[jit] fix trace checking reporting divergent names #37464
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Fixes #23993. There are two fixes here: 1. Previously our name lookup function for the tracer was looking in f.globals for names. For example: ``` sample = torch.ones(1) traced = torch.jit.trace(my_mod, ((sample, sample,),)) ``` This is not great if you are, e.g. trace checking, because a non-local bit of interpreter state is affected the graph produced: ``` traced = torch.jit.trace(my_mod, _clone_inputs((sample, sample,),)) ``` I have removed this functionality, as I don't think it provides huge value. Things that look locally for names will still work, so e.g. inputs, intermediate variables, and the like will be named correctly. 2. Previously, our input cloning for trace checking didn't do a memoized deep copy. So: ``` _clone_inputs((sample, sample, sample)) ``` produces a tuple with three non-aliased tensors. That's wrong! Use copy.deepcopy with a memoization argument to fix this. [ghstack-poisoned]
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Fixes #23993. There are two fixes here: 1. Previously our name lookup function for the tracer was looking in f.globals for names. For example: ``` sample = torch.ones(1) traced = torch.jit.trace(my_mod, ((sample, sample,),)) # produces a graph with something like # %sample, %sample = prim::TupleUnpack(%input) ``` This is not great if you are, e.g. trace checking, because a non-local bit of interpreter state is affected the graph produced: ``` traced = torch.jit.trace(my_mod, _clone_inputs((sample, sample,),)) # produces a graph with something like # %0, %1 = prim::TupleUnpack(%input) ``` I have removed this functionality, as I don't think it provides huge value. Things that look locally for names will still work, so e.g. inputs, intermediate variables, and the like will be named correctly. 2. Previously, our input cloning for trace checking didn't do a memoized deep copy. So: ``` _clone_inputs((sample, sample, sample)) ``` produces a tuple with three non-aliased tensors. That's wrong! Use copy.deepcopy with a memoization argument to fix this. [ghstack-poisoned]
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Fixes #23993. There are two fixes here: 1. Previously our name lookup function for the tracer was looking in f.globals for names. For example: ``` sample = torch.ones(1) traced = torch.jit.trace(my_mod, ((sample, sample,),)) ``` This is not great if you are, e.g. trace checking, because a non-local bit of interpreter state is affected the graph produced: ``` traced = torch.jit.trace(my_mod, _clone_inputs((sample, sample,),)) ``` I have removed this functionality, as I don't think it provides huge value. Things that look locally for names will still work, so e.g. inputs, intermediate variables, and the like will be named correctly. 2. Previously, our input cloning for trace checking didn't do a memoized deep copy. So: ``` _clone_inputs((sample, sample, sample)) ``` produces a tuple with three non-aliased tensors. That's wrong! Use copy.deepcopy with a memoization argument to fix this. ghstack-source-id: 20428a0 Pull Request resolved: #37464
💊 Build failures summary and remediationsAs of commit 3b24b5b (more details on the Dr. CI page):
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. This comment has been revised 2 times. |
zdevito
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Apr 29, 2020
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Stack from ghstack:
Fixes #23993.
There are two fixes here:
f.globals for names. For example:
This is not great if you are, e.g. trace checking, because a non-local
bit of interpreter state is affected the graph produced:
I have removed this functionality, as I don't think it provides huge
value. Things that look locally for names will still work, so e.g.
inputs, intermediate variables, and the like will be named correctly.
deep copy. So:
produces a tuple with three non-aliased tensors. That's wrong! Use
copy.deepcopy with a memoization argument to fix this.
Differential Revision: D21297549