543 lines
24 KiB
Python
543 lines
24 KiB
Python
# Copyright (C) 2018-2021 Intel Corporation
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# SPDX-License-Identifier: Apache-2.0
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import logging as log
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from io import IOBase
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import networkx as nx
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import numpy as np
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from extensions.ops.elementwise import Mul
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from extensions.ops.split import AttributedVariadicSplit
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from mo.front.common.partial_infer.utils import float_array, int64_array
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from mo.front.extractor import add_outputs_identity
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from mo.front.kaldi.loader.utils import find_next_tag, read_placeholder, find_next_component, get_name_from_path, \
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find_end_of_component, end_of_nnet_tag, read_binary_integer32_token, get_parameters, read_token_value, \
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collect_until_token, collect_until_token_and_read, create_edge_attrs, get_args_for_specifier
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from mo.graph.graph import Node, Graph
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from mo.ops.const import Const
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from mo.utils.error import Error
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from mo.utils.utils import refer_to_faq_msg
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def load_parallel_component(file_descr, graph: Graph, prev_layer_id):
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"""
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Load ParallelComponent of the Kaldi model.
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ParallelComponent contains parallel nested networks.
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VariadicSplit is inserted before nested networks.
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Outputs of nested networks concatenate with layer Concat.
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:param file_descr: descriptor of the model file
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:param graph: graph with the topology.
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:param prev_layer_id: id of the input layers for parallel component layer
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:return: id of the concat layer - last layer of the parallel component layers
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"""
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nnet_count = read_token_value(file_descr, b'<NestedNnetCount>')
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log.debug('Model contains parallel component with {} nested networks'.format(nnet_count))
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split_points = []
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outputs = []
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inputs = []
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for i in range(nnet_count):
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read_token_value(file_descr, b'<NestedNnet>')
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collect_until_token(file_descr, b'<Nnet>')
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g = Graph()
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load_kalid_nnet1_model(g, file_descr, 'Nested_net_{}'.format(i))
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# input to nnet1 models is of a rank 1 but we also insert batch_size to 0th axis
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# 1st axis contains input_size of the nested subnetwork
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# we split input from the main network to subnetworks
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input_node = Node(g, 'Parameter')
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split_points.append(input_node['shape'][1])
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g.remove_node(input_node.id)
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mapping = {node: graph.unique_id(node) for node in g.nodes(data=False) if node in graph}
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g = nx.relabel_nodes(g, mapping)
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for val in mapping.values():
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g.node[val]['name'] = val
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graph.add_nodes_from(g.nodes(data=True))
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graph.add_edges_from(g.edges(data=True))
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sorted_nodes = tuple(nx.topological_sort(g))
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outputs.append(Node(graph, sorted_nodes[-1]))
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inputs.append(Node(graph, sorted_nodes[0]))
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split_id = graph.unique_id(prefix='NestedNets/VariadicSplit')
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attrs = {'out_ports_count': nnet_count, 'size_splits': split_points, 'axis': 1, 'name': split_id}
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variadic_split_node = AttributedVariadicSplit(graph, attrs).create_node()
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prev_layer_node = Node(graph, prev_layer_id)
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prev_layer_node.add_output_port(0)
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graph.create_edge(prev_layer_node, variadic_split_node, 0, 0, create_edge_attrs(prev_layer_id, variadic_split_node.id, prev_layer_id))
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concat_id = graph.unique_id(prefix='Concat')
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graph.add_node(concat_id, parameters=None, op='concat', kind='op')
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concat_node = Node(graph, concat_id)
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# Connect each output of variadic_split_node to each subnetwork's inputs in ParallelComponent
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# and each subnetwork's output to concat_node
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for i, (input_node, output_node) in enumerate(zip(inputs, outputs)):
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output_node.add_output_port(0)
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concat_node.add_input_port(i)
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graph.create_edge(output_node, concat_node, 0, i, create_edge_attrs(output_node.id, concat_id, output_node.id, i, 0))
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graph.create_edge(variadic_split_node, input_node, i, 0, create_edge_attrs(variadic_split_node.id, input_node.id, variadic_split_node.id, 0, i))
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return concat_id
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def load_kaldi_model(graph, nnet_path):
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"""
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Structure of the file is the following:
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magic-number(16896)<Nnet> <Next Layer Name> weights etc.
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:param nnet_path:
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:return:
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"""
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nnet_name = None
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if isinstance(nnet_path, str):
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file_desc = open(nnet_path, "rb")
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nnet_name = get_name_from_path(nnet_path)
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elif isinstance(nnet_path, IOBase):
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file_desc = nnet_path
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else:
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raise Error('Unsupported type of Kaldi model')
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tag = find_next_tag(file_desc)
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# start new model / submodel
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if tag == '<Nnet>':
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load_function = load_kalid_nnet1_model
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elif tag == '<TransitionModel>':
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while tag != '<Nnet>' and tag != '<Nnet3>':
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tag = find_next_tag(file_desc)
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if tag == '<Nnet3>':
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load_function = load_kaldi_nnet3_model
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else:
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load_function = load_kalid_nnet2_model
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elif tag == '<Nnet3>':
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load_function = load_kaldi_nnet3_model
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else:
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raise Error('Kaldi model should start with <Nnet> or <TransitionModel> tag. ',
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refer_to_faq_msg(89))
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read_placeholder(file_desc, 1)
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return load_function(graph, file_desc, nnet_name)
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def load_kalid_nnet1_model(graph, file_descr, name):
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prev_layer_id = 'Parameter'
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graph.add_node(prev_layer_id, name=prev_layer_id, kind='op', op='Parameter', parameters=None)
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# find out output layer, it can be only one due to chain structure of nnet1 model
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output_layer = None
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while True:
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component_type = find_next_component(file_descr)
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if component_type == end_of_nnet_tag.lower()[1:-1]:
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break
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layer_o = read_binary_integer32_token(file_descr)
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layer_i = read_binary_integer32_token(file_descr)
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if component_type == 'parallelcomponent':
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prev_layer_id = load_parallel_component(file_descr, graph, prev_layer_id)
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find_end_of_component(file_descr, component_type)
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continue
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start_index = file_descr.tell()
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end_tag, end_index = find_end_of_component(file_descr, component_type)
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end_index -= len(end_tag)
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layer_id = graph.unique_id(prefix=component_type)
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graph.add_node(layer_id,
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parameters=get_parameters(file_descr, start_index, end_index),
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op=component_type,
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kind='op',
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layer_i=layer_i,
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layer_o=layer_o)
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if hasattr(graph, 'op_names_statistic'):
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graph.op_names_statistic[component_type] += 1
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prev_node = Node(graph, prev_layer_id)
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if prev_node.op == 'Parameter':
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prev_node['shape'] = np.array([1, layer_i], dtype=np.int64)
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prev_node.add_output_port(0)
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Node(graph, layer_id).add_input_port(0)
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graph.create_edge(prev_node, Node(graph, layer_id), 0, 0, create_edge_attrs(prev_layer_id, layer_id, prev_layer_id))
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prev_layer_id = layer_id
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output_layer = layer_id
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log.debug('{} (type is {}) was loaded'.format(prev_layer_id, component_type))
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# Tensor names information corresponding to a node is stored on outgoing edges.
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# As output nodes do not have outgoing edges, fake outputs are required. In the following code
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# for each output Identity node is added, and tensor name for the output is kept
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# on (output, fake output) edge. After Result nodes adding transformation fake outputs
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# are deleted from graph.
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assert output_layer is not None, "Output layer is not found in graph"
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add_outputs_identity(graph, [output_layer], lambda g, output, fake_output: g.create_edge(
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Node(g, output), Node(g, fake_output), 0, 0, create_edge_attrs(output, fake_output, output)))
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def load_kalid_nnet2_model(graph, file_descr, nnet_name):
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input_name = 'Input'
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graph.add_node(input_name, name=input_name, kind='op', op='Parameter', parameters=None, shape=None)
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prev_layer_id = input_name
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all_components = load_components(file_descr, graph)
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used_layers = set()
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for layer_id in all_components:
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prev_node = Node(graph, prev_layer_id)
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if prev_node.op == 'Parameter':
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parameters = Node(graph, layer_id).parameters
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input_dim = read_token_value(parameters, b'<InputDim>')
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prev_node['shape'] = np.array([1, input_dim], dtype=np.int64)
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prev_node.add_output_port(0)
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Node(graph, layer_id).add_input_port(0)
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graph.create_edge(prev_node, Node(graph, layer_id), 0, 0, create_edge_attrs(prev_layer_id, layer_id, prev_layer_id))
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used_layers.add(prev_layer_id)
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prev_layer_id = layer_id
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log.debug('{} and {} were connected'.format(prev_layer_id, layer_id))
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# Tensor names information corresponding to a node is stored on outgoing edges.
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# As output nodes do not have outgoing edges, fake outputs are required. In the following code
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# for each output Identity node is added, and tensor name for the output is kept
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# on (output, fake output) edge. After Result nodes adding transformation fake outputs
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# are deleted from graph.
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output_layers = graph.nodes - used_layers
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add_outputs_identity(graph, output_layers, lambda g, output, fake_output: g.create_edge(
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Node(g, output), Node(g, fake_output), 0, 0, create_edge_attrs(output, fake_output, output)))
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def load_kaldi_nnet3_model(graph, file_descr, nnet_name):
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file_descr.read(1)
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component_layer_map = load_topology_map(file_descr, graph)
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# add information for shape calculation for MemoryOffset
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# shape calculation for MemoryOffset can't be done through shape of previous layer because
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# it is separated in 2 parts to remove cycle from graph
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for node in graph.get_op_nodes(**{'op': 'Parameter'}):
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for o_n_name, params in node.get_outputs():
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o_n = Node(graph, o_n_name)
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if o_n['op'] == 'MemoryOffset':
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# don't take batch from Parameter, it will be overwritten
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# take only second dimension because we have only 2 dimensions
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o_n['parameters']['element_size'] = int64_array([1, node.shape[1]])
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load_components(file_descr, graph, component_layer_map)
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def load_components(file_descr, graph, component_layer_map=None):
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num_components = collect_until_token_and_read(file_descr, b'<NumComponents>')
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log.debug('Network contains {} components'.format(num_components))
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is_nnet3 = False if component_layer_map is None else True
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if not is_nnet3:
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collect_until_token(file_descr, b'<Components>')
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all_components = list()
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name = ""
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for _ in range(num_components):
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if is_nnet3:
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name = collect_until_token_and_read(file_descr, b'<ComponentName>', np.string_)
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component_type = find_next_component(file_descr)
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if component_type == end_of_nnet_tag.lower()[1:-1]:
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break
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start_index = file_descr.tell()
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end_tag, end_index = find_end_of_component(file_descr, component_type)
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# read dim info where possible to simplify shape calculation for MemoryOffset
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# shape calculation for MemoryOffset can't be done through shape of previous layer because
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# it is separated in 2 parts to remove cycle from graph
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file_descr.seek(start_index)
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dim = 0
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dim_words = {b'<Dim>', b'<InputDim>'}
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for dim_word in dim_words:
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try:
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collect_until_token(file_descr, dim_word, size_search_zone=end_index - start_index)
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cur_index = file_descr.tell()
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if start_index < cur_index < end_index:
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dim = read_binary_integer32_token(file_descr)
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break
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else:
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file_descr.seek(start_index)
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except Error:
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file_descr.seek(start_index)
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if is_nnet3:
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if name in component_layer_map:
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layer_id = component_layer_map[name][0]
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for layer in component_layer_map[name]:
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node = Node(graph, layer)
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node['parameters'] = get_parameters(file_descr, start_index, end_index)
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node['op'] = component_type
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# Read dim info where possible to simplify shape calculation for MemoryOffset
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for o_n_name, params in node.get_outputs():
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o_n = Node(graph, o_n_name)
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if o_n['op'] == 'MemoryOffset' and dim != 0:
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o_n['parameters']['element_size'] = int64_array([1, dim])
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else:
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raise Error("Something wrong with layer {}".format(name))
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else:
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layer_id = graph.unique_id(prefix=component_type)
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graph.add_node(layer_id,
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parameters=get_parameters(file_descr, start_index, end_index),
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op=component_type,
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kind='op')
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if hasattr(graph, 'op_names_statistic'):
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graph.op_names_statistic[component_type] += 1
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all_components.append(layer_id)
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log.debug('{} (type is {}) was loaded'.format(layer_id, component_type))
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return all_components
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def load_topology_map(file_descr, graph):
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not_finished = True
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component_layer_map = {}
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layer_node_map = {}
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while not_finished:
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not_finished = read_node(file_descr, graph, component_layer_map, layer_node_map)
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return component_layer_map
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def read_node(file_descr, graph, component_layer_map, layer_node_map):
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s = file_descr.readline()
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if s == b'\n':
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return False
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tokens = s.split(b' ')
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if tokens[0] == b'input-node':
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in_name = s[s.find(b'name=') + len(b'name='):].split(b' ')[0]
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in_name = str(in_name).strip('b').replace('\'', "")
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in_shape = np.array([1, s[s.find(b'dim=') + len(b'dim='):].split(b' ')[0]], dtype=np.int)
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if in_name not in layer_node_map:
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graph.add_node(in_name, name=in_name, kind='op', op='Parameter', parameters=None, shape=in_shape)
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layer_node_map[in_name] = in_name
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else:
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Node(graph, in_name)['op'] = 'Parameter'
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Node(graph, in_name)['shape'] = in_shape
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elif tokens[0] == b'component-node':
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layer_name = s[s.find(b'name=') + len(b'name='):].split(b' ')[0]
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layer_name = str(layer_name).strip('b').replace('\'', "")
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component_name = s[s.find(b'component=') + len(b'component='):].split(b' ')[0]
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if layer_name not in layer_node_map:
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node_name = graph.unique_id(prefix=layer_name)
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graph.add_node(node_name,
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parameters=None,
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op=None,
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kind='op')
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layer_node_map[layer_name] = node_name
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else:
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node_name = layer_node_map[layer_name]
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if component_name in component_layer_map:
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component_layer_map[component_name].append(node_name)
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else:
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component_layer_map[component_name] = [node_name]
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# parse input
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in_node_id = parse_input_for_node(s[s.find(b'input=') + 6:], graph, layer_node_map)
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out_port = len(Node(graph, in_node_id).out_nodes())
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in_port = len(Node(graph, node_name).in_nodes())
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Node(graph, node_name).add_input_port(in_port)
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Node(graph, in_node_id).add_output_port(out_port, skip_if_exist=True)
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graph.add_edge(in_node_id, node_name, **create_edge_attrs(in_node_id, node_name, in_node_id, in_port, out_port))
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elif tokens[0] == b'output-node':
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layer_name = s[s.find(b'name=') + len(b'name='):].split(b' ')[0]
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layer_name = str(layer_name).strip('b').replace('\'', "")
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node_name = graph.unique_id(prefix=layer_name)
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graph.add_node(node_name,
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parameters=None,
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op='Identity',
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kind='op')
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out_name = graph.unique_id(prefix=node_name + "_out")
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graph.add_node(out_name,
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parameters=None,
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op='Result',
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kind='op')
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Node(graph, node_name).add_input_port(0)
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Node(graph, node_name).add_output_port(0)
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Node(graph, out_name).add_input_port(0)
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graph.add_edge(node_name, out_name, **create_edge_attrs(node_name, out_name, node_name))
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# parse input
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in_node_id = parse_input_for_node(s[s.find(b'input=') + len(b'input='):], graph, layer_node_map)
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out_port = len(Node(graph, in_node_id).out_nodes())
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Node(graph, in_node_id).add_output_port(out_port)
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graph.create_edge(Node(graph, in_node_id), Node(graph, node_name), out_port, 0, create_edge_attrs(in_node_id, node_name, in_node_id, 0, out_port))
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objective_type = s[s.find(b'objective=') + 10:].split(b' ')[0].split(b'\n')[0]
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if objective_type != b'linear':
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raise Error("Unsupported objective-type for output {}".format(node_name))
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elif tokens[0] == b'dim-range-node':
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layer_name = s[s.find(b'name=') + len(b'name='):].split(b' ')[0]
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layer_name = str(layer_name).strip('b').replace('\'', "")
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offset = int(s[s.find(b'dim-offset=') + len(b'dim-offset='):].split(b' ')[0])
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dim = int(s[s.find(b'dim=') + len(b'dim='):].split(b' ')[0])
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if layer_name in layer_node_map:
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node_name = layer_node_map[layer_name]
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node = Node(graph, node_name)
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node['parameters'] = {'offset': np.array([offset]), 'dim': np.array([dim]), 'axis': np.array([1])}
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node['op'] = 'Crop'
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else:
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node_name = graph.unique_id(prefix=layer_name)
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graph.add_node(node_name,
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parameters={'offset': np.array([offset]), 'dim': np.array([dim]), 'axis': np.array([1])},
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op='Crop',
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kind='op')
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layer_node_map[layer_name] = node_name
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node = Node(graph, node_name)
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in_node_id = parse_input_for_node(s[s.find(b'input-node=') + len(b'input-node='):], graph, layer_node_map)
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out_port = len(Node(graph, in_node_id).out_nodes())
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in_port = len(Node(graph, node_name).in_nodes())
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node.add_input_port(in_port)
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Node(graph, in_node_id).add_output_port(out_port)
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graph.create_edge(Node(graph, in_node_id), node, out_port, in_port, create_edge_attrs(in_node_id, node_name, in_node_id, in_port, out_port))
|
|
|
|
# read dim info where possible to simplify shape calculation for MemoryOffset
|
|
# shape calculation for MemoryOffset can't be done through shape of previous layer because
|
|
# it is separated in 2 parts to remove cycle from graph
|
|
for o_n_name, params in node.get_outputs():
|
|
o_n = Node(graph, o_n_name)
|
|
if o_n['op'] == 'MemoryOffset':
|
|
o_n['parameters']['element_size'] = int64_array([1, dim])
|
|
else:
|
|
raise Error("Unsupported node specifier {}".format(tokens[0]))
|
|
return True
|
|
|
|
|
|
def parse_input_for_node(string, graph, component_layer_map):
|
|
return parse_specifier(string, graph, component_layer_map)
|
|
|
|
|
|
def parse_specifier(string, graph, layer_node_map):
|
|
pos = string.find(b'(')
|
|
if pos == -1:
|
|
# node name
|
|
input_name = str(string.split(b' ')[0]).strip('b').replace("\'", '').replace('\\n', '')
|
|
|
|
if input_name not in layer_node_map:
|
|
node_name = graph.unique_id(prefix=input_name)
|
|
graph.add_node(node_name, parameters=[], op="", kind='op')
|
|
layer_node_map[input_name] = node_name
|
|
else:
|
|
node_name = layer_node_map[input_name]
|
|
return node_name
|
|
|
|
spec = string[:pos]
|
|
args = get_args_for_specifier(string[pos:])
|
|
if spec == b'Append':
|
|
nodes = []
|
|
for i in range(len(args)):
|
|
nodes.append(parse_specifier(args[i], graph, layer_node_map))
|
|
layer_name = 'Append_'
|
|
for node in nodes:
|
|
layer_name = layer_name + node + "_"
|
|
|
|
if layer_name not in layer_node_map:
|
|
concat_name = graph.unique_id(prefix=layer_name)
|
|
graph.add_node(concat_name,
|
|
parameters=None,
|
|
op='concat',
|
|
kind='op')
|
|
layer_node_map[layer_name] = concat_name
|
|
i = 0
|
|
Node(graph, concat_name).add_sequence_of_ports('in', range(len(nodes)))
|
|
for node in nodes:
|
|
out_port = len(Node(graph, node).out_nodes())
|
|
Node(graph, node).add_output_port(out_port)
|
|
graph.create_edge(Node(graph, node), Node(graph, concat_name), out_port, i, create_edge_attrs(node, concat_name, node, i, out_port))
|
|
i = i + 1
|
|
else:
|
|
concat_name = layer_node_map[layer_name]
|
|
return concat_name
|
|
elif spec == b'Offset':
|
|
node = parse_specifier(args[0], graph, layer_node_map)
|
|
t = int(args[1])
|
|
if len(args) > 2:
|
|
raise Error("ModelOptimizer supports only 2 arguments for Offset")
|
|
layer_name = 'Offset_' + node + '_'
|
|
if t < 0:
|
|
layer_name = layer_name + '_' + str(-t)
|
|
else:
|
|
layer_name = layer_name + str(t)
|
|
|
|
if layer_name not in layer_node_map:
|
|
memory_name = graph.unique_id(prefix=layer_name)
|
|
layer_node_map[layer_name] = memory_name
|
|
memory_name_2 = memory_name + '_out'
|
|
graph.add_node(memory_name,
|
|
parameters=dict(t=t, pair_name=memory_name_2, has_default=False),
|
|
op='MemoryOffset',
|
|
kind='op')
|
|
out_port = len(Node(graph, node).out_nodes())
|
|
in_port = len(Node(graph, memory_name).in_nodes())
|
|
Node(graph, memory_name).add_input_port(in_port)
|
|
Node(graph, node).add_output_port(out_port, skip_if_exist=True)
|
|
graph.create_edge(Node(graph, node), Node(graph, memory_name), out_port, in_port, create_edge_attrs(node, memory_name, node, in_port, out_port))
|
|
else:
|
|
memory_name = layer_node_map[layer_name]
|
|
return memory_name
|
|
elif spec == b'Sum':
|
|
nodes = []
|
|
for i in range(len(args)):
|
|
nodes.append(parse_specifier(args[i], graph, layer_node_map))
|
|
|
|
layer_name = 'Sum_'
|
|
for node in nodes:
|
|
layer_name = layer_name + node + "_"
|
|
|
|
if layer_name not in layer_node_map:
|
|
sum_name = graph.unique_id(prefix=layer_name)
|
|
graph.add_node(sum_name, parameters=None, op='Add', kind='op')
|
|
layer_node_map[layer_name] = sum_name
|
|
else:
|
|
sum_name = layer_node_map[layer_name]
|
|
|
|
for i, node in enumerate(nodes):
|
|
out_port = len(Node(graph, node).out_nodes())
|
|
Node(graph, node).add_output_port(out_port, skip_if_exist=True)
|
|
Node(graph, sum_name).add_input_port(i)
|
|
graph.add_edge(node, sum_name, **create_edge_attrs(node, sum_name, node, i))
|
|
|
|
return sum_name
|
|
elif spec == b'IfDefined':
|
|
node_id = parse_specifier(args[0], graph, layer_node_map)
|
|
node = Node(graph, node_id)
|
|
if node.op == 'MemoryOffset':
|
|
node['parameters']['has_default'] = True
|
|
return node_id
|
|
elif spec == b'ReplaceIndex':
|
|
node = parse_specifier(args[0], graph, layer_node_map)
|
|
return node
|
|
elif spec == b'Scale':
|
|
node_name = parse_specifier(args[1], graph, layer_node_map)
|
|
scale_value = float(args[0])
|
|
layer_name = '{}/Mul/{}'.format(node_name, scale_value)
|
|
|
|
if layer_name not in layer_node_map:
|
|
scale_name = graph.unique_id(prefix=layer_name)
|
|
scale_node = Mul(graph, {'name': scale_name}).create_node()
|
|
|
|
layer_node_map[layer_name] = scale_name
|
|
|
|
scale_const_name = 'Const_{}'.format(scale_value)
|
|
const_node = Const(graph, {'name': scale_const_name, 'value': float_array([scale_value])}).create_node()
|
|
|
|
node = Node(graph, node_name)
|
|
graph.create_edge(const_node, scale_node, 0, 0, create_edge_attrs(const_node.id, scale_node.id, const_node.id))
|
|
out_port = len(node.out_nodes())
|
|
graph.create_edge(node, scale_node, out_port, 1, create_edge_attrs(node_name, scale_node.id, node_name, 1, out_port))
|
|
else:
|
|
scale_name = layer_node_map[layer_name]
|
|
|
|
return scale_name
|