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# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import re
import numpy as np
import torch
from transformers import AutoTokenizer, load_tool
from trl import AutoModelForCausalLMWithValueHead, PPOConfig, PPOTrainer, TextEnvironment
def generate_data(n):
"""Generate random arithmetic tasks and answers."""
tasks, answers = [], []
for _ in range(n):
a = np.random.randint(0, 50)
b = np.random.randint(0, 50)
op = np.random.choice(["-", "+", "*"])
tasks.append(f"\n\nWhat is {a} {op} {b}?")
if op == "-":
answers.append(a - b)
elif op == "+":
answers.append(a + b)
else:
answers.append(a * b)
return tasks, answers
def exact_match_reward(responses, answers=None):
"""Reward if generated response contains correct answer."""
rewards = []
pattern = r"Result\s*=\s*(-?\d+(?:\.\d+)?)\s*<submit>" # generated by chatGPT
for response, answer in zip(responses, answers):
reward = 0.0
predicted_number = None
match_pattern = re.findall(pattern, response)
if match_pattern:
predicted_number = float(match_pattern[0])
if predicted_number is not None:
if np.abs(predicted_number - answer) < 0.01:
reward += 1.0
rewards.append(torch.tensor(reward))
return rewards
# set up models
model_id = "gpt2"
model = AutoModelForCausalLMWithValueHead.from_pretrained(model_id)
model_ref = AutoModelForCausalLMWithValueHead.from_pretrained(model_id)
tokenizer = AutoTokenizer.from_pretrained(model_id)
tokenizer.pad_token = tokenizer.eos_token
# system prompt
prompt = """\
What is 13-3?
<request><SimpleCalculatorTool>13-3<call>10.0<response>
Result=10<submit>
What is 4*3?
<request><SimpleCalculatorTool>4*3<call>12.0<response>
Result=12<submit>"""
generation_kwargs = {
"min_length": -1,
"top_k": 0.0,
"top_p": 1.0,
"do_sample": True,
"pad_token_id": tokenizer.eos_token_id,
"eos_token_id": -1,
"max_new_tokens": 32,
}
# trainer
ppo_config = PPOConfig(
batch_size=256,
learning_rate=1.41e-5,
mini_batch_size=64,
log_with="wandb",
)
ppo_trainer = PPOTrainer(ppo_config, model, model_ref, tokenizer)
# text env
text_env = TextEnvironment(
model,
tokenizer,
{"SimpleCalculatorTool": load_tool("ybelkada/simple-calculator")},
exact_match_reward,
prompt,
generation_kwargs=generation_kwargs,
)
# main training loop
for step in range(100):
tasks, answers = generate_data(ppo_config.batch_size)
queries, responses, masks, rewards, histories = text_env.run(tasks, answers=answers)
train_stats = ppo_trainer.step(queries, responses, rewards, masks)
response_texts = [tokenizer.decode(response) for response in responses]
query_texts = [tokenizer.decode(query) for query in queries]
texts = {"query": [qt.split("<submit>")[-1].strip() for qt in query_texts], "response": response_texts}
ppo_trainer.log_stats(train_stats, texts, rewards, columns_to_log=["query", "response", "answer"])
ppo_trainer.save_pretrained(model_id + "-calculator")
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