ViperEkura
1c7369f293
- Propagates MoE load-balancing loss through model outputs - Logs task, auxiliary, and weighted losses across strategies - Computes only explicitly requested callback metrics - Preserves tensor compute_loss API and adds regression tests
286 lines
10 KiB
Python
286 lines
10 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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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 swap_result(self, result):
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self.result = result
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self._fresh = True
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def _make_grpo(device, executor=None):
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model, _ = make_model(device)
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old_model = make_frozen(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=old_model,
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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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def test_dpo_prepare_from_rollout_picks_best_worst(device):
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strat = _make_dpo(device)
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r = _make_rollout_result(B=3, G=4, R=5, device=device)
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batch = strat.prepare_from_rollout(r)
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assert batch["chosen"].shape == (3, 5)
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assert batch["rejected"].shape == (3, 5)
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assert batch["chosen_mask"].shape == (3, 5)
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assert batch["rejected_mask"].shape == (3, 5)
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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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assert torch.equal(batch["chosen"], expected_best)
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assert torch.equal(batch["rejected"], expected_worst)
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def test_call_without_runner_falls_back_to_compute_loss_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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}
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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_syncs_old_model_on_first_rollout(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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with torch.no_grad():
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for p in strat.model.parameters():
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p.add_(0.1)
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old_before = {k: v.clone() for k, v in strat.old_model.state_dict().items()}
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strat({"input_ids": torch.randint(3, 200, (2, 4), device=device)})
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old_after = strat.old_model.state_dict()
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synced = any(
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not torch.allclose(old_before[k], old_after[k])
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for k in old_before
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if k in old_after
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)
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assert synced
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def test_grpo_no_resync_when_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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strat.on_optimizer_step()
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strat({"input_ids": torch.randint(3, 200, (2, 4), device=device)})
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strat.on_optimizer_step()
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assert runner.calls == 2
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assert runner.step_calls == 2
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def test_grpo_resync_when_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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strat.on_optimizer_step()
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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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strat.on_optimizer_step()
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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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strat.on_optimizer_step()
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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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strat.on_optimizer_step()
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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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strat.on_optimizer_step()
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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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