[algo, perf] feat: Vectorize RLOO Advantage Estimator - 20x Speedup

docs/examples/config.rst

@@ -501,7 +501,7 @@ Algorithm
 
 - ``gamma``: discount factor
 - ``lam``: Trade-off between bias and variance in the GAE estimator
-- ``adv_estimator``: Support ``gae``, ``grpo``, ``reinforce_plus_plus``, ``reinforce_plus_plus_baseline``, ``rloo``
+- ``adv_estimator``: Support ``gae``, ``grpo``, ``reinforce_plus_plus``, ``reinforce_plus_plus_baseline``, ``rloo``, ``rloo_vectorized``
 - ``use_kl_in_reward``: Whether to enable in-reward kl penalty. Default is False.
 - ``kl_penalty``: Support ``kl``, ``abs``, ``mse``, ``low_var_kl`` and ``full``. How to
   calculate the kl divergence between actor and reference policy. For

examples/ppo_trainer/README.md

@@ -43,7 +43,7 @@ Most critic configs are similar to those of actors. Note that the critic model i
 
 - `algorithm.lam`: The lambda term that trades off between bias and variance in the GAE estimator
 
-- `algorithm.adv_estimator`: Support gae, grpo, reinforce_plus_plus, reinforce_plus_plus_baseline, rloo
+- `algorithm.adv_estimator`: Support gae, grpo, reinforce_plus_plus, reinforce_plus_plus_baseline, rloo, rloo_vectorized
 
 ## Advanced Extensions
 

tests/trainer/ppo/test_core_algos_on_cpu.py

@@ -15,11 +15,18 @@
 import random
 import unittest
 
+import numpy as np
 import pytest
 import torch
 
 import verl.trainer.ppo.core_algos
-from verl.trainer.ppo.core_algos import compute_gae_advantage_return, get_adv_estimator_fn, register_adv_est
+from verl.trainer.ppo.core_algos import (
+    compute_gae_advantage_return,
+    compute_rloo_outcome_advantage,
+    compute_rloo_vectorized_outcome_advantage,
+    get_adv_estimator_fn,
+    register_adv_est,
+)
 
 
 def mock_test_fn():
@@ -188,5 +195,67 @@ def test_multi_turn_compute_gae_advantage_return():
     print(f" [CORRECT] \n\n{adv1=}, \n\n{ret1=}")
 
 
+def _make_group_index(batch_size: int, num_groups: int) -> np.ndarray:
+    """Create a numpy index array ensuring each group has at least 2 samples."""
+    assert num_groups * 2 <= batch_size, "batch_size must allow >=2 samples per group"
+    counts: list[int] = [2] * num_groups
+    remaining = batch_size - 2 * num_groups
+    for _ in range(remaining):
+        counts[random.randrange(num_groups)] += 1
+    index = []
+    for gid, c in enumerate(counts):
+        index.extend([gid] * c)
+    random.shuffle(index)
+    return np.asarray(index, dtype=np.int64)
+
+
+def _rand_mask(batch_size: int, seq_len: int) -> torch.Tensor:
+    mask = torch.randint(0, 2, (batch_size, seq_len), dtype=torch.int64).float()
+    rows_without_one = (mask.sum(dim=-1) == 0).nonzero(as_tuple=True)[0]
+    if len(rows_without_one) > 0:
+        mask[rows_without_one, -1] = 1.0
+    return mask
+
+
+@pytest.mark.parametrize(
+    "batch_size,seq_len,num_groups,seed",
+    [
+        (64, 128, 5, 0),
+        (128, 256, 8, 1),
+        (512, 512, 10, 2),
+    ],
+)
+def test_rloo_and_vectorized_equivalence(batch_size: int, seq_len: int, num_groups: int, seed: int):
+    torch.manual_seed(seed)
+    random.seed(seed)
+    np.random.seed(seed)
+    index = _make_group_index(batch_size, num_groups)
+    response_mask = _rand_mask(batch_size, seq_len)
+    base_rewards = torch.randn(batch_size, seq_len, dtype=torch.float32)
+    token_level_rewards = base_rewards * response_mask
+    adv1, ret1 = compute_rloo_outcome_advantage(
+        token_level_rewards=token_level_rewards,
+        response_mask=response_mask,
+        index=index,
+    )
+    adv2, ret2 = compute_rloo_vectorized_outcome_advantage(
+        token_level_rewards=token_level_rewards,
+        response_mask=response_mask,
+        index=index,
+    )
+    # Print concise diagnostics for visibility during test runs
+    adv_max_diff = (adv1 - adv2).abs().max().item()
+    ret_max_diff = (ret1 - ret2).abs().max().item()
+    total_mask_tokens = int(response_mask.sum().item())
+    print(
+        f"[RLOO] seed={seed} groups={num_groups} shape={adv1.shape} "
+        f"mask_tokens={total_mask_tokens} adv_max_diff={adv_max_diff:.3e} ret_max_diff={ret_max_diff:.3e}"
+    )
+    assert adv1.shape == adv2.shape == (batch_size, seq_len)
+    assert ret1.shape == ret2.shape == (batch_size, seq_len)
+    assert torch.allclose(adv1, adv2, rtol=1e-5, atol=1e-6)
+    assert torch.allclose(ret1, ret2, rtol=1e-5, atol=1e-6)
+
+
 if __name__ == "__main__":
     unittest.main()

verl/trainer/ppo/core_algos.py

@@ -102,6 +102,7 @@ class AdvantageEstimator(str, Enum):
     OPO = "opo"
     GRPO_PASSK = "grpo_passk"
     GPG = "gpg"
+    RLOO_VECTORIZED = "rloo_vectorized"
 
 
 ADV_ESTIMATOR_REGISTRY: dict[str, Any] = {}
@@ -685,6 +686,44 @@ def compute_gpg_outcome_advantage(
     return scores, scores
 
 
+@register_adv_est(AdvantageEstimator.RLOO_VECTORIZED)  # or simply: @register_adv_est("rloo_vectorized")
+def compute_rloo_vectorized_outcome_advantage(
+    token_level_rewards: torch.Tensor,
+    response_mask: torch.Tensor,
+    index: np.ndarray,
+    epsilon: float = 1e-6,
+    config: Optional[AlgoConfig] = None,
+    **kwargs,
+) -> tuple[torch.Tensor, torch.Tensor]:
+    """
+    Compute advantage for RLOO based on https://arxiv.org/abs/2402.14740
+
+    Args:
+        token_level_rewards: `(torch.Tensor)`
+            shape: (bs, response_length)
+        response_mask: `(torch.Tensor)`
+            shape: (bs, response_length)
+        config: (AlgoConfig) algorithm config
+
+    Returns:
+        advantages: `(torch.Tensor)`
+            shape: (bs, response_length)
+        Returns: `(torch.Tensor)`
+            shape: (bs, response_length)
+    """
+    scores = token_level_rewards.sum(dim=-1)
+
+    with torch.no_grad():
+        inv = torch.from_numpy(np.unique(index, return_inverse=True)[1]).to(scores.device)
+
+        c = torch.bincount(inv)[inv].to(scores.dtype)
+        adv = ((c * scores - torch.bincount(inv, weights=scores)[inv]) / (c - 1).clamp_min(1)) * (c > 1)
+
+        adv = adv.unsqueeze(-1) * response_mask
+
+    return adv, adv
+
+
 def compute_rewards(token_level_scores, old_log_prob, ref_log_prob, kl_ratio):
     """Compute token-level rewards with KL penalty.