mirror of
https://github.com/hpcaitech/ColossalAI.git
synced 2025-09-03 18:19:58 +00:00
[feat] Support DAPO (#6263)
* update help information * update style * fix * minor fix * support PP training * add pp support * remove unused code * address conversation * fix memory leakage support tp+pp * move empty cache * move empty cache * add DAPO support * remove format reward * fix filtering, still buggy * small fix * add DAPO support * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * tested multi-node training; fix bind_batch bug * fix conversation; support sleep mode * support reusing excessive samples * add dynamic batching control flag * add dynamic batching control flag * refactored * fix logging --------- Co-authored-by: Tong Li <tong.li35271158@gmail.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
This commit is contained in:
YeAnbang
GitHub
@@ -1,18 +1,16 @@
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import json
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import os
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import warnings
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from contextlib import nullcontext
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from typing import Optional
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from typing import Any, Optional
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import ray
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import torch
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import torch.distributed as dist
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import wandb
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from coati.distributed.consumer import BaseConsumer
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from coati.distributed.loss import PolicyLoss
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from coati.distributed.reward.reward_fn import math_reward_fn
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from coati.distributed.reward.verifiable_reward import VerifiableReward
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from coati.distributed.utils import calc_action_log_probs
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from coati.trainer.utils import all_reduce_mean
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from coati.trainer.utils import all_reduce_mean, all_reduce_sum
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from transformers import AutoModelForCausalLM, AutoTokenizer
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from colossalai.nn.lr_scheduler import CosineAnnealingWarmupLR
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@@ -34,13 +32,23 @@ class GRPOConsumer(BaseConsumer):
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batch_size,
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model_config,
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plugin_config,
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microbatch_size=1,
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minibatch_size=1,
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num_generations=8,
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use_wandb=True,
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generate_config=None,
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training_config={},
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grpo_config={},
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project_name=None,
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save_interval: int = 100,
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save_dir="./model",
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):
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print(f"Using GRPO config: {grpo_config}")
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if grpo_config.get("loss_variation", "sample_level") == "token_level":
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if batch_size != minibatch_size:
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warnings.warn(
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f"Applied token_level loss, force overwrite mini-batch-size with batch-size: mini-batch-size: {minibatch_size}->{batch_size}",
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UserWarning,
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)
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minibatch_size = batch_size
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super().__init__(
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num_producers,
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num_episodes,
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@@ -53,36 +61,58 @@ class GRPOConsumer(BaseConsumer):
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batch_size,
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model_config,
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plugin_config,
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microbatch_size,
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minibatch_size,
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save_dir=save_dir,
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)
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path = model_config.pop("path")
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self.policy_model = AutoModelForCausalLM.from_pretrained(path, **model_config)
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self.policy_model.train()
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self.policy_model.gradient_checkpointing_enable()
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self.optimizer = HybridAdam(self.policy_model.parameters(), lr=training_config.get("lr", 1e-6))
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self.optimizer = HybridAdam(self.policy_model.parameters(), lr=grpo_config.get("lr", 1e-6))
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self.accum_loss = torch.zeros(1, device=self.device)
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self.accum_reward = torch.zeros(1, device=self.device)
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self.accum_kl = torch.zeros(1, device=self.device)
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self.accum_format_reward = torch.zeros(1, device=self.device)
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self.accum_acc_reward = torch.zeros(1, device=self.device)
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self.accum_format_acc = torch.zeros(1, device=self.device)
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self.accum_ans_acc = torch.zeros(1, device=self.device)
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self.accum_advantages = torch.zeros(1, device=self.device)
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self.accum_response_length = torch.zeros(1, device=self.device)
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self.accum_count = 0
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self.generate_config = generate_config
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self.training_config = training_config
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self.grpo_config = grpo_config
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self.project_name = project_name
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self.effective_sample_count = 0
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self.total_sample_count = 0
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self.policy_loss_fn = PolicyLoss(
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clip_eps_low=grpo_config.get("clip_eps_low", 0.2),
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clip_eps_high=grpo_config.get("clip_eps_high", 0.2),
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beta=grpo_config.get("beta", 0.01),
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loss_variation=grpo_config.get("loss_variation", "sample_level"),
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)
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# Reference model is initialized from policy model.
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self.reference_model = AutoModelForCausalLM.from_pretrained(path, **model_config)
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self.reference_model.eval()
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if self.policy_loss_fn.beta > 0:
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self.reference_model = AutoModelForCausalLM.from_pretrained(path, **model_config)
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self.reference_model.eval()
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self.tokenizer = AutoTokenizer.from_pretrained(path)
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self.pad_token_id = self.tokenizer.pad_token_id
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self.num_generations = num_generations
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self.filter_range = training_config.get("filter_range", None)
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self.filter_range = grpo_config.get("filter_range", None)
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if self.filter_range is not None:
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assert len(self.filter_range) == 2, "Filter range should have 2 values."
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self.filter_truncated_response = grpo_config.get("filter_truncated_response", False)
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if self.filter_truncated_response:
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self.max_length = 0
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if "max_tokens" in self.generate_config:
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self.max_length = self.generate_config["max_tokens"]
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elif "max_new_tokens" in self.generate_config:
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self.max_length = self.generate_config["max_new_tokens"]
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else:
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raise ValueError(
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"either max_tokens (vllm) or max_new_tokens (transformers) must be set in generate_config."
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)
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# Initialize verifiable reward.
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response_format_tags = {
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"think_start": {"text": "<think>", "num_occur": 1},
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@@ -90,11 +120,12 @@ class GRPOConsumer(BaseConsumer):
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"answer_start": {"text": "<answer>", "num_occur": 1},
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"answer_end": {"text": "</answer>", "num_occur": 1},
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}
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reward_model_kwargs = {
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k: v for k, v in grpo_config.items() if k in ["soft_over_length_punishment", "max_length", "cache_length"]
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}
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self.reward_model = VerifiableReward(
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reward_fns=[math_reward_fn], tokenizer=self.tokenizer, tags=response_format_tags
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reward_fns=[math_reward_fn], tokenizer=self.tokenizer, tags=response_format_tags, **reward_model_kwargs
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)
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self.policy_loss_fn = PolicyLoss()
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self.global_step = 0
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self.use_wandb = use_wandb
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@@ -102,7 +133,7 @@ class GRPOConsumer(BaseConsumer):
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optimizer=self.optimizer,
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total_steps=min(self.num_episodes, 4) * self.num_update_per_episode,
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warmup_steps=0,
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eta_min=0.1 * training_config.get("lr", 1e-6),
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eta_min=0.1 * grpo_config.get("lr", 1e-6),
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)
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def setup(self):
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@@ -118,10 +149,11 @@ class GRPOConsumer(BaseConsumer):
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self.policy_model, self.optimizer, _, _, self.lr_scheduler = self.booster.boost(
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self.policy_model, self.optimizer, lr_scheduler=self.lr_scheduler
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)
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self.reference_model, *_ = self.booster.boost(self.reference_model)
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if self.policy_loss_fn.beta > 0:
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self.reference_model, *_ = self.booster.boost(self.reference_model)
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self.plugin.logger.set_level("ERROR")
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def step(self, step_idx: int, **kwargs) -> Optional[float]:
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def step(self, step_idx: int, pbar: Any, **kwargs) -> Optional[float]:
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"""
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Step data from policy model:
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[{
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@@ -132,18 +164,108 @@ class GRPOConsumer(BaseConsumer):
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},
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...]
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Format:
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[batch_size, num_of_generation, prompt_length + response_length] --- <PAD>...<PAD><PROMPT>...<PROMPT><RESPONSE>...<RESPONSE><PAD>...<PAD>.
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[minibatch_size, num_of_generation, prompt_length + response_length] --- <PAD>...<PAD><PROMPT>...<PROMPT><RESPONSE>...<RESPONSE><PAD>...<PAD>.
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"""
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# Reshape to [batch_size x num_of_generation, prompt_length + response_length]
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# Reshape to [minibatch_size x num_of_generation, prompt_length + response_length]
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data = {k: v.view(-1, v.size(-1)) for k, v in kwargs.items()}
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action_mask = data["action_mask"]
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num_action = action_mask.shape[1]
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old_action_log_probs = data["action_log_probs"]
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response_length = torch.sum(action_mask, dim=1).to(torch.float32)
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forward_batch_size = self.training_config.get("train_microbatch_size", data["input_ids"].size(0))
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train_microbatch_size = self.grpo_config.get("train_microbatch_size", data["input_ids"].size(0))
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reward_group = self.reward_model(
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data["input_ids"],
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gt_answer=data["gt_answer"],
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response_idx=data["response_idx"],
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)
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reward = torch.tensor([value[0] for value in reward_group]).to(data["input_ids"].device)
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format_acc = torch.tensor([value[1] for value in reward_group]).to(data["input_ids"].device)
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ans_acc = torch.tensor([value[2] for value in reward_group]).to(data["input_ids"].device)
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# [minibatch_size, num_generations]
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group_reward = reward.view(-1, self.num_generations)
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reward_mean = group_reward.mean(dim=1)
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# [minibatch_size x num_generations]
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reward_mean = reward_mean.repeat_interleave(self.num_generations, dim=0)
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reward_std = group_reward.std(dim=1).repeat_interleave(self.num_generations, dim=0)
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# [minibatch_size x num_generations]
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advantages = ((reward - reward_mean) / (reward_std + 1e-4)).unsqueeze(dim=-1)
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# filter out the reward that is too high (all sample gets full score) or too low (all sample gets 0 score),
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group_ans_acc = (
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ans_acc.view(-1, self.num_generations).mean(dim=1).repeat_interleave(self.num_generations, dim=0)
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)
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loss_mask = (
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torch.ones(action_mask.size(0), device=action_mask.device).bool()
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if self.filter_range is None
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else torch.logical_and(group_ans_acc > self.filter_range[0], group_ans_acc < self.filter_range[1])
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)
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# filter out overlength samples
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if self.filter_truncated_response and action_mask.size(1) == self.max_length:
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loss_mask = torch.logical_and(
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loss_mask,
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action_mask[:, -1] == False,
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)
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effective_tokens_count = torch.sum(action_mask, dim=-1) * loss_mask
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effective_samples = all_reduce_sum(torch.sum(loss_mask), self.plugin)
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total_effective_tokens_count = all_reduce_sum(torch.sum(effective_tokens_count), self.plugin)
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total_samples = all_reduce_sum(torch.sum(torch.ones_like(loss_mask, device=loss_mask.device)), self.plugin)
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self.effective_sample_count += effective_samples.item()
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self.total_sample_count += total_samples.item()
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mean_kl, mean_loss = [], []
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if self.grpo_config.get("dynamic_batching", True):
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need_update = self.effective_sample_count >= self.batch_size * self.dp_size * self.num_generations
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# to exclude the excessive samples from the last batch, the last num_excessive_samples samples are not used for training and will be kept in buffer for the next iteration.
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num_excessive_samples = (
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int(
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(self.effective_sample_count - self.batch_size * self.dp_size * self.num_generations)
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/ self.num_generations
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/ self.dp_size
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)
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* self.num_generations
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)
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if num_excessive_samples > 0:
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data = {
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k: (
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v[: -num_excessive_samples if num_excessive_samples != 0 else v.size(0)]
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if k
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in [
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"input_ids",
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"attention_mask",
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"action_log_probs",
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"action_mask",
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"response_idx",
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"gt_answer",
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]
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else v
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)
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for k, v in data.items()
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}
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action_mask = action_mask[
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: -num_excessive_samples if num_excessive_samples != 0 else action_mask.size(0)
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]
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loss_mask = loss_mask[: -num_excessive_samples if num_excessive_samples != 0 else loss_mask.size(0)]
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advantages = advantages[: -num_excessive_samples if num_excessive_samples != 0 else advantages.size(0)]
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else:
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num_excessive_samples = 0
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else:
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# If dynamic batching is disabled, we need to use all samples for training.
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need_update = (step_idx + 1) % self.num_microbatches == 0
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num_excessive_samples = 0
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pbar.set_postfix(
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{
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"Step": self.global_step + 1,
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"Status": f"Collecting: {self.effective_sample_count}/{self.batch_size * self.dp_size * self.num_generations}",
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}
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)
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need_update = (step_idx + 1) % self.num_microbatches == 0
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# Gradient must be synchronized if zero2 is enabled. https://github.com/hpcaitech/ColossalAI/blob/44d4053fec005fe0b06b6bc755fdc962463145df/colossalai/booster/plugin/hybrid_parallel_plugin.py#L1500
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ctx = (
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nullcontext()
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@@ -151,95 +273,71 @@ class GRPOConsumer(BaseConsumer):
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else self.booster.no_sync(self.policy_model, self.optimizer)
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)
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with ctx:
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reward_group = self.reward_model(
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data["input_ids"], gt_answer=data["gt_answer"], response_idx=data["response_idx"]
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)
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reward = torch.tensor([value[0] for value in reward_group]).to(data["input_ids"].device)
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format_reward = torch.tensor([value[1] for value in reward_group]).to(data["input_ids"].device)
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acc_reward = torch.tensor([value[2] for value in reward_group]).to(data["input_ids"].device)
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# [batch_size, num_generations]
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group_reward = reward.view(-1, self.num_generations)
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reward_mean = group_reward.mean(dim=1)
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# [batch_size x num_generations]
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reward_mean = reward_mean.repeat_interleave(self.num_generations, dim=0)
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reward_std = group_reward.std(dim=1).repeat_interleave(self.num_generations, dim=0)
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# [batch_size x num_generations]
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advantages = ((reward - reward_mean) / (reward_std + 1e-4)).unsqueeze(dim=-1)
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# filter out the reward that is too high (all sample gets full score) or too low (all sample gets 0 score),
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loss_mask = (
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None
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if self.filter_range is None
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else torch.logical_and(
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reward_mean > self.filter_range[0], reward_mean < self.filter_range[1]
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).repeat_interleave(self.num_generations, dim=0)
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)
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mean_kl, mean_loss = [], []
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for forward_micro_batch_start in range(0, data["input_ids"].size(0), forward_batch_size):
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for forward_micro_batch_start in range(0, data["input_ids"].size(0), train_microbatch_size):
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input_ids_forward_micro_batch = data["input_ids"][
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forward_micro_batch_start : forward_micro_batch_start + forward_batch_size
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forward_micro_batch_start : forward_micro_batch_start + train_microbatch_size
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]
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attention_mask_forward_micro_batch = data["attention_mask"][
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forward_micro_batch_start : forward_micro_batch_start + forward_batch_size
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forward_micro_batch_start : forward_micro_batch_start + train_microbatch_size
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]
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action_mask_forward_micro_batch = action_mask[
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forward_micro_batch_start : forward_micro_batch_start + forward_batch_size
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forward_micro_batch_start : forward_micro_batch_start + train_microbatch_size
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]
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loss_mask_forward_micro_batch = (
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loss_mask[forward_micro_batch_start : forward_micro_batch_start + forward_batch_size]
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loss_mask[forward_micro_batch_start : forward_micro_batch_start + train_microbatch_size]
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if loss_mask is not None
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else None
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)
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advantages_forward_micro_batch = advantages[
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forward_micro_batch_start : forward_micro_batch_start + forward_batch_size
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forward_micro_batch_start : forward_micro_batch_start + train_microbatch_size
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]
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if self.plugin.pp_size > 1:
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# Support training with PP.
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if self.policy_loss_fn.beta > 0:
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with torch.no_grad():
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reference_model_outputs = self.booster.execute_pipeline(
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iter(
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[
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{
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"input_ids": input_ids_forward_micro_batch,
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"attention_mask": attention_mask_forward_micro_batch,
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}
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]
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),
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self.reference_model,
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criterion=lambda outputs, inputs: torch.tensor(
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[0.0], device=action_mask.device
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), # dummy criterion
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optimizer=None,
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return_loss=False,
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return_outputs=True,
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)
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with torch.no_grad():
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reference_model_outputs = self.booster.execute_pipeline(
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iter(
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[
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{
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"input_ids": input_ids_forward_micro_batch,
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"attention_mask": attention_mask_forward_micro_batch,
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}
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]
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),
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self.reference_model,
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criterion=lambda outputs, inputs: torch.tensor(
|
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[0.0], device=action_mask.device
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), # dummy criterion
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optimizer=None,
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return_loss=False,
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return_outputs=True,
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)
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|
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if self.booster.plugin.stage_manager.is_last_stage():
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reference_model_logits = reference_model_outputs["outputs"]["logits"]
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reference_action_log_probs = calc_action_log_probs(
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reference_model_logits / self.generate_config["temperature"],
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input_ids_forward_micro_batch,
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num_action,
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self.plugin.shard_config,
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)
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if self.booster.plugin.stage_manager.is_last_stage():
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reference_model_logits = reference_model_outputs["outputs"]["logits"]
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reference_action_log_probs = calc_action_log_probs(
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reference_model_logits / self.generate_config["temperature"],
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input_ids_forward_micro_batch,
|
||||
num_action,
|
||||
self.plugin.shard_config,
|
||||
)
|
||||
else:
|
||||
# Dummy reference logprobs for data iterator.
|
||||
reference_action_log_probs = None
|
||||
else:
|
||||
# Dummy reference logprobs for data iterator.
|
||||
reference_action_log_probs = None
|
||||
|
||||
data_policy_forward = {
|
||||
"input_ids": input_ids_forward_micro_batch,
|
||||
"attention_mask": attention_mask_forward_micro_batch,
|
||||
"action_mask": action_mask_forward_micro_batch,
|
||||
"reference_action_log_probs": reference_action_log_probs,
|
||||
"advantages": advantages_forward_micro_batch,
|
||||
"loss_mask": loss_mask_forward_micro_batch,
|
||||
"source": self.rank,
|
||||
}
|
||||
if reference_action_log_probs is not None:
|
||||
data_policy_forward["reference_action_log_probs"] = reference_action_log_probs
|
||||
|
||||
kl = []
|
||||
|
||||
@@ -251,24 +349,30 @@ class GRPOConsumer(BaseConsumer):
|
||||
num_action,
|
||||
self.plugin.shard_config,
|
||||
)
|
||||
per_token_kl = (
|
||||
torch.exp(inputs["reference_action_log_probs"] - action_log_probs)
|
||||
- (inputs["reference_action_log_probs"] - action_log_probs)
|
||||
- 1
|
||||
)
|
||||
appox_kl = torch.sum(per_token_kl * inputs["action_mask"], dim=-1) / torch.sum(
|
||||
inputs["action_mask"], dim=-1
|
||||
)
|
||||
kl.append(appox_kl.mean())
|
||||
loss, skip_update, _ = self.policy_loss_fn(
|
||||
if "reference_action_log_probs" in inputs:
|
||||
per_token_kl = (
|
||||
torch.exp(inputs["reference_action_log_probs"] - action_log_probs)
|
||||
- (inputs["reference_action_log_probs"] - action_log_probs)
|
||||
- 1
|
||||
)
|
||||
appox_kl = torch.sum(per_token_kl * inputs["action_mask"], dim=-1) / torch.sum(
|
||||
inputs["action_mask"], dim=-1
|
||||
)
|
||||
kl.append(appox_kl.mean())
|
||||
else:
|
||||
per_token_kl = 0.0
|
||||
kl.append(0.0)
|
||||
|
||||
loss, _ = self.policy_loss_fn(
|
||||
action_log_probs,
|
||||
action_log_probs,
|
||||
inputs["advantages"].repeat_interleave(action_log_probs.size(-1), dim=-1),
|
||||
per_token_kl,
|
||||
inputs["action_mask"],
|
||||
loss_mask=inputs["loss_mask"],
|
||||
total_effective_tokens_in_batch=total_effective_tokens_count,
|
||||
)
|
||||
return loss
|
||||
return loss, num_excessive_samples // self.num_generations
|
||||
|
||||
policy_model_outputs = self.booster.execute_pipeline(
|
||||
iter([data_policy_forward]),
|
||||
@@ -298,61 +402,71 @@ class GRPOConsumer(BaseConsumer):
|
||||
self.plugin.shard_config,
|
||||
)
|
||||
|
||||
with torch.no_grad():
|
||||
reference_model_logits = self.reference_model(
|
||||
input_ids=input_ids_forward_micro_batch,
|
||||
attention_mask=attention_mask_forward_micro_batch,
|
||||
).logits
|
||||
reference_action_log_probs = calc_action_log_probs(
|
||||
reference_model_logits / self.generate_config["temperature"],
|
||||
input_ids_forward_micro_batch,
|
||||
num_action,
|
||||
self.plugin.shard_config,
|
||||
)
|
||||
per_token_kl = (
|
||||
torch.exp(reference_action_log_probs - action_log_probs)
|
||||
- (reference_action_log_probs - action_log_probs)
|
||||
- 1
|
||||
)
|
||||
kl = torch.sum(per_token_kl * action_mask_forward_micro_batch, dim=-1) / torch.sum(
|
||||
action_mask_forward_micro_batch, dim=-1
|
||||
)
|
||||
if self.policy_loss_fn.beta > 0:
|
||||
with torch.no_grad():
|
||||
reference_model_logits = self.reference_model(
|
||||
input_ids=input_ids_forward_micro_batch,
|
||||
attention_mask=attention_mask_forward_micro_batch,
|
||||
).logits
|
||||
reference_action_log_probs = calc_action_log_probs(
|
||||
reference_model_logits / self.generate_config["temperature"],
|
||||
input_ids_forward_micro_batch,
|
||||
num_action,
|
||||
self.plugin.shard_config,
|
||||
)
|
||||
per_token_kl = (
|
||||
torch.exp(reference_action_log_probs - action_log_probs)
|
||||
- (reference_action_log_probs - action_log_probs)
|
||||
- 1
|
||||
)
|
||||
kl = torch.sum(per_token_kl * action_mask_forward_micro_batch, dim=-1) / torch.sum(
|
||||
action_mask_forward_micro_batch, dim=-1
|
||||
)
|
||||
else:
|
||||
per_token_kl = 0.0
|
||||
kl = None
|
||||
|
||||
loss, skip_update, _ = self.policy_loss_fn(
|
||||
loss, _ = self.policy_loss_fn(
|
||||
action_log_probs,
|
||||
old_action_log_probs,
|
||||
advantages_forward_micro_batch.repeat_interleave(action_log_probs.size(-1), dim=-1),
|
||||
per_token_kl,
|
||||
action_mask_forward_micro_batch,
|
||||
loss_mask=loss_mask_forward_micro_batch,
|
||||
total_effective_tokens_in_batch=total_effective_tokens_count,
|
||||
)
|
||||
|
||||
if not skip_update:
|
||||
self.booster.backward(loss, self.optimizer)
|
||||
self.booster.backward(loss, self.optimizer)
|
||||
loss = all_reduce_mean(loss, self.plugin)
|
||||
kl = all_reduce_mean(kl.mean(), self.plugin)
|
||||
# Calculate accumulate value.
|
||||
mean_kl.append(kl.data)
|
||||
if kl is not None:
|
||||
kl = all_reduce_mean(kl.mean(), self.plugin)
|
||||
mean_kl.append(kl.data)
|
||||
mean_loss.append(loss.data)
|
||||
if not self.plugin.pp_size > 1 or (
|
||||
self.plugin.pp_size > 1 and self.booster.plugin.stage_manager.is_last_stage() and self.tp_rank == 0
|
||||
):
|
||||
reward = all_reduce_mean(reward.mean(), self.plugin)
|
||||
format_reward = all_reduce_mean(format_reward.mean(), self.plugin)
|
||||
acc_reward = all_reduce_mean(acc_reward.mean(), self.plugin)
|
||||
format_acc = all_reduce_mean(format_acc.mean(), self.plugin)
|
||||
ans_acc = all_reduce_mean(ans_acc.mean(), self.plugin)
|
||||
advantages = all_reduce_mean(advantages.mean(), self.plugin)
|
||||
response_length = all_reduce_mean(response_length.mean(), self.plugin)
|
||||
self.accum_loss.add_(sum(mean_loss) / len(mean_loss))
|
||||
self.accum_kl.add_(sum(mean_kl) / len(mean_kl))
|
||||
if self.policy_loss_fn.beta > 0:
|
||||
self.accum_kl.add_(sum(mean_kl) / len(mean_kl))
|
||||
self.accum_reward.add_(reward.data)
|
||||
self.accum_format_reward.add_(format_reward.data)
|
||||
self.accum_acc_reward.add_(acc_reward.data)
|
||||
self.accum_format_acc.add_(format_acc.data)
|
||||
self.accum_ans_acc.add_(ans_acc.data)
|
||||
self.accum_advantages.add_(advantages.data)
|
||||
self.accum_response_length.add_(response_length.data)
|
||||
self.accum_count += 1
|
||||
if need_update:
|
||||
self.optimizer.step()
|
||||
self.optimizer.zero_grad()
|
||||
self.global_step += 1
|
||||
sample_utilization = self.effective_sample_count / self.total_sample_count
|
||||
self.effective_sample_count = 0
|
||||
self.total_sample_count = 0
|
||||
if not self.plugin.pp_size > 1 or (
|
||||
self.plugin.pp_size > 1 and self.booster.plugin.stage_manager.is_last_stage() and self.tp_rank == 0
|
||||
):
|
||||
@@ -360,167 +474,41 @@ class GRPOConsumer(BaseConsumer):
|
||||
if (not self.plugin.pp_size > 1 and self.rank == 0) or (
|
||||
self.plugin.pp_size > 1 and self.booster.plugin.stage_manager.is_last_stage() and self.tp_rank == 0
|
||||
):
|
||||
print(
|
||||
"Loss:",
|
||||
self.accum_loss.item() / self.accum_count,
|
||||
"\nReward:",
|
||||
self.accum_reward.item() / self.accum_count,
|
||||
"\nFormat Reward:",
|
||||
self.accum_format_reward.item() / self.accum_count,
|
||||
"\nAcc Reward:",
|
||||
self.accum_acc_reward.item() / self.accum_count,
|
||||
"\nKL:",
|
||||
self.accum_kl.item() / self.accum_count,
|
||||
"\nAdvantages:",
|
||||
self.accum_advantages.item() / self.accum_count,
|
||||
"\nResponse Length:",
|
||||
self.accum_response_length.item() / self.accum_count,
|
||||
)
|
||||
self.wandb_run.log(
|
||||
{
|
||||
"metrics/reward": self.accum_reward.item() / self.accum_count,
|
||||
"metrics/format_reward": self.accum_format_reward.item() / self.accum_count,
|
||||
"metrics/acc_reward": self.accum_acc_reward.item() / self.accum_count,
|
||||
"metrics/response_length": self.accum_response_length.item() / self.accum_count,
|
||||
"train/loss": self.accum_loss.item() / self.accum_count,
|
||||
"train/kl": self.accum_kl.item() / self.accum_count,
|
||||
"train/advantages": self.accum_advantages.item() / self.accum_count,
|
||||
"train/learning_rate": self.lr_scheduler.get_last_lr()[0],
|
||||
"rollout/temperature": data["temperature"].cpu().numpy()[0][0],
|
||||
}
|
||||
)
|
||||
to_log_msg = [
|
||||
f"Loss: {self.accum_loss.item() / self.accum_count:.4f}",
|
||||
f"Reward: {self.accum_reward.item() / self.accum_count:.4f}",
|
||||
f"format Reward: {self.accum_format_acc.item() / self.accum_count:.4f}",
|
||||
f"Acc Reward: {self.accum_ans_acc.item() / self.accum_count:.4f}",
|
||||
f"Advantages: {self.accum_advantages.item() / self.accum_count:.4f}",
|
||||
f"Response Length: {self.accum_response_length.item() / self.accum_count:.4f}",
|
||||
] + ([f"KL: {self.accum_kl.item() / self.accum_count:.4f}"] if self.policy_loss_fn.beta > 0 else [])
|
||||
print("\n".join(to_log_msg))
|
||||
metrics = {
|
||||
"metrics/reward": self.accum_reward.item() / self.accum_count,
|
||||
"metrics/format_acc": self.accum_format_acc.item() / self.accum_count,
|
||||
"metrics/ans_acc": self.accum_ans_acc.item() / self.accum_count,
|
||||
"metrics/response_length": self.accum_response_length.item() / self.accum_count,
|
||||
"train/loss": self.accum_loss.item() / self.accum_count,
|
||||
"train/advantages": self.accum_advantages.item() / self.accum_count,
|
||||
"train/learning_rate": self.lr_scheduler.get_last_lr()[0],
|
||||
"train/sample_utilization": sample_utilization,
|
||||
"rollout/temperature": data["temperature"].cpu().numpy()[0][0],
|
||||
}
|
||||
if self.policy_loss_fn.beta > 0:
|
||||
metrics["train/kl"] = self.accum_kl.item() / self.accum_count
|
||||
|
||||
self.wandb_run.log(metrics)
|
||||
self.accum_loss.zero_()
|
||||
self.accum_reward.zero_()
|
||||
self.accum_acc_reward.zero_()
|
||||
self.accum_format_reward.zero_()
|
||||
self.accum_ans_acc.zero_()
|
||||
self.accum_format_acc.zero_()
|
||||
self.accum_kl.zero_()
|
||||
self.accum_advantages.zero_()
|
||||
self.accum_response_length.zero_()
|
||||
|
||||
self.accum_count = 0
|
||||
return loss_scalar
|
||||
|
||||
def state_dict(self):
|
||||
self.policy_model._force_wait_all_gather()
|
||||
model = self.policy_model.unwrap()
|
||||
state_dict = model.state_dict()
|
||||
return state_dict
|
||||
|
||||
|
||||
@ray.remote
|
||||
class GRPOEvalConsumer(BaseConsumer):
|
||||
def __init__(
|
||||
self,
|
||||
num_producers,
|
||||
num_episodes,
|
||||
rank,
|
||||
world_size,
|
||||
master_addr,
|
||||
master_port,
|
||||
num_update_per_episode,
|
||||
num_recv_per_update,
|
||||
batch_size,
|
||||
model_config,
|
||||
plugin_config,
|
||||
microbatch_size=1,
|
||||
num_generations=4,
|
||||
use_wandb=True,
|
||||
log_dir="./results",
|
||||
):
|
||||
super().__init__(
|
||||
num_producers,
|
||||
num_episodes,
|
||||
rank,
|
||||
world_size,
|
||||
master_addr,
|
||||
master_port,
|
||||
num_update_per_episode,
|
||||
num_recv_per_update,
|
||||
batch_size,
|
||||
model_config,
|
||||
plugin_config,
|
||||
microbatch_size,
|
||||
)
|
||||
path = model_config.pop("path")
|
||||
self.policy_model = AutoModelForCausalLM.from_pretrained(path, **model_config)
|
||||
self.policy_model.train()
|
||||
self.accum_reward = torch.zeros(1, device=self.device)
|
||||
self.accum_format_reward = torch.zeros(1, device=self.device)
|
||||
self.accum_acc_reward = torch.zeros(1, device=self.device)
|
||||
self.accum_response_length = torch.zeros(1, device=self.device)
|
||||
self.accum_count = torch.zeros(1, device=self.device)
|
||||
|
||||
self.tokenizer = AutoTokenizer.from_pretrained(path)
|
||||
self.pad_token_id = self.tokenizer.pad_token_id
|
||||
self.num_generations = num_generations
|
||||
|
||||
# Initialize verifiable reward.
|
||||
response_format_tags = {
|
||||
"think_start": {"text": "<think>", "num_occur": 1},
|
||||
"think_end": {"text": "</think>", "num_occur": 1},
|
||||
"answer_start": {"text": "<answer>", "num_occur": 1},
|
||||
"answer_end": {"text": "</answer>", "num_occur": 1},
|
||||
}
|
||||
self.reward_model = VerifiableReward(
|
||||
reward_fns=[math_reward_fn], tokenizer=self.tokenizer, tags=response_format_tags
|
||||
)
|
||||
|
||||
self.log_dir = log_dir
|
||||
if not os.path.exists(self.log_dir):
|
||||
os.makedirs(self.log_dir)
|
||||
return loss_scalar, num_excessive_samples // self.num_generations
|
||||
else:
|
||||
os.system(f"rm -rf {self.log_dir}/*")
|
||||
|
||||
def setup(self):
|
||||
super().setup()
|
||||
self.policy_model, _, *_ = self.booster.boost(self.policy_model)
|
||||
|
||||
def step(self, step_idx: int, **kwargs) -> Optional[float]:
|
||||
rank = dist.get_rank()
|
||||
data = {k: v.view(-1, v.size(-1)).cpu() for k, v in kwargs.items()}
|
||||
kwargs["input_ids"].size(0)
|
||||
reward_group = self.reward_model(
|
||||
data["input_ids"], gt_answer=data["gt_answer"], response_idx=data["response_idx"]
|
||||
)
|
||||
reward = [value[0].item() for value in reward_group]
|
||||
format_reward = [value[1].item() for value in reward_group]
|
||||
acc_reward = [value[2].item() for value in reward_group]
|
||||
response_length = [(data["response_idx"][i][1] - data["response_idx"][i][0]).item() for i in range(len(reward))]
|
||||
|
||||
response = self.tokenizer.batch_decode(data["input_ids"], skip_special_tokens=True)
|
||||
with open(f"{self.log_dir}/eval_results_rank_{rank}.jsonl", "a", encoding="utf8") as f:
|
||||
for i in range(len(response)):
|
||||
f.write(
|
||||
json.dumps(
|
||||
{
|
||||
"response": response[i],
|
||||
"reward": reward[i],
|
||||
"format_reward": format_reward[i],
|
||||
"acc_reward": acc_reward[i],
|
||||
"response_length": response_length[i],
|
||||
},
|
||||
ensure_ascii=False,
|
||||
)
|
||||
+ "\n"
|
||||
)
|
||||
|
||||
self.accum_reward += sum(reward)
|
||||
self.accum_format_reward += sum(format_reward)
|
||||
self.accum_acc_reward += sum(acc_reward)
|
||||
self.accum_response_length += sum(response_length)
|
||||
self.accum_count += len(reward)
|
||||
|
||||
# print results
|
||||
total_count = all_reduce_mean(self.accum_count, self.plugin)
|
||||
mean_reward = all_reduce_mean(self.accum_reward, self.plugin) / total_count
|
||||
mean_format_reward = all_reduce_mean(self.accum_format_reward, self.plugin) / total_count
|
||||
mean_acc_reward = all_reduce_mean(self.accum_acc_reward, self.plugin) / total_count
|
||||
mean_response_length = all_reduce_mean(self.accum_response_length, self.plugin) / total_count
|
||||
if rank == 0:
|
||||
print(
|
||||
f"Step {step_idx}: Mean Reward: {mean_reward}, Mean Format Reward: {mean_format_reward}, Mean Acc Reward: {mean_acc_reward}, Mean Response Length: {mean_response_length}"
|
||||
)
|
||||
return None
|
||||
return None, num_excessive_samples // self.num_generations
|
||||
|
||||
def state_dict(self):
|
||||
self.policy_model._force_wait_all_gather()
|
||||
|
||||
Reference in New Issue
Block a user