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Clear dead code And supplementary notes (#2757)
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* 1.supplementary notes 2.delete dead code

* fix bug of forward meta

* Global modification of forward meta

* fix vl model_runner bug
This commit is contained in:
RAM
2025-07-09 16:17:34 +08:00
committed by GitHub
parent b89180f1cd
commit 03a74995b8
12 changed files with 248 additions and 463 deletions
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54
fastdeploy/worker/gpu_worker.py
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@@ -32,7 +32,6 @@ logger = get_logger("gpu_worker", "gpu_worker.log")
class GpuWorker(WorkerBase):
""" """
def __init__(
self,
@@ -48,7 +47,8 @@ class GpuWorker(WorkerBase):
pass
def init_device(self):
""" Initialize device and Construct model runner
"""
Initialize device and construct model runner
"""
if self.device_config.device_type == "cuda" and paddle.device.is_compiled_with_cuda(
):
@@ -74,10 +74,10 @@ class GpuWorker(WorkerBase):
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
Check whether prefill stage finished
"""
return self.model_runner.prefill_finished()
@@ -115,7 +115,8 @@ class GpuWorker(WorkerBase):
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}"))
f"\nPaddle allocated memory: {paddle_allocated_mem_before_run / Gb}"
))
# 2. Profile run
self.model_runner.profile_run()
@@ -126,15 +127,6 @@ class GpuWorker(WorkerBase):
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
@@ -146,32 +138,31 @@ class GpuWorker(WorkerBase):
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}"))
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:
""" """
""" Load model """
self.model_runner.load_model()
def get_model(self) -> nn.Layer:
""" """
""" Get current model """
return self.model_runner.get_model()
def initialize_cache(self, num_gpu_blocks: int,
num_cpu_blocks: int) -> None:
""" """
""" Initizlize the KV Cache """
pass
def execute_model(
@@ -193,10 +184,7 @@ class GpuWorker(WorkerBase):
"""
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
# Triger cuda grpah capture
self.model_runner.capture_model()
def check_health(self) -> bool:
@@ -204,10 +192,10 @@ class GpuWorker(WorkerBase):
return True
def cal_theortical_kvcache(self) -> int:
""" """
""" Calculate the block memory required """
return self.model_runner.cal_theortical_kvcache()
def reinitialize_kv_cache(self, num_gpu_blocks: int) -> None:
""" """
""" Reinitialize the kv cache using the parameters from the profile """
self.model_runner.update_share_input_block_num(
num_gpu_blocks=num_gpu_blocks)