87ebaef4e4
### What this PR does / why we need it? Based on the design of dual-batch overlap proposed by Deepseek team and also the implementation of fused moe in VLLM project, we implement the multi-stream(also known as dual-batch) overlap for deepseek+mla on Ascend NPU. We split the input batch of model into two microbatches and then overlap the comp/comm ops in attention and moe layers using two streams to improve the performance. Our approach can be easily extended when adding dispatch/combine communications for moe layer. Compared with the previously proposed [draft](https://github.com/vllm-project/vllm-ascend/pull/842), we use one stream for computation ops and the other for communication ops, separately. In out opinions, it is beneficial for arranging the order of executing different ops and thus avoiding the contention of computation/communication resources. ref: [overlap for llama](https://github.com/vllm-project/vllm/pull/15787/files) ref: [dbo in sglang](https://github.com/sgl-project/sglang/pull/4068/files#diff-b4937569fc71f6ad215181b633b2f89c7183a2b4ac39e41fc22635599a9be7de) ### Does this PR introduce _any_ user-facing change? Adding an env variable "VLLM_ENABLE_DBO". Users can enable dbo by setting "VLLM_ASCEND_ENABLE_DBO=1" See /examples/offline_dualbatch_overlap_npu.py for more info. ### How was this patch tested? This patch can be tested with vllm-0.9.0 using its online service with benchmark tests. We have decoupled the func of dbo from vllm and it should be able to run without any modification to the code of vllm(some modifications is better to implement in vllm though). Any advice/discussion is welcome. ### Performance Benchmark We have ran the benchmark_serving script of vllm to test the performance after using dual-batch overlap. `python -m vllm.entrypoints.openai.api_server \ --model=DeepSeek-R1-W8A8 \ --trust-remote-code \ --distributed-executor-backend=mp \ -tp=16 \ --port 8006 \ --max-num-seqs 390 \ --max-model-len 32768 \ --max-num-batched-tokens 65536 \ --block-size 128 \ --compilation_config 0 \ --gpu-memory-utilization 0.90 \ --disable-log-requests \ --additional-config '{"expert_tensor_parallel_size":1,"enable_inter_dp_scheduling":true,"init_torchair_graph_batch_sizes":true,"trace_recompiles":true,"ascend_scheduler_config":{},"enable_graph_mode":false}'` and run benchmark with the parameters of : `--dataset-name random --random-input-len 4096 --random-output-len 1 --num-prompts 200 --max-concurrency 8 --request-rate 5 --metric-percentiles 90` 1. test with the version using allgather+allreduce in Ascend 910B (tp16 ep16 + deepseek r1 w8a8) 2. test with the version using alltoall: prefill qps: 0.90 -> 1.01 Mean TTFT：8226->7432ms The overlap approach when using alltoall communication can be further optimized by overlapping micro-batch1's moe comp with micro-batch2's dispatch a2a comm --------- Signed-off-by: zhuohuan <zxdu1997@gmail.com>
1183 lines
47 KiB
Python
1183 lines
47 KiB
Python
# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
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# Copyright 2023 The vLLM team.
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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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# This file is a part of the vllm-ascend project.
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# Adapted from vllm/tests/kernels/test_moe.py
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from typing import Callable, List, Optional
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import torch
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import torch.distributed as dist
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import torch_npu
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from vllm.config import get_current_vllm_config
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from vllm.distributed import (GroupCoordinator,
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get_tensor_model_parallel_world_size,
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tensor_model_parallel_all_reduce)
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from vllm.distributed.parallel_state import get_dp_group
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from vllm.model_executor.layers.fused_moe.layer import (
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FusedMoE, FusedMoEParallelConfig, MoEConfig, UnquantizedFusedMoEMethod,
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determine_expert_map)
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from vllm.model_executor.layers.quantization.base_config import \
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QuantizationConfig
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import vllm_ascend.envs as envs_ascend
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from vllm_ascend.ascend_config import get_ascend_config
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from vllm_ascend.distributed.parallel_state import get_ep_group, get_etp_group
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VLLM_ENABLE_MC2: bool = envs_ascend.VLLM_ENABLE_MC2
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USING_LCCL_COM: bool = envs_ascend.USING_LCCL_COM
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MOE_ALL2ALL_BUFFER: bool = envs_ascend.MOE_ALL2ALL_BUFFER
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def process_topk_ids(topk_ids: torch.Tensor, expert_num: int, ep_size: int,
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max_row_per_ep_rank: int, num_tokens: int,
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top_k: int) -> tuple[torch.Tensor, torch.Tensor]:
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original_total_elements = num_tokens * top_k
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device = topk_ids.device
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original_dtype = topk_ids.dtype
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if original_total_elements == 0:
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output_len = ep_size * max_row_per_ep_rank
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topk_ids_pad = torch.full((output_len, ),
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expert_num,
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dtype=original_dtype,
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device=device)
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unpad_indices = torch.full((original_total_elements, ),
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-1,
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dtype=torch.long,
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device=device)
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return topk_ids_pad, unpad_indices
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experts_per_ep_rank_val = expert_num // ep_size
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if experts_per_ep_rank_val == 0:
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raise ValueError(
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"expert_num // ep_size is 0, which leads to division by zero in ep_rank calculation. "
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"Ensure expert_num >= ep_size.")
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assigned_ep_rank = (topk_ids.float() /
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experts_per_ep_rank_val).to(original_dtype)
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indices_arange = torch.arange(topk_ids.shape[0], device=device)
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is_new_segment = torch.cat((torch.tensor([True], device=device),
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assigned_ep_rank[1:] != assigned_ep_rank[:-1]))
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temp_start_markers = torch.full_like(indices_arange,
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-1,
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dtype=indices_arange.dtype)
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temp_start_markers[is_new_segment] = indices_arange[is_new_segment]
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start_offset_for_each_token = torch.cummax(temp_start_markers, dim=0)[0]
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token_intra_ep_rank_idx = indices_arange - start_offset_for_each_token
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is_kept_mask = token_intra_ep_rank_idx < max_row_per_ep_rank
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cumsum_kept = torch.cumsum(is_kept_mask.float(), dim=0).to(torch.long)
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indices_in_rec_cond_list_for_all = cumsum_kept - 1
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unpad_indices = torch.where(
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is_kept_mask, indices_in_rec_cond_list_for_all,
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torch.tensor(-1, device=device, dtype=torch.long))
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output_len = ep_size * max_row_per_ep_rank
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topk_ids_pad = torch.full((output_len, ),
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expert_num,
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dtype=original_dtype,
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device=device)
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if topk_ids.shape[0] > 0:
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all_destination_indices = assigned_ep_rank * max_row_per_ep_rank + token_intra_ep_rank_idx
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temp_pad_buffer = torch.full((output_len + 1, ),
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expert_num,
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dtype=original_dtype,
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device=device)
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output_len_tensor = torch.tensor(output_len,
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dtype=torch.long,
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device=device)
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scatter_indices = torch.where(is_kept_mask, all_destination_indices,
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output_len_tensor)
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temp_pad_buffer.scatter_(0, scatter_indices, topk_ids)
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topk_ids_pad = temp_pad_buffer[:output_len]
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return topk_ids_pad, unpad_indices
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def fused_experts_with_mc2(hidden_states: torch.Tensor,
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w1: torch.Tensor,
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w2: torch.Tensor,
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topk_weights: torch.Tensor,
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topk_ids: torch.Tensor,
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top_k: int,
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expert_map: torch.Tensor = None,
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moe_all_to_all_group_name: Optional[str] = None,
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**kwargs) -> torch.Tensor:
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global_bs = 0
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moe_expert_num = len(expert_map)
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kwargs_mc2 = {
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"x": hidden_states,
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"expert_ids": topk_ids,
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"expert_shard_type": 0,
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"shared_expert_rank_num": 0,
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"moe_expert_num": moe_expert_num,
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"global_bs": global_bs,
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}
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rank = torch.distributed.get_rank()
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quant_mode = 0
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ep_group = get_ep_group().device_group
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local_rank = torch.distributed.get_rank(group=ep_group)
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all_to_all_group_size = torch.distributed.get_world_size(ep_group)
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tp_size = get_etp_group().world_size
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tp_rank = rank % tp_size
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stage1_kwargs = {
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"scales": None,
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"quant_mode": quant_mode,
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"group_ep": moe_all_to_all_group_name,
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"ep_world_size": all_to_all_group_size,
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"ep_rank_id": local_rank,
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# "group_tp": self.moe_rs_group_name,
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"group_tp": moe_all_to_all_group_name,
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"tp_world_size": tp_size,
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"tp_rank_id": tp_rank,
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}
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kwargs_mc2.update(stage1_kwargs)
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output = torch_npu.npu_moe_distribute_dispatch(**kwargs_mc2)
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# comm_stream.wait_stream(torch.npu.current_stream())
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expand_x, dynamic_scale, expand_idx, expert_token_nums, ep_recv_counts = output[
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0:5]
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w1 = w1.transpose(1, 2)
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group_list = expert_token_nums.to(torch.int64)
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gate_up_out_list = torch_npu.npu_grouped_matmul(
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x=[expand_x],
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weight=[w1],
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split_item=2,
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# 1 means count mode, to avoid cumulative operation of the group list
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group_list_type=1,
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group_type=0,
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group_list=group_list,
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)
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# TODO: Remove this in the future.
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gate_up_out = torch.cat(gate_up_out_list, dim=0)
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gate_up_out = torch_npu.npu_swiglu(gate_up_out)
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w2 = w2.transpose(1, 2)
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down_out_list = torch_npu.npu_grouped_matmul(
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x=[gate_up_out],
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weight=[w2],
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split_item=2,
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group_list_type=1,
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group_type=0,
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group_list=group_list,
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)
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down_out_list = torch.cat(down_out_list, dim=0)
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# moeCombine
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kwargs_mc2 = {
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"expand_x": down_out_list,
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"expert_ids": topk_ids,
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"expand_idx": expand_idx,
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"expert_scales": topk_weights.to(torch.float32),
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"expert_shard_type": 0,
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"shared_expert_rank_num": 0,
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"moe_expert_num": moe_expert_num,
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"global_bs": 0,
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}
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tp_recv_counts = output[5]
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stage3_kwargs = {
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"ep_send_counts": ep_recv_counts,
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"group_ep": moe_all_to_all_group_name,
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"ep_world_size": all_to_all_group_size,
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"ep_rank_id": local_rank,
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"tp_send_counts": tp_recv_counts,
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# "group_tp": self.moe_rs_group_name,
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"group_tp": moe_all_to_all_group_name,
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"tp_world_size": tp_size,
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"tp_rank_id": tp_rank,
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}
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kwargs_mc2.update(stage3_kwargs)
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hidden_states = torch_npu.npu_moe_distribute_combine(**kwargs_mc2)
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return hidden_states
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def apply_mlp(hidden_states_wrapper: List[torch.Tensor],
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w1: torch.Tensor,
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w2: torch.Tensor,
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group_list: torch.Tensor,
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group_list_type: int = 1) -> torch.Tensor:
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"""
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apply MLP: gate_up_proj -> swiglu -> down_proj
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Args:
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hidden_states_wrapper: wrapper of input hidden states with shape (num_tokens, hidden_size).
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w1: expert weights1 with shape
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(num_experts, hidden_size, intermediate_size * 2)
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w2: expert weights2 with shape
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(num_experts, intermediate_size, hidden_size)
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group_list: number of tokens for each expert, follow cumsum mode, and
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with shape (num_experts).
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transpose_weight:
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w1: (num_experts, intermediate_size * 2, hidden_size) ->
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(num_experts, hidden_size, intermediate_size * 2)
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w2: (num_experts, hidden_size, intermediate_size) ->
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(num_experts, intermediate_size, hidden_size)
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Returns:
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hidden_states: output hidden states after MLP.
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"""
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assert len(hidden_states_wrapper) == 1
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hidden_states = hidden_states_wrapper.pop()
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w1 = w1.transpose(1, 2)
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hidden_states = torch_npu.npu_grouped_matmul(
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x=[hidden_states],
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weight=[w1],
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split_item=2,
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group_list_type=group_list_type,
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group_type=0,
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group_list=group_list,
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)
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hidden_states = torch.cat(hidden_states, dim=0)
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hidden_states = torch_npu.npu_swiglu(hidden_states)
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w2 = w2.transpose(1, 2)
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hidden_states = torch_npu.npu_grouped_matmul(
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x=[hidden_states],
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weight=[w2],
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split_item=2,
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group_list_type=group_list_type,
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group_type=0,
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group_list=group_list,
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)
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hidden_states = torch.cat(hidden_states, dim=0)
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return hidden_states
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def fused_experts_with_all2all(
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hidden_states: torch.Tensor,
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w1: torch.Tensor,
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w2: torch.Tensor,
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topk_weights: torch.Tensor,
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topk_ids: torch.Tensor,
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top_k: int,
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expert_map: torch.Tensor = None,
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ep_group: GroupCoordinator = None,
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):
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original_shape = hidden_states.shape
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if len(original_shape) == 3:
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hidden_states = hidden_states.view(-1, hidden_states.shape[-1])
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num_tokens, _ = hidden_states.shape
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num_experts = w1.shape[0]
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device = hidden_states.device
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if expert_map is not None:
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global_num_experts = len(expert_map)
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local_num_experts = global_num_experts // ep_group.world_size
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row_idx_len = num_tokens * top_k
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row_idx = (torch.arange(0,
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row_idx_len,
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dtype=torch.int32,
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device=device).view(top_k, -1).permute(
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1, 0).contiguous())
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hidden_states, expanded_row_idx, expanded_expert_idx = torch_npu.npu_moe_init_routing(
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hidden_states,
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row_idx=row_idx,
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expert_idx=topk_ids,
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active_num=num_tokens)
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global_expert_tokens = torch.bincount(expanded_expert_idx,
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minlength=global_num_experts)
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scatter_sizes = global_expert_tokens.view(ep_group.world_size,
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-1).sum(-1)
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gather_sizes = torch.empty_like(scatter_sizes)
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dist.all_to_all_single(gather_sizes,
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scatter_sizes,
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group=ep_group.device_group)
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scatter_size_list = scatter_sizes.cpu().tolist()
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gather_size_list = gather_sizes.cpu().tolist()
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expanded_expert_idx = expanded_expert_idx % local_num_experts
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hidden_states = ep_group.all_to_all(hidden_states, 0, 0,
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scatter_size_list,
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gather_size_list)
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local_expert_idx = ep_group.all_to_all(expanded_expert_idx, 0, 0,
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scatter_size_list,
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gather_size_list)
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sorted_local_expert_idx, sorted_idx = torch.sort(local_expert_idx)
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expert_tokens = torch_npu.npu_moe_compute_expert_tokens(
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sorted_local_expert_idx, local_num_experts).to(torch.int64)
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hidden_states = hidden_states[sorted_idx]
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else:
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row_idx_len = num_tokens * top_k
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row_idx = torch.arange(0,
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row_idx_len,
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dtype=torch.int32,
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device=topk_weights.device).view(
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top_k, -1).permute(1, 0).contiguous()
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hidden_states, expanded_row_idx, expanded_expert_idx = torch_npu.npu_moe_init_routing(
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hidden_states,
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row_idx=row_idx,
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expert_idx=topk_ids,
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active_num=num_tokens)
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expert_tokens = torch_npu.npu_moe_compute_expert_tokens(
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expanded_expert_idx, num_experts)
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expert_tokens = expert_tokens.to(torch.int64)
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w1 = w1.transpose(1, 2)
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gate_up_out_list = torch_npu.npu_grouped_matmul(
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x=[hidden_states],
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weight=[w1],
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split_item=2,
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group_list_type=0,
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group_type=0,
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group_list=expert_tokens,
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)
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# TODO: Remove this in the future.
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hidden_states = torch.cat(gate_up_out_list, dim=0)
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hidden_states = torch_npu.npu_swiglu(hidden_states)
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w2 = w2.transpose(1, 2)
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down_out_list = torch_npu.npu_grouped_matmul(
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x=[hidden_states],
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weight=[w2],
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split_item=2,
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group_list_type=0,
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group_type=0,
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group_list=expert_tokens,
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)
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hidden_states = torch.cat(down_out_list, dim=0)
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if expert_map is not None:
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resorted_idx = torch.argsort(sorted_idx)
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hidden_states = hidden_states[resorted_idx]
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hidden_states = ep_group.all_to_all(hidden_states, 0, 0,
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gather_size_list,
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scatter_size_list)
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final_hidden_states = torch_npu.npu_moe_finalize_routing(
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hidden_states,
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skip1=None,
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skip2=None,
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bias=None,
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scales=topk_weights,
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expanded_src_to_dst_row=expanded_row_idx,
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export_for_source_row=topk_ids,
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)
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else:
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# TODO: Reorder device memory 2 times here, replace the current
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# implementation here when suitable operators become available.
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final_hidden_states = torch_npu.npu_moe_finalize_routing(
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hidden_states,
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skip1=None,
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skip2=None,
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bias=None,
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scales=topk_weights,
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expanded_src_to_dst_row=expanded_row_idx,
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export_for_source_row=topk_ids,
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)
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if len(original_shape) == 3:
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final_hidden_states = final_hidden_states.view(original_shape)
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return final_hidden_states
|
|
|
|
|
|
# currently expert parallelism implemented with all2all
|
|
# is under-optimized.
|
|
def fused_experts_with_all2all_buffer(
|
|
hidden_states: torch.Tensor,
|
|
w1: torch.Tensor,
|
|
w2: torch.Tensor,
|
|
topk_weights: torch.Tensor,
|
|
topk_ids: torch.Tensor,
|
|
top_k: int,
|
|
max_model_len: int,
|
|
global_batch_size: int,
|
|
expert_map: torch.Tensor = None,
|
|
ep_group: GroupCoordinator = None,
|
|
):
|
|
original_shape = hidden_states.shape
|
|
if len(original_shape) == 3:
|
|
hidden_states = hidden_states.view(-1, hidden_states.shape[-1])
|
|
|
|
num_tokens, _ = hidden_states.shape
|
|
device = hidden_states.device
|
|
|
|
global_num_experts = len(expert_map)
|
|
local_num_experts = global_num_experts // ep_group.world_size
|
|
row_idx_len = num_tokens * top_k
|
|
row_idx = (torch.arange(0, row_idx_len, dtype=torch.int32,
|
|
device=device).view(top_k,
|
|
-1).permute(1, 0).contiguous())
|
|
hidden_states, expanded_row_idx, expanded_expert_idx = torch_npu.npu_moe_init_routing(
|
|
hidden_states,
|
|
row_idx=row_idx,
|
|
expert_idx=topk_ids,
|
|
active_num=num_tokens)
|
|
|
|
max_row_per_ep_rank = (-(-global_batch_size // ep_group.world_size) *
|
|
max_model_len // ep_group.world_size +
|
|
1) * top_k * 2
|
|
expert_idx_buffer_scatter, unpad_indices = process_topk_ids(
|
|
expanded_expert_idx, global_num_experts, ep_group.world_size,
|
|
max_row_per_ep_rank, num_tokens, top_k)
|
|
hidden_states_pad_idx = torch.zeros(
|
|
expert_idx_buffer_scatter.shape,
|
|
dtype=expert_idx_buffer_scatter.dtype,
|
|
device=expert_idx_buffer_scatter.device)
|
|
non_pad_len = torch.sum(
|
|
(expert_idx_buffer_scatter != global_num_experts).to(torch.int32))
|
|
hidden_states_pad_idx[
|
|
expert_idx_buffer_scatter != global_num_experts] = torch.arange(
|
|
non_pad_len,
|
|
dtype=expert_idx_buffer_scatter.dtype,
|
|
device=hidden_states.device)
|
|
|
|
hidden_states_buffer_scatter = hidden_states[hidden_states_pad_idx]
|
|
expert_idx_buffer_gather = torch.empty_like(
|
|
expert_idx_buffer_scatter,
|
|
dtype=expert_idx_buffer_scatter.dtype,
|
|
device=expert_idx_buffer_scatter.device)
|
|
hidden_states_buffer_gather = torch.empty_like(
|
|
hidden_states_buffer_scatter,
|
|
dtype=hidden_states_buffer_scatter.dtype,
|
|
device=hidden_states_buffer_scatter.device)
|
|
dist.all_to_all_single(expert_idx_buffer_gather,
|
|
expert_idx_buffer_scatter,
|
|
group=ep_group.device_group)
|
|
dist.all_to_all_single(hidden_states_buffer_gather,
|
|
hidden_states_buffer_scatter,
|
|
group=ep_group.device_group)
|
|
mask = expert_idx_buffer_gather != global_num_experts
|
|
local_expert_idx = expert_idx_buffer_gather[mask] - ep_group.rank * (
|
|
global_num_experts // ep_group.world_size)
|
|
hidden_states = hidden_states_buffer_gather[mask]
|
|
idx_type = local_expert_idx.dtype
|
|
sorted_local_expert_idx, sorted_idx = torch.sort(local_expert_idx.float())
|
|
sorted_local_expert_idx = sorted_local_expert_idx.to(idx_type)
|
|
|
|
expert_tokens = torch_npu.npu_moe_compute_expert_tokens(
|
|
sorted_local_expert_idx, local_num_experts).to(torch.int64)
|
|
hidden_states = hidden_states[sorted_idx]
|
|
group_list_type = 0
|
|
|
|
hidden_states_wrapper = [hidden_states]
|
|
del hidden_states
|
|
|
|
hidden_states = apply_mlp(hidden_states_wrapper,
|
|
w1,
|
|
w2,
|
|
expert_tokens,
|
|
group_list_type=group_list_type)
|
|
|
|
resorted_idx = torch.argsort(sorted_idx.float()).to(sorted_idx.dtype)
|
|
hidden_states = hidden_states[resorted_idx]
|
|
hidden_states_scatter = torch.zeros(
|
|
(mask.shape[0], hidden_states.shape[1]),
|
|
dtype=hidden_states.dtype,
|
|
device=hidden_states.device)
|
|
hidden_states_scatter[mask] = hidden_states
|
|
hidden_states_gatter = torch.empty_like(
|
|
hidden_states_scatter,
|
|
dtype=hidden_states_scatter.dtype,
|
|
device=hidden_states_scatter.device)
|
|
dist.all_to_all_single(hidden_states_gatter,
|
|
hidden_states_scatter,
|
|
group=ep_group.device_group)
|
|
hidden_states_gatter = hidden_states_gatter[
|
|
expert_idx_buffer_scatter != global_num_experts]
|
|
if hidden_states_gatter.shape[0] != row_idx_len:
|
|
hidden_states = torch.zeros((row_idx_len, hidden_states.shape[1]),
|
|
dtype=hidden_states.dtype,
|
|
device=hidden_states.device)
|
|
hidden_states[unpad_indices != -1] = hidden_states_gatter
|
|
else:
|
|
# TODO: Reorder device memory 2 times here, replace the current
|
|
hidden_states = hidden_states_gatter
|
|
final_hidden_states = torch_npu.npu_moe_finalize_routing(
|
|
hidden_states,
|
|
skip1=None,
|
|
skip2=None,
|
|
bias=None,
|
|
scales=topk_weights,
|
|
expanded_src_to_dst_row=expanded_row_idx,
|
|
export_for_source_row=topk_ids,
|
|
)
|
|
|
|
if len(original_shape) == 3:
|
|
final_hidden_states = final_hidden_states.view(original_shape)
|
|
return final_hidden_states
|
|
|
|
|
|
def fused_experts(
|
|
hidden_states: torch.Tensor,
|
|
w1: torch.Tensor,
|
|
w2: torch.Tensor,
|
|
topk_weights: torch.Tensor,
|
|
topk_ids: torch.Tensor,
|
|
top_k: int,
|
|
expert_map: torch.Tensor = None,
|
|
apply_router_weight_on_input: bool = False,
|
|
) -> torch.Tensor:
|
|
"""
|
|
Fused experts with top-k routing.
|
|
|
|
Args:
|
|
hidden_states: Hidden states of shape (num_tokens, hidden_size).
|
|
w1: Expert weights1 of shape (num_experts, intermediate_size * 2, hidden_size).
|
|
w2: Expert weights2 of shape (num_experts, hidden_size, intermediate_size).
|
|
topk_weights: Routing weights of shape (num_tokens, top_k).
|
|
topk_ids: Selected expert IDs of shape (num_tokens, top_k).
|
|
top_k: Number of experts to select.
|
|
expert_map: Expert mapping of shape (num_experts,).
|
|
|
|
Returns:
|
|
hidden_states: Hidden states after routing.
|
|
"""
|
|
"""
|
|
# Check constraints.
|
|
assert hidden_states.shape[1] == w1.shape[2], "Hidden size mismatch"
|
|
assert topk_weights.shape == topk_ids.shape, "topk shape mismatch"
|
|
assert hidden_states.is_contiguous(), "Hidden_states must be contiguous"
|
|
assert w1.is_contiguous(), "Expert weights1 must be contiguous"
|
|
assert w2.is_contiguous(), "Expert weights2 must be contiguous"
|
|
"""
|
|
# if torch.distributed.get_rank() == 0:
|
|
# print(w1.shape)
|
|
# print(hidden_states.shape)
|
|
|
|
original_shape = hidden_states.shape
|
|
# assert len(original_shape) == 2
|
|
|
|
num_tokens = hidden_states.shape[:-1].numel()
|
|
num_experts = w1.shape[0]
|
|
dtype = hidden_states.dtype
|
|
device = hidden_states.device
|
|
# assert dtype in [torch.float32, torch.float16, torch.bfloat16
|
|
# ], "Only float32, float16, and bfloat16 are supported"
|
|
|
|
if 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:
|
|
# Generate token indices and flatten
|
|
token_indices = (torch.arange(num_tokens,
|
|
device=device,
|
|
dtype=torch.int64).unsqueeze(1).expand(
|
|
-1, top_k).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]
|
|
|
|
# Filter valid token-expert pairs
|
|
mask = local_experts_flat != -1
|
|
filtered_weights = torch.where(
|
|
mask, weights_flat, torch.zeros_like(weights_flat)).to(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))
|
|
sorted_token_indices = token_indices[sort_indices]
|
|
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=device,
|
|
dtype=torch.int64)
|
|
ones = torch.ones_like(filtered_experts, dtype=torch.int64)
|
|
token_counts.scatter_add_(0, filtered_experts.to(torch.int64), ones)
|
|
token_counts = token_counts[:num_experts]
|
|
expert_tokens = torch.cumsum(token_counts, dim=0, dtype=torch.int64)
|
|
|
|
# Rearrange hidden_states
|
|
sorted_hidden_states = hidden_states[sorted_token_indices]
|
|
else:
|
|
row_idx_len = num_tokens * top_k
|
|
row_idx = (torch.arange(0,
|
|
row_idx_len,
|
|
dtype=torch.int32,
|
|
device=device).view(top_k, -1).permute(
|
|
1, 0).contiguous())
|
|
sorted_hidden_states, expanded_row_idx, expanded_expert_idx = torch_npu.npu_moe_init_routing(
|
|
hidden_states,
|
|
row_idx=row_idx,
|
|
expert_idx=topk_ids,
|
|
active_num=num_tokens)
|
|
|
|
expert_tokens = torch_npu.npu_moe_compute_expert_tokens(
|
|
expanded_expert_idx, num_experts)
|
|
expert_tokens = expert_tokens.to(torch.int64)
|
|
|
|
w1 = w1.transpose(1, 2)
|
|
gate_up_out_list = torch_npu.npu_grouped_matmul(
|
|
x=[sorted_hidden_states],
|
|
weight=[w1],
|
|
split_item=2,
|
|
group_list_type=0,
|
|
group_type=0,
|
|
group_list=expert_tokens,
|
|
)
|
|
|
|
# TODO: Remove this in the future.
|
|
gate_up_out = torch.cat(gate_up_out_list, dim=0)
|
|
gate_up_out = torch_npu.npu_swiglu(gate_up_out)
|
|
|
|
w2 = w2.transpose(1, 2)
|
|
down_out_list = torch_npu.npu_grouped_matmul(
|
|
x=[gate_up_out],
|
|
weight=[w2],
|
|
split_item=2,
|
|
group_list_type=0,
|
|
group_type=0,
|
|
group_list=expert_tokens,
|
|
)
|
|
|
|
down_out_list = torch.cat(down_out_list, dim=0)
|
|
|
|
if expert_map is not None:
|
|
weighted_down_out = down_out_list * sorted_weights.unsqueeze(1)
|
|
|
|
final_hidden_states = torch.zeros(*original_shape,
|
|
device=hidden_states.device,
|
|
dtype=dtype)
|
|
|
|
# TODO: npu_grouped_matmul output random values at [num_valid_tokens:, ...]
|
|
# This created multiple NaN and index_add_ will mix them up which harms accuracy
|
|
# remove this mask and filter after it being fixed
|
|
num_valid_tokens = mask.sum()
|
|
valid_token_mask = torch.arange(
|
|
0, sorted_token_indices.shape[0],
|
|
device=device).unsqueeze(1) < num_valid_tokens
|
|
valid_output = torch.where(
|
|
valid_token_mask, weighted_down_out,
|
|
torch.zeros_like(weighted_down_out)).to(dtype)
|
|
final_hidden_states.index_add_(0, sorted_token_indices, valid_output)
|
|
else:
|
|
scales = torch.ones_like(
|
|
topk_weights) if apply_router_weight_on_input else topk_weights
|
|
# TODO: Reorder device memory 2 times here, replace the current
|
|
# implementation here when suitable operators become available.
|
|
final_hidden_states = torch_npu.npu_moe_finalize_routing(
|
|
down_out_list,
|
|
skip1=None,
|
|
skip2=None,
|
|
bias=None,
|
|
scales=scales,
|
|
expanded_src_to_dst_row=expanded_row_idx,
|
|
export_for_source_row=topk_ids,
|
|
)
|
|
|
|
return final_hidden_states
|
|
|
|
|
|
def native_grouped_topk(
|
|
topk_weights: torch.Tensor,
|
|
num_expert_group: Optional[int],
|
|
topk_group: Optional[int],
|
|
):
|
|
topk_group = 0 if topk_group is None else topk_group
|
|
num_expert_group = 0 if num_expert_group is None else num_expert_group
|
|
|
|
num_token = topk_weights.shape[0]
|
|
grouped_weights = topk_weights.view(num_token, num_expert_group,
|
|
-1).max(dim=-1).values
|
|
topk_group_indices = torch.topk(grouped_weights.to(torch.float32),
|
|
k=topk_group,
|
|
dim=-1,
|
|
sorted=False)[1]
|
|
topk_group_mask = torch.zeros_like(grouped_weights)
|
|
topk_group_mask.scatter_(1, topk_group_indices, 1)
|
|
topk_weight_mask = (topk_group_mask.unsqueeze(-1).expand(
|
|
num_token, num_expert_group,
|
|
topk_weights.shape[-1] // num_expert_group).reshape(num_token, -1))
|
|
topk_weights = topk_weights.masked_fill(~topk_weight_mask.bool(), 0.0)
|
|
|
|
return topk_weights
|
|
|
|
|
|
def select_experts(
|
|
hidden_states: torch.Tensor,
|
|
router_logits: torch.Tensor,
|
|
top_k: int,
|
|
use_grouped_topk: bool,
|
|
renormalize: bool,
|
|
topk_group: Optional[int] = None,
|
|
num_expert_group: Optional[int] = None,
|
|
custom_routing_function: Optional[Callable] = None,
|
|
scoring_func: str = "softmax",
|
|
e_score_correction_bias: Optional[torch.Tensor] = None,
|
|
) -> tuple[torch.Tensor, torch.Tensor]:
|
|
"""
|
|
Select top-k experts based on router logits.
|
|
|
|
Args:
|
|
hidden_states: Hidden states of shape (num_tokens, hidden_size).
|
|
router_logits: Router logits of shape (num_tokens, num_experts).
|
|
top_k: Number of experts to select.
|
|
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.
|
|
|
|
Returns:
|
|
topk_weights: Routing weights of shape (num_tokens, top_k).
|
|
topk_ids: Selected expert IDs of shape (num_tokens, top_k).
|
|
|
|
Raises:
|
|
ValueError: If an unsupported scoring function is provided.
|
|
"""
|
|
|
|
if scoring_func == "softmax":
|
|
# NOTE: vLLM use dtype=torch.float here
|
|
topk_weights = router_logits.softmax(dim=-1)
|
|
elif scoring_func == "sigmoid":
|
|
topk_weights = router_logits.sigmoid()
|
|
else:
|
|
raise ValueError(f"Unsupported scoring function: {scoring_func}")
|
|
|
|
if use_grouped_topk:
|
|
assert topk_group is not None
|
|
assert num_expert_group is not None
|
|
|
|
if e_score_correction_bias is not None:
|
|
# Store original scores before applying correction bias. We use biased
|
|
# scores for expert selection but original scores for routing weights
|
|
original_weights = topk_weights
|
|
topk_weights = topk_weights + e_score_correction_bias.unsqueeze(0)
|
|
|
|
# TODO: Change to npu_group_topk when the latest CANN and NNAL is available
|
|
# >>> torch_npu._npu_group_topk(topk_weights, group_num=num_expert_group, k=topk_group)
|
|
topk_weights = native_grouped_topk(topk_weights, num_expert_group,
|
|
topk_group)
|
|
# TODO bfloat16 is not supported in torch.topk with ge graph.
|
|
if e_score_correction_bias is not None:
|
|
topk_ids = torch.topk(topk_weights.to(torch.float32),
|
|
k=top_k,
|
|
dim=-1,
|
|
sorted=False)[1]
|
|
# Use original unbiased scores for the routing weights
|
|
topk_weights = original_weights.gather(1, topk_ids)
|
|
else:
|
|
topk_weights, topk_ids = torch.topk(topk_weights.to(torch.float32),
|
|
k=top_k,
|
|
dim=-1,
|
|
sorted=False)
|
|
elif custom_routing_function is None:
|
|
topk_weights, topk_ids = topk_weights.topk(top_k, dim=-1)
|
|
topk_weights = topk_weights.to(hidden_states.dtype)
|
|
else:
|
|
topk_weights, topk_ids = custom_routing_function(
|
|
hidden_states=hidden_states,
|
|
gating_output=router_logits,
|
|
topk=top_k,
|
|
renormalize=renormalize)
|
|
# Required by npu_moe_init_routing
|
|
topk_ids = topk_ids.to(torch.int32)
|
|
return topk_weights, topk_ids
|
|
|
|
# Required by npu_moe_init_routing
|
|
topk_ids = topk_ids.to(torch.int32)
|
|
|
|
if renormalize:
|
|
topk_weights = topk_weights / topk_weights.sum(dim=-1, keepdim=True)
|
|
|
|
return topk_weights, topk_ids
|
|
|
|
|
|
class AscendUnquantizedFusedMoEMethod(UnquantizedFusedMoEMethod):
|
|
|
|
def __init__(self, moe: MoEConfig = None):
|
|
|
|
super().__init__(moe=moe)
|
|
vllm_config = get_current_vllm_config()
|
|
|
|
ep_group = get_ep_group()
|
|
self.ep_size = ep_group.world_size
|
|
self.global_batch_size = vllm_config.scheduler_config.max_num_seqs
|
|
self.local_batch_size = self.global_batch_size // self.ep_size
|
|
self.max_model_len = vllm_config.model_config.max_model_len
|
|
|
|
ascend_config = get_ascend_config()
|
|
self.torchair_graph_enabled = ascend_config.torchair_graph_config.enabled
|
|
|
|
try:
|
|
device_group = ep_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)
|
|
except AttributeError:
|
|
self.moe_all_to_all_group_name = None
|
|
|
|
def process_weights_after_loading(self, layer):
|
|
super(UnquantizedFusedMoEMethod,
|
|
self).process_weights_after_loading(layer)
|
|
layer.w13_weight = torch.nn.Parameter(self._maybe_pad_weight(
|
|
layer.w13_weight.data),
|
|
requires_grad=False)
|
|
layer.w2_weight = torch.nn.Parameter(self._maybe_pad_weight(
|
|
layer.w2_weight.data),
|
|
requires_grad=False)
|
|
|
|
def apply(
|
|
self,
|
|
layer: torch.nn.Module,
|
|
x: torch.Tensor,
|
|
router_logits: torch.Tensor,
|
|
top_k: int,
|
|
renormalize: bool,
|
|
use_grouped_topk: bool = False,
|
|
global_num_experts: int = -1,
|
|
expert_map: Optional[torch.Tensor] = None,
|
|
topk_group: Optional[int] = None,
|
|
num_expert_group: Optional[int] = None,
|
|
custom_routing_function: Optional[Callable] = None,
|
|
scoring_func: str = "softmax",
|
|
e_score_correction_bias: Optional[torch.Tensor] = None,
|
|
is_prefill: bool = False,
|
|
enable_force_load_balance: bool = False,
|
|
**kwargs,
|
|
) -> torch.Tensor:
|
|
|
|
# NOTE: now npu_moe_gating_top_k can only support `group_count=256` pattern
|
|
if global_num_experts == 256:
|
|
topk_weights, topk_ids, _ = torch_npu.npu_moe_gating_top_k(
|
|
router_logits,
|
|
k=top_k, # topk当前写8
|
|
bias=e_score_correction_bias,
|
|
k_group=topk_group, # fix: 4
|
|
group_count=num_expert_group, # fix 8
|
|
group_select_mode=1, # 0: group中的最大; 1: topk2.sum(fix)
|
|
renorm=0, # 0: softmax->topk(fix); 1: topk->softmax
|
|
norm_type=1, # 0: softmax; 1: sigmoid(fix)
|
|
# out_flag=False, # todo new api; 第三个输出是否输出
|
|
# y2_flag=False, # old api; 第三个输出是否输出
|
|
routed_scaling_factor=1,
|
|
eps=float(1e-20))
|
|
else:
|
|
topk_weights, topk_ids = select_experts(
|
|
hidden_states=x,
|
|
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,
|
|
)
|
|
|
|
topk_weights = topk_weights.to(x.dtype)
|
|
# this is a naive implementation for experts load balance so as
|
|
# to avoid accumulating too much tokens on a single rank.
|
|
# currently it is only activated when doing profile runs.
|
|
if enable_force_load_balance:
|
|
topk_ids = torch.randint_like(topk_ids, 0, global_num_experts)
|
|
|
|
if VLLM_ENABLE_MC2 and not is_prefill:
|
|
return fused_experts_with_mc2(
|
|
hidden_states=x,
|
|
w1=layer.w13_weight,
|
|
w2=layer.w2_weight,
|
|
topk_weights=topk_weights,
|
|
topk_ids=topk_ids,
|
|
top_k=top_k,
|
|
expert_map=expert_map,
|
|
moe_all_to_all_group_name=self.moe_all_to_all_group_name,
|
|
**kwargs)
|
|
elif self.torchair_graph_enabled or get_ep_group().world_size == 1:
|
|
return fused_experts(hidden_states=x,
|
|
w1=layer.w13_weight,
|
|
w2=layer.w2_weight,
|
|
topk_weights=topk_weights,
|
|
topk_ids=topk_ids,
|
|
top_k=top_k,
|
|
expert_map=expert_map)
|
|
elif MOE_ALL2ALL_BUFFER:
|
|
return fused_experts_with_all2all_buffer(
|
|
hidden_states=x,
|
|
w1=layer.w13_weight,
|
|
w2=layer.w2_weight,
|
|
topk_weights=topk_weights,
|
|
topk_ids=topk_ids,
|
|
top_k=top_k,
|
|
max_model_len=self.max_model_len,
|
|
global_batch_size=self.global_batch_size,
|
|
expert_map=expert_map,
|
|
ep_group=get_ep_group())
|
|
else:
|
|
return fused_experts_with_all2all(hidden_states=x,
|
|
w1=layer.w13_weight,
|
|
w2=layer.w2_weight,
|
|
topk_weights=topk_weights,
|
|
topk_ids=topk_ids,
|
|
top_k=top_k,
|
|
expert_map=expert_map,
|
|
ep_group=get_ep_group())
|
|
|
|
|
|
class AscendFusedMoE(FusedMoE):
|
|
|
|
def __init__(
|
|
self,
|
|
num_experts: int, # Global number of experts
|
|
top_k: int,
|
|
hidden_size: int,
|
|
intermediate_size: int,
|
|
params_dtype: Optional[torch.dtype] = None,
|
|
reduce_results: bool = False,
|
|
renormalize: bool = True,
|
|
use_grouped_topk: bool = False,
|
|
num_expert_group: Optional[int] = None,
|
|
topk_group: Optional[int] = None,
|
|
quant_config: Optional[QuantizationConfig] = None,
|
|
tp_size: Optional[int] = None,
|
|
ep_size: Optional[int] = None,
|
|
dp_size: Optional[int] = None,
|
|
prefix: str = "",
|
|
custom_routing_function: Optional[Callable] = None,
|
|
scoring_func: str = "softmax",
|
|
e_score_correction_bias: Optional[torch.Tensor] = None,
|
|
activation: str = "silu",
|
|
apply_router_weight_on_input: bool = False,
|
|
):
|
|
# TODO: This could not initialize FusedMoE baseclass,
|
|
# fixme and make __init__() of AscendFusedMoE more clear
|
|
super(FusedMoE, self).__init__()
|
|
|
|
if params_dtype is None:
|
|
params_dtype = torch.get_default_dtype()
|
|
|
|
vllm_config = get_current_vllm_config()
|
|
|
|
self.moe_parallel_config: FusedMoEParallelConfig = (
|
|
FusedMoEParallelConfig.make(
|
|
tp_size_=(tp_size if tp_size is not None else
|
|
get_tensor_model_parallel_world_size()),
|
|
dp_size_=(dp_size if dp_size is not None else
|
|
get_dp_group().world_size),
|
|
vllm_parallel_config=vllm_config.parallel_config))
|
|
|
|
self.moe_parallel_config.ep_size = get_ep_group().world_size
|
|
self.moe_parallel_config.tp_size = get_etp_group().world_size
|
|
|
|
self.top_k = top_k
|
|
self.num_experts = num_experts
|
|
self.global_num_experts = num_experts
|
|
assert intermediate_size % self.tp_size == 0
|
|
self.intermediate_size_per_partition = intermediate_size // self.tp_size
|
|
self.reduce_results = reduce_results
|
|
self.renormalize = renormalize
|
|
self.use_grouped_topk = use_grouped_topk
|
|
if self.use_grouped_topk:
|
|
assert num_expert_group is not None and topk_group is not None
|
|
self.num_expert_group = num_expert_group
|
|
self.topk_group = topk_group
|
|
self.custom_routing_function = custom_routing_function
|
|
self.scoring_func = scoring_func
|
|
self.e_score_correction_bias = e_score_correction_bias
|
|
self.expert_map = None
|
|
self.activation = activation
|
|
|
|
# Create a tensor of size num_experts filled with -1
|
|
self.local_num_experts, self.expert_map = determine_expert_map(
|
|
self.ep_size,
|
|
get_ep_group().rank_in_group, self.global_num_experts)
|
|
|
|
self.moe_parallel_config.tp_rank = get_etp_group().rank_in_group
|
|
self.moe_parallel_config.ep_rank = get_ep_group().rank_in_group
|
|
|
|
ascend_config = get_ascend_config()
|
|
self.torchair_graph_enabled = ascend_config.torchair_graph_config.enabled
|
|
self.enable_multistream_shared_expert = \
|
|
ascend_config.torchair_graph_config.enable_multistream_shared_expert
|
|
|
|
if self.scoring_func != "softmax" and not self.use_grouped_topk:
|
|
raise ValueError("Only softmax scoring function is supported for "
|
|
"non-grouped topk.")
|
|
|
|
moe = MoEConfig(
|
|
num_experts=self.global_num_experts,
|
|
experts_per_token=top_k,
|
|
hidden_dim=hidden_size,
|
|
num_local_experts=self.local_num_experts,
|
|
moe_parallel_config=self.moe_parallel_config,
|
|
# TODO (bnell): this needs to be fixed for quantized types.
|
|
in_dtype=params_dtype,
|
|
)
|
|
|
|
if quant_config is None:
|
|
self.quant_method = AscendUnquantizedFusedMoEMethod(moe)
|
|
else:
|
|
self.quant_method = quant_config.get_quant_method(self, prefix)
|
|
|
|
assert self.quant_method is not None
|
|
|
|
local_num_experts = torch.sum(self.expert_map != -1) \
|
|
if self.expert_map is not None else num_experts
|
|
|
|
moe_quant_params = {
|
|
"num_experts": local_num_experts,
|
|
"hidden_size": hidden_size,
|
|
"intermediate_size_per_partition":
|
|
self.intermediate_size_per_partition,
|
|
"params_dtype": params_dtype,
|
|
"weight_loader": self.weight_loader,
|
|
}
|
|
# need full intermediate size pre-sharding for WNA16 act order
|
|
if (self.quant_method.__class__.__name__
|
|
in ("GPTQMarlinMoEMethod", "CompressedTensorsWNA16MoEMethod")):
|
|
moe_quant_params["intermediate_size_full"] = intermediate_size
|
|
|
|
self.ep_group = get_ep_group()
|
|
self.quant_method.create_weights(layer=self, **moe_quant_params)
|
|
|
|
def forward(self,
|
|
hidden_states: torch.Tensor,
|
|
router_logits: torch.Tensor,
|
|
is_prefill: bool,
|
|
enable_force_load_balance: bool = False,
|
|
top_k=None,
|
|
**kwargs):
|
|
assert self.quant_method is not None
|
|
|
|
if top_k:
|
|
real_top_k = top_k
|
|
else:
|
|
real_top_k = self.top_k
|
|
|
|
# MC2 ag/rs broadcast/all_reduce
|
|
# prefill_req x x √
|
|
# decode_req √ x √
|
|
# graph_mode √ √ x
|
|
if self.dp_size > 1:
|
|
if VLLM_ENABLE_MC2 and not is_prefill:
|
|
...
|
|
elif self.torchair_graph_enabled:
|
|
if USING_LCCL_COM: # type: ignore
|
|
hidden_states = get_dp_group().all_gather(
|
|
hidden_states, 0, False)
|
|
router_logits = get_dp_group().all_gather(
|
|
router_logits, 0, False)
|
|
elif self.torchair_graph_enabled and not is_prefill:
|
|
hidden_states = get_dp_group().all_gather(hidden_states, 0)
|
|
router_logits = get_dp_group().all_gather(router_logits, 0)
|
|
else:
|
|
hidden_states, router_logits = get_ep_group().dispatch(
|
|
hidden_states, router_logits)
|
|
|
|
# Matrix multiply.
|
|
hidden_states = self.quant_method.apply(
|
|
layer=self,
|
|
x=hidden_states,
|
|
router_logits=router_logits,
|
|
top_k=real_top_k,
|
|
renormalize=self.renormalize,
|
|
use_grouped_topk=self.use_grouped_topk,
|
|
global_num_experts=self.global_num_experts,
|
|
expert_map=self.expert_map,
|
|
topk_group=self.topk_group,
|
|
num_expert_group=self.num_expert_group,
|
|
custom_routing_function=self.custom_routing_function,
|
|
scoring_func=self.scoring_func,
|
|
e_score_correction_bias=self.e_score_correction_bias,
|
|
is_prefill=is_prefill,
|
|
enable_force_load_balance=enable_force_load_balance,
|
|
**kwargs)
|
|
|
|
if self.enable_multistream_shared_expert and not is_prefill:
|
|
hidden_states, shared_output = hidden_states
|
|
|
|
if self.dp_size > 1:
|
|
if VLLM_ENABLE_MC2 and not is_prefill:
|
|
...
|
|
elif self.torchair_graph_enabled:
|
|
if USING_LCCL_COM: # type: ignore
|
|
hidden_states = dist._functional_collectives.reduce_scatter_tensor(
|
|
hidden_states,
|
|
"sum",
|
|
scatter_dim=0,
|
|
group=get_dp_group().device_group)
|
|
elif self.torchair_graph_enabled and not is_prefill:
|
|
hidden_states = dist._functional_collectives.reduce_scatter_tensor(
|
|
hidden_states,
|
|
"sum",
|
|
scatter_dim=0,
|
|
group=get_dp_group().device_group)
|
|
else:
|
|
hidden_states = get_ep_group().combine(hidden_states)
|
|
|
|
if self.reduce_results and (self.tp_size > 1 or self.ep_size > 1):
|
|
hidden_states = tensor_model_parallel_all_reduce(hidden_states)
|
|
|
|
if self.enable_multistream_shared_expert and not is_prefill:
|
|
return hidden_states, shared_output
|
|
return hidden_states
|
|
|
|
# ----------------------------------------- TBO-related --------------------------------------------
|
|
|
|
def _forward_ms_fused_moe_comp(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
router_logits: torch.Tensor,
|
|
is_prefill: bool,
|
|
real_top_k,
|
|
enable_force_load_balance: bool = False,
|
|
):
|
|
hidden_states = self.quant_method.apply(
|
|
layer=self,
|
|
x=hidden_states,
|
|
router_logits=router_logits,
|
|
top_k=real_top_k,
|
|
renormalize=self.renormalize,
|
|
use_grouped_topk=self.use_grouped_topk,
|
|
global_num_experts=self.global_num_experts,
|
|
expert_map=self.expert_map,
|
|
topk_group=self.topk_group,
|
|
num_expert_group=self.num_expert_group,
|
|
custom_routing_function=self.custom_routing_function,
|
|
scoring_func=self.scoring_func,
|
|
e_score_correction_bias=self.e_score_correction_bias,
|
|
is_prefill=is_prefill,
|
|
enable_force_load_balance=enable_force_load_balance)
|
|
|
|
return hidden_states
|