02886e2641
### What this PR does / why we need it?
This PR introduces support for W8A8 dynamic quantization for
Mixture-of-Experts (MoE) models on Ascend 310P devices. This is achieved
by:
- Implementing a new quantization scheme
`AscendW8A8DynamicFusedMoEMethod310`.
- Adding a unified MLP implementation (`unified_apply_mlp`) for 310P
that handles both quantized and unquantized paths.
- Refactoring the MoE and quantization configuration logic to correctly
route to the new 310P-specific implementations.
- Adding new e2e and unit tests to verify the functionality of MoE W8A8
quantization.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
- Added a new e2e test `test_qwen3_moe_tp2_w8a8` to test MoE W8A8
quantization in a multi-card setup.
- Added several new unit tests for the 310P-specific MoE components,
including `experts_selector`, `fused_moe`, `moe_comm_method`, `moe_mlp`,
and the new `w8a8_dynamic` quantization method.
- vLLM version: v0.15.0
- vLLM main:
d7e17aaacd
---------
Signed-off-by: pu-zhe <zpuaa@outlook.com>
71 lines
2.7 KiB
Python
71 lines
2.7 KiB
Python
#
|
|
# Copyright (c) 2026 Huawei Technologies Co., Ltd. All Rights Reserved.
|
|
# This file is a part of the vllm-ascend project.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
#
|
|
from collections.abc import Callable
|
|
|
|
import torch
|
|
|
|
from vllm_ascend.ops.fused_moe.experts_selector import _native_select_experts
|
|
|
|
|
|
def select_experts(
|
|
hidden_states: torch.Tensor,
|
|
router_logits: torch.Tensor,
|
|
top_k: int,
|
|
use_grouped_topk: bool,
|
|
renormalize: bool,
|
|
topk_group: int | None = None,
|
|
num_expert_group: int | None = None,
|
|
custom_routing_function: Callable | None = None,
|
|
scoring_func: str = "softmax",
|
|
e_score_correction_bias: torch.Tensor | None = None,
|
|
global_num_experts: int = -1,
|
|
):
|
|
"""
|
|
Fused experts with select experts.
|
|
|
|
Args:
|
|
router_logits: router logits of shape (num_tokens, hidden_size).
|
|
hidden_states: Hidden states of shape (num_tokens, hidden_size).
|
|
top_k: number of top k experts.
|
|
use_grouped_topk: Whether to group experts before selecting top-k.
|
|
renormalize: Whether to renormalize the routing weights.
|
|
topk_group: Number of expert groups to select from.
|
|
num_expert_group: Number of experts in each group.
|
|
custom_routing_function: Custom routing function.
|
|
scoring_func: Scoring function to use.
|
|
e_score_correction_bias: Correction bias to apply to expert scores.
|
|
global_num_experts: Global number of experts.
|
|
|
|
Returns:
|
|
topk_weights: router weights of shape (num_tokens, top_k).
|
|
topk_ids: selected expert IDs of shape (num_tokens, top_k).
|
|
"""
|
|
topk_weights, topk_ids = _native_select_experts(
|
|
hidden_states=hidden_states,
|
|
router_logits=router_logits,
|
|
top_k=top_k,
|
|
use_grouped_topk=use_grouped_topk,
|
|
renormalize=renormalize,
|
|
topk_group=topk_group,
|
|
num_expert_group=num_expert_group,
|
|
custom_routing_function=custom_routing_function,
|
|
scoring_func=scoring_func,
|
|
e_score_correction_bias=e_score_correction_bias,
|
|
global_num_experts=global_num_experts,
|
|
)
|
|
return topk_weights, topk_ids
|