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foreach_map_fn: add UX fallback for torch.mm to avoid Dynamo graph breaks #159757

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foreach_map_fn: add UX fallback for torch.mm to avoid Dynamo graph breaks #159757

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0cd4d70
Add matmul (torch.mm) fallback support to foreach_map_fn
Quantum-Kayak Aug 4, 2025
d1f887c
Add test for torch.mm fallback in foreach_map_fn
Quantum-Kayak Aug 7, 2025
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14 changes: 14 additions & 0 deletions test/inductor/test_foreach_map.py
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Original file line number Diff line number Diff line change
@@ -0,0 +1,14 @@
import unittest
import torch
from torch._foreach import foreach_map

class TestForeachMapMatmul(unittest.TestCase):
def test_foreach_map_with_torch_mm(self):
a_list = [torch.randn(3, 4) for _ in range(3)]
b_list = [torch.randn(4, 2) for _ in range(3)]

result = foreach_map(torch.mm, a_list, b_list)
expected = [torch.mm(a, b) for a, b in zip(a_list, b_list)]

for r, e in zip(result, expected):
self.assertTrue(torch.allclose(r, e), msg=f"Expected {e}, got {r}")
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Suggested change
self.assertTrue(torch.allclose(r, e), msg=f"Expected {e}, got {r}")
self.assertEqual(r, e)

6 changes: 6 additions & 0 deletions torch/_dynamo/polyfills/__init__.py
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Expand Up @@ -318,6 +318,12 @@ def foreach_map_fn(*args):
if not at_least_one_list:
return op(*args[1:])

# Special handling for torch.mm
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So before this PR it was an open question whether this was needed or not, can you check what happens in your test without the special handling? It's possible this was already working, but it just needed a test.

if op is torch.mm:
if len(new_args) != 2:
raise ValueError("torch.mm requires exactly two argument lists")
return [torch.mm(a, b) for a, b in zip(new_args[0], new_args[1])]
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wait...does this just forloop over torch.mm with no optimization...

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Yeah this is just a UX improvement so that we no longer need to workaround the matmul. (we can basically just pass a whole optim single-tensor implementation, vs breaking it up into multiple foreach_map calls to workaround the matmul.


out = []
for unpacked in zip(*new_args):
out.append(op(*unpacked))
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