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5dd1bb4 9e64e71 5dd1bb4 9e64e71 5dd1bb4 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 | """E2E-style smoke coverage for the GRPO training notebook."""
from __future__ import annotations
import json
from pathlib import Path
from sql_env.training.config import GRPOConfig
from sql_env.training.notebook_pipeline import (
build_trainer,
run_training_with_metrics,
sample_random_baseline,
)
from sql_env.training.data_loading import filter_questions_by_difficulty
NOTEBOOK_PATH = Path("notebooks/train_grpo.ipynb")
def _read_notebook() -> dict:
return json.loads(NOTEBOOK_PATH.read_text(encoding="utf-8"))
def _code_sources(notebook: dict) -> list[str]:
cells = notebook.get("cells", [])
return [
"".join(cell.get("source", []))
for cell in cells
if cell.get("cell_type") == "code"
]
def test_training_notebook_smoke_structure() -> None:
"""Notebook includes the core GRPO training flow cells."""
assert NOTEBOOK_PATH.exists(), "notebooks/train_grpo.ipynb must exist"
notebook = _read_notebook()
sources = "\n".join(_code_sources(notebook))
assert "GRPOConfig(" in sources
assert "load_model_and_tokenizer(config.model_name)" in sources
assert "grpo_trainer_cls=GRPOTrainer" in sources
assert "run_training_with_metrics" in sources
assert "matplotlib.pyplot as plt" in sources
before_index = sources.find("before_rollouts = sample_random_baseline")
train_index = sources.find("run_training_with_metrics(trainer)")
assert before_index != -1
assert train_index != -1
assert before_index < train_index
def test_question_filtering_by_difficulty() -> None:
"""Difficulty filtering keeps only questions in the allowed set."""
questions = [
{"question_text": "q1", "difficulty": "easy"},
{"question_text": "q2", "difficulty": "medium"},
{"question_text": "q3", "difficulty": "hard"},
]
filtered = filter_questions_by_difficulty(questions, ["easy"])
assert [item["question_text"] for item in filtered] == ["q1"]
class _FakeTRLConfig:
def __init__(self, **kwargs):
self.kwargs = kwargs
class _FakeTrainer:
def __init__(
self,
*,
model,
processing_class,
args,
train_dataset,
reward_funcs,
) -> None:
self.model = model
self.processing_class = processing_class
self.args = args
self.train_dataset = train_dataset
self.reward_funcs = reward_funcs
self.state = type("State", (), {"log_history": []})()
self.train_called = False
def train(self) -> dict[str, str]:
self.train_called = True
self.state.log_history = [{"step": 1, "reward": 0.25}]
return {"status": "ok"}
class _FakeTokenizer:
def apply_chat_template(
self,
messages: list[dict[str, str]],
tokenize: bool = False,
add_generation_prompt: bool = True,
) -> str:
del messages
del tokenize
del add_generation_prompt
return "prompt"
class _FakeModel:
def __init__(self) -> None:
self._count = 0
def generate(self, prompt: str, max_new_tokens: int) -> str:
del prompt
del max_new_tokens
self._count += 1
if self._count == 1:
return "QUERY: SELECT 1"
return "ANSWER: 42"
def test_notebook_pipeline_executes_training_step() -> None:
"""Notebook pipeline helper builds trainer and executes train()."""
config = GRPOConfig(
questions_path="data/questions/questions_train.json",
db_dir="data/databases",
output_dir="outputs/grpo_test",
step_budget=2,
)
tokenizer = _FakeTokenizer()
model = _FakeModel()
trainer = build_trainer(
model=model,
tokenizer=tokenizer,
prompts=[{"prompt": "Count rows"}],
config=config,
trl_grpo_config_cls=_FakeTRLConfig,
grpo_trainer_cls=_FakeTrainer,
reward_funcs=[],
)
output, steps, rewards = run_training_with_metrics(trainer)
assert trainer.train_called is True
assert output == {"status": "ok"}
assert steps == [1]
assert rewards == [0.25]
def test_random_baseline_transcripts_are_generated() -> None:
"""Random baseline helper generates readable transcripts per prompt."""
baseline = sample_random_baseline(["q1", "q2"], step_budget=3, seed=7)
assert len(baseline) == 2
assert all(item["metadata"]["policy"] == "random" for item in baseline)
assert all(item["completion"] for item in baseline)
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