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reasoning-gym-server & cli tool (#154)
* feat: Add initial server structure with configuration, registry, and middleware * feat: Add chain_sum dataset to experiment registry test * fix: Update test_registry to use DatasetSpec for composite config validation * refactor: Update Pydantic config to use json_schema_extra and ConfigDict * feat: Add Pydantic models for API request/response data * feat: Implement basic experiment management endpoints with tests * feat: Implement composite configuration endpoints for experiments * fix: Add missing DatasetConfigUpdate import in server.py * refactor: Update dataset config update method to properly merge config updates * fix: Correctly retrieve current dataset config in composite endpoint * feat: Add basic CLI structure with experiments and config commands * feat: Add initial CLI tool with basic experiment management commands * refactor: Reorganize CLI package structure and fix import paths * refactor: Implement initial CLI commands for experiment management * feat: Implement HTTP client for Reasoning Gym server in RGC CLI tool * fix: Move print statements inside try block to resolve SyntaxError * fix: Resolve SyntaxError in edit_config function by adding missing except block * feat: Add default app instance in server module for easier uvicorn startup * docs: Add README.md with server and RGC tool documentation * remove unused files * refactor: Remove unsupported type annotation in registry.py * refactor: Move ExperimentRegistry to coaching module and add Experiment class * fix: Add missing CompositeDataset import in test_registry.py * refactor: Implement lazy ASGI app creation for server initialization * feat: Add health check command to RGC CLI for server connection * feat: Add version tracking support to CompositeDataset * feat: Add DatasetVersionManager for tracking dataset versions * feat: Add entry_id metadata and score_answer_with_id method to CompositeDataset * feat: Add entry_id metadata combining version and index * fix: Resolve undefined variable by storing version_id before use * test: Add comprehensive unit tests for score_answer_with_id() function * test: Add comprehensive version tracking test for dataset config updates * feat: Validate dataset weights are positive in CompositeDataset initialization * feat: Add weight update and normalization methods to CompositeDataset * refactor: Centralize weight normalization in CompositeDataset and allow zero-weight datasets * feat: Add negative weight validation to CompositeDataset constructor * feat: Add duplicate dataset name check in CompositeDataset and update test * refactor: Move duplicate dataset name check inside dataset iteration loop * refactor: Update CompositeDataset weight management to use config as source of truth * refactor: Move duplicate dataset name check to CompositeConfig.validate() * test: Update composite dataset weight test assertions and validation * feat: Add methods to add and remove datasets in CompositeDataset * refactor: Remove weight normalization and use unnormalized weights directly * refactor: Remove redundant total weight check in update_dataset_weights * feat: Add batch generation and scoring endpoints to server * fix: Import BatchEntry in server.py to resolve undefined name error * refactor: Update ReasoningGymDataset to use server for batch generation and scoring * fix: Add missing List and Dict type imports * feat: Add get_batch() and score_outputs() methods to RGClient * test: Add unit tests for generate_batch and score_outputs endpoints * refactor: Add DatasetVersionManager to Experiment class and CompositeDataset constructor * feat: Add validation for base_index and batch_size in generate_batch endpoint * refactor: Remove unused BatchRequest type from imports * refactor: Convert models to use Pydantic exclusively * test: Update scoring endpoint tests to use correct request model format * refactor: Rename ScoreItem to AnswerItem and update related code * feat: Update scoring endpoint to return ordered ScoringResponse with scores and entry_ids * fix: Add missing ScoringResponse import in server.py * move verl ppo sample with server into own file * refactor: Use Pydantic models for get_batch() and score_outputs() in RGClient * refactor: Update client methods to use Pydantic models for type safety * refactor: Use Pydantic models for experiment and dataset config operations * refactor: Clean up duplicate methods and improve error handling in main.py * first bits of rg server use for verl * refactor: Optimize scoring with single HTTP request in _score_output * fix: Correct experiment creation with ExperimentCreate object * grpo tests with server
This commit is contained in:
Andreas Köpf
GitHub
parent
bec6aefd11
commit
e2702092f4
23 changed files with 1968 additions and 22 deletions
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@ -4,6 +4,7 @@ import pytest
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import yaml
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from reasoning_gym.composite import CompositeConfig, CompositeDataset, DatasetSpec
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from reasoning_gym.version_manager import DatasetVersionManager
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def create_test_config(tmp_path):
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@ -85,13 +86,165 @@ def test_composite_dataset_weights():
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seed=42,
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datasets=[
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DatasetSpec("chain_sum", 2.0, {"min_terms": 2}),
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DatasetSpec("chain_sum", 3.0, {"min_terms": 3}),
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DatasetSpec("products", 3.0, {"min_terms": 2}),
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],
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)
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dataset = CompositeDataset(config)
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assert abs(dataset.weights[0] - 0.4) < 1e-6
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assert abs(dataset.weights[1] - 0.6) < 1e-6
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assert abs(dataset.weights[0] - 2.0) < 1e-6
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assert abs(dataset.weights[1] - 3.0) < 1e-6
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# Test weight updates
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dataset.update_dataset_weight("chain_sum", 1.0)
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print(dataset.weights)
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assert abs(dataset.weights[0] - 1.0) < 1e-6
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assert abs(dataset.weights[1] - 3.0) < 1e-6
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# Test invalid weight
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with pytest.raises(ValueError, match="Weight must be non-negative"):
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dataset.update_dataset_weight("chain_sum", -1.0)
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# Test invalid dataset name
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with pytest.raises(KeyError):
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dataset.update_dataset_weight("invalid_dataset", 1.0)
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# Test zero total weight
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dataset.update_dataset_weight("chain_sum", 0.0)
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with pytest.raises(ValueError, match="Total of weights must be greater than zero"):
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dataset.update_dataset_weight("products", 0.0)
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_ = dataset[0] # access item with all weights 0
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# Test duplicate dataset names
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with pytest.raises(ValueError, match="Duplicate dataset names"):
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CompositeConfig(
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size=1000,
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seed=42,
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datasets=[
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DatasetSpec("chain_sum", 1.0, {"min_terms": 2}),
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DatasetSpec("chain_sum", 1.0, {"min_terms": 3}),
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],
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).validate()
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def test_version_tracking_with_config_updates():
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"""Test that version tracking works correctly when updating dataset configs"""
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# Create composite dataset with version manager
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version_manager = DatasetVersionManager()
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config = CompositeConfig(
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size=10, seed=42, datasets=[DatasetSpec("chain_sum", 1.0, {"min_terms": 2, "max_terms": 4})]
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)
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dataset = CompositeDataset(config, version_manager=version_manager)
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# Get an entry and its id from initial version
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entry_1 = dataset[0]
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entry_id_1 = entry_1["metadata"]["entry_id"]
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answer_1 = entry_1["answer"]
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# Update dataset config
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dataset.update_dataset_config("chain_sum", {"min_terms": 3, "max_terms": 5})
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# Get new entry after config update
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entry_2 = dataset[0]
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entry_id_2 = entry_2["metadata"]["entry_id"]
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answer_2 = entry_2["answer"]
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# Verify entries have different version IDs
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version_1 = int(entry_id_1.split(".")[0])
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version_2 = int(entry_id_2.split(".")[0])
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assert version_1 != version_2, "New config should create new version"
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# Verify original answer still works with original version
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score_1 = dataset.score_answer_with_id(answer_1, entry_id_1)
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assert score_1 == 1.0, "Original answer should still work with original version"
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# Verify new answer works with new version
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score_2 = dataset.score_answer_with_id(answer_2, entry_id_2)
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assert score_2 == 1.0, "New answer should work with new version"
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# Verify original answer fails with new version
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score_3 = dataset.score_answer_with_id(answer_1, entry_id_2)
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assert score_3 < 1.0, "Original answer should not work with new version"
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def test_score_answer_with_id():
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"""Test scoring answers using entry_id"""
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# Create composite dataset with version manager
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version_manager = DatasetVersionManager()
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config = CompositeConfig(
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size=10, seed=42, datasets=[DatasetSpec("chain_sum", 1.0, {"min_terms": 2, "max_terms": 4})]
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)
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dataset = CompositeDataset(config, version_manager=version_manager)
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# Get an entry and its id
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entry = dataset[0]
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entry_id = entry["metadata"]["entry_id"]
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# Test successful scoring
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answer = entry["answer"]
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score = dataset.score_answer_with_id(answer, entry_id)
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assert score == 1.0 # Correct answer should get full score
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# Test wrong answer
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wrong_answer = "wrong"
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score = dataset.score_answer_with_id(wrong_answer, entry_id)
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assert score < 1.0 # Wrong answer should get lower score
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# Test invalid entry_id format
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with pytest.raises(ValueError, match="Invalid entry_id format"):
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dataset.score_answer_with_id(answer, "invalid")
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# Test non-existent version
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with pytest.raises(KeyError, match="Version .* not found"):
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dataset.score_answer_with_id(answer, "999.0")
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# Test without version manager
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dataset_no_vm = CompositeDataset(config)
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with pytest.raises(RuntimeError, match="Version manager required"):
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dataset_no_vm.score_answer_with_id(answer, entry_id)
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def test_add_remove_dataset():
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"""Test adding and removing datasets from composite"""
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config = CompositeConfig(
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size=1000,
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seed=42,
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datasets=[
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DatasetSpec("chain_sum", 1.0, {"min_terms": 2}),
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],
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)
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dataset = CompositeDataset(config)
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# Test adding new dataset
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new_spec = DatasetSpec("products", 2.0, {"min_terms": 2})
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dataset.add_dataset(new_spec)
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assert len(dataset.datasets) == 2
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assert "products" in dataset.datasets
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assert len(dataset.config.datasets) == 2
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assert dataset.dataset_names[0] == "chain_sum"
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assert dataset.dataset_names[1] == "products"
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assert abs(dataset.weights[0] - 1.0) < 1e-6 # chain_sum weight
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assert abs(dataset.weights[1] - 2.0) < 1e-6 # products weight
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# Test duplicate name
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with pytest.raises(ValueError, match="already exists"):
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dataset.add_dataset(new_spec)
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# Test removing dataset
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dataset.remove_dataset("products")
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assert len(dataset.datasets) == 1
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assert "products" not in dataset.datasets
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assert len(dataset.config.datasets) == 1
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# Test removing non-existent dataset
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with pytest.raises(KeyError):
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dataset.remove_dataset("nonexistent")
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# Test removing last dataset
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with pytest.raises(ValueError, match="Cannot remove last dataset"):
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dataset.remove_dataset("chain_sum")
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def test_yaml_loading(tmp_path):
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