mirror of
https://github.com/pytorch/pytorch.git
synced 2025-10-20 21:14:14 +08:00
d5988c5eca
Summary: During development it is common practice to put `type: ignore` comments on lines that are correct, but `mypy` doesn't recognize this. This often stems from the fact, that the used `mypy` version wasn't able to handle the used pattern. With every new release `mypy` gets better at handling complex code. In addition to fix all the previously accepted but now failing patterns, we should also revisit all `type: ignore` comments to see if they are still needed or not. Fortunately, we don't need to do it manually: by adding `warn_unused_ignores = True` to the configuration, `mypy` will error out in case it encounters an `type: ignore` that is no longer needed. Pull Request resolved: https://github.com/pytorch/pytorch/pull/60006 Reviewed By: jbschlosser, malfet Differential Revision: D29133237 Pulled By: albanD fbshipit-source-id: 41e82edc5cd5affa7ccedad044b59b94dad4425a
830 lines
30 KiB
Python
830 lines
30 KiB
Python
import sys
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import os
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import contextlib
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import io
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import re
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import shutil
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import random
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import subprocess
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import tempfile
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import textwrap
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import unittest
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import torch
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import torch.nn as nn
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import torch.utils.data
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from torch.utils.data import DataLoader
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import torch.cuda
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from torch.utils.checkpoint import checkpoint, checkpoint_sequential
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import torch.utils.cpp_extension
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import torch.hub as hub
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from torch.autograd._functions.utils import check_onnx_broadcast
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from torch.onnx.symbolic_opset9 import _prepare_onnx_paddings
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from torch.testing._internal.common_utils import load_tests, retry, IS_SANDCASTLE, IS_WINDOWS
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from urllib.error import URLError
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# load_tests from torch.testing._internal.common_utils is used to automatically filter tests for
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# sharding on sandcastle. This line silences flake warnings
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load_tests = load_tests
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HAS_CUDA = torch.cuda.is_available()
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def check_breakpad():
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try:
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torch._C._get_minidump_directory() # type: ignore[attr-defined]
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return True
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except RuntimeError as e:
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return "Minidump handler is uninintialized, make sure to call" in str(e)
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HAS_BREAKPAD = check_breakpad()
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from torch.testing._internal.common_utils import TestCase, run_tests
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class RandomDatasetMock(torch.utils.data.Dataset):
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def __getitem__(self, index):
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return torch.tensor([torch.rand(1).item(), random.uniform(0, 1)])
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def __len__(self):
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return 1000
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class TestCheckpoint(TestCase):
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# This runs checkpoint_sequential on each of the nets in
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# module_lists_to_compare, and compares them against the uncheckpointed model.
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# To compare, it checks outputs as well as input gradients and parameter gradients
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def _check_checkpoint_sequential(
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self,
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model,
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module_lists_to_compare,
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num_chunks,
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input,
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):
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# not checkpointed
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out = model(input)
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out_not_checkpointed = out.detach().clone()
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model.zero_grad()
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out.sum().backward()
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grad_not_checkpointed = {
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name: param.grad.detach().clone()
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for name, param in model.named_parameters()
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}
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input_grad_not_checkpointed = input.grad.detach().clone()
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for model_to_compare in module_lists_to_compare:
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# checkpointed model by passing list of modules
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detached = input.detach()
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detached.requires_grad = True
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# pass list of modules to checkpoint
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out = checkpoint_sequential(model_to_compare, num_chunks, detached)
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out_checkpointed = out.detach().clone()
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model.zero_grad()
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out.sum().backward()
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grad_checkpointed = {
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name: param.grad.detach().clone()
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for name, param in model.named_parameters()
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}
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input_grad_checkpointed = detached.grad.detach().clone()
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# compare outputs as well as the gradients of input and parameters
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self.assertEqual(out_checkpointed, out_not_checkpointed)
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self.assertEqual(input_grad_not_checkpointed, input_grad_checkpointed)
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for name in grad_checkpointed:
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self.assertEqual(grad_checkpointed[name], grad_not_checkpointed[name])
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# Test whether checkpoint is being triggered or not. For this, we check
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# the number of times forward pass happens
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def test_checkpoint_trigger(self):
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class Net(nn.Module):
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def __init__(self):
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super(Net, self).__init__()
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self.counter = 0
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def forward(self, input_var):
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self.counter += 1
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return input_var
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# checkpointed
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modules = [Net() for _ in range(10)]
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for m in modules:
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self.assertEqual(m.counter, 0)
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input_var = torch.randn(3, 4, requires_grad=True)
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out = checkpoint_sequential(modules, 2, input_var)
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for m in modules:
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self.assertEqual(m.counter, 1)
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out.sum().backward()
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for m in modules[:(len(modules) // 2)]:
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self.assertEqual(m.counter, 2)
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for m in modules[(len(modules) // 2):]:
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self.assertEqual(m.counter, 1)
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def test_checkpoint_valid(self):
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model = nn.Sequential(
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nn.Linear(100, 50),
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nn.ReLU(),
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nn.Linear(50, 20),
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nn.ReLU(),
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nn.Linear(20, 5),
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nn.ReLU()
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)
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input_var = torch.randn(1, 100, requires_grad=True)
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# checkpointed
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chunks = 2
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modules = list(model.children())
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out = checkpoint_sequential(modules, chunks, input_var)
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with self.assertRaisesRegex(RuntimeError, "Checkpointing is not compatible"):
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torch.autograd.grad(
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outputs=[out], grad_outputs=[torch.ones(1, 5)], inputs=[input_var], create_graph=True
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)
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def test_checkpoint(self):
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model = nn.Sequential(
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nn.Linear(100, 50),
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nn.ReLU(),
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nn.Linear(50, 20),
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nn.ReLU(),
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nn.Linear(20, 5),
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nn.ReLU()
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)
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# Compare uncheckpointed model with its checkpointed counterparts
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# In addition to running checkpoint_sequential on the nn.Sequential
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# instance, we also run the function on the list of functions within
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# the module.
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self._check_checkpoint_sequential(
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model,
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[list(model.children()), model],
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2,
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torch.randn(1, 100, requires_grad=True)
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)
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def test_checkpoint_module_list(self):
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class ModuleListNet(nn.Module):
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def __init__(self):
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super(ModuleListNet, self).__init__()
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module_list = [
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nn.Linear(100, 50),
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nn.ReLU(),
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nn.Linear(50, 20),
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nn.ReLU(),
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nn.Linear(20, 5),
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nn.ReLU(),
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]
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self.module_list = nn.ModuleList(module_list)
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def forward(self, input):
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for layer in self.module_list:
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input = layer(input)
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return input
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model = ModuleListNet()
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# Compare uncheckpointed model with its checkpointed counterparts.
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self._check_checkpoint_sequential(
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model,
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[list(model.module_list.children()), model.module_list],
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2,
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torch.randn(1, 100, requires_grad=True),
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)
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def test_checkpoint_sequential_deprecated_multiple_args(self):
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class Two(nn.Module):
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def forward(self, a, b):
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return a, b
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model = nn.Sequential(Two())
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a = torch.randn(1, 100, requires_grad=True)
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b = torch.randn(1, 100, requires_grad=True)
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with self.assertRaises(TypeError):
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checkpoint_sequential(model, 1, a, b) # type: ignore[call-arg]
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def test_checkpoint_sequential_deprecated_no_args(self):
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class Noop(nn.Module):
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def forward(self):
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pass
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model = nn.Sequential(Noop())
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with self.assertRaises(TypeError):
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checkpoint_sequential(model, 1) # type: ignore[call-arg]
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def test_checkpoint_rng_cpu(self):
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for _ in range(5):
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inp = torch.randn(20000, device='cpu').requires_grad_()
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phase1 = torch.nn.Dropout()
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phase2 = torch.nn.Dropout()
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def run_fn(input):
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return phase2(input)
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state = torch.get_rng_state()
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out = phase1(inp)
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out = checkpoint(run_fn, out)
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out.sum().backward()
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grad_with_checkpointing = inp.grad
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torch.set_rng_state(state)
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inp.grad = None
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out = phase1(inp)
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out = run_fn(out)
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out.sum().backward()
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grad_no_checkpointing = inp.grad
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self.assertEqual(grad_with_checkpointing, grad_no_checkpointing)
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@unittest.skipIf(not HAS_CUDA, 'No CUDA')
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def test_checkpoint_rng_cuda(self):
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for _ in range(5):
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inp = torch.randn(20000, device='cuda').requires_grad_()
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phase1 = torch.nn.Dropout()
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phase2 = torch.nn.Dropout()
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def run_fn(input):
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return phase2(input)
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state = torch.cuda.get_rng_state()
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out = phase1(inp)
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out = checkpoint(run_fn, out)
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out.sum().backward()
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grad_with_checkpointing = inp.grad
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torch.cuda.set_rng_state(state)
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inp.grad = None
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out = phase1(inp)
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out = run_fn(out)
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out.sum().backward()
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grad_no_checkpointing = inp.grad
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self.assertEqual(grad_with_checkpointing, grad_no_checkpointing)
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def test_checkpoint_non_tensor(self):
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def run_fn(tensor1, tensor2):
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if tensor2 is None:
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return tensor1
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return tensor1 + tensor2
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input_var = torch.randn(1, 100, requires_grad=True)
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out = checkpoint(run_fn, input_var, None)
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out.sum().backward()
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def test_checkpoint_non_tensor_inputs_outputs(self):
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def foo(t1, t2, scale, t3):
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t4 = t1 + t2 * t3
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t5 = t1 * t2 + t3
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t4 *= scale
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t5 *= scale
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return scale, t4, None, True, t5, "bar", t1
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t1 = torch.rand(10, requires_grad=True)
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t2 = torch.rand(10, requires_grad=True)
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t3 = torch.rand(10)
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scale = random.randint(0, 10)
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res = checkpoint(foo, t1, t2, scale, t3)
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self.assertEqual(scale, res[0])
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self.assertEqual((t1 + t2 * t3) * scale, res[1])
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self.assertEqual(None, res[2])
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self.assertEqual(True, res[3])
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self.assertEqual((t1 * t2 + t3) * scale, res[4])
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self.assertEqual("bar", res[5])
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self.assertEqual(t1, res[6])
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# Validate running backward.
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res[1].sum().backward(retain_graph=True)
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res[4].sum().backward(retain_graph=True)
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res[6].sum().backward()
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with self.assertRaisesRegex(RuntimeError, "Trying to backward through the graph a second time"):
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res[6].sum().backward()
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t1_grad = t1.grad
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t2_grad = t2.grad
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# Reset grads, run without checkpoint and validate we receive same grads.
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t1.grad = None
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t2.grad = None
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res = foo(t1, t2, scale, t3)
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torch.autograd.backward([res[1].sum(), res[4].sum(), res[6].sum()])
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self.assertEqual(t1.grad, t1_grad)
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self.assertEqual(t2.grad, t2_grad)
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def test_checkpoint_no_tensors(self):
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def foo(t1, t2, scale, t3):
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t4 = t1 + t2 * t3
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t5 = t1 * t2 + t3
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t4 *= scale
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t5 *= scale
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return scale, t4, None, True, t5, "bar", t1
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t1 = random.random()
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t2 = random.random()
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t3 = random.random()
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scale = random.randint(0, 10)
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res = checkpoint(foo, t1, t2, scale, t3)
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self.assertEqual(scale, res[0])
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self.assertEqual((t1 + t2 * t3) * scale, res[1])
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self.assertEqual(None, res[2])
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self.assertEqual(True, res[3])
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self.assertEqual((t1 * t2 + t3) * scale, res[4])
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self.assertEqual("bar", res[5])
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self.assertEqual(t1, res[6])
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def test_checkpoint_partial_grad(self):
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def run_fn(tensor1, tensor2):
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# tensor 2 is used for other application logic
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return tensor1, tensor2
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input_var = torch.randn(1, 4, requires_grad=True)
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input_var2 = torch.randn(1, 4, requires_grad=False)
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out = checkpoint(run_fn, input_var, input_var2)
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out[0].sum().backward()
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def run_fn2(tensor1, tensor2):
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return tensor1
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input_var = torch.randn(1, 4, requires_grad=False)
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input_var2 = torch.randn(1, 4, requires_grad=True)
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with self.assertRaisesRegex(
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RuntimeError,
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r"none of output has requires_grad=True, this checkpoint\(\) is not necessary"
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):
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out = checkpoint(run_fn2, input_var, input_var2)
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out.sum().backward()
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class TestDataLoaderUtils(TestCase):
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def setUp(self):
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self.dataset = torch.randn(5, 3, 3, 2)
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self.batch_size = 3
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def test_random_seed(self):
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def run():
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dataloader = torch.utils.data.DataLoader(RandomDatasetMock(),
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batch_size=2,
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num_workers=4,
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shuffle=True)
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return next(iter(dataloader))
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torch.manual_seed(2018)
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x1 = run()
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torch.manual_seed(2018)
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x2 = run()
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self.assertEqual(x1, x2)
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def test_single_keep(self):
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# self.dataset is a Tensor here; technically not a valid input because
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# not a Dataset subclass, but needs to stay working so add ignore's
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# for type checking with mypy
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dataloader : DataLoader = DataLoader(self.dataset, # type: ignore[arg-type]
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batch_size=self.batch_size,
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num_workers=0,
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drop_last=False)
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dataiter = iter(dataloader)
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self.assertEqual(len(list(dataiter)), 2)
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def test_single_drop(self):
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dataloader : DataLoader = DataLoader(self.dataset, # type: ignore[arg-type]
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batch_size=self.batch_size,
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num_workers=0,
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drop_last=True)
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dataiter = iter(dataloader)
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self.assertEqual(len(list(dataiter)), 1)
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@unittest.skip("FIXME: Intermittent CUDA out-of-memory error on Windows and time-out under ASAN")
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def test_multi_keep(self):
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dataloader : DataLoader = DataLoader(self.dataset, # type: ignore[arg-type]
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batch_size=self.batch_size,
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num_workers=2,
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drop_last=False)
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dataiter = iter(dataloader)
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self.assertEqual(len(list(dataiter)), 2)
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def test_multi_drop(self):
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dataloader : DataLoader = DataLoader(self.dataset, # type: ignore[arg-type]
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batch_size=self.batch_size,
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num_workers=2,
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drop_last=True)
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dataiter = iter(dataloader)
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self.assertEqual(len(list(dataiter)), 1)
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test_dir = os.path.abspath(os.path.dirname(str(__file__)))
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class TestFFI(TestCase):
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def test_deprecated(self):
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with self.assertRaisesRegex(ImportError, "torch.utils.ffi is deprecated. Please use cpp extensions instead."):
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from torch.utils.ffi import create_extension # type: ignore[attr-defined] # noqa: F401
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@unittest.skipIf('SKIP_TEST_BOTTLENECK' in os.environ.keys(), 'SKIP_TEST_BOTTLENECK is set')
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class TestBottleneck(TestCase):
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def _run(self, command, timeout=30):
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"""Returns (return-code, stdout, stderr)"""
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import subprocess
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p = subprocess.Popen(command, stdout=subprocess.PIPE, # noqa: P204
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stderr=subprocess.PIPE, shell=True)
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try:
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output, err = p.communicate(timeout=timeout)
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except subprocess.TimeoutExpired:
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p.kill()
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output, err = p.communicate()
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rc = p.returncode
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output_str = output.decode("ascii")
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err_str = err.decode("ascii")
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return (rc, output_str, err_str)
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def _run_bottleneck(self, test_file, scriptargs=''):
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curdir = os.path.dirname(os.path.abspath(__file__))
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filepath = '{}/{}'.format(curdir, test_file)
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if scriptargs != '':
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scriptargs = ' {}'.format(scriptargs)
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rc, out, err = self._run(
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'{} -m torch.utils.bottleneck {}{}'.format(sys.executable, filepath, scriptargs))
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return rc, out, err
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def _check_run_args(self):
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# Check that this fails due to missing args
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rc, out, err = self._run_bottleneck('bottleneck_test/test_args.py')
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self.assertEqual(rc, 2, atol=0, rtol=0, msg=self._fail_msg('Missing args should error', out + err))
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# This should succeed
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rc, out, err = self._run_bottleneck('bottleneck_test/test_args.py', '--foo foo --bar bar')
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self.assertEqual(rc, 0, atol=0, rtol=0, msg=self._fail_msg('Should pass args to script', out + err))
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def _fail_msg(self, msg, output):
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return '{}, output was:\n{}'.format(msg, output)
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def _check_environment_summary(self, output):
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results = re.search('Environment Summary', output)
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self.assertIsNotNone(results, self._fail_msg('Should have Environment Summary', output))
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# Up to five lines away from the heading, there should be the version number
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results = re.search(r'Environment Summary.*(\n.*){,5}\nPyTorch \d+\.\d+', output)
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self.assertIsNotNone(results, self._fail_msg('Should have PyTorch version', output))
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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)
|
|
|
|
@retry(URLError, tries=3, skip_after_retries=True)
|
|
def test_load_commit_from_forked_repo(self):
|
|
with self.assertRaisesRegex(
|
|
ValueError,
|
|
'If it\'s a commit from a forked repo'):
|
|
model = torch.hub.load('pytorch/vision:4e2c216', 'resnet18', force_reload=True)
|
|
|
|
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))
|
|
|
|
|
|
class TestCrashHandler(TestCase):
|
|
@unittest.skipIf(not HAS_BREAKPAD, "Crash handler lib was not linked in")
|
|
def test_python_exception_writing(self):
|
|
with tempfile.TemporaryDirectory() as temp_dir:
|
|
torch.utils._crash_handler.enable_minidumps(temp_dir)
|
|
torch.utils._crash_handler.enable_minidumps_on_exceptions()
|
|
|
|
files = os.listdir(temp_dir)
|
|
self.assertEqual(len(files), 0)
|
|
|
|
f = io.StringIO()
|
|
with contextlib.redirect_stderr(f):
|
|
try:
|
|
@torch.jit.script
|
|
def x(i: int):
|
|
return i + "2" # type: ignore[operator]
|
|
except RuntimeError as e:
|
|
pass
|
|
|
|
files = os.listdir(temp_dir)
|
|
self.assertEqual(len(files), 1)
|
|
self.assertTrue(files[0].endswith(".dmp"))
|
|
torch.utils._crash_handler.disable_minidumps()
|
|
|
|
|
|
@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()
|