OSSForks/pytorch
1
0
Fork 0
mirror of https://github.com/zebrajr/pytorch.git synced 2025-12-06 12:20:52 +01:00
Code Issues Actions 38 Packages Projects Releases Wiki Activity
8769fb854d
pytorch/test/dynamo/test_misc.py
Aaron Gokaslan 8769fb854d [BE] Fix flake8 B027 errors - missing abstractmethod decorator (#100715) 
Enables B027 and applies fixes by adding abstract method decorators. Autofix generated by ruff master.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/100715
Approved by: https://github.com/ezyang
2023-05-09 17:28:48 +00:00

5238 lines
163 KiB
Python
Raw Blame History

# Owner(s): ["module: dynamo"]
import abc
import collections
import copy
import dataclasses
import dis
import enum
import logging
import math
import operator
import os
import sys
import typing
import unittest
import unittest.mock as mock
import weakref
from unittest.mock import patch
import numpy as np
import torch
import torch._dynamo.test_case
import torch._dynamo.testing
import torch.onnx.operators
from torch._C import FileCheck
from torch._dynamo import bytecode_analysis, bytecode_transformation
from torch._dynamo.output_graph import OutputGraph
from torch._dynamo.source import GetItemSource, LocalSource
from torch._dynamo.testing import (
CompileCounter,
requires_static_shapes,
same,
skipIfNotPy311,
unsupported,
)
from torch._dynamo.utils import CompileProfiler, ifdyn, ifdynstaticdefault, ifunspec
from torch.ao.quantization import MinMaxObserver
from torch.ao.quantization.fake_quantize import FakeQuantize
from torch.ao.quantization.qconfig import QConfig
from torch.ao.quantization.quantize_fx import prepare_qat_fx
from torch.autograd.profiler import _enable_dynamo_cache_lookup_profiler
from torch.fx.experimental.symbolic_shapes import ConstraintViolationError
from torch.nn import functional as F
from torch.testing._internal.common_cuda import (
PLATFORM_SUPPORTS_FUSED_SDPA,
SM80OrLater,
)
from torch.testing._internal.common_utils import freeze_rng_state
from torch.testing._internal.jit_utils import JitTestCase
mytuple = collections.namedtuple("mytuple", ["a", "b", "ab"])
class MyPickledModule(torch.nn.Module):
def __init__(self, z):
super().__init__()
self.z = z
def forward(self, x, y):
return x * x * x + y + self.z
# These are used for test_{cond/map}_with_quantization
default_symmetric_fake_quant = FakeQuantize.with_args(
observer=MinMaxObserver, qscheme=torch.per_tensor_symmetric, dtype=torch.quint8
)
default_weight_symmetric_fake_quant = FakeQuantize.with_args(
observer=MinMaxObserver, qscheme=torch.per_tensor_symmetric, dtype=torch.qint8
)
uniform_qconfig_8bit = QConfig(
activation=default_symmetric_fake_quant,
weight=default_weight_symmetric_fake_quant.with_args,
)
qconfig_dict = {"object_type": [(torch.nn.Linear, uniform_qconfig_8bit)]}
class MiscTests(torch._dynamo.test_case.TestCase):
def test_boolarg(self):
def boolarg(aa, bb, flag):
if flag:
return aa - bb
else:
return bb - aa
a = torch.randn(10, 10)
b = torch.randn(10, 10)
correct1 = boolarg(a, b, True)
correct2 = boolarg(a, b, False)
correct3 = boolarg(a, b, None)
counter = CompileCounter()
opt_boolarg = torch._dynamo.optimize_assert(counter)(boolarg)
val1 = opt_boolarg(a, b, True)
val2 = opt_boolarg(a, b, False)
val3 = opt_boolarg(a, b, None)
val4 = opt_boolarg(a, b, True)
self.assertTrue(same(val1, correct1))
self.assertTrue(same(val2, correct2))
self.assertTrue(same(val3, correct3))
self.assertTrue(same(val4, correct1))
self.assertEqual(counter.frame_count, 3)
def test_callpacked(self):
def call_packed(args):
a, b, c = args
return a - b * c
counter = CompileCounter()
a = torch.randn(10, 10)
b = torch.randn(10, 10)
c = torch.randn(10, 10)
correct = call_packed([a, b, c])
opt_call_packed = torch._dynamo.optimize_assert(counter)(call_packed)
val1 = opt_call_packed([a, b, c])
val2 = opt_call_packed((a, b, c))
val3 = opt_call_packed([a, b, c])
val4 = opt_call_packed((a, b, c))
self.assertTrue(same(val1, correct))
self.assertTrue(same(val2, correct))
self.assertTrue(same(val3, correct))
self.assertTrue(same(val4, correct))
self.assertEqual(counter.frame_count, 2)
def test_raises(self):
def fn(a, b, c, cls):
x = a + b - c * 10
raise cls(str(x))
counter = CompileCounter()
a = torch.randn(10, 10)
b = torch.randn(10, 10)
c = torch.randn(10, 10)
opt_fn = torch._dynamo.optimize(counter)(fn)
self.assertRaises(AssertionError, lambda: opt_fn(a, b, c, AssertionError))
self.assertEqual(counter.frame_count, 1)
self.assertEqual(counter.op_count, 3)
def test_inplace(self):
def inplace1(a, b):
o = torch.empty((10, 10))
o.copy_(a)
o -= b
return o
torch._dynamo.testing.standard_test(self, inplace1, 2, expected_ops=3)
def test_unpack4(self):
def unpack4(a, b):
a = a[:5, :]
b = b[:5, :]
x, y = a.size()
o = torch.empty((x, y))
o.copy_(a / b)
return o
torch._dynamo.testing.standard_test(
self,
unpack4,
2,
expected_ops=5,
expected_ops_dynamic=ifdynstaticdefault(6, 7),
)
def test_unpack5(self):
def unpack5(a, b):
a = a[:5, :]
b = b[:5, :]
x, y = a.shape
o = torch.empty((x, y))
o.copy_(a / b)
return o
torch._dynamo.testing.standard_test(
self,
unpack5,
2,
expected_ops=5,
expected_ops_dynamic=ifdynstaticdefault(6, 7),
)
def test_matmul1(self):
def matmul_op1(a, b):
return a @ b
# TODO(jansel): FX doesn't support this, should add upstream support
torch._dynamo.testing.standard_test(self, matmul_op1, 2, expected_ops=1)
def test_int_shape_binops(self):
def fn(x):
# Test reversal by putting int arg first.
y = 15 - x.shape[0]
y = 4 + y
y = 5 * y
y = 2 % y
y = 3**y
y = 10 // y
y = pow(2, y)
y = 10 / y
return x + y
torch._dynamo.testing.standard_test(
self, fn, 1, expected_ops=1, expected_ops_dynamic=ifdynstaticdefault(2, 11)
)
def test_shape_int_inplace_binops(self):
def fn(x):
p = x.shape[0]
p += 2
p -= 2
p **= 2
p /= 2
p *= 2
p //= 2
p %= 2
return x + p
torch._dynamo.testing.standard_test(
self, fn, 1, expected_ops=1, expected_ops_dynamic=ifdynstaticdefault(2, 10)
)
def test_int_shape_inplace_binops(self):
def fn(x):
p = x.shape[0]
# Test reversal by putting constant first
y = 2
y += p
y = 2
y -= p
y = 2
y **= p
y = 2
y /= p
y = 2
y *= p
y = 2
y //= p
y = 2
y %= p
return x + y
torch._dynamo.testing.standard_test(
self, fn, 1, expected_ops=1, expected_ops_dynamic=ifdynstaticdefault(2, 10)
)
def test_int_int_comparisons(self):
def fn(x):
if 2 != 2:
out = 1
elif 2 < 1:
out = 1
elif 1 > 2:
out = 1
elif 1 >= 2:
out = 1
elif 2 <= 1:
out = 1
elif 2 == 2:
out = 2
else:
out = 1
return x + out
torch._dynamo.testing.standard_test(self, fn, 1, expected_ops=1)
def test_shape_int_comparisons(self):
def fn(x):
a = x.shape[0]
# Ensure support for constant on right side
if a != 10:
out = 1
elif a < 2:
out = 1
elif a > 12:
out = 1
elif a >= 12:
out = 1
elif a <= 2:
out = 1
elif a == 10:
out = 2
else:
out = 1
return x + out
# expect for dynamic: size, index, 6 comparison ops, add
torch._dynamo.testing.standard_test(
self, fn, 1, expected_ops=1, expected_ops_dynamic=ifdynstaticdefault(2, 9)
)
def test_int_shape_comparisons(self):
def fn(x):
a = x.shape[0]
# Ensure support for constant on left side
if 10 != a:
out = 1
elif 12 < a:
out = 1
elif 2 > a:
out = 1
elif 2 >= a:
out = 1
elif 12 <= a:
out = 1
elif 10 == a:
out = 2
else:
out = 1
return x + out
# expect for dynamic: size, index, 6 comparison ops, add
torch._dynamo.testing.standard_test(
self, fn, 1, expected_ops=1, expected_ops_dynamic=ifdynstaticdefault(2, 9)
)
def test_param_shape_binops(self):
class MyModule(torch.nn.Module):
def __init__(self):
super().__init__()
self.param = torch.nn.Parameter(torch.randn(15))
def forward(self, x):
# Test reversal by putting param shape arg first.
p = self.param.shape[0]
y = p - x.shape[0]
y = p + y
y = p * y
y = p % y
y = p**y
y = p // y
y = pow(p, y)
y = p / y
return x + y
counts = torch._dynamo.testing.CompileCounter()
mod = MyModule()
optimized_mod = torch._dynamo.optimize(counts, nopython=True)(mod)
x = torch.randn(3)
ref = mod(x)
res = optimized_mod(x)
self.assertTrue(same(ref, res))
self.assertEqual(counts.frame_count, 1)
expected_op_count = (
ifdynstaticdefault(3, 12)
if torch._dynamo.testing.config.dynamic_shapes
else 1
)
self.assertEqual(counts.op_count, expected_op_count)
def test_user_defined_binop(self):
class MyClass:
def __init__(self, value):
self.value = value
def __radd__(self, other):
return self.value + other
def fn(x, c):
y = x.shape[0] + c
return x + y
counts = torch._dynamo.testing.CompileCounter()
opt_fn = torch._dynamo.optimize(counts)(fn)
x = torch.randn(3)
c = MyClass(4)
ref = fn(x, c)
res = opt_fn(x, c)
self.assertTrue(same(ref, res))
self.assertEqual(counts.frame_count, 1)
expected_op_count = (
ifdynstaticdefault(2, 4)
if torch._dynamo.testing.config.dynamic_shapes
else 1
)
self.assertEqual(counts.op_count, expected_op_count)
def test_compare_shapes_eq(self):
def compare_shapes(a, b, to_list):
x = list(a.unsqueeze(-1).shape) if to_list else a.shape
y = list(b.unsqueeze(-1).shape) if to_list else b.shape
if x == y:
return a + 1
else:
return a + 2
# Test both ListVariable and ShapeVariable
torch._dynamo.testing.standard_test(
self, lambda a, b: compare_shapes(a, b, to_list=True), 2
)
torch._dynamo.testing.standard_test(
self, lambda a, b: compare_shapes(a, b, to_list=False), 2
)
def test_compare_shapes_tuple_eq(self):
def compare_shapes(a, b):
x = tuple(a.unsqueeze(-1).shape)
y = tuple(b.unsqueeze(-1).shape)
if x == y:
return a + 1
else:
return a + 2
torch._dynamo.testing.standard_test(self, lambda a, b: compare_shapes(a, b), 2)
def test_compare_shapes_tuple_neq(self):
def compare_shapes(a, b):
x = tuple(a.unsqueeze(-1).shape)
y = tuple(b.unsqueeze(-1).shape)
if x != y:
return a + 1
else:
return a + 2
torch._dynamo.testing.standard_test(self, lambda a, b: compare_shapes(a, b), 2)
def test_compare_shapes_neq(self):
def compare_shapes(a, b, to_list):
x = list(a.unsqueeze(-1).shape) if to_list else a.shape
y = list(b.unsqueeze(-1).shape) if to_list else b.shape
if x != y:
return a + 1
else:
return a + 2
# Test both ListVariable and ShapeVariable
torch._dynamo.testing.standard_test(
self, lambda a, b: compare_shapes(a, b, to_list=True), 2
)
torch._dynamo.testing.standard_test(
self, lambda a, b: compare_shapes(a, b, to_list=False), 2
)
@patch.object(torch._dynamo.config, "dynamic_shapes", True)
def test_compare_shapes_with_constant(self):
def compare_shapes(a):
x = a.shape
if x[0] != 3:
return a * 4
return a * 3
guard_failure = None
def guard_failures(failure):
nonlocal guard_failure
guard_failure = failure
opt_fn = torch._dynamo.optimize(
"eager", nopython=True, guard_fail_fn=guard_failures
)(compare_shapes)
opt_fn(torch.randn([3, 4]))
opt_fn(torch.randn([4, 3]))
self.assertExpectedInline(
guard_failure.reason,
"""tensor 'L['a']' size mismatch at index 0. expected 3, actual 4""",
)
def test_builtin_isinstance(self):
def fn(x):
t = torch.arange(1, 3)
a = isinstance(x, torch.Tensor)
b = isinstance(t, torch.Tensor)
c = isinstance(x, int)
d = isinstance(3, int)
e = isinstance([1, 2, 3], list)
f = isinstance({"foo": 1, "bar": 2}, dict)
res = [a, b, c, d, e, f]
# Can't run yet due to other unimplemented instructions
# res += [isinstance(torch.nn.LazyLinear(2, 3), torch.nn.Linear)]
return res
torch._dynamo.testing.standard_test(self, fn, 1, expected_ops=1)
def test_fold(self):
def fn(a):
return a + math.sqrt(63)
torch._dynamo.testing.standard_test(self, fn, 1, expected_ops=1)
def test_shape_unpack(self):
def fn(x):
a, b = x.size()
return x * b
i = torch.randn(5, 10)
r1 = fn(i)
opt_fn = torch._dynamo.optimize("eager")(fn)
r2 = opt_fn(i)
self.assertTrue(same(r1, r2))
def test_tensor_iter(self):
def fn(x):
for y in x:
y.add_(1.0)
return y
# expect extra size node for dynamic
torch._dynamo.testing.standard_test(
self, fn, 1, expected_ops=20, expected_ops_dynamic=21
)
def test_empty_list(self):
def fn(x, ll):
if len(ll) == 0 and not ll and ll is not None:
return x + 1
i = torch.randn(5, 10)
r1 = fn(i, [])
opt_fn = torch._dynamo.optimize("eager")(fn)
r2 = opt_fn(i, [])
r3 = opt_fn(i, tuple())
self.assertTrue(same(r1, r2))
self.assertTrue(same(r1, r3))
def test_min_max_over_iterable(self):
def get_test_fn(func):
def _fn(a, b, func=func):
# try all of list, iterator, tuple, vararg.
lst = [a.shape[0] + 1, 8, a.shape[0]]
x = func(lst)
y = func(iter(lst))
z = func(tuple(lst))
w = func(*lst)
return a + (x + y + z + w)
return _fn
torch._dynamo.testing.standard_test(
self,
get_test_fn(func=min),
2,
expected_ops=1,
expected_ops_dynamic=ifdynstaticdefault(3, 14),
)
torch._dynamo.testing.standard_test(
self,
get_test_fn(func=max),
2,
expected_ops=1,
expected_ops_dynamic=ifdynstaticdefault(3, 17),
)
def test_config_obj(self):
class Cfg:
def __init__(self):
self.val = 0.5
self.count = 3
def fn(x, cfg):
for i in range(cfg.count):
x = x + cfg.val
return x
cfg1 = Cfg()
cfg1.val = 1.0
cfg2 = Cfg()
v = torch.zeros(1)
cnts = torch._dynamo.testing.CompileCounter()
opt_fn = torch._dynamo.optimize(cnts)(fn)
v = opt_fn(v, cfg1) # 3
v = opt_fn(v, cfg2) # 4.5
cfg2.count = 1
v = opt_fn(v, cfg2) # 5
cfg2.val = 2.0
v = opt_fn(v, cfg2) # 7
self.assertEqual(v[0], 7)
self.assertEqual(cnts.op_count, 8)
def test_config_getattr_default(self):
class Cfg:
def __init__(self):
self.val = 0.5
self.count = 10
def fn(x, cfg):
if getattr(cfg, "just_add_7", False):
return x + 7
for i in range(cfg.count):
x = x + cfg.val
return x
cfg1 = Cfg()
v = torch.zeros(1)
cnts = torch._dynamo.testing.CompileCounter()
opt_fn = torch._dynamo.optimize(cnts)(fn)
self.assertEqual(opt_fn(v, cfg1)[0], 5)
self.assertEqual(opt_fn(v, cfg1)[0], 5)
cfg1.just_add_7 = True
self.assertEqual(opt_fn(v, cfg1)[0], 7)
self.assertEqual(opt_fn(v, cfg1)[0], 7)
cfg1.just_add_7 = False
self.assertEqual(opt_fn(v, cfg1)[0], 5)
self.assertEqual(opt_fn(v, cfg1)[0], 5)
self.assertEqual(cnts.frame_count, 3)
def test_size_input(self):
def fn(x, s):
a, b = s
return x + (a - b)
v = torch.zeros(10, 20)
cnts = torch._dynamo.testing.CompileCounter()
opt_fn = torch._dynamo.optimize(cnts)(fn)
self.assertEqual(opt_fn(v, v.size())[0, 0], -10)
self.assertEqual(opt_fn(v, (10, 20))[0, 0], -10)
self.assertEqual(opt_fn(v, [10, 20])[0, 0], -10)
# One recompile per differing input type
self.assertEqual(cnts.frame_count, 3)
def test_cell_output1(self):
out = None
def fn(a, b):
nonlocal out
out = a + b * 10
v = torch.Tensor([100])
cnts = torch._dynamo.testing.CompileCounter()
opt_fn = torch._dynamo.optimize(cnts)(fn)
self.assertIsNone(opt_fn(v, v))
self.assertEqual(out[0], 1100)
self.assertEqual(cnts.op_count, 2)
def test_cell_output2(self):
out = None
def fn(a, b):
nonlocal out
c = unsupported(a, b)
out = a + b * 10 + c
v = torch.Tensor([100])
cnts = torch._dynamo.testing.CompileCounter()
opt_fn = torch._dynamo.optimize(cnts)(fn)
self.assertIsNone(opt_fn(v, v))
self.assertEqual(out[0], 1200)
self.assertEqual(cnts.op_count, 3)
def test_return_nested_function(self):
out = None
def fn(a, b):
nonlocal out
c = a + b
d = a + 1.0
def fn2(f: int = 7, g: float = 9.0):
nonlocal out
out = a + b * 10
return c * f - d * g
return fn2
v1 = torch.Tensor([100])
v2 = torch.Tensor([200])
cnts = torch._dynamo.testing.CompileCounter()
opt_fn = torch._dynamo.optimize(cnts)(fn)
opt_fn_ret = torch._dynamo.optimize(cnts)(opt_fn(v1, v2))
self.assertEqual(opt_fn_ret(1.5)[0], -459)
self.assertEqual(out[0], 2100)
self.assertEqual(cnts.frame_count, 2)
self.assertEqual(cnts.op_count, 7)
def test_tensor_dict1(self):
def fn(inputs):
return inputs["a"] - inputs["b"] * 1.5
v1 = torch.Tensor([100])
v2 = torch.Tensor([200])
cnts = torch._dynamo.testing.CompileCounter()
opt_fn = torch._dynamo.optimize(cnts)(fn)
self.assertEqual(opt_fn({"a": v1, "b": v2})[0], -200)
self.assertEqual(cnts.frame_count, 1)
self.assertEqual(cnts.op_count, 2)
def test_tensor_dict2(self):
def fn1(inputs):
total = torch.zeros(1)
for k, v in inputs.items():
total += v
return total
def fn2(inputs):
total = torch.zeros(1)
for v in inputs.values():
total += v
return total
def fn3(inputs):
total = torch.zeros(1)
for k in inputs.keys():
total += inputs[k]
return total
v1 = torch.Tensor([100])
v2 = torch.Tensor([200])
cnts = torch._dynamo.testing.CompileCounter()
opt_fn1 = torch._dynamo.optimize(cnts)(fn1)
opt_fn2 = torch._dynamo.optimize(cnts)(fn2)
opt_fn3 = torch._dynamo.optimize(cnts)(fn3)
self.assertEqual(opt_fn1({"a": v1, "b": v2})[0], 300)
self.assertEqual(opt_fn2({"a": v1, "b": v2})[0], 300)
self.assertEqual(opt_fn3({"a": v1, "b": v2})[0], 300)
self.assertEqual(cnts.frame_count, 3)
self.assertEqual(cnts.op_count, 9)
def test_dictcomp(self):
def fn1(inputs):
return {k: v + 1 for k, v in inputs.items()}
v1 = torch.Tensor([100])
v2 = torch.Tensor([200])
cnts = torch._dynamo.testing.CompileCounter()
opt_fn1 = torch._dynamo.optimize(cnts)(fn1)
self.assertEqual(opt_fn1({"a": v1, "b": v2})["a"], 101)
self.assertEqual(opt_fn1({"a": v1, "b": v2})["b"], 201)
self.assertEqual(cnts.frame_count, 1)
self.assertEqual(cnts.op_count, 2)
def test_listcomp(self):
def fn2(inputs):
return torch.sum(torch.cat([v + 1 for k, v in inputs.items()], 0))
v1 = torch.Tensor([100])
v2 = torch.Tensor([200])
cnts = torch._dynamo.testing.CompileCounter()
opt_fn2 = torch._dynamo.optimize(cnts)(fn2)
self.assertEqual(opt_fn2({"a": v1, "b": v2}), 302)
self.assertEqual(cnts.frame_count, 1)
self.assertEqual(cnts.op_count, 4)
def test_is_floating_point(self):
def fn(a, b):
x = a + 1.0
if torch.is_floating_point(b):
x = x + b
return x + 2.0
return torch._dynamo.testing.standard_test(self, fn=fn, nargs=2, expected_ops=3)
def test_is_floating_point2(self):
def fn(a, b):
x = a + 1.0
if b.is_floating_point():
x = x + b
return x + 2.0
return torch._dynamo.testing.standard_test(self, fn=fn, nargs=2, expected_ops=3)
def test_is_tensor(self):
def fn(a, b):
x = a + 1.0
if torch.is_tensor(b):
x = x + b
return x + 2.0
return torch._dynamo.testing.standard_test(self, fn=fn, nargs=2, expected_ops=3)
def test_is_tensor2(self):
def fn(x):
if torch.is_tensor(x):
return x + 1
else:
return torch.ones([2, 3])
x1 = {"input": torch.rand(2, 3)}
x2 = torch.rand(2, 3)
ref1 = fn(x1)
ref2 = fn(x2)
opt_fn = torch._dynamo.optimize("eager")(fn)
res1 = opt_fn(x1)
res2 = opt_fn(x2)
self.assertEqual(ref1, res1)
self.assertEqual(ref2, res2)
def test_numel(self):
def fn(a):
return (a + a.numel() + torch.numel(a), a + a.nelement())
return torch._dynamo.testing.standard_test(
self, fn=fn, nargs=1, expected_ops=3, expected_ops_dynamic=6
)
def test_pair(self):
def fn(a):
return (
torch.zeros(torch.nn.modules.utils._pair(a.size()))
+ a
+ torch.ones(torch.nn.modules.utils._ntuple(3)(3)).sum()
)
return torch._dynamo.testing.standard_test(
self,
fn=fn,
nargs=1,
expected_ops=5,
expected_ops_dynamic=ifdynstaticdefault(6, 8),
)
@patch.object(torch._dynamo.config, "dynamic_shapes", True)
@patch.object(torch._dynamo.config, "capture_scalar_outputs", True)
def test_tensor_item_capture(self):
def fn(a, b):
return (a + b).sum().item()
v1 = torch.randn((10, 10))
v2 = torch.randn((10, 10))
correct = fn(v1, v2)
cnts = torch._dynamo.testing.CompileCounter()
opt_fn = torch._dynamo.optimize((cnts))(fn)
self.assertEqual(opt_fn(v1, v2), correct)
self.assertEqual(cnts.frame_count, 1)
self.assertEqual(cnts.op_count, 3)
@patch.object(torch._dynamo.config, "dynamic_shapes", True)
@patch.object(torch._dynamo.config, "capture_scalar_outputs", False)
def test_tensor_item_no_capture(self):
def fn(a, b):
return (a + b).sum().item()
v1 = torch.randn((10, 10))
v2 = torch.randn((10, 10))
correct = fn(v1, v2)
cnts = torch._dynamo.testing.CompileCounter()
opt_fn = torch._dynamo.optimize((cnts))(fn)
self.assertEqual(opt_fn(v1, v2), correct)
self.assertEqual(cnts.frame_count, 1)
self.assertEqual(cnts.op_count, 2)
def test_namedtuple1(self):
def fn(a, b):
tmp = mytuple(a, b, a + b)
return mytuple(tmp.a, tmp[1], tmp.ab + b)
v1 = torch.Tensor([10])
v2 = torch.Tensor([20])
cnts = torch._dynamo.testing.CompileCounter()
opt_fn = torch._dynamo.optimize(cnts)(fn)
self.assertEqual(opt_fn(v1, v2).ab, 50)
self.assertEqual(cnts.frame_count, 1)
self.assertEqual(cnts.op_count, 2)
def test_namedtuple2(self):
def fn(packed):
a, b, c = packed
if hasattr(packed, "b"):
b = packed.b + 1
c = packed[2]
return a + b + c
v1 = torch.Tensor([1])
v2 = torch.Tensor([2])
v3 = torch.Tensor([3])
cnts = torch._dynamo.testing.CompileCounter()
opt_fn = torch._dynamo.optimize(cnts)(fn)
self.assertEqual(opt_fn(mytuple(v1, v2, v3))[0], 7)
self.assertEqual(cnts.frame_count, 1)
self.assertEqual(cnts.op_count, 3)
def test_namedtuple3(self):
def fn(x, packed):
if isinstance(packed, mytuple):
return x + 1
else:
return x - 1
x = torch.rand([2, 3])
packed = mytuple(1, 2, 3)
ref = fn(x, packed)
opt_fn = torch._dynamo.optimize("eager")(fn)
res = opt_fn(x, packed)
self.assertTrue(same(ref, res))
def test_range_input(self):
def fn(a, rng):
x = a
for i in rng:
x = x + i
return x
def fn1(a):
return fn(a, rng=range(3))
return torch._dynamo.testing.standard_test(
self, fn=fn1, nargs=1, expected_ops=3
)
def test_range_with_shape(self):
def fn(a):
for i in range(1, a.shape[0]):
a += 1
return a
# expect 1 more op (size call) for dynamic
return torch._dynamo.testing.standard_test(
self, fn=fn, nargs=1, expected_ops=9, expected_ops_dynamic=10
)
def test_build_tuple_unpack(self):
def fn1(a, b, c):
return a - b / c
def fn2(a, b, c):
tmp1 = (a,)
tmp2 = (b, c)
args = (*tmp1, *tmp2)
return fn1(*args)
def fn3(a, *args):
return fn1(a, *args)
torch._dynamo.testing.standard_test(self, fn=fn2, nargs=3, expected_ops=2)
torch._dynamo.testing.standard_test(self, fn=fn3, nargs=3, expected_ops=2)
def test_list_mul(self):
def fn(count):
head_mask = count * [None] * count
return head_mask
cnts = torch._dynamo.testing.CompileCounter()
opt_fn = torch._dynamo.optimize(cnts)(fn)
self.assertEqual(opt_fn(2), [None] * 4)
# TODO: the captured frame here is a bit goofy, because we don't
# output anything and none of the traced operations have side
# effects. Probably need better heuristic for bailing on
# dynamo if there are no outputs
self.assertEqual(cnts.frame_count, ifunspec(1, 0))
self.assertEqual(cnts.op_count, ifunspec(2, 0))
def test_list_slice_mul(self):
def fn(count):
a = [1, 2, 3]
head_mask = count * a[1:] * count
return head_mask
cnts = torch._dynamo.testing.CompileCounter()
opt_fn = torch._dynamo.optimize(cnts)(fn)
self.assertEqual(opt_fn(2), [2, 3] * 4)
self.assertEqual(cnts.frame_count, ifunspec(1, 0))
self.assertEqual(cnts.op_count, ifunspec(2, 0))
def test_tuple_mul(self):
def fn(count):
head_mask = count * (2, 3) * count
return head_mask
cnts = torch._dynamo.testing.CompileCounter()
opt_fn = torch._dynamo.optimize(cnts)(fn)
self.assertEqual(opt_fn(2), (2, 3) * 4)
self.assertEqual(cnts.frame_count, ifunspec(1, 0))
self.assertEqual(cnts.op_count, ifunspec(ifdynstaticdefault(2, 2), 0))
def test_tuple_mul_with_shape(self):
def fn(a):
x = a.shape[0]
y = 2 * (x, 3) * 2
return a + y[4]
# expect 3 ops post folding for dynamic case: size, index, add
torch._dynamo.testing.standard_test(
self, fn, 1, expected_ops=1, expected_ops_dynamic=ifdynstaticdefault(2, 3)
)
def test_tuple_iadd_with_shape(self):
def fn(a):
output = (a + a.shape[0], a - a.shape[0])
# tuple += tuple
output += (a - a.shape[0], a + a.shape[0])
# tuple += constant tuple
output += (2, 3)
return output
# expect 4 add / subs for static, 4 * 3 (size, index, math op) for dynamic
torch._dynamo.testing.standard_test(
self, fn, 1, expected_ops=4, expected_ops_dynamic=ifdynstaticdefault(8, 12)
)
def test_list_iadd_with_shape(self):
def fn(a):
output = [a + a.shape[0], a - a.shape[0]]
# list += list
output += [a - a.shape[0], a + a.shape[0]]
# list += tuple
output += (a + a.shape[0], a - a.shape[0])
return output
# expect 6 add / subs for static, 6 * 3 (size, index, math op) for dynamic
torch._dynamo.testing.standard_test(
self, fn, 1, expected_ops=6, expected_ops_dynamic=ifdynstaticdefault(12, 18)
)
def test_user_getattr1(self):
class MyConfig(dict):
def __getattr__(self, name):
return self[name]
def fn(cfg, x, y):
return x + y + cfg.offset
x = torch.randn(10)
cfg = MyConfig(offset=5)
cnts = torch._dynamo.testing.CompileCounter()
opt_fn = torch._dynamo.optimize(cnts)(fn)
self.assertTrue(same(opt_fn(cfg, x, x), 2 * x + 5))
self.assertEqual(cnts.frame_count, 1)
self.assertEqual(cnts.op_count, 2)
def test_user_getattr2(self):
class MyConfig:
defined_on_class = 1
def __init__(self):
self.defined_on_object = 2
def __getattr__(self, name):
return 3
def fn(cfg, x):
return x + cfg.defined_on_class - cfg.defined_on_object + cfg.not_defined
x = torch.randn(10)
cfg = MyConfig()
cnts = torch._dynamo.testing.CompileCounter()
opt_fn = torch._dynamo.optimize(cnts)(fn)
self.assertTrue(same(opt_fn(cfg, x), x + 1 - 2 + 3))
self.assertEqual(cnts.frame_count, 1)
self.assertEqual(cnts.op_count, 3)
def test_user_getattribute(self):
class MyObject:
def __init__(self):
self.custom_dict = {"a": torch.rand((2, 2))}
self.my_number = 42
def __getattribute__(self, name):
custom_dict = super().__getattribute__("custom_dict")
if name in custom_dict:
return custom_dict[name]
return super().__getattribute__(name)
def run(self, x):
return self.my_number * x + self.a * x
def fn(obj, x):
return obj.run(x)
obj = MyObject()
x = torch.rand((2, 2))
cnts = torch._dynamo.testing.CompileCounter()
opt_fn = torch._dynamo.optimize(cnts)(fn)
self.assertTrue(same(opt_fn(obj, x), fn(obj, x)))
def test_nn_module_getattr(self):
class MyMod(torch.nn.Module):
def __init__(self):
super().__init__()
self.custom_dict = {"queue": [torch.rand((2, 2)) for _ in range(3)]}
self.other_attr = torch.rand((2, 2))
def __getattr__(self, name):
custom_dict = self.custom_dict
if name in custom_dict:
return custom_dict[name]
return super().__getattr__(name)
def forward(self, x):
return x @ self.other_attr + self.queue[-1]
x = torch.rand((2, 2))
mod = MyMod()
cnts = torch._dynamo.testing.CompileCounter()
opt_mod = torch._dynamo.optimize(cnts)(mod)
self.assertTrue(same(opt_mod(x), mod(x)))
self.assertTrue(cnts.frame_count, 1)
self.assertTrue(cnts.op_count, 2)
def test_nn_module_getattribute(self):
class MyMod(torch.nn.Module):
def __init__(self):
super().__init__()
self.my_number = 42
def __getattribute__(self, name):
if name == "special_attr":
return torch.tensor([[1, 2], [3, 4]])
return super().__getattribute__(name)
def forward(self, x):
return self.my_number * x + self.special_attr * x
def fn(mod, x):
return mod(x)
mod = MyMod()
x = torch.rand((2, 2))
cnts = torch._dynamo.testing.CompileCounter()
opt_fn = torch._dynamo.optimize(cnts)(fn)
self.assertTrue(same(opt_fn(mod, x), fn(mod, x)))
def test_constant_getattr(self):
# https://github.com/pytorch/pytorch/issues/97480
def fn():
return getattr(None, "arg", 3)
cnt = torch._dynamo.testing.CompileCounter()
optimized_fn = torch._dynamo.optimize(cnt)(fn)
res = optimized_fn()
self.assertTrue(same(res, 3))
def test_user_property(self):
class MyConfig:
@property
def prop5(self):
return 5
def fn(cfg, x, y):
return x + y + cfg.prop5
x = torch.randn(10)
cfg = MyConfig()
cnts = torch._dynamo.testing.CompileCounter()
opt_fn = torch._dynamo.optimize(cnts)(fn)
self.assertTrue(same(opt_fn(cfg, x, x), 2 * x + 5))
self.assertEqual(cnts.frame_count, 1)
self.assertEqual(cnts.op_count, 2)
def test_dataclass_fields(self):
@dataclasses.dataclass
class MyDataClass:
a: torch.Tensor
b: torch.Tensor = None
c: torch.Tensor = None
d: torch.Tensor = None
e: torch.Tensor = None
def fn(obj):
class_fields = dataclasses.fields(obj)
assert len(class_fields)
assert all(field.default is None for field in class_fields[1:])
other_fields_are_none = all(
getattr(obj, field.name) is None for field in class_fields[1:]
)
assert not other_fields_are_none
total = getattr(obj, class_fields[0].name)
for field in class_fields[1:]:
v = getattr(obj, field.name)
if v is not None:
total += v
return total
obj1 = MyDataClass(torch.randn(10), torch.randn(10), torch.randn(10))
obj2 = MyDataClass(torch.randn(10), e=torch.randn(10))
correct1 = fn(obj1)
correct2 = fn(obj2)
cnts = torch._dynamo.testing.CompileCounter()
opt_fn = torch._dynamo.optimize(cnts)(fn)
self.assertTrue(same(opt_fn(obj1), correct1))
self.assertEqual(cnts.frame_count, 1)
self.assertEqual(cnts.op_count, 2)
torch._dynamo.reset()
cnts = torch._dynamo.testing.CompileCounter()
opt_fn = torch._dynamo.optimize(cnts)(fn)
self.assertTrue(same(opt_fn(obj2), correct2))
self.assertEqual(cnts.frame_count, 1)
self.assertEqual(cnts.op_count, 1)
@requires_static_shapes
def test_tensor_build_list_unpack(self):
def fn(x):
# seen in fastNLP_Bert
return torch.cat([*x], dim=-1)
val = torch.randn([1, 1, 473, 768])
correct = fn(val)
cnts = torch._dynamo.testing.CompileCounter()
opt_fn = torch._dynamo.optimize(cnts)(fn)
self.assertTrue(same(opt_fn(val), correct))
self.assertEqual(cnts.frame_count, 1)
self.assertEqual(cnts.op_count, 2)
def test_numpy_int_constant(self):
def fn(x, a, b):
return x + (a % b)
args = [torch.randn(10), 4096, np.int64(8)]
correct = fn(*args)
cnts = torch._dynamo.testing.CompileCounter()
opt_fn = torch._dynamo.optimize(cnts)(fn)
self.assertTrue(same(opt_fn(*args), correct))
self.assertTrue(same(opt_fn(*args), correct))
self.assertEqual(cnts.frame_count, 1)
self.assertEqual(cnts.op_count, 2)
def test_inplace_resize_on_graph_input(self):
cnts = torch._dynamo.testing.CompileCounter()
# graph break when calling resize_() on graph input
def f1(x):
x.resize_(6)
x.mul_(2)
return x
@torch.compile(backend=cnts)
def f2(x):
x.resize_(6)
x.mul_(2)
return x
x = torch.ones(4)
y = torch.ones(4)
self.assertTrue(same(f1(x).shape, f2(y).shape))
self.assertEqual(cnts.frame_count, 1)
self.assertEqual(cnts.op_count, 1) # mul_
def test_dict_mutation_side_effect(self):
def fn(d):
d["c"] = d["a"] + d.pop("b")
return d
args1 = {"a": torch.randn(10), "b": torch.randn(10)}
args2 = dict(args1)
assert fn(args1) is args1
cnts = torch._dynamo.testing.CompileCounter()
opt_fn = torch._dynamo.optimize(cnts)(fn)
self.assertIs(opt_fn(args2), args2)
self.assertTrue(same(args1, args2))
self.assertEqual(cnts.frame_count, 1)
self.assertEqual(cnts.op_count, 1)
def test_module_deepcopy(self):
m1 = torch.nn.Sequential(
torch.nn.Linear(10, 10),
torch.nn.ReLU(),
torch.nn.Linear(10, 10),
torch.nn.ReLU(),
)
m2 = torch.nn.Sequential(
torch.nn.Linear(10, 10),
torch.nn.ReLU(),
torch.nn.Linear(10, 10),
torch.nn.ReLU(),
)
def fn(m, x):
m_copy = copy.deepcopy(m)
return m_copy(x)
v = torch.randn(10)
correct1 = fn(m1, v)
correct2 = fn(m2, v)
cnts = torch._dynamo.testing.CompileCounter()
opt_fn = torch._dynamo.optimize(cnts)(fn)
for _ in range(10):
self.assertTrue(same(opt_fn(m1, v), correct1))
for _ in range(10):
self.assertTrue(same(opt_fn(m2, v), correct2))
self.assertEqual(cnts.frame_count, 1)
self.assertEqual(cnts.op_count, 4)
def test_type_copy(self):
def fn(seq):
a, b = seq
return type(seq)([a + 1, b + 2, a + b])
args1 = [torch.randn(10), torch.randn(10)]
args2 = (torch.randn(10), torch.randn(10))
correct1 = fn(args1)
correct2 = fn(args2)
cnts = torch._dynamo.testing.CompileCounter()
opt_fn = torch._dynamo.optimize(cnts)(fn)
self.assertTrue(same(opt_fn(args1), correct1))
self.assertTrue(same(opt_fn(args2), correct2))
self.assertIsInstance(opt_fn(args1), list)
self.assertIsInstance(opt_fn(args2), tuple)
self.assertEqual(cnts.frame_count, 2)
self.assertEqual(cnts.op_count, 6)
def test_setattr_mutation1(self):
class MyObj: # noqa: B903
def __init__(self, a, b):
self.a = a
self.b = b
def fn(obj):
obj.c = obj.a * obj.b + 1
obj.b = obj.a * obj.c + 2
obj.a = obj.b * obj.c + 3
obj.c = obj.a * obj.b + 4
obj.b = obj.a * obj.c + 5
obj.a = obj.b * obj.c + 6
return obj
x1 = torch.randn(10)
x2 = torch.randn(10)
obj1 = MyObj(x1, x2)
obj2 = MyObj(x1, x2)
fn(obj2)
cnts = torch._dynamo.testing.CompileCounter()
opt_fn = torch._dynamo.optimize(cnts)(fn)
self.assertIs(opt_fn(obj1), obj1)
self.assertTrue(same(obj1.a, obj2.a))
self.assertTrue(same(obj1.b, obj2.b))
self.assertTrue(same(obj1.c, obj2.c))
self.assertEqual(cnts.frame_count, 1)
self.assertEqual(cnts.op_count, 12)
def test_setattr_mutation2(self):
class MyObj:
def __init__(self, x):
self.a = x + 1
self.b = x + 2
def fn(x):
x = x / 3.0
obj = MyObj(x)
obj.c = obj.a * obj.b + 1
obj.b = obj.a * obj.c + 2
obj.a = obj.b * obj.c + 3
return obj
x1 = torch.randn(10)
obj2 = fn(x1)
cnts = torch._dynamo.testing.CompileCounter()
opt_fn = torch._dynamo.optimize(cnts)(fn)
obj1 = opt_fn(x1)
self.assertTrue(same(obj1.a, obj2.a))
self.assertTrue(same(obj1.b, obj2.b))
self.assertTrue(same(obj1.c, obj2.c))
self.assertEqual(cnts.frame_count, 1)
self.assertEqual(cnts.op_count, 9)
def test_setattr_mutation3(self):
# TODO(jansel): dead code eliminate the object creation
class MyObj:
def __init__(self, x):
super().__init__()
self.a = x + 1
self.b = x + 2
def fn(x):
x = x / 3.0
obj = MyObj(x)
obj.c = obj.a * obj.b + 1
obj.b = obj.a * obj.c + 2
obj.a = obj.b * obj.c + 3
return obj.a, obj.b, obj.c
x1 = torch.randn(10)
obj2 = fn(x1)
cnts = torch._dynamo.testing.CompileCounter()
opt_fn = torch._dynamo.optimize(cnts)(fn)
obj1 = opt_fn(x1)
self.assertTrue(same(obj1, obj2))
self.assertEqual(cnts.frame_count, 1)
self.assertEqual(cnts.op_count, 9)
def test_user_defined_class_name(self):
class MyClassFoo:
pass
def fn1(a, b, c):
tmp = MyClassFoo()
if tmp.__class__.__name__ == "MyClassFoo":
return a - b / c
torch._dynamo.testing.standard_test(self, fn=fn1, nargs=3)
def test_user_defined_class_python_type(self):
class MyClass1:
pass
class ExampleMeta(type):
pass
class MyClass2(metaclass=ExampleMeta):
pass
def fn(x, c):
if isinstance(c, MyClass1):
return x + 1
elif isinstance(c, MyClass2):
return x + 2
else:
return x + 3
x = torch.rand(3)
opt_fn = torch._dynamo.optimize("eager")(fn)
for c in [MyClass1, MyClass2]:
ref = fn(x, c)
res = opt_fn(x, c)
self.assertTrue(same(ref, res))
def test_super_calling_with_metaclass(self):
class ExampleMeta(type):
pass
class MyClass1(metaclass=ExampleMeta):
@classmethod
def add(cls, x):
return x + 1
class MyClass2(MyClass1):
@classmethod
def add(cls, x):
torch._dynamo.graph_break()
return x + super().add(x)
def fn(x, obj):
return x + obj.add(x)
x = torch.rand(3)
obj = MyClass2()
opt_fn = torch._dynamo.optimize("eager")(fn)
ref = fn(x, obj)
res = opt_fn(x, obj)
self.assertTrue(same(ref, res))
def test_manual_seed(self):
def fn(a, b):
x = a + b
torch.manual_seed(9000)
return x + 1
torch._dynamo.testing.standard_test(self, fn=fn, nargs=2, expected_ops=3)
def test_usr_cls_staticmethod(self):
class Foo:
@staticmethod
def bar(a, b):
return a + b
def fn(a, b):
return Foo.bar(a, b) - 1
torch._dynamo.testing.standard_test(self, fn=fn, nargs=2)
def test_usr_cls_classmethod(self):
class Foo:
@classmethod
def bar(cls, a, b):
return a + b
def fn(a, b):
return Foo.bar(a, b) - 1
torch._dynamo.testing.standard_test(self, fn=fn, nargs=2)
def test_dunder_methods(self):
class Foo:
def __init__(self, val):
super().__init__()
self.val = val
def __add__(self, other):
return Foo(self.val + other.val)
def __mul__(self, other):
return Foo(self.val * other.val)
def __truediv__(self, other):
return Foo(self.val / other.val)
def __sub__(self, other):
return Foo(self.val - other.val)
def fn(a, b, c):
return Foo(a) + Foo(b) * Foo(c) / Foo(a) - Foo(b)
torch._dynamo.testing.standard_test(self, fn=fn, nargs=3, expected_ops=4)
def test_function_annotation(self):
class Variable:
pass
def fn(x):
x = x / 3.0
def inner(y: typing.List[Variable]):
return x + 1
return inner
x1 = torch.randn(10)
obj2 = fn(x1)([])
cnts = torch._dynamo.testing.CompileCounter()
opt_fn = torch._dynamo.optimize_assert(cnts)(fn)
opt_fn_inner = torch._dynamo.optimize_assert(cnts)(opt_fn(x1))
obj1 = opt_fn_inner([])
self.assertTrue(same(obj1, obj2))
self.assertEqual(cnts.frame_count, 2)
self.assertEqual(cnts.op_count, 2)
def test_nested_closure(self):
v0 = torch.randn(10)
def fn1():
v1 = torch.randn(10)
def fn2(*args, **kwargs):
assert len(args) == 1
assert len(kwargs) == 1
v2 = torch.randn(10) + args[0] + kwargs["b"]
def fn3(v3=torch.randn(10)):
def fn4():
return v0 + v1 + v2 + v3 + 1
return fn4
return fn3
return fn2(1, b=2)()
cnts = torch._dynamo.testing.CompileCounter()
opt_fn1 = torch._dynamo.optimize_assert(cnts)(fn1)
tmp1 = torch._dynamo.optimize_assert(cnts)(opt_fn1())
tmp2 = torch._dynamo.optimize_assert(cnts)(opt_fn1())
self.assertTrue(tmp1().shape, (10,))
self.assertTrue(same(tmp1(), tmp1()))
self.assertFalse(same(tmp1(), tmp2()))
self.assertEqual(cnts.frame_count, 2)
self.assertEqual(cnts.op_count, 9)
def test_nested_closure_mutation(self):
def fn1():
v1 = torch.randn(10)
def fn2():
v2 = torch.randn(10)
def fn3():
nonlocal v1, v2
v1 += 1
v2 += 2
return v1 + v2
return fn3
rv = fn2()
rv()
rv()
return rv
torch.manual_seed(9000)
counter1 = fn1()
result1 = [counter1(), counter1(), counter1()]
torch.manual_seed(9000)
cnts = torch._dynamo.testing.CompileCounter()
opt_fn1 = torch._dynamo.optimize_assert(cnts)(fn1)
counter2 = torch._dynamo.optimize_assert(cnts)(opt_fn1())
result2 = [counter2(), counter2(), counter2()]
result1.append(counter1())
result2.append(counter2())
self.assertTrue(same(result1, result2))
self.assertEqual(cnts.frame_count, 2)
self.assertEqual(cnts.op_count, 11)
def test_write_to_closures_in_inlining(self):
out = []
for use_dynamo in [False, True]:
def make_counter():
x = torch.randn(10)
def counter():
nonlocal x
x = x + 1
return x
return counter
torch.manual_seed(0)
counter = make_counter()
if not use_dynamo:
out.append(counter() + counter())
else:
cnts = torch._dynamo.testing.CompileCounter()
@torch._dynamo.optimize(cnts, nopython=True)
def fn(counter):
return counter() + counter()
out.append(fn(counter))
self.assertEqual(cnts.frame_count, 1)
self.assertEqual(cnts.op_count, 3)
self.assertFalse(same(counter() + counter(), out[-1]))
self.assertTrue(same(out[0], out[1]))
def test_top_package_import(self):
def fn(x):
import torch.fx
assert not isinstance(x, torch.fx.Proxy)
return torch.sin(x)
x = torch.randn(4, 5)
ref = fn(x)
cnts = torch._dynamo.testing.CompileCounter()
opt_fn = torch._dynamo.optimize_assert(cnts)(fn)
res = opt_fn(x)
self.assertTrue(same(ref, res))
def test_typing_union_and_optional(self):
def fn(x):
a = torch.jit.annotate(typing.Dict[str, typing.Optional[torch.Tensor]], {})
b = torch.jit.annotate(
typing.Dict[str, typing.Union[torch.Tensor, None]], {}
)
return a, b, x + 1
x = torch.randn(3)
ref = fn(x)
opt_fn = torch._dynamo.optimize("eager")(fn)
res = opt_fn(x)
self.assertTrue(same(ref, res))
def test_optimize_on_module(self):
class MockModule(torch.nn.Module):
def __init__(self):
super().__init__()
self.relu = torch.nn.ReLU()
def custom_member(self):
# Just for checking that Dynamo returned mod object can redirect
# to this method
pass
def forward(self, x):
return self.relu(x)
cnts1 = torch._dynamo.testing.CompileCounter()
mod = MockModule()
optimized_mod = torch._dynamo.optimize(cnts1, nopython=True)(mod)
a = torch.randn(10)
ref = mod(a)
res = optimized_mod(a)
optimized_mod.custom_member()
self.assertTrue(same(ref, res))
def test_nested_optimize_decorator(self):
cnts2 = torch._dynamo.testing.CompileCounter()
cnts3 = torch._dynamo.testing.CompileCounter()
@torch._dynamo.run()
def fn1(x):
return torch.sin(x) * 10
@torch._dynamo.optimize(cnts2, nopython=True)
def fn2(x):
return fn1(x) + 1
@torch._dynamo.optimize(cnts3, nopython=True)
def fn3(x):
return torch.relu(fn2(x))
fn3(torch.randn(4, 5))
self.assertEqual(cnts2.frame_count, 0)
self.assertEqual(cnts3.frame_count, 1)
self.assertEqual(cnts3.op_count, 4)
def test_nested_optimize_run(self):
cnts = torch._dynamo.testing.CompileCounter()
@torch._dynamo.optimize(cnts, nopython=True)
def fn(x):
return torch.relu(torch.cos(x) + torch.sin(x))
fn(torch.randn(4))
self.assertEqual(cnts.frame_count, 1)
fn(torch.randn(4, 4))
self.assertEqual(cnts.frame_count, 2)
# Test that run works on a decorated fn
fn = torch._dynamo.run(fn)
fn(torch.randn(4, 4, 4))
self.assertEqual(cnts.frame_count, 2)
def test_nested_optimize(self):
cnts1 = torch._dynamo.testing.CompileCounter()
cnts2 = torch._dynamo.testing.CompileCounter()
def fn(x):
return torch.relu(torch.cos(x) + torch.sin(x))
fn1 = torch._dynamo.optimize(cnts1, nopython=True)(fn)
fn2 = torch._dynamo.optimize(cnts2, nopython=True)(fn1)
# The first optimize in the nesting should be ignored
fn2(torch.randn(4))
self.assertEqual(cnts2.frame_count, 1)
self.assertEqual(cnts1.frame_count, 0)
# Since the fn code object is already compiled, calling fn1 should
# directly call the compiled_fn callable.
torch._dynamo.run()(fn1)(torch.randn(4))
self.assertEqual(cnts1.frame_count, 0)
# Test same behavior by reversing the calls
torch._dynamo.reset()
cnts1 = torch._dynamo.testing.CompileCounter()
cnts2 = torch._dynamo.testing.CompileCounter()
fn1 = torch._dynamo.optimize(cnts1, nopython=True)(fn)
fn2 = torch._dynamo.optimize(cnts2, nopython=True)(fn1)
fn1(torch.randn(4))
self.assertEqual(cnts1.frame_count, 1)
torch._dynamo.run()(fn2)(torch.randn(4))
self.assertEqual(cnts2.frame_count, 0)
def test_torch_size(self):
cnts = torch._dynamo.testing.CompileCounter()
def fn(x):
output_size = torch.Size([10, 10])
x = x.view(*output_size)
return (x,)
x = torch.randn(100, requires_grad=True)
x_clone = x.clone()
ref = fn(x)
opt_fn = torch._dynamo.optimize(cnts, nopython=True)(fn)
res = opt_fn(x_clone)
self.assertTrue(same(ref, res))
def test_size_dim(self):
cnts = torch._dynamo.testing.CompileCounter()
def fn(x, dim):
return x.size(dim=dim)
opt_fn = torch._dynamo.optimize(cnts, nopython=True)(fn)
x = torch.empty([4, 9, 8])
self.assertTrue(opt_fn(x, 1) == 9)
self.assertTrue(opt_fn(x, -2) == 9)
def test_stride_dim(self):
cnts = torch._dynamo.testing.CompileCounter()
def fn(x, dim):
return x.stride(dim=dim)
opt_fn = torch._dynamo.optimize(cnts, nopython=True)(fn)
x = torch.empty([4, 9, 8])
self.assertTrue(opt_fn(x, 0) == 72)
self.assertTrue(opt_fn(x, -2) == 8)
def test_torch_seed(self):
cnts = torch._dynamo.testing.CompileCounter()
def fn(x):
attention_seed = int(torch.seed() % sys.maxsize)
torch.manual_seed(attention_seed)
return (x,)
x = torch.randn(100, requires_grad=True)
ref = fn(x)
opt_fn = torch._dynamo.optimize(cnts, nopython=True)(fn)
res = opt_fn(x)
self.assertTrue(same(ref, res))
def test_is_tensor_like(self):
cnts = torch._dynamo.testing.CompileCounter()
def f(x):
if torch.overrides.is_tensor_like(x):
return (x * 2,)
return (torch.ones(10) + x,)
x = torch.randn(10)
ref0 = f(x)
ref1 = f(4)
opt_f = torch._dynamo.optimize(cnts, nopython=True)(f)
res0 = opt_f(x)
res1 = opt_f(4)
self.assertTrue(same(ref0, res0))
self.assertTrue(same(ref1, res1))
def test_is_tensor_like2(self):
class MyTensor:
@classmethod
def __torch_function__(cls, func, types, args=(), kwargs=None):
if kwargs is None:
kwargs = {}
if func is torch.max:
return torch.tensor(123)
return func(*args, **kwargs)
def fn(x):
if torch.overrides.is_tensor_like(x):
return torch.max(x)
else:
return torch.zeros(1)
x = MyTensor()
ref0 = fn(x)
ref1 = fn(4)
opt_fn = torch._dynamo.optimize("eager")(fn)
res0 = opt_fn(x)
res1 = opt_fn(4)
self.assertTrue(same(ref0, res0))
self.assertTrue(same(ref1, res1))
def test_tensor_data(self):
def fn(x, y):
return x[y.data]
x = torch.rand(8)
y = torch.ones(8).to(torch.int)
ref = fn(x, y)
opt_fn = torch._dynamo.optimize("eager", nopython=True)(fn)
res = opt_fn(x, y)
self.assertTrue(same(ref, res))
def test_tensor_layout(self):
def fn(x):
return torch.zeros(
[x.size()[0], x.size()[1]],
dtype=x.dtype,
layout=x.layout,
device=x.device,
)
x = torch.rand(2, 3)
ref = fn(x)
opt_fn = torch._dynamo.optimize("eager", nopython=True)(fn)
res = opt_fn(x)
self.assertTrue(same(ref, res))
def test_version_ci(self):
# temporary test to check that the ci torch version is set correctly
self.assertTrue(hasattr(torch, "_subclasses"))
@unittest.skipIf(not torch.cuda.is_available(), "requires cuda")
def test_rand(self):
cnts = torch._dynamo.testing.CompileCounter()
device = "cuda"
def fn():
return torch.randn(10, device=device)
torch.manual_seed(10)
ref_run1 = fn()
torch.manual_seed(10)
ref_run2 = fn()
self.assertTrue(same(ref_run1, ref_run2))
torch.manual_seed(10)
opt_fn = torch._dynamo.optimize(cnts, nopython=True)(fn)
res = opt_fn()
self.assertTrue(same(res, ref_run1))
def test_slice_input(self):
cnts = torch._dynamo.testing.CompileCounter()
def getitem(a, idx):
if isinstance(idx, slice):
return (
torch.zeros(1),
a[idx]
+ [
100,
],
)
else:
return (torch.zeros(1), a[idx])
layers = list(range(10))
ref0 = getitem(layers, slice(0, 2, 1))
ref1 = getitem(layers, 2)
ref2 = getitem(layers, slice(3, 8, 2))
opt_getitem = torch._dynamo.optimize(cnts, nopython=True)(getitem)
res0 = opt_getitem(layers, slice(0, 2, 1))
res1 = opt_getitem(layers, 2)
res2 = opt_getitem(layers, slice(3, 8, 2))
self.assertTrue(ref0 == res0)
self.assertTrue(ref1 == res1)
self.assertTrue(ref2 == res2)
def test_grad(self):
cnts = torch._dynamo.testing.CompileCounter()
def fn(a, b):
out = a * b
out.sum().backward()
real_out = torch.sigmoid(a.grad + b)
return real_out
inps = [torch.randn(4, requires_grad=True) for _ in range(2)]
for inp in inps:
inp.grad = None
ref = fn(*inps)
for inp in inps:
inp.grad = None
opt_fn = torch._dynamo.optimize(cnts)(fn)
res = opt_fn(*inps)
self.assertTrue(same(ref, res))
@skipIfNotPy311
def test_linetable_311_writer1(self):
def fn():
a = 10
b = 20
c = a + b
f = "linetable_writer"
return f"Test if {f} generates correct co_linetable: {c}"
# Dynamo doesn't deal with column locations or end line numbers,
# so we only check that start line numbers in the linetables match.
keys = bytecode_transformation.get_code_keys()
code_options = {k: getattr(fn.__code__, k) for k in keys}
result = bytecode_transformation.clean_and_assemble_instructions(
bytecode_transformation.cleaned_instructions(fn.__code__),
keys,
code_options,
)
l1, l2 = list(fn.__code__.co_positions()), list(result[1].co_positions())
self.assertEqual(len(l1), len(l2))
for p1, p2 in zip(l1, l2):
# check that start line numbers match
self.assertEqual(p1[0], p2[0])
self.assertEqual(fn.__code__.co_lnotab, result[1].co_lnotab)
@skipIfNotPy311
def test_linetable_311_writer2(self):
"""
test large ops (LOAD_METHOD) and EXTENDED_ARGS
fn_str is in the form:
def fn():
...
x0 = 1
x1 = 1
...
l = [x0, x1, ...]
"""
fn_str = f"""\
def fn():
foo.bar(1, 2, 3)
{str(chr(10)).join(' ' * 4 + 'x' + str(i) + ' = 1' for i in range(1 << 9))}
l = [{str(' ').join('x' + str(i) + ',' for i in range(1 << 9))}]
"""
locals = {}
exec(fn_str, {}, locals)
fn = locals["fn"]
orig_inst_str = "\n".join(list(map(str, dis.get_instructions(fn))))
self.assertIn("EXTENDED_ARG", orig_inst_str)
self.assertIn("LOAD_METHOD", orig_inst_str)
keys = bytecode_transformation.get_code_keys()
code_options = {k: getattr(fn.__code__, k) for k in keys}
result = bytecode_transformation.clean_and_assemble_instructions(
bytecode_transformation.cleaned_instructions(fn.__code__),
keys,
code_options,
)
new_inst_str = "\n".join(list(map(str, result[0])))
self.assertIn("EXTENDED_ARG", new_inst_str)
self.assertIn("LOAD_METHOD", new_inst_str)
l1, l2 = list(fn.__code__.co_positions()), list(result[1].co_positions())
self.assertEqual(len(l1), len(l2))
for p1, p2 in zip(l1, l2):
# check that start line numbers match
self.assertEqual(p1[0], p2[0])
self.assertEqual(fn.__code__.co_lnotab, result[1].co_lnotab)
@unittest.skipIf(
sys.version_info < (3, 10) or sys.version_info >= (3, 11),
"linetable test for Python 3.10",
)
def test_linetable_310_writer(self):
def fn():
a = 10
b = 20
c = a + b
f = "linetable_writer"
return f"Test if {f} generates correct co_linetable: {c}"
inst = dis.get_instructions(fn)
result = bytecode_transformation.assemble(inst, fn.__code__.co_firstlineno)
self.assertTrue(result[1] == fn.__code__.co_linetable)
@unittest.skipIf(sys.version_info >= (3, 10), "use lnotab when python < 3.10")
def test_lnotab_writer(self):
def fn():
a = 10
b = 20
c = a + b
f = "lnotab_writer"
return f"Test if {f} generates correct co_lnotab: {c}"
inst = dis.get_instructions(fn)
result = bytecode_transformation.assemble(inst, fn.__code__.co_firstlineno)
self.assertTrue(result[1] == fn.__code__.co_lnotab)
def test_profiler_cache_lookup(self):
def fn(x):
y = x**2
y = y + 2
z = y**3
return z
for profiler, get_events in (
(torch.autograd.profiler.profile, lambda prof: prof.function_events),
(torch.profiler.profiler.profile, lambda prof: prof.events()),
):
x = torch.randn((2, 2), requires_grad=True)
ref = fn(x)
opt_fn = torch.compile(fn, backend="aot_eager")
# warmup
opt_fn(x)
# whenver we enter the profiler context, hooks are automatically registered
with profiler() as prof:
res = opt_fn(x)
events = list(
filter(
lambda event: event.name == "TorchDynamo Cache Lookup",
get_events(prof),
)
)
self.assertTrue(same(ref, res))
self.assertTrue(
len(events) == 1,
"Expected one lookup profiler event for one opt_fn run",
)
with profiler() as prof:
# just make sure the disable functionality works
_enable_dynamo_cache_lookup_profiler(False)
res = opt_fn(x)
events = list(
filter(
lambda event: event.name == "TorchDynamo Cache Lookup",
get_events(prof),
)
)
self.assertTrue(same(ref, res))
self.assertTrue(len(events) == 0, "Expected disabled profiling")
def test_tensor_is_contiguous(self):
def fn(x):
input = torch.randn((1, 16, 1, 1))
weight = torch.randn((8, 16, 3, 3))
weight = weight.to(memory_format=x)
output = torch.conv2d(input, weight, None, (2, 1), (1, 1), (1, 1), 1)
return output.is_contiguous(memory_format=x)
opt_fn = torch._dynamo.optimize("eager")(fn)
for x in [torch.contiguous_format, torch.channels_last]:
self.assertEqual(fn(x), opt_fn(x))
def test_python_slice(self):
def f1(input):
y = 0
for i, x in enumerate(input[2:], 1):
y = y + x
return y
def f2(input):
y = 0
for i, x in enumerate(input.shape[2:], 1):
y = y + x
return y
cnts = torch._dynamo.testing.CompileCounter()
opt_f1 = torch._dynamo.optimize(cnts)(f1)
opt_f2 = torch._dynamo.optimize(cnts)(f2)
res1 = opt_f1([1, 2, 3, 5])
res2 = opt_f2(torch.rand([2, 3, 4, 5]))
self.assertEqual(res1, 8)
self.assertEqual(res2, 9)
def test_enum_as_dict_key(self):
class MyEnum(enum.Enum):
FOO = 10
BAR = 20
def fn(x):
y = x + 2
z = {
MyEnum.FOO: torch.tensor(1),
MyEnum.BAR: 10,
"MyEnum.BAR": torch.tensor(8),
5: torch.rand(3),
}
torch._dynamo.graph_break()
a = z[MyEnum.FOO] + z["MyEnum.BAR"]
b = y * 2
return a, b
cnts = torch._dynamo.testing.CompileCounter()
opt_fn = torch._dynamo.optimize(cnts)(fn)
for _ in range(10):
x = torch.rand(3)
ref = fn(x)
res = opt_fn(x)
self.assertTrue(same(ref, res))
self.assertEqual(cnts.frame_count, 2)
def test_const_dict_variable_python_type(self):
from torch._dynamo.variables import ConstantVariable, ConstDictVariable
d1 = {"a": ConstantVariable(10), "b": ConstantVariable(20)}
d2 = collections.OrderedDict(
[("x", ConstantVariable(12)), ("y", ConstantVariable(22))]
)
self.assertEqual(ConstDictVariable(d1, dict).python_type(), dict)
self.assertEqual(
ConstDictVariable(d2, collections.OrderedDict).python_type(),
collections.OrderedDict,
)
def test_builtin_subclasses_as_method_on_class_type(self):
class Foo:
def __init__(self, name):
self.ame_ = name
def get_name(self):
return "Foo " + self.name_
class Bar(Foo):
def __init__(self, name):
self.name_ = name
def get_name(self):
return "Bar " + self.name_
class Baz(Foo):
def __init__(self, name): # noqa: B903
self.name_ = name
def get_name(self):
return "Baz " + self.name_
subs_of_foo_reg = Foo.__subclasses__()
counter = CompileCounter()
@torch._dynamo.optimize_assert(counter)
def fn():
return Foo.__subclasses__()
subs_of_foo_optim = fn()
self.assertEqual(len(subs_of_foo_reg), 2)
self.assertEqual(subs_of_foo_reg, subs_of_foo_optim)
def test_builtin_subclasses_as_method_on_var(self):
class Foo:
def __init__(self, name):
self.name_ = name
def get_name(self):
return "Foo " + self.name_
class Bar(Foo):
def __init__(self, name):
self.name_ = name
def get_name(self):
return "Bar " + self.name_
class Baz(Bar):
def __init__(self, name):
self.name_ = name
def get_name(self):
return "Baz " + self.name_
subs_of_foo_reg = Foo.__subclasses__()
sub_of_foo_subclass_var_reg = subs_of_foo_reg[0].__subclasses__()
sub_of_foo_subclass_var_optim = list()
counter = CompileCounter()
@torch._dynamo.optimize_assert(counter)
def fn():
return Foo.__subclasses__()
@torch._dynamo.optimize_assert(counter)
def fn_single(subs_of_foo_optim):
return subs_of_foo_optim[0].__subclasses__()
subs_of_foo_optim = fn()
sub_of_foo_subclass_var_optim = fn_single(subs_of_foo_optim)
self.assertEqual(len(sub_of_foo_subclass_var_optim), 1)
self.assertEqual(sub_of_foo_subclass_var_optim, sub_of_foo_subclass_var_reg)
def test_enum_no_graphbreaks(self):
class Foo(enum.Enum):
FOO = 0
BAR = 1
def fn(x, foo):
if foo is Foo.FOO:
x = torch.add(x, 1.0)
x = torch.mul(x, 1.0)
return x
x = torch.randn(1)
cnts = torch._dynamo.testing.CompileCounter()
opt_fn = torch._dynamo.optimize(cnts, nopython=True)(fn)
opt_fn(x, Foo.FOO)
self.assertEqual(cnts.op_count, 2)
torch._dynamo.reset()
cnts = torch._dynamo.testing.CompileCounter()
opt_fn = torch._dynamo.optimize(cnts, nopython=True)(fn)
opt_fn(x, Foo.BAR)
self.assertEqual(cnts.op_count, 1)
@patch.object(torch._dynamo.config, "dynamic_shapes", True)
def test_repeat_interleave_graphbreaks(self):
def fn_no_breaks(x):
# no breaks on self_int
x += 1
x = torch.repeat_interleave(x, 2, 3)
x += 1
return x
def fn_has_breaks(x):
# breaks on self_Tensor
x += 1
x = torch.repeat_interleave(x, torch.tensor(2), 3)
x += 1
return x
x = torch.randn([4, 16, 1, 64])
cnts = torch._dynamo.testing.CompileCounter()
opt_fn = torch._dynamo.optimize(cnts)(fn_no_breaks)
opt_fn(x)
self.assertEqual(cnts.frame_count, 1)
torch._dynamo.reset()
cnts = torch._dynamo.testing.CompileCounter()
opt_fn = torch._dynamo.optimize(cnts)(fn_has_breaks)
opt_fn(x)
self.assertEqual(cnts.frame_count, 2)
def test_id_of_nn_module(self):
class M(torch.nn.Module):
def forward(self, x, ref_id):
self_id = id(self)
if self_id == ref_id:
x = torch.mul(x, 1.0)
x = torch.add(x, 1.0)
return x
m = M().eval()
data = torch.randn(1)
cnts = torch._dynamo.testing.CompileCounter()
correct_ref_id = id(m)
opt_m = torch._dynamo.optimize(cnts, nopython=True)(m)
opt_m(data, correct_ref_id)
# Extra op is the recorded equality test (although once
# the trace is flattened this is dead!)
self.assertEqual(cnts.op_count, ifunspec(3, 2))
torch._dynamo.reset()
cnts = torch._dynamo.testing.CompileCounter()
incorrect_ref_id = id(m) + 1
opt_m = torch._dynamo.optimize(cnts, nopython=True)(m)
opt_m(data, incorrect_ref_id)
self.assertEqual(cnts.op_count, ifunspec(2, 1))
def test_inline_func_jump_on_tensor_condition(self):
def f1(input):
if input == 0:
return input + 1
else:
return input + 2
def f2(input):
return f1(input)
cnts = torch._dynamo.testing.CompileCounter()
opt_f2 = torch._dynamo.optimize(cnts)(f2)
res1 = opt_f2(torch.tensor([1.0]))
res2 = opt_f2(torch.tensor([0.0]))
self.assertEqual(res1, 3)
self.assertEqual(res2, 1)
def test_frozenset_torch_func_contains(self):
funcs = frozenset([torch.add])
def fn(x, func):
if func in funcs:
x = torch.add(x, 1.0)
x = torch.mul(x, 1.0)
return x
x = torch.randn(1)
cnts = torch._dynamo.testing.CompileCounter()
opt_fn = torch._dynamo.optimize(cnts, nopython=True)(fn)
opt_fn(x, torch.add)
self.assertEqual(cnts.op_count, 2)
torch._dynamo.reset()
cnts = torch._dynamo.testing.CompileCounter()
opt_fn = torch._dynamo.optimize(cnts, nopython=True)(fn)
opt_fn(x, torch.mul)
self.assertEqual(cnts.op_count, 1)
def test_inline_list_mutation(self):
def f1(x):
x.append(torch.ones(8))
return x
def f2():
x = [torch.ones(6)]
f1(x)
return x
res1 = f2()
cnts = torch._dynamo.testing.CompileCounter()
opt_f2 = torch._dynamo.optimize(cnts)(f2)
res2 = opt_f2()
self.assertTrue(same(res1, res2))
def test_inline_dict_mutation(self):
def f1(d):
d["c"] = d["a"] + d.pop("b")
return d
def f2():
d = {"a": torch.ones(5), "b": torch.ones(5)}
f1(d)
return d
res1 = f2()
cnts = torch._dynamo.testing.CompileCounter()
opt_f2 = torch._dynamo.optimize(cnts)(f2)
res2 = opt_f2()
self.assertTrue(same(res1, res2))
def test_recursive_inline_list_mutation(self):
def f1(x, y):
x.append(torch.tensor([1.1]))
y.append(torch.tensor([1.2]))
return x, y
def f2(x, y):
x.append(torch.tensor([2.1]))
y.append(torch.tensor([2.2]))
f1(x, y)
return x, y
def f3(x):
x.append(torch.tensor([3.1]))
y = [torch.tensor([3.2])]
f2(x, y)
return x, y
def f4():
x = [torch.tensor([4.1])]
return f3(x)
res1 = f4()
cnts = torch._dynamo.testing.CompileCounter()
opt_f4 = torch._dynamo.optimize(cnts)(f4)
res2 = opt_f4()
self.assertTrue(same(res1, res2))
def test_sample_input(self):
from torch.testing._internal.common_methods_invocations import SampleInput
def fn(sample):
if isinstance(sample.input, torch.Tensor):
return sample.input * 2
return torch.zeros(())
sample = SampleInput(torch.ones(2))
ref = fn(sample)
opt_fn = torch._dynamo.optimize("eager")(fn)
res = opt_fn(sample)
self.assertTrue(same(ref, res))
def test_release_input_memory(self):
x = torch.rand([4])
x_ref = weakref.ref(x)
cnts = torch._dynamo.testing.CompileCounter()
@torch._dynamo.optimize(cnts)
def foo(x):
return x + x
out = foo(x)
self.assertTrue(same(out, x + x))
del x
self.assertIs(x_ref(), None)
def test_release_module_memory(self):
mod = torch.nn.Linear(10, 10)
x = torch.rand([10, 10])
mod_weight_ref = weakref.ref(mod.weight)
mod_ref = weakref.ref(mod)
# Modules that are passed into torch._dynamo optimized functions
# will normally be held onto through the generated GraphModule,
# which contains the modules. remove the reference in this backend
# and test that no additional references are being held.
class NoLeakBackend:
def __call__(self, gm: torch.fx.GraphModule, example_inputs):
gm.mod = None
def foo(*args, **kwargs):
return (1,)
return foo
no_leak_backend = NoLeakBackend()
@torch._dynamo.optimize(no_leak_backend)
def foo(mod, x):
return mod(x)
foo(mod, x)
del mod
del x
self.assertIsNone(mod_ref(), None)
self.assertIsNone(mod_weight_ref(), None)
def test_update_locals_and_stack_uses_shared_cache(self):
def fn(x):
perm = [0, 3, 5]
perm = list(range(min(perm))) + perm
perm.extend(i for i in range(x.dim()) if i not in perm)
return perm
x = torch.rand([2, 2, 2, 2, 2, 2])
res1 = fn(x)
cnts = torch._dynamo.testing.CompileCounter()
opt_fn = torch._dynamo.optimize(cnts)(fn)
res2 = opt_fn(x)
self.assertTrue(same(res1, res2))
def test_dict_reconstruct_keeps_original_order(self):
def fn():
modules = collections.OrderedDict([("act", torch.nn.ReLU())])
module_dict = torch.nn.ModuleDict(modules)
next_modules = {"fc4": torch.nn.Linear(5, 6), "act3": torch.nn.Sigmoid()}
modules.update(next_modules.items())
module_dict.update(next_modules)
return modules, module_dict
cnts = torch._dynamo.testing.CompileCounter()
opt_fn = torch._dynamo.optimize(cnts)(fn)
modules, module_dict = opt_fn()
self.assertEqual(len(module_dict), len(modules))
for k1, m2 in zip(modules, module_dict.children()):
self.assertTrue(modules[k1] is m2)
def test_side_effects_codegen_update_mutated(self):
# codegen to update mutated variables with side effect
# should after stack value's codegen
def f1(x):
alist = [x]
alist.append(x + 1)
alist[0].sum().item() # graph break
res = alist.pop()
res.sum().item() # graph break
return res
def f2(a, b):
d = {"a": a + 1, "b": b + 2}
x = d.pop("b")
x.sum().item() # graph break
y = d["a"] + x
y.sum().item() # graph break
d["c"] = y
return d
x = torch.rand([2, 3])
a = torch.rand([5, 6])
b = torch.rand([5, 6])
res11 = f1(x)
res21 = f2(a, b)
cnts = torch._dynamo.testing.CompileCounter()
opt_f1 = torch._dynamo.optimize(cnts)(f1)
opt_f2 = torch._dynamo.optimize(cnts)(f2)
res12 = opt_f1(x)
res22 = opt_f2(a, b)
self.assertTrue(same(res11, res12))
self.assertTrue(same(res21, res22))
def test_list_append_return_none(self):
def fn(x):
alist = []
blist = alist.append(x + 1)
return alist, blist
x = torch.tensor([2.3])
res = fn(x)
cnts = torch._dynamo.testing.CompileCounter()
opt_fn = torch._dynamo.optimize(cnts)(fn)
res2 = opt_fn(x)
self.assertEqual(res, res2)
def test_tensor_types(self):
def fn(dtype, tensor_type):
x = torch.empty(4, dtype=dtype)
assert isinstance(x, tensor_type)
opt_fn = torch._dynamo.optimize("eager")(fn)
opt_fn(torch.float32, torch.FloatTensor)
opt_fn(torch.float64, torch.DoubleTensor)
opt_fn(torch.float16, torch.HalfTensor)
opt_fn(torch.bfloat16, torch.BFloat16Tensor)
opt_fn(torch.uint8, torch.ByteTensor)
opt_fn(torch.int8, torch.CharTensor)
opt_fn(torch.int64, torch.LongTensor)
opt_fn(torch.int, torch.IntTensor)
opt_fn(torch.int16, torch.ShortTensor)
opt_fn(torch.bool, torch.BoolTensor)
def test_nan(self):
def f(x, n):
return x * 2 + n
x = torch.randn(4)
n = float("nan")
cnts = torch._dynamo.testing.CompileCounter()
opt_f = torch._dynamo.optimize(cnts)(f)
opt_f(x, n)
opt_f(x, n)
self.assertEqual(cnts.frame_count, 1)
@patch.object(torch._dynamo.config, "dynamic_shapes", True)
@patch.object(torch._dynamo.config, "capture_scalar_outputs", True)
def test_item(self):
class MyMod(torch.nn.Module):
def forward(self, x):
z = torch.max(x)
return z.int().item()
x = torch.tensor([[10.6763, 11.7445, -2.2369]])
model = MyMod()
y = torch._dynamo.optimize("eager", nopython=True)(model)(x)
self.assertEqual(y, 11)
@patch.object(torch._dynamo.config, "dynamic_shapes", True)
@patch.object(torch._dynamo.config, "capture_scalar_outputs", True)
def test_item_changes(self):
class MyMod(torch.nn.Module):
def forward(self, x):
z = torch.max(x)
return z.int().item()
x = torch.tensor([[10.6763, 11.7445, -2.2369]])
model = MyMod()
opt_model = torch._dynamo.optimize("eager", nopython=True)(model)
y = opt_model(x)
z = opt_model(torch.tensor([[y - 5, y + 10, y + 50]]))
self.assertEqual(y, 11)
self.assertEqual(z, 61)
@patch.object(torch._dynamo.config, "dynamic_shapes", True)
@patch.object(torch._dynamo.config, "capture_scalar_outputs", True)
def test_item_changes_new_shape(self):
class MyMod(torch.nn.Module):
def forward(self, x):
z = torch.max(x)
return z.int().item()
x = torch.tensor([[10.6763, 11.7445, -2.2369]])
model = MyMod()
opt_model = torch._dynamo.optimize("eager", nopython=True)(model)
y = opt_model(x)
z = opt_model(torch.tensor([[y - 5, y + 50], [y + 5, y - 50]]))
self.assertEqual(y, 11)
self.assertEqual(z, 61)
@unittest.skip("https://github.com/pytorch/pytorch/issues/99726")
def test_cross_entropy_loss_fancy_ctor1(self):
rand_5 = torch.randn(5)
rand_3_5 = torch.randn(3, 5)
target = torch.empty(3, dtype=torch.long).random_(5)
loss = torch.nn.CrossEntropyLoss(
weight=rand_5, reduce=False, label_smoothing=0.5
)
opt_loss = torch._dynamo.optimize("eager", nopython=True)(loss)
input = rand_3_5
dynamo_output = opt_loss(input, target)
loss = torch.nn.CrossEntropyLoss(
weight=rand_5, reduce=False, label_smoothing=0.5
)
input = rand_3_5
output = loss(input, target)
self.assertTrue(torch.allclose(dynamo_output, output))
@requires_static_shapes
def test_cross_entropy_loss_fancy_ctor2(self):
rand_3_5 = torch.randn(3, 5)
target = torch.empty(3, dtype=torch.long).random_(5)
loss = torch.nn.CrossEntropyLoss(reduce=False, label_smoothing=0.5)
opt_loss = torch._dynamo.optimize("eager", nopython=True)(loss)
input = rand_3_5
dynamo_output = opt_loss(input, target)
loss = torch.nn.CrossEntropyLoss(reduce=False, label_smoothing=0.5)
input = rand_3_5
output = loss(input, target)
self.assertTrue(torch.allclose(dynamo_output, output))
def test_cross_entropy_loss_simple_ctor(self):
output = None
rand_3_5 = torch.randn(3, 5)
target = torch.empty(3, dtype=torch.long).random_(5)
loss = torch.nn.CrossEntropyLoss()
opt_loss = torch._dynamo.optimize("eager", nopython=True)(loss)
input = rand_3_5
dynamo_output = opt_loss(input, target)
loss = torch.nn.CrossEntropyLoss()
input = rand_3_5
output = loss(input, target)
self.assertTrue(torch.allclose(dynamo_output, output))
def test_nn_functional_reduction(self):
def fn(loss, reduction):
reduction_enum = F._Reduction.get_enum(reduction)
if reduction_enum == 0:
return loss
elif reduction_enum == 1:
return loss.mean()
elif reduction_enum == 2:
return loss.sum()
x = torch.rand([3, 5])
y = "mean"
ref = fn(x, y)
opt_fn = torch._dynamo.optimize("eager", nopython=True)(fn)
res = opt_fn(x, y)
self.assertTrue(torch.allclose(ref, res))
def test_large_reduction_list(self):
dtype = torch.float32
device = "cpu"
def check_sum_all(tensor: torch.Tensor) -> None:
pylist = tensor.reshape(-1).tolist()
self.assertTrue(same(tensor.sum(), torch.tensor(sum(pylist))))
check_sum_all(torch.randn(200000, dtype=dtype, device=device))
def test_raise_on_backend_error(self):
def my_compiler(gm, _):
raise RuntimeError("duck!")
@torch._dynamo.optimize(my_compiler)
def fn(a, b):
return a + b / (a - b)
self.assertRaises(
torch._dynamo.exc.BackendCompilerFailed,
lambda: fn(torch.randn(10), torch.randn(10)),
)
def test_named_parameters(self):
n_embd = 768
block_size = 128
vocab_size = 65
embd_pdrop = 0.1
class MyModel2(torch.nn.Module):
def __init__(self):
super().__init__()
self.tok_emb = torch.nn.Embedding(vocab_size, n_embd)
self.pos_emb = torch.nn.Parameter(torch.zeros(1, block_size, n_embd))
self.drop = torch.nn.Dropout(embd_pdrop)
def forward(self, x):
return x
class MyModel(torch.nn.Module):
def __init__(self):
super().__init__()
self.tok_emb = torch.nn.Embedding(vocab_size, n_embd)
self.pos_emb = torch.nn.Parameter(torch.zeros(1, block_size, n_embd))
self.drop = torch.nn.Dropout(embd_pdrop)
self.submod2 = MyModel2()
def forward(self, x):
return x
# Regular
params = []
mod = MyModel()
actual_params = list(mod.named_parameters())
@torch._dynamo.optimize("eager", nopython=True)
def fn():
return list(mod.named_parameters())
params = fn()
self.assertEqual(len(actual_params), len(params))
for idx in range(len(params)):
k_a, v_a = actual_params[idx]
k, v = params[idx]
self.assertEqual(k_a, k)
self.assertTrue(torch.allclose(v_a, v))
# Prefix
params = []
mod = MyModel()
actual_params = list(mod.named_parameters(prefix="foo"))
@torch._dynamo.optimize("eager", nopython=True)
def fn1():
return list(mod.named_parameters(prefix="foo"))
params = fn1()
self.assertEqual(len(actual_params), len(params))
for idx in range(len(params)):
k_a, v_a = actual_params[idx]
k, v = params[idx]
self.assertEqual(k_a, k)
self.assertTrue(torch.allclose(v_a, v))
def test_module_complex_iter(self):
n_embd = 768
block_size = 128
vocab_size = 65
embd_pdrop = 0.1
class FakeGPT(torch.nn.Module):
def __init__(self):
super().__init__()
self.tok_emb = torch.nn.Embedding(vocab_size, n_embd)
self.pos_emb = torch.nn.Parameter(torch.zeros(1, block_size, n_embd))
self.drop = torch.nn.Dropout(embd_pdrop)
self.ln_f = torch.nn.LayerNorm(n_embd)
self.head = torch.nn.Linear(n_embd, vocab_size, bias=False)
self.block_size = block_size
self.names = []
def forward(self, idx, targets=None):
b, t = idx.size()
assert (
t <= self.block_size
), "Cannot forward, model block size is exhausted."
# forward the GPT model
token_embeddings = self.tok_emb(
idx
) # each index maps to a (learnable) vector
position_embeddings = self.pos_emb[
:, :t, :
] # each position maps to a (learnable) vector
x = self.drop(token_embeddings + position_embeddings)
x = self.blocks(x)
x = self.ln_f(x)
logits = self.head(x)
# if we are given some desired targets also calculate the loss
loss = None
if targets is not None:
loss = F.cross_entropy(
logits.view(-1, logits.size(-1)), targets.view(-1)
)
return logits, loss
def foo(self, memo=None, prefix="", remove_duplicate=False):
for mn, m in self.named_modules(
memo=memo, prefix=prefix, remove_duplicate=remove_duplicate
):
for pn, p in self.named_parameters():
fpn = "%s.%s" % (mn, pn) if mn else pn
self.names.append(fpn)
# Test plain recurse
model_a = FakeGPT()
model_a.foo()
a_names = model_a.names
model_b = FakeGPT()
opt_model_b = torch._dynamo.optimize("eager", nopython=True)(model_b)
opt_model_b.foo()
self.assertEqual(a_names, model_b.names)
# Test with prefix
model_a = FakeGPT()
model_a.foo(prefix="abc")
a_names = model_a.names
model_b = FakeGPT()
opt_model_b = torch._dynamo.optimize("eager", nopython=True)(model_b)
opt_model_b.foo(prefix="abc")
self.assertEqual(a_names, model_b.names)
def test_numpy_variable_isinstance(self):
def fn(x, m):
if isinstance(m, np.ndarray):
return x + 1
else:
return x - 1
x = torch.tensor([2.3])
m = np.array([1, 2, 3])
ref = fn(x, m)
cnts = torch._dynamo.testing.CompileCounter()
opt_fn = torch._dynamo.optimize(cnts)(fn)
res = opt_fn(x, m)
self.assertEqual(ref, res)
def test_tensor_dot_grad_no_graph_break(self):
def fn(a, b):
y = 3 * a**3 - b**2
y.backward(gradient=torch.tensor([1.0, 1.0]))
b.grad.zero_()
return a.grad, b.grad
a = torch.tensor([2.0, 3.0], requires_grad=True)
b = torch.tensor([6.0, 4.0], requires_grad=True)
cnts = torch._dynamo.testing.CompileCounter()
opt_fn = torch._dynamo.optimize(cnts)(fn)
_, b_grad = opt_fn(a, b)
self.assertTrue(same(b_grad, torch.tensor([0.0, 0.0])))
self.assertEqual(cnts.frame_count, 2)
def test_torch_nn_parameter_isinstance(self):
def fn(x):
a = torch.nn.Parameter(torch.rand(2, 3))
if isinstance(a, torch.Tensor):
return x + 1
else:
return x - 1
x = torch.tensor([2.5])
ref = fn(x)
opt_fn = torch._dynamo.optimize("eager")(fn)
res = opt_fn(x)
self.assertEqual(ref, res)
@torch._dynamo.config.patch(raise_on_backend_change=True)
def test_change_backends(self):
@torch._dynamo.optimize("eager", nopython=True)
def fn1():
return x + 1
@torch._dynamo.optimize("ts")
def fn2():
return x + 2
@torch._dynamo.optimize("eager", nopython=False)
def fn3():
return x + 1
x = torch.tensor([3, 5])
fn1()
fn1()
fn3()
self.assertRaises(torch._dynamo.exc.ResetRequired, fn2)
fn1()
torch._dynamo.reset()
fn2()
fn2()
self.assertRaises(torch._dynamo.exc.ResetRequired, fn1)
self.assertRaises(torch._dynamo.exc.ResetRequired, fn3)
fn2()
def test_dynamo_min_operator_with_shape(self):
@torch._dynamo.optimize("eager", nopython=True)
def f(x, a):
return min(x.shape[0], a)
result = f(torch.ones(6), 3)
self.assertEqual(result, 3)
@patch.object(torch._dynamo.config, "dynamic_shapes", True)
def test_onnx_shape_as_tensor(self):
@torch._dynamo.optimize("eager", nopython=True)
def f(x):
return 1 + torch._shape_as_tensor(x)[0]
gm, _ = torch._dynamo.export(f, torch.ones(6))
input_one_dim = torch.ones(6)
input_two_dims = torch.ones(7, 4)
self.assertEqual(f(input_one_dim), 7)
self.assertEqual(f(input_two_dims), 8)
self.assertEqual(f(input_two_dims), 8)
@torch._dynamo.optimize("eager", nopython=True)
def f_onnx(x):
return 1 + torch.onnx.operators.shape_as_tensor(x)[0]
self.assertEqual(f_onnx(input_one_dim), 7)
self.assertEqual(f_onnx(input_two_dims), 8)
self.assertEqual(f_onnx(input_two_dims), 8)
def test_cond(self):
from functorch.experimental.control_flow import cond
def true_fn(x):
return x.sin()
def false_fn(x):
return x.cos()
def f(pred, x):
return cond(pred, true_fn, false_fn, [x])
opt_fn = torch._dynamo.optimize("eager")(f)
a = opt_fn(torch.tensor(False), torch.tensor([0.25, 0.25]))
self.assertTrue(same(torch.cos(torch.tensor([0.25, 0.25])), a))
b = opt_fn(torch.tensor(True), torch.tensor([0.25, 0.25]))
self.assertTrue(same(torch.sin(torch.tensor([0.25, 0.25])), b))
def test_nonzero_static(self):
# invalid size
with self.assertRaisesRegex(
RuntimeError, "nonzero_static: 'size' must be an non-negative integer"
):
torch.nonzero_static(torch.tensor([8]), size=-2)
with self.assertRaisesRegex(
RuntimeError, "nonzero_static: 'size' must be an non-negative integer"
):
torch.nonzero_static(torch.tensor([8]), size=-2, out=torch.tensor(0))
# nonzero_static.out: out dtype mismatch
input_tensor = torch.tensor([8])
static_size = 1
out_tensor = torch.empty((static_size, input_tensor.dim()), dtype=torch.float)
with self.assertRaisesRegex(
RuntimeError, "nonzero_static: Expected out tensor to have scalar type Long"
):
torch.nonzero_static(input_tensor, size=static_size, out=out_tensor)
# nonzero_static.out: out resize (shrink)
input_tensor = torch.tensor([8])
static_size = 1
out_tensor = torch.empty((10, 10, 10, 10), dtype=torch.long)
self.assertTrue(
same(
torch.nonzero_static(input_tensor, size=static_size, out=out_tensor),
torch.tensor([0]),
)
)
self.assertTrue(
same(
out_tensor,
torch.tensor([0]),
)
)
# nonzero_static.out: out resize (enlarge)
input_tensor = torch.tensor([8])
static_size = 1
out_tensor = torch.empty((0), dtype=torch.long)
self.assertTrue(
same(
torch.nonzero_static(input_tensor, size=static_size, out=out_tensor),
torch.tensor([0]),
)
)
self.assertTrue(
same(
out_tensor,
torch.tensor([0]),
)
)
# 0 rank
input_tensor = torch.tensor(6)
static_size = 2
self.assertTrue(
same(
torch.nonzero_static(input_tensor, size=static_size),
torch.empty((static_size, input_tensor.dim()), dtype=torch.long),
)
)
# 0 size
input_tensor = torch.tensor([[[1]]])
static_size = 0
self.assertTrue(
same(
torch.nonzero_static(input_tensor, size=static_size),
torch.empty((static_size, input_tensor.dim()), dtype=torch.long),
)
)
# 1D input
input_tensor = torch.tensor([0, 8])
static_size = 1
self.assertTrue(
same(
torch.nonzero_static(input_tensor, size=static_size),
torch.tensor([1]),
)
)
input_tensor = torch.tensor([8, 0])
static_size = 2
self.assertTrue(
same(
torch.nonzero_static(input_tensor, size=static_size),
torch.tensor([[0], [-1]]), # padded with default fill_value "-1"
)
)
# 2D input
input_tensor = torch.tensor([[1.2, 0], [3.4, 5.6]])
static_size = 5
fill_value = -100
self.assertTrue(
torch._dynamo.utils.same(
torch.nonzero_static(
input_tensor, size=static_size, fill_value=fill_value
),
torch.tensor(
[
[0, 0],
[1, 0],
[1, 1],
[fill_value, fill_value],
[fill_value, fill_value],
]
),
)
)
input_tensor = torch.tensor([[1.2, 0], [3.4, 5.6]])
static_size = 2
fill_value = -100
self.assertTrue(
torch._dynamo.utils.same(
torch.nonzero_static(
input_tensor, size=static_size, fill_value=fill_value
),
torch.tensor([[0, 0], [1, 0]]),
)
)
# 3D input
input_tensor = torch.tensor([[[0, 0], [0, -3]], [[0, 0], [5, 0]]])
static_size = 4
fill_value = -999
self.assertTrue(
torch._dynamo.utils.same(
torch.nonzero_static(
input_tensor,
size=static_size,
fill_value=fill_value,
),
torch.tensor(
[
[0, 1, 1],
[1, 1, 0],
[fill_value, fill_value, fill_value],
[fill_value, fill_value, fill_value],
]
),
)
)
def test_cond_with_quantization(self):
from functorch.experimental.control_flow import cond
class MyModule(torch.nn.Module):
def __init__(self):
super().__init__()
example_inputs = (torch.randn(5, 5),)
self.model = torch.nn.Linear(5, 5)
self.quantized_model = prepare_qat_fx(
self.model, qconfig_dict, example_inputs=example_inputs
)
def forward(self, pred, x):
def true_fn(x):
return x.sin() + self.quantized_model(x)
def false_fn(x):
return x.cos() + self.model(x)
return cond(pred, true_fn, false_fn, [x])
module = MyModule()
opt_m = torch._dynamo.optimize("eager", nopython=True)(module)
x = torch.rand((5, 5))
pred = torch.tensor(True)
self.assertTrue(same(module(pred, x), opt_m(pred, x)))
pred = torch.tensor(False)
self.assertTrue(same(module(pred, x), opt_m(pred, x)))
def test_map_with_quantization(self):
from functorch.experimental.control_flow import map
class MyModule(torch.nn.Module):
def __init__(self):
super().__init__()
example_inputs = (torch.randn(5, 5),)
self.model = torch.nn.Linear(5, 5)
self.quantized_model = prepare_qat_fx(
self.model, qconfig_dict, example_inputs=example_inputs
)
def forward(self, x):
def body(x):
return x.sin() + self.quantized_model(x)
return map(body, x)
module = MyModule()
opt_m = torch._dynamo.optimize("eager", nopython=True)(module)
x = torch.rand((5, 5))
self.assertTrue(same(module(x), opt_m(x)))
def test_cond_side_effects(self):
from functorch.experimental.control_flow import cond
c = 0
def true_fn(x):
return x - c
def false_fn(x):
return x + c
def f(pred, x):
nonlocal c
c = 1
return cond(pred, true_fn, false_fn, [x])
opt_fn = torch._dynamo.optimize("eager")(f)
c = 0
a = opt_fn(torch.tensor(False), torch.tensor([0.25, 0.25]))
self.assertTrue(same(torch.tensor([1.25, 1.25]), a))
def test_map_side_effects(self):
from functorch.experimental.control_flow import map
class Module(torch.nn.Module):
def __init__(self):
super().__init__()
self.w = torch.tensor(1)
def forward(self, xs):
def body(x):
self.w += 1
return x
return map(body, xs)
mod = Module()
with self.assertRaisesRegex(
TypeError, "missing 1 required positional argument"
):
opt_fn = torch._dynamo.optimize("eager", nopython=True)(mod)
opt_fn(torch.randn(3, 2))
def test_cond_nested(self):
from functorch.experimental.control_flow import cond
def true_fn_nested(x):
return x * 10
def false_fn_nested(x):
return x * -1
def true_fn(pred2, x):
return x.sin()
def false_fn(pred2, x):
return x + cond(pred2, true_fn_nested, false_fn_nested, [x])
def f(pred, pred2, x):
return cond(pred, true_fn, false_fn, [pred2, x])
cc = torch._dynamo.testing.CompileCounter()
opt_fn = torch._dynamo.optimize(cc)(f)
true_true_sin = opt_fn(
torch.tensor(True), torch.tensor(True), torch.tensor([0.25, 0.25])
)
self.assertTrue(same(torch.sin(torch.tensor([0.25, 0.25])), true_true_sin))
true_false_sin = opt_fn(
torch.tensor(True), torch.tensor(False), torch.tensor([0.25, 0.25])
)
self.assertTrue(same(torch.sin(torch.tensor([0.25, 0.25])), true_false_sin))
false_true_sum_mult = opt_fn(
torch.tensor(False), torch.tensor(True), torch.tensor([0.25, 0.25])
)
self.assertTrue(
same(torch.tensor([2.75, 2.75]), false_true_sum_mult)
) # * 10 then add x
false_false_sum_neg = opt_fn(
torch.tensor(False), torch.tensor(False), torch.tensor([0.25, 0.25])
)
self.assertTrue(
same(torch.tensor([0.0, 0.0]), false_false_sum_neg)
) # * -1 then add x
self.assertTrue(cc.frame_count, 2)
def test_cond_export(self):
from functorch.experimental.control_flow import cond
def true_fn_nested(x):
return x * 10
def false_fn_nested(x):
return x * -1
def true_fn(pred2, x):
return x.sin()
def false_fn(pred2, x):
return x + cond(pred2, true_fn_nested, false_fn_nested, [x])
def f(pred, pred2, x):
return cond(pred, true_fn, false_fn, [pred2, x])
graph, guard = torch._dynamo.export(
f, torch.tensor(False), torch.tensor(True), torch.tensor([0.25, 0.25])
)
true_true_sin = graph(
torch.tensor(True), torch.tensor(True), torch.tensor([0.25, 0.25])
)
self.assertTrue(same(torch.sin(torch.tensor([0.25, 0.25])), true_true_sin))
true_false_sin = graph(
torch.tensor(True), torch.tensor(False), torch.tensor([0.25, 0.25])
)
self.assertTrue(same(torch.sin(torch.tensor([0.25, 0.25])), true_false_sin))
false_true_sum_mult = graph(
torch.tensor(False), torch.tensor(True), torch.tensor([0.25, 0.25])
)
self.assertTrue(
same(torch.tensor([2.75, 2.75]), false_true_sum_mult)
) # * 10 then add x
false_false_sum_neg = graph(
torch.tensor(False), torch.tensor(False), torch.tensor([0.25, 0.25])
)
self.assertTrue(
same(torch.tensor([0.0, 0.0]), false_false_sum_neg)
) # * -1 then add x
def test_cond_export_single_arg(self):
from functorch.experimental.control_flow import cond
def true_fn(x):
return x
def false_fn(x):
return x.sin()
def f(pred, x):
return cond(pred, true_fn, false_fn, [x])
graph, guard = torch._dynamo.export(
f, torch.tensor(False), torch.tensor([0.25, 0.25])
)
true_mirror = graph(torch.tensor(True), torch.tensor([0.25, 0.25]))
self.assertTrue(same(torch.tensor([0.25, 0.25]), true_mirror))
true_mirror_2 = graph(torch.tensor(True), torch.tensor([0.33, 0.33, 0.33]))
self.assertTrue(same(torch.tensor([0.33, 0.33, 0.33]), true_mirror_2))
false_sin = graph(torch.tensor(False), torch.tensor([0.5, 0.5]))
self.assertTrue(same(torch.sin(torch.tensor([0.5, 0.5])), false_sin))
def test_enum_guards(self):
class MyEnum(enum.Enum):
FOO = 10
BAR = 20
def fn(x, y):
if y == MyEnum.FOO:
return x + 1
else:
return x - 1
x = torch.rand(3)
y = MyEnum.BAR
ref = fn(x, y)
opt_fn = torch.compile(backend="eager")(fn)
res = opt_fn(x, y)
self.assertTrue(same(ref, res))
@patch.object(torch._dynamo.config, "print_graph_breaks", True)
def test_duplicate_graph_break_warning(self):
@torch._dynamo.optimize("eager")
def f1(a, b):
f2(a, b)
def f2(a, b):
c = a + b
print("break")
return a + b + c
@torch._dynamo.optimize("eager")
def g1(a, b):
g2(a, b)
def g2(a, b):
c = a + b
print("break")
return a + b + c
def count_graph_break_msgs(msgs):
return sum(msg.find("Graph break") != -1 for msg in msgs)
with self.assertLogs(logger="torch._dynamo", level=logging.WARNING) as log:
torch._dynamo.config.verbose = True
f1(torch.randn(10), torch.randn(10))
self.assertGreater(count_graph_break_msgs(log.output), 1)
with self.assertLogs(logger="torch._dynamo", level=logging.WARNING) as log:
torch._dynamo.config.verbose = False
g1(torch.randn(10), torch.randn(10))
self.assertEqual(count_graph_break_msgs(log.output), 1)
def test_inplace_param_update(self):
def fn(param, y):
prev_grad = torch.is_grad_enabled()
try:
torch.set_grad_enabled(False)
torch.set_grad_enabled(True)
torch.set_grad_enabled(False)
param.add_(y)
finally:
torch.set_grad_enabled(prev_grad)
y = torch.randn(4)
x = torch.nn.Parameter(torch.randn(4))
fn(x, y)
cnts = torch._dynamo.testing.CompileCounter()
opt_fn = torch._dynamo.optimize(cnts, nopython=True)(fn)
opt_fn(x, y)
self.assertEqual(cnts.frame_count, 1)
self.assertEqual(cnts.op_count, 3)
@unittest.skipIf(
not PLATFORM_SUPPORTS_FUSED_SDPA or not SM80OrLater,
"Can't run fused SDPA on this platform",
)
def test_parsing_sdpa(self):
class MyModule(torch.nn.Module):
def forward(self, query, key, value):
out = F.scaled_dot_product_attention(query, key, value, None, 0, True)
out = F.scaled_dot_product_attention(
query, key, value, None, 0, True, scale=8
)
out = F.scaled_dot_product_attention(
query=query,
key=key,
value=value,
attn_mask=None,
dropout_p=0,
is_causal=True,
)
out = F.scaled_dot_product_attention(
query,
key=key,
value=value,
attn_mask=None,
dropout_p=0,
is_causal=True,
)
out = F.scaled_dot_product_attention(
query, key, value, None, dropout_p=0, is_causal=True
)
out = F.scaled_dot_product_attention(query, key, value, None, scale=8)
return out
device = "cuda"
dtype = torch.float16
seq_len_q = 1
seq_len_k = 1
head_dim = 8
query = torch.ones(
1, 8, seq_len_q, head_dim, device=device, dtype=dtype, requires_grad=True
)
key = torch.ones(
1, 8, seq_len_k, head_dim, device=device, dtype=dtype, requires_grad=True
)
value = torch.ones(
1, 8, seq_len_k, head_dim, device=device, dtype=dtype, requires_grad=True
)
module = MyModule()
opt_mod = torch._dynamo.optimize("inductor")(module)
opt_mod(query, key, value)
def test_generate_tensor_from_list_of_numpy_primitive_type(self):
# Test sth like torch.LongTensor(list(np.int64, np.int64, ...))
def fn():
x = np.array([1, 2, 3, 4, 5, 6], dtype=np.int64)
y = [x[0], x[2], x[4]]
z = torch.LongTensor(y)
return z
ref = fn()
opt_fn = torch._dynamo.optimize("eager")(fn)
res = opt_fn()
self.assertTrue(same(ref, res))
def test_autograd_function_equivalence(self):
for i in range(1, 5):
model = globals()[f"Module{i}"]()
opt_model = torch._dynamo.optimize("eager", nopython=True)(model)
self.assertTrue(
torch.allclose(opt_model(torch.ones(2, 3)), torch.tensor([2.0]))
)
def test_autograd_function_has_graph_break(self):
x = torch.randn(10)
for model in [Module5(), Module6()]:
torch._dynamo.reset()
cnts = torch._dynamo.testing.CompileCounter()
opt_model = torch._dynamo.optimize(cnts)(model)
for _ in range(3):
ref = model(x)
res = opt_model(x)
self.assertTrue(torch.allclose(ref, res))
self.assertEqual(cnts.frame_count, 2)
def test_object_classmethod(self):
class C:
@classmethod
def fn(cls, x):
return x + x
@torch._dynamo.optimize("eager", nopython=True)
def f():
return C().fn(torch.ones(2, 3))
self.assertTrue(torch.allclose(f(), torch.tensor([2.0])))
def test_object_staticmethod(self):
class C:
@staticmethod
def fn(x):
return x + x
@torch._dynamo.optimize("eager", nopython=True)
def f():
return C().fn(torch.ones(2, 3))
self.assertTrue(torch.allclose(f(), torch.tensor([2.0])))
def test_user_function_variable_supports_enum_argument(self):
class Foo(enum.Enum):
FOO = 0
BAR = 1
def gn(x, y=Foo.FOO):
if y is Foo.FOO:
return x
else:
return x + 1
def fn(x):
return gn(x)
x = torch.randn(2, 3)
ref = fn(x)
opt_fn = torch._dynamo.optimize("eager", nopython=True)(fn)
res = opt_fn(x)
self.assertTrue(torch.allclose(ref, res))
def test_user_function_variable_supports_type_abcmeta_argument(self):
class Foo(metaclass=abc.ABCMeta):
@abc.abstractclassmethod
def read(self): # noqa: B027
pass
class Bar(Foo):
def read(self):
return "Hello World!"
class Baz:
pass
def gn(x, tys=(Bar, Baz)):
if Bar in tys:
return x - 1
else:
return x + 1
def fn(x):
return gn(x)
x = torch.randn(2, 3)
ref = fn(x)
opt_fn = torch._dynamo.optimize("eager", nopython=True)(fn)
res = opt_fn(x)
self.assertTrue(torch.allclose(ref, res))
def test_user_function_variable_supports_function_argument(self):
# Test user defined function default arguments can be:
# 1, user defined functions (e.g, add1)
# 2, torch functions (e.g, torch.sin)
# 3, python builtin functions (e.g, operator.neg)
def add1(x):
return x + 1
def gn(x, f1=add1, f2=torch.sin, f3=operator.neg):
return f3(f2(f1(x)))
def fn(x):
return gn(x)
x = torch.randn(2, 3)
ref = fn(x)
opt_fn = torch._dynamo.optimize("eager", nopython=True)(fn)
res = opt_fn(x)
self.assertTrue(torch.allclose(ref, res))
def test_typing_variable_isinstance(self):
def fn(x, m):
if isinstance(m, typing.Mapping):
return x + 1
else:
return x - 1
x = torch.randn(2, 3)
m = {"x": torch.randn(3)}
ref = fn(x, m)
opt_fn = torch._dynamo.optimize("eager")(fn)
res = opt_fn(x, m)
self.assertTrue(torch.allclose(ref, res))
def test_repro_graph_breaks_in__get_item_by_idx(self):
class Mod(torch.nn.Module):
def __init__(self):
super().__init__()
self.mod = torch.nn.Sequential(
torch.nn.Linear(3, 3), torch.nn.Linear(3, 3)
)
def forward(self, x):
return self.mod[0](x)
m = Mod()
graph, _ = torch._dynamo.export(m, torch.randn(3, 3))
def test_nn_sequential_invocation(self):
with freeze_rng_state():
class TestModel(torch.nn.Module):
def __init__(self) -> None:
super().__init__()
self.linears = torch.nn.Sequential(
torch.nn.Linear(2, 2),
torch.nn.Linear(2, 2),
torch.nn.Linear(2, 2),
torch.nn.Linear(2, 2),
)
def forward(self, x):
all_but_last = self.linears[:-1]
return all_but_last(x)
m = TestModel()
x = torch.rand((2, 2))
real = m(x)
graph, _ = torch._dynamo.export(m, x)
dynamo_result = graph(x)
self.assertTrue(same(real, dynamo_result))
def test_nn_sequential_invocation_reposition_indices(self):
with freeze_rng_state():
class TestModel(torch.nn.Module):
def __init__(self) -> None:
super().__init__()
self.linears = torch.nn.Sequential(
torch.nn.Linear(2, 2),
torch.nn.Linear(2, 2),
torch.nn.Linear(2, 2),
torch.nn.Linear(2, 2),
)
def forward(self, x):
all_but_last = self.linears[1:3]
return all_but_last(x)
m = TestModel()
x = torch.rand((2, 2))
real = m(x)
graph, _ = torch._dynamo.export(m, x)
dynamo_result = graph(x)
self.assertTrue(same(real, dynamo_result))
def test_error_on_nested_fx_trace(self):
input = torch.rand(2, 3)
def f(x):
x + x
real = f(input)
optimized = torch._dynamo.optimize("eager")(f)
self.assertTrue(same(optimized(input), real))
with self.assertRaisesRegex(RuntimeError, "Detected that you are using FX"):
gm = torch.fx.symbolic_trace(optimized)
@patch.object(torch._dynamo.config, "error_on_nested_fx_trace", False)
def test_no_error_on_nested_fx_trace(self):
input = torch.rand(2, 3)
def f(x):
x + x
real = f(input)
optimized = torch._dynamo.optimize("eager")(f)
self.assertTrue(same(optimized(input), real))
# should not error
gm = torch.fx.symbolic_trace(optimized)
self.assertTrue(same(gm(input), real))
def test_not_dynamic_scope(self):
def f(y):
x = 1
def g():
x = 2
return lambda: x
return y + g()()
input = torch.zeros(1)
real = f(input)
optimized = torch._dynamo.optimize("eager")(f)
opt = optimized(input)
self.assertTrue(same(opt, real))
def test_inference_mode(self):
@torch.inference_mode()
def func(x, y):
return x.add(1.0) + y
x = torch.ones(4, requires_grad=True)
y = torch.ones(4, requires_grad=True)
ref = func(x, y)
opt_func = torch._dynamo.optimize("eager")(func)
x1 = torch.ones(4, requires_grad=True)
res = opt_func(x1, y)
self.assertTrue(same(ref, res))
self.assertTrue(same(x, x1))
def test_if_cond_nn_mod(self):
class MockModule(torch.nn.Module):
def __init__(self, output_relu=True):
super().__init__()
self.relu = torch.nn.ReLU() if output_relu else None
def forward(self, x):
x = torch.sin(x)
if self.relu:
x = self.relu(x)
return x
model = MockModule()
opt_model = torch._dynamo.optimize("eager", nopython=True)(model)
x = torch.rand(4)
ref = model(x)
res = opt_model(x)
self.assertTrue(same(ref, res))
model = MockModule(output_relu=False)
opt_model = torch._dynamo.optimize("eager", nopython=True)(model)
x = torch.rand(4)
ref = model(x)
res = opt_model(x)
self.assertTrue(same(ref, res))
def test_if_cond_user_defined_object(self):
# obj.__bool__ is not existed
class A: # noqa: B903
def __init__(self, x):
self.x = x
# obj.__bool__ is function and returns bool type
class B:
def __init__(self, x):
self.x = x
def __bool__(self):
return self.x > 0
# obj.__bool__ is non-function
class C:
def __init__(self, x):
self.x = x
self.__bool__ = False
def fn(x, obj):
if not obj:
return x + 1
else:
return x - 1
x = torch.rand(4)
cnts = torch._dynamo.testing.CompileCounter()
opt_fn = torch._dynamo.optimize(cnts, nopython=True)(fn)
obj1 = A(0.5)
obj2 = B(0.5)
obj3 = B(-0.5)
obj4 = C(0.5)
for obj in [obj1, obj2, obj3, obj4, obj3, obj2]:
ref = fn(x, obj)
res = opt_fn(x, obj)
self.assertTrue(same(ref, res))
self.assertEqual(cnts.frame_count, 4)
def test_if_cond_user_defined_object2(self):
# obj.__bool__ is function and returns non-bool type
class MyObj:
def __init__(self, x):
self.x = x
def __bool__(self):
self.x = 1
return self.x
def fn(a, obj):
if not obj:
return a + obj.x
else:
return a - obj.x
x = torch.rand(4)
obj = MyObj(0.5)
opt_fn = torch._dynamo.optimize("eager")(fn)
try:
opt_fn(x, obj)
self.assertFalse(True)
except TypeError as e:
self.assertIn("__bool__ should return bool, returned int", str(e))
def test_class_has_instancecheck_method(self):
class A:
pass
class ExampleMeta(type):
def __instancecheck__(cls, instance):
return True
class B(metaclass=ExampleMeta):
pass
def fn(x, obj):
if isinstance(obj, B):
return x + 1
else:
return x - 1
x = torch.rand(4)
obj = A()
ref = fn(x, obj)
opt_fn = torch._dynamo.optimize("eager", nopython=True)(fn)
res = opt_fn(x, obj)
self.assertTrue(same(ref, res))
def test_torch_cuda_is_available(self):
def fn(x):
if torch.cuda.is_available():
return x + 1
else:
return x - 1
x = torch.rand(4)
ref = fn(x)
opt_fn = torch._dynamo.optimize("eager", nopython=True)(fn)
res = opt_fn(x)
self.assertTrue(same(ref, res))
@unittest.skipIf(not torch.cuda.is_available(), "requires cuda")
@unittest.skipIf(not torch.backends.cudnn.is_available(), "requires cudnn")
def test_torch_cudnn_is_acceptable(self):
def fn(x):
if torch.backends.cudnn.is_acceptable(tensor=x):
return x + 1
return x
x = torch.rand(4).cuda()
ref = fn(x)
opt_fn = torch._dynamo.optimize("eager", nopython=True)(fn)
res = opt_fn(x)
self.assertTrue(same(ref, res))
@unittest.skipIf(not torch.cuda.is_available(), "requires cuda")
@unittest.skipIf(not torch.backends.cudnn.is_available(), "requires cudnn")
def test_torch_cudnn_is_acceptable_bad_inputs(self):
def fn1(x):
if torch.backends.cudnn.is_acceptable("invalid"):
return x + 1
return x
def fn2(x):
if torch.backends.cudnn.is_acceptable(x, 3.14):
return x + 1
return x
with self.assertRaisesRegex(
AssertionError, "Expect input to cudnn.is_acceptable to be a tensor"
):
x1 = torch.rand(4).cuda()
opt_fn1 = torch._dynamo.optimize("eager", nopython=True)(fn1)
res1 = opt_fn1(x1)
with self.assertRaisesRegex(
AssertionError, "Expect 1 input to cudnn.is_acceptable"
):
x2 = torch.rand(4).cuda()
opt_fn2 = torch._dynamo.optimize("eager", nopython=True)(fn2)
res = opt_fn2(x2)
@unittest.skipIf(not torch.cuda.is_available(), "requires cuda")
def test_get_device(self):
def fn(x, y):
x = x + 1
y = y + 1
return x.get_device(), y.get_device()
x = torch.rand(4, device="cuda")
y = torch.rand(4, device="cpu")
ref = fn(x, y)
opt_fn = torch._dynamo.optimize("eager", nopython=True)(fn)
res = opt_fn(x, y)
self.assertTrue(same(ref, res))
def test_disable_flag(self):
cnt = torch._dynamo.testing.CompileCounter()
with patch.dict(os.environ, {"TORCH_COMPILE_DISABLE": "1"}):
def fn(x, y):
x = x + 1
y = y + 1
opt_fn = torch._dynamo.optimize(cnt)
self.assertEqual(cnt.frame_count, 0)
def test_is_compiling(self):
def f():
if torch._dynamo.is_compiling():
return torch.ones(2, 2)
else:
return torch.zeros(2, 2)
opt_f = torch._dynamo.optimize("eager")(f)
self.assertEqual(f(), torch.zeros(2, 2))
self.assertEqual(opt_f(), torch.ones(2, 2))
def test_torch_generator_set_state(self):
def fn():
default_state = torch.default_generator.get_state()
x = torch.rand([2, 3])
torch._dynamo.graph_break()
torch.default_generator.set_state(default_state)
y = torch.rand([2, 3])
return x, y
opt_fn = torch._dynamo.optimize("eager")(fn)
x, y = opt_fn()
self.assertEqual(x, y)
def test_guard_failure_fn(self):
def fn(x, y, k):
x = x + 1
y = y + 1
return x * y * k
x = torch.tensor([0.5, 0.5])
y = torch.tensor([1.0, 1.0])
guard_failure = None
def guard_failures(failure):
nonlocal guard_failure
guard_failure = failure
opt_fn = torch._dynamo.optimize(
"eager", nopython=True, guard_fail_fn=guard_failures
)(fn)
x2 = torch.tensor([0.5, 0.5, 1.0])
y2 = torch.tensor([0.5, 0.5, 0.5])
opt_fn(x, y, 3)
opt_fn(x2, y2, 5)
if (
torch._dynamo.config.dynamic_shapes
and not torch._dynamo.config.specialize_int
and not torch._dynamo.config.assume_static_by_default
):
# we didn't actually test guard_failure_fn here but whatever,
# nice to see no guard failure on the test
self.assertTrue(guard_failure is None)
else:
self.assertTrue(guard_failure is not None)
if not torch._dynamo.config.dynamic_shapes:
self.assertExpectedInline(guard_failure[0], """L['k'] == 3""")
@patch.object(torch._dynamo.config, "dynamic_shapes", True)
def test_guard_failure_fn_shape_control(self):
def fn(x, y):
if x.shape[0] < 3:
if y.shape[0] < 3:
return x * y
else:
return x + y
else:
return -1
x = torch.randn([2, 2])
y = torch.randn([2, 2])
guard_failure = None
def guard_failures(failure):
nonlocal guard_failure
guard_failure = failure
opt_fn = torch._dynamo.optimize(
"eager", nopython=True, guard_fail_fn=guard_failures
)(fn)
x2 = torch.randn([5, 5])
y2 = torch.randn([5, 5])
opt_fn(x, y)
opt_fn(x2, y2)
self.assertTrue(guard_failure is not None)
if torch._dynamo.config.assume_static_by_default:
self.assertExpectedInline(
guard_failure[0],
"""tensor 'L['x']' size mismatch at index 0. expected 2, actual 5""",
)
else:
self.assertExpectedInline(guard_failure[0], """L['x'].size()[0] < 3""")
def test_guard_failure_fn2(self):
def fn(x, y):
x = x + 1
y = y + 1
return x * y
x = torch.tensor([0.5, 0.5])
y = torch.tensor([1.0, 1.0])
guard_failure = None
def guard_failures(failure):
nonlocal guard_failure
guard_failure = failure
opt_fn = torch._dynamo.optimize(
"eager", nopython=True, guard_fail_fn=guard_failures
)(fn)
x2 = torch.tensor([0.5, 0.5, 1.0])
y2 = torch.tensor([0.5, 0.5, 0.5])
opt_fn(x, y)
opt_fn(x2, y2)
if torch._dynamo.config.dynamic_shapes:
if torch._dynamo.config.assume_static_by_default:
self.assertExpectedInline(
guard_failure[0],
"""tensor 'L['x']' size mismatch at index 0. expected 2, actual 3""",
)
else:
self.assertTrue(guard_failure is None)
else:
self.assertTrue(guard_failure is not None)
self.assertExpectedInline(
guard_failure[0],
"""tensor 'L['x']' size mismatch at index 0. expected 2, actual 3""",
)
def test_guard_failure_fn_tensor_iter(self):
def fn(x):
for y in x:
y.add_(1.0)
return y
guard_failure = None
def guard_failures(failure):
nonlocal guard_failure
guard_failure = failure
opt_fn = torch._dynamo.optimize(
"eager", nopython=True, guard_fail_fn=guard_failures
)(fn)
args1 = torch.randn(10, 10)
out = fn(args1)
opt_out = opt_fn(args1)
self.assertTrue(same(out, opt_out))
args2 = torch.randn(9, 10)
out = fn(args2)
opt_out = opt_fn(args2)
self.assertTrue(same(out, opt_out))
# guard is expected for both static and dynamic shapes
self.assertTrue(guard_failure is not None)
self.assertExpectedInline(guard_failure[0], """len(L['x']) == 10""")
def test_restore_graphstate(self):
# This function does some guard accumulation,
# and then rolls back due to control flow.
# The idea is that if one were printing guards as they appear,
# they would see this insert a guard that does not show up in the final set of
# guards as we rolled back from it.
def nested_fn(s):
if x[0] < 10:
return s * s
return s
def fn(x, y):
x = x + 1
y = nested_fn(y)
y = y + 10
return x * y
all_guards = []
def guard_export_print(guards):
nonlocal all_guards
all_guards.extend(guards)
opt_fn = torch._dynamo.optimize("eager", guard_export_fn=guard_export_print)(fn)
x = torch.tensor([0.5, 0.5])
y = torch.tensor([1.0, 1.0])
opt_fn(x, y)
for guard in all_guards:
# This guard was created
self.assertTrue(guard.name != "nested_fn.__closure__[0].cell_contents")
# Note - here be mild dragons.
# This test relies a ton on internal implementation. Future refactor efforts
# are welcome to delete it if necessary, rewriting this test constantly is a chore, not
# a feature. We kept it around with some amount of saddness, as it was extremely useful in debugging.
def test_restore_graphstate_internals(self):
def fn(x, y):
x = x + 1
y = y + 1
return x * y
_, guards = torch._dynamo.export(
fn, torch.tensor([0.25, 0.25]), torch.tensor([0.25, 0.25])
)
# Dummy ctor
graph = OutputGraph(
f_globals={},
code_options={},
compiler_fn=None,
root_tx=None,
export=False,
export_constraints=None,
frame_state={"_id": 0},
)
graph.nn_modules_sources = {}
# Contrived generation timestamp
graph.timestamp = 4
# Contrived guards
graph.tracing_context.guards_context.dynamo_guards = guards
# Save the state
state = graph.copy_graphstate()
# Saving increments the generation
self.assertEqual(graph.timestamp, 5)
# Assure that the saved state is valid
self.assertEqual(state.timestamp, 4)
# Ensure that the guards reflect the expected state
self.assertEqual(graph.tracing_context.guards_context.dynamo_guards, guards)
self.assertEqual(graph.guards, guards)
# Mess around with the state
graph.tracing_context.guards_context.dynamo_guards = set()
self.assertEqual(graph.guards, set())
# Restore the state
graph.restore_graphstate(state)
# Make sure it restored correctly
self.assertEqual(graph.timestamp, 4)
self.assertEqual(graph.guards, guards)
self.assertEqual(graph.tracing_context.guards_context.dynamo_guards, guards)
def test_call_parent_non_class_methods_from_child(self):
class A:
def add(self, x):
return x + 10
def mul(self, x):
return x * 0.1
class B(A):
def add(self, x):
return x + 20
def mul(self, x):
return x * 0.2
class C(B):
def add(self, x):
y = A.add(self, x)
z = B.mul(self, y)
return z + 30
x = torch.rand(4)
fn = C().add
ref = fn(x)
opt_fn = torch._dynamo.optimize("eager", nopython=True)(fn)
res = opt_fn(x)
self.assertTrue(same(ref, res))
def test_builder_for_class_with_metaclass(self):
class ExampleMeta(type):
pass
class MyClass(metaclass=ExampleMeta):
pass
def fn(x, y):
if isinstance(y, MyClass):
return x + 1
else:
return x - 1
x = torch.rand([4, 4])
y = MyClass()
ref = fn(x, y)
opt_fn = torch._dynamo.optimize("eager")(fn)
res = opt_fn(x, y)
self.assertTrue(same(ref, res))
def test_tuple_from_tuple_iter(self):
def inner_fn(*args):
acc = torch.ones(10, 10)
for arg in args:
acc.add_(arg)
return acc
@torch._dynamo.optimize("eager")
def fn(inputs, params):
y = tuple(inputs) + tuple(params)
return inner_fn(*y)
inputs = [torch.randn(10, 10) for _ in range(3)]
fn(inputs, iter(tuple(inputs)))
def test_torch_package_working_with_trace(self):
# from torch._dynamo.test_case import run_tests
inputs = [torch.randn([2, 2]), torch.randn([2, 2])]
optimized_model = torch._dynamo.optimize(backend="eager")(
MyPickledModule(torch.randn([2, 2]))
)
from torch import package
path = "/tmp/MyPickledModule.pt"
package_name = "MyPickledModule"
resource_name = "MyPickledModule.pkl"
model = MyPickledModule(torch.randn([2, 2]))
with package.PackageExporter(path) as exp:
exp.extern("**")
exp.save_pickle(package_name, resource_name, model)
imp = package.PackageImporter(path)
loaded_model = imp.load_pickle(package_name, resource_name)
optimized_loaded_model = torch._dynamo.optimize("eager")(loaded_model)(*inputs)
def test_shape_and_tuple_equality(self):
def fn(x, y, t):
z = x * y
if x.size() == t:
return z.cos()
return z.sin()
torch._dynamo.optimize("eager", nopython=True)(fn)(
torch.randn([4, 4]), torch.randn([4, 4]), (4, 4)
)
def test_int_list(self):
# if dynamic_shapes == True: unspec int list
# if dynamic_shapes == False: spec int list
def fn(x, y):
return torch.sin(x + y[1] % 2)
x = torch.randn(6)
cnt = torch._dynamo.testing.CompileCounter()
opt_fn = torch._dynamo.optimize(cnt)(fn)
for i in range(10, 25, 3):
y = [i, i + 1, i + 2]
ref = fn(x, y)
res = opt_fn(x, y)
self.assertTrue(same(ref, res))
self.assertEqual(cnt.frame_count, ifunspec(ifdyn(1, 5), 5))
# specifically test for tensor.attribute -> torch.something()
def test_real_imag_tensor_attribute(self):
def fn(x, y):
a = x.real
b = x.imag
return torch.mul(torch.add(a, y), b)
x_real = torch.rand((4, 4))
x_imag = torch.rand((4, 4))
x = torch.complex(x_real, x_imag)
y = torch.rand((4, 4))
ref = fn(x, y)
opt_fn = torch._dynamo.optimize("eager")(fn)
res = opt_fn(x, y)
self.assertTrue(same(ref, res))
def test_T_tensor_attribute(self):
def fn(x, y):
a = x.T
return torch.add(a, y)
x = torch.rand((4, 4))
y = torch.rand((4, 4))
ref = fn(x, y)
opt_fn = torch._dynamo.optimize("eager")(fn)
res = opt_fn(x, y)
self.assertTrue(same(ref, res))
def test_recursive_tensor_attribute(self):
def fn(x, y):
a = x.real.T
b = x.imag
return torch.mul(torch.add(a, y), b)
x_real = torch.rand((4, 4))
x_imag = torch.rand((4, 4))
x = torch.complex(x_real, x_imag)
y = torch.rand((4, 4))
ref = fn(x, y)
opt_fn = torch._dynamo.optimize("eager")(fn)
res = opt_fn(x, y)
self.assertTrue(same(ref, res))
def test_tagging_tensors_simple(self):
def foo(x, y):
return x * y, x, y
a = torch.randn([3, 3])
a.tag = "a"
a.frog = "ribbity ribbit"
b = torch.randn([3, 3])
b.tag = "b"
b.frog = "ribbit"
exported = torch._dynamo.export(foo, a, b)
out_graph = exported[0]
nodes = list(out_graph.graph.nodes)
placeholders = [node for node in nodes if node.op == "placeholder"]
all_tags = []
all_frogs = []
for placeholder in placeholders:
if "tensor_dict" in placeholder.meta:
all_tags.append(placeholder.meta["tensor_dict"]["tag"])
all_frogs.append(placeholder.meta["tensor_dict"]["frog"])
self.assertEqual(all_tags, ["a", "b"])
self.assertEqual(all_frogs, ["ribbity ribbit", "ribbit"])
def test_tagging_tensors_mix_used_unused_structure(self):
def pre_attention_state_ops(input, mems, state):
lc_key = state[0]
lc_val = state[1]
bar = []
for i in range(0, 4):
bar2 = []
for j in range(0, 3):
bar2.append(
lc_key + lc_val + torch.tensor([0.1, 0.25, 0.4, 0.5, 0.1])
)
bar.append(bar2)
return bar
mems = torch.tensor([[[1.8364, 0.2724, -1.4917, -0.4367, 0.8640]]])
state = [
torch.tensor([[[1.0517, 0.3848, -0.6472, 0.0823, 0.9116]]]),
torch.tensor([[[1.0517, 0.3848, -0.6472, 0.0823, 0.9116]]]),
]
i = torch.tensor(
[
[0.0313, -0.1487, -0.3846, -0.5321],
[-1.7073, 1.3331, -0.0890, -1.4935],
[-0.8314, -0.1862, -0.5935, 1.5232],
]
)
mems.tag = "MEMS"
i.tag = "FOO"
state[0].tag = "STATE_0"
state[1].tag = "HMMM"
exported = torch._dynamo.export(pre_attention_state_ops, i, mems, state)
out_graph = exported[0]
nodes = list(out_graph.graph.nodes)
placeholders = [node for node in nodes if node.op == "placeholder"]
all_tags = []
for placeholder in placeholders:
if "tensor_dict" in placeholder.meta:
all_tags.append(placeholder.meta["tensor_dict"]["tag"])
self.assertEqual(all_tags, ["STATE_0", "HMMM"])
def test_get_custom_tensor_attribute(self):
def fn(x):
return x.custom_attr * x
x = torch.rand((2, 2))
x.custom_attr = 3.14
ref = fn(x)
opt_fn = torch._dynamo.optimize("eager")(fn)
res = opt_fn(x)
self.assertTrue(same(ref, res))
def test_set_custom_tensor_attribute(self):
def fn(x):
x.custom_attr = 3.14
return x.custom_attr * x
x = torch.rand((2, 2))
ref = fn(x)
opt_fn = torch._dynamo.optimize("eager")(fn)
res = opt_fn(x)
self.assertTrue(same(ref, res))
def test_if_tensor_is_none(self):
"""
Python 3.11 adds new jump instructions that check if
TOS is None. We do not support these instructions.
"""
def f(x, y):
z = 1
if x is None:
z *= 2
if y is not None:
z *= 3
return z
opt_f = torch._dynamo.optimize("eager", nopython=True)(f)
self.assertEqual(opt_f(None, torch.ones(2)), 6)
if sys.version_info >= (3, 11):
insts = bytecode_transformation.cleaned_instructions(f.__code__)
for inst in insts:
self.assertNotIn("_NONE", inst.opname)
@skipIfNotPy311
def test_py311_jump_offset(self):
new_inst = bytecode_transformation.create_instruction
load_global = bytecode_transformation.create_load_global
consts = (None, 1, 2, 3, 4)
def create_test_code(jump_opname, target_idx):
targets = [
new_inst("LOAD_CONST", argval=1),
new_inst("LOAD_CONST", argval=3),
]
jump_to_target_inst = new_inst(jump_opname, target=targets[target_idx])
"""
pseudocode of generated bytecode:
def test_py311_fn():
goto target1
target0:
return 1
target1:
goto [target0/target2] (via fwd or bwd jump)
return 2
target2:
return 3
return 4
"""
# test with LOAD_GLOBAL since it has a different instruction size
insts = [
new_inst("RESUME", arg=0),
new_inst("JUMP_FORWARD", target=jump_to_target_inst),
targets[0],
load_global("print", False),
new_inst("POP_TOP"),
new_inst("RETURN_VALUE"),
jump_to_target_inst,
new_inst("LOAD_CONST", argval=2),
load_global("print", False),
new_inst("POP_TOP"),
new_inst("RETURN_VALUE"),
targets[1],
new_inst("RETURN_VALUE"),
new_inst("LOAD_CONST", argval=4),
new_inst("RETURN_VALUE"),
]
code_options = collections.OrderedDict(
[
("co_argcount", 0),
("co_posonlyargcount", 0),
("co_kwonlyargcount", 0),
("co_nlocals", 0),
("co_stacksize", 2),
("co_flags", 3),
("co_code", b""),
("co_consts", consts),
("co_names", ("print",)),
("co_varnames", ()),
("co_filename", __file__),
("co_name", "test_py311_fn"),
("co_qualname", "test_py311_fn"),
("co_firstlineno", 1),
("co_linetable", b""),
("co_exceptiontable", b""),
("co_freevars", ()),
("co_cellvars", ()),
]
)
return bytecode_transformation.clean_and_assemble_instructions(
insts,
list(code_options.keys()),
code_options,
)
# format: jump_opname, target_idx, expected forward jump, expected return value
test_args = (
("JUMP_FORWARD", 0, False, 1),
("JUMP_FORWARD", 1, True, 3),
("JUMP_BACKWARD", 0, False, 1),
("JUMP_BACKWARD", 1, True, 3),
)
for test in test_args:
insts, code = create_test_code(test[0], test[1])
# check if offset of latest jump instruction is forward/backward
for inst in reversed(insts):
if inst.opname.startswith("JUMP"):
if test[2]:
self.assertIn("FORWARD", inst.opname)
else:
self.assertIn("BACKWARD", inst.opname)
break
# run the code and check result
def dummy_fn():
pass
dummy_fn.__code__ = code
self.assertEqual(dummy_fn(), test[3])
dummy_opt = torch._dynamo.optimize("eager")(dummy_fn)
self.assertEqual(dummy_opt(), test[3])
def test_exception_table_encode_varint(self):
# these numbers have no real meaning to them
nums = [
0b111_101010_000000,
0b1100_111000_010101_101010,
]
b = bytecode_transformation.encode_exception_table_varint(
nums[0]
) + bytecode_transformation.encode_exception_table_varint(nums[1])
nums_new = []
b_iter = iter(bytes(b))
while True:
try:
nums_new.append(
bytecode_transformation.decode_exception_table_varint(b_iter)
)
except StopIteration:
break
self.assertEqual(nums, nums_new)
@skipIfNotPy311
def test_exception_table_parsing(self):
def fn():
try:
with a():
b()
c()
except Exception:
d()
finally:
e()
f()
tab = bytecode_transformation.parse_exception_table(
fn.__code__.co_exceptiontable
)
b = bytecode_transformation.assemble_exception_table(tab)
self.assertEqual(b, fn.__code__.co_exceptiontable)
@skipIfNotPy311
def test_exception_table_e2e(self):
def fn():
try:
with a():
b()
c()
except Exception:
d()
finally:
e()
f()
def nothing(*args):
pass
code = bytecode_transformation.transform_code_object(fn.__code__, nothing)
self.assertEqual(code.co_exceptiontable, fn.__code__.co_exceptiontable)
@skipIfNotPy311
def test_exception_table_e2e_2(self):
# last instructions of an exn_table entry is a large instruction
# i.e., LOAD_GLOBAL a
def fn():
try:
return a
except Exception:
pass
def nothing(*args):
pass
code = bytecode_transformation.transform_code_object(fn.__code__, nothing)
self.assertEqual(code.co_exceptiontable, fn.__code__.co_exceptiontable)
@skipIfNotPy311
def test_exception_table_entry_propagation(self):
insts = []
for _ in range(10):
insts.append(bytecode_transformation.create_instruction("NOP"))
insts[8].exn_tab_entry = bytecode_transformation.InstructionExnTabEntry(
insts[0], insts[9], insts[0], 0, True
)
insts[0].exn_tab_entry = bytecode_transformation.InstructionExnTabEntry(
insts[0], insts[0], insts[1], 0, True
)
insts[1].exn_tab_entry = bytecode_transformation.InstructionExnTabEntry(
insts[0], insts[2], insts[2], 0, True
)
insts[5].exn_tab_entry = bytecode_transformation.InstructionExnTabEntry(
insts[4], insts[6], insts[3], 0, True
)
insts[9].exn_tab_entry = bytecode_transformation.InstructionExnTabEntry(
insts[9], insts[9], insts[4], 0, True
)
insts[7].exn_tab_entry = bytecode_transformation.InstructionExnTabEntry(
insts[7], insts[9], insts[5], 0, True
)
bytecode_transformation.propagate_inst_exn_table_entries(insts)
expected = [1, 2, 2, 0, 3, 3, 3, 5, 5, 4]
for inst, exp in zip(insts, expected):
self.assertIsNotNone(inst.exn_tab_entry)
self.assertIs(inst.exn_tab_entry.target, insts[exp])
@skipIfNotPy311
def test_compute_exception_table_nested(self):
insts = []
for _ in range(20):
insts.append(bytecode_transformation.create_instruction("NOP"))
insts[10].exn_tab_entry = bytecode_transformation.InstructionExnTabEntry(
insts[1], insts[10], insts[0], 0, True
)
insts[0].exn_tab_entry = bytecode_transformation.InstructionExnTabEntry(
insts[1], insts[1], insts[1], 0, True
)
insts[1].exn_tab_entry = bytecode_transformation.InstructionExnTabEntry(
insts[1], insts[3], insts[2], 0, True
)
insts[5].exn_tab_entry = bytecode_transformation.InstructionExnTabEntry(
insts[5], insts[7], insts[3], 0, True
)
insts[9].exn_tab_entry = bytecode_transformation.InstructionExnTabEntry(
insts[10], insts[10], insts[4], 0, True
)
insts[7].exn_tab_entry = bytecode_transformation.InstructionExnTabEntry(
insts[8], insts[10], insts[5], 0, True
)
insts[14].exn_tab_entry = bytecode_transformation.InstructionExnTabEntry(
insts[13], insts[17], insts[6], 0, True
)
insts[16].exn_tab_entry = bytecode_transformation.InstructionExnTabEntry(
insts[15], insts[16], insts[7], 0, True
)
bytecode_transformation.update_offsets(insts)
tab = bytecode_transformation.compute_exception_table(insts)
expected = [
(1, 1, 1),
(2, 3, 2),
(4, 4, 0),
(5, 7, 3),
(8, 9, 5),
(10, 10, 4),
(13, 14, 6),
(15, 16, 7),
(17, 17, 6),
]
self.assertEquals(len(tab), len(expected))
for entry, exp in zip(tab, expected):
self.assertEquals(entry.start, exp[0] * 2)
self.assertEquals(entry.end, exp[1] * 2)
self.assertEquals(entry.target, exp[2] * 2)
@skipIfNotPy311
def test_remove_dead_code_with_exn_table_entries(self):
create_instruction = bytecode_transformation.create_instruction
target1 = create_instruction("NOP")
target2 = create_instruction("NOP")
target3 = create_instruction("NOP")
exn_start = create_instruction("NOP")
exn_end = create_instruction("NOP")
insts = [
create_instruction("JUMP_FORWARD", target=target1),
exn_start, # dead
target1,
create_instruction("JUMP_FORWARD", target=target3),
exn_end, # dead
target2,
target3,
]
exn_start.exn_tab_entry = bytecode_transformation.InstructionExnTabEntry(
exn_start, exn_end, target2, 0, True
)
bytecode_transformation.propagate_inst_exn_table_entries(insts)
insts = bytecode_analysis.remove_dead_code(insts)
self.assertEquals(len(insts), 5)
self.assertNotIn(exn_start, insts)
self.assertNotIn(exn_end, insts)
self.assertIn(target2, insts)
self.assertIn(target3, insts)
bytecode_transformation.update_offsets(insts)
tab = bytecode_transformation.compute_exception_table(insts)
self.assertEquals(len(tab), 1)
self.assertEquals(tab[0].start, 2)
self.assertEquals(tab[0].end, 4)
self.assertEquals(tab[0].target, 6)
def test_ordered_dict_alias_reconstruct(self):
od = collections.OrderedDict
def fn():
d1 = dict()
d1["a"] = 1
d2 = od(d1)
d2["b"] = 2
torch._dynamo.graph_break()
if isinstance(d2, od):
return d2["a"] + d2["b"]
else:
return 0
dis.dis(fn)
self.assertEqual(torch._dynamo.optimize("eager")(fn)(), 3)
@torch._dynamo.config.patch(dynamic_shapes=True)
def test_raise_guard_full_constraint(self):
y = torch.randn([3, 3, 3])
def my_dyn_fn(x):
if x.shape[0] == 3:
return x.sin()
return x.cos()
torch._dynamo.mark_dynamic(y, 0)
with self.assertRaises(ConstraintViolationError):
torch._dynamo.optimize("eager")(my_dyn_fn)(y)
def test_mark_static(self):
counter = CompileCounter()
def my_dyn_fn(x):
return x.cos()
y = torch.randn([3])
torch._dynamo.mark_static(y, 0)
torch._dynamo.optimize(counter)(my_dyn_fn)(y)
z = torch.randn([4])
torch._dynamo.optimize(counter)(my_dyn_fn)(z)
self.assertEqual(counter.frame_count, 2)
@torch._dynamo.config.patch(dynamic_shapes=True)
def test_no_raise_guard_partial_constraint(self):
y = torch.randn([3, 3, 3])
def my_dyn_fn(x):
if x.shape[0] > 3:
return x.sin()
return x.cos()
torch._dynamo.optimize("eager")(my_dyn_fn)(y)
torch._dynamo.mark_dynamic(y, 0)
torch._dynamo.reset()
torch._dynamo.optimize("eager")(my_dyn_fn)(y)
@torch._dynamo.config.patch(dynamic_shapes=True)
def test_no_raise_guard_partial_constraint_across_break(self):
y = torch.randn([3, 3, 3])
def my_dyn_fn(x, y):
z = x * y
torch._dynamo.graph_break()
if z.shape[0] > 2:
return z.cos()
return x.cos()
torch._dynamo.optimize("eager")(my_dyn_fn)(y, y)
torch._dynamo.mark_dynamic(y, 0)
torch._dynamo.reset()
torch._dynamo.optimize("eager")(my_dyn_fn)(y, y)
# Sadly, this does not throw - we do not prop correctly across the graph break
@unittest.expectedFailure
@torch._dynamo.config.patch(dynamic_shapes=True)
def test_raise_guard_partial_constraint_across_break(self):
y = torch.randn([3, 3, 3])
def my_dyn_fn(x, y):
z = x * y
torch._dynamo.graph_break()
if z.shape[0] == 3:
return z.cos()
return x.cos()
torch._dynamo.optimize("eager")(my_dyn_fn)(y, y)
torch._dynamo.mark_dynamic(y, 0)
torch._dynamo.reset()
with self.assertRaisesRegex(
Exception,
):
torch._dynamo.optimize("eager")(my_dyn_fn)(y, y)
@torch._dynamo.config.patch(dynamic_shapes=True)
def test_raise_guard_partial_constraint_no_graph_break(self):
y = torch.randn([3, 3, 3])
def my_dyn_fn(x, y):
z = x * y
if z.shape[0] == 3:
return z.cos()
return x.cos()
torch._dynamo.mark_dynamic(y, 0)
with self.assertRaises(ConstraintViolationError):
torch._dynamo.optimize("eager")(my_dyn_fn)(y, y)
def test_cannot_trace_mark_dynamic(self):
y = torch.randn([3, 3, 3])
def my_dyn_fn(x):
torch._dynamo.mark_dynamic(x, 0)
return x * x
with self.assertRaisesRegex(
AssertionError, "Attempt to trace forbidden callable"
):
torch._dynamo.optimize("eager")(my_dyn_fn)(y)
def test_cannot_trace_mark_dynamic_safe_unreached(self):
y = torch.randn([3, 3, 3])
def my_dyn_fn(x):
if x.shape[0] == 3:
return x
print("Running", torch._dynamo.mark_dynamic(x, 0))
return x * x
torch._dynamo.optimize("eager")(my_dyn_fn)(y)
@torch._dynamo.config.patch(dynamic_shapes=True)
def test_py_guards_mark_dynamic(self):
def my_dyn_fn(a):
if a.shape[0] > 2:
return a.cos()
return a.sin()
counter = CompileCounter()
# Run with dynamic
x0 = torch.randn([3, 3, 3])
torch._dynamo.mark_dynamic(x0, 0)
torch._dynamo.optimize(counter)(my_dyn_fn)(x0)
self.assertEqual(counter.frame_count, 1)
# Run without dynamic, no recompile
x = torch.randn([3, 3, 3])
torch._dynamo.optimize(counter)(my_dyn_fn)(x)
self.assertEqual(counter.frame_count, 1)
# Mark a new dim, 1, as dynamic
x1 = torch.randn([3, 3, 3])
torch._dynamo.mark_dynamic(x1, 1)
torch._dynamo.optimize(counter)(my_dyn_fn)(x1)
# Recompile triggered because we marked a new dym as dynamic
self.assertEqual(counter.frame_count, 2)
# Reset
torch._dynamo.reset()
# Reset counter
counter = CompileCounter()
# Run with dynamic 1
torch._dynamo.optimize(counter)(my_dyn_fn)(x1)
self.assertEqual(counter.frame_count, 1)
# Run with dynamic 0, not subset
torch._dynamo.optimize(counter)(my_dyn_fn)(x0)
self.assertEqual(counter.frame_count, 2)
# Run with dynamic 0, 1, 2, not subset
x012 = torch.randn([3, 3, 3])
torch._dynamo.mark_dynamic(x012, 0)
torch._dynamo.mark_dynamic(x012, 1)
torch._dynamo.mark_dynamic(x012, 2)
torch._dynamo.optimize(counter)(my_dyn_fn)(x012)
self.assertEqual(counter.frame_count, 3)
def test_torch_compile_ctx_on_forward_and_training_step(self):
class MyModel(torch.nn.Module):
def forward(self):
...
def training_step(self):
self()
model = MyModel()
compiled_model = torch.compile(model)
model.forward = compiled_model.dynamo_ctx(model.forward)
model.training_step = compiled_model.dynamo_ctx(model.training_step)
model.training_step()
def test_torch_guards_stack_frame_register_inlining(self):
x = torch.tensor([0.5, 0.5])
y = torch.tensor([0.75, 0.75, 0.75, 0.75])
z = torch.tensor([0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25])
def uwu_inline_me(x, y, z):
r = torch.cat((x, x)) + y
r2 = torch.cat((y, y)) + z
return r, r2
def fn(x, y, z):
r, r2 = uwu_inline_me(x, y, z)
return torch.mul(r, r), torch.mul(r2, r2)
seen_frames = []
import contextlib
@contextlib.contextmanager
def global_context_capture_fn(frame_summary):
seen_frames.append(frame_summary)
yield
with mock.patch(
"torch._guards.TracingContext.current_frame",
side_effect=global_context_capture_fn,
):
torch._dynamo.optimize("eager")(fn)(x, y, z)
self.assertEqual(len(seen_frames), 1)
self.assertEqual(seen_frames[0].name, "fn")
self.assertEqual(seen_frames[0].line, "r, r2 = uwu_inline_me(x, y, z)")
def test_torch_guards_stack_frame_register_inlining_deep(self):
x = torch.tensor([0.5, 0.5])
y = torch.tensor([0.75, 0.75, 0.75, 0.75])
z = torch.tensor([0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25])
def uwu_inline_me_deep(x, y):
return torch.cat((x, x)) + y
def uwu_inline_me(x, y, z):
r = uwu_inline_me_deep(x, y)
r2 = uwu_inline_me_deep(y, z)
return r, r2
def fn(x, y, z):
r, r2 = uwu_inline_me(x, y, z)
return torch.mul(r, r), torch.mul(r2, r2)
seen_frames = []
import contextlib
@contextlib.contextmanager
def global_context_capture_fn(frame_summary):
seen_frames.append(frame_summary)
yield
with mock.patch(
"torch._guards.TracingContext.current_frame",
side_effect=global_context_capture_fn,
):
torch._dynamo.optimize("eager")(fn)(x, y, z)
self.assertEqual(len(seen_frames), 3)
self.assertEqual(seen_frames[0].name, "fn")
self.assertEqual(seen_frames[1].name, "uwu_inline_me")
self.assertEqual(seen_frames[2].line, "r2 = uwu_inline_me_deep(y, z)")
def test_error_on_recompile(self):
@torch._dynamo.optimize("eager")
def fn(a, b):
return a + b
with unittest.mock.patch("torch._dynamo.config.error_on_recompile", True):
with self.assertRaises(torch._dynamo.exc.RecompileError):
fn(torch.rand(2, 3), torch.rand(2, 3))
fn(torch.rand(2, 3), (1, 2, 3))
def test_compile_profiler(self):
class Model(torch.nn.Module):
def forward(self, input):
return input + input
model = Model()
with CompileProfiler() as prof:
compiled = torch.compile(model, backend=prof)
base_checker = (
lambda: FileCheck()
.check("Torchdynamo Profiler Report")
.check("Graph Breaks")
.check("No graph breaks detected.")
.check("Recompilation")
)
input = torch.rand((2, 3, 4))
_ = compiled(input)
base_checker().check("No recompilation detected.").run(prof.report())
new_shape_input = torch.rand((3, 3, 4))
_ = compiled(new_shape_input)
# Not an exhaustive test of dynamic shapes behavior, but some sanity
if (
not torch._dynamo.config.dynamic_shapes
or torch._dynamo.config.assume_static_by_default
):
base_checker().check("Recompile Reasons").check("'forward'").check(
"cache_size_limit to 1"
).run(prof.report())
else:
base_checker().check("No recompilation detected.").run(prof.report())
# Ensure correct guard fail message is selected to show to user
if not torch._dynamo.config.dynamic_shapes:
new_shape_input = torch.rand((4, 3, 4))
_ = compiled(new_shape_input)
base_checker().check("Recompile Reasons").check("'forward'").check(
"tensor 'L['input']' size mismatch at index 0. expected 2, actual 3"
).check(
"tensor 'L['input']' size mismatch at index 0. expected 3, actual 4"
).run(
prof.report()
)
def test_guards_strip_function_call(self):
from torch._dynamo.guards import strip_function_call
test_case = [
("___odict_getitem(a, 1)", "a"),
("a.layers[slice(2)][0]._xyz", "a"),
("getattr(a.layers[slice(2)][0]._abc, '0')", "a"),
("getattr(getattr(a.x[3], '0'), '3')", "a"),
("a.layers[slice(None, -1, None)][0]._xyz", "a"),
("a.layers[func('offset', -1, None)][0]._xyz", "a"),
]
# strip_function_call should extract the object from the string.
for name, expect_obj in test_case:
self.assertEqual(strip_function_call(name), expect_obj)
def test_int_neg(self):
def int_neg(a, b):
x = a.shape[0]
y = b.shape[0]
return -x * -y * a * b
torch._dynamo.testing.standard_test(self, int_neg, 2)
def test_hash_getitem_slice(self):
s = GetItemSource(LocalSource("foo"), slice(None, -1, None))
s2 = GetItemSource(LocalSource("foo"), slice(None, -1, None))
s3 = GetItemSource(LocalSource("foo"), slice(None, -1, 2))
some_set = set()
self.assertTrue(s not in some_set)
self.assertTrue(s2 not in some_set)
self.assertTrue(s3 not in some_set)
some_set.add(s)
self.assertTrue(s in some_set)
# s and s2 should hash the same
self.assertTrue(s2 in some_set)
# s3 should be different
self.assertTrue(s3 not in some_set)
self.assertTrue(s == s2)
self.assertTrue(s != s3)
def test_add_sizes(self):
def func(x):
y = x.size()
return y + y
eager_out = func(torch.ones(10, 10, 3))
compile_out = torch._dynamo.optimize("eager")(func)(torch.ones(10, 10, 3))
self.assertTrue(isinstance(compile_out, torch.Size))
self.assertEqual(eager_out, compile_out)
def test_nested_function_resuming_with_correct_globals(self):
# https://github.com/pytorch/pytorch/issues/99665
try:
from .utils import outer_func
except ImportError:
from utils import outer_func
def gn(x, y):
return x + y
def fn(x, y):
return outer_func(gn)(x, y)
x = torch.rand([3])
y = torch.rand([3])
opt_fn = torch.compile(backend="eager")(fn)
ref = fn(x, y)
res = opt_fn(x, y)
self.assertTrue(same(ref, res))
class CustomFunc1(torch.autograd.Function):
@staticmethod
def forward(ctx, foo):
return foo + foo
@staticmethod
def backward(ctx, grad_output):
return grad_output
class CustomFunc2(torch.autograd.Function):
# the forward function can be staticmethod or classmethod
@classmethod
def forward(cls, ctx, foo):
return foo + foo
@staticmethod
def backward(ctx, grad_output):
return grad_output
class CustomFunc3(torch.autograd.Function):
# Test there is graph break in forward function
@staticmethod
def forward(ctx, foo):
result = foo + foo
torch._dynamo.graph_break()
result = result + foo
ctx.save_for_backward(result)
return result
@staticmethod
def backward(ctx, grad_output):
(result,) = ctx.saved_tensors
return grad_output * math.sqrt(result.numel())
class Module1(torch.nn.Module):
def forward(self, foo):
return CustomFunc1().apply(foo)
class Module2(torch.nn.Module):
def __init__(self):
super().__init__()
self.fn = CustomFunc1.apply
def forward(self, foo):
return self.fn(foo)
class Module3(torch.nn.Module):
def forward(self, foo):
return CustomFunc2().apply(foo)
class Module4(torch.nn.Module):
def __init__(self):
super().__init__()
self.fn = CustomFunc2.apply
def forward(self, foo):
return self.fn(foo)
class Module5(torch.nn.Module):
def forward(self, foo):
return CustomFunc3().apply(foo)
class Module6(torch.nn.Module):
def __init__(self):
super().__init__()
self.fn = CustomFunc3.apply
def forward(self, foo):
return self.fn(foo)
class TestTracer(JitTestCase):
def test_jit_save(self):
def fn():
class Foo(torch.nn.Module):
def __init__(self):
super().__init__()
self.a = 3
@torch.jit.export
def __getstate__(self):
return (3, self.training)
@torch.jit.export
def __setstate__(self, state):
self.a = state[0]
self.training = state[1]
def forward(self, x):
return x + self.a
f = Foo()
return torch.jit.trace(f, (torch.rand(3, 4),))
fn()
opt_fn = torch._dynamo.optimize("eager")(fn)
opt_fn()
if __name__ == "__main__":
from torch._dynamo.test_case import run_tests
run_tests()
Reference in New Issue View Git Blame Copy Permalink