background：
i tried a github repo, found problem[RuntimeError: CPU dispatcher tracer already initlized].

[docker_image] easydl/xpu-timer-dev:cuda12.4-torch250
cd /build/demo/train_fsdp/
bash train.sh

# train.sh
export XPU_TIMER_HOST_TRACING_FUNC=transformers.models.gpt_neox.modeling_gpt_neox@GPTNeoXAttention@forward,transformers.models.gpt_neox.modeling_gpt_neox@GPTNeoXRotaryEmbedding@forward,transformers.models.gpt_neox.modeling_gpt_neox@GPTNeoXMLP@forward,transformers.models.gpt_neox.modeling_gpt_neox@GPTNeoXLayer@forward
xpu_timer_launch python -m torch.distributed.launch --nproc_per_node=8 train_fsdp.py

# train_fsdp.py
import os
import time
import functools

import torch
import torch.distributed as dist
from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
from torch.utils.data import DataLoader, Dataset
from transformers import GPTNeoXConfig, GPTNeoXForCausalLM
from transformers.models.gpt_neox.modeling_gpt_neox import GPTNeoXLayer
from torch.distributed.fsdp.wrap import transformer_auto_wrap_policy
from torch.distributed.fsdp import MixedPrecision


class DummyDataset(Dataset):
    def __init__(self, vocab_size=1000, max_length=128, data_size=100000):
        self.vocab_size = vocab_size
        self.max_length = max_length
        self.data_size = data_size

    def __len__(self):
        return self.data_size

    def __getitem__(self, idx):
        text = torch.randint(low=0, high=self.vocab_size, size=(self.max_length,))
        return text, text


def main():
    # Initialize the process group
    dist.init_process_group(backend="nccl")

    # Get local rank and world size
    local_rank = int(os.environ["LOCAL_RANK"])
    rank = int(os.environ["RANK"])
    world_size = int(os.environ["WORLD_SIZE"])

    torch.cuda.set_device(local_rank)
    config = GPTNeoXConfig(
        vocab_size=50257,
        hidden_size=2048,
        num_hidden_layers=24,
        num_attention_heads=16,
        intermediate_size=8192,
        max_position_embeddings=2048,
        hidden_act="gelu_new",
        initializer_range=0.02,
        layer_norm_eps=1e-5,
        use_cache=True,
        bos_token_id=50256,
        eos_token_id=50256,
        attn_implementation="sdpa",
    )
    model = GPTNeoXForCausalLM(config)

    # Initialize Dummy DataLoader
    dataset = DummyDataset()
    sampler = torch.utils.data.distributed.DistributedSampler(dataset, num_replicas=world_size, rank=rank)
    dataloader = DataLoader(dataset, batch_size=32, shuffle=False, sampler=sampler)

    # Wrap Model with FSDP
    wrap_policy = functools.partial(transformer_auto_wrap_policy, transformer_layer_cls={GPTNeoXLayer,},)
    model = FSDP(model, device_id=local_rank, auto_wrap_policy=wrap_policy,
                 mixed_precision=MixedPrecision(param_dtype=torch.float16, cast_forward_inputs=True))

    optimizer = torch.optim.Adam(model.parameters(), lr=1e-5)

    # Training Loop
    epoch = 0
    iters = 0
    while True:
        model.train()
        for input_ids, labels in dataloader:
            start = time.time()
            input_ids, labels = input_ids.to(local_rank), labels.to(local_rank)
            optimizer.zero_grad()
            loss = model(input_ids=input_ids, labels=labels).loss
            loss.backward()
            optimizer.step()
            torch.cuda.synchronize()
            if local_rank == 0 and iters % 10 == 0:
                print(f"Epoch {epoch}, Loss: {loss.item()} time {time.time() - start}")
            iters += 1
        epoch += 1

    print("Training Complete")
    dist.destroy_process_group()


if __name__ == "__main__":
    main()

Traceback (most recent call last):
  File "/build/demo/train_fsdp/train_fsdp.py", line 5, in <module>
    import torch
  File "/opt/conda/lib/python3.11/site-packages/torch/__init__.py", line 1706, in <module>
    from torch import storage as storage  # usort: skip
    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/opt/conda/lib/python3.11/site-packages/torch/storage.py", line 23, in <module>
    import numpy as np
  File "/opt/conda/lib/python3.11/site-packages/numpy/__init__.py", line 114, in <module>
    from numpy.__config__ import show as show_config
  File "/opt/conda/lib/python3.11/site-packages/numpy/__config__.py", line 4, in <module>
    from numpy._core._multiarray_umath import (
  File "/opt/conda/lib/python3.11/site-packages/numpy/_core/__init__.py", line 23, in <module>
    from . import multiarray
  File "/opt/conda/lib/python3.11/site-packages/numpy/_core/multiarray.py", line 10, in <module>
    from . import overrides
  File "/opt/conda/lib/python3.11/site-packages/numpy/_core/overrides.py", line 8, in <module>
    from numpy._core._multiarray_umath import (
RuntimeError: CPU dispatcher tracer already initlized

attemp: 1.change numpy version and torch version, not work.
2.rebuild docker image, not work.

anyone can explain this error, and solve it?