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import os
import sys
import urllib
from queue import Queue
from typing import Any, Optional
import torch
from langchain.callbacks.base import BaseCallbackHandler
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
from langchain.callbacks.tracers import LangChainTracer
from langchain.chains import ConversationalRetrievalChain
from langchain.chat_models import ChatOpenAI
from langchain.llms import GPT4All, HuggingFacePipeline, LlamaCpp
from langchain.schema import LLMResult
from langchain.vectorstores import VectorStore
from langchain.vectorstores.base import VectorStore
from transformers import (
AutoConfig,
AutoModelForCausalLM,
AutoModelForSeq2SeqLM,
AutoTokenizer,
BitsAndBytesConfig,
StoppingCriteria,
StoppingCriteriaList,
T5Tokenizer,
TextStreamer,
pipeline,
)
from app_modules.instruct_pipeline import InstructionTextGenerationPipeline
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,
**decode_kwargs,
):
super().__init__(tokenizer, skip_prompt, **decode_kwargs)
self.text_queue = Queue()
self.stop_signal = None
self.timeout = timeout
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)
if stream_end:
print("\n")
self.text_queue.put("\n", timeout=self.timeout)
self.text_queue.put(self.stop_signal, timeout=self.timeout)
def on_llm_new_token(self, token: str, **kwargs: Any) -> None:
sys.stdout.write(token)
sys.stdout.flush()
self.text_queue.put(token, timeout=self.timeout)
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()
class QAChain:
llm_model_type: str
vectorstore: VectorStore
llm: any
streamer: any
def __init__(self, vectorstore, llm_model_type):
self.vectorstore = vectorstore
self.llm_model_type = llm_model_type
self.llm = None
self.streamer = TextIteratorStreamer("")
self.max_tokens_limit = 2048
self.search_kwargs = {"k": 4}
def _init_streamer(self, tokenizer, custom_handler):
self.streamer = (
TextIteratorStreamer(
tokenizer,
timeout=10.0,
skip_prompt=True,
skip_special_tokens=True,
)
if custom_handler is None
else TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
)
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")
torch_dtype = torch.float16 if using_cuda else torch.float32
if 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}")
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 = [self.streamer]
if custom_handler is not None:
callbacks.append(custom_handler)
if self.llm is None:
if self.llm_model_type == "openai":
self.llm = ChatOpenAI(
model_name="gpt-4",
streaming=True,
callbacks=callbacks,
verbose=True,
temperature=0,
)
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.startswith("huggingface"):
MODEL_NAME_OR_PATH = os.environ.get("HUGGINGFACE_MODEL_NAME_OR_PATH")
print(f" loading model: {MODEL_NAME_OR_PATH}")
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
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
)
# 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)
if is_t5
else AutoTokenizer.from_pretrained(
MODEL_NAME_OR_PATH,
use_fast=use_fast,
trust_remote_code=True,
padding_side=padding_side,
)
)
self._init_streamer(tokenizer, custom_handler)
task = "text2text-generation" if is_t5 else "text-generation"
return_full_text = True if "dolly" in MODEL_NAME_OR_PATH else None
repetition_penalty = (
1.15
if "falcon" in MODEL_NAME_OR_PATH
else (1.25 if "dolly" in MODEL_NAME_OR_PATH else 1.1)
)
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,
)
if is_t5
else AutoModelForCausalLM.from_pretrained(
MODEL_NAME_OR_PATH,
config=config,
quantization_config=double_quant_config,
trust_remote_code=True,
)
)
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 = (
InstructionTextGenerationPipeline(
task=task,
model=model,
tokenizer=tokenizer,
streamer=self.streamer,
max_new_tokens=2048,
temperature=temperature,
return_full_text=return_full_text, # langchain expects the full text
repetition_penalty=repetition_penalty,
)
if "dolly" in MODEL_NAME_OR_PATH
else (
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,
# verbose=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,
)
)
)
elif "dolly" in MODEL_NAME_OR_PATH:
model = AutoModelForCausalLM.from_pretrained(
MODEL_NAME_OR_PATH,
device_map=hf_pipeline_device_type,
torch_dtype=torch_dtype,
)
pipe = InstructionTextGenerationPipeline(
task=task,
model=model,
tokenizer=tokenizer,
streamer=self.streamer,
max_new_tokens=2048,
temperature=temperature,
return_full_text=True,
repetition_penalty=repetition_penalty,
)
else:
pipe = pipeline(
task, # model=model,
model=MODEL_NAME_OR_PATH,
tokenizer=tokenizer,
streamer=self.streamer,
return_full_text=return_full_text, # langchain expects the full text
device=hf_pipeline_device_type,
torch_dtype=torch_dtype,
max_new_tokens=2048,
trust_remote_code=True,
# verbose=True,
temperature=temperature,
top_p=0.95,
top_k=0, # select from top 0 tokens (because zero, relies on top_p)
repetition_penalty=1.115,
)
self.llm = HuggingFacePipeline(pipeline=pipe, callbacks=callbacks)
elif self.llm_model_type == "mosaicml":
MODEL_NAME_OR_PATH = os.environ.get("MOSAICML_MODEL_NAME_OR_PATH")
print(f" loading model: {MODEL_NAME_OR_PATH}")
config = AutoConfig.from_pretrained(
MODEL_NAME_OR_PATH, trust_remote_code=True
)
# config.attn_config["attn_impl"] = "triton"
config.max_seq_len = 16384 if "30b" in MODEL_NAME_OR_PATH else 4096
config.init_device = hf_pipeline_device_type
model = (
AutoModelForCausalLM.from_pretrained(
MODEL_NAME_OR_PATH,
config=config,
quantization_config=double_quant_config,
trust_remote_code=True,
)
if load_quantized_model is not None
else AutoModelForCausalLM.from_pretrained(
MODEL_NAME_OR_PATH,
config=config,
torch_dtype=torch_dtype,
trust_remote_code=True,
)
)
print(f"Model loaded on {config.init_device}")
print(f"Model memory footprint: {model.get_memory_footprint()}")
tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-neox-20b")
self._init_streamer(tokenizer, custom_handler)
# mtp-7b is trained to add "<|endoftext|>" at the end of generations
stop_token_ids = tokenizer.convert_tokens_to_ids(["<|endoftext|>"])
# define custom stopping criteria object
class StopOnTokens(StoppingCriteria):
def __call__(
self,
input_ids: torch.LongTensor,
scores: torch.FloatTensor,
**kwargs,
) -> bool:
for stop_id in stop_token_ids:
if input_ids[0][-1] == stop_id:
return True
return False
stopping_criteria = StoppingCriteriaList([StopOnTokens()])
max_new_tokens = 8192 if "30b" in MODEL_NAME_OR_PATH else 2048
self.max_tokens_limit = max_new_tokens
self.search_kwargs = (
{"k": 8} if "30b" in MODEL_NAME_OR_PATH else self.search_kwargs
)
repetition_penalty = 1.0005 if "30b" in MODEL_NAME_OR_PATH else 1.02
pipe = (
pipeline(
model=model,
tokenizer=tokenizer,
streamer=self.streamer,
return_full_text=True, # langchain expects the full text
task="text-generation",
device_map="auto",
# we pass model parameters here too
stopping_criteria=stopping_criteria, # without this model will ramble
temperature=0, # 'randomness' of outputs, 0.0 is the min and 1.0 the max
top_p=0.95, # select from top tokens whose probability add up to 15%
top_k=0, # select from top 0 tokens (because zero, relies on top_p)
max_new_tokens=max_new_tokens, # mex number of tokens to generate in the output
repetition_penalty=repetition_penalty, # without this output begins repeating
)
if load_quantized_model is not None
else pipeline(
model=model,
tokenizer=tokenizer,
streamer=self.streamer,
return_full_text=True, # langchain expects the full text
task="text-generation",
device=config.init_device,
# we pass model parameters here too
stopping_criteria=stopping_criteria, # without this model will ramble
temperature=0, # 'randomness' of outputs, 0.0 is the min and 1.0 the max
top_p=0.95, # select from top tokens whose probability add up to 15%
top_k=0, # select from top 0 tokens (because zero, relies on top_p)
max_new_tokens=max_new_tokens, # mex number of tokens to generate in the output
repetition_penalty=repetition_penalty, # without this output begins repeating
)
)
self.llm = HuggingFacePipeline(pipeline=pipe, callbacks=callbacks)
elif self.llm_model_type == "stablelm":
MODEL_NAME_OR_PATH = os.environ.get("STABLELM_MODEL_NAME_OR_PATH")
print(f" loading model: {MODEL_NAME_OR_PATH}")
config = AutoConfig.from_pretrained(
MODEL_NAME_OR_PATH, trust_remote_code=True
)
# config.attn_config["attn_impl"] = "triton"
# config.max_seq_len = 4096
config.init_device = hf_pipeline_device_type
model = (
AutoModelForCausalLM.from_pretrained(
MODEL_NAME_OR_PATH,
config=config,
quantization_config=double_quant_config,
trust_remote_code=True,
)
if load_quantized_model is not None
else AutoModelForCausalLM.from_pretrained(
MODEL_NAME_OR_PATH,
config=config,
torch_dtype=torch_dtype,
trust_remote_code=True,
)
)
print(f"Model loaded on {config.init_device}")
print(f"Model memory footprint: {model.get_memory_footprint()}")
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME_OR_PATH)
self._init_streamer(tokenizer, custom_handler)
class StopOnTokens(StoppingCriteria):
def __call__(
self,
input_ids: torch.LongTensor,
scores: torch.FloatTensor,
**kwargs,
) -> bool:
stop_ids = [50278, 50279, 50277, 1, 0]
for stop_id in stop_ids:
if input_ids[0][-1] == stop_id:
return True
return False
stopping_criteria = StoppingCriteriaList([StopOnTokens()])
pipe = (
pipeline(
model=model,
tokenizer=tokenizer,
streamer=self.streamer,
return_full_text=True, # langchain expects the full text
task="text-generation",
device_map="auto",
# we pass model parameters here too
stopping_criteria=stopping_criteria, # without this model will ramble
temperature=0, # 'randomness' of outputs, 0.0 is the min and 1.0 the max
top_p=0.95, # select from top tokens whose probability add up to 15%
top_k=0, # select from top 0 tokens (because zero, relies on top_p)
max_new_tokens=2048, # mex number of tokens to generate in the output
repetition_penalty=1.25, # without this output begins repeating
)
if load_quantized_model is not None
else pipeline(
model=model,
tokenizer=tokenizer,
streamer=self.streamer,
return_full_text=True, # langchain expects the full text
task="text-generation",
device=config.init_device,
# we pass model parameters here too
stopping_criteria=stopping_criteria, # without this model will ramble
temperature=0, # 'randomness' of outputs, 0.0 is the min and 1.0 the max
top_p=0.95, # select from top tokens whose probability add up to 15%
top_k=0, # select from top 0 tokens (because zero, relies on top_p)
max_new_tokens=2048, # mex number of tokens to generate in the output
repetition_penalty=1.05, # without this output begins repeating
)
)
self.llm = HuggingFacePipeline(pipeline=pipe, callbacks=callbacks)
print("initialization complete")
def get_chain(self, tracing: bool = False) -> ConversationalRetrievalChain:
if tracing:
tracer = LangChainTracer()
tracer.load_default_session()
if self.llm is None:
self.init()
qa = ConversationalRetrievalChain.from_llm(
self.llm,
self.vectorstore.as_retriever(search_kwargs=self.search_kwargs),
max_tokens_limit=self.max_tokens_limit,
return_source_documents=True,
)
return qa
def call(self, inputs, q: Queue = None, tracing: bool = False):
print(inputs)
if self.streamer is not None and isinstance(
self.streamer, TextIteratorStreamer
):
self.streamer.reset(q)
qa = self.get_chain(tracing)
result = qa(inputs)
base_url = os.environ.get("PDF_FILE_BASE_URL")
if base_url is not None:
documents = result["source_documents"]
for doc in documents:
source = doc.metadata["source"]
title = source.split("/")[-1]
doc.metadata["url"] = f"{base_url}{urllib.parse.quote(title)}"
return result
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