Initial commit

test_npu_quantized.py

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+#!/usr/bin/env python3
+"""
+Test VitisAI EP with the quantized nomic-embed-text-v1.5 model.
+Checks whether the NPU VAIML pass achieves meaningful GOPs coverage.
+"""
+
+import os
+import json
+import time
+import numpy as np
+from pathlib import Path
+
+MODEL_PATH = Path.home() / ".cache/breadsearch/models/model_quantized_static.onnx"
+CACHE_DIR = Path.home() / ".cache/breadsearch/npu/nomic-quantized-static"
+VAIP_CONFIG = Path.home() / ".config/breadsearch/vaip_config.json"
+RYZEN_AI_LIB = Path.home() / ".local/share/ryzen-ai-1.7.1/lib"
+
+# libvaiml.so must be discoverable
+os.environ["RYZEN_AI_INSTALLATION_PATH"] = str(RYZEN_AI_LIB)
+os.environ["LD_LIBRARY_PATH"] = str(RYZEN_AI_LIB) + ":" + os.environ.get("LD_LIBRARY_PATH", "")
+
+CACHE_DIR.mkdir(parents=True, exist_ok=True)
+
+if not VAIP_CONFIG.exists():
+    print(f"ERROR: vaip_config.json not found at {VAIP_CONFIG}")
+    print("Check breadmill embed.rs for the find_vaip_config() paths")
+    exit(1)
+
+print(f"Testing quantized model: {MODEL_PATH}")
+print(f"Cache dir: {CACHE_DIR}")
+print(f"VAIP config: {VAIP_CONFIG}")
+print(f"RYZEN_AI_INSTALLATION_PATH: {os.environ['RYZEN_AI_INSTALLATION_PATH']}")
+
+import onnxruntime as ort
+
+providers = [
+    ("VitisAIExecutionProvider", {
+        "config_file": str(VAIP_CONFIG),
+        "cacheDir": str(CACHE_DIR),
+        "cacheKey": "nomic-quantized-static",
+    }),
+    "CPUExecutionProvider",
+]
+
+print("\nCreating InferenceSession with VitisAI EP...")
+t0 = time.time()
+try:
+    sess = ort.InferenceSession(str(MODEL_PATH), providers=providers)
+    t1 = time.time()
+    print(f"Session created in {t1-t0:.1f}s")
+    print(f"Active providers: {sess.get_providers()}")
+except Exception as e:
+    print(f"ERROR creating session: {e}")
+    exit(1)
+
+# Check for the VAIML pass summary
+summary_path = CACHE_DIR / "nomic-quantized" / "preliminary-vaiml-pass-summary.txt"
+if not summary_path.exists():
+    # Try variations
+    for p in CACHE_DIR.rglob("preliminary-vaiml-pass-summary.txt"):
+        summary_path = p
+        break
+
+if summary_path.exists():
+    print(f"\n--- VAIML Pass Summary ---")
+    print(summary_path.read_text())
+else:
+    print(f"\nNo VAIML summary found at {summary_path}")
+    print("Files in cache dir:")
+    for f in CACHE_DIR.rglob("*"):
+        if f.is_file():
+            print(f"  {f}")
+
+# Run a quick inference test
+print("\nRunning inference test...")
+seq_len = 128
+dummy_ids = np.ones((1, seq_len), dtype=np.int64)
+dummy_mask = np.ones((1, seq_len), dtype=np.int64)
+dummy_types = np.zeros((1, seq_len), dtype=np.int64)
+
+input_names = [inp.name for inp in sess.get_inputs()]
+print(f"Input names: {input_names}")
+
+feed = {}
+for name in input_names:
+    if "type" in name:
+        feed[name] = dummy_types
+    elif "mask" in name:
+        feed[name] = dummy_mask
+    else:
+        feed[name] = dummy_ids
+
+t0 = time.time()
+outputs = sess.run(None, feed)
+t1 = time.time()
+print(f"Inference completed in {(t1-t0)*1000:.1f}ms")
+print(f"Output shape: {outputs[0].shape}")