Integrate new winograd APIs from MLTech
Resolves: COMPMID-5400
Signed-off-by: Ramy Elgammal <ramy.elgammal@arm.com>
Change-Id: Ib4428436dd7a6e40d8b2d8a2f8dac1b079154551
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/7894
Reviewed-by: Pablo Marquez Tello <pablo.tello@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Benchmark: Arm Jenkins <bsgcomp@arm.com>
diff --git a/src/cpu/operators/CpuWinogradConv2d.cpp b/src/cpu/operators/CpuWinogradConv2d.cpp
index dcc18ce..7be2d6d 100644
--- a/src/cpu/operators/CpuWinogradConv2d.cpp
+++ b/src/cpu/operators/CpuWinogradConv2d.cpp
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2021 Arm Limited.
+ * Copyright (c) 2021-2022 Arm Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -31,19 +31,19 @@
#include "arm_compute/runtime/NEON/NEScheduler.h"
#include "src/common/utils/Log.h"
#include "src/core/CPP/Validate.h"
+#include "src/core/NEON/kernels/assembly/winograd.hpp"
+#include "src/core/NEON/kernels/convolution/common/tensor.hpp"
#include "src/core/NEON/kernels/convolution/common/utils.hpp"
-#include "src/core/NEON/kernels/convolution/winograd/winograd.hpp"
#include "src/core/helpers/MemoryHelpers.h"
+#include "src/core/helpers/WindowHelpers.h"
+#include "src/core/utils/AssemblyUtils.h"
#include "src/cpu/kernels/CpuWinogradConv2dKernel.h"
+#include "src/cpu/kernels/assembly/arm_gemm.hpp"
#include "src/cpu/operators/CpuActivation.h"
#include "src/cpu/operators/CpuPermute.h"
-#include "src/cpu/operators/CpuWinogradConv2d.h"
#include "src/cpu/utils/CpuAuxTensorHandler.h"
-
#include "support/Cast.h"
-#include <set>
-
namespace arm_compute
{
namespace cpu
@@ -53,174 +53,20 @@
namespace
{
-arm_gemm::Activation arm_gemm_activation_from_acl_activation(const ActivationLayerInfo &act_info)
+inline Tensor4DShape internal_get_shape(const ITensorInfo *in)
{
- switch(act_info.activation())
- {
- case ActivationLayerInfo::ActivationFunction::RELU:
- {
- return arm_gemm::Activation(arm_gemm::Activation::Type::ReLU, act_info.a(), act_info.b());
- }
- case ActivationLayerInfo::ActivationFunction::BOUNDED_RELU:
- {
- return arm_gemm::Activation(arm_gemm::Activation::Type::BoundedReLU, act_info.a(), act_info.b());
- }
- default:
- {
- return arm_gemm::Activation(arm_gemm::Activation::Type::None);
- }
- }
-}
-
-inline Status validate_kernel_3x3(const Size2D input_dims, const ITensorInfo *src, const TensorInfo *input0, const TensorInfo *input1, const TensorInfo *batched_mm_output,
- const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *dst, const WinogradInfo &winograd_info, const ActivationLayerInfo &act_info)
-{
- ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(src);
- ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src, 1, DataType::F16, DataType::F32);
-
- if(src->data_type() == DataType::F32)
- {
- if(input_dims.width > 4 && input_dims.height > 4)
- {
- ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformInputKernel<float, 4, 4, 3, 3>::validate(src, input0, winograd_info)));
- ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformWeightsKernel<float, 4, 4, 3, 3>::validate(weights, input1, winograd_info)));
- ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformOutputKernel<float, 4, 4, 3, 3>::validate(batched_mm_output, biases, dst, winograd_info)));
- }
- else
- {
- ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformInputKernel<float, 2, 2, 3, 3>::validate(src, input0, winograd_info)));
- ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformWeightsKernel<float, 2, 2, 3, 3>::validate(weights, input1, winograd_info)));
- ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformOutputKernel<float, 2, 2, 3, 3>::validate(batched_mm_output, biases, dst, winograd_info)));
- }
- }
-#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
- else if(src->data_type() == DataType::F16)
- {
- ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformInputKernel<__fp16, 4, 4, 3, 3>::validate(src, input0, winograd_info)));
- ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformWeightsKernel<__fp16, 4, 4, 3, 3>::validate(weights, input1, winograd_info)));
- ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformOutputKernel<__fp16, 4, 4, 3, 3>::validate(batched_mm_output, biases, dst, winograd_info)));
- }
-#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */
-
- if(act_info.enabled())
- {
- CpuActivation::validate(dst, nullptr, act_info);
- }
- return Status{};
-}
-
-inline Status validate_kernel_5x5(const ITensorInfo *src, const TensorInfo *input0, const TensorInfo *input1, const TensorInfo *batched_mm_output,
- const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *dst, const WinogradInfo &winograd_info, const ActivationLayerInfo &act_info)
-{
- ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformInputKernel<float, 2, 2, 5, 5>::validate(src, input0, winograd_info)));
- ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformWeightsKernel<float, 2, 2, 5, 5>::validate(weights, input1, winograd_info)));
- ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformOutputKernel<float, 2, 2, 5, 5>::validate(batched_mm_output, biases, dst, winograd_info)));
- if(act_info.enabled())
- {
- CpuActivation::validate(dst, nullptr, act_info);
- }
- return Status{};
-}
-
-inline Status validate_kernel_3x1(const ITensorInfo *src, const TensorInfo *input0, const TensorInfo *input1, const TensorInfo *batched_mm_output,
- const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *dst, const WinogradInfo &winograd_info, const ActivationLayerInfo &act_info)
-{
- ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src, 1, DataType::F32);
- ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformInputKernel<float, 1, 6, 1, 3>::validate(src, input0, winograd_info)));
- ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformWeightsKernel<float, 1, 6, 1, 3>::validate(weights, input1, winograd_info)));
- ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformOutputKernel<float, 1, 6, 1, 3>::validate(batched_mm_output, biases, dst, winograd_info)));
- if(act_info.enabled())
- {
- CpuActivation::validate(dst, nullptr, act_info);
- }
- return Status{};
-}
-
-inline Status validate_kernel_1x3(const ITensorInfo *src, const TensorInfo *input0, const TensorInfo *input1, const TensorInfo *batched_mm_output,
- const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *dst, const WinogradInfo &winograd_info, const ActivationLayerInfo &act_info)
-{
- ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src, 1, DataType::F32);
- ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformInputKernel<float, 6, 1, 3, 1>::validate(src, input0, winograd_info)));
- ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformWeightsKernel<float, 6, 1, 3, 1>::validate(weights, input1, winograd_info)));
- ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformOutputKernel<float, 6, 1, 3, 1>::validate(batched_mm_output, biases, dst, winograd_info)));
-
- if(act_info.enabled())
- {
- CpuActivation::validate(dst, nullptr, act_info);
- }
- return Status{};
-}
-
-inline Status validate_kernel_5x1(const ITensorInfo *src, const TensorInfo *input0, const TensorInfo *input1, const TensorInfo *batched_mm_output,
- const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *dst, const WinogradInfo &winograd_info, const ActivationLayerInfo &act_info)
-{
- ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src, 1, DataType::F32);
- ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformInputKernel<float, 1, 4, 1, 5>::validate(src, input0, winograd_info)));
- ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformWeightsKernel<float, 1, 4, 1, 5>::validate(weights, input1, winograd_info)));
- ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformOutputKernel<float, 1, 4, 1, 5>::validate(batched_mm_output, biases, dst, winograd_info)));
- if(act_info.enabled())
- {
- CpuActivation::validate(dst, nullptr, act_info);
- }
- return Status{};
-}
-inline Status validate_kernel_1x5(const ITensorInfo *src, const TensorInfo *input0, const TensorInfo *input1, const TensorInfo *batched_mm_output,
- const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *dst, const WinogradInfo &winograd_info, const ActivationLayerInfo &act_info)
-{
- ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src, 1, DataType::F32);
- ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformInputKernel<float, 4, 1, 5, 1>::validate(src, input0, winograd_info)));
- ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformWeightsKernel<float, 4, 1, 5, 1>::validate(weights, input1, winograd_info)));
- ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformOutputKernel<float, 4, 1, 5, 1>::validate(batched_mm_output, biases, dst, winograd_info)));
- if(act_info.enabled())
- {
- CpuActivation::validate(dst, nullptr, act_info);
- }
- return Status{};
-}
-
-inline Status validate_kernel_7x1(const ITensorInfo *src, const TensorInfo *input0, const TensorInfo *input1, const TensorInfo *batched_mm_output,
- const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *dst, const WinogradInfo &winograd_info, const ActivationLayerInfo &act_info)
-{
- ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src, 1, DataType::F32);
- ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformInputKernel<float, 1, 2, 1, 7>::validate(src, input0, winograd_info)));
- ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformWeightsKernel<float, 1, 2, 1, 7>::validate(weights, input1, winograd_info)));
- ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformOutputKernel<float, 1, 2, 1, 7>::validate(batched_mm_output, biases, dst, winograd_info)));
- if(act_info.enabled())
- {
- CpuActivation::validate(dst, nullptr, act_info);
- }
- return Status{};
-}
-
-inline Status validate_kernel_1x7(const ITensorInfo *src, const TensorInfo *input0, const TensorInfo *input1, const TensorInfo *batched_mm_output,
- const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *dst, const WinogradInfo &winograd_info, const ActivationLayerInfo &act_info)
-{
- ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src, 1, DataType::F32);
- ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformInputKernel<float, 2, 1, 7, 1>::validate(src, input0, winograd_info)));
- ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformWeightsKernel<float, 2, 1, 7, 1>::validate(weights, input1, winograd_info)));
- ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformOutputKernel<float, 2, 1, 7, 1>::validate(batched_mm_output, biases, dst, winograd_info)));
-
- if(act_info.enabled())
- {
- CpuActivation::validate(dst, nullptr, act_info);
- }
- return Status{};
-}
-
-inline Tensor4DShape internal_get_input_shape(const ITensorInfo *src)
-{
- const DataLayout data_layout = src->data_layout();
- const int in_width = src->dimension(get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH));
- const int in_height = src->dimension(get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT));
- const int in_channels = src->dimension(get_data_layout_dimension_index(data_layout, DataLayoutDimension::CHANNEL));
- const int in_batches = src->dimension(3);
+ const DataLayout data_layout = in->data_layout();
+ const int in_width = in->dimension(get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH));
+ const int in_height = in->dimension(get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT));
+ const int in_channels = in->dimension(get_data_layout_dimension_index(data_layout, DataLayoutDimension::CHANNEL));
+ const int in_batches = in->dimension(get_data_layout_dimension_index(data_layout, DataLayoutDimension::BATCHES));
return Tensor4DShape{ in_batches, in_height, in_width, in_channels };
}
Status validate_arguments(const ITensorInfo *src, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *dst, const PadStrideInfo &conv_info)
{
- ARM_COMPUTE_UNUSED(dst);
+ ARM_COMPUTE_UNUSED(dst, weights);
ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED(src);
ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.stride().first != 1 || conv_info.stride().second != 1, "Winograd layer only supports unit strides.");
@@ -229,108 +75,85 @@
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src, biases);
ARM_COMPUTE_RETURN_ERROR_ON(biases->num_dimensions() > 1);
}
- return ICpuWinogradConv2dTransformWeightsKernel::validate(src, weights);
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src, 1, DataType::F16, DataType::F32);
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src, weights);
+ return Status{};
}
-Size2D winograd_output_tile(const Size2D &input_dims, const Size2D &kernel_dims, DataType data_type)
+
+bool get_winograd_kernel_implementation(const ITensorInfo *src, const ITensorInfo *weights, const ITensorInfo *dst,
+ const PadStrideInfo &conv_info, const ActivationLayerInfo &act_info, bool enable_fast_math,
+ arm_conv::winograd::WinogradImpl *winograd_impl, std::unique_ptr<arm_conv::ConvolutionArgs> &conv_args)
{
- Size2D output_tile = Size2D{};
- if(kernel_dims == Size2D(3U, 3U))
+ arm_conv::winograd::WinogradConfig winograd_cfg;
+ arm_gemm::GemmConfig cfg;
+
+ const DataType data_type = src->data_type();
+ Tensor4DShape in_shape{ internal_get_shape(src) };
+ Tensor4DShape out_shape{ internal_get_shape(dst) };
+ Tensor4DShape kernel_shape{ internal_get_shape(weights) };
+ uint32_t nthreads = NEScheduler::get().num_threads();
+ // Get configuration arguments for Winograd
+ winograd_cfg.output_rows = 0;
+ winograd_cfg.output_cols = 0;
+ conv_args = std::make_unique<arm_conv::ConvolutionArgs>(
+ in_shape.n_batches,
+ arm_conv::Shape2D{ static_cast<uint32_t>(in_shape.n_rows), static_cast<uint32_t>(in_shape.n_cols) },
+ in_shape.n_channels,
+ conv_info.pad_top(),
+ conv_info.pad_left(),
+ arm_conv::Shape2D{ static_cast<uint32_t>(out_shape.n_rows), static_cast<uint32_t>(out_shape.n_cols) },
+ out_shape.n_channels,
+ arm_conv::Shape2D{ static_cast<uint32_t>(kernel_shape.n_rows), static_cast<uint32_t>(kernel_shape.n_cols) },
+ assembly_utils::map_to_arm_gemm_activation(act_info));
+
+ bool success = false;
+ if(data_type == DataType::F32)
{
- output_tile = (input_dims.width <= 4 || input_dims.height <= 4) ? Size2D(2U, 2U) : Size2D(4U, 4U);
- if(data_type == DataType::F16)
- {
- output_tile = Size2D(4U, 4U);
- }
+ success = arm_conv::winograd::get_implementation<float>(
+ *winograd_impl, &CPUInfo::get(), *conv_args, nthreads, enable_fast_math, &winograd_cfg, nullptr);
}
- else if(kernel_dims == Size2D(5U, 5U))
+#if defined(__aarch64__) && defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC)
+ else if(data_type == DataType::F16)
{
- output_tile = Size2D(2U, 2U);
+ success = arm_conv::winograd::get_implementation<__fp16>(
+ *winograd_impl, &CPUInfo::get(), *conv_args, nthreads, enable_fast_math, &winograd_cfg, nullptr);
}
- else if(kernel_dims == Size2D(1U, 3U))
+#endif // defined(__aarch64__) && defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC)
+ else
{
- output_tile = Size2D(1U, 6U);
+ success = false;
}
- else if(kernel_dims == Size2D(3U, 1U))
- {
- output_tile = Size2D(6U, 1U);
- }
- else if(kernel_dims == Size2D(1U, 5U))
- {
- output_tile = Size2D(1U, 4U);
- }
- else if(kernel_dims == Size2D(5U, 1U))
- {
- output_tile = Size2D(4U, 1U);
- }
- else if(kernel_dims == Size2D(7U, 1U))
- {
- output_tile = Size2D(2U, 1U);
- }
- else if(kernel_dims == Size2D(1U, 7U))
- {
- output_tile = Size2D(1U, 2U);
- }
- return output_tile;
+ return success;
}
-
-bool check_support_fast_math(const Size2D &output_tile, const Size2D &kernel_size, DataType data_type)
-{
- // 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>>;
-
- const std::vector<WinogradConfiguration> fast_math_winograd_f16 =
- {
- WinogradConfiguration(std::pair<int, int>(4, 4), std::pair<int, int>(3, 3))
- };
-
- const std::vector<WinogradConfiguration> fast_math_winograd_f32 =
- {
- WinogradConfiguration(std::pair<int, int>(2, 2), std::pair<int, int>(5, 5)),
- WinogradConfiguration(std::pair<int, int>(4, 4), std::pair<int, int>(5, 5))
- };
-
- 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));
-
- switch(data_type)
- {
- case DataType::F16:
- return std::find(fast_math_winograd_f16.begin(), fast_math_winograd_f16.end(), p) != fast_math_winograd_f16.end();
- case DataType::F32:
- return std::find(fast_math_winograd_f32.begin(), fast_math_winograd_f32.end(), p) != fast_math_winograd_f32.end();
- default:
- return false;
- }
-}
-
inline bool fuse_function_supported(const ActivationLayerInfo &act_info)
{
return act_info.activation() == ActivationLayerInfo::ActivationFunction::RELU || act_info.activation() == ActivationLayerInfo::ActivationFunction::BOUNDED_RELU;
}
-
} // namespace
CpuWinogradConv2d::CpuWinogradConv2d()
+
: _gemm_function(std::make_unique<CpuGemm>()),
_activation_func(std::make_unique<CpuActivation>()),
+ _transform_input_kernel(nullptr),
+ _transform_output_kernel(nullptr),
_permute_input(std::make_unique<CpuPermute>()),
_permute_output(std::make_unique<CpuPermute>()),
_permute_weights(std::make_unique<CpuPermute>()),
- _transform_input_kernel(nullptr),
- _transform_weights_kernel(nullptr),
- _transform_output_kernel(nullptr),
- _data_layout(),
_aux_mem(AuxTensorIdx::Count),
+ _conv_args{ nullptr },
+ _winograd_impl{},
+ _data_layout(),
+ _winograd_transformed_input{},
+ _winograd_transformed_output{},
+ _winograd_transformed_weights{},
+ _input_workspace(),
+ _output_workspace(),
+ _weights_hwio(),
_input_nhwc(),
_output_nhwc(),
- _input_workspace(),
- _kernel_storage(),
- _output_workspace(),
- _input_transformed(),
- _output_transformed(),
- _weights_hwio(),
- _run_activation(false),
- _is_prepared(false)
+ _is_prepared{ false },
+ _run_activation{ false }
{
}
@@ -342,464 +165,199 @@
ARM_COMPUTE_ERROR_ON_NULLPTR(src, weights, dst);
ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(src, weights, biases, dst, conv_info));
ARM_COMPUTE_LOG_PARAMS(src, weights, biases, dst, conv_info, act_info, enable_fast_math);
+ ARM_COMPUTE_UNUSED(biases);
+ const DataType data_type = src->data_type();
+ uint32_t nthreads = NEScheduler::get().num_threads();
+ _data_layout = src->data_layout();
+ const Tensor4DShape kernel_shape{ internal_get_shape(weights) };
- // Get indices for the width and height
- _data_layout = src->data_layout();
- const unsigned int width_idx = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::WIDTH);
- const unsigned int height_idx = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::HEIGHT);
- const unsigned int channel_idx = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::CHANNEL);
+ bool success = get_winograd_kernel_implementation(src, weights, dst, conv_info, act_info, enable_fast_math, &_winograd_impl, _conv_args);
- const Size2D input_dims = Size2D(src->dimension(width_idx), src->dimension(height_idx));
- const Size2D kernel_size = Size2D(weights->dimension(width_idx), weights->dimension(height_idx));
- const DataType data_type = src->data_type();
- const Size2D output_tile = winograd_output_tile(input_dims, kernel_size, data_type);
+ ARM_COMPUTE_EXIT_ON_MSG_VAR(!success, "Unsupported kernel size: %d x %d.\n", kernel_shape.n_rows, kernel_shape.n_cols);
+ ARM_COMPUTE_LOG_MSG_WITH_FORMAT_ACL(arm_compute::logging::LogLevel::INFO, "Using input transform: %s\n", _winograd_impl.input_transform->get_name().c_str());
+ ARM_COMPUTE_LOG_MSG_WITH_FORMAT_ACL(arm_compute::logging::LogLevel::INFO, "Using weight transform: %s\n", _winograd_impl.input_transform->get_name().c_str());
+ ARM_COMPUTE_LOG_MSG_WITH_FORMAT_ACL(arm_compute::logging::LogLevel::INFO, "Using output transform: %s\n", _winograd_impl.input_transform->get_name().c_str());
- // Check if the Winograd configuration requires fast math
- if(!enable_fast_math)
+ const bool has_impl = ((_winograd_impl.input_transform != nullptr) && (_winograd_impl.output_transform != nullptr) && (_winograd_impl.gemm_args != nullptr));
+ if(has_impl)
{
- ARM_COMPUTE_ERROR_ON_MSG(check_support_fast_math(output_tile, kernel_size, data_type),
- "This Winograd configuration requires enable_fast_math=true");
- }
+ // Determine how much working space is required, allocate it.
+ const size_t input_workspace_size = _winograd_impl.input_transform->get_working_space_size(*_conv_args, nthreads);
+ const size_t output_workspace_size = _winograd_impl.output_transform->get_working_space_size(*_conv_args, nthreads);
- _is_prepared = false;
+ TensorInfo input_workspace_info(TensorShape(input_workspace_size), 1, DataType::U8);
+ TensorInfo output_workspace_info(TensorShape(output_workspace_size), 1, DataType::U8);
+ _input_workspace = input_workspace_info;
+ _output_workspace = output_workspace_info;
- std::unique_ptr<ICpuWinogradConv2dTransformInputKernel> transform_input_kernel;
- std::unique_ptr<ICpuWinogradConv2dTransformWeightsKernel> transform_weights_kernel;
- std::unique_ptr<ICpuWinogradConv2dTransformOutputKernel> transform_output_kernel;
+ const auto &wds = _winograd_impl.winograd_spec;
- int n_gemms = 1;
- int N_BLOCK = 1; // Size of block used by GEMM.
- if(data_type == DataType::F32)
- {
- if(kernel_size == Size2D(3, 3))
+ // Preparing winograd transformed input tensor
+ const size_t data_type_size = src->element_size();
+ const uint32_t m = _winograd_impl.gemm_args->_Msize; // Total number of tiles
+ const uint32_t k = _winograd_impl.gemm_args->_Ksize; // Input channels
+ const uint32_t n = _winograd_impl.gemm_args->_Nsize; // Output channels
+ const uint32_t n_gemms = _winograd_impl.gemm_args->_nmulti;
+ const uint32_t n_batches = _winograd_impl.gemm_args->_nbatches;
+ constexpr size_t storage_alignment = 64;
+
+ const TensorShape a_shape(k, m, n_batches, n_gemms);
+ Strides a_strides(data_type_size);
+ a_strides.set(1, data_type_size * _winograd_impl.winograd_spec.input_ld_row);
+ a_strides.set(2, data_type_size * _winograd_impl.winograd_spec.input_ld_batch);
+ a_strides.set(3, data_type_size * _winograd_impl.winograd_spec.input_ld_matrix);
+
+ const TensorShape b_shape(n, k, n_gemms);
+ Strides b_strides(data_type_size);
+ b_strides.set(1, data_type_size * _winograd_impl.winograd_spec.weight_ld_row);
+ b_strides.set(2, data_type_size * _winograd_impl.winograd_spec.weight_ld_matrix);
+
+ const TensorShape d_shape(n, m, n_batches, n_gemms);
+ Strides d_strides(data_type_size);
+ d_strides.set(1, data_type_size * _winograd_impl.winograd_spec.output_ld_row);
+ d_strides.set(2, data_type_size * _winograd_impl.winograd_spec.output_ld_batch);
+ d_strides.set(3, data_type_size * _winograd_impl.winograd_spec.output_ld_matrix);
+
+ TensorInfo a_info{};
+ TensorInfo b_info{};
+ TensorInfo d_info{};
+ a_info.init(a_shape, 1, data_type, a_strides, 0, wds.input_matrix_size_bytes);
+ b_info.init(b_shape, 1, data_type, b_strides, 0, wds.weight_matrix_size_bytes);
+ d_info.init(d_shape, 1, data_type, d_strides, 0, wds.output_matrix_size_bytes);
+
+ _winograd_transformed_input = a_info;
+ _winograd_transformed_weights = b_info;
+ _winograd_transformed_output = d_info;
+
+ PermutationVector weights_permutation_vector(3U, 0U, 1U, 2U);
+
+ // Configure the kernel to transform the input tensor from NCHW -> NHWC
+ if(_data_layout == DataLayout::NCHW)
{
- if(src->dimension(width_idx) > 4 && src->dimension(height_idx) > 4)
- {
- using config = CpuWinogradConv2dConfiguration<float, float, 4, 4, 3, 3>;
- transform_input_kernel = std::make_unique<config::TransformInputKernel>();
- transform_weights_kernel = std::make_unique<config::TransformWeightsKernel>();
- transform_output_kernel = std::make_unique<config::TransformOutputKernel>();
- n_gemms = config::WinogradBase::N_GEMMS;
- N_BLOCK = config::WinogradConv::N_BLOCK;
- }
- else
- {
- using config = CpuWinogradConv2dConfiguration<float, float, 2, 2, 3, 3>;
- transform_input_kernel = std::make_unique<config::TransformInputKernel>();
- transform_weights_kernel = std::make_unique<config::TransformWeightsKernel>();
- transform_output_kernel = std::make_unique<config::TransformOutputKernel>();
- n_gemms = config::WinogradBase::N_GEMMS;
- N_BLOCK = config::WinogradConv::N_BLOCK;
- }
+ _permute_input->configure(src, &_input_nhwc, PermutationVector(2U, 0U, 1U));
+ weights_permutation_vector = PermutationVector(3U, 2U, 0U, 1U);
}
- else if(kernel_size == Size2D(5, 5))
+
+ // Re-order a weight tensor from [Output feature map x Input feature map x Height x Width] to [Height x Width x Input feature map x Output feature map]
+ _permute_weights->configure(weights, &_weights_hwio, weights_permutation_vector);
+
+ // Reorder the convoluted output to ACL's ordering NCHW
+ if(_data_layout == DataLayout::NCHW)
{
- using config = CpuWinogradConv2dConfiguration<float, float, 2, 2, 5, 5>;
- transform_input_kernel = std::make_unique<config::TransformInputKernel>();
- transform_weights_kernel = std::make_unique<config::TransformWeightsKernel>();
- transform_output_kernel = std::make_unique<config::TransformOutputKernel>();
- n_gemms = config::WinogradBase::N_GEMMS;
- N_BLOCK = config::WinogradConv::N_BLOCK;
+ // configure and allocate dst tensor to be used to convert from winograd domain to spatial domain when calling to reshape_output()
+ TensorInfo info(TensorShape(dst->dimension(2), dst->dimension(0),
+ dst->dimension(1), dst->dimension(3)),
+ 1, dst->data_type());
+ _output_nhwc = info;
+ _permute_output->configure(&_output_nhwc, dst, PermutationVector(1U, 2U, 0U));
}
- else if(kernel_size == Size2D(1, 3))
+
+ // Configure GEMM function
+ _gemm_function->configure(&_winograd_transformed_input, &_winograd_transformed_weights, nullptr, &_winograd_transformed_output, 1.0f, 0.f);
+
+ //Configure Activation Layer
+ _run_activation = act_info.enabled() && !fuse_function_supported(act_info);
+ if(_run_activation)
{
- using config = CpuWinogradConv2dConfiguration<float, float, 6, 1, 3, 1>;
- transform_input_kernel = std::make_unique<config::TransformInputKernel>();
- transform_weights_kernel = std::make_unique<config::TransformWeightsKernel>();
- transform_output_kernel = std::make_unique<config::TransformOutputKernel>();
- n_gemms = config::WinogradBase::N_GEMMS;
- N_BLOCK = config::WinogradConv::N_BLOCK;
+ _activation_func->configure(dst, nullptr, act_info);
}
- else if(kernel_size == Size2D(3, 1))
+
+ auto asm_mem_req = _gemm_function->workspace();
+ _aux_mem[GemmWorkspace] = asm_mem_req[GemmWorkspace];
+ _aux_mem[Pretranspose] = asm_mem_req[Pretranspose];
+ _aux_mem[InterleavedLHS] = asm_mem_req[InterleavedLHS];
+ _aux_mem[TransposedRHS] = asm_mem_req[TransposedRHS];
+ _aux_mem[TempResult] = asm_mem_req[TempResult];
+
+ // Request temporary memory. Overlap memory needed for Input/Output transformations as they run on different non-overlapping time-steps.
+ _aux_mem[TransformedInput] = MemoryInfo(offset_int_vec(TransformedInput), MemoryLifetime::Temporary, wds.input_matrix_size_bytes, storage_alignment);
+ _aux_mem[TransformedOutput] = MemoryInfo(offset_int_vec(TransformedOutput), MemoryLifetime::Temporary, wds.output_matrix_size_bytes, storage_alignment);
+ _aux_mem[WorkspaceIO] = MemoryInfo(offset_int_vec(WorkspaceIO), MemoryLifetime::Temporary, std::max(input_workspace_size, output_workspace_size));
+ _aux_mem[PermutedWeights] = MemoryInfo(offset_int_vec(PermutedWeights), MemoryLifetime::Prepare, _weights_hwio.total_size());
+ _aux_mem[TransformedWeights] = MemoryInfo(offset_int_vec(TransformedWeights), MemoryLifetime::Persistent, wds.weight_matrix_size_bytes, storage_alignment);
+ if(_data_layout == DataLayout::NCHW)
{
- using config = CpuWinogradConv2dConfiguration<float, float, 1, 6, 1, 3>;
- transform_input_kernel = std::make_unique<config::TransformInputKernel>();
- transform_weights_kernel = std::make_unique<config::TransformWeightsKernel>();
- transform_output_kernel = std::make_unique<config::TransformOutputKernel>();
- n_gemms = config::WinogradBase::N_GEMMS;
- N_BLOCK = config::WinogradConv::N_BLOCK;
+ _aux_mem[PermutedInput].merge(offset_int_vec(PermutedInput), src->total_size());
+ _aux_mem[PermutedOutput].merge(offset_int_vec(PermutedOutput), dst->total_size());
}
- else if(kernel_size == Size2D(1, 5))
- {
- using config = CpuWinogradConv2dConfiguration<float, float, 4, 1, 5, 1>;
- transform_input_kernel = std::make_unique<config::TransformInputKernel>();
- transform_weights_kernel = std::make_unique<config::TransformWeightsKernel>();
- transform_output_kernel = std::make_unique<config::TransformOutputKernel>();
- n_gemms = config::WinogradBase::N_GEMMS;
- N_BLOCK = config::WinogradConv::N_BLOCK;
- }
- else if(kernel_size == Size2D(5, 1))
- {
- using config = CpuWinogradConv2dConfiguration<float, float, 1, 4, 1, 5>;
- transform_input_kernel = std::make_unique<config::TransformInputKernel>();
- transform_weights_kernel = std::make_unique<config::TransformWeightsKernel>();
- transform_output_kernel = std::make_unique<config::TransformOutputKernel>();
- n_gemms = config::WinogradBase::N_GEMMS;
- N_BLOCK = config::WinogradConv::N_BLOCK;
- }
- else if(kernel_size == Size2D(1, 7))
- {
- using config = CpuWinogradConv2dConfiguration<float, float, 2, 1, 7, 1>;
- transform_input_kernel = std::make_unique<config::TransformInputKernel>();
- transform_weights_kernel = std::make_unique<config::TransformWeightsKernel>();
- transform_output_kernel = std::make_unique<config::TransformOutputKernel>();
- n_gemms = config::WinogradBase::N_GEMMS;
- N_BLOCK = config::WinogradConv::N_BLOCK;
- }
- else if(kernel_size == Size2D(7, 1))
- {
- using config = CpuWinogradConv2dConfiguration<float, float, 1, 2, 1, 7>;
- transform_input_kernel = std::make_unique<config::TransformInputKernel>();
- transform_weights_kernel = std::make_unique<config::TransformWeightsKernel>();
- transform_output_kernel = std::make_unique<config::TransformOutputKernel>();
- n_gemms = config::WinogradBase::N_GEMMS;
- N_BLOCK = config::WinogradConv::N_BLOCK;
- }
- else
- {
- ARM_COMPUTE_ERROR("Not supported.");
- }
- }
-#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
- else if(data_type == DataType::F16)
- {
- if(kernel_size == Size2D(3, 3))
- {
- using config = CpuWinogradConv2dConfiguration<__fp16, __fp16, 4, 4, 3, 3>;
- transform_input_kernel = std::make_unique<config::TransformInputKernel>();
- transform_weights_kernel = std::make_unique<config::TransformWeightsKernel>();
- transform_output_kernel = std::make_unique<config::TransformOutputKernel>();
- n_gemms = config::WinogradBase::N_GEMMS;
- N_BLOCK = config::WinogradConv::N_BLOCK;
- }
- else
- {
- ARM_COMPUTE_ERROR("Not supported.");
- }
- }
-#endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
- else
- {
- ARM_COMPUTE_ERROR("Not supported.");
- }
-
- const PaddingType use_padding_type = (conv_info.pad_top() != 0u || conv_info.pad_left() != 0) ? PADDING_SAME : PADDING_VALID;
- const bool use_same_padding = use_padding_type == PADDING_SAME;
-
- // Get convolved dimensions
- const int in_channels = src->dimension(channel_idx);
- const int out_channels = dst->dimension(channel_idx);
-
- const Tensor4DShape in_shape(internal_get_input_shape(src));
- const size_t data_type_size = src->element_size();
- // Get the memory required to instantiate a new Winograd operator.
- constexpr size_t storage_alignment = 64;
-
- // Kernel Storage
- const size_t kernel_storage_size = transform_weights_kernel->get_weight_storage_size(out_channels, in_channels) * data_type_size;
-
- // Input storage
- const size_t input_storage_size = transform_input_kernel->get_input_storage_size(in_shape.n_batches, in_shape.n_channels, in_shape.n_rows, in_shape.n_cols, use_same_padding) * data_type_size;
-
- // Output storage
- const size_t output_storage_size = transform_output_kernel->get_output_storage_size(in_shape.n_batches, in_shape.n_rows, in_shape.n_cols, out_channels) * data_type_size;
- const int kernel_matrix_stride = transform_weights_kernel->get_matrix_stride(out_channels, in_channels);
- const int output_matrix_stride = transform_output_kernel->get_matrix_stride(in_shape.n_batches, in_shape.n_rows, in_shape.n_cols, out_channels);
- const auto output_shape = transform_output_kernel->get_output_shape(in_shape.n_rows, in_shape.n_cols, use_padding_type == PADDING_SAME);
- const int input_matrix_stride = transform_input_kernel->get_matrix_stride(in_shape.n_batches, in_channels, in_shape.n_rows, in_shape.n_cols, use_padding_type == PADDING_SAME);
-
- // Configure GEMM
- const int tile_rows = iceildiv(output_shape.first, output_tile.height);
- const int tile_cols = iceildiv(output_shape.second, output_tile.width);
- const int m = in_shape.n_batches * tile_rows * tile_cols;
- const int k = in_shape.n_channels;
- const int n = out_channels;
- const int kernel_matrix_row_stride = roundup(out_channels, N_BLOCK);
- const int output_matrix_row_stride = kernel_matrix_row_stride;
-
- TensorShape a_shape(k, m, 1, n_gemms);
- Strides a_strides(data_type_size);
- a_strides.set(1, a_strides[0] * k);
- //a_strides.set(2, data_type_size * input_matrix_stride / n_gemms); FIXME: This is the real batch size, but RSH's code crashes if it's not 0.
- a_strides.set(2, 0);
- a_strides.set(3, data_type_size * input_matrix_stride);
-
- TensorShape b_shape(n, k, n_gemms);
- Strides b_strides(data_type_size);
- b_strides.set(1, data_type_size * kernel_matrix_row_stride);
- b_strides.set(2, data_type_size * kernel_matrix_stride);
-
- TensorShape d_shape(n, m, 1, n_gemms);
- Strides d_strides(data_type_size);
- d_strides.set(1, data_type_size * output_matrix_row_stride);
- //d_strides.set(2, data_type_size * output_matrix_stride / n_gemms); FIXME: This is the real batch size, but RSH's code crashes if it's not 0.
- d_strides.set(2, 0);
- d_strides.set(3, data_type_size * output_matrix_stride);
-
- TensorInfo a_info{};
- TensorInfo b_info{};
- TensorInfo d_info{};
- a_info.init(a_shape, 1, data_type, a_strides, 0, input_storage_size);
- b_info.init(b_shape, 1, data_type, b_strides, 0, kernel_storage_size);
- d_info.init(d_shape, 1, data_type, d_strides, 0, output_storage_size);
-
- _input_transformed = a_info;
- _kernel_storage = b_info;
- _output_transformed = d_info;
-
- const ITensorInfo *input_to_use = src;
- ITensorInfo *output_to_use = dst;
- PermutationVector weights_permutation_vector(3U, 0U, 1U, 2U);
- const unsigned int max_num_threads = NEScheduler::get().num_threads();
-
- // Configure the kernel to transform the input tensor from NCHW -> NHWC
- if(_data_layout == DataLayout::NCHW)
- {
- _permute_input->configure(src, &_input_nhwc, PermutationVector(2U, 0U, 1U));
- input_to_use = &_input_nhwc;
- weights_permutation_vector = PermutationVector(3U, 2U, 0U, 1U);
- }
-
- // Configure input transform kernel
- transform_input_kernel->configure(input_to_use, in_shape.n_batches, in_shape.n_rows, in_shape.n_cols, in_shape.n_channels, use_padding_type,
- &_input_transformed, input_matrix_stride, &_input_workspace);
- const size_t input_workspace_size = transform_input_kernel->get_working_space_size(max_num_threads);
- TensorInfo input_workspace_info(TensorShape(input_workspace_size), 1, DataType::U8);
- _input_workspace = input_workspace_info;
-
- // Re-order a weight tensor from [Output feature map x Input feature map x Height x Width] to [Height x Width x Input feature map x Output feature map]
- _permute_weights->configure(weights, &_weights_hwio, weights_permutation_vector);
- transform_weights_kernel->configure(&_weights_hwio, &_kernel_storage, kernel_matrix_stride, out_channels, in_channels);
-
- // Configure GEMM function
- _gemm_function->configure(&_input_transformed, &_kernel_storage, nullptr, &_output_transformed, 1.0f, 0.f);
-
- // Configure output transform function
- // The biases tensor has not been allocated at this point in time, the output transform will add the biases to the final result in the run() method
- if(_data_layout == DataLayout::NCHW)
- {
- // configure and allocate dst tensor to be used to convert from winograd domain to spatial domain when calling to reshape_output()
- TensorInfo info(TensorShape(dst->dimension(2), dst->dimension(0),
- dst->dimension(1), dst->dimension(3)),
- 1, dst->data_type());
- _output_nhwc = info;
- output_to_use = &_output_nhwc;
- }
- const arm_gemm::Activation activation = arm_gemm_activation_from_acl_activation(act_info);
-
- transform_output_kernel->configure(biases,
- &_output_transformed,
- output_matrix_stride,
- output_to_use,
- in_shape.n_batches,
- output_shape.first,
- output_shape.second,
- out_channels,
- &_output_workspace,
- activation);
-
- const size_t output_workspace_size = transform_output_kernel->get_working_space_size(max_num_threads);
- TensorInfo output_workspace_info(TensorShape(output_workspace_size), 1, DataType::U8);
- _output_workspace = output_workspace_info;
-
- // Reorder the convoluted output to ACL's ordering NCHW
- if(_data_layout == DataLayout::NCHW)
- {
- _permute_output->configure(&_output_nhwc, dst, PermutationVector(1U, 2U, 0U));
- }
-
- _transform_input_kernel = std::move(transform_input_kernel);
- _transform_weights_kernel = std::move(transform_weights_kernel);
- _transform_output_kernel = std::move(transform_output_kernel);
-
- //Configure Activation Layer
- _run_activation = act_info.enabled() && !fuse_function_supported(act_info);
- if(_run_activation)
- {
- _activation_func->configure(dst, nullptr, act_info);
- }
-
- auto asm_mem_req = _gemm_function->workspace();
- _aux_mem[GemmWorkspace] = asm_mem_req[GemmWorkspace];
- _aux_mem[Pretranspose] = asm_mem_req[Pretranspose];
- _aux_mem[InterleavedLHS] = asm_mem_req[InterleavedLHS];
- _aux_mem[TransposedRHS] = asm_mem_req[TransposedRHS];
- _aux_mem[TempResult] = asm_mem_req[TempResult];
-
- // Request temporary memory. Overlap memory needed for Input/Output transformations as they run on different non-overlapping time-steps.
- _aux_mem[TransformedInput] = MemoryInfo(offset_int_vec(TransformedInput), MemoryLifetime::Temporary, input_storage_size, storage_alignment);
- _aux_mem[TransformedOutput] = MemoryInfo(offset_int_vec(TransformedOutput), MemoryLifetime::Temporary, output_storage_size, storage_alignment);
- _aux_mem[WorkspaceIO] = MemoryInfo(offset_int_vec(WorkspaceIO), MemoryLifetime::Temporary, std::max(input_workspace_size, output_workspace_size));
- _aux_mem[PermutedWeights] = MemoryInfo(offset_int_vec(PermutedWeights), MemoryLifetime::Prepare, _weights_hwio.total_size());
- _aux_mem[TransformedWeights] = MemoryInfo(offset_int_vec(TransformedWeights), MemoryLifetime::Persistent, kernel_storage_size, storage_alignment);
- if(_data_layout == DataLayout::NCHW)
- {
- _aux_mem[PermutedInput].merge(offset_int_vec(PermutedInput), src->total_size());
- _aux_mem[PermutedOutput].merge(offset_int_vec(PermutedOutput), dst->total_size());
}
}
-
Status CpuWinogradConv2d::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_ERROR_ON_NULLPTR(src, weights, dst);
ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(src, weights, biases, dst, conv_info));
- // Get indices for the width and height
- const size_t idx_width = get_data_layout_dimension_index(src->data_layout(), DataLayoutDimension::WIDTH);
- const size_t idx_height = get_data_layout_dimension_index(src->data_layout(), DataLayoutDimension::HEIGHT);
+ const Tensor4DShape kernel_shape{ internal_get_shape(weights) };
+ arm_conv::winograd::WinogradImpl winograd_impl{};
- // Input shape, kernel size and output tile
- const Size2D input_dims = Size2D(src->dimension(idx_width), src->dimension(idx_height));
- const Size2D kernel_size = Size2D(weights->dimension(idx_width), weights->dimension(idx_height));
- const DataType data_type = src->data_type();
- const Size2D output_tile = winograd_output_tile(input_dims, kernel_size, data_type);
+ std::unique_ptr<arm_conv::ConvolutionArgs> conv_args;
+ const bool success = get_winograd_kernel_implementation(src, weights, dst, conv_info, act_info, enable_fast_math, &winograd_impl, conv_args);
- // Check if the Winograd configuration requires fast math
- if(!enable_fast_math)
- {
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(check_support_fast_math(output_tile, kernel_size, data_type),
- "This Winograd configuration requires enable_fast_math=true");
- }
-
- 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);
- // 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);
- // 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);
-
- if(kernel_size == Size2D(3, 3))
- {
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_top() != 0u && conv_info.pad_top() != 1, "Only SAME or VALID padding supported");
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_bottom() != 0u && conv_info.pad_bottom() != 1, "Only SAME or VALID padding supported");
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_left() != 0u && conv_info.pad_left() != 1, "Only SAME or VALID padding supported");
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_right() != 0u && conv_info.pad_right() != 1, "Only SAME or VALID padding supported");
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_right() != conv_info.pad_left(), "Only SAME or VALID padding supported");
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_top() != conv_info.pad_bottom(), "Only SAME or VALID padding supported");
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_top() != conv_info.pad_left(), "Only SAME or VALID padding supported");
- return validate_kernel_3x3(input_dims, src, &input0, &input1, &batched_mm_output, weights, biases, dst, winograd_info, act_info);
- }
- else if(kernel_size == Size2D(5, 5))
- {
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_top() != 0u && conv_info.pad_top() != 2, "Only SAME or VALID padding supported");
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_left() != 0u && conv_info.pad_left() != 2, "Only SAME or VALID padding supported");
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_bottom() != 0u && conv_info.pad_bottom() != 2, "Only SAME or VALID padding supported");
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_right() != 0u && conv_info.pad_right() != 2, "Only SAME or VALID padding supported");
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_right() != conv_info.pad_left(), "Only SAME or VALID padding supported");
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_top() != conv_info.pad_bottom(), "Only SAME or VALID padding supported");
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_top() != conv_info.pad_left(), "Only SAME or VALID padding supported");
- return validate_kernel_5x5(src, &input0, &input1, &batched_mm_output, weights, biases, dst, winograd_info, act_info);
- }
- if(kernel_size == Size2D(3, 1))
- {
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_left() != 0u && conv_info.pad_left() != 1, "Only SAME or VALID padding supported");
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_right() != 0u && conv_info.pad_right() != 1, "Only SAME or VALID padding supported");
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_top() != 0u && conv_info.pad_bottom() != 0, "Only SAME or VALID padding supported");
- return validate_kernel_3x1(src, &input0, &input1, &batched_mm_output, weights, biases, dst, winograd_info, act_info);
- }
- else if(kernel_size == Size2D(1, 3))
- {
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_top() != 0u && conv_info.pad_top() != 1, "Only SAME or VALID padding supported");
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_bottom() != 0u && conv_info.pad_bottom() != 1, "Only SAME or VALID padding supported");
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_left() != 0u && conv_info.pad_right() != 0, "Only SAME or VALID padding supported");
- return validate_kernel_1x3(src, &input0, &input1, &batched_mm_output, weights, biases, dst, winograd_info, act_info);
- }
- else if(kernel_size == Size2D(5, 1))
- {
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_left() != 0u && conv_info.pad_left() != 2, "Only SAME or VALID padding supported");
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_right() != 0u && conv_info.pad_right() != 2, "Only SAME or VALID padding supported");
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_top() != 0u && conv_info.pad_bottom() != 0, "Only SAME or VALID padding supported");
- return validate_kernel_5x1(src, &input0, &input1, &batched_mm_output, weights, biases, dst, winograd_info, act_info);
- }
- else if(kernel_size == Size2D(1, 5))
- {
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_top() != 0u && conv_info.pad_top() != 2, "Only SAME or VALID padding supported");
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_bottom() != 0u && conv_info.pad_bottom() != 2, "Only SAME or VALID padding supported");
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_left() != 0u && conv_info.pad_right() != 0, "Only SAME or VALID padding supported");
- return validate_kernel_1x5(src, &input0, &input1, &batched_mm_output, weights, biases, dst, winograd_info, act_info);
- }
- else if(kernel_size == Size2D(7, 1))
- {
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_left() != 0u && conv_info.pad_left() != 3, "Only SAME or VALID padding supported");
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_right() != 0u && conv_info.pad_right() != 3, "Only SAME or VALID padding supported");
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_top() != 0u && conv_info.pad_bottom() != 0, "Only SAME or VALID padding supported");
- return validate_kernel_7x1(src, &input0, &input1, &batched_mm_output, weights, biases, dst, winograd_info, act_info);
- }
- else if(kernel_size == Size2D(1, 7))
- {
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_top() != 0u && conv_info.pad_top() != 3, "Only SAME or VALID padding supported");
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_bottom() != 0u && conv_info.pad_bottom() != 3, "Only SAME or VALID padding supported");
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_left() != 0u && conv_info.pad_right() != 0, "Only SAME or VALID padding supported");
- return validate_kernel_1x7(src, &input0, &input1, &batched_mm_output, weights, biases, dst, winograd_info, act_info);
- }
- else
- {
- ARM_COMPUTE_RETURN_ERROR_MSG("Kernel shape not supported");
- }
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG_VAR(success == false, "Unsupported kernel size: %d x %d.\n", kernel_shape.n_rows, kernel_shape.n_cols);
+ ARM_COMPUTE_LOG_MSG_WITH_FORMAT_ACL(arm_compute::logging::LogLevel::INFO, "Using input transform: %s\n", winograd_impl.input_transform->get_name().c_str());
+ ARM_COMPUTE_LOG_MSG_WITH_FORMAT_ACL(arm_compute::logging::LogLevel::INFO, "Using weight transform: %s\n", winograd_impl.input_transform->get_name().c_str());
+ ARM_COMPUTE_LOG_MSG_WITH_FORMAT_ACL(arm_compute::logging::LogLevel::INFO, "Using output transform: %s\n", winograd_impl.input_transform->get_name().c_str());
+ return Status{};
}
void CpuWinogradConv2d::run(ITensorPack &tensors)
{
prepare(tensors);
+ auto src = tensors.get_const_tensor(ACL_SRC_0);
+ auto biases = tensors.get_const_tensor(ACL_SRC_2);
+ auto output = tensors.get_tensor(ACL_DST);
+ Window win;
- auto a = tensors.get_const_tensor(ACL_SRC_0);
- auto c = tensors.get_const_tensor(ACL_SRC_2);
- auto d = tensors.get_tensor(ACL_DST);
+ const uint32_t nthreads = NEScheduler::get().num_threads();
+ // The Winograd transform implementation does fine-grain threading inside the transforms. Just pass thread_id and nthreads.
+ win.set(Window::DimX, Window::Dimension(0, nthreads, 1));
+
+ // Wrap the winograd-domain tensorInfos created in configuration in tensors and allocate the required memory.
CpuAuxTensorHandler input_nhwc(offset_int_vec(PermutedInput), _input_nhwc, tensors, true);
- CpuAuxTensorHandler input_transformed(offset_int_vec(TransformedInput), _input_transformed, tensors, true);
+ CpuAuxTensorHandler winograd_input_transformed(offset_int_vec(TransformedInput), _winograd_transformed_input, tensors, true);
CpuAuxTensorHandler input_workspace(offset_int_vec(WorkspaceIO), _input_workspace, tensors, true);
-
- const bool is_nchw = _data_layout == DataLayout::NCHW;
+ const bool is_nchw = _data_layout == DataLayout::NCHW;
if(is_nchw)
{
//Bring channels to the front as Winograd code expects the tensor to be in the format NHWC
- ITensorPack pack{ { ACL_SRC, a }, { ACL_DST, input_nhwc.get() } };
+ ITensorPack pack{ { ACL_SRC, src }, { ACL_DST, input_nhwc.get() } };
_permute_input->run(pack);
}
- // Transform input tensor to the winograd domain
- ITensorPack transform_input_pack{ { ACL_SRC, is_nchw ? input_nhwc.get() : a }, { ACL_DST, input_transformed.get() }, { ACL_INT, input_workspace.get() } };
- NEScheduler::get().schedule_op(_transform_input_kernel.get(), Window::DimX, _transform_input_kernel->window(), transform_input_pack);
+ CpuAuxTensorHandler winograd_output_transformed(offset_int_vec(TransformedOutput), _winograd_transformed_output, tensors, true);
+ CpuAuxTensorHandler output_workspace(offset_int_vec(WorkspaceIO), _output_workspace, tensors, true);
+ CpuAuxTensorHandler output_nhwc(offset_int_vec(PermutedOutput), _output_nhwc, tensors, true);
- CpuAuxTensorHandler output_transformed(offset_int_vec(TransformedOutput), _output_transformed, tensors, true);
- CpuAuxTensorHandler weights_transformed(offset_int_vec(TransformedWeights), _kernel_storage, tensors, true);
+ ITensorPack transform_input_pack{ { ACL_SRC, is_nchw ? input_nhwc.get() : src }, { ACL_DST, winograd_input_transformed.get() }, { ACL_INT, input_workspace.get() } };
+ _transform_input_kernel = std::make_unique<CpuWinogradConv2dTransformInputKernel>(_winograd_impl, *_conv_args, nthreads);
+
+ NEScheduler::get().schedule_op(_transform_input_kernel.get(), Window::DimX, win, transform_input_pack);
+
+ CpuAuxTensorHandler winograd_weights_transformed(offset_int_vec(TransformedWeights), _winograd_transformed_weights, tensors, true);
// Run 16 GEMMs in multiple threads, each kernel runs one or more GEMMs
ITensorPack gemm_pack = tensors;
- gemm_pack.add_const_tensor(ACL_SRC, input_transformed.get());
- gemm_pack.add_const_tensor(ACL_SRC_1, weights_transformed.get());
+ gemm_pack.add_const_tensor(ACL_SRC, winograd_input_transformed.get());
+ gemm_pack.add_const_tensor(ACL_SRC_1, winograd_weights_transformed.get());
gemm_pack.add_const_tensor(ACL_BIAS, nullptr);
- gemm_pack.add_tensor(ACL_DST, output_transformed.get());
+ gemm_pack.add_tensor(ACL_DST, winograd_output_transformed.get());
_gemm_function->run(gemm_pack);
- // Transform output tensor to the spatial domain
- CpuAuxTensorHandler output_workspace(offset_int_vec(WorkspaceIO), _output_workspace, tensors, true);
- CpuAuxTensorHandler output_nhwc(offset_int_vec(PermutedOutput), _output_nhwc, tensors, true);
- ITensorPack transform_output_pack{ { ACL_SRC_0, c }, { ACL_SRC_1, output_transformed.get() }, { ACL_DST, is_nchw ? output_nhwc.get() : d }, { ACL_INT, output_workspace.get() } };
- NEScheduler::get().schedule_op(_transform_output_kernel.get(), Window::DimX, _transform_output_kernel->window(), transform_output_pack);
-
+ // Output transform
+ _transform_output_kernel = std::make_unique<CpuWinogradConv2dTransformOutputKernel>(_winograd_impl, *_conv_args, nthreads);
+ ITensorPack transform_output_pack{ { ACL_SRC_0, winograd_output_transformed.get() }, { ACL_DST, is_nchw ? output_nhwc.get() : output }, { ACL_SRC_1, biases }, { ACL_INT, output_workspace.get() } };
+ NEScheduler::get().schedule_op(_transform_output_kernel.get(), Window::DimX, win, transform_output_pack);
if(is_nchw)
{
// Reorder the convoluted output to ACL's ordering NCHW
- ITensorPack pack{ { ACL_SRC, output_nhwc.get() }, { ACL_DST, d } };
+ ITensorPack pack{ { ACL_SRC, output_nhwc.get() }, { ACL_DST, output } };
_permute_output->run(pack);
}
-
if(_run_activation)
{
- ITensorPack pack{ { ACL_SRC, d }, { ACL_DST, d } };
+ ITensorPack pack{ { ACL_SRC, output }, { ACL_DST, output } };
_activation_func->run(pack);
}
}
@@ -808,34 +366,54 @@
{
if(!_is_prepared)
{
- // Permute weights
const ITensor *weights = tensors.get_const_tensor(ACL_SRC_1);
ITensor *weights_aux = utils::cast::polymorphic_cast<ITensor *>(tensors.get_tensor(offset_int_vec(PermutedWeights)));
- ARM_COMPUTE_ERROR_ON_NULLPTR(weights, weights_aux);
CpuAuxTensorHandler permuted_weights(_weights_hwio, *weights_aux);
ITensorPack permute_tensors{ { ACL_SRC, weights }, { ACL_DST, permuted_weights.get() } };
_permute_weights->run(permute_tensors);
+ const int element_size_in_bytes = permuted_weights.get()->info()->element_size();
+ // Weights were in OHWI format, before being permuted "permuted_weights" to be in HWIO format.
+ const unsigned int height_idx = 3; // H in HWIO
+ const unsigned int width_idx = 2; // W in HWIO
+ const unsigned int channel_idx = 1; // I in HWIO
- // Transform weights
+ const int permuted_weight_row_stride = permuted_weights.get()->info()->strides_in_bytes()[height_idx] / element_size_in_bytes;
+ const int permuted_weight_col_stride = permuted_weights.get()->info()->strides_in_bytes()[width_idx] / element_size_in_bytes;
+ const int permuted_weight_channel_stride = permuted_weights.get()->info()->strides_in_bytes()[channel_idx] / element_size_in_bytes;
+
+ // Wrap the winograd-domain transformed weight TensorInfo in Auxiliary tensor and allocate the required memory.
ITensor *weights_transf = utils::cast::polymorphic_cast<ITensor *>(tensors.get_tensor(offset_int_vec(TransformedWeights)));
ARM_COMPUTE_ERROR_ON_NULLPTR(weights_transf);
+ CpuAuxTensorHandler winograd_transformed_weights(_winograd_transformed_weights, *weights_transf);
- CpuAuxTensorHandler transformed_weights(_kernel_storage, *weights_transf);
- ITensorPack transform_tensors{ { ACL_SRC, permuted_weights.get() }, { ACL_DST, transformed_weights.get() } };
- NEScheduler::get().schedule_op(_transform_weights_kernel.get(), Window::DimX, _transform_weights_kernel->window(), transform_tensors);
+ const void *permuted_weights_ptr;
+ void *win_wght_transf_ptr;
+ permuted_weights_ptr = reinterpret_cast<const void *>(permuted_weights.get()->buffer() + permuted_weights.get()->info()->offset_first_element_in_bytes());
+ win_wght_transf_ptr = reinterpret_cast<void *>(winograd_transformed_weights.get()->buffer() + winograd_transformed_weights.get()->info()->offset_first_element_in_bytes());
+
+ // Prepare Weights
+ _winograd_impl.weight_transform->execute(
+ *_conv_args,
+ permuted_weights_ptr,
+ permuted_weight_row_stride,
+ permuted_weight_col_stride,
+ permuted_weight_channel_stride,
+ win_wght_transf_ptr,
+ _winograd_impl.winograd_spec,
+ 0, 1 // Thread 1 of 1
+ );
ITensorPack gemm_pack = tensors;
- gemm_pack.add_const_tensor(ACL_SRC_1, transformed_weights.get());
+ gemm_pack.add_const_tensor(ACL_SRC_1, winograd_transformed_weights.get());
_gemm_function->prepare(gemm_pack);
-
- _is_prepared = true;
+ _is_prepared = 1;
}
}
-
experimental::MemoryRequirements CpuWinogradConv2d::workspace() const
{
return _aux_mem;
}
+
} // namespace cpu
-} // namespace arm_compute
\ No newline at end of file
+} // namespace arm_compute
diff --git a/src/cpu/operators/CpuWinogradConv2d.h b/src/cpu/operators/CpuWinogradConv2d.h
index 0abd110..e0df34e 100644
--- a/src/cpu/operators/CpuWinogradConv2d.h
+++ b/src/cpu/operators/CpuWinogradConv2d.h
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2021 Arm Limited.
+ * Copyright (c) 2021-2022 Arm Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -29,6 +29,7 @@
#include "src/core/common/Macros.h"
#include "src/cpu/ICpuOperator.h"
#include "src/cpu/kernels/CpuWinogradConv2dKernel.h"
+#include "src/cpu/kernels/assembly/gemm_common.hpp"
#include "src/cpu/operators/CpuActivation.h"
#include "src/cpu/operators/CpuGemm.h"
#include "src/cpu/operators/CpuPermute.h"
@@ -59,13 +60,13 @@
* |F16 |F16 |F16 |F16 |
* |F32 |F32 |F32 |F32 |
*
- * @param[in] src Source tensor info. 3 lower dimensions represent a single input [width, height, IFM],
+ * @param[in] src Source tensor Info. 3 lower dimensions represent a single input [width, height, IFM],
* while every optional dimension from 4 and above represent a batch of inputs.
* Data types supported: F16/F32.
- * @param[in] weights Weights tensor info. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM]. Data type supported: Same as @p input.
+ * @param[in] weights Weights tensor Info. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM]. Data type supported: Same as @p input.
* Currently only 3x3 and 5x5 kernels are supported.
- * @param[in] biases Biases tensor info. Shared biases supported. Biases are 1D tensor with dimensions [OFM]. Data type supported: Same as @p weights.
- * @param[out] dst Destination tensor info. 3 lower dimensions represent a single output [width, height, OFM], while the rest represent batch of outputs.
+ * @param[in] biases Biases tensor Info. Shared biases supported. Biases are 1D tensor with dimensions [OFM]. Data type supported: Same as @p weights.
+ * @param[out] dst Destination tensor Info. 3 lower dimensions represent a single output [width, height, OFM], while the rest represent batch of outputs.
* Data types supported: Same as @p input.
* @param[in] conv_info Contains padding and stride information described in @ref PadStrideInfo. Currently only unit strides are supported.
* @param[in] act_info (Optional) Activation layer information in case of a fused activation.
@@ -107,28 +108,27 @@
PermutedOutput = TransformedInput,
Count = 10
};
-
- std::unique_ptr<CpuGemm> _gemm_function;
- std::unique_ptr<CpuActivation> _activation_func;
- std::unique_ptr<CpuPermute> _permute_input;
- std::unique_ptr<CpuPermute> _permute_output;
- std::unique_ptr<CpuPermute> _permute_weights;
- std::unique_ptr<ICPPKernel> _transform_input_kernel;
- std::unique_ptr<ICPPKernel> _transform_weights_kernel;
- std::unique_ptr<ICPPKernel> _transform_output_kernel;
-
- DataLayout _data_layout;
- experimental::MemoryRequirements _aux_mem{ Count };
- TensorInfo _input_nhwc;
- TensorInfo _output_nhwc;
- TensorInfo _input_workspace;
- TensorInfo _kernel_storage;
- TensorInfo _output_workspace;
- TensorInfo _input_transformed;
- TensorInfo _output_transformed;
- TensorInfo _weights_hwio;
- bool _run_activation;
- bool _is_prepared;
+ std::unique_ptr<CpuGemm> _gemm_function;
+ std::unique_ptr<CpuActivation> _activation_func;
+ std::unique_ptr<ICPPKernel> _transform_input_kernel;
+ std::unique_ptr<ICPPKernel> _transform_output_kernel;
+ std::unique_ptr<CpuPermute> _permute_input;
+ std::unique_ptr<CpuPermute> _permute_output;
+ std::unique_ptr<CpuPermute> _permute_weights;
+ experimental::MemoryRequirements _aux_mem{ Count };
+ std::unique_ptr<arm_conv::ConvolutionArgs> _conv_args; // Make it unique ptr because this type does not have a default constructor
+ arm_conv::winograd::WinogradImpl _winograd_impl;
+ DataLayout _data_layout;
+ TensorInfo _winograd_transformed_input;
+ TensorInfo _winograd_transformed_output;
+ TensorInfo _winograd_transformed_weights;
+ TensorInfo _input_workspace;
+ TensorInfo _output_workspace;
+ TensorInfo _weights_hwio;
+ TensorInfo _input_nhwc;
+ TensorInfo _output_nhwc;
+ bool _is_prepared;
+ bool _run_activation;
};
} // namespace cpu
} // namespace arm_compute