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

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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
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53
fastdeploy/worker/dcu_worker.py
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@@ -13,6 +13,7 @@
# See the License for the specific language governing permissions and
# limitations under the License.
"""
import time
import paddle
@@ -58,30 +59,28 @@ class DcuWorker(GpuWorker):
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
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}"))
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)
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)
@@ -89,18 +88,24 @@ class DcuWorker(GpuWorker):
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 = (
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.total_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}"))
(
"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