[CPU] misc updates (#11906)
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# CPU Servers
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The document addresses how to set up the [SGLang](https://github.com/sgl-project/sglang) environment and run LLM inference on CPU servers.
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Specifically, SGLang is well optimized on the CPUs equipped with Intel® AMX® Instructions,
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SGLang is enabled and optimized on the CPUs equipped with Intel® AMX® Instructions,
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which are 4th generation or newer Intel® Xeon® Scalable Processors.
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## Optimized Model List
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A list of popular LLMs are optimized and run efficiently on CPU,
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including the most notable open-source models like Llama series, Qwen series,
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and the phenomenal high-quality reasoning model DeepSeek-R1.
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and DeepSeek series like DeepSeek-R1 and DeepSeek-V3.1-Terminus.
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| Model Name | BF16 | w8a8_int8 | FP8 |
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| Model Name | BF16 | W8A8_INT8 | FP8 |
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|:---:|:---:|:---:|:---:|
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| DeepSeek-R1 | | [meituan/DeepSeek-R1-Channel-INT8](https://huggingface.co/meituan/DeepSeek-R1-Channel-INT8) | [deepseek-ai/DeepSeek-R1](https://huggingface.co/deepseek-ai/DeepSeek-R1) |
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| DeepSeek-V3.1-Terminus | | [IntervitensInc/DeepSeek-V3.1-Terminus-Channel-int8](https://huggingface.co/IntervitensInc/DeepSeek-V3.1-Terminus-Channel-int8) | [deepseek-ai/DeepSeek-V3.1-Terminus](https://huggingface.co/deepseek-ai/DeepSeek-V3.1-Terminus) |
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| Llama-3.2-3B | [meta-llama/Llama-3.2-3B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct) | [RedHatAI/Llama-3.2-3B-quantized.w8a8](https://huggingface.co/RedHatAI/Llama-3.2-3B-Instruct-quantized.w8a8) | |
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| Llama-3.1-8B | [meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct) | [RedHatAI/Meta-Llama-3.1-8B-quantized.w8a8](https://huggingface.co/RedHatAI/Meta-Llama-3.1-8B-quantized.w8a8) | |
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| QwQ-32B | | [RedHatAI/QwQ-32B-quantized.w8a8](https://huggingface.co/RedHatAI/QwQ-32B-quantized.w8a8) | |
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@@ -36,7 +37,7 @@ git clone https://github.com/sgl-project/sglang.git
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cd sglang/docker
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# Build the docker image
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docker build -t sglang-cpu:main -f Dockerfile.xeon .
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docker build -t sglang-cpu:latest -f Dockerfile.xeon .
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# Initiate a docker container
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docker run \
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@@ -48,7 +49,7 @@ docker run \
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-v ~/.cache/huggingface:/root/.cache/huggingface \
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-p 30000:30000 \
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-e "HF_TOKEN=<secret>" \
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sglang-cpu:main /bin/bash
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sglang-cpu:latest /bin/bash
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```
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### Install From Source
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@@ -121,9 +122,9 @@ Notes:
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2. The flag `--tp 6` specifies that tensor parallelism will be applied using 6 ranks (TP6).
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The number of TP specified is how many TP ranks will be used during the execution.
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In a CPU platform, a TP rank means a sub-NUMA cluster (SNC).
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Usually we can get the SNC information (How many available) from Operation System.
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User can specify TP to be no more than the total available SNCs in current system.
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On a CPU platform, a TP rank means a sub-NUMA cluster (SNC).
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Usually we can get the SNC information (How many available) from the Operating System.
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Users can specify TP to be no more than the total available SNCs in current system.
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If the specified TP rank number differs from the total SNC count,
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the system will automatically utilize the first `n` SNCs.
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@@ -175,29 +176,29 @@ Additionally, the requests can be formed with
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[OpenAI Completions API](https://docs.sglang.ai/basic_usage/openai_api_completions.html)
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and sent via the command line (e.g. using `curl`) or via your own script.
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## Example: Running DeepSeek-R1
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## Example: Running DeepSeek-V3.1-Terminus
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An example command to launch service for W8A8 DeepSeek-R1 on a Xeon® 6980P server
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An example command to launch service for W8A8_INT8 DeepSeek-V3.1-Terminus on a Xeon® 6980P server:
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```bash
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python -m sglang.launch_server \
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--model meituan/DeepSeek-R1-Channel-INT8 \
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--trust-remote-code \
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--disable-overlap-schedule \
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--device cpu \
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--quantization w8a8_int8 \
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--host 0.0.0.0 \
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--mem-fraction-static 0.8 \
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--enable-torch-compile \
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--torch-compile-max-bs 4 \
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python -m sglang.launch_server \
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--model IntervitensInc/DeepSeek-V3.1-Terminus-Channel-int8 \
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--trust-remote-code \
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--disable-overlap-schedule \
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--device cpu \
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--quantization w8a8_int8 \
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--host 0.0.0.0 \
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--mem-fraction-static 0.8 \
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--enable-torch-compile \
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--torch-compile-max-bs 4 \
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--tp 6
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```
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Similarly, an example command to launch service for FP8 DeepSeek-R1 would be
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Similarly, an example command to launch service for FP8 DeepSeek-V3.1-Terminus would be:
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```bash
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python -m sglang.launch_server \
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--model deepseek-ai/DeepSeek-R1 \
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--model deepseek-ai/DeepSeek-V3.1-Terminus \
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--trust-remote-code \
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--disable-overlap-schedule \
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--device cpu \
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@@ -1623,13 +1623,18 @@ def get_cpu_memory_capacity():
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for numa_id in range(n_numa_node):
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file_meminfo = f"node{numa_id}/meminfo"
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with open(os.path.join(file_prefix, file_meminfo), "r") as f:
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# 1st line contains 'MemTotal'
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line = f.read().split("\n")[0]
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numa_mem_list.append(int(line.split()[3]))
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# MemTotal info is at the 1st line
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line = f.readline()
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# Expected format: "Node 0 MemTotal: 100000000 kB"
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parts = line.split()
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if len(parts) >= 4 and parts[2] == "MemTotal:":
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numa_mem_list.append(int(parts[3]))
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else:
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raise ValueError(f"Unexpected format in {file_meminfo}: {line}")
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# Retrieved value in KB, need MB
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numa_mem = float(min(numa_mem_list) // 1024)
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return numa_mem
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except FileNotFoundError:
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except (FileNotFoundError, ValueError, IndexError):
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numa_mem = psutil.virtual_memory().total / n_numa_node
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# Retrieved value in Byte, need MB
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return float(numa_mem // (1 << 20))
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@@ -15,8 +15,7 @@ requires-python = ">=3.10"
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license = { file = "LICENSE" }
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classifiers = [
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"Programming Language :: Python :: 3",
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"License :: OSI Approved :: Apache Software License",
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"Environment :: CPU"
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"License :: OSI Approved :: Apache Software License"
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]
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dependencies = []
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