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# Owner(s): ["module: dynamo"]
import contextlib
import dis
import unittest
from typing import List
import torch
import torch._dynamo.test_case
from torch.testing._internal.common_utils import IS_FBCODE
def _filter_instructions(instructions, opname):
return list(filter(lambda x: x.opname == opname, instructions))
class ReconstructTest(torch._dynamo.test_case.TestCase):
@contextlib.contextmanager
def register_bytecode_hook(self, fn):
def hook(code, out_code):
fn(list(dis.get_instructions(out_code)))
return code
torch._dynamo.reset()
handle = torch._dynamo.convert_frame.register_bytecode_hook(hook)
try:
yield
finally:
handle.remove()
def test_ConstDict_optimize_reconstruct(self):
"""
Emit code to reconstruct only the key that changed
"""
def hook(instructions: List[dis.Instruction]):
build_map = _filter_instructions(instructions, "BUILD_MAP")
self.assertEqual(len(build_map), 1)
# reconstruct only d[40]
self.assertEqual(build_map[0].argval, 1)
def f(d, t):
d[40] = t + 1
t = torch.randn(3, 4)
d = {1: t}
d_opt = d.copy()
f(d, t)
with self.register_bytecode_hook(hook):
opt_f = torch.compile(f, backend="eager", fullgraph=True)
opt_f(d_opt, t)
self.assertEqual(d, d_opt)
def test_ConstDict_pop_reconstruct(self):
"""
If something is pop'ed from the dict, we reconstruct everything
"""
def hook(instructions: List[dis.Instruction]):
build_map = _filter_instructions(instructions, "BUILD_MAP")
self.assertEqual(len(build_map), 1)
# reconstruct everything
self.assertEqual(build_map[0].argval, 2)
def f(d, t):
d.pop(2)
d[40] = t + 1
t = torch.randn(3, 4)
d = {1: t, 2: t + 1}
d_opt = d.copy()
f(d, t)
with self.register_bytecode_hook(hook):
opt_f = torch.compile(f, backend="eager", fullgraph=True)
opt_f(d_opt, t)
self.assertEqual(d, d_opt)
@unittest.expectedFailure
def test_ConstDict_popitem_reconstruct(self):
"""
If something is pop'ed from the dict, we reconstruct everything
"""
def hook(instructions: List[dis.Instruction]):
build_map = _filter_instructions(instructions, "BUILD_MAP")
self.assertEqual(len(build_map), 1)
# reconstruct everything
self.assertEqual(build_map[0].argval, 1)
def f(d, t):
d.popitem()
t = torch.randn(3, 4)
d = {1: t, 2: t + 1}
d_opt = d.copy()
f(d, t)
with self.register_bytecode_hook(hook):
opt_f = torch.compile(f, backend="eager", fullgraph=True)
opt_f(d_opt, t)
self.assertEqual(d, d_opt)
def test_ConstDict_popitem_reconstruct_graph_break(self):
"""
If something is pop'ed from the dict, we reconstruct everything.
Calling dict.popitem will graph break.
"""
def f(d, t):
d.popitem()
t = torch.randn(3, 4)
d = {1: t, 2: t + 1}
d_opt = d.copy()
f(d, t)
opt_f = torch.compile(backend="eager")(f)
opt_f(d_opt, t)
self.assertEqual(d, d_opt)
def test_ConstDict_del_reconstruct(self):
"""
If something is deleted from the dict, we reconstruct everything
"""
def hook(instructions: List[dis.Instruction]):
build_map = _filter_instructions(instructions, "BUILD_MAP")
self.assertEqual(len(build_map), 1)
# reconstruct everything
self.assertEqual(build_map[0].argval, 2)
def f(d, t):
del d[2]
d[40] = t + 1
t = torch.randn(3, 4)
d = {1: t, 2: t + 1}
d_opt = d.copy()
f(d, t)
with self.register_bytecode_hook(hook):
opt_f = torch.compile(f, backend="eager", fullgraph=True)
opt_f(d_opt, t)
self.assertEqual(d, d_opt)
def test_ConstDict_get_reconstruct(self):
"""
dict.get shouldn't affect anything
"""
def hook(instructions: List[dis.Instruction]):
build_map = _filter_instructions(instructions, "BUILD_MAP")
self.assertEqual(len(build_map), 1)
self.assertEqual(build_map[0].argval, 1)
load_const = _filter_instructions(instructions, "LOAD_CONST")
self.assertNotIn(123, load_const)
def f(d, t):
d[456] = d.get(456) + t
t = torch.randn(3, 4)
d = {123: t, 456: t + 1}
d_opt = d.copy()
f(d, t)
with self.register_bytecode_hook(hook):
opt_f = torch.compile(f, backend="eager", fullgraph=True)
opt_f(d_opt, t)
self.assertEqual(d, d_opt)
def test_ConstDict_clear_reconstruct(self):
"""
If dict.clear() is used, we reconstruct everything
"""
def hook(instructions: List[dis.Instruction]):
build_map = _filter_instructions(instructions, "BUILD_MAP")
self.assertEqual(len(build_map), 1)
# reconstruct everything
self.assertEqual(build_map[0].argval, 1)
def f(d, t):
d.clear()
d[3] = t + 3
t = torch.randn(3, 4)
d = {1: t, 2: t + 1}
d_opt = d.copy()
f(d, t)
with self.register_bytecode_hook(hook):
opt_f = torch.compile(f, backend="eager", fullgraph=True)
opt_f(d_opt, t)
self.assertEqual(d, d_opt)
def test_create_dict_reconstruct(self):
"""
If dict is created inside a function, everything needs to be reconstructed
"""
def hook(instructions: List[dis.Instruction]):
build_map = _filter_instructions(instructions, "BUILD_MAP")
self.assertEqual(len(build_map), 1)
# reconstruct everything
self.assertEqual(build_map[0].argval, 2)
def f(t):
return {1: t, 2: t + 1}
t = torch.randn(3, 4)
d = f(t)
with self.register_bytecode_hook(hook):
opt_f = torch.compile(f, backend="eager", fullgraph=True)
d_opt = opt_f(t)
self.assertEqual(d, d_opt)
@unittest.skipIf(
IS_FBCODE, "capturing functional_call is not enabled by default in FB_CODE"
)
def test_functional_call_reconstruct(self):
"""
PyTorch shouldn't codegen any key/value when functional_call is used
"""
def hook(instructions: List[dis.Instruction]):
build_map = _filter_instructions(instructions, "BUILD_MAP")
# don't reconstruct anything
self.assertEqual(len(build_map), 0)
m = torch.nn.Linear(3, 3)
new_bias = torch.randn(3)
new_weight = torch.randn(3, 3)
def fn(new_weight, new_bias, x):
return torch.func.functional_call(
m, {"weight": new_weight, "bias": new_bias}, x
)
x = torch.randn(2, 3)
expected = torch.nn.functional.linear(x, new_weight, new_bias)
with self.register_bytecode_hook(hook):
opt_fn = torch.compile(fn, backend="eager", fullgraph=True)
got = opt_fn(new_weight, new_bias, x)
self.assertEqual(expected, got)
@unittest.skipIf(
IS_FBCODE, "capturing functional_call is not enabled by default in FB_CODE"
)
def test_functional_call_reconstruct_2(self):
"""
PyTorch shouldn't codegen any key/value when functional_call is used
"""
def hook(instructions: List[dis.Instruction]):
build_map = _filter_instructions(instructions, "BUILD_MAP")
# don't reconstruct anything
self.assertEqual(len(build_map), 0)
class DummyModule(torch.nn.Module):
def __init__(self):
super().__init__()
self.a = torch.nn.ModuleDict(
{
"b": torch.nn.ModuleDict(
{
"c": torch.nn.ModuleDict(
{
"d": torch.nn.ModuleDict(
{"e": torch.nn.Linear(10, 10, bias=False)}
)
}
)
}
)
}
)
def forward(self, x):
return self.a.b.c.d.e(x)
model = DummyModule()
def fn(model, states, x):
return torch.func.functional_call(model, states, x)
x = torch.randn(2, 3)
states = model.state_dict()
x = torch.randn(10, 10)
expected = fn(model, states, x)
with self.register_bytecode_hook(hook):
opt_fn = torch.compile(fn, backend="eager", fullgraph=True)
got = opt_fn(model, states, x)
self.assertEqual(expected, got)
if __name__ == "__main__":
from torch._dynamo.test_case import run_tests
run_tests()
|
