| # Owner(s): ["module: intel"] |
| |
| import collections |
| import sys |
| import tempfile |
| import unittest |
| |
| import torch |
| import torch.xpu._gpu_trace as gpu_trace |
| from torch.testing._internal.autocast_test_lists import AutocastTestLists |
| from torch.testing._internal.common_device_type import ( |
| instantiate_device_type_tests, |
| onlyXPU, |
| OpDTypes, |
| ops, |
| ) |
| from torch.testing._internal.common_methods_invocations import ops_and_refs |
| from torch.testing._internal.common_utils import ( |
| NoTest, |
| run_tests, |
| suppress_warnings, |
| TEST_WITH_UBSAN, |
| TEST_XPU, |
| TestCase, |
| ) |
| |
| if not TEST_XPU: |
| print("XPU not available, skipping tests", file=sys.stderr) |
| TestCase = NoTest # noqa: F811 |
| |
| TEST_MULTIXPU = torch.xpu.device_count() > 1 |
| |
| cpu_device = torch.device("cpu") |
| xpu_device = torch.device("xpu") |
| |
| any_common_cpu_xpu_one = OpDTypes.any_common_cpu_cuda_one |
| _xpu_computation_op_list = [ |
| "fill", |
| "zeros", |
| "zeros_like", |
| "clone", |
| "view_as_real", |
| "view_as_complex", |
| "view", |
| "resize_", |
| "resize_as_", |
| "add", |
| "sub", |
| "mul", |
| "div", |
| "abs", |
| ] |
| _xpu_tensor_factory_op_list = [ |
| "as_strided", |
| "empty", |
| "empty_strided", |
| ] |
| _xpu_not_test_dtype_op_list = [ |
| "resize_", # Skipped by CPU |
| "resize_as_", # Skipped by CPU |
| "abs", # Not aligned dtype |
| ] |
| _xpu_all_op_list = _xpu_computation_op_list + _xpu_tensor_factory_op_list |
| _xpu_all_ops = [op for op in ops_and_refs if op.name in _xpu_all_op_list] |
| _xpu_computation_ops = [ |
| op for op in ops_and_refs if op.name in _xpu_computation_op_list |
| ] |
| |
| |
| class TestXpu(TestCase): |
| def test_device_behavior(self): |
| current_device = torch.xpu.current_device() |
| torch.xpu.set_device(current_device) |
| self.assertEqual(current_device, torch.xpu.current_device()) |
| |
| @unittest.skipIf(not TEST_MULTIXPU, "only one GPU detected") |
| def test_multi_device_behavior(self): |
| current_device = torch.xpu.current_device() |
| target_device = (current_device + 1) % torch.xpu.device_count() |
| |
| with torch.xpu.device(target_device): |
| self.assertEqual(target_device, torch.xpu.current_device()) |
| self.assertEqual(current_device, torch.xpu.current_device()) |
| |
| with torch.xpu._DeviceGuard(target_device): |
| self.assertEqual(target_device, torch.xpu.current_device()) |
| self.assertEqual(current_device, torch.xpu.current_device()) |
| |
| def test_get_device_properties(self): |
| current_device = torch.xpu.current_device() |
| device_properties = torch.xpu.get_device_properties(current_device) |
| self.assertEqual(device_properties, torch.xpu.get_device_properties(None)) |
| self.assertEqual(device_properties, torch.xpu.get_device_properties()) |
| |
| device_name = torch.xpu.get_device_name(current_device) |
| self.assertEqual(device_name, torch.xpu.get_device_name(None)) |
| self.assertEqual(device_name, torch.xpu.get_device_name()) |
| |
| device_capability = torch.xpu.get_device_capability(current_device) |
| self.assertTrue(device_capability["max_work_group_size"] > 0) |
| self.assertTrue(device_capability["max_num_sub_groups"] > 0) |
| self.assertEqual( |
| device_properties.driver_version, device_capability["driver_version"] |
| ) |
| self.assertEqual(device_properties.has_fp16, device_capability["has_fp16"]) |
| self.assertEqual(device_properties.has_fp64, device_capability["has_fp64"]) |
| self.assertEqual( |
| device_properties.has_atomic64, device_capability["has_atomic64"] |
| ) |
| |
| def test_wrong_xpu_fork(self): |
| stderr = TestCase.runWithPytorchAPIUsageStderr( |
| """\ |
| import torch |
| from torch.multiprocessing import Process |
| def run(rank): |
| torch.xpu.set_device(rank) |
| if __name__ == "__main__": |
| size = 2 |
| processes = [] |
| for rank in range(size): |
| # it would work fine without the line below |
| torch.xpu.set_device(0) |
| p = Process(target=run, args=(rank,)) |
| p.start() |
| processes.append(p) |
| for p in processes: |
| p.join() |
| """ |
| ) |
| self.assertRegex(stderr, "Cannot re-initialize XPU in forked subprocess.") |
| |
| def test_streams(self): |
| s0 = torch.xpu.Stream() |
| torch.xpu.set_stream(s0) |
| s1 = torch.xpu.current_stream() |
| self.assertEqual(s0, s1) |
| s2 = torch.xpu.Stream() |
| self.assertFalse(s0 == s2) |
| torch.xpu.set_stream(s2) |
| with torch.xpu.stream(s0): |
| self.assertEqual(s0, torch.xpu.current_stream()) |
| self.assertEqual(s2, torch.xpu.current_stream()) |
| |
| def test_stream_priority(self): |
| low, high = torch.xpu.Stream.priority_range() |
| s0 = torch.xpu.Stream(device=0, priority=low) |
| |
| self.assertEqual(low, s0.priority) |
| self.assertEqual(torch.device("xpu:0"), s0.device) |
| |
| s1 = torch.xpu.Stream(device=0, priority=high) |
| |
| self.assertEqual(high, s1.priority) |
| self.assertEqual(torch.device("xpu:0"), s1.device) |
| |
| def test_stream_event_repr(self): |
| s = torch.xpu.current_stream() |
| self.assertTrue("torch.xpu.Stream" in str(s)) |
| e = torch.xpu.Event() |
| self.assertTrue("torch.xpu.Event(uninitialized)" in str(e)) |
| s.record_event(e) |
| self.assertTrue("torch.xpu.Event" in str(e)) |
| |
| def test_events(self): |
| stream = torch.xpu.current_stream() |
| event = torch.xpu.Event() |
| self.assertTrue(event.query()) |
| stream.record_event(event) |
| event.synchronize() |
| self.assertTrue(event.query()) |
| |
| def test_generic_stream_event(self): |
| stream = torch.Stream("xpu") |
| self.assertEqual(stream.device_index, torch.xpu.current_device()) |
| xpu_stream = torch.xpu.Stream( |
| stream_id=stream.stream_id, |
| device_index=stream.device_index, |
| device_type=stream.device_type, |
| ) |
| self.assertEqual(stream.stream_id, xpu_stream.stream_id) |
| self.assertNotEqual(stream.stream_id, torch.xpu.current_stream().stream_id) |
| |
| event1 = torch.Event("xpu") |
| event2 = torch.Event("xpu") |
| self.assertEqual(event1.event_id, 0) |
| a = torch.randn(1000) |
| b = torch.randn(1000) |
| with torch.xpu.stream(xpu_stream): |
| a_xpu = a.to("xpu", non_blocking=True) |
| b_xpu = b.to("xpu", non_blocking=True) |
| self.assertEqual(stream.stream_id, torch.xpu.current_stream().stream_id) |
| event1.record(stream) |
| event1.synchronize() |
| self.assertTrue(event1.query()) |
| c_xpu = a_xpu + b_xpu |
| event2.record() |
| event2.synchronize() |
| self.assertTrue(event2.query()) |
| self.assertNotEqual(event1.event_id, event2.event_id) |
| self.assertEqual(c_xpu.cpu(), a + b) |
| with self.assertRaisesRegex( |
| NotImplementedError, "elapsedTime is not supported by XPU backend." |
| ): |
| event1.elapsed_time(event2) |
| |
| def test_generator(self): |
| torch.manual_seed(2024) |
| g_state0 = torch.xpu.get_rng_state() |
| torch.manual_seed(1234) |
| g_state1 = torch.xpu.get_rng_state() |
| self.assertNotEqual(g_state0, g_state1) |
| |
| torch.xpu.manual_seed(2024) |
| g_state2 = torch.xpu.get_rng_state() |
| self.assertEqual(g_state0, g_state2) |
| |
| torch.xpu.set_rng_state(g_state1) |
| self.assertEqual(g_state1, torch.xpu.get_rng_state()) |
| |
| torch.manual_seed(1234) |
| torch.xpu.set_rng_state(g_state0) |
| self.assertEqual(2024, torch.xpu.initial_seed()) |
| |
| @onlyXPU |
| @suppress_warnings |
| @ops(_xpu_computation_ops, dtypes=any_common_cpu_xpu_one) |
| def test_compare_cpu(self, device, dtype, op): |
| def to_cpu(arg): |
| if isinstance(arg, torch.Tensor): |
| return arg.to(device="cpu") |
| return arg |
| |
| samples = op.reference_inputs(device, dtype) |
| |
| for sample in samples: |
| cpu_sample = sample.transform(to_cpu) |
| xpu_results = op(sample.input, *sample.args, **sample.kwargs) |
| cpu_results = op(cpu_sample.input, *cpu_sample.args, **cpu_sample.kwargs) |
| |
| xpu_results = sample.output_process_fn_grad(xpu_results) |
| cpu_results = cpu_sample.output_process_fn_grad(cpu_results) |
| |
| # Lower tolerance because we are running this as a `@slowTest` |
| # Don't want the periodic tests to fail frequently |
| self.assertEqual(xpu_results, cpu_results, atol=1e-4, rtol=1e-4) |
| |
| @onlyXPU |
| @ops(_xpu_computation_ops, allowed_dtypes=(torch.bool,)) |
| @unittest.skipIf(TEST_WITH_UBSAN, "Test uses undefined behavior") |
| def test_non_standard_bool_values(self, device, dtype, op): |
| # Test boolean values other than 0x00 and 0x01 (gh-54789) |
| def convert_boolean_tensors(x): |
| if not isinstance(x, torch.Tensor) or x.dtype != torch.bool: |
| return x |
| |
| # Map False -> 0 and True -> Random value in [2, 255] |
| true_vals = torch.randint( |
| 2, 255, x.shape, dtype=torch.uint8, device=x.device |
| ) |
| false_vals = torch.zeros((), dtype=torch.uint8, device=x.device) |
| x_int = torch.where(x, true_vals, false_vals) |
| |
| ret = x_int.view(torch.bool) |
| self.assertEqual(ret, x) |
| return ret |
| |
| for sample in op.sample_inputs(device, dtype): |
| expect = op(sample.input, *sample.args, **sample.kwargs) |
| |
| transformed = sample.transform(convert_boolean_tensors) |
| actual = op(transformed.input, *transformed.args, **transformed.kwargs) |
| |
| self.assertEqual(expect, actual) |
| |
| def test_serialization_array_with_storage(self): |
| x = torch.randn(5, 5).xpu() |
| y = torch.zeros(2, 5, dtype=torch.int, device="xpu") |
| q = [x, y, x, y.storage()] |
| with tempfile.NamedTemporaryFile() as f: |
| torch.save(q, f) |
| f.seek(0) |
| q_copy = torch.load(f) |
| self.assertEqual(q_copy, q, atol=0, rtol=0) |
| q_copy[0].fill_(5) |
| self.assertEqual(q_copy[0], q_copy[2], atol=0, rtol=0) |
| self.assertEqual(q_copy[0].dtype, torch.float) |
| self.assertEqual(q_copy[1].dtype, torch.int) |
| self.assertEqual(q_copy[2].dtype, torch.float) |
| self.assertTrue(isinstance(q_copy[3], torch.storage.TypedStorage)) |
| self.assertTrue(isinstance(q_copy[3]._untyped_storage, torch.UntypedStorage)) |
| q_copy[1].fill_(10) |
| y.fill_(10) |
| self.assertEqual(q_copy[3], y.storage()) |
| |
| def test_serialization_array_with_empty(self): |
| x = [ |
| torch.randn(4, 4).xpu(), |
| torch.tensor([], dtype=torch.float, device=torch.device("xpu")), |
| ] |
| with tempfile.NamedTemporaryFile() as f: |
| torch.save(x, f) |
| f.seek(0) |
| x_copy = torch.load(f) |
| for original, copy in zip(x, x_copy): |
| self.assertEqual(copy, original) |
| self.assertIs(type(copy), type(original)) |
| self.assertEqual(copy.get_device(), original.get_device()) |
| |
| |
| instantiate_device_type_tests(TestXpu, globals(), only_for="xpu") |
| |
| |
| class TestXpuAutocast(TestCase): |
| # These operators are not implemented on XPU backend and we can NOT fall back |
| # them to CPU. So we have to skip them at this moment. |
| # TODO: remove these operators from skip list when they are implemented on XPU backend. |
| skip_list = ["gru_cell"] |
| |
| def setUp(self): |
| super().setUp() |
| self.autocast_lists = AutocastTestLists(torch.device("xpu")) |
| |
| def tearDown(self): |
| del self.autocast_lists |
| super().tearDown() |
| |
| def _run_autocast_outofplace( |
| self, op, args, run_as_type, out_type=None, module=torch, add_kwargs=None |
| ): |
| # helper to cast args |
| def cast(val, to_type): |
| if isinstance(val, torch.Tensor): |
| return val.to(to_type) if val.is_floating_point() else val |
| elif isinstance(val, collections.abc.Iterable): |
| return type(val)(cast(v, to_type) for v in val) |
| else: |
| return val |
| |
| if add_kwargs is None: |
| add_kwargs = {} |
| fast_dtype = torch.bfloat16 if run_as_type == torch.bfloat16 else torch.float16 |
| self.assertFalse(torch.is_autocast_enabled("xpu")) |
| with torch.amp.autocast("xpu", dtype=fast_dtype): |
| self.assertTrue(torch.is_autocast_enabled("xpu")) |
| |
| out_type = out_type if out_type is not None else run_as_type |
| output = output_method = None |
| |
| # Try module.* variant, if requested: |
| if module is not None and hasattr(module, op): |
| output = getattr(module, op)(*args, **add_kwargs) |
| if isinstance(output, torch.Tensor): |
| self.assertTrue( |
| out_type == output.dtype, |
| f"autocast for torch.{op} produced {output.dtype}, should produce {out_type}", |
| ) |
| |
| # Try Tensor.* variant: |
| if hasattr(torch.Tensor, op): |
| output_method = getattr(args[0], op)(*args[1:], **add_kwargs) |
| if isinstance(output_method, torch.Tensor): |
| self.assertTrue( |
| out_type == output_method.dtype, |
| f"autocast for torch.{op} produced {output_method.dtype}, should produce torch.{out_type}", |
| ) |
| |
| self.assertTrue( |
| (output is not None) or (output_method is not None), |
| f"{op} not found as an attribute on either Tensor or the requested module {module}", |
| ) |
| |
| # Accounts for ops that return Tensors, iterables, and other non-Tensors. |
| # For example, lstm_cell returns a tuple and equal returns bool. |
| def compare(first, second): |
| if isinstance(first, torch.Tensor): |
| return torch.equal(first, second) |
| elif isinstance(first, collections.abc.Iterable): |
| return all(compare(f, s) for f, s in zip(first, second)) |
| else: |
| return first == second |
| |
| # If both torch.* and Tensor.* variants were found, check outputs are identical |
| if (output is not None) and (output_method is not None): |
| self.assertTrue(type(output) == type(output_method)) |
| comparison = compare(output, output_method) |
| self.assertTrue( |
| comparison, f"torch.{op} result did not match Tensor.{op} result" |
| ) |
| |
| # Compare numerics to Python-side "autocasting" that (we expect) does the same thing |
| # as the C++-side autocasting, and should be bitwise accurate. |
| output_to_compare = output if output is not None else output_method |
| with torch.amp.autocast("xpu", enabled=False): |
| self.assertFalse(torch.is_autocast_enabled("xpu")) |
| |
| if module is not None and hasattr(module, op): |
| control = getattr(module, op)( |
| *cast(args, run_as_type), **add_kwargs |
| ) |
| else: |
| control = getattr(args[0].to(run_as_type), op)( |
| *cast(args[1:], run_as_type), **add_kwargs |
| ) |
| self.assertTrue(type(output_to_compare) == type(control)) |
| comparison = compare(output_to_compare, control) |
| self.assertTrue(comparison, f"torch.{op} result did not match control") |
| self.assertTrue(torch.is_autocast_enabled("xpu")) |
| self.assertFalse(torch.is_autocast_enabled("xpu")) |
| |
| def test_autocast_torch_fp16(self): |
| for op_with_args in self.autocast_lists.torch_fp16: |
| skip_test = False |
| op, args = op_with_args[0], op_with_args[1] |
| if op in self.skip_list: |
| skip_test = True # skip unimplemented op |
| if len(op_with_args) == 3: |
| skip_test = True # skip cudnn op |
| if not skip_test: |
| self._run_autocast_outofplace(op, args, torch.float16) |
| |
| def test_autocast_torch_bf16(self): |
| for op_with_args in self.autocast_lists.torch_fp16: |
| skip_test = False |
| op, args = op_with_args[0], op_with_args[1] |
| if op in self.skip_list: |
| skip_test = True # skip unimplemented op |
| if len(op_with_args) == 3: |
| skip_test = True # skip cudnn op |
| if not skip_test: |
| self._run_autocast_outofplace(op, args, torch.bfloat16) |
| |
| def test_autocast_torch_need_autocast_promote(self): |
| for op, args in self.autocast_lists.torch_need_autocast_promote: |
| self._run_autocast_outofplace(op, args, torch.float32) |
| |
| def test_autocast_torch_expect_builtin_promote(self): |
| for op, args, out_type in self.autocast_lists.torch_expect_builtin_promote: |
| self._run_autocast_outofplace(op, args, torch.float32, out_type=out_type) |
| |
| def test_xpu_autocast_dtype(self): |
| dtype = torch.get_autocast_dtype("xpu") |
| self.assertEqual(dtype, torch.float16) |
| mat0_fp32 = torch.randn((10, 10), dtype=torch.float32, device="xpu") |
| mat1_fp32 = torch.randn((10, 10), dtype=torch.float32, device="xpu") |
| with torch.amp.autocast("xpu"): |
| result = torch.mm(mat0_fp32, mat1_fp32) |
| self.assertEqual(result.dtype, torch.float16) |
| |
| |
| class TestXpuTrace(TestCase): |
| def setUp(self): |
| torch._C._activate_gpu_trace() |
| self.mock = unittest.mock.MagicMock() |
| |
| def test_event_creation_callback(self): |
| gpu_trace.register_callback_for_event_creation(self.mock) |
| |
| event = torch.xpu.Event() |
| event.record() |
| self.mock.assert_called_once_with(event._as_parameter_.value) |
| |
| def test_event_deletion_callback(self): |
| gpu_trace.register_callback_for_event_deletion(self.mock) |
| |
| event = torch.xpu.Event() |
| event.record() |
| event_id = event._as_parameter_.value |
| del event |
| self.mock.assert_called_once_with(event_id) |
| |
| def test_event_record_callback(self): |
| gpu_trace.register_callback_for_event_record(self.mock) |
| |
| event = torch.xpu.Event() |
| event.record() |
| self.mock.assert_called_once_with( |
| event._as_parameter_.value, torch.xpu.current_stream().sycl_queue |
| ) |
| |
| def test_event_wait_callback(self): |
| gpu_trace.register_callback_for_event_wait(self.mock) |
| |
| event = torch.xpu.Event() |
| event.record() |
| event.wait() |
| self.mock.assert_called_once_with( |
| event._as_parameter_.value, torch.xpu.current_stream().sycl_queue |
| ) |
| |
| def test_device_synchronization_callback(self): |
| gpu_trace.register_callback_for_device_synchronization(self.mock) |
| |
| torch.xpu.synchronize() |
| self.mock.assert_called() |
| |
| def test_stream_synchronization_callback(self): |
| gpu_trace.register_callback_for_stream_synchronization(self.mock) |
| |
| stream = torch.xpu.Stream() |
| stream.synchronize() |
| self.mock.assert_called_once_with(stream.sycl_queue) |
| |
| def test_event_synchronization_callback(self): |
| gpu_trace.register_callback_for_event_synchronization(self.mock) |
| |
| event = torch.xpu.Event() |
| event.record() |
| event.synchronize() |
| self.mock.assert_called_once_with(event._as_parameter_.value) |
| |
| |
| if __name__ == "__main__": |
| run_tests() |
