openvino/model-optimizer/extensions/ops/lstm_cell.py

"""
 Copyright (C) 2017-2020 Intel Corporation

 Licensed under the Apache License, Version 2.0 (the "License");
 you may not use this file except in compliance with the License.
 You may obtain a copy of the License at

      http://www.apache.org/licenses/LICENSE-2.0

 Unless required by applicable law or agreed to in writing, software
 distributed under the License is distributed on an "AS IS" BASIS,
 WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 See the License for the specific language governing permissions and
 limitations under the License.
"""

from mo.front.common.partial_infer.utils import mark_input_bins
from mo.graph.graph import Node, Graph
from mo.ops.op import Op
from mo.utils.error import Error


class LSTMCell(Op):
    ''' A single LSTM cell (without a loop).

        3 inputs:
            - [0, required] input data (2D),
            - [1, required] initial hidden state (2D),
            - [2, required] initial cell state (2D),
        
        2 blobs:
            - [3, required] LSTM FC weights
            - [4, required] LSTM FC biases
        
        2 outputs:
            - [required] output data / resulting hidden state (2D)
            - [required] resulting cell state (2D)
    '''
    op = 'LSTMCell'

    def __init__(self, graph: Graph, attrs: dict):
        mandatory_props = {
            'type': __class__.op,
            'op': __class__.op,
            'version': 'opset1',
            'infer': __class__.infer,
            'in_ports_count': 5,
            'out_ports_count': 2,
            'wr_input_id': 3,
            'gates_count': 4
        }
        super().__init__(graph, mandatory_props, attrs)

    def supported_attrs(self):
        return [
            'hidden_size',  # number of the elements in hidden cell size
            'activations',
            'activation_alpha',
            'activation_beta',
            'clip',
        ]

    def backend_attrs(self):
        return [
            'hidden_size',  # number of the elements in hidden cell size
            ('activations', lambda node: ','.join(node.activations) if node.activations is not None else None),
            'activation_alpha',
            'activation_beta',
            'clip',
        ]

    @staticmethod
    def infer(node: Node):
        if node.has_and_set('extra_inputs'):
            assert len(node.in_nodes()) == 8
        else:
            assert len(node.in_nodes()) == 5
        assert len(node.out_nodes()) in [1, 2]

        hidden_shape = node.in_node(1).shape.copy()
        cell_shape = node.in_node(2).shape.copy()

        mark_input_bins(node, start_port=3)
        node.out_node(0).shape = hidden_shape
        if len(node.out_nodes()) == 2:
            node.out_node(1).shape = cell_shape

        hidden_size = hidden_shape[1]

        if node.has_valid('hidden_size'):
            if node.hidden_size != hidden_size:
                raise Error("Input shape {} for hidden size doesn't match pre-defined hidden_size in node {}".format(
                    node.in_node(1).shape, node.soft_get('name')))
        else:
            node['hidden_size'] = hidden_size

        assert cell_shape[1] == hidden_size

        input_shape = node.in_node(0).shape
        assert input_shape is not None
        assert hidden_shape[0] == cell_shape[0] == input_shape[0], 'States are not broadcastable by batch'