Device-generalize more dynamo tests to run on accelerator backends - #87
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Device-generalize more dynamo tests to run on accelerator backends#87aws-alexk wants to merge 1 commit into
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Follow-up to the earlier device-generalization pass. These test/dynamo tests
still hardcode CPU input tensors (no device= argument). torch.compile is
device-preserving, but accelerator backends that execute the compiled graph
on-device return device tensors, so autograd rejects the device mismatch on
backward, e.g.:
RuntimeError: Function CompiledFunctionBackward returned an invalid
gradient at index 0 - expected device cpu but got <accel>:0
or the compiled region raises a plain device mismatch when a CPU input meets an
on-device intermediate.
Create inputs on the current accelerator via the module-level device_type
(torch.accelerator.current_accelerator, falling back to "cpu"), adding
device=device_type to input tensor factories and .to(device_type) to nn.Module
instances in the affected tests. test_activation_checkpointing.py and
test_wrap_inductor_compiled_regions.py gain the module-level device_type
definition (the other files already have it); the latter also generalizes its
DTensor device mesh. Because device_type resolves to "cpu" when no accelerator
is present, these edits are a no-op on CPU and CUDA CI and only take effect on
accelerator backends.
Test Plan:
On CPU (no accelerator; device_type == "cpu", edits are a no-op):
```
python test/dynamo/test_autograd_function.py AutogradFunctionTests.test_apply_kwargs_old_style
python test/dynamo/test_hooks.py HooksTests.test_input_hooks_same
python test/dynamo/test_repros.py ReproTests.test_intermediate_leaf_requires_grad
python test/dynamo/test_fwd_loss_bwd.py TestForwardLossBackward.test_backward_dict_inputs
```
On an accelerator backend, the same nodeids (and their _nested_graph_breaks
variants) that previously failed with a device mismatch now pass.
Authored with an AI assistant (Claude).
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Follow-up to the earlier device-generalization pass. These test/dynamo tests still hardcode CPU input tensors (no device= argument). torch.compile is device-preserving, but accelerator backends that execute the compiled graph on-device return device tensors, so autograd rejects the device mismatch on backward, e.g.:
or the compiled region raises a plain device mismatch when a CPU input meets an on-device intermediate.
Create inputs on the current accelerator via the module-level device_type (torch.accelerator.current_accelerator, falling back to "cpu"), adding device=device_type to input tensor factories and .to(device_type) to nn.Module instances in the affected tests. test_activation_checkpointing.py and test_wrap_inductor_compiled_regions.py gain the module-level device_type definition (the other files already have it); the latter also generalizes its DTensor device mesh. Because device_type resolves to "cpu" when no accelerator is present, these edits are a no-op on CPU and CUDA CI and only take effect on accelerator backends.
Issue #, if available:
Description of changes:
from this PR: #81
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