-
Notifications
You must be signed in to change notification settings - Fork 5.5k
/
sparse_bw_api.yaml
114 lines (99 loc) · 4.64 KB
/
sparse_bw_api.yaml
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
- backward_api : add_grad
forward : add(Tensor x, Tensor y) -> Tensor(out)
args : (Tensor x, Tensor y, Tensor out_grad)
output : Tensor(x_grad), Tensor(y_grad)
kernel :
func : add_coo_coo_grad{sparse_coo, sparse_coo, sparse_coo -> sparse_coo, sparse_coo},
add_csr_csr_grad{sparse_csr, sparse_csr, sparse_csr -> sparse_csr, sparse_csr}
- backward_api : conv3d_grad
forward : conv3d (Tensor x, Tensor kernel, int[] paddings, int[] dilations, int[] strides, int groups, bool subm) -> Tensor(out@SparseCooTensor), Tensor(rulebook@DenseTensor)
args : (Tensor x, Tensor kernel, Tensor rulebook, Tensor out_grad, int[] paddings, int[] dilations, int[] strides, int groups, bool subm)
output : Tensor(x_grad), Tensor(kernel_grad)
kernel :
func : sparse_conv3d_grad{sparse_coo, dense, dense, sparse_coo -> sparse_coo, dense}
- backward_api : coo_to_dense_grad
forward : coo_to_dense(Tensor x) -> Tensor(out)
args : (Tensor x, Tensor out_grad)
output : Tensor(x_grad)
kernel :
func : sparse_coo_to_dense_grad{sparse_coo, dense-> sparse_coo}
- backward_api : create_sparse_coo_tensor_grad
forward : create_sparse_coo_tensor(Tensor values, Tensor indices, IntArray dense_shape) -> Tensor(out)
args : (Tensor indices, Tensor out_grad)
output : Tensor(values_grad)
kernel :
func : sparse_coo_tensor_grad{dense, sparse_coo -> dense}
- backward_api : dense_to_coo_grad
forward : dense_to_coo(Tensor x, int64_t sparse_dim) -> Tensor(out)
args : (Tensor out_grad)
output : Tensor(x_grad)
invoke : to_dense_impl(out_grad)
- backward_api : divide_grad
forward : divide(Tensor x, Tensor y) -> Tensor(out)
args : (Tensor x, Tensor y, Tensor out, Tensor out_grad)
output : Tensor(x_grad), Tensor(y_grad)
kernel :
func : divide_coo_coo_grad{sparse_coo, sparse_coo, sparse_coo, sparse_coo -> sparse_coo, sparse_coo},
divide_csr_csr_grad{sparse_csr, sparse_csr, sparse_csr, sparse_csr -> sparse_csr, sparse_csr}
- backward_api : masked_matmul_grad
forward : masked_matmul(Tensor x, Tensor y, Tensor mask) -> Tensor(out)
args : (Tensor x, Tensor y, Tensor out_grad)
output : Tensor(x_grad), Tensor(y_grad)
kernel :
func : csr_masked_matmul_grad{dense, dense, sparse_csr -> dense, dense}
- backward_api : matmul_grad
forward : matmul(Tensor x, Tensor y) -> Tensor(out)
args : (Tensor x, Tensor y, Tensor out_grad)
output : Tensor(x_grad), Tensor(y_grad)
kernel :
func : csr_dense_matmul_grad{sparse_csr, dense, dense -> sparse_csr, dense}
- backward_api : multiply_grad
forward : multiply(Tensor x, Tensor y) -> Tensor(out)
args : (Tensor x, Tensor y, Tensor out_grad)
output : Tensor(x_grad), Tensor(y_grad)
kernel :
func : multiply_coo_coo_grad{sparse_coo, sparse_coo, sparse_coo -> sparse_coo, sparse_coo},
multiply_csr_csr_grad{sparse_csr, sparse_csr, sparse_csr -> sparse_csr, sparse_csr}
- backward_api : relu_grad
forward : relu(Tensor x) -> Tensor(out)
args : (Tensor out, Tensor out_grad)
output : Tensor(x_grad)
kernel :
func : sparse_coo_relu_grad {sparse_coo, sparse_coo -> sparse_coo}
- backward_api : sin_grad
forward : sin(Tensor x) -> Tensor(out)
args : (Tensor x, Tensor out_grad)
output : Tensor(x_grad)
kernel :
func : sparse_coo_sin_grad {sparse_coo, sparse_coo -> sparse_coo}
- backward_api : sparse_maxpool_grad
forward : sparse_maxpool(Tensor x, int[] kernel_sizes, int[] paddings, int[] dilations, int[] strides) -> Tensor(out), Tensor(rulebook)
args : (Tensor x, Tensor rulebook, Tensor out, Tensor out_grad, int[] kernel_sizes)
output : Tensor(x_grad)
kernel :
func : sparse_maxpool_grad {sparse_coo, dense, sparse_coo, sparse_coo -> sparse_coo}
- backward_api : sqrt_grad
forward : sqrt(Tensor x) -> Tensor(out)
args : (Tensor out, Tensor out_grad)
output : Tensor(x_grad)
kernel :
func : sparse_coo_sqrt_grad {sparse_coo, sparse_coo -> sparse_coo}
- backward_api : subtract_grad
forward : subtract(Tensor x, Tensor y) -> Tensor(out)
args : (Tensor x, Tensor y, Tensor out_grad)
output : Tensor(x_grad), Tensor(y_grad)
kernel :
func : subtract_coo_coo_grad{sparse_coo, sparse_coo, sparse_coo -> sparse_coo, sparse_coo},
subtract_csr_csr_grad{sparse_csr, sparse_csr, sparse_csr -> sparse_csr, sparse_csr}
- backward_api : tanh_grad
forward : tanh(Tensor x) -> Tensor(out)
args : (Tensor out, Tensor out_grad)
output : Tensor(x_grad)
kernel :
func : sparse_coo_tanh_grad {sparse_coo, sparse_coo -> sparse_coo}
- backward_api : values_grad
forward : coo_values(Tensor x) -> Tensor(out)
args : (Tensor x, Tensor out_grad)
output : Tensor(x_grad)
kernel :
func : coo_values_grad{sparse_coo, dense-> sparse_coo}