File: SymIntArrayRef.h

package info (click to toggle)
pytorch-cuda 2.6.0%2Bdfsg-7
  • links: PTS, VCS
  • area: contrib
  • in suites: forky, sid, trixie
  • size: 161,620 kB
  • sloc: python: 1,278,832; cpp: 900,322; ansic: 82,710; asm: 7,754; java: 3,363; sh: 2,811; javascript: 2,443; makefile: 597; ruby: 195; xml: 84; objc: 68
file content (89 lines) | stat: -rw-r--r-- 2,714 bytes parent folder | download | duplicates (3)
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
#pragma once

#include <c10/core/SymInt.h>
#include <c10/util/ArrayRef.h>
#include <c10/util/DimVector.h>
#include <c10/util/Exception.h>
#include <c10/util/irange.h>
#include <cstdint>
#include <optional>

namespace c10 {
using SymIntArrayRef = ArrayRef<SymInt>;

inline at::IntArrayRef asIntArrayRefUnchecked(c10::SymIntArrayRef ar) {
  return IntArrayRef(reinterpret_cast<const int64_t*>(ar.data()), ar.size());
}

// TODO: a SymIntArrayRef containing a heap allocated large negative integer
// can actually technically be converted to an IntArrayRef... but not with
// the non-owning API we have here.  We can't reinterpet cast; we have to
// allocate another buffer and write the integers into it.  If you need it,
// we can do it.  But I don't think you need it.

inline std::optional<at::IntArrayRef> asIntArrayRefSlowOpt(
    c10::SymIntArrayRef ar) {
  for (const c10::SymInt& sci : ar) {
    if (sci.is_heap_allocated()) {
      return std::nullopt;
    }
  }

  return {asIntArrayRefUnchecked(ar)};
}

inline at::IntArrayRef asIntArrayRefSlow(
    c10::SymIntArrayRef ar,
    const char* file,
    int64_t line) {
  for (const c10::SymInt& sci : ar) {
    TORCH_CHECK(
        !sci.is_heap_allocated(),
        file,
        ":",
        line,
        ": SymIntArrayRef expected to contain only concrete integers");
  }
  return asIntArrayRefUnchecked(ar);
}

// Even slower than asIntArrayRefSlow, as it forces an allocation for a
// destination int, BUT it is able to force specialization (it never errors)
inline c10::DimVector asIntArrayRefSlowAlloc(
    c10::SymIntArrayRef ar,
    const char* file,
    int64_t line) {
  c10::DimVector res(ar.size(), 0);
  for (const auto i : c10::irange(ar.size())) {
    res[i] = ar[i].guard_int(file, line);
  }
  return res;
}

#define C10_AS_INTARRAYREF_SLOW(a) c10::asIntArrayRefSlow(a, __FILE__, __LINE__)
#define C10_AS_INTARRAYREF_SLOW_ALLOC(a) \
  c10::asIntArrayRefSlowAlloc(a, __FILE__, __LINE__)

// Prefer using a more semantic constructor, like
// fromIntArrayRefKnownNonNegative
inline SymIntArrayRef fromIntArrayRefUnchecked(IntArrayRef array_ref) {
  return SymIntArrayRef(
      reinterpret_cast<const SymInt*>(array_ref.data()), array_ref.size());
}

inline SymIntArrayRef fromIntArrayRefKnownNonNegative(IntArrayRef array_ref) {
  return fromIntArrayRefUnchecked(array_ref);
}

inline SymIntArrayRef fromIntArrayRefSlow(IntArrayRef array_ref) {
  for (long i : array_ref) {
    TORCH_CHECK(
        SymInt::check_range(i),
        "IntArrayRef contains an int that cannot be represented as a SymInt: ",
        i);
  }
  return SymIntArrayRef(
      reinterpret_cast<const SymInt*>(array_ref.data()), array_ref.size());
}

} // namespace c10