COMPMID-3108: Add Winograd 3x3,4x4 FP16 support for NEON
Change-Id: I20680dc74a3d709297539e2132417308a7aecc9d
Signed-off-by: Georgios Pinitas <georgios.pinitas@arm.com>
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/3159
Reviewed-by: Michele Di Giorgio <michele.digiorgio@arm.com>
Reviewed-by: Gian Marco Iodice <gianmarco.iodice@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
diff --git a/src/runtime/NEON/functions/NEWinogradConvolutionLayer.cpp b/src/runtime/NEON/functions/NEWinogradConvolutionLayer.cpp
index 81190fb..d567a18 100644
--- a/src/runtime/NEON/functions/NEWinogradConvolutionLayer.cpp
+++ b/src/runtime/NEON/functions/NEWinogradConvolutionLayer.cpp
@@ -23,11 +23,11 @@
*/
#include "arm_compute/runtime/NEON/functions/NEWinogradConvolutionLayer.h"
+#include "arm_compute/core/CPP/Validate.h"
#include "arm_compute/core/Error.h"
#include "arm_compute/core/NEON/kernels/NEWinogradConvolutionLayerKernel.h"
#include "arm_compute/core/Utils.h"
#include "arm_compute/core/Validate.h"
-#include "arm_compute/core/Validate.h"
#include "arm_compute/core/utils/misc/ShapeCalculator.h"
#include "arm_compute/runtime/NEON/NEScheduler.h"
#include "arm_compute/runtime/NEON/functions/NEGEMMAssemblyDispatch.h"
@@ -43,18 +43,32 @@
inline Status validate_kernel_3x3(const Size2D input_dims, const ITensorInfo *input, const TensorInfo *input0, const TensorInfo *input1, const TensorInfo *batched_mm_output,
const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const WinogradInfo &winograd_info, const ActivationLayerInfo &act_info)
{
- if(input_dims.width > 4 && input_dims.height > 4)
+ ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input);
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F16, DataType::F32);
+
+ if(input->data_type() == DataType::F32)
{
- ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformInputKernel<float, 4, 4, 3, 3>::validate(input, input0, winograd_info)));
- ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformWeightsKernel<float, 4, 4, 3, 3>::validate(weights, input1, winograd_info)));
- ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformOutputKernel<float, 4, 4, 3, 3>::validate(batched_mm_output, biases, output, winograd_info)));
+ if(input_dims.width > 4 && input_dims.height > 4)
+ {
+ ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformInputKernel<float, 4, 4, 3, 3>::validate(input, input0, winograd_info)));
+ ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformWeightsKernel<float, 4, 4, 3, 3>::validate(weights, input1, winograd_info)));
+ ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformOutputKernel<float, 4, 4, 3, 3>::validate(batched_mm_output, biases, output, winograd_info)));
+ }
+ else
+ {
+ ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformInputKernel<float, 2, 2, 3, 3>::validate(input, input0, winograd_info)));
+ ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformWeightsKernel<float, 2, 2, 3, 3>::validate(weights, input1, winograd_info)));
+ ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformOutputKernel<float, 2, 2, 3, 3>::validate(batched_mm_output, biases, output, winograd_info)));
+ }
}
- else
+#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
+ else if(input->data_type() == DataType::F32)
{
- ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformInputKernel<float, 2, 2, 3, 3>::validate(input, input0, winograd_info)));
- ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformWeightsKernel<float, 2, 2, 3, 3>::validate(weights, input1, winograd_info)));
- ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformOutputKernel<float, 2, 2, 3, 3>::validate(batched_mm_output, biases, output, winograd_info)));
+ ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformInputKernel<__fp16, 4, 4, 3, 3>::validate(input, input0, winograd_info)));
+ ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformWeightsKernel<__fp16, 4, 4, 3, 3>::validate(weights, input1, winograd_info)));
+ ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformOutputKernel<__fp16, 4, 4, 3, 3>::validate(batched_mm_output, biases, output, winograd_info)));
}
+#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */
if(act_info.enabled())
{
@@ -79,6 +93,7 @@
inline Status validate_kernel_3x1(const ITensorInfo *input, const TensorInfo *input0, const TensorInfo *input1, const TensorInfo *batched_mm_output,
const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const WinogradInfo &winograd_info, const ActivationLayerInfo &act_info)
{
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32);
ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformInputKernel<float, 1, 6, 1, 3>::validate(input, input0, winograd_info)));
ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformWeightsKernel<float, 1, 6, 1, 3>::validate(weights, input1, winograd_info)));
ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformOutputKernel<float, 1, 6, 1, 3>::validate(batched_mm_output, biases, output, winograd_info)));
@@ -92,6 +107,7 @@
inline Status validate_kernel_1x3(const ITensorInfo *input, const TensorInfo *input0, const TensorInfo *input1, const TensorInfo *batched_mm_output,
const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const WinogradInfo &winograd_info, const ActivationLayerInfo &act_info)
{
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32);
ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformInputKernel<float, 6, 1, 3, 1>::validate(input, input0, winograd_info)));
ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformWeightsKernel<float, 6, 1, 3, 1>::validate(weights, input1, winograd_info)));
ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformOutputKernel<float, 6, 1, 3, 1>::validate(batched_mm_output, biases, output, winograd_info)));
@@ -106,6 +122,7 @@
inline Status validate_kernel_5x1(const ITensorInfo *input, const TensorInfo *input0, const TensorInfo *input1, const TensorInfo *batched_mm_output,
const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const WinogradInfo &winograd_info, const ActivationLayerInfo &act_info)
{
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32);
ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformInputKernel<float, 1, 4, 1, 5>::validate(input, input0, winograd_info)));
ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformWeightsKernel<float, 1, 4, 1, 5>::validate(weights, input1, winograd_info)));
ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformOutputKernel<float, 1, 4, 1, 5>::validate(batched_mm_output, biases, output, winograd_info)));
@@ -118,6 +135,7 @@
inline Status validate_kernel_1x5(const ITensorInfo *input, const TensorInfo *input0, const TensorInfo *input1, const TensorInfo *batched_mm_output,
const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const WinogradInfo &winograd_info, const ActivationLayerInfo &act_info)
{
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32);
ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformInputKernel<float, 4, 1, 5, 1>::validate(input, input0, winograd_info)));
ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformWeightsKernel<float, 4, 1, 5, 1>::validate(weights, input1, winograd_info)));
ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformOutputKernel<float, 4, 1, 5, 1>::validate(batched_mm_output, biases, output, winograd_info)));
@@ -131,6 +149,7 @@
inline Status validate_kernel_7x1(const ITensorInfo *input, const TensorInfo *input0, const TensorInfo *input1, const TensorInfo *batched_mm_output,
const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const WinogradInfo &winograd_info, const ActivationLayerInfo &act_info)
{
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32);
ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformInputKernel<float, 1, 2, 1, 7>::validate(input, input0, winograd_info)));
ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformWeightsKernel<float, 1, 2, 1, 7>::validate(weights, input1, winograd_info)));
ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformOutputKernel<float, 1, 2, 1, 7>::validate(batched_mm_output, biases, output, winograd_info)));
@@ -144,6 +163,7 @@
inline Status validate_kernel_1x7(const ITensorInfo *input, const TensorInfo *input0, const TensorInfo *input1, const TensorInfo *batched_mm_output,
const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const WinogradInfo &winograd_info, const ActivationLayerInfo &act_info)
{
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32);
ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformInputKernel<float, 2, 1, 7, 1>::validate(input, input0, winograd_info)));
ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformWeightsKernel<float, 2, 1, 7, 1>::validate(weights, input1, winograd_info)));
ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformOutputKernel<float, 2, 1, 7, 1>::validate(batched_mm_output, biases, output, winograd_info)));
@@ -169,21 +189,27 @@
Status validate_arguments(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info)
{
ARM_COMPUTE_UNUSED(output);
+ ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED(input);
+
ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.stride().first != 1 || conv_info.stride().second != 1, "Winograd layer only supports unit strides.");
if(biases != nullptr)
{
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, biases);
ARM_COMPUTE_RETURN_ERROR_ON(biases->num_dimensions() > 1);
}
- return INEWinogradLayerTransformWeightsKernel<float>::validate(input, weights);
+ return INEWinogradLayerTransformWeightsKernel::validate(input, weights);
}
-Size2D winograd_output_tile(const Size2D &input_dims, const Size2D &kernel_dims)
+Size2D winograd_output_tile(const Size2D &input_dims, const Size2D &kernel_dims, DataType data_type)
{
Size2D output_tile = Size2D{};
if(kernel_dims == Size2D(3U, 3U))
{
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);
+ }
}
else if(kernel_dims == Size2D(5U, 5U))
{
@@ -216,12 +242,17 @@
return output_tile;
}
-bool check_support_fast_math(const Size2D &output_tile, const Size2D &kernel_size)
+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 =
+ 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))
@@ -230,7 +261,15 @@
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));
- return std::find(fast_math_winograd.begin(), fast_math_winograd.end(), p) != fast_math_winograd.end();
+ 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)
@@ -256,7 +295,6 @@
}
}
}
-
} //namespace
NEWinogradConvolutionLayer::NEWinogradConvolutionLayer(const std::shared_ptr<IMemoryManager> &memory_manager)
@@ -278,14 +316,16 @@
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);
- const Size2D input_dims = Size2D(input->info()->dimension(width_idx), input->info()->dimension(height_idx));
- const Size2D kernel_size = Size2D(weights->info()->dimension(width_idx), weights->info()->dimension(height_idx));
- const Size2D output_tile = winograd_output_tile(input_dims, kernel_size);
+ const Size2D input_dims = Size2D(input->info()->dimension(width_idx), input->info()->dimension(height_idx));
+ const Size2D kernel_size = Size2D(weights->info()->dimension(width_idx), weights->info()->dimension(height_idx));
+ const DataType data_type = input->info()->data_type();
+ const Size2D output_tile = winograd_output_tile(input_dims, kernel_size, data_type);
// Check if the Winograd configuration requires fast math
if(!enable_fast_math)
{
- 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_MSG(check_support_fast_math(output_tile, kernel_size, data_type),
+ "This Winograd configuration requires enable_fast_math=true");
}
_weights = weights;
@@ -293,18 +333,93 @@
_output = output;
_is_prepared = false;
- std::unique_ptr<INEWinogradLayerTransformInputKernel<float>> transform_input_kernel;
- std::unique_ptr<INEWinogradLayerTransformWeightsKernel<float>> transform_weights_kernel;
- std::unique_ptr<INEWinogradLayerTransformOutputKernel<float>> transform_output_kernel;
-
int n_gemms = 0;
int N_BLOCK = 0; // Size of block used by GEMM.
- if(kernel_size == Size2D(3, 3))
+ std::unique_ptr<INEWinogradLayerTransformInputKernel> transform_input_kernel;
+ std::unique_ptr<INEWinogradLayerTransformWeightsKernel> transform_weights_kernel;
+ std::unique_ptr<INEWinogradLayerTransformOutputKernel> transform_output_kernel;
+
+ if(data_type == DataType::F32)
{
- if(input->info()->dimension(width_idx) > 4 && input->info()->dimension(height_idx) > 4)
+ if(kernel_size == Size2D(3, 3))
{
- using config = NEWinogradLayerConfiguration<float, float, 4, 4, 3, 3>;
+ if(input->info()->dimension(width_idx) > 4 && input->info()->dimension(height_idx) > 4)
+ {
+ using config = NEWinogradLayerConfiguration<float, float, 4, 4, 3, 3>;
+ transform_input_kernel = support::cpp14::make_unique<config::TransformInputKernel>();
+ transform_weights_kernel = support::cpp14::make_unique<config::TransformWeightsKernel>();
+ transform_output_kernel = support::cpp14::make_unique<config::TransformOutputKernel>();
+ n_gemms = config::WinogradBase::N_GEMMS;
+ N_BLOCK = config::WinogradConv::N_BLOCK;
+ }
+ else
+ {
+ using config = NEWinogradLayerConfiguration<float, float, 2, 2, 3, 3>;
+ transform_input_kernel = support::cpp14::make_unique<config::TransformInputKernel>();
+ transform_weights_kernel = support::cpp14::make_unique<config::TransformWeightsKernel>();
+ transform_output_kernel = support::cpp14::make_unique<config::TransformOutputKernel>();
+ n_gemms = config::WinogradBase::N_GEMMS;
+ N_BLOCK = config::WinogradConv::N_BLOCK;
+ }
+ }
+ else if(kernel_size == Size2D(5, 5))
+ {
+ using config = NEWinogradLayerConfiguration<float, float, 2, 2, 5, 5>;
+ transform_input_kernel = support::cpp14::make_unique<config::TransformInputKernel>();
+ transform_weights_kernel = support::cpp14::make_unique<config::TransformWeightsKernel>();
+ transform_output_kernel = support::cpp14::make_unique<config::TransformOutputKernel>();
+ n_gemms = config::WinogradBase::N_GEMMS;
+ N_BLOCK = config::WinogradConv::N_BLOCK;
+ }
+ else if(kernel_size == Size2D(1, 3))
+ {
+ using config = NEWinogradLayerConfiguration<float, float, 6, 1, 3, 1>;
+ transform_input_kernel = support::cpp14::make_unique<config::TransformInputKernel>();
+ transform_weights_kernel = support::cpp14::make_unique<config::TransformWeightsKernel>();
+ transform_output_kernel = support::cpp14::make_unique<config::TransformOutputKernel>();
+ n_gemms = config::WinogradBase::N_GEMMS;
+ N_BLOCK = config::WinogradConv::N_BLOCK;
+ }
+ else if(kernel_size == Size2D(3, 1))
+ {
+ using config = NEWinogradLayerConfiguration<float, float, 1, 6, 1, 3>;
+ transform_input_kernel = support::cpp14::make_unique<config::TransformInputKernel>();
+ transform_weights_kernel = support::cpp14::make_unique<config::TransformWeightsKernel>();
+ transform_output_kernel = support::cpp14::make_unique<config::TransformOutputKernel>();
+ n_gemms = config::WinogradBase::N_GEMMS;
+ N_BLOCK = config::WinogradConv::N_BLOCK;
+ }
+ else if(kernel_size == Size2D(1, 5))
+ {
+ using config = NEWinogradLayerConfiguration<float, float, 4, 1, 5, 1>;
+ transform_input_kernel = support::cpp14::make_unique<config::TransformInputKernel>();
+ transform_weights_kernel = support::cpp14::make_unique<config::TransformWeightsKernel>();
+ transform_output_kernel = support::cpp14::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 = NEWinogradLayerConfiguration<float, float, 1, 4, 1, 5>;
+ transform_input_kernel = support::cpp14::make_unique<config::TransformInputKernel>();
+ transform_weights_kernel = support::cpp14::make_unique<config::TransformWeightsKernel>();
+ transform_output_kernel = support::cpp14::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 = NEWinogradLayerConfiguration<float, float, 2, 1, 7, 1>;
+ transform_input_kernel = support::cpp14::make_unique<config::TransformInputKernel>();
+ transform_weights_kernel = support::cpp14::make_unique<config::TransformWeightsKernel>();
+ transform_output_kernel = support::cpp14::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 = NEWinogradLayerConfiguration<float, float, 1, 2, 1, 7>;
transform_input_kernel = support::cpp14::make_unique<config::TransformInputKernel>();
transform_weights_kernel = support::cpp14::make_unique<config::TransformWeightsKernel>();
transform_output_kernel = support::cpp14::make_unique<config::TransformOutputKernel>();
@@ -313,81 +428,27 @@
}
else
{
- using config = NEWinogradLayerConfiguration<float, float, 2, 2, 3, 3>;
+ 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 = NEWinogradLayerConfiguration<__fp16, __fp16, 4, 4, 3, 3>;
transform_input_kernel = support::cpp14::make_unique<config::TransformInputKernel>();
transform_weights_kernel = support::cpp14::make_unique<config::TransformWeightsKernel>();
transform_output_kernel = support::cpp14::make_unique<config::TransformOutputKernel>();
n_gemms = config::WinogradBase::N_GEMMS;
N_BLOCK = config::WinogradConv::N_BLOCK;
}
+ else
+ {
+ ARM_COMPUTE_ERROR("Not supported.");
+ }
}
- else if(kernel_size == Size2D(5, 5))
- {
- using config = NEWinogradLayerConfiguration<float, float, 2, 2, 5, 5>;
- transform_input_kernel = support::cpp14::make_unique<config::TransformInputKernel>();
- transform_weights_kernel = support::cpp14::make_unique<config::TransformWeightsKernel>();
- transform_output_kernel = support::cpp14::make_unique<config::TransformOutputKernel>();
- n_gemms = config::WinogradBase::N_GEMMS;
- N_BLOCK = config::WinogradConv::N_BLOCK;
- }
- else if(kernel_size == Size2D(1, 3))
- {
- using config = NEWinogradLayerConfiguration<float, float, 6, 1, 3, 1>;
- transform_input_kernel = support::cpp14::make_unique<config::TransformInputKernel>();
- transform_weights_kernel = support::cpp14::make_unique<config::TransformWeightsKernel>();
- transform_output_kernel = support::cpp14::make_unique<config::TransformOutputKernel>();
- n_gemms = config::WinogradBase::N_GEMMS;
- N_BLOCK = config::WinogradConv::N_BLOCK;
- }
- else if(kernel_size == Size2D(3, 1))
- {
- using config = NEWinogradLayerConfiguration<float, float, 1, 6, 1, 3>;
- transform_input_kernel = support::cpp14::make_unique<config::TransformInputKernel>();
- transform_weights_kernel = support::cpp14::make_unique<config::TransformWeightsKernel>();
- transform_output_kernel = support::cpp14::make_unique<config::TransformOutputKernel>();
- n_gemms = config::WinogradBase::N_GEMMS;
- N_BLOCK = config::WinogradConv::N_BLOCK;
- }
- else if(kernel_size == Size2D(1, 5))
- {
- using config = NEWinogradLayerConfiguration<float, float, 4, 1, 5, 1>;
- transform_input_kernel = support::cpp14::make_unique<config::TransformInputKernel>();
- transform_weights_kernel = support::cpp14::make_unique<config::TransformWeightsKernel>();
- transform_output_kernel = support::cpp14::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 = NEWinogradLayerConfiguration<float, float, 1, 4, 1, 5>;
- transform_input_kernel = support::cpp14::make_unique<config::TransformInputKernel>();
- transform_weights_kernel = support::cpp14::make_unique<config::TransformWeightsKernel>();
- transform_output_kernel = support::cpp14::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 = NEWinogradLayerConfiguration<float, float, 2, 1, 7, 1>;
- transform_input_kernel = support::cpp14::make_unique<config::TransformInputKernel>();
- transform_weights_kernel = support::cpp14::make_unique<config::TransformWeightsKernel>();
- transform_output_kernel = support::cpp14::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 = NEWinogradLayerConfiguration<float, float, 1, 2, 1, 7>;
- transform_input_kernel = support::cpp14::make_unique<config::TransformInputKernel>();
- transform_weights_kernel = support::cpp14::make_unique<config::TransformWeightsKernel>();
- transform_output_kernel = support::cpp14::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
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;
@@ -397,7 +458,6 @@
const int out_channels = output->info()->dimension(channel_idx);
const Tensor4DShape in_shape(internal_get_input_shape(input));
- const DataType data_type = input->info()->data_type();
const size_t data_type_size = input->info()->element_size();
// Get the memory required to instantiate a new Winograd operator.
constexpr size_t storage_alignment = 64;
@@ -592,14 +652,16 @@
const size_t idx_height = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::HEIGHT);
// Input shape, kernel size and output tile
- const Size2D input_dims = Size2D(input->dimension(idx_width), input->dimension(idx_height));
- const Size2D kernel_size = Size2D(weights->dimension(idx_width), weights->dimension(idx_height));
- const Size2D output_tile = winograd_output_tile(input_dims, kernel_size);
+ const Size2D input_dims = Size2D(input->dimension(idx_width), input->dimension(idx_height));
+ const Size2D kernel_size = Size2D(weights->dimension(idx_width), weights->dimension(idx_height));
+ const DataType data_type = input->data_type();
+ const Size2D output_tile = winograd_output_tile(input_dims, kernel_size, data_type);
// 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), "This Winograd configuration requires enable_fast_math=true");
+ 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,
@@ -706,5 +768,4 @@
_is_prepared = true;
}
}
-
} // namespace arm_compute