add XPU support for qwen3moe local chat

ktransformers/optimize/optimize_rules/xpu/Qwen3Moe-Chat.yaml

@@ -0,0 +1,80 @@
+- match:
+    name: "rotary_emb$"
+  replace:
+    class: ktransformers.operators.RoPE.KQwen3MoeRotaryEmbedding
+    kwargs:
+      generate_device: "xpu"
+      prefill_device: "xpu"
+- match:
+    name: "^lm_head$"  # regular expression
+    class: torch.nn.Linear  # only match modules matching name and class simultaneously
+  replace:
+    class: ktransformers.operators.linear.KTransformersLinear  # optimized Kernel on quantized data types
+    kwargs:
+      generate_device: "xpu"
+      prefill_device: "xpu"
+      generate_op: "KLinearIPEXLLM"
+      prefill_op: "KLinearIPEXLLM"
+- match:
+    name: "^model\\.layers\\.(?!.*mlp\\.gate).*$"  # regular expression
+    class: torch.nn.Linear  # only match modules matching name and class simultaneously
+  replace:
+    class: ktransformers.operators.linear.KTransformersLinear  # optimized Kernel on quantized data types
+    kwargs:
+      generate_device: "xpu"
+      prefill_device: "xpu"
+      generate_op: "KLinearIPEXLLM"
+      prefill_op: "KLinearIPEXLLM"
+- match:
+    name: "^model\\.layers\\..*\\.mlp$"
+    class: transformers.models.qwen3_moe.modeling_qwen3_moe.Qwen3MoeSparseMoeBlock
+  replace:
+    class: ktransformers.operators.experts.KQwen3MoeSparseMoeBlockV2     # mlp module with custom forward function
+    kwargs:
+      generate_device: "xpu"
+      prefill_device: "xpu"
+- match:
+    name: "^model\\.layers\\..*\\.mlp\\.experts$"
+  replace:
+    class: ktransformers.operators.experts.KTransformersExpertsV2     # custom MoE Kernel with expert paralleism
+    kwargs:
+      prefill_device: "xpu"
+      prefill_op: "KExpertsTorch"
+      generate_device: "cpu"
+      generate_op: "KExpertsCPU"
+      out_device: "xpu"
+  recursive: False # don't recursively inject submodules of this module
+- match:
+    name: "^model\\.layers\\..*\\.self_attn$"
+  replace:
+    class: ktransformers.operators.attention.KQwen3MoeAttentionIPEXLLM
+    kwargs:
+      generate_device: "xpu"
+      prefill_device: "xpu"
+- match:
+    name: "^model$"
+  replace:
+    class: "ktransformers.operators.models.KQwen2MoeModel"
+    kwargs:
+      per_layer_prefill_intput_threshold: 0 # 0 is close layer wise prefill
+- match:
+    name: "^model.embed_tokens"
+  replace:
+    class: "default"
+    kwargs:
+      generate_device: "cpu"
+      prefill_device: "cpu"
+- match:
+    class: transformers.models.qwen3_moe.modeling_qwen3_moe.Qwen3MoeRMSNorm
+  replace:
+    class: ktransformers.operators.layernorm.KDeepseekRMSNormIPEXLLM
+    kwargs:
+      generate_device: "xpu"
+      prefill_device: "xpu"
+- match:
+    class: transformers.models.qwen3_moe.modeling_qwen3_moe.Qwen3MoeMLP
+  replace:
+    class:  ktransformers.operators.mlp.KQwen2MoeMLP
+    kwargs:
+      generate_device: "xpu"
+      prefill_device: "xpu"