ViperEkura
5ba21f4eb3
- Add tests/helpers.py with shared config, dataset, tokenizer, executor, and assertion helpers - Replace 15 copies of device one-liner with session-scoped fixture - Collapse 5 near-identical Dataset subclasses into RandomTokenDataset - Remove duplicate _make_config/_make_model/_make_frozen and FakeTokenizer/FakeExecutor definitions - Make test_callbacks and test_early_stopping use existing train_config_factory - Replace 6 duplicate meta.json read blocks with load_shard_meta - Fix mkdtemp leaks in test_lora.py with TemporaryDirectory
312 lines
9.9 KiB
Python
312 lines
9.9 KiB
Python
"""Unit tests for the online rollout module."""
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import pytest
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import torch
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from astrai.inference.core.scheduler import InferenceScheduler
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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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from tests.helpers import FakeTokenizer, make_model
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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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class BadShapeRewardModel(BaseRewardModel):
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def score(self, prompts, responses):
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return torch.zeros(len(prompts))
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class NonFiniteRewardModel(BaseRewardModel):
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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("nan"))
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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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)
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def _make_instruction_batch(n=2):
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"""Build a batch of instruction+input prompts as lists of strings."""
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instructions = [f"Tell me about topic {i}" for i in range(n)]
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inputs = [f"context {i}" for i in range(n)]
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return {"instruction": instructions, "input": inputs}
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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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prompt_mask=torch.ones(2, 4, dtype=torch.bool),
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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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prompt_mask=torch.ones(2, 4, dtype=torch.bool),
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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_mask.shape == (2, 4)
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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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def _make_generator(device, **kw):
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model, _ = make_model(device, max_position_embeddings=128)
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tokenizer = FakeTokenizer(with_chat_template=True)
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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_position_embeddings", 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_instruction_batch(n=2)
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r = gen.generate(batch)
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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 r.prompt_mask.shape == r.prompts.shape
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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_uses_eval_and_restores_mode(device):
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gen, model = _make_generator(device, group_size=1, max_tokens=2)
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model.train()
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seen_training = []
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original = gen.scheduler.run_batch
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def recording_run_batch(*args, **kwargs):
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seen_training.append(model.training)
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return original(*args, **kwargs)
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gen.scheduler.run_batch = recording_run_batch
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gen.generate(_make_instruction_batch(n=1))
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assert seen_training == [False]
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assert model.training is True
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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_instruction_batch(n=2)
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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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assert r.responses[i, g, real:].sum() == 0
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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_instruction_batch(n=1)
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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 test_rollout_generator_instruction_role_mapping(device):
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"""instruction -> system, input -> user, output -> assistant."""
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gen, _ = _make_generator(device, group_size=1, max_tokens=4)
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batch = {
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"instruction": ["Be helpful"],
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"input": ["What is 2+2?"],
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"output": ["Four"],
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}
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r = gen.generate(batch)
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text = r.prompt_texts[0]
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assert "SYSTEM: Be helpful" in text
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assert "USER: What is 2+2?" in text
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assert "ASSISTANT: Four" in text
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def test_rollout_generator_messages_format(device):
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"""Rollout also accepts pre-built messages."""
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gen, _ = _make_generator(device, group_size=2, max_tokens=4)
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batch = {
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"messages": [
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[{"role": "user", "content": "Hello"}],
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[{"role": "user", "content": "Goodbye"}],
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]
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}
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r = gen.generate(batch)
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assert r.responses.shape[0] == 2
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assert len(r.prompt_texts) == 2
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assert "Hello" in r.prompt_texts[0] or "USER" in r.prompt_texts[0]
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def test_rollout_generator_bad_batch_raises(device):
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"""Batch without messages or instruction raises a clear error."""
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gen, _ = _make_generator(device)
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with pytest.raises(
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ValueError, match="must contain either 'messages' or 'instruction'"
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):
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gen.generate({"input_ids": torch.zeros(2, 4, dtype=torch.long)})
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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_position_embeddings", 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_instruction_batch(n=2)
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r, is_fresh = runner(batch)
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assert is_fresh
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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_instruction_batch()
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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_refreshes_for_different_batch(device):
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runner, _ = _make_runner(device, rollout_interval=100)
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r1, fresh1 = runner(_make_instruction_batch(n=1))
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batch2 = {"instruction": ["Different prompt"], "input": [""]}
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r2, fresh2 = runner(batch2)
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assert fresh1 is True
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assert fresh2 is True
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assert r2 is not r1
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@pytest.mark.parametrize("reward_model", [BadShapeRewardModel, NonFiniteRewardModel])
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def test_rollout_runner_rejects_invalid_rewards(device, reward_model):
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generator, _ = _make_generator(device, group_size=2, max_tokens=2)
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runner = RolloutRunner(generator, reward_model(), rollout_interval=1)
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with pytest.raises(ValueError):
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runner(_make_instruction_batch(n=1))
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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_instruction_batch()
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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_instruction_batch()
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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_instruction_batch()
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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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