bc1622338c
### What this PR does / why we need it? This version has no divisibility constraint between tp and mtp+1. However, cudagraph_capture_sizes must be a common multiple of tp and mtp+1, with a maximum of tp * (mtp+1). Therefore, we fixed cudagraph_capture_sizes. We added a long-sequence test (64k input, 3k output) for the two-node mixed deployment scenario. Due to the excessive time required for performance benchmarking, we are only verifying functionality. The single-node scenario is skipped because VRAM limitations prevent launching the model with a max-model-len of 68,000. and we also add aime2025 test for dual-node deepseek 3.2 nightly test. ### How was this patch tested? test at nightly environment. - vLLM version: v0.15.0 - vLLM main: https://github.com/vllm-project/vllm/commit/v0.15.0 Signed-off-by: guozr <guozr1997@hotmail.com> Co-authored-by: guozr <guozr1997@hotmail.com>
126 lines
3.8 KiB
YAML
126 lines
3.8 KiB
YAML
test_name: "test DeepSeek-V3.2-W8A8 on A3"
|
|
model: "vllm-ascend/DeepSeek-V3.2-W8A8"
|
|
num_nodes: 2
|
|
npu_per_node: 16
|
|
env_common:
|
|
HCCL_OP_EXPANSION_MODE: "AIV"
|
|
|
|
VLLM_USE_MODELSCOPE: true
|
|
HCCL_BUFFSIZE: 1024
|
|
SERVER_PORT: 8080
|
|
OMP_PROC_BIND: false
|
|
OMP_NUM_THREADS: 1
|
|
PYTORCH_NPU_ALLOC_CONF: "expandable_segments:True"
|
|
VLLM_ASCEND_ENABLE_MLAPO: 1
|
|
VLLM_ASCEND_ENABLE_FLASHCOMM1: 1
|
|
ASCEND_A3_EBA_ENABLE: 1
|
|
|
|
# TODO: need to identify why TP and mtp+1 divisibility rules break on dual-node case
|
|
|
|
deployment:
|
|
-
|
|
server_cmd: >
|
|
vllm serve vllm-ascend/DeepSeek-V3.2-W8A8
|
|
--host 0.0.0.0
|
|
--port $SERVER_PORT
|
|
--data-parallel-size 4
|
|
--data-parallel-size-local 2
|
|
--data-parallel-address $LOCAL_IP
|
|
--data-parallel-rpc-port 13399
|
|
--tensor-parallel-size 8
|
|
--quantization ascend
|
|
--seed 1024
|
|
--enable-expert-parallel
|
|
--max-num-seqs 16
|
|
--max-model-len 68000
|
|
--max-num-batched-tokens 4096
|
|
--no-enable-prefix-caching
|
|
--gpu-memory-utilization 0.85
|
|
--trust-remote-code
|
|
--speculative-config '{"num_speculative_tokens": 3, "method":"deepseek_mtp"}'
|
|
--compilation-config '{"cudagraph_capture_sizes": [8, 16, 24, 32, 40, 48], "cudagraph_mode": "FULL_DECODE_ONLY"}'
|
|
--additional-config '{"layer_sharding": ["q_b_proj", "o_proj"]}'
|
|
--tokenizer-mode deepseek_v32
|
|
--reasoning-parser deepseek_v3
|
|
|
|
-
|
|
server_cmd: >
|
|
vllm serve vllm-ascend/DeepSeek-V3.2-W8A8
|
|
--headless
|
|
--data-parallel-size 4
|
|
--data-parallel-rpc-port 13399
|
|
--data-parallel-size-local 2
|
|
--data-parallel-start-rank 2
|
|
--data-parallel-address $MASTER_IP
|
|
--tensor-parallel-size 8
|
|
--quantization ascend
|
|
--seed 1024
|
|
--enable-expert-parallel
|
|
--max-num-seqs 16
|
|
--max-model-len 68000
|
|
--max-num-batched-tokens 4096
|
|
--no-enable-prefix-caching
|
|
--gpu-memory-utilization 0.85
|
|
--trust-remote-code
|
|
--speculative-config '{"num_speculative_tokens": 3, "method":"deepseek_mtp"}'
|
|
--compilation-config '{"cudagraph_capture_sizes": [8, 16, 24, 32, 40, 48], "cudagraph_mode": "FULL_DECODE_ONLY"}'
|
|
--additional-config '{"layer_sharding": ["q_b_proj", "o_proj"]}'
|
|
--tokenizer-mode deepseek_v32
|
|
--reasoning-parser deepseek_v3
|
|
benchmarks:
|
|
perf_short_warmup:
|
|
case_type: performance
|
|
dataset_path: vllm-ascend/GSM8K-in3500-bs2800
|
|
request_conf: vllm_api_stream_chat
|
|
dataset_conf: gsm8k/gsm8k_gen_0_shot_cot_str_perf
|
|
num_prompts: 1
|
|
max_out_len: 3000
|
|
batch_size: 512
|
|
request_rate: 11.2
|
|
baseline: 1253.8466
|
|
threshold: 0.97
|
|
|
|
perf_long_warmup:
|
|
case_type: performance
|
|
dataset_path: vllm-ascend/GSM8K-in64000-bs2800
|
|
request_conf: vllm_api_stream_chat
|
|
dataset_conf: gsm8k/gsm8k_gen_0_shot_cot_str_perf
|
|
num_prompts: 1
|
|
max_out_len: 3000
|
|
batch_size: 1
|
|
request_rate: 11.2
|
|
baseline: 1253.8466
|
|
threshold: 0.97
|
|
|
|
perf_short:
|
|
case_type: performance
|
|
dataset_path: vllm-ascend/GSM8K-in3500-bs2800
|
|
request_conf: vllm_api_stream_chat
|
|
dataset_conf: gsm8k/gsm8k_gen_0_shot_cot_str_perf
|
|
num_prompts: 512
|
|
max_out_len: 3000
|
|
batch_size: 1
|
|
request_rate: 11.2
|
|
baseline: 148 # after switch vllm to 0.15.0, the baseline reduced significantly, need to confirm if it's a regression or just a more strict measurement
|
|
threshold: 0.97
|
|
|
|
acc:
|
|
case_type: accuracy
|
|
dataset_path: vllm-ascend/gsm8k-lite
|
|
request_conf: vllm_api_general_chat
|
|
dataset_conf: gsm8k/gsm8k_gen_0_shot_cot_chat_prompt
|
|
max_out_len: 4096
|
|
batch_size: 64
|
|
baseline: 95
|
|
threshold: 5
|
|
|
|
acc_aime2025:
|
|
case_type: accuracy
|
|
dataset_path: vllm-ascend/aime2025
|
|
request_conf: vllm_api_general_chat
|
|
dataset_conf: aime2025/aime2025_gen_0_shot_chat_prompt
|
|
max_out_len: 80000
|
|
batch_size: 32
|
|
baseline: 40
|
|
threshold: 7
|