diff options
Diffstat (limited to 'src/runtime/cpu/operators/CpuFullyConnected.cpp')
| -rw-r--r-- | src/runtime/cpu/operators/CpuFullyConnected.cpp | 496 |
1 files changed, 0 insertions, 496 deletions
diff --git a/src/runtime/cpu/operators/CpuFullyConnected.cpp b/src/runtime/cpu/operators/CpuFullyConnected.cpp deleted file mode 100644 index eeabce0753..0000000000 --- a/src/runtime/cpu/operators/CpuFullyConnected.cpp +++ /dev/null @@ -1,496 +0,0 @@ -/* - * Copyright (c) 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/cpu/operators/CpuFullyConnected.h" - -#include "arm_compute/core/Helpers.h" -#include "arm_compute/core/ITensorPack.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/NEON/NEScheduler.h" -#include "src/core/cpu/kernels/CpuTransposeKernel.h" -#include "src/core/helpers/AutoConfiguration.h" -#include "src/core/helpers/MemoryHelpers.h" -#include "src/runtime/cpu/operators/CpuConvertFullyConnectedWeights.h" -#include "src/runtime/cpu/operators/CpuFlatten.h" -#include "src/runtime/cpu/operators/CpuGemm.h" -#include "src/runtime/cpu/operators/CpuGemmLowpMatrixMultiplyCore.h" -#include "src/runtime/cpu/utils/CpuAuxTensorHandler.h" - -namespace arm_compute -{ -namespace cpu -{ -using namespace arm_compute::experimental; -using namespace arm_compute::misc::shape_calculator; - -namespace -{ -// Get min, max bound of a quantized asymmetric dst tensor, with the effect of fused activation -std::pair<PixelValue, PixelValue> get_quantized_asymmetric_output_min_max(const QuantizationInfo &q_info, const ActivationLayerInfo &act_info, DataType data_type) -{ - PixelValue type_min{}; - PixelValue type_max{}; - std::tie(type_min, type_max) = get_min_max(data_type); - const UniformQuantizationInfo q_unif = q_info.uniform(); - - if(act_info.enabled()) - { - switch(act_info.activation()) - { - case ActivationLayerInfo::ActivationFunction::RELU: - type_min = PixelValue(q_unif.offset); - break; - case ActivationLayerInfo::ActivationFunction::BOUNDED_RELU: - type_min = PixelValue(q_unif.offset); - type_max = PixelValue(act_info.a(), data_type, q_info); - break; - case ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU: - type_min = PixelValue(act_info.b(), data_type, q_info); - type_max = PixelValue(act_info.a(), data_type, q_info); - break; - default: - ARM_COMPUTE_ERROR("Activation function not supported."); - break; - } - } - - return std::make_pair(type_min, type_max); -} - -Status get_gemmlowp_output_stage_info(const ITensorInfo *src, const ITensorInfo *weights, const ITensorInfo *dst, const ActivationLayerInfo &act, - GEMMLowpOutputStageInfo &gemmlowp_output_stage_info) -{ - const auto data_type = src->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(); - - float multiplier = (iq_unif.scale * wq_unif.scale) / oq_unif.scale; - int32_t output_multiplier; - int32_t output_shift; - - 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_quantized_asymmetric_output_min_max(oq_info, act, data_type); - - gemmlowp_output_stage_info.gemmlowp_multiplier = output_multiplier; - gemmlowp_output_stage_info.gemmlowp_shift = output_shift; - gemmlowp_output_stage_info.gemmlowp_offset = oq_unif.offset; - gemmlowp_output_stage_info.type = GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT; - gemmlowp_output_stage_info.gemmlowp_min_bound = type_min.get<int32_t>(); - gemmlowp_output_stage_info.gemmlowp_max_bound = type_max.get<int32_t>(); - - return Status{}; -} - -Status validate_mm(const ITensorInfo *src, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *dst, const ActivationLayerInfo &act) -{ - if(is_data_type_quantized_asymmetric(src->data_type())) - { - // 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(src->quantization_info().uniform().scale, -src->quantization_info().uniform().offset); - const QuantizationInfo weights_quantization_info(weights->quantization_info().uniform().scale, -weights->quantization_info().uniform().offset); - - GEMMLowpOutputStageInfo gemmlowp_output_stage_info; - ARM_COMPUTE_RETURN_ON_ERROR(get_gemmlowp_output_stage_info(src, weights, dst, act, gemmlowp_output_stage_info)); - - GEMMInfo gemm_info; - gemm_info.set_gemmlowp_output_stage(gemmlowp_output_stage_info); - - // Validate gemmlowp function - TensorInfo src_info = src->clone()->set_quantization_info(src_quantization_info); - TensorInfo weights_info = weights->clone()->set_quantization_info(weights_quantization_info); - ARM_COMPUTE_RETURN_ON_ERROR(CpuGemmLowpMatrixMultiplyCore::validate(&src_info, - &weights_info, - biases, - dst, - gemm_info)); - } - else - { - ARM_COMPUTE_RETURN_ON_ERROR(CpuGemm::validate(src, weights, biases, dst, 1.f, 1.0f, GEMMInfo(false, false, true /* Reshape weights only for the first run */))); - } - - return Status{}; -} -} // namespace - -CpuFullyConnected::CpuFullyConnected() - : _flatten(nullptr), - _convert_weights(nullptr), - _transpose_weights(nullptr), - _mm_gemm(nullptr), - _mm_gemmlowp(nullptr), - _flattened_src(), - _converted_weights(), - _reshaped_weights(), - _trans_weights(), - _trans_weights_idx(AuxTensorIdx::Count), - _aux_mem(Count), - _needs_weights_conversion(false), - _needs_weights_reshape(false), - _is_fc_after_conv(false), - _is_quantized_asymmetric(false), - _is_prepared(false) - -{ -} - -CpuFullyConnected::~CpuFullyConnected() = default; - -void CpuFullyConnected::configure_mm(const ITensorInfo *src, const ITensorInfo *weights, const ITensorInfo *biases, ITensorInfo *dst, const ActivationLayerInfo &act) -{ - if(_is_quantized_asymmetric) - { - // 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(src->quantization_info().uniform().scale, -src->quantization_info().uniform().offset); - const QuantizationInfo weights_quantization_info(weights->quantization_info().uniform().scale, -weights->quantization_info().uniform().offset); - - TensorInfo src_info = src->clone()->set_quantization_info(src_quantization_info); - TensorInfo weights_info = weights->clone()->set_quantization_info(weights_quantization_info); - - // Configure gemmlowp function and output stage for asymmetric quantized types - GEMMLowpOutputStageInfo gemmlowp_output_stage_info; - const Status status = get_gemmlowp_output_stage_info(&src_info, &weights_info, dst, act, gemmlowp_output_stage_info); - ARM_COMPUTE_ERROR_ON(status.error_code() != ErrorCode::OK); - - GEMMInfo gemm_info; - gemm_info.set_gemmlowp_output_stage(gemmlowp_output_stage_info); - gemm_info.set_activation_info(act); - _mm_gemmlowp = std::make_unique<CpuGemmLowpMatrixMultiplyCore>(); - _mm_gemmlowp->configure(&src_info, &weights_info, biases, dst, gemm_info); - } - else - { - // Configure matrix multiply kernel - GEMMInfo gemm_info(false, false, true /* Reshape weights only for the first run */); - gemm_info.set_activation_info(act); - _mm_gemm = std::make_unique<CpuGemm>(); - _mm_gemm->configure(src, weights, biases, dst, 1.f, 1.0f, gemm_info); - } -} - -void CpuFullyConnected::configure_conv_fc(const ITensorInfo *src, const ITensorInfo *weights, const ITensorInfo *biases, ITensorInfo *dst, const ActivationLayerInfo &act) -{ - 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 src tensor must be linearized - - // Initialize output tensor for flatten - auto_init_if_empty(_flattened_src, src->clone()->set_tensor_shape(compute_flatten_shape(src))); - - _flatten = std::make_unique<CpuFlatten>(); - _flatten->configure(src, &_flattened_src); - - // Configure matrix multiply kernel - configure_mm(&_flattened_src, weights, biases, dst, act); -} - -void CpuFullyConnected::configure_fc_fc(const ITensorInfo *src, const ITensorInfo *weights, const ITensorInfo *biases, ITensorInfo *dst, const ActivationLayerInfo &act) -{ - ARM_COMPUTE_ERROR_ON(src->dimension(0) != weights->dimension(1)); - - // Configure matrix multiply kernel - configure_mm(src, weights, biases, dst, act); -} - -void CpuFullyConnected::configure(const ITensorInfo *src, const ITensorInfo *weights, const ITensorInfo *biases, ITensorInfo *dst, - FullyConnectedLayerInfo fc_info) -{ - // Perform validate step - ARM_COMPUTE_ERROR_ON_NULLPTR(src, weights, dst); - ARM_COMPUTE_ERROR_THROW_ON(CpuFullyConnected::validate(src, - weights, - biases != nullptr ? biases : nullptr, - dst, - fc_info)); - - _needs_weights_conversion = false; - _needs_weights_reshape = fc_info.transpose_weights ? !fc_info.are_weights_reshaped : false; - _needs_weights_reshape = _needs_weights_reshape && !fc_info.retain_internal_weights; - _is_fc_after_conv = true; - _is_quantized_asymmetric = is_data_type_quantized_asymmetric(src->data_type()); - _is_prepared = false; - _trans_weights_idx = AuxTensorIdx::Count; - - // 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 *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; - } - - // Reshape weights if needed - if(_needs_weights_reshape) - { - // Reshape the weights - _transpose_weights = std::make_unique<kernels::CpuTransposeKernel>(); - _transpose_weights->configure(weights, &_reshaped_weights); - weights_to_use = &_reshaped_weights; - _trans_weights_idx = AuxTensorIdx::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<CpuConvertFullyConnectedWeights>(); - _convert_weights->configure(weights_to_use, - &_converted_weights, - src->tensor_shape(), - fc_info.weights_trained_layout); - - weights_to_use = &_converted_weights; - _needs_weights_conversion = true; - _trans_weights_idx = AuxTensorIdx::ConvertedWeights; - } - - if(_is_fc_after_conv) - { - // Fully Connected layer after a Convolution Layer without batches - configure_conv_fc(src, weights_to_use, biases, dst, fc_info.activation_info); - } - else - { - // Fully Connected layer after a Fully Connected Layer without batches - configure_fc_fc(src, weights_to_use, biases, dst, fc_info.activation_info); - } - - // Retain the tensorinfo with the weights to use - if(_needs_weights_reshape || _needs_weights_conversion) - { - _trans_weights = *weights_to_use; - } - - // Set auxiliary memory requirements - auto gemm_mem_req = (_is_quantized_asymmetric) ? _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[Pretranspose].size > 0) - { - // 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 - { - _aux_mem[TransposedWeights] = MemoryInfo(offset_int_vec(TransposedWeights), _needs_weights_conversion ? MemoryLifetime::Prepare : MemoryLifetime::Persistent, _reshaped_weights.total_size()); - _aux_mem[ConvertedWeights] = MemoryInfo(offset_int_vec(ConvertedWeights), MemoryLifetime::Persistent, _converted_weights.total_size()); - } - _aux_mem[FlattenedSrc] = MemoryInfo(offset_int_vec(FlattenedSrc), MemoryLifetime::Temporary, _flattened_src.total_size()); -} - -Status CpuFullyConnected::validate(const ITensorInfo *src, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *dst, - FullyConnectedLayerInfo fc_info) -{ - ARM_COMPUTE_UNUSED(fc_info.retain_internal_weights); - 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(biases != nullptr && biases->num_dimensions() > 1); - 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_MSG(!fc_info.constant_weights, "Non-constant weights are currently not supported"); - - 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))); - 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(kernels::CpuTransposeKernel::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(CpuConvertFullyConnectedWeights::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(CpuFlatten::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.activation_info)); - - return Status{}; -} - -void CpuFullyConnected::run(ITensorPack &tensors) -{ - prepare(tensors); - - auto src = tensors.get_const_tensor(ACL_SRC_0); - - CpuAuxTensorHandler flattened_src(offset_int_vec(FlattenedSrc), _flattened_src, tensors, false); - CpuAuxTensorHandler transformed_wei(offset_int_vec(_trans_weights_idx), _trans_weights, tensors, false); - - // Linearize src 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(_needs_weights_reshape || _needs_weights_conversion) - { - gemm_pack.add_const_tensor(ACL_SRC_1, transformed_wei.get()); - } - - // Run matrix multiply - if(_is_quantized_asymmetric) - { - _mm_gemmlowp->run(gemm_pack); - } - else - { - _mm_gemm->run(gemm_pack); - } -} - -void CpuFullyConnected::prepare(ITensorPack &tensors) -{ - if(!_is_prepared) - { - auto weights = tensors.get_const_tensor(ACL_SRC_1); - - CpuAuxTensorHandler reshaped_weights(offset_int_vec(TransposedWeights), _reshaped_weights, tensors, false); - CpuAuxTensorHandler converted_weights(offset_int_vec(ConvertedWeights), _converted_weights, tensors, false); - - // Pointer to current weights - const ITensor *cur_weights = weights; - - // Reshape of the weights (happens only once) - if(_needs_weights_reshape) - { - // Run reshape weights kernel and mark weights as unused - ITensorPack transpose_pack{ { ACL_SRC, weights }, { ACL_DST, reshaped_weights.get() } }; - NEScheduler::get().schedule_op(_transpose_weights.get(), Window::DimY, _transpose_weights->window(), transpose_pack); - - cur_weights->mark_as_unused(); - cur_weights = reshaped_weights.get(); - } - - // Convert weights if needed (happens only once) - if(_needs_weights_conversion) - { - 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(); - } - - ITensorPack gemm_pack = tensors; - gemm_pack.add_const_tensor(ACL_SRC_1, cur_weights); - - // Prepare GEMM prepare and release unused weights - if(!_is_quantized_asymmetric) - { - _mm_gemm->prepare(gemm_pack); - } - else - { - _mm_gemmlowp->prepare(gemm_pack); - } - - _is_prepared = true; - } -} - -experimental::MemoryRequirements CpuFullyConnected::workspace() const -{ - return _aux_mem; -} -} // namespace cpu -} // namespace arm_compute |