[WARNING] PRE_ACT(505232,7efd06d50700,python):2023-07-14-16:49:38.341.247 [mindspore/ccsrc/backend/common/mem_reuse/mem_dynamic_allocator.cc:267] CalMemBlockAllocSize] Memory not enough: current free memory size[13904707584] is smaller than required size[17374927872].
[WARNING] PRE_ACT(505232,7efd06d50700,python):2023-07-14-16:49:38.341.300 [mindspore/ccsrc/backend/common/mem_reuse/mem_dynamic_allocator.cc:564] DumpDynamicMemPoolDebugInfo] Start dump dynamic memory pool debug info.
[WARNING] PRE_ACT(505232,7efd06d50700,python):2023-07-14-16:49:38.341.318 [mindspore/ccsrc/backend/common/mem_reuse/mem_dynamic_allocator.cc:523] operator()] Common mem all mem_block info: counts[2].
[WARNING] PRE_ACT(505232,7efd06d50700,python):2023-07-14-16:49:38.341.336 [mindspore/ccsrc/backend/common/mem_reuse/mem_dynamic_allocator.cc:527] operator()]  MemBlock info: number[0] mem_buf_counts[4] base_address[0x7ef18c000000] block_size[8589934592].
[WARNING] PRE_ACT(505232,7efd06d50700,python):2023-07-14-16:49:38.341.359 [mindspore/ccsrc/backend/common/mem_reuse/mem_dynamic_allocator.cc:527] operator()]  MemBlock info: number[1] mem_buf_counts[1] base_address[0x7ef408000000] block_size[1073741824].
[WARNING] PRE_ACT(505232,7efd06d50700,python):2023-07-14-16:49:38.341.376 [mindspore/ccsrc/backend/common/mem_reuse/mem_dynamic_allocator.cc:544] operator()] Common mem all idle mem_buf info: counts[2].
[WARNING] PRE_ACT(505232,7efd06d50700,python):2023-07-14-16:49:38.341.394 [mindspore/ccsrc/backend/common/mem_reuse/mem_dynamic_allocator.cc:554] operator()] Common mem total allocated memory[9663676416], used memory[7904168960], idle memory[1759507456].
[WARNING] PRE_ACT(505232,7efd06d50700,python):2023-07-14-16:49:38.341.410 [mindspore/ccsrc/backend/common/mem_reuse/mem_dynamic_allocator.cc:523] operator()] Persistent mem all mem_block info: counts[0].
[WARNING] PRE_ACT(505232,7efd06d50700,python):2023-07-14-16:49:38.341.426 [mindspore/ccsrc/backend/common/mem_reuse/mem_dynamic_allocator.cc:544] operator()] Persistent mem all idle mem_buf info: counts[0].
[WARNING] PRE_ACT(505232,7efd06d50700,python):2023-07-14-16:49:38.341.441 [mindspore/ccsrc/backend/common/mem_reuse/mem_dynamic_allocator.cc:554] operator()] Persistent mem total allocated memory[0], used memory[0], idle memory[0].
[WARNING] PRE_ACT(505232,7efd06d50700,python):2023-07-14-16:49:38.341.457 [mindspore/ccsrc/backend/common/mem_reuse/mem_dynamic_allocator.cc:567] DumpDynamicMemPoolDebugInfo] Finish dump dynamic memory pool debug info.
[ERROR] DEVICE(505232,7efd06d50700,python):2023-07-14-16:49:38.341.481 [mindspore/ccsrc/runtime/pynative/run_op_helper.cc:372] MallocForKernelOutput] Allocate output memory failed, node:Default/PadV3-op0
[WARNING] DEVICE(505232,7efd06d50700,python):2023-07-14-16:49:38.348.445 [mindspore/ccsrc/runtime/pynative/async/async_queue.cc:67] WorkerLoop] Run task failed, error msg:Malloc for kernel output failed, Memory isn't enough, node:Default/PadV3-op0

----------------------------------------------------
- C++ Call Stack: (For framework developers)
----------------------------------------------------
mindspore/ccsrc/runtime/pynative/run_op_helper.cc:643 LaunchKernels

---------------------------------------------------------------------------
RuntimeError                              Traceback (most recent call last)
/data1/myz/netsv/utils/Test.py in <module>
     42     print("mindspore time: ", end_time - start_time)
     43 
---> 44     print(dead.shape)
     45 
     46 

~/miniconda3/lib/python3.7/site-packages/mindspore/common/_stub_tensor.py in shape(self)
     88         if self.stub:
     89             if not hasattr(self, "stub_shape"):
---> 90                 self.stub_shape = self.stub.get_shape()
     91             return self.stub_shape
     92         return self.tensor.shape

RuntimeError: Malloc for kernel output failed, Memory isn't enough, node:Default/PadV3-op0

----------------------------------------------------
- C++ Call Stack: (For framework developers)
----------------------------------------------------
mindspore/ccsrc/runtime/pynative/run_op_helper.cc:643 LaunchKernels

import mindspore
import mindspore.ops as ops

mindspore.set_context(device_target="GPU", device_id=1)
np_tensor = np.random.randn(20, 999, 999, 99)
dead = mindspore.Tensor(np_tensor, mindspore.float32)
padding = (0, 99, 99, 0)
start_time = time.time()
dead = ops.pad(dead, padding, mode="constant", value=0)
end_time = time.time()
print("mindspore time: ", end_time - start_time)

print(dead.shape)