mirror of
https://github.com/meta-llama/llama-stack.git
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151 lines
4.7 KiB
Python
151 lines
4.7 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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import asyncio
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import json
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from typing import Any, AsyncGenerator, List, Optional
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import fire
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import httpx
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from llama_models.llama3.api.datatypes import ImageMedia, URL
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from PIL import Image as PIL_Image
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from pydantic import BaseModel
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from llama_models.llama3.api import * # noqa: F403
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from llama_stack.apis.inference import * # noqa: F403
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from termcolor import cprint
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from llama_stack.distribution.datatypes import RemoteProviderConfig
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from .event_logger import EventLogger
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async def get_client_impl(config: RemoteProviderConfig, _deps: Any) -> Inference:
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return InferenceClient(config.url)
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def encodable_dict(d: BaseModel):
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return json.loads(d.json())
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class InferenceClient(Inference):
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def __init__(self, base_url: str):
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self.base_url = base_url
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async def initialize(self) -> None:
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pass
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async def shutdown(self) -> None:
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pass
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async def completion(self, request: CompletionRequest) -> AsyncGenerator:
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raise NotImplementedError()
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async def chat_completion(
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self,
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model: str,
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messages: List[Message],
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sampling_params: Optional[SamplingParams] = SamplingParams(),
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tools: Optional[List[ToolDefinition]] = None,
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tool_choice: Optional[ToolChoice] = ToolChoice.auto,
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tool_prompt_format: Optional[ToolPromptFormat] = ToolPromptFormat.json,
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stream: Optional[bool] = False,
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logprobs: Optional[LogProbConfig] = None,
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) -> AsyncGenerator:
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request = ChatCompletionRequest(
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model=model,
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messages=messages,
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sampling_params=sampling_params,
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tools=tools or [],
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tool_choice=tool_choice,
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tool_prompt_format=tool_prompt_format,
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stream=stream,
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logprobs=logprobs,
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)
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async with httpx.AsyncClient() as client:
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async with client.stream(
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"POST",
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f"{self.base_url}/inference/chat_completion",
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json=encodable_dict(request),
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headers={"Content-Type": "application/json"},
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timeout=20,
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) as response:
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if response.status_code != 200:
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content = await response.aread()
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cprint(
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f"Error: HTTP {response.status_code} {content.decode()}", "red"
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)
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return
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async for line in response.aiter_lines():
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if line.startswith("data:"):
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data = line[len("data: ") :]
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try:
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if request.stream:
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if "error" in data:
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cprint(data, "red")
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continue
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yield ChatCompletionResponseStreamChunk(
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**json.loads(data)
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)
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else:
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yield ChatCompletionResponse(**json.loads(data))
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except Exception as e:
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print(data)
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print(f"Error with parsing or validation: {e}")
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async def run_main(host: str, port: int, stream: bool):
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client = InferenceClient(f"http://{host}:{port}")
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message = UserMessage(
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content="hello world, write me a 2 sentence poem about the moon"
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)
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cprint(f"User>{message.content}", "green")
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iterator = client.chat_completion(
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model="Llama3.1-8B-Instruct",
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messages=[message],
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stream=stream,
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)
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async for log in EventLogger().log(iterator):
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log.print()
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async def run_mm_main(host: str, port: int, stream: bool, path: str):
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client = InferenceClient(f"http://{host}:{port}")
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with open(path, "rb") as f:
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img = PIL_Image.open(f).convert("RGB")
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message = UserMessage(
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content=[
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ImageMedia(image=URL(uri=f"file://{path}")),
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# ImageMedia(image=img),
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"Describe this image in two sentences",
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],
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)
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cprint(f"User>{message.content}", "green")
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iterator = client.chat_completion(
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model="Llama3.2-11B-Vision-Instruct",
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messages=[message],
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stream=stream,
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)
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async for log in EventLogger().log(iterator):
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log.print()
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def main(host: str, port: int, stream: bool = True, mm: bool = False, file: str = None):
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if mm:
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asyncio.run(run_mm_main(host, port, stream, file))
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else:
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asyncio.run(run_main(host, port, stream))
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if __name__ == "__main__":
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fire.Fire(main)
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