init src 0.9.2

vllm/model_executor/layers/fused_moe/prepare_finalize.py

@@ -0,0 +1,66 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+from typing import Optional
+
+import torch
+
+import vllm.model_executor.layers.fused_moe.modular_kernel as mk
+from vllm.model_executor.layers.fused_moe.config import FusedMoEQuantConfig
+from vllm.model_executor.layers.fused_moe.moe_permute_unpermute import (
+    _moe_unpermute_and_reduce)
+from vllm.model_executor.layers.fused_moe.utils import (
+    moe_kernel_quantize_input)
+
+
+class MoEPrepareAndFinalizeNoEP(mk.FusedMoEPrepareAndFinalize):
+
+    @property
+    def activation_format(self) -> mk.FusedMoEActivationFormat:
+        return mk.FusedMoEActivationFormat.Standard
+
+    def max_num_tokens_per_rank(self) -> Optional[int]:
+        return None
+
+    def topk_indices_dtype(self) -> Optional[torch.dtype]:
+        return None
+
+    def num_dispatchers(self) -> int:
+        return 1
+
+    def prepare(
+        self,
+        a1: torch.Tensor,
+        a1_scale: Optional[torch.Tensor],
+        a2_scale: Optional[torch.Tensor],
+        topk_weights: torch.Tensor,
+        topk_ids: torch.Tensor,
+        num_experts: int,
+        expert_map: Optional[torch.Tensor],
+        apply_router_weight_on_input: bool,
+        quant_config: FusedMoEQuantConfig,
+    ) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[torch.Tensor],
+               Optional[torch.Tensor], Optional[torch.Tensor]]:
+
+        if apply_router_weight_on_input:
+            topk = topk_ids.size(1)
+            # TODO: this only works for topK=1, will need to update for topK>1
+            assert topk == 1, \
+                "apply_router_weight_on_input is only implemented for topk=1"
+            a1.mul_(topk_weights.to(a1.dtype))
+
+        a1q, a1q_scale = moe_kernel_quantize_input(
+            a1, a1_scale, quant_config.quant_dtype,
+            quant_config.per_act_token_quant, quant_config.block_shape)
+
+        return a1q, a1q_scale, None, None, None
+
+    def finalize(
+        self,
+        output: torch.Tensor,
+        fused_expert_output: torch.Tensor,
+        topk_weights: torch.Tensor,
+        topk_ids: torch.Tensor,
+        apply_router_weight_on_input: bool,
+    ) -> None:
+        _moe_unpermute_and_reduce(output, fused_expert_output, None,
+                                  topk_weights, apply_router_weight_on_input)