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80 lines
2.4 KiB
Python
80 lines
2.4 KiB
Python
# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the terms described in the LICENSE file in
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# the root directory of this source tree.
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from typing import Optional
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from llama_models.schema_utils import json_schema_type
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from pydantic import BaseModel, Field
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from llama_stack.providers.utils.docker.service_config import DockerComposeServiceConfig
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DEFAULT_VLLM_PORT = 8000
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@json_schema_type
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class VLLMInferenceAdapterConfig(BaseModel):
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url: Optional[str] = Field(
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default=None,
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description="The URL for the vLLM model serving endpoint",
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)
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max_tokens: int = Field(
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default=4096,
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description="Maximum number of tokens to generate.",
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)
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api_token: Optional[str] = Field(
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default="fake",
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description="The API token",
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)
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@classmethod
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def sample_run_config(
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cls,
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url: str = "${env.VLLM_URL:http://host.docker.internal:5100/v1}",
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):
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return {
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"url": url,
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"max_tokens": "${env.VLLM_MAX_TOKENS:4096}",
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"api_token": "${env.VLLM_API_TOKEN:fake}",
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}
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@classmethod
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def sample_docker_compose_config(
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cls,
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port: int = DEFAULT_VLLM_PORT,
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cuda_visible_devices: str = "0",
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model: str = "meta-llama/Llama-3.2-3B-Instruct",
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) -> Optional[DockerComposeServiceConfig]:
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return DockerComposeServiceConfig(
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image="vllm/vllm-openai:latest",
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volumes=["$HOME/.cache/huggingface:/root/.cache/huggingface"],
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devices=["nvidia.com/gpu=all"],
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deploy={
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"resources": {
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"reservations": {
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"devices": [{"driver": "nvidia", "capabilities": ["gpu"]}]
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}
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}
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},
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runtime="nvidia",
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ports=[f"{port}:{port}"],
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environment={
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"CUDA_VISIBLE_DEVICES": cuda_visible_devices,
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"HUGGING_FACE_HUB_TOKEN": "$HF_TOKEN",
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},
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command=(
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" ".join(
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[
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"--gpu-memory-utilization 0.75",
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f"--model {model}",
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"--enforce-eager",
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"--max-model-len 8192",
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"--max-num-seqs 16",
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f"--port {port}",
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]
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)
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),
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)
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