class torch.nn.LSTM(*args, **kwargs)[source]

Applies a multi-layer long short-term memory (LSTM) RNN to an input sequence.

For each element in the input sequence, each layer computes the following function:

it=σ(Wiixt+bii+Whiht1+bhi)ft=σ(Wifxt+bif+Whfht1+bhf)gt=tanh(Wigxt+big+Whght1+bhg)ot=σ(Wioxt+bio+Whoht1+bho)ct=ftct1+itgtht=ottanh(ct)\begin{array}{ll} \\ i_t = \sigma(W_{ii} x_t + b_{ii} + W_{hi} h_{t-1} + b_{hi}) \\ f_t = \sigma(W_{if} x_t + b_{if} + W_{hf} h_{t-1} + b_{hf}) \\ g_t = \tanh(W_{ig} x_t + b_{ig} + W_{hg} h_{t-1} + b_{hg}) \\ o_t = \sigma(W_{io} x_t + b_{io} + W_{ho} h_{t-1} + b_{ho}) \\ c_t = f_t \odot c_{t-1} + i_t \odot g_t \\ h_t = o_t \odot \tanh(c_t) \\ \end{array}

where hth_t is the hidden state at time t, ctc_t is the cell state at time t, xtx_t is the input at time t, ht1h_{t-1} is the hidden state of the layer at time t-1 or the initial hidden state at time 0, and iti_t , ftf_t , gtg_t , oto_t are the input, forget, cell, and output gates, respectively. σ\sigma is the sigmoid function, and \odot is the Hadamard product.

In a multilayer LSTM, the input xt(l)x^{(l)}_t of the ll -th layer (l>=2l >= 2 ) is the hidden state ht(l1)h^{(l-1)}_t of the previous layer multiplied by dropout δt(l1)\delta^{(l-1)}_t where each δt(l1)\delta^{(l-1)}_t is a Bernoulli random variable which is 00 with probability dropout.

  • input_size – The number of expected features in the input x

  • hidden_size – The number of features in the hidden state h

  • num_layers – Number of recurrent layers. E.g., setting num_layers=2 would mean stacking two LSTMs together to form a stacked LSTM, with the second LSTM taking in outputs of the first LSTM and computing the final results. Default: 1

  • bias – If False, then the layer does not use bias weights b_ih and b_hh. Default: True

  • batch_first – If True, then the input and output tensors are provided as (batch, seq, feature). Default: False

  • dropout – If non-zero, introduces a Dropout layer on the outputs of each LSTM layer except the last layer, with dropout probability equal to dropout. Default: 0

  • bidirectional – If True, becomes a bidirectional LSTM. Default: False

Inputs: input, (h_0, c_0)
  • input of shape (seq_len, batch, input_size): tensor containing the features of the input sequence. The input can also be a packed variable length sequence. See torch.nn.utils.rnn.pack_padded_sequence() or torch.nn.utils.rnn.pack_sequence() for details.

  • h_0 of shape (num_layers * num_directions, batch, hidden_size): tensor containing the initial hidden state for each element in the batch. If the LSTM is bidirectional, num_directions should be 2, else it should be 1.

  • c_0 of shape (num_layers * num_directions, batch, hidden_size): tensor containing the initial cell state for each element in the batch.

    If (h_0, c_0) is not provided, both h_0 and c_0 default to zero.

Outputs: output, (h_n, c_n)
  • output of shape (seq_len, batch, num_directions * hidden_size): tensor containing the output features (h_t) from the last layer of the LSTM, for each t. If a torch.nn.utils.rnn.PackedSequence has been given as the input, the output will also be a packed sequence.

    For the unpacked case, the directions can be separated using output.view(seq_len, batch, num_directions, hidden_size), with forward and backward being direction 0 and 1 respectively. Similarly, the directions can be separated in the packed case.

  • h_n of shape (num_layers * num_directions, batch, hidden_size): tensor containing the hidden state for t = seq_len.

    Like output, the layers can be separated using h_n.view(num_layers, num_directions, batch, hidden_size) and similarly for c_n.

  • c_n of shape (num_layers * num_directions, batch, hidden_size): tensor containing the cell state for t = seq_len.

  • ~LSTM.weight_ih_l[k] – the learnable input-hidden weights of the kth\text{k}^{th} layer (W_ii|W_if|W_ig|W_io), of shape (4*hidden_size, input_size) for k = 0. Otherwise, the shape is (4*hidden_size, num_directions * hidden_size)

  • ~LSTM.weight_hh_l[k] – the learnable hidden-hidden weights of the kth\text{k}^{th} layer (W_hi|W_hf|W_hg|W_ho), of shape (4*hidden_size, hidden_size)

  • ~LSTM.bias_ih_l[k] – the learnable input-hidden bias of the kth\text{k}^{th} layer (b_ii|b_if|b_ig|b_io), of shape (4*hidden_size)

  • ~LSTM.bias_hh_l[k] – the learnable hidden-hidden bias of the kth\text{k}^{th} layer (b_hi|b_hf|b_hg|b_ho), of shape (4*hidden_size)


All the weights and biases are initialized from U(k,k)\mathcal{U}(-\sqrt{k}, \sqrt{k}) where k=1hidden_sizek = \frac{1}{\text{hidden\_size}}


There are known non-determinism issues for RNN functions on some versions of cuDNN and CUDA. You can enforce deterministic behavior by setting the following environment variables:

On CUDA 10.1, set environment variable CUDA_LAUNCH_BLOCKING=1. This may affect performance.

On CUDA 10.2 or later, set environment variable (note the leading colon symbol) CUBLAS_WORKSPACE_CONFIG=:16:8 or CUBLAS_WORKSPACE_CONFIG=:4096:2.

See the cuDNN 8 Release Notes for more information.



If the following conditions are satisfied: 1) cudnn is enabled, 2) input data is on the GPU 3) input data has dtype torch.float16 4) V100 GPU is used, 5) input data is not in PackedSequence format persistent algorithm can be selected to improve performance.


>>> rnn = nn.LSTM(10, 20, 2)
>>> input = torch.randn(5, 3, 10)
>>> h0 = torch.randn(2, 3, 20)
>>> c0 = torch.randn(2, 3, 20)
>>> output, (hn, cn) = rnn(input, (h0, c0))


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