71 lines
2.1 KiB
Python
71 lines
2.1 KiB
Python
# Copyright (C) 2018-2021 Intel Corporation
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# SPDX-License-Identifier: Apache-2.0
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import numpy as np
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from extensions.ops.RNN import rnn_infer
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from mo.graph.graph import Node, Graph
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from mo.ops.op import Op
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class GRU(Op):
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op = 'GRU'
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def __init__(self, graph: Graph, attrs: dict):
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mandatory_props = {
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'type': 'RNNSequence', # should be never emitted to IR; for debugging purposes
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'op': __class__.op,
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'blobs_wrb': False,
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'has_num_directions': False,
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'direction': 'forward',
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'infer': __class__.infer,
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'multiplier': 3,
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'multilayers': False,
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'gate_order': np.array([0, 1, 2]), # TODO: change it later
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'normalized': False,
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'activation_alpha': None,
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'activation_beta': None,
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'activations': None,
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'clip': None,
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'linear_before_reset': None,
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'in_ports_count': 6,
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'out_ports_count': 2,
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}
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super().__init__(graph, mandatory_props, attrs)
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@staticmethod
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def supported_attrs():
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return [
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'hidden_size', # number of the elements in hidden cell size
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'direction', # one of 'forward', 'reverse', or 'bidirectional'
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'axis',
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'activation_alpha',
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'activation_beta',
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'activations',
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'clip',
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'linear_before_reset',
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]
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def backend_attrs(self):
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return [
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'hidden_size', # number of the elements in hidden cell size
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'direction', # one of 'forward', 'reverse', or 'bidirectional'
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'axis',
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'activation_alpha',
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'activation_beta',
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('activations', lambda node: ','.join(node.activations) if node.activations is not None else None),
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'clip',
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'linear_before_reset',
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]
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@staticmethod
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def infer(node: Node):
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assert len(node.in_nodes()) >= 3 # X, W and R
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assert len(node.in_nodes()) <= 5
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assert len(node.out_nodes()) <= 2
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rnn_infer(node, [1])
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