# SPDX-License-Identifier: Apache-2.0 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project import pytest import torch from vllm.utils.torch_utils import ( common_broadcastable_dtype, current_stream, is_lossless_cast, ) @pytest.mark.parametrize( ("src_dtype", "tgt_dtype", "expected_result"), [ # Different precision_levels (torch.bool, torch.int8, True), (torch.bool, torch.float16, True), (torch.bool, torch.complex32, True), (torch.int64, torch.bool, False), (torch.int64, torch.float16, True), (torch.int64, torch.complex32, True), (torch.float64, torch.bool, False), (torch.float64, torch.int8, False), (torch.float64, torch.complex32, True), (torch.complex128, torch.bool, False), (torch.complex128, torch.int8, False), (torch.complex128, torch.float16, False), # precision_level=0 (torch.bool, torch.bool, True), # precision_level=1 (torch.int8, torch.int16, True), (torch.int16, torch.int8, False), (torch.uint8, torch.int8, False), (torch.int8, torch.uint8, False), # precision_level=2 (torch.float16, torch.float32, True), (torch.float32, torch.float16, False), (torch.bfloat16, torch.float32, True), (torch.float32, torch.bfloat16, False), # precision_level=3 (torch.complex32, torch.complex64, True), (torch.complex64, torch.complex32, False), ], ) def test_is_lossless_cast(src_dtype, tgt_dtype, expected_result): assert is_lossless_cast(src_dtype, tgt_dtype) == expected_result @pytest.mark.parametrize( ("dtypes", "expected_result"), [ ([torch.bool], torch.bool), ([torch.bool, torch.int8], torch.int8), ([torch.bool, torch.int8, torch.float16], torch.float16), ([torch.bool, torch.int8, torch.float16, torch.complex32], torch.complex32), # noqa: E501 ], ) def test_common_broadcastable_dtype(dtypes, expected_result): assert common_broadcastable_dtype(dtypes) == expected_result def _test_stream_thread(main_expected_stream: torch.cuda.Stream): import threading child_stream = torch.cuda.Stream() thread_stream_ready = threading.Event() thread_can_exit = threading.Event() def child_thread_func(): with torch.cuda.stream(child_stream): thread_stream_ready.set() thread_can_exit.wait(timeout=10) child_thread = threading.Thread(target=child_thread_func) child_thread.start() try: assert thread_stream_ready.wait(timeout=5), ( "Child thread failed to enter stream context in time" ) main_current_stream = current_stream() assert main_current_stream != child_stream, ( "Main thread's current_stream was contaminated by child thread" ) assert main_current_stream == main_expected_stream, ( f"Main thread's stream changed unexpectedly. " f"Expected {main_expected_stream}, got {main_current_stream}" ) thread_can_exit.set() finally: child_thread.join(timeout=5) if child_thread.is_alive(): pytest.fail("Child thread failed to exit properly") def test_current_stream_multithread(): from vllm.platforms import current_platform if not torch.cuda.is_available(): pytest.skip("CUDA not available") if current_platform.is_rocm(): main_dedicated_stream = current_stream() assert main_dedicated_stream.cuda_stream != 0, ( "ROCm should create a dedicated stream, not use default stream (0x0)" ) main_stream_again = current_stream() assert main_stream_again == main_dedicated_stream, ( "Multiple calls to current_stream should return the same dedicated stream" ) _test_stream_thread(main_dedicated_stream) else: main_default_stream = torch.cuda.default_stream() main_initial_stream = current_stream() assert main_initial_stream == main_default_stream, ( "First call to current_stream should return default stream on CUDA" ) _test_stream_thread(main_default_stream)