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npu_dynamic_rnn

Description

Creates a high-performance neural network specified by RNNCell.

Prototype

def npu_dynamic_rnn(cell,
inputs,
initial_state=None,
dtype=None,
sequence_length=None,
scope=None)

Options

Option

Input/Output

Description

cell

Input

RNNCell instance, which is the memory unit of long short-term memory (LSTM) and gated recurrent unit (GRU).

inputs

Input

An input list whose length is T. Each input is a tuple whose shape is [max_time, batch_size, input_size], or a nested tuple of this shape.

initial_state

Input

(Optional) Initial state of the recurrent neural network (RNN). If cell.state_size is an integer, it must be a tensor whose shape is [batch_size, cell.state_size]. If cell.state_size is a tuple, it must be a tuple of the [batch_size, s] tensor in cell.state_size.

dtype

Input

(Optional) Data type of the initial state and expected output. This option is required if initial_state is empty, or there is a heterogeneous data type in the RNN state.

sequence_length

Input

Length of each sequence for an input. It is an int32 or int64 vector (tensor) whose size is [batch_size]. The value range is [0, T).

scope

Input

VariableScope of the subgraph. Defaults to rnn.

Returns

Result tensor of the RNN.

Final state.

Restrictions

This API applies to the neural machine translation (NMT) network training script in the while_loop expansion scenario.

Example

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from npu_bridge.npu_init import *
Original code:
inputs = npu_unstack(self.encoder_emb_inp, axis=0)
encoder_outputs , encoder_state = static_rnn(
    cell,
    inputs,
    dtype= dtype,
    sequence_length = sequence_length
     )
encoder_outputs = npu_stack( encoder_outputs, axis=0 )
Modified code:
encoder_outputs , encoder_state = npu_rnn.npu_dynamic_rnn(
    cell,
    inputs=self.encoder_emb_inp,
    dtype= dtype,
    sequence_length= sequence_length)