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vllm_npu/ops/common_fused_moe.py

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+#
+# 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.
+#
+import os.path
+from typing import Any, Callable, Optional
+
+import torch
+import torch_npu
+from vllm.config import get_current_vllm_config
+from vllm.distributed import (get_dp_group, get_ep_group, get_tp_group,
+                              tensor_model_parallel_all_reduce)
+from vllm.forward_context import get_forward_context
+from vllm.logger import logger
+from vllm.model_executor.layers.fused_moe.config import FusedMoEConfig
+from vllm.model_executor.layers.fused_moe.layer import (
+    FusedMoE, UnquantizedFusedMoEMethod, determine_expert_map,
+    get_compressed_expert_map)
+from vllm.model_executor.layers.shared_fused_moe import SharedFusedMoE
+
+from vllm_npu.ascend_config import get_ascend_config
+from vllm_npu.ascend_forward_context import MoECommType
+from vllm_npu.distributed.parallel_state import get_mc2_group
+from vllm_npu.eplb.core.eplb_utils import determine_default_log2phy_map
+from vllm_npu.ops.expert_load_balancer import ExpertLoadBalancer
+from vllm_npu.ops.moe.experts_selector import select_experts
+from vllm_npu.ops.moe.moe_comm_method import setup_moe_comm_method
+from vllm_npu.quantization.w8a8_dynamic import \
+    AscendW8A8DynamicFusedMoEMethod
+from vllm_npu.utils import (ACL_FORMAT_FRACTAL_NZ, enable_sp, is_310p,
+                               is_enable_nz, npu_stream_switch,
+                               shared_expert_dp_enabled,
+                               shared_experts_compute_stream)
+
+
+class AscendUnquantizedFusedMoEMethod(UnquantizedFusedMoEMethod):
+
+    def __init__(self, moe: FusedMoEConfig = None):
+
+        super().__init__(moe=moe)
+        self.dynamic_eplb = get_ascend_config().dynamic_eplb
+        self.transpose = True
+
+    def process_weights_after_loading(self, layer):
+        super(UnquantizedFusedMoEMethod,
+              self).process_weights_after_loading(layer)
+        if self.transpose:
+            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)
+
+            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)
+
+            self.transpose = False
+        else:
+            w13_data = self._maybe_pad_weight(layer.w13_weight.data)
+            layer.w13_weight = torch.nn.Parameter(w13_data,
+                                                  requires_grad=False)
+
+            w2_data = self._maybe_pad_weight(layer.w2_weight.data)
+            layer.w2_weight = torch.nn.Parameter(w2_data, requires_grad=False)
+
+        if not is_310p() and is_enable_nz(layer.w13_weight.data.dtype):
+            layer.w13_weight.data = torch_npu.npu_format_cast(
+                layer.w13_weight.data, ACL_FORMAT_FRACTAL_NZ)
+            layer.w2_weight.data = torch_npu.npu_format_cast(
+                layer.w2_weight.data, ACL_FORMAT_FRACTAL_NZ)
+
+    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: Optional[int] = None,
+              num_expert_group: Optional[int] = None,
+              custom_routing_function: Optional[Callable] = None,
+              scoring_func: str = "softmax",
+              routed_scaling_factor: float = 1.0,
+              e_score_correction_bias: Optional[torch.Tensor] = None,
+              global_num_experts: int = -1,
+              expert_map: Optional[torch.Tensor] = None,
+              apply_router_weight_on_input: bool = False,
+              enable_force_load_balance: bool = False,
+              shared_experts: Optional[Any] = None,
+              **kwargs) -> torch.Tensor:
+
+        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,
+            routed_scaling_factor=routed_scaling_factor,
+            e_score_correction_bias=e_score_correction_bias,
+            global_num_experts=global_num_experts)
+
+        topk_weights = topk_weights.to(x.dtype)
+        # this is a naive implementation for experts load balance so as
+        # to avoid accumulating too much tokens on a single rank.
+        # currently it is only activated when doing profile runs.
+        if enable_force_load_balance:
+            topk_ids = torch.randint_like(topk_ids, 0, global_num_experts)
+
+        moe_comm_method = get_forward_context().moe_comm_method
+        return moe_comm_method.fused_experts(
+            hidden_states=x,
+            w1=layer.w13_weight,
+            w2=layer.w2_weight,
+            topk_weights=topk_weights,
+            topk_ids=topk_ids,
+            global_num_experts=global_num_experts,
+            expert_map=expert_map,
+            shared_experts=shared_experts,
+            apply_router_weight_on_input=apply_router_weight_on_input,
+            dynamic_eplb=self.dynamic_eplb)
+
+
+class AscendFusedMoE(FusedMoE):
+    moe_counter = -1
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+        num_experts = kwargs["num_experts"]
+        intermediate_size = kwargs["intermediate_size"]
+
+        AscendFusedMoE.moe_counter += 1
+        self.moe_instance_id = AscendFusedMoE.moe_counter
+
+        self.expert_map = None
+        self.log2phy = None
+
+        if self.quant_config is None:
+            self.quant_method = AscendUnquantizedFusedMoEMethod(
+                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.mc2_group = get_mc2_group()
+        ascend_config = get_ascend_config()
+        self.dynamic_eplb = ascend_config.dynamic_eplb or ascend_config.expert_map_record_path
+        self.expert_map_path = ascend_config.expert_map_path
+        self.global_redundant_expert_num = ascend_config.init_redundancy_expert
+        self.global_num_experts = num_experts + self.global_redundant_expert_num
+        # TODO: Flag for static expert placement. This is a temporary workaround
+        # to allow dynamic EPLB with float weights by skipping quantization checks.
+        self.static_eplb_enabled = False
+        if self.custom_routing_function is None and self.e_score_correction_bias is not None:
+            vllm_config = get_current_vllm_config()
+            self.e_score_correction_bias.data = self.e_score_correction_bias.data.to(
+                dtype=vllm_config.model_config.dtype)
+        # static eplb initializing with expert_map_path
+        init_eplb_enable = False
+        if self.expert_map_path and os.path.exists(
+                self.expert_map_path) and os.access(self.expert_map_path,
+                                                    os.R_OK):
+            self.expert_load_balancer = ExpertLoadBalancer(
+                self.expert_map_path, num_experts)
+            self.expert_load_balancer.check_expert_map_tensor()
+            self.global_redundant_expert_num = (
+                self.expert_load_balancer.get_global_redundant_expert_num())
+            self.global_num_experts = num_experts + self.global_redundant_expert_num
+            try:
+                self.local_num_experts, self.expert_map = (
+                    self.expert_load_balancer.get_rank_placement_map(
+                        self.moe_instance_id, self.ep_rank))
+                self.log2phy = self.expert_load_balancer.get_rank_log2phy_map(
+                    self.moe_instance_id, self.ep_rank).npu()
+                init_eplb_enable = True
+            except Exception as e:
+                logger.warning(
+                    f"Init expert map of mtp/eagle when using sample.{e}")
+                self.local_num_experts, self.expert_map = determine_expert_map(
+                    self.ep_size, self.ep_rank, self.global_num_experts)
+                self.log2phy = determine_default_log2phy_map(
+                    self.global_num_experts, self.ep_size, self.ep_rank).npu()
+        else:
+            # init moe.
+            self.local_num_experts, self.expert_map = determine_expert_map(
+                self.ep_size, self.ep_rank, self.global_num_experts)
+            # dynamic eplb initializing with not expert_map_path
+            if self.dynamic_eplb:
+                self.log2phy = determine_default_log2phy_map(
+                    self.global_num_experts, self.ep_size, self.ep_rank).npu()
+        if self.expert_map is not None and isinstance(self.expert_map,
+                                                      torch.Tensor):
+            logger.info_once(
+                "[EP Rank %s/%s] Expert parallelism is enabled. Local/global"
+                " number of experts: %s/%s. Experts local to global index map:"
+                " %s.", self.ep_rank, self.ep_size, self.local_num_experts,
+                self.global_num_experts,
+                get_compressed_expert_map(self.expert_map))
+        local_num_experts = (torch.sum(
+            self.expert_map != -1) if self.expert_map is not None else
+                             self.global_num_experts)
+        if self.dynamic_eplb:
+            self.moe_load = torch.zeros(local_num_experts,
+                                        dtype=torch.int64).npu()
+
+        if init_eplb_enable and (
+                not hasattr(self.quant_method, "quant_method")
+                or not isinstance(self.quant_method.quant_method,
+                                  AscendW8A8DynamicFusedMoEMethod)):
+            raise ValueError("Eplb supports only w8a8_dynamic quantization.")
+
+        self.moe_config.num_experts = self.global_num_experts
+        self.moe_config.num_local_experts = self.local_num_experts
+        self.moe_config.original_num_experts = num_experts
+
+        moe_quant_params = {
+            "num_experts": 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,
+        }
+        # need full intermediate size pre-sharding for WNA16 act order
+        if (self.quant_method.__class__.__name__
+                in ("GPTQMarlinMoEMethod", "CompressedTensorsWNA16MoEMethod")):
+            moe_quant_params["intermediate_size_full"] = intermediate_size
+        self.quant_method.create_weights(layer=self, **moe_quant_params)
+
+        self.enable_shared_expert_dp = ascend_config.enable_shared_expert_dp
+
+        setup_moe_comm_method(self.moe_config)
+
+    def update_expert_map(self, new_expert_map):
+        self.expert_map = new_expert_map
+
+    def get_map(self):
+        return self.expert_map
+
+    def get_log2phy_map(self):
+        return self.log2phy
+
+    def clear_moe_load(self):
+        if self.moe_load is not None:
+            self.moe_load.zero_()
+
+    def maybe_all_reduce_tensor_model_parallel(
+            self, final_hidden_states: torch.Tensor):
+        """NOTE(Yizhou): This is to override the parent class method. In `mc2commimpl`,
+        and `alltoallcommimpl`, we do not need to all-reduce the final outputs since
+        the outputs are already aggregated across tensor parallel ranks in the
+        `finalize` function. In `allgathercommimpl`, we still need to all-reduce the
+        outputs since each rank only has partial outputs.
+        """
+        return torch.ops.vllm.maybe_all_reduce_tensor_model_parallel(
+            final_hidden_states)
+
+    def forward_impl(self, hidden_states: torch.Tensor,
+                     router_logits: torch.Tensor):
+        assert self.quant_method is not None
+
+        # For w8a8 dynamic we can do npu_dynamic_quant and gate in parallel.
+        quantized_x_for_share, dynamic_scale_for_share = None, None
+
+        forward_context = get_forward_context()
+
+        # Load balancing for token distribution among experts in dummy_run
+        # TODO: The community only considers load balancing when DP > 1.
+        # This approach may overlook some extreme scenarios.
+        enable_force_load_balance = forward_context.in_profile_run
+
+        hidden_states, router_logits = forward_context.moe_comm_method.prepare(
+            hidden_states=hidden_states,
+            router_logits=router_logits,
+            replace_allreduce=forward_context.sp_enabled,
+            enable_shared_expert_dp=self.enable_shared_expert_dp)
+
+        # Matrix multiply.
+        final_hidden_states = self.quant_method.apply(
+            layer=self,
+            x=hidden_states,
+            router_logits=router_logits,
+            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.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,
+            e_score_correction_bias=self.e_score_correction_bias,
+            activation=self.activation,
+            apply_router_weight_on_input=self.apply_router_weight_on_input,
+            quantized_x_for_share=quantized_x_for_share,
+            dynamic_scale_for_share=dynamic_scale_for_share,
+            shared_experts=None,
+            enable_force_load_balance=enable_force_load_balance,
+            log2phy=self.log2phy,
+            global_redundant_expert_num=self.global_redundant_expert_num)
+
+        if isinstance(final_hidden_states, tuple):
+            final_hidden_states, group_list_type, expert_tokens = final_hidden_states
+
+            if self.dynamic_eplb:
+                self.moe_load += expert_tokens if group_list_type == 1 else \
+                    torch.cat([expert_tokens[:1], expert_tokens[1:] - expert_tokens[:-1]])
+
+        final_hidden_states = forward_context.moe_comm_method.finalize(
+            hidden_states=final_hidden_states,
+            reduce_results=self.reduce_results)
+
+        return final_hidden_states
+
+    def transpose_weight(self, loaded_weight, expert_data, shard_dim):
+        # Ensure training and inference weight shapes match during RL weight updates
+        if (
+            loaded_weight.shape[1] != expert_data.shape[1] and \
+            loaded_weight.shape[0] != expert_data.shape[0]
+        ):
+            shard_dim = int(not shard_dim)
+            loaded_weight = loaded_weight.transpose(0, 1).contiguous()
+        return loaded_weight, shard_dim
+
+    def _load_w13(self,
+                  expert_data: torch.Tensor,
+                  shard_dim: int,
+                  shard_id: str,
+                  loaded_weight: torch.Tensor,
+                  tp_rank: int,
+                  load_full: bool = False):
+        # Index the loaded weight for tp sharding.
+        # gate_up_proj: "MergedColumnParallel", so tp sharding on output_dim
+        loaded_weight, shard_dim = self.transpose_weight(
+            loaded_weight, expert_data, shard_dim)
+        shard_size = expert_data.shape[shard_dim] // 2
+        if not load_full:
+            loaded_weight = loaded_weight.narrow(shard_dim,
+                                                 shard_size * tp_rank,
+                                                 shard_size)
+        # Narrow parameter and load.
+        # w1, gate_proj: Load into first logical weight of w13.
+        if shard_id == "w1":
+            expert_data = expert_data.narrow(shard_dim, 0, shard_size)
+        # w3, up_proj: Load into second logical weight of w13.
+        else:
+            assert shard_id == "w3"
+            expert_data = expert_data.narrow(shard_dim, shard_size, shard_size)
+        expert_data.copy_(loaded_weight)
+
+    def _load_w2(self,
+                 expert_data: torch.Tensor,
+                 shard_dim: int,
+                 loaded_weight: torch.Tensor,
+                 tp_rank: int,
+                 load_full: bool = False):
+        # Index the loaded weight for tp sharding.
+        # down_proj: "RowParallel" so tp sharding on input_dim
+        # Narrow parameter and load.
+        loaded_weight, shard_dim = self.transpose_weight(
+            loaded_weight, expert_data, shard_dim)
+        shard_size = expert_data.shape[shard_dim]
+        if not load_full:
+            loaded_weight = loaded_weight.narrow(shard_dim,
+                                                 shard_size * tp_rank,
+                                                 shard_size)
+        # w2, down_proj: Load into only logical weight of w2.
+        expert_data.copy_(loaded_weight)
+
+
+class AscendSharedFusedMoE(SharedFusedMoE, AscendFusedMoE):
+
+    def __init__(
+        self,
+        shared_experts: torch.nn.Module,
+        use_overlapped: bool = True,
+        **kwargs,
+    ):
+        AscendFusedMoE.__init__(self, **kwargs)
+        self._shared_experts = shared_experts
+        self.use_overlapped = use_overlapped
+        self.shared_expert_stream = None
+        ascend_config = get_ascend_config()
+        self.multistream_overlap_shared_expert = ascend_config.multistream_overlap_shared_expert
+        if enable_sp():
+            logger.info_once(
+                "Sequence parallelism is enabled, shared experts are replicated for best performance."
+            )
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        router_logits: torch.Tensor,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        shared_out, fused_out = AscendFusedMoE.forward(
+            self,
+            hidden_states=hidden_states,
+            router_logits=router_logits,
+        )
+        return shared_out, fused_out
+
+    def forward_impl(self, hidden_states: torch.Tensor,
+                     router_logits: torch.Tensor):
+        # Make sure the shared experts stream begins after hidden_states are ready.
+        if self.multistream_overlap_shared_expert:
+            shared_experts_compute_stream().wait_stream(  # type: ignore
+                torch.npu.current_stream())
+        with npu_stream_switch(shared_experts_compute_stream(),
+                               enabled=self.multistream_overlap_shared_expert):
+            # Use a separate stream to run shared experts.
+            # Note that currently we only support calculations in separate streams with aclgraph.
+            # Communication operations in another stream might cause unknown errors.
+            shared_out = self._shared_experts(hidden_states)
+
+        fused_output = AscendFusedMoE.forward_impl(
+            self,
+            hidden_states=hidden_states,
+            router_logits=router_logits,
+        )
+        # Make sure the default stream waits for the shared experts stream to finish.
+        if self.multistream_overlap_shared_expert:
+            torch.npu.current_stream().wait_stream(
+                shared_experts_compute_stream())
+        # NOTE: This is exactly the opposite of `maybe_all_reduce_tensor_model_parallel`
+        forward_context = get_forward_context()
+        moe_comm_type = forward_context.moe_comm_type
+        if moe_comm_type in {MoECommType.ALLTOALL, MoECommType.MC2} \
+                and not shared_expert_dp_enabled():
+            shared_out = tensor_model_parallel_all_reduce(shared_out)
+        return shared_out, fused_output