[gpt-oss] Add gpt-oss bf16 support
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94
vllm/plugins/__init__.py
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94
vllm/plugins/__init__.py
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# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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import logging
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import os
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from typing import Any, Callable
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import torch
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import vllm.envs as envs
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logger = logging.getLogger(__name__)
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DEFAULT_PLUGINS_GROUP = 'vllm.general_plugins'
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# make sure one process only loads plugins once
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plugins_loaded = False
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def load_plugins_by_group(group: str) -> dict[str, Callable[[], Any]]:
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import sys
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if sys.version_info < (3, 10):
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from importlib_metadata import entry_points
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else:
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from importlib.metadata import entry_points
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allowed_plugins = envs.VLLM_PLUGINS
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discovered_plugins = entry_points(group=group)
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if len(discovered_plugins) == 0:
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logger.debug("No plugins for group %s found.", group)
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return {}
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# Check if the only discovered plugin is the default one
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is_default_group = (group == DEFAULT_PLUGINS_GROUP)
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# Use INFO for non-default groups and DEBUG for the default group
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log_level = logger.debug if is_default_group else logger.info
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log_level("Available plugins for group %s:", group)
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for plugin in discovered_plugins:
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log_level("- %s -> %s", plugin.name, plugin.value)
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if allowed_plugins is None:
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log_level("All plugins in this group will be loaded. "
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"Set `VLLM_PLUGINS` to control which plugins to load.")
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plugins = dict[str, Callable[[], Any]]()
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for plugin in discovered_plugins:
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if allowed_plugins is None or plugin.name in allowed_plugins:
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if allowed_plugins is not None:
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log_level("Loading plugin %s", plugin.name)
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try:
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func = plugin.load()
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plugins[plugin.name] = func
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except Exception:
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logger.exception("Failed to load plugin %s", plugin.name)
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return plugins
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def load_general_plugins():
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"""WARNING: plugins can be loaded for multiple times in different
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processes. They should be designed in a way that they can be loaded
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multiple times without causing issues.
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"""
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global plugins_loaded
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if plugins_loaded:
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return
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plugins_loaded = True
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# some platform-specific configurations
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from vllm.platforms import current_platform
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if current_platform.is_xpu():
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# see https://github.com/pytorch/pytorch/blob/43c5f59/torch/_dynamo/config.py#L158
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torch._dynamo.config.disable = True
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elif current_platform.is_hpu():
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# NOTE(kzawora): PT HPU lazy backend (PT_HPU_LAZY_MODE = 1)
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# does not support torch.compile
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# Eager backend (PT_HPU_LAZY_MODE = 0) must be selected for
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# torch.compile support
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is_lazy = os.environ.get('PT_HPU_LAZY_MODE', '1') == '1'
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if is_lazy:
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torch._dynamo.config.disable = True
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# NOTE(kzawora) multi-HPU inference with HPUGraphs (lazy-only)
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# requires enabling lazy collectives
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# see https://docs.habana.ai/en/latest/PyTorch/Inference_on_PyTorch/Inference_Using_HPU_Graphs.html # noqa: E501
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os.environ['PT_HPU_ENABLE_LAZY_COLLECTIVES'] = 'true'
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plugins = load_plugins_by_group(group=DEFAULT_PLUGINS_GROUP)
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# general plugins, we only need to execute the loaded functions
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for func in plugins.values():
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func()
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