Spaces:
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Sleeping
claudiubarbu
commited on
Commit
•
76a36b2
1
Parent(s):
db72730
added streaming endpoint
Browse files- Dockerfile +13 -0
- app.py +119 -0
- requirements.txt +3 -0
Dockerfile
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FROM python:3.9
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RUN useradd -m -u 1000 user
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USER user
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ENV PATH="/home/user/.local/bin:$PATH"
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WORKDIR /app
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COPY --chown=user ./requirements.txt requirements.txt
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RUN pip install --no-cache-dir --upgrade -r requirements.txt
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COPY --chown=user . /app
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CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]
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app.py
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from fastapi import FastAPI, Request
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from fastapi.responses import StreamingResponse
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from pydantic import BaseModel
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from vllm import AsyncLLMEngine, SamplingParams
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from vllm.engine.arg_utils import AsyncEngineArgs
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import json
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import uuid
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app = FastAPI()
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# TODO: In the AsyncEngineArgs select the additional parameters
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# to make this deployment efficient. Specifically, consider:
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# - max_num_batched_tokens: Sets the maximum number of tokens that can be processed
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# in a single batch. Make sure to accommodate for the memory constraints of GPU hosting the application.
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# - max_num_seqs: Limits the maximum number of sequences that can
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# be processed concurrently. Smaller numbers will reduce the memory pressure on the GPU.
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# - gpu_memory_utilization: Sets the target GPU memory utilization.
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# Adjust to make more efficient use of available GPU memory.
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# - max_model_len: Specifies the maximum sequence length the model can handle.
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# - enforce_eager: Disables or enables CUDA graph optimization. This can be useful
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# for debugging or when CUDA graph optimization causes issues.
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# - dtype='half': Sets the data type for model parameters to half-precision
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# (float16). This reduces memory usage and can speed up computations, especially on GPUs with good half-precision performance.
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engine = AsyncLLMEngine.from_engine_args(
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AsyncEngineArgs(
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model='claudiubarbu/HW2-orpo',
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max_num_batched_tokens=1024,
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max_num_seqs=8,
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gpu_memory_utilization=0.8,
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max_model_len=512,
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enforce_eager=True,
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dtype='half',
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)
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)
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class GenerationRequest(BaseModel):
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# FastAPI uses classes like GenerationRequest for several important reasons:
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# - Automatic Request Parsing
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# - Data Validation
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# - Default Values
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# - Self-Documenting APIs
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# - Type Safety in Your Code
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prompt: str
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max_tokens: int = 100
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temperature: float = 0.7
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async def generate_stream(prompt: str, max_tokens: int, temperature: float):
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"""
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The function generate_stream is an asynchronous generator that produces a stream of
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text from a language model. Asynchronous functions can pause their execution,
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allowing other code to run while waiting for operations to complete.
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prompt: The initial text to start the generation.
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max_tokens: The maximum number of tokens (words or word pieces) to generate.
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temperature: Controls the randomness of the generation. Higher values (e.g., 1.0)
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make output more random, while lower values (e.g., 0.1) make it more deterministic.
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"""
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# SamplingParams configures how the text generation will behave.
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# It uses the temperature and max_tokens values passed to the function.
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sampling_params = SamplingParams(
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temperature=temperature,
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max_tokens=max_tokens
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)
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# The request_id is used by vLLM to track different generation requests,
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# especially useful in scenarios with multiple concurrent requests.
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# Using a UUID ensures that each request has a unique identifier,
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# preventing conflicts between different generation tasks.
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request_id = str(uuid.uuid4())
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# async for is an asynchronous loop that works with asynchronous generators.
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# engine.generate() is an instance of the language model that generates text
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# based on the given prompt and parameters. The loop will receive chunks of
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# generated text one at a time rather than waiting for the entire text to be generated.
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# The generate function requires a request_id, which I set to 1
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async for output in engine.generate(prompt, sampling_params, request_id=request_id):
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# yield is used in generator functions to produce a series of values
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# over time rather than computing them all at once. The yielded string
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# follows the Server-Sent Events (SSE) format:
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# - It starts with "data: ".
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# - The content is a JSON string containing the generated text.
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# - It ends with two newlines (\n\n) to signal the end of an SSE message.
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yield f"data: {json.dumps({'text': output.outputs[0].text})}\n\n"
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# After the generation is complete, we yield a special "DONE" signal,
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# also in SSE format, to indicate that the stream has ended.
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yield "data: [DONE]\n\n"
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# This line tells FastAPI that this function should handle POST requests
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# to the "/generate-stream" endpoint.
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@app.post("/generate-stream")
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async def generate_text(request: GenerationRequest):
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"""
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The function generate_text is a FastAPI route that handles POST requests to "/generate-stream".
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It's designed to stream generated text back to the client as it's being produced
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rather than waiting for all the text to be generated before sending a response.
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"""
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try:
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# StreamingResponse is used to send a streaming response back to the client.
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# generate_stream() is called with the parameters from the request. This function is expected to be a generator that yields chunks of text.
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# media_type="text/event-stream" indicates that this is a Server-Sent Events (SSE) stream, a format for sending real-time updates from server to client.
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return StreamingResponse(
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generate_stream(request.prompt, request.max_tokens, request.temperature),
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media_type="text/event-stream"
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)
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except Exception as e:
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# If an exception occurs, it returns a streaming response with an error message,
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# maintaining the SSE format.
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return StreamingResponse(
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iter([f"data: {json.dumps({'error': str(e)})}\n\n"]),
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media_type="text/event-stream"
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)
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@app.get("/")
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def greet_json():
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return {"Hello": "World!"}
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requirements.txt
ADDED
@@ -0,0 +1,3 @@
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fastapi
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uvicorn[standard]
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vllm
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