aws_test / app.py
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import os
import torch
from fastapi import FastAPI, HTTPException
from fastapi.responses import JSONResponse, StreamingResponse
from pydantic import BaseModel, field_validator
from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, GenerationConfig, StoppingCriteriaList
import boto3
import uvicorn
from io import BytesIO
from transformers import pipeline
AWS_ACCESS_KEY_ID = os.getenv("AWS_ACCESS_KEY_ID")
AWS_SECRET_ACCESS_KEY = os.getenv("AWS_SECRET_ACCESS_KEY")
AWS_REGION = os.getenv("AWS_REGION")
S3_BUCKET_NAME = os.getenv("S3_BUCKET_NAME")
HUGGINGFACE_HUB_TOKEN = os.getenv("HUGGINGFACE_HUB_TOKEN")
s3_client = boto3.client('s3', aws_access_key_id=AWS_ACCESS_KEY_ID, aws_secret_access_key=AWS_SECRET_ACCESS_KEY, region_name=AWS_REGION)
app = FastAPI()
SPECIAL_TOKENS = {
"bos_token": "<|startoftext|>",
"eos_token": "<|endoftext|>",
"pad_token": "[PAD]",
"unk_token": "[UNK]",
}
class GenerateRequest(BaseModel):
model_name: str
input_text: str = ""
task_type: str
temperature: float = 1.0
max_new_tokens: int = 10
top_p: float = 1.0
top_k: int = 50
repetition_penalty: float = 1.1
num_return_sequences: int = 1
do_sample: bool = True
stop_sequences: list[str] = []
no_repeat_ngram_size: int = 2
@field_validator("model_name")
def model_name_cannot_be_empty(cls, v):
if not v:
raise ValueError("model_name cannot be empty.")
return v
@field_validator("task_type")
def task_type_must_be_valid(cls, v):
valid_types = ["text-to-text", "text-to-image", "text-to-speech", "text-to-video"]
if v not in valid_types:
raise ValueError(f"task_type must be one of: {valid_types}")
return v
@field_validator("max_new_tokens")
def max_new_tokens_must_be_within_limit(cls, v):
if v > 500:
raise ValueError("max_new_tokens cannot be greater than 500.")
return v
class S3ModelLoader:
def __init__(self, bucket_name, s3_client):
self.bucket_name = bucket_name
self.s3_client = s3_client
def _get_s3_uri(self, model_name):
return f"s3://{self.bucket_name}/{model_name.replace('/', '-')}"
async def load_model_and_tokenizer(self, model_name):
s3_uri = self._get_s3_uri(model_name)
try:
config = AutoConfig.from_pretrained(s3_uri, local_files_only=True)
model = AutoModelForCausalLM.from_pretrained(s3_uri, config=config, local_files_only=True)
tokenizer = AutoTokenizer.from_pretrained(s3_uri, config=config, local_files_only=True)
tokenizer.add_special_tokens(SPECIAL_TOKENS)
model.resize_token_embeddings(len(tokenizer))
if tokenizer.pad_token_id is None:
tokenizer.pad_token_id = tokenizer.eos_token_id
return model, tokenizer
except EnvironmentError:
try:
config = AutoConfig.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name, config=config)
tokenizer.add_special_tokens(SPECIAL_TOKENS)
model = AutoModelForCausalLM.from_pretrained(model_name, config=config)
model.resize_token_embeddings(len(tokenizer))
if tokenizer.pad_token_id is None:
tokenizer.pad_token_id = tokenizer.eos_token_id
model.save_pretrained(s3_uri)
tokenizer.save_pretrained(s3_uri)
return model, tokenizer
except Exception as e:
raise HTTPException(status_code=500, detail=f"Error loading model: {e}")
model_loader = S3ModelLoader(S3_BUCKET_NAME, s3_client)
@app.post("/generate")
async def generate(request: GenerateRequest):
try:
model_name = request.model_name
input_text = request.input_text
task_type = request.task_type
temperature = request.temperature
max_new_tokens = request.max_new_tokens
top_p = request.top_p
top_k = request.top_k
repetition_penalty = request.repetition_penalty
num_return_sequences = request.num_return_sequences
do_sample = request.do_sample
stop_sequences = request.stop_sequences
no_repeat_ngram_size = request.no_repeat_ngram_size
model, tokenizer = await model_loader.load_model_and_tokenizer(model_name)
device = "cuda" if torch.cuda.is_available() else "cpu"
model.to(device)
generation_config = GenerationConfig(
temperature=temperature,
max_new_tokens=max_new_tokens,
top_p=top_p,
top_k=top_k,
repetition_penalty=repetition_penalty,
do_sample=do_sample,
num_return_sequences=num_return_sequences,
no_repeat_ngram_size=no_repeat_ngram_size,
pad_token_id=tokenizer.pad_token_id
)
generated_text = generate_text(model, tokenizer, input_text, generation_config, stop_sequences, device)
return JSONResponse({"text": generated_text})
except Exception as e:
raise HTTPException(status_code=500, detail=f"Internal server error: {str(e)}")
def generate_text(model, tokenizer, input_text, generation_config, stop_sequences, device):
max_model_length = model.config.max_position_embeddings
encoded_input = tokenizer(input_text, return_tensors="pt", max_length=max_model_length, truncation=True).to(device)
stopping_criteria = StoppingCriteriaList()
class CustomStoppingCriteria(StoppingCriteriaList):
def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
decoded_output = tokenizer.decode(input_ids[0], skip_special_tokens=True)
for stop in stop_sequences:
if decoded_output.endswith(stop):
return True
return False
stopping_criteria.append(CustomStoppingCriteria())
outputs = model.generate(
encoded_input.input_ids,
generation_config=generation_config,
stopping_criteria=stopping_criteria,
pad_token_id=generation_config.pad_token_id
)
generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
return generated_text
@app.post("/generate-image")
async def generate_image(request: GenerateRequest):
try:
validated_body = request
device = "cuda" if torch.cuda.is_available() else "cpu"
image_generator = pipeline("text-to-image", model=validated_body.model_name, device=device)
image = image_generator(validated_body.input_text)[0]
img_byte_arr = BytesIO()
image.save(img_byte_arr, format="PNG")
img_byte_arr.seek(0)
return StreamingResponse(img_byte_arr, media_type="image/png")
except Exception as e:
raise HTTPException(status_code=500, detail=f"Internal server error: {str(e)}")
@app.post("/generate-text-to-speech")
async def generate_text_to_speech(request: GenerateRequest):
try:
validated_body = request
device = "cuda" if torch.cuda.is_available() else "cpu"
audio_generator = pipeline("text-to-speech", model=validated_body.model_name, device=device)
audio = audio_generator(validated_body.input_text)[0]
audio_byte_arr = BytesIO()
# It is expected that the audio is saved as wav.
# Saving like this will not always work. Please check how your
# audio_generator model is working.
audio_generator.save_audio(audio_byte_arr, audio)
audio_byte_arr.seek(0)
return StreamingResponse(audio_byte_arr, media_type="audio/wav")
except Exception as e:
raise HTTPException(status_code=500, detail=f"Internal server error: {str(e)}")
@app.post("/generate-video")
async def generate_video(request: GenerateRequest):
try:
validated_body = request
device = "cuda" if torch.cuda.is_available() else "cpu"
video_generator = pipeline("text-to-video", model=validated_body.model_name, device=device)
video = video_generator(validated_body.input_text)[0]
video_byte_arr = BytesIO()
# Same as above. Please check how your video model is returning the
# videos and save them accordingly.
# It is expected that the video is saved as MP4
video_generator.save_video(video_byte_arr, video)
video_byte_arr.seek(0)
return StreamingResponse(video_byte_arr, media_type="video/mp4")
except Exception as e:
raise HTTPException(status_code=500, detail=f"Internal server error: {str(e)}")
if __name__ == "__main__":
uvicorn.run(app, host="0.0.0.0", port=7860)