mirror of
https://github.com/zebrajr/pytorch.git
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e4fd0bf771
As the title stated. **Involved testcases**: - test_open_device_storage_pin_memory - test_open_device_serialization Pull Request resolved: https://github.com/pytorch/pytorch/pull/155101 Approved by: https://github.com/albanD ghstack dependencies: #153947, #154018, #154019, #154106, #154181
315 lines
14 KiB
Python
315 lines
14 KiB
Python
# Owner(s): ["module: cpp-extensions"]
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import _codecs
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import io
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import os
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import unittest
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from unittest.mock import patch
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import numpy as np
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import pytorch_openreg # noqa: F401
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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 TemporaryFileName
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@unittest.skipIf(common.TEST_XPU, "XPU does not support cppextension currently")
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@common.markDynamoStrictTest
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class TestCppExtensionOpenRegistration(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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common.remove_cpp_extensions_build_root()
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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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def test_open_device_storage_resize(self):
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cpu_tensor = torch.randn([8])
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openreg_tensor = cpu_tensor.openreg()
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openreg_storage = openreg_tensor.storage()
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self.assertTrue(openreg_storage.size() == 8)
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# Only register tensor resize_ function.
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openreg_tensor.resize_(8)
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self.assertTrue(openreg_storage.size() == 8)
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with self.assertRaisesRegex(TypeError, "Overflow"):
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openreg_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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openreg_tensor = cpu_tensor.openreg()
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openreg_storage = openreg_tensor.storage()
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self.assertEqual(openreg_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.openreg.FloatStorage = CustomFloatStorage()
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self.assertEqual(openreg_storage.type(), "torch.openreg.FloatStorage")
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# test custom int storage after defining FloatStorage
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openreg_tensor2 = torch.randn([8]).int().openreg()
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openreg_storage2 = openreg_tensor2.storage()
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self.assertEqual(openreg_storage2.type(), "torch.storage.TypedStorage")
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finally:
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torch.openreg.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="openreg")
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b = torch.empty(1, device="openreg:0")
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result = a + b # noqa: F841
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def test_open_device_named_tensor(self):
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torch.empty([2, 3, 4, 5], device="openreg", names=["N", "C", "H", "W"])
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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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x_test = x_ref.detach().clone().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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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
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# for testing torch.compile on custom C++ operators
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@common.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.detach().clone().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()
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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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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="openreg")
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self.assertEqual(z_cpu, z)
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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("openreg")
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y = torch.Tensor([1, 0, 2]).to("openreg")
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# create result tensor located in cpu
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z_cpu = torch.Tensor([[0, 2, 1], [1, 3, 2]])
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# Check that our device is correct.
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device = self.module.custom_device()
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self.assertTrue(x.device == device)
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self.assertFalse(x.is_cpu)
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# call sub op, which will fallback to cpu
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z = torch.sub(x, y)
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self.assertEqual(z_cpu, z)
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# call index op, which will fallback to cpu
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z_cpu = torch.Tensor([3, 1])
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y = torch.Tensor([1, 0]).long().to("openreg")
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z = x[y, y]
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self.assertEqual(z_cpu, z)
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def test_open_device_tensorlist_type_fallback(self):
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# create tensors located in custom device
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v_openreg = torch.Tensor([1, 2, 3]).to("openreg")
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# create result tensor located in cpu
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z_cpu = torch.Tensor([2, 4, 6])
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# create tensorlist for foreach_add op
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x = (v_openreg, v_openreg)
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y = (v_openreg, v_openreg)
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# Check that our device is correct.
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device = self.module.custom_device()
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self.assertTrue(v_openreg.device == device)
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self.assertFalse(v_openreg.is_cpu)
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# call _foreach_add op, which will fallback to cpu
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z = torch._foreach_add(x, y)
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self.assertEqual(z_cpu, z[0])
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self.assertEqual(z_cpu, z[1])
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# call _fused_adamw_ with undefined tensor.
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self.module.fallback_with_undefined_tensor()
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@common.skipIfTorchDynamo()
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@unittest.skipIf(
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np.__version__ < "1.25",
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"versions < 1.25 serialize dtypes differently from how it's serialized in data_legacy_numpy",
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)
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def test_open_device_numpy_serialization(self):
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"""
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This tests the legacy _rebuild_device_tensor_from_numpy serialization path
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"""
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device = self.module.custom_device()
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# Legacy data saved with _rebuild_device_tensor_from_numpy on f80ed0b8 via
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# with patch.object(torch._C, "_has_storage", return_value=False):
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# x = torch.tensor([[1, 2, 3], [4, 5, 6]], dtype=torch.float32, device=device)
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# x_foo = x.to(device)
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# sd = {"x": x_foo}
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# rebuild_func = x_foo._reduce_ex_internal(default_protocol)[0]
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# self.assertTrue(
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# rebuild_func is torch._utils._rebuild_device_tensor_from_numpy
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# )
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# with open("foo.pt", "wb") as f:
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# torch.save(sd, f)
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data_legacy_numpy = (
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b"PK\x03\x04\x00\x00\x08\x08\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00"
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b"\x00\x00\x00\x10\x00\x12\x00archive/data.pklFB\x0e\x00ZZZZZZZZZZZZZZ\x80\x02}q\x00X\x01"
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b"\x00\x00\x00xq\x01ctorch._utils\n_rebuild_device_tensor_from_numpy\nq\x02(cnumpy.core.m"
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b"ultiarray\n_reconstruct\nq\x03cnumpy\nndarray\nq\x04K\x00\x85q\x05c_codecs\nencode\nq\x06"
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b"X\x01\x00\x00\x00bq\x07X\x06\x00\x00\x00latin1q\x08\x86q\tRq\n\x87q\x0bRq\x0c(K\x01K\x02K"
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b"\x03\x86q\rcnumpy\ndtype\nq\x0eX\x02\x00\x00\x00f4q\x0f\x89\x88\x87q\x10Rq\x11(K\x03X\x01"
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b"\x00\x00\x00<q\x12NNNJ\xff\xff\xff\xffJ\xff\xff\xff\xffK\x00tq\x13b\x89h\x06X\x1c\x00\x00"
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b"\x00\x00\x00\xc2\x80?\x00\x00\x00@\x00\x00@@\x00\x00\xc2\x80@\x00\x00\xc2\xa0@\x00\x00\xc3"
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b"\x80@q\x14h\x08\x86q\x15Rq\x16tq\x17bctorch\nfloat32\nq\x18X\t\x00\x00\x00openreg:0q\x19\x89"
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b"tq\x1aRq\x1bs.PK\x07\x08\xdfE\xd6\xcaS\x01\x00\x00S\x01\x00\x00PK\x03\x04\x00\x00\x08"
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b"\x08\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x11\x00.\x00"
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b"archive/byteorderFB*\x00ZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZlittlePK\x07\x08"
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b"\x85=\xe3\x19\x06\x00\x00\x00\x06\x00\x00\x00PK\x03\x04\x00\x00\x08\x08\x00\x00\x00\x00"
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b"\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x0f\x00=\x00archive/versionFB9\x00"
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b"ZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZ3\nPK\x07\x08\xd1\x9egU\x02\x00\x00"
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b"\x00\x02\x00\x00\x00PK\x03\x04\x00\x00\x08\x08\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00"
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b"\x00\x00\x00\x00\x00\x00\x00\x1e\x002\x00archive/.data/serialization_idFB.\x00ZZZZZZZZZZZZZ"
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b"ZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZ0636457737946401051300000025273995036293PK\x07\x08\xee(\xcd"
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b"\x8d(\x00\x00\x00(\x00\x00\x00PK\x01\x02\x00\x00\x00\x00\x08\x08\x00\x00\x00\x00\x00\x00"
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b"\xdfE\xd6\xcaS\x01\x00\x00S\x01\x00\x00\x10\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00"
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b"\x00\x00\x00\x00\x00\x00archive/data.pklPK\x01\x02\x00\x00\x00\x00\x08\x08\x00\x00\x00\x00"
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b"\x00\x00\x85=\xe3\x19\x06\x00\x00\x00\x06\x00\x00\x00\x11\x00\x00\x00\x00\x00\x00\x00\x00"
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b"\x00\x00\x00\x00\x00\xa3\x01\x00\x00archive/byteorderPK\x01\x02\x00\x00\x00\x00\x08\x08\x00"
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b"\x00\x00\x00\x00\x00\xd1\x9egU\x02\x00\x00\x00\x02\x00\x00\x00\x0f\x00\x00\x00\x00\x00\x00"
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b"\x00\x00\x00\x00\x00\x00\x00\x16\x02\x00\x00archive/versionPK\x01\x02\x00\x00\x00\x00\x08"
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b"\x08\x00\x00\x00\x00\x00\x00\xee(\xcd\x8d(\x00\x00\x00(\x00\x00\x00\x1e\x00\x00\x00\x00"
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b"\x00\x00\x00\x00\x00\x00\x00\x00\x00\x92\x02\x00\x00archive/.data/serialization_idPK\x06"
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b"\x06,\x00\x00\x00\x00\x00\x00\x00\x1e\x03-\x00\x00\x00\x00\x00\x00\x00\x00\x00\x04\x00\x00"
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b"\x00\x00\x00\x00\x00\x04\x00\x00\x00\x00\x00\x00\x00\x06\x01\x00\x00\x00\x00\x00\x008\x03\x00"
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b"\x00\x00\x00\x00\x00PK\x06\x07\x00\x00\x00\x00>\x04\x00\x00\x00\x00\x00\x00\x01\x00\x00\x00"
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b"PK\x05\x06\x00\x00\x00\x00\x04\x00\x04\x00\x06\x01\x00\x008\x03\x00\x00\x00\x00"
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)
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buf_data_legacy_numpy = io.BytesIO(data_legacy_numpy)
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with safe_globals(
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[
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(np.core.multiarray._reconstruct, "numpy.core.multiarray._reconstruct")
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if np.__version__ >= "2.1"
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else np.core.multiarray._reconstruct,
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np.ndarray,
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np.dtype,
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_codecs.encode,
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np.dtypes.Float32DType,
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]
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):
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sd_loaded = torch.load(buf_data_legacy_numpy, weights_only=True)
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buf_data_legacy_numpy.seek(0)
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# Test map_location
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sd_loaded_cpu = torch.load(
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buf_data_legacy_numpy, weights_only=True, map_location="cpu"
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)
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expected = torch.tensor(
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[[1, 2, 3], [4, 5, 6]], dtype=torch.float32, device=device
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)
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self.assertEqual(sd_loaded["x"].cpu(), expected.cpu())
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self.assertFalse(sd_loaded["x"].is_cpu)
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self.assertTrue(sd_loaded_cpu["x"].is_cpu)
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def test_open_device_cpu_serialization(self):
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torch.utils.rename_privateuse1_backend("openreg")
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device = self.module.custom_device()
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default_protocol = torch.serialization.DEFAULT_PROTOCOL
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with patch.object(torch._C, "_has_storage", return_value=False):
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x = torch.randn(2, 3)
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x_openreg = x.to(device)
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sd = {"x": x_openreg}
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rebuild_func = x_openreg._reduce_ex_internal(default_protocol)[0]
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self.assertTrue(
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rebuild_func is torch._utils._rebuild_device_tensor_from_cpu_tensor
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)
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# Test map_location
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with TemporaryFileName() as f:
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torch.save(sd, f)
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sd_loaded = torch.load(f, weights_only=True)
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# Test map_location
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sd_loaded_cpu = torch.load(f, weights_only=True, map_location="cpu")
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self.assertFalse(sd_loaded["x"].is_cpu)
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self.assertEqual(sd_loaded["x"].cpu(), x)
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self.assertTrue(sd_loaded_cpu["x"].is_cpu)
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# Test metadata_only
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with TemporaryFileName() as f:
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with self.assertRaisesRegex(
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RuntimeError,
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"Cannot serialize tensors on backends with no storage under skip_data context manager",
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):
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with torch.serialization.skip_data():
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torch.save(sd, f)
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def test_open_device_dlpack(self):
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t = torch.randn(2, 3).to("openreg")
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capsule = torch.utils.dlpack.to_dlpack(t)
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t1 = torch.from_dlpack(capsule)
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self.assertTrue(t1.device == t.device)
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t = t.to("cpu")
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t1 = t1.to("cpu")
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self.assertEqual(t, t1)
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if __name__ == "__main__":
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common.run_tests()
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