[PyTorch] Float8Tensor uses cached transpose if available #524
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Signed-off-by: Tim Moon <tmoon@nvidia.com>
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/te-ci pytorch |
8 tasks
ptrendx
reviewed
Nov 20, 2023
| @@ -263,7 +263,7 @@ def test_transpose( | |||
| dims: DimsType, | |||
| transpose_dims: Tuple[int, int], | |||
| fp8_dtype: tex.DType = tex.DType.kFloat8E4M3, | |||
| scale: float = 1, | |||
| scale: float = 0.5, | |||
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I thought there was a correctness issue that was hidden by scale=1, but I don't think it's actually an issue. Making this non-one does a better job stress-testing this in any case though.
Signed-off-by: Tim Moon <tmoon@nvidia.com>
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/te-ci pytorch |
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Test failures are ONNX-related. This is ready to go. |
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@timmoon10 |
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The work in this PR was merged in #529. |
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This PR changes the transpose behavior of
Float8Tensor:update_cache == True, it will compute the transpose and update the cacheupdate_cache == Falseand the cache is empty, it will compute the transposeupdate_cache == Falseand the cache is populated, it will return the cached transposeThis is somewhat of a kludge to support transpose caching with Megatron GPT (see NVIDIA/NeMo#7909). Its
forwardfunction doesn't keep track of gradient accumulation steps, so it doesn't passis_first_microbatchtoLayerNormLinearorLinear. E.g.:https://github.com/NVIDIA/Megatron-LM/blob/9290c730d04b482be8fae92a4186fe4ff0c95270/megatron/core/transformer/attention.py#L271C31-L271C31
Compare to NeMo GPT, which contains TE-specific logic like
is_first_microbatch:https://github.com/NVIDIA/NeMo/blob/d81beac52423dbd04b48e4e04567b17df2428e3a/nemo/collections/nlp/modules/common/megatron/transformer.py#L1556
Discussion would be appreciated. This design ping-ponged a few times in #452, e.g. 00b9c31. This approach is convenient with an FP8-aware optimizer since the optimizer doesn't need any access to the TE modules, just the FP8 params. There are also some alternative approaches:
is_first_microbatch, to keep the current API