Fix collection inputs to postproc modules #2733
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Summary:
Postproc modules with collection inputs (list or dict) with non-static (derived from input or other postproc) elements were not properly rewritten - input elements remained fx.Nodes even during the actual model forward (i.e. outside rewrite, during pipeline execution)
To illustrate:
```
def forward(model_input: ...) -> ...:
modified_input = model_input.float_features + 1
sharded_module_input = self.postproc(model_input, modified_input) # works
sharded_module_input = self.postproc(model_input, [123]) # works
sharded_module_input = self.postproc(model_input, [torch.ones_like(modified_input)]) # fails
sharded_module_input = self.postproc(model_input, [modified_input]) # fails
sharded_module_input = self.postproc(model_input, { 'a': 123 }) # works
sharded_module_input = self.postproc(model_input, { 'a': torch.ones_like(modified_input) }) # fails
sharded_module_input = self.postproc(model_input, { 'a': modified_input }) # fails
return self.ebc(sharded_module_input)
```
Differential Revision: D69292525
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…postproc modules (meta-pytorch#2733) Summary: Postproc modules with collection inputs (list or dict) with non-static (derived from input or other postproc) elements were not properly rewritten - input elements remained fx.Nodes even during the actual model forward (i.e. outside rewrite, during pipeline execution) To illustrate: ``` def forward(model_input: ...) -> ...: modified_input = model_input.float_features + 1 sharded_module_input = self.postproc(model_input, modified_input) # works sharded_module_input = self.postproc(model_input, [123]) # works sharded_module_input = self.postproc(model_input, [torch.ones_like(modified_input)]) # fails sharded_module_input = self.postproc(model_input, [modified_input]) # fails sharded_module_input = self.postproc(model_input, { 'a': 123 }) # works sharded_module_input = self.postproc(model_input, { 'a': torch.ones_like(modified_input) }) # fails sharded_module_input = self.postproc(model_input, { 'a': modified_input }) # fails return self.ebc(sharded_module_input) ``` Differential Revision: D69292525
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…stproc modules (meta-pytorch#2733) Summary: Postproc modules with collection inputs (list or dict) with non-static (derived from input or other postproc) elements were not properly rewritten - input elements remained fx.Nodes even during the actual model forward (i.e. outside rewrite, during pipeline execution) To illustrate: ``` def forward(model_input: ...) -> ...: modified_input = model_input.float_features + 1 sharded_module_input = self.postproc(model_input, modified_input) # works sharded_module_input = self.postproc(model_input, [123]) # works sharded_module_input = self.postproc(model_input, [torch.ones_like(modified_input)]) # fails sharded_module_input = self.postproc(model_input, [modified_input]) # fails sharded_module_input = self.postproc(model_input, { 'a': 123 }) # works sharded_module_input = self.postproc(model_input, { 'a': torch.ones_like(modified_input) }) # fails sharded_module_input = self.postproc(model_input, { 'a': modified_input }) # fails return self.ebc(sharded_module_input) ``` Differential Revision: D69292525
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…stproc modules (meta-pytorch#2733) Summary: Postproc modules with collection inputs (list or dict) with non-static (derived from input or other postproc) elements were not properly rewritten - input elements remained fx.Nodes even during the actual model forward (i.e. outside rewrite, during pipeline execution) To illustrate: ``` def forward(model_input: ...) -> ...: modified_input = model_input.float_features + 1 sharded_module_input = self.postproc(model_input, modified_input) # works sharded_module_input = self.postproc(model_input, [123]) # works sharded_module_input = self.postproc(model_input, [torch.ones_like(modified_input)]) # fails sharded_module_input = self.postproc(model_input, [modified_input]) # fails sharded_module_input = self.postproc(model_input, { 'a': 123 }) # works sharded_module_input = self.postproc(model_input, { 'a': torch.ones_like(modified_input) }) # fails sharded_module_input = self.postproc(model_input, { 'a': modified_input }) # fails return self.ebc(sharded_module_input) ``` Differential Revision: D69292525
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…stproc modules (meta-pytorch#2733) Summary: Postproc modules with collection inputs (list or dict) with non-static (derived from input or other postproc) elements were not properly rewritten - input elements remained fx.Nodes even during the actual model forward (i.e. outside rewrite, during pipeline execution) To illustrate: ``` def forward(model_input: ...) -> ...: modified_input = model_input.float_features + 1 sharded_module_input = self.postproc(model_input, modified_input) # works sharded_module_input = self.postproc(model_input, [123]) # works sharded_module_input = self.postproc(model_input, [torch.ones_like(modified_input)]) # fails sharded_module_input = self.postproc(model_input, [modified_input]) # fails sharded_module_input = self.postproc(model_input, { 'a': 123 }) # works sharded_module_input = self.postproc(model_input, { 'a': torch.ones_like(modified_input) }) # fails sharded_module_input = self.postproc(model_input, { 'a': modified_input }) # fails return self.ebc(sharded_module_input) ``` Differential Revision: D69292525
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…stproc modules (meta-pytorch#2733) Summary: Postproc modules with collection inputs (list or dict) with non-static (derived from input or other postproc) elements were not properly rewritten - input elements remained fx.Nodes even during the actual model forward (i.e. outside rewrite, during pipeline execution) To illustrate: ``` def forward(model_input: ...) -> ...: modified_input = model_input.float_features + 1 sharded_module_input = self.postproc(model_input, modified_input) # works sharded_module_input = self.postproc(model_input, [123]) # works sharded_module_input = self.postproc(model_input, [torch.ones_like(modified_input)]) # fails sharded_module_input = self.postproc(model_input, [modified_input]) # fails sharded_module_input = self.postproc(model_input, { 'a': 123 }) # works sharded_module_input = self.postproc(model_input, { 'a': torch.ones_like(modified_input) }) # fails sharded_module_input = self.postproc(model_input, { 'a': modified_input }) # fails return self.ebc(sharded_module_input) ``` Differential Revision: D69292525
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…stproc modules (meta-pytorch#2733) Summary: Postproc modules with collection inputs (list or dict) with non-static (derived from input or other postproc) elements were not properly rewritten - input elements remained fx.Nodes even during the actual model forward (i.e. outside rewrite, during pipeline execution) To illustrate: ``` def forward(model_input: ...) -> ...: modified_input = model_input.float_features + 1 sharded_module_input = self.postproc(model_input, modified_input) # works sharded_module_input = self.postproc(model_input, [123]) # works sharded_module_input = self.postproc(model_input, [torch.ones_like(modified_input)]) # fails sharded_module_input = self.postproc(model_input, [modified_input]) # fails sharded_module_input = self.postproc(model_input, { 'a': 123 }) # works sharded_module_input = self.postproc(model_input, { 'a': torch.ones_like(modified_input) }) # fails sharded_module_input = self.postproc(model_input, { 'a': modified_input }) # fails return self.ebc(sharded_module_input) ``` Differential Revision: D69292525
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…stproc modules (meta-pytorch#2733) Summary: Postproc modules with collection inputs (list or dict) with non-static (derived from input or other postproc) elements were not properly rewritten - input elements remained fx.Nodes even during the actual model forward (i.e. outside rewrite, during pipeline execution) To illustrate: ``` def forward(model_input: ...) -> ...: modified_input = model_input.float_features + 1 sharded_module_input = self.postproc(model_input, modified_input) # works sharded_module_input = self.postproc(model_input, [123]) # works sharded_module_input = self.postproc(model_input, [torch.ones_like(modified_input)]) # fails sharded_module_input = self.postproc(model_input, [modified_input]) # fails sharded_module_input = self.postproc(model_input, { 'a': 123 }) # works sharded_module_input = self.postproc(model_input, { 'a': torch.ones_like(modified_input) }) # fails sharded_module_input = self.postproc(model_input, { 'a': modified_input }) # fails return self.ebc(sharded_module_input) ``` Differential Revision: D69292525
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…stproc modules (meta-pytorch#2733) Summary: Postproc modules with collection inputs (list or dict) with non-static (derived from input or other postproc) elements were not properly rewritten - input elements remained fx.Nodes even during the actual model forward (i.e. outside rewrite, during pipeline execution) To illustrate: ``` def forward(model_input: ...) -> ...: modified_input = model_input.float_features + 1 sharded_module_input = self.postproc(model_input, modified_input) # works sharded_module_input = self.postproc(model_input, [123]) # works sharded_module_input = self.postproc(model_input, [torch.ones_like(modified_input)]) # fails sharded_module_input = self.postproc(model_input, [modified_input]) # fails sharded_module_input = self.postproc(model_input, { 'a': 123 }) # works sharded_module_input = self.postproc(model_input, { 'a': torch.ones_like(modified_input) }) # fails sharded_module_input = self.postproc(model_input, { 'a': modified_input }) # fails return self.ebc(sharded_module_input) ``` Differential Revision: D69292525
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…stproc modules (meta-pytorch#2733) Summary: Postproc modules with collection inputs (list or dict) with non-static (derived from input or other postproc) elements were not properly rewritten - input elements remained fx.Nodes even during the actual model forward (i.e. outside rewrite, during pipeline execution) To illustrate: ``` def forward(model_input: ...) -> ...: modified_input = model_input.float_features + 1 sharded_module_input = self.postproc(model_input, modified_input) # works sharded_module_input = self.postproc(model_input, [123]) # works sharded_module_input = self.postproc(model_input, [torch.ones_like(modified_input)]) # fails sharded_module_input = self.postproc(model_input, [modified_input]) # fails sharded_module_input = self.postproc(model_input, { 'a': 123 }) # works sharded_module_input = self.postproc(model_input, { 'a': torch.ones_like(modified_input) }) # fails sharded_module_input = self.postproc(model_input, { 'a': modified_input }) # fails return self.ebc(sharded_module_input) ``` Differential Revision: D69292525
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…stproc modules (meta-pytorch#2733) Summary: Postproc modules with collection inputs (list or dict) with non-static (derived from input or other postproc) elements were not properly rewritten - input elements remained fx.Nodes even during the actual model forward (i.e. outside rewrite, during pipeline execution) To illustrate: ``` def forward(model_input: ...) -> ...: modified_input = model_input.float_features + 1 sharded_module_input = self.postproc(model_input, modified_input) # works sharded_module_input = self.postproc(model_input, [123]) # works sharded_module_input = self.postproc(model_input, [torch.ones_like(modified_input)]) # fails sharded_module_input = self.postproc(model_input, [modified_input]) # fails sharded_module_input = self.postproc(model_input, { 'a': 123 }) # works sharded_module_input = self.postproc(model_input, { 'a': torch.ones_like(modified_input) }) # fails sharded_module_input = self.postproc(model_input, { 'a': modified_input }) # fails return self.ebc(sharded_module_input) ``` Differential Revision: D69292525
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…stproc modules (meta-pytorch#2733) Summary: Postproc modules with collection inputs (list or dict) with non-static (derived from input or other postproc) elements were not properly rewritten - input elements remained fx.Nodes even during the actual model forward (i.e. outside rewrite, during pipeline execution) To illustrate: ``` def forward(model_input: ...) -> ...: modified_input = model_input.float_features + 1 sharded_module_input = self.postproc(model_input, modified_input) # works sharded_module_input = self.postproc(model_input, [123]) # works sharded_module_input = self.postproc(model_input, [torch.ones_like(modified_input)]) # fails sharded_module_input = self.postproc(model_input, [modified_input]) # fails sharded_module_input = self.postproc(model_input, { 'a': 123 }) # works sharded_module_input = self.postproc(model_input, { 'a': torch.ones_like(modified_input) }) # fails sharded_module_input = self.postproc(model_input, { 'a': modified_input }) # fails return self.ebc(sharded_module_input) ``` Differential Revision: D69292525
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…stproc modules (meta-pytorch#2733) Summary: Postproc modules with collection inputs (list or dict) with non-static (derived from input or other postproc) elements were not properly rewritten - input elements remained fx.Nodes even during the actual model forward (i.e. outside rewrite, during pipeline execution) To illustrate: ``` def forward(model_input: ...) -> ...: modified_input = model_input.float_features + 1 sharded_module_input = self.postproc(model_input, modified_input) # works sharded_module_input = self.postproc(model_input, [123]) # works sharded_module_input = self.postproc(model_input, [torch.ones_like(modified_input)]) # fails sharded_module_input = self.postproc(model_input, [modified_input]) # fails sharded_module_input = self.postproc(model_input, { 'a': 123 }) # works sharded_module_input = self.postproc(model_input, { 'a': torch.ones_like(modified_input) }) # fails sharded_module_input = self.postproc(model_input, { 'a': modified_input }) # fails return self.ebc(sharded_module_input) ``` Differential Revision: D69292525
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…stproc modules (meta-pytorch#2733) Summary: Postproc modules with collection inputs (list or dict) with non-static (derived from input or other postproc) elements were not properly rewritten - input elements remained fx.Nodes even during the actual model forward (i.e. outside rewrite, during pipeline execution) To illustrate: ``` def forward(model_input: ...) -> ...: modified_input = model_input.float_features + 1 sharded_module_input = self.postproc(model_input, modified_input) # works sharded_module_input = self.postproc(model_input, [123]) # works sharded_module_input = self.postproc(model_input, [torch.ones_like(modified_input)]) # fails sharded_module_input = self.postproc(model_input, [modified_input]) # fails sharded_module_input = self.postproc(model_input, { 'a': 123 }) # works sharded_module_input = self.postproc(model_input, { 'a': torch.ones_like(modified_input) }) # fails sharded_module_input = self.postproc(model_input, { 'a': modified_input }) # fails return self.ebc(sharded_module_input) ``` Differential Revision: D69292525
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…stproc modules (meta-pytorch#2733) Summary: Postproc modules with collection inputs (list or dict) with non-static (derived from input or other postproc) elements were not properly rewritten - input elements remained fx.Nodes even during the actual model forward (i.e. outside rewrite, during pipeline execution) To illustrate: ``` def forward(model_input: ...) -> ...: modified_input = model_input.float_features + 1 sharded_module_input = self.postproc(model_input, modified_input) # works sharded_module_input = self.postproc(model_input, [123]) # works sharded_module_input = self.postproc(model_input, [torch.ones_like(modified_input)]) # fails sharded_module_input = self.postproc(model_input, [modified_input]) # fails sharded_module_input = self.postproc(model_input, { 'a': 123 }) # works sharded_module_input = self.postproc(model_input, { 'a': torch.ones_like(modified_input) }) # fails sharded_module_input = self.postproc(model_input, { 'a': modified_input }) # fails return self.ebc(sharded_module_input) ``` Differential Revision: D69292525
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…stproc modules (meta-pytorch#2733) Summary: Postproc modules with collection inputs (list or dict) with non-static (derived from input or other postproc) elements were not properly rewritten - input elements remained fx.Nodes even during the actual model forward (i.e. outside rewrite, during pipeline execution) To illustrate: ``` def forward(model_input: ...) -> ...: modified_input = model_input.float_features + 1 sharded_module_input = self.postproc(model_input, modified_input) # works sharded_module_input = self.postproc(model_input, [123]) # works sharded_module_input = self.postproc(model_input, [torch.ones_like(modified_input)]) # fails sharded_module_input = self.postproc(model_input, [modified_input]) # fails sharded_module_input = self.postproc(model_input, { 'a': 123 }) # works sharded_module_input = self.postproc(model_input, { 'a': torch.ones_like(modified_input) }) # fails sharded_module_input = self.postproc(model_input, { 'a': modified_input }) # fails return self.ebc(sharded_module_input) ``` Differential Revision: D69292525
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…stproc modules (meta-pytorch#2733) Summary: Postproc modules with collection inputs (list or dict) with non-static (derived from input or other postproc) elements were not properly rewritten - input elements remained fx.Nodes even during the actual model forward (i.e. outside rewrite, during pipeline execution) To illustrate: ``` def forward(model_input: ...) -> ...: modified_input = model_input.float_features + 1 sharded_module_input = self.postproc(model_input, modified_input) # works sharded_module_input = self.postproc(model_input, [123]) # works sharded_module_input = self.postproc(model_input, [torch.ones_like(modified_input)]) # fails sharded_module_input = self.postproc(model_input, [modified_input]) # fails sharded_module_input = self.postproc(model_input, { 'a': 123 }) # works sharded_module_input = self.postproc(model_input, { 'a': torch.ones_like(modified_input) }) # fails sharded_module_input = self.postproc(model_input, { 'a': modified_input }) # fails return self.ebc(sharded_module_input) ``` Differential Revision: D69292525
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…stproc modules (meta-pytorch#2733) Summary: Postproc modules with collection inputs (list or dict) with non-static (derived from input or other postproc) elements were not properly rewritten - input elements remained fx.Nodes even during the actual model forward (i.e. outside rewrite, during pipeline execution) To illustrate: ``` def forward(model_input: ...) -> ...: modified_input = model_input.float_features + 1 sharded_module_input = self.postproc(model_input, modified_input) # works sharded_module_input = self.postproc(model_input, [123]) # works sharded_module_input = self.postproc(model_input, [torch.ones_like(modified_input)]) # fails sharded_module_input = self.postproc(model_input, [modified_input]) # fails sharded_module_input = self.postproc(model_input, { 'a': 123 }) # works sharded_module_input = self.postproc(model_input, { 'a': torch.ones_like(modified_input) }) # fails sharded_module_input = self.postproc(model_input, { 'a': modified_input }) # fails return self.ebc(sharded_module_input) ``` Differential Revision: D69292525
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…stproc modules (meta-pytorch#2733) Summary: Postproc modules with collection inputs (list or dict) with non-static (derived from input or other postproc) elements were not properly rewritten - input elements remained fx.Nodes even during the actual model forward (i.e. outside rewrite, during pipeline execution) To illustrate: ``` def forward(model_input: ...) -> ...: modified_input = model_input.float_features + 1 sharded_module_input = self.postproc(model_input, modified_input) # works sharded_module_input = self.postproc(model_input, [123]) # works sharded_module_input = self.postproc(model_input, [torch.ones_like(modified_input)]) # fails sharded_module_input = self.postproc(model_input, [modified_input]) # fails sharded_module_input = self.postproc(model_input, { 'a': 123 }) # works sharded_module_input = self.postproc(model_input, { 'a': torch.ones_like(modified_input) }) # fails sharded_module_input = self.postproc(model_input, { 'a': modified_input }) # fails return self.ebc(sharded_module_input) ``` Differential Revision: D69292525
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…stproc modules (meta-pytorch#2733) Summary: Postproc modules with collection inputs (list or dict) with non-static (derived from input or other postproc) elements were not properly rewritten - input elements remained fx.Nodes even during the actual model forward (i.e. outside rewrite, during pipeline execution) To illustrate: ``` def forward(model_input: ...) -> ...: modified_input = model_input.float_features + 1 sharded_module_input = self.postproc(model_input, modified_input) # works sharded_module_input = self.postproc(model_input, [123]) # works sharded_module_input = self.postproc(model_input, [torch.ones_like(modified_input)]) # fails sharded_module_input = self.postproc(model_input, [modified_input]) # fails sharded_module_input = self.postproc(model_input, { 'a': 123 }) # works sharded_module_input = self.postproc(model_input, { 'a': torch.ones_like(modified_input) }) # fails sharded_module_input = self.postproc(model_input, { 'a': modified_input }) # fails return self.ebc(sharded_module_input) ``` Differential Revision: D69292525
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…stproc modules (meta-pytorch#2733) Summary: Postproc modules with collection inputs (list or dict) with non-static (derived from input or other postproc) elements were not properly rewritten - input elements remained fx.Nodes even during the actual model forward (i.e. outside rewrite, during pipeline execution) To illustrate: ``` def forward(model_input: ...) -> ...: modified_input = model_input.float_features + 1 sharded_module_input = self.postproc(model_input, modified_input) # works sharded_module_input = self.postproc(model_input, [123]) # works sharded_module_input = self.postproc(model_input, [torch.ones_like(modified_input)]) # fails sharded_module_input = self.postproc(model_input, [modified_input]) # fails sharded_module_input = self.postproc(model_input, { 'a': 123 }) # works sharded_module_input = self.postproc(model_input, { 'a': torch.ones_like(modified_input) }) # fails sharded_module_input = self.postproc(model_input, { 'a': modified_input }) # fails return self.ebc(sharded_module_input) ``` Differential Revision: D69292525
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…stproc modules (meta-pytorch#2733) Summary: Postproc modules with collection inputs (list or dict) with non-static (derived from input or other postproc) elements were not properly rewritten - input elements remained fx.Nodes even during the actual model forward (i.e. outside rewrite, during pipeline execution) To illustrate: ``` def forward(model_input: ...) -> ...: modified_input = model_input.float_features + 1 sharded_module_input = self.postproc(model_input, modified_input) # works sharded_module_input = self.postproc(model_input, [123]) # works sharded_module_input = self.postproc(model_input, [torch.ones_like(modified_input)]) # fails sharded_module_input = self.postproc(model_input, [modified_input]) # fails sharded_module_input = self.postproc(model_input, { 'a': 123 }) # works sharded_module_input = self.postproc(model_input, { 'a': torch.ones_like(modified_input) }) # fails sharded_module_input = self.postproc(model_input, { 'a': modified_input }) # fails return self.ebc(sharded_module_input) ``` Differential Revision: D69292525
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Feb 20, 2025
…stproc modules (meta-pytorch#2733) Summary: Postproc modules with collection inputs (list or dict) with non-static (derived from input or other postproc) elements were not properly rewritten - input elements remained fx.Nodes even during the actual model forward (i.e. outside rewrite, during pipeline execution) To illustrate: ``` def forward(model_input: ...) -> ...: modified_input = model_input.float_features + 1 sharded_module_input = self.postproc(model_input, modified_input) # works sharded_module_input = self.postproc(model_input, [123]) # works sharded_module_input = self.postproc(model_input, [torch.ones_like(modified_input)]) # fails sharded_module_input = self.postproc(model_input, [modified_input]) # fails sharded_module_input = self.postproc(model_input, { 'a': 123 }) # works sharded_module_input = self.postproc(model_input, { 'a': torch.ones_like(modified_input) }) # fails sharded_module_input = self.postproc(model_input, { 'a': modified_input }) # fails return self.ebc(sharded_module_input) ``` Reviewed By: sarckk Differential Revision: D69292525
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…stproc modules (meta-pytorch#2733) Summary: Postproc modules with collection inputs (list or dict) with non-static (derived from input or other postproc) elements were not properly rewritten - input elements remained fx.Nodes even during the actual model forward (i.e. outside rewrite, during pipeline execution) To illustrate: ``` def forward(model_input: ...) -> ...: modified_input = model_input.float_features + 1 sharded_module_input = self.postproc(model_input, modified_input) # works sharded_module_input = self.postproc(model_input, [123]) # works sharded_module_input = self.postproc(model_input, [torch.ones_like(modified_input)]) # fails sharded_module_input = self.postproc(model_input, [modified_input]) # fails sharded_module_input = self.postproc(model_input, { 'a': 123 }) # works sharded_module_input = self.postproc(model_input, { 'a': torch.ones_like(modified_input) }) # fails sharded_module_input = self.postproc(model_input, { 'a': modified_input }) # fails return self.ebc(sharded_module_input) ``` Reviewed By: sarckk Differential Revision: D69292525
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Feb 20, 2025
…stproc modules (meta-pytorch#2733) Summary: Postproc modules with collection inputs (list or dict) with non-static (derived from input or other postproc) elements were not properly rewritten - input elements remained fx.Nodes even during the actual model forward (i.e. outside rewrite, during pipeline execution) To illustrate: ``` def forward(model_input: ...) -> ...: modified_input = model_input.float_features + 1 sharded_module_input = self.postproc(model_input, modified_input) # works sharded_module_input = self.postproc(model_input, [123]) # works sharded_module_input = self.postproc(model_input, [torch.ones_like(modified_input)]) # fails sharded_module_input = self.postproc(model_input, [modified_input]) # fails sharded_module_input = self.postproc(model_input, { 'a': 123 }) # works sharded_module_input = self.postproc(model_input, { 'a': torch.ones_like(modified_input) }) # fails sharded_module_input = self.postproc(model_input, { 'a': modified_input }) # fails return self.ebc(sharded_module_input) ``` Reviewed By: sarckk Differential Revision: D69292525
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che-sh
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Feb 20, 2025
…stproc modules (meta-pytorch#2733) Summary: Postproc modules with collection inputs (list or dict) with non-static (derived from input or other postproc) elements were not properly rewritten - input elements remained fx.Nodes even during the actual model forward (i.e. outside rewrite, during pipeline execution) To illustrate: ``` def forward(model_input: ...) -> ...: modified_input = model_input.float_features + 1 sharded_module_input = self.postproc(model_input, modified_input) # works sharded_module_input = self.postproc(model_input, [123]) # works sharded_module_input = self.postproc(model_input, [torch.ones_like(modified_input)]) # fails sharded_module_input = self.postproc(model_input, [modified_input]) # fails sharded_module_input = self.postproc(model_input, { 'a': 123 }) # works sharded_module_input = self.postproc(model_input, { 'a': torch.ones_like(modified_input) }) # fails sharded_module_input = self.postproc(model_input, { 'a': modified_input }) # fails return self.ebc(sharded_module_input) ``` Reviewed By: sarckk Differential Revision: D69292525
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Feb 20, 2025
…stproc modules (meta-pytorch#2733) Summary: Postproc modules with collection inputs (list or dict) with non-static (derived from input or other postproc) elements were not properly rewritten - input elements remained fx.Nodes even during the actual model forward (i.e. outside rewrite, during pipeline execution) To illustrate: ``` def forward(model_input: ...) -> ...: modified_input = model_input.float_features + 1 sharded_module_input = self.postproc(model_input, modified_input) # works sharded_module_input = self.postproc(model_input, [123]) # works sharded_module_input = self.postproc(model_input, [torch.ones_like(modified_input)]) # fails sharded_module_input = self.postproc(model_input, [modified_input]) # fails sharded_module_input = self.postproc(model_input, { 'a': 123 }) # works sharded_module_input = self.postproc(model_input, { 'a': torch.ones_like(modified_input) }) # fails sharded_module_input = self.postproc(model_input, { 'a': modified_input }) # fails return self.ebc(sharded_module_input) ``` Reviewed By: sarckk Differential Revision: D69292525
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Feb 20, 2025
…stproc modules (meta-pytorch#2733) Summary: Postproc modules with collection inputs (list or dict) with non-static (derived from input or other postproc) elements were not properly rewritten - input elements remained fx.Nodes even during the actual model forward (i.e. outside rewrite, during pipeline execution) To illustrate: ``` def forward(model_input: ...) -> ...: modified_input = model_input.float_features + 1 sharded_module_input = self.postproc(model_input, modified_input) # works sharded_module_input = self.postproc(model_input, [123]) # works sharded_module_input = self.postproc(model_input, [torch.ones_like(modified_input)]) # fails sharded_module_input = self.postproc(model_input, [modified_input]) # fails sharded_module_input = self.postproc(model_input, { 'a': 123 }) # works sharded_module_input = self.postproc(model_input, { 'a': torch.ones_like(modified_input) }) # fails sharded_module_input = self.postproc(model_input, { 'a': modified_input }) # fails return self.ebc(sharded_module_input) ``` Reviewed By: sarckk Differential Revision: D69292525
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Feb 20, 2025
…stproc modules (meta-pytorch#2733) Summary: Postproc modules with collection inputs (list or dict) with non-static (derived from input or other postproc) elements were not properly rewritten - input elements remained fx.Nodes even during the actual model forward (i.e. outside rewrite, during pipeline execution) To illustrate: ``` def forward(model_input: ...) -> ...: modified_input = model_input.float_features + 1 sharded_module_input = self.postproc(model_input, modified_input) # works sharded_module_input = self.postproc(model_input, [123]) # works sharded_module_input = self.postproc(model_input, [torch.ones_like(modified_input)]) # fails sharded_module_input = self.postproc(model_input, [modified_input]) # fails sharded_module_input = self.postproc(model_input, { 'a': 123 }) # works sharded_module_input = self.postproc(model_input, { 'a': torch.ones_like(modified_input) }) # fails sharded_module_input = self.postproc(model_input, { 'a': modified_input }) # fails return self.ebc(sharded_module_input) ``` Reviewed By: sarckk Differential Revision: D69292525
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Feb 20, 2025
…stproc modules (meta-pytorch#2733) Summary: Postproc modules with collection inputs (list or dict) with non-static (derived from input or other postproc) elements were not properly rewritten - input elements remained fx.Nodes even during the actual model forward (i.e. outside rewrite, during pipeline execution) To illustrate: ``` def forward(model_input: ...) -> ...: modified_input = model_input.float_features + 1 sharded_module_input = self.postproc(model_input, modified_input) # works sharded_module_input = self.postproc(model_input, [123]) # works sharded_module_input = self.postproc(model_input, [torch.ones_like(modified_input)]) # fails sharded_module_input = self.postproc(model_input, [modified_input]) # fails sharded_module_input = self.postproc(model_input, { 'a': 123 }) # works sharded_module_input = self.postproc(model_input, { 'a': torch.ones_like(modified_input) }) # fails sharded_module_input = self.postproc(model_input, { 'a': modified_input }) # fails return self.ebc(sharded_module_input) ``` Reviewed By: sarckk Differential Revision: D69292525
…stproc modules (meta-pytorch#2733) Summary: Postproc modules with collection inputs (list or dict) with non-static (derived from input or other postproc) elements were not properly rewritten - input elements remained fx.Nodes even during the actual model forward (i.e. outside rewrite, during pipeline execution) To illustrate: ``` def forward(model_input: ...) -> ...: modified_input = model_input.float_features + 1 sharded_module_input = self.postproc(model_input, modified_input) # works sharded_module_input = self.postproc(model_input, [123]) # works sharded_module_input = self.postproc(model_input, [torch.ones_like(modified_input)]) # fails sharded_module_input = self.postproc(model_input, [modified_input]) # fails sharded_module_input = self.postproc(model_input, { 'a': 123 }) # works sharded_module_input = self.postproc(model_input, { 'a': torch.ones_like(modified_input) }) # fails sharded_module_input = self.postproc(model_input, { 'a': modified_input }) # fails return self.ebc(sharded_module_input) ``` Reviewed By: sarckk Differential Revision: D69292525
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Summary:
Postproc modules with collection inputs (list or dict) with non-static (derived from input or other postproc) elements were not properly rewritten - input elements remained fx.Nodes even during the actual model forward (i.e. outside rewrite, during pipeline execution)
To illustrate: