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import os
from functools import lru_cache
import pipmaster as pm # Pipmaster for dynamic library install
# install specific modules
if not pm.is_installed("transformers"):
pm.install("transformers")
if not pm.is_installed("torch"):
pm.install("torch")
if not pm.is_installed("numpy"):
pm.install("numpy")
from transformers import AutoTokenizer, AutoModelForCausalLM
from tenacity import (
retry,
stop_after_attempt,
wait_exponential,
retry_if_exception_type,
)
from lightrag.exceptions import (
APIConnectionError,
RateLimitError,
APITimeoutError,
)
import torch
import numpy as np
os.environ["TOKENIZERS_PARALLELISM"] = "false"
@lru_cache(maxsize=1)
def initialize_hf_model(model_name):
hf_tokenizer = AutoTokenizer.from_pretrained(
model_name, device_map="auto", trust_remote_code=True
)
hf_model = AutoModelForCausalLM.from_pretrained(
model_name, device_map="auto", trust_remote_code=True
)
if hf_tokenizer.pad_token is None:
hf_tokenizer.pad_token = hf_tokenizer.eos_token
return hf_model, hf_tokenizer
@retry(
stop=stop_after_attempt(3),
wait=wait_exponential(multiplier=1, min=4, max=10),
retry=retry_if_exception_type(
(RateLimitError, APIConnectionError, APITimeoutError)
),
)
async def hf_model_if_cache(
model,
prompt,
system_prompt=None,
history_messages=[],
enable_cot: bool = False,
**kwargs,
) -> str:
if enable_cot:
from lightrag.utils import logger
logger.debug(
"enable_cot=True is not supported for Hugging Face local models and will be ignored."
)
model_name = model
hf_model, hf_tokenizer = initialize_hf_model(model_name)
messages = []
if system_prompt:
messages.append({"role": "system", "content": system_prompt})
messages.extend(history_messages)
messages.append({"role": "user", "content": prompt})
kwargs.pop("hashing_kv", None)
input_prompt = ""
try:
input_prompt = hf_tokenizer.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
except Exception:
try:
ori_message = copy.deepcopy(messages)
if messages[0]["role"] == "system":
messages[1]["content"] = (
"<system>"
+ messages[0]["content"]
+ "</system>\n"
+ messages[1]["content"]
)
messages = messages[1:]
input_prompt = hf_tokenizer.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
except Exception:
len_message = len(ori_message)
for msgid in range(len_message):
input_prompt = (
input_prompt
+ "<"
+ ori_message[msgid]["role"]
+ ">"
+ ori_message[msgid]["content"]
+ "</"
+ ori_message[msgid]["role"]
+ ">\n"
)
input_ids = hf_tokenizer(
input_prompt, return_tensors="pt", padding=True, truncation=True
).to("cuda")
inputs = {k: v.to(hf_model.device) for k, v in input_ids.items()}
output = hf_model.generate(
**input_ids, max_new_tokens=512, num_return_sequences=1, early_stopping=True
)
response_text = hf_tokenizer.decode(
output[0][len(inputs["input_ids"][0]) :], skip_special_tokens=True
)
return response_text
async def hf_model_complete(
prompt,
system_prompt=None,
history_messages=[],
keyword_extraction=False,
enable_cot: bool = False,
**kwargs,
) -> str:
kwargs.pop("keyword_extraction", None)
model_name = kwargs["hashing_kv"].global_config["llm_model_name"]
result = await hf_model_if_cache(
model_name,
prompt,
system_prompt=system_prompt,
history_messages=history_messages,
enable_cot=enable_cot,
**kwargs,
)
return result
async def hf_embed(texts: list[str], tokenizer, embed_model) -> np.ndarray:
# Detect the appropriate device
if torch.cuda.is_available():
device = next(embed_model.parameters()).device # Use CUDA if available
elif torch.backends.mps.is_available():
device = torch.device("mps") # Use MPS for Apple Silicon
else:
device = torch.device("cpu") # Fallback to CPU
# Move the model to the detected device
embed_model = embed_model.to(device)
# Tokenize the input texts and move them to the same device
encoded_texts = tokenizer(
texts, return_tensors="pt", padding=True, truncation=True
).to(device)
# Perform inference
with torch.no_grad():
outputs = embed_model(
input_ids=encoded_texts["input_ids"],
attention_mask=encoded_texts["attention_mask"],
)
embeddings = outputs.last_hidden_state.mean(dim=1)
# Convert embeddings to NumPy
if embeddings.dtype == torch.bfloat16:
return embeddings.detach().to(torch.float32).cpu().numpy()
else:
return embeddings.detach().cpu().numpy()
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