fix multiproc executor determine kv cache memory & update Dockerfile

Dockerfile

@@ -21,12 +21,6 @@ ARG PIP_INDEX_URL="https://mirrors.tuna.tsinghua.edu.cn/pypi/web/simple"
 ARG MOONCAKE_TAG="v0.3.8.post1"
 ARG SOC_VERSION="ascend910b1"
 
-# Define environments
-ENV DEBIAN_FRONTEND=noninteractive
-ENV SOC_VERSION=$SOC_VERSION \
-    TASK_QUEUE_ENABLE=1 \
-    OMP_NUM_THREADS=1
-
 WORKDIR /workspace
 
 COPY . /vllm-workspace/vllm-ascend/
@@ -58,6 +52,12 @@ RUN VLLM_TARGET_DEVICE="empty" python3 -m pip install -v -e /vllm-workspace/vllm
     python3 -m pip cache purge
 
 # Install vllm-ascend
+ENV DEBIAN_FRONTEND=noninteractive
+ENV SOC_VERSION=$SOC_VERSION \
+    TASK_QUEUE_ENABLE=1 \
+    OMP_NUM_THREADS=1 \
+    VLLM_WORKER_MULTIPROC_METHOD=spawn \
+    VLLM_ASCEND_ENABLE_VNPU=1
 # Append `libascend_hal.so` path (devlib) to LD_LIBRARY_PATH
 # Installing vllm-ascend on x86 can pull upstream triton back in alongside triton-ascend. Remove it immediately after this step.
 RUN export PIP_EXTRA_INDEX_URL=https://mirrors.huaweicloud.com/ascend/repos/pypi && \
@@ -86,9 +86,6 @@ RUN export PIP_EXTRA_INDEX_URL=https://mirrors.huaweicloud.com/ascend/repos/pypi
     python3 -m pip install "https://vllm-ascend.obs.cn-north-4.myhuaweicloud.com/vllm-ascend/${PTA_WHEEL}" && \
     python3 -m pip cache purge
 
-ENV VLLM_WORKER_MULTIPROC_METHOD=spawn \
-    VLLM_ASCEND_ENABLE_VNPU=1
-
 # Install clang-15 (for triton-ascend)
 RUN apt-get update -y && \
     apt-get -y install clang-15 && \

Dockerfile.310p

@@ -20,13 +20,6 @@ FROM quay.io/ascend/cann:8.5.1-310p-ubuntu22.04-py3.11
 ARG PIP_INDEX_URL="https://mirrors.tuna.tsinghua.edu.cn/pypi/web/simple"
 ARG SOC_VERSION="ascend310p1"
 
-# Define environments
-ENV DEBIAN_FRONTEND=noninteractive
-ENV SOC_VERSION=$SOC_VERSION \
-    TASK_QUEUE_ENABLE=1 \
-    OMP_NUM_THREADS=1
-    
-
 RUN apt-get update -y && \
     apt-get install -y python3-pip git vim wget net-tools gcc g++ cmake numactl libnuma-dev libjemalloc2 && \
     rm -rf /var/cache/apt/* && \
@@ -48,6 +41,12 @@ RUN VLLM_TARGET_DEVICE="empty" python3 -m pip install -v -e /vllm-workspace/vllm
     python3 -m pip cache purge
 
 # Install vllm-ascend
+ENV DEBIAN_FRONTEND=noninteractive
+ENV SOC_VERSION=$SOC_VERSION \
+    TASK_QUEUE_ENABLE=1 \
+    OMP_NUM_THREADS=1 \
+    VLLM_WORKER_MULTIPROC_METHOD=spawn \
+    VLLM_ASCEND_ENABLE_VNPU=1
 # Append `libascend_hal.so` path (devlib) to LD_LIBRARY_PATH
 # Installing vllm-ascend on x86 can pull upstream triton back in alongside triton-ascend. Remove it immediately after this step.
 RUN export PIP_EXTRA_INDEX_URL=https://mirrors.huaweicloud.com/ascend/repos/pypi && \
@@ -70,9 +69,6 @@ RUN export PIP_EXTRA_INDEX_URL=https://mirrors.huaweicloud.com/ascend/repos/pypi
     python3 -m pip install "https://vllm-ascend.obs.cn-north-4.myhuaweicloud.com/vllm-ascend/${PTA_WHEEL}" && \
     python3 -m pip cache purge
 
-ENV VLLM_WORKER_MULTIPROC_METHOD=spawn \
-    VLLM_ASCEND_ENABLE_VNPU=1
-
 # Install modelscope (for fast download) and ray (for multinode)
 RUN python3 -m pip install modelscope 'ray>=2.47.1,<=2.48.0' 'protobuf>3.20.0' && \
     python3 -m pip cache purge

Dockerfile.310p.openEuler

@@ -20,10 +20,6 @@ FROM quay.io/ascend/cann:8.5.1-310p-openeuler24.03-py3.11
 ARG PIP_INDEX_URL="https://mirrors.tuna.tsinghua.edu.cn/pypi/web/simple"
 ARG SOC_VERSION="ascend310p1"
 
-ENV SOC_VERSION=$SOC_VERSION \
-    TASK_QUEUE_ENABLE=1 \
-    OMP_NUM_THREADS=1
-
 RUN yum update -y && \
     yum install -y python3-pip git vim wget net-tools gcc gcc-c++ make cmake numactl numactl-devel jemalloc && \
     rm -rf /var/cache/yum
@@ -44,6 +40,11 @@ RUN VLLM_TARGET_DEVICE="empty" python3 -m pip install -e /vllm-workspace/vllm/[a
     python3 -m pip cache purge
 
 # Install vllm-ascend
+ENV SOC_VERSION=$SOC_VERSION \
+    TASK_QUEUE_ENABLE=1 \
+    OMP_NUM_THREADS=1 \
+    VLLM_WORKER_MULTIPROC_METHOD=spawn \
+    VLLM_ASCEND_ENABLE_VNPU=1
 # Installing vllm-ascend on x86 can pull upstream triton back in alongside triton-ascend. Remove it immediately after this step.
 RUN export PIP_EXTRA_INDEX_URL=https://mirrors.huaweicloud.com/ascend/repos/pypi && \
     source /usr/local/Ascend/ascend-toolkit/set_env.sh && \
@@ -66,9 +67,6 @@ RUN export PIP_EXTRA_INDEX_URL=https://mirrors.huaweicloud.com/ascend/repos/pypi
     python3 -m pip install "https://vllm-ascend.obs.cn-north-4.myhuaweicloud.com/vllm-ascend/${PTA_WHEEL}" && \
     python3 -m pip cache purge
 
-ENV VLLM_WORKER_MULTIPROC_METHOD=spawn \
-    VLLM_ASCEND_ENABLE_VNPU=1
-
 # Install modelscope (for fast download) and ray (for multinode)
 RUN python3 -m pip install modelscope 'ray>=2.47.1,<=2.48.0' 'protobuf>3.20.0' && \
     python3 -m pip cache purge

Dockerfile.a3

@@ -22,11 +22,6 @@ ARG MOONCAKE_TAG=v0.3.8.post1
 ARG SOC_VERSION="ascend910_9391"
 
 COPY . /vllm-workspace/vllm-ascend/
-# Define environments
-ENV DEBIAN_FRONTEND=noninteractive
-ENV SOC_VERSION=$SOC_VERSION \
-    TASK_QUEUE_ENABLE=1 \
-    OMP_NUM_THREADS=1
 
 RUN pip config set global.index-url ${PIP_INDEX_URL}
 
@@ -57,6 +52,12 @@ RUN VLLM_TARGET_DEVICE="empty" python3 -m pip install -v -e /vllm-workspace/vllm
     python3 -m pip cache purge
 
 # Install vllm-ascend
+ENV DEBIAN_FRONTEND=noninteractive
+ENV SOC_VERSION=$SOC_VERSION \
+    TASK_QUEUE_ENABLE=1 \
+    OMP_NUM_THREADS=1 \
+    VLLM_WORKER_MULTIPROC_METHOD=spawn \
+    VLLM_ASCEND_ENABLE_VNPU=1
 # Append `libascend_hal.so` path (devlib) to LD_LIBRARY_PATH
 # Installing vllm-ascend on x86 can pull upstream triton back in alongside triton-ascend. Remove it immediately after this step.
 RUN export PIP_EXTRA_INDEX_URL=https://mirrors.huaweicloud.com/ascend/repos/pypi && \
@@ -85,9 +86,6 @@ RUN export PIP_EXTRA_INDEX_URL=https://mirrors.huaweicloud.com/ascend/repos/pypi
     python3 -m pip install "https://vllm-ascend.obs.cn-north-4.myhuaweicloud.com/vllm-ascend/${PTA_WHEEL}" && \
     python3 -m pip cache purge
 
-ENV VLLM_WORKER_MULTIPROC_METHOD=spawn \
-    VLLM_ASCEND_ENABLE_VNPU=1
-
 # Install clang-15 (for triton-ascend)
 RUN apt-get update -y && \
     apt-get -y install clang-15 && \

Dockerfile.a3.openEuler

@@ -21,10 +21,6 @@ ARG PIP_INDEX_URL="https://mirrors.tuna.tsinghua.edu.cn/pypi/web/simple"
 ARG MOONCAKE_TAG="v0.3.8.post1"
 ARG SOC_VERSION="ascend910_9391"
 
-ENV SOC_VERSION=$SOC_VERSION \
-    TASK_QUEUE_ENABLE=1 \
-    OMP_NUM_THREADS=1
-
 RUN pip config set global.index-url ${PIP_INDEX_URL}
 
 WORKDIR /workspace
@@ -58,6 +54,11 @@ RUN VLLM_TARGET_DEVICE="empty" python3 -m pip install -e /vllm-workspace/vllm/[a
     python3 -m pip cache purge
 
 # Install vllm-ascend
+ENV SOC_VERSION=$SOC_VERSION \
+    TASK_QUEUE_ENABLE=1 \
+    OMP_NUM_THREADS=1 \
+    VLLM_WORKER_MULTIPROC_METHOD=spawn \
+    VLLM_ASCEND_ENABLE_VNPU=1
 # Installing vllm-ascend on x86 can pull upstream triton back in alongside triton-ascend. Remove it immediately after this step.
 RUN export PIP_EXTRA_INDEX_URL=https://mirrors.huaweicloud.com/ascend/repos/pypi && \
     source /usr/local/Ascend/ascend-toolkit/set_env.sh && \
@@ -86,9 +87,6 @@ RUN export PIP_EXTRA_INDEX_URL=https://mirrors.huaweicloud.com/ascend/repos/pypi
     python3 -m pip install "https://vllm-ascend.obs.cn-north-4.myhuaweicloud.com/vllm-ascend/${PTA_WHEEL}" && \
     python3 -m pip cache purge
 
-ENV VLLM_WORKER_MULTIPROC_METHOD=spawn \
-    VLLM_ASCEND_ENABLE_VNPU=1
-
 # Install clang (for triton-ascend)
 RUN yum update -y && \
     yum install -y clang && \

Dockerfile.openEuler

@@ -21,10 +21,6 @@ ARG PIP_INDEX_URL="https://mirrors.tuna.tsinghua.edu.cn/pypi/web/simple"
 ARG MOONCAKE_TAG="v0.3.8.post1"
 ARG SOC_VERSION="ascend910b1"
 
-ENV SOC_VERSION=$SOC_VERSION \
-    TASK_QUEUE_ENABLE=1 \
-    OMP_NUM_THREADS=1
-
 RUN pip config set global.index-url ${PIP_INDEX_URL}
 
 WORKDIR /workspace
@@ -58,6 +54,11 @@ RUN VLLM_TARGET_DEVICE="empty" python3 -m pip install -e /vllm-workspace/vllm/[a
     python3 -m pip cache purge
 
 # Install vllm-ascend
+ENV SOC_VERSION=$SOC_VERSION \
+    TASK_QUEUE_ENABLE=1 \
+    OMP_NUM_THREADS=1 \
+    VLLM_WORKER_MULTIPROC_METHOD=spawn \
+    VLLM_ASCEND_ENABLE_VNPU=1
 # Installing vllm-ascend on x86 can pull upstream triton back in alongside triton-ascend. Remove it immediately after this step.
 RUN export PIP_EXTRA_INDEX_URL=https://mirrors.huaweicloud.com/ascend/repos/pypi && \
     source /usr/local/Ascend/ascend-toolkit/set_env.sh && \
@@ -86,9 +87,6 @@ RUN export PIP_EXTRA_INDEX_URL=https://mirrors.huaweicloud.com/ascend/repos/pypi
     python3 -m pip install "https://vllm-ascend.obs.cn-north-4.myhuaweicloud.com/vllm-ascend/${PTA_WHEEL}" && \
     python3 -m pip cache purge
 
-ENV VLLM_WORKER_MULTIPROC_METHOD=spawn \
-    VLLM_ASCEND_ENABLE_VNPU=1
-
 # Install clang (for triton-ascend)
 RUN yum update -y && \
     yum install -y clang && \

vllm_ascend/patch/platform/patch_core.py

@@ -1,20 +1,103 @@
 from logging import DEBUG
-import os
+import signal
 import queue
-import time
 
-import vllm.envs as envs
 from vllm.config import ParallelConfig, VllmConfig
 from vllm.logger import logger
-from vllm.v1.kv_cache_interface import KVCacheConfig
+from vllm.tracing import maybe_init_worker_tracer
-from vllm.v1.core.kv_cache_utils import (generate_scheduler_kv_cache_config,
+from vllm.transformers_utils.config import \
-                                         get_kv_cache_configs)
+    maybe_register_config_serialize_by_value
-from vllm.v1.engine.core import EngineCoreProc, EngineCore
+from vllm.utils.system_utils import decorate_logs, set_process_title
-from vllm.tracing import instrument
+from vllm.v1.engine.core import EngineCoreProc, DPEngineCoreProc, EngineShutdownState
+from vllm.v1.engine import EngineCoreRequestType
+from vllm.v1.engine.utils import SignalCallback
 
 import vllm_ascend.envs as envs_ascend
 
 
+def run_engine_core(*args, dp_rank: int = 0, local_dp_rank: int = 0, **kwargs):
+    """Launch EngineCore busy loop in background process."""
+
+    # Ensure we can serialize transformer config after spawning
+    maybe_register_config_serialize_by_value()
+
+    engine_core: EngineCoreProc | None = None
+    signal_callback: SignalCallback | None = None
+    try:
+        vllm_config: VllmConfig = kwargs["vllm_config"]
+        parallel_config: ParallelConfig = vllm_config.parallel_config
+        data_parallel = parallel_config.data_parallel_size > 1 or dp_rank > 0
+        if data_parallel:
+            parallel_config.data_parallel_rank_local = local_dp_rank
+            process_title = f"EngineCore_DP{dp_rank}"
+        else:
+            process_title = "EngineCore"
+        set_process_title(process_title)
+        maybe_init_worker_tracer("vllm.engine_core", "engine_core", process_title)
+        decorate_logs()
+
+        if data_parallel and vllm_config.kv_transfer_config is not None:
+            # modify the engine_id and append the local_dp_rank to it to ensure
+            # that the kv_transfer_config is unique for each DP rank.
+            vllm_config.kv_transfer_config.engine_id = (
+                f"{vllm_config.kv_transfer_config.engine_id}_dp{local_dp_rank}"
+            )
+            logger.debug(
+                "Setting kv_transfer_config.engine_id to %s",
+                vllm_config.kv_transfer_config.engine_id,
+            )
+
+        parallel_config.data_parallel_index = dp_rank
+        if data_parallel and vllm_config.model_config.is_moe:
+            # Set data parallel rank for this engine process.
+            parallel_config.data_parallel_rank = dp_rank
+            engine_core = DPEngineCoreProc(*args, **kwargs)
+        else:
+            # Non-MoE DP ranks are completely independent, so treat like DP=1.
+            # Note that parallel_config.data_parallel_index will still reflect
+            # the original DP rank.
+            parallel_config.data_parallel_size = 1
+            parallel_config.data_parallel_size_local = 1
+            parallel_config.data_parallel_rank = 0
+            engine_core = EngineCoreProc(*args, engine_index=dp_rank, **kwargs)
+
+        assert engine_core is not None
+
+        def wakeup_engine():
+            # Wakes up idle engine via input_queue when shutdown is requested
+            # Not safe in a signal handler - we may interrupt the main thread
+            # while it is holding the non-reentrant input_queue.mutex
+            engine_core.input_queue.put_nowait((EngineCoreRequestType.WAKEUP, None))
+
+        signal_callback = SignalCallback(wakeup_engine)
+
+        def signal_handler(signum, frame):
+            engine_core.shutdown_state = EngineShutdownState.REQUESTED
+            signal_callback.trigger()
+
+        signal.signal(signal.SIGTERM, signal_handler)
+        signal.signal(signal.SIGINT, signal_handler)
+
+        engine_core.run_busy_loop()
+
+    except SystemExit:
+        logger.debug("EngineCore exiting.")
+        raise
+    except Exception as e:
+        if engine_core is None:
+            logger.exception("EngineCore failed to start.")
+        else:
+            logger.exception("EngineCore encountered a fatal error.")
+            engine_core._send_engine_dead()
+        raise e
+    finally:
+        signal.signal(signal.SIGTERM, signal.SIG_DFL)
+        signal.signal(signal.SIGINT, signal.SIG_DFL)
+        if signal_callback is not None:
+            signal_callback.stop()
+        if engine_core is not None:
+            engine_core.shutdown()
+
 def run_busy_loop(self):
     """Core busy loop of the EngineCore."""
     while self._handle_shutdown():
@@ -77,75 +160,8 @@ def _process_input_queue(self):
         self._handle_client_request(*req)
 
 
-@instrument(span_name="Prepare model")
+# to make multi-proc enginecore get patched
-def _initialize_kv_caches(self, vllm_config: VllmConfig) -> KVCacheConfig:
+EngineCoreProc.run_engine_core = run_engine_core
-    start = time.time()
-
-    # Get all kv cache needed by the model
-    kv_cache_specs = self.model_executor.get_kv_cache_specs()
-
-    has_kv_cache = any(kv_cache_spec for kv_cache_spec in kv_cache_specs)
-    if has_kv_cache:
-        if envs_ascend.VLLM_ASCEND_ENABLE_VNPU:
-            # get available memory in idle offload mode
-            available_gpu_memory = (
-                self.model_executor.determine_available_memory_vnpu_offload_mode())
-            self.available_gpu_memory_for_kv_cache = \
-                available_gpu_memory[0]
-        elif envs.VLLM_ELASTIC_EP_SCALE_UP_LAUNCH:
-            # NOTE(yongji): should already be set
-            # during _eep_scale_up_before_kv_init
-            assert self.available_gpu_memory_for_kv_cache > 0
-            available_gpu_memory = [self.available_gpu_memory_for_kv_cache] * len(
-                kv_cache_specs
-            )
-        else:
-            # Profiles the peak memory usage of the model to determine how
-            # much memory can be allocated for kv cache.
-            available_gpu_memory = self.model_executor.determine_available_memory()
-            self.available_gpu_memory_for_kv_cache = available_gpu_memory[0]
-    else:
-        # Attention free models don't need memory for kv cache
-        available_gpu_memory = [0] * len(kv_cache_specs)
-
-    assert len(kv_cache_specs) == len(available_gpu_memory)
-
-    # Track max_model_len before KV cache config to detect auto-fit changes
-    max_model_len_before = vllm_config.model_config.max_model_len
-
-    kv_cache_configs = get_kv_cache_configs(
-        vllm_config, kv_cache_specs, available_gpu_memory
-    )
-
-    # If auto-fit reduced max_model_len, sync the new value to workers.
-    # This is needed because workers were spawned before memory profiling
-    # and have the original (larger) max_model_len cached.
-    max_model_len_after = vllm_config.model_config.max_model_len
-    if max_model_len_after != max_model_len_before:
-        self.collective_rpc("update_max_model_len", args=(max_model_len_after,))
-
-    scheduler_kv_cache_config = generate_scheduler_kv_cache_config(kv_cache_configs)
-    vllm_config.cache_config.num_gpu_blocks = scheduler_kv_cache_config.num_blocks
-    kv_cache_groups = scheduler_kv_cache_config.kv_cache_groups
-    if kv_cache_groups:
-        vllm_config.cache_config.block_size = min(
-            g.kv_cache_spec.block_size for g in kv_cache_groups
-        )
-
-    vllm_config.validate_block_size()
-
-    # Initialize kv cache and warmup the execution
-    self.model_executor.initialize_from_config(kv_cache_configs)
-
-    elapsed = time.time() - start
-    logger.info_once(
-        "init engine (profile, create kv cache, warmup model) took %.2f seconds",
-        elapsed,
-        scope="local",
-    )
-    return scheduler_kv_cache_config
-
 
 EngineCoreProc.run_busy_loop = run_busy_loop
 EngineCoreProc._process_input_queue = _process_input_queue
-EngineCore._initialize_kv_caches = _initialize_kv_caches

vllm_ascend/patch/platform/patch_executor.py

@@ -42,11 +42,6 @@ def reload_vram(self) -> bool:
             time.sleep(0.001)
 
 
-def determine_available_memory_vnpu_offload_mode(self) -> int:
-    return self.collective_rpc("determine_available_memory_vnpu_offload_mode")
-
-
 Executor.is_offloaded = is_offloaded
 Executor.offload_vram = offload_vram
 Executor.reload_vram = reload_vram
-Executor.determine_available_memory_vnpu_offload_mode = determine_available_memory_vnpu_offload_mode

vllm_ascend/worker/worker.py

@@ -336,6 +336,25 @@ class NPUWorker(WorkerBase):
         bytes.
         """
         GiB = lambda b: b / GiB_bytes
+        if envs_ascend.VLLM_ASCEND_ENABLE_VNPU:
+            allocator = CaMemAllocator.get_instance()
+            free, total = allocator.get_pool_mem_info()
+            if self.cache_config.gpu_memory_utilization <= 0.9:
+                logger.warning(
+                    "GPU memory utilization is set to %.2f. For VNPU mode, it is recommended to set gpu_memory_utilization to a larger value",
+                    self.cache_config.gpu_memory_utilization,
+                )
+            available_kv_cache_memory = int(
+                total * self.cache_config.gpu_memory_utilization - (total - free)
+            )
+            available_kv_cache_memory = int(max(available_kv_cache_memory, 0))
+            self.available_kv_cache_memory_bytes = available_kv_cache_memory
+            logger.info_once(
+                "Available KV cache memory: %.2f GiB",
+                GiB(self.available_kv_cache_memory_bytes),
+                scope="local",
+            )
+            return int(self.available_kv_cache_memory_bytes)
 
         # Execute a forward pass with dummy inputs to profile the memory usage
         # of the model.
@@ -363,28 +382,6 @@ class NPUWorker(WorkerBase):
 
         return int(self.available_kv_cache_memory_bytes)
 
-    @torch.inference_mode()
-    def determine_available_memory_vnpu_offload_mode(self) -> int:
-        GiB = lambda b: b / GiB_bytes
-        allocator = CaMemAllocator.get_instance()
-        free, total = allocator.get_pool_mem_info()
-        if self.cache_config.gpu_memory_utilization <= 0.9:
-            logger.warning(
-                "GPU memory utilization is set to %.2f. For VNPU mode, it is recommended to set gpu_memory_utilization to a larger value",
-                self.cache_config.gpu_memory_utilization,
-            )
-        available_kv_cache_memory = int(
-            total * self.cache_config.gpu_memory_utilization - (total - free)
-        )
-        available_kv_cache_memory = int(max(available_kv_cache_memory, 0))
-        self.available_kv_cache_memory_bytes = available_kv_cache_memory
-        logger.info_once(
-            "Available KV cache memory: %.2f GiB",
-            GiB(self.available_kv_cache_memory_bytes),
-            scope="local",
-        )
-        return int(self.available_kv_cache_memory_bytes)
-
     def execute_model(
         self,
         scheduler_output: "SchedulerOutput",