LSTM
- class torchrl.modules.LSTM(input_size: int, hidden_size: int, num_layers: int = 1, batch_first: bool = True, bias: bool = True, dropout: float = 0.0, bidirectional: float = False, proj_size: int = 0, device=None, dtype=None)[source]
A PyTorch module for executing multiple steps of a multi-layer LSTM. The module behaves exactly like
torch.nn.LSTM
, but this implementation is exclusively coded in Python.Note
This class is implemented without relying on CuDNN, which makes it compatible with
torch.vmap()
andtorch.compile()
.Examples
>>> import torch >>> from torchrl.modules.tensordict_module.rnn import LSTM
>>> device = torch.device("cuda") if torch.cuda.device_count() else torch.device("cpu") >>> B = 2 >>> T = 4 >>> N_IN = 10 >>> N_OUT = 20 >>> N_LAYERS = 2 >>> V = 4 # vector size >>> lstm = LSTM( ... input_size=N_IN, ... hidden_size=N_OUT, ... device=device, ... num_layers=N_LAYERS, ... )
# single call >>> x = torch.randn(B, T, N_IN, device=device) >>> h0 = torch.zeros(N_LAYERS, B, N_OUT, device=device) >>> c0 = torch.zeros(N_LAYERS, B, N_OUT, device=device) >>> with torch.no_grad(): … h1, c1 = lstm(x, (h0, c0))
# vectorised call - not possible with nn.LSTM >>> def call_lstm(x, h, c): … h_out, c_out = lstm(x, (h, c)) … return h_out, c_out >>> batched_call = torch.vmap(call_lstm) >>> x = torch.randn(V, B, T, 10, device=device) >>> h0 = torch.zeros(V, N_LAYERS, B, N_OUT, device=device) >>> c0 = torch.zeros(V, N_LAYERS, B, N_OUT, device=device) >>> with torch.no_grad(): … h1, c1 = batched_call(x, h0, c0)
__init__(input_size,hidden_size,num_layers=1,bias=True,batch_first=False,dropout=0.0,bidirectional=False,proj_size=0,device=None,dtype=None)
Apply 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:
where
is the hidden state at time t, is the cell state at time t, is the input at time t, is the hidden state of the layer at time t-1 or the initial hidden state at time 0, and , , , are the input, forget, cell, and output gates, respectively. is the sigmoid function, and is the Hadamard product.In a multilayer LSTM, the input
of the -th layer ( ) is the hidden state of the previous layer multiplied by dropout where each is a Bernoulli random variable which is with probabilitydropout
.If
proj_size > 0
is specified, LSTM with projections will be used. This changes the LSTM cell in the following way. First, the dimension of will be changed fromhidden_size
toproj_size
(dimensions of will be changed accordingly). Second, the output hidden state of each layer will be multiplied by a learnable projection matrix: . Note that as a consequence of this, the output of LSTM network will be of different shape as well. See Inputs/Outputs sections below for exact dimensions of all variables. You can find more details in https://arxiv.org/abs/1402.1128.- Parameters:
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: 1bias – 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) instead of (seq, batch, feature). Note that this does not apply to hidden or cell states. See the Inputs/Outputs sections below for details. 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: 0bidirectional – If
True
, becomes a bidirectional LSTM. Default:False
proj_size – If
> 0
, will use LSTM with projections of corresponding size. Default: 0
- Inputs: input, (h_0, c_0)
input: tensor of shape
for unbatched input, whenbatch_first=False
or whenbatch_first=True
containing the features of the input sequence. The input can also be a packed variable length sequence. Seetorch.nn.utils.rnn.pack_padded_sequence()
ortorch.nn.utils.rnn.pack_sequence()
for details.h_0: tensor of shape
for unbatched input or containing the initial hidden state for each element in the input sequence. Defaults to zeros if (h_0, c_0) is not provided.c_0: tensor of shape
for unbatched input or containing the initial cell state for each element in the input sequence. Defaults to zeros if (h_0, c_0) is not provided.
where:
- Outputs: output, (h_n, c_n)
output: tensor of shape
for unbatched input, whenbatch_first=False
or whenbatch_first=True
containing the output features (h_t) from the last layer of the LSTM, for each t. If atorch.nn.utils.rnn.PackedSequence
has been given as the input, the output will also be a packed sequence. Whenbidirectional=True
, output will contain a concatenation of the forward and reverse hidden states at each time step in the sequence.h_n: tensor of shape
for unbatched input or containing the final hidden state for each element in the sequence. Whenbidirectional=True
, h_n will contain a concatenation of the final forward and reverse hidden states, respectively.c_n: tensor of shape
for unbatched input or containing the final cell state for each element in the sequence. Whenbidirectional=True
, c_n will contain a concatenation of the final forward and reverse cell states, respectively.
- Variables:
weight_ih_l[k] – the learnable input-hidden weights of the
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). Ifproj_size > 0
was specified, the shape will be (4*hidden_size, num_directions * proj_size) for k > 0weight_hh_l[k] – the learnable hidden-hidden weights of the
layer (W_hi|W_hf|W_hg|W_ho), of shape (4*hidden_size, hidden_size). Ifproj_size > 0
was specified, the shape will be (4*hidden_size, proj_size).bias_ih_l[k] – the learnable input-hidden bias of the
layer (b_ii|b_if|b_ig|b_io), of shape (4*hidden_size)bias_hh_l[k] – the learnable hidden-hidden bias of the
layer (b_hi|b_hf|b_hg|b_ho), of shape (4*hidden_size)weight_hr_l[k] – the learnable projection weights of the
layer of shape (proj_size, hidden_size). Only present whenproj_size > 0
was specified.weight_ih_l[k]_reverse – Analogous to weight_ih_l[k] for the reverse direction. Only present when
bidirectional=True
.weight_hh_l[k]_reverse – Analogous to weight_hh_l[k] for the reverse direction. Only present when
bidirectional=True
.bias_ih_l[k]_reverse – Analogous to bias_ih_l[k] for the reverse direction. Only present when
bidirectional=True
.bias_hh_l[k]_reverse – Analogous to bias_hh_l[k] for the reverse direction. Only present when
bidirectional=True
.weight_hr_l[k]_reverse – Analogous to weight_hr_l[k] for the reverse direction. Only present when
bidirectional=True
andproj_size > 0
was specified.
Note
All the weights and biases are initialized from
whereNote
For bidirectional LSTMs, forward and backward are directions 0 and 1 respectively. Example of splitting the output layers when
batch_first=False
:output.view(seq_len, batch, num_directions, hidden_size)
.Note
For bidirectional LSTMs, h_n is not equivalent to the last element of output; the former contains the final forward and reverse hidden states, while the latter contains the final forward hidden state and the initial reverse hidden state.
Note
batch_first
argument is ignored for unbatched inputs.Note
proj_size
should be smaller thanhidden_size
.Examples:
>>> 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))
- forward(input, hx=None)[source]
Define the computation performed at every call.
Should be overridden by all subclasses.
Note
Although the recipe for forward pass needs to be defined within this function, one should call the
Module
instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.