8000 add support for numpy by tugsbayasgalan · Pull Request #149288 · pytorch/pytorch · GitHub
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add support for numpy #149288

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add support for numpy #149288

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594b3ed
add support for numpy
tugsbayasgalan Mar 17, 2025
cb6dbd9
Update on "add support for numpy"
tugsbayasgalan Mar 17, 2025
4846e37
Update on "add support for numpy"
tugsbayasgalan Mar 17, 2025
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24 changes: 24 additions & 0 deletions test/export/test_export.py
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Original file line number Diff line number Diff line change
Expand Up @@ -17,6 +17,7 @@
import torch._dynamo as torchdynamo
import torch.nn.functional as F
from functorch.experimental.control_flow import cond, map
import numpy as np
from torch import Tensor
from torch._decomp import decomposition_table
from torch._dynamo.test_case import TestCase
Expand Down Expand Up @@ -1267,6 +1268,29 @@ def false_fn(x):
)
torch.export.export(M(), args)

def test_numpy(self):
class Foo(torch.nn.Module):
def forward(self, x):
a = x.numpy()
return x + np.sin(x.numpy().sum())

foo = Foo()
foo(torch.randn(10, 10))

non_strict_graph = (
8000 export(foo, (torch.randn(10, 10),), strict=False)
.run_decompositions({})
.graph
)
# strict_graph = (
# export(foo, (torch.randn(10, 10),), strict=True)
# .run_decompositions({})
# .graph
# )
# g = torch.export._trace._export_to_torch_ir(foo, (torch.randn(10, 10),))
# print("GRAPH", g.graph)
# self.assertEqual(str(non_strict_graph), str(strict_graph))

def test_cond_int_closure(self):
class M(torch.nn.Module):
def __init__(self):
Expand Down
9 changes: 9 additions & 0 deletions torch/_export/non_strict_utils.py
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Expand Up @@ -625,6 +625,8 @@ class _NonStrictTorchFunctionHandler(torch.overrides.TorchFunctionMode):
"""

def _override(self, func, args, kwargs):
if "__array__" in str(func):
breakpoint()
if torch.distributed.is_available():
from torch.distributed._functional_collectives import (
REDUCE_OP_TO_STR,
Expand Down Expand Up @@ -660,6 +662,13 @@ def _override(self, func, args, kwargs):
# Redirect to torch.select for indexing with symint.
if isinstance(args[1], torch.SymInt):
return torch.select, [args[0], 0, args[1]], {}

if func.__name__ == "numpy" and isinstance(args[0], torch.Tensor):
return torch._refs.view_as, [args[0], args[0]], {}

if func.__name__ == "__array__" and isinstance(args[0], torch.Tensor):
return torch._numpy.ndarray, [args[0]], {}

return func, args, kwargs

def __torch_function__(self, func, types, args=(), kwargs=None):
Expand Down
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