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Validating State Manager Configuration Parameters in DeepSpeed

This test suite validates configuration parameters for the DeepSpeed State Manager, focusing on boundary conditions and invalid inputs for ragged batch processing. It ensures robust error handling for sequence tracking and batch size management in inference scenarios.

Test Coverage Overview

The test suite provides comprehensive validation of DSStateManagerConfig parameters, specifically targeting invalid configurations.

Validates negative and zero values for max_tracked_sequences
Tests boundary conditions for max_ragged_batch_size
Verifies max_ragged_sequence_count constraints
Ensures proper relationship between batch size and sequence counts

Implementation Analysis

The implementation uses pytest’s exception handling mechanisms to verify validation errors. Each test case focuses on a specific configuration parameter, employing a systematic approach to validate input constraints.

Uses pytest.mark.inference_v2 for test categorization
Implements ValidationError checks from pydantic
Follows isolated test case pattern for each parameter

Technical Details

Testing Framework: pytest
Validation Library: pydantic
Test Scope: Unit tests
Component: DSStateManagerConfig
Module: deepspeed.inference.v2.ragged

Best Practices Demonstrated

The test suite exemplifies strong testing practices through clear separation of concerns and thorough validation coverage.

Individual test cases for each failure condition
Consistent test naming convention
Proper use of pytest markers
Clear validation error expectations
Type hints for function signatures

microsoft/deepspeed

tests/unit/inference/v2/ragged/test_manager_configs.py

            
# Copyright (c) Microsoft Corporation.
# SPDX-License-Identifier: Apache-2.0

# DeepSpeed Team

import pytest

from pydantic import ValidationError

from deepspeed.inference.v2.ragged import DSStateManagerConfig


@pytest.mark.inference_v2
def test_negative_max_tracked_sequences() -> None:
    with pytest.raises(ValidationError):
        DSStateManagerConfig(max_tracked_sequences=-1)


@pytest.mark.inference_v2
def test_zero_max_tracked_sequences() -> None:
    with pytest.raises(ValidationError):
        DSStateManagerConfig(max_tracked_sequences=0)


@pytest.mark.inference_v2
def test_negative_max_ragged_batch_size() -> None:
    with pytest.raises(ValidationError):
        DSStateManagerConfig(max_ragged_batch_size=-1)


@pytest.mark.inference_v2
def test_zero_max_ragged_batch_size() -> None:
    with pytest.raises(ValidationError):
        DSStateManagerConfig(max_ragged_batch_size=0)


@pytest.mark.inference_v2
def test_negative_max_ragged_sequence_count() -> None:
    with pytest.raises(ValidationError):
        DSStateManagerConfig(max_ragged_sequence_count=-1)


@pytest.mark.inference_v2
def test_zero_max_ragged_sequence_count() -> None:
    with pytest.raises(ValidationError):
        DSStateManagerConfig(max_ragged_sequence_count=0)


@pytest.mark.inference_v2
def test_too_small_max_ragged_batch_size() -> None:
    with pytest.raises(ValidationError):
        DSStateManagerConfig(max_ragged_batch_size=512, max_ragged_sequence_count=1024)


@pytest.mark.inference_v2
def test_too_small_max_tracked_sequences() -> None:
    with pytest.raises(ValidationError):
        DSStateManagerConfig(max_tracked_sequences=512, max_ragged_sequence_count=1024)