mirror of
https://github.com/pytorch/pytorch.git
synced 2025-10-20 21:14:14 +08:00
4261e26a8b
---- - move fallback tests into test_operneg - remove the test_cpp_extensions_open_device_registration.py Pull Request resolved: https://github.com/pytorch/pytorch/pull/158441 Approved by: https://github.com/albanD ghstack dependencies: #158415, #158440
627 lines
26 KiB
Python
627 lines
26 KiB
Python
# Owner(s): ["module: PrivateUse1"]
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import _codecs
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import io
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import os
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import tempfile
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import types
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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 psutil
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import torch_openreg # noqa: F401
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import torch
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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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run_tests,
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skipIfTorchDynamo,
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skipIfXpu,
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TemporaryFileName,
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TestCase,
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)
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class TestPrivateUse1(TestCase):
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"""Tests of third-parth device integration mechinasm based PrivateUse1"""
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def test_backend_name(self):
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self.assertEqual(torch._C._get_privateuse1_backend_name(), "openreg")
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# backend can be renamed to the same name multiple times
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torch.utils.rename_privateuse1_backend("openreg")
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with self.assertRaisesRegex(RuntimeError, "has already been set"): # type: ignore[misc]
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torch.utils.rename_privateuse1_backend("dev")
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def test_backend_module_registration(self):
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def generate_faked_module():
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return types.ModuleType("fake_module")
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with self.assertRaisesRegex(RuntimeError, "Expected one of cpu"): # type: ignore[misc]
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torch._register_device_module("dev", generate_faked_module())
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with self.assertRaisesRegex(RuntimeError, "The runtime module of"): # type: ignore[misc]
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torch._register_device_module("openreg", generate_faked_module())
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def test_backend_generate_methods(self):
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with self.assertRaisesRegex(RuntimeError, "The custom device module of"): # type: ignore[misc]
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torch.utils.generate_methods_for_privateuse1_backend() # type: ignore[misc]
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self.assertTrue(hasattr(torch.Tensor, "is_openreg"))
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self.assertTrue(hasattr(torch.Tensor, "openreg"))
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self.assertTrue(hasattr(torch.TypedStorage, "is_openreg"))
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self.assertTrue(hasattr(torch.TypedStorage, "openreg"))
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self.assertTrue(hasattr(torch.UntypedStorage, "is_openreg"))
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self.assertTrue(hasattr(torch.UntypedStorage, "openreg"))
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self.assertTrue(hasattr(torch.nn.Module, "openreg"))
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self.assertTrue(hasattr(torch.nn.utils.rnn.PackedSequence, "is_openreg"))
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self.assertTrue(hasattr(torch.nn.utils.rnn.PackedSequence, "openreg"))
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def test_backend_module_function(self):
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with self.assertRaisesRegex(RuntimeError, "Try to call torch.openreg"): # type: ignore[misc]
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torch.utils.backend_registration._get_custom_mod_func("func_name_") # type: ignore[misc]
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self.assertTrue(
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torch.utils.backend_registration._get_custom_mod_func("device_count")() == 2 # type: ignore[misc]
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)
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@skipIfTorchDynamo()
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def test_backend_operator_registration(self):
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self.assertTrue(
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torch._C._dispatch_has_kernel_for_dispatch_key(
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"aten::empty.memory_format", torch.DispatchKey.PrivateUse1
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)
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)
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x = torch.empty(3, 3, device="openreg")
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self.assertTrue(x.device.type, "openreg")
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self.assertTrue(x.shape, torch.Size([3, 3]))
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def test_backend_dispatchstub(self):
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x_cpu = torch.randn(2, 2, 3, dtype=torch.float32, device="cpu")
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x_openreg = x_cpu.to("openreg")
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y_cpu = torch.abs(x_cpu)
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y_openreg = torch.abs(x_openreg)
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self.assertEqual(y_cpu, y_openreg.cpu())
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o_cpu = torch.randn(2, 2, 6, dtype=torch.float32, device="cpu")
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o_openreg = o_cpu.to("openreg")
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# output operand with resize flag is False in TensorIterator.
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torch.abs(x_cpu, out=o_cpu[:, :, 0:6:2])
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torch.abs(x_openreg, out=o_openreg[:, :, 0:6:2])
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self.assertEqual(o_cpu, o_openreg.cpu())
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# output operand with resize flag is True in TensorIterator and
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# convert output to contiguous tensor in TensorIterator.
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torch.abs(x_cpu, out=o_cpu[:, :, 0:6:3])
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torch.abs(x_openreg, out=o_openreg[:, :, 0:6:3])
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self.assertEqual(o_cpu, o_openreg.cpu())
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def test_backend_tensor_type(self):
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dtypes_map = {
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torch.bool: "torch.openreg.BoolTensor",
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torch.double: "torch.openreg.DoubleTensor",
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torch.float32: "torch.openreg.FloatTensor",
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torch.half: "torch.openreg.HalfTensor",
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torch.int32: "torch.openreg.IntTensor",
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torch.int64: "torch.openreg.LongTensor",
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torch.int8: "torch.openreg.CharTensor",
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torch.short: "torch.openreg.ShortTensor",
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torch.uint8: "torch.openreg.ByteTensor",
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}
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for dtype, str in dtypes_map.items():
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x = torch.empty(4, 4, dtype=dtype, device="openreg")
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self.assertTrue(x.type() == str)
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# Note that all dtype-d Tensor objects here are only for legacy reasons
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# and should NOT be used.
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def test_backend_type_methods(self):
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# Tensor
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tensor_cpu = torch.randn([8]).float()
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self.assertEqual(tensor_cpu.type(), "torch.FloatTensor")
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tensor_openreg = tensor_cpu.openreg()
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self.assertEqual(tensor_openreg.type(), "torch.openreg.FloatTensor")
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# Storage
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storage_cpu = tensor_cpu.storage()
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self.assertEqual(storage_cpu.type(), "torch.FloatStorage")
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tensor_openreg = tensor_cpu.openreg()
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storage_openreg = tensor_openreg.storage()
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self.assertEqual(storage_openreg.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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try:
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torch.openreg.FloatStorage = CustomFloatStorage()
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self.assertEqual(storage_openreg.type(), "torch.openreg.FloatStorage")
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# test custom int storage after defining FloatStorage
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tensor_openreg = tensor_cpu.int().openreg()
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storage_openreg = tensor_openreg.storage()
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self.assertEqual(storage_openreg.type(), "torch.storage.TypedStorage")
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finally:
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torch.openreg.FloatStorage = None
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def test_backend_tensor_methods(self):
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x = torch.empty(4, 4)
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self.assertFalse(x.is_openreg) # type: ignore[misc]
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y = x.openreg(torch.device("openreg")) # type: ignore[misc]
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self.assertTrue(y.is_openreg) # type: ignore[misc]
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z = x.openreg(torch.device("openreg:0")) # type: ignore[misc]
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self.assertTrue(z.is_openreg) # type: ignore[misc]
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n = x.openreg(0) # type: ignore[misc]
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self.assertTrue(n.is_openreg) # type: ignore[misc]
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@unittest.skip("Need to support Parameter in openreg")
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def test_backend_module_methods(self):
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class FakeModule(torch.nn.Module):
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def __init__(self):
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super().__init__()
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self.x = torch.nn.Parameter(torch.randn(3, 3))
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def forward(self):
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pass
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module = FakeModule()
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self.assertEqual(module.x.device.type, "cpu")
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module.openreg() # type: ignore[misc]
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self.assertEqual(module.x.device.type, "openreg")
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@unittest.skip("Need to support untyped_storage in openreg")
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def test_backend_storage_methods(self):
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x = torch.empty(4, 4)
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x_cpu = x.storage()
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self.assertFalse(x_cpu.is_openreg) # type: ignore[misc]
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x_openreg = x_cpu.openreg() # type: ignore[misc]
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self.assertTrue(x_openreg.is_openreg) # type: ignore[misc]
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y = torch.empty(4, 4)
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y_cpu = y.untyped_storage()
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self.assertFalse(y_cpu.is_openreg) # type: ignore[misc]
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y_openreg = y_cpu.openreg() # type: ignore[misc]
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self.assertTrue(y_openreg.is_openreg) # type: ignore[misc]
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def test_backend_packed_sequence_methods(self):
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x = torch.rand(5, 3)
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y = torch.tensor([1, 1, 1, 1, 1])
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z_cpu = torch.nn.utils.rnn.PackedSequence(x, y)
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self.assertFalse(z_cpu.is_openreg) # type: ignore[misc]
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z_openreg = z_cpu.openreg() # type: ignore[misc]
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self.assertTrue(z_openreg.is_openreg) # type: ignore[misc]
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class TestOpenReg(TestCase):
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"""Tests of mimic accelerator named OpenReg based on PrivateUse1"""
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# Stream & Event
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def test_stream_synchronize(self):
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stream = torch.Stream(device="openreg:1")
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stream.synchronize()
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self.assertEqual(True, stream.query())
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def test_stream_wait_stream(self):
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stream_1 = torch.Stream(device="openreg:0")
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stream_2 = torch.Stream(device="openreg:1")
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# Does not crash!
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stream_2.wait_stream(stream_1)
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@skipIfTorchDynamo()
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def test_record_event(self):
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stream = torch.Stream(device="openreg:1")
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event1 = stream.record_event()
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self.assertNotEqual(0, event1.event_id)
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event2 = stream.record_event()
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self.assertNotEqual(0, event2.event_id)
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self.assertNotEqual(event1.event_id, event2.event_id)
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@skipIfTorchDynamo()
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def test_event_elapsed_time(self):
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stream = torch.Stream(device="openreg:1")
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e1 = torch.Event(device="openreg:1", enable_timing=True)
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e1.record(stream)
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e2 = torch.Event(device="openreg:1", enable_timing=True)
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e2.record(stream)
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e2.synchronize()
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self.assertTrue(e2.query())
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ms = e1.elapsed_time(e2)
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self.assertTrue(ms > 0)
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@skipIfTorchDynamo()
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def test_stream_wait_event(self):
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s1 = torch.Stream(device="openreg")
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s2 = torch.Stream(device="openreg")
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e = s1.record_event()
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s2.wait_event(e)
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@skipIfTorchDynamo()
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def test_event_wait_stream(self):
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s1 = torch.Stream(device="openreg")
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s2 = torch.Stream(device="openreg")
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e1 = s1.record_event()
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e1.wait(s2)
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# Copy
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def test_cross_device_copy(self):
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a = torch.rand(10)
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b = a.to(device="openreg").add(2).to(device="cpu")
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self.assertEqual(b, a + 2)
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def test_copy_same_device(self):
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a = torch.ones(10, device="openreg").clone()
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self.assertEqual(a, torch.ones(10, device="openreg"))
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def test_cross_diff_devices_copy(self):
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a = torch.ones(10, device="openreg:0").to(device="openreg:1").to(device="cpu")
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self.assertEqual(a, torch.ones(10))
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# RNG
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def test_generator(self):
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generator = torch.Generator(device="openreg:1")
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self.assertEqual(generator.device.type, "openreg")
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self.assertEqual(generator.device.index, 1)
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def test_rng_state(self):
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state = torch.openreg.get_rng_state(0) # type: ignore[misc]
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torch.openreg.set_rng_state(state, 0) # type: ignore[misc]
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def test_manual_seed(self):
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torch.openreg.manual_seed_all(2024) # type: ignore[misc]
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self.assertEqual(torch.openreg.initial_seed(), 2024) # type: ignore[misc]
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# Autograd
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def test_autograd_init(self):
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# Make sure autograd is initialized
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torch.ones(2, requires_grad=True, device="openreg").sum().backward()
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pid = os.getpid()
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task_path = f"/proc/{pid}/task"
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all_threads = psutil.Process(pid).threads()
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all_thread_names = set()
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for t in all_threads:
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with open(f"{task_path}/{t.id}/comm") as file:
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thread_name = file.read().strip()
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all_thread_names.add(thread_name)
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for i in range(torch.accelerator.device_count()):
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self.assertIn(f"pt_autograd_{i}", all_thread_names)
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# Storage & Pin Memory
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@skipIfTorchDynamo("unsupported aten.is_pinned.default")
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def test_pin_memory(self):
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tensor = torch.randn(10)
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self.assertFalse(tensor.is_pinned())
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pinned_tensor = tensor.pin_memory()
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self.assertTrue(pinned_tensor.is_pinned())
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slice_tensor = pinned_tensor[2:5]
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self.assertTrue(slice_tensor.is_pinned())
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tensor = torch.randn(10)
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storage = tensor.storage()
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self.assertFalse(storage.is_pinned("openreg"))
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pinned_storage = storage.pin_memory("openreg")
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self.assertTrue(pinned_storage.is_pinned("openreg"))
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tensor = torch.randn(10)
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untyped_storage = tensor.untyped_storage()
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self.assertFalse(untyped_storage.is_pinned("openreg"))
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pinned_untyped_storage = untyped_storage.pin_memory("openreg")
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self.assertTrue(pinned_untyped_storage.is_pinned("openreg"))
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@skipIfTorchDynamo("unsupported aten.is_pinned.default")
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def test_rewrapped_storage(self):
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pinned_a = torch.randn(10).pin_memory()
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rewrapped_a = torch.tensor((), dtype=torch.float32).set_(
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pinned_a.untyped_storage()[2:],
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size=(5,),
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stride=(1,),
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storage_offset=0,
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)
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self.assertTrue(rewrapped_a.is_pinned())
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self.assertNotEqual(pinned_a.data_ptr(), rewrapped_a.data_ptr())
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# Serialization
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def test_serialization(self):
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storage = torch.UntypedStorage(4, device=torch.device("openreg"))
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self.assertEqual(torch.serialization.location_tag(storage), "openreg:0")
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storage = torch.UntypedStorage(4, device=torch.device("openreg:0"))
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self.assertEqual(torch.serialization.location_tag(storage), "openreg:0")
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storage_cpu = torch.empty(4, 4).storage()
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storage_openreg = torch.serialization.default_restore_location(
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storage_cpu, "openreg:0"
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)
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self.assertTrue(storage_openreg.is_openreg) # type: ignore[misc]
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tensor = torch.empty(3, 3, device="openreg")
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self.assertEqual(torch._utils.get_tensor_metadata(tensor), {}) # type: ignore[misc]
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metadata = {"version_number": True, "format_number": True}
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torch._utils.set_tensor_metadata(tensor, metadata) # type: ignore[misc]
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self.assertEqual(torch._utils.get_tensor_metadata(tensor), metadata) # type: ignore[misc]
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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(tensor, path)
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tensor_openreg = torch.load(path)
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self.assertTrue(tensor_openreg.is_openreg)
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self.assertEqual(torch._utils.get_tensor_metadata(tensor_openreg), metadata) # type: ignore[misc]
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tensor_cpu = torch.load(path, map_location="cpu")
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self.assertFalse(tensor_cpu.is_openreg)
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self.assertEqual(torch._utils.get_tensor_metadata(tensor_cpu), {}) # type: ignore[misc]
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@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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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"
|
|
b"\x00\x02\x00\x00\x00PK\x03\x04\x00\x00\x08\x08\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00"
|
|
b"\x00\x00\x00\x00\x00\x00\x00\x1e\x002\x00archive/.data/serialization_idFB.\x00ZZZZZZZZZZZZZ"
|
|
b"ZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZ0636457737946401051300000025273995036293PK\x07\x08\xee(\xcd"
|
|
b"\x8d(\x00\x00\x00(\x00\x00\x00PK\x01\x02\x00\x00\x00\x00\x08\x08\x00\x00\x00\x00\x00\x00"
|
|
b"\xdfE\xd6\xcaS\x01\x00\x00S\x01\x00\x00\x10\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00"
|
|
b"\x00\x00\x00\x00\x00\x00archive/data.pklPK\x01\x02\x00\x00\x00\x00\x08\x08\x00\x00\x00\x00"
|
|
b"\x00\x00\x85=\xe3\x19\x06\x00\x00\x00\x06\x00\x00\x00\x11\x00\x00\x00\x00\x00\x00\x00\x00"
|
|
b"\x00\x00\x00\x00\x00\xa3\x01\x00\x00archive/byteorderPK\x01\x02\x00\x00\x00\x00\x08\x08\x00"
|
|
b"\x00\x00\x00\x00\x00\xd1\x9egU\x02\x00\x00\x00\x02\x00\x00\x00\x0f\x00\x00\x00\x00\x00\x00"
|
|
b"\x00\x00\x00\x00\x00\x00\x00\x16\x02\x00\x00archive/versionPK\x01\x02\x00\x00\x00\x00\x08"
|
|
b"\x08\x00\x00\x00\x00\x00\x00\xee(\xcd\x8d(\x00\x00\x00(\x00\x00\x00\x1e\x00\x00\x00\x00"
|
|
b"\x00\x00\x00\x00\x00\x00\x00\x00\x00\x92\x02\x00\x00archive/.data/serialization_idPK\x06"
|
|
b"\x06,\x00\x00\x00\x00\x00\x00\x00\x1e\x03-\x00\x00\x00\x00\x00\x00\x00\x00\x00\x04\x00\x00"
|
|
b"\x00\x00\x00\x00\x00\x04\x00\x00\x00\x00\x00\x00\x00\x06\x01\x00\x00\x00\x00\x00\x008\x03\x00"
|
|
b"\x00\x00\x00\x00\x00PK\x06\x07\x00\x00\x00\x00>\x04\x00\x00\x00\x00\x00\x00\x01\x00\x00\x00"
|
|
b"PK\x05\x06\x00\x00\x00\x00\x04\x00\x04\x00\x06\x01\x00\x008\x03\x00\x00\x00\x00"
|
|
)
|
|
buf_data_legacy_numpy = io.BytesIO(data_legacy_numpy)
|
|
|
|
with safe_globals(
|
|
[
|
|
(
|
|
(
|
|
np.core.multiarray._reconstruct,
|
|
"numpy.core.multiarray._reconstruct",
|
|
)
|
|
if np.__version__ >= "2.1"
|
|
else np.core.multiarray._reconstruct
|
|
),
|
|
np.ndarray,
|
|
np.dtype,
|
|
_codecs.encode,
|
|
np.dtypes.Float32DType,
|
|
]
|
|
):
|
|
sd_loaded = torch.load(buf_data_legacy_numpy, weights_only=True)
|
|
buf_data_legacy_numpy.seek(0)
|
|
# Test map_location
|
|
sd_loaded_cpu = torch.load(
|
|
buf_data_legacy_numpy, weights_only=True, map_location="cpu"
|
|
)
|
|
|
|
expected = torch.tensor(
|
|
[[1, 2, 3], [4, 5, 6]], dtype=torch.float32, device="openreg"
|
|
)
|
|
self.assertEqual(sd_loaded["x"].cpu(), expected.cpu())
|
|
self.assertFalse(sd_loaded["x"].is_cpu)
|
|
self.assertTrue(sd_loaded_cpu["x"].is_cpu)
|
|
|
|
def test_open_device_cpu_serialization(self):
|
|
default_protocol = torch.serialization.DEFAULT_PROTOCOL
|
|
|
|
with patch.object(torch._C, "_has_storage", return_value=False):
|
|
x = torch.randn(2, 3)
|
|
x_openreg = x.to("openreg")
|
|
sd = {"x": x_openreg}
|
|
rebuild_func = x_openreg._reduce_ex_internal(default_protocol)[0]
|
|
self.assertTrue(
|
|
rebuild_func is torch._utils._rebuild_device_tensor_from_cpu_tensor
|
|
)
|
|
|
|
# Test map_location
|
|
with TemporaryFileName() as f:
|
|
torch.save(sd, f)
|
|
sd_loaded = torch.load(f, weights_only=True)
|
|
# Test map_location
|
|
sd_loaded_cpu = torch.load(f, weights_only=True, map_location="cpu")
|
|
self.assertFalse(sd_loaded["x"].is_cpu)
|
|
self.assertEqual(sd_loaded["x"].cpu(), x)
|
|
self.assertTrue(sd_loaded_cpu["x"].is_cpu)
|
|
|
|
# Test metadata_only
|
|
with TemporaryFileName() as f:
|
|
with self.assertRaisesRegex(
|
|
RuntimeError,
|
|
"Cannot serialize tensors on backends with no storage under skip_data context manager",
|
|
):
|
|
with torch.serialization.skip_data():
|
|
torch.save(sd, f)
|
|
|
|
# Operators
|
|
def test_factory(self):
|
|
x = torch.empty(3, device="openreg")
|
|
self.assertEqual(x.device.type, "openreg")
|
|
self.assertEqual(x.shape, torch.Size([3]))
|
|
|
|
y = torch.zeros(3, device="openreg")
|
|
self.assertEqual(y.device.type, "openreg")
|
|
self.assertEqual(y.shape, torch.Size([3]))
|
|
|
|
z = torch.tensor((), device="openreg")
|
|
self.assertEqual(z.device.type, "openreg")
|
|
self.assertEqual(z.shape, torch.Size([0]))
|
|
|
|
def test_fake_tensor(self):
|
|
with torch._subclasses.fake_tensor.FakeTensorMode():
|
|
a = torch.empty(1, device="openreg")
|
|
b = torch.empty(1, device="openreg:0")
|
|
result = a + b # noqa: F841
|
|
|
|
def test_named_tensor(self):
|
|
return torch.empty([2, 3, 4, 5], device="openreg", names=["N", "C", "H", "W"])
|
|
|
|
def test_printing(self):
|
|
a = torch.ones(20, device="openreg")
|
|
# Does not crash!
|
|
str(a)
|
|
|
|
def test_data_dependent_output(self):
|
|
cpu_a = torch.randn(10)
|
|
a = cpu_a.to(device="openreg")
|
|
mask = a.gt(0)
|
|
out = torch.masked_select(a, mask)
|
|
|
|
self.assertEqual(out, cpu_a.masked_select(cpu_a.gt(0)))
|
|
|
|
def test_expand(self):
|
|
x = torch.tensor([[1], [2], [3]], device="openreg")
|
|
y = x.expand(3, 2)
|
|
self.assertEqual(y.to(device="cpu"), torch.tensor([[1, 1], [2, 2], [3, 3]]))
|
|
self.assertEqual(x.data_ptr(), y.data_ptr())
|
|
|
|
def test_resize(self):
|
|
tensor_cpu = torch.randn([4, 4])
|
|
|
|
tensor_openreg = tensor_cpu.openreg()
|
|
self.assertTrue(tensor_openreg.size() == torch.Size([4, 4]))
|
|
|
|
storage_openreg = tensor_openreg.storage()
|
|
self.assertTrue(storage_openreg.size() == 16)
|
|
|
|
tensor_openreg.resize_(2, 2, 2, 2)
|
|
self.assertTrue(tensor_openreg.size() == torch.Size([2, 2, 2, 2]))
|
|
|
|
storage_openreg = tensor_openreg.storage()
|
|
self.assertTrue(storage_openreg.size() == 16)
|
|
|
|
# Quantize
|
|
@skipIfXpu(msg="missing kernel for openreg")
|
|
def test_quantize(self):
|
|
x = torch.randn(3, 4, 5, dtype=torch.float32, device="openreg")
|
|
quantized_tensor = torch.quantize_per_tensor(x, 0.1, 10, torch.qint8)
|
|
self.assertEqual(quantized_tensor.device, torch.device("openreg:0"))
|
|
self.assertEqual(quantized_tensor.dtype, torch.qint8)
|
|
|
|
# custom autograd
|
|
def test_compile_autograd_function_returns_self(self):
|
|
in_ref = torch.randn(4, device="openreg", requires_grad=True)
|
|
out_ref = torch.ops.openreg.custom_autograd_fn_returns_self(in_ref)
|
|
out_ref.sum().backward()
|
|
|
|
in_test = in_ref.detach().clone().requires_grad_(True)
|
|
# TODO(FFFrog): Need to support inductor for OpenReg first.
|
|
out_test = torch.compile(backend="aot_eager")(
|
|
torch.ops.openreg.custom_autograd_fn_returns_self
|
|
)(in_test)
|
|
out_test.sum().backward()
|
|
|
|
self.assertEqual(out_ref, out_test)
|
|
self.assertEqual(in_ref.grad, in_test.grad)
|
|
|
|
@skipIfTorchDynamo("Temporary disabled due to torch._ops.OpOverloadPacket")
|
|
def test_compile_autograd_function_aliasing(self):
|
|
in_ref = torch.randn(4, device="openreg", requires_grad=True)
|
|
out_ref = torch.ops.openreg.custom_autograd_fn_aliasing(in_ref)
|
|
out_ref.sum().backward()
|
|
|
|
in_test = in_ref.detach().clone().requires_grad_(True)
|
|
# TODO(FFFrog): Need to support inductor for OpenReg first.
|
|
out_test = torch.compile(backend="aot_eager")(
|
|
torch.ops.openreg.custom_autograd_fn_aliasing
|
|
)(in_test)
|
|
out_test.sum().backward()
|
|
|
|
self.assertEqual(out_ref, out_test)
|
|
self.assertEqual(in_ref.grad, in_test.grad)
|
|
|
|
def test_open_device_dlpack(self):
|
|
x_in = torch.randn(2, 3).to("openreg")
|
|
capsule = torch.utils.dlpack.to_dlpack(x_in)
|
|
x_out = torch.from_dlpack(capsule)
|
|
self.assertTrue(x_out.device == x_in.device)
|
|
|
|
x_in = x_in.to("cpu")
|
|
x_out = x_out.to("cpu")
|
|
self.assertEqual(x_in, x_out)
|
|
|
|
# fallback
|
|
def test_scalar_type_fallback(self):
|
|
x_cpu = torch.Tensor([[0, 0, 0, 1, 1, 2], [0, 1, 2, 1, 2, 2]]).to(torch.int64)
|
|
x = torch.triu_indices(3, 3, device="openreg")
|
|
self.assertEqual(x_cpu, x)
|
|
|
|
def test_tensor_type_fallback(self):
|
|
x = torch.Tensor([[1, 2, 3], [2, 3, 4]]).to("openreg")
|
|
y = torch.Tensor([1, 0, 2]).to("openreg")
|
|
self.assertTrue(x.device.type, "openreg")
|
|
self.assertFalse(x.is_cpu)
|
|
|
|
z_cpu = torch.Tensor([[0, 2, 1], [1, 3, 2]])
|
|
# 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("openreg")
|
|
z = x[y, y]
|
|
self.assertEqual(z_cpu, z)
|
|
|
|
def test_tensorlist_type_fallback(self):
|
|
# create tensors located in custom device
|
|
v_openreg = torch.Tensor([1, 2, 3]).to("openreg")
|
|
# create result tensor located in cpu
|
|
z_cpu = torch.Tensor([2, 4, 6])
|
|
# create tensorlist for foreach_add op
|
|
x = (v_openreg, v_openreg)
|
|
y = (v_openreg, v_openreg)
|
|
|
|
# Check that our device is correct.
|
|
self.assertTrue(v_openreg.device.type == "openreg")
|
|
self.assertFalse(v_openreg.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])
|
|
|
|
|
|
if __name__ == "__main__":
|
|
run_tests()
|