forked from PaddlePaddle/Paddle
/
sparse_bw_api.yaml
272 lines (238 loc) · 10.9 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
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
- backward_api : abs_grad
forward : tanh(Tensor x) -> Tensor(out)
args : (Tensor x, Tensor out_grad)
output : Tensor(x_grad)
kernel :
func : abs_coo_grad {sparse_coo, sparse_coo -> sparse_coo},
abs_csr_grad {sparse_csr, sparse_csr -> sparse_csr}
- backward_api : acos_grad
forward : acos(Tensor x) -> Tensor(out)
args : (Tensor x, Tensor out_grad)
output : Tensor(x_grad)
kernel :
func : acos_coo_grad {sparse_coo, sparse_coo -> sparse_coo},
acos_csr_grad {sparse_csr, sparse_csr -> sparse_csr}
- backward_api : acosh_grad
forward : acosh(Tensor x) -> Tensor(out)
args : (Tensor x, Tensor out_grad)
output : Tensor(x_grad)
kernel :
func : acosh_coo_grad {sparse_coo, sparse_coo -> sparse_coo},
acosh_csr_grad {sparse_csr, sparse_csr -> sparse_csr}
- 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 : addmm_grad
forward : addmm(Tensor input, Tensor x, Tensor y, float alpha=1.0, float beta=1.0) -> Tensor(out)
args : (Tensor input, Tensor x, Tensor y, Tensor out_grad, float alpha=1.0, float beta=1.0)
output : Tensor(input_grad), Tensor(x_grad), Tensor(y_grad)
kernel :
func : addmm_csr_dense_grad {dense, sparse_csr, dense, dense -> dense, sparse_csr, dense},
addmm_csr_csr_grad {sparse_csr, sparse_csr, sparse_csr, sparse_csr -> sparse_csr, sparse_csr, sparse_csr},
addmm_coo_dense_grad {dense, sparse_coo, dense, dense -> dense, sparse_coo, dense},
addmm_coo_coo_grad {sparse_coo, sparse_coo, sparse_coo, sparse_coo -> sparse_coo, sparse_coo, sparse_coo}
- backward_api : asin_grad
forward : asin(Tensor x) -> Tensor(out)
args : (Tensor x, Tensor out_grad)
output : Tensor(x_grad)
kernel :
func : asin_coo_grad {sparse_coo, sparse_coo -> sparse_coo},
asin_csr_grad {sparse_csr, sparse_csr -> sparse_csr}
- backward_api : asinh_grad
forward : asinh(Tensor x) -> Tensor(out)
args : (Tensor x, Tensor out_grad)
output : Tensor(x_grad)
kernel :
func : asinh_coo_grad {sparse_coo, sparse_coo -> sparse_coo},
asinh_csr_grad {sparse_csr, sparse_csr -> sparse_csr}
- backward_api : atan_grad
forward : atan(Tensor x) -> Tensor(out)
args : (Tensor x, Tensor out_grad)
output : Tensor(x_grad)
kernel :
func : atan_coo_grad {sparse_coo, sparse_coo -> sparse_coo},
atan_csr_grad {sparse_csr, sparse_csr -> sparse_csr}
- backward_api : atanh_grad
forward : atanh(Tensor x) -> Tensor(out)
args : (Tensor x, Tensor out_grad)
output : Tensor(x_grad)
kernel :
func : atanh_coo_grad {sparse_coo, sparse_coo -> sparse_coo},
atanh_csr_grad {sparse_csr, sparse_csr -> sparse_csr}
- backward_api : cast_grad
forward : cast(Tensor x, DataType index_dtype, DataType value_dtype) -> Tensor(out)
args : (Tensor x, Tensor out_grad, DataType value_dtype)
output : Tensor(x_grad)
kernel :
func : cast_coo_grad {sparse_coo, sparse_coo -> sparse_coo},
cast_csr_grad {sparse_csr, sparse_csr -> sparse_csr}
data_type : out_grad
- 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 : conv3d_coo_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 : divide_scalar_grad
forward : divide_scalar (Tensor x, float scalar) -> Tensor(out)
args : (Tensor out_grad, float scalar)
output : Tensor(x_grad)
invoke : divide_scalar(out_grad, scalar)
- backward_api : log1p_grad
forward : log1p(Tensor x) -> Tensor(out)
args : (Tensor x, Tensor out_grad)
output : Tensor(x_grad)
kernel :
func : log1p_coo_grad {sparse_coo, sparse_coo -> sparse_coo},
log1p_csr_grad {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 : masked_matmul_csr_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 : matmul_csr_dense_grad {sparse_csr, dense, dense -> sparse_csr, dense},
matmul_csr_csr_grad {sparse_csr, sparse_csr, sparse_csr -> sparse_csr, sparse_csr},
matmul_coo_dense_grad {sparse_coo, dense, dense -> sparse_coo, dense},
matmul_coo_coo_grad {sparse_coo, sparse_coo, sparse_coo -> sparse_coo, sparse_coo}
- backward_api : maxpool_grad
forward : 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 : maxpool_coo_grad {sparse_coo, dense, sparse_coo, sparse_coo -> sparse_coo}
- 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 : mv_grad
forward : mv(Tensor x, Tensor vec) -> Tensor(out)
args : (Tensor x, Tensor vec, Tensor out_grad)
output : Tensor(x_grad), Tensor(vec_grad)
kernel :
func : mv_coo_grad{sparse_coo, dense, dense -> sparse_coo, dense},
mv_csr_grad{sparse_csr, dense, dense -> sparse_csr, dense}
- backward_api : pow_grad
forward : pow(Tensor x, float factor) -> Tensor(out)
args : (Tensor x, Tensor out_grad, float factor)
output : Tensor(x_grad)
kernel :
func : pow_coo_grad {sparse_coo, sparse_coo -> sparse_coo},
pow_csr_grad {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 : relu_coo_grad {sparse_coo, sparse_coo -> sparse_coo},
relu_csr_grad {sparse_csr, sparse_csr -> sparse_csr}
- backward_api : scale_grad
forward : scale(Tensor x, float scale, float bias, bool bias_after_scale) -> Tensor(out)
args : (Tensor out_grad, float scale)
output : Tensor(x_grad)
invoke : scale(out_grad, scale, 0.0, true)
- backward_api : sin_grad
forward : sin(Tensor x) -> Tensor(out)
args : (Tensor x, Tensor out_grad)
output : Tensor(x_grad)
kernel :
func : sin_coo_grad {sparse_coo, sparse_coo -> sparse_coo},
sin_csr_grad {sparse_csr, sparse_csr -> sparse_csr}
- backward_api : sinh_grad
forward : sinh(Tensor x) -> Tensor(out)
args : (Tensor x, Tensor out_grad)
output : Tensor(x_grad)
kernel :
func : sinh_coo_grad {sparse_coo, sparse_coo -> sparse_coo},
sinh_csr_grad {sparse_csr, sparse_csr -> sparse_csr}
- backward_api : softmax_grad
forward : softmax(Tensor x, int axis=-1) -> Tensor(out)
args : (Tensor out, Tensor out_grad, int axis)
output : Tensor(x_grad)
kernel :
func : softmax_csr_grad{sparse_csr, sparse_csr -> sparse_csr}
- backward_api : sqrt_grad
forward : sqrt(Tensor x) -> Tensor(out)
args : (Tensor out, Tensor out_grad)
output : Tensor(x_grad)
kernel :
func : sqrt_coo_grad {sparse_coo, sparse_coo -> sparse_coo},
sqrt_csr_grad {sparse_csr, sparse_csr -> sparse_csr}
- backward_api : square_grad
forward : square(Tensor x) -> Tensor(out)
args : (Tensor x, Tensor out_grad)
output : Tensor(x_grad)
kernel :
func : square_coo_grad {sparse_coo, sparse_coo -> sparse_coo},
square_csr_grad {sparse_csr, sparse_csr -> sparse_csr}
- 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 : tan_grad
forward : tan(Tensor x) -> Tensor(out)
args : (Tensor x, Tensor out_grad)
output : Tensor(x_grad)
kernel :
func : tan_coo_grad {sparse_coo, sparse_coo -> sparse_coo},
tan_csr_grad {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 : tanh_coo_grad {sparse_coo, sparse_coo -> sparse_coo},
tanh_csr_grad {sparse_csr, sparse_csr -> sparse_csr}
- 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}
- backward_api: fused_attention_grad
forward : fused_attention_csr(Tensor query, Tensor key, Tensor value, Tensor sparse_mask, Tensor key_padding_mask, Tensor attn_mask) -> Tensor(out), Tensor(softmax)
args: (Tensor query, Tensor key, Tensor value, Tensor softmax, Tensor out_grad)
output : Tensor(query_grad), Tensor(key_grad), Tensor(value_grad)
kernel :
func : fused_attention_csr_grad{dense, dense, dense, sparse_csr, dense -> dense, dense, dense}
layout : softmax
data_type: query