| # Owner(s): ["module: unknown"] |
| |
| import sys |
| import os |
| import re |
| import shutil |
| import random |
| import subprocess |
| import tempfile |
| import textwrap |
| import unittest |
| from typing import List |
| 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 |
| 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, IS_SANDCASTLE, IS_WINDOWS |
| |
| # 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) |
| |
| @unittest.skipIf(not HAS_CUDA, 'No CUDA') |
| def test_checkpoint_not_preserve_rng_state_and_without_reentrant(self): |
| inp = torch.randn(2, device='cuda').requires_grad_() |
| layer = torch.nn.Dropout() |
| |
| def run_fn(input): |
| return layer(input) |
| |
| out = checkpoint(run_fn, inp, use_reentrant=False, preserve_rng_state=False) |
| out.sum().backward() |
| # This should run without error |
| |
| |
| 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() |
| |
| @unittest.skipIf(not torch.cuda.is_available(), "Test requires CUDA") |
| def test_checkpointing_without_reentrant_early_free(self): |
| # I don't know how to check if the temporary saved variable buffer |
| # get de-allocated directly. So using cuda memory usage as a proxy |
| |
| def _do_test(fn, should_free): |
| stats: List[int] = [] |
| |
| def track(x, idx): |
| # Track that at each step of the backward, some Tensor were |
| # de-allocated (which correspond to the checkpoint storage being |
| # emptied at each step) |
| def hook(_unused): |
| self.assertEqual(len(stats), idx) |
| torch.cuda.synchronize() |
| stats.append(torch.cuda.memory_allocated()) |
| if idx > 0: |
| if should_free: |
| self.assertLess(stats[idx], stats[idx - 1]) |
| else: |
| self.assertEqual(stats[idx], stats[idx - 1]) |
| |
| x.register_hook(hook) |
| |
| def test_fn(x): |
| # The main property of this function is that it contains multiple |
| # operations that save gradients in a chain. |
| x = x ** 2 |
| track(x, 2) |
| x = x ** 2 |
| track(x, 1) |
| x = x ** 2 |
| track(x, 0) |
| x = x ** 2 |
| return x.sum() |
| |
| fn(test_fn) |
| |
| return stats |
| |
| x = torch.zeros(10, device="cuda", requires_grad=True) |
| x.grad = torch.zeros_like(x) |
| |
| # In a regular backward, buffers get eagerly freed |
| non_retain_stats = _do_test(lambda fn: fn(x).backward(), True) |
| |
| # In a retain_grad backward, buffers get preserved |
| retain_stats = _do_test(lambda fn: fn(x).backward(retain_graph=True), False) |
| |
| # In a regular backward with checkpoint, buffers get eagerly freed |
| checkpoint_non_retain_stats = _do_test(lambda fn: checkpoint(fn, x, use_reentrant=False).backward(), True) |
| |
| # In a retain_grad backward with checkpoint, buffers get preserved |
| checkpoint_retain_stats = _do_test(lambda fn: checkpoint(fn, x, use_reentrant=False).backward(retain_graph=True), False) |
| |
| self.assertEqual(non_retain_stats, checkpoint_non_retain_stats) |
| self.assertEqual(retain_stats, checkpoint_retain_stats) |
| |
| class TestDataLoaderUtils(TestCase): |
| def setUp(self): |
| super().setUp() |
| 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__))) |
| |
| |
| @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) |
| |
| |
| 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"): |
| 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) |
| |
| |
| class DummyXPUModule(object): |
| @staticmethod |
| def is_available(): |
| return True |
| |
| |
| class TestExtensionUtils(TestCase): |
| def test_external_module_register(self): |
| # Built-in module |
| with self.assertRaisesRegex(RuntimeError, "The runtime module of"): |
| torch._register_device_module('cuda', torch.cuda) |
| |
| # Wrong device type |
| with self.assertRaisesRegex(RuntimeError, "Expected one of cpu"): |
| torch._register_device_module('dummmy', DummyXPUModule) |
| |
| with self.assertRaises(AttributeError): |
| torch.xpu.is_available() # type: ignore[attr-defined] |
| |
| torch._register_device_module('xpu', DummyXPUModule) |
| |
| torch.xpu.is_available() # type: ignore[attr-defined] |
| |
| # No supporting for override |
| with self.assertRaisesRegex(RuntimeError, "The runtime module of"): |
| torch._register_device_module('xpu', DummyXPUModule) |
| |
| |
| class TestCppExtensionUtils(TestCase): |
| def test_cpp_compiler_is_ok(self): |
| self.assertTrue(torch.utils.cpp_extension.check_compiler_ok_for_platform('c++')) |
| |
| def test_cc_compiler_is_ok(self): |
| self.assertTrue(torch.utils.cpp_extension.check_compiler_ok_for_platform('cc')) |
| |
| |
| if __name__ == '__main__': |
| run_tests() |
