} + +//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// // jit_constants //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// class JitConstants @@ -383,12 +333,12 @@ class JitConstants public: JitConstants(std::initializer_list<std::shared_ptr<JitConstant>> constants) :_constants(constants) {} - void AddConstant(std::shared_ptr<JitConstant> constant) + inline void AddConstant(std::shared_ptr<JitConstant> constant) { _constants.push_back(constant); } - void AddConstants(const std::vector<std::shared_ptr<JitConstant>>& constants) + inline void AddConstants(const std::vector<std::shared_ptr<JitConstant>>& constants) { for (const auto& c : constants) { @@ -396,81 +346,16 @@ public: } } - void Merge(const JitConstants& jit) + inline void Merge(const JitConstants& jit) { AddConstants(jit._constants); } - JitDefinitions GetDefinitions() const - { - JitDefinitions definitons; - definitons.reserve(_constants.size() * 6); //assuming max 6 pairs per jit_constant - - for (auto& constant : _constants) { - auto def = constant->GetDefinitions(); - definitons.insert(definitons.end(), def.begin(), def.end()); - } - return definitons; - } + JitDefinitions GetDefinitions() const; }; -//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// -// MakeBaseParamsJitConstants -//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// -inline JitConstants MakeBaseParamsJitConstants(const base_params& params) -{ - bool bFP16Used = params.output.GetDType() == Datatype::F16; - bool bInt8Used = params.output.GetDType() == Datatype::INT8; - for (const auto& i : params.inputs) - { - bFP16Used |= i.GetDType() == Datatype::F16; - bInt8Used |= i.GetDType() == Datatype::INT8; - - } - - JitConstants jit{ - MakeJitConstant("OUTPUT", params.output), - MakeJitConstant("FP64_SUPPORTED", params.engineInfo.bFP64Support), - MakeJitConstant("FP16_SUPPORTED", params.engineInfo.bFP16Support), - MakeJitConstant("FP16_UNIT_USED", bFP16Used), - MakeJitConstant("INT8_UNIT_USED", bInt8Used), - MakeJitConstant("UNIT_TYPE", bInt8Used ? "char" : bFP16Used ? "half" : "float"), - MakeJitConstant("NL_M", params.activationParams.m), - MakeJitConstant("NL_N", params.activationParams.n), - MakeJitConstant("ACTIVATION_FUNCTION_" + toString(params.activationFunc), ""), - MakeJitConstant("GRADIENT", params.gradient), - }; - - for (size_t i = 0; i < params.inputs.size(); i++) - { - jit.AddConstant(MakeJitConstant("INPUT" + toCodeString(i), params.inputs[i])); - } - - return jit; -} - -//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// -// MakeLoopUnrollParamsJitConstants -//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// -inline JitConstants MakeLoopUnrollParamsJitConstants(uint32_t loopCount) -{ - JitConstants jit{ - MakeJitConstant("LOOP0(VAR, STMT)", ""), - MakeJitConstant("LOOP1(VAR, STMT)", "(STMT); (VAR)++;"), - }; - - for (uint32_t i = 2; i <= loopCount + 1; i++) - { - jit.AddConstant({ - MakeJitConstant("LOOP" + toCodeString(i) + "(VAR, STMT)", "LOOP" + toCodeString(i - 1) + "(VAR, STMT); (STMT); (VAR)++;"), - }); - } - - jit.AddConstant({ - MakeJitConstant("LOOP(N, VAR, STMT)", "CAT(LOOP, N)((VAR), (STMT))"), - }); - - return jit; -} +JitConstants MakeBaseParamsJitConstants(const base_params& params); +JitConstants MakeLoopUnrollParamsJitConstants(uint32_t loopCount); +JitConstants MakeUnitTypeJitConstants(Datatype dataType); } diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/common/kernel_selector_utils.cpp b/inference-engine/thirdparty/clDNN/kernel_selector/core/common/kernel_selector_utils.cpp new file mode 100644 index 000000000..92933f832 --- /dev/null +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/common/kernel_selector_utils.cpp @@ -0,0 +1,313 @@ +/* +// Copyright (c) 2018 Intel Corporation +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +*/ + +#include "kernel_selector_utils.h" +#include "reorder/reorder_weights_kernel_selector.h" +#include "reorder/reorder_kernel_base.h" +#include "convolution/convolution_params.h" + +namespace kernel_selector { + + bool CheckConvolutionPaddedInputDesc(const convolution_params& params, const DataTensor& reqDesc) + { + assert(params.inputs.size() == 1); + + bool properPadding = + reqDesc.X().pad.before <= params.inputs[0].X().pad.before && + reqDesc.Y().pad.before <= params.inputs[0].Y().pad.before && + reqDesc.Feature().pad.before <= params.inputs[0].Feature().pad.before && + reqDesc.Batch().pad.before <= params.inputs[0].Batch().pad.before; + + properPadding &= + reqDesc.X().pad.after <= params.inputs[0].X().pad.after && + reqDesc.Y().pad.after <= params.inputs[0].Y().pad.after && + reqDesc.Feature().pad.after <= params.inputs[0].Feature().pad.after && + reqDesc.Batch().pad.after <= params.inputs[0].Batch().pad.after; + + properPadding &= ((params.padding.x == 0 && params.padding.y == 0) || params.inputs[0].GetPaddedVal() == 0.f); + + return properPadding; + } + + DataTensor GetConvolutionBFYXPaddedTensor(const convolution_params& cp) + { + assert(cp.inputs.size() == 1); + assert(cp.inputs[0].GetDims().size() == 4U); + + DataTensor t = cp.inputs[0]; + std::vector<Tensor::Pad> pad{ { 0,0 },{ 0,0 },{ 0,0 },{ 0,0 } }; + + pad[0].before = cp.padding.x; + pad[1].before = cp.padding.y; + + const auto inputLimitX = (cp.output.X().v - 1) * cp.stride.x + (cp.filterSize.x - 1) * cp.dilation.x + 1; + const auto inputLimitY = (cp.output.Y().v - 1) * cp.stride.y + (cp.filterSize.y - 1) * cp.dilation.y + 1; + + pad[0].after = (size_t)std::max((int)inputLimitX - (int)t.X().v - (int)pad[0].before, (int)0); + pad[1].after = (size_t)std::max((int)inputLimitY - (int)t.Y().v - (int)pad[1].before, (int)0); + + Tensor::NDims dims(4); + const Tensor::NDims& orgDims = cp.inputs[0].GetDims(); + size_t pitch = 1; + for (size_t i = 0; i < dims.size(); i++) + { + dims[i].pad = pad[i]; + dims[i].v = orgDims[i].v; + dims[i].pitch = pitch; + pitch *= dims[i].LogicalDimPadded(); + } + + return{ dims, t.GetDType(), t.GetLayout() }; + } + + bool CovolutionCheckInput(const Params& p, const optional_params& o) + { + const convolution_params& params = static_cast<const convolution_params&>(p); + const convolution_optional_params& optParams = static_cast<const convolution_optional_params&>(o); + + const auto req_input = GetConvolutionBFYXPaddedTensor(params); + const bool bProperInputDesc = CheckConvolutionPaddedInputDesc(params, req_input); + const bool bInputPadded = optParams.allowInputReordering || bProperInputDesc; + + if (!bInputPadded) + { + return false; + } + + return true; + } + + bool CovolutionUpdateInputParams(convolution_params& params) + { + const auto req_input = GetConvolutionBFYXPaddedTensor(params); + const bool bProperInputDesc = CheckConvolutionPaddedInputDesc(params, req_input); + + if (!bProperInputDesc) + { + params.inputs[0] = req_input; + return true; + } + + return false; + } + + WeightsType DataTypeToWeightsType(Datatype t) + { + switch (t) + { + case Datatype::INT8: return WeightsType::INT8; + case Datatype::F16: return WeightsType::F16; + case Datatype::F32: return WeightsType::F32; + default: + return WeightsType::UNSUPPORTED; + } + } + + bool CheckWeights(const WeightsTensor& tensor, WeightsType reqType, std::vector<WeightsLayout> reqLayouts) + { + if (reqType != tensor.GetDType()) + { + return false; + } + + bool bProperWeightsLayout = std::find(reqLayouts.begin(), reqLayouts.end(), tensor.GetLayout()) != reqLayouts.end(); + if (!bProperWeightsLayout && tensor.PitchesDifferFromLogicalDims() == false) + { + bProperWeightsLayout = + (std::find(reqLayouts.begin(), reqLayouts.end(), WeightsLayout::io) != reqLayouts.end() && tensor.GetLayout() == WeightsLayout::iyxo) || + (std::find(reqLayouts.begin(), reqLayouts.end(), WeightsLayout::oi) != reqLayouts.end() && tensor.GetLayout() == WeightsLayout::oiyx); + } + + return bProperWeightsLayout; + } + + std::vector<size_t> GetImageSizes(const kernel_selector::WeightsTensor& dimensions, const WeightsLayout layout) + { + auto ofm = dimensions.OFM().v; + auto ifm = dimensions.IFM().v; + auto x = dimensions.X().v; + auto y = dimensions.Y().v; + + switch (layout) + { + case WeightsLayout::image_2d_weights_c1_b_fyx: + case WeightsLayout::image_2d_weights_c4_fyx_b: + return { ofm, ifm * x * y }; + case WeightsLayout::image_2d_weights_winograd_6x3_s1_fbxyb: + return { ofm * x * y * 8 / 3, ifm }; + case WeightsLayout::image_2d_weights_winograd_6x3_s1_xfbyb: + return { ofm * y, ifm * x * 8 / 3 }; + default: + return { 0, 0 }; + } + } + + bool CheckImageSize(const weight_bias_params& newParams, const WeightsLayout layout) + { + if (!newParams.engineInfo.bImageSupport) + return false; + + auto image_sizes = GetImageSizes(newParams.weights, layout); + if (image_sizes[0] == 0 || + image_sizes[1] == 0 || + image_sizes[0] > newParams.engineInfo.maxImage2dWidth || + image_sizes[1] > newParams.engineInfo.maxImage2dHeight) + return false; + + return true; + } + + bool UpdateWeightsParams(weight_bias_params& newParams, const optional_params& options, std::vector<WeightsLayout> layouts, WeightsReorderParams& weightsReorderParams) + { + //validate if weights type is image and if device supports requested sizes + for (auto& requested_layout : layouts) + { + if (Tensor::IsImageType(requested_layout)) + { + if (!CheckImageSize(newParams, requested_layout)) + return false; + } + } + const weight_bias_optional_params& optParams = static_cast<const weight_bias_optional_params&>(options); + + const auto dtype = DataTypeToWeightsType(newParams.inputs[0].GetDType()); + bool bProperWeights = CheckWeights(newParams.weights, dtype, layouts); + + if (!bProperWeights) + { + if (!optParams.allowStaticInputReordering) + { + return false; + } + + auto& reorderKS = ReorderWeightsKernelSelctor::Instance(); + reorder_weights_params r_params; + + r_params.layerID = newParams.layerID + "_reorder_"; + r_params.input = newParams.weights; + r_params.output = newParams.weights.TransformIgnorePadding(layouts[0], dtype); + + reorder_optional_params op; + KernelsData kernels_data = reorderKS.GetBestKernels(r_params, op); + + if (kernels_data.empty()) + { + return false; + } + + weightsReorderParams.engine = WeightsReorderParams::Engine::GPU; + weightsReorderParams.clKernel = std::make_shared<clKernelData>(kernels_data[0].kernels[0]); + weightsReorderParams.newBufferSize = r_params.output.PhysicalSizeInBytes(); + weightsReorderParams.dtype = dtype; + weightsReorderParams.destLayout = r_params.output.GetLayout(); + weightsReorderParams.toImageType = Tensor::IsImageType(r_params.output.GetLayout()); + + newParams.weights = r_params.output; + } + + return true; + } + + JitConstants GetTensorFriendlyWorkGroupsJit(const DataTensor& t) + { + auto b = DataTensor::Channelndex(t.GetLayout(), Tensor::DataChannelName::BATCH); + auto f = DataTensor::Channelndex(t.GetLayout(), Tensor::DataChannelName::FEATURE); + auto x = DataTensor::Channelndex(t.GetLayout(), Tensor::DataChannelName::X); + + if (x == -1) + { + x = 2; + } + else + { + b = (b < x) ? b : b - 1; + f = (f < x) ? f : f - 1; + } + + JitConstants jit{ + MakeJitConstant("GWS_BATCH", b), + MakeJitConstant("GWS_FEATURE", f), + MakeJitConstant("GWS_YX", x), + }; + + return jit; + } + + std::vector<size_t> GetTensorFriendlyWorkGroups(const DataTensor& t) + { + std::vector<size_t> sizes; + auto y = DataTensor::Channelndex(t.GetLayout(), Tensor::DataChannelName::Y); + for (size_t i = 0; i < t.GetDims().size(); i++) + { + const auto& o = t.GetDims()[i]; + if (y == (int)i) + { + sizes.back() *= o.v; + } + else + { + sizes.push_back(o.v); + } + } + + for (size_t i = sizes.size(); i < 3; i++) + { + sizes.push_back(1U); + } + + return sizes; + } + + std::vector<size_t> GetOptimalLocalWorkGroupSizes(std::vector<size_t> gws) + { + const size_t lws_max = 256; + const size_t optimal_lws_values[] = { 256, 224, 192, 160, 128, 96, 64, 32, 16, 8, 7, 6, 5, 4, 3, 2, 1 }; + size_t total_lws = 1; + std::vector<size_t> lws; + for (size_t i = 0; i < gws.size(); ++i) + { + auto rest_lws = lws_max / total_lws; + size_t lws_idx = 0; + while (rest_lws < optimal_lws_values[lws_idx]) lws_idx++; + + while (gws[i] % optimal_lws_values[lws_idx]) lws_idx++; + + lws.push_back(optimal_lws_values[lws_idx]); + total_lws *= optimal_lws_values[lws_idx]; + } + + return lws; + } + + bool CheckInputsOutputNoPitchSameDims(const base_params& params) + { + bool no_pitch_same_dims = true; + + if (params.inputs.size()) + { + no_pitch_same_dims = !params.inputs[0].PitchesDifferFromLogicalDims(); + + for (size_t i = 1; i < params.inputs.size(); i++) + { + no_pitch_same_dims = no_pitch_same_dims && (params.inputs[0] == params.inputs[i]); + } + + no_pitch_same_dims = no_pitch_same_dims && (params.inputs[0] == params.output); + } + + return no_pitch_same_dims; + } +}
\ No newline at end of file diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/common/kernel_selector_utils.h b/inference-engine/thirdparty/clDNN/kernel_selector/core/common/kernel_selector_utils.h index bc58ad933..e7cc7cf10 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/common/kernel_selector_utils.h +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/common/kernel_selector_utils.h @@ -18,300 +18,23 @@ #include "jitter.h" #include "tensor_type.h" -#include "kernel_selector_common.h" -#include "reorder/reorder_weights_kernel_selector.h" -#include "reorder/reorder_kernel_base.h" -#include "convolution/convolution_params.h" namespace kernel_selector { - inline bool CheckConvolutionPaddedInputDesc(const convolution_params& params, const DataTensor& reqDesc) - { - assert(params.inputs.size() == 1); - - bool properPadding = - reqDesc.X().pad.before <= params.inputs[0].X().pad.before && - reqDesc.Y().pad.before <= params.inputs[0].Y().pad.before && - reqDesc.Feature().pad.before <= params.inputs[0].Feature().pad.before && - reqDesc.Batch().pad.before <= params.inputs[0].Batch().pad.before; - - properPadding &= - reqDesc.X().pad.after <= params.inputs[0].X().pad.after && - reqDesc.Y().pad.after <= params.inputs[0].Y().pad.after && - reqDesc.Feature().pad.after <= params.inputs[0].Feature().pad.after && - reqDesc.Batch().pad.after <= params.inputs[0].Batch().pad.after; - - properPadding &= ((params.padding.x == 0 && params.padding.y == 0) || params.inputs[0].GetPaddedVal() == 0.f); - - return properPadding; - } - - inline DataTensor GetConvolutionBFYXPaddedTensor(const convolution_params& cp) - { - assert(cp.inputs.size() == 1); - assert(cp.inputs[0].GetDims().size() == 4U); - - DataTensor t = cp.inputs[0]; - std::vector<Tensor::Pad> pad{ { 0,0 },{ 0,0 },{ 0,0 },{ 0,0 } }; - - pad[0].before = cp.padding.x; - pad[1].before = cp.padding.y; - - const auto inputLimitX = (cp.output.X().v - 1) * cp.stride.x + (cp.filterSize.x - 1) * cp.dilation.x + 1; - const auto inputLimitY = (cp.output.Y().v - 1) * cp.stride.y + (cp.filterSize.y - 1) * cp.dilation.y + 1; - - pad[0].after = (size_t)std::max((int)inputLimitX - (int)t.X().v - (int)pad[0].before, (int)0); - pad[1].after = (size_t)std::max((int)inputLimitY - (int)t.Y().v - (int)pad[1].before, (int)0); - - Tensor::NDims dims(4); - const Tensor::NDims& orgDims = cp.inputs[0].GetDims(); - size_t pitch = 1; - for (size_t i = 0; i < dims.size(); i++) - { - dims[i].pad = pad[i]; - dims[i].v = orgDims[i].v; - dims[i].pitch = pitch; - pitch *= dims[i].LogicalDimPadded(); - } - - return{dims, t.GetDType(), t.GetLayout()}; - } - - inline bool CovolutionCheckInput(const Params& p, const optional_params& o) - { - const convolution_params& params = static_cast<const convolution_params&>(p); - const convolution_optional_params& optParams = static_cast<const convolution_optional_params&>(o); - - const auto req_input = GetConvolutionBFYXPaddedTensor(params); - const bool bProperInputDesc = CheckConvolutionPaddedInputDesc(params, req_input); - const bool bInputPadded = optParams.allowInputReordering || bProperInputDesc; - - if (!bInputPadded) - { - return false; - } - - return true; - } - - inline bool CovolutionUpdateInputParams(convolution_params& params) - { - const auto req_input = GetConvolutionBFYXPaddedTensor(params); - const bool bProperInputDesc = CheckConvolutionPaddedInputDesc(params, req_input); - - if (!bProperInputDesc) - { - params.inputs[0] = req_input; - return true; - } - - return false; - } - - inline WeightsType DataTypeToWeightsType(Datatype t) - { - switch (t) - { - case Datatype::INT8: return WeightsType::INT8; - case Datatype::F16: return WeightsType::F16; - case Datatype::F32: return WeightsType::F32; - default: - return WeightsType::UNSUPPORTED; - } - } - - inline bool CheckWeights(const WeightsTensor& tensor, WeightsType reqType, std::vector<WeightsLayout> reqLayouts) - { - if (reqType != tensor.GetDType()) - { - return false; - } - - bool bProperWeightsLayout = std::find(reqLayouts.begin(), reqLayouts.end(), tensor.GetLayout()) != reqLayouts.end(); - if (!bProperWeightsLayout && tensor.PitchesDifferFromLogicalDims() == false) - { - bProperWeightsLayout = - (std::find(reqLayouts.begin(), reqLayouts.end(), WeightsLayout::io) != reqLayouts.end() && tensor.GetLayout() == WeightsLayout::iyxo) || - (std::find(reqLayouts.begin(), reqLayouts.end(), WeightsLayout::oi) != reqLayouts.end() && tensor.GetLayout() == WeightsLayout::oiyx); - } - - return bProperWeightsLayout; - } - - inline std::vector<size_t> GetImageSizes(const kernel_selector::WeightsTensor& dimensions, const WeightsLayout layout) - { - auto ofm = dimensions.OFM().v; - auto ifm = dimensions.IFM().v; - auto x = dimensions.X().v; - auto y = dimensions.Y().v; - - switch (layout) - { - case WeightsLayout::image_2d_weights_c1_b_fyx: - case WeightsLayout::image_2d_weights_c4_fyx_b: - return { ofm, ifm * x * y }; - case WeightsLayout::image_2d_weights_winograd_6x3_s1_fbxyb: - return { ofm * x * y * 8 / 3, ifm }; - case WeightsLayout::image_2d_weights_winograd_6x3_s1_xfbyb: - return { ofm * y, ifm * x * 8 / 3 }; - default: - return { 0, 0 }; - } - } - - inline bool CheckImageSize(const weight_bias_params& newParams, const WeightsLayout layout) - { - if (!newParams.engineInfo.bImageSupport) - return false; - - auto image_sizes = GetImageSizes(newParams.weights, layout); - if (image_sizes[0] == 0 || - image_sizes[1] == 0 || - image_sizes[0] > newParams.engineInfo.maxImage2dWidth || - image_sizes[1] > newParams.engineInfo.maxImage2dHeight) - return false; - - return true; - } - - inline bool UpdateWeightsParams(weight_bias_params& newParams, const optional_params& options, std::vector<WeightsLayout> layouts, WeightsReorderParams& weightsReorderParams) - { - //validate if weights type is image and if device supports requested sizes - for (auto& requested_layout : layouts) - { - if (Tensor::IsImageType(requested_layout)) - { - if (!CheckImageSize(newParams, requested_layout)) - return false; - } - } - const weight_bias_optional_params& optParams = static_cast<const weight_bias_optional_params&>(options); - - const auto dtype = DataTypeToWeightsType(newParams.inputs[0].GetDType()); - bool bProperWeights = CheckWeights(newParams.weights, dtype, layouts); - - if (!bProperWeights) - { - if (!optParams.allowStaticInputReordering) - { - return false; - } - - auto& reorderKS = ReorderWeightsKernelSelctor::Instance(); - reorder_weights_params r_params; - - r_params.layerID = newParams.layerID + "_reorder_"; - r_params.input = newParams.weights; - r_params.output = newParams.weights.TransformIgnorePadding(layouts[0], dtype); - - reorder_optional_params op; - KernelsData kernels_data = reorderKS.GetBestKernels(r_params, op); - - if (kernels_data.empty()) - { - return false; - } - - weightsReorderParams.engine = WeightsReorderParams::Engine::GPU; - weightsReorderParams.clKernel = std::make_shared<clKernelData>(kernels_data[0].kernels[0]); - weightsReorderParams.newBufferSize = r_params.output.PhysicalSizeInBytes(); - weightsReorderParams.dtype = dtype; - weightsReorderParams.destLayout = r_params.output.GetLayout(); - weightsReorderParams.toImageType = Tensor::IsImageType(r_params.output.GetLayout()); - - newParams.weights = r_params.output; - } - - return true; - } - - inline JitConstants GetTensorFriendlyWorkGroupsJit(const DataTensor& t) - { - auto b = DataTensor::Channelndex(t.GetLayout(), Tensor::DataChannelName::BATCH); - auto f = DataTensor::Channelndex(t.GetLayout(), Tensor::DataChannelName::FEATURE); - auto x = DataTensor::Channelndex(t.GetLayout(), Tensor::DataChannelName::X); - - if (x == -1) - { - x = 2; - } - else - { - b = (b < x) ? b : b - 1; - f = (f < x) ? f : f - 1; - } - - JitConstants jit{ - MakeJitConstant("GWS_BATCH", b), - MakeJitConstant("GWS_FEATURE", f), - MakeJitConstant("GWS_YX", x), - }; - - return jit; - } - - inline std::vector<size_t> GetTensorFriendlyWorkGroups(const DataTensor& t) - { - std::vector<size_t> sizes; - auto y = DataTensor::Channelndex(t.GetLayout(), Tensor::DataChannelName::Y); - for (size_t i = 0; i < t.GetDims().size(); i++) - { - const auto& o = t.GetDims()[i]; - if (y == (int)i) - { - sizes.back() *= o.v; - } - else - { - sizes.push_back(o.v); - } - } - - for (size_t i = sizes.size(); i < 3; i++) - { - sizes.push_back(1U); - } - - return sizes; - } - - inline std::vector<size_t> GetOptimalLocalWorkGroupSizes(std::vector<size_t> gws) - { - const size_t lws_max = 256; - const size_t optimal_lws_values[] = { 256, 224, 192, 160, 128, 96, 64, 32, 16, 8, 7, 6, 5, 4, 3, 2, 1 }; - size_t total_lws = 1; - std::vector<size_t> lws; - for (size_t i = 0; i < gws.size(); ++i) - { - auto rest_lws = lws_max / total_lws; - size_t lws_idx = 0; - while (rest_lws < optimal_lws_values[lws_idx]) lws_idx++; - - while (gws[i] % optimal_lws_values[lws_idx]) lws_idx++; - - lws.push_back(optimal_lws_values[lws_idx]); - total_lws *= optimal_lws_values[lws_idx]; - } - - return lws; - } - - inline bool CheckInputsOutputNoPitchSameDims(const base_params& params) - { - bool no_pitch_same_dims = true; - - if (params.inputs.size()) - { - no_pitch_same_dims = !params.inputs[0].PitchesDifferFromLogicalDims(); - - for (size_t i = 1; i < params.inputs.size(); i++) - { - no_pitch_same_dims = no_pitch_same_dims && (params.inputs[0] == params.inputs[i]); - } - - no_pitch_same_dims = no_pitch_same_dims && (params.inputs[0] == params.output); - } - - return no_pitch_same_dims; - } + struct weight_bias_params; + struct convolution_params; + + bool CheckConvolutionPaddedInputDesc(const convolution_params& params, const DataTensor& reqDesc); + DataTensor GetConvolutionBFYXPaddedTensor(const convolution_params& cp); + bool CovolutionCheckInput(const Params& p, const optional_params& o); + bool CovolutionUpdateInputParams(convolution_params& params); + WeightsType DataTypeToWeightsType(Datatype t); + bool CheckWeights(const WeightsTensor& tensor, WeightsType reqType, std::vector<WeightsLayout> reqLayouts); + std::vector<size_t> GetImageSizes(const kernel_selector::WeightsTensor& dimensions, const WeightsLayout layout); + bool CheckImageSize(const weight_bias_params& newParams, const WeightsLayout layout); + bool UpdateWeightsParams(weight_bias_params& newParams, const optional_params& options, std::vector<WeightsLayout> layouts, WeightsReorderParams& weightsReorderParams); + JitConstants GetTensorFriendlyWorkGroupsJit(const DataTensor& t); + std::vector<size_t> GetTensorFriendlyWorkGroups(const DataTensor& t); + std::vector<size_t> GetOptimalLocalWorkGroupSizes(std::vector<size_t> gws); + bool CheckInputsOutputNoPitchSameDims(const base_params& params); }
\ No newline at end of file diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/kernel_base.h b/inference-engine/thirdparty/clDNN/kernel_selector/core/kernel_base.h index 6c8c1d5fc..80b501e8d 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/kernel_base.h +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/kernel_base.h @@ -18,9 +18,12 @@ #include "kernel_selector_common.h" #include "kernel_selector_params.h" - +#include "primitive_db.h" + namespace kernel_selector { + using primitive_db = kernel_selector::gpu::cache::primitive_db; + class KernelBase { public: diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/kernel_selector.cpp b/inference-engine/thirdparty/clDNN/kernel_selector/core/kernel_selector.cpp index edcaac239..6e938d006 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/kernel_selector.cpp +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/kernel_selector.cpp @@ -14,10 +14,10 @@ // limitations under the License. */ +#include "kernel_base.h" #include "kernel_selector_common.h" #include "kernel_selector.h" #include <type_traits> -#include <iostream> #include <sstream> #include <fstream> @@ -150,7 +150,7 @@ namespace kernel_selector { if (params.GetType() == kType && options.GetType() == kType) { - std::string hash = std::to_string(std::hash<std::string>{}(params.to_string())); + std::string hash = std::to_string(create_hash(params.to_string())); ParamsKey requireKey = params.GetParamsKey().Merge(options.GetSupportedKey()); std::tuple<std::string, int> cachedKernelConfig; @@ -232,7 +232,39 @@ namespace kernel_selector { // we have to handle it in order to avoid exception in KernelSelector as much we can } } + } + + //try to fallback to reference kernels if no optimized were found during tuning + if (!kernelsData.size()) + { + for (const auto& implementation : implementations) + { + const ParamsKey implKey = implementation->GetSupportedKey(); + //this time, check only implementations that have disabled tuning + if (implKey.Support(requireKey) && !implKey.TuningSupport()) + { + try + { + KernelsData kds = implementation->GetKernelsDataForAutoTune(params, options); + std::vector<uint64_t> runTimes = options.tuningParams.runner->run_kernels(kds); + + for (size_t i = 0; i < kds.size(); i++) + { + kds[i].runTime = runTimes[i]; + if (kernelsData.size() == 0 || kds[i].runTime < kernelsData[0].runTime) + { + kernelsData = { kds[i] }; + kernelName = implementation->GetName(); + } + } + } + catch (std::runtime_error&) + { + // we have to handle it in order to avoid exception in KernelSelector as much we can + } + } + } } if (kernelsData.size()) diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/kernel_selector.h b/inference-engine/thirdparty/clDNN/kernel_selector/core/kernel_selector.h index 4ba7f7314..3091aee40 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/kernel_selector.h +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/kernel_selector.h @@ -16,14 +16,14 @@ #pragma once -#include "kernel_base.h" #include "kernel_selector_common.h" -#include "kernel_selector_params.h" #include "kernel_runner_interface.h" #include "auto_tuner.h" namespace kernel_selector { + class KernelBase; + using KernelList = std::vector<std::shared_ptr<KernelBase>>; using ForceList = std::map<std::string, bool>; @@ -39,7 +39,7 @@ namespace kernel_selector template<typename T> inline void Attach() { - implementations.push_back(std::make_shared<T>(T())); + implementations.push_back(std::make_shared<T>()); } virtual KernelsData GetNaiveBestKernel(const Params& params, const optional_params& options, KernelType kType) const; diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/kernel_selector_common.cpp b/inference-engine/thirdparty/clDNN/kernel_selector/core/kernel_selector_common.cpp index eefeb4e52..c4f964a07 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/kernel_selector_common.cpp +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/kernel_selector_common.cpp @@ -15,8 +15,9 @@ */ #include "kernel_selector_common.h" - -namespace kernel_selector +#include <sstream> + +namespace kernel_selector { std::string GetStringEnv(const char* varName) { @@ -43,4 +44,374 @@ namespace kernel_selector return str; } -}
\ No newline at end of file + + std::string toString(ActivationFunction activation) + { + std::string method("LINEAR"); + switch (activation) + { + case ActivationFunction::LOGISTIC: method = "LOGISTIC"; break; + case ActivationFunction::HYPERBOLIC_TAN: method = "HYPERBOLIC_TAN"; break; + case ActivationFunction::RELU: method = "RELU"; break; + case ActivationFunction::RELU_NEGATIVE_SLOPE: method = "RELU_NEGATIVE_SLOPE"; break; + case ActivationFunction::CLAMP: method = "CLAMP"; break; + case ActivationFunction::SOFTRELU: method = "SOFTRELU"; break; + case ActivationFunction::ABS: method = "ABS"; break; + case ActivationFunction::SQUARE: method = "SQUARE"; break; + case ActivationFunction::SQRT: method = "SQRT"; break; + case ActivationFunction::LINEAR: method = "LINEAR"; break; + case ActivationFunction::ELU: method = "ELU"; break; + case ActivationFunction::RELU_GRAD: method = "RELU_GRAD"; break; + case ActivationFunction::RELU_NEGATIVE_SLOPE_GRAD: method = "RELU_NEGATIVE_SLOPE_GRAD"; break; + case ActivationFunction::SIN: method = "SIN"; break; + case ActivationFunction::ASIN: method = "ASIN"; break; + case ActivationFunction::SINH: method = "SINH"; break; + case ActivationFunction::COS: method = "COS"; break; + case ActivationFunction::ACOS: method = "ACOS"; break; + case ActivationFunction::COSH: method = "COSH"; break; + case ActivationFunction::LOG: method = "LOG"; break; + case ActivationFunction::LOG2: method = "LOG2"; break; + case ActivationFunction::EXP: method = "EXP"; break; + case ActivationFunction::NONE: method = "NONE"; break; + case ActivationFunction::NONE_GRAD: method = "NONE_GRAD"; break; + default: break; + } + return method; + } + + std::string toString(DataLayout l) + { + switch (l) + { + case kernel_selector::DataLayout::bf: return "BF"; + case kernel_selector::DataLayout::fb: return "FB"; + case kernel_selector::DataLayout::bfyx: return "BFYX"; + case kernel_selector::DataLayout::yxfb: return "YXFB"; + case kernel_selector::DataLayout::byxf: return "BYXF"; + case kernel_selector::DataLayout::fyxb: return "FYXB"; + case kernel_selector::DataLayout::bs_f_bsv8__af8: return "BS_F_BSV8__AF8"; + case kernel_selector::DataLayout::bs_f_bsv16__af8: return "BS_F_BSV16__AF8"; + case kernel_selector::DataLayout::bf8_xy16: return "BF8_XY16"; + case kernel_selector::DataLayout::brfyx: return "BRFYX"; + case kernel_selector::DataLayout::winograd_2x3_s1_data: return "WINOGRAD_2x3_S1_DATA"; + case kernel_selector::DataLayout::byxf_af32: return "BYXF_AF32"; + case kernel_selector::DataLayout::fs_bs_yx_bsv4_fsv32: return "FS_BS_YX_BSV4_FSV32"; + default: return ""; + } + } + + std::string toString(Datatype dType) + { + switch (dType) + { + case Datatype::INT8: return "INT8"; + case Datatype::UINT8: return "UINT8"; + case Datatype::INT16: return "INT16"; + case Datatype::UINT16: return "UINT16"; + case Datatype::INT32: return "INT32"; + case Datatype::UINT32: return "UINT32"; + case Datatype::INT64: return "INT64"; + case Datatype::F16: return "F16"; + case Datatype::F32: return "F32"; + default: return ""; + } + } + + std::string toString(WeightsType wType) + { + switch (wType) + { + case WeightsType::F16: return "F16"; + case WeightsType::F32: return "F32"; + case WeightsType::INT8: return "INT8"; + default: return ""; + } + } + + std::string toString(KernelType kt) + { + switch (kt) + { + case KernelType::UNKNOWN: return "UNKNOWN"; + case KernelType::CONVOLUTION: return "CONVOLUTION"; + case KernelType::LRN: return "LRN"; + case KernelType::POOLING: return "POOLING"; + case KernelType::ROI_POOLING: return "ROI_POOLING"; + case KernelType::FULLY_CONNECTED: return "FULLY_CONNECTED"; + case KernelType::ACTIVATION: return "ACTIVATION"; + case KernelType::SOFT_MAX: return "SOFT_MAX"; + case KernelType::REGION_YOLO: return "REGION_YOLO"; + case KernelType::REORG_YOLO: return "REORG_YOLO"; + case KernelType::ELTWISE: return "ELTWISE"; + case KernelType::REORDER: return "REORDER"; + case KernelType::SELECT: return "SELECT"; + default: + return ""; + } + } + + std::string toString(EltwiseMode b_mode) + { + switch (b_mode) + { + case EltwiseMode::ADD: return "ADD"; + case EltwiseMode::SUB: return "SUB"; + case EltwiseMode::MUL: return "MUL"; + case EltwiseMode::DIV: return "DIV"; + case EltwiseMode::MIN: return "MIN"; + case EltwiseMode::MAX: return "MAX"; + case EltwiseMode::POW: return "POW"; + case EltwiseMode::MODULU: return "MODULU"; + case EltwiseMode::SQRT: return "SQRT"; + case EltwiseMode::RSQRT: return "RSQRT"; + case EltwiseMode::ASSIGN: return "ASSIGN"; + default: + return ""; + } + } + + std::string toString(ReorderMode mode) + { + switch (mode) + { + case ReorderMode::xyzw: return "XYZW"; + case ReorderMode::xywz: return "XYWZ"; + case ReorderMode::xwyz: return "XWYZ"; + case ReorderMode::wxyz: return "WXYZ"; + case ReorderMode::xzyw: return "XZYW"; + case ReorderMode::zyxw: return "ZYXW"; + case ReorderMode::yxzw: return "YXZW"; + default: return "XYZW"; + } + } + + std::string toString(MeanSubtractMode mode) + { + switch (mode) + { + case MeanSubtractMode::NONE: return "NONE"; + case MeanSubtractMode::INSIDE_PARAMS: return "INSIDE_PARAMS"; + case MeanSubtractMode::IN_BUFFER: return "IN_BUFFER"; + default: return ""; + } + } + + std::string toString(ArgMaxMinOut mode) + { + switch (mode) + { + case ArgMaxMinOut::MAX: return "MAX"; + case ArgMaxMinOut::MIN: return "MIN"; + default: return ""; + } + } + + std::string toString(ArgMaxMinAxis mode) + { + switch (mode) + { + case ArgMaxMinAxis::BATCH: return "BATCH"; + case ArgMaxMinAxis::FEATURE: return "FEATURE"; + case ArgMaxMinAxis::X: return "X"; + case ArgMaxMinAxis::Y: return "Y"; + case ArgMaxMinAxis::XYF: return "XYF"; + default: return ""; + } + } + + std::string toString(LookUpTableAxis mode) + { + switch (mode) + { + case LookUpTableAxis::BATCH: return "BATCH"; + case LookUpTableAxis::FEATURE: return "FEATURE"; + case LookUpTableAxis::X: return "X"; + case LookUpTableAxis::Y: return "Y"; + case LookUpTableAxis::XYF: return "XYF"; + default: return ""; + } + } + + std::string toString(PoolType mode) + { + switch (mode) + { + case PoolType::MAX: return "MAX"; + case PoolType::AVG: return "AVG"; + case PoolType::MAX_WITH_ARGMAX: return "MAX_WITH_ARGMAX"; + case PoolType::BILINEAR: return "BILINEAR"; + default: return ""; + } + } + + std::string toString(LRNMode mode) + { + switch (mode) + { + case LRNMode::ACROSS_CHANNEL: return "ACROSS_CHANNEL"; + case LRNMode::WITHIN_CHANNEL: return "WITHIN_CHANNEL"; + default: return ""; + } + } + + std::string toString(KernelDividerMode mode) + { + switch (mode) + { + case KernelDividerMode::DONT_CARE: return "DONT_CARE"; + case KernelDividerMode::FIXED: return "FIXED"; + case KernelDividerMode::DYNAMIC: return "DYNAMIC"; + case KernelDividerMode::DYNAMIC_WITH_PADDING: return "DYNAMIC_WITH_PADDING"; + default: return ""; + } + } + + std::string toString(SoftmaxDim d) + { + switch (d) + { + case SoftmaxDim::X: return "X"; + case SoftmaxDim::Y: return "Y"; + case SoftmaxDim::FEATURE: return "FEATURE"; + default: return ""; + } + } + + std::string toString(NormalizeMode mode) + { + switch (mode) + { + case NormalizeMode::ACROSS_SPATIAL: return "ACROSS_SPATIAL"; + case NormalizeMode::WITHIN_SPATIAL: return "WITHIN_SPATIAL"; + default: return ""; + } + } + + std::string toString(MVNMode mode) + { + switch (mode) + { + case MVNMode::ACROSS_CHANNELS: return "ACROSS_CHANNELS"; + case MVNMode::WITHIN_CHANNELS: return "WITHIN_CHANNELS"; + default: return ""; + } + } + + std::string toString(WeightsLayout layout) + { + switch (layout) + { + case WeightsLayout::oi: return "OI"; + case WeightsLayout::io: return "IO"; + case WeightsLayout::oiyx: return "OIYX"; + case WeightsLayout::oyxi: return "OYXI"; + case WeightsLayout::iyxo: return "IYXO"; + case WeightsLayout::yxio: return "YXIO"; + case WeightsLayout::os_iyx_osv16: return "OS_IYX_OSV16"; + case WeightsLayout::os_iyx_osv16_rotate_180: return "OS_IYX_OSV16_ROTATE_180"; + case WeightsLayout::os_i_osv16: return "OS_I_OSV16"; + case WeightsLayout::os_i_osv8__ai8: return "OS_I_OSV8__AI8"; + case WeightsLayout::os_i_osv16__ai8: return "OS_I_OSV16__AI8"; + case WeightsLayout::i_yxs_os_yxsv2_osv16: return "I_YXS_OS_YXSV2_OSV16"; + case WeightsLayout::iy_xs_os_xsv2_osv16__ao32: return "IY_XS_OS_XSV2_OSV16__AO32"; + case WeightsLayout::iy_xs_os_xsv2_osv8__ao32: return "IY_XS_OS_XSV2_OSV8__AO32"; + case WeightsLayout::image_2d_weights_c4_fyx_b: return "IMAGE_2D_WEIGHTS_C4_FYX_B"; + case WeightsLayout::image_2d_weights_c1_b_fyx: return "IMAGE_2D_WEIGHTS_C1_B_FYX"; + case WeightsLayout::winograd_2x3_s1_weights: return "WINOGRAD_2x3_S1_WEIGHTS"; + case WeightsLayout::winograd_2x3_s1_fused_weights: return "WINOGRAD_2x3_S1_FUSED_WEIGHTS"; + case WeightsLayout::winograd_6x3_s1_fused_weights: return "WINOGRAD_6x3_S1_FUSED_WEIGHTS"; + case WeightsLayout::image_2d_weights_winograd_6x3_s1_fbxyb: return "IMAGE_2D_WEIGHTS_WINOGRAD_6x3_S1_FBXYB"; + case WeightsLayout::image_2d_weights_winograd_6x3_s1_xfbyb: return "IMAGE_2D_WEIGHTS_WINOGRAD_6x3_S1_XFBYB"; + case WeightsLayout::os_is_yx_isa8_osv8_isv4: return "OS_IS_YX_ISA8_OSV8_ISV4"; + case WeightsLayout::is_o_yx_isv32: return "IS_O_YX_ISV32"; + default: + return ""; + break; + } + } + + std::string toString(ConcatAxis a) + { + switch (a) + { + case ConcatAxis::X: return "X"; + case ConcatAxis::Y: return "Y"; + case ConcatAxis::FEATURE: return "FEATURE"; + case ConcatAxis::BATCH: return "BATCH"; + default: return ""; + } + } + + std::string toString(TileAxis a) + { + switch (a) + { + case TileAxis::X: return "X"; + case TileAxis::Y: return "Y"; + case TileAxis::FEATURE: return "FEATURE"; + case TileAxis::BATCH: return "BATCH"; + default: return ""; + } + } + + std::string toString(SampleType type) + { + switch (type) + { + case SampleType::NEAREST: return "SAMPLE_TYPE_NEAREST"; + case SampleType::BILINEAR: return "SAMPLE_TYPE_BILINEAR"; + default: return ""; + } + } + + std::string toString(const BorderType type) + { + switch (type) + { + case BorderType::ZERO: return "BORDER_TYPE_ZERO"; + case BorderType::MIRROR: return "BORDER_TYPE_MIRROR"; + case BorderType::MIRROR_101: return "BORDER_TYPE_MIRROR_101"; + default: return ""; + } + } + + std::string toString(const IndexSelectAxis& axis) + { + switch (axis) + { + case IndexSelectAxis::BATCH: return "INDEX_SELECT_AXIS_BATCH"; + case IndexSelectAxis::FEATURE: return "INDEX_SELECT_AXIS_FEATURE"; + case IndexSelectAxis::X: return "INDEX_SELECT_AXIS_X"; + case IndexSelectAxis::Y: return "INDEX_SELECT_AXIS_Y"; + default: return ""; + } + } + + std::string toString(NonLinearParams params) + { + std::stringstream s; + s << "m" << params.m << "_n" << params.n; + return s.str(); + } + + std::string toString(const Tensor::Dim& dim) + { + std::stringstream s; + s << "v" << dim.v << "_p" << dim.pitch << "_" << dim.pad.before << "_" << dim.pad.after; + return s.str(); + } + + std::string toString(const DataTensor& tensor) + { + std::stringstream s; + s << toString(tensor.GetDType()) << "_"; + s << toString(tensor.GetLayout()) << "_"; + int i = 0; + for (auto dim : tensor.GetDims()) + { + s << "d" << i << "_" << toString(dim) << "_"; + i++; + } + return s.str(); + } +} diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/kernel_selector_common.h b/inference-engine/thirdparty/clDNN/kernel_selector/core/kernel_selector_common.h index a5fbe572e..ef12a74b9 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/kernel_selector_common.h +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/kernel_selector_common.h @@ -15,13 +15,18 @@ */ #pragma once + +#include "kernel_selector_params.h" +#include "primitive_db.h" + +#include <cfloat> +#include <cstdint> #include <limits> -#include <string> #include <memory> -#include "primitive_db.h" -#include "kernel_selector_params.h" -#include <float.h> #include <sstream> +#include <cfloat> +#include <string> +#include <vector> #define AGE_BASED "-cl-no-subgroup-ifp" #define ROUND_ROBIN "" @@ -47,11 +52,6 @@ namespace kernel_selector { #define TUTORIAL_PRIORITY (DONT_USE_IF_HAVE_SOMETHING_ELSE + 1.f) #define NOT_SUPPORTED (FLT_MAX) - //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// - // usings - //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// - using primitive_db = kernel_selector::gpu::cache::primitive_db; - std::string GetStringEnv(const char* varName); //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// @@ -259,325 +259,57 @@ namespace kernel_selector { //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// // to string functions //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// - inline std::string toString(ActivationFunction activation) + std::string toString(ActivationFunction activation); + std::string toString(DataLayout l); + std::string toString(Datatype dType); + std::string toString(WeightsType wType); + std::string toString(KernelType kt); + std::string toString(EltwiseMode b_mode); + std::string toString(ReorderMode mode); + std::string toString(MeanSubtractMode mode); + std::string toString(ArgMaxMinOut mode); + std::string toString(ArgMaxMinAxis mode); + std::string toString(LookUpTableAxis mode); + std::string toString(PoolType mode); + std::string toString(LRNMode mode); + std::string toString(KernelDividerMode mode); + std::string toString(SoftmaxDim d); + std::string toString(NormalizeMode mode); + std::string toString(MVNMode mode); + std::string toString(WeightsLayout layout); + std::string toString(ConcatAxis a); + std::string toString(TileAxis a); + std::string toString(SampleType type); + std::string toString(const BorderType type); + std::string toString(NonLinearParams params); + std::string toString(const Tensor::Dim& dim); + std::string toString(const DataTensor& tensor); + std::string toString(const IndexSelectAxis& axis); + inline std::uint64_t create_hash(const unsigned char* begin, const unsigned char* end) { - std::string method("LINEAR"); - switch (activation) - { - case ActivationFunction::LOGISTIC: method = "LOGISTIC"; break; - case ActivationFunction::HYPERBOLIC_TAN: method = "HYPERBOLIC_TAN"; break; - case ActivationFunction::RELU: method = "RELU"; break; - case ActivationFunction::RELU_NEGATIVE_SLOPE: method = "RELU_NEGATIVE_SLOPE"; break; - case ActivationFunction::CLAMP: method = "CLAMP"; break; - case ActivationFunction::SOFTRELU: method = "SOFTRELU"; break; - case ActivationFunction::ABS: method = "ABS"; break; - case ActivationFunction::SQUARE: method = "SQUARE"; break; - case ActivationFunction::SQRT: method = "SQRT"; break; - case ActivationFunction::LINEAR: method = "LINEAR"; break; - case ActivationFunction::ELU: method = "ELU"; break; - case ActivationFunction::RELU_GRAD: method = "RELU_GRAD"; break; - case ActivationFunction::RELU_NEGATIVE_SLOPE_GRAD: method = "RELU_NEGATIVE_SLOPE_GRAD"; break; - case ActivationFunction::NONE: method = "NONE"; break; - case ActivationFunction::NONE_GRAD: method = "NONE_GRAD"; break; - default: break; - } - return method; - } + // Compatible with VS std::hash. + constexpr auto start_acc = static_cast<std::uint64_t>(UINT64_C(14695981039346656037)); + constexpr auto mul_factor = static_cast<std::uint64_t>(UINT64_C(1099511628211)); - inline std::string toString(DataLayout l) - { - switch (l) + std::uint64_t acc = start_acc; + for (auto elem_it = begin; elem_it != end; ++elem_it) { - case kernel_selector::DataLayout::bf: return "BF"; - case kernel_selector::DataLayout::fb: return "FB"; - case kernel_selector::DataLayout::bfyx: return "BFYX"; - case kernel_selector::DataLayout::yxfb: return "YXFB"; - case kernel_selector::DataLayout::byxf: return "BYXF"; - case kernel_selector::DataLayout::fyxb: return "FYXB"; - case kernel_selector::DataLayout::bs_f_bsv8__af8: return "BS_F_BSV8__AF8"; - case kernel_selector::DataLayout::bs_f_bsv16__af8: return "BS_F_BSV16__AF8"; - case kernel_selector::DataLayout::bf8_xy16: return "BF8_XY16"; - case kernel_selector::DataLayout::brfyx: return "BRFYX"; - case kernel_selector::DataLayout::winograd_2x3_s1_data: return "WINOGRAD_2x3_S1_DATA"; - case kernel_selector::DataLayout::byxf_af32: return "BYXF_AF32"; - default: return ""; + acc ^= static_cast<std::uint64_t>(*elem_it); + acc *= mul_factor; } - } - inline std::string toString(Datatype dType) - { - switch (dType) - { - case Datatype::INT8: return "INT8"; - case Datatype::UINT8: return "UINT8"; - case Datatype::INT16: return "INT16"; - case Datatype::UINT16: return "UINT16"; - case Datatype::INT32: return "INT32"; - case Datatype::UINT32: return "UINT32"; - case Datatype::F16: return "F16"; - case Datatype::F32: return "F32"; - default: return ""; - } + return acc; } - inline std::string toString(WeightsType wType) + template <typename ElemTy> + std::uint64_t create_hash(const ElemTy* begin, const std::size_t size) { - switch (wType) - { - case WeightsType::F16: return "F16"; - case WeightsType::F32: return "F32"; - case WeightsType::INT8: return "INT8"; - default: return ""; - } + return create_hash(reinterpret_cast<const unsigned char*>(begin), reinterpret_cast<const unsigned char*>(begin + size)); } - inline std::string toString(KernelType kt) + template <typename CharTy, typename CharTraits, typename AllocatorTy> + std::uint64_t create_hash(const std::basic_string<CharTy, CharTraits, AllocatorTy>& value) { - switch (kt) - { - case KernelType::UNKNOWN: return "UNKNOWN"; - case KernelType::CONVOLUTION: return "CONVOLUTION"; - case KernelType::LRN: return "LRN"; - case KernelType::POOLING: return "POOLING"; - case KernelType::ROI_POOLING: return "ROI_POOLING"; - case KernelType::FULLY_CONNECTED: return "FULLY_CONNECTED"; - case KernelType::ACTIVATION: return "ACTIVATION"; - case KernelType::SOFT_MAX: return "SOFT_MAX"; - case KernelType::REGION_YOLO: return "REGION_YOLO"; - case KernelType::REORG_YOLO: return "REORG_YOLO"; - case KernelType::ELTWISE: return "ELTWISE"; - case KernelType::REORDER: return "REORDER"; - default: - return ""; - } - } - - inline std::string toString(EltwiseMode b_mode) - { - switch (b_mode) - { - case EltwiseMode::ADD: return "ADD"; - case EltwiseMode::SUB: return "SUB"; - case EltwiseMode::MUL: return "MUL"; - case EltwiseMode::DIV: return "DIV"; - case EltwiseMode::MIN: return "MIN"; - case EltwiseMode::MAX: return "MAX"; - case EltwiseMode::POW: return "POW"; - case EltwiseMode::MODULU: return "MODULU"; - case EltwiseMode::SQRT: return "SQRT"; - case EltwiseMode::RSQRT: return "RSQRT"; - case EltwiseMode::ASSIGN: return "ASSIGN"; - default: - return ""; - } - } - - inline std::string toString(ReorderMode mode) - { - switch (mode) - { - case ReorderMode::xyzw: return "XYZW"; - case ReorderMode::xywz: return "XYWZ"; - case ReorderMode::xwyz: return "XWYZ"; - case ReorderMode::wxyz: return "WXYZ"; - case ReorderMode::xzyw: return "XZYW"; - case ReorderMode::zyxw: return "ZYXW"; - case ReorderMode::yxzw: return "YXZW"; - default: return "XYZW"; - } - } - - inline std::string toString(MeanSubtractMode mode) - { - switch (mode) - { - case MeanSubtractMode::NONE: return "NONE"; - case MeanSubtractMode::INSIDE_PARAMS: return "INSIDE_PARAMS"; - case MeanSubtractMode::IN_BUFFER: return "IN_BUFFER"; - default: return ""; - } - } - - inline std::string toString(ArgMaxMinOut mode) - { - switch (mode) - { - case ArgMaxMinOut::MAX: return "MAX"; - case ArgMaxMinOut::MIN: return "MIN"; - default: return ""; - } - } - - inline std::string toString(ArgMaxMinAxis mode) - { - switch (mode) - { - case ArgMaxMinAxis::BATCH: return "BATCH"; - case ArgMaxMinAxis::FEATURE: return "FEATURE"; - case ArgMaxMinAxis::X: return "X"; - case ArgMaxMinAxis::Y: return "Y"; - case ArgMaxMinAxis::XYF: return "XYF"; - default: return ""; - } - } - - inline std::string toString(LookUpTableAxis mode) - { - switch (mode) - { - case LookUpTableAxis::BATCH: return "BATCH"; - case LookUpTableAxis::FEATURE: return "FEATURE"; - case LookUpTableAxis::X: return "X"; - case LookUpTableAxis::Y: return "Y"; - case LookUpTableAxis::XYF: return "XYF"; - default: return ""; - } - } - - inline std::string toString(PoolType mode) - { - switch (mode) - { - case PoolType::MAX: return "MAX"; - case PoolType::AVG: return "AVG"; - case PoolType::MAX_WITH_ARGMAX: return "MAX_WITH_ARGMAX"; - default: return ""; - } + return create_hash<CharTy>(value.data(), value.size()); } - - inline std::string toString(LRNMode mode) - { - switch (mode) - { - case LRNMode::ACROSS_CHANNEL: return "ACROSS_CHANNEL"; - case LRNMode::WITHIN_CHANNEL: return "WITHIN_CHANNEL"; - default: return ""; - } - } - - inline std::string toString(KernelDividerMode mode) - { - switch (mode) - { - case KernelDividerMode::DONT_CARE: return "DONT_CARE"; - case KernelDividerMode::FIXED: return "FIXED"; - case KernelDividerMode::DYNAMIC: return "DYNAMIC"; - case KernelDividerMode::DYNAMIC_WITH_PADDING: return "DYNAMIC_WITH_PADDING"; - default: return ""; - } - } - - inline std::string toString(SoftmaxDim d) - { - switch (d) - { - case SoftmaxDim::X: return "X"; - case SoftmaxDim::Y: return "Y"; - case SoftmaxDim::FEATURE: return "FEATURE"; - default: return ""; - } - } - - inline std::string toString(NormalizeMode mode) - { - switch (mode) - { - case NormalizeMode::ACROSS_SPATIAL: return "ACROSS_SPATIAL"; - case NormalizeMode::WITHIN_SPATIAL: return "WITHIN_SPATIAL"; - default: return ""; - } - } - - inline std::string toString(MVNMode mode) - { - switch (mode) - { - case MVNMode::ACROSS_CHANNELS: return "ACROSS_CHANNELS"; - case MVNMode::WITHIN_CHANNELS: return "WITHIN_CHANNELS"; - default: return ""; - } - } - - inline std::string toString(WeightsLayout layout) - { - switch (layout) - { - case WeightsLayout::oi: return "OI"; - case WeightsLayout::io: return "IO"; - case WeightsLayout::oiyx: return "OIYX"; - case WeightsLayout::oyxi: return "OYXI"; - case WeightsLayout::iyxo: return "IYXO"; - case WeightsLayout::yxio: return "YXIO"; - case WeightsLayout::os_iyx_osv16: return "OS_IYX_OSV16"; - case WeightsLayout::os_iyx_osv16_rotate_180: return "OS_IYX_OSV16_ROTATE_180"; - case WeightsLayout::os_i_osv16: return "OS_I_OSV16"; - case WeightsLayout::os_i_osv8__ai8: return "OS_I_OSV8__AI8"; - case WeightsLayout::os_i_osv16__ai8: return "OS_I_OSV16__AI8"; - case WeightsLayout::i_yxs_os_yxsv2_osv16: return "I_YXS_OS_YXSV2_OSV16"; - case WeightsLayout::iy_xs_os_xsv2_osv16__ao32: return "IY_XS_OS_XSV2_OSV16__AO32"; - case WeightsLayout::iy_xs_os_xsv2_osv8__ao32: return "IY_XS_OS_XSV2_OSV8__AO32"; - case WeightsLayout::image_2d_weights_c4_fyx_b: return "IMAGE_2D_WEIGHTS_C4_FYX_B"; - case WeightsLayout::image_2d_weights_c1_b_fyx: return "IMAGE_2D_WEIGHTS_C1_B_FYX"; - case WeightsLayout::winograd_2x3_s1_weights: return "WINOGRAD_2x3_S1_WEIGHTS"; - case WeightsLayout::winograd_2x3_s1_fused_weights: return "WINOGRAD_2x3_S1_FUSED_WEIGHTS"; - case WeightsLayout::winograd_6x3_s1_fused_weights: return "WINOGRAD_6x3_S1_FUSED_WEIGHTS"; - case WeightsLayout::image_2d_weights_winograd_6x3_s1_fbxyb: return "IMAGE_2D_WEIGHTS_WINOGRAD_6x3_S1_FBXYB"; - case WeightsLayout::image_2d_weights_winograd_6x3_s1_xfbyb: return "IMAGE_2D_WEIGHTS_WINOGRAD_6x3_S1_XFBYB"; - case WeightsLayout::os_is_yx_isa8_osv8_isv4: return "OS_IS_YX_ISA8_OSV8_ISV4"; - default: - return ""; - break; - } - } - - inline std::string toString(ConcatAxis a) - { - switch (a) - { - case ConcatAxis::X: return "X"; - case ConcatAxis::Y: return "Y"; - case ConcatAxis::FEATURE: return "FEATURE"; - case ConcatAxis::BATCH: return "BATCH"; - default: return ""; - } - } - - inline std::string toString(SampleType type) - { - switch (type) - { - case SampleType::NEAREST: return "SAMPLE_TYPE_NEAREST"; - case SampleType::BILINEAR: return "SAMPLE_TYPE_BILINEAR"; - default: return ""; - } - } - - inline std::string toString(NonLinearParams params) - { - std::stringstream s; - s << "m" << params.m << "_n" << params.n; - return s.str(); - } - - inline std::string toString(Tensor::Dim dim) - { - std::stringstream s; - s << "v" << dim.v << "_p" << dim.pitch << "_" << dim.pad.before << "_" << dim.pad.after; - return s.str(); - } - - inline std::string toString(DataTensor tensor) - { - std::stringstream s; - s << toString(tensor.GetDType()) << "_"; - s << toString(tensor.GetLayout()) << "_"; - int i = 0; - for (auto dim : tensor.GetDims()) - { - s << "d" << i << "_" << toString(dim) << "_"; - i++; - } - return s.str(); - } - } diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/kernel_selector_params.cpp b/inference-engine/thirdparty/clDNN/kernel_selector/core/kernel_selector_params.cpp index 2baff0735..bd718c17a 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/kernel_selector_params.cpp +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/kernel_selector_params.cpp @@ -17,9 +17,439 @@ #include "kernel_selector_params.h" #include "kernel_selector_common.h" #include <sstream> - + namespace kernel_selector { + //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// + // ParamsKey + //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// + void ParamsKey::EnableInputDataType(Datatype dt) + { + switch (dt) + { + case Datatype::INT8: + key.inputType.val.int8 = 1; + break; + case Datatype::UINT8: + key.inputType.val.uint8 = 1; + break; + case Datatype::INT16: + key.inputType.val.int16 = 1; + break; + case Datatype::UINT16: + key.inputType.val.uint16 = 1; + break; + case Datatype::INT32: + key.inputType.val.int32 = 1; + break; + case Datatype::UINT32: + key.inputType.val.uint32 = 1; + break; + case Datatype::INT64: + key.inputType.val.int64 = 1; + break; + case Datatype::F16: + key.inputType.val.F16 = 1; + break; + case Datatype::F32: + key.inputType.val.F32 = 1; + break; + default: + break; + } + } + + void ParamsKey::EnableAllInputDataType() + { + key.inputType.raw = 0xffffffff; + } + + void ParamsKey::EnableOutputDataType(Datatype dt) + { + switch (dt) + { + case Datatype::INT8: + key.outputType.val.int8 = 1; + break; + case Datatype::UINT8: + key.outputType.val.uint8 = 1; + break; + case Datatype::INT16: + key.outputType.val.int16 = 1; + break; + case Datatype::UINT16: + key.outputType.val.uint16 = 1; + break; + case Datatype::INT32: + key.outputType.val.int32 = 1; + break; + case Datatype::UINT32: + key.outputType.val.uint32 = 1; + break; + case Datatype::INT64: + key.outputType.val.int64 = 1; + break; + case Datatype::F16: + key.outputType.val.F16 = 1; + break; + case Datatype::F32: + key.outputType.val.F32 = 1; + break; + default: + break; + } + } + + void ParamsKey::EnableAllOutputDataType() + { + key.outputType.raw = 0xffffffff; + } + + void ParamsKey::EnableInputWeightsType(WeightsType wt) + { + switch (wt) + { + case WeightsType::F16: + key.inputWeightsType.val.F16 = 1; + break; + case WeightsType::F32: + key.inputWeightsType.val.F32 = 1; + break; + case WeightsType::INT8: + key.inputWeightsType.val.int8 = 1; + break; + default: + break; + } + } + + void ParamsKey::EnableAllInputWeightsType() + { + key.inputWeightsType.raw = 0xffffffff; + } + + void ParamsKey::EnableOutputWeightsType(WeightsType wt) + { + switch (wt) + { + case WeightsType::F16: + key.outputWeightsType.val.F16 = 1; + break; + case WeightsType::F32: + key.outputWeightsType.val.F32 = 1; + break; + case WeightsType::INT8: + key.outputWeightsType.val.int8 = 1; + break; + default: + break; + } + } + + void ParamsKey::EnableAllOutputWeightsType() + { + key.outputWeightsType.raw = 0xffffffff; + } + + void ParamsKey::EnableLRNMode(LRNMode m) + { + switch (m) + { + case LRNMode::ACROSS_CHANNEL: + key.restrict.val.dedicated.norm.across = 1; + break; + case LRNMode::WITHIN_CHANNEL: + key.restrict.val.dedicated.norm.within = 1; + break; + default: + break; + } + } + + void ParamsKey::EnableLookUpTableAxis(LookUpTableAxis m) + { + switch (m) + { + case kernel_selector::LookUpTableAxis::BATCH: + key.restrict.val.dedicated.lookt.axisBatch = 1; + break; + case kernel_selector::LookUpTableAxis::FEATURE: + key.restrict.val.dedicated.lookt.axisFeature = 1; + break; + case kernel_selector::LookUpTableAxis::X: + key.restrict.val.dedicated.lookt.axisX = 1; + break; + case kernel_selector::LookUpTableAxis::Y: + key.restrict.val.dedicated.lookt.axisY = 1; + break; + case kernel_selector::LookUpTableAxis::XYF: + key.restrict.val.dedicated.lookt.axisXYF = 1; + break; + default: + break; + } + } + + void ParamsKey::EnableNormalizeMode(NormalizeMode m) + { + switch (m) + { + case NormalizeMode::ACROSS_SPATIAL: + key.restrict.val.dedicated.norm.across = 1; + break; + case NormalizeMode::WITHIN_SPATIAL: + key.restrict.val.dedicated.norm.within = 1; + break; + default: + break; + } + } + + void ParamsKey::EnableMVNMode(MVNMode m) + { + switch (m) + { + case MVNMode::ACROSS_CHANNELS: + key.restrict.val.dedicated.mvn.across = 1; + break; + case MVNMode::WITHIN_CHANNELS: + key.restrict.val.dedicated.mvn.within = 1; + break; + default: + break; + } + } + + void ParamsKey::EnableMVNNormalizeVariance() + { + key.restrict.val.dedicated.mvn.normalize_variance = 1; + } + + void ParamsKey::EnableLRNKernelDividerMode(KernelDividerMode m) + { + switch (m) + { + case KernelDividerMode::FIXED: + key.restrict.val.dedicated.norm.fixedKenrelDivider = 1; + break; + case KernelDividerMode::DYNAMIC: + key.restrict.val.dedicated.norm.dynamicKenrelDivider = 1; + break; + default: + break; + } + } + + void ParamsKey::EnablePoolKernelDividerMode(KernelDividerMode m) + { + switch (m) + { + case KernelDividerMode::FIXED: + key.restrict.val.dedicated.pooling.fixedKenrelDivider = 1; + break; + case KernelDividerMode::DYNAMIC: + key.restrict.val.dedicated.pooling.dynamicKenrelDivider = 1; + break; + case KernelDividerMode::DYNAMIC_WITH_PADDING: + key.restrict.val.dedicated.pooling.dynamicKenrelDividerWithPadding = 1; + break; + default: + break; + } + } + + void ParamsKey::EnablePoolType(PoolType t) + { + switch (t) + { + case PoolType::MAX: + key.restrict.val.dedicated.pooling.max = 1; + break; + case PoolType::AVG: + key.restrict.val.dedicated.pooling.avg = 1; + break; + case PoolType::MAX_WITH_ARGMAX: + key.restrict.val.dedicated.pooling.max_with_argmax = 1; + break; + case PoolType::BILINEAR: + key.restrict.val.dedicated.pooling.bilinear = 1; + default: + break; + } + } + + void ParamsKey::EnablePoolRemainder(PoolRemainder r) + { + switch (r) + { + case PoolRemainder::FLOOR: + key.restrict.val.dedicated.pooling.floor = 1; + break; + case PoolRemainder::CEIL: + key.restrict.val.dedicated.pooling.ceil = 1; + break; + default: + break; + } + } + + void ParamsKey::EnableSoftmaxDim(SoftmaxDim d) + { + switch (d) + { + case SoftmaxDim::X: + key.restrict.val.dedicated.softmax.dimX = 1; + break; + case SoftmaxDim::Y: + key.restrict.val.dedicated.softmax.dimY = 1; + break; + case SoftmaxDim::FEATURE: + key.restrict.val.dedicated.softmax.dimFeature = 1; + break; + default: + break; + } + } + + void ParamsKey::EnableConcatAxis(ConcatAxis a) + { + switch (a) + { + case ConcatAxis::X: + key.restrict.val.dedicated.concat.axisX = 1; + break; + case ConcatAxis::Y: + key.restrict.val.dedicated.concat.axisY = 1; + break; + case ConcatAxis::FEATURE: + key.restrict.val.dedicated.concat.axisFeature = 1; + break; + case ConcatAxis::BATCH: + key.restrict.val.dedicated.concat.axisBatch = 1; + break; + default: + break; + } + } + + void ParamsKey::EnableUpSamplingSampleType(SampleType a) + { + switch (a) + { + case SampleType::NEAREST: + key.restrict.val.dedicated.upsample.nearest = 1; + break; + case SampleType::BILINEAR: + key.restrict.val.dedicated.upsample.bilinear = 1; + break; + default: + break; + } + } + + void ParamsKey::EnableArgMaxMinAxis(ArgMaxMinAxis a) + { + switch (a) + { + case ArgMaxMinAxis::X: + key.restrict.val.dedicated.argm.axisX = 1; + break; + case ArgMaxMinAxis::Y: + key.restrict.val.dedicated.argm.axisY = 1; + break; + case ArgMaxMinAxis::FEATURE: + key.restrict.val.dedicated.argm.axisFeature = 1; + break; + case ArgMaxMinAxis::BATCH: + key.restrict.val.dedicated.argm.axisBatch = 1; + break; + case ArgMaxMinAxis::XYF: + key.restrict.val.dedicated.argm.axisXYF = 1; + break; + default: + break; + } + } + + void ParamsKey::EnableIndexSelectAxis(IndexSelectAxis a) + { + switch (a) + { + case IndexSelectAxis::X: + key.restrict.val.dedicated.idxsel.axisX = 1; + break; + case IndexSelectAxis::Y: + key.restrict.val.dedicated.idxsel.axisY = 1; + break; + case IndexSelectAxis::FEATURE: + key.restrict.val.dedicated.idxsel.axisFeature = 1; + break; + case IndexSelectAxis::BATCH: + key.restrict.val.dedicated.idxsel.axisBatch = 1; + break; + default: + break; + } + } + + void ParamsKey::EnableLookUpTableIndicesFormat(Datatype a) + { + if (a == Datatype::F32) + key.restrict.val.dedicated.lookt.indicesF32 = 1; + else + key.restrict.val.dedicated.lookt.indicesOther = 1; + } + + bool ParamsKey::Support(const ParamsKey& k) const + { + return + ((key.restrict.raw & k.key.restrict.raw) == k.key.restrict.raw) && // check if this kernel supports this params + ((key.machineInfo.raw & k.key.machineInfo.raw) == key.machineInfo.raw) && // check if machine supports this kernel + ((key.inputType.raw & k.key.inputType.raw) == k.key.inputType.raw) && + ((key.outputType.raw & k.key.outputType.raw) == k.key.outputType.raw) && + ((key.inputWeightsType.raw & k.key.inputWeightsType.raw) == k.key.inputWeightsType.raw) && + ((key.outputWeightsType.raw & k.key.outputWeightsType.raw) == k.key.outputWeightsType.raw) && + ((key.inputLayout & k.key.inputLayout) != 0 || key.inputLayout == k.key.inputLayout) && + ((key.outputLayout & k.key.outputLayout) != 0 || key.outputLayout == k.key.outputLayout) && + ((key.weightsInputLayout & k.key.weightsInputLayout) != 0 || key.weightsInputLayout == k.key.weightsInputLayout) && + ((key.weightsOutputLayout & k.key.weightsOutputLayout) != 0 || key.weightsOutputLayout == k.key.weightsOutputLayout); + } + + ParamsKey ParamsKey::Merge(const ParamsKey& k) const + { + ParamsKey ret; + ret.key.restrict.raw = key.restrict.raw | k.key.restrict.raw; + ret.key.machineInfo.raw = key.machineInfo.raw | k.key.machineInfo.raw; + ret.key.inputType.raw = key.inputType.raw | k.key.inputType.raw; + ret.key.outputType.raw = key.outputType.raw | k.key.outputType.raw; + ret.key.inputWeightsType.raw = key.inputWeightsType.raw | k.key.inputWeightsType.raw; + ret.key.outputWeightsType.raw = key.outputWeightsType.raw | k.key.outputWeightsType.raw; + ret.key.inputLayout = key.inputLayout | k.key.inputLayout; + ret.key.outputLayout = key.outputLayout | k.key.outputLayout; + ret.key.weightsInputLayout = key.weightsInputLayout | k.key.weightsInputLayout; + ret.key.weightsOutputLayout = key.weightsOutputLayout | k.key.weightsOutputLayout; + return ret; + } + //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// + // Params + //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// + ParamsKey Params::GetParamsKey() const + { + ParamsKey k; + + if (engineInfo.bSubGroupSupport) + { + k.EnableSubGroup(); + } + + if (engineInfo.bSubGroupShortSupport) + { + k.EnableSubGroupShort(); + } + + return k; + } + std::string Params::to_string() const { std::stringstream s; @@ -27,6 +457,91 @@ namespace kernel_selector { return s.str(); } + //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// + // optional_params + //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// + ParamsKey optional_params::GetSupportedKey() const + { + ParamsKey k; + + for (auto l : inputLayouts) + { + k.EnableInputLayout(l); + } + + for (auto l : outputLayouts) + { + k.EnableOutputLayout(l); + } + + return k; + } + + //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// + // base_params + //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// + ParamsKey base_params::GetParamsKey() const + { + ParamsKey k = Params::GetParamsKey(); + + bool bBatching = false; + bool bPitches = false; + bool bOffests = false; + bool bDifferentTypes = false; + bool bFP16Used = (output.GetDType() == Datatype::F16); + + for (const auto& i : inputs) + { + k.EnableInputDataType(i.GetDType()); + k.EnableInputLayout(i.GetLayout()); + + bBatching |= (i.Batch().v > 1); + bPitches |= (i.PitchesDifferFromLogicalDims()); + bOffests |= (i.GetFirstElementOffset() != 0); + bDifferentTypes |= (i.GetDType() != output.GetDType()); + bFP16Used |= (i.GetDType() == Datatype::F16); + } + + k.EnableOutputDataType(output.GetDType()); + k.EnableOutputLayout(output.GetLayout()); + + if (bBatching) + { + k.EnableBatching(); + } + + if (bPitches || + output.PitchesDifferFromLogicalDims()) + { + k.EnableTensorPitches(); + } + + if (bDifferentTypes) + { + k.EnableDifferentTypes(); + } + + if (bOffests || + output.GetFirstElementOffset() != 0) + { + k.EnableTensorOffset(); + } + + if (!engineInfo.bFP16Support && + bFP16Used) + { + // I'm not sure it's the best idea, but we can live with it right now + k.EnableFP16Emulation(); + } + + if (gradient) + { + k.EnableGradient(); + } + + return k; + } + std::string base_params::to_string() const { std::stringstream s; @@ -42,5 +557,4 @@ namespace kernel_selector { return s.str(); } - -}
\ No newline at end of file +} diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/kernel_selector_params.h b/inference-engine/thirdparty/clDNN/kernel_selector/core/kernel_selector_params.h index 39a379855..d4351f2b4 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/kernel_selector_params.h +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/kernel_selector_params.h @@ -17,9 +17,8 @@ #pragma once #include <string> -#include <cstddef> #include <memory> -#include <map> +#include <cstddef> #include "common_types.h" #include "common_tools.h" #include "tensor_type.h" @@ -90,6 +89,13 @@ namespace kernel_selector uint32_t axisBatch : 1; uint32_t axisXYF : 1; } argm; + struct idxsel_t + { + uint32_t axisX : 1; + uint32_t axisY : 1; + uint32_t axisFeature : 1; + uint32_t axisBatch : 1; + } idxsel; struct norm_t { uint32_t across : 1; @@ -110,11 +116,12 @@ namespace kernel_selector uint32_t floor : 1; uint32_t max_with_argmax : 1; uint32_t ceil : 1; + uint32_t bilinear : 1; uint32_t fixedKenrelDivider : 1; uint32_t dynamicKenrelDivider : 1; uint32_t dynamicKenrelDividerWithPadding : 1; } pooling; - struct conv_t + struct conv_t { uint32_t split : 1; uint32_t dilation : 1; @@ -124,7 +131,7 @@ namespace kernel_selector uint32_t calibration : 1; } conv; struct fc_t {} fc; - struct softmax_t + struct softmax_t { uint32_t dimX : 1; uint32_t dimY : 1; @@ -198,6 +205,7 @@ namespace kernel_selector uint32_t uint16 : 1; uint32_t int32 : 1; uint32_t uint32 : 1; + uint32_t int64 : 1; uint32_t F16 : 1; uint32_t F32 : 1; } val; @@ -215,559 +223,71 @@ namespace kernel_selector uint32_t weightsOutputLayout; }; - void EnableInputDataType(Datatype dt) - { - switch (dt) - { - case Datatype::INT8: - key.inputType.val.int8 = 1; - break; - case Datatype::UINT8: - key.inputType.val.uint8 = 1; - break; - case Datatype::INT16: - key.inputType.val.int16 = 1; - break; - case Datatype::UINT16: - key.inputType.val.uint16 = 1; - break; - case Datatype::INT32: - key.inputType.val.int32 = 1; - break; - case Datatype::UINT32: - key.inputType.val.uint32 = 1; - break; - case Datatype::F16: - key.inputType.val.F16 = 1; - break; - case Datatype::F32: - key.inputType.val.F32 = 1; - break; - default: - break; - } - } - - void EnableAllInputDataType() - { - key.inputType.raw = 0xffffffff; - } - - void EnableOutputDataType(Datatype dt) - { - switch (dt) - { - case Datatype::INT8: - key.outputType.val.int8 = 1; - break; - case Datatype::UINT8: - key.outputType.val.uint8 = 1; - break; - case Datatype::INT16: - key.outputType.val.int16 = 1; - break; - case Datatype::UINT16: - key.outputType.val.uint16 = 1; - break; - case Datatype::INT32: - key.outputType.val.int32 = 1; - break; - case Datatype::UINT32: - key.outputType.val.uint32 = 1; - break; - case Datatype::F16: - key.outputType.val.F16 = 1; - break; - case Datatype::F32: - key.outputType.val.F32 = 1; - break; - default: - break; - } - } - - void EnableAllOutputDataType() - { - key.outputType.raw = 0xffffffff; - } - - void EnableInputWeightsType(WeightsType wt) - { - switch (wt) - { - case WeightsType::F16: - key.inputWeightsType.val.F16 = 1; - break; - case WeightsType::F32: - key.inputWeightsType.val.F32 = 1; - break; - case WeightsType::INT8: - key.inputWeightsType.val.int8 = 1; - break; - default: - break; - } - } - - void EnableAllInputWeightsType() - { - key.inputWeightsType.raw = 0xffffffff; - } - - void EnableOutputWeightsType(WeightsType wt) - { - switch (wt) - { - case WeightsType::F16: - key.outputWeightsType.val.F16 = 1; - break; - case WeightsType::F32: - key.outputWeightsType.val.F32 = 1; - break; - case WeightsType::INT8: - key.outputWeightsType.val.int8 = 1; - break; - default: - break; - } - } - - void EnableAllOutputWeightsType() - { - key.outputWeightsType.raw = 0xffffffff; - } - - void EnableFP16Emulation() - { - key.restrict.val.FP16Emulation = 1; - } - - void EnableDifferentTypes() - { - key.restrict.val.different_types = 1; - } - - void EnableInputLayout(DataLayout l) - { - key.inputLayout |= (1 << l); - } - - void EnableAllInputLayout() - { - key.inputLayout = 0xffffffff; - } - - void EnableOutputLayout(DataLayout l) - { - key.outputLayout |= (1 << l); - } - - void EnableAllOutputLayout() - { - key.outputLayout = 0xffffffff; - } - - void EnableInputWeightsLayout(WeightsLayout l) - { - key.weightsInputLayout |= (1 << l); - } - - void EnableAllInputWeightsLayout() - { - key.weightsInputLayout = 0xffffffff; - } - - void EnableOutputWeightsLayout(WeightsLayout l) - { - key.weightsOutputLayout |= (1 << l); - } - - void EnableAllOutputWeightsLayout() - { - key.weightsOutputLayout = 0xffffffff; - } - - void EnableTensorOffset() - { - key.restrict.val.offset = 1; - } - - void EnableTensorPitches() - { - key.restrict.val.pitches = 1; - } - - void EnableBatching() - { - key.restrict.val.batching = 1; - } - - void EnableGradient() - { - key.restrict.val.gradient = 1; - } - - void EnableSubGroup() - { - key.machineInfo.val.subgroup = 1; - } - - void EnableSubGroupShort() - { - key.machineInfo.val.subgroupShort = 1; - } - - void EnableNonBiasTerm() - { - key.restrict.val.nonBias = 1; - } - - void EnableBiasPerFeature() - { - key.restrict.val.biasPerFeatureMap = 1; - } - - void EnableBiasPerOutput() - { - key.restrict.val.biasPerOutput = 1; - } - - void EnableActivationAdditionalParamsAsInput() - { - key.restrict.val.activationAdditionalParamsAsInput = 1; - } - - void EnableMomentum() - { - key.restrict.val.momentum = 1; - } - - void EnableLRNMode(LRNMode m) - { - switch (m) - { - case LRNMode::ACROSS_CHANNEL: - key.restrict.val.dedicated.norm.across = 1; - break; - case LRNMode::WITHIN_CHANNEL: - key.restrict.val.dedicated.norm.within = 1; - break; - default: - break; - } - } - - void EnableLookUpTableAxis(LookUpTableAxis m) - { - switch (m) - { - case kernel_selector::LookUpTableAxis::BATCH: - key.restrict.val.dedicated.lookt.axisBatch = 1; - break; - case kernel_selector::LookUpTableAxis::FEATURE: - key.restrict.val.dedicated.lookt.axisFeature = 1; - break; - case kernel_selector::LookUpTableAxis::X: - key.restrict.val.dedicated.lookt.axisX = 1; - break; - case kernel_selector::LookUpTableAxis::Y: - key.restrict.val.dedicated.lookt.axisY = 1; - break; - case kernel_selector::LookUpTableAxis::XYF: - key.restrict.val.dedicated.lookt.axisXYF = 1; - break; - default: - break; - } - } - - void EnableNormalizeMode(NormalizeMode m) - { - switch (m) - { - case NormalizeMode::ACROSS_SPATIAL: - key.restrict.val.dedicated.norm.across = 1; - break; - case NormalizeMode::WITHIN_SPATIAL: - key.restrict.val.dedicated.norm.within = 1; - break; - default: - break; - } - } - - void EnableMVNMode(MVNMode m) - { - switch (m) - { - case MVNMode::ACROSS_CHANNELS: - key.restrict.val.dedicated.mvn.across = 1; - break; - case MVNMode::WITHIN_CHANNELS: - key.restrict.val.dedicated.mvn.within = 1; - break; - default: - break; - } - } - - void EnableMVNNormalizeVariance() - { - key.restrict.val.dedicated.mvn.normalize_variance = 1; - } - - void EnableLRNKernelDividerMode(KernelDividerMode m) - { - switch (m) - { - case KernelDividerMode::FIXED: - key.restrict.val.dedicated.norm.fixedKenrelDivider = 1; - break; - case KernelDividerMode::DYNAMIC: - key.restrict.val.dedicated.norm.dynamicKenrelDivider = 1; - break; - default: - break; - } - } - - void EnablePoolKernelDividerMode(KernelDividerMode m) - { - switch (m) - { - case KernelDividerMode::FIXED: - key.restrict.val.dedicated.pooling.fixedKenrelDivider = 1; - break; - case KernelDividerMode::DYNAMIC: - key.restrict.val.dedicated.pooling.dynamicKenrelDivider = 1; - break; - case KernelDividerMode::DYNAMIC_WITH_PADDING: - key.restrict.val.dedicated.pooling.dynamicKenrelDividerWithPadding = 1; - break; - default: - break; - } - } - - void EnablePoolType(PoolType t) - { - switch (t) - { - case PoolType::MAX: - key.restrict.val.dedicated.pooling.max = 1; - break; - case PoolType::AVG: - key.restrict.val.dedicated.pooling.avg = 1; - break; - case PoolType::MAX_WITH_ARGMAX: - key.restrict.val.dedicated.pooling.max_with_argmax = 1; - break; - default: - break; - } - } - - void EnablePoolRemainder(PoolRemainder r) - { - switch (r) - { - case PoolRemainder::FLOOR: - key.restrict.val.dedicated.pooling.floor = 1; - break; - case PoolRemainder::CEIL: - key.restrict.val.dedicated.pooling.ceil = 1; - break; - default: - break; - } - } - - void EnableSplitSupport() - { - key.restrict.val.dedicated.conv.split = 1; - } - - void EnableDilation() - { - key.restrict.val.dedicated.conv.dilation = 1; - } - - void EnableDepthwiseSeparableOpt() - { - key.restrict.val.dedicated.conv.depthwiseSeparableOpt = 1; - } - - void EnableTranspose() - { - key.restrict.val.dedicated.conv.transposed = 1; - } - - void EnableInt8Quantization() - { - key.restrict.val.dedicated.conv.quantization = 1; - } - - void EnableOutputCalibration() - { - key.restrict.val.dedicated.conv.calibration = 1; - } - - void EnableWinogradReorder() - { - key.restrict.val.dedicated.reorder.winograd = 1; - } - - void EnableSoftmaxDim(SoftmaxDim d) - { - switch (d) - { - case SoftmaxDim::X: - key.restrict.val.dedicated.softmax.dimX = 1; - break; - case SoftmaxDim::Y: - key.restrict.val.dedicated.softmax.dimY = 1; - break; - case SoftmaxDim::FEATURE: - key.restrict.val.dedicated.softmax.dimFeature = 1; - break; - default: - break; - } - } - - void EnableConcatAxis(ConcatAxis a) - { - switch (a) - { - case ConcatAxis::X: - key.restrict.val.dedicated.concat.axisX = 1; - break; - case ConcatAxis::Y: - key.restrict.val.dedicated.concat.axisY = 1; - break; - case ConcatAxis::FEATURE: - key.restrict.val.dedicated.concat.axisFeature = 1; - break; - case ConcatAxis::BATCH: - key.restrict.val.dedicated.concat.axisBatch = 1; - break; - default: - break; - } - } - - void EnableUpSamplingSampleType(SampleType a) - { - switch (a) - { - case SampleType::NEAREST: - key.restrict.val.dedicated.upsample.nearest = 1; - break; - case SampleType::BILINEAR: - key.restrict.val.dedicated.upsample.bilinear = 1; - break; - default: - break; - } - } - - void EnableLSTMGEMMBias() { - key.restrict.val.dedicated.lstm_gemm.bias = 1; - } - - void EnableLSTMGEMMHidden() { - key.restrict.val.dedicated.lstm_gemm.hidden = 1; - } - - void EnableLSTMEltCell() { - key.restrict.val.dedicated.lstm_elt.cell = 1; - } - - - void EnableConcatKernelPerInput() - { - key.restrict.val.dedicated.concat.kernelPerInput = 1; - } - - void DisableTuning() - { - key.enableTuning = 0; - } - - void EnableConcatOneKernel() - { - key.restrict.val.dedicated.concat.oneKernel = 1; - } - - void EnableArgMaxMinAxis(ArgMaxMinAxis a) - { - switch (a) - { - case ArgMaxMinAxis::X: - key.restrict.val.dedicated.argm.axisX = 1; - break; - case ArgMaxMinAxis::Y: - key.restrict.val.dedicated.argm.axisY = 1; - break; - case ArgMaxMinAxis::FEATURE: - key.restrict.val.dedicated.argm.axisFeature = 1; - break; - case ArgMaxMinAxis::BATCH: - key.restrict.val.dedicated.argm.axisBatch = 1; - break; - case ArgMaxMinAxis::XYF: - key.restrict.val.dedicated.argm.axisXYF = 1; - break; - default: - break; - } - } - - void EnableLookUpTableIndicesFormat(Datatype a) - { - if (a == Datatype::F32) - key.restrict.val.dedicated.lookt.indicesF32 = 1; - else - key.restrict.val.dedicated.lookt.indicesOther = 1; - } - - bool Support(const ParamsKey& k) const - { - return - ((key.restrict.raw & k.key.restrict.raw) == k.key.restrict.raw) && // check if this kernel supports this params - ((key.machineInfo.raw & k.key.machineInfo.raw) == key.machineInfo.raw) && // check if machine supports this kernel - ((key.inputType.raw & k.key.inputType.raw) == k.key.inputType.raw) && - ((key.outputType.raw & k.key.outputType.raw) == k.key.outputType.raw) && - ((key.inputWeightsType.raw & k.key.inputWeightsType.raw) == k.key.inputWeightsType.raw) && - ((key.outputWeightsType.raw & k.key.outputWeightsType.raw) == k.key.outputWeightsType.raw) && - ((key.inputLayout & k.key.inputLayout) != 0 || key.inputLayout == k.key.inputLayout) && - ((key.outputLayout & k.key.outputLayout) != 0 || key.outputLayout == k.key.outputLayout) && - ((key.weightsInputLayout & k.key.weightsInputLayout) != 0 || key.weightsInputLayout == k.key.weightsInputLayout) && - ((key.weightsOutputLayout & k.key.weightsOutputLayout) != 0 || key.weightsOutputLayout == k.key.weightsOutputLayout); - } - + void EnableInputDataType(Datatype dt); + void EnableAllInputDataType(); + void EnableOutputDataType(Datatype dt); + void EnableAllOutputDataType(); + void EnableInputWeightsType(WeightsType wt); + void EnableAllInputWeightsType(); + void EnableOutputWeightsType(WeightsType wt); + void EnableAllOutputWeightsType(); + void EnableFP16Emulation() { key.restrict.val.FP16Emulation = 1; } + void EnableDifferentTypes() { key.restrict.val.different_types = 1; } + void EnableInputLayout(DataLayout l) { key.inputLayout |= (1 << l); } + void EnableAllInputLayout() { key.inputLayout = 0xffffffff; } + void EnableOutputLayout(DataLayout l) { key.outputLayout |= (1 << l); } + void EnableAllOutputLayout() { key.outputLayout = 0xffffffff; } + void EnableInputWeightsLayout(WeightsLayout l) { key.weightsInputLayout |= (1 << l); } + void EnableAllInputWeightsLayout() { key.weightsInputLayout = 0xffffffff; } + void EnableOutputWeightsLayout(WeightsLayout l) { key.weightsOutputLayout |= (1 << l); } + void EnableAllOutputWeightsLayout() { key.weightsOutputLayout = 0xffffffff; } + void EnableTensorOffset() { key.restrict.val.offset = 1; } + void EnableTensorPitches() { key.restrict.val.pitches = 1; } + void EnableBatching() { key.restrict.val.batching = 1; } + void EnableGradient() { key.restrict.val.gradient = 1; } + void EnableSubGroup() { key.machineInfo.val.subgroup = 1; } + void EnableSubGroupShort() { key.machineInfo.val.subgroupShort = 1; } + void EnableNonBiasTerm() { key.restrict.val.nonBias = 1; } + void EnableBiasPerFeature() { key.restrict.val.biasPerFeatureMap = 1; } + void EnableBiasPerOutput() { key.restrict.val.biasPerOutput = 1; } + void EnableActivationAdditionalParamsAsInput() { key.restrict.val.activationAdditionalParamsAsInput = 1; } + void EnableMomentum() { key.restrict.val.momentum = 1; } + void EnableLRNMode(LRNMode m); + void EnableLookUpTableAxis(LookUpTableAxis m); + void EnableNormalizeMode(NormalizeMode m); + void EnableMVNMode(MVNMode m); + void EnableMVNNormalizeVariance(); + void EnableLRNKernelDividerMode(KernelDividerMode m); + void EnablePoolKernelDividerMode(KernelDividerMode m); + void EnablePoolType(PoolType t); + void EnablePoolRemainder(PoolRemainder r); + void EnableSplitSupport() { key.restrict.val.dedicated.conv.split = 1; } + void EnableDilation() { key.restrict.val.dedicated.conv.dilation = 1; } + void EnableDepthwiseSeparableOpt() { key.restrict.val.dedicated.conv.depthwiseSeparableOpt = 1; } + void EnableTranspose() { key.restrict.val.dedicated.conv.transposed = 1; } + void EnableInt8Quantization() { key.restrict.val.dedicated.conv.quantization = 1; } + void EnableOutputCalibration() { key.restrict.val.dedicated.conv.calibration = 1; } + void EnableWinogradReorder() { key.restrict.val.dedicated.reorder.winograd = 1; } + void EnableSoftmaxDim(SoftmaxDim d); + void EnableConcatAxis(ConcatAxis a); + void EnableUpSamplingSampleType(SampleType a); + void EnableLSTMGEMMBias() { key.restrict.val.dedicated.lstm_gemm.bias = 1; } + void EnableLSTMGEMMHidden() { key.restrict.val.dedicated.lstm_gemm.hidden = 1; } + void EnableLSTMEltCell() { key.restrict.val.dedicated.lstm_elt.cell = 1; } + void EnableConcatKernelPerInput() { key.restrict.val.dedicated.concat.kernelPerInput = 1; } + void DisableTuning() { key.enableTuning = 0; } + void EnableConcatOneKernel() { key.restrict.val.dedicated.concat.oneKernel = 1; } + void EnableArgMaxMinAxis(ArgMaxMinAxis a); + void EnableLookUpTableIndicesFormat(Datatype a); + void EnableIndexSelectAxis(IndexSelectAxis a); + bool Support(const ParamsKey& k) const; bool TuningSupport() const { if (key.enableTuning == 1) return true; return false; } - - ParamsKey Merge(const ParamsKey& k) const - { - ParamsKey ret; - ret.key.restrict.raw = key.restrict.raw | k.key.restrict.raw; - ret.key.machineInfo.raw = key.machineInfo.raw | k.key.machineInfo.raw; - ret.key.inputType.raw = key.inputType.raw | k.key.inputType.raw; - ret.key.outputType.raw = key.outputType.raw | k.key.outputType.raw; - ret.key.inputWeightsType.raw = key.inputWeightsType.raw | k.key.inputWeightsType.raw; - ret.key.outputWeightsType.raw = key.outputWeightsType.raw | k.key.outputWeightsType.raw; - ret.key.inputLayout = key.inputLayout | k.key.inputLayout; - ret.key.outputLayout = key.outputLayout | k.key.outputLayout; - ret.key.weightsInputLayout = key.weightsInputLayout | k.key.weightsInputLayout; - ret.key.weightsOutputLayout = key.weightsOutputLayout | k.key.weightsOutputLayout; - return ret; - } + ParamsKey Merge(const ParamsKey& k) const; private: Key key; @@ -783,6 +303,8 @@ namespace kernel_selector bool bFP16Support = false; bool bFP64Support = false; bool bImageSupport = false; + bool bIMADSupport = false; + bool bIMMADSupport = false; uint64_t maxWorkGroupSize = 0; uint64_t maxLocalMemSize = 0; uint64_t maxImage2dWidth = 0; @@ -800,22 +322,7 @@ namespace kernel_selector virtual ~Params() {} KernelType GetType() const { return kType; } - virtual ParamsKey GetParamsKey() const - { - ParamsKey k; - - if (engineInfo.bSubGroupSupport) - { - k.EnableSubGroup(); - } - - if (engineInfo.bSubGroupShortSupport) - { - k.EnableSubGroupShort(); - } - - return k; - } + virtual ParamsKey GetParamsKey() const; protected: Params(KernelType kt, const std::string& id) : kType(kt), layerID(id) {} @@ -842,69 +349,7 @@ namespace kernel_selector bool gradient = false; virtual std::string to_string() const; - - virtual ParamsKey GetParamsKey() const - { - ParamsKey k = Params::GetParamsKey(); - - bool bBatching = false; - bool bPitches = false; - bool bOffests = false; - bool bDifferentTypes = false; - bool bFP16Used = (output.GetDType() == Datatype::F16); - - for (const auto& i : inputs) - { - k.EnableInputDataType(i.GetDType()); - k.EnableInputLayout(i.GetLayout()); - - bBatching |= (i.Batch().v > 1); - bPitches |= (i.PitchesDifferFromLogicalDims()); - bOffests |= (i.GetFirstElementOffset() != 0); - bDifferentTypes |= (i.GetDType() != output.GetDType()); - bFP16Used |= (i.GetDType() == Datatype::F16); - } - - k.EnableOutputDataType(output.GetDType()); - k.EnableOutputLayout(output.GetLayout()); - - if (bBatching) - { - k.EnableBatching(); - } - - if (bPitches || - output.PitchesDifferFromLogicalDims()) - { - k.EnableTensorPitches(); - } - - if (bDifferentTypes) - { - k.EnableDifferentTypes(); - } - - if (bOffests || - output.GetFirstElementOffset() != 0) - { - k.EnableTensorOffset(); - } - - if (!engineInfo.bFP16Support && - bFP16Used) - { - // I'm not sure it's the best idea, but we can live with it right now - k.EnableFP16Emulation(); - } - - if (gradient) - { - k.EnableGradient(); - } - - return k; - } - + virtual ParamsKey GetParamsKey() const; protected: base_params(KernelType kt) : Params(kt, ""), inputs(1){} @@ -942,23 +387,7 @@ namespace kernel_selector TuningParams tuningParams; - virtual ParamsKey GetSupportedKey() const - { - ParamsKey k; - - for (auto l : inputLayouts) - { - k.EnableInputLayout(l); - } - - for (auto l : outputLayouts) - { - k.EnableOutputLayout(l); - } - - return k; - } - + virtual ParamsKey GetSupportedKey() const; protected: optional_params(KernelType kt) : kType(kt) {} KernelType kType; |