app_modules/llm_loader.py

import os
import sys
import threading
from queue import Queue
from typing import Any, Dict, List, Optional

import torch
from langchain.callbacks.base import BaseCallbackHandler
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
from langchain_openai.chat_models import ChatOpenAI
from langchain_openai.llms import OpenAI
from langchain_google_genai import (
    ChatGoogleGenerativeAI,
    HarmBlockThreshold,
    HarmCategory,
)
from langchain_community.llms import (
    HuggingFaceTextGenInference,
    CTransformers,
    GPT4All,
    HuggingFacePipeline,
    LlamaCpp,
    VLLM,
)
from langchain_community.chat_models import ChatOllama
from langchain.schema import LLMResult
from transformers import (
    AutoConfig,
    AutoModelForCausalLM,
    AutoModelForSeq2SeqLM,
    AutoTokenizer,
    BitsAndBytesConfig,
    StoppingCriteria,
    StoppingCriteriaList,
    T5Tokenizer,
    TextStreamer,
    pipeline,
)

from app_modules.utils import ensure_model_is_downloaded


class TextIteratorStreamer(TextStreamer, StreamingStdOutCallbackHandler):
    def __init__(
        self,
        tokenizer: "AutoTokenizer",
        skip_prompt: bool = False,
        timeout: Optional[float] = None,
        for_huggingface: bool = False,
        **decode_kwargs,
    ):
        super().__init__(tokenizer, skip_prompt, **decode_kwargs)
        self.text_queue = Queue()
        self.stop_signal = None
        self.timeout = timeout
        self.total_tokens = 0
        self.for_huggingface = for_huggingface
        self.end_token = ""

    def on_finalized_text(self, text: str, stream_end: bool = False):
        super().on_finalized_text(text, stream_end=stream_end)

        """Put the new text in the queue. If the stream is ending, also put a stop signal in the queue."""
        self.text_queue.put(text, timeout=self.timeout)
        self.total_tokens = self.total_tokens + 1
        if stream_end:
            print("\n")
            self.text_queue.put("\n", timeout=self.timeout)
            self.text_queue.put(self.stop_signal, timeout=self.timeout)

    def check_end_token(self, token):
        new_token = self.end_token + token
        if "<|im_end|>".startswith(new_token):
            self.end_token = "" if new_token == "<|im_end|>" else new_token
            return None
        elif self.end_token != "":
            self.end_token = ""

        return new_token

    def on_llm_new_token(self, token: str, **kwargs: Any) -> None:
        token = self.check_end_token(token)
        if token:
            sys.stdout.write(token)
            sys.stdout.flush()
            self.text_queue.put(token, timeout=self.timeout)
            self.total_tokens = self.total_tokens + 1

    def on_llm_start(
        self, serialized: Dict[str, Any], prompts: List[str], **kwargs: Any
    ) -> Any:
        # print("on_llm_start:", serialized, prompts)
        pass

    def on_llm_end(self, response: LLMResult, **kwargs: Any) -> None:
        print("\n")
        self.text_queue.put("\n", timeout=self.timeout)
        self.text_queue.put(self.stop_signal, timeout=self.timeout)

    def __iter__(self):
        return self

    def __next__(self):
        value = self.text_queue.get(timeout=self.timeout)
        if value == self.stop_signal:
            raise StopIteration()
        else:
            return value

    def reset(self, q: Queue = None):
        # print("resetting TextIteratorStreamer")
        self.text_queue = q if q is not None else Queue()
        self.end_token = ""

    def empty(self):
        return self.text_queue.empty()


class LLMLoader:
    def __init__(self, llm_model_type):
        self.llm_model_type = llm_model_type
        self.llm = None
        self.streamer = TextIteratorStreamer(
            "",
            for_huggingface=True,
        )
        self.max_tokens_limit = 4096
        self.search_kwargs = {"k": 8}
        self.lock = threading.Lock()
        self.model_name = os.getenv("HUGGINGFACE_MODEL_NAME_OR_PATH").split("/")[-1]
        self.repetition_penalty = ""
        self.batch_size = int(os.getenv("BATCH_SIZE", "1"))

    def _init_hf_streamer(self, tokenizer):
        if self.batch_size == 1:
            self.streamer = TextIteratorStreamer(
                tokenizer,
                timeout=10.0,
                skip_prompt=True,
                skip_special_tokens=True,
                for_huggingface=True,
            )
        else:
            self.streamer = None

    def huggingfaceStreamingEnabled(self):
        return self.streamer is not None

    def init(
        self,
        custom_handler: Optional[BaseCallbackHandler] = None,
        n_threds: int = 4,
        hf_pipeline_device_type: str = None,
    ):
        print("initializing LLM: " + self.llm_model_type)

        if hf_pipeline_device_type is None:
            hf_pipeline_device_type = "cpu"

        using_cuda = hf_pipeline_device_type.startswith("cuda")
        using_mps = hf_pipeline_device_type.startswith("mps")
        torch_dtype = torch.float16 if using_cuda or using_mps else torch.float32
        if not using_mps and os.environ.get("USING_TORCH_BFLOAT16") == "true":
            torch_dtype = torch.bfloat16

        load_quantized_model = os.environ.get("LOAD_QUANTIZED_MODEL")

        print(f"  hf_pipeline_device_type: {hf_pipeline_device_type}")
        print(f"     load_quantized_model: {load_quantized_model}")
        print(f"              torch_dtype: {torch_dtype}")
        print(f"                 n_threds: {n_threds}")

        torch.set_default_dtype(torch_dtype)

        double_quant_config = BitsAndBytesConfig(
            load_in_4bit=load_quantized_model == "4bit",
            bnb_4bit_use_double_quant=load_quantized_model == "4bit",
            load_in_8bit=load_quantized_model == "8bit",
            bnb_8bit_use_double_quant=load_quantized_model == "8bit",
        )

        callbacks = []
        if self.streamer is not None and self.streamer.for_huggingface:
            callbacks.append(self.streamer)
        if custom_handler is not None:
            callbacks.append(custom_handler)

        if self.llm is None:
            if self.llm_model_type == "openai":
                MODEL_NAME = os.environ.get("OPENAI_MODEL_NAME") or "gpt-3.5-turbo"
                print(f"              using model: {MODEL_NAME}")
                self.model_name = MODEL_NAME
                self.llm = (
                    OpenAI(
                        model_name=MODEL_NAME,
                        streaming=True,
                        callbacks=callbacks,
                        verbose=True,
                        temperature=0,
                    )
                    if "instruct" in MODEL_NAME
                    else ChatOpenAI(
                        model_name=MODEL_NAME,
                        streaming=True,
                        callbacks=callbacks,
                        verbose=True,
                        temperature=0,
                    )
                )
            elif self.llm_model_type == "google":
                MODEL_NAME = os.environ.get("GOOGLE_MODEL_NAME") or "gemini-pro"
                print(f"              using model: {MODEL_NAME}")
                self.llm = ChatGoogleGenerativeAI(
                    model=MODEL_NAME,
                    convert_system_message_to_human=True,
                    callbacks=callbacks,
                    streaming=True,
                    safety_settings={
                        HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT: HarmBlockThreshold.BLOCK_NONE,
                    },
                )
            elif self.llm_model_type.startswith("gpt4all"):
                MODEL_PATH = ensure_model_is_downloaded(self.llm_model_type)
                self.llm = GPT4All(
                    model=MODEL_PATH,
                    max_tokens=2048,
                    n_threads=n_threds,
                    backend="gptj" if self.llm_model_type == "gpt4all-j" else "llama",
                    callbacks=callbacks,
                    verbose=True,
                    use_mlock=True,
                )
            elif self.llm_model_type == "llamacpp":
                MODEL_PATH = ensure_model_is_downloaded(self.llm_model_type)
                self.llm = LlamaCpp(
                    model_path=MODEL_PATH,
                    n_ctx=8192,
                    n_threads=n_threds,
                    seed=0,
                    temperature=0,
                    max_tokens=2048,
                    callbacks=callbacks,
                    verbose=True,
                    use_mlock=True,
                )
            elif self.llm_model_type == "ctransformers":
                MODEL_PATH = ensure_model_is_downloaded(self.llm_model_type)
                config = {
                    "max_new_tokens": self.max_tokens_limit,
                    "repetition_penalty": 1.1,
                }
                self.llm = CTransformers(
                    model=MODEL_PATH,
                    model_type="llama",
                    config=config,
                    callbacks=callbacks,
                )
            elif self.llm_model_type == "hftgi":
                HFTGI_SERVER_URL = os.environ.get("HFTGI_SERVER_URL")
                HFTGI_RP = os.environ.get("HFTGI_RP")
                repetition_penalty = 1.120 if HFTGI_RP is None else float(HFTGI_RP)
                print(f"       repetition_penalty: {repetition_penalty}")
                self.repetition_penalty = repetition_penalty
                self.max_tokens_limit = 4096
                self.llm = HuggingFaceTextGenInference(
                    inference_server_url=HFTGI_SERVER_URL,
                    max_new_tokens=self.max_tokens_limit / 2,
                    # top_k=0,
                    top_p=0.95,
                    # typical_p=0.95,
                    temperature=0.01,
                    repetition_penalty=repetition_penalty,
                    callbacks=callbacks,
                    timeout=600,
                    streaming=True,
                )
            elif self.llm_model_type == "ollama":
                MODEL_NAME = os.environ.get("OLLAMA_MODEL_NAME") or "mistral"
                self.model_name = MODEL_NAME
                print(f"            loading model: {MODEL_NAME}")

                OLLAMA_RP = os.getenv("OLLAMA_RP")
                repetition_penalty = float(OLLAMA_RP) if OLLAMA_RP else 1.15
                self.repetition_penalty = repetition_penalty
                print(f"       repetition_penalty: {repetition_penalty}")

                self.llm = ChatOllama(
                    model=MODEL_NAME,
                    callbacks=callbacks,
                    temperature=0,
                    repeat_penalty=repetition_penalty,
                    max_new_tokens=2048,
                    max_tokens=8192,
                )
            elif self.llm_model_type == "vllm":
                MODEL_NAME = (
                    os.environ.get("HUGGINGFACE_MODEL_NAME_OR_PATH")
                    or "google/gemma-1.1-2b-it"
                )
                print(f"            loading model: {MODEL_NAME}")

                VLLM_RP = os.getenv("HF_RP")
                repetition_penalty = float(VLLM_RP) if VLLM_RP else 1.15
                self.repetition_penalty = repetition_penalty
                print(f"       repetition_penalty: {repetition_penalty}")

                vllm_kwargs = {
                    "max_model_len": 4096,
                    "enforce_eager": True,
                }

                quantization = os.getenv("VLLM_QUANTIZATION")
                if quantization:
                    vllm_kwargs["quantization"] = quantization

                self.llm = VLLM(
                    model=MODEL_NAME,
                    callbacks=callbacks,
                    temperature=0,
                    repeat_penalty=repetition_penalty,
                    top_p=0.95,
                    max_new_tokens=2048,
                    max_tokens=8192,
                    tensor_parallel_size=torch.cuda.device_count(),
                    trust_remote_code=True,
                    vllm_kwargs=vllm_kwargs,
                )
            elif self.llm_model_type.startswith("huggingface"):
                MODEL_NAME_OR_PATH = os.environ.get("HUGGINGFACE_MODEL_NAME_OR_PATH")
                print(f"            loading model: {MODEL_NAME_OR_PATH}")

                hf_auth_token = (
                    os.environ.get("HUGGINGFACE_AUTH_TOKEN")
                    if "Llama-2" in MODEL_NAME_OR_PATH
                    or "gemma" in MODEL_NAME_OR_PATH
                    or "Mistral" in MODEL_NAME_OR_PATH
                    else None
                )
                transformers_offline = os.environ.get("TRANSFORMERS_OFFLINE") == "1"
                token = (
                    hf_auth_token
                    if hf_auth_token is not None
                    and len(hf_auth_token) > 0
                    and not transformers_offline
                    else None
                )
                print(f"            HF auth token: {str(token)[-5:]}")

                if "Llama-2" in MODEL_NAME_OR_PATH:
                    self.max_tokens_limit = 4096
                elif "TinyLlama" in MODEL_NAME_OR_PATH:
                    self.max_tokens_limit = 1024

                    class StopOnTokens(StoppingCriteria):
                        def __call__(
                            self,
                            input_ids: torch.LongTensor,
                            scores: torch.FloatTensor,
                            **kwargs,
                        ) -> bool:
                            stop_ids = [
                                2
                            ]  # IDs of tokens where the generation should stop.
                            for stop_id in stop_ids:
                                if (
                                    input_ids[0][-1] == stop_id
                                ):  # Checking if the last generated token is a stop token.
                                    return True
                            return False

                    stopping_criteria = StoppingCriteriaList([StopOnTokens()])

                is_t5 = "t5" in MODEL_NAME_OR_PATH
                temperature = (
                    0.01
                    if "gpt4all-j" in MODEL_NAME_OR_PATH
                    or "dolly" in MODEL_NAME_OR_PATH
                    or "Qwen" in MODEL_NAME_OR_PATH
                    or "Llama" in MODEL_NAME_OR_PATH
                    or "Orca-2" in MODEL_NAME_OR_PATH
                    or "phi-2" in MODEL_NAME_OR_PATH
                    or "Phi-3" in MODEL_NAME_OR_PATH
                    or "Mistral" in MODEL_NAME_OR_PATH
                    or "gemma" in MODEL_NAME_OR_PATH
                    else 0
                )

                use_fast = (
                    "stable" in MODEL_NAME_OR_PATH
                    or "RedPajama" in MODEL_NAME_OR_PATH
                    or "dolly" in MODEL_NAME_OR_PATH
                )
                padding_side = "left"  # if "dolly" in MODEL_NAME_OR_PATH else None

                config = (
                    AutoConfig.from_pretrained(
                        MODEL_NAME_OR_PATH,
                        trust_remote_code=True,
                        token=token,
                        fp32=hf_pipeline_device_type == "cpu",
                        bf16=(
                            hf_pipeline_device_type != "cpu"
                            and torch_dtype == torch.bfloat16
                        ),
                        fp16=(
                            hf_pipeline_device_type != "cpu"
                            and torch_dtype != torch.bfloat16
                        ),
                    )
                    if "Qwen" in MODEL_NAME_OR_PATH
                    else AutoConfig.from_pretrained(
                        MODEL_NAME_OR_PATH,
                        trust_remote_code=True,
                        token=token,
                    )
                )

                # config.attn_config["attn_impl"] = "triton"
                # config.max_seq_len = 4096
                # config.init_device = hf_pipeline_device_type

                tokenizer = (
                    T5Tokenizer.from_pretrained(
                        MODEL_NAME_OR_PATH,
                        token=token,
                    )
                    if is_t5
                    else AutoTokenizer.from_pretrained(
                        MODEL_NAME_OR_PATH,
                        use_fast=use_fast,
                        trust_remote_code=True,
                        padding_side=padding_side,
                        token=token,
                    )
                )

                self._init_hf_streamer(tokenizer)

                task = "text2text-generation" if is_t5 else "text-generation"

                return_full_text = True if "dolly" in MODEL_NAME_OR_PATH else False

                repetition_penalty = (
                    1.15
                    if "falcon" in MODEL_NAME_OR_PATH
                    else (1.25 if "dolly" in MODEL_NAME_OR_PATH else 1.1)
                )

                HF_RP = os.environ.get("HF_RP")
                if HF_RP is not None and len(HF_RP) > 0:
                    repetition_penalty = float(HF_RP)
                print(f"       repetition_penalty: {repetition_penalty}")
                self.repetition_penalty = repetition_penalty
                self.model_name = MODEL_NAME_OR_PATH.split("/")[-1]

                if load_quantized_model is not None:
                    model = (
                        AutoModelForSeq2SeqLM.from_pretrained(
                            MODEL_NAME_OR_PATH,
                            config=config,
                            quantization_config=double_quant_config,
                            trust_remote_code=True,
                            token=token,
                        )
                        if is_t5
                        else AutoModelForCausalLM.from_pretrained(
                            MODEL_NAME_OR_PATH,
                            config=config,
                            quantization_config=double_quant_config,
                            trust_remote_code=True,
                            token=token,
                        )
                    )

                    print(f"Model memory footprint: {model.get_memory_footprint()}")

                    eos_token_id = -1
                    # starchat-beta uses a special <|end|> token with ID 49155 to denote ends of a turn
                    if "starchat" in MODEL_NAME_OR_PATH:
                        eos_token_id = 49155
                    pad_token_id = eos_token_id

                    pipe = (
                        pipeline(
                            task,
                            model=model,
                            tokenizer=tokenizer,
                            eos_token_id=eos_token_id,
                            pad_token_id=pad_token_id,
                            streamer=self.streamer,
                            return_full_text=return_full_text,  # langchain expects the full text
                            device_map="auto",
                            trust_remote_code=True,
                            max_new_tokens=2048,
                            do_sample=True,
                            temperature=0.01,
                            top_p=0.95,
                            top_k=50,
                            repetition_penalty=repetition_penalty,
                        )
                        if eos_token_id != -1
                        else pipeline(
                            task,
                            model=model,
                            tokenizer=tokenizer,
                            streamer=self.streamer,
                            return_full_text=return_full_text,  # langchain expects the full text
                            device_map="auto",
                            trust_remote_code=True,
                            max_new_tokens=2048,
                            do_sample=True,
                            temperature=temperature,
                            top_p=0.95,
                            top_k=0,  # select from top 0 tokens (because zero, relies on top_p)
                            repetition_penalty=repetition_penalty,
                        )
                    )
                else:
                    if os.environ.get("DISABLE_MODEL_PRELOADING") != "true":
                        model = (
                            AutoModelForSeq2SeqLM.from_pretrained(
                                MODEL_NAME_OR_PATH,
                                config=config,
                                trust_remote_code=True,
                            )
                            if is_t5
                            else (
                                AutoModelForCausalLM.from_pretrained(
                                    MODEL_NAME_OR_PATH,
                                    config=config,
                                    trust_remote_code=True,
                                )
                                if "Qwen" in MODEL_NAME_OR_PATH
                                else (
                                    AutoModelForCausalLM.from_pretrained(
                                        MODEL_NAME_OR_PATH,
                                        config=config,
                                        trust_remote_code=True,
                                    )
                                    if token is None
                                    else AutoModelForCausalLM.from_pretrained(
                                        MODEL_NAME_OR_PATH,
                                        config=config,
                                        trust_remote_code=True,
                                        token=token,
                                    )
                                )
                            )
                        )
                        print(f"Model memory footprint: {model.get_memory_footprint()}")
                        model = model.eval()
                        # print(f"Model memory footprint: {model.get_memory_footprint()}")
                    else:
                        model = MODEL_NAME_OR_PATH

                    pipe = pipeline(
                        task,
                        model=model,
                        tokenizer=tokenizer,
                        streamer=self.streamer,
                        return_full_text=return_full_text,  # langchain expects the full text
                        device_map="auto",
                        torch_dtype=torch_dtype,
                        max_new_tokens=2048,
                        trust_remote_code=True,
                        do_sample=True,
                        temperature=temperature,
                        top_p=0.95,
                        top_k=0,  # select from top 0 tokens (because zero, relies on top_p)
                        repetition_penalty=repetition_penalty,
                        token=token,
                        batch_size=self.batch_size,
                    )

                pipe.model.config.pad_token_id = pipe.model.config.eos_token_id
                pipe.tokenizer.pad_token_id = pipe.model.config.eos_token_id
                self.llm = HuggingFacePipeline(pipeline=pipe, callbacks=callbacks)

        print("initialization complete")