forked from pytorch/pytorch
-
Notifications
You must be signed in to change notification settings - Fork 0
/
shape_functions.h
477 lines (394 loc) · 13 KB
/
shape_functions.h
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
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
R"=====(" ### DO NOT REMOVE THIS STRING!!!
# this file is included in torch/csrc/jit/runtime/symbolic_shape_registry.cpp
# at compile time and turned into a "raw" string
# there's a matching one at the bottom
# mypy: ignore-errors
# flake8: noqa
from typing import List, Any, Optional, Tuple, TypeVar, Union
number = TypeVar('number', bound=Union[int, float])
import torch
import inspect
import warnings
from importlib.machinery import SourceFileLoader
import os
shape_function_fp = f"{os.path.dirname(os.path.realpath(__file__))}/shape_functions_1.h"
try:
_shapes_1 = SourceFileLoader("shape_functions", shape_function_fp).load_module() # type: ignore
globals().update(inspect.getmembers(_shapes_1))
except Exception as e:
warnings.warn(f"Couldn't load shape functions from {shape_function_fp}")
#### SHAPE COMPUTE FUNCTIONS START ###
def index_select(self: List[int], dim: int, index: List[int]):
dim = maybe_wrap_dim(dim, len(self))
numel = multiply_integers(index)
assert len(index) <= 1
assert dim == 0 or dim < len(self)
result_size: List[int] = []
for i in range(len(self)):
if dim == i:
result_size.append(numel)
else:
result_size.append(self[i])
return result_size
def embedding(
weight: List[int],
indices: List[int],
padding_idx: int = -1,
scale_grad_by_freq: bool = False,
sparse: bool = False,
):
assert len(weight) == 2
if len(indices) == 1:
return index_select(weight, 0, indices)
size = _copy(indices)
size.append(weight[1])
return size
def max_int():
return 9223372036854775807
def slice(
self: List[int], dim: int, start: Optional[int], end: Optional[int], step: int
):
ndim = len(self)
assert ndim != 0
dim = maybe_wrap_dim(dim, ndim)
start_val = start if start is not None else 0
end_val = end if end is not None else max_int()
assert step > 0
if start_val == max_int():
start_val = 0
if start_val < 0:
start_val += self[dim]
if end_val < 0:
end_val += self[dim]
if start_val < 0:
start_val = 0
elif start_val >= self[dim]:
start_val = self[dim]
if end_val < start_val:
end_val = start_val
elif end_val >= self[dim]:
end_val = self[dim]
len = end_val - start_val
out = _copy(self)
out[dim] = (len + step - 1) // step
return out
def check_cat_no_zero_dim(tensors: List[List[int]]):
for tensor in tensors:
assert len(tensor) > 0
def legacy_cat_wrap_dim(dim: int, tensor_sizes: List[List[int]]):
out_dim: Optional[int] = None
for size in tensor_sizes:
if not (len(size) == 1 and size[0] == 0):
if out_dim is None:
out_dim = maybe_wrap_dim(dim, len(size))
if out_dim is None:
out_dim = dim
return out_dim
def should_skip(tensor: List[int]):
return numel(tensor) == 0 and len(tensor) == 1
def check_cat_shape_except_dim(
first: List[int], second: List[int], dimension: int, index: int
):
first_dims = len(first)
second_dims = len(second)
assert first_dims == second_dims, "Tensors must have same number of dimensions"
for dim in range(0, first_dims):
if dim != dimension:
assert (
first[dim] == second[dim]
), "Sizes of tensors must match except in dimension"
def cat(tensors: List[List[int]], dim: int):
check_cat_no_zero_dim(tensors)
dim = legacy_cat_wrap_dim(dim, tensors)
assert len(tensors) > 0
not_skipped_tensor: Optional[List[int]] = None
for tensor in tensors:
if not should_skip(tensor):
not_skipped_tensor = tensor
if not_skipped_tensor is None:
return [0]
cat_dim_size = 0
for i in range(len(tensors)):
tensor = tensors[i]
if not should_skip(tensor):
check_cat_shape_except_dim(not_skipped_tensor, tensor, dim, i)
cat_dim_size = cat_dim_size + tensor[dim]
result_size = _copy(not_skipped_tensor)
result_size[dim] = cat_dim_size
return result_size
def select(self: List[int], dim: int, index: int):
ndim = len(self)
assert ndim != 0
dim = maybe_wrap_dim(dim, ndim)
size = self[dim]
assert not (index < -size or index >= size)
if index < 0:
index += size
out: List[int] = []
for i in range(ndim):
if i != dim:
out.append(self[i])
return out
def matmul(tensor1: List[int], tensor2: List[int]):
dim_tensor1 = len(tensor1)
dim_tensor2 = len(tensor2)
if dim_tensor1 == 1 and dim_tensor2 == 1:
return dot(tensor1, tensor2)
elif dim_tensor1 == 2 and dim_tensor2 == 1:
return mv(tensor1, tensor2)
elif dim_tensor1 == 1 and dim_tensor2 == 2:
return squeeze(mm(unsqueeze(tensor1, 0), tensor2), 0)
elif dim_tensor1 == 2 and dim_tensor2 == 2:
return mm(tensor1, tensor2)
elif dim_tensor1 >= 1 and dim_tensor2 >= 1:
# We are multiplying b1 x n x m1 by x2 x m2 x p (where b1 can be a list);
# we track m1 vs m2 separately even though they must match for nicer error messages
n = tensor1[-2] if dim_tensor1 > 1 else 1
m1 = tensor1[-1]
batch_tensor1: List[int] = []
# TODO: handling of slice
for i in range(dim_tensor1 - 2):
batch_tensor1.append(tensor1[i])
m2 = tensor2[-1] if dim_tensor2 > 1 else 1
p = tensor2[-1]
batch_tensor2: List[int] = []
# TODO: handling of slice
for i in range(dim_tensor2 - 2):
batch_tensor2.append(tensor2[i])
# expand the batch portion (i.e. cut off matrix dimensions and expand rest)
expand_batch_portion = broadcast(batch_tensor1, batch_tensor2)
# todo: copy ?
output_shape = expand_batch_portion
if dim_tensor1 > 1:
output_shape.append(n)
if dim_tensor2 > 1:
output_shape.append(p)
return output_shape
else:
assert False, "both arguments to matmul need to be at least 1D"
def t(self: List[int]):
assert len(self) <= 2
self_len = len(self)
if self_len == 0:
out: List[int] = []
return out
elif self_len == 1:
return [self[0]]
else:
return [self[1], self[0]]
def transpose(self: List[int], dim0: int, dim1: int):
ndims = len(self)
dim0 = maybe_wrap_dim(dim0, ndims)
dim1 = maybe_wrap_dim(dim1, ndims)
if dim0 == dim1:
return _copy(self)
out: List[int] = []
for i in range(ndims):
if i == dim0:
out.append(self[dim1])
elif i == dim1:
out.append(self[dim0])
else:
out.append(self[i])
return out
def linear(input: List[int], weight: List[int], bias: Optional[List[int]]):
out = matmul(input, t(weight))
if bias is not None:
assert broadcast(bias, out) == out
return out
def addmm(self: List[int], mat1: List[int], mat2: List[int], beta: Any, alpha: Any):
return broadcast(self, mm(mat1, mat2))
def check_non_negative(array: List[int]) -> bool:
# TODO: look into rewriting with early return and getting loop unrolling to fire
non_negative = False
for val in array:
if val < 0:
non_negative = True
return non_negative
def check_shape_forward(
input: List[int],
weight_sizes: List[int],
bias: Optional[List[int]],
stride: List[int],
padding: List[int],
dilation: List[int],
groups: int,
):
k = len(input)
weight_dim = len(weight_sizes)
# TODO: assertions could be expanded with the error messages
assert not check_non_negative(padding)
assert not check_non_negative(stride)
assert weight_dim == k
assert weight_sizes[0] >= groups
assert (weight_sizes[0] % groups) == 0
# only handling not transposed
assert input[1] == weight_sizes[1] * groups
assert bias is None or (len(bias) == 1 and bias[0] == weight_sizes[0])
for i in range(2, k):
assert (input[i] + 2 * padding[i - 2]) >= (
dilation[i - 2] * (weight_sizes[i] - 1) + 1
)
# this is not handling transposed convolution yet
def conv_output_size(
input_size: List[int],
weight_size: List[int],
bias: Optional[List[int]],
stride: List[int],
padding: List[int],
dilation: List[int],
groups: int,
):
check_shape_forward(
input_size, weight_size, bias, stride, padding, dilation, groups
)
has_dilation = len(dilation) > 0
dim = len(input_size)
output_size: List[int] = []
input_batch_size_dim = 0
weight_output_channels_dim = 0
output_size.append(input_size[input_batch_size_dim])
output_size.append(weight_size[weight_output_channels_dim])
for d in range(2, dim):
dilation_ = dilation[d - 2] if has_dilation else 1
kernel = dilation_ * (weight_size[d] - 1) + 1
output_size.append(
(input_size[d] + (2 * padding[d - 2]) - kernel) // stride[d - 2] + 1
)
return output_size
def conv1d(
input: List[int],
weight: List[int],
bias: Optional[List[int]],
stride: List[int],
padding: List[int],
dilation: List[int],
groups: int,
):
assert len(weight) == 3
assert len(input) == 3
return conv_output_size(input, weight, bias, stride, padding, dilation, groups)
def conv2d(
input: List[int],
weight: List[int],
bias: Optional[List[int]],
stride: List[int],
padding: List[int],
dilation: List[int],
groups: int,
):
assert len(weight) == 4
assert len(input) == 4
return conv_output_size(input, weight, bias, stride, padding, dilation, groups)
def batch_norm(
input: List[int],
weight: List[int],
bias: Optional[List[int]],
running_mean: Optional[List[int]],
running_var: Optional[List[int]],
training: bool,
momentum: float,
eps: float,
cudnn_enabled: bool,
):
out: List[int] = []
for elem in input:
out.append(elem)
return out
def conv3d(
input: List[int],
weight: List[int],
bias: Optional[List[int]],
stride: List[int],
padding: List[int],
dilation: List[int],
groups: int,
):
assert len(weight) == 5
assert len(input) == 5
return conv_output_size(input, weight, bias, stride, padding, dilation, groups)
def maybe_wrap_dim(dim: int, dim_post_expr: int, wrap_scalar: bool = True):
if dim_post_expr <= 0:
assert wrap_scalar
dim_post_expr = 1
min = -dim_post_expr
max = dim_post_expr - 1
assert not (dim < min or dim > max)
if dim < 0:
dim += dim_post_expr
return dim
def zero_dim_tensor(input: Any):
out: List[int] = []
return out
def multiply_integers(li: List[int]):
out = 1
for elem in li:
out = out * elem
return out
def arange_end(end: number, inp0: Any, inp1: Any, inp2: Any, inp3: Any):
assert end >= 0
return [int(torch.ceil(end))]
def arange_start(
start: number, end: number, inp0: Any, inp1: Any, inp2: Any, inp3: Any
):
assert end >= 0
assert end >= start
return [int(torch.ceil(end - start))]
def arange_start_step(
start: number, end: number, step: number, inp0: Any, inp1: Any, inp2: Any, inp3: Any
):
assert step != 0
if step < 0:
assert start >= end
else:
assert end >= start
return [int(torch.ceil((end - start) / step))]
def permute(input: List[int], dims: List[int]):
assert len(input) == len(dims)
ndim = len(dims)
seen_dims: List[int] = []
newSizes: List[int] = []
for i in range(ndim):
dim = maybe_wrap_dim(dims[i], ndim)
seen_dims.append(dim)
newSizes.append(input[dim])
for i in range(1, ndim):
for j in range(i):
assert seen_dims[i] != seen_dims[j]
return newSizes
def flatten(input: List[int], start_dim: int, end_dim: int):
start_dim = maybe_wrap_dim(start_dim, len(input))
end_dim = maybe_wrap_dim(end_dim, len(input))
assert start_dim <= end_dim
if len(input) == 0:
return [1]
if start_dim == end_dim:
# TODO: return self
out: List[int] = []
for elem in input:
out.append(elem)
return out
slice_numel = 1
for i in range(start_dim, end_dim + 1):
slice_numel *= input[i]
# TODO: use slicing when slice optimization has landed
# slice_numel = multiply_integers(input[start_dim:end_dim - start_dim + 1])
shape: List[int] = []
for i in range(start_dim):
shape.append(input[i])
shape.append(slice_numel)
for i in range(end_dim + 1, len(input)):
shape.append(input[i])
return shape
def quantized_prepacked_conv2d(input: List[int], conv2dOpContext: Any):
assert isinstance(
conv2dOpContext, __torch__.torch.classes.quantized.Conv2dPackedParamsBase
)
(weight, bias, stride, padding, dilation, groups) = unchecked_cast(
Tuple[List[int], Optional[List[int]], List[int], List[int], List[int], int],
ops.quantized.conv2d_unpack_sizes(conv2dOpContext),
)
return conv2d(input, weight, bias, stride, padding, dilation, groups)
#### SHAPE COMPUTE FUNCTIONS END ###
### DO NOT REMOVE THIS STRING!!!
")====="