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
@@ -0,0 +1,125 @@
+import torch
+import time
+import os
+import sys
+
+print(f'PyTorch: {torch.__version__}')
+print(f'CUDA: {torch.cuda.is_available()}')
+if torch.cuda.is_available():
+    print(f'Device: {torch.cuda.get_device_name(0)}')
+else:
+    print('NO CUDA AVAILABLE')
+    os._exit(1)
+
+print()
+tests = []
+
+# Test 1: Small matmul
+print('=== Test 1: Small matmul 256x256 fp32 ===')
+try:
+    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()
+    elapsed = time.time()-t
+    print(f'  OK: {elapsed:.3f}s')
+    tests.append(('small_mm', True))
+    del a, b, c
+except Exception as e:
+    print(f'  FAIL: {e}')
+    tests.append(('small_mm', False))
+
+# Test 2: Medium matmul
+print('=== Test 2: matmul 1024x1024 fp32 ===')
+try:
+    a = torch.randn(1024, 1024, device='cuda', dtype=torch.float32)
+    b = torch.randn(1024, 1024, device='cuda', dtype=torch.float32)
+    torch.cuda.synchronize()
+    t = time.time()
+    c = torch.mm(a, b)
+    torch.cuda.synchronize()
+    elapsed = time.time()-t
+    print(f'  OK: {elapsed:.3f}s')
+    tests.append(('med_mm', True))
+    del a, b, c
+except Exception as e:
+    print(f'  FAIL: {e}')
+    tests.append(('med_mm', False))
+
+# Test 3: fp16
+print('=== Test 3: matmul 1024x1024 fp16 ===')
+try:
+    a = torch.randn(1024, 1024, device='cuda', dtype=torch.float16)
+    b = torch.randn(1024, 1024, device='cuda', dtype=torch.float16)
+    torch.cuda.synchronize()
+    t = time.time()
+    c = torch.mm(a, b)
+    torch.cuda.synchronize()
+    elapsed = time.time()-t
+    print(f'  OK: {elapsed:.3f}s')
+    tests.append(('fp16_mm', True))
+    del a, b, c
+except Exception as e:
+    print(f'  FAIL: {e}')
+    tests.append(('fp16_mm', False))
+
+# Test 4: Conv2d
+print('=== Test 4: Conv2d 32ch fp32 ===')
+try:
+    conv = torch.nn.Conv2d(32, 32, 3, padding=1).cuda().float()
+    x = torch.randn(1, 32, 32, 32, device='cuda', dtype=torch.float32)
+    torch.cuda.synchronize()
+    t = time.time()
+    y = conv(x)
+    torch.cuda.synchronize()
+    elapsed = time.time()-t
+    print(f'  OK: {elapsed:.3f}s')
+    tests.append(('conv2d', True))
+    del conv, x, y
+except Exception as e:
+    print(f'  FAIL: {e}')
+    tests.append(('conv2d', False))
+
+# Test 5: BMM (attention-like)
+print('=== Test 5: BMM 4x512x64 fp32 ===')
+try:
+    q = torch.randn(1, 4, 512, 64, device='cuda', dtype=torch.float32)
+    k = torch.randn(1, 4, 512, 64, device='cuda', dtype=torch.float32)
+    torch.cuda.synchronize()
+    t = time.time()
+    attn = torch.matmul(q, k.transpose(-2, -1))
+    torch.cuda.synchronize()
+    elapsed = time.time()-t
+    print(f'  OK: {elapsed:.3f}s')
+    tests.append(('bmm', True))
+    del q, k, attn
+except Exception as e:
+    print(f'  FAIL: {e}')
+    tests.append(('bmm', False))
+
+# Test 6: Linear (typical model layer)
+print('=== Test 6: Linear 3072->3072 fp32 ===')
+try:
+    lin = torch.nn.Linear(3072, 3072).cuda().float()
+    x = torch.randn(1, 256, 3072, device='cuda', dtype=torch.float32)
+    torch.cuda.synchronize()
+    t = time.time()
+    y = lin(x)
+    torch.cuda.synchronize()
+    elapsed = time.time()-t
+    print(f'  OK: {elapsed:.3f}s')
+    tests.append(('linear', True))
+    del lin, x, y
+except Exception as e:
+    print(f'  FAIL: {e}')
+    tests.append(('linear', False))
+
+print()
+passed = sum(1 for _, ok in tests if ok)
+print(f'=== {passed}/{len(tests)} tests passed ===')
+sys.stdout.flush()
+torch.cuda.synchronize()
+os._exit(0)