The following issues were found

test/typing/fail/creation_ops.py
3 issues
Unable to import 'torch'
Error

Line: 2 Column: 1 

              # flake8: noqa
import torch

torch.tensor([3], dtype='int32')  # E: expected "Optional[dtype]"
torch.ones(3, dtype='int32')  # E: No overload variant of "ones" matches argument types "int", "str"
torch.zeros(3, dtype='int32')  # E: No overload variant of "zeros" matches argument types "int", "str"

Reported by Pylint.

Missing module docstring
Error

Line: 1 Column: 1 

              # flake8: noqa
import torch

torch.tensor([3], dtype='int32')  # E: expected "Optional[dtype]"
torch.ones(3, dtype='int32')  # E: No overload variant of "ones" matches argument types "int", "str"
torch.zeros(3, dtype='int32')  # E: No overload variant of "zeros" matches argument types "int", "str"

Reported by Pylint.

Line too long (102/100)
Error

Line: 6 Column: 1 

              
torch.tensor([3], dtype='int32')  # E: expected "Optional[dtype]"
torch.ones(3, dtype='int32')  # E: No overload variant of "ones" matches argument types "int", "str"
torch.zeros(3, dtype='int32')  # E: No overload variant of "zeros" matches argument types "int", "str"

Reported by Pylint.

test/typing/fail/bitwise_ops.py
3 issues
Unable to import 'torch'
Error

Line: 2 Column: 1 

              # flake8: noqa
import torch

# binary ops: <<, >>, |, &, ~, ^

a = torch.ones(3, dtype=torch.float64)
i = int()

i | a  # E: Unsupported operand types

Reported by Pylint.

Statement seems to have no effect
Error

Line: 9 Column: 1 

              a = torch.ones(3, dtype=torch.float64)
i = int()

i | a  # E: Unsupported operand types

Reported by Pylint.

Missing module docstring
Error

Line: 1 Column: 1 

              # flake8: noqa
import torch

# binary ops: <<, >>, |, &, ~, ^

a = torch.ones(3, dtype=torch.float64)
i = int()

i | a  # E: Unsupported operand types

Reported by Pylint.

torch/ao/nn/sparse/quantized/__init__.py
3 issues
Unable to import '__init__.linear'
Error

Line: 3 Column: 1 

              from torch.ao.nn.sparse.quantized import dynamic

from .linear import Linear
from .linear import LinearPackedParams

__all__ = [
    "dynamic",
    "Linear",
    "LinearPackedParams",

Reported by Pylint.

Unable to import '__init__.linear'
Error

Line: 4 Column: 1 

              from torch.ao.nn.sparse.quantized import dynamic

from .linear import Linear
from .linear import LinearPackedParams

__all__ = [
    "dynamic",
    "Linear",
    "LinearPackedParams",

Reported by Pylint.

Missing module docstring
Error

Line: 1 Column: 1 

              from torch.ao.nn.sparse.quantized import dynamic

from .linear import Linear
from .linear import LinearPackedParams

__all__ = [
    "dynamic",
    "Linear",
    "LinearPackedParams",

Reported by Pylint.

torch/ao/sparsity/_mappings.py
3 issues
Missing module docstring
Error

Line: 1 Column: 1 

              import torch

def get_static_sparse_quantized_mapping():
    _static_sparse_quantized_mapping = dict({
        torch.nn.Linear: torch.ao.nn.sparse.quantized.Linear,
    })
    return _static_sparse_quantized_mapping

def get_dynamic_sparse_quantized_mapping():

Reported by Pylint.

Missing function or method docstring
Error

Line: 3 Column: 1 

              import torch

def get_static_sparse_quantized_mapping():
    _static_sparse_quantized_mapping = dict({
        torch.nn.Linear: torch.ao.nn.sparse.quantized.Linear,
    })
    return _static_sparse_quantized_mapping

def get_dynamic_sparse_quantized_mapping():

Reported by Pylint.

Missing function or method docstring
Error

Line: 9 Column: 1 

                  })
    return _static_sparse_quantized_mapping

def get_dynamic_sparse_quantized_mapping():
    _dynamic_sparse_quantized_mapping = dict({
        torch.nn.Linear: torch.ao.nn.sparse.quantized.dynamic.Linear,
    })
    return _dynamic_sparse_quantized_mapping

Reported by Pylint.

torch/backends/mkl/__init__.py
3 issues
Access to a protected member _C of a client class
Error

Line: 6 Column: 12 

              
def is_available():
    r"""Returns whether PyTorch is built with MKL support."""
    return torch._C.has_mkl

Reported by Pylint.

Missing module docstring
Error

Line: 1 Column: 1 

              import torch


def is_available():
    r"""Returns whether PyTorch is built with MKL support."""
    return torch._C.has_mkl

Reported by Pylint.

Module 'torch._C' has no 'has_mkl' member, but source is unavailable. Consider adding this module to extension-pkg-whitelist if you want to perform analysis based on run-time introspection of living objects.
Error

Line: 6 Column: 12 

              
def is_available():
    r"""Returns whether PyTorch is built with MKL support."""
    return torch._C.has_mkl

Reported by Pylint.

torch/backends/openmp/__init__.py
3 issues
Access to a protected member _C of a client class
Error

Line: 6 Column: 12 

              
def is_available():
    r"""Returns whether PyTorch is built with OpenMP support."""
    return torch._C.has_openmp

Reported by Pylint.

Missing module docstring
Error

Line: 1 Column: 1 

              import torch


def is_available():
    r"""Returns whether PyTorch is built with OpenMP support."""
    return torch._C.has_openmp

Reported by Pylint.

Module 'torch._C' has no 'has_openmp' member, but source is unavailable. Consider adding this module to extension-pkg-whitelist if you want to perform analysis based on run-time introspection of living objects.
Error

Line: 6 Column: 12 

              
def is_available():
    r"""Returns whether PyTorch is built with OpenMP support."""
    return torch._C.has_openmp

Reported by Pylint.

torch/csrc/Exceptions.cpp
3 issues
vsnprintf - If format strings can be influenced by an attacker, they can be exploited, and note that sprintf variations do not always \0-terminate
Security

Line: 134 Column: 3 CWE codes: 134
Suggestion: Use a constant for the format specification 

                static const size_t ERROR_BUF_SIZE = 1024;
  // NOLINTNEXTLINE(modernize-avoid-c-arrays,cppcoreguidelines-avoid-c-arrays)
  char error_buf[ERROR_BUF_SIZE];
  vsnprintf(error_buf, ERROR_BUF_SIZE, format, fmt_args);

  // Ensure that the string is null terminated
  error_buf[sizeof(error_buf) / sizeof(*error_buf) - 1] = 0;

  return std::string(error_buf);

Reported by FlawFinder.

getenv - Environment variables are untrustable input if they can be set by an attacker. They can have any content and length, and the same variable can be set more than once
Security

Line: 25 Column: 21 CWE codes: 807 20
Suggestion: Check environment variables carefully before using them 

              namespace torch {

static bool compute_cpp_stack_traces_enabled() {
  auto envar = std::getenv("TORCH_SHOW_CPP_STACKTRACES");
  if (envar) {
    if (strcmp(envar, "0") == 0) {
      return false;
    }
    if (strcmp(envar, "1") == 0) {

Reported by FlawFinder.

char - Statically-sized arrays can be improperly restricted, leading to potential overflows or other issues
Security

Line: 133 Column: 3 CWE codes: 119 120
Suggestion: Perform bounds checking, use functions that limit length, or ensure that the size is larger than the maximum possible length 

              static std::string formatMessage(const char *format, va_list fmt_args) {
  static const size_t ERROR_BUF_SIZE = 1024;
  // NOLINTNEXTLINE(modernize-avoid-c-arrays,cppcoreguidelines-avoid-c-arrays)
  char error_buf[ERROR_BUF_SIZE];
  vsnprintf(error_buf, ERROR_BUF_SIZE, format, fmt_args);

  // Ensure that the string is null terminated
  error_buf[sizeof(error_buf) / sizeof(*error_buf) - 1] = 0;

Reported by FlawFinder.

torch/csrc/api/include/torch/nn/functional/activation.h
3 issues
equal - Function does not check the second iterator for over-read conditions
Security

Line: 627 Column: 16 CWE codes: 126
Suggestion: This function is often discouraged by most C++ coding standards in favor of its safer alternatives provided since C++14. Consider using a form of this function that checks the second iterator before potentially overflowing it 

              
  Tensor q, k, v;
  if (!use_separate_proj_weight) {
    if (torch::equal(query, key) && torch::equal(key, value)) {
      // self-attention
      const auto chunks =
        F::linear(query, in_proj_weight, in_proj_bias).chunk(3, /*dim=*/-1);
      q = chunks[0];
      k = chunks[1];

Reported by FlawFinder.

equal - Function does not check the second iterator for over-read conditions
Security

Line: 627 Column: 44 CWE codes: 126
Suggestion: This function is often discouraged by most C++ coding standards in favor of its safer alternatives provided since C++14. Consider using a form of this function that checks the second iterator before potentially overflowing it 

              
  Tensor q, k, v;
  if (!use_separate_proj_weight) {
    if (torch::equal(query, key) && torch::equal(key, value)) {
      // self-attention
      const auto chunks =
        F::linear(query, in_proj_weight, in_proj_bias).chunk(3, /*dim=*/-1);
      q = chunks[0];
      k = chunks[1];

Reported by FlawFinder.

equal - Function does not check the second iterator for over-read conditions
Security

Line: 634 Column: 23 CWE codes: 126
Suggestion: This function is often discouraged by most C++ coding standards in favor of its safer alternatives provided since C++14. Consider using a form of this function that checks the second iterator before potentially overflowing it 

                    q = chunks[0];
      k = chunks[1];
      v = chunks[2];
    } else if (torch::equal(key, value)) {
      // encoder-decoder attention
      // This is inline in_proj function with in_proj_weight and in_proj_bias
      auto _b = in_proj_bias;
      auto _start = 0;
      auto _end = embed_dim;

Reported by FlawFinder.

torch/csrc/api/src/data/datasets/mnist.cpp
3 issues
read - Check buffer boundaries if used in a loop including recursive loops
Security

Line: 42 Column: 20 CWE codes: 120 20 

                static const bool is_little_endian = check_is_little_endian();
  // NOLINTNEXTLINE(cppcoreguidelines-init-variables)
  uint32_t value;
  AT_ASSERT(stream.read(reinterpret_cast<char*>(&value), sizeof value));
  return is_little_endian ? flip_endianness(value) : value;
}

uint32_t expect_int32(std::ifstream& stream, uint32_t expected) {
  const auto value = read_int32(stream);

Reported by FlawFinder.

read - Check buffer boundaries if used in a loop including recursive loops
Security

Line: 79 Column: 10 CWE codes: 120 20 

              
  auto tensor =
      torch::empty({count, 1, kImageRows, kImageColumns}, torch::kByte);
  images.read(reinterpret_cast<char*>(tensor.data_ptr()), tensor.numel());
  return tensor.to(torch::kFloat32).div_(255);
}

Tensor read_targets(const std::string& root, bool train) {
  const auto path =

Reported by FlawFinder.

read - Check buffer boundaries if used in a loop including recursive loops
Security

Line: 95 Column: 11 CWE codes: 120 20 

                expect_int32(targets, count);

  auto tensor = torch::empty(count, torch::kByte);
  targets.read(reinterpret_cast<char*>(tensor.data_ptr()), count);
  return tensor.to(torch::kInt64);
}
} // namespace

MNIST::MNIST(const std::string& root, Mode mode)

Reported by FlawFinder.

torch/csrc/api/src/data/samplers/distributed.cpp
3 issues
read - Check buffer boundaries if used in a loop including recursive loops
Security

Line: 84 Column: 11 CWE codes: 120 20 

              
void DistributedRandomSampler::load(serialize::InputArchive& archive) {
  auto tensor = torch::empty(1, torch::kInt64);
  archive.read("epoch_", tensor, /*is_buffer=*/true);
  epoch_ = tensor.item<int64_t>();
  // call reset() after loading epoch_ to populate indices.
  reset(size_);

  tensor = torch::empty(1, torch::kInt64);

Reported by FlawFinder.

read - Check buffer boundaries if used in a loop including recursive loops
Security

Line: 90 Column: 11 CWE codes: 120 20 

                reset(size_);

  tensor = torch::empty(1, torch::kInt64);
  archive.read("sample_index_", tensor, /*is_buffer=*/true);
  sample_index_ = tensor.item<int64_t>();
}

size_t DistributedRandomSampler::index() const noexcept {
  return sample_index_;

Reported by FlawFinder.

read - Check buffer boundaries if used in a loop including recursive loops
Security

Line: 158 Column: 11 CWE codes: 120 20 

              
void DistributedSequentialSampler::load(serialize::InputArchive& archive) {
  auto tensor = torch::empty(1, torch::kInt64);
  archive.read("sample_index_", tensor, /*is_buffer=*/true);
  sample_index_ = tensor.item<int64_t>();
}

size_t DistributedSequentialSampler::index() const noexcept {
  return sample_index_;

Reported by FlawFinder.