ViperEkura
95c43368ae
- RolloutGenerator now delegates prefill/decode to InferenceScheduler.run_batch (sync API, no background thread), sharing one KV-cache code path with the inference server and eliminating O(n^2) recompute in rollout - Add sample(return_logprobs=) and Executor.execute_decode(return_logprobs=) to expose behaviour-policy log-probs through the engine; Task gains output_logprobs - RolloutResult now subclasses RawRollout (adds rewards only), removing duplicated fields - RolloutRunner.__call__ returns (result, is_fresh) instead of relying on object identity, removing the fragile refresh-detection contract - Remove O(n^2) generate_responses helper and dead code (_tokenize_prompts, unused old_model arg) - train_context.py wires InferenceScheduler directly instead of hand-rolling SamplingPipeline - Tests: +11 covering return_logprobs, run_batch, and KV-cache-backed rollout semantics; 404 pass
282 lines
8.5 KiB
Python
282 lines
8.5 KiB
Python
"""Unit tests for the online rollout module.
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Covers :class:`RolloutResult` / :class:`RawRollout`, :class:`BaseRewardModel`,
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:class:`RolloutGenerator` (KV-cache-backed via :class:`InferenceScheduler.run_batch`)
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and :class:`RolloutRunner` including its internal cache and rollout-interval
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trigger logic.
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"""
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import pytest
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import torch
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from astrai.config.model_config import AutoRegressiveLMConfig
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from astrai.inference.core.scheduler import InferenceScheduler
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from astrai.model.transformer import AutoRegressiveLM
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from astrai.trainer.rollout import (
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BaseRewardModel,
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RawRollout,
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RolloutGenerator,
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RolloutResult,
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RolloutRunner,
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)
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class FakeTokenizer:
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"""Minimal char-level tokenizer stub for rollout tests.
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Vocab: 0 = pad, 1..255 = byte values. ``stop_ids = [2]`` (a fake
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EOS) so tests can verify early-stopping behaviour.
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"""
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stop_ids = [2]
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def encode(self, texts, out_ids=True, **_):
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if isinstance(texts, str):
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texts = [texts]
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return [[b for b in t.encode("utf-8")] for t in texts]
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def decode(self, ids, skip_special_tokens=True):
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out = bytes(b for b in ids if b > 2 or not skip_special_tokens).decode(
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"utf-8", errors="ignore"
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)
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return out
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class ConstantRewardModel(BaseRewardModel):
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"""Returns a constant reward for every response."""
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def __init__(self, value: float = 1.0):
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self.value = value
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def score(self, prompts, responses):
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B = len(prompts)
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G = len(responses[0]) if B else 0
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return torch.full((B, G), float(self.value))
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def _make_config(vocab_size=200, max_len=128):
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return AutoRegressiveLMConfig(
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vocab_size=vocab_size,
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dim=16,
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n_heads=2,
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n_kv_heads=1,
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dim_ffn=32,
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max_len=max_len,
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n_layers=2,
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norm_eps=1e-5,
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)
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def _make_model(device):
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cfg = _make_config()
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m = AutoRegressiveLM(cfg).to(device=device)
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m.eval()
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return m, cfg
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def _make_scheduler(model, tokenizer, max_batch_size=8, max_len=128):
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return InferenceScheduler(
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model=model,
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tokenizer=tokenizer,
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max_batch_size=max_batch_size,
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max_seq_len=max_len,
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max_prompt_len=max_len,
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)
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def _make_prompt_batch(batch_size=2, prompt_len=6, device="cpu"):
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ids = torch.randint(3, 200, (batch_size, prompt_len), device=device)
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mask = torch.ones(batch_size, prompt_len, dtype=torch.bool, device=device)
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return {"input_ids": ids, "attention_mask": mask}
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def test_raw_rollout_fields():
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r = RawRollout(
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prompts=torch.zeros(2, 4, dtype=torch.long),
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responses=torch.zeros(2, 3, 5, dtype=torch.long),
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response_mask=torch.ones(2, 3, 5, dtype=torch.bool),
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logprobs_old=torch.zeros(2, 3, 5),
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)
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assert r.prompts.shape == (2, 4)
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assert r.responses.shape == (2, 3, 5)
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assert r.prompt_texts == []
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assert r.response_texts == []
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def test_rollout_result_inherits_raw_rollout_fields():
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r = RolloutResult(
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prompts=torch.zeros(2, 4, dtype=torch.long),
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responses=torch.zeros(2, 3, 5, dtype=torch.long),
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response_mask=torch.ones(2, 3, 5, dtype=torch.bool),
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logprobs_old=torch.zeros(2, 3, 5),
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rewards=torch.zeros(2, 3),
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)
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assert r.rewards.shape == (2, 3)
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assert r.prompts.shape == (2, 4)
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assert r.responses.shape == (2, 3, 5)
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assert r.prompt_texts == []
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assert r.response_texts == []
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def test_base_reward_model_is_abstract():
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with pytest.raises(TypeError):
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BaseRewardModel()
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def test_constant_reward_model_shape():
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rm = ConstantRewardModel(0.5)
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out = rm.score(["a", "b"], [["x", "y", "z"], ["p", "q", "r"]])
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assert out.shape == (2, 3)
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assert torch.all(out == 0.5)
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@pytest.fixture
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def device():
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return "cuda" if torch.cuda.is_available() else "cpu"
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def _make_generator(device, **kw):
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model, _ = _make_model(device)
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tokenizer = FakeTokenizer()
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scheduler = _make_scheduler(
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model,
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tokenizer,
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max_batch_size=kw.get("max_batch_size", 8),
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max_len=kw.get("max_len", 128),
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)
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generator = RolloutGenerator(
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scheduler=scheduler,
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tokenizer=tokenizer,
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max_tokens=kw.get("max_tokens", 8),
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group_size=kw.get("group_size", 2),
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temperature=kw.get("temperature", 1.0),
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top_k=kw.get("top_k", 0),
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top_p=kw.get("top_p", 1.0),
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)
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return generator, model
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def test_rollout_generator_shapes(device):
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gen, _ = _make_generator(device, group_size=3, max_tokens=5)
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batch = _make_prompt_batch(batch_size=2, prompt_len=4, device=device)
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r = gen.generate(batch)
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assert r.prompts.shape == (2, 4)
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assert r.responses.shape == (2, 3, 5)
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assert r.response_mask.shape == (2, 3, 5)
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assert r.logprobs_old.shape == (2, 3, 5)
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assert len(r.prompt_texts) == 2
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assert len(r.response_texts) == 2
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assert len(r.response_texts[0]) == 3
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def test_rollout_generator_mask_matches_responses(device):
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"""Positions beyond a response's length are pad (mask False)."""
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gen, _ = _make_generator(device, group_size=2, max_tokens=6)
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batch = _make_prompt_batch(batch_size=2, prompt_len=4, device=device)
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r = gen.generate(batch)
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for i in range(2):
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for g in range(2):
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real = r.response_mask[i, g].sum().item()
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# Pad positions should be 0
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assert r.responses[i, g, real:].sum() == 0
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# logprobs after the real tokens are 0 (padding)
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if real < r.logprobs_old.size(-1):
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assert torch.all(r.logprobs_old[i, g, real:] == 0)
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def test_rollout_generator_logprobs_are_nonpositive(device):
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"""Behaviour-policy logprobs of sampled tokens should be ≤ 0."""
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gen, _ = _make_generator(device, group_size=2, max_tokens=4)
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batch = _make_prompt_batch(batch_size=1, prompt_len=3, device=device)
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r = gen.generate(batch)
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for i in range(1):
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for g in range(2):
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mask = r.response_mask[i, g]
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lp = r.logprobs_old[i, g][mask]
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assert torch.all(lp <= 1e-5)
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def _make_runner(device, **kw):
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generator, model = _make_generator(
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device,
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group_size=kw.get("group_size", 2),
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max_tokens=kw.get("max_tokens", 8),
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max_batch_size=kw.get("max_batch_size", 8),
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max_len=kw.get("max_len", 128),
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)
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rm = ConstantRewardModel(1.0)
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return (
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RolloutRunner(
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generator=generator,
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reward_model=rm,
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rollout_interval=kw.get("rollout_interval", 2),
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),
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model,
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)
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def test_rollout_runner_shapes(device):
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runner, _ = _make_runner(device, group_size=3, max_tokens=5)
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batch = _make_prompt_batch(batch_size=2, prompt_len=4, device=device)
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r, is_fresh = runner(batch)
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assert is_fresh
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assert r.prompts.shape == (2, 4)
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assert r.responses.shape == (2, 3, 5)
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assert r.response_mask.shape == (2, 3, 5)
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assert r.rewards.shape == (2, 3)
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assert r.logprobs_old.shape == (2, 3, 5)
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assert len(r.prompt_texts) == 2
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assert len(r.response_texts) == 2
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assert len(r.response_texts[0]) == 3
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def test_rollout_runner_cache_returns_stale_flag(device):
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runner, _ = _make_runner(device, rollout_interval=10)
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batch = _make_prompt_batch(device=device)
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r1, fresh1 = runner(batch)
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r2, fresh2 = runner(batch)
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assert r1 is r2
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assert fresh1 is True
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assert fresh2 is False
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def test_rollout_runner_step_triggers_new_rollout(device):
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runner, _ = _make_runner(device, rollout_interval=2)
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batch = _make_prompt_batch(device=device)
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r1, fresh1 = runner(batch)
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assert fresh1 is True
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runner.step()
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# interval=2 means trigger when _steps_since_rollout >= 2; 1 step not enough
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r2, fresh2 = runner(batch)
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assert r2 is r1
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assert fresh2 is False
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runner.step()
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# Now _steps_since_rollout == 2 -> re-rollout
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r3, fresh3 = runner(batch)
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assert r3 is not r1
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assert fresh3 is True
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def test_rollout_runner_clear_cache_forces_rerun(device):
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runner, _ = _make_runner(device, rollout_interval=100)
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batch = _make_prompt_batch(device=device)
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r1, _ = runner(batch)
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runner.clear_cache()
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r2, fresh2 = runner(batch)
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assert r2 is not r1
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assert fresh2 is True
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def test_rollout_runner_step_resets_counter(device):
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runner, _ = _make_runner(device, rollout_interval=1)
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batch = _make_prompt_batch(device=device)
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r1, _ = runner(batch)
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runner.step()
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r2, fresh2 = runner(batch)
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assert r2 is not r1
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assert fresh2 is True
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# Counter reset after rollout; second call w/o step should be cached.
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r3, fresh3 = runner(batch)
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assert r3 is r2
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assert fresh3 is False
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