fix: rewrite GRPO data pipeline for offline record-level access
- process_list_field returns List[List[int]] preserving per-response boundaries - GRPODataset rewritten to record-level __getitem__ (no windowing/stride) - grpo_collate_fn pads variable-length responses into [B, G, R] tensors - JsonlStore detects nested List[List[int]] and stores List[Tensor] per record - Store._normalize skips nested-list keys from cumsum bookkeeping - Pipeline._flush handles nested lists without cross-record flattening - Export grpo_collate_fn from astrai.dataset - 6 new GRPO tests + 2 updated builder tests, 114 total pass
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+290
-23
@@ -327,43 +327,82 @@ def test_normalize_mixed_empty_key():
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def test_grpo_dataset_dtype(base_test_env):
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"""GRPO dataset returns correct dtypes for per-record structured data."""
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from astrai.dataset.dataset import GRPODataset
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test_dir = base_test_env["test_dir"]
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dummy_data = {
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"prompts": [torch.randint(0, 100, (100,), dtype=torch.int32)],
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"responses": [torch.randint(0, 100, (100,), dtype=torch.int32)],
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"masks": [torch.ones(100, dtype=torch.int32)],
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"rewards": [torch.ones(100, dtype=torch.float32)],
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}
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dataset = _make_seq_dataset(
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test_dir, "grpo_dtype", train_type="grpo", data=dummy_data, window_size=32
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)
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G = 4
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dataset = GRPODataset()
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dataset.storage = type(
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"FakeStore",
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(),
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{
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"keys": ["prompts", "responses", "masks", "rewards"],
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"_data": {
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"prompts": [torch.randint(0, 100, (10,), dtype=torch.int32)],
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"responses": [
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[torch.randint(0, 100, (5,), dtype=torch.int32) for _ in range(G)]
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],
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"masks": [[torch.ones(5, dtype=torch.int32) for _ in range(G)]],
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"rewards": [torch.rand(G, dtype=torch.float32)],
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},
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},
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)()
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dataset._build_records()
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item = dataset[0]
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assert item["prompts"].dtype == torch.long
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assert item["responses"].dtype == torch.long
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assert item["masks"].dtype == torch.bool
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assert all(r.dtype == torch.long for r in item["responses"])
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assert all(m.dtype == torch.bool for m in item["masks"])
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assert item["rewards"].dtype == torch.float32
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def test_grpo_dataset_load(base_test_env):
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"""GRPO dataset loads record-structured data with per-response boundaries."""
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from astrai.dataset.dataset import GRPODataset
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test_dir = base_test_env["test_dir"]
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dummy_data = {
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"prompts": [_rand_seq(200)],
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"responses": [_rand_seq(200)],
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"masks": [torch.ones(200, dtype=torch.int64)],
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"rewards": [torch.rand(200, dtype=torch.float32)],
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}
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dataset = _make_seq_dataset(
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test_dir, "grpo_test", train_type="grpo", data=dummy_data
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)
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assert len(dataset) > 0
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G = 3
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prompt_len = 8
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resp_lens = [5, 7, 4]
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dataset = GRPODataset()
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dataset.storage = type(
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"FakeStore",
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(),
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{
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"keys": ["prompts", "responses", "masks", "rewards"],
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"_data": {
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"prompts": [torch.randint(0, 100, (prompt_len,))],
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"responses": [[torch.randint(0, 100, (rl,)) for rl in resp_lens]],
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"masks": [[torch.ones(rl, dtype=torch.int64) for rl in resp_lens]],
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"rewards": [torch.tensor([0.9, 0.3, 0.7], dtype=torch.float32)],
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},
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},
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)()
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dataset._build_records()
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assert len(dataset) == 1
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item = dataset[0]
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assert "prompts" in item
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assert "responses" in item
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assert "masks" in item
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assert "rewards" in item
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assert item["prompts"].shape[0] == 64
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assert item["responses"].shape[0] == 64
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# Prompts is 1-D
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assert item["prompts"].shape == (prompt_len,)
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# Responses is a list of G tensors with correct lengths
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assert len(item["responses"]) == G
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for i, r in enumerate(item["responses"]):
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assert r.shape == (resp_lens[i],)
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# Masks align with responses
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assert len(item["masks"]) == G
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for i, m in enumerate(item["masks"]):
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assert m.shape == (resp_lens[i],)
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# Rewards has G elements
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assert item["rewards"].shape == (G,)
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def test_detect_format_bin_dir(base_test_env):
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@@ -621,3 +660,231 @@ def test_jsonl_store_pipeline_config_roundtrip(base_test_env):
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config = PipelineConfig.from_dict(raw)
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assert config.output.position_ids_mode == "doc_reset"
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assert config.preprocessing.max_seq_len == 64
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# ---------------------------------------------------------------------------
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# GRPO end-to-end: builder → JsonlStore → GRPODataset → collate_fn
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# ---------------------------------------------------------------------------
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def _write_grpo_jsonl(test_dir, tokenizer_path, records):
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"""Write a GRPO JSONL dataset directory with config."""
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data_dir = os.path.join(test_dir, "grpo_jsonl")
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os.makedirs(data_dir, exist_ok=True)
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with open(os.path.join(data_dir, "data.jsonl"), "w", encoding="utf-8") as f:
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for rec in records:
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f.write(json.dumps(rec, ensure_ascii=False) + "\n")
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config = {
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"tokenizer_path": tokenizer_path,
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"version": 1,
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"input": {
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"sources": {
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"prompts": {
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"sections": [
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{
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"field": "prompt",
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"action": "mask",
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"add_special_tokens": True,
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}
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]
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},
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"responses": {
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"sections": [{"field": "responses", "action": "train"}],
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"list_field": True,
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"mask_key": "masks",
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},
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"rewards": {
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"sections": [{"field": "rewards", "action": "value"}],
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},
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}
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},
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"mask": {"user": "mask", "assistant": "train"},
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"mask_default": "mask",
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"preprocessing": {"max_seq_len": 128},
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"output": {"position_ids_mode": "none"},
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}
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with open(
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os.path.join(data_dir, "dataset_config.json"), "w", encoding="utf-8"
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) as f:
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json.dump(config, f, ensure_ascii=False, indent=2)
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return data_dir
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def test_grpo_builder_preserves_response_boundaries(base_test_env):
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"""MultiOutputMaskBuilder with list_field returns List[List[int]] for responses."""
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from astrai.preprocessing.builder import SectionedMaskBuilder
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from tests.data.conftest import make_grpo_no_template_config
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tokenizer = base_test_env["tokenizer"]
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tokenizer_path = _save_test_tokenizer(base_test_env["test_dir"], tokenizer)
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builder = SectionedMaskBuilder()
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config = make_grpo_no_template_config()
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config.preprocessing.max_seq_len = 128
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item = {
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"prompt": "What is 2+2?",
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"responses": ["4", "four", "2+2=4"],
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"rewards": [0.9, 0.1, 0.5],
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}
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result = builder.build(item, config, tokenizer)
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assert result is not None
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# prompts should be flat list of ints
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assert isinstance(result["prompts"], list)
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assert isinstance(result["prompts"][0], int)
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# responses should be list of lists (one per response)
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assert isinstance(result["responses"], list)
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assert isinstance(result["responses"][0], list)
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assert isinstance(result["responses"][0][0], int)
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assert len(result["responses"]) == 3
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# masks should match responses structure
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assert isinstance(result["masks"], list)
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assert len(result["masks"]) == 3
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for i in range(3):
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assert len(result["masks"][i]) == len(result["responses"][i])
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# rewards should be flat list of floats
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assert isinstance(result["rewards"], list)
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assert all(isinstance(r, float) for r in result["rewards"])
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assert len(result["rewards"]) == 3
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def test_grpo_end_to_end_jsonl(base_test_env):
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"""Full GRPO pipeline: JSONL → JsonlStore → GRPODataset → collate_fn."""
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from astrai.dataset.dataset import grpo_collate_fn
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test_dir = base_test_env["test_dir"]
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tokenizer = base_test_env["tokenizer"]
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tokenizer_path = _save_test_tokenizer(test_dir, tokenizer)
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records = [
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{
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"prompt": "What is 2+2?",
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"responses": ["4", "four", "The answer is 4"],
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"rewards": [0.9, 0.1, 0.5],
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},
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{
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"prompt": "Write a haiku",
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"responses": ["Leaves fall", "Cherry blossoms bloom in spring"],
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"rewards": [0.3, 0.8],
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},
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]
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data_dir = _write_grpo_jsonl(test_dir, tokenizer_path, records)
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dataset = DatasetFactory.load("grpo", data_dir, window_size=0)
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assert len(dataset) == 2
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# Item 0: 3 responses
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item0 = dataset[0]
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assert item0["prompts"].ndim == 1
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assert len(item0["responses"]) == 3
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assert len(item0["masks"]) == 3
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assert item0["rewards"].shape == (3,)
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for r, m in zip(item0["responses"], item0["masks"]):
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assert r.shape == m.shape
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# Item 1: 2 responses (different group size)
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item1 = dataset[1]
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assert len(item1["responses"]) == 2
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assert item1["rewards"].shape == (2,)
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# Collate: batch records with same G (item0 has G=3)
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batch = grpo_collate_fn([item0, item0])
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assert batch["prompts"].shape[0] == 2
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assert batch["responses"].ndim == 3
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assert batch["responses"].shape[0] == 2
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assert batch["responses"].shape[1] == 3 # G=3
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assert batch["masks"].shape == batch["responses"].shape
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assert batch["rewards"].shape == (2, 3)
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def test_grpo_collate_variable_lengths():
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"""collate_fn pads variable-length responses to [B, G, R_max]."""
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from astrai.dataset.dataset import grpo_collate_fn
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batch = [
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{
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"prompts": torch.tensor([1, 2, 3]),
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"responses": [torch.tensor([4, 5]), torch.tensor([6, 7, 8, 9])],
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"masks": [torch.tensor([1, 1]), torch.tensor([1, 1, 1, 1])],
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"rewards": torch.tensor([0.9, 0.1]),
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},
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{
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"prompts": torch.tensor([10, 11]),
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"responses": [torch.tensor([12]), torch.tensor([13, 14, 15])],
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"masks": [torch.tensor([1]), torch.tensor([1, 1, 1])],
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"rewards": torch.tensor([0.5, 0.5]),
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},
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]
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result = grpo_collate_fn(batch)
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assert result["prompts"].shape == (2, 3) # B=2, P_max=3
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assert result["responses"].shape == (2, 2, 4) # B=2, G=2, R_max=4
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assert result["masks"].shape == (2, 2, 4)
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assert result["rewards"].shape == (2, 2)
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# Check padding: item 1 prompt is length 2, padded to 3
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assert result["prompts"][1, 2] == 0
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# Check response content: item 0, response 0 is [4,5] padded to 4
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assert result["responses"][0, 0, 0] == 4
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assert result["responses"][0, 0, 1] == 5
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assert result["responses"][0, 0, 2] == 0 # padded
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assert result["masks"][0, 0, 2] == False # padded
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# Check response content: item 0, response 1 is [6,7,8,9] no padding
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assert result["responses"][0, 1, 3] == 9
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assert result["masks"][0, 1, 3] == True
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def test_grpo_multiple_records(base_test_env):
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"""GRPODataset loads multiple records with correct structure."""
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from astrai.dataset.dataset import GRPODataset
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G = 4
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n_records = 5
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dummy_responses = [
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[torch.randint(0, 100, (np.random.randint(3, 8),)) for _ in range(G)]
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for _ in range(n_records)
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]
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dataset = GRPODataset()
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dataset.storage = type(
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"FakeStore",
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(),
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{
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"keys": ["prompts", "responses", "masks", "rewards"],
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"_data": {
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"prompts": [torch.randint(0, 100, (10,)) for _ in range(n_records)],
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"responses": dummy_responses,
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"masks": [
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[torch.ones(r.shape[0], dtype=torch.int64) for r in resps]
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for resps in dummy_responses
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],
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"rewards": [
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torch.rand(G, dtype=torch.float32) for _ in range(n_records)
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],
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},
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},
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)()
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dataset._build_records()
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assert len(dataset) == n_records
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for i in range(n_records):
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item = dataset[i]
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assert len(item["responses"]) == G
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assert len(item["masks"]) == G
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assert item["rewards"].shape == (G,)
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for g in range(G):
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assert item["responses"][g].shape == item["masks"][g].shape
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@@ -349,7 +349,17 @@ def test_grpo_basic(chat_tokenizer, builder):
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assert "responses" in result
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assert "masks" in result
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assert "rewards" in result
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assert len(result["responses"]) == len(result["masks"])
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# responses is List[List[int]] — one per response
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assert len(result["responses"]) == 4
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assert all(isinstance(r, list) for r in result["responses"])
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assert all(isinstance(r[0], int) for r in result["responses"])
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# masks is List[List[int]] — one per response, matching length
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assert len(result["masks"]) == 4
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for i in range(4):
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assert len(result["masks"][i]) == len(result["responses"][i])
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assert result["rewards"] == [1.0, 0.5, 0.8, 0.2]
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@@ -362,8 +372,11 @@ def test_grpo_response_tokens_all_trained(chat_tokenizer, builder):
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}
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result = builder.build(item, config, chat_tokenizer)
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masks = result["masks"]
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assert all(m == 1 for m in masks)
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assert len(masks) == len(result["responses"])
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# masks is List[List[int]] — each response's mask should be all 1s
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assert len(masks) == 2
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for m in masks:
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assert all(v == 1 for v in m)
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assert len(m) == len(result["responses"][masks.index(m)])
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def test_grpo_single_reward(chat_tokenizer, builder):
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