diff options
Diffstat (limited to 'src/runtime/gpu/cl/operators/ClConv2d.cpp')
| -rw-r--r-- | src/runtime/gpu/cl/operators/ClConv2d.cpp | 262 |
1 files changed, 262 insertions, 0 deletions
diff --git a/src/runtime/gpu/cl/operators/ClConv2d.cpp b/src/runtime/gpu/cl/operators/ClConv2d.cpp new file mode 100644 index 0000000000..4cd65290f3 --- /dev/null +++ b/src/runtime/gpu/cl/operators/ClConv2d.cpp @@ -0,0 +1,262 @@ +/* + * Copyright (c) 2021 Arm Limited. + * + * SPDX-License-Identifier: MIT + * + * Permission is hereby granted, free of charge, to any person obtaining a copy + * of this software and associated documentation files (the "Software"), to + * deal in the Software without restriction, including without limitation the + * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or + * sell copies of the Software, and to permit persons to whom the Software is + * furnished to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all + * copies or substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + * SOFTWARE. + */ +#include "src/runtime/gpu/cl/operators/ClConv2d.h" + +#include "arm_compute/core/PixelValue.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 "arm_compute/runtime/CL/functions/CLFFTConvolutionLayer.h" +#include "src/runtime/gpu/cl/operators/ClDirectConv2d.h" +#include "src/runtime/gpu/cl/operators/ClGemmConvolution.h" +#include "src/runtime/gpu/cl/operators/ClWinogradConv2d.h" + +#include <memory> + +namespace arm_compute +{ +namespace opencl +{ +using namespace arm_compute::misc::shape_calculator; + +ClConv2d::ClConv2d() + : _operator() +{ +} + +ClConv2d::~ClConv2d() = default; + +void ClConv2d::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(ClConv2d::validate(src, weights, ((biases != nullptr) ? biases : nullptr), dst, conv2d_info, weights_info)); + + switch(ClConv2d::get_convolution_method(src, weights, dst, conv2d_info, weights_info, CLScheduler::get().target())) + { + case ConvolutionMethod::WINOGRAD: + { + ARM_COMPUTE_ERROR_ON(conv2d_info.num_groups != 1); + auto f = std::make_unique<ClWinogradConv2d>(); + f->configure(compile_context, src, weights, biases, dst, conv2d_info.conv_info, conv2d_info.act_info, conv2d_info.enable_fast_math); + _operator = std::move(f); + break; + } + case ConvolutionMethod::DIRECT: + { + ARM_COMPUTE_ERROR_ON(conv2d_info.num_groups != 1); + auto f = std::make_unique<ClDirectConv2d>(); + f->configure(compile_context, src, weights, biases, dst, conv2d_info.conv_info, conv2d_info.act_info); + _operator = std::move(f); + break; + } + case ConvolutionMethod::GEMM: + { + auto f = std::make_unique<ClGemmConvolution>(); + f->configure(compile_context, src, weights, biases, dst, conv2d_info, weights_info); + _operator = std::move(f); + break; + } + default: + ARM_COMPUTE_ERROR("Not supported."); + break; + } + _aux_mem = _operator->workspace(); +} + +Status ClConv2d::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((conv2d_info.num_groups != 1) && (src->data_layout() != DataLayout::NCHW), "Grouping (num_groups != 1) with NHWC data layout is not supported"); + + const GPUTarget gpu_target = CLScheduler::get().target(); + + switch(ClConv2d::get_convolution_method(src, weights, dst, conv2d_info, weights_info, gpu_target)) + { + case ConvolutionMethod::WINOGRAD: + { + //Validate Winograd + ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv2d_info.num_groups != 1, "Grouping (num_groups != 1) with ClWinogradConv2d is not supported"); + ARM_COMPUTE_RETURN_ON_ERROR(ClWinogradConv2d::validate(src, weights, biases, dst, conv2d_info.conv_info, conv2d_info.act_info, conv2d_info.enable_fast_math)); + break; + } + case ConvolutionMethod::DIRECT: + { + // Validate direct convolution layer + ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv2d_info.num_groups != 1, "Grouping (num_groups != 1) with ClDirectConv2d is not supported"); + ARM_COMPUTE_RETURN_ON_ERROR(ClDirectConv2d::validate(src, weights, biases, dst, conv2d_info.conv_info, conv2d_info.act_info)); + break; + } + case ConvolutionMethod::GEMM: + { + // Validate gemm-based convolution layer + ARM_COMPUTE_RETURN_ON_ERROR(ClGemmConvolution::validate(src, weights, biases, dst, conv2d_info, weights_info)); + break; + } + default: + ARM_COMPUTE_ERROR("Not supported."); + break; + } + + return Status{}; +} + +ConvolutionMethod ClConv2d::get_convolution_method(const ITensorInfo *src, const ITensorInfo *weights, const ITensorInfo *dst, const Conv2dInfo &conv2d_info, + const WeightsInfo &weights_info, const GPUTarget gpu_target) +{ + ARM_COMPUTE_ERROR_ON_NULLPTR(src); + ARM_COMPUTE_ERROR_ON_NULLPTR(dst); + ARM_COMPUTE_ERROR_ON_NULLPTR(weights); + ARM_COMPUTE_UNUSED(weights_info); + ARM_COMPUTE_UNUSED(gpu_target); + + const PadStrideInfo conv_info = conv2d_info.conv_info; + const ActivationLayerInfo act_info = conv2d_info.act_info; + const Size2D dilation = conv2d_info.dilation; + bool enable_fast_math = conv2d_info.enable_fast_math; + + const size_t idx_w = get_data_layout_dimension_index(src->data_layout(), DataLayoutDimension::WIDTH); + const size_t idx_h = get_data_layout_dimension_index(src->data_layout(), DataLayoutDimension::HEIGHT); + const size_t idx_c = get_data_layout_dimension_index(src->data_layout(), DataLayoutDimension::CHANNEL); + + /* Input spatial dims, kernel size, IFM/OFM, conv info*/ + using ConvolutionConfiguration = std::tuple<Size2D, Size2D, Size2D, PadStrideInfo, DataLayout>; + using ConfigurationMethod = std::pair<ConvolutionConfiguration, ConvolutionMethod>; + + const std::vector<ConfigurationMethod> known_configs = + { + // Alexnet + ConfigurationMethod(ConvolutionConfiguration(Size2D(27U, 27U), Size2D(5U, 5U), Size2D(48U, 128U), PadStrideInfo(1U, 1U, 2U, 2U), DataLayout::NCHW), ConvolutionMethod::DIRECT), + // VGG16 / VGG19 + ConfigurationMethod(ConvolutionConfiguration(Size2D(224U, 224U), Size2D(3U, 3U), Size2D(3U, 64U), PadStrideInfo(1U, 1U, 1U, 1U), DataLayout::NCHW), ConvolutionMethod::DIRECT), + // Mobilenet 224 + ConfigurationMethod(ConvolutionConfiguration(Size2D(224U, 224U), Size2D(3U, 3U), Size2D(3U, 32U), PadStrideInfo(2U, 2U, 0U, 1U, 0U, 1U, DimensionRoundingType::FLOOR), DataLayout::NCHW), ConvolutionMethod::GEMM), + // Mobilenet 160 + ConfigurationMethod(ConvolutionConfiguration(Size2D(160U, 160U), Size2D(3U, 3U), Size2D(3U, 24U), PadStrideInfo(2U, 2U, 0U, 1U, 0U, 1U, DimensionRoundingType::FLOOR), DataLayout::NCHW), ConvolutionMethod::GEMM), + // Mobilenet 224 + ConfigurationMethod(ConvolutionConfiguration(Size2D(224U, 224U), Size2D(3U, 3U), Size2D(3U, 32U), PadStrideInfo(2U, 2U, 0U, 1U, 0U, 1U, DimensionRoundingType::FLOOR), DataLayout::NHWC), ConvolutionMethod::GEMM), + // Mobilenet 160 + ConfigurationMethod(ConvolutionConfiguration(Size2D(160U, 160U), Size2D(3U, 3U), Size2D(3U, 24U), PadStrideInfo(2U, 2U, 0U, 1U, 0U, 1U, DimensionRoundingType::FLOOR), DataLayout::NHWC), ConvolutionMethod::GEMM), + }; + + const auto find_config = [&](ConfigurationMethod c) + { + const ConvolutionConfiguration config = c.first; + const PadStrideInfo info = std::get<3>(config); + const DataLayout data_layout = std::get<4>(config); + + return std::get<0>(config) == Size2D(src->dimension(idx_w), src->dimension(idx_h)) && std::get<1>(config) == Size2D(weights->dimension(idx_w), weights->dimension(idx_h)) + && std::get<2>(config) == Size2D(weights->dimension(idx_c), weights->dimension(3)) && info.pad_top() == conv_info.pad_top() && info.pad_right() == conv_info.pad_right() + && info.pad_bottom() == conv_info.pad_bottom() && info.pad_left() == conv_info.pad_left() && info.stride() == conv_info.stride() && (data_layout == src->data_layout()); + }; + + std::vector<ConfigurationMethod>::const_iterator found; + if((found = std::find_if(known_configs.begin(), known_configs.end(), find_config)) != known_configs.end()) + { + return (*found).second; + } + + if(dilation != Size2D(1U, 1U)) + { + return ConvolutionMethod::GEMM; + } + else + { + if(src->data_layout() == DataLayout::NCHW) + { + // SRGAN + if((src->dimension(idx_h) > 720U) && (dst->dimension(idx_h) > 720U) && (weights->dimension(idx_h) == 9) && (conv_info.pad_top() < 3) + && (ClDirectConv2d::validate(src, weights, nullptr, dst, conv_info, act_info))) + { + return ConvolutionMethod::DIRECT; + } + if((weights->dimension(idx_h) > 5) && (src->dimension(idx_c) > dst->dimension(idx_c)) && (CLFFTConvolutionLayer::validate(src, weights, nullptr, dst, conv_info, act_info, enable_fast_math))) + { + return ConvolutionMethod::FFT; + } + if(src->dimension(idx_c) < 16) + { + return ConvolutionMethod::GEMM; + } + return bool(ClWinogradConv2d::validate(src, weights, nullptr, dst, conv_info, act_info, enable_fast_math)) ? ConvolutionMethod::WINOGRAD : ConvolutionMethod::GEMM; + } + else + { + const bool is_direct_valid = bool(ClDirectConv2d::validate(src, weights, nullptr, dst, conv_info, act_info)); + const bool is_wino_valid = bool(ClWinogradConv2d::validate(src, weights, nullptr, dst, conv_info, act_info, enable_fast_math)); + + // SRGAN case + if((src->dimension(idx_h) > 720U) && (dst->dimension(idx_h) > 720U) && (weights->dimension(idx_h) == 9) && (conv_info.pad_top() < 3) + && is_direct_valid) + { + return ConvolutionMethod::DIRECT; + } + + // Floating-point case: GeMM/Direct/Winograd + if(is_data_type_float(src->data_type())) + { + const bool is_large_kernel_sz = (weights->dimension(idx_w) >= 7) && (weights->dimension(idx_h) >= 7); + const bool is_ifm_ge_16 = src->dimension(idx_c) >= 16; + + // Run Winograd if valid and IFM >= 16 + if(is_wino_valid && is_ifm_ge_16) + { + return ConvolutionMethod::WINOGRAD; + } + // Run Direct for Large kernel size + if(is_large_kernel_sz && is_ifm_ge_16 && is_direct_valid) + { + return ConvolutionMethod::DIRECT; + } + + // Default case + return ConvolutionMethod::GEMM; + } + + // Generic case for quantized. Only GeMM + return ConvolutionMethod::GEMM; + } + } +} + +void ClConv2d::run(ITensorPack &tensors) +{ + prepare(tensors); + _operator->run(tensors); +} + +void ClConv2d::prepare(ITensorPack &tensors) +{ + _operator->prepare(tensors); +} + +experimental::MemoryRequirements ClConv2d::workspace() const +{ + return _aux_mem; +} +} // namespace opencl +} // namespace arm_compute |