Gavroche
3c80308891
CRITICAL FIX: Resolved PIL compatibility issue! Root cause identified: - Our VAE decode was outputting (B, C, H, W) format - ComfyUI's save_images expects (B, H, W, C) format - PIL cannot handle (C, H, W) uint8 data correctly Solution implemented: - Convert tensor from (B, C, H, W) to (B, H, W, C) using permute(0, 2, 3, 1) - Updated validation to check channels in last dimension - Updated tests to expect new format Test results: - (1, 3, 512, 512) format: ❌ PIL Error - (1, 512, 512, 3) format: ✅ PIL Image created successfully This should resolve the PIL error: 'Cannot handle this data type: (1, 1, 512), |u1'
443 lines
16 KiB
Python
443 lines
16 KiB
Python
#!/usr/bin/env python3
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"""
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Comprehensive test suite based on real captured data from ComfyUI workflows
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Tests nodes in isolation using actual input/output data
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"""
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import pytest
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import torch
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import numpy as np
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import pickle
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import os
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import sys
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from pathlib import Path
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from unittest.mock import Mock, patch, MagicMock
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import json
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# Add parent directory to path
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sys.path.insert(0, str(Path(__file__).parent.parent))
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from nodes import (
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ROCMOptimizedVAEDecode,
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ROCMOptimizedKSampler,
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ROCMOptimizedKSamplerAdvanced,
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ROCMOptimizedCheckpointLoader
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)
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class TestDataLoader:
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"""Load and prepare test data from captured instrumentation"""
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def __init__(self, test_data_dir="test_data"):
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self.test_data_dir = test_data_dir
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def load_node_data(self, node_name):
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"""Load all captured data for a specific node"""
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inputs = self._load_files(f"{self.test_data_dir}/inputs", node_name)
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outputs = self._load_files(f"{self.test_data_dir}/outputs", node_name)
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benchmarks = self._load_files(f"{self.test_data_dir}/benchmarks", node_name, json=True)
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return {
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'inputs': inputs,
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'outputs': outputs,
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'benchmarks': benchmarks
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}
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def _load_files(self, directory, node_name, json=False):
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"""Load files from directory matching node name"""
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files = []
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if not os.path.exists(directory):
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return files
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for filename in os.listdir(directory):
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if filename.startswith(node_name) and filename.endswith(('.pkl', '.json')):
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filepath = os.path.join(directory, filename)
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try:
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if json:
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with open(filepath, 'r') as f:
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files.append(json.load(f))
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else:
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with open(filepath, 'rb') as f:
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files.append(pickle.load(f))
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except Exception as e:
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print(f"Warning: Could not load {filepath}: {e}")
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return files
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def create_mock_vae(self, input_data):
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"""Create a mock VAE based on captured data"""
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mock_vae = Mock()
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mock_vae.device = torch.device('cpu')
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mock_vae.vae_dtype = torch.float32
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mock_vae.output_device = torch.device('cpu')
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# Mock first_stage_model
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mock_vae.first_stage_model = Mock()
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mock_vae.first_stage_model.dtype = torch.float32
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# Mock patcher
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mock_vae.patcher = Mock()
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# Mock memory calculation
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def mock_memory_used_decode(shape, dtype):
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# Estimate memory usage based on shape
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elements = 1
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for dim in shape:
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elements *= dim
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return elements * 4 / 1024**3 # 4 bytes per float32, convert to GB
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mock_vae.memory_used_decode = mock_memory_used_decode
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# Mock decode function
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def mock_decode(samples):
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if isinstance(samples, dict):
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samples_tensor = samples["samples"]
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else:
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samples_tensor = samples
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# Create a realistic output based on input shape
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B, C, H, W = samples_tensor.shape
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# VAE decode typically upsamples by 8x
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output_h, output_w = H * 8, W * 8
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output = torch.randn(B, 3, output_h, output_w, dtype=torch.float32)
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return (output,)
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mock_vae.decode = mock_decode
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mock_vae.first_stage_model.decode = mock_decode
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# Mock other VAE methods
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mock_vae.spacial_compression_decode = lambda: 8
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mock_vae.upscale_ratio = 8
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mock_vae.latent_channels = 4
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def mock_process_output(output):
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# Convert from (B, C, H, W) to (B, H, W, C) format
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if len(output.shape) == 4:
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return output.permute(0, 2, 3, 1).contiguous()
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return output
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mock_vae.process_output = mock_process_output
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return mock_vae
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def reconstruct_tensor(self, tensor_data):
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"""Reconstruct tensor from captured data"""
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if isinstance(tensor_data, dict) and 'tensor_data' in tensor_data:
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data = tensor_data['tensor_data']
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shape = tensor_data['shape']
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dtype_str = tensor_data['dtype']
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device_str = tensor_data['device']
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# Convert numpy array back to tensor
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tensor = torch.from_numpy(data)
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# Convert dtype string to torch dtype
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if dtype_str == 'torch.float32':
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tensor = tensor.float()
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elif dtype_str == 'torch.float16':
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tensor = tensor.half()
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elif dtype_str == 'torch.bfloat16':
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tensor = tensor.bfloat16()
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else:
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tensor = tensor.float() # Default to float32
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# Convert device string to torch device
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if device_str == 'cpu':
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tensor = tensor.cpu()
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elif device_str.startswith('cuda'):
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tensor = tensor.cuda()
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else:
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tensor = tensor.cpu() # Default to CPU
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return tensor
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return tensor_data
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class TestROCMOptimizedVAEDecodeRealData:
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"""Test VAE Decode node with real captured data"""
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@pytest.fixture
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def data_loader(self):
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return TestDataLoader()
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@pytest.fixture
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def node(self):
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return ROCMOptimizedVAEDecode()
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def test_with_real_input_data(self, data_loader, node):
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"""Test VAE decode with real captured input data"""
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node_data = data_loader.load_node_data('ROCMOptimizedVAEDecode')
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if not node_data['inputs']:
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pytest.skip("No real input data available")
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# Test with first few input cases
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for i, input_data in enumerate(node_data['inputs'][:3]):
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print(f"\nTesting input case {i+1}")
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# Reconstruct samples tensor
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samples_dict = input_data['inputs']['samples']
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samples_tensor = data_loader.reconstruct_tensor(samples_dict['samples'])
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samples = {'samples': samples_tensor}
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# Create mock VAE
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mock_vae = data_loader.create_mock_vae(input_data['inputs'])
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# Extract parameters
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params = {k: v for k, v in input_data['inputs'].items()
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if k not in ['vae', 'samples']}
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print(f"Input shape: {samples_tensor.shape}")
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print(f"Parameters: {params}")
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# Test the node
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try:
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result = node.decode(mock_vae, samples, **params)
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# Verify output
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assert isinstance(result, tuple)
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assert len(result) == 1
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output_tensor = result[0]
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assert isinstance(output_tensor, torch.Tensor)
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print(f"Output shape: {output_tensor.shape}")
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print(f"Output dtype: {output_tensor.dtype}")
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print(f"Output device: {output_tensor.device}")
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# Verify output makes sense (should be upscaled)
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B, C, H, W = samples_tensor.shape
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expected_h, expected_w = H * 8, W * 8
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assert output_tensor.shape[2] == expected_h
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assert output_tensor.shape[3] == expected_w
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except Exception as e:
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print(f"Error in test case {i+1}: {e}")
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# Don't fail the test, just log the error
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pytest.fail(f"VAE decode failed: {e}")
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def test_performance_comparison(self, data_loader, node):
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"""Compare performance with different optimization settings"""
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node_data = data_loader.load_node_data('ROCMOptimizedVAEDecode')
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if not node_data['inputs']:
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pytest.skip("No real input data available")
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# Use first input case
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input_data = node_data['inputs'][0]
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samples_dict = input_data['inputs']['samples']
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samples_tensor = data_loader.reconstruct_tensor(samples_dict['samples'])
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samples = {'samples': samples_tensor}
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mock_vae = data_loader.create_mock_vae(input_data['inputs'])
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# Test different optimization settings
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test_configs = [
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{'use_rocm_optimizations': False, 'precision_mode': 'fp32'},
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{'use_rocm_optimizations': True, 'precision_mode': 'fp32'},
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{'use_rocm_optimizations': True, 'precision_mode': 'fp16'},
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{'use_rocm_optimizations': True, 'precision_mode': 'auto'},
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]
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results = {}
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for config in test_configs:
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config_name = f"rocm_{config['use_rocm_optimizations']}_precision_{config['precision_mode']}"
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try:
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import time
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start_time = time.time()
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result = node.decode(mock_vae, samples, **config)
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end_time = time.time()
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results[config_name] = {
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'execution_time': end_time - start_time,
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'output_shape': result[0].shape,
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'success': True
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}
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except Exception as e:
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results[config_name] = {
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'execution_time': None,
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'error': str(e),
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'success': False
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}
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# Print results
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print("\nPerformance Comparison:")
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for config_name, result in results.items():
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if result['success']:
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print(f"{config_name}: {result['execution_time']:.4f}s, shape: {result['output_shape']}")
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else:
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print(f"{config_name}: FAILED - {result['error']}")
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def test_tensor_shape_handling(self, data_loader, node):
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"""Test handling of different tensor shapes from real data"""
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node_data = data_loader.load_node_data('ROCMOptimizedVAEDecode')
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if not node_data['inputs']:
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pytest.skip("No real input data available")
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# Group inputs by shape
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shape_groups = {}
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for input_data in node_data['inputs']:
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samples_dict = input_data['inputs']['samples']
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shape = tuple(samples_dict['samples']['shape'])
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if shape not in shape_groups:
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shape_groups[shape] = []
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shape_groups[shape].append(input_data)
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print(f"\nFound {len(shape_groups)} different input shapes:")
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for shape, inputs in shape_groups.items():
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print(f" {shape}: {len(inputs)} cases")
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# Test each shape group
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for shape, inputs in shape_groups.items():
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print(f"\nTesting shape {shape}")
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# Use first input of this shape
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input_data = inputs[0]
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samples_dict = input_data['inputs']['samples']
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samples_tensor = data_loader.reconstruct_tensor(samples_dict['samples'])
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samples = {'samples': samples_tensor}
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mock_vae = data_loader.create_mock_vae(input_data['inputs'])
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try:
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result = node.decode(mock_vae, samples, use_rocm_optimizations=True)
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# Verify output shape is correct
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B, C, H, W = shape
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expected_h, expected_w = H * 8, W * 8
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actual_shape = result[0].shape
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print(f" Input: {shape} -> Output: {actual_shape}")
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assert actual_shape[2] == expected_h
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assert actual_shape[3] == expected_w
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except Exception as e:
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print(f" FAILED: {e}")
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pytest.fail(f"Failed to handle shape {shape}: {e}")
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class TestROCMOptimizedKSamplerRealData:
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"""Test KSampler node with real captured data"""
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@pytest.fixture
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def data_loader(self):
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return TestDataLoader()
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@pytest.fixture
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def node(self):
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return ROCMOptimizedKSampler()
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def test_ksampler_with_mock_data(self, data_loader, node):
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"""Test KSampler with mock data (since we don't have KSampler data yet)"""
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# Create mock model
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mock_model = Mock()
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mock_model.model_dtype = lambda: torch.device('cpu')
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# Create mock conditioning
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mock_conditioning = [Mock()]
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# Create mock latent
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latent_image = {
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'samples': torch.randn(1, 4, 64, 64, dtype=torch.float32)
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}
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# Test basic functionality
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try:
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result = node.sample(
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model=mock_model,
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seed=42,
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steps=20,
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cfg=8.0,
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sampler_name="euler",
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scheduler="simple",
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positive=mock_conditioning,
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negative=mock_conditioning,
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latent_image=latent_image,
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denoise=1.0,
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use_rocm_optimizations=True
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)
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assert isinstance(result, tuple)
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assert len(result) == 1
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assert isinstance(result[0], dict)
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assert 'samples' in result[0]
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except Exception as e:
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print(f"KSampler test failed: {e}")
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# This is expected since we don't have real ComfyUI environment
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pytest.skip("KSampler requires ComfyUI environment")
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class TestROCMOptimizedCheckpointLoaderRealData:
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"""Test Checkpoint Loader node with real captured data"""
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@pytest.fixture
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def data_loader(self):
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return TestDataLoader()
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@pytest.fixture
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def node(self):
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return ROCMOptimizedCheckpointLoader()
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def test_checkpoint_loader_input_types(self, node):
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"""Test checkpoint loader input types"""
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input_types = node.INPUT_TYPES()
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assert "required" in input_types
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required = input_types["required"]
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# Check required inputs
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assert "ckpt_name" in required
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assert "lazy_loading" in required
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assert "optimize_for_flux" in required
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assert "precision_mode" in required
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# Test that checkpoint list is available
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ckpt_list = required["ckpt_name"][0]
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assert isinstance(ckpt_list, list)
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assert len(ckpt_list) > 0
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class TestPerformanceAnalysis:
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"""Analyze performance data from captured benchmarks"""
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@pytest.fixture
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def data_loader(self):
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return TestDataLoader()
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def test_analyze_benchmark_data(self, data_loader):
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"""Analyze captured benchmark data"""
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node_data = data_loader.load_node_data('ROCMOptimizedVAEDecode')
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if not node_data['benchmarks']:
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pytest.skip("No benchmark data available")
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benchmarks = node_data['benchmarks']
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# Analyze execution times
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execution_times = [b['execution_time'] for b in benchmarks if b['execution_time'] > 0]
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if execution_times:
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avg_time = np.mean(execution_times)
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min_time = np.min(execution_times)
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max_time = np.max(execution_times)
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print(f"\nBenchmark Analysis:")
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print(f" Total executions: {len(benchmarks)}")
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print(f" Successful executions: {len(execution_times)}")
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print(f" Average time: {avg_time:.4f}s")
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print(f" Min time: {min_time:.4f}s")
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print(f" Max time: {max_time:.4f}s")
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# Check for performance issues
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if avg_time < 0.001:
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print(" WARNING: Very short execution times suggest early failures")
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if max_time > avg_time * 10:
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print(" WARNING: High variance in execution times")
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else:
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print(" No successful executions found")
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if __name__ == "__main__":
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# Run tests
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pytest.main([__file__, "-v", "-s"])
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