diff options
Diffstat (limited to 'src/gpu/cl/operators/ClWinogradConv2d.cpp')
| -rw-r--r-- | src/gpu/cl/operators/ClWinogradConv2d.cpp | 175 |
1 files changed, 96 insertions, 79 deletions
diff --git a/src/gpu/cl/operators/ClWinogradConv2d.cpp b/src/gpu/cl/operators/ClWinogradConv2d.cpp index b4163a5986..8ec96b247e 100644 --- a/src/gpu/cl/operators/ClWinogradConv2d.cpp +++ b/src/gpu/cl/operators/ClWinogradConv2d.cpp @@ -24,20 +24,19 @@ #include "src/gpu/cl/operators/ClWinogradConv2d.h" #include "arm_compute/core/CL/ICLTensor.h" -#include "arm_compute/core/Utils.h" -#include "arm_compute/core/Validate.h" #include "arm_compute/core/experimental/Types.h" +#include "arm_compute/core/Utils.h" #include "arm_compute/core/utils/misc/ShapeCalculator.h" +#include "arm_compute/core/Validate.h" #include "arm_compute/runtime/CL/CLScheduler.h" -#include "src/core/CL/kernels/CLFillBorderKernel.h" + +#include "src/common/utils/Log.h" #include "src/core/CL/kernels/CLFillBorderKernel.h" #include "src/core/helpers/MemoryHelpers.h" #include "src/gpu/cl/kernels/ClWinogradFilterTransformKernel.h" #include "src/gpu/cl/kernels/ClWinogradInputTransformKernel.h" #include "src/gpu/cl/kernels/ClWinogradOutputTransformKernel.h" #include "src/gpu/cl/utils/ClAuxTensorHandler.h" - -#include "src/common/utils/Log.h" #include "support/Cast.h" using namespace arm_compute::experimental; @@ -55,15 +54,16 @@ Size2D winograd_output_tile(const Size2D &input_dims, const Size2D &kernel_dims, const unsigned int kernel_max_dim = std::max(kernel_dims.width, kernel_dims.height); // Check if the input spatial dimensions are smaller than 4 - const bool is_input_lt4_nchw = (input_dims.width <= 4 && input_dims.height <= 4) && (data_layout == DataLayout::NCHW); + const bool is_input_lt4_nchw = + (input_dims.width <= 4 && input_dims.height <= 4) && (data_layout == DataLayout::NCHW); - if(kernel_max_dim == 3U) + if (kernel_max_dim == 3U) { - if(kernel_dims == Size2D(3U, 3U)) + if (kernel_dims == Size2D(3U, 3U)) { output_tile = is_input_lt4_nchw ? Size2D(2U, 2U) : Size2D(4U, 4U); } - else if(kernel_dims == Size2D(3U, 1U)) + else if (kernel_dims == Size2D(3U, 1U)) { output_tile = is_input_lt4_nchw ? Size2D(2U, 1U) : Size2D(4U, 1U); } @@ -72,15 +72,13 @@ Size2D winograd_output_tile(const Size2D &input_dims, const Size2D &kernel_dims, output_tile = is_input_lt4_nchw ? Size2D(1U, 2U) : Size2D(1U, 4U); } } - else if(kernel_max_dim == 5U) + else if (kernel_max_dim == 5U) { - output_tile = Size2D(kernel_dims.width == 1 ? 1U : 4U, - kernel_dims.height == 1 ? 1U : 4U); + output_tile = Size2D(kernel_dims.width == 1 ? 1U : 4U, kernel_dims.height == 1 ? 1U : 4U); } - else if(kernel_max_dim == 7U) + else if (kernel_max_dim == 7U) { - output_tile = Size2D(kernel_dims.width == 1 ? 1U : 2U, - kernel_dims.height == 1 ? 1U : 2U); + output_tile = Size2D(kernel_dims.width == 1 ? 1U : 2U, kernel_dims.height == 1 ? 1U : 2U); } return output_tile; @@ -91,11 +89,9 @@ bool check_support_fast_math(const Size2D &output_tile, const Size2D &kernel_siz // Check if we want to configure a Winograd configuration which requires fast math using WinogradConfiguration = std::pair<std::pair<int, int>, std::pair<int, int>>; - std::vector<WinogradConfiguration> fast_math_winograd = - { + std::vector<WinogradConfiguration> fast_math_winograd = { WinogradConfiguration(std::pair<int, int>(4, 4), std::pair<int, int>(5, 5)), - WinogradConfiguration(std::pair<int, int>(2, 2), std::pair<int, int>(7, 7)) - }; + WinogradConfiguration(std::pair<int, int>(2, 2), std::pair<int, int>(7, 7))}; auto p = std::make_pair(std::pair<int, int>(output_tile.width, output_tile.height), std::pair<int, int>(kernel_size.width, kernel_size.height)); @@ -103,8 +99,13 @@ bool check_support_fast_math(const Size2D &output_tile, const Size2D &kernel_siz return std::find(fast_math_winograd.begin(), fast_math_winograd.end(), p) != fast_math_winograd.end(); } -Status validate_arguments(const ITensorInfo *src, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *dst, const PadStrideInfo &conv_info, - const ActivationLayerInfo &act_info, bool enable_fast_math) +Status validate_arguments(const ITensorInfo *src, + const ITensorInfo *weights, + const ITensorInfo *biases, + const ITensorInfo *dst, + const PadStrideInfo &conv_info, + const ActivationLayerInfo &act_info, + bool enable_fast_math) { // Get indeces for the width and height const size_t idx_width = get_data_layout_dimension_index(src->data_layout(), DataLayoutDimension::WIDTH); @@ -115,41 +116,49 @@ Status validate_arguments(const ITensorInfo *src, const ITensorInfo *weights, co const Size2D kernel_size = Size2D(weights->tensor_shape()[idx_width], weights->tensor_shape()[idx_height]); const Size2D output_tile = winograd_output_tile(input_dims, kernel_size, src->data_layout()); - ARM_COMPUTE_RETURN_ERROR_ON_MSG(((conv_info.pad_left() > (kernel_size.x() / 2u)) || (conv_info.pad_right() > (kernel_size.x() / 2u))), "Winograd only supports padding up to half kernel size"); - ARM_COMPUTE_RETURN_ERROR_ON_MSG(((conv_info.pad_top() > (kernel_size.y() / 2u)) || (conv_info.pad_bottom() > (kernel_size.y() / 2u))), "Winograd only supports padding up to half kernel size"); + ARM_COMPUTE_RETURN_ERROR_ON_MSG( + ((conv_info.pad_left() > (kernel_size.x() / 2u)) || (conv_info.pad_right() > (kernel_size.x() / 2u))), + "Winograd only supports padding up to half kernel size"); + ARM_COMPUTE_RETURN_ERROR_ON_MSG( + ((conv_info.pad_top() > (kernel_size.y() / 2u)) || (conv_info.pad_bottom() > (kernel_size.y() / 2u))), + "Winograd only supports padding up to half kernel size"); // Check if the Winograd configuration requires fast math - if(!enable_fast_math) + if (!enable_fast_math) { - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src, 1, DataType::F32); //disable winograd for fp16 if fast math is false. - ARM_COMPUTE_RETURN_ERROR_ON_MSG(check_support_fast_math(output_tile, kernel_size), "This Winograd configuration requires enable_fast_math=true"); + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN( + src, 1, DataType::F32); //disable winograd for fp16 if fast math is false. + ARM_COMPUTE_RETURN_ERROR_ON_MSG(check_support_fast_math(output_tile, kernel_size), + "This Winograd configuration requires enable_fast_math=true"); } - const WinogradInfo winograd_info = WinogradInfo(output_tile, - kernel_size, - input_dims, - conv_info, - src->data_layout()); + const WinogradInfo winograd_info = + WinogradInfo(output_tile, kernel_size, input_dims, conv_info, src->data_layout()); // Validate input transform - const TensorShape input0_shape = misc::shape_calculator::compute_winograd_input_transform_shape(*src, winograd_info); - const TensorInfo input0 = src->clone()->set_tensor_shape(input0_shape); + const TensorShape input0_shape = + misc::shape_calculator::compute_winograd_input_transform_shape(*src, winograd_info); + const TensorInfo input0 = src->clone()->set_tensor_shape(input0_shape); ARM_COMPUTE_RETURN_ON_ERROR(kernels::ClWinogradInputTransformKernel::validate(src, &input0, winograd_info)); // Validate filter transform - const TensorShape input1_shape = misc::shape_calculator::compute_winograd_filter_transform_shape(*weights, winograd_info); - const TensorInfo input1 = weights->clone()->set_tensor_shape(input1_shape); + const TensorShape input1_shape = + misc::shape_calculator::compute_winograd_filter_transform_shape(*weights, winograd_info); + const TensorInfo input1 = weights->clone()->set_tensor_shape(input1_shape); ARM_COMPUTE_RETURN_ON_ERROR(kernels::ClWinogradFilterTransformKernel::validate(weights, &input1, winograd_info)); // Validate batched matrix multiply TensorShape batched_mm_output_shape = input0.tensor_shape(); batched_mm_output_shape[0] = input1.tensor_shape()[0]; const TensorInfo batched_mm_output = input0.clone()->set_tensor_shape(batched_mm_output_shape); - ARM_COMPUTE_RETURN_ON_ERROR(ClGemm::validate(&input0, &input1, nullptr, &batched_mm_output, 1.0f, 0.0f, GEMMInfo(false, false, true /* Reshape weights only for the first run*/, 0, false, false, - GEMMLowpOutputStageInfo(), (src->data_type() == DataType::F16)))); + ARM_COMPUTE_RETURN_ON_ERROR( + ClGemm::validate(&input0, &input1, nullptr, &batched_mm_output, 1.0f, 0.0f, + GEMMInfo(false, false, true /* Reshape weights only for the first run*/, 0, false, false, + GEMMLowpOutputStageInfo(), (src->data_type() == DataType::F16)))); // Configure output transform - ARM_COMPUTE_RETURN_ON_ERROR(kernels::ClWinogradOutputTransformKernel::validate(&batched_mm_output, biases, dst, winograd_info, act_info)); + ARM_COMPUTE_RETURN_ON_ERROR( + kernels::ClWinogradOutputTransformKernel::validate(&batched_mm_output, biases, dst, winograd_info, act_info)); return Status{}; } @@ -171,8 +180,14 @@ ClWinogradConv2d::ClWinogradConv2d() ClWinogradConv2d::~ClWinogradConv2d() = default; -void ClWinogradConv2d::configure(const ClCompileContext &compile_context, ITensorInfo *src, ITensorInfo *weights, ITensorInfo *biases, ITensorInfo *dst, - const PadStrideInfo &conv_info, const ActivationLayerInfo &act_info, bool enable_fast_math) +void ClWinogradConv2d::configure(const ClCompileContext &compile_context, + ITensorInfo *src, + ITensorInfo *weights, + ITensorInfo *biases, + ITensorInfo *dst, + const PadStrideInfo &conv_info, + const ActivationLayerInfo &act_info, + bool enable_fast_math) { ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(src, weights, biases, dst, conv_info, act_info, enable_fast_math)); ARM_COMPUTE_LOG_PARAMS(src, weights, biases, dst, conv_info, act_info, enable_fast_math); @@ -187,50 +202,53 @@ void ClWinogradConv2d::configure(const ClCompileContext &compile_context, ITenso const Size2D output_tile = winograd_output_tile(input_dims, kernel_size, src->data_layout()); // Check if the Winograd configuration requires fast math - if(!enable_fast_math) + if (!enable_fast_math) { - ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src, 1, DataType::F32); //disable winograd for fp16 if fast math is false. - ARM_COMPUTE_ERROR_ON_MSG(check_support_fast_math(output_tile, kernel_size), "This Winograd configuration requires enable_fast_math=true"); + ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src, 1, + DataType::F32); //disable winograd for fp16 if fast math is false. + ARM_COMPUTE_ERROR_ON_MSG(check_support_fast_math(output_tile, kernel_size), + "This Winograd configuration requires enable_fast_math=true"); } - const WinogradInfo winograd_info = WinogradInfo(output_tile, - kernel_size, - input_dims, - conv_info, - src->data_layout()); + const WinogradInfo winograd_info = + WinogradInfo(output_tile, kernel_size, input_dims, conv_info, src->data_layout()); _is_prepared = false; // Configure input transform _input_transform->configure(compile_context, src, &_input0, winograd_info); - _border_handler.configure(compile_context, src, _input_transform->border_size(), BorderMode::CONSTANT, PixelValue()); + _border_handler.configure(compile_context, src, _input_transform->border_size(), BorderMode::CONSTANT, + PixelValue()); // Configure filter transform _filter_transform->configure(compile_context, weights, &_input1, winograd_info); // Configure batched matrix multiply - _batched_mm.configure(compile_context, &_input0, &_input1, nullptr, &_batched_mm_output, 1.0f, 0.0f, GEMMInfo(false, false, true /* Reshape weights only for the first run*/, 0, - false, false, - GEMMLowpOutputStageInfo(), - (src->data_type() == DataType::F16))); + _batched_mm.configure(compile_context, &_input0, &_input1, nullptr, &_batched_mm_output, 1.0f, 0.0f, + GEMMInfo(false, false, true /* Reshape weights only for the first run*/, 0, false, false, + GEMMLowpOutputStageInfo(), (src->data_type() == DataType::F16))); // Configure output transform _output_transform->set_target(CLScheduler::get().target()); _output_transform->configure(compile_context, &_batched_mm_output, biases, dst, winograd_info, act_info); - _aux_mem = _batched_mm.workspace(); - const MemoryLifetime wino_wei_lifetm = std::any_of(std::begin(_aux_mem), std::end(_aux_mem), [](const auto & r) - { - return (r.lifetime == MemoryLifetime::Persistent) && (r.size > 0); - }) ? - MemoryLifetime::Prepare : - MemoryLifetime::Persistent; + _aux_mem = _batched_mm.workspace(); + const MemoryLifetime wino_wei_lifetm = + std::any_of(std::begin(_aux_mem), std::end(_aux_mem), + [](const auto &r) { return (r.lifetime == MemoryLifetime::Persistent) && (r.size > 0); }) + ? MemoryLifetime::Prepare + : MemoryLifetime::Persistent; _aux_mem.push_back(MemoryInfo(offset_int_vec(2), MemoryLifetime::Temporary, _input0.total_size())); _aux_mem.push_back(MemoryInfo(offset_int_vec(3), wino_wei_lifetm, _input1.total_size())); _aux_mem.push_back(MemoryInfo(offset_int_vec(4), MemoryLifetime::Temporary, _batched_mm_output.total_size())); } -Status ClWinogradConv2d::validate(const ITensorInfo *src, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *dst, const PadStrideInfo &conv_info, - const ActivationLayerInfo &act_info, bool enable_fast_math) +Status ClWinogradConv2d::validate(const ITensorInfo *src, + const ITensorInfo *weights, + const ITensorInfo *biases, + const ITensorInfo *dst, + const PadStrideInfo &conv_info, + const ActivationLayerInfo &act_info, + bool enable_fast_math) { ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(src, weights, biases, dst, conv_info, act_info, enable_fast_math)); return Status{}; @@ -251,10 +269,9 @@ void ClWinogradConv2d::run(ITensorPack &tensors) prepare(tensors); // Run input transform - ITensorPack pack_it - { - { TensorType::ACL_SRC, src }, - { TensorType::ACL_DST, input0.get() }, + ITensorPack pack_it{ + {TensorType::ACL_SRC, src}, + {TensorType::ACL_DST, input0.get()}, }; CLScheduler::get().enqueue_op(_border_handler, pack_it, false); CLScheduler::get().enqueue_op(*_input_transform, pack_it, false); @@ -263,31 +280,31 @@ void ClWinogradConv2d::run(ITensorPack &tensors) ITensorPack pack_mm = tensors; pack_mm.add_const_tensor(TensorType::ACL_SRC_0, input0.get()); pack_mm.add_tensor(TensorType::ACL_DST, batched_mm_output.get()); - is_gemm_reshaped ? pack_mm.remove_tensor(TensorType::ACL_SRC_1) : pack_mm.add_const_tensor(TensorType::ACL_SRC_1, input1.get()); + is_gemm_reshaped ? pack_mm.remove_tensor(TensorType::ACL_SRC_1) + : pack_mm.add_const_tensor(TensorType::ACL_SRC_1, input1.get()); _batched_mm.run(pack_mm); // Run output transform - ITensorPack pack_ot - { - { TensorType::ACL_SRC_0, batched_mm_output.get() }, - { TensorType::ACL_SRC_1, biases }, - { TensorType::ACL_DST, dst }, + ITensorPack pack_ot{ + {TensorType::ACL_SRC_0, batched_mm_output.get()}, + {TensorType::ACL_SRC_1, biases}, + {TensorType::ACL_DST, dst}, }; CLScheduler::get().enqueue_op(*_output_transform, pack_ot); } void ClWinogradConv2d::prepare(ITensorPack &tensors) { - if(!_is_prepared) + if (!_is_prepared) { - auto weights = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC_1)); + auto weights = + utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC_1)); ICLTensor *in1_aux = utils::cast::polymorphic_downcast<ICLTensor *>(tensors.get_tensor(offset_int_vec(3))); CLAuxTensorHandler input1(_input1, *in1_aux); - ITensorPack pack_ft - { - { TensorType::ACL_SRC, weights }, - { TensorType::ACL_DST, input1.get() }, + ITensorPack pack_ft{ + {TensorType::ACL_SRC, weights}, + {TensorType::ACL_DST, input1.get()}, }; // Run filter transform and mark original weights as unused CLScheduler::get().enqueue_op(*_filter_transform, pack_ft, false); @@ -308,4 +325,4 @@ experimental::MemoryRequirements ClWinogradConv2d::workspace() const return _aux_mem; } } // namespace opencl -} // namespace arm_compute
\ No newline at end of file +} // namespace arm_compute |