annotator/oneformer/detectron2/utils/collect_env.py

# Copyright (c) Facebook, Inc. and its affiliates.
import importlib
import numpy as np
import os
import re
import subprocess
import sys
from collections import defaultdict
import PIL
import torch
import torchvision
from tabulate import tabulate

__all__ = ["collect_env_info"]


def collect_torch_env():
    try:
        import torch.__config__

        return torch.__config__.show()
    except ImportError:
        # compatible with older versions of pytorch
        from torch.utils.collect_env import get_pretty_env_info

        return get_pretty_env_info()


def get_env_module():
    var_name = "DETECTRON2_ENV_MODULE"
    return var_name, os.environ.get(var_name, "<not set>")


def detect_compute_compatibility(CUDA_HOME, so_file):
    try:
        cuobjdump = os.path.join(CUDA_HOME, "bin", "cuobjdump")
        if os.path.isfile(cuobjdump):
            output = subprocess.check_output(
                "'{}' --list-elf '{}'".format(cuobjdump, so_file), shell=True
            )
            output = output.decode("utf-8").strip().split("\n")
            arch = []
            for line in output:
                line = re.findall(r"\.sm_([0-9]*)\.", line)[0]
                arch.append(".".join(line))
            arch = sorted(set(arch))
            return ", ".join(arch)
        else:
            return so_file + "; cannot find cuobjdump"
    except Exception:
        # unhandled failure
        return so_file


def collect_env_info():
    has_gpu = torch.cuda.is_available()  # true for both CUDA & ROCM
    torch_version = torch.__version__

    # NOTE that CUDA_HOME/ROCM_HOME could be None even when CUDA runtime libs are functional
    from torch.utils.cpp_extension import CUDA_HOME, ROCM_HOME

    has_rocm = False
    if (getattr(torch.version, "hip", None) is not None) and (ROCM_HOME is not None):
        has_rocm = True
    has_cuda = has_gpu and (not has_rocm)

    data = []
    data.append(("sys.platform", sys.platform))  # check-template.yml depends on it
    data.append(("Python", sys.version.replace("\n", "")))
    data.append(("numpy", np.__version__))

    try:
        import annotator.oneformer.detectron2  # noqa

        data.append(
            ("detectron2", detectron2.__version__ + " @" + os.path.dirname(detectron2.__file__))
        )
    except ImportError:
        data.append(("detectron2", "failed to import"))
    except AttributeError:
        data.append(("detectron2", "imported a wrong installation"))

    try:
        import annotator.oneformer.detectron2._C as _C
    except ImportError as e:
        data.append(("detectron2._C", f"not built correctly: {e}"))

        # print system compilers when extension fails to build
        if sys.platform != "win32":  # don't know what to do for windows
            try:
                # this is how torch/utils/cpp_extensions.py choose compiler
                cxx = os.environ.get("CXX", "c++")
                cxx = subprocess.check_output("'{}' --version".format(cxx), shell=True)
                cxx = cxx.decode("utf-8").strip().split("\n")[0]
            except subprocess.SubprocessError:
                cxx = "Not found"
            data.append(("Compiler ($CXX)", cxx))

            if has_cuda and CUDA_HOME is not None:
                try:
                    nvcc = os.path.join(CUDA_HOME, "bin", "nvcc")
                    nvcc = subprocess.check_output("'{}' -V".format(nvcc), shell=True)
                    nvcc = nvcc.decode("utf-8").strip().split("\n")[-1]
                except subprocess.SubprocessError:
                    nvcc = "Not found"
                data.append(("CUDA compiler", nvcc))
        if has_cuda and sys.platform != "win32":
            try:
                so_file = importlib.util.find_spec("detectron2._C").origin
            except (ImportError, AttributeError):
                pass
            else:
                data.append(
                    ("detectron2 arch flags", detect_compute_compatibility(CUDA_HOME, so_file))
                )
    else:
        # print compilers that are used to build extension
        data.append(("Compiler", _C.get_compiler_version()))
        data.append(("CUDA compiler", _C.get_cuda_version()))  # cuda or hip
        if has_cuda and getattr(_C, "has_cuda", lambda: True)():
            data.append(
                ("detectron2 arch flags", detect_compute_compatibility(CUDA_HOME, _C.__file__))
            )

    data.append(get_env_module())
    data.append(("PyTorch", torch_version + " @" + os.path.dirname(torch.__file__)))
    data.append(("PyTorch debug build", torch.version.debug))
    try:
        data.append(("torch._C._GLIBCXX_USE_CXX11_ABI", torch._C._GLIBCXX_USE_CXX11_ABI))
    except Exception:
        pass

    if not has_gpu:
        has_gpu_text = "No: torch.cuda.is_available() == False"
    else:
        has_gpu_text = "Yes"
    data.append(("GPU available", has_gpu_text))
    if has_gpu:
        devices = defaultdict(list)
        for k in range(torch.cuda.device_count()):
            cap = ".".join((str(x) for x in torch.cuda.get_device_capability(k)))
            name = torch.cuda.get_device_name(k) + f" (arch={cap})"
            devices[name].append(str(k))
        for name, devids in devices.items():
            data.append(("GPU " + ",".join(devids), name))

        if has_rocm:
            msg = " - invalid!" if not (ROCM_HOME and os.path.isdir(ROCM_HOME)) else ""
            data.append(("ROCM_HOME", str(ROCM_HOME) + msg))
        else:
            try:
                from torch.utils.collect_env import get_nvidia_driver_version, run as _run

                data.append(("Driver version", get_nvidia_driver_version(_run)))
            except Exception:
                pass
            msg = " - invalid!" if not (CUDA_HOME and os.path.isdir(CUDA_HOME)) else ""
            data.append(("CUDA_HOME", str(CUDA_HOME) + msg))

            cuda_arch_list = os.environ.get("TORCH_CUDA_ARCH_LIST", None)
            if cuda_arch_list:
                data.append(("TORCH_CUDA_ARCH_LIST", cuda_arch_list))
    data.append(("Pillow", PIL.__version__))

    try:
        data.append(
            (
                "torchvision",
                str(torchvision.__version__) + " @" + os.path.dirname(torchvision.__file__),
            )
        )
        if has_cuda:
            try:
                torchvision_C = importlib.util.find_spec("torchvision._C").origin
                msg = detect_compute_compatibility(CUDA_HOME, torchvision_C)
                data.append(("torchvision arch flags", msg))
            except (ImportError, AttributeError):
                data.append(("torchvision._C", "Not found"))
    except AttributeError:
        data.append(("torchvision", "unknown"))

    try:
        import fvcore

        data.append(("fvcore", fvcore.__version__))
    except (ImportError, AttributeError):
        pass

    try:
        import iopath

        data.append(("iopath", iopath.__version__))
    except (ImportError, AttributeError):
        pass

    try:
        import cv2

        data.append(("cv2", cv2.__version__))
    except (ImportError, AttributeError):
        data.append(("cv2", "Not found"))
    env_str = tabulate(data) + "\n"
    env_str += collect_torch_env()
    return env_str


def test_nccl_ops():
    num_gpu = torch.cuda.device_count()
    if os.access("/tmp", os.W_OK):
        import torch.multiprocessing as mp

        dist_url = "file:///tmp/nccl_tmp_file"
        print("Testing NCCL connectivity ... this should not hang.")
        mp.spawn(_test_nccl_worker, nprocs=num_gpu, args=(num_gpu, dist_url), daemon=False)
        print("NCCL succeeded.")


def _test_nccl_worker(rank, num_gpu, dist_url):
    import torch.distributed as dist

    dist.init_process_group(backend="NCCL", init_method=dist_url, rank=rank, world_size=num_gpu)
    dist.barrier(device_ids=[rank])


if __name__ == "__main__":
    try:
        from annotator.oneformer.detectron2.utils.collect_env import collect_env_info as f

        print(f())
    except ImportError:
        print(collect_env_info())

    if torch.cuda.is_available():
        num_gpu = torch.cuda.device_count()
        for k in range(num_gpu):
            device = f"cuda:{k}"
            try:
                x = torch.tensor([1, 2.0], dtype=torch.float32)
                x = x.to(device)
            except Exception as e:
                print(
                    f"Unable to copy tensor to device={device}: {e}. "
                    "Your CUDA environment is broken."
                )
        if num_gpu > 1:
            test_nccl_ops()