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Unverified Commit ba1141e0 authored by Ewa Tusień's avatar Ewa Tusień Committed by GitHub
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Merge branch 'master' into etusien/depth_to_space

parents cad5a6b7 31fae943
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doc/sphinx/ngraph_theme/ngversions.html
View file @ ba1141e0
......@@ -9,11 +9,11 @@
<dt>{{ _('Recent Versions') }}</dt>
<dd><!-- Until our https://docs.ngraph.ai/ publishing is set up, we link to GitHub -->
<ul>
<li><a href="https://github.com/NervanaSystems/ngraph/releases/tag/v0.24.0">0.24.0</a></li>
<li><a href="https://github.com/NervanaSystems/ngraph/releases/tag/v0.23.0">0.23.0</a></li>
<li><a href="https://github.com/NervanaSystems/ngraph/releases/tag/v0.22.0">0.22.0</a></li>
<li><a href="https://github.com/NervanaSystems/ngraph/releases/tag/v0.21.0">0.21.0</a></li>
<li><a href="https://github.com/NervanaSystems/ngraph/releases/tag/v0.20.0">0.20.0</a></li>
<li><a href="https://github.com/NervanaSystems/ngraph/releases/tag/v0.19.0">0.19.0</a></li>
</ul></dd>
</dl>
<dl>
......
doc/sphinx/source/conf.py
View file @ ba1141e0
......@@ -73,11 +73,11 @@ author = 'Intel Corporation'
# built documents.
#
# The short X.Y version.
version = '0.24'
version = '0.25'
# The Documentation full version, including alpha/beta/rc tags. Some features
# available in the latest code will not necessarily be documented first
release = '0.24.0'
release = '0.25.0'
# The language for content autogenerated by Sphinx. Refer to documentation
# for a list of supported languages.
......
doc/sphinx/source/project/release-notes.rst
View file @ ba1141e0
......@@ -16,14 +16,11 @@ We are pleased to announce the release of version |version|-doc.
Core updates for |version|
~~~~~~~~~~~~~~~~~~~~~~~~~~~
+ More ONNX ops
+ Elementwise divide defaults to Python semantics
+ GenerateMask seed optional
+ Graph visualization improvements
+ Preserve control dependencies in more places
+ GetOutputElement has single input
+ Better PlaidML support
+ Double-buffering support
+ Constant folding
+ Support for static linking
+ Additional ops
.. Latest doc updates
.. ~~~~~~~~~~~~~~~~~~
......@@ -37,6 +34,19 @@ Core updates for |version|
Changelog on Previous Releases
==============================
0.24
----
+ Fixes reshape sink/swim issue
+ More ONNX ops
+ Elementwise divide defaults to Python semantics
+ GenerateMask seed optional
+ Graph visualization improvements
+ Preserve control dependencies in more places
+ GetOutputElement has single input
.. + Add instructions how to build ``NGRAPH_PLAIDML`` backend.
0.23
----
......
src/ngraph/op/pad.cpp
View file @ ba1141e0
......@@ -23,12 +23,12 @@ using namespace ngraph;
const string op::Pad::type_name{"Pad"};
op::Pad::Pad(const shared_ptr<Node>& arg,
const shared_ptr<Node>& arg_pad_value,
op::Pad::Pad(const Output<Node>& arg,
const Output<Node>& arg_pad_value,
const CoordinateDiff& padding_below,
const CoordinateDiff& padding_above,
PadMode pad_mode)
: Op(check_single_output_args({arg, arg_pad_value}))
: Op({arg, arg_pad_value})
, m_padding_below(padding_below)
, m_padding_above(padding_above)
, m_padding_interior_fake(padding_below.size())
......
src/ngraph/op/pad.hpp
View file @ ba1141e0
......@@ -32,14 +32,16 @@ namespace ngraph
static const std::string type_name;
const std::string& description() const override { return type_name; }
/// \brief Constructs a generic padding operation.
Pad() = default;
/// \brief Constructs a generic padding operation.
///
/// \param arg The node producing input tensor to be padded.
/// \param arg_pad_value The node producing the scalar value to be inserted for padding.
/// \param padding_below The padding-below widths.
/// \param padding_above The padding-above widths.
/// \param pad_mode The padding mode: CONSTANT(default), EDGE, REFLECT or SYMMETRIC.
Pad(const std::shared_ptr<Node>& arg,
const std::shared_ptr<Node>& arg_pad_value,
Pad(const Output<Node>& arg,
const Output<Node>& arg_pad_value,
const CoordinateDiff& padding_below,
const CoordinateDiff& padding_above,
PadMode pad_mode = PadMode::CONSTANT);
......@@ -49,14 +51,24 @@ namespace ngraph
void validate_and_infer_types() override;
/// \return The padding-below sizes.
const CoordinateDiff& get_padding_below() const { return m_padding_below; }
void set_padding_below(const CoordinateDiff& padding_below)
{
m_padding_below = padding_below;
}
/// \return The padding-above sizes.
const CoordinateDiff& get_padding_above() const { return m_padding_above; }
void set_padding_above(const CoordinateDiff& padding_above)
{
m_padding_below = padding_above;
}
/// \brief DEPRECATED. This is just a stub for backends that used to implement the
/// interior padding feature, which is no longer supported.
/// \return Returns a shape full of zeros, with the same rank as get_padding_below().
const Shape& get_padding_interior() const { return m_padding_interior_fake; }
/// \return The padding mode.
PadMode get_pad_mode() const { return m_pad_mode; }
void set_pad_mode(PadMode pad_mode) { m_pad_mode = pad_mode; }
/// \return The default value for Pad.
virtual std::shared_ptr<Node> get_default_value() const override;
......
src/ngraph/op/parameter.hpp
View file @ ba1141e0
......@@ -38,6 +38,7 @@ namespace ngraph
NGRAPH_API
static const std::string type_name;
const std::string& description() const override { return type_name; }
Parameter() = default;
/// \brief Constructions a tensor-typed parameter node.
///
/// \param element_type The element type of the parameter.
......
src/ngraph/op/passthrough.cpp
View file @ ba1141e0
......@@ -38,6 +38,21 @@ ngraph::op::Passthrough::Passthrough(const std::string& logical_type,
constructor_validate_and_infer_types();
}
ngraph::op::Passthrough::Passthrough(const std::string& logical_type,
const std::string& language,
const std::string& function,
const OutputVector& args,
std::vector<std::tuple<element::Type, PartialShape>> outputs)
: Op{args}
, m_logical_type{logical_type}
, m_language{language}
, m_function{function}
, m_output_shapes{std::move(outputs)}
{
set_output_size(m_output_shapes.size());
constructor_validate_and_infer_types();
}
void ngraph::op::Passthrough::validate_and_infer_types()
{
// N.B. It would be useful to have the backend deduce the output
......
src/ngraph/op/passthrough.hpp
View file @ ba1141e0
......@@ -41,12 +41,19 @@ public:
NGRAPH_API
static const std::string type_name;
const std::string& description() const override { return type_name; }
Passthrough() = default;
Passthrough(const std::string& logical_type, // aka "What this operation is doing"
const std::string& language, // The language the implementation is written in
const std::string& function, // The operation implementation
const NodeVector& args,
std::vector<std::tuple<element::Type, PartialShape>> outputs);
Passthrough(const std::string& logical_type, // aka "What this operation is doing"
const std::string& language, // The language the implementation is written in
const std::string& function, // The operation implementation
const OutputVector& args,
std::vector<std::tuple<element::Type, PartialShape>> outputs);
void validate_and_infer_types() final;
std::shared_ptr<Node> copy_with_new_args(const NodeVector& new_args) const final;
......
src/ngraph/op/power.cpp
View file @ ba1141e0
......@@ -24,9 +24,7 @@ using namespace ngraph;
const string op::Power::type_name{"Power"};
op::Power::Power(const shared_ptr<Node>& arg0,
const shared_ptr<Node>& arg1,
const AutoBroadcastSpec& autob)
op::Power::Power(const Output<Node>& arg0, const Output<Node>& arg1, const AutoBroadcastSpec& autob)
: BinaryElementwiseArithmetic(arg0, arg1, autob)
{
constructor_validate_and_infer_types();
......
src/ngraph/op/power.hpp
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......@@ -42,13 +42,14 @@ namespace ngraph
NGRAPH_API
static const std::string type_name;
const std::string& description() const override { return type_name; }
Power() = default;
/// \brief Constructs an exponentiation operation.
///
/// \param arg0 Node that produces the first input tensor.
/// \param arg1 Node that produces the second input tensor.
/// \param autob Auto broadcast specification
Power(const std::shared_ptr<Node>& arg0,
const std::shared_ptr<Node>& arg1,
Power(const Output<Node>& arg0,
const Output<Node>& arg1,
const AutoBroadcastSpec& autob = AutoBroadcastSpec());
virtual std::shared_ptr<Node>
......
src/ngraph/op/product.cpp
View file @ ba1141e0
......@@ -21,10 +21,6 @@ using namespace ngraph;
const string op::Product::type_name{"Product"};
op::Product::Product()
{
}
op::Product::Product(const Output<Node>& arg, const AxisSet& reduction_axes)
: ArithmeticReduction(arg, reduction_axes)
{
......
src/ngraph/op/product.hpp
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......@@ -33,7 +33,7 @@ namespace ngraph
static const std::string type_name;
const std::string& description() const override { return type_name; }
/// \brief Constructs a product reduction operation.
Product();
Product() = default;
/// \brief Constructs a product reduction operation.
///
/// \param arg The tensor to be reduced.
......
src/ngraph/op/quantize.hpp
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......@@ -92,6 +92,8 @@ namespace ngraph
const ngraph::AxisSet& axes,
RoundMode round_mode);
Quantize() = default;
void validate_and_infer_types() override;
virtual std::shared_ptr<Node>
......
src/ngraph/op/quantized_convolution.hpp
View file @ ba1141e0
......@@ -66,6 +66,8 @@ namespace ngraph
const ngraph::AxisSet& filter_axes = ngraph::AxisSet{},
const ngraph::AxisSet& output_axes = ngraph::AxisSet{});
QuantizedConvolution() = default;
const Strides& get_window_movement_strides() const { return m_window_movement_strides; }
const Strides& get_window_dilation_strides() const { return m_window_dilation_strides; }
const CoordinateDiff& get_padding_below() const { return m_padding_below; }
......
src/ngraph/op/relu.cpp
View file @ ba1141e0
......@@ -23,7 +23,7 @@ using namespace ngraph;
const string op::Relu::type_name{"Relu"};
const string op::ReluBackprop::type_name{"ReluBackprop"};
op::Relu::Relu(shared_ptr<Node> arg)
op::Relu::Relu(const Output<Node>& arg)
: UnaryElementwiseArithmetic(arg)
{
constructor_validate_and_infer_types();
......
src/ngraph/op/relu.hpp
View file @ ba1141e0
......@@ -36,10 +36,11 @@ namespace ngraph
NGRAPH_API
static const std::string type_name;
const std::string& description() const override { return type_name; }
Relu() = default;
/// \brief Constructs a Relu operation.
///
/// \param arg Node that produces the input tensor.
Relu(std::shared_ptr<ngraph::Node> arg);
Relu(const Output<ngraph::Node>& arg);
virtual std::shared_ptr<Node>
copy_with_new_args(const NodeVector& new_args) const override;
......
src/ngraph/op/replace_slice.hpp
View file @ ba1141e0
......@@ -53,6 +53,7 @@ namespace ngraph
NGRAPH_API
static const std::string type_name;
const std::string& description() const override { return type_name; }
ReplaceSlice() = default;
/// \brief Constructs a tensor slice replacement operation.
///
/// \param arg0 The tensor to overwrite into.
......@@ -85,10 +86,16 @@ namespace ngraph
/// \return The inclusive lower-bound coordinates.
const Coordinate& get_lower_bounds() const { return m_lower_bounds; }
void set_lower_bounds(const Coordinate& lower_bounds) { m_lower_bounds = lower_bounds; }
/// \return The exclusive upper-bound coordinates.
const Coordinate& get_upper_bounds() const { return m_upper_bounds; }
void set_uppper_bounds(const Coordinate& upper_bounds)
{
m_upper_bounds = upper_bounds;
}
/// \return The slicing strides.
const Strides& get_strides() const { return m_strides; }
void set_strides(const Strides& strides) { m_strides = strides; }
protected:
virtual void generate_adjoints(autodiff::Adjoints& adjoints,
const NodeVector& deltas) override;
......
src/ngraph/op/reverse.cpp
View file @ ba1141e0
......@@ -25,8 +25,8 @@ using namespace ngraph;
const string op::Reverse::type_name{"Reverse"};
op::Reverse::Reverse(const shared_ptr<Node>& arg, const AxisSet& reversed_axes)
: Op(check_single_output_args({arg}))
op::Reverse::Reverse(const Output<Node>& arg, const AxisSet& reversed_axes)
: Op({arg})
, m_reversed_axes(reversed_axes)
{
constructor_validate_and_infer_types();
......@@ -65,7 +65,7 @@ void op::Reverse::generate_adjoints(autodiff::Adjoints& adjoints, const NodeVect
{
auto delta = deltas.at(0);
auto x = get_argument(0);
auto x = input(0).get_source_output();
adjoints.add_delta(x, make_shared<op::Reverse>(delta, m_reversed_axes));
}
src/ngraph/op/reverse.hpp
View file @ ba1141e0
......@@ -49,11 +49,12 @@ namespace ngraph
NGRAPH_API
static const std::string type_name;
const std::string& description() const override { return type_name; }
Reverse() = default;
/// \brief Constructs a reverse operation.
///
/// \param arg The input tensor, some of whose axes are to be reversed.
/// \param reversed_axes The axes to reverse.
Reverse(const std::shared_ptr<Node>& arg, const AxisSet& reversed_axes);
Reverse(const Output<Node>& arg, const AxisSet& reversed_axes);
void validate_and_infer_types() override;
......@@ -62,11 +63,16 @@ namespace ngraph
/// \return The set of axes to reverse.
const AxisSet& get_reversed_axes() const { return m_reversed_axes; }
void set_reversed_axes(const AxisSet& reversed_axes)
{
m_reversed_axes = reversed_axes;
}
protected:
virtual void generate_adjoints(autodiff::Adjoints& adjoints,
const NodeVector& deltas) override;
const AxisSet m_reversed_axes;
AxisSet m_reversed_axes;
};
}
}
src/ngraph/op/reverse_sequence.cpp
View file @ ba1141e0
......@@ -27,11 +27,11 @@ using namespace ngraph;
const string op::ReverseSequence::type_name{"ReverseSequence"};
op::ReverseSequence::ReverseSequence(const std::shared_ptr<Node> arg,
const std::shared_ptr<Node> seq_indices,
op::ReverseSequence::ReverseSequence(const Output<Node>& arg,
const Output<Node>& seq_indices,
size_t batch_axis,
size_t seq_axis)
: Op(check_single_output_args({arg, seq_indices}))
: Op({arg, seq_indices})
, m_batch_axis(batch_axis)
, m_seq_axis(seq_axis)
{
......@@ -104,8 +104,8 @@ shared_ptr<Node> op::ReverseSequence::copy_with_new_args(const NodeVector& new_a
void op::ReverseSequence::generate_adjoints(autodiff::Adjoints& adjoints, const NodeVector& deltas)
{
auto x = get_argument(0);
auto rs_delta =
make_shared<ReverseSequence>(deltas.at(0), get_argument(1), m_batch_axis, m_seq_axis);
auto x = input(0).get_source_output();
auto rs_delta = make_shared<ReverseSequence>(
deltas.at(0), input(1).get_source_output(), m_batch_axis, m_seq_axis);
adjoints.add_delta(x, rs_delta);
}
src/ngraph/op/reverse_sequence.hpp
View file @ ba1141e0
......@@ -28,11 +28,12 @@ namespace ngraph
NGRAPH_API
static const std::string type_name;
const std::string& description() const override { return type_name; }
ReverseSequence() = default;
/// \brief Constructs an arcsin operation.
///
/// \param arg Node that produces the input tensor.
ReverseSequence(const std::shared_ptr<Node> arg,
const std::shared_ptr<Node> seq_lengths,
ReverseSequence(const Output<Node>& arg,
const Output<Node>& seq_lengths,
size_t batch_axis,
size_t seq_axis);
......@@ -42,7 +43,9 @@ namespace ngraph
copy_with_new_args(const NodeVector& new_args) const override;
size_t get_batch_axis() const { return m_batch_axis; }
void set_batch_axis(size_t batch_axis) { m_batch_axis = batch_axis; }
size_t get_sequence_axis() const { return m_seq_axis; }
void set_sequence_axis(size_t sequence_axis) { m_seq_axis = sequence_axis; }
protected:
virtual void generate_adjoints(autodiff::Adjoints& adjoints,
const NodeVector& deltas) override;
......
src/ngraph/op/scatter_add.hpp
View file @ ba1141e0
......@@ -29,13 +29,14 @@ namespace ngraph
NGRAPH_API
static const std::string type_name;
const std::string& description() const override { return type_name; }
ScatterAdd() = default;
/// \param inputs Tensor
/// \param indices Index tensor: Data type must be `element::i32` or `element::i64`
/// \param updates Tensor: Must have same type as inputs
ScatterAdd(const std::shared_ptr<Node>& inputs,
const std::shared_ptr<Node>& indices,
const std::shared_ptr<Node>& updates)
: Op(check_single_output_args({inputs, indices, updates}))
ScatterAdd(const Output<Node>& inputs,
const Output<Node>& indices,
const Output<Node>& updates)
: Op({inputs, indices, updates})
{
constructor_validate_and_infer_types();
}
......
src/ngraph/op/scatter_nd_add.hpp
View file @ ba1141e0
......@@ -29,13 +29,14 @@ namespace ngraph
NGRAPH_API
static const std::string type_name;
const std::string& description() const override { return type_name; }
ScatterNDAdd() = default;
/// \param inputs Tensor
/// \param indices Index tensor: Data type must be `element::i32` or `element::i64`
/// \param updates Tensor: Must have same type as inputs
ScatterNDAdd(const std::shared_ptr<Node>& inputs,
const std::shared_ptr<Node>& indices,
const std::shared_ptr<Node>& updates)
: Op(check_single_output_args({inputs, indices, updates}))
ScatterNDAdd(const Output<Node>& inputs,
const Output<Node>& indices,
const Output<Node>& updates)
: Op({inputs, indices, updates})
{
constructor_validate_and_infer_types();
}
......
src/ngraph/op/select.cpp
View file @ ba1141e0
......@@ -72,12 +72,12 @@ void op::Select::generate_adjoints(autodiff::Adjoints& adjoints, const NodeVecto
{
auto delta = deltas.at(0);
auto p = get_argument(0);
auto x = get_argument(1);
auto y = get_argument(2);
auto p = input(0).get_source_output();
auto x = input(1).get_source_output();
auto y = input(2).get_source_output();
auto p_as_x_type = make_shared<op::Convert>(p, x->get_element_type());
auto not_p_as_y_type = make_shared<op::Convert>(make_shared<op::Not>(p), y->get_element_type());
auto p_as_x_type = make_shared<op::Convert>(p, x.get_element_type());
auto not_p_as_y_type = make_shared<op::Convert>(make_shared<op::Not>(p), y.get_element_type());
adjoints.add_delta(x, delta * p_as_x_type);
adjoints.add_delta(y, delta * not_p_as_y_type);
......
src/ngraph/op/sigmoid.cpp
View file @ ba1141e0
......@@ -30,13 +30,13 @@ shared_ptr<Node> op::Sigmoid::copy_with_new_args(const NodeVector& new_args) con
return make_shared<Sigmoid>(new_args.at(0));
}
op::Sigmoid::Sigmoid(shared_ptr<Node> arg)
op::Sigmoid::Sigmoid(const Output<Node>& arg)
: UnaryElementwiseArithmetic(arg)
{
constructor_validate_and_infer_types();
}
op::SigmoidBackprop::SigmoidBackprop(shared_ptr<Node> arg, shared_ptr<Node> delta)
op::SigmoidBackprop::SigmoidBackprop(const Output<Node>& arg, const Output<Node>& delta)
: BinaryElementwiseArithmetic(arg, delta)
{
constructor_validate_and_infer_types();
......@@ -52,6 +52,6 @@ void op::Sigmoid::generate_adjoints(autodiff::Adjoints& adjoints, const NodeVect
{
auto delta = deltas.at(0);
auto backprop = make_shared<op::SigmoidBackprop>(get_argument(0), delta);
adjoints.add_delta(get_argument(0), backprop);
auto backprop = make_shared<op::SigmoidBackprop>(input(0).get_source_output(), delta);
adjoints.add_delta(input(0).get_source_output(), backprop);
}
src/ngraph/op/sigmoid.hpp
View file @ ba1141e0
......@@ -31,7 +31,8 @@ namespace ngraph
NGRAPH_API
static const std::string type_name;
const std::string& description() const override { return type_name; }
Sigmoid(std::shared_ptr<Node> arg);
Sigmoid(const Output<Node>& arg);
Sigmoid() = default;
virtual std::shared_ptr<Node>
copy_with_new_args(const NodeVector& new_args) const override;
virtual void generate_adjoints(autodiff::Adjoints& adjoints,
......@@ -46,10 +47,11 @@ namespace ngraph
NGRAPH_API
static const std::string type_name;
const std::string& description() const override { return type_name; }
SigmoidBackprop() = default;
/// \brief Constructs a SigmoidBackprop operation.
///
/// \param arg Node that produces the Sigmoid forward input tensor.
SigmoidBackprop(std::shared_ptr<ngraph::Node> arg, std::shared_ptr<ngraph::Node> delta);
SigmoidBackprop(const Output<Node>& arg, const Output<Node>& delta);
virtual std::shared_ptr<Node>
copy_with_new_args(const NodeVector& new_args) const override;
......
src/ngraph/op/sign.cpp
View file @ ba1141e0
......@@ -21,7 +21,7 @@ using namespace ngraph;
const string op::Sign::type_name{"Sign"};
op::Sign::Sign(const shared_ptr<Node>& arg)
op::Sign::Sign(const Output<Node>& arg)
: UnaryElementwiseArithmetic(arg)
{
constructor_validate_and_infer_types();
......
src/ngraph/op/sign.hpp
View file @ ba1141e0
......@@ -30,10 +30,11 @@ namespace ngraph
NGRAPH_API
static const std::string type_name;
const std::string& description() const override { return type_name; }
Sign() = default;
/// \brief Constructs an elementwise sign operation.
///
/// \param arg Node that produces the input tensor.
Sign(const std::shared_ptr<Node>& arg);
Sign(const Output<Node>& arg);
virtual std::shared_ptr<Node>
copy_with_new_args(const NodeVector& new_args) const override;
......
src/ngraph/op/sin.cpp
View file @ ba1141e0
......@@ -23,7 +23,7 @@ using namespace ngraph;
const string op::Sin::type_name{"Sin"};
op::Sin::Sin(const shared_ptr<Node>& arg)
op::Sin::Sin(const Output<Node>& arg)
: UnaryElementwiseArithmetic(arg)
{
constructor_validate_and_infer_types();
......@@ -39,7 +39,7 @@ void op::Sin::generate_adjoints(autodiff::Adjoints& adjoints, const NodeVector&
{
auto delta = deltas.at(0);
auto x = get_argument(0);
auto x = input(0).get_source_output();
adjoints.add_delta(x, delta * (make_shared<op::Cos>(x)));
}
src/ngraph/op/sin.hpp
View file @ ba1141e0
......@@ -44,7 +44,8 @@ namespace ngraph
/// \brief Constructs a sine operation.
///
/// \param arg Node that produces the input tensor.
Sin(const std::shared_ptr<Node>& arg);
Sin(const Output<Node>& arg);
Sin() = default;
virtual std::shared_ptr<Node>
copy_with_new_args(const NodeVector& new_args) const override;
......
src/ngraph/op/sinh.cpp
View file @ ba1141e0
......@@ -23,7 +23,7 @@ using namespace ngraph;
const string op::Sinh::type_name{"Sinh"};
op::Sinh::Sinh(const shared_ptr<Node>& arg)
op::Sinh::Sinh(const Output<Node>& arg)
: UnaryElementwiseArithmetic(arg)
{
constructor_validate_and_infer_types();
......
src/ngraph/op/sinh.hpp
View file @ ba1141e0
......@@ -32,7 +32,8 @@ namespace ngraph
/// \brief Constructs a hyperbolic sine operation.
///
/// \param arg Node that produces the input tensor.
Sinh(const std::shared_ptr<Node>& arg);
Sinh(const Output<Node>& arg);
Sinh() = default;
virtual std::shared_ptr<Node>
copy_with_new_args(const NodeVector& new_args) const override;
......
src/ngraph/op/slice.cpp
View file @ ba1141e0
......@@ -21,10 +21,6 @@ using namespace ngraph;
const string op::Slice::type_name{"Slice"};
op::Slice::Slice()
{
}
op::Slice::Slice(const Output<Node>& arg,
const Coordinate& lower_bounds,
const Coordinate& upper_bounds,
......@@ -139,7 +135,7 @@ void op::Slice::generate_adjoints(autodiff::Adjoints& adjoints, const NodeVector
{
auto delta = deltas.at(0);
auto x = get_argument(0);
auto x = input(0).get_source_output();
adjoints.add_delta_to_slice(x, delta, m_lower_bounds, m_upper_bounds, m_strides);
}
src/ngraph/op/slice.hpp
View file @ ba1141e0
......@@ -32,7 +32,7 @@ namespace ngraph
static const std::string type_name;
const std::string& description() const override { return type_name; }
/// \brief Constructs a tensor slice operation
Slice();
Slice() = default;
/// \brief Constructs a tensor slice operation.
///
/// \param arg The tensor to be sliced.
......
src/ngraph/op/softmax.cpp
View file @ ba1141e0
......@@ -31,7 +31,7 @@ using namespace ngraph;
const string op::Softmax::type_name{"Softmax"};
op::Softmax::Softmax(const shared_ptr<Node>& arg, const AxisSet& axes)
op::Softmax::Softmax(const Output<Node>& arg, const AxisSet& axes)
: UnaryElementwiseArithmetic(arg)
, m_axes(axes)
{
......@@ -88,6 +88,6 @@ void op::Softmax::generate_adjoints(autodiff::Adjoints& adjoints, const NodeVect
auto adjoint = z - builder::make_with_numpy_broadcast<op::Multiply>(output(0), zreshape);
auto x = get_argument(0);
auto x = input(0).get_source_output();
adjoints.add_delta(x, adjoint);
}
src/ngraph/op/softmax.hpp
View file @ ba1141e0
......@@ -30,6 +30,7 @@ namespace ngraph
NGRAPH_API
static const std::string type_name;
const std::string& description() const override { return type_name; }
Softmax() = default;
/// \brief Constructs a softmax operation.
///
/// \param arg Node that produces the first input tensor.<br>
......@@ -38,12 +39,13 @@ namespace ngraph
///
/// Output `[d0, ...]`
///
Softmax(const std::shared_ptr<Node>& arg, const AxisSet& axes);
Softmax(const Output<Node>& arg, const AxisSet& axes);
virtual std::shared_ptr<Node>
copy_with_new_args(const NodeVector& new_args) const override;
const AxisSet& get_axes() const { return m_axes; }
void set_axes(const AxisSet& axes) { m_axes = axes; }
protected:
virtual void generate_adjoints(autodiff::Adjoints& adjoints,
const NodeVector& deltas) override;
......
src/ngraph/op/sqrt.cpp
View file @ ba1141e0
......@@ -23,7 +23,7 @@ using namespace ngraph;
const string op::Sqrt::type_name{"Sqrt"};
op::Sqrt::Sqrt(const shared_ptr<Node>& arg)
op::Sqrt::Sqrt(const Output<Node>& arg)
: UnaryElementwiseArithmetic(arg)
{
constructor_validate_and_infer_types();
......@@ -39,7 +39,7 @@ void op::Sqrt::generate_adjoints(autodiff::Adjoints& adjoints, const NodeVector&
{
auto delta = deltas.at(0);
auto x = get_argument(0);
auto x = input(0).get_source_output();
adjoints.add_delta(x, delta / (shared_from_this() + shared_from_this()));
}
src/ngraph/op/sqrt.hpp
View file @ ba1141e0
......@@ -44,7 +44,8 @@ namespace ngraph
/// \brief Constructs a square operation.
///
/// \param arg Node that produces the input tensor.
Sqrt(const std::shared_ptr<Node>& arg);
Sqrt(const Output<Node>& arg);
Sqrt() = default;
virtual std::shared_ptr<Node>
copy_with_new_args(const NodeVector& new_args) const override;
......
src/ngraph/op/stop_gradient.cpp
View file @ ba1141e0
......@@ -23,7 +23,7 @@ using namespace ngraph;
const string op::StopGradient::type_name{"StopGradient"};
op::StopGradient::StopGradient(const shared_ptr<Node>& arg)
op::StopGradient::StopGradient(const Output<Node>& arg)
: UnaryElementwiseArithmetic(arg)
{
constructor_validate_and_infer_types();
......
src/ngraph/op/stop_gradient.hpp
View file @ ba1141e0
......@@ -32,7 +32,8 @@ namespace ngraph
/// \brief Constructs StopGradient
///
/// \param arg Node that produces the input tensor.
StopGradient(const std::shared_ptr<Node>& arg);
StopGradient(const Output<Node>& arg);
StopGradient() = default;
virtual std::shared_ptr<Node>
copy_with_new_args(const NodeVector& new_args) const override;
......
src/ngraph/op/subtract.cpp
View file @ ba1141e0
......@@ -45,8 +45,8 @@ void op::Subtract::generate_adjoints(autodiff::Adjoints& adjoints, const NodeVec
auto delta = deltas.at(0);
auto x = get_argument(0);
auto y = get_argument(1);
auto x = input(0).get_source_output();
auto y = input(1).get_source_output();
adjoints.add_delta(x, delta);
adjoints.add_delta(y, -delta);
......
src/ngraph/op/sum.cpp
View file @ ba1141e0
......@@ -22,10 +22,6 @@ using namespace ngraph;
const string op::Sum::type_name{"Sum"};
op::Sum::Sum()
{
}
op::Sum::Sum(const Output<Node>& arg, const AxisSet& reduction_axes)
: ArithmeticReduction(arg, reduction_axes)
{
......
src/ngraph/op/sum.hpp
View file @ ba1141e0
......@@ -78,7 +78,7 @@ namespace ngraph
static const std::string type_name;
const std::string& description() const override { return type_name; }
/// \brief Constructs a summation operation.
Sum();
Sum() = default;
/// \brief Constructs a summation operation.
///
/// \param arg The tensor to be summed.
......
src/ngraph/op/tan.cpp
View file @ ba1141e0
......@@ -24,7 +24,7 @@ using namespace ngraph;
const string op::Tan::type_name{"Tan"};
op::Tan::Tan(const shared_ptr<Node>& arg)
op::Tan::Tan(const Output<Node>& arg)
: UnaryElementwiseArithmetic(arg)
{
constructor_validate_and_infer_types();
......@@ -40,7 +40,7 @@ void op::Tan::generate_adjoints(autodiff::Adjoints& adjoints, const NodeVector&
{
auto delta = deltas.at(0);
auto x = get_argument(0);
auto x = input(0).get_source_output();
auto c = make_shared<op::Cos>(x);
......
src/ngraph/op/tan.hpp
View file @ ba1141e0
......@@ -44,7 +44,8 @@ namespace ngraph
/// \brief Constructs a tangent operation.
///
/// \param arg Node that produces the input tensor.
Tan(const std::shared_ptr<Node>& arg);
Tan(const Output<Node>& arg);
Tan() = default;
virtual std::shared_ptr<Node>
copy_with_new_args(const NodeVector& new_args) const override;
......
src/ngraph/op/tanh.cpp
View file @ ba1141e0
......@@ -23,7 +23,7 @@ using namespace ngraph;
const string op::Tanh::type_name{"Tanh"};
op::Tanh::Tanh(const shared_ptr<Node>& arg)
op::Tanh::Tanh(const Output<Node>& arg)
: UnaryElementwiseArithmetic(arg)
{
constructor_validate_and_infer_types();
......@@ -39,7 +39,7 @@ void op::Tanh::generate_adjoints(autodiff::Adjoints& adjoints, const NodeVector&
{
auto delta = deltas.at(0);
auto x = get_argument(0);
auto x = input(0).get_source_output();
adjoints.add_delta(x, delta - (delta * (shared_from_this() * shared_from_this())));
}
src/ngraph/op/tanh.hpp
View file @ ba1141e0
......@@ -32,7 +32,8 @@ namespace ngraph
/// \brief Constructs a hyperbolic tangent operation.
///
/// \param arg Node that produces the input tensor.
Tanh(const std::shared_ptr<Node>& arg);
Tanh(const Output<Node>& arg);
Tanh() = default;
virtual std::shared_ptr<Node>
copy_with_new_args(const NodeVector& new_args) const override;
......
src/ngraph/op/topk.cpp
View file @ ba1141e0
......@@ -26,10 +26,6 @@ using namespace ngraph;
const string op::TopK::type_name{"TopK"};
op::TopK::TopK()
{
}
op::TopK::TopK(const Output<Node>& arg,
size_t top_k_axis,
const element::Type& index_element_type,
......@@ -63,7 +59,8 @@ op::TopK::TopK(const Output<Node>& arg,
size_t op::TopK::get_k() const
{
size_t k = 0;
if (auto const_op = dynamic_pointer_cast<op::Constant>(get_argument(1)))
if (auto const_op =
dynamic_pointer_cast<op::Constant>(input(1).get_source_output().get_node_shared_ptr()))
{
k = const_op->get_vector<int64_t>()[0];
}
......
src/ngraph/op/topk.hpp
View file @ ba1141e0
......@@ -44,7 +44,7 @@ namespace ngraph
static const std::string type_name;
const std::string& description() const override { return type_name; }
/// \brief Constructs a TopK operation
TopK();
TopK() = default;
/// \brief Constructs a TopK operation.
///
/// \param arg The input tensor
......
src/ngraph/pass/constant_folding.cpp
View file @ ba1141e0
This diff is collapsed.
src/ngraph/runtime/interpreter/int_executable.cpp
View file @ ba1141e0
......@@ -24,7 +24,6 @@
#include "ngraph/pass/assign_layout.hpp"
#include "ngraph/pass/core_fusion.hpp"
#include "ngraph/pass/fused_op_decomposition.hpp"
#include "ngraph/pass/implicit_broadcast_elimination.hpp"
#include "ngraph/pass/like_replacement.hpp"
#include "ngraph/pass/liveness.hpp"
#include "ngraph/pass/manager.hpp"
......@@ -48,7 +47,6 @@ runtime::interpreter::INTExecutable::INTExecutable(const shared_ptr<Function>& f
pass::Manager pass_manager;
pass_manager.register_pass<pass::LikeReplacement>();
pass_manager.register_pass<pass::FusedOpDecomposition>();
pass_manager.register_pass<pass::ImplicitBroadcastElimination>();
pass_manager.register_pass<pass::AssignLayout<DenseTensorLayout>>();
pass_manager.register_pass<pass::Liveness>();
pass_manager.run_passes(m_function);
......
src/ngraph/runtime/reference/add.hpp
View file @ ba1141e0
......@@ -18,6 +18,11 @@
#include <cstddef>
#include "ngraph/coordinate_transform.hpp"
#include "ngraph/op/util/attr_types.hpp"
#include "ngraph/runtime/reference/autobroadcast_binop.hpp"
#include "ngraph/shape_util.hpp"
namespace ngraph
{
namespace runtime
......@@ -32,6 +37,20 @@ namespace ngraph
out[i] = arg0[i] + arg1[i];
}
}
template <typename T>
void add(const T* arg0,
const T* arg1,
T* out,
const Shape& arg0_shape,
const Shape& arg1_shape,
const op::AutoBroadcastSpec& broadcast_spec)
{
autobroadcast_binop(
arg0, arg1, out, arg0_shape, arg1_shape, broadcast_spec, [](T x, T y) -> T {
return x + y;
});
}
}
}
}
src/ngraph/runtime/reference/and.hpp
View file @ ba1141e0
......@@ -32,6 +32,20 @@ namespace ngraph
out[i] = static_cast<T>(arg0[i] && arg1[i]);
}
}
template <typename T>
void logical_and(const T* arg0,
const T* arg1,
T* out,
const Shape& arg0_shape,
const Shape& arg1_shape,
const op::AutoBroadcastSpec& broadcast_spec)
{
autobroadcast_binop(
arg0, arg1, out, arg0_shape, arg1_shape, broadcast_spec, [](T x, T y) -> T {
return static_cast<T>(x && y);
});
}
}
}
}
src/ngraph/runtime/reference/autobroadcast_binop.hpp 0 → 100644
View file @ ba1141e0
//*****************************************************************************
// Copyright 2017-2019 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.
//*****************************************************************************
#pragma once
#include <cstddef>
#include "ngraph/coordinate_transform.hpp"
#include "ngraph/op/util/attr_types.hpp"
#include "ngraph/shape_util.hpp"
namespace ngraph
{
namespace runtime
{
namespace reference
{
/// \brief Helper function to implement autobroadcasting elementwise binop references.
///
/// \tparam T Element type of the input tensors.
/// \tparam U Element type of the output tensor.
/// \tparam Functor Type of the functor for the elementwise operation. Must support
/// operator()(T,T), and operator()(T,T) must return a value of type
/// U.
///
/// \param arg0 Pointer to the buffer for left operand input tensor.
/// \param arg1 Pointer to the buffer for right operand input tensor.
/// \param out Pointer to the buffer for output tensor. This must be pre-allocated by
/// the caller, and must be large enough to hold a tensor of the correct
/// shape.
/// \param broadcast_spec Specification of the auto-broadcasting scheme.
/// \param elementwise_functor Functor implementing the elementwise operation to be
/// applied across the input tensors. Must accept two
/// arguments of type T, and return a value of type U.
template <typename T, typename U, typename Functor>
void autobroadcast_binop(const T* arg0,
const T* arg1,
U* out,
const Shape& arg0_shape,
const Shape& arg1_shape,
const op::AutoBroadcastSpec& broadcast_spec,
Functor elementwise_functor)
{
switch (broadcast_spec.m_type)
{
case op::AutoBroadcastType::NONE:
for (size_t i = 0; i < shape_size(arg0_shape); i++)
{
out[i] = elementwise_functor(arg0[i], arg1[i]);
}
break;
case op::AutoBroadcastType::NUMPY:
// We'll be using CoordinateTransform to handle the broadcasting. The general
// procedure is as follows:
//
// (1) Left pad the shorter of the two shapes with ones.
// (2) Squeeze (remove ones from) both shapes, and record the squeezed axis
// indices.
// (3) Using CoordinateTransform, broadcast both args to the final output
// shape. The "broadcasted axes" will be those that were squeezed in step
// 2.
//
// Example:
//
// Input shape->Padded shape->Squeezed Shape/Squeezed Axes
// ----------- ------------ ----------------------------
// a: [ 3, 2, 1] [ 3, 2, 1] [ 3, 2 ] {2}
// b: [ 1, 6] [ 1, 1, 6] [ 6] {0,1}
// | | |
// v v v
// Output shape
// ------------
// [ 3, 2, 6]
Shape arg0_padded_shape = arg0_shape;
Shape arg1_padded_shape = arg1_shape;
while (arg0_padded_shape.size() < arg1_padded_shape.size())
{
arg0_padded_shape.insert(arg0_padded_shape.begin(), 1);
}
while (arg1_padded_shape.size() < arg0_padded_shape.size())
{
arg1_padded_shape.insert(arg1_padded_shape.begin(), 1);
}
Shape arg0_squeezed_shape;
Shape arg1_squeezed_shape;
AxisSet arg0_squeezed_axes;
AxisSet arg1_squeezed_axes;
Shape output_shape;
for (size_t i = 0; i < arg0_padded_shape.size(); i++)
{
if (arg0_padded_shape[i] == 1)
{
arg0_squeezed_axes.insert(i);
}
else
{
arg0_squeezed_shape.push_back(arg0_padded_shape[i]);
}
if (arg1_padded_shape[i] == 1)
{
arg1_squeezed_axes.insert(i);
}
else
{
arg1_squeezed_shape.push_back(arg1_padded_shape[i]);
}
output_shape.push_back(arg0_padded_shape[i] == 1 ? arg1_padded_shape[i]
: arg0_padded_shape[i]);
}
CoordinateTransform arg0_transform(arg0_squeezed_shape);
CoordinateTransform arg1_transform(arg1_squeezed_shape);
CoordinateTransform output_transform(output_shape);
for (const Coordinate& output_coord : output_transform)
{
Coordinate arg0_coord = reduce(output_coord, arg0_squeezed_axes);
Coordinate arg1_coord = reduce(output_coord, arg1_squeezed_axes);
out[output_transform.index(output_coord)] =
elementwise_functor(arg0[arg0_transform.index(arg0_coord)],
arg1[arg1_transform.index(arg1_coord)]);
}
}
}
}
}
}
src/ngraph/runtime/reference/divide.hpp
View file @ ba1141e0
......@@ -67,6 +67,47 @@ namespace ngraph
}
}
template <typename T>
typename std::enable_if<std::is_integral<T>::value>::type
divide(const T* arg0,
const T* arg1,
T* out,
const Shape& arg0_shape,
const Shape& arg1_shape,
const op::AutoBroadcastSpec& broadcast_spec,
bool pythondiv)
{
auto functor = [pythondiv](T x, T y) -> T {
if (pythondiv)
{
if (y == 0)
{
throw std::domain_error("integer division by zero");
}
T quot = x / y;
T rem = x % y;
if ((rem != 0) && ((x < 0) != (y < 0)))
{
return quot - 1;
}
else
{
return quot;
}
}
else
{
if (y == 0)
{
throw std::domain_error("integer division by zero");
}
return x / y;
}
};
autobroadcast_binop(
arg0, arg1, out, arg0_shape, arg1_shape, broadcast_spec, functor);
}
// In English: return type is void and T must be a standard floating point type, or
// bfloat16, or float16.
template <typename T>
......@@ -83,6 +124,25 @@ namespace ngraph
out[i] = arg0[i] / arg1[i];
}
}
template <typename T>
typename std::enable_if<std::is_floating_point<T>::value ||
std::is_same<T, bfloat16>::value ||
std::is_same<T, float16>::value>::type
divide(const T* arg0,
const T* arg1,
T* out,
const Shape& arg0_shape,
const Shape& arg1_shape,
const op::AutoBroadcastSpec& broadcast_spec,
bool pythondiv)
{
(void)pythondiv;
autobroadcast_binop(
arg0, arg1, out, arg0_shape, arg1_shape, broadcast_spec, [](T x, T y) -> T {
return x / y;
});
}
}
}
}
src/ngraph/runtime/reference/equal.hpp
View file @ ba1141e0
......@@ -40,6 +40,20 @@ namespace ngraph
out[i] = arg0[i] == arg1[i];
}
}
template <typename T>
void equal(const T* arg0,
const T* arg1,
char* out,
const Shape& arg0_shape,
const Shape& arg1_shape,
const op::AutoBroadcastSpec& broadcast_spec)
{
autobroadcast_binop(
arg0, arg1, out, arg0_shape, arg1_shape, broadcast_spec, [](T x, T y) -> T {
return x == y;
});
}
}
}
}
......
src/ngraph/runtime/reference/greater.hpp
View file @ ba1141e0
......@@ -35,6 +35,20 @@ namespace ngraph
out[i] = arg0[i] > arg1[i];
}
}
template <typename T>
void greater(const T* arg0,
const T* arg1,
char* out,
const Shape& arg0_shape,
const Shape& arg1_shape,
const op::AutoBroadcastSpec& broadcast_spec)
{
autobroadcast_binop(
arg0, arg1, out, arg0_shape, arg1_shape, broadcast_spec, [](T x, T y) -> T {
return x > y;
});
}
}
}
}
src/ngraph/runtime/reference/greater_eq.hpp
View file @ ba1141e0
......@@ -35,6 +35,20 @@ namespace ngraph
out[i] = arg0[i] >= arg1[i];
}
}
template <typename T>
void greater_eq(const T* arg0,
const T* arg1,
char* out,
const Shape& arg0_shape,
const Shape& arg1_shape,
const op::AutoBroadcastSpec& broadcast_spec)
{
autobroadcast_binop(
arg0, arg1, out, arg0_shape, arg1_shape, broadcast_spec, [](T x, T y) -> T {
return x >= y;
});
}
}
}
}
src/ngraph/runtime/reference/less.hpp
View file @ ba1141e0
......@@ -35,6 +35,20 @@ namespace ngraph
out[i] = arg0[i] < arg1[i];
}
}
template <typename T>
void less(const T* arg0,
const T* arg1,
char* out,
const Shape& arg0_shape,
const Shape& arg1_shape,
const op::AutoBroadcastSpec& broadcast_spec)
{
autobroadcast_binop(
arg0, arg1, out, arg0_shape, arg1_shape, broadcast_spec, [](T x, T y) -> T {
return x < y;
});
}
}
}
}
src/ngraph/runtime/reference/less_eq.hpp
View file @ ba1141e0
......@@ -35,6 +35,20 @@ namespace ngraph
out[i] = arg0[i] <= arg1[i];
}
}
template <typename T>
void less_eq(const T* arg0,
const T* arg1,
char* out,
const Shape& arg0_shape,
const Shape& arg1_shape,
const op::AutoBroadcastSpec& broadcast_spec)
{
autobroadcast_binop(
arg0, arg1, out, arg0_shape, arg1_shape, broadcast_spec, [](T x, T y) -> T {
return x <= y;
});
}
}
}
}
src/ngraph/runtime/reference/maximum.hpp
View file @ ba1141e0
......@@ -32,6 +32,20 @@ namespace ngraph
out[i] = arg0[i] > arg1[i] ? arg0[i] : arg1[i];
}
}
template <typename T>
void maximum(const T* arg0,
const T* arg1,
T* out,
const Shape& arg0_shape,
const Shape& arg1_shape,
const op::AutoBroadcastSpec& broadcast_spec)
{
autobroadcast_binop(
arg0, arg1, out, arg0_shape, arg1_shape, broadcast_spec, [](T x, T y) -> T {
return x > y ? x : y;
});
}
}
}
}
src/ngraph/runtime/reference/minimum.hpp
View file @ ba1141e0
......@@ -32,6 +32,20 @@ namespace ngraph
out[i] = arg0[i] < arg1[i] ? arg0[i] : arg1[i];
}
}
template <typename T>
void minimum(const T* arg0,
const T* arg1,
T* out,
const Shape& arg0_shape,
const Shape& arg1_shape,
const op::AutoBroadcastSpec& broadcast_spec)
{
autobroadcast_binop(
arg0, arg1, out, arg0_shape, arg1_shape, broadcast_spec, [](T x, T y) -> T {
return x < y ? x : y;
});
}
}
}
}
src/ngraph/runtime/reference/multiply.hpp
View file @ ba1141e0
......@@ -32,6 +32,20 @@ namespace ngraph
out[i] = arg0[i] * arg1[i];
}
}
template <typename T>
void multiply(const T* arg0,
const T* arg1,
T* out,
const Shape& arg0_shape,
const Shape& arg1_shape,
const op::AutoBroadcastSpec& broadcast_spec)
{
autobroadcast_binop(
arg0, arg1, out, arg0_shape, arg1_shape, broadcast_spec, [](T x, T y) -> T {
return x * y;
});
}
}
}
}
src/ngraph/runtime/reference/not_equal.hpp
View file @ ba1141e0
......@@ -40,6 +40,20 @@ namespace ngraph
out[i] = arg0[i] != arg1[i];
}
}
template <typename T>
void not_equal(const T* arg0,
const T* arg1,
char* out,
const Shape& arg0_shape,
const Shape& arg1_shape,
const op::AutoBroadcastSpec& broadcast_spec)
{
autobroadcast_binop(
arg0, arg1, out, arg0_shape, arg1_shape, broadcast_spec, [](T x, T y) -> T {
return x != y;
});
}
}
}
}
......
src/ngraph/runtime/reference/or.hpp
View file @ ba1141e0
......@@ -32,6 +32,20 @@ namespace ngraph
out[i] = static_cast<T>(arg0[i] || arg1[i]);
}
}
template <typename T>
void logical_or(const T* arg0,
const T* arg1,
T* out,
const Shape& arg0_shape,
const Shape& arg1_shape,
const op::AutoBroadcastSpec& broadcast_spec)
{
autobroadcast_binop(
arg0, arg1, out, arg0_shape, arg1_shape, broadcast_spec, [](T x, T y) -> T {
return static_cast<T>(x || y);
});
}
}
}
}
src/ngraph/runtime/reference/power.hpp
View file @ ba1141e0
......@@ -33,6 +33,20 @@ namespace ngraph
out[i] = std::pow(arg0[i], arg1[i]);
}
}
template <typename T>
void power(const T* arg0,
const T* arg1,
T* out,
const Shape& arg0_shape,
const Shape& arg1_shape,
const op::AutoBroadcastSpec& broadcast_spec)
{
autobroadcast_binop(
arg0, arg1, out, arg0_shape, arg1_shape, broadcast_spec, [](T x, T y) -> T {
return std::pow(x, y);
});
}
}
}
}
src/ngraph/runtime/reference/subtract.hpp
View file @ ba1141e0
......@@ -32,6 +32,20 @@ namespace ngraph
out[i] = arg0[i] - arg1[i];
}
}
template <typename T>
void subtract(const T* arg0,
const T* arg1,
T* out,
const Shape& arg0_shape,
const Shape& arg1_shape,
const op::AutoBroadcastSpec& broadcast_spec)
{
autobroadcast_binop(
arg0, arg1, out, arg0_shape, arg1_shape, broadcast_spec, [](T x, T y) -> T {
return x - y;
});
}
}
}
}
src/ngraph/runtime/reference/xor.hpp
View file @ ba1141e0
......@@ -32,6 +32,20 @@ namespace ngraph
out[i] = static_cast<T>((arg0[i] || arg1[i]) && !(arg0[i] && arg1[i]));
}
}
template <typename T>
void logical_xor(const T* arg0,
const T* arg1,
T* out,
const Shape& arg0_shape,
const Shape& arg1_shape,
const op::AutoBroadcastSpec& broadcast_spec)
{
autobroadcast_binop(
arg0, arg1, out, arg0_shape, arg1_shape, broadcast_spec, [](T x, T y) -> T {
return static_cast<T>((x || y) && !(x && y));
});
}
}
}
}
src/ngraph/serializer.cpp
View file @ ba1141e0
......@@ -1576,7 +1576,7 @@ shared_ptr<Node> JSONDeserializer::deserialize_node(json node_js)
node = make_shared<op::Passthrough>(node_js.at("logical_type"),
node_js.at("language"),
node_js.at("function"),
args,
static_cast<OutputVector>(args),
std::move(outputs));
break;
}
......
test/constant_folding.cpp
View file @ ba1141e0
......@@ -168,15 +168,24 @@ static std::vector<T> get_result_constant(std::shared_ptr<Function> f, size_t po
TEST(constant_folding, constant_unary_binary)
{
Shape shape_in{4};
vector<int> values_a{1, 2, 3, 4};
vector<int> values_b{1, 2, 3, 4};
vector<int> values_c{-1, -1, -1, -1};
vector<int> values_d{1, 4, 9, 16};
auto a = make_shared<op::Constant>(element::i32, shape_in, values_a);
auto b = make_shared<op::Constant>(element::i32, shape_in, values_b);
auto c = make_shared<op::Constant>(element::i32, shape_in, values_c);
auto d = make_shared<op::Constant>(element::i32, shape_in, values_d);
vector<int> values_e{5, 6};
vector<int> values_f{0, 10};
vector<int> values_g{1, 4};
vector<char> values_h{0, 0, 1, 1};
vector<char> values_i{0, 1};
auto a = make_shared<op::Constant>(element::i32, Shape{2, 2}, values_a);
auto b = make_shared<op::Constant>(element::i32, Shape{2, 2}, values_b);
auto c = make_shared<op::Constant>(element::i32, Shape{2, 2}, values_c);
auto d = make_shared<op::Constant>(element::i32, Shape{2, 2}, values_d);
auto e = make_shared<op::Constant>(element::i32, Shape{2}, values_e);
auto f = make_shared<op::Constant>(element::i32, Shape{2}, values_f);
auto g = make_shared<op::Constant>(element::i32, Shape{2}, values_g);
auto h = make_shared<op::Constant>(element::boolean, Shape{2, 2}, values_h);
auto i = make_shared<op::Constant>(element::boolean, Shape{2}, values_i);
auto add = a + b;
auto sub = a - b;
......@@ -187,15 +196,54 @@ TEST(constant_folding, constant_unary_binary)
auto absn = make_shared<op::Abs>(c);
auto neg = make_shared<op::Negative>(c);
auto sqrt = make_shared<op::Sqrt>(d);
auto add_autob_numpy = make_shared<op::Add>(a, e, op::AutoBroadcastType::NUMPY);
auto sub_autob_numpy = make_shared<op::Subtract>(a, e, op::AutoBroadcastType::NUMPY);
auto mul_autob_numpy = make_shared<op::Multiply>(a, e, op::AutoBroadcastType::NUMPY);
auto div_autob_numpy = make_shared<op::Divide>(a, g, op::AutoBroadcastType::NUMPY);
auto min_autob_numpy = make_shared<op::Minimum>(a, f, op::AutoBroadcastType::NUMPY);
auto max_autob_numpy = make_shared<op::Maximum>(a, f, op::AutoBroadcastType::NUMPY);
auto equal_autob_numpy = make_shared<op::Equal>(a, g, op::AutoBroadcastType::NUMPY);
auto not_equal_autob_numpy = make_shared<op::NotEqual>(a, g, op::AutoBroadcastType::NUMPY);
auto greater_autob_numpy = make_shared<op::Greater>(a, g, op::AutoBroadcastType::NUMPY);
auto greater_eq_autob_numpy = make_shared<op::GreaterEq>(a, g, op::AutoBroadcastType::NUMPY);
auto less_autob_numpy = make_shared<op::Less>(a, g, op::AutoBroadcastType::NUMPY);
auto less_eq_autob_numpy = make_shared<op::LessEq>(a, g, op::AutoBroadcastType::NUMPY);
auto logical_and_autob_numpy = make_shared<op::And>(h, i, op::AutoBroadcastType::NUMPY);
auto logical_or_autob_numpy = make_shared<op::Or>(h, i, op::AutoBroadcastType::NUMPY);
auto logical_xor_autob_numpy = make_shared<op::Xor>(h, i, op::AutoBroadcastType::NUMPY);
auto neg_sqrt = make_shared<op::Sqrt>(c);
auto f = make_shared<Function>(NodeVector{add, sub, mul, divn, min, max, absn, neg, sqrt},
ParameterVector{});
auto f_error = make_shared<Function>(NodeVector{neg_sqrt}, ParameterVector{});
auto func = make_shared<Function>(NodeVector{add,
sub,
mul,
divn,
min,
max,
absn,
neg,
sqrt,
add_autob_numpy,
sub_autob_numpy,
mul_autob_numpy,
div_autob_numpy,
min_autob_numpy,
max_autob_numpy,
equal_autob_numpy,
not_equal_autob_numpy,
greater_autob_numpy,
greater_eq_autob_numpy,
less_autob_numpy,
less_eq_autob_numpy,
logical_and_autob_numpy,
logical_or_autob_numpy,
logical_xor_autob_numpy},
ParameterVector{});
auto func_error = make_shared<Function>(NodeVector{neg_sqrt}, ParameterVector{});
pass::Manager pass_manager;
pass_manager.register_pass<pass::ConstantFolding>();
pass_manager.run_passes(f);
pass_manager.run_passes(func);
//expected values
vector<int> add_expected{2, 4, 6, 8};
......@@ -206,17 +254,47 @@ TEST(constant_folding, constant_unary_binary)
vector<int> max_expected{1, 2, 3, 4};
vector<int> abs_neg_expected{1, 1, 1, 1};
vector<int> sqrt_expected{1, 2, 3, 4};
ASSERT_EQ(get_result_constant<int>(f, 0), add_expected);
ASSERT_EQ(get_result_constant<int>(f, 1), sub_expected);
ASSERT_EQ(get_result_constant<int>(f, 2), mul_expected);
ASSERT_EQ(get_result_constant<int>(f, 3), div_expected);
ASSERT_EQ(get_result_constant<int>(f, 4), min_expected);
ASSERT_EQ(get_result_constant<int>(f, 5), max_expected);
ASSERT_EQ(get_result_constant<int>(f, 6), abs_neg_expected);
ASSERT_EQ(get_result_constant<int>(f, 7), abs_neg_expected);
ASSERT_EQ(get_result_constant<int>(f, 8), sqrt_expected);
ASSERT_ANY_THROW(pass_manager.run_passes(f_error));
vector<int> add_autob_numpy_expected{6, 8, 8, 10};
vector<int> sub_autob_numpy_expected{-4, -4, -2, -2};
vector<int> mul_autob_numpy_expected{5, 12, 15, 24};
vector<int> div_autob_numpy_expected{1, 0, 3, 1};
vector<int> min_autob_numpy_expected{0, 2, 0, 4};
vector<int> max_autob_numpy_expected{1, 10, 3, 10};
vector<char> equal_autob_numpy_expected{1, 0, 0, 1};
vector<char> not_equal_autob_numpy_expected{0, 1, 1, 0};
vector<char> greater_autob_numpy_expected{0, 0, 1, 0};
vector<char> greater_eq_autob_numpy_expected{1, 0, 1, 1};
vector<char> less_autob_numpy_expected{0, 1, 0, 0};
vector<char> less_eq_autob_numpy_expected{1, 1, 0, 1};
vector<char> logical_and_autob_numpy_expected{0, 0, 0, 1};
vector<char> logical_or_autob_numpy_expected{0, 1, 1, 1};
vector<char> logical_xor_autob_numpy_expected{0, 1, 1, 0};
ASSERT_EQ(get_result_constant<int>(func, 0), add_expected);
ASSERT_EQ(get_result_constant<int>(func, 1), sub_expected);
ASSERT_EQ(get_result_constant<int>(func, 2), mul_expected);
ASSERT_EQ(get_result_constant<int>(func, 3), div_expected);
ASSERT_EQ(get_result_constant<int>(func, 4), min_expected);
ASSERT_EQ(get_result_constant<int>(func, 5), max_expected);
ASSERT_EQ(get_result_constant<int>(func, 6), abs_neg_expected);
ASSERT_EQ(get_result_constant<int>(func, 7), abs_neg_expected);
ASSERT_EQ(get_result_constant<int>(func, 8), sqrt_expected);
ASSERT_EQ(get_result_constant<int>(func, 9), add_autob_numpy_expected);
ASSERT_EQ(get_result_constant<int>(func, 10), sub_autob_numpy_expected);
ASSERT_EQ(get_result_constant<int>(func, 11), mul_autob_numpy_expected);
ASSERT_EQ(get_result_constant<int>(func, 12), div_autob_numpy_expected);
ASSERT_EQ(get_result_constant<int>(func, 13), min_autob_numpy_expected);
ASSERT_EQ(get_result_constant<int>(func, 14), max_autob_numpy_expected);
ASSERT_EQ(get_result_constant<char>(func, 15), equal_autob_numpy_expected);
ASSERT_EQ(get_result_constant<char>(func, 16), not_equal_autob_numpy_expected);
ASSERT_EQ(get_result_constant<char>(func, 17), greater_autob_numpy_expected);
ASSERT_EQ(get_result_constant<char>(func, 18), greater_eq_autob_numpy_expected);
ASSERT_EQ(get_result_constant<char>(func, 19), less_autob_numpy_expected);
ASSERT_EQ(get_result_constant<char>(func, 20), less_eq_autob_numpy_expected);
ASSERT_EQ(get_result_constant<char>(func, 21), logical_and_autob_numpy_expected);
ASSERT_EQ(get_result_constant<char>(func, 22), logical_or_autob_numpy_expected);
ASSERT_EQ(get_result_constant<char>(func, 23), logical_xor_autob_numpy_expected);
ASSERT_ANY_THROW(pass_manager.run_passes(func_error));
}
TEST(constant_folding, const_dequantize)
......
test/cpu_test.cpp
View file @ ba1141e0
......@@ -1172,7 +1172,7 @@ static std::vector<T> get_result_constant(std::shared_ptr<Function> f, size_t po
TEST(cpu_test, constant_unary_binary)
{
Shape shape_in{4};
Shape shape_in{2, 2};
vector<int> values_a{1, 2, 3, 4};
vector<int> values_b{1, 2, 3, 4};
vector<int> values_c{-1, -1, -1, -1};
......@@ -1184,6 +1184,7 @@ TEST(cpu_test, constant_unary_binary)
vector<char> values_i{0, 0, 1, 1};
vector<char> values_j{0, 1, 0, 1};
vector<float> values_k{-0.1f, 0.0f, -1.5f, 2.6f};
vector<int> values_l{1, 2};
auto a = make_shared<op::Constant>(element::i32, shape_in, values_a);
auto b = make_shared<op::Constant>(element::i32, shape_in, values_b);
auto c = make_shared<op::Constant>(element::i32, shape_in, values_c);
......@@ -1195,6 +1196,7 @@ TEST(cpu_test, constant_unary_binary)
auto i = make_shared<op::Constant>(element::boolean, shape_in, values_i);
auto j = make_shared<op::Constant>(element::boolean, shape_in, values_j);
auto k = make_shared<op::Constant>(element::f32, shape_in, values_k);
auto l = make_shared<op::Constant>(element::i32, Shape{2}, values_l);
auto add = a + b;
auto sub = a - b;
......@@ -1220,12 +1222,17 @@ TEST(cpu_test, constant_unary_binary)
auto ceil = make_shared<op::Ceiling>(k);
auto floor = make_shared<op::Floor>(k);
auto logical_not = make_shared<op::Not>(j);
// Note: The CPU functors do not actually support autobroadcast yet; instead the pass itself
// falls back if autobroadcasting is in use. Putting this check here just to make sure the
// fallback works as expected, but if direct support for autobroadcast is added to the CPU
// folders we should add more comprehensive tests here. --amprocte
auto add_autob_numpy = make_shared<op::Add>(a, l, op::AutoBroadcastType::NUMPY);
auto func = make_shared<Function>(
NodeVector{add, sub, mul, divn, min, max,
absn, neg, sqrt, relu, sign, equal,
not_equal, greater, greater_eq, less, less_eq, logical_and,
logical_or, logical_xor, ceil, floor, logical_not},
NodeVector{add, sub, mul, divn, min, max,
absn, neg, sqrt, relu, sign, equal,
not_equal, greater, greater_eq, less, less_eq, logical_and,
logical_or, logical_xor, ceil, floor, logical_not, add_autob_numpy},
ParameterVector{});
auto func_error = make_shared<Function>(NodeVector{neg_sqrt}, ParameterVector{});
......@@ -1282,6 +1289,7 @@ TEST(cpu_test, constant_unary_binary)
vector<float> ceil_expected{0.0f, 0.0f, -1.0f, 3.0f};
vector<float> floor_expected{-1.0f, 0.0f, -2.0f, 2.0f};
vector<char> not_expected{1, 0, 1, 0};
vector<int> add_autob_numpy_expected{2, 4, 4, 6};
ASSERT_EQ(get_result_constant<int>(func, 0), add_expected);
ASSERT_EQ(get_result_constant<int>(func, 1), sub_expected);
......@@ -1308,6 +1316,7 @@ TEST(cpu_test, constant_unary_binary)
ASSERT_TRUE(test::all_close_f(
get_result_constant<float>(func, 21), floor_expected, MIN_FLOAT_TOLERANCE_BITS));
ASSERT_EQ(get_result_constant<char>(func, 22), not_expected);
ASSERT_EQ(get_result_constant<int>(func, 23), add_autob_numpy_expected);
ASSERT_ANY_THROW(pass_manager.run_passes(func_error));
}
......
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