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author | Adnan AlSinan <adnan.alsinan@arm.com> | 2021-07-05 13:12:52 +0100 |
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committer | Georgios Pinitas <georgios.pinitas@arm.com> | 2021-07-25 13:04:23 +0000 |
commit | 7075fe2c5ee6f7cfe7cfd9454d905235e70b9ac4 (patch) | |
tree | b65671bdf37eb1ef8cc30ef64ab572da795546fa /src/core/CL/cl_kernels/nchw/direct_convolution3x3.cl | |
parent | 22f5ed51f1b01f7cf6993a556a0b763e437926fc (diff) | |
download | ComputeLibrary-7075fe2c5ee6f7cfe7cfd9454d905235e70b9ac4.tar.gz |
Reorganize the kernels into nhwc, nchw and common folders
The Following kernels have been split into nchw/nhwc kernels files:
- batchnormalization_layer
- batch_to_space
- channel_shuffle
- depth_to_space
- dequantization_layer
- im2col
- normalization_layer
- normalize_planar_yuv_layer
- normalize_planar_yuv_layer_quantized
- pooling_layer
- pooling_layer_quantized
- remap
- reorg_layer
- scale
- scale_quantized
- space_to_batch
- space_to_depth
- upsample_layer
- winograd_filter_transform
- winograd_input_transform
- winograd_output_transform
The following kernels have been moved to nchw folder:
- direct_convolution1x1
- direct_convolution3x3
- direct_convolution5x5
- direct_convolution_quantized
- prior_box_layer
The following kernels have been moved to nhwc folder:
- direct_convolution
- dwc_native_fp_nhwc
- dwc_native_quantized_nhwc
The following kernels have been removed:
- sobel_filter
While the rest kerenls have been moved to the common folder.
Partially resolves COMPMID-4453
Signed-off-by: Adnan AlSinan <adnan.alsinan@arm.com>
Change-Id: Ic327ac935687ec351c610c65a3c6357f364a5a58
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/5919
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Diffstat (limited to 'src/core/CL/cl_kernels/nchw/direct_convolution3x3.cl')
-rw-r--r-- | src/core/CL/cl_kernels/nchw/direct_convolution3x3.cl | 291 |
1 files changed, 291 insertions, 0 deletions
diff --git a/src/core/CL/cl_kernels/nchw/direct_convolution3x3.cl b/src/core/CL/cl_kernels/nchw/direct_convolution3x3.cl new file mode 100644 index 0000000000..811df053c4 --- /dev/null +++ b/src/core/CL/cl_kernels/nchw/direct_convolution3x3.cl @@ -0,0 +1,291 @@ +/* + * 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(STRIDE_X) && defined(WEIGHTS_DEPTH) + +#if STRIDE_X == 1 +#define CONVOLUTION1x3(acc, src_row_ptr, weights_row_ptr) CONVOLUTION1x3_STRIDE1(acc, src_row_ptr, weights_row_ptr) +#elif STRIDE_X == 2 /* STRIDE_X == 1 */ +#define CONVOLUTION1x3(acc, src_row_ptr, weights_row_ptr) CONVOLUTION1x3_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 CONVOLUTION1x3_STRIDE1(acc, src_row_ptr, weights_row_ptr) \ + ({ \ + VEC_DATA_TYPE(DATA_TYPE, 3) \ + weights_values0 = vload3(0, weights_row_ptr); \ + VEC_DATA_TYPE(DATA_TYPE, 8) \ + src0 = vload8(0, src_row_ptr); \ + VEC_DATA_TYPE(DATA_TYPE, 2) \ + src1 = vload2(0, src_row_ptr + 8); \ + \ + acc = ADD_OP(acc, MUL_OP(src0, (VEC_DATA_TYPE(DATA_TYPE, 8))weights_values0.s0)); \ + acc = ADD_OP(acc, MUL_OP((VEC_DATA_TYPE(DATA_TYPE, 8))(src0.s1234, src0.s567, src1.s0), (VEC_DATA_TYPE(DATA_TYPE, 8))weights_values0.s1)); \ + acc = ADD_OP(acc, MUL_OP((VEC_DATA_TYPE(DATA_TYPE, 8))(src0.s234, src0.s567, src1.s01), (VEC_DATA_TYPE(DATA_TYPE, 8))weights_values0.s2)); \ + }) + +#define CONVOLUTION1x3_STRIDE2(acc, src_row_ptr, weights_row_ptr) \ + ({ \ + VEC_DATA_TYPE(DATA_TYPE, 3) \ + weights_values0 = vload3(0, weights_row_ptr); \ + VEC_DATA_TYPE(DATA_TYPE, 16) \ + src0 = vload16(0, src_row_ptr); \ + DATA_TYPE src1 = *(src_row_ptr + 16); \ + \ + acc = ADD_OP(acc, MUL_OP(src0.even, (VEC_DATA_TYPE(DATA_TYPE, 8))weights_values0.s0)); \ + acc = ADD_OP(acc, MUL_OP((VEC_DATA_TYPE(DATA_TYPE, 8))(src0.s1357, src0.s9BDF), (VEC_DATA_TYPE(DATA_TYPE, 8))weights_values0.s1)); \ + acc = ADD_OP(acc, MUL_OP((VEC_DATA_TYPE(DATA_TYPE, 8))(src0.s2468, src0.sACE, src1), (VEC_DATA_TYPE(DATA_TYPE, 8))weights_values0.s2)); \ + }) + +/** This kernel performs a direct convolution to convolve the low three dimensions. + * + * @note This OpenCL kernel works with stride_x = 1 and 2 + * @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_convolution3x3( + 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_PROMOTED, 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) + { + CONVOLUTION1x3(values0, (__global DATA_TYPE *)(src_addr + 0 * src_stride_y), (__global DATA_TYPE *)(weights_addr + 0 * weights_stride_y)); + CONVOLUTION1x3(values0, (__global DATA_TYPE *)(src_addr + 1 * src_stride_y), (__global DATA_TYPE *)(weights_addr + 1 * weights_stride_y)); + CONVOLUTION1x3(values0, (__global DATA_TYPE *)(src_addr + 2 * src_stride_y), (__global DATA_TYPE *)(weights_addr + 2 * 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 = ADD_OP(values0, (VEC_DATA_TYPE(DATA_TYPE_PROMOTED, 8)) * ((__global DATA_TYPE *)(vector_offset(&biases, kernel_index)))); +#endif /* defined(HAS_BIAS) */ + + vstore8(CONVERT_SAT(values0, VEC_DATA_TYPE(DATA_TYPE, 8)), 0, (__global DATA_TYPE *)dst.ptr); +} +#endif //defined(DATA_TYPE) && defined(STRIDE_X) && defined(WEIGHTS_DEPTH) + +#if defined(WEIGHTS_DEPTH) + +#define CONVOLUTION1x3_BIFROST(acc, src0, src1, weights_row0) \ + ({ \ + acc.s0 = mad(src0.s0, weights_row0.s0, acc.s0); \ + acc.s1 = mad(src0.s1, weights_row0.s0, acc.s1); \ + acc.s2 = mad(src0.s2, weights_row0.s0, acc.s2); \ + acc.s3 = mad(src0.s3, weights_row0.s0, acc.s3); \ + acc.s0 = mad(src0.s1, weights_row0.s1, acc.s0); \ + acc.s1 = mad(src0.s2, weights_row0.s1, acc.s1); \ + acc.s2 = mad(src0.s3, weights_row0.s1, acc.s2); \ + acc.s3 = mad(src1.s0, weights_row0.s1, acc.s3); \ + acc.s0 = mad(src0.s2, weights_row0.s2, acc.s0); \ + acc.s1 = mad(src0.s3, weights_row0.s2, acc.s1); \ + acc.s2 = mad(src1.s0, weights_row0.s2, acc.s2); \ + acc.s3 = mad(src1.s1, weights_row0.s2, acc.s3); \ + }) + +/** An optimized direct convolution 3x3 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_convolution3x3_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; + float4 values1 = 0; + float4 values2 = 0; + + __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 4x3 elements, we need to load 5 rows from the input tensor + + for(ushort d = 0; d < (ushort)WEIGHTS_DEPTH; ++d) + { + // Load the weights + float3 weights_row0 = vload3(0, (__global float *)(weights_addr + 0 * weights_stride_y)); + float3 weights_row1 = vload3(0, (__global float *)(weights_addr + 1 * weights_stride_y)); + float3 weights_row2 = vload3(0, (__global float *)(weights_addr + 2 * weights_stride_y)); + float4 src0; + float2 src1; + + // Load values from row0 of input tensor + src0 = vload4(0, (__global float *)(src_addr + 0 * src_stride_y)); + src1 = vload2(0, (__global float *)(src_addr + 0 * src_stride_y) + 4); + + CONVOLUTION1x3_BIFROST(values0, src0, src1, weights_row0); + + // Load values from row1 of input tensor + src0 = vload4(0, (__global float *)(src_addr + 1 * src_stride_y)); + src1 = vload2(0, (__global float *)(src_addr + 1 * src_stride_y) + 4); + + // Accumulate + CONVOLUTION1x3_BIFROST(values0, src0, src1, weights_row1); + CONVOLUTION1x3_BIFROST(values1, src0, src1, weights_row0); + + // Load values from row2 of input tensor + src0 = vload4(0, (__global float *)(src_addr + 2 * src_stride_y)); + src1 = vload2(0, (__global float *)(src_addr + 2 * src_stride_y) + 4); + + // Accumulate + CONVOLUTION1x3_BIFROST(values0, src0, src1, weights_row2); + CONVOLUTION1x3_BIFROST(values1, src0, src1, weights_row1); + CONVOLUTION1x3_BIFROST(values2, src0, src1, weights_row0); + + // Load values from row3 of input tensor + src0 = vload4(0, (__global float *)(src_addr + 3 * src_stride_y)); + src1 = vload2(0, (__global float *)(src_addr + 3 * src_stride_y) + 4); + + // Accumulate + CONVOLUTION1x3_BIFROST(values1, src0, src1, weights_row2); + CONVOLUTION1x3_BIFROST(values2, src0, src1, weights_row1); + + // Row4 + src0 = vload4(0, (__global float *)(src_addr + 4 * src_stride_y)); + src1 = vload2(0, (__global float *)(src_addr + 4 * src_stride_y) + 4); + + // Accumulate + CONVOLUTION1x3_BIFROST(values2, src0, src1, weights_row2); + + 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))); + + values0 += (float4)bias; + values1 += (float4)bias; + values2 += (float4)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)); + vstore4(values2, 0, (__global float *)(dst.ptr + 2 * dst_stride_y)); +} +#endif // defined(WEIGHTS_DEPTH) |