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add release note for 0.12.0 (#4995)

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Add release note for v0.12.0rc1
Update deepseek3.2 tutorial doc

- vLLM version: v0.12.0
- vLLM main:
ad32e3e19c

Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
This commit is contained in:
wangxiyuan
2025-12-13 22:09:59 +08:00
committed by GitHub
parent 0f92d34a70
commit 42ceaf08a1
10 changed files with 530 additions and 278 deletions
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docs/source/tutorials/DeepSeek-V3.2.md
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@@ -2,7 +2,7 @@
## Introduction
DeepSeek-V3.2-Exp is a sparse attention model. The main architecture is similar to DeepSeek-V3.1, but with a sparse attention mechanism, which is designed to explore and validate optimizations for training and inference efficiency in long-context scenarios.
DeepSeek-V3.2 is a sparse attention model. The main architecture is similar to DeepSeek-V3.1, but with a sparse attention mechanism, which is designed to explore and validate optimizations for training and inference efficiency in long-context scenarios.
This document will show the main verification steps of the model, including supported features, feature configuration, environment preparation, single-node and multi-node deployment, accuracy and performance evaluation.
@@ -18,6 +18,8 @@ Refer to [feature guide](../user_guide/feature_guide/index.md) to get the featur
- `DeepSeek-V3.2-Exp`(BF16 version): require 2 Atlas 800 A3 (64G × 16) nodes or 4 Atlas 800 A2 (64G × 8) nodes. [Download model weight](https://modelers.cn/models/Modelers_Park/DeepSeek-V3.2-Exp-BF16)
- `DeepSeek-V3.2-Exp-w8a8`(Quantized version): require 1 Atlas 800 A3 (64G × 16) node or 2 Atlas 800 A2 (64G × 8) nodes. [Download model weight](https://modelers.cn/models/Modelers_Park/DeepSeek-V3.2-Exp-w8a8)
- `DeepSeek-V3.2`(BF16 version): require 2 Atlas 800 A3 (64G × 16) nodes or 4 Atlas 800 A2 (64G × 8) nodes. [Download model weight](https://modelscope.cn/models/deepseek-ai/DeepSeek-V3.2/)
- `DeepSeek-V3.2-w8a8`(Quantized version): require 1 Atlas 800 A3 (64G × 16) node or 2 Atlas 800 A2 (64G × 8) nodes. [Download model weight](https://modelers.cn/models/Eco-Tech/DeepSeek-V3.2-w8a8-QuaRot)
It is recommended to download the model weight to the shared directory of multiple nodes, such as `/root/.cache/`
@@ -27,10 +29,10 @@ If you want to deploy multi-node environment, you need to verify multi-node comm
### Installation
You can using our official docker image and install extra operator for supporting `DeepSeek-V3.2-Exp`.
You can using our official docker image and install extra operator for supporting `DeepSeek-V3.2`.
:::{note}
Only AArch64 architecture are supported currently due to extra operator's installation limitations.
We strongly recommend you to install triton ascend package to speed up the inference.
:::
:::::{tab-set}
@@ -39,7 +41,7 @@ Only AArch64 architecture are supported currently due to extra operator's instal
::::{tab-item} A3 series
:sync: A3
1. Start the docker image on your each node.
Start the docker image on your each node.
```{code-block} bash
:substitutions:
@@ -78,23 +80,11 @@ docker run --rm \
-it $IMAGE bash
```
2. Install the package `custom-ops` to make the kernels available.
```shell
wget https://vllm-ascend.obs.cn-north-4.myhuaweicloud.com/vllm-ascend/a3/CANN-custom_ops-sfa-linux.aarch64.run
chmod +x ./CANN-custom_ops-sfa-linux.aarch64.run
./CANN-custom_ops-sfa-linux.aarch64.run --quiet
export ASCEND_CUSTOM_OPP_PATH=/usr/local/Ascend/ascend-toolkit/latest/opp/vendors/customize:${ASCEND_CUSTOM_OPP_PATH}
export LD_LIBRARY_PATH=/usr/local/Ascend/ascend-toolkit/latest/opp/vendors/customize/op_api/lib/:${LD_LIBRARY_PATH}
wget https://vllm-ascend.obs.cn-north-4.myhuaweicloud.com/vllm-ascend/a3/custom_ops-1.0-cp311-cp311-linux_aarch64.whl
pip install custom_ops-1.0-cp311-cp311-linux_aarch64.whl
```
::::
::::{tab-item} A2 series
:sync: A2
1. Start the docker image on your each node.
Start the docker image on your each node.
```{code-block} bash
:substitutions:
@@ -125,18 +115,6 @@ docker run --rm \
-it $IMAGE bash
```
2. Install the package `custom-ops` to make the kernels available.
```shell
wget https://vllm-ascend.obs.cn-north-4.myhuaweicloud.com/vllm-ascend/a2/CANN-custom_ops-sfa-linux.aarch64.run
chmod +x ./CANN-custom_ops-sfa-linux.aarch64.run
./CANN-custom_ops-sfa-linux.aarch64.run --quiet
export ASCEND_CUSTOM_OPP_PATH=/usr/local/Ascend/ascend-toolkit/latest/opp/vendors/customize:${ASCEND_CUSTOM_OPP_PATH}
export LD_LIBRARY_PATH=/usr/local/Ascend/ascend-toolkit/latest/opp/vendors/customize/op_api/lib/:${LD_LIBRARY_PATH}
wget https://vllm-ascend.obs.cn-north-4.myhuaweicloud.com/vllm-ascend/a2/custom_ops-1.0-cp311-cp311-linux_aarch64.whl
pip install custom_ops-1.0-cp311-cp311-linux_aarch64.whl
```
::::
:::::
@@ -144,229 +122,457 @@ In addition, if you don't want to use the docker image as above, you can also bu
- Install `vllm-ascend` from source, refer to [installation](../installation.md).
- Install extra operator for supporting `DeepSeek-V3.2-Exp`, refer to the above tab.
If you want to deploy multi-node environment, you need to set up environment on each node.
## Deployment
### Single-node Deployment
Only the quantized model `DeepSeek-V3.2-Exp-w8a8` can be deployed on 1 Atlas 800 A3.
Run the following script to execute online inference.
```shell
#!/bin/sh
export VLLM_USE_MODELSCOPE=true
vllm serve vllm-ascend/DeepSeek-V3.2-Exp-W8A8 \
--host 0.0.0.0 \
--port 8000 \
--tensor-parallel-size 16 \
--seed 1024 \
--quantization ascend \
--served-model-name deepseek_v3.2 \
--max-num-seqs 16 \
--max-model-len 17450 \
--max-num-batched-tokens 17450 \
--enable-expert-parallel \
--trust-remote-code \
--no-enable-prefix-caching \
--gpu-memory-utilization 0.92
```
### Multi-node Deployment
- `DeepSeek-V3.2-Exp`: require 2 Atlas 800 A3 (64G × 16) nodes or 4 Atlas 800 A2 (64G × 8).
- `DeepSeek-V3.2-Exp-w8a8`: require 2 Atlas 800 A2 (64G × 8).
:::::{tab-set}
:sync-group: install
::::{tab-item} DeepSeek-V3.2-Exp A3 series
:sync: A3
Run the following scripts on two nodes respectively.
**Node 0**
```shell
#!/bin/sh
# this obtained through ifconfig
# nic_name is the network interface name corresponding to local_ip of the current node
nic_name="xxxx"
local_ip="xxxx"
export VLLM_USE_MODELSCOPE=True
export HCCL_IF_IP=$local_ip
export GLOO_SOCKET_IFNAME=$nic_name
export TP_SOCKET_IFNAME=$nic_name
export HCCL_SOCKET_IFNAME=$nic_name
export OMP_PROC_BIND=false
export OMP_NUM_THREADS=10
export HCCL_BUFFSIZE=1024
vllm serve /root/.cache/Modelers_Park/DeepSeek-V3.2-Exp \
--host 0.0.0.0 \
--port 8000 \
--data-parallel-size 2 \
--data-parallel-size-local 1 \
--data-parallel-address $local_ip \
--data-parallel-rpc-port 13389 \
--tensor-parallel-size 16 \
--seed 1024 \
--served-model-name deepseek_v3.2 \
--enable-expert-parallel \
--max-num-seqs 16 \
--max-model-len 17450 \
--max-num-batched-tokens 17450 \
--trust-remote-code \
--no-enable-prefix-caching \
--gpu-memory-utilization 0.9
```
**Node 1**
```shell
#!/bin/sh
# this obtained through ifconfig
# nic_name is the network interface name corresponding to local_ip of the current node
nic_name="xxx"
local_ip="xxx"
# The value of node0_ip must be consistent with the value of local_ip set in node0 (master node)
node0_ip="xxxx"
export VLLM_USE_MODELSCOPE=True
export HCCL_IF_IP=$local_ip
export GLOO_SOCKET_IFNAME=$nic_name
export TP_SOCKET_IFNAME=$nic_name
export HCCL_SOCKET_IFNAME=$nic_name
export OMP_PROC_BIND=false
export OMP_NUM_THREADS=10
export HCCL_BUFFSIZE=1024
vllm serve /root/.cache/Modelers_Park/DeepSeek-V3.2-Exp \
--host 0.0.0.0 \
--port 8000 \
--headless \
--data-parallel-size 2 \
--data-parallel-size-local 1 \
--data-parallel-start-rank 1 \
--data-parallel-address $node0_ip \
--data-parallel-rpc-port 13389 \
--tensor-parallel-size 16 \
--seed 1024 \
--served-model-name deepseek_v3.2 \
--max-num-seqs 16 \
--max-model-len 17450 \
--max-num-batched-tokens 17450 \
--enable-expert-parallel \
--trust-remote-code \
--no-enable-prefix-caching \
--gpu-memory-utilization 0.92
```
::::
::::{tab-item} DeepSeek-V3.2-Exp-W8A8 A2 series
:sync: A2
Run the following scripts on two nodes respectively.
**Node 0**
```shell
#!/bin/sh
# this obtained through ifconfig
# nic_name is the network interface name corresponding to local_ip of the current node
nic_name="xxxx"
local_ip="xxxx"
export VLLM_USE_MODELSCOPE=True
export HCCL_IF_IP=$local_ip
export GLOO_SOCKET_IFNAME=$nic_name
export TP_SOCKET_IFNAME=$nic_name
export HCCL_SOCKET_IFNAME=$nic_name
export OMP_PROC_BIND=false
export OMP_NUM_THREADS=10
export HCCL_BUFFSIZE=1024
export PYTORCH_NPU_ALLOC_CONF="expandable_segments:True"
vllm serve vllm-ascend/DeepSeek-V3.2-Exp-W8A8 \
--host 0.0.0.0 \
--port 8000 \
--data-parallel-size 2 \
--data-parallel-size-local 1 \
--data-parallel-address $local_ip \
--data-parallel-rpc-port 13389 \
--tensor-parallel-size 8 \
--seed 1024 \
--served-model-name deepseek_v3.2 \
--enable-expert-parallel \
--max-num-seqs 16 \
--max-model-len 17450 \
--max-num-batched-tokens 17450 \
--trust-remote-code \
--quantization ascend \
--no-enable-prefix-caching \
--gpu-memory-utilization 0.9
```
**Node 1**
```shell
#!/bin/sh
# this obtained through ifconfig
# nic_name is the network interface name corresponding to local_ip of the current node
nic_name="xxx"
local_ip="xxx"
# The value of node0_ip must be consistent with the value of local_ip set in node0 (master node)
node0_ip="xxxx"
export VLLM_USE_MODELSCOPE=True
export HCCL_IF_IP=$local_ip
export GLOO_SOCKET_IFNAME=$nic_name
export TP_SOCKET_IFNAME=$nic_name
export HCCL_SOCKET_IFNAME=$nic_name
export OMP_PROC_BIND=false
export OMP_NUM_THREADS=10
export HCCL_BUFFSIZE=1024
export PYTORCH_NPU_ALLOC_CONF="expandable_segments:True"
vllm serve vllm-ascend/DeepSeek-V3.2-Exp-W8A8 \
--host 0.0.0.0 \
--port 8000 \
--headless \
--data-parallel-size 2 \
--data-parallel-size-local 1 \
--data-parallel-start-rank 1 \
--data-parallel-address $node0_ip \
--data-parallel-rpc-port 13389 \
--tensor-parallel-size 8 \
--seed 1024 \
--served-model-name deepseek_v3.2 \
--max-num-seqs 16 \
--max-model-len 17450 \
--max-num-batched-tokens 17450 \
--enable-expert-parallel \
--trust-remote-code \
--quantization ascend \
--no-enable-prefix-caching \
--gpu-memory-utilization 0.92
```
::::
:::::
:::{note}
In this tutorial, we suppose you downloaded the model weight to `/root/.cache/`. Feel free to change it to your own path.
:::
### Prefill-Decode Disaggregation
Not supported yet.
We'd like to show the deployment guide of `DeepSeek-V3.2` on multi-node environment with 1P1D for better performance.
Before you start, please
1. prepare the script `launch_online_dp.py` on each node.
```
import argparse
import multiprocessing
import os
import subprocess
import sys
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument(
"--dp-size",
type=int,
required=True,
help="Data parallel size."
)
parser.add_argument(
"--tp-size",
type=int,
default=1,
help="Tensor parallel size."
)
parser.add_argument(
"--dp-size-local",
type=int,
default=-1,
help="Local data parallel size."
)
parser.add_argument(
"--dp-rank-start",
type=int,
default=0,
help="Starting rank for data parallel."
)
parser.add_argument(
"--dp-address",
type=str,
required=True,
help="IP address for data parallel master node."
)
parser.add_argument(
"--dp-rpc-port",
type=str,
default=12345,
help="Port for data parallel master node."
)
parser.add_argument(
"--vllm-start-port",
type=int,
default=9000,
help="Starting port for the engine."
)
return parser.parse_args()
args = parse_args()
dp_size = args.dp_size
tp_size = args.tp_size
dp_size_local = args.dp_size_local
if dp_size_local == -1:
dp_size_local = dp_size
dp_rank_start = args.dp_rank_start
dp_address = args.dp_address
dp_rpc_port = args.dp_rpc_port
vllm_start_port = args.vllm_start_port
def run_command(visiable_devices, dp_rank, vllm_engine_port):
command = [
"bash",
"./run_dp_template.sh",
visiable_devices,
str(vllm_engine_port),
str(dp_size),
str(dp_rank),
dp_address,
dp_rpc_port,
str(tp_size),
]
subprocess.run(command, check=True)
if __name__ == "__main__":
template_path = "./run_dp_template.sh"
if not os.path.exists(template_path):
print(f"Template file {template_path} does not exist.")
sys.exit(1)
processes = []
num_cards = dp_size_local * tp_size
for i in range(dp_size_local):
dp_rank = dp_rank_start + i
vllm_engine_port = vllm_start_port + i
visiable_devices = ",".join(str(x) for x in range(i * tp_size, (i + 1) * tp_size))
process = multiprocessing.Process(target=run_command,
args=(visiable_devices, dp_rank,
vllm_engine_port))
processes.append(process)
process.start()
for process in processes:
process.join()
```
2. prepare the script `run_dp_template.sh` on each node.
1. Prefill node 0
```
nic_name="enp48s3u1u1" # change to your own nic name
local_ip=141.61.39.105 # change to your own ip
export HCCL_OP_EXPANSION_MODE="AIV"
export HCCL_IF_IP=$local_ip
export GLOO_SOCKET_IFNAME=$nic_name
export TP_SOCKET_IFNAME=$nic_name
export HCCL_SOCKET_IFNAME=$nic_name
export OMP_PROC_BIND=false
export OMP_NUM_THREADS=10
export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
export VLLM_USE_V1=1
export HCCL_BUFFSIZE=256
export VLLM_TORCH_PROFILER_DIR="./vllm_profile"
export VLLM_TORCH_PROFILER_WITH_STACK=0
export ASCEND_AGGREGATE_ENABLE=1
export ASCEND_TRANSPORT_PRINT=1
export ACL_OP_INIT_MODE=1
export ASCEND_A3_ENABLE=1
export VLLM_NIXL_ABORT_REQUEST_TIMEOUT=300000
export ASCEND_RT_VISIBLE_DEVICES=$1
export VLLM_ASCEND_ENABLE_FLASHCOMM1=1
vllm serve /root/.cache/Eco-Tech/DeepSeek-V3.2-w8a8-QuaRot \
--host 0.0.0.0 \
--port $2 \
--data-parallel-size $3 \
--data-parallel-rank $4 \
--data-parallel-address $5 \
--data-parallel-rpc-port $6 \
--tensor-parallel-size $7 \
--enable-expert-parallel \
--speculative-config '{"num_speculative_tokens": 2, "method":"deepseek_mtp"}' \
--seed 1024 \
--served-model-name dsv3 \
--max-model-len 68000 \
--max-num-batched-tokens 32550 \
--trust-remote-code \
--max-num-seqs 64 \
--gpu-memory-utilization 0.82 \
--quantization ascend \
--enforce-eager \
--no-enable-prefix-caching \
--kv-transfer-config \
'{"kv_connector": "MooncakeConnector",
"kv_role": "kv_producer",
"kv_port": "30000",
"engine_id": "0",
"kv_connector_module_path": "vllm_ascend.distributed.mooncake_connector",
"kv_connector_extra_config": {
"use_ascend_direct": true,
"prefill": {
"dp_size": 2,
"tp_size": 16
},
"decode": {
"dp_size": 8,
"tp_size": 4
}
}
}'
```
2. Prefill node 1
```
nic_name="enp48s3u1u1" # change to your own nic name
local_ip=141.61.39.113 # change to your own ip
export HCCL_OP_EXPANSION_MODE="AIV"
export HCCL_IF_IP=$local_ip
export GLOO_SOCKET_IFNAME=$nic_name
export TP_SOCKET_IFNAME=$nic_name
export HCCL_SOCKET_IFNAME=$nic_name
export OMP_PROC_BIND=false
export OMP_NUM_THREADS=10
export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
export VLLM_USE_V1=1
export HCCL_BUFFSIZE=256
export VLLM_TORCH_PROFILER_DIR="./vllm_profile"
export VLLM_TORCH_PROFILER_WITH_STACK=0
export ASCEND_AGGREGATE_ENABLE=1
export ASCEND_TRANSPORT_PRINT=1
export ACL_OP_INIT_MODE=1
export ASCEND_A3_ENABLE=1
export VLLM_NIXL_ABORT_REQUEST_TIMEOUT=300000
export ASCEND_RT_VISIBLE_DEVICES=$1
export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
export VLLM_ASCEND_ENABLE_FLASHCOMM1=1
vllm serve /root/.cache/Eco-Tech/DeepSeek-V3.2-w8a8-QuaRot \
--host 0.0.0.0 \
--port $2 \
--data-parallel-size $3 \
--data-parallel-rank $4 \
--data-parallel-address $5 \
--data-parallel-rpc-port $6 \
--tensor-parallel-size $7 \
--enable-expert-parallel \
--speculative-config '{"num_speculative_tokens": 2, "method":"deepseek_mtp"}' \
--seed 1024 \
--served-model-name dsv3 \
--max-model-len 68000 \
--max-num-batched-tokens 32550 \
--trust-remote-code \
--max-num-seqs 64 \
--gpu-memory-utilization 0.82 \
--quantization ascend \
--enforce-eager \
--no-enable-prefix-caching \
--kv-transfer-config \
'{"kv_connector": "MooncakeConnector",
"kv_role": "kv_producer",
"kv_port": "30000",
"engine_id": "0",
"kv_connector_module_path": "vllm_ascend.distributed.mooncake_connector",
"kv_connector_extra_config": {
"use_ascend_direct": true,
"prefill": {
"dp_size": 2,
"tp_size": 16
},
"decode": {
"dp_size": 8,
"tp_size": 4
}
}
}'
```
3. Decode node 0
```
nic_name="enp48s3u1u1" # change to your own nic name
local_ip=141.61.39.117 # change to your own ip
export HCCL_OP_EXPANSION_MODE="AIV"
export HCCL_IF_IP=$local_ip
export GLOO_SOCKET_IFNAME=$nic_name
export TP_SOCKET_IFNAME=$nic_name
export HCCL_SOCKET_IFNAME=$nic_name
#Mooncake
export OMP_PROC_BIND=false
export OMP_NUM_THREADS=10
export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
export VLLM_USE_V1=1
export HCCL_BUFFSIZE=256
export VLLM_TORCH_PROFILER_DIR="./vllm_profile"
export VLLM_TORCH_PROFILER_WITH_STACK=0
export ASCEND_AGGREGATE_ENABLE=1
export ASCEND_TRANSPORT_PRINT=1
export ACL_OP_INIT_MODE=1
export ASCEND_A3_ENABLE=1
export VLLM_NIXL_ABORT_REQUEST_TIMEOUT=300000
export TASK_QUEUE_ENABLE=1
export ASCEND_RT_VISIBLE_DEVICES=$1
export VLLM_ASCEND_ENABLE_MLAPO=1
vllm serve /root/.cache/Eco-Tech/DeepSeek-V3.2-w8a8-QuaRot \
--host 0.0.0.0 \
--port $2 \
--data-parallel-size $3 \
--data-parallel-rank $4 \
--data-parallel-address $5 \
--data-parallel-rpc-port $6 \
--tensor-parallel-size $7 \
--enable-expert-parallel \
--speculative-config '{"num_speculative_tokens": 2, "method":"deepseek_mtp"}' \
--seed 1024 \
--served-model-name dsv3 \
--max-model-len 68000 \
--max-num-batched-tokens 4 \
--compilation-config '{"cudagraph_mode":"FULL_DECODE_ONLY", "cudagraph_capture_sizes":[2, 4, 6, 8]}' \
--trust-remote-code \
--max-num-seqs 1 \
--gpu-memory-utilization 0.95 \
--no-enable-prefix-caching \
--async-scheduling \
--quantization ascend \
--kv-transfer-config \
'{"kv_connector": "MooncakeConnector",
"kv_role": "kv_consumer",
"kv_port": "30100",
"engine_id": "1",
"kv_connector_module_path": "vllm_ascend.distributed.mooncake_connector",
"kv_connector_extra_config": {
"use_ascend_direct": true,
"prefill": {
"dp_size": 2,
"tp_size": 16
},
"decode": {
"dp_size": 8,
"tp_size": 4
}
}
}'
```
4. Decode node 1
```
nic_name="enp48s3u1u1" # change to your own nic name
local_ip=141.61.39.181 # change to your own ip
export HCCL_OP_EXPANSION_MODE="AIV"
export HCCL_IF_IP=$local_ip
export GLOO_SOCKET_IFNAME=$nic_name
export TP_SOCKET_IFNAME=$nic_name
export HCCL_SOCKET_IFNAME=$nic_name
#Mooncake
export OMP_PROC_BIND=false
export OMP_NUM_THREADS=10
export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
export VLLM_USE_V1=1
export HCCL_BUFFSIZE=256
export VLLM_TORCH_PROFILER_DIR="./vllm_profile"
export VLLM_TORCH_PROFILER_WITH_STACK=0
export ASCEND_AGGREGATE_ENABLE=1
export ASCEND_TRANSPORT_PRINT=1
export ACL_OP_INIT_MODE=1
export ASCEND_A3_ENABLE=1
export VLLM_NIXL_ABORT_REQUEST_TIMEOUT=300000
export TASK_QUEUE_ENABLE=1
export ASCEND_RT_VISIBLE_DEVICES=$1
export VLLM_ASCEND_ENABLE_MLAPO=1
vllm serve /root/.cache/Eco-Tech/DeepSeek-V3.2-w8a8-QuaRot \
--host 0.0.0.0 \
--port $2 \
--data-parallel-size $3 \
--data-parallel-rank $4 \
--data-parallel-address $5 \
--data-parallel-rpc-port $6 \
--tensor-parallel-size $7 \
--enable-expert-parallel \
--speculative-config '{"num_speculative_tokens": 2, "method":"deepseek_mtp"}' \
--seed 1024 \
--served-model-name dsv3 \
--max-model-len 68000 \
--max-num-batched-tokens 4 \
--compilation-config '{"cudagraph_mode":"FULL_DECODE_ONLY", "cudagraph_capture_sizes":[2, 4, 6, 8]}' \
--trust-remote-code \
--async-scheduling \
--max-num-seqs 1 \
--gpu-memory-utilization 0.95 \
--no-enable-prefix-caching \
--quantization ascend \
--kv-transfer-config \
'{"kv_connector": "MooncakeConnector",
"kv_role": "kv_consumer",
"kv_port": "30100",
"engine_id": "1",
"kv_connector_module_path": "vllm_ascend.distributed.mooncake_connector",
"kv_connector_extra_config": {
"use_ascend_direct": true,
"prefill": {
"dp_size": 2,
"tp_size": 16
},
"decode": {
"dp_size": 8,
"tp_size": 4
}
}
}'
```
Once the preparation is done, you can start the server with the following command on each node:
1. Prefill node 0
```
# change ip to your own
python launch_online_dp.py --dp-size 2 --tp-size 16 --dp-size-local 1 --dp-rank-start 0 --dp-address 141.61.39.105 --dp-rpc-port 12890 --vllm-start-port 9100
```
2. Prefill node 1
```
# change ip to your own
python launch_online_dp.py --dp-size 2 --tp-size 16 --dp-size-local 1 --dp-rank-start 1 --dp-address 141.61.39.105 --dp-rpc-port 12890 --vllm-start-port 9100
```
3. Decode node 0
```
# change ip to your own
python launch_online_dp.py --dp-size 8 --tp-size 4 --dp-size-local 4 --dp-rank-start 0 --dp-address 141.61.39.117 --dp-rpc-port 12777 --vllm-start-port 9100
```
4. Decode node 1
```
# change ip to your own
python launch_online_dp.py --dp-size 8 --tp-size 4 --dp-size-local 4 --dp-rank-start 4 --dp-address 141.61.39.117 --dp-rpc-port 12777 --vllm-start-port 9100
```
## Functional Verification
@@ -391,15 +597,11 @@ Here are two accuracy evaluation methods.
1. Refer to [Using AISBench](../developer_guide/evaluation/using_ais_bench.md) for details.
2. After execution, you can get the result, here is the result of `DeepSeek-V3.2-Exp-W8A8` in `vllm-ascend:0.11.0rc0` for reference only.
| dataset | version | metric | mode | vllm-api-general-chat |
|----- | ----- | ----- | ----- | -----|
| cevaldataset | - | accuracy | gen | 92.20 |
2. After execution, you can get the result.
### Using Language Model Evaluation Harness
As an example, take the `gsm8k` dataset as a test dataset, and run accuracy evaluation of `DeepSeek-V3.2-Exp-W8A8` in online mode.
As an example, take the `gsm8k` dataset as a test dataset, and run accuracy evaluation of `DeepSeek-V3.2-W8A8` in online mode.
1. Refer to [Using lm_eval](../developer_guide/evaluation/using_lm_eval.md) for `lm_eval` installation.
@@ -408,17 +610,12 @@ As an example, take the `gsm8k` dataset as a test dataset, and run accuracy eval
```shell
lm_eval \
--model local-completions \
--model_args model=/root/.cache/modelscope/hub/models/vllm-ascend/DeepSeek-V3.2-Exp-W8A8,base_url=http://127.0.0.1:8000/v1/completions,tokenized_requests=False,trust_remote_code=True \
--model_args model=/root/.cache/Eco-Tech/DeepSeek-V3.2-w8a8-QuaRot,base_url=http://127.0.0.1:8000/v1/completions,tokenized_requests=False,trust_remote_code=True \
--tasks gsm8k \
--output_path ./
```
3. After execution, you can get the result, here is the result of `DeepSeek-V3.2-Exp-W8A8` in `vllm-ascend:0.11.0rc0` for reference only.
|Tasks|Version| Filter |n-shot| Metric | |Value | |Stderr|
|-----|------:|----------------|-----:|-----------|---|-----:|---|-----:|
|gsm8k| 3|flexible-extract| 5|exact_match|↑ |0.9591|± |0.0055|
|gsm8k| 3|strict-match | 5|exact_match|↑ |0.9583|± |0.0055|
3. After execution, you can get the result.
## Performance
@@ -428,7 +625,7 @@ Refer to [Using AISBench for performance evaluation](../developer_guide/evaluati
### Using vLLM Benchmark
Run performance evaluation of `DeepSeek-V3.2-Exp-W8A8` as an example.
Run performance evaluation of `DeepSeek-V3.2-W8A8` as an example.
Refer to [vllm benchmark](https://docs.vllm.ai/en/latest/contributing/benchmarks.html) for more details.
@@ -441,7 +638,15 @@ Take the `serve` as an example. Run the code as follows.
```shell
export VLLM_USE_MODELSCOPE=true
vllm bench serve --model vllm-ascend/DeepSeek-V3.2-Exp-W8A8 --dataset-name random --random-input 200 --num-prompt 200 --request-rate 1 --save-result --result-dir ./
vllm bench serve --model vllm-ascend/DeepSeek-V3.2-W8A8 --dataset-name random --random-input 200 --num-prompt 200 --request-rate 1 --save-result --result-dir ./
```
After about several minutes, you can get the performance evaluation result.
After about several minutes, you can get the performance evaluation result. With this tutorial, the performance result is:
**Hardware**: A3-752T, 4 node
**Deployment**: 1P1D, Prefill node: DP2+TP16, Decode Node: DP8+TP4
**Input/Output**: 64k/3k
**Performance**: 255tps, TPOT 23ms