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Fixed a memory leak when calling from_numpy on a numpy array with an … #121156

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Fixed a memory leak when calling from_numpy on a numpy array with an … #121156

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d23ae5c
Fixed a memory leak when calling from_numpy on a numpy array with an …
tringwald Mar 4, 2024
fde130a
Skip dynamo tests for invalid dtypes.
tringwald Mar 4, 2024
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13 changes: 13 additions & 0 deletions test/test_numpy_interop.py
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Original file line number Diff line number Diff line change
Expand Up @@ -6,6 +6,7 @@
import numpy as np

from itertools import product
import sys

from torch.testing._internal.common_utils import \
(skipIfTorchDynamo, TestCase, run_tests)
Expand Down Expand Up @@ -257,6 +258,18 @@ def test_from_numpy(self, device) -> None:
x.strides = (3,)
self.assertRaises(ValueError, lambda: torch.from_numpy(x))

@skipIfTorchDynamo("No need to test invalid dtypes that should fail by design.")
def test_from_numpy_no_leak_on_invalid_dtype(self):
# This used to leak memory as the `from_numpy` call raised an exception and didn't decref the temporary
# object. See https://github.com/pytorch/pytorch/issues/121138
x = np.array("value".encode('ascii'))
for _ in range(1000):
try:
torch.from_numpy(x)
except TypeError:
pass
self.assertTrue(sys.getrefcount(x) == 2)

@skipMeta
def test_from_list_of_ndarray_warning(self, device):
warning_msg = r"Creating a tensor from a list of numpy.ndarrays is extremely slow"
Expand Down
5 changes: 4 additions & 1 deletion torch/csrc/utils/tensor_numpy.cpp
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Expand Up @@ -258,6 +258,9 @@ at::Tensor tensor_from_numpy(
PyArray_EquivByteorders(PyArray_DESCR(array)->byteorder, NPY_NATIVE),
"given numpy array has byte order different from the native byte order. "
"Conversion between byte orders is currently not supported.");
// This has to go before the INCREF in case the dtype mapping doesn't
// exist and an exception is thrown
auto torch_dtype = numpy_dtype_to_aten(PyArray_TYPE(array));
Py_INCREF(obj);
return at::lift_fresh(at::from_blob(
data_ptr,
Expand All @@ -267,7 +270,7 @@ at::Tensor tensor_from_numpy(
pybind11::gil_scoped_acquire gil;
Py_DECREF(obj);
},
at::device(kCPU).dtype(numpy_dtype_to_aten(PyArray_TYPE(array)))));
at::device(kCPU).dtype(torch_dtype)));
}

int aten_to_numpy_dtype(const ScalarType scalar_type) {
Expand Down