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author | Georgios Pinitas <georgios.pinitas@arm.com> | 2021-08-16 12:38:54 +0100 |
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committer | Georgios Pinitas <georgios.pinitas@arm.com> | 2021-08-20 16:26:16 +0000 |
commit | 19884630c37ae9de2f65a88ea2cda5630a551bad (patch) | |
tree | 0fdffe84c5d66ffe8a1d320b798a247afa1dfdae /src/runtime/gpu/cl/operators/ClGemmConvolution.cpp | |
parent | 73df9310f4e94e43597f283307e3cde0653d8bae (diff) | |
download | ComputeLibrary-19884630c37ae9de2f65a88ea2cda5630a551bad.tar.gz |
Rename [Cl|Cpu]GemmConvolution to [Cl|Gpu]GemmConv2d
Renaming the gemm-based convolution operators to accomodate for new
operators with higher convolution dimensonality
Signed-off-by: Georgios Pinitas <georgios.pinitas@arm.com>
Change-Id: Id2f2cf11404221f0e87baa0e5d08ad5d63eaf78e
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/6113
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Diffstat (limited to 'src/runtime/gpu/cl/operators/ClGemmConvolution.cpp')
-rw-r--r-- | src/runtime/gpu/cl/operators/ClGemmConvolution.cpp | 628 |
1 files changed, 0 insertions, 628 deletions
diff --git a/src/runtime/gpu/cl/operators/ClGemmConvolution.cpp b/src/runtime/gpu/cl/operators/ClGemmConvolution.cpp deleted file mode 100644 index 1926cbbe4d..0000000000 --- a/src/runtime/gpu/cl/operators/ClGemmConvolution.cpp +++ /dev/null @@ -1,628 +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/ClGemmConvolution.h" - -#include "arm_compute/core/CL/ICLTensor.h" -#include "arm_compute/core/PixelValue.h" -#include "arm_compute/core/Size2D.h" -#include "arm_compute/core/TensorInfo.h" -#include "arm_compute/core/Utils.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/gpu/cl/kernels/ClActivationKernel.h" -#include "src/core/gpu/cl/kernels/ClCol2ImKernel.h" -#include "src/core/gpu/cl/kernels/ClIm2ColKernel.h" -#include "src/core/gpu/cl/kernels/ClWeightsReshapeKernel.h" -#include "src/core/helpers/AutoConfiguration.h" -#include "src/core/helpers/MemoryHelpers.h" -#include "src/runtime/gpu/cl/operators/ClGemm.h" -#include "src/runtime/gpu/cl/operators/ClGemmLowpMatrixMultiplyCore.h" -#include "src/runtime/gpu/cl/utils/ClAuxTensorHandler.h" -#include "support/Cast.h" - -namespace arm_compute -{ -using namespace experimental; -using namespace misc::shape_calculator; -using namespace utils::cast; -namespace opencl -{ -ClGemmConvolution::ClGemmConvolution() - : _weights_reshape_kernel(nullptr), _im2col_kernel(nullptr), _mm_gemm(nullptr), _mm_gemmlowp(nullptr), _col2im_kernel(nullptr), _activation_kernel(nullptr), _im2col_output(), _weights_reshaped(), - _gemm_output(), _skip_im2col(false), _skip_col2im(false), _is_quantized(false), _fuse_activation(true), _append_bias(false), _is_prepared(false), _aux_mem(AuxTensorIdx::Count) -{ -} -ClGemmConvolution::~ClGemmConvolution() = default; - -void ClGemmConvolution::configure_mm(const ClCompileContext &compile_context, const ITensorInfo *src, ITensorInfo *weights, ITensorInfo *biases, ITensorInfo *dst, - const GEMMLowpOutputStageInfo &gemmlowp_output_stage, - int gemm_3d_depth, const ActivationLayerInfo &act_info) -{ - ARM_COMPUTE_ERROR_ON_NULLPTR(src, weights); - ARM_COMPUTE_ERROR_THROW_ON(validate_mm(src, weights, biases, dst, gemmlowp_output_stage, gemm_3d_depth, _skip_im2col, act_info)); - - const GEMMInfo &gemm_info = GEMMInfo(false, // is_a_reshaped - false, // is_b_reshaped - true, // reshape_b_only_on_first_run - gemm_3d_depth, // depth_output_gemm3d - _skip_im2col, // reinterpret_input_as_3d - false, // retain_internal_weights - gemmlowp_output_stage, // gemmlowp_output_stage - false, // fast_math - false, // fp_mixed_precision - true, // broadcast_bias - act_info); // activation_info - - TensorInfo tmp_src{ *src }; - 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 = src->quantization_info(); - const QuantizationInfo weights_quantization_info = weights->quantization_info(); - - tmp_src.set_quantization_info(QuantizationInfo(input_quantization_info.uniform().scale, -input_quantization_info.uniform().offset)); - weights->set_quantization_info(QuantizationInfo(weights_quantization_info.uniform().scale, -weights_quantization_info.uniform().offset)); - - _mm_gemmlowp = std::make_unique<ClGemmLowpMatrixMultiplyCore>(); - _mm_gemmlowp->configure(compile_context, &tmp_src, weights, biases, dst, gemm_info); - - // Revert back QuantizatioInfo as weights could be used in other convolution layers - weights->set_quantization_info(weights_quantization_info); - - auto mm_mem_req = _mm_gemmlowp->workspace(); - for(unsigned int cont = 0; cont < mm_mem_req.size(); ++cont) - { - _aux_mem[cont] = mm_mem_req[cont]; - } - } - else - { - // Configure matrix multiply function - _mm_gemm = std::make_unique<ClGemm>(); - _mm_gemm->configure(compile_context, &tmp_src, weights, biases, dst, 1.0f, 1.0f, gemm_info); - auto mm_mem_req = _mm_gemm->workspace(); - for(unsigned int cont = 0; cont < mm_mem_req.size(); ++cont) - { - _aux_mem[cont] = mm_mem_req[cont]; - } - } -} - -Status ClGemmConvolution::validate_mm(const ITensorInfo *src, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *dst, - const GEMMLowpOutputStageInfo &gemmlowp_output_stage, int gemm_3d_depth, bool skip_im2col, const ActivationLayerInfo &act_info) -{ - const bool is_quantized = is_data_type_quantized_asymmetric(src->data_type()); - - const GEMMInfo &gemm_info = GEMMInfo(false, // is_a_reshaped - false, // is_b_reshaped - true, // reshape_b_only_on_first_run - gemm_3d_depth, // depth_output_gemm3d - skip_im2col, // reinterpret_input_as_3d - false, // retain_internal_weights - gemmlowp_output_stage, // gemmlowp_output_stage - false, // fast_math - false, // fp_mixed_precision - true, // broadcast_bias - act_info); // 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 = src->quantization_info(); - const QuantizationInfo weights_quantization_info = weights->quantization_info(); - - std::unique_ptr<ITensorInfo> src_qa = src->clone(); - std::unique_ptr<ITensorInfo> weights_qa = weights->clone(); - src_qa->set_quantization_info(QuantizationInfo(input_quantization_info.uniform().scale, -input_quantization_info.uniform().offset)); - weights_qa->set_quantization_info(QuantizationInfo(weights_quantization_info.uniform().scale, -weights_quantization_info.uniform().offset)); - - // Perform validation step on GEMMLowp - return ClGemmLowpMatrixMultiplyCore::validate(src_qa.get(), weights_qa.get(), biases, dst, gemm_info); - } - else - { - // Perform validation step on Matrix multiply function - return ClGemm::validate(src, weights, biases, dst, 1.0f, 1.0f, gemm_info); - } -} - -void ClGemmConvolution::configure(const CLCompileContext &compile_context, ITensorInfo *src, ITensorInfo *weights, ITensorInfo *biases, ITensorInfo *dst, - const Conv2dInfo &conv2d_info, const WeightsInfo &weights_info) -{ - ARM_COMPUTE_ERROR_ON_NULLPTR(src, weights, dst); - - ARM_COMPUTE_ERROR_THROW_ON(ClGemmConvolution::validate(src, weights, biases, dst, - conv2d_info, - weights_info)); - - const DataType data_type = src->data_type(); - const DataLayout data_layout = src->data_layout(); - const int idx_width = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH); - const int idx_height = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT); - const int idx_kernels = get_data_layout_dimension_index(data_layout, DataLayoutDimension::BATCHES); - - const unsigned int kernel_width = weights->dimension(idx_width); - const unsigned int kernel_height = weights->dimension(idx_height); - const unsigned int num_kernels = weights->dimension(idx_kernels); - - const UniformQuantizationInfo iq_info = src->quantization_info().uniform(); - const UniformQuantizationInfo oq_info = dst->quantization_info().uniform(); - - _is_prepared = weights_info.retain_internal_weights(); - _is_quantized = is_data_type_quantized_asymmetric(src->data_type()); - _skip_im2col = (data_layout == DataLayout::NHWC && kernel_width == 1 && kernel_height == 1 && conv2d_info.conv_info.stride().first == 1 && conv2d_info.conv_info.stride().second == 1); - _skip_col2im = data_layout == DataLayout::NHWC; - - // Only for quantize there are few cases where we cannot fuse the activation function in GEMM - _fuse_activation = true; - - const ITensorInfo *gemm_input_to_use = src; - ITensorInfo *gemm_output_to_use = dst; - - // Get parameters from conv_info - unsigned int stride_x = 0; - unsigned int stride_y = 0; - std::tie(stride_x, stride_y) = conv2d_info.conv_info.stride(); - - // Get convolved dimensions - unsigned int conv_w = 0; - unsigned int conv_h = 0; - std::tie(conv_w, conv_h) = scaled_dimensions(src->dimension(idx_width), - src->dimension(idx_height), - kernel_width, - kernel_height, - conv2d_info.conv_info, - conv2d_info.dilation); - - unsigned int mat_weights_cols = num_kernels / conv2d_info.num_groups; - - ITensorInfo *biases_to_use = biases; - _append_bias = false; - - _weights_reshape_kernel = std::make_unique<kernels::ClWeightsReshapeKernel>(); - if(conv2d_info.num_groups != 1 && biases != nullptr) - { - // num_groups != 1 can only be for NCHW - // Since it is missing an utility function to reshape the biases, we append the biases into the weights tensor - biases_to_use = nullptr; - _append_bias = true; - _weights_reshape_kernel->configure(compile_context, weights, biases, &_weights_reshaped, conv2d_info.num_groups); - } - else - { - _weights_reshape_kernel->configure(compile_context, weights, nullptr, &_weights_reshaped, conv2d_info.num_groups); - } - - // Create tensor to store im2col reshaped inputs - if(!_skip_im2col) - { - // Configure and tune im2col. im2col output shape is auto-initialized - _im2col_kernel = std::make_unique<opencl::kernels::ClIm2ColKernel>(); - - // Set the GPU target for im2col - _im2col_kernel->set_target(CLScheduler::get().target()); - _im2col_kernel->configure(compile_context, src, &_im2col_output, Size2D(kernel_width, kernel_height), conv2d_info.conv_info, _append_bias, conv2d_info.dilation, conv2d_info.num_groups); - - // Set quantization info - _im2col_output.set_quantization_info(src->quantization_info()); - CLScheduler::get().tune_kernel_static(*_im2col_kernel); - - // Update GEMM input - gemm_input_to_use = &_im2col_output; - } - - // Create GEMM output tensor - if(!_skip_col2im) - { - TensorShape shape_gemm; - - // If we cannot skip col2im it means we run im2col as well - shape_gemm = _im2col_output.tensor_shape(); - shape_gemm.set(0, mat_weights_cols); - shape_gemm.set(1, conv_w * conv_h); - - _gemm_output = TensorInfo(shape_gemm, 1, data_type); - _gemm_output.set_quantization_info(dst->quantization_info()).set_data_layout(src->data_layout()); - - // Update GEMM output - gemm_output_to_use = &_gemm_output; - } - - GEMMLowpOutputStageInfo gemmlowp_output_stage; - gemmlowp_output_stage.type = GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT; - gemmlowp_output_stage.gemmlowp_offset = 0; - - // Configure output stage for quantized case - if(_is_quantized) - { - const auto output_quant_info = (dst->total_size() == 0) ? iq_info : oq_info; - const bool is_quantized_per_channel = is_data_type_quantized_per_channel(weights->data_type()); - const unsigned int num_filters = (is_quantized_per_channel) ? num_kernels : 1; - - gemmlowp_output_stage.is_quantized_per_channel = is_quantized_per_channel; - - gemmlowp_output_stage.gemmlowp_multipliers.resize(num_filters); - gemmlowp_output_stage.gemmlowp_shifts.resize(num_filters); - quantization::compute_quantized_multipliers_and_shifts(src, weights, dst, - gemmlowp_output_stage.gemmlowp_multipliers.data(), - gemmlowp_output_stage.gemmlowp_shifts.data()); - gemmlowp_output_stage.gemmlowp_multiplier = gemmlowp_output_stage.gemmlowp_multipliers[0]; - gemmlowp_output_stage.gemmlowp_shift = gemmlowp_output_stage.gemmlowp_shifts[0]; - - PixelValue min_val{}; - PixelValue max_val{}; - std::tie(min_val, max_val) = get_min_max(dst->data_type()); - - auto min_activation = min_val.get<int32_t>(); - auto max_activation = max_val.get<int32_t>(); - - const std::set<ActivationLayerInfo::ActivationFunction> supported_acts = { ActivationLayerInfo::ActivationFunction::RELU, - ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, - ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU - }; - - if(conv2d_info.act_info.enabled()) - { - if(supported_acts.count(conv2d_info.act_info.activation()) != 0) - { - std::tie(min_activation, max_activation) = get_quantized_activation_min_max(conv2d_info.act_info, data_type, output_quant_info); - } - else - { - _fuse_activation = false; - } - } - - // Set the GEMMLowp output stage info - gemmlowp_output_stage.gemmlowp_offset = output_quant_info.offset; - gemmlowp_output_stage.gemmlowp_min_bound = min_activation; - gemmlowp_output_stage.gemmlowp_max_bound = max_activation; - } - - // Configure and tune GEMM - // In case of NHWC, we need to run GEMM3D (gemm_3d_depth != 0) in order to avoid reshaping the output matrix - const unsigned int gemm_3d_depth = (data_layout == DataLayout::NHWC) ? conv_h : 0; - - configure_mm(compile_context, gemm_input_to_use, &_weights_reshaped, biases_to_use, gemm_output_to_use, gemmlowp_output_stage, gemm_3d_depth, conv2d_info.act_info); - - if(!_skip_col2im) - { - // Set the GPU target for col2im - _col2im_kernel = std::make_unique<opencl::kernels::ClCol2ImKernel>(); - _col2im_kernel->set_target(CLScheduler::get().target()); - // Configure and tune Col2Im - _col2im_kernel->configure(compile_context, gemm_output_to_use, dst, Size2D(conv_w, conv_h), conv2d_info.num_groups); - CLScheduler::get().tune_kernel_static(*_col2im_kernel.get()); - } - - ARM_COMPUTE_ERROR_ON_MSG((dst->dimension(idx_width) != conv_w) || (dst->dimension(idx_height) != conv_h), - "Output shape does not match the expected one"); - - if(!_fuse_activation) - { - _activation_kernel = std::make_unique<opencl::kernels::ClActivationKernel>(); - _activation_kernel->configure(compile_context, dst, nullptr, conv2d_info.act_info); - } - - _aux_mem[Im2ColOutput] = MemoryInfo(offset_int_vec(Im2ColOutput), MemoryLifetime::Temporary, _im2col_output.total_size()); - _aux_mem[WeightsReshaped] = MemoryInfo(offset_int_vec(WeightsReshaped), MemoryLifetime::Persistent, _weights_reshaped.total_size()); - _aux_mem[GemmOutput] = MemoryInfo(offset_int_vec(GemmOutput), MemoryLifetime::Temporary, _gemm_output.total_size()); -} - -Status ClGemmConvolution::validate(const ITensorInfo *src, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *dst, const Conv2dInfo &conv2d_info, - const WeightsInfo &weights_info) -{ - ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(src, weights, dst); - ARM_COMPUTE_RETURN_ERROR_ON_MSG(weights_info.are_reshaped(), "Weights already reshaped are not supported!"); - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED, DataType::F16, DataType::F32); - const bool is_quantized_per_channel = is_data_type_quantized_per_channel(weights->data_type()); - - if(!is_quantized_per_channel) - { - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src, weights); - } - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT(src, weights); - ARM_COMPUTE_RETURN_ERROR_ON_MSG((conv2d_info.num_groups != 1) && (src->data_layout() != DataLayout::NCHW), "Grouping (num_groups != 1) with NHWC data layout is not supported"); - ARM_COMPUTE_RETURN_ERROR_ON_MSG((conv2d_info.num_groups != 1) && (src->data_type() == DataType::QASYMM8), "Grouping (num_groups != 1) is not supported with QASYMM8"); - ARM_COMPUTE_RETURN_ERROR_ON(((src->dimension(2) / weights->dimension(2)) != conv2d_info.num_groups) && (src->data_layout() == DataLayout::NCHW)); - - const DataLayout data_layout = src->data_layout(); - const DataType data_type = src->data_type(); - const int idx_width = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH); - const int idx_height = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT); - const int idx_channel = get_data_layout_dimension_index(data_layout, DataLayoutDimension::CHANNEL); - const int idx_kernels = get_data_layout_dimension_index(data_layout, DataLayoutDimension::BATCHES); - - const unsigned int kernel_width = weights->dimension(idx_width); - const unsigned int kernel_height = weights->dimension(idx_height); - const unsigned int num_kernels = weights->dimension(idx_kernels); - - TensorInfo im2col_reshaped_info{}; - TensorInfo info_gemm{}; - TensorInfo weights_reshaped_info{}; - const ITensorInfo *gemm_input_to_use = src; - const ITensorInfo *gemm_output_to_use = dst; - const ITensorInfo *weights_to_use = weights; - const bool is_quantized = is_data_type_quantized_asymmetric(data_type); - const bool skip_im2col = (data_layout == DataLayout::NHWC && kernel_width == 1 && kernel_height == 1 && conv2d_info.conv_info.stride().first == 1 - && conv2d_info.conv_info.stride().second == 1); - const bool skip_col2im = data_layout == DataLayout::NHWC; - bool fuse_activation = true; - - ARM_COMPUTE_RETURN_ERROR_ON((weights->dimension(idx_channel) * conv2d_info.num_groups) != src->dimension(idx_channel)); - ARM_COMPUTE_RETURN_ERROR_ON(weights->num_dimensions() > 4); - - // Validate biases - if(biases != nullptr) - { - if(is_quantized) - { - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(biases, 1, DataType::S32); - } - else - { - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src, biases); - } - ARM_COMPUTE_RETURN_ERROR_ON(biases->dimension(0) != weights->dimension(idx_kernels)); - ARM_COMPUTE_RETURN_ERROR_ON(biases->num_dimensions() > 1); - } - - if(conv2d_info.act_info.enabled()) - { - ARM_COMPUTE_ERROR_ON(conv2d_info.act_info.b() > conv2d_info.act_info.a()); - } - - // Get convolved dimensions - unsigned int conv_w = 0; - unsigned int conv_h = 0; - - std::tie(conv_w, conv_h) = scaled_dimensions(src->dimension(idx_width), - src->dimension(idx_height), - kernel_width, - kernel_height, - conv2d_info.conv_info, - conv2d_info.dilation); - - unsigned int mat_weights_cols = num_kernels / conv2d_info.num_groups; - - const ITensorInfo *biases_to_use = biases; - bool append_bias = false; - - if(conv2d_info.num_groups != 1 && biases != nullptr) - { - // num_groups != 1 can only be for NCHW - // Since it is missing an utility function to reshape the biases, we append the biases into the weights tensor - biases_to_use = nullptr; - append_bias = true; - weights_reshaped_info = TensorInfo(compute_weights_reshaped_shape(*weights, true, conv2d_info.num_groups), 1, data_type); - } - else - { - weights_reshaped_info = TensorInfo(compute_weights_reshaped_shape(*weights, false, conv2d_info.num_groups), 1, data_type); - } - - weights_to_use = &weights_reshaped_info; - - if(!skip_im2col) - { - const Size2D kernel_dims(kernel_width, kernel_height); - - // Output tensor auto initialization if not yet initialized - TensorShape expected_output_shape = compute_im2col_conv_shape(src, kernel_dims, conv2d_info.conv_info, append_bias, conv2d_info.dilation, conv2d_info.num_groups == 1, conv2d_info.num_groups); - - auto_init_if_empty(im2col_reshaped_info, src->clone()->set_tensor_shape(expected_output_shape)); - - ARM_COMPUTE_RETURN_ON_ERROR(opencl::kernels::ClIm2ColKernel::validate(src, &im2col_reshaped_info, kernel_dims, conv2d_info.conv_info, append_bias, conv2d_info.dilation, conv2d_info.num_groups)); - gemm_input_to_use = &im2col_reshaped_info; - } - - // Create GEMM output tensor - if(!skip_col2im) - { - TensorShape shape_gemm; - - shape_gemm = gemm_input_to_use->tensor_shape(); - shape_gemm.set(0, mat_weights_cols); - shape_gemm.set(1, conv_w * conv_h); - - info_gemm = TensorInfo(shape_gemm, 1, data_type); - info_gemm.set_quantization_info(dst->quantization_info()).set_data_layout(src->data_layout()); - gemm_output_to_use = &info_gemm; - } - - GEMMLowpOutputStageInfo gemmlowp_output_stage; - gemmlowp_output_stage.type = GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT; - gemmlowp_output_stage.gemmlowp_offset = 0; - gemmlowp_output_stage.is_quantized_per_channel = is_quantized_per_channel; - - if(is_quantized) - { - const UniformQuantizationInfo iq_info = src->quantization_info().uniform(); - const UniformQuantizationInfo oq_info = dst->quantization_info().uniform(); - const auto output_quant_info = (dst->total_size() == 0) ? iq_info : oq_info; - const unsigned int num_filters = (is_quantized_per_channel) ? num_kernels : 1; - - gemmlowp_output_stage.gemmlowp_multipliers.resize(num_filters); - gemmlowp_output_stage.gemmlowp_shifts.resize(num_filters); - quantization::compute_quantized_multipliers_and_shifts(src, weights, dst, - gemmlowp_output_stage.gemmlowp_multipliers.data(), - gemmlowp_output_stage.gemmlowp_shifts.data()); - gemmlowp_output_stage.gemmlowp_multiplier = gemmlowp_output_stage.gemmlowp_multipliers[0]; - gemmlowp_output_stage.gemmlowp_shift = gemmlowp_output_stage.gemmlowp_shifts[0]; - - int min_activation = 0; - int max_activation = 0; - - const std::set<ActivationLayerInfo::ActivationFunction> supported_acts = { ActivationLayerInfo::ActivationFunction::RELU, - ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, - ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU - }; - - if(conv2d_info.act_info.enabled()) - { - if(supported_acts.count(conv2d_info.act_info.activation()) != 0) - { - std::tie(min_activation, max_activation) = get_quantized_activation_min_max(conv2d_info.act_info, data_type, output_quant_info); - } - else - { - fuse_activation = false; - } - } - - // Set the GEMMLowp output stage info - gemmlowp_output_stage.gemmlowp_offset = output_quant_info.offset; - gemmlowp_output_stage.gemmlowp_min_bound = min_activation; - gemmlowp_output_stage.gemmlowp_max_bound = max_activation; - } - - // In case of NHWC, we need to run GEMM3D (gemm_3d_depth != 0) in order to avoid reshaping the output matrix - const unsigned int gemm_3d_depth = (data_layout == DataLayout::NHWC) ? conv_h : 0; - - ARM_COMPUTE_RETURN_ON_ERROR(validate_mm(gemm_input_to_use, weights_to_use, biases_to_use, gemm_output_to_use, gemmlowp_output_stage, gemm_3d_depth, skip_im2col, conv2d_info.act_info)); - - // Validate Col2Im - if(!skip_col2im) - { - ARM_COMPUTE_RETURN_ON_ERROR(kernels::ClCol2ImKernel::validate(gemm_output_to_use, dst, Size2D(conv_w, conv_h), conv2d_info.num_groups)); - } - - //Validate Activation Layer - if(!fuse_activation) - { - ARM_COMPUTE_RETURN_ON_ERROR(kernels::ClActivationKernel::validate(dst, nullptr, conv2d_info.act_info)); - } - - return Status{}; -} - -void ClGemmConvolution::run(ITensorPack &tensors) -{ - prepare(tensors); - - auto src = tensors.get_const_tensor(ACL_SRC_0); - auto biases = tensors.get_const_tensor(ACL_SRC_2); - auto dst = tensors.get_tensor(ACL_DST); - auto gemm_input_to_use = src; - auto gemm_output_to_use = dst; - - CLAuxTensorHandler im2col_output(offset_int_vec(Im2ColOutput), _im2col_output, tensors, false); - CLAuxTensorHandler gemm_output(offset_int_vec(GemmOutput), _gemm_output, tensors, false); - CLAuxTensorHandler weights_reshaped(offset_int_vec(WeightsReshaped), _weights_reshaped, tensors, false); - - // Run im2col - if(!_skip_im2col) - { - ITensorPack pack = - { - { TensorType::ACL_SRC, src }, - { TensorType::ACL_DST, im2col_output.get() } - }; - CLScheduler::get().enqueue_op(*_im2col_kernel, pack, false); - gemm_input_to_use = im2col_output.get(); - } - if(!_skip_col2im) - { - gemm_output_to_use = gemm_output.get(); - } - ITensorPack pack_mm = tensors; - pack_mm.add_const_tensor(TensorType::ACL_SRC_0, gemm_input_to_use); - pack_mm.add_const_tensor(TensorType::ACL_SRC_1, weights_reshaped.get()); - if(!_append_bias) - { - pack_mm.add_const_tensor(TensorType::ACL_SRC_2, biases); - } - pack_mm.add_tensor(TensorType::ACL_DST, gemm_output_to_use); - // Runs ClGemm or ClGemmLowpMatrixMultiplyCore functions - if(_is_quantized) - { - // Run gemmlowp - _mm_gemmlowp->run(pack_mm); - } - else - { - // Run gemm - _mm_gemm->run(pack_mm); - } - - // Reshape output matrix - if(!_skip_col2im) - { - ITensorPack pack = - { - { TensorType::ACL_SRC, gemm_output_to_use }, - { TensorType::ACL_DST, dst } - }; - CLScheduler::get().enqueue_op(*_col2im_kernel.get(), pack, false); - } - - //Run Activation Layer if we cannot fuse in GEMM - if(!_fuse_activation) - { - ITensorPack pack = - { - { TensorType::ACL_SRC, dst }, - { TensorType::ACL_DST, dst } - }; - CLScheduler::get().enqueue_op(*_activation_kernel.get(), pack, false); - } -} - -void ClGemmConvolution::prepare(ITensorPack &tensors) -{ - if(!_is_prepared) - { - // Run weights reshaping and mark original weights tensor as unused - ICLTensor *weights_reshaped_p = utils::cast::polymorphic_downcast<ICLTensor *>(tensors.get_tensor(offset_int_vec(WeightsReshaped))); - CLAuxTensorHandler weights_reshaped(_weights_reshaped, *weights_reshaped_p); - auto weights = tensors.get_const_tensor(TensorType::ACL_SRC_1); - ITensorPack pack = - { - { TensorType::ACL_SRC, weights }, - { TensorType::ACL_DST, weights_reshaped.get() } - }; - - if(_append_bias) - { - const auto biases = tensors.get_const_tensor(TensorType::ACL_SRC_2); - pack.add_const_tensor(TensorType::ACL_BIAS, biases); - } - CLScheduler::get().enqueue_op(*_weights_reshape_kernel.get(), pack, true); - tensors.add_const_tensor(TensorType::ACL_SRC_1, weights_reshaped.get()); - - // Prepare GEMM - _is_quantized ? _mm_gemmlowp->prepare(tensors) : _mm_gemm->prepare(tensors); - _is_prepared = true; - } -} -experimental::MemoryRequirements ClGemmConvolution::workspace() const -{ - return _aux_mem; -} -} // namespace opencl -} // namespace arm_compute |