Trouble Converting LSTM Pytorch Model to ONNX
I am trying to export my LSTM Anomally-Detection Pytorch model to ONNX, but I'm experiencing errors. Please take a look at my code below.

Note: My data is shaped as [2685, 5, 6].
Here is where I define my model:

class Model(torch.nn.Module):
    def __init__(self, input_dim, hidden_dim, layer_dim):
        super(Model, self).__init__()
        self.hidden_dim = hidden_dim 
        self.layer_dim = layer_dim
        self.lstm = nn.LSTM(input_dim, hidden_dim, layer_dim, batch_first=True)
        self.fc1 = torch.nn.Linear(hidden_dim, hidden_dim)
        self.fc2 = torch.nn.Linear(hidden_dim, input_dim)   
    def forward(self, x):
        h0 = torch.zeros(self.layer_dim, x.size(0), self.hidden_dim).requires_grad_()
        c0 = torch.zeros(self.layer_dim, x.size(0), self.hidden_dim).requires_grad_()
        out, (hn, cn) = self.lstm(x, (h0.detach(), c0.detach()))
        out = self.fc1(out) 
        out = self.fc2(out)
        return out

input_dim = 6
hidden_dim = 3
layer_dim = 2
model = Model(input_dim, hidden_dim, layer_dim)


I can train it and test with it fine. But the problem comes when exporting:

model.eval()
import torch.onnx
torch_out = torch.onnx.export(model, 
                         torch.randn(2685, 5, 6), 
                         "onnx_model.onnx", 
                         export_params = True
                        )


But I have the following error:

LSTM(6, 3, num_layers=2, batch_first=True)
Linear(in_features=3, out_features=3, bias=True)
Linear(in_features=3, out_features=6, bias=True)
['input_1', 'l_lstm_LSTM', 'l_fc1_Linear', 'l_fc2_Linear', 'l_lstm_LSTM', 'l_fc1_Linear', 'l_fc2_Linear', 'l_lstm_LSTM', 'l_fc1_Linear', 'l_fc2_Linear', 'l_lstm_LSTM', 'l_fc1_Linear', 'l_fc2_Linear', 'l_lstm_LSTM', 'l_fc1_Linear', 'l_fc2_Linear', 'l_lstm_LSTM', 'l_fc1_Linear', 'l_fc2_Linear', 'l_lstm_LSTM', 'l_fc1_Linear', 'l_fc2_Linear', 'l_lstm_LSTM', 'l_fc1_Linear', 'l_fc2_Linear', 'l_lstm_LSTM', 'l_fc1_Linear', 'l_fc2_Linear', 'l_lstm_LSTM', 'l_fc1_Linear', 'l_fc2_Linear']

/home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/torch/onnx/symbolic.py:173: UserWarning: ONNX export failed on RNN/GRU/LSTM because batch_first not supported
  warnings.warn("ONNX export failed on " + op + " because " + msg + " not supported")

---------------------------------------------------------------------------
RuntimeError                              Traceback (most recent call last)
&lt;ipython-input-264-28c6c55537ab&gt; in &lt;module&gt;()
     10                          torch.randn(2685, 5, 6),
     11                          "onnx_model.onnx",
---&gt; 12                          export_params = True
     13                         )

~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/torch/onnx/__init__.py in export(*args, **kwargs)
     23 def export(*args, **kwargs):
     24     from torch.onnx import utils
---&gt; 25     return utils.export(*args, **kwargs)
     26 
     27 

~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/torch/onnx/utils.py in export(model, args, f, export_params, verbose, training, input_names, output_names, aten, export_raw_ir, operator_export_type, opset_version, _retain_param_name, do_constant_folding, strip_doc_string)
    129             operator_export_type=operator_export_type, opset_version=opset_version,
    130             _retain_param_name=_retain_param_name, do_constant_folding=do_constant_folding,
--&gt; 131             strip_doc_string=strip_doc_string)
    132 
    133 

~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/torch/onnx/utils.py in _export(model, args, f, export_params, verbose, training, input_names, output_names, operator_export_type, export_type, example_outputs, propagate, opset_version, _retain_param_name, do_constant_folding, strip_doc_string)
    367         if export_params:
    368             proto, export_map = graph._export_onnx(params_dict, opset_version, defer_weight_export, operator_export_type,
--&gt; 369                                                    strip_doc_string)
    370         else:
    371             proto, export_map = graph._export_onnx({}, opset_version, False, operator_export_type, strip_doc_string)

RuntimeError: ONNX export failed: Couldn't export operator aten::lstm

Defined at:
/home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/torch/nn/modules/rnn.py(522): forward_impl
/home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/torch/nn/modules/rnn.py(539): forward_tensor
/home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/torch/nn/modules/rnn.py(559): forward
/home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/torch/nn/modules/module.py(481): _slow_forward
/home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/torch/nn/modules/module.py(491): __call__
&lt;ipython-input-255-468cef410a2c&gt;(14): forward
/home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/torch/nn/modules/module.py(481): _slow_forward
/home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/torch/nn/modules/module.py(491): __call__
/home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/torch/jit/__init__.py(294): forward
/home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/torch/nn/modules/module.py(493): __call__
/home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/torch/jit/__init__.py(231): get_trace_graph
/home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/torch/onnx/utils.py(225): _trace_and_get_graph_from_model
/home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/torch/onnx/utils.py(266): _model_to_graph
/home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/torch/onnx/utils.py(363): _export
/home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/torch/onnx/utils.py(131): export
/home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/torch/onnx/__init__.py(25): export
&lt;ipython-input-264-28c6c55537ab&gt;(12): &lt;module&gt;
/home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/IPython/core/interactiveshell.py(2963): run_code
/home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/IPython/core/interactiveshell.py(2903): run_ast_nodes
/home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/IPython/core/interactiveshell.py(2785): _run_cell
/home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/IPython/core/interactiveshell.py(2662): run_cell
/home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/ipykernel/zmqshell.py(537): run_cell
/home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/ipykernel/ipkernel.py(208): do_execute
/home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/ipykernel/kernelbase.py(399): execute_request
/home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/ipykernel/kernelbase.py(233): dispatch_shell
/home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/ipykernel/kernelbase.py(283): dispatcher
/home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/tornado/stack_context.py(276): null_wrapper
/home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/zmq/eventloop/zmqstream.py(432): _run_callback
/home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/zmq/eventloop/zmqstream.py(480): _handle_recv
/home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/zmq/eventloop/zmqstream.py(450): _handle_events
/home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/tornado/stack_context.py(276): null_wrapper
/home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/tornado/platform/asyncio.py(117): _handle_events
/home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/asyncio/events.py(145): _run
/home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/asyncio/base_events.py(1432): _run_once
/home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/asyncio/base_events.py(422): run_forever
/home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/tornado/platform/asyncio.py(127): start
/home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/ipykernel/kernelapp.py(486): start
/home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/traitlets/config/application.py(658): launch_instance
/home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/ipykernel/__main__.py(3): &lt;module&gt;
/home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/runpy.py(85): _run_code
/home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/runpy.py(193): _run_module_as_main


Graph we tried to export:
graph(%input.1 : Float(2685, 5, 6),
      %lstm.weight_ih_l0 : Float(12, 6),
      %lstm.weight_hh_l0 : Float(12, 3),
      %lstm.bias_ih_l0 : Float(12),
      %lstm.bias_hh_l0 : Float(12),
      %lstm.weight_ih_l1 : Float(12, 3),
      %lstm.weight_hh_l1 : Float(12, 3),
      %lstm.bias_ih_l1 : Float(12),
      %lstm.bias_hh_l1 : Float(12),
      %fc1.weight : Float(3, 3),
      %fc1.bias : Float(3),
      %fc2.weight : Float(6, 3),
      %fc2.bias : Float(6)):
  %13 : Long() = onnx::Constant[value={0}](), scope: Model
  %14 : Tensor = onnx::Shape(%input.1), scope: Model
  %15 : Long() = onnx::Gather[axis=0](%14, %13), scope: Model
  %16 : Long() = onnx::Constant[value={2}](), scope: Model
  %17 : Long() = onnx::Constant[value={3}](), scope: Model
  %18 : Tensor = onnx::Unsqueeze[axes=[0]](%16)
  %19 : Tensor = onnx::Unsqueeze[axes=[0]](%15)
  %20 : Tensor = onnx::Unsqueeze[axes=[0]](%17)
  %21 : Tensor = onnx::Concat[axis=0](%18, %19, %20)
  %22 : Float(2, 2685, 3) = onnx::ConstantOfShape[value={0}](%21), scope: Model
  %23 : Long() = onnx::Constant[value={0}](), scope: Model
  %24 : Tensor = onnx::Shape(%input.1), scope: Model
  %25 : Long() = onnx::Gather[axis=0](%24, %23), scope: Model
  %26 : Long() = onnx::Constant[value={2}](), scope: Model
  %27 : Long() = onnx::Constant[value={3}](), scope: Model
  %28 : Tensor = onnx::Unsqueeze[axes=[0]](%26)
  %29 : Tensor = onnx::Unsqueeze[axes=[0]](%25)
  %30 : Tensor = onnx::Unsqueeze[axes=[0]](%27)
  %31 : Tensor = onnx::Concat[axis=0](%28, %29, %30)
  %32 : Float(2, 2685, 3) = onnx::ConstantOfShape[value={0}](%31), scope: Model
  %33 : Long() = onnx::Constant[value={1}](), scope: Model/LSTM[lstm]
  %34 : Long() = onnx::Constant[value={2}](), scope: Model/LSTM[lstm]
  %35 : Double() = onnx::Constant[value={0}](), scope: Model/LSTM[lstm]
  %36 : Long() = onnx::Constant[value={0}](), scope: Model/LSTM[lstm]
  %37 : Long() = onnx::Constant[value={0}](), scope: Model/LSTM[lstm]
  %38 : Long() = onnx::Constant[value={1}](), scope: Model/LSTM[lstm]
  %input.2 : Float(2685!, 5!, 3), %40 : Float(2, 2685, 3), %41 : Float(2, 2685, 3) = aten::lstm(%input.1, %22, %32, %lstm.weight_ih_l0, %lstm.weight_hh_l0, %lstm.bias_ih_l0, %lstm.bias_hh_l0, %lstm.weight_ih_l1, %lstm.weight_hh_l1, %lstm.bias_ih_l1, %lstm.bias_hh_l1, %33, %34, %35, %36, %37, %38), scope: Model/LSTM[lstm]
  %42 : Float(3!, 3!) = onnx::Transpose[perm=[1, 0]](%fc1.weight), scope: Model/Linear[fc1]
  %43 : Float(2685, 5, 3) = onnx::MatMul(%input.2, %42), scope: Model/Linear[fc1]
  %44 : Float(2685, 5, 3) = onnx::Add(%43, %fc1.bias), scope: Model/Linear[fc1]
  %45 : Float(3!, 6!) = onnx::Transpose[perm=[1, 0]](%fc2.weight), scope: Model/Linear[fc2]
  %46 : Float(2685, 5, 6) = onnx::MatMul(%44, %45), scope: Model/Linear[fc2]
  %47 : Float(2685, 5, 6) = onnx::Add(%46, %fc2.bias), scope: Model/Linear[fc2]
  return (%47)


What does this mean? What should I do to export properly?

If you're coming here from Google the previous answers are no longer up to date. ONNX now supports an LSTM operator. Take care as exporting from PyTorch will fix the input sequence length by default unless you use the dynamic_axes parameter.
Below is a minimal LSTM export example I adapted from the torch.onnx FAQ
import torch
import onnx
from torch import nn
import numpy as np
import onnxruntime.backend as backend
import numpy as np

torch.manual_seed(0)

layer_count = 4

model = nn.LSTM(10, 20, num_layers=layer_count, bidirectional=True)
model.eval()

with torch.no_grad():
    input = torch.randn(1, 3, 10)
    h0 = torch.randn(layer_count * 2, 3, 20)
    c0 = torch.randn(layer_count * 2, 3, 20)
    output, (hn, cn) = model(input, (h0, c0))

    # default export
    torch.onnx.export(model, (input, (h0, c0)), 'lstm.onnx')
    onnx_model = onnx.load('lstm.onnx')
    # input shape [5, 3, 10]
    print(onnx_model.graph.input[0])

    # export with `dynamic_axes`
    torch.onnx.export(model, (input, (h0, c0)), 'lstm.onnx',
                    input_names=['input', 'h0', 'c0'],
                    output_names=['output', 'hn', 'cn'],
                    dynamic_axes={'input': {0: 'sequence'}, 'output': {0: 'sequence'}})
    onnx_model = onnx.load('lstm.onnx')
    # input shape ['sequence', 3, 10]

# Check export
y, (hn, cn) = model(input, (h0, c0))
y_onnx, hn_onnx, cn_onnx = backend.run(
    onnx_model, 
    [input.numpy(), h0.numpy(), c0.numpy()],
    device='CPU'
)

np.testing.assert_almost_equal(y_onnx, y.detach(), decimal=5)
np.testing.assert_almost_equal(hn_onnx, hn.detach(), decimal=5)
np.testing.assert_almost_equal(cn_onnx, cn.detach(), decimal=5)

I've tested this example with:
torch==1.4.0,
onnx=1.7.0
