feat: centralize model configurations and benchmark settings into a new models.py module and update Dockerfile and scripts to use it.
Este commit está contenido en:
Donato Capitella
+7
-3
@@ -125,8 +125,9 @@ RUN chmod -R a+rwX /opt && \
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COPY scripts/01-rocm-env-for-triton.sh /etc/profile.d/01-rocm-env-for-triton.sh
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COPY scripts/99-toolbox-banner.sh /etc/profile.d/99-toolbox-banner.sh
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COPY scripts/zz-venv-last.sh /etc/profile.d/zz-venv-last.sh
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COPY scripts/start_vllm.py /usr/local/bin/start-vllm
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COPY scripts/start_vllm_cluster.py /usr/local/bin/start-vllm-cluster
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COPY scripts/start_vllm.py /opt/start-vllm
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COPY scripts/start_vllm_cluster.py /opt/start-vllm-cluster
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COPY scripts/models.py /opt/models.py
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COPY benchmarks/max_context_results.json /opt/max_context_results.json
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COPY benchmarks/run_vllm_bench.py /opt/run_vllm_bench.py
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COPY benchmarks/vllm_cluster_bench.py /opt/vllm_cluster_bench.py
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@@ -134,7 +135,10 @@ COPY benchmarks/find_max_context.py /opt/find_max_context.py
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COPY rdma_cluster/compare_eth_vs_rdma.sh /opt/compare_eth_vs_rdma.sh
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COPY scripts/configure_cluster.sh /opt/configure_cluster.sh
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RUN chmod +x /opt/configure_cluster.sh
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RUN chmod 0644 /etc/profile.d/*.sh && chmod +x /usr/local/bin/start-vllm && chmod +x /usr/local/bin/start-vllm-cluster && chmod +x /opt/vllm_cluster_bench.py && chmod +x /opt/compare_eth_vs_rdma.sh && chmod +x /opt/find_max_context.py && chmod 0644 /opt/max_context_results.json
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RUN chmod +x /opt/start-vllm /opt/start-vllm-cluster /opt/vllm_cluster_bench.py /opt/compare_eth_vs_rdma.sh /opt/find_max_context.py /opt/run_vllm_bench.py && \
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ln -s /opt/start-vllm /usr/local/bin/start-vllm && \
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ln -s /opt/start-vllm-cluster /usr/local/bin/start-vllm-cluster && \
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chmod 0644 /etc/profile.d/*.sh /opt/max_context_results.json /opt/models.py
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RUN chmod 0644 /etc/profile.d/*.sh
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RUN printf 'ulimit -S -c 0\n' > /etc/profile.d/90-nocoredump.sh && chmod 0644 /etc/profile.d/90-nocoredump.sh
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@@ -2,17 +2,32 @@
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import subprocess, time, json, sys, os, requests, argparse
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from pathlib import Path
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# =========================
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# ⚙️ GLOBAL SETTINGS
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# =========================
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# HARDWARE: 1x Strix Halo (128GB, RDNA 3.5)
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GPU_UTIL = "0.90"
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# 1. THROUGHPUT CONFIG
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OFF_NUM_PROMPTS = 200
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OFF_FORCED_OUTPUT = "512"
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# Default fallback if not specified in MODEL_TABLE
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DEFAULT_BATCH_TOKENS = "8192"
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try:
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import models
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except ImportError:
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# If running locally and models.py is in ../scripts?
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# Or if running in /opt where models.py is alongside.
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# We will try adding current dir to path just in case
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sys.path.append(os.getcwd())
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try:
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import models
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except ImportError:
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# Fallback for local structure: assuming this is in benchmarks/ and models is in scripts/
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sys.path.append(str(Path(__file__).parent.parent / "scripts"))
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import models
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# Import from shared config
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MODEL_TABLE = models.MODEL_TABLE
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MODELS_TO_RUN = models.MODELS_TO_RUN
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GPU_UTIL = models.GPU_UTIL
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OFF_NUM_PROMPTS = models.OFF_NUM_PROMPTS
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OFF_FORCED_OUTPUT = models.OFF_FORCED_OUTPUT
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DEFAULT_BATCH_TOKENS = models.DEFAULT_BATCH_TOKENS
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# Fallbacks
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FALLBACK_INPUT_LEN = 1024
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@@ -21,84 +36,6 @@ FALLBACK_OUTPUT_LEN = 512
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RESULTS_DIR = Path("benchmark_results")
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RESULTS_DIR.mkdir(exist_ok=True)
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# =========================
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# 🛠️ MODEL CONFIGURATION 🛠️
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# =========================
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MODEL_TABLE = {
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# 1. Llama 3.1 8B Instruct
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# MAD uses 131k tokens. We scale to 32k for 32GB VRAM safety.
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"meta-llama/Meta-Llama-3.1-8B-Instruct": {
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"trust_remote": False,
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"valid_tp": [1, 2],
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"max_num_seqs": "64",
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"max_tokens": "32768"
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},
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"google/gemma-3-12b-it": {
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"trust_remote": False,
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"valid_tp": [1, 2],
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"max_num_seqs": "64",
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"max_tokens": "32768"
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},
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# 2. GPT-OSS 20B (MXFP4)
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# MAD Row 0 uses 8192. We match this exactly.
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"openai/gpt-oss-20b": {
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"trust_remote": True,
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"valid_tp": [1, 2],
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"max_num_seqs": "64",
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"max_tokens": "8192"
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},
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"openai/gpt-oss-120b": {
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"trust_remote": True,
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"valid_tp": [1],
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"max_num_seqs": "64",
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"max_tokens": "8192"
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},
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"Qwen/Qwen3-14B-AWQ": {
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"trust_remote": True,
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"valid_tp": [1], # Too big for single GPU
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"max_num_seqs": "32", # Lower concurrency for safety
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"max_tokens": "16384", # Lower batch size because Eager mode is CPU intensive
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"enforce_eager": False,
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"env": {"VLLM_USE_TRITON_AWQ": "1"} # Fixes "Unsupported Hardware" error
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},
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# 4. Qwen 30B 4-bit
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"cpatonn/Qwen3-Coder-30B-A3B-Instruct-GPTQ-4bit": {
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"trust_remote": True,
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"enforce_eager": False,
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"valid_tp": [1, 2],
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"max_num_seqs": "64",
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"max_tokens": "32768"
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},
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# 5. Qwen 80B AWQ
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# Size: ~48GB. Fits on 2x32GB (64GB). Leftover for Cache: ~16GB.
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# Config: 20k ctx fits in that cache. Eager mode required for stability.
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"dazipe/Qwen3-Next-80B-A3B-Instruct-GPTQ-Int4A16": {
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"trust_remote": True,
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"valid_tp": [1], # Too big for single GPU
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"max_num_seqs": "32", # Lower concurrency for safety
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"max_tokens": "16384", # Lower batch size because Eager mode is CPU intensive
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"enforce_eager": True,
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"env": {"VLLM_USE_TRITON_AWQ": "1"} # Fixes "Unsupported Hardware" error
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},
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}
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MODELS_TO_RUN = [
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"meta-llama/Meta-Llama-3.1-8B-Instruct",
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"google/gemma-3-12b-it",
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"Qwen/Qwen3-14B-AWQ",
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"openai/gpt-oss-20b",
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"openai/gpt-oss-120b",
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"cpatonn/Qwen3-Coder-30B-A3B-Instruct-GPTQ-4bit",
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"dazipe/Qwen3-Next-80B-A3B-Instruct-GPTQ-Int4A16",
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]
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# =========================
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# UTILS
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@@ -25,12 +25,21 @@ RESULTS_DIR.mkdir(exist_ok=True)
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# Since this is a new file in root/benchmarks? No, likely scripts/ or same dir.
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# Let's assume it's in the same dir as run_vllm_bench.py.
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try:
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from run_vllm_bench import MODEL_TABLE, MODELS_TO_RUN
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import models
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except ImportError:
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# Fallback if run directly and path issues
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sys.path.append(os.path.dirname(__file__))
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from run_vllm_bench import MODEL_TABLE, MODELS_TO_RUN
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# If in /opt, this should work if path includes ., otherwise:
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sys.path.append(os.getcwd())
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try:
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import models
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# Also try parent/scripts for local dev if above failed?
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except ImportError:
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sys.path.append(str(Path(__file__).parent.parent / "scripts"))
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import models
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MODEL_TABLE = models.MODEL_TABLE
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MODELS_TO_RUN = models.MODELS_TO_RUN
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# =========================
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# UTILS (Adapted for Cluster)
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@@ -0,0 +1,98 @@
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MODEL_TABLE = {
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# 1. Llama 3.1 8B Instruct
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# MAD uses 131k tokens. We scale to 32k for 32GB VRAM safety.
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"meta-llama/Meta-Llama-3.1-8B-Instruct": {
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"trust_remote": False,
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"valid_tp": [1, 2],
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"max_num_seqs": "64",
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"max_tokens": "32768"
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},
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"google/gemma-3-12b-it": {
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"trust_remote": False,
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"valid_tp": [1, 2],
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"max_num_seqs": "64",
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"max_tokens": "32768"
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},
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# 2. GPT-OSS 20B (MXFP4)
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# MAD Row 0 uses 8192. We match this exactly.
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"openai/gpt-oss-20b": {
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"trust_remote": True,
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"valid_tp": [1, 2],
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"max_num_seqs": "64",
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"max_tokens": "8192"
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},
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"openai/gpt-oss-120b": {
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"trust_remote": True,
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"valid_tp": [1],
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"max_num_seqs": "64",
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"max_tokens": "8192"
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},
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"Qwen/Qwen3-14B-AWQ": {
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"trust_remote": True,
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"valid_tp": [1], # Too big for single GPU
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"max_num_seqs": "32", # Lower concurrency for safety
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"max_tokens": "16384", # Lower batch size because Eager mode is CPU intensive
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"enforce_eager": False,
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"env": {"VLLM_USE_TRITON_AWQ": "1"} # Fixes "Unsupported Hardware" error
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},
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# 4. Qwen 30B 4-bit
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"btbtyler09/Qwen3-Coder-30B-A3B-Instruct-gptq-4bit": {
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"trust_remote": True,
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"enforce_eager": False,
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"valid_tp": [1, 2],
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"max_num_seqs": "64",
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"max_tokens": "32768"
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},
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"btbtyler09/Qwen3-Coder-30B-A3B-Instruct-gptq-8bit": {
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"trust_remote": True,
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"enforce_eager": False,
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"valid_tp": [1, 2],
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"max_num_seqs": "64",
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"max_tokens": "32768"
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},
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"zai-org/GLM-4.7-Flash": {
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"trust_remote": True,
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"enforce_eager": False,
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"valid_tp": [1, 2],
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"max_num_seqs": "64",
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"max_tokens": "32768",
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},
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# 5. Qwen 80B AWQ
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# Size: ~48GB. Fits on 2x32GB (64GB). Leftover for Cache: ~16GB.
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# Config: 20k ctx fits in that cache. Eager mode required for stability.
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"dazipe/Qwen3-Next-80B-A3B-Instruct-GPTQ-Int4A16": {
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"trust_remote": True,
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"valid_tp": [1], # Too big for single GPU
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"max_num_seqs": "32", # Lower concurrency for safety
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"max_tokens": "16384", # Lower batch size because Eager mode is CPU intensive
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"enforce_eager": True,
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"env": {"VLLM_USE_TRITON_AWQ": "1"} # Fixes "Unsupported Hardware" error
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},
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}
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MODELS_TO_RUN = [
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"meta-llama/Meta-Llama-3.1-8B-Instruct",
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"google/gemma-3-12b-it",
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"Qwen/Qwen3-14B-AWQ",
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"openai/gpt-oss-20b",
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"openai/gpt-oss-120b",
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"zai-org/GLM-4.7-Flash",
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"btbtyler09/Qwen3-Coder-30B-A3B-Instruct-gptq-4bit",
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"btbtyler09/Qwen3-Coder-30B-A3B-Instruct-gptq-8bit",
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"dazipe/Qwen3-Next-80B-A3B-Instruct-GPTQ-Int4A16",
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]
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# Hardware / Global Defaults
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GPU_UTIL = "0.90"
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OFF_NUM_PROMPTS = 200
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OFF_FORCED_OUTPUT = "512"
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DEFAULT_BATCH_TOKENS = "8192"
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@@ -12,16 +12,21 @@ SCRIPT_DIR = Path(__file__).parent.resolve()
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BENCH_DIR = SCRIPT_DIR.parent / "benchmarks"
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OPT_DIR = Path("/opt")
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# Check /opt first (Container), then local fallback
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# Check /opt first (Container), then local fallback for results file location
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if (OPT_DIR / "run_vllm_bench.py").exists():
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sys.path.append(str(OPT_DIR))
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else:
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sys.path.append(str(BENCH_DIR))
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# Also ensure current script dir is in path for local 'models' import if not already
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sys.path.append(str(SCRIPT_DIR))
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try:
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from run_vllm_bench import MODEL_TABLE, MODELS_TO_RUN
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import models
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MODEL_TABLE = models.MODEL_TABLE
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MODELS_TO_RUN = models.MODELS_TO_RUN
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except ImportError:
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print("Error: Could not import run_vllm_bench.py config.")
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print("Error: Could not import models.py config.")
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sys.exit(1)
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if (OPT_DIR / "max_context_results.json").exists():
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@@ -13,16 +13,20 @@ SCRIPT_DIR = Path(__file__).parent.resolve()
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BENCH_DIR = SCRIPT_DIR.parent / "benchmarks"
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OPT_DIR = Path("/opt")
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# Check /opt first (Container), then local fallback
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if (OPT_DIR / "run_vllm_bench.py").exists():
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sys.path.append(str(OPT_DIR))
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else:
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sys.path.append(str(BENCH_DIR))
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sys.path.append(str(SCRIPT_DIR))
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try:
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from run_vllm_bench import MODEL_TABLE, MODELS_TO_RUN
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import models
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MODEL_TABLE = models.MODEL_TABLE
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MODELS_TO_RUN = models.MODELS_TO_RUN
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except ImportError:
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print("Error: Could not import run_vllm_bench.py config.")
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print("Error: Could not import models.py config.")
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sys.exit(1)
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if (OPT_DIR / "max_context_results.json").exists():
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Referencia en una nueva incidencia
Block a user