path: root/compute/ARMComputeEx/src/runtime/CL/functions/CLFullyConnectedLayerEx.cpp

/*
 * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved
 *
 * 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.
 */

/*
 * Copyright (c) 2017-2019 ARM Limited.
 *
 * SPDX-License-Identifier: MIT
 *
 * Permission is hereby granted, free of charge, to any person obtaining a copy
 * of this software and associated documentation files (the "Software"), to
 * deal in the Software without restriction, including without limitation the
 * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
 * sell copies of the Software, and to permit persons to whom the Software is
 * furnished to do so, subject to the following conditions:
 *
 * The above copyright notice and this permission notice shall be included in all
 * copies or substantial portions of the Software.
 *
 * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
 * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
 * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
 * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
 * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
 * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
 * SOFTWARE.
 */

#include "arm_compute/runtime/CL/functions/CLFullyConnectedLayerEx.h"

#include "arm_compute/core/Size2D.h"
#include "arm_compute/core/Validate.h"
#include "arm_compute/core/utils/misc/Cast.h"
#include "arm_compute/core/utils/misc/ShapeCalculator.h"
#include "arm_compute/core/utils/quantization/AsymmHelpers.h"
#include "arm_compute/runtime/CL/CLScheduler.h"
#include "support/MemorySupport.h"

#include <algorithm>

namespace arm_compute
{
using namespace arm_compute::misc::shape_calculator;
using namespace arm_compute::utils::cast;

namespace
{
Status construct_gemmlowp_output_stage(const ITensorInfo &input, const ITensorInfo &weights,
                                       const ITensorInfo &output,
                                       GEMMLowpOutputStageInfo &gemmlowp_output_stage)
{
  gemmlowp_output_stage.type = GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT;
  gemmlowp_output_stage.gemmlowp_offset = 0;
  gemmlowp_output_stage.gemmlowp_multiplier = 0;
  gemmlowp_output_stage.gemmlowp_shift = 0;

  // Configure output stage for quantized case
  if (is_data_type_quantized_asymmetric(input.data_type()))
  {
    const UniformQuantizationInfo iq_info = input.quantization_info().uniform();
    const UniformQuantizationInfo wq_info = weights.quantization_info().uniform();
    const UniformQuantizationInfo oq_info = output.quantization_info().uniform();

    const auto output_quant_info = (output.total_size() == 0) ? iq_info : oq_info;

    const float multiplier = (iq_info.scale * wq_info.scale) / output_quant_info.scale;
    int output_multiplier = 0;
    int output_shift = 0;
    ARM_COMPUTE_RETURN_ON_ERROR(quantization::calculate_quantized_multiplier_less_than_one(
        multiplier, &output_multiplier, &output_shift));

    // Set the GEMMLowp output stage info
    gemmlowp_output_stage.gemmlowp_offset = output_quant_info.offset;
    gemmlowp_output_stage.gemmlowp_multiplier = output_multiplier;
    gemmlowp_output_stage.gemmlowp_shift = output_shift;
    gemmlowp_output_stage.gemmlowp_min_bound = 0;
    gemmlowp_output_stage.gemmlowp_max_bound = 255;
    gemmlowp_output_stage.gemmlowp_multipliers.push_back(output_multiplier);
    gemmlowp_output_stage.gemmlowp_shifts.push_back(output_shift);
  }

  return Status{};
}

Status validate_mm(const ITensorInfo &input, const ITensorInfo &weights, const ITensorInfo *bias,
                   const ITensorInfo &output, const FullyConnectedLayerInfo &fc_info)
{
  GEMMLowpOutputStageInfo gemmlowp_output_stage;
  ARM_COMPUTE_RETURN_ON_ERROR(
      construct_gemmlowp_output_stage(input, weights, output, gemmlowp_output_stage));

  const GEMMInfo &gemm_info = GEMMInfo(false, // is_a_reshaped
                                       false, // is_b_reshaped
                                       true,  // reshape_b_only_on_first_run
                                       0,     // depth_output_gemm3d
                                       false, // reinterpret_input_as_3d
                                       fc_info.retain_internal_weights, // retain_internal_weights
                                       gemmlowp_output_stage,           // gemmlowp_output_stage
                                       fc_info.fp_mixed_precision,      // fp_mixed_precision
                                       true,                            // broadcast_bias
                                       ActivationLayerInfo());          // activation_info

  if (is_data_type_quantized_asymmetric(input.data_type()))
  {
    const UniformQuantizationInfo iq_info = input.quantization_info().uniform();
    const UniformQuantizationInfo wq_info = weights.quantization_info().uniform();

    // Since we need negative offsets for computing convolution, we need to change
    // QuantizationInfo()
    // Extract and negate input and weights offset
    const QuantizationInfo input_quantization_info(iq_info.scale, -iq_info.offset);
    const QuantizationInfo weights_quantization_info(wq_info.scale, -wq_info.offset);

    // Validate gemmlowp function
    ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMLowpMatrixMultiplyCore::validate(
        &input.clone()->set_quantization_info(input_quantization_info),
        &weights.clone()->set_quantization_info(weights_quantization_info), bias, &output,
        gemm_info));
  }
  else
  {
    ARM_COMPUTE_RETURN_ON_ERROR(
        CLGEMM::validate(&input, &weights, bias, &output, 1.f, 1.f, gemm_info));
  }

  return Status{};
}
} // namespace

void CLFullyConnectedLayerReshapeWeightsEx::configure(const ICLTensor *input, ICLTensor *output)
{
  auto k = support::cpp14::make_unique<CLTransposeKernel>();
  k->configure(input, output);
  _kernel = std::move(k);
}

Status CLFullyConnectedLayerReshapeWeightsEx::validate(const ITensorInfo *input,
                                                       const ITensorInfo *output)
{
  return CLTransposeKernel::validate(input, output);
}

CLFullyConnectedLayerEx::CLFullyConnectedLayerEx(std::shared_ptr<IMemoryManager> memory_manager,
                                                 IWeightsManager *weights_manager)
    : _memory_group(memory_manager), _weights_manager(weights_manager), _convert_weights(),
      _convert_weights_managed(), _reshape_weights_managed_function(), _flatten_layer(),
      _reshape_weights_function(), _mm_gemm(memory_manager, weights_manager),
      _mm_gemmlowp(memory_manager), _flatten_output(), _converted_weights_output(),
      _reshape_weights_output(), _are_weights_converted(true), _are_weights_reshaped(true),
      _is_fc_after_conv(true), _is_quantized(false), _is_prepared(false), _original_weights(nullptr)
{
}
void CLFullyConnectedLayerEx::configure_mm(const ICLTensor *input, const ICLTensor *weights,
                                           const ICLTensor *bias, ICLTensor *output,
                                           const FullyConnectedLayerInfo &fc_info)
{
  GEMMLowpOutputStageInfo gemmlowp_output_stage;
  construct_gemmlowp_output_stage(*input->info(), *weights->info(), *output->info(),
                                  gemmlowp_output_stage);

  const GEMMInfo &gemm_info = GEMMInfo(false, // is_a_reshaped
                                       false, // is_b_reshaped
                                       true,  // reshape_b_only_on_first_run
                                       0,     // depth_output_gemm3d
                                       false, // reinterpret_input_as_3d
                                       fc_info.retain_internal_weights, // retain_internal_weights
                                       gemmlowp_output_stage,           // gemmlowp_output_stage
                                       fc_info.fp_mixed_precision,      // fp_mixed_precision
                                       true,                            // broadcast_bias
                                       ActivationLayerInfo());          // activation_info

  if (_is_quantized)
  {
    // Since we need negative offsets for computing convolution, we need to change
    // QuantizationInfo()
    // Extract and negate input and weights offset
    const QuantizationInfo input_quantization_info = input->info()->quantization_info();
    const QuantizationInfo weights_quantization_info = weights->info()->quantization_info();

    input->info()->set_quantization_info(QuantizationInfo(
        input_quantization_info.uniform().scale, -input_quantization_info.uniform().offset));
    weights->info()->set_quantization_info(QuantizationInfo(
        weights_quantization_info.uniform().scale, -weights_quantization_info.uniform().offset));

    // Configure gemmlowp function
    _mm_gemmlowp.configure(input, weights, bias, output, gemm_info);

    // Revert back QuantizatioInfo as input and weights could be used in other fully connected
    // layers
    input->info()->set_quantization_info(input_quantization_info);
    weights->info()->set_quantization_info(weights_quantization_info);
  }
  else
  {
    // Configure matrix multiply kernel
    _mm_gemm.configure(input, weights, bias, output, 1.f, 1.f, gemm_info);
  }
}

void CLFullyConnectedLayerEx::configure_conv_fc(const ICLTensor *input, const ICLTensor *weights,
                                                const ICLTensor *bias, ICLTensor *output,
                                                const FullyConnectedLayerInfo &fc_info)
{
  ARM_COMPUTE_ERROR_ON(
      (weights->info()->dimension(1) !=
       (input->info()->dimension(0) * input->info()->dimension(1) * input->info()->dimension(2))));

  // If the fully connected layer is called after a convolution layer, the input tensor must be
  // linearized

  // Initialize output tensor for flatten
  TensorShape shape_flatten = compute_flatten_shape(input->info());
  _flatten_output.allocator()->init(input->info()
                                        ->clone()
                                        ->set_is_resizable(true)
                                        .reset_padding()
                                        .set_tensor_shape(shape_flatten)
                                        .set_data_layout(DataLayout::NCHW));

  // Configure flatten kernel
  _memory_group.manage(&_flatten_output);
  _flatten_layer.configure(input, &_flatten_output);

  // Configure matrix multiply kernel
  configure_mm(&_flatten_output, weights, bias, output, fc_info);

  // Allocate the output tensor for flatten once all the configure methods have been called
  _flatten_output.allocator()->allocate();
}

void CLFullyConnectedLayerEx::configure_fc_fc(const ICLTensor *input, const ICLTensor *weights,
                                              const ICLTensor *bias, ICLTensor *output,
                                              const FullyConnectedLayerInfo &fc_info)
{
  ARM_COMPUTE_ERROR_ON(input->info()->dimension(0) != weights->info()->dimension(1));

  // Configure matrix multiply kernel
  configure_mm(input, weights, bias, output, fc_info);
}

void CLFullyConnectedLayerEx::configure(const ICLTensor *input, const ICLTensor *weights,
                                        const ICLTensor *biases, ICLTensor *output,
                                        FullyConnectedLayerInfo fc_info)
{
  ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, output);

  // Perform validate step
  ARM_COMPUTE_ERROR_THROW_ON(CLFullyConnectedLayerEx::validate(
      input->info(), weights->info(), biases != nullptr ? biases->info() : nullptr, output->info(),
      fc_info));

  _are_weights_converted = true;
  _are_weights_reshaped = fc_info.transpose_weights ? fc_info.are_weights_reshaped : true;
  _is_fc_after_conv = true;
  _is_quantized = is_data_type_quantized_asymmetric(input->info()->data_type());
  _is_prepared = fc_info.retain_internal_weights;
  _original_weights = weights;

  if (_weights_manager)
  {
    _weights_manager->manage(weights);
  }

  const ICLTensor *weights_to_use = weights;

  // With the Fully Connected layer we can have 4 different cases:
  //  1) Convolution layer -> Fully Connected layer without batches
  //  2) Fully Connected layer -> Fully Connected layer without batches
  //  3) Convolution layer -> Fully Connected layer with batches
  //  4) Fully Connected layer -> Fully Connected layer with batches

  // Check if we have a fully connected layer with batches
  const bool is_batched_fc_layer = output->info()->dimension(1) > 1;
  if (is_batched_fc_layer)
  {
    _is_fc_after_conv = (TensorShape::num_max_dimensions >= 4) &&
                        (std::equal(input->info()->tensor_shape().cbegin() + 3,
                                    input->info()->tensor_shape().cend(),
                                    output->info()->tensor_shape().cbegin() + 1));
  }
  else
  {
    _is_fc_after_conv = input->info()->num_dimensions() > 1;
  }

  // Reshape weights if needed
  if (!_are_weights_reshaped)
  {
    if (_weights_manager && _weights_manager->are_weights_managed(weights))
    {
      _reshape_weights_managed_function.configure(weights);
      weights_to_use = utils::cast::polymorphic_downcast<ICLTensor *>(
          _weights_manager->acquire(weights, &_reshape_weights_managed_function));
    }
    else
    {
      // Reshape the weights
      _reshape_weights_function.configure(weights, &_reshape_weights_output);
      weights_to_use = &_reshape_weights_output;
    }
  }

  // Convert weights if needed
  if (_is_fc_after_conv && (input->info()->data_layout() != fc_info.weights_trained_layout))
  {
    if (_weights_manager && _weights_manager->are_weights_managed(weights_to_use))
    {
      _convert_weights_managed.configure(weights_to_use, input->info()->tensor_shape(),
                                         fc_info.weights_trained_layout);
      weights_to_use = utils::cast::polymorphic_downcast<ICLTensor *>(
          _weights_manager->acquire(weights, &_convert_weights_managed));
    }
    else
    {
      // Convert weights
      _convert_weights.configure(weights_to_use, &_converted_weights_output,
                                 input->info()->tensor_shape(), fc_info.weights_trained_layout);

      weights_to_use = &_converted_weights_output;
    }
    _are_weights_converted = false;
  }

  if (_is_fc_after_conv)
  {
    // Fully Connected layer after a Convolution Layer without batches
    configure_conv_fc(input, weights_to_use, biases, output, fc_info);
  }
  else
  {
    // Fully Connected layer after a Fully Connected Layer without batches
    configure_fc_fc(input, weights_to_use, biases, output, fc_info);
  }
}

Status CLFullyConnectedLayerEx::validate(const ITensorInfo *input, const ITensorInfo *weights,
                                         const ITensorInfo *biases, const ITensorInfo *output,
                                         FullyConnectedLayerInfo fc_info)
{
  ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, weights, output);
  ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::F16,
                                                       DataType::F32);
  ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights, output);
  ARM_COMPUTE_RETURN_ERROR_ON(weights->num_dimensions() > 2);

  bool weights_reshaped = fc_info.transpose_weights ? fc_info.are_weights_reshaped : true;
  bool is_fc_after_conv = true;

  const ITensorInfo &flatten_input = TensorInfo(input->clone()
                                                    ->set_is_resizable(true)
                                                    .reset_padding()
                                                    .set_tensor_shape(compute_flatten_shape(input))
                                                    .set_data_layout(DataLayout::NCHW));
  const ITensorInfo &reshaped_weights =
      TensorInfo(weights->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(
          compute_transposed_shape(*weights)));
  const ITensorInfo &converted_weights =
      weights_reshaped ? TensorInfo(weights->clone()->set_is_resizable(true).reset_padding())
                       : TensorInfo(*reshaped_weights.clone());

  // With the Fully Connected layer we can have 4 different cases:
  //  1) Convolution layer -> Fully Connected layer without batches
  //  2) Fully Connected layer -> Fully Connected layer without batches
  //  3) Convolution layer -> Fully Connected layer with batches
  //  4) Fully Connected layer -> Fully Connected layer with batches

  const ITensorInfo *input_to_use = input;
  const ITensorInfo *weights_to_use = weights;

  // Check if we have a fully connected layer with batches
  const bool is_batched_fc_layer = output->dimension(1) > 1;
  if (is_batched_fc_layer)
  {
    is_fc_after_conv = (TensorShape::num_max_dimensions >= 4) &&
                       (std::equal(input->tensor_shape().cbegin() + 3, input->tensor_shape().cend(),
                                   output->tensor_shape().cbegin() + 1));
  }
  else
  {
    is_fc_after_conv = input->num_dimensions() > 1;
  }

  if (!weights_reshaped)
  {
    // Validate reshape weights kernel
    ARM_COMPUTE_RETURN_ON_ERROR(
        CLFullyConnectedLayerReshapeWeightsEx::validate(weights, &reshaped_weights));
    weights_to_use = &reshaped_weights;
  }

  if (is_fc_after_conv && (input->data_layout() != fc_info.weights_trained_layout))
  {
    // Validate convert weights kernel
    ARM_COMPUTE_RETURN_ON_ERROR(CLConvertFullyConnectedWeights::validate(
        weights_to_use, &converted_weights, input->tensor_shape(), fc_info.weights_trained_layout));
    weights_to_use = &converted_weights;
  }

  if (is_fc_after_conv)
  {
    // Fully Connected layer after a Convolution Layer without batches
    ARM_COMPUTE_RETURN_ERROR_ON(
        (weights_to_use->dimension(1) !=
         (input->dimension(0) * input->dimension(1) * input->dimension(2))));

    // Validate flatten kernel
    ARM_COMPUTE_RETURN_ON_ERROR(CLFlattenLayer::validate(input, &flatten_input));
    input_to_use = &flatten_input;
  }
  else
  {
    // Fully Connected layer after a Fully Connected Layer without batches
    ARM_COMPUTE_RETURN_ERROR_ON(input->dimension(0) != weights_to_use->dimension(1));
  }

  // Validate matrix multiply kernel
  ARM_COMPUTE_RETURN_ON_ERROR(
      validate_mm(*input_to_use, *weights_to_use, biases, *output, fc_info));

  return Status{};
}

void CLFullyConnectedLayerEx::run()
{
  if (!_is_prepared)
  {
    if (!_are_weights_reshaped)
      _reshape_weights_output.allocator()->allocate();
    if (!_are_weights_converted)
      _converted_weights_output.allocator()->allocate();
    _is_prepared = true;
  }

  {
    if (!_weights_manager)
    {
      ARM_COMPUTE_ERROR_ON(!_original_weights->is_used());
    }

    // Pointer to current weights
    const ICLTensor *cur_weights = _original_weights;
    // Reshape of the weights
    if (!_are_weights_reshaped)
    {
      if (_weights_manager && _weights_manager->are_weights_managed(cur_weights))
      {
        _original_weights = utils::cast::polymorphic_downcast<ICLTensor *>(
            _weights_manager->run(cur_weights, &_reshape_weights_managed_function));
      }
      else
      {
        _reshape_weights_function.run();
        cur_weights = &_reshape_weights_output;
      }
    }

    // Convert weights if needed
    if (!_are_weights_converted)
    {
      if (_weights_manager && _weights_manager->are_weights_managed(cur_weights))
      {
        _weights_manager->run(cur_weights, &_convert_weights_managed);
      }
      else
      {
        _convert_weights.run();
      }
    }

    // Prepare GEMM prepare
    if (!_is_quantized)
    {
      _mm_gemm.prepare();
    }
  }

  MemoryGroupResourceScope scope_mg(_memory_group);

  // Linearize input if it comes from a convolutional layer
  if (_is_fc_after_conv)
  {
    _flatten_layer.run();
  }

  // Run matrix multiply
  if (_is_quantized)
  {
    _mm_gemmlowp.run();
  }
  else
  {
    _mm_gemm.run();
  }
}

void CLFullyConnectedLayerEx::prepare()
{
#if 0 // TODO Remove this block
    if(!_is_prepared)
    {
        if(!_weights_manager)
        {
            ARM_COMPUTE_ERROR_ON(!_original_weights->is_used());
        }

        auto release_unused = [](CLTensor * w)
        {
            if(!w->is_used())
            {
                CLScheduler::get().queue().finish();
                w->allocator()->free();
            }
        };

        // Pointer to current weights
        const ICLTensor *cur_weights = _original_weights;

        // Reshape of the weights if needed (happens only once)
        if(!_are_weights_reshaped)
        {
            if(_weights_manager && _weights_manager->are_weights_managed(_original_weights))
            {
                cur_weights = utils::cast::polymorphic_downcast<ICLTensor *>(_weights_manager->run(cur_weights, &_reshape_weights_managed_function));
            }
            else
            {
                // Run reshape weights kernel and mark weights as unused
                _reshape_weights_output.allocator()->allocate();
                _reshape_weights_function.run();

                cur_weights->mark_as_unused();
                cur_weights = &_reshape_weights_output;
            }
            _are_weights_reshaped = true;
        }

        // Convert weights if needed (happens only once)
        if(!_are_weights_converted)
        {
            if(_weights_manager && _weights_manager->are_weights_managed(cur_weights))
            {
                _weights_manager->run(cur_weights, &_convert_weights_managed);
            }
            else
            {
                _converted_weights_output.allocator()->allocate();
                _convert_weights.run();
                cur_weights->mark_as_unused();
            }

            _are_weights_converted = true;
        }

        // Release reshaped weights if unused
        release_unused(&_reshape_weights_output);

        // Prepare GEMM prepare and release unused weights
        if(!_is_quantized)
        {
            _mm_gemm.prepare();
        }

        // Release converted weights if unused
        release_unused(&_reshape_weights_output);
        release_unused(&_converted_weights_output);

        _is_prepared = true;
    }
#endif
}
} // namespace arm_compute