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Testing Balanced Partition Algorithm Implementation in DeepSpeed

This test suite evaluates DeepSpeed’s partition balancing utility functionality, focusing on the balanced distribution of weights across multiple partitions. The tests verify that the partition_balanced function correctly divides weight arrays while maintaining specified difference constraints between partition sums.

Test Coverage Overview

The test suite provides comprehensive coverage of the partition_balanced utility function in DeepSpeed.

Key areas tested include:

Balanced partitioning of different sized weight arrays
Handling of various partition counts
Verification of target difference constraints between partition sums
Edge cases with zero values and uneven distributions

Implementation Analysis

The testing approach employs a helper function check_partition that validates partition results against expected target differences. The implementation uses direct assertions to verify partition sum differences, with each test case providing specific weight arrays, partition counts, and expected difference targets.

The testing pattern focuses on numerical validation of partition balance properties.

Technical Details

Testing components include:

Python’s built-in assertion mechanism
DeepSpeed runtime utilities module
Custom check_partition validation function
Array-based weight distributions
Numerical difference calculations between partition sums

Best Practices Demonstrated

The test suite exemplifies several testing best practices.

Notable aspects include:

Clear test case organization with specific input-output pairs
Robust validation of numerical constraints
Comprehensive error messages with test case details
Efficient test helper function reuse
Coverage of both standard and edge cases

microsoft/deepspeed

tests/unit/utils/test_partition_balanced.py

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

# DeepSpeed Team

from deepspeed.runtime import utils as ds_utils


def check_partition(weights, num_parts, target_diff):
    result = ds_utils.partition_balanced(weights=weights, num_parts=num_parts)

    parts_sum = []
    for b, e in zip(result[:-1], result[1:]):
        parts_sum.append(sum(weights[b:e]))

    assert max(parts_sum) - min(
        parts_sum
    ) == target_diff, f"ds_utils.partition_balanced(weights={weights}, num_parts={num_parts}) return {result}"


def test_partition_balanced():
    check_partition([1, 2, 1], 4, target_diff=2)
    check_partition([1, 1, 1, 1], 4, target_diff=0)
    check_partition([1, 1, 1, 1, 1], 4, target_diff=1)
    check_partition([1, 1, 1, 1, 0, 1], 4, target_diff=1)