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https://github.com/ROCm/composable_kernel.git
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Test comprehensive dataset (#2685)
* Add CSV-driven convolution test pipeline - Add test_grouped_convnd_fwd_dataset_xdl.cpp with CSV reader functionality - Add complete dataset generation toolchain in test_data/ - Add Jenkins integration with RUN_CONV_COMPREHENSIVE_DATASET parameter - Ready for comprehensive convolution testing with scalable datasets * Update convolution test dataset generation pipeline * add 2d, 3d dataset csv files * Remove CSV test dataset files from repository * Update generate_test_dataset.sh * Fix channel division for MIOpen to CK conversion * Remove unnecessary test files * Fix clang-format-18 formatting issues * TEST: Enable comprehensive dataset tests by default * Fix test_data path in Jenkins - build runs from build directory * Add Python dependencies and debug output for CSV generation * Remove Python package installation - not needed * Add better debugging for generate_test_dataset.sh execution * Fix Jenkinsfile syntax error - escape dollar signs * Add PyTorch to Docker image for convolution test dataset generation - Install PyTorch CPU version for lightweight model execution - Fixes Jenkins CI failures where CSV files were empty due to missing PyTorch - Model generation scripts require PyTorch to extract convolution parameters * Add debugging to understand Jenkins directory structure and CSV file status - Print current working directory - List CSV files in test_data directory - Show line counts of CSV files - Will help diagnose why tests fail in Jenkins * Fix clang-format-18 formatting issues - Applied clang-format-18 to test file - Fixed brace placement and whitespace issues * Add detailed debugging for CSV dataset investigation - Check generated_datasets directory contents - List all CSV files with line counts - Show first 5 lines of main CSV file - Applied clang-format-18 formatting - This will help identify why CSV files are empty in Jenkins * keep testing add pytorch installation in shell script * Use virtual environment for PyTorch installation - Jenkins user doesn't have permission to write to /.local - Create virtual environment in current directory (./pytorch_venv) - Install PyTorch in virtual environment to avoid permission issues - Use PYTHON_CMD variable to run all Python scripts with correct interpreter - Virtual environment will be reused if it already exists * Remove debug code and reduce verbose logging in Jenkins - Remove bash -x and debug commands from Jenkinsfile execute_args - Remove all debug system() calls and getcwd from C++ test file - Remove unistd.h include that was only needed for getcwd - Remove debug print in CSV parser - Add set +x to generate_test_dataset.sh to disable command echo - Redirect Python script stdout to /dev/null for cleaner output This makes Jenkins logs much cleaner while still showing progress messages. * install gpu torch * Clean up and optimize comprehensive dataset test pipeline - Reorder Jenkinsfile execution: build -> generate data -> run test - Remove commented-out debug code from generate_test_dataset.sh - Ensure all files end with proper newline character (POSIX compliance) - Keep useful status messages while removing development debug prints - Set MAX_ITERATIONS=0 for unlimited test generation in production * Add configuration modes to reduce test execution time - Add --mode option (half/full) to generate_model_configs.py - half mode (default): ~278 configs (224 2D + 54 3D) -> ~1,058 total tests - full mode: ~807 configs (672 2D + 135 3D) -> ~3,093 total tests - Update generate_test_dataset.sh to use CONFIG_MODE environment variable - Keeps all model types but reduces parameter combinations intelligently - Fixes Jenkins timeout issue (was running 3,669 tests taking 17+ hours) - Default half mode should complete in ~4-5 hours instead of 17+ hours * Add small mode for quick testing of comprehensive dataset * jenkins pipeline test done * jenkins test done * Trigger CI build * remove test comment and update data generation option as half --------- Co-authored-by: Bartłomiej Kocot <barkocot@amd.com>
This commit is contained in:
JH-Leon-KIM-AMD
@@ -10,8 +10,12 @@ import csv
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import itertools
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import argparse
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def generate_2d_configs():
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"""Generate all 2D model configuration combinations"""
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def generate_2d_configs(mode='full'):
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"""Generate all 2D model configuration combinations
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Args:
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mode: 'small' for minimal set (~50 configs), 'half' for reduced set (~250 configs), 'full' for comprehensive set (~500 configs)
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"""
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# Define parameter ranges
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models_2d = [
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@@ -24,15 +28,37 @@ def generate_2d_configs():
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'shufflenet_v2_x1_0'
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]
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batch_sizes = [1, 4, 8, 16, 32]
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# Input dimensions: (height, width)
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input_dims = [
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(64, 64), (128, 128), (224, 224), (256, 256), (512, 512), # Square
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(224, 320), (224, 448), (320, 224), (448, 224), # Rectangular
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(227, 227), # AlexNet preferred
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(299, 299) # Inception preferred
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]
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if mode == 'small':
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# Minimal set for quick testing
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batch_sizes = [1, 8] # Just two batch sizes
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# Very limited input dimensions - only 2 key sizes
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input_dims = [
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(224, 224), # Standard (most common)
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(256, 256), # Medium
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]
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# Use only first 3 models for minimal testing
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models_2d = models_2d[:3] # Only resnet18, resnet34, resnet50
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elif mode == 'half':
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# Reduced set for faster testing
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batch_sizes = [1, 8, 32] # Small, medium, large
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# Reduced input dimensions - 5 key sizes
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input_dims = [
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(64, 64), # Small
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(224, 224), # Standard (most common)
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(512, 512), # Large
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(224, 320), # Rectangular
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(227, 227), # AlexNet preferred
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]
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else: # full mode
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# More comprehensive but still limited
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batch_sizes = [1, 4, 8, 16, 32]
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# More dimensions but skip some redundant ones
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input_dims = [
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(64, 64), (128, 128), (224, 224), (256, 256), (512, 512), # Square
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(224, 320), (320, 224), # Rectangular (reduced from 4)
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(227, 227), # AlexNet preferred
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(299, 299) # Inception preferred
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]
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precisions = ['fp32'] #, 'fp16', 'bf16']
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channels = [3] # Most models expect RGB
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@@ -68,19 +94,44 @@ def generate_2d_configs():
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return configs
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def generate_3d_configs():
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"""Generate all 3D model configuration combinations"""
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def generate_3d_configs(mode='full'):
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"""Generate all 3D model configuration combinations
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Args:
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mode: 'small' for minimal set (~10 configs), 'half' for reduced set (~50 configs), 'full' for comprehensive set (~100 configs)
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"""
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models_3d = ['r3d_18', 'mc3_18', 'r2plus1d_18']
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batch_sizes = [1, 2, 4, 8] # 3D models are more memory intensive
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temporal_sizes = [8, 16, 32]
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# 3D input dimensions: (height, width)
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input_dims = [
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(112, 112), (224, 224), (256, 256), # Standard sizes
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(224, 320), (320, 224) # Rectangular
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]
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if mode == 'small':
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# Minimal set for quick testing
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batch_sizes = [1, 4] # Just two batch sizes
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temporal_sizes = [8] # Only smallest temporal size
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# Very limited spatial dimensions
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input_dims = [
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(112, 112), # Standard for 3D
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]
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# Use only first model for minimal testing
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models_3d = models_3d[:1] # Only r3d_18
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elif mode == 'half':
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# Reduced set for faster testing
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batch_sizes = [1, 4, 8] # Skip batch_size=2
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temporal_sizes = [8, 16] # Skip 32 (most expensive)
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# Reduced spatial dimensions
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input_dims = [
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(112, 112), # Small (common for video)
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(224, 224), # Standard
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(224, 320) # Rectangular
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]
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else: # full mode
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# More comprehensive but still reasonable
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batch_sizes = [1, 2, 4, 8] # 3D models are more memory intensive
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temporal_sizes = [8, 16, 32]
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# More dimensions
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input_dims = [
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(112, 112), (224, 224), (256, 256), # Standard sizes
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(224, 320), (320, 224) # Rectangular
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]
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precisions = ['fp32'] #, 'fp16'] # Skip bf16 for 3D to reduce combinations
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channels = [3]
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@@ -142,19 +193,23 @@ def main():
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help='Output file for 2D configurations')
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parser.add_argument('--output-3d', type=str, default='model_configs_3d.csv',
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help='Output file for 3D configurations')
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parser.add_argument('--mode', choices=['small', 'half', 'full'], default='full',
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help='Configuration mode: small (~60 total), half (~300 total) or full (~600 total) (default: half)')
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parser.add_argument('--limit', type=int,
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help='Limit number of configurations per type (for testing)')
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args = parser.parse_args()
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print(f"Generating {args.mode} model configurations...")
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print("Generating 2D model configurations...")
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configs_2d = generate_2d_configs()
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configs_2d = generate_2d_configs(mode=args.mode)
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if args.limit:
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configs_2d = configs_2d[:args.limit]
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save_configs_to_csv(configs_2d, args.output_2d, "2D")
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print("Generating 3D model configurations...")
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configs_3d = generate_3d_configs()
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configs_3d = generate_3d_configs(mode=args.mode)
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if args.limit:
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configs_3d = configs_3d[:args.limit]
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save_configs_to_csv(configs_3d, args.output_3d, "3D")
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@@ -164,4 +219,4 @@ def main():
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print(" Update generate_test_dataset.sh to read from these CSV files")
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if __name__ == "__main__":
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main()
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main()
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