From 1246b63ca04cb067f26ae860688647224d6ba24e Mon Sep 17 00:00:00 2001 From: Gian Marco Iodice Date: Wed, 16 Aug 2017 18:38:32 +0100 Subject: COMPMID-477 - Optimized Direct Convolution 3x3 and 5x5 (f32) for Bifrost. Each work-item computes 4x3 output elements in case of 3x3 convolution and 4x2 in case of 5x5 convolution Change-Id: I6ebbaff8b7e971c1f90d5845c0b58d2a40f39df5 Reviewed-on: http://mpd-gerrit.cambridge.arm.com/84345 Reviewed-by: Anthony Barbier Tested-by: Kaizen --- src/core/CL/cl_kernels/direct_convolution5x5.cl | 168 +++++++++++++++++++++++- 1 file changed, 166 insertions(+), 2 deletions(-) (limited to 'src/core/CL/cl_kernels/direct_convolution5x5.cl') diff --git a/src/core/CL/cl_kernels/direct_convolution5x5.cl b/src/core/CL/cl_kernels/direct_convolution5x5.cl index d8c0d891d7..496da97a09 100644 --- a/src/core/CL/cl_kernels/direct_convolution5x5.cl +++ b/src/core/CL/cl_kernels/direct_convolution5x5.cl @@ -25,6 +25,8 @@ #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 */ @@ -71,7 +73,7 @@ * * @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 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. + * @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) @@ -103,7 +105,6 @@ * @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 */ -#if defined(DATA_TYPE) && defined(STRIDE_X) && defined(WEIGHTS_DEPTH) __kernel void direct_convolution5x5( TENSOR3D_DECLARATION(src), TENSOR3D_DECLARATION(dst), @@ -147,3 +148,166 @@ __kernel void direct_convolution5x5( 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) -- cgit v1.2.1