eltwise_ops.cu
14.8 KB
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
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
// This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html.
#include <cuda_runtime.h>
#include <cuda_fp16.h>
#include "array.hpp"
#include "functors.hpp"
#include "grid_stride_range.hpp"
#include "execution.hpp"
#include "vector_traits.hpp"
#include "kernel_dispatcher.hpp"
#include "../cuda4dnn/csl/stream.hpp"
#include "../cuda4dnn/csl/span.hpp"
#include "../cuda4dnn/csl/tensor.hpp"
#include <opencv2/core.hpp>
using namespace cv::dnn::cuda4dnn::csl;
using namespace cv::dnn::cuda4dnn::csl::device;
namespace cv { namespace dnn { namespace cuda4dnn { namespace kernels {
namespace raw {
template <class T, class EltwiseOp, std::size_t N>
__global__ void eltwise_op_vec(Span<T> output, View<T> x, View<T> y, const typename EltwiseOp::Params params) {
using vector_type = get_vector_type_t<T, N>;
auto output_vPtr = vector_type::get_pointer(output.data());
auto x_vPtr = vector_type::get_pointer(x.data());
auto y_vPtr = vector_type::get_pointer(y.data());
EltwiseOp eltwise_op(params);
for (auto i : grid_stride_range(output.size() / vector_type::size())) {
vector_type vec_x, vec_y;
v_load(vec_x, x_vPtr[i]);
v_load(vec_y, y_vPtr[i]);
for (int j = 0; j < vector_type::size(); j++)
vec_x.data[j] = eltwise_op(vec_x.data[j], vec_y.data[j]);
v_store(output_vPtr[i], vec_x);
}
}
template <class T, class EltwiseOp, std::size_t Rank>
__global__ void eltwise_op_bcast(
Span<T> output, array<size_type, Rank> out_strides,
View<T> x, array<size_type, Rank> x_strides, array<bool, Rank> x_bcast,
View<T> y, array<size_type, Rank> y_strides, array<bool, Rank> y_bcast,
const typename EltwiseOp::Params params) {
EltwiseOp eltwise_op(params);
for (auto i : grid_stride_range(output.size())) {
index_type out_index = i / out_strides[0];
index_type x_index = x_bcast[0] ? 0 : out_index * x_strides[0];
index_type y_index = y_bcast[0] ? 0 : out_index * y_strides[0];
for (int j = 1; j < Rank; j++)
{
out_index = (i % out_strides[j - 1]) / out_strides[j];
if (!x_bcast[j])
x_index += out_index * x_strides[j];
if (!y_bcast[j])
y_index += out_index * y_strides[j];
}
output[i] = eltwise_op(x[x_index], y[y_index]);
}
}
}
template <class T, class EltwiseOp, std::size_t N> static
void launch_vectorized_eltwise_op(const Stream& stream, Span<T> output, View<T> x, View<T> y, const typename EltwiseOp::Params& params) {
CV_Assert(x.size() == y.size());
CV_Assert(x.size() == output.size());
CV_Assert(is_fully_aligned<T>(output, N));
CV_Assert(is_fully_aligned<T>(x, N));
CV_Assert(is_fully_aligned<T>(y, N));
auto kernel = raw::eltwise_op_vec<T, EltwiseOp, N>;
auto policy = make_policy(kernel, output.size() / N, 0, stream);
launch_kernel(kernel, policy, output, x, y, params);
}
template <class T, class EltwiseOp, std::size_t Rank> static
void launch_eltwise_op_bcast(
const Stream& stream,
Span<T> output, const std::vector<std::size_t>& outStride,
View<T> x, const std::vector<std::size_t>& inStride1, const std::vector<int>& inBcast1,
View<T> y, const std::vector<std::size_t>& inStride2, const std::vector<int>& inBcast2,
const typename EltwiseOp::Params& params)
{
CV_Assert(outStride.size() == Rank);
CV_Assert(inStride1.size() == Rank);
CV_Assert(inStride2.size() == Rank);
CV_Assert(inBcast1.size() == Rank);
CV_Assert(inBcast2.size() == Rank);
array<size_type, Rank> outStride_k, inStride1_k, inStride2_k;
outStride_k.assign(std::begin(outStride), std::end(outStride));
inStride1_k.assign(std::begin(inStride1), std::end(inStride1));
inStride2_k.assign(std::begin(inStride2), std::end(inStride2));
array<bool, Rank> inBcast1_k, inBcast2_k;
inBcast1_k.assign(std::begin(inBcast1), std::end(inBcast1));
inBcast2_k.assign(std::begin(inBcast2), std::end(inBcast2));
auto kernel = raw::eltwise_op_bcast<T, EltwiseOp, Rank>;
auto policy = make_policy(kernel, output.size(), 0, stream);
launch_kernel(kernel, policy, output, outStride_k, x, inStride1_k, inBcast1_k, y, inStride2_k, inBcast2_k, params);
}
GENERATE_KERNEL_DISPATCHER_2TP(eltwise_op_bcast_dispatcher, launch_eltwise_op_bcast);
template <class T, class EltwiseOp> static
void eltwise_op(const Stream& stream, TensorSpan<T> output, TensorView<T> x, TensorView<T> y, const typename EltwiseOp::Params& params = {}) {
if (is_shape_same(output, x) && is_shape_same(output, y))
{
/* no broadcasting; use fast path */
CV_Assert(x.size() == y.size());
CV_Assert(x.size() == output.size());
if (is_fully_aligned<T>(output, 4) && is_fully_aligned<T>(x, 4) && is_fully_aligned<T>(y, 4)) {
launch_vectorized_eltwise_op<T, EltwiseOp, 4>(stream, output, x, y, params);
} else if (is_fully_aligned<T>(output, 2) && is_fully_aligned<T>(x, 2) && is_fully_aligned<T>(y, 2)) {
launch_vectorized_eltwise_op<T, EltwiseOp, 2>(stream, output, x, y, params);
} else {
launch_vectorized_eltwise_op<T, EltwiseOp, 1>(stream, output, x, y, params);
}
}
else
{
CV_Assert(is_shape_compatible(output, x));
CV_Assert(is_shape_compatible(output, y));
/* matching singleton axes in both input tensors can be eliminated
*
* Reasoning:
* ----------
* Singleton axes do not contribute towards address calculation. They are redundant
* unless there is broadcasting. If both input tensors have singleton axis at a
* specified position, there is no broadcasting on that axis.
*
* Example:
* ---------
* x: [1, 256, 32, 32] -> [256, 32, 32]
* y: [1, 256, 1, 1] -> [256, 1, 1]
*/
for (int r = 0; r < output.rank(); r++)
{
while (x.get_axis_size(r) == 1 && y.get_axis_size(r) == 1) {
CV_Assert(output.get_axis_size(r) == 1);
x.squeeze(r);
y.squeeze(r);
output.squeeze(r);
}
}
auto inShape1 = x.shape_as_vector();
auto inShape2 = y.shape_as_vector();
auto outShape = output.shape_as_vector();
/* contiguous axes that do not broadcast can be merged into one axis
*
* Example:
* ---------
* x: [32, 8, 8] -> [32, 64]
* y: [1, 8, 8] -> [1, 64]
*/
for (int i = 0; i < inShape1.size(); i++) {
/* check if axis `i` requires any broadcasting */
if (inShape1[i] == inShape2[i]) {
/* loop invariant: `i` is the first axis in the contiguous axis sequence */
int j = i + 1; /* `j` is the axis which we will attempt to merge */
while (j < inShape1.size() && inShape1[j] == inShape2[j]) {
CV_Assert(outShape[j] == inShape1[j]);
/* `j` axis is also used fully; merge `i` and `j` */
auto new_size = inShape1[i] * inShape1[j];
inShape1[i] = new_size;
inShape2[i] = new_size;
/* delete axis `j` */
inShape1.erase(std::begin(inShape1) + j);
inShape2.erase(std::begin(inShape2) + j);
outShape.erase(std::begin(outShape) + j);
/* optimizations should not break the invariants */
CV_Assert(inShape1.size() == outShape.size());
CV_Assert(inShape2.size() == outShape.size());
CV_Assert(inShape1[i] == outShape[i]);
CV_Assert(inShape2[i] == outShape[i]);
}
}
}
/* contiguous broadcasting axes on the same tensor can be merged into one axis
*
* Example:
* ---------
* x: [256, 8, 8] -> [256, 64]
* y: [256, 1, 1] -> [256, 1]
*/
for (int i = 0; i < inShape1.size(); i++) {
/* check if axis `i` requires any broadcasting in tensor 1 */
if (inShape1[i] == 1 && inShape2[i] != 1) {
/* loop invariant: `i` is the first axis in the contiguous axis sequence */
int j = i + 1; /* `j` is the axis which we will attempt to merge */
while (j < inShape1.size() && inShape1[j] == 1 && inShape2[j] != 1) {
CV_Assert(outShape[j] == inShape2[j]);
/* `j` axis is also used fully; merge `i` and `j` */
inShape1[i] = 1;
inShape2[i] = inShape2[i] * inShape2[j];
outShape[i] = inShape2[i];
/* delete axis `j` */
inShape1.erase(std::begin(inShape1) + j);
inShape2.erase(std::begin(inShape2) + j);
outShape.erase(std::begin(outShape) + j);
/* optimizations should not break the invariants */
CV_Assert(inShape1.size() == outShape.size());
CV_Assert(inShape2.size() == outShape.size());
CV_Assert(inShape1[i] == 1);
CV_Assert(inShape2[i] == outShape[i]);
}
}
/* check if axis `i` requires any broadcasting in tensor 2 */
if (inShape1[i] != 1 && inShape2[i] == 1) {
/* loop invariant: `i` is the first axis in the contiguous axis sequence */
int j = i + 1; /* `j` is the axis which we will attempt to merge */
while (j < inShape1.size() && inShape1[j] != 1 && inShape2[j] == 1) {
CV_Assert(outShape[j] == inShape1[j]);
/* `j` axis is also used fully; merge `i` and `j` */
inShape1[i] = inShape1[i] * inShape1[j];
inShape2[i] = 1;
outShape[i] = inShape1[i];
/* delete axis `j` */
inShape1.erase(std::begin(inShape1) + j);
inShape2.erase(std::begin(inShape2) + j);
outShape.erase(std::begin(outShape) + j);
/* optimizations should not break the invariants */
CV_Assert(inShape1.size() == outShape.size());
CV_Assert(inShape2.size() == outShape.size());
CV_Assert(inShape1[i] == outShape[i]);
CV_Assert(inShape2[i] == 1);
}
}
}
auto rank = outShape.size();
std::vector<std::size_t> inStride1(rank), inStride2(rank), outStride(rank);
inStride1.back() = 1;
inStride2.back() = 1;
outStride.back() = 1;
/* garbage, ..., garbage, 1 */
std::copy(std::begin(inShape1) + 1, std::end(inShape1), std::begin(inStride1));
std::copy(std::begin(inShape2) + 1, std::end(inShape2), std::begin(inStride2));
std::copy(std::begin(outShape) + 1, std::end(outShape), std::begin(outStride));
/* dim[0], dim[1], ..., dim[-1], 1 */
std::partial_sum(inStride1.rbegin(), inStride1.rend(), inStride1.rbegin(), std::multiplies<std::size_t>());
std::partial_sum(inStride2.rbegin(), inStride2.rend(), inStride2.rbegin(), std::multiplies<std::size_t>());
std::partial_sum(outStride.rbegin(), outStride.rend(), outStride.rbegin(), std::multiplies<std::size_t>());
/* stride[0], stride[1], ..., stride[-2], 1 */
std::vector<int> inBcast1(rank), inBcast2(rank);
std::transform(std::begin(inShape1), std::end(inShape1), std::begin(inBcast1), [](std::size_t sz) { return sz == 1; });
std::transform(std::begin(inShape2), std::end(inShape2), std::begin(inBcast2), [](std::size_t sz) { return sz == 1; });
CV_Assert(1 <= rank && rank <= CSL_MAX_TENSOR_RANK);
eltwise_op_bcast_dispatcher<T, EltwiseOp, 1, CSL_MAX_TENSOR_RANK>(rank, stream, output, outStride, x, inStride1, inBcast1, y, inStride2, inBcast2, params);
}
}
template <class T>
void eltwise_max_2(const Stream& stream, TensorSpan<T> output, TensorView<T> x, TensorView<T> y) {
eltwise_op<T, MaxFunctor<T>>(stream, output, x, y);
}
template <class T>
void eltwise_min_2(const Stream& stream, TensorSpan<T> output, TensorView<T> x, TensorView<T> y) {
eltwise_op<T, MinFunctor<T>>(stream, output, x, y);
}
template <class T>
void eltwise_sum_2(const Stream& stream, TensorSpan<T> output, TensorView<T> x, TensorView<T> y) {
eltwise_op<T, SumFunctor<T>>(stream, output, x, y);
}
template <class T>
void eltwise_sum_coeff_2(const Stream& stream, TensorSpan<T> output, T coeff_x, TensorView<T> x, T coeff_y, TensorView<T> y) {
eltwise_op<T, ScaledSumFunctor<T>>(stream, output, x, y, {coeff_x, coeff_y});
}
template <class T>
void eltwise_prod_2(const Stream& stream, TensorSpan<T> output, TensorView<T> x, TensorView<T> y) {
eltwise_op<T, ProductFunctor<T>>(stream, output, x, y);
}
template <class T>
void eltwise_div_2(const Stream& stream, TensorSpan<T> output, TensorView<T> x, TensorView<T> y) {
eltwise_op<T, DivFunctor<T>>(stream, output, x, y);
}
#if !defined(__CUDA_ARCH__) || (__CUDA_ARCH__ >= 530)
template void eltwise_div_2(const Stream& stream, TensorSpan<__half> output, TensorView<__half> x, TensorView<__half> y);
template void eltwise_prod_2(const Stream& stream, TensorSpan<__half> output, TensorView<__half> x, TensorView<__half> y);
template void eltwise_sum_coeff_2(const Stream&, TensorSpan<__half>, __half, TensorView<__half>, __half, TensorView<__half>);
template void eltwise_sum_2(const Stream& stream, TensorSpan<__half> output, TensorView<__half> x, TensorView<__half> y);
template void eltwise_max_2(const Stream& stream, TensorSpan<__half> output, TensorView<__half> x, TensorView<__half> y);
template void eltwise_min_2(const Stream& stream, TensorSpan<__half> output, TensorView<__half> x, TensorView<__half> y);
#endif
template void eltwise_div_2(const Stream& stream, TensorSpan<float> output, TensorView<float> x, TensorView<float> y);
template void eltwise_prod_2(const Stream& stream, TensorSpan<float> output, TensorView<float> x, TensorView<float> y);
template void eltwise_sum_coeff_2(const Stream&, TensorSpan<float>, float, TensorView<float>, float, TensorView<float>);
template void eltwise_sum_2(const Stream& stream, TensorSpan<float> output, TensorView<float> x, TensorView<float> y);
template void eltwise_max_2(const Stream& stream, TensorSpan<float> output, TensorView<float> x, TensorView<float> y);
template void eltwise_min_2(const Stream& stream, TensorSpan<float> output, TensorView<float> x, TensorView<float> y);
}}}} /* namespace cv::dnn::cuda4dnn::kernels */