background
I want to convert model to ONNX, but there is the mv operator in my model, so when run torch.onnx.export, console output the error:
RuntimeError:exporting the operator mv to ONNX opset version 11 is not supported. Please feel free to request support or submit a pull request on Pytorch Github
so, I have to implement mv operator for ONNX support.
question
- how to confirm the operator is an ATen operator.
This document says thatIf the operator is an ATen operator, which means you can find the declaration of the function in torch/csrc/autograd/generated/VariableType.h, but in this header file, these is not any operator declaration, if I have the mistaken about thefunction declaration.
VariableType.h info
#pragma once
// @generated from tools/autograd/templates/VariableType.h
#include <ATen/ATen.h>
#include <c10/util/intrusive_ptr.h>
#include <torch/csrc/WindowsTorchApiMacro.h>
#include <cstdint> // for size_t
#include <functional> // for function
#include <memory> // for unique_ptr
#include <string>
#include <vector>
namespace at {
struct Quantizer;
};
namespace torch { namespace autograd {
using Variable = at::Tensor;
using at::Context;
using at::Device;
using at::Dimname;
using at::DimnameList;
using at::Generator;
using at::IntArrayRef;
using at::MemoryFormat;
using at::QScheme;
using at::Scalar;
using at::ScalarType;
using at::Storage;
using at::Tensor;
using at::TensorList;
using at::TensorOptions;
using at::Quantizer;
// This is temporary typedef to enable Quantizer in aten native function API
// we'll remove them when we are actually exposing Quantizer class
// to frontend
using ConstQuantizerPtr = const c10::intrusive_ptr<Quantizer>&;
using c10::optional;
namespace VariableType {
TORCH_API std::vector<at::DeprecatedTypeProperties*> allCUDATypes();
TORCH_API std::vector<at::DeprecatedTypeProperties*> allCPUTypes();
at::Tensor & unpack(Tensor & t, const char * name, int pos);
const at::Tensor & unpack(const Tensor & t, const char * name, int pos);
at::Tensor unpack_opt(const Tensor & t, const char * name, int pos);
std::vector<at::Tensor> unpack(at::TensorList tl, const char *name, int pos);
};
}} // namespace torch::autograd
the code for reproduce the error
from torch.autograd import Variable
import torch
class custom_net(torch.nn.Module):
def __init__(self,):
super(custom_net, self).__init__()
self.sigmoid = torch.nn.Sigmoid()
def forward(self, martix, vector):
martix = self.sigmoid(martix)
vector = self.sigmoid(vector)
outputs = torch.mv(martix, vector)
return outputs
if __name__ == "__main__":
net = custom_net()
net = net.eval().cuda()
martix = Variable(torch.randn(2, 3)).type(torch.FloatTensor)
vector = Variable(torch.randn(3)).type(torch.FloatTensor)
martix = martix.cuda()
vector = vector.cuda()
input_name = ["martix", "vector"]
output_name = ["output"]
torch.onnx.export(net, (martix, vector), "net.onnx", input_names=input_name, output_names=output_name,
opset_version=11)
print("done!")