ONNX RNN/GRU enable dynamic input shape (#4241)
This commit is contained in:
Katarzyna Mitrus
GitHub
parent
b800f08c0c
commit
ead6427097
@ -41,26 +41,25 @@ namespace ngraph
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m_map[OpInput::W] = ng_inputs.at(1);
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m_map[OpInput::R] = ng_inputs.at(2);
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const auto el_type = ng_inputs.at(0).get_element_type();
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const auto x_pshape = m_map[OpInput::X].get_partial_shape();
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const auto w_pshape = m_map[OpInput::W].get_partial_shape();
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const auto r_pshape = m_map[OpInput::R].get_partial_shape();
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NGRAPH_CHECK(x_pshape.rank().is_static() && x_pshape[0].is_static() &&
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x_pshape[1].is_static(),
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"RecurrentSequence input X must have static \"seq_length\" and "
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"\"batch_size\" dimensions.");
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NGRAPH_CHECK(w_pshape.rank().is_static() && w_pshape[0].is_static(),
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"RecurrentSequence input W must have static \"num_directions\" "
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"(outermost) dimension.");
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NGRAPH_CHECK(r_pshape.rank().is_static() && r_pshape[2].is_static(),
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"RecurrentSequence input R must have static \"hidden_size\" "
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"(innermost) dimension.");
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const std::size_t hidden_size = m_map[OpInput::R].get_shape().back();
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const std::size_t batch_size = m_map[OpInput::X].get_shape().at(0);
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const std::size_t num_directions = m_map[OpInput::W].get_shape().front();
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// Get dimensions needed for default inputs creation
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auto shape_of_x = std::make_shared<default_opset::ShapeOf>(m_map[OpInput::X]);
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auto axes = default_opset::Constant::create(element::i32, Shape{1}, {0});
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auto batch_size_node = std::make_shared<default_opset::Gather>(
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shape_of_x, default_opset::Constant::create(element::i32, Shape{1}, {0}), axes);
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auto seq_length_node = std::make_shared<default_opset::Gather>(
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shape_of_x, default_opset::Constant::create(element::i32, Shape{1}, {1}), axes);
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auto shape_of_r = std::make_shared<default_opset::ShapeOf>(m_map[OpInput::R]);
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auto num_directions_node = std::make_shared<default_opset::Gather>(
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shape_of_r, default_opset::Constant::create(element::i32, Shape{1}, {0}), axes);
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auto hidden_size_node = std::make_shared<default_opset::Gather>(
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shape_of_r, default_opset::Constant::create(element::i32, Shape{1}, {2}), axes);
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// ------ Optional inputs ------
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if (ng_inputs.size() > 3 && !ngraph::op::is_null(ng_inputs.at(3)))
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{
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auto bias = ng_inputs.at(3);
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@ -72,8 +71,17 @@ namespace ngraph
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}
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else
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{
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m_map[OpInput::B] = std::make_shared<default_opset::Constant>(
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el_type, Shape{num_directions, gates_count * hidden_size}, 0.f);
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auto b_shape = std::make_shared<default_opset::Concat>(
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OutputVector{num_directions_node,
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std::make_shared<default_opset::Multiply>(
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default_opset::Constant::create(
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element::Type_t::i64, Shape{1}, {gates_count}),
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hidden_size_node)},
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0);
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m_map[OpInput::B] = std::make_shared<default_opset::Broadcast>(
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default_opset::Constant::create(
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m_map[OpInput::X].get_element_type(), Shape{}, {0}),
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b_shape);
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}
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if (ng_inputs.size() > 4 && !ngraph::op::is_null(ng_inputs.at(4)))
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{
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@ -81,8 +89,8 @@ namespace ngraph
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}
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else
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{
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m_map[OpInput::SEQ_LENGTHS] = std::make_shared<default_opset::Constant>(
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element::i32, Shape{batch_size}, m_map[OpInput::X].get_shape().at(1));
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m_map[OpInput::SEQ_LENGTHS] = std::make_shared<default_opset::Broadcast>(
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seq_length_node, batch_size_node);
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}
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// The initial value of the hidden.
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if (ng_inputs.size() > 5 && !ngraph::op::is_null(ng_inputs.at(5)))
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@ -92,8 +100,12 @@ namespace ngraph
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}
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else
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{
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m_map[OpInput::INIT_H] = std::make_shared<default_opset::Constant>(
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el_type, Shape{batch_size, num_directions, hidden_size}, 0.f);
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auto init_h_shape = std::make_shared<default_opset::Concat>(
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OutputVector{batch_size_node, num_directions_node, hidden_size_node}, 0);
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m_map[OpInput::INIT_H] = std::make_shared<default_opset::Broadcast>(
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default_opset::Constant::create(
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m_map[OpInput::X].get_element_type(), Shape{}, {0}),
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init_h_shape);
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}
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}
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@ -0,0 +1,220 @@
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ir_version: 3
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producer_name: "nGraph ONNX Importer"
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graph {
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node {
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output: "W"
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op_type: "Constant"
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attribute {
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name: "value"
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t {
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dims: 1 # num_directions
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dims: 15 # gates_count*hidden_size
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dims: 2 # input_size
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data_type: 1
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float_data: 0.31403765
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float_data: -0.16793324
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float_data: 1.388258
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float_data: -0.6902954
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float_data: -0.3994045
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float_data: -0.7833511
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float_data: -0.30992958
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float_data: 0.3557573
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float_data: -0.4682631
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float_data: 1.1741459
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float_data: -2.414789
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float_data: -0.42783254
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float_data: -0.82199496
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float_data: -0.0390086
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float_data: -0.43670088
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float_data: -0.53810567
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float_data: -0.10769883
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float_data: 0.75242394
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float_data: -0.2507971
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float_data: 1.0447186
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float_data: -1.4777364
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float_data: 0.19993274
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float_data: 0.925649
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float_data: -2.282516
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float_data: 0.95039636
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float_data: 1.5379831
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float_data: -0.88576007
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float_data: 0.28566247
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float_data: 0.79292643
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float_data: -0.04261953
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name: "W_tensor"
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}
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type: TENSOR
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}
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}
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node {
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output: "R"
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op_type: "Constant"
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attribute {
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name: "value"
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t {
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dims: 1 # num_directions
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dims: 15 # gates_count*hidden_size
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dims: 5 # input_size
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data_type: 1
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float_data: 0.8490583
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float_data: 0.45121244
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float_data: -1.1799014
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float_data: 0.13536449
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float_data: 0.81328654
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float_data: 0.6017516
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float_data: 0.48475724
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float_data: -1.2136037
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float_data: 0.16383322
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float_data: 1.5106261
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float_data: 1.1177503
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float_data: 0.23582461
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float_data: 0.5754652
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float_data: 0.43879887
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float_data: 0.7399294
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float_data: 0.4517558
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float_data: 1.3536783
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float_data: -0.4843166
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float_data: -1.1503736
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float_data: -0.2458678
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float_data: 0.54523313
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float_data: -0.08649993
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float_data: -0.6936281
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float_data: 1.002422
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float_data: -1.770847
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float_data: -0.94642
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float_data: -1.8135757
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float_data: 1.8819852
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float_data: -0.10852333
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float_data: -0.26120332
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float_data: 1.0223165
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float_data: -0.7468837
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float_data: 0.28566906
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float_data: 0.92321056
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float_data: 0.22521864
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float_data: 1.1123824
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float_data: -0.9298287
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float_data: 1.2141289
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float_data: 1.3470556
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float_data: -0.32972014
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float_data: -1.6552197
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float_data: -1.0998285
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float_data: 0.71901864
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float_data: 0.962846
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float_data: -0.1366851
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float_data: -2.6534476
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float_data: -1.4992771
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float_data: -0.45793465
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float_data: 0.4290477
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float_data: 0.9893151
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float_data: 0.2511034
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float_data: 0.12906462
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float_data: 0.7491512
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float_data: 0.3316756
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float_data: 1.0576645
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float_data: -0.04618666
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float_data: 1.3556088
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float_data: 1.2842374
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float_data: 0.7103014
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float_data: 0.52889013
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float_data: 0.30327162
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float_data: 1.5069056
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float_data: 0.16591893
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float_data: 1.5719851
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float_data: -2.099427
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float_data: -1.010277
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float_data: -0.52800924
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float_data: -0.22292352
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float_data: -0.55177474
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float_data: 1.3432894
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float_data: 0.8731192
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float_data: -0.01055307
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float_data: -0.01138215
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float_data: 0.85698843
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float_data: -1.2615703
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name: "R_tensor"
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}
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type: TENSOR
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}
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}
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node {
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input: "X"
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input: "W"
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input: "R"
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output: "Y"
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output: "Y_h"
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op_type: "GRU"
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attribute {
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name: "hidden_size"
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i: 5
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type: INT
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}
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}
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name: "test_gru_defaults_const"
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input {
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name: "X"
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type {
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tensor_type {
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elem_type: 1
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shape {
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dim {
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dim_value: -1 # seq_length
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}
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dim {
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dim_value: -1 # batch size
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}
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dim {
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dim_value: 2 # input size
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}
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}
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}
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}
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}
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output {
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name: "Y"
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type {
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tensor_type {
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elem_type: 1
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shape {
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dim {
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dim_value: -1 # seq_length
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}
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dim {
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dim_value: 1 # num_directions
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}
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dim {
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dim_value: -1 # batch_size
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}
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dim {
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dim_value: 5 # hidden_size
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}
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}
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}
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}
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}
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output {
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name: "Y_h"
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type {
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tensor_type {
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elem_type: 1
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shape {
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dim {
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dim_value: 1 # num_directions
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}
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dim {
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dim_value: -1 # batch_size
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}
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dim {
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dim_value: 5 # hidden_size
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}
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}
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}
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}
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}
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}
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opset_import {
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version: 9
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}
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@ -0,0 +1,146 @@
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ir_version: 3
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producer_name: "nGraph ONNX Importer"
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graph {
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node {
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output: "W"
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op_type: "Constant"
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attribute {
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name: "value"
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t {
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dims: 1
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dims: 5
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dims: 2
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data_type: 1
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float_data: 0.31403765
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float_data: -0.16793324
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float_data: 1.388258
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float_data: -0.6902954
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float_data: -0.3994045
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float_data: -0.7833511
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float_data: -0.30992958
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float_data: 0.3557573
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float_data: -0.4682631
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float_data: 1.1741459
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name: "W_tensor"
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}
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type: TENSOR
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}
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}
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node {
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output: "R"
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op_type: "Constant"
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attribute {
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name: "value"
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t {
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dims: 1 # num_directions
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dims: 5 # gates_count*hidden_size
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dims: 5 # input_size
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data_type: 1
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float_data: -2.414789
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float_data: -0.42783254
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float_data: -0.82199496
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float_data: -0.03900861
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float_data: -0.43670088
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float_data: -0.53810567
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float_data: -0.10769883
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float_data: 0.75242394
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float_data: -0.2507971
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float_data: 1.0447186
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float_data: -1.4777364
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float_data: 0.19993274
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float_data: 0.925649
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float_data: -2.282516
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float_data: 0.95039636
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float_data: 1.5379831
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float_data: -0.88576007
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float_data: 0.28566247
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float_data: 0.79292643
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float_data: -0.04261953
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float_data: 0.8490583
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float_data: 0.45121244
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float_data: -1.1799014
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float_data: 0.13536449
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float_data: 0.81328654
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name: "R_tensor"
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}
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type: TENSOR
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}
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}
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node {
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input: "X"
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input: "W"
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input: "R"
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output: "Y"
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output: "Y_h"
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op_type: "RNN"
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attribute {
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name: "hidden_size"
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i: 5
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type: INT
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}
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}
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name: "test_rnn_defaults"
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input {
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name: "X"
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type {
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tensor_type {
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elem_type: 1
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shape {
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dim {
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dim_value: -1 # seq_length
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}
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dim {
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dim_value: -1 # batch size
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}
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dim {
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dim_value: 2 # input size
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}
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}
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}
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}
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}
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output {
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name: "Y"
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type {
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tensor_type {
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elem_type: 1
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shape {
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dim {
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dim_value: -1 # seq_length
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}
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dim {
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dim_value: 1 # num_directions
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}
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dim {
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dim_value: -1 # batch_size
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}
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dim {
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dim_value: 5 # hidden_size
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}
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}
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}
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}
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}
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output {
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name: "Y_h"
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type {
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tensor_type {
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elem_type: 1
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shape {
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dim {
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dim_value: 1 # num_directions
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}
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dim {
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dim_value: -1 # batch_size
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}
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dim {
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dim_value: 5 # hidden_size
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}
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}
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}
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}
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}
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}
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opset_import {
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version: 9
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}
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@ -1551,6 +1551,70 @@ NGRAPH_TEST_F(${BACKEND_NAME}, GRUSequenceOp, onnx_model_gru_fwd_linear_before_r
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test_case.run(DEFAULT_FLOAT_TOLERANCE_BITS + 4);
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}
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NGRAPH_TEST_F(${BACKEND_NAME}, GRUSequenceOp, onnx_model_gru_defaults_fwd_const_dynamic)
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{
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auto function = onnx_import::import_onnx_model(file_util::path_join(
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SERIALIZED_ZOO, "onnx/dynamic_shapes/gru_defaults_fwd_const_dynamic.prototxt"));
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auto test_case = test::TestCase<TestEngine, test::TestCaseType::DYNAMIC>(function);
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test_case.add_input<float>(Shape{4, 3, 2}, in_X);
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// Y
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test_case.add_expected_output<float>(
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Shape{4, 1, 3, 5},
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std::vector<float>{
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-0.3224981f, -0.44282594f, 0.7499796f, -0.12240417f, 0.12079421f, 0.02534253f,
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0.02504562f, -0.0463777f, 0.01204534f, -0.01497037f, -0.04651929f, -0.6264307f,
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0.7236632f, 0.06250653f, 0.02594197f, -0.06868916f, -0.5412897f, 0.49794048f,
|
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0.22239858f, -0.11257736f, -0.23071964f, 0.26079988f, -0.07375772f, -0.21816255f,
|
||||
0.18764113f, -0.5228772f, 0.00575754f, 0.2514028f, -0.58864325f, 0.49843538f,
|
||||
-0.6129046f, -0.10794663f, 0.6544055f, -0.70105773f, 0.5397687f, -0.35791716f,
|
||||
0.3885092f, -0.15291792f, -0.22324723f, 0.11557932f, -0.42112932f, 0.26772985f,
|
||||
-0.38304564f, -0.05039781f, -0.5057976f, 0.5775348f, -0.6736855f, -0.20032284f,
|
||||
0.03698462f, -0.7693824f, -0.5831348f, 0.25767964f, 0.7121098f, -0.35951245f,
|
||||
0.39223647f, -0.6645166f, 0.37950075f, 0.59931314f, -0.4741001f, 0.21156166f,
|
||||
});
|
||||
// Y_h
|
||||
test_case.add_expected_output<float>(Shape{1, 3, 5},
|
||||
std::vector<float>{
|
||||
0.5775348f,
|
||||
-0.6736855f,
|
||||
-0.20032284f,
|
||||
0.03698462f,
|
||||
-0.7693824f,
|
||||
-0.5831348f,
|
||||
0.25767964f,
|
||||
0.7121098f,
|
||||
-0.35951245f,
|
||||
0.39223647f,
|
||||
-0.6645166f,
|
||||
0.37950075f,
|
||||
0.59931314f,
|
||||
-0.4741001f,
|
||||
0.21156166f,
|
||||
});
|
||||
test_case.run(DEFAULT_FLOAT_TOLERANCE_BITS + 7);
|
||||
}
|
||||
|
||||
NGRAPH_TEST_F(${BACKEND_NAME}, GRUSequenceOp, onnx_model_import_only_gru_defaults_fwd_const_dynamic)
|
||||
{
|
||||
auto function = onnx_import::import_onnx_model(file_util::path_join(
|
||||
SERIALIZED_ZOO, "onnx/dynamic_shapes/gru_defaults_fwd_const_dynamic.prototxt"));
|
||||
|
||||
auto batch_size = Dimension::dynamic();
|
||||
auto seq_length = Dimension::dynamic();
|
||||
int64_t hidden_size = 5;
|
||||
int64_t num_directions = 1;
|
||||
auto Y_expected_output = PartialShape{batch_size, num_directions, seq_length, hidden_size};
|
||||
auto Y_h_expected_output = PartialShape{num_directions, batch_size, hidden_size};
|
||||
|
||||
EXPECT_EQ(function->get_output_size(), 2);
|
||||
EXPECT_EQ(function->get_output_partial_shape(0), Y_expected_output);
|
||||
EXPECT_EQ(function->get_output_partial_shape(1), Y_h_expected_output);
|
||||
|
||||
EXPECT_EQ(count_ops_of_type<op::v5::GRUSequence>(function), 1);
|
||||
}
|
||||
|
||||
// RNNLikeSequenceOp test fixture for test setup reuse
|
||||
class RNNSequenceOp : public testing::Test
|
||||
{
|
||||
@ -2386,3 +2450,67 @@ NGRAPH_TEST_F(${BACKEND_NAME}, RNNSequenceOp, onnx_model_rnn_bidirectional_const
|
||||
});
|
||||
test_case.run(DEFAULT_FLOAT_TOLERANCE_BITS + 6);
|
||||
}
|
||||
|
||||
NGRAPH_TEST_F(${BACKEND_NAME}, RNNSequenceOp, onnx_model_rnn_defaults_fwd_const_dynamic)
|
||||
{
|
||||
auto function = onnx_import::import_onnx_model(file_util::path_join(
|
||||
SERIALIZED_ZOO, "onnx/dynamic_shapes/rnn_defaults_fwd_const_dynamic.prototxt"));
|
||||
|
||||
auto test_case = test::TestCase<TestEngine, test::TestCaseType::DYNAMIC>(function);
|
||||
test_case.add_input<float>(Shape{4, 3, 2}, in_X);
|
||||
|
||||
// Y
|
||||
test_case.add_expected_output<float>(
|
||||
Shape{4, 1, 3, 5},
|
||||
std::vector<float>{
|
||||
0.02254748f, 0.15776646f, -0.8229023f, 0.19205809f, 0.76984656f, -0.00603169f,
|
||||
-0.02861464f, 0.04512155f, -0.0011912f, -0.02572936f, -0.13703543f, -0.49651444f,
|
||||
-0.78868157f, 0.3566854f, 0.8758509f, 0.20788848f, 0.13481987f, -0.756822f,
|
||||
-0.121436f, 0.97542346f, 0.16959739f, 0.63496053f, 0.1245538f, -0.1970138f,
|
||||
-0.56581646f, 0.8225869f, 0.9611373f, -0.42990375f, -0.22925597f, 0.2226491f,
|
||||
0.08246052f, 0.9798831f, -0.13415998f, -0.5567714f, 0.78594816f, -0.34759718f,
|
||||
0.11376679f, -0.07107389f, -0.5420871f, -0.58504283f, -0.96065646f, 0.18588805f,
|
||||
-0.4870671f, -0.1475982f, 0.82456505f, -0.80264574f, -0.46370947f, 0.9719335f,
|
||||
-0.7374159f, 0.94937694f, 0.8814341f, 0.67015004f, 0.21958017f, -0.8332769f,
|
||||
-0.487742f, 0.9918536f, 0.99563396f, 0.94866276f, -0.98504806f, -0.42824882f,
|
||||
});
|
||||
// Y_h
|
||||
test_case.add_expected_output<float>(Shape{1, 3, 5},
|
||||
std::vector<float>{
|
||||
-0.80264574f,
|
||||
-0.46370947f,
|
||||
0.9719335f,
|
||||
-0.7374159f,
|
||||
0.94937694f,
|
||||
0.8814341f,
|
||||
0.67015004f,
|
||||
0.21958017f,
|
||||
-0.8332769f,
|
||||
-0.487742f,
|
||||
0.9918536f,
|
||||
0.99563396f,
|
||||
0.94866276f,
|
||||
-0.98504806f,
|
||||
-0.42824882f,
|
||||
});
|
||||
test_case.run(DEFAULT_FLOAT_TOLERANCE_BITS + 4);
|
||||
}
|
||||
|
||||
NGRAPH_TEST_F(${BACKEND_NAME}, RNNSequenceOp, onnx_model_import_only_rnn_defaults_fwd_const_dynamic)
|
||||
{
|
||||
auto function = onnx_import::import_onnx_model(file_util::path_join(
|
||||
SERIALIZED_ZOO, "onnx/dynamic_shapes/rnn_defaults_fwd_const_dynamic.prototxt"));
|
||||
|
||||
auto batch_size = Dimension::dynamic();
|
||||
auto seq_length = Dimension::dynamic();
|
||||
int64_t hidden_size = 5;
|
||||
int64_t num_directions = 1;
|
||||
auto Y_expected_output = PartialShape{batch_size, num_directions, seq_length, hidden_size};
|
||||
auto Y_h_expected_output = PartialShape{num_directions, batch_size, hidden_size};
|
||||
|
||||
EXPECT_EQ(function->get_output_size(), 2);
|
||||
EXPECT_EQ(function->get_output_partial_shape(0), Y_expected_output);
|
||||
EXPECT_EQ(function->get_output_partial_shape(1), Y_h_expected_output);
|
||||
|
||||
EXPECT_EQ(count_ops_of_type<op::v5::RNNSequence>(function), 1);
|
||||
}
|
||||
|
||||
@ -249,6 +249,8 @@ IE_CPU.nothing_to_reverse
|
||||
|
||||
# Unsupported dynamic ops
|
||||
onnx_size_dyn_op
|
||||
onnx_model_gru_defaults_fwd_const_dynamic
|
||||
onnx_model_rnn_defaults_fwd_const_dynamic
|
||||
|
||||
# Constant network
|
||||
# MKLDNNGraph::CreateGraph: No inputs for the topology
|
||||
|
||||
Loading…
Reference in New Issue
Block a user