forked from pytorch/pytorch
-
Notifications
You must be signed in to change notification settings - Fork 0
/
symbolic_opset8.py
278 lines (221 loc) · 10.8 KB
/
symbolic_opset8.py
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
277
278
from __future__ import absolute_import, division, print_function, unicode_literals
import torch
import torch.onnx.symbolic_helper as sym_help
import torch.onnx.symbolic_opset9 as sym_opset9
from torch.onnx.symbolic_helper import parse_args, _unimplemented, _black_list_in_opset, _try_get_scalar_type
from torch.onnx.symbolic_opset9 import _cast_Float
import warnings
# Note [ONNX operators that are added/updated from opset 8 to opset 9]
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# New operators:
# Compress
# ConstantOfShape
# EyeLike
# MaxUnpool
# OneHot
# Sinh
# Cosh
# Asinh
# Acosh
# Atanh
# Shrink
# IsNaN
# Sign
# Erf
# Scatter
# Where
# NonZero
# TfIdfVectorizer
# MeanVarianceNormalization
#
# Updated operators:
# BatchNormalization: removed spatial attribute.
# Greater, Less, Constant, MatMul, PRelu, Gemm, Flatten: more data types{integers} supported.
# Cast: more data types{string} supported.
# Upsample: moved scales from attribute to input.
# Scan
black_listed_operators = [
"nonzero", "where", "scatter", "scatter_add", "erf", "sign", "isnan", "gather",
"arange", "masked_fill",
"index_fill", "index_copy"
]
for black_listed_op in black_listed_operators:
vars()[black_listed_op] = _black_list_in_opset(black_listed_op)
def _interpolate(name, dim, interpolate_mode):
def symbolic_fn(g, input, output_size, align_corners=None):
sym_help._interpolate_warning(interpolate_mode)
align_corners = sym_help._maybe_get_scalar(align_corners)
if align_corners:
return _unimplemented(name, "align_corners == True")
output_size = sym_help._maybe_get_const(output_size, 'is')
if sym_help._is_value(output_size):
return _unimplemented(name, "torch._C.Value (output_size) indexing")
else:
scales = [1. if i < 2 else
float(output_size[-(dim - i)]) / float(input.type().sizes()[-(dim - i)])
for i in range(0, dim)]
return g.op("Upsample", input, mode_s=interpolate_mode, scales_f=scales)
return symbolic_fn
upsample_nearest1d = _interpolate('upsample_nearest1d', 3, "nearest")
upsample_nearest2d = _interpolate('upsample_nearest2d', 4, "nearest")
upsample_nearest3d = _interpolate('upsample_nearest3d', 5, "nearest")
upsample_linear1d = _interpolate('upsample_linear1d', 3, "linear")
upsample_bilinear2d = _interpolate('upsample_bilinear2d', 4, "linear")
upsample_trilinear3d = _interpolate('upsample_trilinear3d', 5, "linear")
def __interpolate(g, input, size, scale_factor, mode , align_corners):
align_corners = sym_help._maybe_get_const(align_corners, 'b')
if not sym_help._is_none(align_corners) and align_corners:
return _unimplemented("interpolate", "align_corners == True")
if not sym_help._is_none(scale_factor) and sym_help._is_value(scale_factor):
return _unimplemented("interpolate", "dynamic scales in opset 8")
if not sym_help._is_none(size) and sym_help._is_value(size):
return _unimplemented("interpolate", "dynamic size in opset 8")
scales, mode = sym_help._interpolate_get_scales_and_mode(g, input, size, scale_factor,
mode , align_corners)
return g.op("Upsample", input, mode_s=mode, scales_f=scales)
# NOTE: We should create a wrapper for this kind of operation, after resolving the shape/type propagation
# issue for "cast" operators. Some symbolic functions depend on shape information of input tensor, which
# is lost after casting.
def _try_cast_integer_to_float(g, *args):
floating_scalar_types = ['Half', 'Float', 'Double']
old_type = None
# Cast the input tensor to Float if its scalarType is known and is not floating number.
# If casting is performed, return the old scalarType, otherwise return None.
arg0_type = args[0].type().scalarType()
if arg0_type is not None:
old_type = arg0_type
if old_type not in floating_scalar_types:
args = tuple(_cast_Float(g, arg, False) for arg in args)
else:
return (None,) + args
else:
warnings.warn("Only floating datatype is supported for these operators: "
"{Greater, Less, MatMul, PRelu, Gemm, Flatten}. This might cause "
"the onnx model to be incorrect, if inputs have integer datatypes.")
return (old_type,) + args
def _cast_to_type(g, input, to_type):
if to_type is None:
return input
return getattr(sym_opset9, '_cast_{}'.format(to_type))(g, input, False)
def _comparison_operator(g, input, other, op_name):
other = sym_help._maybe_get_scalar(other)
other = sym_help._if_scalar_type_as(g, other, input)
_, input, other = _try_cast_integer_to_float(g, input, other)
return g.op(op_name, input, other)
# NOTE: For symbolics {gt, lt, bmm, matmul, prelu, mm, addmm, view, flatten},
# integer input type not supported in opset8. Cast to float if possible.
def gt(g, input, other):
return _comparison_operator(g, input, other, "Greater")
def lt(g, input, other):
return _comparison_operator(g, input, other, "Less")
def bmm(g, self, other):
if _try_get_scalar_type(self):
old_type, self, other = _try_cast_integer_to_float(g, self, other)
return _cast_to_type(g, g.op("MatMul", self, other), old_type)
else:
return g.op("MatMul", self, other)
def matmul(g, self, other):
return bmm(g, self, other)
def prelu(g, self, weight):
if self.isCompleteTensor():
self_sizes = self.type().sizes()
if self_sizes and len(self_sizes) > 2:
weight = g.op("Unsqueeze", weight, axes_i=list(range(1, len(self_sizes) - 1)))
if _try_get_scalar_type(self):
old_type, self, weight = _try_cast_integer_to_float(g, self, weight)
return _cast_to_type(g, g.op("PRelu", self, weight), old_type)
else:
return g.op("PRelu", self, weight)
def mm(g, self, other):
# Create a dummy C tensor. Only needed for API purposes, the value is
# since beta = 0
ty = sym_help._try_get_scalar_type(self, other).lower()
C = g.constant(0, [1], ty)
if _try_get_scalar_type(self):
old_type, self, other, C = _try_cast_integer_to_float(g, self, other, C)
return _cast_to_type(g, g.op("Gemm", self, other, C, beta_f=0.0, alpha_f=1.0), old_type)
else:
return g.op("Gemm", self, other, C, beta_f=0.0, alpha_f=1.0)
@parse_args('v', 'v', 'v', 't', 't')
def addmm(g, self, mat1, mat2, beta, alpha):
if _try_get_scalar_type(self):
old_type, self, mat1, mat2 = _try_cast_integer_to_float(g, self, mat1, mat2)
return _cast_to_type(
g, g.op("Gemm", mat1, mat2, self,
beta_f=sym_help._scalar(beta), alpha_f=sym_help._scalar(alpha)), old_type)
else:
return g.op("Gemm", mat1, mat2, self, beta_f=sym_help._scalar(beta), alpha_f=sym_help._scalar(alpha))
def view(g, self, size):
size = sym_help._maybe_get_const(size, 'is')
if sym_help._is_value(size):
shape = size
else:
if self.isCompleteTensor():
self_sizes = self.type().sizes()
if self_sizes and len(size) == 2 and self_sizes[0] == size[0]:
old_type, self = _try_cast_integer_to_float(g, self)
return _cast_to_type(g, g.op("Flatten", self, axis_i=1), old_type)
shape = g.op("Constant", value_t=torch.LongTensor(size))
return g.op("Reshape", self, shape)
def flatten(g, input, start_dim, end_dim):
start_dim_i = sym_help._get_const(start_dim, 'i', 'start_dim')
end_dim_i = sym_help._get_const(end_dim, 'i', 'end_dim')
dim = input.type().dim()
if end_dim_i < 0 :
end_dim_i = dim + end_dim_i
# use ONNX's Flatten operator for cases where the output shape is 2D
if start_dim_i == 1 and end_dim_i == dim - 1 :
if _try_get_scalar_type(input):
old_type, input = _try_cast_integer_to_float(g, input)
return _cast_to_type(g, g.op("Flatten", input, axis_i=start_dim_i), old_type)
else:
return g.op("Flatten", input, axis_i=start_dim_i)
if start_dim_i == 0 and end_dim_i == dim - 2 :
if _try_get_scalar_type(input):
old_type, input = _try_cast_integer_to_float(g, input)
return _cast_to_type(g, g.op("Flatten", input, axis_i=end_dim_i + 1), old_type)
else:
return g.op("Flatten", input, axis_i=end_dim_i + 1)
return sym_opset9.flatten(g, input, start_dim, end_dim)
def _constant_fill(g, sizes, dtype, const_value):
if dtype is None:
dtype = 6 # float
if not sym_help.scalar_type_to_pytorch_type[dtype].is_floating_point:
result = g.op(
"ConstantFill", sizes, dtype_i=sym_help.cast_pytorch_to_onnx["Float"], input_as_shape_i=1, value_f=const_value)
return sym_help._cast_func_template(sym_help.scalar_type_to_onnx[dtype], g, result, None)
else:
return g.op("ConstantFill", sizes, dtype_i=sym_help.scalar_type_to_onnx[dtype], input_as_shape_i=1, value_f=const_value)
@parse_args('v', 'i', 'v', 'v', 'v', 'v')
def empty(g, sizes, dtype, layout, device, pin_memory=False, memory_format=None):
return zeros(g, sizes, dtype, layout, device, pin_memory)
@parse_args('v', 'i', 'v', 'v', 'v', 'v')
def empty_like(g, input, dtype, layout, device, pin_memory=False, memory_format=None):
return zeros_like(g, input, dtype, layout, device, pin_memory)
@parse_args('v', 'i', 'v', 'v', 'v')
def zeros(g, sizes, dtype, layout, device, pin_memory=False):
# NOTE: no way to set device and layout in ONNX, so we ignore it
return _constant_fill(g, sizes, dtype, 0)
@parse_args('v', 'i', 'v', 'v', 'v', 'v')
def zeros_like(g, input, dtype, layout, device, pin_memory=False, memory_format=None):
shape = g.op("Shape", input)
return _constant_fill(g, shape, dtype, 0)
@parse_args('v', 'i', 'v', 'v', 'v')
def ones(g, sizes, dtype, layout, device, pin_memory=False):
return _constant_fill(g, sizes, dtype, 1)
@parse_args('v', 'i', 'v', 'v', 'v', 'v')
def ones_like(g, input, dtype, layout, device, pin_memory=False, memory_format=None):
shape = g.op("Shape", input)
return _constant_fill(g, shape, dtype, 1)
def full(g, sizes, value, dtype, layout, device, pin_memory=False):
const_value = sym_help._maybe_get_const(value, 't')
if sym_help._is_value(const_value):
tmp = zeros(g, sizes, dtype, layout, device)
return sym_opset9.add(g, tmp, value, g.op("Constant", value_t=torch.tensor(1)))
else:
dtype = sym_help._get_const(dtype, 'i', 'dtype')
return _constant_fill(g, sizes, dtype, const_value)
@parse_args('v', 'f', 'i', 'v', 'v', 'v', 'v')
def full_like(g, input, fill_value, dtype, layout, device, pin_memory=False, memory_format=None):
shape = g.op("Shape", input)
return _constant_fill(g, shape, dtype, fill_value)