1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
|
/*
* Copyright (c) 2017-2021 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.h"
#if defined(DATA_TYPE) && defined(EPSILON)
/** OpenCL kernel to fuse the weights of convolution or depthwise convolution layer with batch normalization when the data layout is either NCHW or NHWC
*
* @note The input weights tensor is assumed 4D with the OFMs in the fourth dimension
* @note Data type should be passed at compile time using the -DDATA_TYPE, e.g. -DDATA_TYPE=float
* @note The third dimension of the input tensor should be passed at compile time when weights belong to a convolution layer using -DDIM2=size. e.g. -DDIM2=16.
* For depthwise convolution weight do not pass DIM2
* @note Data layout NHWC should be passed at compile time with -DNHWC. For data layout NCHW it is not required to pass any parameter
* @note Batch normalization epsilon parameter should be passed at compile time using -DEPSILON=value. e.g. -DEPSILON=0.001f
*
* @param[in] w_ptr Pointer to the weights tensor. Supported data types: F16/F32
* @param[in] w_stride_x Stride of the weights tensor in X dimension (in bytes)
* @param[in] w_step_x w_stride_x * number of elements along X processed per workitem(in bytes)
* @param[in] w_stride_y Stride of the weights tensor in Y dimension (in bytes)
* @param[in] w_step_y w_stride_y * number of elements along Y processed per workitem(in bytes)
* @param[in] w_stride_z Stride of the weights tensor in Z dimension (in bytes)
* @param[in] w_step_z w_stride_z * number of elements along Z processed per workitem(in bytes)
* @param[in] w_offset_first_element_in_bytes The offset of the first element in the weights tensor
* @param[in] b_ptr (Optional) Pointer to the bias tensor. Supported data types: same as @p w_ptr
* @param[in] b_stride_x (Optional) Stride of the bias tensor in X dimension (in bytes)
* @param[in] b_step_x (Optional) b_stride_x * number of elements along X processed per workitem(in bytes)
* @param[in] b_stride_y (Optional) Stride of the bias tensor in Y dimension (in bytes)
* @param[in] b_step_y (Optional) b_stride_y * number of elements along Y processed per workitem(in bytes)
* @param[in] b_stride_z (Optional) Stride of the bias tensor in Z dimension (in bytes)
* @param[in] b_step_z (Optional) b_stride_z * number of elements along Z processed per workitem(in bytes)
* @param[in] b_offset_first_element_in_bytes (Optional) The offset of the first element in the bias tensor
* @param[in] mean_ptr Pointer to the mean source tensor. Supported data types: same as @p w_ptr
* @param[in] mean_stride_x Stride of the mean source tensor in X dimension (in bytes)
* @param[in] mean_step_x mean_stride_x * number of elements along X processed per workitem(in bytes)
* @param[in] mean_offset_first_element_in_bytes The offset of the first element in the mean source tensor
* @param[in] var_ptr Pointer to the var tensor. Supported data types: same as @p w_ptr
* @param[in] var_stride_x Stride of the var tensor in X dimension (in bytes)
* @param[in] var_step_x var_stride_x * number of elements along X processed per workitem(in bytes)
* @param[in] var_offset_first_element_in_bytes The offset of the first element in the var source tensor
* @param[out] w_fused_ptr (Optional) Pointer to the destination weights tensors. Supported data types: same as @p w_ptr
* @param[in] w_fused_stride_x (Optional) Stride of the destination weights tensor in X dimension (in bytes)
* @param[in] w_fused_step_x (Optional) w_fused_stride_x * number of elements along X processed per workitem(in bytes)
* @param[in] w_fused_stride_y (Optional) Stride of the destination weights tensor in Y dimension (in bytes)
* @param[in] w_fused_step_y (Optional) w_fused_stride_y * number of elements along Y processed per workitem(in bytes)
* @param[in] w_fused_stride_z (Optional) Stride of the destination weights tensor in Z dimension (in bytes)
* @param[in] w_fused_step_z (Optional) w_fused_stride_z * number of elements along Z processed per workitem(in bytes)
* @param[in] w_fused_offset_first_element_in_bytes (Optional) The offset of the first element in the destination weights tensor
* @param[in] b_fused_ptr (Optional) Pointer to the destination bias tensor. Supported data types: same as @p w_ptr
* @param[in] b_fused_stride_x (Optional) Stride of the destination bias tensor in X dimension (in bytes)
* @param[in] b_fused_step_x (Optional) b_fused_stride_x * number of elements along X processed per workitem(in bytes)
* @param[in] b_fused_offset_first_element_in_bytes (Optional) The offset of the first element in the destination bias tensor
* @param[in] beta_ptr (Optional) Pointer to the beta source tensor. Supported data types: same as @p w_ptr
* @param[in] beta_stride_x (Optional) Stride of the beta source tensor in X dimension (in bytes)
* @param[in] beta_step_x (Optional) beta_stride_x * number of elements along X processed per workitem(in bytes)
* @param[in] beta_offset_first_element_in_bytes (Optional) The offset of the first element in the beta source tensor
* @param[in] gamma_ptr (Optional) Pointer to the gamma source tensor. Supported data types: same as @p w_ptr
* @param[in] gamma_stride_x (Optional) Stride of the gamma source tensor in X dimension (in bytes)
* @param[in] gamma_step_x (Optional) gamma_stride_x * number of elements along X processed per workitem(in bytes)
* @param[in] gamma_offset_first_element_in_bytes (Optional) The offset of the first element in the gamma source tensor
*/
__kernel void fuse_batchnormalization_layer(TENSOR3D_DECLARATION(w),
#if defined(BIAS)
VECTOR_DECLARATION(b),
#endif // defined(BIAS)
VECTOR_DECLARATION(mean),
VECTOR_DECLARATION(var)
#ifndef IN_PLACE_W
,
TENSOR3D_DECLARATION(w_fused)
#endif // ifndef IN_PLACE_W
#ifndef IN_PLACE_B
,
VECTOR_DECLARATION(b_fused)
#endif // ifndef IN_PLACE_B
#if defined(BETA)
,
VECTOR_DECLARATION(beta)
#endif // defined(BETA)
#if defined(GAMMA)
,
VECTOR_DECLARATION(gamma)
#endif // defined(GAMMA)
)
{
int x = get_global_id(0);
int y = get_global_id(1);
int z = get_global_id(2);
#if defined(DIM2)
int c0 = z % DIM2;
int c1 = z / DIM2;
#else // ! defined(DIM2)
int c0 = 0;
#if defined(NHWC)
int c1 = x;
#else // defined(NHWC)
int c1 = z;
#endif // defined(NHWC)
#endif // defined(DIM2)
int w_offset = x * sizeof(DATA_TYPE) + y * w_stride_y + z * w_stride_z;
int v_offset = c1 * sizeof(DATA_TYPE);
DATA_TYPE w_old = 0.0f;
DATA_TYPE b_old = 0.0f;
DATA_TYPE w_new = 0.0f;
DATA_TYPE b_new = 0.0f;
DATA_TYPE gamma = 1.0f;
DATA_TYPE mean = 0.0f;
DATA_TYPE var = 1.0f;
DATA_TYPE beta = 0.0f;
w_old = *((__global DATA_TYPE *)(w_ptr + w_offset + w_offset_first_element_in_bytes));
var = *((__global DATA_TYPE *)(var_ptr + v_offset + var_offset_first_element_in_bytes));
mean = *((__global DATA_TYPE *)(mean_ptr + v_offset + mean_offset_first_element_in_bytes));
#if defined(GAMMA)
gamma = *((__global DATA_TYPE *)(gamma_ptr + v_offset + gamma_offset_first_element_in_bytes));
#endif // defined(GAMMA)
// Compute new weight
w_new = (gamma * w_old) / (sqrt(var + EPSILON));
#if defined(IN_PLACE_W)
*((__global DATA_TYPE *)(w_ptr + w_offset + w_offset_first_element_in_bytes)) = w_new;
#else // defined(IN_PLACE_W)
*((__global DATA_TYPE *)(w_fused_ptr + w_offset + w_fused_offset_first_element_in_bytes)) = w_new;
#endif // defined(IN_PLACE_W)
// Compute bias
#if !defined(DIM2) && defined(NHWC)
if(z == 0 && y == 0)
#else // !defined(DIM2) && defined(NHWC)
if(x == 0 && y == 0 && c0 == 0)
#endif // !defined(DIM2) && defined(NHWC)
{
#if defined(BIAS)
b_old = *((__global DATA_TYPE *)(b_ptr + v_offset + b_offset_first_element_in_bytes));
#endif // defined(BIAS)
#if defined(BETA)
beta = *((__global DATA_TYPE *)(beta_ptr + v_offset + beta_offset_first_element_in_bytes));
#endif // defined(BETA)
b_new = ((gamma * (b_old - mean)) / (sqrt(var + EPSILON))) + beta;
#if defined(BIAS)
#if defined(IN_PLACE_B)
*((__global DATA_TYPE *)(b_ptr + v_offset + b_offset_first_element_in_bytes)) = b_new;
#else // defined(IN_PLACE_B)
*((__global DATA_TYPE *)(b_fused_ptr + v_offset + b_fused_offset_first_element_in_bytes)) = b_new;
#endif // defined(IN_PLACE_B)
#else // defined(BIAS)
#ifndef IN_PLACE_B
*((__global DATA_TYPE *)(b_fused_ptr + v_offset + b_fused_offset_first_element_in_bytes)) = b_new;
#endif // ifndef IN_PLACE_B
#endif // defined(BIAS)
}
}
#endif // defined(DATA_TYPE) && defined(EPSILON)
|
