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f6e5decc10
Upgrade to vllm 0.9.0. 0.8.5 will not be supported any more. Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
855 lines
33 KiB
Python
855 lines
33 KiB
Python
# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
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# Copyright 2023 The vLLM team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# This file is a part of the vllm-ascend project.
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# Adapted from vllm/tests/kernels/test_moe.py
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from typing import Callable, Optional
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import torch
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import torch.distributed as dist
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import torch_npu
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from vllm.config import get_current_vllm_config
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from vllm.distributed import (GroupCoordinator,
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get_tensor_model_parallel_world_size,
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tensor_model_parallel_all_reduce)
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from vllm.distributed.parallel_state import get_dp_group
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from vllm.model_executor.layers.fused_moe.layer import (
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FusedMoE, FusedMoEParallelConfig, MoEConfig, UnquantizedFusedMoEMethod,
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determine_expert_map)
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from vllm.model_executor.layers.quantization.base_config import \
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QuantizationConfig
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import vllm_ascend.envs as envs_ascend
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from vllm_ascend.distributed.parallel_state import get_ep_group, get_etp_group
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VLLM_ENABLE_MC2: bool = envs_ascend.VLLM_ENABLE_MC2
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USING_LCCL_COM: bool = envs_ascend.USING_LCCL_COM
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def fused_experts_with_mc2(
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hidden_states: torch.Tensor,
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w1: torch.Tensor,
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w2: torch.Tensor,
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topk_weights: torch.Tensor,
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topk_ids: torch.Tensor,
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top_k: int,
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expert_map: torch.Tensor = None,
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moe_all_to_all_group_name: Optional[str] = None,
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) -> torch.Tensor:
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global_bs = 0
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moe_expert_num = len(expert_map)
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kwargs = {
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"x": hidden_states,
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"expert_ids": topk_ids,
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"expert_shard_type": 0,
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"shared_expert_rank_num": 0,
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"moe_expert_num": moe_expert_num,
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"global_bs": global_bs,
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}
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rank = torch.distributed.get_rank()
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quant_mode = 0
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ep_group = get_ep_group().device_group
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local_rank = torch.distributed.get_rank(group=ep_group)
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all_to_all_group_size = torch.distributed.get_world_size(ep_group)
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world_szie = torch.distributed.get_world_size()
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tp_size = world_szie // all_to_all_group_size
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tp_rank = rank % tp_size
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stage1_kwargs = {
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"scales": None,
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"quant_mode": quant_mode,
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"group_ep": moe_all_to_all_group_name,
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"ep_world_size": all_to_all_group_size,
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"ep_rank_id": local_rank,
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# "group_tp": self.moe_rs_group_name,
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"group_tp": moe_all_to_all_group_name,
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"tp_world_size": tp_size,
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"tp_rank_id": tp_rank,
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}
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kwargs.update(stage1_kwargs)
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output = torch_npu.npu_moe_distribute_dispatch(**kwargs)
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# comm_stream.wait_stream(torch.npu.current_stream())
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expand_x, dynamic_scale, expand_idx, expert_token_nums, ep_recv_counts = output[
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0:5]
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w1 = w1.transpose(1, 2)
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expert_token_nums = torch.cumsum(expert_token_nums,
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dim=0,
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dtype=torch.int64)
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group_list = expert_token_nums.to(torch.int64)
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gate_up_out_list = torch_npu.npu_grouped_matmul(
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x=[expand_x],
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weight=[w1],
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split_item=2,
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group_list_type=0,
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group_type=0,
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group_list=group_list,
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)
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# TODO: Remove this in the future.
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gate_up_out = torch.cat(gate_up_out_list, dim=0)
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gate_up_out = torch_npu.npu_swiglu(gate_up_out)
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w2 = w2.transpose(1, 2)
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down_out_list = torch_npu.npu_grouped_matmul(
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x=[gate_up_out],
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weight=[w2],
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split_item=2,
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group_list_type=0,
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group_type=0,
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group_list=group_list,
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)
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down_out_list = torch.cat(down_out_list, dim=0)
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# moeCombine
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kwargs = {
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"expand_x": down_out_list,
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"expert_ids": topk_ids,
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"expand_idx": expand_idx,
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"expert_scales": topk_weights.to(torch.float32),
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"expert_shard_type": 0,
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"shared_expert_rank_num": 0,
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"moe_expert_num": moe_expert_num,
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"global_bs": 0,
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}
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tp_recv_counts = output[5]
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stage3_kwargs = {
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"ep_send_counts": ep_recv_counts,
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"group_ep": moe_all_to_all_group_name,
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"ep_world_size": all_to_all_group_size,
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"ep_rank_id": local_rank,
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"tp_send_counts": tp_recv_counts,
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# "group_tp": self.moe_rs_group_name,
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"group_tp": moe_all_to_all_group_name,
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"tp_world_size": tp_size,
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"tp_rank_id": tp_rank,
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}
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kwargs.update(stage3_kwargs)
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hidden_states = torch_npu.npu_moe_distribute_combine(**kwargs)
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return hidden_states
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# currently expert parallelism implemented with all2all
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# is under-optimized.
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def fused_experts_with_all2all(
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hidden_states: torch.Tensor,
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w1: torch.Tensor,
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w2: torch.Tensor,
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topk_weights: torch.Tensor,
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topk_ids: torch.Tensor,
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top_k: int,
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expert_map: torch.Tensor = None,
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ep_group: GroupCoordinator = None,
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):
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original_shape = hidden_states.shape
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if len(original_shape) == 3:
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hidden_states = hidden_states.view(-1, hidden_states.shape[-1])
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num_tokens, _ = hidden_states.shape
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num_experts = w1.shape[0]
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device = hidden_states.device
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if expert_map is not None:
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global_num_experts = len(expert_map)
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local_num_experts = global_num_experts // ep_group.world_size
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row_idx_len = num_tokens * top_k
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row_idx = (torch.arange(0,
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row_idx_len,
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dtype=torch.int32,
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device=device).view(top_k, -1).permute(
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1, 0).contiguous())
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hidden_states, expanded_row_idx, expanded_expert_idx = torch_npu.npu_moe_init_routing(
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hidden_states,
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row_idx=row_idx,
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expert_idx=topk_ids,
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active_num=num_tokens)
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global_expert_tokens = torch.bincount(expanded_expert_idx,
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minlength=global_num_experts)
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scatter_sizes = global_expert_tokens.view(ep_group.world_size,
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-1).sum(-1)
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gather_sizes = torch.empty_like(scatter_sizes)
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dist.all_to_all_single(gather_sizes,
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scatter_sizes,
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group=ep_group.device_group)
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scatter_size_list = scatter_sizes.cpu().tolist()
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gather_size_list = gather_sizes.cpu().tolist()
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expanded_expert_idx = expanded_expert_idx % local_num_experts
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hidden_states = ep_group.all_to_all(hidden_states, 0, 0,
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scatter_size_list,
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gather_size_list)
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local_expert_idx = ep_group.all_to_all(expanded_expert_idx, 0, 0,
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scatter_size_list,
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gather_size_list)
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sorted_local_expert_idx, sorted_idx = torch.sort(local_expert_idx)
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expert_tokens = torch_npu.npu_moe_compute_expert_tokens(
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sorted_local_expert_idx, local_num_experts).to(torch.int64)
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hidden_states = hidden_states[sorted_idx]
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else:
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row_idx_len = num_tokens * top_k
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row_idx = torch.arange(0,
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row_idx_len,
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dtype=torch.int32,
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device=topk_weights.device).view(
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top_k, -1).permute(1, 0).contiguous()
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hidden_states, expanded_row_idx, expanded_expert_idx = torch_npu.npu_moe_init_routing(
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hidden_states,
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row_idx=row_idx,
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expert_idx=topk_ids,
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active_num=num_tokens)
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expert_tokens = torch_npu.npu_moe_compute_expert_tokens(
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expanded_expert_idx, num_experts)
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expert_tokens = expert_tokens.to(torch.int64)
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w1 = w1.transpose(1, 2)
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gate_up_out_list = torch_npu.npu_grouped_matmul(
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x=[hidden_states],
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weight=[w1],
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split_item=2,
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group_list_type=0,
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group_type=0,
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group_list=expert_tokens,
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)
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# TODO: Remove this in the future.
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hidden_states = torch.cat(gate_up_out_list, dim=0)
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hidden_states = torch_npu.npu_swiglu(hidden_states)
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w2 = w2.transpose(1, 2)
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down_out_list = torch_npu.npu_grouped_matmul(
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x=[hidden_states],
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weight=[w2],
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split_item=2,
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group_list_type=0,
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group_type=0,
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group_list=expert_tokens,
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)
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hidden_states = torch.cat(down_out_list, dim=0)
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if expert_map is not None:
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resorted_idx = torch.argsort(sorted_idx)
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hidden_states = hidden_states[resorted_idx]
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hidden_states = ep_group.all_to_all(hidden_states, 0, 0,
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gather_size_list,
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scatter_size_list)
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final_hidden_states = torch_npu.npu_moe_finalize_routing(
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hidden_states,
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skip1=None,
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skip2=None,
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bias=None,
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scales=topk_weights,
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expanded_src_to_dst_row=expanded_row_idx,
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export_for_source_row=topk_ids,
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)
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else:
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# TODO: Reorder device memory 2 times here, replace the current
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# implementation here when suitable operators become available.
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final_hidden_states = torch_npu.npu_moe_finalize_routing(
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hidden_states,
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skip1=None,
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skip2=None,
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bias=None,
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scales=topk_weights,
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expanded_src_to_dst_row=expanded_row_idx,
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export_for_source_row=topk_ids,
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)
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if len(original_shape) == 3:
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final_hidden_states = final_hidden_states.view(original_shape)
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return final_hidden_states
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def fused_experts(
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hidden_states: torch.Tensor,
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w1: torch.Tensor,
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w2: torch.Tensor,
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topk_weights: torch.Tensor,
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topk_ids: torch.Tensor,
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top_k: int,
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expert_map: torch.Tensor = None,
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apply_router_weight_on_input: bool = False,
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) -> torch.Tensor:
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"""
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Fused experts with top-k routing.
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Args:
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hidden_states: Hidden states of shape (num_tokens, hidden_size).
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w1: Expert weights1 of shape (num_experts, intermediate_size * 2, hidden_size).
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w2: Expert weights2 of shape (num_experts, hidden_size, intermediate_size).
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topk_weights: Routing weights of shape (num_tokens, top_k).
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topk_ids: Selected expert IDs of shape (num_tokens, top_k).
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top_k: Number of experts to select.
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expert_map: Expert mapping of shape (num_experts,).
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Returns:
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hidden_states: Hidden states after routing.
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"""
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"""
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# Check constraints.
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assert hidden_states.shape[1] == w1.shape[2], "Hidden size mismatch"
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assert topk_weights.shape == topk_ids.shape, "topk shape mismatch"
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assert hidden_states.is_contiguous(), "Hidden_states must be contiguous"
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assert w1.is_contiguous(), "Expert weights1 must be contiguous"
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assert w2.is_contiguous(), "Expert weights2 must be contiguous"
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"""
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# if torch.distributed.get_rank() == 0:
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# print(w1.shape)
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# print(hidden_states.shape)
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original_shape = hidden_states.shape
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# assert len(original_shape) == 2
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num_tokens = hidden_states.shape[:-1].numel()
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num_experts = w1.shape[0]
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dtype = hidden_states.dtype
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device = hidden_states.device
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# assert dtype in [torch.float32, torch.float16, torch.bfloat16
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# ], "Only float32, float16, and bfloat16 are supported"
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if apply_router_weight_on_input:
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assert (topk_weights.dim() == 2
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), "`topk_weights` should be in shape (num_tokens, topk)"
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_, topk = topk_weights.shape
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assert (
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topk == 1
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), "Only support topk=1 when `apply_router_weight_on_input` is True"
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hidden_states = hidden_states * topk_weights.to(hidden_states.dtype)
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if expert_map is not None:
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# Generate token indices and flatten
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token_indices = (torch.arange(num_tokens,
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device=device,
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dtype=torch.int64).unsqueeze(1).expand(
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-1, top_k).reshape(-1))
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# Flatten token-to-expert mappings and map to local experts
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weights_flat = topk_weights.view(-1)
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experts_flat = topk_ids.view(-1)
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local_experts_flat = expert_map[experts_flat]
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# Filter valid token-expert pairs
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mask = local_experts_flat != -1
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filtered_weights = torch.where(
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mask, weights_flat, torch.zeros_like(weights_flat)).to(dtype)
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filtered_experts = torch.where(
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mask, local_experts_flat,
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torch.full_like(local_experts_flat,
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num_experts)).to(topk_ids.dtype)
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# Sort by local expert IDs
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sort_indices = torch.argsort(filtered_experts)
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sorted_token_indices = token_indices[sort_indices]
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sorted_weights = filtered_weights[sort_indices]
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# Compute token counts with minlength of num_experts
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# This is equivalent to but faster than:
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# >>> token_counts = torch.bincount(filtered_experts, minlength=num_experts)[:-1]
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token_counts = torch.zeros(num_experts + 1,
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device=device,
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dtype=torch.int64)
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ones = torch.ones_like(filtered_experts, dtype=torch.int64)
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token_counts.scatter_add_(0, filtered_experts.to(torch.int64), ones)
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token_counts = token_counts[:num_experts]
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expert_tokens = torch.cumsum(token_counts, dim=0, dtype=torch.int64)
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# Rearrange hidden_states
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sorted_hidden_states = hidden_states[sorted_token_indices]
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else:
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row_idx_len = num_tokens * top_k
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row_idx = (torch.arange(0,
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row_idx_len,
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dtype=torch.int32,
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device=device).view(top_k, -1).permute(
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1, 0).contiguous())
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sorted_hidden_states, expanded_row_idx, expanded_expert_idx = torch_npu.npu_moe_init_routing(
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hidden_states,
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row_idx=row_idx,
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expert_idx=topk_ids,
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active_num=num_tokens)
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expert_tokens = torch_npu.npu_moe_compute_expert_tokens(
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expanded_expert_idx, num_experts)
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expert_tokens = expert_tokens.to(torch.int64)
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w1 = w1.transpose(1, 2)
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gate_up_out_list = torch_npu.npu_grouped_matmul(
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x=[sorted_hidden_states],
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weight=[w1],
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split_item=2,
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group_list_type=0,
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group_type=0,
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group_list=expert_tokens,
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)
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# TODO: Remove this in the future.
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gate_up_out = torch.cat(gate_up_out_list, dim=0)
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gate_up_out = torch_npu.npu_swiglu(gate_up_out)
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w2 = w2.transpose(1, 2)
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down_out_list = torch_npu.npu_grouped_matmul(
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x=[gate_up_out],
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weight=[w2],
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split_item=2,
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group_list_type=0,
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group_type=0,
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group_list=expert_tokens,
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)
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down_out_list = torch.cat(down_out_list, dim=0)
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if expert_map is not None:
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weighted_down_out = down_out_list * sorted_weights.unsqueeze(1)
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final_hidden_states = torch.zeros(*original_shape,
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device=hidden_states.device,
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dtype=dtype)
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# TODO: npu_grouped_matmul output random values at [num_valid_tokens:, ...]
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# This created multiple NaN and index_add_ will mix them up which harms accuracy
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# remove this mask and filter after it being fixed
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num_valid_tokens = mask.sum()
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valid_token_mask = torch.arange(
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0, sorted_token_indices.shape[0],
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device=device).unsqueeze(1) < num_valid_tokens
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valid_output = torch.where(
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valid_token_mask, weighted_down_out,
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torch.zeros_like(weighted_down_out)).to(dtype)
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final_hidden_states.index_add_(0, sorted_token_indices, valid_output)
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else:
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scales = torch.ones_like(
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topk_weights) if apply_router_weight_on_input else topk_weights
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# TODO: Reorder device memory 2 times here, replace the current
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# implementation here when suitable operators become available.
|
|
final_hidden_states = torch_npu.npu_moe_finalize_routing(
|
|
down_out_list,
|
|
skip1=None,
|
|
skip2=None,
|
|
bias=None,
|
|
scales=scales,
|
|
expanded_src_to_dst_row=expanded_row_idx,
|
|
export_for_source_row=topk_ids,
|
|
)
|
|
|
|
return final_hidden_states
|
|
|
|
|
|
def native_grouped_topk(
|
|
topk_weights: torch.Tensor,
|
|
num_expert_group: Optional[int],
|
|
topk_group: Optional[int],
|
|
):
|
|
topk_group = 0 if topk_group is None else topk_group
|
|
num_expert_group = 0 if num_expert_group is None else num_expert_group
|
|
|
|
num_token = topk_weights.shape[0]
|
|
grouped_weights = topk_weights.view(num_token, num_expert_group,
|
|
-1).max(dim=-1).values
|
|
topk_group_indices = torch.topk(grouped_weights.to(torch.float32),
|
|
k=topk_group,
|
|
dim=-1,
|
|
sorted=False)[1]
|
|
topk_group_mask = torch.zeros_like(grouped_weights)
|
|
topk_group_mask.scatter_(1, topk_group_indices, 1)
|
|
topk_weight_mask = (topk_group_mask.unsqueeze(-1).expand(
|
|
num_token, num_expert_group,
|
|
topk_weights.shape[-1] // num_expert_group).reshape(num_token, -1))
|
|
topk_weights = topk_weights.masked_fill(~topk_weight_mask.bool(), 0.0)
|
|
|
|
return topk_weights
|
|
|
|
|
|
def select_experts(
|
|
hidden_states: torch.Tensor,
|
|
router_logits: torch.Tensor,
|
|
top_k: int,
|
|
use_grouped_topk: bool,
|
|
renormalize: bool,
|
|
topk_group: Optional[int] = None,
|
|
num_expert_group: Optional[int] = None,
|
|
custom_routing_function: Optional[Callable] = None,
|
|
scoring_func: str = "softmax",
|
|
e_score_correction_bias: Optional[torch.Tensor] = None,
|
|
) -> tuple[torch.Tensor, torch.Tensor]:
|
|
"""
|
|
Select top-k experts based on router logits.
|
|
|
|
Args:
|
|
hidden_states: Hidden states of shape (num_tokens, hidden_size).
|
|
router_logits: Router logits of shape (num_tokens, num_experts).
|
|
top_k: Number of experts to select.
|
|
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.
|
|
|
|
Returns:
|
|
topk_weights: Routing weights of shape (num_tokens, top_k).
|
|
topk_ids: Selected expert IDs of shape (num_tokens, top_k).
|
|
|
|
Raises:
|
|
ValueError: If an unsupported scoring function is provided.
|
|
"""
|
|
|
|
if scoring_func == "softmax":
|
|
# NOTE: vLLM use dtype=torch.float here
|
|
topk_weights = router_logits.softmax(dim=-1)
|
|
elif scoring_func == "sigmoid":
|
|
topk_weights = router_logits.sigmoid()
|
|
else:
|
|
raise ValueError(f"Unsupported scoring function: {scoring_func}")
|
|
|
|
if use_grouped_topk:
|
|
assert topk_group is not None
|
|
assert num_expert_group is not None
|
|
|
|
if e_score_correction_bias is not None:
|
|
# Store original scores before applying correction bias. We use biased
|
|
# scores for expert selection but original scores for routing weights
|
|
original_weights = topk_weights
|
|
topk_weights = topk_weights + e_score_correction_bias.unsqueeze(0)
|
|
|
|
# TODO: Change to npu_group_topk when the latest CANN and NNAL is available
|
|
# >>> torch_npu._npu_group_topk(topk_weights, group_num=num_expert_group, k=topk_group)
|
|
topk_weights = native_grouped_topk(topk_weights, num_expert_group,
|
|
topk_group)
|
|
# TODO bfloat16 is not supported in torch.topk with ge graph.
|
|
if e_score_correction_bias is not None:
|
|
topk_ids = torch.topk(topk_weights.to(torch.float32),
|
|
k=top_k,
|
|
dim=-1,
|
|
sorted=False)[1]
|
|
# Use original unbiased scores for the routing weights
|
|
topk_weights = original_weights.gather(1, topk_ids)
|
|
else:
|
|
topk_weights, topk_ids = torch.topk(topk_weights.to(torch.float32),
|
|
k=top_k,
|
|
dim=-1,
|
|
sorted=False)
|
|
elif custom_routing_function is None:
|
|
topk_weights, topk_ids = topk_weights.topk(top_k, dim=-1)
|
|
topk_weights = topk_weights.to(hidden_states.dtype)
|
|
else:
|
|
topk_weights, topk_ids = custom_routing_function(
|
|
hidden_states=hidden_states,
|
|
gating_output=router_logits,
|
|
topk=top_k,
|
|
renormalize=renormalize)
|
|
# Required by npu_moe_init_routing
|
|
topk_ids = topk_ids.to(torch.int32)
|
|
return topk_weights, topk_ids
|
|
|
|
# Required by npu_moe_init_routing
|
|
topk_ids = topk_ids.to(torch.int32)
|
|
|
|
if renormalize:
|
|
topk_weights = topk_weights / topk_weights.sum(dim=-1, keepdim=True)
|
|
|
|
return topk_weights, topk_ids
|
|
|
|
|
|
class AscendUnquantizedFusedMoEMethod(UnquantizedFusedMoEMethod):
|
|
|
|
def __init__(self, moe: MoEConfig = None):
|
|
|
|
super().__init__(moe=moe)
|
|
vllm_config = get_current_vllm_config()
|
|
|
|
ep_group = get_ep_group()
|
|
self.ep_size = ep_group.world_size
|
|
self.global_batch_size = vllm_config.scheduler_config.max_num_seqs
|
|
self.local_batch_size = self.global_batch_size // self.ep_size
|
|
|
|
try:
|
|
device_group = ep_group.device_group
|
|
# TODO: Try local_rank = ep_group.rank_in_group
|
|
local_rank = torch.distributed.get_rank(group=device_group)
|
|
backend = device_group._get_backend(torch.device("npu"))
|
|
self.moe_all_to_all_group_name = backend.get_hccl_comm_name(
|
|
local_rank)
|
|
except AttributeError:
|
|
self.moe_all_to_all_group_name = None
|
|
|
|
def process_weights_after_loading(self, layer):
|
|
super(UnquantizedFusedMoEMethod,
|
|
self).process_weights_after_loading(layer)
|
|
layer.w13_weight = torch.nn.Parameter(self._maybe_pad_weight(
|
|
layer.w13_weight.data),
|
|
requires_grad=False)
|
|
layer.w2_weight = torch.nn.Parameter(self._maybe_pad_weight(
|
|
layer.w2_weight.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: Optional[int] = None,
|
|
num_expert_group: Optional[int] = None,
|
|
global_num_experts: int = -1,
|
|
expert_map: Optional[torch.Tensor] = None,
|
|
custom_routing_function: Optional[Callable] = None,
|
|
scoring_func: str = "softmax",
|
|
e_score_correction_bias: Optional[torch.Tensor] = None,
|
|
is_prefill: bool = False,
|
|
**kwargs,
|
|
):
|
|
# NOTE: now npu_moe_gating_top_k can only support `group_count=256` pattern
|
|
if global_num_experts == 256:
|
|
topk_weights, topk_ids, _ = torch_npu.npu_moe_gating_top_k(
|
|
router_logits,
|
|
k=top_k, # topk当前写8
|
|
bias=e_score_correction_bias,
|
|
k_group=topk_group, # fix: 4
|
|
group_count=num_expert_group, # fix 8
|
|
group_select_mode=1, # 0: group中的最大; 1: topk2.sum(fix)
|
|
renorm=0, # 0: softmax->topk(fix); 1: topk->softmax
|
|
norm_type=1, # 0: softmax; 1: sigmoid(fix)
|
|
# out_flag=False, # todo new api; 第三个输出是否输出
|
|
# y2_flag=False, # old api; 第三个输出是否输出
|
|
routed_scaling_factor=1,
|
|
eps=float(1e-20))
|
|
else:
|
|
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,
|
|
)
|
|
|
|
if VLLM_ENABLE_MC2 and not is_prefill:
|
|
return fused_experts_with_mc2(
|
|
hidden_states=x,
|
|
w1=layer.w13_weight,
|
|
w2=layer.w2_weight,
|
|
topk_weights=topk_weights,
|
|
topk_ids=topk_ids,
|
|
top_k=top_k,
|
|
expert_map=expert_map,
|
|
moe_all_to_all_group_name=self.moe_all_to_all_group_name)
|
|
elif get_ep_group().world_size == 1:
|
|
return fused_experts(hidden_states=x,
|
|
w1=layer.w13_weight,
|
|
w2=layer.w2_weight,
|
|
topk_weights=topk_weights,
|
|
topk_ids=topk_ids,
|
|
top_k=top_k,
|
|
expert_map=expert_map)
|
|
else:
|
|
# The current implementation of deepseek moe splits hidden_states
|
|
# according to tp_size before they are feed into fused_moe module.
|
|
# Therefore, all2all is needed no matter how dp/tp is set so as to
|
|
# dispatch/combine tokens.
|
|
return fused_experts_with_all2all(hidden_states=x,
|
|
w1=layer.w13_weight,
|
|
w2=layer.w2_weight,
|
|
topk_weights=topk_weights,
|
|
topk_ids=topk_ids,
|
|
top_k=top_k,
|
|
expert_map=expert_map,
|
|
ep_group=get_ep_group())
|
|
|
|
|
|
class AscendFusedMoE(FusedMoE):
|
|
|
|
def __init__(
|
|
self,
|
|
num_experts: int, # Global number of experts
|
|
top_k: int,
|
|
hidden_size: int,
|
|
intermediate_size: int,
|
|
params_dtype: Optional[torch.dtype] = None,
|
|
reduce_results: bool = False,
|
|
renormalize: bool = True,
|
|
use_grouped_topk: bool = False,
|
|
num_expert_group: Optional[int] = None,
|
|
topk_group: Optional[int] = None,
|
|
quant_config: Optional[QuantizationConfig] = None,
|
|
tp_size: Optional[int] = None,
|
|
ep_size: Optional[int] = None,
|
|
dp_size: Optional[int] = None,
|
|
prefix: str = "",
|
|
custom_routing_function: Optional[Callable] = None,
|
|
scoring_func: str = "softmax",
|
|
e_score_correction_bias: Optional[torch.Tensor] = None,
|
|
activation: str = "silu",
|
|
apply_router_weight_on_input: bool = False,
|
|
):
|
|
# TODO: This could not initialize FusedMoE baseclass,
|
|
# fixme and make __init__() of AscendFusedMoE more clear
|
|
super(FusedMoE, self).__init__()
|
|
|
|
if params_dtype is None:
|
|
params_dtype = torch.get_default_dtype()
|
|
|
|
vllm_config = get_current_vllm_config()
|
|
|
|
self.moe_parallel_config: FusedMoEParallelConfig = (
|
|
FusedMoEParallelConfig.make(
|
|
tp_size_=(tp_size if tp_size is not None else
|
|
get_tensor_model_parallel_world_size()),
|
|
dp_size_=(dp_size if dp_size is not None else
|
|
get_dp_group().world_size),
|
|
vllm_parallel_config=vllm_config.parallel_config))
|
|
|
|
self.moe_parallel_config.ep_size = get_ep_group().world_size
|
|
self.moe_parallel_config.tp_size = get_etp_group().world_size
|
|
|
|
self.top_k = top_k
|
|
self.num_experts = num_experts
|
|
self.global_num_experts = num_experts
|
|
assert intermediate_size % self.tp_size == 0
|
|
self.intermediate_size_per_partition = intermediate_size // self.tp_size
|
|
self.reduce_results = reduce_results
|
|
self.renormalize = renormalize
|
|
self.use_grouped_topk = use_grouped_topk
|
|
if self.use_grouped_topk:
|
|
assert num_expert_group is not None and topk_group is not None
|
|
self.num_expert_group = num_expert_group
|
|
self.topk_group = topk_group
|
|
self.custom_routing_function = custom_routing_function
|
|
self.scoring_func = scoring_func
|
|
self.e_score_correction_bias = e_score_correction_bias
|
|
self.expert_map = None
|
|
self.activation = activation
|
|
|
|
if self.ep_size > 1:
|
|
# Create a tensor of size num_experts filled with -1
|
|
self.local_num_experts, self.expert_map = determine_expert_map(
|
|
self.ep_size,
|
|
get_ep_group().rank_in_group, self.global_num_experts)
|
|
|
|
self.moe_parallel_config.tp_rank = get_etp_group().rank_in_group
|
|
self.moe_parallel_config.ep_rank = get_ep_group().rank_in_group
|
|
|
|
else:
|
|
# Adjust TP size for DP attention
|
|
# haven't test its functionality yet, may remove in the future
|
|
|
|
self.moe_parallel_config.tp_rank = self.tp_size * self.dp_rank
|
|
self.moe_parallel_config.ep_rank = 0
|
|
self.moe_parallel_config.tp_size = self.tp_size * self.dp_size
|
|
self.moe_parallel_config.ep_size = 1
|
|
|
|
self.local_num_experts, self.expert_map = (self.global_num_experts,
|
|
None)
|
|
if self.scoring_func != "softmax" and not self.use_grouped_topk:
|
|
raise ValueError("Only softmax scoring function is supported for "
|
|
"non-grouped topk.")
|
|
|
|
moe = MoEConfig(
|
|
num_experts=self.global_num_experts,
|
|
experts_per_token=top_k,
|
|
hidden_dim=hidden_size,
|
|
num_local_experts=self.local_num_experts,
|
|
moe_parallel_config=self.moe_parallel_config,
|
|
# TODO (bnell): this needs to be fixed for quantized types.
|
|
in_dtype=params_dtype,
|
|
)
|
|
|
|
if quant_config is None:
|
|
self.quant_method = AscendUnquantizedFusedMoEMethod(moe)
|
|
else:
|
|
self.quant_method = quant_config.get_quant_method(self, prefix)
|
|
|
|
assert self.quant_method is not None
|
|
|
|
local_num_experts = torch.sum(self.expert_map != -1) \
|
|
if self.expert_map is not None else num_experts
|
|
|
|
moe_quant_params = {
|
|
"num_experts": local_num_experts,
|
|
"hidden_size": hidden_size,
|
|
"intermediate_size_per_partition":
|
|
self.intermediate_size_per_partition,
|
|
"params_dtype": 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)
|
|
|
|
def forward(self,
|
|
hidden_states: torch.Tensor,
|
|
router_logits: torch.Tensor,
|
|
is_prefill: bool,
|
|
enable_force_load_balance: bool = False,
|
|
top_k=None):
|
|
assert self.quant_method is not None
|
|
|
|
if top_k:
|
|
real_top_k = top_k
|
|
else:
|
|
real_top_k = self.top_k
|
|
|
|
if VLLM_ENABLE_MC2 and not is_prefill:
|
|
...
|
|
|
|
# Matrix multiply.
|
|
final_hidden_states = self.quant_method.apply(
|
|
layer=self,
|
|
x=hidden_states,
|
|
router_logits=router_logits,
|
|
top_k=real_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,
|
|
is_prefill=is_prefill,
|
|
enable_force_load_balance=enable_force_load_balance)
|
|
|
|
if VLLM_ENABLE_MC2 and not is_prefill:
|
|
...
|
|
|
|
if self.reduce_results and (self.tp_size > 1 or self.ep_size > 1):
|
|
final_hidden_states = tensor_model_parallel_all_reduce(
|
|
final_hidden_states)
|
|
|
|
return final_hidden_states
|