forked from pytorch/pytorch
-
Notifications
You must be signed in to change notification settings - Fork 0
/
library.cpp
276 lines (263 loc) · 39.5 KB
/
library.cpp
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
#include <torch/library.h>
int register_linear_params();
template <int kSpatialDim = 2>
int register_conv_params();
extern template int register_conv_params<2>();
extern template int register_conv_params<3>();
int register_embedding_params();
TORCH_LIBRARY(quantized, m) {
register_linear_params();
register_conv_params<2>();
register_conv_params<3>();
register_embedding_params();
m.def(TORCH_SELECTIVE_SCHEMA("quantized::add(Tensor qa, Tensor qb, float scale, int zero_point) -> Tensor qc"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::add.out(Tensor qa, Tensor qb, Tensor(a!) out) -> Tensor(a!) out"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::add.Scalar(Tensor qa, Scalar b) -> Tensor qc"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::add.Scalar2(Scalar b, Tensor qa) -> Tensor qc"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::add.Scalar_out(Tensor qa, Scalar b, Tensor(a!) out) -> Tensor(a!) out"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::add_relu(Tensor qa, Tensor qb, float scale, int zero_point) -> Tensor qc"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::add_relu.Scalar(Tensor qa, Scalar b) -> Tensor qc"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::add_relu.Scalar2(Scalar b, Tensor qa) -> Tensor qc"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::add_relu.out(Tensor qa, Tensor qb, Tensor(a!) out) -> Tensor(a!) out"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::add_relu.Scalar_out(Tensor qa, Scalar b, Tensor(a!) out) -> Tensor(a!) out"), {at::Tag::pt2_compliant_tag});
// deprecated functions, kept for backward compatibility
m.def(TORCH_SELECTIVE_SCHEMA("quantized::add_out(Tensor qa, Tensor qb, Tensor(a!) out) -> Tensor(a!) out"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::add_relu_out(Tensor qa, Tensor qb, Tensor(a!) out) -> Tensor(a!) out"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::add_scalar(Tensor qa, Scalar b) -> Tensor qc"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::add_scalar_relu(Tensor qa, Scalar b) -> Tensor qc"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::add_scalar_out(Tensor qa, Scalar b, Tensor(a!) out) -> Tensor(a!) out"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::add_scalar_relu_out(Tensor qa, Scalar b, Tensor(a!) out) -> Tensor(a!) out"), {at::Tag::pt2_compliant_tag});
// TODO: remove after broadcasting is supported
m.def(TORCH_SELECTIVE_SCHEMA("quantized::add_scalar_out.Tensor(Tensor qa, Tensor b, Tensor(a!) out) -> Tensor(a!) out"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::add_scalar.Tensor(Tensor qa, Tensor b) -> Tensor qc"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::add_scalar_relu.Tensor(Tensor qa, Tensor b) -> Tensor qc"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::add_scalar_relu_out.Tensor(Tensor qa, Tensor b, Tensor(a!) out) -> Tensor(a!) out"), {at::Tag::pt2_compliant_tag});
// This is needed for graph mode quantization, when we fuse
// dequant - aten::batch_norm - quant into quantized::batch_norm
// and dimension is unknown given only the aten op call
// quantized::batch_norm supports both 2d and 3d batch norm right now
// it should also support 1d batch_norm after quantized::batch_norm1d is
// implemented
m.def(TORCH_SELECTIVE_SCHEMA("quantized::batch_norm(Tensor qx, Tensor? weight, Tensor? bias, Tensor mean, Tensor var, float eps, float output_scale, int output_zero_point) -> Tensor"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::batch_norm_relu(Tensor qx, Tensor? weight, Tensor? bias, Tensor mean, Tensor var, float eps, float output_scale, int output_zero_point) -> Tensor"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::batch_norm1d(Tensor qx, Tensor? weight, Tensor? bias, Tensor mean, Tensor var, float eps, float output_scale, int output_zero_point) -> Tensor"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::batch_norm1d_relu(Tensor qx, Tensor? weight, Tensor? bias, Tensor mean, Tensor var, float eps, float output_scale, int output_zero_point) -> Tensor"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::batch_norm2d(Tensor qx, Tensor? weight, Tensor? bias, Tensor mean, Tensor var, float eps, float output_scale, int output_zero_point) -> Tensor"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::batch_norm2d_relu(Tensor qx, Tensor? weight, Tensor? bias, Tensor mean, Tensor var, float eps, float output_scale, int output_zero_point) -> Tensor"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::batch_norm3d(Tensor qx, Tensor? weight, Tensor? bias, Tensor mean, Tensor var, float eps, float output_scale, int output_zero_point) -> Tensor"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::batch_norm3d_relu(Tensor qx, Tensor? weight, Tensor? bias, Tensor mean, Tensor var, float eps, float output_scale, int output_zero_point) -> Tensor"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::clamp(Tensor qx, Scalar? min=None, Scalar? max=None) -> Tensor qy"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::threshold(Tensor qx, Scalar threshold, Scalar value) -> Tensor qy"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::cat(Tensor[] qx, int dim, float? scale, int? zero_point) -> Tensor"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::cat_relu(Tensor[] qx, int dim, float? scale, int? zero_point) -> Tensor"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::cat_out(Tensor[] qx, int dim, Tensor(a!) out) -> Tensor(a!)"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::cat_relu_out(Tensor[] qx, int dim, Tensor(a!) out) -> Tensor(a!)"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv1d(Tensor qx, __torch__.torch.classes.quantized.Conv2dPackedParamsBase packed_weight, float output_scale, int output_zero_point) -> Tensor"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv1d_relu(Tensor qx, __torch__.torch.classes.quantized.Conv2dPackedParamsBase packed_weight, float output_scale, int output_zero_point) -> Tensor"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv2d.new(Tensor qx, __torch__.torch.classes.quantized.Conv2dPackedParamsBase packed_weight, float output_scale, int output_zero_point) -> Tensor"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv2d_relu.new(Tensor qx, __torch__.torch.classes.quantized.Conv2dPackedParamsBase packed_weight, float output_scale, int output_zero_point) -> Tensor"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv2d_add(Tensor qx, Tensor qaccum, __torch__.torch.classes.quantized.Conv2dPackedParamsBase packed_weight, float output_scale, int output_zero_point) -> Tensor"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv2d_add_relu(Tensor qx, Tensor qaccum, __torch__.torch.classes.quantized.Conv2dPackedParamsBase packed_weight, float output_scale, int output_zero_point) -> Tensor"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv3d.new(Tensor qx, __torch__.torch.classes.quantized.Conv3dPackedParamsBase packed_weight, float output_scale, int output_zero_point) -> Tensor"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv3d_relu.new(Tensor qx, __torch__.torch.classes.quantized.Conv3dPackedParamsBase packed_weight, float output_scale, int output_zero_point) -> Tensor"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv2d(Tensor qx, __torch__.torch.classes.quantized.Conv2dPackedParamsBase weight, int[] stride, int[] padding, int[] dilation, int groups, float output_scale, int output_zero_point) -> Tensor"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv2d_relu(Tensor qx, __torch__.torch.classes.quantized.Conv2dPackedParamsBase weight, int[] stride, int[] padding, int[] dilation, int groups, float output_scale, int output_zero_point) -> Tensor"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv3d(Tensor qx, __torch__.torch.classes.quantized.Conv3dPackedParamsBase weight, int[] stride, int[] padding, int[] dilation, int groups, float output_scale, int output_zero_point) -> Tensor"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv3d_relu(Tensor qx, __torch__.torch.classes.quantized.Conv3dPackedParamsBase weight, int[] stride, int[] padding, int[] dilation, int groups, float output_scale, int output_zero_point) -> Tensor"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv1d_dynamic(Tensor qx, __torch__.torch.classes.quantized.Conv2dPackedParamsBase packed_weight, bool reduce_range=False) -> Tensor"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv2d_dynamic(Tensor qx, __torch__.torch.classes.quantized.Conv2dPackedParamsBase packed_weight, bool reduce_range=False) -> Tensor"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv3d_dynamic(Tensor qx, __torch__.torch.classes.quantized.Conv3dPackedParamsBase packed_weight, bool reduce_range=False) -> Tensor"), {at::Tag::pt2_compliant_tag});
// conv_prepack is deprecated, please use conv2d_prepack for 2D conv.
m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv_prepack(Tensor weight, Tensor? bias, int[] stride, int[] padding, int[] dilation, int groups) -> __torch__.torch.classes.quantized.Conv2dPackedParamsBase"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv1d_prepack(Tensor weight, Tensor? bias, int[] stride, int[] padding, int[] dilation, int groups) -> __torch__.torch.classes.quantized.Conv2dPackedParamsBase"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv2d_prepack(Tensor weight, Tensor? bias, int[] stride, int[] padding, int[] dilation, int groups) -> __torch__.torch.classes.quantized.Conv2dPackedParamsBase"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv3d_prepack(Tensor weight, Tensor? bias, int[] stride, int[] padding, int[] dilation, int groups) -> __torch__.torch.classes.quantized.Conv3dPackedParamsBase"), {at::Tag::pt2_compliant_tag});
// conv_unpack is deprecated, please use conv2d_unpack for 2D conv.
m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv_unpack(__torch__.torch.classes.quantized.Conv2dPackedParamsBase packed_weights) -> (Tensor unpacked_weights, Tensor? B_origin)"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv1d_unpack(__torch__.torch.classes.quantized.Conv2dPackedParamsBase packed_weights) -> (Tensor unpacked_weights, Tensor? B_origin)"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv2d_unpack(__torch__.torch.classes.quantized.Conv2dPackedParamsBase packed_weights) -> (Tensor unpacked_weights, Tensor? B_origin)"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv2d_unpack_sizes(Any packed_weights) -> (Any)"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv3d_unpack(__torch__.torch.classes.quantized.Conv3dPackedParamsBase packed_weights) -> (Tensor unpacked_weights, Tensor? B_origin)"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv2d_stride(__torch__.torch.classes.quantized.Conv2dPackedParamsBase packed_weights) -> int[]"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv2d_padding(__torch__.torch.classes.quantized.Conv2dPackedParamsBase packed_weights) -> int[]"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv2d_output_padding(__torch__.torch.classes.quantized.Conv2dPackedParamsBase packed_weights) -> int[]"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv2d_dilation(__torch__.torch.classes.quantized.Conv2dPackedParamsBase packed_weights) -> int[]"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv2d_groups(__torch__.torch.classes.quantized.Conv2dPackedParamsBase packed_weights) -> int"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv2d_transpose(__torch__.torch.classes.quantized.Conv2dPackedParamsBase packed_weights) -> int"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv3d_stride(__torch__.torch.classes.quantized.Conv3dPackedParamsBase packed_weights) -> int[]"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv3d_padding(__torch__.torch.classes.quantized.Conv3dPackedParamsBase packed_weights) -> int[]"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv3d_output_padding(__torch__.torch.classes.quantized.Conv3dPackedParamsBase packed_weights) -> int[]"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv3d_dilation(__torch__.torch.classes.quantized.Conv3dPackedParamsBase packed_weights) -> int[]"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv3d_groups(__torch__.torch.classes.quantized.Conv3dPackedParamsBase packed_weights) -> int"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv3d_transpose(__torch__.torch.classes.quantized.Conv3dPackedParamsBase packed_weights) -> int"), {at::Tag::pt2_compliant_tag});
// conv_transpose
m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv_transpose1d(Tensor qx, __torch__.torch.classes.quantized.Conv2dPackedParamsBase packed_weight, float output_scale, int output_zero_point) -> Tensor"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv_transpose2d(Tensor qx, __torch__.torch.classes.quantized.Conv2dPackedParamsBase packed_weight, float output_scale, int output_zero_point) -> Tensor"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv_transpose3d(Tensor qx, __torch__.torch.classes.quantized.Conv3dPackedParamsBase packed_weight, float output_scale, int output_zero_point) -> Tensor"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv_transpose1d_dynamic(Tensor qx, __torch__.torch.classes.quantized.Conv2dPackedParamsBase packed_weight, bool reduce_range=False) -> Tensor"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv_transpose2d_dynamic(Tensor qx, __torch__.torch.classes.quantized.Conv2dPackedParamsBase packed_weight, bool reduce_range=False) -> Tensor"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv_transpose3d_dynamic(Tensor qx, __torch__.torch.classes.quantized.Conv3dPackedParamsBase packed_weight, bool reduce_range=False) -> Tensor"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv_transpose1d_prepack(Tensor weight, Tensor? bias, int[] stride, int[] padding, int[] output_padding, int[] dilation, int groups) -> __torch__.torch.classes.quantized.Conv2dPackedParamsBase"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv_transpose1d_unpack(__torch__.torch.classes.quantized.Conv2dPackedParamsBase packed_weights) -> (Tensor unpacked_weights, Tensor? B_origin)"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv_transpose2d_prepack(Tensor weight, Tensor? bias, int[] stride, int[] padding, int[] output_padding, int[] dilation, int groups) -> __torch__.torch.classes.quantized.Conv2dPackedParamsBase"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv_transpose2d_unpack(__torch__.torch.classes.quantized.Conv2dPackedParamsBase packed_weights) -> (Tensor unpacked_weights, Tensor? B_origin)"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv_transpose2d_stride(__torch__.torch.classes.quantized.Conv2dPackedParamsBase packed_weights) -> int[]"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv_transpose2d_padding(__torch__.torch.classes.quantized.Conv2dPackedParamsBase packed_weights) -> int[]"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv_transpose2d_output_padding(__torch__.torch.classes.quantized.Conv2dPackedParamsBase packed_weights) -> int[]"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv_transpose2d_dilation(__torch__.torch.classes.quantized.Conv2dPackedParamsBase packed_weights) -> int[]"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv_transpose2d_groups(__torch__.torch.classes.quantized.Conv2dPackedParamsBase packed_weights) -> int"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv_transpose2d_transpose(__torch__.torch.classes.quantized.Conv2dPackedParamsBase packed_weights) -> int"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv_transpose3d_prepack(Tensor weight, Tensor? bias, int[] stride, int[] padding, int[] output_padding, int[] dilation, int groups) -> __torch__.torch.classes.quantized.Conv3dPackedParamsBase"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv_transpose3d_unpack(__torch__.torch.classes.quantized.Conv3dPackedParamsBase packed_weights) -> (Tensor unpacked_weights, Tensor? B_origin)"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv_transpose3d_stride(__torch__.torch.classes.quantized.Conv3dPackedParamsBase packed_weights) -> int[]"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv_transpose3d_padding(__torch__.torch.classes.quantized.Conv3dPackedParamsBase packed_weights) -> int[]"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv_transpose3d_output_padding(__torch__.torch.classes.quantized.Conv3dPackedParamsBase packed_weights) -> int[]"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv_transpose3d_dilation(__torch__.torch.classes.quantized.Conv3dPackedParamsBase packed_weights) -> int[]"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv_transpose3d_groups(__torch__.torch.classes.quantized.Conv3dPackedParamsBase packed_weights) -> int"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv_transpose3d_transpose(__torch__.torch.classes.quantized.Conv3dPackedParamsBase packed_weights) -> int"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::elu(Tensor self, float output_scale, int output_zero_point, Scalar alpha=1, Scalar scale=1, Scalar input_scale=1) -> Tensor"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::dropout(Tensor self, float output_scale, int output_zero_point, Scalar p=0.5, bool training=False) -> Tensor"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::embedding_bag_prepack(Tensor weight) -> __torch__.torch.classes.quantized.EmbeddingPackedParamsBase W_prepack"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::embedding_bag_unpack(__torch__.torch.classes.quantized.EmbeddingPackedParamsBase W_prepack) -> Tensor W_origin"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::embedding_bag_byte_prepack(Tensor weight) -> Tensor"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::embedding_bag_byte_unpack(Tensor weight) -> Tensor"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::embedding_bag_4bit_prepack(Tensor weight, bool optimized_qparams=False, int nbins=200, float ratio=0.16) -> Tensor"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::embedding_bag_4bit_unpack(Tensor weight) -> Tensor"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::embedding_bag_2bit_prepack(Tensor weight, bool optimized_qparams=False, int nbins=200, float ratio=0.16) -> Tensor"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::embedding_bag_2bit_unpack(Tensor weight) -> Tensor"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::embedding_bag_byte_rowwise_offsets(Tensor weight, Tensor indices, Tensor? offsets=None, bool scale_grad_by_freq=False, int mode=0, bool pruned_weights=False, Tensor? per_sample_weights=None, Tensor? compressed_indices_mapping=None, bool include_last_offset=False) -> Tensor"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::embedding_bag_4bit_rowwise_offsets(Tensor weight, Tensor indices, Tensor? offsets=None, bool scale_grad_by_freq=False, int mode=0, bool pruned_weights=False, Tensor? per_sample_weights=None, Tensor? compressed_indices_mapping=None, bool include_last_offset=False) -> Tensor"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::embedding_bag_2bit_rowwise_offsets(Tensor weight, Tensor indices, Tensor? offsets=None, bool scale_grad_by_freq=False, int mode=0, bool pruned_weights=False, Tensor? per_sample_weights=None, Tensor? compressed_indices_mapping=None, bool include_last_offset=False) -> Tensor"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::embedding_bag_byte(__torch__.torch.classes.quantized.EmbeddingPackedParamsBase weight, Tensor indices, Tensor? offsets=None, bool scale_grad_by_freq=False, int mode=0, bool pruned_weights=False, Tensor? per_sample_weights=None, Tensor? compressed_indices_mapping=None, bool include_last_offset=False) -> Tensor"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::embedding_bag_4bit(__torch__.torch.classes.quantized.EmbeddingPackedParamsBase weight, Tensor indices, Tensor? offsets=None, bool scale_grad_by_freq=False, int mode=0, bool pruned_weights=False, Tensor? per_sample_weights=None, Tensor? compressed_indices_mapping=None, bool include_last_offset=False) -> Tensor"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::embedding_byte(__torch__.torch.classes.quantized.EmbeddingPackedParamsBase weight, Tensor indices, bool pruned_weights=False) -> Tensor"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::embedding_4bit(__torch__.torch.classes.quantized.EmbeddingPackedParamsBase weight, Tensor indices, bool pruned_weights=False) -> Tensor"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::celu(Tensor self, float output_scale, int output_zero_point, Scalar alpha=1) -> Tensor"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::group_norm(Tensor input, int num_groups, Tensor? weight, Tensor? bias, float eps, float output_scale, int output_zero_point) -> Tensor"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::hardswish(Tensor input, float output_scale, int output_zero_point) -> Tensor"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::instance_norm(Tensor input, Tensor? weight, Tensor? bias, float eps, float output_scale, int output_zero_point) -> Tensor"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::layer_norm(Tensor input, int[] normalized_shape, Tensor? weight, Tensor? bias, float eps, float output_scale, int output_zero_point) -> Tensor"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::linear(Tensor X, __torch__.torch.classes.quantized.LinearPackedParamsBase W_prepack, float Y_scale_i, int Y_zero_point_i) -> Tensor Y"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::linear_relu(Tensor X, __torch__.torch.classes.quantized.LinearPackedParamsBase W_prepack, float Y_scale_i, int Y_zero_point_i) -> Tensor Y"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::linear_dynamic(Tensor X, __torch__.torch.classes.quantized.LinearPackedParamsBase W_prepack, bool reduce_range=False) -> Tensor Y"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::linear_relu_dynamic(Tensor X, __torch__.torch.classes.quantized.LinearPackedParamsBase W_prepack, bool reduce_range=False) -> Tensor Y"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::linear_dynamic_fp16(Tensor X, __torch__.torch.classes.quantized.LinearPackedParamsBase W_prepack) -> Tensor Y"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::linear_dynamic_fp16_unpacked_weight(Tensor X, Tensor weight, Tensor bias) -> Tensor Y"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::linear_relu_dynamic_fp16(Tensor X, __torch__.torch.classes.quantized.LinearPackedParamsBase W_prepack) -> Tensor Y"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::linear_leaky_relu(Tensor X, __torch__.torch.classes.quantized.LinearPackedParamsBase W_prepack, float Y_scale_i, int Y_zero_point_i, float negative_slope) -> Tensor Y"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::linear_tanh(Tensor X, __torch__.torch.classes.quantized.LinearPackedParamsBase W_prepack, float Y_scale_i, int Y_zero_point_i) -> Tensor Y"), {at::Tag::pt2_compliant_tag});
// Corresponding pattern (the ops with `*` are part of the pattern that
// represents the computation of quantized::linear_with_input_q_dq_qweight_dq_output_fp32):
// input -> q* -> dq* -> linear* ->
// qweight -> dq* /
//
// After fusion:
// input -> quantized::linear_with_input_q_dq_qweight_dq_output_fp32* ->
// qweight /
//
// Additional Note: the weight is packed as well
// Params:
// X: float32 Tensor, will be quantized to quint8 in the op
// W_prepack: packed qint8 quantized weight and bias
// Returns:
// Y: float32 Tensor
m.def(TORCH_SELECTIVE_SCHEMA("quantized::linear_with_input_q_dq_qweight_dq_output_fp32(Tensor X, float X_scale, int X_zero_point, __torch__.torch.classes.quantized.LinearPackedParamsBase W_prepack) -> Tensor Y"), {at::Tag::pt2_compliant_tag});
// Corresponding pattern (the ops with `*` are part of the pattern that
// represents the computation of quantized::linear_with_input_q_dq_qweight_dq_relu_output_fp32):
// input -> q* -> dq* -> linear* -> relu* ->
// qweight -> dq* /
//
// After fusion:
// input -> quantized::linear_with_input_q_dq_qweight_dq_relu_output_fp32* ->
// qweight /
//
// Additional Note: the weight is packed as well
// Params:
// X: float32 Tensor, will be quantized to quint8 in the op
// W_prepack: packed qint8 quantized weight and bias
// Returns:
// Y: float32 Tensor
m.def(TORCH_SELECTIVE_SCHEMA("quantized::linear_with_input_q_dq_qweight_dq_relu_output_fp32(Tensor X, float X_scale, int X_zero_point, __torch__.torch.classes.quantized.LinearPackedParamsBase W_prepack) -> Tensor Y"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::linear_prepack(Tensor W, Tensor? B=None) -> __torch__.torch.classes.quantized.LinearPackedParamsBase W_prepack"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::linear_prepack_fp16(Tensor W, Tensor? B=None) -> __torch__.torch.classes.quantized.LinearPackedParamsBase W_prepack"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::linear_prepack_legacy(Tensor W, Tensor? B=None) -> Tensor W_prepack"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::linear_prepack_fp16_legacy(Tensor W, Tensor? B=None) -> Tensor W_prepack"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::linear_unpack(__torch__.torch.classes.quantized.LinearPackedParamsBase W_prepack) -> (Tensor W_origin, Tensor? B_origin)"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::linear_unpack_fp16(__torch__.torch.classes.quantized.LinearPackedParamsBase W_prepack) -> (Tensor W_origin, Tensor? B_origin)"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::linear_unpack.legacy(Tensor W_prepack) -> (Tensor W_origin, Tensor? B_origin)"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::linear_unpack_fp16.legacy(Tensor W_prepack) -> (Tensor W_origin, Tensor? B_origin)"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::matmul(Tensor qa, Tensor qb, float scale, int zero_point)-> Tensor qc"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::mul(Tensor qa, Tensor qb, float scale, int zero_point)-> Tensor qc"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::mul.out(Tensor qa, Tensor qb, Tensor(a!) out)-> Tensor(a!) out"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::mul.Scalar(Tensor qa, Scalar b)-> Tensor qc"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::mul.Scalar2(Scalar b, Tensor qa)-> Tensor qc"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::mul.Scalar_out(Tensor qa, Scalar b, Tensor(a!) out)-> Tensor(a!) out"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::mul_relu(Tensor qa, Tensor qb, float scale, int zero_point)-> Tensor qc"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::mul_relu.out(Tensor qa, Tensor qb, Tensor(a!) out)-> Tensor(a!) out"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::mul_relu.Scalar(Tensor qa, Scalar b)-> Tensor qc"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::mul_relu.Scalar2(Scalar b, Tensor qa)-> Tensor qc"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::mul_relu.Scalar_out(Tensor qa, Scalar b, Tensor(a!) out)-> Tensor(a!) out"), {at::Tag::pt2_compliant_tag});
// deprecated functions, kept for backward compatibility
m.def(TORCH_SELECTIVE_SCHEMA("quantized::mul_out(Tensor qa, Tensor qb, Tensor(a!) out)-> Tensor(a!) out"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::mul_relu_out(Tensor qa, Tensor qb, Tensor(a!) out)-> Tensor(a!) out"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::mul_scalar(Tensor qa, Scalar b)-> Tensor qc"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::mul_scalar_relu(Tensor qa, Scalar b)-> Tensor qc"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::mul_scalar_out(Tensor qa, Scalar b, Tensor(a!) out)-> Tensor(a!) out"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::mul_scalar_relu_out(Tensor qa, Scalar b, Tensor(a!) out)-> Tensor(a!) out"), {at::Tag::pt2_compliant_tag});
// TODO: remove after broadcasting is supported
m.def(TORCH_SELECTIVE_SCHEMA("quantized::mul_scalar.Tensor(Tensor qa, Tensor b)-> Tensor qc"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::mul_scalar_relu.Tensor(Tensor qa, Tensor b)-> Tensor qc"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::mul_scalar_out.Tensor(Tensor qa, Tensor b, Tensor(a!) out)-> Tensor(a!) out"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::mul_scalar_relu_out.Tensor(Tensor qa, Tensor b, Tensor(a!) out)-> Tensor(a!) out"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::max_pool1d(Tensor qx, int[] kernel_size, int[] stride, int[] padding, int[] dilation, bool ceil_mode) -> Tensor"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::max_pool2d(Tensor qx, int[] kernel_size, int[] stride, int[] padding, int[] dilation, bool ceil_mode) -> Tensor"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::relu6(Tensor qx, bool inplace=False) -> Tensor"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::leaky_relu(Tensor qx, Scalar negative_slope, bool inplace, float output_scale, int output_zero_point) -> Tensor"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::prelu(Tensor qx, Tensor weight, float output_scale, int output_zero_point) -> Tensor"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::sigmoid(Tensor qx, float output_scale, int output_zero_point) -> Tensor"), {at::Tag::pt2_compliant_tag});
m.def(TORCH_SELECTIVE_SCHEMA("quantized::softmax(Tensor qx, int dim, float output_scale, int output_zero_point) -> Tensor"), {at::Tag::pt2_compliant_tag});
}
// According to #33294: The "_" prefix registration will be
// removed when the operators are all migrated to mobile.
// https://github.com/pytorch/pytorch/issues/36510
TORCH_LIBRARY(_quantized, m) {
m.def(TORCH_SELECTIVE_SCHEMA("_quantized::add(Tensor qa, Tensor qb, float scale, int zero_point) -> Tensor qc"));
m.def(TORCH_SELECTIVE_SCHEMA("_quantized::conv2d(Tensor qx, __torch__.torch.classes.quantized.Conv2dPackedParamsBase packed_weight, float output_scale, int output_zero_point) -> Tensor"));
m.def(TORCH_SELECTIVE_SCHEMA("_quantized::conv2d_relu(Tensor qx, __torch__.torch.classes.quantized.Conv2dPackedParamsBase packed_weight, float output_scale, int output_zero_point) -> Tensor"));
m.def(TORCH_SELECTIVE_SCHEMA("_quantized::conv2d_prepack(Tensor weight, Tensor? bias, int[] stride, int[] padding, int[] dilation, int groups) -> __torch__.torch.classes.quantized.Conv2dPackedParamsBase"));
m.def(TORCH_SELECTIVE_SCHEMA("_quantized::conv3d(Tensor qx, __torch__.torch.classes.quantized.Conv3dPackedParamsBase packed_weight, float output_scale, int output_zero_point) -> Tensor"));
m.def(TORCH_SELECTIVE_SCHEMA("_quantized::conv3d_relu(Tensor qx, __torch__.torch.classes.quantized.Conv3dPackedParamsBase packed_weight, float output_scale, int output_zero_point) -> Tensor"));
m.def(TORCH_SELECTIVE_SCHEMA("_quantized::conv3d_prepack(Tensor weight, Tensor? bias, int[] stride, int[] padding, int[] dilation, int groups) -> __torch__.torch.classes.quantized.Conv3dPackedParamsBase"));
m.def(TORCH_SELECTIVE_SCHEMA("_quantized::conv_transpose1d(Tensor qx, __torch__.torch.classes.quantized.Conv2dPackedParamsBase packed_weight, float output_scale, int output_zero_point) -> Tensor"));
m.def(TORCH_SELECTIVE_SCHEMA("_quantized::conv_transpose2d(Tensor qx, __torch__.torch.classes.quantized.Conv2dPackedParamsBase packed_weight, float output_scale, int output_zero_point) -> Tensor"));
m.def(TORCH_SELECTIVE_SCHEMA("_quantized::conv_transpose1d_prepack(Tensor weight, Tensor? bias, int[] stride, int[] padding, int[] output_padding, int[] dilation, int groups) -> __torch__.torch.classes.quantized.Conv2dPackedParamsBase"));
m.def(TORCH_SELECTIVE_SCHEMA("_quantized::conv_transpose2d_prepack(Tensor weight, Tensor? bias, int[] stride, int[] padding, int[] output_padding, int[] dilation, int groups) -> __torch__.torch.classes.quantized.Conv2dPackedParamsBase"));
m.def(TORCH_SELECTIVE_SCHEMA("_quantized::conv_transpose3d_prepack(Tensor weight, Tensor? bias, int[] stride, int[] padding, int[] output_padding, int[] dilation, int groups) -> __torch__.torch.classes.quantized.Conv3dPackedParamsBase"));
m.def(TORCH_SELECTIVE_SCHEMA("_quantized::linear(Tensor X, __torch__.torch.classes.quantized.LinearPackedParamsBase W_prepack, float Y_scale_i, int Y_zero_point_i) -> Tensor Y"));
m.def(TORCH_SELECTIVE_SCHEMA("_quantized::linear_dynamic(Tensor X, __torch__.torch.classes.quantized.LinearPackedParamsBase W_prepack, bool reduce_range=False) -> Tensor Y"));
m.def(TORCH_SELECTIVE_SCHEMA("_quantized::linear_prepack(Tensor W, Tensor? B=None) -> __torch__.torch.classes.quantized.LinearPackedParamsBase W_prepack"));
m.def(TORCH_SELECTIVE_SCHEMA("_quantized::linear_prepack_fp16(Tensor W, Tensor? B=None) -> __torch__.torch.classes.quantized.LinearPackedParamsBase W_prepack"));
m.def(TORCH_SELECTIVE_SCHEMA("_quantized::linear_prepack_legacy(Tensor W, Tensor? B=None) -> Tensor W_prepack"));
m.def(TORCH_SELECTIVE_SCHEMA("_quantized::linear_prepack_fp16_legacy(Tensor W, Tensor? B=None) -> Tensor W_prepack"));
m.def(TORCH_SELECTIVE_SCHEMA("_quantized::wrapped_fbgemm_pack_gemm_matrix_fp16(Tensor W) -> Tensor"));
m.def(TORCH_SELECTIVE_SCHEMA("_quantized::wrapped_fbgemm_linear_fp16_weight(Tensor X, Tensor W, Tensor B, int out_channel) -> Tensor"));
}
TORCH_LIBRARY(onednn, m) {
// New OP definition for Quantization in PyTorch 2.0 Export
// Weight Prepack
m.def(TORCH_SELECTIVE_SCHEMA("onednn::qconv_prepack(Tensor weight, Tensor w_scales, float x_scale, int x_zp, int[] stride, int[] padding, int[] dilation, int groups, int[]? x_shape=None) -> Tensor"));
// Conv1D/2D/3D with unary postop
m.def(TORCH_SELECTIVE_SCHEMA("onednn::qconv1d_pointwise(Tensor qx, float x_scale, int x_zero_point, Tensor qw, Tensor w_scale, Tensor w_zero_point, Tensor? bias, int[] stride, int[] padding, int[] dilation, int groups, float output_scale, int output_zero_point, ScalarType? output_dtype, str attr, Scalar?[] scalars, str? algorithm) -> Tensor"));
m.def(TORCH_SELECTIVE_SCHEMA("onednn::qconv2d_pointwise(Tensor qx, float x_scale, int x_zero_point, Tensor qw, Tensor w_scale, Tensor w_zero_point, Tensor? bias, int[] stride, int[] padding, int[] dilation, int groups, float output_scale, int output_zero_point, ScalarType? output_dtype, str attr, Scalar?[] scalars, str? algorithm) -> Tensor"));
m.def(TORCH_SELECTIVE_SCHEMA("onednn::qconv3d_pointwise(Tensor qx, float x_scale, int x_zero_point, Tensor qw, Tensor w_scale, Tensor w_zero_point, Tensor? bias, int[] stride, int[] padding, int[] dilation, int groups, float output_scale, int output_zero_point, ScalarType? output_dtype, str attr, Scalar?[] scalars, str? algorithm) -> Tensor"));
// Conv2D with binary postop
m.def(TORCH_SELECTIVE_SCHEMA("onednn::qconv2d_pointwise.binary(Tensor qx, float x_scale, int x_zero_point, Tensor qaccum, float accum_scale, int accum_zero_point, Tensor qw, Tensor w_scale, Tensor w_zero_point, Tensor? bias, int[] stride, int[] padding, int[] dilation, int groups, float output_scale, int output_zero_point, ScalarType? output_dtype, str binary_attr, Scalar? alpha, str? unary_attr, Scalar?[] unary_scalars, str? unary_algorithm) -> Tensor"));
// Linear prepack
m.def(TORCH_SELECTIVE_SCHEMA("onednn::qlinear_prepack(Tensor weight, int[]? x_shape) -> Tensor"));
// Linear with unary postop
m.def(TORCH_SELECTIVE_SCHEMA("onednn::qlinear_pointwise(Tensor qx, float x_scale, int x_zero_point, Tensor qw, Tensor w_scale, Tensor w_zero_point, Tensor? bias, float output_scale, int output_zero_point, ScalarType? output_dtype, str post_op_name, Scalar?[] post_op_args, str post_op_algorithm) -> Tensor"));
m.def(TORCH_SELECTIVE_SCHEMA("onednn::qlinear_pointwise.tensor(Tensor qx, Tensor x_scale, Tensor x_zero_point, Tensor qw, Tensor w_scale, Tensor w_zero_point, Tensor? bias, float output_scale, int output_zero_point, ScalarType? output_dtype, str post_op_name, Scalar?[] post_op_args, str post_op_algorithm) -> Tensor"));
// Linear with binary postop
m.def(TORCH_SELECTIVE_SCHEMA("onednn::qlinear_pointwise.binary(Tensor qx, float x_scale, int x_zero_point, Tensor qw, Tensor w_scale, Tensor w_zero_point, Tensor? bias, float output_scale, int output_zero_point, ScalarType? output_dtype, Tensor? other, float other_scale, int other_zp, str binary_post_op, float binary_alpha, str unary_post_op, Scalar?[] unary_post_op_args, str unary_post_op_algorithm) -> Tensor"));
m.def(TORCH_SELECTIVE_SCHEMA("onednn::qlinear_pointwise.binary_tensor(Tensor qx, Tensor x_scale, Tensor x_zero_point, Tensor qw, Tensor w_scale, Tensor w_zero_point, Tensor? bias, float output_scale, int output_zero_point, ScalarType? output_dtype, Tensor? other, float other_scale, int other_zp, str binary_post_op, float binary_alpha, str unary_post_op, Scalar?[] unary_post_op_args, str unary_post_op_algorithm) -> Tensor"));
}