Fix decode cuda graph for Qwen-Image-2.1 model-level cpu offload (#7801)
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@@ -284,6 +284,49 @@ def test_component_selective_model_offload_requires_swap_counterpart(component,
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backend.enable(pipeline)
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def test_persistent_dit_staging_reuses_fixed_device_storage(accelerator_device) -> None:
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dit = _create_simple_module().to(accelerator_device)
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encoder = _create_simple_module().to(accelerator_device)
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original_weight = dit.linear.weight.detach().clone()
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apply_sequential_offload(
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dit_modules=[dit],
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encoder_modules=[encoder],
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device=accelerator_device,
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pin_memory=False,
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persistent_dit_staging=True,
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)
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dit_hook = dit._hook_registry.get_hook(SequentialOffloadHook._HOOK_NAME)
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encoder_hook = encoder._hook_registry.get_hook(SequentialOffloadHook._HOOK_NAME)
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assert dit_hook._stager is not None
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assert encoder_hook._stager is None # encoders keep plain move semantics
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# Registration snapshots the CPU master and rebinds to it.
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assert dit.linear.weight.device.type == "cpu"
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dit_hook._to_gpu(dit)
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first_ptr = dit.linear.weight.data_ptr()
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assert dit.linear.weight.device == accelerator_device
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torch.testing.assert_close(dit.linear.weight.data, original_weight)
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# Offload via a *different* module's hook (the cross-module swap path),
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# then reload: the same fixed device storage must be reused so that
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# CUDA-graph-captured weight pointers stay valid.
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encoder_hook._to_cpu(dit)
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assert dit.linear.weight.device.type == "cpu"
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dit_hook._to_gpu(dit)
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assert dit.linear.weight.data_ptr() == first_ptr
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torch.testing.assert_close(dit.linear.weight.data, original_weight)
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# A second offload/load cycle through the DiT hook itself stays put too.
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dit_hook._to_cpu(dit)
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dit_hook._to_gpu(dit)
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assert dit.linear.weight.data_ptr() == first_ptr
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remove_sequential_offload([dit, encoder])
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def test_sequential_offload_can_begin_with_dit_on_cpu(monkeypatch: pytest.MonkeyPatch) -> None:
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dit = _create_simple_module()
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encoder = _create_simple_module()
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