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import os
from typing import Annotated, List
from fastapi import Request, HTTPException, Form
import modal
import torch
import torch.nn.functional as F
from torch import Tensor
from transformers import AutoTokenizer, AutoModel
image = (
modal.Image.debian_slim(python_version="3.11")
.pip_install(
"transformers",
"huggingface_hub[hf_transfer]==0.26.2",
"flashinfer-python==0.2.0.post2", # pinning, very unstable
"fastapi[standard]",
extra_index_url="https://flashinfer.ai/whl/cu124/torch2.5",
)
.env({"HF_HUB_ENABLE_HF_TRANSFER": "1"}) # faster model transfers
)
MODELS_DIR = "/llamas"
MODEL_NAME = "intfloat/multilingual-e5-large-instruct"
MODEL_REVISION = "84344a23ee1820ac951bc365f1e91d094a911763"
hf_cache_vol = modal.Volume.from_name("huggingface-cache", create_if_missing=True)
vllm_cache_vol = modal.Volume.from_name("vllm-cache", create_if_missing=True)
app = modal.App("intfloat-multilingual-e5-large-instruct-embeddings")
def get_device():
return torch.device('cuda' if torch.cuda.is_available() else 'cpu')
def load_model():
print("Loading model...")
device = get_device()
print(f"Using device: {device}")
tokenizer = AutoTokenizer.from_pretrained('intfloat/multilingual-e5-large-instruct')
model = AutoModel.from_pretrained('intfloat/multilingual-e5-large-instruct').to(device)
print("Model loaded successfully.")
return tokenizer, model, device
N_GPU = 1
MINUTES = 60 # seconds
VLLM_PORT = 8000
def average_pool(last_hidden_states: Tensor,
attention_mask: Tensor) -> Tensor:
last_hidden = last_hidden_states.masked_fill(~attention_mask[..., None].bool(), 0.0)
return last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None]
@app.function(
image=image,
gpu=f"L40S:{N_GPU}",
# gpu=f"A10G:{N_GPU}",
# how long should we stay up with no requests?
scaledown_window=3 * MINUTES,
volumes={
"/root/.cache/huggingface": hf_cache_vol,
"/root/.cache/vllm": vllm_cache_vol,
},
secrets=[modal.Secret.from_name("document-qa-embedding-key")]
)
@modal.concurrent(
max_inputs=5
) # how many requests can one replica handle? tune carefully!
@modal.fastapi_endpoint(method="POST")
def embed(request: Request, text: Annotated[str, Form()]):
api_key = request.headers.get("x-api-key")
expected_key = os.environ["API_KEY"]
if api_key != expected_key:
raise HTTPException(status_code=401, detail="Unauthorized")
texts = [t for t in text.split("\n") if t.strip()]
if not texts:
return []
tokenizer, model, device = load_model()
model.eval()
print(f"Start embedding {len(texts)} texts")
try:
with torch.no_grad():
# Move inputs to the same device as model
batch_dict = tokenizer(texts, padding=True, truncation=True, return_tensors='pt')
batch_dict = {k: v.to(device) for k, v in batch_dict.items()}
# Forward pass
outputs = model(**batch_dict)
# Process embeddings
embeddings = average_pool(
outputs.last_hidden_state,
batch_dict['attention_mask']
)
embeddings = F.normalize(embeddings, p=2, dim=1)
# Move to CPU and convert to list for serialization
embeddings = embeddings.cpu().numpy().tolist()
print("Finished embedding texts.")
return embeddings
except RuntimeError as e:
print(f"Error during embedding: {str(e)}")
if "CUDA out of memory" in str(e):
print("CUDA out of memory error. Try reducing batch size or using a smaller model.")
raise
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