mirror of
https://github.com/hpcaitech/ColossalAI.git
synced 2025-06-22 13:41:43 +00:00
add DAPO support
This commit is contained in:
YeAnbang
parent
1723a02860
commit
6e71e2a3ce
@ -12,7 +12,7 @@ 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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@ -38,7 +38,7 @@ class GRPOConsumer(BaseConsumer):
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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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):
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super().__init__(
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@ -59,7 +59,7 @@ class GRPOConsumer(BaseConsumer):
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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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@ -69,8 +69,9 @@ class GRPOConsumer(BaseConsumer):
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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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# Reference model is initialized from policy model.
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self.reference_model = AutoModelForCausalLM.from_pretrained(path, **model_config)
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@ -79,10 +80,21 @@ class GRPOConsumer(BaseConsumer):
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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 +102,20 @@ 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.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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skip_threshold=grpo_config.get("skip_threshold", 20.0),
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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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self.global_step = 0
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self.use_wandb = use_wandb
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@ -102,7 +123,7 @@ 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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@ -141,9 +162,65 @@ class GRPOConsumer(BaseConsumer):
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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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forward_batch_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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int(step_idx / self.num_microbatches),
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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_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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reward_mean_no_length_penalty = (
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(format_reward + acc_reward)
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.view(-1, self.num_generations)
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.mean(dim=1)
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.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(
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reward_mean_no_length_penalty > self.filter_range[0], reward_mean < self.filter_range[1]
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)
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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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# for i in range(loss_mask.size(0)):
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# if loss_mask[i] == False:
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# print(data["input_ids"].size(), data["input_ids"][i], action_mask[i], "mean reward", reward_mean_no_length_penalty.size(), reward_mean_no_length_penalty[i])
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effective_samples = all_reduce_sum(torch.sum(loss_mask), self.plugin)
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self.effective_sample_count += effective_samples.item()
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mean_kl, mean_loss = [], []
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# update gradient only if at least 0.7*batch_size*num_generation valid samples are collected in case a lot of samples are invalid and got filtered out.
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# balance between efficiency and accuracy
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need_update = self.effective_sample_count >= self.batch_size * self.dp_size * self.num_generations * 0.75
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if need_update:
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print(f"***** Update gradient based on {self.effective_sample_count} valid samples *****")
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self.effective_sample_count = 0
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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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@ -151,32 +228,6 @@ 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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input_ids_forward_micro_batch = data["input_ids"][
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@ -199,47 +250,50 @@ class GRPOConsumer(BaseConsumer):
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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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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,
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num_action,
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self.plugin.shard_config,
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)
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else:
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# Dummy reference logprobs for data iterator.
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reference_action_log_probs = None
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else:
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# Dummy reference logprobs for data iterator.
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reference_action_log_probs = None
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data_policy_forward = {
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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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"action_mask": action_mask_forward_micro_batch,
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"reference_action_log_probs": reference_action_log_probs,
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"advantages": advantages_forward_micro_batch,
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"loss_mask": loss_mask_forward_micro_batch,
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"source": self.rank,
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}
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if reference_action_log_probs is not None:
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data_policy_forward["reference_action_log_probs"] = reference_action_log_probs
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kl = []
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@ -251,15 +305,20 @@ class GRPOConsumer(BaseConsumer):
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num_action,
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self.plugin.shard_config,
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)
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per_token_kl = (
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torch.exp(inputs["reference_action_log_probs"] - action_log_probs)
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- (inputs["reference_action_log_probs"] - action_log_probs)
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- 1
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)
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appox_kl = torch.sum(per_token_kl * inputs["action_mask"], dim=-1) / torch.sum(
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inputs["action_mask"], dim=-1
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)
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kl.append(appox_kl.mean())
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if "reference_action_log_probs" in inputs:
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per_token_kl = (
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torch.exp(inputs["reference_action_log_probs"] - action_log_probs)
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- (inputs["reference_action_log_probs"] - action_log_probs)
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- 1
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)
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appox_kl = torch.sum(per_token_kl * inputs["action_mask"], dim=-1) / torch.sum(
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inputs["action_mask"], dim=-1
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)
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kl.append(appox_kl.mean())
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else:
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per_token_kl = 0.0
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kl.append(0.0)
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loss, skip_update, _ = self.policy_loss_fn(
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action_log_probs,
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action_log_probs,
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@ -298,25 +357,29 @@ class GRPOConsumer(BaseConsumer):
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self.plugin.shard_config,
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)
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with torch.no_grad():
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reference_model_logits = self.reference_model(
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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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).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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per_token_kl = (
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torch.exp(reference_action_log_probs - action_log_probs)
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- (reference_action_log_probs - action_log_probs)
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- 1
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)
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kl = torch.sum(per_token_kl * action_mask_forward_micro_batch, dim=-1) / torch.sum(
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action_mask_forward_micro_batch, dim=-1
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)
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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_logits = self.reference_model(
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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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).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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per_token_kl = (
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torch.exp(reference_action_log_probs - action_log_probs)
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- (reference_action_log_probs - action_log_probs)
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- 1
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)
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kl = torch.sum(per_token_kl * action_mask_forward_micro_batch, dim=-1) / torch.sum(
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action_mask_forward_micro_batch, dim=-1
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)
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else:
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per_token_kl = 0.0
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kl = None
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loss, skip_update, _ = self.policy_loss_fn(
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action_log_probs,
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@ -330,9 +393,10 @@ class GRPOConsumer(BaseConsumer):
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if not skip_update:
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self.booster.backward(loss, self.optimizer)
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loss = all_reduce_mean(loss, self.plugin)
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kl = all_reduce_mean(kl.mean(), self.plugin)
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# Calculate accumulate value.
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mean_kl.append(kl.data)
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if kl is not None:
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kl = all_reduce_mean(kl.mean(), self.plugin)
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mean_kl.append(kl.data)
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mean_loss.append(loss.data)
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if not self.plugin.pp_size > 1 or (
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self.plugin.pp_size > 1 and self.booster.plugin.stage_manager.is_last_stage() and self.tp_rank == 0
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@ -343,7 +407,8 @@ class GRPOConsumer(BaseConsumer):
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advantages = all_reduce_mean(advantages.mean(), self.plugin)
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response_length = all_reduce_mean(response_length.mean(), self.plugin)
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self.accum_loss.add_(sum(mean_loss) / len(mean_loss))
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self.accum_kl.add_(sum(mean_kl) / len(mean_kl))
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if self.policy_loss_fn.beta > 0:
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self.accum_kl.add_(sum(mean_kl) / len(mean_kl))
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self.accum_reward.add_(reward.data)
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self.accum_format_reward.add_(format_reward.data)
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self.accum_acc_reward.add_(acc_reward.data)
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@ -360,35 +425,32 @@ class GRPOConsumer(BaseConsumer):
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if (not self.plugin.pp_size > 1 and self.rank == 0) or (
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self.plugin.pp_size > 1 and self.booster.plugin.stage_manager.is_last_stage() and self.tp_rank == 0
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):
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print(
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"Loss:",
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self.accum_loss.item() / self.accum_count,
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"\nReward:",
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self.accum_reward.item() / self.accum_count,
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"\nFormat Reward:",
|
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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} \
|
||||
Reward: {self.accum_reward.item() / self.accum_count:.4f} \
|
||||
Format Reward: {self.accum_format_reward.item() / self.accum_count:.4f} \
|
||||
Acc Reward: {self.accum_acc_reward.item() / self.accum_count:.4f} \
|
||||
Advantages: {self.accum_advantages.item() / self.accum_count:.4f} \
|
||||
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(to_log_msg)
|
||||
metrics = {
|
||||
"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/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],
|
||||
}
|
||||
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_()
|
||||
|
||||
@ -40,6 +40,7 @@ def launch_distributed(
|
||||
inference_model_config: Dict[str, Any],
|
||||
generate_config: Dict[str, Any],
|
||||
train_model_config: Dict[str, Any],
|
||||
grpo_config: Dict[str, Any],
|
||||
plugin_config: Dict[str, Any],
|
||||
tokenizer_config: Optional[Dict[str, Any]] = None,
|
||||
inference_backend: str = "transformers",
|
||||
@ -103,11 +104,7 @@ def launch_distributed(
|
||||
plugin_config=plugin_config,
|
||||
microbatch_size=train_minibatch_size,
|
||||
generate_config=generate_config_consumer,
|
||||
training_config={
|
||||
"filter_range": [0.05, 9.0],
|
||||
"lr": 1e-6,
|
||||
"train_microbatch_size": train_microbatch_size,
|
||||
},
|
||||
grpo_config=grpo_config,
|
||||
num_generations=num_generations,
|
||||
project_name=project_name,
|
||||
)
|
||||
|
||||
@ -2,7 +2,7 @@ from typing import Optional
|
||||
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
from coati.distributed.utils import masked_mean
|
||||
from coati.distributed.utils import masked_mean, masked_sum
|
||||
|
||||
|
||||
class PolicyLoss(nn.Module):
|
||||
@ -10,11 +10,21 @@ class PolicyLoss(nn.Module):
|
||||
Policy Loss for PPO
|
||||
"""
|
||||
|
||||
def __init__(self, clip_eps: float = 0.2, skip_threshold: float = 20.0, beta: float = 0.01) -> None:
|
||||
def __init__(
|
||||
self,
|
||||
clip_eps_low: float = 0.2,
|
||||
clip_eps_high: float = 0.2,
|
||||
skip_threshold: float = 20.0,
|
||||
beta: float = 0.01,
|
||||
loss_variation: str = "sample_level",
|
||||
) -> None:
|
||||
super().__init__()
|
||||
self.clip_eps = clip_eps
|
||||
self.clip_eps_low = clip_eps_low
|
||||
self.clip_eps_high = clip_eps_high
|
||||
self.skip_threshold = skip_threshold
|
||||
self.beta = beta
|
||||
self.loss_variation = loss_variation
|
||||
assert loss_variation in ["sample_level", "token_level"], f"Unsupported loss variation: {loss_variation}"
|
||||
|
||||
def forward(
|
||||
self,
|
||||
@ -32,14 +42,31 @@ class PolicyLoss(nn.Module):
|
||||
ratio = ((log_probs - log_probs.detach()) * action_mask).exp()
|
||||
|
||||
surr1 = ratio * advantages
|
||||
surr2 = ratio.clamp(1 - self.clip_eps, 1 + self.clip_eps) * advantages
|
||||
surr2 = ratio.clamp(1 - self.clip_eps_low, 1 + self.clip_eps_high) * advantages
|
||||
if self.beta <= 0:
|
||||
# skip kl term if kl coefficient is zero
|
||||
per_token_kl = 0.0
|
||||
loss = -torch.min(surr1, surr2) + self.beta * per_token_kl
|
||||
|
||||
if action_mask is not None:
|
||||
loss = masked_mean(loss, action_mask)
|
||||
else:
|
||||
loss = loss.mean(dim=1)
|
||||
if loss_mask is not None:
|
||||
loss = loss * loss_mask
|
||||
loss = loss.mean()
|
||||
if self.loss_variation == "sample_level":
|
||||
if action_mask is not None:
|
||||
loss = masked_mean(loss, action_mask)
|
||||
else:
|
||||
loss = loss.mean(dim=1)
|
||||
if loss_mask is not None:
|
||||
loss = loss * loss_mask
|
||||
loss = loss.mean()
|
||||
elif self.loss_variation == "token_level":
|
||||
total_tokens = 0
|
||||
if action_mask is not None:
|
||||
loss = masked_sum(loss, action_mask)
|
||||
total_tokens = action_mask.sum(dim=1)
|
||||
else:
|
||||
loss = loss.sum(dim=1)
|
||||
total_tokens = torch.ones_like(loss, device=loss.device) * log_probs.size(1)
|
||||
if loss_mask is not None:
|
||||
loss = loss * loss_mask
|
||||
total_tokens = total_tokens * loss_mask
|
||||
loss = loss.sum() / (total_tokens.sum() + 1e-8)
|
||||
|
||||
return loss, skip, ratio.max()
|
||||
|
||||
@ -124,12 +124,12 @@ class BaseProducer:
|
||||
self.load_state_dict(state_dict)
|
||||
del state_dict
|
||||
torch.cuda.empty_cache()
|
||||
# linear annealing for 1 episode, temperature from initial to 0.7
|
||||
# linear annealing for 1 episode, temperature from initial to 0.9
|
||||
if episode <= 0:
|
||||
ratio = 1 - (len(self.dataloader) - i) / len(self.dataloader)
|
||||
self.model.generate_config.temperature = (1 - ratio) * self.generate_config[
|
||||
"temperature"
|
||||
] + ratio * 0.7
|
||||
] + ratio * 0.9
|
||||
|
||||
|
||||
@ray.remote
|
||||
|
||||
@ -3,14 +3,29 @@ import torch
|
||||
from .reward_utils import extract_solution, validate_response_structure
|
||||
|
||||
|
||||
def math_reward_fn(input_ids, gt_answer, response_idx, **kwargs):
|
||||
def math_reward_fn(step, input_ids, gt_answer, response_idx, **kwargs):
|
||||
tokenizer = kwargs["tokenizer"]
|
||||
soft_over_length_punishment = kwargs["soft_over_length_punishment"]
|
||||
format_score = 1.0
|
||||
acc_score = 9.0
|
||||
tokenizer = kwargs["tokenizer"]
|
||||
if step > 30:
|
||||
format_score = 0.0
|
||||
acc_score = 10.0
|
||||
reward = torch.tensor(0.0)
|
||||
format_reward = torch.tensor(0.0)
|
||||
acc_reward = torch.tensor(0.0)
|
||||
s, e = response_idx[0], response_idx[1]
|
||||
|
||||
length_reward = 0.0
|
||||
if soft_over_length_punishment:
|
||||
max_length = kwargs.get("max_length", 1024 * 4)
|
||||
cache_length = kwargs.get("cache_length", 512)
|
||||
res_length = e.item() - s.item() + 1
|
||||
if res_length >= max_length:
|
||||
length_reward = -1.0 * 2
|
||||
elif res_length > max_length - cache_length:
|
||||
length_reward = ((max_length - cache_length) - res_length) / cache_length * 2
|
||||
|
||||
if gt_answer is None:
|
||||
return reward
|
||||
|
||||
@ -33,6 +48,8 @@ def math_reward_fn(input_ids, gt_answer, response_idx, **kwargs):
|
||||
acc_reward += acc_score
|
||||
reward += acc_score
|
||||
|
||||
reward = reward + length_reward
|
||||
|
||||
return torch.tensor([reward, format_reward, acc_reward]).to(input_ids.device)
|
||||
|
||||
|
||||
|
||||
@ -14,6 +14,7 @@ class VerifiableReward:
|
||||
|
||||
def __call__(
|
||||
self,
|
||||
step: int,
|
||||
input_ids: torch.LongTensor,
|
||||
gt_answer: List[torch.Tensor] = None,
|
||||
response_idx: List[torch.Tensor] = None,
|
||||
@ -29,6 +30,7 @@ class VerifiableReward:
|
||||
reward_batch = torch.stack(
|
||||
[
|
||||
reward_fn(
|
||||
step,
|
||||
input_ids[i],
|
||||
gt_answer=gt_answer[i],
|
||||
response_idx=response_idx[i],
|
||||
|
||||
@ -113,3 +113,20 @@ def masked_mean(tensor: torch.Tensor, mask: torch.Tensor, dim: int = 1) -> torch
|
||||
mask_sum = mask.sum(dim=dim)
|
||||
mean = tensor / (mask_sum + 1e-8)
|
||||
return mean
|
||||
|
||||
|
||||
def masked_sum(tensor: torch.Tensor, mask: torch.Tensor, dim: int = 1) -> torch.Tensor:
|
||||
"""
|
||||
Compute the masked sum of a tensor along a specified dimension.
|
||||
|
||||
Args:
|
||||
tensor (torch.Tensor): The input tensor.
|
||||
mask (torch.Tensor): The mask tensor with the same shape as the input tensor.
|
||||
dim (int, optional): The dimension along which to compute the sum. Default is 1.
|
||||
|
||||
Returns:
|
||||
torch.Tensor: The masked sum tensor.
|
||||
|
||||
"""
|
||||
tensor = tensor * mask
|
||||
return tensor.sum(dim=dim)
|
||||
|
||||
@ -128,7 +128,21 @@ def all_reduce_mean(tensor: torch.Tensor, plugin: Plugin = None) -> torch.Tensor
|
||||
return tensor
|
||||
|
||||
|
||||
def all_reduce_sum(tensor: torch.Tensor) -> torch.Tensor:
|
||||
# def all_reduce_sum(tensor: torch.Tensor, ) -> torch.Tensor:
|
||||
# """
|
||||
# Performs an all-reduce operation to sum the values of the given tensor across all processes.
|
||||
|
||||
# Args:
|
||||
# tensor (torch.Tensor): The input tensor to be reduced.
|
||||
|
||||
# Returns:
|
||||
# torch.Tensor: The reduced tensor with the sum of values across all processes.
|
||||
# """
|
||||
# dist.all_reduce(tensor=tensor, op=dist.ReduceOp.SUM)
|
||||
# return tensor
|
||||
|
||||
|
||||
def all_reduce_sum(tensor: torch.Tensor, plugin: Plugin = None) -> torch.Tensor:
|
||||
"""
|
||||
Performs an all-reduce operation to sum the values of the given tensor across all processes.
|
||||
|
||||
@ -138,5 +152,9 @@ def all_reduce_sum(tensor: torch.Tensor) -> torch.Tensor:
|
||||
Returns:
|
||||
torch.Tensor: The reduced tensor with the sum of values across all processes.
|
||||
"""
|
||||
dist.all_reduce(tensor=tensor, op=dist.ReduceOp.SUM)
|
||||
# All reduce sum across DP group
|
||||
if plugin is not None:
|
||||
dist.all_reduce(tensor=tensor, op=dist.ReduceOp.SUM, group=plugin.dp_group)
|
||||
else:
|
||||
dist.all_reduce(tensor=tensor, op=dist.ReduceOp.SUM)
|
||||
return tensor
|
||||
|
||||
@ -60,8 +60,8 @@ if __name__ == "__main__":
|
||||
ray.init(address="local", namespace="ray-example")
|
||||
|
||||
inference_model_config = dict(path=args.model)
|
||||
train_model_config = dict(path=args.model, use_flash_attention_2=True, use_cache=False)
|
||||
generate_config = dict(top_k=50, top_p=0.75, temperature=0.9)
|
||||
train_model_config = dict(path=args.model, use_flash_attention_2=False, use_cache=False)
|
||||
generate_config = dict(top_k=-1, top_p=1.0, temperature=1.0)
|
||||
|
||||
if args.backend == "transformers":
|
||||
inference_model_config.update(
|
||||
@ -102,6 +102,29 @@ if __name__ == "__main__":
|
||||
)
|
||||
)
|
||||
|
||||
# Default Settings
|
||||
# grpo_config = {
|
||||
# "filter_range": [0.05, 9.0],
|
||||
# "lr": 1e-6,
|
||||
# "train_microbatch_size": train_microbatch_size,
|
||||
# }
|
||||
|
||||
# DAPO variant settings
|
||||
grpo_config = {
|
||||
"filter_range": [0.05, 9.0],
|
||||
"lr": 1e-6,
|
||||
"train_microbatch_size": args.train_microbatch_size,
|
||||
"clip_eps_low": 0.2,
|
||||
"clip_eps_high": 0.28,
|
||||
"skip_threshold": 20.0,
|
||||
"beta": 0.0, # no KL penalty
|
||||
"loss_variation": "token_level",
|
||||
"soft_over_length_punishment": True,
|
||||
"max_length": 1024 * 2,
|
||||
"cache_length": 256,
|
||||
"filter_truncated_response": True,
|
||||
}
|
||||
|
||||
launch_distributed(
|
||||
num_producers=args.num_inferencer,
|
||||
num_proc_per_producer=1,
|
||||
@ -118,14 +141,17 @@ if __name__ == "__main__":
|
||||
generate_config=generate_config,
|
||||
num_generations=args.num_generations,
|
||||
train_model_config=train_model_config,
|
||||
# plugin_config={}, # for zero
|
||||
grpo_config=grpo_config,
|
||||
plugin_config={
|
||||
"pp_size": 2,
|
||||
"tp_size": 2,
|
||||
"microbatch_size": args.train_microbatch_size // 2,
|
||||
"zero_stage": 0,
|
||||
"max_norm": 1.0,
|
||||
}, # for pp
|
||||
"zero_stage": 2,
|
||||
}, # for zero
|
||||
# plugin_config={
|
||||
# "pp_size": 2,
|
||||
# "tp_size": 2,
|
||||
# "microbatch_size": args.train_microbatch_size // 2,
|
||||
# "zero_stage": 0,
|
||||
# "max_norm": 1.0,
|
||||
# }, # for pp
|
||||
inference_backend=args.backend,
|
||||
master_addr="localhost",
|
||||
master_port=29506,
|
||||
|
||||
Loading…
Reference in New Issue
Block a user