[GitHub] [incubator-mxnet] xnnxax527 edited a comment on issue #15796: Model Quantization with CUDNN

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xnnxax527 edited a comment on issue #15796: Model Quantization with CUDNN
URL: https://github.com/apache/incubator-mxnet/issues/15796#issuecomment-520865110
 
 
   Hi @xinyu-intel , I see you release the calibration and quantization code on Intel MKLDNN, thanks.
   I am so interested in quantizing gluoncv SSD model on GPU, but the mkldnn quantized ssd model can't run on ctx=mx.gpu(), what's the problem? 
   the model should quantized with cudnn if i want to run on GPU?
   
   When run net(x), the error log is:
   `ipdb> net(x)
   *** mxnet.base.MXNetError: [00:17:29] src/imperative/./imperative_utils.h:558: Check failed: fcompute != nullptr: One of FStatefulCompute and FStatefulComputeEx must be registered for stateful operator _sg_mkldnn_conv
   Stack trace:
     [bt] (0) /home/jiashen.hjs/anaconda2/envs/py35/lib/python3.5/site-packages/mxnet/libmxnet.so(+0x4f710b) [0x7f8a1582b10b]
     [bt] (1) /home/jiashen.hjs/anaconda2/envs/py35/lib/python3.5/site-packages/mxnet/libmxnet.so(mxnet::imperative::PushOperator(mxnet::OpStatePtr const&, nnvm::Op const*, nnvm::NodeAttrs const&, mxnet::Context const&, std::vector<mxn
   et::engine::Var*, std::allocator<mxnet::engine::Var*> > const&, std::vector<mxnet::engine::Var*, std::allocator<mxnet::engine::Var*> > const&, std::vector<mxnet::Resource, std::allocator<mxnet::Resource> > const&, std::vector<mxnet:
   :NDArray*, std::allocator<mxnet::NDArray*> > const&, std::vector<mxnet::NDArray*, std::allocator<mxnet::NDArray*> > const&, std::vector<unsigned int, std::allocator<unsigned int> > const&, std::vector<mxnet::OpReqType, std::allocato
   r<mxnet::OpReqType> > const&, mxnet::DispatchMode)+0x9cf) [0x7f8a17e3447f]
     [bt] (2) /home/jiashen.hjs/anaconda2/envs/py35/lib/python3.5/site-packages/mxnet/libmxnet.so(mxnet::Imperative::InvokeOp(mxnet::Context const&, nnvm::NodeAttrs const&, std::vector<mxnet::NDArray*, std::allocator<mxnet::NDArray*> >
    const&, std::vector<mxnet::NDArray*, std::allocator<mxnet::NDArray*> > const&, std::vector<mxnet::OpReqType, std::allocator<mxnet::OpReqType> > const&, mxnet::DispatchMode, mxnet::OpStatePtr)+0xa71) [0x7f8a17e368d1]
     [bt] (3) /home/jiashen.hjs/anaconda2/envs/py35/lib/python3.5/site-packages/mxnet/libmxnet.so(+0x2b0c990) [0x7f8a17e40990]
     [bt] (4) /home/jiashen.hjs/anaconda2/envs/py35/lib/python3.5/site-packages/mxnet/libmxnet.so(+0x2b0e5c4) [0x7f8a17e425c4]
     [bt] (5) /home/jiashen.hjs/anaconda2/envs/py35/lib/python3.5/site-packages/mxnet/libmxnet.so(mxnet::imperative::RunGraph(bool, nnvm::IndexedGraph const&, std::vector<mxnet::NDArray*, std::allocator<mxnet::NDArray*> > const&, unsig
   ned long, unsigned long, std::vector<mxnet::OpReqType, std::allocator<mxnet::OpReqType> >&&, std::vector<unsigned int, std::allocator<unsigned int> >&&, std::vector<mxnet::OpStatePtr, std::allocator<mxnet::OpStatePtr> >*, std::vecto
   r<mxnet::DispatchMode, std::allocator<mxnet::DispatchMode> > const&, bool, std::vector<mxnet::TShape, std::allocator<mxnet::TShape> >*)+0x208) [0x7f8a17e42a58]
     [bt] (6) /home/jiashen.hjs/anaconda2/envs/py35/lib/python3.5/site-packages/mxnet/libmxnet.so(mxnet::CachedOp::DynamicForward(mxnet::Context const&, std::vector<mxnet::NDArray*, std::allocator<mxnet::NDArray*> > const&, std::vector
   <mxnet::NDArray*, std::allocator<mxnet::NDArray*> > const&, bool)+0x124e) [0x7f8a17e10e6e]
     [bt] (7) /home/jiashen.hjs/anaconda2/envs/py35/lib/python3.5/site-packages/mxnet/libmxnet.so(mxnet::CachedOp::Forward(std::shared_ptr<mxnet::CachedOp> const&, std::vector<mxnet::NDArray*, std::allocator<mxnet::NDArray*> > const&,
   std::vector<mxnet::NDArray*, std::allocator<mxnet::NDArray*> > const&)+0xb55) [0x7f8a17e16825]
     [bt] (8) /home/jiashen.hjs/anaconda2/envs/py35/lib/python3.5/site-packages/mxnet/libmxnet.so(MXInvokeCachedOp+0x4ab) [0x7f8a17d2ac9b]`
   

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