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import warnings
import os
import json
import time
import torch
from typing import Dict, List, Any, Tuple
from concurrent.futures import ThreadPoolExecutor, as_completed
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
from accelerate import Accelerator
# Configure logging
logging.basicConfig(
level=logging.DEBUG,
format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
)
logging.captureWarnings(True)
logger = logging.getLogger(__name__)
logger.debug("Starting the handler.py script and initializing components.")
class InvalidInputError(Exception):
"""Custom exception for invalid input errors."""
pass
class EndpointHandler:
"""
EndpointHandler class responsible for processing incoming data, generating model outputs,
and returning results in a backwards-compatible format with detailed metrics.
"""
# Path to the fully merged fine-tuned model on Hugging Face
path = "imsevolve/llama3-8b-tag-extractor"
def __init__(self, model_dir: str = path):
"""
Initialize the EndpointHandler, setting up the model, tokenizer, and accelerator.
"""
self.accelerator = Accelerator()
hf_token = os.environ.get("HF_TOKEN")
if not hf_token:
raise RuntimeError("HF_TOKEN environment variable is missing!")
quantization_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_compute_dtype=torch.float16,
bnb_4bit_quant_type="nf4",
bnb_4bit_use_double_quant=True,
)
# Load tokenizer from the merged model
self.tokenizer = AutoTokenizer.from_pretrained(model_dir, token=hf_token)
self.tokenizer.pad_token = self.tokenizer.eos_token
# Load the fully merged model
self.model = AutoModelForCausalLM.from_pretrained(
model_dir,
quantization_config=quantization_config,
device_map="auto",
token=hf_token
)
self.model = self.accelerator.prepare(self.model)
logger.info(f"Loaded merged model from {model_dir}")
def format_prompt(self, instruction: str) -> str:
"""
Formats the instruction into the prompt template required by the model.
"""
prompt_template = """
### Instruction:
{}
### Response:
"""
return prompt_template.format(instruction)
def extract_tags_from_response(self, response: str) -> List[str]:
"""
Extracts tags from the model's response, cleans them, and ensures they are unique.
"""
try:
# Extract the part after "### Response:"
response_part = response.split("### Response:")[-1].strip()
# Split into tags, clean, and remove duplicates
tags = []
for tag in response_part.split(","):
clean_tag = tag.split("/")[0].split("\n")[0].strip()
if clean_tag:
tags.append(clean_tag)
unique_tags = list(set(tags))
return unique_tags
except Exception as e:
logger.error(f"extract_tags_from_response: Error while parsing response: {e}")
return ["extract_tags_from_response: error_extraction"]
def tokenize_prompts(self, prompts: List[str]) -> Dict[str, torch.Tensor]:
"""
Tokenizes a list of prompts for input to the model.
"""
try:
tokenized_batch = self.tokenizer(
prompts,
return_tensors="pt",
padding=True,
truncation=True,
max_length=512,
pad_to_multiple_of=8,
add_special_tokens=False,
).to(self.accelerator.device)
logger.debug(f"tokenize_prompts: Tokenized batch input_ids shape: {tokenized_batch['input_ids'].shape}")
return tokenized_batch
except Exception as e:
logger.error(f"tokenize_prompts: Error during tokenization: {e}")
raise RuntimeError("tokenize_prompts: Tokenization failed") from e
def format_and_tokenize_prompts(self, inputs: List[Dict[str, str]]) -> Tuple[Dict[str, Any], List[str]]:
"""
Formats and tokenizes prompts from the input data.
"""
prompts = [self.format_prompt(input_dict["prompt"]) for input_dict in inputs]
tokenized_batch = self.tokenize_prompts(prompts)
dataPointIds = [input_dict["dataPointId"] for input_dict in inputs]
return tokenized_batch, dataPointIds
def generate_model_outputs(self, tokenized_batch: Dict[str, Any]) -> List[torch.Tensor]:
"""
Generates model outputs from the tokenized batch.
"""
try:
outputs = self.model.generate(
**tokenized_batch,
max_new_tokens=54,
do_sample=True,
temperature=0.7,
top_p=0.9,
repetition_penalty=1.1,
)
return outputs
except Exception as e:
logger.error(f"generate_model_outputs: Error during model generation: {e}")
return []
def process_model_outputs(self, outputs: List[torch.Tensor], dataPointIds: List[str]) -> List[Dict[str, Any]]:
"""
Processes model outputs, extracts tags, and associates with data point IDs.
"""
results = []
for idx, output in enumerate(outputs):
try:
generated_text = self.tokenizer.decode(output, skip_special_tokens=True)
tags = self.extract_tags_from_response(generated_text)
results.append({
"dataPointId": dataPointIds[idx],
"generated_text": tags
})
except Exception as e:
logger.error(f"Error processing output for dataPointId {dataPointIds[idx]}: {e}")
results.append({
"dataPointId": dataPointIds[idx],
"error": "Failed to process output"
})
return results
def process_batch_inputs(self, inputs: List[Dict[str, str]]) -> List[Dict[str, Any]]:
"""
Processes a batch of inputs: formats, tokenizes, generates outputs, and processes results.
"""
tokenized_batch, dataPointIds = self.format_and_tokenize_prompts(inputs)
outputs = self.generate_model_outputs(tokenized_batch)
results = self.process_model_outputs(outputs, dataPointIds)
return results
def extract_and_validate_inputs(self, data: Dict[str, Any], metrics: Dict[str, Any]) -> List[Dict[str, str]]:
"""
Extracts and validates inputs from the received data.
"""
inputs = data.get("inputs", [])
metrics["total_inputs"] = len(inputs)
if not isinstance(inputs, list) or not inputs:
raise InvalidInputError("No inputs provided or inputs are not a list.")
return inputs
def handle_batching(self, inputs: List[Dict[str, str]], metrics: Dict[str, Any]) -> List[List[Dict[str, str]]]:
"""
Handles batching of inputs based on the batch size environment variable.
"""
batch_size = min(int(os.environ.get("BATCH_SIZE", 10)), 50)
prompt_batches = self.create_batches(inputs, batch_size)
metrics["parallel_batches"] = len(prompt_batches)
return prompt_batches
def process_batches_in_parallel(self, prompt_batches: List[List[Dict[str, str]]], metrics: Dict[str, Any]) -> List[Dict[str, Any]]:
"""
Processes batches of inputs in parallel using ThreadPoolExecutor.
"""
results = []
try:
max_workers = min(int(os.environ.get("MAX_WORKERS", 4)), 4)
with ThreadPoolExecutor(max_workers=max_workers) as executor:
futures = {executor.submit(self.process_batch_inputs, batch): batch for batch in prompt_batches}
for future in as_completed(futures):
try:
torch.cuda.empty_cache()
batch_results = future.result()
metrics["successfully_processed"] += sum(1 for result in batch_results if "error" not in result)
metrics["failed_processed"] += sum(1 for result in batch_results if "error" in result)
results.extend(batch_results)
except Exception as e:
logger.error(f"process_batches_in_parallel: Exception in future result: {e}")
results.append({"error": str(e)})
metrics["failed_processed"] += 1
return results
except Exception as e:
logger.error(f"process_batches_in_parallel: Failed to process batches in parallel: {e}")
raise
def gather_and_finalize_results(self, results: List[Dict[str, Any]], metrics: Dict[str, Any], start_time: float) -> Dict[str, Any]:
"""
Gathers results and finalizes metrics.
"""
metrics["total_processing_time"] = time.time() - start_time
return {"results": results, "metrics": metrics}
def __call__(self, data: Dict[str, Any]) -> Dict[str, Any]:
"""
Main entry point for processing incoming data.
"""
metrics = self.initialize_metrics()
start_time = time.time()
try:
inputs = self.extract_and_validate_inputs(data, metrics)
prompt_batches = self.handle_batching(inputs, metrics)
self.accelerator.wait_for_everyone()
results = self.process_batches_in_parallel(prompt_batches, metrics)
return self.gather_and_finalize_results(results, metrics, start_time)
except InvalidInputError as e:
logger.error(f"__call__: Invalid input error: {e}")
return {"results": [{"error": str(e)}], "metrics": metrics}
except Exception as e:
logger.error(f"__call__: Unexpected error: {e}")
return {"results": [{"error": str(e)}], "metrics": metrics}
@staticmethod
def initialize_metrics() -> Dict[str, Any]:
"""
Initializes the metrics dictionary.
"""
return {
"total_processing_time": 0,
"memory_allocated": torch.cuda.memory_allocated(),
"memory_reserved": torch.cuda.memory_reserved(),
"parallel_batches": 0,
"total_inputs": 0,
"successfully_processed": 0,
"failed_processed": 0
}
@staticmethod
def create_batches(inputs: List[Dict[str, str]], batch_size: int) -> List[List[Dict[str, str]]]:
"""
Splits inputs into batches.
"""
return [inputs[i:i + batch_size] for i in range(0, len(inputs), batch_size)] |