0z5a
4019ddac31
Feed sampler-aligned behavior log-probabilities directly into online GRPO instead of allocating, synchronizing, and forwarding a duplicate old-policy model. Keep the old-model path as an offline compatibility fallback and validate supplied rollout tensors before loss computation.
142 lines
4.1 KiB
Python
142 lines
4.1 KiB
Python
"""End-to-end integration tests for online GRPO/DPO rollout."""
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import os
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from functools import partial
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import pytest
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import torch
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from torch.utils.data import Dataset
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import astrai.trainer.train_context as train_context
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from astrai.config import TrainConfig
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from astrai.model.transformer import AutoRegressiveLM
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from astrai.trainer.rollout import BaseRewardModel
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from astrai.trainer.schedule import SchedulerFactory
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from astrai.trainer.trainer import Trainer
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from tests.helpers import CHAT_TEMPLATE
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class InstructionDataset(Dataset):
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"""Toy instruction/input dataset for online RL rollout.
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Each sample has an ``instruction`` and an optional ``input``; the
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RolloutGenerator renders both through the tokenizer's chat template
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so the prompt matches the SFT-trained format.
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"""
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_SAMPLES = [
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{"instruction": "Hello", "input": ""},
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{"instruction": "Tell me a story", "input": "about dragons"},
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{"instruction": "Summarize", "input": "the article"},
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{"instruction": "Translate", "input": "to French: hi"},
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]
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def __len__(self):
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return len(self._SAMPLES)
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def __getitem__(self, idx):
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return dict(self._SAMPLES[idx])
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class LengthRewardModel(BaseRewardModel):
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"""Rewards each response by its (non-pad) token count.
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Gives the group-normalized advantage a non-degenerate signal.
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"""
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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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rewards = torch.zeros(B, G)
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for i in range(B):
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for g in range(G):
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rewards[i, g] = float(len(responses[i][g]))
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return rewards
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def instruction_collate_fn(batch):
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"""Stack a list of instruction/input dicts into a batch dict of lists."""
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return {
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"instruction": [b["instruction"] for b in batch],
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"input": [b.get("input", "") for b in batch],
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}
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def _model_fn(model_config):
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return AutoRegressiveLM(model_config).to(dtype=torch.float32)
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def _optimizer_fn(m):
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return torch.optim.AdamW(m.parameters(), lr=1e-4)
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def _scheduler_fn(optim):
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return SchedulerFactory.create(
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"cosine", optim, warmup_steps=1, lr_decay_steps=4, min_rate=0.05
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)
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_ONLINE_STRATEGIES = [
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pytest.param(
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"online_grpo",
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{"clip_eps": 0.2, "kl_coef": 0.01, "group_size": 2},
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id="grpo",
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),
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pytest.param("online_dpo", {"beta": 0.1, "group_size": 2}, id="dpo"),
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]
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@pytest.mark.integration
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@pytest.mark.parametrize(("strategy", "strategy_kwargs"), _ONLINE_STRATEGIES)
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def test_online_rollout_end_to_end(
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base_test_env, strategy, strategy_kwargs, monkeypatch
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):
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"""Run one epoch of online RL rollout with KV-cache-backed generation."""
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created_reference_models = []
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create_ref_model = train_context.create_ref_model
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def track_reference_model(*args, **kwargs):
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created_reference_models.append(strategy)
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return create_ref_model(*args, **kwargs)
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monkeypatch.setattr(train_context, "create_ref_model", track_reference_model)
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test_dir = base_test_env["test_dir"]
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device = base_test_env["device"]
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tokenizer = base_test_env["tokenizer"]
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model_config = base_test_env["transformer_config"]
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tokenizer.set_chat_template(CHAT_TEMPLATE)
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tokenizer.save_pretrained(test_dir)
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train_config = TrainConfig(
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strategy=strategy,
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model_fn=partial(_model_fn, model_config),
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dataset=InstructionDataset(),
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optimizer_fn=_optimizer_fn,
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scheduler_fn=_scheduler_fn,
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ckpt_dir=os.path.join(test_dir, "ckpt"),
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n_epoch=1,
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batch_per_device=2,
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ckpt_interval=100,
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grad_accum_steps=1,
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random_seed=42,
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device_type=device,
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nprocs=1,
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parallel_mode="none",
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strategy_kwargs=strategy_kwargs,
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rollout_interval=1,
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rollout_temperature=1.0,
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rollout_top_k=0,
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rollout_top_p=1.0,
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rollout_max_tokens=4,
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reward_model_fn=LengthRewardModel,
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collate_fn=instruction_collate_fn,
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)
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trainer = Trainer(train_config)
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trainer.train(param_path=test_dir)
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assert os.path.isdir(os.path.join(test_dir, "ckpt"))
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assert len(created_reference_models) == 1
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