[CPU] fix OOM when mem-fraction is not set (#9090)

docker/Dockerfile.xeon

@@ -31,8 +31,7 @@ ENV PIP_ROOT_USER_ACTION=ignore
 ENV CONDA_PREFIX=/sgl-workspace/miniforge3
 
 RUN pip config set global.index-url https://download.pytorch.org/whl/cpu && \
-    pip config set global.extra-index-url https://pypi.org/simple && \
-    pip install intel-openmp
+    pip config set global.extra-index-url https://pypi.org/simple
 
 RUN git clone https://github.com/sgl-project/sglang.git && \
     cd sglang && \
@@ -41,7 +40,7 @@ RUN git clone https://github.com/sgl-project/sglang.git && \
     pip install torch==${VER_TORCH} torchvision==${VER_TORCHVISION} triton==${VER_TRITON} --force-reinstall && \
     cd sgl-kernel && \
     cp pyproject_cpu.toml pyproject.toml && \
-    pip install -v .
+    pip install .
 
 ENV SGLANG_USE_CPU_ENGINE=1
 ENV LD_PRELOAD=/sgl-workspace/miniforge3/lib/libiomp5.so:/sgl-workspace/miniforge3/lib/libtcmalloc.so:/sgl-workspace/miniforge3/lib/libtbbmalloc.so.2

docs/platforms/cpu_server.md

@@ -84,13 +84,13 @@ git checkout <YOUR-DESIRED-VERSION>
 # Install SGLang dependent libs, and build SGLang main package
 pip install --upgrade pip setuptools
 conda install -y libsqlite==3.48.0 gperftools tbb libnuma numactl
-pip install intel-openmp
 pip install -e "python[all_cpu]"
+pip install torch==2.7.1 torchvision==0.22.1 triton==3.3.1 --force-reinstall
 
 # Build the CPU backend kernels
 cd sgl-kernel
 cp pyproject_cpu.toml pyproject.toml
-pip install -v .
+pip install .
 
 # Other required environment variables
 # Recommend to set these in ~/.bashrc in order not to set every time in a new terminal
@@ -134,13 +134,17 @@ Notes:
     export SGLANG_CPU_OMP_THREADS_BIND="0-39|43-82|86-125|128-167|171-210|214-253"
     ```
 
+    Please beware that with SGLANG_CPU_OMP_THREADS_BIND set,
+    the available memory amounts of the ranks may not be determined in prior.
+    You may need to set proper `--max-total-tokens` to avoid the out-of-memory error.
+
 3. For optimizing decoding with torch.compile, please add the flag `--enable-torch-compile`.
     To specify the maximum batch size when using torch compile, set the flag `--torch-compile-max-bs`.
     For example, `--enable-torch-compile --torch-compile-max-bs 4` means using torch compile and setting the
     maximum batch size to 4.
 
 4. A warmup step is automatically triggered when the service is started.
-The server is ready when you see the log `The server is fired up and ready to roll!`.
+    The server is ready when you see the log `The server is fired up and ready to roll!`.
 
 ## Benchmarking with Requests
 
@@ -164,7 +168,7 @@ python -m sglang.bench_serving -h
 ```
 
 Additionally, the requests can be formed with
-[OpenAI Completions API](https://docs.sglang.ai/backend/openai_api_completions.html)
+[OpenAI Completions API](https://docs.sglang.ai/basic_usage/openai_api_completions.html)
 and sent via the command line (e.g. using `curl`) or via your own script.
 
 ## Example: Running DeepSeek-R1
@@ -180,7 +184,6 @@ python -m sglang.launch_server                 \
     --quantization w8a8_int8                   \
     --host 0.0.0.0                             \
     --mem-fraction-static 0.8                  \
-    --max-total-token 65536                    \
     --tp 6
 ```
 
@@ -194,7 +197,6 @@ python -m sglang.launch_server                 \
     --device cpu                               \
     --host 0.0.0.0                             \
     --mem-fraction-static 0.8                  \
-    --max-total-token 65536                    \
     --tp 6
 ```
 

python/pyproject.toml

@@ -87,7 +87,7 @@ srt_hip = [
 ]
 
 # https://docs.sglang.ai/platforms/cpu_server.html
-srt_cpu = ["sglang[runtime_common]"]
+srt_cpu = ["sglang[runtime_common]", "intel-openmp"]
 
 # https://docs.sglang.ai/platforms/ascend_npu.html
 srt_npu = ["sglang[runtime_common]"]

python/sglang/srt/model_executor/model_runner.py

@@ -1673,10 +1673,9 @@ class ModelRunner:
 
     def init_threads_binding(self):
         omp_cpuids = os.environ.get("SGLANG_CPU_OMP_THREADS_BIND", "all")
+        cpu_ids_by_node = get_cpu_ids_by_node()
+        n_numa_node = len(cpu_ids_by_node)
         if omp_cpuids == "all":
-            cpu_ids_by_node = get_cpu_ids_by_node()
-            n_numa_node = len(cpu_ids_by_node)
-
             assert self.tp_size <= n_numa_node, (
                 f"SGLANG_CPU_OMP_THREADS_BIND is not set, in this case, "
                 f"tp_size {self.tp_size} should be smaller than or equal to number of numa node on the machine {n_numa_node}. "
@@ -1693,7 +1692,18 @@ class ModelRunner:
                 )
             self.local_omp_cpuid = cpu_ids_by_node[self.tp_rank]
         else:
-            self.local_omp_cpuid = omp_cpuids.split("|")[self.tp_rank]
+            threads_bind_list = omp_cpuids.split("|")
+            assert self.tp_size == len(threads_bind_list), (
+                f"SGLANG_CPU_OMP_THREADS_BIND setting must be aligned with TP size parameter ({self.tp_size}). "
+                f"Please double check your settings."
+            )
+            self.local_omp_cpuid = threads_bind_list[self.tp_rank]
+            if self.tp_size > n_numa_node:
+                logger.warning(
+                    f"TP size ({self.tp_size})is larger than numa node number ({n_numa_node}), "
+                    f"in this case the available memory amount of each rank cannot be determined in prior. "
+                    f"Please set proper `--max-total-tokens` to avoid the out-of-memory error."
+                )
 
     def apply_torch_tp(self):
         logger.info(f"Enabling torch tensor parallelism on {self.tp_size} devices.")

python/sglang/srt/utils.py

@@ -434,7 +434,9 @@ def get_available_gpu_memory(
 
     elif device == "cpu":
         # TODO: rename the variables in the current function to be not GPU specific
-        free_gpu_memory = psutil.virtual_memory().available
+        total_free_memory = psutil.virtual_memory().available
+        n_numa_node: int = len(get_cpu_ids_by_node())
+        free_gpu_memory = round(total_free_memory / n_numa_node, 3)
     elif device == "npu":
         num_gpus = torch.npu.device_count()
         assert gpu_id < num_gpus

test/srt/test_intel_amx_attention_backend.py

@@ -109,7 +109,7 @@ class TestIntelAMXAttnBackend(CustomTestCase):
                 "--attention-backend",
                 "intel_amx",
                 "--mem-fraction-static",
-                "0.05",
+                "0.3",
                 "--disable-radix",
                 "--trust-remote-code",
                 "--disable-overlap-schedule",