Fix

onnxruntime/contrib_ops/cpu/moe/moe_utils.cc

@@ -30,39 +30,39 @@ float ApplyActivation(float x, ActivationType activation_type) {
 void ApplySwiGLUActivation(float* data, int64_t inter_size, bool is_interleaved_format) {
   constexpr float swiglu_alpha = 1.702f;
   // Create a temporary buffer for the result
-  auto result_buffer = std::make_unique<float[]>(inter_size);
+  auto result_buffer = std::make_unique<float[]>(static_cast<size_t>(inter_size));
 
   if (is_interleaved_format) {
     // For interleaved format [linear, gate, linear, gate, ...], process directly
     for (int64_t i = 0; i < inter_size; ++i) {
-      float linear_val = data[2 * i];    // Interleaved: even index
-      float gate_val = data[2 * i + 1];  // Interleaved: odd index
+      float linear_val = data[2 * static_cast<size_t>(i)];    // Interleaved: even index
+      float gate_val = data[2 * static_cast<size_t>(i) + 1];  // Interleaved: odd index
 
       // SwiGLU: gate * sigmoid(alpha * gate) * (linear + 1)
       float sigmoid_arg = swiglu_alpha * gate_val;
       float sigmoid_out = 1.0f / (1.0f + std::exp(-sigmoid_arg));
       float swish_out = gate_val * sigmoid_out;
-      result_buffer[i] = swish_out * (linear_val + 1.0f);
+      result_buffer[static_cast<size_t>(i)] = swish_out * (linear_val + 1.0f);
     }
   } else {
     // For chunked layout [linear..., gate...], handle separately
     float* linear_part = data;
-    float* gate_part = data + inter_size;
+    float* gate_part = data + static_cast<size_t>(inter_size);
 
     for (int64_t i = 0; i < inter_size; ++i) {
-      float linear_val = linear_part[i];
-      float gate_val = gate_part[i];
+      float linear_val = linear_part[static_cast<size_t>(i)];
+      float gate_val = gate_part[static_cast<size_t>(i)];
 
       // SwiGLU: gate * sigmoid(alpha * gate) * (linear + 1)
       float sigmoid_arg = swiglu_alpha * gate_val;
       float sigmoid_out = 1.0f / (1.0f + std::exp(-sigmoid_arg));
       float swish_out = gate_val * sigmoid_out;
-      result_buffer[i] = swish_out * (linear_val + 1.0f);
+      result_buffer[static_cast<size_t>(i)] = swish_out * (linear_val + 1.0f);
     }
   }
 
   // Copy result back to data (first inter_size elements only - rest is overwritten by GEMM)
-  std::memcpy(data, result_buffer.get(), inter_size * sizeof(float));
+  std::memcpy(data, result_buffer.get(), static_cast<size_t>(inter_size) * sizeof(float));
 }
 
 }  // namespace contrib

onnxruntime/test/python/transformers/test_qmoe_cpu.py

@@ -466,13 +466,11 @@ def __init__(self, quant_bits=0, onnx_dtype=None):
         self.np_type = numpy.float16 if self.onnx_dtype == TensorProto.FLOAT16 else numpy.float32
 
     def create_ort_session(self, moe_onnx_graph):
-        from onnxruntime import InferenceSession, SessionOptions  # noqa: PLC0415
-
-        sess_options = SessionOptions()
+        sess_options = onnxruntime.SessionOptions()
         sess_options.log_severity_level = 2
 
         try:
-            ort_session = InferenceSession(moe_onnx_graph, sess_options, providers=ort_provider)
+            ort_session = onnxruntime.InferenceSession(moe_onnx_graph, sess_options, providers=ort_provider)
         except Exception as e:
             print(f"Failed to create ONNX Runtime session with provider {ort_provider}: {e}")
             print("Skipping ONNX Runtime execution for this test case.")