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pytorch/test/jit/test_freezing.py
Anthony Barbier bf7e290854 Add __main__ guards to jit tests (#154725) 
This PR is part of a series attempting to re-submit https://github.com/pytorch/pytorch/pull/134592 as smaller PRs.

In jit tests:

- Add and use a common raise_on_run_directly method for when a user runs a test file directly which should not be run this way. Print the file which the user should have run.
- Raise a RuntimeError on tests which have been disabled (not run)

Pull Request resolved: https://github.com/pytorch/pytorch/pull/154725
Approved by: https://github.com/clee2000
2025-06-16 10:28:45 +00:00

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# Owner(s): ["oncall: jit"]
# ruff: noqa: F841
import io
import unittest
from itertools import product
from typing import Any
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.jit._recursive import wrap_cpp_module
from torch.testing import FileCheck
from torch.testing._internal.common_cuda import TEST_CUDA, TEST_CUDNN
from torch.testing._internal.common_quantization import skipIfNoFBGEMM
from torch.testing._internal.common_quantized import override_quantized_engine
from torch.testing._internal.common_utils import (
raise_on_run_directly,
set_default_dtype,
skipCUDAMemoryLeakCheckIf,
skipIfTorchDynamo,
TEST_WITH_ROCM,
)
from torch.testing._internal.jit_utils import JitTestCase
from torch.utils import mkldnn as mkldnn_utils
try:
import torchvision
HAS_TORCHVISION = True
except ImportError:
HAS_TORCHVISION = False
skipIfNoTorchVision = unittest.skipIf(not HAS_TORCHVISION, "no torchvision")
TEST_ROCM = torch.cuda.is_available() and torch.version.hip is not None
def removeExceptions(graph):
for n in graph.findAllNodes("prim::RaiseException"):
n.destroy()
class TestFreezing(JitTestCase):
def test_freeze_module(self):
class M(nn.Module):
def __init__(self) -> None:
super().__init__()
self.a = 1 # folded
self.b = 1.2 # folded
self.c = "hello" # folded
self.c2 = "hi\xa1" # not folded
self.d = [1, 1] # folded
self.e = [1.0, 1.1] # folded
self.f = ["hello", "world"] # folded
self.f2 = [(1, "Over \u0e55\u0e57 57")]
self.g = (
[1, 2],
3.2,
"4.4",
torch.tensor([5.5], requires_grad=True),
) # folded
self.h = {"layer": [torch.tensor([7.7], requires_grad=True)]}
self.h2 = {"layer\xb1": [torch.tensor([8.8], requires_grad=True)]}
self.t = torch.tensor([1.2, 2.4], requires_grad=True) # folded
self.ts = [
torch.tensor([1.0, 2.0], requires_grad=True),
torch.tensor([3.0, 4.0], requires_grad=True),
] # folded
self.tt = [[torch.tensor([3.3, 2.3], requires_grad=True), None]]
def forward(self, x):
return (
str(self.a)
+ str(self.b)
+ self.c
+ self.c2
+ str(self.d)
+ str(self.e)
+ str(self.f)
+ str(self.f2)
+ str(self.g)
+ str(self.h)
+ str(self.h2)
+ str(self.t)
+ str(self.ts)
+ str(self.tt)
)
m = torch.jit.script(M())
m.eval()
input = torch.randn(2, 2)
output_s = m.forward(input)
m._c = torch._C._freeze_module(m._c)
buffer = io.BytesIO()
torch.jit.save(m._c, buffer)
buffer.seek(0)
m2 = torch.jit.load(buffer)
# Check if frozen module looks as below:
# module m {
# attributes {
# tt = ...
# }
# ...
# }
self.assertFalse(m2._c.hasattr("a"))
self.assertFalse(m2._c.hasattr("b"))
self.assertFalse(m2._c.hasattr("c"))
self.assertFalse(m2._c.hasattr("c2"))
self.assertFalse(m2._c.hasattr("d"))
self.assertFalse(m2._c.hasattr("e"))
self.assertFalse(m2._c.hasattr("f"))
self.assertFalse(m2._c.hasattr("f2"))
self.assertFalse(m2._c.hasattr("g"))
self.assertFalse(m2._c.hasattr("h"))
self.assertFalse(m2._c.hasattr("h2"))
self.assertFalse(m2._c.hasattr("t"))
self.assertFalse(m2._c.hasattr("ts"))
self.assertFalse(m2._c.hasattr("tt"))
output_f = m2.forward(input)
self.assertEqual(output_s, output_f)
def test_freeze_module_with_submodule(self):
class SubModule(nn.Module):
def __init__(self) -> None:
super().__init__()
self.a = 11
self.b = 2
def forward(self, x):
return self.a + self.b
class SubModule2(nn.Module):
def __init__(self) -> None:
super().__init__()
self.a = 12
self.b = 2
def forward(self, x):
self.b = 30
return self.a + self.b
class TestModule(nn.Module):
def __init__(self) -> None:
super().__init__()
self.sub1 = SubModule()
self.sub2 = SubModule2()
self.a = 3
self.b = 4
def forward(self, x):
self.b = 20
return self.sub1(x) + self.a + self.b + self.sub2(x)
m = torch.jit.script(TestModule())
m.eval()
input = torch.randn(2, 2)
output_s = m.forward(input)
mf = torch.jit.freeze(m)
# Check if frozen module looks as below:
# module m {
# attributes {
# sub2 = ...
# b =
# }
# ...
# submodule {
# module m {
# attributes {
# sub2 = ...
# b =
# }
# ...
# }
# }
# }
mf = mf._c
self.assertFalse(mf.hasattr("sub1"))
self.assertFalse(mf.hasattr("a"))
self.assertTrue(mf.hasattr("b"))
self.assertTrue(mf.hasattr("sub2"))
self.assertTrue(mf.sub2.hasattr("b")) # verify b is preserved in sub2
self.assertFalse(mf.sub2.hasattr("a")) # verify a is removed in sub2
output_f = mf.forward(input)
self.assertEqual(output_s, output_f)
def test_freeze_module_with_fork(self):
class SubModule(nn.Module):
def __init__(self) -> None:
super().__init__()
self.a = torch.ones(20, 20)
self.b = torch.ones(20, 20)
def forward(self, x):
return self.a * self.b + x
class TestModule(nn.Module):
def __init__(self) -> None:
super().__init__()
self.sub = SubModule()
def forward(self, x):
fut = torch.jit._fork(self.sub.forward, x)
y_hat = self.sub(x)
y = torch.jit._wait(fut)
return y_hat + y
m = torch.jit.script(TestModule())
m.eval()
input = torch.randn(20, 20)
output_s = m.forward(input)
mf = torch._C._freeze_module(m._c)
# Check if frozen module looks as below:
# module m {
# attributes {
# }
# ...
# submodule {
# }
# }
self.assertFalse(mf.hasattr("a"))
self.assertFalse(mf.hasattr("b"))
output_f = mf.forward(input)
self.assertEqual(output_s, output_f)
def test_freeze_module_with_nested_fork(self):
class SubModule(nn.Module):
def __init__(self) -> None:
super().__init__()
self.a = torch.ones(20, 20)
self.b = torch.ones(20, 20)
def forward(self, x):
return self.a * self.b + x
class SubModule2(nn.Module):
def __init__(self) -> None:
super().__init__()
self.sub = SubModule()
self.c = torch.ones(20, 20)
def forward(self, x):
fut = torch.jit._fork(self.sub.forward, x)
y_hat = self.sub(x)
y = torch.jit._wait(fut)
return y_hat + y + self.c
class TestModule(nn.Module):
def __init__(self) -> None:
super().__init__()
self.sub = SubModule2()
self.d = 1
def forward(self, x):
fut = torch.jit._fork(self.sub.forward, x)
y_hat = self.sub(x)
y = torch.jit._wait(fut)
self.d = 2
return y_hat * y + self.d
m = torch.jit.script(TestModule())
m.eval()
input = torch.randn(20, 20)
output_s = m.forward(input)
mf = torch._C._freeze_module(m._c)
# Check if frozen module looks as below:
# module m {
# attributes {
# }
# ...
# submodule {
# }
# }
self.assertFalse(mf.hasattr("a"))
self.assertFalse(mf.hasattr("b"))
self.assertFalse(mf.hasattr("c"))
self.assertTrue(mf.hasattr("d"))
output_f = mf.forward(input)
self.assertEqual(output_s, output_f)
def test_freeze_module_with_fork2(self):
@torch.jit.script
def foo(x):
return x * 2
class TestModule(nn.Module):
def __init__(self) -> None:
super().__init__()
self.a = torch.ones(20, 20)
self.b = torch.ones(20, 20)
def forward(self, x):
fut = torch.jit._fork(foo, self.a)
y_hat = foo(self.b)
y = torch.jit._wait(fut)
return y_hat + y
m = torch.jit.script(TestModule())
m.eval()
input = torch.randn(2, 2)
output_s = m.forward(input)
mf = torch._C._freeze_module(m._c)
# Check if frozen module looks as below:
# module m {
# attributes {
# self.a = ...
# self.b = ..
# }
# ...
# submodule {
# }
# }
# TODO: Although there are no mutation, the alias analysis
# conservatively assumes there is a mutation because attributes are
# passed to fork subgraph. both 'a' and 'b' are preserved.
self.assertTrue(mf.hasattr("a"))
self.assertFalse(mf.hasattr("b"))
output_f = mf.forward(input)
self.assertEqual(output_s, output_f)
def test_freeze_module_with_fork_calling_module_method(self):
@torch.jit.script
def foo(x, y):
return x * y
class TestModule(nn.Module):
def __init__(self) -> None:
super().__init__()
self.a = torch.ones(20, 20)
self.b = torch.ones(20, 20)
@torch.jit.export
def foo(self, x):
return x * self.a
@torch.jit.export
def bar(self, x):
return x * self.b
def forward(self, x):
fut = torch.jit._fork(self.foo, self.b)
y_hat = self.bar(self.a)
y = torch.jit._wait(fut)
return y_hat + y
m = torch.jit.script(TestModule())
m.eval()
input = torch.randn(2, 2)
output_s = m.forward(input)
mf = torch._C._freeze_module(m._c)
# Check if frozen module looks as below:
# module m {
# attributes {
# self.b = ..
# }
# ...
# TODO: Although there are no mutation, the alias analysis
# conservatively assumes there is a mutation because attributes are
# passed to fork subgraph. 'b' is preserved.
self.assertFalse(mf.hasattr("a"))
self.assertTrue(mf.hasattr("b"))
output_f = mf.forward(input)
self.assertEqual(output_s, output_f)
def test_freeze_module_with_sharedclasstype(self):
class SubModule(nn.Module):
def __init__(self) -> None:
super().__init__()
self.a = torch.tensor([1.1])
self.b = torch.tensor([2.2])
def forward(self, x):
return self.a + self.b
@torch.jit.export
def modify_a(self, x):
self.a[0] += 10
return self.b
@torch.jit.export
def modify_b(self, x):
self.b[0] += 20
return self.a
class SubModule2(nn.Module):
def __init__(self) -> None:
super().__init__()
self.sub = SubModule()
self.b = torch.tensor([3.3])
def forward(self, x):
y = self.sub.modify_b(x)
return y + self.b
class TestModule(nn.Module):
def __init__(self) -> None:
super().__init__()
self.sub1 = SubModule() # sub1 and sub2.sub shared same class type.
self.sub2 = SubModule2()
self.a = torch.tensor([4.4])
def forward(self, x):
z = self.sub1.modify_a(x)
return self.sub2(x) + z + self.a
m = torch.jit.script(TestModule())
m.eval()
input = torch.randn(2, 2)
output_s = m.forward(input)
mf = torch._C._freeze_module(m._c)
# Checking if Frozen module looks as below
# module mf {
# attributes {
# sub1 = ...
# sub2 = ...
# }
# ...
# submodules {
# module sub1 {
# attributes {
# a = ...
# b = ...
# }
# ...
# }
# module sub2 {
# attributes {
# sub = ...
# }
# ...
# submodule {
# module sub {
# attributes {
# a = ...
# b = ...
# }
# ...
# }
# }
# }
# }
# }
self.assertTrue(mf.hasattr("sub1"))
self.assertTrue(mf.sub1.hasattr("a"))
self.assertTrue(mf.sub1.hasattr("b"))
self.assertFalse(mf.hasattr("a"))
self.assertTrue(mf.hasattr("sub2"))
self.assertTrue(mf.sub2.hasattr("sub"))
self.assertFalse(mf.sub2.hasattr("b"))
self.assertTrue(mf.sub2.sub.hasattr("a"))
self.assertTrue(mf.sub2.sub.hasattr("b"))
output_f = mf.forward(input)
self.assertEqual(output_s, output_f)
def test_freeze_module_with_nestedaliasing(self):
class SubModule(nn.Module):
def __init__(self) -> None:
super().__init__()
self.a = torch.tensor([1.1])
self.b = torch.tensor([2.2])
def forward(self, x):
return self.a + self.b
@torch.jit.export
def modify_a(self, x):
self.a[0] = 10
return self.b
@torch.jit.export
def modify_b(self, x):
self.b[0] = 20
return self.a
Sub = SubModule()
class SubModule2(nn.Module):
def __init__(self) -> None:
super().__init__()
self.sub = Sub # aliasing
def forward(self, x):
return self.sub.a
class TestModule(nn.Module):
def __init__(self) -> None:
super().__init__()
self.sub1 = Sub # aliasing
self.sub2 = SubModule2()
def forward(self, x):
z = self.sub1.modify_a(x)
return self.sub2(x) + z
m = torch.jit.script(TestModule())
m.eval()
mf = torch._C._freeze_module(m._c)
self.assertTrue(mf.hasattr("sub1"))
self.assertTrue(mf.sub1.hasattr("a"))
self.assertFalse(mf.sub1.hasattr("b"))
self.assertTrue(mf.hasattr("sub2"))
self.assertTrue(mf.sub2.hasattr("sub"))
self.assertTrue(
mf.sub2.sub.hasattr("a")
) # Freezing detects that self.sub2.sub.a and self.sub1.a are alias
self.assertFalse(mf.sub2.sub.hasattr("b"))
input = torch.randn(2, 2)
output_s = m.forward(input)
output_f = mf.forward(input)
self.assertEqual(output_s, output_f)
# FIXME: JIT is not honoring aliasing. 'Sub' module is copied. As a result
# Eager and Script modules produce different output.
def test_freeze_module_with_nestedaliasingscalar(self):
class SubModule(nn.Module):
def __init__(self) -> None:
super().__init__()
self.a = 1.1
self.b = 2.2
def forward(self, x):
return self.a + self.b
@torch.jit.export
def modify_a(self, x):
self.a = 10.0
return self.b
@torch.jit.export
def modify_b(self, x):
self.b = 20.0
return self.a
Sub = SubModule()
class SubModule2(nn.Module):
def __init__(self) -> None:
super().__init__()
self.sub = Sub # aliasing
def forward(self, x):
return self.sub.a
class TestModule(nn.Module):
def __init__(self) -> None:
super().__init__()
self.sub1 = Sub # aliasing
self.sub2 = SubModule2()
def forward(self, x):
z = self.sub1.modify_a(x)
return self.sub2(x) + z
m = TestModule()
ms = torch.jit.script(m)
ms.eval()
mf = torch._C._freeze_module(ms._c)
self.assertTrue(mf.hasattr("sub1"))
self.assertTrue(mf.sub1.hasattr("a"))
self.assertFalse(mf.sub1.hasattr("b"))
# sub2 is fully folded becasue self.sub1 and self.sub2.sub are not alias (Scripting bug)
self.assertFalse(mf.hasattr("sub2"))
input = torch.randn(2, 2)
output = m.forward(input)
output_s = ms.forward(input)
output_f = mf.forward(input)
# Should be equal
self.assertNotEqual(output, output_s)
self.assertEqual(output_s, output_f)
def test_freeze_module_with_preserve_sub_module(self):
class SubModule(nn.Module):
def __init__(self) -> None:
super().__init__()
self.a = torch.tensor([1.1])
self.b = 2.2
def forward(self, x):
return self.a
class TestModule(nn.Module):
def __init__(self) -> None:
super().__init__()
self.sub1 = SubModule() # aliasing
self.sub2 = SubModule()
def forward(self, x):
return self.sub2(x) + self.sub1(x)
m = TestModule()
ms = torch.jit.script(m)
ms.eval()
mf = torch._C._freeze_module(ms._c, ["sub1"])
# Test that 'sub1' is preserved entirely and 'sub2' is completely folded
self.assertTrue(mf.hasattr("sub1"))
self.assertTrue(mf.sub1.hasattr("a"))
self.assertTrue(mf.sub1.hasattr("b"))
self.assertFalse(mf.hasattr("sub2"))
input = torch.randn(2, 2)
output_s = ms.forward(input)
output_f = mf.forward(input)
self.assertEqual(output_s, output_f)
def test_freeze_module_with_preserve_sub_module_and_mutation(self):
class SubModule(nn.Module):
def __init__(self) -> None:
super().__init__()
self.a = torch.tensor([1.1])
self.b = 2.2
def forward(self, x):
self.a[0] = 3.3
return self.a
class TestModule(nn.Module):
def __init__(self) -> None:
super().__init__()
self.sub1 = SubModule() # aliasing
self.sub2 = SubModule()
def forward(self, x):
return self.sub2(x) + self.sub1(x)
m = TestModule()
ms = torch.jit.script(m)
ms.eval()
mf = torch._C._freeze_module(ms._c, ["sub1"])
# Test that be both sub1 and sub1 are preserved and 'b' is preserved
# even if it is not used. To fulfill user request to preserve 'sub1'
self.assertTrue(mf.hasattr("sub1"))
self.assertTrue(mf.sub1.hasattr("a"))
self.assertTrue(mf.sub1.hasattr("b"))
self.assertTrue(mf.hasattr("sub2"))
self.assertTrue(mf.sub2.hasattr("a"))
self.assertTrue(mf.sub2.hasattr("b"))
input = torch.randn(2, 2)
output_s = ms.forward(input)
output_f = mf.forward(input)
self.assertEqual(output_s, output_f)
def test_freeze_module_with_helperfunction(self):
class SubModule(nn.Module):
def __init__(self) -> None:
super().__init__()
self.a = 11
self.b = 2
def forward(self, x):
return self.a + self.b
class TestModule(nn.Module):
def __init__(self) -> None:
super().__init__()
self.sub = SubModule()
self.a = 3
self.b = 4
def forward(self, x):
self.b = 20
return self._forward(x) + self.a + self.b
def _forward(self, x):
return self.sub(x)
m = torch.jit.script(TestModule())
m.eval()
input = torch.randn(2, 2)
mf = torch._C._freeze_module(m._c)
self.assertFalse(mf.hasattr("sub"))
self.assertFalse(mf.hasattr("a"))
self.assertTrue(mf.hasattr("b"))
with self.assertRaisesRegex(
AttributeError, "TestModule (.*) does not have a field with name '_forward'"
):
mf._forward(x) # noqa: F821
def test_freeze_module_with_inplace_mutable(self):
class FreezeMe(torch.jit.ScriptModule):
def __init__(self) -> None:
super().__init__()
self.a = [11, 22]
@torch.jit.script_method
def forward(self, x):
for i in range(3):
self.a.append(i)
return self.a
m = FreezeMe()
m.eval()
m_f = torch._C._freeze_module(m._c)
self.assertTrue(m_f.hasattr("a"))
m.forward(torch.tensor([3]))
out = m_f.forward(torch.tensor([5]))
expected = [11, 22, 0, 1, 2, 0, 1, 2]
self.assertEqual(out, expected)
# Mutable attributes
def test_freeze_module_with_mutable_list(self):
class FreezeMe(nn.Module):
def __init__(self) -> None:
super().__init__()
self.a = [1, 2]
def forward(self, x):
return self.a
m = FreezeMe()
m.eval()
m.a.append(3)
m_s = torch.jit.script(m)
v = m_s.a
v.append(4)
m_s.a = v
m_s.eval()
m_f = torch._C._freeze_module(m_s._c)
# Post-freezing mutating m_s.a does not affect m_f (m_f has its own copy).
v = m_s.a
v.append(5)
m_s.a = v
self.assertFalse(m_f.hasattr("a"))
out = m_f.forward(torch.tensor([5]))
expected = [1, 2, 3, 4]
self.assertEqual(out, expected)
def test_freeze_module_with_mutable_dict(self):
class FreezeMe(nn.Module):
def __init__(self) -> None:
super().__init__()
self.a = {"layer": "4"}
def forward(self, x):
return self.a
@torch.jit.export
def modify_a(self, x):
self.a["layer"] = self.a["layer"] + "1"
return self.a
m = FreezeMe()
m.eval()
m.a["layer2"] = "3"
m_s = torch.jit.script(m)
t = torch.tensor(5)
m_s.modify_a(t)
m_s.eval()
m_f = torch._C._freeze_module(m_s._c)
m.a["layer2"] += "2"
m_s.modify_a(t)
self.assertFalse(m_f.hasattr("a"))
out = m_f.forward(t)
expected = {"layer": "411", "layer2": "3"}
self.assertEqual(out, expected)
def test_freeze_module_with_mutable_tensor(self):
class FreezeMe(nn.Module):
def __init__(self) -> None:
super().__init__()
self.a = torch.tensor([1.0, 2.0, 3.0])
def forward(self, x):
return self.a
m = FreezeMe()
m_s = torch.jit.script(m)
m_s.a[1] += 3.0
m_s.eval()
m_f = torch._C._freeze_module(m_s._c)
# Post-freezing tensor attribute mutations affect m_f.
# FIXME: deep copy all folded attributes so that m_f has full ownership.
m_s.a[0] += 5.0
self.assertFalse(m_f.hasattr("a"))
out = m_f.forward(torch.tensor([5]))
expected = [6.0, 5.0, 3.0]
self.assertEqual(out, expected)
def test_freeze_module_with_tuple(self):
class FreezeMe(nn.Module):
def __init__(self) -> None:
super().__init__()
self.a = (torch.tensor([1, 2, 3, 4, 5, 6]), "hi")
def forward(self, x):
if x[0] == 2.0:
self.a[0][0] = 10
return self.a[0].sum()
m = FreezeMe()
m_s = torch.jit.script(m)
m_s.eval()
inp = torch.tensor([2.0])
expected = m_s.forward(inp)
m_s.a[0][0] = 1
m_f = torch._C._freeze_module(m_s._c)
self.assertFalse(m_f.hasattr("a"))
out = m_f.forward(inp)
self.assertEqual(out, expected)
def test_freeze_module_with_tensor(self):
class FreezeMe(nn.Module):
def __init__(self) -> None:
super().__init__()
self.a = torch.tensor([1, 2, 3, 4, 5, 6])
def forward(self, x):
x = self.a.view(2, 3)
x[0][0] += 10
return self.a.sum()
m = FreezeMe()
m_s = torch.jit.script(m)
m_s.eval()
inp = torch.tensor([5])
expected = m_s.forward(inp)
m_f = torch._C._freeze_module(m_s._c)
self.assertTrue(m_f.hasattr("a"))
m_f.a[0] -= 10
out = m_f.forward(inp)
self.assertEqual(out, expected)
def test_freeze_module_with_list(self):
class FreezeMe(nn.Module):
def __init__(self) -> None:
super().__init__()
self.a = [torch.tensor([1, 2, 3, 4, 5, 6])]
def forward(self, x):
self.a[0][1] += 10
return self.a[0].sum()
m = FreezeMe()
m_s = torch.jit.script(m)
m_s.eval()
inp = torch.tensor([5])
expected = m_s.forward(inp)
m_s.a[0][1] -= 10
m_f = torch._C._freeze_module(m_s._c)
self.assertFalse(m_f.hasattr("a"))
out = m_f.forward(inp)
self.assertEqual(out, expected)
def test_freeze_module_with_aliased_tensor_attr(self):
class FreezeMe(nn.Module):
def __init__(self) -> None:
super().__init__()
self.a = torch.tensor([1, 2, 3, 4, 5, 6])
self.b = self.a.view(2, 3)
def forward(self, x):
self.b[1] += 10
return self.a.sum()
m = FreezeMe()
m_s = torch.jit.script(m)
m_s.eval()
m_f = torch._C._freeze_module(m_s._c)
self.assertTrue(m_f.hasattr("a"))
inp = torch.tensor([5])
out = m_f.forward(inp)
expected = torch.tensor(51) # 1+2+3+14+15+16
self.assertEqual(out, expected)
def test_freeze_module_with_aliased_tensor_attr2(self):
class FreezeMe(nn.Module):
def __init__(self) -> None:
super().__init__()
self.a = torch.tensor([1, 2, 3, 4, 5, 6])
self.b = {"layer": ([self.a.view(2, 3), torch.tensor([10])], 20)}
self.c = ([self.a.view(2, 3), torch.tensor([10])], 20)
self.d = (self.a.view(2, 3), 20)
def forward(self, x):
self.d[0][0] += 10
return self.a.sum()
m = FreezeMe()
m_s = torch.jit.script(m)
m_s.eval()
inp = torch.tensor([5])
expected = m_s.forward(inp)
with self.assertRaisesRegex(
RuntimeError, "module contains attributes values that overlaps"
):
m_f = torch._C._freeze_module(m_s._c)
def test_freeze_module_with_aliased_tensor_attr3(self):
class FreezeMe(nn.Module):
def __init__(self) -> None:
super().__init__()
self.a = torch.tensor([1, 2, 3, 4, 5, 6])
self.b = [self.a, torch.tensor([10])]
def forward(self, x):
self.a[1] += 10
return self.b[0].sum()
m = FreezeMe()
m_s = torch.jit.script(m)
m_s.eval()
inp = torch.tensor([5])
expected = m_s.forward(inp)
m_f = torch._C._freeze_module(m_s._c)
self.assertTrue(m_f.hasattr("a"))
self.assertTrue(m_f.hasattr("b"))
out = m_f.forward(inp)
expected += 10 # account for self.a += 10.
self.assertEqual(out, expected)
def test_freeze_module_with_aliased_tensor_attr4(self):
class FreezeMe(nn.Module):
def __init__(self) -> None:
super().__init__()
self.a = torch.tensor([1, 2, 3, 4, 5, 6])
self.b = [self.a, torch.tensor([10])]
def forward(self, x):
self.b[0][0] += 10
return self.a.sum()
m = FreezeMe()
m_s = torch.jit.script(m)
m_s.eval()
inp = torch.tensor([5])
expected = m_s.forward(inp)
m_s.a[0] -= 10
with self.assertRaisesRegex(
RuntimeError, "module contains attributes values that overlaps"
):
m_f = torch._C._freeze_module(m_s._c)
def test_freeze_module_with_overlapping_attrs(self):
a = torch.tensor([1, 2, 3, 4, 5, 6])
class FreezeMe(nn.Module):
def __init__(self) -> None:
super().__init__()
self.b = [a.view(3, 2), torch.tensor([10])]
self.c = (20, a.view(2, 3))
def forward(self, x):
self.b[0][0] += 10
return self.c[1].sum()
m = FreezeMe()
m_s = torch.jit.script(m)
m_s.eval()
inp = torch.tensor([5])
expected = m_s.forward(inp)
a[0] -= 10
with self.assertRaisesRegex(
RuntimeError, "module contains attributes values that overlaps"
):
m_f = torch._C._freeze_module(m_s._c)
def test_freeze_module_with_aliased_attr(self):
class FreezeMe(nn.Module):
def __init__(self) -> None:
super().__init__()
self.a = [1, 2, 3, 4, 5, 6]
self.b = self.a
self.c = (self.a, 10)
def forward(self, x):
self.b[1] += 10
return str(self.a) + str(self.c)
m = FreezeMe()
m_s = torch.jit.script(m)
m_s.eval()
m_f = torch._C._freeze_module(m_s._c)
# FIXME: It should be assertTrue. Currently scripting is making a copy for setting self.b (see #33034)
self.assertFalse(m_f.hasattr("a"))
self.assertFalse(m_f.hasattr("c"))
inp = torch.tensor([5])
out = m_f.forward(inp)
expected = m_s.forward(inp)
self.assertEqual(out, expected)
# Check attribute a is preserved. Alias analysis detects that 'a' has output writers.
# In this example, 'a' is not mutated. However, we do not track which sub
# values of a composite ivalue is mutated.
def test_freeze_module_with_aliased_attr2(self):
class FreezeMe(nn.Module):
def __init__(self) -> None:
super().__init__()
self.a = [1, 2, 3, 4, 5, 6]
self.b = ([11], [10])
def forward(self, x):
v = self.a
self.b = (v, [12])
v2 = self.b[1]
v2.append(7)
return str(v) + str(v2)
m = FreezeMe()
m_s = torch.jit.script(m)
m_s.eval()
m_f = torch._C._freeze_module(m_s._c)
self.assertTrue(m_f.hasattr("a"))
inp = torch.tensor([5])
out = m_f.forward(inp)
expected = m.forward(inp)
self.assertEqual(out, expected)
def test_freeze_module_with_aliased_attr3(self):
class FreezeMe(nn.Module):
def __init__(self) -> None:
super().__init__()
self.a = [1, 2, 3, 4, 5, 6]
self.b = ([11], [10])
def forward(self, x):
v = self.a
v2 = (v, [12])
v3 = v2[0]
v3.append(7)
return str(self.a)
m = FreezeMe()
m_s = torch.jit.script(m)
m_s.eval()
m_f = torch._C._freeze_module(m_s._c)
self.assertTrue(m_f.hasattr("a"))
inp = torch.tensor([5])
out = m_f.forward(inp)
expected = m.forward(inp)
self.assertEqual(out, expected)
def test_freeze_module_return_self(self):
class FreezeMe(nn.Module):
def __init__(self) -> None:
super().__init__()
self.a = torch.tensor([1.0, 2.0, 3.0])
def forward(self, x):
return self
m = FreezeMe()
m_s = torch.jit.script(m)
m_s.eval()
with self.assertRaisesRegex(
RuntimeError, "attempted to freeze a module that return itself"
):
m_f = torch._C._freeze_module(m_s._c)
def test_freeze_module_inlining(self):
@torch.jit.script # noqa: B903
class Obj: # noqa: B903
def __init__(self, x: int, y: int):
self.x = x
self.y = y
class Mod(nn.Module):
def __init__(self) -> None:
super().__init__()
self.obj = Obj(2, 3)
def forward(self, i: int):
print(self.obj)
return i
mod = torch.jit.freeze(torch.jit.script(Mod().eval()))
obj = mod.graph.findNode("prim::Constant")
self.assertTrue(torch._C._jit_object_is_non_holding(obj))
buffer = io.BytesIO()
torch.jit.save(mod, buffer)
buffer.seek(0)
loaded = torch.jit.load(buffer)
obj = mod.graph.findNode("prim::Constant")
self.assertTrue(torch._C._jit_object_is_non_holding(obj))
def test_freeze_module_return_sub_module(self):
class FreezeMe(nn.Module):
def __init__(self) -> None:
super().__init__()
self.conv1 = nn.Conv2d(1, 32, 3, 1)
def forward(self, x):
return self.conv1
m = FreezeMe()
m_s = torch.jit.script(m)
m_s.eval()
m_f = torch._C._freeze_module(m_s._c)
self.assertTrue(m_f.hasattr("conv1"))
def test_freeze_module_no_forward(self):
class FreezeMe(nn.Module):
def __init__(self) -> None:
super().__init__()
self.lin = nn.Linear(10, 1)
@torch.jit.export
def foo(self, x):
return self.lin(x)
m = FreezeMe()
m_s = torch.jit.script(m)
m_s.eval()
m_f = torch._C._freeze_module(m_s._c, preservedAttrs=["foo"])
input = torch.ones(10)
self.assertEqual(m_s.foo(input), m_f.foo(input))
def test_freeze_no_forward(self):
class FreezeMe(nn.Module):
def __init__(self) -> None:
super().__init__()
self.lin = nn.Linear(10, 1)
@torch.jit.export
def foo(self, x):
return self.lin(x)
m = FreezeMe()
m_s = torch.jit.script(m)
m_s.eval()
m_f = torch.jit.freeze(m_s, preserved_attrs=["foo"])
input = torch.ones(10)
self.assertEqual(m_s.foo(input), m_f.foo(input))
def test_freeze_module_in_training_mode(self):
class Net(nn.Module):
def __init__(self) -> None:
super().__init__()
self.conv1 = nn.Conv2d(1, 32, 3, 1)
self.conv2 = nn.Conv2d(32, 64, 3, 1)
self.dropout1 = nn.Dropout2d(0.25)
self.dropout2 = nn.Dropout2d(0.5)
self.fc1 = nn.Linear(9216, 128)
self.fc2 = nn.Linear(128, 10)
def forward(self, x):
x = self.conv1(x)
x = nn.functional.relu(x)
x = self.conv2(x)
x = nn.functional.max_pool2d(x, 2)
x = self.dropout1(x)
x = torch.flatten(x, 1)
x = self.fc1(x)
x = nn.functional.relu(x)
x = self.dropout2(x)
x = self.fc2(x)
output = nn.functional.log_softmax(x, dim=1)
return output
model = torch.jit.script(Net())
model.train()
mTrain_freezed = torch._C._freeze_module(model._c)
# verify mTrain_freezed looks exactly as:
# module {
# attributes {
# conv1 = ...
# conv2 = ...
# dropout1 = ...
# dropout2 = ...
# fc1 = ...
# fc2 = ...
# }
# ...
# submodules {
# module conv1 {
# attributes {
# weight = ...
# bias = ...
# }
# ...
# }
# module conv2 {
# attributes {
# weight = ...
# bias = ...
# }
# ...
# }
# module dropout1 {
# attributes {
# training = ...
# }
# ...
# }
# module dropout2 {
# attributes {
# training = ...
# }
# ...
# }
# module fc1 {
# attributes {
# weight = ...
# bias = ...
# }
# ...
# }
# module fc2 {
# attributes {
# weight = ...
# bias = ...
# }
# ...
# }
self.assertFalse(mTrain_freezed.hasattr("training"))
self.assertTrue(mTrain_freezed.hasattr("conv1"))
self.assertFalse(mTrain_freezed.conv1.hasattr("training"))
self.assertTrue(mTrain_freezed.conv1.hasattr("weight"))
self.assertTrue(mTrain_freezed.conv1.hasattr("bias"))
self.assertTrue(mTrain_freezed.hasattr("conv2"))
self.assertFalse(mTrain_freezed.conv2.hasattr("training"))
self.assertTrue(mTrain_freezed.conv2.hasattr("weight"))
self.assertTrue(mTrain_freezed.conv2.hasattr("bias"))
self.assertTrue(mTrain_freezed.hasattr("dropout1"))
self.assertTrue(mTrain_freezed.dropout1.hasattr("training"))
self.assertTrue(mTrain_freezed.hasattr("dropout2"))
self.assertTrue(mTrain_freezed.dropout2.hasattr("training"))
self.assertTrue(mTrain_freezed.hasattr("fc1"))
self.assertTrue(mTrain_freezed.fc1.hasattr("weight"))
self.assertTrue(mTrain_freezed.fc1.hasattr("bias"))
self.assertTrue(mTrain_freezed.hasattr("fc2"))
self.assertTrue(mTrain_freezed.fc2.hasattr("weight"))
self.assertTrue(mTrain_freezed.fc2.hasattr("bias"))
model.eval()
mEval_freezed = torch._C._freeze_module(model._c)
self.assertFalse(mEval_freezed.hasattr("conv1"))
self.assertFalse(mEval_freezed.hasattr("conv2"))
self.assertFalse(mEval_freezed.hasattr("dropout1"))
self.assertFalse(mEval_freezed.hasattr("training"))
self.assertFalse(mEval_freezed.hasattr("fc1"))
self.assertFalse(mEval_freezed.hasattr("dropout2"))
self.assertFalse(mEval_freezed.hasattr("fc2"))
with self.assertRaisesRegex(
AttributeError, "does not have a field with name 'state_dict'"
):
print(mEval_freezed.state_dict())
buffer = io.BytesIO()
torch.jit.save(mEval_freezed, buffer)
buffer.seek(0)
m = torch.jit.load(buffer)
FileCheck().check_not("GetAttr[name=").run(m._c._get_method("forward").graph)
m2 = torch._C._freeze_module(model._c, preserveParameters=True)
self.assertTrue(m2.hasattr("conv1"))
self.assertTrue(m2.hasattr("conv2"))
self.assertFalse(m2.hasattr("dropout1"))
self.assertFalse(m2.hasattr("training"))
self.assertTrue(m2.hasattr("fc1"))
self.assertFalse(m2.hasattr("dropout2"))
self.assertTrue(m2.hasattr("fc2"))
def test_freeze_module_detach_gradient(self):
mod = nn.Conv2d(8, 3, 4, 2, 1)
self.assertTrue(mod.weight.requires_grad)
smod = torch.jit.script(mod)
smod.eval()
fmod = torch._C._freeze_module(smod._c)
self.assertTrue(mod.weight.requires_grad)
self.assertTrue(smod.weight.requires_grad)
self.assertFalse(fmod.hasattr("weight"))
inp = torch.ones(1, 8, 32, 32)
out1 = fmod.forward(inp)
# FIXME: frozen module mutated from outside (original module).
with torch.no_grad():
smod.weight[0, 0, 0, 0] += 100.0
out2 = fmod.forward(inp)
out3 = smod(inp)
self.assertNotEqual(out1, out2)
self.assertEqual(out2, out3)
def test_freeze_module_with_user_preserved_attr(self):
class Module(nn.Module):
def __init__(self) -> None:
super().__init__()
self.a = torch.tensor([1.1])
self.b = torch.tensor([2.2])
def forward(self, x):
return self.a + self.b
m = torch.jit.script(Module())
m.eval()
fm = torch._C._freeze_module(m._c, ["a"])
# Attribute "a" is preserved
self.assertTrue(fm.hasattr("a"))
self.assertFalse(fm.hasattr("b"))
def test_freeze_module_with_user_preserved_method(self):
class Module(nn.Module):
def __init__(self) -> None:
super().__init__()
self.a = torch.tensor([1.1])
self.b = torch.tensor([2.2])
def forward(self, x):
return self.a + self.b
@torch.jit.export
def modify_a(self, x):
self.a[0] += 10
return self.b
@torch.jit.export
def modify_b(self, x):
self.b[0] += 20
return self.a
m = torch.jit.script(Module())
m.eval()
fm = torch._C._freeze_module(m._c, ["modify_a"])
# Both attribute "a" and method "modify_a" are preserved
self.assertTrue(fm.hasattr("a"))
self.assertFalse(fm.hasattr("b"))
input = torch.randn(2, 2)
expected = m.forward(input)
out = fm.forward(input)
self.assertEqual(out, expected)
def test_freeze_module_with_user_preserved_method2(self):
class Module(nn.Module):
def __init__(self) -> None:
super().__init__()
self.a = torch.tensor([1.1])
self.b = torch.tensor([2.2])
def forward(self, x):
self.b += 10
return self.a + self.b
@torch.jit.export
def modify_a(self, x):
self.a[0] += 10
return self.b + self.a
m = torch.jit.script(Module())
m.eval()
fm = torch._C._freeze_module(m._c, ["modify_a"])
FileCheck().check('prim::GetAttr[name="a"]').run(fm.forward.graph)
FileCheck().check('prim::GetAttr[name="b"]').run(fm.modify_a.graph)
def test_freeze_module_with_user_preserved_attribute_on_submodule(self):
class SubModule(nn.Module):
def __init__(self) -> None:
super().__init__()
self.a = 1
self.b = 2
def forward(self):
return self.a + self.b
class Module(nn.Module):
def __init__(self) -> None:
super().__init__()
self.sub1 = SubModule()
self.sub2 = SubModule()
def forward(self):
return self.sub1() + self.sub2()
m = torch.jit.script(Module())
m.eval()
m = torch.jit.freeze(m, preserved_attrs=["sub1.a", "sub2.a"])
fm = m._c
self.assertTrue(fm.hasattr("sub1"))
self.assertTrue(fm.sub1.hasattr("a"))
self.assertFalse(fm.sub1.hasattr("b"))
self.assertTrue(fm.hasattr("sub2"))
self.assertTrue(fm.sub2.hasattr("a"))
self.assertFalse(fm.sub2.hasattr("b"))
self.assertEqual(m(), 6)
m.sub1.a += 1
self.assertEqual(m(), 7)
def test_freeze_module_with_user_preserved_attribute_on_unused_submodule(self):
class SubModule(nn.Module):
def __init__(self) -> None:
super().__init__()
self.a = 1
self.b = 2
def forward(self):
return self.a + self.b
@torch.jit.export
def method_a(self):
return 42
class Module(nn.Module):
def __init__(self) -> None:
super().__init__()
self.sub = SubModule()
def forward(self):
return 1
m = torch.jit.script(Module())
m.eval()
fm = torch.jit.freeze(m, preserved_attrs=["sub.a", "sub.method_a"])._c
self.assertTrue(fm.hasattr("sub"))
self.assertTrue(fm.sub.hasattr("a"))
self.assertFalse(fm.sub.hasattr("b"))
self.assertTrue(fm.sub._has_method("method_a"))
def test_freeze_module_with_user_preserved_method_on_submodule(self):
class SubModule(nn.Module):
def forward(self, x):
return self.method_a(x) + self.method_b(x)
def method_a(self, x):
return x * x
def method_b(self, x):
return x + x
class Module(nn.Module):
def __init__(self) -> None:
super().__init__()
self.sub = SubModule()
def forward(self, x):
return self.sub(x)
m = torch.jit.script(Module())
m.eval()
fm = torch.jit.freeze(m, preserved_attrs=["sub.method_a"])._c
self.assertTrue(fm.hasattr("sub"))
self.assertTrue(fm.sub._has_method("method_a"))
self.assertFalse(fm.sub._has_method("method_b"))
@skipIfNoFBGEMM
def test_module_with_shared_type_instances(self):
class Child(nn.Module):
def __init__(self) -> None:
super().__init__()
self.conv1 = nn.Conv2d(1, 1, 1).to(dtype=torch.float32)
def forward(self, x):
x = self.conv1(x)
return x
class Parent(nn.Module):
def __init__(self) -> None:
super().__init__()
self.quant = torch.ao.quantization.QuantStub()
self.conv1 = nn.Conv2d(1, 1, 1).to(dtype=torch.float32)
self.child = Child()
self.child2 = Child()
self.dequant = torch.ao.quantization.DeQuantStub()
def forward(self, x):
x = self.quant(x)
x = self.conv1(x)
x = self.child(x)
x = self.child2(x)
x = self.dequant(x)
return x
def _static_quant(model):
qModel = torch.ao.quantization.QuantWrapper(model)
qModel.qconfig = torch.ao.quantization.default_qconfig
torch.ao.quantization.prepare(qModel, inplace=True)
qModel(torch.rand(4, 1, 4, 4, dtype=torch.float32))
torch.ao.quantization.convert(qModel, inplace=True)
return model
with override_quantized_engine("fbgemm"):
data = torch.randn(4, 1, 4, 4, dtype=torch.float32)
m = Parent().to(torch.float32)
m = _static_quant(m)
m = torch.jit.script(m)
m.eval()
torch._C._jit_pass_inline(m.graph)
m_frozen = wrap_cpp_module(torch._C._freeze_module(m._c))
# Earlier bug resulted in _packed_params set to false.
FileCheck().check_not("_packed_params = False").run(
m_frozen._c.dump_to_str(True, True, False)
)
m_res = m(data)
# It used to segfault while running frozen module.
m_frozen_res = m_frozen(data)
self.assertEqual(m_res, m_frozen_res)
def test_module_getattr_indirection(self):
@torch.jit.script
class ValHolder:
def __init__(self, val: int):
self.val: int = val
class Mod(nn.Module):
def __init__(self) -> None:
super().__init__()
self.mod1 = ValHolder(1)
self.mod2 = ValHolder(2)
def forward(self, cond: bool):
if cond:
mod = self.mod1
else:
mod = self.mod2
return mod.val
mod = Mod()
mod.eval()
frozen_mod = torch.jit.freeze(torch.jit.script(mod))
mod_eager = Mod()
self.assertEqual(mod_eager(True), frozen_mod(True))
self.assertEqual(mod_eager(False), frozen_mod(False))
def test_freeze_module_with_non_static_module_container_index(self):
"""
Test that Modules containing non-static ModuleDict or ModuleList
indexing cannot be frozen.
"""
@torch.jit.interface
class ModuleInterface(torch.nn.Module):
def forward(self, inp: Any) -> Any:
pass
class ImplementsInterface(torch.nn.Module):
def forward(self, inp: Any) -> Any:
if isinstance(inp, torch.Tensor):
return torch.max(inp, dim=0)
return inp
class ModWithDict(torch.nn.Module):
def __init__(self) -> None:
super().__init__()
self.d = torch.nn.ModuleDict({"module": ImplementsInterface()})
def forward(self, x: torch.Tensor, key: str) -> Any:
value: ModuleInterface = self.d[key]
return value.forward(x)
m = torch.jit.script(ModWithDict())
m.eval()
with self.assertRaisesRegex(
RuntimeError,
"Freezing modules containing prim::ModuleContainerIndex is not supported",
):
mf = torch._C._freeze_module(m._c)
class ModWithList(torch.nn.Module):
def __init__(self) -> None:
super().__init__()
self.l = torch.nn.ModuleList([ImplementsInterface()])
def forward(self, x: torch.Tensor, idx: int) -> Any:
value: ModuleInterface = self.l[idx]
return value.forward(x)
m = torch.jit.script(ModWithList())
m.eval()
with self.assertRaisesRegex(
RuntimeError,
"Freezing modules containing prim::ModuleContainerIndex is not supported",
):
mf = torch._C._freeze_module(m._c)
def test_freeze_with_interface_mutable(self):
@torch.jit.interface
class ModuleInterface(torch.nn.Module):
def forward(self, inp: torch.Tensor) -> torch.Tensor:
pass
class ImplementsInterface(torch.nn.Module):
def __init__(self) -> None:
super().__init__()
self.sum = torch.zeros((2, 2))
def forward(self, inp: torch.Tensor) -> torch.Tensor:
self.sum += inp.relu()
return self.sum
class WrapperModule(torch.nn.Module):
impl: ModuleInterface
def __init__(self) -> None:
super().__init__()
self.impl = ImplementsInterface()
def forward(self, x: torch.Tensor) -> torch.Tensor:
return self.impl.forward(x)
m = torch.jit.script(WrapperModule())
m.eval()
m_frozen = torch.jit.freeze(m)
x = torch.rand((2, 2))
m_frozen(x)
self.assertEqual(m_frozen.impl.sum, x.relu())
def test_freeze_with_swapping_interfaces(self):
@torch.jit.interface
class ModuleInterface(torch.nn.Module):
def forward(self, inp: torch.Tensor) -> torch.Tensor:
pass
class Implementation1(torch.nn.Module):
def forward(self, inp: torch.Tensor) -> torch.Tensor:
return inp.relu()
class Implementation2(torch.nn.Module):
def forward(self, inp: torch.Tensor) -> torch.Tensor:
return inp.sin()
class WrapperModule(torch.nn.Module):
impl: ModuleInterface
def __init__(self) -> None:
super().__init__()
self.option1 = Implementation1()
self.option2 = Implementation2()
self.impl = self.option1
self.idx = 0
def forward(self, x: torch.Tensor) -> torch.Tensor:
self.idx += 1
if self.idx % 2 == 1:
self.impl = self.option1
else:
self.impl = self.option2
return self.impl(x)
m = torch.jit.script(WrapperModule())
m.eval()
with self.assertRaisesRegex(
RuntimeError, "Freezing does not support SetAttr on an interface type"
):
m_frozen = torch.jit.freeze(m)
def test_freeze_recursive_interfaces(self):
@torch.jit.interface
class InnerInterface(torch.nn.Module):
def forward(self, inp: torch.Tensor) -> torch.Tensor:
pass
@torch.jit.interface
class OuterInterface(torch.nn.Module):
def forward(self, inp: torch.Tensor) -> torch.Tensor:
pass
class InnerImpl(torch.nn.Module):
def __init__(self) -> None:
super().__init__()
self.x = torch.ones((2, 2))
def forward(self, inp):
return inp.cos() * self.x
class OuterImpl(torch.nn.Module):
inner_impl: InnerInterface
def __init__(self) -> None:
super().__init__()
self.inner_impl = InnerImpl()
def forward(self, inp):
return inp.relu() + self.inner_impl(inp.sin())
class WrapperModule(torch.nn.Module):
outer_impl: OuterInterface
def __init__(self) -> None:
super().__init__()
self.outer_impl = OuterImpl()
def forward(self, inp):
return self.outer_impl(inp) + inp
m = WrapperModule()
x = torch.rand((2, 2))
expected = m(x)
m_s = torch.jit.script(m)
m_s.eval()
m_s = torch.jit.freeze(m_s)
actual = m_s(x)
self.assertEqual(expected, actual)
def test_freeze_recursive_interfaces_with_reassignment(self):
@torch.jit.interface
class InnerInterface(torch.nn.Module):
def forward(self, inp: torch.Tensor) -> torch.Tensor:
pass
@torch.jit.interface
class OuterInterface(torch.nn.Module):
def forward(self, inp: torch.Tensor) -> torch.Tensor:
pass
class InnerImpl1(torch.nn.Module):
def __init__(self) -> None:
super().__init__()
self.x = torch.ones((2, 2))
def forward(self, inp):
return inp.cos() * self.x
class InnerImpl2(torch.nn.Module):
def __init__(self) -> None:
super().__init__()
self.x = torch.ones((2, 2)) * 2
def forward(self, inp):
return inp.sin() / self.x
class OuterImpl(torch.nn.Module):
inner_impl: InnerInterface
def __init__(self) -> None:
super().__init__()
self.inner_impl = InnerImpl1()
self.impl1 = InnerImpl1()
self.impl2 = InnerImpl1()
self.idx = 0
def forward(self, inp):
self.idx += 1
if self.idx % 2 == 0:
self.inner_impl = self.impl1
else:
self.inner_impl = self.impl2
return inp.relu() + self.inner_impl(inp.sin())
class WrapperModule(torch.nn.Module):
outer_impl: OuterInterface
def __init__(self) -> None:
super().__init__()
self.outer_impl = OuterImpl()
def forward(self, inp):
return self.outer_impl(inp) + inp
m = WrapperModule()
m_s = torch.jit.script(m)
m_s.eval()
with self.assertRaisesRegex(
RuntimeError, "Freezing does not support SetAttr on an interface type"
):
m_s = torch.jit.freeze(m_s)
def test_freeze_interface_swapping_two_methods(self):
@torch.jit.interface
class MyInterface(torch.nn.Module):
def forward(self, inp: torch.Tensor) -> torch.Tensor:
pass
class Impl1(torch.nn.Module):
def forward(self, inp):
return inp.cos()
class Impl2(torch.nn.Module):
def forward(self, inp):
return inp.sin()
class WrapperModule1(torch.nn.Module):
interface_impl: MyInterface
def __init__(self) -> None:
super().__init__()
self.interface_impl = Impl1()
self.impl1 = Impl1()
self.impl2 = Impl2()
self.idx = 0
def forward(self, x):
return self.interface_impl(x)
@torch.jit.export
def other_method(self, x):
self.idx += 1
if self.idx % 2 == 0:
self.interface_impl = self.impl1
else:
self.interface_impl = self.impl2
return self.interface_impl(x)
class WrapperModule2(torch.nn.Module):
interface_impl: MyInterface
def __init__(self) -> None:
super().__init__()
self.interface_impl = Impl1()
self.impl1 = Impl1()
self.impl2 = Impl2()
self.idx = 0
def forward(self, x):
self.idx += 1
if self.idx % 2 == 0:
self.interface_impl = self.impl1
else:
self.interface_impl = self.impl2
return self.interface_impl(x)
@torch.jit.export
def other_method(self, x):
return self.interface_impl(x)
m1 = torch.jit.script(WrapperModule1())
m2 = torch.jit.script(WrapperModule2())
m1.eval()
m2.eval()
with self.assertRaisesRegex(
RuntimeError, "Freezing does not support SetAttr on an interface type"
):
torch.jit.freeze(m1, preserved_attrs=["other_method"])
with self.assertRaisesRegex(
RuntimeError, "Freezing does not support SetAttr on an interface type"
):
torch.jit.freeze(m2, preserved_attrs=["other_method"])
def test_freeze_recursive_interfaces_same_name(self):
@torch.jit.interface
class InnerInterface(torch.nn.Module):
def forward(self, inp: torch.Tensor) -> torch.Tensor:
pass
@torch.jit.interface
class OuterInterface(torch.nn.Module):
def forward(self, inp: torch.Tensor) -> torch.Tensor:
pass
class InnerImpl(torch.nn.Module):
def __init__(self) -> None:
super().__init__()
self.x = torch.ones((2, 2))
def forward(self, inp):
return inp.cos() * self.x
class OuterImpl(torch.nn.Module):
impl: InnerInterface
def __init__(self) -> None:
super().__init__()
self.impl = InnerImpl()
self.x = torch.ones((2, 2)) * 5
def forward(self, inp):
return self.other_method(inp)
def other_method(self, inp):
return inp.relu() + self.impl(inp.sin()) + self.x
class WrapperModule(torch.nn.Module):
impl: OuterInterface
def __init__(self) -> None:
super().__init__()
self.impl = OuterImpl()
def forward(self, inp):
return self.impl(inp) + inp
m = WrapperModule()
x = torch.rand((2, 2))
expected = m(x)
m_s = torch.jit.script(m)
m_s.eval()
m_s = torch.jit.freeze(m_s)
actual = m_s(x)
self.assertEqual(expected, actual)
def test_freeze_non_interface_module_swap(self):
class InnerModule(torch.nn.Module):
def __init__(self, x):
super().__init__()
self.x = x
def forward(self, inp: torch.Tensor) -> torch.Tensor:
return inp.relu() + self.x
class WrapperModule(torch.nn.Module):
def __init__(self) -> None:
super().__init__()
self.option1 = InnerModule(torch.rand((2, 2)))
self.option2 = InnerModule(torch.rand((2, 2)))
self.impl = self.option1
self.idx = 0
def forward(self, x: torch.Tensor) -> torch.Tensor:
self.idx += 1
if self.idx % 2 == 1:
self.impl = self.option1
else:
self.impl = self.option2
return self.impl(x)
unfrozen = WrapperModule()
m = torch.jit.script(unfrozen)
m.eval()
m_frozen = torch.jit.freeze(m)
x = torch.rand((2, 2))
expected = unfrozen(x)
actual = m_frozen(x)
self.assertEqual(expected, actual)
@unittest.expectedFailure
def test_freeze_interface_within_object(self):
# I don't think there's any way to create a plain python object that
# contains a torch.nn.Module inside it, but just in case... I'm not
# sure freezing would handle this case correctly, so marking as xfail
# so that if this ever _does_ start working someone will need to
# investigate to make sure this is handled correctly.
class MyIface(torch.nn.Module):
def forward(self, inp: torch.Tensor) -> torch.Tensor:
pass
class MyImpl(torch.nn.Module):
def forward(self, inp: torch.Tensor) -> torch.Tensor:
return inp.sin()
class MyObject:
impl: MyIface
def run(self, x):
return self.impl(x)
class WrapperModule(torch.nn.Module):
impl: MyObject
def __init__(self) -> None:
super().__init__()
self.impl = MyObject()
self.impl.impl = MyImpl()
def forward(self, x: torch.Tensor) -> torch.Tensor:
return self.impl(x)
unfrozen = WrapperModule()
m = torch.jit.script(unfrozen)
m.eval()
m_frozen = torch.jit.freeze(m)
x = torch.rand((2, 2))
expected = unfrozen(x)
actual = m_frozen(x)
self.expectEqual(expected, actual)
def test_freeze_non_module_class_getattr(self):
class BoxCoder:
def __init__(self, bbox_xform_clip):
# type: (float) -> None
self.bbox_xform_clip = bbox_xform_clip
def decode(self, input):
return input * self.bbox_xform_clip
class MyModule(torch.nn.Module):
__annotations__ = {
"box_coder": BoxCoder,
}
def __init__(self) -> None:
super().__init__()
self.box_coder = BoxCoder(50.0)
def forward(self, input):
return self.box_coder.decode(input)
model = MyModule()
model.eval()
script_model = torch.jit.freeze(torch.jit.script(model))
inp = torch.randn([4, 4])
output_eager = model(inp)
self.assertEqual(model(inp), script_model(inp))
FileCheck().check_not("GetAttr").run(script_model.graph)
def test_freeze_module_with_tupleoutput_submodule(self):
class SubModule(nn.Module):
def forward(self, x):
return (x + 1, x + 2)
class TestModule(nn.Module):
def __init__(self) -> None:
super().__init__()
self.sub = SubModule()
def forward(self, x):
y1, y2 = self.sub(x)
return y1 + y2
m = torch.jit.script(TestModule())
m = m.eval()
mf = torch.jit.freeze(m)
inp = torch.randn(2, 2)
expected = m.forward(inp)
output = mf.forward(inp)
# Check if prim::TupleConstruct and prim::TupleUnpack
# Don't exist in frozen graph
FileCheck().check_not("prim::TupleConstruct").run(mf.graph)
FileCheck().check_not("prim::TupleUnpack").run(mf.graph)
self.assertEqual(output, expected)
def test_freeze_module_with_call_method(self):
class Mod(nn.Module):
def __init__(self, val):
super().__init__()
self.param = nn.Parameter(val)
def forward(self, x):
# this method will change during freezing
return x + self.param
@torch.jit.export
def make_prediction(self, x):
y = x + x
return self.forward(y)
param = torch.rand([2, 2])
x = torch.rand([2, 2])
unscripted_mod = Mod(param)
mod = torch.jit.script(unscripted_mod)
mod.eval()
mod = torch.jit.freeze(mod, preserved_attrs=["make_prediction"])
self.assertEqual(
mod.forward(x), unscripted_mod.forward(x), atol=1e-5, rtol=1e-5
)
@skipIfTorchDynamo("somehow causing hanging during python shutdown")
class TestFrozenOptimizations(JitTestCase):
def setUp(self):
super().setUp()
self.default_dtype = torch.get_default_dtype()
torch.set_default_dtype(torch.double)
def tearDown(self):
torch.set_default_dtype(self.default_dtype)
super().tearDown()
def test_conv_bn_folding(self):
conv_bias = [True, False]
module_pairs = [
(nn.Conv1d, nn.BatchNorm1d),
(nn.Conv2d, nn.BatchNorm2d),
(nn.Conv3d, nn.BatchNorm3d),
]
use_tracing = [True, False]
bn_running_stats = [True, False]
for use_bias, modules, tracing, track_stats in product(
conv_bias, module_pairs, use_tracing, bn_running_stats
):
class ConvBN(torch.nn.Module):
def __init__(self, in_channels, out_channels, **kwargs):
super().__init__()
self.conv = modules[0](
in_channels, out_channels, bias=use_bias, **kwargs
)
self.bn = modules[1](
out_channels, eps=0.001, track_running_stats=track_stats
)
def forward(self, x):
x = self.conv(x)
return self.bn(x)
mod_eager = ConvBN(3, 32, kernel_size=3, stride=2).eval()
inps = [4, 3, 4]
if modules[0] == nn.Conv2d:
inps.append(inps[-1])
if modules[0] == nn.Conv3d:
inps.append(inps[-1])
inps.append(inps[-1])
inp = torch.rand(inps)
if tracing:
scripted_mod = torch.jit.trace(mod_eager, (inp))
else:
scripted_mod = torch.jit.script(mod_eager)
self.run_pass("inline", scripted_mod.graph)
self.run_pass("peephole", scripted_mod.graph)
self.run_pass("constant_propagation", scripted_mod.graph)
FileCheck().check("conv").check("batch").run(scripted_mod.graph)
# successfully no-ops with non-const inputs
self.run_pass("fold_frozen_conv_bn", scripted_mod.graph)
FileCheck().check("conv").check("aten::batch_norm").run(scripted_mod.graph)
scripted_mod = torch.jit.freeze(scripted_mod)
self.run_pass("fold_frozen_conv_bn", scripted_mod.graph)
if track_stats:
FileCheck().check("conv").check_not("aten::batch_norm").run(
scripted_mod.graph
)
else:
FileCheck().check("conv").check("aten::batch_norm").run(
scripted_mod.graph
)
self.assertEqual(mod_eager(inp), scripted_mod(inp))
self.assertEqual(mod_eager(inp), scripted_mod(inp))
def test_conv_bn_folding_not_forward(self):
class ConvBN(torch.nn.Module):
def __init__(self, in_channels, out_channels, **kwargs):
super().__init__()
self.conv = torch.nn.Conv2d(
in_channels, out_channels, bias=True, **kwargs
)
self.bn = torch.nn.BatchNorm2d(out_channels, eps=0.001)
self.amt = 3.2
def forward(self, x):
x = self.conv(x)
return self.bn(x)
@torch.jit.export
def make_prediction(self, x):
return self.forward(x) + self.amt
mod_eager = ConvBN(3, 32, kernel_size=3, stride=2).eval()
scripted_mod = torch.jit.script(mod_eager)
torch._C._jit_pass_inline(scripted_mod.make_prediction.graph)
FileCheck().check("conv").check("aten::batch_norm").run(
scripted_mod.make_prediction.graph
)
# _jit_pass_optimize_frozen_graph should not be called on non-method attributes (e.g. "amt")
scripted_mod = torch.jit.freeze(
scripted_mod, preserved_attrs=["make_prediction", "amt"]
)
FileCheck().check("conv").check_not("aten::batch_norm").run(
scripted_mod.make_prediction.graph
)
# During freezing this creates tensors constants that are attached to the frozen graph,
# which is then kept alive by the compilation unit (which causes a leak)
@skipCUDAMemoryLeakCheckIf(True)
@unittest.skipIf(not TEST_CUDA, "Optimization currently only run for GPU")
def test_conv_bn_folding_autocast_scenario_cuda(self):
# CUDA conv takes input tensors which must all be the same dtype,
# which can cause issues if folding produces inputs of different dtypes.
class ConvBN(torch.nn.Module):
def __init__(self, in_channels, out_channels, **kwargs):
super().__init__()
self.conv = torch.nn.Conv2d(
in_channels, out_channels, bias=False, dtype=torch.half, **kwargs
)
self.bn = torch.nn.BatchNorm2d(
out_channels, eps=0.001, dtype=torch.float
)
def forward(self, x):
return self.bn(self.conv(x))
mod_eager = ConvBN(3, 32, kernel_size=3, stride=2).cuda().eval()
scripted_mod = torch.jit.script(mod_eager)
scripted_mod = torch.jit.freeze(scripted_mod)
FileCheck().check("conv").check_not("aten::batch_norm").run(scripted_mod.graph)
conv_node = scripted_mod.graph.findNode("aten::conv2d", True)
self.assertTrue(conv_node is not None)
bias_input = conv_node.namedInput("bias")
self.assertTrue(bias_input is not None)
self.assertTrue(bias_input.type().dtype() == torch.half)
x = torch.rand((3, 3, 32, 32), dtype=torch.half).cuda()
self.assertEqual(mod_eager(x), scripted_mod(x), atol=1e-2, rtol=1e-2)
self.assertEqual(mod_eager(x), scripted_mod(x), atol=1e-2, rtol=1e-2)
def test_conv_add_folding(self):
@torch.no_grad()
def test_conv_fusion(
use_bias, module, tracing, op, scalar, add_tensor, expect_success
):
class ConvOp(torch.nn.Module):
__constants__ = ["use_scalar"]
def __init__(self, in_channels, out_channels, tensor=None, **kwargs):
super().__init__()
self.conv = module(
in_channels, out_channels, bias=use_bias, **kwargs
)
self.conv2 = module(
in_channels, out_channels, bias=use_bias, **kwargs
)
self.use_scalar = scalar
tensor_size = [1 for _ in range(self.conv.weight.ndim)]
tensor_size[1] = self.conv.weight.size(0)
self.tensor = (
add_tensor
if add_tensor is not None
else torch.rand(tensor_size)
)
self.op = op
def forward(self, x):
x = self.conv(x)
if self.use_scalar:
return self.op(x, 2.0)
else:
return self.op(x, self.tensor)
mod_eager = ConvOp(3, 32, kernel_size=3, stride=2).eval()
inps = [4, 3, 4]
if module == nn.Conv2d:
inps.append(inps[-1])
if module == nn.Conv3d:
inps.append(inps[-1])
inps.append(inps[-1])
inp = torch.rand(inps)
if tracing:
scripted_mod = torch.jit.trace(mod_eager, (inp,))
else:
scripted_mod = torch.jit.script(mod_eager)
self.run_pass("inline", scripted_mod.graph)
op_str = "aten::" + op.__name__
FileCheck().check("conv").check(op_str).run(scripted_mod.graph)
# successively no-ops with non-const inputs
self.run_pass("fold_frozen_conv_mul_or_div", scripted_mod.graph)
self.run_pass("fold_frozen_conv_add_or_sub", scripted_mod.graph)
FileCheck().check("conv").check(op_str).run(scripted_mod.graph)
scripted_mod = torch.jit.freeze(scripted_mod)
self.run_pass("fold_frozen_conv_mul_or_div", scripted_mod.graph)
self.run_pass("fold_frozen_conv_add_or_sub", scripted_mod.graph)
if expect_success:
FileCheck().check("conv").check_not(op_str).run(scripted_mod.graph)
else:
FileCheck().check("conv").check(op_str).run(scripted_mod.graph)
self.assertEqual(mod_eager(inp), scripted_mod(inp))
self.assertEqual(mod_eager(inp), scripted_mod(inp))
conv_bias = [True, False]
modules = [nn.Conv1d, nn.Conv2d, nn.Conv3d]
use_tracing = [False, True]
use_scalar = [False, True]
ops = [torch.add, torch.sub, torch.mul, torch.div]
for use_bias, module, tracing, pytorch_op, scalar in product(
conv_bias, modules, use_tracing, ops, use_scalar
):
test_conv_fusion(
use_bias,
module,
tracing,
pytorch_op,
scalar,
add_tensor=None,
expect_success=True,
)
for use_bias, pytorch_op in product(conv_bias, ops):
# broadcasting add
test_conv_fusion(
use_bias,
nn.Conv2d,
False,
pytorch_op,
False,
add_tensor=torch.rand(32, 1, 32),
expect_success=False,
)
# broadcasting add
test_conv_fusion(
use_bias,
nn.Conv2d,
False,
pytorch_op,
False,
add_tensor=torch.rand(1, 1),
expect_success=True,
)
# add with different dtype
test_conv_fusion(
use_bias,
nn.Conv2d,
False,
pytorch_op,
False,
add_tensor=torch.tensor([2]).to(torch.int),
expect_success=True,
)
def test_conv_mul_add_bn(self):
class Conv_Mul_Add_Bn(nn.Module):
def __init__(self, in_channels, out_channels, **kwargs):
super().__init__()
self.conv = nn.Conv2d(in_channels, out_channels, **kwargs)
self.bn = nn.BatchNorm2d(out_channels, eps=0.001)
self.tensor1 = torch.tensor(2.2)
self.tensor2 = torch.tensor(2)
def forward(self, x):
return self.bn(
torch.add(torch.mul(self.conv(x), self.tensor1), self.tensor2)
)
input = torch.randn(8, 3, 64, 64)
model = Conv_Mul_Add_Bn(3, 32, kernel_size=3, stride=1).eval()
with torch.no_grad():
result = model(input)
traced_model = torch.jit.trace(model, input).eval()
traced_model = torch.jit.freeze(traced_model)
tresult = traced_model(input)
self.assertEqual(result, tresult)
FileCheck().check("conv").check_not("aten::batch_norm").run(
traced_model.graph
)
FileCheck().check("conv").check_not("aten::add").run(traced_model.graph)
def test_linear_bn_folding(self):
module_pairs = [
(nn.Linear, nn.BatchNorm1d),
(nn.Linear, nn.BatchNorm2d),
(nn.Linear, nn.BatchNorm3d),
]
use_tracing = [True, False]
bn_running_stats = [True, False]
for modules, tracing, track_stats in product(
module_pairs, use_tracing, bn_running_stats
):
class LinearBN(torch.nn.Module):
def __init__(self, in_features, out_features):
super().__init__()
self.linear = modules[0](in_features, out_features)
self.bn = modules[1](
out_features, eps=0.001, track_running_stats=track_stats
)
def forward(self, x):
x = self.linear(x)
return self.bn(x)
mod_eager = LinearBN(32, 32).eval()
inps = [3, 32]
if modules[1] == nn.BatchNorm2d:
inps.append(inps[-1])
inps.append(inps[-1])
if modules[1] == nn.BatchNorm3d:
inps.append(inps[-1])
inps.append(inps[-1])
inps.append(inps[-1])
inp = torch.rand(inps)
if tracing:
scripted_mod = torch.jit.trace(mod_eager, (inp))
else:
scripted_mod = torch.jit.script(mod_eager)
self.run_pass("inline", scripted_mod.graph)
self.run_pass("peephole", scripted_mod.graph)
self.run_pass("constant_propagation", scripted_mod.graph)
FileCheck().check("linear").check("batch").run(scripted_mod.graph)
# successfully no-ops with non-const inputs
self.run_pass("fold_frozen_linear_bn", scripted_mod.graph)
FileCheck().check("linear").check("aten::batch_norm").run(
scripted_mod.graph
)
scripted_mod = torch.jit.freeze(scripted_mod)
self.run_pass("fold_frozen_linear_bn", scripted_mod.graph)
if track_stats:
FileCheck().check("linear").check_not("aten::batch_norm").run(
scripted_mod.graph
)
else:
FileCheck().check("linear").check("aten::batch_norm").run(
scripted_mod.graph
)
self.assertEqual(mod_eager(inp), scripted_mod(inp))
self.assertEqual(mod_eager(inp), scripted_mod(inp))
def test_bn_not_broadcast_with_linear(self):
module_pairs = [
(nn.Linear, nn.BatchNorm1d),
(nn.Linear, nn.BatchNorm2d),
(nn.Linear, nn.BatchNorm3d),
]
use_tracing = [True, False]
linear_in = 3
# (linear_out, bn_in)
# case 1: linear_out < bn_in
# case 2: linear_out > bn_in
# case 3: linear_out != bn_in && linear_out = 1
dims = [(2, 4), (4, 2), (1, 2)]
for modules, tracing, dim in product(module_pairs, use_tracing, dims):
linear_out, bn_in = dim[0], dim[1]
linear = modules[0](linear_in, linear_out)
bn = modules[1](bn_in)
mod_eager = nn.Sequential(linear, bn).eval()
N, C = 3, bn_in
input_shape = [N, C]
if modules[1] == nn.BatchNorm1d:
H = linear_in
input_shape.append(H)
elif modules[1] == nn.BatchNorm2d:
H, W = 4, linear_in
input_shape.append(H)
input_shape.append(W)
elif modules[1] == nn.BatchNorm3d:
D, H, W = 4, 4, linear_in
input_shape.append(D)
input_shape.append(H)
input_shape.append(W)
inp = torch.rand(input_shape)
if tracing:
scripted_mod = torch.jit.trace(mod_eager, (inp))
else:
scripted_mod = torch.jit.script(mod_eager)
self.run_pass("inline", scripted_mod.graph)
self.run_pass("peephole", scripted_mod.graph)
self.run_pass("constant_propagation", scripted_mod.graph)
FileCheck().check("linear").check("batch").run(scripted_mod.graph)
self.run_pass("fold_frozen_linear_bn", scripted_mod.graph)
FileCheck().check("linear").check("aten::batch_norm").run(
scripted_mod.graph
)
frozen_mod = torch.jit.freeze(scripted_mod)
self.run_pass("fold_frozen_linear_bn", frozen_mod.graph)
# successfully skipped folding
FileCheck().check("linear").check("aten::batch_norm").run(frozen_mod.graph)
self.assertEqual(mod_eager(inp), frozen_mod(inp))
self.assertEqual(mod_eager(inp), frozen_mod(inp))
# successfully failed folding
with self.assertRaisesRegex(
AssertionError,
"To fuse, linear.out_features == bn.num_features or bn.num_features == 1",
):
nn.utils.fusion.fuse_linear_bn_eval(linear, bn)
@skipCUDAMemoryLeakCheckIf(True)
@unittest.skipIf(not TEST_CUDA, "Optimization currently only run for GPU")
def test_linear_bn_folding_autocast_scenario_cuda(self):
module_pairs = [
(nn.Linear, nn.BatchNorm1d),
(nn.Linear, nn.BatchNorm2d),
(nn.Linear, nn.BatchNorm3d),
]
use_tracing = [True, False]
bn_running_stats = [True, False]
for modules, tracing, track_stats in product(
module_pairs, use_tracing, bn_running_stats
):
class LinearBN(torch.nn.Module):
def __init__(self, in_features, out_features):
super().__init__()
self.linear = modules[0](
in_features, out_features, bias=False, dtype=torch.half
)
self.bn = modules[1](out_features, eps=0.001, dtype=torch.float)
def forward(self, x):
x = self.linear(x)
return self.bn(x)
mod_eager = LinearBN(32, 32).cuda().eval()
inps = [3, 32]
if modules[1] == nn.BatchNorm2d:
inps.append(inps[-1])
inps.append(inps[-1])
if modules[1] == nn.BatchNorm3d:
inps.append(inps[-1])
inps.append(inps[-1])
inps.append(inps[-1])
x = torch.rand(inps, dtype=torch.half).cuda()
if tracing:
scripted_mod = torch.jit.trace(mod_eager, (x))
else:
scripted_mod = torch.jit.script(mod_eager)
scripted_mod = torch.jit.freeze(scripted_mod)
FileCheck().check("linear").check_not("aten::batch_norm").run(
scripted_mod.graph
)
lin_node = scripted_mod.graph.findNode("aten::linear", True)
self.assertTrue(lin_node is not None)
weight_input = lin_node.namedInput("weight")
bias_input = lin_node.namedInput("bias")
self.assertTrue(bias_input is not None)
self.assertTrue(weight_input.type().dtype() == torch.half)
self.assertTrue(bias_input.type().dtype() == torch.half)
self.assertEqual(mod_eager(x), scripted_mod(x), atol=1e-2, rtol=1e-2)
self.assertEqual(mod_eager(x), scripted_mod(x), atol=1e-2, rtol=1e-2)
@unittest.skipIf(not TEST_CUDA, "Optimization currently only run for GPU")
def test_linear_concat(self):
out_dimms = [[5, 10], [1, 5]]
for w1_dim, w2_dim in out_dimms:
class ModMultLinear(nn.Module):
def __init__(self, w1_dim, w2_dim):
super().__init__()
self.w1 = nn.Parameter(torch.rand([w1_dim, 5]))
self.b1 = nn.Parameter(torch.rand([w1_dim]))
self.w2 = nn.Parameter(torch.rand([w2_dim, 5]))
self.b2 = nn.Parameter(torch.rand([w2_dim]))
def forward(self, in_tensor1):
res1 = torch._C._nn.linear(in_tensor1, self.w1, self.b1)
res2 = torch._C._nn.linear(in_tensor1, self.w2, self.b2)
return res1, res2
mod_eager = ModMultLinear(w1_dim, w2_dim).eval()
test_val1 = torch.rand([50, 5])
self.check_linear_optimizations(mod_eager, 2, 1, (test_val1,))
@unittest.skipIf(not TEST_CUDA, "Optimization currently only run for GPU")
def test_linear_concat_complex(self):
"""
Testing that the interleaving of multiple optimizations does not
cause errors, and gets optimized as expected
"""
class ModMultLinear(nn.Module):
def __init__(self) -> None:
super().__init__()
w1_dim = 5
w2_dim = 10
self.w1 = nn.Parameter(torch.rand([w1_dim, 5]))
self.b1 = nn.Parameter(torch.rand([w1_dim]))
self.w2 = nn.Parameter(torch.rand([w2_dim, 5]))
self.b2 = nn.Parameter(torch.rand([w2_dim]))
def forward(self, in_tensor1):
res1 = torch._C._nn.linear(in_tensor1, self.w1, self.b1)
res3 = torch._C._nn.linear(res1, self.w2, self.b2)
res2 = torch._C._nn.linear(in_tensor1, self.w2, self.b2)
res4 = torch._C._nn.linear(res1, self.w1, self.b1)
return res2, res3, res4
mod_eager = ModMultLinear().eval()
test_val1 = torch.rand([50, 5])
self.check_linear_optimizations(mod_eager, 4, 2, (test_val1,))
@unittest.skipIf(not TEST_CUDA, "Optimization currently only run for GPU")
def test_linear_concat_different_input(self):
"""
There should be no change to the graph due to the optimization pass
due to the two input tensors being different
"""
# Freezing requires that the graph be a module
class ModMultLinear(nn.Module):
def __init__(self, w1_dim, w2_dim):
super().__init__()
self.w1 = nn.Parameter(torch.rand([w1_dim, 5]))
self.b1 = nn.Parameter(torch.rand([w1_dim]))
self.w2 = nn.Parameter(torch.rand([w2_dim, 5]))
self.b2 = nn.Parameter(torch.rand([w2_dim]))
def forward(self, in_tensor1, in_tensor2):
res1 = torch._C._nn.linear(in_tensor1, self.w1, self.b1)
res2 = torch._C._nn.linear(in_tensor2, self.w2, self.b2)
return res1, res2
mod_eager = ModMultLinear(5, 5).eval()
test_val1 = torch.rand([50, 5])
test_val2 = torch.rand([50, 5])
self.check_linear_optimizations(mod_eager, 2, 2, (test_val1, test_val2))
@unittest.skipIf(not TEST_CUDA, "Optimization currently only run for GPU")
def test_linear_multiple_blocks(self):
class ModMultLinear(nn.Module):
def __init__(self, w1_dim, w2_dim):
super().__init__()
self.w1 = nn.Parameter(torch.rand([w1_dim, 5]))
self.b1 = nn.Parameter(torch.rand([w1_dim]))
self.w2 = nn.Parameter(torch.rand([w2_dim, 5]))
self.b2 = nn.Parameter(torch.rand([w2_dim]))
def forward(self, in_tensor1, in_tensor2, cond: bool):
res1 = torch._C._nn.linear(in_tensor1, self.w1, self.b1)
if cond:
res3 = torch._C._nn.linear(in_tensor2, self.w2, self.b2)
res4 = torch._C._nn.linear(in_tensor1, self.w2, self.b1)
else:
raise AssertionError
res2 = torch._C._nn.linear(in_tensor1, self.w2, self.b1)
return res1, res2, res3, res4
mod_eager = ModMultLinear(5, 5).eval()
test_val1 = torch.rand([50, 5])
test_val2 = torch.rand([50, 5])
self.check_linear_optimizations(mod_eager, 4, 3, (test_val1, test_val2, True))
def check_linear_optimizations(
self, eager_mod, orig_linears, new_linears, test_vals
):
for is_cuda in [False, True]:
if is_cuda:
mod_to_device = eager_mod.cuda()
test_vals_to_device = [
t.cuda() if isinstance(t, torch.Tensor) else t for t in test_vals
]
else:
mod_to_device = eager_mod
test_vals_to_device = test_vals
script_mod = torch.jit.script(mod_to_device)
op_graph = script_mod.graph
FileCheck().check_count("aten::linear", orig_linears, exactly=True).run(
op_graph
)
# successively no-ops with non-const inputs
self.run_pass("concat_frozen_linear", op_graph)
FileCheck().check_count("aten::linear", orig_linears, exactly=True).run(
op_graph
)
script_mod = torch.jit.freeze(script_mod)
op_graph = script_mod.graph
self.run_pass("concat_frozen_linear", op_graph)
if is_cuda:
FileCheck().check_count("aten::linear", new_linears, exactly=True).run(
op_graph
)
else:
FileCheck().check_count("aten::linear", orig_linears, exactly=True).run(
op_graph
)
self.assertEqual(
mod_to_device(*test_vals_to_device), script_mod(*test_vals_to_device)
)
def test_optimize_freeze_module(self):
in_channels, out_channels = 3, 32
conv = torch.nn.Conv2d(
in_channels, out_channels, kernel_size=3, stride=2, bias=True
)
bn = torch.nn.BatchNorm2d(out_channels, eps=0.001)
mod = torch.nn.Sequential(conv, bn)
# set optimize to False here, by default freezing runs run_frozen_optimizations
frozen_mod = torch.jit.freeze(
torch.jit.script(mod.eval()), optimize_numerics=False
)
# inspect frozen mod
FileCheck().check("batch_norm").run(frozen_mod.graph)
torch.jit.run_frozen_optimizations(frozen_mod)
FileCheck().check_not("batch_norm").run(frozen_mod.graph)
# run_frozen_optimizations should be run
frozen_mod = torch.jit.freeze(torch.jit.script(mod.eval()))
FileCheck().check_not("batch_norm").run(frozen_mod.graph)
def test_freeze_remove_dropout(self):
class Net(nn.Module):
def __init__(self) -> None:
super().__init__()
self.dropout = nn.Dropout(0.5)
def forward(self, x):
return self.dropout(x)
mod = torch.jit.script(Net())
# inspect mod
torch._C._jit_pass_inline(mod.graph)
FileCheck().check("aten::dropout").run(mod.graph)
frozen_mod = torch.jit.freeze(mod.eval())
FileCheck().check_not("aten::dropout").run(frozen_mod.graph)
input = torch.randn(2)
output_s = mod.forward(input)
output_f = frozen_mod.forward(input)
self.assertEqual(output_s, output_f)
def test_freeze_remove_feature_dropout(self):
class Net(nn.Module):
def __init__(self) -> None:
super().__init__()
self.dropout = nn.Dropout2d(0.5)
def forward(self, x):
return self.dropout(x)
mod = torch.jit.script(Net().eval())
# inspect mod
torch._C._jit_pass_inline(mod.graph)
FileCheck().check("aten::feature_dropout").run(mod.graph)
frozen_mod = torch.jit.freeze(mod)
FileCheck().check_not("aten::feature_dropout").run(frozen_mod.graph)
input = torch.randn(2, 2, 1, 1)
output_s = mod.forward(input)
output_f = frozen_mod.forward(input)
self.assertEqual(output_s, output_f)
@unittest.skipIf(
not torch.backends.mkldnn.is_available(), "MKL-DNN build is disabled"
)
def test_freeze_mkdlnn(self):
conv = torch.nn.Conv2d(3, 32, kernel_size=3, stride=2).eval().float()
convmkl = mkldnn_utils.to_mkldnn(conv)
out = torch.jit.freeze(torch.jit.script(convmkl.eval()))
inp = torch.rand([4, 3, 4, 4]).float()
self.assertEqual(out(inp.to_mkldnn()).to_dense(), conv(inp))
@unittest.skipIf(
not torch.backends.mkldnn.is_available(), "MKL-DNN build is disabled"
)
def test_conv_to_mkldnn(self):
with set_default_dtype(torch.float):
for module, trace in product([nn.Conv2d, nn.Conv3d], [False, True]):
mod = module(3, 32, kernel_size=3, stride=2).eval()
inps = [4, 3, 4]
if module == nn.Conv2d:
inps.append(inps[-1])
if module == nn.Conv3d:
inps.append(inps[-1])
inps.append(inps[-1])
inp = torch.rand(inps)
if trace:
scripted_mod = torch.jit.script(mod)
else:
scripted_mod = torch.jit.trace(mod, (inp,))
self.run_pass("inline", scripted_mod.graph)
FileCheck().check("conv").run(scripted_mod.graph)
# successfully no-ops with non-const inputs
self.run_pass("convert_frozen_ops_to_mkldnn", scripted_mod.graph)
FileCheck().check_not("to_mkldnn").run(scripted_mod.graph)
scripted_mod = torch.jit.freeze(scripted_mod)
self.run_pass("convert_frozen_ops_to_mkldnn", scripted_mod.graph)
FileCheck().check("to_mkldnn").check("prim::mkldnn_convolution").check(
"to_dense"
).run(scripted_mod.graph)
self.assertEqual(mod(inp), scripted_mod(inp))
self.assertEqual(mod(inp), scripted_mod(inp))
def test_linear_transpose(self):
class ModLinear(torch.nn.Module):
def __init__(self) -> None:
super().__init__()
self.bias = torch.nn.Parameter(torch.rand(30))
self.weight = torch.nn.Parameter(torch.rand([30, 20]))
def forward(self, x):
return torch._C._nn.linear(x, self.weight, self.bias)
mod_eager = ModLinear().eval()
test_val = torch.rand([50, 20])
self.check_linear_optimizations_2(
mod_eager, 1, 0, "transpose_frozen_linear", (test_val,)
)
def test_linear_non_constant_weight(self):
class ModLinear(torch.nn.Module):
def __init__(self) -> None:
super().__init__()
self.bias = torch.nn.Parameter(torch.rand(30))
def forward(self, x, weight):
return torch._C._nn.linear(x, weight, self.bias)
mod_eager = ModLinear().eval()
test_val = torch.rand([50, 20])
test_weight = torch.rand([30, 20])
self.check_linear_optimizations_2(
mod_eager, 1, 1, "transpose_frozen_linear", (test_val, test_weight)
)
def check_linear_optimizations_2(
self, eager_mod, orig_linears, new_linears, opt_pass, test_vals
):
# TODO: merge with check_linear_optimizations once both diffs land
mod_to_device = eager_mod
test_vals_to_device = test_vals
script_mod = torch.jit.script(mod_to_device)
op_graph = script_mod.graph
FileCheck().check_count("aten::linear", orig_linears, exactly=True).run(
op_graph
)
# successively no-ops with non-const inputs
self.run_pass(opt_pass, op_graph)
FileCheck().check_count("aten::linear", orig_linears, exactly=True).run(
op_graph
)
script_mod = torch.jit.freeze(script_mod)
op_graph = script_mod.graph
self.run_pass(opt_pass, op_graph)
FileCheck().check_count("aten::linear", new_linears, exactly=True).run(op_graph)
self.assertEqual(
mod_to_device(*test_vals_to_device), script_mod(*test_vals_to_device)
)
@staticmethod
def conv():
# Generic composable conv for testing purposes
return nn.Conv2d(8, 8, 1)
@unittest.skipIf(
not torch.backends.mkldnn.is_available(), "MKL-DNN build is disabled"
)
def test_collapse_adjacent_conversions(self):
with set_default_dtype(torch.float):
mod = nn.Sequential(self.conv(), self.conv()).eval()
scripted_mod = torch.jit.script(mod)
scripted_mod = torch.jit.freeze(scripted_mod)
self.run_pass("convert_frozen_ops_to_mkldnn", scripted_mod.graph)
FileCheck().check("to_mkldnn").check("prim::mkldnn_convolution").check(
"prim::mkldnn_convolution"
).check("to_dense").run(scripted_mod.graph)
FileCheck().check_count("to_mkldnn", 1, exactly=True).run(
scripted_mod.graph
)
inp = torch.rand([1, 8, 8, 8])
self.assertEqual(scripted_mod(inp), mod(inp))
self.assertEqual(scripted_mod(inp), mod(inp))
@unittest.skipIf(
not torch.backends.mkldnn.is_available(), "MKL-DNN build is disabled"
)
def test_mkldnn_fuser_broadcasting(self):
class Add(nn.Module):
def __init__(self, tensor):
super().__init__()
self.tensor = tensor
def forward(self, x):
return x + self.tensor
with set_default_dtype(torch.float):
for add_inp in [8], [8, 8, 1]:
mod = nn.Sequential(self.conv(), Add(torch.rand(add_inp))).eval()
scripted_mod = torch.jit.script(mod)
scripted_mod = torch.jit.freeze(scripted_mod)
self.run_pass("convert_frozen_ops_to_mkldnn", scripted_mod.graph)
FileCheck().check("prim::BroadcastMKLDNNTensors").run(
scripted_mod.graph
)
inp = torch.rand([1, 8, 8, 8])
self.assertEqual(scripted_mod(inp), mod(inp))
self.assertEqual(scripted_mod(inp), mod(inp))
# for good measure, check that broadcasting does not work without this op
# so we can remove the op if it ever gets supported
with self.assertRaisesRegex(RuntimeError, ""):
(
torch.rand([1, 8, 8, 8]).to_mkldnn()
+ torch.rand(add_inp).to_mkldnn()
)
@unittest.skipIf(
not torch.backends.mkldnn.is_available(), "MKL-DNN build is disabled"
)
def test_mkldnn_inplace_removal(self):
class AddMul(nn.Module):
def __init__(self, tensor):
super().__init__()
self.tensor = tensor
def forward(self, x):
return x.add_(self.tensor).div_(self.tensor) - 4
with set_default_dtype(torch.float):
mod = nn.Sequential(self.conv(), AddMul(torch.rand([8]))).eval()
scripted_mod = torch.jit.script(mod)
scripted_mod = torch.jit.freeze(scripted_mod)
self.run_pass("convert_frozen_ops_to_mkldnn", scripted_mod.graph)
# add gets uninplaced and reinplaced
FileCheck().check("aten::to_mkldnn").check("aten::add_").check(
"aten::div_"
).run(scripted_mod.graph)
inp = torch.rand([1, 8, 8, 8])
self.assertEqual(scripted_mod(inp), mod(inp))
self.assertEqual(scripted_mod(inp), mod(inp))
@unittest.skipIf(
not torch.backends.mkldnn.is_available(), "MKL-DNN build is disabled"
)
@skipIfNoTorchVision
def test_maxpool_mkldnn(self):
with set_default_dtype(torch.float):
model = torchvision.models.resnet18()
sub_model = torch.nn.Sequential(
model.conv1, model.bn1, model.relu, model.maxpool
)
mod = torch.jit.freeze(torch.jit.script(sub_model.eval()))
(
N,
C,
H,
W,
) = (
10,
3,
224,
224,
)
inp = torch.randn(N, C, H, W)
self.run_pass("convert_frozen_ops_to_mkldnn", mod.graph)
FileCheck().check("max_pool").check("to_dense").run(mod.graph)
FileCheck().check_count("to_dense", 1, exactly=True).run(mod.graph)
self.assertEqual(mod(inp), sub_model(inp))
@unittest.skipIf(torch.backends.mkldnn.is_available(), "Testing no mkldnn")
def test_conv_to_mkldnn_no_mkldnn(self):
# test no error when mkldnn not available
with set_default_dtype(torch.float):
mod = torch.jit.script(nn.Conv2d(3, 32, kernel_size=3, stride=2).eval())
frozen = torch.jit.freeze(mod)
self.run_pass("convert_frozen_ops_to_mkldnn", frozen.graph)
inp = torch.rand([4, 3, 4, 4])
self.assertEqual(frozen(inp), mod(inp))
@unittest.skipIf(not (TEST_CUDNN or TEST_WITH_ROCM), "requires CUDNN")
def test_freeze_conv_relu_fusion(self):
with set_default_dtype(torch.float):
conv_bias = [True, False]
conv_ops = [nn.Conv2d, nn.Conv3d]
use_add_z = [True, False]
use_tracing = [True, False]
for use_bias, conv, add_z, tracing in product(
conv_bias, conv_ops, use_add_z, use_tracing
):
class Net(nn.Module):
def __init__(self, in_channels, out_channels, **kwargs):
super().__init__()
self.conv = conv(
in_channels, out_channels, bias=use_bias, **kwargs
)
self.relu = nn.ReLU(inplace=True)
self.add_z = add_z
def forward(self, x):
z = self.conv(x)
out = self.conv(x)
if self.add_z:
out += z
out = self.relu(out)
return out
mod_eager = Net(3, 6, kernel_size=3, stride=2).eval().cuda()
inps = [5, 3, 4, 4]
if conv == nn.Conv3d:
inps.append(inps[-1])
inp = torch.rand(inps).cuda()
if tracing:
scripted_mod = torch.jit.trace(mod_eager, (inp))
else:
scripted_mod = torch.jit.script(mod_eager)
frozen_mod = torch.jit.optimize_for_inference(scripted_mod)
if TEST_WITH_ROCM:
if add_z:
FileCheck().check("aten::miopen_convolution_add_relu").run(
frozen_mod.graph
)
else:
FileCheck().check("aten::miopen_convolution_relu").run(
frozen_mod.graph
)
else:
if add_z:
FileCheck().check("aten::cudnn_convolution_add_relu").run(
frozen_mod.graph
)
else:
FileCheck().check("aten::cudnn_convolution_relu").run(
frozen_mod.graph
)
self.assertEqual(mod_eager(inp), frozen_mod(inp))
@unittest.skipIf(not (TEST_CUDNN or TEST_WITH_ROCM), "requires CUDNN")
def test_freeze_conv_relu_fusion_not_forward(self):
with set_default_dtype(torch.float):
class Net(nn.Module):
def __init__(self, in_channels, out_channels, **kwargs):
super().__init__()
self.conv = nn.Conv2d(
in_channels, out_channels, bias=None, **kwargs
)
self.relu = nn.ReLU(inplace=True)
def forward(self, x):
z = self.conv(x)
out = self.conv(x)
out = self.relu(out)
return out
@torch.jit.export
def make_prediction(self, x):
return self.forward(x)
mod_eager = Net(3, 6, kernel_size=3, stride=2).eval().cuda()
inps = [5, 3, 4, 4]
inp = torch.rand(inps).cuda()
scripted_mod = torch.jit.script(mod_eager)
frozen_mod = torch.jit.freeze(
scripted_mod, preserved_attrs=["make_prediction"]
)
optimized_mod = torch.jit.optimize_for_inference(
frozen_mod, other_methods=["make_prediction"]
)
if TEST_WITH_ROCM:
FileCheck().check("aten::miopen_convolution_relu").run(
optimized_mod.make_prediction.graph
)
else:
FileCheck().check("aten::cudnn_convolution_relu").run(
optimized_mod.make_prediction.graph
)
self.assertEqual(
mod_eager.make_prediction(inp), optimized_mod.make_prediction(inp)
)
@unittest.skipIf(
not torch.backends.mkldnn.is_available(), "MKL-DNN build is disabled"
)
def test_numel_less_than_size_with_padding(self):
with set_default_dtype(torch.float):
class MyModule(nn.Module):
def __init__(self) -> None:
super().__init__()
self.conv1 = nn.Conv2d(
1,
2,
kernel_size=(2, 4),
stride=2,
padding=2,
dilation=(2, 1),
)
def forward(self, i0):
x = self.conv1(i0)
o0 = torch.max(x, i0)
o1 = torch.clip(x, -1.5, 1.5)
return o0, o1
i0 = torch.zeros((1, 1, 1, 2), dtype=torch.float32)
mod = MyModule()
out = mod(i0)
exported = torch.jit.trace(mod, [i0])
exported = torch.jit.optimize_for_inference(exported)
eout = exported(i0)
self.assertTrue(all(torch.allclose(x, y) for x, y in zip(out, eout)))
@unittest.skipIf(
not torch.backends.mkldnn.is_available(), "MKL-DNN build is disabled"
)
def test_incompatible_perf_formats(self):
with set_default_dtype(torch.float):
class Mod(nn.Module):
def __init__(self) -> None:
super().__init__()
self.conv = torch.nn.Conv2d(3, 64, 3, 2)
self.max_pool = torch.nn.MaxPool2d(111, 111)
def forward(self, x):
a = self.conv(x)
b = self.max_pool(a)
return a + b
model = Mod()
model.eval()
mod = torch.jit.freeze(torch.jit.script(model))
(
N,
C,
H,
W,
) = (
10,
3,
224,
224,
)
inp = torch.randn(N, C, H, W)
self.run_pass("convert_frozen_ops_to_mkldnn", mod.graph)
self.assertEqual(model(inp), mod(inp))
@unittest.skipIf(
not torch.backends.mkldnn.is_available(), "MKL-DNN build is disabled"
)
def test_pool2d_batchnorm(self):
with set_default_dtype(torch.float):
pooling_layers = [
torch.nn.AdaptiveAvgPool2d(4),
# torch.nn.AdaptiveMaxPool2d(4), # return tuples
torch.nn.MaxPool2d(4),
torch.nn.AvgPool2d(4),
torch.nn.BatchNorm2d(64).eval(),
]
for pl in pooling_layers:
sub_model = torch.nn.Sequential(
torch.nn.Conv2d(3, 64, 2, 2),
torch.nn.ReLU(),
pl,
torch.nn.Hardswish(),
)
sub_model.eval()
mod = torch.jit.freeze(torch.jit.script(sub_model))
(
N,
C,
H,
W,
) = (
10,
3,
224,
224,
)
inp = torch.randn(N, C, H, W)
# these two passes needed to remove
# a size check in BatchNorm2d
removeExceptions(mod.graph)
self.run_pass("dce", mod.graph)
self.run_pass("convert_frozen_ops_to_mkldnn", mod.graph)
FileCheck().check("aten::to_dense").check_next("return").run(mod.graph)
self.assertEqual(sub_model(inp), mod(inp))
@unittest.skipIf(
not torch.backends.mkldnn.is_available(), "MKL-DNN build is disabled"
)
def test_pool3d_batchnorm(self):
with set_default_dtype(torch.float):
pooling_layers = [
torch.nn.MaxPool3d(4),
# torch.nn.AdaptiveAvgPool3d(4), # no ideep bindings
# torch.nn.AdaptiveMaxPool3d(4), # return tuples
torch.nn.AvgPool3d(4),
torch.nn.BatchNorm3d(64).eval(),
]
for pl in pooling_layers:
sub_model = torch.nn.Sequential(
torch.nn.Conv3d(3, 64, 2, 2),
torch.nn.ReLU(),
pl,
torch.nn.Hardswish(),
)
sub_model.eval()
mod = torch.jit.freeze(torch.jit.script(sub_model))
N, C, H, W, D = 10, 3, 64, 64, 64
inp = torch.randn(N, C, D, H, W)
# these two passes needed to remove
# a size check in BatchNorm2d
removeExceptions(mod.graph)
self.run_pass("dce", mod.graph)
self.run_pass("convert_frozen_ops_to_mkldnn", mod.graph)
FileCheck().check("aten::to_dense").check_next("return").run(mod.graph)
self.assertEqual(sub_model(inp), mod(inp))
@unittest.skipIf(
not torch.backends.mkldnn.is_available(), "MKL-DNN build is disabled"
)
@skipIfNoTorchVision
def test_conv_hardswish(self):
with set_default_dtype(torch.float):
class Clamp(torch.nn.Module):
def __init__(self, min_val, max_val, **kwargs):
super().__init__()
self.min_val = min_val
self.max_val = max_val
def forward(self, x):
return torch.clamp(x, self.min_val, self.max_val)
(
N,
C,
H,
W,
) = (
10,
3,
224,
224,
)
activations = [
torch.nn.Hardswish(),
torch.nn.Hardsigmoid(),
torch.nn.ReLU6(),
torch.nn.Tanh(),
torch.nn.Hardtanh(0.0, 6.0),
torch.nn.Hardtanh(1.0, 100.0),
torch.nn.Hardtanh(-100.0, -1.0),
torch.nn.GELU(),
Clamp(-100.0, -1.0),
Clamp(1.0, 100.0),
Clamp(0.0, 6.0),
Clamp(-1.0, 0.0),
]
model = torchvision.models.resnet18()
for activation in activations:
sub_model = torch.nn.Sequential(model.conv1, activation)
sub_model.eval()
mod = torch.jit.freeze(torch.jit.script(sub_model))
inp = torch.randn(N, C, H, W)
self.run_pass("convert_frozen_ops_to_mkldnn", mod.graph)
FileCheck().check_count("aten::to_dense", 1, exactly=True).run(
mod.graph
)
self.assertEqual(sub_model(inp), mod(inp))
@unittest.skipIf(
not torch.backends.mkldnn.is_available(), "MKL-DNN build is disabled"
)
def test_hardswish_hardsigmoid(self):
with set_default_dtype(torch.float):
op_map = {
"prim::MKLDNNHardSwish": F.hardswish,
"prim::MKLDNNHardSigmoid": F.hardsigmoid,
}
input_sizes = ([0], [1], [3], [1, 3, 8, 8])
for mkldnn_opname, aten_op in op_map.items():
for size in input_sizes:
for inplace in (True, False):
inplace_str = "_" if inplace else ""
inplace_tgt = "%34" if inplace else "%35"
graph_str = f"""graph(%input.1 : Tensor):
%33 : None = prim::Constant()
%34 : Tensor = aten::to_mkldnn(%input.1, %33)
%35 : Tensor = {mkldnn_opname}{inplace_str}(%34)
return ({inplace_tgt})
"""
g = torch._C.parse_ir(graph_str)
m = self.createFunctionFromGraph(g)
x = torch.rand(size)
# `inplace=False` is intentional, otherwise we modify the input
# and we aren't testing aten impls anyways
self.assertEqual(aten_op(x, inplace=False), m(x).to_dense())
@unittest.skipIf(
not torch.backends.mkldnn.is_available(), "MKL-DNN build is disabled"
)
def test_scalar_mul(self):
with set_default_dtype(torch.float):
class Mod(nn.Module):
def __init__(self) -> None:
super().__init__()
self.mod = nn.Conv2d(8, 8, 1, padding=1)
def forward(self, x):
a1 = self.mod(x) * 4
return a1 * 4 + a1 * 5.0
mod = Mod().eval()
scripted = torch.jit.freeze(torch.jit.script(mod))
optimized = torch.jit.optimize_for_inference(scripted)
inp = torch.rand([1, 8, 8, 8])
# a1 cant be inplaced for first use, can for second
FileCheck().check("ScalarMul(").check("ScalarMul_").run(optimized.graph)
self.assertEqual(optimized(inp), mod(inp))
def test_remove_detach(self):
class Mod(nn.Module):
def forward(self, x):
y = x.detach()
return y * y
mod = Mod().eval()
frozen_mod = torch.jit.freeze(torch.jit.script(mod))
inp = torch.randn((2, 2))
FileCheck().check_not("aten::detach").run(frozen_mod.graph)
self.assertEqual(frozen_mod(inp), mod(inp))
def test_remove_detach_not_applied(self):
class Mod(nn.Module):
def forward(self, x):
y = x.detach()
return x is y
mod = Mod().eval()
frozen_mod = torch.jit.freeze(torch.jit.script(mod))
inp = torch.randn((2, 2))
FileCheck().check("aten::detach").run(frozen_mod.graph)
self.assertEqual(frozen_mod(inp), mod(inp))
@skipIfTorchDynamo("somehow causing hanging during python shutdown")
@unittest.skipIf(not torch.backends.mkldnn.is_available(), "MKL-DNN build is disabled")
class TestMKLDNNReinplacing(JitTestCase):
def setUp(self):
super().setUp()
self.default_dtype = torch.get_default_dtype()
torch.set_default_dtype(torch.float)
def tearDown(self):
super().tearDown()
torch.set_default_dtype(self.default_dtype)
def getConv(self):
return nn.Conv2d(3, 32, kernel_size=3, stride=2).eval()
def getInput(self):
return torch.rand([4, 3, 4, 4])
def freezeAndConvert(self, mod):
mod = torch.jit.freeze(torch.jit.script(mod.eval()))
self.run_pass("convert_frozen_ops_to_mkldnn", mod.graph)
return mod
def checkResults(self, mod1, mod2):
inp = self.getInput()
self.assertEqual(mod1(inp), mod2(inp))
def test_successful(self):
# simple conv-relu
mod_eager = nn.Sequential(self.getConv(), nn.Hardswish(), nn.ReLU())
mod = self.freezeAndConvert(mod_eager)
FileCheck().check("mkldnn_convolution").check_next(
"prim::MKLDNNHardSwish_"
).check_next("aten::relu_").run(mod.graph)
self.checkResults(mod_eager, mod)
def test_merge_liveness(self):
class Mod(nn.Module):
def __init__(self, tensor):
super().__init__()
self.tensor = tensor
def forward(self, x):
# this mul can be inplaced since x is dead after this use
temporary = x * self.tensor
# temporary livespan is the return node,
# add can not be inplaced
return temporary + temporary, temporary
mod_eager = nn.Sequential(self.getConv(), Mod(torch.rand([4, 32, 1, 1])))
mod = self.freezeAndConvert(mod_eager)
FileCheck().check("aten::mul_").check_not("aten::add_").run(mod.graph)
self.checkResults(mod_eager, mod)
def test_always_alive_values(self):
class Mod(nn.Module):
def __init__(self, tensor):
super().__init__()
self.tensor = tensor
def forward(self, x):
# x can't be inplaced because its a return value,
# check that the inplacing pass doesnt try to inplace
# self.tensor because its always alive
return x * self.tensor, x
mod_eager = nn.Sequential(self.getConv(), Mod(torch.rand([4, 32, 1, 1])))
mod = self.freezeAndConvert(mod_eager)
FileCheck().check_not("aten::mul_").run(mod.graph)
self.checkResults(mod_eager, mod)
conv = self.getConv()
class Mod(nn.Module):
def __init__(self) -> None:
super().__init__()
self.tensor = torch.rand([4, 32, 1, 1])
self.conv = conv
def forward(self, x):
# the shapes dont add up on this just testing a particular pattern
conv_output = self.conv(x)
return conv_output, self.conv(torch.add(x, x))
mod = self.freezeAndConvert(Mod())
# x is an input to the graph, and so it should not be inplaced
# in the torch.add(x, x) call
FileCheck().check_not("aten::add_").run(mod.graph)
def test_switch_inputs_to_inplace(self):
class Mod(nn.Module):
def __init__(self, tensor):
super().__init__()
self.tensor = tensor
def forward(self, x):
# self.tensor cannot be inplaced, however x can,
# and bc add is commutative we can reverse inputs to add_
return self.tensor + x
mod_eager = nn.Sequential(self.getConv(), Mod(torch.rand([4, 32, 1, 1])))
mod = self.freezeAndConvert(mod_eager)
FileCheck().check("aten::add_").run(mod.graph)
self.checkResults(mod_eager, mod)
if __name__ == "__main__":
raise_on_run_directly("test/test_jit.py")
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