adapt to vllm-ascend v0.18.0rc1

vllm_ascend/patch/platform/__init__.py

@@ -37,3 +37,6 @@ if os.getenv("DYNAMIC_EPLB", "false").lower() in ("true", "1") or os.getenv("EXP
 
 if envs.VLLM_ASCEND_BALANCE_SCHEDULING:
     import vllm_ascend.patch.platform.patch_balance_schedule  # noqa
+
+import vllm_ascend.patch.platform.patch_executor  # noqa
+import vllm_ascend.patch.platform.patch_core  # noqa

vllm_ascend/patch/platform/patch_core.py

@@ -0,0 +1,151 @@
+from logging import DEBUG
+import os
+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.v1.core.kv_cache_utils import (generate_scheduler_kv_cache_config,
+                                         get_kv_cache_configs)
+from vllm.v1.engine.core import EngineCoreProc, EngineCore
+from vllm.tracing import instrument
+
+import vllm_ascend.envs as envs_ascend
+
+
+def run_busy_loop(self):
+    """Core busy loop of the EngineCore."""
+    while self._handle_shutdown():
+        # 1) Poll the input queue until there is work to do.
+        self._process_input_queue()
+        if (
+            envs_ascend.VLLM_ASCEND_ENABLE_VNPU
+            and self.has_work()
+            and self.model_executor.is_offloaded()
+        ):
+            prev_is_self = self.model_executor.reload_vram()
+            if not prev_is_self:
+                self.reset_prefix_cache()
+        # 2) Step the engine core and return the outputs.
+        self._process_engine_step()
+        if (
+            envs_ascend.VLLM_ASCEND_ENABLE_VNPU
+            and not self.has_work()
+            and not self.model_executor.is_offloaded()
+        ):
+            self.model_executor.offload_vram()
+
+    raise SystemExit
+
+def _process_input_queue(self):
+    """Exits when an engine step needs to be performed."""
+
+    waited = False
+    while not self.has_work() and self.is_running():
+        # Notify callbacks waiting for engine to become idle.
+        self._notify_idle_state_callbacks()
+        if self.input_queue.empty():
+            # Drain aborts queue; all aborts are also processed via input_queue.
+            with self.aborts_queue.mutex:
+                self.aborts_queue.queue.clear()
+            if logger.isEnabledFor(DEBUG):
+                logger.debug("EngineCore waiting for work.")
+                waited = True
+            # vnpu offload if idle
+            if (
+                envs_ascend.VLLM_ASCEND_ENABLE_VNPU
+                and not self.model_executor.is_offloaded()
+            ):
+                self.model_executor.offload_vram()
+        block = self.process_input_queue_block
+        try:
+            req = self.input_queue.get(block=block)
+            self._handle_client_request(*req)
+        except queue.Empty:
+            break
+        if not block:
+            break
+
+    if waited:
+        logger.debug("EngineCore loop active.")
+
+    # Handle any more client requests.
+    while not self.input_queue.empty():
+        req = self.input_queue.get_nowait()
+        self._handle_client_request(*req)
+
+
+@instrument(span_name="Prepare model")
+def _initialize_kv_caches(self, vllm_config: VllmConfig) -> KVCacheConfig:
+    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

@@ -0,0 +1,52 @@
+import time
+
+from vllm.v1.executor.abstract import logger, Executor
+
+
+def is_offloaded(self) -> bool:
+    if not hasattr(self, "_is_offloaded"):
+        self._is_offloaded = False
+    return self._is_offloaded
+
+def offload_vram(self):
+    if self.is_offloaded():
+        logger.warning("Executor is already offloaded.")
+        return
+    time_before_offload = time.perf_counter()
+    self.collective_rpc("offload_vram")
+    time_after_offload = time.perf_counter()
+
+    self._is_offloaded = True
+    logger.info(f"Offloading VRAM costs {time_after_offload - time_before_offload:.6f} seconds.")
+
+
+def reload_vram(self) -> bool:
+    if not self.is_offloaded():
+        logger.warning("Executor is not offloaded.")
+        return True
+
+    while True:
+        time_before_reload = time.perf_counter()
+        res = self.collective_rpc("try_reload_vram")
+        time_after_reload = time.perf_counter()
+
+        succ = all(x[0] for x in res)
+        if succ:
+            self._is_offloaded = False
+            logger.info(f"Reloading VRAM costs {time_after_reload - time_before_reload:.6f} seconds.")
+            prev_is_self = all(x[1] for x in res)
+            return prev_is_self
+        else:
+            # some workers not get lock
+            self.collective_rpc("vnpu_unlock_gpu")
+            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