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Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/68769 att, since we want to use this type in fuser_method_mapping in later PRs Test Plan: no change to logic, just regression test on ci ``` python test/test_quantization.py ``` Imported from OSS Reviewed By: vkuzo Differential Revision: D32602636 fbshipit-source-id: 15b95241431dfca9b1088d0920bf75705b37aa9a
22 lines
975 B
Markdown
22 lines
975 B
Markdown
# Fusion Pattern Format
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The patterns are we matching against is float modules types, functional operators and pytorch operators in reverse order:
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```
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operator = module_type | functional | torch op | native op | MatchAllNode
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Pattern = (operator, Pattern, Pattern, ...) | operator
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```
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where the first item for Pattern is the operator we want to match, and the rest are the patterns for the arguments of the operator.
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For example, pattern (nn.ReLU, (operator.add, MatchAllNode, (nn.BatchNorm2d, nn.Conv2d))) would match the following graph:
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```
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tensor_1 tensor_2
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*(MatchAllNode) nn.Conv2d
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\ /
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-- operator.add --
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nn.ReLU
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```
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we’ll match the last node as the anchor point of the match, and we can retrieve the whole graph by tracing back from the node, e.g. in the example above, we matched nn.ReLU node, then node.args[0] is the operator.add node.
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