香橙派上静态图模式加载microsoft/phi-2前向传播报RuntimeError: Call aclnnGelu failed

[xuhangscut]

Describe the bug/ 问题描述 (Mandatory / 必填)
香橙派上静态图模式加载microsoft/phi-2前向传播报RuntimeError: Call aclnnGelu failed

• Hardware Environment(Ascend/GPU/CPU) / 硬件环境:
  Orange Pi Ai Pro

• Software Environment / 软件环境 (Mandatory / 必填):
  -- MindSpore version (e.g., 1.7.0.Bxxx) :2.4.1
  -- Python version (e.g., Python 3.7.5) :3.9.21
  -- OS platform and distribution (e.g., Linux Ubuntu 16.04):Ubuntu 22.04.3 LTS (GNU/Linux 5.10.0+ aarch64)
  -- GCC/Compiler version (if compiled from source):

• Excute Mode / 执行模式 (Mandatory / 必填)(PyNative/Graph):

Please delete the mode not involved / 请删除不涉及的模式:
/mode graph

To Reproduce / 重现步骤 (Mandatory / 必填)
`
import mindspore
from mindnlp.transformers import AutoTokenizer, AutoModelForCausalLM, StaticCache
from mindnlp.core import ops
from mindnlp.configs import set_pyboost, ON_ORANGE_PI

import time

if ON_ORANGE_PI:
mindspore.set_context(
enable_graph_kernel=True,
mode=mindspore.GRAPH_MODE,
jit_config={
"jit_level": "O2",
},
)

prompts = [
"Simply put, the theory of relativity states that ",
"My favorite all time favorite condiment is ketchup.",
]

NUM_TOKENS_TO_GENERATE = 40

model_id = "microsoft/phi-2"
tokenizer = AutoTokenizer.from_pretrained(model_id, pad_token="<|endoftext|>", padding_side="right")
model = AutoModelForCausalLM.from_pretrained(model_id, ms_dtype=mindspore.float16, low_cpu_mem_usage=True)

model.jit()

inputs = tokenizer(prompts, return_tensors="ms", padding=True)
set_pyboost(False)

@mindspore.jit(jit_config=mindspore.JitConfig(jit_syntax_level='STRICT'))
def decode_one_tokens(model, cur_token, input_pos, cache_position, past_key_values):
logits = model(
cur_token,
position_ids=input_pos,
cache_position=cache_position,
past_key_values=past_key_values,
return_dict=False,
use_cache=True
)[0]
new_token = ops.argmax(logits[:, -1], dim=-1)[:, None]
return new_token

batch_size, seq_length = inputs["input_ids"].shape
past_key_values = StaticCache(
config=model.config, max_batch_size=2, max_cache_len=11, dtype=model.dtype
)
cache_position = ops.arange(seq_length)
generated_ids = ops.zeros(
batch_size, seq_length + NUM_TOKENS_TO_GENERATE + 1, dtype=mindspore.int32
)
generated_ids[:, cache_position] = inputs["input_ids"].to(mindspore.int32)

logits = model(
**inputs, cache_position=cache_position, past_key_values=past_key_values,return_dict=False, use_cache=True
)[0]
next_token = ops.argmax(logits[:, -1], dim=-1)[:, None]
generated_ids[:, seq_length] = next_token[:, 0]

cache_position = mindspore.tensor([seq_length + 1])
for _ in range(1, NUM_TOKENS_TO_GENERATE):
s = time.time()
next_token = decode_one_tokens(model, next_token, None, cache_position, past_key_values)
generated_ids[:, cache_position] = next_token.int()
cache_position += 1
t = time.time()
print(t - s)

text = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
print(text)

`

Expected behavior / 预期结果 (Mandatory / 必填)
正常运行

Screenshots/ 日志 / 截图 (Mandatory / 必填)