Adapt for iluvatar gpu (#2684)

fastdeploy/worker/iluvatar_worker.py

@@ -0,0 +1,143 @@
+"""
+# 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 os
+from typing import List, Optional
+
+import paddle
+import paddle.nn as nn
+
+from fastdeploy.config import FDConfig
+from fastdeploy.engine.request import Request
+from fastdeploy.utils import get_logger
+from fastdeploy.worker.iluvatar_model_runner import IluvatarModelRunner
+from fastdeploy.worker.output import ModelRunnerOutput
+from fastdeploy.worker.worker_base import WorkerBase
+
+logger = get_logger("iluvatar_worker", "iluvatar_worker.log")
+
+
+class IluvatarWorker(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 paddle.is_compiled_with_custom_device("iluvatar_gpu"):
+            # Set evironment variable
+            self.device = f"iluvatar_gpu:{self.local_rank}"
+            paddle.device.set_device(self.device)
+            paddle.set_default_dtype(self.parallel_config.dtype)
+            self.device_ids = self.parallel_config.device_ids.split(",")
+
+            gc.collect()
+        else:
+            raise RuntimeError(
+                f"Not support device type: {self.device_config.device}")
+
+        # Construct model runner
+        self.model_runner: IluvatarModelRunner = IluvatarModelRunner(
+            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
+        return int(float(os.getenv("FD_ILUVATAR_KVCACHE_MEM", "3")) * 1024**3)
+
+    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)