Implement Tensor.to batching rule #43206
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The batching rule is the same as the unary pointwise batching rules: given a BatchedTensor, we unwrap it, call Tensor.to, and then re-wrap it. Test Plan: - `pytest test/test_vmap.py -v -k` [ghstack-poisoned]
💊 CI failures summary and remediationsAs of commit 1e734c1 (more details on the Dr. CI page):
🚧 1 ongoing upstream failure:These were probably caused by upstream breakages that are not fixed yet:
ci.pytorch.org: 1 failedThis comment was automatically generated by Dr. CI (expand for details).Follow this link to opt-out of these comments for your Pull Requests.Please report bugs/suggestions on the GitHub issue tracker or post in the (internal) Dr. CI Users group. This comment has been revised 6 times. |
The batching rule is the same as the unary pointwise batching rules: given a BatchedTensor, we unwrap it, call Tensor.to, and then re-wrap it. Test Plan: - `pytest test/test_vmap.py -v -k` Differential Revision: [D23189053](https://our.internmc.facebook.com/intern/diff/D23189053) [ghstack-poisoned]
ezyang
approved these changes
Aug 18, 2020
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Stack from ghstack:
The batching rule is the same as the unary pointwise batching rules:
given a BatchedTensor, we unwrap it, call Tensor.to, and then re-wrap
it.
Test Plan:
pytest test/test_vmap.py -v -kDifferential Revision: D23189053