dcu adapter ernie45t (#2756)

Co-authored-by: lifu <lifu@sugon.com>
Co-authored-by: yongqiangma <xing.wo@163.com>

fastdeploy/worker/dcu_worker.py

@@ -0,0 +1,112 @@
+"""
+# 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
+
+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.gpu_worker import GpuWorker
+
+logger = get_logger("dcu_worker", "dcu_worker.log")
+
+
+class DcuWorker(GpuWorker):
+    """ """
+
+    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 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
+        Gb = 1024**3
+        start_time = time.perf_counter()
+        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
+
+        total_gpu_memory = paddle.device.cuda.get_device_properties(self.local_rank).total_memory
+        before_used_gpu_memory = paddle.device.cuda.memory_allocated(self.local_rank)
+
+
+        logger.info((
+            "Before running the profile, the memory usage info is as follows:",
+            f"\nDevice Total memory: {total_gpu_memory / Gb}",
+            f"\nDevice used memory: {before_used_gpu_memory / 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)
+
+        after_used_gpu_memory = paddle.device.cuda.memory_allocated(self.local_rank)
+
+        # v0 worker
+        model_block_memory_used = self.cal_theortical_kvcache()
+        paddle.device.cuda.empty_cache()
+        paddle_peak_increase = paddle_reserved_mem_after_run - paddle_allocated_mem_before_run
+        available_kv_cache_memory = total_gpu_memory * \
+            self.parallel_config.gpu_memory_utilization - after_used_gpu_memory - 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: {total_gpu_memory / Gb}",
+             f"\nDevice used memory: {after_used_gpu_memory / 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