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Testing Layer Parameter Inheritance Implementation in DeepSpeed

This test suite validates parameter inheritance functionality in DeepSpeed’s inference v2 implementation, focusing on layer containers and parameter management. It ensures proper inheritance behavior between parent and child layers while maintaining parameter initialization states and type consistency.

Test Coverage Overview

The test suite covers layer inheritance patterns in DeepSpeed’s inference framework, specifically testing parameter handling and initialization states.

Validates parent-child layer relationships
Verifies parameter count inheritance
Tests initialization state tracking
Confirms parameter type consistency

Implementation Analysis

The testing approach utilizes pytest fixtures to verify layer container inheritance mechanics. It implements a hierarchical structure with ParentLayer and ChildLayer classes, testing parameter initialization and type verification using torch tensors.

Class inheritance validation
Parameter initialization checks
Type assertion verification
State management testing

Technical Details

Testing Framework: pytest
Core Components: InferenceParameter, LayerContainer
Test Dependencies: torch, deepspeed.inference.v2
Custom Classes: SimpleParam, DummyInferenceModel
Decorator: @pytest.mark.inference_v2

Best Practices Demonstrated

The test implementation showcases robust testing practices for inheritance-based architectures in machine learning frameworks.

Clear test case isolation
Systematic state verification
Type safety checks
Hierarchical class testing
Proper assertion usage

microsoft/deepspeed

tests/unit/inference/v2/model_implementations/parameters/test_layer_inheritance.py

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

# DeepSpeed Team

import pytest
import torch

from deepspeed.inference.v2.inference_parameter import InferenceParameter
from deepspeed.inference.v2.model_implementations.layer_container_base import LayerContainer

from .utils import SimpleParam, DummyInferenceModel


class ParentLayer(LayerContainer):
    """
    A layer that has a dependency on a simple parameter.
    """

    param_1: SimpleParam


class ChildLayer(ParentLayer):
    """
    A layer that inherits from another layer.
    """

    param_2: SimpleParam


@pytest.mark.inference_v2
def test_layer_inheritance():
    inference_model = DummyInferenceModel()

    multi_param_layer = ChildLayer(inference_model)

    assert multi_param_layer.n_params == 2
    assert multi_param_layer.is_initialized is False

    multi_param_layer.param_1.param = torch.ones(16, 16)

    assert multi_param_layer.is_initialized is False

    multi_param_layer.param_2.param = torch.full((16, 16), 2.0)

    assert multi_param_layer.is_populated is True
    assert isinstance(multi_param_layer.param_1, InferenceParameter)
    assert isinstance(multi_param_layer.param_2, InferenceParameter)