Ivan Tikhonov
5c0225d358
* Enable SequenceFusion, TensorIteratorToSequence, GRUCellFusion transformation in MOC, the transformations have been deleted from plugin pipelines * clean-up: delete augru transformation header * Temporary disabled TensorIterator IR reader tests * temporary disable tests, it's not TI issue, usqueeze doesn't have reference impl for fp64 and bf16 * Update ConvertTensorItearatorToSequence transformations: add Unsqueeze to pattern * Codestyle * Specify negative values for LSTM/GRU/RNN Sequence ops * fix conflicts with master branch * Insert ShapeOf subgraph in ConvertSequencesToTensorIterator transformation in case of dynamic input shape * codestyle * fix conflict with master branch * fix onednn version * Update low latency v2 to support GRU/RNN/LSTM Sequence ops, fix accuracy issue * fix tensor names * fix tensor name issue on gna * fix transformation tests, fix copying rt_info in the transformation * fix warning, enable bf16, f64 ti tests * codestyle * fix functional tests * EliminateDuplicateTIInputs transformation, fix copy_with_new_inputs method of TI op * move EliminateDuplicateTIInputs transformation * codestyle
135 lines
5.4 KiB
Markdown
135 lines
5.4 KiB
Markdown
# GRUSequence {#openvino_docs_ops_sequence_GRUSequence_5}
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**Versioned name**: *GRUSequence-5*
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**Category**: *Sequence processing*
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**Short description**: *GRUSequence* operation represents a series of GRU cells. Each cell is implemented as <a href="#GRUCell">GRUCell</a> operation.
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**Detailed description**
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A single cell in the sequence is implemented in the same way as in <a href="#GRUCell">GRUCell</a> operation. *GRUSequence* represents a sequence of GRU cells. The sequence can be connected differently depending on `direction` attribute that specifies the direction of traversing of input data along sequence dimension or specifies whether it should be a bidirectional sequence. The most of the attributes are in sync with the specification of ONNX GRU operator defined <a href="https://github.com/onnx/onnx/blob/master/docs/Operators.md#gru">GRUCell</a>.
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**Attributes**
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* *hidden_size*
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* **Description**: *hidden_size* specifies hidden state size.
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* **Range of values**: a positive integer
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* **Type**: `int`
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* **Required**: *yes*
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* *activations*
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* **Description**: *activations* specifies activation functions for gates, there are two gates, so two activation functions should be specified as a value for this attributes
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* **Range of values**: any combination of *relu*, *sigmoid*, *tanh*
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* **Type**: a list of strings
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* **Default value**: *sigmoid,tanh*
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* **Required**: *no*
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* *activations_alpha, activations_beta*
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* **Description**: *activations_alpha, activations_beta* attributes of functions; applicability and meaning of these attributes depends on chosen activation functions
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* **Range of values**: a list of floating-point numbers
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* **Type**: `float[]`
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* **Default value**: None
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* **Required**: *no*
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* *clip*
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* **Description**: *clip* specifies bound values *[-C, C]* for tensor clipping. Clipping is performed before activations.
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* **Range of values**: a positive floating-point number
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* **Type**: `float`
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* **Default value**: *infinity* that means that the clipping is not applied
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* **Required**: *no*
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* *direction*
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* **Description**: Specify if the RNN is forward, reverse, or bidirectional. If it is one of *forward* or *reverse* then `num_directions = 1`, if it is *bidirectional*, then `num_directions = 2`. This `num_directions` value specifies input/output shape requirements.
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* **Range of values**: *forward*, *reverse*, *bidirectional*
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* **Type**: `string`
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* **Required**: *yes*
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* *linear_before_reset*
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* **Description**: *linear_before_reset* flag denotes if the layer behaves according to the modification of *GRUCell* described in the formula in the [ONNX documentation](https://github.com/onnx/onnx/blob/master/docs/Operators.md#GRU).
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* **Range of values**: True or False
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* **Type**: `boolean`
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* **Default value**: False
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* **Required**: *no*
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**Inputs**
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* **1**: `X` - 3D tensor of type *T1* `[batch_size, seq_length, input_size]`, input data. It differs from GRUCell 1st input only by additional axis with size `seq_length`. **Required.**
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* **2**: `initial_hidden_state` - 3D tensor of type *T1* `[batch_size, num_directions, hidden_size]`, input hidden state data. **Required.**
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* **3**: `sequence_lengths` - 1D tensor of type *T2* `[batch_size]`, specifies real sequence lengths for each batch element. In case of negative values in this input, the operation behavior is undefined. **Required.**
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* **4**: `W` - 3D tensor of type *T1* `[num_directions, 3 * hidden_size, input_size]`, the weights for matrix multiplication, gate order: zrh. **Required.**
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* **5**: `R` - 3D tensor of type *T1* `[num_directions, 3 * hidden_size, hidden_size]`, the recurrence weights for matrix multiplication, gate order: zrh. **Required.**
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* **6**: `B` - 2D tensor of type *T*. If *linear_before_reset* is set to 1, then the shape is `[num_directions, 4 * hidden_size]` - the sum of biases for z and r gates (weights and recurrence weights), the biases for h gate are placed separately. Otherwise the shape is `[num_directions, 3 * hidden_size]`, the sum of biases (weights and recurrence weights). **Required.**
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**Outputs**
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* **1**: `Y` - 4D tensor of type *T1* `[batch_size, num_directions, seq_len, hidden_size]`, concatenation of all the intermediate output values of the hidden.
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* **2**: `Ho` - 3D tensor of type *T1* `[batch_size, num_directions, hidden_size]`, the last output value of hidden state.
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**Types**
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* *T1*: any supported floating-point type.
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* *T2*: any supported integer type.
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**Example**
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```xml
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<layer ... type="GRUSequence" ...>
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<data hidden_size="128"/>
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<input>
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<port id="0">
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<dim>1</dim>
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<dim>4</dim>
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<dim>16</dim>
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</port>
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<port id="1">
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<dim>1</dim>
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<dim>1</dim>
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<dim>128</dim>
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</port>
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<port id="2">
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<dim>1</dim>
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</port>
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<port id="3">
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<dim>1</dim>
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<dim>384</dim>
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<dim>16</dim>
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</port>
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<port id="4">
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<dim>1</dim>
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<dim>384</dim>
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<dim>128</dim>
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</port>
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<port id="5">
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<dim>1</dim>
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<dim>384</dim>
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</port>
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</input>
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<output>
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<port id="6">
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<dim>1</dim>
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<dim>1</dim>
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<dim>4</dim>
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<dim>128</dim>
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</port>
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<port id="7">
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<dim>1</dim>
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<dim>1</dim>
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<dim>128</dim>
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</port>
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</output>
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</layer>
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```
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