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https://github.com/zebrajr/pytorch.git
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d92bf921db
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/46953 Test Plan: Imported from OSS Reviewed By: supriyar Differential Revision: D24579402 fbshipit-source-id: 5e0b8abf682287ab3c7dd54c2fc2cf309295e147
69 lines
2.3 KiB
Python
69 lines
2.3 KiB
Python
from torch.fx import (
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GraphModule,
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map_arg
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)
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from torch.fx.graph import Graph
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from .pattern_utils import (
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is_match,
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get_default_fusion_patterns,
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)
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from .fusion_patterns import * # noqa: F401
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class Fuser:
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def fuse(self, model, fuse_custom_config_dict=None):
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if fuse_custom_config_dict is None:
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fuse_custom_config_dict = {}
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input_root = model
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input_graph = model.graph
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self.modules = dict(input_root.named_modules())
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additional_fusion_patterns = fuse_custom_config_dict.get("additional_quant_pattern", {})
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fusion_patterns = get_default_fusion_patterns().copy()
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for k, v in additional_fusion_patterns.items():
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fusion_patterns[k] = v
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# find fusion
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fusion_pairs = self._find_matches(input_root, input_graph, fusion_patterns)
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self.fused_graph = Graph()
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env = {}
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def load_arg(a):
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return map_arg(a, lambda node: env[node.name])
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for node in input_graph.nodes:
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root_node, obj = fusion_pairs.get(node.name, (None, None))
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if root_node is node:
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env[node.name] = obj.fuse(self, load_arg)
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elif root_node is None:
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env[node.name] = self.fused_graph.node_copy(node, load_arg)
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# node matched in patterns and is not root is removed here
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model = GraphModule(input_root, self.fused_graph)
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return model
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def _find_matches(self, root, graph, patterns):
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modules = dict(root.named_modules())
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match_map = {} # node name -> (root_node, match_value?)
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def apply_match(pattern, node, match):
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if isinstance(pattern, tuple):
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s, *args = pattern
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apply_match(s, node, match)
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for subpattern, arg in zip(args, node.args):
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apply_match(subpattern, arg, match)
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else:
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# the first pattern matches will take precedence
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if node.name not in match_map:
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match_map[node.name] = match
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for node in reversed(graph.nodes):
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if node.name not in match_map:
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for pattern, value in patterns.items():
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if is_match(modules, node, pattern):
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apply_match(pattern, node, (node, value(self, node)))
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return match_map
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