[Feature] Add RLHF code #736

hui-zhao-1 · 2024-05-31T10:53:18Z

No description provided.

hijkzzz · 2024-06-03T00:58:37Z

Hi, XTuner Team

Could you please add a citation for the source of the Ray+vLLM-based RLHF architecture - OpenRLHF, such as in the README.md file: https://github.com/InternLM/xtuner?tab=readme-ov-file#%EF%B8%8F-acknowledgement.
We noticed that most RLHF-related code, particularly the Ray RLHF architecture in XTuner, are refactored from OpenRLHF. According to the Apache License 2.0 of OpenRLHF, the original copyright statement must be included.

An example:

XTuner's RLHF solution mainly references OpenRLHF, for which we are grateful.

Related MR:
#736
#764

Thank you

RangiLyu · 2024-06-14T03:12:39Z

examples/rlhf/four_model_8gpu.py

+    ),
+)
+
+dataset_config = {


建议把经常会调整的配置放在配置文件靠前的地方，并且配置文件中多加一些注释方便用户理解。可以参考xtuner里的配置。

RangiLyu · 2024-06-14T03:31:51Z

xtuner/rlhf/envs/txt_env.py

+            if sample_data[i].rm_meta != 'default':
+                cur_rm_data = [{
+                    'role': 'system',
+                    'content': META_PROMPT[sample_data[i].rm_meta]


这里可以在注释里说明下这个是conditional system prompt，可以加一下paper的链接。
以及META_PROMPT这个变量名太抽象了，最好还是按paper里的名字来吧。

RangiLyu · 2024-06-14T03:34:19Z

xtuner/rlhf/logger.py

+# Adapted from
+# https://github.com/skypilot-org/skypilot/blob/86dc0f6283a335e4aa37b3c10716f90999f48ab6/sky/sky_logging.py
+"""Logging configuration."""
+import logging


为啥有地方用了loguru的logger，这里又用了自定义的logger？最好统一一下吧

RangiLyu · 2024-06-14T03:39:37Z

xtuner/rlhf/policy_output.py

+    if not gather or labels is None:
+        return logp
+    logpy = torch.gather(logp, -1, labels.unsqueeze(2)).squeeze(-1)
+    return logpy.cuda()


为啥这儿要cuda一下？会变到cpu上吗？

RangiLyu · 2024-06-14T03:41:47Z

xtuner/rlhf/policy_output.py

+        if policy_output[key] is None:
+            continue
+        batch_size, seq_len = policy_output[
+            key].shape  # assert: only support 2d tensor


Suggested change

key].shape # assert: only support 2d tensor

key].shap[:2]

这样是不是兼容性会好点

RangiLyu · 2024-06-14T03:43:05Z

xtuner/rlhf/policy_output.py

+def padding_policy_outputs(policy_outputs: list[PolicyOutput],
+                           padding_token_map={}):
+    DEFAULT_PADDING_ID = 0
+    RIGHT_PADDING = True


这几个是准备做成可配置的吗？现在是写死了的

RangiLyu · 2024-06-14T03:43:44Z

xtuner/rlhf/repeaters/base.py

+import time
+
+import torch
+from loguru import logger


统一一下logger用哪个吧

RangiLyu · 2024-06-14T03:47:39Z

xtuner/rlhf/repeaters/base.py

+        num_actions = action_mask.size(1)
+        if sft_model is not None:
+            self.sft_model: BaseModelServer = sft_model
+        kl_rewards, entropy, kl_distance, policy_logprobs, sft_logprobs = self._get_kl_rewards(  # noqa: E501


Suggested change

kl_rewards, entropy, kl_distance, policy_logprobs, sft_logprobs = self._get_kl_rewards( # noqa: E501

(kl_rewards, entropy, kl_distance, policy_logprobs, sft_logprobs) = self._get_kl_rewards(

这儿加个括号之后yapf就能帮你换行了

RangiLyu · 2024-06-14T04:03:54Z

xtuner/rlhf/repeaters/base.py

+        s_t = time.time()
+        value_output = value_model.infer_get(value_output_ref)
+        raw_values = value_output.logits.squeeze(-1)
+        logger.info(


我看代码里好多这种计时的需求，往utils里面加个计时上下文是不是用起来方便点

class TimeLogger: def __init__(self, message: str): self.message = message def __enter__(self): self.start_time = time.time() return self def __exit__(self, exc_type, exc_val, exc_tb): duration = round(time.time() - self.start_time, 2) logger.info(f'{self.message} duration: {duration} s')

RangiLyu · 2024-06-14T04:05:54Z

xtuner/rlhf/timer.py

+from loguru import logger
+
+
+class Timer:


诶，这里有个计时上下文呀，咋没用上

C1rN09 · 2024-06-14T06:00:53Z

setup.py

+            'rlhf':
+            parse_requirements('requirements/rlhf.txt'),


Suggested change

'rlhf':

parse_requirements('requirements/rlhf.txt'),

'rlhf':

parse_requirements('requirments/deepspeed.txt') +

parse_requirements('requirements/rlhf.txt'),

C1rN09 · 2024-06-14T06:01:21Z

requirements/rlhf.txt

+-r requirements/deepspeed.txt
+loguru
+ray[default,train]==2.9.1


Suggested change

-r requirements/deepspeed.txt

loguru

ray[default,train]==2.9.1

loguru

ray[default,train]==2.9.1

C1rN09 · 2024-06-14T06:19:17Z

xtuner/rlhf/config/config_utils.py

+    num_gpus = 1
+    if 'parallel' in trainer_config:
+        parallel = trainer_config['parallel']
+        data = parallel.get('data', {'size': 1})
+        tensor = parallel.get('tensor', {'size': 1})
+        pipeline = parallel.get('pipeline', {'size': 1})
+        num_gpus = data['size'] * tensor['size'] * pipeline['size']
+    return num_gpus


Suggested change

num_gpus = 1

if 'parallel' in trainer_config:

parallel = trainer_config['parallel']

data = parallel.get('data', {'size': 1})

tensor = parallel.get('tensor', {'size': 1})

pipeline = parallel.get('pipeline', {'size': 1})

num_gpus = data['size'] * tensor['size'] * pipeline['size']

return num_gpus

return get_dp_size(trainer_config) * get_tp_size(trainer_config) * get_pp_size(trainer_config)

C1rN09 · 2024-06-14T06:33:59Z

xtuner/rlhf/coordinator.py

+            logger.info(
+                f'{model_name} {model.__class__.__name__}.is_initialized: {model.is_initialized}'  # noqa: E501
+            )


感觉这个打印不是非常必要，还容易引起误解

C1rN09 · 2024-06-14T06:39:56Z

xtuner/rlhf/coordinator.py

+        try:
+            client_context = ray.init(
+                address=self.cluster_address,
+                runtime_env=runtime_env,
+                ignore_reinit_error=True,
+            )
+            logger.info(
+                f'Connected to a running ray cluster at {self.cluster_address}'
+            )
+            self.context_type = 'client'
+            self.context = client_context
+
+        except ConnectionError:
+            logger.info(
+                f'Error connecting to {self.cluster_address}, try initializing a new ray cluster.'  # noqa: E501
+            )
+            ray_context = ray.init(
+                address=None,
+                resources=resources,
+                runtime_env=runtime_env,
+                ignore_reinit_error=True,
+            )
+            node_ip_address = ray_context.address_info['node_ip_address']
+            logger.info(f'Initialize a ray cluster at {node_ip_address}')
+            self.context_type = 'server'
+            self.context = ray_context


所以这是一个自动 fallback 到本地 ray server 的方案吗，感觉不要 fallback 直接抛出会更好

C1rN09 · 2024-06-14T08:19:34Z

xtuner/rlhf/dataset/base.py

+class FileDataset(IterableDataset):
+    """Single json file dataset."""
+
+    def __init__(self,
+                 filename,
+                 tokenizer,
+                 sys_meta='default',
+                 rm_meta='default'):
+        self._filename = filename
+        self.tokenizer = tokenizer
+        self.data_list = []
+        self.sys_meta = sys_meta
+        self.rm_meta = rm_meta
+        with open_file(self._filename) as fin:
+            for lineno, line in enumerate(fin):
+                data = json.loads(line)
+                self.data_list.append(data)
+
+    def __len__(self):
+        return len(self.data_list)
+
+    def __getitem__(self, index: int):
+        data = self.data_list[index]
+        try:
+            self.tokenizer.apply_chat_template(data, tokenize=True)
+            return {
+                'data': data,
+                'sys_meta': self.sys_meta,
+                'rm_meta': self.rm_meta
+            }
+        except Exception:
+            print(f'[data tokenize check] skip dirty data: {data}')
+            return None
+
+    def __iter__(self):
+        with open_file(self._filename) as fin:
+            for lineno, line in enumerate(fin):
+                data = json.loads(line)
+                try:
+                    self.tokenizer.apply_chat_template(data, tokenize=True)
+                except Exception:
+                    print(f'[data tokenize check] skip dirty data: {data}')
+                    continue
+                if data is None:
+                    continue
+                yield {
+                    'data': data,
+                    'sys_meta': self.sys_meta,
+                    'rm_meta': self.rm_meta
+                }


作为一个 IterableDataset 实现 __len__, __getitem__ 这些 MapDataset 的接口，感觉不是很合理

作为 IterableDataset，在 __init__ 阶段将整个数据集从磁盘加载到内存，而在 __iter__ 中仍然从磁盘读取，是一种低效的行为

改个名字&docstring 可能更加合理。读取的文件是 Json line 而非 json，建议改名 JsonlDataset

下面的代码也存在同样问题

C1rN09 · 2024-06-14T08:35:24Z

xtuner/rlhf/dataset/base.py

+    def __iter__(self):
+        while True:
+            self.rng.shuffle(self.indices)
+            for i in self.indices:
+                yield self.data[i]


可以考虑在 epoch 结束的时候，输出一下日志，方便 debug

C1rN09 · 2024-06-14T08:36:36Z

xtuner/rlhf/dataset/open_datasets/Anthropic_hh_rlhf.py

需要重构。建议

__init__ 改成和 Anthropic/hh-rlhf usage 一致的用法，避免从 path 中解析 data_dir

删除 save_to_disk 和 load_from_disk

改一下 docstring

C1rN09 · 2024-06-14T08:41:56Z

xtuner/rlhf/dataset/txt_loader.py

+        self.epoch_index = 0
+
+    def _init_in_data(self):
+        print(f"========================= Init in data sampler =========================")


这个文件的打印信息要清理一下，去除不必要的

C1rN09 · 2024-06-14T08:43:18Z

xtuner/rlhf/dataset/txt_loader.py

+                 prompt_datasets: list[str] = None,
+                 pretrain_datasets: list[str] = None,


讨论一下：prompt_datasets 和 pretrain_datasets 分开可能更好？

C1rN09 · 2024-06-14T08:47:31Z

xtuner/rlhf/envs/prompt_utils.py

讨论：是否保留，或者换种形式。

初衷是 RM 训练和 PPO 训练之间共享。但目前的仓库结构，RM 和 PPO 不在一起。这样没有意义

C1rN09 · 2024-06-14T08:49:56Z

xtuner/rlhf/envs/txt_env.py

+
+    def __init__(
+        self,
+        dataloader: IterableDataset,


dataloader 是一个 Dataset 感觉不是非常合理

C1rN09 · 2024-06-14T08:51:04Z

xtuner/rlhf/envs/txt_env.py

+            dataloader (IterableDataset): generate rl data iteratively
+            reward_function: reward function that computes scalar reward for each episode  # noqa: E501
+        """
+        self.dataloader: IterableDataset = iter(dataloader)


如果入参 dataloader 实际上是一个 Dataset，这里用 torch.utils.data.DataLoader 更合理，可以享受更多特性

C1rN09 · 2024-06-14T09:06:01Z

xtuner/rlhf/envs/txt_env.py

+                    'role':
+                    'assistant',
+                    'content':
+                    policyout.output_ans_str[i]
+                }]
+            else:
+                cur_rm_data = sample_data[i].message + [{
+                    'role':
+                    'assistant',
+                    'content':
+                    policyout.output_ans_str[i]


格式有点问题？

C1rN09 · 2024-06-14T09:11:40Z

xtuner/rlhf/loss/actor_loss.py

+from ..policy_output import logprobs_from_logits
+
+
+class ActorLoss(torch.nn.Module):


Suggested change

class ActorLoss(torch.nn.Module):

class PPOPolicyLoss(torch.nn.Module):

C1rN09 · 2024-06-14T09:14:19Z

xtuner/rlhf/loss/actor_loss.py

+    def forward(self, logits: torch.Tensor, labels: dict[str, Any]):
+        """Forward function of ActorLoss.
+
+        Args:
+            logits (Tensor): Forward result of the model. Its shape may be varied.  # noqa: E501
+                For packed forward: (micro_bsz * seqlen, 1), where micro_bsz = 1  # noqa: E501
+                For non packed forward: (micro_bsz, seqlen, 1)
+
+            labels (tuple[dict]): Label values which are split by pipeline
+                schedule into pieces. The length of the list is micro_bsz. Each
+                element is a dict, representing labels to a batch.
+
+        Note:
+            The parameter `labels` seems strange because of pj-colossalai's
+            pipeline schedule mechanism. Labels are delivered to colosslai.Engine  # noqa: E501
+            in List format, so pipeline schedule split it into micro_bsz pieces,  # noqa: E501
+            and deliver them to loss_fn by `*args`.
+
+        Returns:
+            Tensor: Return the final loss


docstring mismatch

C1rN09 · 2024-06-14T09:17:05Z

xtuner/rlhf/loss/actor_loss.py

讨论：这段代码看来经过了重构，但可能会和 InterEvo 的 megatron-style 训练方式不兼容。需要额外确认

以及 per_seq 和 per_token 模式是否保留，是否需要额外说明

C1rN09 · 2024-06-17T11:28:26Z

xtuner/rlhf/trainer/ppo.py

+    def policy_learn(self, trajectories, policy_model: BaseModelServer):
+        if self.policy_minibatch is None:
+            self.policy_minibatch = len(trajectories.output_ids)
+        policy_updates = len(trajectories.output_ids) // self.policy_minibatch


如果不是整除，会丢数据。最好在这里 assert / logger.warning 一下，或者改一下 minibatch 的逻辑，保证数据都使用到

C1rN09 · 2024-06-17T11:31:55Z

xtuner/rlhf/trainer/ppo.py

+                assert len(
+                    trajectories.output_ids[begin:end, :]
+                ) == self.policy_minibatch, '[Policy learn] make sure len(policy_batch_inputs) == self.policy_minibatch'  # noqa: E501


这个检查似乎是多余的？

C1rN09 · 2024-06-17T12:01:59Z

xtuner/rlhf/trainer/ppo.py

+    def value_learn_async(self, trajectories, value_model: BaseModelServer):
+        if self.value_minibatch is None:
+            self.value_minibatch = len(trajectories.output_ids)
+        value_updates = len(trajectories.output_ids) // self.value_minibatch
+        value_loss = []
+        assert value_updates == 1 and self.policy_learn_time == 1, f'value_updates={value_updates} * self.policy_learn_time={self.policy_learn_time} > 1'  # noqa: E501
+        s_t = time.time()
+        value_batch_inputs, labels = self._value_learn_prepare(
+            0, 0, trajectories, value_updates)
+        v_loss_ref = value_model.train_async(
+            input_ids=value_batch_inputs['input_ids'],
+            labels=labels,
+            attention_mask=value_batch_inputs['attention_mask'],
+            criterion=self.value_criterion,
+            micro_batch_size=self.critic_micro_bs,
+        )
+        logger.info(
+            f'[critic train] async duration: {round(time.time() - s_t, 2)} s, {self.value_minibatch} batch'  # noqa: E501
+        )
+        value_loss.append(v_loss_ref)
+        return value_loss
+
+    def value_learn_get(self, value_loss_ref, value_model: BaseModelServer):
+        with Timer('value_model.train_get'):
+            return [
+                value_model.train_get(ref).item() for ref in value_loss_ref
+            ]
+
+    def value_learn(self, trajectories, value_model: BaseModelServer):
+        if self.value_minibatch is None:
+            self.value_minibatch = len(trajectories.output_ids)
+        value_updates = len(trajectories.output_ids) // self.value_minibatch
+        value_loss = []
+
+        for learn_i in range(self.policy_learn_time):
+            for step_i in range(value_updates):
+                s_t = time.time()
+                value_batch_inputs, labels = self._value_learn_prepare(
+                    step_i, learn_i, trajectories, value_updates)
+                v_loss = value_model.train(
+                    input_ids=value_batch_inputs['input_ids'],
+                    labels=labels,
+                    attention_mask=value_batch_inputs['attention_mask'],
+                    criterion=self.value_criterion,
+                    micro_batch_size=self.critic_micro_bs,
+                )
+                logger.info(
+                    f'[critic train] duration: {round(time.time() - s_t, 2)} s, {self.value_minibatch} batch,value loss: {v_loss.item()}'  # noqa: E501
+                )
+                value_loss.append(v_loss.item())
+        return value_loss


为什么 value_learn_async 不支持 value_updates / value_learn_time(代码 typo 注意改正）> 1，而 value_learn 支持？两者应该具有完全相同的功能，只是 sync/async 的区别

C1rN09 · 2024-06-17T13:03:42Z

xtuner/rlhf/repeaters/base.py

+        clip_reward_max: int = 5,
+        norm_rewards=True,
+        reward_scale: bool = False,
+        fine_grained_rm: bool = False,


fine_grained_rm 看起来还没实现，要不先去掉，等实现后再新写一个 class

C1rN09 · 2024-06-17T13:07:57Z

xtuner/rlhf/repeaters/base.py

+from .running_mean_std import RunningStates
+
+
+class BaseRepeater:


如果是作为基类的话，可以考虑更加 Abstract 一些。现在的这个实现可以继承基类之后改个名字，比如 OutcomeKLGAERepeater 之类的（仅供参考）

C1rN09 · 2024-06-17T13:10:40Z

xtuner/rlhf/repeaters/base.py

+        policy_model: BaseModelServer,
+        value_model: BaseModelServer,
+        sft_model: BaseModelServer = None,
+        # only used for async reward model.infer_get() in _get_kl_rewards
+        env=None,


xxx_model 和 env 放在 __init__ 里面可能更加合理一些，让这个 process 接口函数更加 consistent。我的理解是，并非所有 Repeater 都 exactly 需要这几个 xxx_model

C1rN09 · 2024-06-17T13:16:15Z

xtuner/rlhf/policy_output.py

+            if isinstance(v, torch.Tensor):
+                if not torch.equal(v, vother):
+                    return False
+            elif isinstance(v, tuple):  # tuple(torch.Tensor)


存在 v 是 Tuple[torch.Tensor] 的情况吗，似乎 v 只能是 torch.Tensor 或 None
如果存在，下面的 to(self, device) 的实现需要更改

C1rN09 · 2024-06-17T13:18:30Z

xtuner/rlhf/policy_output.py

+        if len(self.keys()) != len(other.keys()):
+            return False
+        for k, v in self.items():
+            if k not in other:
+                return False
+            vother = other[k]


如果 other 的 keys 是 self 的超集，也可以通过检查，是否是预期的行为？
如果不是，可以考虑

if self.keys() != other.keys(): return False

C1rN09 · 2024-06-17T13:22:37Z

xtuner/rlhf/model_server/base_model_server.py

+        if model_path == 'internlm/internlm2-chat-1_8b-sft':
+            return InternLM2ForCausalLM


这里的 hardcode 有什么特别的原因吗？

C1rN09 · 2024-06-17T13:23:48Z

xtuner/rlhf/model_server/base_model_server.py

+        trainer_type = self.trainer_config.get('trainer_type',
+                                               'huggingface').lower()


Suggested change

trainer_type = self.trainer_config.get('trainer_type',

'huggingface').lower()

trainer_type = self.trainer_config.get('trainer_type',

ENGINE_HUGGINGFACE).lower()

C1rN09 · 2024-06-17T13:30:52Z

xtuner/rlhf/model_server/reward_model_server.py

+    def init_tokenizer_and_config(self, model_config):
+        super().init_tokenizer_and_config(self.model_config)
+
+        self.reward_token_id = self.tokenizer.pad_token_id


默认 self.reward_token_id = self.tokenizer.pad_token_id 很巧妙，但感觉可以加一行注释说明一下原因

RangiLyu · 2024-06-25T12:17:26Z

examples/rlhf/quick_start.md

+
+# 启动任务，首次启动建议添加 HF_ENDPOINT=https://hf-mirror.com 方便数据集加载
+HF_ENDPOINT=https://hf-mirror.com xtuner rlhf -c examples/rlhf/internlm2_chat_1_8b_ppo_ds_vllm_8gpu.py
+```


记得添加read the docs里详细文档的链接

RangiLyu · 2024-06-25T12:26:31Z

xtuner/rlhf/dataset/message_iter.py

+        """Post process sequence: tokenization & truncation."""
+        message_data = message['data']
+        new_meaasage_data = []
+        if self.message_type == 'prompt':


move post-process to dataset class

RangiLyu · 2024-06-25T12:37:27Z

xtuner/rlhf/envs/txt_env.py

+            trajectories['rewards'] = rewards
+
+        # pretrain data
+        if self.pretrain_mes_iter is not None:


move pretrain loss tokenize to pretrain dataset

ShuaibinLi force-pushed the rlhf branch 4 times, most recently from 45702b6 to 1ffe51e Compare June 14, 2024 03:07

RangiLyu reviewed Jun 14, 2024

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xtuner/rlhf/repeaters/base.py Outdated

import time

import torch

from loguru import logger

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RangiLyu Jun 14, 2024

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统一一下logger用哪个吧

RangiLyu reviewed Jun 14, 2024

View reviewed changes

xtuner/rlhf/timer.py

from loguru import logger

class Timer:

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诶，这里有个计时上下文呀，咋没用上

C1rN09 reviewed Jun 14, 2024

View reviewed changes

C1rN09 reviewed Jun 17, 2024

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zhaohui and others added 11 commits June 25, 2024 02:22

add rlhf

0e1453a

add/fix pretrain_loss

14ea454

dataloader, resolve comments

72ede3b

rename,remove

6adc32c

save tokenizer

9599838

rm/move models

bb96eef

fix: zero3 for bigger models

5d6fb35

precommit check

795995d

resume

1ff6ab3

fix resume/pretrain_data

e1c8c61

fix tokenizer bug

89bfe9d

hui-zhao-1 force-pushed the rlhf branch from 5a0d927 to 89bfe9d Compare June 25, 2024 02:41

qzweng force-pushed the rlhf branch 2 times, most recently from 9a4609f to 69fef1f Compare June 25, 2024 11:45

async learn

82a3366

qzweng force-pushed the rlhf branch from 69fef1f to 4e731ec Compare June 25, 2024 11:49

add and rename ppo configs

6531b5d

qzweng force-pushed the rlhf branch from 4e731ec to 6531b5d Compare June 25, 2024 11:55

RangiLyu reviewed Jun 25, 2024

View reviewed changes

	kl_rewards, entropy, kl_distance, policy_logprobs, sft_logprobs = self._get_kl_rewards( # noqa: E501
	(kl_rewards, entropy, kl_distance, policy_logprobs, sft_logprobs) = self._get_kl_rewards(

		prompt_datasets: list[str] = None,
		pretrain_datasets: list[str] = None,

		from ..policy_output import logprobs_from_logits


		class ActorLoss(torch.nn.Module):

	class ActorLoss(torch.nn.Module):
	class PPOPolicyLoss(torch.nn.Module):

		from .running_mean_std import RunningStates


		class BaseRepeater:

		if model_path == 'internlm/internlm2-chat-1_8b-sft':
		return InternLM2ForCausalLM

		trainer_type = self.trainer_config.get('trainer_type',
		'huggingface').lower()

[Feature] Add RLHF code #736

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[Feature] Add RLHF code #736

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hui-zhao-1 commented May 31, 2024

hijkzzz commented Jun 3, 2024 • edited Loading

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C1rN09 Jun 14, 2024 • edited Loading

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hijkzzz commented Jun 3, 2024 •

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C1rN09 Jun 14, 2024 •

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C1rN09 Jun 17, 2024 •

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C1rN09 Jun 17, 2024 •

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C1rN09 Jun 17, 2024 •

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C1rN09 Jun 17, 2024 •

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