[Feat.]: 310p support MOE models

vllm_ascend/_310p/fused_moe/experts_selector.py

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+#
+# Copyright (c) 2026 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 collections.abc import Callable
+
+import torch
+
+from vllm_ascend.ops.fused_moe.experts_selector import _native_select_experts
+from vllm_ascend.utils import get_weight_prefetch_method
+
+
+def select_experts(
+    hidden_states: torch.Tensor,
+    router_logits: torch.Tensor,
+    top_k: int,
+    use_grouped_topk: bool,
+    renormalize: bool,
+    topk_group: int | None = None,
+    num_expert_group: int | None = None,
+    custom_routing_function: Callable | None = None,
+    scoring_func: str = "softmax",
+    e_score_correction_bias: torch.Tensor | None = None,
+    global_num_experts: int = -1,
+):
+    """
+    Fused experts with select experts.
+
+    Args:
+        router_logits: router logits of shape (num_tokens, hidden_size).
+        hidden_states: Hidden states of shape (num_tokens, hidden_size).
+        top_k: number of top k experts.
+        use_grouped_topk: Whether to group experts before selecting top-k.
+        renormalize: Whether to renormalize the routing weights.
+        topk_group: Number of expert groups to select from.
+        num_expert_group: Number of experts in each group.
+        custom_routing_function: Custom routing function.
+        scoring_func: Scoring function to use.
+        e_score_correction_bias: Correction bias to apply to expert scores.
+        global_num_experts: Global number of experts.
+
+    Returns:
+        topk_weights: router weights of shape (num_tokens, top_k).
+        topk_ids: selected expert IDs of shape (num_tokens, top_k).
+    """
+    # prefetch w1_w3_proj.weight preprocess
+    weight_prefetch_method = get_weight_prefetch_method()
+    if weight_prefetch_method:
+        weight_prefetch_method.maybe_prefetch_moe_weight_preprocess(hidden_states, "gate_up")
+    topk_weights, topk_ids = _native_select_experts(
+        hidden_states=hidden_states,
+        router_logits=router_logits,
+        top_k=top_k,
+        use_grouped_topk=use_grouped_topk,
+        renormalize=renormalize,
+        topk_group=topk_group,
+        num_expert_group=num_expert_group,
+        custom_routing_function=custom_routing_function,
+        scoring_func=scoring_func,
+        e_score_correction_bias=e_score_correction_bias,
+        global_num_experts=global_num_experts,
+    )
+    return topk_weights, topk_ids

vllm_ascend/_310p/fused_moe/fused_moe.py

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+#
+# Copyright (c) 2026 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 collections.abc import Callable
+
+import torch
+from vllm.distributed import get_dp_group, get_ep_group, get_tp_group
+from vllm.forward_context import get_forward_context
+from vllm.model_executor.layers.fused_moe.config import FusedMoEConfig
+from vllm.model_executor.layers.fused_moe.layer import FusedMoE, UnquantizedFusedMoEMethod
+from vllm.model_executor.layers.fused_moe.shared_fused_moe import SharedFusedMoE
+
+from vllm_ascend.ascend_forward_context import MoECommType
+from vllm_ascend.ops.fused_moe.experts_selector import zero_experts_compute
+from vllm_ascend.ops.fused_moe.moe_comm_method import FusedExpertsResult, _MoECommMethods
+from vllm_ascend.quantization.methods.base import QuantType
+
+from .experts_selector import select_experts
+from .moe_comm_method import AllGatherCommImpl310
+
+
+class AscendUnquantizedFusedMoEMethod310(UnquantizedFusedMoEMethod):
+    def __init__(self, moe: FusedMoEConfig = None):
+        super().__init__(moe=moe)
+
+    def process_weights_after_loading(self, layer):
+        super().process_weights_after_loading(layer)
+
+        # Fused gate_up_proj (column parallel)
+        w13_data = self._maybe_pad_weight(layer.w13_weight.data).transpose(1, 2).contiguous()
+        layer.w13_weight = torch.nn.Parameter(w13_data, requires_grad=False)
+        # down_proj (row parallel)
+        w2_data = self._maybe_pad_weight(layer.w2_weight.data).transpose(1, 2).contiguous()
+        layer.w2_weight = torch.nn.Parameter(w2_data, requires_grad=False)
+
+    def apply(
+        self,
+        layer: torch.nn.Module,
+        x: torch.Tensor,
+        use_grouped_topk: bool,
+        top_k: int,
+        router_logits: torch.Tensor,
+        renormalize: bool,
+        topk_group: int | None = None,
+        num_expert_group: int | None = None,
+        custom_routing_function: Callable | None = None,
+        scoring_func: str = "softmax",
+        routed_scaling_factor: float = 1.0,
+        e_score_correction_bias: torch.Tensor | None = None,
+        global_num_experts: int = -1,
+        expert_map: torch.Tensor | None = None,
+        apply_router_weight_on_input: bool = False,
+        **kwargs,
+    ) -> torch.Tensor:
+        zero_expert_num = getattr(layer, "zero_expert_num", 0)
+        zero_expert_type = getattr(layer, "zero_expert_type", None)
+        assert routed_scaling_factor == 1.0
+
+        topk_weights, topk_ids = select_experts(
+            hidden_states=x,
+            router_logits=router_logits,
+            top_k=top_k,
+            use_grouped_topk=use_grouped_topk,
+            renormalize=renormalize,
+            topk_group=topk_group,
+            num_expert_group=num_expert_group,
+            custom_routing_function=custom_routing_function,
+            scoring_func=scoring_func,
+            e_score_correction_bias=e_score_correction_bias,
+            global_num_experts=global_num_experts,
+        )
+
+        if zero_expert_num > 0 and zero_expert_type is not None:
+            topk_ids, topk_weights, zero_expert_result = zero_experts_compute(
+                expert_indices=topk_ids,
+                expert_scales=topk_weights,
+                num_experts=global_num_experts,
+                zero_expert_type=zero_expert_type,
+                hidden_states=x,
+            )
+
+        topk_weights = topk_weights.to(x.dtype)
+
+        moe_comm_method = get_forward_context().moe_comm_method
+        final_hidden_states = moe_comm_method.fused_experts(
+            hidden_states=x,
+            w1=layer.w13_weight,
+            w2=layer.w2_weight,
+            topk_weights=topk_weights,
+            topk_ids=topk_ids,
+            expert_map=expert_map,
+            apply_router_weight_on_input=apply_router_weight_on_input,
+        )
+        if zero_expert_num > 0 and zero_expert_type is not None:
+            final_hidden_states += zero_expert_result
+        return final_hidden_states
+
+
+class AscendFusedMoE310(FusedMoE):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+        self.global_num_experts = kwargs["num_experts"]
+
+        if self.quant_config is None:
+            self.quant_method = AscendUnquantizedFusedMoEMethod310(self.moe_config)
+        else:
+            self.quant_method = self.quant_config.get_quant_method(self, self.layer_name)
+
+        assert self.quant_method is not None
+
+        self.moe_config.tp_group = get_tp_group()
+        self.moe_config.dp_group = get_dp_group()
+        self.moe_config.ep_group = get_ep_group()
+        self.moe_config.supports_eplb = False
+
+        # init moe
+        self.global_expert_map = None
+        self.local_expert_map = None
+        if self.moe_config.ep_size > 1:
+            self.global_expert_map, self.local_expert_map = self.init_experts_map(self.moe_config)
+        self.local_num_experts = (
+            torch.sum(self.local_expert_map != -1).item()
+            if self.local_expert_map is not None
+            else self.global_num_experts
+        )
+
+        self.moe_config.num_experts = self.global_num_experts
+        self.moe_config.num_local_experts = self.local_num_experts
+        self.moe_config.global_redundant_expert_num = 0
+
+        moe_quant_params = {
+            "num_experts": self.local_num_experts,
+            "hidden_size": self.hidden_size,
+            "intermediate_size_per_partition": self.intermediate_size_per_partition,
+            "params_dtype": self.params_dtype,
+            "weight_loader": self.weight_loader,
+        }
+
+        self.quant_method.create_weights(layer=self, **moe_quant_params)
+        self.quant_type = self.get_quant_type()
+
+        _MoECommMethods[MoECommType.ALLGATHER] = AllGatherCommImpl310(self.moe_config)
+
+    def init_experts_map(self, moe_config):
+        """
+        Initialize expert mapping for MoE (Mixture of Experts) model.
+
+        This function creates mappings between global expert indices and local expert indices
+        for each rank in the expert parallel group. It divides the total experts among
+        different ranks and creates both global and local expert maps that are used
+        during MoE computation to determine which experts are handled by which rank.
+
+        Args:
+            moe_config: Configuration object containing MoE parameters including
+                       number of experts, expert parallel size, and expert parallel rank.
+
+        Returns:
+            tuple: A tuple containing:
+                   - global_expert_map: Stack of expert maps for all ranks
+                   - local_expert_map: Expert map for the current rank (transferred to NPU)
+        """
+        n_experts = moe_config.num_experts
+        ep_size = moe_config.ep_size
+        all_experts = torch.arange(n_experts, dtype=torch.int32)
+        experts_groups = all_experts.chunk(ep_size)
+        global_expert_map = []
+        local_expert_map = None
+        for rankid in range(ep_size):
+            expert_map = torch.full((n_experts,), -1, dtype=torch.int32)
+            local_experts = experts_groups[rankid]
+            expert_map[local_experts] = torch.arange(local_experts.shape[0], dtype=torch.int32)
+            global_expert_map.append(expert_map)
+            if rankid == moe_config.ep_rank:
+                local_expert_map = expert_map.npu()
+        return torch.stack(global_expert_map), local_expert_map
+
+    def get_quant_type(self) -> QuantType:
+        quant_method = self.quant_method
+        if not hasattr(quant_method, "quant_method") or quant_method.quant_method is None:
+            return QuantType.NONE
+
+        method = quant_method.quant_method
+        quant_type = getattr(method, "quant_type", QuantType.NONE)
+        if quant_type != QuantType.NONE:
+            # TODO: w8a8 quantization will be supported soon, and only reject w4a8 here.
+            raise RuntimeError("W8A8 is not supported currently.")
+        return QuantType.NONE
+
+    def forward_impl(  # type: ignore[override]
+        self, hidden_states: torch.Tensor, router_logits: torch.Tensor
+    ) -> torch.Tensor:
+        assert self.quant_method is not None
+        forward_context = get_forward_context()
+
+        hidden_states, router_logits, _, context_metadata = forward_context.moe_comm_method.prepare(
+            hidden_states=hidden_states, router_logits=router_logits, quant_type=self.quant_type
+        )
+
+        if isinstance(hidden_states, tuple):
+            hidden_states, pertoken_scale = hidden_states
+        else:
+            pertoken_scale = None
+
+        # Matrix multiply.
+        fused_experts_results: FusedExpertsResult = self.quant_method.apply(
+            layer=self,
+            x=hidden_states,
+            router_logits=router_logits,
+            pertoken_scale=pertoken_scale,
+            top_k=self.top_k,
+            renormalize=self.renormalize,
+            use_grouped_topk=self.use_grouped_topk,
+            global_num_experts=self.global_num_experts,
+            expert_map=self.local_expert_map,
+            topk_group=self.topk_group,
+            num_expert_group=self.num_expert_group,
+            custom_routing_function=self.custom_routing_function,
+            scoring_func=self.scoring_func,
+            routed_scaling_factor=self.routed_scaling_factor,
+            e_score_correction_bias=self.e_score_correction_bias,
+            activation=self.activation,
+            apply_router_weight_on_input=self.apply_router_weight_on_input,
+        )
+
+        routed_out = forward_context.moe_comm_method.finalize(
+            hidden_states=fused_experts_results.routed_out,
+            reduce_results=self.reduce_results,
+            context_metadata=context_metadata,
+        )
+
+        return routed_out
+
+
+class AscendSharedFusedMoE310(SharedFusedMoE, AscendFusedMoE310):
+    def __init__(
+        self,
+        shared_experts: torch.nn.Module,
+        gate: torch.nn.Module | None = None,
+        use_overlapped: bool = True,
+        **kwargs,
+    ):
+        AscendFusedMoE310.__init__(self, **kwargs)
+        self._shared_experts = shared_experts
+        self.use_overlapped = use_overlapped
+        self.shared_expert_stream = None
+        self._gate = gate
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        router_logits: torch.Tensor,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        if self._shared_experts is None:
+            fused_out = AscendFusedMoE310.forward(
+                self,
+                hidden_states=hidden_states,
+                router_logits=router_logits,
+            )
+            shared_out = None
+            return shared_out, fused_out
+        shared_out, fused_out = AscendFusedMoE310.forward(
+            self,
+            hidden_states=hidden_states,
+            router_logits=router_logits,
+        )
+        return shared_out, fused_out
+
+    def _forward_shared_experts(self, hidden_states: torch.Tensor):
+        if self._shared_experts is None:
+            return None
+        part1_out = self._shared_experts_part1(hidden_states)
+        shared_out = self._shared_experts_part2(hidden_states, part1_out)
+        return shared_out
+
+    def forward_impl(  # type: ignore[override]
+        self, hidden_states: torch.Tensor, router_logits: torch.Tensor
+    ):
+        routed_out = AscendFusedMoE310.forward_impl(
+            self,
+            hidden_states=hidden_states,
+            router_logits=router_logits,
+        )
+        if self._shared_experts is None:
+            return routed_out
+        shared_out = self._forward_shared_experts(hidden_states)
+        return shared_out, routed_out