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Revert "[LLM] fix multinode bugs (#2945)" (#2971)

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This reverts commit b0f1e0eef4.
This commit is contained in:
Jiang-Jia-Jun
2025-07-22 21:23:48 +08:00
committed by GitHub
parent 8c43bc8176
commit e5804b1d98
9 changed files with 87 additions and 68 deletions
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15
fastdeploy/worker/gpu_worker.py
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@@ -100,17 +100,16 @@ class GpuWorker(WorkerBase):
# 1. Record memory state before profile run
start_time = time.perf_counter()
Gb = 1024**3
local_rank = self.local_rank % self.max_chips_per_node
paddle.device.cuda.reset_max_memory_reserved(local_rank)
paddle.device.cuda.reset_max_memory_allocated(local_rank)
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(
local_rank)
self.local_rank)
paddle_allocated_mem_before_run = paddle.device.cuda.max_memory_allocated(
local_rank) # not reserved
self.local_rank) # not reserved
pynvml.nvmlInit()
handle = pynvml.nvmlDeviceGetHandleByIndex(
int(self.device_ids[local_rank]))
int(self.device_ids[self.local_rank]))
before_run_meminfo = pynvml.nvmlDeviceGetMemoryInfo(handle)
logger.info((
@@ -127,9 +126,9 @@ class GpuWorker(WorkerBase):
# 3. Statistical memory information
paddle_reserved_mem_after_run = paddle.device.cuda.max_memory_reserved(
local_rank)
self.local_rank)
paddle_allocated_mem_after_run = paddle.device.cuda.max_memory_allocated(
local_rank)
self.local_rank)
model_block_memory_used = self.cal_theortical_kvcache()
paddle_peak_increase = paddle_reserved_mem_after_run - paddle_allocated_mem_before_run