mirror of https://github.com/razor-ai/soup.git
feat(schema): DataConfig.replay* + _validate_replay_compat (v0.71.36 Part E)
data.replay / replay_ratio / replay_seed for continual-learning rehearsal,
plus the cross-validator gate. 540 existing schema tests still green.
Ratio semantics are pinned in the field description and by test: r is the
fraction of the FINAL mixed train set, so n_replay = round(r/(1-r)*n_new).
At r=0.1 over 1000 new rows that is 111 replay rows -> 1111 total -> 10.0%.
The naive r*n_new gives 9.09% and is wrong.
Gates, each mutation-verified to fail a named test:
- task in {sft, pretrain}: replay on dpo would be silently ignored
- packing / multipack rejected: both concatenate rows into fixed blocks, so
the ratio stops being meaningful at block boundaries -- reject rather than
silently mis-mix
- footgun: replay_ratio/replay_seed without data.replay silently no-op
Provenance needs no extra plumbing: data.replay* rides model_dump(), so the
experiment tracker, the registry's config_json, soup card and the repro
receipt already capture it. Per-row _replay keys would be a coin-flip bug --
sft.py computes remove_columns from dataset["train"][0], so whether the
column survives into TRL depends on whether row 0 happens to be a replay row.
This commit is contained in:
Alpamys
parent
72c1d4aa7f
commit
686e885858
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@ -288,6 +288,68 @@ class DataConfig(BaseModel):
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),
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)
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# --- v0.71.36 Data Moat II: continual-learning rehearsal ---------------
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replay: Optional[str] = Field(
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default=None,
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description=(
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"Path to an OLD dataset to interleave into training as "
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"continual-learning rehearsal, so fine-tuning on a new task does "
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"not erase the previous one. Rows are mixed into train ONLY "
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"(never val, which stays pure new-task). sft / pretrain only; "
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"incompatible with packing / multipack. (v0.71.36)"
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),
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)
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replay_ratio: float = Field(
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default=0.1,
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gt=0.0,
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le=0.5,
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description=(
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"Fraction of the FINAL mixed train set that is replay rows: "
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"n_replay = round(r/(1-r) * n_new). At 0.1 over 1000 new rows "
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"that is 111 replay rows -> 1111 total -> 10.0%. (v0.71.36)"
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),
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)
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replay_seed: Optional[int] = Field(
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default=None,
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ge=0,
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le=2_147_483_647,
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description=(
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"Seed for the replay sample + interleave. None = seed 0. "
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"(v0.71.36)"
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),
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)
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@field_validator("replay")
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@classmethod
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def _validate_replay_path(cls, v):
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if v is None:
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return None
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if not isinstance(v, str):
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raise ValueError("data.replay must be a string path")
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cleaned = v.strip()
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if not cleaned:
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raise ValueError("data.replay must be a non-empty path")
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if "\x00" in cleaned:
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raise ValueError("data.replay must not contain null bytes")
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if len(cleaned) > 4096:
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raise ValueError("data.replay path too long (max 4096 chars)")
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return cleaned
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@field_validator("replay_ratio", mode="before")
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@classmethod
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def _validate_replay_ratio(cls, v):
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# Bool is a subclass of int/float — reject before coercion.
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if isinstance(v, bool):
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raise ValueError("data.replay_ratio must not be a bool")
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return v
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@field_validator("replay_seed", mode="before")
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@classmethod
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def _validate_replay_seed(cls, v):
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if isinstance(v, bool):
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raise ValueError("data.replay_seed must not be a bool")
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return v
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# --- v0.42.0 Data Pipeline Pro -----------------------------------------
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video_dir: Optional[str] = Field(
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default=None,
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@ -4592,6 +4654,47 @@ class SoupConfig(BaseModel):
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)
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return self
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@model_validator(mode="after")
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def _validate_replay_compat(self) -> "SoupConfig":
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"""v0.71.36 — continual-learning rehearsal gate.
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Replay interleaves rows from an OLD dataset into train so the model
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does not forget the previous task. v1 covers the plain
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instruction / continued-pretraining paths only.
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packing / multipack concatenate rows into fixed-length blocks, so
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the replay ratio stops being meaningful at block boundaries —
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reject rather than silently mis-mix. Setting replay_ratio /
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replay_seed without data.replay silently no-ops, so reject that as
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a footgun.
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"""
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data = self.data
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replay_knobs_set = (
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data.replay_ratio != 0.1 or data.replay_seed is not None
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)
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if data.replay is not None:
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if self.task not in ("sft", "pretrain"):
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raise ValueError(
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"data.replay requires task='sft' or task='pretrain'; "
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f"got task={self.task!r}"
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)
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if self.training.packing:
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raise ValueError(
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"data.replay is incompatible with training.packing "
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"(packing concatenates rows into fixed blocks, so the "
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"replay ratio stops being meaningful)"
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)
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if getattr(self.training, "multipack", False):
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raise ValueError(
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"data.replay is incompatible with training.multipack "
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"(bin-packing breaks the replay ratio)"
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)
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elif replay_knobs_set:
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raise ValueError(
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"data.replay_ratio / data.replay_seed require data.replay"
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)
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return self
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@model_validator(mode="after")
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def _validate_vllm_sleep_mode(self) -> "SoupConfig":
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"""v0.50.0 Part B — ``vllm_sleep_mode`` requires task='grpo' and a
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@ -1939,3 +1939,137 @@ class TestDataCanaryCli:
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)
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assert res.exit_code == 1
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assert "no canaries" in _clean(res.output).lower()
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_REPLAY_YAML = """
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base: HuggingFaceTB/SmolLM2-135M-Instruct
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task: sft
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data:
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train: train.jsonl
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replay: old.jsonl
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replay_ratio: 0.2
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training:
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epochs: 1
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"""
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_PLAIN_YAML = (
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"base: m\ntask: sft\ndata:\n train: t.jsonl\ntraining:\n epochs: 1\n"
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)
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class TestReplaySchema:
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def _load(self, yaml_str):
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from soup_cli.config.loader import load_config_from_string
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return load_config_from_string(yaml_str)
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def test_happy_path(self):
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cfg = self._load(_REPLAY_YAML)
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assert cfg.data.replay == "old.jsonl"
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assert cfg.data.replay_ratio == 0.2
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def test_default_is_off(self):
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cfg = self._load(_PLAIN_YAML)
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assert cfg.data.replay is None
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assert cfg.data.replay_ratio == 0.1
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assert cfg.data.replay_seed is None
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def test_pretrain_allowed(self):
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cfg = self._load(
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_REPLAY_YAML.replace("task: sft", "task: pretrain").replace(
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" train: train.jsonl",
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" train: train.jsonl\n format: plaintext",
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)
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)
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assert cfg.data.replay == "old.jsonl"
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def test_rejected_on_dpo(self):
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with pytest.raises(Exception, match="replay"):
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self._load(
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_REPLAY_YAML.replace("task: sft", "task: dpo").replace(
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" train: train.jsonl",
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" train: train.jsonl\n format: dpo",
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)
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)
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def test_footgun_ratio_without_replay(self):
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yaml_str = (
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"base: m\ntask: sft\ndata:\n train: t.jsonl\n"
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" replay_ratio: 0.3\ntraining:\n epochs: 1\n"
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)
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with pytest.raises(Exception, match="data.replay"):
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self._load(yaml_str)
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def test_footgun_seed_without_replay(self):
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yaml_str = (
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"base: m\ntask: sft\ndata:\n train: t.jsonl\n"
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" replay_seed: 7\ntraining:\n epochs: 1\n"
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)
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with pytest.raises(Exception, match="data.replay"):
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self._load(yaml_str)
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def test_mutually_exclusive_with_packing(self):
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yaml_str = _REPLAY_YAML.replace(
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"training:\n epochs: 1", "training:\n epochs: 1\n packing: true"
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)
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with pytest.raises(Exception, match="packing"):
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self._load(yaml_str)
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def test_mutually_exclusive_with_multipack(self):
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yaml_str = _REPLAY_YAML.replace(
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"training:\n epochs: 1",
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"training:\n epochs: 1\n multipack: true",
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)
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with pytest.raises(Exception, match="multipack"):
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self._load(yaml_str)
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@pytest.mark.parametrize("bad", ["0.0", "0.6", "1.0", "-0.1"])
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def test_ratio_bounds(self, bad):
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with pytest.raises(Exception):
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self._load(
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_REPLAY_YAML.replace("replay_ratio: 0.2", f"replay_ratio: {bad}")
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)
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def test_ratio_boundary_0_5_allowed(self):
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cfg = self._load(
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_REPLAY_YAML.replace("replay_ratio: 0.2", "replay_ratio: 0.5")
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)
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assert cfg.data.replay_ratio == 0.5
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@pytest.mark.parametrize("bad", ['""', '" "'])
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def test_replay_field_validator_rejects_blank(self, bad):
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with pytest.raises(Exception):
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self._load(_REPLAY_YAML.replace("replay: old.jsonl", f"replay: {bad}"))
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def test_replay_rejects_null_byte(self):
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from soup_cli.config.schema import DataConfig
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with pytest.raises(Exception, match="null"):
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DataConfig(train="t.jsonl", replay="a\x00b")
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def test_replay_rejects_overlong_path(self):
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from soup_cli.config.schema import DataConfig
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with pytest.raises(Exception, match="too long"):
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DataConfig(train="t.jsonl", replay="x" * 5000)
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def test_ratio_rejects_bool(self):
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with pytest.raises(Exception):
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self._load(
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_REPLAY_YAML.replace("replay_ratio: 0.2", "replay_ratio: true")
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)
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def test_seed_rejects_bool(self):
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with pytest.raises(Exception):
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self._load(
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_REPLAY_YAML.replace(
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"replay_ratio: 0.2", "replay_seed: true"
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)
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)
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def test_replay_survives_model_dump(self):
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"""Provenance rides the schema — the tracker/registry capture it."""
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cfg = self._load(_REPLAY_YAML)
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dumped = cfg.model_dump()
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assert dumped["data"]["replay"] == "old.jsonl"
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assert dumped["data"]["replay_ratio"] == 0.2
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