[BREAKING][vllm, fsdp] feat: add Rollout-Training Mismatch Fix -- Truncated importance sampling

recipe/dapo/run_dapo_qwen2.5_32b_tis.sh

@@ -0,0 +1,143 @@
+#!/usr/bin/env bash
+set -xeuo pipefail
+
+project_name='DAPO'
+exp_name='DAPO-Qwen2.5-32B-TIS'  # Truncated Importance Sampling (TIS) -> https://fengyao.notion.site/off-policy-rl
+
+adv_estimator=grpo
+
+use_kl_in_reward=False
+kl_coef=0.0
+use_kl_loss=False
+kl_loss_coef=0.0
+
+clip_ratio_low=0.2
+clip_ratio_high=0.28
+
+max_prompt_length=$((1024 * 2))
+max_response_length=$((1024 * 20))
+enable_overlong_buffer=True
+overlong_buffer_len=$((1024 * 4))
+overlong_penalty_factor=1.0
+
+loss_agg_mode="token-mean"
+
+enable_filter_groups=True
+filter_groups_metric=acc
+max_num_gen_batches=10
+train_prompt_bsz=512
+gen_prompt_bsz=$((train_prompt_bsz * 3))
+n_resp_per_prompt=16
+train_prompt_mini_bsz=32
+
+# Ray
+RAY_ADDRESS=${RAY_ADDRESS:-"http://localhost:8265"}
+WORKING_DIR=${WORKING_DIR:-"${PWD}"}
+RUNTIME_ENV=${RUNTIME_ENV:-"${WORKING_DIR}/verl/trainer/runtime_env.yaml"}
+NNODES=${NNODES:-16}
+# Paths
+RAY_DATA_HOME=${RAY_DATA_HOME:-"${HOME}/verl"}
+MODEL_PATH=${MODEL_PATH:-"${RAY_DATA_HOME}/models/Qwen2.5-32B"}
+CKPTS_DIR=${CKPTS_DIR:-"${RAY_DATA_HOME}/ckpts/${project_name}/${exp_name}"}
+TRAIN_FILE=${TRAIN_FILE:-"${RAY_DATA_HOME}/data/dapo-math-17k.parquet"}
+TEST_FILE=${TEST_FILE:-"${RAY_DATA_HOME}/data/aime-2024.parquet"}
+
+# Algorithm
+temperature=1.0
+top_p=1.0
+top_k=-1 # 0 for HF rollout, -1 for vLLM rollout
+val_top_p=0.7
+
+# Performance Related Parameter
+sp_size=8
+use_dynamic_bsz=True
+actor_ppo_max_token_len=$((max_prompt_length + max_response_length))
+infer_ppo_max_token_len=$((max_prompt_length + max_response_length))
+offload=True
+gen_tp=4
+
+
+# Truncated Importance Sampling (TIS) -> https://fengyao.notion.site/off-policy-rl
+
+# Please note that server mode(agent loop) hasn't return rollout_log_probs for now.
+# so currently, server mode is not supported for TIS.
+
+# To turn on TIS, you need to set the following parameters:
+# 1. rollout.calculate_log_probs=True
+# 2. rollout.imp_ratio_cap > 0 (the value can be tuned)
+
+ray job submit --no-wait --runtime-env="${RUNTIME_ENV}" \
+    --working-dir "${WORKING_DIR}" \
+    -- python3 -m recipe.dapo.main_dapo \
+    data.train_files="${TRAIN_FILE}" \
+    data.val_files="${TEST_FILE}" \
+    data.prompt_key=prompt \
+    data.truncation='left' \
+    data.max_prompt_length=${max_prompt_length} \
+    data.max_response_length=${max_response_length} \
+    data.gen_batch_size=${gen_prompt_bsz} \
+    data.train_batch_size=${train_prompt_bsz} \
+    actor_rollout_ref.rollout.n=${n_resp_per_prompt} \
+    algorithm.adv_estimator=${adv_estimator} \
+    algorithm.use_kl_in_reward=${use_kl_in_reward} \
+    algorithm.kl_ctrl.kl_coef=${kl_coef} \
+    actor_rollout_ref.actor.use_kl_loss=${use_kl_loss} \
+    actor_rollout_ref.actor.kl_loss_coef=${kl_loss_coef} \
+    actor_rollout_ref.actor.clip_ratio_low=${clip_ratio_low} \
+    actor_rollout_ref.actor.clip_ratio_high=${clip_ratio_high} \
+    actor_rollout_ref.actor.clip_ratio_c=10.0 \
+    algorithm.filter_groups.enable=${enable_filter_groups} \
+    algorithm.filter_groups.max_num_gen_batches=${max_num_gen_batches} \
+    algorithm.filter_groups.metric=${filter_groups_metric} \
+    actor_rollout_ref.model.use_remove_padding=True \
+    actor_rollout_ref.actor.use_dynamic_bsz=${use_dynamic_bsz} \
+    actor_rollout_ref.ref.log_prob_use_dynamic_bsz=${use_dynamic_bsz} \
+    actor_rollout_ref.rollout.log_prob_use_dynamic_bsz=${use_dynamic_bsz} \
+    actor_rollout_ref.actor.ppo_max_token_len_per_gpu=${actor_ppo_max_token_len} \
+    actor_rollout_ref.ref.log_prob_max_token_len_per_gpu=${infer_ppo_max_token_len} \
+    actor_rollout_ref.rollout.log_prob_max_token_len_per_gpu=${infer_ppo_max_token_len} \
+    actor_rollout_ref.model.path="${MODEL_PATH}" \
+    actor_rollout_ref.model.enable_gradient_checkpointing=True \
+    actor_rollout_ref.actor.optim.lr=1e-6 \
+    actor_rollout_ref.actor.optim.lr_warmup_steps=10 \
+    actor_rollout_ref.actor.optim.weight_decay=0.1 \
+    actor_rollout_ref.actor.ppo_mini_batch_size=${train_prompt_mini_bsz} \
+    actor_rollout_ref.actor.fsdp_config.param_offload=${offload} \
+    actor_rollout_ref.actor.fsdp_config.optimizer_offload=${offload} \
+    actor_rollout_ref.actor.entropy_coeff=0 \
+    actor_rollout_ref.actor.grad_clip=1.0 \
+    actor_rollout_ref.actor.loss_agg_mode=${loss_agg_mode} \
+    actor_rollout_ref.actor.ulysses_sequence_parallel_size=${sp_size} \
+    actor_rollout_ref.rollout.gpu_memory_utilization=0.80 \
+    actor_rollout_ref.rollout.tensor_model_parallel_size=${gen_tp} \
+    actor_rollout_ref.rollout.enable_chunked_prefill=True \
+    actor_rollout_ref.rollout.max_num_batched_tokens=$((max_prompt_length + max_response_length)) \
+    actor_rollout_ref.rollout.temperature=${temperature} \
+    actor_rollout_ref.rollout.top_p=${top_p} \
+    actor_rollout_ref.rollout.top_k="${top_k}" \
+    actor_rollout_ref.rollout.val_kwargs.temperature=${temperature} \
+    actor_rollout_ref.rollout.val_kwargs.top_p=${val_top_p} \
+    actor_rollout_ref.rollout.val_kwargs.top_k=${top_k} \
+    actor_rollout_ref.rollout.val_kwargs.do_sample=True \
+    actor_rollout_ref.rollout.val_kwargs.n=1 \
+    actor_rollout_ref.rollout.name=vllm \
+    actor_rollout_ref.ref.fsdp_config.param_offload=${offload} \
+    actor_rollout_ref.ref.ulysses_sequence_parallel_size=${sp_size} \
+    actor_rollout_ref.actor.fsdp_config.fsdp_size=-1 \
+    reward_model.reward_manager=dapo \
+    reward_model.overlong_buffer.enable=${enable_overlong_buffer} \
+    reward_model.overlong_buffer.len=${overlong_buffer_len} \
+    reward_model.overlong_buffer.penalty_factor=${overlong_penalty_factor} \
+    trainer.logger='["console","wandb"]' \
+    trainer.project_name="${project_name}" \
+    trainer.experiment_name="${exp_name}" \
+    trainer.n_gpus_per_node=8 \
+    trainer.nnodes="${NNODES}" \
+    trainer.val_before_train=True \
+    trainer.test_freq=5 \
+    trainer.save_freq=5 \
+    trainer.total_epochs=1 \
+    trainer.default_local_dir="${CKPTS_DIR}" \
+    trainer.resume_mode=auto \
+    rollout.calculate_log_probs=True \
+    +rollout.imp_ratio_cap=2.0    # remember to turn on calculate_log_probs=True first, and set imp_ratio_cap > 0. The value can be tuned.

verl/trainer/config/actor/actor.yaml

@@ -71,6 +71,9 @@ loss_agg_mode: token-mean
 # Entropy regularization coefficient in PPO loss
 entropy_coeff: 0
 
+# whether to apply the truncated Importance Sampling (-1 for no importance sampling)
+imp_ratio_cap: -1
+
 # Whether to use KL loss instead of KL reward penalty. True for GRPO
 use_kl_loss: false
 

verl/trainer/config/rollout/rollout.yaml

@@ -159,7 +159,8 @@ multi_turn:
   format: hermes
 
 # support logging rollout prob for debugging purpose
-calculate_log_probs: False
+# "Truncated importance sampling" requires rollout log probs, set to True when turning on Truncated importance sampling
+calculate_log_probs: False 
 
 # [Experimental] agent loop based rollout configs
 agent:

verl/trainer/ppo/core_algos.py

@@ -747,6 +747,8 @@ def compute_policy_loss(
     cliprange_high=None,
     clip_ratio_c=3.0,
     loss_agg_mode: str = "token-mean",
+    rollout_log_probs=None,
+    imp_ratio_cap=-1,
 ):
     """
     Compute the clipped policy objective and related metrics for PPO.
@@ -807,6 +809,13 @@ def compute_policy_loss(
     )
 
     pg_losses = torch.where(advantages < 0, clip_pg_losses2, clip_pg_losses1)
+
+    if imp_ratio_cap > 0 and rollout_log_probs is not None:
+        # Apply truncated importance sampling -> https://fengyao.notion.site/off-policy-rl
+        imp_ratio = torch.exp(old_log_prob - rollout_log_probs)
+        imp_ratio = torch.clamp(imp_ratio, max=imp_ratio_cap)
+        pg_losses = pg_losses * imp_ratio
+
     pg_loss = agg_loss(loss_mat=pg_losses, loss_mask=response_mask, loss_agg_mode=loss_agg_mode)
 
     return pg_loss, pg_clipfrac, ppo_kl, pg_clipfrac_lower
@@ -820,6 +829,8 @@ def compute_policy_loss_vanilla(
     response_mask: torch.Tensor,
     loss_agg_mode: str = "token-mean",
     config: Optional[DictConfig | AlgoConfig] = None,
+    rollout_log_probs=None,
+    imp_ratio_cap=-1,
 ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
     """
     Compute the clipped policy objective and related metrics for PPO.
@@ -884,6 +895,13 @@ def compute_policy_loss_vanilla(
     )
 
     pg_losses = torch.where(advantages < 0, clip_pg_losses2, clip_pg_losses1)
+
+    if imp_ratio_cap > 0 and rollout_log_probs is not None:
+        # Apply truncated importance sampling -> https://fengyao.notion.site/off-policy-rl
+        imp_ratio = torch.exp(old_log_prob - rollout_log_probs)
+        imp_ratio = torch.clamp(imp_ratio, max=imp_ratio_cap)
+        pg_losses = pg_losses * imp_ratio
+
     pg_loss = agg_loss(loss_mat=pg_losses, loss_mask=response_mask, loss_agg_mode=loss_agg_mode)
 
     return pg_loss, pg_clipfrac, ppo_kl, pg_clipfrac_lower
@@ -1270,6 +1288,19 @@ def compute_value_loss(
 
 
 def kl_penalty(logprob: torch.FloatTensor, ref_logprob: torch.FloatTensor, kl_penalty) -> torch.FloatTensor:
+    """
+    The expectation of k1 and k3 estimator is the expectaed value of KL, 
+    but the expected gradient of k1 and k3 estimator is not the expectaed gradient of KL!
+    On the other hand k2 estimator gives right gradient estimator, 
+    so we use a straight through trick here
+    """
+    forward_score = kl_penalty_forward(logprob, ref_logprob, kl_penalty)
+    backward_score = 0.5 * (logprob - ref_logprob).square()
+
+    return backward_score - backward_score.detach() + forward_score.detach()
+
+
+def kl_penalty_forward(logprob: torch.FloatTensor, ref_logprob: torch.FloatTensor, kl_penalty) -> torch.FloatTensor:
     """Compute KL divergence given logprob and ref_logprob.
     Copied from https://github.com/huggingface/trl/blob/main/trl/trainer/ppo_trainer.py#L1104
     See more description in http://joschu.net/blog/kl-approx.html

verl/workers/actor/dp_actor.py

@@ -376,6 +376,8 @@ def update_policy(self, data: DataProto):
         ]
         if self.config.use_kl_loss:
             select_keys.append("ref_log_prob")
+        if self.config.imp_ratio_cap > 0 and "rollout_log_probs" in data.batch.keys():
+            select_keys.append("rollout_log_probs")
 
         has_multi_modal_inputs = "multi_modal_inputs" in data.non_tensor_batch.keys()
         non_tensor_select_keys = ["multi_modal_inputs"] if has_multi_modal_inputs else []
@@ -405,6 +407,7 @@ def update_policy(self, data: DataProto):
                     model_inputs = {**micro_batch.batch, **micro_batch.non_tensor_batch}
                     response_mask = model_inputs["response_mask"]
                     old_log_prob = model_inputs["old_log_probs"]
+                    rollout_log_probs = model_inputs["rollout_log_probs"] if self.config.imp_ratio_cap > 0 else None
                     advantages = model_inputs["advantages"]
 
                     entropy_coeff = self.config.entropy_coeff
@@ -435,6 +438,8 @@ def update_policy(self, data: DataProto):
                         response_mask=response_mask,
                         loss_agg_mode=loss_agg_mode,
                         config=self.config,
+                        rollout_log_probs=rollout_log_probs,
+                        imp_ratio_cap=self.config.imp_ratio_cap,
                     )
 
                     if entropy_coeff != 0: