mirror of
https://github.com/zebrajr/pytorch.git
synced 2025-12-07 00:21:07 +01:00
d6a25a58f5
Summary: TODO: post the numbers for mobilenetv2 Pull Request resolved: https://github.com/pytorch/pytorch/pull/56203 Reviewed By: malfet Differential Revision: D27917557 Pulled By: Krovatkin fbshipit-source-id: acea0f933a7e8c7a036a494295f68222c46a36f7
2014 lines
73 KiB
Python
2014 lines
73 KiB
Python
import torch
|
|
import torch.nn as nn
|
|
import torch.nn.functional as F
|
|
import unittest
|
|
from torch.testing._internal.jit_utils import JitTestCase
|
|
from torch._C import parse_ir
|
|
|
|
from torch.testing import FileCheck
|
|
from torch.testing._internal.common_quantized import override_quantized_engine
|
|
from torch.testing._internal.common_quantization import skipIfNoFBGEMM
|
|
from torch.testing._internal.common_utils import set_default_dtype
|
|
from torch.utils import mkldnn as mkldnn_utils
|
|
|
|
|
|
from torch.jit._recursive import wrap_cpp_module
|
|
from typing import Any
|
|
from itertools import product
|
|
|
|
import io
|
|
|
|
try:
|
|
import torchvision
|
|
HAS_TORCHVISION = True
|
|
except ImportError:
|
|
HAS_TORCHVISION = False
|
|
skipIfNoTorchVision = unittest.skipIf(not HAS_TORCHVISION, "no torchvision")
|
|
|
|
if __name__ == '__main__':
|
|
raise RuntimeError("This test file is not meant to be run directly, use:\n\n"
|
|
"\tpython test/test_jit.py TESTNAME\n\n"
|
|
"instead.")
|
|
|
|
TEST_CUDA = torch.cuda.is_available()
|
|
TEST_ROCM = torch.cuda.is_available() and torch.version.hip is not None
|
|
TEST_CUDNN = False
|
|
if TEST_CUDA and not TEST_ROCM: # Skip ROCM
|
|
torch.ones(1).cuda() # initialize cuda context
|
|
TEST_CUDNN = TEST_CUDA and torch.backends.cudnn.is_acceptable(torch.tensor(1., device=torch.device('cuda:0')))
|
|
|
|
class TestFreezing(JitTestCase):
|
|
def test_freeze_module(self):
|
|
class M(nn.Module):
|
|
def __init__(self):
|
|
super(M, self).__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):
|
|
super(SubModule, self).__init__()
|
|
self.a = 11
|
|
self.b = 2
|
|
|
|
def forward(self, x):
|
|
return self.a + self.b
|
|
|
|
class SubModule2(nn.Module):
|
|
def __init__(self):
|
|
super(SubModule2, self).__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):
|
|
super(TestModule, self).__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):
|
|
super(SubModule, self).__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):
|
|
super(TestModule, self).__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):
|
|
super(SubModule, self).__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):
|
|
super(SubModule2, self).__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):
|
|
super(TestModule, self).__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):
|
|
super(TestModule, self).__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):
|
|
super(TestModule, self).__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):
|
|
super(SubModule, self).__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):
|
|
super(SubModule2, self).__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):
|
|
super(TestModule, self).__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):
|
|
super(SubModule, self).__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):
|
|
super(SubModule2, self).__init__()
|
|
self.sub = Sub # aliasing
|
|
|
|
def forward(self, x):
|
|
return self.sub.a
|
|
|
|
class TestModule(nn.Module):
|
|
def __init__(self):
|
|
super(TestModule, self).__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):
|
|
super(SubModule, self).__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):
|
|
super(SubModule2, self).__init__()
|
|
self.sub = Sub # aliasing
|
|
|
|
def forward(self, x):
|
|
return self.sub.a
|
|
|
|
class TestModule(nn.Module):
|
|
def __init__(self):
|
|
super(TestModule, self).__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):
|
|
super(SubModule, self).__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):
|
|
super(TestModule, self).__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):
|
|
super(SubModule, self).__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):
|
|
super(TestModule, self).__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):
|
|
super(SubModule, self).__init__()
|
|
self.a = 11
|
|
self.b = 2
|
|
|
|
def forward(self, x):
|
|
return self.a + self.b
|
|
|
|
class TestModule(nn.Module):
|
|
def __init__(self):
|
|
super(TestModule, self).__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'"): # noqa: W605
|
|
mf._forward(x)
|
|
|
|
def test_freeze_module_with_inplace_mutable(self):
|
|
class FreezeMe(torch.jit.ScriptModule):
|
|
def __init__(self):
|
|
super(FreezeMe, self).__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):
|
|
super(FreezeMe, self).__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):
|
|
super(FreezeMe, self).__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):
|
|
super(FreezeMe, self).__init__()
|
|
self.a = torch.tensor([1., 2., 3.])
|
|
|
|
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., 5., 3.]
|
|
self.assertEqual(out, expected)
|
|
|
|
def test_freeze_module_with_tuple(self):
|
|
class FreezeMe(nn.Module):
|
|
def __init__(self):
|
|
super(FreezeMe, self).__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):
|
|
super(FreezeMe, self).__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):
|
|
super(FreezeMe, self).__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):
|
|
super(FreezeMe, self).__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):
|
|
super(FreezeMe, self).__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):
|
|
super(FreezeMe, self).__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):
|
|
super(FreezeMe, self).__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):
|
|
super(FreezeMe, self).__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):
|
|
super(FreezeMe, self).__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):
|
|
super(FreezeMe, self).__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):
|
|
super(FreezeMe, self).__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):
|
|
super(FreezeMe, self).__init__()
|
|
self.a = torch.tensor([1., 2., 3.])
|
|
|
|
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_return_sub_module(self):
|
|
|
|
class FreezeMe(nn.Module):
|
|
def __init__(self):
|
|
super(FreezeMe, self).__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):
|
|
super(FreezeMe, self).__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_module_in_training_mode(self):
|
|
class Net(nn.Module):
|
|
def __init__(self):
|
|
super(Net, self).__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):
|
|
super(Module, self).__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):
|
|
super(Module, self).__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):
|
|
super(Module, self).__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)
|
|
|
|
@skipIfNoFBGEMM
|
|
def test_module_with_shared_type_instances(self):
|
|
class Child(nn.Module):
|
|
def __init__(self):
|
|
super(Child, self).__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):
|
|
super(Parent, self).__init__()
|
|
self.quant = torch.quantization.QuantStub()
|
|
self.conv1 = nn.Conv2d(1, 1, 1).to(dtype=torch.float32)
|
|
self.child = Child()
|
|
self.child2 = Child()
|
|
self.dequant = torch.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.quantization.QuantWrapper(model)
|
|
qModel.qconfig = torch.quantization.default_qconfig
|
|
torch.quantization.prepare(qModel, inplace=True)
|
|
qModel(torch.rand(4, 1, 4, 4, dtype=torch.float32))
|
|
torch.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(object):
|
|
def __init__(self, val: int):
|
|
self.val: int = val
|
|
|
|
class Mod(nn.Module):
|
|
def __init__(self):
|
|
super(Mod, self).__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):
|
|
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):
|
|
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_non_module_class_getattr(self):
|
|
class BoxCoder(object):
|
|
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):
|
|
super(MyModule, self).__init__()
|
|
self.box_coder = BoxCoder(50.)
|
|
|
|
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)
|
|
|
|
class TestFrozenOptimizations(JitTestCase):
|
|
def setUp(self):
|
|
self.default_dtype = torch.get_default_dtype()
|
|
torch.set_default_dtype(torch.double)
|
|
|
|
def tearDown(self):
|
|
torch.set_default_dtype(self.default_dtype)
|
|
|
|
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]
|
|
|
|
for use_bias, modules, tracing in product(conv_bias, module_pairs, use_tracing):
|
|
class ConvBN(torch.nn.Module):
|
|
def __init__(self, in_channels, out_channels, **kwargs):
|
|
super(ConvBN, self).__init__()
|
|
self.conv = modules[0](in_channels, out_channels, bias=use_bias, **kwargs)
|
|
self.bn = modules[1](out_channels, eps=0.001)
|
|
|
|
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)
|
|
FileCheck().check("conv").check_not("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_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(ConvOp, self).__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.)
|
|
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.rand(1).to(torch.int), expect_success=False)
|
|
|
|
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=.001)
|
|
mod = torch.nn.Sequential(conv, bn)
|
|
# set optimize to False here, by default freezing runs optimize_frozen_module
|
|
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.optimize_frozen_module(frozen_mod)
|
|
FileCheck().check_not("batch_norm").run(frozen_mod.graph)
|
|
|
|
# optimize_frozen_module 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):
|
|
super(Net, self).__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):
|
|
super(Net, self).__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)
|
|
output_s = mod.forward(input)
|
|
output_f = frozen_mod.forward(input)
|
|
self.assertEqual(output_s, output_f)
|
|
|
|
@unittest.skipIf(not torch._C.has_mkldnn, "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._C.has_mkldnn, "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))
|
|
|
|
@unittest.skipIf(not torch._C.has_mkldnn, "MKL-DNN build is disabled")
|
|
def test_linear_to_mkldnn(self):
|
|
|
|
with set_default_dtype(torch.float):
|
|
# make sure mkldnn handles broadcast rules
|
|
inp_shapes = [[20], [20, 20], [1, 20, 20]]
|
|
for inp_shape in inp_shapes:
|
|
mod = nn.Linear(20, 30).eval()
|
|
scripted_mod = torch.jit.script(mod)
|
|
inp = torch.rand(inp_shape)
|
|
|
|
self.run_pass("inline", scripted_mod.graph)
|
|
FileCheck().check("aten::linear").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("ConvertToMKLDNN").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("aten::linear").check("to_dense").run(scripted_mod.graph)
|
|
|
|
self.assertEqual(mod(inp), scripted_mod(inp))
|
|
self.assertEqual(mod(inp), scripted_mod(inp))
|
|
|
|
@unittest.skipIf(not torch._C.has_mkldnn, "MKL-DNN build is disabled")
|
|
def test_collapse_adjacent_conversions(self):
|
|
|
|
with set_default_dtype(torch.float):
|
|
mod = nn.Sequential(nn.Linear(20, 20), nn.Linear(20, 20)).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("aten::linear").check("aten::linear").check("to_dense").run(scripted_mod.graph)
|
|
FileCheck().check_count("to_mkldnn", 1, exactly=True).run(scripted_mod.graph)
|
|
|
|
inp = torch.rand([20, 20])
|
|
self.assertEqual(scripted_mod(inp), mod(inp))
|
|
self.assertEqual(scripted_mod(inp), mod(inp))
|
|
|
|
# testing unsupported behavior
|
|
class Add(nn.Module):
|
|
def __init__(self, tensor):
|
|
super().__init__()
|
|
self.tensor = tensor
|
|
|
|
def forward(self, x):
|
|
return x + self.tensor
|
|
|
|
def test_unsupported(module, preserved_attrs=None):
|
|
mod = torch.jit.freeze(torch.jit.script(module.eval()), preserved_attrs)
|
|
self.run_pass("convert_frozen_ops_to_mkldnn", mod.graph)
|
|
FileCheck().check("to_mkldnn").check("linear").check("to_dense").check("add").run(mod.graph)
|
|
|
|
lin = nn.Linear(20, 20)
|
|
# Scalar-Tensor not supported
|
|
test_unsupported(nn.Sequential(lin, Add(.5)))
|
|
# # 0-dim not supported
|
|
test_unsupported(nn.Sequential(lin, Add(torch.tensor(.5))))
|
|
# tensor of unknown dtype (getAttr node here) not supported
|
|
test_unsupported(nn.Sequential(lin, Add(torch.tensor([20]))), ['1'])
|
|
|
|
@unittest.skipIf(not torch._C.has_mkldnn, "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 [20], [20, 20, 1]:
|
|
mod = nn.Sequential(nn.Linear(20, 20), 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([20, 20])
|
|
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([20, 20]).to_mkldnn() + torch.rand(add_inp).to_mkldnn()
|
|
|
|
@unittest.skipIf(not torch._C.has_mkldnn, "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(nn.Linear(20, 20), AddMul(torch.rand([20]))).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([20, 20])
|
|
self.assertEqual(scripted_mod(inp), mod(inp))
|
|
self.assertEqual(scripted_mod(inp), mod(inp))
|
|
|
|
@unittest.skipIf(not torch._C.has_mkldnn, "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._C.has_mkldnn, "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, "requires CUDNN")
|
|
def test_freeze_conv_relu_fusion(self):
|
|
conv_bias = [True, False]
|
|
conv_ops = [nn.Conv2d, nn.Conv3d]
|
|
add_z = [True, False]
|
|
use_tracing = [True, False]
|
|
for use_bias, conv, add_z, tracing in product(conv_bias, conv_ops, add_z, use_tracing):
|
|
class Net(nn.Module):
|
|
def __init__(self, in_channels, out_channels, **kwargs):
|
|
super(Net, self).__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.freeze(scripted_mod)
|
|
FileCheck().check("aten::relu").run(frozen_mod.graph)
|
|
self.run_pass("fuse_frozen_conv_add_relu", frozen_mod.graph)
|
|
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 torch._C.has_mkldnn, "MKL-DNN build is disabled")
|
|
def test_incompatible_perf_formats(self):
|
|
with set_default_dtype(torch.float):
|
|
class Mod(nn.Module):
|
|
def __init__(self):
|
|
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.assertTrue(torch.allclose(model(inp), mod(inp)))
|
|
|
|
@unittest.skipIf(not torch._C.has_mkldnn, "MKL-DNN build is disabled")
|
|
def test_adaptive_avgpool2d(self):
|
|
with set_default_dtype(torch.float):
|
|
|
|
sub_model = torch.nn.Sequential(torch.nn.Conv2d(3, 64, 2, 2), torch.nn.AdaptiveAvgPool2d(4), 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)
|
|
self.run_pass("convert_frozen_ops_to_mkldnn", mod.graph)
|
|
self.assertTrue(torch.allclose(sub_model(inp), mod(inp)))
|
|
|
|
@unittest.skipIf(not torch._C.has_mkldnn, "MKL-DNN build is disabled")
|
|
@skipIfNoTorchVision
|
|
def test_conv_hardswish(self):
|
|
with set_default_dtype(torch.float):
|
|
activations = [
|
|
torch.nn.Hardswish(),
|
|
torch.nn.Hardsigmoid(),
|
|
torch.nn.ReLU6(),
|
|
torch.nn.Hardtanh(0., 6.),
|
|
torch.nn.Hardtanh(1., 100.),
|
|
torch.nn.Hardtanh(-100., -1.),
|
|
]
|
|
|
|
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))
|
|
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.assertTrue(torch.allclose(sub_model(inp), mod(inp)))
|
|
|
|
@unittest.skipIf(not torch._C.has_mkldnn, "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 = 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.assertTrue(torch.allclose(aten_op(x, inplace=False), m(x).to_dense()))
|
|
|
|
@unittest.skipIf(not torch._C.has_mkldnn, "MKL-DNN build is disabled")
|
|
class TestMKLDNNReinplacing(JitTestCase):
|
|
def setUp(self):
|
|
self.default_dtype = torch.get_default_dtype()
|
|
torch.set_default_dtype(torch.float)
|
|
|
|
def tearDown(self):
|
|
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):
|
|
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)
|