60 lines
2.3 KiB
Python
60 lines
2.3 KiB
Python
#!/usr/bin/env python3
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"""
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DeepSeek V4 Pro NVFP4 — vLLM OpenAI-compatible server.
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Run from the venv on the B200 node:
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source /root/nvidia-meeting/venv/bin/activate
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python3 /root/nvidia-meeting/deepseek-v4-quant/scripts/serve_vllm.py
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Or in the background:
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nohup python3 /root/nvidia-meeting/deepseek-v4-quant/scripts/serve_vllm.py \
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> /root/nvidia-meeting/vllm_serve.log 2>&1 &
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"""
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import subprocess
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import sys
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MODEL = "/root/nvidia-meeting/DeepSeek-V4-Pro-NVFP4"
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# These flags are critical for V4 — do not change without understanding why:
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# --trust-remote-code V4 needs custom modeling code
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# --kv-cache-dtype fp8 Match our kv_cache_qformat=fp8_cast quantization
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# --block-size 256 V4 recommended block size
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# --enable-expert-parallel Distribute expert computation across GPUs (critical for 256-expert MoE)
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# --tensor-parallel-size 8 8× B200
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# --compilation-config CUDA graphs for throughput — FULL_AND_PIECEWISE + all custom ops
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# --attention_config FP4 indexer cache for V4 MLA attention
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# --moe-backend deep_gemm_mega_moe — optimized MoE kernel for Blackwell
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# --tokenizer-mode deepseek_v4 — V4-specific tokenizer
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# --tool-call-parser deepseek_v4 — native tool calling
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# --enable-auto-tool-choice Auto tool choice for function calling
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# --reasoning-parser deepseek_v4 — reasoning/thinking output parsing
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# --speculative_config MTP speculative decoding (2 speculative tokens)
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cmd = [
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sys.executable, "-m", "vllm.entrypoints.openai.api_server",
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"--model", MODEL,
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"--trust-remote-code",
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"--kv-cache-dtype", "fp8",
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"--block-size", "256",
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"--enable-expert-parallel",
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"--tensor-parallel-size", "8",
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"--compilation-config", '{"cudagraph_mode":"FULL_AND_PIECEWISE", "custom_ops":["all"]}',
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"--attention_config.use_fp4_indexer_cache=True",
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"--moe-backend", "deep_gemm_mega_moe",
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"--tokenizer-mode", "deepseek_v4",
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"--tool-call-parser", "deepseek_v4",
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"--enable-auto-tool-choice",
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"--reasoning-parser", "deepseek_v4",
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"--speculative_config", '{"method":"mtp","num_speculative_tokens":2}',
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"--host", "0.0.0.0",
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"--port", "8000",
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]
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print(f"Starting vLLM server for {MODEL}")
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print(f"Command: {' '.join(cmd)}")
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print(f"Log: /root/nvidia-meeting/vllm_serve.log")
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print()
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sys.exit(subprocess.call(cmd))
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