init v0.23.0

Signed-off-by: Sun Ruoxi <sunruoxi@4paradigm.com>

vllm_ascend/ops/fused_moe/gate_linear.py

@@ -0,0 +1,63 @@
+#
+# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
+# This file is a part of the vllm-ascend project.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+from __future__ import annotations
+
+import torch
+from vllm.model_executor.layers.fused_moe.router.gate_linear import GateLinear
+from vllm.model_executor.layers.linear import ReplicatedLinear
+
+
+class AscendGateLinear(GateLinear):
+    """Ascend replacement for vLLM GateLinear.
+    Router logits are sensitive to numerical precision because they directly
+    affect expert selection in MoE models. On NPU, computing the router gate in
+    lower precision may lead to accuracy issues in some agent workloads.
+    Therefore, this layer forces the gate input and weights to fp32 for the
+    router linear computation, and keeps the router logits in fp32.
+    """
+
+    def __init__(
+        self,
+        input_size: int,
+        output_size: int,
+        bias: bool = False,
+        out_dtype: torch.dtype | None = None,
+        params_dtype: torch.dtype | None = None,
+        force_fp32_compute: bool = False,
+        prefix: str = "",
+    ):
+        super().__init__(
+            input_size=input_size,
+            output_size=output_size,
+            bias=bias,
+            params_dtype=torch.float32,
+            out_dtype=out_dtype,
+            force_fp32_compute=True,
+            prefix=prefix,
+        )
+
+    def forward(self, x: torch.Tensor):
+        # TODO: Remove this workaround after upgrading to a vLLM version that
+        # no longer forces router logits to bf16 via
+        # self.gate.set_out_dtype(torch.bfloat16).
+        if x.dtype != torch.float32:
+            x = x.to(torch.float32)
+
+        output, output_bias = ReplicatedLinear.forward(self, x)
+
+        return output, output_bias