forked from phoenix-oss/llama-stack-mirror
99 lines
3.3 KiB
Python
99 lines
3.3 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 os
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from copy import deepcopy
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from functools import partial
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from typing import Generator, List, Optional
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from llama_models.llama3.api.chat_format import ChatFormat
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from llama_models.llama3.api.datatypes import Message, ToolPromptFormat
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from llama_models.llama3.api.tokenizer import Tokenizer
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from llama_models.sku_list import resolve_model
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from .config import MetaReferenceInferenceConfig
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from .generation import Llama, model_checkpoint_dir
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from .parallel_utils import InferenceArgs, ModelParallelProcessGroup
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class ModelRunner:
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def __init__(self, llama):
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self.llama = llama
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# the `task` object is the same that is sent to `ModelParallelProcessGroup.run_inference()`
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def __call__(self, task: InferenceArgs):
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return self.llama.chat_completion(
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task.messages,
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task.temperature,
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task.top_p,
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task.max_gen_len,
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task.logprobs,
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task.tool_prompt_format,
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)
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def init_model_cb(config: MetaReferenceInferenceConfig):
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llama = Llama.build(config)
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return ModelRunner(llama)
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class LlamaModelParallelGenerator:
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"""
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This abstraction exists so
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- we can run model parallel code without needing to run the CLIs via torchrun
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- this also enables use model parallel code within a notebook context.
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A Context Manager is used to ensure that the model parallel process is started and stopped
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correctly. This does make the ergonomics a little awkward, because it isn't immediately
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clear at the callsite why we need to use a context manager.
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"""
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def __init__(self, config: MetaReferenceInferenceConfig):
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self.config = config
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self.model = resolve_model(self.config.model)
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# this is a hack because Agent's loop uses this to tokenize and check if input is too long
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# while the tool-use loop is going
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checkpoint_dir = model_checkpoint_dir(self.model)
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tokenizer_path = os.path.join(checkpoint_dir, "tokenizer.model")
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self.formatter = ChatFormat(Tokenizer(tokenizer_path))
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def start(self):
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self.__enter__()
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def stop(self):
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self.__exit__(None, None, None)
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def __enter__(self):
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self.group = ModelParallelProcessGroup(
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self.config.model_parallel_size,
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init_model_cb=partial(init_model_cb, self.config),
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)
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self.group.start()
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return self
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def __exit__(self, exc_type, exc_value, exc_traceback):
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self.group.stop()
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def chat_completion(
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self,
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messages: List[Message],
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temperature: float = 0.6,
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top_p: float = 0.9,
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max_gen_len: Optional[int] = None,
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logprobs: bool = False,
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tool_prompt_format: ToolPromptFormat = ToolPromptFormat.json,
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) -> Generator:
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req_obj = InferenceArgs(
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messages=deepcopy(messages),
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temperature=temperature,
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top_p=top_p,
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max_gen_len=max_gen_len,
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logprobs=logprobs or False,
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tool_prompt_format=tool_prompt_format,
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)
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gen = self.group.run_inference(req_obj)
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yield from gen
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