Sync v2.0 version of code to github repo

fastdeploy/worker/gpu_worker.py

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+"""
+# Copyright (c) 2025  PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+import gc
+import time
+from typing import List, Optional
+
+import paddle
+import paddle.nn as nn
+import pynvml
+
+from fastdeploy.config import FDConfig
+from fastdeploy.engine.request import Request
+from fastdeploy.utils import get_logger
+from fastdeploy.worker.gpu_model_runner import GPUModelRunner
+from fastdeploy.worker.output import ModelRunnerOutput
+from fastdeploy.worker.worker_base import WorkerBase
+
+logger = get_logger("gpu_worker", "gpu_worker.log")
+
+
+class GpuWorker(WorkerBase):
+    """ """
+
+    def __init__(
+        self,
+        fd_config: FDConfig,
+        local_rank: int,
+        rank: int,
+    ):
+        super().__init__(
+            fd_config=fd_config,
+            local_rank=local_rank,
+            rank=rank,
+        )
+        pass
+
+    def init_device(self):
+        """ Initialize device and Construct model runner
+        """
+        if self.device_config.device_type == "cuda" and paddle.device.is_compiled_with_cuda(
+        ):
+            # Set evironment variable
+            self.device_ids = self.parallel_config.device_ids.split(",")
+            self.device = f"gpu:{self.local_rank}"
+            paddle.device.set_device(self.device)
+            paddle.set_default_dtype(self.parallel_config.dtype)
+
+            gc.collect()
+            paddle.device.cuda.empty_cache()
+        else:
+            raise RuntimeError(
+                f"Not support device type: {self.device_config.device}")
+
+        # Construct model runner
+        self.model_runner: GPUModelRunner = GPUModelRunner(
+            fd_config=self.fd_config,
+            device=self.device,
+            device_id=self.device_ids[self.local_rank],
+            rank=self.rank,
+            local_rank=self.local_rank)
+    
+    def prefill_finished(self):
+        """
+        check whether prefill stage finished
+        """
+        return self.model_runner.prefill_finished()
+
+    def determine_available_memory(self) -> int:
+        """
+        Profiles the peak memory usage of the model to determine how much
+        memory can be used for KV cache without OOMs.
+
+        The engine will first conduct a profiling of the existing memory usage.
+        Then, it calculate the maximum possible number of GPU and CPU blocks
+        that can be allocated with the remaining free memory.
+
+        Tip:
+            You may limit the usage of GPU memory
+            by adjusting the `gpu_memory_utilization` parameter.
+        """
+        # 1. Record memory state before profile run
+        start_time = time.perf_counter()
+        Gb = 1024**3
+        paddle.device.cuda.reset_max_memory_reserved(self.local_rank)
+        paddle.device.cuda.reset_max_memory_allocated(self.local_rank)
+        paddle_reserved_mem_before_run = paddle.device.cuda.max_memory_reserved(
+            self.local_rank)
+        paddle_allocated_mem_before_run = paddle.device.cuda.max_memory_allocated(
+            self.local_rank)  # not reserved
+
+        pynvml.nvmlInit()
+        handle = pynvml.nvmlDeviceGetHandleByIndex(
+            int(self.device_ids[self.local_rank]))
+        before_run_meminfo = pynvml.nvmlDeviceGetMemoryInfo(handle)
+
+        logger.info((
+            "Before running the profile, the memory usage info is as follows:",
+            f"\nDevice Total memory: {before_run_meminfo.total / Gb}",
+            f"\nDevice used memory: {before_run_meminfo.used / Gb}",
+            f"\nDevice free memory: {before_run_meminfo.free / Gb}",
+            f"\nPaddle reserved memory: {paddle_reserved_mem_before_run / Gb}",
+            f"\nPaddle allocated memory: {paddle_allocated_mem_before_run / Gb}"))
+
+        # 2. Profile run
+        self.model_runner.profile_run()
+
+        # 3. Statistical memory information
+        paddle_reserved_mem_after_run = paddle.device.cuda.max_memory_reserved(
+            self.local_rank)
+        paddle_allocated_mem_after_run = paddle.device.cuda.max_memory_allocated(
+            self.local_rank)
+
+        
+
+        # NOTE(gongshaotian): v1 worker
+        # not_paddle_use_mem = after_run_meminfo.used - paddle_reserved_mem_after_run
+        # peak_memory = paddle_allocated_mem_after_run + not_paddle_use_mem
+        # available_kv_cache_memory = after_run_meminfo.total * \
+        #    self.parallel_config.gpu_memory_utilization - peak_memory
+
+        # v0 worker
+        model_block_memory_used = self.cal_theortical_kvcache()
+        paddle_peak_increase = paddle_reserved_mem_after_run - paddle_allocated_mem_before_run
+
+        paddle.device.cuda.empty_cache()
+
+        after_run_meminfo = pynvml.nvmlDeviceGetMemoryInfo(handle)
+        pynvml.nvmlShutdown()
+
+        available_kv_cache_memory = after_run_meminfo.total * \
+            self.parallel_config.gpu_memory_utilization - after_run_meminfo.used - paddle_peak_increase
+        available_kv_cache_memory += model_block_memory_used * self.parallel_config.max_block_num
+        
+
+        end_time = time.perf_counter()
+        logger.info(
+            ("After running the profile, the memory usage info is as follows:",
+             f"\nDevice Total memory: {after_run_meminfo.total / Gb}",
+             f"\nDevice used memory: {after_run_meminfo.used / Gb}",
+             f"\nDevice free memory: {after_run_meminfo.free / Gb}",
+             f"\nPaddle reserved memory: {paddle_reserved_mem_after_run / Gb}",
+             f"\nPaddle allocated memory: {paddle_allocated_mem_after_run / Gb}",
+             f"\nAvailable KV Cache meomory: {available_kv_cache_memory / Gb}",
+             f"Profile time: {end_time - start_time}"))
+
+        return available_kv_cache_memory  # return to caculate the block num in this device
+
+    def load_model(self) -> None:
+        """ """
+        self.model_runner.load_model()
+
+    def get_model(self) -> nn.Layer:
+        """ """
+        return self.model_runner.get_model()
+
+    def initialize_cache(self, num_gpu_blocks: int,
+                         num_cpu_blocks: int) -> None:
+        """ """
+        pass
+
+    def execute_model(
+        self,
+        model_forward_batch: Optional[List[Request]] = None,
+    ) -> Optional[ModelRunnerOutput]:
+        """ """
+        output = self.model_runner.execute_model(model_forward_batch)
+        return output
+
+    def preprocess_new_task(self, req_dicts: List[Request]) -> None:
+        """ Process new requests and then start the decode loop
+        TODO(gongshaotian):The scheduler should schedule the handling of prefill,
+        and workers and modelrunners should not perceive it.
+        """
+        self.model_runner.insert_prefill_inputs(req_dicts=req_dicts)
+
+    def graph_optimize_and_warm_up_model(self) -> None:
+        """
+        Perform the warm-up and the graph optimization
+        """
+        # 1. Warm up model
+        # NOTE(gongshaotian): may be not need warm_up at this place
+
+        # 2. Triger cuda grpah capture
+        self.model_runner.capture_model()
+
+    def check_health(self) -> bool:
+        """ """
+        return True
+
+    def cal_theortical_kvcache(self) -> int:
+        """ """
+        return self.model_runner.cal_theortical_kvcache()
+
+    def reinitialize_kv_cache(self, num_gpu_blocks: int) -> None:
+        """ """
+        self.model_runner.update_share_input_block_num(
+            num_gpu_blocks=num_gpu_blocks)