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/kernels/CpuWinogradConv2dKernel.h b/src/cpu/kernels/CpuWinogradConv2dKernel.h
index 6909216..0170dca 100644
--- a/src/cpu/kernels/CpuWinogradConv2dKernel.h
+++ b/src/cpu/kernels/CpuWinogradConv2dKernel.h
@@ -24,550 +24,79 @@
#ifndef ARM_COMPUTE_CPUWINOGRADCONV2DKERNEL_H
#define ARM_COMPUTE_CPUWINOGRADCONV2DKERNEL_H
-#include "src/core/NEON/kernels/convolution/common/convolution.hpp"
+#include "arm_compute/core/Helpers.h"
+#include "arm_compute/core/ITensor.h"
+#include "arm_compute/core/ITensorPack.h"
+#include "arm_compute/core/Steps.h"
+#include "arm_compute/core/TensorInfo.h"
+#include "arm_compute/runtime/Tensor.h"
+#include "src/core/NEON/kernels/assembly/winograd.hpp"
#include "src/core/NEON/kernels/convolution/common/tensor.hpp"
#include "src/cpu/ICpuKernel.h"
-#include "src/core/NEON/kernels/convolution/winograd/winograd_layer.hpp"
-
namespace arm_compute
{
namespace cpu
{
-/** Interface for the kernel to perform Winograd input transform. */
-class ICpuWinogradConv2dTransformInputKernel : public ICpuKernel<ICpuWinogradConv2dTransformInputKernel>
-{
-public:
- /** Get the working space required to perform the transformation.
- *
- * Note, the working space is only required when performing the
- * transformation - hence it can be reused whenever the transformation is
- * not running.
- *
- * @param num_threads The greatest number of threads that will be used to execute the transform.
- * @return Size of working space required in bytes.
- */
- virtual unsigned int get_working_space_size(unsigned int num_threads) const = 0;
-
- /** Determine how much memory (in units of TIn) to allocate for the
- * transformed input.
- *
- * @param[in] num_batches Number of batches in the input tensor.
- * @param[in] num_channels Number of feature maps in the input tensor.
- * @param[in] num_rows Number of rows in each feature map.
- * @param[in] num_cols Number of columns in each feature map.
- * @param[in] same_padding Use "SAME" padding, otherwise use "VALID".
- *
- * @return Storage size (in units of TIn) required.
- */
- virtual unsigned int get_input_storage_size(int num_batches, int num_channels, int num_rows, int num_cols, bool same_padding) const = 0;
-
- /** Gets the stride between matrices in the input worspace
- *
- * @param[in] num_batches Number of batches in the input tensor.
- * @param[in] num_channels Number of feature maps in the input tensor.
- * @param[in] num_rows Number of rows in each feature map.
- * @param[in] num_cols Number of columns in each feature map.
- * @param[in] same_padding Use "SAME" padding, otherwise use "VALID".
- *
- * @return Stride expressed in bytes.
- */
- virtual int get_matrix_stride(int num_batches, int num_channels, int num_rows, int num_cols, bool same_padding) const = 0;
-
- /** Configure the output transform kernel.
- *
- * @param[in] input_nhwc Input tensor in NHWC data layout format.
- * @param[in] num_batches Number of batches in input tensor.
- * @param[in] num_rows Number of rows in input tensor.
- * @param[in] num_cols Number of columns in input tensor.
- * @param[in] num_channels Number of channels in input tensor.
- * @param[in] padding Padding type.
- * @param[out] output Base of output matrices.
- * @param[in] matrix_stride Stride between output matrices.
- * @param[in] workspace Tensor to be used as the working space during the computation.
- */
- virtual void configure(const ITensorInfo *input_nhwc, const int num_batches, const int num_rows, const int num_cols, const int num_channels,
- const PaddingType padding, ITensorInfo *output, const int matrix_stride, ITensorInfo *workspace) = 0;
-
- /** Destructor */
- virtual ~ICpuWinogradConv2dTransformInputKernel()
- {
- }
-};
-
-/** Kernel to perform Winograd input transform. */
-template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
-class CpuWinogradConv2dTransformInputKernel : public ICpuWinogradConv2dTransformInputKernel
+class CpuWinogradConv2dTransformInputKernel final : public ICpuKernel<CpuWinogradConv2dTransformInputKernel>
{
public:
/** Prevent instances of this class from being copied (As this class contains pointers) */
CpuWinogradConv2dTransformInputKernel(const CpuWinogradConv2dTransformInputKernel &) = delete;
+
/** Prevent instances of this class from being copied (As this class contains pointers) */
CpuWinogradConv2dTransformInputKernel &operator=(const CpuWinogradConv2dTransformInputKernel &) = delete;
- /** Allow instances of this class to be moved */
- CpuWinogradConv2dTransformInputKernel(CpuWinogradConv2dTransformInputKernel &&) = default;
- /** Allow instances of this class to be moved */
- CpuWinogradConv2dTransformInputKernel &operator=(CpuWinogradConv2dTransformInputKernel &&) = default;
- /** Default destructor */
- ~CpuWinogradConv2dTransformInputKernel() = default;
- /** Determine how much memory (in units of TIn) to allocate for the
- * transformed input.
- *
- * @param[in] num_batches Number of batches in the input tensor.
- * @param[in] num_channels Number of feature maps in the input tensor.
- * @param[in] num_rows Number of rows in each feature map.
- * @param[in] num_cols Number of columns in each feature map.
- * @param[in] same_padding Use "SAME" padding, otherwise use "VALID".
- *
- * @return Storage size (in units of TIn) required.
- */
- unsigned int get_input_storage_size(
- int num_batches,
- int num_channels,
- int num_rows,
- int num_cols,
- bool same_padding) const override;
+ /** Prevent instances of this class from being moved it contains references.*/
+ CpuWinogradConv2dTransformInputKernel(CpuWinogradConv2dTransformInputKernel &&) = delete;
- /** Get the working space required to perform the transformation.
- *
- * Note, the working space is only required when performing the
- * transformation - hence it can be reused whenever the transformation is
- * not running.
- *
- * @param[in] num_threads The greatest number of threads that will be used to execute the transform.
- *
- * @return Size of working space required in bytes.
- */
- unsigned int get_working_space_size(unsigned int num_threads) const override;
+ /** Prevent instances of this class from being moved it contains references.*/
+ CpuWinogradConv2dTransformInputKernel &operator=(CpuWinogradConv2dTransformInputKernel &&) = delete;
- /** Gets the stride between matrices in the input worspace
- *
- * @param[in] num_batches Number of batches in the input tensor.
- * @param[in] num_channels Number of feature maps in the input tensor.
- * @param[in] num_rows Number of rows in each feature map.
- * @param[in] num_cols Number of columns in each feature map.
- * @param[in] same_padding Use "SAME" padding, otherwise use "VALID".
- *
- * @return Stride expressed in bytes.
- */
- int get_matrix_stride(
- int num_batches,
- int num_channels,
- int num_rows,
- int num_cols,
- bool same_padding) const override;
+ CpuWinogradConv2dTransformInputKernel(arm_conv::winograd::WinogradImpl &w_impl, arm_conv::ConvolutionArgs &_c_args, uint32_t nthreads);
- /** Default constructor */
- CpuWinogradConv2dTransformInputKernel();
+ // Inherited methods overridden:
+ void run_op(ITensorPack &tensors, const Window &window, const ThreadInfo &info) override;
const char *name() const override
{
return "CpuWinogradConv2dTransformInputKernel";
}
- /** Configure the output transform kernel.
- *
- * @param[in] input_nhwc Input tensor. Data types supported: F16/F32. Layout supported NHWC.
- * @param[in] num_batches Number of batches in input tensor.
- * @param[in] num_rows Number of rows in input tensor.
- * @param[in] num_cols Number of columns in input tensor.
- * @param[in] num_channels Number of channels in input tensor.
- * @param[in] padding Padding type.
- * @param[out] output Base of output matrices.
- * @param[in] matrix_stride Stride between output matrices.
- * @param[in] workspace Tensor to be used as the working space during the computation.
- */
- void configure(
- const ITensorInfo *input_nhwc,
- const int num_batches,
- const int num_rows,
- const int num_cols,
- const int num_channels,
- const PaddingType padding,
- ITensorInfo *output,
- const int matrix_stride,
- ITensorInfo *workspace) override;
+private:
+ arm_conv::winograd::WinogradImpl &_winograd_impl;
+ arm_conv::ConvolutionArgs &_conv_args;
+ uint32_t _nthreads;
+};
+class CpuWinogradConv2dTransformOutputKernel : public ICpuKernel<CpuWinogradConv2dTransformOutputKernel>
+{
+public:
+ /** Prevent instances of this class from being copied (As this class contains pointers) */
+ CpuWinogradConv2dTransformOutputKernel(const CpuWinogradConv2dTransformOutputKernel &) = delete;
+
+ /** Prevent instances of this class from being copied (As this class contains pointers) */
+ CpuWinogradConv2dTransformOutputKernel &operator=(const CpuWinogradConv2dTransformOutputKernel &) = delete;
+
+ /** Prevent instances of this class from being moved it contains references.*/
+ CpuWinogradConv2dTransformOutputKernel(CpuWinogradConv2dTransformOutputKernel &&) = delete;
+
+ /** Prevent instances of this class from being moved it contains references.*/
+ CpuWinogradConv2dTransformOutputKernel &operator=(CpuWinogradConv2dTransformOutputKernel &&) = delete;
+
+ CpuWinogradConv2dTransformOutputKernel(arm_conv::winograd::WinogradImpl &w_impl, arm_conv::ConvolutionArgs &_c_args, uint32_t nthreads);
// Inherited methods overridden:
void run_op(ITensorPack &tensors, const Window &window, const ThreadInfo &info) override;
- /** Winograd base kernel */
- using WinogradBase = winograd::WinogradGEMM<OutputTileRows, OutputTileCols, KernelRows, KernelCols, winograd::WinogradRoots::Integers>;
- /** Winograd convolution kernel */
- using WinogradConv = typename WinogradBase::template Convolution<T, T>;
-
- /** Static function to check if given info will lead to a valid configuration of @ref CpuWinogradConv2dTransformInputKernel
- *
- * @param[in] input First tensor input info. Data types supported: F16/F32.
- * @param[in] output Output tensor info. Data types supported: same as @p input.
- * @param[in] winograd_info Contains Winograd's information described in @ref WinogradInfo
- *
- * @return a status
- */
- static Status validate(const ITensorInfo *input, const ITensorInfo *output, const WinogradInfo &winograd_info);
-
-private:
- using InputTransform = typename WinogradBase::template InputTransform<T, T>;
-
- std::unique_ptr<InputTransform> _transform{ nullptr };
- int _num_channels; /**< Number of channels in input tensor. */
- int _matrix_stride; /**< Stride between output matrices. */
-};
-
-/** Interface for the kernel to perform Winograd output transform. */
-class ICpuWinogradConv2dTransformOutputKernel : public ICpuKernel<ICpuWinogradConv2dTransformOutputKernel>
-{
-public:
- /** Get the working space required to perform the transformation.
- *
- * Note, the working space is only required when performing the
- * transformation - hence it can be reused whenever the transformation is
- * not running.
- *
- * @param[in] num_threads The greatest number of threads that will be used to execute the transform.
- *
- * @return Size of working space required in bytes.
- */
- virtual unsigned int get_working_space_size(unsigned int num_threads) const = 0;
-
- /** Determine how much memory (in units of TOut) to allocate for the
- * (Winograd domain) output.
- *
- * @param[in] num_batches Number of batches in the output tensor.
- * @param[in] num_rows Number of rows in each feature map of the input tensor.
- * @param[in] num_cols Number of columns in each feature map of the input tensor.
- * @param[in] num_output_channels Number of feature maps in the output tensor.
- *
- * @return Storage size (in units of TOut) required.
- */
- virtual unsigned int get_output_storage_size(int num_batches, int num_rows, int num_cols, int num_output_channels) const = 0;
-
- /** Gets the stride between matrices in the output worspace
- *
- * @param[in] num_batches Number of batches in the output tensor.
- * @param[in] num_rows Number of rows in each feature map of the input tensor.
- * @param[in] num_cols Number of columns in each feature map of the input tensor.
- * @param[in] num_output_channels Number of feature maps in the output tensor.
- *
- * @return Stride expressed in bytes.
- */
- virtual int get_matrix_stride(int num_batches, int num_rows, int num_cols, int num_output_channels) const = 0;
-
- /** Get the output shape of a convolution.
- *
- * @param[in] num_rows Number of rows in each feature map of the input tensor.
- * @param[in] num_cols Number of columns in each feature map of the input tensor.
- * @param[in] padding_same True if padding is SAME, false otherwise
- *
- * @return Shape of the output tensor
- */
- virtual std::pair<unsigned int, unsigned int> get_output_shape(
- int num_rows, /* Number of rows in each feature map of the input tensor. */
- int num_cols, /* Number of columns in each feature map of the input tensor. */
- bool padding_same /* True if padding is SAME, false otherwise */
- ) const = 0;
-
- /** Configure the output transform kernel.
- *
- * @param[in] biases Pointer to the biases tensor.
- * @param[in] transformed_output Pointer to working space for the output tensor in the Winograd domain.
- * @param[in] matrix_stride Output matrix stride, can be computed with winograd::WinogradGEMM<2, 2, 3, 3>::Convolution<float, float>::get_output_matrix_stride()
- * @param[out] output_nhwc Pointer to a tensor in NHWC data layout ordered output tensor, in the spatial domain.
- * @param[in] num_batches Number of batches in the input tensor.
- * @param[in] num_rows Number of rows in output tensor.
- * @param[in] num_cols Number of columns in output tensor.
- * @param[in] num_channels Number of feature maps in the output tensor.
- * @param[in] workspace Tensor to be used as the working space during the computation.
- * @param[in] activation Activation to be used
- */
- virtual void configure(
- const ITensorInfo *biases,
- const ITensorInfo *transformed_output,
- const int matrix_stride,
- ITensorInfo *output_nhwc,
- const int num_batches,
- const int num_rows,
- const int num_cols,
- const int num_channels,
- ITensorInfo *workspace,
- const arm_gemm::Activation &activation) = 0;
-
- virtual ~ICpuWinogradConv2dTransformOutputKernel()
- {
- }
-};
-
-/** Kernel to perform Winograd output transform. */
-template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
-class CpuWinogradConv2dTransformOutputKernel : public ICpuWinogradConv2dTransformOutputKernel
-{
-public:
const char *name() const override
{
return "CpuWinogradConv2dTransformOutputKernel";
}
- /** Constructor */
- CpuWinogradConv2dTransformOutputKernel();
-
- /** Prevent instances of this class from being copied (As this class contains pointers) */
- CpuWinogradConv2dTransformOutputKernel(const CpuWinogradConv2dTransformOutputKernel &) = delete;
- /** Prevent instances of this class from being copied (As this class contains pointers) */
- CpuWinogradConv2dTransformOutputKernel &operator=(const CpuWinogradConv2dTransformOutputKernel &) = delete;
- /** Allow instances of this class to be moved */
- CpuWinogradConv2dTransformOutputKernel(CpuWinogradConv2dTransformOutputKernel &&) = default;
- /** Allow instances of this class to be moved */
- CpuWinogradConv2dTransformOutputKernel &operator=(CpuWinogradConv2dTransformOutputKernel &&) = default;
- /** Default destructor */
- ~CpuWinogradConv2dTransformOutputKernel() = default;
-
- // Inherited methods overridden:
- /** Determine how much memory (in units of TOut) to allocate for the
- * (Winograd domain) output.
- *
- * @param[in] num_batches Number of batches in the output tensor.
- * @param[in] num_rows Number of rows in each feature map of the input tensor.
- * @param[in] num_cols Number of columns in each feature map of the input tensor.
- * @param[in] num_output_channels Number of feature maps in the output tensor.
- *
- * @return Storage size (in units of TOut) required.
- */
- unsigned int get_output_storage_size(int num_batches, int num_rows, int num_cols, int num_output_channels) const override;
-
- /** Gets the stride between matrices in the output worspace
- *
- * @param[in] num_batches Number of batches in the output tensor.
- * @param[in] num_rows Number of rows in each feature map of the input tensor.
- * @param[in] num_cols Number of columns in each feature map of the input tensor.
- * @param[in] num_output_channels Number of feature maps in the output tensor.
- *
- * @return Stride expressed in bytes.
- */
- int get_matrix_stride(int num_batches, int num_rows, int num_cols, int num_output_channels) const override;
- /** Get the output shape of a convolution.
- *
- * @param[in] num_rows Number of rows in each feature map of the input tensor.
- * @param[in] num_cols Number of columns in each feature map of the input tensor.
- * @param[in] padding_same True if padding is SAME, false otherwise
- *
- * @return Shape of the output tensor
- */
- std::pair<unsigned int, unsigned int> get_output_shape(
- int num_rows, /* Number of rows in each feature map of the input tensor. */
- int num_cols, /* Number of columns in each feature map of the input tensor. */
- bool padding_same) const override;
-
- /** Get the working space required to perform the transformation.
- *
- * Note, the working space is only required when performing the
- * transformation - hence it can be reused whenever the transformation is
- * not running.
- *
- * @param[in] num_threads The greatest number of threads that will be used to execute the transform.
- *
- * @return Size of working space required in bytes.
- */
- unsigned int get_working_space_size(unsigned int num_threads) const override;
-
- /** Configure the output transform kernel.
- *
- * @param[in] biases Pointer to the biases tensor.
- * @param[in] transformed_output Pointer to working space for the output tensor in the Winograd domain.
- * @param[in] matrix_stride Output matrix stride, can be computed with winograd::WinogradGEMM<2, 2, 3, 3>::Convolution<float, float>::get_output_matrix_stride()
- * @param[out] output_nhwc Pointer to a tensor with NHWC data layout, in the spatial domain.
- * @param[in] num_batches Number of batches in the input tensor.
- * @param[in] num_rows Number of rows in output tensor.
- * @param[in] num_cols Number of columns in output tensor.
- * @param[in] num_channels Number of feature maps in the output tensor.
- * @param[in] workspace Tensor to be used as the working space during the computation.
- * @param[in] activation Activation to be used
- */
- void configure(
- const ITensorInfo *biases,
- const ITensorInfo *transformed_output,
- const int matrix_stride,
- ITensorInfo *output_nhwc,
- const int num_batches,
- const int num_rows,
- const int num_cols,
- const int num_channels,
- ITensorInfo *workspace,
- const arm_gemm::Activation &activation) override;
-
- void run_op(ITensorPack &tensors, const Window &window, const ThreadInfo &info) override;
-
- /** Static function to check if given info will lead to a valid configuration of @ref CpuWinogradConv2dTransformOutputKernel
- *
- * @param[in] input Source tensor info with shape [C, N, 16, batches] or [C, N, 36, batches]. Data types supported: F16/F32.
- * @param[in] bias Biases tensor info. Shared biases supported. Biases are 1D tensor with dimensions [OFM]. It can be a nullptr. Data type supported: as @p input
- * @param[in] output Destination tensor info with shape [output_convolved_dims.width, output_convolved_dims.height, C, batches]. Data type supported: same as @p input
- * @param[in] winograd_info Contains Winograd's information described in @ref WinogradInfo
- *
- * @return a status
- */
- static Status validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, const WinogradInfo &winograd_info);
private:
- using WinogradBase = winograd::WinogradGEMM<OutputTileRows, OutputTileCols, KernelRows, KernelCols, winograd::WinogradRoots::Integers>;
- using WinogradConv = typename WinogradBase::template Convolution<T, T>;
- using OutputTransform = typename WinogradBase::template OutputTransform<T, T>;
-
- std::unique_ptr<OutputTransform> _transform{ nullptr };
- int _matrix_stride;
- int _matrix_row_stride;
-};
-
-/** Interface for the kernel to perform Winograd weights transform. */
-class ICpuWinogradConv2dTransformWeightsKernel : public ICpuKernel<ICpuWinogradConv2dTransformWeightsKernel>
-{
-public:
- /** Prevent instances of this class from being copied (As this class contains pointers) */
- ICpuWinogradConv2dTransformWeightsKernel(const ICpuWinogradConv2dTransformWeightsKernel &) = default;
- /** Prevent instances of this class from being copied (As this class contains pointers) */
- ICpuWinogradConv2dTransformWeightsKernel &operator=(const ICpuWinogradConv2dTransformWeightsKernel &) = default;
- /** Allow instances of this class to be moved */
- ICpuWinogradConv2dTransformWeightsKernel(ICpuWinogradConv2dTransformWeightsKernel &&) = default;
- /** Allow instances of this class to be moved */
- ICpuWinogradConv2dTransformWeightsKernel &operator=(ICpuWinogradConv2dTransformWeightsKernel &&) = default;
-
- ICpuWinogradConv2dTransformWeightsKernel()
- {
- }
- virtual ~ICpuWinogradConv2dTransformWeightsKernel()
- {
- }
- /** Determine how much memory (in units of T) to allocate for the
- * transformed weights.
- *
- * @param[in] num_output_channels Number of output feature maps.
- * @param[in] num_input_channels Number of input feature maps.
- *
- * @return Storage size (in units of T) required.
- */
- virtual unsigned int get_weight_storage_size(int num_output_channels, int num_input_channels) const = 0;
- /** Gets the stride between matrices in the kernel worspace
- *
- * @param[in] num_output_channels Number of output feature maps.
- * @param[in] num_input_channels Number of input feature maps.
- *
- * @return Stride expressed in bytes.
- */
- virtual int get_matrix_stride(int num_output_channels, int num_input_channels) const = 0;
-
- /** Configure the weights transform kernel.
- *
- * @param[in] weights_hwio Pointer to the weights tensor info
- * @param[out] output Pointer to working space for the output tensor in the Winograd domain.
- * @param[in] matrix_stride Stride across matrices in the output workspace.
- * @param[in] num_output_channels Number of filters.
- * @param[in] num_input_channels Number of channels in each filter.
- */
-
- virtual void configure(const ITensorInfo *weights_hwio, ITensorInfo *output, const int matrix_stride, const int num_output_channels, const int num_input_channels) = 0;
-
- /** Static function to check if given info will lead to a valid configuration of @ref CpuWinogradConv2dTransformWeightsKernel
- *
- * @param[in] input First tensor input info. Data types supported: F16/F32.
- * @param[in] weights Weights tensor info. Data types supported: same as @p input.
- *
- * @return a status
- */
- static Status validate(const ITensorInfo *input, const ITensorInfo *weights);
-};
-
-/** Kernel to perform Winograd weights transform. */
-template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
-class CpuWinogradConv2dTransformWeightsKernel final : public ICpuWinogradConv2dTransformWeightsKernel
-{
-public:
- /** Prevent instances of this class from being copied (As this class contains pointers) */
- CpuWinogradConv2dTransformWeightsKernel(const CpuWinogradConv2dTransformWeightsKernel &) = delete;
- /** Prevent instances of this class from being copied (As this class contains pointers) */
- CpuWinogradConv2dTransformWeightsKernel &operator=(const CpuWinogradConv2dTransformWeightsKernel &) = delete;
- /** Allow instances of this class to be moved */
- CpuWinogradConv2dTransformWeightsKernel(CpuWinogradConv2dTransformWeightsKernel &&) = default;
- /** Allow instances of this class to be moved */
- CpuWinogradConv2dTransformWeightsKernel &operator=(CpuWinogradConv2dTransformWeightsKernel &&) = default;
- /** Default destructor */
- ~CpuWinogradConv2dTransformWeightsKernel() = default;
-
- /** Default constructor. */
- CpuWinogradConv2dTransformWeightsKernel();
- const char *name() const override
- {
- return "CpuWinogradConv2dTransformWeightsKernel";
- }
-
- /** Static function to check if given info will lead to a valid configuration of @ref CpuWinogradConv2dTransformWeightsKernel
- *
- * @param[in] input Source tensor info. The input is a 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM] (NCHW data layout).
- * kernel_x must be 3 and equal to kernel_y. Data types supported: F16/F32.
- * @param[in] output Destination tensor info. The output is a 3D tensor with dimensions [OFM, IFM, 16] or [OFM, IFM, 36]. Data type supported: same as @p input
- * @param[in] winograd_info Contains Winograd's information described in @ref WinogradInfo
- *
- * @return a status
- */
- static Status validate(const ITensorInfo *input, const ITensorInfo *output, const WinogradInfo &winograd_info);
-
- // Inherited methods overridden:
-
-#ifndef DOXYGEN_SKIP_THIS
- /** Configure the weights transform kernel.
- *
- * @param[in] weights_hwio Pointer to the weights tensor info
- * @param[out] output Pointer to working space for the output tensor in the Winograd domain.
- * @param[in] matrix_stride Stride across matrices in the output workspace.
- * @param[in] num_output_channels Number of filters.
- * @param[in] num_input_channels Number of channels in each filter.
- */
- void configure(const ITensorInfo *weights_hwio, ITensorInfo *output, const int matrix_stride, const int num_output_channels, const int num_input_channels) override;
-#endif /* DOXYGEN_SKIP_THIS */
-
- /** Determine how much memory (in units of T) to allocate for the
- * transformed weights.
- *
- * @param[in] num_output_channels Number of output feature maps.
- * @param[in] num_input_channels Number of input feature maps.
- *
- * @return Storage size (in units of T) required.
- */
- unsigned int get_weight_storage_size(int num_output_channels, int num_input_channels) const override;
-
- /** Gets the stride between matrices in the input worspace
- *
- * @param[in] num_output_channels Number of output feature maps.
- * @param[in] num_input_channels Number of input feature maps.
- *
- * @return Stride expressed in bytes.
- */
- int get_matrix_stride(int num_output_channels, int num_input_channels) const override;
- void run_op(ITensorPack &tensors, const Window &window, const ThreadInfo &info) override;
- bool is_parallelisable() const override;
-
-private:
- using WinogradBase = winograd::WinogradGEMM<OutputTileRows, OutputTileCols, KernelRows, KernelCols, winograd::WinogradRoots::Integers>;
- using WinogradConv = typename WinogradBase::template Convolution<T, T>;
- using WeightsTransform = typename WinogradBase::template WeightsTransform<T, T>;
-
- std::unique_ptr<WeightsTransform> _transform{ nullptr };
- int _num_output_channels;
- int _matrix_stride;
-};
-
-/** Kernel to perform Winograd. */
-template <typename TIn, typename TOut, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
-class CpuWinogradConv2dConfiguration
-{
-public:
- /** Winograd base kernel */
- using WinogradBase = winograd::WinogradGEMM<OutputTileRows, OutputTileCols, KernelRows, KernelCols, winograd::WinogradRoots::Integers>;
- /** Winograd convolution kernel */
-
- using WinogradConv = typename WinogradBase::template Convolution<TIn, TOut>;
-
- using TransformInputKernel = CpuWinogradConv2dTransformInputKernel<TIn, OutputTileRows, OutputTileCols, KernelRows, KernelCols>;
- using TransformWeightsKernel = CpuWinogradConv2dTransformWeightsKernel<TIn, OutputTileRows, OutputTileCols, KernelRows, KernelCols>;
- using TransformOutputKernel = CpuWinogradConv2dTransformOutputKernel<TOut, OutputTileRows, OutputTileCols, KernelRows, KernelCols>;
+ arm_conv::winograd::WinogradImpl &_winograd_impl;
+ const arm_conv::ConvolutionArgs &_conv_args;
+ uint32_t _nthreads;
};
} // namespace cpu