metadata
base_model: google/gemma-2-9b
tags:
- text-generation-inference
- transformers
- torch
- mlx_lm
license: apache-2.0
language:
- en
推論用コード
Hugging Faceにアップロードしたモデルを用いてELYZA-tasks-100-TVの出力を得るためのコードです。 このコードで生成されたjsonlファイルは課題の成果として提出可能なフォーマットになっております。
!pip install -U bitsandbytes
!pip install -U transformers
!pip install -U accelerate
!pip install -U datasets
!pip install -U peft
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
import json
from pathlib import Path
from typing import Dict, Any, Optional
from tqdm import tqdm
import time
from datetime import datetime
class GPUPredictions:
def __init__(self,
model_id="testmoto/gemma-2-lora-dpo-moe-1",
adapter_path=None,
max_tokens=1024,
temp=0.0,
top_p=0.9,
seed=3407):
self.model_id = model_id
self.adapter_path = adapter_path
self.max_tokens = max_tokens
self.temp = temp
self.top_p = top_p
self.seed = seed
print(f"Loading model: {model_id}")
torch.cuda.empty_cache()
# GPU設定
n_gpus = torch.cuda.device_count()
max_memory = {i: "20GiB" for i in range(n_gpus)}
max_memory["cpu"] = "100GiB"
# トークナイザーの初期化
self.tokenizer = AutoTokenizer.from_pretrained(
model_id,
trust_remote_code=True
)
if self.tokenizer.pad_token is None:
self.tokenizer.pad_token = self.tokenizer.eos_token
try:
self.model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype=torch.float16,
device_map="auto",
max_memory=max_memory,
low_cpu_mem_usage=True,
trust_remote_code=True
)
except Exception as e:
print(f"First loading attempt failed: {str(e)}")
print("Trying alternative loading method...")
self.model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype=torch.float16,
device_map="balanced",
low_cpu_mem_usage=True,
trust_remote_code=True
)
if adapter_path:
print(f"Loading adapter from {adapter_path}")
self.model.load_adapter(adapter_path)
# Generate設定
self.gen_config = {
"max_new_tokens": max_tokens,
"temperature": temp,
"top_p": top_p,
"do_sample": temp > 0,
"pad_token_id": self.tokenizer.pad_token_id,
"eos_token_id": self.tokenizer.eos_token_id
}
print("Model loaded successfully")
self.device = next(self.model.parameters()).device
print(f"Model is on device: {self.device}")
@torch.inference_mode()
def generate_response(self, prompt: str) -> str:
"""効率的な応答生成"""
try:
inputs = self.tokenizer(prompt, return_tensors="pt", padding=True)
inputs = {k: v.to(self.device) for k, v in inputs.items()}
with torch.cuda.amp.autocast():
outputs = self.model.generate(
**inputs,
**self.gen_config
)
response = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
if prompt in response:
response = response[len(prompt):].strip()
return response
except Exception as e:
print(f"Error during generation: {str(e)}")
raise
def load_tasks(self, file_path: str) -> list:
"""ELYZAタスクの読み込み"""
datasets = []
with open(file_path, "r") as f:
for line in f:
if line.strip():
datasets.append(json.loads(line.strip()))
return datasets
def run_inference(self, input_file="elyza-tasks-100-TV_0.jsonl", output_file="gpu_results.jsonl"):
"""バッチ処理による効率的な推論実行"""
tasks = self.load_tasks(input_file)
results = []
start_time = time.time()
execution_date = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
print(f"Execution started at: {execution_date}")
print(f"Total tasks: {len(tasks)}")
print("-" * 50)
for task in tqdm(tasks, desc="Processing tasks"):
task_start_time = time.time()
prompt = f"""### Instruction:
{task['input']}
<eos>
### Response: """
try:
response = self.generate_response(prompt)
try:
answer = response.split('### Response: ')[-1]
except:
answer = response
task_end_time = time.time()
task_duration = task_end_time - task_start_time
result = {
"task_id": task["task_id"],
"input": task["input"],
"output": answer
}
results.append(result)
print(f"\nTask {task['task_id']} completed in {task_duration:.2f} seconds")
print(f"Input: {task['input'][:100]}...")
print(f"Output: {answer[:100]}...")
print("-" * 50)
with open(output_file, 'a', encoding='utf-8') as f:
json.dump(result, f, ensure_ascii=False)
f.write('\n')
if task["task_id"] % 5 == 0:
torch.cuda.empty_cache()
except Exception as e:
print(f"Error processing task {task['task_id']}: {str(e)}")
continue
total_time = time.time() - start_time
avg_time = total_time / len(tasks)
summary = {
"execution_date": execution_date,
"total_tasks": len(tasks),
"total_time": round(total_time, 2),
"average_time_per_task": round(avg_time, 2),
"model_id": self.model_id,
"adapter_used": self.adapter_path is not None
}
print("\nExecution Summary:")
print(f"Total execution time: {total_time:.2f} seconds")
print(f"Average time per task: {avg_time:.2f} seconds")
print(f"Results saved to: {output_file}")
summary_file = output_file.replace('.jsonl', '_summary.json')
with open(summary_file, 'w', encoding='utf-8') as f:
json.dump(summary, f, ensure_ascii=False, indent=2)
return results
from GPUPredictions import GPUPredictions
predictor = GPUPredictions(
model_id="testmoto/gemma-2-lora-dpo-moe-1"
)
results = predictor.run_inference(
input_file="elyza-tasks-100-TV_0.jsonl",
output_file="llm_2024_elyza_tv_0.jsonl"
)