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Testing Matrix Dequantization Implementation in DeepSpeed

This test suite validates DeepSpeed’s dequantization functionality for deep learning model optimization. It focuses on verifying the accuracy of dequantization operations across different matrix dimensions and group configurations, ensuring compatibility with hardware acceleration.

Test Coverage Overview

The test suite provides comprehensive coverage of the dequantization process in DeepSpeed.

Key areas tested include:

Matrix dequantization with varying dimensions (14336×7168, 14336×1792, 768×768)
Different group configurations (32 and 48 groups)
Comparison between custom backend implementation and PyTorch reference
Hardware-specific acceleration compatibility

Implementation Analysis

The testing approach utilizes pytest framework with distributed testing capabilities. The implementation employs a systematic verification pattern comparing dequantized outputs between DeepSpeed’s optimized backend and PyTorch reference implementation.

Key patterns include:

Device-aware testing using accelerator abstraction
Random input generation for comprehensive validation
Precision-specific conversions (int8 to float16)

Technical Details

Testing infrastructure includes:

PyTest framework with DistributedTest base class
DeepSpeed InferenceBuilder for backend operations
CUDA/GPU device management
Tensor operations with specific dtype handling (int8, float16)
Custom dequantization operation validation

Best Practices Demonstrated

The test suite exemplifies high-quality testing practices for deep learning operations.

Notable practices include:

Systematic validation across different matrix dimensions
Hardware compatibility checks
Precise numerical comparisons using torch.allclose
Clean separation of initialization and test execution
Proper error handling and skip conditions

microsoft/deepspeed

tests/unit/compression/test_dequantization.py

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

# DeepSpeed Team

# Copyright (c) 2023, 2023, Oracle and/or its affiliates.

import os
import torch
import pytest
from unit.common import DistributedTest
import deepspeed
from deepspeed.accelerator import get_accelerator


class TestDequantization(DistributedTest):

    def init(self):
        local_rank = int(os.getenv("LOCAL_RANK", "0"))
        self.device = torch.device(get_accelerator().device_name(local_rank))

        from deepspeed.ops.op_builder import InferenceBuilder
        if not deepspeed.ops.__compatible_ops__[InferenceBuilder.NAME]:
            pytest.skip("InferenceBuilder is not implemented")
        else:
            self.dequantize_func = InferenceBuilder().load().dequantize_fp16

    def run_dequantize_test(self, M, N, num_groups):
        weight = torch.randint(-255, 255, (M, N)).to(dtype=torch.int8, device=self.device)
        scale = torch.rand(num_groups, 1).to(device=self.device)

        weight_deq = (weight.reshape(num_groups, -1) * scale).reshape(M, N).to(torch.float16).contiguous()
        weight_deq_backend = self.dequantize_func(weight, scale, num_groups)

        assert torch.allclose(weight_deq, weight_deq_backend)

    def test_dequantize(self):
        self.init()

        self.run_dequantize_test(14336, 7168, 32)
        self.run_dequantize_test(14336, 1792, 32)
        self.run_dequantize_test(768, 768, 32)
        self.run_dequantize_test(768, 768, 48)