test: add online GRPO end-to-end training test

tests/trainer/test_grpo_e2e.py

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+"""End-to-end integration test for online GRPO rollout."""
+
+import os
+from functools import partial
+
+import pytest
+import torch
+from torch.utils.data import Dataset
+
+from astrai.config import TrainConfig
+from astrai.model.transformer import AutoRegressiveLM
+from astrai.trainer.rollout import BaseRewardModel
+from astrai.trainer.schedule import SchedulerFactory
+from astrai.trainer.trainer import Trainer
+from tests.helpers import CHAT_TEMPLATE
+
+
+class InstructionDataset(Dataset):
+    """Toy instruction/input dataset for online RL rollout.
+
+    Each sample has an ``instruction`` and an optional ``input``; the
+    RolloutGenerator renders both through the tokenizer's chat template
+    so the prompt matches the SFT-trained format.
+    """
+
+    _SAMPLES = [
+        {"instruction": "Hello", "input": ""},
+        {"instruction": "Tell me a story", "input": "about dragons"},
+        {"instruction": "Summarize", "input": "the article"},
+        {"instruction": "Translate", "input": "to French: hi"},
+    ]
+
+    def __len__(self):
+        return len(self._SAMPLES)
+
+    def __getitem__(self, idx):
+        return dict(self._SAMPLES[idx])
+
+
+class LengthRewardModel(BaseRewardModel):
+    """Rewards each response by its (non-pad) token count.
+
+    Gives the group-normalized advantage a non-degenerate signal.
+    """
+
+    def score(self, prompts, responses):
+        B = len(prompts)
+        G = len(responses[0]) if B else 0
+        rewards = torch.zeros(B, G)
+        for i in range(B):
+            for g in range(G):
+                rewards[i, g] = float(len(responses[i][g]))
+        return rewards
+
+
+def instruction_collate_fn(batch):
+    """Stack a list of instruction/input dicts into a batch dict of lists."""
+    return {
+        "instruction": [b["instruction"] for b in batch],
+        "input": [b.get("input", "") for b in batch],
+    }
+
+
+def _model_fn(model_config):
+    return AutoRegressiveLM(model_config).to(dtype=torch.float32)
+
+
+def _optimizer_fn(m):
+    return torch.optim.AdamW(m.parameters(), lr=1e-4)
+
+
+def _scheduler_fn(optim):
+    return SchedulerFactory.create(
+        "cosine", optim, warmup_steps=1, lr_decay_steps=4, min_rate=0.05
+    )
+
+
+@pytest.mark.integration
+def test_online_grpo_end_to_end(base_test_env):
+    """Run one epoch of online GRPO with KV-cache-backed rollout."""
+    test_dir = base_test_env["test_dir"]
+    device = base_test_env["device"]
+    tokenizer = base_test_env["tokenizer"]
+    model_config = base_test_env["transformer_config"]
+
+    tokenizer.set_chat_template(CHAT_TEMPLATE)
+    tokenizer.save_pretrained(test_dir)
+
+    model_fn = partial(_model_fn, model_config)
+    optimizer_fn = _optimizer_fn
+    scheduler_fn = _scheduler_fn
+
+    dataset = InstructionDataset()
+
+    train_config = TrainConfig(
+        strategy="online_grpo",
+        model_fn=model_fn,
+        dataset=dataset,
+        optimizer_fn=optimizer_fn,
+        scheduler_fn=scheduler_fn,
+        ckpt_dir=os.path.join(test_dir, "ckpt"),
+        n_epoch=1,
+        batch_per_device=2,
+        ckpt_interval=100,
+        grad_accum_steps=1,
+        random_seed=42,
+        device_type=device,
+        nprocs=1,
+        parallel_mode="none",
+        extra_kwargs={"clip_eps": 0.2, "kl_coef": 0.01, "group_size": 2},
+        rollout_interval=1,
+        rollout_temperature=1.0,
+        rollout_top_k=0,
+        rollout_top_p=1.0,
+        rollout_max_tokens=4,
+        reward_model_fn=LengthRewardModel,
+        collate_fn=instruction_collate_fn,
+    )
+
+    trainer = Trainer(train_config)
+    trainer.train(param_path=test_dir)
+
+    assert os.path.isdir(os.path.join(test_dir, "ckpt"))