mirror of
https://github.com/zebrajr/pytorch.git
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d9576c9440
Tests on XLA shard not fixed yet but there is an issue here https://github.com/pytorch/xla/issues/7799 Pull Request resolved: https://github.com/pytorch/pytorch/pull/127627 Approved by: https://github.com/albanD ghstack dependencies: #132349
575 lines
22 KiB
Python
575 lines
22 KiB
Python
# Owner(s): ["module: cpp-extensions"]
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import _codecs
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import os
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import shutil
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import sys
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import tempfile
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import types
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import unittest
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from typing import Union
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from unittest.mock import patch
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import numpy as np
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import torch
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import torch.testing._internal.common_utils as common
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import torch.utils.cpp_extension
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from torch.serialization import safe_globals
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from torch.testing._internal.common_utils import (
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IS_ARM64,
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skipIfTorchDynamo,
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TemporaryFileName,
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TEST_CUDA,
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TEST_XPU,
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)
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from torch.utils.cpp_extension import CUDA_HOME, ROCM_HOME
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TEST_CUDA = TEST_CUDA and CUDA_HOME is not None
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TEST_ROCM = TEST_CUDA and torch.version.hip is not None and ROCM_HOME is not None
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def remove_build_path():
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if sys.platform == "win32":
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# Not wiping extensions build folder because Windows
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return
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default_build_root = torch.utils.cpp_extension.get_default_build_root()
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if os.path.exists(default_build_root):
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shutil.rmtree(default_build_root, ignore_errors=True)
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def generate_faked_module():
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def device_count() -> int:
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return 1
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def get_rng_state(device: Union[int, str, torch.device] = "foo") -> torch.Tensor:
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# create a tensor using our custom device object.
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return torch.empty(4, 4, device="foo")
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def set_rng_state(
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new_state: torch.Tensor, device: Union[int, str, torch.device] = "foo"
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) -> None:
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pass
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def is_available():
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return True
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def current_device():
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return 0
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# create a new module to fake torch.foo dynamicaly
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foo = types.ModuleType("foo")
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foo.device_count = device_count
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foo.get_rng_state = get_rng_state
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foo.set_rng_state = set_rng_state
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foo.is_available = is_available
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foo.current_device = current_device
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foo._lazy_init = lambda: None
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foo.is_initialized = lambda: True
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return foo
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@unittest.skipIf(IS_ARM64, "Does not work on arm")
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@unittest.skipIf(TEST_XPU, "XPU does not support cppextension currently")
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@torch.testing._internal.common_utils.markDynamoStrictTest
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class TestCppExtensionOpenRgistration(common.TestCase):
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"""Tests Open Device Registration with C++ extensions."""
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module = None
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def setUp(self):
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super().setUp()
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# cpp extensions use relative paths. Those paths are relative to
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# this file, so we'll change the working directory temporarily
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self.old_working_dir = os.getcwd()
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os.chdir(os.path.dirname(os.path.abspath(__file__)))
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assert self.module is not None
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def tearDown(self):
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super().tearDown()
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# return the working directory (see setUp)
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os.chdir(self.old_working_dir)
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@classmethod
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def setUpClass(cls):
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remove_build_path()
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cls.module = torch.utils.cpp_extension.load(
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name="custom_device_extension",
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sources=[
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"cpp_extensions/open_registration_extension.cpp",
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],
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extra_include_paths=["cpp_extensions"],
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extra_cflags=["-g"],
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verbose=True,
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)
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# register torch.foo module and foo device to torch
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torch.utils.rename_privateuse1_backend("foo")
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torch.utils.generate_methods_for_privateuse1_backend(for_storage=True)
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torch._register_device_module("foo", generate_faked_module())
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def test_base_device_registration(self):
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self.assertFalse(self.module.custom_add_called())
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# create a tensor using our custom device object
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device = self.module.custom_device()
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x = torch.empty(4, 4, device=device)
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y = torch.empty(4, 4, device=device)
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# Check that our device is correct.
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self.assertTrue(x.device == device)
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self.assertFalse(x.is_cpu)
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self.assertFalse(self.module.custom_add_called())
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# calls out custom add kernel, registered to the dispatcher
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z = x + y
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# check that it was called
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self.assertTrue(self.module.custom_add_called())
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z_cpu = z.to(device="cpu")
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# Check that our cross-device copy correctly copied the data to cpu
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self.assertTrue(z_cpu.is_cpu)
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self.assertFalse(z.is_cpu)
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self.assertTrue(z.device == device)
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self.assertEqual(z, z_cpu)
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def test_common_registration(self):
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# check unsupported device and duplicated registration
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with self.assertRaisesRegex(RuntimeError, "Expected one of cpu"):
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torch._register_device_module("dev", generate_faked_module())
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with self.assertRaisesRegex(RuntimeError, "The runtime module of"):
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torch._register_device_module("foo", generate_faked_module())
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# backend name can be renamed to the same name multiple times
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torch.utils.rename_privateuse1_backend("foo")
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# backend name can't be renamed multiple times to different names.
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with self.assertRaisesRegex(
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RuntimeError, "torch.register_privateuse1_backend()"
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):
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torch.utils.rename_privateuse1_backend("dev")
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# generator tensor and module can be registered only once
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with self.assertRaisesRegex(RuntimeError, "The custom device module of"):
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torch.utils.generate_methods_for_privateuse1_backend()
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# check whether torch.foo have been registered correctly
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self.assertTrue(
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torch.utils.backend_registration._get_custom_mod_func("device_count")() == 1
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)
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with self.assertRaisesRegex(RuntimeError, "Try to call torch.foo"):
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torch.utils.backend_registration._get_custom_mod_func("func_name_")
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# check attributes after registered
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self.assertTrue(hasattr(torch.Tensor, "is_foo"))
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self.assertTrue(hasattr(torch.Tensor, "foo"))
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self.assertTrue(hasattr(torch.TypedStorage, "is_foo"))
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self.assertTrue(hasattr(torch.TypedStorage, "foo"))
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self.assertTrue(hasattr(torch.UntypedStorage, "is_foo"))
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self.assertTrue(hasattr(torch.UntypedStorage, "foo"))
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self.assertTrue(hasattr(torch.nn.Module, "foo"))
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self.assertTrue(hasattr(torch.nn.utils.rnn.PackedSequence, "is_foo"))
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self.assertTrue(hasattr(torch.nn.utils.rnn.PackedSequence, "foo"))
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def test_open_device_generator_registration_and_hooks(self):
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device = self.module.custom_device()
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# None of our CPU operations should call the custom add function.
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self.assertFalse(self.module.custom_add_called())
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# check generator registered before using
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with self.assertRaisesRegex(
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RuntimeError,
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"Please register a generator to the PrivateUse1 dispatch key",
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):
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torch.Generator(device=device)
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self.module.register_generator_first()
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gen = torch.Generator(device=device)
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self.assertTrue(gen.device == device)
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# generator can be registered only once
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with self.assertRaisesRegex(
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RuntimeError,
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"Only can register a generator to the PrivateUse1 dispatch key once",
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):
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self.module.register_generator_second()
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if self.module.is_register_hook() is False:
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self.module.register_hook()
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default_gen = self.module.default_generator(0)
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self.assertTrue(
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default_gen.device.type == torch._C._get_privateuse1_backend_name()
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)
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def test_open_device_dispatchstub(self):
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# test kernels could be reused by privateuse1 backend through dispatchstub
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input_data = torch.randn(2, 2, 3, dtype=torch.float32, device="cpu")
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foo_input_data = input_data.to("foo")
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output_data = torch.abs(input_data)
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foo_output_data = torch.abs(foo_input_data)
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self.assertEqual(output_data, foo_output_data.cpu())
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output_data = torch.randn(2, 2, 6, dtype=torch.float32, device="cpu")
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# output operand will resize flag is True in TensorIterator.
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foo_input_data = input_data.to("foo")
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foo_output_data = output_data.to("foo")
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# output operand will resize flag is False in TensorIterator.
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torch.abs(input_data, out=output_data[:, :, 0:6:2])
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torch.abs(foo_input_data, out=foo_output_data[:, :, 0:6:2])
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self.assertEqual(output_data, foo_output_data.cpu())
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# output operand will resize flag is True in TensorIterator.
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# and convert output to contiguous tensor in TensorIterator.
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output_data = torch.randn(2, 2, 6, dtype=torch.float32, device="cpu")
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foo_input_data = input_data.to("foo")
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foo_output_data = output_data.to("foo")
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torch.abs(input_data, out=output_data[:, :, 0:6:3])
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torch.abs(foo_input_data, out=foo_output_data[:, :, 0:6:3])
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self.assertEqual(output_data, foo_output_data.cpu())
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def test_open_device_quantized(self):
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input_data = torch.randn(3, 4, 5, dtype=torch.float32, device="cpu").to("foo")
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quantized_tensor = torch.quantize_per_tensor(input_data, 0.1, 10, torch.qint8)
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self.assertEqual(quantized_tensor.device, torch.device("foo:0"))
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self.assertEqual(quantized_tensor.dtype, torch.qint8)
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def test_open_device_random(self):
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# check if torch.foo have implemented get_rng_state
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with torch.random.fork_rng(device_type="foo"):
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pass
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def test_open_device_tensor(self):
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device = self.module.custom_device()
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# check whether print tensor.type() meets the expectation
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dtypes = {
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torch.bool: "torch.foo.BoolTensor",
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torch.double: "torch.foo.DoubleTensor",
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torch.float32: "torch.foo.FloatTensor",
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torch.half: "torch.foo.HalfTensor",
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torch.int32: "torch.foo.IntTensor",
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torch.int64: "torch.foo.LongTensor",
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torch.int8: "torch.foo.CharTensor",
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torch.short: "torch.foo.ShortTensor",
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torch.uint8: "torch.foo.ByteTensor",
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}
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for tt, dt in dtypes.items():
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test_tensor = torch.empty(4, 4, dtype=tt, device=device)
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self.assertTrue(test_tensor.type() == dt)
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# check whether the attributes and methods of the corresponding custom backend are generated correctly
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x = torch.empty(4, 4)
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self.assertFalse(x.is_foo)
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x = x.foo(torch.device("foo"))
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self.assertFalse(self.module.custom_add_called())
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self.assertTrue(x.is_foo)
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# test different device type input
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y = torch.empty(4, 4)
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self.assertFalse(y.is_foo)
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y = y.foo(torch.device("foo:0"))
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self.assertFalse(self.module.custom_add_called())
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self.assertTrue(y.is_foo)
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# test different device type input
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z = torch.empty(4, 4)
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self.assertFalse(z.is_foo)
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z = z.foo(0)
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self.assertFalse(self.module.custom_add_called())
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self.assertTrue(z.is_foo)
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def test_open_device_packed_sequence(self):
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device = self.module.custom_device()
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a = torch.rand(5, 3)
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b = torch.tensor([1, 1, 1, 1, 1])
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input = torch.nn.utils.rnn.PackedSequence(a, b)
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self.assertFalse(input.is_foo)
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input_foo = input.foo()
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self.assertTrue(input_foo.is_foo)
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def test_open_device_storage(self):
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# check whether the attributes and methods for storage of the corresponding custom backend are generated correctly
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x = torch.empty(4, 4)
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z1 = x.storage()
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self.assertFalse(z1.is_foo)
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z1 = z1.foo()
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self.assertFalse(self.module.custom_add_called())
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self.assertTrue(z1.is_foo)
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with self.assertRaisesRegex(RuntimeError, "Invalid device"):
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z1.foo(torch.device("cpu"))
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z1 = z1.cpu()
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self.assertFalse(self.module.custom_add_called())
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self.assertFalse(z1.is_foo)
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z1 = z1.foo(device="foo:0", non_blocking=False)
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self.assertFalse(self.module.custom_add_called())
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self.assertTrue(z1.is_foo)
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with self.assertRaisesRegex(RuntimeError, "Invalid device"):
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z1.foo(device="cuda:0", non_blocking=False)
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# check UntypedStorage
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y = torch.empty(4, 4)
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z2 = y.untyped_storage()
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self.assertFalse(z2.is_foo)
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z2 = z2.foo()
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self.assertFalse(self.module.custom_add_called())
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self.assertTrue(z2.is_foo)
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# check custom StorageImpl create
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self.module.custom_storage_registry()
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z3 = y.untyped_storage()
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self.assertFalse(self.module.custom_storageImpl_called())
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z3 = z3.foo()
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self.assertTrue(self.module.custom_storageImpl_called())
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self.assertFalse(self.module.custom_storageImpl_called())
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z3 = z3[0:3]
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self.assertTrue(self.module.custom_storageImpl_called())
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@skipIfTorchDynamo("unsupported aten.is_pinned.default")
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def test_open_device_storage_pin_memory(self):
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# Check if the pin_memory is functioning properly on custom device
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cpu_tensor = torch.empty(3)
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self.assertFalse(cpu_tensor.is_foo)
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self.assertFalse(cpu_tensor.is_pinned("foo"))
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cpu_tensor_pin = cpu_tensor.pin_memory("foo")
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self.assertTrue(cpu_tensor_pin.is_pinned("foo"))
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# Test storage pin_memory and is_pin
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cpu_storage = cpu_tensor.storage()
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# We implement a dummy pin_memory of no practical significance
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# for custom device. Once tensor.pin_memory() has been called,
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# then tensor.is_pinned() will always return true no matter
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# what tensor it's called on.
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self.assertTrue(cpu_storage.is_pinned("foo"))
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cpu_storage_pinned = cpu_storage.pin_memory("foo")
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self.assertTrue(cpu_storage_pinned.is_pinned("foo"))
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# Test untyped storage pin_memory and is_pin
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cpu_tensor = torch.randn([3, 2, 1, 4])
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cpu_untyped_storage = cpu_tensor.untyped_storage()
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self.assertTrue(cpu_untyped_storage.is_pinned("foo"))
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cpu_untyped_storage_pinned = cpu_untyped_storage.pin_memory("foo")
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self.assertTrue(cpu_untyped_storage_pinned.is_pinned("foo"))
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@unittest.skip(
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"Temporarily disable due to the tiny differences between clang++ and g++ in defining static variable in inline function"
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)
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def test_open_device_serialization(self):
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self.module.set_custom_device_index(-1)
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storage = torch.UntypedStorage(4, device=torch.device("foo"))
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self.assertEqual(torch.serialization.location_tag(storage), "foo")
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self.module.set_custom_device_index(0)
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storage = torch.UntypedStorage(4, device=torch.device("foo"))
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self.assertEqual(torch.serialization.location_tag(storage), "foo:0")
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cpu_storage = torch.empty(4, 4).storage()
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foo_storage = torch.serialization.default_restore_location(cpu_storage, "foo:0")
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self.assertTrue(foo_storage.is_foo)
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# test tensor MetaData serialization
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x = torch.empty(4, 4).long()
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y = x.foo()
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self.assertFalse(self.module.check_backend_meta(y))
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self.module.custom_set_backend_meta(y)
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self.assertTrue(self.module.check_backend_meta(y))
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self.module.custom_serialization_registry()
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with tempfile.TemporaryDirectory() as tmpdir:
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path = os.path.join(tmpdir, "data.pt")
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torch.save(y, path)
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z1 = torch.load(path)
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# loads correctly onto the foo backend device
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self.assertTrue(z1.is_foo)
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# loads BackendMeta data correctly
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self.assertTrue(self.module.check_backend_meta(z1))
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# cross-backend
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z2 = torch.load(path, map_location="cpu")
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# loads correctly onto the cpu backend device
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self.assertFalse(z2.is_foo)
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# loads BackendMeta data correctly
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self.assertFalse(self.module.check_backend_meta(z2))
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def test_open_device_storage_resize(self):
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cpu_tensor = torch.randn([8])
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foo_tensor = cpu_tensor.foo()
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foo_storage = foo_tensor.storage()
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self.assertTrue(foo_storage.size() == 8)
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# Only register tensor resize_ function.
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foo_tensor.resize_(8)
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self.assertTrue(foo_storage.size() == 8)
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with self.assertRaisesRegex(TypeError, "Overflow"):
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foo_tensor.resize_(8**29)
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def test_open_device_storage_type(self):
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# test cpu float storage
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cpu_tensor = torch.randn([8]).float()
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cpu_storage = cpu_tensor.storage()
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self.assertEqual(cpu_storage.type(), "torch.FloatStorage")
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# test custom float storage before defining FloatStorage
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foo_tensor = cpu_tensor.foo()
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foo_storage = foo_tensor.storage()
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self.assertEqual(foo_storage.type(), "torch.storage.TypedStorage")
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class CustomFloatStorage:
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@property
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def __module__(self):
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return "torch." + torch._C._get_privateuse1_backend_name()
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@property
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def __name__(self):
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return "FloatStorage"
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# test custom float storage after defining FloatStorage
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try:
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torch.foo.FloatStorage = CustomFloatStorage()
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self.assertEqual(foo_storage.type(), "torch.foo.FloatStorage")
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# test custom int storage after defining FloatStorage
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foo_tensor2 = torch.randn([8]).int().foo()
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foo_storage2 = foo_tensor2.storage()
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self.assertEqual(foo_storage2.type(), "torch.storage.TypedStorage")
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finally:
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torch.foo.FloatStorage = None
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def test_open_device_faketensor(self):
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with torch._subclasses.fake_tensor.FakeTensorMode.push():
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a = torch.empty(1, device="foo")
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b = torch.empty(1, device="foo:0")
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result = a + b
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def test_open_device_named_tensor(self):
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torch.empty([2, 3, 4, 5], device="foo", names=["N", "C", "H", "W"])
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|
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# Not an open registration test - this file is just very convenient
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|
# for testing torch.compile on custom C++ operators
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def test_compile_autograd_function_returns_self(self):
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x_ref = torch.randn(4, requires_grad=True)
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out_ref = self.module.custom_autograd_fn_returns_self(x_ref)
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out_ref.sum().backward()
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|
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x_test = x_ref.clone().detach().requires_grad_(True)
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f_compiled = torch.compile(self.module.custom_autograd_fn_returns_self)
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out_test = f_compiled(x_test)
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out_test.sum().backward()
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|
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self.assertEqual(out_ref, out_test)
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self.assertEqual(x_ref.grad, x_test.grad)
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|
|
|
# Not an open registration test - this file is just very convenient
|
|
# for testing torch.compile on custom C++ operators
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|
@skipIfTorchDynamo("Temporary disabled due to torch._ops.OpOverloadPacket")
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|
def test_compile_autograd_function_aliasing(self):
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x_ref = torch.randn(4, requires_grad=True)
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out_ref = torch.ops._test_funcs.custom_autograd_fn_aliasing(x_ref)
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|
out_ref.sum().backward()
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|
|
|
x_test = x_ref.clone().detach().requires_grad_(True)
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|
f_compiled = torch.compile(torch.ops._test_funcs.custom_autograd_fn_aliasing)
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|
out_test = f_compiled(x_test)
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|
out_test.sum().backward()
|
|
|
|
self.assertEqual(out_ref, out_test)
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|
self.assertEqual(x_ref.grad, x_test.grad)
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|
|
|
def test_open_device_scalar_type_fallback(self):
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z_cpu = torch.Tensor([[0, 0, 0, 1, 1, 2], [0, 1, 2, 1, 2, 2]]).to(torch.int64)
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|
z = torch.triu_indices(3, 3, device="foo")
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|
self.assertEqual(z_cpu, z)
|
|
|
|
def test_open_device_tensor_type_fallback(self):
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|
# create tensors located in custom device
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|
x = torch.Tensor([[1, 2, 3], [2, 3, 4]]).to("foo")
|
|
y = torch.Tensor([1, 0, 2]).to("foo")
|
|
# create result tensor located in cpu
|
|
z_cpu = torch.Tensor([[0, 2, 1], [1, 3, 2]])
|
|
# Check that our device is correct.
|
|
device = self.module.custom_device()
|
|
self.assertTrue(x.device == device)
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|
self.assertFalse(x.is_cpu)
|
|
|
|
# call sub op, which will fallback to cpu
|
|
z = torch.sub(x, y)
|
|
self.assertEqual(z_cpu, z)
|
|
|
|
# call index op, which will fallback to cpu
|
|
z_cpu = torch.Tensor([3, 1])
|
|
y = torch.Tensor([1, 0]).long().to("foo")
|
|
z = x[y, y]
|
|
self.assertEqual(z_cpu, z)
|
|
|
|
def test_open_device_tensorlist_type_fallback(self):
|
|
# create tensors located in custom device
|
|
v_foo = torch.Tensor([1, 2, 3]).to("foo")
|
|
# create result tensor located in cpu
|
|
z_cpu = torch.Tensor([2, 4, 6])
|
|
# create tensorlist for foreach_add op
|
|
x = (v_foo, v_foo)
|
|
y = (v_foo, v_foo)
|
|
# Check that our device is correct.
|
|
device = self.module.custom_device()
|
|
self.assertTrue(v_foo.device == device)
|
|
self.assertFalse(v_foo.is_cpu)
|
|
|
|
# call _foreach_add op, which will fallback to cpu
|
|
z = torch._foreach_add(x, y)
|
|
self.assertEqual(z_cpu, z[0])
|
|
self.assertEqual(z_cpu, z[1])
|
|
|
|
# call _fused_adamw_ with undefined tensor.
|
|
self.module.fallback_with_undefined_tensor()
|
|
|
|
def test_open_device_numpy_serialization_map_location(self):
|
|
torch.utils.rename_privateuse1_backend("foo")
|
|
device = self.module.custom_device()
|
|
default_protocol = torch.serialization.DEFAULT_PROTOCOL
|
|
# This is a hack to test serialization through numpy
|
|
with patch.object(torch._C, "_has_storage", return_value=False):
|
|
x = torch.randn(2, 3)
|
|
x_foo = x.to(device)
|
|
sd = {"x": x_foo}
|
|
rebuild_func = x_foo._reduce_ex_internal(default_protocol)[0]
|
|
self.assertTrue(
|
|
rebuild_func is torch._utils._rebuild_device_tensor_from_numpy
|
|
)
|
|
with TemporaryFileName() as f:
|
|
torch.save(sd, f)
|
|
with safe_globals(
|
|
[
|
|
np.core.multiarray._reconstruct,
|
|
np.ndarray,
|
|
np.dtype,
|
|
_codecs.encode,
|
|
type(np.dtype(np.float32))
|
|
if np.__version__ < "1.25.0"
|
|
else np.dtypes.Float32DType,
|
|
]
|
|
):
|
|
sd_loaded = torch.load(f, map_location="cpu")
|
|
self.assertTrue(sd_loaded["x"].is_cpu)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
common.run_tests()
|