File size: 15,874 Bytes
5c93746 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 | """
Test cases for the communication module.
This module provides comprehensive tests for all communication abstractions.
"""
import unittest
import logging
import torch
import torch.distributed as dist
import tempfile
import os
import sys
from unittest.mock import Mock, patch, MagicMock
# Add the parent directory to the path to import our modules
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from communication.data_containers import LatentData, KVCacheData, CommunicationConfig, BlockInterval, PerformanceMetrics
from communication.buffer_manager import BufferManager
from communication.utils import CommunicationTags, setup_logging, compute_balanced_split
from communication.distributed_communicator import DistributedCommunicator
from communication.kv_cache_manager import KVCacheManager
from communication.model_data_transfer import ModelDataTransfer
class TestDataContainers(unittest.TestCase):
"""Test cases for data container classes."""
def setUp(self):
"""Set up test fixtures."""
self.device = torch.device('cpu')
self.sample_latents = torch.randn(1, 4, 16, 16, device=self.device)
self.sample_original_latents = torch.randn(1, 4, 16, 16, 16, device=self.device)
self.sample_current_start = torch.tensor([0, 1, 2], device=self.device)
self.sample_current_end = torch.tensor([1, 2, 3], device=self.device)
self.sample_patched_x_shape = torch.tensor([1, 4, 16, 16, 16], device=self.device)
def test_latent_data_creation(self):
"""Test LatentData creation and validation."""
latent_data = LatentData(
chunk_idx=0,
latents=self.sample_latents,
original_latents=self.sample_original_latents,
current_start=self.sample_current_start,
current_end=self.sample_current_end,
current_step=100,
patched_x_shape=self.sample_patched_x_shape
)
self.assertEqual(latent_data.chunk_idx, 0)
self.assertEqual(latent_data.current_step, 100)
self.assertTrue(torch.equal(latent_data.latents, self.sample_latents))
def test_latent_data_validation(self):
"""Test LatentData validation with invalid inputs."""
with self.assertRaises(TypeError):
LatentData(
chunk_idx=0,
latents="invalid", # Should be torch.Tensor
original_latents=self.sample_original_latents,
current_start=self.sample_current_start,
current_end=self.sample_current_end,
current_step=100,
patched_x_shape=self.sample_patched_x_shape
)
def test_communication_config(self):
"""Test CommunicationConfig creation and validation."""
config = CommunicationConfig(
max_outstanding=5,
buffer_pool_size=20,
enable_buffer_reuse=True,
communication_timeout=60.0
)
self.assertEqual(config.max_outstanding, 5)
self.assertEqual(config.buffer_pool_size, 20)
self.assertTrue(config.enable_buffer_reuse)
self.assertEqual(config.communication_timeout, 60.0)
def test_communication_config_validation(self):
"""Test CommunicationConfig validation with invalid inputs."""
with self.assertRaises(ValueError):
CommunicationConfig(max_outstanding=0) # Should be at least 1
with self.assertRaises(ValueError):
CommunicationConfig(buffer_pool_size=0) # Should be at least 1
with self.assertRaises(ValueError):
CommunicationConfig(communication_timeout=0) # Should be positive
def test_block_interval(self):
"""Test BlockInterval creation and methods."""
interval = BlockInterval(start=0, end=10, rank=0)
self.assertEqual(interval.start, 0)
self.assertEqual(interval.end, 10)
self.assertEqual(interval.rank, 0)
self.assertEqual(interval.size, 10)
self.assertTrue(interval.contains(5))
self.assertFalse(interval.contains(10))
self.assertFalse(interval.contains(-1))
def test_block_interval_validation(self):
"""Test BlockInterval validation with invalid inputs."""
with self.assertRaises(ValueError):
BlockInterval(start=-1, end=10, rank=0) # Start should be non-negative
with self.assertRaises(ValueError):
BlockInterval(start=10, end=5, rank=0) # End should be greater than start
with self.assertRaises(ValueError):
BlockInterval(start=0, end=10, rank=-1) # Rank should be non-negative
def test_performance_metrics(self):
"""Test PerformanceMetrics creation and methods."""
metrics = PerformanceMetrics(
dit_time=1.0,
total_time=2.0,
communication_time=0.5,
buffer_allocation_time=0.1
)
self.assertEqual(metrics.dit_time, 1.0)
self.assertEqual(metrics.total_time, 2.0)
self.assertEqual(metrics.communication_time, 0.5)
self.assertEqual(metrics.buffer_allocation_time, 0.1)
self.assertEqual(metrics.efficiency, 0.75) # (2.0 - 0.5) / 2.0
class TestBufferManager(unittest.TestCase):
"""Test cases for BufferManager."""
def setUp(self):
"""Set up test fixtures."""
self.device = torch.device('cpu')
self.config = CommunicationConfig(buffer_pool_size=5)
self.buffer_manager = BufferManager(self.device, self.config)
def test_buffer_allocation(self):
"""Test buffer allocation and reuse."""
shape = (1, 4, 16, 16)
dtype = torch.float32
# Allocate a buffer
buffer1 = self.buffer_manager.get_buffer(shape, dtype, "latent")
self.assertEqual(buffer1.shape, shape)
self.assertEqual(buffer1.dtype, dtype)
self.assertEqual(buffer1.device, self.device)
# Return the buffer
self.buffer_manager.return_buffer(buffer1, "latent")
# Get another buffer of the same shape - should reuse
buffer2 = self.buffer_manager.get_buffer(shape, dtype, "latent")
self.assertEqual(buffer2.shape, shape)
self.assertEqual(buffer2.dtype, dtype)
def test_buffer_statistics(self):
"""Test buffer manager statistics."""
shape = (1, 4, 16, 16)
dtype = torch.float32
# Allocate and return some buffers
buffer1 = self.buffer_manager.get_buffer(shape, dtype, "latent")
self.buffer_manager.return_buffer(buffer1, "latent")
buffer2 = self.buffer_manager.get_buffer(shape, dtype, "latent")
self.buffer_manager.return_buffer(buffer2, "latent")
stats = self.buffer_manager.get_statistics()
self.assertEqual(stats['allocation_count'], 1)
self.assertEqual(stats['reuse_count'], 1)
self.assertGreater(stats['total_allocated_memory_bytes'], 0)
def test_buffer_cleanup(self):
"""Test buffer cleanup."""
shape = (1, 4, 16, 16)
dtype = torch.float32
# Allocate and return some buffers
buffer1 = self.buffer_manager.get_buffer(shape, dtype, "latent")
self.buffer_manager.return_buffer(buffer1, "latent")
# Clear buffers
self.buffer_manager.clear_buffers("latent")
stats = self.buffer_manager.get_statistics()
self.assertEqual(stats['total_free_buffers'], 0)
class TestUtils(unittest.TestCase):
"""Test cases for utility functions."""
def test_compute_balanced_split(self):
"""Test the compute_balanced_split function."""
total_blocks = 30
rank_times = [1.0, 2.0, 1.5] # Rank 1 is slower
dit_times = [0.8, 1.6, 1.2]
current_block_nums = [[0, 10], [10, 20], [20, 30]]
new_block_nums = compute_balanced_split(total_blocks, rank_times, dit_times, current_block_nums)
# Should have same number of ranks
self.assertEqual(len(new_block_nums), len(current_block_nums))
# Should sum to total_blocks
total_allocated = sum(end - start for start, end in new_block_nums)
self.assertEqual(total_allocated, total_blocks)
# Should be contiguous
for i in range(len(new_block_nums) - 1):
self.assertEqual(new_block_nums[i][1], new_block_nums[i + 1][0])
def test_compute_balanced_split_edge_cases(self):
"""Test compute_balanced_split with edge cases."""
# Empty input
result = compute_balanced_split(0, [], [], [])
self.assertEqual(result, [])
# Single rank
result = compute_balanced_split(10, [1.0], [0.8], [[0, 10]])
self.assertEqual(result, [[0, 10]])
# Invalid input lengths
result = compute_balanced_split(10, [1.0], [0.8], [[0, 10], [10, 20]])
self.assertEqual(result, [[0, 10], [10, 20]]) # Should return original
class TestDistributedCommunicator(unittest.TestCase):
"""Test cases for DistributedCommunicator."""
def setUp(self):
"""Set up test fixtures."""
self.device = torch.device('cpu')
self.config = CommunicationConfig()
# Mock distributed environment
with patch('torch.distributed.is_initialized', return_value=True):
self.communicator = DistributedCommunicator(0, 2, self.device, self.config)
def test_communicator_initialization(self):
"""Test communicator initialization."""
self.assertEqual(self.communicator.rank, 0)
self.assertEqual(self.communicator.world_size, 2)
self.assertEqual(self.communicator.device, self.device)
def test_communicator_initialization_without_distributed(self):
"""Test communicator initialization without distributed."""
with patch('torch.distributed.is_initialized', return_value=False):
with self.assertRaises(RuntimeError):
DistributedCommunicator(0, 2, self.device, self.config)
def test_create_header(self):
"""Test header creation and parsing."""
chunk_idx = 5
shape = (1, 4, 16, 16)
header = self.communicator._create_header(chunk_idx, shape)
self.assertEqual(header.shape, (5,)) # chunk_idx + 4 shape dimensions
self.assertEqual(header.dtype, torch.int64)
parsed_chunk_idx, parsed_shape = self.communicator._parse_header(header)
self.assertEqual(parsed_chunk_idx, chunk_idx)
self.assertEqual(parsed_shape, shape)
def test_communicator_statistics(self):
"""Test communicator statistics."""
stats = self.communicator.get_statistics()
self.assertEqual(stats['rank'], 0)
self.assertEqual(stats['world_size'], 2)
self.assertEqual(stats['outstanding_operations'], 0)
self.assertEqual(stats['max_outstanding'], 1)
class TestKVCacheManager(unittest.TestCase):
"""Test cases for KVCacheManager."""
def setUp(self):
"""Set up test fixtures."""
self.device = torch.device('cpu')
# Mock pipeline with KV cache
self.mock_pipeline = Mock()
self.mock_pipeline.frame_seq_length = 16
self.mock_pipeline.denoising_step_list = [700, 500, 0]
self.mock_pipeline.kv_cache1 = [
{
'k': torch.randn(1, 8, 16, 64, device=self.device),
'v': torch.randn(1, 8, 16, 64, device=self.device),
'global_end_index': torch.tensor([16], device=self.device),
'local_end_index': torch.tensor([16], device=self.device)
}
for _ in range(30)
]
self.kv_cache_manager = KVCacheManager(self.mock_pipeline, self.device)
def test_compute_block_owners(self):
"""Test block owner computation."""
block_intervals = torch.tensor([[0, 10], [10, 20], [20, 30]], device=self.device)
total_blocks = 30
owners = self.kv_cache_manager.compute_block_owners(block_intervals, total_blocks)
self.assertEqual(owners.shape, (30,))
self.assertTrue(torch.all(owners[:10] == 0))
self.assertTrue(torch.all(owners[10:20] == 1))
self.assertTrue(torch.all(owners[20:30] == 2))
def test_kv_cache_statistics(self):
"""Test KV cache statistics."""
block_intervals = torch.tensor([[0, 10], [10, 20], [20, 30]], device=self.device)
total_blocks = 30
stats = self.kv_cache_manager.get_kv_cache_statistics(block_intervals, total_blocks)
self.assertEqual(stats['total_blocks'], 30)
self.assertEqual(stats['block_counts'][0], 10)
self.assertEqual(stats['block_counts'][1], 10)
self.assertEqual(stats['block_counts'][2], 10)
self.assertGreater(stats['memory_per_block_bytes'], 0)
def test_validate_kv_cache_consistency(self):
"""Test KV cache consistency validation."""
block_intervals = torch.tensor([[0, 10], [10, 20], [20, 30]], device=self.device)
total_blocks = 30
is_consistent = self.kv_cache_manager.validate_kv_cache_consistency(block_intervals, total_blocks)
self.assertTrue(is_consistent)
# Test with invalid intervals
invalid_intervals = torch.tensor([[0, 10], [10, 20], [20, 25]], device=self.device) # Missing blocks
is_consistent = self.kv_cache_manager.validate_kv_cache_consistency(invalid_intervals, total_blocks)
self.assertFalse(is_consistent)
class TestModelDataTransfer(unittest.TestCase):
"""Test cases for ModelDataTransfer."""
def setUp(self):
"""Set up test fixtures."""
self.device = torch.device('cpu')
self.config = CommunicationConfig()
# Mock components
with patch('torch.distributed.is_initialized', return_value=True):
self.communicator = DistributedCommunicator(0, 2, self.device, self.config)
self.buffer_manager = BufferManager(self.device, self.config)
self.mock_pipeline = Mock()
self.mock_pipeline.frame_seq_length = 16
self.mock_pipeline.denoising_step_list = [700, 500, 0]
self.mock_pipeline.kv_cache1 = []
self.kv_cache_manager = KVCacheManager(self.mock_pipeline, self.device)
self.data_transfer = ModelDataTransfer(
self.communicator,
self.buffer_manager,
self.kv_cache_manager,
self.config
)
def test_data_transfer_initialization(self):
"""Test data transfer initialization."""
self.assertEqual(self.data_transfer.comm, self.communicator)
self.assertEqual(self.data_transfer.buffer_mgr, self.buffer_manager)
self.assertEqual(self.data_transfer.kv_cache_mgr, self.kv_cache_manager)
self.assertEqual(self.data_transfer.transfer_count, 0)
def test_data_transfer_statistics(self):
"""Test data transfer statistics."""
stats = self.data_transfer.get_statistics()
self.assertEqual(stats['transfer_count'], 0)
self.assertEqual(stats['total_transfer_time'], 0.0)
self.assertIsNotNone(stats['communicator_stats'])
self.assertIsNotNone(stats['buffer_manager_stats'])
def test_cleanup(self):
"""Test data transfer cleanup."""
# Should not raise any exceptions
self.data_transfer.cleanup()
if __name__ == '__main__':
# Set up logging for tests
logging.basicConfig(level=logging.INFO)
# Run tests
unittest.main(verbosity=2)
|