[Model] Support DeepSeek-V4

tools/ray_mlu/mlu.py

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+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])