pytorch/torch/quantization/fx/pattern_utils.py
Jerry Zhang ffcb0989e7 [quant][graphmode][fx] Merge all quantization mode (#45292)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/45292

This PR merges all quantization mode and will only expose the following top level functions:
```
prepare_fx
prepare_qat_fx
convert_fx
```

Test Plan: Imported from OSS

Reviewed By: vkuzo

Differential Revision: D23913105

fbshipit-source-id: 4e335286d6de225839daf51d1df54322d52d68e5
2020-09-30 21:20:34 -07:00

75 lines
2.4 KiB
Python

import torch
import sys
from collections import OrderedDict
# pattern for conv bn fusion
FUSION_PATTERNS = OrderedDict()
def register_fusion_pattern(pattern):
def insert(fn):
FUSION_PATTERNS[pattern] = fn
return fn
return insert
def get_fusion_patterns():
return FUSION_PATTERNS
QUANTIZATION_PATTERNS = OrderedDict()
# Register pattern for both static quantization and qat
def register_quant_pattern(pattern):
def insert(fn):
QUANTIZATION_PATTERNS[pattern] = fn
return fn
return insert
# Get patterns for both static quantization and qat
def get_quant_patterns():
return QUANTIZATION_PATTERNS
# Example use of register pattern function:
# @register_fusion_pattern(torch.nn.ReLU, (torch.nn.BatchNorm2d, torch.nn.Conv2d)))
# class ConvBNReLUFusion():
# def __init__(...):
# ...
#
# Note: The order of patterns is important! match function will take whatever is matched first, so we'll
# need to put the fusion patterns before single patterns. For example, add_relu should be registered come before relu.
# decorators are applied in the reverse order we see. Also when we match the nodes in the graph with these patterns,
# we'll start from the last node of the graph and traverse back.
def is_match(modules, node, pattern, max_uses=sys.maxsize):
""" Matches a node in fx against a pattern
"""
if isinstance(pattern, tuple):
self_match, *arg_matches = pattern
if self_match is getattr:
assert len(pattern) == 2, 'Expecting getattr pattern to have two elements'
arg_matches = []
else:
self_match = pattern
arg_matches = []
if node.uses > max_uses:
return False
if isinstance(self_match, type) and issubclass(self_match, torch.nn.Module):
if node.op != 'call_module':
return False
if not type(modules[node.target]) == self_match:
return False
elif callable(self_match):
if node.op != 'call_function' or node.target is not self_match:
return False
elif node.target is getattr:
if node.args[1] != pattern[1]:
return False
elif node.target != self_match:
return False
if not arg_matches:
return True
if len(arg_matches) != len(node.args):
return False
return all(is_match(modules, node, arg_match, max_uses=1) for node, arg_match in zip(node.args, arg_matches))