From c799ed85bcf0b18269e65a64f34382073a68bd93 Mon Sep 17 00:00:00 2001 From: Gian Marco Date: Thu, 1 Feb 2018 16:57:48 +0000 Subject: COMPMID-895 - Optimizing CLDepthwiseConvolution3x3Kernel This patch brings the MACs utilisation up to 25 % when both stride_x and stride_y are equal to 1 Performance reported in the following confluence page: https://confluence.arm.com/display/MLENG/Depthwise+convolution+3x3+FP32+performance%3A+ACL+18.02 Change-Id: Ida1b64be9a88805902a3d90194559b58eb1224a3 Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/119068 Reviewed-by: Michalis Spyrou Tested-by: Jenkins --- src/core/CL/cl_kernels/depthwise_convolution.cl | 219 +++++++++++++++++++++++- 1 file changed, 211 insertions(+), 8 deletions(-) (limited to 'src/core/CL/cl_kernels/depthwise_convolution.cl') diff --git a/src/core/CL/cl_kernels/depthwise_convolution.cl b/src/core/CL/cl_kernels/depthwise_convolution.cl index 89555a0cb6..ac94b693e3 100644 --- a/src/core/CL/cl_kernels/depthwise_convolution.cl +++ b/src/core/CL/cl_kernels/depthwise_convolution.cl @@ -1,5 +1,5 @@ /* - * Copyright (c) 2017 ARM Limited. + * Copyright (c) 2017-2018 ARM Limited. * * SPDX-License-Identifier: MIT * @@ -144,9 +144,9 @@ inline float2 convolution3x3( return pixels; } -/** This function computes the horizontal integral of the image. +/** This OpenCL kernel computes the depthwise convolution 3x3 * - * @param[in] src_ptr Pointer to the source image. Supported data types: U8 + * @param[in] src_ptr Pointer to the source image. Supported data types: F32 * @param[in] src_stride_x Stride of the source image 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 image in Y dimension (in bytes) @@ -154,7 +154,7 @@ inline float2 convolution3x3( * @param[in] src_offset_first_element_in_bytes The offset of the first element in the source image * @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 Y processed per workitem(in bytes) - * @param[in] dst_ptr Pointer to the destination tensor. Supported data types: F16/F32 + * @param[in] dst_ptr Pointer to the destination tensor. Supported data types: F32 * @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) @@ -162,7 +162,7 @@ inline float2 convolution3x3( * @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 Y 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: F16/F32 + * @param[in] weights_ptr Pointer to the weights tensor. Supported data types: F32 * @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) @@ -175,7 +175,6 @@ inline float2 convolution3x3( * @param[in] biases_step_x (Optional) biases_stride_x * number of elements along X processed per workitem(in bytes) * @param[in] biases_offset_first_element_in_bytes (Optional) The offset of the first element in the biases vector */ - __kernel void depthwise_convolution_3x3( TENSOR3D_DECLARATION(src), TENSOR3D_DECLARATION(dst), @@ -207,9 +206,214 @@ __kernel void depthwise_convolution_3x3( vstore2(pixels, 0, (__global float *)dst.ptr); } - #endif //defined(CONV_STRIDE_X) +#define CONVOLUTION1x3_BIFROST2X1_STRIDE1(acc, src0, weights_row0) \ + ({ \ + acc.s0 = fma(src0.s0, weights_row0.s0, acc.s0); \ + acc.s0 = fma(src0.s1, weights_row0.s1, acc.s0); \ + acc.s0 = fma(src0.s2, weights_row0.s2, acc.s0); \ + acc.s1 = fma(src0.s1, weights_row0.s0, acc.s1); \ + acc.s1 = fma(src0.s2, weights_row0.s1, acc.s1); \ + acc.s1 = fma(src0.s3, weights_row0.s2, acc.s1); \ + }) + +#define CONVOLUTION1x3_BIFROST2X1_STRIDE2(acc, src0, src1, weights_row0) \ + ({ \ + acc.s0 = fma(src0.s0, weights_row0.s0, acc.s0); \ + acc.s0 = fma(src0.s1, weights_row0.s1, acc.s0); \ + acc.s0 = fma(src0.s2, weights_row0.s2, acc.s0); \ + acc.s1 = fma(src0.s2, weights_row0.s0, acc.s1); \ + acc.s1 = fma(src0.s3, weights_row0.s1, acc.s1); \ + acc.s1 = fma(src1.s0, weights_row0.s2, acc.s1); \ + }) + +/** This OpenCL kernel is optimized for Bifrost architectures and computes the depthwise convolution 3x3 when both + * stride_x and stride_y are equal to 1 + * + * @param[in] src_ptr Pointer to the source image. Supported data types: F32 + * @param[in] src_stride_x Stride of the source image 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 image 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_offset_first_element_in_bytes The offset of the first element in the source image + * @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 Y processed per workitem(in bytes) + * @param[in] dst_ptr Pointer to the destination tensor. Supported data types: F32 + * @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 Y 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 Y 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: F32 + * @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 Y processed per workitem(in bytes) + * @param[in] weights_offset_first_element_in_bytes The offset of the first element in the biases vector + * @param[in] biases_ptr (Optional) Pointer to the biases vector. Supported data types: F32 + * @param[in] biases_stride_x (Optional) Stride of the biases vector in X dimension (in bytes) + * @param[in] biases_step_x (Optional) biases_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] biases_offset_first_element_in_bytes (Optional) The offset of the first element in the biases vector + */ +__kernel void depthwise_convolution_3x3_stridex1_stridey1_bifrost( + TENSOR3D_DECLARATION(src), + TENSOR3D_DECLARATION(dst), + TENSOR3D_DECLARATION(weights) +#if defined(HAS_BIAS) + , + VECTOR_DECLARATION(biases) +#endif //defined(HAS_BIAS) +) +{ + Image src = CONVERT_TENSOR3D_TO_IMAGE_STRUCT(src); + Image dst = CONVERT_TENSOR3D_TO_IMAGE_STRUCT(dst); + Tensor3D weights = CONVERT_TO_TENSOR3D_STRUCT(weights); + + float2 pixels0 = 0.0f; + float2 pixels1 = 0.0f; + float2 pixels2 = 0.0f; + float2 pixels3 = 0.0f; + + __global uchar *weights_addr = (__global uchar *)weights.ptr; + __global uchar *src_addr = (__global uchar *)offset(&src, 0, 0); + + // 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)); + + // Note: Since each work-item computes 4x2 elements, we need to load 4 rows from the input tensor + float4 src00 = vload4(0, (__global float *)(src_addr + 0 * src_stride_y)); // Row0 + float4 src10 = vload4(0, (__global float *)(src_addr + 1 * src_stride_y)); // Row1 + float4 src20 = vload4(0, (__global float *)(src_addr + 2 * src_stride_y)); // Row2 + float4 src30 = vload4(0, (__global float *)(src_addr + 3 * src_stride_y)); // Row3 + float4 src40 = vload4(0, (__global float *)(src_addr + 4 * src_stride_y)); // Row3 + float4 src50 = vload4(0, (__global float *)(src_addr + 5 * src_stride_y)); // Row3 + + CONVOLUTION1x3_BIFROST2X1_STRIDE1(pixels0, src00, weights_row0); + CONVOLUTION1x3_BIFROST2X1_STRIDE1(pixels0, src10, weights_row1); + CONVOLUTION1x3_BIFROST2X1_STRIDE1(pixels0, src20, weights_row2); + CONVOLUTION1x3_BIFROST2X1_STRIDE1(pixels1, src10, weights_row0); + CONVOLUTION1x3_BIFROST2X1_STRIDE1(pixels1, src20, weights_row1); + CONVOLUTION1x3_BIFROST2X1_STRIDE1(pixels1, src30, weights_row2); + CONVOLUTION1x3_BIFROST2X1_STRIDE1(pixels2, src20, weights_row0); + CONVOLUTION1x3_BIFROST2X1_STRIDE1(pixels2, src30, weights_row1); + CONVOLUTION1x3_BIFROST2X1_STRIDE1(pixels2, src40, weights_row2); + CONVOLUTION1x3_BIFROST2X1_STRIDE1(pixels3, src30, weights_row0); + CONVOLUTION1x3_BIFROST2X1_STRIDE1(pixels3, src40, weights_row1); + CONVOLUTION1x3_BIFROST2X1_STRIDE1(pixels3, src50, weights_row2); + +#ifdef HAS_BIAS + Vector biases = CONVERT_TO_VECTOR_STRUCT_NO_STEP(biases); + + float bias = *((__global float *)(vector_offset(&biases, get_global_id(2)))); + + pixels0 += (float2)bias; + pixels1 += (float2)bias; + pixels2 += (float2)bias; + pixels3 += (float2)bias; +#endif /* defined(HAS_BIAS) */ + + vstore2(pixels0, 0, (__global float *)(dst.ptr + 0 * dst_stride_y)); + vstore2(pixels1, 0, (__global float *)(dst.ptr + 1 * dst_stride_y)); + vstore2(pixels2, 0, (__global float *)(dst.ptr + 2 * dst_stride_y)); + vstore2(pixels3, 0, (__global float *)(dst.ptr + 3 * dst_stride_y)); +} + +/** This OpenCL kernel is optimized for Bifrost architectures and computes the depthwise convolution 3x3 when both + * stride_x and stride_y are equal to 2 + * + * @param[in] src_ptr Pointer to the source image. Supported data types: F32 + * @param[in] src_stride_x Stride of the source image 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 image 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_offset_first_element_in_bytes The offset of the first element in the source image + * @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 Y processed per workitem(in bytes) + * @param[in] dst_ptr Pointer to the destination tensor. Supported data types: F32 + * @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 Y 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 Y 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: F32 + * @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 Y processed per workitem(in bytes) + * @param[in] weights_offset_first_element_in_bytes The offset of the first element in the biases vector + * @param[in] biases_ptr (Optional) Pointer to the biases vector. Supported data types: F32 + * @param[in] biases_stride_x (Optional) Stride of the biases vector in X dimension (in bytes) + * @param[in] biases_step_x (Optional) biases_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] biases_offset_first_element_in_bytes (Optional) The offset of the first element in the biases vector + */ +__kernel void depthwise_convolution_3x3_stridex2_stridey2_bifrost( + TENSOR3D_DECLARATION(src), + TENSOR3D_DECLARATION(dst), + TENSOR3D_DECLARATION(weights) +#if defined(HAS_BIAS) + , + VECTOR_DECLARATION(biases) +#endif //defined(HAS_BIAS) +) +{ + Image src = CONVERT_TENSOR3D_TO_IMAGE_STRUCT(src); + Image dst = CONVERT_TENSOR3D_TO_IMAGE_STRUCT(dst); + Tensor3D weights = CONVERT_TO_TENSOR3D_STRUCT(weights); + + float2 pixels0 = 0.0f; + float2 pixels1 = 0.0f; + + __global uchar *weights_addr = (__global uchar *)weights.ptr; + __global uchar *src_addr = (__global uchar *)offset(&src, 0, 0); + + // 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)); + + // Note: Since each work-item computes 4x2 elements, we need to load 5 rows from the input tensor + float4 src00 = vload4(0, (__global float *)(src_addr + 0 * src_stride_y)); // Row0 + float2 src01 = vload2(2, (__global float *)(src_addr + 0 * src_stride_y)); // Row0 + float4 src10 = vload4(0, (__global float *)(src_addr + 1 * src_stride_y)); // Row1 + float2 src11 = vload2(2, (__global float *)(src_addr + 1 * src_stride_y)); // Row1 + float4 src20 = vload4(0, (__global float *)(src_addr + 2 * src_stride_y)); // Row2 + float2 src21 = vload2(2, (__global float *)(src_addr + 2 * src_stride_y)); // Row2 + float4 src30 = vload4(0, (__global float *)(src_addr + 3 * src_stride_y)); // Row3 + float2 src31 = vload2(2, (__global float *)(src_addr + 3 * src_stride_y)); // Row3 + float4 src40 = vload4(0, (__global float *)(src_addr + 4 * src_stride_y)); // Row4 + float2 src41 = vload2(2, (__global float *)(src_addr + 4 * src_stride_y)); // Row4 + + CONVOLUTION1x3_BIFROST2X1_STRIDE2(pixels0, src00, src01, weights_row0); + CONVOLUTION1x3_BIFROST2X1_STRIDE2(pixels0, src10, src11, weights_row1); + CONVOLUTION1x3_BIFROST2X1_STRIDE2(pixels0, src20, src21, weights_row2); + CONVOLUTION1x3_BIFROST2X1_STRIDE2(pixels1, src20, src21, weights_row0); + CONVOLUTION1x3_BIFROST2X1_STRIDE2(pixels1, src30, src31, weights_row1); + CONVOLUTION1x3_BIFROST2X1_STRIDE2(pixels1, src40, src41, weights_row2); + +#ifdef HAS_BIAS + Vector biases = CONVERT_TO_VECTOR_STRUCT_NO_STEP(biases); + + float bias = *((__global float *)(vector_offset(&biases, get_global_id(2)))); + + pixels0 += (float2)bias; + pixels1 += (float2)bias; +#endif /* defined(HAS_BIAS) */ + + vstore2(pixels0, 0, (__global float *)(dst.ptr + 0 * dst_stride_y)); + vstore2(pixels1, 0, (__global float *)(dst.ptr + 1 * dst_stride_y)); +} + #if defined(SRC_WIDTH) && defined(DATA_TYPE) /** This kernel reshapes each of the tensor's low three dimensions to single rows. * @@ -288,7 +492,6 @@ __kernel void depthwise_weights_reshape( * @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 */ - __kernel void depthwise_im2col(TENSOR3D_DECLARATION(src), TENSOR3D_DECLARATION(dst)) { Tensor3D dst = CONVERT_TO_TENSOR3D_STRUCT(dst); -- cgit v1.2.1