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
|
/**
* Copyright (c) 2016-present, Facebook, Inc.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#include "caffe2/operators/unique_ops.h"
#include <thrust/device_vector.h>
#include <thrust/sequence.h>
#include <thrust/sort.h>
#include <thrust/system/cuda/execution_policy.h>
#include <thrust/unique.h>
#include <thrust/version.h>
#include "caffe2/core/context_gpu.h"
namespace caffe2 {
#if THRUST_VERSION >= 100800
namespace {
__global__ void remap_kernel(
thrust::device_ptr<int> second_order,
thrust::device_ptr<int> order,
int* output,
int N,
int K) {
int i = blockDim.x * blockIdx.x + threadIdx.x;
if (i >= K)
return;
int idx = second_order[i];
output[order[idx]] = i;
// Maybe cuda 1D kernel?
for (idx++; idx < N && (i == K - 1 || idx != second_order[i + 1]); idx++) {
output[order[idx]] = i;
}
return;
}
} // namespace
template <>
template <typename T>
bool UniqueOp<CUDAContext>::DoRunWithType() {
auto& inputTensor = Input(0);
// use dim32 to enforce that it's fine to have remapping of type int
int N = inputTensor.dim32(0);
CAFFE_ENFORCE_EQ(inputTensor.dim(), 1, "Input should be a vector");
int* remapping = nullptr;
if (REMAPPING < OutputSize()) {
auto* remappingTensor =
Output(REMAPPING, inputTensor.sizes(), at::dtype<int>());
remapping = remappingTensor->template mutable_data<int>();
}
if (N <= 0) {
// if the input is empty, we have nothing to do, not even launch kernel.
/* auto* uniqueTensor = */ Output(UNIQUE, {0}, at::dtype<T>());
return true;
}
const T* input = inputTensor.template data<T>();
ReinitializeTensor(&thrust_unique_buffer_, {N}, at::dtype<T>().device(CUDA));
auto* buffer = thrust_unique_buffer_.template mutable_data<T>();
context_.CopyItemsSameDevice(inputTensor.meta(), N, input, buffer);
// Create two vectors of {0, 1, ..., N-1} on CUDA device
thrust::device_vector<int> order1(N), order2(N);
thrust::sequence(
thrust::cuda::par.on(context_.cuda_stream()),
order1.begin(),
order1.end());
thrust::sequence(
thrust::cuda::par.on(context_.cuda_stream()),
order2.begin(),
order2.end());
// Sort the input along with order vector. So now we know where each element
// is permutated to. For example:
// input1 = 1,3,5,1,5,7,9
// order1 = 0,1,2,3,4,5,6
// Now we have:
// output = 1,1,3,5,5,7,9
// order1 = 0,3,1,2,4,5,6
thrust::sort_by_key(
thrust::cuda::par.on(context_.cuda_stream()),
buffer,
buffer + N,
order1.begin());
// Use consequent unique op to get another order_buffer
// input2 = 1,1,3,5,5,7,9
// order2 = 0,1,2,3,4,5,6
// Now we have:
// output = 1,3,5,7,9
// order2 = 0,2,3,5,6
auto new_last = thrust::unique_by_key(
thrust::cuda::par.on(context_.cuda_stream()),
buffer,
buffer + N,
order2.begin());
int K = new_last.first - buffer;
auto* uniqueTensor = Output(UNIQUE, {K}, at::dtype<T>());
T* unique = uniqueTensor->template mutable_data<T>();
context_.CopyItemsSameDevice(thrust_unique_buffer_.meta(), K, buffer, unique);
// Compute the remapping. For example, for the number 1, if we look at
// order2[0] and order2[1], we know that input2[0:2) are all 1. They are all
// remapped to 0 in final input. And from order1, we know where they come
// from. The rest is easy.
if (remapping != nullptr) {
// record remap
remap_kernel<<<
CAFFE_GET_BLOCKS(K),
CAFFE_CUDA_NUM_THREADS,
0,
context_.cuda_stream()>>>(
order2.data(), order1.data(), remapping, N, K);
C10_CUDA_KERNEL_LAUNCH_CHECK();
}
return true;
}
REGISTER_CUDA_OPERATOR(Unique, UniqueOp<CUDAContext>);
#endif // THRUST_VERSION >= 100800
} // namespace caffe2
|
