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119 lines
3.6 KiB
Python
119 lines
3.6 KiB
Python
# Copyright 2020-2025 The HuggingFace Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import re
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import numpy as np
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import torch
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from transformers import AutoTokenizer, load_tool
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from trl import AutoModelForCausalLMWithValueHead, PPOConfig, PPOTrainer, TextEnvironment
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def generate_data(n):
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"""Generate random arithmetic tasks and answers."""
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tasks, answers = [], []
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for _ in range(n):
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a = np.random.randint(0, 50)
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b = np.random.randint(0, 50)
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op = np.random.choice(["-", "+", "*"])
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tasks.append(f"\n\nWhat is {a} {op} {b}?")
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if op == "-":
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answers.append(a - b)
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elif op == "+":
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answers.append(a + b)
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else:
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answers.append(a * b)
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return tasks, answers
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def exact_match_reward(responses, answers=None):
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"""Reward if generated response contains correct answer."""
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rewards = []
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pattern = r"Result\s*=\s*(-?\d+(?:\.\d+)?)\s*<submit>" # generated by chatGPT
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for response, answer in zip(responses, answers):
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reward = 0.0
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predicted_number = None
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match_pattern = re.findall(pattern, response)
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if match_pattern:
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predicted_number = float(match_pattern[0])
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if predicted_number is not None:
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if np.abs(predicted_number - answer) < 0.01:
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reward += 1.0
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rewards.append(torch.tensor(reward))
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return rewards
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# set up models
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model_id = "gpt2"
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model = AutoModelForCausalLMWithValueHead.from_pretrained(model_id)
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ref_model = AutoModelForCausalLMWithValueHead.from_pretrained(model_id)
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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tokenizer.pad_token = tokenizer.eos_token
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# system prompt
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prompt = """\
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What is 13-3?
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<request><SimpleCalculatorTool>13-3<call>10.0<response>
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Result=10<submit>
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What is 4*3?
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<request><SimpleCalculatorTool>4*3<call>12.0<response>
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Result=12<submit>"""
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generation_kwargs = {
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"min_length": -1,
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"top_k": 0.0,
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"top_p": 1.0,
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"do_sample": True,
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"pad_token_id": tokenizer.eos_token_id,
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"eos_token_id": -1,
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"max_new_tokens": 32,
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}
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# trainer
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ppo_config = PPOConfig(
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batch_size=256,
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learning_rate=1.41e-5,
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mini_batch_size=64,
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log_with="wandb",
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)
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ppo_trainer = PPOTrainer(ppo_config, model, ref_model, tokenizer)
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# text env
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text_env = TextEnvironment(
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model,
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tokenizer,
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{"SimpleCalculatorTool": load_tool("ybelkada/simple-calculator")},
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exact_match_reward,
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prompt,
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generation_kwargs=generation_kwargs,
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)
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# main training loop
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for _step in range(100):
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tasks, answers = generate_data(ppo_config.batch_size)
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queries, responses, masks, rewards, histories = text_env.run(tasks, answers=answers)
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train_stats = ppo_trainer.step(queries, responses, rewards, masks)
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response_texts = [tokenizer.decode(response) for response in responses]
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query_texts = [tokenizer.decode(query) for query in queries]
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texts = {"query": [qt.split("<submit>")[-1].strip() for qt in query_texts], "response": response_texts}
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ppo_trainer.log_stats(train_stats, texts, rewards, columns_to_log=["query", "response", "answer"])
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ppo_trainer.save_pretrained(model_id + "-calculator")
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