66b60c9440
### What this PR does / why we need it?
1. [Refact] Refact MLA/SFA weight prefetch to consist with moe weight
prefetch
2. Remove duplicated o_proj weight prefetch in forward for MLA/SFA
### Does this PR introduce _any_ user-facing change?
NA
### How was this patch tested?
1) Performance result:
Perf test data:
*) MLA:
| | 1st test | 2nd test | Output Token Throughput(Avg) | Performance
improvement percentage |
| --- | --- | --- | --- | --- |
| o_proj duplicate prefetch | 11.9669 token/s | 12.0287 token/s |
11.9978 |
| o_proj no duplicate prefetch | 12.5594 token/s | 12.6216 token/s |
12.5905 | 4.94%| |
single layer performace improve: 5%~8%
*) SFA:
| | 1st test | 2nd test | Output Token Throughput(Avg) | Performance
improvement percentage |
| --- | --- | --- | --- | --- |
| o_proj duplicate prefetch | 13.0523 token/s | 13.1084 token/s |
13.08035 | |
| o_proj no duplicate prefetch | 13.9844 token/s | 14.1678 token/s |
14.0761 | 7.6% |
- vLLM version: v0.15.0
- vLLM main:
d7e17aaacd
---------
Signed-off-by: leo-pony <nengjunma@outlook.com>
333 lines
13 KiB
Python
333 lines
13 KiB
Python
#
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# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
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# This file is a part of the vllm-ascend project.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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from collections.abc import Callable
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import torch
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from vllm_ascend.utils import get_weight_prefetch_method
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def select_experts(
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hidden_states: torch.Tensor,
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router_logits: torch.Tensor,
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top_k: int,
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use_grouped_topk: bool,
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renormalize: bool,
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topk_group: int | None = None,
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num_expert_group: int | None = None,
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custom_routing_function: Callable | None = None,
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scoring_func: str = "softmax",
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routed_scaling_factor=1.0,
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e_score_correction_bias: torch.Tensor | None = None,
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indices_type: torch.dtype | None = None,
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global_num_experts: int = -1,
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):
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"""
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Fused experts with select experts.
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Args:
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router_logits: router logits of shape (num_tokens, hidden_size).
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hidden_states: Hidden states of shape (num_tokens, hidden_size).
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top_k: number of top k experts.
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use_grouped_topk: Whether to group experts before selecting top-k.
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renormalize: Whether to renormalize the routing weights.
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topk_group: Number of expert groups to select from.
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num_expert_group: Number of experts in each group.
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custom_routing_function: Custom routing function.
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scoring_func: Scoring function to use.
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e_score_correction_bias: Correction bias to apply to expert scores.
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indices_type: dtype of indices
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global_num_experts: Global number of experts.
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Returns:
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topk_weights: router weights of shape (num_tokens, top_k).
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topk_ids: selected expert IDs of shape (num_tokens, top_k).
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"""
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# prefetch w1_w3_proj.weight preprocess
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weight_prefetch_method = get_weight_prefetch_method()
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weight_prefetch_method.maybe_prefetch_moe_weight_preprocess(hidden_states, "gate_up")
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is_support_npu_moe_gating_top_k = check_npu_moe_gating_top_k(
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hidden_states=hidden_states,
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top_k=top_k,
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renormalize=renormalize,
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topk_group=topk_group,
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num_expert_group=num_expert_group,
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scoring_func=scoring_func,
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custom_routing_function=custom_routing_function,
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)
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if is_support_npu_moe_gating_top_k:
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topk_weights, topk_ids = _select_experts_with_fusion_ops(
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hidden_states=hidden_states,
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router_logits=router_logits,
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top_k=top_k,
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use_grouped_topk=use_grouped_topk,
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topk_group=topk_group,
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renormalize=renormalize,
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e_score_correction_bias=e_score_correction_bias,
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num_expert_group=num_expert_group,
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scoring_func=scoring_func,
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routed_scaling_factor=routed_scaling_factor,
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global_num_experts=global_num_experts,
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)
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else:
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topk_weights, topk_ids = _native_select_experts(
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hidden_states=hidden_states,
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router_logits=router_logits,
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top_k=top_k,
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use_grouped_topk=use_grouped_topk,
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renormalize=renormalize,
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topk_group=topk_group,
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num_expert_group=num_expert_group,
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custom_routing_function=custom_routing_function,
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scoring_func=scoring_func,
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e_score_correction_bias=e_score_correction_bias,
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global_num_experts=global_num_experts,
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)
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return topk_weights, topk_ids
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def check_npu_moe_gating_top_k(
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hidden_states: torch.Tensor,
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top_k: int,
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renormalize: bool,
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topk_group: int | None = None,
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num_expert_group: int | None = None,
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scoring_func: str = "softmax",
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custom_routing_function: Callable | None = None,
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):
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if scoring_func == "sigmoid" and not renormalize: # sigmoid + renorm=0 is not supported in current branch
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return False
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if custom_routing_function is not None:
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return False
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if scoring_func != "softmax" and scoring_func != "sigmoid":
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return False
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topk_group = topk_group if topk_group is not None else 1
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num_expert_group = num_expert_group if num_expert_group is not None else 1
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if not (
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num_expert_group > 0
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and hidden_states.shape[-1] % num_expert_group == 0
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and hidden_states.shape[-1] // num_expert_group > 2
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):
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return False
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if topk_group < 1 or topk_group > num_expert_group:
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return False
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if top_k < 1 or top_k > (hidden_states.shape[-1] / (num_expert_group * topk_group)):
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return False
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if topk_group * hidden_states.shape[-1] / num_expert_group < top_k: # noqa: SIM103
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return False
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return True
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def _native_grouped_topk(
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topk_weights: torch.Tensor,
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num_expert_group: int | None,
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topk_group: int | None,
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):
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topk_group = 0 if topk_group is None else topk_group
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num_expert_group = 0 if num_expert_group is None else num_expert_group
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num_token = topk_weights.shape[0]
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grouped_weights = topk_weights.view(num_token, num_expert_group, -1).max(dim=-1).values
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topk_group_indices = torch.topk(grouped_weights.to(torch.float32), k=topk_group, dim=-1, sorted=False)[1]
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topk_group_mask = torch.zeros_like(grouped_weights)
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topk_group_mask.scatter_(1, topk_group_indices, 1)
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topk_weight_mask = (
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topk_group_mask.unsqueeze(-1)
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.expand(num_token, num_expert_group, topk_weights.shape[-1] // num_expert_group)
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.reshape(num_token, -1)
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)
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topk_weights = topk_weights.masked_fill(~topk_weight_mask.bool(), 0.0)
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return topk_weights
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def _renormalize_topk_weights(
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topk_weights: torch.Tensor,
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renormalize: bool,
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):
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if renormalize:
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topk_weights = topk_weights / topk_weights.sum(dim=-1, keepdim=True)
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return topk_weights
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def _select_expert_use_group_topk(
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topk_weights: torch.Tensor,
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topk_group: int | None,
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renormalize: bool,
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top_k: int,
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num_expert_group: int | None,
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e_score_correction_bias: torch.Tensor | None,
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):
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assert topk_group is not None
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assert num_expert_group is not None
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if e_score_correction_bias is not None:
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# Store original scores before applying correction bias. We use biased
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# scores for expert selection but original scores for routing weights
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original_weights = topk_weights
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topk_weights = topk_weights + e_score_correction_bias.unsqueeze(0)
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# TODO: Change to npu_group_topk when the latest CANN and NNAL is available
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# >>> torch_npu._npu_group_topk(topk_weights, group_num=num_expert_group, k=topk_group)
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topk_weights = _native_grouped_topk(topk_weights, num_expert_group, topk_group)
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# TODO bfloat16 is not supported in torch.topk with ge graph.
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if e_score_correction_bias is not None:
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topk_ids = torch.topk(topk_weights.to(torch.float32), k=top_k, dim=-1, sorted=False)[1]
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# Use original unbiased scores for the routing weights
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topk_weights = original_weights.gather(1, topk_ids)
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else:
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topk_weights, topk_ids = torch.topk(topk_weights.to(torch.float32), k=top_k, dim=-1, sorted=False)
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topk_ids = topk_ids.to(torch.int32)
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topk_weights = _renormalize_topk_weights(topk_weights, renormalize)
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return topk_weights, topk_ids
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def _select_experts_with_fusion_ops(
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hidden_states: torch.Tensor,
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router_logits: torch.Tensor,
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top_k: int,
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use_grouped_topk: bool,
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renormalize: bool,
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e_score_correction_bias: torch.Tensor | None,
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topk_group: int | None,
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num_expert_group: int | None,
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scoring_func: str = "softmax",
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routed_scaling_factor=1.0,
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global_num_experts: int = -1,
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):
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topk_group = topk_group if topk_group is not None else 1
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num_expert_group = num_expert_group if num_expert_group is not None else 1
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renorm = int(renormalize)
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norm_type = 0 if scoring_func == "softmax" else 1
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if e_score_correction_bias is not None and e_score_correction_bias.dtype != router_logits.dtype:
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e_score_correction_bias = e_score_correction_bias.to(router_logits.dtype)
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topk_weights, topk_ids, _ = torch.ops._C_ascend.moe_gating_top_k(
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router_logits,
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k=top_k,
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k_group=topk_group,
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group_count=num_expert_group,
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group_select_mode=1,
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renorm=renorm,
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norm_type=norm_type, # 0: softmax; 1: sigmoid
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out_flag=False,
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routed_scaling_factor=routed_scaling_factor,
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eps=1e-20,
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bias_opt=e_score_correction_bias,
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)
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return topk_weights, topk_ids
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def _native_select_experts(
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hidden_states: torch.Tensor,
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router_logits: torch.Tensor,
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top_k: int,
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use_grouped_topk: bool,
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renormalize: bool,
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topk_group: int | None = None,
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num_expert_group: int | None = None,
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custom_routing_function: Callable | None = None,
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scoring_func: str = "softmax",
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e_score_correction_bias: torch.Tensor | None = None,
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global_num_experts: torch.Tensor | None = None,
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) -> tuple[torch.Tensor, torch.Tensor]:
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"""
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Select top-k experts based on router logits.
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Args:
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hidden_states: Hidden states of shape (num_tokens, hidden_size).
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router_logits: Router logits of shape (num_tokens, num_experts).
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top_k: Number of experts to select.
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use_grouped_topk: Whether to group experts before selecting top-k.
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renormalize: Whether to renormalize the routing weights.
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topk_group: Number of expert groups to select from.
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num_expert_group: Number of experts in each group.
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custom_routing_function: Custom routing function.
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scoring_func: Scoring function to use.
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e_score_correction_bias: Correction bias to apply to expert scores.
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Returns:
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topk_weights: Routing weights of shape (num_tokens, top_k).
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topk_ids: Selected expert IDs of shape (num_tokens, top_k).
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Raises:
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ValueError: If an unsupported scoring function is provided.
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"""
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if scoring_func == "softmax":
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topk_weights = router_logits.softmax(dim=-1)
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elif scoring_func == "sigmoid":
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topk_weights = router_logits.sigmoid()
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else:
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raise ValueError(f"Unsupported scoring function: {scoring_func}")
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if use_grouped_topk:
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return _select_expert_use_group_topk(
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topk_weights=topk_weights,
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top_k=top_k,
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renormalize=renormalize,
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topk_group=topk_group,
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num_expert_group=num_expert_group,
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e_score_correction_bias=e_score_correction_bias,
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)
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if custom_routing_function is not None:
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topk_weights, topk_ids = custom_routing_function(
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hidden_states=hidden_states,
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gating_output=router_logits,
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topk=top_k,
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renormalize=renormalize,
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global_num_experts=global_num_experts,
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)
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# Required by npu_moe_init_routing
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topk_ids = topk_ids.to(torch.int32)
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return topk_weights, topk_ids
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topk_weights, topk_ids = topk_weights.topk(top_k, dim=-1)
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topk_weights = topk_weights.to(hidden_states.dtype)
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# Required by npu_moe_init_routing
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topk_ids = topk_ids.to(torch.int32)
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topk_weights = _renormalize_topk_weights(topk_weights, renormalize)
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return topk_weights, topk_ids
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def zero_experts_compute(
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expert_indices: torch.Tensor,
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expert_scales: torch.Tensor,
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num_experts: int,
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zero_expert_type: str,
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hidden_states: torch.Tensor,
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) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
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if zero_expert_type == "identity":
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zero_expert_mask = expert_indices < num_experts
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zero_expert_scales = expert_scales.clone()
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zero_expert_scales = torch.where(zero_expert_mask, 0.0, zero_expert_scales)
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hidden_states = hidden_states.unsqueeze(1)
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zero_expert_scales = zero_expert_scales.unsqueeze(2)
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result = hidden_states * zero_expert_scales
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result = result.sum(dim=1)
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normal_expert_mask = expert_indices >= num_experts
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expert_indices = torch.where(normal_expert_mask, 0, expert_indices)
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expert_scales = torch.where(normal_expert_mask, 0.0, expert_scales)
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return expert_indices, expert_scales, result
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