fix multiproc executor determine kv cache memory

vllm_ascend/patch/platform/patch_core.py

@@ -77,75 +77,5 @@ def _process_input_queue(self):
         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

@@ -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",