fix: rewrite GRPO data pipeline for offline record-level access
- process_list_field returns List[List[int]] preserving per-response boundaries - GRPODataset rewritten to record-level __getitem__ (no windowing/stride) - grpo_collate_fn pads variable-length responses into [B, G, R] tensors - JsonlStore detects nested List[List[int]] and stores List[Tensor] per record - Store._normalize skips nested-list keys from cumsum bookkeeping - Pipeline._flush handles nested lists without cross-record flattening - Export grpo_collate_fn from astrai.dataset - 6 new GRPO tests + 2 updated builder tests, 114 total pass
This commit is contained in:
@@ -1,6 +1,7 @@
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from astrai.dataset.dataset import (
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BaseDataset,
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DatasetFactory,
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grpo_collate_fn,
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)
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from astrai.dataset.sampler import ResumableDistributedSampler
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from astrai.dataset.storage import (
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@@ -21,6 +22,7 @@ from astrai.serialization import (
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__all__ = [
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"BaseDataset",
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"DatasetFactory",
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"grpo_collate_fn",
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"Store",
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"StoreFactory",
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"H5Store",
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+116
-16
@@ -15,6 +15,49 @@ from astrai.dataset.storage import (
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from astrai.factory import BaseFactory
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def grpo_collate_fn(batch: List[Dict[str, Tensor]]) -> Dict[str, Tensor]:
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"""Collate variable-length GRPO samples into padded 3-D tensors.
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Input: list of dicts, each with:
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- prompts: [P_i]
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- responses: list of G tensors, each [R_ij]
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- masks: list of G tensors, each [R_ij]
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- rewards: [G]
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Output:
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- prompts: [B, P_max]
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- responses: [B, G, R_max]
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- masks: [B, G, R_max]
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- rewards: [B, G]
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"""
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B = len(batch)
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G = len(batch[0]["responses"])
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P_max = max(b["prompts"].size(0) for b in batch)
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R_max = max(r.size(0) for b in batch for r in b["responses"])
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prompts = torch.zeros(B, P_max, dtype=torch.long)
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responses = torch.zeros(B, G, R_max, dtype=torch.long)
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masks = torch.zeros(B, G, R_max, dtype=torch.bool)
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rewards = torch.zeros(B, G, dtype=torch.float32)
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for i, b in enumerate(batch):
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p_len = b["prompts"].size(0)
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prompts[i, :p_len] = b["prompts"]
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rewards[i, : b["rewards"].size(0)] = b["rewards"]
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for g in range(min(G, len(b["responses"]))):
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r_len = b["responses"][g].size(0)
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responses[i, g, :r_len] = b["responses"][g]
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if g < len(b["masks"]):
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masks[i, g, :r_len] = b["masks"][g]
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return {
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"prompts": prompts,
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"responses": responses,
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"masks": masks,
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"rewards": rewards,
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}
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class BaseDataset(Dataset, ABC):
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"""Abstract base class for all dataset types.
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@@ -250,28 +293,85 @@ class DPODataset(BaseDataset):
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@DatasetFactory.register("grpo")
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class GRPODataset(BaseDataset):
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"""Dataset for Group Relative Policy Optimization training."""
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"""Dataset for offline Group Relative Policy Optimization.
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Unlike the window-based datasets (SEQ/SFT/DPO), GRPO data is
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record-structured: each sample is one prompt with its group of
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responses and scalar rewards. There is no windowing or stride —
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every record is an independent training unit.
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Expected storage layout (produced by JsonlStore or pre-tokenized):
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- ``prompts``: List[Tensor] — one 1-D token tensor per record
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- ``responses``: List[List[Tensor]] — G response tensors per record
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- ``masks``: List[List[Tensor]] — G mask tensors per record
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- ``rewards``: List[Tensor] — one 1-D float tensor (len G) per record
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"""
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def __init__(self, window_size: int = 0, stride: int = 0, **kwargs):
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super().__init__(window_size=window_size, stride=stride or window_size)
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self._records: List[dict] = []
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@property
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def required_keys(self) -> List[str]:
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return ["prompts", "responses", "masks", "rewards"]
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def _fetch_data(self, begin_idx: int, end_idx: int, key: str) -> Tensor:
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return self.storage.fetch(begin_idx, end_idx, key)
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def load(self, load_path: str, storage_type: Optional[str] = None, **kwargs):
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if storage_type is None:
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storage_type = detect_format(load_path)
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self.storage = StoreFactory.create(storage_type, **kwargs)
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self._load_path = load_path
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self.storage.load(load_path, **kwargs)
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self._validate_keys()
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self._build_records()
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def _validate_keys(self):
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actual_keys = set(self.storage.keys)
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missing = [k for k in self.required_keys if k not in actual_keys]
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if missing:
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raise KeyError(
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f"GRPODataset requires keys {self.required_keys}, "
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f"but storage only has {sorted(actual_keys)}. Missing: {missing}"
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)
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def _build_records(self):
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"""Unfold segmented storage into per-record lists.
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``prompts`` is a flat list of 1-D tensors (one per record).
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``responses`` / ``masks`` are nested lists (G tensors per record).
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``rewards`` is a flat list of 1-D tensors (len G per record).
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"""
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prompt_segs = self.storage._data.get("prompts", [])
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response_segs = self.storage._data.get("responses", [])
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mask_segs = self.storage._data.get("masks", [])
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reward_segs = self.storage._data.get("rewards", [])
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n_records = len(prompt_segs)
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self._records = []
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for i in range(n_records):
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self._records.append(
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{
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"prompts": prompt_segs[i],
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"responses": response_segs[i] if i < len(response_segs) else [],
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"masks": mask_segs[i] if i < len(mask_segs) else [],
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"rewards": reward_segs[i]
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if i < len(reward_segs)
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else torch.tensor([]),
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}
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)
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@property
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def count(self) -> int:
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return len(self._records)
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def __len__(self) -> int:
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return len(self._records)
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def __getitem__(self, index: int) -> Dict[str, Tensor]:
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begin_idx, end_idx = self.get_index(index)
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prompts = self._fetch_data(begin_idx, end_idx, "prompts").to(dtype=torch.long)
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responses = self._fetch_data(begin_idx, end_idx, "responses").to(
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dtype=torch.long
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)
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masks = self._fetch_data(begin_idx, end_idx, "masks").to(dtype=torch.bool)
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rewards = self._fetch_data(begin_idx, end_idx, "rewards")
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rec = self._records[index]
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return {
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"prompts": prompts,
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"responses": responses,
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"masks": masks,
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"rewards": rewards,
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"prompts": rec["prompts"].to(dtype=torch.long),
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"responses": [r.to(dtype=torch.long) for r in rec["responses"]],
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"masks": [m.to(dtype=torch.bool) for m in rec["masks"]],
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"rewards": rec["rewards"].to(dtype=torch.float32),
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}
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@@ -148,26 +148,37 @@ class Store(ABC):
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return results[0] if len(results) == 1 else torch.cat(results, dim=0)
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def _normalize(self, raw: Dict[str, List[Tensor]]):
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def _normalize(self, raw: Dict[str, list]):
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"""Register segments and pre-compute cumulative lengths.
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Does NOT concatenate — segments are kept as-is to avoid OOM on
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large datasets. Sets ``self._length`` to the minimum total
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element count across all keys.
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element count across all flat-tensor keys.
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For GRPO multi-response keys, values may be ``List[List[Tensor]]``
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(one list of G tensors per record). These are stored as-is and
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excluded from the cumulative-length bookkeeping since they are
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accessed record-by-record via ``_data`` rather than via ``fetch``.
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"""
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flat_lengths = []
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for key, tensors in raw.items():
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self._data[key] = tensors
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if not tensors:
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self._cum[key] = []
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flat_lengths.append(0)
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continue
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# Skip nested lists (GRPO responses/masks) — record-level access
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if isinstance(tensors[0], list):
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self._cum[key] = []
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continue
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cum = []
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total = 0
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for t in tensors:
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total += t.shape[0]
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cum.append(total)
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self._cum[key] = cum
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self._length = (
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min((cum[-1] if cum else 0) for cum in self._cum.values())
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if self._cum
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else 0
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)
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flat_lengths.append(cum[-1] if cum else 0)
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self._length = min(flat_lengths) if flat_lengths else 0
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class StoreFactory(BaseFactory["Store"]):
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@@ -274,7 +285,13 @@ class JsonlStore(Store):
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for key, ids in result.items():
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if key not in raw:
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raw[key] = []
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raw[key].append(torch.tensor(ids, dtype=self._infer_dtype(ids)))
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if ids and isinstance(ids[0], list):
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# GRPO multi-response: List[List[int]] → List[Tensor]
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raw[key].append(
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[torch.tensor(sub, dtype=self._infer_dtype(sub)) for sub in ids]
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)
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else:
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raw[key].append(torch.tensor(ids, dtype=self._infer_dtype(ids)))
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for jsonl_path in sorted(root.glob("*.jsonl")):
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with open(jsonl_path, "r", encoding="utf-8") as f:
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@@ -314,7 +331,7 @@ class JsonlStore(Store):
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@staticmethod
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def _primary_ids(result: dict) -> List[int]:
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"""Return the first integer list in *result* as the primary id sequence."""
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"""Return the first flat integer list in *result* as the primary id sequence."""
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for val in result.values():
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if isinstance(val, list) and val and isinstance(val[0], int):
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return val
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@@ -95,8 +95,15 @@ class SectionRenderer:
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return all_ids, loss_mask
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def process_list_field(self, item: dict, sections: list, config, tokenizer):
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all_ids: list[int] = []
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loss_mask: list[int] = []
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"""Tokenize a list-valued field, preserving per-element boundaries.
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Returns ``(list_of_id_lists, list_of_mask_lists)`` where each
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inner list corresponds to one element of the source list. This
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is critical for GRPO where each response must stay a separate
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sequence so the strategy can form a ``[G, R]`` tensor.
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"""
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per_item_ids: list[list[int]] = []
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per_item_masks: list[list[int]] = []
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for sec in sections:
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field = sec["field"]
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@@ -108,17 +115,13 @@ class SectionRenderer:
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continue
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for val in values:
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ids: list[int] = []
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mask: list[int] = []
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if use_template:
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if isinstance(val, list):
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wrapper = {field: val}
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self._append_template(
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wrapper,
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field,
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action,
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tokenizer,
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config,
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all_ids,
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loss_mask,
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wrapper, field, action, tokenizer, config, ids, mask
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)
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else:
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wrapper = {field: str(val)}
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@@ -130,17 +133,19 @@ class SectionRenderer:
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False,
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False,
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config,
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all_ids,
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loss_mask,
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ids,
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mask,
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)
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if ids:
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max_len = config.preprocessing.max_seq_len
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ids = ids[:max_len]
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mask = mask[: len(ids)]
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per_item_ids.append(ids)
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per_item_masks.append(mask)
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max_len = config.preprocessing.max_seq_len
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all_ids = all_ids[:max_len]
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loss_mask = loss_mask[: len(all_ids)]
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if not all_ids:
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if not per_item_ids:
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return None, None
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return all_ids, loss_mask
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return per_item_ids, per_item_masks
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@staticmethod
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def is_value_section(sections: list) -> bool:
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@@ -282,10 +287,18 @@ class MultiOutputMaskBuilder(BaseMaskBuilder):
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ids, mask = self.renderer.process_list_field(
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item, sections, config, tokenizer
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)
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else:
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ids, mask = self.renderer.process_sections(
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item, sections, config, tokenizer, is_top_level=True
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)
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if ids is None:
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continue
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# ids is List[List[int]] — preserve per-response structure
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result[output_key] = ids
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if mask is not None:
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result[mask_key] = mask
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any_output = True
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continue
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ids, mask = self.renderer.process_sections(
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item, sections, config, tokenizer, is_top_level=True
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)
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if ids is None:
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continue
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@@ -180,9 +180,24 @@ class Pipeline:
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dt = _STR_TO_DTYPE.get(
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self.config.output.dtype.get(key, "int32"), torch.int32
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)
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tensors[key] = [
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torch.tensor(list(chain.from_iterable(ids_list)), dtype=dt)
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]
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# GRPO multi-response keys store List[List[int]] per record
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# (responses/masks). Rewards store List[float] per record.
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# Both produce List[Tensor] (one tensor per record), but
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# responses need inner flattening while rewards do not.
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if ids_list and isinstance(ids_list[0], list):
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tensors[key] = [
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torch.tensor(
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list(chain.from_iterable(ids))
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if ids and isinstance(ids[0], list)
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else ids,
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dtype=dt,
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)
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for ids in ids_list
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]
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else:
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tensors[key] = [
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torch.tensor(list(chain.from_iterable(ids_list)), dtype=dt)
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]
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if mode == "continuous" and original_sequences:
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pos_ids = self._position_id.generate(keys.get("sequence", []))
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