| /* |
| * 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/CLGEMMConvolutionLayer.h" |
| |
| #include "arm_compute/core/PixelValue.h" |
| #include "arm_compute/core/Size2D.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 <cmath> |
| #include <memory> |
| #include <tuple> |
| |
| using namespace arm_compute; |
| using namespace arm_compute::misc::shape_calculator; |
| |
| CLConvolutionLayerReshapeWeights::CLConvolutionLayerReshapeWeights() |
| : _weights_reshape_kernel() |
| { |
| } |
| |
| void CLConvolutionLayerReshapeWeights::configure(const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, unsigned int num_groups) |
| { |
| // Perform validation step |
| ARM_COMPUTE_ERROR_ON_NULLPTR(weights, output); |
| ARM_COMPUTE_ERROR_THROW_ON(CLConvolutionLayerReshapeWeights::validate(weights->info(), |
| (biases != nullptr) ? biases->info() : nullptr, |
| output->info(), |
| num_groups)); |
| |
| const bool append_biases = (biases != nullptr) && !is_data_type_quantized_asymmetric(weights->info()->data_type()); |
| const ICLTensor *biases_to_use = (append_biases) ? biases : nullptr; |
| |
| _weights_reshape_kernel.configure(weights, biases_to_use, output, num_groups); |
| |
| output->info()->set_quantization_info(weights->info()->quantization_info()); |
| } |
| |
| Status CLConvolutionLayerReshapeWeights::validate(const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, unsigned int num_groups) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(weights); |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(weights, 1, DataType::QASYMM8, DataType::F16, DataType::F32); |
| ARM_COMPUTE_RETURN_ERROR_ON(weights->num_dimensions() > 4); |
| |
| if(biases != nullptr) |
| { |
| const int idx_kernels = get_data_layout_dimension_index(weights->data_layout(), DataLayoutDimension::BATCHES); |
| ARM_COMPUTE_RETURN_ERROR_ON(is_data_type_quantized_asymmetric(weights->data_type())); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(weights, biases); |
| ARM_COMPUTE_RETURN_ERROR_ON(biases->dimension(0) != weights->dimension(idx_kernels)); |
| ARM_COMPUTE_RETURN_ERROR_ON(biases->num_dimensions() > 1); |
| } |
| |
| if((output != nullptr) && (output->total_size() != 0)) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(weights, output); |
| |
| CLWeightsReshapeKernel::validate(weights, biases, output, num_groups); |
| } |
| |
| return Status{}; |
| } |
| |
| void CLConvolutionLayerReshapeWeights::run() |
| { |
| CLScheduler::get().enqueue(_weights_reshape_kernel); |
| } |
| |
| CLGEMMConvolutionLayer::CLGEMMConvolutionLayer(std::shared_ptr<IMemoryManager> memory_manager) |
| : _memory_group(memory_manager), _reshape_weights(), _im2col_kernel(), _mm_gemm(memory_manager), _mm_gemmlowp(memory_manager), _col2im_kernel(), _activationlayer_function(), |
| _original_weights(nullptr), _im2col_output(), _weights_reshaped(), _gemm_output(), _skip_im2col(false), _skip_col2im(false), _is_quantized(false), _fuse_activation(true), _is_prepared(false) |
| { |
| } |
| |
| void CLGEMMConvolutionLayer::configure_mm(const ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, const GEMMLowpOutputStageInfo &gemmlowp_output_stage, |
| int gemm_3d_depth, const ActivationLayerInfo &act_info) |
| { |
| ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights); |
| ARM_COMPUTE_ERROR_THROW_ON(validate_mm(input->info(), weights->info(), biases != nullptr ? biases->info() : nullptr, output->info(), 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, // 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 = 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)); |
| |
| _mm_gemmlowp.configure(input, weights, biases, output, gemm_info); |
| |
| // Revert back QuantizatioInfo as input and weights could be used in other convolution layers |
| input->info()->set_quantization_info(input_quantization_info); |
| weights->info()->set_quantization_info(weights_quantization_info); |
| } |
| else |
| { |
| // Configure matrix multiply function |
| _mm_gemm.configure(input, weights, biases, output, 1.0f, 1.0f, gemm_info); |
| } |
| } |
| |
| Status CLGEMMConvolutionLayer::validate_mm(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, |
| 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(input->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, // 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 = input->quantization_info(); |
| const QuantizationInfo weights_quantization_info = weights->quantization_info(); |
| |
| std::unique_ptr<ITensorInfo> input_qa = input->clone(); |
| std::unique_ptr<ITensorInfo> weights_qa = weights->clone(); |
| input_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(input_qa.get(), weights_qa.get(), biases, output, gemm_info); |
| } |
| else |
| { |
| // Perform validation step on Matrix multiply function |
| return CLGEMM::validate(input, weights, biases, output, 1.0f, 1.0f, gemm_info); |
| } |
| } |
| |
| void CLGEMMConvolutionLayer::configure(const ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, const PadStrideInfo &conv_info, const WeightsInfo &weights_info, |
| const Size2D &dilation, const ActivationLayerInfo &act_info, unsigned int num_groups) |
| { |
| ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, output); |
| |
| ARM_COMPUTE_ERROR_THROW_ON(CLGEMMConvolutionLayer::validate(input->info(), |
| weights->info(), |
| biases != nullptr ? biases->info() : nullptr, |
| output->info(), |
| conv_info, |
| weights_info, |
| dilation, |
| act_info, |
| num_groups)); |
| |
| const DataType data_type = input->info()->data_type(); |
| const DataLayout data_layout = input->info()->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->info()->dimension(idx_width); |
| const unsigned int kernel_height = weights->info()->dimension(idx_height); |
| |
| const UniformQuantizationInfo iq_info = input->info()->quantization_info().uniform(); |
| const UniformQuantizationInfo wq_info = weights->info()->quantization_info().uniform(); |
| const UniformQuantizationInfo oq_info = output->info()->quantization_info().uniform(); |
| |
| _is_prepared = weights_info.retain_internal_weights(); |
| _original_weights = weights; |
| _is_quantized = is_data_type_quantized_asymmetric(input->info()->data_type()); |
| _skip_im2col = (data_layout == DataLayout::NHWC && kernel_width == 1 && kernel_height == 1 && conv_info.stride().first == 1 && 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; |
| |
| // Set the GPU target for im2col and col2im |
| _im2col_kernel.set_target(CLScheduler::get().target()); |
| _col2im_kernel.set_target(CLScheduler::get().target()); |
| |
| const ICLTensor *gemm_input_to_use = input; |
| ICLTensor *gemm_output_to_use = output; |
| |
| // Get parameters from conv_info |
| unsigned int stride_x = 0; |
| unsigned int stride_y = 0; |
| std::tie(stride_x, stride_y) = conv_info.stride(); |
| |
| // Get convolved dimensions |
| unsigned int conv_w = 0; |
| unsigned int conv_h = 0; |
| std::tie(conv_w, conv_h) = scaled_dimensions(input->info()->dimension(idx_width), |
| input->info()->dimension(idx_height), |
| kernel_width, |
| kernel_height, |
| conv_info, |
| dilation); |
| |
| unsigned int mat_weights_cols = weights->info()->dimension(idx_kernels) / num_groups; |
| |
| const ICLTensor *biases_to_use = biases; |
| bool append_bias = false; |
| |
| if(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; |
| |
| _reshape_weights.configure(weights, biases, &_weights_reshaped, num_groups); |
| } |
| else |
| { |
| _reshape_weights.configure(weights, nullptr, &_weights_reshaped, num_groups); |
| } |
| |
| // Create tensor to store im2col reshaped inputs |
| if(!_skip_im2col) |
| { |
| _memory_group.manage(&_im2col_output); |
| |
| // Configure and tune im2col. im2col output shape is auto-initialized |
| _im2col_kernel.configure(input, &_im2col_output, Size2D(kernel_width, kernel_height), conv_info, append_bias, dilation, num_groups); |
| |
| // Set quantization info |
| _im2col_output.info()->set_quantization_info(input->info()->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.info()->tensor_shape(); |
| shape_gemm.set(0, mat_weights_cols); |
| shape_gemm.set(1, conv_w * conv_h); |
| |
| // TODO(COMPMID-2078): input->clone() doesn't work with subtensors for grouped convolutions. |
| TensorInfo info_gemm(shape_gemm, 1, data_type); |
| info_gemm.set_quantization_info(output->info()->quantization_info()).set_data_layout(input->info()->data_layout()); |
| _gemm_output.allocator()->init(info_gemm); |
| _memory_group.manage(&_gemm_output); |
| |
| // 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; |
| gemmlowp_output_stage.gemmlowp_multiplier = 0; |
| gemmlowp_output_stage.gemmlowp_shift = 0; |
| |
| // Configure output stage for quantized case |
| if(_is_quantized) |
| { |
| const auto output_quant_info = (output->info()->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; |
| quantization::calculate_quantized_multiplier_less_than_one(multiplier, &output_multiplier, &output_shift); |
| |
| 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(act_info.enabled()) |
| { |
| if(supported_acts.count(act_info.activation()) != 0) |
| { |
| const int a_const_int = quantize_qasymm8(act_info.a(), output_quant_info); |
| const int b_const_int = quantize_qasymm8(act_info.b(), output_quant_info); |
| |
| min_activation = act_info.activation() != ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU ? output_quant_info.offset : b_const_int; |
| max_activation = act_info.activation() == ActivationLayerInfo::ActivationFunction::RELU ? 255 : a_const_int; |
| } |
| else |
| { |
| _fuse_activation = false; |
| } |
| } |
| |
| // 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 = 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(gemm_input_to_use, &_weights_reshaped, biases_to_use, gemm_output_to_use, gemmlowp_output_stage, gemm_3d_depth, act_info); |
| |
| if(!_skip_im2col) |
| { |
| _im2col_output.allocator()->allocate(); |
| } |
| |
| if(!_skip_col2im) |
| { |
| // Configure and tune Col2Im |
| _col2im_kernel.configure(gemm_output_to_use, output, Size2D(conv_w, conv_h), num_groups); |
| CLScheduler::get().tune_kernel_static(_col2im_kernel); |
| } |
| |
| if(!_skip_col2im) |
| { |
| _gemm_output.allocator()->allocate(); |
| } |
| |
| ARM_COMPUTE_ERROR_ON_MSG((output->info()->dimension(idx_width) != conv_w) || (output->info()->dimension(idx_height) != conv_h), |
| "Output shape does not match the expected one"); |
| |
| if(!_fuse_activation) |
| { |
| _activationlayer_function.configure(output, nullptr, act_info); |
| } |
| |
| ARM_COMPUTE_UNUSED(weights_info); |
| } |
| |
| Status CLGEMMConvolutionLayer::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info, |
| const WeightsInfo &weights_info, const Size2D &dilation, const ActivationLayerInfo &act_info, unsigned int num_groups) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, weights, output); |
| 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(input, 1, DataType::QASYMM8, DataType::F16, DataType::F32); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT(input, weights); |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG((num_groups != 1) && (input->data_layout() != DataLayout::NCHW), "Grouping (num_groups != 1) with NHWC data layout is not supported"); |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG((num_groups != 1) && (input->data_type() == DataType::QASYMM8), "Grouping (num_groups != 1) is not supported with QASYMM8"); |
| ARM_COMPUTE_RETURN_ERROR_ON(((input->dimension(2) / weights->dimension(2)) != num_groups) && (input->data_layout() == DataLayout::NCHW)); |
| |
| const DataLayout data_layout = input->data_layout(); |
| const DataType data_type = input->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); |
| |
| TensorInfo im2col_reshaped_info{}; |
| TensorInfo info_gemm{}; |
| TensorInfo weights_reshaped_info{}; |
| const ITensorInfo *gemm_input_to_use = input; |
| const ITensorInfo *gemm_output_to_use = output; |
| 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 && conv_info.stride().first == 1 && conv_info.stride().second == 1); |
| const bool skip_col2im = data_layout == DataLayout::NHWC; |
| bool fuse_activation = true; |
| |
| const UniformQuantizationInfo iq_info = input->quantization_info().uniform(); |
| const UniformQuantizationInfo wq_info = weights->quantization_info().uniform(); |
| const UniformQuantizationInfo oq_info = output->quantization_info().uniform(); |
| |
| ARM_COMPUTE_RETURN_ERROR_ON((weights->dimension(idx_channel) * num_groups) != input->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(input, biases); |
| } |
| ARM_COMPUTE_RETURN_ERROR_ON(biases->dimension(0) != weights->dimension(idx_kernels)); |
| ARM_COMPUTE_RETURN_ERROR_ON(biases->num_dimensions() > 1); |
| } |
| |
| if(act_info.enabled()) |
| { |
| ARM_COMPUTE_ERROR_ON(act_info.b() > act_info.a()); |
| } |
| |
| // Get convolved dimensions |
| unsigned int conv_w = 0; |
| unsigned int conv_h = 0; |
| |
| std::tie(conv_w, conv_h) = scaled_dimensions(input->dimension(idx_width), |
| input->dimension(idx_height), |
| kernel_width, |
| kernel_height, |
| conv_info, |
| dilation); |
| |
| unsigned int mat_weights_cols = weights->dimension(idx_kernels) / num_groups; |
| |
| const ITensorInfo *biases_to_use = biases; |
| bool append_bias = false; |
| |
| if(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; |
| |
| ARM_COMPUTE_RETURN_ON_ERROR(CLConvolutionLayerReshapeWeights::validate(weights, biases, nullptr, num_groups)); |
| weights_reshaped_info = TensorInfo(compute_weights_reshaped_shape(*weights, true, num_groups), 1, data_type); |
| } |
| else |
| { |
| ARM_COMPUTE_RETURN_ON_ERROR(CLConvolutionLayerReshapeWeights::validate(weights, nullptr, nullptr, num_groups)); |
| weights_reshaped_info = TensorInfo(compute_weights_reshaped_shape(*weights, false, 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(input, kernel_dims, conv_info, append_bias, dilation, num_groups == 1, num_groups); |
| |
| auto_init_if_empty(im2col_reshaped_info, input->clone()->set_tensor_shape(expected_output_shape)); |
| |
| ARM_COMPUTE_RETURN_ON_ERROR(CLIm2ColKernel::validate(input, &im2col_reshaped_info, kernel_dims, conv_info, append_bias, dilation, 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(output->quantization_info()).set_data_layout(input->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.gemmlowp_multiplier = 0; |
| gemmlowp_output_stage.gemmlowp_shift = 0; |
| |
| if(is_quantized) |
| { |
| 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)); |
| |
| 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(act_info.enabled()) |
| { |
| if(supported_acts.count(act_info.activation()) != 0) |
| { |
| const int a_const_int = quantize_qasymm8(act_info.a(), output_quant_info); |
| const int b_const_int = quantize_qasymm8(act_info.b(), output_quant_info); |
| |
| min_activation = act_info.activation() != ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU ? output_quant_info.offset : b_const_int; |
| max_activation = act_info.activation() == ActivationLayerInfo::ActivationFunction::RELU ? 255 : a_const_int; |
| } |
| else |
| { |
| fuse_activation = false; |
| } |
| } |
| |
| // 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 = 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, act_info)); |
| |
| // Validate Col2Im |
| if(!skip_col2im) |
| { |
| ARM_COMPUTE_RETURN_ON_ERROR(CLCol2ImKernel::validate(gemm_output_to_use, output, Size2D(conv_w, conv_h), num_groups)); |
| } |
| |
| //Validate Activation Layer |
| if(!fuse_activation) |
| { |
| ARM_COMPUTE_RETURN_ON_ERROR(CLActivationLayer::validate(output, nullptr, act_info)); |
| } |
| |
| return Status{}; |
| } |
| |
| void CLGEMMConvolutionLayer::run() |
| { |
| prepare(); |
| |
| MemoryGroupResourceScope scope_mg(_memory_group); |
| |
| // Run im2col |
| if(!_skip_im2col) |
| { |
| CLScheduler::get().enqueue(_im2col_kernel); |
| } |
| |
| // Runs CLGEMM or CLGEMMLowpMatrixMultiplyCore functions |
| if(_is_quantized) |
| { |
| // Run gemmlowp |
| _mm_gemmlowp.run(); |
| } |
| else |
| { |
| // Run gemm |
| _mm_gemm.run(); |
| } |
| |
| // Reshape output matrix |
| if(!_skip_col2im) |
| { |
| CLScheduler::get().enqueue(_col2im_kernel, false); |
| } |
| |
| //Run Activation Layer if we cannot fuse in GEMM |
| if(!_fuse_activation) |
| { |
| _activationlayer_function.run(); |
| } |
| } |
| |
| void CLGEMMConvolutionLayer::prepare() |
| { |
| if(!_is_prepared) |
| { |
| ARM_COMPUTE_ERROR_ON(!_original_weights->is_used()); |
| |
| // Run weights reshaping and mark original weights tensor as unused |
| _weights_reshaped.allocator()->allocate(); |
| _reshape_weights.run(); |
| _original_weights->mark_as_unused(); |
| |
| // Prepare GEMM |
| _is_quantized ? _mm_gemmlowp.prepare() : _mm_gemm.prepare(); |
| if(!_weights_reshaped.is_used()) |
| { |
| _weights_reshaped.allocator()->free(); |
| } |
| |
| CLScheduler::get().queue().finish(); |
| _is_prepared = true; |
| } |
| } |