8a59367d0c
### What this PR does / why we need it?
Supports Deepseek-R1 w4a8 quantization.
Since R1 w4a8 uses mixed quantization, only the MOE layer uses
w4a8_dynamic quantization, so we added the w4a8_dynamic.py file, which
includes the AscendW4A8DynamicFusedMoEMethod class.
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
Adding ut case in `tests/ut/quantization/test_w4a8_dynamic.py` and
`tests/ut/quantization/test_quantizer.py`
Adding e2e case in
`tests/e2e/multicard/test_offline_inference_distributed.py::test_models_distributed_DeepSeek_W4A8DYNAMIC`
to test deepseek w4a8_dynamic quantized model
#### 1.How to get weights using Modelslim
##### Installation steps
Use the branch master, the commit id is:
298e175d69b3b855111a1e09bbe2fcd12fdb4e24
git clone https://gitee.com/ascend/msit.git
cd msit/msmodelslim
bash install.sh
##### The required transformers environment
transformers>=4.48.2
##### Generate w4a8 weights
cd /example/DeepSeek
Command reference: msmodelslim/example/DeepSeek/README.md Execute the
[pre-check](https://gitee.com/ascend/msit/blob/master/msmodelslim/example/DeepSeek/README.md#%E8%BF%90%E8%A1%8C%E5%89%8D%E5%BF%85%E6%A3%80)
and [DeepSeek-R1 w4a8 mix
quantization](https://gitee.com/ascend/msit/blob/master/msmodelslim/example/DeepSeek/README.md#deepseek-r1-w4a8-%E6%B7%B7%E5%90%88%E9%87%8F%E5%8C%96%E5%89%8D%E4%B8%89%E5%B1%82-mlpw8a8-dynamic-%E9%87%8F%E5%8C%96mla%E5%85%B1%E4%BA%AB%E4%B8%93%E5%AE%B6w8a8%E9%87%8F%E5%8C%96%E8%B7%AF%E7%94%B1%E4%B8%93%E5%AE%B6w4a8-dynamic%E9%87%8F%E5%8C%96)
chapter
Reference command：python3 quant_deepseek_w4a8.py --model_path {Original
weight path} --save_path {Generate weight path} --mindie_format
##### Adapt to vllm-ascend
Since mindie_format generates mindie format, some adaptation
modifications are needed for vllm-ascend to use it:
`quant_model_description_w8a8_dynamic.json` rename to
`quant_model_description.json`, and add `"group_size": 256`
Modification in `config.json`：`"model_type":deepseekv2` is changed to
`"model_type":deepseek_v3`; `quantization_config` is removed;
tips:The group_size and weights match. If the w4a8 weights are not
generated using msmodelslim, you can check the group_size in
quantization_config in config.json.
#### 2.How to run w4a8
##### a.How to run eager mode
export VLLM_USE_V1=1 # v1
python -m vllm.entrypoints.openai.api_server --model=$1
--trust-remote-code -tp $2 -dp $3 --enable_expert_parallel
--quantization ascend --port $4 --max-model-len $5 --max-num-seqs $6
--enforce-eager
eg: python -m vllm.entrypoints.openai.api_server
--model=/weightpath/w4a8_4_layer --trust-remote-code -tp 4 -dp 4
--enable_expert_parallel --quantization ascend --port 8002
--max-model-len 5120 --max-num-seqs 128 --enforce-eager
##### b.How to run graph mode
export VLLM_USE_V1=1 # v1
export HCCL_BUFFSIZE=1024
python -m vllm.entrypoints.openai.api_server --model=$1
--trust-remote-code -tp $2 -dp $3 --enable_expert_parallel
--quantization ascend --port $4 --max-model-len $5
--additional_config='{"ascend_scheduler_config":{"enabled":true},"torchair_graph_config":{"enabled":true}}'
eg: python -m vllm.entrypoints.openai.api_server
--model=/weight/dsr1_w4a8_vllm --trust-remote-code -tp 4 -dp 4
--enable_expert_parallel --quantization ascend --port 8002
--max-model-len 5120
--additional_config='{"ascend_scheduler_config":{"enabled":true},"torchair_graph_config":{"enabled":true}}'
- vLLM version: v0.10.0
- vLLM main:
c494f96fbc
---------
Signed-off-by: Wang Kunpeng <1289706727@qq.com>
397 lines
17 KiB
Python
397 lines
17 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 typing import Any, Callable, Dict, Optional
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import numpy as np
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import torch
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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 get_ep_group
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from vllm.forward_context import get_forward_context
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from vllm_ascend.ascend_config import get_ascend_config
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from vllm_ascend.ascend_forward_context import FusedMoEState
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from vllm_ascend.distributed.parallel_state import get_mc2_group
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from vllm_ascend.ops.fused_moe import select_experts
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from vllm_ascend.quantization.w8a8_dynamic import (fused_experts_with_all2all,
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fused_experts_with_mc2)
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from vllm_ascend.torchair.utils import npu_stream_switch, npu_wait_tensor
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class AscendW4A8DynamicLinearMethod:
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"""Linear method for Ascend W4A8_DYNAMIC
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"""
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def __init__(self):
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self.transpose_weight = True
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try:
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self.group_size = get_current_vllm_config(
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).quant_config.quant_description.get("group_size", 256)
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except AttributeError:
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self.group_size = 256
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@staticmethod
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def get_weight(input_size: int, output_size: int,
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params_dtype: torch.dtype) -> Dict[str, Any]:
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params_dict = {
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"weight": torch.empty(output_size, input_size, dtype=torch.int8)
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}
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return params_dict
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@staticmethod
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def get_pertensor_param(params_dtype: torch.dtype) -> Dict[str, Any]:
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return {}
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@staticmethod
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def get_perchannel_param(output_size: int,
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params_dtype: torch.dtype) -> Dict[str, Any]:
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return {}
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def get_pergroup_param(self, input_size: int, output_size: int,
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params_dtype: torch.dtype) -> Dict[str, Any]:
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params_dict = {}
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params_dict["weight_scale"] = torch.empty(output_size,
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1,
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dtype=params_dtype)
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params_dict["weight_offset"] = torch.empty(output_size,
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1,
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dtype=params_dtype)
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params_dict["weight_scale_second"] = torch.empty(output_size,
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input_size //
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self.group_size,
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dtype=params_dtype)
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params_dict["weight_offset_second"] = torch.empty(output_size,
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input_size //
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self.group_size,
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dtype=params_dtype)
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return params_dict
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@staticmethod
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def process_scale_second(weight: torch.Tensor, scale: torch.Tensor,
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per_group_scale: torch.Tensor):
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k, n = weight.shape
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group_num, n = per_group_scale.shape
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weight_high = weight.to(torch.float32).reshape(
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group_num, -1, n) * per_group_scale.reshape(group_num, 1, n)
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weight_high = weight_high.reshape(k, n)
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bias = 8 * (weight_high.to(torch.float32) * scale).sum(dim=0)
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antiquant_scale = (scale * per_group_scale).reshape(group_num, n)
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return antiquant_scale.npu(), bias
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def apply(
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self,
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layer: torch.nn.Module,
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x: torch.Tensor,
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bias: Optional[torch.Tensor] = None,
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tp_rank: Optional[int] = None,
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) -> torch.Tensor:
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return torch_npu.npu_weight_quant_batchmatmul(
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x,
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layer.weight,
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antiquant_scale=layer.weight_scale_second.to(x.dtype),
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antiquant_group_size=self.group_size,
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)
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def process_weights_after_loading(self, layer: torch.nn.Module):
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if self.transpose_weight:
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layer.weight.data = layer.weight.data.transpose(0, 1).contiguous()
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layer.weight_scale.data = layer.weight_scale.data.flatten().to(
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torch.float32)
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layer.weight_offset.data = layer.weight_offset.data.flatten()
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layer.weight_scale_second.data, scale_bias = self.process_scale_second(
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layer.weight.data,
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layer.weight_scale.data,
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layer.weight_scale_second.data.transpose(0, 1).contiguous(),
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)
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param = torch.nn.Parameter(scale_bias, requires_grad=False)
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layer.register_parameter("weight_scale_bias", param)
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layer.weight.data = torch_npu.npu_convert_weight_to_int4pack(
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layer.weight.data.to(torch.int32))
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class AscendW4A8DynamicFusedMoEMethod:
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"""FusedMoe method for Ascend W4A8_DYNAMIC.
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"""
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def __init__(self):
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self.transpose_weight = True
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self.ep_group = get_ep_group()
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ascend_config = get_ascend_config()
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self.torchair_graph_enabled = ascend_config.torchair_graph_config.enabled
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try:
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device_group = get_mc2_group().device_group
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# TODO: Try local_rank = ep_group.rank_in_group
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local_rank = torch.distributed.get_rank(group=device_group)
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backend = device_group._get_backend(torch.device("npu"))
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self.moe_all_to_all_group_name = backend.get_hccl_comm_name(
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local_rank)
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except AttributeError:
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self.moe_all_to_all_group_name = ""
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@staticmethod
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def get_weight(num_experts: int, intermediate_size_per_partition: int,
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hidden_sizes: int,
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params_dtype: torch.dtype) -> Dict[str, Any]:
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param_dict = {}
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param_dict["w13_weight"] = torch.empty(num_experts,
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2 *
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intermediate_size_per_partition,
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hidden_sizes,
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dtype=torch.int8)
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param_dict["w2_weight"] = torch.empty(num_experts,
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hidden_sizes,
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intermediate_size_per_partition,
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dtype=torch.int8)
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return param_dict
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@staticmethod
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def get_dynamic_quant_param(num_experts: int,
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intermediate_size_per_partition: int,
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hidden_sizes: int,
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params_dtype: torch.dtype) -> Dict[str, Any]:
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param_dict = {}
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config = get_current_vllm_config()
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group_size = config.quant_config.quant_description.get(
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"group_size", 256)
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param_dict["w13_weight_scale"] = torch.empty(
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num_experts,
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2 * intermediate_size_per_partition,
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1,
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dtype=params_dtype)
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param_dict["w13_weight_offset"] = torch.empty(
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num_experts,
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2 * intermediate_size_per_partition,
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1,
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dtype=params_dtype)
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param_dict["w13_weight_scale_second"] = torch.empty(
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num_experts,
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2 * intermediate_size_per_partition,
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hidden_sizes // group_size,
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dtype=params_dtype)
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param_dict["w13_weight_offset_second"] = torch.empty(
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num_experts,
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2 * intermediate_size_per_partition,
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hidden_sizes // group_size,
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dtype=params_dtype)
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param_dict["w2_weight_scale"] = torch.empty(num_experts,
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hidden_sizes,
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1,
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dtype=params_dtype)
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param_dict["w2_weight_offset"] = torch.empty(num_experts,
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hidden_sizes,
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1,
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dtype=params_dtype)
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param_dict["w2_weight_scale_second"] = torch.empty(
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num_experts,
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hidden_sizes,
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intermediate_size_per_partition // group_size,
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dtype=params_dtype)
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param_dict["w2_weight_offset_second"] = torch.empty(
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num_experts,
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hidden_sizes,
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intermediate_size_per_partition // group_size,
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dtype=params_dtype)
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return param_dict
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def apply(
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self,
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layer: torch.nn.Module,
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x: torch.Tensor,
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router_logits: torch.Tensor,
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top_k: int,
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renormalize: bool,
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use_grouped_topk: bool = False,
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global_num_experts: int = -1,
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expert_map: Optional[torch.Tensor] = None,
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topk_group: Optional[int] = None,
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num_expert_group: Optional[int] = None,
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custom_routing_function: Optional[Callable] = None,
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scoring_func: str = "softmax",
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e_score_correction_bias: Optional[torch.Tensor] = None,
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is_prefill: bool = True,
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enable_force_load_balance: bool = True,
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log2phy: torch.Tensor = None,
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global_redundant_expert_num: int = 0,
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shared_experts: Optional[Any] = None,
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quantized_x_for_share: Optional[Any] = None,
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dynamic_scale_for_share: Optional[Any] = None,
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**kwargs,
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) -> torch.Tensor:
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assert router_logits.shape[
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1] == global_num_experts, "Number of global experts mismatch"
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# NOTE: now npu_moe_gating_top_k can only support `group_count=256` pattern
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if global_num_experts == 256:
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topk_weights, topk_ids, _ = torch_npu.npu_moe_gating_top_k(
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router_logits,
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k=top_k, # topk currently is 8
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bias=e_score_correction_bias,
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k_group=topk_group, # fix: 4
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group_count=num_expert_group, # fix 8
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group_select_mode=
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1, # 0: the maximum in the group; 1: topk2.sum(fix)
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renorm=0, # 0: softmax->topk(fix); 1: topk->softmax
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norm_type=1, # 0: softmax; 1: sigmoid(fix)
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# out_flag=False, # todo new api; should the third output be output
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# y2_flag=False, # old api; should the third output be output
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routed_scaling_factor=1,
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eps=float(1e-20))
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else:
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topk_weights, topk_ids = select_experts(
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hidden_states=x,
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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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)
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fused_moe_state = get_forward_context().fused_moe_state
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shared_gate_up, shared_dequant_scale = None, None
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if shared_experts is not None and fused_moe_state == FusedMoEState.MC2:
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with npu_stream_switch("moe_secondary", 0):
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npu_wait_tensor(quantized_x_for_share, router_logits)
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share_up_out, _ = shared_experts.gate_up_proj(
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(quantized_x_for_share, dynamic_scale_for_share))
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shared_gate_up, shared_dequant_scale = share_up_out[
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0], share_up_out[1]
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# this is a naive implementation for experts load balance so as
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# to avoid accumulating too much tokens on a single rank.
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# currently it is only activated when doing profile runs.
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if enable_force_load_balance:
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topk_ids = torch.randint_like(topk_ids, 0, global_num_experts)
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topk_weights = topk_weights.to(x.dtype)
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if fused_moe_state == FusedMoEState.MC2:
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return fused_experts_with_mc2(
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hidden_states=x,
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w1=layer.w13_weight,
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w2=layer.w2_weight,
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w1_scale=layer.w13_weight_scale_second,
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w2_scale=layer.w2_weight_scale_second,
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w1_scale_bias=layer.w13_scale_bias,
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w2_scale_bias=layer.w2_scale_bias,
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topk_weights=topk_weights,
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topk_ids=topk_ids,
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top_k=top_k,
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expert_map=expert_map,
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moe_all_to_all_group_name=self.moe_all_to_all_group_name,
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log2phy=log2phy,
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global_redundant_expert_num=global_redundant_expert_num,
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shared_experts=shared_experts,
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is_torchair=self.torchair_graph_enabled,
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quantized_x_for_share=shared_gate_up,
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dynamic_scale_for_share=shared_dequant_scale,
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mc2_mask=kwargs.get("mc2_mask", None))
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else:
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# The current implementation of deepseek moe splits hidden_states
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# according to tp_size before they are feed into fused_moe module.
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# Therefore, all2all is needed no matter how dp/tp is set so as to
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# dispatch/combine tokens.
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return fused_experts_with_all2all(
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hidden_states=x,
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w1=layer.w13_weight,
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w2=layer.w2_weight,
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w1_scale=layer.w13_weight_scale_second,
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w2_scale=layer.w2_weight_scale_second,
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w1_scale_bias=layer.w13_scale_bias,
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w2_scale_bias=layer.w2_scale_bias,
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topk_weights=topk_weights,
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topk_ids=topk_ids,
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top_k=top_k,
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expert_map=expert_map,
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ep_group=self.ep_group,
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log2phy=log2phy,
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global_redundant_expert_num=global_redundant_expert_num,
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)
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def process_scale(self, weight: torch.Tensor, scale, per_group_scale):
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group_num, k, n = weight.shape
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per_group_scale = per_group_scale.reshape(group_num, -1, n)
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group_num, quantgroup_num, n = per_group_scale.shape
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weight_high = weight.to(torch.float32).reshape([group_num, quantgroup_num, -1, n]) * \
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per_group_scale.reshape([group_num, quantgroup_num, 1, n])
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weight_high = weight_high.reshape([group_num, k, n])
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bias = 8 * (weight_high.to(torch.float32) * scale).sum(axis=1)
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scale_fp32 = (scale * per_group_scale).to(torch.float16).to(
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torch.float32)
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scale_fp32_np = scale_fp32.cpu().numpy()
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scale_fp32_np.dtype = np.uint32
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sscale_uint64 = np.zeros((group_num, quantgroup_num, n * 2),
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dtype=np.uint32)
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sscale_uint64[..., ::2] = scale_fp32_np
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sscale_uint64_buffer = np.frombuffer(sscale_uint64.tobytes(),
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dtype=np.int64).copy()
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sscale_uint64_tensor = torch.from_numpy(sscale_uint64_buffer).reshape(
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group_num, quantgroup_num, n)
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sscale_uint64_tensor = sscale_uint64_tensor.npu()
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return sscale_uint64_tensor, bias
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def process_weights_after_loading(self, layer):
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if self.transpose_weight:
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layer.w13_weight.data = layer.w13_weight.data.transpose(
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1, 2).contiguous()
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layer.w2_weight.data = layer.w2_weight.data.transpose(
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1, 2).contiguous()
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layer.w13_weight_scale.data = layer.w13_weight_scale.data.transpose(
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1, 2).contiguous()
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layer.w2_weight_scale.data = layer.w2_weight_scale.data.transpose(
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1, 2).contiguous()
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layer.w13_weight_offset.data = layer.w13_weight_offset.data.view(
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layer.w13_weight_offset.data.shape[0], -1)
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layer.w2_weight_offset.data = layer.w2_weight_offset.data.view(
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layer.w2_weight_offset.data.shape[0], -1)
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layer.w13_weight_scale_second.data = layer.w13_weight_scale_second.data.transpose(
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1, 2).contiguous()
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layer.w2_weight_scale_second.data = layer.w2_weight_scale_second.data.transpose(
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1, 2).contiguous()
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layer.w13_weight_scale_second.data, bias = self.process_scale(
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layer.w13_weight, layer.w13_weight_scale.data,
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layer.w13_weight_scale_second.data)
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param = torch.nn.Parameter(bias, requires_grad=False)
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layer.register_parameter("w13_scale_bias", param)
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layer.w2_weight_scale_second.data, bias1 = self.process_scale(
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layer.w2_weight, layer.w2_weight_scale.data,
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layer.w2_weight_scale_second.data)
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param = torch.nn.Parameter(bias1, requires_grad=False)
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layer.register_parameter("w2_scale_bias", param)
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layer.w13_weight.data = torch_npu.npu_quantize(
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layer.w13_weight.data.to(torch.float32),
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torch.tensor([1.]).npu(), None, torch.quint4x2, -1, False)
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layer.w2_weight.data = torch_npu.npu_quantize(
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layer.w2_weight.data.to(torch.float32),
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torch.tensor([1.]).npu(), None, torch.quint4x2, -1, False)
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