[Hardware][PPC64LE] Enable V1 for ppc64le and ARM (#20554)

Akashcodes732 · Akash Kaothalkar · nikhil-arm · web-flow · commit 6db31e7a2735 · 2025-07-08T20:00:41.000-07:00
Signed-off-by: Akash Kaothalkar &lt;akash.kaothalkar@ibm.com&gt;
Co-authored-by: Akash Kaothalkar &lt;akash.kaothalkar@ibm.com&gt;
Co-authored-by: Nikhil Gupta &lt;nikhil.gupta2@arm.com&gt;
diff --git a/vllm/engine/arg_utils.py b/vllm/engine/arg_utils.py
@@ -36,6 +36,7 @@
 from vllm.executor.executor_base import ExecutorBase
 from vllm.logger import init_logger
 from vllm.model_executor.layers.quantization import QuantizationMethods
+from vllm.platforms import CpuArchEnum, current_platform
 from vllm.plugins import load_general_plugins
 from vllm.reasoning import ReasoningParserManager
 from vllm.test_utils import MODEL_WEIGHTS_S3_BUCKET, MODELS_ON_S3
@@ -1096,7 +1097,6 @@ def create_engine_config(
         If VLLM_USE_V1 is specified by the user but the VllmConfig
         is incompatible, we raise an error.
         """
-        from vllm.platforms import current_platform
         current_platform.pre_register_and_update()
 
         device_config = DeviceConfig(
@@ -1123,9 +1123,16 @@ def create_engine_config(
         # Set default arguments for V0 or V1 Engine.
         if use_v1:
             self._set_default_args_v1(usage_context, model_config)
+            # Disable chunked prefill for POWER (ppc64le)/ARM CPUs in V1
+            if current_platform.is_cpu(
+            ) and current_platform.get_cpu_architecture() in (
+                    CpuArchEnum.POWERPC, CpuArchEnum.ARM):
+                logger.info(
+                    "Chunked prefill is not supported for ARM and POWER CPUs; "
+                    "disabling it for V1 backend.")
+                self.enable_chunked_prefill = False
         else:
             self._set_default_args_v0(model_config)
-
         assert self.enable_chunked_prefill is not None
 
         if envs.VLLM_ATTENTION_BACKEND in [STR_DUAL_CHUNK_FLASH_ATTN_VAL]:
@@ -1242,7 +1249,6 @@ def create_engine_config(
             if self.enable_chunked_prefill and self.pipeline_parallel_size > 1:
                 raise ValueError("Multi-Step Chunked-Prefill is not supported "
                                  "for pipeline-parallel-size > 1")
-            from vllm.platforms import current_platform
             if current_platform.is_cpu():
                 logger.warning("Multi-Step (--num-scheduler-steps > 1) is "
                                "currently not supported for CPUs and has been "
@@ -1391,7 +1397,6 @@ def _is_v1_supported_oracle(self, model_config: ModelConfig) -> bool:
         # Skip this check if we are running on a non-GPU platform,
         # or if the device capability is not available
         # (e.g. in a Ray actor without GPUs).
-        from vllm.platforms import current_platform
         if (current_platform.is_cuda()
                 and current_platform.get_device_capability()
                 and current_platform.get_device_capability().major < 8):
@@ -1652,7 +1657,6 @@ def _set_default_args_v1(self, usage_context: UsageContext,
         # as the platform that vLLM is running on (e.g. the case of scaling
         # vLLM with Ray) and has no GPUs. In this case we use the default
         # values for non-H100/H200 GPUs.
-        from vllm.platforms import current_platform
         try:
             device_memory = current_platform.get_device_total_memory()
             device_name = current_platform.get_device_name().lower()
@@ -1755,7 +1759,6 @@ def add_cli_args(parser: FlexibleArgumentParser,
         parser.add_argument('--disable-log-requests',
                             action='store_true',
                             help='Disable logging requests.')
-        from vllm.platforms import current_platform
         current_platform.pre_register_and_update(parser)
         return parser
 
diff --git a/vllm/platforms/cpu.py b/vllm/platforms/cpu.py
@@ -271,5 +271,6 @@ def default_v1(cls, model_config) -> bool:
         """Returns whether the current platform can use v1 by default for the
         supplied model configuration.
         """
-        return cls.supports_v1(
-            model_config) and cls.get_cpu_architecture() == CpuArchEnum.X86
+        arch = cls.get_cpu_architecture()
+        return (cls.supports_v1(model_config) and arch
+                in (CpuArchEnum.X86, CpuArchEnum.POWERPC, CpuArchEnum.ARM))
diff --git a/vllm/v1/attention/backends/cpu_attn.py b/vllm/v1/attention/backends/cpu_attn.py
@@ -316,7 +316,6 @@ def __init__(self, runner: CPUModelRunner, kv_cache_spec: AttentionSpec,
                  block_table: BlockTable) -> None:
         self.runner = runner
         self.block_table = block_table
-
         # For reorder
         self.reorder_prompt_req_index_list = np.empty(self.runner.max_num_reqs,
                                                       dtype=np.int64)
@@ -401,11 +400,14 @@ def build(self, common_prefix_len: int,
             num_prefill_tokens=num_prefill_tokens,
             num_decode_tokens=num_decode_tokens,
             slot_mapping=slot_mapping,
+            # to ensure inference when chunked_prefill is disabled
+            seq_lens=runner.seq_lens_cpu[:num_reqs].tolist(),
             seq_lens_tensor=runner.
             seq_lens_cpu[num_prompt_req:num_reqs],  # decode
             max_decode_seq_len=max_decode_seq_len,  # decode
             block_tables=block_table_tensor[num_prompt_req:num_reqs],  # decode
-            chunked_prefill=True,
+            chunked_prefill=self.runner.scheduler_config.
+            chunked_prefill_enabled,
             max_query_len=max_query_len,
             max_kv_len=max_prefill_seq_len,
             prefill_query_start_loc=runner.
diff --git a/vllm/v1/worker/cpu_worker.py b/vllm/v1/worker/cpu_worker.py
@@ -11,7 +11,7 @@
 from vllm.distributed.parallel_state import get_pp_group, get_tp_group
 from vllm.logger import init_logger
 from vllm.model_executor.utils import set_random_seed
-from vllm.platforms import current_platform
+from vllm.platforms import CpuArchEnum, current_platform
 from vllm.sequence import IntermediateTensors
 from vllm.v1.core.sched.output import SchedulerOutput
 from vllm.v1.outputs import ModelRunnerOutput
@@ -43,8 +43,12 @@ def init_device(self):
         omp_cpuids = envs.VLLM_CPU_OMP_THREADS_BIND
         self.local_omp_cpuid = "all"
         if omp_cpuids == "auto":
-            self.local_omp_cpuid = self.get_cpus_id_binding_based_on_numa_nodes(
-            )
+            if current_platform.get_cpu_architecture() == CpuArchEnum.POWERPC:
+                self.local_omp_cpuid = (
+                    self.get_cpus_id_binding_based_on_numa_nodes_ppc64le())
+            else:
+                self.local_omp_cpuid = (
+                    self.get_cpus_id_binding_based_on_numa_nodes())
         else:
             self.local_omp_cpuid = omp_cpuids.split("|")[self.rank]
 
@@ -153,3 +157,57 @@ def get_cpus_id_binding_based_on_numa_nodes(self) -> str:
                 "fallback to no thread-binding. To get better performance,"
                 "please try to manually bind threads.")
         return rank_to_cpus
+
+    def get_cpus_id_binding_based_on_numa_nodes_ppc64le(self) -> str:
+        """
+        Power (ppc64le) specific: Selects a subset of threads per core for 
+        each NUMA node.This is robust to SMT mode (SMT-8, SMT-4, etc) 
+        because the OS only exposes available threads.This maximizes 
+        performance by avoiding oversubscription of logical CPUs on Power.
+        """
+
+        def select_threads_per_power_core(node_cpu_ids):
+            return [cpu for cpu in node_cpu_ids if cpu % 8 < 4]
+
+        rank_to_cpus = self.local_omp_cpuid
+        world_size = self.vllm_config.parallel_config.world_size
+        libnuma_found = util.find_spec("numa") is not None
+        psutil_found = util.find_spec("psutil") is not None
+        if libnuma_found and psutil_found:
+            import psutil
+            from numa import info
+            cpus_allow_list = psutil.Process().cpu_affinity()
+            numa_size = info.get_num_configured_nodes()
+
+            node_to_cpus = []
+            for i in range(numa_size):
+                node_intersect = set(
+                    info.node_to_cpus(i)).intersection(cpus_allow_list)
+                if bool(node_intersect):
+                    node_to_cpus.append(sorted(list(node_intersect)))
+
+            if world_size > len(node_to_cpus):
+                logger.error(
+                    "Auto thread-binding failed due to "
+                    "world size: %d is larger than "
+                    "allowed NUMA nodes number: %d."
+                    "Please try to bind threads manually.", world_size,
+                    len(node_to_cpus))
+            else:
+                node_cpus_this_rank = node_to_cpus[self.rank]
+                node_cpus_this_rank = select_threads_per_power_core(
+                    node_cpus_this_rank)
+                cpu_count_per_numa = len(node_cpus_this_rank)
+                num_of_reserved_cpu = min(envs.VLLM_CPU_NUM_OF_RESERVED_CPU,
+                                          cpu_count_per_numa // 2)
+                end = cpu_count_per_numa - num_of_reserved_cpu
+                rank_to_cpus_list = node_cpus_this_rank[:end]
+                rank_to_cpus = ','.join(str(x) for x in rank_to_cpus_list)
+                logger.info("ppc64le thread-binding list: %s", rank_to_cpus)
+        else:
+            logger.warning(
+                "Auto thread-binding is not supported due to "
+                "the lack of package numa and psutil,"
+                "fallback to no thread-binding. To get better performance,"
+                "please try to manually bind threads.")
+        return rank_to_cpus
