ab676413e6
### What this PR does / why we need it?
1) Default enable MLAPO for deepseek MLA Attention W8A8 models on PD
disagregation D Instance, for example: DeepSeekV3-W8A8,
DeepSeek-R1-W8A8.
2) Default enable MLAPO for DeepSeek SFA Attention W8A8 models,
currently is DeepSeek-V3.2-W8A8.
### Does this PR introduce _any_ user-facing change?
Don't need use manully to VLLM_ASCEND_ENABLE_MLAPO=1, to enable MLAPO
feature for deepseek w8a8 model
The effect of enabling MLAPO SFA model deployed on a single A3 Node:
Test
with:tests/e2e/nightly/single_node/models/test_deepseek_v3_2_exp_w8a8.py
dataset: gsm8k-lite，without set MTP, FULL GRAPH, has 19% promote：
未默认开启 MLAPO 时：
├─────────────────────────┤
│ TTFT │ 14055.8836 ms │
├─────────────────────────┤
│ ITL │ 66.8171 ms. │
├─────────────────────────┤
│ Output Token Throughput │ 104.9105 token/s │
├─────────────────────────┤
默认开启 MLAPO 时：
├─────────────────────────┤
│ TTFT │ 3753.1547 ms │
├─────────────────────────┤
│ ITL. │ 61.4236 ms. │
├─────────────────────────┤
│ Output Token Throughput │ 125.2075 token/s│
├─────────────────────────┤
- vLLM version: v0.13.0
- vLLM main:
2c24bc6996
---------
Signed-off-by: leo-pony <nengjunma@outlook.com>
87 lines
2.6 KiB
YAML
87 lines
2.6 KiB
YAML
test_name: "test DeepSeek-V3.2-W8A8 on A3"
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model: "vllm-ascend/DeepSeek-V3.2-W8A8"
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num_nodes: 2
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npu_per_node: 16
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env_common:
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HCCL_OP_EXPANSION_MODE: "AIV"
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VLLM_USE_MODELSCOPE: true
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HCCL_BUFFSIZE: 1024
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SERVER_PORT: 8080
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OMP_PROC_BIND: false
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OMP_NUM_THREADS: 1
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PYTORCH_NPU_ALLOC_CONF: "expandable_segments:True"
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VLLM_ASCEND_ENABLE_FLASHCOMM1: 0
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ASCEND_A3_EBA_ENABLE: 1
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deployment:
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-
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server_cmd: >
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vllm serve vllm-ascend/DeepSeek-V3.2-W8A8
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--host 0.0.0.0
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--port $SERVER_PORT
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--data-parallel-size 4
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--data-parallel-size-local 2
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--data-parallel-address $LOCAL_IP
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--data-parallel-rpc-port 13399
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--tensor-parallel-size 8
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--quantization ascend
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--seed 1024
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--enable-expert-parallel
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--max-num-seqs 16
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--max-model-len 8192
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--max-num-batched-tokens 4096
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--no-enable-prefix-caching
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--gpu-memory-utilization 0.85
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--trust-remote-code
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--speculative-config '{"num_speculative_tokens": 2, "method":"deepseek_mtp"}'
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--compilation-config '{"cudagraph_capture_sizes": [3, 6, 9, 12, 15, 18, 21, 24, 27, 30, 33, 36, 39, 42, 45, 48], "cudagraph_mode": "FULL_DECODE_ONLY"}'
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--tokenizer-mode deepseek_v32
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--reasoning-parser deepseek_v3
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-
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server_cmd: >
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vllm serve vllm-ascend/DeepSeek-V3.2-W8A8
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--headless
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--data-parallel-size 4
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--data-parallel-rpc-port 13399
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--data-parallel-size-local 2
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--data-parallel-start-rank 2
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--data-parallel-address $MASTER_IP
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--tensor-parallel-size 8
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--quantization ascend
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--seed 1024
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--enable-expert-parallel
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--max-num-seqs 16
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--max-model-len 8192
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--max-num-batched-tokens 4096
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--no-enable-prefix-caching
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--gpu-memory-utilization 0.85
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--trust-remote-code
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--speculative-config '{"num_speculative_tokens": 2, "method":"deepseek_mtp"}'
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--compilation-config '{"cudagraph_capture_sizes": [3, 6, 9, 12, 15, 18, 21, 24, 27, 30, 33, 36, 39, 42, 45, 48], "cudagraph_mode": "FULL_DECODE_ONLY"}'
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--tokenizer-mode deepseek_v32
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--reasoning-parser deepseek_v3
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benchmarks:
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perf:
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case_type: performance
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dataset_path: vllm-ascend/GSM8K-in3500-bs2800
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request_conf: vllm_api_stream_chat
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dataset_conf: gsm8k/gsm8k_gen_0_shot_cot_str_perf
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num_prompts: 512
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max_out_len: 3000
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batch_size: 512
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request_rate: 11.2
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baseline: 905.6805
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threshold: 0.97
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acc:
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case_type: accuracy
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dataset_path: vllm-ascend/gsm8k-lite
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request_conf: vllm_api_general_chat
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dataset_conf: gsm8k/gsm8k_gen_0_shot_cot_chat_prompt
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max_out_len: 4096
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batch_size: 64
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baseline: 95
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threshold: 5
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