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Diffstat (limited to 'src/gpu/cl/operators/ClFullyConnected.cpp')
| -rw-r--r-- | src/gpu/cl/operators/ClFullyConnected.cpp | 496 |
1 files changed, 496 insertions, 0 deletions
diff --git a/src/gpu/cl/operators/ClFullyConnected.cpp b/src/gpu/cl/operators/ClFullyConnected.cpp new file mode 100644 index 0000000000..8b7e336c9f --- /dev/null +++ b/src/gpu/cl/operators/ClFullyConnected.cpp @@ -0,0 +1,496 @@ +/* + * 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/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/gpu/cl/operators/ClConvertFullyConnectedWeights.h" +#include "src/gpu/cl/operators/ClFlatten.h" +#include "src/gpu/cl/operators/ClGemm.h" +#include "src/gpu/cl/operators/ClGemmLowpMatrixMultiplyCore.h" +#include "src/gpu/cl/operators/ClTranspose.h" +#include "src/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 |