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.
38 lines
1.5 KiB
Python
38 lines
1.5 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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import pytest
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import data_designer.lazy_heavy_imports as lazy
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from data_designer.engine.sampling_gen.utils import check_random_state
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@pytest.mark.parametrize(
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"test_case,input_value,expected_type,expected_seed",
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[
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("none_input", None, "np.random.mtrand._rand", None),
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("np_random_input", lazy.np.random, "np.random.mtrand._rand", None),
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("integer_input", 42, "np.random.RandomState", 42),
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("random_state_input", lazy.np.random.RandomState(123), "np.random.RandomState", 123),
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],
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)
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def test_check_random_state_scenarios(test_case, input_value, expected_type, expected_seed):
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if test_case == "random_state_input":
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result = check_random_state(input_value)
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assert result is input_value
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else:
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result = check_random_state(input_value)
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if expected_type == "np.random.mtrand._rand":
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assert result is lazy.np.random.mtrand._rand
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elif expected_type == "np.random.RandomState":
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assert isinstance(result, lazy.np.random.RandomState)
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if expected_seed is not None:
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assert result.get_state()[1][0] == expected_seed
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def test_check_random_state_invalid():
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with pytest.raises(ValueError, match="'invalid' cannot be used to seed a numpy.random.RandomState instance"):
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check_random_state("invalid")
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