ViperEkura
7e98a419a7
- re-register the linear family with the operator dispatcher (ASTR_OPS / op_backend / resolve) - fix bf16 gemv misaligned-address faults and element mispairing for offset weights - reject misaligned bf16_swiglu inputs with a clear error and fall back in the backend gate - make the rollout reuse decision, validation, and return atomic under one policy snapshot - add the documented post-scoring rollout version check - derive live+1 under the scheduler lock in optimizer_step via apply_weight_update(None, ...) - reject rollout_max_policy_lag below rollout_interval - 1 at config time - sync gemv stream-test inputs before switching streams; drop dead loader imports
370 lines
13 KiB
Python
370 lines
13 KiB
Python
"""Unit tests for online rollout integration in :class:`BaseStrategy`.
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Covers the shared rollout-trigger logic in ``BaseStrategy.__call__``
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(runner injection, cache-driven refresh hook, ``step()`` callback) and
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the per-strategy ``prepare_from_rollout`` mappings for both
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:class:`GRPOStrategy` and :class:`DPOStrategy`.
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"""
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import pytest
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import torch
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from astrai.model.transformer import AutoRegressiveLM
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from astrai.trainer.rollout import RolloutResult
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from astrai.trainer.strategy import (
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DPOStrategy,
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GRPOStrategy,
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StrategyFactory,
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)
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from tests.helpers import FakeExecutor, make_frozen, make_model, make_rollout_config
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def _make_rollout_result(B=2, G=4, P=6, R=8, device="cpu"):
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return RolloutResult(
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prompts=torch.randint(3, 200, (B, P), device=device),
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prompt_mask=torch.ones(B, P, dtype=torch.bool, device=device),
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responses=torch.randint(3, 200, (B, G, R), device=device),
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response_mask=torch.ones(B, G, R, dtype=torch.bool, device=device),
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rewards=torch.randn(B, G, device=device),
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logprobs_old=torch.zeros(B, G, R, device=device),
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)
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class _RecordingRunner:
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"""Fake RolloutRunner returning a fixed result with freshness tracking.
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Freshness is ``True`` on the first call after construction or after
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:meth:`swap_result`; ``False`` on subsequent cached calls -- mirroring
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the real ``RolloutRunner`` contract without invoking generation.
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"""
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def __init__(self, result):
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self.result = result
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self.calls = 0
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self.step_calls = 0
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self._fresh = True
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self.policy_version = result.policy_version
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self.weight_updates = []
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def __call__(self, batch):
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self.calls += 1
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fresh = self._fresh
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self._fresh = False
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return self.result, fresh
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def step(self):
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self.step_calls += 1
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def update_weights(self, policy_version):
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self.policy_version = policy_version
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self.weight_updates.append(policy_version)
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return policy_version
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def apply_weight_update(self, policy_version, update):
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result = update()
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if policy_version is None:
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# Mirror the scheduler: None derives live+1 under the lock.
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policy_version = self.policy_version + 1
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self.update_weights(policy_version)
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return result
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def swap_result(self, result):
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self.result = result
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self._fresh = True
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class _NoOpOptimizer:
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def step(self):
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return None
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def _step(strat):
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strat.optimizer_step(_NoOpOptimizer())
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def _make_grpo(device, executor=None):
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model, _ = make_model(device)
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ref_model = make_frozen(model, device)
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return GRPOStrategy(
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model=model,
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device=device,
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old_model=None,
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ref_model=ref_model,
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clip_eps=0.2,
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kl_coef=0.01,
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group_size=4,
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model_fn=lambda c=make_rollout_config(): AutoRegressiveLM(c).to(device=device),
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executor=executor or FakeExecutor(),
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)
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def _make_dpo(device, executor=None):
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model, _ = make_model(device)
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ref_model = make_frozen(model, device)
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return DPOStrategy(
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model=model,
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device=device,
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ref_model=ref_model,
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beta=0.1,
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reduction="sum",
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model_fn=lambda c=make_rollout_config(): AutoRegressiveLM(c).to(device=device),
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executor=executor or FakeExecutor(),
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)
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def test_factory_registers_online_aliases():
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assert StrategyFactory.is_registered("online_grpo")
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assert StrategyFactory.is_registered("online_dpo")
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assert StrategyFactory.get_component_class("online_grpo") is GRPOStrategy
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assert StrategyFactory.get_component_class("online_dpo") is DPOStrategy
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@pytest.mark.parametrize("make_fn", ["_make_grpo", "_make_dpo"])
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def test_online_strategies_support_online(device, make_fn):
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maker = {"_make_grpo": _make_grpo, "_make_dpo": _make_dpo}[make_fn]
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assert maker(device).supports_online() is True
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def test_base_strategy_prepare_from_rollout_raises_by_default(device):
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from astrai.trainer.strategy import BaseStrategy
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class _Offline(BaseStrategy):
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def compute_loss(self, batch):
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return torch.tensor(0.0)
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strat = _Offline(model=torch.nn.Linear(1, 1), device="cpu")
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with pytest.raises(NotImplementedError):
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strat.prepare_from_rollout(_make_rollout_result(device="cpu"))
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def test_base_strategy_supports_online_default_false():
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from astrai.trainer.strategy import BaseStrategy
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class _Offline(BaseStrategy):
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def compute_loss(self, batch):
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return torch.tensor(0.0)
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strat = _Offline(model=torch.nn.Linear(1, 1), device="cpu")
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assert strat.supports_online() is False
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def test_grpo_prepare_from_rollout_mapping(device):
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strat = _make_grpo(device)
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r = _make_rollout_result(device=device)
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batch = strat.prepare_from_rollout(r)
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assert batch["prompts"] is r.prompts
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assert batch["prompt_mask"] is r.prompt_mask
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assert batch["responses"] is r.responses
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assert batch["masks"] is r.response_mask
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assert batch["rewards"] is r.rewards
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assert batch["logprobs_old"] is r.logprobs_old
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def test_dpo_prepare_from_rollout_conditions_responses_on_prompt(device):
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strat = _make_dpo(device)
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r = _make_rollout_result(B=3, G=4, P=6, R=5, device=device)
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r.prompt_mask[0, :2] = False
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r.prompts[0, :2] = 0
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r.response_mask[1, :, -2:] = False
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r.responses[1, :, -2:] = 0
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batch = strat.prepare_from_rollout(r)
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assert batch["chosen"].shape == (3, 11)
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assert batch["rejected"].shape == (3, 11)
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assert batch["chosen_mask"].shape == (3, 11)
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assert batch["rejected_mask"].shape == (3, 11)
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idx = torch.arange(3, device=device)
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expected_best = r.responses[idx, r.rewards.argmax(dim=-1)]
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expected_worst = r.responses[idx, r.rewards.argmin(dim=-1)]
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expected_best_mask = r.response_mask[idx, r.rewards.argmax(dim=-1)]
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expected_worst_mask = r.response_mask[idx, r.rewards.argmin(dim=-1)]
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assert torch.equal(batch["chosen"][:, :6], r.prompts)
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assert torch.equal(batch["rejected"][:, :6], r.prompts)
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assert torch.equal(batch["chosen"][:, 6:], expected_best)
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assert torch.equal(batch["rejected"][:, 6:], expected_worst)
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assert not batch["chosen_mask"][:, :6].any()
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assert not batch["rejected_mask"][:, :6].any()
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assert torch.equal(batch["chosen_mask"][:, 6:], expected_best_mask)
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assert torch.equal(batch["rejected_mask"][:, 6:], expected_worst_mask)
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assert torch.equal(batch["chosen_attention_mask"][:, :6], r.prompt_mask)
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assert torch.equal(batch["rejected_attention_mask"][:, :6], r.prompt_mask)
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assert torch.equal(batch["chosen_attention_mask"][:, 6:], expected_best_mask)
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assert torch.equal(batch["rejected_attention_mask"][:, 6:], expected_worst_mask)
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def test_dpo_prepare_from_rollout_same_response_keeps_distinct_prompts():
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strat = _make_dpo("cpu")
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r = _make_rollout_result(B=2, G=2, P=3, R=2, device="cpu")
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r.prompts = torch.tensor([[0, 11, 12], [21, 22, 23]])
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r.prompt_mask = torch.tensor([[False, True, True], [True, True, True]])
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shared_response = torch.tensor([101, 102])
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r.responses[:] = shared_response
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r.response_mask[:] = True
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r.rewards = torch.tensor([[1.0, 0.0], [1.0, 0.0]])
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batch = strat.prepare_from_rollout(r)
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assert torch.equal(batch["chosen"][:, 3:], shared_response.expand(2, -1))
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assert torch.equal(batch["rejected"][:, 3:], shared_response.expand(2, -1))
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assert torch.equal(batch["chosen"][:, :3], r.prompts)
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assert torch.equal(batch["rejected"][:, :3], r.prompts)
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assert not torch.equal(batch["chosen"][0], batch["chosen"][1])
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assert not batch["chosen_mask"][:, :3].any()
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assert not batch["rejected_mask"][:, :3].any()
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def test_call_without_runner_accepts_behavior_logprobs_grpo(device):
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strat = _make_grpo(device)
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batch = {
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"prompts": torch.randint(3, 200, (2, 4), device=device),
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"responses": torch.randint(3, 200, (2, 4, 6), device=device),
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"masks": torch.ones(2, 4, 6, device=device),
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"rewards": torch.randn(2, 4, device=device),
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"logprobs_old": torch.zeros(2, 4, 6, device=device),
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}
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loss = strat(batch)["loss"]
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assert torch.isfinite(loss).item()
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def test_call_with_runner_returns_finite_loss_grpo(device):
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strat = _make_grpo(device)
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strat.set_rollout_runner(_RecordingRunner(_make_rollout_result(device=device)))
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loss = strat({"input_ids": torch.randint(3, 200, (2, 4), device=device)})["loss"]
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assert torch.isfinite(loss).item()
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def test_call_with_runner_returns_finite_loss_dpo(device):
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strat = _make_dpo(device)
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strat.set_rollout_runner(_RecordingRunner(_make_rollout_result(device=device)))
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loss = strat({"input_ids": torch.randint(3, 200, (2, 4), device=device)})["loss"]
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assert torch.isfinite(loss).item()
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def test_call_invokes_runner_each_time(device):
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strat = _make_grpo(device)
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runner = _RecordingRunner(_make_rollout_result(device=device))
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strat.set_rollout_runner(runner)
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strat({"input_ids": torch.randint(3, 200, (2, 4), device=device)})
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strat({"input_ids": torch.randint(3, 200, (2, 4), device=device)})
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assert runner.calls == 2
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def test_grpo_reuses_rollout_logprobs_without_old_model(device):
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strat = _make_grpo(device)
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result = _make_rollout_result(device=device)
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result.logprobs_old.normal_().requires_grad_()
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runner = _RecordingRunner(result)
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strat.set_rollout_runner(runner)
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assert strat.old_model is None
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loss = strat({"input_ids": torch.randint(3, 200, (2, 4), device=device)})["loss"]
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loss.backward()
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assert result.logprobs_old.grad is None
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def test_grpo_reuses_same_cached_result(device):
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strat = _make_grpo(device)
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runner = _RecordingRunner(_make_rollout_result(device=device))
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strat.set_rollout_runner(runner)
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strat({"input_ids": torch.randint(3, 200, (2, 4), device=device)})
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_step(strat)
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strat({"input_ids": torch.randint(3, 200, (2, 4), device=device)})
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_step(strat)
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assert runner.calls == 2
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assert runner.step_calls == 2
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def test_grpo_accepts_new_rollout_result(device):
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strat = _make_grpo(device)
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runner = _RecordingRunner(_make_rollout_result(device=device))
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strat.set_rollout_runner(runner)
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strat({"input_ids": torch.randint(3, 200, (2, 4), device=device)})
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_step(strat)
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runner.swap_result(_make_rollout_result(device=device))
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strat({"input_ids": torch.randint(3, 200, (2, 4), device=device)})
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_step(strat)
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assert runner.calls == 2
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assert runner.step_calls == 2
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def test_dpo_no_sync_hook_when_new_rollout_result(device):
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"""DPO has no old_model, so ``_on_rollout_refresh`` must be a no-op.
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We verify by ensuring no AttributeError is raised (DPO has no
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old_model) and that step is still called.
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"""
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strat = _make_dpo(device)
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runner = _RecordingRunner(_make_rollout_result(device=device))
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strat.set_rollout_runner(runner)
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strat({"input_ids": torch.randint(3, 200, (2, 4), device=device)})
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_step(strat)
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runner.swap_result(_make_rollout_result(device=device))
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strat({"input_ids": torch.randint(3, 200, (2, 4), device=device)})
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_step(strat)
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assert runner.step_calls == 2
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def test_step_not_called_when_sync_gradients_false(device):
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executor = FakeExecutor(sync_gradients=False)
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strat = _make_grpo(device, executor=executor)
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runner = _RecordingRunner(_make_rollout_result(device=device))
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strat.set_rollout_runner(runner)
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strat({"input_ids": torch.randint(3, 200, (2, 4), device=device)})
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assert runner.step_calls == 0
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def test_step_called_when_sync_gradients_true(device):
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executor = FakeExecutor(sync_gradients=True)
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strat = _make_grpo(device, executor=executor)
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runner = _RecordingRunner(_make_rollout_result(device=device))
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strat.set_rollout_runner(runner)
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strat({"input_ids": torch.randint(3, 200, (2, 4), device=device)})
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_step(strat)
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assert runner.step_calls == 1
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assert runner.weight_updates == [1]
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assert strat.policy_version == 1
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def test_post_hoc_online_optimizer_step_is_rejected(device):
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strat = _make_grpo(device)
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strat.set_rollout_runner(_RecordingRunner(_make_rollout_result(device=device)))
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with pytest.raises(RuntimeError, match="strategy.optimizer_step"):
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strat.on_optimizer_step()
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def test_optimizer_step_publishes_version_with_weight_update(device):
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strat = _make_grpo(device)
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runner = _RecordingRunner(_make_rollout_result(device=device))
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strat.set_rollout_runner(runner)
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parameter = next(strat.model.parameters())
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parameter.grad = torch.ones_like(parameter)
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optimizer = torch.optim.SGD(strat.model.parameters(), lr=0.1)
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before = parameter.detach().clone()
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strat.optimizer_step(optimizer)
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assert not torch.equal(parameter, before)
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assert runner.weight_updates == [1]
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assert runner.step_calls == 1
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def test_loss_is_differentiable_dpo(device):
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strat = _make_dpo(device)
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strat.set_rollout_runner(_RecordingRunner(_make_rollout_result(device=device)))
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loss = strat({"input_ids": torch.randint(3, 200, (2, 4), device=device)})["loss"]
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loss.backward()
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has_grad = any(
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p.grad is not None and p.grad.abs().sum() > 0 for p in strat.model.parameters()
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)
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assert has_grad
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def test_ref_model_not_updated_by_backward_dpo(device):
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strat = _make_dpo(device)
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strat.set_rollout_runner(_RecordingRunner(_make_rollout_result(device=device)))
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loss = strat({"input_ids": torch.randint(3, 200, (2, 4), device=device)})["loss"]
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loss.backward()
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for p in strat.ref_model.parameters():
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assert p.grad is None
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