How to convert model from double to float

[inocsin]

When I try to complie torchscript model, I get this log

DEBUG: [TRTorch Conversion Context] - Found IValue containing object of type Double(requires_grad=0, device=cpu)
terminate called after throwing an instance of 'trtorch::Error'
  what():  [enforce fail at core/util/trt_util.cpp:293] Expected aten_trt_type_map.find(t) != aten_trt_type_map.end() to be true but got false
Unsupported Aten datatype

So I try to convert model to float using this

script_model = torch.jit.load(path)
script_model = script_model.eval()
script_model = script_model.float()
script_model.save(new_path)

And it still throw this error

[narendasan]

can you provide more of the log / the graph?

[inocsin]

can you provide more of the log / the graph?

Log file is here
double.log

gdb backtrace

Thread 1 "trtorchc" received signal SIGABRT, Aborted.
0x00007fff63987438 in __GI_raise (sig=sig@entry=6) at ../sysdeps/unix/sysv/linux/raise.c:54
54      ../sysdeps/unix/sysv/linux/raise.c: No such file or directory.
(gdb) bt
#0  0x00007fff63987438 in __GI_raise (sig=sig@entry=6) at ../sysdeps/unix/sysv/linux/raise.c:54
#1  0x00007fff6398903a in __GI_abort () at abort.c:89
#2  0x00007ffff7a8ddde in ?? () from /usr/lib/x86_64-linux-gnu/libstdc++.so.6
#3  0x00007ffff7a99896 in ?? () from /usr/lib/x86_64-linux-gnu/libstdc++.so.6
#4  0x00007ffff7a99901 in std::terminate() () from /usr/lib/x86_64-linux-gnu/libstdc++.so.6
#5  0x00007ffff7a99b55 in __cxa_throw () from /usr/lib/x86_64-linux-gnu/libstdc++.so.6
#6  0x000000000055a628 in trtorch::core::util::toTRTDataType (t=c10::ScalarType::Double) at core/util/trt_util.cpp:293
#7  0x000000000055a714 in trtorch::core::util::toTRTDataType (dtype=...) at core/util/trt_util.cpp:299
#8  0x000000000052f888 in trtorch::core::conversion::converters::Weights::Weights (this=0x7fffffffa300, ctx=0x7fffffffb010, t=...)
    at core/conversion/converters/Weights.cpp:76
#9  0x000000000052e40e in trtorch::core::conversion::Var::ITensorOrFreeze (this=0x67243178, ctx=0x7fffffffb010) at core/conversion/var/Var.cpp:99
#10 0x00000000004a5f32 in trtorch::core::conversion::converters::impl::(anonymous namespace)::<lambda(trtorch::core::conversion::ConversionCtx*, const torch::jit::Node*, trtorch::core::conversion::converters::args&)>::operator()(trtorch::core::conversion::ConversionCtx *, const torch::jit::Node *, trtorch::core::conversion::converters::args &) const (__closure=0x7fffffffaae0, ctx=0x7fffffffb010, n=0x5e391a60, args=std::vector of length 2, capacity 2 = {...})
    at core/conversion/converters/impl/element_wise.cpp:325
#11 0x00000000004aeb5e in std::_Function_handler<bool(trtorch::core::conversion::ConversionCtx*, const torch::jit::Node*, std::vector<trtorch::core::conversion::Var, std::allocator<trtorch::core::conversion::Var> >&), trtorch::core::conversion::converters::impl::(anonymous namespace)::<lambda(trtorch::core::conversion::ConversionCtx*, const torch::jit::Node*, trtorch::core::conversion::converters::args&)> >::_M_invoke(const std::_Any_data &, <unknown type in /home/vincent/.cache/bazel/_bazel_vincent/6ed89a01738d85eb9ea1a1afec2b71b5/execroot/TRTorch/bazel-out/k8-dbg/bin/cpp/trtorchc/trtorchc, CU 0x939ab0, DIE 0x9ff895>, <unknown type in /home/vincent/.cache/bazel/_bazel_vincent/6ed89a01738d85eb9ea1a1afec2b71b5/execroot/TRTorch/bazel-out/k8-dbg/bin/cpp/trtorchc/trtorchc, CU 0x939ab0, DIE 0x9ff8a4>, std::vector<trtorch::core::conversion::Var, std::allocator<trtorch::core::conversion::Var> > &) (__functor=...,
    __args#0=<unknown type in /home/vincent/.cache/bazel/_bazel_vincent/6ed89a01738d85eb9ea1a1afec2b71b5/execroot/TRTorch/bazel-out/k8-dbg/bin/cpp/trtorchc/trtorchc, CU 0x939ab0, DIE 0x9ff895>,
    __args#1=<unknown type in /home/vincent/.cache/bazel/_bazel_vincent/6ed89a01738d85eb9ea1a1afec2b71b5/execroot/TRTorch/bazel-out/k8-dbg/bin/cpp/trtorchc/trtorchc, CU 0x939ab0, DIE 0x9ff8a4>, __args#2=std::vector of length 2, capacity 2 = {...}) at /usr/include/c++/7/bits/std_function.h:302
#12 0x000000000047cd02 in std::function<bool (trtorch::core::conversion::ConversionCtx*, torch::jit::Node const*, std::vector<trtorch::core::conversion::Var, std::allocator<trtorch::core::conversion::Var> >&)>::operator()(trtorch::core::conversion::ConversionCtx*, torch::jit::Node const*, std::vector<trtorch::core::conversion::Var, std::allocator<trtorch::core::conversion::Var> >&) const (this=0x7fffffffaae0, __args#0=0x7fffffffb010, __args#1=0x5e391a60, __args#2=std::vector of length 2, capacity 2 = {...})
    at /usr/include/c++/7/bits/std_function.h:706
#13 0x00000000004769d8 in trtorch::core::conversion::AddLayer (ctx=0x7fffffffb010, n=0x5e391a60) at core/conversion/conversion.cpp:116
#14 0x000000000047a91b in trtorch::core::conversion::ConvertBlockToNetDef (ctx=0x7fffffffb010, b=0x5e30a510, build_info=..., static_params=std::map with 0 elements)
    at core/conversion/conversion.cpp:353
#15 0x000000000047adaf in trtorch::core::conversion::ConvertBlockToEngine[abi:cxx11](torch::jit::Block const*, trtorch::core::conversion::ConversionInfo, std::map<torch::jit::Value*, c10::IValue, std::less<torch::jit::Value*>, std::allocator<std::pair<torch::jit::Value* const, c10::IValue> > >&) (b=0x5e30a510, build_info=...,
    static_params=std::map with 0 elements) at core/conversion/conversion.cpp:380
#16 0x000000000045da60 in trtorch::core::ConvertGraphToTRTEngine (mod=..., method_name="forward", cfg=...) at core/compiler.cpp:150
#17 0x000000000045de12 in trtorch::core::CompileGraph (mod=..., cfg=...) at core/compiler.cpp:163
#18 0x000000000045b033 in trtorch::CompileGraph (module=..., info=...) at cpp/api/src/trtorch.cpp:31
#19 0x000000000042196c in main (argc=5, argv=0x7fffffffdf88) at cpp/trtorchc/main.cpp:382

[peri044]

This error is from aten_to_trt_type mapping. The pytorch tensor in your graph has a double datatype and TRT doesn't support beyond float datatype.
My guess:
Looking at your log, it looks like the second input to the mul node is a double node.

%22 : Tensor = prim::Constant[value={0.2}]()
%523 : Tensor = aten::mul(%x5.1, %22)

Adding Layer %523 : Tensor = aten::mul(%x5.1, %22) # /data00/home/roy.he/projects/super_res/infere/models/modules/RRDBNet_arch.py:28:0 (ctx.AddLayer)
�[1;35mDEBUG: �[0m[TRTorch Conversion Context] - Node input is an already converted tensor
�[1;35mDEBUG: �[0m[TRTorch Conversion Context] - Node input is a result of a previously evaluated value
�[1;35mDEBUG: �[0mFrozen tensor shape: [1, 64, 1000, 1000]
�[1;35mDEBUG: �[0m[TRTorch Conversion Context] - Found IValue containing object of type Double(requires_grad=0, device=cpu)
terminate called after throwing an instance of 'trtorch::Error'
  what():  [enforce fail at core/util/trt_util.cpp:293] Expected aten_trt_type_map.find(t) != aten_trt_type_map.end() to be true but got false
Unsupported Aten datatype

Maybe this %22 is a double value which might be causing the problem. If you have the source code, can you force this constant to be float, regenerate the torchscript and try ?
There are other similar constants in your model as follows which are represented as explicit float constants unlike %22 tensor

 %13 : float = prim::Constant[value=0.20000000000000001]()

[inocsin]

This error is from aten_to_trt_type mapping. The pytorch tensor in your graph has a double datatype and TRT doesn't support beyond float datatype.
My guess:
Looking at your log, it looks like the second input to the mul node is a double node.

%22 : Tensor = prim::Constant[value={0.2}]()
%523 : Tensor = aten::mul(%x5.1, %22)

Adding Layer %523 : Tensor = aten::mul(%x5.1, %22) # /data00/home/roy.he/projects/super_res/infere/models/modules/RRDBNet_arch.py:28:0 (ctx.AddLayer)
�[1;35mDEBUG: �[0m[TRTorch Conversion Context] - Node input is an already converted tensor
�[1;35mDEBUG: �[0m[TRTorch Conversion Context] - Node input is a result of a previously evaluated value
�[1;35mDEBUG: �[0mFrozen tensor shape: [1, 64, 1000, 1000]
�[1;35mDEBUG: �[0m[TRTorch Conversion Context] - Found IValue containing object of type Double(requires_grad=0, device=cpu)
terminate called after throwing an instance of 'trtorch::Error'
  what():  [enforce fail at core/util/trt_util.cpp:293] Expected aten_trt_type_map.find(t) != aten_trt_type_map.end() to be true but got false
Unsupported Aten datatype

Maybe this %22 is a double value which might be causing the problem. If you have the source code, can you force this constant to be float, regenerate the torchscript and try ?
There are other similar constants in your model as follows which are represented as explicit float constants unlike %22 tensor

 %13 : float = prim::Constant[value=0.20000000000000001]()

Yes, I tried to print the constant value in the script model using

script_model = torch.jit.load(path)
script_model.eval()
script_model.float()
print(script_model.forward.code_with_constants[1])

the output is

{'c0': tensor(0.2000, dtype=torch.float64), 'c1': tensor(2.)}

I also try to modify the costant value by

script_model.code_with_constants[1].c0 = script_model.code_with_constants[1].c0.float()

but it didn't change the type. So the only way is to modify the orignal python module definition? There this nothing can do to modify the constant type throuhg torchscript?

[peri044]

@inocsin Can you try with torch.fx https://pytorch.org/docs/stable/fx.html#direct-graph-manipulation ?
I'm not sure if that would work but we can basically modify layers/add new layers etc. Hopefully we can modify these constant layers to the desired precision ?

[github-actions]

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[codeislife99]

Hi is there any way to convert to float within trt itself ? Because changing every model is not feasible. Or scoping out every line of huge models can be very tedious and non-scalable.

[apivovarov]

Related issues:
inception_v3 pretrained compilation - Unsupported ATen data type Double - #1096

Fix Inception transform_input to use Float tensors - pytorch/vision#6120

torch_tensorrt does not even support basic inception_v3 model!!! Just because it has the following statement

x_ch0 = torch.unsqueeze(x[:, 0], 1) * (0.229 / 0.5) + (0.485 - 0.5) / 0.5

Which should be replaced with

x_ch0 = torch.unsqueeze(x[:, 0], 1) * torch.tensor(0.229 / 0.5) + torch.tensor((0.485 - 0.5) / 0.5)

Torchvision Model developers thinks that the fix should be done in torch_tensorrt - the right thing to do is to add a strength reduction pass in torch_tensorrt