Hugging Face's logo

Spaces:
wyjlu
/
Grounded-Segment-Anything
Runtime error

App Files Community
Grounded-Segment-Anything
File size: 4,072 Bytes 
1ce5e18
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
 
#include <torch/extension.h>
#include <ATen/ATen.h>
#include "cuda_launch.h"
#include "cuda_kernel.h"
#include <vector>

//////////////////////////////////////////////////////////////////////////////////////////////////
//////////////////////////////////////////////////////////////////////////////////////////////////

std::vector<at::Tensor> index_max_kernel(
  at::Tensor index_vals,  // [batch_size, 32, num_block]
  at::Tensor indices,     // [batch_size, num_block],
  int A_num_block,
  int B_num_block
) {
  int batch_size = indices.size(0);
  int num_block = indices.size(1);

  at::Tensor max_vals = at::zeros({batch_size, A_num_block * 32}, index_vals.options());
  at::Tensor max_vals_scatter = at::zeros({batch_size, 32, num_block}, index_vals.options());

  dim3 threads(256);
  dim3 blocks(batch_size);
  int shared_mem = A_num_block * 32 * sizeof(float);

  index_max_cuda_kernel<<<blocks, threads, shared_mem>>>(
    index_vals.data_ptr<float>(),
    indices.data_ptr<int>(),
    max_vals.data_ptr<float>(),
    max_vals_scatter.data_ptr<float>(),
    batch_size,
    A_num_block,
    B_num_block,
    num_block
  );

  return {max_vals, max_vals_scatter};
}

at::Tensor mm_to_sparse_kernel(
  at::Tensor dense_A,  // [batch_size, A_num_block, dim, 32]
  at::Tensor dense_B,  // [batch_size, B_num_block, dim, 32]
  at::Tensor indices   // [batch_size, num_block]
) {
  int batch_size = dense_A.size(0);
  int A_num_block = dense_A.size(1);
  int B_num_block = dense_B.size(1);
  int dim = dense_A.size(2);
  int num_block = indices.size(1);

  at::Tensor sparse_C = at::zeros({batch_size, num_block, 32, 32}, dense_A.options());

  dim3 threads(64, 4);
  dim3 blocks(num_block / 4, batch_size);

  mm_to_sparse_cuda_kernel<<<blocks, threads>>>(
    dense_A.data_ptr<float>(),
    dense_B.data_ptr<float>(),
    indices.data_ptr<int>(),
    sparse_C.data_ptr<float>(),
    batch_size,
    A_num_block,
    B_num_block,
    dim,
    num_block
  );

  return sparse_C;
}

at::Tensor sparse_dense_mm_kernel(
  at::Tensor sparse_A,  // [batch_size, num_block, 32, 32]
  at::Tensor indices,   // [batch_size, num_block]
  at::Tensor dense_B,   // [batch_size, B_num_block, dim, 32]
  int A_num_block
) {
  int batch_size = sparse_A.size(0);
  int num_block = sparse_A.size(1);
  int B_num_block = dense_B.size(1);
  int dim = dense_B.size(2);

  at::Tensor dense_C = at::zeros({batch_size, A_num_block, dim, 32}, dense_B.options());

  dim3 threads(128, 2);
  dim3 blocks(num_block / 2, batch_size);

  sparse_dense_mm_cuda_kernel<<<blocks, threads>>>(
    sparse_A.data_ptr<float>(),
    indices.data_ptr<int>(),
    dense_B.data_ptr<float>(),
    dense_C.data_ptr<float>(),
    batch_size,
    A_num_block,
    B_num_block,
    dim,
    num_block
  );

  return dense_C;
}

at::Tensor reduce_sum_kernel(
  at::Tensor sparse_A,  // [batch_size, num_block, 32, 32]
  at::Tensor indices,   // [batch_size, num_block]
  int A_num_block,
  int B_num_block
) {
  int batch_size = sparse_A.size(0);
  int num_block = sparse_A.size(1);

  at::Tensor dense_C = at::zeros({batch_size, A_num_block, 32}, sparse_A.options());

  dim3 threads(32, 4);
  dim3 blocks(num_block / 4, batch_size);

  reduce_sum_cuda_kernel<<<blocks, threads>>>(
    sparse_A.data_ptr<float>(),
    indices.data_ptr<int>(),
    dense_C.data_ptr<float>(),
    batch_size,
    A_num_block,
    B_num_block,
    num_block
  );

  return dense_C;
}

at::Tensor scatter_kernel(
  at::Tensor dense_A,   // [batch_size, A_num_block, 32]
  at::Tensor indices,   // [batch_size, num_block]
  int B_num_block
) {
  int batch_size = dense_A.size(0);
  int A_num_block = dense_A.size(1);
  int num_block = indices.size(1);

  at::Tensor sparse_C = at::zeros({batch_size, num_block, 32, 32}, dense_A.options());

  dim3 threads(32, 4);
  dim3 blocks(num_block / 4, batch_size);

  scatter_cuda_kernel<<<blocks, threads>>>(
    dense_A.data_ptr<float>(),
    indices.data_ptr<int>(),
    sparse_C.data_ptr<float>(),
    batch_size,
    A_num_block,
    B_num_block,
    num_block
  );

  return sparse_C;
}