Uploaded sanitized BC250/ROCm Repository.

2026-08-20 00:45:43 +02:00
parent 7d2184f1e8
commit d7d22e93b3
678 changed files with 65963 additions and 1 deletions
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+#!/bin/bash
+# GPU benchmark - test various operations
+export HSA_OVERRIDE_GFX_VERSION=10.1.0
+export HSA_ENABLE_SDMA=0
+export HIP_VISIBLE_DEVICES=0
+
+cd /home/fabian/ComfyUI
+source venv/bin/activate
+
+python3 -c "
+import torch
+import time
+
+print(f'PyTorch: {torch.__version__}')
+print(f'CUDA: {torch.cuda.is_available()}')
+print(f'Device: {torch.cuda.get_device_name(0)}')
+print()
+
+# Test 1: Small matmul (should be fast)
+print('=== Test 1: Small matmul 256x256 ===')
+a = torch.randn(256, 256, device='cuda', dtype=torch.float32)
+b = torch.randn(256, 256, device='cuda', dtype=torch.float32)
+torch.cuda.synchronize()
+t = time.time()
+for _ in range(10):
+    c = torch.mm(a, b)
+torch.cuda.synchronize()
+print(f'  10x matmul 256x256 fp32: {time.time()-t:.3f}s')
+
+# Test 2: Large matmul (typical for model inference)
+print('=== Test 2: Large matmul 2048x2048 ===')
+a = torch.randn(2048, 2048, device='cuda', dtype=torch.float32)
+b = torch.randn(2048, 2048, device='cuda', dtype=torch.float32)
+torch.cuda.synchronize()
+t = time.time()
+c = torch.mm(a, b)
+torch.cuda.synchronize()
+print(f'  1x matmul 2048x2048 fp32: {time.time()-t:.3f}s')
+
+# Test 3: fp16 matmul
+print('=== Test 3: fp16 matmul 2048x2048 ===')
+a = torch.randn(2048, 2048, device='cuda', dtype=torch.float16)
+b = torch.randn(2048, 2048, device='cuda', dtype=torch.float16)
+torch.cuda.synchronize()
+t = time.time()
+c = torch.mm(a, b)
+torch.cuda.synchronize()
+print(f'  1x matmul 2048x2048 fp16: {time.time()-t:.3f}s')
+
+# Test 4: Conv2d (typical for VAE)
+print('=== Test 4: Conv2d 64ch ===')
+conv = torch.nn.Conv2d(64, 64, 3, padding=1).cuda().float()
+x = torch.randn(1, 64, 64, 64, device='cuda', dtype=torch.float32)
+torch.cuda.synchronize()
+t = time.time()
+y = conv(x)
+torch.cuda.synchronize()
+print(f'  Conv2d 64x64x64 fp32: {time.time()-t:.3f}s')
+
+# Test 5: Attention-like operation
+print('=== Test 5: Attention (batch matmul) ===')
+q = torch.randn(1, 8, 1024, 64, device='cuda', dtype=torch.float32)
+k = torch.randn(1, 8, 1024, 64, device='cuda', dtype=torch.float32)
+torch.cuda.synchronize()
+t = time.time()
+attn = torch.matmul(q, k.transpose(-2, -1))
+torch.cuda.synchronize()
+print(f'  BMM 8x1024x64 fp32: {time.time()-t:.3f}s')
+
+# Test 6: Larger attention (closer to model size)
+print('=== Test 6: Large attention ===')
+q = torch.randn(1, 16, 4096, 128, device='cuda', dtype=torch.float32)
+k = torch.randn(1, 16, 4096, 128, device='cuda', dtype=torch.float32)
+torch.cuda.synchronize()
+t = time.time()
+attn = torch.matmul(q, k.transpose(-2, -1))
+torch.cuda.synchronize()
+print(f'  BMM 16x4096x128 fp32: {time.time()-t:.3f}s')
+
+# Test 7: bf16
+print('=== Test 7: bf16 matmul ===')
+try:
+    a = torch.randn(2048, 2048, device='cuda', dtype=torch.bfloat16)
+    b = torch.randn(2048, 2048, device='cuda', dtype=torch.bfloat16)
+    torch.cuda.synchronize()
+    t = time.time()
+    c = torch.mm(a, b)
+    torch.cuda.synchronize()
+    print(f'  bf16 2048x2048: {time.time()-t:.3f}s')
+except Exception as e:
+    print(f'  bf16 FAILED: {e}')
+
+print()
+print('=== All tests done ===')
+" 2>&1