Blame - src/core/CL/cl_kernels/direct_convolution5x5.cl - ml/ComputeLibrary

2017-08-09 16:26:22 +0100

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/*

Gian Marco Iodice

ff1fe3e

2021-01-02 09:58:51 +0000

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steniu01

2017-08-09 16:26:22 +0100

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*

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* SPDX-License-Identifier: MIT

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*

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* Permission is hereby granted, free of charge, to any person obtaining a copy

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* of this software and associated documentation files (the "Software"), to

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* deal in the Software without restriction, including without limitation the

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* rights to use, copy, modify, merge, publish, distribute, sublicense, and/or

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* sell copies of the Software, and to permit persons to whom the Software is

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* furnished to do so, subject to the following conditions:

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*

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* The above copyright notice and this permission notice shall be included in all

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* copies or substantial portions of the Software.

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*

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* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR

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* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,

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* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE

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* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER

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* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,

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* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE

* SOFTWARE.

*/

#include "helpers.h"

#undef CONVERT_SAT

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#if defined(DATA_TYPE) && defined(STRIDE_X) && defined(WEIGHTS_DEPTH)

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#if STRIDE_X == 1

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#define CONVOLUTION1x5(acc, src_row_ptr, weights_row_ptr) CONVOLUTION1x5_STRIDE1(acc, src_row_ptr, weights_row_ptr)

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#elif STRIDE_X == 2 /* STRIDE_X == 1 */

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#define CONVOLUTION1x5(acc, src_row_ptr, weights_row_ptr) CONVOLUTION1x5_STRIDE2(acc, src_row_ptr, weights_row_ptr)

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#else /* STRIDE_X not equals 1 or 2 */

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#error "STRIDE_X larger than 2 is not supported"

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#endif /* STRIDE_X == 2 */

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#define CONVOLUTION1x5_STRIDE1(acc, src_row_ptr, weights_row_ptr) \

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({ \

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VEC_DATA_TYPE(DATA_TYPE, 4) \

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weights_values0 = vload4(0, weights_row_ptr); \

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DATA_TYPE weights_value1 = *(weights_row_ptr + 4); \

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VEC_DATA_TYPE(DATA_TYPE, 8) \

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src0 = vload8(0, src_row_ptr); \

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VEC_DATA_TYPE(DATA_TYPE, 4) \

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src1 = vload4(0, src_row_ptr + 8); \

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\

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acc += src0 * (VEC_DATA_TYPE(DATA_TYPE, 8))weights_values0.s0; \

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acc += (VEC_DATA_TYPE(DATA_TYPE, 8))(src0.s1234, src0.s567, src1.s0) * (VEC_DATA_TYPE(DATA_TYPE, 8))weights_values0.s1; \

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acc += (VEC_DATA_TYPE(DATA_TYPE, 8))(src0.s234, src0.s567, src1.s01) * (VEC_DATA_TYPE(DATA_TYPE, 8))weights_values0.s2; \

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acc += (VEC_DATA_TYPE(DATA_TYPE, 8))(src0.s345, src0.s67, src1.s012) * (VEC_DATA_TYPE(DATA_TYPE, 8))weights_values0.s3; \

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acc += (VEC_DATA_TYPE(DATA_TYPE, 8))(src0.s45, src0.s67, src1.s0123) * (VEC_DATA_TYPE(DATA_TYPE, 8))weights_value1; \

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})

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#define CONVOLUTION1x5_STRIDE2(acc, src_row_ptr, weights_row_ptr) \

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({ \

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VEC_DATA_TYPE(DATA_TYPE, 4) \

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weights_values0 = vload4(0, weights_row_ptr); \

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DATA_TYPE weights_value1 = *(weights_row_ptr + 4); \

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VEC_DATA_TYPE(DATA_TYPE, 16) \

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src0 = vload16(0, src_row_ptr); \

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VEC_DATA_TYPE(DATA_TYPE, 4) \

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src1 = vload4(0, src_row_ptr + 16); \

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acc += src0.even * (VEC_DATA_TYPE(DATA_TYPE, 8))weights_values0.s0; \

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acc += (VEC_DATA_TYPE(DATA_TYPE, 8))(src0.s1357, src0.s9BDF) * (VEC_DATA_TYPE(DATA_TYPE, 8))weights_values0.s1; \

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acc += (VEC_DATA_TYPE(DATA_TYPE, 8))(src0.s2468, src0.sACE, src1.s0) * (VEC_DATA_TYPE(DATA_TYPE, 8))weights_values0.s2; \

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\

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acc += (VEC_DATA_TYPE(DATA_TYPE, 8))(src0.s3579, src0.sBDF, src1.s1) * (VEC_DATA_TYPE(DATA_TYPE, 8))weights_values0.s3; \

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acc += (VEC_DATA_TYPE(DATA_TYPE, 8))(src0.s468a, src0.sCE, src1.s02) * (VEC_DATA_TYPE(DATA_TYPE, 8))weights_value1; \

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})

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/** This kernel performs a direct convolution to convolve the low three dimensions.

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*

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* @note The data type must be passed at compile time using -DDATA_TYPE: e.g. -DDATA_TYPE=float

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* @note The third dimensions of the weights tensors must be passed at compile time using -DWEIGHTS_DEPTH

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* @note If biases are used then -DHAS_BIAS has to be passed at compile time

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*

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* @param[in] src_ptr Pointer to the source tensor. Supported data types: F16/F32

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* @param[in] src_stride_x Stride of the source tensor in X dimension (in bytes)

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* @param[in] src_step_x src_stride_x * number of elements along X processed per workitem(in bytes)

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* @param[in] src_stride_y Stride of the source tensor in Y dimension (in bytes)

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* @param[in] src_step_y src_stride_y * number of elements along Y processed per workitem(in bytes)

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* @param[in] src_stride_z Stride of the source tensor in Z dimension (in bytes)

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* @param[in] src_step_z src_stride_z * number of elements along Z processed per workitem(in bytes)

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* @param[in] src_offset_first_element_in_bytes The offset of the first element in the source tensor

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* @param[out] dst_ptr Pointer to the destination tensor. Supported data types: same as @p src_ptr

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* @param[in] dst_stride_x Stride of the destination tensor in X dimension (in bytes)

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* @param[in] dst_step_x dst_stride_x * number of elements along X processed per workitem(in bytes)

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* @param[in] dst_stride_y Stride of the destination tensor in Y dimension (in bytes)

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* @param[in] dst_step_y dst_stride_y * number of elements along Z processed per workitem(in bytes)

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* @param[in] dst_stride_z Stride of the destination tensor in Z dimension (in bytes)

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* @param[in] dst_step_z dst_stride_z * number of elements along Z processed per workitem(in bytes)

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* @param[in] dst_offset_first_element_in_bytes The offset of the first element in the destination tensor

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* @param[in] weights_ptr Pointer to the weights tensor. Supported data types: same as @p src_ptr

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* @param[in] weights_stride_x Stride of the weights tensor in X dimension (in bytes)

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* @param[in] weights_step_x weights_stride_x * number of elements along X processed per workitem(in bytes)

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* @param[in] weights_stride_y Stride of the weights tensor in Y dimension (in bytes)

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* @param[in] weights_step_y weights_stride_y * number of elements along y processed per workitem(in bytes)

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* @param[in] weights_stride_z Stride of the weights tensor in Z dimension (in bytes)

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* @param[in] weights_step_z weights_stride_z * number of elements along Z processed per workitem(in bytes)

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* @param[in] weights_offset_first_element_in_bytes The offset of the first element in the weights tensor

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* @param[in] biases_ptr Pointer to the biases tensor. Same as @p src_ptr

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* @param[in] biases_stride_x Stride of the biases tensor in X dimension (in bytes)

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* @param[in] biases_step_x biases_stride_x * number of elements along X processed per workitem(in bytes)

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* @param[in] biases_offset_first_element_in_bytes The offset of the first element in the biases tensor

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* @param[in] weights_stride_w Stride of the weights tensor in the 4th dimension

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*/

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__kernel void direct_convolution5x5(

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TENSOR3D_DECLARATION(src),

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TENSOR3D_DECLARATION(dst),

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TENSOR3D_DECLARATION(weights),

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#ifdef HAS_BIAS

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VECTOR_DECLARATION(biases),

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#endif /* defined(HAS_BIAS) */

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unsigned int weights_stride_w)

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{

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Image src = CONVERT_TO_IMAGE_STRUCT(src);

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Tensor3D weights = CONVERT_TO_TENSOR3D_STRUCT_NO_STEP(weights);

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Tensor3D dst = CONVERT_TO_TENSOR3D_STRUCT(dst);

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VEC_DATA_TYPE(DATA_TYPE, 8)

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values0 = 0;

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__global uchar *weights_addr = (__global uchar *)tensor3D_offset(&weights, 0, 0, 0);

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__global uchar *src_addr = (__global uchar *)offset(&src, 0, 0);

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const int kernel_index = get_global_id(2);

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weights_addr += kernel_index * weights_stride_w;

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for(volatile int d = 0; d < WEIGHTS_DEPTH; ++d)

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{

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CONVOLUTION1x5(values0, (__global DATA_TYPE *)src_addr, (__global DATA_TYPE *)weights_addr);

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CONVOLUTION1x5(values0, (__global DATA_TYPE *)(src_addr + 1 * src_stride_y), (__global DATA_TYPE *)(weights_addr + 1 * weights_stride_y));

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CONVOLUTION1x5(values0, (__global DATA_TYPE *)(src_addr + 2 * src_stride_y), (__global DATA_TYPE *)(weights_addr + 2 * weights_stride_y));

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CONVOLUTION1x5(values0, (__global DATA_TYPE *)(src_addr + 3 * src_stride_y), (__global DATA_TYPE *)(weights_addr + 3 * weights_stride_y));

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CONVOLUTION1x5(values0, (__global DATA_TYPE *)(src_addr + 4 * src_stride_y), (__global DATA_TYPE *)(weights_addr + 4 * weights_stride_y));

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src_addr += src_stride_z;

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weights_addr += weights_stride_z;

}

#ifdef HAS_BIAS

Vector biases = CONVERT_TO_VECTOR_STRUCT_NO_STEP(biases);

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values0 += (VEC_DATA_TYPE(DATA_TYPE, 8)) * ((__global DATA_TYPE *)(vector_offset(&biases, kernel_index)));

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#endif /* defined(HAS_BIAS) */

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vstore8(values0, 0, (__global DATA_TYPE *)dst.ptr);

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}

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#endif // defined(DATA_TYPE) && defined(STRIDE_X) && defined(WEIGHTS_DEPTH)

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#if defined(WEIGHTS_DEPTH)

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#define CONVOLUTION1x5_BIFROST(acc, src0, weights_row00, weights_row01) \

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({ \

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acc.s0 = mad(src0.s0, weights_row00.s0, acc.s0); \

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acc.s1 = mad(src0.s1, weights_row00.s0, acc.s1); \

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acc.s2 = mad(src0.s2, weights_row00.s0, acc.s2); \

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acc.s3 = mad(src0.s3, weights_row00.s0, acc.s3); \

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acc.s0 = mad(src0.s1, weights_row00.s1, acc.s0); \

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acc.s1 = mad(src0.s2, weights_row00.s1, acc.s1); \

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acc.s2 = mad(src0.s3, weights_row00.s1, acc.s2); \

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acc.s3 = mad(src0.s4, weights_row00.s1, acc.s3); \

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acc.s0 = mad(src0.s2, weights_row00.s2, acc.s0); \

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acc.s1 = mad(src0.s3, weights_row00.s2, acc.s1); \

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acc.s2 = mad(src0.s4, weights_row00.s2, acc.s2); \

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acc.s3 = mad(src0.s5, weights_row00.s2, acc.s3); \

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acc.s0 = mad(src0.s3, weights_row00.s3, acc.s0); \

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acc.s1 = mad(src0.s4, weights_row00.s3, acc.s1); \

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acc.s2 = mad(src0.s5, weights_row00.s3, acc.s2); \

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acc.s3 = mad(src0.s6, weights_row00.s3, acc.s3); \

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acc.s0 = mad(src0.s4, weights_row01, acc.s0); \

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acc.s1 = mad(src0.s5, weights_row01, acc.s1); \

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acc.s2 = mad(src0.s6, weights_row01, acc.s2); \

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acc.s3 = mad(src0.s7, weights_row01, acc.s3); \

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})

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/** An optimized direct convolution 5x5 OpenCL kernel for Bifrost architectures when the data type is F32

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*

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* @note This OpenCL kernel works only with stride_x and stride_y equal to 1

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* @note The third dimensions of the weights tensors must be passed at compile time using -DWEIGHTS_DEPTH

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* @note If biases are used then -DHAS_BIAS has to be passed at compile time

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*

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* @param[in] src_ptr Pointer to the source tensor. Supported data types: F32

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* @param[in] src_stride_x Stride of the source tensor in X dimension (in bytes)

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* @param[in] src_step_x src_stride_x * number of elements along X processed per workitem(in bytes)

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* @param[in] src_stride_y Stride of the source tensor in Y dimension (in bytes)

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* @param[in] src_step_y src_stride_y * number of elements along Y processed per workitem(in bytes)

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* @param[in] src_stride_z Stride of the source tensor in Z dimension (in bytes)

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* @param[in] src_step_z src_stride_z * number of elements along Z processed per workitem(in bytes)

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* @param[in] src_offset_first_element_in_bytes The offset of the first element in the source tensor

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* @param[out] dst_ptr Pointer to the destination tensor. Supported data types: same as @p src_ptr

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* @param[in] dst_stride_x Stride of the destination tensor in X dimension (in bytes)

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* @param[in] dst_step_x dst_stride_x * number of elements along X processed per workitem(in bytes)

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* @param[in] dst_stride_y Stride of the destination tensor in Y dimension (in bytes)

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* @param[in] dst_step_y dst_stride_y * number of elements along Z processed per workitem(in bytes)

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* @param[in] dst_stride_z Stride of the destination tensor in Z dimension (in bytes)

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* @param[in] dst_step_z dst_stride_z * number of elements along Z processed per workitem(in bytes)

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* @param[in] dst_offset_first_element_in_bytes The offset of the first element in the destination tensor

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* @param[in] weights_ptr Pointer to the weights tensor. Supported data types: same as @p src_ptr

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* @param[in] weights_stride_x Stride of the weights tensor in X dimension (in bytes)

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* @param[in] weights_step_x weights_stride_x * number of elements along X processed per workitem(in bytes)

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* @param[in] weights_stride_y Stride of the weights tensor in Y dimension (in bytes)

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* @param[in] weights_step_y weights_stride_y * number of elements along y processed per workitem(in bytes)

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* @param[in] weights_stride_z Stride of the weights tensor in Z dimension (in bytes)

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* @param[in] weights_step_z weights_stride_z * number of elements along Z processed per workitem(in bytes)

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* @param[in] weights_offset_first_element_in_bytes The offset of the first element in the weights tensor

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* @param[in] biases_ptr Pointer to the biases tensor. Same as @p src_ptr

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* @param[in] biases_stride_x Stride of the biases tensor in X dimension (in bytes)

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* @param[in] biases_step_x biases_stride_x * number of elements along X processed per workitem(in bytes)

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* @param[in] biases_offset_first_element_in_bytes The offset of the first element in the biases tensor

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* @param[in] weights_stride_w Stride of the weights tensor in the 4th dimension

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*/

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__kernel void direct_convolution5x5_f32_bifrost(

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TENSOR3D_DECLARATION(src),

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TENSOR3D_DECLARATION(dst),

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TENSOR3D_DECLARATION(weights),

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#ifdef HAS_BIAS

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VECTOR_DECLARATION(biases),

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#endif /* defined(HAS_BIAS) */

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unsigned int weights_stride_w)

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{

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// Get the kernel index

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const int kernel_index = get_global_id(2);

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Image src = CONVERT_TO_IMAGE_STRUCT(src);

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Tensor3D dst = CONVERT_TO_TENSOR3D_STRUCT(dst);

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float4 values0 = 0.0f;

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float4 values1 = 0.0f;

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__global uchar *weights_addr = (__global uchar *)(weights_ptr + weights_offset_first_element_in_bytes + kernel_index * weights_stride_w);

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__global uchar *src_addr = (__global uchar *)offset(&src, 0, 0);

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// Note: Since each work-item computes 4x2 elements, we need to load 6 rows from the input tensor

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for(ushort d = 0; d < (ushort)WEIGHTS_DEPTH; ++d)

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{

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// Load the weights from row0 and row1

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float4 weights_row00 = vload4(0, (__global float *)(weights_addr + 0 * weights_stride_y));

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float weights_row01 = *((__global float *)(weights_addr + 0 * weights_stride_y) + 4);

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float4 weights_row10 = vload4(0, (__global float *)(weights_addr + 1 * weights_stride_y));

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float weights_row11 = *((__global float *)(weights_addr + 1 * weights_stride_y) + 4);

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float8 src0;

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// Load values from row0 of input tensor

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src0 = vload8(0, (__global float *)(src_addr + 0 * src_stride_y));

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// Accumulate

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CONVOLUTION1x5_BIFROST(values0, src0, weights_row00, weights_row01);

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// Load values from row1 of input tensor

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src0 = vload8(0, (__global float *)(src_addr + 1 * src_stride_y));

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// Accumulate

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CONVOLUTION1x5_BIFROST(values0, src0, weights_row10, weights_row11);

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CONVOLUTION1x5_BIFROST(values1, src0, weights_row00, weights_row01);

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// Load values from row2 of input tensor

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src0 = vload8(0, (__global float *)(src_addr + 2 * src_stride_y));

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// Load weights from row2

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weights_row00 = vload4(0, (__global float *)(weights_addr + 2 * weights_stride_y));

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weights_row01 = *((__global float *)(weights_addr + 2 * weights_stride_y) + 4);

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// Accumulate

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CONVOLUTION1x5_BIFROST(values0, src0, weights_row00, weights_row01);

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CONVOLUTION1x5_BIFROST(values1, src0, weights_row10, weights_row11);

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// Load values from row3 of input tensor

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src0 = vload8(0, (__global float *)(src_addr + 3 * src_stride_y));

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// Load weights from row3

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weights_row10 = vload4(0, (__global float *)(weights_addr + 3 * weights_stride_y));

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weights_row11 = *((__global float *)(weights_addr + 3 * weights_stride_y) + 4);

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// Accumulate

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CONVOLUTION1x5_BIFROST(values0, src0, weights_row10, weights_row11);

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CONVOLUTION1x5_BIFROST(values1, src0, weights_row00, weights_row01);

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// Load values from row4 of input tensor

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src0 = vload8(0, (__global float *)(src_addr + 4 * src_stride_y));

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// Load weights from row4

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weights_row00 = vload4(0, (__global float *)(weights_addr + 4 * weights_stride_y));

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weights_row01 = *((__global float *)(weights_addr + 4 * weights_stride_y) + 4);

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CONVOLUTION1x5_BIFROST(values0, src0, weights_row00, weights_row01);

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CONVOLUTION1x5_BIFROST(values1, src0, weights_row10, weights_row11);

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// Load values from row5 of input tensor

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src0 = vload8(0, (__global float *)(src_addr + 5 * src_stride_y));

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// Accumulate

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CONVOLUTION1x5_BIFROST(values1, src0, weights_row00, weights_row01);

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src_addr += src_stride_z;

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weights_addr += weights_stride_z;

}

#ifdef HAS_BIAS

Vector biases = CONVERT_TO_VECTOR_STRUCT_NO_STEP(biases);

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float4 bias = (float4) * ((__global float *)(vector_offset(&biases, kernel_index)));

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values0 += bias;

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values1 += bias;

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#endif /* defined(HAS_BIAS) */

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vstore4(values0, 0, (__global float *)(dst.ptr + 0 * dst_stride_y));

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vstore4(values1, 0, (__global float *)(dst.ptr + 1 * dst_stride_y));

Gian Marco Iodice