test_utils.py

from __future__ import print_function
import sys
import os
import re
import math
import shutil
import random
import tempfile
import unittest
import traceback
import torch
import torch.nn as nn
import torch.utils.data
import torch.cuda
import warnings
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 IS_WINDOWS, IS_PPC, 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

try:
    import torchvision.models as models
    HAS_TORCHVISION = True
except ImportError:
    HAS_TORCHVISION = False


skipIfNoTorchVision = unittest.skipIf(not HAS_TORCHVISION, "no torchvision")

HAS_CUDA = torch.cuda.is_available()

from common_utils import TestCase, run_tests, download_file


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_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


@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)


class TestHub(TestCase):
    @classmethod
    @skipIfNoTorchVision
    def setUpClass(cls):
        cls.resnet18_pretrained = models.__dict__['resnet18'](pretrained=True).state_dict()

    @skipIfNoTorchVision
    def test_load_from_github(self):
        hub_model = hub.load(
            'pytorch/vision',
            'resnet18',
            pretrained=True)
        self.assertEqual(self.resnet18_pretrained, hub_model.state_dict())

    @skipIfNoTorchVision
    def test_set_dir(self):
        temp_dir = tempfile.gettempdir()
        hub.set_dir(temp_dir)
        hub_model = hub.load(
            'pytorch/vision',
            'resnet18',
            pretrained=True)
        self.assertEqual(self.resnet18_pretrained, hub_model.state_dict())
        assert os.path.exists(temp_dir + '/vision_master')
        shutil.rmtree(temp_dir + '/vision_master')


if __name__ == '__main__':
    run_tests()