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Link error when building flex:tensorflowlite_flex in debug mode for Windows
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[
"@OleksiyA Could you try to build flex:tensorflowlite_flex in release mode (without -c dbg flag) and see if the error persists. If it disappears, the issue is likely related to missing debug information.\r\nThank you!",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"Release mode build work without issue.",
"@OleksiyA Thank you for the confirmation. Could you please move this issue to closed status if it is resolved?\r\nThank you!",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/65688\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/65688\">No</a>\n"
] | 2024-04-15T01:37:01 | 2024-05-21T01:48:48 | 2024-05-21T01:48:45 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
master
### Custom code
Yes
### OS platform and distribution
_No response_
### Mobile device
_No response_
### Python version
_No response_
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
Build should produce dll.
### Standalone code to reproduce the issue
```shell
Command used to build:
bazelisk-windows-amd64.exe build -c dbg --config=monolithic tensorflow/lite/delegates/flex:tensorflowlite_flex
Trying to build debug to figure out why registering flex delegate crashes, Crash in TfLiteDelegatePrepareInternal.
```
### Relevant log output
```shell
Longer log https://gist.github.com/OleksiyA/dd93cb7dc7fbd1d132afb8afe8fcb8af
.....
LINK : warning LNK4217: symbol 'TF_ShapeInferenceContextDim' defined in 'ops.lo.lib(ops.obj)' is imported by 'bitcast_op_lib.lo.lib(bitcast.obj)' in function '"void __cdecl ComputeNewShape(struct TF_ShapeInferenceContext *,struct TF_ShapeHandle *,enum TF_DataType,enum TF_DataType,struct TSL_Status *)" (?ComputeNewShape@@YAXPEAUTF_ShapeInferenceContext@@PEAUTF_ShapeHandle@@W4TF_DataType@@2PEAUTSL_Status@@@Z)'
LINK : warning LNK4217: symbol 'TF_ShapeInferenceContextSubshape' defined in 'ops.lo.lib(ops.obj)' is imported by 'bitcast_op_lib.lo.lib(bitcast.obj)' in function '"void __cdecl ComputeNewShape(struct TF_ShapeInferenceContext *,struct TF_ShapeHandle *,enum TF_DataType,enum TF_DataType,struct TSL_Status *)" (?ComputeNewShape@@YAXPEAUTF_ShapeInferenceContext@@PEAUTF_ShapeHandle@@W4TF_DataType@@2PEAUTSL_Status@@@Z)'
LINK : warning LNK4217: symbol 'TF_ShapeInferenceContextSetUnknownShape' defined in 'ops.lo.lib(ops.obj)' is imported by 'bitcast_op_lib.lo.lib(bitcast.obj)' in function '"void __cdecl bitcast_shape_inference_fn(struct TF_ShapeInferenceContext *,struct TSL_Status *)" (?bitcast_shape_inference_fn@@YAXPEAUTF_ShapeInferenceContext@@PEAUTSL_Status@@@Z)'
LINK : warning LNK4217: symbol 'TF_DimensionHandleValueKnown' defined in 'ops.lo.lib(ops.obj)' is imported by 'bitcast_op_lib.lo.lib(bitcast.obj)' in function '"void __cdecl ComputeNewShape(struct TF_ShapeInferenceContext *,struct TF_ShapeHandle *,enum TF_DataType,enum TF_DataType,struct TSL_Status *)" (?ComputeNewShape@@YAXPEAUTF_ShapeInferenceContext@@PEAUTF_ShapeHandle@@W4TF_DataType@@2PEAUTSL_Status@@@Z)'
LINK : warning LNK4217: symbol 'TF_DimensionHandleValue' defined in 'ops.lo.lib(ops.obj)' is imported by 'bitcast_op_lib.lo.lib(bitcast.obj)' in function '"void __cdecl ComputeNewShape(struct TF_ShapeInferenceContext *,struct TF_ShapeHandle *,enum TF_DataType,enum TF_DataType,struct TSL_Status *)" (?ComputeNewShape@@YAXPEAUTF_ShapeInferenceContext@@PEAUTF_ShapeHandle@@W4TF_DataType@@2PEAUTSL_Status@@@Z)'
LINK : warning LNK4217: symbol 'TF_ShapeInferenceContextConcatenateShapes' defined in 'ops.lo.lib(ops.obj)' is imported by 'bitcast_op_lib.lo.lib(bitcast.obj)' in function '"void __cdecl ComputeNewShape(struct TF_ShapeInferenceContext *,struct TF_ShapeHandle *,enum TF_DataType,enum TF_DataType,struct TSL_Status *)" (?ComputeNewShape@@YAXPEAUTF_ShapeInferenceContext@@PEAUTF_ShapeHandle@@W4TF_DataType@@2PEAUTSL_Status@@@Z)'
LINK : warning LNK4217: symbol 'TF_DeleteDimensionHandle' defined in 'ops.lo.lib(ops.obj)' is imported by 'bitcast_op_lib.lo.lib(bitcast.obj)' in function '"void __cdecl ComputeNewShape(struct TF_ShapeInferenceContext *,struct TF_ShapeHandle *,enum TF_DataType,enum TF_DataType,struct TSL_Status *)" (?ComputeNewShape@@YAXPEAUTF_ShapeInferenceContext@@PEAUTF_ShapeHandle@@W4TF_DataType@@2PEAUTSL_Status@@@Z)'
depth_space_ops.lo.lib(spacetodepth_op.obj) : error LNK2019: unresolved external symbol "public: void __cdecl tensorflow::functor::SpaceToDepthOpFunctor<struct Eigen::ThreadPoolDevice,int,1>::operator()(struct Eigen::ThreadPoolDevice const &,class Eigen::TensorMap<class Eigen::Tensor<int const ,4,1,__int64>,16,struct Eigen::MakePointer>,int,class Eigen::TensorMap<class Eigen::Tensor<int,4,1,__int64>,16,struct Eigen::MakePointer>)" (??R?$SpaceToDepthOpFunctor@UThreadPoolDevice@Eigen@@H$00@functor@tensorflow@@QEAAXAEBUThreadPoolDevice@Eigen@@V?$TensorMap@V?$Tensor@$$CBH$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@HV?$TensorMap@V?$Tensor@H$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@@Z) referenced in function "public: virtual void __cdecl tensorflow::SpaceToDepthOp<struct Eigen::ThreadPoolDevice,signed char>::Compute(class tensorflow::OpKernelContext *)" (?Compute@?$SpaceToDepthOp@UThreadPoolDevice@Eigen@@C@tensorflow@@UEAAXPEAVOpKernelContext@2@@Z)
depth_space_ops.lo.lib(spacetodepth_op.obj) : error LNK2019: unresolved external symbol "public: void __cdecl tensorflow::functor::SpaceToDepthOpFunctor<struct Eigen::ThreadPoolDevice,unsigned char,1>::operator()(struct Eigen::ThreadPoolDevice const &,class Eigen::TensorMap<class Eigen::Tensor<unsigned char const ,4,1,__int64>,16,struct Eigen::MakePointer>,int,class Eigen::TensorMap<class Eigen::Tensor<unsigned char,4,1,__int64>,16,struct Eigen::MakePointer>)" (??R?$SpaceToDepthOpFunctor@UThreadPoolDevice@Eigen@@E$00@functor@tensorflow@@QEAAXAEBUThreadPoolDevice@Eigen@@V?$TensorMap@V?$Tensor@$$CBE$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@HV?$TensorMap@V?$Tensor@E$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@@Z) referenced in function "public: virtual void __cdecl tensorflow::SpaceToDepthOp<struct Eigen::ThreadPoolDevice,unsigned char>::Compute(class tensorflow::OpKernelContext *)" (?Compute@?$SpaceToDepthOp@UThreadPoolDevice@Eigen@@E@tensorflow@@UEAAXPEAVOpKernelContext@2@@Z)
depth_space_ops.lo.lib(spacetodepth_op.obj) : error LNK2019: unresolved external symbol "public: void __cdecl tensorflow::functor::SpaceToDepthOpFunctor<struct Eigen::ThreadPoolDevice,class tensorflow::Variant,1>::operator()(struct Eigen::ThreadPoolDevice const &,class Eigen::TensorMap<class Eigen::Tensor<class tensorflow::Variant const ,4,1,__int64>,16,struct Eigen::MakePointer>,int,class Eigen::TensorMap<class Eigen::Tensor<class tensorflow::Variant,4,1,__int64>,16,struct Eigen::MakePointer>)" (??R?$SpaceToDepthOpFunctor@UThreadPoolDevice@Eigen@@VVariant@tensorflow@@$00@functor@tensorflow@@QEAAXAEBUThreadPoolDevice@Eigen@@V?$TensorMap@V?$Tensor@$$CBVVariant@tensorflow@@$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@HV?$TensorMap@V?$Tensor@VVariant@tensorflow@@$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@@Z) referenced in function "public: virtual void __cdecl tensorflow::SpaceToDepthOp<struct Eigen::ThreadPoolDevice,class tensorflow::Variant>::Compute(class tensorflow::OpKernelContext *)" (?Compute@?$SpaceToDepthOp@UThreadPoolDevice@Eigen@@VVariant@tensorflow@@@tensorflow@@UEAAXPEAVOpKernelContext@2@@Z)
depth_space_ops.lo.lib(spacetodepth_op.obj) : error LNK2019: unresolved external symbol "public: void __cdecl tensorflow::functor::SpaceToDepthOpFunctor<struct Eigen::ThreadPoolDevice,class tensorflow::ResourceHandle,1>::operator()(struct Eigen::ThreadPoolDevice const &,class Eigen::TensorMap<class Eigen::Tensor<class tensorflow::ResourceHandle const ,4,1,__int64>,16,struct Eigen::MakePointer>,int,class Eigen::TensorMap<class Eigen::Tensor<class tensorflow::ResourceHandle,4,1,__int64>,16,struct Eigen::MakePointer>)" (??R?$SpaceToDepthOpFunctor@UThreadPoolDevice@Eigen@@VResourceHandle@tensorflow@@$00@functor@tensorflow@@QEAAXAEBUThreadPoolDevice@Eigen@@V?$TensorMap@V?$Tensor@$$CBVResourceHandle@tensorflow@@$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@HV?$TensorMap@V?$Tensor@VResourceHandle@tensorflow@@$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@@Z) referenced in function "public: virtual void __cdecl tensorflow::SpaceToDepthOp<struct Eigen::ThreadPoolDevice,class tensorflow::ResourceHandle>::Compute(class tensorflow::OpKernelContext *)" (?Compute@?$SpaceToDepthOp@UThreadPoolDevice@Eigen@@VResourceHandle@tensorflow@@@tensorflow@@UEAAXPEAVOpKernelContext@2@@Z)
depth_space_ops.lo.lib(spacetodepth_op.obj) : error LNK2019: unresolved external symbol "public: void __cdecl tensorflow::functor::SpaceToDepthOpFunctor<struct Eigen::ThreadPoolDevice,class tsl::tstring,1>::operator()(struct Eigen::ThreadPoolDevice const &,class Eigen::TensorMap<class Eigen::Tensor<class tsl::tstring const ,4,1,__int64>,16,struct Eigen::MakePointer>,int,class Eigen::TensorMap<class Eigen::Tensor<class tsl::tstring,4,1,__int64>,16,struct Eigen::MakePointer>)" (??R?$SpaceToDepthOpFunctor@UThreadPoolDevice@Eigen@@Vtstring@tsl@@$00@functor@tensorflow@@QEAAXAEBUThreadPoolDevice@Eigen@@V?$TensorMap@V?$Tensor@$$CBVtstring@tsl@@$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@HV?$TensorMap@V?$Tensor@Vtstring@tsl@@$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@@Z) referenced in function "public: virtual void __cdecl tensorflow::SpaceToDepthOp<struct Eigen::ThreadPoolDevice,class tsl::tstring>::Compute(class tensorflow::OpKernelContext *)" (?Compute@?$SpaceToDepthOp@UThreadPoolDevice@Eigen@@Vtstring@tsl@@@tensorflow@@UEAAXPEAVOpKernelContext@2@@Z)
depth_space_ops.lo.lib(spacetodepth_op.obj) : error LNK2019: unresolved external symbol "public: void __cdecl tensorflow::functor::SpaceToDepthOpFunctor<struct Eigen::ThreadPoolDevice,bool,1>::operator()(struct Eigen::ThreadPoolDevice const &,class Eigen::TensorMap<class Eigen::Tensor<bool const ,4,1,__int64>,16,struct Eigen::MakePointer>,int,class Eigen::TensorMap<class Eigen::Tensor<bool,4,1,__int64>,16,struct Eigen::MakePointer>)" (??R?$SpaceToDepthOpFunctor@UThreadPoolDevice@Eigen@@_N$00@functor@tensorflow@@QEAAXAEBUThreadPoolDevice@Eigen@@V?$TensorMap@V?$Tensor@$$CB_N$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@HV?$TensorMap@V?$Tensor@_N$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@@Z) referenced in function "public: virtual void __cdecl tensorflow::SpaceToDepthOp<struct Eigen::ThreadPoolDevice,bool>::Compute(class tensorflow::OpKernelContext *)" (?Compute@?$SpaceToDepthOp@UThreadPoolDevice@Eigen@@_N@tensorflow@@UEAAXPEAVOpKernelContext@2@@Z)
depth_space_ops.lo.lib(spacetodepth_op.obj) : error LNK2019: unresolved external symbol "public: void __cdecl tensorflow::functor::SpaceToDepthOpFunctor<struct Eigen::ThreadPoolDevice,class std::complex<double>,1>::operator()(struct Eigen::ThreadPoolDevice const &,class Eigen::TensorMap<class Eigen::Tensor<class std::complex<double> const ,4,1,__int64>,16,struct Eigen::MakePointer>,int,class Eigen::TensorMap<class Eigen::Tensor<class std::complex<double>,4,1,__int64>,16,struct Eigen::MakePointer>)" (??R?$SpaceToDepthOpFunctor@UThreadPoolDevice@Eigen@@V?$complex@N@std@@$00@functor@tensorflow@@QEAAXAEBUThreadPoolDevice@Eigen@@V?$TensorMap@V?$Tensor@$$CBV?$complex@N@std@@$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@HV?$TensorMap@V?$Tensor@V?$complex@N@std@@$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@@Z) referenced in function "public: virtual void __cdecl tensorflow::SpaceToDepthOp<struct Eigen::ThreadPoolDevice,class std::complex<double> >::Compute(class tensorflow::OpKernelContext *)" (?Compute@?$SpaceToDepthOp@UThreadPoolDevice@Eigen@@V?$complex@N@std@@@tensorflow@@UEAAXPEAVOpKernelContext@2@@Z)
depth_space_ops.lo.lib(spacetodepth_op.obj) : error LNK2019: unresolved external symbol "public: void __cdecl tensorflow::functor::SpaceToDepthOpFunctor<struct Eigen::ThreadPoolDevice,class std::complex<float>,1>::operator()(struct Eigen::ThreadPoolDevice const &,class Eigen::TensorMap<class Eigen::Tensor<class std::complex<float> const ,4,1,__int64>,16,struct Eigen::MakePointer>,int,class Eigen::TensorMap<class Eigen::Tensor<class std::complex<float>,4,1,__int64>,16,struct Eigen::MakePointer>)" (??R?$SpaceToDepthOpFunctor@UThreadPoolDevice@Eigen@@V?$complex@M@std@@$00@functor@tensorflow@@QEAAXAEBUThreadPoolDevice@Eigen@@V?$TensorMap@V?$Tensor@$$CBV?$complex@M@std@@$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@HV?$TensorMap@V?$Tensor@V?$complex@M@std@@$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@@Z) referenced in function "public: virtual void __cdecl tensorflow::SpaceToDepthOp<struct Eigen::ThreadPoolDevice,class std::complex<float> >::Compute(class tensorflow::OpKernelContext *)" (?Compute@?$SpaceToDepthOp@UThreadPoolDevice@Eigen@@V?$complex@M@std@@@tensorflow@@UEAAXPEAVOpKernelContext@2@@Z)
depth_space_ops.lo.lib(spacetodepth_op.obj) : error LNK2019: unresolved external symbol "public: void __cdecl tensorflow::functor::SpaceToDepthOpFunctor<struct Eigen::ThreadPoolDevice,double,1>::operator()(struct Eigen::ThreadPoolDevice const &,class Eigen::TensorMap<class Eigen::Tensor<double const ,4,1,__int64>,16,struct Eigen::MakePointer>,int,class Eigen::TensorMap<class Eigen::Tensor<double,4,1,__int64>,16,struct Eigen::MakePointer>)" (??R?$SpaceToDepthOpFunctor@UThreadPoolDevice@Eigen@@N$00@functor@tensorflow@@QEAAXAEBUThreadPoolDevice@Eigen@@V?$TensorMap@V?$Tensor@$$CBN$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@HV?$TensorMap@V?$Tensor@N$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@@Z) referenced in function "public: virtual void __cdecl tensorflow::SpaceToDepthOp<struct Eigen::ThreadPoolDevice,double>::Compute(class tensorflow::OpKernelContext *)" (?Compute@?$SpaceToDepthOp@UThreadPoolDevice@Eigen@@N@tensorflow@@UEAAXPEAVOpKernelContext@2@@Z)
depth_space_ops.lo.lib(spacetodepth_op.obj) : error LNK2019: unresolved external symbol "public: void __cdecl tensorflow::functor::SpaceToDepthOpFunctor<struct Eigen::ThreadPoolDevice,float,1>::operator()(struct Eigen::ThreadPoolDevice const &,class Eigen::TensorMap<class Eigen::Tensor<float const ,4,1,__int64>,16,struct Eigen::MakePointer>,int,class Eigen::TensorMap<class Eigen::Tensor<float,4,1,__int64>,16,struct Eigen::MakePointer>)" (??R?$SpaceToDepthOpFunctor@UThreadPoolDevice@Eigen@@M$00@functor@tensorflow@@QEAAXAEBUThreadPoolDevice@Eigen@@V?$TensorMap@V?$Tensor@$$CBM$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@HV?$TensorMap@V?$Tensor@M$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@@Z) referenced in function "public: virtual void __cdecl tensorflow::SpaceToDepthOp<struct Eigen::ThreadPoolDevice,float>::Compute(class tensorflow::OpKernelContext *)" (?Compute@?$SpaceToDepthOp@UThreadPoolDevice@Eigen@@M@tensorflow@@UEAAXPEAVOpKernelContext@2@@Z)
depth_space_ops.lo.lib(spacetodepth_op.obj) : error LNK2019: unresolved external symbol "public: void __cdecl tensorflow::functor::SpaceToDepthOpFunctor<struct Eigen::ThreadPoolDevice,struct Eigen::bfloat16,1>::operator()(struct Eigen::ThreadPoolDevice const &,class Eigen::TensorMap<class Eigen::Tensor<struct Eigen::bfloat16 const ,4,1,__int64>,16,struct Eigen::MakePointer>,int,class Eigen::TensorMap<class Eigen::Tensor<struct Eigen::bfloat16,4,1,__int64>,16,struct Eigen::MakePointer>)" (??R?$SpaceToDepthOpFunctor@UThreadPoolDevice@Eigen@@Ubfloat16@2@$00@functor@tensorflow@@QEAAXAEBUThreadPoolDevice@Eigen@@V?$TensorMap@V?$Tensor@$$CBUbfloat16@Eigen@@$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@HV?$TensorMap@V?$Tensor@Ubfloat16@Eigen@@$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@@Z) referenced in function "public: virtual void __cdecl tensorflow::SpaceToDepthOp<struct Eigen::ThreadPoolDevice,struct Eigen::bfloat16>::Compute(class tensorflow::OpKernelContext *)" (?Compute@?$SpaceToDepthOp@UThreadPoolDevice@Eigen@@Ubfloat16@2@@tensorflow@@UEAAXPEAVOpKernelContext@2@@Z)
depth_space_ops.lo.lib(spacetodepth_op.obj) : error LNK2019: unresolved external symbol "public: void __cdecl tensorflow::functor::SpaceToDepthOpFunctor<struct Eigen::ThreadPoolDevice,struct Eigen::half,1>::operator()(struct Eigen::ThreadPoolDevice const &,class Eigen::TensorMap<class Eigen::Tensor<struct Eigen::half const ,4,1,__int64>,16,struct Eigen::MakePointer>,int,class Eigen::TensorMap<class Eigen::Tensor<struct Eigen::half,4,1,__int64>,16,struct Eigen::MakePointer>)" (??R?$SpaceToDepthOpFunctor@UThreadPoolDevice@Eigen@@Uhalf@2@$00@functor@tensorflow@@QEAAXAEBUThreadPoolDevice@Eigen@@V?$TensorMap@V?$Tensor@$$CBUhalf@Eigen@@$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@HV?$TensorMap@V?$Tensor@Uhalf@Eigen@@$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@@Z) referenced in function "public: virtual void __cdecl tensorflow::SpaceToDepthOp<struct Eigen::ThreadPoolDevice,struct Eigen::half>::Compute(class tensorflow::OpKernelContext *)" (?Compute@?$SpaceToDepthOp@UThreadPoolDevice@Eigen@@Uhalf@2@@tensorflow@@UEAAXPEAVOpKernelContext@2@@Z)
depth_space_ops.lo.lib(spacetodepth_op.obj) : error LNK2019: unresolved external symbol "public: void __cdecl tensorflow::functor::SpaceToDepthOpFunctor<struct Eigen::ThreadPoolDevice,signed char,1>::operator()(struct Eigen::ThreadPoolDevice const &,class Eigen::TensorMap<class Eigen::Tensor<signed char const ,4,1,__int64>,16,struct Eigen::MakePointer>,int,class Eigen::TensorMap<class Eigen::Tensor<signed char,4,1,__int64>,16,struct Eigen::MakePointer>)" (??R?$SpaceToDepthOpFunctor@UThreadPoolDevice@Eigen@@C$00@functor@tensorflow@@QEAAXAEBUThreadPoolDevice@Eigen@@V?$TensorMap@V?$Tensor@$$CBC$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@HV?$TensorMap@V?$Tensor@C$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@@Z) referenced in function "public: virtual void __cdecl tensorflow::SpaceToDepthOp<struct Eigen::ThreadPoolDevice,signed char>::Compute(class tensorflow::OpKernelContext *)" (?Compute@?$SpaceToDepthOp@UThreadPoolDevice@Eigen@@C@tensorflow@@UEAAXPEAVOpKernelContext@2@@Z)
depth_space_ops.lo.lib(spacetodepth_op.obj) : error LNK2019: unresolved external symbol "public: void __cdecl tensorflow::functor::SpaceToDepthOpFunctor<struct Eigen::ThreadPoolDevice,short,1>::operator()(struct Eigen::ThreadPoolDevice const &,class Eigen::TensorMap<class Eigen::Tensor<short const ,4,1,__int64>,16,struct Eigen::MakePointer>,int,class Eigen::TensorMap<class Eigen::Tensor<short,4,1,__int64>,16,struct Eigen::MakePointer>)" (??R?$SpaceToDepthOpFunctor@UThreadPoolDevice@Eigen@@F$00@functor@tensorflow@@QEAAXAEBUThreadPoolDevice@Eigen@@V?$TensorMap@V?$Tensor@$$CBF$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@HV?$TensorMap@V?$Tensor@F$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@@Z) referenced in function "public: virtual void __cdecl tensorflow::SpaceToDepthOp<struct Eigen::ThreadPoolDevice,short>::Compute(class tensorflow::OpKernelContext *)" (?Compute@?$SpaceToDepthOp@UThreadPoolDevice@Eigen@@F@tensorflow@@UEAAXPEAVOpKernelContext@2@@Z)
depth_space_ops.lo.lib(spacetodepth_op.obj) : error LNK2019: unresolved external symbol "public: void __cdecl tensorflow::functor::SpaceToDepthOpFunctor<struct Eigen::ThreadPoolDevice,unsigned short,1>::operator()(struct Eigen::ThreadPoolDevice const &,class Eigen::TensorMap<class Eigen::Tensor<unsigned short const ,4,1,__int64>,16,struct Eigen::MakePointer>,int,class Eigen::TensorMap<class Eigen::Tensor<unsigned short,4,1,__int64>,16,struct Eigen::MakePointer>)" (??R?$SpaceToDepthOpFunctor@UThreadPoolDevice@Eigen@@G$00@functor@tensorflow@@QEAAXAEBUThreadPoolDevice@Eigen@@V?$TensorMap@V?$Tensor@$$CBG$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@HV?$TensorMap@V?$Tensor@G$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@@Z) referenced in function "public: virtual void __cdecl tensorflow::SpaceToDepthOp<struct Eigen::ThreadPoolDevice,unsigned short>::Compute(class tensorflow::OpKernelContext *)" (?Compute@?$SpaceToDepthOp@UThreadPoolDevice@Eigen@@G@tensorflow@@UEAAXPEAVOpKernelContext@2@@Z)
depth_space_ops.lo.lib(spacetodepth_op.obj) : error LNK2019: unresolved external symbol "public: void __cdecl tensorflow::functor::SpaceToDepthOpFunctor<struct Eigen::ThreadPoolDevice,unsigned int,1>::operator()(struct Eigen::ThreadPoolDevice const &,class Eigen::TensorMap<class Eigen::Tensor<unsigned int const ,4,1,__int64>,16,struct Eigen::MakePointer>,int,class Eigen::TensorMap<class Eigen::Tensor<unsigned int,4,1,__int64>,16,struct Eigen::MakePointer>)" (??R?$SpaceToDepthOpFunctor@UThreadPoolDevice@Eigen@@I$00@functor@tensorflow@@QEAAXAEBUThreadPoolDevice@Eigen@@V?$TensorMap@V?$Tensor@$$CBI$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@HV?$TensorMap@V?$Tensor@I$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@@Z) referenced in function "public: virtual void __cdecl tensorflow::SpaceToDepthOp<struct Eigen::ThreadPoolDevice,unsigned int>::Compute(class tensorflow::OpKernelContext *)" (?Compute@?$SpaceToDepthOp@UThreadPoolDevice@Eigen@@I@tensorflow@@UEAAXPEAVOpKernelContext@2@@Z)
depth_space_ops.lo.lib(spacetodepth_op.obj) : error LNK2019: unresolved external symbol "public: void __cdecl tensorflow::functor::SpaceToDepthOpFunctor<struct Eigen::ThreadPoolDevice,__int64,1>::operator()(struct Eigen::ThreadPoolDevice const &,class Eigen::TensorMap<class Eigen::Tensor<__int64 const ,4,1,__int64>,16,struct Eigen::MakePointer>,int,class Eigen::TensorMap<class Eigen::Tensor<__int64,4,1,__int64>,16,struct Eigen::MakePointer>)" (??R?$SpaceToDepthOpFunctor@UThreadPoolDevice@Eigen@@_J$00@functor@tensorflow@@QEAAXAEBUThreadPoolDevice@Eigen@@V?$TensorMap@V?$Tensor@$$CB_J$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@HV?$TensorMap@V?$Tensor@_J$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@@Z) referenced in function "public: virtual void __cdecl tensorflow::SpaceToDepthOp<struct Eigen::ThreadPoolDevice,__int64>::Compute(class tensorflow::OpKernelContext *)" (?Compute@?$SpaceToDepthOp@UThreadPoolDevice@Eigen@@_J@tensorflow@@UEAAXPEAVOpKernelContext@2@@Z)
depth_space_ops.lo.lib(spacetodepth_op.obj) : error LNK2019: unresolved external symbol "public: void __cdecl tensorflow::functor::SpaceToDepthOpFunctor<struct Eigen::ThreadPoolDevice,unsigned __int64,1>::operator()(struct Eigen::ThreadPoolDevice const &,class Eigen::TensorMap<class Eigen::Tensor<unsigned __int64 const ,4,1,__int64>,16,struct Eigen::MakePointer>,int,class Eigen::TensorMap<class Eigen::Tensor<unsigned __int64,4,1,__int64>,16,struct Eigen::MakePointer>)" (??R?$SpaceToDepthOpFunctor@UThreadPoolDevice@Eigen@@_K$00@functor@tensorflow@@QEAAXAEBUThreadPoolDevice@Eigen@@V?$TensorMap@V?$Tensor@$$CB_K$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@HV?$TensorMap@V?$Tensor@_K$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@@Z) referenced in function "public: virtual void __cdecl tensorflow::SpaceToDepthOp<struct Eigen::ThreadPoolDevice,unsigned __int64>::Compute(class tensorflow::OpKernelContext *)" (?Compute@?$SpaceToDepthOp@UThreadPoolDevice@Eigen@@_K@tensorflow@@UEAAXPEAVOpKernelContext@2@@Z)
depth_space_ops.lo.lib(depthtospace_op.obj) : error LNK2019: unresolved external symbol "public: void __cdecl tensorflow::functor::DepthToSpaceOpFunctor<struct Eigen::ThreadPoolDevice,int,1>::operator()(struct Eigen::ThreadPoolDevice const &,class Eigen::TensorMap<class Eigen::Tensor<int const ,4,1,__int64>,16,struct Eigen::MakePointer>,int,class Eigen::TensorMap<class Eigen::Tensor<int,4,1,__int64>,16,struct Eigen::MakePointer>)" (??R?$DepthToSpaceOpFunctor@UThreadPoolDevice@Eigen@@H$00@functor@tensorflow@@QEAAXAEBUThreadPoolDevice@Eigen@@V?$TensorMap@V?$Tensor@$$CBH$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@HV?$TensorMap@V?$Tensor@H$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@@Z) referenced in function "public: virtual void __cdecl tensorflow::DepthToSpaceOp<struct Eigen::ThreadPoolDevice,signed char>::Compute(class tensorflow::OpKernelContext *)" (?Compute@?$DepthToSpaceOp@UThreadPoolDevice@Eigen@@C@tensorflow@@UEAAXPEAVOpKernelContext@2@@Z)
depth_space_ops.lo.lib(depthtospace_op.obj) : error LNK2019: unresolved external symbol "public: void __cdecl tensorflow::functor::DepthToSpaceOpFunctor<struct Eigen::ThreadPoolDevice,class tensorflow::Variant,1>::operator()(struct Eigen::ThreadPoolDevice const &,class Eigen::TensorMap<class Eigen::Tensor<class tensorflow::Variant const ,4,1,__int64>,16,struct Eigen::MakePointer>,int,class Eigen::TensorMap<class Eigen::Tensor<class tensorflow::Variant,4,1,__int64>,16,struct Eigen::MakePointer>)" (??R?$DepthToSpaceOpFunctor@UThreadPoolDevice@Eigen@@VVariant@tensorflow@@$00@functor@tensorflow@@QEAAXAEBUThreadPoolDevice@Eigen@@V?$TensorMap@V?$Tensor@$$CBVVariant@tensorflow@@$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@HV?$TensorMap@V?$Tensor@VVariant@tensorflow@@$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@@Z) referenced in function "public: virtual void __cdecl tensorflow::DepthToSpaceOp<struct Eigen::ThreadPoolDevice,class tensorflow::Variant>::Compute(class tensorflow::OpKernelContext *)" (?Compute@?$DepthToSpaceOp@UThreadPoolDevice@Eigen@@VVariant@tensorflow@@@tensorflow@@UEAAXPEAVOpKernelContext@2@@Z)
depth_space_ops.lo.lib(depthtospace_op.obj) : error LNK2019: unresolved external symbol "public: void __cdecl tensorflow::functor::DepthToSpaceOpFunctor<struct Eigen::ThreadPoolDevice,class tensorflow::ResourceHandle,1>::operator()(struct Eigen::ThreadPoolDevice const &,class Eigen::TensorMap<class Eigen::Tensor<class tensorflow::ResourceHandle const ,4,1,__int64>,16,struct Eigen::MakePointer>,int,class Eigen::TensorMap<class Eigen::Tensor<class tensorflow::ResourceHandle,4,1,__int64>,16,struct Eigen::MakePointer>)" (??R?$DepthToSpaceOpFunctor@UThreadPoolDevice@Eigen@@VResourceHandle@tensorflow@@$00@functor@tensorflow@@QEAAXAEBUThreadPoolDevice@Eigen@@V?$TensorMap@V?$Tensor@$$CBVResourceHandle@tensorflow@@$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@HV?$TensorMap@V?$Tensor@VResourceHandle@tensorflow@@$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@@Z) referenced in function "public: virtual void __cdecl tensorflow::DepthToSpaceOp<struct Eigen::ThreadPoolDevice,class tensorflow::ResourceHandle>::Compute(class tensorflow::OpKernelContext *)" (?Compute@?$DepthToSpaceOp@UThreadPoolDevice@Eigen@@VResourceHandle@tensorflow@@@tensorflow@@UEAAXPEAVOpKernelContext@2@@Z)
depth_space_ops.lo.lib(depthtospace_op.obj) : error LNK2019: unresolved external symbol "public: void __cdecl tensorflow::functor::DepthToSpaceOpFunctor<struct Eigen::ThreadPoolDevice,class tsl::tstring,1>::operator()(struct Eigen::ThreadPoolDevice const &,class Eigen::TensorMap<class Eigen::Tensor<class tsl::tstring const ,4,1,__int64>,16,struct Eigen::MakePointer>,int,class Eigen::TensorMap<class Eigen::Tensor<class tsl::tstring,4,1,__int64>,16,struct Eigen::MakePointer>)" (??R?$DepthToSpaceOpFunctor@UThreadPoolDevice@Eigen@@Vtstring@tsl@@$00@functor@tensorflow@@QEAAXAEBUThreadPoolDevice@Eigen@@V?$TensorMap@V?$Tensor@$$CBVtstring@tsl@@$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@HV?$TensorMap@V?$Tensor@Vtstring@tsl@@$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@@Z) referenced in function "public: virtual void __cdecl tensorflow::DepthToSpaceOp<struct Eigen::ThreadPoolDevice,class tsl::tstring>::Compute(class tensorflow::OpKernelContext *)" (?Compute@?$DepthToSpaceOp@UThreadPoolDevice@Eigen@@Vtstring@tsl@@@tensorflow@@UEAAXPEAVOpKernelContext@2@@Z)
depth_space_ops.lo.lib(depthtospace_op.obj) : error LNK2019: unresolved external symbol "public: void __cdecl tensorflow::functor::DepthToSpaceOpFunctor<struct Eigen::ThreadPoolDevice,bool,1>::operator()(struct Eigen::ThreadPoolDevice const &,class Eigen::TensorMap<class Eigen::Tensor<bool const ,4,1,__int64>,16,struct Eigen::MakePointer>,int,class Eigen::TensorMap<class Eigen::Tensor<bool,4,1,__int64>,16,struct Eigen::MakePointer>)" (??R?$DepthToSpaceOpFunctor@UThreadPoolDevice@Eigen@@_N$00@functor@tensorflow@@QEAAXAEBUThreadPoolDevice@Eigen@@V?$TensorMap@V?$Tensor@$$CB_N$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@HV?$TensorMap@V?$Tensor@_N$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@@Z) referenced in function "public: virtual void __cdecl tensorflow::DepthToSpaceOp<struct Eigen::ThreadPoolDevice,bool>::Compute(class tensorflow::OpKernelContext *)" (?Compute@?$DepthToSpaceOp@UThreadPoolDevice@Eigen@@_N@tensorflow@@UEAAXPEAVOpKernelContext@2@@Z)
depth_space_ops.lo.lib(depthtospace_op.obj) : error LNK2019: unresolved external symbol "public: void __cdecl tensorflow::functor::DepthToSpaceOpFunctor<struct Eigen::ThreadPoolDevice,class std::complex<double>,1>::operator()(struct Eigen::ThreadPoolDevice const &,class Eigen::TensorMap<class Eigen::Tensor<class std::complex<double> const ,4,1,__int64>,16,struct Eigen::MakePointer>,int,class Eigen::TensorMap<class Eigen::Tensor<class std::complex<double>,4,1,__int64>,16,struct Eigen::MakePointer>)" (??R?$DepthToSpaceOpFunctor@UThreadPoolDevice@Eigen@@V?$complex@N@std@@$00@functor@tensorflow@@QEAAXAEBUThreadPoolDevice@Eigen@@V?$TensorMap@V?$Tensor@$$CBV?$complex@N@std@@$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@HV?$TensorMap@V?$Tensor@V?$complex@N@std@@$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@@Z) referenced in function "public: virtual void __cdecl tensorflow::DepthToSpaceOp<struct Eigen::ThreadPoolDevice,class std::complex<double> >::Compute(class tensorflow::OpKernelContext *)" (?Compute@?$DepthToSpaceOp@UThreadPoolDevice@Eigen@@V?$complex@N@std@@@tensorflow@@UEAAXPEAVOpKernelContext@2@@Z)
depth_space_ops.lo.lib(depthtospace_op.obj) : error LNK2019: unresolved external symbol "public: void __cdecl tensorflow::functor::DepthToSpaceOpFunctor<struct Eigen::ThreadPoolDevice,class std::complex<float>,1>::operator()(struct Eigen::ThreadPoolDevice const &,class Eigen::TensorMap<class Eigen::Tensor<class std::complex<float> const ,4,1,__int64>,16,struct Eigen::MakePointer>,int,class Eigen::TensorMap<class Eigen::Tensor<class std::complex<float>,4,1,__int64>,16,struct Eigen::MakePointer>)" (??R?$DepthToSpaceOpFunctor@UThreadPoolDevice@Eigen@@V?$complex@M@std@@$00@functor@tensorflow@@QEAAXAEBUThreadPoolDevice@Eigen@@V?$TensorMap@V?$Tensor@$$CBV?$complex@M@std@@$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@HV?$TensorMap@V?$Tensor@V?$complex@M@std@@$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@@Z) referenced in function "public: virtual void __cdecl tensorflow::DepthToSpaceOp<struct Eigen::ThreadPoolDevice,class std::complex<float> >::Compute(class tensorflow::OpKernelContext *)" (?Compute@?$DepthToSpaceOp@UThreadPoolDevice@Eigen@@V?$complex@M@std@@@tensorflow@@UEAAXPEAVOpKernelContext@2@@Z)
depth_space_ops.lo.lib(depthtospace_op.obj) : error LNK2019: unresolved external symbol "public: void __cdecl tensorflow::functor::DepthToSpaceOpFunctor<struct Eigen::ThreadPoolDevice,double,1>::operator()(struct Eigen::ThreadPoolDevice const &,class Eigen::TensorMap<class Eigen::Tensor<double const ,4,1,__int64>,16,struct Eigen::MakePointer>,int,class Eigen::TensorMap<class Eigen::Tensor<double,4,1,__int64>,16,struct Eigen::MakePointer>)" (??R?$DepthToSpaceOpFunctor@UThreadPoolDevice@Eigen@@N$00@functor@tensorflow@@QEAAXAEBUThreadPoolDevice@Eigen@@V?$TensorMap@V?$Tensor@$$CBN$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@HV?$TensorMap@V?$Tensor@N$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@@Z) referenced in function "public: virtual void __cdecl tensorflow::DepthToSpaceOp<struct Eigen::ThreadPoolDevice,double>::Compute(class tensorflow::OpKernelContext *)" (?Compute@?$DepthToSpaceOp@UThreadPoolDevice@Eigen@@N@tensorflow@@UEAAXPEAVOpKernelContext@2@@Z)
depth_space_ops.lo.lib(depthtospace_op.obj) : error LNK2019: unresolved external symbol "public: void __cdecl tensorflow::functor::DepthToSpaceOpFunctor<struct Eigen::ThreadPoolDevice,float,1>::operator()(struct Eigen::ThreadPoolDevice const &,class Eigen::TensorMap<class Eigen::Tensor<float const ,4,1,__int64>,16,struct Eigen::MakePointer>,int,class Eigen::TensorMap<class Eigen::Tensor<float,4,1,__int64>,16,struct Eigen::MakePointer>)" (??R?$DepthToSpaceOpFunctor@UThreadPoolDevice@Eigen@@M$00@functor@tensorflow@@QEAAXAEBUThreadPoolDevice@Eigen@@V?$TensorMap@V?$Tensor@$$CBM$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@HV?$TensorMap@V?$Tensor@M$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@@Z) referenced in function "public: virtual void __cdecl tensorflow::DepthToSpaceOp<struct Eigen::ThreadPoolDevice,float>::Compute(class tensorflow::OpKernelContext *)" (?Compute@?$DepthToSpaceOp@UThreadPoolDevice@Eigen@@M@tensorflow@@UEAAXPEAVOpKernelContext@2@@Z)
depth_space_ops.lo.lib(depthtospace_op.obj) : error LNK2019: unresolved external symbol "public: void __cdecl tensorflow::functor::DepthToSpaceOpFunctor<struct Eigen::ThreadPoolDevice,struct Eigen::bfloat16,1>::operator()(struct Eigen::ThreadPoolDevice const &,class Eigen::TensorMap<class Eigen::Tensor<struct Eigen::bfloat16 const ,4,1,__int64>,16,struct Eigen::MakePointer>,int,class Eigen::TensorMap<class Eigen::Tensor<struct Eigen::bfloat16,4,1,__int64>,16,struct Eigen::MakePointer>)" (??R?$DepthToSpaceOpFunctor@UThreadPoolDevice@Eigen@@Ubfloat16@2@$00@functor@tensorflow@@QEAAXAEBUThreadPoolDevice@Eigen@@V?$TensorMap@V?$Tensor@$$CBUbfloat16@Eigen@@$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@HV?$TensorMap@V?$Tensor@Ubfloat16@Eigen@@$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@@Z) referenced in function "public: virtual void __cdecl tensorflow::DepthToSpaceOp<struct Eigen::ThreadPoolDevice,struct Eigen::bfloat16>::Compute(class tensorflow::OpKernelContext *)" (?Compute@?$DepthToSpaceOp@UThreadPoolDevice@Eigen@@Ubfloat16@2@@tensorflow@@UEAAXPEAVOpKernelContext@2@@Z)
depth_space_ops.lo.lib(depthtospace_op.obj) : error LNK2019: unresolved external symbol "public: void __cdecl tensorflow::functor::DepthToSpaceOpFunctor<struct Eigen::ThreadPoolDevice,struct Eigen::half,1>::operator()(struct Eigen::ThreadPoolDevice const &,class Eigen::TensorMap<class Eigen::Tensor<struct Eigen::half const ,4,1,__int64>,16,struct Eigen::MakePointer>,int,class Eigen::TensorMap<class Eigen::Tensor<struct Eigen::half,4,1,__int64>,16,struct Eigen::MakePointer>)" (??R?$DepthToSpaceOpFunctor@UThreadPoolDevice@Eigen@@Uhalf@2@$00@functor@tensorflow@@QEAAXAEBUThreadPoolDevice@Eigen@@V?$TensorMap@V?$Tensor@$$CBUhalf@Eigen@@$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@HV?$TensorMap@V?$Tensor@Uhalf@Eigen@@$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@@Z) referenced in function "public: virtual void __cdecl tensorflow::DepthToSpaceOp<struct Eigen::ThreadPoolDevice,struct Eigen::half>::Compute(class tensorflow::OpKernelContext *)" (?Compute@?$DepthToSpaceOp@UThreadPoolDevice@Eigen@@Uhalf@2@@tensorflow@@UEAAXPEAVOpKernelContext@2@@Z)
depth_space_ops.lo.lib(depthtospace_op.obj) : error LNK2019: unresolved external symbol "public: void __cdecl tensorflow::functor::DepthToSpaceOpFunctor<struct Eigen::ThreadPoolDevice,signed char,1>::operator()(struct Eigen::ThreadPoolDevice const &,class Eigen::TensorMap<class Eigen::Tensor<signed char const ,4,1,__int64>,16,struct Eigen::MakePointer>,int,class Eigen::TensorMap<class Eigen::Tensor<signed char,4,1,__int64>,16,struct Eigen::MakePointer>)" (??R?$DepthToSpaceOpFunctor@UThreadPoolDevice@Eigen@@C$00@functor@tensorflow@@QEAAXAEBUThreadPoolDevice@Eigen@@V?$TensorMap@V?$Tensor@$$CBC$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@HV?$TensorMap@V?$Tensor@C$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@@Z) referenced in function "public: virtual void __cdecl tensorflow::DepthToSpaceOp<struct Eigen::ThreadPoolDevice,signed char>::Compute(class tensorflow::OpKernelContext *)" (?Compute@?$DepthToSpaceOp@UThreadPoolDevice@Eigen@@C@tensorflow@@UEAAXPEAVOpKernelContext@2@@Z)
depth_space_ops.lo.lib(depthtospace_op.obj) : error LNK2019: unresolved external symbol "public: void __cdecl tensorflow::functor::DepthToSpaceOpFunctor<struct Eigen::ThreadPoolDevice,unsigned char,1>::operator()(struct Eigen::ThreadPoolDevice const &,class Eigen::TensorMap<class Eigen::Tensor<unsigned char const ,4,1,__int64>,16,struct Eigen::MakePointer>,int,class Eigen::TensorMap<class Eigen::Tensor<unsigned char,4,1,__int64>,16,struct Eigen::MakePointer>)" (??R?$DepthToSpaceOpFunctor@UThreadPoolDevice@Eigen@@E$00@functor@tensorflow@@QEAAXAEBUThreadPoolDevice@Eigen@@V?$TensorMap@V?$Tensor@$$CBE$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@HV?$TensorMap@V?$Tensor@E$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@@Z) referenced in function "public: virtual void __cdecl tensorflow::DepthToSpaceOp<struct Eigen::ThreadPoolDevice,unsigned char>::Compute(class tensorflow::OpKernelContext *)" (?Compute@?$DepthToSpaceOp@UThreadPoolDevice@Eigen@@E@tensorflow@@UEAAXPEAVOpKernelContext@2@@Z)
depth_space_ops.lo.lib(depthtospace_op.obj) : error LNK2019: unresolved external symbol "public: void __cdecl tensorflow::functor::DepthToSpaceOpFunctor<struct Eigen::ThreadPoolDevice,short,1>::operator()(struct Eigen::ThreadPoolDevice const &,class Eigen::TensorMap<class Eigen::Tensor<short const ,4,1,__int64>,16,struct Eigen::MakePointer>,int,class Eigen::TensorMap<class Eigen::Tensor<short,4,1,__int64>,16,struct Eigen::MakePointer>)" (??R?$DepthToSpaceOpFunctor@UThreadPoolDevice@Eigen@@F$00@functor@tensorflow@@QEAAXAEBUThreadPoolDevice@Eigen@@V?$TensorMap@V?$Tensor@$$CBF$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@HV?$TensorMap@V?$Tensor@F$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@@Z) referenced in function "public: virtual void __cdecl tensorflow::DepthToSpaceOp<struct Eigen::ThreadPoolDevice,short>::Compute(class tensorflow::OpKernelContext *)" (?Compute@?$DepthToSpaceOp@UThreadPoolDevice@Eigen@@F@tensorflow@@UEAAXPEAVOpKernelContext@2@@Z)
depth_space_ops.lo.lib(depthtospace_op.obj) : error LNK2019: unresolved external symbol "public: void __cdecl tensorflow::functor::DepthToSpaceOpFunctor<struct Eigen::ThreadPoolDevice,unsigned short,1>::operator()(struct Eigen::ThreadPoolDevice const &,class Eigen::TensorMap<class Eigen::Tensor<unsigned short const ,4,1,__int64>,16,struct Eigen::MakePointer>,int,class Eigen::TensorMap<class Eigen::Tensor<unsigned short,4,1,__int64>,16,struct Eigen::MakePointer>)" (??R?$DepthToSpaceOpFunctor@UThreadPoolDevice@Eigen@@G$00@functor@tensorflow@@QEAAXAEBUThreadPoolDevice@Eigen@@V?$TensorMap@V?$Tensor@$$CBG$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@HV?$TensorMap@V?$Tensor@G$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@@Z) referenced in function "public: virtual void __cdecl tensorflow::DepthToSpaceOp<struct Eigen::ThreadPoolDevice,unsigned short>::Compute(class tensorflow::OpKernelContext *)" (?Compute@?$DepthToSpaceOp@UThreadPoolDevice@Eigen@@G@tensorflow@@UEAAXPEAVOpKernelContext@2@@Z)
depth_space_ops.lo.lib(depthtospace_op.obj) : error LNK2019: unresolved external symbol "public: void __cdecl tensorflow::functor::DepthToSpaceOpFunctor<struct Eigen::ThreadPoolDevice,unsigned int,1>::operator()(struct Eigen::ThreadPoolDevice const &,class Eigen::TensorMap<class Eigen::Tensor<unsigned int const ,4,1,__int64>,16,struct Eigen::MakePointer>,int,class Eigen::TensorMap<class Eigen::Tensor<unsigned int,4,1,__int64>,16,struct Eigen::MakePointer>)" (??R?$DepthToSpaceOpFunctor@UThreadPoolDevice@Eigen@@I$00@functor@tensorflow@@QEAAXAEBUThreadPoolDevice@Eigen@@V?$TensorMap@V?$Tensor@$$CBI$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@HV?$TensorMap@V?$Tensor@I$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@@Z) referenced in function "public: virtual void __cdecl tensorflow::DepthToSpaceOp<struct Eigen::ThreadPoolDevice,unsigned int>::Compute(class tensorflow::OpKernelContext *)" (?Compute@?$DepthToSpaceOp@UThreadPoolDevice@Eigen@@I@tensorflow@@UEAAXPEAVOpKernelContext@2@@Z)
depth_space_ops.lo.lib(depthtospace_op.obj) : error LNK2019: unresolved external symbol "public: void __cdecl tensorflow::functor::DepthToSpaceOpFunctor<struct Eigen::ThreadPoolDevice,__int64,1>::operator()(struct Eigen::ThreadPoolDevice const &,class Eigen::TensorMap<class Eigen::Tensor<__int64 const ,4,1,__int64>,16,struct Eigen::MakePointer>,int,class Eigen::TensorMap<class Eigen::Tensor<__int64,4,1,__int64>,16,struct Eigen::MakePointer>)" (??R?$DepthToSpaceOpFunctor@UThreadPoolDevice@Eigen@@_J$00@functor@tensorflow@@QEAAXAEBUThreadPoolDevice@Eigen@@V?$TensorMap@V?$Tensor@$$CB_J$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@HV?$TensorMap@V?$Tensor@_J$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@@Z) referenced in function "public: virtual void __cdecl tensorflow::DepthToSpaceOp<struct Eigen::ThreadPoolDevice,__int64>::Compute(class tensorflow::OpKernelContext *)" (?Compute@?$DepthToSpaceOp@UThreadPoolDevice@Eigen@@_J@tensorflow@@UEAAXPEAVOpKernelContext@2@@Z)
depth_space_ops.lo.lib(depthtospace_op.obj) : error LNK2019: unresolved external symbol "public: void __cdecl tensorflow::functor::DepthToSpaceOpFunctor<struct Eigen::ThreadPoolDevice,unsigned __int64,1>::operator()(struct Eigen::ThreadPoolDevice const &,class Eigen::TensorMap<class Eigen::Tensor<unsigned __int64 const ,4,1,__int64>,16,struct Eigen::MakePointer>,int,class Eigen::TensorMap<class Eigen::Tensor<unsigned __int64,4,1,__int64>,16,struct Eigen::MakePointer>)" (??R?$DepthToSpaceOpFunctor@UThreadPoolDevice@Eigen@@_K$00@functor@tensorflow@@QEAAXAEBUThreadPoolDevice@Eigen@@V?$TensorMap@V?$Tensor@$$CB_K$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@HV?$TensorMap@V?$Tensor@_K$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@@Z) referenced in function "public: virtual void __cdecl tensorflow::DepthToSpaceOp<struct Eigen::ThreadPoolDevice,unsigned __int64>::Compute(class tensorflow::OpKernelContext *)" (?Compute@?$DepthToSpaceOp@UThreadPoolDevice@Eigen@@_K@tensorflow@@UEAAXPEAVOpKernelContext@2@@Z)
bazel-out\x64_windows-dbg\bin\tensorflow\lite\delegates\flex\tensorflowlite_flex.dll : fatal error LNK1120: 36 unresolved externals
[9,471 / 9,471] checking cached actions
Target //tensorflow/lite/delegates/flex:tensorflowlite_flex failed to build
INFO: Elapsed time: 3988.941s, Critical Path: 515.75s
INFO: 4147 processes: 40 internal, 4107 local.
FAILED: Build did NOT complete successfully
```
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Automated Code Change
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Provide a runtime option to lower bound the number of batch threads.
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Provide a runtime option to lower bound the number of batch threads.
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Delete unused lowering code.
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I_kwDOArmXAs6FpwNl
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Memory leak in tf.data when iterating over Dataset.from_generator
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"Hi **@cohaegen** ,\r\nI tried to run your code on colab using TF v2.15, 2.16.1, and nightly. But i am not facing any issue. Please find the [gist](https://colab.sandbox.google.com/gist/Venkat6871/b13bb1502f15e0d47eeb043f96a44be0/65675_2-15-2-16-nightly.ipynb) here for reference.\r\n\r\nThank you!",
"Hi Venkat- thanks for checking it out. I’ve only seen the issue in newer\r\nversions of TF with Python 3.11- it seems ok with 3.10. It looks like you\r\nran it with 3.10 (correct me if I’m mistaken). Can you try with 3.11?\r\n\r\nThanks,\r\nNick\r\n\r\nOn Mon, Apr 15, 2024 at 2:16 AM Venkat6871 ***@***.***> wrote:\r\n\r\n> Hi ***@***.*** <https://github.com/cohaegen>* ,\r\n> I tried to run your code on colab using TF v2.15, 2.16.1, and nightly. But\r\n> i am not facing any issue. Please find the gist\r\n> <https://colab.sandbox.google.com/gist/Venkat6871/b13bb1502f15e0d47eeb043f96a44be0/65675_2-15-2-16-nightly.ipynb>\r\n> here for reference.\r\n>\r\n> Thank you!\r\n>\r\n> —\r\n> Reply to this email directly, view it on GitHub\r\n> <https://github.com/tensorflow/tensorflow/issues/65675#issuecomment-2056340290>,\r\n> or unsubscribe\r\n> <https://github.com/notifications/unsubscribe-auth/AJBJI645INTV5YBHGK4QYT3Y5OLHDAVCNFSM6AAAAABGGJBR46VHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMZDANJWGM2DAMRZGA>\r\n> .\r\n> You are receiving this because you were mentioned.Message ID:\r\n> ***@***.***>\r\n>\r\n",
"Hi **@cohaegen** ,\r\n- I tried to run your code on windows with python 3.11 and facing the same issue. Here i am adding screenshots for reference.\r\n\r\n\r\n\r\n\r\nThank you!"
] | 2024-04-14T19:02:53 | 2024-06-04T10:12:11 | null |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
binary
### TensorFlow version
v1.12.1-108954-g88310ddcbdd 2.17.0-dev20240412
### Custom code
Yes
### OS platform and distribution
Docker: tensorflow/tensorflow:nightly
### Mobile device
_No response_
### Python version
3.11.0rc1
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
I discovered what appears to be a memory leak when iterating over a tf.data.Dataset created with from_generator. Process memory usage continues to grow out of hand. The effect only appears in certain combinations of Tensorflow and Python and it may have appeared in Python 3.11. Here are some examples I've tested:
Python 3.10.10, tensorflow 2.13.0: yes
Python 3.10.10, tensorflow 2.16.1: no
Python 3.10.12, tensorflow v2.15.0-0-g6887368d6d4: no
Python 3.11, tensorflow 2.16.1: yes
Python: 3.11.0rc1, tensorflow v1.12.1-109002-g2c2c0a17f05: yes
Maybe related to https://docs.python.org/3/whatsnew/3.11.html#faster-cpython? I thought maybe Python is re-using the memory and not freeing it, but usage grows ridiculously (I noticed it because it started taking up tens of GB in one case) and it seems like it shouldn't with a generator. Odd that 2.13.0 experiences the problem with Python 3.10.10 too though.
### Standalone code to reproduce the issue
```shell
https://colab.research.google.com/drive/1LmdIqWME19GLFG0E7dsCtRtscLLFF89R?usp=sharing
```
### Relevant log output
```shell
2024-04-14 16:12:10.319053: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
Python: 3.11.0rc1 (main, Aug 12 2022, 10:02:14) [GCC 11.2.0]
TensorFlow: ('v1.12.1-109002-g2c2c0a17f05', '2.17.0-dev20240413')
Iterations: 10000 Memory use: 489308160
Iterations: 20000 Memory use: 496451584
Iterations: 30000 Memory use: 503353344
Iterations: 40000 Memory use: 510517248
Iterations: 50000 Memory use: 517464064
Iterations: 60000 Memory use: 524591104
...
Iterations: 980000 Memory use: 1173454848
Iterations: 990000 Memory use: 1180618752
Iterations: 1000000 Memory use: 1187770368
2024-04-14 16:19:00.851851: I tensorflow/core/framework/local_rendezvous.cc:404] Local rendezvous is aborting with status: OUT_OF_RANGE: End of sequence
379
Output from running valgrind:
PYTHONMALLOC=malloc valgrind --track-origins=yes --leak-check=full --show-leak-kinds=definite --trace-children=yes python ./tfdata_test.py
I stopped it at about 80,000 iterations because it's much slower running under valgrind
==324== 24,434,160 (4,660,032 direct, 19,774,128 indirect) bytes in 72,813 blocks are definitely lost in loss record 188,561 of 188,562
==324== at 0x4848899: malloc (in /usr/libexec/valgrind/vgpreload_memcheck-amd64-linux.so)
==324== by 0x4D4AC1: ??? (in /usr/bin/python3.11)
==324== by 0x5BA613: ??? (in /usr/bin/python3.11)
==324== by 0x5BB4C4: ??? (in /usr/bin/python3.11)
==324== by 0x4FE1B5: _PyEval_EvalFrameDefault (in /usr/bin/python3.11)
==324== by 0x531822: _PyFunction_Vectorcall (in /usr/bin/python3.11)
==324== by 0x530FB8: ??? (in /usr/bin/python3.11)
==324== by 0x5DA693: _PyObject_CallMethod_SizeT (in /usr/bin/python3.11)
==324== by 0x26C7748A: _descriptor_from_pep3118_format (in /usr/local/lib/python3.11/dist-packages/numpy/core/_multiarray_umath.cpython-311-x86_64-linux-gnu.so)
==324== by 0x26C8CBD5: _array_from_buffer_3118.part.0 (in /usr/local/lib/python3.11/dist-packages/numpy/core/_multiarray_umath.cpython-311-x86_64-linux-gnu.so)
==324== by 0x26C8DF4E: _array_from_array_like (in /usr/local/lib/python3.11/dist-packages/numpy/core/_multiarray_umath.cpython-311-x86_64-linux-gnu.so)
==324== by 0x26C6FCD4: PyArray_DiscoverDTypeAndShape_Recursive (in /usr/local/lib/python3.11/dist-packages/numpy/core/_multiarray_umath.cpython-311-x86_64-linux-gnu.so)
==324==
==324== 26,302,320 bytes in 73,062 blocks are definitely lost in loss record 188,562 of 188,562
==324== at 0x484DA83: calloc (in /usr/libexec/valgrind/vgpreload_memcheck-amd64-linux.so)
==324== by 0x625128: ??? (in /usr/bin/python3.11)
==324== by 0x6250BA: PyThreadState_New (in /usr/bin/python3.11)
==324== by 0x64E44F: PyGILState_Ensure (in /usr/bin/python3.11)
==324== by 0xA597C58: tensorflow::PyFuncOp::Compute(tensorflow::OpKernelContext*) (in /usr/local/lib/python3.11/dist-packages/tensorflow/python/_pywrap_tensorflow_internal.so)
==324== by 0x92F0F3F: tensorflow::ThreadPoolDevice::Compute(tensorflow::OpKernel*, tensorflow::OpKernelContext*) (in /usr/local/lib/python3.11/dist-packages/tensorflow/libtensorflow_framework.so.2)
==324== by 0x927A539: tensorflow::(anonymous namespace)::ExecutorState<tensorflow::SimplePropagatorState>::Process(tensorflow::SimplePropagatorState::TaggedNode const&, long) (in /usr/local/lib/python3.11/dist-packages/tensorflow/libtensorflow_framework.so.2)
==324== by 0x9A3D39D: std::_Function_handler<void (), std::_Bind<tensorflow::data::RunnerWithMaxParallelism(std::function<void (std::function<void ()>)>, int)::$_0::operator()(std::function<void (std::function<void ()>)> const&, std::function<void ()>) const::{lambda(std::function<void ()> const&)#1} (std::function<void ()>)> >::_M_invoke(std::_Any_data const&) (in /usr/local/lib/python3.11/dist-packages/tensorflow/libtensorflow_framework.so.2)
==324== by 0x9C2299F: Eigen::ThreadPoolTempl<tsl::thread::EigenEnvironment>::WorkerLoop(int) (in /usr/local/lib/python3.11/dist-packages/tensorflow/libtensorflow_framework.so.2)
==324== by 0x9C223D0: void std::__invoke_impl<void, tsl::thread::EigenEnvironment::CreateThread(std::function<void ()>)::{lambda()#1}&>(std::__invoke_other, tsl::thread::EigenEnvironment::CreateThread(std::function<void ()>)::{lambda()#1}&) (in /usr/local/lib/python3.11/dist-packages/tensorflow/libtensorflow_framework.so.2)
==324== by 0x960E55A: tsl::(anonymous namespace)::PThread::ThreadFn(void*) (in /usr/local/lib/python3.11/dist-packages/tensorflow/libtensorflow_framework.so.2)
==324== by 0x4A27AC2: start_thread (pthread_create.c:442)
==324==
==324== LEAK SUMMARY:
==324== definitely lost: 30,984,576 bytes in 146,328 blocks
==324== indirectly lost: 19,783,920 bytes in 72,736 blocks
==324== possibly lost: 136,684,089 bytes in 1,082,632 blocks
==324== still reachable: 17,341,568 bytes in 182,582 blocks
==324== of which reachable via heuristic:
==324== stdstring : 341 bytes in 8 blocks
==324== newarray : 104,387 bytes in 320 blocks
==324== multipleinheritance: 7,168 bytes in 24 blocks
==324== suppressed: 0 bytes in 0 blocks
```
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Some `Check Failed` errors in `tf.raw_ops.MatrixSetDiagV2`
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[
"@xl1043237213,\r\nI was able to reproduce the issue on TensorFlow v2.15, [v2.16](https://colab.research.google.com/gist/tilakrayal/c019e315bb40fc13963c9d977d851f70/untitled1901.ipynb) and tf-nightly. Kindly find the gist of it [here](https://colab.research.google.com/gist/tilakrayal/669d5d627371da576ee766ddea6d96fb/-6_tf_raw_ops_matrixsetdiagv2.ipynb).\r\n\r\n\r\n"
] | 2024-04-14T17:41:03 | 2024-05-20T05:20:01 | null |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
binary
### TensorFlow version
tf 2.15
### Custom code
Yes
### OS platform and distribution
Linux Ubuntu 20.04
### Mobile device
_No response_
### Python version
3.10
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
Some `Check Failed` errors in `tf.raw_ops.MatrixSetDiagV2`, which causes the program to crash. See the colab link below for details.
### Standalone code to reproduce the issue
```shell
https://colab.research.google.com/drive/1-yHRqhkbV3J77AaBD4GXYh3nSF49Inep?usp=sharing
```
### Relevant log output
```shell
# Check failed: d < dims() (1 vs. 1)
# Check failed: d < dims() (2 vs. 2)
```
|
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| 2,242,279,198 |
I_kwDOArmXAs6Fpnce
| 65,673 |
Some `Check Failed` errors in `tf.raw_ops.MatrixSetDiagV3`
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[
"Hi @xl1043237213 ,\r\n\r\nI have replicated the reported behaviour with tf-nightly and attached below is screenshot of same. Will check and keep you posted.\r\n\r\n<img width=\"1512\" alt=\"Screenshot 2024-04-15 at 9 44 40 AM\" src=\"https://github.com/tensorflow/tensorflow/assets/116063290/98aadccb-1974-4611-99ce-f6f9227591e0\">\r\n\r\n\r\n"
] | 2024-04-14T17:33:52 | 2024-04-15T08:08:31 | null |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
binary
### TensorFlow version
tf 2.15
### Custom code
No
### OS platform and distribution
Linux Ubuntu 20.04
### Mobile device
_No response_
### Python version
3.10
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
Some `Check Failed` errors in `tf.raw_ops.MatrixSetDiagV3`, which causes the program to crash. See the colab link below for details.
### Standalone code to reproduce the issue
```shell
https://colab.research.google.com/drive/11e72xipp_lz6fHcp7bN2MQy3Kw5KqHpL?usp=sharing
```
### Relevant log output
```shell
# Check failed: d < dims() (1 vs. 1)
# Check failed: d < dims() (2 vs. 2)
```
|
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| 2,242,267,308 |
I_kwDOArmXAs6Fpkis
| 65,672 |
`Segmentation Fault` in `tf.raw_ops.TensorListScatter` and `tf.raw_ops.TensorListScatterV2` when the value of `indices` is too large.
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[
"@SuryanarayanaY I was able to replicate the issue reported [here](https://colab.research.google.com/gist/sushreebarsa/9cb856ac2db2feb72375f90072155146/-11_tf_raw_ops_tensorlistscatter-v2.ipynb). Thank you!"
] | 2024-04-14T17:14:00 | 2024-04-30T04:49:11 | null |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
binary
### TensorFlow version
tf 2.15
### Custom code
No
### OS platform and distribution
Linux Ubuntu 20.04
### Mobile device
_No response_
### Python version
3.10
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
When the value of `indices` is too large, `tf.raw_ops.TensorListScatter` and `tf.raw_ops.TensorListScatterV2` triggers `Segmentation Fault` due to RAM being full.
### Standalone code to reproduce the issue
```shell
https://colab.research.google.com/drive/1BOVVbofLSB2x_chg_WuFIdTWZXS9hiw0?usp=sharing
```
### Relevant log output
```shell
segmentation fault (core dumped)
```
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I_kwDOArmXAs6Fphrm
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`Check failed` in `tf.raw_ops.TensorScatterAdd` and `tf.tensor_scatter_nd_add` when the rank of `indices` > 2.
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[
"Hi **@xl1043237213** ,\r\n\r\nI tried to run your code on colab using TF v2.15, 2.16.1, nightly and faced the same issue. Please find the [gist](https://colab.sandbox.google.com/gist/Venkat6871/6500f73bbd6aa7e4549e1a46bc82d58b/65671_2-15-2-16-nightly-v.ipynb) here for reference.\r\n\r\nThank you!"
] | 2024-04-14T16:46:04 | 2024-04-30T04:52:49 | null |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
binary
### TensorFlow version
tf 2.15
### Custom code
Yes
### OS platform and distribution
Linux Ubuntu 20.04
### Mobile device
_No response_
### Python version
3.10
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
`Check failed` in `tf.raw_ops.TensorScatterAdd` and `tf.tensor_scatter_nd_add` when the rank of `indices` > 2, which causes the program to crash. See the colab link below for details.
### Standalone code to reproduce the issue
```shell
https://colab.research.google.com/drive/18JWN3lSTdwvwuG2yka6Uek7CHmwH9ffi?usp=sharing
```
### Relevant log output
```shell
Check failed: d < dims() (1 vs. 1)
```
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[
"@xl1043237213,\r\nI was able to reproduce the issue on TensorFlow v2.15, [v2.16](https://colab.research.google.com/gist/tilakrayal/7aa51c492663fd71355d8fe946381b12/untitled1902.ipynb) and tf-nightly. Kindly find the gist of it [here](https://colab.research.google.com/gist/tilakrayal/1b071af1716f863529c76ed51c43f368/-6_tf_raw_ops_tensorscattersub.ipynb).\r\n\r\n\r\n"
] | 2024-04-14T16:41:30 | 2024-05-20T05:25:02 | null |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
binary
### TensorFlow version
tf 2.15
### Custom code
No
### OS platform and distribution
_No response_
### Mobile device
_No response_
### Python version
3.10
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
`Check failed` in `tf.raw_ops.TensorScatterSub` and `tf.tensor_scatter_nd_sub` when the rank of `indices` > 2, which causes the program to crash. See the colab link below for details.
### Standalone code to reproduce the issue
```shell
https://colab.research.google.com/drive/1wqe_qe71oLucYiq1WS8s9GkZiVsNlaMz?usp=sharing
```
### Relevant log output
```shell
Check failed: d < dims() (1 vs. 1)
```
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I_kwDOArmXAs6Fpfax
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`Check failed` in `tf.raw_ops.TensorScatterMin` and `tf.tensor_scatter_nd_min` when the rank of `indices` > 2.
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[
"Hi @xl1043237213 ,\r\n\r\nThanks for reporting. I have reproduced the reported check fail and attached screenshot below.\r\n\r\n",
"<img width=\"1512\" alt=\"Screenshot 2024-04-15 at 1 46 43 PM\" src=\"https://github.com/tensorflow/tensorflow/assets/116063290/ccd4ab71-c0fa-4330-b964-f8b5310628a5\">\r\n",
"This seems related to PR #63725 ",
"@xl1043237213 ,\r\nThe PR https://github.com/tensorflow/tensorflow/pull/63725 which was raised was closed and mentioned that \r\n``` python\r\nThe assert that should fail is the last one, which has an equality assertion on the sizes of updates.shape == batch_shape + inner_shape\r\n```\r\n\r\nhttps://github.com/tensorflow/tensorflow/pull/63725\r\n\r\nCould you please take a look and provide the context to fulfill and get the PR merged. Thank you!"
] | 2024-04-14T16:26:52 | 2024-06-10T10:59:54 | null |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
binary
### TensorFlow version
tf 2.15
### Custom code
No
### OS platform and distribution
Linux Ubuntu 20.04
### Mobile device
_No response_
### Python version
3.10
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
`Check failed` in `tf.raw_ops.TensorScatterMin` and `tf.tensor_scatter_nd_min` when the rank of `indices` > 2, which causes the program to crash. See the colab link below for details.
### Standalone code to reproduce the issue
```shell
https://colab.research.google.com/drive/1An8M5c6VvjjZNvG_xx94Oy-H93suc6mD?usp=sharing
```
### Relevant log output
```shell
Check failed: d < dims() (1 vs. 1)
```
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[
"@SuryanarayanaY I was able to replicate the issue reported [here](https://colab.research.google.com/gist/sushreebarsa/1d9902ec75874e268bd7af4045154969/-6_tf_raw_ops_tensorscattermax.ipynb).\r\nThank you!"
] | 2024-04-14T16:16:43 | 2024-04-30T04:50:15 | null |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
binary
### TensorFlow version
tf 2.15
### Custom code
No
### OS platform and distribution
Linux Ubuntu 20.04
### Mobile device
_No response_
### Python version
3.10
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
`Check failed` in `tf.raw_ops.TensorScatterMax` and `tf.tensor_scatter_nd_max` when the rank of `indices` > 2, which causes the program to crash. See the colab link below for details.
### Standalone code to reproduce the issue
```shell
https://colab.research.google.com/drive/1BvyImiXvq4Sr0CMTt--t6lRFy6bkGY8P?usp=sharing
```
### Relevant log output
```shell
Check failed: d < dims() (1 vs. 1)
```
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[
"Hi **@xl1043237213** ,\r\n\r\nI tried to run your code on colab using TF v2.15, 2.16, nightly and faced the same issue. Please find the [gist](https://colab.sandbox.google.com/gist/Venkat6871/2c96b6260a4669cf88c684810251a99b/65667_2-15-2-16-1-nightly.ipynb) here for reference.\r\n\r\nThank you!"
] | 2024-04-14T16:11:54 | 2024-04-30T04:49:39 | null |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
binary
### TensorFlow version
tf 2.15
### Custom code
No
### OS platform and distribution
Linux Ubuntu 20.04
### Mobile device
_No response_
### Python version
3.10
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
`Check failed` in `tf.raw_ops.TensorScatterUpdate` and `tf.tensor_scatter_nd_update` when the rank of `indices` > 2, which causes the program to crash. See the colab link below for details.
### Standalone code to reproduce the issue
```shell
https://colab.research.google.com/drive/1PLVxv-jQDZ15P4lrd3AhyVU-RAesammI?usp=sharing
```
### Relevant log output
```shell
Check failed: d < dims() (1 vs. 1)
```
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[
"@xl1043237213,\r\nI was able to reproduce the issue on TensorFlow v2.15, [v2.16](https://colab.research.google.com/gist/tilakrayal/8013e3b310acafecc64a545fe8b07481/untitled1903.ipynb) and tf-nightly. Kindly find the gist of it [here](https://colab.research.google.com/gist/tilakrayal/1e46cf6271298b394bc293b3e5e58380/-6_tf_raw_ops_scatternd.ipynb).\r\n\r\n\r\n"
] | 2024-04-14T15:25:42 | 2024-05-20T05:31:20 | null |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
binary
### TensorFlow version
tf 2.15
### Custom code
No
### OS platform and distribution
Linux Ubuntu 20.04
### Mobile device
_No response_
### Python version
3.10
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
`Check failed` in `tf.raw_ops.ScatterNd` and `tf.scatter_nd` when the rank of `indices` > 2, which causes the program to crash. See the colab link below for details.
### Standalone code to reproduce the issue
```shell
https://colab.research.google.com/drive/1rr8w5IOzVdjs07YECnyOsnFu3ouUFkBs?usp=sharing
```
### Relevant log output
```shell
Check failed: d < dims() (1 vs. 1)
```
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Error when trring a second training : WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer.iter
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[
"Hi @garywillcodeit ,\r\n\r\nThe error message seems generating from the destructor method from [here](https://github.com/tensorflow/tensorflow/blob/cb348a09747c484a987d3c4f5765c641e5b6489f/tensorflow/python/checkpoint/checkpoint.py#L234).\r\n\r\nI am not sure how and why destructor method being called here. I request you to provide more context and minimal repro code for same.\r\n\r\nThanks.",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/65657\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/65657\">No</a>\n"
] | 2024-04-14T09:14:40 | 2024-05-01T01:49:32 | 2024-05-01T01:49:29 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
2.13.0
### Custom code
Yes
### OS platform and distribution
MAC OS 13.5 Ventura
### Mobile device
_No response_
### Python version
3.11.9
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
Hi everyone !
I am trying to train a pre-trained TensorFlow 2 Detection Model Zoo. Everything works well after the first training, new checkpoints are generated and when I try detecting objects, it finds perfectly the objects in every images.
The problem is that when I start a new training (a second training), I get this message and the training stops :
**WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer.iter**
Here is my tree structure:
_tensorflow
____models (TF API)
____workspace
________annotations
_____________label_map.pbtxt
_____________test.record
_____________train.record
________images
_____________train
_________________IMG1.jpg
_________________IMG1.xml
...
_________________IMG50.jpg
_________________IMG50.xml
_____________test
_________________IMG1.jpg
_________________IMG1.xml
...
_________________IMG20.jpg
_________________IMG20.xml
________models
_____________my_ssd_mobnet
_________________eval
_________________train
_________________checkpoint (file)
_________________ckpt-1.data-00000-of-00001
_________________ckpt-1.index
_________________ckpt-2.data-00000-of-00001
_________________ckpt-2.index
_________________ckpt-3.data-00000-of-00001
_________________ckpt-3.index
_________________pipeline.config
________pre-trained-model
_____________checkpoint (dir)
_________________checkpoint (file)
_________________ckpt-0.data-00000-of-00001
_________________ckpt-0.index
_____________saved_model
_________________variables
_____________________variables.data-00000-of-00001
_____________________variables.index
_________________saved_model.pb
_____________pipeline.config
Here is my process:
1. I create the label map
2. I generate the TF records
3. I update pipeline.config: - num_classes (depending on labels' length); - fine_tune_checkpoint (I set the pre-trained-model checkpoint dir); - label_map_path (label_map.pbtxt path); - tf record for tain and eval
4. I run this code
```
command = [
"python",
training_script, // This is the "model_main_tf2.py" path
f"--model_dir={my_ssd_mobnet_path}",
f"--pipeline_config_path={trained_pipeline}",
"--num_train_steps=2000",
]
subprocess.run(command, text=True)
```
5. I evaluate the model
As I said before, everything works very well during the first training. The training goes until 2000 steps, the evaluation returns great data and when I try the trained model, it manages to find all objects that I trained it for.
But when I try to run a new (second) training, I get the warning message and the training stops.
I read [here](https://stackoverflow.com/questions/71929036/value-in-checkpoint-could-not-be-found-in-the-restored-object-root-optimizer) that changing the steps number works but not for me.
I also read that adding this line of code works but I don't see where to put it in mine 🤷🏼♂️
```
model.load_weights(MY_CHECKPOINT_FILE).expect_partial()
```
Things that work :
The only thing that works is to delete all the checkpoint that has been generated during the first training. But I finally understood that I also remove all data from the first training.
Has anyone experienced this problem and found a solution?
### Standalone code to reproduce the issue
```shell
command = [
"python",
training_script, // This is the "model_main_tf2.py" path
f"--model_dir={my_ssd_mobnet_path}",
f"--pipeline_config_path={trained_pipeline}",
"--num_train_steps=2000",
]
subprocess.run(command, text=True)
```
### Relevant log output
```shell
WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer.iter
```
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`Check failed` and `segmentation fault` in `tf.raw_ops.AudioSpectrogram`.
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[
"@SuryanarayanaY I was able to replicate the issue reported [here](https://colab.research.google.com/gist/sushreebarsa/70e69afd98868af8080befdf9cee448a/-11_-6_tf_raw_ops_audiospectrogram.ipynb). Thank you!"
] | 2024-04-14T05:32:07 | 2024-04-25T07:34:18 | null |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
binary
### TensorFlow version
tf 2.15
### Custom code
No
### OS platform and distribution
Linux Ubuntu 20.04
### Mobile device
_No response_
### Python version
3.10
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
When the value of `window_size` is negative, `tf.raw_ops.AudioSpectrogram` triggers `segmentation fault`. When the value of `window_size` is too large, `tf.raw_ops.AudioSpectrogram` triggers`Check failed` due to RAM being full.
### Standalone code to reproduce the issue
```shell
https://colab.research.google.com/drive/18-XyT4GBDN-o5SZ8jNSR01a9wR-SwScR?usp=sharing
```
### Relevant log output
segmentation fault (core dumped)
or
Check failed: fft_length_ >= window_length_ Aborted (core dumped)
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`Check failed` in `tf.image.crop_and_resize` and `tf.compat.v1.image.crop_and_resize` when `boxes` and `box_indices` are empty.
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[
"can you assign this issue to me ? i am new bie this will be my first contribution for open source . ",
"@JiteshSinghShekhawat I'm sorry, I don't have permission to assign. You might need to wait for response of @Venkat6871.",
"Hi **@xl1043237213** ,\r\n\r\nI tried to run your code on colab using TF v2.15, 2.16.1, nightly and faced the same issue. Please find the [gist](https://colab.sandbox.google.com/gist/Venkat6871/d4946bc2d9f50d4b7436b1adfe9a02d4/65652_2-15-2-16-nightly.ipynb) here for reference.\r\n\r\nThank you!",
"Hi @JiteshSinghShekhawat ,\r\n\r\nPlease feel free to contribute."
] | 2024-04-14T05:10:59 | 2024-04-30T04:52:10 | null |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
binary
### TensorFlow version
tf 2.15
### Custom code
No
### OS platform and distribution
Linux Ubuntu 20.04
### Mobile device
_No response_
### Python version
3.10
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
`Check failed` in `tf.image.crop_and_resize` when `boxes` and `box_indices` are empty, which causes the program to crash. See the colab link below for details.
### Standalone code to reproduce the issue
```shell
https://colab.research.google.com/drive/1FDl2zKTLnCEA8aSIAjsOHg4B3CIPJNCn?usp=sharing
```
### Relevant log output
```shell
Check failed: NDIMS == dims() (2 vs. 1)Asking for tensor of 2 dimensions from a tensor of 1 dimensions
```
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`Check failed` in `tf.transpose`, `tf.raw_ops.Transpose` and `tf.compat.v1.transpose` when the values of `perm` have negative numbers.
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[
"@xl1043237213,\r\nI was able to reproduce the issue on TensorFlow v2.15, [v2.16](https://colab.research.google.com/gist/tilakrayal/db022d1ed439e7d1c3a6978de02d2afc/untitled1904.ipynb) and [tf-nightly](https://colab.research.google.com/gist/tilakrayal/a1be72080f11a9f46247bdadf17ff625/-6_tf_transpose.ipynb). Kindly find the gist of it here.\r\n\r\n\r\n",
"@xl1043237213,\r\nApologies for the delay. I request you to take a look at this [issue](https://github.com/tensorflow/tensorflow/issues/69213\r\n) where a similar feature has been proposed and it is still open. Also for the same fix, PR was also raised and it is under reviews\r\nhttps://github.com/tensorflow/tensorflow/pull/63064\r\n\r\nAlso I request to follow the similar PR and issue which has been proposed to have the updates on the same. Thank you!"
] | 2024-04-14T04:53:07 | 2024-05-20T05:37:03 | null |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
tf 2.15
### Custom code
No
### OS platform and distribution
Linux Ubuntu 20.04
### Mobile device
_No response_
### Python version
3.10
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
`Check failed` in `tf.transpose` when the values of `perm` have negative numbers, which causes the program to crash. See the colab link below for details.
### Standalone code to reproduce the issue
```shell
https://colab.research.google.com/drive/1GtoXacWSEuyLXVE5GD9IIB-YcMOxV6sP?usp=sharing
```
### Relevant log output
```shell
Check failed: d >= 0 (0 vs. -2)
Aborted (core dumped)
```
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Getting [java.lang.IllegalArgumentException: Internal error: Error applying delegate] error while applying GPU Delegate
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[
"```\r\n\r\n\r\noverride suspend fun initModel(): InitModelResult {\r\n return withContext(dispatcher) {\r\n val loadResult = loadModelFile(context)\r\n\r\n if (loadResult.isFailure) {\r\n val exc = loadResult.exceptionOrNull()\r\n return@withContext if (exc is FileNotFoundException) {\r\n InitModelResult.Error(AutoCompleteServiceError.MODEL_FILE_NOT_FOUND)\r\n } else {\r\n InitModelResult.Error(AutoCompleteServiceError.MODEL_NOT_INITIALIZED)\r\n }\r\n }\r\n\r\n val options = Interpreter.Options()\r\n val myDelegate = GpuDelegate()\r\n options.addDelegate(myDelegate)\r\n\r\n try {\r\n\r\n val model = loadResult.getOrNull()\r\n isInitialized = model?.let {\r\n interpreter = Interpreter(it, options)\r\n true\r\n } ?: false\r\n\r\n return@withContext if (isInitialized) {\r\n InitModelResult.Success\r\n } else {\r\n InitModelResult.Error(AutoCompleteServiceError.MODEL_NOT_INITIALIZED)\r\n }\r\n } catch (e: Exception) {\r\n \r\n e.printStackTrace()\r\n return@withContext InitModelResult.Error(AutoCompleteServiceError.MODEL_INITIALIZATION_FAILED)\r\n }\r\n }\r\n}\r\n// Try this code snippet \r\n```",
"> ```\r\n> \r\n> \r\n> override suspend fun initModel(): InitModelResult {\r\n> return withContext(dispatcher) {\r\n> val loadResult = loadModelFile(context)\r\n> \r\n> if (loadResult.isFailure) {\r\n> val exc = loadResult.exceptionOrNull()\r\n> return@withContext if (exc is FileNotFoundException) {\r\n> InitModelResult.Error(AutoCompleteServiceError.MODEL_FILE_NOT_FOUND)\r\n> } else {\r\n> InitModelResult.Error(AutoCompleteServiceError.MODEL_NOT_INITIALIZED)\r\n> }\r\n> }\r\n> \r\n> val options = Interpreter.Options()\r\n> val myDelegate = GpuDelegate()\r\n> options.addDelegate(myDelegate)\r\n> \r\n> try {\r\n> \r\n> val model = loadResult.getOrNull()\r\n> isInitialized = model?.let {\r\n> interpreter = Interpreter(it, options)\r\n> true\r\n> } ?: false\r\n> \r\n> return@withContext if (isInitialized) {\r\n> InitModelResult.Success\r\n> } else {\r\n> InitModelResult.Error(AutoCompleteServiceError.MODEL_NOT_INITIALIZED)\r\n> }\r\n> } catch (e: Exception) {\r\n> \r\n> e.printStackTrace()\r\n> return@withContext InitModelResult.Error(AutoCompleteServiceError.MODEL_INITIALIZATION_FAILED)\r\n> }\r\n> }\r\n> }\r\n> // Try this code snippet \r\n> ```\r\n\r\nThank you for your reply. I have tried this code above. It does catch the exception. However, the log convert from 'Error' to 'Warning\":\r\n```\r\ncom.google.tensorflowdemo.debug W java.lang.IllegalArgumentException: Internal error: Error applying delegate: \r\n2024-04-14 18:08:11.198 29026-29253 System.err com.google.tensorflowdemo.debug W \tat org.tensorflow.lite.NativeInterpreterWrapper.createInterpreter(Native Method)\r\n2024-04-14 18:08:11.198 29026-29253 System.err com.google.tensorflowdemo.debug W \tat org.tensorflow.lite.NativeInterpreterWrapper.init(NativeInterpreterWrapper.java:110)\r\n2024-04-14 18:08:11.198 29026-29253 System.err com.google.tensorflowdemo.debug W \tat org.tensorflow.lite.NativeInterpreterWrapper.<init>(NativeInterpreterWrapper.java:73)\r\n2024-04-14 18:08:11.198 29026-29253 System.err com.google.tensorflowdemo.debug W \tat org.tensorflow.lite.NativeInterpreterWrapperExperimental.<init>(NativeInterpreterWrapperExperimental.java:36)\r\n2024-04-14 18:08:11.198 29026-29253 System.err com.google.tensorflowdemo.debug W \tat org.tensorflow.lite.Interpreter.<init>(Interpreter.java:214)\r\n2024-04-14 18:08:11.198 29026-29253 System.err com.google.tensorflowdemo.debug W \tat com.google.tensorflowdemo.data.autocomplete.AutoCompleteServiceImpl$initModel$2.invokeSuspend(AutoCompleteService.kt:156)\r\n2024-04-14 18:08:11.198 29026-29253 System.err com.google.tensorflowdemo.debug W \tat kotlin.coroutines.jvm.internal.BaseContinuationImpl.resumeWith(ContinuationImpl.kt:33)\r\n2024-04-14 18:08:11.198 29026-29253 System.err com.google.tensorflowdemo.debug W \tat kotlinx.coroutines.DispatchedTask.run(DispatchedTask.kt:106)\r\n2024-04-14 18:08:11.198 29026-29253 System.err com.google.tensorflowdemo.debug W \tat kotlinx.coroutines.internal.LimitedDispatcher.run(LimitedDispatcher.kt:42)\r\n2024-04-14 18:08:11.198 29026-29253 System.err com.google.tensorflowdemo.debug W \tat kotlinx.coroutines.scheduling.TaskImpl.run(Tasks.kt:95)\r\n2024-04-14 18:08:11.198 29026-29253 System.err com.google.tensorflowdemo.debug W \tat kotlinx.coroutines.scheduling.CoroutineScheduler.runSafely(CoroutineScheduler.kt:570)\r\n2024-04-14 18:08:11.198 29026-29253 System.err com.google.tensorflowdemo.debug W \tat kotlinx.coroutines.scheduling.CoroutineScheduler$Worker.executeTask(CoroutineScheduler.kt:750)\r\n2024-04-14 18:08:11.198 29026-29253 System.err com.google.tensorflowdemo.debug W \tat kotlinx.coroutines.scheduling.CoroutineScheduler$Worker.runWorker(CoroutineScheduler.kt:677)\r\n2024-04-14 18:08:11.198 29026-29253 System.err com.google.tensorflowdemo.debug W \tat kotlinx.coroutines.scheduling.CoroutineScheduler$Worker.run(CoroutineScheduler.kt:664)\r\n```\r\n\r\nThe problem seems to still exist. ",
"Hi @walker-ai ,\r\n\r\nI have been trying to replicate your issue but i don't have autocomplete.tflite file, can you provide me the autocomplete.tflite file or the script you used to create it .",
"> Hi @walker-ai ,\r\n> \r\n> I have been trying to replicate your issue but i don't have autocomplete.tflite file, can you provide me the autocomplete.tflite file or the script you used to create it .\r\n\r\nThank you for your reply. The **autocomplete.tflite** was converted from `quant_generate_tflite`, you can check the [example](https://github.com/tensorflow/examples/tree/master/lite/examples/generative_ai/android):\r\n\r\n```python\r\ngpt2_lm.jit_compile = False\r\nconverter = tf.lite.TFLiteConverter.from_concrete_functions(\r\n [concrete_func],\r\n gpt2_lm)\r\n\r\nconverter.target_spec.supported_ops = [\r\n tf.lite.OpsSet.TFLITE_BUILTINS, # enable TFLite ops\r\n tf.lite.OpsSet.SELECT_TF_OPS, # enable TF ops\r\n]\r\nconverter.allow_custom_ops = True\r\nconverter.optimizations = [tf.lite.Optimize.DEFAULT]\r\nconverter.target_spec.experimental_select_user_tf_ops = [\r\n \"UnsortedSegmentJoin\",\r\n \"UpperBound\"\r\n]\r\nconverter._experimental_guarantee_all_funcs_one_use = True\r\nquant_generate_tflite = converter.convert()\r\nrun_inference(\"I'm enjoying a\", quant_generate_tflite)\r\n\r\nwith open('quantized_gpt2.tflite', 'wb') as f:\r\n f.write(quant_generate_tflite) # autocomplete.tflite\r\n```",
"Hi @walker-ai ,\r\n\r\nI followed the same example while creating the autocomplete.tflite file , for some reason the \"generate\" function under the decorator \"tf.function\" gave me numpy related error . I am trying to debug that, but if you have a dummy autocomplete.tflite file that you can provide, it will save me some time.",
"> Hi @walker-ai ,\r\n> \r\n> I followed the same example while creating the autocomplete.tflite file , for some reason the \"generate\" function under the decorator \"tf.function\" gave me numpy related error . I am trying to debug that, but if you have a dummy autocomplete.tflite file that you can provide, it will save me some time.\r\n\r\nSorry, I haven't provided more details. You can open the [colab](https://colab.research.google.com/github/tensorflow/codelabs/blob/main/KerasNLP/io2023_workshop.ipynb?hl=zh-cn#scrollTo=G6CAygGok6BQ) and run through the notebook to get the `autocomplete.tflite`. And I have got the [file](https://drive.google.com/file/d/1aGCY3I0f3ZaZ6FTnZ4ihDq5Vdf8Z1Rco/view?usp=drive_link) for you.",
"Hi @walker-ai ,\r\n\r\nThank you for the model file. I replicated your issue on my emulator with android 10 and it also crashed when i tried running the model on GPU. I will try out some other tensorflow examples which make use of gpu acceleration and i will get back to you .",
"Hi @walker-ai, I modified your code to check for compatibility as shown here: https://www.tensorflow.org/lite/android/delegates/gpu#enable_gpu_acceleration_2\r\n\r\nMy final code looks like this:\r\n```kts\r\noverride suspend fun initModel(): InitModelResult {\r\n return withContext(dispatcher) {\r\n // Load model file\r\n val loadResult = loadModelFile(context)\r\n\r\n // Determine if load was successful\r\n if (loadResult.isFailure) {\r\n val exc = loadResult.exceptionOrNull()\r\n return@withContext if (exc is FileNotFoundException) {\r\n InitModelResult.Error(AutoCompleteServiceError.MODEL_FILE_NOT_FOUND)\r\n } else {\r\n InitModelResult.Error(AutoCompleteServiceError.MODEL_NOT_INITIALIZED)\r\n }\r\n }\r\n\r\n val compatList = CompatibilityList()\r\n\r\n val options = Interpreter.Options().apply{\r\n if(compatList.isDelegateSupportedOnThisDevice){\r\n // if the device has a supported GPU, add the GPU delegate\r\n val delegateOptions = compatList.bestOptionsForThisDevice\r\n this.addDelegate(GpuDelegate(delegateOptions))\r\n } else {\r\n // if the GPU is not supported, run on 4 threads\r\n this.setNumThreads(4)\r\n }\r\n }\r\n\r\n\r\n // Instantiate interpreter with loaded model\r\n val model = loadResult.getOrNull()\r\n isInitialized = model?.let {\r\n interpreter = Interpreter(it, options)\r\n true\r\n } ?: false\r\n\r\n if (isInitialized) InitModelResult.Success\r\n else InitModelResult.Error(AutoCompleteServiceError.MODEL_NOT_INITIALIZED)\r\n }\r\n }\r\n```\r\n\r\nIf I didn't include this compatibility check, I get your same error.... this tells me your GPU or system is not compatible with your setup (probably b/c of emulation). Perhaps try on a real device which is supported. Let me know if you have any questions.",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/65648\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/65648\">No</a>\n"
] | 2024-04-14T04:30:41 | 2024-06-04T01:49:57 | 2024-06-04T01:49:54 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
tf 2.12.0
### Custom code
Yes
### OS platform and distribution
Linux Ubuntu 20.04
### Mobile device
android 10.0 emulator
### Python version
3.9
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
CUDA 11.4 cuDNN 8
### GPU model and memory
_No response_
### Current behavior?
**Sorry about the type 'bug', I have no idea to solve it, I don' mean to offend that.**
I am using the official tf example named generative_ai which from:
https://github.com/tensorflow/examples/tree/master/lite/examples/generative_ai/android.
And I tried to add GPU delegate to the interpreter, and my dependencies's version are:
```
tflite-gpu = { module = "org.tensorflow:tensorflow-lite-gpu", version = "2.12.0" }
tflite-gpu-api = { module = "org.tensorflow:tensorflow-lite-gpu-api", version = "2.12.0" }
```
The test case is:
```kotlin
override suspend fun initModel(): InitModelResult {
return withContext(dispatcher) {
// Load model file
val loadResult = loadModelFile(context)
/**
* wyt
* create interpreter option for gpu delegate
*/
val options = Interpreter.Options()
val myDelegate = GpuDelegate()
options.addDelegate(myDelegate)
// Determine if load was successful
if (loadResult.isFailure) {
val exc = loadResult.exceptionOrNull()
return@withContext if (exc is FileNotFoundException) {
InitModelResult.Error(AutoCompleteServiceError.MODEL_FILE_NOT_FOUND)
} else {
InitModelResult.Error(AutoCompleteServiceError.MODEL_NOT_INITIALIZED)
}
}
/**
* wyt
* add options to interpreter
*/
// Instantiate interpreter with loaded model
val model = loadResult.getOrNull()
isInitialized = model?.let {
interpreter = Interpreter(it, options)
true
} ?: false
if (isInitialized) InitModelResult.Success
else InitModelResult.Error(AutoCompleteServiceError.MODEL_NOT_INITIALIZED)
}
}
```
I have only add these lines(Added `wyt` commented section).
But the app cannot be installed rightly. It will crash.
But when I delete the param `options` to `interpreter = Interpreter(it)`, it works well.
It stands to reason that gpu delegate should not be supported.
### Standalone code to reproduce the issue
```shell
The standalone code as described above(Current behavior).
```
### Relevant log output
```shell
java.lang.IllegalArgumentException: Internal error: Error applying delegate:
at org.tensorflow.lite.NativeInterpreterWrapper.createInterpreter(Native Method)
at org.tensorflow.lite.NativeInterpreterWrapper.init(NativeInterpreterWrapper.java:110)
at org.tensorflow.lite.NativeInterpreterWrapper.<init>(NativeInterpreterWrapper.java:73)
at org.tensorflow.lite.NativeInterpreterWrapperExperimental.<init>(NativeInterpreterWrapperExperimental.java:36)
at org.tensorflow.lite.Interpreter.<init>(Interpreter.java:214)
at com.google.tensorflowdemo.data.autocomplete.AutoCompleteServiceImpl$initModel$2.invokeSuspend(AutoCompleteService.kt:167)
at kotlin.coroutines.jvm.internal.BaseContinuationImpl.resumeWith(ContinuationImpl.kt:33)
at kotlinx.coroutines.DispatchedTask.run(DispatchedTask.kt:106)
at kotlinx.coroutines.internal.LimitedDispatcher.run(LimitedDispatcher.kt:42)
at kotlinx.coroutines.scheduling.TaskImpl.run(Tasks.kt:95)
at kotlinx.coroutines.scheduling.CoroutineScheduler.runSafely(CoroutineScheduler.kt:570)
at kotlinx.coroutines.scheduling.CoroutineScheduler$Worker.executeTask(CoroutineScheduler.kt:750)
at kotlinx.coroutines.scheduling.CoroutineScheduler$Worker.runWorker(CoroutineScheduler.kt:677)
at kotlinx.coroutines.scheduling.CoroutineScheduler$Worker.run(CoroutineScheduler.kt:664)
```
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`segmentation fault` in `tf.raw_ops.AddManySparseToTensorsMap` when `sparse_indices` and `sparse_values` are empty and `sparse_shape` is too large.
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"@SuryanarayanaY I was able to replicate this issue [here](https://colab.research.google.com/gist/sushreebarsa/3cd42ccbfb34479e2e718d0db63ec60a/-11_tf_raw_ops_addmanysparsetotensorsmap.ipynb). Thank you!"
] | 2024-04-14T04:28:10 | 2024-04-25T07:33:20 | null |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
binary
### TensorFlow version
tf 2.15
### Custom code
No
### OS platform and distribution
Linux Ubuntu 20.04
### Mobile device
_No response_
### Python version
3.10
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
`segmentation fault` in `tf.raw_ops.AddManySparseToTensorsMap` when `sparse_indices` and `sparse_values` are empty and `sparse_shape` is too large, which causes the program to crash. See the colab link below for details.
### Standalone code to reproduce the issue
```shell
https://colab.research.google.com/drive/1KNuiyCMsSo32teT3lNwYyYbQkD9tEBET?usp=sharing
```
### Relevant log output
```shell
segmentation fault (core dumped)
```
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Use `xla::ifrt::Array::FullyReplicatedShard()` for materializing fully-replicated arrays
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Use `xla::ifrt::Array::FullyReplicatedShard()` for materializing fully-replicated arrays
This allows us to avoid potentially expensive device array explosion when we know the array is fully replicated. To make repeated calls to `PyArray::FullyReplicatedShard()` as efficient as retrieving `py_arrays`, this CL also adds caching for `PyArray::FullyReplicatedShard()`.
Note: Most `FullyRepliciatedShard()` implementations today do not return an error if the array is actually not fully replicated and instead choose one shard, unlike `FetchSingleShard` which does have an (incomplete) shape-based check. This is acceptable because `_single_device_array_to_np_array` and `_copy_single_device_array_to_host_async` are internal APIs that are called only if JAX knows that the array is fully sharded, and its Python implementation does not have such a check either.
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Suggestion: Chatbot Integration for TensorFlow API Assistance
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"Hi **@Praveer1981** ,\r\n\r\nThanks for your suggestion, We will discuss this with our engineering team for update belongs to your suggestion.\r\n\r\nThank you!"
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Hi,
I'm learning TensorFlow and sometimes struggle with finding the correct API name. Could a chatbot be integrated into the tensorflow.org website to assist with this? I could ask it a question, and it would provide the accurate API name with all the necessary information, and in fact, it could give a link to navigate to the API page.
Kind Regards,
Praveer.
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Fix a bug in quantization driver to skip the qtype propagation only for per-axis quantized type and let the quantization propagate for all other cases.
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`Check failed` in `tf.raw_ops.UnbatchGrad` when the input of `original_input` is empty.
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"@xl1043237213,\r\nI was able to reproduce the issue on TensorFlow v2.15, [v2.16](https://colab.research.google.com/gist/tilakrayal/0461ab61356c744575934ecba7116d29/untitled1905.ipynb) and tf-nightly. Kindly find the gist of it [here](https://colab.research.google.com/gist/tilakrayal/bf5b58fbc42531de76cb219ad2aab98d/-6_tf_raw_ops_unbatchgrad.ipynb).\r\n\r\n\r\n"
] | 2024-04-13T18:25:26 | 2024-05-20T05:42:05 | null |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
binary
### TensorFlow version
tf 2.15
### Custom code
No
### OS platform and distribution
Linux Ubuntu 20.04
### Mobile device
_No response_
### Python version
3.10
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
When the input of `original_input` is empty, `tf.raw_ops.UnbatchGrad` will trigger `Check failed` of the input tensor dimension.
### Standalone code to reproduce the issue
```shell
https://colab.research.google.com/drive/1ekseH5mJBvt3_SeK4MuAAM47U8qi5BEB?usp=sharing
```
### Relevant log output
```shell
Check failed: d < dims() (0 vs. 0)
Aborted (core dumped)
```
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Clean up TF deps.
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"Hi @xl1043237213 ,\r\n\r\nThanks for reporting. I have reproduced the reported behaviour and attached screenshot below.\r\n\r\n<img width=\"1500\" alt=\"Screenshot 2024-04-15 at 1 52 35 PM\" src=\"https://github.com/tensorflow/tensorflow/assets/116063290/2c29f38f-acf9-4207-847f-af3f1c2fa26d\">\r\n"
] | 2024-04-13T18:07:22 | 2024-04-23T07:17:08 | null |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
binary
### TensorFlow version
tf 2.15
### Custom code
No
### OS platform and distribution
Linux Ubuntu 20.04
### Mobile device
_No response_
### Python version
3.10
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
When the value of `size` is close to the maximum value for the dtype, an integer overflow error will occur in following multiplication calculations.
### Standalone code to reproduce the issue
```shell
https://colab.research.google.com/drive/1HOwF3BTWBrP2y9hpnFD9STu86vG7PrFX?usp=sharing
```
### Relevant log output
```shell
Non-OK-status: InitDims(dim_sizes) status: INVALID_ARGUMENT: Encountered overflow when multiplying 3 with 6522107765268123892, result: -1
Aborted (core dumped)
```
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| 2,241,676,224 |
I_kwDOArmXAs6FnUPA
| 65,633 |
Some `Check Failed` errors in `tf.raw_ops.Unbatch`
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[
"@xl1043237213 I was able to replicate the [issue](https://colab.research.google.com/gist/sushreebarsa/68c6ea1108e51ca696a00e2fcbc4c3ae/-6_tf_raw_ops_unbatch.ipynb#scrollTo=SunncZz3kS9g) here. Could you please ensure the input tensor has a batch dimension greater than 0. If you're working with a single sample, try to reshape it to have a batch dimension of 1. You can use tf.expand_dims(tensor, axis=0).\r\nThank you!",
"Thanks for your answe.\r\nI can avoid such errors by checking the input or modifying the input, but I think it's an unexpected execution behavior and hoping that TensorFlow can fix this bug."
] | 2024-04-13T17:50:28 | 2024-04-18T16:14:22 | null |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
binary
### TensorFlow version
tf 2.15
### Custom code
No
### OS platform and distribution
Linux Ubuntu 20.04
### Mobile device
_No response_
### Python version
3.10
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
Some `Check Failed` errors in `tf.raw_ops.Unbatch`, which causes the program to crash. See the colab link below for details.
### Standalone code to reproduce the issue
```shell
https://colab.research.google.com/drive/1JRw_-UwodqwPx87naMM04nnFLaLaZcXn?usp=sharing
```
### Relevant log output
```shell
Check failed: size >= 0 (0 vs. -1) Aborted (core dumped)
Check failed: d < dims() (1 vs. 1) Aborted (core dumped)
Check failed: new_num_elements == NumElements() (6 vs. 18) Aborted (core dumped)
```
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I_kwDOArmXAs6FnMsu
| 65,632 |
`Check failed` in `tf.strings.unsorted_segment_join` when the rank of `inputs` is larger than 1 and the value of `num_segments` is too large.
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[
"Hi **@xl1043237213** ,\r\n\r\nI tried to run your code on colab using TF v2.15, 2.16.1, nightly and faced the same issue. Please find the [gist](https://colab.sandbox.google.com/gist/Venkat6871/ffd39afa22b162d49b6a4475728a3145/65632_2-15-2-16-nightlty-v.ipynb) here for reference.\r\n\r\nThank you!"
] | 2024-04-13T16:27:05 | 2024-04-24T06:46:38 | null |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
binary
### TensorFlow version
tf 2.15
### Custom code
No
### OS platform and distribution
Linux Ubuntu 20.04
### Mobile device
_No response_
### Python version
_No response_
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
`Check failed` in `tf.strings.unsorted_segment_join` when the rank of `inputs` is larger than 1 and the value of `num_segments` is too large, which causes the program to crash.
### Standalone code to reproduce the issue
```shell
https://colab.research.google.com/drive/1y6fNdHnw5hjfOdxTa1ojr0HCwh8nN94c?usp=sharing
```
### Relevant log output
```shell
Check failed: 0 <= new_num_elements (0 vs. -1)
Aborted (core dumped)
```
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| 2,241,639,737 |
PR_kwDOArmXAs5ski1n
| 65,631 |
[pjrt] Use single PjRtFuture<>::Promise::Set() API to set completion success or error
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[pjrt] Use single PjRtFuture<>::Promise::Set() API to set completion success or error
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`Check failed` and other errors in `tf.unstack` when dtype of `axis` is `uint64`.
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"@xl1043237213,\r\nThe args axis for the API **tf.unstack** should work with int as mentioned with the document. Here you are trying with the **uint64** which is not supported. I tried with the **int** and it was executed as expected. Kindly find the gist of it [here](https://colab.research.google.com/gist/tilakrayal/f3e31d29b12d9c5889b7cc62228fb752/untitled1850.ipynb).\r\n\r\nhttps://www.tensorflow.org/api_docs/python/tf/unstack#args\r\n\r\nThank you!",
"> ### Issue type\r\n> Bug\r\n> \r\n> ### Have you reproduced the bug with TensorFlow Nightly?\r\n> Yes\r\n> \r\n> ### Source\r\n> binary\r\n> \r\n> ### TensorFlow version\r\n> tf 2.15\r\n> \r\n> ### Custom code\r\n> No\r\n> \r\n> ### OS platform and distribution\r\n> Linux Ubuntu 20.04\r\n> \r\n> ### Mobile device\r\n> _No response_\r\n> \r\n> ### Python version\r\n> _No response_\r\n> \r\n> ### Bazel version\r\n> _No response_\r\n> \r\n> ### GCC/compiler version\r\n> _No response_\r\n> \r\n> ### CUDA/cuDNN version\r\n> _No response_\r\n> \r\n> ### GPU model and memory\r\n> _No response_\r\n> \r\n> ### Current behavior?\r\n> `axis` of `tf.unstack` does support inputs with dtype `uint64`, which produces odd error messages such as \"ValueError: Argument `axis` = 1 not in range [-2, 2)\". Meanwhile, rather oddly, the `axis = tf.constant(1, dtype=tf.uint64)` triggers `OverflowError`, and and repeating this error triggers `Check failed`. See the colab link below for details.\r\n> \r\n> ### Standalone code to reproduce the issue\r\n> ```shell\r\n> https://colab.research.google.com/drive/10Eruvnb1cArC2SBz010lDNIKVd9VRd3i#scrollTo=ZOa0hboFxZ8B\r\n> ```\r\n> \r\n> ### Relevant log output\r\n> ```shell\r\n> Check failed: !PyErr_Occurred()\r\n> Aborted (core dumped)\r\n> ```\r\n\r\nUsers expect it to raise some exception incase of invalid input and not definitely the crash."
] | 2024-04-13T15:18:37 | 2024-04-18T13:55:20 | null |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
binary
### TensorFlow version
tf 2.15
### Custom code
No
### OS platform and distribution
Linux Ubuntu 20.04
### Mobile device
_No response_
### Python version
_No response_
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
`axis` of `tf.unstack` does support inputs with dtype `uint64`, which produces odd error messages such as "ValueError: Argument `axis` = 1 not in range [-2, 2)". Meanwhile, rather oddly, the `axis = tf.constant(1, dtype=tf.uint64)` triggers `OverflowError`, and and repeating this error triggers `Check failed`. See the colab link below for details.
### Standalone code to reproduce the issue
```shell
https://colab.research.google.com/drive/10Eruvnb1cArC2SBz010lDNIKVd9VRd3i#scrollTo=ZOa0hboFxZ8B
```
### Relevant log output
```shell
Check failed: !PyErr_Occurred()
Aborted (core dumped)
```
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I_kwDOArmXAs6Fm4Un
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`Check failed` in `tf.raw_ops.CropAndResize` when `boxes` and `box_ind` are empty.
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[
"Hi @xl1043237213 ,\r\n\r\nThanks for reporting. I have reproduced the reported check fail and attached screenshot below.\r\n\r\n<img width=\"1505\" alt=\"Screenshot 2024-04-15 at 1 56 32 PM\" src=\"https://github.com/tensorflow/tensorflow/assets/116063290/00ad5a07-01ba-4d98-8648-a57f731339aa\">\r\n",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/65629\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/65629\">No</a>\n"
] | 2024-04-13T13:26:57 | 2024-04-18T18:02:46 | 2024-04-18T18:02:43 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
binary
### TensorFlow version
tf 2.15
### Custom code
No
### OS platform and distribution
Linux Ubuntu 20.04
### Mobile device
_No response_
### Python version
3.10
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
`Check failed` in `tf.raw_ops.CropAndResize` when `boxes` and `box_ind` are empty. `boxes` should be a 2-D tensor of shape [`num_boxes`, 4] and `box_ind` should be 1-D tensor of shape [`num_boxes`], but `num_boxes` can be 0.
### Standalone code to reproduce the issue
```shell
https://colab.research.google.com/drive/17OaE1dOW1PJH48RSBlpk7YKN1Q3ZYhRW?usp=sharing
```
### Relevant log output
```shell
Check failed: NDIMS == dims() (2 vs. 1)Asking for tensor of 2 dimensions from a tensor of 1 dimensions
Aborted (core dumped)
```
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Automated Code Change
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IntegerLookup layer performance issue when inited with vocabulary
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"Hi, @jamm1985!\r\nCould you please have a look at this [gist](https://colab.research.google.com/gist/sushreebarsa/fec0fc1cc1c00c82eced3bc83fc7d035/65610.ipynb) where I could reproduce this output ? Kindly confirm the results?\r\n```\r\n8.22 ms ± 363 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\r\n509 µs ± 91.1 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\r\n421 µs ± 7.34 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\r\n``` \r\n\r\nThank you!",
"Hi @sushreebarsa!\r\n\r\nThis is the same code snippet from the bug report. My timings are slightly different as it depends on system load, but I confirm that `tf.keras.layers.IntegerLookup(vocabulary=vocab.tolist())` is an order of magnitude slower.",
"@sushreebarsa from your colab gist:\r\n\r\n```\r\nprint(tf.__version__)\r\n# 2.15.0\r\n```\r\n\r\nthis bug also affects version 2.15.",
"Hi @jamm1985 ,\r\n\r\nI have checked with tf-nightly and there seems no performance issue. Please check the attached [gist](https://colab.sandbox.google.com/gist/SuryanarayanaY/b615a3abfd92852654817695db057ecb/65610_tf-nightly.ipynb) and confirm.",
"@SuryanarayanaY \r\n\r\nI confirm that this bug does not affect tf-nightly.",
"Hi @jamm1985 ,\r\n\r\nThanks for confirmation. Since the issue resolved in nightly can we mark this as closed? Please feel free to close the issue. Thanks!",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"Hi @SuryanarayanaY :\r\n\r\nI hope this bug can be fixed in versions 2.14 and 1.15 as these are the most heavily used versions in production right now."
] | 2024-04-13T09:08:11 | 2024-05-11T08:31:00 | null |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
binary
### TensorFlow version
2.14.0
### Custom code
No
### OS platform and distribution
Ubuntu 20.04
### Mobile device
_No response_
### Python version
3.9
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
I ran into a performance issue when calling an IntegerLookup layer initialized with a Python list vocabulary. Please see code snippet.
Note that passing a numpy array as a vocabulary does not cause performance issues, but hangs during serialization since only the list in [code](https://github.com/keras-team/keras/blob/68f9af408a1734704746f7e6fa9cfede0d6879d8/keras/layers/preprocessing/index_lookup.py#L839) is checked.
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
import numpy as np
vocab = np.arange(0,10_000,1)
data = np.array([[10,20,30,40], [50,60,70,20_000]])
layer_1 = tf.keras.layers.IntegerLookup(vocabulary=vocab.tolist())
%timeit layer_1(data)
# 6.21 ms ± 349 µs per loop (mean ± std. dev. of 7 runs, 100 loops each) # <- that is issue
layer_2 = tf.keras.layers.IntegerLookup(vocabulary=vocab) # pass as numpy
timeit layer_2(data)
# 285 µs ± 6.04 µs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)
layer_3 = tf.keras.layers.IntegerLookup()
layer_3.adapt(vocab.tolist())
%timeit layer_3(data)
# 286 µs ± 1.31 µs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)
```
```
### Relevant log output
_No response_
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