EngineX/xc-llm-ascend
3
0
Fork 0
Code Issues Pull Requests Projects Releases Wiki Activity
Files
82fdd40d49667166ab4d227ae47fd7ea4e8a82e4
xc-llm-ascend/vllm_ascend/ops/fused_moe/token_dispatcher.py
Eric-dot 3c66a970f2 add mxfp8 moe quantization (#6670) 
### What this PR does / why we need it?
support mxfp8 quantization (Qwen MOE )
Using adaptor to make the hardware-specific behavior clearer and more
maintainable
### How was this patch tested?

- vLLM version: v0.15.0
- vLLM main:
13397841ab

---------

Signed-off-by: fangrongcan <17343701736@163.com>
Signed-off-by: wangyao-i <iwangyao@outlook.com>
Signed-off-by: linfeng-yuan <1102311262@qq.com>
Signed-off-by: Eric-dot <60131170+Eric-dot@users.noreply.github.com>
Co-authored-by: fangrongcan <f00876277@china.huawei.com>
Co-authored-by: wangyao-i <iwangyao@outlook.com>
Co-authored-by: linfeng-yuan <1102311262@qq.com>
2026-03-02 11:04:06 +08:00

632 lines
26 KiB
Python
Raw Blame History

# 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 abc import ABC, abstractmethod
from dataclasses import dataclass, field
import torch
import torch_npu
from vllm.config import get_current_vllm_config
from vllm.distributed.parallel_state import get_ep_group
from vllm_ascend.device.device_op import DeviceOperator
from vllm_ascend.distributed.parallel_state import get_mc2_group
from vllm_ascend.ops.fused_moe.comm_utils import async_all_to_all, gather_from_sequence_parallel_region
from vllm_ascend.utils import AscendDeviceType, get_ascend_device_type, is_hierarchical_communication_enabled
@dataclass
class TokenDispatchResult:
hidden_states: torch.Tensor
group_list: torch.Tensor
group_list_type: int
dynamic_scale: torch.Tensor | None = field(default=None)
topk_scales: torch.Tensor | None = field(default=None)
context_metadata: dict = field(default_factory=dict)
@dataclass
class TokenCombineResult:
routed_out: torch.Tensor
class MoETokenDispatcher(ABC):
def __init__(self, **kwargs) -> None:
"""
Initialize the MoE Token Dispatcher.
"""
self.top_k = kwargs.get("top_k", 0)
self.num_experts = kwargs.get("num_experts", 0)
@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
@abstractmethod
def token_dispatch(
self,
hidden_states: torch.Tensor,
topk_weights: torch.Tensor,
topk_ids: torch.Tensor,
expert_map: torch.Tensor | None = None,
global_redundant_expert_num: int = 0,
mc2_mask: torch.Tensor | None = None,
apply_router_weight_on_input: bool = False,
with_quant: bool = False,
dynamic_eplb: bool = False,
pertoken_scale: torch.Tensor | None = None,
) -> TokenDispatchResult:
raise NotImplementedError("Dispatch function not implemented.")
@abstractmethod
def token_combine(
self, hidden_states: torch.Tensor, context_metadata: dict, bias: torch.Tensor | None = None
) -> TokenCombineResult:
raise NotImplementedError("Combine function not implemented.")
class TokenDispatcherWithMC2(MoETokenDispatcher):
def __init__(self, **kwargs):
super().__init__(**kwargs)
device_group = get_mc2_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)
self.ep_rank_id = get_mc2_group().rank_in_group
self.ep_world_size = get_mc2_group().world_size
self.enable_dispatch_v2 = hasattr(torch_npu, "npu_moe_distribute_dispatch_v2")
self.need_extra_args = get_ascend_device_type() in [AscendDeviceType.A3, AscendDeviceType.A5]
self.a5_need_extra_args = get_ascend_device_type() == AscendDeviceType.A5
# NOTE: When in A2, setting the environment variables HCCL_INTRA_PCIE_ENABLE=1 and
# HCCL_INTRA_ROCE_ENABLE=0 can reduce cross-machine communication traffic and significantly
# improve communication performance.
self.need_expert_scale = is_hierarchical_communication_enabled()
self.with_quant = False
# Here we need to calculate the global_bs = max_bs_per_rank * ep_world_size to execute
# dispatch & combine operators with different input num_tokens per rank.
vllm_config = get_current_vllm_config()
scheduler_config = vllm_config.scheduler_config
compilation_config = vllm_config.compilation_config
speculative_config = vllm_config.speculative_config
tp_size = vllm_config.parallel_config.tensor_parallel_size
uniform_decode_query_len = 1 if not speculative_config else 1 + speculative_config.num_speculative_tokens
decode_max_num_seqs = getattr(scheduler_config, "decode_max_num_seqs", 0)
max_num_reqs = max(scheduler_config.max_num_seqs, decode_max_num_seqs)
if compilation_config.cudagraph_capture_sizes:
max_num_tokens = compilation_config.max_cudagraph_capture_size
else:
max_num_tokens = min(max_num_reqs * uniform_decode_query_len, 512)
num_tokens_per_tp_rank = (max_num_tokens + tp_size - 1) // tp_size
self.global_bs = num_tokens_per_tp_rank * self.ep_world_size
def get_dispatch_mc2_kwargs(
self,
hidden_states: torch.Tensor,
topk_weights: torch.Tensor,
topk_ids: torch.Tensor,
expert_map: torch.Tensor,
mc2_mask: torch.Tensor,
global_redundant_expert_num: int = 0,
**kwargs,
):
use_mxfp_quant = kwargs.get("use_mxfp_quant", False)
comm_quant_mode = kwargs.get("comm_quant_mode")
# NOTE: quant_mode differs by quant feature:
# - Legacy int communication quantization uses quant_mode=2.
# - A5 MXFP8 communication uses quant_mode=4.
# TODO(linfeng): The quantization-related parameters need to be consolidated into a single
# dataclass, and the FP8 MoE code path should be integrated into it going forward.
if comm_quant_mode is not None:
quant_mode = comm_quant_mode
elif self.with_quant:
quant_mode = 4 if self.a5_need_extra_args and use_mxfp_quant else 2
else:
quant_mode = 0
self.moe_expert_num = len(expert_map) + global_redundant_expert_num
kwargs_mc2 = {
"x": hidden_states,
"expert_ids": topk_ids,
"expert_shard_type": 0,
"shared_expert_rank_num": 0,
"moe_expert_num": self.moe_expert_num,
"global_bs": self.global_bs,
"expert_token_nums_type": 0,
}
stage1_kwargs = {
"scales": None,
"quant_mode": quant_mode,
"group_ep": self.moe_all_to_all_group_name,
"ep_world_size": self.ep_world_size,
"ep_rank_id": self.ep_rank_id,
}
if self.need_extra_args:
stage1_kwargs.update(
{
"group_tp": self.moe_all_to_all_group_name,
"tp_world_size": 1,
"tp_rank_id": 0,
}
)
if self.a5_need_extra_args and use_mxfp_quant:
y_dtype = kwargs.get("y_dtype")
if self.with_quant:
y_dtype = torch.float8_e4m3fn if y_dtype is None else y_dtype
stage1_kwargs.update({"tp_world_size": 1, "tp_rank_id": 0, "y_dtype": y_dtype})
if self.need_expert_scale or self.a5_need_extra_args:
stage1_kwargs.update(
{
"expert_scales": topk_weights.to(torch.float32),
}
)
kwargs_mc2.update(stage1_kwargs)
return kwargs_mc2
def token_dispatch(
self,
hidden_states: torch.Tensor,
topk_weights: torch.Tensor,
topk_ids: torch.Tensor,
expert_map: torch.Tensor | None = None,
global_redundant_expert_num: int = 0,
mc2_mask: torch.Tensor | None = None,
apply_router_weight_on_input: bool = False,
with_quant: bool = False,
dynamic_eplb: bool = False,
pertoken_scale: torch.Tensor | None = None,
**kwargs,
):
self.with_quant = with_quant
kwargs_mc2 = self.get_dispatch_mc2_kwargs(
hidden_states, topk_weights, topk_ids, expert_map, mc2_mask, global_redundant_expert_num, **kwargs
)
output = (
torch_npu.npu_moe_distribute_dispatch_v2(**kwargs_mc2)
if self.enable_dispatch_v2
else torch_npu.npu_moe_distribute_dispatch(**kwargs_mc2)
)
# comm_stream.wait_stream(torch.npu.current_stream())
(
expand_x,
dynamic_scale,
assist_info_for_combine,
expert_token_nums,
ep_recv_counts,
tp_recv_counts,
expand_scales,
) = output[0:7]
context_metadata = {
"topk_ids": topk_ids,
"topk_weights": topk_weights,
"expert_map": expert_map,
"ep_recv_counts": ep_recv_counts,
"tp_recv_counts": tp_recv_counts,
"assist_info_for_combine": assist_info_for_combine,
"expand_scales": expand_scales,
}
group_list_type = 0
return TokenDispatchResult(
hidden_states=expand_x,
dynamic_scale=dynamic_scale,
group_list=expert_token_nums,
group_list_type=group_list_type,
context_metadata=context_metadata,
)
def get_combine_mc_kwargs(self, hidden_states: torch.Tensor, context_metadata: dict):
expert_map = context_metadata["expert_map"]
topk_ids = context_metadata["topk_ids"]
topk_weights = context_metadata["topk_weights"]
ep_recv_counts = context_metadata["ep_recv_counts"]
tp_recv_counts = context_metadata["tp_recv_counts"]
assist_info_for_combine = context_metadata["assist_info_for_combine"]
expand_scales = context_metadata["expand_scales"]
assert expert_map is not None
kwargs_mc2 = {
"expand_x": hidden_states,
"expert_ids": topk_ids,
"expert_scales": topk_weights.to(torch.float32),
"expert_shard_type": 0,
"shared_expert_rank_num": 0,
"moe_expert_num": self.moe_expert_num,
"global_bs": self.global_bs,
}
if self.with_quant:
tp_recv_counts = torch.empty(1, dtype=torch.int32, device=hidden_states.device)
stage3_kwargs = {
"ep_send_counts": ep_recv_counts,
"group_ep": self.moe_all_to_all_group_name,
"ep_world_size": self.ep_world_size,
"ep_rank_id": self.ep_rank_id,
"expand_scales": expand_scales,
}
if self.enable_dispatch_v2:
stage3_kwargs["assist_info_for_combine"] = assist_info_for_combine
else:
stage3_kwargs["expand_idx"] = assist_info_for_combine
if self.need_extra_args:
stage3_kwargs.update(
{
"tp_send_counts": tp_recv_counts,
"group_tp": self.moe_all_to_all_group_name,
"tp_world_size": 1,
"tp_rank_id": 0,
}
)
kwargs_mc2.update(stage3_kwargs)
return kwargs_mc2
def token_combine(self, hidden_states, context_metadata, bias=None):
assert bias is None, "Bias is not supported in MoEAlltoAllvTokenDispatcher."
kwargs_mc2 = self.get_combine_mc_kwargs(hidden_states, context_metadata)
combined_output = (
torch_npu.npu_moe_distribute_combine_v2(**kwargs_mc2)
if self.enable_dispatch_v2
else torch_npu.npu_moe_distribute_combine(**kwargs_mc2)
)
return TokenCombineResult(
routed_out=combined_output,
)
class TokenDispatcherWithAllGather(MoETokenDispatcher):
def __init__(self, **kwargs):
super().__init__(**kwargs)
self.apply_router_weight_on_input = False
self.max_num_tokens = kwargs.get("max_num_tokens")
num_experts_local = kwargs.get("num_local_experts", 0)
self.num_experts_local = (
num_experts_local.item() if torch.is_tensor(num_experts_local) else int(num_experts_local)
)
self.original_shape = None
self.with_quant = False
def token_dispatch(
self,
hidden_states: torch.Tensor,
topk_weights: torch.Tensor,
topk_ids: torch.Tensor,
expert_map: torch.Tensor | None = None,
global_redundant_expert_num: int = 0,
mc2_mask: torch.Tensor | None = None,
apply_router_weight_on_input: bool = False,
with_quant: bool = False,
dynamic_eplb: bool = False,
pertoken_scale: torch.Tensor | None = None,
):
self.with_quant = with_quant
self.original_shape = hidden_states.shape
num_tokens = hidden_states.shape[:-1].numel()
self.apply_router_weight_on_input = apply_router_weight_on_input
if self.apply_router_weight_on_input:
assert topk_weights.dim() == 2, "`topk_weights` should be in shape (num_tokens, topk)"
_, topk = topk_weights.shape
assert topk == 1, "Only support topk=1 when `apply_router_weight_on_input` is True"
hidden_states = hidden_states * topk_weights.to(hidden_states.dtype)
if expert_map is not None:
global_num_experts = len(expert_map) + global_redundant_expert_num
mask = expert_map[topk_ids] != -1
topk_weights = topk_weights * mask
first_expert_idx = get_ep_group().rank_in_group * self.num_experts_local
last_expert_idx = first_expert_idx + self.num_experts_local
else:
first_expert_idx = 0
last_expert_idx = self.num_experts_local
global_num_experts = self.num_experts_local
sorted_hidden_states, expanded_row_idx, expert_tokens, pertoken_scale = DeviceOperator.npu_moe_init_routing(
hidden_states,
topk_ids,
scale=pertoken_scale,
active_num=num_tokens * self.top_k,
expert_num=global_num_experts,
expert_tokens_num_type=1,
expert_tokens_num_flag=True,
active_expert_range=[first_expert_idx, last_expert_idx],
quant_mode=1 if self.with_quant and pertoken_scale is None else -1,
)
expert_tokens = expert_tokens.to(torch.int64)
group_list_type = 1 # `count` mode
context_metadata = {"topk_weights": topk_weights, "expanded_row_idx": expanded_row_idx}
return TokenDispatchResult(
hidden_states=sorted_hidden_states,
dynamic_scale=pertoken_scale if self.with_quant else None,
group_list=expert_tokens,
group_list_type=group_list_type,
context_metadata=context_metadata,
)
def token_combine(self, hidden_states, context_metadata, bias=None):
assert self.original_shape is not None
final_hidden_states = torch_npu.npu_moe_token_unpermute(
permuted_tokens=hidden_states,
sorted_indices=torch.abs(context_metadata["expanded_row_idx"]),
probs=context_metadata["topk_weights"],
)
if len(self.original_shape) == 3:
final_hidden_states = final_hidden_states.view(self.original_shape)
# these values are no longer used, so they need to be set to None for memory release.
return TokenCombineResult(routed_out=final_hidden_states)
class TokenDispatcherWithAll2AllV(MoETokenDispatcher):
"""
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, **kwargs):
super().__init__(**kwargs)
self.with_quant = False
self.num_local_experts = kwargs.get("num_local_experts", 0)
self.hidden_shape = None
self.hidden_shape_before_permute = None
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"
)
# TODO: Try local_rank = ep_group.rank_in_group
local_rank = torch.distributed.get_rank(group=self.ep_group)
backend = self.ep_group._get_backend(torch.device("npu"))
self.moe_all_to_all_group_name = backend.get_hccl_comm_name(local_rank)
def token_dispatch(
self,
hidden_states: torch.Tensor,
topk_weights: torch.Tensor,
topk_ids: torch.Tensor,
expert_map: torch.Tensor | None = None,
global_redundant_expert_num: int = 0,
mc2_mask: torch.Tensor | None = None,
apply_router_weight_on_input: bool = False,
with_quant: bool = False,
dynamic_eplb: bool = False,
pertoken_scale: torch.Tensor | None = None,
):
self.with_quant = with_quant
self.hidden_shape = hidden_states.shape
(
permutated_local_input_tokens,
reversed_local_input_permutation_mapping,
tokens_per_expert,
input_splits,
output_splits,
num_global_tokens_per_local_expert,
global_input_tokens_local_experts_indices,
) = self._dispatch_preprocess(hidden_states, topk_ids)
dynamic_scale_after_all2all = None
if self.with_quant:
permutated_local_input_tokens, dynamic_scale = torch_npu.npu_dynamic_quant(permutated_local_input_tokens)
_, dynamic_scale_after_all2all, permute2_ep_all_to_all_handle = async_all_to_all(
dynamic_scale, output_splits, input_splits, self.ep_group
)
permute2_ep_all_to_all_handle.wait()
dynamic_scale.untyped_storage().resize_(0)
_, global_input_tokens, permute1_ep_all_to_all_handle = async_all_to_all(
permutated_local_input_tokens, output_splits, input_splits, self.ep_group
)
permute1_ep_all_to_all_handle.wait()
permutated_local_input_tokens.untyped_storage().resize_(0)
# Postprocess
global_input_tokens, dynamic_scale_final, reversed_global_input_permutation_mapping = (
self._dispatch_postprocess(
global_input_tokens, dynamic_scale_after_all2all, global_input_tokens_local_experts_indices
)
)
context_metadata = {
"input_splits": input_splits,
"output_splits": output_splits,
"topk_weights": topk_weights,
"reversed_local_input_permutation_mapping": reversed_local_input_permutation_mapping,
"reversed_global_input_permutation_mapping": reversed_global_input_permutation_mapping,
}
return TokenDispatchResult(
hidden_states=global_input_tokens,
dynamic_scale=dynamic_scale_final,
group_list=tokens_per_expert,
group_list_type=1,
context_metadata=context_metadata,
)
def token_combine(self, hidden_states, context_metadata, bias=None):
assert bias is None, "Bias is not supported in MoEAlltoAllvTokenDispatcher."
# 1. Preprocess using metadata
hidden_states = self._combine_preprocess(hidden_states, context_metadata)
# 2. AllToAll
_, permutated_local_input_tokens, handle = async_all_to_all(
hidden_states,
context_metadata["input_splits"],
context_metadata["output_splits"],
self.ep_group,
)
handle.wait()
hidden_states.untyped_storage().resize_(0)
# 3. Postprocess using metadata
output = self._combine_postprocess(permutated_local_input_tokens, context_metadata)
return TokenCombineResult(routed_out=output)
def _dispatch_preprocess(self, hidden_states, topk_ids):
assert self.hidden_shape is not None
hidden_states = hidden_states.view(-1, hidden_states.size(-1))
(
tokens_per_expert,
input_splits,
output_splits,
num_global_tokens_per_local_expert,
global_input_tokens_local_experts_indices,
) = self._preprocess(topk_ids)
self.hidden_shape_before_permute = hidden_states.shape
permutated_local_input_tokens, reversed_local_input_permutation_mapping = torch_npu.npu_moe_token_permute(
tokens=hidden_states,
indices=topk_ids,
num_out_tokens=self.num_out_tokens,
)
return (
permutated_local_input_tokens,
reversed_local_input_permutation_mapping,
tokens_per_expert,
input_splits,
output_splits,
num_global_tokens_per_local_expert,
global_input_tokens_local_experts_indices,
)
def _preprocess(self, topk_ids: torch.Tensor):
num_local_tokens_per_expert = torch.histc(topk_ids, bins=self.num_experts, min=0, max=self.num_experts)
ep_size = self.ep_size
self.num_out_tokens = topk_ids.numel()
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)
num_global_tokens_per_local_expert = num_global_tokens_per_expert[
:, self.local_expert_indices[0] : self.local_expert_indices[-1] + 1
]
if num_global_tokens_per_local_expert is None:
raise ValueError("num_global_tokens_per_local_expert must be set before sum.")
output_splits = (
num_global_tokens_per_local_expert.sum(axis=-1).to(torch.device("cpu"), non_blocking=True).numpy()
)
num_tokens_per_local_expert = num_global_tokens_per_local_expert.sum(axis=0)
global_input_tokens_local_experts_indices = None
if self.num_local_experts > 1:
if num_global_tokens_per_local_expert is None:
raise ValueError("num_global_tokens_per_local_expert must be set before operations.")
global_input_tokens_local_experts_indices = torch.repeat_interleave(
self.expert_ids_per_ep_rank, num_global_tokens_per_local_expert.ravel()
)
else:
torch.npu.synchronize()
return (
num_tokens_per_local_expert,
input_splits,
output_splits,
num_global_tokens_per_local_expert,
global_input_tokens_local_experts_indices,
)
def _dispatch_postprocess(
self, global_input_tokens, dynamic_scale_after_all2all, global_input_tokens_local_experts_indices
):
# Early return if no local experts or no tokens
if self.num_local_experts <= 1:
return global_input_tokens, dynamic_scale_after_all2all, None
# Handle quantized case
if self.with_quant:
assert global_input_tokens_local_experts_indices is not None, (
"global_input_tokens_local_experts_indices must be provided"
)
dynamic_scale_after_all2all, _ = torch_npu.npu_moe_token_permute(
dynamic_scale_after_all2all.unsqueeze(-1), global_input_tokens_local_experts_indices
)
dynamic_scale_after_all2all = dynamic_scale_after_all2all.squeeze(-1)
# Non-quantized case
global_input_tokens, reversed_global_input_permutation_mapping = torch_npu.npu_moe_token_permute(
global_input_tokens, global_input_tokens_local_experts_indices
)
return global_input_tokens, dynamic_scale_after_all2all, reversed_global_input_permutation_mapping
def _combine_preprocess(self, hidden_states: torch.Tensor, context_metadata: dict) -> torch.Tensor:
# Unpermutation 2: expert output to AlltoAll input
if hidden_states.shape[0] > 0 and self.num_local_experts > 1:
rev_global = context_metadata["reversed_global_input_permutation_mapping"]
hidden_states = torch_npu.npu_moe_token_unpermute(hidden_states, rev_global)
return hidden_states
def _combine_postprocess(self, permutated_local_input_tokens: torch.Tensor, context_metadata: dict) -> torch.Tensor:
# Unpermutation 1: AlltoAll output to output
output = torch_npu.npu_moe_token_unpermute(
permuted_tokens=permutated_local_input_tokens,
sorted_indices=context_metadata["reversed_local_input_permutation_mapping"].to(torch.int32),
probs=context_metadata["topk_weights"],
restore_shape=self.hidden_shape_before_permute,
)
output = output.view(self.hidden_shape)
return output
Reference in New Issue View Git Blame Copy Permalink