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Diffstat (limited to 'src/core/CL/cl_kernels/nchw/direct_convolution1x1.cl')
| -rw-r--r-- | src/core/CL/cl_kernels/nchw/direct_convolution1x1.cl | 316 |
1 files changed, 316 insertions, 0 deletions
diff --git a/src/core/CL/cl_kernels/nchw/direct_convolution1x1.cl b/src/core/CL/cl_kernels/nchw/direct_convolution1x1.cl new file mode 100644 index 0000000000..8ab2d1d4ea --- /dev/null +++ b/src/core/CL/cl_kernels/nchw/direct_convolution1x1.cl @@ -0,0 +1,316 @@ +/* + * 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 + +#define ADD_OP(a, b) ((a) + (b)) +#define MUL_OP(a, b) ((a) * (b)) +#define CONVERT_SAT(a, b) ((a)) + +#if defined(DATA_TYPE) && defined(DATA_SIZE) && defined(STRIDE_X) && defined(WEIGHTS_DEPTH) + +#if STRIDE_X == 3 +#define INPUT_PIXEL_STR(data_size) extract_input_stride3_##data_size +#define INPUT_PIXEL(data_size) INPUT_PIXEL_STR(data_size) +#elif STRIDE_X == 2 +#define INPUT_PIXEL(data_size) extract_input_stride2 +#elif STRIDE_X == 1 +#define INPUT_PIXEL(data_size) extract_input_stride1 +#else /* STRIDE_X not equals 1, 2 or 3 */ +#error "Only support strides 1, 2 and 3" +#endif /* STRIDE_X == 3 */ + +/** Extracts a 1D horizontal vector from the input tensor with stride as 1. + * + * @param[in] input_pixel Pointer to the first pixel. + * + * @return extracted input values. + */ +inline VEC_DATA_TYPE(DATA_TYPE, 8) extract_input_stride1(__global const DATA_TYPE *input_pixel) +{ + return vload8(0, input_pixel); +} + +/** Extracts a 1D horizontal vector from the input tensor with stride as 2. + * + * @param[in] input_pixel Pointer to the first pixel. + * + * @return extracted input values. + */ +inline VEC_DATA_TYPE(DATA_TYPE, 8) extract_input_stride2(__global const DATA_TYPE *input_pixel) +{ + VEC_DATA_TYPE(DATA_TYPE, 16) + temp = vload16(0, input_pixel); + return temp.s02468ace; +} + +/** Extracts a 1D horizontal vector from the input tensor with stride as 3 and 32-bit data size. + * + * @param[in] input_pixel Pointer to the first pixel. + * + * @return extracted input values. + */ +inline VEC_DATA_TYPE(DATA_TYPE, 8) extract_input_stride3_32(__global const DATA_TYPE *input_pixel) +{ + VEC_DATA_TYPE(DATA_TYPE, 4) + temp1 = vload4(0, input_pixel); + VEC_DATA_TYPE(DATA_TYPE, 4) + temp2 = vload4(0, input_pixel + 6); + VEC_DATA_TYPE(DATA_TYPE, 4) + temp3 = vload4(0, input_pixel + 12); + VEC_DATA_TYPE(DATA_TYPE, 4) + temp4 = vload4(0, input_pixel + 18); + return (VEC_DATA_TYPE(DATA_TYPE, 8))(temp1.s03, temp2.s03, temp3.s03, temp4.s03); +} + +/** Extracts a 1D horizontal vector from the input tensor with stride as 3 and 16-bit data size. + * + * @param[in] input_pixel Pointer to the first pixel. + * + * @return extracted input values. + */ +inline VEC_DATA_TYPE(DATA_TYPE, 8) extract_input_stride3_16(__global const DATA_TYPE *input_pixel) +{ + VEC_DATA_TYPE(DATA_TYPE, 8) + temp1 = vload8(0, input_pixel); + VEC_DATA_TYPE(DATA_TYPE, 8) + temp2 = vload8(0, input_pixel + 8); + VEC_DATA_TYPE(DATA_TYPE, 8) + temp3 = vload8(0, input_pixel + 16); + return (VEC_DATA_TYPE(DATA_TYPE, 8))(temp1.s036, temp2.s147, temp3.s25); +} + +/** Extracts a 1D horizontal vector from the input tensor with stride as 3 and 8-bit data size. + * + * @param[in] input_pixel Pointer to the first pixel. + * + * @return extracted input values. + */ +inline VEC_DATA_TYPE(DATA_TYPE, 8) extract_input_stride3_8(__global const DATA_TYPE *input_pixel) +{ + VEC_DATA_TYPE(DATA_TYPE, 16) + temp1 = vload16(0, input_pixel); + VEC_DATA_TYPE(DATA_TYPE, 16) + temp2 = vload16(0, input_pixel + 12); + return (VEC_DATA_TYPE(DATA_TYPE, 8))(temp1.s0369, temp2.s0369); +} + +/** 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 data size must be passed at compile time using -DDATA_SIZE e.g. -DDATA_SIZE=32 + * @note The convolution stride x must be passed at compile time using -DSTRIDE_X e.g. -DSTRIDE_X=1 + * @note The third dimensions of the weights tensors must be passed at compile time using -DWEIGHTS_DEPTH + * @note In case biases will be added to the convolution -DHAS_BIAS has to be passed to append the final matrix with 1 in each row. + * + * @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_convolution1x1( + 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); + +#ifdef HAS_BIAS + Vector biases = CONVERT_TO_VECTOR_STRUCT_NO_STEP(biases); +#endif /* defined(HAS_BIAS) */ + + VEC_DATA_TYPE(DATA_TYPE_PROMOTED, 8) + values = 0; + + const uint z_index = get_global_id(2); + + weights.ptr += z_index * weights_stride_w; + for(volatile int d = 0; d < WEIGHTS_DEPTH; ++d) + { + DATA_TYPE weight = *(__global DATA_TYPE *)weights.ptr; + VEC_DATA_TYPE(DATA_TYPE, 8) + input_pixel = INPUT_PIXEL(DATA_SIZE)((__global DATA_TYPE *)src.ptr); + values = ADD_OP(values, MUL_OP((VEC_DATA_TYPE(DATA_TYPE, 8))weight, input_pixel)); + src.ptr += src_stride_z; + weights.ptr += weights_stride_z; + } + +#ifdef HAS_BIAS + values = ADD_OP(values, (VEC_DATA_TYPE(DATA_TYPE_PROMOTED, 8)) * ((__global DATA_TYPE *)(vector_offset(&biases, z_index)))); +#endif /* defined(HAS_BIAS) */ + + vstore8(CONVERT_SAT(values, VEC_DATA_TYPE(DATA_TYPE, 8)), 0, (__global DATA_TYPE *)dst.ptr); +} +#endif // defined(DATA_TYPE) && defined(DATA_SIZE) && defined(STRIDE_X) && defined(WEIGHTS_DEPTH) + +#if defined(WEIGHTS_DEPTH) + +#define CONVOLUTION1x1_BIFROST(acc, src, weight_value) \ + ({ \ + acc.s0 = mad(src.s0, weight_value, acc.s0); \ + acc.s1 = mad(src.s1, weight_value, acc.s1); \ + acc.s2 = mad(src.s2, weight_value, acc.s2); \ + acc.s3 = mad(src.s3, weight_value, acc.s3); \ + }) + +/** An optimized direct convolution 1x1 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 In case biases, -DHAS_BIAS must to be passed at compile + * + * @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_convolution1x1_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 acc0 = 0.0f; + float4 acc1 = 0.0f; + float4 acc2 = 0.0f; + float4 acc3 = 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); + + for(ushort d = 0; d < (ushort)WEIGHTS_DEPTH; ++d) + { + // Load the weights + float weight = *((__global float *)weights_addr); + + // Load values from row0 of input tensor + float4 src0 = vload4(0, (__global float *)(src_addr + 0 * src_stride_y)); + float4 src1 = vload4(0, (__global float *)(src_addr + 1 * src_stride_y)); + float4 src2 = vload4(0, (__global float *)(src_addr + 2 * src_stride_y)); + float4 src3 = vload4(0, (__global float *)(src_addr + 3 * src_stride_y)); + + CONVOLUTION1x1_BIFROST(acc0, src0, weight); + CONVOLUTION1x1_BIFROST(acc1, src1, weight); + CONVOLUTION1x1_BIFROST(acc2, src2, weight); + CONVOLUTION1x1_BIFROST(acc3, src3, weight); + + src_addr += src_stride_z; + weights_addr += weights_stride_z; + } + +#ifdef HAS_BIAS + Vector biases = CONVERT_TO_VECTOR_STRUCT_NO_STEP(biases); + + float bias = (float) * ((__global float *)(vector_offset(&biases, kernel_index))); + + acc0.s0 += bias; + acc0.s1 += bias; + acc0.s2 += bias; + acc0.s3 += bias; + acc1.s0 += bias; + acc1.s1 += bias; + acc1.s2 += bias; + acc1.s3 += bias; + acc2.s0 += bias; + acc2.s1 += bias; + acc2.s2 += bias; + acc2.s3 += bias; + acc3.s0 += bias; + acc3.s1 += bias; + acc3.s2 += bias; + acc3.s3 += bias; +#endif /* defined(HAS_BIAS) */ + + vstore4(acc0, 0, (__global float *)(dst.ptr + 0 * dst_stride_y)); + vstore4(acc1, 0, (__global float *)(dst.ptr + 1 * dst_stride_y)); + vstore4(acc2, 0, (__global float *)(dst.ptr + 2 * dst_stride_y)); + vstore4(acc3, 0, (__global float *)(dst.ptr + 3 * dst_stride_y)); +} +#endif // defined(WEIGHTS_DEPTH) |