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# Owner(s): ["module: fx"]
import os
import sys
from typing import Callable
import torch
import torch.nn.functional as F
from torch.export import export_for_training
from torch.fx import symbolic_trace
from torch.fx.experimental.proxy_tensor import make_fx
pytorch_test_dir = os.path.dirname(os.path.dirname(os.path.realpath(__file__)))
sys.path.append(pytorch_test_dir)
import unittest
from torch.fx.passes.utils.matcher_utils import SubgraphMatcher
from torch.fx.passes.utils.matcher_with_name_node_map_utils import (
SubgraphMatcherWithNameNodeMap,
)
from torch.testing._internal.common_utils import IS_WINDOWS, run_tests
from torch.testing._internal.jit_utils import JitTestCase
class WrapperModule(torch.nn.Module):
def __init__(self, fn: Callable):
super().__init__()
self.fn = fn
def forward(self, *args, **kwargs):
return self.fn(*args, **kwargs)
class TestMatcher(JitTestCase):
def test_subgraph_matcher_with_attributes(self):
class LargeModel(torch.nn.Module):
def __init__(self) -> None:
super().__init__()
self._weight = torch.nn.Parameter(torch.ones(3, 3))
self._bias = torch.nn.Parameter(torch.ones(3, 3))
def forward(self, x):
return torch.ops.aten.addmm.default(self._bias, x, self._weight)
# Large Model graph:
# opcode name target args kwargs
# ------------- ------------- ------------------ ------------------- --------
# placeholder x x () {}
# get_attr _bias _bias () {}
# get_attr _weight _weight () {}
# call_function addmm_default aten.addmm.default (_bias, x, _weight) {}
# output output output (addmm_default,) {}
large_model_graph = symbolic_trace(LargeModel()).graph
class PatternModel(torch.nn.Module):
def __init__(self) -> None:
super().__init__()
self._weight_1 = torch.nn.Parameter(torch.ones(5, 5))
self._bias_1 = torch.nn.Parameter(torch.ones(5, 5))
def forward(self, x):
return torch.ops.aten.addmm.default(self._bias_1, x, self._weight_1)
pattern_graph = torch.fx.symbolic_trace(PatternModel()).graph
subgraph_matcher = SubgraphMatcher(pattern_graph)
match_result = subgraph_matcher.match(large_model_graph)
self.assertEqual(len(match_result), 1)
def test_subgraph_matcher_with_list(self):
def original(x, y):
return torch.ops.aten.view(x, [5, y.shape[0]])
original_graph = torch.fx.symbolic_trace(original).graph
def pattern(x, y, z):
return torch.ops.aten.view(x, [z, y.shape[0]])
pattern_graph = torch.fx.symbolic_trace(pattern).graph
subgraph_matcher = SubgraphMatcher(pattern_graph)
match_result = subgraph_matcher.match(original_graph)
self.assertEqual(len(match_result), 1)
def test_subgraph_matcher_with_list_bad(self):
def original(x, y):
return torch.ops.aten._reshape_alias_copy.default(
x, [1, y.shape[0]], [y.shape[1], y.shape[1]]
)
original_graph = torch.fx.symbolic_trace(original).graph
def pattern(x, y, b):
return torch.ops.aten._reshape_alias_copy.default(
x, [b, y.shape[0], y.shape[1]], [y.shape[1]]
)
pattern_graph = torch.fx.symbolic_trace(pattern).graph
subgraph_matcher = SubgraphMatcher(pattern_graph)
match_result = subgraph_matcher.match(original_graph)
self.assertEqual(len(match_result), 0)
def test_subgraph_matcher_ignore_literals(self):
def original(x):
return x + 1
original_graph = make_fx(original)(torch.ones(3, 3)).graph
original_graph.eliminate_dead_code()
def pattern(x):
return x + 2
pattern_graph = make_fx(pattern)(torch.ones(4, 4)).graph
pattern_graph.eliminate_dead_code()
subgraph_matcher = SubgraphMatcher(pattern_graph)
match_result = subgraph_matcher.match(original_graph)
self.assertEqual(len(match_result), 0)
subgraph_matcher = SubgraphMatcher(pattern_graph, ignore_literals=True)
match_result = subgraph_matcher.match(original_graph)
self.assertEqual(len(match_result), 1)
def test_variatic_arg_matching(self):
inputs = (torch.randn(20, 16, 50, 32),)
def maxpool(x, kernel_size, stride, padding, dilation):
return torch.ops.aten.max_pool2d_with_indices.default(
x, kernel_size, stride, padding, dilation
)
maxpool_graph = torch.fx.symbolic_trace(maxpool).graph
maxpool_matcher = SubgraphMatcher(maxpool_graph)
match_result = maxpool_matcher.match(maxpool_graph)
self.assertEqual(len(match_result), 1)
# Graph only contains "stride" argument
maxpool_s = torch.nn.MaxPool2d(kernel_size=2, stride=1).eval()
maxpool_s_graph = make_fx(maxpool_s)(*inputs).graph
match_s_result = maxpool_matcher.match(maxpool_s_graph)
self.assertEqual(len(match_s_result), 1)
# Graph only contains "padding" argument
maxpool_p = torch.nn.MaxPool2d(kernel_size=2, padding=1)
maxpool_p_graph = make_fx(maxpool_p)(*inputs).graph
match_p_result = maxpool_matcher.match(maxpool_p_graph)
self.assertEqual(len(match_p_result), 1)
# Graph only contains "stride, padding" argument
maxpool_sp = torch.nn.MaxPool2d(kernel_size=2, stride=1, padding=1)
maxpool_sp_graph = make_fx(maxpool_sp)(*inputs).graph
match_sp_result = maxpool_matcher.match(maxpool_sp_graph)
self.assertEqual(len(match_sp_result), 1)
@unittest.skipIf(IS_WINDOWS, "Windows not yet supported for torch.compile")
def test_split_to_graph_and_name_node_map(self):
"""Testing the internal helper function for splitting the pattern graph"""
from torch.fx.passes.utils.matcher_with_name_node_map_utils import (
_split_to_graph_and_name_node_map,
)
def pattern(x, weight):
conv = F.conv2d(x, weight)
relu = F.relu(conv)
relu_mul_by_two = relu * 2
return relu, relu_mul_by_two, {"conv": conv, "relu": relu}
example_inputs = (
torch.randn(1, 3, 3, 3) * 10,
torch.randn(3, 3, 3, 3),
)
pattern_gm = export_for_training(
WrapperModule(pattern), example_inputs
).module()
before_split_res = pattern_gm(*example_inputs)
pattern_gm, name_node_map = _split_to_graph_and_name_node_map(pattern_gm)
after_split_res = pattern_gm(*example_inputs)
self.assertEqual(before_split_res[0], after_split_res[0])
self.assertEqual(before_split_res[1], after_split_res[1])
@unittest.skipIf(IS_WINDOWS, "Windows not yet supported for torch.compile")
def test_matcher_with_name_node_map_function(self):
"""Testing SubgraphMatcherWithNameNodeMap with function pattern"""
def target_graph(x, weight):
x = x * 2
weight = weight * 3
conv = F.conv2d(x, weight)
relu = F.relu(conv)
relu2 = relu * 2
return relu + relu2
def pattern(x, weight):
conv = F.conv2d(x, weight)
relu = F.relu(conv)
relu_mul_by_two = relu * 2
return relu, relu_mul_by_two, {"conv": conv, "relu": relu}
example_inputs = (
torch.randn(1, 3, 3, 3) * 10,
torch.randn(3, 3, 3, 3),
)
pattern_gm = export_for_training(
WrapperModule(pattern), example_inputs
).module()
matcher = SubgraphMatcherWithNameNodeMap(pattern_gm)
target_gm = export_for_training(
WrapperModule(target_graph), example_inputs
).module()
internal_matches = matcher.match(target_gm.graph)
for internal_match in internal_matches:
name_node_map = internal_match.name_node_map
assert "conv" in name_node_map
assert "relu" in name_node_map
name_node_map["conv"].meta["custom_annotation"] = "annotation"
# check if we correctly annotated the target graph module
for n in target_gm.graph.nodes:
if n == name_node_map["conv"]:
assert (
"custom_annotation" in n.meta
and n.meta["custom_annotation"] == "annotation"
)
@unittest.skipIf(IS_WINDOWS, "Windows not yet supported for torch.compile")
def test_matcher_with_name_node_map_module(self):
"""Testing SubgraphMatcherWithNameNodeMap with module pattern"""
class M(torch.nn.Module):
def __init__(self) -> None:
super().__init__()
self.linear = torch.nn.Linear(5, 5)
def forward(self, x):
return self.linear(x)
class Pattern(torch.nn.Module):
def __init__(self) -> None:
super().__init__()
self.linear = torch.nn.Linear(5, 5)
def forward(self, x):
linear = self.linear(x)
# Note: we can't put "weight": self.linear.weight in dictionary since
# nn.Parameter is not an allowed output type in dynamo
return linear, {"linear": linear, "x": x}
example_inputs = (torch.randn(3, 5),)
pattern_gm = export_for_training(Pattern(), example_inputs).module()
matcher = SubgraphMatcherWithNameNodeMap(pattern_gm)
target_gm = export_for_training(M(), example_inputs).module()
internal_matches = matcher.match(target_gm.graph)
for internal_match in internal_matches:
name_node_map = internal_match.name_node_map
assert "linear" in name_node_map
assert "x" in name_node_map
name_node_map["linear"].meta["custom_annotation"] = "annotation"
# check if we correctly annotated the target graph module
for n in target_gm.graph.nodes:
if n == name_node_map["linear"]:
assert (
"custom_annotation" in n.meta
and n.meta["custom_annotation"] == "annotation"
)
if __name__ == "__main__":
run_tests()
|
