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- **Add SPDX license headers to python source files**
- **Check for SPDX headers using pre-commit**
commit 9d7ef44c3cfb72ca4c32e1c677d99259d10d4745
Author: Russell Bryant <rbryant@redhat.com>
Date: Fri Jan 31 14:18:24 2025 -0500
Add SPDX license headers to python source files
This commit adds SPDX license headers to python source files as
recommended to
the project by the Linux Foundation. These headers provide a concise way
that is
both human and machine readable for communicating license information
for each
source file. It helps avoid any ambiguity about the license of the code
and can
also be easily used by tools to help manage license compliance.
The Linux Foundation runs license scans against the codebase to help
ensure
we are in compliance with the licenses of the code we use, including
dependencies. Having these headers in place helps that tool do its job.
More information can be found on the SPDX site:
- https://spdx.dev/learn/handling-license-info/
Signed-off-by: Russell Bryant <rbryant@redhat.com>
commit 5a1cf1cb3b80759131c73f6a9dddebccac039dea
Author: Russell Bryant <rbryant@redhat.com>
Date: Fri Jan 31 14:36:32 2025 -0500
Check for SPDX headers using pre-commit
Signed-off-by: Russell Bryant <rbryant@redhat.com>
---------
Signed-off-by: Russell Bryant <rbryant@redhat.com>
179 lines
7.4 KiB
Python
179 lines
7.4 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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import logging
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from typing import Any, Optional, Set, Type
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import torch
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from vllm.adapter_commons.utils import (add_adapter_worker,
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apply_adapters_worker,
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list_adapters_worker,
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set_active_adapters_worker)
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from vllm.adapter_commons.worker_manager import AbstractWorkerManager
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from vllm.config import PromptAdapterConfig
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from vllm.prompt_adapter.models import (LRUCachePromptAdapterModelManager,
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PromptAdapterModel,
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PromptAdapterModelManager,
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create_prompt_adapter_manager)
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from vllm.prompt_adapter.request import PromptAdapterRequest
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logger = logging.getLogger(__name__)
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class WorkerPromptAdapterManager(AbstractWorkerManager):
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"""WorkerPromptAdapterManager that manages
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prompt_adapter models on the worker side.
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Every request, the requested prompt_adapters will be
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loaded (unless they are already loaded),
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and every other prompt_adapter will be unloaded."""
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_manager_cls: Type[PromptAdapterModelManager] = PromptAdapterModelManager
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def __init__(
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self,
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max_num_seqs: int,
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max_num_batched_tokens: int,
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device: torch.device,
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prompt_adapter_config: PromptAdapterConfig,
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prompt_adapter_model_cls: Type[PromptAdapterModel] = PromptAdapterModel
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):
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self._adapter_manager: PromptAdapterModelManager
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self.max_num_seqs = max_num_seqs
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self.max_num_batched_tokens = max_num_batched_tokens
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self._prompt_adapter_model_cls = prompt_adapter_model_cls
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self.prompt_adapter_config = prompt_adapter_config
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super().__init__(device)
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@property
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def is_enabled(self) -> bool:
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return True
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def create_prompt_adapter_manager(
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self,
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model: torch.nn.Module,
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) -> Any:
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prompt_adapter_manager = create_prompt_adapter_manager(
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model,
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max_num_seqs=self.max_num_seqs,
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max_num_batched_tokens=self.max_num_batched_tokens,
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prompt_adapter_config=self.prompt_adapter_config,
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prompt_adapter_manager_cls=self._manager_cls,
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)
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self._adapter_manager = prompt_adapter_manager
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return prompt_adapter_manager.model
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def _load_adapter(
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self, prompt_adapter_request: PromptAdapterRequest
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) -> PromptAdapterModel:
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try:
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prompt_adapter = (
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self._prompt_adapter_model_cls.from_local_checkpoint(
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prompt_adapter_request.prompt_adapter_local_path,
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prompt_adapter_id=prompt_adapter_request.prompt_adapter_id,
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num_virtual_tokens=prompt_adapter_request.
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prompt_adapter_num_virtual_tokens,
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config=self.prompt_adapter_config,
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device=str(self.device),
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))
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except Exception as e:
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raise RuntimeError(
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f"Loading prompt_adapter "
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f"{prompt_adapter_request.prompt_adapter_local_path}"
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f" failed") from e
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return prompt_adapter
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def add_dummy_prompt_adapter(
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self, prompt_adapter_request: PromptAdapterRequest) -> bool:
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return True
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def pin_adapter(self, adapter_id: int) -> bool:
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return self._adapter_manager.pin_adapter(adapter_id)
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def set_active_adapters(self, requests: Set[Any],
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mapping: Optional[Any]) -> None:
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set_active_adapters_worker(requests, mapping, self._apply_adapters,
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self._adapter_manager.set_adapter_mapping)
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def add_adapter(self, adapter_request: Any) -> bool:
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return add_adapter_worker(adapter_request, self.list_adapters,
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self._load_adapter,
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self._adapter_manager.add_adapter,
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self._adapter_manager.activate_adapter)
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def _apply_adapters(self, adapter_requests: Set[Any]) -> None:
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apply_adapters_worker(adapter_requests, self.list_adapters,
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self._adapter_manager.adapter_slots,
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self.remove_adapter, self.add_adapter)
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def remove_adapter(self, adapter_id: int) -> bool:
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return self._adapter_manager.remove_adapter(adapter_id)
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def remove_all_adapters(self):
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self._adapter_manager.remove_all_adapters()
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def list_adapters(self) -> Set[int]:
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return list_adapters_worker(self._adapter_manager.list_adapters)
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class LRUCacheWorkerPromptAdapterManager(WorkerPromptAdapterManager):
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"""WorkerPromptAdapterManager that manages
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prompt_adapter models on the worker side.
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Uses an LRU Cache. Every request, the requested
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prompt_adapters will be loaded (unless they are already loaded)
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and least recently used prompt_adapters will
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be unloaded if the cache is above capacity."""
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_prompt_adapter_manager_cls: Type[
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LRUCachePromptAdapterModelManager] = LRUCachePromptAdapterModelManager
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def create_prompt_adapter_manager(
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self,
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model: torch.nn.Module,
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) -> Any:
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prompt_adapter_manager = create_prompt_adapter_manager(
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model,
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max_num_seqs=self.max_num_seqs,
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max_num_batched_tokens=self.max_num_batched_tokens,
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prompt_adapter_config=self.prompt_adapter_config,
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prompt_adapter_manager_cls=self._prompt_adapter_manager_cls)
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self._adapter_manager: LRUCachePromptAdapterModelManager = (
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prompt_adapter_manager)
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return prompt_adapter_manager.model
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def _apply_adapters(
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self, prompt_adapter_requests: Set[PromptAdapterRequest]) -> None:
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prompt_adapters_map = {
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prompt_adapter_request.prompt_adapter_id: prompt_adapter_request
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for prompt_adapter_request in prompt_adapter_requests
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if prompt_adapter_request
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}
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if len(prompt_adapters_map
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) > self._adapter_manager.prompt_adapter_slots:
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raise RuntimeError(
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f"Number of requested prompt_adapters "
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f"({len(prompt_adapters_map)}) is greater "
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"than the number of GPU prompt_adapter slots "
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f"({self._adapter_manager.prompt_adapter_slots}).")
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for prompt_adapter in prompt_adapters_map.values():
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self.add_adapter(prompt_adapter)
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def add_adapter(self,
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prompt_adapter_request: PromptAdapterRequest) -> bool:
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if prompt_adapter_request.prompt_adapter_id not in self.list_adapters(
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):
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# Remove before we load the new prompt_adapter to save memory
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if len(self._adapter_manager) + 1 > self._adapter_manager.capacity:
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self._adapter_manager.remove_oldest_adapter()
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prompt_adapter = self._load_adapter(prompt_adapter_request)
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loaded = self._adapter_manager.add_adapter(prompt_adapter)
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else:
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# If the prompt_adapter is already loaded, just touch it to
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# update its position in the caches
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loaded = self._adapter_manager.get_adapter(
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prompt_adapter_request.prompt_adapter_id) is not None
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self._adapter_manager.activate_adapter(
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prompt_adapter_request.prompt_adapter_id)
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return loaded
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