forked from pytorch/pytorch
-
Notifications
You must be signed in to change notification settings - Fork 0
/
MemoryOverlap.cpp
98 lines (82 loc) · 3.27 KB
/
MemoryOverlap.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
#include <ATen/MemoryOverlap.h>
#include <c10/core/Layout.h>
namespace at {
MemOverlap has_internal_overlap(const Tensor& tensor) {
return has_internal_overlap(tensor.unsafeGetTensorImpl());
}
MemOverlap has_internal_overlap(TensorImpl* t) {
TORCH_INTERNAL_ASSERT_DEBUG_ONLY(t->layout() == kStrided);
if (t->is_non_overlapping_and_dense()) {
return MemOverlap::NO;
}
auto strides = t->strides();
auto sizes = t->sizes();
for (size_t i = 0; i < strides.size(); ++i) {
if (strides[i] == 0 && sizes[i] > 1) {
return MemOverlap::YES;
}
}
return MemOverlap::TOO_HARD;
}
void assert_no_internal_overlap(const Tensor& t) {
assert_no_internal_overlap(t.unsafeGetTensorImpl());
}
void assert_no_internal_overlap(TensorImpl* t) {
TORCH_CHECK(has_internal_overlap(t) != MemOverlap::YES,
"unsupported operation: more than one element of the written-to tensor "
"refers to a single memory location. Please clone() the tensor before "
"performing the operation.");
}
MemOverlapStatus get_overlap_status(const Tensor& a, const Tensor& b) {
return get_overlap_status(a.unsafeGetTensorImpl(), b.unsafeGetTensorImpl());
}
MemOverlapStatus get_overlap_status(TensorImpl* a, TensorImpl* b) {
if (a == b) return MemOverlapStatus::FULL;
if (a->numel() == 0 || b->numel() == 0) {
return MemOverlapStatus::NO;
}
if (!a->is_non_overlapping_and_dense() || !b->is_non_overlapping_and_dense()) {
return MemOverlapStatus::TOO_HARD;
}
if (!a->has_storage() || !b->has_storage()) {
return MemOverlapStatus::NO;
}
// Test for storage equality, rather than pointer equality.
// This reduces precision, but if people are aliasing the
// same pointer across multiple storages there are many
// similar situations (e.g., storage().data() == storage().data()+1)
// which we will miss.
if (a->storage().is_alias_of(b->storage())) {
const auto a_begin = static_cast<char*>(a->data());
const auto a_end = a_begin + a->numel() * a->itemsize();
const auto b_begin = static_cast<char*>(b->data());
const auto b_end = b_begin + b->numel() * b->itemsize();
if (a_begin == b_begin && a_end == b_end) {
return MemOverlapStatus::FULL;
}
if (a_begin < b_end && b_begin < a_end) {
return MemOverlapStatus::PARTIAL;
}
}
return MemOverlapStatus::NO;
}
void assert_no_partial_overlap(const Tensor& a, const Tensor& b) {
assert_no_partial_overlap(a.unsafeGetTensorImpl(), b.unsafeGetTensorImpl());
}
void assert_no_partial_overlap(TensorImpl* a, TensorImpl* b) {
TORCH_CHECK(get_overlap_status(a, b) != MemOverlapStatus::PARTIAL,
"unsupported operation: some elements of the input tensor and "
"the written-to tensor refer to a single memory location. "
"Please clone() the tensor before performing the operation.");
}
void assert_no_overlap(const Tensor& a, const Tensor& b) {
assert_no_overlap(a.unsafeGetTensorImpl(), b.unsafeGetTensorImpl());
}
void assert_no_overlap(TensorImpl* a, TensorImpl* b) {
const auto lap = get_overlap_status(a, b);
TORCH_CHECK(lap != MemOverlapStatus::PARTIAL && lap != MemOverlapStatus::FULL,
"unsupported operation: some elements of the input tensor and "
"the written-to tensor refer to a single memory location. "
"Please clone() the tensor before performing the operation.");
}
}