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[main][Feature]Moe alltoallv communication optimization for unquantized RL training sence (#2088)

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It comes from 0.9.1dev
[0.9.1][Feature]Moe alltoallv communication optimization for unquantized
RL training sence & alltoallv support dpo (#1547)

- vLLM version: v0.10.0
- vLLM main:
97608dc276

---------

Signed-off-by: weijinqian_v1 <weijinqian@huawei.com>
Signed-off-by: whx-sjtu <2952154980@qq.com>
Signed-off-by: curryliu <120010041@link.cuhk.edu.cn>
Signed-off-by: wangli <wangli858794774@gmail.com>
Signed-off-by: ChenTaoyu-SJTU <ctynb@qq.com>
Signed-off-by: taoxudonghaha <justsheldon@163.com>
Signed-off-by: shen-shanshan <467638484@qq.com>
Signed-off-by: Shanshan Shen <87969357+shen-shanshan@users.noreply.github.com>
Signed-off-by: leo-pony <nengjunma@outlook.com>
Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
Signed-off-by: MengqingCao <cmq0113@163.com>
Co-authored-by: weijinqian_v1 <weijinqian@huawei.com>
Co-authored-by: whx <56632993+whx-sjtu@users.noreply.github.com>
Co-authored-by: curryliu <99582471+Irving11-BKN@users.noreply.github.com>
Co-authored-by: Li Wang <wangli858794774@gmail.com>
Co-authored-by: TaoYu Chen <ctynb@qq.com>
Co-authored-by: taoxudonghaha <justsheldon@163.com>
Co-authored-by: Shanshan Shen <467638484@qq.com>
Co-authored-by: leo-pony <nengjunma@outlook.com>
Co-authored-by: wangxiyuan <wangxiyuan1007@gmail.com>
Co-authored-by: Mengqing Cao <cmq0113@163.com>
This commit is contained in:
weijinqian0
2025-08-02 09:49:10 +08:00
committed by GitHub
parent f0c1f0c828
commit 6e00aed4d5
14 changed files with 1265 additions and 17 deletions
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62
vllm_ascend/ops/comm_utils.py Normal file
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@@ -0,0 +1,62 @@
# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
# Copyright 2023 The vLLM team.
#
# 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.
# This file is a part of the vllm-ascend project.
import torch
import torch.distributed
import torch.distributed as dist
import torch_npu
COMM_STREAM = None
def async_all_to_all(input_,
output_split_sizes,
input_split_sizes,
group,
event=None):
if output_split_sizes is None:
# Equal split (all2all)
a2a_out = torch.empty_like(input_)
else:
# Unequal split (all2all-v)
a2a_out = input_.new_empty(
size=[sum(output_split_sizes)] + list(input_.size()[1:]),
dtype=input_.dtype,
device=torch.npu.current_device(),
)
if event:
# multi stream wait event
global COMM_STREAM
if COMM_STREAM is None:
COMM_STREAM = torch_npu.npu.Stream(
device=torch.npu.current_device())
with torch_npu.npu.stream(COMM_STREAM):
event.wait()
handle = dist.all_to_all_single(
a2a_out,
input_.contiguous(),
output_split_sizes=output_split_sizes,
input_split_sizes=input_split_sizes,
group=group,
async_op=True)
else:
handle = dist.all_to_all_single(a2a_out,
input_.contiguous(),
output_split_sizes=output_split_sizes,
input_split_sizes=input_split_sizes,
group=group,
async_op=True)
return input_, a2a_out, handle

158
vllm_ascend/ops/fused_moe.py
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@@ -16,12 +16,14 @@
# Adapted from vllm/tests/kernels/test_moe.py
import os
from typing import Any, Callable, List, Optional, Tuple, Union
from typing import Any, Callable, Optional, Tuple, Union
import torch
import torch.distributed as dist
import torch_npu
from torch import nn
from transformers import PretrainedConfig
from vllm.attention import AttentionMetadata
from vllm.config import get_current_vllm_config
from vllm.distributed import (GroupCoordinator, get_tensor_model_parallel_rank,
get_tensor_model_parallel_world_size,
@@ -35,6 +37,7 @@ from vllm.model_executor.layers.fused_moe.config import \
FusedMoEParallelConfig # isort: skip
from vllm.model_executor.layers.fused_moe.layer import (
FusedMoE, UnquantizedFusedMoEMethod, determine_expert_map)
from vllm.model_executor.layers.linear import ReplicatedLinear
from vllm.model_executor.layers.quantization.base_config import \
QuantizationConfig
@@ -45,6 +48,8 @@ from vllm_ascend.distributed.communication_op import \
data_parallel_reduce_scatter
from vllm_ascend.distributed.parallel_state import get_mc2_group
from vllm_ascend.ops.expert_load_balancer import ExpertLoadBalancer
from vllm_ascend.ops.moe_dispatcher.token_dispatcher import (
MoEAlltoAllSeqOverLapDispatcher, MoEDispatcherConfig)
from vllm_ascend.torchair.utils import npu_stream_switch, npu_wait_tensor
from vllm_ascend.utils import (AscendSocVersion, dispose_tensor,
get_all_reduce_merge_state,
@@ -273,11 +278,13 @@ def fused_experts_with_mc2(
return hidden_states, shared_hidden_states
def apply_mlp(hidden_states_wrapper: List[torch.Tensor],
w1: torch.Tensor,
w2: torch.Tensor,
group_list: torch.Tensor,
group_list_type: int = 1) -> torch.Tensor:
def apply_mlp(
hidden_states: torch.Tensor,
w1: torch.Tensor,
w2: torch.Tensor,
group_list: torch.Tensor,
group_list_type: int = 1,
) -> torch.Tensor:
"""
apply MLP: gate_up_proj -> swiglu -> down_proj
@@ -299,9 +306,6 @@ def apply_mlp(hidden_states_wrapper: List[torch.Tensor],
hidden_states: output hidden states after MLP.
"""
assert len(hidden_states_wrapper) == 1
hidden_states = hidden_states_wrapper.pop()
w1 = w1.transpose(1, 2)
hidden_states = torch_npu.npu_grouped_matmul(
x=[hidden_states],
@@ -329,6 +333,8 @@ def apply_mlp(hidden_states_wrapper: List[torch.Tensor],
return hidden_states
# currently expert parallelism implemented with all2all
# is under-optimized.
def fused_experts_with_all2all(
hidden_states: torch.Tensor,
w1: torch.Tensor,
@@ -543,10 +549,7 @@ def fused_experts_with_all2all_buffer(
hidden_states = hidden_states[sorted_idx]
group_list_type = 0
hidden_states_wrapper = [hidden_states]
del hidden_states
hidden_states = apply_mlp(hidden_states_wrapper,
hidden_states = apply_mlp(hidden_states,
w1,
w2,
expert_tokens,
@@ -682,6 +685,24 @@ def fused_experts_moge(
return final_hidden_states
def fused_experts_with_all2allv(
token_dispatcher,
probs,
routing_map,
hidden_states: torch.Tensor,
w1: torch.Tensor,
w2: torch.Tensor,
):
# Enable moe alltoallv, it's a balanced policy for precision and efficiency.
(share_experts_output, dispatched_input,
tokens_per_expert) = (token_dispatcher.token_permutation(
hidden_states, probs, routing_map))
expert_output = apply_mlp(dispatched_input, w1, w2, tokens_per_expert)
output, mlp_bias = token_dispatcher.token_unpermutation(expert_output)
return output
def fused_experts(
hidden_states: torch.Tensor,
w1: torch.Tensor,
@@ -1124,6 +1145,16 @@ class AscendUnquantizedFusedMoEMethod(UnquantizedFusedMoEMethod):
global_batch_size=self.global_batch_size,
expert_map=expert_map,
ep_group=get_ep_group())
elif fused_moe_state == FusedMoEState.All2AllSeq:
token_dispatcher = kwargs.get("token_dispatcher")
return fused_experts_with_all2allv(
token_dispatcher=token_dispatcher,
probs=topk_weights,
routing_map=topk_ids,
hidden_states=x,
w1=layer.w13_weight,
w2=layer.w2_weight,
)
else:
return fused_experts_with_all2all(hidden_states=x,
w1=layer.w13_weight,
@@ -1275,6 +1306,25 @@ class AscendFusedMoE(FusedMoE):
# NOTE: self.tp_group is not expert_tp_group
self.tp_group = get_tp_group().device_group
self.quant_method.create_weights(layer=self, **moe_quant_params)
self.token_dispatcher = None
if envs_ascend.VLLM_ASCEND_ENABLE_MOE_ALL2ALL_SEQ and isinstance(
self.quant_method, AscendUnquantizedFusedMoEMethod):
self.reduce_results = False
moe_dispatcher_config = (
MoEDispatcherConfig().set_num_moe_experts(
self.global_num_experts).set_num_local_experts(
self.local_num_experts).set_moe_router_topk(
top_k).set_group_topk(topk_group).
set_num_groups(num_expert_group).set_expert_bias(
e_score_correction_bias).set_scaling_factor(1.0).build())
self.token_dispatcher = MoEAlltoAllSeqOverLapDispatcher(
moe_dispatcher_config)
if envs_ascend.VLLM_ASCEND_ENABLE_DBO:
token_dispatcher1 = MoEAlltoAllSeqOverLapDispatcher(
moe_dispatcher_config)
self.token_dispatchers = [
self.token_dispatcher, token_dispatcher1
]
def naive_multicast(self, x: torch.Tensor,
cu_tokens_across_dp_cpu: torch.Tensor):
@@ -1414,6 +1464,7 @@ class AscendFusedMoE(FusedMoE):
shared_experts=shared_experts if self.torchair_graph_enabled
and self.enable_multistream_moe and not is_prefill else None,
mc2_mask=mc2_mask,
token_dispatcher=self.token_dispatcher,
quantized_x_for_share=quantized_x_for_share,
dynamic_scale_for_share=dynamic_scale_for_share,
)
@@ -1430,11 +1481,11 @@ class AscendFusedMoE(FusedMoE):
dist.all_gather(list(chunk_hidden_states), e_hidden_states,
self.tp_group)
final_hidden_states = torch.cat(chunk_hidden_states, dim=0)
dispose_tensor(e_hidden_states)
else:
final_hidden_states = e_hidden_states
if num_tokens < padding_size:
final_hidden_states = final_hidden_states[:num_tokens]
dispose_tensor(e_hidden_states)
elif self.dp_size > 1:
if fused_moe_state == FusedMoEState.NaiveMulticast:
start = 0 if self.dp_rank == 0 else cu_tokens_across_dp_cpu[
@@ -1491,6 +1542,83 @@ class AscendFusedMoE(FusedMoE):
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)
enable_force_load_balance=enable_force_load_balance,
)
return hidden_states
class AscendSparseMoeBlock(nn.Module):
def __init__(
self,
config: PretrainedConfig,
quant_config: Optional[QuantizationConfig] = None,
prefix: str = "",
):
super().__init__()
self.tp_size = get_tensor_model_parallel_world_size()
if self.tp_size > config.num_experts:
raise ValueError(
f"Tensor parallel size {self.tp_size} is greater than "
f"the number of experts {config.num_experts}.")
ascend_config = get_ascend_config()
self.torchair_graph_enabled = ascend_config.torchair_graph_config.enabled
self.enable_multistream_moe = (
ascend_config.torchair_graph_config.enable_multistream_moe)
self.gate = ReplicatedLinear(
config.hidden_size,
config.num_experts,
bias=False,
quant_config=None,
prefix=f"{prefix}.gate",
)
self.experts = AscendFusedMoE(
num_experts=config.num_experts,
top_k=config.num_experts_per_tok,
hidden_size=config.hidden_size,
intermediate_size=config.moe_intermediate_size,
reduce_results=False,
renormalize=config.norm_topk_prob,
quant_config=quant_config,
prefix=f"{prefix}.experts",
)
self.top_k = config.num_experts_per_tok
self.dp_size = get_dp_group().world_size
self.tp_group = get_tp_group().device_group
self.tp_rank = get_tp_group().rank_in_group
self.ep_group = get_ep_group()
self.params_dtype = torch.get_default_dtype()
def forward(
self,
hidden_states: torch.Tensor,
attn_metadata: Optional[AttentionMetadata] = None,
) -> torch.Tensor:
if attn_metadata is None:
attn_metadata = get_forward_context().attn_metadata
# when profile runs, force experts to load balanced tokens
# to avoid high memory consumption on a single rank.
enable_force_load_balance = get_forward_context().in_profile_run
is_prefill = get_forward_context().with_prefill
# router_logits: (num_tokens, n_experts)
router_logits, _ = self.gate(hidden_states)
hidden_states = self.experts(
hidden_states=hidden_states,
router_logits=router_logits,
is_prefill=is_prefill,
top_k=self.top_k,
enable_force_load_balance=enable_force_load_balance,
shared_experts=None,
)
return hidden_states

0
vllm_ascend/ops/moe_dispatcher/__init__.py Normal file
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453
vllm_ascend/ops/moe_dispatcher/token_dispatcher.py Normal file
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@@ -0,0 +1,453 @@
# SPDX-License-Identifier: Apache-2.0
# Copyright (c) 2024; NVIDIA CORPORATION. All rights reserved.
# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
# Copyright 2023 The vLLM team.
# Copyright 2023 DeepSeek-AI and the HuggingFace Inc. team. All rights reserved.
#
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
# and OPT implementations in this library. It has been modified from its
# original forms to accommodate minor architectural differences compared
# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
#
# 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 typing import Optional
import torch
import torch_npu
from vllm.distributed.parallel_state import get_ep_group
from vllm_ascend.distributed.tensor_parallel import (
all_gather_last_dim_from_tensor_parallel_region, all_to_all_hp2sp,
all_to_all_sp2hp, gather_from_sequence_parallel_region,
reduce_scatter_last_dim_to_tensor_parallel_region)
from vllm_ascend.ops.comm_utils import async_all_to_all
class MoEDispatcherConfig:
def __init__(self):
self.num_local_experts: int = 0
self.num_moe_experts: int = 0
self.moe_pad_expert_input_to_capacity: bool = False
self.moe_expert_capacity_factor: Optional[float] = None
self.moe_router_topk: int = 2
self.moe_grouped_gemm: bool = False
self.group_topk: int = 0
self.num_groups: int = 1
self.expert_bias: torch.Tensor = None
self.scaling_factor: Optional[float] = None
self.is_fused: bool = True
def set_num_local_experts(self, num_local_experts):
self.num_local_experts = num_local_experts
return self
def set_num_moe_experts(self, num_moe_experts):
self.num_moe_experts = num_moe_experts
return self
def set_moe_pad_expert_input_to_capacity(self,
moe_pad_expert_input_to_capacity):
self.moe_pad_expert_input_to_capacity = moe_pad_expert_input_to_capacity
return self
def set_moe_expert_capacity_factor(self, moe_expert_capacity_factor):
self.moe_expert_capacity_factor = moe_expert_capacity_factor
return self
def set_moe_router_topk(self, moe_router_topk):
self.moe_router_topk = moe_router_topk
return self
def set_moe_grouped_gemm(self, moe_grouped_gemm):
self.moe_grouped_gemm = moe_grouped_gemm
return self
def set_group_topk(self, group_topk):
self.group_topk = group_topk
return self
def set_num_groups(self, num_groups):
self.num_groups = num_groups
return self
def set_expert_bias(self, expert_bias):
self.expert_bias = expert_bias
return self
def set_scaling_factor(self, scaling_factor):
self.scaling_factor = scaling_factor
return self
def set_is_fused(self, is_fused):
self.is_fused = is_fused
return self
def build(self):
return self
class MoEDispatcher:
def __init__(self, config: MoEDispatcherConfig) -> None:
"""
Initialize the MoE Token Dispatcher.
"""
self.config = config
self.shared_experts = None
def set_shared_experts(self, shared_experts):
self.shared_experts = shared_experts
@property
def ep_group(self):
"""Get expert model parallel group."""
return get_ep_group().device_group
@property
def ep_rank(self):
return get_ep_group().rank_in_group
@property
def ep_size(self):
return get_ep_group().world_size
@property
def tp_ep_group(self):
"""Get expert tensor and model parallel group."""
return None
@property
def tp_ep_size(self):
return 1
class MoEAlltoAllSeqOverLapDispatcher(MoEDispatcher):
overlap_stream = None
"""
The implementation of the AlltoAll-based token dispatcher, which handles token
dispatching on the sequence level instead of token level. The core of this implementation
lies in each device dispatching on the entire sequence, with the hidden state being partitioned.
"""
def __init__(self, config: MoEDispatcherConfig):
"""
Initialize the AlltoAllSeq token dispatcher.
Args:
config (MoEDispatcherConfig): Configuration for the transformer model.
"""
super().__init__(config)
self.num_local_experts = config.num_local_experts
self.config = config
# use MOEAlltoAllSEQTokenDispatcher to init
self.hidden_shape = None
self.num_input_tokens = None
self.num_experts = config.num_moe_experts
assert self.num_local_experts > 0, "Expected at least one expert"
if self.num_local_experts > 1:
self.expert_ids_per_ep_rank = torch.tensor(
[i % self.num_local_experts for i in range(self.num_experts)],
dtype=torch.int32,
device=torch.npu.current_device(),
)
local_expert_indices_offset = (self.ep_rank * self.num_local_experts)
self.local_expert_indices = [
local_expert_indices_offset + i
for i in range(self.num_local_experts)
]
assert (len(self.local_expert_indices) == self.num_local_experts
), "Invalid local expert indices"
for i in range(len(self.local_expert_indices) - 1):
assert (self.local_expert_indices[i] ==
self.local_expert_indices[i + 1] -
1), "local_expert_indices must be continuous"
self.probs = None
self.input_splits = None
self.output_splits = None
self.routing_map = None
self.hidden_shape_before_permute = None
# [tp_ep_size * ep_size, num_local_experts]. Represents the number of tokens sent
# to each local expert by all ranks.
self.num_global_tokens_per_local_expert_cpu = None
self.num_global_tokens_per_local_expert = None
# A cuda stream synchronization is needed in self.token_permutation()
# in some cases, because there are several non-blocking DtoH data
# transfers called in self.preprocess(). The synchronization happens
# at different points based on MoE settings as late as possible.
# Valid sync points are "before_permutation_1", "before_ep_alltoall",
# "before_finish", and "no_sync".
self.device_sync_point = "no_sync"
# cached intermediate tensors.
self.cached_permutated_local_input_tokens = None
self.cached_global_input_tokens = None
self.cached_shared_expert_output = None
self.tokens_per_expert = None
self.perm1_finish_event = None
self.global_input_tokens_local_experts_indices = None
if MoEAlltoAllSeqOverLapDispatcher.overlap_stream is None:
MoEAlltoAllSeqOverLapDispatcher.overlap_stream = torch.npu.Stream()
self.overlap_stream = MoEAlltoAllSeqOverLapDispatcher.overlap_stream
def preprocess(self,
indices: torch.Tensor,
with_sync=True) -> torch.Tensor:
"""
Preprocess routing map for AlltoAll communication and token permutation.
This method computes the number of tokens assigned to each expert based on
the routing map. It also initializes the necessary data structures for
AlltoAll communication, such as input and output splits, and the mapping
between global tokens and local experts.
Args:
routing_map (torch.Tensor): The mapping of tokens to experts, with shape
[num_tokens, num_experts].
Returns:
torch.Tensor: Tensor containing the number of tokens assigned to local expert.
"""
num_local_tokens_per_expert = torch.histc(indices,
bins=self.num_experts,
min=0,
max=self.num_experts)
# num_local_tokens_per_expert: [num_experts]
ep_size = self.ep_size
# Dropless
self.num_out_tokens = indices.numel()
if self.ep_size > 1 or self.num_local_experts > 1:
# Token dropless and enable ep. A synchronization is needed before expert parallel
# AlltoAll communication to get the `input_splits` and `output_splits` CPU values.
self.device_sync_point = "before_ep_alltoall"
else:
# Token dropless and no ep. A synchronization is needed to get the
# `tokens_per_expert` CPU value.
self.device_sync_point = "before_finish"
if ep_size > 1:
# ===================================================
# Calculate input_splits, output_splits for alltoall-v.
# ===================================================
self.input_splits = (num_local_tokens_per_expert.reshape(
ep_size, self.num_local_experts).sum(axis=1).to(
torch.device("cpu"), non_blocking=True).numpy())
num_global_tokens_per_expert = gather_from_sequence_parallel_region(
num_local_tokens_per_expert,
group=self.ep_group).reshape(ep_size, self.num_experts)
self.num_global_tokens_per_local_expert = num_global_tokens_per_expert[:, self.local_expert_indices[
0]:self.local_expert_indices[-1] + 1]
if self.num_global_tokens_per_local_expert is None:
raise ValueError(
"num_global_tokens_per_local_expert must be set before sum."
)
self.output_splits = (self.num_global_tokens_per_local_expert.sum(
axis=-1).to(torch.device("cpu"), non_blocking=True).numpy())
num_tokens_per_local_expert = self.num_global_tokens_per_local_expert.sum(
axis=0)
# ===================================================
# num_global_tokens_per_expert: [ep_size, num_experts]
# num_global_tokens_per_local_expert: [ep_size, num_local_experts]
# num_tokens_per_local_expert: [num_local_experts]
# ===================================================
else:
self.num_global_tokens_per_local_expert = num_local_tokens_per_expert.reshape(
-1, self.num_experts)
num_tokens_per_local_expert = num_local_tokens_per_expert
if self.num_local_experts > 1 and with_sync:
if self.num_global_tokens_per_local_expert is None:
raise ValueError(
"num_global_tokens_per_local_expert must be set before operations."
)
self.device_sync_point = "no_sync"
self.global_input_tokens_local_experts_indices = torch.repeat_interleave(
self.expert_ids_per_ep_rank,
self.num_global_tokens_per_local_expert.ravel())
return num_tokens_per_local_expert
def token_permutation(
self,
hidden_states: torch.Tensor,
probs: torch.Tensor,
routing_map: torch.Tensor,
):
"""
Dispatch tokens to local experts using AlltoAllSeq communication.
Args:
hidden_states (torch.Tensor): Input token embeddings.
probs (torch.Tensor): Probs of tokens assigned to experts.
Shape: [num_tokens, num_experts].
routing_map (torch.Tensor): Mapping of tokens assigned to experts.
Shape: [num_tokens, num_experts].
Returns:
Tuple[torch.Tensor, torch.Tensor]:
- Permuted token embeddings for local experts.
- Number of tokens per expert.
"""
self.hidden_shape = hidden_states.shape
self.probs = probs
self.top_indices = routing_map
assert probs.dim() == 2, "Expected 2D tensor for probs"
assert routing_map.dim() == 2, "Expected 2D tensor for routing map"
# Permutation 1: input to AlltoAll input
def alltoall_token_permutation1(hidden_states, routing_map):
assert self.hidden_shape is not None
hidden_states = hidden_states.view(-1, self.hidden_shape[-1])
tokens_per_expert = self.preprocess(routing_map)
if self.tp_ep_size > 1:
hidden_states = all_to_all_sp2hp(hidden_states,
group=self.tp_ep_group)
self.hidden_shape_before_permute = hidden_states.shape
if self.device_sync_point == "before_permutation_1":
torch.npu.current_stream().synchronize()
permutated_local_input_tokens, reversed_local_input_permutation_mapping = torch_npu.npu_moe_token_permute(
tokens=hidden_states,
indices=self.top_indices,
num_out_tokens=self.num_out_tokens,
)
return permutated_local_input_tokens, reversed_local_input_permutation_mapping, tokens_per_expert
permutated_local_input_tokens, reversed_local_input_permutation_mapping, tokens_per_expert = alltoall_token_permutation1(
hidden_states, routing_map)
self.reversed_local_input_permutation_mapping = reversed_local_input_permutation_mapping
# permute 1
ep_group = self.ep_group
# Perform expert parallel AlltoAll communication
if self.device_sync_point == "before_ep_alltoall":
torch.npu.current_stream().synchronize()
_, global_input_tokens, permute1_ep_all_to_all_handle = async_all_to_all(
permutated_local_input_tokens,
self.output_splits,
self.input_splits,
ep_group,
)
# shared experts compute
if self.shared_experts is not None:
(share_experts_output), *_ = self.shared_experts(hidden_states)
else:
share_experts_output = None
permute1_ep_all_to_all_handle.wait()
permutated_local_input_tokens.untyped_storage().resize_(0)
def alltoall_token_permutation2(global_input_tokens):
# Permutation 2: Sort tokens by local expert.
if self.num_local_experts > 1:
global_input_tokens, self.reversed_global_input_permutation_mapping = torch_npu.npu_moe_token_permute(
global_input_tokens,
self.global_input_tokens_local_experts_indices)
# Perform tensor parallel AllGather on the hidden dimension to obtain the input tokens.
# global_input_tokens: [SEQL, H/TP] -> [SEQL, H]
if self.tp_ep_size > 1 and self.config.moe_grouped_gemm:
global_input_tokens = all_gather_last_dim_from_tensor_parallel_region(
global_input_tokens, self.tp_ep_group)
if self.device_sync_point == "before_finish":
torch.npu.current_stream().synchronize()
return global_input_tokens
# token premute2 input
global_input_tokens = alltoall_token_permutation2(global_input_tokens)
return share_experts_output, global_input_tokens, tokens_per_expert
def token_unpermutation(self,
hidden_states: torch.Tensor,
bias: torch.Tensor = None):
"""
Reverse the token permutation to restore the original order.
Args:
hidden_states (torch.Tensor): Output from local experts.
bias (torch.Tensor, optional): Bias tensor (not supported).
Returns:
Tuple[torch.Tensor, Optional[torch.Tensor]]:
- Unpermuted token embeddings in the original order.
- None (bias is not supported).
"""
def alltoall_token_unpermutation1(hidden_states):
assert bias is None, "Bias is not supported in MoEAlltoAllSeqTokenDispatcher"
# Perform tensor parallel Reduce-Scatter
# hidden_states: [SEQL, H] -> [SEQL, H/TP]
if self.tp_ep_size > 1:
hidden_states = reduce_scatter_last_dim_to_tensor_parallel_region(
hidden_states, group=self.tp_ep_group)
# Unpermutation 2: expert output to AlltoAll input
if hidden_states.shape[0] > 0 and self.num_local_experts > 1:
hidden_states = torch_npu.npu_moe_token_unpermute(
hidden_states,
self.reversed_global_input_permutation_mapping)
return hidden_states
hidden_states = alltoall_token_unpermutation1(hidden_states)
ep_group = self.ep_group
# Perform expert parallel AlltoAll communication
# hidden_states: [SEQL, H] -> [SEQL, H/TP]
_, permutated_local_input_tokens, handle = async_all_to_all(
hidden_states, self.input_splits, self.output_splits, ep_group)
handle.wait()
hidden_states.untyped_storage().resize_(0)
def alltoall_token_unpermutation2(permutated_local_input_tokens):
# Unpermutation 1: AlltoAll output to output
output = torch_npu.npu_moe_token_unpermute(
permuted_tokens=permutated_local_input_tokens,
sorted_indices=self.reversed_local_input_permutation_mapping.
to(torch.int32),
probs=self.probs,
restore_shape=self.hidden_shape_before_permute)
# Perform tensor parallel AlltoAll communication
# output: [S*B, H/TP] -> [S*B/TP, H]
if self.tp_ep_size > 1:
output = all_to_all_hp2sp(output, self.tp_ep_group)
# Reshape the output tensor
output = output.view(self.hidden_shape)
return output
output = alltoall_token_unpermutation2(permutated_local_input_tokens)
self.input_splits = None
self.output_splits = None
self.num_global_tokens_per_local_expert = None
self.num_global_tokens_per_local_expert_cpu = None
return output, None