propagate .meta info when replacing subgraphs in fx#87255
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bdhirsh wants to merge 13 commits intogh/bdhirsh/329/basefrom
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propagate .meta info when replacing subgraphs in fx#87255bdhirsh wants to merge 13 commits intogh/bdhirsh/329/basefrom
bdhirsh wants to merge 13 commits intogh/bdhirsh/329/basefrom
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🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/87255
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Fixes pytorch/torchdynamo#1708 Our FX subgraph partitioner works by taking all of the original output nodes from a subgraph, and replacing it with a new `call_module` node in the graph. If the original subgraph outputs had fake tensors and other metadata stored in their `.meta` attribute though, then this information was getting lost when we spliced in the subgraph. Losing metadata on an FX graph also seems like an easy trap to fall into, so I'm wondering if there are any better guardrails that we can add. I ended up fixing in this PR by adding an optional kwarg to propagate meta info directly in the `fx.Node.replace_all_uses_with`, just because propagating metadata seems like a pretty core thing. [ghstack-poisoned]
wconstab
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Oct 19, 2022
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seems like a good change, but i wonder how we can tell if we're still missing cases, should the default be true?
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Yeah - I was pretty torn about whether or not copying metadata should be explicit vs. implicit. Right now it's explicit - you specify I wasn't sure about making it implicit, because I guess I could flip it so that we copy metadata by default, but only if there's no metadata field set on the new node (and if there is, maybe we should just silently not copy metadata instead of erroring). I'm open to either / other opinions though. |
bdhirsh
commented
Oct 21, 2022
| @@ -468,7 +468,9 @@ def format_node(self, | |||
| @compatibility(is_backward_compatible=True) | |||
| def replace_all_uses_with(self, | |||
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this is an FX public API change (it shouldn't be BC breaking though) - so I'll have to update the tests.
Who else should review public FX API changes? @SherlockNoMad ?
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This is not a BC breaking change. So it should be fine.
SherlockNoMad
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Oct 21, 2022
Fixes pytorch/torchdynamo#1708 Our FX subgraph partitioner works by taking all of the original output nodes from a subgraph, and replacing it with a new `call_module` node in the graph. If the original subgraph outputs had fake tensors and other metadata stored in their `.meta` attribute though, then this information was getting lost when we spliced in the subgraph. Losing metadata on an FX graph also seems like an easy trap to fall into, so I'm wondering if there are any better guardrails that we can add. I ended up fixing in this PR by adding an optional kwarg to propagate meta info directly in the `fx.Node.replace_all_uses_with`, just because propagating metadata seems like a pretty core thing. [ghstack-poisoned]
Fixes pytorch/torchdynamo#1708 Our FX subgraph partitioner works by taking all of the original output nodes from a subgraph, and replacing it with a new `call_module` node in the graph. If the original subgraph outputs had fake tensors and other metadata stored in their `.meta` attribute though, then this information was getting lost when we spliced in the subgraph. Losing metadata on an FX graph also seems like an easy trap to fall into, so I'm wondering if there are any better guardrails that we can add. I ended up fixing in this PR by adding an optional kwarg to propagate meta info directly in the `fx.Node.replace_all_uses_with`, just because propagating metadata seems like a pretty core thing. [ghstack-poisoned]
This was referenced Oct 24, 2022
Fixes pytorch/torchdynamo#1708 Our FX subgraph partitioner works by taking all of the original output nodes from a subgraph, and replacing it with a new `call_module` node in the graph. If the original subgraph outputs had fake tensors and other metadata stored in their `.meta` attribute though, then this information was getting lost when we spliced in the subgraph. Losing metadata on an FX graph also seems like an easy trap to fall into, so I'm wondering if there are any better guardrails that we can add. I ended up fixing in this PR by adding an optional kwarg to propagate meta info directly in the `fx.Node.replace_all_uses_with`, just because propagating metadata seems like a pretty core thing. [ghstack-poisoned]
Fixes pytorch/torchdynamo#1708 Our FX subgraph partitioner works by taking all of the original output nodes from a subgraph, and replacing it with a new `call_module` node in the graph. If the original subgraph outputs had fake tensors and other metadata stored in their `.meta` attribute though, then this information was getting lost when we spliced in the subgraph. Losing metadata on an FX graph also seems like an easy trap to fall into, so I'm wondering if there are any better guardrails that we can add. I ended up fixing in this PR by adding an optional kwarg to propagate meta info directly in the `fx.Node.replace_all_uses_with`, just because propagating metadata seems like a pretty core thing. [ghstack-poisoned]
Fixes pytorch/torchdynamo#1708 Our FX subgraph partitioner works by taking all of the original output nodes from a subgraph, and replacing it with a new `call_module` node in the graph. If the original subgraph outputs had fake tensors and other metadata stored in their `.meta` attribute though, then this information was getting lost when we spliced in the subgraph. Losing metadata on an FX graph also seems like an easy trap to fall into, so I'm wondering if there are any better guardrails that we can add. I ended up fixing in this PR by adding an optional kwarg to propagate meta info directly in the `fx.Node.replace_all_uses_with`, just because propagating metadata seems like a pretty core thing. [ghstack-poisoned]
Fixes pytorch/torchdynamo#1708 Our FX subgraph partitioner works by taking all of the original output nodes from a subgraph, and replacing it with a new `call_module` node in the graph. If the original subgraph outputs had fake tensors and other metadata stored in their `.meta` attribute though, then this information was getting lost when we spliced in the subgraph. Losing metadata on an FX graph also seems like an easy trap to fall into, so I'm wondering if there are any better guardrails that we can add. I ended up fixing in this PR by adding an optional kwarg to propagate meta info directly in the `fx.Node.replace_all_uses_with`, just because propagating metadata seems like a pretty core thing. [ghstack-poisoned]
Fixes pytorch/torchdynamo#1708 Our FX subgraph partitioner works by taking all of the original output nodes from a subgraph, and replacing it with a new `call_module` node in the graph. If the original subgraph outputs had fake tensors and other metadata stored in their `.meta` attribute though, then this information was getting lost when we spliced in the subgraph. Losing metadata on an FX graph also seems like an easy trap to fall into, so I'm wondering if there are any better guardrails that we can add. I ended up fixing in this PR by adding an optional kwarg to propagate meta info directly in the `fx.Node.replace_all_uses_with`, just because propagating metadata seems like a pretty core thing. [ghstack-poisoned]
This was referenced Nov 1, 2022
Fixes pytorch/torchdynamo#1708 Our FX subgraph partitioner works by taking all of the original output nodes from a subgraph, and replacing it with a new `call_module` node in the graph. If the original subgraph outputs had fake tensors and other metadata stored in their `.meta` attribute though, then this information was getting lost when we spliced in the subgraph. Losing metadata on an FX graph also seems like an easy trap to fall into, so I'm wondering if there are any better guardrails that we can add. I ended up fixing in this PR by adding an optional kwarg to propagate meta info directly in the `fx.Node.replace_all_uses_with`, just because propagating metadata seems like a pretty core thing. [ghstack-poisoned]
Fixes pytorch/torchdynamo#1708 Our FX subgraph partitioner works by taking all of the original output nodes from a subgraph, and replacing it with a new `call_module` node in the graph. If the original subgraph outputs had fake tensors and other metadata stored in their `.meta` attribute though, then this information was getting lost when we spliced in the subgraph. Losing metadata on an FX graph also seems like an easy trap to fall into, so I'm wondering if there are any better guardrails that we can add. I ended up fixing in this PR by adding an optional kwarg to propagate meta info directly in the `fx.Node.replace_all_uses_with`, just because propagating metadata seems like a pretty core thing. [ghstack-poisoned]
Fixes pytorch/torchdynamo#1708 Our FX subgraph partitioner works by taking all of the original output nodes from a subgraph, and replacing it with a new `call_module` node in the graph. If the original subgraph outputs had fake tensors and other metadata stored in their `.meta` attribute though, then this information was getting lost when we spliced in the subgraph. Losing metadata on an FX graph also seems like an easy trap to fall into, so I'm wondering if there are any better guardrails that we can add. I ended up fixing in this PR by adding an optional kwarg to propagate meta info directly in the `fx.Node.replace_all_uses_with`, just because propagating metadata seems like a pretty core thing. [ghstack-poisoned]
Fixes pytorch/torchdynamo#1708 Our FX subgraph partitioner works by taking all of the original output nodes from a subgraph, and replacing it with a new `call_module` node in the graph. If the original subgraph outputs had fake tensors and other metadata stored in their `.meta` attribute though, then this information was getting lost when we spliced in the subgraph. Losing metadata on an FX graph also seems like an easy trap to fall into, so I'm wondering if there are any better guardrails that we can add. I ended up fixing in this PR by adding an optional kwarg to propagate meta info directly in the `fx.Node.replace_all_uses_with`, just because propagating metadata seems like a pretty core thing. [ghstack-poisoned]
kulinseth
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Nov 5, 2022
Fixes pytorch/torchdynamo#1708 Our FX subgraph partitioner works by taking all of the original output nodes from a subgraph, and replacing it with a new `call_module` node in the graph. If the original subgraph outputs had fake tensors and other metadata stored in their `.meta` attribute though, then this information was getting lost when we spliced in the subgraph. Losing metadata on an FX graph also seems like an easy trap to fall into, so I'm wondering if there are any better guardrails that we can add. I ended up fixing in this PR by adding an optional kwarg to propagate meta info directly in the `fx.Node.replace_all_uses_with`, just because propagating metadata seems like a pretty core thing. Pull Request resolved: pytorch#87255 Approved by: https://github.com/wconstab, https://github.com/SherlockNoMad
kulinseth
pushed a commit
to kulinseth/pytorch
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Dec 10, 2022
Fixes pytorch/torchdynamo#1708 Our FX subgraph partitioner works by taking all of the original output nodes from a subgraph, and replacing it with a new `call_module` node in the graph. If the original subgraph outputs had fake tensors and other metadata stored in their `.meta` attribute though, then this information was getting lost when we spliced in the subgraph. Losing metadata on an FX graph also seems like an easy trap to fall into, so I'm wondering if there are any better guardrails that we can add. I ended up fixing in this PR by adding an optional kwarg to propagate meta info directly in the `fx.Node.replace_all_uses_with`, just because propagating metadata seems like a pretty core thing. Pull Request resolved: pytorch#87255 Approved by: https://github.com/wconstab, https://github.com/SherlockNoMad
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Fixes pytorch/torchdynamo#1708
Our FX subgraph partitioner works by taking all of the original output nodes from a subgraph, and replacing it with a new
call_modulenode in the graph.If the original subgraph outputs had fake tensors and other metadata stored in their
.metaattribute though, then this information was getting lost when we spliced in the subgraph.Losing metadata on an FX graph also seems like an easy trap to fall into, so I'm wondering if there are any better guardrails that we can add. I ended up fixing in this PR by adding an optional kwarg to propagate meta info directly in the
fx.Node.replace_all_uses_with, just because propagating metadata seems like a pretty core thing.Stack from ghstack (oldest at bottom):