apps/FastDeploy
1
0
Fork 0
mirror of https://github.com/PaddlePaddle/FastDeploy.git synced 2025-10-27 18:41:02 +08:00
Code Issues Actions 5 Packages Projects Releases Wiki Activity

polish code with new pre-commit rule (#2923)

Browse Source
This commit is contained in:
Zero Rains
2025-07-19 23:19:27 +08:00
committed by GitHub
parent b8676d71a8
commit 25698d56d1
424 changed files with 14307 additions and 13518 deletions
Download Patch File Download Diff File Expand all files Collapse all files

40
fastdeploy/worker/xpu_worker.py
View File

@@ -13,11 +13,12 @@
# See the License for the specific language governing permissions and
# limitations under the License.
"""
import gc
from typing import List, Optional
import paddle
import paddle.nn as nn
from paddle import nn
from fastdeploy.config import FDConfig
from fastdeploy.engine.request import Request
@@ -46,8 +47,7 @@ class XpuWorker(WorkerBase):
pass
def init_device(self):
""" Initialize device and Construct model runner
"""
"""Initialize device and Construct model runner"""
if paddle.is_compiled_with_xpu():
# Set evironment variable
self.device = f"xpu:{self.local_rank}"
@@ -57,19 +57,19 @@ class XpuWorker(WorkerBase):
gc.collect()
else:
raise RuntimeError(
f"Not support device type: {self.device_config.device}")
raise RuntimeError(f"Not support device type: {self.device_config.device}")
# Construct model runner
self.model_runner: XPUModelRunner = XPUModelRunner(
fd_config=self.fd_config,
device=self.device,
rank=self.rank,
local_rank=self.local_rank)
local_rank=self.local_rank,
)
def graph_optimize_and_warm_up_model(self) -> None:
"""
Optimizes the inference graph using the specified optimization options.
Optimizes the inference graph using the specified optimization options.
"""
logger.warn("XPU current could not graph optimize and warm up model")
@@ -87,15 +87,19 @@ class XpuWorker(WorkerBase):
by adjusting the `gpu_memory_utilization` parameter.
"""
from fastdeploy.model_executor.ops.xpu import (
xpu_get_free_global_memory, xpu_get_total_global_memory,
xpu_get_used_global_memory)
xpu_get_free_global_memory,
xpu_get_total_global_memory,
xpu_get_used_global_memory,
)
total_memory = xpu_get_total_global_memory(self.local_rank)
used_memory = xpu_get_used_global_memory(self.local_rank)
free_memory = xpu_get_free_global_memory(self.local_rank)
logger.info(f"Before warm up, total_memory: {total_memory}, \
used_memory: {used_memory}, free_memory: {free_memory}")
logger.info(
f"Before warm up, total_memory: {total_memory}, \
used_memory: {used_memory}, free_memory: {free_memory}"
)
self.model_runner.prepare_profile()
self.model_runner.profile_run()
@@ -108,8 +112,10 @@ class XpuWorker(WorkerBase):
self.model_runner.clear_block_table()
logger.info(f"After warm up, total_available_memory: {total_available_memory}, \
used_memory: {used_memory}, available_kv_cache_memory: {available_kv_cache_memory}")
logger.info(
f"After warm up, total_available_memory: {total_available_memory}, \
used_memory: {used_memory}, available_kv_cache_memory: {available_kv_cache_memory}"
)
paddle.device.xpu.empty_cache()
return available_kv_cache_memory # approximate value
@@ -125,8 +131,7 @@ class XpuWorker(WorkerBase):
""" """
return self.model_runner.get_model()
def initialize_cache(self, num_gpu_blocks: int,
num_cpu_blocks: int) -> None:
def initialize_cache(self, num_gpu_blocks: int, num_cpu_blocks: int) -> None:
""" """
pass
@@ -145,7 +150,7 @@ class XpuWorker(WorkerBase):
return self.model_runner.prefill_finished()
def preprocess_new_task(self, req_dicts: List[Request]) -> None:
""" Process new requests and then start the decode loop
"""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.
"""
@@ -157,5 +162,4 @@ class XpuWorker(WorkerBase):
def reinitialize_kv_cache(self, num_gpu_blocks: int) -> None:
""" """
self.model_runner.update_share_input_block_num(
num_gpu_blocks=num_gpu_blocks)
self.model_runner.update_share_input_block_num(num_gpu_blocks=num_gpu_blocks)