├── .gitignore ├── .python-version ├── LICENSE ├── README.md ├── config.yaml ├── config_24GB.yaml ├── countdown_task.py ├── data_types.py ├── grpo.py ├── optimizer.py ├── pyproject.toml ├── qwen2_model.py ├── tokenizer.py ├── train.py └── uv.lock /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 11 | build/ 12 | develop-eggs/ 13 | dist/ 14 | downloads/ 15 | eggs/ 16 | .eggs/ 17 | lib/ 18 | lib64/ 19 | parts/ 20 | sdist/ 21 | var/ 22 | wheels/ 23 | share/python-wheels/ 24 | *.egg-info/ 25 | .installed.cfg 26 | *.egg 27 | MANIFEST 28 | 29 | # PyInstaller 30 | # Usually these files are written by a python script from a template 31 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 32 | *.manifest 33 | *.spec 34 | 35 | # Installer logs 36 | pip-log.txt 37 | pip-delete-this-directory.txt 38 | 39 | # Unit test / coverage reports 40 | htmlcov/ 41 | .tox/ 42 | .nox/ 43 | .coverage 44 | .coverage.* 45 | .cache 46 | nosetests.xml 47 | coverage.xml 48 | *.cover 49 | *.py,cover 50 | .hypothesis/ 51 | .pytest_cache/ 52 | cover/ 53 | 54 | # Translations 55 | *.mo 56 | *.pot 57 | 58 | # Django stuff: 59 | *.log 60 | local_settings.py 61 | db.sqlite3 62 | db.sqlite3-journal 63 | 64 | # Flask stuff: 65 | instance/ 66 | .webassets-cache 67 | 68 | # Scrapy stuff: 69 | .scrapy 70 | 71 | # Sphinx documentation 72 | docs/_build/ 73 | 74 | # PyBuilder 75 | .pybuilder/ 76 | target/ 77 | 78 | # Jupyter Notebook 79 | .ipynb_checkpoints 80 | 81 | # IPython 82 | profile_default/ 83 | ipython_config.py 84 | 85 | # pyenv 86 | # For a library or package, you might want to ignore these files since the code is 87 | # intended to run in multiple environments; otherwise, check them in: 88 | # .python-version 89 | 90 | # pipenv 91 | # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. 92 | # However, in case of collaboration, if having platform-specific dependencies or dependencies 93 | # having no cross-platform support, pipenv may install dependencies that don't work, or not 94 | # install all needed dependencies. 95 | #Pipfile.lock 96 | 97 | # UV 98 | # Similar to Pipfile.lock, it is generally recommended to include uv.lock in version control. 99 | # This is especially recommended for binary packages to ensure reproducibility, and is more 100 | # commonly ignored for libraries. 101 | #uv.lock 102 | 103 | # poetry 104 | # Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. 105 | # This is especially recommended for binary packages to ensure reproducibility, and is more 106 | # commonly ignored for libraries. 107 | # https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control 108 | #poetry.lock 109 | 110 | # pdm 111 | # Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. 112 | #pdm.lock 113 | # pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it 114 | # in version control. 115 | # https://pdm.fming.dev/latest/usage/project/#working-with-version-control 116 | .pdm.toml 117 | .pdm-python 118 | .pdm-build/ 119 | 120 | # PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm 121 | __pypackages__/ 122 | 123 | # Celery stuff 124 | celerybeat-schedule 125 | celerybeat.pid 126 | 127 | # SageMath parsed files 128 | *.sage.py 129 | 130 | # Environments 131 | .env 132 | .venv 133 | env/ 134 | venv/ 135 | ENV/ 136 | env.bak/ 137 | venv.bak/ 138 | 139 | # Spyder project settings 140 | .spyderproject 141 | .spyproject 142 | 143 | # Rope project settings 144 | .ropeproject 145 | 146 | # mkdocs documentation 147 | /site 148 | 149 | # mypy 150 | .mypy_cache/ 151 | .dmypy.json 152 | dmypy.json 153 | 154 | # Pyre type checker 155 | .pyre/ 156 | 157 | # pytype static type analyzer 158 | .pytype/ 159 | 160 | # Cython debug symbols 161 | cython_debug/ 162 | 163 | # PyCharm 164 | # JetBrains specific template is maintained in a separate JetBrains.gitignore that can 165 | # be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore 166 | # and can be added to the global gitignore or merged into this file. 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We also recommend that a 185 | file or class name and description of purpose be included on the 186 | same "printed page" as the copyright notice for easier 187 | identification within third-party archives. 188 | 189 | Copyright [yyyy] [name of copyright owner] 190 | 191 | Licensed under the Apache License, Version 2.0 (the "License"); 192 | you may not use this file except in compliance with the License. 193 | You may obtain a copy of the License at 194 | 195 | http://www.apache.org/licenses/LICENSE-2.0 196 | 197 | Unless required by applicable law or agreed to in writing, software 198 | distributed under the License is distributed on an "AS IS" BASIS, 199 | WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 200 | See the License for the specific language governing permissions and 201 | limitations under the License. 202 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # GRPO:Zero 2 | 3 | GRPO training with minimal dependencies (and low GPU memory usage!). We implement almost everything from scratch and only depend on `tokenizers` for tokenization and `pytorch` for training. 4 | - No `transformers` and `vLLM` dependencies! 5 | - The default config is set to run on a single A40 GPU (48GB VRAM) for a few hours to get good results. (An A40 costs `$0.44` per hour if you rent it from RunPod.) 6 | - We also support training with a 24GB VRAM GPU (e.g., an RTX 4090 GPU) by offloading the optimizer to CPU. Fortunately, this only adds a small overhead to the training because we only update the policy network a few hundred times during the entire training process. 7 | - We support several improvements over the original GRPO algorithm from the [DAPO project](https://arxiv.org/abs/2503.14476), including: 8 | - **Token-level policy gradient loss**: every token is equally weighted in the policy gradient loss. 9 | - **Removing KL Divergence**: the KL divergence is not used in the policy gradient loss. This reduces GPU memory usage as we no longer need the reference policy network. 10 | - **Overlong episode filtering**: skips unfinished episodes that exceed context length limits. This stabilizes training. Though we disabled it by default to observe model learning under limited context length. Set `skip_unfinished_episodes` to `true` to enable it. 11 | 12 | ## Algorithm 13 | 14 | Group Relative Policy Optimization (GRPO) is an algorithm proposed by Deepseek for training large language models with reinforcement learning. The idea is simple: for each question, we randomly sample multiple answers. The advantage of an answer is then defined as the normalized reward. This gets rid of the value estimation network. In particular, we implement the following algorithm: 15 | 16 | 1. For each training step, randomly sample $N$ questions $q_1, q_2, \cdots, q_N$. 17 | 2. For each question $q_i$, sample $M$ answers $a_{i,1}, a_{i,2}, \cdots, a_{i,M}$. 18 | 3. Compute the reward $r_{i,j}$ for each answer $a_{i,j}$. 19 | 4. Compute the mean and std of the rewards for each question $q_i$. 20 | 21 | $$ 22 | \begin{aligned} 23 | \mu_i &\leftarrow \text{mean}(r_{i,1}, r_{i,2}, \cdots, r_{i,M}) \\ 24 | \sigma_i &\leftarrow \text{std}(r_{i,1}, r_{i,2}, \cdots, r_{i,M}) 25 | \end{aligned} 26 | $$ 27 | 28 | 5. For each token $t$ in the answer $a_{i,j}$, compute the advantage as 29 | 30 | $$A_{i,j}[t] \leftarrow \frac{r_{i,j} - \mu_i}{\sigma_i}$$ 31 | 32 | 6. Compute policy gradient using PPO surrogate objective. For simplicity, we will only do one policy update per iteration, in which the gradient of the PPO objective is equivalent to following vanilla policy gradient estimation (per token). 33 | 34 | $$ 35 | \nabla_\theta \log \pi_\theta(a_{i,j}[t]) \cdot A_{i,j}[t] 36 | $$ 37 | 38 | 7. Update the policy network $\pi(\theta)$ using the gradient. Go back to step 1. 39 | 40 | ## CountDown Task 41 | 42 | We are going to train the Qwen2.5 models on the [CountDown task](https://huggingface.co/datasets/Jiayi-Pan/Countdown-Tasks-3to4). Given a list of 3 or 4 numbers and a target number, the model needs to generate a mathematical expression using simple arithmetic operations (+, -, *, /) that evaluates to the target number. For example: 43 | 44 | ``` 45 | Question: Given 1 2 3 4 and a target number 11. Show an expression that evaluates to 11. 46 | Answer: 1 + (2 * 3) + 4 47 | ``` 48 | 49 | ## Reward Function 50 | 51 | To solve the CountDown task, we will use the GRPO algorithm to train the model to generate the chain of thought reasoning before generating the final expression. Specifically, the model is trained to follow the format: 52 | 53 | ``` 54 | Model step by step reasoning 55 | Final answer 56 | ``` 57 | 58 | The reward is the sum of two components: 59 | 60 | 1. **Format Reward**: The model earns a reward of `0.1` when it correctly follows the specified format with thinking and answer tags, and `0` otherwise. 61 | 2. **Answer Reward**: The model receives a reward of `1` if its final answer uses each provided number exactly once and correctly evaluates to the target value, otherwise it receives `0`. 62 | 63 | 64 | ## Training 65 | 66 | We use the `Qwen2.5-3B-Instruct` model for training. To train the model, run the following commands: 67 | 68 | ```bash 69 | # initialize the environment 70 | pip install uv 71 | uv sync 72 | 73 | # install git-lfs 74 | apt update; apt install git-lfs -y; git lfs install 75 | 76 | # download the dataset 77 | git clone https://huggingface.co/datasets/Jiayi-Pan/Countdown-Tasks-3to4 78 | 79 | # download the pretrained model 80 | git clone https://huggingface.co/Qwen/Qwen2.5-3B-Instruct 81 | # train the model 82 | uv run train.py 83 | # train the model with a 24GB VRAM GPU (e.g., an RTX 4090 GPU) 84 | uv run train.py --config config_24GB.yaml 85 | ``` 86 | ## Acknowledgements 87 | 88 | This project builds upon the work of several outstanding projects: 89 | 90 | - [DeepSeekMath](https://arxiv.org/abs/2402.03300) for pioneering the GRPO algorithm. 91 | - [DAPO](https://arxiv.org/abs/2503.14476) for their enhancements to the original GRPO algorithm. 92 | - [TinyZero](https://github.com/Jiayi-Pan/TinyZero) for their implementation of GRPO and creation of the [CountDown-Tasks-3to4](https://huggingface.co/datasets/Jiayi-Pan/Countdown-Tasks-3to4) dataset. 93 | - [nano-aha-moment](https://github.com/McGill-NLP/nano-aha-moment/tree/main) for their clear implementation and tutorial on the GRPO algorithm. 94 | - [Qwen2.5](https://huggingface.co/Qwen/Qwen2.5-3B-Instruct) for developing the high-quality pretrained model used in this project. -------------------------------------------------------------------------------- /config.yaml: -------------------------------------------------------------------------------- 1 | model: 2 | pretrained_model_path: "Qwen2.5-3B-Instruct" 3 | device: "cuda" 4 | dtype: "bfloat16" 5 | data: 6 | path: "Countdown-Tasks-3to4" 7 | test_size: 128 8 | training: 9 | random_seed: 1337 10 | max_prompt_len: 256 11 | max_gen_len: 1024 12 | batch_size: 256 13 | num_questions_per_batch: 32 14 | # Number of examples per gradient accumulation step 15 | micro_batch_size: 2 16 | max_grad_norm: 1.0 17 | learning_rate: 1.0e-5 18 | weight_decay: 0.0 19 | betas: [0.9, 0.999] 20 | ckpt_dir: "ckpt" 21 | log_dir: "logs" 22 | skip_unfinished_episodes: false 23 | ckpt_save_interval: 100 24 | eval_interval: 10 25 | memory_efficient_adamw: false -------------------------------------------------------------------------------- /config_24GB.yaml: -------------------------------------------------------------------------------- 1 | model: 2 | pretrained_model_path: "Qwen2.5-3B-Instruct" 3 | device: "cuda" 4 | dtype: "bfloat16" 5 | data: 6 | path: "Countdown-Tasks-3to4" 7 | test_size: 128 8 | training: 9 | random_seed: 1337 10 | max_prompt_len: 256 11 | max_gen_len: 1024 12 | batch_size: 256 13 | num_questions_per_batch: 32 14 | # Number of examples per gradient accumulation step 15 | micro_batch_size: 2 16 | max_grad_norm: 1.0 17 | learning_rate: 1.0e-5 18 | weight_decay: 0.0 19 | betas: [0.9, 0.999] 20 | ckpt_dir: "ckpt" 21 | log_dir: "logs" 22 | skip_unfinished_episodes: false 23 | ckpt_save_interval: 100 24 | eval_interval: 10 25 | # save GPU memory by offloading the optimizer states to CPU 26 | memory_efficient_adamw: true -------------------------------------------------------------------------------- /countdown_task.py: -------------------------------------------------------------------------------- 1 | import re 2 | from pathlib import Path 3 | from typing import Any, Dict, List, Optional 4 | 5 | import pandas as pd 6 | from torch.utils.data import Dataset 7 | 8 | from data_types import MiniBatch 9 | from tokenizer import Tokenizer 10 | 11 | SYSTEM_MESSAGE = ( 12 | "You are a helpful assistant. You first think about the reasoning process " 13 | "in your mind and then provide the user with the answer." 14 | ) 15 | USER_TEMPLATE = ( 16 | "Using the numbers {numbers}, create an equation that equals {target}. " 17 | "You can use basic arithmetic operations (+, -, *, /) and each number can only be used once. " 18 | "Show your work in tags. " 19 | "And return the final answer in tags, for example (1 + 2) / 3 ." 20 | ) 21 | RESPONSE_PROMPT = "Let me solve this step by step.\n" 22 | 23 | 24 | class CountdownTasksDataset(Dataset): 25 | """Prepare Countdown Tasks for training""" 26 | 27 | def __init__( 28 | self, 29 | tokenizer: Tokenizer, 30 | data_path: str, 31 | split: str = "train", 32 | test_size: int = 100, 33 | ): 34 | data = pd.read_parquet(Path(data_path) / "data") 35 | # use the last `test_size` examples for testing 36 | self.data = ( 37 | data.iloc[:-test_size] if split == "train" else data.iloc[-test_size:] 38 | ) 39 | self.tokenizer = tokenizer 40 | 41 | def __len__(self): 42 | return len(self.data) 43 | 44 | def __getitem__(self, idx): 45 | item = self.data.iloc[idx].to_dict() 46 | item.update(self.encode_prefix(item["nums"], item["target"])) 47 | return item 48 | 49 | def encode_prefix(self, numbers: List[int], target: int): 50 | """Prefix is the *actual* input to the model.""" 51 | user_message = USER_TEMPLATE.format(numbers=numbers, target=target) 52 | prefix = self.tokenizer.encode_chat_with_response_prompt( 53 | [ 54 | {"role": "system", "content": SYSTEM_MESSAGE}, 55 | {"role": "user", "content": user_message}, 56 | ], 57 | RESPONSE_PROMPT, 58 | ) 59 | tokens = self.tokenizer.tokenize(prefix) 60 | return { 61 | "prefix": prefix, 62 | "prefix_tokens": tokens.tokens, 63 | "prefix_token_ids": tokens.ids, 64 | } 65 | 66 | @staticmethod 67 | def collate_fn(batch: List[Dict[str, Any]]) -> MiniBatch: 68 | """Collate examples into a batch.""" 69 | numbers = [item["nums"] for item in batch] 70 | target = [item["target"] for item in batch] 71 | prefix = [item["prefix"] for item in batch] 72 | prefix_tokens = [item["prefix_tokens"] for item in batch] 73 | prefix_token_ids = [item["prefix_token_ids"] for item in batch] 74 | return MiniBatch( 75 | numbers=numbers, 76 | target=target, 77 | prefix=prefix, 78 | prefix_tokens=prefix_tokens, 79 | prefix_token_ids=prefix_token_ids, 80 | ) 81 | 82 | 83 | def format_reward_function(response: str, end_token: Optional[str] = None) -> float: 84 | """ 85 | Checks if the response follows the format ...... 86 | """ 87 | # Strip end token if present 88 | if end_token and response.endswith(end_token): 89 | response = response[: -len(end_token)] 90 | 91 | think_regex = r".*?<\/think>" 92 | answer_regex = r".*?<\/answer>" 93 | full_format_regex = r"^.*?<\/think>\n.*?<\/answer>$" 94 | 95 | think_match = re.search(think_regex, response, re.DOTALL) 96 | answer_match = re.search(answer_regex, response, re.DOTALL) 97 | full_format_match = re.match(full_format_regex, response, re.DOTALL) 98 | 99 | if full_format_match: 100 | return 1.0 101 | 102 | reward = 0.0 103 | 104 | if think_match: 105 | reward += 0.1 106 | 107 | if answer_match: 108 | reward += 0.5 109 | 110 | return reward 111 | 112 | 113 | def answer_reward_function( 114 | response: str, numbers: List[int] = None, target: int = None 115 | ) -> float: 116 | """ 117 | Checks if the answer uses all numbers exactly once and evaluates to the target 118 | """ 119 | answer_regex = r"(.*?)<\/answer>" 120 | answer_match = re.search(answer_regex, response, re.DOTALL) 121 | if not answer_match: 122 | return 0.0 123 | 124 | answer_content = answer_match.group(1) 125 | if not answer_content: 126 | return 0.0 127 | 128 | allowed_chars = r"^[0-9+\-*/() ]+$" 129 | if not re.match(allowed_chars, answer_content): 130 | return 0.0 131 | 132 | # Check if the answer uses all numbers exactly once 133 | used_numbers = [int(n) for n in re.findall(r"\d+", answer_content)] 134 | if sorted(used_numbers) != sorted(numbers): 135 | return 0.0 136 | 137 | # Check if the answer evaluates to the target 138 | try: 139 | result = eval(answer_content, {"__builtins__": None}, {}) 140 | if abs(float(result) - float(target)) < 1e-5: 141 | return 1.0 142 | except: 143 | pass 144 | 145 | return 0.0 146 | 147 | 148 | def reward_function( 149 | response: str, 150 | numbers: List[int] = None, 151 | target: int = None, 152 | end_token: str = None, 153 | ) -> Dict[str, Any]: 154 | """Reward function for Countdown Tasks. 155 | 156 | Total reward = 0.1 * format_reward + answer_reward 157 | """ 158 | format_reward = format_reward_function("" + response, end_token) 159 | answer_reward = answer_reward_function(response, numbers, target) 160 | return { 161 | "reward": format_reward * 0.1 + answer_reward, 162 | "reward_info": { 163 | "format_reward": format_reward, 164 | "answer_reward": answer_reward, 165 | }, 166 | } 167 | -------------------------------------------------------------------------------- /data_types.py: -------------------------------------------------------------------------------- 1 | from dataclasses import dataclass 2 | from typing import Dict, List 3 | 4 | 5 | @dataclass 6 | class Episode: 7 | """Store all relevant information of an episode.""" 8 | 9 | prefix: str 10 | text: str 11 | prefix_token_ids: List[int] 12 | prefix_tokens: List[str] 13 | generated_token_ids: List[int] 14 | is_finished: bool 15 | reward: float 16 | reward_info: Dict[str, float] 17 | 18 | 19 | @dataclass 20 | class MiniBatch: 21 | """Batch of data for each training step.""" 22 | 23 | prefix: List[str] 24 | prefix_tokens: List[List[str]] 25 | prefix_token_ids: List[List[int]] 26 | numbers: List[List[int]] 27 | target: List[int] 28 | -------------------------------------------------------------------------------- /grpo.py: -------------------------------------------------------------------------------- 1 | import dataclasses 2 | import gc 3 | import math 4 | from collections import defaultdict 5 | from typing import Callable, List 6 | 7 | import numpy as np 8 | import torch 9 | 10 | from data_types import Episode, MiniBatch 11 | from qwen2_model import Transformer 12 | from tokenizer import Tokenizer 13 | 14 | 15 | @torch.no_grad() 16 | def rollout( 17 | model: Transformer, 18 | batch: MiniBatch, 19 | tokenizer: Tokenizer, 20 | max_gen_len: int, 21 | num_answer_per_question: int, 22 | reward_function: Callable, 23 | device: torch.device, 24 | dtype: torch.dtype, 25 | ) -> List[Episode]: 26 | end_token = tokenizer.eos_token 27 | end_token_id = tokenizer.eos_token_id 28 | pad_token_id = tokenizer.pad_token_id 29 | prefix_token_ids = batch.prefix_token_ids 30 | bsz = len(batch.prefix) * num_answer_per_question 31 | min_prompt_len = min(len(t) for t in prefix_token_ids) 32 | max_prompt_len = max(len(t) for t in prefix_token_ids) 33 | total_len = max_gen_len + max_prompt_len 34 | model.init_kv_cache( 35 | max_batch_size=bsz, 36 | max_seq_len=total_len, 37 | device=device, 38 | dtype=dtype, 39 | ) 40 | tokens = torch.full((bsz, total_len), pad_token_id, dtype=torch.long, device=device) 41 | for k, t in enumerate(prefix_token_ids): 42 | offset = k * num_answer_per_question 43 | for i in range(num_answer_per_question): 44 | tokens[offset + i, : len(t)] = torch.tensor( 45 | t, dtype=torch.long, device=device 46 | ) 47 | 48 | prev_pos = 0 49 | input_text_mask = tokens != pad_token_id 50 | assert min_prompt_len < total_len 51 | is_finished = torch.zeros((bsz,), dtype=torch.bool, device=device) 52 | 53 | for cur_pos in range(min_prompt_len, total_len): 54 | print( 55 | f"\r* Generating trajectories: {cur_pos-min_prompt_len:>4d}/{total_len-min_prompt_len:>4d}", 56 | flush=True, 57 | end="", 58 | ) 59 | with torch.autocast(device_type=device.type, dtype=dtype): 60 | logits = model.inference(tokens[:, prev_pos:cur_pos], prev_pos) 61 | probs = torch.softmax(logits[:, -1], dim=-1) 62 | next_token = torch.multinomial(probs, num_samples=1) 63 | next_token = next_token.reshape(-1) 64 | next_token = torch.where( 65 | input_text_mask[:, cur_pos], tokens[:, cur_pos], next_token 66 | ) 67 | # if an rollout is finished, we fill the rest of the tokens with pad_token_id 68 | next_token = torch.where(is_finished, pad_token_id, next_token) 69 | tokens[:, cur_pos] = next_token 70 | if end_token_id is not None: 71 | is_end_token = next_token == end_token_id 72 | is_generated_token = ~input_text_mask[:, cur_pos] 73 | is_finished = is_finished | (is_end_token & is_generated_token) 74 | prev_pos = cur_pos 75 | if is_finished.all(): 76 | break 77 | model.del_kv_cache() 78 | gc.collect() 79 | torch.cuda.empty_cache() 80 | is_finished_list = is_finished.tolist() 81 | tokens_list = tokens.tolist() 82 | 83 | # prepare the output episodes 84 | episodes = [] 85 | for i in range(bsz // num_answer_per_question): 86 | for j in range(num_answer_per_question): 87 | idx = i * num_answer_per_question + j 88 | generated_token_ids = tokens_list[idx][len(batch.prefix_token_ids[i]) :] 89 | # remove padding tokens 90 | if pad_token_id in generated_token_ids: 91 | generated_token_ids = generated_token_ids[ 92 | : generated_token_ids.index(pad_token_id) 93 | ] 94 | generated_text = tokenizer.detokenize(generated_token_ids) 95 | rewards = reward_function( 96 | response=generated_text, 97 | numbers=batch.numbers[i], 98 | target=batch.target[i], 99 | end_token=end_token, 100 | ) 101 | episode = Episode( 102 | prefix=batch.prefix[i], 103 | text=batch.prefix[i] + generated_text, 104 | prefix_token_ids=batch.prefix_token_ids[i], 105 | prefix_tokens=batch.prefix_tokens[i], 106 | generated_token_ids=generated_token_ids, 107 | is_finished=is_finished_list[idx], 108 | reward=rewards["reward"], 109 | reward_info=rewards["reward_info"], 110 | ) 111 | episodes.append(episode) 112 | # clear the output line 113 | print("\r", end=" " * 100, flush=True) 114 | return episodes 115 | 116 | 117 | def normalize_rewards_per_group(episodes: List[Episode]) -> List[Episode]: 118 | """Normalize rewards per group. A group is defined by the prefix.""" 119 | groups = defaultdict(list) 120 | for episode in episodes: 121 | groups[tuple(episode.prefix)].append(episode) 122 | output = [] 123 | for group in groups.values(): 124 | group_rewards = [item.reward for item in group] 125 | mean_reward = np.mean(group_rewards) 126 | std_reward = np.std(group_rewards) 127 | for episode in group: 128 | normalized_reward = (episode.reward - mean_reward) / (std_reward + 1e-4) 129 | episode = dataclasses.replace(episode, reward=normalized_reward) 130 | output.append(episode) 131 | return output 132 | 133 | 134 | def compute_entropy(logits: torch.Tensor) -> torch.Tensor: 135 | probs = torch.nn.functional.softmax(logits, dim=-1) 136 | entropy = torch.logsumexp(logits, dim=-1) - torch.sum(probs * logits, dim=-1) 137 | return entropy 138 | 139 | 140 | def update_policy( 141 | model, 142 | optimizer, 143 | episodes: List[Episode], 144 | micro_batch_size: int, 145 | pad_token_id: int, 146 | max_grad_norm: float, 147 | device: torch.device, 148 | dtype: torch.dtype, 149 | ): 150 | """Update the policy using the GRPO algorithm.""" 151 | episodes = normalize_rewards_per_group(episodes) 152 | # sort episodes by token length for efficient (micro-)batching 153 | episodes.sort(key=lambda x: len(x.prefix_token_ids) + len(x.generated_token_ids)) 154 | num_micro_batches = math.ceil(len(episodes) / micro_batch_size) 155 | num_target_tokens = sum(len(episode.generated_token_ids) for episode in episodes) 156 | entropy = 0.0 157 | 158 | for i in range(0, len(episodes), micro_batch_size): 159 | print( 160 | f"\r* Computing policy gradient: {i:>2d}/{len(episodes):>2d}", 161 | flush=True, 162 | end="", 163 | ) 164 | j = min(i + micro_batch_size, len(episodes)) 165 | batch_episodes = episodes[i:j] 166 | batch_lengths = [ 167 | len(episode.prefix_token_ids) + len(episode.generated_token_ids) 168 | for episode in batch_episodes 169 | ] 170 | batch_max_length = max(batch_lengths) 171 | batch_token_ids = [ 172 | episode.prefix_token_ids 173 | + episode.generated_token_ids 174 | + [pad_token_id] * (batch_max_length - batch_lengths[i]) 175 | for i, episode in enumerate(batch_episodes) 176 | ] 177 | batch_masks = [ 178 | [0] * len(episode.prefix_token_ids) 179 | + [1] * len(episode.generated_token_ids) 180 | + [0] * (batch_max_length - batch_lengths[i]) 181 | for i, episode in enumerate(batch_episodes) 182 | ] 183 | batch_advantages = [episode.reward for episode in batch_episodes] 184 | batch_token_ids = torch.tensor(batch_token_ids, device=device, dtype=torch.long) 185 | batch_masks = torch.tensor(batch_masks, device=device, dtype=torch.bool) 186 | batch_advantages = torch.tensor( 187 | batch_advantages, device=device, dtype=torch.float32 188 | ) 189 | 190 | with torch.autocast(device_type=device.type, dtype=dtype): 191 | input_token_ids = batch_token_ids[:, :-1] 192 | target_token_ids = batch_token_ids[:, 1:] 193 | target_masks = batch_masks[:, 1:] 194 | logits = model.forward(input_token_ids).float() 195 | 196 | log_probs = -torch.nn.functional.cross_entropy( 197 | logits.reshape(-1, logits.size(-1)), 198 | target_token_ids.reshape(-1), 199 | ignore_index=pad_token_id, 200 | reduction="none", 201 | ).reshape(input_token_ids.shape[0], -1) 202 | 203 | with torch.no_grad(): 204 | token_entropy = compute_entropy(logits) 205 | entropy = entropy + (token_entropy * target_masks).sum() / num_target_tokens 206 | 207 | obj = log_probs * batch_advantages[:, None] 208 | # per-token objective 209 | obj = (obj * target_masks).sum() / num_target_tokens 210 | loss = -obj 211 | loss.backward() 212 | 213 | # update the policy 214 | grad_norm = torch.nn.utils.clip_grad_norm_( 215 | model.parameters(), max_norm=max_grad_norm 216 | ) 217 | optimizer.step() 218 | optimizer.zero_grad(set_to_none=True) 219 | return { 220 | "loss": loss.item(), 221 | "grad_norm": grad_norm.item(), 222 | "entropy": entropy.item(), 223 | } 224 | -------------------------------------------------------------------------------- /optimizer.py: -------------------------------------------------------------------------------- 1 | import math 2 | 3 | import torch 4 | from torch.optim import AdamW 5 | 6 | 7 | class MemoryEfficientAdamW(AdamW): 8 | """ 9 | Memory Efficient AdamW optimizer that keeps parameters and gradients on GPU 10 | but optimizer states on CPU when enabled. 11 | When disabled, behaves exactly like standard AdamW. 12 | """ 13 | 14 | def __init__( 15 | self, 16 | params, 17 | lr=1e-3, 18 | betas=(0.9, 0.999), 19 | eps=1e-8, 20 | weight_decay=1e-2, 21 | amsgrad=False, 22 | pin_memory=True, 23 | enabled=True, 24 | ): 25 | super(MemoryEfficientAdamW, self).__init__( 26 | params, 27 | lr=lr, 28 | betas=betas, 29 | eps=eps, 30 | weight_decay=weight_decay, 31 | amsgrad=amsgrad, 32 | ) 33 | self.pin_memory = pin_memory 34 | self.enabled = enabled 35 | 36 | @torch.no_grad() 37 | def step(self, closure=None): 38 | """Performs a single optimization step.""" 39 | if not self.enabled: 40 | # Use the parent AdamW implementation when disabled 41 | return super(MemoryEfficientAdamW, self).step(closure) 42 | 43 | loss = None 44 | if closure is not None: 45 | with torch.enable_grad(): 46 | loss = closure() 47 | 48 | for group in self.param_groups: 49 | params_with_grad = [] 50 | grads = [] 51 | exp_avgs = [] 52 | exp_avg_sqs = [] 53 | max_exp_avg_sqs = [] 54 | state_steps = [] 55 | beta1, beta2 = group["betas"] 56 | 57 | for p in group["params"]: 58 | if p.grad is None: 59 | continue 60 | 61 | params_with_grad.append(p) 62 | grads.append(p.grad) 63 | 64 | # Initialize state if needed 65 | state = self.state[p] 66 | if len(state) == 0: 67 | state["step"] = 0 68 | # Store optimizer states on CPU with pinned memory 69 | device = "cpu" 70 | pin_memory = self.pin_memory 71 | dtype = torch.float32 72 | 73 | state["exp_avg"] = torch.zeros_like( 74 | p.data, device=device, pin_memory=pin_memory, dtype=dtype 75 | ) 76 | state["exp_avg_sq"] = torch.zeros_like( 77 | p.data, device=device, pin_memory=pin_memory, dtype=dtype 78 | ) 79 | if group["amsgrad"]: 80 | state["max_exp_avg_sq"] = torch.zeros_like( 81 | p.data, device=device, pin_memory=pin_memory, dtype=dtype 82 | ) 83 | 84 | # Get state values 85 | exp_avgs.append(state["exp_avg"]) 86 | exp_avg_sqs.append(state["exp_avg_sq"]) 87 | 88 | if group["amsgrad"]: 89 | max_exp_avg_sqs.append(state["max_exp_avg_sq"]) 90 | 91 | state["step"] += 1 92 | state_steps.append(state["step"]) 93 | 94 | # Process all parameters in the group 95 | self._memory_efficient_update( 96 | params_with_grad, 97 | grads, 98 | exp_avgs, 99 | exp_avg_sqs, 100 | max_exp_avg_sqs, 101 | state_steps, 102 | amsgrad=group["amsgrad"], 103 | beta1=beta1, 104 | beta2=beta2, 105 | lr=group["lr"], 106 | weight_decay=group["weight_decay"], 107 | eps=group["eps"], 108 | ) 109 | 110 | return loss 111 | 112 | def _memory_efficient_update( 113 | self, 114 | params, 115 | grads, 116 | exp_avgs, 117 | exp_avg_sqs, 118 | max_exp_avg_sqs, 119 | state_steps, 120 | amsgrad, 121 | beta1, 122 | beta2, 123 | lr, 124 | weight_decay, 125 | eps, 126 | ): 127 | """ 128 | Performs the AdamW parameter update on GPU with CPU-stored optimizer states. 129 | Uses pinned memory for efficient CPU-to-GPU transfer of optimizer states. 130 | """ 131 | for i, param in enumerate(params): 132 | grad = grads[i] 133 | param_device = param.device 134 | 135 | # Access optimizer states - they'll transfer efficiently due to pin_memory 136 | exp_avg = exp_avgs[i].to(param_device, non_blocking=True) 137 | exp_avg_sq = exp_avg_sqs[i].to(param_device, non_blocking=True) 138 | 139 | step = state_steps[i] 140 | 141 | # Decay the first and second moment running averages 142 | exp_avg.mul_(beta1).add_(grad, alpha=1 - beta1) 143 | exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=1 - beta2) 144 | 145 | if amsgrad: 146 | # Access max_exp_avg_sq - transfers efficiently with pin_memory 147 | max_exp_avg_sq = max_exp_avg_sqs[i].to(param_device, non_blocking=True) 148 | # Maintains the maximum of all 2nd moment running avg. till now 149 | torch.maximum(max_exp_avg_sq, exp_avg_sq, out=max_exp_avg_sq) 150 | # Use the max for normalizing running avg of gradient 151 | denom = max_exp_avg_sq.sqrt().add_(eps) 152 | # Store back to CPU 153 | max_exp_avg_sqs[i].copy_(max_exp_avg_sq, non_blocking=True) 154 | else: 155 | denom = exp_avg_sq.sqrt().add_(eps) 156 | 157 | bias_correction1 = 1 - beta1**step 158 | bias_correction2 = 1 - beta2**step 159 | step_size = lr * math.sqrt(bias_correction2) / bias_correction1 160 | 161 | # Apply weight decay directly to the parameter (AdamW) 162 | if weight_decay != 0: 163 | param.mul_(1 - lr * weight_decay) 164 | 165 | # Update parameters (directly on GPU) 166 | param.addcdiv_(exp_avg, denom, value=-step_size) 167 | 168 | # Store optimizer states back to CPU 169 | exp_avgs[i].copy_(exp_avg, non_blocking=True) 170 | exp_avg_sqs[i].copy_(exp_avg_sq, non_blocking=True) 171 | -------------------------------------------------------------------------------- /pyproject.toml: -------------------------------------------------------------------------------- 1 | [project] 2 | name = "GRPO-Zero" 3 | version = "0.2.0" 4 | description = "GRPO from scratch." 5 | readme = "README.md" 6 | requires-python = ">=3.11" 7 | dependencies = [ 8 | "jinja2>=3.1.6", 9 | "pandas>=2.2.3", 10 | "pyarrow>=19.0.1", 11 | "pyyaml>=6.0.2", 12 | "safetensors>=0.5.3", 13 | "tensorboard>=2.19.0", 14 | "tokenizers>=0.21.1", 15 | "torch>=2.6.0", 16 | ] 17 | 18 | [dependency-groups] 19 | dev = [ 20 | "pytype>=2024.10.11", 21 | ] 22 | -------------------------------------------------------------------------------- /qwen2_model.py: -------------------------------------------------------------------------------- 1 | import json 2 | from dataclasses import dataclass 3 | from pathlib import Path 4 | from typing import Optional, Tuple, Union 5 | 6 | import torch 7 | import torch.nn.functional as F 8 | from torch import nn 9 | 10 | 11 | @dataclass 12 | class Qwen2Config: 13 | attention_dropout: float = 0.0 14 | bos_token_id: int = 151643 15 | eos_token_id: int = 151645 16 | hidden_act: str = "silu" 17 | hidden_size: int = 2048 18 | initializer_range: float = 0.02 19 | intermediate_size: int = 11008 20 | max_position_embeddings: int = 32768 21 | max_window_layers: int = 70 22 | model_type: str = "qwen2" 23 | num_attention_heads: int = 16 24 | num_hidden_layers: int = 36 25 | num_key_value_heads: int = 2 26 | rms_norm_eps: float = 1e-06 27 | rope_theta: float = 1000000.0 28 | sliding_window: int = 32768 29 | tie_word_embeddings: bool = True 30 | torch_dtype: str = "bfloat16" 31 | use_cache: bool = True 32 | use_sliding_window: bool = False 33 | vocab_size: int = 151936 34 | 35 | 36 | class RMSNorm(torch.nn.Module): 37 | def __init__(self, dim: int, eps: float = 1e-6): 38 | super().__init__() 39 | self.eps = eps 40 | self.weight = nn.Parameter(torch.ones(dim)) 41 | 42 | def _norm(self, x): 43 | return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps) 44 | 45 | def forward(self, x): 46 | input_dtype = x.dtype 47 | x = x.to(torch.float32) 48 | x = self._norm(x).type_as(x) 49 | x = self.weight * x.to(input_dtype) 50 | return x 51 | 52 | 53 | def rotate_half(x): 54 | """Rotates half the hidden dims of the input.""" 55 | x1 = x[..., : x.shape[-1] // 2] 56 | x2 = x[..., x.shape[-1] // 2 :] 57 | return torch.cat((-x2, x1), dim=-1) 58 | 59 | 60 | def apply_rotary_pos_emb(q, k, cos, sin, unsqueeze_dim=2): 61 | cos = cos.unsqueeze(unsqueeze_dim) 62 | sin = sin.unsqueeze(unsqueeze_dim) 63 | q_embed = (q * cos) + (rotate_half(q) * sin) 64 | k_embed = (k * cos) + (rotate_half(k) * sin) 65 | return q_embed, k_embed 66 | 67 | 68 | class Attention(nn.Module): 69 | def __init__(self, args: Qwen2Config): 70 | super().__init__() 71 | self.n_kv_heads = ( 72 | args.num_attention_heads 73 | if args.num_key_value_heads is None 74 | else args.num_key_value_heads 75 | ) 76 | self.n_heads = args.num_attention_heads 77 | self.n_kv_heads = self.n_kv_heads 78 | self.n_rep = self.n_heads // self.n_kv_heads 79 | self.head_dim = args.hidden_size // args.num_attention_heads 80 | 81 | self.q_proj = nn.Linear( 82 | args.hidden_size, 83 | args.num_attention_heads * self.head_dim, 84 | bias=True, 85 | ) 86 | self.k_proj = nn.Linear( 87 | args.hidden_size, 88 | args.num_key_value_heads * self.head_dim, 89 | bias=True, 90 | ) 91 | self.v_proj = nn.Linear( 92 | args.hidden_size, 93 | args.num_key_value_heads * self.head_dim, 94 | bias=True, 95 | ) 96 | self.o_proj = nn.Linear( 97 | args.num_attention_heads * self.head_dim, 98 | args.hidden_size, 99 | bias=False, 100 | ) 101 | self.args = args 102 | 103 | def init_kv_cache( 104 | self, 105 | max_batch_size: int, 106 | max_seq_len: int, 107 | dtype: torch.dtype, 108 | device: torch.device, 109 | ): 110 | cache_shape = (max_batch_size, max_seq_len, self.n_kv_heads, self.head_dim) 111 | cache_k = torch.zeros(cache_shape, dtype=dtype, device=device) 112 | cache_v = torch.zeros(cache_shape, dtype=dtype, device=device) 113 | self.register_buffer("cache_k", cache_k, persistent=False) 114 | self.register_buffer("cache_v", cache_v, persistent=False) 115 | 116 | def del_kv_cache(self): 117 | self.cache_k = None 118 | self.cache_v = None 119 | 120 | def forward( 121 | self, 122 | x: torch.Tensor, 123 | pos_embed: Tuple[torch.Tensor, torch.Tensor], 124 | start_pos: Optional[Union[int, torch.Tensor]] = None, 125 | ): 126 | bsz, seqlen, _ = x.shape 127 | xq, xk, xv = self.q_proj(x), self.k_proj(x), self.v_proj(x) 128 | xq = xq.view(bsz, seqlen, self.n_heads, self.head_dim) 129 | xk = xk.view(bsz, seqlen, self.n_kv_heads, self.head_dim) 130 | xv = xv.view(bsz, seqlen, self.n_kv_heads, self.head_dim) 131 | 132 | cos, sin = pos_embed 133 | xq, xk = apply_rotary_pos_emb(xq, xk, cos, sin, unsqueeze_dim=2) 134 | if start_pos is not None: 135 | # inference mode 136 | end_pos = start_pos + seqlen 137 | self.cache_k[:bsz, start_pos:end_pos, :, :] = xk 138 | self.cache_v[:bsz, start_pos:end_pos, :, :] = xv 139 | output = torch.nn.functional.scaled_dot_product_attention( 140 | query=xq.transpose(1, 2), 141 | key=self.cache_k[:bsz, :end_pos].transpose(1, 2), 142 | value=self.cache_v[:bsz, :end_pos].transpose(1, 2), 143 | is_causal=True if seqlen > 1 else False, 144 | enable_gqa=True, 145 | ).transpose(1, 2) 146 | else: 147 | # training mode 148 | output = torch.nn.functional.scaled_dot_product_attention( 149 | query=xq.transpose(1, 2), 150 | key=xk.transpose(1, 2), 151 | value=xv.transpose(1, 2), 152 | is_causal=True, 153 | enable_gqa=True, 154 | ).transpose(1, 2) 155 | output = output.reshape(bsz, seqlen, -1) 156 | return self.o_proj(output) 157 | 158 | 159 | class FeedForward(nn.Module): 160 | def __init__( 161 | self, 162 | dim: int, 163 | intermediate_size: int, 164 | ): 165 | super().__init__() 166 | self.up_proj = nn.Linear(dim, intermediate_size, bias=False) 167 | self.down_proj = nn.Linear(intermediate_size, dim, bias=False) 168 | self.gate_proj = nn.Linear(dim, intermediate_size, bias=False) 169 | 170 | def forward(self, x): 171 | x = self.down_proj(F.silu(self.gate_proj(x)) * self.up_proj(x)) 172 | return x 173 | 174 | 175 | class TransformerBlock(nn.Module): 176 | def __init__(self, layer_id: int, args: Qwen2Config): 177 | super().__init__() 178 | self.n_heads = args.num_attention_heads 179 | self.dim = args.hidden_size 180 | self.head_dim = args.hidden_size // args.num_attention_heads 181 | self.self_attn = Attention(args) 182 | self.mlp = FeedForward( 183 | dim=args.hidden_size, 184 | intermediate_size=args.intermediate_size, 185 | ) 186 | self.layer_id = layer_id 187 | self.input_layernorm = RMSNorm(args.hidden_size, eps=args.rms_norm_eps) 188 | self.post_attention_layernorm = RMSNorm(args.hidden_size, eps=args.rms_norm_eps) 189 | 190 | def forward( 191 | self, 192 | x: torch.Tensor, 193 | pos_embed: Tuple[torch.Tensor, torch.Tensor], 194 | start_pos: Optional[Union[int, torch.Tensor]] = None, 195 | ): 196 | h = x + self.self_attn(self.input_layernorm(x), pos_embed, start_pos=start_pos) 197 | out = h + self.mlp(self.post_attention_layernorm(h)) 198 | return out 199 | 200 | 201 | class Qwen2RotaryEmbedding(nn.Module): 202 | def __init__(self, config: Qwen2Config, device: torch.device): 203 | super().__init__() 204 | self.config = config 205 | base = config.rope_theta 206 | dim = config.hidden_size // config.num_attention_heads 207 | with torch.autocast(device_type=device.type, dtype=torch.float32): 208 | inv_freq = 1.0 / ( 209 | base 210 | ** (torch.arange(0, dim, 2, dtype=torch.int64).float().to(device) / dim) 211 | ) 212 | self.register_buffer("inv_freq", inv_freq, persistent=False) 213 | 214 | @torch.no_grad() 215 | def forward(self, x, pos): 216 | inv_freq = self.inv_freq[None, :, None].float().expand(pos.shape[0], -1, 1) 217 | pos = pos[:, None, :].float() 218 | device_type = x.device.type 219 | with torch.autocast(device_type=device_type, enabled=False): 220 | freqs = (inv_freq.float().to(x.device) @ pos.float()).transpose(1, 2) 221 | emb = torch.cat((freqs, freqs), dim=-1) 222 | cos = emb.cos() 223 | sin = emb.sin() 224 | return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype) 225 | 226 | 227 | class Transformer(nn.Module): 228 | def __init__(self, params: Qwen2Config, device: torch.device): 229 | super().__init__() 230 | self.params = params 231 | self.vocab_size = params.vocab_size 232 | self.n_layers = params.num_hidden_layers 233 | 234 | self.embed_tokens = torch.nn.Embedding(params.vocab_size, params.hidden_size) 235 | with torch.device(device): 236 | self.rotary_emb = Qwen2RotaryEmbedding(config=params, device=device) 237 | 238 | self.layers = torch.nn.ModuleList() 239 | for layer_id in range(params.num_hidden_layers): 240 | self.layers.append(TransformerBlock(layer_id, params)) 241 | 242 | self.norm = RMSNorm(params.hidden_size, eps=params.rms_norm_eps) 243 | if not params.tie_word_embeddings: 244 | self.lm_head = nn.Linear(params.hidden_size, params.vocab_size, bias=False) 245 | 246 | def output_proj(self, x): 247 | if self.params.tie_word_embeddings: 248 | return x @ self.embed_tokens.weight.T 249 | else: 250 | return self.lm_head(x) 251 | 252 | def forward(self, tokens: torch.Tensor): 253 | _bsz, seqlen = tokens.shape 254 | h = self.embed_tokens(tokens) 255 | pos = torch.arange(0, seqlen, device=tokens.device, dtype=torch.int32) 256 | pos_emb = self.rotary_emb(h, pos[None, :]) 257 | 258 | pipe = [] 259 | for layer in self.layers: 260 | pipe.append(lambda x, layer=layer: layer(x, pos_emb)) 261 | pipe.append(self.norm.forward) 262 | pipe.append(self.output_proj) 263 | return torch.utils.checkpoint.checkpoint_sequential( 264 | pipe, len(pipe), h, use_reentrant=False 265 | ) 266 | 267 | def inference(self, tokens: torch.Tensor, start_pos: Union[int, torch.Tensor]): 268 | _bsz, seqlen = tokens.shape 269 | del _bsz 270 | h = self.embed_tokens(tokens) 271 | 272 | pos = torch.arange(0, seqlen, device=tokens.device, dtype=torch.int32)[None, :] 273 | if isinstance(start_pos, torch.Tensor): 274 | pos = pos + start_pos[:, None] 275 | else: # int 276 | pos.add_(start_pos) 277 | pos_emb = self.rotary_emb(h, pos) 278 | 279 | for layer in self.layers: 280 | h = layer(h, pos_emb, start_pos=start_pos) 281 | 282 | # only need the hidden state of the last token 283 | # to predict the next token 284 | h = h[:, -1:, :] 285 | h = self.norm(h) 286 | 287 | output = self.output_proj(h) 288 | return output 289 | 290 | def init_kv_cache( 291 | self, 292 | max_batch_size: int, 293 | max_seq_len: int, 294 | device: torch.device, 295 | dtype: torch.dtype, 296 | ): 297 | for layer in self.layers: 298 | layer.self_attn.init_kv_cache( 299 | max_batch_size, max_seq_len, dtype=dtype, device=device 300 | ) 301 | 302 | def del_kv_cache(self): 303 | for layer in self.layers: 304 | layer.self_attn.del_kv_cache() 305 | 306 | @classmethod 307 | def from_pretrained(cls, ckpt_path, device: torch.device): 308 | config_file = Path(ckpt_path) / "config.json" 309 | with open(config_file, "r") as f: 310 | config = json.load(f) 311 | args = Qwen2Config( 312 | attention_dropout=config["attention_dropout"], 313 | bos_token_id=config["bos_token_id"], 314 | eos_token_id=config["eos_token_id"], 315 | hidden_act=config["hidden_act"], 316 | hidden_size=config["hidden_size"], 317 | initializer_range=config["initializer_range"], 318 | intermediate_size=config["intermediate_size"], 319 | max_position_embeddings=config["max_position_embeddings"], 320 | max_window_layers=config["max_window_layers"], 321 | model_type=config["model_type"], 322 | num_hidden_layers=config["num_hidden_layers"], 323 | num_attention_heads=config["num_attention_heads"], 324 | num_key_value_heads=config["num_key_value_heads"], 325 | vocab_size=config["vocab_size"], 326 | rms_norm_eps=config["rms_norm_eps"], 327 | rope_theta=config["rope_theta"], 328 | sliding_window=config["sliding_window"], 329 | use_sliding_window=config["use_sliding_window"], 330 | use_cache=config["use_cache"], 331 | tie_word_embeddings=config["tie_word_embeddings"], 332 | torch_dtype=config["torch_dtype"], 333 | ) 334 | with torch.device("meta"): 335 | model = cls(params=args, device=device) 336 | 337 | import safetensors.torch 338 | 339 | model_weight_files = sorted(Path(ckpt_path).glob("model*.safetensors")) 340 | weights = {} 341 | for file in model_weight_files: 342 | weights.update(safetensors.torch.load_file(file, device="cpu")) 343 | # remove "model." prefix from keys 344 | weights = {k.replace("model.", ""): v for k, v in weights.items()} 345 | model.load_state_dict(weights, strict=True, assign=True) 346 | return model.to(device) 347 | -------------------------------------------------------------------------------- /tokenizer.py: -------------------------------------------------------------------------------- 1 | import json 2 | from pathlib import Path 3 | from typing import Dict, List 4 | 5 | from jinja2 import Environment 6 | from tokenizers import Encoding 7 | from tokenizers import Tokenizer as TokenizerBase 8 | 9 | 10 | class Tokenizer: 11 | """Tokenizer with chat template supported using jinja2 engine""" 12 | 13 | def __init__(self, tokenizer_path: str): 14 | super().__init__() 15 | tokenizer_config_path = Path(tokenizer_path).parent / "tokenizer_config.json" 16 | self.tokenizer_config = json.load(open(tokenizer_config_path)) 17 | self.tokenizer = TokenizerBase.from_file(tokenizer_path) 18 | self.chat_template = Environment().from_string( 19 | self.tokenizer_config["chat_template"] 20 | ) 21 | self.eos_token = self.tokenizer_config["eos_token"] 22 | self.eos_token_id = self.tokenizer.token_to_id(self.eos_token) 23 | self.pad_token = self.tokenizer_config["pad_token"] 24 | self.pad_token_id = self.tokenizer.token_to_id(self.pad_token) 25 | 26 | def encode_chat(self, messages: List[Dict[str, str]]) -> str: 27 | return self.chat_template.render(messages=messages, add_generation_prompt=True) 28 | 29 | def encode_chat_with_response_prompt( 30 | self, messages: List[Dict[str, str]], prompt: str 31 | ) -> str: 32 | return self.encode_chat(messages) + prompt 33 | 34 | def tokenize(self, text: str) -> Encoding: 35 | return self.tokenizer.encode(text) 36 | 37 | def detokenize(self, token_ids: List[int]) -> str: 38 | return self.tokenizer.decode(token_ids, skip_special_tokens=False) 39 | -------------------------------------------------------------------------------- /train.py: -------------------------------------------------------------------------------- 1 | import html 2 | import time 3 | from argparse import ArgumentParser 4 | from datetime import datetime 5 | from pathlib import Path 6 | 7 | import numpy as np 8 | import torch 9 | import yaml 10 | from torch.utils.data import DataLoader 11 | from torch.utils.tensorboard.writer import SummaryWriter 12 | 13 | from countdown_task import CountdownTasksDataset, reward_function 14 | from grpo import rollout, update_policy 15 | from optimizer import MemoryEfficientAdamW 16 | from qwen2_model import Transformer 17 | from tokenizer import Tokenizer 18 | 19 | 20 | def evaluate(model, tokenizer, device, dtype, config): 21 | test_dataset = CountdownTasksDataset( 22 | data_path=config["data"]["path"], 23 | tokenizer=tokenizer, 24 | split="test", 25 | test_size=config["data"]["test_size"], 26 | ) 27 | generator = torch.Generator(device=device) 28 | # We reduce the batch size by half as we want to 29 | # generate twice as long trajectories. 30 | dataloader = DataLoader( 31 | test_dataset, 32 | shuffle=False, 33 | collate_fn=CountdownTasksDataset.collate_fn, 34 | generator=generator, 35 | batch_size=config["training"]["batch_size"] // 2, 36 | drop_last=False, 37 | ) 38 | success = [] 39 | for batch in dataloader: 40 | episodes = rollout( 41 | model=model, 42 | tokenizer=tokenizer, 43 | batch=batch, 44 | max_gen_len=config["training"]["max_gen_len"] * 2, 45 | num_answer_per_question=1, 46 | reward_function=reward_function, 47 | device=device, 48 | dtype=dtype, 49 | ) 50 | success.extend([episode.reward_info["answer_reward"] for episode in episodes]) 51 | return np.mean(success) 52 | 53 | 54 | def main(config_path: str): 55 | with open(config_path, "r") as f: 56 | config = yaml.safe_load(f) 57 | 58 | pretrained_model_path = Path(config["model"]["pretrained_model_path"]) 59 | device = torch.device(config["model"]["device"]) 60 | dtype_map = { 61 | "bfloat16": torch.bfloat16, 62 | "float16": torch.float16, 63 | "float32": torch.float32, 64 | } 65 | dtype = dtype_map.get(config["model"]["dtype"], torch.bfloat16) 66 | torch.set_default_device(device) 67 | torch.random.manual_seed(config["training"]["random_seed"]) 68 | BATCH_SIZE = config["training"]["batch_size"] 69 | NUM_QUESTIONS_PER_BATCH = config["training"]["num_questions_per_batch"] 70 | NUM_ANSWERS_PER_QUESTION = BATCH_SIZE // NUM_QUESTIONS_PER_BATCH 71 | 72 | current_time = datetime.now().strftime(r"%Y%m%d-%H%M%S") 73 | tb_writer = SummaryWriter(log_dir=f"{config['training']['log_dir']}/{current_time}") 74 | tokenizer = Tokenizer(str(pretrained_model_path / "tokenizer.json")) 75 | 76 | train_dataset = CountdownTasksDataset( 77 | data_path=config["data"]["path"], 78 | tokenizer=tokenizer, 79 | split="train", 80 | test_size=config["data"]["test_size"], 81 | ) 82 | generator = torch.Generator(device=device) 83 | train_dataloader = DataLoader( 84 | train_dataset, 85 | shuffle=True, 86 | collate_fn=CountdownTasksDataset.collate_fn, 87 | generator=generator, 88 | batch_size=NUM_QUESTIONS_PER_BATCH, 89 | ) 90 | 91 | model = Transformer.from_pretrained(pretrained_model_path, device=device).train() 92 | 93 | optimizer = MemoryEfficientAdamW( 94 | model.parameters(), 95 | lr=config["training"]["learning_rate"], 96 | weight_decay=config["training"]["weight_decay"], 97 | betas=config["training"]["betas"], 98 | enabled=config["training"]["memory_efficient_adamw"], 99 | ) 100 | 101 | start_time = time.time() 102 | ckpt_dir = Path(config["training"]["ckpt_dir"]) 103 | ckpt_dir.mkdir(parents=True, exist_ok=True) 104 | 105 | for step, batch in enumerate(train_dataloader, start=1): 106 | episodes = rollout( 107 | model=model, 108 | tokenizer=tokenizer, 109 | batch=batch, 110 | max_gen_len=config["training"]["max_gen_len"], 111 | num_answer_per_question=NUM_ANSWERS_PER_QUESTION, 112 | reward_function=reward_function, 113 | device=device, 114 | dtype=dtype, 115 | ) 116 | if config["training"]["skip_unfinished_episodes"]: 117 | episodes = [episode for episode in episodes if episode.is_finished] 118 | results = update_policy( 119 | model=model, 120 | optimizer=optimizer, 121 | episodes=episodes, 122 | micro_batch_size=config["training"]["micro_batch_size"], 123 | pad_token_id=tokenizer.pad_token_id, 124 | max_grad_norm=config["training"]["max_grad_norm"], 125 | device=device, 126 | dtype=dtype, 127 | ) 128 | torch.cuda.synchronize() 129 | end_time = time.time() 130 | duration = end_time - start_time 131 | start_time = end_time 132 | 133 | # compute and log important metrics 134 | reward = [episode.reward for episode in episodes] 135 | formatted_reward = [ 136 | episode.reward_info["format_reward"] for episode in episodes 137 | ] 138 | answer_reward = [episode.reward_info["answer_reward"] for episode in episodes] 139 | num_finished_episodes = sum(episode.is_finished for episode in episodes) 140 | mean_reward = np.mean(reward) 141 | std_reward = np.std(reward) 142 | success_rate = np.mean(answer_reward) 143 | format_reward = np.mean(formatted_reward) 144 | grad_norm = results["grad_norm"] 145 | entropy = results["entropy"] 146 | lr = optimizer.param_groups[0]["lr"] 147 | loss = results["loss"] 148 | mean_response_len = np.mean( 149 | [len(episode.generated_token_ids) for episode in episodes] 150 | ) 151 | print( 152 | f"\rStep {step}, mean_reward: {mean_reward:.2f}, " 153 | f"train success_rate: {success_rate:.2f}, " 154 | f"grad_norm: {grad_norm:.2f}, duration: {duration:.2f}, " 155 | f"num_finished_episodes: {num_finished_episodes}, " 156 | f"mean_response_len: {mean_response_len:.2f}, " 157 | f"entropy: {entropy:.2f}" 158 | ) 159 | if step % config["training"]["eval_interval"] == 0: 160 | eval_success_rate = evaluate(model, tokenizer, device, dtype, config) 161 | print(f"\rEval success rate: {eval_success_rate:.2f}" + " " * 100) 162 | tb_writer.add_scalar("success_rate/eval", eval_success_rate, step) 163 | 164 | tb_writer.add_scalar("loss", loss, step) 165 | tb_writer.add_scalar("mean_reward", mean_reward, step) 166 | tb_writer.add_scalar("std_reward", std_reward, step) 167 | tb_writer.add_scalar("success_rate/train", success_rate, step) 168 | tb_writer.add_scalar("format_reward", format_reward, step) 169 | tb_writer.add_scalar("grad_norm", grad_norm, step) 170 | tb_writer.add_scalar("duration", duration, step) 171 | tb_writer.add_scalar("num_finished_episodes", num_finished_episodes, step) 172 | tb_writer.add_scalar("learning_rate", lr, step) 173 | tb_writer.add_scalar("mean_response_len", mean_response_len, step) 174 | tb_writer.add_scalar("entropy", entropy, step) 175 | for i, episode in enumerate(episodes): 176 | # TensorBoard treats text as markdown. 177 | text = html.escape(episode.text) 178 | tb_writer.add_text(f"text_{i}", f"
{text}
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