mirror of
https://github.com/NVIDIA-NeMo/DataDesigner
synced 2026-05-24 09:48:29 +00:00
1439bbea7e
* perf: defer heavy imports to improve CLI startup time Move expensive imports (engine, models, controllers) out of the module-level import path so that data-designer --help and other non-generation commands no longer pay the full startup cost. Key changes: - Defer controller imports to inside command functions - Remove eager re-export chains from CLI package __init__ files - Move default-settings bootstrap into load_config_builder() and DataDesigner.__init__() instead of running at import time - Add lazy __getattr__ exports in interface/__init__.py - Replace module-level tokenizer init with cached lazy getter - Fix ModelProvider import to use config layer instead of engine - Update test mock paths to match new import locations Reduces CLI import-time from ~1.67s to ~0.46s. * perf: defer pandas/numpy in io_helpers and add config_list benchmark - Replace eager `from lazy_heavy_imports import pd, np` in io_helpers with module-level __getattr__ (for backwards-compatible external access / test mocks) and function-level imports in the 3 functions that actually use them (read_parquet_dataset, smart_load_dataframe, _convert_to_serializable). Importing io_helpers no longer triggers pandas/numpy loading. - Defer heavy imports in list and reset CLI commands into function bodies to avoid loading repositories, Rich, and prompt_toolkit at module import time. - Add `config_list` (data-designer config list) measurement to the CLI startup benchmark with isolated cold measurement in a separate venv and a --skip-config-list-check flag. - Update test mock paths to match new import locations. * Refine lazy import usage and TYPE_CHECKING cleanup * Run license header updater on PR-touched files * fix: update sqlfluff mock target for lazy imports in test_sql * perf: cache globals() in lazy __getattr__ to avoid repeated lookups Add globals() caching and explanatory comment to all three lazy __getattr__ implementations (lazy_heavy_imports, config/__init__, interface/__init__) so subsequent attribute accesses bypass __getattr__. * perf: lazy CLI command loading and deferred heavy import evaluations - Add LazyTyperGroup to defer command module loading until invocation, allowing module-level imports in all CLI command files - Split DataFrameSeedSource into seed_source_dataframe.py to isolate pandas dependency from other seed source classes - Move TypeVar/TypeAlias definitions (DataT, NumpyArray1dT, RadomStateT, EngineT) to TYPE_CHECKING blocks with runtime fallbacks - Wrap module-level constants in lru_cache (phone_number parquet data, jsonschema validator) to defer I/O and heavy imports to first use - Update test mock targets to patch at usage-site for module-level imports * refactor: use direct pandas import in seed_source_dataframe Drop lazy-loading for pandas in DataFrameSeedSource; use direct import for simplicity. * update lazy import pattern * update tests to use lazy import namespace Switch test modules to import data_designer.lazy_heavy_imports as lazy and reference heavy libraries through that namespace. This keeps heavy imports deferred during module import and aligns tests with the new lazy-import usage pattern. * tighten import perf test thresholds Document recent baseline timings and lower the allowed average import time and timeout so regressions are detected sooner. * document pandas import requirement Clarify that Pydantic needs DataFrame resolved at module load and that keeping the direct import preserves IDE typing support. * increase timeout time * use lazy pandas imports in visualization tests - replace direct pandas usage with lazy.pd in visualization tests to avoid eager imports - add TYPE_CHECKING pandas import and keep CLI controller imports sorted * fix lazy pandas runtime usage and preview mocks Switch sample-record handling to lazy pandas types so runtime paths no longer depend on TYPE_CHECKING imports. Align preview controller tests to patch the module-local DataDesigner symbol, preventing real engine invocation in save results scenarios.
112 lines
3.6 KiB
Python
112 lines
3.6 KiB
Python
# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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# SPDX-License-Identifier: Apache-2.0
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from __future__ import annotations
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from unittest.mock import Mock
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import pytest
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from data_designer.config.base import ConfigBase
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from data_designer.engine.configurable_task import ConfigurableTask, DataT, TaskConfigT
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from data_designer.engine.models.registry import ModelRegistry
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from data_designer.engine.resources.resource_provider import ResourceProvider
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from data_designer.engine.storage.artifact_storage import ArtifactStorage
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def test_configurable_task_generic_type_variables() -> None:
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# DataT constraints are deferred to TYPE_CHECKING to avoid eagerly importing pandas.
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# At runtime it is an unconstrained TypeVar; type checkers still see the constraints.
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assert DataT.__constraints__ == ()
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assert TaskConfigT.__bound__ == ConfigBase
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def test_configurable_task_concrete_implementation(tmp_path) -> None:
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class TestConfig(ConfigBase):
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value: str
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class TestTask(ConfigurableTask[TestConfig]):
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@classmethod
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def get_config_type(cls) -> type[TestConfig]:
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return TestConfig
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def _validate(self) -> None:
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pass
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def _initialize(self) -> None:
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pass
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config = TestConfig(value="test")
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artifact_storage = ArtifactStorage(artifact_path=tmp_path)
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resource_provider = ResourceProvider(artifact_storage=artifact_storage)
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task = TestTask(config=config, resource_provider=resource_provider)
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assert task._config == config
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assert task._resource_provider == resource_provider
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def test_configurable_task_config_validation(tmp_path) -> None:
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class TestConfig(ConfigBase):
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value: str
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class TestTask(ConfigurableTask[TestConfig]):
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@classmethod
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def get_config_type(cls) -> type[TestConfig]:
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return TestConfig
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def _validate(self) -> None:
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if self._config.value == "invalid":
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raise ValueError("Invalid config")
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config = TestConfig(value="test")
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artifact_storage = ArtifactStorage(artifact_path=tmp_path)
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resource_provider = ResourceProvider(artifact_storage=artifact_storage)
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task = TestTask(config=config, resource_provider=resource_provider)
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assert task._config.value == "test"
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invalid_config = TestConfig(value="invalid")
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with pytest.raises(ValueError, match="Invalid config"):
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TestTask(config=invalid_config, resource_provider=resource_provider)
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def test_configurable_task_resource_validation(tmp_path) -> None:
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class TestConfig(ConfigBase):
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value: str
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class TestTask(ConfigurableTask[TestConfig]):
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@classmethod
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def get_config_type(cls) -> type[TestConfig]:
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return TestConfig
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def _validate(self) -> None:
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pass
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def _initialize(self) -> None:
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pass
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config = TestConfig(value="test")
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artifact_storage = ArtifactStorage(artifact_path=tmp_path)
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mock_model_registry = Mock(spec=ModelRegistry)
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resource_provider = ResourceProvider(artifact_storage=artifact_storage, model_registry=mock_model_registry)
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task = TestTask(config=config, resource_provider=resource_provider)
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assert task._resource_provider == resource_provider
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def test_configurable_task_resource_provider_is_none() -> None:
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class TestConfig(ConfigBase):
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value: str
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class TestTask(ConfigurableTask[TestConfig]):
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def _validate(self) -> None:
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pass
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def _initialize(self) -> None:
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pass
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config = TestConfig(value="test")
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task = TestTask(config=config, resource_provider=None)
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assert task._resource_provider is None
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