ViperEkura
350e4a1849
- register online_ppo train type backed by PPOStrategy: token-level clipped surrogate over GAE advantages plus masked value regression against rollout-pinned returns, with explained-variance metrics - fold the reference-KL penalty (k3 estimator) into per-token rewards before GAE and pin advantages/returns on RolloutResult so replayed gradient steps optimize fixed targets - add self-contained ValueModel critic with a zero-initialized value head and backbone warm-started from policy weights; AutoRegressiveLM stays untouched and trunk parity is pinned by tests - step the critic's own optimizer outside the policy-version lock with the same max_grad_norm clipping as the policy - persist critic state as value_model.pt/value_optimizer.pt checkpoint extras; resume restores it, fails loudly when missing, and the train.sh completeness check requires the extras for online_ppo configs - extract shared rollout sequence/logprob helpers from GRPO (behavior unchanged) and add ppo_gamma/ppo_gae_lambda/ppo_vf_coef CLI options
574 lines
22 KiB
Python
574 lines
22 KiB
Python
"""Online rollout runner for RL training.
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Provides:
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- :class:`RawRollout` — generation output container (no reward yet)
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- :class:`RolloutResult` — a :class:`RawRollout` with rewards attached
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- :class:`BaseRewardModel` — pluggable reward interface
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- :class:`RolloutGenerator` — KV-cache-backed generation of grouped
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responses + decoding (no reward); delegates the generation loop to
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:class:`~astrai.inference.scheduler.InferenceScheduler.run_batch`
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so rollout and the production inference server share one code path
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- :class:`RolloutRunner` — orchestrates generation + scoring with a
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step-driven cache; its ``__call__`` returns ``(RolloutResult, is_fresh)``
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so callers do not need to rely on object identity to detect refreshes.
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"""
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import threading
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from abc import ABC, abstractmethod
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from dataclasses import dataclass, field
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from typing import Callable, Dict, List, Optional, Tuple, TypeVar
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import torch
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from torch import Tensor
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from astrai.inference.scheduler import InferenceScheduler
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from astrai.inference.task import GenerationResult
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@dataclass(kw_only=True)
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class RawRollout:
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"""Generation output before reward scoring.
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Produced by :class:`RolloutGenerator`; consumed by :class:`RolloutRunner`
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to assemble a :class:`RolloutResult` once rewards are attached.
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Fields are designed to cover all common RL algorithms:
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GRPO, PPO, Online DPO, Rejection Sampling, etc.
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Fields:
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prompts: Tokenized prompts, shape ``[B, P_len]``.
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prompt_mask: Boolean mask for real prompt tokens, shape ``[B, P_len]``.
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responses: Generated response token IDs, shape ``[B, G, R_max]``.
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response_mask: Boolean mask for real (non-pad) response tokens,
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shape ``[B, G, R_max]``.
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logprobs_old: Per-token log-probs under the behaviour policy,
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shape ``[B, G, R_max]``.
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prompt_texts: Decoded prompt strings (for reward models that
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need text).
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response_texts: Decoded response strings, shape ``[B, G]``
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(for reward models).
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"""
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prompts: Tensor
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prompt_mask: Tensor
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responses: Tensor
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response_mask: Tensor
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logprobs_old: Tensor
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policy_version: int = 0
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prompt_texts: List[str] = field(default_factory=list)
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response_texts: List[List[str]] = field(default_factory=list)
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@dataclass(kw_only=True)
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class RolloutResult(RawRollout):
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"""A :class:`RawRollout` with reward scoring attached.
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Produced by :class:`RolloutRunner` once the :class:`BaseRewardModel`
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has scored the decoded responses.
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Fields:
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rewards: Reward per response, shape ``[B, G]``.
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advantages: Optional GAE advantages ``[B, G, R_max]`` pinned at
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rollout time by actor-critic strategies (PPO). ``None`` until
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a strategy computes them.
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returns: Optional GAE value targets ``[B, G, R_max]`` matching
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``advantages``.
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"""
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rewards: Tensor
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advantages: Optional[Tensor] = None
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returns: Optional[Tensor] = None
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class BaseRewardModel(ABC):
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"""Pluggable reward model interface.
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Subclasses should implement ``score()`` to return a ``[B, G]`` float
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tensor of rewards. Implementations can be:
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* A loaded reward model (e.g. ArmoRM, Skywork-Reward)
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* An external API call
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* A rule-based function (format, length, keyword matching)
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"""
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@abstractmethod
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def score(self, prompts: List[str], responses: List[List[str]]) -> Tensor:
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"""Score each generated response.
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Args:
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prompts: Raw prompt strings, length ``B``.
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responses: Generated response strings, shape ``[B, G]``.
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Returns:
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Float tensor of shape ``[B, G]``.
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"""
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...
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_PAD = 0
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T = TypeVar("T")
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class RolloutVersionError(RuntimeError):
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"""A rollout cannot be attributed to an acceptable policy version."""
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class RolloutGenerator:
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"""Pure generation + decoding for a group of responses per prompt.
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Delegates the prefill/decode loop to
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:meth:`~astrai.inference.scheduler.InferenceScheduler.run_batch`,
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which uses a real KV cache (no O(n²) recompute). Has no dependency
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on any reward model; can be reused in isolation for offline
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generation, qualitative sampling, or eval pipelines.
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"""
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def __init__(
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self,
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scheduler: InferenceScheduler,
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tokenizer,
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max_tokens: int = 1024,
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group_size: int = 8,
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temperature: float = 1.0,
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top_k: int = 0,
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top_p: float = 1.0,
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frequency_penalty: float = 0.0,
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rep_window: int = 64,
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):
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self.scheduler = scheduler
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self.tokenizer = tokenizer
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self.max_tokens = max_tokens
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self.group_size = group_size
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self.temperature = temperature
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self.top_k = top_k
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self.top_p = top_p
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self.frequency_penalty = frequency_penalty
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self.rep_window = rep_window
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self._weight_lock = threading.RLock()
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@property
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def policy_version(self) -> int:
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return self.scheduler.policy_version
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def update_weights(self, policy_version: int) -> int:
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"""Acknowledge shared-model weights and invalidate older scheduler KV."""
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with self._weight_lock:
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return self.scheduler.update_weights(policy_version)
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def apply_weight_update(
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self, policy_version: Optional[int], update: Callable[[], T]
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) -> T:
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"""Apply a shared-model mutation at an atomic generation boundary.
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``policy_version=None`` lets the scheduler derive ``live + 1`` under
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the policy lock, closing the read-compute-write race for callers
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that only need to advance by one.
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"""
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with self._weight_lock:
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return self.scheduler.apply_weight_update(policy_version, update)
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def with_policy_snapshot(self, inspect: Callable[[int], T]) -> T:
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"""Inspect a version stable against generator and scheduler updates."""
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if not callable(inspect):
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raise TypeError("inspect must be callable")
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with self._weight_lock:
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return self.scheduler.with_policy_snapshot(inspect)
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@torch.no_grad()
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def generate(self, batch: Dict) -> RawRollout:
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"""Expand prompts by ``group_size`` and generate one response each.
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Accepted batch formats (per sample, repeated B times):
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- **messages**: ``{"messages": [{"role": "user", "content": "..."}, ...]}``
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- **instruction + input + output**: ``{"instruction": "...",
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"input": "...", "output": "..."}`` — mapped to ``system`` /
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``user`` / ``assistant`` messages; ``input`` and ``output``
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are optional and skipped when empty.
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Both are rendered through the tokenizer's chat template with
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``add_generation_prompt=True`` so rollout prompts match the
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format the policy was SFT-trained on.
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"""
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with self._weight_lock:
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def generate_snapshot(generation_version: int) -> RawRollout:
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model = self.scheduler._executor.model
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was_training = model.training
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model.eval()
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try:
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return self._generate_eval(batch, generation_version)
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finally:
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model.train(was_training)
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# Capture the version under the scheduler lock as well as the
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# generator lock. This also serializes callers that update the
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# scheduler directly instead of going through this wrapper.
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return self.scheduler.with_policy_snapshot(generate_snapshot)
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def _generate_eval(self, batch: Dict, generation_version: int) -> RawRollout:
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prompt_texts, flat_prompt_ids = self._prepare_prompts(batch)
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B = len(prompt_texts)
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G = self.group_size
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# Re-expand flat list to G copies per prompt for run_batch.
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expanded_prompt_ids: List[List[int]] = []
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for ids in flat_prompt_ids:
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expanded_prompt_ids.extend([list(ids)] * G)
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results = self.scheduler.run_batch(
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expanded_prompt_ids,
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max_tokens=self.max_tokens,
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temperature=self.temperature,
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top_k=self.top_k,
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top_p=self.top_p,
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frequency_penalty=self.frequency_penalty,
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rep_window=self.rep_window,
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return_logprobs=True,
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return_details=True,
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)
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if len(results) != B * G:
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raise RuntimeError(
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f"Rollout scheduler returned {len(results)} results, expected {B * G}"
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)
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for result in results:
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if not isinstance(result, GenerationResult):
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raise RuntimeError("Rollout scheduler returned an invalid result type")
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failures = [
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(index, result)
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for index, result in enumerate(results)
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if result.error_reason is not None
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or result.finish_reason in ("cancelled", "rejected")
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]
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if failures:
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reasons = ", ".join(
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f"request {index}: {result.error_reason or result.finish_reason}"
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for index, result in failures
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)
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raise RuntimeError(f"Rollout generation failed: {reasons}")
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for result in results:
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if len(result.token_ids) != len(result.logprobs):
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raise RuntimeError(
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"Rollout scheduler returned misaligned token IDs and logprobs"
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)
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# Pad successful structured results to a uniform response length.
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max_len = max((len(result.token_ids) for result in results), default=0)
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max_len = max(max_len, 1)
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device = self.scheduler.device
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P_len = max(len(ids) for ids in flat_prompt_ids)
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prompts_tensor = torch.zeros(B, P_len, dtype=torch.long, device=device)
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prompt_mask = torch.zeros(B, P_len, dtype=torch.bool, device=device)
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for i, ids in enumerate(flat_prompt_ids):
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prompts_tensor[i, -len(ids) :] = torch.tensor(
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ids, dtype=torch.long, device=device
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)
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prompt_mask[i, -len(ids) :] = True
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responses = torch.full((B, G, max_len), _PAD, dtype=torch.long, device=device)
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response_mask = torch.zeros((B, G, max_len), dtype=torch.bool, device=device)
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logprobs_old = torch.zeros((B, G, max_len), dtype=torch.float, device=device)
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flat_idx = 0
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response_texts: List[List[str]] = [[] for _ in range(B)]
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for i in range(B):
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for g in range(G):
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result = results[flat_idx]
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token_ids, lps = result.token_ids, result.logprobs
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flat_idx += 1
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n = len(token_ids)
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if n:
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responses[i, g, :n] = torch.tensor(
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token_ids, dtype=torch.long, device=device
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)
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response_mask[i, g, :n] = True
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logprobs_old[i, g, :n] = torch.tensor(
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lps, dtype=torch.float, device=device
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)
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response_texts[i].append(
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self.tokenizer.decode(token_ids, skip_special_tokens=True)
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)
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return RawRollout(
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prompts=prompts_tensor,
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prompt_mask=prompt_mask,
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responses=responses,
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response_mask=response_mask,
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logprobs_old=logprobs_old,
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policy_version=generation_version,
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prompt_texts=prompt_texts,
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response_texts=response_texts,
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)
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def _prepare_prompts(self, batch: Dict) -> Tuple[List[str], List[List[int]]]:
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"""Render batch prompts to ``(texts, token_id_lists)``.
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Returns two parallel lists of length B (number of prompts in
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the batch). Dispatches by batch keys:
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- ``"messages"``: treated as a pre-built message list per sample.
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- ``"instruction"`` (optionally ``"input"`` and ``"output"``): mapped
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to ``system`` / ``user`` / ``assistant`` messages respectively.
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Both paths go through the tokenizer's chat template with
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``add_generation_prompt=True``.
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"""
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if "messages" in batch:
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messages_list = batch["messages"]
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elif "instruction" in batch:
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instructions = batch["instruction"]
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B = len(instructions)
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inputs = batch.get("input") or [""] * B
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outputs = batch.get("output") or [""] * B
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messages_list = [
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self._instruction_to_messages(i, u, o)
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for i, u, o in zip(instructions, inputs, outputs)
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]
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else:
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raise ValueError(
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"Rollout batch must contain either 'messages' or "
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"'instruction' (optionally 'input'/'output'); got keys: "
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f"{list(batch.keys())}"
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)
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try:
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prompt_texts = self.tokenizer.apply_chat_template(
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messages_list, tokenize=False, add_generation_prompt=True
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)
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if (
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not isinstance(prompt_texts, list)
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or len(prompt_texts) != len(messages_list)
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or not all(isinstance(text, str) for text in prompt_texts)
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):
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raise TypeError("Tokenizer does not support batched chat templates")
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flat_prompt_ids = self.tokenizer.encode(prompt_texts)
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if len(flat_prompt_ids) != len(messages_list) or not all(
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isinstance(ids, list) for ids in flat_prompt_ids
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):
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raise TypeError("Tokenizer does not support batched encoding")
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except (TypeError, IndexError, KeyError):
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# Keep compatibility with lightweight tokenizer adapters that only
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# implement the single-conversation template API.
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prompt_texts = []
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flat_prompt_ids = []
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for messages in messages_list:
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text = self.tokenizer.apply_chat_template(
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messages, tokenize=False, add_generation_prompt=True
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)
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ids = self.tokenizer.apply_chat_template(
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messages, tokenize=True, add_generation_prompt=True
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)
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prompt_texts.append(text)
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flat_prompt_ids.append(list(ids))
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return prompt_texts, flat_prompt_ids
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@staticmethod
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def _instruction_to_messages(
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instruction: str, inp: str = "", output: str = ""
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) -> List[Dict[str, str]]:
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"""Map instruction/input/output to chat messages.
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Role mapping follows the convention used throughout the
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preprocessing pipeline: ``instruction`` → system, ``input`` →
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user, ``output`` → assistant. Empty fields are skipped so a
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bare instruction produces a ``[system]`` list and the chat
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template's ``add_generation_prompt`` adds the assistant header
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for sampling.
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"""
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messages: List[Dict[str, str]] = []
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if instruction:
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messages.append({"role": "system", "content": instruction})
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if inp:
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messages.append({"role": "user", "content": inp})
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if output:
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messages.append({"role": "assistant", "content": output})
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return messages
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class RolloutRunner:
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"""Produces :class:`RolloutResult` from a prompt batch.
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Composes a :class:`RolloutGenerator` (generation + decoding) with a
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:class:`BaseRewardModel` (scoring). Maintains an internal cache so
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the same batch prompt can be replayed for multiple gradient steps.
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A new rollout is triggered every ``rollout_interval`` calls to
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:meth:`step` (or after :meth:`clear_cache`).
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The ``__call__`` contract returns a ``(RolloutResult, is_fresh)``
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tuple — callers must use the boolean to detect a refreshed rollout
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rather than relying on object identity.
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Usage::
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generator = RolloutGenerator(policy, tokenizer, pipeline, ...)
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runner = RolloutRunner(generator, reward_model, rollout_interval=512)
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result, is_fresh = runner(prompt_batch)
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if is_fresh:
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... # e.g. sync behaviour policy
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"""
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def __init__(
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self,
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generator: RolloutGenerator,
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reward_model: BaseRewardModel,
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rollout_interval: int = 512,
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max_policy_lag: Optional[int] = None,
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):
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if rollout_interval <= 0:
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raise ValueError("rollout_interval must be positive")
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if max_policy_lag is not None and max_policy_lag < 0:
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raise ValueError("max_policy_lag must be non-negative or None")
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self.generator = generator
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self.reward_model = reward_model
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self.rollout_interval = rollout_interval
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self.max_policy_lag = (
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rollout_interval - 1 if max_policy_lag is None else max_policy_lag
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)
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self._cache: Optional[RolloutResult] = None
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self._cache_key = None
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self._steps_since_rollout: int = 0
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@property
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def policy_version(self) -> int:
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return self.generator.policy_version
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def update_weights(self, policy_version: int) -> int:
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"""Publish the shared policy's new version to the rollout backend."""
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return self.generator.update_weights(policy_version)
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def apply_weight_update(
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self, policy_version: Optional[int], update: Callable[[], T]
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) -> T:
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"""Apply a model update and publish its version as one operation."""
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return self.generator.apply_weight_update(policy_version, update)
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def step(self):
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"""Advance the internal counter (call once per optimizer step)."""
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self._steps_since_rollout += 1
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def clear_cache(self):
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"""Force next call to re-run rollout."""
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self._cache = None
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self._cache_key = None
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@staticmethod
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def _batch_key(batch: Dict):
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"""Build a stable key for the prompt fields accepted by the generator."""
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def freeze(value):
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if isinstance(value, dict):
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return tuple(sorted((key, freeze(val)) for key, val in value.items()))
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if isinstance(value, (list, tuple)):
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return tuple(freeze(item) for item in value)
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return value
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fields = ("messages", "instruction", "input", "output")
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return tuple(
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(field, freeze(batch[field])) for field in fields if field in batch
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)
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def _score(self, raw: RawRollout) -> RolloutResult:
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rewards = self.reward_model.score(raw.prompt_texts, raw.response_texts)
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if not isinstance(rewards, Tensor):
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rewards = torch.as_tensor(rewards, dtype=torch.float32)
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expected_shape = raw.responses.shape[:2]
|
|
if rewards.shape != expected_shape:
|
|
raise ValueError(
|
|
f"Reward model returned shape {tuple(rewards.shape)}, "
|
|
f"expected {tuple(expected_shape)}"
|
|
)
|
|
if not torch.isfinite(rewards).all():
|
|
raise ValueError("Reward model returned non-finite values")
|
|
device = raw.prompts.device
|
|
return RolloutResult(
|
|
prompts=raw.prompts,
|
|
prompt_mask=raw.prompt_mask,
|
|
responses=raw.responses,
|
|
response_mask=raw.response_mask,
|
|
rewards=rewards.to(device=device),
|
|
logprobs_old=raw.logprobs_old,
|
|
policy_version=raw.policy_version,
|
|
prompt_texts=raw.prompt_texts,
|
|
response_texts=raw.response_texts,
|
|
)
|
|
|
|
def _validate_policy_version(
|
|
self, result: RawRollout, *, live_version: Optional[int] = None
|
|
) -> None:
|
|
version = result.policy_version
|
|
if isinstance(version, bool) or not isinstance(version, int) or version < 0:
|
|
raise RolloutVersionError(f"rollout has invalid policy version {version!r}")
|
|
if live_version is None:
|
|
live_version = self.policy_version
|
|
if version > live_version:
|
|
raise RolloutVersionError(
|
|
f"rollout has future policy version {version}; "
|
|
f"live policy version is {live_version}"
|
|
)
|
|
lag = live_version - version
|
|
if lag > self.max_policy_lag:
|
|
raise RolloutVersionError(
|
|
f"rollout policy lag {lag} exceeds max_policy_lag="
|
|
f"{self.max_policy_lag} (rollout={version}, live={live_version})"
|
|
)
|
|
|
|
def __call__(self, batch: Dict[str, Tensor]) -> Tuple[RolloutResult, bool]:
|
|
"""Return ``(cached or fresh) RolloutResult`` plus an ``is_fresh`` flag.
|
|
|
|
Triggers a new rollout when ``_steps_since_rollout >= rollout_interval``
|
|
or when the cache is empty. The reuse decision, its version
|
|
validation, and the returned object are all captured inside one
|
|
policy snapshot, so a concurrent commit, refresh, or cache clear
|
|
can never hand out an object the snapshot has already invalidated.
|
|
"""
|
|
cache_key = self._batch_key(batch)
|
|
|
|
def reuse(live_version: int) -> Optional[Tuple[RolloutResult, bool]]:
|
|
cached = self._cache
|
|
if (
|
|
cached is None
|
|
or self._cache_key != cache_key
|
|
or self._steps_since_rollout >= self.rollout_interval
|
|
):
|
|
return None
|
|
self._validate_policy_version(cached, live_version=live_version)
|
|
return cached, False
|
|
|
|
outcome = self.generator.with_policy_snapshot(reuse)
|
|
if outcome is not None:
|
|
return outcome
|
|
|
|
raw = self.generator.generate(batch)
|
|
self._validate_policy_version(raw)
|
|
scored = self._score(raw)
|
|
# Post-scoring check: reward scoring may call slow external services;
|
|
# surface an over-lag policy move before the commit critical section.
|
|
self._validate_policy_version(scored)
|
|
|
|
def commit(live_version: int) -> Tuple[RolloutResult, bool]:
|
|
self._validate_policy_version(scored, live_version=live_version)
|
|
self._cache = scored
|
|
self._cache_key = cache_key
|
|
self._steps_since_rollout = 0
|
|
return scored, True
|
|
|
|
# A weight update cannot land between the final version check and
|
|
# cache publication. Reward scoring itself intentionally remains
|
|
# outside the policy lock because it may call an external service.
|
|
return self.generator.with_policy_snapshot(commit)
|
|
|
|
def evaluate(self, batch: Dict) -> RolloutResult:
|
|
"""One-off rollout + scoring that leaves the replay cache untouched.
|
|
|
|
Used by validation on online strategies: the training cache, its
|
|
cadence counter, and the cache key stay intact, so evaluation
|
|
prompts never disturb the rollout replay schedule.
|
|
"""
|
|
raw = self.generator.generate(batch)
|
|
self._validate_policy_version(raw)
|
|
scored = self._score(raw)
|
|
self._validate_policy_version(scored)
|
|
return scored
|