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| author | Gian Marco Iodice <gianmarco.iodice@arm.com> | 2018-04-11 15:59:10 +0100 |
|---|---|---|
| committer | Anthony Barbier <anthony.barbier@arm.com> | 2018-11-02 16:49:37 +0000 |
| commit | e52a3000d2c13bc1b66ca66b3d12b6b836982394 (patch) | |
| tree | 70e8ef5ba216762604f84228805aac9bd65747b6 | |
| parent | dd03870b63784abe499761da2b26b209b33f2db2 (diff) | |
| download | ComputeLibrary-e52a3000d2c13bc1b66ca66b3d12b6b836982394.tar.gz | |
COMPMID-1026 - Add support for 4x4 output tile in CLWinogradConvolutionLayer
The performance achieved can be found at the following confluence page:
https://confluence.arm.com/display/MLENG/GEMM-based+convolution+vs+Winograd-based+convolution+on+OpenCL
Change-Id: I4b690cfdd4eb4ff0cd17b14fdd49ccaa1d1dc85c
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/127729
Tested-by: Jenkins <bsgcomp@arm.com>
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>
19 files changed, 601 insertions, 107 deletions
diff --git a/arm_compute/core/CL/kernels/CLWinogradFilterTransformKernel.h b/arm_compute/core/CL/kernels/CLWinogradFilterTransformKernel.h index 7115710d59..828e2e521a 100644 --- a/arm_compute/core/CL/kernels/CLWinogradFilterTransformKernel.h +++ b/arm_compute/core/CL/kernels/CLWinogradFilterTransformKernel.h @@ -49,8 +49,7 @@ public: /** Set the input and output tensor. * * @note Winograd filter transform supports the following configurations: - * Output tile size: 2x2, 4x4 - * Kernel size: 3x3 + * F(output tile, kernel size):F(2x2, 3x3), F(4x4, 3x3), F(4x4, 5x5) * Strides: only unit strides * * @param[in] input Source tensor. The input is a 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM] (NCHW data layout). Data types supported: F32. @@ -61,8 +60,7 @@ public: /** Static function to check if given info will lead to a valid configuration of @ref CLWinogradFilterTransformKernel * * @note Winograd filter transform supports the following configurations: - * Output tile size: 2x2, 4x4 - * Kernel size: 3x3 + * F(output tile, kernel size):F(2x2, 3x3), F(4x4, 3x3), F(4x4, 5x5) * Strides: only unit strides * * @param[in] input Source tensor. The input is a 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM] (NCHW data layout). Data types supported: F32. diff --git a/arm_compute/core/CL/kernels/CLWinogradInputTransformKernel.h b/arm_compute/core/CL/kernels/CLWinogradInputTransformKernel.h index 2d1eadf3cf..b92ff2f60c 100644 --- a/arm_compute/core/CL/kernels/CLWinogradInputTransformKernel.h +++ b/arm_compute/core/CL/kernels/CLWinogradInputTransformKernel.h @@ -47,8 +47,7 @@ public: /** Set the input and output of the kernel. * * @note Winograd input transform supports the following configurations: - * Output tile size: 2x2 - * Kernel size: 3x3 + * F(output tile, kernel size):F(2x2, 3x3), F(4x4, 3x3), F(4x4, 5x5) * Strides: only unit strides * * @param[in] input The input tensor to transform. Data types supported: F32 @@ -59,8 +58,7 @@ public: /** Static function to check if given info will lead to a valid configuration of @ref CLWinogradInputTransformKernel * * @note Winograd input transform supports the following configurations: - * Output tile size: 2x2 - * Kernel size: 3x3 + * F(output tile, kernel size):F(2x2, 3x3), F(4x4, 3x3), F(4x4, 5x5) * Strides: only unit strides * * @param[in] input The input tensor to transform. Data types supported: F32 diff --git a/arm_compute/core/CL/kernels/CLWinogradOutputTransformKernel.h b/arm_compute/core/CL/kernels/CLWinogradOutputTransformKernel.h index b0d0bbeeaa..5e64a82e48 100644 --- a/arm_compute/core/CL/kernels/CLWinogradOutputTransformKernel.h +++ b/arm_compute/core/CL/kernels/CLWinogradOutputTransformKernel.h @@ -49,11 +49,10 @@ public: /** Set the input and output tensor. * * @note Winograd output transform supports the following configurations: - * Output tile size: 2x2 - * Kernel size: 3x3 + * F(output tile, kernel size):F(2x2, 3x3), F(4x4, 3x3), F(4x4, 5x5) * Strides: only unit strides * - * @param[in] input Source tensor with shape [C, N, 16, batches]. Data types supported: F32. + * @param[in] input Source tensor with shape [C, N, K, batches]. Data types supported: F32. * @param[in] bias Biases tensor. Shared biases supported. Biases are 1D tensor with dimensions [OFM]. It can be a nullptr. Data type supported: as @p input * @param[out] output The output tensor. The shape for this tensor can be calculated using the utility function @p compute_winograd_output_transform_shape. Data types supported: Same as @p input * @param[in] winograd_info Contains Winograd's information described in @ref WinogradInfo @@ -62,11 +61,10 @@ public: /** Static function to check if given info will lead to a valid configuration of @ref CLWinogradOutputTransformKernel * * @note Winograd output transform supports the following configurations: - * Output tile size: 2x2 - * Kernel size: 3x3 + * F(output tile, kernel size):F(2x2, 3x3), F(4x4, 3x3), F(4x4, 5x5) * Strides: only unit strides * - * @param[in] input Source tensor with shape [C, N, 16, batches]. Data types supported: F32. + * @param[in] input Source tensor with shape [C, N, K, batches]. Data types supported: F32. * @param[in] bias Biases tensor. Shared biases supported. Biases are 1D tensor with dimensions [OFM]. It can be a nullptr. Data type supported: as @p input * @param[out] output The output tensor. The shape for this tensor can be calculated using the utility function @p compute_winograd_output_transform_shape. Data types supported: Same as @p input * @param[in] winograd_info Contains Winograd's information described in @ref WinogradInfo diff --git a/arm_compute/runtime/CL/functions/CLConvolutionLayer.h b/arm_compute/runtime/CL/functions/CLConvolutionLayer.h index 7df765988f..a1cd15515f 100644 --- a/arm_compute/runtime/CL/functions/CLConvolutionLayer.h +++ b/arm_compute/runtime/CL/functions/CLConvolutionLayer.h @@ -26,6 +26,7 @@ #include "arm_compute/runtime/CL/functions/CLDirectConvolutionLayer.h" #include "arm_compute/runtime/CL/functions/CLGEMMConvolutionLayer.h" +#include "arm_compute/runtime/CL/functions/CLWinogradConvolutionLayer.h" #include "arm_compute/runtime/IFunction.h" #include "arm_compute/runtime/IMemoryManager.h" @@ -84,7 +85,6 @@ public: * while every optional dimension from 4 and above represent a batch of inputs. * Data types supported: QS8/QASYMM8/QS16/F16/F32. * @param[in] weights Weights tensor. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM]. Data type supported:Same as @p input. - * @param[in] biases Biases tensor. Shared biases supported. Biases are 1D tensor with dimensions [OFM]. Data type supported:Same as @p input. * @param[in] output Destination tensor. 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. @@ -95,7 +95,7 @@ public: * * @return a status */ - static ConvolutionMethod get_convolution_method(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info, + static ConvolutionMethod get_convolution_method(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *output, const PadStrideInfo &conv_info, const WeightsInfo &weights_info, const ActivationLayerInfo &act_info, const GPUTarget gpu_target, const Size2D &dilation = Size2D(1U, 1U)); // Inherited methods overridden: void run() override; diff --git a/arm_compute/runtime/CL/functions/CLWinogradInputTransform.h b/arm_compute/runtime/CL/functions/CLWinogradInputTransform.h index 0e0d6bf284..64d3e80bc9 100644 --- a/arm_compute/runtime/CL/functions/CLWinogradInputTransform.h +++ b/arm_compute/runtime/CL/functions/CLWinogradInputTransform.h @@ -40,8 +40,7 @@ public: /** Set the input and output tensors. * * @note Winograd input transform supports the following configurations: - * Output tile size: 2x2 - * Kernel size: 3x3 + * F(output tile, kernel size):F(2x2, 3x3), F(4x4, 3x3), F(4x4, 5x5) * Strides: only unit strides * * @param[in] input The input tensor to transform. Data types supported: F32 @@ -52,8 +51,7 @@ public: /** Static function to check if given info will lead to a valid configuration of @ref CLWinogradInputTransform. * * @note Winograd input transform supports the following configurations: - * Output tile size: 2x2 - * Kernel size: 3x3 + * F(output tile, kernel size):F(2x2, 3x3), F(4x4, 3x3), F(4x4, 5x5) * Strides: only unit strides * * @param[in] input The input tensor to transform. Data types supported: F32 diff --git a/src/core/CL/CLKernelLibrary.cpp b/src/core/CL/CLKernelLibrary.cpp index 50f623fffb..59be956ad8 100644 --- a/src/core/CL/CLKernelLibrary.cpp +++ b/src/core/CL/CLKernelLibrary.cpp @@ -363,7 +363,9 @@ const std::map<std::string, std::string> CLKernelLibrary::_kernel_program_map = { "winograd_input_transform_4x4_5x5_stepz1_nchw", "winograd.cl" }, { "winograd_input_transform_2x2_3x3_stepz1_nchw", "winograd.cl" }, { "winograd_input_transform_2x2_3x3_stepz2_nchw", "winograd.cl" }, + { "winograd_input_transform_4x4_3x3_stepz1_nchw", "winograd.cl" }, { "winograd_output_transform_2x2_3x3_nchw", "winograd.cl" }, + { "winograd_output_transform_4x4_3x3_nchw", "winograd.cl" }, { "winograd_output_transform_4x4_5x5_nchw", "winograd.cl" }, { "YUYV422_to_IYUV_bt709", "color_convert.cl" }, { "YUYV422_to_NV12_bt709", "color_convert.cl" }, diff --git a/src/core/CL/cl_kernels/winograd.cl b/src/core/CL/cl_kernels/winograd.cl index cda23b0155..f40a969ea0 100644 --- a/src/core/CL/cl_kernels/winograd.cl +++ b/src/core/CL/cl_kernels/winograd.cl @@ -708,6 +708,265 @@ __kernel void winograd_input_transform_2x2_3x3_stepz2_nchw( vstore2(out33, 0, (__global float *)(dst_addr + 15 * dst_stride_z)); } +/** This OpenCL kernel computes the input transform when the output tile is 4x4, the filter size 3x3 and the data format is NCHW + * + * @note The number of tiles in the x axis must be passed at compile time using -DNUM_TILES_X (i.e.-DNUM_TILES_X=5). + * @note The pad left and pad top must be passed at compile time using -DPAD_LEFT and -DPAD_TOP (i.e.-DPAD_LEFT=1 and -DPAD_TOP=0). + * + * @param[in] src_ptr Pointer to the source image. Supported data types: F32 + * @param[in] src_stride_x Stride of the source image in X dimension (in bytes) + * @param[in] src_step_x src_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] src_stride_y Stride of the source image in Y dimension (in bytes) + * @param[in] src_step_y src_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in] src_offset_first_element_in_bytes The offset of the first element in the source image + * @param[in] src_stride_z Stride of the source tensor in Z dimension (in bytes) + * @param[in] src_step_z src_stride_z * number of elements along Y processed per workitem(in bytes) + * @param[in] dst_ptr Pointer to the destination tensor. Supported data types: as @p src_ptr + * @param[in] dst_stride_x Stride of the destination tensor in X dimension (in bytes) + * @param[in] dst_step_x dst_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] dst_stride_y Stride of the destination tensor in Y dimension (in bytes) + * @param[in] dst_step_y dst_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in] dst_stride_z Stride of the destination tensor in Z dimension (in bytes) + * @param[in] dst_step_z dst_stride_z * number of elements along Y processed per workitem(in bytes) + * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the destination tensor + */ +__kernel void winograd_input_transform_4x4_3x3_stepz1_nchw( + TENSOR3D_DECLARATION(src), + TENSOR3D_DECLARATION(dst)) +{ + int x = get_global_id(0); + int y = get_global_id(1); + int z = get_global_id(2); + + // Compute input address + __global uchar *src_addr = src_ptr + src_offset_first_element_in_bytes + x * 4 * src_stride_x + y * 4 * src_stride_y + z * src_stride_z; + + src_addr = src_addr - ((int)PAD_LEFT * src_stride_x) - ((int)PAD_TOP * src_stride_y); + + // Row4 + float4 d40 = vload4(0, (__global float *)(src_addr + 4 * src_stride_y)); + float2 d41 = vload2(2, (__global float *)(src_addr + 4 * src_stride_y)); + + float k0 = d41.s0; + float k1 = d41.s0; + float k2 = d41.s0; + float k3 = d41.s0; + float k4 = d41.s0; + float k5 = 0.0f; + + k0 += 4.0f * d40.s0 - 5.0f * d40.s2; + k1 += -4.0f * d40.s1 - 4.0f * d40.s2 + d40.s3; + k2 += 4.0f * d40.s1 - 4.0f * d40.s2 - d40.s3; + k3 += -2.0f * d40.s1 + 2.0f * d40.s3 - d40.s2; + k4 += 2.0f * d40.s1 - 2.0f * d40.s3 - d40.s2; + k5 += 4.0f * d40.s1 - 5.0f * d40.s3 + d41.s1; + + // Row0 + float4 d00 = vload4(0, (__global float *)(src_addr + 0 * src_stride_y)); + float2 d01 = vload2(2, (__global float *)(src_addr + 0 * src_stride_y)); + + // Row2 + float4 d20 = vload4(0, (__global float *)(src_addr + 2 * src_stride_y)); + float2 d21 = vload2(2, (__global float *)(src_addr + 2 * src_stride_y)); + + // Compute destination address + __global float *dst_addr = (__global float *)(dst_ptr + dst_offset_first_element_in_bytes + z * dst_stride_x + (x + y * (int)NUM_TILES_X) * dst_stride_y); + + uint dst_plane_stride = dst_stride_z / sizeof(float); + + float out0 = k0; + float out1 = k1; + float out2 = k2; + float out3 = k3; + float out4 = k4; + float out5 = k5; + float out6 = k0; + float out7 = k1; + float out8 = k2; + float out9 = k3; + float out10 = k4; + float out11 = k5; + float out12 = k0; + float out13 = k1; + float out14 = k2; + float out15 = k3; + float out16 = k4; + float out17 = k5; + float out18 = k0; + float out19 = k1; + float out20 = k2; + float out21 = k3; + float out22 = k4; + float out23 = k5; + float out24 = k0; + float out25 = k1; + float out26 = k2; + float out27 = k3; + float out28 = k4; + float out29 = k5; + + // Channels [0, 5]: [out00, out01, out02, out03, out04, out05] + out0 += 16.0f * d00.s0 - 20.0f * d00.s2 - 20.0f * d20.s0 + 25.0f * d20.s2 + 4.0f * d01.s0 - 5.0f * d21.s0; + out1 += -16.0f * d00.s1 - 16.0f * d00.s2 + 4.0f * d00.s3 + 20.0f * d20.s1 + 20.0f * d20.s2 - 5.0f * d20.s3 + 4.0f * d01.s0 - 5.0f * d21.s0; + out2 += 16.0f * d00.s1 - 16.0f * d00.s2 - 4.0f * d00.s3 - 20.0f * d20.s1 + 20.0f * d20.s2 + 5.0f * d20.s3 + 4.0f * d01.s0 - 5.0f * d21.s0; + out3 += -8.0f * d00.s1 - 4.0f * d00.s2 + 8.0f * d00.s3 + 10.0f * d20.s1 + 5.0f * d20.s2 - 10.0f * d20.s3 + 4.0f * d01.s0 - 5.0f * d21.s0; + out4 += 8.0f * d00.s1 - 4.0f * d00.s2 - 8.0f * d00.s3 - 10.0f * d20.s1 + 5.0f * d20.s2 + 10.0f * d20.s3 + 4.0f * d01.s0 - 5.0f * d21.s0; + out5 += 16.0f * d00.s1 - 20.0f * d00.s3 - 20.0f * d20.s1 + 4.0f * d01.s1 + 25.0f * d20.s3 - 5.0f * d21.s1; + + *(dst_addr) = out0; + dst_addr += dst_plane_stride; + *(dst_addr) = out1; + dst_addr += dst_plane_stride; + *(dst_addr) = out2; + dst_addr += dst_plane_stride; + *(dst_addr) = out3; + dst_addr += dst_plane_stride; + *(dst_addr) = out4; + dst_addr += dst_plane_stride; + *(dst_addr) = out5; + dst_addr += dst_plane_stride; + + // Row1 + float4 d10 = vload4(0, (__global float *)(src_addr + 1 * src_stride_y)); + float2 d11 = vload2(2, (__global float *)(src_addr + 1 * src_stride_y)); + + // Row3 + float4 d30 = vload4(0, (__global float *)(src_addr + 3 * src_stride_y)); + float2 d31 = vload2(2, (__global float *)(src_addr + 3 * src_stride_y)); + + // Compute common parts for the channels between [6, 29] + // Channels [6, 11]: [out10, out11, out12, out13, out14, out15] + // Channels [12, 17]: [out20, out21, out22, out23, out24, out25] + float part0 = -16.0f * d20.s0 + 20.0f * d20.s2 - 4.0f * d21.s0; + float part1 = 16.0f * d10.s0 - 20.0f * d10.s2 + 4.0f * d11.s0 - 4.0f * d30.s0 + 5.0f * d30.s2 - d31.s0; + float part2 = 16.0f * d20.s2 - 4.0f * d21.s0; + float part3 = 16.0f * d20.s1 - 4.0f * d20.s3; + float part4 = 16.0f * d10.s2 - 4.0f * d11.s0 - 4.0f * d30.s2 + d31.s0; + float part5 = 16.0f * d10.s1 - 4.0f * d10.s3 - 4.0f * d30.s1 + d30.s3; + float part6 = 4.0f * d20.s2 - 4.0f * d21.s0; + float part7 = 8.0f * d10.s1 - 8.0f * d10.s3 - 2.0f * d30.s1 + 2.0f * d30.s3; + float part8 = 4.0f * d10.s2 - 4.0f * d11.s0 - d30.s2 + d31.s0; + float part9 = 8.0f * d20.s1 - 8.0f * d20.s3; + float part10 = -16.0f * d20.s1 + 20.0f * d20.s3 - 4.0f * d21.s1; + float part11 = -16.0f * d10.s1 + 20.0f * d10.s3 - 4.0f * d11.s1 + 4.0f * d30.s1 - 5.0f * d30.s3 + d31.s1; + + // Channels [18, 23]: [out30, out31, out32, out33, out34, out35] + // Channels [24, 29]: [out40, out41, out42, out43, out44, out45] + float part12 = 8.0f * d10.s0 - 10.0f * d10.s2 + 2.0f * d11.s0 - 8.0f * d30.s0 + 10.0f * d30.s2 - 2.0f * d31.s0; + float part13 = part0 * 0.25f; // -4.0f * d20.s0 + 5.0f * d20.s2 - d21.s0 + float part14 = part2 * 0.25f; // 4.0f * d20.s2 - d21.s0 + float part15 = 8.0f * d10.s1 - 2.0f * d10.s3 - 8.0f * d30.s1 + 2.0f * d30.s3; + float part16 = 8.0f * d10.s2 - 2.0f * d11.s0 - 8.0f * d30.s2 + 2.0f * d31.s0; + float part17 = part3 * 0.25f; // 4.0f * d20.s1 - d20.s3 + float part18 = part6 * 0.25f; // d20.s2 - d21.s0 + float part19 = 4.0f * d10.s1 - 4.0f * d10.s3 - 4.0f * d30.s1 + 4.0f * d30.s3; + float part20 = 2.0f * d10.s2 - 2.0f * d11.s0 - 2.0f * d30.s2 + 2.0f * d31.s0; + float part21 = part9 * 0.25f; // 2.0f * (d20.s1 - d20.s3) + float part22 = part10 * 0.25f; // - 4.0f * d20.s1 + 5.0f * d20.s3 - d21.s1 + float part23 = part11 * 0.5f + 6.0f * d30.s1 - 7.5f * d30.s3 + 1.5f * d31.s1; // - 8.0f * d10.s1 + 10.0f * d10.s3 - 2.0f * d11.s1 + 8.0f * d30.s1 - 10.0f * d30.s3 + 2.0f * d31.s1; + + out6 += part0 - part1; + out12 += part0 + part1; + out7 += part2 + part3 + part4 + part5; + out8 += part2 - part3 + part4 - part5; + out13 += part2 + part3 - part4 - part5; + out14 += part2 - part3 - part4 + part5; + out9 += part6 + part7 + part8 + part9; + out10 += part6 - part7 + part8 - part9; + out15 += part6 - part7 - part8 + part9; + out16 += part6 + part7 - part8 - part9; + out11 += part10 + part11; + out17 += part10 - part11; + + out18 += part13 - part12; + out24 += part13 + part12; + out19 += part14 + part15 + part16 + part17; + out20 += part14 - part15 + part16 - part17; + out25 += part14 - part15 - part16 + part17; + out26 += part14 + part15 - part16 - part17; + out21 += part18 + part19 + part20 + part21; + out22 += part18 - part19 + part20 - part21; + out27 += part18 - part19 - part20 + part21; + out28 += part18 + part19 - part20 - part21; + out23 += part22 + part23; + out29 += part22 - part23; + + *(dst_addr) = out6; + dst_addr += dst_plane_stride; + *(dst_addr) = out7; + dst_addr += dst_plane_stride; + *(dst_addr) = out8; + dst_addr += dst_plane_stride; + *(dst_addr) = out9; + dst_addr += dst_plane_stride; + *(dst_addr) = out10; + dst_addr += dst_plane_stride; + *(dst_addr) = out11; + dst_addr += dst_plane_stride; + *(dst_addr) = out12; + dst_addr += dst_plane_stride; + *(dst_addr) = out13; + dst_addr += dst_plane_stride; + *(dst_addr) = out14; + dst_addr += dst_plane_stride; + *(dst_addr) = out15; + dst_addr += dst_plane_stride; + *(dst_addr) = out16; + dst_addr += dst_plane_stride; + *(dst_addr) = out17; + dst_addr += dst_plane_stride; + + *(dst_addr) = out18; + dst_addr += dst_plane_stride; + *(dst_addr) = out19; + dst_addr += dst_plane_stride; + *(dst_addr) = out20; + dst_addr += dst_plane_stride; + *(dst_addr) = out21; + dst_addr += dst_plane_stride; + *(dst_addr) = out22; + dst_addr += dst_plane_stride; + *(dst_addr) = out23; + dst_addr += dst_plane_stride; + *(dst_addr) = out24; + dst_addr += dst_plane_stride; + *(dst_addr) = out25; + dst_addr += dst_plane_stride; + *(dst_addr) = out26; + dst_addr += dst_plane_stride; + *(dst_addr) = out27; + dst_addr += dst_plane_stride; + *(dst_addr) = out28; + dst_addr += dst_plane_stride; + *(dst_addr) = out29; + dst_addr += dst_plane_stride; + + // Row5 + float4 d50 = vload4(0, (__global float *)(src_addr + 5 * src_stride_y)); + float2 d51 = vload2(2, (__global float *)(src_addr + 5 * src_stride_y)); + + // Channels [30, 35] + out0 = 16.0f * d10.s0 - 20.0f * d10.s2 - 20.0f * d30.s0 + 25.0f * d30.s2 + 4.0f * d50.s0 - 5.0f * d50.s2 + d51.s0 + 4.0f * d11.s0 - 5.0f * d31.s0; + out1 = -16.0f * d10.s1 - 16.0f * d10.s2 + 4.0f * d10.s3 + 20.0f * d30.s1 + 20.0f * d30.s2 - 5.0f * d30.s3 - 4.0f * d50.s1 - 4.0f * d50.s2 + d50.s3 + d51.s0 + 4.0f * d11.s0 - 5.0f * d31.s0; + out2 = 16.0f * d10.s1 - 16.0f * d10.s2 - 4.0f * d10.s3 - 20.0f * d30.s1 + 20.0f * d30.s2 + 5.0f * d30.s3 + 4.0f * d50.s1 - 4.0f * d50.s2 - d50.s3 + d51.s0 + 4.0f * d11.s0 - 5.0f * d31.s0; + out3 = -8.0f * d10.s1 - 4.0f * d10.s2 + 8.0f * d10.s3 + 10.0f * d30.s1 - 10.0f * d30.s3 + 5.0f * d30.s2 - 2.0f * d50.s1 + 2.0f * d50.s3 - d50.s2 + d51.s0 + 4.0f * d11.s0 - 5.0f * d31.s0; + out4 = 8.0f * d10.s1 - 4.0f * d10.s2 - 8.0f * d10.s3 - 10.0f * d30.s1 + 5.0f * d30.s2 + 10.0f * d30.s3 + 2.0f * d50.s1 - 2.0f * d50.s3 - d50.s2 + d51.s0 + 4.0f * d11.s0 - 5.0f * d31.s0; + out5 = 16.0f * d10.s1 - 20.0f * d10.s3 + 4.0f * d11.s1 - 20.0f * d30.s1 + 25.0f * d30.s3 - 5.0f * d31.s1 + 4.0f * d50.s1 - 5.0f * d50.s3 + d51.s1; + + *(dst_addr) = out0; + dst_addr += dst_plane_stride; + *(dst_addr) = out1; + dst_addr += dst_plane_stride; + *(dst_addr) = out2; + dst_addr += dst_plane_stride; + *(dst_addr) = out3; + dst_addr += dst_plane_stride; + *(dst_addr) = out4; + dst_addr += dst_plane_stride; + *(dst_addr) = out5; + dst_addr += dst_plane_stride; +} + #define OUTPUT_ROW_4x4_5x5(out, tmp, comm_fact) \ ({ \ comm_fact.s0 = tmp.s2 - 4.25f * tmp.s4 + tmp.s6; \ @@ -981,6 +1240,183 @@ __kernel void winograd_output_transform_2x2_3x3_nchw( vstore2((float2)(out10, out11), 0, (__global float *)(dst_addr + 1 * dst_stride_y)); } +/** This OpenCL kernel performs Winograd output transform when the output tile is 4x4, the filter size 3x3 and the data format is NCHW + * + * @note The number of tiles along the X direction must be passed at compile time using -DNUM_TILES_X: e.g. -DNUM_TILES_X=16 + * + * @param[in] src_ptr Pointer to the source tensor. Supported data types: F32 + * @param[in] src_stride_x Stride of the source tensor in X dimension (in bytes) + * @param[in] src_step_x src_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] src_stride_y Stride of the source tensor in Y dimension (in bytes) + * @param[in] src_step_y src_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in] src_stride_z Stride of the source tensor in Z dimension (in bytes) + * @param[in] src_step_z src_stride_z * number of elements along Z processed per workitem(in bytes) + * @param[in] src_offset_first_element_in_bytes The offset of the first element in the source tensor + * @param[out] dst_ptr Pointer to the destination tensor. Supported data types: same as @p src_ptr + * @param[in] dst_stride_x Stride of the destination tensor in X dimension (in bytes) + * @param[in] dst_step_x dst_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] dst_stride_y Stride of the destination tensor in Y dimension (in bytes) + * @param[in] dst_step_y dst_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in] src_stride_z Stride of the source tensor in Z dimension (in bytes) + * @param[in] src_step_z src_stride_z * number of elements along Z processed per workitem(in bytes) + * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the destination tensor + */ +__kernel void winograd_output_transform_4x4_3x3_nchw( + TENSOR3D_DECLARATION(src), + TENSOR3D_DECLARATION(dst) +#if defined(HAS_BIAS) + , + VECTOR_DECLARATION(bias) +#endif // defined(HAS_BIAS) +) +{ + // Each thread stores a 4x4 tile + Tensor3D src = CONVERT_TO_TENSOR3D_STRUCT(src); + + const __global uchar *src_addr = tensor3D_offset(&src, 0, 0, 0); + + // Load the values across the 36 channels to compose the 6x6 tile + float d00 = *((__global float *)(src_addr + 0 * src_stride_z)); + float d01 = *((__global float *)(src_addr + 1 * src_stride_z)); + float d02 = *((__global float *)(src_addr + 2 * src_stride_z)); + float d03 = *((__global float *)(src_addr + 3 * src_stride_z)); + float d04 = *((__global float *)(src_addr + 4 * src_stride_z)); + float d05 = *((__global float *)(src_addr + 5 * src_stride_z)); + + float d10 = *((__global float *)(src_addr + 6 * src_stride_z)); + float d11 = *((__global float *)(src_addr + 7 * src_stride_z)); + float d12 = *((__global float *)(src_addr + 8 * src_stride_z)); + float d13 = *((__global float *)(src_addr + 9 * src_stride_z)); + float d14 = *((__global float *)(src_addr + 10 * src_stride_z)); + float d15 = *((__global float *)(src_addr + 11 * src_stride_z)); + + float d20 = *((__global float *)(src_addr + 12 * src_stride_z)); + float d21 = *((__global float *)(src_addr + 13 * src_stride_z)); + float d22 = *((__global float *)(src_addr + 14 * src_stride_z)); + float d23 = *((__global float *)(src_addr + 15 * src_stride_z)); + float d24 = *((__global float *)(src_addr + 16 * src_stride_z)); + float d25 = *((__global float *)(src_addr + 17 * src_stride_z)); + + float d30 = *((__global float *)(src_addr + 18 * src_stride_z)); + float d31 = *((__global float *)(src_addr + 19 * src_stride_z)); + float d32 = *((__global float *)(src_addr + 20 * src_stride_z)); + float d33 = *((__global float *)(src_addr + 21 * src_stride_z)); + float d34 = *((__global float *)(src_addr + 22 * src_stride_z)); + float d35 = *((__global float *)(src_addr + 23 * src_stride_z)); + + float d40 = *((__global float *)(src_addr + 24 * src_stride_z)); + float d41 = *((__global float *)(src_addr + 25 * src_stride_z)); + float d42 = *((__global float *)(src_addr + 26 * src_stride_z)); + float d43 = *((__global float *)(src_addr + 27 * src_stride_z)); + float d44 = *((__global float *)(src_addr + 28 * src_stride_z)); + float d45 = *((__global float *)(src_addr + 29 * src_stride_z)); + + float d50 = *((__global float *)(src_addr + 30 * src_stride_z)); + float d51 = *((__global float *)(src_addr + 31 * src_stride_z)); + float d52 = *((__global float *)(src_addr + 32 * src_stride_z)); + float d53 = *((__global float *)(src_addr + 33 * src_stride_z)); + float d54 = *((__global float *)(src_addr + 34 * src_stride_z)); + float d55 = *((__global float *)(src_addr + 35 * src_stride_z)); + + // Compute out00, out01, out02 and out03 + float out00 = d01 + d21 + d41 + d11 + d31; + float out01 = d01 + d21 + d41 + d11 + d31; + float out02 = d01 + d21 + d41 + d11 + d31; + float out03 = d01 + d21 + d41 + d11 + d31; + + float k0 = d03 + d04 + d13 + d14 + d23 + d24 + d33 + d34 + d43 + d44; + float k1 = 2.0f * d03 - 2.0f * d04 + 2.0f * d13 - 2.0f * d14 + 2.0f * d23 - 2.0f * d24 + 2.0f * d33 - 2.0f * d34 + 2.0f * d43 - 2.0f * d44; + + out00 += k0 + d00 + d02 + d10 + d12 + d20 + d22 + d30 + d32 + d40 + d42; + out01 += k1 - d02 - d12 - d22 - d32 - d42; + out02 += 4.0f * k0 + d02 + d12 + d22 + d32 + d42; + out03 += 4.0f * k1 - d02 - d12 - d22 - d32 - d42 + d05 + d15 + d25 + d35 + d45; + + // Compute out10, out11, out12 and out13 + float out10 = d11 - d21 + 2.0f * d31 - 2.0f * d41; + float out11 = d11 - d21 + 2.0f * d31 - 2.0f * d41; + float out12 = d11 - d21 + 2.0f * d31 - 2.0f * d41; + float out13 = d11 - d21 + 2.0f * d31 - 2.0f * d41; + + k0 = d13 + d14 - d23 - d24 + 2.0f * d33 + 2.0f * d34 - 2.0f * d43 - 2.0f * d44; + k1 = 2.0f * d13 - 2.0f * d14 - 2.0f * d23 + 2.0f * d24 + 4.0f * d33 - 4.0f * d34 - 4.0f * d43 + 4.0f * d44; + + out10 += k0 + d10 + d12 - d20 - d22 + 2.0f * d30 + 2.0f * d32 - 2.0f * d40 - 2.0f * d42; + out11 += k1 - d12 + d22 - 2.0f * d32 + 2.0f * d42; + out12 += 4.0f * k0 + d12 - d22 + 2.0f * d32 - 2.0f * d42; + out13 += 4.0f * k1 - d12 + d15 + d22 - d25 - 2.0f * d32 + 2.0f * d35 + 2.0f * d42 - 2.0f * d45; + + // Compute out20, out21, out22 and out23 + float out20 = d11 + d21 + 4.0f * d31 + 4.0f * d41; + float out21 = d11 + d21 + 4.0f * d31 + 4.0f * d41; + float out22 = d11 + d21 + 4.0f * d31 + 4.0f * d41; + float out23 = d11 + d21 + 4.0f * d31 + 4.0f * d41; + + k0 = d13 + d14 + d23 + d24 + 4.0f * d33 + 4.0f * d34 + 4.0f * d43 + 4.0f * d44; + k1 = 2.0f * d13 - 2.0f * d14 + 2.0f * d23 - 2.0f * d24 + 8.0f * d33 - 8.0f * d34 + 8.0f * d43 - 8.0f * d44; + + out20 += k0 + d10 + d12 + d20 + d22 + 4.0f * d30 + 4.0f * d32 + 4.0f * d40 + 4.0f * d42; + out21 += k1 - d12 - d22 - 4.0f * d32 - 4.0f * d42; + out22 += 4.0f * k0 + d12 + d22 + 4.0f * d32 + 4.0f * d42; + out23 += 4.0f * k1 - d12 + d15 - d22 + d25 - 4.0f * d32 + 4.0f * d35 - 4.0f * d42 + 4.0f * d45; + + // Compute out30, out31, out32 and out33 + float out30 = d11 - d21 + 8.0f * d31 - 8.0f * d41 + d51; + float out31 = d11 - d21 + 8.0f * d31 - 8.0f * d41 + d51; + float out32 = d11 - d21 + 8.0f * d31 - 8.0f * d41 + d51; + float out33 = d11 - d21 + 8.0f * d31 - 8.0f * d41 + d51; + + k0 = d13 + d14 - d23 - d24 + 8.0f * d33 + 8.0f * d34 - 8.0f * d43 - 8.0f * d44 + d53 + d54; + k1 = 2.0f * d13 - 2.0f * d14 - 2.0f * d23 + 2.0f * d24 + 16.0f * d33 - 16.0f * d34 - 16.0f * d43 + 16.0f * d44 + 2.0f * d53 - 2.0f * d54; + + out30 += k0 + d10 + d12 - d20 - d22 + 8.0f * d30 + 8.0f * d32 - 8.0f * d40 - 8.0f * d42 + d50 + d52; + out31 += k1 - d12 + d22 - 8.0f * d32 + 8.0f * d42 - d52; + out32 += 4.0f * k0 + d12 - d22 + 8.0f * d32 - 8.0f * d42 + d52; + out33 += 4.0f * k1 - d12 + d15 + d22 - d25 - 8.0f * d32 + 8.0f * d35 + 8.0f * d42 - 8.0f * d45 - d52 + d55; + + int y_in = get_global_id(1); + int x_out = (y_in % NUM_TILES_X) * 4; + int y_out = (y_in / NUM_TILES_X) * 4; + int z_out = get_global_id(0); + +#if defined(HAS_BIAS) + // Add bias + Vector bias = CONVERT_TO_VECTOR_STRUCT_NO_STEP(bias); + + float b = (float) * ((__global float *)(vector_offset(&bias, z_out))); + + out00 += (float)b; + out01 += (float)b; + out02 += (float)b; + out03 += (float)b; + + out10 += (float)b; + out11 += (float)b; + out12 += (float)b; + out13 += (float)b; + + out20 += (float)b; + out21 += (float)b; + out22 += (float)b; + out23 += (float)b; + + out30 += (float)b; + out31 += (float)b; + out32 += (float)b; + out33 += (float)b; + +#endif // defined(HAS_BIAS) + + // Get output address + __global uchar *dst_addr = dst_ptr + dst_offset_first_element_in_bytes + x_out * dst_stride_x + y_out * dst_stride_y + z_out * dst_stride_z; + + // Store the 4x4 output tile + vstore4((float4)(out00, out01, out02, out03), 0, (__global float *)(dst_addr + 0 * dst_stride_y)); + vstore4((float4)(out10, out11, out12, out13), 0, (__global float *)(dst_addr + 1 * dst_stride_y)); + vstore4((float4)(out20, out21, out22, out23), 0, (__global float *)(dst_addr + 2 * dst_stride_y)); + vstore4((float4)(out30, out31, out32, out33), 0, (__global float *)(dst_addr + 3 * dst_stride_y)); +} + #define COMPUTE_TMP_COL(col, d0, d1, d2, d3, d4, d5, d6, d7, comm_fact) \ ({ \ comm_fact.s0 = d1 + d2; \ diff --git a/src/core/CL/kernels/CLGEMMMatrixMultiplyKernel.cpp b/src/core/CL/kernels/CLGEMMMatrixMultiplyKernel.cpp index 0a0de7ad4e..805a594af6 100644 --- a/src/core/CL/kernels/CLGEMMMatrixMultiplyKernel.cpp +++ b/src/core/CL/kernels/CLGEMMMatrixMultiplyKernel.cpp @@ -286,17 +286,23 @@ void CLGEMMMatrixMultiplyKernel::configure(const ICLTensor *input0, const ICLTen else // The input tensors have not been reshaped { build_opts.add_option("-DCOLS_A=" + support::cpp11::to_string(input0->info()->dimension(0))); + build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(data_type)); // Create kernels according to the architecture, data type and input size. if(gpu_target_is_in(gpu_target, GPUTarget::G71, GPUTarget::G72, GPUTarget::G51, GPUTarget::G51BIG, GPUTarget::G51LIT, GPUTarget::TNOX) && is_data_type_float(data_type)) { - kernel_name = "gemm_mm_floating_point_" + lower_string(string_from_data_type(data_type)) + "_bifrost"; - // The first kernel is optimized for the case of 1000 or less output elements (e.g. FC8 of AlexNet and VGG-16, and - // FC1 of Inception v3). The second kernel is optimized for the case of greater than 1000 output elements (e.g. - // FC6 and FC7 of AlexNet and VGG-16). - if(input1->info()->dimension(0) <= 1000 && input0->info()->num_dimensions() == 1 && data_type == DataType::F32) + kernel_name = "gemm_mm_floating_point"; + + if(input0->info()->num_dimensions() != 1) + { + kernel_name += "_" + lower_string(string_from_data_type(data_type)) + "_bifrost"; + } + else if(input1->info()->dimension(0) <= 1000 && data_type == DataType::F32) { - kernel_name += "_1000"; + // The first kernel is optimized for the case of 1000 or less output elements (e.g. FC8 of AlexNet and VGG-16, and + // FC1 of Inception v3). The second kernel is optimized for the case of greater than 1000 output elements (e.g. + // FC6 and FC7 of AlexNet and VGG-16). + kernel_name += "_" + lower_string(string_from_data_type(data_type)) + "_bifrost_1000"; } // The work-group size equal to the Bifrost quad size has been proved to be optimal for these kernels @@ -309,7 +315,6 @@ void CLGEMMMatrixMultiplyKernel::configure(const ICLTensor *input0, const ICLTen } else // (MIDGARD and F32) or (F16) { - build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(data_type)); kernel_name = "gemm_mm_floating_point"; } build_opts.add_option("-DNUM_ELEMS_PROCESSED_PER_THREAD_Y=" + support::cpp11::to_string(num_elements_processed.y())); diff --git a/src/core/CL/kernels/CLWinogradFilterTransformKernel.cpp b/src/core/CL/kernels/CLWinogradFilterTransformKernel.cpp index d3a33c01f9..41b3ac50b5 100644 --- a/src/core/CL/kernels/CLWinogradFilterTransformKernel.cpp +++ b/src/core/CL/kernels/CLWinogradFilterTransformKernel.cpp @@ -55,9 +55,11 @@ Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, c const size_t idx_w = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::WIDTH); const size_t idx_h = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::HEIGHT); - ARM_COMPUTE_RETURN_ERROR_ON(kernel_size != Size2D(3U, 3U) && kernel_size != Size2D(5U, 5U)); - ARM_COMPUTE_RETURN_ERROR_ON(kernel_size == Size2D(3U, 3U) && output_tile_size != Size2D(2U, 2U) && output_tile_size != Size2D(4U, 4U)); - ARM_COMPUTE_RETURN_ERROR_ON(kernel_size == Size2D(5U, 5U) && output_tile_size != Size2D(4U, 4U)); + ARM_COMPUTE_RETURN_ERROR_ON_MSG(kernel_size != Size2D(3U, 3U) && kernel_size != Size2D(5U, 5U), "Winograd filter transform only supports 3x3 and 5x5 kernels"); + ARM_COMPUTE_RETURN_ERROR_ON_MSG(kernel_size == Size2D(3U, 3U) && output_tile_size != Size2D(2U, 2U) + && output_tile_size != Size2D(4U, 4U), + "Winograd filter transform only supports 2x2 or 4x4 output tile for 3x3 kernels"); + ARM_COMPUTE_RETURN_ERROR_ON_MSG(kernel_size == Size2D(5U, 5U) && output_tile_size != Size2D(4U, 4U), "Winograd filter transform only supports 4x4 output tile for 5x5 kernels"); ARM_COMPUTE_RETURN_ERROR_ON(input->dimension(idx_w) != kernel_size.width || input->dimension(idx_h) != kernel_size.height); ARM_COMPUTE_RETURN_ERROR_ON(input->num_dimensions() > 4); diff --git a/src/core/CL/kernels/CLWinogradInputTransformKernel.cpp b/src/core/CL/kernels/CLWinogradInputTransformKernel.cpp index a47590d20f..febd22b04e 100644 --- a/src/core/CL/kernels/CLWinogradInputTransformKernel.cpp +++ b/src/core/CL/kernels/CLWinogradInputTransformKernel.cpp @@ -47,7 +47,9 @@ Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, c const Size2D kernel_size = winograd_info.kernel_size; ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.stride().first != 1 || conv_info.stride().second != 1, "Winograd input transform only supports unit strides"); ARM_COMPUTE_RETURN_ERROR_ON_MSG(kernel_size != Size2D(3U, 3U) && kernel_size != Size2D(5U, 5U), "Winograd input transform only supports 3x3 and 5x5 kernels"); - ARM_COMPUTE_RETURN_ERROR_ON_MSG(kernel_size == Size2D(3U, 3U) && output_tile_size != Size2D(2U, 2U), "Winograd input transform only supports 2x2 output tile for 3x3 kernels"); + ARM_COMPUTE_RETURN_ERROR_ON_MSG(kernel_size == Size2D(3U, 3U) && output_tile_size != Size2D(2U, 2U) + && output_tile_size != Size2D(4U, 4U), + "Winograd input transform only supports 2x2 or 4x4 output tile for 3x3 kernels"); ARM_COMPUTE_RETURN_ERROR_ON_MSG(kernel_size == Size2D(5U, 5U) && output_tile_size != Size2D(4U, 4U), "Winograd input transform only supports 4x4 output tile for 5x5 kernels"); ARM_COMPUTE_UNUSED(conv_info); ARM_COMPUTE_UNUSED(output_tile_size); @@ -111,7 +113,6 @@ void CLWinogradInputTransformKernel::configure(const ICLTensor *input, ICLTensor const int num_elements_y = input->info()->dimension(1) - (kernel_size.height - 1) + conv_info.pad_top() + conv_info.pad_bottom(); // Check if we need to extend the right or bottom border - // FIXME: This actually is not needed. Added just for validating the result; const unsigned int extra_border_right = ((num_elements_x % output_tile_size.width) == 0) ? 0u : static_cast<unsigned int>(output_tile_size.width - 1); const unsigned int extra_border_bottom = ((num_elements_y % output_tile_size.height) == 0) ? 0u : static_cast<unsigned int>(output_tile_size.height - 1); @@ -137,19 +138,13 @@ void CLWinogradInputTransformKernel::configure(const ICLTensor *input, ICLTensor std::string kernel_name = "winograd_input_transform_" + output_tile_size.to_string() + "_" + kernel_size.to_string(); // Check optimized kernel if output_dims == 2x2 - if(output_tile_size.width == 2 && output_tile_size.height == 2) + if(output_tile_size == Size2D(2U, 2U)) { - if((_input->info()->dimension(2) % 2) != 0) - { - _step_z = 1; - } - else - { - _step_z = 2; - _lws_hint = cl::NDRange(1, 1, 8); - } + _step_z = (_input->info()->dimension(2) % 2) != 0 ? 1 : 2; } + _lws_hint = cl::NDRange(1, 1, 8); + // Append stepz and data layout kernel_name += "_stepz"; kernel_name += support::cpp11::to_string(_step_z); diff --git a/src/core/CL/kernels/CLWinogradOutputTransformKernel.cpp b/src/core/CL/kernels/CLWinogradOutputTransformKernel.cpp index 8ee1a82209..c5d2528aa2 100644 --- a/src/core/CL/kernels/CLWinogradOutputTransformKernel.cpp +++ b/src/core/CL/kernels/CLWinogradOutputTransformKernel.cpp @@ -58,6 +58,7 @@ Status validate_arguments(const ITensorInfo *input, const ITensorInfo *bias, con ARM_COMPUTE_RETURN_ERROR_ON_MSG(kernel_size != Size2D(3U, 3U) && kernel_size != Size2D(5U, 5U), "Only 3x3 and 5x5 kernels are supported"); ARM_COMPUTE_RETURN_ERROR_ON_MSG(kernel_size == Size2D(3U, 3U) && output_tile_size == Size2D(2U, 2U) && input->dimension(2) != 16, "Wrong number of batches"); + ARM_COMPUTE_RETURN_ERROR_ON_MSG(kernel_size == Size2D(3U, 3U) && output_tile_size == Size2D(4U, 4U) && input->dimension(2) != 36, "Wrong number of batches"); ARM_COMPUTE_RETURN_ERROR_ON_MSG(kernel_size == Size2D(5U, 5U) && output_tile_size == Size2D(4U, 4U) && input->dimension(2) != 64, "Wrong number of batches"); // Compute number of elements to process in the X and Y direction @@ -67,7 +68,6 @@ Status validate_arguments(const ITensorInfo *input, const ITensorInfo *bias, con const int num_tiles_y = std::ceil(num_elements_y / static_cast<float>(output_tile_size.height)); ARM_COMPUTE_RETURN_ERROR_ON(input->dimension(1) != static_cast<unsigned int>((num_tiles_x * num_tiles_y))); - ARM_COMPUTE_UNUSED(output_tile_size); if(bias != nullptr) { @@ -207,4 +207,4 @@ void CLWinogradOutputTransformKernel::run(const Window &window, cl::CommandQueue enqueue(queue, *this, slice, _lws_hint); } while(window.slide_window_slice_3D(slice) && window.slide_window_slice_3D(slice_out)); -}
\ No newline at end of file +} diff --git a/src/runtime/CL/functions/CLConvolutionLayer.cpp b/src/runtime/CL/functions/CLConvolutionLayer.cpp index bcb5424aab..643e24d638 100644 --- a/src/runtime/CL/functions/CLConvolutionLayer.cpp +++ b/src/runtime/CL/functions/CLConvolutionLayer.cpp @@ -48,9 +48,16 @@ void CLConvolutionLayer::configure(ICLTensor *input, const ICLTensor *weights, c ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, output); ARM_COMPUTE_ERROR_THROW_ON(CLConvolutionLayer::validate(input->info(), weights->info(), ((biases != nullptr) ? biases->info() : nullptr), output->info(), conv_info, weights_info, dilation, act_info)); - switch(CLConvolutionLayer::get_convolution_method(input->info(), weights->info(), ((biases != nullptr) ? biases->info() : nullptr), output->info(), conv_info, + switch(CLConvolutionLayer::get_convolution_method(input->info(), weights->info(), output->info(), conv_info, weights_info, act_info, CLScheduler::get().target(), dilation)) { + case ConvolutionMethod::WINOGRAD: + { + auto f = arm_compute::support::cpp14::make_unique<CLWinogradConvolutionLayer>(); + f->configure(input, weights, biases, output, conv_info); + _function = std::move(f); + break; + } case ConvolutionMethod::DIRECT: { auto f = arm_compute::support::cpp14::make_unique<CLDirectConvolutionLayer>(); @@ -79,8 +86,14 @@ Status CLConvolutionLayer::validate(const ITensorInfo *input, const ITensorInfo //Configure if the parameters match the direct convolution or the gemm-based const GPUTarget gpu_target = CLScheduler::get().target(); - switch(CLConvolutionLayer::get_convolution_method(input, weights, biases, output, conv_info, weights_info, act_info, gpu_target, dilation)) + switch(CLConvolutionLayer::get_convolution_method(input, weights, output, conv_info, weights_info, act_info, gpu_target, dilation)) { + case ConvolutionMethod::WINOGRAD: + { + //Validate Winograd + CLWinogradConvolutionLayer::validate(input, weights, biases, output, conv_info); + break; + } case ConvolutionMethod::DIRECT: { // Validate direct convolution layer @@ -101,19 +114,25 @@ Status CLConvolutionLayer::validate(const ITensorInfo *input, const ITensorInfo return Status{}; } -ConvolutionMethod CLConvolutionLayer::get_convolution_method(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info, +ConvolutionMethod CLConvolutionLayer::get_convolution_method(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *output, const PadStrideInfo &conv_info, const WeightsInfo &weights_info, const ActivationLayerInfo &act_info, const GPUTarget gpu_target, const Size2D &dilation) { - ARM_COMPUTE_UNUSED(input); - ARM_COMPUTE_UNUSED(weights); - ARM_COMPUTE_UNUSED(biases); + ARM_COMPUTE_ERROR_ON_NULLPTR(input); + ARM_COMPUTE_ERROR_ON_NULLPTR(output); + ARM_COMPUTE_ERROR_ON_NULLPTR(weights); ARM_COMPUTE_UNUSED(output); - ARM_COMPUTE_UNUSED(conv_info); ARM_COMPUTE_UNUSED(weights_info); ARM_COMPUTE_UNUSED(gpu_target); - ARM_COMPUTE_UNUSED(dilation); - ARM_COMPUTE_UNUSED(act_info); + const size_t idx_w = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::WIDTH); + const size_t idx_h = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::HEIGHT); + const size_t idx_c = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::CHANNEL); + + if((input->data_type() == DataType::F32) && (input->data_layout() == DataLayout::NCHW) && (input->dimension(idx_c) > 3) && (weights->dimension(idx_w) == 3) && (weights->dimension(idx_h) == 3) + && (weights->num_dimensions() <= 4) && (conv_info.stride().first == 1) && (conv_info.stride().second == 1) && (dilation == Size2D(1U, 1U)) && (!act_info.enabled())) + { + return ConvolutionMethod::WINOGRAD; + } return ConvolutionMethod::GEMM; } diff --git a/src/runtime/CL/functions/CLWinogradConvolutionLayer.cpp b/src/runtime/CL/functions/CLWinogradConvolutionLayer.cpp index 0aa7f8d1b5..86ccddac88 100644 --- a/src/runtime/CL/functions/CLWinogradConvolutionLayer.cpp +++ b/src/runtime/CL/functions/CLWinogradConvolutionLayer.cpp @@ -44,13 +44,18 @@ void CLWinogradConvolutionLayer::configure(ICLTensor *input, const ICLTensor *we const size_t idx_height = get_data_layout_dimension_index(input->info()->data_layout(), DataLayoutDimension::HEIGHT); // Input shape - const TensorShape input_shape = input->info()->tensor_shape(); + const TensorShape input_shape = input->info()->tensor_shape(); + const unsigned int input_w = input->info()->tensor_shape()[idx_width]; + const unsigned int input_h = input->info()->tensor_shape()[idx_height]; // Kernel size const unsigned int kernel_w = weights->info()->tensor_shape()[idx_width]; const unsigned int kernel_h = weights->info()->tensor_shape()[idx_height]; - const WinogradInfo winograd_info = WinogradInfo(Size2D(2, 2), + //Winograd output tile + const Size2D output_tile = (Size2D(kernel_w, kernel_h) == Size2D(3U, 3U) && input_w <= 4 && input_h <= 4) ? Size2D(2U, 2U) : Size2D(4U, 4U); + + const WinogradInfo winograd_info = WinogradInfo(output_tile, Size2D(kernel_w, kernel_h), Size2D(input_shape[idx_width], input_shape[idx_height]), conv_info, @@ -95,13 +100,18 @@ Status CLWinogradConvolutionLayer::validate(const ITensorInfo *input, const ITen const size_t idx_height = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::HEIGHT); // Input shape - const TensorShape input_shape = input->tensor_shape(); + const TensorShape input_shape = input->tensor_shape(); + const unsigned int input_w = input->tensor_shape()[idx_width]; + const unsigned int input_h = input->tensor_shape()[idx_height]; // Kernel size const unsigned int kernel_w = weights->tensor_shape()[idx_width]; const unsigned int kernel_h = weights->tensor_shape()[idx_height]; - const WinogradInfo winograd_info = WinogradInfo(Size2D(2, 2), + //Winograd output tile + const Size2D output_tile = (Size2D(kernel_w, kernel_h) == Size2D(3U, 3U) && input_w <= 4 && input_h <= 4) ? Size2D(2U, 2U) : Size2D(4U, 4U); + + const WinogradInfo winograd_info = WinogradInfo(output_tile, Size2D(kernel_w, kernel_h), Size2D(input_shape[idx_width], input_shape[idx_height]), conv_info, diff --git a/src/runtime/NEON/functions/NEConvolutionLayer.cpp b/src/runtime/NEON/functions/NEConvolutionLayer.cpp index afc354533d..b0603e92d2 100644 --- a/src/runtime/NEON/functions/NEConvolutionLayer.cpp +++ b/src/runtime/NEON/functions/NEConvolutionLayer.cpp @@ -109,10 +109,12 @@ ConvolutionMethod NEConvolutionLayer::get_convolution_method(const ITensorInfo * ARM_COMPUTE_ERROR_ON_NULLPTR(weights); ARM_COMPUTE_UNUSED(output); ARM_COMPUTE_UNUSED(weights_info); - ARM_COMPUTE_UNUSED(act_info); - if((input->data_type() == DataType::F32) && (weights->dimension(0) == 3) && (weights->dimension(1) == 3) && (weights->num_dimensions() <= 4) && (conv_info.stride().first == 1) - && (conv_info.stride().second == 1) && (dilation == Size2D(1U, 1U))) + const size_t idx_w = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::WIDTH); + const size_t idx_h = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::HEIGHT); + + if((input->data_type() == DataType::F32) && (input->data_layout() == DataLayout::NCHW) && (weights->dimension(idx_w) == 3) && (weights->dimension(idx_h) == 3) && (weights->num_dimensions() <= 4) + && (conv_info.stride().first == 1) && (conv_info.stride().second == 1) && (dilation == Size2D(1U, 1U)) && (!act_info.enabled())) { //FIXME Until COMPMID-1041 is implemented Winograd is slower than GEMM on A53. if(Scheduler::get().cpu_info().get_cpu_model() != CPUModel::A53) diff --git a/tests/datasets/WinogradInputTransformDataset.h b/tests/datasets/WinogradInputTransformDataset.h index cbe63645de..59fb2adbd6 100644 --- a/tests/datasets/WinogradInputTransformDataset.h +++ b/tests/datasets/WinogradInputTransformDataset.h @@ -102,6 +102,7 @@ class SmallWinogradInputTransformDataset final : public WinogradInputTransformDa public: SmallWinogradInputTransformDataset() { + // (2x2, 3x3) add_config(TensorShape(9U, 9U), WinogradInfo(Size2D(2U, 2U), Size2D(3U, 3U), Size2D(9U, 9U), PadStrideInfo(1, 1, 1, 1), DataLayout::NCHW)); add_config(TensorShape(27U, 13U, 2U), WinogradInfo(Size2D(2U, 2U), Size2D(3U, 3U), Size2D(27U, 13U), PadStrideInfo(1, 1, 0, 0), DataLayout::NCHW)); add_config(TensorShape(128U, 64U, 1U, 3U), WinogradInfo(Size2D(2U, 2U), Size2D(3U, 3U), Size2D(128U, 64U), PadStrideInfo(1, 1, 1, 1), DataLayout::NCHW)); @@ -109,6 +110,17 @@ public: add_config(TensorShape(27U, 13U, 2U, 4U), WinogradInfo(Size2D(2U, 2U), Size2D(3U, 3U), Size2D(27U, 13U), PadStrideInfo(1, 1, 1, 1), DataLayout::NCHW)); add_config(TensorShape(9U, 9U, 3U, 5U), WinogradInfo(Size2D(2U, 2U), Size2D(3U, 3U), Size2D(9U, 9U), PadStrideInfo(1, 1, 0, 0), DataLayout::NCHW)); add_config(TensorShape(14U, 14U, 512U, 2U), WinogradInfo(Size2D(2U, 2U), Size2D(3U, 3U), Size2D(14U, 14U), PadStrideInfo(1, 1, 1, 1), DataLayout::NCHW)); + + // (4x4, 3x3) + add_config(TensorShape(9U, 9U), WinogradInfo(Size2D(4U, 4U), Size2D(3U, 3U), Size2D(9U, 9U), PadStrideInfo(1, 1, 1, 1), DataLayout::NCHW)); + add_config(TensorShape(27U, 13U, 2U), WinogradInfo(Size2D(4U, 4U), Size2D(3U, 3U), Size2D(27U, 13U), PadStrideInfo(1, 1, 0, 0), DataLayout::NCHW)); + add_config(TensorShape(128U, 64U, 1U, 3U), WinogradInfo(Size2D(4U, 4U), Size2D(3U, 3U), Size2D(128U, 64U), PadStrideInfo(1, 1, 1, 1), DataLayout::NCHW)); + add_config(TensorShape(9U, 9U, 3U, 4U), WinogradInfo(Size2D(4U, 4U), Size2D(3U, 3U), Size2D(9U, 9U), PadStrideInfo(1, 1, 0, 0), DataLayout::NCHW)); + add_config(TensorShape(27U, 13U, 2U, 4U), WinogradInfo(Size2D(4U, 4U), Size2D(3U, 3U), Size2D(27U, 13U), PadStrideInfo(1, 1, 1, 1), DataLayout::NCHW)); + add_config(TensorShape(9U, 9U, 3U, 5U), WinogradInfo(Size2D(4U, 4U), Size2D(3U, 3U), Size2D(9U, 9U), PadStrideInfo(1, 1, 0, 0), DataLayout::NCHW)); + add_config(TensorShape(14U, 14U, 512U, 2U), WinogradInfo(Size2D(4U, 4U), Size2D(3U, 3U), Size2D(14U, 14U), PadStrideInfo(1, 1, 1, 1), DataLayout::NCHW)); + + // (4x4, 5x5) add_config(TensorShape(9U, 9U), WinogradInfo(Size2D(4U, 4U), Size2D(5U, 5U), Size2D(9U, 9U), PadStrideInfo(1, 1, 1, 1), DataLayout::NCHW)); add_config(TensorShape(27U, 13U, 2U), WinogradInfo(Size2D(4U, 4U), Size2D(5U, 5U), Size2D(27U, 13U), PadStrideInfo(1, 1, 0, 0), DataLayout::NCHW)); add_config(TensorShape(128U, 64U, 1U, 3U), WinogradInfo(Size2D(4U, 4U), Size2D(5U, 5U), Size2D(128U, 64U), PadStrideInfo(1, 1, 1, 1), DataLayout::NCHW)); @@ -124,10 +136,19 @@ class LargeWinogradInputTransformDataset final : public WinogradInputTransformDa public: LargeWinogradInputTransformDataset() { + // (2x2, 3x3) add_config(TensorShape(42U, 37U, 8U, 15U), WinogradInfo(Size2D(2U, 2U), Size2D(3U, 3U), Size2D(42U, 37U), PadStrideInfo(1, 1, 1, 1), DataLayout::NCHW)); add_config(TensorShape(57U, 60U, 13U, 8U), WinogradInfo(Size2D(2U, 2U), Size2D(3U, 3U), Size2D(57U, 60U), PadStrideInfo(1, 1, 1, 1), DataLayout::NCHW)); add_config(TensorShape(128U, 64U, 21U, 13U), WinogradInfo(Size2D(2U, 2U), Size2D(3U, 3U), Size2D(128U, 64U), PadStrideInfo(1, 1, 0, 0), DataLayout::NCHW)); add_config(TensorShape(83U, 72U, 14U, 5U), WinogradInfo(Size2D(2U, 2U), Size2D(3U, 3U), Size2D(83U, 72U), PadStrideInfo(1, 1, 0, 0), DataLayout::NCHW)); + + // (4x4, 3x3) + add_config(TensorShape(42U, 37U, 8U, 15U), WinogradInfo(Size2D(4U, 4U), Size2D(3U, 3U), Size2D(42U, 37U), PadStrideInfo(1, 1, 1, 1), DataLayout::NCHW)); + add_config(TensorShape(57U, 60U, 13U, 8U), WinogradInfo(Size2D(4U, 4U), Size2D(3U, 3U), Size2D(57U, 60U), PadStrideInfo(1, 1, 1, 1), DataLayout::NCHW)); + add_config(TensorShape(128U, 64U, 21U, 13U), WinogradInfo(Size2D(4U, 4U), Size2D(3U, 3U), Size2D(128U, 64U), PadStrideInfo(1, 1, 0, 0), DataLayout::NCHW)); + add_config(TensorShape(83U, 72U, 14U, 5U), WinogradInfo(Size2D(4U, 4U), Size2D(3U, 3U), Size2D(83U, 72U), PadStrideInfo(1, 1, 0, 0), DataLayout::NCHW)); + + // (4x4, 5x5) add_config(TensorShape(42U, 37U, 8U, 15U), WinogradInfo(Size2D(4U, 4U), Size2D(5U, 5U), Size2D(42U, 37U), PadStrideInfo(1, 1, 1, 1), DataLayout::NCHW)); add_config(TensorShape(57U, 60U, 13U, 8U), WinogradInfo(Size2D(4U, 4U), Size2D(5U, 5U), Size2D(57U, 60U), PadStrideInfo(1, 1, 1, 1), DataLayout::NCHW)); add_config(TensorShape(128U, 64U, 21U, 13U), WinogradInfo(Size2D(4U, 4U), Size2D(5U, 5U), Size2D(128U, 64U), PadStrideInfo(1, 1, 0, 0), DataLayout::NCHW)); diff --git a/tests/datasets/WinogradOutputTransformDataset.h b/tests/datasets/WinogradOutputTransformDataset.h index aa36f153c5..b28df390d1 100644 --- a/tests/datasets/WinogradOutputTransformDataset.h +++ b/tests/datasets/WinogradOutputTransformDataset.h @@ -110,6 +110,13 @@ public: add_config(TensorShape(1U, 442U, 16U), WinogradInfo(Size2D(2U, 2U), Size2D(3U, 3U), Size2D(53U, 33U), PadStrideInfo(1, 1, 0, 1), DataLayout::NCHW)); add_config(TensorShape(7U, 12U, 16U, 3U), WinogradInfo(Size2D(2U, 2U), Size2D(3U, 3U), Size2D(8U, 10U), PadStrideInfo(1, 1, 0, 0), DataLayout::NCHW)); add_config(TensorShape(24U, 49U, 16U, 2U), WinogradInfo(Size2D(2U, 2U), Size2D(3U, 3U), Size2D(14U, 14U), PadStrideInfo(1, 1, 1, 1), DataLayout::NCHW)); + + // (4x4, 3x3) + add_config(TensorShape(13U, 4U, 36U), WinogradInfo(Size2D(4U, 4U), Size2D(3U, 3U), Size2D(10U, 9U), PadStrideInfo(1, 1, 0, 0), DataLayout::NCHW)); + add_config(TensorShape(13U, 6U, 36U), WinogradInfo(Size2D(4U, 4U), Size2D(3U, 3U), Size2D(10U, 11U), PadStrideInfo(1, 1, 0, 0), DataLayout::NCHW)); + add_config(TensorShape(7U, 117U, 36U), WinogradInfo(Size2D(4U, 4U), Size2D(3U, 3U), Size2D(53U, 33U), PadStrideInfo(1, 1, 0, 1), DataLayout::NCHW)); + add_config(TensorShape(7U, 4U, 36U, 3U), WinogradInfo(Size2D(4U, 4U), Size2D(3U, 3U), Size2D(8U, 10U), PadStrideInfo(1, 1, 0, 0), DataLayout::NCHW)); + add_config(TensorShape(24U, 16U, 36U, 2U), WinogradInfo(Size2D(4U, 4U), Size2D(3U, 3U), Size2D(14U, 14U), PadStrideInfo(1, 1, 1, 1), DataLayout::NCHW)); add_config(TensorShape(7U, 12U, 16U, 5U), WinogradInfo(Size2D(2U, 2U), Size2D(3U, 3U), Size2D(8U, 10U), PadStrideInfo(1, 1, 0, 0), DataLayout::NCHW)); // (4x4, 5x5) @@ -127,12 +134,21 @@ class LargeWinogradOutputTransformDataset final : public WinogradOutputTransform public: LargeWinogradOutputTransformDataset() { + // (2x2, 3x3) add_config(TensorShape(64U, 12544U, 16U), WinogradInfo(Size2D(2U, 2U), Size2D(3U, 3U), Size2D(224U, 224U), PadStrideInfo(1, 1, 1, 1), DataLayout::NCHW)); add_config(TensorShape(32U, 3080U, 16U), WinogradInfo(Size2D(2U, 2U), Size2D(3U, 3U), Size2D(112U, 112U), PadStrideInfo(1, 1, 1, 0), DataLayout::NCHW)); add_config(TensorShape(13U, 756U, 16U), WinogradInfo(Size2D(2U, 2U), Size2D(3U, 3U), Size2D(56U, 56U), PadStrideInfo(1, 1, 0, 1), DataLayout::NCHW)); add_config(TensorShape(64U, 12544U, 16U, 3U), WinogradInfo(Size2D(2U, 2U), Size2D(3U, 3U), Size2D(224U, 224U), PadStrideInfo(1, 1, 1, 1), DataLayout::NCHW)); add_config(TensorShape(32U, 3080U, 16U, 2U), WinogradInfo(Size2D(2U, 2U), Size2D(3U, 3U), Size2D(112U, 112U), PadStrideInfo(1, 1, 1, 0), DataLayout::NCHW)); add_config(TensorShape(13U, 756U, 16U, 5U), WinogradInfo(Size2D(2U, 2U), Size2D(3U, 3U), Size2D(56U, 56U), PadStrideInfo(1, 1, 0, 1), DataLayout::NCHW)); + + // (4x4, 3x3) + add_config(TensorShape(64U, 3136U, 36U), WinogradInfo(Size2D(4U, 4U), Size2D(3U, 3U), Size2D(224U, 224U), PadStrideInfo(1, 1, 1, 1), DataLayout::NCHW)); + add_config(TensorShape(32U, 784U, 36U), WinogradInfo(Size2D(4U, 4U), Size2D(3U, 3U), Size2D(112U, 112U), PadStrideInfo(1, 1, 1, 0), DataLayout::NCHW)); + add_config(TensorShape(13U, 196U, 36U), WinogradInfo(Size2D(4U, 4U), Size2D(3U, 3U), Size2D(56U, 56U), PadStrideInfo(1, 1, 0, 1), DataLayout::NCHW)); + add_config(TensorShape(64U, 3136U, 36U, 3U), WinogradInfo(Size2D(4U, 4U), Size2D(3U, 3U), Size2D(224U, 224U), PadStrideInfo(1, 1, 1, 1), DataLayout::NCHW)); + add_config(TensorShape(32U, 784U, 36U, 2U), WinogradInfo(Size2D(4U, 4U), Size2D(3U, 3U), Size2D(112U, 112U), PadStrideInfo(1, 1, 1, 0), DataLayout::NCHW)); + add_config(TensorShape(13U, 196U, 36U, 5U), WinogradInfo(Size2D(4U, 4U), Size2D(3U, 3U), Size2D(56U, 56U), PadStrideInfo(1, 1, 0, 1), DataLayout::NCHW)); } }; } // namespace datasets diff --git a/tests/validation/CL/ConvolutionLayer.cpp b/tests/validation/CL/ConvolutionLayer.cpp index b50bb94bbb..52ad417cd0 100644 --- a/tests/validation/CL/ConvolutionLayer.cpp +++ b/tests/validation/CL/ConvolutionLayer.cpp @@ -72,25 +72,19 @@ const auto ActivationFunctionsDataset = framework::dataset::make("ActivationInfo TEST_SUITE(CL) TEST_SUITE(ConvolutionLayer) -DATA_TEST_CASE(ValidateConvolutionMethod, framework::DatasetMode::ALL, zip(zip(zip(zip(zip(zip( - framework::dataset::make("InputInfo", { TensorInfo(TensorShape(17U, 31U, 2U), 1, DataType::F32, 0), - TensorInfo(TensorShape(17U, 31U, 2U), 1, DataType::F32, 0), - TensorInfo(TensorShape(23U, 27U, 5U, 4U), 1, DataType::F32, 0), - TensorInfo(TensorShape(3U, 3U, 2U, 1U), 1, DataType::F32, 0), - TensorInfo(TensorShape(33U, 27U, 7U, 4U), 1, DataType::F32, 0) - }), - framework::dataset::make("WeightsInfo", { TensorInfo(TensorShape(5U, 5U, 2U, 19U), 1, DataType::F32, 0), - TensorInfo(TensorShape(5U, 5U, 2U, 19U), 1, DataType::F32, 0), - TensorInfo(TensorShape(3U, 3U, 5U, 21U), 1, DataType::F32, 0), - TensorInfo(TensorShape(3U, 3U, 5U, 21U), 1, DataType::F32, 0), - TensorInfo(TensorShape(5U, 5U, 7U, 16U), 1, DataType::F16, 0) - })), - framework::dataset::make("BiasesInfo", { TensorInfo(TensorShape(19U), 1, DataType::F32, 0), - TensorInfo(TensorShape(19U), 1, DataType::F32, 0), - TensorInfo(TensorShape(21U), 1, DataType::F32, 0), - TensorInfo(TensorShape(21U), 1, DataType::F32, 0), - TensorInfo(TensorShape(16U), 1, DataType::F32, 0) - })), +DATA_TEST_CASE(ValidateConvolutionMethod, framework::DatasetMode::ALL, zip(zip(zip(zip(zip( + framework::dataset::make("InputInfo", { TensorInfo(TensorShape(17U, 31U, 2U), 1, DataType::F32, 0), + TensorInfo(TensorShape(17U, 31U, 2U), 1, DataType::F32, 0), + TensorInfo(TensorShape(23U, 27U, 5U, 4U), 1, DataType::F32, 0), + TensorInfo(TensorShape(3U, 3U, 2U, 1U), 1, DataType::F32, 0), + TensorInfo(TensorShape(33U, 27U, 7U, 4U), 1, DataType::F32, 0) + }), + framework::dataset::make("WeightsInfo", { TensorInfo(TensorShape(5U, 5U, 2U, 19U), 1, DataType::F32, 0), + TensorInfo(TensorShape(5U, 5U, 2U, 19U), 1, DataType::F32, 0), + TensorInfo(TensorShape(3U, 3U, 5U, 21U), 1, DataType::F32, 0), + TensorInfo(TensorShape(3U, 3U, 5U, 21U), 1, DataType::F32, 0), + TensorInfo(TensorShape(5U, 5U, 7U, 16U), 1, DataType::F16, 0) + })), framework::dataset::make("OutputInfo", { TensorInfo(TensorShape(15U, 15U, 19U), 1, DataType::F32, 0), TensorInfo(TensorShape(15U, 15U, 19U), 1, DataType::F32, 0), TensorInfo(TensorShape(21U, 25U, 21U, 4U), 1, DataType::F32, 0), @@ -110,12 +104,11 @@ DATA_TEST_CASE(ValidateConvolutionMethod, framework::DatasetMode::ALL, zip(zip(z GPUTarget::BIFROST })), - framework::dataset::make("Expected", { ConvolutionMethod::GEMM, ConvolutionMethod::GEMM, ConvolutionMethod::GEMM, ConvolutionMethod::GEMM, ConvolutionMethod::GEMM })), - input_info, weights_info, biases_info, output_info, conv_info, gpu_target, expected) + framework::dataset::make("Expected", { ConvolutionMethod::GEMM, ConvolutionMethod::GEMM, ConvolutionMethod::WINOGRAD, ConvolutionMethod::GEMM, ConvolutionMethod::GEMM })), + input_info, weights_info, output_info, conv_info, gpu_target, expected) { ConvolutionMethod is_valid = CLConvolutionLayer::get_convolution_method(&input_info.clone()->set_is_resizable(false), &weights_info.clone()->set_is_resizable(false), - &biases_info.clone()->set_is_resizable(false), &output_info.clone()->set_is_resizable(false), conv_info, WeightsInfo(), ActivationLayerInfo(), gpu_target); ARM_COMPUTE_EXPECT(is_valid == expected, framework::LogLevel::ERRORS); } diff --git a/tests/validation/CL/DilatedConvolutionLayer.cpp b/tests/validation/CL/DilatedConvolutionLayer.cpp index 532d04b431..e6a765bbe1 100644 --- a/tests/validation/CL/DilatedConvolutionLayer.cpp +++ b/tests/validation/CL/DilatedConvolutionLayer.cpp @@ -63,25 +63,19 @@ const auto CNNDataTypes = framework::dataset::make("DataType", TEST_SUITE(CL) TEST_SUITE(DilatedConvolutionLayer) -DATA_TEST_CASE(ValidateConvolutionMethod, framework::DatasetMode::ALL, zip(zip(zip(zip(zip(zip(zip( - framework::dataset::make("InputInfo", { TensorInfo(TensorShape(17U, 31U, 2U), 1, DataType::F32, 0), - TensorInfo(TensorShape(17U, 31U, 2U), 1, DataType::F32, 0), - TensorInfo(TensorShape(23U, 27U, 5U, 4U), 1, DataType::F32, 0), - TensorInfo(TensorShape(3U, 3U, 2U, 1U), 1, DataType::F32, 0), - TensorInfo(TensorShape(33U, 27U, 7U, 4U), 1, DataType::F32, 0) - }), - framework::dataset::make("WeightsInfo", { TensorInfo(TensorShape(5U, 5U, 2U, 19U), 1, DataType::F32, 0), - TensorInfo(TensorShape(5U, 5U, 2U, 19U), 1, DataType::F32, 0), - TensorInfo(TensorShape(3U, 3U, 5U, 21U), 1, DataType::F32, 0), - TensorInfo(TensorShape(3U, 3U, 5U, 21U), 1, DataType::F32, 0), - TensorInfo(TensorShape(5U, 5U, 7U, 16U), 1, DataType::F16, 0) - })), - framework::dataset::make("BiasesInfo", { TensorInfo(TensorShape(19U), 1, DataType::F32, 0), - TensorInfo(TensorShape(19U), 1, DataType::F32, 0), - TensorInfo(TensorShape(21U), 1, DataType::F32, 0), - TensorInfo(TensorShape(21U), 1, DataType::F32, 0), - TensorInfo(TensorShape(16U), 1, DataType::F32, 0) - })), +DATA_TEST_CASE(ValidateConvolutionMethod, framework::DatasetMode::ALL, zip(zip(zip(zip(zip(zip( + framework::dataset::make("InputInfo", { TensorInfo(TensorShape(17U, 31U, 2U), 1, DataType::F32, 0), + TensorInfo(TensorShape(17U, 31U, 2U), 1, DataType::F32, 0), + TensorInfo(TensorShape(23U, 27U, 5U, 4U), 1, DataType::F32, 0), + TensorInfo(TensorShape(3U, 3U, 2U, 1U), 1, DataType::F32, 0), + TensorInfo(TensorShape(33U, 27U, 7U, 4U), 1, DataType::F32, 0) + }), + framework::dataset::make("WeightsInfo", { TensorInfo(TensorShape(5U, 5U, 2U, 19U), 1, DataType::F32, 0), + TensorInfo(TensorShape(5U, 5U, 2U, 19U), 1, DataType::F32, 0), + TensorInfo(TensorShape(3U, 3U, 5U, 21U), 1, DataType::F32, 0), + TensorInfo(TensorShape(3U, 3U, 5U, 21U), 1, DataType::F32, 0), + TensorInfo(TensorShape(5U, 5U, 7U, 16U), 1, DataType::F16, 0) + })), framework::dataset::make("OutputInfo", { TensorInfo(TensorShape(15U, 15U, 19U), 1, DataType::F32, 0), TensorInfo(TensorShape(15U, 15U, 19U), 1, DataType::F32, 0), TensorInfo(TensorShape(21U, 25U, 21U, 4U), 1, DataType::F32, 0), @@ -107,12 +101,11 @@ DATA_TEST_CASE(ValidateConvolutionMethod, framework::DatasetMode::ALL, zip(zip(z Size2D(3U, 3U) })), - framework::dataset::make("Expected", { ConvolutionMethod::GEMM, ConvolutionMethod::GEMM, ConvolutionMethod::GEMM, ConvolutionMethod::GEMM, ConvolutionMethod::GEMM })), - input_info, weights_info, biases_info, output_info, conv_info, gpu_target, dilation, expected) + framework::dataset::make("Expected", { ConvolutionMethod::GEMM, ConvolutionMethod::GEMM, ConvolutionMethod::WINOGRAD, ConvolutionMethod::GEMM, ConvolutionMethod::GEMM })), + input_info, weights_info, output_info, conv_info, gpu_target, dilation, expected) { ConvolutionMethod is_valid = CLConvolutionLayer::get_convolution_method(&input_info.clone()->set_is_resizable(false), &weights_info.clone()->set_is_resizable(false), - &biases_info.clone()->set_is_resizable(false), &output_info.clone()->set_is_resizable(false), conv_info, WeightsInfo(), ActivationLayerInfo(), gpu_target, dilation); ARM_COMPUTE_EXPECT(is_valid == expected, framework::LogLevel::ERRORS); } diff --git a/tests/validation/CL/Winograd.cpp b/tests/validation/CL/Winograd.cpp index 4bfe4c8f42..6e673a5f96 100644 --- a/tests/validation/CL/Winograd.cpp +++ b/tests/validation/CL/Winograd.cpp @@ -236,7 +236,9 @@ DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(zip( TensorInfo(TensorShape(512U, 49U, 16U, 5U), 1, DataType::F32), // Valid TensorInfo(TensorShape(13U, 108U, 16U, 4U), 1, DataType::F32), // Padding needed TensorInfo(TensorShape(7U, 20U, 16U, 7U), 1, DataType::F32), // Valid - TensorInfo(TensorShape(7U, 20U, 16U, 7U), 1, DataType::F32) // Wrong WinogradInfo + TensorInfo(TensorShape(7U, 20U, 16U, 7U), 1, DataType::F32), // Wrong WinogradInfo + TensorInfo(TensorShape(7U, 256U, 36U, 3U), 1, DataType::F32), // Valid + TensorInfo(TensorShape(7U, 256U, 16U, 3U), 1, DataType::F32) // Wrong number of batches }), framework::dataset::make("BiasInfo", { TensorInfo(TensorShape(512U), 1, DataType::F16), @@ -245,6 +247,8 @@ DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(zip( TensorInfo(TensorShape(512U), 1, DataType::F32), TensorInfo(TensorShape(13U), 1, DataType::F32), TensorInfo(TensorShape(7U), 1, DataType::F32), + TensorInfo(TensorShape(7U), 1, DataType::F32), + TensorInfo(TensorShape(7U), 1, DataType::F32), TensorInfo(TensorShape(7U), 1, DataType::F32) })), framework::dataset::make("OutputInfo", { @@ -254,7 +258,9 @@ DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(zip( TensorInfo(TensorShape(14U, 14U, 512U, 5U), 1, DataType::F32), TensorInfo(TensorShape(17U, 23U, 13U, 4U), 1, DataType::F32), TensorInfo(TensorShape(8U, 10U, 7U, 7U), 1, DataType::F32), - TensorInfo(TensorShape(7U, 9U, 7U, 7U), 1, DataType::F32) + TensorInfo(TensorShape(7U, 9U, 7U, 7U), 1, DataType::F32), + TensorInfo(TensorShape(64U, 64U, 7U, 3U), 1, DataType::F32), + TensorInfo(TensorShape(64U, 64U, 7U, 3U), 1, DataType::F32) })), framework::dataset::make("WinogradInfo", { WinogradInfo(Size2D(2U, 2U), Size2D(3U, 3U), Size2D(14U, 14U), PadStrideInfo(1, 1, 1, 1), DataLayout::NCHW), @@ -264,8 +270,10 @@ DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(zip( WinogradInfo(Size2D(2U, 2U), Size2D(3U, 3U), Size2D(17U, 23U), PadStrideInfo(1, 1, 1, 1), DataLayout::NCHW), WinogradInfo(Size2D(2U, 2U), Size2D(3U, 3U), Size2D(8U, 10U), PadStrideInfo(1, 1, 1, 1), DataLayout::NCHW), WinogradInfo(Size2D(2U, 3U), Size2D(3U, 3U), Size2D(8U, 10U), PadStrideInfo(1, 1, 0, 0), DataLayout::NCHW), + WinogradInfo(Size2D(4U, 4U), Size2D(3U, 3U), Size2D(64U, 64U), PadStrideInfo(1, 1, 1, 1), DataLayout::NCHW), + WinogradInfo(Size2D(4U, 4U), Size2D(3U, 3U), Size2D(64U, 64U), PadStrideInfo(1, 1, 1, 1), DataLayout::NCHW) })), - framework::dataset::make("Expected", { false, false, false, true, false, true, false })), + framework::dataset::make("Expected", { false, false, false, true, false, true, false, true, false })), input_info, bias_info, output_info, winograd_info, expected) { ARM_COMPUTE_EXPECT(bool(CLWinogradOutputTransformKernel::validate(&input_info.clone()->set_is_resizable(false), &bias_info.clone()->set_is_resizable(false), &output_info.clone()->set_is_resizable(false), winograd_info)) == expected, framework::LogLevel::ERRORS); 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