103 lines
3.3 KiB
Python
103 lines
3.3 KiB
Python
#!/usr/bin/env python3
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"""
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Direct test of fused pipeline primitive.
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Compares performance of:
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1. Fused kernel (single CUDA kernel for all effects)
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2. Separate kernels (one CUDA kernel per effect)
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"""
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import time
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import sys
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# Check for CuPy
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try:
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import cupy as cp
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print("[test] CuPy available")
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except ImportError:
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print("[test] CuPy not available - can't run test")
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sys.exit(1)
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# Add path for imports
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sys.path.insert(0, '/app')
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from streaming.sexp_to_cuda import compile_frame_pipeline
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from streaming.jit_compiler import fast_rotate, fast_hue_shift, fast_ripple
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def test_fused_vs_separate():
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"""Compare fused vs separate kernel performance."""
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width, height = 1920, 1080
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n_frames = 100
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# Create test frame
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frame = cp.random.randint(0, 255, (height, width, 3), dtype=cp.uint8)
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# Define effects pipeline
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effects = [
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{'op': 'rotate', 'angle': 45.0},
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{'op': 'hue_shift', 'degrees': 30.0},
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{'op': 'ripple', 'amplitude': 15, 'frequency': 10, 'decay': 2, 'phase': 0, 'center_x': 960, 'center_y': 540},
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]
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print(f"\n[test] Testing {n_frames} frames at {width}x{height}")
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print(f"[test] Effects: rotate, hue_shift, ripple\n")
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# ========== Test fused kernel ==========
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print("[test] Compiling fused kernel...")
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pipeline = compile_frame_pipeline(effects, width, height)
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# Warmup
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output = pipeline(frame, rotate_angle=45, ripple_phase=0)
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cp.cuda.Stream.null.synchronize()
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print("[test] Running fused kernel benchmark...")
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start = time.time()
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for i in range(n_frames):
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output = pipeline(frame, rotate_angle=i * 3.6, ripple_phase=i * 0.1)
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cp.cuda.Stream.null.synchronize()
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fused_time = time.time() - start
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fused_ms = fused_time / n_frames * 1000
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fused_fps = n_frames / fused_time
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print(f"[test] Fused kernel: {fused_ms:.2f}ms/frame ({fused_fps:.0f} fps)")
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# ========== Test separate kernels ==========
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print("\n[test] Running separate kernels benchmark...")
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# Warmup
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temp = fast_rotate(frame, 45.0)
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temp = fast_hue_shift(temp, 30.0)
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temp = fast_ripple(temp, 15, frequency=10, decay=2, phase=0, center_x=960, center_y=540)
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cp.cuda.Stream.null.synchronize()
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start = time.time()
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for i in range(n_frames):
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temp = fast_rotate(frame, i * 3.6)
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temp = fast_hue_shift(temp, 30.0)
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temp = fast_ripple(temp, 15, frequency=10, decay=2, phase=i * 0.1, center_x=960, center_y=540)
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cp.cuda.Stream.null.synchronize()
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separate_time = time.time() - start
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separate_ms = separate_time / n_frames * 1000
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separate_fps = n_frames / separate_time
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print(f"[test] Separate kernels: {separate_ms:.2f}ms/frame ({separate_fps:.0f} fps)")
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# ========== Summary ==========
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speedup = separate_time / fused_time
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print(f"\n{'='*50}")
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print(f"SPEEDUP: {speedup:.1f}x faster with fused kernel")
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print(f"")
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print(f"Fused: {fused_ms:.2f}ms ({fused_fps:.0f} fps)")
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print(f"Separate: {separate_ms:.2f}ms ({separate_fps:.0f} fps)")
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print(f"{'='*50}")
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# Compare with original Python sexp interpreter baseline (126-205ms)
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python_baseline_ms = 150 # Approximate from profiling
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vs_python = python_baseline_ms / fused_ms
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print(f"\nVs Python sexp interpreter (~{python_baseline_ms}ms): {vs_python:.0f}x faster!")
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if __name__ == '__main__':
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test_fused_vs_separate()
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