* [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>
90 lines
4.4 KiB
Python
90 lines
4.4 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 unittest
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import numpy as np
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from extensions.ops.embedding_bag import EmbeddingBagOffsetsSum, EmbeddingBagPackedSum, EmbeddingSegmentsSum
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from mo.front.common.partial_infer.utils import int64_array
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from mo.graph.graph import Node
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from mo.utils.unittest.graph import build_graph, regular_op_with_shaped_data, valued_const_with_data, result, \
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connect, FakeAttr
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nodes = {
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**valued_const_with_data('data', np.random.randn(3000, 8)),
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**regular_op_with_shaped_data('indices1d', [100], {'type': 'Parameter', 'value': None,
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'_out_port_data_type': {0: np.int32}}),
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**regular_op_with_shaped_data('indices2d', [30, 3], {'type': 'Parameter', 'value': None,
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'_out_port_data_type': {0: np.int32}}),
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**regular_op_with_shaped_data('offsets', [30], {'type': 'Parameter', 'value': None,
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'_out_port_data_type': {0: np.int32}}),
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**regular_op_with_shaped_data('segment_ids', [100], {'type': 'Parameter', 'value': None,
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'_out_port_data_type': {0: np.int32}}),
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**valued_const_with_data('num_segments', np.array(30, dtype=np.int32)),
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**regular_op_with_shaped_data('embedding_bag_offsets', None,
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{'op': 'EmbeddingBagOffsetsSum', 'type': 'EmbeddingBagOffsetsSum',
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'name': 'embedding_bag_offsets'}),
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**regular_op_with_shaped_data('embedding_bag_packed', None,
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{'op': 'EmbeddingBagPackedSum', 'type': 'EmbeddingBagPackedSum',
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'name': 'embedding_bag_packed'}),
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**regular_op_with_shaped_data('embedding_segments', None,
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{'op': 'EmbeddingSegmentsSum', 'type': 'EmbeddingSegmentsSum',
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'name': 'embedding_bag_packed'}),
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**result('output'),
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}
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class TestEmbeddingInfer(unittest.TestCase):
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def test_embedding_bag_offsets_sum(self):
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graph = build_graph(nodes, [
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*connect('data', '0:embedding_bag_offsets'),
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*connect('indices1d', '1:embedding_bag_offsets'),
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*connect('offsets', '2:embedding_bag_offsets'),
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('embedding_bag_offsets', 'embedding_bag_offsets_d', {'out': 0}),
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('embedding_bag_offsets_d', 'output'),
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], nodes_with_edges_only=True)
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eb_node = Node(graph, 'embedding_bag_offsets')
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EmbeddingBagOffsetsSum.infer(eb_node)
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self.assertTrue(np.array_equal(eb_node.out_port(0).data.get_shape(), int64_array([30, 8])))
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def test_embedding_bag_packed_sum(self):
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graph = build_graph(nodes, [
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*connect('data', '0:embedding_bag_packed'),
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*connect('indices2d', '1:embedding_bag_packed'),
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('embedding_bag_packed', 'embedding_bag_packed_d', {'out': 0}),
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('embedding_bag_packed_d', 'output'),
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], nodes_with_edges_only=True)
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eb_node = Node(graph, 'embedding_bag_packed')
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EmbeddingBagPackedSum.infer(eb_node)
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self.assertTrue(np.array_equal(eb_node.out_port(0).data.get_shape(), int64_array([30, 8])))
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def test_embedding_segments_sum(self):
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graph = build_graph(nodes, [
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*connect('data', '0:embedding_segments'),
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*connect('indices1d', '1:embedding_segments'),
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*connect('segment_ids', '2:embedding_segments'),
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*connect('num_segments', '3:embedding_segments'),
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('embedding_segments', 'embedding_segments_d', {'out': 0}),
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('embedding_segments_d', 'output'),
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], nodes_with_edges_only=True)
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eb_node = Node(graph, 'embedding_segments')
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EmbeddingSegmentsSum.infer(eb_node)
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self.assertTrue(np.array_equal(eb_node.out_port(0).data.get_shape(), int64_array([30, 8])))
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