Support skipping tracing of selected pure modules #308
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- Support nested tuples in `assume_pure(mark_sharding)` - Add a `PureModule` from AI-Hypercomputer/torchprime#308 - Support `PureModule(EinsumLinear)` which uses `torch.ops.xla.einsum_linear_forward`
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- Support nested tuples in `assume_pure(mark_sharding)` - Add a `PureModule` from AI-Hypercomputer/torchprime#308 - Support `PureModule(EinsumLinear)` which uses `torch.ops.xla.einsum_linear_forward`
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- Support nested tuples in `assume_pure(mark_sharding)` - Add a `PureModule` from AI-Hypercomputer/torchprime#308 - Support `PureModule(EinsumLinear)` which uses `torch.ops.xla.einsum_linear_forward`
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- Support nested tuples in `assume_pure(mark_sharding)` - Add a `PureModule` from AI-Hypercomputer/torchprime#308 - Support `PureModule(EinsumLinear)` which uses `torch.ops.xla.einsum_linear_forward`
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TESTED: python3 torchprime/torch_xla_models/train.py model.pure_modules=[LlamaMLP,Linear]
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This PR adds a
model.pure_modulesoption to the trainer, which indicates which modules are run with@assume_pure1.The primary benefit is making available rich profiles during the backward pass, because
jax.vjppreserves framework scopes in the backward pass.This PR updates PyTorch/XLA pin to Jun 17 because it relies on pytorch/xla#9360.
Regular profile
Profile with
assume_pure