| /* |
| * 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/ClGemmConv2d.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/helpers/AutoConfiguration.h" |
| #include "src/core/helpers/MemoryHelpers.h" |
| #include "src/gpu/cl/kernels/ClActivationKernel.h" |
| #include "src/gpu/cl/kernels/ClCol2ImKernel.h" |
| #include "src/gpu/cl/kernels/ClIm2ColKernel.h" |
| #include "src/gpu/cl/kernels/ClWeightsReshapeKernel.h" |
| #include "src/gpu/cl/operators/ClGemm.h" |
| #include "src/gpu/cl/operators/ClGemmLowpMatrixMultiplyCore.h" |
| #include "src/gpu/cl/utils/ClAuxTensorHandler.h" |
| |
| #include "src/common/utils/Log.h" |
| #include "support/Cast.h" |
| |
| namespace arm_compute |
| { |
| using namespace experimental; |
| using namespace misc::shape_calculator; |
| using namespace utils::cast; |
| namespace opencl |
| { |
| ClGemmConv2d::ClGemmConv2d() |
| : _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), _use_post_ops(false), _aux_mem(AuxTensorIdx::Count) |
| { |
| } |
| ClGemmConv2d::~ClGemmConv2d() = default; |
| |
| void ClGemmConv2d::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, const experimental::PostOpList<ITensorInfo *> &post_ops) |
| { |
| 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 |
| post_ops // post ops |
| ); |
| |
| TensorInfo tmp_src{ *src }; |
| if(_is_quantized) |
| { |
| ARM_COMPUTE_ERROR_ON_MSG(post_ops.size() > 0, "ClGemmConv2d quantized types do not support post ops"); |
| // 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 ClGemmConv2d::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 experimental::PostOpList<ITensorInfo *> &post_ops) |
| { |
| 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 |
| post_ops // post ops |
| ); |
| |
| if(is_quantized) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(post_ops.size() > 0, "ClGemmConv2d quantized types do not support post ops"); |
| // 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 ClGemmConv2d::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(ClGemmConv2d::validate(src, weights, biases, dst, |
| conv2d_info, |
| weights_info)); |
| ARM_COMPUTE_LOG_PARAMS(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; |
| _use_post_ops = conv2d_info.post_ops.size() > 0; |
| |
| 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, conv2d_info.post_ops); |
| |
| if(!_skip_col2im) |
| { |
| ARM_COMPUTE_ERROR_ON_MSG(conv2d_info.post_ops.size() > 0, "ClGemmConv2d does not support post ops with col2im operation"); // Post ops must be performed after every other op |
| // 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"); |
| |
| // Disable running of activation kernel if post ops are used |
| if(!_fuse_activation && !_use_post_ops) |
| { |
| _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 ClGemmConv2d::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; |
| bool use_post_ops = conv2d_info.post_ops.size() > 0; |
| |
| 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); |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(!skip_im2col |
| && conv2d_info.post_ops.size() > 0, |
| "ClGemmConv2d does not support post ops with col2im or im2col operation"); // Post ops must be performed after every other op |
| |
| // 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, |
| conv2d_info.post_ops)); |
| |
| // 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 |
| // Disable running (thus validation) of activation kernel if post ops are used |
| if(!fuse_activation && !use_post_ops) |
| { |
| ARM_COMPUTE_RETURN_ON_ERROR(kernels::ClActivationKernel::validate(dst, nullptr, conv2d_info.act_info)); |
| } |
| |
| return Status{}; |
| } |
| |
| void ClGemmConv2d::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 |
| // Disable running of activation kernel if post ops are used |
| if(!_fuse_activation && !_use_post_ops) |
| { |
| ITensorPack pack = |
| { |
| { TensorType::ACL_SRC, dst }, |
| { TensorType::ACL_DST, dst } |
| }; |
| CLScheduler::get().enqueue_op(*_activation_kernel.get(), pack, false); |
| } |
| } |
| |
| void ClGemmConv2d::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 ClGemmConv2d::workspace() const |
| { |
| return _aux_mem; |
| } |
| } // namespace opencl |
| } // namespace arm_compute |