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[main] [refactor] refactor common_fused_moe.py (#2706)

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### What this PR does / why we need it?
1. Move prepare/finalize operation from moe_comm_method to
/ops/moe/fused_moe_prepare_and_finalize
2. Adapt to token_dispatcher in moe_comm_method
3. Move
moe_comm_method/experts_selector/token_dispatcher/fused_moe_prepare_and_finalize
to /ops/moe
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
e2e & ut

- vLLM version: v0.10.1.1
- vLLM main:
f4962a6d55

Signed-off-by: weichen <calvin_zhu0210@outlook.com>
Signed-off-by: Pr0Wh1teGivee <calvin_zhu0210@outlook.com>
Co-authored-by: weijinqian0 <12153182+weijinqian0@users.noreply.github.com>
This commit is contained in:
weichen
2025-09-08 20:09:50 +08:00
committed by GitHub
parent 1a82b16355
commit a041d4f328
21 changed files with 1052 additions and 932 deletions
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3
vllm_ascend/ascend_forward_context.py
View File

@@ -94,8 +94,7 @@ def set_ascend_forward_context(
forward_context.fused_moe_state = fused_moe_state
forward_context.in_profile_run = in_profile_run
from vllm_ascend.ops.moe_dispatcher.token_dispatcher import \
get_token_dispatcher
from vllm_ascend.ops.moe.token_dispatcher import get_token_dispatcher
dispatcher_name = get_dispatcher_name(ep_size, with_prefill)
dispatcher = get_token_dispatcher(dispatcher_name)
forward_context.token_dispatcher = dispatcher

555
vllm_ascend/distributed/moe_comm_method.py
View File

@@ -1,555 +0,0 @@
from abc import ABC, abstractmethod
from typing import Optional
import torch
import torch.distributed as dist
import torch.nn as nn
import torch_npu
from vllm.distributed import tensor_model_parallel_all_reduce
from vllm.distributed.parallel_state import (
get_dp_group, get_tensor_model_parallel_rank,
get_tensor_model_parallel_world_size)
from vllm.forward_context import get_forward_context
from vllm.model_executor.layers.fused_moe import FusedMoEConfig
from vllm_ascend.distributed.parallel_state import get_mc2_group
from vllm_ascend.utils import AscendSocVersion, get_ascend_soc_version
class MoECommMethod(ABC):
"""Base class for MoE communication methods."""
def __init__(self, moe_config: FusedMoEConfig):
self.moe_config = moe_config
@abstractmethod
def prepare(
self, hidden_states: torch.Tensor,
router_logits: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
"""Prepare the MoE communication method.
This method is called before quant_method.apply to prepare the
communication method. It can be used to initialize any necessary
resources or configurations.
"""
pass
@abstractmethod
def finalize(self, hidden_states: torch.Tensor,
reduce_results: bool) -> torch.Tensor:
"""Finalize the MoE communication method.
This method is called after quant_method.apply to finalize the
communication method. It can be used to clean up any resources or
configurations.
"""
pass
@abstractmethod
def permute(
self,
hidden_states: torch.Tensor,
topk_ids: torch.Tensor,
topk_weights: torch.Tensor,
expert_map: torch.Tensor,
num_experts: int,
apply_a8_quantization: bool,
) -> tuple[torch.Tensor, torch.Tensor, Optional[torch.Tensor], int]:
"""Pre-process before MLP.
Args:
hidden_states (torch.Tensor): Tensor of shape (num_tokens, hidden_size)
topk_ids (torch.Tensor): Tensor of shape (num_tokens, top_k_num)
topk_weights (torch.Tensor): Tensor of shape (num_tokens, top_k_num)
expert_map (torch.Tensor): Tensor of shape (global_num_experts, )
Mapping from global expert IDs to local expert IDs.
num_experts (int): Number of local experts (experts on this device).
apply_a8_quantization (bool): Whether to apply A8 quantization (W4A8 and W8A8).
Returns:
tuple[torch.Tensor, torch.Tensor, int]: Return a tuple containing:
- permuted_hidden_states (torch.Tensor): Tensor of shape
(num_tokens * top_k_num, hidden_size) after permuting
hidden_states based on topk_ids.
- expert_tokens (torch.Tensor): Tensor of shape (num_experts, )
Number of tokens assigned to each expert.
- dynamic_scale (torch.Tensor, optional): Tensor of shape (num_experts, )
Dynamic scale for each expert, used for quantization.
- group_list_type (int): Type of group list, 0 for `cumsum`
and 1 for `count`. This is mainly for `npu_grouped_matmul`
to determine how to handle the output.
Raises:
NotImplementedError: If the method is not implemented in the subclass.
"""
pass
@abstractmethod
def unpermute(self, mlp_output: torch.Tensor,
hidden_states: torch.Tensor) -> None:
"""Post-process after MLP.
Args:
mlp_output (torch.Tensor): Tensor of shape
(num_tokens * top_k_num, hidden_size) after MLP.
hidden_states (torch.Tensor): Tensor of shape
(num_tokens, hidden_size) to be updated with the final output.
"""
pass
class AllGatherCommImpl(MoECommMethod):
"""This implementation is the same as NativeAllGatherCommImpl,
but uses NPU-specific ops for better performance.
This implementation should be compatible with all scenarios, and
thus it is the default implementation for MoE communication methods.
It uses `torch_npu.npu_moe_init_routing_v2` for pre-processing
and `torch_npu.npu_moe_token_unpermute` for post-processing
to handle the token-to-expert mapping and communication efficiently.
NOTE(Yizhou): TBH, it is really weird that we were supposed to use
`torch_npu.npu_moe_init_routing_v2` and `torch_npu.npu_moe_finalize_routing`
or `torch_npu.npu_moe_token_permute` and `torch_npu.npu_moe_token_unpermute`
for pre-processing and post-processing, respectively.
But `npu_moe_finalize_routing` will lead to accuracy issues so we have to
use `torch_npu.npu_moe_token_unpermute` instead.
This is a workaround and should be removed after the issue is fixed.
"""
def prepare(
self, hidden_states: torch.Tensor,
router_logits: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
"""When DP size > 1, pad the hidden states and router logits for communication."""
if self.moe_config.dp_size > 1:
forward_context = get_forward_context()
max_tokens_across_dp = forward_context.max_tokens_across_dp
self.num_tokens = hidden_states.shape[0]
pad_size = max_tokens_across_dp - self.num_tokens
if pad_size > 0:
hidden_states = nn.functional.pad(hidden_states,
(0, 0, 0, pad_size))
router_logits = nn.functional.pad(router_logits,
(0, 0, 0, pad_size))
hidden_states = self.moe_config.dp_group.all_gather(
hidden_states, 0)
router_logits = self.moe_config.dp_group.all_gather(
router_logits, 0)
return hidden_states, router_logits
def finalize(self, hidden_states: torch.Tensor,
reduce_results: bool) -> torch.Tensor:
"""When DP size > 1, reduce-scatter the hidden states to get the final output.
When TP size > 1, all-reduce the hidden states to get the final output.
"""
if self.moe_config.dp_size > 1:
hidden_states = get_dp_group().reduce_scatter(hidden_states, 0)
hidden_states = hidden_states[:self.num_tokens]
if reduce_results and (self.moe_config.tp_size > 1
or self.moe_config.ep_size > 1):
hidden_states = tensor_model_parallel_all_reduce(hidden_states)
return hidden_states
def permute(
self,
hidden_states: torch.Tensor,
topk_ids: torch.Tensor,
topk_weights: torch.Tensor,
expert_map: torch.Tensor, # noqa: F841
num_experts: int,
apply_a8_quantization: bool,
) -> tuple[torch.Tensor, torch.Tensor, Optional[torch.Tensor], int]:
num_tokens = hidden_states.shape[0]
self.topk_weights = topk_weights
self.topk_ids = topk_ids
first_expert_idx = 0
if expert_map is not None:
# FIXME: npu_grouped_matmul output random values at [num_valid_tokens:, ...]
# So we need to filter out invalid tokens by zeroing their weights.
# This is a workaround and should be removed after the issue is fixed
mask = expert_map[topk_ids] != -1
# NOTE: This is equivalent to self.topk_weights[~mask] = 0.0,
# but ~mask will dispatch to aclnnNonzeroV2, which is not supported in ACL Graph
self.topk_weights = torch.where(mask, topk_weights, 0.0)
first_expert_idx = self.moe_config.ep_rank * num_experts
last_expert_idx = first_expert_idx + num_experts
permuted_hidden_states, expanded_row_idx, expert_tokens, _ = (
torch_npu.npu_moe_init_routing_v2(
hidden_states,
topk_ids,
active_num=num_tokens * self.moe_config.experts_per_token,
expert_num=self.moe_config.num_experts,
expert_tokens_num_type=1, # Only support `count` mode now
expert_tokens_num_flag=True, # Output `expert_tokens`
active_expert_range=[first_expert_idx, last_expert_idx],
quant_mode=-1,
))
self.expanded_row_idx = expanded_row_idx
permuted_hidden_states = permuted_hidden_states
group_list_type = 1 # `count` mode
return permuted_hidden_states, expert_tokens, None, group_list_type
def unpermute(self, mlp_output: torch.Tensor,
hidden_states: torch.Tensor) -> None:
hidden_states[:] = torch_npu.npu_moe_token_unpermute(
permuted_tokens=mlp_output,
sorted_indices=self.expanded_row_idx,
probs=self.topk_weights)
class NativeAllGatherCommImpl(AllGatherCommImpl):
"""This implementation should be compatible with all scenarios.
Note that this implementation purely consists of native PyTorch ops
and does not use any NPU-specific ops. So the performance may not be optimal.
But it is a good fallback for scenarios where NPU-specific ops are not available.
"""
def permute(
self,
hidden_states: torch.Tensor,
topk_ids: torch.Tensor,
topk_weights: torch.Tensor,
expert_map: torch.Tensor,
num_experts: int,
apply_a8_quantization: bool,
) -> tuple[torch.Tensor, torch.Tensor, Optional[torch.Tensor], int]:
num_tokens = hidden_states.shape[0]
# Generate token indices and flatten
token_indices = torch.arange(num_tokens,
device=hidden_states.device,
dtype=torch.int64)
token_indices = (token_indices.unsqueeze(1).expand(
-1, self.moe_config.experts_per_token).reshape(-1))
# Flatten token-to-expert mappings and map to local experts
weights_flat = topk_weights.view(-1)
experts_flat = topk_ids.view(-1)
local_experts_flat = (expert_map[experts_flat]
if expert_map is not None else experts_flat)
# Filter valid token-expert pairs
mask = local_experts_flat != -1
# FIXME: npu_grouped_matmul output random values at [num_valid_tokens:, ...]
# So we need to filter out invalid tokens by zeroing their weights.
# This is a workaround and should be removed after the issue is fixed
filtered_weights = torch.where(mask, weights_flat,
torch.zeros_like(weights_flat)).to(
topk_weights.dtype)
filtered_experts = torch.where(
mask,
local_experts_flat,
torch.full_like(local_experts_flat, num_experts),
).to(topk_ids.dtype)
# Sort by local expert IDs
sort_indices = torch.argsort(filtered_experts.view(torch.float32))
self.sorted_token_indices = token_indices[sort_indices]
self.sorted_weights = filtered_weights[sort_indices]
# Compute token counts with minlength of num_experts
# This is equivalent to but faster than:
# >>> token_counts = torch.bincount(filtered_experts, minlength=num_experts)[:-1]
token_counts = torch.zeros(num_experts + 1,
device=hidden_states.device,
dtype=torch.int64)
ones = torch.ones_like(filtered_experts, dtype=torch.int64)
token_counts.scatter_add_(0, filtered_experts.to(torch.int64), ones)
expert_tokens = token_counts[:num_experts]
# Rearrange hidden_states
permuted_hidden_states = hidden_states[self.sorted_token_indices]
group_list_type = 1 # `count` mode
return permuted_hidden_states, expert_tokens, None, group_list_type
def unpermute(self, mlp_output: torch.Tensor,
hidden_states: torch.Tensor) -> None:
mlp_output = mlp_output * self.sorted_weights.unsqueeze(1)
final_hidden_states = torch.zeros_like(hidden_states)
final_hidden_states.index_add_(0, self.sorted_token_indices,
mlp_output)
hidden_states[:] = final_hidden_states
class MC2CommImpl(MoECommMethod):
"""This implementation is for the scenarios listed below:
1. `enable_expert_parallel=True`.
2. `npu_moe_distribute_dispatch` and `npu_moe_distribute_combine` are available.
3. `enable_expert_parallel=False` is not supported.
This implementation uses the MC2 communication method, which is optimized for
Communication and Computation parallelism on Ascend devices.
"""
def __init__(self, moe_config: Optional[FusedMoEConfig]):
super().__init__(moe_config)
# NOTE: We do not need to use mc2_group's rank and world size
# because ep_group and mc2_group basically have the same init params.
# We only init another group because of the restriction of MC2:
# "No other groups can be used in the same process as the MC2 group."
self.mc2_comm_name = get_mc2_group().device_group._get_backend(
torch.device("npu")).get_hccl_comm_name(self.moe_config.ep_rank)
# Feature flags
self.enable_dispatch_v2 = hasattr(torch_npu,
"npu_moe_distribute_dispatch_v2")
self.is_ascend_a3 = get_ascend_soc_version() == AscendSocVersion.A3
self.need_extra_args = self.is_ascend_a3
self._restore_tp_across_dp()
def _restore_tp_across_dp(self):
# NOTE: Since vLLM flatten tp across dp, we need to restore the original
# tp_size and tp_rank.
self.tp_size = get_tensor_model_parallel_world_size()
self.tp_rank = get_tensor_model_parallel_rank()
def prepare(
self, hidden_states: torch.Tensor,
router_logits: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
"""The target_pad_length is calculated in forward_context, here we pad the
hidden states and router logits. And if TP size > 1, we also need to split
the tensors accordingly.
"""
self.num_tokens, _ = hidden_states.shape
forward_context = get_forward_context()
self.mc2_mask = forward_context.mc2_mask
target_pad_length = forward_context.padded_num_tokens
pad_size = target_pad_length - self.num_tokens
if pad_size > 0:
hidden_states = nn.functional.pad(hidden_states,
(0, 0, 0, pad_size))
router_logits = nn.functional.pad(router_logits,
(0, 0, 0, pad_size))
if self.tp_size > 1:
split_hidden_states = torch.tensor_split(hidden_states,
self.tp_size,
dim=0)
split_router_logits = torch.tensor_split(router_logits,
self.tp_size,
dim=0)
split_mc2_mask = torch.tensor_split(self.mc2_mask,
self.tp_size,
dim=0)
self.split_hidden_states = split_hidden_states
hidden_states = split_hidden_states[self.tp_rank]
router_logits = split_router_logits[self.tp_rank]
self.mc2_mask = split_mc2_mask[self.tp_rank]
return hidden_states, router_logits
def finalize(self, hidden_states: torch.Tensor,
reduce_results: bool) -> torch.Tensor:
"""If TP size > 1, all-gather the hidden states to get the final output.
Also, unpad the hidden states if needed.
"""
if self.tp_size > 1:
dist.all_gather(list(self.split_hidden_states), hidden_states,
self.moe_config.tp_group.device_group)
hidden_states = torch.cat(self.split_hidden_states, dim=0)
if self.num_tokens < hidden_states.shape[0]:
hidden_states = hidden_states[:self.num_tokens]
return hidden_states
def permute(
self,
hidden_states: torch.Tensor,
topk_ids: torch.Tensor,
topk_weights: torch.Tensor,
expert_map: torch.Tensor,
num_experts: int,
apply_a8_quantization: bool,
) -> tuple[torch.Tensor, torch.Tensor, Optional[torch.Tensor], int]:
# Store tensors needed for post_process
self.topk_ids = topk_ids
self.topk_weights = topk_weights.to(torch.float32)
dispatch_kwargs = {
"x": hidden_states,
"expert_ids": self.topk_ids,
"expert_shard_type": 0,
"shared_expert_rank_num": 0,
"moe_expert_num": self.moe_config.num_experts,
"global_bs": 0,
"scales": None,
"quant_mode": 2 if apply_a8_quantization else 0,
"group_ep": self.mc2_comm_name,
"ep_world_size": self.moe_config.ep_size,
"ep_rank_id": self.moe_config.ep_rank,
}
if self.need_extra_args:
dispatch_kwargs.update({
"group_tp": self.mc2_comm_name,
"tp_world_size": 1,
"tp_rank_id": 0,
})
if self.is_ascend_a3 and self.enable_dispatch_v2:
dispatch_kwargs.update({
"x_active_mask": self.mc2_mask,
})
dispatch = torch_npu.npu_moe_distribute_dispatch_v2 if self.enable_dispatch_v2 else torch_npu.npu_moe_distribute_dispatch
(
permuted_hidden_states,
dynamic_scale,
self.assist_info_for_combine,
expert_tokens,
self.ep_recv_counts,
self.tp_recv_counts,
) = dispatch(**dispatch_kwargs)[:6]
group_list_type = 1
return permuted_hidden_states, expert_tokens, dynamic_scale, group_list_type
def unpermute(self, mlp_output: torch.Tensor,
hidden_states: torch.Tensor) -> None:
combine_kwargs = {
"expand_x": mlp_output,
"expert_ids": self.topk_ids,
"expert_scales": self.topk_weights,
"expert_shard_type": 0,
"shared_expert_rank_num": 0,
"moe_expert_num": self.moe_config.num_experts,
"global_bs": 0,
"ep_send_counts": self.ep_recv_counts,
"group_ep": self.mc2_comm_name,
"ep_world_size": self.moe_config.ep_size,
"ep_rank_id": self.moe_config.ep_rank,
}
if self.enable_dispatch_v2:
combine_kwargs[
"assist_info_for_combine"] = self.assist_info_for_combine
else:
combine_kwargs["expand_idx"] = self.assist_info_for_combine
if self.need_extra_args:
combine_kwargs.update({
"tp_send_counts": self.tp_recv_counts,
"group_tp": self.mc2_comm_name,
"tp_world_size": 1,
"tp_rank_id": 0,
})
if self.is_ascend_a3 and self.enable_dispatch_v2:
combine_kwargs.update({
"x_active_mask": self.mc2_mask,
})
combine = torch_npu.npu_moe_distribute_combine_v2 if self.enable_dispatch_v2 else torch_npu.npu_moe_distribute_combine
hidden_states[:] = combine(**combine_kwargs)
class AlltoAllCommImpl(MoECommMethod):
"""This implementation is for the scenarios listed below:
1. `enable_expert_parallel=True`.
2. `npu_grouped_matmul` is available.
This implementation uses all-to-all communication to exchange tokens
between data parallel ranks before and after the MLP computation. It should
have better performance than AllGatherCommImpl when DP size > 1.
"""
def __init__(self, moe_config: Optional[FusedMoEConfig]):
super().__init__(moe_config)
from vllm_ascend.ops.moe_dispatcher.token_dispatcher import \
get_token_dispatcher
self.token_dispatcher = get_token_dispatcher(
"TokenDispatcherWithAll2AllV")
self._restore_tp_across_dp()
def _restore_tp_across_dp(self):
# NOTE: Since vLLM flatten tp across dp, we need to restore the original
# tp_size and tp_rank.
self.tp_size = get_tensor_model_parallel_world_size()
self.tp_rank = get_tensor_model_parallel_rank()
def prepare(
self, hidden_states: torch.Tensor,
router_logits: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
self.num_tokens, _ = hidden_states.shape
pad_size = self.tp_size - self.num_tokens
if pad_size > 0:
hidden_states = nn.functional.pad(hidden_states,
(0, 0, 0, pad_size))
router_logits = nn.functional.pad(router_logits,
(0, 0, 0, pad_size))
if self.tp_size > 1:
split_hidden_states = torch.tensor_split(hidden_states,
self.tp_size,
dim=0)
split_router_logits = torch.tensor_split(router_logits,
self.tp_size,
dim=0)
self.split_hidden_states = split_hidden_states
hidden_states = split_hidden_states[self.tp_rank]
router_logits = split_router_logits[self.tp_rank]
return hidden_states, router_logits
def finalize(self, hidden_states: torch.Tensor,
reduce_results: bool) -> torch.Tensor:
"""If TP size > 1, all-gather the hidden states to get the final output.
Also, unpad the hidden states if needed.
"""
if self.tp_size > 1:
dist.all_gather(list(self.split_hidden_states), hidden_states,
self.moe_config.tp_group.device_group)
hidden_states = torch.cat(self.split_hidden_states, dim=0)
if self.num_tokens < hidden_states.shape[0]:
hidden_states = hidden_states[:self.num_tokens]
return hidden_states
def permute(
self,
hidden_states: torch.Tensor,
topk_ids: torch.Tensor,
topk_weights: torch.Tensor,
expert_map: torch.Tensor,
num_experts: int,
apply_a8_quantization: bool,
) -> tuple[torch.Tensor, torch.Tensor, Optional[torch.Tensor], int]:
results = self.token_dispatcher.token_dispatch(
hidden_states,
topk_weights,
topk_ids,
None,
log2phy=None,
with_quant=apply_a8_quantization)
return results["hidden_states"], results["group_list"], results[
"dynamic_scale"], results["group_list_type"]
def unpermute(self, mlp_output: torch.Tensor,
hidden_states: torch.Tensor) -> None:
hidden_states[:] = self.token_dispatcher.token_combine(mlp_output)

152
vllm_ascend/ops/common_fused_moe.py
View File

@@ -15,7 +15,7 @@
# limitations under the License.
#
from typing import Any, Callable, Optional
from typing import Callable, Optional
import torch
import torch_npu
@@ -28,118 +28,16 @@ from vllm.model_executor.layers.fused_moe.layer import (
FusedMoE, UnquantizedFusedMoEMethod)
from vllm_ascend.ascend_config import get_ascend_config
from vllm_ascend.distributed.moe_comm_method import (AllGatherCommImpl,
AlltoAllCommImpl,
MC2CommImpl)
from vllm_ascend.distributed.parallel_state import get_mc2_group
from vllm_ascend.ops.layers.experts_selector import select_experts
from vllm_ascend.ops.moe_dispatcher.token_dispatcher import \
setup_token_dispatchers
from vllm_ascend.ops.moe.experts_selector import select_experts
from vllm_ascend.ops.moe.moe_comm_method import (AllGatherCommImpl,
AlltoAllCommImpl, MC2CommImpl)
from vllm_ascend.ops.moe.token_dispatcher import setup_token_dispatchers
from vllm_ascend.utils import ACL_FORMAT_FRACTAL_NZ, is_310p, vllm_version_is
original_unquantized_fused_moe_init_func = UnquantizedFusedMoEMethod.__init__
def fused_experts(
hidden_states: torch.Tensor,
w1: torch.Tensor,
w2: torch.Tensor,
topk_weights: torch.Tensor,
topk_ids: torch.Tensor,
activation: str = "silu",
apply_router_weight_on_input: bool = False,
use_int8_w8a8: bool = False,
use_int4_w4a8: bool = False,
global_num_experts: Optional[int] = None,
expert_map: Optional[torch.Tensor] = None,
w1_scale: Optional[torch.Tensor] = None,
w2_scale: Optional[torch.Tensor] = None,
w1_scale_bias: torch.Tensor = None,
w2_scale_bias: torch.Tensor = None,
# For TorchAir graph
is_torchair: bool = False,
# For Cube/Vector parallel
shared_experts: Optional[Any] = None,
quantized_x_for_share: Optional[Any] = None,
dynamic_scale_for_share: Optional[Any] = None,
# For load balance
log2phy: torch.Tensor = None,
global_redundant_expert_num: int = 0,
) -> torch.Tensor:
# Check constraints
assert hidden_states.shape[1] == w1.shape[1], (
f"Hidden size mismatch {hidden_states.shape[1]} != {w1.shape[1]}")
assert topk_weights.shape == topk_ids.shape, "topk shape mismatch"
assert hidden_states.is_contiguous(), "Hidden_states must be contiguous"
assert w1.stride(-1) == 1, "Stride of last dimension must be 1"
assert w2.stride(-1) == 1, "Stride of last dimension must be 1"
assert hidden_states.dtype in [
torch.float32, torch.float16, torch.bfloat16
]
if (use_int8_w8a8 or use_int4_w4a8):
assert w1_scale is not None and w2_scale is not None, \
"INT8 quantization requires weight scales."
w1_scale = w1_scale.to(torch.float32)
down_scale = [w2_scale]
down_output_dtype = w2_scale.dtype
else:
down_scale = None
down_output_dtype = None
moe_comm_method = get_forward_context().moe_comm_method
assert moe_comm_method is not None, "Missing communication context"
num_experts = w1.shape[0]
permuted_hidden_states, expert_tokens, dynamic_scale, group_list_type = moe_comm_method.permute(
hidden_states, topk_ids, topk_weights, expert_map, num_experts,
use_int8_w8a8 or use_int4_w4a8)
gate_up_output = torch_npu.npu_grouped_matmul(
x=[permuted_hidden_states],
weight=[w1],
split_item=2,
group_list_type=group_list_type,
group_type=0,
group_list=expert_tokens,
output_dtype=torch.int32 if use_int8_w8a8 else None,
)[0]
if (use_int8_w8a8 or use_int4_w4a8):
activated_output, activated_output_scale = torch_npu.npu_dequant_swiglu_quant(
x=gate_up_output,
weight_scale=w1_scale,
activation_scale=dynamic_scale,
bias=None,
quant_scale=None,
quant_offset=None,
group_index=expert_tokens,
activate_left=True,
quant_mode=1,
)
activated_output_scale = [activated_output_scale]
else:
activated_output = torch_npu.npu_swiglu(gate_up_output)
activated_output_scale = None
down_output = torch_npu.npu_grouped_matmul(
x=[activated_output],
weight=[w2],
scale=down_scale,
per_token_scale=activated_output_scale,
split_item=2,
group_list_type=group_list_type,
group_type=0,
group_list=expert_tokens,
output_dtype=down_output_dtype,
)[0]
moe_comm_method.unpermute(down_output, hidden_states)
return hidden_states
def fused_experts_moge(
hidden_states: torch.Tensor,
w1: torch.Tensor,
@@ -259,7 +157,7 @@ def forward_oot_v01011(
logical_to_physical_map: Optional[torch.Tensor] = None,
logical_replica_count: Optional[torch.Tensor] = None) -> torch.Tensor:
topk_weights, topk_ids, _ = select_experts(
topk_weights, topk_ids, row_idx = select_experts(
hidden_states=x,
router_logits=router_logits,
top_k=top_k,
@@ -287,15 +185,15 @@ def forward_oot_v01011(
expert_map=expert_map,
apply_router_weight_on_input=apply_router_weight_on_input)
return fused_experts(
hidden_states=x,
w1=layer.w13_weight,
w2=layer.w2_weight,
topk_weights=topk_weights,
topk_ids=topk_ids,
global_num_experts=global_num_experts,
expert_map=expert_map,
)
moe_comm_method = get_forward_context().moe_comm_method
return moe_comm_method.fused_experts(hidden_states=x,
w1=layer.w13_weight,
w2=layer.w2_weight,
topk_weights=topk_weights,
topk_ids=topk_ids,
row_idx=row_idx,
global_num_experts=global_num_experts,
expert_map=expert_map)
def forward_oot(
@@ -321,7 +219,7 @@ def forward_oot(
logical_to_physical_map: Optional[torch.Tensor] = None,
logical_replica_count: Optional[torch.Tensor] = None) -> torch.Tensor:
topk_weights, topk_ids, _ = select_experts(
topk_weights, topk_ids, row_idx = select_experts(
hidden_states=x,
router_logits=router_logits,
top_k=top_k,
@@ -349,15 +247,15 @@ def forward_oot(
expert_map=expert_map,
apply_router_weight_on_input=apply_router_weight_on_input)
return fused_experts(
hidden_states=x,
w1=layer.w13_weight,
w2=layer.w2_weight,
topk_weights=topk_weights,
topk_ids=topk_ids,
global_num_experts=global_num_experts,
expert_map=expert_map,
)
moe_comm_method = get_forward_context().moe_comm_method
return moe_comm_method.fused_experts(hidden_states=x,
w1=layer.w13_weight,
w2=layer.w2_weight,
topk_weights=topk_weights,
topk_ids=topk_ids,
row_idx=row_idx,
global_num_experts=global_num_experts,
expert_map=expert_map)
def process_weights_after_loading(self, layer):

6
vllm_ascend/ops/fused_moe.py
View File

@@ -42,8 +42,8 @@ from vllm_ascend.ascend_config import get_ascend_config
from vllm_ascend.ascend_forward_context import FusedMoEState
from vllm_ascend.distributed.parallel_state import get_mc2_group
from vllm_ascend.ops.expert_load_balancer import ExpertLoadBalancer
from vllm_ascend.ops.layers.experts_selector import select_experts
from vllm_ascend.ops.layers.moe_mlp import unified_apply_mlp
from vllm_ascend.ops.moe.experts_selector import select_experts
from vllm_ascend.ops.moe.moe_mlp import unified_apply_mlp
from vllm_ascend.ops.sequence_parallel import MetadataForPadding
from vllm_ascend.utils import (ACL_FORMAT_FRACTAL_NZ, dispose_tensor,
get_all_reduce_merge_state,
@@ -358,7 +358,7 @@ class AscendFusedMoE(FusedMoE):
ep_size = (get_ep_group().world_size if
vllm_config.parallel_config.enable_expert_parallel else 1)
from vllm_ascend.ops.moe_dispatcher.token_dispatcher import \
from vllm_ascend.ops.moe.token_dispatcher import \
setup_token_dispatchers
setup_token_dispatchers(
ep_size,

0
vllm_ascend/ops/layers/__init__.py → vllm_ascend/ops/moe/__init__.py
View File

0
vllm_ascend/ops/layers/experts_selector.py → vllm_ascend/ops/moe/experts_selector.py
View File

240
vllm_ascend/ops/moe/fused_moe_prepare_and_finalize.py Normal file
View File

@@ -0,0 +1,240 @@
# 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.
from abc import ABC, abstractmethod
import torch
import torch.distributed as dist
import torch.nn as nn
from vllm.distributed import tensor_model_parallel_all_reduce
from vllm.distributed.parallel_state import (
get_dp_group, get_tensor_model_parallel_rank,
get_tensor_model_parallel_world_size)
from vllm.forward_context import get_forward_context
from vllm.model_executor.layers.fused_moe import FusedMoEConfig
class FusedMoEPrepareAndFinalize(ABC):
def __init__(self, moe_config: FusedMoEConfig):
self.moe_config = moe_config
@abstractmethod
def prepare(self,
hidden_states: torch.Tensor,
router_logits: torch.Tensor,
enable_shared_expert_dp: bool = False,
rm_router_logits: bool = False,
replace_allreduce: bool = False,
gate=None) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
raise NotImplementedError("Prepare not implemented.")
def finalize(self, hidden_states: torch.Tensor,
reduce_results: bool) -> torch.Tensor:
raise NotImplementedError("Combine function not implemented.")
class FusedMoEPrepareAndFinalizeWithMC2(FusedMoEPrepareAndFinalize):
def __init__(self, moe_config: FusedMoEConfig):
super().__init__(moe_config)
self._restore_tp_across_dp()
def _restore_tp_across_dp(self):
# NOTE: Since vLLM flatten tp across dp, we need to restore the original
# tp_size and tp_rank.
self.tp_size = get_tensor_model_parallel_world_size()
self.tp_rank = get_tensor_model_parallel_rank()
def prepare(self,
hidden_states: torch.Tensor,
router_logits: torch.Tensor,
enable_shared_expert_dp: bool = False,
rm_router_logits: bool = False,
replace_allreduce: bool = False,
gate=None) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
"""The target_pad_length is calculated in forward_context, here we pad the
hidden states and router logits. And if TP size > 1, we also need to split
the tensors accordingly.
"""
self.replace_allreduce = replace_allreduce
self.enable_shared_expert_dp = enable_shared_expert_dp
if not self.replace_allreduce:
self.num_tokens, _ = hidden_states.shape
forward_context = get_forward_context()
mc2_mask = forward_context.mc2_mask
target_pad_length = forward_context.padded_num_tokens
pad_size = target_pad_length - self.num_tokens
if pad_size > 0 and not self.enable_shared_expert_dp:
hidden_states = nn.functional.pad(hidden_states,
(0, 0, 0, pad_size))
router_logits = nn.functional.pad(router_logits,
(0, 0, 0, pad_size))
if self.tp_size > 1:
if not self.enable_shared_expert_dp:
split_hidden_states = torch.tensor_split(hidden_states,
self.tp_size,
dim=0)
split_router_logits = torch.tensor_split(router_logits,
self.tp_size,
dim=0)
hidden_states = split_hidden_states[self.tp_rank]
router_logits = split_router_logits[self.tp_rank]
self.split_hidden_states = split_hidden_states
split_mc2_mask = torch.tensor_split(mc2_mask,
self.tp_size,
dim=0)
mc2_mask = split_mc2_mask[self.tp_rank]
return hidden_states, router_logits, mc2_mask
def finalize(self, hidden_states: torch.Tensor,
reduce_results: bool) -> torch.Tensor:
"""If TP size > 1, all-gather the hidden states to get the final output.
Also, unpad the hidden states if needed.
"""
if not (self.enable_shared_expert_dp or self.replace_allreduce):
if self.tp_size > 1:
dist.all_gather(list(self.split_hidden_states), hidden_states,
self.moe_config.tp_group.device_group)
hidden_states = torch.cat(self.split_hidden_states, dim=0)
if self.num_tokens < hidden_states.shape[0]:
hidden_states = hidden_states[:self.num_tokens]
return hidden_states
class FusedMoEPrepareAndFinalizeWithAll2All(FusedMoEPrepareAndFinalize):
def __init__(self, moe_config: FusedMoEConfig):
super().__init__(moe_config)
self._restore_tp_across_dp()
def _restore_tp_across_dp(self):
# NOTE: Since vLLM flatten tp across dp, we need to restore the original
# tp_size and tp_rank.
self.tp_size = get_tensor_model_parallel_world_size()
self.tp_rank = get_tensor_model_parallel_rank()
def prepare(self,
hidden_states: torch.Tensor,
router_logits: torch.Tensor,
enable_shared_expert_dp: bool = False,
rm_router_logits: bool = False,
replace_allreduce: bool = False,
gate=None) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
self.replace_allreduce = replace_allreduce
self.enable_shared_expert_dp = enable_shared_expert_dp
if not (self.replace_allreduce or self.enable_shared_expert_dp):
self.num_tokens, _ = hidden_states.shape
pad_size = self.tp_size - self.num_tokens
if pad_size > 0:
hidden_states = nn.functional.pad(hidden_states,
(0, 0, 0, pad_size))
router_logits = nn.functional.pad(router_logits,
(0, 0, 0, pad_size))
if self.tp_size > 1:
split_hidden_states = torch.tensor_split(hidden_states,
self.tp_size,
dim=0)
split_router_logits = torch.tensor_split(router_logits,
self.tp_size,
dim=0)
self.split_hidden_states = split_hidden_states
hidden_states = split_hidden_states[self.tp_rank]
router_logits = split_router_logits[self.tp_rank]
return hidden_states, router_logits, None
def finalize(self, hidden_states: torch.Tensor,
reduce_results: bool) -> torch.Tensor:
"""If TP size > 1, all-gather the hidden states to get the final output.
Also, unpad the hidden states if needed.
"""
if not (self.enable_shared_expert_dp or self.replace_allreduce):
if self.tp_size > 1:
dist.all_gather(list(self.split_hidden_states), hidden_states,
self.moe_config.tp_group.device_group)
hidden_states = torch.cat(self.split_hidden_states, dim=0)
if self.num_tokens < hidden_states.shape[0]:
hidden_states = hidden_states[:self.num_tokens]
return hidden_states
class FusedMoEPrepareAndFinalizeWithAllGather(FusedMoEPrepareAndFinalize):
def prepare(self,
hidden_states: torch.Tensor,
router_logits: torch.Tensor,
enable_shared_expert_dp: bool = False,
rm_router_logits: bool = False,
replace_allreduce: bool = False,
gate=None) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
"""When DP size > 1, pad the hidden states and router logits for communication."""
self.rm_router_logits = rm_router_logits
self.enable_shared_expert_dp = enable_shared_expert_dp
if self.moe_config.dp_size > 1:
forward_context = get_forward_context()
max_tokens_across_dp = forward_context.max_tokens_across_dp
self.num_tokens = hidden_states.shape[0]
pad_size = max_tokens_across_dp - self.num_tokens
if pad_size > 0:
hidden_states = nn.functional.pad(hidden_states,
(0, 0, 0, pad_size))
if not self.rm_router_logits:
router_logits = nn.functional.pad(router_logits,
(0, 0, 0, pad_size))
hidden_states = self.moe_config.dp_group.all_gather(
hidden_states, 0)
if self.rm_router_logits:
router_logits, _ = gate(hidden_states)
else:
router_logits = self.moe_config.dp_group.all_gather(
router_logits, 0)
return hidden_states, router_logits, None
def finalize(self, hidden_states: torch.Tensor,
reduce_results: bool) -> torch.Tensor:
"""When DP size > 1, reduce-scatter the hidden states to get the final output.
When TP size > 1, all-reduce the hidden states to get the final output.
"""
if self.moe_config.dp_size > 1 and not self.enable_shared_expert_dp:
hidden_states = get_dp_group().reduce_scatter(hidden_states, 0)
hidden_states = hidden_states[:self.num_tokens]
if reduce_results and (self.moe_config.tp_size > 1
or self.moe_config.ep_size > 1):
hidden_states = tensor_model_parallel_all_reduce(hidden_states)
return hidden_states

298
vllm_ascend/ops/moe/moe_comm_method.py Normal file
View File

@@ -0,0 +1,298 @@
# 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.
from abc import ABC, abstractmethod
from typing import Any, Optional
import torch
from vllm.forward_context import get_forward_context
from vllm.model_executor.layers.fused_moe import FusedMoEConfig
from vllm_ascend.ops.moe.fused_moe_prepare_and_finalize import (
FusedMoEPrepareAndFinalizeWithAll2All,
FusedMoEPrepareAndFinalizeWithAllGather, FusedMoEPrepareAndFinalizeWithMC2)
from vllm_ascend.ops.moe.moe_mlp import unified_apply_mlp
from vllm_ascend.ops.moe.token_dispatcher import (TokenDispatcherWithAll2AllV,
TokenDispatcherWithAllGather,
TokenDispatcherWithMC2)
class MoECommMethod(ABC):
"""Base class for MoE communication methods."""
def __init__(self, moe_config: FusedMoEConfig):
self.moe_config = moe_config
self.mc2_mask = None
self.token_dispatcher = self._get_token_dispatcher()
self.fused_moe_prepare_finalize = self._get_fused_moe_prepare_finalize(
)
def prepare(self,
hidden_states: torch.Tensor,
router_logits: torch.Tensor,
enable_shared_expert_dp: bool = False,
rm_router_logits: bool = False,
replace_allreduce: bool = False,
gate=None) -> tuple[torch.Tensor, torch.Tensor]:
hidden_states, router_logits, mc2_mask = self.fused_moe_prepare_finalize.prepare(
hidden_states, router_logits, enable_shared_expert_dp,
rm_router_logits, replace_allreduce, gate)
self.mc2_mask = mc2_mask
return hidden_states, router_logits
def finalize(self, hidden_states: torch.Tensor,
reduce_results: bool) -> torch.Tensor:
hidden_states = self.fused_moe_prepare_finalize.finalize(
hidden_states, reduce_results)
return hidden_states
def fused_experts(
self,
hidden_states: torch.Tensor,
w1: torch.Tensor,
w2: torch.Tensor,
topk_weights: torch.Tensor,
topk_ids: torch.Tensor,
row_idx: torch.Tensor,
activation: str = "silu",
apply_router_weight_on_input: bool = False,
use_int8_w8a8: bool = False,
use_int4_w4a8: bool = False,
global_num_experts: Optional[int] = None,
expert_map: Optional[torch.Tensor] = None,
w1_scale: Optional[torch.Tensor] = None,
w2_scale: Optional[torch.Tensor] = None,
w1_scale_bias: torch.Tensor = None,
w2_scale_bias: torch.Tensor = None,
# For TorchAir graph
is_torchair: bool = False,
# For Cube/Vector parallel
shared_experts: Optional[Any] = None,
shared_gate_up: Optional[Any] = None,
shared_dequant_scale: Optional[Any] = None,
quantized_x_for_share: Optional[Any] = None,
dynamic_scale_for_share: Optional[Any] = None,
# For load balance
log2phy: torch.Tensor = None,
global_redundant_expert_num: int = 0,
need_trans: bool = False) -> torch.Tensor:
# Check constraints
assert hidden_states.shape[1] == w1.shape[1], (
f"Hidden size mismatch {hidden_states.shape[1]} != {w1.shape[1]}")
assert topk_weights.shape == topk_ids.shape, "topk shape mismatch"
assert hidden_states.is_contiguous(
), "Hidden_states must be contiguous"
assert w1.stride(-1) == 1, "Stride of last dimension must be 1"
assert w2.stride(-1) == 1, "Stride of last dimension must be 1"
assert hidden_states.dtype in [
torch.float32, torch.float16, torch.bfloat16
]
moe_comm_method = get_forward_context().moe_comm_method
assert moe_comm_method is not None, "Missing communication context"
results = self.token_dispatcher.token_dispatch(
hidden_states=hidden_states,
topk_weights=topk_weights,
topk_ids=topk_ids,
row_idx=row_idx,
expert_map=expert_map,
log2phy=log2phy,
global_redundant_expert_num=global_redundant_expert_num,
shared_experts=shared_experts,
shared_gate_up=shared_gate_up,
shared_dequant_scale=shared_dequant_scale,
mc2_mask=self.mc2_mask,
apply_router_weight_on_input=apply_router_weight_on_input,
with_quant=use_int8_w8a8 or use_int4_w4a8)
permuted_hidden_states, expert_tokens, dynamic_scale, group_list_type = \
results["hidden_states"], results["group_list"], results.get("dynamic_scale"), results["group_list_type"]
mlp_output = unified_apply_mlp(hidden_states=permuted_hidden_states,
w1=w1,
w1_scale=w1_scale,
w2=w2,
w2_scale=w2_scale,
group_list=expert_tokens,
dynamic_scale=dynamic_scale,
group_list_type=group_list_type,
w1_scale_bias=w1_scale_bias,
w2_scale_bias=w2_scale_bias,
with_quant=use_int8_w8a8
or use_int4_w4a8,
need_trans=need_trans)
hidden_states[:] = self.token_dispatcher.token_combine(
hidden_states=mlp_output)
return hidden_states
@abstractmethod
def _get_token_dispatcher(self):
raise NotImplementedError(
"_get_token_dispatcher function not implemented.")
@abstractmethod
def _get_fused_moe_prepare_finalize(self):
raise NotImplementedError(
"_get_fused_moe_prepare_finalize function not implemented.")
class AllGatherCommImpl(MoECommMethod):
"""This implementation is the same as NativeAllGatherCommImpl,
but uses NPU-specific ops for better performance.
This implementation should be compatible with all scenarios, and
thus it is the default implementation for MoE communication methods.
It uses `torch_npu.npu_moe_init_routing_v2` for pre-processing
and `torch_npu.npu_moe_token_unpermute` for post-processing
to handle the token-to-expert mapping and communication efficiently.
NOTE(Yizhou): TBH, it is really weird that we were supposed to use
`torch_npu.npu_moe_init_routing_v2` and `torch_npu.npu_moe_finalize_routing`
or `torch_npu.npu_moe_token_permute` and `torch_npu.npu_moe_token_unpermute`
for pre-processing and post-processing, respectively.
But `npu_moe_finalize_routing` will lead to accuracy issues so we have to
use `torch_npu.npu_moe_token_unpermute` instead.
This is a workaround and should be removed after the issue is fixed.
"""
def _get_token_dispatcher(self):
return TokenDispatcherWithAllGather(
top_k=self.moe_config.experts_per_token,
num_experts=self.moe_config.num_experts,
num_local_experts=self.moe_config.num_local_experts)
def _get_fused_moe_prepare_finalize(self):
return FusedMoEPrepareAndFinalizeWithAllGather(self.moe_config)
class NativeAllGatherCommImpl(AllGatherCommImpl):
"""This implementation should be compatible with all scenarios.
Note that this implementation purely consists of native PyTorch ops
and does not use any NPU-specific ops. So the performance may not be optimal.
But it is a good fallback for scenarios where NPU-specific ops are not available.
"""
def permute(
self,
hidden_states: torch.Tensor,
topk_ids: torch.Tensor,
topk_weights: torch.Tensor,
expert_map: torch.Tensor,
num_experts: int,
apply_a8_quantization: bool,
) -> tuple[torch.Tensor, torch.Tensor, Optional[torch.Tensor], int]:
num_tokens = hidden_states.shape[0]
# Generate token indices and flatten
token_indices = torch.arange(num_tokens,
device=hidden_states.device,
dtype=torch.int64)
token_indices = (token_indices.unsqueeze(1).expand(
-1, self.moe_config.experts_per_token).reshape(-1))
# Flatten token-to-expert mappings and map to local experts
weights_flat = topk_weights.view(-1)
experts_flat = topk_ids.view(-1)
local_experts_flat = (expert_map[experts_flat]
if expert_map is not None else experts_flat)
# Filter valid token-expert pairs
mask = local_experts_flat != -1
# FIXME: npu_grouped_matmul output random values at [num_valid_tokens:, ...]
# So we need to filter out invalid tokens by zeroing their weights.
# This is a workaround and should be removed after the issue is fixed
filtered_weights = torch.where(mask, weights_flat,
torch.zeros_like(weights_flat)).to(
topk_weights.dtype)
filtered_experts = torch.where(
mask,
local_experts_flat,
torch.full_like(local_experts_flat, num_experts),
).to(topk_ids.dtype)
# Sort by local expert IDs
sort_indices = torch.argsort(filtered_experts.view(torch.float32))
self.sorted_token_indices = token_indices[sort_indices]
self.sorted_weights = filtered_weights[sort_indices]
# Compute token counts with minlength of num_experts
# This is equivalent to but faster than:
# >>> token_counts = torch.bincount(filtered_experts, minlength=num_experts)[:-1]
token_counts = torch.zeros(num_experts + 1,
device=hidden_states.device,
dtype=torch.int64)
ones = torch.ones_like(filtered_experts, dtype=torch.int64)
token_counts.scatter_add_(0, filtered_experts.to(torch.int64), ones)
expert_tokens = token_counts[:num_experts]
# Rearrange hidden_states
permuted_hidden_states = hidden_states[self.sorted_token_indices]
group_list_type = 1 # `count` mode
return permuted_hidden_states, expert_tokens, None, group_list_type
def unpermute(self, mlp_output: torch.Tensor,
hidden_states: torch.Tensor) -> None:
mlp_output = mlp_output * self.sorted_weights.unsqueeze(1)
final_hidden_states = torch.zeros_like(hidden_states)
final_hidden_states.index_add_(0, self.sorted_token_indices,
mlp_output)
hidden_states[:] = final_hidden_states
class MC2CommImpl(MoECommMethod):
"""This implementation is for the scenarios listed below:
1. `enable_expert_parallel=True`.
2. `npu_moe_distribute_dispatch` and `npu_moe_distribute_combine` are available.
3. `enable_expert_parallel=False` is not supported.
This implementation uses the MC2 communication method, which is optimized for
Communication and Computation parallelism on Ascend devices.
"""
def _get_token_dispatcher(self):
return TokenDispatcherWithMC2()
def _get_fused_moe_prepare_finalize(self):
return FusedMoEPrepareAndFinalizeWithMC2(self.moe_config)
class AlltoAllCommImpl(MoECommMethod):
"""This implementation is for the scenarios listed below:
1. `enable_expert_parallel=True`.
2. `npu_grouped_matmul` is available.
This implementation uses all-to-all communication to exchange tokens
between data parallel ranks before and after the MLP computation. It should
have better performance than AllGatherCommImpl when DP size > 1.
"""
def _get_token_dispatcher(self):
return TokenDispatcherWithAll2AllV(
top_k=self.moe_config.experts_per_token,
num_experts=self.moe_config.num_experts,
num_local_experts=self.moe_config.num_local_experts)
def _get_fused_moe_prepare_finalize(self):
return FusedMoEPrepareAndFinalizeWithAll2All(self.moe_config)

27
vllm_ascend/ops/layers/moe_mlp.py → vllm_ascend/ops/moe/moe_mlp.py
View File

@@ -176,14 +176,18 @@ def quant_apply_mlp(hidden_states: torch.Tensor,
return hidden_states
def unquant_apply_mlp(
hidden_states: torch.Tensor,
w1: torch.Tensor,
w2: torch.Tensor,
group_list: torch.Tensor,
group_list_type: int = 1,
topk_scales: Optional[torch.Tensor] = None) -> torch.Tensor:
w1 = w1.transpose(1, 2)
def unquant_apply_mlp(hidden_states: torch.Tensor,
w1: torch.Tensor,
w2: torch.Tensor,
group_list: torch.Tensor,
group_list_type: int = 1,
topk_scales: Optional[torch.Tensor] = None,
need_trans: bool = True) -> torch.Tensor:
if need_trans:
w1 = w1.transpose(1, 2)
w2 = w2.transpose(1, 2)
gate_up_out = torch_npu.npu_grouped_matmul(
x=[hidden_states],
weight=[w1],
@@ -201,7 +205,6 @@ def unquant_apply_mlp(
if topk_scales is not None:
gate_up_out *= topk_scales
w2 = w2.transpose(1, 2)
hidden_states = torch_npu.npu_grouped_matmul(
x=[gate_up_out],
weight=[w2],
@@ -225,7 +228,8 @@ def unified_apply_mlp(hidden_states: torch.Tensor,
w2_scale_bias: torch.Tensor = None,
topk_scales: Optional[torch.Tensor] = None,
with_quant: bool = False,
fusion: bool = False) -> torch.Tensor:
fusion: bool = False,
need_trans: bool = True) -> torch.Tensor:
if with_quant:
return quant_apply_mlp(hidden_states=hidden_states,
w1=w1,
@@ -244,4 +248,5 @@ def unified_apply_mlp(hidden_states: torch.Tensor,
w2=w2,
group_list=group_list,
group_list_type=group_list_type,
topk_scales=topk_scales)
topk_scales=topk_scales,
need_trans=need_trans)

0
vllm_ascend/ops/moe_dispatcher/token_dispatcher.py → vllm_ascend/ops/moe/token_dispatcher.py
View File

0
vllm_ascend/ops/moe_dispatcher/__init__.py
View File

2
vllm_ascend/quantization/w4a8_dynamic.py
View File

@@ -27,7 +27,7 @@ from vllm.forward_context import get_forward_context
from vllm_ascend.ascend_forward_context import FusedMoEState
from vllm_ascend.distributed.parallel_state import get_mc2_group
from vllm_ascend.ops.fused_moe import unified_fused_experts_eager
from vllm_ascend.ops.layers.experts_selector import select_experts
from vllm_ascend.ops.moe.experts_selector import select_experts
class AscendW4A8DynamicLinearMethod:

2
vllm_ascend/quantization/w8a8.py
View File

@@ -23,7 +23,7 @@ from vllm.attention.backends.abstract import AttentionType
from vllm.distributed.parallel_state import get_ep_group
from vllm_ascend.attention.attention_v1 import AscendAttentionState
from vllm_ascend.ops.layers.experts_selector import select_experts
from vllm_ascend.ops.moe.experts_selector import select_experts
from vllm_ascend.utils import ACL_FORMAT_FRACTAL_NZ, is_310p

8
vllm_ascend/quantization/w8a8_dynamic.py
View File

@@ -27,10 +27,8 @@ import vllm_ascend.envs as envs_ascend
from vllm_ascend.ascend_config import get_ascend_config
from vllm_ascend.ascend_forward_context import FusedMoEState
from vllm_ascend.distributed.parallel_state import get_mc2_group
from vllm_ascend.ops.common_fused_moe import \
fused_experts as unified_fused_experts
from vllm_ascend.ops.fused_moe import unified_fused_experts_eager
from vllm_ascend.ops.layers.experts_selector import select_experts
from vllm_ascend.ops.moe.experts_selector import select_experts
from vllm_ascend.utils import ACL_FORMAT_FRACTAL_NZ
@@ -221,12 +219,14 @@ class AscendW8A8DynamicFusedMoEMethod:
global_num_experts=global_num_experts)
if self.use_aclgraph:
return unified_fused_experts(
moe_comm_method = get_forward_context().moe_comm_method
return moe_comm_method.fused_experts(
hidden_states=x,
w1=layer.w13_weight,
w2=layer.w2_weight,
topk_weights=topk_weights,
topk_ids=topk_ids,
row_idx=row_idx,
use_int8_w8a8=True,
w1_scale=layer.w13_weight_scale,
w2_scale=layer.w2_weight_scale,