Iluvatar-mrv100 SDK 4.3.0

vllm/plugins/__init__.py

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+# SPDX-License-Identifier: Apache-2.0
+
+import logging
+import os
+from typing import Callable, Dict
+
+import torch
+
+import vllm.envs as envs
+
+logger = logging.getLogger(__name__)
+
+# make sure one process only loads plugins once
+plugins_loaded = False
+
+
+def load_plugins_by_group(group: str) -> Dict[str, Callable]:
+    import sys
+    if sys.version_info < (3, 10):
+        from importlib_metadata import entry_points
+    else:
+        from importlib.metadata import entry_points
+
+    allowed_plugins = envs.VLLM_PLUGINS
+
+    discovered_plugins = entry_points(group=group)
+    if len(discovered_plugins) == 0:
+        logger.debug("No plugins for group %s found.", group)
+        return {}
+    logger.info("Available plugins for group %s:", group)
+    for plugin in discovered_plugins:
+        logger.info("name=%s, value=%s", plugin.name, plugin.value)
+    if allowed_plugins is None:
+        logger.info("all available plugins for group %s will be loaded.",
+                    group)
+        logger.info("set environment variable VLLM_PLUGINS to control"
+                    " which plugins to load.")
+    plugins = {}
+    for plugin in discovered_plugins:
+        if allowed_plugins is None or plugin.name in allowed_plugins:
+            try:
+                func = plugin.load()
+                plugins[plugin.name] = func
+                logger.info("plugin %s loaded.", plugin.name)
+            except Exception:
+                logger.exception("Failed to load plugin %s", plugin.name)
+    return plugins
+
+
+def load_general_plugins():
+    """WARNING: plugins can be loaded for multiple times in different
+    processes. They should be designed in a way that they can be loaded
+    multiple times without causing issues.
+    """
+    global plugins_loaded
+    if plugins_loaded:
+        return
+    plugins_loaded = True
+
+    # some platform-specific configurations
+    from vllm.platforms import current_platform
+
+    if current_platform.is_xpu():
+        # see https://github.com/pytorch/pytorch/blob/43c5f59/torch/_dynamo/config.py#L158
+        torch._dynamo.config.disable = True
+    elif current_platform.is_hpu():
+        # NOTE(kzawora): PT HPU lazy backend (PT_HPU_LAZY_MODE = 1)
+        # does not support torch.compile
+        # Eager backend (PT_HPU_LAZY_MODE = 0) must be selected for
+        # torch.compile support
+        is_lazy = os.environ.get('PT_HPU_LAZY_MODE', '1') == '1'
+        if is_lazy:
+            torch._dynamo.config.disable = True
+            # NOTE(kzawora) multi-HPU inference with HPUGraphs (lazy-only)
+            # requires enabling lazy collectives
+            # see https://docs.habana.ai/en/latest/PyTorch/Inference_on_PyTorch/Inference_Using_HPU_Graphs.html # noqa: E501
+            os.environ['PT_HPU_ENABLE_LAZY_COLLECTIVES'] = 'true'
+
+    plugins = load_plugins_by_group(group='vllm.general_plugins')
+    # general plugins, we only need to execute the loaded functions
+    for func in plugins.values():
+        func()