mirror of
https://github.com/zebrajr/pytorch.git
synced 2025-12-06 12:20:52 +01:00
e3900d2ba5
Summary:
As this diff shows, currently there are a couple hundred instances of raw `noqa` in the codebase, which just ignore all errors on a given line. That isn't great, so this PR changes all existing instances of that antipattern to qualify the `noqa` with respect to a specific error code, and adds a lint to prevent more of this from happening in the future.
Interestingly, some of the examples the `noqa` lint catches are genuine attempts to qualify the `noqa` with a specific error code, such as these two:
```
test/jit/test_misc.py:27: print(f"{hello + ' ' + test}, I'm a {test}") # noqa E999
test/jit/test_misc.py:28: print(f"format blank") # noqa F541
```
However, those are still wrong because they are [missing a colon](https://flake8.pycqa.org/en/3.9.1/user/violations.html#in-line-ignoring-errors), which actually causes the error code to be completely ignored:
- If you change them to anything else, the warnings will still be suppressed.
- If you add the necessary colons then it is revealed that `E261` was also being suppressed, unintentionally:
```
test/jit/test_misc.py:27:57: E261 at least two spaces before inline comment
test/jit/test_misc.py:28:35: E261 at least two spaces before inline comment
```
I did try using [flake8-noqa](https://pypi.org/project/flake8-noqa/) instead of a custom `git grep` lint, but it didn't seem to work. This PR is definitely missing some of the functionality that flake8-noqa is supposed to provide, though, so if someone can figure out how to use it, we should do that instead.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/56272
Test Plan:
CI should pass on the tip of this PR, and we know that the lint works because the following CI run (before this PR was finished) failed:
- https://github.com/pytorch/pytorch/runs/2365189927
Reviewed By: janeyx99
Differential Revision: D27830127
Pulled By: samestep
fbshipit-source-id: d6dcf4f945ebd18cd76c46a07f3b408296864fcb
786 lines
28 KiB
Python
786 lines
28 KiB
Python
import sys
|
|
import os
|
|
import re
|
|
import shutil
|
|
import random
|
|
import subprocess
|
|
import tempfile
|
|
import textwrap
|
|
import unittest
|
|
import torch
|
|
import torch.nn as nn
|
|
import torch.utils.data
|
|
from torch.utils.data import DataLoader
|
|
import torch.cuda
|
|
from torch.utils.checkpoint import checkpoint, checkpoint_sequential
|
|
import torch.utils.cpp_extension
|
|
import torch.hub as hub
|
|
from torch.autograd._functions.utils import check_onnx_broadcast
|
|
from torch.onnx.symbolic_opset9 import _prepare_onnx_paddings
|
|
from torch.testing._internal.common_utils import load_tests, retry, IS_SANDCASTLE, IS_WINDOWS
|
|
from urllib.error import URLError
|
|
|
|
# load_tests from torch.testing._internal.common_utils is used to automatically filter tests for
|
|
# sharding on sandcastle. This line silences flake warnings
|
|
load_tests = load_tests
|
|
|
|
HAS_CUDA = torch.cuda.is_available()
|
|
|
|
from torch.testing._internal.common_utils import TestCase, run_tests
|
|
|
|
|
|
class RandomDatasetMock(torch.utils.data.Dataset):
|
|
|
|
def __getitem__(self, index):
|
|
return torch.tensor([torch.rand(1).item(), random.uniform(0, 1)])
|
|
|
|
def __len__(self):
|
|
return 1000
|
|
|
|
|
|
class TestCheckpoint(TestCase):
|
|
|
|
# This runs checkpoint_sequential on each of the nets in
|
|
# module_lists_to_compare, and compares them against the uncheckpointed model.
|
|
# To compare, it checks outputs as well as input gradients and parameter gradients
|
|
def _check_checkpoint_sequential(
|
|
self,
|
|
model,
|
|
module_lists_to_compare,
|
|
num_chunks,
|
|
input,
|
|
):
|
|
|
|
# not checkpointed
|
|
out = model(input)
|
|
out_not_checkpointed = out.detach().clone()
|
|
model.zero_grad()
|
|
out.sum().backward()
|
|
grad_not_checkpointed = {
|
|
name: param.grad.detach().clone()
|
|
for name, param in model.named_parameters()
|
|
}
|
|
input_grad_not_checkpointed = input.grad.detach().clone()
|
|
for model_to_compare in module_lists_to_compare:
|
|
# checkpointed model by passing list of modules
|
|
detached = input.detach()
|
|
detached.requires_grad = True
|
|
|
|
# pass list of modules to checkpoint
|
|
out = checkpoint_sequential(model_to_compare, num_chunks, detached)
|
|
out_checkpointed = out.detach().clone()
|
|
model.zero_grad()
|
|
out.sum().backward()
|
|
grad_checkpointed = {
|
|
name: param.grad.detach().clone()
|
|
for name, param in model.named_parameters()
|
|
}
|
|
input_grad_checkpointed = detached.grad.detach().clone()
|
|
# compare outputs as well as the gradients of input and parameters
|
|
self.assertEqual(out_checkpointed, out_not_checkpointed)
|
|
self.assertEqual(input_grad_not_checkpointed, input_grad_checkpointed)
|
|
for name in grad_checkpointed:
|
|
self.assertEqual(grad_checkpointed[name], grad_not_checkpointed[name])
|
|
|
|
# Test whether checkpoint is being triggered or not. For this, we check
|
|
# the number of times forward pass happens
|
|
def test_checkpoint_trigger(self):
|
|
|
|
class Net(nn.Module):
|
|
|
|
def __init__(self):
|
|
super(Net, self).__init__()
|
|
self.counter = 0
|
|
|
|
def forward(self, input_var):
|
|
self.counter += 1
|
|
return input_var
|
|
|
|
# checkpointed
|
|
modules = [Net() for _ in range(10)]
|
|
for m in modules:
|
|
self.assertEqual(m.counter, 0)
|
|
input_var = torch.randn(3, 4, requires_grad=True)
|
|
out = checkpoint_sequential(modules, 2, input_var)
|
|
for m in modules:
|
|
self.assertEqual(m.counter, 1)
|
|
out.sum().backward()
|
|
for m in modules[:(len(modules) // 2)]:
|
|
self.assertEqual(m.counter, 2)
|
|
for m in modules[(len(modules) // 2):]:
|
|
self.assertEqual(m.counter, 1)
|
|
|
|
def test_checkpoint_valid(self):
|
|
model = nn.Sequential(
|
|
nn.Linear(100, 50),
|
|
nn.ReLU(),
|
|
nn.Linear(50, 20),
|
|
nn.ReLU(),
|
|
nn.Linear(20, 5),
|
|
nn.ReLU()
|
|
)
|
|
|
|
input_var = torch.randn(1, 100, requires_grad=True)
|
|
|
|
# checkpointed
|
|
chunks = 2
|
|
modules = list(model.children())
|
|
out = checkpoint_sequential(modules, chunks, input_var)
|
|
with self.assertRaisesRegex(RuntimeError, "Checkpointing is not compatible"):
|
|
torch.autograd.grad(
|
|
outputs=[out], grad_outputs=[torch.ones(1, 5)], inputs=[input_var], create_graph=True
|
|
)
|
|
|
|
def test_checkpoint(self):
|
|
model = nn.Sequential(
|
|
nn.Linear(100, 50),
|
|
nn.ReLU(),
|
|
nn.Linear(50, 20),
|
|
nn.ReLU(),
|
|
nn.Linear(20, 5),
|
|
nn.ReLU()
|
|
)
|
|
|
|
# Compare uncheckpointed model with its checkpointed counterparts
|
|
# In addition to running checkpoint_sequential on the nn.Sequential
|
|
# instance, we also run the function on the list of functions within
|
|
# the module.
|
|
self._check_checkpoint_sequential(
|
|
model,
|
|
[list(model.children()), model],
|
|
2,
|
|
torch.randn(1, 100, requires_grad=True)
|
|
)
|
|
|
|
def test_checkpoint_module_list(self):
|
|
class ModuleListNet(nn.Module):
|
|
def __init__(self):
|
|
super(ModuleListNet, self).__init__()
|
|
module_list = [
|
|
nn.Linear(100, 50),
|
|
nn.ReLU(),
|
|
nn.Linear(50, 20),
|
|
nn.ReLU(),
|
|
nn.Linear(20, 5),
|
|
nn.ReLU(),
|
|
]
|
|
self.module_list = nn.ModuleList(module_list)
|
|
|
|
def forward(self, input):
|
|
for layer in self.module_list:
|
|
input = layer(input)
|
|
return input
|
|
|
|
model = ModuleListNet()
|
|
|
|
# Compare uncheckpointed model with its checkpointed counterparts.
|
|
self._check_checkpoint_sequential(
|
|
model,
|
|
[list(model.module_list.children()), model.module_list],
|
|
2,
|
|
torch.randn(1, 100, requires_grad=True),
|
|
)
|
|
|
|
def test_checkpoint_sequential_deprecated_multiple_args(self):
|
|
class Two(nn.Module):
|
|
def forward(self, a, b):
|
|
return a, b
|
|
|
|
model = nn.Sequential(Two())
|
|
a = torch.randn(1, 100, requires_grad=True)
|
|
b = torch.randn(1, 100, requires_grad=True)
|
|
|
|
with self.assertRaises(TypeError):
|
|
checkpoint_sequential(model, 1, a, b) # type: ignore[call-arg]
|
|
|
|
def test_checkpoint_sequential_deprecated_no_args(self):
|
|
class Noop(nn.Module):
|
|
def forward(self):
|
|
pass
|
|
|
|
model = nn.Sequential(Noop())
|
|
|
|
with self.assertRaises(TypeError):
|
|
checkpoint_sequential(model, 1) # type: ignore[call-arg]
|
|
|
|
def test_checkpoint_rng_cpu(self):
|
|
for _ in range(5):
|
|
inp = torch.randn(20000, device='cpu').requires_grad_()
|
|
phase1 = torch.nn.Dropout()
|
|
phase2 = torch.nn.Dropout()
|
|
|
|
def run_fn(input):
|
|
return phase2(input)
|
|
|
|
state = torch.get_rng_state()
|
|
|
|
out = phase1(inp)
|
|
out = checkpoint(run_fn, out)
|
|
out.sum().backward()
|
|
grad_with_checkpointing = inp.grad
|
|
|
|
torch.set_rng_state(state)
|
|
|
|
inp.grad = None
|
|
|
|
out = phase1(inp)
|
|
out = run_fn(out)
|
|
out.sum().backward()
|
|
grad_no_checkpointing = inp.grad
|
|
|
|
self.assertEqual(grad_with_checkpointing, grad_no_checkpointing)
|
|
|
|
@unittest.skipIf(not HAS_CUDA, 'No CUDA')
|
|
def test_checkpoint_rng_cuda(self):
|
|
for _ in range(5):
|
|
inp = torch.randn(20000, device='cuda').requires_grad_()
|
|
phase1 = torch.nn.Dropout()
|
|
phase2 = torch.nn.Dropout()
|
|
|
|
def run_fn(input):
|
|
return phase2(input)
|
|
|
|
state = torch.cuda.get_rng_state()
|
|
|
|
out = phase1(inp)
|
|
out = checkpoint(run_fn, out)
|
|
out.sum().backward()
|
|
grad_with_checkpointing = inp.grad
|
|
|
|
torch.cuda.set_rng_state(state)
|
|
|
|
inp.grad = None
|
|
|
|
out = phase1(inp)
|
|
out = run_fn(out)
|
|
out.sum().backward()
|
|
grad_no_checkpointing = inp.grad
|
|
|
|
self.assertEqual(grad_with_checkpointing, grad_no_checkpointing)
|
|
|
|
def test_checkpoint_non_tensor(self):
|
|
|
|
def run_fn(tensor1, tensor2):
|
|
if tensor2 is None:
|
|
return tensor1
|
|
return tensor1 + tensor2
|
|
|
|
input_var = torch.randn(1, 100, requires_grad=True)
|
|
out = checkpoint(run_fn, input_var, None)
|
|
out.sum().backward()
|
|
|
|
def test_checkpoint_non_tensor_inputs_outputs(self):
|
|
def foo(t1, t2, scale, t3):
|
|
t4 = t1 + t2 * t3
|
|
t5 = t1 * t2 + t3
|
|
t4 *= scale
|
|
t5 *= scale
|
|
return scale, t4, None, True, t5, "bar", t1
|
|
|
|
t1 = torch.rand(10, requires_grad=True)
|
|
t2 = torch.rand(10, requires_grad=True)
|
|
t3 = torch.rand(10)
|
|
scale = random.randint(0, 10)
|
|
res = checkpoint(foo, t1, t2, scale, t3)
|
|
self.assertEqual(scale, res[0])
|
|
self.assertEqual((t1 + t2 * t3) * scale, res[1])
|
|
self.assertEqual(None, res[2])
|
|
self.assertEqual(True, res[3])
|
|
self.assertEqual((t1 * t2 + t3) * scale, res[4])
|
|
self.assertEqual("bar", res[5])
|
|
self.assertEqual(t1, res[6])
|
|
|
|
# Validate running backward.
|
|
res[1].sum().backward(retain_graph=True)
|
|
res[4].sum().backward(retain_graph=True)
|
|
res[6].sum().backward()
|
|
with self.assertRaisesRegex(RuntimeError, "Trying to backward through the graph a second time"):
|
|
res[6].sum().backward()
|
|
t1_grad = t1.grad
|
|
t2_grad = t2.grad
|
|
|
|
# Reset grads, run without checkpoint and validate we receive same grads.
|
|
t1.grad = None
|
|
t2.grad = None
|
|
res = foo(t1, t2, scale, t3)
|
|
torch.autograd.backward([res[1].sum(), res[4].sum(), res[6].sum()])
|
|
self.assertEqual(t1.grad, t1_grad)
|
|
self.assertEqual(t2.grad, t2_grad)
|
|
|
|
def test_checkpoint_no_tensors(self):
|
|
def foo(t1, t2, scale, t3):
|
|
t4 = t1 + t2 * t3
|
|
t5 = t1 * t2 + t3
|
|
t4 *= scale
|
|
t5 *= scale
|
|
return scale, t4, None, True, t5, "bar", t1
|
|
|
|
t1 = random.random()
|
|
t2 = random.random()
|
|
t3 = random.random()
|
|
scale = random.randint(0, 10)
|
|
res = checkpoint(foo, t1, t2, scale, t3)
|
|
self.assertEqual(scale, res[0])
|
|
self.assertEqual((t1 + t2 * t3) * scale, res[1])
|
|
self.assertEqual(None, res[2])
|
|
self.assertEqual(True, res[3])
|
|
self.assertEqual((t1 * t2 + t3) * scale, res[4])
|
|
self.assertEqual("bar", res[5])
|
|
self.assertEqual(t1, res[6])
|
|
|
|
def test_checkpoint_partial_grad(self):
|
|
def run_fn(tensor1, tensor2):
|
|
# tensor 2 is used for other application logic
|
|
return tensor1, tensor2
|
|
input_var = torch.randn(1, 4, requires_grad=True)
|
|
input_var2 = torch.randn(1, 4, requires_grad=False)
|
|
out = checkpoint(run_fn, input_var, input_var2)
|
|
out[0].sum().backward()
|
|
|
|
def run_fn2(tensor1, tensor2):
|
|
return tensor1
|
|
input_var = torch.randn(1, 4, requires_grad=False)
|
|
input_var2 = torch.randn(1, 4, requires_grad=True)
|
|
with self.assertRaisesRegex(
|
|
RuntimeError,
|
|
r"none of output has requires_grad=True, this checkpoint\(\) is not necessary"
|
|
):
|
|
out = checkpoint(run_fn2, input_var, input_var2)
|
|
out.sum().backward()
|
|
|
|
class TestDataLoaderUtils(TestCase):
|
|
def setUp(self):
|
|
self.dataset = torch.randn(5, 3, 3, 2)
|
|
self.batch_size = 3
|
|
|
|
def test_random_seed(self):
|
|
def run():
|
|
dataloader = torch.utils.data.DataLoader(RandomDatasetMock(),
|
|
batch_size=2,
|
|
num_workers=4,
|
|
shuffle=True)
|
|
return next(iter(dataloader))
|
|
|
|
torch.manual_seed(2018)
|
|
x1 = run()
|
|
torch.manual_seed(2018)
|
|
x2 = run()
|
|
self.assertEqual(x1, x2)
|
|
|
|
def test_single_keep(self):
|
|
# self.dataset is a Tensor here; technically not a valid input because
|
|
# not a Dataset subclass, but needs to stay working so add ignore's
|
|
# for type checking with mypy
|
|
dataloader : DataLoader = DataLoader(self.dataset, # type: ignore[arg-type]
|
|
batch_size=self.batch_size,
|
|
num_workers=0,
|
|
drop_last=False)
|
|
dataiter = iter(dataloader)
|
|
self.assertEqual(len(list(dataiter)), 2)
|
|
|
|
def test_single_drop(self):
|
|
dataloader : DataLoader = DataLoader(self.dataset, # type: ignore[arg-type]
|
|
batch_size=self.batch_size,
|
|
num_workers=0,
|
|
drop_last=True)
|
|
dataiter = iter(dataloader)
|
|
self.assertEqual(len(list(dataiter)), 1)
|
|
|
|
@unittest.skip("FIXME: Intermittent CUDA out-of-memory error on Windows and time-out under ASAN")
|
|
def test_multi_keep(self):
|
|
dataloader : DataLoader = DataLoader(self.dataset, # type: ignore[arg-type]
|
|
batch_size=self.batch_size,
|
|
num_workers=2,
|
|
drop_last=False)
|
|
dataiter = iter(dataloader)
|
|
self.assertEqual(len(list(dataiter)), 2)
|
|
|
|
def test_multi_drop(self):
|
|
dataloader : DataLoader = DataLoader(self.dataset, # type: ignore[arg-type]
|
|
batch_size=self.batch_size,
|
|
num_workers=2,
|
|
drop_last=True)
|
|
dataiter = iter(dataloader)
|
|
self.assertEqual(len(list(dataiter)), 1)
|
|
|
|
|
|
test_dir = os.path.abspath(os.path.dirname(str(__file__)))
|
|
|
|
|
|
class TestFFI(TestCase):
|
|
def test_deprecated(self):
|
|
with self.assertRaisesRegex(ImportError, "torch.utils.ffi is deprecated. Please use cpp extensions instead."):
|
|
from torch.utils.ffi import create_extension # type: ignore # noqa: F401
|
|
|
|
|
|
@unittest.skipIf('SKIP_TEST_BOTTLENECK' in os.environ.keys(), 'SKIP_TEST_BOTTLENECK is set')
|
|
class TestBottleneck(TestCase):
|
|
def _run(self, command, timeout=30):
|
|
"""Returns (return-code, stdout, stderr)"""
|
|
import subprocess
|
|
|
|
p = subprocess.Popen(command, stdout=subprocess.PIPE, # noqa: P204
|
|
stderr=subprocess.PIPE, shell=True)
|
|
try:
|
|
output, err = p.communicate(timeout=timeout)
|
|
except subprocess.TimeoutExpired:
|
|
p.kill()
|
|
output, err = p.communicate()
|
|
rc = p.returncode
|
|
output_str = output.decode("ascii")
|
|
err_str = err.decode("ascii")
|
|
return (rc, output_str, err_str)
|
|
|
|
def _run_bottleneck(self, test_file, scriptargs=''):
|
|
curdir = os.path.dirname(os.path.abspath(__file__))
|
|
filepath = '{}/{}'.format(curdir, test_file)
|
|
if scriptargs != '':
|
|
scriptargs = ' {}'.format(scriptargs)
|
|
rc, out, err = self._run(
|
|
'{} -m torch.utils.bottleneck {}{}'.format(sys.executable, filepath, scriptargs))
|
|
return rc, out, err
|
|
|
|
def _check_run_args(self):
|
|
# Check that this fails due to missing args
|
|
rc, out, err = self._run_bottleneck('bottleneck_test/test_args.py')
|
|
self.assertEqual(rc, 2, atol=0, rtol=0, msg=self._fail_msg('Missing args should error', out + err))
|
|
|
|
# This should succeed
|
|
rc, out, err = self._run_bottleneck('bottleneck_test/test_args.py', '--foo foo --bar bar')
|
|
self.assertEqual(rc, 0, atol=0, rtol=0, msg=self._fail_msg('Should pass args to script', out + err))
|
|
|
|
def _fail_msg(self, msg, output):
|
|
return '{}, output was:\n{}'.format(msg, output)
|
|
|
|
def _check_environment_summary(self, output):
|
|
results = re.search('Environment Summary', output)
|
|
self.assertIsNotNone(results, self._fail_msg('Should have Environment Summary', output))
|
|
|
|
# Up to five lines away from the heading, there should be the version number
|
|
results = re.search(r'Environment Summary.*(\n.*){,5}\nPyTorch \d+\.\d+', output)
|
|
self.assertIsNotNone(results, self._fail_msg('Should have PyTorch version', output))
|
|
|
|
def _check_cprof_summary(self, output):
|
|
results = re.search('cProfile output', output)
|
|
self.assertIsNotNone(results, self._fail_msg('Should have cProfile output', output))
|
|
|
|
# This assumes that after the cProfile output section we have
|
|
# the autograd profiler output
|
|
results = re.search(r'cProfile output.*(\n.*){6,50}\n.*autograd profiler output', output)
|
|
self.assertIsNotNone(results, self._fail_msg(
|
|
'Distance between cProfile and autograd prof out not in [6, 50] lines', output))
|
|
|
|
def _check_autograd_summary(self, output):
|
|
results = re.search('autograd profiler output', output)
|
|
self.assertIsNotNone(results, self._fail_msg('Should have autograd profiler output', output))
|
|
|
|
# This assumes that after the autograd profiler output is the end of the
|
|
# output.
|
|
results = re.search(r'autograd profiler output.*(\n.*){6,100}', output)
|
|
self.assertIsNotNone(results, self._fail_msg(
|
|
'Distance between autograd prof output and end of output not in [6, 100] lines', output))
|
|
|
|
def _check_cuda(self, output):
|
|
if HAS_CUDA:
|
|
results = re.search('CUDA mode', output)
|
|
self.assertIsNotNone(results, self._fail_msg('Should tell users CUDA', output))
|
|
else:
|
|
results = re.search('CUDA mode', output)
|
|
self.assertIsNone(results, self._fail_msg('Should not tell users about CUDA', output))
|
|
|
|
@unittest.skipIf(HAS_CUDA, 'CPU-only test')
|
|
def test_bottleneck_cpu_only(self):
|
|
rc, out, err = self._run_bottleneck('bottleneck_test/test.py')
|
|
self.assertEqual(rc, 0, msg='Run failed with\n{}'.format(err))
|
|
|
|
self._check_run_args()
|
|
self._check_environment_summary(out)
|
|
self._check_autograd_summary(out)
|
|
self._check_cprof_summary(out)
|
|
self._check_cuda(out)
|
|
|
|
@unittest.skipIf(not HAS_CUDA, 'No CUDA')
|
|
def test_bottleneck_cuda(self):
|
|
rc, out, err = self._run_bottleneck('bottleneck_test/test_cuda.py')
|
|
self.assertEqual(rc, 0, msg='Run failed with\n{}'.format(err))
|
|
|
|
self._check_run_args()
|
|
self._check_environment_summary(out)
|
|
self._check_autograd_summary(out)
|
|
self._check_cprof_summary(out)
|
|
self._check_cuda(out)
|
|
|
|
|
|
from torch.utils.collect_env import get_pretty_env_info
|
|
|
|
|
|
class TestCollectEnv(TestCase):
|
|
def test_smoke(self):
|
|
info_output = get_pretty_env_info()
|
|
self.assertTrue(info_output.count('\n') >= 17)
|
|
|
|
|
|
class TestONNXUtils(TestCase):
|
|
def test_prepare_onnx_paddings(self):
|
|
sizes = [2, 3, 4]
|
|
pad = [1, 2, 3, 4]
|
|
paddings = _prepare_onnx_paddings(len(sizes), pad)
|
|
self.assertEqual(paddings, [0, 3, 1, 0, 4, 2])
|
|
|
|
def test_check_onnx_broadcast(self):
|
|
|
|
def try_check_onnx_broadcast(dims1, dims2, expect_broadcast, expect_fail):
|
|
broadcast = True
|
|
fail = False
|
|
try:
|
|
broadcast = check_onnx_broadcast(dims1, dims2)
|
|
except ValueError:
|
|
fail = True
|
|
self.assertEqual(broadcast, expect_broadcast)
|
|
self.assertEqual(fail, expect_fail)
|
|
|
|
# Case 1, check the case when len(dims1) < len(dims2) and numel(dims2) > 1
|
|
dims1 = [3, 4]
|
|
dims2 = [2, 3, 4]
|
|
try_check_onnx_broadcast(dims1, dims2, True, True)
|
|
|
|
# Case 2, check the case when len(dims1) < len(dims2) and numel(dims2) == 1
|
|
dims1 = [3, 4]
|
|
dims2 = [1, 1, 1]
|
|
try_check_onnx_broadcast(dims1, dims2, True, False)
|
|
|
|
# Case 3, check the case when len(dims1) > len(dims2) and numel(dims2) == 1
|
|
dims1 = [1, 1]
|
|
dims2 = [1]
|
|
try_check_onnx_broadcast(dims1, dims2, True, False)
|
|
|
|
# Case 4, check the case when len(dims1) > len(dims2) and dims1[x:] == dims2
|
|
dims1 = [2, 3, 4]
|
|
dims2 = [3, 4]
|
|
try_check_onnx_broadcast(dims1, dims2, True, False)
|
|
|
|
# Case 5, check the case when len(dims1) > len(dims2), but dims1[x:] != dims2
|
|
dims1 = [2, 3, 4]
|
|
dims2 = [1, 4]
|
|
try_check_onnx_broadcast(dims1, dims2, True, True)
|
|
|
|
# Case 6, check the equal case, no broadcast
|
|
dims1 = [3, 4]
|
|
dims2 = [3, 4]
|
|
try_check_onnx_broadcast(dims1, dims2, False, False)
|
|
|
|
# Case 7, check the case when len(dims1) == len(dims2), but dims1 != dims2
|
|
dims1 = [3, 4]
|
|
dims2 = [1, 4]
|
|
try_check_onnx_broadcast(dims1, dims2, True, True)
|
|
|
|
# Case 8, check the case when len(dims1) == len(dims2) and numel(s2) == 1
|
|
dims1 = [3, 4]
|
|
dims2 = [1, 1]
|
|
try_check_onnx_broadcast(dims1, dims2, True, False)
|
|
|
|
|
|
def sum_of_state_dict(state_dict):
|
|
s = 0
|
|
for _, v in state_dict.items():
|
|
s += v.sum()
|
|
return s
|
|
|
|
SUM_OF_HUB_EXAMPLE = 431080
|
|
TORCHHUB_EXAMPLE_RELEASE_URL = 'https://github.com/ailzhang/torchhub_example/releases/download/0.1/mnist_init_ones'
|
|
|
|
@unittest.skipIf(IS_SANDCASTLE, 'Sandcastle cannot ping external')
|
|
class TestHub(TestCase):
|
|
@retry(URLError, tries=3, skip_after_retries=True)
|
|
def test_load_from_github(self):
|
|
hub_model = hub.load(
|
|
'ailzhang/torchhub_example',
|
|
'mnist',
|
|
source='github',
|
|
pretrained=True,
|
|
verbose=False)
|
|
self.assertEqual(sum_of_state_dict(hub_model.state_dict()),
|
|
SUM_OF_HUB_EXAMPLE)
|
|
|
|
@retry(URLError, tries=3, skip_after_retries=True)
|
|
def test_load_from_local_dir(self):
|
|
local_dir = hub._get_cache_or_reload(
|
|
'ailzhang/torchhub_example', force_reload=False)
|
|
hub_model = hub.load(
|
|
local_dir,
|
|
'mnist',
|
|
source='local',
|
|
pretrained=True,
|
|
verbose=False)
|
|
self.assertEqual(sum_of_state_dict(hub_model.state_dict()),
|
|
SUM_OF_HUB_EXAMPLE)
|
|
|
|
@retry(URLError, tries=3, skip_after_retries=True)
|
|
def test_load_from_branch(self):
|
|
hub_model = hub.load(
|
|
'ailzhang/torchhub_example:ci/test_slash',
|
|
'mnist',
|
|
pretrained=True,
|
|
verbose=False)
|
|
self.assertEqual(sum_of_state_dict(hub_model.state_dict()),
|
|
SUM_OF_HUB_EXAMPLE)
|
|
|
|
@retry(URLError, tries=3, skip_after_retries=True)
|
|
def test_set_dir(self):
|
|
temp_dir = tempfile.gettempdir()
|
|
hub.set_dir(temp_dir)
|
|
hub_model = hub.load(
|
|
'ailzhang/torchhub_example',
|
|
'mnist',
|
|
pretrained=True,
|
|
verbose=False)
|
|
self.assertEqual(sum_of_state_dict(hub_model.state_dict()),
|
|
SUM_OF_HUB_EXAMPLE)
|
|
assert os.path.exists(temp_dir + '/ailzhang_torchhub_example_master')
|
|
shutil.rmtree(temp_dir + '/ailzhang_torchhub_example_master')
|
|
|
|
@retry(URLError, tries=3, skip_after_retries=True)
|
|
def test_list_entrypoints(self):
|
|
entry_lists = hub.list('ailzhang/torchhub_example', force_reload=True)
|
|
self.assertObjectIn('mnist', entry_lists)
|
|
|
|
@retry(URLError, tries=3, skip_after_retries=True)
|
|
def test_download_url_to_file(self):
|
|
temp_file = os.path.join(tempfile.gettempdir(), 'temp')
|
|
hub.download_url_to_file(TORCHHUB_EXAMPLE_RELEASE_URL, temp_file, progress=False)
|
|
loaded_state = torch.load(temp_file)
|
|
self.assertEqual(sum_of_state_dict(loaded_state),
|
|
SUM_OF_HUB_EXAMPLE)
|
|
|
|
@retry(URLError, tries=3, skip_after_retries=True)
|
|
def test_load_state_dict_from_url(self):
|
|
loaded_state = hub.load_state_dict_from_url(TORCHHUB_EXAMPLE_RELEASE_URL)
|
|
self.assertEqual(sum_of_state_dict(loaded_state),
|
|
SUM_OF_HUB_EXAMPLE)
|
|
|
|
@retry(URLError, tries=3, skip_after_retries=True)
|
|
def test_load_zip_checkpoint(self):
|
|
hub_model = hub.load(
|
|
'ailzhang/torchhub_example',
|
|
'mnist_zip',
|
|
pretrained=True,
|
|
verbose=False)
|
|
self.assertEqual(sum_of_state_dict(hub_model.state_dict()),
|
|
SUM_OF_HUB_EXAMPLE)
|
|
|
|
# Test the default zipfile serialization format produced by >=1.6 release.
|
|
@retry(URLError, tries=3, skip_after_retries=True)
|
|
def test_load_zip_1_6_checkpoint(self):
|
|
hub_model = hub.load(
|
|
'ailzhang/torchhub_example',
|
|
'mnist_zip_1_6',
|
|
pretrained=True,
|
|
verbose=False)
|
|
self.assertEqual(sum_of_state_dict(hub_model.state_dict()),
|
|
SUM_OF_HUB_EXAMPLE)
|
|
|
|
|
|
def test_hub_dir(self):
|
|
with tempfile.TemporaryDirectory('hub_dir') as dirname:
|
|
torch.hub.set_dir(dirname)
|
|
self.assertEqual(torch.hub.get_dir(), dirname)
|
|
|
|
@retry(URLError, tries=3, skip_after_retries=True)
|
|
def test_load_state_dict_from_url_with_name(self):
|
|
with tempfile.TemporaryDirectory('hub_dir') as dirname:
|
|
torch.hub.set_dir(dirname)
|
|
file_name = 'test_file'
|
|
loaded_state = hub.load_state_dict_from_url(TORCHHUB_EXAMPLE_RELEASE_URL, file_name=file_name)
|
|
self.assertTrue(os.path.exists(os.path.join(dirname, 'checkpoints', file_name)))
|
|
self.assertEqual(sum_of_state_dict(loaded_state),
|
|
SUM_OF_HUB_EXAMPLE)
|
|
|
|
class TestHipify(TestCase):
|
|
def test_import_hipify(self):
|
|
from torch.utils.hipify import hipify_python # noqa: F401
|
|
|
|
|
|
class TestAssert(TestCase):
|
|
def test_assert_true(self):
|
|
# verify assertions work as expected
|
|
# bool argument
|
|
torch._assert(True, "foo")
|
|
with self.assertRaisesRegex(AssertionError, "bar"):
|
|
torch._assert(False, "bar")
|
|
# tensor argument
|
|
torch._assert(torch.tensor([True], dtype=torch.bool), "foo")
|
|
with self.assertRaisesRegex(AssertionError, "bar"):
|
|
torch._assert(torch.tensor([False], dtype=torch.bool), "bar")
|
|
|
|
def test_assert_scriptable(self):
|
|
class M(torch.nn.Module):
|
|
def forward(self, x):
|
|
torch._assert(x.sum() > 0, "foo")
|
|
return x
|
|
|
|
m = M()
|
|
# scriptable
|
|
ms = torch.jit.script(m)
|
|
# data can be passed without errors
|
|
x = torch.randn(4, 4).fill_(1.0)
|
|
ms(x)
|
|
with self.assertRaisesRegex(torch.jit.Error, "foo"): # type: ignore[type-var]
|
|
ms(torch.tensor([False], dtype=torch.bool))
|
|
|
|
|
|
@unittest.skipIf(IS_SANDCASTLE, "cpp_extension is OSS only")
|
|
class TestStandaloneCPPJIT(TestCase):
|
|
def test_load_standalone(self):
|
|
build_dir = tempfile.mkdtemp()
|
|
try:
|
|
src_path = os.path.join(build_dir, "main.cpp")
|
|
src = textwrap.dedent("""\
|
|
#include <iostream>
|
|
#include <torch/torch.h>
|
|
int main() {
|
|
auto x = torch::eye(3);
|
|
std::cout << x << std::endl;
|
|
}
|
|
""")
|
|
with open(src_path, "wt") as f:
|
|
f.write(src)
|
|
|
|
exec_path = torch.utils.cpp_extension.load(
|
|
"standalone_load_test",
|
|
src_path,
|
|
build_directory=build_dir,
|
|
is_python_module=False,
|
|
is_standalone=True,
|
|
)
|
|
|
|
ext = ".exe" if IS_WINDOWS else ""
|
|
self.assertEqual(
|
|
exec_path,
|
|
os.path.join(build_dir, f"standalone_load_test{ext}")
|
|
)
|
|
|
|
for shell in [True, False]:
|
|
r = subprocess.run(
|
|
[exec_path],
|
|
shell=shell,
|
|
stdout=subprocess.PIPE,
|
|
)
|
|
self.assertEqual(r.returncode, 0)
|
|
self.assertEqual(
|
|
# Windows prints "\r\n" for newlines.
|
|
textwrap.dedent(r.stdout.decode("utf-8")).replace("\r\n", "\n"),
|
|
textwrap.dedent("""\
|
|
1 0 0
|
|
0 1 0
|
|
0 0 1
|
|
[ CPUFloatType{3,3} ]
|
|
""")
|
|
)
|
|
|
|
finally:
|
|
shutil.rmtree(build_dir)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
run_tests()
|