[dtensor][4/n] don't use make_fx for strategy propagation #108262
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We were using make_fx for strategy based propagation so that we can get a graph and the shape related metadata, this becomes too much overkill for the sharding propagation purpose. This change refactors the strategy propagation to remove the graph based propagation, instead just use the op to index to the strategy functions. We also just use a fake shape prop instead of relying on fx tracing for the shape/stride propagation. for a future possible decomposed propagation, we will exercise different codepath to enable that NOTE that this would also greatly reduce the latency of: 1. first time dtensor operations when populating the cache, the first iter would become faster again! 2. greatly reduce the test_dtensor_ops.py time again, right now the whole test finished within 2-3 mins again. [ghstack-poisoned]
wanchaol
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This was referenced Aug 30, 2023
🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/108262
Note: Links to docs will display an error until the docs builds have been completed. ✅ You can merge normally! (1 Unrelated Failure)As of commit 5bdc291 with merge base 6dc56d3 ( UNSTABLE - The following job failed but was likely due to flakiness present on trunk and has been marked as unstable:
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We were using make_fx for strategy based propagation so that we can get a graph and the shape related metadata, this becomes too much overkill for the sharding propagation purpose. This change refactors the strategy propagation to remove the graph based propagation, instead just use the op to index to the strategy functions. We also just use a fake shape prop instead of relying on fx tracing for the shape/stride propagation. for a future possible decomposed propagation, we will exercise different codepath to enable that NOTE that this would also greatly reduce the latency of: 1. first time dtensor operations when populating the cache, the first iter would become faster again! 2. greatly reduce the test_dtensor_ops.py time again, right now the whole test finished within 2-3 mins again. [ghstack-poisoned]
fduwjj
reviewed
Sep 11, 2023
| return None | ||
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| def _wrap_output_spec_tensor_meta( | ||
| self, output_spec: OutputSpecType, output_tensor_meta: object |
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Does this mean output_spec is optional?
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I think it probably won't be None. I'll submit a follow up PR to do assertion here instead.
fduwjj
reviewed
Sep 11, 2023
Comment on lines
+65
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| with FakeTensorMode(): | ||
| fake_args = op_schema.gen_fake_args() | ||
| fake_kwargs = op_schema.gen_fake_kwargs() | ||
| fake_out = op_schema.op(*fake_args, **fake_kwargs) |
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This will help view,split and chunk op a lot.
We were using make_fx for strategy based propagation so that we can get a graph and the shape related metadata, this becomes too much overkill for the sharding propagation purpose. This change refactors the strategy propagation to remove the graph based propagation, instead just use the op to index to the strategy functions. We also just use a fake shape prop instead of relying on fx tracing for the shape/stride propagation. for a future possible decomposed propagation, we will exercise different codepath to enable that NOTE that this would also greatly reduce the latency of: 1. first time dtensor operations when populating the cache, the first iter would become faster again! 2. greatly reduce the test_dtensor_ops.py time again, right now the whole test finished within 2-3 mins again. [ghstack-poisoned]
This was referenced Sep 12, 2023
wanchaol
added
ciflow/trunk
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@pytorchbot merge |
Merge startedYour change will be merged once all checks pass (ETA 0-4 Hours). Learn more about merging in the wiki. Questions? Feedback? Please reach out to the PyTorch DevX Team |
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Stack from ghstack (oldest at bottom):
We were using make_fx for strategy based propagation so that we can get
a graph and the shape related metadata, this becomes too much overkill
for the sharding propagation purpose. This change refactors the strategy
propagation to remove the graph based propagation, instead just use the
op to index to the strategy functions.
We also just use a fake shape prop instead of relying on fx tracing for
the shape/stride propagation.
for a future possible decomposed propagation, we will exercise different
codepath to enable that
NOTE that this would also greatly reduce the latency of:
iter would become faster again!
whole test finished within 2-3 mins again.