forked from pytorch/pytorch
-
Notifications
You must be signed in to change notification settings - Fork 1
/
Copy pathlibrary.cpp
178 lines (171 loc) · 15.2 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
#include <torch/library.h>
#include <ATen/native/quantized/cpu/conv_packed_params.h>
#include <ATen/native/quantized/cpu/packed_params.h>
#include <ATen/native/quantized/cpu/embedding_packed_params.h>
#include <torch/custom_class.h>
torch::class_<LinearPackedParamsBase> register_linear_params();
template <int kSpatialDim = 2>
torch::class_<ConvPackedParamsBase<kSpatialDim>> register_conv_params();
extern template torch::class_<ConvPackedParamsBase<2>> register_conv_params<2>();
extern template torch::class_<ConvPackedParamsBase<3>> register_conv_params<3>();
torch::class_<EmbeddingPackedParamsBase> register_embedding_params();
TORCH_LIBRARY(quantized, m) {
register_linear_params();
register_conv_params<2>();
register_conv_params<3>();
register_embedding_params();
m.def("add(Tensor qa, Tensor qb, float scale, int zero_point) -> Tensor qc");
m.def("add.out(Tensor qa, Tensor qb, Tensor(a!) out) -> Tensor(a!) out");
m.def("add.Scalar(Tensor qa, Scalar b) -> Tensor qc");
m.def("add.Scalar_out(Tensor qa, Scalar b, Tensor(a!) out) -> Tensor(a!) out");
m.def("add_relu(Tensor qa, Tensor qb, float scale, int zero_point) -> Tensor qc");
m.def("add_relu.Scalar(Tensor qa, Scalar b) -> Tensor qc");
m.def("add_relu.out(Tensor qa, Tensor qb, Tensor(a!) out) -> Tensor(a!) out");
m.def("add_relu.Scalar_out(Tensor qa, Scalar b, Tensor(a!) out) -> Tensor(a!) out");
// deprecated functions, kept for backward compatibility
m.def("add_out(Tensor qa, Tensor qb, Tensor(a!) out) -> Tensor(a!) out");
m.def("add_relu_out(Tensor qa, Tensor qb, Tensor(a!) out) -> Tensor(a!) out");
m.def("add_scalar(Tensor qa, Scalar b) -> Tensor qc");
m.def("add_scalar_relu(Tensor qa, Scalar b) -> Tensor qc");
m.def("add_scalar_out(Tensor qa, Scalar b, Tensor(a!) out) -> Tensor(a!) out");
m.def("add_scalar_relu_out(Tensor qa, Scalar b, Tensor(a!) out) -> Tensor(a!) out");
// TODO: remove after broadcasting is supported
m.def("add_scalar_out.Tensor(Tensor qa, Tensor b, Tensor(a!) out) -> Tensor(a!) out");
m.def("add_scalar.Tensor(Tensor qa, Tensor b) -> Tensor qc");
m.def("add_scalar_relu.Tensor(Tensor qa, Tensor b) -> Tensor qc");
m.def("add_scalar_relu_out.Tensor(Tensor qa, Tensor b, Tensor(a!) out) -> Tensor(a!) out");
// 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("batch_norm(Tensor qx, Tensor? weight, Tensor? bias, Tensor mean, Tensor var, float eps, float output_scale, int output_zero_point) -> Tensor");
m.def("batch_norm_relu(Tensor qx, Tensor? weight, Tensor? bias, Tensor mean, Tensor var, float eps, float output_scale, int output_zero_point) -> Tensor");
m.def("batch_norm1d(Tensor qx, Tensor? weight, Tensor? bias, Tensor mean, Tensor var, float eps, float output_scale, int output_zero_point) -> Tensor");
m.def("batch_norm1d_relu(Tensor qx, Tensor? weight, Tensor? bias, Tensor mean, Tensor var, float eps, float output_scale, int output_zero_point) -> Tensor");
m.def("batch_norm2d(Tensor qx, Tensor? weight, Tensor? bias, Tensor mean, Tensor var, float eps, float output_scale, int output_zero_point) -> Tensor");
m.def("batch_norm2d_relu(Tensor qx, Tensor? weight, Tensor? bias, Tensor mean, Tensor var, float eps, float output_scale, int output_zero_point) -> Tensor");
m.def("batch_norm3d(Tensor qx, Tensor? weight, Tensor? bias, Tensor mean, Tensor var, float eps, float output_scale, int output_zero_point) -> Tensor");
m.def("batch_norm3d_relu(Tensor qx, Tensor? weight, Tensor? bias, Tensor mean, Tensor var, float eps, float output_scale, int output_zero_point) -> Tensor");
m.def("clamp(Tensor qx, Scalar? min, Scalar? max) -> Tensor qy");
m.def("threshold(Tensor qx, Scalar threshold, Scalar value) -> Tensor qy");
m.def("cat(Tensor[] qx, int dim, float? scale, int? zero_point) -> Tensor");
m.def("cat_relu(Tensor[] qx, int dim, float? scale, int? zero_point) -> Tensor");
m.def("cat_out(Tensor[] qx, int dim, Tensor(a!) out) -> Tensor(a!)");
m.def("cat_relu_out(Tensor[] qx, int dim, Tensor(a!) out) -> Tensor(a!)");
m.def("conv1d(Tensor qx, __torch__.torch.classes.quantized.Conv2dPackedParamsBase packed_weight, float output_scale, int output_zero_point) -> Tensor");
m.def("conv1d_relu(Tensor qx, __torch__.torch.classes.quantized.Conv2dPackedParamsBase packed_weight, float output_scale, int output_zero_point) -> Tensor");
m.def("conv2d.new(Tensor qx, __torch__.torch.classes.quantized.Conv2dPackedParamsBase packed_weight, float output_scale, int output_zero_point) -> Tensor");
m.def("conv2d_relu.new(Tensor qx, __torch__.torch.classes.quantized.Conv2dPackedParamsBase packed_weight, float output_scale, int output_zero_point) -> Tensor");
m.def("conv3d.new(Tensor qx, __torch__.torch.classes.quantized.Conv3dPackedParamsBase packed_weight, float output_scale, int output_zero_point) -> Tensor");
m.def("conv3d_relu.new(Tensor qx, __torch__.torch.classes.quantized.Conv3dPackedParamsBase packed_weight, float output_scale, int output_zero_point) -> Tensor");
m.def("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");
m.def("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");
m.def("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");
m.def("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");
// conv_prepack is deprecated, please use conv2d_prepack for 2D conv.
m.def("conv_prepack(Tensor weight, Tensor? bias, int[] stride, int[] padding, int[] dilation, int groups) -> __torch__.torch.classes.quantized.Conv2dPackedParamsBase");
m.def("conv1d_prepack(Tensor weight, Tensor? bias, int[] stride, int[] padding, int[] dilation, int groups) -> __torch__.torch.classes.quantized.Conv2dPackedParamsBase");
m.def("conv2d_prepack(Tensor weight, Tensor? bias, int[] stride, int[] padding, int[] dilation, int groups) -> __torch__.torch.classes.quantized.Conv2dPackedParamsBase");
m.def("conv3d_prepack(Tensor weight, Tensor? bias, int[] stride, int[] padding, int[] dilation, int groups) -> __torch__.torch.classes.quantized.Conv3dPackedParamsBase");
// conv_unpack is deprecated, please use conv2d_unpack for 2D conv.
m.def("conv_unpack(__torch__.torch.classes.quantized.Conv2dPackedParamsBase packed_weights) -> (Tensor unpacked_weights, Tensor? B_origin)");
m.def("conv1d_unpack(__torch__.torch.classes.quantized.Conv2dPackedParamsBase packed_weights) -> (Tensor unpacked_weights, Tensor? B_origin)");
m.def("conv2d_unpack(__torch__.torch.classes.quantized.Conv2dPackedParamsBase packed_weights) -> (Tensor unpacked_weights, Tensor? B_origin)");
m.def("conv3d_unpack(__torch__.torch.classes.quantized.Conv3dPackedParamsBase packed_weights) -> (Tensor unpacked_weights, Tensor? B_origin)");
m.def("conv2d_stride(__torch__.torch.classes.quantized.Conv2dPackedParamsBase packed_weights) -> int[]");
m.def("conv2d_padding(__torch__.torch.classes.quantized.Conv2dPackedParamsBase packed_weights) -> int[]");
m.def("conv2d_dilation(__torch__.torch.classes.quantized.Conv2dPackedParamsBase packed_weights) -> int[]");
m.def("conv2d_groups(__torch__.torch.classes.quantized.Conv2dPackedParamsBase packed_weights) -> int");
m.def("conv3d_stride(__torch__.torch.classes.quantized.Conv3dPackedParamsBase packed_weights) -> int[]");
m.def("conv3d_padding(__torch__.torch.classes.quantized.Conv3dPackedParamsBase packed_weights) -> int[]");
m.def("conv3d_dilation(__torch__.torch.classes.quantized.Conv3dPackedParamsBase packed_weights) -> int[]");
m.def("conv3d_groups(__torch__.torch.classes.quantized.Conv3dPackedParamsBase packed_weights) -> int");
m.def("elu(Tensor self, float output_scale, int output_zero_point, Scalar alpha=1, Scalar scale=1, Scalar input_scale=1) -> Tensor");
m.def("embedding_bag_prepack(Tensor weight) -> __torch__.torch.classes.quantized.EmbeddingPackedParamsBase W_prepack");
m.def("embedding_bag_unpack(__torch__.torch.classes.quantized.EmbeddingPackedParamsBase W_prepack) -> Tensor W_origin");
m.def("embedding_bag_byte_prepack(Tensor weight) -> Tensor");
m.def("embedding_bag_byte_unpack(Tensor weight) -> Tensor");
m.def("embedding_bag_4bit_prepack(Tensor weight) -> Tensor");
m.def("embedding_bag_4bit_unpack(Tensor weight) -> Tensor");
m.def("embedding_bag_2bit_prepack(Tensor weight) -> Tensor");
m.def("embedding_bag_2bit_unpack(Tensor weight) -> Tensor");
m.def("embedding_bag_byte_rowwise_offsets(Tensor weight, Tensor indices, Tensor? offsets=None, bool scale_grad_by_freq=False, int mode=0, bool sparse=False, Tensor? per_sample_weights=None, bool include_last_offset=False) -> Tensor");
m.def("embedding_bag_4bit_rowwise_offsets(Tensor weight, Tensor indices, Tensor? offsets=None, bool scale_grad_by_freq=False, int mode=0, bool sparse=False, Tensor? per_sample_weights=None, Tensor? compressed_indices_mapping=None, bool include_last_offset=False) -> Tensor");
m.def("embedding_bag_byte(__torch__.torch.classes.quantized.EmbeddingPackedParamsBase weight, Tensor indices, Tensor? offsets=None, bool scale_grad_by_freq=False, int mode=0, bool sparse=False, Tensor? per_sample_weights=None, Tensor? compressed_indices_mapping=None, bool include_last_offset=False) -> Tensor");
m.def("celu(Tensor self, float output_scale, int output_zero_point, Scalar alpha=1) -> Tensor");
m.def("hardswish(Tensor input, float output_scale, int output_zero_point) -> Tensor");
m.def("group_norm(Tensor input, int num_groups, Tensor? weight, Tensor? bias, float eps, float output_scale, int output_zero_point) -> Tensor");
m.def("hardswish(Tensor input, float output_scale, int output_zero_point) -> Tensor");
m.def("instance_norm(Tensor input, Tensor? weight, Tensor? bias, float eps, float output_scale, int output_zero_point) -> Tensor");
m.def("layer_norm(Tensor input, int[] normalized_shape, Tensor? weight, Tensor? bias, float eps, float output_scale, int output_zero_point) -> Tensor");
m.def(
"linear(Tensor X, __torch__.torch.classes.quantized.LinearPackedParamsBase W_prepack, float Y_scale_i, int Y_zero_point_i) -> Tensor Y");
m.def(
"linear_relu(Tensor X, __torch__.torch.classes.quantized.LinearPackedParamsBase W_prepack, float Y_scale_i, int Y_zero_point_i) -> Tensor Y");
m.def(
"linear_dynamic(Tensor X, __torch__.torch.classes.quantized.LinearPackedParamsBase W_prepack, bool reduce_range=False) -> Tensor Y");
m.def(
"linear_relu_dynamic(Tensor X, __torch__.torch.classes.quantized.LinearPackedParamsBase W_prepack, bool reduce_range=False) -> Tensor Y");
m.def(
"linear_dynamic_fp16(Tensor X, __torch__.torch.classes.quantized.LinearPackedParamsBase W_prepack) -> Tensor Y");
m.def(
"linear_prepack(Tensor W, Tensor? B=None) -> __torch__.torch.classes.quantized.LinearPackedParamsBase W_prepack");
m.def(
"linear_prepack_fp16(Tensor W, Tensor? B=None) -> __torch__.torch.classes.quantized.LinearPackedParamsBase W_prepack");
m.def("linear_prepack_legacy(Tensor W, Tensor? B=None) -> Tensor W_prepack");
m.def(
"linear_prepack_fp16_legacy(Tensor W, Tensor? B=None) -> Tensor W_prepack");
m.def(
"linear_unpack(__torch__.torch.classes.quantized.LinearPackedParamsBase W_prepack) -> (Tensor W_origin, Tensor? B_origin)");
m.def(
"linear_unpack_fp16(__torch__.torch.classes.quantized.LinearPackedParamsBase W_prepack) -> (Tensor W_origin, Tensor? B_origin)");
m.def(
"linear_unpack.legacy(Tensor W_prepack) -> (Tensor W_origin, Tensor? B_origin)");
m.def(
"linear_unpack_fp16.legacy(Tensor W_prepack) -> (Tensor W_origin, Tensor? B_origin)");
m.def("mul(Tensor qa, Tensor qb, float scale, int zero_point)-> Tensor qc");
m.def("mul.out(Tensor qa, Tensor qb, Tensor(a!) out)-> Tensor(a!) out");
m.def("mul.Scalar(Tensor qa, Scalar b)-> Tensor qc");
m.def("mul.Scalar_out(Tensor qa, Scalar b, Tensor(a!) out)-> Tensor(a!) out");
m.def("mul_relu(Tensor qa, Tensor qb, float scale, int zero_point)-> Tensor qc");
m.def("mul_relu.out(Tensor qa, Tensor qb, Tensor(a!) out)-> Tensor(a!) out");
m.def("mul_relu.Scalar(Tensor qa, Scalar b)-> Tensor qc");
m.def("mul_relu.Scalar_out(Tensor qa, Scalar b, Tensor(a!) out)-> Tensor(a!) out");
// deprecated functions, kept for backward compatibility
m.def("mul_out(Tensor qa, Tensor qb, Tensor(a!) out)-> Tensor(a!) out");
m.def("mul_relu_out(Tensor qa, Tensor qb, Tensor(a!) out)-> Tensor(a!) out");
m.def("mul_scalar(Tensor qa, Scalar b)-> Tensor qc");
m.def("mul_scalar_relu(Tensor qa, Scalar b)-> Tensor qc");
m.def("mul_scalar_out(Tensor qa, Scalar b, Tensor(a!) out)-> Tensor(a!) out");
m.def("mul_scalar_relu_out(Tensor qa, Scalar b, Tensor(a!) out)-> Tensor(a!) out");
// TODO: remove after broadcasting is supported
m.def("mul_scalar.Tensor(Tensor qa, Tensor b)-> Tensor qc");
m.def("mul_scalar_relu.Tensor(Tensor qa, Tensor b)-> Tensor qc");
m.def("mul_scalar_out.Tensor(Tensor qa, Tensor b, Tensor(a!) out)-> Tensor(a!) out");
m.def("mul_scalar_relu_out.Tensor(Tensor qa, Tensor b, Tensor(a!) out)-> Tensor(a!) out");
// NB: missing a space after comma here...
m.def("max_pool2d(Tensor qx, int[] kernel_size, int[] stride, int[] padding, int[] dilation,bool ceil_mode) -> Tensor");
m.def("relu6(Tensor qx, bool inplace=False) -> Tensor");
}
// 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("add(Tensor qa, Tensor qb, float scale, int zero_point) -> Tensor qc");
m.def("conv2d(Tensor qx, __torch__.torch.classes.quantized.Conv2dPackedParamsBase packed_weight, float output_scale, int output_zero_point) -> Tensor");
m.def("conv2d_relu(Tensor qx, __torch__.torch.classes.quantized.Conv2dPackedParamsBase packed_weight, float output_scale, int output_zero_point) -> Tensor");
m.def("conv2d_prepack(Tensor weight, Tensor? bias, int[] stride, int[] padding, int[] dilation, int groups) -> __torch__.torch.classes.quantized.Conv2dPackedParamsBase");
m.def(
"linear(Tensor X, __torch__.torch.classes.quantized.LinearPackedParamsBase W_prepack, float Y_scale_i, int Y_zero_point_i) -> Tensor Y");
m.def(
"linear_dynamic(Tensor X, __torch__.torch.classes.quantized.LinearPackedParamsBase W_prepack, bool reduce_range=False) -> Tensor Y");
m.def(
"linear_prepack(Tensor W, Tensor? B=None) -> __torch__.torch.classes.quantized.LinearPackedParamsBase W_prepack");
m.def(
"linear_prepack_fp16(Tensor W, Tensor? B=None) -> __torch__.torch.classes.quantized.LinearPackedParamsBase W_prepack");
m.def("linear_prepack_legacy(Tensor W, Tensor? B=None) -> Tensor W_prepack");
m.def(
"linear_prepack_fp16_legacy(Tensor W, Tensor? B=None) -> Tensor W_prepack");
}