Donato Capitella
201 рядки
7.8 KiB
Bash
201 рядки
7.8 KiB
Bash
#!/usr/bin/env bash
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set -euo pipefail
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# start_vllm — interactive helper to launch vLLM on AMD Strix Halo (gfx1151)
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# - Presents a curated list of recent HF models that fit within ~100GB memory (with FP16 or AWQ)
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# - Asks for context length, concurrency, kv‑cache dtype, port, etc.
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# - Starts vLLM with sensible ROCm defaults for Strix Halo
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#
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# Requirements inside the toolbox/container:
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# - vLLM installed in /torch-therock/.venv (this image has it)
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# - internet for first model download (or pre‑downloaded into ~/vllm-models)
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# - optional: ~/.cache/vllm mapped to persist compile cache when using Podman/Docker
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#
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# Notes on quantization:
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# - vLLM supports weight‑only quantized models like AWQ and GPTQ (load pre‑quantized repos).
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# - For AMD GPUs, FP8 KV‑cache can be supported but is experimental on consumer APUs; INT8 KV‑cache is a safer saver.
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# - Qwen3 provides AWQ variants officially; using them can materially reduce memory use. (You do NOT need GGUF; that is for llama.cpp.)
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#
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# Model memory rule of thumb (VERY rough):
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# - FP16 weights ≈ 2 bytes/parameter. So 12B ≈ ~24 GB; 27B ≈ ~54 GB; 32B ≈ ~64 GB (weights only).
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# - Plus KV‑cache, which grows with context & concurrency. If you OOM, lower max context or max concurrent requests.
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#
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# Default directories
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DOWNLOAD_DIR="${DOWNLOAD_DIR:-$HOME/vllm-models}"
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CACHE_DIR_DEFAULT="$HOME/.cache/vllm"
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PORT_DEFAULT="8000"
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HOST_DEFAULT="0.0.0.0"
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GPU_UTIL_DEFAULT="0.92"
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MAX_NUM_SEQS_DEFAULT="4"
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MAX_MODEL_LEN_DEFAULT="16384"
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KV_CACHE_DTYPE_DEFAULT="auto" # choices: auto|int8|fp8 (fp8_e4m3)
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DTYPE_DEFAULT="float16" # choices: float16|bfloat16
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VENV_ACTIVATE="/torch-therock/.venv/bin/activate"
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if [[ -f "$VENV_ACTIVATE" ]]; then
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# shellcheck disable=SC1090
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source "$VENV_ACTIVATE"
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fi
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print_divider() { printf '\n%s\n' "────────────────────────────────────────────────────────"; }
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# --- curated model list (recent, likely to fit <= ~100GB with sane settings) ---
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# Format: label|hf_repo|quant_hint|compat|note
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MODELS=(
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"Llama-4 Scout 17B-16E Instruct FP4|nvidia/Llama-4-Scout-17B-16E-Instruct-FP4|modelopt|nvidia_only|Optimized for NVIDIA; FP4 path may not work on AMD/ROCm"
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"Llama-4 Scout 17B-16E Instruct FP8|nvidia/Llama-4-Scout-17B-16E-Instruct-FP8|modelopt|nvidia_only|Optimized for NVIDIA; FP8 ModelOpt path may not work on AMD/ROCm"
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"OpenAI GPT-OSS 20B (MXFP4)|openai/gpt-oss-20b|mxfp4|experimental|MXFP4 support requires recent vLLM; performance/compat on AMD RDNA iGPU varies"
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"OpenAI GPT-OSS 120B (MXFP4, huge)|openai/gpt-oss-120b|mxfp4|too_large|~120B total params; not practical on a single APU"
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"GLM-4.5-Air FP8 (12B active)|zai-org/GLM-4.5-Air-FP8|fp8|multi_gpu_fp8|Published FP8; vendor recommends multi-GPU with native FP8"
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"Gemma 3 12B IT (FP16)|google/gemma-3-12b-it|fp16|amd_ok|Good baseline"
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"Gemma 3 27B IT (FP16)|google/gemma-3-27b-it|fp16|borderline|Large; consider GPTQ variant if memory tight"
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"Gemma 3 27B IT (GPTQ 4bit)|ISTA-DASLab/gemma-3-27b-it-GPTQ-4b-128g|gptq|amd_ok|Weight-only INT4 reduces memory; throughput may drop"
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"Qwen3 8B Instruct (FP16)|Qwen/Qwen3-8B-Instruct|fp16|amd_ok|Solid quality, easy fit"
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"Qwen3 8B Instruct (AWQ 4bit)|Qwen/Qwen3-8B-AWQ|awq|amd_ok|Official AWQ"
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"Qwen3 14B Instruct (FP16)|Qwen/Qwen3-14B-Instruct|fp16|amd_ok|"
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"Qwen3 14B Instruct (AWQ 4bit)|Qwen/Qwen3-14B-AWQ|awq|amd_ok|"
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"Qwen3 30B A3B Instruct (FP16)|Qwen/Qwen3-30B-A3B-Instruct-2507|fp16|amd_ok|MoE; fits with careful context/concurrency"
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"Qwen3 30B A3B Instruct (AWQ 4bit)|cpatonn/Qwen3-30B-A3B-Instruct-2507-AWQ-4bit|awq|community|Community AWQ; quality varies"
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)
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cat <<'HDR'
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Start vLLM — AMD Strix Halo (gfx1151)
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This helper will:
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1) Let you pick a model (FP16 or AWQ when available)
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2) Ask for context length, concurrency, and KV‑cache dtype
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3) Launch vLLM with Strix‑friendly defaults
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HDR
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print_divider
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printf 'Model download dir (persisted on host) [%s]: ' "$DOWNLOAD_DIR"
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read -r REPLY_DL
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[[ -n "${REPLY_DL:-}" ]] && DOWNLOAD_DIR="$REPLY_DL"
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mkdir -p "$DOWNLOAD_DIR"
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printf 'Cache dir for compiled kernels [%s]: ' "$CACHE_DIR_DEFAULT"
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read -r REPLY_CACHE
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[[ -n "${REPLY_CACHE:-}" ]] && export VLLM_CACHE_DIR="$REPLY_CACHE" || export VLLM_CACHE_DIR="$CACHE_DIR_DEFAULT"
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mkdir -p "$VLLM_CACHE_DIR"
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print_divider
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printf 'Select a model:\n'
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idx=1
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for m in "${MODELS[@]}"; do
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IFS='|' read -r label _ _ <<<"$m"
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printf ' [%d] %s\n' "$idx" "$label"
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idx=$((idx+1))
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done
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printf 'Enter number: '
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read -r CHOICE
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if ! [[ "$CHOICE" =~ ^[0-9]+$ ]] || (( CHOICE < 1 || CHOICE > ${#MODELS[@]} )); then
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echo 'Invalid choice.'; exit 1
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fi
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SEL="${MODELS[$((CHOICE-1))]}"
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IFS='|' read -r SEL_LABEL HF_REPO QUANT_HINT COMPAT NOTE <<<"$SEL"
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# Quantization flag heuristic
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QUANT_FLAG=()
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case "$QUANT_HINT" in
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awq) QUANT_FLAG=(--quantization awq) ;;
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gptq) QUANT_FLAG=(--quantization gptq) ;;
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mxfp4) QUANT_FLAG=(--quantization mxfp4) ;;
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modelopt) QUANT_FLAG=(--quantization modelopt) ;;
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fp16|fp8|bf16|auto|'') ;; # rely on model config
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esac
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# Compatibility warnings
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case "$COMPAT" in
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nvidia_only)
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echo "WARNING: This checkpoint is optimized for NVIDIA (TensorRT/ModelOpt). It may not run on AMD ROCm (RDNA iGPU)." ;;
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multi_gpu_fp8)
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echo "WARNING: Vendor docs indicate multi‑GPU FP8 is recommended. On a single Strix Halo APU this is likely impractical." ;;
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too_large)
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echo "WARNING: 120B‑class model is far beyond single‑APU capacity. Expect failure unless heavy offload/sharding is used." ;;
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borderline)
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echo "Note: Large model — keep context/concurrency modest or use a quantized variant." ;;
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community)
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echo "Note: Community quantization — quality/perf may vary." ;;
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amd_ok|*) ;;
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esac
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[[ -n "$NOTE" ]] && echo "Note: $NOTE"
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print_divider
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printf 'Max context tokens (--max-model-len) [%s]: ' "$MAX_MODEL_LEN_DEFAULT"
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read -r REPLY_CTX
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MAX_MODEL_LEN="${REPLY_CTX:-$MAX_MODEL_LEN_DEFAULT}"
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printf 'Max concurrent requests (--max-num-seqs) [%s]: ' "$MAX_NUM_SEQS_DEFAULT"
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read -r REPLY_CONC
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MAX_NUM_SEQS="${REPLY_CONC:-$MAX_NUM_SEQS_DEFAULT}"
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printf 'KV cache dtype (auto|int8|fp8) [%s]: ' "$KV_CACHE_DTYPE_DEFAULT"
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read -r REPLY_KV
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KV_CACHE_DTYPE="${REPLY_KV:-$KV_CACHE_DTYPE_DEFAULT}"
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printf 'Model dtype (float16|bfloat16) [%s]: ' "$DTYPE_DEFAULT"
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read -r REPLY_DTYPE
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DTYPE="${REPLY_DTYPE:-$DTYPE_DEFAULT}"
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printf 'GPU memory utilization (0.50‑0.98) [%s]: ' "$GPU_UTIL_DEFAULT"
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read -r REPLY_UTIL
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GPU_UTIL="${REPLY_UTIL:-$GPU_UTIL_DEFAULT}"
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printf 'Host bind address [%s]: ' "$HOST_DEFAULT"
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read -r REPLY_HOST
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HOST="${REPLY_HOST:-$HOST_DEFAULT}"
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printf 'Optional CPU offload in GB (0 to disable) [0]: '
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read -r REPLY_OFF
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CPU_OFFLOAD_GB="${REPLY_OFF:-0}"
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printf 'Port [%s]: ' "$PORT_DEFAULT"
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read -r REPLY_PORT
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PORT="${REPLY_PORT:-$PORT_DEFAULT}"
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print_divider
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CMD=(
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vllm serve "$HF_REPO"
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--host "$HOST"
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--port "$PORT"
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--download-dir "$DOWNLOAD_DIR"
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--dtype "$DTYPE"
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--max-model-len "$MAX_MODEL_LEN"
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--max-num-seqs "$MAX_NUM_SEQS"
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--gpu-memory-utilization "$GPU_UTIL"
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)
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# Add CPU offload if requested
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if [[ "$CPU_OFFLOAD_GB" =~ ^[0-9]+$ ]] && (( CPU_OFFLOAD_GB > 0 )); then
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CMD+=(--cpu-offload-gb "$CPU_OFFLOAD_GB")
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fi
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# kv‑cache dtype
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if [[ "$KV_CACHE_DTYPE" != "auto" ]]; then
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# Map fp8 -> fp8_e4m3 for AMD unless user typed explicit subtype already
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if [[ "$KV_CACHE_DTYPE" == "fp8" ]]; then
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CMD+=(--kv-cache-dtype fp8_e4m3)
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else
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CMD+=(--kv-cache-dtype "$KV_CACHE_DTYPE")
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fi
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fi
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# quantization flags (if any)
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CMD+=("${QUANT_FLAG[@]}")
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# AMD ROCm/AOTriton helpful env
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export PYTORCH_ROCM_ARCH="${PYTORCH_ROCM_ARCH:-gfx1151}"
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export TORCH_ROCM_AOTRITON_ENABLE_EXPERIMENTAL=1
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printf 'About to run:\n\n %q' "${CMD[0]}"; for ((i=1;i<${#CMD[@]};i++)); do printf ' \\\n %q' "${CMD[$i]}"; done; printf '\n\n'
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read -r -p "Proceed? [Y/n] " yn
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yn=${yn:-Y}
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if [[ "$yn" =~ ^[Yy]$ ]]; then
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exec "${CMD[@]}"
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else
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echo "Canceled."
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fi
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