lsdustc opened a new issue #19628:
URL: https://github.com/apache/incubator-mxnet/issues/19628
## Description
(A clear and concise description of what the bug is.)
### Error Message
(Paste the complete error message. Please also include stack trace by
setting environment variable `DMLC_LOG_STACK_TRACE_DEPTH=100` before running
your script.)
`[14:22:42] /home/super/software/incubator-mxnet/src/storage/storage.cc:199:
Using Pooled (Naive) StorageManager for GPU
Traceback (most recent call last):
File "/data/workspace/mxnet_project/ime/test.py", line 53, in <module>
mx.nd.waitall()
File
"/home/super/python-latest/lib/python3.9/site-packages/mxnet-2.0.0-py3.9.egg/mxnet/ndarray/ndarray.py",
line 240, in waitall
check_call(_LIB.MXNDArrayWaitAll())
File
"/home/super/python-latest/lib/python3.9/site-packages/mxnet-2.0.0-py3.9.egg/mxnet/base.py",
line 246, in check_call
raise get_last_ffi_error()
mxnet.base.MXNetError: Traceback (most recent call last):
[bt] (13) /lib/x86_64-linux-gnu/libc.so.6(clone+0x3f) [0x7fd72e61d4cf]
[bt] (12) /lib/x86_64-linux-gnu/libpthread.so.0(+0x7fa3) [0x7fd72e87bfa3]
[bt] (11) /lib/x86_64-linux-gnu/libstdc++.so.6(+0xbbb2f) [0x7fd6b1f1db2f]
[bt] (10)
/home/super/python-latest/lib/python3.9/site-packages/mxnet-2.0.0-py3.9.egg/mxnet/libmxnet.so(std::thread::_State_impl<std::thread::_Invoker<std::tuple<std::function<void
(std::shared_ptr<dmlc::ManualEvent>)>, std::shared_ptr<dmlc::ManualEvent> > >
>::_M_run()+0x33) [0x7fd6fe279b63]
[bt] (9)
/home/super/python-latest/lib/python3.9/site-packages/mxnet-2.0.0-py3.9.egg/mxnet/libmxnet.so(std::_Function_handler<void
(std::shared_ptr<dmlc::ManualEvent>),
mxnet::engine::ThreadedEnginePerDevice::PushToExecute(mxnet::engine::OprBlock*,
bool)::{lambda()#4}::operator()()
const::{lambda(std::shared_ptr<dmlc::ManualEvent>)#1}>::_M_invoke(std::_Any_data
const&, std::shared_ptr<dmlc::ManualEvent>&&)+0x37) [0x7fd6fe27e3e7]
[bt] (8)
/home/super/python-latest/lib/python3.9/site-packages/mxnet-2.0.0-py3.9.egg/mxnet/libmxnet.so(void
mxnet::engine::ThreadedEnginePerDevice::GPUWorker<(dmlc::ConcurrentQueueType)0>(mxnet::Context,
bool,
mxnet::engine::ThreadedEnginePerDevice::ThreadWorkerBlock<(dmlc::ConcurrentQueueType)0>*,
std::shared_ptr<dmlc::ManualEvent> const&)+0x17e) [0x7fd6fe27e12e]
[bt] (7)
/home/super/python-latest/lib/python3.9/site-packages/mxnet-2.0.0-py3.9.egg/mxnet/libmxnet.so(mxnet::engine::ThreadedEngine::ExecuteOprBlock(mxnet::RunContext,
mxnet::engine::OprBlock*)+0x111) [0x7fd6fe27a9b1]
[bt] (6)
/home/super/python-latest/lib/python3.9/site-packages/mxnet-2.0.0-py3.9.egg/mxnet/libmxnet.so(+0x1e095b0)
[0x7fd6fe26f5b0]
[bt] (5)
/home/super/python-latest/lib/python3.9/site-packages/mxnet-2.0.0-py3.9.egg/mxnet/libmxnet.so(std::_Function_handler<void
(mxnet::RunContext), mxnet::imperative::PushFCompute(std::function<void
(nnvm::NodeAttrs const&, mxnet::OpContext const&, std::vector<mxnet::TBlob,
std::allocator<mxnet::TBlob> > const&, std::vector<mxnet::OpReqType,
std::allocator<mxnet::OpReqType> > const&, std::vector<mxnet::TBlob,
std::allocator<mxnet::TBlob> > const&)> const&, nnvm::Op const*,
nnvm::NodeAttrs const&, mxnet::Context const&, std::vector<mxnet::engine::Var*,
std::allocator<mxnet::engine::Var*> > const&, std::vector<mxnet::engine::Var*,
std::allocator<mxnet::engine::Var*> > const&, std::vector<mxnet::Resource,
std::allocator<mxnet::Resource> > const&, std::vector<mxnet::NDArray*,
std::allocator<mxnet::NDArray*> > const&, std::vector<mxnet::NDArray*,
std::allocator<mxnet::NDArray*> > const&, std::vector<unsigned int,
std::allocator<unsigned int> > const&, std::vector<mxnet::OpReqType
, std::allocator<mxnet::OpReqType> >
const&)::{lambda(mxnet::RunContext)#1}>::_M_invoke(std::_Any_data const&,
mxnet::RunContext&&)+0x17) [0x7fd6fe2f0867]
[bt] (4)
/home/super/python-latest/lib/python3.9/site-packages/mxnet-2.0.0-py3.9.egg/mxnet/libmxnet.so(mxnet::imperative::PushFCompute(std::function<void
(nnvm::NodeAttrs const&, mxnet::OpContext const&, std::vector<mxnet::TBlob,
std::allocator<mxnet::TBlob> > const&, std::vector<mxnet::OpReqType,
std::allocator<mxnet::OpReqType> > const&, std::vector<mxnet::TBlob,
std::allocator<mxnet::TBlob> > const&)> const&, nnvm::Op const*,
nnvm::NodeAttrs const&, mxnet::Context const&, std::vector<mxnet::engine::Var*,
std::allocator<mxnet::engine::Var*> > const&, std::vector<mxnet::engine::Var*,
std::allocator<mxnet::engine::Var*> > const&, std::vector<mxnet::Resource,
std::allocator<mxnet::Resource> > const&, std::vector<mxnet::NDArray*,
std::allocator<mxnet::NDArray*> > const&, std::vector<mxnet::NDArray*,
std::allocator<mxnet::NDArray*> > const&, std::vector<unsigned int,
std::allocator<unsigned int> > const&, std::vector<mxnet::OpReqType,
std::allocator<mxnet::OpReqType> > const&)::{la
mbda(mxnet::RunContext)#1}::operator()(mxnet::RunContext) const+0x9f2)
[0x7fd6fe2eff02]
[bt] (3)
/home/super/python-latest/lib/python3.9/site-packages/mxnet-2.0.0-py3.9.egg/mxnet/libmxnet.so(void
mxnet::op::CTCLossOpForward<mshadow::gpu>(nnvm::NodeAttrs const&,
mxnet::OpContext const&, std::vector<mxnet::TBlob, std::allocator<mxnet::TBlob>
> const&, std::vector<mxnet::OpReqType, std::allocator<mxnet::OpReqType> >
const&, std::vector<mxnet::TBlob, std::allocator<mxnet::TBlob> >
const&)+0x121c) [0x7fd7042c8072]
[bt] (2)
/home/super/python-latest/lib/python3.9/site-packages/mxnet-2.0.0-py3.9.egg/mxnet/libmxnet.so(mshadow::Tensor<mshadow::gpu,
3, float> mxnet::TBlob::get<mshadow::gpu, 3,
float>(mshadow::Stream<mshadow::gpu>*) const+0xf7) [0x7fd7038cc645]
[bt] (1)
/home/super/python-latest/lib/python3.9/site-packages/mxnet-2.0.0-py3.9.egg/mxnet/libmxnet.so(float*
mxnet::TBlob::dptr<float>() const+0x11d) [0x7fd6fe21e5ed]
[bt] (0)
/home/super/python-latest/lib/python3.9/site-packages/mxnet-2.0.0-py3.9.egg/mxnet/libmxnet.so(dmlc::LogMessageFatal::~LogMessageFatal()+0x6b)
[0x7fd6fe05d72b]
File
"/home/super/software/incubator-mxnet/include/mxnet/././tensor_blob.h", line 256
MXNetError: Check failed: mshadow: :DataType<DType>::kFlag == type_flag_:
TBlob.get_with_shape: data type do not match specified type.Expected: half v.s.
given float`
## To Reproduce
(If you developed your own code, please provide a short script that
reproduces the error. For existing examples, please provide link.)
### Steps to reproduce
(Paste the commands you ran that produced the error.)
1.
2.
## What have you tried to solve it?
1.
2.
## Environment
***We recommend using our script for collecting the diagnostic information
with the following command***
`curl --retry 10 -s
https://raw.githubusercontent.com/apache/incubator-mxnet/master/tools/diagnose.py
| python3`
<details>
<summary>Environment Information</summary>
```
# Paste the diagnose.py command output here
```
</details>
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