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Diffstat (limited to 'src/core/CL/cl_kernels/direct_convolution5x5.cl')
| -rw-r--r-- | src/core/CL/cl_kernels/direct_convolution5x5.cl | 313 |
1 files changed, 0 insertions, 313 deletions
diff --git a/src/core/CL/cl_kernels/direct_convolution5x5.cl b/src/core/CL/cl_kernels/direct_convolution5x5.cl deleted file mode 100644 index 59d668f0bf..0000000000 --- a/src/core/CL/cl_kernels/direct_convolution5x5.cl +++ /dev/null @@ -1,313 +0,0 @@ -/* - * Copyright (c) 2016-2021 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 "helpers.h" - -#undef CONVERT_SAT - -#if defined(DATA_TYPE) && defined(STRIDE_X) && defined(WEIGHTS_DEPTH) - -#if STRIDE_X == 1 -#define CONVOLUTION1x5(acc, src_row_ptr, weights_row_ptr) CONVOLUTION1x5_STRIDE1(acc, src_row_ptr, weights_row_ptr) -#elif STRIDE_X == 2 /* STRIDE_X == 1 */ -#define CONVOLUTION1x5(acc, src_row_ptr, weights_row_ptr) CONVOLUTION1x5_STRIDE2(acc, src_row_ptr, weights_row_ptr) -#else /* STRIDE_X not equals 1 or 2 */ -#error "STRIDE_X larger than 2 is not supported" -#endif /* STRIDE_X == 2 */ - -#define CONVOLUTION1x5_STRIDE1(acc, src_row_ptr, weights_row_ptr) \ - ({ \ - VEC_DATA_TYPE(DATA_TYPE, 4) \ - weights_values0 = vload4(0, weights_row_ptr); \ - DATA_TYPE weights_value1 = *(weights_row_ptr + 4); \ - VEC_DATA_TYPE(DATA_TYPE, 8) \ - src0 = vload8(0, src_row_ptr); \ - VEC_DATA_TYPE(DATA_TYPE, 4) \ - src1 = vload4(0, src_row_ptr + 8); \ - \ - acc += src0 * (VEC_DATA_TYPE(DATA_TYPE, 8))weights_values0.s0; \ - acc += (VEC_DATA_TYPE(DATA_TYPE, 8))(src0.s1234, src0.s567, src1.s0) * (VEC_DATA_TYPE(DATA_TYPE, 8))weights_values0.s1; \ - acc += (VEC_DATA_TYPE(DATA_TYPE, 8))(src0.s234, src0.s567, src1.s01) * (VEC_DATA_TYPE(DATA_TYPE, 8))weights_values0.s2; \ - acc += (VEC_DATA_TYPE(DATA_TYPE, 8))(src0.s345, src0.s67, src1.s012) * (VEC_DATA_TYPE(DATA_TYPE, 8))weights_values0.s3; \ - acc += (VEC_DATA_TYPE(DATA_TYPE, 8))(src0.s45, src0.s67, src1.s0123) * (VEC_DATA_TYPE(DATA_TYPE, 8))weights_value1; \ - }) - -#define CONVOLUTION1x5_STRIDE2(acc, src_row_ptr, weights_row_ptr) \ - ({ \ - VEC_DATA_TYPE(DATA_TYPE, 4) \ - weights_values0 = vload4(0, weights_row_ptr); \ - DATA_TYPE weights_value1 = *(weights_row_ptr + 4); \ - VEC_DATA_TYPE(DATA_TYPE, 16) \ - src0 = vload16(0, src_row_ptr); \ - VEC_DATA_TYPE(DATA_TYPE, 4) \ - src1 = vload4(0, src_row_ptr + 16); \ - acc += src0.even * (VEC_DATA_TYPE(DATA_TYPE, 8))weights_values0.s0; \ - acc += (VEC_DATA_TYPE(DATA_TYPE, 8))(src0.s1357, src0.s9BDF) * (VEC_DATA_TYPE(DATA_TYPE, 8))weights_values0.s1; \ - acc += (VEC_DATA_TYPE(DATA_TYPE, 8))(src0.s2468, src0.sACE, src1.s0) * (VEC_DATA_TYPE(DATA_TYPE, 8))weights_values0.s2; \ - \ - acc += (VEC_DATA_TYPE(DATA_TYPE, 8))(src0.s3579, src0.sBDF, src1.s1) * (VEC_DATA_TYPE(DATA_TYPE, 8))weights_values0.s3; \ - acc += (VEC_DATA_TYPE(DATA_TYPE, 8))(src0.s468a, src0.sCE, src1.s02) * (VEC_DATA_TYPE(DATA_TYPE, 8))weights_value1; \ - }) - -/** This kernel performs a direct convolution to convolve the low three dimensions. - * - * @note The data type must be passed at compile time using -DDATA_TYPE: e.g. -DDATA_TYPE=float - * @note The third dimensions of the weights tensors must be passed at compile time using -DWEIGHTS_DEPTH - * @note If biases are used then -DHAS_BIAS has to be passed at compile time - * - * @param[in] src_ptr Pointer to the source tensor. Supported data types: F16/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 Z processed per workitem(in bytes) - * @param[in] dst_stride_z Stride of the destination tensor in Z dimension (in bytes) - * @param[in] dst_step_z dst_stride_z * number of elements along Z processed per workitem(in bytes) - * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the destination tensor - * @param[in] weights_ptr Pointer to the weights tensor. Supported data types: same as @p src_ptr - * @param[in] weights_stride_x Stride of the weights tensor in X dimension (in bytes) - * @param[in] weights_step_x weights_stride_x * number of elements along X processed per workitem(in bytes) - * @param[in] weights_stride_y Stride of the weights tensor in Y dimension (in bytes) - * @param[in] weights_step_y weights_stride_y * number of elements along y processed per workitem(in bytes) - * @param[in] weights_stride_z Stride of the weights tensor in Z dimension (in bytes) - * @param[in] weights_step_z weights_stride_z * number of elements along Z processed per workitem(in bytes) - * @param[in] weights_offset_first_element_in_bytes The offset of the first element in the weights tensor - * @param[in] biases_ptr Pointer to the biases tensor. Same as @p src_ptr - * @param[in] biases_stride_x Stride of the biases tensor in X dimension (in bytes) - * @param[in] biases_step_x biases_stride_x * number of elements along X processed per workitem(in bytes) - * @param[in] biases_offset_first_element_in_bytes The offset of the first element in the biases tensor - * @param[in] weights_stride_w Stride of the weights tensor in the 4th dimension - */ -__kernel void direct_convolution5x5( - TENSOR3D_DECLARATION(src), - TENSOR3D_DECLARATION(dst), - TENSOR3D_DECLARATION(weights), -#ifdef HAS_BIAS - VECTOR_DECLARATION(biases), -#endif /* defined(HAS_BIAS) */ - unsigned int weights_stride_w) -{ - Image src = CONVERT_TO_IMAGE_STRUCT(src); - Tensor3D weights = CONVERT_TO_TENSOR3D_STRUCT_NO_STEP(weights); - Tensor3D dst = CONVERT_TO_TENSOR3D_STRUCT(dst); - - VEC_DATA_TYPE(DATA_TYPE, 8) - values0 = 0; - - __global uchar *weights_addr = (__global uchar *)tensor3D_offset(&weights, 0, 0, 0); - __global uchar *src_addr = (__global uchar *)offset(&src, 0, 0); - - const int kernel_index = get_global_id(2); - weights_addr += kernel_index * weights_stride_w; - - for(volatile int d = 0; d < WEIGHTS_DEPTH; ++d) - { - CONVOLUTION1x5(values0, (__global DATA_TYPE *)src_addr, (__global DATA_TYPE *)weights_addr); - CONVOLUTION1x5(values0, (__global DATA_TYPE *)(src_addr + 1 * src_stride_y), (__global DATA_TYPE *)(weights_addr + 1 * weights_stride_y)); - CONVOLUTION1x5(values0, (__global DATA_TYPE *)(src_addr + 2 * src_stride_y), (__global DATA_TYPE *)(weights_addr + 2 * weights_stride_y)); - CONVOLUTION1x5(values0, (__global DATA_TYPE *)(src_addr + 3 * src_stride_y), (__global DATA_TYPE *)(weights_addr + 3 * weights_stride_y)); - CONVOLUTION1x5(values0, (__global DATA_TYPE *)(src_addr + 4 * src_stride_y), (__global DATA_TYPE *)(weights_addr + 4 * weights_stride_y)); - - src_addr += src_stride_z; - weights_addr += weights_stride_z; - } - -#ifdef HAS_BIAS - Vector biases = CONVERT_TO_VECTOR_STRUCT_NO_STEP(biases); - - values0 += (VEC_DATA_TYPE(DATA_TYPE, 8)) * ((__global DATA_TYPE *)(vector_offset(&biases, kernel_index))); -#endif /* defined(HAS_BIAS) */ - - vstore8(values0, 0, (__global DATA_TYPE *)dst.ptr); -} -#endif // defined(DATA_TYPE) && defined(STRIDE_X) && defined(WEIGHTS_DEPTH) - -#if defined(WEIGHTS_DEPTH) - -#define CONVOLUTION1x5_BIFROST(acc, src0, weights_row00, weights_row01) \ - ({ \ - acc.s0 = mad(src0.s0, weights_row00.s0, acc.s0); \ - acc.s1 = mad(src0.s1, weights_row00.s0, acc.s1); \ - acc.s2 = mad(src0.s2, weights_row00.s0, acc.s2); \ - acc.s3 = mad(src0.s3, weights_row00.s0, acc.s3); \ - acc.s0 = mad(src0.s1, weights_row00.s1, acc.s0); \ - acc.s1 = mad(src0.s2, weights_row00.s1, acc.s1); \ - acc.s2 = mad(src0.s3, weights_row00.s1, acc.s2); \ - acc.s3 = mad(src0.s4, weights_row00.s1, acc.s3); \ - acc.s0 = mad(src0.s2, weights_row00.s2, acc.s0); \ - acc.s1 = mad(src0.s3, weights_row00.s2, acc.s1); \ - acc.s2 = mad(src0.s4, weights_row00.s2, acc.s2); \ - acc.s3 = mad(src0.s5, weights_row00.s2, acc.s3); \ - acc.s0 = mad(src0.s3, weights_row00.s3, acc.s0); \ - acc.s1 = mad(src0.s4, weights_row00.s3, acc.s1); \ - acc.s2 = mad(src0.s5, weights_row00.s3, acc.s2); \ - acc.s3 = mad(src0.s6, weights_row00.s3, acc.s3); \ - acc.s0 = mad(src0.s4, weights_row01, acc.s0); \ - acc.s1 = mad(src0.s5, weights_row01, acc.s1); \ - acc.s2 = mad(src0.s6, weights_row01, acc.s2); \ - acc.s3 = mad(src0.s7, weights_row01, acc.s3); \ - }) - -/** An optimized direct convolution 5x5 OpenCL kernel for Bifrost architectures when the data type is F32 - * - * @note This OpenCL kernel works only with stride_x and stride_y equal to 1 - * @note The third dimensions of the weights tensors must be passed at compile time using -DWEIGHTS_DEPTH - * @note If biases are used then -DHAS_BIAS has to be passed at compile time - * - * @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 Z processed per workitem(in bytes) - * @param[in] dst_stride_z Stride of the destination tensor in Z dimension (in bytes) - * @param[in] dst_step_z dst_stride_z * number of elements along Z processed per workitem(in bytes) - * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the destination tensor - * @param[in] weights_ptr Pointer to the weights tensor. Supported data types: same as @p src_ptr - * @param[in] weights_stride_x Stride of the weights tensor in X dimension (in bytes) - * @param[in] weights_step_x weights_stride_x * number of elements along X processed per workitem(in bytes) - * @param[in] weights_stride_y Stride of the weights tensor in Y dimension (in bytes) - * @param[in] weights_step_y weights_stride_y * number of elements along y processed per workitem(in bytes) - * @param[in] weights_stride_z Stride of the weights tensor in Z dimension (in bytes) - * @param[in] weights_step_z weights_stride_z * number of elements along Z processed per workitem(in bytes) - * @param[in] weights_offset_first_element_in_bytes The offset of the first element in the weights tensor - * @param[in] biases_ptr Pointer to the biases tensor. Same as @p src_ptr - * @param[in] biases_stride_x Stride of the biases tensor in X dimension (in bytes) - * @param[in] biases_step_x biases_stride_x * number of elements along X processed per workitem(in bytes) - * @param[in] biases_offset_first_element_in_bytes The offset of the first element in the biases tensor - * @param[in] weights_stride_w Stride of the weights tensor in the 4th dimension - */ -__kernel void direct_convolution5x5_f32_bifrost( - TENSOR3D_DECLARATION(src), - TENSOR3D_DECLARATION(dst), - TENSOR3D_DECLARATION(weights), -#ifdef HAS_BIAS - VECTOR_DECLARATION(biases), -#endif /* defined(HAS_BIAS) */ - unsigned int weights_stride_w) -{ - // Get the kernel index - const int kernel_index = get_global_id(2); - - Image src = CONVERT_TO_IMAGE_STRUCT(src); - Tensor3D dst = CONVERT_TO_TENSOR3D_STRUCT(dst); - - float4 values0 = 0.0f; - float4 values1 = 0.0f; - - __global uchar *weights_addr = (__global uchar *)(weights_ptr + weights_offset_first_element_in_bytes + kernel_index * weights_stride_w); - __global uchar *src_addr = (__global uchar *)offset(&src, 0, 0); - - // Note: Since each work-item computes 4x2 elements, we need to load 6 rows from the input tensor - - for(ushort d = 0; d < (ushort)WEIGHTS_DEPTH; ++d) - { - // Load the weights from row0 and row1 - float4 weights_row00 = vload4(0, (__global float *)(weights_addr + 0 * weights_stride_y)); - float weights_row01 = *((__global float *)(weights_addr + 0 * weights_stride_y) + 4); - float4 weights_row10 = vload4(0, (__global float *)(weights_addr + 1 * weights_stride_y)); - float weights_row11 = *((__global float *)(weights_addr + 1 * weights_stride_y) + 4); - float8 src0; - - // Load values from row0 of input tensor - src0 = vload8(0, (__global float *)(src_addr + 0 * src_stride_y)); - - // Accumulate - CONVOLUTION1x5_BIFROST(values0, src0, weights_row00, weights_row01); - - // Load values from row1 of input tensor - src0 = vload8(0, (__global float *)(src_addr + 1 * src_stride_y)); - - // Accumulate - CONVOLUTION1x5_BIFROST(values0, src0, weights_row10, weights_row11); - CONVOLUTION1x5_BIFROST(values1, src0, weights_row00, weights_row01); - - // Load values from row2 of input tensor - src0 = vload8(0, (__global float *)(src_addr + 2 * src_stride_y)); - - // Load weights from row2 - weights_row00 = vload4(0, (__global float *)(weights_addr + 2 * weights_stride_y)); - weights_row01 = *((__global float *)(weights_addr + 2 * weights_stride_y) + 4); - - // Accumulate - CONVOLUTION1x5_BIFROST(values0, src0, weights_row00, weights_row01); - CONVOLUTION1x5_BIFROST(values1, src0, weights_row10, weights_row11); - - // Load values from row3 of input tensor - src0 = vload8(0, (__global float *)(src_addr + 3 * src_stride_y)); - - // Load weights from row3 - weights_row10 = vload4(0, (__global float *)(weights_addr + 3 * weights_stride_y)); - weights_row11 = *((__global float *)(weights_addr + 3 * weights_stride_y) + 4); - - // Accumulate - CONVOLUTION1x5_BIFROST(values0, src0, weights_row10, weights_row11); - CONVOLUTION1x5_BIFROST(values1, src0, weights_row00, weights_row01); - - // Load values from row4 of input tensor - src0 = vload8(0, (__global float *)(src_addr + 4 * src_stride_y)); - - // Load weights from row4 - weights_row00 = vload4(0, (__global float *)(weights_addr + 4 * weights_stride_y)); - weights_row01 = *((__global float *)(weights_addr + 4 * weights_stride_y) + 4); - - CONVOLUTION1x5_BIFROST(values0, src0, weights_row00, weights_row01); - CONVOLUTION1x5_BIFROST(values1, src0, weights_row10, weights_row11); - - // Load values from row5 of input tensor - src0 = vload8(0, (__global float *)(src_addr + 5 * src_stride_y)); - - // Accumulate - CONVOLUTION1x5_BIFROST(values1, src0, weights_row00, weights_row01); - - src_addr += src_stride_z; - weights_addr += weights_stride_z; - } - -#ifdef HAS_BIAS - Vector biases = CONVERT_TO_VECTOR_STRUCT_NO_STEP(biases); - - float4 bias = (float4) * ((__global float *)(vector_offset(&biases, kernel_index))); - - values0 += bias; - values1 += bias; -#endif /* defined(HAS_BIAS) */ - - vstore4(values0, 0, (__global float *)(dst.ptr + 0 * dst_stride_y)); - vstore4(values1, 0, (__global float *)(dst.ptr + 1 * dst_stride_y)); -} -#endif // defined(WEIGHTS_DEPTH) |