Fabian
51 lines
1.7 KiB
Bash
51 lines
1.7 KiB
Bash
#!/bin/bash
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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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export TORCHDYNAMO_DISABLE=1
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export PYTORCH_HIP_ALLOC_CONF=garbage_collection_threshold:0.8,max_split_size_mb:128
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export TORCH_BLAS_PREFER_HIPBLASLT=0
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/home/fabian/ComfyUI/venv/bin/python -c "
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import torch
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import time
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print('=== BC-250 Transfer Test (no empty_cache) ===')
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print(f'Device: {torch.cuda.get_device_name(0)}')
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# Test: Keep all tensors on GPU, no empty_cache(), do compute
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print()
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print('Test: 50x transfer+matmul [2560x2560 f16], NO empty_cache')
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t0 = time.time()
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for i in range(50):
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a = torch.randn(2560, 2560, dtype=torch.float16).to('cuda:0', non_blocking=False)
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b = torch.randn(2560, 2560, dtype=torch.float16).to('cuda:0', non_blocking=False)
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c = torch.matmul(a, b)
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torch.cuda.synchronize()
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del a, b, c
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if (i+1) % 10 == 0:
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mem = torch.cuda.memory_allocated() / 1024**2
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print(f' {i+1}/50 done, GPU mem: {mem:.0f}MB ({time.time()-t0:.1f}s)')
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print(f' Total: {time.time()-t0:.1f}s')
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# Test 2: Simulate lowvram layer loading pattern
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print()
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print('Test2: Simulated UNET forward (453 layers)')
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t0 = time.time()
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for i in range(453):
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# Simulate dequant on CPU -> transfer to GPU
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w = torch.randn(1024, 1024, dtype=torch.float16).to('cuda:0', non_blocking=False)
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# Simulate compute
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x = torch.randn(1, 1024, dtype=torch.float16, device='cuda:0')
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y = torch.matmul(x, w.T)
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torch.cuda.synchronize()
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del w, x, y
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if (i+1) % 100 == 0:
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mem = torch.cuda.memory_allocated() / 1024**2
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print(f' Layer {i+1}/453, GPU mem: {mem:.0f}MB ({time.time()-t0:.1f}s)')
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print(f' Total: {time.time()-t0:.1f}s')
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print()
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print('ALL TESTS PASSED')
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"
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