add qwen3

2026-02-04 17:22:39 +08:00
parent d1c0f68ab4
commit 8511fe8530
1932 changed files with 300426 additions and 0 deletions
--- a/vllm-v0.6.2/ray_mlu/README.md
+++ b/vllm-v0.6.2/ray_mlu/README.md
@@ -0,0 +1,17 @@
+这个文件夹里包含 ray 适配 mlu 所需的内容. 其 diff 同时放在了 `diff.patch` 里.
+
+原始适配基于官方 commit: 457d6e930e2d87354c9462b150be26a592508ea1, 其对应的 wheel 包在:
+`https://s3-us-west-2.amazonaws.com/ray-wheels/master/457d6e930e2d87354c9462b150be26a592508ea1/ray-3.0.0.dev0-cp310-cp310-manyli
+nux2014_x86_64.whl`
+
+安装 ray 的 mlu 适配的步骤为:
+1. 安装官方commit的wheel包:
+   ```bash
+   pip install https://s3-us-west-2.amazonaws.com/ray-wheels/master/457d6e930e2d87354c9462b150be26a592508ea1/ray-3.0.0.dev0-cp310-cp310-manylinux2014_x86_64.whl
+   ```
+2. 把 `__init__.py` 和 `mlu.py` 拷到包安装的地方. 其中,
+   `__init__.py` 会把已有的 `__init__.py` 覆盖, `mlu.py` 会是一个全新的文件. 比如这样:
+   ```bash
+   cp python/ray/_private/accelerators/__init__.py /path/to/python3.10/site-packages/ray/_private/accelerators/__init__.py && \
+   cp python/ray/_private/accelerators/mlu.py /path/to/python3.10/site-packages/ray/_private/accelerators/
+   ```
--- a/vllm-v0.6.2/ray_mlu/init.py
+++ b/vllm-v0.6.2/ray_mlu/init.py
@@ -0,0 +1,82 @@
+from typing import Set, Optional
+
+from ray._private.accelerators.accelerator import AcceleratorManager
+from ray._private.accelerators.nvidia_gpu import NvidiaGPUAcceleratorManager
+from ray._private.accelerators.intel_gpu import IntelGPUAcceleratorManager
+from ray._private.accelerators.amd_gpu import AMDGPUAcceleratorManager
+from ray._private.accelerators.tpu import TPUAcceleratorManager
+from ray._private.accelerators.neuron import NeuronAcceleratorManager
+from ray._private.accelerators.hpu import HPUAcceleratorManager
+from ray._private.accelerators.npu import NPUAcceleratorManager
+from ray._private.accelerators.mlu import MLUAcceleratorManager
+
+
+def get_all_accelerator_managers() -> Set[AcceleratorManager]:
+    """Get all accelerator managers supported by Ray."""
+    return {
+        NvidiaGPUAcceleratorManager,
+        IntelGPUAcceleratorManager,
+        AMDGPUAcceleratorManager,
+        TPUAcceleratorManager,
+        NeuronAcceleratorManager,
+        HPUAcceleratorManager,
+        NPUAcceleratorManager,
+        MLUAcceleratorManager,
+    }
+
+
+def get_all_accelerator_resource_names() -> Set[str]:
+    """Get all resource names for accelerators."""
+    return {
+        accelerator_manager.get_resource_name()
+        for accelerator_manager in get_all_accelerator_managers()
+    }
+
+
+def get_accelerator_manager_for_resource(
+    resource_name: str,
+) -> Optional[AcceleratorManager]:
+    """Get the corresponding accelerator manager for the given
+    accelerator resource name
+
+    E.g., TPUAcceleratorManager is returned if resource name is "TPU"
+    """
+    try:
+        return get_accelerator_manager_for_resource._resource_name_to_accelerator_manager.get(  # noqa: E501
+            resource_name, None
+        )
+    except AttributeError:
+        # Lazy initialization.
+        resource_name_to_accelerator_manager = {
+            accelerator_manager.get_resource_name(): accelerator_manager
+            for accelerator_manager in get_all_accelerator_managers()
+        }
+        # Special handling for GPU resource name since multiple accelerator managers
+        # have the same GPU resource name.
+        if AMDGPUAcceleratorManager.get_current_node_num_accelerators() > 0:
+            resource_name_to_accelerator_manager["GPU"] = AMDGPUAcceleratorManager
+        elif IntelGPUAcceleratorManager.get_current_node_num_accelerators() > 0:
+            resource_name_to_accelerator_manager["GPU"] = IntelGPUAcceleratorManager
+        elif MLUAcceleratorManager.get_current_node_num_accelerators() > 0:
+            resource_name_to_accelerator_manager["GPU"] = MLUAcceleratorManager
+        else:
+            resource_name_to_accelerator_manager["GPU"] = NvidiaGPUAcceleratorManager
+        get_accelerator_manager_for_resource._resource_name_to_accelerator_manager = (
+            resource_name_to_accelerator_manager
+        )
+        return resource_name_to_accelerator_manager.get(resource_name, None)
+
+
+__all__ = [
+    "NvidiaGPUAcceleratorManager",
+    "IntelGPUAcceleratorManager",
+    "AMDGPUAcceleratorManager",
+    "TPUAcceleratorManager",
+    "NeuronAcceleratorManager",
+    "HPUAcceleratorManager",
+    "NPUAcceleratorManager",
+    "MLUAcceleratorManager",
+    "get_all_accelerator_managers",
+    "get_all_accelerator_resource_names",
+    "get_accelerator_manager_for_resource",
+]
--- a/vllm-v0.6.2/ray_mlu/diff.patch
+++ b/vllm-v0.6.2/ray_mlu/diff.patch
@@ -0,0 +1,243 @@
+commit 7376225d16e381ecae5cc07d84db9eed043ed06a
+Author: tanhaojue <tanhaojue@cambricon.com>
+Date:   Thu Mar 7 15:54:09 2024 +0800
+
+    support mlu
+
+diff --git a/python/ray/_private/accelerators/__init__.py b/python/ray/_private/accelerators/__init__.py
+index 71550bc..07bdcd6 100644
+--- a/python/ray/_private/accelerators/__init__.py
+++ b/python/ray/_private/accelerators/__init__.py
+@@ -8,6 +8,7 @@ from ray._private.accelerators.tpu import TPUAcceleratorManager
+ from ray._private.accelerators.neuron import NeuronAcceleratorManager
+ from ray._private.accelerators.hpu import HPUAcceleratorManager
+ from ray._private.accelerators.npu import NPUAcceleratorManager
+from ray._private.accelerators.mlu import MLUAcceleratorManager
+ 
+ 
+ def get_all_accelerator_managers() -> Set[AcceleratorManager]:
+@@ -20,6 +21,7 @@ def get_all_accelerator_managers() -> Set[AcceleratorManager]:
+         NeuronAcceleratorManager,
+         HPUAcceleratorManager,
+         NPUAcceleratorManager,
+        MLUAcceleratorManager,
+     }
+ 
+ 
+@@ -55,6 +57,8 @@ def get_accelerator_manager_for_resource(
+             resource_name_to_accelerator_manager["GPU"] = AMDGPUAcceleratorManager
+         elif IntelGPUAcceleratorManager.get_current_node_num_accelerators() > 0:
+             resource_name_to_accelerator_manager["GPU"] = IntelGPUAcceleratorManager
+        elif MLUAcceleratorManager.get_current_node_num_accelerators() > 0:
+            resource_name_to_accelerator_manager["GPU"] = MLUAcceleratorManager
+         else:
+             resource_name_to_accelerator_manager["GPU"] = NvidiaGPUAcceleratorManager
+         get_accelerator_manager_for_resource._resource_name_to_accelerator_manager = (
+@@ -71,6 +75,7 @@ __all__ = [
+     "NeuronAcceleratorManager",
+     "HPUAcceleratorManager",
+     "NPUAcceleratorManager",
+    "MLUAcceleratorManager",
+     "get_all_accelerator_managers",
+     "get_all_accelerator_resource_names",
+     "get_accelerator_manager_for_resource",
+diff --git a/python/ray/_private/accelerators/mlu.py b/python/ray/_private/accelerators/mlu.py
+new file mode 100755
+index 0000000..21a5771
+--- /dev/null
+++ b/python/ray/_private/accelerators/mlu.py
+@@ -0,0 +1,92 @@
+import os
+import glob
+import logging
+from typing import Optional, List, Tuple
+import torch
+import torch_mlu
+from ray._private.accelerators.accelerator import AcceleratorManager
+
+logger = logging.getLogger(__name__)
+
+MLU_VISIBLE_DEVICES_ENV_VAR = "MLU_VISIBLE_DEVICES"
+NOSET_MLU_VISIBLE_DEVICES_ENV_VAR = "RAY_EXPERIMENTAL_NOSET_MLU_VISIBLE_DEVICES"
+
+
+class MLUAcceleratorManager(AcceleratorManager):
+    """Cambricon MLU accelerators."""
+
+    @staticmethod
+    def get_resource_name() -> str:
+        return "GPU"
+
+    @staticmethod
+    def get_visible_accelerator_ids_env_var() -> str:
+        return MLU_VISIBLE_DEVICES_ENV_VAR
+
+    @staticmethod
+    def get_current_process_visible_accelerator_ids() -> Optional[List[str]]:
+        mlu_visible_devices = os.environ.get(
+            MLUAcceleratorManager.get_visible_accelerator_ids_env_var(), None
+        )
+
+        if mlu_visible_devices is None:
+            return None
+
+        if mlu_visible_devices == "":
+            return []
+
+        if mlu_visible_devices == "NoDevFiles":
+            return []
+
+        return list(mlu_visible_devices.split(","))
+
+    @staticmethod
+    def get_current_node_num_accelerators() -> int:
+        """Attempt to detect the number of MLUs on this machine.
+
+        MLU chips are represented as devices within `/dev/`, either as `/dev/davinci?`.
+
+        Returns:
+             The number of MLUs if any were detected, otherwise 0.
+        """
+        try:
+            return torch.mlu.device_count()
+        except Exception as e:
+            logger.debug("Could not import CambriconCL: %s", e)
+
+        try:
+            mlu_files = glob.glob("/dev/cambricon_dev?")
+            return len(mlu_files)
+        except Exception as e:
+            logger.debug("Failed to detect number of MLUs: %s", e)
+        return 0
+
+    @staticmethod
+    def get_current_node_accelerator_type() -> Optional[str]:
+        """Get the type of the Cambricon MLU on the current node.
+
+        Returns:
+            A string of the type, such as "MLU370".
+        """
+        try:
+            return torch.mlu.get_device_name(0)
+        except Exception:
+            logger.exception("Failed to detect MLU type.")
+        return None
+
+    @staticmethod
+    def validate_resource_request_quantity(
+        quantity: float,
+    ) -> Tuple[bool, Optional[str]]:
+        return (True, None)
+
+    @staticmethod
+    def set_current_process_visible_accelerator_ids(
+        visible_mlu_devices: List[str],
+    ) -> None:
+        if os.environ.get(NOSET_MLU_VISIBLE_DEVICES_ENV_VAR):
+            return
+
+        os.environ[
+            MLUAcceleratorManager.get_visible_accelerator_ids_env_var()
+        ] = ",".join([str(i) for i in visible_mlu_devices])
+diff --git a/python/ray/tests/accelerators/test_mlu.py b/python/ray/tests/accelerators/test_mlu.py
+new file mode 100755
+index 0000000..70e81f7
+--- /dev/null
+++ b/python/ray/tests/accelerators/test_mlu.py
+@@ -0,0 +1,92 @@
+import os
+import sys
+import pytest
+from unittest.mock import patch
+
+import ray
+from ray._private.accelerators import MLUAcceleratorManager as Accelerator
+
+
+@patch("glob.glob")
+@patch("os.listdir")
+def test_autodetect_num_mlus(mock_list, mock_glob):
+    mock_glob.return_value = [f"/dev/davinci{i}" for i in range(4)]
+    # mock_list.return_value = []
+    assert Accelerator.get_current_node_num_accelerators() == 4
+
+
+@patch("glob.glob")
+@patch("os.listdir")
+def test_autodetect_num_mlus_without_devices(mock_list, mock_glob):
+    mock_glob.side_effect = Exception
+    # mock_list.return_value = []
+    assert Accelerator.get_current_node_num_accelerators() == 0
+
+
+def test_mlu_accelerator_manager_api():
+    assert Accelerator.get_resource_name() == "MLU"
+    assert Accelerator.get_visible_accelerator_ids_env_var() == "MLU_VISIBLE_DEVICES"
+    assert Accelerator.validate_resource_request_quantity(0.5) == (True, None)
+    assert Accelerator.validate_resource_request_quantity(1) == (True, None)
+
+
+def test_visible_mlu_type(monkeypatch, shutdown_only):
+    with patch.object(
+        Accelerator, "get_current_node_num_accelerators", return_value=4
+    ), patch.object(
+        Accelerator, "get_current_node_accelerator_type", return_value="MLU370"
+    ):
+        monkeypatch.setenv("MLU_VISIBLE_DEVICES", "0,1,2")
+        manager = ray._private.accelerators.get_accelerator_manager_for_resource("MLU")
+        assert manager.get_current_node_accelerator_type() == "MLU370"
+
+@pytest.mark.skipif(sys.platform == "win32", reason="Not supported mock on Windows")
+def test_visible_mlu_ids(monkeypatch, shutdown_only):
+    monkeypatch.setenv("MLU_VISIBLE_DEVICES", "0,1,2")
+    with patch.object(Accelerator, "get_current_node_num_accelerators", return_value=4):
+
+        ray.init()
+        manager = ray._private.accelerators.get_accelerator_manager_for_resource("MLU")
+        assert manager.get_current_node_num_accelerators() == 4
+        assert manager.__name__ == "MLUAcceleratorManager"
+        assert ray.available_resources()["MLU"] == 3
+
+def test_get_current_process_visible_accelerator_ids(monkeypatch, shutdown_only):
+    monkeypatch.setenv("MLU_VISIBLE_DEVICES", "0,1,2")
+    assert Accelerator.get_current_process_visible_accelerator_ids() == ["0", "1", "2"]
+
+    monkeypatch.delenv("MLU_VISIBLE_DEVICES")
+    assert Accelerator.get_current_process_visible_accelerator_ids() is None
+
+    monkeypatch.setenv("MLU_VISIBLE_DEVICES", "")
+    assert Accelerator.get_current_process_visible_accelerator_ids() == []
+
+    monkeypatch.setenv("MLU_VISIBLE_DEVICES", "NoDevFiles")
+    assert Accelerator.get_current_process_visible_accelerator_ids() == []
+
+
+def test_set_current_process_visible_accelerator_ids(shutdown_only):
+    Accelerator.set_current_process_visible_accelerator_ids(["0"])
+    assert os.environ["MLU_VISIBLE_DEVICES"] == "0"
+
+    Accelerator.set_current_process_visible_accelerator_ids(["0", "1"])
+    assert os.environ["MLU_VISIBLE_DEVICES"] == "0,1"
+
+    Accelerator.set_current_process_visible_accelerator_ids(["0", "1", "2"])
+    assert os.environ["MLU_VISIBLE_DEVICES"] == "0,1,2"
+
+
+@pytest.mark.skipif(sys.platform == "win32", reason="Not supported mock on Windows")
+def test_auto_detected_more_than_visible(monkeypatch, shutdown_only):
+    with patch.object(Accelerator, "get_current_node_num_accelerators", return_value=4):
+        # If more MLUs are detected than visible.
+        monkeypatch.setenv("MLU_VISIBLE_DEVICES", "0,1,2")
+
+        ray.init()
+        assert ray.available_resources()["MLU"] == 3
+
+if __name__ == "__main__":
+    if os.environ.get("PARALLEL_CI"):
+        sys.exit(pytest.main(["-n", "auto", "--boxed", "-vs", __file__]))
+    else:
+        sys.exit(pytest.main(["-sv", __file__]))
+diff --git a/ray-3.0.0.dev0-cp310-cp310-manylinux2014_x86_64.whl b/ray-3.0.0.dev0-cp310-cp310-manylinux2014_x86_64.whl
+new file mode 100644
+index 0000000..8628a88
+Binary files /dev/null and b/ray-3.0.0.dev0-cp310-cp310-manylinux2014_x86_64.whl differ
--- a/vllm-v0.6.2/ray_mlu/diff_for_dump_info.patch
+++ b/vllm-v0.6.2/ray_mlu/diff_for_dump_info.patch
@@ -0,0 +1,11 @@
+diff --git a/ray_mlu/mlu.py b/ray_mlu/mlu.py
+index 21a57719..2c63fd5b 100755
+--- a/ray_mlu/mlu.py
+++ b/ray_mlu/mlu.py
+@@ -87,6 +87,3 @@ class MLUAcceleratorManager(AcceleratorManager):
+         if os.environ.get(NOSET_MLU_VISIBLE_DEVICES_ENV_VAR):
+             return
+ 
+-        os.environ[
+-            MLUAcceleratorManager.get_visible_accelerator_ids_env_var()
+-        ] = ",".join([str(i) for i in visible_mlu_devices])
--- a/vllm-v0.6.2/ray_mlu/mlu.py
+++ b/vllm-v0.6.2/ray_mlu/mlu.py
@@ -0,0 +1,92 @@
+import os
+import glob
+import logging
+from typing import Optional, List, Tuple
+import torch
+import torch_mlu
+from ray._private.accelerators.accelerator import AcceleratorManager
+
+logger = logging.getLogger(__name__)
+
+MLU_VISIBLE_DEVICES_ENV_VAR = "MLU_VISIBLE_DEVICES"
+NOSET_MLU_VISIBLE_DEVICES_ENV_VAR = "RAY_EXPERIMENTAL_NOSET_MLU_VISIBLE_DEVICES"
+
+
+class MLUAcceleratorManager(AcceleratorManager):
+    """Cambricon MLU accelerators."""
+
+    @staticmethod
+    def get_resource_name() -> str:
+        return "GPU"
+
+    @staticmethod
+    def get_visible_accelerator_ids_env_var() -> str:
+        return MLU_VISIBLE_DEVICES_ENV_VAR
+
+    @staticmethod
+    def get_current_process_visible_accelerator_ids() -> Optional[List[str]]:
+        mlu_visible_devices = os.environ.get(
+            MLUAcceleratorManager.get_visible_accelerator_ids_env_var(), None
+        )
+
+        if mlu_visible_devices is None:
+            return None
+
+        if mlu_visible_devices == "":
+            return []
+
+        if mlu_visible_devices == "NoDevFiles":
+            return []
+
+        return list(mlu_visible_devices.split(","))
+
+    @staticmethod
+    def get_current_node_num_accelerators() -> int:
+        """Attempt to detect the number of MLUs on this machine.
+
+        MLU chips are represented as devices within `/dev/`, either as `/dev/davinci?`.
+
+        Returns:
+             The number of MLUs if any were detected, otherwise 0.
+        """
+        try:
+            return torch.mlu.device_count()
+        except Exception as e:
+            logger.debug("Could not import CambriconCL: %s", e)
+
+        try:
+            mlu_files = glob.glob("/dev/cambricon_dev?")
+            return len(mlu_files)
+        except Exception as e:
+            logger.debug("Failed to detect number of MLUs: %s", e)
+        return 0
+
+    @staticmethod
+    def get_current_node_accelerator_type() -> Optional[str]:
+        """Get the type of the Cambricon MLU on the current node.
+
+        Returns:
+            A string of the type, such as "MLU370".
+        """
+        try:
+            return torch.mlu.get_device_name(0)
+        except Exception:
+            logger.exception("Failed to detect MLU type.")
+        return None
+
+    @staticmethod
+    def validate_resource_request_quantity(
+        quantity: float,
+    ) -> Tuple[bool, Optional[str]]:
+        return (True, None)
+
+    @staticmethod
+    def set_current_process_visible_accelerator_ids(
+        visible_mlu_devices: List[str],
+    ) -> None:
+        if os.environ.get(NOSET_MLU_VISIBLE_DEVICES_ENV_VAR):
+            return
+
+        os.environ[
+            MLUAcceleratorManager.get_visible_accelerator_ids_env_var()
+        ] = ",".join([str(i) for i in visible_mlu_devices])
--- a/vllm-v0.6.2/ray_mlu/node.py
+++ b/vllm-v0.6.2/ray_mlu/node.py
--- a/vllm-v0.6.2/ray_mlu/nsight.py
+++ b/vllm-v0.6.2/ray_mlu/nsight.py
@@ -0,0 +1,154 @@
+import os
+import sys
+import logging
+import asyncio
+import subprocess
+import copy
+from pathlib import Path
+from typing import Tuple, List, Dict, Optional
+
+from ray._private.runtime_env.context import RuntimeEnvContext
+from ray._private.runtime_env.plugin import RuntimeEnvPlugin
+from ray._private.utils import (
+    try_to_create_directory,
+)
+from ray.exceptions import RuntimeEnvSetupError
+
+default_logger = logging.getLogger(__name__)
+
+# Nsight options used when runtime_env={"_nsight": "default"}
+NSIGHT_DEFAULT_CONFIG = {
+    "o": "'worker_process_%p'",
+    # "cudabacktrace": "all",
+    # "stop-on-exit": "true",
+    "force_overwrite": "false"
+}
+
+
+def parse_nsight_config(nsight_config: Dict[str, str]) -> List[str]:
+    """
+    Function to convert dictionary of nsight options into
+    nsight command line
+
+    The function returns:
+    - List[str]: nsys profile cmd line split into list of str
+    """
+    # nsight_cmd = ["nsys", "profile"]
+    nsight_cmd = ["cnperf-cli", "record"]
+    for option, option_val in nsight_config.items():
+        # option standard based on
+        # https://www.gnu.org/software/libc/manual/html_node/Argument-Syntax.html
+        if len(option) > 1:
+            nsight_cmd.append(f"--{option}={option_val}")
+        else:
+            nsight_cmd += [f"-{option}", option_val]
+    return nsight_cmd
+
+
+class NsightPlugin(RuntimeEnvPlugin):
+    name = "_nsight"
+
+    def __init__(self, resources_dir: str):
+        self.nsight_cmd = []
+
+        # replace this with better way to get logs dir
+        session_dir, runtime_dir = os.path.split(resources_dir)
+        self._nsight_dir = Path(session_dir) / "logs" / "nsight"
+        try_to_create_directory(self._nsight_dir)
+
+    async def _check_nsight_script(
+        self, nsight_config: Dict[str, str]
+    ) -> Tuple[bool, str]:
+        """
+        Function to validate if nsight_config is a valid nsight profile options
+        Args:
+            nsight_config: dictionary mapping nsight option to it's value
+        Returns:
+            a tuple consists of a boolean indicating if the nsight_config
+            is valid option and an error message if the nsight_config is invalid
+        """
+
+        # use empty as nsight report test filename
+        nsight_config_copy = copy.deepcopy(nsight_config)
+        try_to_create_directory(Path(self._nsight_dir) / "empty")
+        nsight_config_copy["o"] = str(Path(self._nsight_dir) / "empty")
+        nsight_cmd = parse_nsight_config(nsight_config_copy)
+        try:
+            nsight_cmd = nsight_cmd + ["python", "-c", '""']
+            process = await asyncio.create_subprocess_exec(
+                *nsight_cmd,
+                stdout=subprocess.PIPE,
+                stderr=subprocess.PIPE,
+            )
+            stdout, stderr = await process.communicate()
+            error_msg = stderr.strip() if stderr.strip() != "" else stdout.strip()
+
+            # cleanup test.nsys-rep file
+            # clean_up_cmd = ["rm", f"{nsight_config_copy['o']}.nsys-rep"]
+            clean_up_cmd = ["rm", f"{nsight_config_copy['o']}", "-rf"]
+            cleanup_process = await asyncio.create_subprocess_exec(
+                *clean_up_cmd,
+                stdout=subprocess.PIPE,
+                stderr=subprocess.PIPE,
+            )
+            _, _ = await cleanup_process.communicate()
+            if process.returncode == 0:
+                return True, None
+            else:
+                return False, error_msg
+        except FileNotFoundError:
+            return False, ("nsight is not installed")
+
+    async def create(
+        self,
+        uri: Optional[str],
+        runtime_env: "RuntimeEnv",  # noqa: F821
+        context: RuntimeEnvContext,
+        logger: logging.Logger = default_logger,
+    ) -> int:
+        nsight_config = runtime_env.nsight()
+        if not nsight_config:
+            return 0
+
+        if nsight_config and sys.platform != "linux":
+            raise RuntimeEnvSetupError(
+                "Cnperf CLI is only available in Linux.\n"
+                "More information can be found in "
+                "https://docs.nvidia.com/nsight-compute/NsightComputeCli/index.html"
+            )
+
+        if isinstance(nsight_config, str):
+            if nsight_config == "default":
+                nsight_config = NSIGHT_DEFAULT_CONFIG
+            else:
+                raise RuntimeEnvSetupError(
+                    f"Unsupported nsight config: {nsight_config}. "
+                    "The supported config is 'default' or "
+                    "Dictionary of nsight options"
+                )
+
+        is_valid_nsight_cmd, error_msg = await self._check_nsight_script(nsight_config)
+        if not is_valid_nsight_cmd:
+            logger.warning(error_msg)
+            raise RuntimeEnvSetupError(
+                "cnperf profile failed to run with the following "
+                f"error message:\n {error_msg}"
+            )
+        # add set output path to logs dir
+        # nsight_config["o"] = str(
+        #     Path(self._nsight_dir) / nsight_config.get("o", NSIGHT_DEFAULT_CONFIG["o"])
+        # )
+        try_to_create_directory(nsight_config["o"])
+
+        self.nsight_cmd = parse_nsight_config(nsight_config)
+        return 0
+
+    def modify_context(
+        self,
+        uris: List[str],
+        runtime_env: "RuntimeEnv",  # noqa: F821
+        context: RuntimeEnvContext,
+        logger: Optional[logging.Logger] = default_logger,
+    ):
+        logger.info("Running nsight profiler")
+        context.py_executable = " ".join(self.nsight_cmd) + " python"
--- a/vllm-v0.6.2/ray_mlu/test_mlu.py
+++ b/vllm-v0.6.2/ray_mlu/test_mlu.py
@@ -0,0 +1,92 @@
+import os
+import sys
+import pytest
+from unittest.mock import patch
+
+import ray
+from ray._private.accelerators import MLUAcceleratorManager as Accelerator
+
+
+@patch("glob.glob")
+@patch("os.listdir")
+def test_autodetect_num_mlus(mock_list, mock_glob):
+    mock_glob.return_value = [f"/dev/davinci{i}" for i in range(4)]
+    # mock_list.return_value = []
+    assert Accelerator.get_current_node_num_accelerators() == 4
+
+
+@patch("glob.glob")
+@patch("os.listdir")
+def test_autodetect_num_mlus_without_devices(mock_list, mock_glob):
+    mock_glob.side_effect = Exception
+    # mock_list.return_value = []
+    assert Accelerator.get_current_node_num_accelerators() == 0
+
+
+def test_mlu_accelerator_manager_api():
+    assert Accelerator.get_resource_name() == "MLU"
+    assert Accelerator.get_visible_accelerator_ids_env_var() == "MLU_VISIBLE_DEVICES"
+    assert Accelerator.validate_resource_request_quantity(0.5) == (True, None)
+    assert Accelerator.validate_resource_request_quantity(1) == (True, None)
+
+
+def test_visible_mlu_type(monkeypatch, shutdown_only):
+    with patch.object(
+        Accelerator, "get_current_node_num_accelerators", return_value=4
+    ), patch.object(
+        Accelerator, "get_current_node_accelerator_type", return_value="MLU370"
+    ):
+        monkeypatch.setenv("MLU_VISIBLE_DEVICES", "0,1,2")
+        manager = ray._private.accelerators.get_accelerator_manager_for_resource("MLU")
+        assert manager.get_current_node_accelerator_type() == "MLU370"
+
+@pytest.mark.skipif(sys.platform == "win32", reason="Not supported mock on Windows")
+def test_visible_mlu_ids(monkeypatch, shutdown_only):
+    monkeypatch.setenv("MLU_VISIBLE_DEVICES", "0,1,2")
+    with patch.object(Accelerator, "get_current_node_num_accelerators", return_value=4):
+
+        ray.init()
+        manager = ray._private.accelerators.get_accelerator_manager_for_resource("MLU")
+        assert manager.get_current_node_num_accelerators() == 4
+        assert manager.__name__ == "MLUAcceleratorManager"
+        assert ray.available_resources()["MLU"] == 3
+
+def test_get_current_process_visible_accelerator_ids(monkeypatch, shutdown_only):
+    monkeypatch.setenv("MLU_VISIBLE_DEVICES", "0,1,2")
+    assert Accelerator.get_current_process_visible_accelerator_ids() == ["0", "1", "2"]
+
+    monkeypatch.delenv("MLU_VISIBLE_DEVICES")
+    assert Accelerator.get_current_process_visible_accelerator_ids() is None
+
+    monkeypatch.setenv("MLU_VISIBLE_DEVICES", "")
+    assert Accelerator.get_current_process_visible_accelerator_ids() == []
+
+    monkeypatch.setenv("MLU_VISIBLE_DEVICES", "NoDevFiles")
+    assert Accelerator.get_current_process_visible_accelerator_ids() == []
+
+
+def test_set_current_process_visible_accelerator_ids(shutdown_only):
+    Accelerator.set_current_process_visible_accelerator_ids(["0"])
+    assert os.environ["MLU_VISIBLE_DEVICES"] == "0"
+
+    Accelerator.set_current_process_visible_accelerator_ids(["0", "1"])
+    assert os.environ["MLU_VISIBLE_DEVICES"] == "0,1"
+
+    Accelerator.set_current_process_visible_accelerator_ids(["0", "1", "2"])
+    assert os.environ["MLU_VISIBLE_DEVICES"] == "0,1,2"
+
+
+@pytest.mark.skipif(sys.platform == "win32", reason="Not supported mock on Windows")
+def test_auto_detected_more_than_visible(monkeypatch, shutdown_only):
+    with patch.object(Accelerator, "get_current_node_num_accelerators", return_value=4):
+        # If more MLUs are detected than visible.
+        monkeypatch.setenv("MLU_VISIBLE_DEVICES", "0,1,2")
+
+        ray.init()
+        assert ray.available_resources()["MLU"] == 3
+
+if __name__ == "__main__":
+    if os.environ.get("PARALLEL_CI"):
+        sys.exit(pytest.main(["-n", "auto", "--boxed", "-vs", __file__]))
+    else:
+        sys.exit(pytest.main(["-sv", __file__]))