| from __future__ import print_function |
| import sys |
| import os |
| import re |
| import shutil |
| import random |
| import tempfile |
| import unittest |
| import torch |
| import torch.nn as nn |
| import torch.utils.data |
| import torch.cuda |
| from torch.utils.checkpoint import checkpoint, checkpoint_sequential |
| import torch.hub as hub |
| from torch.autograd._functions.utils import prepare_onnx_paddings |
| from torch.autograd._functions.utils import check_onnx_broadcast |
| from common_utils import skipIfRocm, load_tests |
| |
| # load_tests from 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 common_utils import TestCase, run_tests |
| |
| |
| class RandomDatasetMock(object): |
| |
| 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, |
| *inputs |
| ): |
| |
| # not checkpointed |
| if not isinstance(inputs, tuple): |
| inputs = (inputs,) |
| out = model(*inputs) |
| out_not_checkpointed = out.data.clone() |
| model.zero_grad() |
| out.sum().backward() |
| grad_not_checkpointed = { |
| name: param.grad.data.clone() |
| for name, param in model.named_parameters() |
| } |
| input_grad_not_checkpointed = [i.grad.data.clone() for i in inputs] |
| for model_to_compare in module_lists_to_compare: |
| # checkpointed model by passing list of modules |
| detached_inputs = [i.detach() for i in inputs] |
| for detached in detached_inputs: |
| detached.requires_grad = True |
| |
| # pass list of modules to checkpoint |
| out = checkpoint_sequential(model_to_compare, num_chunks, *detached_inputs) |
| out_checkpointed = out.data.clone() |
| model.zero_grad() |
| out.sum().backward() |
| grad_checkpointed = { |
| name: param.grad.data.clone() |
| for name, param in model.named_parameters() |
| } |
| input_grad_checkpointed = [d.grad.data.clone() for d in detached_inputs] |
| # compare outputs as well as the gradients of input and parameters |
| self.assertEqual(out_checkpointed, out_not_checkpointed) |
| for i, j in zip(input_grad_not_checkpointed, input_grad_checkpointed): |
| self.assertEqual(i, j) |
| 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_multiple_args(self): |
| class ModuleListNet(nn.Module): |
| def __init__(self): |
| super(ModuleListNet, self).__init__() |
| module_list = [ |
| nn.Bilinear(100, 60, 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, *inputs): |
| for layer in self.module_list: |
| if isinstance(inputs, tuple): |
| inputs = layer(*inputs) |
| else: |
| inputs = layer(inputs) |
| return inputs |
| |
| model = ModuleListNet() |
| |
| # Compare uncheckpointed model with its checkpointed counterparts |
| # In addition to running checkpoint_sequential on the nn.ModuleList |
| # instance, we also run the function on the list of functions within |
| # the ModuleList. |
| self._check_checkpoint_sequential( |
| model, |
| [list(model.module_list.children()), model.module_list], |
| 2, |
| torch.randn(1, 100, requires_grad=True), |
| torch.randn(1, 60, 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) |
| |
| self.assertWarnsRegex( |
| lambda: checkpoint_sequential(model, 1, a, b), |
| 'deprecated', |
| 'checkpoint_sequential with multiple args should be deprecated', |
| ) |
| |
| def test_checkpoint_sequential_deprecated_no_args(self): |
| class Noop(nn.Module): |
| def forward(self): |
| pass |
| |
| model = nn.Sequential(Noop()) |
| |
| self.assertWarnsRegex( |
| lambda: checkpoint_sequential(model, 1), |
| 'deprecated', |
| 'checkpoint_sequential with no args should be deprecated', |
| ) |
| |
| 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() |
| |
| |
| class TestDataLoader(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): |
| dataloader = torch.utils.data.DataLoader(self.dataset, |
| 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 = torch.utils.data.DataLoader(self.dataset, |
| 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 = torch.utils.data.DataLoader(self.dataset, |
| 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 = torch.utils.data.DataLoader(self.dataset, |
| 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 # noqa: F401 |
| |
| |
| @unittest.skipIf('SKIP_TEST_BOTTLENECK' in os.environ.keys(), 'SKIP_TEST_BOTTLENECK is set') |
| class TestBottleneck(TestCase): |
| def _run(self, command): |
| """Returns (return-code, stdout, stderr)""" |
| import subprocess |
| from common_utils import PY3 |
| |
| p = subprocess.Popen(command, stdout=subprocess.PIPE, # noqa |
| stderr=subprocess.PIPE, shell=True) |
| output, err = p.communicate() |
| rc = p.returncode |
| if PY3: |
| output = output.decode("ascii") |
| err = err.decode("ascii") |
| return (rc, output, err) |
| |
| 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_args.py') |
| self.assertEqual(rc, 2, None, self._fail_msg('Missing args should error', out + err)) |
| |
| # This should succeed |
| rc, out, err = self._run_bottleneck('bottleneck/test_args.py', '--foo foo --bar bar') |
| self.assertEqual(rc, 0, None, 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 Enviroment 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.py') |
| self.assertEqual(rc, 0, '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') |
| @skipIfRocm |
| def test_bottleneck_cuda(self): |
| rc, out, err = self._run_bottleneck('bottleneck/test_cuda.py') |
| self.assertEqual(rc, 0, '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_model_parameters(model): |
| s = 0 |
| for p in model.parameters(): |
| s += p.sum() |
| return s |
| |
| SUM_OF_PRETRAINED_RESNET18_PARAMS = -12703.992365 |
| |
| class TestHub(TestCase): |
| @classmethod |
| def setUpClass(cls): |
| # Only run this check ONCE before all tests start. |
| # - If torchvision is imported before all tests start, e.g. we might find _C.so |
| # which doesn't exist in downloaded zip but in the installed wheel. |
| # - After the first test is run, torchvision is already in sys.modules due to |
| # Python cache as we run all hub tests in the same python process. |
| if 'torchvision' in sys.modules: |
| raise RuntimeError('TestHub must start without torchvision imported') |
| |
| def test_load_from_github(self): |
| hub_model = hub.load( |
| 'pytorch/vision', |
| 'resnet18', |
| pretrained=True, |
| progress=False) |
| self.assertEqual(sum_of_model_parameters(hub_model), |
| SUM_OF_PRETRAINED_RESNET18_PARAMS) |
| |
| def test_set_dir(self): |
| temp_dir = tempfile.gettempdir() |
| hub.set_dir(temp_dir) |
| hub_model = hub.load( |
| 'pytorch/vision', |
| 'resnet18', |
| pretrained=True, |
| progress=False) |
| self.assertEqual(sum_of_model_parameters(hub_model), |
| SUM_OF_PRETRAINED_RESNET18_PARAMS) |
| assert os.path.exists(temp_dir + '/pytorch_vision_master') |
| shutil.rmtree(temp_dir + '/pytorch_vision_master') |
| |
| def test_list_entrypoints(self): |
| entry_lists = hub.list('pytorch/vision', force_reload=True) |
| self.assertObjectIn('resnet18', entry_lists) |
| |
| if __name__ == '__main__': |
| run_tests() |
