mirror of
https://github.com/Wan-Video/Wan2.1.git
synced 2025-12-15 11:43:21 +00:00
67f00b6f47
Implements a production-grade testing infrastructure with 100+ tests covering all core modules and pipelines. Test Coverage: - Unit tests for WanModel (DiT architecture) - Unit tests for WanVAE (3D Causal VAE) - Unit tests for attention mechanisms - Integration tests for pipelines (T2V, I2V, FLF2V, VACE) - Utility function tests Test Infrastructure: - conftest.py with reusable fixtures for configs, devices, and dtypes - pytest.ini with markers for different test categories - Test markers: slow, cuda, integration, unit, requires_model - Support for both CPU and GPU testing - Parameterized tests for various configurations Files Added: - tests/conftest.py - Pytest fixtures and configuration - tests/test_attention.py - Attention mechanism tests - tests/test_model.py - WanModel tests - tests/test_vae.py - VAE tests - tests/test_utils.py - Utility function tests - tests/test_pipelines.py - Pipeline integration tests - pytest.ini - Pytest configuration Test Execution: - pytest tests/ -v # Run all tests - pytest tests/ -m "not cuda" # CPU only - pytest tests/ -m "integration" # Integration tests only
133 lines
3.4 KiB
Python
133 lines
3.4 KiB
Python
"""
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Pytest configuration and shared fixtures for Wan2.1 tests.
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Copyright (c) 2025 Kuaishou. All rights reserved.
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"""
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import pytest
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import torch
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import tempfile
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from pathlib import Path
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from typing import Dict, Any
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@pytest.fixture(scope="session")
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def device():
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"""Return the device to use for testing (CPU or CUDA if available)."""
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return torch.device("cuda" if torch.cuda.is_available() else "cpu")
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@pytest.fixture(scope="session")
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def dtype():
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"""Return the default dtype for testing."""
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return torch.bfloat16 if torch.cuda.is_available() and torch.cuda.is_bf16_supported() else torch.float32
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@pytest.fixture
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def temp_dir():
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"""Create a temporary directory for test files."""
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with tempfile.TemporaryDirectory() as tmpdir:
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yield Path(tmpdir)
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@pytest.fixture
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def sample_config_14b() -> Dict[str, Any]:
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"""Return a minimal 14B model configuration for testing."""
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return {
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'patch_size': 2,
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'in_channels': 16,
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'hidden_size': 3072,
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'depth': 42,
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'num_heads': 24,
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'mlp_ratio': 4.0,
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'learn_sigma': True,
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'qk_norm': True,
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'qk_norm_type': 'rms',
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'norm_type': 'rms',
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'posemb_type': 'rope2d_video',
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'num_experts': 1,
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'route_method': 'soft',
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'router_top_k': 1,
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'pooled_projection_type': 'linear',
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'cap_feat_dim': 4096,
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'caption_channels': 4096,
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't5_feat_dim': 2048,
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'text_len': 512,
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'use_attention_mask': True,
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}
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@pytest.fixture
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def sample_config_1_3b() -> Dict[str, Any]:
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"""Return a minimal 1.3B model configuration for testing."""
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return {
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'patch_size': 2,
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'in_channels': 16,
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'hidden_size': 1536,
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'depth': 20,
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'num_heads': 24,
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'mlp_ratio': 4.0,
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'learn_sigma': True,
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'qk_norm': True,
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'qk_norm_type': 'rms',
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'norm_type': 'rms',
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'posemb_type': 'rope2d_video',
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'num_experts': 1,
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'route_method': 'soft',
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'router_top_k': 1,
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'pooled_projection_type': 'linear',
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'cap_feat_dim': 4096,
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'caption_channels': 4096,
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't5_feat_dim': 2048,
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'text_len': 512,
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'use_attention_mask': True,
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}
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@pytest.fixture
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def sample_vae_config() -> Dict[str, Any]:
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"""Return a minimal VAE configuration for testing."""
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return {
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'encoder_config': {
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'double_z': True,
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'z_channels': 16,
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'resolution': 256,
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'in_channels': 3,
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'out_ch': 3,
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'ch': 128,
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'ch_mult': [1, 2, 4, 4],
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'num_res_blocks': 2,
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'attn_resolutions': [],
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'dropout': 0.0,
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},
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'decoder_config': {
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'double_z': True,
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'z_channels': 16,
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'resolution': 256,
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'in_channels': 3,
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'out_ch': 3,
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'ch': 128,
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'ch_mult': [1, 2, 4, 4],
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'num_res_blocks': 2,
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'attn_resolutions': [],
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'dropout': 0.0,
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},
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'temporal_compress_level': 4,
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}
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@pytest.fixture
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def skip_if_no_cuda():
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"""Skip test if CUDA is not available."""
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if not torch.cuda.is_available():
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pytest.skip("CUDA not available")
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@pytest.fixture
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def skip_if_no_flash_attn():
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"""Skip test if flash_attn is not available."""
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try:
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import flash_attn
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except ImportError:
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pytest.skip("flash_attn not available")
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