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# Owner(s): ["module: fx"]
import torch
import torch.fx as fx
from torch.fx.passes.infra.pass_base import PassBase, PassResult
from torch.fx.passes.infra.pass_manager import (
_topological_sort_passes,
pass_result_wrapper,
PassManager,
this_before_that_pass_constraint,
)
from torch.testing._internal.common_utils import TestCase
# Pass that uses PassBase and returns a PassResult (best scenario)
class ReplaceAddWithMulPass(PassBase):
def call(self, gm) -> PassResult:
modified = False
for node in gm.graph.nodes:
if node.op == "call_function" and node.target == torch.add:
node.target = torch.mul
modified = True
return PassResult(gm, modified)
# Pass that is a callable and returns a PassResult
def replace_mul_with_div_pass(gm) -> PassResult:
modified = False
for node in gm.graph.nodes:
if node.op == "call_function" and node.target == torch.mul:
node.target = torch.div
modified = True
return PassResult(gm, modified)
# Pass that is a PassBase and does not return a PassResult
# Need to wrap with pass_result_wrapper or else it will fail
class ReplaceDivWithSubPass(PassBase):
def call(self, gm) -> PassResult:
for node in gm.graph.nodes:
if node.op == "call_function" and node.target == torch.div:
node.target = torch.sub
# Pass that is a callable and does not return a PassResult
# Need to wrap with pass_result_wrapper or else it will fail
def replace_sub_with_add_pass(gm) -> PassResult:
for node in gm.graph.nodes:
if node.op == "call_function" and node.target == torch.sub:
node.target = torch.add
class AddModule(torch.nn.Module):
def forward(self, x):
y = torch.add(x, x)
z = torch.add(y, x)
return z
class TestPassManager(TestCase):
def test_pass_manager(self):
"""
Tests that the pass manager runs the passes correctly.
"""
m = AddModule()
traced_m = torch.fx.symbolic_trace(m)
pm = PassManager(
passes=[
ReplaceAddWithMulPass(),
replace_mul_with_div_pass,
pass_result_wrapper(ReplaceDivWithSubPass()),
pass_result_wrapper(replace_sub_with_add_pass),
],
steps=5,
)
pm.validate_constraints()
self.assertEqual(len(pm.passes), 4)
res = pm(traced_m)
modified_m = res.graph_module
assert isinstance(modified_m, fx.GraphModule)
# Check that all call_function nodes are divs
for node in modified_m.graph.nodes:
if node.op == "call_function":
self.assertEqual(node.target, torch.add)
def test_this_before_that_pass_constraint(self):
"""
Tests the construction of constraints
"""
passes = [lambda x: 2 * x for _ in range(10)]
pm = PassManager(passes)
# add unfulfillable constraint
pm.add_constraint(this_before_that_pass_constraint(passes[-1], passes[0]))
with self.assertRaises(RuntimeError):
pm.validate_constraints()
def test_pass_manager_checks(self):
"""
Tests that users can add in check functions correctly
"""
m = AddModule()
traced_m = fx.symbolic_trace(m)
pm = PassManager(passes=[ReplaceAddWithMulPass(), replace_mul_with_div_pass])
def check_div_target(graph_module):
for node in graph_module.graph.nodes:
if node.op == "call_function" and node.target != torch.div:
raise ValueError("Target should be div!")
pm.add_checks(check_div_target)
with self.assertRaises(ValueError):
pm(traced_m)
def test_pass_manager_bad_checks(self):
"""
Checks that we error if we pass in a check function with the wrong parameters
"""
def check_bad_args(graph_module, i):
pass
pm = PassManager()
self.assertRaises(TypeError, pm.add_checks, check_bad_args)
def test_topological_sort(self):
"""
Tests that passes are correctly ordered based on contraints.
"""
def pass0(x):
return x
def pass1(x):
return x + 1
def pass2(x):
return x + 2
def pass3(x):
return x + 3
def pass4(x):
return x + 4
def pass5(x):
return x + 5
# Not passing any constraints should keep the original order
passes = [pass0, pass1, pass2, pass3, pass4, pass5]
sorted = _topological_sort_passes(passes, [])
self.assertEqual(sorted, passes)
# Graph that we are constructing:
# 5 ----> 0 <---- 4
# | |
# +-> 2 -> 3 -> 1 <-+
# Which has a possible topological order of: [4, 5, 0, 2, 3, 1]
passes = [pass0, pass1, pass2, pass3, pass4, pass5]
constraints = [
this_before_that_pass_constraint(pass5, pass0),
this_before_that_pass_constraint(pass5, pass2),
this_before_that_pass_constraint(pass4, pass0),
this_before_that_pass_constraint(pass4, pass1),
this_before_that_pass_constraint(pass2, pass3),
this_before_that_pass_constraint(pass3, pass1),
]
sorted = _topological_sort_passes(passes, constraints)
self.assertEqual(sorted, [pass4, pass5, pass0, pass2, pass3, pass1])
# Circular dependency should result in the circular_dep flag being set
passes = [pass0, pass1, pass2]
constraints = [
this_before_that_pass_constraint(passes[0], passes[1]),
this_before_that_pass_constraint(passes[1], passes[2]),
this_before_that_pass_constraint(passes[2], passes[0]),
]
with self.assertRaises(RuntimeError) as e:
_topological_sort_passes(passes, constraints)
expected_error_msg = (
f"Circular dependency detected within the following passes: {passes}"
)
self.assertEqual(e.exception.args[0], expected_error_msg)
def test_pass_manager_error(self):
"""
Tests error catching + debug
"""
def pass_fail(graph_module):
raise RuntimeError("bad")
m = AddModule()
traced_m = torch.fx.symbolic_trace(m)
pm = PassManager(
passes=[
ReplaceAddWithMulPass(),
replace_mul_with_div_pass,
ReplaceDivWithSubPass(),
pass_result_wrapper(replace_sub_with_add_pass),
],
)
# Comment out this line to see the actual error message
error_msg = (
"ReplaceDivWithSubPass.*ReplaceAddWithMulPass.*replace_mul_with_div_pass"
)
with self.assertRaisesRegex(Exception, error_msg):
pm(traced_m)
pm = PassManager(
passes=[
ReplaceAddWithMulPass(),
replace_mul_with_div_pass,
pass_result_wrapper(ReplaceDivWithSubPass()),
pass_result_wrapper(replace_sub_with_add_pass),
pass_fail,
],
)
# Comment out this line to see the actual error message
error_msg = "pass_fail.*ReplaceAddWithMulPass.*replace_mul_with_div_pass.*ReplaceDivWithSubPass.*replace_sub_with_add_pass"
with self.assertRaisesRegex(Exception, error_msg):
pm(traced_m)
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