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
|
# mypy: allow-untyped-decorators
# mypy: allow-untyped-defs
from typing import List, Optional, Tuple, Union
import sympy
import torch
from .ir import Pointwise, TensorBox
from .lowering import fallback_handler, is_integer_type, register_lowering
from .virtualized import ops
# pyre-ignore[2,3]
def dense_idx_to_jagged_idx(batch_idx, seq_idx, offsets_loader, jagged_len):
# jagged_len + 1 is used as the upper bound,
# because the last sequence length may be zero
begin_idx = ops.indirect_indexing(
offsets_loader([batch_idx]),
jagged_len + 1,
)
end_idx = offsets_loader([batch_idx + 1])
jagged_idx = begin_idx + seq_idx
return jagged_idx, end_idx
def get_inverse_offsets(
offsets: TensorBox,
jagged_len: Union[int, sympy.Expr],
realize: bool = True,
) -> TensorBox:
"""
Returns "inverse_offsets" - the inverse of the offsets array.
offsets maps batch index (dense) to jagged index (i.e. offset into jagged tensor).
inverse_offsets maps jagged index to batch index.
e.g. for offsets [0, 3, 4, 9, 10] this will return
inverse_offsets = [0, 0, 0, 1, 2, 2, 2, 2, 2, 3]
For the given offsets, the computed inverse_offsets are cached
on the first call and reused in the further calls.
"""
if hasattr(offsets, "inverse_offsets"):
# inverse_offsets are already computed
# for these offsets: can reuse
return offsets.inverse_offsets
# ops.bucketize takes offsets.get_name() which doesn't exist on Pointwise
# kernels, i.e. we need to realize it before using. In other words, we need
# offsets to be in global memory so that we can binary search over the
# entire tensor
offsets.realize()
device: torch.device = offsets.get_device_or_error()
dtype: torch.dtype = offsets.get_dtype()
# pyre-ignore[2,3]
def inner_fn(index):
idx = index[0]
bucket = ops.bucketize(
values=ops.index_expr(idx, dtype),
boundaries=(
offsets.get_name(),
offsets.get_size()[-1],
offsets.get_size()[0] * offsets.get_stride()[0],
offsets.get_stride()[-1],
),
boundary_indices=0,
indexing_dtype=dtype,
right=True,
)
# ops.bucketize above returns 1-based bucket indices,
# but we need 0-based, hence we subtract 1 from batch
return bucket - 1
inverse_offsets = Pointwise.create(
device=device,
dtype=dtype,
inner_fn=inner_fn,
ranges=[jagged_len],
)
if realize:
# "freeze" the node so that it doesn't get inlined downstream.
inverse_offsets.realize()
# cache inverse_offsets for further reuse
offsets.inverse_offsets = inverse_offsets # type: ignore[attr-defined]
return inverse_offsets
def jagged_idx_to_dense_idx(
jagged_idx, # pyre-ignore[2]
inverse_offsets_loader, # pyre-ignore[2]
offsets_loader, # pyre-ignore[2]
batch_size: Union[int, sympy.Expr],
max_seq_len: Union[int, sympy.Expr],
offsets_dtype: torch.dtype,
) -> Tuple[sympy.Expr, sympy.Expr]:
batch_idx = ops.indirect_indexing(
inverse_offsets_loader([jagged_idx]),
batch_size + 1,
)
batch_start = offsets_loader([batch_idx])
seq = ops.index_expr(jagged_idx, offsets_dtype) - batch_start
# check=False because there may be sequences longer than max_seq_len
seq_idx = ops.indirect_indexing(seq, max_seq_len, check=False)
return batch_idx, seq_idx
def register_jagged_ops():
# pyre-ignore[56]
@register_lowering(torch.ops.aten._jagged_to_padded_dense_forward.default)
def _jagged_to_padded_dense_forward(
jagged_values: TensorBox,
jagged_offsets: List[TensorBox],
max_lengths: List[int], # list of ints/SymInts
padding_value: float = 0.0,
) -> TensorBox:
device = jagged_values.get_device_or_error()
dtype = jagged_values.get_dtype()
jagged_values_size = jagged_values.get_size()
# only handle the common case of a single jagged dimension
if (
len(jagged_offsets) != 1
or device.type != "cuda"
or device != jagged_offsets[0].get_device()
or len(jagged_values_size) != 2
or len(jagged_offsets[0].get_size()) != 1
or len(max_lengths) != len(jagged_offsets)
or not is_integer_type(jagged_offsets[0])
):
return fallback_handler(
torch.ops.aten._jagged_to_padded_dense_forward.default,
add_to_fallback_set=False,
)(
jagged_values,
jagged_offsets,
max_lengths,
padding_value,
)
offsets: TensorBox = jagged_offsets[0]
offsets_len = offsets.get_size()[0]
offsets_dtype = offsets.get_dtype()
batch_size = offsets_len - 1
max_seq_len = max_lengths[0]
embedding_len = jagged_values_size[1]
jagged_len = jagged_values_size[0]
output_size = [batch_size, max_seq_len, embedding_len]
values_loader = jagged_values.make_loader()
offsets_loader = offsets.make_loader()
# pyre-ignore[2,3,53]
def inner_fn(index):
# dense tensor size: [B, N, D]
batch_idx, seq_idx, emb_idx = index
jagged_idx, end_idx = dense_idx_to_jagged_idx(
batch_idx=batch_idx,
seq_idx=seq_idx,
offsets_loader=offsets_loader,
jagged_len=jagged_len,
)
return ops.masked(
ops.lt(
ops.index_expr(jagged_idx, offsets_dtype),
end_idx,
),
lambda: values_loader([jagged_idx, emb_idx]),
padding_value,
)
return Pointwise.create(
device=device,
dtype=dtype,
inner_fn=inner_fn,
ranges=output_size,
)
def _dense_to_jagged_forward_impl(
fallback_op, # pyre-ignore[2]
dense: TensorBox,
jagged_offsets: List[TensorBox],
jagged_len: Optional[int] = None,
) -> TensorBox:
device = dense.get_device_or_error()
dtype = dense.get_dtype()
dense_size = dense.get_size()
# only handle the common case of a single jagged dimension
if (
len(jagged_offsets) != 1
or device.type != "cuda"
or device != jagged_offsets[0].get_device()
or len(jagged_offsets[0].get_size()) != 1
or len(dense_size) != 3
or jagged_len is None
or not is_integer_type(jagged_offsets[0])
):
return fallback_handler(fallback_op, add_to_fallback_set=False)(
dense,
jagged_offsets,
jagged_len,
)
offsets: TensorBox = jagged_offsets[0]
offsets_dtype = offsets.get_dtype()
batch_size = dense_size[0]
max_seq_len = dense_size[1]
embedding_len = dense_size[-1]
output_size = [jagged_len, embedding_len]
dense_loader = dense.make_loader()
offsets_loader = offsets.make_loader()
inverse_offsets = get_inverse_offsets(
offsets=offsets,
jagged_len=jagged_len,
)
inverse_offsets_loader = inverse_offsets.make_loader()
# pyre-ignore[2,3,53]
def inner_fn(index):
# jagged tensor size: [sum_B(N_B), D]
jagged_idx, emb_idx = index
batch_idx, seq_idx = jagged_idx_to_dense_idx(
jagged_idx=jagged_idx,
offsets_loader=offsets_loader,
inverse_offsets_loader=inverse_offsets_loader,
batch_size=batch_size,
max_seq_len=max_seq_len,
offsets_dtype=offsets_dtype,
)
return ops.masked(
ops.lt(
ops.index_expr(seq_idx, offsets_dtype),
ops.index_expr(max_seq_len, offsets_dtype),
),
lambda: dense_loader([batch_idx, seq_idx, emb_idx]),
0.0, # jagged sequence longer than max_seq_len
)
return Pointwise.create(
device=device,
dtype=dtype,
inner_fn=inner_fn,
ranges=output_size,
)
# pyre-ignore[56]
@register_lowering(torch.ops.aten._padded_dense_to_jagged_forward)
def _dense_to_jagged_forward(
dense: TensorBox,
jagged_offsets: List[TensorBox],
jagged_len: Optional[int] = None,
) -> TensorBox:
return _dense_to_jagged_forward_impl(
fallback_op=torch.ops.aten._padded_dense_to_jagged_forward.default,
dense=dense,
jagged_offsets=jagged_offsets,
jagged_len=jagged_len,
)
|
