Linear with DID loop split #3650
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!build |
Priya2698
requested review from
wujingyue,
samnordmann and
cowanmeg
and removed request for
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samnordmann
wujingyue
approved these changes
Dec 29, 2024
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| self.weight = self.define_tensor([d * e, e], contiguity=[True, True]) | ||
| self.bias = self.define_tensor([d * e], contiguity=[True]) |
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Suggested change
| self.weight = self.define_tensor([d * e, e], contiguity=[True, True]) | |
| self.bias = self.define_tensor([d * e], contiguity=[True]) | |
| self.weight = self.define_tensor([d * e, e], contiguity=True) | |
| self.bias = self.define_tensor([d * e], contiguity=True) |
Also, a question for you: is contiguity=True necessary here? Is there a problem with non-contiguous inputs?
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| expected_out_tensor = unsharded_out_tensor.view([b, s, d, e]).permute(2, 0, 1, 3)[ | ||
| rank : rank + 1 | ||
| ] |
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Can we use shard_tensor here?
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This PR adds a test case demonstrating DID loop split for linear.
The test does not require any changes to the
LinearOp::evaluatemethod and works out of the box. DID split on logical domain continues to work for linear using the additional WARs present that squeeze/unsqueeze the DID dimension. Those WARs can be removed once we completely switch to representing device parallelism using allocation and loop domain.