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Adjust ...mark_unbacked() -> ...decorators.mark_unbacked() in logs. (#164131)

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Pull Request resolved: https://github.com/pytorch/pytorch/pull/164131
Approved by: https://github.com/albanD, https://github.com/Skylion007
This commit is contained in:
Fabian
2025-09-29 17:43:56 +00:00
committed by PyTorch MergeBot
parent a56e7a1920
commit 8701f18bc0
3 changed files with 4 additions and 4 deletions
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2
docs/source/torch.compiler_dynamic_shapes.md
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@ -216,7 +216,7 @@ you never specialize.
#### `mark_unbacked(tensor, dim)`
The {func}`torch._dynamo.mark_unbacked` function marks a tensor dimension as unbacked. It is unlikely
The {func}`torch._dynamo.decorators.mark_unbacked` function marks a tensor dimension as unbacked. It is unlikely
to be the tool you need, but it could be useful if the specialization occurs inside
a condition `guard_size_oblivious(x)`, and if using it removes the specialization.
Ensure it fixes the specialization and does not introduce a data-dependent error

4
test/dynamo/test_logging.py
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@ -733,7 +733,7 @@ TRACE FX call mul from test_logging.py:N in fn (LoggingTests.test_trace_call_pre
+- __SHAPE_GUARD__: L['x'].size()[0] == 2*L['y'].size()[0] # return x + torch.cat([y, z]) # #:# in # #:# in #
+- __SHAPE_GUARD__: L['z'].size()[0] == L['y'].size()[0] # duck sizing added this equality because these variables had the same size 3 (to avoid this specialization, set torch.fx.experimental._config.use_duck_shape = False)
+- __SHAPE_GUARD__: ((2*L['y'].size()[0]) % 3) == 0 # if x.size(0) % 3 == 0: # #:# in # #:# in #
+- __SHAPE_GUARD__: 2 <= L['y'].size()[0] # return x + torch.cat([y, z]) # #:# in # (user code shown is first use of this value--the guard itself is not due user code but due to 0/1 specialization in the framework; to avoid specialization try torch._dynamo.mark_unbacked(tensor, dim))""", # noqa: B950
+- __SHAPE_GUARD__: 2 <= L['y'].size()[0] # return x + torch.cat([y, z]) # #:# in # (user code shown is first use of this value--the guard itself is not due user code but due to 0/1 specialization in the framework; to avoid specialization try torch._dynamo.decorators.mark_unbacked(tensor, dim))""", # noqa: B950
)
@make_logging_test(guards=True)
@ -749,7 +749,7 @@ TRACE FX call mul from test_logging.py:N in fn (LoggingTests.test_trace_call_pre
munge_shape_guards(record.getMessage()),
"""\
+- __SHAPE_GUARD__: L['x'].size()[0] == 2*L['y'].size()[0] # return any([x.size(0) == y.size(0) * 2]) # #:# in # #:# in #
+- __SHAPE_GUARD__: 2 <= L['y'].size()[0] # return any([x.size(0) == y.size(0) * 2]) # #:# in # (user code shown is first use of this value--the guard itself is not due user code but due to 0/1 specialization in the framework; to avoid specialization try torch._dynamo.mark_unbacked(tensor, dim))""", # noqa: B950
+- __SHAPE_GUARD__: 2 <= L['y'].size()[0] # return any([x.size(0) == y.size(0) * 2]) # #:# in # (user code shown is first use of this value--the guard itself is not due user code but due to 0/1 specialization in the framework; to avoid specialization try torch._dynamo.decorators.mark_unbacked(tensor, dim))""", # noqa: B950
)
@make_logging_test(guards=True)

2
torch/fx/experimental/symbolic_shapes.py
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@ -5077,7 +5077,7 @@ class ShapeEnv:
self._get_sloc(
"user code shown is first use of this value--the guard itself is not "
"due user code but due to 0/1 specialization in the framework; to "
"avoid specialization try torch._dynamo.mark_unbacked(tensor, dim)"
"avoid specialization try torch._dynamo.decorators.mark_unbacked(tensor, dim)"
if self.specialize_zero_one
else None
),