/* * Copyright (c) 2017-2018 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_asymm.h" #undef CONVERT_SAT #if defined(DATA_TYPE) && defined(STRIDE_X) && defined(WEIGHTS_DEPTH) #if KERNEL_SIZE == 5 #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 #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 */ #define CONVOLUTION1x5_STRIDE1(acc, src_row_ptr, weights_row_ptr) \ ({ \ int4 weights_values0 = convert_int4(vload4(0, weights_row_ptr)); \ int weights_value1 = convert_int(*(weights_row_ptr + 4)); \ int8 src0 = convert_int8(vload8(0, src_row_ptr)); \ int4 src1 = convert_int4(vload4(0, src_row_ptr + 8)); \ acc += (src0 + input_offset) * ((int8)weights_values0.s0 + weight_offset); \ acc += ((int8)(src0.s1234, src0.s567, src1.s0) + input_offset) * ((int8)weights_values0.s1 + weight_offset); \ acc += ((int8)(src0.s234, src0.s567, src1.s01) + input_offset) * ((int8)weights_values0.s2 + weight_offset); \ acc += ((int8)(src0.s345, src0.s67, src1.s012) + input_offset) * ((int8)weights_values0.s3 + weight_offset); \ acc += ((int8)(src0.s45, src0.s67, src1.s0123) + input_offset) * ((int8)weights_value1 + weight_offset); \ }) #define CONVOLUTION1x5_STRIDE2(acc, src_row_ptr, weights_row_ptr) \ ({ \ int4 weights_values0 = convert_int4(vload4(0, weights_row_ptr)); \ int weights_value1 = convert_int(*(weights_row_ptr + 4)); \ int16 src0 = convert_int16(vload16(0, src_row_ptr)); \ int4 src1 = convert_int4(vload4(0, src_row_ptr + 16)); \ acc += (src0.even + input_offset) * ((int8)weights_values0.s0 + weight_offset); \ acc += ((int8)(src0.s1357, src0.s9BDF) + input_offset) * ((int8)weights_values0.s1 + weight_offset); \ acc += ((int8)(src0.s2468, src0.sACE, src1.s0) + input_offset) * ((int8)weights_values0.s2 + weight_offset); \ acc += ((int8)(src0.s3579, src0.sBDF, src1.s1) + input_offset) * ((int8)weights_values0.s3 + weight_offset); \ acc += ((int8)(src0.s468a, src0.sCE, src1.s02) + input_offset) * ((int8)weights_value1 + weight_offset); \ }) #elif KERNEL_SIZE == 3 #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 #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 */ #define CONVOLUTION1x3_STRIDE1(acc, src_row_ptr, weights_row_ptr) \ ({ \ int3 weights_values0 = convert_int3(vload3(0, weights_row_ptr)); \ int8 src0 = convert_int8(vload8(0, src_row_ptr)); \ int2 src1 = convert_int2(vload2(0, src_row_ptr + 8)); \ acc += (src0 + input_offset) * ((int8)weights_values0.s0 + weight_offset); \ acc += ((int8)(src0.s1234, src0.s567, src1.s0) + input_offset) * ((int8)weights_values0.s1 + weight_offset); \ acc += ((int8)(src0.s234, src0.s567, src1.s01) + input_offset) * ((int8)weights_values0.s2 + weight_offset); \ }) #define CONVOLUTION1x3_STRIDE2(acc, src_row_ptr, weights_row_ptr) \ ({ \ int3 weights_values0 = convert_int3(vload3(0, weights_row_ptr)); \ int16 src0 = convert_int16(vload16(0, src_row_ptr)); \ int src1 = convert_int(*(src_row_ptr + 16)); \ acc += (src0.even + input_offset) * ((int8)weights_values0.s0 + weight_offset); \ acc += ((int8)(src0.s1357, src0.s9BDF) + input_offset) * ((int8)weights_values0.s1 + weight_offset); \ acc += ((int8)(src0.s2468, src0.sACE, src1) + input_offset) * ((int8)weights_values0.s2 + weight_offset); \ }) #elif KERNEL_SIZE == 1 #if STRIDE_X == 3 #define INPUT_PIXEL extract_input_stride3 #elif STRIDE_X == 2 #define INPUT_PIXEL extract_input_stride2 #elif STRIDE_X == 1 #define INPUT_PIXEL extract_input_stride1 #else /* STRIDE_X not equals 1, 2 or 3 */ #error "Only support strides 1, 2 and 3" #endif /* STRIDE_X */ /** 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 pixels. */ inline uchar8 extract_input_stride1(__global const uchar *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 pixels. */ inline uchar8 extract_input_stride2(__global const uchar *input_pixel) { uchar16 temp = vload16(0, input_pixel); return temp.s02468ace; } /** 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 pixels. */ inline uchar8 extract_input_stride3(__global const uchar *input_pixel) { uchar16 temp1 = vload16(0, input_pixel); uchar16 temp2 = vload16(0, input_pixel + 12); return (uchar8)(temp1.s0369, temp2.s0369); } #else /* KERNEL_SIZE not equals 1, 3 or 5 */ #error "Only kernel sizes 1, 3 and 5 are supported" #endif /* KERNEL_SIZE */ /** This kernel performs a direct convolution to convolve the low three dimensions. * * @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 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: QASYMM8 * @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 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. Supported data types: S32 * @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 * @param[in] input_offset Input offset quantization parameter * @param[in] weight_offset Weights offset quantization parameter * @param[in] output_offset Output offset quantization parameter * @param[in] output_multiplier Output integer multiplier quantization parameter * @param[in] output_shift Output integer shift quantization parameter */ __kernel void direct_convolution_1x1_3x3_5x5_quantized( TENSOR3D_DECLARATION(src), TENSOR3D_DECLARATION(dst), TENSOR3D_DECLARATION(weights), #ifdef HAS_BIAS VECTOR_DECLARATION(biases), #endif /* defined(HAS_BIAS) */ unsigned int weights_stride_w, int input_offset, int weight_offset, int output_offset, int output_multiplier, int output_shift) { Image src = CONVERT_TO_IMAGE_STRUCT(src); Tensor3D weights = CONVERT_TO_TENSOR3D_STRUCT_NO_STEP(weights); Tensor3D dst = CONVERT_TO_TENSOR3D_STRUCT(dst); int8 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) { #if KERNEL_SIZE == 5 CONVOLUTION1x5(pixels0, (__global uchar *)src_addr, (__global uchar *)weights_addr); CONVOLUTION1x5(pixels0, (__global uchar *)(src_addr + 1 * src_stride_y), (__global uchar *)(weights_addr + 1 * weights_stride_y)); CONVOLUTION1x5(pixels0, (__global uchar *)(src_addr + 2 * src_stride_y), (__global uchar *)(weights_addr + 2 * weights_stride_y)); CONVOLUTION1x5(pixels0, (__global uchar *)(src_addr + 3 * src_stride_y), (__global uchar *)(weights_addr + 3 * weights_stride_y)); CONVOLUTION1x5(pixels0, (__global uchar *)(src_addr + 4 * src_stride_y), (__global uchar *)(weights_addr + 4 * weights_stride_y)); #elif KERNEL_SIZE == 3 CONVOLUTION1x3(pixels0, (__global uchar *)(src_addr + 0 * src_stride_y), (__global uchar *)(weights_addr + 0 * weights_stride_y)); CONVOLUTION1x3(pixels0, (__global uchar *)(src_addr + 1 * src_stride_y), (__global uchar *)(weights_addr + 1 * weights_stride_y)); CONVOLUTION1x3(pixels0, (__global uchar *)(src_addr + 2 * src_stride_y), (__global uchar *)(weights_addr + 2 * weights_stride_y)); #elif KERNEL_SIZE == 1 int weight = convert_int(*(__global uchar *)weights_addr); int8 input_pixel = convert_int8(INPUT_PIXEL((__global uchar *)src_addr)); pixels0 += (input_pixel + input_offset) * ((int8)weight + weight_offset); #endif /* (KERNEL_SIZE == 1) || (KERNEL_SIZE == 3) || (KERNEL_SIZE == 5) */ src_addr += src_stride_z; weights_addr += weights_stride_z; } #ifdef HAS_BIAS Vector biases = CONVERT_TO_VECTOR_STRUCT_NO_STEP(biases); __global int *bias_addr = ((__global int *)(vector_offset(&biases, kernel_index))); pixels0 += (int8)(*bias_addr); #endif /* defined(HAS_BIAS) */ pixels0 = ASYMM_MULT_BY_QUANT_MULTIPLIER_LESS_THAN_ONE(pixels0, output_multiplier, output_shift, 8); pixels0 = pixels0 + output_offset; vstore8(convert_uchar8_sat(pixels0), 0, (__global uchar *)dst.ptr); } #endif // defined(DATA_TYPE) && defined(STRIDE_X) && defined(WEIGHTS_DEPTH) #if defined(VEC_SIZE) #define VEC_INT VEC_DATA_TYPE(int, VEC_SIZE) #define CONVERT_SAT_UCHAR_STR(x, size) (convert_uchar##size##_sat((x))) #define CONVERT_SAT_UCHAR(x, size) CONVERT_SAT_UCHAR_STR(x, size) /** This function computes the output stage of a depthwise convolution. * * @param[in] src_ptr Pointer to the source image. Supported data types: QASYMM8 * @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: QASYMM8 * @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] bias_ptr (Optional) Pointer to the biases vector. Supported data types: S32 * @param[in] bias_stride_x (Optional) Stride of the biases vector in X dimension (in bytes) * @param[in] bias_step_x (Optional) bias_stride_x * number of elements along X processed per workitem(in bytes) * @param[in] bias_offset_first_element_in_bytes (Optional) The offset of the first element in the biases vector * @param[in] output_offset Quantized offset of zero point of the output tensor data range * @param[in] output_multiplier Output scale multiplier * @param[in] output_shift Output scale divisor exponent */ __kernel void output_stage_quantized( TENSOR3D_DECLARATION(src), TENSOR3D_DECLARATION(dst), #if defined(HAS_BIAS) VECTOR_DECLARATION(bias), #endif //defined(HAS_BIAS) int output_offset, int output_multiplier, int output_shift) { Image src = CONVERT_TENSOR3D_TO_IMAGE_STRUCT(src); Image dst = CONVERT_TENSOR3D_TO_IMAGE_STRUCT(dst); #if defined(HAS_BIAS) Vector bias = CONVERT_TO_VECTOR_STRUCT_NO_STEP(bias); #endif //defined(HAS_BIAS) // Load input VEC_INT vals = VLOAD(VEC_SIZE)(0, (__global int *)(src.ptr)); #if defined(HAS_BIAS) // Load and add bias #if defined(NCHW) int bias_value = *((__global int *)(vector_offset(&bias, get_global_id(2)))); #else // defined(NCHW) VEC_INT bias_value = VLOAD(VEC_SIZE)(0, ((__global int *)(vector_offset(&bias, get_global_id(0) * VEC_SIZE)))); #endif // defined(NCHW) vals += (VEC_INT)(bias_value); #endif //defined(HAS_BIAS) vals = ASYMM_MULT_BY_QUANT_MULTIPLIER_LESS_THAN_ONE(vals, output_multiplier, output_shift, VEC_SIZE); vals = vals + output_offset; // Store result in dst VSTORE(VEC_SIZE) (CONVERT_SAT_UCHAR(vals, VEC_SIZE), 0, (__global uchar *)dst.ptr); } #undef VEC_INT #undef CONVERT_SAT_UCHAR_STR #undef CONVERT_SAT_UCHAR #endif // defined(VEC_SIZE)