Standardize misc graph interface - #4485
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mathemakitten
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| @@ -1126,11 +1144,18 @@ def teardown_method(self, method): | |||
| CudaGraphManager.global_mempool = None | |||
| Utils.destroy_model_parallel() | |||
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|
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| @pytest.mark.parametrize( | |||
| "make_module", | |||
| [ | |||
| pytest.param(_make_simple_module, id="nn_module"), | |||
| pytest.param(_make_simple_non_module, id="plain_class"), | |||
| ], | |||
| ) | |||
| @torch.inference_mode() | |||
| def test_inline_capture_matches_eager(self): | |||
| def test_inline_capture_matches_eager(self, make_module): | |||
| """Inline-captured graph output must match eager execution.""" | |||
| config = self._make_config() | |||
| module = _SimpleModule(config).cuda().eval() | |||
| module = make_module(config) | |||
|
|
|||
| # Get eager reference before wrapping | |||
| x = torch.randn(4, config.hidden_size, device="cuda") | |||
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Can we use the existing model building logic for this? Seems more rigorous than a dummy model.
kvareddy
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What does this PR do ?
#4425 added syntactic sugar to CudaGraphManager to make it easy to graph arbitrary, non-layer, torch ops which may be keyed on things other than
padded_batch_dimensions. This common sugar will be shared by several pieces of upcoming code.In parallel, #4260 added similar syntactic sugar, but special-cased specifically to MTP CUDA Graphs. The implementation is very similar to that of #4425.
This PR subsumes MTP CUDA graphs into the same sugar as #4425, reconciling #4260's implementation with that of other planned work.
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