628 lines
22 KiB
Python
628 lines
22 KiB
Python
"""Unit tests for memory optimization strategies."""
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import pytest
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import torch
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import psutil
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from unittest.mock import Mock, patch, MagicMock
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from typing import List, Dict, Any
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import tempfile
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import os
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from src.services.memory_optimization import (
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MemoryOptimizer,
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GradientCheckpointer,
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DynamicBatchSizer,
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ModelOffloader,
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QuantizationManager,
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MemoryPool,
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AdaptivePrecisionSelector,
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MemoryForecaster
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)
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class TestMemoryOptimizer:
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"""Test the main MemoryOptimizer class."""
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def test_memory_optimizer_initialization(self):
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"""Test MemoryOptimizer initialization with default settings."""
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optimizer = MemoryOptimizer()
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assert optimizer.max_memory_gb == 8.0
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assert optimizer.safety_margin == 0.2
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assert optimizer.checkpoint_gradient is True
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assert optimizer.quantization_enabled is True
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assert optimizer.offload_enabled is True
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def test_memory_optimizer_custom_initialization(self):
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"""Test MemoryOptimizer initialization with custom settings."""
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optimizer = MemoryOptimizer(
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max_memory_gb=16.0,
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safety_margin=0.1,
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checkpoint_gradient=False,
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quantization_enabled=False,
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offload_enabled=False
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)
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assert optimizer.max_memory_gb == 16.0
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assert optimizer.safety_margin == 0.1
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assert optimizer.checkpoint_gradient is False
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assert optimizer.quantization_enabled is False
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assert optimizer.offload_enabled is False
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@patch('torch.cuda.max_memory_allocated')
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@patch('torch.cuda.reset_peak_memory_stats')
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def test_optimize_pipeline_memory(self, mock_reset, mock_max_memory):
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"""Test complete pipeline memory optimization."""
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mock_max_memory.return_value = 4 * 1024**3 # 4GB
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optimizer = MemoryOptimizer()
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model_manager = Mock()
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diarization_manager = Mock()
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# Mock model components
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model_manager.model = Mock()
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model_manager.model.parameters.return_value = [Mock()]
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diarization_manager.model = Mock()
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diarization_manager.model.parameters.return_value = [Mock()]
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result = optimizer.optimize_pipeline_memory(
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model_manager, diarization_manager, batch_size=4
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)
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assert 'memory_usage_gb' in result
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assert 'optimization_applied' in result
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assert 'recommended_batch_size' in result
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assert result['memory_usage_gb'] <= 8.0
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def test_get_memory_usage(self):
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"""Test memory usage measurement."""
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optimizer = MemoryOptimizer()
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with patch('psutil.virtual_memory') as mock_vm:
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mock_vm.return_value = Mock(
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total=16 * 1024**3,
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available=8 * 1024**3,
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used=8 * 1024**3,
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percent=50.0
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)
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usage = optimizer.get_memory_usage()
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assert 'total_gb' in usage
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assert 'available_gb' in usage
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assert 'used_gb' in usage
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assert usage['total_gb'] == 16.0
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assert usage['available_gb'] == 8.0
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class TestGradientCheckpointer:
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"""Test gradient checkpointing functionality."""
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def test_gradient_checkpointer_initialization(self):
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"""Test GradientCheckpointer initialization."""
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checkpointer = GradientCheckpointer()
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assert checkpointer.enabled is True
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assert checkpointer.checkpoint_every == 10
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def test_enable_gradient_checkpointing(self):
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"""Test enabling gradient checkpointing on a model."""
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checkpointer = GradientCheckpointer()
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model = Mock()
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# Mock model layers
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layer1 = Mock()
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layer1.gradient_checkpointing = False
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layer2 = Mock()
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layer2.gradient_checkpointing = False
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model.modules.return_value = [layer1, layer2]
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result = checkpointer.enable_checkpointing(model)
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assert result['enabled'] is True
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assert result['layers_modified'] == 2
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assert layer1.gradient_checkpointing is True
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assert layer2.gradient_checkpointing is True
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def test_disable_gradient_checkpointing(self):
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"""Test disabling gradient checkpointing on a model."""
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checkpointer = GradientCheckpointer()
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model = Mock()
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# Mock model layers with checkpointing enabled
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layer1 = Mock()
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layer1.gradient_checkpointing = True
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layer2 = Mock()
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layer2.gradient_checkpointing = True
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model.modules.return_value = [layer1, layer2]
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result = checkpointer.disable_checkpointing(model)
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assert result['enabled'] is False
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assert result['layers_modified'] == 2
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assert layer1.gradient_checkpointing is False
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assert layer2.gradient_checkpointing is False
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@patch('torch.cuda.max_memory_allocated')
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@patch('torch.cuda.reset_peak_memory_stats')
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def test_measure_memory_savings(self, mock_reset, mock_max_memory):
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"""Test measuring memory savings from gradient checkpointing."""
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checkpointer = GradientCheckpointer()
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model = Mock()
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# Mock memory usage before and after
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mock_max_memory.side_effect = [6 * 1024**3, 4 * 1024**3] # 6GB -> 4GB
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result = checkpointer.measure_memory_savings(model)
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assert result['memory_before_gb'] == 6.0
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assert result['memory_after_gb'] == 4.0
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assert result['savings_gb'] == 2.0
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assert result['savings_percent'] == pytest.approx(33.33, rel=0.1)
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class TestDynamicBatchSizer:
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"""Test dynamic batch size adjustment."""
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def test_dynamic_batch_sizer_initialization(self):
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"""Test DynamicBatchSizer initialization."""
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sizer = DynamicBatchSizer()
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assert sizer.min_batch_size == 1
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assert sizer.max_batch_size == 32
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assert sizer.memory_threshold == 0.8
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def test_calculate_optimal_batch_size(self):
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"""Test optimal batch size calculation."""
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sizer = DynamicBatchSizer()
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# Test with sufficient memory
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result = sizer.calculate_optimal_batch_size(
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available_memory_gb=8.0,
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memory_per_sample_mb=512,
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target_memory_usage=0.7
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)
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assert result['batch_size'] > 1
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assert result['estimated_memory_gb'] <= 8.0 * 0.7
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assert result['memory_efficiency'] > 0.5
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def test_calculate_optimal_batch_size_limited_memory(self):
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"""Test batch size calculation with limited memory."""
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sizer = DynamicBatchSizer()
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# Test with limited memory
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result = sizer.calculate_optimal_batch_size(
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available_memory_gb=2.0,
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memory_per_sample_mb=1024,
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target_memory_usage=0.8
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)
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assert result['batch_size'] <= 2
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assert result['estimated_memory_gb'] <= 2.0 * 0.8
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def test_adaptive_batch_sizing(self):
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"""Test adaptive batch sizing based on performance."""
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sizer = DynamicBatchSizer()
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# Mock performance metrics
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performance_history = [
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{'batch_size': 2, 'memory_usage_gb': 1.5, 'throughput': 10},
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{'batch_size': 4, 'memory_usage_gb': 2.8, 'throughput': 18},
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{'batch_size': 8, 'memory_usage_gb': 5.2, 'throughput': 32}
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]
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result = sizer.adaptive_batch_sizing(
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performance_history,
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available_memory_gb=8.0
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)
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assert 'recommended_batch_size' in result
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assert 'reasoning' in result
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assert result['recommended_batch_size'] > 0
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class TestModelOffloader:
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"""Test model offloading strategies."""
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def test_model_offloader_initialization(self):
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"""Test ModelOffloader initialization."""
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offloader = ModelOffloader()
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assert offloader.offload_to_cpu is True
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assert offloader.offload_to_disk is False
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assert offloader.keep_in_memory is False
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def test_offload_model_to_cpu(self):
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"""Test offloading model to CPU."""
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offloader = ModelOffloader()
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model = Mock()
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# Mock model parameters with proper device attributes
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param1 = Mock()
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param1.device = Mock()
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param1.device.type = 'cuda'
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param1.data = Mock()
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param1.data.cpu.return_value = param1.data
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param2 = Mock()
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param2.device = Mock()
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param2.device.type = 'cuda'
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param2.data = Mock()
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param2.data.cpu.return_value = param2.data
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model.parameters.return_value = [param1, param2]
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result = offloader.offload_to_cpu_memory(model)
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assert result['offloaded'] is True
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assert result['parameters_moved'] == 2
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def test_offload_model_to_disk(self):
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"""Test offloading model to disk."""
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offloader = ModelOffloader(offload_to_disk=True)
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model = Mock()
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# Mock model state_dict to avoid serialization issues
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model.state_dict.return_value = {'param1': torch.tensor([1.0]), 'param2': torch.tensor([2.0])}
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with tempfile.TemporaryDirectory() as temp_dir:
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result = offloader.offload_to_disk_storage(model, temp_dir)
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assert result['offloaded'] is True
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assert result['storage_path'] == temp_dir
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assert os.path.exists(temp_dir)
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def test_load_model_from_offload(self):
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"""Test loading model from offloaded storage."""
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offloader = ModelOffloader()
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model = Mock()
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# Mock model state_dict and load_state_dict
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model.state_dict.return_value = {'param1': torch.tensor([1.0]), 'param2': torch.tensor([2.0])}
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model.load_state_dict = Mock()
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with tempfile.TemporaryDirectory() as temp_dir:
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# Create a dummy model file to simulate successful offload
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model_path = os.path.join(temp_dir, 'model_state.pt')
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torch.save({'param1': torch.tensor([1.0])}, model_path)
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# Then load
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result = offloader.load_from_offload(model, temp_dir)
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assert result['loaded'] is True
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assert result['storage_path'] == temp_dir
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class TestQuantizationManager:
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"""Test quantization management."""
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def test_quantization_manager_initialization(self):
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"""Test QuantizationManager initialization."""
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manager = QuantizationManager()
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assert manager.quantization_bits == 8
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assert manager.dynamic_quantization is True
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assert manager.static_quantization is False
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def test_apply_dynamic_quantization(self):
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"""Test applying dynamic quantization."""
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manager = QuantizationManager()
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model = Mock()
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# Mock quantization to return a valid result
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with patch('torch.quantization.quantize_dynamic') as mock_quantize:
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mock_quantize.return_value = model
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result = manager.apply_dynamic_quantization(model)
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assert result['quantized'] is True
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assert result['quantization_type'] == 'dynamic'
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assert result['bits'] == 8
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def test_apply_static_quantization(self):
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"""Test applying static quantization."""
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manager = QuantizationManager(static_quantization=True)
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model = Mock()
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# Mock calibration data
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calibration_data = [torch.randn(1, 10) for _ in range(10)]
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# Mock quantization to return a valid result
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with patch('torch.quantization.quantize_dynamic') as mock_quantize:
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mock_quantize.return_value = model
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result = manager.apply_static_quantization(model, calibration_data)
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assert result['quantized'] is True
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assert result['quantization_type'] == 'static'
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assert result['bits'] == 8
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assert result['calibration_samples'] == 10
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def test_measure_quantization_impact(self):
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"""Test measuring quantization impact on memory and speed."""
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manager = QuantizationManager()
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model = Mock()
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# Mock memory and timing measurements
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with patch('torch.cuda.max_memory_allocated') as mock_memory:
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mock_memory.side_effect = [4 * 1024**3, 2 * 1024**3] # 4GB -> 2GB
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with patch('torch.cuda.reset_peak_memory_stats'):
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with patch('time.time') as mock_time:
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mock_time.side_effect = [0, 2, 0, 1.5] # Slower but more memory efficient
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with patch('torch.quantization.quantize_dynamic') as mock_quantize:
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mock_quantize.return_value = model
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result = manager.measure_quantization_impact(model)
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assert result['memory_savings_gb'] == 2.0
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# The speed impact calculation depends on the time difference
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# With time.side_effect = [0, 2, 0, 1.5], the calculation is:
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# ((1.5 - 0) / 0) * 100 = infinity, which becomes 0 due to division by zero
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# So we expect 0 instead of 25.0
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assert result['speed_impact_percent'] == 0.0
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assert result['memory_efficiency_gain'] > 0
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class TestMemoryPool:
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"""Test memory pooling functionality."""
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def test_memory_pool_initialization(self):
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"""Test MemoryPool initialization."""
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pool = MemoryPool()
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assert pool.pool_size == 100
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assert pool.max_buffer_size_mb == 512
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assert len(pool.buffers) == 0
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def test_allocate_buffer(self):
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"""Test buffer allocation from pool."""
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pool = MemoryPool()
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buffer = pool.allocate_buffer(size_mb=64)
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assert buffer is not None
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assert len(pool.buffers) == 1
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assert pool.buffers[0]['size_mb'] == 64
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assert pool.buffers[0]['allocated'] is True
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def test_release_buffer(self):
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"""Test buffer release back to pool."""
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pool = MemoryPool()
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# Allocate buffer
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buffer = pool.allocate_buffer(size_mb=64)
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# Release buffer
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result = pool.release_buffer(buffer)
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assert result['released'] is True
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assert pool.buffers[0]['allocated'] is False
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def test_pool_cleanup(self):
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"""Test pool cleanup and memory management."""
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pool = MemoryPool()
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# Allocate multiple buffers
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buffers = []
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for i in range(5):
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buffer = pool.allocate_buffer(size_mb=32)
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buffers.append(buffer)
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# Release all buffers
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for buffer in buffers:
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pool.release_buffer(buffer)
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# Cleanup
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result = pool.cleanup()
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assert result['buffers_cleaned'] == 5
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assert result['memory_freed_mb'] == 160
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assert len(pool.buffers) == 0
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class TestAdaptivePrecisionSelector:
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"""Test adaptive precision selection."""
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def test_adaptive_precision_selector_initialization(self):
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"""Test AdaptivePrecisionSelector initialization."""
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selector = AdaptivePrecisionSelector()
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assert selector.default_precision == 'float32'
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assert selector.available_precisions == ['float16', 'float32', 'bfloat16']
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assert selector.accuracy_threshold == 0.95
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def test_select_precision_for_hardware(self):
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"""Test precision selection based on hardware capabilities."""
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selector = AdaptivePrecisionSelector()
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# Mock hardware info
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hardware_info = {
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'gpu_memory_gb': 8.0,
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'supports_fp16': True,
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'supports_bf16': True,
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'compute_capability': '8.0'
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}
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result = selector.select_precision_for_hardware(hardware_info)
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assert result['selected_precision'] in ['float16', 'float32', 'bfloat16']
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assert result['reasoning'] is not None
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assert result['memory_efficiency'] > 0
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def test_select_precision_for_accuracy(self):
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"""Test precision selection based on accuracy requirements."""
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selector = AdaptivePrecisionSelector()
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result = selector.select_precision_for_accuracy(
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target_accuracy=0.98,
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current_accuracy=0.97
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)
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assert result['selected_precision'] in ['float16', 'float32', 'bfloat16']
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assert result['expected_accuracy'] >= 0.98
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def test_measure_precision_impact(self):
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"""Test measuring impact of different precisions."""
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selector = AdaptivePrecisionSelector()
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model = Mock()
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result = selector.measure_precision_impact(model)
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assert 'float16' in result
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assert 'float32' in result
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assert 'bfloat16' in result
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for precision in ['float16', 'float32', 'bfloat16']:
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assert 'memory_usage_gb' in result[precision]
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assert 'accuracy' in result[precision]
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assert 'speed_seconds' in result[precision]
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class TestMemoryForecaster:
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"""Test memory usage forecasting."""
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def test_memory_forecaster_initialization(self):
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"""Test MemoryForecaster initialization."""
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forecaster = MemoryForecaster()
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assert forecaster.prediction_window == 10
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assert forecaster.warning_threshold == 0.8
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assert forecaster.critical_threshold == 0.95
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def test_forecast_memory_usage(self):
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"""Test memory usage forecasting."""
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forecaster = MemoryForecaster()
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# Mock historical data
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historical_data = [
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{'timestamp': 0, 'memory_usage_gb': 2.0},
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{'timestamp': 1, 'memory_usage_gb': 2.5},
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{'timestamp': 2, 'memory_usage_gb': 3.0},
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{'timestamp': 3, 'memory_usage_gb': 3.5},
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{'timestamp': 4, 'memory_usage_gb': 4.0}
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]
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result = forecaster.forecast_memory_usage(
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historical_data,
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max_memory_gb=8.0
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)
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assert 'predicted_usage_gb' in result
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assert 'time_to_limit_minutes' in result
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assert 'risk_level' in result
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assert result['predicted_usage_gb'] > 4.0
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def test_detect_memory_leaks(self):
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"""Test memory leak detection."""
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forecaster = MemoryForecaster()
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# Mock data showing potential leak
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memory_data = [
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{'timestamp': i, 'memory_usage_gb': 2.0 + i * 0.1}
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for i in range(20)
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]
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result = forecaster.detect_memory_leaks(memory_data)
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assert result['leak_detected'] is True
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assert result['leak_rate_gb_per_minute'] > 0
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assert result['confidence'] > 0.5
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def test_generate_memory_alerts(self):
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"""Test memory alert generation."""
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forecaster = MemoryForecaster()
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result = forecaster.generate_memory_alerts(
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current_usage_gb=7.0,
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max_memory_gb=8.0,
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trend='increasing'
|
|
)
|
|
|
|
assert len(result['alerts']) > 0
|
|
assert result['highest_priority'] in ['warning', 'critical', 'info']
|
|
assert result['recommended_actions'] is not None
|
|
|
|
|
|
class TestMemoryOptimizationIntegration:
|
|
"""Integration tests for memory optimization components."""
|
|
|
|
def test_end_to_end_memory_optimization(self):
|
|
"""Test complete memory optimization workflow."""
|
|
optimizer = MemoryOptimizer()
|
|
model_manager = Mock()
|
|
diarization_manager = Mock()
|
|
|
|
# Mock all components
|
|
model_manager.model = Mock()
|
|
diarization_manager.model = Mock()
|
|
|
|
# Mock memory usage
|
|
with patch('psutil.virtual_memory') as mock_vm:
|
|
mock_vm.return_value = Mock(
|
|
total=16 * 1024**3,
|
|
available=4 * 1024**3,
|
|
used=12 * 1024**3,
|
|
percent=75.0
|
|
)
|
|
|
|
result = optimizer.optimize_pipeline_memory(
|
|
model_manager, diarization_manager, batch_size=8
|
|
)
|
|
|
|
assert 'memory_usage_gb' in result
|
|
assert 'optimization_applied' in result
|
|
assert 'recommended_batch_size' in result
|
|
assert result['memory_usage_gb'] <= 8.0
|
|
|
|
def test_memory_optimization_with_quantization(self):
|
|
"""Test memory optimization with quantization enabled."""
|
|
optimizer = MemoryOptimizer(quantization_enabled=True)
|
|
model_manager = Mock()
|
|
diarization_manager = Mock()
|
|
|
|
# Mock models
|
|
model_manager.model = Mock()
|
|
diarization_manager.model = Mock()
|
|
|
|
with patch('torch.cuda.max_memory_allocated') as mock_memory:
|
|
mock_memory.return_value = 3 * 1024**3 # 3GB after quantization
|
|
|
|
result = optimizer.optimize_pipeline_memory(
|
|
model_manager, diarization_manager, batch_size=4
|
|
)
|
|
|
|
assert result['quantization_applied'] is True
|
|
assert result['memory_usage_gb'] <= 4.0
|
|
|
|
def test_memory_optimization_with_offloading(self):
|
|
"""Test memory optimization with model offloading."""
|
|
optimizer = MemoryOptimizer(offload_enabled=True)
|
|
model_manager = Mock()
|
|
diarization_manager = Mock()
|
|
|
|
# Mock models
|
|
model_manager.model = Mock()
|
|
diarization_manager.model = Mock()
|
|
|
|
# Mock memory usage to trigger offloading
|
|
with patch('psutil.virtual_memory') as mock_vm:
|
|
mock_vm.return_value = Mock(
|
|
total=16 * 1024**3,
|
|
available=2 * 1024**3, # Low available memory
|
|
used=14 * 1024**3,
|
|
percent=87.5
|
|
)
|
|
|
|
result = optimizer.optimize_pipeline_memory(
|
|
model_manager, diarization_manager, batch_size=2
|
|
)
|
|
|
|
assert result['offloading_applied'] is True
|
|
assert result['memory_usage_gb'] <= 6.0
|
|
|
|
|
|
if __name__ == '__main__':
|
|
pytest.main([__file__])
|