// Copyright (c) ONNX Project Contributors // // SPDX-License-Identifier: Apache-2.0 #include #include #include "onnx/defs/doc_strings.h" #include "onnx/defs/schema.h" namespace ONNX_NAMESPACE { ONNX_API void RNNShapeInference(InferenceContext& ctx) { TensorShapeProto::Dimension num_directions, seq_length, batch_size, hidden_size; auto direction = getAttribute(ctx, "direction", "forward"); if ((direction == "forward") || (direction == "reverse")) num_directions.set_dim_value(1); else if (direction == "bidirectional") num_directions.set_dim_value(2); // else leave num_directions unknown in case of incorrect attribute value auto hidden_size_value = getAttribute(ctx, "hidden_size", -1); if (hidden_size_value > 0) hidden_size.set_dim_value(hidden_size_value); auto layout_value = getAttribute(ctx, "layout", 0); if (hasInputShape(ctx, 0)) { const auto& first_input_shape = getInputShape(ctx, 0); if (first_input_shape.dim_size() != 3) { fail_shape_inference("First input tensor must have rank 3"); } seq_length = first_input_shape.dim((layout_value == 0) ? 0 : 1); batch_size = first_input_shape.dim((layout_value == 0) ? 1 : 0); } auto num_outputs = ctx.getNumOutputs(); if (num_outputs > 0) { // Y propagateElemTypeFromInputToOutput(ctx, 0, 0); if (layout_value == 0) { auto dims = {seq_length, num_directions, batch_size, hidden_size}; updateOutputShape(ctx, 0, dims); } else { auto dims = {batch_size, seq_length, num_directions, hidden_size}; updateOutputShape(ctx, 0, dims); } } if (num_outputs > 1) { // Y_h propagateElemTypeFromInputToOutput(ctx, 0, 1); if (layout_value == 0) { auto dims = {num_directions, batch_size, hidden_size}; updateOutputShape(ctx, 1, dims); } else { auto dims = {batch_size, num_directions, hidden_size}; updateOutputShape(ctx, 1, dims); } } if (num_outputs > 2) { // Y_c : only in the case of LSTM propagateElemTypeFromInputToOutput(ctx, 0, 2); if (layout_value == 0) { auto dims = {num_directions, batch_size, hidden_size}; updateOutputShape(ctx, 2, dims); } else { auto dims = {batch_size, num_directions, hidden_size}; updateOutputShape(ctx, 2, dims); } } } static std::function RNNDocGenerator(const char* /*name*/) { return [=](OpSchema& schema) { schema.Attr( "direction", "Specify if the RNN is forward, reverse, or bidirectional. " "Must be one of forward (default), reverse, or bidirectional.", AttributeProto::STRING, std::string("forward")); schema.Attr( "layout", "The shape format of inputs X, initial_h and outputs Y, Y_h. " "If 0, the following shapes are expected: " "X.shape = [seq_length, batch_size, input_size], " "Y.shape = [seq_length, num_directions, batch_size, hidden_size], " "initial_h.shape = Y_h.shape = [num_directions, batch_size, hidden_size]. " "If 1, the following shapes are expected: " "X.shape = [batch_size, seq_length, input_size], " "Y.shape = [batch_size, seq_length, num_directions, hidden_size], " "initial_h.shape = Y_h.shape = [batch_size, num_directions, hidden_size].", AttributeProto::INT, static_cast(0)); schema.Attr("hidden_size", "Number of neurons in the hidden layer", AttributeProto::INT, OPTIONAL_VALUE); schema.Attr( "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.", AttributeProto::FLOATS, OPTIONAL_VALUE); schema.Attr( "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.", AttributeProto::FLOATS, OPTIONAL_VALUE); schema.Attr( "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.", AttributeProto::FLOAT, OPTIONAL_VALUE); schema.Input( 0, "X", "The input sequences packed (and potentially padded) into one 3-D " "tensor with the shape of `[seq_length, batch_size, input_size]`.", "T", OpSchema::Single, true, 1, OpSchema::Differentiable); schema.Input( 4, "sequence_lens", "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]`.", "T1", OpSchema::Optional, true, 1, OpSchema::NonDifferentiable); schema.Input( 5, "initial_h", "Optional initial value of the hidden. If not specified - assumed " "to be 0. It has shape `[num_directions, batch_size, hidden_size]`.", "T", OpSchema::Optional, true, 1, OpSchema::NonDifferentiable); schema.Output( 0, "Y", "A tensor that concats all the intermediate output values of the hidden. " "It has shape `[seq_length, num_directions, batch_size, hidden_size]`. ", "T", OpSchema::Optional, true, 1, OpSchema::Differentiable); schema.Output( 1, "Y_h", "The last output value of the hidden. It has shape " "`[num_directions, batch_size, hidden_size]`.", "T", OpSchema::Optional, true, 1, OpSchema::Differentiable); schema.TypeConstraint("T", OpSchema::all_float_types_ir4(), "Constrain input and output types to float tensors."); schema.TypeConstraint("T1", {"tensor(int32)"}, "Constrain seq_lens to integer tensor."); schema.TypeAndShapeInferenceFunction(RNNShapeInference); }; } static const char* const RNN_ver22_doc = kDoc_RNN_ver14; ONNX_OPERATOR_SET_SCHEMA( RNN, 22, OpSchema() .SetDoc(GET_OP_DOC_STR(std::string(RNN_ver22_doc) + GenerateOptionalArgumentsDoc())) .Attr( "activations", "One (or two if bidirectional) activation function for " "input gate. The activation function must be one of the activation " "functions specified above. Optional: Default `Tanh` if not specified.", AttributeProto::STRINGS, std::vector{"Tanh", "Tanh"}) .Input( 1, "W", "The weight tensor for input gate. Concatenation of `Wi` and `WBi` " "(if bidirectional). The tensor has shape " "`[num_directions, hidden_size, input_size]`.", "T", OpSchema::Single, true, 1, OpSchema::Differentiable) .Input( 2, "R", "The recurrence weight tensor. Concatenation of `Ri` and `RBi` " "(if bidirectional). The tensor has shape " "`[num_directions, hidden_size, hidden_size]`.", "T", OpSchema::Single, true, 1, OpSchema::Differentiable) .Input( 3, "B", "The bias tensor for input gate. Concatenation of `[Wbi, Rbi]` " "and `[WBbi, RBbi]` (if bidirectional). The tensor has shape " "`[num_directions, 2*hidden_size]`. Optional: If not specified - assumed " "to be 0.", "T", OpSchema::Optional, true, 1, OpSchema::Differentiable) .FillUsing(RNNDocGenerator("RNN"))); static const char* const GRU_ver22_doc = kDoc_GRU_ver14; ONNX_OPERATOR_SET_SCHEMA( GRU, 22, OpSchema() .SetDoc(GET_OP_DOC_STR(std::string(GRU_ver22_doc) + GenerateOptionalArgumentsDoc())) .Attr( "activations", "A list of 2 (or 4 if bidirectional) activation functions " "for update, reset, and hidden gates. The activation functions must be one " "of the activation functions specified above. Optional: See the equations " "for default if not specified.", AttributeProto::STRINGS, OPTIONAL_VALUE) .Attr( "linear_before_reset", "When computing the output of the hidden gate, " "apply the linear transformation before multiplying by the output of the " "reset gate.", AttributeProto::INT, static_cast(0)) .Input( 1, "W", "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]`.", "T", OpSchema::Single, true, 1, OpSchema::Differentiable) .Input( 2, "R", "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]`.", "T", OpSchema::Single, true, 1, OpSchema::Differentiable) .Input( 3, "B", "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", "T", OpSchema::Optional, true, 1, OpSchema::Differentiable) .FillUsing(RNNDocGenerator("GRU"))); static const char* const LSTM_ver22_doc = kDoc_LSTM_ver14; ONNX_OPERATOR_SET_SCHEMA( LSTM, 22, OpSchema() .SetDoc(GET_OP_DOC_STR(std::string(LSTM_ver22_doc) + GenerateOptionalArgumentsDoc())) .Attr( "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.", AttributeProto::STRINGS, OPTIONAL_VALUE) .Attr( "layout", "The shape format of inputs X, initial_h, initial_c and outputs Y, Y_h, Y_c. " "If 0, the following shapes are expected: " "X.shape = [seq_length, batch_size, input_size], " "Y.shape = [seq_length, num_directions, batch_size, hidden_size], " "initial_h.shape = Y_h.shape = initial_c.shape = Y_c.shape = " "[num_directions, batch_size, hidden_size]. " "If 1, the following shapes are expected: " "X.shape = [batch_size, seq_length, input_size], " "Y.shape = [batch_size, seq_length, num_directions, hidden_size], " "initial_h.shape = Y_h.shape = initial_c.shape = Y_c.shape = " "[batch_size, num_directions, hidden_size].", AttributeProto::INT, static_cast(0)) .Attr("input_forget", "Couple the input and forget gates if 1.", AttributeProto::INT, static_cast(0)) .Input( 1, "W", "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]`.", "T", OpSchema::Single, true, 1, OpSchema::Differentiable) .Input( 2, "R", "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]`.", "T", OpSchema::Single, true, 1, OpSchema::Differentiable) .Input( 3, "B", "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.", "T", OpSchema::Optional, true, 1, OpSchema::Differentiable) .Input( 6, "initial_c", "Optional initial value of the cell. If not specified - assumed " "to be 0. It has shape `[num_directions, batch_size, hidden_size]`.", "T", OpSchema::Optional, true, 1, OpSchema::NonDifferentiable) .Input( 7, "P", "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.", "T", OpSchema::Optional, true, 1, OpSchema::Differentiable) .FillUsing(RNNDocGenerator("LSTM")) .Output( 2, "Y_c", "The last output value of the cell. It has shape " "`[num_directions, batch_size, hidden_size]`.", "T", OpSchema::Optional, true, 1, OpSchema::Differentiable)); } // namespace ONNX_NAMESPACE