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import importlib |
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import numpy as np |
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import os |
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import re |
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import subprocess |
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import sys |
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from collections import defaultdict |
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import PIL |
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import torch |
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import torchvision |
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from tabulate import tabulate |
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__all__ = ["collect_env_info"] |
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def collect_torch_env(): |
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try: |
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import torch.__config__ |
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return torch.__config__.show() |
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except ImportError: |
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from torch.utils.collect_env import get_pretty_env_info |
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return get_pretty_env_info() |
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def get_env_module(): |
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var_name = "DETECTRON2_ENV_MODULE" |
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return var_name, os.environ.get(var_name, "<not set>") |
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def detect_compute_compatibility(CUDA_HOME, so_file): |
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try: |
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cuobjdump = os.path.join(CUDA_HOME, "bin", "cuobjdump") |
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if os.path.isfile(cuobjdump): |
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output = subprocess.check_output( |
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"'{}' --list-elf '{}'".format(cuobjdump, so_file), shell=True |
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) |
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output = output.decode("utf-8").strip().split("\n") |
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arch = [] |
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for line in output: |
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line = re.findall(r"\.sm_([0-9]*)\.", line)[0] |
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arch.append(".".join(line)) |
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arch = sorted(set(arch)) |
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return ", ".join(arch) |
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else: |
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return so_file + "; cannot find cuobjdump" |
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except Exception: |
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return so_file |
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def collect_env_info(): |
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has_gpu = torch.cuda.is_available() |
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torch_version = torch.__version__ |
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from torch.utils.cpp_extension import CUDA_HOME, ROCM_HOME |
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has_rocm = False |
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if (getattr(torch.version, "hip", None) is not None) and (ROCM_HOME is not None): |
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has_rocm = True |
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has_cuda = has_gpu and (not has_rocm) |
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data = [] |
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data.append(("sys.platform", sys.platform)) |
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data.append(("Python", sys.version.replace("\n", ""))) |
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data.append(("numpy", np.__version__)) |
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try: |
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import annotator.oneformer.detectron2 |
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data.append( |
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("detectron2", detectron2.__version__ + " @" + os.path.dirname(detectron2.__file__)) |
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) |
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except ImportError: |
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data.append(("detectron2", "failed to import")) |
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except AttributeError: |
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data.append(("detectron2", "imported a wrong installation")) |
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try: |
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import annotator.oneformer.detectron2._C as _C |
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except ImportError as e: |
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data.append(("detectron2._C", f"not built correctly: {e}")) |
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if sys.platform != "win32": |
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try: |
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cxx = os.environ.get("CXX", "c++") |
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cxx = subprocess.check_output("'{}' --version".format(cxx), shell=True) |
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cxx = cxx.decode("utf-8").strip().split("\n")[0] |
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except subprocess.SubprocessError: |
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cxx = "Not found" |
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data.append(("Compiler ($CXX)", cxx)) |
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if has_cuda and CUDA_HOME is not None: |
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try: |
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nvcc = os.path.join(CUDA_HOME, "bin", "nvcc") |
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nvcc = subprocess.check_output("'{}' -V".format(nvcc), shell=True) |
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nvcc = nvcc.decode("utf-8").strip().split("\n")[-1] |
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except subprocess.SubprocessError: |
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nvcc = "Not found" |
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data.append(("CUDA compiler", nvcc)) |
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if has_cuda and sys.platform != "win32": |
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try: |
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so_file = importlib.util.find_spec("detectron2._C").origin |
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except (ImportError, AttributeError): |
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pass |
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else: |
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data.append( |
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("detectron2 arch flags", detect_compute_compatibility(CUDA_HOME, so_file)) |
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) |
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else: |
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data.append(("Compiler", _C.get_compiler_version())) |
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data.append(("CUDA compiler", _C.get_cuda_version())) |
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if has_cuda and getattr(_C, "has_cuda", lambda: True)(): |
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data.append( |
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("detectron2 arch flags", detect_compute_compatibility(CUDA_HOME, _C.__file__)) |
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) |
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data.append(get_env_module()) |
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data.append(("PyTorch", torch_version + " @" + os.path.dirname(torch.__file__))) |
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data.append(("PyTorch debug build", torch.version.debug)) |
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try: |
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data.append(("torch._C._GLIBCXX_USE_CXX11_ABI", torch._C._GLIBCXX_USE_CXX11_ABI)) |
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except Exception: |
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pass |
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if not has_gpu: |
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has_gpu_text = "No: torch.cuda.is_available() == False" |
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else: |
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has_gpu_text = "Yes" |
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data.append(("GPU available", has_gpu_text)) |
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if has_gpu: |
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devices = defaultdict(list) |
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for k in range(torch.cuda.device_count()): |
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cap = ".".join((str(x) for x in torch.cuda.get_device_capability(k))) |
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name = torch.cuda.get_device_name(k) + f" (arch={cap})" |
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devices[name].append(str(k)) |
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for name, devids in devices.items(): |
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data.append(("GPU " + ",".join(devids), name)) |
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if has_rocm: |
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msg = " - invalid!" if not (ROCM_HOME and os.path.isdir(ROCM_HOME)) else "" |
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data.append(("ROCM_HOME", str(ROCM_HOME) + msg)) |
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else: |
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try: |
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from torch.utils.collect_env import get_nvidia_driver_version, run as _run |
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data.append(("Driver version", get_nvidia_driver_version(_run))) |
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except Exception: |
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pass |
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msg = " - invalid!" if not (CUDA_HOME and os.path.isdir(CUDA_HOME)) else "" |
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data.append(("CUDA_HOME", str(CUDA_HOME) + msg)) |
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cuda_arch_list = os.environ.get("TORCH_CUDA_ARCH_LIST", None) |
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if cuda_arch_list: |
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data.append(("TORCH_CUDA_ARCH_LIST", cuda_arch_list)) |
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data.append(("Pillow", PIL.__version__)) |
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try: |
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data.append( |
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( |
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"torchvision", |
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str(torchvision.__version__) + " @" + os.path.dirname(torchvision.__file__), |
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) |
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) |
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if has_cuda: |
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try: |
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torchvision_C = importlib.util.find_spec("torchvision._C").origin |
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msg = detect_compute_compatibility(CUDA_HOME, torchvision_C) |
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data.append(("torchvision arch flags", msg)) |
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except (ImportError, AttributeError): |
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data.append(("torchvision._C", "Not found")) |
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except AttributeError: |
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data.append(("torchvision", "unknown")) |
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try: |
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import fvcore |
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data.append(("fvcore", fvcore.__version__)) |
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except (ImportError, AttributeError): |
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pass |
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try: |
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import iopath |
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data.append(("iopath", iopath.__version__)) |
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except (ImportError, AttributeError): |
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pass |
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try: |
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import cv2 |
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data.append(("cv2", cv2.__version__)) |
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except (ImportError, AttributeError): |
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data.append(("cv2", "Not found")) |
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env_str = tabulate(data) + "\n" |
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env_str += collect_torch_env() |
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return env_str |
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def test_nccl_ops(): |
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num_gpu = torch.cuda.device_count() |
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if os.access("/tmp", os.W_OK): |
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import torch.multiprocessing as mp |
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dist_url = "file:///tmp/nccl_tmp_file" |
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print("Testing NCCL connectivity ... this should not hang.") |
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mp.spawn(_test_nccl_worker, nprocs=num_gpu, args=(num_gpu, dist_url), daemon=False) |
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print("NCCL succeeded.") |
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def _test_nccl_worker(rank, num_gpu, dist_url): |
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import torch.distributed as dist |
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dist.init_process_group(backend="NCCL", init_method=dist_url, rank=rank, world_size=num_gpu) |
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dist.barrier(device_ids=[rank]) |
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if __name__ == "__main__": |
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try: |
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from annotator.oneformer.detectron2.utils.collect_env import collect_env_info as f |
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print(f()) |
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except ImportError: |
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print(collect_env_info()) |
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if torch.cuda.is_available(): |
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num_gpu = torch.cuda.device_count() |
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for k in range(num_gpu): |
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device = f"cuda:{k}" |
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try: |
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x = torch.tensor([1, 2.0], dtype=torch.float32) |
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x = x.to(device) |
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except Exception as e: |
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print( |
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f"Unable to copy tensor to device={device}: {e}. " |
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"Your CUDA environment is broken." |
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) |
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if num_gpu > 1: |
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test_nccl_ops() |
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