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/*
* 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)
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