wehub-resource-sync
138 lines
4.3 KiB
Python
138 lines
4.3 KiB
Python
# __model_def_start__
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import asyncio
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from functools import partial
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from queue import Empty
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from typing import Dict, Any
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from starlette.requests import Request
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from starlette.responses import StreamingResponse
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import torch
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from ray import serve
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# Define the Ray Serve deployment
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@serve.deployment(ray_actor_options={"num_cpus": 10, "resources": {"HPU": 1}})
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class LlamaModel:
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def __init__(self, model_id_or_path: str):
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from transformers import AutoTokenizer, AutoModelForCausalLM, AutoConfig
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from optimum.habana.transformers.modeling_utils import (
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adapt_transformers_to_gaudi,
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)
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# Tweak transformers to optimize performance
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adapt_transformers_to_gaudi()
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self.device = torch.device("hpu")
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self.tokenizer = AutoTokenizer.from_pretrained(
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model_id_or_path, use_fast=False, use_auth_token=""
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)
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hf_config = AutoConfig.from_pretrained(
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model_id_or_path,
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torchscript=True,
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use_auth_token="",
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trust_remote_code=False,
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)
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# Load the model in Gaudi
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model = AutoModelForCausalLM.from_pretrained(
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model_id_or_path,
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config=hf_config,
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torch_dtype=torch.float32,
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low_cpu_mem_usage=True,
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use_auth_token="",
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)
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model = model.eval().to(self.device)
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from habana_frameworks.torch.hpu import wrap_in_hpu_graph
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# Enable hpu graph runtime
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self.model = wrap_in_hpu_graph(model)
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# Set pad token, etc.
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self.tokenizer.pad_token_id = self.model.generation_config.pad_token_id
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self.tokenizer.padding_side = "left"
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# Use async loop in streaming
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self.loop = asyncio.get_running_loop()
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def tokenize(self, prompt: str):
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"""Tokenize the input and move to HPU."""
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input_tokens = self.tokenizer(prompt, return_tensors="pt", padding=True)
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return input_tokens.input_ids.to(device=self.device)
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def generate(self, prompt: str, **config: Dict[str, Any]):
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"""Take a prompt and generate a response."""
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input_ids = self.tokenize(prompt)
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gen_tokens = self.model.generate(input_ids, **config)
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return self.tokenizer.batch_decode(gen_tokens, skip_special_tokens=True)[0]
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async def consume_streamer_async(self, streamer):
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"""Consume the streamer asynchronously."""
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while True:
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try:
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for token in streamer:
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yield token
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break
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except Empty:
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await asyncio.sleep(0.001)
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def streaming_generate(self, prompt: str, streamer, **config: Dict[str, Any]):
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"""Generate a streamed response given an input."""
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input_ids = self.tokenize(prompt)
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self.model.generate(input_ids, streamer=streamer, **config)
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async def __call__(self, http_request: Request):
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"""Handle HTTP requests."""
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# Load fields from the request
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json_request: str = await http_request.json()
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text = json_request["text"]
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# Config used in generation
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config = json_request.get("config", {})
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streaming_response = json_request["stream"]
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# Prepare prompts
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prompts = []
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if isinstance(text, list):
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prompts.extend(text)
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else:
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prompts.append(text)
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# Process config
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config.setdefault("max_new_tokens", 128)
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# Enable HPU graph runtime
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config["hpu_graphs"] = True
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# Lazy mode should be True when using HPU graphs
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config["lazy_mode"] = True
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# Non-streaming case
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if not streaming_response:
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return self.generate(prompts, **config)
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# Streaming case
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from transformers import TextIteratorStreamer
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streamer = TextIteratorStreamer(
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self.tokenizer, skip_prompt=True, timeout=0, skip_special_tokens=True
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)
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# Convert the streamer into a generator
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self.loop.run_in_executor(
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None, partial(self.streaming_generate, prompts, streamer, **config)
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)
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return StreamingResponse(
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self.consume_streamer_async(streamer),
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status_code=200,
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media_type="text/plain",
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)
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# Replace the model ID with path if necessary
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entrypoint = LlamaModel.bind("meta-llama/Llama-2-7b-chat-hf")
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# __model_def_end__
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