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Diffstat (limited to 'src/runtime/gpu/cl/operators/ClFullyConnected.cpp')
-rw-r--r-- | src/runtime/gpu/cl/operators/ClFullyConnected.cpp | 496 |
1 files changed, 0 insertions, 496 deletions
diff --git a/src/runtime/gpu/cl/operators/ClFullyConnected.cpp b/src/runtime/gpu/cl/operators/ClFullyConnected.cpp deleted file mode 100644 index 377168d864..0000000000 --- a/src/runtime/gpu/cl/operators/ClFullyConnected.cpp +++ /dev/null @@ -1,496 +0,0 @@ -/* - * Copyright (c) 2017-2021 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 "src/runtime/gpu/cl/operators/ClFullyConnected.h" - -#include "arm_compute/core/Size2D.h" -#include "arm_compute/core/Validate.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 "src/core/CL/kernels/CLFillBorderKernel.h" - -#include "src/core/helpers/MemoryHelpers.h" -#include "src/runtime/gpu/cl/operators/ClConvertFullyConnectedWeights.h" -#include "src/runtime/gpu/cl/operators/ClFlatten.h" -#include "src/runtime/gpu/cl/operators/ClGemm.h" -#include "src/runtime/gpu/cl/operators/ClGemmLowpMatrixMultiplyCore.h" -#include "src/runtime/gpu/cl/operators/ClTranspose.h" -#include "src/runtime/gpu/cl/utils/ClAuxTensorHandler.h" - -#include "support/Cast.h" - -#include <algorithm> - -namespace arm_compute -{ -namespace opencl -{ -using namespace arm_compute::experimental; -using namespace arm_compute::misc::shape_calculator; - -namespace -{ -Status construct_gemmlowp_output_stage(const ITensorInfo &src, const ITensorInfo &weights, const ITensorInfo &dst, - GEMMLowpOutputStageInfo &gemmlowp_output_stage, ActivationLayerInfo activation_info) -{ - 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; - - const auto data_type = src.data_type(); - - // Configure output stage for quantized case - if(is_data_type_quantized_asymmetric(data_type)) - { - const QuantizationInfo oq_info = dst.quantization_info(); - const UniformQuantizationInfo iq_unif = src.quantization_info().uniform(); - const UniformQuantizationInfo wq_unif = weights.quantization_info().uniform(); - const UniformQuantizationInfo oq_unif = oq_info.uniform(); - - const auto output_quant_info = (dst.total_size() == 0) ? iq_unif : oq_unif; - - const float multiplier = (iq_unif.scale * wq_unif.scale) / output_quant_info.scale; - int output_multiplier = 0; - int output_shift = 0; - ARM_COMPUTE_RETURN_ON_ERROR(quantization::calculate_quantized_multiplier(multiplier, &output_multiplier, &output_shift)); - - PixelValue type_min{}; - PixelValue type_max{}; - std::tie(type_min, type_max) = get_min_max(data_type); - - if(activation_info.enabled()) - { - std::tie(type_min, type_max) = get_quantized_activation_min_max(activation_info, data_type, output_quant_info); - } - - // 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_multipliers.push_back(output_multiplier); - gemmlowp_output_stage.gemmlowp_shifts.push_back(output_shift); - type_min.get(gemmlowp_output_stage.gemmlowp_min_bound); - type_max.get(gemmlowp_output_stage.gemmlowp_max_bound); - } - - return Status{}; -} - -Status validate_mm(const ITensorInfo &src, const ITensorInfo &weights, const ITensorInfo *bias, const ITensorInfo &dst, const FullyConnectedLayerInfo &fc_info) -{ - GEMMLowpOutputStageInfo gemmlowp_output_stage; - ARM_COMPUTE_RETURN_ON_ERROR(construct_gemmlowp_output_stage(src, weights, dst, gemmlowp_output_stage, fc_info.activation_info)); - - 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 - false, // fast_math - true, // broadcast_bias - ActivationLayerInfo()); // activation_info - - if(is_data_type_quantized_asymmetric(src.data_type())) - { - const UniformQuantizationInfo iq_info = src.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 src and weights offset - const QuantizationInfo src_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(&src.clone()->set_quantization_info(src_quantization_info), - &weights.clone()->set_quantization_info(weights_quantization_info), - bias, - &dst, - gemm_info)); - } - else - { - ARM_COMPUTE_RETURN_ON_ERROR(ClGemm::validate(&src, &weights, bias, &dst, 1.f, 1.f, gemm_info)); - } - - return Status{}; -} -} // namespace - -ClFullyConnected::ClFullyConnected() - : _convert_weights(nullptr), - _flatten(nullptr), - _reshape_weights(nullptr), - _mm_gemm(nullptr), - _mm_gemmlowp(nullptr), - _aux_mem(Count) -{ -} - -ClFullyConnected::~ClFullyConnected() = default; - -void ClFullyConnected::configure_mm(const CLCompileContext &compile_context, ITensorInfo *src, ITensorInfo *weights, ITensorInfo *bias, ITensorInfo *dst, - const FullyConnectedLayerInfo &fc_info) -{ - GEMMLowpOutputStageInfo gemmlowp_output_stage; - construct_gemmlowp_output_stage(*src, *weights, *dst, gemmlowp_output_stage, fc_info.activation_info); - - 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 - false, // fast_math - true, // broadcast_bias - fc_info.activation_info, // activation_info - fc_info.constant_weights); // constant_weights - - 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 src_quantization_info = src->quantization_info(); - const QuantizationInfo weights_quantization_info = weights->quantization_info(); - - TensorInfo src_info = src->clone()->set_quantization_info(src_quantization_info); - TensorInfo weights_info = weights->clone()->set_quantization_info(weights_quantization_info); - - src_info.set_quantization_info(QuantizationInfo(src_quantization_info.uniform().scale, -src_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 = std::make_unique<ClGemmLowpMatrixMultiplyCore>(); - _mm_gemmlowp->configure(compile_context, &src_info, &weights_info, bias, dst, gemm_info); - } - else - { - // Configure matrix multiply kernel - _mm_gemm = std::make_unique<ClGemm>(); - _mm_gemm->configure(compile_context, src, weights, bias, dst, 1.f, 1.f, gemm_info); - } -} - -void ClFullyConnected::configure_conv_fc(const CLCompileContext &compile_context, ITensorInfo *src, ITensorInfo *weights, ITensorInfo *bias, ITensorInfo *dst, - const FullyConnectedLayerInfo &fc_info) -{ - ARM_COMPUTE_ERROR_ON((weights->dimension(1) != (src->dimension(0) * src->dimension(1) * src->dimension(2)))); - - // If the fully connected layer is called after a convolution layer, the input tensor must be linearized - - // Initialize output tensor for flatten - _flattened_src = src->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(compute_flatten_shape(src)).set_data_layout(DataLayout::NCHW); - - // Configure flatten kernel - _flatten = std::make_unique<ClFlatten>(); - _flatten->configure(compile_context, src, &_flattened_src); - - // Configure matrix multiply kernel - configure_mm(compile_context, &_flattened_src, weights, bias, dst, fc_info); -} - -void ClFullyConnected::configure_fc_fc(const CLCompileContext &compile_context, ITensorInfo *src, ITensorInfo *weights, ITensorInfo *bias, ITensorInfo *dst, - const FullyConnectedLayerInfo &fc_info) -{ - ARM_COMPUTE_ERROR_ON(src->dimension(0) != weights->dimension(1)); - - // Configure matrix multiply kernel - configure_mm(compile_context, src, weights, bias, dst, fc_info); -} - -void ClFullyConnected::configure(const CLCompileContext &compile_context, ITensorInfo *src, ITensorInfo *weights, ITensorInfo *biases, ITensorInfo *dst, - FullyConnectedLayerInfo fc_info) -{ - ARM_COMPUTE_ERROR_ON_NULLPTR(src, weights, dst); - - // Perform validate step - ARM_COMPUTE_ERROR_THROW_ON(ClFullyConnected::validate(src, weights, biases, dst, 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(src->data_type()); - _is_prepared = fc_info.retain_internal_weights; - _weights_to_use = TensorInfo(*weights); - _weights_to_use_idx = ACL_SRC_1; - - // 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 = dst->dimension(1) > 1; - if(is_batched_fc_layer) - { - _is_fc_after_conv = (TensorShape::num_max_dimensions >= 4) && (std::equal(src->tensor_shape().cbegin() + 3, - src->tensor_shape().cend(), - dst->tensor_shape().cbegin() + 1)); - } - else - { - _is_fc_after_conv = src->num_dimensions() > 1; - } - - ITensorInfo *weights_used = weights; - - // Reshape weights if needed - if(!_are_weights_reshaped) - { - // Reshape the weights - _reshape_weights = std::make_unique<ClTranspose>(); - _reshape_weights->configure(compile_context, weights, &_reshaped_weights); - weights_used = &_reshaped_weights; - _weights_to_use_idx = offset_int_vec(TransposedWeights); - } - - // Convert weights if needed - if(_is_fc_after_conv && (src->data_layout() != fc_info.weights_trained_layout)) - { - // Convert weights - _convert_weights = std::make_unique<ClConvertFullyConnectedWeights>(); - _convert_weights->configure(compile_context, - weights_used, - &_converted_weights, - src->tensor_shape(), - fc_info.weights_trained_layout); - - weights_used = &_converted_weights; - _weights_to_use_idx = offset_int_vec(ConvertedWeights); - _are_weights_converted = false; - } - - if(_is_fc_after_conv) - { - // Fully Connected layer after a Convolution Layer without batches - configure_conv_fc(compile_context, src, weights_used, biases, dst, fc_info); - } - else - { - // Fully Connected layer after a Fully Connected Layer without batches - configure_fc_fc(compile_context, src, weights_used, biases, dst, fc_info); - } - // Update TensorInfo of final weights used (Need to be done in the end due to padding expansion) - _weights_to_use = *weights_used; - - // Set auxiliary memory requirements - auto gemm_mem_req = (_is_quantized) ? _mm_gemmlowp->workspace() : _mm_gemm->workspace(); - for(unsigned int i = 0; i < gemm_mem_req.size(); ++i) - { - _aux_mem[i] = gemm_mem_req[i]; - } - if(_aux_mem[1].size > 0 || _aux_mem[2].size > 0) // Persistent weights memory on GEMMs - { - // Release permuted weights at the of prepare as they are further transposed by the assembly dispatch - _aux_mem[TransposedWeights] = MemoryInfo(offset_int_vec(TransposedWeights), MemoryLifetime::Prepare, _reshaped_weights.total_size()); - _aux_mem[ConvertedWeights] = MemoryInfo(offset_int_vec(ConvertedWeights), MemoryLifetime::Prepare, _converted_weights.total_size()); - } - else - { - // Release permuted weights at the of prepare as they are further transposed by the assembly dispatch - const auto transposed_wei_lft = (_weights_to_use_idx == offset_int_vec(TransposedWeights)) ? MemoryLifetime::Persistent : MemoryLifetime::Prepare; - const auto converted_wei_lft = (_weights_to_use_idx == offset_int_vec(ConvertedWeights)) ? MemoryLifetime::Persistent : MemoryLifetime::Prepare; - - _aux_mem[TransposedWeights] = MemoryInfo(offset_int_vec(TransposedWeights), transposed_wei_lft, _reshaped_weights.total_size()); - _aux_mem[ConvertedWeights] = MemoryInfo(offset_int_vec(ConvertedWeights), converted_wei_lft, _converted_weights.total_size()); - } - _aux_mem[FlattenedSrc] = MemoryInfo(offset_int_vec(FlattenedSrc), MemoryLifetime::Temporary, _flattened_src.total_size()); -} - -Status ClFullyConnected::validate(const ITensorInfo *src, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *dst, - FullyConnectedLayerInfo fc_info) -{ - ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(src, weights, dst); - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED, DataType::F16, DataType::F32); - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src, weights, dst); - ARM_COMPUTE_RETURN_ERROR_ON(weights->num_dimensions() > 2); - ARM_COMPUTE_RETURN_ERROR_ON(fc_info.activation_info.enabled() && is_data_type_quantized(src->data_type()) && fc_info.activation_info.activation() != ActivationLayerInfo::ActivationFunction::RELU - && fc_info.activation_info.activation() != ActivationLayerInfo::ActivationFunction::BOUNDED_RELU && fc_info.activation_info.activation() != ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU); - ARM_COMPUTE_RETURN_ERROR_ON(!fc_info.constant_weights && (!fc_info.are_weights_reshaped || fc_info.transpose_weights)); - - bool weights_reshaped = fc_info.transpose_weights ? fc_info.are_weights_reshaped : true; - bool is_fc_after_conv = true; - - const ITensorInfo &flatten_src = TensorInfo(src->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(compute_flatten_shape(src)).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 *src_to_use = src; - const ITensorInfo *weights_to_use = weights; - - // Check if we have a fully connected layer with batches - const bool is_batched_fc_layer = dst->dimension(1) > 1; - if(is_batched_fc_layer) - { - is_fc_after_conv = (TensorShape::num_max_dimensions >= 4) && (std::equal(src->tensor_shape().cbegin() + 3, - src->tensor_shape().cend(), - dst->tensor_shape().cbegin() + 1)); - } - else - { - is_fc_after_conv = src->num_dimensions() > 1; - } - - if(!weights_reshaped) - { - // Validate reshape weights kernel - ARM_COMPUTE_RETURN_ON_ERROR(ClTranspose::validate(weights, &reshaped_weights)); - weights_to_use = &reshaped_weights; - } - - if(is_fc_after_conv && (src->data_layout() != fc_info.weights_trained_layout)) - { - // Validate convert weights kernel - ARM_COMPUTE_RETURN_ON_ERROR(ClConvertFullyConnectedWeights::validate(weights_to_use, - &converted_weights, - src->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) != (src->dimension(0) * src->dimension(1) * src->dimension(2)))); - - // Validate flatten kernel - ARM_COMPUTE_RETURN_ON_ERROR(ClFlatten::validate(src, &flatten_src)); - src_to_use = &flatten_src; - } - else - { - // Fully Connected layer after a Fully Connected Layer without batches - ARM_COMPUTE_RETURN_ERROR_ON(src->dimension(0) != weights_to_use->dimension(1)); - } - - // Validate matrix multiply kernel - ARM_COMPUTE_RETURN_ON_ERROR(validate_mm(*src_to_use, *weights_to_use, biases, *dst, fc_info)); - - return Status{}; -} - -void ClFullyConnected::run(ITensorPack &tensors) -{ - prepare(tensors); - - auto src = tensors.get_const_tensor(ACL_SRC_0); - - CLAuxTensorHandler flattened_src(offset_int_vec(FlattenedSrc), _flattened_src, tensors, false); - CLAuxTensorHandler weights(_weights_to_use_idx, _weights_to_use, tensors, false); - - // Linearize input if it comes from a convolutional layer - if(_is_fc_after_conv) - { - ITensorPack flatten_pack{ { ACL_SRC, src }, { ACL_DST, flattened_src.get() } }; - _flatten->run(flatten_pack); - } - - ITensorPack gemm_pack = tensors; - gemm_pack.add_const_tensor(ACL_SRC_0, (_is_fc_after_conv) ? flattened_src.get() : src); - if(_weights_to_use_idx != ACL_SRC_1) - { - gemm_pack.add_const_tensor(ACL_SRC_1, weights.get()); - } - - // Run matrix multiply - if(_is_quantized) - { - _mm_gemmlowp->run(gemm_pack); - } - else - { - _mm_gemm->run(gemm_pack); - } -} - -void ClFullyConnected::prepare(ITensorPack &tensors) -{ - if(!_is_prepared) - { - auto weights = tensors.get_const_tensor(ACL_SRC_1); - - CLAuxTensorHandler reshaped_weights(offset_int_vec(TransposedWeights), _reshaped_weights, tensors, false); - CLAuxTensorHandler converted_weights(offset_int_vec(ConvertedWeights), _converted_weights, tensors, false); - - // Pointer to current weights - const ITensor *cur_weights = weights; - - // Reshape of the weights if needed (happens only once) - if(!_are_weights_reshaped) - { - // Run reshape weights kernel and mark weights as unused - ITensorPack transpose_pack{ { ACL_SRC, weights }, { ACL_DST, reshaped_weights.get() } }; - _reshape_weights->run(transpose_pack); - - cur_weights->mark_as_unused(); - cur_weights = reshaped_weights.get(); - - _are_weights_reshaped = true; - } - - // Convert weights if needed (happens only once) - if(!_are_weights_converted) - { - ITensorPack convert_pack{ { ACL_SRC, cur_weights }, { ACL_DST, converted_weights.get() } }; - _convert_weights->run(convert_pack); - - cur_weights->mark_as_unused(); - cur_weights = converted_weights.get(); - - _are_weights_converted = true; - } - - tensors.add_const_tensor(ACL_SRC_1, cur_weights); - - // Prepare GEMM prepare and release unused weights - if(!_is_quantized) - { - _mm_gemm->prepare(tensors); - } - else - { - _mm_gemmlowp->prepare(tensors); - } - _is_prepared = true; - } -} - -experimental::MemoryRequirements ClFullyConnected::workspace() const -{ - return _aux_mem; -} -} // namespace opencl -} // namespace arm_compute |