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
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
|
/*
* Copyright (c) 2021-2023 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"
#include "tile_helpers.h"
// *INDENT-OFF*
// clang-format off
#define CALCULATE_WEIGHTS_OFFSET_CORRECTION(A_DATA_TYPE, B_DATA_TYPE) CALCULATE_WEIGHTS_OFFSET_CORRECTION_STR(A_DATA_TYPE, B_DATA_TYPE)
#define CALCULATE_WEIGHTS_OFFSET_CORRECTION_STR(A_DATA_TYPE, B_DATA_TYPE) CALCULATE_WEIGHTS_OFFSET_CORRECTION_##A_DATA_TYPE##_##B_DATA_TYPE
#define CALCULATE_WEIGHTS_OFFSET_CORRECTION_char_char (0)
#define CALCULATE_WEIGHTS_OFFSET_CORRECTION_uchar_uchar (0)
#define CALCULATE_WEIGHTS_OFFSET_CORRECTION_uchar_char (128)
#define CALCULATE_WEIGHTS_OFFSET_CORRECTION_char_uchar (-128)
#define T_LOAD_MULTIPLIERS_SHIFT_PER_TENSOR() \
({})
#define T_LOAD_MULTIPLIERS_SHIFT_PER_CHANNEL() \
TILE(DST_MULTIPLIERS_DATA_TYPE, 1, N0, multipliers); \
TILE(DST_SHIFTS_DATA_TYPE, 1, N0, shifts); \
T_LOAD(DST_MULTIPLIERS_DATA_TYPE, 1, N0, BUFFER, dst_multipliers, cout, 0, 0, 0, multipliers); \
T_LOAD(DST_SHIFTS_DATA_TYPE, 1, N0, BUFFER, dst_shifts, cout, 0, 0, 0, shifts);
#define T_LOAD_MULTIPLIERS_SHIFT(QUANTIZATION_TYPE) T_LOAD_MULTIPLIERS_SHIFT_STR(QUANTIZATION_TYPE)
#define T_LOAD_MULTIPLIERS_SHIFT_STR(QUANTIZATION_TYPE) T_LOAD_MULTIPLIERS_SHIFT_##QUANTIZATION_TYPE()
#if defined(WEI_WIDTH) && defined(WEI_HEIGHT) && defined(N0) && defined(M0) && defined(DILATION_X) && defined(DILATION_Y) && defined(STRIDE_X) && defined(STRIDE_Y) && defined(PAD_LEFT) && defined(PAD_TOP)
//! @cond Doxygen_Suppress
/** OpenCL kernel to compute the depthwise convolution for quantized data types
*
* @note Data layout supported: NHWC
* @note Data type supported: QSYMM8/QASYMM8/QASYMM8_SIGNED/QSYMM8_PER_CHANNEL
* @note The convolution padding (left and top) must be passed at compile time using -DPAD_LEFT and -DPAD_TOP (e.g. -DPAD_LEFT=2, -DPAD_TOP=2)
* @note The convolution strides must be passed at compile time using -DSTRIDE_X and -DSTRIDE_Y (e.g. -DSTRIDE_X=2, -DSTRIDE_Y=2)
* @note The convolution dilations must be passed at compile time using -DDILATION_X and -DDILATION_Y (e.g. -DDILATION_X=2, -DDILATION_Y=2)
* @note The spatial dimensions of the weights must be passed at compile time using -DWEI_WIDTH and -DWEI_HEIGHT (e.g. -DWEI_WIDTH=9, -DWEI_HEIGHT=9)
* @note The tensor type ("BUFFER" or "IMAGE") of the source tensor must be passed at compile time using -DSRC_TENSOR_TYPE (e.g. -DSRC_TENSOR_TYPE=BUFFER)
* @note The tensor type ("BUFFER" or "IMAGE") of the weights tensor must be passed at compile time using -DWEI_TENSOR_TYPE (e.g. -DWEI_TENSOR_TYPE=BUFFER)
* @note The tensor type ("BUFFER" or "IMAGE") of the destination tensor must be passed at compile time using -DDST_TENSOR_TYPE (e.g. -DDST_TENSOR_TYPE=BUFFER)
* @note The data type of the source tensor must be passed at compile time using -DSRC_DATA_TYPE (e.g. -DSRC_DATA_TYPE=int8)
* @note The data type of the weights tensor must be passed at compile time using -DWEI_DATA_TYPE (e.g. -DWEI_DATA_TYPE=int8)
* @note The data type of the destination tensor must be passed at compile time using -DDST_DATA_TYPE (e.g. -DDST_DATA_TYPE=int8)
* @note The data type of the accumulators must be passed at compile time using -DACC_DATA_TYPE (e.g. -DACC_DATA_TYPE=int)
* @note The number of M0 rows (width) to process must be passed at compile time using -DM0 (e.g. -DM0=2)
* @note The number of N0 output channels to process must be passed at compile time using -DN0 (e.g. -DN0=2)
* @note The size of the partial store block in the first dimension must be passed at compile time using -DPARTIAL_N0 (e.g. -DPARTIAL_N0=1)
* @note The activation type must be passed at compile using -DACTIVATION_TYPE e.g. -DACTIVATION_TYPE=relu
* @note The A and B variables required by some activation functions must be passed at compile time using -DA_VAL= and -DB_VAL= respectively
* @note The quantization offset used for both the per-tensor and per-channel quantization must be passed at compile using -DDST_OFFSET (e.g., -DDST_OFFSET=3)
* @note The quantization shift for the per-tensor quantization must be passed at compile time using -DDST_SHIFT (e.g., -DDST_SHIFT=1)
* @note The quantization multiplier for the per-tensor quantization must be passed at compile using -DDST_MULTIPLIER (e.g., -DDST_MULTIPLER=121432)
* @note Only the following configurations of M0 and N0 are currently supported:
* - M0 = 1, 2, 3, 4, 5, .... n (M0 != 1 with STRIDE_X == 1 && DILATION_X == 1 only)
* - N0 = 2, 3, 4, 8, 16
* @note The number of rows to read from the src tensor must be passed at compile time using -DM0_A (e.g., -DM0_A=3). M0_A must be equal to WEI_WIDTH + (M0 - 1)
* @note The number of columns to read from the src tensor must be passed at compile time using -DN0_A. It can either be 1 (for DEPTH_MULTIPLIER > 1) or N0 (for DEPTH_MULTIPLIER == 1)
*
* @param[in] src_img (Not supported) Read only cl_image object for the source tensor. Included when SRC_TENSOR_TYPE=IMAGE
* @param[in] src_ptr Pointer to the source tensor. Supported data type: QSYMM8/QASYMM8/QASYMM8_SIGNED/QSYMM8_PER_CHANNEL
* @param[in] src_stride_y Stride of the source tensor in Y dimension (in bytes)
* @param[in] src_stride_z Stride of the source tensor in Z dimension (in bytes)
* @param[in] src_stride_w Stride of the source tensor in W dimension (in bytes)
* @param[in] src_c The size of the channels dimension of the source tensor
* @param[in] src_w The size of the width dimension of the source tensor
* @param[in] src_h The size of the height dimension of the source tensor
* @param[in] src_n The size of the batches dimension of the source tensor
* @param[in] src_offset_first_element_in_bytes The offset of the first element in the source tensor
* @param[out] dst_img (Not supported) Write only cl_image object for the destination tensor. Included when DST_TENSOR_TYPE=IMAGE
* @param[out] dst_ptr Pointer to the destination tensor. Supported data type: same as @p src_ptr
* @param[in] dst_stride_y Stride of the destination tensor in Y dimension (in bytes)
* @param[in] dst_stride_z Stride of the destination tensor in Z dimension (in bytes)
* @param[in] dst_stride_w Stride of the destination tensor in W dimension (in bytes)
* @param[in] dst_c The size of the channels dimension of the destination tensor
* @param[in] dst_w The size of the width dimension of the destination tensor
* @param[in] dst_h The size of the height dimension of the destination tensor
* @param[in] dst_n The size of the batches dimension of the destination tensor
* @param[in] dst_offset_first_element_in_bytes The offset of the first element in the destination tensor
* @param[in] wei_img (Not supported) Read only cl_image object for the weights tensor. Included when WEI_TENSOR_TYPE=IMAGE
* @param[in] wei_ptr Pointer to the weights tensor. Supported data type: same as @p src_ptr
* @param[in] wei_stride_y Stride of the weights tensor in Y dimension (in bytes)
* @param[in] wei_stride_z Stride of the weights tensor in Z dimension (in bytes)
* @param[in] wei_stride_w Stride of the weights tensor in W dimension (in bytes)
* @param[in] wei_c The size of the channels dimension of the weights tensor
* @param[in] wei_w The size of the width dimension of the weights tensor
* @param[in] wei_h The size of the height dimension of the weights tensor
* @param[in] wei_n The size of the batches dimension of the weights tensor
* @param[in] wei_step_w wei_stride_w * number of elements along W processed per workitem(in bytes)
* @param[in] wei_offset_first_element_in_bytes The offset of the first element in the weights tensor
* @param[in] dst_multipliers_ptr Pointer to the destination multipliers tensor for the per-channel quantization. Supported data type: S32
* @param[in] dst_multipliers_stride_x Stride of the destination multipliers tensor in X dimension (in bytes)
* @param[in] dst_multipliers_step_x dst_multipliers_stride_x * number of elements along X processed per workitem(in bytes)
* @param[in] dst_multipliers_offset_first_element_in_bytes The offset of the first element in the destination multipliers tensor
* @param[in] dst_shifts_ptr Pointer to the destination shifts tensor for the per-channel quantization. Supported data type: S32
* @param[in] dst_shifts_stride_x Stride of the destination shifts tensor in X dimension (in bytes)
* @param[in] dst_shifts_step_x dst_shifts_stride_x * number of elements along X processed per workitem(in bytes)
* @param[in] dst_shifts_offset_first_element_in_bytes The offset of the first element in the destination shifts tensor
* @param[in] bia_ptr (Optional) Pointer to the bias tensor Supported data type: S32
* @param[in] bia_stride_x (Optional) Stride of the bias tensor in X dimension (in bytes)
* @param[in] bia_step_x (Optional) bia_stride_x * number of elements along X processed per workitem(in bytes)
* @param[in] bia_offset_first_element_in_bytes (Optional) The offset of the first element in the bias tensor
*/
//! @endcond
__kernel void dwc_native_quantized_nhwc(
TENSOR4D_RO_T(src, SRC_TENSOR_TYPE),
TENSOR4D_WO_T(dst, DST_TENSOR_TYPE),
TENSOR4D_RO_T(wei, WEI_TENSOR_TYPE),
VECTOR_DECLARATION(dst_multipliers),
VECTOR_DECLARATION(dst_shifts)
#if defined(HAS_BIAS)
,
VECTOR_DECLARATION(bia)
#endif // defined(HAS_BIAS)
)
{
// Only the weight tensor dimensions are passed at compile time.
// In case of dynamic tensor support, the following dimensions should be passed as function argument.
#define _IWEI_WIDTH WEI_WIDTH
#define _IWEI_HEIGHT WEI_HEIGHT
#define _IM0_A M0_A // _IWEI_WIDTH + (M0 - 1) Rows tile A (If M0 != 1, the tiles overlap of 1 element on the X dimension)
#define _IN0_A N0_A // Cols tile A. It can be either 1 (for DEPTH_MULTIPLIER > 1) or N0 (for DEPTH_MULTIPLIER == 1)
#define _IM0_B _IWEI_WIDTH // Rows tile B
#define _IN0_B N0 // Cols tile B
#define _IBOUNDARY_CHECK (!((WEI_WIDTH == 1 && WEI_HEIGHT == 1 && PAD_LEFT == 0 && PAD_TOP == 0 && M0 == 1)))
const int cout = GET_SPATIAL_IDX(0, N0, PARTIAL_N0); // OFM
const int xo = GET_SPATIAL_IDX(1, M0, 0); // WIDTH
#if defined(BATCHED_EXECUTION)
const int yo = GET_SPATIAL_IDX(2, 1, 0) % dst_h; // HEIGHT
const int bout = GET_SPATIAL_IDX(2, 1, 0) / dst_h; // BATCH SIZE IDX
#else // defined(BATCHED_EXECUTION)
const int yo = GET_SPATIAL_IDX(2, 1, 0); // HEIGHT
const int bout = 0; // BATCH SIZE IDX
#endif // defined(BATCHED_EXECUTION)
int xi = xo * STRIDE_X;
int yi = yo * STRIDE_Y;
xi -= PAD_LEFT;
yi -= PAD_TOP;
TILE(ACC_DATA_TYPE, M0, N0, c);
// Reset accumulators
LOOP_UNROLLING(int, i, 0, 1, M0,
{
c[i].v = 0;
})
#if _IWEI_HEIGHT <= 5
LOOP_UNROLLING(int, yk, 0, 1, _IWEI_HEIGHT,
#else // _IWEI_HEIGHT <= 5
for(int yk = 0; yk < _IWEI_HEIGHT; yk++)
#endif // _IWEI_HEIGHT <= 5
{
TILE(SRC_DATA_TYPE, _IM0_A, _IN0_A, a);
LOOP_UNROLLING(int, i, 0, 1, _IM0_A,
{
a[i].v = ZERO_VALUE;
})
// Load tile from the src tensor (TILE A)
T_LOAD_NHWC_WITH_DILATION(SRC_DATA_TYPE, 1, _IM0_A, _IN0_A, SRC_TENSOR_TYPE, src, bout, yi + yk * DILATION_Y, xi, (cout / DEPTH_MULTIPLIER), src_w, src_h, DILATION_X, 1, _IBOUNDARY_CHECK, a);
TILE(WEI_DATA_TYPE, _IM0_B, _IN0_B, b);
// Load tile from the weights tensor (TILE B)
T_LOAD(WEI_DATA_TYPE, _IM0_B, _IN0_B, WEI_TENSOR_TYPE, wei, cout, yk * _IM0_B, 1, wei_stride_y, b);
// Optimized path for STRIDE_X == 1
// If M0 != 1, we can skip the common loads between the two applied kernels on the X (WIDTH) dimension
LOOP_UNROLLING(int, m0, 0, 1, M0,
{
LOOP_UNROLLING(int, n0, 0, 1, N0,
{
#if _IWEI_WIDTH <= 16
#define DOT_DATA_TYPE SRC_DATA_TYPE
#define WEI_OFFSET_CORRECTION (CALCULATE_WEIGHTS_OFFSET_CORRECTION(SRC_DATA_TYPE, WEI_DATA_TYPE))
// Optimized path for the dot instruction
TILE(DOT_DATA_TYPE, 1, _IWEI_WIDTH, x0);
TILE(DOT_DATA_TYPE, 1, _IWEI_WIDTH, y0);
ACC_DATA_TYPE offset_a = 0;
ACC_DATA_TYPE offset_b = 0;
LOOP_UNROLLING(int, xk, 0, 1, _IWEI_WIDTH,
{
x0[0].s[xk] = a[xk + m0].s[n0];
y0[0].s[xk] = b[xk].s[n0] + (int)WEI_OFFSET_CORRECTION;
})
DOT_PRODUCT_INTEGER8(DOT_DATA_TYPE, DOT_DATA_TYPE, ACC_DATA_TYPE, _IWEI_WIDTH, x0[0].v, y0[0].v, c[m0].s[n0]);
REDUCE_INTEGER8(DOT_DATA_TYPE, DOT_DATA_TYPE, ACC_DATA_TYPE, _IWEI_WIDTH, x0[0].v, offset_a);
REDUCE_INTEGER8(DOT_DATA_TYPE, DOT_DATA_TYPE, ACC_DATA_TYPE, _IWEI_WIDTH, y0[0].v, offset_b);
c[m0].s[n0] += offset_a * (ACC_DATA_TYPE)(WEI_OFFSET - (ACC_DATA_TYPE)WEI_OFFSET_CORRECTION) + offset_b * (ACC_DATA_TYPE)SRC_OFFSET;
#else // _IWEI_WIDTH <= 16
LOOP_UNROLLING(int, xk, 0, 1, _IWEI_WIDTH,
{
c[m0].s[n0] += ((ACC_DATA_TYPE)a[xk + m0].s[n0] + (ACC_DATA_TYPE)(SRC_OFFSET)) * ((ACC_DATA_TYPE)b[xk].s[n0] + (ACC_DATA_TYPE)(WEI_OFFSET));
})
#endif // _IWEI_WIDTH <= 16
})
})
}
#if _IWEI_HEIGHT <= 5
)
#endif // _IWEI_HEIGHT <= 5
#if _IWEI_WIDTH <= 16
T_ADD_CONSTANT(ACC_DATA_TYPE, M0, N0, c, (_IWEI_WIDTH * _IWEI_HEIGHT * SRC_OFFSET * (ACC_DATA_TYPE)(WEI_OFFSET - (ACC_DATA_TYPE)WEI_OFFSET_CORRECTION)), c);
#endif // _IWEI_WIDTH <= 16
#if defined(HAS_BIAS)
TILE(BIA_DATA_TYPE, 1, N0, bias0);
// Load bias
T_LOAD(BIA_DATA_TYPE, 1, N0, BUFFER, bia, cout, 0, 0, 0, bias0);
// c = c + bias[broadcasted]
T_ELTWISE_BROADCAST_ADD_X(ACC_DATA_TYPE, M0, N0, c, bias0, c);
#endif // HAS_BIAS
T_LOAD_MULTIPLIERS_SHIFT(QUANTIZATION_TYPE);
// Quantize the tile
TILE(DST_DATA_TYPE, M0, N0, cq);
T_QUANTIZE8(ACC_DATA_TYPE, DST_DATA_TYPE, QUANTIZATION_TYPE, M0, N0, DST_OFFSET, DST_SHIFT, DST_MULTIPLIER, c, multipliers, shifts, cq);
// Perform activation
T_ACTIVATION_QUANTIZED(DST_DATA_TYPE, M0, N0, ACTIVATION_TYPE, DST_OFFSET, A_VAL, B_VAL, cq, cq);
bool x_cond = PARTIAL_N0 != 0 && get_global_id(0) == 0;
if(x_cond)
{
LOOP_UNROLLING(int, m0, 0, 1, M0,
{
int xi_out = min(xo + M0 - 1 - m0, (int)(dst_w) - 1);
VSTORE_PARTIAL(N0, PARTIAL_N0)
(cq[M0 - 1 - m0].v, 0, (__global DST_DATA_TYPE *)(dst_ptr + dst_offset_first_element_in_bytes + (uint)cout * sizeof(DST_DATA_TYPE) + (uint)xi_out * dst_stride_y + (uint)yo * dst_stride_z + (uint)bout * dst_stride_w));
})
}
else
{
LOOP_UNROLLING(int, m0, 0, 1, M0,
{
int xi_out = min(xo + M0 - 1 - m0, (int)(dst_w) - 1);
VSTORE(N0)
(cq[M0 - 1 - m0].v, 0, (__global DST_DATA_TYPE *)(dst_ptr + dst_offset_first_element_in_bytes + (uint)cout * sizeof(DST_DATA_TYPE) + (uint)xi_out * dst_stride_y + (uint)yo * dst_stride_z + (uint)bout * dst_stride_w));
})
}
}
#endif // defined(WEI_WIDTH) && defined(WEI_HEIGHT) && defined(N0) && defined(M0) && defined(DILATION_X) && defined(DILATION_Y) && defined(STRIDE_X) && defined(STRIDE_Y) && defined(PAD_LEFT) && defined(PAD_TOP)
// *INDENT-ON*
// clang-format on
|
