2f5a28d44f
* validate_and_infer_types() implementation * input parameter validation for LSTM, GRU and RNN * style-check applied * Add LSTMSequence dynamic shape validation and test props for RNNCell, GRUCell, LSTMCell and LSTMSequence. * recurrent_sequence.hpp moved to ngraph/core/include/ngraph/op/util/ * style check applied * removed unused variable from LSTMSequence::validate_and_infer_types * Add missing newline mark at the end of file. * Add supression macro for FusedOp deprecation. * Add element type initialization * transpose,rnn cell reference implementations * Apply PR review remarks * reference implementations for cells op, single layer tests, align lstm cell/sequence according to the spec * lstm/gru/rnn cell decompostion transformations * ngraph codestyle * clean up * ngraph code style * change inheritance of Cells, fix build * fix build * fix build again * remove Peepholes from LSTMSeq, fix copy_runtime_info in transformations * Rewrite tests to use gtest exception assertions. * resolve tests issues * ngraph codestyle * add missed files * fix typeprop tests * fix lstm sequence checks * fix arm build * fix arm again * delete unnecessary file * add convert weghts format function, enable lstm test, resolve review comments * add ngraph builders * ngraph codestyle * fix unit tests * revert transpose reference implementation * revert LSTM Cell v0, add LSTMCell v1, update transformation lstm_cell_to_cell_ie * v1 version of LSTMCell op * LSTMSequence v1 operation, exclude LSTMSeq from opset4 * fix python api tests * resolve review comments, tests for decomposition transformations, switch lstm cell to opset4 in mo Co-authored-by: Szymon Durawa <szymon.durawa@intel.com>
102 lines
3.3 KiB
Python
102 lines
3.3 KiB
Python
"""
|
|
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': 'opset4',
|
|
'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'
|