From dd03870b63784abe499761da2b26b209b33f2db2 Mon Sep 17 00:00:00 2001 From: Giorgio Arena Date: Mon, 16 Apr 2018 11:20:11 +0100 Subject: COMPMID-1037 Add support for F(4x4, 5x5) in CLWinogradOutputTransformKernel Change-Id: I0b126f03028f08687497b0d79d2e2764f7ed07c8 Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/128001 Tested-by: Jenkins Reviewed-by: Anthony Barbier Reviewed-by: Gian Marco Iodice --- src/core/CL/CLKernelLibrary.cpp | 1 + src/core/CL/cl_kernels/winograd.cl | 192 +++++++++++++++++++++ .../CL/kernels/CLWinogradOutputTransformKernel.cpp | 5 +- 3 files changed, 196 insertions(+), 2 deletions(-) (limited to 'src/core') diff --git a/src/core/CL/CLKernelLibrary.cpp b/src/core/CL/CLKernelLibrary.cpp index 48316ce991..50f623fffb 100644 --- a/src/core/CL/CLKernelLibrary.cpp +++ b/src/core/CL/CLKernelLibrary.cpp @@ -364,6 +364,7 @@ const std::map CLKernelLibrary::_kernel_program_map = { "winograd_input_transform_2x2_3x3_stepz1_nchw", "winograd.cl" }, { "winograd_input_transform_2x2_3x3_stepz2_nchw", "winograd.cl" }, { "winograd_output_transform_2x2_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" }, { "YUYV422_to_RGB888_bt709", "color_convert.cl" }, diff --git a/src/core/CL/cl_kernels/winograd.cl b/src/core/CL/cl_kernels/winograd.cl index 9932119003..cda23b0155 100644 --- a/src/core/CL/cl_kernels/winograd.cl +++ b/src/core/CL/cl_kernels/winograd.cl @@ -980,4 +980,196 @@ __kernel void winograd_output_transform_2x2_3x3_nchw( vstore2((float2)(out00, out01), 0, (__global float *)(dst_addr + 0 * dst_stride_y)); vstore2((float2)(out10, out11), 0, (__global float *)(dst_addr + 1 * dst_stride_y)); } + +#define COMPUTE_TMP_COL(col, d0, d1, d2, d3, d4, d5, d6, d7, comm_fact) \ + ({ \ + comm_fact.s0 = d1 + d2; \ + comm_fact.s1 = d3 + d4; \ + comm_fact.s2 = d5 + d6; \ + \ + col.s0 = comm_fact.s0 + comm_fact.s1 + 8.f * comm_fact.s2 + d0; \ + col.s2 = comm_fact.s0 + 4.f * comm_fact.s1 + 2.f * comm_fact.s2; \ + \ + comm_fact.s0 = d1 - d2; \ + comm_fact.s1 = d3 - d4; \ + comm_fact.s2 = d5 - d6; \ + \ + col.s1 = comm_fact.s0 + 2.f * comm_fact.s1 + 4.f * comm_fact.s2; \ + col.s3 = comm_fact.s0 + 8.f * comm_fact.s1 + comm_fact.s2 + d7; \ + }) + +/** This OpenCL kernel performs Winograd output transform when the output tile is 4x4, the filter size 5x5 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_5x5_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 64 channels to compose the 8x8 input 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 d06 = *((__global float *)(src_addr + 6 * src_stride_z)); + float d07 = *((__global float *)(src_addr + 7 * src_stride_z)); + + float d10 = *((__global float *)(src_addr + 8 * src_stride_z)); + float d11 = *((__global float *)(src_addr + 9 * src_stride_z)); + float d12 = *((__global float *)(src_addr + 10 * src_stride_z)); + float d13 = *((__global float *)(src_addr + 11 * src_stride_z)); + float d14 = *((__global float *)(src_addr + 12 * src_stride_z)); + float d15 = *((__global float *)(src_addr + 13 * src_stride_z)); + float d16 = *((__global float *)(src_addr + 14 * src_stride_z)); + float d17 = *((__global float *)(src_addr + 15 * src_stride_z)); + + float d20 = *((__global float *)(src_addr + 16 * src_stride_z)); + float d21 = *((__global float *)(src_addr + 17 * src_stride_z)); + float d22 = *((__global float *)(src_addr + 18 * src_stride_z)); + float d23 = *((__global float *)(src_addr + 19 * src_stride_z)); + float d24 = *((__global float *)(src_addr + 20 * src_stride_z)); + float d25 = *((__global float *)(src_addr + 21 * src_stride_z)); + float d26 = *((__global float *)(src_addr + 22 * src_stride_z)); + float d27 = *((__global float *)(src_addr + 23 * src_stride_z)); + + float d30 = *((__global float *)(src_addr + 24 * src_stride_z)); + float d31 = *((__global float *)(src_addr + 25 * src_stride_z)); + float d32 = *((__global float *)(src_addr + 26 * src_stride_z)); + float d33 = *((__global float *)(src_addr + 27 * src_stride_z)); + float d34 = *((__global float *)(src_addr + 28 * src_stride_z)); + float d35 = *((__global float *)(src_addr + 29 * src_stride_z)); + float d36 = *((__global float *)(src_addr + 30 * src_stride_z)); + float d37 = *((__global float *)(src_addr + 31 * src_stride_z)); + + float d40 = *((__global float *)(src_addr + 32 * src_stride_z)); + float d41 = *((__global float *)(src_addr + 33 * src_stride_z)); + float d42 = *((__global float *)(src_addr + 34 * src_stride_z)); + float d43 = *((__global float *)(src_addr + 35 * src_stride_z)); + float d44 = *((__global float *)(src_addr + 36 * src_stride_z)); + float d45 = *((__global float *)(src_addr + 37 * src_stride_z)); + float d46 = *((__global float *)(src_addr + 38 * src_stride_z)); + float d47 = *((__global float *)(src_addr + 39 * src_stride_z)); + + float d50 = *((__global float *)(src_addr + 40 * src_stride_z)); + float d51 = *((__global float *)(src_addr + 41 * src_stride_z)); + float d52 = *((__global float *)(src_addr + 42 * src_stride_z)); + float d53 = *((__global float *)(src_addr + 43 * src_stride_z)); + float d54 = *((__global float *)(src_addr + 44 * src_stride_z)); + float d55 = *((__global float *)(src_addr + 45 * src_stride_z)); + float d56 = *((__global float *)(src_addr + 46 * src_stride_z)); + float d57 = *((__global float *)(src_addr + 47 * src_stride_z)); + + float d60 = *((__global float *)(src_addr + 48 * src_stride_z)); + float d61 = *((__global float *)(src_addr + 49 * src_stride_z)); + float d62 = *((__global float *)(src_addr + 50 * src_stride_z)); + float d63 = *((__global float *)(src_addr + 51 * src_stride_z)); + float d64 = *((__global float *)(src_addr + 52 * src_stride_z)); + float d65 = *((__global float *)(src_addr + 53 * src_stride_z)); + float d66 = *((__global float *)(src_addr + 54 * src_stride_z)); + float d67 = *((__global float *)(src_addr + 55 * src_stride_z)); + + float d70 = *((__global float *)(src_addr + 56 * src_stride_z)); + float d71 = *((__global float *)(src_addr + 57 * src_stride_z)); + float d72 = *((__global float *)(src_addr + 58 * src_stride_z)); + float d73 = *((__global float *)(src_addr + 59 * src_stride_z)); + float d74 = *((__global float *)(src_addr + 60 * src_stride_z)); + float d75 = *((__global float *)(src_addr + 61 * src_stride_z)); + float d76 = *((__global float *)(src_addr + 62 * src_stride_z)); + float d77 = *((__global float *)(src_addr + 63 * src_stride_z)); + + // Compute the 8x4 intermediate tensor + float4 comm_fact0, comm_fact1, comm_fact2; + float4 tmp_col0, tmp_col1, tmp_col2, tmp_col3, tmp_col4, tmp_col5, tmp_col6, tmp_col7; + + COMPUTE_TMP_COL(tmp_col0, d00, d10, d20, d30, d40, d50, d60, d70, comm_fact0); + COMPUTE_TMP_COL(tmp_col1, d01, d11, d21, d31, d41, d51, d61, d71, comm_fact0); + COMPUTE_TMP_COL(tmp_col2, d02, d12, d22, d32, d42, d52, d62, d72, comm_fact0); + COMPUTE_TMP_COL(tmp_col3, d03, d13, d23, d33, d43, d53, d63, d73, comm_fact0); + COMPUTE_TMP_COL(tmp_col4, d04, d14, d24, d34, d44, d54, d64, d74, comm_fact0); + COMPUTE_TMP_COL(tmp_col5, d05, d15, d25, d35, d45, d55, d65, d75, comm_fact0); + COMPUTE_TMP_COL(tmp_col6, d06, d16, d26, d36, d46, d56, d66, d76, comm_fact0); + COMPUTE_TMP_COL(tmp_col7, d07, d17, d27, d37, d47, d57, d67, d77, comm_fact0); + + // Compute the 4x4 output tile + comm_fact0 = tmp_col1 + tmp_col2; + comm_fact1 = tmp_col3 + tmp_col4; + comm_fact2 = tmp_col5 + tmp_col6; + + float4 out_col0 = comm_fact0 + comm_fact1 + 8.f * comm_fact2 + tmp_col0; + float4 out_col2 = comm_fact0 + 4.f * comm_fact1 + 2.f * comm_fact2; + + comm_fact0 = tmp_col1 - tmp_col2; + comm_fact1 = tmp_col3 - tmp_col4; + comm_fact2 = tmp_col5 - tmp_col6; + + float4 out_col1 = comm_fact0 + 2.f * comm_fact1 + 4.f * comm_fact2; + float4 out_col3 = comm_fact0 + 8.f * comm_fact1 + comm_fact2 + tmp_col7; + + 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))); + + out_col0 += (float4)b; + out_col1 += (float4)b; + out_col2 += (float4)b; + out_col3 += (float4)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 + *(__global float *)(dst_addr + 0 * dst_stride_x + 0 * dst_stride_y) = out_col0.s0; + *(__global float *)(dst_addr + 1 * dst_stride_x + 0 * dst_stride_y) = out_col1.s0; + *(__global float *)(dst_addr + 2 * dst_stride_x + 0 * dst_stride_y) = out_col2.s0; + *(__global float *)(dst_addr + 3 * dst_stride_x + 0 * dst_stride_y) = out_col3.s0; + *(__global float *)(dst_addr + 0 * dst_stride_x + 1 * dst_stride_y) = out_col0.s1; + *(__global float *)(dst_addr + 1 * dst_stride_x + 1 * dst_stride_y) = out_col1.s1; + *(__global float *)(dst_addr + 2 * dst_stride_x + 1 * dst_stride_y) = out_col2.s1; + *(__global float *)(dst_addr + 3 * dst_stride_x + 1 * dst_stride_y) = out_col3.s1; + *(__global float *)(dst_addr + 0 * dst_stride_x + 2 * dst_stride_y) = out_col0.s2; + *(__global float *)(dst_addr + 1 * dst_stride_x + 2 * dst_stride_y) = out_col1.s2; + *(__global float *)(dst_addr + 2 * dst_stride_x + 2 * dst_stride_y) = out_col2.s2; + *(__global float *)(dst_addr + 3 * dst_stride_x + 2 * dst_stride_y) = out_col3.s2; + *(__global float *)(dst_addr + 0 * dst_stride_x + 3 * dst_stride_y) = out_col0.s3; + *(__global float *)(dst_addr + 1 * dst_stride_x + 3 * dst_stride_y) = out_col1.s3; + *(__global float *)(dst_addr + 2 * dst_stride_x + 3 * dst_stride_y) = out_col2.s3; + *(__global float *)(dst_addr + 3 * dst_stride_x + 3 * dst_stride_y) = out_col3.s3; +} #endif // defined(NUM_TILES_X) diff --git a/src/core/CL/kernels/CLWinogradOutputTransformKernel.cpp b/src/core/CL/kernels/CLWinogradOutputTransformKernel.cpp index b59bc79327..8ee1a82209 100644 --- a/src/core/CL/kernels/CLWinogradOutputTransformKernel.cpp +++ b/src/core/CL/kernels/CLWinogradOutputTransformKernel.cpp @@ -56,8 +56,9 @@ Status validate_arguments(const ITensorInfo *input, const ITensorInfo *bias, con const Size2D kernel_size = winograd_info.kernel_size; const Size2D input_dimensions = winograd_info.input_dimensions; - ARM_COMPUTE_RETURN_ERROR_ON_MSG(kernel_size != Size2D(3U, 3U), "Only 3x3 kernels are supported"); - ARM_COMPUTE_RETURN_ERROR_ON_MSG(input->dimension(2) != 16, "Only 2x2 output tile is supported"); + 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(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 const int num_elements_x = input_dimensions.width - (kernel_size.width - 1) + conv_info.pad_left() + conv_info.pad_right(); -- cgit v1.2.1