.. _l-onnx-doccom.microsoft-AttnLSTM: ======================== com.microsoft - AttnLSTM ======================== .. contents:: :local: .. _l-onnx-opcom-microsoft-attnlstm-1: AttnLSTM - 1 (com.microsoft) ============================ **Version** * **name**: `AttnLSTM (GitHub) `_ * **domain**: **com.microsoft** * **since_version**: **1** * **function**: * **support_level**: * **shape inference**: This version of the operator has been available **since version 1 of domain com.microsoft**. **Summary** Computes an one-layer RNN where its RNN Cell is an AttentionWrapper wrapped a LSTM Cell. The RNN layer contains following basic component: LSTM Cell, Bahdanau Attention Mechanism, AttentionWrapp. 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) Softmax(x) - exp(x) / sum(exp(x)) Bahdanau Attention Mechanism: `M` - Memory tensor. `VALUES` - masked Memory by its real sequence length. `MW` - Memory layer weight. `KEYS` - Processed memory tensor by the memory layer. KEYS = M * MW `Query` - Query tensor, normally at specific time step in sequence. `QW` - Query layer weight in the attention mechanism `PQ` - processed query, = `Query` * `QW` `V' - attention vector `ALIGN` - calculated alignment based on Query and KEYS ALIGN = softmax(reduce_sum(`V` * Tanh(`KEYS` + `PQ`))) `CONTEXT` - context based on `ALIGN` and `VALUES` CONTEXT = `ALIGN` * `VALUES` LSTM Cell: `X` - input tensor concat with attention state in the attention wrapper `i` - input gate `o` - output gate `f` - forget gate `c` - cell gate `t` - time step (t-1 means previous time step) `W[iofc]` - W parameter weight matrix for input, output, forget, and cell gates `R[iofc]` - R recurrence weight matrix for input, output, forget, and cell gates `Wb[iofc]` - W bias vectors for input, output, forget, and cell gates `Rb[iofc]` - R bias vectors for input, output, forget, and cell gates `P[iof]` - P peephole weight vector for input, output, and forget gates `WB[iofc]` - W parameter weight matrix for backward input, output, forget, and cell gates `RB[iofc]` - R recurrence weight matrix for backward input, output, forget, and cell gates `WBb[iofc]` - W bias vectors for backward input, output, forget, and cell gates `RBb[iofc]` - R bias vectors for backward input, output, forget, and cell gates `PB[iof]` - P peephole weight vector for backward input, output, and forget gates `H` - Hidden state `num_directions` - 2 if direction == bidirectional else 1 Equations (Default: f=Sigmoid, g=Tanh, h=Tanh): - it = f(Xt*(Wi^T) + Ht-1*(Ri^T) + Pi (.) Ct-1 + Wbi + Rbi) - ft = f(Xt*(Wf^T) + Ht-1*(Rf^T) + Pf (.) Ct-1 + Wbf + Rbf) - ct = g(Xt*(Wc^T) + Ht-1*(Rc^T) + Wbc + Rbc) - Ct = ft (.) Ct-1 + it (.) ct - ot = f(Xt*(Wo^T) + Ht-1*(Ro^T) + Po (.) Ct + Wbo + Rbo) - Ht = ot (.) h(Ct) AttentionWrapp Notations: `lstm()' - wrapped inner cell. Ht, Ct = lstm(concat(Xt, ATTNt-1), Ct-1) `am()` - attention mechanism the wrapper used. CONTEXTt, ALIGNt = am(Ht, ALIGNt-1) `AW` - attention layer weights, optional. `ATTN` - attention state, initial is zero. If `AW` provided, it is the output of the attention layer, ATTNt = concat(Ht, CONTEXTt) * AW otherwise, ATTNt = CONTEXTt RNN layer output: `Y` - if needed is the sequence of Ht from lstm cell. `Y_h` - is the last valid H from lstm cell. `Y_c` - is the last valid C from lstm cell. **Attributes** * **activation_alpha**: Optional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM. Default values are the same as of corresponding ONNX operators.For example with LeakyRelu, the default alpha is 0.01. Default value is ``?``. * **activation_beta**: Optional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM. Default values are the same as of corresponding ONNX operators. Default value is ``?``. * **activations**: A list of 3 (or 6 if bidirectional) activation functions for input, output, forget, cell, and hidden. The activation functions must be one of the activation functions specified above. Optional: See the equations for default if not specified. Default value is ``?``. * **clip**: Cell clip threshold. Clipping bounds the elements of a tensor in the range of [-threshold, +threshold] and is applied to the input of activations. No clip if not specified. Default value is ``?``. * **direction**: Specify if the RNN is forward, reverse, or bidirectional. Must be one of forward (default), reverse, or bidirectional. Default value is ``?``. * **hidden_size**: Number of neurons in the hidden layer. Default value is ``?``. * **input_forget**: Couple the input and forget gates if 1, default 0. Default value is ``?``. **Inputs** Between 3 and 14 inputs. * **X** (heterogeneous) - **T**: The input sequences packed (and potentially padded) into one 3-D tensor with the shape of `[seq_length, batch_size, input_size]` * **W** (heterogeneous) - **T**: The weight tensor for the gates. Concatenation of `W[iofc]` and `WB[iofc]` (if bidirectional) along dimension 0. The tensor has shape `[num_directions, 4*hidden_size, input_size]`. * **R** (heterogeneous) - **T**: The recurrence weight tensor. Concatenation of `R[iofc]` and `RB[iofc]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 4*hidden_size, hidden_size]`. * **B** (optional, heterogeneous) - **T**: The bias tensor for input gate. Concatenation of `[Wb[iofc], Rb[iofc]]`, and `[WBb[iofc], RBb[iofc]]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 8*hidden_size]`. Optional: If not specified - assumed to be 0. * **sequence_lens** (optional, heterogeneous) - **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** (optional, heterogeneous) - **T**: Optional initial value of the hidden. If not specified - assumed to be 0. It has shape `[num_directions, batch_size, hidden_size]`. * **initial_c** (optional, heterogeneous) - **T**: Optional initial value of the cell. If not specified - assumed to be 0. It has shape `[num_directions, batch_size, hidden_size]`. * **P** (optional, heterogeneous) - **T**: The weight tensor for peepholes. Concatenation of `P[iof]` and `PB[iof]` (if bidirectional) along dimension 0. It has shape `[num_directions, 3*hidde_size]`. Optional: If not specified - assumed to be 0. * **QW** (optional, heterogeneous) - **T**: The weight tensor of the query layer in the attention mechanism. Should be of shape `[num_directions, am_query_depth(hidden_size of lstm), am_attn_size]` * **MW** (optional, heterogeneous) - **T**: The weight tensor of the memory layer in the attention mechanism. Should be of shape `[num_directions, memory_depth, am_attn_size]` * **V** (optional, heterogeneous) - **T**: The attention_v tensor in the attention mechanism. Should be of shape `[num_directions, am_attn_size]` * **M** (optional, heterogeneous) - **T**: The sequence of the memory (input) for attention mechanism. Should be of `[batch_size, max_memory_step, memory_depth]` * **memory_seq_lens** (optional, heterogeneous) - **T1**: The sequence length of the input memory for the attention mechanism. Should be of `[batch_size]` * **AW** (optional, heterogeneous) - **T**: The weights of attention layer in the attention wrapper. If exists, should be of shape `[num_directions, memory_depth+hidden_size, aw_attn_size]. Please note that attention mechanism context depth is also memory_depth in the attention mechanism.` **Outputs** Between 0 and 3 outputs. * **Y** (optional, heterogeneous) - **T**: A tensor that concats all the intermediate output values of the hidden. It has shape `[seq_length, num_directions, batch_size, hidden_size]` * **Y_h** (optional, heterogeneous) - **T**: The last output value of the hidden. It has shape `[num_directions, batch_size, hidden_size]`. * **Y_c** (optional, heterogeneous) - **T**: The last output value of the cell. It has shape `[num_directions, batch_size, hidden_size]`. **Examples**