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Diffstat (limited to 'src/core/CL/cl_kernels/nchw/winograd_output_transform.cl')
| -rw-r--r-- | src/core/CL/cl_kernels/nchw/winograd_output_transform.cl | 1082 |
1 files changed, 1082 insertions, 0 deletions
diff --git a/src/core/CL/cl_kernels/nchw/winograd_output_transform.cl b/src/core/CL/cl_kernels/nchw/winograd_output_transform.cl new file mode 100644 index 0000000000..861ed50651 --- /dev/null +++ b/src/core/CL/cl_kernels/nchw/winograd_output_transform.cl @@ -0,0 +1,1082 @@ +/* + * Copyright (c) 2018-2021 Arm Limited. + * + * SPDX-License-Identifier: MIT + * + * Permission is hereby granted, free of charge, to any person obtaining a copy + * of this software and associated documentation files (the "Software"), to + * deal in the Software without restriction, including without limitation the + * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or + * sell copies of the Software, and to permit persons to whom the Software is + * furnished to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all + * copies or substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + * SOFTWARE. + */ +#include "activation_float_helpers.h" +#include "helpers.h" +#include "tile_helpers.h" + +#if defined(NUM_TILES_X) && defined(OUTPUT_TILE_W) && defined(OUTPUT_TILE_H) +#if defined(VEC_SIZE) && VEC_SIZE == 2 +/** This OpenCL kernel performs Winograd output transform when the output tile is 2x2/2x1 or 1x2, the filter size 3x3/3x1 or 1x3 and the data layout 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 + * @note The width of the output tile must be passed at compile time using -DOUTPUT_TILE_W: e.g. -DOUTPUT_TILE_W=2 + * @note The height of the output tile must be passed at compile time using -DOUTPUT_TILE_H: e.g. -DOUTPUT_TILE_H=2 + * @note If this kernel is used to perform Winograd output transform 3x1, -DWINOGRAD_OUTPUT_TRANSFORM_HORIZONTAL has to be passed at compile time + * @note If this kernel is used to perform Winograd output transform 1x3, -DWINOGRAD_OUTPUT_TRANSFORM_VERTICAL has to be passed at compile time + * @note The data type must be passed at compile time using -DDATA_TYPE e.g. -DDATA_TYPE=float. Supported data types: float/half. + * @note It is possible to select the activation function to apply using -DACTIVATION_TYPE e.g. -DACTIVATION_TYPE=relu + * @note A, B variables required by some activation functions are set using -DA_VAL= and -DB_VAL= respectively. + * @note Vector size should be given as a preprocessor argument using -DVEC_SIZE=size. Accepted values are -DVEC_SIZE=2 (for output_tile_size 2x2, 2x1, 1x2) and -DVEC_SIZE=4 (for output_tile_size 4x4, 4x1, 1x4) + * + * @param[in] src_ptr Pointer to the source tensor. Supported data types: F32/F16 + * @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_stride_w Stride of the source tensor in W dimension (in bytes) + * @param[in] src_step_w src_stride_w * number of elements along W 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] dst_stride_z Stride of the source tensor in Z dimension (in bytes) + * @param[in] dst_step_z dst_stride_z * number of elements along Z processed per workitem(in bytes) + * @param[in] dst_stride_w Stride of the source tensor in W dimension (in bytes) + * @param[in] dst_step_w dst_stride_w * number of elements along W 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_2x2_3x3_nchw( + TENSOR4D_DECLARATION(src), + TENSOR4D_DECLARATION(dst) +#if defined(HAS_BIAS) + , + VECTOR_DECLARATION(bias) +#endif // defined(HAS_BIAS) +) +{ + // Each thread stores a 2x2/2x1 or 1x2 tile accordingly with the filter size +#if defined(SRC_DEPTH) + Tensor4D src = CONVERT_TO_TENSOR4D_STRUCT(src, SRC_DEPTH); + const __global uchar *src_addr = tensor4D_offset(&src, 0, 0, 0, 0); +#else /* defined(SRC_DEPTH) */ + Tensor3D src = CONVERT_TO_TENSOR3D_STRUCT(src); + const __global uchar *src_addr = tensor3D_offset(&src, 0, 0, 0); +#endif /* defined(SRC_DEPTH) */ + + // Load the values across the 16 or 4 channels to compose the 4x4 or 4x1 tile + DATA_TYPE d00 = *((__global DATA_TYPE *)(src_addr + 0 * src_stride_z)); + DATA_TYPE d01 = *((__global DATA_TYPE *)(src_addr + 1 * src_stride_z)); + DATA_TYPE d02 = *((__global DATA_TYPE *)(src_addr + 2 * src_stride_z)); + DATA_TYPE d03 = *((__global DATA_TYPE *)(src_addr + 3 * src_stride_z)); + +#if defined(WINOGRAD_OUTPUT_TRANSFORM_HORIZONTAL) || defined(WINOGRAD_OUTPUT_TRANSFORM_VERTICAL) + // Compute the 2x1 or 1x2 output tile + // out00 = d00 + d01 + d02 + // out01 = d01 - d02 - d03 + + float out00 = d00 + d01 + d02; + float out01 = d01 - d02 - d03; +#else // defined(WINOGRAD_OUTPUT_TRANSFORM_HORIZONTAL) || defined(WINOGRAD_OUTPUT_TRANSFORM_VERTICAL) + + DATA_TYPE d10 = *((__global DATA_TYPE *)(src_addr + 4 * src_stride_z)); + DATA_TYPE d11 = *((__global DATA_TYPE *)(src_addr + 5 * src_stride_z)); + DATA_TYPE d12 = *((__global DATA_TYPE *)(src_addr + 6 * src_stride_z)); + DATA_TYPE d13 = *((__global DATA_TYPE *)(src_addr + 7 * src_stride_z)); + + DATA_TYPE d20 = *((__global DATA_TYPE *)(src_addr + 8 * src_stride_z)); + DATA_TYPE d21 = *((__global DATA_TYPE *)(src_addr + 9 * src_stride_z)); + DATA_TYPE d22 = *((__global DATA_TYPE *)(src_addr + 10 * src_stride_z)); + DATA_TYPE d23 = *((__global DATA_TYPE *)(src_addr + 11 * src_stride_z)); + + DATA_TYPE d30 = *((__global DATA_TYPE *)(src_addr + 12 * src_stride_z)); + DATA_TYPE d31 = *((__global DATA_TYPE *)(src_addr + 13 * src_stride_z)); + DATA_TYPE d32 = *((__global DATA_TYPE *)(src_addr + 14 * src_stride_z)); + DATA_TYPE d33 = *((__global DATA_TYPE *)(src_addr + 15 * src_stride_z)); + + // Compute the 2x2 output tile + float k0 = d01 + d11 + d21; + float k1 = d02 + d12 + d22; + float k2 = d11 - d21 - d31; + float k3 = d12 - d22 - d32; + + // out00 = d00 + d10 + d20 + d01 + d11 + d21 + d02 + d12 + d22 + // out01 = d01 + d11 + d21 - (d02 + d12 + d22) - (d03 + d13 + d23) + // out10 = d10 - d20 - d30 + (d11 - d21 - d31) + (d12 - d22 - d32) + // out11 = d11 - d21 - d31 - (d12 - d22 - d32) - (d13 - d23 - d33) + + float out00 = d10; + float out01 = -d13; + float out10 = d10; + float out11 = -d13; + + out00 += d00 + d20 + k0 + k1; + out01 += k0 - k1 - (d03 + d23); + out10 += -d20 - d30 + k2 + k3; + out11 += k2 - k3 + d23 + d33; +#endif // defined(WINOGRAD_OUTPUT_TRANSFORM_HORIZONTAL) || defined(WINOGRAD_OUTPUT_TRANSFORM_VERTICAL) + + int y_in = get_global_id(1); + int x_out = (y_in % NUM_TILES_X) * OUTPUT_TILE_W; + int y_out = (y_in / NUM_TILES_X) * OUTPUT_TILE_H; + int z_out = get_global_id(0); +#if defined(SRC_DEPTH) + int batch = get_global_id(2) / SRC_DEPTH; +#endif /* defined(SRC_DEPTH) */ + +#if defined(HAS_BIAS) + // Add bias + Vector bias = CONVERT_TO_VECTOR_STRUCT_NO_STEP(bias); + + float b = (float) * ((__global DATA_TYPE *)(vector_offset(&bias, z_out))); + + out00 += (float)b; + out01 += (float)b; +#endif // defined(HAS_BIAS) + + // Get output address +#if defined(SRC_DEPTH) + __global uchar *dst_addr = dst_ptr + dst_offset_first_element_in_bytes + x_out * sizeof(DATA_TYPE) + y_out * dst_stride_y + z_out * dst_stride_z + batch * dst_stride_w; +#else /* defined(SRC_DEPTH) */ + __global uchar *dst_addr = dst_ptr + dst_offset_first_element_in_bytes + x_out * sizeof(DATA_TYPE) + y_out * dst_stride_y + z_out * dst_stride_z; +#endif /* defined(SRC_DEPTH) */ + + // Store the output tile +#if defined(WINOGRAD_OUTPUT_TRANSFORM_VERTICAL) + const VEC_DATA_TYPE(DATA_TYPE, 2) + out0_dt = ACTIVATION(ACTIVATION_TYPE, DATA_TYPE, VEC_SIZE, CONVERT((VEC_DATA_TYPE(float, 2))(out00, out01), VEC_DATA_TYPE(DATA_TYPE, 2)), A_VAL, B_VAL); + *((__global DATA_TYPE *)(dst_addr + 0 * dst_stride_y)) = out0_dt.s0; + *((__global DATA_TYPE *)(dst_addr + 1 * dst_stride_y)) = out0_dt.s1; +#else // defined(WINOGRAD_OUTPUT_TRANSFORM_VERTICAL) + vstore2(ACTIVATION(ACTIVATION_TYPE, DATA_TYPE, VEC_SIZE, CONVERT((VEC_DATA_TYPE(float, 2))(out00, out01), VEC_DATA_TYPE(DATA_TYPE, 2)), A_VAL, B_VAL), 0, + (__global DATA_TYPE *)(dst_addr + 0 * dst_stride_y)); +#endif // defined(WINOGRAD_OUTPUT_TRANSFORM_VERTICAL) + +#if !defined(WINOGRAD_OUTPUT_TRANSFORM_HORIZONTAL) && !defined(WINOGRAD_OUTPUT_TRANSFORM_VERTICAL) +#if defined(HAS_BIAS) + // Add bias + out10 += (DATA_TYPE)b; + out11 += (DATA_TYPE)b; +#endif // defined(HAS_BIAS) + vstore2(ACTIVATION(ACTIVATION_TYPE, DATA_TYPE, VEC_SIZE, CONVERT((VEC_DATA_TYPE(float, 2))(out10, out11), VEC_DATA_TYPE(DATA_TYPE, 2)), A_VAL, B_VAL), 0, + (__global DATA_TYPE *)(dst_addr + 1 * dst_stride_y)); +#endif // !defined(WINOGRAD_OUTPUT_TRANSFORM_HORIZONTAL) && !defined(WINOGRAD_OUTPUT_TRANSFORM_VERTICAL) +} +#endif // defined(VEC_SIZE) && VEC_SIZE == 2 + +#if defined(VEC_SIZE) && VEC_SIZE == 4 +/** This OpenCL kernel performs Winograd output transform when the output tile is 4x4, the filter size 3x3 and the data layout 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 + * @note The width of the output tile must be passed at compile time using -DOUTPUT_TILE_W: e.g. -DOUTPUT_TILE_W=4 + * @note The height of the output tile must be passed at compile time using -DOUTPUT_TILE_H: e.g. -DOUTPUT_TILE_H=4 + * @note If this kernel is used to perform Winograd output transform 3x1, -DWINOGRAD_OUTPUT_TRANSFORM_HORIZONTAL has to be passed at compile time + * @note If this kernel is used to perform Winograd output transform 1x3, -DWINOGRAD_OUTPUT_TRANSFORM_VERTICAL has to be passed at compile time + * @note The data type must be passed at compile time using -DDATA_TYPE e.g. -DDATA_TYPE=float. Supported data types: float/half. + * + * @param[in] src_ptr Pointer to the source tensor. Supported data types: F32/F16 + * @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_stride_w Stride of the source tensor in W dimension (in bytes) + * @param[in] src_step_w src_stride_w * number of elements along W 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] dst_stride_z Stride of the source tensor in Z dimension (in bytes) + * @param[in] dst_step_z dst_stride_z * number of elements along Z processed per workitem(in bytes) + * @param[in] dst_stride_w Stride of the source tensor in W dimension (in bytes) + * @param[in] dst_step_w dst_stride_w * number of elements along W 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( + TENSOR4D_DECLARATION(src), + TENSOR4D_DECLARATION(dst) +#if defined(HAS_BIAS) + , + VECTOR_DECLARATION(bias) +#endif // defined(HAS_BIAS) +) +{ + // Each thread stores a 4x4/4x1 or 1x4 tile +#if defined(SRC_DEPTH) + Tensor4D src = CONVERT_TO_TENSOR4D_STRUCT(src, SRC_DEPTH); + const __global uchar *src_addr = tensor4D_offset(&src, 0, 0, 0, 0); +#else /* defined(SRC_DEPTH) */ + Tensor3D src = CONVERT_TO_TENSOR3D_STRUCT(src); + const __global uchar *src_addr = tensor3D_offset(&src, 0, 0, 0); +#endif /* defined(SRC_DEPTH) */ + + // Load the values across the channels to compose the 6x6 or 6x1 tile + DATA_TYPE d00 = *((__global DATA_TYPE *)(src_addr + 0 * src_stride_z)); + DATA_TYPE d01 = *((__global DATA_TYPE *)(src_addr + 1 * src_stride_z)); + DATA_TYPE d02 = *((__global DATA_TYPE *)(src_addr + 2 * src_stride_z)); + DATA_TYPE d03 = *((__global DATA_TYPE *)(src_addr + 3 * src_stride_z)); + DATA_TYPE d04 = *((__global DATA_TYPE *)(src_addr + 4 * src_stride_z)); + DATA_TYPE d05 = *((__global DATA_TYPE *)(src_addr + 5 * src_stride_z)); + +#if defined(WINOGRAD_OUTPUT_TRANSFORM_HORIZONTAL) || defined(WINOGRAD_OUTPUT_TRANSFORM_VERTICAL) + // Compute out00, out01, out02 and out03 + float out00 = d00 + d01 + d02 + d03 + d04; + float out01 = d01 - d02 + 2.0f * d03 - 2.0f * d04; + float out02 = d01 + d02 + 4.0f * d03 + 4.0f * d04; + float out03 = d01 - d02 + 8.0f * d03 - 8.0f * d04 + d05; +#else // defined(WINOGRAD_OUTPUT_TRANSFORM_HORIZONTAL) || defined(WINOGRAD_OUTPUT_TRANSFORM_VERTICAL) + + DATA_TYPE d10 = *((__global DATA_TYPE *)(src_addr + 6 * src_stride_z)); + DATA_TYPE d11 = *((__global DATA_TYPE *)(src_addr + 7 * src_stride_z)); + DATA_TYPE d12 = *((__global DATA_TYPE *)(src_addr + 8 * src_stride_z)); + DATA_TYPE d13 = *((__global DATA_TYPE *)(src_addr + 9 * src_stride_z)); + DATA_TYPE d14 = *((__global DATA_TYPE *)(src_addr + 10 * src_stride_z)); + DATA_TYPE d15 = *((__global DATA_TYPE *)(src_addr + 11 * src_stride_z)); + + DATA_TYPE d20 = *((__global DATA_TYPE *)(src_addr + 12 * src_stride_z)); + DATA_TYPE d21 = *((__global DATA_TYPE *)(src_addr + 13 * src_stride_z)); + DATA_TYPE d22 = *((__global DATA_TYPE *)(src_addr + 14 * src_stride_z)); + DATA_TYPE d23 = *((__global DATA_TYPE *)(src_addr + 15 * src_stride_z)); + DATA_TYPE d24 = *((__global DATA_TYPE *)(src_addr + 16 * src_stride_z)); + DATA_TYPE d25 = *((__global DATA_TYPE *)(src_addr + 17 * src_stride_z)); + + DATA_TYPE d30 = *((__global DATA_TYPE *)(src_addr + 18 * src_stride_z)); + DATA_TYPE d31 = *((__global DATA_TYPE *)(src_addr + 19 * src_stride_z)); + DATA_TYPE d32 = *((__global DATA_TYPE *)(src_addr + 20 * src_stride_z)); + DATA_TYPE d33 = *((__global DATA_TYPE *)(src_addr + 21 * src_stride_z)); + DATA_TYPE d34 = *((__global DATA_TYPE *)(src_addr + 22 * src_stride_z)); + DATA_TYPE d35 = *((__global DATA_TYPE *)(src_addr + 23 * src_stride_z)); + + DATA_TYPE d40 = *((__global DATA_TYPE *)(src_addr + 24 * src_stride_z)); + DATA_TYPE d41 = *((__global DATA_TYPE *)(src_addr + 25 * src_stride_z)); + DATA_TYPE d42 = *((__global DATA_TYPE *)(src_addr + 26 * src_stride_z)); + DATA_TYPE d43 = *((__global DATA_TYPE *)(src_addr + 27 * src_stride_z)); + DATA_TYPE d44 = *((__global DATA_TYPE *)(src_addr + 28 * src_stride_z)); + DATA_TYPE d45 = *((__global DATA_TYPE *)(src_addr + 29 * src_stride_z)); + + DATA_TYPE d50 = *((__global DATA_TYPE *)(src_addr + 30 * src_stride_z)); + DATA_TYPE d51 = *((__global DATA_TYPE *)(src_addr + 31 * src_stride_z)); + DATA_TYPE d52 = *((__global DATA_TYPE *)(src_addr + 32 * src_stride_z)); + DATA_TYPE d53 = *((__global DATA_TYPE *)(src_addr + 33 * src_stride_z)); + DATA_TYPE d54 = *((__global DATA_TYPE *)(src_addr + 34 * src_stride_z)); + DATA_TYPE d55 = *((__global DATA_TYPE *)(src_addr + 35 * src_stride_z)); + + // Compute out00, out01, out02 and out03 + float out00 = (float)d01 + (float)d21 + (float)d41 + (float)d11 + (float)d31; + float out01 = (float)d01 + (float)d21 + (float)d41 + (float)d11 + (float)d31; + float out02 = (float)d01 + (float)d21 + (float)d41 + (float)d11 + (float)d31; + float out03 = (float)d01 + d21 + (float)d41 + (float)d11 + (float)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; +#endif // defined(WINOGRAD_OUTPUT_TRANSFORM_HORIZONTAL) || defined(WINOGRAD_OUTPUT_TRANSFORM_VERTICAL) + + int y_in = get_global_id(1); + int x_out = (y_in % NUM_TILES_X) * OUTPUT_TILE_W; + int y_out = (y_in / NUM_TILES_X) * OUTPUT_TILE_H; + int z_out = get_global_id(0); +#if defined(SRC_DEPTH) + int batch = get_global_id(2) / SRC_DEPTH; +#endif /* defined(SRC_DEPTH) */ + +#if defined(HAS_BIAS) + // Add bias + Vector bias = CONVERT_TO_VECTOR_STRUCT_NO_STEP(bias); + + float b = (float) * ((__global DATA_TYPE *)(vector_offset(&bias, z_out))); + + out00 += (float)b; + out01 += (float)b; + out02 += (float)b; + out03 += (float)b; +#endif // defined(HAS_BIAS) + + // Get output address +#if defined(SRC_DEPTH) + __global uchar *dst_addr = dst_ptr + dst_offset_first_element_in_bytes + x_out * sizeof(DATA_TYPE) + y_out * dst_stride_y + z_out * dst_stride_z + batch * dst_stride_w; +#else /* defined(SRC_DEPTH) */ + __global uchar *dst_addr = dst_ptr + dst_offset_first_element_in_bytes + x_out * sizeof(DATA_TYPE) + y_out * dst_stride_y + z_out * dst_stride_z; +#endif /* defined(SRC_DEPTH) */ + + // Store the output tile + const VEC_DATA_TYPE(DATA_TYPE, 4) + out0_dt = CONVERT(ACTIVATION(ACTIVATION_TYPE, float, VEC_SIZE, (VEC_DATA_TYPE(float, 4))(out00, out01, out02, out03), A_VAL, B_VAL), VEC_DATA_TYPE(DATA_TYPE, 4)); + +#if defined(WINOGRAD_OUTPUT_TRANSFORM_VERTICAL) + *((__global DATA_TYPE *)(dst_addr + 0 * dst_stride_y)) = out0_dt.s0; + *((__global DATA_TYPE *)(dst_addr + 1 * dst_stride_y)) = out0_dt.s1; + *((__global DATA_TYPE *)(dst_addr + 2 * dst_stride_y)) = out0_dt.s2; + *((__global DATA_TYPE *)(dst_addr + 3 * dst_stride_y)) = out0_dt.s3; +#else // defined(WINOGRAD_OUTPUT_TRANSFORM_VERTICAL) + vstore4(out0_dt, 0, (__global DATA_TYPE *)(dst_addr + 0 * dst_stride_y)); +#endif // defined(WINOGRAD_OUTPUT_TRANSFORM_VERTICAL) + +#if !defined(WINOGRAD_OUTPUT_TRANSFORM_HORIZONTAL) && !defined(WINOGRAD_OUTPUT_TRANSFORM_VERTICAL) +#if defined(HAS_BIAS) + // Add bias + 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) + vstore4(CONVERT(ACTIVATION(ACTIVATION_TYPE, float, VEC_SIZE, (VEC_DATA_TYPE(float, 4))(out10, out11, out12, out13), A_VAL, B_VAL), VEC_DATA_TYPE(DATA_TYPE, 4)), 0, + (__global DATA_TYPE *)(dst_addr + 1 * dst_stride_y)); + vstore4(CONVERT(ACTIVATION(ACTIVATION_TYPE, float, VEC_SIZE, (VEC_DATA_TYPE(float, 4))(out20, out21, out22, out23), A_VAL, B_VAL), VEC_DATA_TYPE(DATA_TYPE, 4)), 0, + (__global DATA_TYPE *)(dst_addr + 2 * dst_stride_y)); + vstore4(CONVERT(ACTIVATION(ACTIVATION_TYPE, float, VEC_SIZE, (VEC_DATA_TYPE(float, 4))(out30, out31, out32, out33), A_VAL, B_VAL), VEC_DATA_TYPE(DATA_TYPE, 4)), 0, + (__global DATA_TYPE *)(dst_addr + 3 * dst_stride_y)); +#endif // !defined(WINOGRAD_OUTPUT_TRANSFORM_HORIZONTAL) && !defined(WINOGRAD_OUTPUT_TRANSFORM_VERTICAL) +} + +#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 4x4/4x1 or 1x4, the filter size 5x5/5x1 or 1x5 and the data layout 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 + * @note The width of the output tile must be passed at compile time using -DOUTPUT_TILE_W: e.g. -DOUTPUT_TILE_W=4 + * @note The height of the output tile must be passed at compile time using -DOUTPUT_TILE_H: e.g. -DOUTPUT_TILE_H=4 + * @note If this kernel is used to perform Winograd output transform 3x1, -DWINOGRAD_OUTPUT_TRANSFORM_HORIZONTAL has to be passed at compile time + * @note If this kernel is used to perform Winograd output transform 1x3, -DWINOGRAD_OUTPUT_TRANSFORM_VERTICAL has to be passed at compile time + * @note The data type must be passed at compile time using -DDATA_TYPE e.g. -DDATA_TYPE=float. Supported data types: float/half. + * + * @param[in] src_ptr Pointer to the source tensor. Supported data types: F32/F16 + * @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_stride_w Stride of the source tensor in W dimension (in bytes) + * @param[in] src_step_w src_stride_w * number of elements along W 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] dst_stride_z Stride of the source tensor in Z dimension (in bytes) + * @param[in] dst_step_z dst_stride_z * number of elements along Z processed per workitem(in bytes) + * @param[in] dst_stride_w Stride of the source tensor in W dimension (in bytes) + * @param[in] dst_step_w dst_stride_w * number of elements along W 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( + TENSOR4D_DECLARATION(src), + TENSOR4D_DECLARATION(dst) +#if defined(HAS_BIAS) + , + VECTOR_DECLARATION(bias) +#endif // defined(HAS_BIAS) +) +{ + // Each thread stores a 4x4/4x1 or 1x4 tile +#if defined(SRC_DEPTH) + Tensor4D src = CONVERT_TO_TENSOR4D_STRUCT(src, SRC_DEPTH); + const __global uchar *src_addr = tensor4D_offset(&src, 0, 0, 0, 0); +#else /* defined(SRC_DEPTH) */ + + Tensor3D src = CONVERT_TO_TENSOR3D_STRUCT(src); + const __global uchar *src_addr = tensor3D_offset(&src, 0, 0, 0); +#endif /* defined(SRC_DEPTH) */ + + // Compute output address + int y_in = get_global_id(1); + int x_out = (y_in % NUM_TILES_X) * OUTPUT_TILE_W; + int y_out = (y_in / NUM_TILES_X) * OUTPUT_TILE_H; + int z_out = get_global_id(0); +#if defined(SRC_DEPTH) + int batch = get_global_id(2) / SRC_DEPTH; +#endif /* defined(SRC_DEPTH) */ + +#if defined(SRC_DEPTH) + __global uchar *dst_addr = dst_ptr + dst_offset_first_element_in_bytes + x_out * sizeof(DATA_TYPE) + y_out * dst_stride_y + z_out * dst_stride_z + batch * dst_stride_w; +#else /* defined(SRC_DEPTH) */ + + __global uchar *dst_addr = dst_ptr + dst_offset_first_element_in_bytes + x_out * sizeof(DATA_TYPE) + y_out * dst_stride_y + z_out * dst_stride_z; +#endif /* defined(SRC_DEPTH) */ + + // Load the values across the channels to compose the input tile + DATA_TYPE d00 = *((__global DATA_TYPE *)(src_addr + 0 * src_stride_z)); + DATA_TYPE d01 = *((__global DATA_TYPE *)(src_addr + 1 * src_stride_z)); + DATA_TYPE d02 = *((__global DATA_TYPE *)(src_addr + 2 * src_stride_z)); + DATA_TYPE d03 = *((__global DATA_TYPE *)(src_addr + 3 * src_stride_z)); + DATA_TYPE d04 = *((__global DATA_TYPE *)(src_addr + 4 * src_stride_z)); + DATA_TYPE d05 = *((__global DATA_TYPE *)(src_addr + 5 * src_stride_z)); + DATA_TYPE d06 = *((__global DATA_TYPE *)(src_addr + 6 * src_stride_z)); + DATA_TYPE d07 = *((__global DATA_TYPE *)(src_addr + 7 * src_stride_z)); + +#if defined(WINOGRAD_OUTPUT_TRANSFORM_HORIZONTAL) || defined(WINOGRAD_OUTPUT_TRANSFORM_VERTICAL) + // Compute out00, out01, out02 and out03 + float out00 = d00 + d01 + d02 + d03 + d04 + 8.0f * d05 + 8.0f * d06; + float out01 = d01 - d02 + 2.0f * d03 - 2.0f * d04 + 4.0f * d05 - 4.0f * d06; + float out02 = d01 + d02 + 4.0f * d03 + 4.0f * d04 + 2.0f * d05 + 2.0f * d06; + float out03 = d01 - d02 + 8.0f * d03 - 8.0f * d04 + d05 - d06 + d07; + +#if defined(HAS_BIAS) + // Add bias + Vector bias = CONVERT_TO_VECTOR_STRUCT_NO_STEP(bias); + + float b = (float) * ((__global DATA_TYPE *)(vector_offset(&bias, z_out))); + + out00 += (DATA_TYPE)b; + out01 += (DATA_TYPE)b; + out02 += (DATA_TYPE)b; + out03 += (DATA_TYPE)b; +#endif // defined(HAS_BIAS) + + // Store the output tile +#if defined(WINOGRAD_OUTPUT_TRANSFORM_VERTICAL) + VEC_DATA_TYPE(DATA_TYPE, 4) + out0_dt = CONVERT(ACTIVATION(ACTIVATION_TYPE, float, VEC_SIZE, (VEC_DATA_TYPE(float, 4))(out00, out01, out02, out03), A_VAL, + B_VAL), + VEC_DATA_TYPE(DATA_TYPE, 4)); + *((__global DATA_TYPE *)(dst_addr + 0 * dst_stride_y)) = out0_dt.s0; + *((__global DATA_TYPE *)(dst_addr + 1 * dst_stride_y)) = out0_dt.s1; + *((__global DATA_TYPE *)(dst_addr + 2 * dst_stride_y)) = out0_dt.s2; + *((__global DATA_TYPE *)(dst_addr + 3 * dst_stride_y)) = out0_dt.s3; +#else // defined(WINOGRAD_OUTPUT_TRANSFORM_VERTICAL) + vstore4(CONVERT(ACTIVATION(ACTIVATION_TYPE, float, VEC_SIZE, (VEC_DATA_TYPE(float, 4))(out00, out01, out02, out03), A_VAL, B_VAL), VEC_DATA_TYPE(DATA_TYPE, 4)), + 0, (__global DATA_TYPE *)(dst_addr)); +#endif // defined(WINOGRAD_OUTPUT_TRANSFORM_VERTICAL) + +#else // defined(WINOGRAD_OUTPUT_TRANSFORM_HORIZONTAL) || defined(WINOGRAD_OUTPUT_TRANSFORM_VERTICAL) + + DATA_TYPE d10 = *((__global DATA_TYPE *)(src_addr + 8 * src_stride_z)); + DATA_TYPE d11 = *((__global DATA_TYPE *)(src_addr + 9 * src_stride_z)); + DATA_TYPE d12 = *((__global DATA_TYPE *)(src_addr + 10 * src_stride_z)); + DATA_TYPE d13 = *((__global DATA_TYPE *)(src_addr + 11 * src_stride_z)); + DATA_TYPE d14 = *((__global DATA_TYPE *)(src_addr + 12 * src_stride_z)); + DATA_TYPE d15 = *((__global DATA_TYPE *)(src_addr + 13 * src_stride_z)); + DATA_TYPE d16 = *((__global DATA_TYPE *)(src_addr + 14 * src_stride_z)); + DATA_TYPE d17 = *((__global DATA_TYPE *)(src_addr + 15 * src_stride_z)); + + DATA_TYPE d20 = *((__global DATA_TYPE *)(src_addr + 16 * src_stride_z)); + DATA_TYPE d21 = *((__global DATA_TYPE *)(src_addr + 17 * src_stride_z)); + DATA_TYPE d22 = *((__global DATA_TYPE *)(src_addr + 18 * src_stride_z)); + DATA_TYPE d23 = *((__global DATA_TYPE *)(src_addr + 19 * src_stride_z)); + DATA_TYPE d24 = *((__global DATA_TYPE *)(src_addr + 20 * src_stride_z)); + DATA_TYPE d25 = *((__global DATA_TYPE *)(src_addr + 21 * src_stride_z)); + DATA_TYPE d26 = *((__global DATA_TYPE *)(src_addr + 22 * src_stride_z)); + DATA_TYPE d27 = *((__global DATA_TYPE *)(src_addr + 23 * src_stride_z)); + + DATA_TYPE d30 = *((__global DATA_TYPE *)(src_addr + 24 * src_stride_z)); + DATA_TYPE d31 = *((__global DATA_TYPE *)(src_addr + 25 * src_stride_z)); + DATA_TYPE d32 = *((__global DATA_TYPE *)(src_addr + 26 * src_stride_z)); + DATA_TYPE d33 = *((__global DATA_TYPE *)(src_addr + 27 * src_stride_z)); + DATA_TYPE d34 = *((__global DATA_TYPE *)(src_addr + 28 * src_stride_z)); + DATA_TYPE d35 = *((__global DATA_TYPE *)(src_addr + 29 * src_stride_z)); + DATA_TYPE d36 = *((__global DATA_TYPE *)(src_addr + 30 * src_stride_z)); + DATA_TYPE d37 = *((__global DATA_TYPE *)(src_addr + 31 * src_stride_z)); + + DATA_TYPE d40 = *((__global DATA_TYPE *)(src_addr + 32 * src_stride_z)); + DATA_TYPE d41 = *((__global DATA_TYPE *)(src_addr + 33 * src_stride_z)); + DATA_TYPE d42 = *((__global DATA_TYPE *)(src_addr + 34 * src_stride_z)); + DATA_TYPE d43 = *((__global DATA_TYPE *)(src_addr + 35 * src_stride_z)); + DATA_TYPE d44 = *((__global DATA_TYPE *)(src_addr + 36 * src_stride_z)); + DATA_TYPE d45 = *((__global DATA_TYPE *)(src_addr + 37 * src_stride_z)); + DATA_TYPE d46 = *((__global DATA_TYPE *)(src_addr + 38 * src_stride_z)); + DATA_TYPE d47 = *((__global DATA_TYPE *)(src_addr + 39 * src_stride_z)); + + DATA_TYPE d50 = *((__global DATA_TYPE *)(src_addr + 40 * src_stride_z)); + DATA_TYPE d51 = *((__global DATA_TYPE *)(src_addr + 41 * src_stride_z)); + DATA_TYPE d52 = *((__global DATA_TYPE *)(src_addr + 42 * src_stride_z)); + DATA_TYPE d53 = *((__global DATA_TYPE *)(src_addr + 43 * src_stride_z)); + DATA_TYPE d54 = *((__global DATA_TYPE *)(src_addr + 44 * src_stride_z)); + DATA_TYPE d55 = *((__global DATA_TYPE *)(src_addr + 45 * src_stride_z)); + DATA_TYPE d56 = *((__global DATA_TYPE *)(src_addr + 46 * src_stride_z)); + DATA_TYPE d57 = *((__global DATA_TYPE *)(src_addr + 47 * src_stride_z)); + + DATA_TYPE d60 = *((__global DATA_TYPE *)(src_addr + 48 * src_stride_z)); + DATA_TYPE d61 = *((__global DATA_TYPE *)(src_addr + 49 * src_stride_z)); + DATA_TYPE d62 = *((__global DATA_TYPE *)(src_addr + 50 * src_stride_z)); + DATA_TYPE d63 = *((__global DATA_TYPE *)(src_addr + 51 * src_stride_z)); + DATA_TYPE d64 = *((__global DATA_TYPE *)(src_addr + 52 * src_stride_z)); + DATA_TYPE d65 = *((__global DATA_TYPE *)(src_addr + 53 * src_stride_z)); + DATA_TYPE d66 = *((__global DATA_TYPE *)(src_addr + 54 * src_stride_z)); + DATA_TYPE d67 = *((__global DATA_TYPE *)(src_addr + 55 * src_stride_z)); + + DATA_TYPE d70 = *((__global DATA_TYPE *)(src_addr + 56 * src_stride_z)); + DATA_TYPE d71 = *((__global DATA_TYPE *)(src_addr + 57 * src_stride_z)); + DATA_TYPE d72 = *((__global DATA_TYPE *)(src_addr + 58 * src_stride_z)); + DATA_TYPE d73 = *((__global DATA_TYPE *)(src_addr + 59 * src_stride_z)); + DATA_TYPE d74 = *((__global DATA_TYPE *)(src_addr + 60 * src_stride_z)); + DATA_TYPE d75 = *((__global DATA_TYPE *)(src_addr + 61 * src_stride_z)); + DATA_TYPE d76 = *((__global DATA_TYPE *)(src_addr + 62 * src_stride_z)); + DATA_TYPE d77 = *((__global DATA_TYPE *)(src_addr + 63 * src_stride_z)); + + // Compute the 8x4 intermediate tensor + VEC_DATA_TYPE(float, 4) + comm_fact0, comm_fact1, comm_fact2; + VEC_DATA_TYPE(float, 4) + 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; + + VEC_DATA_TYPE(float, 4) + out_col0 = comm_fact0 + comm_fact1 + (float)8.f * comm_fact2 + tmp_col0; + VEC_DATA_TYPE(float, 4) + out_col2 = comm_fact0 + (float)4.f * comm_fact1 + (float)2.f * comm_fact2; + + comm_fact0 = tmp_col1 - tmp_col2; + comm_fact1 = tmp_col3 - tmp_col4; + comm_fact2 = tmp_col5 - tmp_col6; + + VEC_DATA_TYPE(float, 4) + out_col1 = comm_fact0 + (float)2.f * comm_fact1 + (float)4.f * comm_fact2; + VEC_DATA_TYPE(float, 4) + out_col3 = comm_fact0 + (float)8.f * comm_fact1 + comm_fact2 + tmp_col7; + +#if defined(HAS_BIAS) + // Add bias + Vector bias = CONVERT_TO_VECTOR_STRUCT_NO_STEP(bias); + + float b = (float) * ((__global DATA_TYPE *)(vector_offset(&bias, z_out))); + + out_col0 += (VEC_DATA_TYPE(float, 4))b; + out_col1 += (VEC_DATA_TYPE(float, 4))b; + out_col2 += (VEC_DATA_TYPE(float, 4))b; + out_col3 += (VEC_DATA_TYPE(float, 4))b; +#endif // defined(HAS_BIAS) + + // Store the output tile + vstore4(CONVERT(ACTIVATION(ACTIVATION_TYPE, float, VEC_SIZE, (VEC_DATA_TYPE(float, 4))(out_col0.s0, out_col1.s0, out_col2.s0, out_col3.s0), A_VAL, B_VAL), + VEC_DATA_TYPE(DATA_TYPE, 4)), + 0, (__global DATA_TYPE *)(dst_addr + 0 * dst_stride_y)); + vstore4(CONVERT(ACTIVATION(ACTIVATION_TYPE, float, VEC_SIZE, (VEC_DATA_TYPE(float, 4))(out_col0.s1, out_col1.s1, out_col2.s1, out_col3.s1), A_VAL, B_VAL), + VEC_DATA_TYPE(DATA_TYPE, 4)), + 0, (__global DATA_TYPE *)(dst_addr + 1 * dst_stride_y)); + vstore4(CONVERT(ACTIVATION(ACTIVATION_TYPE, float, VEC_SIZE, (VEC_DATA_TYPE(float, 4))(out_col0.s2, out_col1.s2, out_col2.s2, out_col3.s2), A_VAL, B_VAL), + VEC_DATA_TYPE(DATA_TYPE, 4)), + 0, (__global DATA_TYPE *)(dst_addr + 2 * dst_stride_y)); + vstore4(CONVERT(ACTIVATION(ACTIVATION_TYPE, float, VEC_SIZE, (VEC_DATA_TYPE(float, 4))(out_col0.s3, out_col1.s3, out_col2.s3, out_col3.s3), A_VAL, B_VAL), + VEC_DATA_TYPE(DATA_TYPE, 4)), + 0, (__global DATA_TYPE *)(dst_addr + 3 * dst_stride_y)); +#endif // !defined(WINOGRAD_OUTPUT_TRANSFORM_HORIZONTAL) && !defined(WINOGRAD_OUTPUT_TRANSFORM_VERTICAL) +} +#endif // defined(VEC_SIZE) && VEC_SIZE == 4 + +#if defined(WINOGRAD_OUTPUT_TRANSFORM_HORIZONTAL) +#if defined(VEC_SIZE) && VEC_SIZE == 2 +/** This OpenCL kernel performs Winograd output transform when the output tile is 2x1, the filter size 3x1 and the data layout 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 + * @note The width of the output tile must be passed at compile time using -DOUTPUT_TILE_W: e.g. -DOUTPUT_TILE_W=2 + * @note The height of the output tile must be passed at compile time using -DOUTPUT_TILE_H: e.g. -DOUTPUT_TILE_H=1 + * @note -DWINOGRAD_OUTPUT_TRANSFORM_HORIZONTAL has to be passed at compile time + * @note The data type must be passed at compile time using -DDATA_TYPE e.g. -DDATA_TYPE=float. Supported data types: float/half. + * + * @param[in] src_ptr Pointer to the source tensor. Supported data types: F32/F16 + * @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_stride_w Stride of the source tensor in W dimension (in bytes) + * @param[in] src_step_w src_stride_w * number of elements along W 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] dst_stride_z Stride of the source tensor in Z dimension (in bytes) + * @param[in] dst_step_z dst_stride_z * number of elements along Z processed per workitem(in bytes) + * @param[in] dst_stride_w Stride of the source tensor in W dimension (in bytes) + * @param[in] dst_step_w dst_stride_w * number of elements along W 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_2x1_3x1_nchw( + TENSOR4D_DECLARATION(src), + TENSOR4D_DECLARATION(dst) +#if defined(HAS_BIAS) + , + VECTOR_DECLARATION(bias) +#endif // defined(HAS_BIAS) +) +{ + winograd_output_transform_2x2_3x3_nchw(src_ptr, + src_stride_x, + src_step_x, + src_stride_y, + src_step_y, + src_stride_z, + src_step_z, + src_stride_w, + src_step_w, + src_offset_first_element_in_bytes, + dst_ptr, + dst_stride_x, + dst_step_x, + dst_stride_y, + dst_step_y, + dst_stride_z, + dst_step_z, + dst_stride_w, + dst_step_w, + dst_offset_first_element_in_bytes +#if defined(HAS_BIAS) + , + bias_ptr, + bias_stride_x, + bias_step_x, + bias_offset_first_element_in_bytes +#endif // defined(HAS_BIAS) + ); +} + +#endif // defined(VEC_SIZE) && VEC_SIZE == 2 + +#if defined(VEC_SIZE) && VEC_SIZE == 4 +/** This OpenCL kernel performs Winograd output transform when the output tile is 4x1, the filter size 3x1 and the data layout 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 + * @note The width of the output tile must be passed at compile time using -DOUTPUT_TILE_W: e.g. -DOUTPUT_TILE_W=4 + * @note The height of the output tile must be passed at compile time using -DOUTPUT_TILE_H: e.g. -DOUTPUT_TILE_H=1 + * @note -DWINOGRAD_OUTPUT_TRANSFORM_HORIZONTAL has to be passed at compile time + * @note The data type must be passed at compile time using -DDATA_TYPE e.g. -DDATA_TYPE=float. Supported data types: float/half. + * + * @param[in] src_ptr Pointer to the source tensor. Supported data types: F32/F16 + * @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_stride_w Stride of the source tensor in W dimension (in bytes) + * @param[in] src_step_w src_stride_w * number of elements along W 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] dst_stride_z Stride of the source tensor in Z dimension (in bytes) + * @param[in] dst_step_z dst_stride_z * number of elements along Z processed per workitem(in bytes) + * @param[in] dst_stride_w Stride of the source tensor in W dimension (in bytes) + * @param[in] dst_step_w dst_stride_w * number of elements along W 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_4x1_3x1_nchw( + TENSOR4D_DECLARATION(src), + TENSOR4D_DECLARATION(dst) +#if defined(HAS_BIAS) + , + VECTOR_DECLARATION(bias) +#endif // defined(HAS_BIAS) +) +{ + winograd_output_transform_4x4_3x3_nchw(src_ptr, + src_stride_x, + src_step_x, + src_stride_y, + src_step_y, + src_stride_z, + src_step_z, + src_stride_w, + src_step_w, + src_offset_first_element_in_bytes, + dst_ptr, + dst_stride_x, + dst_step_x, + dst_stride_y, + dst_step_y, + dst_stride_z, + dst_step_z, + dst_stride_w, + dst_step_w, + dst_offset_first_element_in_bytes +#if defined(HAS_BIAS) + , + bias_ptr, + bias_stride_x, + bias_step_x, + bias_offset_first_element_in_bytes +#endif // defined(HAS_BIAS) + ); +} + +/** This OpenCL kernel performs Winograd output transform when the output tile is 4x1, the filter size 5x1 and the data layout 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 + * @note The width of the output tile must be passed at compile time using -DOUTPUT_TILE_W: e.g. -DOUTPUT_TILE_W=4 + * @note The height of the output tile must be passed at compile time using -DOUTPUT_TILE_H: e.g. -DOUTPUT_TILE_H=1 + * @note -DWINOGRAD_OUTPUT_TRANSFORM_HORIZONTAL has to be passed at compile time + * @note The data type must be passed at compile time using -DDATA_TYPE e.g. -DDATA_TYPE=float. Supported data types: float/half. + * + * @param[in] src_ptr Pointer to the source tensor. Supported data types: F32/F16 + * @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_stride_w Stride of the source tensor in W dimension (in bytes) + * @param[in] src_step_w src_stride_w * number of elements along W 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] dst_stride_z Stride of the source tensor in Z dimension (in bytes) + * @param[in] dst_step_z dst_stride_z * number of elements along Z processed per workitem(in bytes) + * @param[in] dst_stride_w Stride of the source tensor in W dimension (in bytes) + * @param[in] dst_step_w dst_stride_w * number of elements along W 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_4x1_5x1_nchw( + TENSOR4D_DECLARATION(src), + TENSOR4D_DECLARATION(dst) +#if defined(HAS_BIAS) + , + VECTOR_DECLARATION(bias) +#endif // defined(HAS_BIAS) +) +{ + winograd_output_transform_4x4_5x5_nchw(src_ptr, + src_stride_x, + src_step_x, + src_stride_y, + src_step_y, + src_stride_z, + src_step_z, + src_stride_w, + src_step_w, + src_offset_first_element_in_bytes, + dst_ptr, + dst_stride_x, + dst_step_x, + dst_stride_y, + dst_step_y, + dst_stride_z, + dst_step_z, + dst_stride_w, + dst_step_w, + dst_offset_first_element_in_bytes +#if defined(HAS_BIAS) + , + bias_ptr, + bias_stride_x, + bias_step_x, + bias_offset_first_element_in_bytes +#endif // defined(HAS_BIAS) + ); +} + +#endif // defined(VEC_SIZE) && VEC_SIZE == 4 +#endif // defined(WINOGRAD_OUTPUT_TRANSFORM_HORIZONTAL) + +#if defined(WINOGRAD_OUTPUT_TRANSFORM_VERTICAL) +#if defined(VEC_SIZE) && VEC_SIZE == 2 +/** This OpenCL kernel performs Winograd output transform when the output tile is 1x2, the filter size 1x3 and the data layout 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 + * @note The width of the output tile must be passed at compile time using -DOUTPUT_TILE_W: e.g. -DOUTPUT_TILE_W=1 + * @note The height of the output tile must be passed at compile time using -DOUTPUT_TILE_H: e.g. -DOUTPUT_TILE_H=2 + * @note -DWINOGRAD_OUTPUT_TRANSFORM_VERTICAL has to be passed at compile time + * @note The data type must be passed at compile time using -DDATA_TYPE e.g. -DDATA_TYPE=float. Supported data types: float/half. + * + * @param[in] src_ptr Pointer to the source tensor. Supported data types: F32/F16 + * @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_stride_w Stride of the source tensor in W dimension (in bytes) + * @param[in] src_step_w src_stride_w * number of elements along W 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] dst_stride_z Stride of the source tensor in Z dimension (in bytes) + * @param[in] dst_step_z dst_stride_z * number of elements along Z processed per workitem(in bytes) + * @param[in] dst_stride_w Stride of the source tensor in W dimension (in bytes) + * @param[in] dst_step_w dst_stride_w * number of elements along W 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_1x2_1x3_nchw( + TENSOR4D_DECLARATION(src), + TENSOR4D_DECLARATION(dst) +#if defined(HAS_BIAS) + , + VECTOR_DECLARATION(bias) +#endif // defined(HAS_BIAS) +) +{ + winograd_output_transform_2x2_3x3_nchw(src_ptr, + src_stride_x, + src_step_x, + src_stride_y, + src_step_y, + src_stride_z, + src_step_z, + src_stride_w, + src_step_w, + src_offset_first_element_in_bytes, + dst_ptr, + dst_stride_x, + dst_step_x, + dst_stride_y, + dst_step_y, + dst_stride_z, + dst_step_z, + dst_stride_w, + dst_step_w, + dst_offset_first_element_in_bytes +#if defined(HAS_BIAS) + , + bias_ptr, + bias_stride_x, + bias_step_x, + bias_offset_first_element_in_bytes +#endif // defined(HAS_BIAS) + ); +} + +#endif // defined(VEC_SIZE) && VEC_SIZE == 2 + +#if defined(VEC_SIZE) && VEC_SIZE == 4 +/** This OpenCL kernel performs Winograd output transform when the output tile is 1x4, the filter size 1x3 and the data layout 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 + * @note The width of the output tile must be passed at compile time using -DOUTPUT_TILE_W: e.g. -DOUTPUT_TILE_W=1 + * @note The height of the output tile must be passed at compile time using -DOUTPUT_TILE_H: e.g. -DOUTPUT_TILE_H=4 + * @note -DWINOGRAD_OUTPUT_TRANSFORM_VERTICAL has to be passed at compile time + * @note The data type must be passed at compile time using -DDATA_TYPE e.g. -DDATA_TYPE=float. Supported data types: float/half. + * + * @param[in] src_ptr Pointer to the source tensor. Supported data types: F32/F16 + * @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_stride_w Stride of the source tensor in W dimension (in bytes) + * @param[in] src_step_w src_stride_w * number of elements along W 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] dst_stride_z Stride of the source tensor in Z dimension (in bytes) + * @param[in] dst_step_z dst_stride_z * number of elements along Z processed per workitem(in bytes) + * @param[in] dst_stride_w Stride of the source tensor in W dimension (in bytes) + * @param[in] dst_step_w dst_stride_w * number of elements along W 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_1x4_1x3_nchw( + TENSOR4D_DECLARATION(src), + TENSOR4D_DECLARATION(dst) +#if defined(HAS_BIAS) + , + VECTOR_DECLARATION(bias) +#endif // defined(HAS_BIAS) +) +{ + winograd_output_transform_4x4_3x3_nchw(src_ptr, + src_stride_x, + src_step_x, + src_stride_y, + src_step_y, + src_stride_z, + src_step_z, + src_stride_w, + src_step_w, + src_offset_first_element_in_bytes, + dst_ptr, + dst_stride_x, + dst_step_x, + dst_stride_y, + dst_step_y, + dst_stride_z, + dst_step_z, + dst_stride_w, + dst_step_w, + dst_offset_first_element_in_bytes +#if defined(HAS_BIAS) + , + bias_ptr, + bias_stride_x, + bias_step_x, + bias_offset_first_element_in_bytes +#endif // defined(HAS_BIAS) + ); +} + +/** This OpenCL kernel performs Winograd output transform when the output tile is 1x4, the filter size 1x5 and the data layout 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 + * @note The width of the output tile must be passed at compile time using -DOUTPUT_TILE_W: e.g. -DOUTPUT_TILE_W=1 + * @note The height of the output tile must be passed at compile time using -DOUTPUT_TILE_H: e.g. -DOUTPUT_TILE_H=4 + * @note -DWINOGRAD_OUTPUT_TRANSFORM_VERTICAL has to be passed at compile time + * @note The data type must be passed at compile time using -DDATA_TYPE e.g. -DDATA_TYPE=float. Supported data types: float/half. + * + * @param[in] src_ptr Pointer to the source tensor. Supported data types: F32/F16 + * @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_stride_w Stride of the source tensor in W dimension (in bytes) + * @param[in] src_step_w src_stride_w * number of elements along W 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] dst_stride_z Stride of the source tensor in Z dimension (in bytes) + * @param[in] dst_step_z dst_stride_z * number of elements along Z processed per workitem(in bytes) + * @param[in] dst_stride_w Stride of the source tensor in W dimension (in bytes) + * @param[in] dst_step_w dst_stride_w * number of elements along W 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_1x4_1x5_nchw( + TENSOR4D_DECLARATION(src), + TENSOR4D_DECLARATION(dst) +#if defined(HAS_BIAS) + , + VECTOR_DECLARATION(bias) +#endif // defined(HAS_BIAS) +) +{ + winograd_output_transform_4x4_5x5_nchw(src_ptr, + src_stride_x, + src_step_x, + src_stride_y, + src_step_y, + src_stride_z, + src_step_z, + src_stride_w, + src_step_w, + src_offset_first_element_in_bytes, + dst_ptr, + dst_stride_x, + dst_step_x, + dst_stride_y, + dst_step_y, + dst_stride_z, + dst_step_z, + dst_stride_w, + dst_step_w, + dst_offset_first_element_in_bytes +#if defined(HAS_BIAS) + , + bias_ptr, + bias_stride_x, + bias_step_x, + bias_offset_first_element_in_bytes +#endif // defined(HAS_BIAS) + ); +} + +#endif // defined(VEC_SIZE) && VEC_SIZE == 4 +#endif // defined(WINOGRAD_OUTPUT_TRANSFORM_VERTICAL) +#endif // defined(NUM_TILES_X) && defined(OUTPUT_TILE_W) && defined(OUTPUT_TILE_H) |