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steniu0127b386c2017-07-18 17:37:43 +01001/*
2 * Copyright (c) 2016, 2017 ARM Limited.
3 *
4 * SPDX-License-Identifier: MIT
5 *
6 * Permission is hereby granted, free of charge, to any person obtaining a copy
7 * of this software and associated documentation files (the "Software"), to
8 * deal in the Software without restriction, including without limitation the
9 * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
10 * sell copies of the Software, and to permit persons to whom the Software is
11 * furnished to do so, subject to the following conditions:
12 *
13 * The above copyright notice and this permission notice shall be included in all
14 * copies or substantial portions of the Software.
15 *
16 * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
17 * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
18 * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
19 * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
20 * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
21 * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
22 * SOFTWARE.
23 */
24#include "helpers.h"
25
Gian Marco Iodice5cb4d6a2017-08-08 10:53:00 +010026#if STRIDE_X == 1
27#define CONVOLUTION1x3(acc, src_row_ptr, weights_row_ptr) CONVOLUTION1x3_STRIDE1(acc, src_row_ptr, weights_row_ptr)
28#elif STRIDE_X == 2 /* STRIDE_X == 1 */
29#define CONVOLUTION1x3(acc, src_row_ptr, weights_row_ptr) CONVOLUTION1x3_STRIDE2(acc, src_row_ptr, weights_row_ptr)
steniu0127b386c2017-07-18 17:37:43 +010030#else /* STRIDE_X not equals 1 or 2 */
31#error "STRIDE_X larger than 2 is not supported"
32#endif /* STRIDE_X == 2 */
33
Gian Marco Iodice5cb4d6a2017-08-08 10:53:00 +010034#define CONVOLUTION1x3_STRIDE1(acc, src_row_ptr, weights_row_ptr) \
35 ({ \
36 VEC_DATA_TYPE(DATA_TYPE, 4) \
37 weights_values0 = vload4(0, weights_row_ptr); \
38 VEC_DATA_TYPE(DATA_TYPE, 8) \
39 src0 = vload8(0, src_row_ptr); \
40 VEC_DATA_TYPE(DATA_TYPE, 2) \
41 src1 = vload2(0, src_row_ptr + 8); \
42 \
43 acc += src0 * (VEC_DATA_TYPE(DATA_TYPE, 8))weights_values0.s0; \
44 acc += (VEC_DATA_TYPE(DATA_TYPE, 8))(src0.s1234, src0.s567, src1.s0) * (VEC_DATA_TYPE(DATA_TYPE, 8))weights_values0.s1; \
45 acc += (VEC_DATA_TYPE(DATA_TYPE, 8))(src0.s234, src0.s567, src1.s01) * (VEC_DATA_TYPE(DATA_TYPE, 8))weights_values0.s2; \
46 })
steniu0127b386c2017-07-18 17:37:43 +010047
Gian Marco Iodice5cb4d6a2017-08-08 10:53:00 +010048#define CONVOLUTION1x3_STRIDE2(acc, src_row_ptr, weights_row_ptr) \
49 ({ \
50 VEC_DATA_TYPE(DATA_TYPE, 4) \
51 weights_values0 = vload4(0, weights_row_ptr); \
52 VEC_DATA_TYPE(DATA_TYPE, 16) \
53 src0 = vload16(0, src_row_ptr); \
54 DATA_TYPE src1 = *(src_row_ptr + 16); \
55 \
56 acc += src0.even * (VEC_DATA_TYPE(DATA_TYPE, 8))weights_values0.s0; \
57 acc += (VEC_DATA_TYPE(DATA_TYPE, 8))(src0.s1357, src0.s9BDF) * (VEC_DATA_TYPE(DATA_TYPE, 8))weights_values0.s1; \
58 acc += (VEC_DATA_TYPE(DATA_TYPE, 8))(src0.s2468, src0.sACE, src1) * (VEC_DATA_TYPE(DATA_TYPE, 8))weights_values0.s2; \
59 })
steniu0127b386c2017-07-18 17:37:43 +010060
61/** This kernel performs a direct convolution to convolve the low three dimensions.
62 *
63 * @note The data type must be passed at compile time using -DDATA_TYPE: e.g. -DDATA_TYPE=float
Gian Marco Iodice5cb4d6a2017-08-08 10:53:00 +010064 * @note The third dimensions of the weights tensors must be passed at compile time using -DWEIGHTS_DEPTH
steniu0127b386c2017-07-18 17:37:43 +010065 * @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.
66 *
Gian Marco Iodice5cb4d6a2017-08-08 10:53:00 +010067 * @param[in] src_ptr Pointer to the source tensor. Supported data types: F16/F32
steniu0127b386c2017-07-18 17:37:43 +010068 * @param[in] src_stride_x Stride of the source tensor in X dimension (in bytes)
69 * @param[in] src_step_x src_stride_x * number of elements along X processed per workitem(in bytes)
70 * @param[in] src_stride_y Stride of the source tensor in Y dimension (in bytes)
71 * @param[in] src_step_y src_stride_y * number of elements along Y processed per workitem(in bytes)
72 * @param[in] src_stride_z Stride of the source tensor in Z dimension (in bytes)
73 * @param[in] src_step_z src_stride_z * number of elements along Z processed per workitem(in bytes)
74 * @param[in] src_offset_first_element_in_bytes The offset of the first element in the source tensor
75 * @param[out] dst_ptr Pointer to the destination tensor. Supported data types: same as @p src_ptr
76 * @param[in] dst_stride_x Stride of the destination tensor in X dimension (in bytes)
77 * @param[in] dst_step_x dst_stride_x * number of elements along X processed per workitem(in bytes)
78 * @param[in] dst_stride_y Stride of the destination tensor in Y dimension (in bytes)
79 * @param[in] dst_step_y dst_stride_y * number of elements along Z processed per workitem(in bytes)
80 * @param[in] dst_stride_z Stride of the destination tensor in Z dimension (in bytes)
81 * @param[in] dst_step_z dst_stride_z * number of elements along Z processed per workitem(in bytes)
82 * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the destination tensor
83 * @param[out] weights_ptr Pointer to the weights tensor. Supported data types: same as @p weights_ptr
84 * @param[in] weights_stride_x Stride of the weights tensor in X dimension (in bytes)
85 * @param[in] weights_step_x weights_stride_x * number of elements along X processed per workitem(in bytes)
86 * @param[in] weights_stride_y Stride of the weights tensor in Y dimension (in bytes)
87 * @param[in] weights_step_y weights_stride_y * number of elements along y processed per workitem(in bytes)
88 * @param[in] weights_stride_z Stride of the weights tensor in Z dimension (in bytes)
89 * @param[in] weights_step_z weights_stride_z * number of elements along Z processed per workitem(in bytes)
90 * @param[in] weights_offset_first_element_in_bytes The offset of the first element in the weights tensor
91 * @param[in] biases_ptr Pointer to the biases tensor. Same as @p src_ptr
92 * @param[in] biases_stride_x Stride of the biases tensor in X dimension (in bytes)
93 * @param[in] biases_step_x biases_stride_x * number of elements along X processed per workitem(in bytes)
94 * @param[in] biases_offset_first_element_in_bytes The offset of the first element in the biases tensor
Gian Marco Iodice5cb4d6a2017-08-08 10:53:00 +010095 * @param[in] weights_stride_w Stride of the weights tensor in the 4th dimension
steniu0127b386c2017-07-18 17:37:43 +010096 */
Gian Marco Iodice5cb4d6a2017-08-08 10:53:00 +010097#if defined(DATA_TYPE) && defined(STRIDE_X) && defined(WEIGHTS_DEPTH)
steniu0127b386c2017-07-18 17:37:43 +010098__kernel void direct_convolution3x3(
99 TENSOR3D_DECLARATION(src),
100 TENSOR3D_DECLARATION(dst),
101 TENSOR3D_DECLARATION(weights),
102#ifdef HAS_BIAS
103 VECTOR_DECLARATION(biases),
104#endif /* defined(HAS_BIAS) */
Gian Marco Iodice5cb4d6a2017-08-08 10:53:00 +0100105 unsigned int weights_stride_w)
steniu0127b386c2017-07-18 17:37:43 +0100106{
107 Image src = CONVERT_TO_IMAGE_STRUCT(src);
108 Tensor3D weights = CONVERT_TO_TENSOR3D_STRUCT_NO_STEP(weights);
109 Tensor3D dst = CONVERT_TO_TENSOR3D_STRUCT(dst);
110
steniu0127b386c2017-07-18 17:37:43 +0100111 VEC_DATA_TYPE(DATA_TYPE, 8)
Gian Marco Iodice5cb4d6a2017-08-08 10:53:00 +0100112 pixels0 = 0;
steniu0127b386c2017-07-18 17:37:43 +0100113
Gian Marco Iodice5cb4d6a2017-08-08 10:53:00 +0100114 __global uchar *weights_addr = (__global uchar *)tensor3D_offset(&weights, 0, 0, 0);
115 __global uchar *src_addr = (__global uchar *)offset(&src, 0, 0);
steniu0127b386c2017-07-18 17:37:43 +0100116
Gian Marco Iodice5cb4d6a2017-08-08 10:53:00 +0100117 const int kernel_index = get_global_id(2);
118 weights_addr += kernel_index * weights_stride_w;
steniu0127b386c2017-07-18 17:37:43 +0100119
Gian Marco Iodice5cb4d6a2017-08-08 10:53:00 +0100120 for(int d = 0; d < WEIGHTS_DEPTH; ++d)
steniu0127b386c2017-07-18 17:37:43 +0100121 {
Gian Marco Iodice5cb4d6a2017-08-08 10:53:00 +0100122 CONVOLUTION1x3(pixels0, (__global DATA_TYPE *)(src_addr + 0 * src_stride_y), (__global DATA_TYPE *)(weights_addr + 0 * weights_stride_y));
123 CONVOLUTION1x3(pixels0, (__global DATA_TYPE *)(src_addr + 1 * src_stride_y), (__global DATA_TYPE *)(weights_addr + 1 * weights_stride_y));
124 CONVOLUTION1x3(pixels0, (__global DATA_TYPE *)(src_addr + 2 * src_stride_y), (__global DATA_TYPE *)(weights_addr + 2 * weights_stride_y));
steniu0127b386c2017-07-18 17:37:43 +0100125
Gian Marco Iodice5cb4d6a2017-08-08 10:53:00 +0100126 src_addr += src_stride_z;
127 weights_addr += weights_stride_z;
steniu0127b386c2017-07-18 17:37:43 +0100128 }
129
130#ifdef HAS_BIAS
Gian Marco Iodice5cb4d6a2017-08-08 10:53:00 +0100131 Vector biases = CONVERT_TO_VECTOR_STRUCT_NO_STEP(biases);
132
133 pixels0 += (VEC_DATA_TYPE(DATA_TYPE, 8)) * ((__global DATA_TYPE *)(vector_offset(&biases, kernel_index)));
steniu0127b386c2017-07-18 17:37:43 +0100134#endif /* defined(HAS_BIAS) */
135
Gian Marco Iodice5cb4d6a2017-08-08 10:53:00 +0100136 vstore8(pixels0, 0, (__global DATA_TYPE *)dst.ptr);
steniu0127b386c2017-07-18 17:37:43 +0100137}
Gian Marco Iodice5cb4d6a2017-08-08 10:53:00 +0100138#endif // defined(DATA_TYPE) && defined(STRIDE_X) && defined(WEIGHTS_DEPTH)