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review.mlplatform.org / ml / ComputeLibrary / 6a98a6e322bfb03f98ac9c4dfdc932ec4bea1fd7 / . / src / core / CL / cl_kernels / batchnormalization_layer.cl
blob: 66d371c02fa45f324722c12dd22920393f4bce35 [file] [log] [blame]
Anthony Barbier6ff3b192017-09-04 18:44:23 +0100[diff] [blame]1/*
giuros01acce5042019-02-21 17:32:34 +0000[diff] [blame]2 * Copyright (c) 2017-2019 ARM Limited.
Anthony Barbier6ff3b192017-09-04 18:44:23 +0100[diff] [blame]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
Michalis Spyrou172e5702017-06-26 14:18:47 +0100[diff] [blame]26#define ADD_OP(a, b) ((a) + (b))
27#define SUB_OP(a, b) ((a) - (b))
28#define MUL_OP(a, b) ((a) * (b))
29#define INVSQRT_OP(a) rsqrt((a))
30#define SQCVT_SAT(a) (a)
31
Usama Arif6a98a6e2019-05-10 17:07:27 +0100[diff] [blame^]32#if defined(VEC_SIZE) && defined(DATA_TYPE) && defined(ACTIVATION_TYPE)
33#include "activation_float_helpers.h"
Giorgio Arena11674872018-02-07 15:38:12 +0000[diff] [blame]34
Anthony Barbier6ff3b192017-09-04 18:44:23 +0100[diff] [blame]35/** Apply batch normalization.
36 *
Usama Arif6a98a6e2019-05-10 17:07:27 +0100[diff] [blame^]37 * @note It is possible to select the activation function to apply using -DACTIVATION_TYPE e.g. -DACTIVATION_TYPE=relu
38 * @note A, B variables required by some activation functions are set using -DA_VAL= and -DB_VAL= respectively
39 *
Vidhya Sudhan Loganathan7485d5a2018-07-04 09:34:00 +0100[diff] [blame]40 * @param[in] input_ptr Pointer to the first source tensor. Supported data types: F16/F32
Anthony Barbier6ff3b192017-09-04 18:44:23 +0100[diff] [blame]41 * @param[in] input_stride_x Stride of the first source tensor in X dimension (in bytes)
42 * @param[in] input_step_x input_stride_x * number of elements along X processed per workitem(in bytes)
43 * @param[in] input_stride_y Stride of the first source tensor in Y dimension (in bytes)
44 * @param[in] input_step_y input_stride_y * number of elements along Y processed per workitem(in bytes)
45 * @param[in] input_stride_z Stride of the first source tensor in Z dimension (in bytes)
46 * @param[in] input_step_z input_stride_z * number of elements along Z processed per workitem(in bytes)
47 * @param[in] input_offset_first_element_in_bytes The offset of the first element in the first source tensor
Michalis Spyrou172e5702017-06-26 14:18:47 +0100[diff] [blame]48 * @param[out] output_ptr Pointer to the destination tensor. Supported data types: same as @p input_ptr
Anthony Barbier6ff3b192017-09-04 18:44:23 +0100[diff] [blame]49 * @param[in] output_stride_x Stride of the destination tensor in X dimension (in bytes)
50 * @param[in] output_step_x output_stride_x * number of elements along X processed per workitem(in bytes)
51 * @param[in] output_stride_y Stride of the destination tensor in Y dimension (in bytes)
52 * @param[in] output_step_y output_stride_y * number of elements along Y processed per workitem(in bytes)
53 * @param[in] output_stride_z Stride of the destination tensor in Z dimension (in bytes)
54 * @param[in] output_step_z output_stride_z * number of elements along Z processed per workitem(in bytes)
55 * @param[in] output_offset_first_element_in_bytes The offset of the first element in the destination tensor
Michalis Spyrou172e5702017-06-26 14:18:47 +0100[diff] [blame]56 * @param[in] mean_ptr Pointer to the mean source tensor. Supported data types: same as @p input_ptr
Anthony Barbier6ff3b192017-09-04 18:44:23 +0100[diff] [blame]57 * @param[in] mean_stride_x Stride of the mean source tensor in X dimension (in bytes)
58 * @param[in] mean_step_x mean_stride_x * number of elements along X processed per workitem(in bytes)
59 * @param[in] mean_offset_first_element_in_bytes The offset of the first element in the mean source tensor
Michalis Spyrou172e5702017-06-26 14:18:47 +0100[diff] [blame]60 * @param[in] var_ptr Pointer to the var tensor. Supported data types: same as @p input_ptr
Anthony Barbier6ff3b192017-09-04 18:44:23 +0100[diff] [blame]61 * @param[in] var_stride_x Stride of the var tensor in X dimension (in bytes)
62 * @param[in] var_step_x var_stride_x * number of elements along X processed per workitem(in bytes)
63 * @param[in] var_offset_first_element_in_bytes The offset of the first element in the var source tensor
Michalis Spyrou172e5702017-06-26 14:18:47 +0100[diff] [blame]64 * @param[in] beta_ptr Pointer to the beta source tensor. Supported data types: same as @p input_ptr
Anthony Barbier6ff3b192017-09-04 18:44:23 +0100[diff] [blame]65 * @param[in] beta_stride_x Stride of the beta source tensor in X dimension (in bytes)
66 * @param[in] beta_step_x beta_stride_x * number of elements along X processed per workitem(in bytes)
67 * @param[in] beta_offset_first_element_in_bytes The offset of the first element in the beta source tensor
Michalis Spyrou172e5702017-06-26 14:18:47 +0100[diff] [blame]68 * @param[in] gamma_ptr Pointer to the gamma source tensor. Supported data types: same as @p input_ptr
Anthony Barbier6ff3b192017-09-04 18:44:23 +0100[diff] [blame]69 * @param[in] gamma_stride_x Stride of the gamma source tensor in X dimension (in bytes)
70 * @param[in] gamma_step_x gamma_stride_x * number of elements along X processed per workitem(in bytes)
71 * @param[in] gamma_offset_first_element_in_bytes The offset of the first element in the gamma source tensor
72 * @param[in] epsilon Epsilon parameter in the batch normalization equation
73 */
Michele Di Giorgiobf3c6622018-03-08 11:52:27 +0000[diff] [blame]74__kernel void batchnormalization_layer_nchw(TENSOR3D_DECLARATION(input),
Georgios Pinitas409ee0a2017-08-18 10:16:09 +0100[diff] [blame]75#ifndef IN_PLACE
Michele Di Giorgiobf3c6622018-03-08 11:52:27 +0000[diff] [blame]76 TENSOR3D_DECLARATION(output),
Georgios Pinitas409ee0a2017-08-18 10:16:09 +0100[diff] [blame]77#endif /* not IN_PLACE */
Michele Di Giorgiobf3c6622018-03-08 11:52:27 +0000[diff] [blame]78 VECTOR_DECLARATION(mean),
79 VECTOR_DECLARATION(var),
Michele Di Giorgio4d336302018-03-02 09:43:54 +0000[diff] [blame]80#ifndef USE_DEFAULT_BETA
Michele Di Giorgiobf3c6622018-03-08 11:52:27 +0000[diff] [blame]81 VECTOR_DECLARATION(beta),
Michele Di Giorgio4d336302018-03-02 09:43:54 +0000[diff] [blame]82#endif /* USE_DEFAULT_BETA */
83#ifndef USE_DEFAULT_GAMMA
Michele Di Giorgiobf3c6622018-03-08 11:52:27 +0000[diff] [blame]84 VECTOR_DECLARATION(gamma),
Michele Di Giorgio4d336302018-03-02 09:43:54 +0000[diff] [blame]85#endif /* USE_DEFAULT_GAMMA */
Michele Di Giorgiobf3c6622018-03-08 11:52:27 +0000[diff] [blame]86 float epsilon)
Anthony Barbier6ff3b192017-09-04 18:44:23 +0100[diff] [blame]87{
Georgios Pinitas409ee0a2017-08-18 10:16:09 +0100[diff] [blame]88 Tensor3D in = CONVERT_TO_TENSOR3D_STRUCT(input);
89#ifdef IN_PLACE
90 Tensor3D out = in;
91#else /* IN_PLACE */
92 Tensor3D out = CONVERT_TO_TENSOR3D_STRUCT(output);
93#endif /* IN_PLACE */
Michele Di Giorgio4d336302018-03-02 09:43:54 +0000[diff] [blame]94 Vector mean = CONVERT_TO_VECTOR_STRUCT(mean);
95 Vector var = CONVERT_TO_VECTOR_STRUCT(var);
96#ifndef USE_DEFAULT_BETA
97 Vector beta = CONVERT_TO_VECTOR_STRUCT(beta);
98#endif /* USE_DEFAULT_BETA */
99#ifndef USE_DEFAULT_GAMMA
Georgios Pinitas409ee0a2017-08-18 10:16:09 +0100[diff] [blame]100 Vector gamma = CONVERT_TO_VECTOR_STRUCT(gamma);
Michele Di Giorgio4d336302018-03-02 09:43:54 +0000[diff] [blame]101#endif /* USE_DEFAULT_GAMMA */
Anthony Barbier6ff3b192017-09-04 18:44:23 +0100[diff] [blame]102
Michalis Spyrou172e5702017-06-26 14:18:47 +0100[diff] [blame]103 VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE)
Gian Marco Iodice349feef2017-09-28 11:21:29 +0100[diff] [blame]104 data = 0;
Michalis Spyrou172e5702017-06-26 14:18:47 +0100[diff] [blame]105 VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE)
106 denominator = 0;
107 VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE)
108 numerator = 0;
109 VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE)
110 x_bar = 0;
111 VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE)
Michele Di Giorgio4d336302018-03-02 09:43:54 +0000[diff] [blame]112 res = 0;
Anthony Barbier6ff3b192017-09-04 18:44:23 +0100[diff] [blame]113
114 const int current_slice = get_global_id(2);
115
Gian Marco Iodice349feef2017-09-28 11:21:29 +0100[diff] [blame]116 data = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)in.ptr);
Michalis Spyrou172e5702017-06-26 14:18:47 +0100[diff] [blame]117 denominator = *((__global DATA_TYPE *)(var.ptr + current_slice * var.stride_x));
Gian Marco Iodice349feef2017-09-28 11:21:29 +0100[diff] [blame]118 denominator = INVSQRT_OP(ADD_OP(denominator, ((VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE))SQCVT_SAT(epsilon))));
Anthony Barbier6ff3b192017-09-04 18:44:23 +0100[diff] [blame]119
120 // Calculate x bar and store results
Michalis Spyrou172e5702017-06-26 14:18:47 +0100[diff] [blame]121 numerator = *((__global DATA_TYPE *)(mean.ptr + current_slice * mean.stride_x));
Gian Marco Iodice349feef2017-09-28 11:21:29 +0100[diff] [blame]122 numerator = SUB_OP(data, numerator);
Michalis Spyrou172e5702017-06-26 14:18:47 +0100[diff] [blame]123 x_bar = MUL_OP(numerator, denominator);
Anthony Barbier6ff3b192017-09-04 18:44:23 +0100[diff] [blame]124
Michele Di Giorgio4d336302018-03-02 09:43:54 +0000[diff] [blame]125#ifndef USE_DEFAULT_GAMMA
Giorgio Arena11674872018-02-07 15:38:12 +0000[diff] [blame]126 VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE)
Michele Di Giorgio4d336302018-03-02 09:43:54 +0000[diff] [blame]127 gamma_vec = *((__global DATA_TYPE *)(gamma.ptr + current_slice * gamma.stride_x));
128
129 res = MUL_OP(gamma_vec, x_bar);
130#else /* USE_DEFAULT_GAMMA */
131 // gamma is equal to 1, no need to perform multiplications
Michele Di Giorgiobf3c6622018-03-08 11:52:27 +0000[diff] [blame]132 res = x_bar;
Michele Di Giorgio4d336302018-03-02 09:43:54 +0000[diff] [blame]133#endif /* USE_DEFAULT_GAMMA */
134
135#ifndef USE_DEFAULT_BETA
136 VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE)
137 beta_vec = *((__global DATA_TYPE *)(beta.ptr + current_slice * beta.stride_x));
138 // beta is not zero, hence we need to perform the addition
139 res = ADD_OP(res, beta_vec);
140#endif /* USE_DEFAULT_BETA */
Giorgio Arena11674872018-02-07 15:38:12 +0000[diff] [blame]141
Usama Arif6a98a6e2019-05-10 17:07:27 +0100[diff] [blame^]142 res = ACTIVATION(ACTIVATION_TYPE, DATA_TYPE, res, A_VAL, B_VAL);
Giorgio Arena11674872018-02-07 15:38:12 +0000[diff] [blame]143
Michalis Spyrou172e5702017-06-26 14:18:47 +0100[diff] [blame]144 VSTORE(VEC_SIZE)
Giorgio Arena11674872018-02-07 15:38:12 +0000[diff] [blame]145 (res, 0, (__global DATA_TYPE *)out.ptr);
Anthony Barbier6ff3b192017-09-04 18:44:23 +0100[diff] [blame]146}
Giorgio Arena11674872018-02-07 15:38:12 +0000[diff] [blame]147
Michele Di Giorgiobf3c6622018-03-08 11:52:27 +0000[diff] [blame]148/** Apply batch normalization on tensors with NHWC format.
149 *
Usama Arif6a98a6e2019-05-10 17:07:27 +0100[diff] [blame^]150 * @note It is possible to select the activation function to apply using -DACTIVATION_TYPE e.g. -DACTIVATION_TYPE=relu
151 * @note A, B variables required by some activation functions are set using -DA_VAL= and -DB_VAL= respectively
152 *
Vidhya Sudhan Loganathan7485d5a2018-07-04 09:34:00 +0100[diff] [blame]153 * @param[in] input_ptr Pointer to the first source tensor. Supported data types: F16/F32
Michele Di Giorgiobf3c6622018-03-08 11:52:27 +0000[diff] [blame]154 * @param[in] input_stride_x Stride of the first source tensor in X dimension (in bytes)
155 * @param[in] input_step_x input_stride_x * number of elements along X processed per workitem(in bytes)
156 * @param[in] input_stride_y Stride of the first source tensor in Y dimension (in bytes)
157 * @param[in] input_step_y input_stride_y * number of elements along Y processed per workitem(in bytes)
158 * @param[in] input_stride_z Stride of the first source tensor in Z dimension (in bytes)
159 * @param[in] input_step_z input_stride_z * number of elements along Z processed per workitem(in bytes)
160 * @param[in] input_offset_first_element_in_bytes The offset of the first element in the first source tensor
161 * @param[out] output_ptr Pointer to the destination tensor. Supported data types: same as @p input_ptr
162 * @param[in] output_stride_x Stride of the destination tensor in X dimension (in bytes)
163 * @param[in] output_step_x output_stride_x * number of elements along X processed per workitem(in bytes)
164 * @param[in] output_stride_y Stride of the destination tensor in Y dimension (in bytes)
165 * @param[in] output_step_y output_stride_y * number of elements along Y processed per workitem(in bytes)
166 * @param[in] output_stride_z Stride of the destination tensor in Z dimension (in bytes)
167 * @param[in] output_step_z output_stride_z * number of elements along Z processed per workitem(in bytes)
168 * @param[in] output_offset_first_element_in_bytes The offset of the first element in the destination tensor
169 * @param[in] mean_ptr Pointer to the mean source tensor. Supported data types: same as @p input_ptr
170 * @param[in] mean_stride_x Stride of the mean source tensor in X dimension (in bytes)
171 * @param[in] mean_step_x mean_stride_x * number of elements along X processed per workitem(in bytes)
172 * @param[in] mean_offset_first_element_in_bytes The offset of the first element in the mean source tensor
173 * @param[in] var_ptr Pointer to the var tensor. Supported data types: same as @p input_ptr
174 * @param[in] var_stride_x Stride of the var tensor in X dimension (in bytes)
175 * @param[in] var_step_x var_stride_x * number of elements along X processed per workitem(in bytes)
176 * @param[in] var_offset_first_element_in_bytes The offset of the first element in the var source tensor
177 * @param[in] beta_ptr Pointer to the beta source tensor. Supported data types: same as @p input_ptr
178 * @param[in] beta_stride_x Stride of the beta source tensor in X dimension (in bytes)
179 * @param[in] beta_step_x beta_stride_x * number of elements along X processed per workitem(in bytes)
180 * @param[in] beta_offset_first_element_in_bytes The offset of the first element in the beta source tensor
181 * @param[in] gamma_ptr Pointer to the gamma source tensor. Supported data types: same as @p input_ptr
182 * @param[in] gamma_stride_x Stride of the gamma source tensor in X dimension (in bytes)
183 * @param[in] gamma_step_x gamma_stride_x * number of elements along X processed per workitem(in bytes)
184 * @param[in] gamma_offset_first_element_in_bytes The offset of the first element in the gamma source tensor
185 * @param[in] epsilon Epsilon parameter in the batch normalization equation
186 */
187__kernel void batchnormalization_layer_nhwc(TENSOR3D_DECLARATION(input),
188#ifndef IN_PLACE
189 TENSOR3D_DECLARATION(output),
190#endif /* not IN_PLACE */
191 VECTOR_DECLARATION(mean),
192 VECTOR_DECLARATION(var),
193#ifndef USE_DEFAULT_BETA
194 VECTOR_DECLARATION(beta),
195#endif /* USE_DEFAULT_BETA */
196#ifndef USE_DEFAULT_GAMMA
197 VECTOR_DECLARATION(gamma),
198#endif /* USE_DEFAULT_GAMMA */
199 float epsilon)
200{
201 Tensor3D in = CONVERT_TO_TENSOR3D_STRUCT(input);
202#ifdef IN_PLACE
203 Tensor3D out = in;
204#else /* IN_PLACE */
205 Tensor3D out = CONVERT_TO_TENSOR3D_STRUCT(output);
206#endif /* IN_PLACE */
207 Vector mean = CONVERT_TO_VECTOR_STRUCT(mean);
208 Vector var = CONVERT_TO_VECTOR_STRUCT(var);
209#ifndef USE_DEFAULT_BETA
210 Vector beta = CONVERT_TO_VECTOR_STRUCT(beta);
211#endif /* USE_DEFAULT_BETA */
212#ifndef USE_DEFAULT_GAMMA
213 Vector gamma = CONVERT_TO_VECTOR_STRUCT(gamma);
214#endif /* USE_DEFAULT_GAMMA */
215
216 VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE)
217 data = 0;
218 VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE)
219 denominator = 0;
220 VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE)
221 numerator = 0;
222 VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE)
223 x_bar = 0;
224 VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE)
225 res = 0;
226
227 const int current_slice = get_global_id(0);
228
229 data = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)in.ptr);
230 denominator = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(var.ptr + current_slice * VEC_SIZE * var.stride_x));
231 denominator = INVSQRT_OP(ADD_OP(denominator, ((VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE))SQCVT_SAT(epsilon))));
232
233 // Calculate x bar and store results
234 numerator = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(mean.ptr + current_slice * VEC_SIZE * mean.stride_x));
235 numerator = SUB_OP(data, numerator);
236 x_bar = MUL_OP(numerator, denominator);
237
238#ifndef USE_DEFAULT_GAMMA
239 VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE)
240 gamma_vec = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(gamma.ptr + current_slice * VEC_SIZE * gamma.stride_x));
241
242 res = MUL_OP(gamma_vec, x_bar);
243#else /* USE_DEFAULT_GAMMA */
244 // gamma is equal to 1, no need to perform multiplications
245 res = x_bar;
246#endif /* USE_DEFAULT_GAMMA */
247
248#ifndef USE_DEFAULT_BETA
249 VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE)
250 beta_vec = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(beta.ptr + current_slice * VEC_SIZE * beta.stride_x));
251 // beta is not zero, hence we need to perform the addition
252 res = ADD_OP(res, beta_vec);
253#endif /* USE_DEFAULT_BETA */
254
Usama Arif6a98a6e2019-05-10 17:07:27 +0100[diff] [blame^]255 res = ACTIVATION(ACTIVATION_TYPE, DATA_TYPE, res, A_VAL, B_VAL);
Michele Di Giorgiobf3c6622018-03-08 11:52:27 +0000[diff] [blame]256
257 VSTORE(VEC_SIZE)
258 (res, 0, (__global DATA_TYPE *)out.ptr);
259}
Usama Arif6a98a6e2019-05-10 17:07:27 +0100[diff] [blame^]260#endif /* defined(VEC_SIZE) && defined(DATA_TYPE) && defined(DATA_TYPE)*/
Georgios Pinitasc9369172018-09-26 11:25:40 +0100[diff] [blame]261
262#if defined(NUM_CHANNELS) && defined(DATA_TYPE) && defined(EPSILON)
263/** Fuse batchnorm parameters to convolution layer parameters
264 *
265 * @attention Data type should be passed using the -DDATA_TYPE compile flag, e.g. -DDATA_TYPE=float
266 * @attention Input tensor depth should be given as a preprocessor argument using -DNUM_CHANNELS=size. e.g. -DNUM_CHANNELS=16
267 * @attention Batch normalization epsilon parameter should be given as a preprocessor argument with -DEPSILON=value. e.g. -DEPSILON=0.001f
268 *
269 * @param[in] conv_w_ptr Pointer to the source tensor. Supported data types: F16/F32
270 * @param[in] conv_w_stride_x Stride of the source tensor in X dimension (in bytes)
271 * @param[in] conv_w_step_x input_stride_x * number of elements along X processed per workitem(in bytes)
272 * @param[in] conv_w_stride_y Stride of the source tensor in Y dimension (in bytes)
273 * @param[in] conv_w_step_y input_stride_y * number of elements along Y processed per workitem(in bytes)
274 * @param[in] conv_w_stride_z Stride of the source tensor in Z dimension (in bytes)
275 * @param[in] conv_w_step_z input_stride_z * number of elements along Z processed per workitem(in bytes)
Georgios Pinitasc55beee2018-10-23 15:23:23 +0100[diff] [blame]276 * @param[in] conv_w_stride_w Stride of the source tensor in W dimension (in bytes)
277 * @param[in] conv_w_step_w input_stride_w * number of elements along W processed per workitem(in bytes)
Georgios Pinitasc9369172018-09-26 11:25:40 +0100[diff] [blame]278 * @param[in] conv_w_offset_first_element_in_bytes The offset of the first element in the source tensor
279 * @param[in] bn_mean_ptr Pointer to the mean source tensor. Supported data types: same as @p input_ptr
280 * @param[in] bn_mean_stride_x Stride of the mean source tensor in X dimension (in bytes)
281 * @param[in] bn_mean_step_x bn_mean_stride_x * number of elements along X processed per workitem(in bytes)
282 * @param[in] bn_mean_offset_first_element_in_bytes The offset of the first element in the mean source tensor
283 * @param[in] bn_var_ptr Pointer to the var tensor. Supported data types: same as @p input_ptr
284 * @param[in] bn_var_stride_x Stride of the var tensor in X dimension (in bytes)
285 * @param[in] bn_var_step_x bn_var_stride_x * number of elements along X processed per workitem(in bytes)
286 * @param[in] bn_var_offset_first_element_in_bytes The offset of the first element in the var source tensor
287 * @param[out] fused_w_ptr Pointer to the destination weights tensors. Supported data types: same as @p input_ptr
288 * @param[in] fused_w_stride_x Stride of the destination tensor in X dimension (in bytes)
289 * @param[in] fused_w_step_x fused_w_stride_x * number of elements along X processed per workitem(in bytes)
290 * @param[in] fused_w_stride_y Stride of the destination tensor in Y dimension (in bytes)
291 * @param[in] fused_w_step_y fused_w_stride_y * number of elements along Y processed per workitem(in bytes)
292 * @param[in] fused_w_stride_z Stride of the destination tensor in Z dimension (in bytes)
293 * @param[in] fused_w_step_z fused_w_stride_z * number of elements along Z processed per workitem(in bytes)
294 * @param[in] fused_w_stride_w Stride of the destination tensor in W dimension (in bytes)
295 * @param[in] fused_w_step_w fused_w_stride_w * number of elements along W processed per workitem(in bytes)
296 * @param[in] fused_w_offset_first_element_in_bytes The offset of the first element in the destination tensor
297 * @param[in] fused_b_ptr Pointer to the destination bias tensor. Supported data types: same as @p input_ptr
298 * @param[in] fused_b_stride_x Stride of the bias source tensor in X dimension (in bytes)
299 * @param[in] fused_b_step_x fused_b_stride_x * number of elements along X processed per workitem(in bytes)
300 * @param[in] fused_b_offset_first_element_in_bytes The offset of the first element in the destination tensor
301 * @param[in] conv_b_ptr Pointer to the source bias tensor. Supported data types: same as @p input_ptr
302 * @param[in] conv_b_stride_x Stride of the beta source tensor in X dimension (in bytes)
303 * @param[in] conv_b_step_x conv_b_beta_stride_x * number of elements along X processed per workitem(in bytes)
304 * @param[in] conv_b_offset_first_element_in_bytes The offset of the first element in the source bias tensor
305 * @param[in] bn_beta_ptr Pointer to the beta source tensor. Supported data types: same as @p input_ptr
306 * @param[in] bn_beta_stride_x Stride of the beta source tensor in X dimension (in bytes)
307 * @param[in] bn_beta_step_x bn_beta_stride_x * number of elements along X processed per workitem(in bytes)
308 * @param[in] bn_beta_offset_first_element_in_bytes The offset of the first element in the beta source tensor
309 * @param[in] bn_gamma_ptr Pointer to the gamma source tensor. Supported data types: same as @p input_ptr
310 * @param[in] bn_gamma_stride_x Stride of the gamma source tensor in X dimension (in bytes)
311 * @param[in] bn_gamma_step_x bn_gamma_stride_x * number of elements along X processed per workitem(in bytes)
312 * @param[in] bn_gamma_offset_first_element_in_bytes The offset of the first element in the gamma source tensor
313 * @param[in] epsilon Epsilon parameter in the batch normalization equation
314 */
315__kernel void fuse_batchnormalization_layer(TENSOR4D_DECLARATION(conv_w),
316 VECTOR_DECLARATION(bn_mean),
317 VECTOR_DECLARATION(bn_var)
318#ifndef IN_PLACE_W
319 ,
320 TENSOR4D_DECLARATION(fused_w)
321#endif /* not IN_PLACE_W */
322#ifndef IN_PLACE_B
323 ,
324 VECTOR_DECLARATION(fused_b)
325#endif /* not IN_PLACE_B */
326#ifdef HAS_BIAS
327 ,
328 VECTOR_DECLARATION(conv_b)
329#endif /* HAS_BIAS */
330#ifndef USE_DEFAULT_BETA
331 ,
332 VECTOR_DECLARATION(bn_beta)
333#endif /* USE_DEFAULT_BETA */
334#ifndef USE_DEFAULT_GAMMA
335 ,
336 VECTOR_DECLARATION(bn_gamma)
337#endif /* USE_DEFAULT_GAMMA */
338 )
339{
340 Tensor4D conv_w = CONVERT_TO_TENSOR4D_STRUCT(conv_w, NUM_CHANNELS);
341 Vector bn_mean = CONVERT_TO_VECTOR_STRUCT_NO_STEP(bn_mean);
342 Vector bn_var = CONVERT_TO_VECTOR_STRUCT_NO_STEP(bn_var);
343
Georgios Pinitasc9369172018-09-26 11:25:40 +0100[diff] [blame]344 // Conditional ops
345#ifdef HAS_BIAS
346 Vector conv_b = CONVERT_TO_VECTOR_STRUCT_NO_STEP(conv_b);
giuros01acce5042019-02-21 17:32:34 +0000[diff] [blame]347#endif /* HAS_BIAS */
Georgios Pinitasc9369172018-09-26 11:25:40 +0100[diff] [blame]348#ifndef USE_DEFAULT_BETA
349 Vector bn_beta = CONVERT_TO_VECTOR_STRUCT_NO_STEP(bn_beta);
350#endif /* USE_DEFAULT_BETA */
351#ifndef USE_DEFAULT_GAMMA
352 Vector bn_gamma = CONVERT_TO_VECTOR_STRUCT_NO_STEP(bn_gamma);
353#endif /* USE_DEFAULT_GAMMA */
354
giuros01acce5042019-02-21 17:32:34 +0000[diff] [blame]355 // In-place ops
356#ifdef IN_PLACE_W
357 Tensor4D fused_w = conv_w;
358 uint fused_w_stride_x = conv_w_stride_x;
359#else /* IN_PLACE_W */
360 Tensor4D fused_w = CONVERT_TO_TENSOR4D_STRUCT(fused_w, NUM_CHANNELS);
361#endif /* IN_PLACE_W */
362#ifdef IN_PLACE_B
363 Vector fused_b = conv_b;
364#else /* IN_PLACE_B */
365 Vector fused_b = CONVERT_TO_VECTOR_STRUCT_NO_STEP(fused_b);
366#endif /* IN_PLACE_B */
367
Georgios Pinitasc9369172018-09-26 11:25:40 +0100[diff] [blame]368 const int current_slice = get_global_id(2) / NUM_CHANNELS;
369
370#if defined(VEC_SIZE) && defined(LAST_ACCESSED_X)
371 // Check if access on width gets out of bounds
372 // If it does shift access vector to access elements within bounds
373 const int xi = (int)(get_global_id(0) * VEC_SIZE);
374 conv_w.ptr -= max(xi - (int)LAST_ACCESSED_X, 0) * conv_w_stride_x;
375 fused_w.ptr -= max(xi - (int)LAST_ACCESSED_X, 0) * fused_w_stride_x;
376
377 // Load W
378 VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE)
379 wn = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)conv_w.ptr);
380#else // !defined(VEC_SIZE) || !defined(LAST_ACCESSED_X)
381 DATA_TYPE wn = *((__global DATA_TYPE *)(conv_w.ptr));
382#endif // defined(VEC_SIZE) && defined(LAST_ACCESSED_X)
383
384 // rvar = 1 / sqrt(var + epsilon)
385 const DATA_TYPE var = *((__global DATA_TYPE *)(bn_var.ptr + current_slice * bn_var.stride_x));
386 const DATA_TYPE rvar = INVSQRT_OP(ADD_OP(var, SQCVT_SAT((float)EPSILON)));
387 wn *= rvar;
388
389 // Load b
390 const DATA_TYPE mean = *((__global DATA_TYPE *)(bn_mean.ptr + current_slice * bn_mean.stride_x));
391 DATA_TYPE bn = 0;
392#ifdef HAS_BIAS
393 bn = *((__global DATA_TYPE *)(conv_b.ptr + current_slice * conv_b.stride_x));
394#endif /* HAS_BIAS */
395 bn = (bn - mean) * rvar;
396
397#ifndef USE_DEFAULT_GAMMA
398 const DATA_TYPE gamma_scalar = *((__global DATA_TYPE *)(bn_gamma.ptr + current_slice * bn_gamma.stride_x));
399 wn *= gamma_scalar;
400 bn *= gamma_scalar;
401#endif /* USE_DEFAULT_GAMMA */
402
403#ifndef USE_DEFAULT_BETA
404 const DATA_TYPE beta_scalar = *((__global DATA_TYPE *)(bn_beta.ptr + current_slice * bn_beta.stride_x));
405 bn += beta_scalar;
406#endif /* USE_DEFAULT_BETA */
407
408#if defined(VEC_SIZE) && defined(LAST_ACCESSED_X)
409 // Store updated weights
410 VSTORE(VEC_SIZE)
411 (wn, 0, (__global DATA_TYPE *)fused_w.ptr);
412#else // !defined(VEC_SIZE) || !defined(LAST_ACCESSED_X)
413 *((__global DATA_TYPE *)(fused_w.ptr)) = wn;
414#endif // defined(VEC_SIZE) && defined(LAST_ACCESSED_X)
415
416 // Store updated bias
417 *((__global DATA_TYPE *)(fused_b.ptr + current_slice * fused_b.stride_x)) = bn;
418}
419#endif /* defined(NUM_CHANNELS) && defined(DATA_TYPE) && defined(EPSILON) */