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Diffstat (limited to 'runtimes/nn/common/operations/internal/optimized/depthwiseconv_float.h')
-rw-r--r-- | runtimes/nn/common/operations/internal/optimized/depthwiseconv_float.h | 792 |
1 files changed, 792 insertions, 0 deletions
diff --git a/runtimes/nn/common/operations/internal/optimized/depthwiseconv_float.h b/runtimes/nn/common/operations/internal/optimized/depthwiseconv_float.h new file mode 100644 index 000000000..5c05bf20f --- /dev/null +++ b/runtimes/nn/common/operations/internal/optimized/depthwiseconv_float.h @@ -0,0 +1,792 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright (C) 2017 The Android Open Source Project + * + * 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. + */ + +#ifndef __NNFW_RT_OPTIMIZED_OPS_DEPTHWISECONV_FLOAT_H__ +#define __NNFW_RT_OPTIMIZED_OPS_DEPTHWISECONV_FLOAT_H__ + +#include "gemmlowp.h" +#include "../common.h" +#include "../types.h" + +namespace nnfw { +namespace rt { +namespace optimized_ops { + +// Implementation of float DepthwiseConv + +template <bool kAllowStrided, int kFixedInputDepth, int kFixedDepthMultiplier> +struct FloatDepthwiseConvKernel {}; + +#ifdef USE_NEON + +template <> +struct FloatDepthwiseConvKernel<false, 8, 1> { + static void Run(int num_output_pixels, int input_depth, int depth_multiplier, + const float* input_ptr, int input_ptr_increment, + const float* filter_ptr, float* acc_buffer_ptr) { + // Load the filters + float32x4_t filter[2]; + for (int i = 0; i < 2; i++) { + filter[i] = vld1q_f32(filter_ptr + 4 * i); + } + int outp = 0; + // Handle 2 output pixels at a time. + for (; outp <= num_output_pixels - 2; outp += 2) { + // Load the inputs + float32x4_t input[4]; + for (int i = 0; i < 4; i++) { + input[i] = vld1q_f32(input_ptr + 4 * i); + } + input_ptr += 16; + // Load the accumulators from acc_buffer + float32x4_t acc[4]; + for (int i = 0; i < 4; i++) { + acc[i] = vld1q_f32(acc_buffer_ptr + 4 * i); + } + // Multiply-accumulate + acc[0] = vmlaq_f32(acc[0], input[0], filter[0]); + acc[1] = vmlaq_f32(acc[1], input[1], filter[1]); + acc[2] = vmlaq_f32(acc[2], input[2], filter[0]); + acc[3] = vmlaq_f32(acc[3], input[3], filter[1]); + // Store the accumulators back to acc_buffer + for (int i = 0; i < 4; i++) { + vst1q_f32(acc_buffer_ptr + 4 * i, acc[i]); + } + acc_buffer_ptr += 16; + } + // Handle one output pixel at a time. + for (; outp < num_output_pixels; outp++) { + // Load the inputs + float32x4_t input[2]; + for (int i = 0; i < 2; i++) { + input[i] = vld1q_f32(input_ptr + 4 * i); + } + input_ptr += 8; + // Load the accumulators from acc_buffer + float32x4_t acc[2]; + for (int i = 0; i < 2; i++) { + acc[i] = vld1q_f32(acc_buffer_ptr + 4 * i); + } + // Multiply-accumulate + for (int i = 0; i < 2; i++) { + acc[i] = vmlaq_f32(acc[i], input[i], filter[i]); + } + // Store the accumulators back to acc_buffer + for (int i = 0; i < 2; i++) { + vst1q_f32(acc_buffer_ptr + 4 * i, acc[i]); + } + acc_buffer_ptr += 8; + } + } +}; + +template <> +struct FloatDepthwiseConvKernel<false, 2, 1> { + static void Run(int num_output_pixels, int input_depth, int depth_multiplier, + const float* input_ptr, int input_ptr_increment, + const float* filter_ptr, float* acc_buffer_ptr) { + const float32x2_t filters = vld1_f32(filter_ptr); + const float32x4_t filters_dup2 = vcombine_f32(filters, filters); + int outp = 0; + // Handle 8 output pixels at a time. + for (; outp <= num_output_pixels - 8; outp += 8) { + // Load the inputs + float32x4_t input[4]; + for (int i = 0; i < 4; i++) { + input[i] = vld1q_f32(input_ptr + 4 * i); + } + input_ptr += 16; + // Load the accumulators from acc_buffer + float32x4_t acc[4]; + for (int i = 0; i < 4; i++) { + acc[i] = vld1q_f32(acc_buffer_ptr + 4 * i); + } + // Multiply-accumulate + for (int i = 0; i < 4; i++) { + acc[i] = vmlaq_f32(acc[i], input[i], filters_dup2); + } + // Store the accumulators back to acc_buffer + for (int i = 0; i < 4; i++) { + vst1q_f32(acc_buffer_ptr + 4 * i, acc[i]); + } + acc_buffer_ptr += 16; + } + // Handle 4 output pixels at a time. + for (; outp <= num_output_pixels - 4; outp += 4) { + // Load the inputs + float32x4_t input[2]; + for (int i = 0; i < 2; i++) { + input[i] = vld1q_f32(input_ptr + 4 * i); + } + input_ptr += 8; + // Load the accumulators from acc_buffer + float32x4_t acc[2]; + for (int i = 0; i < 2; i++) { + acc[i] = vld1q_f32(acc_buffer_ptr + 4 * i); + } + // Multiply-accumulate + for (int i = 0; i < 2; i++) { + acc[i] = vmlaq_f32(acc[i], input[i], filters_dup2); + } + // Store the accumulators back to acc_buffer + for (int i = 0; i < 2; i++) { + vst1q_f32(acc_buffer_ptr + 4 * i, acc[i]); + } + acc_buffer_ptr += 8; + } + // Handle 2 output pixels at a time. + for (; outp <= num_output_pixels - 2; outp += 2) { + // Load the inputs + const float32x4_t input = vld1q_f32(input_ptr); + input_ptr += 4; + // Load the accumulators from acc_buffer + float32x4_t acc = vld1q_f32(acc_buffer_ptr); + // Multiply-accumulate + acc = vmlaq_f32(acc, input, filters_dup2); + // Store the accumulators back to acc_buffer + vst1q_f32(acc_buffer_ptr, acc); + acc_buffer_ptr += 4; + } + // Handle 1 output pixel at a time + for (; outp < num_output_pixels; outp++) { + // Load the inputs + const float32x2_t input = vld1_f32(input_ptr); + input_ptr += 2; + // Load the accumulators from acc_buffer + float32x2_t acc = vld1_f32(acc_buffer_ptr); + // Multiply-accumulate + acc = vmla_f32(acc, input, filters); + // Store the accumulators back to acc_buffer + vst1_f32(acc_buffer_ptr, acc); + acc_buffer_ptr += 2; + } + } +}; + +template <> +struct FloatDepthwiseConvKernel<true, 0, 1> { + static void Run(int num_output_pixels, int input_depth, int depth_multiplier, + const float* input_ptr, int input_ptr_increment, + const float* filter_ptr, float* acc_buffer_ptr) { + // Handle one output pixel at a time. + for (int outp = 0; outp < num_output_pixels; outp++) { + const float* local_filter_ptr = filter_ptr; + const float* local_input_ptr = input_ptr; + int ic = 0; + // Handle 16 input channels at a time. + for (; ic <= input_depth - 16; ic += 16) { + // Load the filters + float32x4_t filter[4]; + for (int i = 0; i < 4; i++) { + filter[i] = vld1q_f32(local_filter_ptr + 4 * i); + } + local_filter_ptr += 16; + // Load the inputs + float32x4_t input[4]; + for (int i = 0; i < 4; i++) { + input[i] = vld1q_f32(local_input_ptr + 4 * i); + } + local_input_ptr += 16; + // Load the accumulators from acc_buffer + float32x4_t acc[4]; + for (int i = 0; i < 4; i++) { + acc[i] = vld1q_f32(acc_buffer_ptr + 4 * i); + } + // Multiply-accumulate + for (int i = 0; i < 4; i++) { + acc[i] = vmlaq_f32(acc[i], input[i], filter[i]); + } + // Store the accumulators back to acc_buffer + for (int i = 0; i < 4; i++) { + vst1q_f32(acc_buffer_ptr + 4 * i, acc[i]); + } + acc_buffer_ptr += 16; + } + // Handle 4 input channels at a time. + for (; ic <= input_depth - 4; ic += 4) { + // Load the filters + float32x4_t filter; + filter = vld1q_f32(local_filter_ptr); + local_filter_ptr += 4; + // Load the inputs + float32x4_t input; + input = vld1q_f32(local_input_ptr); + local_input_ptr += 4; + // Load the accumulators from acc_buffer + float32x4_t acc; + acc = vld1q_f32(acc_buffer_ptr); + // Multiply-accumulate + acc = vmlaq_f32(acc, input, filter); + // Store the accumulators back to acc_buffer + vst1q_f32(acc_buffer_ptr, acc); + acc_buffer_ptr += 4; + } + // Handle one input channel at a time. + for (; ic < input_depth; ic++) { + const float input_val = *local_input_ptr++; + const float filter_val = *local_filter_ptr++; + *acc_buffer_ptr++ += filter_val * input_val; + } + input_ptr += input_ptr_increment; + } + } +}; + +template <> +struct FloatDepthwiseConvKernel<true, 0, 8> { + static void Run(int num_output_pixels, int input_depth, int depth_multiplier, + const float* input_ptr, int input_ptr_increment, + const float* filter_ptr, float* acc_buffer_ptr) { + // Handle one output pixel at a time. + for (int outp = 0; outp < num_output_pixels; outp++) { + const float* local_filter_ptr = filter_ptr; + const float* local_input_ptr = input_ptr; + int ic = 0; + // Handle 2 input channels at a time. + for (; ic <= input_depth - 2; ic += 2) { + // Load the filters + float32x4_t filter[4]; + for (int i = 0; i < 4; i++) { + filter[i] = vld1q_f32(local_filter_ptr + 4 * i); + } + local_filter_ptr += 16; + // Load the inputs + const float32x2_t input = vld1_f32(local_input_ptr); + local_input_ptr += 2; + // Load the accumulators from acc_buffer + float32x4_t acc[4]; + for (int i = 0; i < 4; i++) { + acc[i] = vld1q_f32(acc_buffer_ptr + 4 * i); + } + // Multiply-accumulate + acc[0] = vmlaq_lane_f32(acc[0], filter[0], input, 0); + acc[1] = vmlaq_lane_f32(acc[1], filter[1], input, 0); + acc[2] = vmlaq_lane_f32(acc[2], filter[2], input, 1); + acc[3] = vmlaq_lane_f32(acc[3], filter[3], input, 1); + // Store the accumulators back to acc_buffer + for (int i = 0; i < 4; i++) { + vst1q_f32(acc_buffer_ptr + 4 * i, acc[i]); + } + acc_buffer_ptr += 16; + } + // Handle one input channel at a time. + for (; ic < input_depth; ic++) { + // Load the filters + float32x4_t filter[2]; + for (int i = 0; i < 2; i++) { + filter[i] = vld1q_f32(local_filter_ptr + 4 * i); + } + local_filter_ptr += 8; + // Load the inputs + const float input_val = *local_input_ptr++; + // Load the accumulators from acc_buffer + float32x4_t acc[2]; + for (int i = 0; i < 2; i++) { + acc[i] = vld1q_f32(acc_buffer_ptr + 4 * i); + } + // Multiply-accumulate + for (int i = 0; i < 2; i++) { + acc[i] = vmlaq_n_f32(acc[i], filter[i], input_val); + } + // Store the accumulators back to acc_buffer + for (int i = 0; i < 2; i++) { + vst1q_f32(acc_buffer_ptr + 4 * i, acc[i]); + } + acc_buffer_ptr += 8; + } + input_ptr += input_ptr_increment; + } + } +}; + +template <> +struct FloatDepthwiseConvKernel<true, 0, 2> { + static void Run(int num_output_pixels, int input_depth, int depth_multiplier, + const float* input_ptr, int input_ptr_increment, + const float* filter_ptr, float* acc_buffer_ptr) { + // Handle one output pixel at a time. + for (int outp = 0; outp < num_output_pixels; outp++) { + const float* local_filter_ptr = filter_ptr; + const float* local_input_ptr = input_ptr; + int ic = 0; + // Handle 8 input channels at a time. + for (; ic <= input_depth - 8; ic += 8) { + // Load the filters + float32x4_t filter[4]; + for (int i = 0; i < 4; i++) { + filter[i] = vld1q_f32(local_filter_ptr + 4 * i); + } + local_filter_ptr += 16; + // Load the inputs + float32x4x2_t input_dup2[2]; + for (int i = 0; i < 2; i++) { + const float32x4_t input = vld1q_f32(local_input_ptr + 4 * i); + input_dup2[i] = vzipq_f32(input, input); + } + local_input_ptr += 8; + // Load the accumulators from acc_buffer + float32x4_t acc[4]; + for (int i = 0; i < 4; i++) { + acc[i] = vld1q_f32(acc_buffer_ptr + 4 * i); + } + // Multiply-accumulate + acc[0] = vmlaq_f32(acc[0], filter[0], input_dup2[0].val[0]); + acc[1] = vmlaq_f32(acc[1], filter[1], input_dup2[0].val[1]); + acc[2] = vmlaq_f32(acc[2], filter[2], input_dup2[1].val[0]); + acc[3] = vmlaq_f32(acc[3], filter[3], input_dup2[1].val[1]); + // Store the accumulators back to acc_buffer + for (int i = 0; i < 4; i++) { + vst1q_f32(acc_buffer_ptr + 4 * i, acc[i]); + } + acc_buffer_ptr += 16; + } + // Handle 4 input channels at a time. + for (; ic <= input_depth - 4; ic += 4) { + // Load the filters + float32x2_t filter[4]; + for (int i = 0; i < 4; i++) { + filter[i] = vld1_f32(local_filter_ptr + 2 * i); + } + local_filter_ptr += 8; + // Load the inputs + const float32x4_t input = vld1q_f32(local_input_ptr); + local_input_ptr += 4; + // Load the accumulators from acc_buffer + float32x2_t acc[4]; + for (int i = 0; i < 4; i++) { + acc[i] = vld1_f32(acc_buffer_ptr + 2 * i); + } + // Multiply-accumulate + acc[0] = vmla_lane_f32(acc[0], filter[0], vget_low_f32(input), 0); + acc[1] = vmla_lane_f32(acc[1], filter[1], vget_low_f32(input), 1); + acc[2] = vmla_lane_f32(acc[2], filter[2], vget_high_f32(input), 0); + acc[3] = vmla_lane_f32(acc[3], filter[3], vget_high_f32(input), 1); + // Store the accumulators back to acc_buffer + for (int i = 0; i < 4; i++) { + vst1_f32(acc_buffer_ptr + 2 * i, acc[i]); + } + acc_buffer_ptr += 8; + } + // Handle 2 input channels at a time. + for (; ic <= input_depth - 2; ic += 2) { + // Load the filters + const float32x4_t filter = vld1q_f32(local_filter_ptr); + local_filter_ptr += 4; + // Load the inputs + const float32x2_t input = vld1_f32(local_input_ptr); + local_input_ptr += 2; + // Load the accumulators from acc_buffer + float32x2_t acc[2]; + for (int i = 0; i < 2; i++) { + acc[i] = vld1_f32(acc_buffer_ptr + 2 * i); + } + // Multiply-accumulate + acc[0] = vmla_lane_f32(acc[0], vget_low_f32(filter), input, 0); + acc[1] = vmla_lane_f32(acc[1], vget_high_f32(filter), input, 1); + // Store the accumulators back to acc_buffer + for (int i = 0; i < 2; i++) { + vst1_f32(acc_buffer_ptr + 2 * i, acc[i]); + } + acc_buffer_ptr += 4; + } + // Handle one input channel at a time. + for (; ic < input_depth; ic++) { + // Load the inputs + const float input_val = *local_input_ptr++; + // Multiply-accumulate + for (int i = 0; i < 2; i++) { + acc_buffer_ptr[i] += local_filter_ptr[i] * input_val; + } + local_filter_ptr += 2; + acc_buffer_ptr += 2; + } + input_ptr += input_ptr_increment; + } + } +}; + +template <> +struct FloatDepthwiseConvKernel<true, 1, 8> { + static void Run(int num_output_pixels, int input_depth, int depth_multiplier, + const float* input_ptr, int input_ptr_increment, + const float* filter_ptr, float* acc_buffer_ptr) { + // Handle one output pixel at a time. + for (int outp = 0; outp < num_output_pixels; outp++) { + // Load the filters + float32x4_t filter[2]; + for (int i = 0; i < 2; i++) { + filter[i] = vld1q_f32(filter_ptr + 4 * i); + } + // Load the inputs + const float input_val = *input_ptr; + input_ptr += input_ptr_increment; + // Load the accumulators from acc_buffer + float32x4_t acc[2]; + for (int i = 0; i < 2; i++) { + acc[i] = vld1q_f32(acc_buffer_ptr + 4 * i); + } + // Multiply-accumulate + for (int i = 0; i < 2; i++) { + acc[i] = vmlaq_n_f32(acc[i], filter[i], input_val); + } + // Store the accumulators back to acc_buffer + for (int i = 0; i < 2; i++) { + vst1q_f32(acc_buffer_ptr + 4 * i, acc[i]); + } + acc_buffer_ptr += 8; + } + } +}; + +template <> +struct FloatDepthwiseConvKernel<true, 0, 16> { + static void Run(int num_output_pixels, int input_depth, int depth_multiplier, + const float* input_ptr, int input_ptr_increment, + const float* filter_ptr, float* acc_buffer_ptr) { + // Handle one output pixel at a time. + for (int outp = 0; outp < num_output_pixels; outp++) { + const float* local_filter_ptr = filter_ptr; + const float* local_input_ptr = input_ptr; + for (int ic = 0; ic < input_depth; ic++) { + // Load the filters + float32x4_t filter[4]; + for (int i = 0; i < 4; i++) { + filter[i] = vld1q_f32(local_filter_ptr + 4 * i); + } + local_filter_ptr += 16; + // Load the inputs + const float input_val = *local_input_ptr++; + // Load the accumulators from acc_buffer + float32x4_t acc[4]; + for (int i = 0; i < 4; i++) { + acc[i] = vld1q_f32(acc_buffer_ptr + 4 * i); + } + // Multiply-accumulate + for (int i = 0; i < 4; i++) { + acc[i] = vmlaq_n_f32(acc[i], filter[i], input_val); + } + // Store the accumulators back to acc_buffer + for (int i = 0; i < 4; i++) { + vst1q_f32(acc_buffer_ptr + 4 * i, acc[i]); + } + acc_buffer_ptr += 16; + } + input_ptr += input_ptr_increment; + } + } +}; +#endif + +// Accumulates the effect of one row of the filter, on a segment of one row +// of the output, accessing the corresponding one row of the input. +template <bool kAllowStrided, int kFixedInputDepth, int kFixedDepthMultiplier> +void FloatDepthwiseConvAccumRow(int stride, int input_depth, int input_width, + const float* input_data, int pad_width, + int depth_multiplier, int filter_width, + const float* filter_data, + int out_x_buffer_start, int out_x_buffer_end, + int output_depth, float* acc_buffer) { +#ifdef GEMMLOWP_PROFILING + gemmlowp::ScopedProfilingLabel label(__PRETTY_FUNCTION__); +#endif + // Sanity check parameters. This is important in particular to ensure + // that we keep the number of template instantiations minimal, so we don't + // increase binary size unnecessarily. + static_assert(kFixedDepthMultiplier || !kFixedInputDepth, ""); + static_assert(kFixedInputDepth || kAllowStrided, ""); + DCHECK(stride == 1 || kAllowStrided); + if (kFixedInputDepth) { + DCHECK_EQ(input_depth, kFixedInputDepth); + } + if (kFixedDepthMultiplier) { + DCHECK_EQ(depth_multiplier, kFixedDepthMultiplier); + } + DCHECK_EQ(output_depth, input_depth * depth_multiplier); + const int input_ptr_increment = stride * input_depth; + const float* filter_base_ptr = filter_data; + for (int filter_x = 0; filter_x < filter_width; ++filter_x) { + // For the current (filter_x, filter_y) point in the filter, + // compute the boundaries of the corresponding output row segment. + int out_x_loop_start_unclampled = 0; + int out_x_loop_end_unclampled = 0; + if (kAllowStrided) { + if (stride == 2) { + out_x_loop_start_unclampled = (pad_width - filter_x + 1) / 2; + out_x_loop_end_unclampled = + (pad_width + input_width - filter_x + 1) / 2; + } else if (stride == 4) { + out_x_loop_start_unclampled = (pad_width - filter_x + 3) / 4; + out_x_loop_end_unclampled = + (pad_width + input_width - filter_x + 3) / 4; + } else { + out_x_loop_start_unclampled = + (pad_width - filter_x + stride - 1) / stride; + out_x_loop_end_unclampled = + (pad_width + input_width - filter_x + stride - 1) / stride; + } + } else { + out_x_loop_start_unclampled = pad_width - filter_x; + out_x_loop_end_unclampled = pad_width + input_width - filter_x; + } + // The kernel will have to iterate on the segment of the + // output row that starts at out_x_loop_start and out_x_loop_end. + const int out_x_loop_start = + std::max(out_x_buffer_start, out_x_loop_start_unclampled); + const int out_x_loop_end = + std::min(out_x_buffer_end, out_x_loop_end_unclampled); + + float* acc_buffer_ptr = + acc_buffer + (out_x_loop_start - out_x_buffer_start) * output_depth; + const int in_x_origin = (out_x_loop_start * stride) - pad_width + filter_x; + const float* input_ptr = input_data + in_x_origin * input_depth; + const int num_output_pixels = out_x_loop_end - out_x_loop_start; + FloatDepthwiseConvKernel<kAllowStrided, kFixedInputDepth, + kFixedDepthMultiplier>::Run(num_output_pixels, + input_depth, + depth_multiplier, + input_ptr, + input_ptr_increment, + filter_base_ptr, + acc_buffer_ptr); + filter_base_ptr += output_depth; + } +} + +// generic fallback of FloatDepthwiseConvAccumRow, portable, non-templatized. +inline void FloatDepthwiseConvAccumRowGeneric( + int stride, int input_depth, int input_width, const float* input_data, + int pad_width, int depth_multiplier, int filter_width, + const float* filter_data, int out_x_buffer_start, int out_x_buffer_end, + int output_depth, float* acc_buffer) { + gemmlowp::ScopedProfilingLabel label("DepthwiseConvAccumRowGeneric (slow)"); + const float* filter_base_ptr = filter_data; + for (int filter_x = 0; filter_x < filter_width; ++filter_x) { + const int out_x_loop_start = std::max( + out_x_buffer_start, (pad_width - filter_x + stride - 1) / stride); + const int out_x_loop_end = + std::min(out_x_buffer_end, + (pad_width + input_width - filter_x + stride - 1) / stride); + + float* acc_buffer_ptr = + acc_buffer + (out_x_loop_start - out_x_buffer_start) * output_depth; + const int in_x_origin = (out_x_loop_start * stride) - pad_width + filter_x; + const float* input_ptr = input_data + in_x_origin * input_depth; + const int input_ptr_increment = (stride - 1) * input_depth; + for (int out_x = out_x_loop_start; out_x < out_x_loop_end; out_x++) { + const float* filter_ptr = filter_base_ptr; + for (int ic = 0; ic < input_depth; ++ic) { + const float input_val = *input_ptr++; + for (int m = 0; m < depth_multiplier; m++) { + const float filter_val = *filter_ptr++; + *acc_buffer_ptr++ += filter_val * input_val; + } + } + input_ptr += input_ptr_increment; + } + filter_base_ptr += output_depth; + } +} + +// Initializes the accumulator buffer with bias values. +inline void DepthwiseConvInitAccBuffer(int num_output_pixels, int output_depth, + const float* bias_data, + float* acc_buffer) { + for (int i = 0; i < num_output_pixels; i++) { + memcpy(acc_buffer + i * output_depth, bias_data, + sizeof(acc_buffer[0]) * output_depth); + } +} + +template <FusedActivationFunctionType Ac> +void DepthwiseConv(const float* input_data, const Dims<4>& input_dims, + const float* filter_data, const Dims<4>& filter_dims, + const float* bias_data, const Dims<4>& bias_dims, + int stride_width, int stride_height, + int pad_width, int pad_height, int depth_multiplier, + float* output_data, const Dims<4>& output_dims) { + gemmlowp::ScopedProfilingLabel label("DepthwiseConv"); + static_assert(Ac == FusedActivationFunctionType::kNone || + Ac == FusedActivationFunctionType::kRelu || + Ac == FusedActivationFunctionType::kRelu6 || + Ac == FusedActivationFunctionType::kRelu1, + ""); + const int batches = MatchingArraySize(input_dims, 3, output_dims, 3); + const int output_depth = MatchingArraySize(filter_dims, 0, output_dims, 0); + const int input_height = ArraySize(input_dims, 2); + const int input_width = ArraySize(input_dims, 1); + const int input_depth = ArraySize(input_dims, 0); + const int filter_height = ArraySize(filter_dims, 2); + const int filter_width = ArraySize(filter_dims, 1); + const int output_height = ArraySize(output_dims, 2); + const int output_width = ArraySize(output_dims, 1); +#if 0 // TODO-NNRT : Check if assertion is needed, output depth some times not equal to input * depthmultiplier + DCHECK(output_depth == input_depth * depth_multiplier); +#endif + + static const int kAccBufferMaxSize = 1024; + float acc_buffer[kAccBufferMaxSize]; + DCHECK_GE(kAccBufferMaxSize, output_depth); + const int kOutputPixelsInAccBuffer = kAccBufferMaxSize / output_depth; + const int kAccBufferActualSize = kOutputPixelsInAccBuffer * output_depth; + DCHECK_LE(kOutputPixelsInAccBuffer * output_depth, kAccBufferActualSize); + DCHECK_LE(kAccBufferActualSize, kAccBufferMaxSize); + DCHECK_GE(kOutputPixelsInAccBuffer, 1); + + // row_accum_func will point to the core accumulation function to be used + // for this DepthwiseConv op. + auto* row_accum_func = FloatDepthwiseConvAccumRowGeneric; + + const int kMaxFixedDepthMultiplier = 16; + int fixed_depth_multiplier = 0; + if (depth_multiplier <= kMaxFixedDepthMultiplier) { + fixed_depth_multiplier = depth_multiplier; + } + // kMaxUnrolling is the max number of output values that we aim to handle + // in one unrolled iteration of the inner loop. For practical performance + // reasons, it is limited by the number of available registers. We could + // fine-tune it depending on the architecture, but that's not worth doing + // since this whole code is not very optimized to begin with. The + // present value reflects what's realistic on ARM 32bit NEON with 16 128-bit + // vector registers. + const int kMaxUnrolling = 8; + int fixed_input_depth = 0; + if (fixed_depth_multiplier && + input_depth * fixed_depth_multiplier <= kMaxUnrolling) { + fixed_input_depth = input_depth; + } +#define TFMINI_USE_DEPTHWISECONV_KERNEL(ALLOW_STRIDED, FIXED_INPUT_DEPTH, \ + FIXED_DEPTH_MULTIPLIER) \ + if ((stride_width == 1 || ALLOW_STRIDED) && \ + fixed_input_depth == FIXED_INPUT_DEPTH && \ + fixed_depth_multiplier == FIXED_DEPTH_MULTIPLIER) { \ + row_accum_func = \ + FloatDepthwiseConvAccumRow<ALLOW_STRIDED, FIXED_INPUT_DEPTH, \ + FIXED_DEPTH_MULTIPLIER>; \ + } + +#ifdef USE_NEON + TFMINI_USE_DEPTHWISECONV_KERNEL(true, 0, 1) + TFMINI_USE_DEPTHWISECONV_KERNEL(true, 0, 8) + TFMINI_USE_DEPTHWISECONV_KERNEL(true, 0, 2) + TFMINI_USE_DEPTHWISECONV_KERNEL(false, 8, 1) + TFMINI_USE_DEPTHWISECONV_KERNEL(false, 2, 1) + TFMINI_USE_DEPTHWISECONV_KERNEL(true, 0, 16) + TFMINI_USE_DEPTHWISECONV_KERNEL(true, 1, 8) +#endif // USE_NEON + +#undef TFMINI_USE_DEPTHWISECONV_KERNEL + + // Now that we have determined row_accum_func, we can start work. + float* output_ptr = output_data; + for (int b = 0; b < batches; ++b) { + for (int out_y = 0; out_y < output_height; ++out_y) { + const int in_y_origin = (out_y * stride_height) - pad_height; + const int filter_y_start = std::max(0, -in_y_origin); + const int filter_y_end = + std::min(filter_height, input_height - in_y_origin); + for (int out_x_buffer_start = 0; out_x_buffer_start < output_width; + out_x_buffer_start += kOutputPixelsInAccBuffer) { + const int out_x_buffer_end = std::min( + output_width, out_x_buffer_start + kOutputPixelsInAccBuffer); + // We call a 'pixel' a group of activation that share all but the + // 'depth'/'channel' coordinate. num_output_pixels is the number of + // output pixels that we will accumulate in this loop iteration. + const int num_output_pixels = out_x_buffer_end - out_x_buffer_start; + // Initialize our local accumulator with the bias values, so we don't + // have to add them later. + DepthwiseConvInitAccBuffer(num_output_pixels, output_depth, bias_data, + acc_buffer); + // Accumulation loop. Most of the time should be spent in here. + for (int filter_y = filter_y_start; filter_y < filter_y_end; + ++filter_y) { + const int in_y = in_y_origin + filter_y; + row_accum_func(stride_width, input_depth, input_width, + input_data + in_y * input_dims.strides[2] + + b * input_dims.strides[3], + pad_width, depth_multiplier, filter_width, + filter_data + filter_y * filter_dims.strides[2], + out_x_buffer_start, out_x_buffer_end, output_depth, + acc_buffer); + } + // Finished accumulating. Now store to destination. + const int num_output_values = output_depth * num_output_pixels; + int i = 0; +#ifdef USE_NEON + // Handle 16 values at a time + for (; i <= num_output_values - 16; i += 16) { + float32x4_t acc[4]; + for (int k = 0; k < 4; k++) { + acc[k] = vld1q_f32(acc_buffer + i + 4 * k); + } + if (Ac == FusedActivationFunctionType::kRelu) { + for (int k = 0; k < 4; k++) { + acc[k] = vmaxq_f32(vdupq_n_f32(0.f), acc[k]); + } + } else if (Ac == FusedActivationFunctionType::kRelu6) { + for (int k = 0; k < 4; k++) { + acc[k] = vmaxq_f32(vdupq_n_f32(0.f), + vminq_f32(vdupq_n_f32(6.f), acc[k])); + } + } else if (Ac == FusedActivationFunctionType::kRelu1) { + for (int k = 0; k < 4; k++) { + acc[k] = vmaxq_f32(vdupq_n_f32(-1.f), + vminq_f32(vdupq_n_f32(1.f), acc[k])); + } + } + for (int k = 0; k < 4; k++) { + vst1q_f32(output_ptr + 4 * k, acc[k]); + } + output_ptr += 16; + } + // Handle 4 values at a time + for (; i <= num_output_values - 4; i += 4) { + float32x4_t acc = vld1q_f32(acc_buffer + i); + if (Ac == FusedActivationFunctionType::kRelu) { + acc = vmaxq_f32(vdupq_n_f32(0.f), acc); + } else if (Ac == FusedActivationFunctionType::kRelu6) { + acc = vmaxq_f32(vdupq_n_f32(0.f), vminq_f32(vdupq_n_f32(6.f), acc)); + } else if (Ac == FusedActivationFunctionType::kRelu1) { + acc = + vmaxq_f32(vdupq_n_f32(-1.f), vminq_f32(vdupq_n_f32(1.f), acc)); + } + vst1q_f32(output_ptr, acc); + output_ptr += 4; + } +#endif + // Handle leftover values, one by one. This is very slow. + for (; i < num_output_values; i++) { + float acc = acc_buffer[i]; + if (Ac == FusedActivationFunctionType::kRelu) { + acc = std::max(0.f, acc); + } else if (Ac == FusedActivationFunctionType::kRelu6) { + acc = std::max(0.f, std::min(6.f, acc)); + } else if (Ac == FusedActivationFunctionType::kRelu1) { + acc = std::max(-1.f, std::min(1.f, acc)); + } + *output_ptr++ = acc; + } + } + } + } +} + +} // namespace optimized_ops +} // namespace rt +} // namespace nnfw + + +#endif // __NNFW_RT_OPTIMIZED_OPS_DEPTHWISECONV_FLOAT_H__ |