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diff --git a/src/runtime/gpu/cl/operators/ClConv2d.cpp b/src/runtime/gpu/cl/operators/ClConv2d.cpp
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-/*
- * 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/ClGemmConv2d.h"
-#include "src/runtime/gpu/cl/operators/ClWinogradConv2d.h"
-
-#include <memory>
-
-namespace
-{
-/** Get the suitable kernel size for using direct convolution method with NHWC data layout.
- *
- * @note Direct convolution should be executed when the kernel has the spatial dimensions greater than or equal to the value returned by this function
- *
- * @param[in] gpu_target GPU target
- *
- * @return the suitable kernel size for using direct convolution method with NHWC data layout
- */
-size_t get_direct_conv_kernel_threshold_nhwc(arm_compute::GPUTarget gpu_target)
-{
- switch(gpu_target)
- {
- case arm_compute::GPUTarget::G76:
- case arm_compute::GPUTarget::G77:
- case arm_compute::GPUTarget::G78:
- return 5;
- case arm_compute::GPUTarget::G71:
- case arm_compute::GPUTarget::G72:
- case arm_compute::GPUTarget::MIDGARD:
- case arm_compute::GPUTarget::BIFROST:
- return 7;
- default:
- return 5;
- }
-}
-} // namespace
-
-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<ClGemmConv2d>();
- 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(ClGemmConv2d::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);
-
- 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));
- const size_t kernel_sz_direct_conv_thr = get_direct_conv_kernel_threshold_nhwc(gpu_target);
-
- // 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) >= kernel_sz_direct_conv_thr) && (weights->dimension(idx_h) >= kernel_sz_direct_conv_thr);
- const bool is_ifm_ge_16 = src->dimension(idx_c) >= 16;
- const bool is_ifm_gt_ofm = src->dimension(idx_c) > weights->dimension(3U);
-
- // 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 && is_ifm_gt_ofm)
- {
- 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
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