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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+"""Test if torch.cuda.synchronize() crashes after F.linear on gfx1010"""
+import torch
+import torch.nn.functional as F
+import time, os, signal, sys
+
+os.environ['HSA_OVERRIDE_GFX_VERSION'] = '10.1.0'
+os.environ['HSA_ENABLE_SDMA'] = '0'
+os.environ['TORCHDYNAMO_DISABLE'] = '1'
+
+# Catch SIGSEGV
+def segfault_handler(signum, frame):
+    print(f"\n*** SIGNAL {signum} (SIGSEGV) CAUGHT ***", flush=True)
+    sys.exit(1)
+signal.signal(signal.SIGSEGV, segfault_handler)
+
+print(f"Device: {torch.cuda.get_device_name(0)}", flush=True)
+
+# Simulate exactly what the model does:
+# For each layer: transfer from CPU → F.linear → sync → next layer
+shapes = [
+    ("CBW505", (1024, 256)),
+    ("CBW506", (256, 1024)),
+    ("CBW507", (3840, 2560)),
+    ("CBW508", (3840, 64)),
+    ("CBW509", (11520, 3840)),   # This is where ComfyUI crashes
+    ("CBW510", (3840, 3840)),
+    ("CBW511", (3840, 3840)),
+    ("CBW512", (11520, 3840)),
+    ("CBW513", (3840, 11520)),
+]
+
+# Start with latent-like input
+x = torch.randn(1, 1024, 256, dtype=torch.float16, device='cuda:0')
+torch.cuda.synchronize()
+print("Initial input on GPU", flush=True)
+
+for i, (name, (out_feat, in_feat)) in enumerate(shapes):
+    print(f"\n--- {name}: Linear({in_feat} -> {out_feat}) ---", flush=True)
+    
+    # Step 1: Pre-sync (like v20 patch)
+    print(f"  pre-sync...", flush=True) 
+    torch.cuda.synchronize()
+    print(f"  pre-sync OK", flush=True)
+    
+    # Step 2: CPU dequant simulation → transfer to GPU
+    w_cpu = torch.randn(out_feat, in_feat, dtype=torch.float16)
+    b_cpu = torch.randn(out_feat, dtype=torch.float16)
+    print(f"  CPU weights created", flush=True)
+    
+    w_gpu = w_cpu.to('cuda:0', non_blocking=False)
+    b_gpu = b_cpu.to('cuda:0', non_blocking=False)
+    print(f"  transferred to GPU", flush=True)
+    
+    # Step 3: Adjust input dimension
+    if x.shape[-1] != in_feat:
+        x = torch.randn(1, 1024, in_feat, dtype=torch.float16, device='cuda:0')
+        torch.cuda.synchronize()
+        print(f"  input resized to [{1}, {1024}, {in_feat}]", flush=True)
+    
+    # Step 4: F.linear (dispatches GPU kernel)
+    print(f"  F.linear starting...", flush=True)
+    x = F.linear(x, w_gpu, b_gpu)
+    print(f"  F.linear dispatched, out={list(x.shape)}", flush=True)
+    
+    # Step 5: Post-sync (forces GPU to finish and report errors)
+    print(f"  post-sync...", flush=True)
+    torch.cuda.synchronize()
+    print(f"  post-sync OK", flush=True)
+    
+    del w_cpu, b_cpu, w_gpu, b_gpu
+    print(f"  {name} DONE", flush=True)
+
+print("\n=== ALL PASSED ===")