Fabian
96 lines
3.0 KiB
Bash
96 lines
3.0 KiB
Bash
#!/bin/bash
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# GPU benchmark - test various operations
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export HSA_OVERRIDE_GFX_VERSION=10.1.0
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export HSA_ENABLE_SDMA=0
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export HIP_VISIBLE_DEVICES=0
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cd /home/fabian/ComfyUI
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source venv/bin/activate
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python3 -c "
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import torch
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import time
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print(f'PyTorch: {torch.__version__}')
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print(f'CUDA: {torch.cuda.is_available()}')
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print(f'Device: {torch.cuda.get_device_name(0)}')
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print()
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# Test 1: Small matmul (should be fast)
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print('=== Test 1: Small matmul 256x256 ===')
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a = torch.randn(256, 256, device='cuda', dtype=torch.float32)
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b = torch.randn(256, 256, device='cuda', dtype=torch.float32)
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torch.cuda.synchronize()
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t = time.time()
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for _ in range(10):
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c = torch.mm(a, b)
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torch.cuda.synchronize()
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print(f' 10x matmul 256x256 fp32: {time.time()-t:.3f}s')
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# Test 2: Large matmul (typical for model inference)
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print('=== Test 2: Large matmul 2048x2048 ===')
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a = torch.randn(2048, 2048, device='cuda', dtype=torch.float32)
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b = torch.randn(2048, 2048, device='cuda', dtype=torch.float32)
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torch.cuda.synchronize()
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t = time.time()
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c = torch.mm(a, b)
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torch.cuda.synchronize()
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print(f' 1x matmul 2048x2048 fp32: {time.time()-t:.3f}s')
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# Test 3: fp16 matmul
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print('=== Test 3: fp16 matmul 2048x2048 ===')
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a = torch.randn(2048, 2048, device='cuda', dtype=torch.float16)
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b = torch.randn(2048, 2048, device='cuda', dtype=torch.float16)
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torch.cuda.synchronize()
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t = time.time()
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c = torch.mm(a, b)
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torch.cuda.synchronize()
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print(f' 1x matmul 2048x2048 fp16: {time.time()-t:.3f}s')
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# Test 4: Conv2d (typical for VAE)
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print('=== Test 4: Conv2d 64ch ===')
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conv = torch.nn.Conv2d(64, 64, 3, padding=1).cuda().float()
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x = torch.randn(1, 64, 64, 64, device='cuda', dtype=torch.float32)
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torch.cuda.synchronize()
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t = time.time()
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y = conv(x)
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torch.cuda.synchronize()
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print(f' Conv2d 64x64x64 fp32: {time.time()-t:.3f}s')
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# Test 5: Attention-like operation
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print('=== Test 5: Attention (batch matmul) ===')
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q = torch.randn(1, 8, 1024, 64, device='cuda', dtype=torch.float32)
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k = torch.randn(1, 8, 1024, 64, device='cuda', dtype=torch.float32)
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torch.cuda.synchronize()
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t = time.time()
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attn = torch.matmul(q, k.transpose(-2, -1))
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torch.cuda.synchronize()
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print(f' BMM 8x1024x64 fp32: {time.time()-t:.3f}s')
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# Test 6: Larger attention (closer to model size)
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print('=== Test 6: Large attention ===')
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q = torch.randn(1, 16, 4096, 128, device='cuda', dtype=torch.float32)
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k = torch.randn(1, 16, 4096, 128, device='cuda', dtype=torch.float32)
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torch.cuda.synchronize()
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t = time.time()
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attn = torch.matmul(q, k.transpose(-2, -1))
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torch.cuda.synchronize()
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print(f' BMM 16x4096x128 fp32: {time.time()-t:.3f}s')
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# Test 7: bf16
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print('=== Test 7: bf16 matmul ===')
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try:
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a = torch.randn(2048, 2048, device='cuda', dtype=torch.bfloat16)
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b = torch.randn(2048, 2048, device='cuda', dtype=torch.bfloat16)
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torch.cuda.synchronize()
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t = time.time()
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c = torch.mm(a, b)
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torch.cuda.synchronize()
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print(f' bf16 2048x2048: {time.time()-t:.3f}s')
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except Exception as e:
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print(f' bf16 FAILED: {e}')
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print()
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print('=== All tests done ===')
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" 2>&1
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