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# Owner(s): ["oncall: fx"]
import torch
from torch.testing._internal.common_utils import (
TestCase, run_tests)
from torch.fx.experimental.proxy_tensor import make_fx
from torch.fx.passes.dialect.common.cse_pass import CSEPass, get_CSE_banned_ops
from torch.fx import symbolic_trace
import random
banned_ops = get_CSE_banned_ops()
P_default = CSEPass(banned_ops=banned_ops)
def check(self, f, t, delta, check_val=True, graph_input=False, P=None):
"""
check if the CSE modified graph of ``f``
1) has delta less nodes, and
2) do not reduce the number of nodes further on a second pass, and
3) modified returned is true only if the number of nodes decreases.
Args:
f: function to be checked
t: tensor to be passed to f
delta: an integer >= -1.
If delta = -1, it only checks if the new graph has less or equal number of nodes
check_val: if True, check if the output of f is correct
graph_input: True is f is type GraphModule
P: the pass to use. If None, use P_default
"""
if graph_input:
fx_g = f
else:
fx_g = make_fx(f)(t)
if P is None:
P = P_default
res = P(fx_g)
new_g = res.graph_module
new_graph = new_g.graph
modified = res.modified
# the number of nodes decrease/ or stay the same
old_num_nodes = len(fx_g.graph.nodes)
new_num_nodes = len(new_graph.nodes)
assert (new_num_nodes < old_num_nodes) == modified, "modified should be True if the number of nodes decrease"
if delta == -1:
self.assertTrue(old_num_nodes >= new_num_nodes, (
f"number of nodes increased {old_num_nodes}, {new_num_nodes}"))
else:
self.assertTrue(old_num_nodes == new_num_nodes + delta, (
f"number of nodes not the same {old_num_nodes - delta}, {new_num_nodes}\n {fx_g.graph} \n {new_graph}"))
# a second pass should not reduce more nodes
res = P(new_g)
pass_2_graph = res.graph_module.graph
pass_2_num_nodes = len(pass_2_graph.nodes)
self.assertTrue(pass_2_num_nodes == new_num_nodes, (
f"second pass graph has less node {pass_2_num_nodes}, {new_num_nodes}\n {new_graph} \n {pass_2_graph}"))
# check correctness
if check_val:
true_result = fx_g(t)
our_result = new_g(t)
if true_result is None: # both return None
self.assertTrue(our_result is None, f"true result is None, CSE result is {our_result}")
else: # results returned are the same
self.assertTrue(torch.all(true_result == our_result), (
f"results are different {true_result}, {our_result}")) # check results are the same
class TestCSEPass(TestCase):
def test_nochange(self):
def f(x):
a = x + 1
b = x + a
a = x
d = x + a
return b + d
t = torch.randn(2, 2)
check(self, f, t, 0)
def test_empty(self):
def f(x):
pass
t = torch.randn(2, 2)
check(self, f, t, 0)
def test_immutable_list_type(self):
def f(x):
a = x.sum(dim=1)
b = x.sum(dim=1)
c = x.sum()
d = x.sum()
return a + b + c + d
t = torch.randn(2, 2)
check(self, f, t, 2)
def test_immutable_list_multiple_entries(self):
def f(x):
a = x.sum(dim=[0, 1])
b = x.sum(dim=[0, 1])
c = x.sum(dim=1)
d = x.sum(dim=1)
return a + b + c + d
t = torch.randn(2, 2)
check(self, f, t, 2)
def test_simple(self):
def f(x):
a = x.cos()
b = x.cos()
c = a + a
d = b + b
return c + d
t = torch.randn(2, 2)
check(self, f, t, 2)
def test_simple_2(self):
def f(x):
a = x.cos().sin()
b = x.cos().sin()
c = a + a
d = b + b
return c + d
t = torch.randn(1)
check(self, f, t, 3)
def test_two_args_default(self):
def f(x):
a = x.sum(dim=1)
b = x.sum(dim=1, keepdim=False)
c = x.sum(dim=1, keepdim=False)
d = x.sum(dim=1)
return a + b + c + d
t = torch.randn(2, 2)
check(self, f, t, 3)
def test_two_args(self):
def f(x):
a = x.sum(dim=1)
b = x.sum(dim=1, keepdim=True)
c = x.sum(dim=1, keepdim=True)
d = x.sum(dim=1)
return a + b + c + d
t = torch.randn(2, 2)
check(self, f, t, 2)
def test_simple_multiple_same_ops(self):
def f(x):
a = x.sum()
b = x.sum()
c = x.sum()
d = x.sum()
return a + b + c + d
t = torch.randn(2, 2)
check(self, f, t, 3)
def test_nested_immutable_list_type(self):
def f(x):
a = torch.cat((x, x))
b = torch.cat((x, x))
return a + b
t = torch.randn(2, 2)
check(self, f, t, 1)
def test_kwarg(self):
def f(x):
a = torch.ones_like(x)
b = torch.ones_like(x)
return a + b
t = torch.randn(2, 2)
check(self, f, t, 1)
"""
Generate function with random ops and check if the result is the same
"""
def test_random(self):
def f(x):
vals = [x]
ops = [torch.clone, torch.cos, torch.tanh, torch.nn.functional.gelu]
for _ in range(100):
new_val = random.choice(ops)(random.choice(vals))
vals.append(new_val)
return vals[-1]
fx_g = symbolic_trace(f)
fx_g.graph.eliminate_dead_code()
fx_g.recompile()
t = torch.randn(2, 2)
for _ in range(30):
check(self, fx_g, t, -1, graph_input=True)
"""
Test that banned list ban ops as expected.
"""
def test_banned_list(self):
def f(x):
a = x + 1
b = x + 1
return a + b
t = torch.randn(2, 2)
P_ban_add = P = CSEPass(banned_ops=[torch.ops.aten.add])
check(self, f, t, 0, P=P_ban_add) # check that add is banned
check(self, f, t, 1) # check that add is not banned by default
def test_rand_like(self):
def f(x):
a = torch.rand_like(x)
b = torch.rand_like(x)
return a + b
t = torch.randn(2, 2)
check(self, f, t, 0, check_val=False)
def test_rand_n(self):
def f(x):
a = torch.randn(4)
b = torch.randn(4)
return a + b
t = torch.randn(2, 2)
check(self, f, t, 0, check_val=False)
if __name__ == '__main__':
run_tests()
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