GRU - version 3#

This page documents version 3 of operator GRU. See GRU for the latest version (since version 22).

  • Domain: ai.onnx

  • Since version: 3

Computes an one-layer GRU. This operator is usually supported via some custom implementation such as CuDNN.

Notations:

  • X - input tensor

  • z - update gate

  • r - reset gate

  • h - hidden gate

  • t - time step (t-1 means previous time step)

  • W[zrh] - W parameter weight matrix for update, reset, and hidden gates

  • R[zrh] - R recurrence weight matrix for update, reset, and hidden gates

  • Wb[zrh] - W bias vectors for update, reset, and hidden gates

  • Rb[zrh] - R bias vectors for update, reset, and hidden gates

  • WB[zrh] - W parameter weight matrix for backward update, reset, and hidden gates

  • RB[zrh] - R recurrence weight matrix for backward update, reset, and hidden gates

  • WBb[zrh] - W bias vectors for backward update, reset, and hidden gates

  • RBb[zrh] - R bias vectors for backward update, reset, and hidden gates

  • H - Hidden state

  • num_directions - 2 if direction == bidirectional else 1

Activation functions:

  • Relu(x) - max(0, x)

  • Tanh(x) - (1 - e^{-2x})/(1 + e^{-2x})

  • Sigmoid(x) - 1/(1 + e^{-x})

NOTE:

Below are optional

  • Affine(x) - alpha * x + beta

  • LeakyRelu(x) - x if x >= 0 else alpha * x

  • ThresholdedRelu(x) - x if x >= alpha else 0

  • ScaledTanh(x) - alpha * Tanh(beta * x)

  • HardSigmoid(x) - min(max(alpha * x + beta, 0), 1)

  • Elu(x) - x if x >= 0 else alpha * (e^x - 1)

  • Softsign(x) - x/(1 + |x|)

  • Softplus(x) - log(1 + e^x)

Equations (Default: f=Sigmoid, g=Tanh):

  • zt = f(Xt*(Wz^T) + Ht-1*(Rz^T) + Wbz + Rbz)

  • rt = f(Xt*(Wr^T) + Ht-1*(Rr^T) + Wbr + Rbr)

  • ht = g(Xt*(Wh^T) + (rt (.) Ht-1)*(Rh^T) + Rbh + Wbh) # default, when linear_before_reset = 0

  • ht = g(Xt*(Wh^T) + (rt (.) (Ht-1*(Rh^T) + Rbh)) + Wbh) # when linear_before_reset != 0

  • Ht = (1 - zt) (.) ht + zt (.) Ht-1

Inputs

  • X (T): The input sequences packed (and potentially padded) into one 3-D tensor with the shape of [seq_length, batch_size, input_size].

  • W (T): The weight tensor for the gates. Concatenation of W[zrh] and WB[zrh] (if bidirectional) along dimension 0. This tensor has shape [num_directions, 3*hidden_size, input_size].

  • R (T): The recurrence weight tensor. Concatenation of R[zrh] and RB[zrh] (if bidirectional) along dimension 0. This tensor has shape [num_directions, 3*hidden_size, hidden_size].

  • B (T): The bias tensor for the gates. Concatenation of [Wb[zrh], Rb[zrh]] and [WBb[zrh], RBb[zrh]] (if bidirectional) along dimension 0. This tensor has shape [num_directions, 6*hidden_size]. Optional: If not specified - assumed to be 0

  • sequence_lens (T1): Optional tensor specifying lengths of the sequences in a batch. If not specified - assumed all sequences in the batch to have length seq_length. It has shape [batch_size].

  • initial_h (T): Optional initial value of the hidden. If not specified - assumed to be 0. It has shape [num_directions, batch_size, hidden_size].

Outputs

  • Y (T): A tensor that concats all the intermediate output values of the hidden. It has shape [seq_length, num_directions, batch_size, hidden_size]. It is optional if output_sequence is 0.

  • Y_h (T): The last output value of the hidden. It has shape [num_directions, batch_size, hidden_size].

Type Constraints

  • T: Constrain input and output types to float tensors. Allowed types: tensor(double), tensor(float), tensor(float16).

  • T1: Constrain seq_lens to integer tensor. Allowed types: tensor(int32).

Differences with previous version (1)#

SchemaDiff: GRU (domain 'ai.onnx')

  • old version: 1

  • new version: 3

  • breaking: no