Qwen3.5: Smoke test

tests/smoke_qwen3_5_arch.py

@@ -0,0 +1,94 @@
+#!/usr/bin/env python3
+"""
+Lightweight smoke checks for Qwen3.5 architecture integration.
+
+Checks:
+1) Config/architecture resolution
+2) Model graph construction
+3) One linear-attn block forward
+4) One full-attn block forward
+
+Optional:
+5) Attempt full model load (can fail on low VRAM for FP16 checkpoints)
+"""
+
+import argparse
+import sys
+import torch
+
+from exllamav3 import Config, Model
+
+
+def fail(msg: str) -> None:
+    print(f"[FAIL] {msg}")
+    sys.exit(1)
+
+
+def main() -> None:
+    ap = argparse.ArgumentParser()
+    ap.add_argument("--model_dir", required=True)
+    ap.add_argument("--device", default="cuda:0")
+    ap.add_argument("--full_load", action="store_true")
+    ap.add_argument("--reserve_per_device", default=None, help="e.g. 0.25,0.25")
+    args = ap.parse_args()
+
+    cfg = Config.from_directory(args.model_dir)
+    print("[INFO] architecture:", cfg.architecture)
+    if cfg.architecture not in ("Qwen3_5ForConditionalGeneration", "Qwen3_5MoeForConditionalGeneration"):
+        fail(f"Unexpected architecture: {cfg.architecture}")
+
+    model = Model.from_config(cfg)
+    print("[INFO] model graph ok")
+
+    # Verify split projection path exists for linear attention blocks
+    first_block = model.modules[model.first_block_idx]
+    if not hasattr(first_block.attn, "qkv_proj") and not hasattr(first_block.attn, "qkvz_proj"):
+        fail("Expected Qwen3.5 linear attention projection attributes not found")
+
+    if not torch.cuda.is_available():
+        fail("CUDA is required for this smoke check")
+
+    device = torch.device(args.device)
+    hidden_size = cfg.hidden_size
+
+    # First linear-attn block
+    idx_linear = model.first_block_idx
+    blk_linear = model.modules[idx_linear]
+    blk_linear.load(device)
+    x = torch.randn((1, 8, hidden_size), device=device, dtype=torch.half)
+    with torch.inference_mode():
+        y = blk_linear.forward(x, params={})
+    print("[INFO] linear block forward:", blk_linear.key, tuple(y.shape))
+    blk_linear.unload()
+
+    # First full-attn block
+    if "full_attention" not in cfg.layer_types:
+        fail("No full_attention layer found in layer_types")
+    idx_full = model.first_block_idx + cfg.layer_types.index("full_attention")
+    blk_full = model.modules[idx_full]
+    blk_full.load(device)
+    x = torch.randn((1, 8, hidden_size), device=device, dtype=torch.half)
+    with torch.inference_mode():
+        y = blk_full.forward(x, params={})
+    print("[INFO] full block forward:", blk_full.key, tuple(y.shape))
+    blk_full.unload()
+
+    if args.full_load:
+        reserve = None
+        if args.reserve_per_device:
+            reserve = [float(x) for x in args.reserve_per_device.split(",")]
+        print("[INFO] attempting full model load")
+        try:
+            model.load(reserve_per_device=reserve)
+            print("[INFO] full model load ok")
+            model.unload()
+        except Exception as e:
+            print("[WARN] full model load failed:", repr(e))
+
+    if torch.cuda.is_available():
+        torch.cuda.empty_cache()
+    print("[PASS] qwen3.5 smoke checks complete")
+
+
+if __name__ == "__main__":
+    main()