| /* |
| * Copyright (c) 2016, 2017 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[out] weights_ptr Pointer to the weights tensor. Supported data types: same as @p weights_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) |
| pixels0 = 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(pixels0, (__global DATA_TYPE *)src_addr, (__global DATA_TYPE *)weights_addr); |
| CONVOLUTION1x5(pixels0, (__global DATA_TYPE *)(src_addr + 1 * src_stride_y), (__global DATA_TYPE *)(weights_addr + 1 * weights_stride_y)); |
| CONVOLUTION1x5(pixels0, (__global DATA_TYPE *)(src_addr + 2 * src_stride_y), (__global DATA_TYPE *)(weights_addr + 2 * weights_stride_y)); |
| CONVOLUTION1x5(pixels0, (__global DATA_TYPE *)(src_addr + 3 * src_stride_y), (__global DATA_TYPE *)(weights_addr + 3 * weights_stride_y)); |
| CONVOLUTION1x5(pixels0, (__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); |
| |
| pixels0 += (VEC_DATA_TYPE(DATA_TYPE, 8)) * ((__global DATA_TYPE *)(vector_offset(&biases, kernel_index))); |
| #endif /* defined(HAS_BIAS) */ |
| |
| vstore8(pixels0, 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[out] weights_ptr Pointer to the weights tensor. Supported data types: same as @p weights_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 pixels0 = 0.0f; |
| float4 pixels1 = 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(pixels0, 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(pixels0, src0, weights_row10, weights_row11); |
| CONVOLUTION1x5_BIFROST(pixels1, 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(pixels0, src0, weights_row00, weights_row01); |
| CONVOLUTION1x5_BIFROST(pixels1, 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(pixels0, src0, weights_row10, weights_row11); |
| CONVOLUTION1x5_BIFROST(pixels1, 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(pixels0, src0, weights_row00, weights_row01); |
| CONVOLUTION1x5_BIFROST(pixels1, 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(pixels1, 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))); |
| |
| pixels0 += bias; |
| pixels1 += bias; |
| #endif /* defined(HAS_BIAS) */ |
| |
| vstore4(pixels0, 0, (__global float *)(dst.ptr + 0 * dst_stride_y)); |
| vstore4(pixels1, 0, (__global float *)(dst.ptr + 1 * dst_stride_y)); |
| } |
| #endif // defined(WEIGHTS_DEPTH) |
