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 +1,101 @@
+#!/bin/bash
+export HSA_OVERRIDE_GFX_VERSION=10.1.0
+export HSA_ENABLE_SDMA=0
+export HIP_VISIBLE_DEVICES=0
+export TORCHDYNAMO_DISABLE=1
+export PYTORCH_HIP_ALLOC_CONF=garbage_collection_threshold:0.8,max_split_size_mb:128
+export TORCH_BLAS_PREFER_HIPBLASLT=0
+
+/home/fabian/ComfyUI/venv/bin/python -c "
+import torch
+import time
+import gc
+
+print('=== BC-250 GPU Transfer Stress Test ===')
+print(f'Device: {torch.cuda.get_device_name(0)}')
+print(f'VRAM: {torch.cuda.get_device_properties(0).total_memory / 1024**3:.1f} GB')
+
+# Test 1: Many small .to(cuda) transfers
+print()
+print('Test 1: 100x small .to(cuda) [1024x1024 f16]')
+t0 = time.time()
+for i in range(100):
+    cpu_t = torch.randn(1024, 1024, dtype=torch.float16)
+    gpu_t = cpu_t.to('cuda:0', non_blocking=False)
+    del gpu_t, cpu_t
+    if (i+1) % 10 == 0:
+        print(f'  {i+1}/100 done ({time.time()-t0:.1f}s)')
+torch.cuda.synchronize()
+print(f'  Total: {time.time()-t0:.1f}s')
+gc.collect()
+torch.cuda.empty_cache()
+
+# Test 2: Larger tensors like attention weights
+print()
+print('Test 2: 50x medium .to(cuda) [3840x2560 f16]')
+t0 = time.time()
+for i in range(50):
+    cpu_t = torch.randn(3840, 2560, dtype=torch.float16)
+    gpu_t = cpu_t.to('cuda:0', non_blocking=False)
+    del gpu_t, cpu_t
+    if (i+1) % 10 == 0:
+        print(f'  {i+1}/50 done ({time.time()-t0:.1f}s)')
+torch.cuda.synchronize()
+print(f'  Total: {time.time()-t0:.1f}s')
+gc.collect()
+torch.cuda.empty_cache()
+
+# Test 3: .to(cuda) + matmul (actual compute)
+print()
+print('Test 3: 20x transfer + matmul [2560x2560 f16]')
+t0 = time.time()
+for i in range(20):
+    a = torch.randn(2560, 2560, dtype=torch.float16).to('cuda:0', non_blocking=False)
+    b = torch.randn(2560, 2560, dtype=torch.float16).to('cuda:0', non_blocking=False)
+    c = torch.matmul(a, b)
+    torch.cuda.synchronize()
+    del a, b, c
+    if (i+1) % 5 == 0:
+        print(f'  {i+1}/20 done ({time.time()-t0:.1f}s)')
+torch.cuda.empty_cache()
+print(f'  Total: {time.time()-t0:.1f}s')
+
+# Test 4: Keep tensors on GPU (like lowvram loads many layers)
+print()
+print('Test 4: Load 30 layers to GPU simultaneously [1024x2560 f16]')
+t0 = time.time()
+layers = []
+for i in range(30):
+    cpu_t = torch.randn(1024, 2560, dtype=torch.float16)
+    gpu_t = cpu_t.to('cuda:0', non_blocking=False)
+    layers.append(gpu_t)
+    del cpu_t
+    if (i+1) % 10 == 0:
+        mem = torch.cuda.memory_allocated() / 1024**2
+        print(f'  {i+1}/30 done, GPU mem: {mem:.0f}MB ({time.time()-t0:.1f}s)')
+torch.cuda.synchronize()
+print(f'  Total: {time.time()-t0:.1f}s')
+
+# Cleanup
+del layers
+torch.cuda.empty_cache()
+gc.collect()
+
+# Test 5: Rapid alloc/free cycle (simulating lowvram)
+print()
+print('Test 5: 50x rapid alloc-compute-free cycle [2560x2560 f16]')
+t0 = time.time()
+for i in range(50):
+    a = torch.randn(2560, 2560, dtype=torch.float16).to('cuda:0', non_blocking=False)
+    b = torch.randn(2560, 2560, dtype=torch.float16).to('cuda:0', non_blocking=False)
+    c = torch.matmul(a, b)
+    torch.cuda.synchronize()
+    del a, b, c
+    torch.cuda.empty_cache()
+    if (i+1) % 10 == 0:
+        print(f'  {i+1}/50 done ({time.time()-t0:.1f}s)')
+print(f'  Total: {time.time()-t0:.1f}s')
+
+print()
+print('ALL TESTS PASSED')
+"