Maxim VafinandRoman Kazantsev
f1811ad060
* [MO] Implement EmbeddingBag_3 * Transform dynamic sub-graph of Wide and Deep into EmbeddingSegmentsSum - Expressed SparseWeightedSum sub-graph through EmbeddingSegmentsSum - Removed experimental SparseWeightedSum layer - Implemented tests for the transformation Signed-off-by: Roman Kazantsev <roman.kazantsev@intel.com> * Fix EmbeddingBag shape infer * Fix EmbeddingSegmentsSum transformation for Wide and Deep Signed-off-by: Roman Kazantsev <roman.kazantsev@intel.com> * Fix EmbeddingSegmentSum replacer after ports swap Signed-off-by: Roman Kazantsev <roman.kazantsev@intel.com> * Update package_BOM.txt Signed-off-by: Roman Kazantsev <roman.kazantsev@intel.com> * Add unit tests for EmbeddingXXX shape infer * Fix ATen resolver * Remove deleted files from BOM * Add opset version to embedding_bag * Use base class for EmbeddingBag * Fix per_sample_weights case * Fix EmbeddingSegmentsSum transformation Signed-off-by: Roman Kazantsev <roman.kazantsev@intel.com> * Fix EmbeddingBag checks * Fix ATen front transformation and merge conflicts * Fix BOM * Work around limitation for I64 input of W&D model Signed-off-by: Roman Kazantsev <roman.kazantsev@intel.com> * Cleanup where operation to fix affect of WhereDecomposition transform Signed-off-by: Roman Kazantsev <roman.kazantsev@intel.com> * Fix BOM * Correct EmbeddingSegmentSum transform for Wide and Deep Add casting segment ids to i32 and remove ConstToResult sub-graph. Signed-off-by: Roman Kazantsev <roman.kazantsev@intel.com> * Update BOM with RemoveConstToResult transform Signed-off-by: Roman Kazantsev <roman.kazantsev@intel.com> * Add more comments for RemoveConstToResult transformation Signed-off-by: Roman Kazantsev <roman.kazantsev@intel.com> * Remove useless logging in EmbeddingSegmentsSum transformation Signed-off-by: Roman Kazantsev <roman.kazantsev@intel.com> * Small fixes * Move EmbeddingBag resolving back to front phase * Improve error messages * Fix typo in unittests * Reimplement sparse_reshape middle transform Avoid deprecated API. Signed-off-by: Roman Kazantsev <roman.kazantsev@intel.com> * Clean-up graph after sparse_reshape and ConstToResult transformation Signed-off-by: Roman Kazantsev <roman.kazantsev@intel.com> * Fix clean-up for transformations Signed-off-by: Roman Kazantsev <roman.kazantsev@intel.com> * Fix clean-up for transformation #2 Signed-off-by: Roman Kazantsev <roman.kazantsev@intel.com> Co-authored-by: Roman Kazantsev <roman.kazantsev@intel.com>
67 lines
2.5 KiB
Python
67 lines
2.5 KiB
Python
"""
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Copyright (C) 2018-2020 Intel Corporation
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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"""
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import numpy as np
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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 SparseReshape(Op):
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"""
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SparseReshape operation reshapes a sparse tensor. It recomputes indices for a new dense shape.
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"""
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op = 'SparseReshape'
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def __init__(self, graph: Graph, attrs: dict):
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mandatory_props = {
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'type': None,
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'op': __class__.op,
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'infer': self.infer,
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'in_ports_count': 3,
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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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def supported_attrs(self):
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return []
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@staticmethod
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def infer(node: Node):
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input_indices_shape = node.in_port(0).data.get_shape()
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input_indices_value = node.in_port(0).data.get_value()
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input_shape_value = node.in_port(1).data.get_value()
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new_shape_value = node.in_port(2).data.get_value()
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new_shape_shape = node.in_port(2).data.get_shape()
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assert input_shape_value is not None and new_shape_value is not None, \
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"Values for input shape and new shape must be defined"
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assert np.count_nonzero(new_shape_value == -1) <= 1, \
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"Value -1 occurs in new shape value more than once"
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node.out_port(1).data.set_shape(new_shape_shape)
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output_shape_value = new_shape_value
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if np.count_nonzero(output_shape_value == -1) == 1:
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elem = np.prod(input_shape_value) // np.prod(new_shape_value[new_shape_value != -1])
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output_shape_value[output_shape_value == -1] = elem
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node.out_port(1).data.set_value(output_shape_value)
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output_indices_shape = np.concatenate((input_indices_shape[0:1], new_shape_shape))
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node.out_port(0).data.set_shape(output_indices_shape)
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# TODO: implement constant value propogation for common case
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if np.array_equal(input_shape_value, output_shape_value) and input_indices_value is not None:
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node.out_port(0).data.set_value(input_indices_value)
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