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Unverified Commit 956e8b3a authored by Robert Kimball's avatar Robert Kimball Committed by GitHub
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Merge branch 'master' into nmostafa/recompile

parents a2b9c6b8 5d3456e4
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Showing with 932 additions and 290 deletions
CODEOWNERS
View file @ 956e8b3a
......@@ -61,7 +61,7 @@ project/doc-contributor-README.rst @indie
/src/ngraph/type/ @diyessi
/src/ngraph/serializer.*pp @rkimballn1
/test/distributed.in.cpp @wenzhe-nrv @diyessi @jianyinglang
/test/backend/distributed.in.cpp @wenzhe-nrv @diyessi @jianyinglang
# Putting this last so it's not overridden by directory rules
CMakeLists.txt @rkimballn1 @silee2
......
src/contrib/mlir/compiler.cpp
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......@@ -14,6 +14,9 @@
// limitations under the License.
//*****************************************************************************
// NOTE: This file follows nGraph format style and naming convention since it
// exposes a public API to the rest of nGraph codebase.
#include "compiler.hpp"
#include "dialect/dialect.hpp"
......@@ -423,6 +426,12 @@ namespace ngraph
compiler.m_builder->getI64IntegerAttr(ng_node_gather->get_axis()));
return op;
}
template <>
mlir::Operation* MLIRCompiler::COMPILE_OP_DECL(ngraph::op::Relu)
{
return compiler.create_generic_op<mlir::NGReluOp>(ng_node);
}
}
}
}
......
src/contrib/mlir/compiler.hpp
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......@@ -14,6 +14,9 @@
// limitations under the License.
//*****************************************************************************
// NOTE: This file follows nGraph format style and naming convention since it
// exposes a public API to the rest of nGraph codebase.
#pragma once
#include "memory_manager.hpp"
......
src/contrib/mlir/dialect/dialect.cpp
View file @ 956e8b3a
......@@ -14,6 +14,9 @@
// limitations under the License.
//*****************************************************************************
// NOTE: This file follows nGraph format style and MLIR naming convention since it does
// not expose public API to the rest of nGraph codebase and heavily depends on MLIR API.
#include "dialect.hpp"
#include "ngraph/check.hpp"
#include "ops.hpp"
......@@ -41,12 +44,12 @@ void NGraphOpsDialect::printType(mlir::Type type, raw_ostream& os) const
case NG_TENSOR_TYPE_ID:
{
os << "tensor<";
auto tensor_ty = type.cast<NGTensorType>();
for (auto dim : tensor_ty.getShape())
auto tensorTy = type.cast<NGTensorType>();
for (auto dim : tensorTy.getShape())
{
os << dim << 'x';
}
os << tensor_ty.getElementType() << '>';
os << tensorTy.getElementType() << '>';
return;
}
case NG_I8_TYPE_ID:
......@@ -58,8 +61,8 @@ void NGraphOpsDialect::printType(mlir::Type type, raw_ostream& os) const
case NG_U32_TYPE_ID:
case NG_U64_TYPE_ID:
{
auto int_ty = type.cast<NGIntegerType>();
os << "i" << int_ty.getWidth();
auto intTy = type.cast<NGIntegerType>();
os << "i" << intTy.getWidth();
return;
}
case NG_BOOL_TYPE_ID:
......
src/contrib/mlir/dialect/dialect.hpp
View file @ 956e8b3a
......@@ -13,6 +13,10 @@
// See the License for the specific language governing permissions and
// limitations under the License.
//*****************************************************************************
// NOTE: This file follows nGraph format style and MLIR naming convention since it does
// not expose public API to the rest of nGraph codebase and heavily depends on MLIR API.
#pragma once
#include "mlir/IR/Dialect.h"
......@@ -23,6 +27,7 @@
#include "mlir/IR/TypeSupport.h"
#include "mlir/IR/Types.h"
#include "ngraph/check.hpp"
namespace mlir
{
class NGraphOpsDialect : public mlir::Dialect
......
src/contrib/mlir/dialect/ops.cpp
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......@@ -13,6 +13,10 @@
// See the License for the specific language governing permissions and
// limitations under the License.
//*****************************************************************************
// NOTE: This file follows nGraph format style and MLIR naming convention since it does
// not expose public API to the rest of nGraph codebase and heavily depends on MLIR API.
#include "ops.hpp"
#include "assertion.hpp"
#include "llvm/Support/ErrorHandling.h"
......
src/contrib/mlir/dialect/ops.hpp
View file @ 956e8b3a
......@@ -13,6 +13,10 @@
// See the License for the specific language governing permissions and
// limitations under the License.
//*****************************************************************************
// NOTE: This file follows nGraph format style and MLIR naming convention since it does
// not expose public API to the rest of nGraph codebase and heavily depends on MLIR API.
#pragma once
#include <cstdarg>
......
src/contrib/mlir/dialect/ops.td
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......@@ -18,6 +18,9 @@
//
//===----------------------------------------------------------------------===//
// NOTE: This file follows nGraph format style and MLIR naming convention since it does
// not expose public API to the rest of nGraph codebase and heavily depends on MLIR API.
include "mlir/IR/OpBase.td"
// nGraph Dialect operations definitions
......
src/contrib/mlir/dialect/type.cpp
View file @ 956e8b3a
......@@ -12,6 +12,10 @@
// 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.
//*****************************************************************************
// NOTE: This file follows nGraph format style and MLIR naming convention since it does
// not expose public API to the rest of nGraph codebase and heavily depends on MLIR API.
#include "type.hpp"
......
src/contrib/mlir/dialect/type.hpp
View file @ 956e8b3a
......@@ -13,6 +13,10 @@
// See the License for the specific language governing permissions and
// limitations under the License.
//*****************************************************************************
// NOTE: This file follows nGraph format style and MLIR naming convention since it does
// not expose public API to the rest of nGraph codebase and heavily depends on MLIR API.
#pragma once
#include "mlir/IR/Dialect.h"
......@@ -198,19 +202,19 @@ namespace mlir
return new (storage) NGTensorTypeStorage(eltType, shape);
}
Shape getShape() const { return m_shape; }
int64_t getRank() const { return m_shape.size(); }
EltType getElementType() const { return m_eltType; }
Shape getShape() const { return shape; }
int64_t getRank() const { return shape.size(); }
EltType getElementType() const { return eltType; }
private:
NGTensorTypeStorage(EltType eltType, Shape shape)
: m_eltType(eltType)
, m_shape(shape)
: eltType(eltType)
, shape(shape)
{
}
private:
EltType m_eltType;
Shape m_shape;
EltType eltType;
Shape shape;
};
/// NGraph Tensor Type
......
src/contrib/mlir/lowerer.cpp
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src/contrib/mlir/lowerer.hpp
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......@@ -14,6 +14,9 @@
// limitations under the License.
//*****************************************************************************
// NOTE: This file follows nGraph format style and MLIR naming convention since it does
// not expose public API to the rest of nGraph codebase and heavily depends on MLIR API.
#pragma once
#include "contrib/mlir/compiler.hpp"
......
src/contrib/mlir/memory_manager.cpp
View file @ 956e8b3a
......@@ -14,6 +14,9 @@
// limitations under the License.
//*****************************************************************************
// NOTE: This file follows nGraph format style and MLIR naming convention since it does
// not expose public API to the rest of nGraph codebase and heavily depends on MLIR API.
#include "memory_manager.hpp"
#include <memory>
#include "ngraph/ngraph_visibility.hpp"
......@@ -21,9 +24,9 @@
using namespace ngraph::runtime::ngmlir;
/// Call back to allocate memory for temps from JIT'ed code
extern "C" NGRAPH_API void* __mlir_allocate(MLIRMemMgr* mem_mgr, size_t size)
extern "C" NGRAPH_API void* __mlir_allocate(MLIRMemMgr* memMgr, size_t size)
{
return mem_mgr->allocate(size);
return memMgr->allocate(size);
}
void* MLIRMemMgr::allocate(size_t size)
......
src/contrib/mlir/memory_manager.hpp
View file @ 956e8b3a
......@@ -14,11 +14,15 @@
// limitations under the License.
//*****************************************************************************
// NOTE: This file follows nGraph format style and MLIR naming convention since it does
// not expose public API to the rest of nGraph codebase and heavily depends on MLIR API.
#pragma once
#include <stdint.h>
#include <stdlib.h>
#include <vector>
namespace ngraph
{
namespace runtime
......
src/contrib/mlir/pass/mlir_subgraph_extraction.cpp
View file @ 956e8b3a
......@@ -14,6 +14,9 @@
// limitations under the License.
//*****************************************************************************
// NOTE: This file follows nGraph format style and naming convention since it
// exposes a public API to the rest of nGraph codebase.
#include "mlir_subgraph_extraction.hpp"
#include "ngraph/assertion.hpp"
#include "ngraph/graph_util.hpp"
......
src/contrib/mlir/pass/mlir_subgraph_extraction.hpp
View file @ 956e8b3a
......@@ -14,6 +14,9 @@
// limitations under the License.
//*****************************************************************************
// NOTE: This file follows nGraph format style and naming convention since it
// exposes a public API to the rest of nGraph codebase.
#pragma once
#include <mutex>
......
src/ngraph/builder/quantization.cpp
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src/ngraph/builder/quantization.hpp
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src/ngraph/builder/quantization/quantized_linear_convolution.cpp
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......@@ -36,25 +36,25 @@ namespace ngraph
{
namespace quantization
{
shared_ptr<Node> QuantizedLinearConvolutionBias(const shared_ptr<Node>& input,
const shared_ptr<Node>& filter,
const shared_ptr<Node>& bias,
shared_ptr<Node> QuantizedLinearConvolutionBias(const Output<Node>& input,
const Output<Node>& filter,
const Output<Node>& bias,
const Strides& window_movement_strides,
const Strides& window_dilation_strides,
const CoordinateDiff& padding_below,
const CoordinateDiff& padding_above,
const Strides& data_dilation_strides,
const shared_ptr<Node>& input_scale,
const shared_ptr<Node>& filter_scale,
const shared_ptr<Node>& output_scale)
const Output<Node>& input_scale,
const Output<Node>& filter_scale,
const Output<Node>& output_scale)
{
// TODO: need to establish cross-nGraph view of scale (mult or div)
auto requantization_scale = (input_scale * filter_scale) / output_scale;
auto mybias = bias;
if (bias->get_element_type() != element::i32)
if (bias.get_element_type() != element::i32)
{
const auto zero = make_constant(element::i32, input_scale->get_shape(), 0);
const auto zero = make_constant(element::i32, input_scale.get_shape(), 0);
const AxisSet quantization_axes;
const auto bias_scale = input_scale * filter_scale;
op::Quantize::RoundMode round_mode =
......
src/ngraph/builder/quantization/quantized_linear_convolution.hpp
View file @ 956e8b3a
......@@ -26,17 +26,17 @@ namespace ngraph
namespace quantization
{
std::shared_ptr<Node>
QuantizedLinearConvolutionBias(const std::shared_ptr<Node>& input,
const std::shared_ptr<Node>& filter,
const std::shared_ptr<Node>& bias,
QuantizedLinearConvolutionBias(const Output<Node>& input,
const Output<Node>& filter,
const Output<Node>& bias,
const Strides& window_movement_strides,
const Strides& window_dilation_strides,
const CoordinateDiff& padding_below,
const CoordinateDiff& padding_above,
const Strides& data_dilation_strides,
const std::shared_ptr<Node>& input_scale,
const std::shared_ptr<Node>& filter_scale,
const std::shared_ptr<Node>& output_scale);
const Output<Node>& input_scale,
const Output<Node>& filter_scale,
const Output<Node>& output_scale);
}
}
}
src/ngraph/builder/quantization/quantized_linear_matmul.cpp
View file @ 956e8b3a
......@@ -39,14 +39,14 @@ namespace ngraph
{
// TODO: this code is falling back to fp32 dot
// 1) add support in reference kernel for zero point
shared_ptr<Node> QuantizedLinearMatmul(const shared_ptr<Node>& input0,
const shared_ptr<Node>& input1,
const shared_ptr<Node>& input0_scale,
const shared_ptr<Node>& input0_zero_point,
const shared_ptr<Node>& input1_scale,
const shared_ptr<Node>& input1_zero_point,
const shared_ptr<Node>& output_scale,
const shared_ptr<Node>& output_zero_point)
shared_ptr<Node> QuantizedLinearMatmul(const Output<Node>& input0,
const Output<Node>& input1,
const Output<Node>& input0_scale,
const Output<Node>& input0_zero_point,
const Output<Node>& input1_scale,
const Output<Node>& input1_zero_point,
const Output<Node>& output_scale,
const Output<Node>& output_zero_point)
{
// Check if zero point is constant and zero
if (ngraph::is_zero(input0_zero_point) && ngraph::is_zero(input1_zero_point) &&
......@@ -62,13 +62,13 @@ namespace ngraph
auto dq_input0 = make_shared<op::Dequantize>(input0,
input0_scale,
input0_zero_point,
input0_scale->get_element_type(),
input0_scale.get_element_type(),
axes);
auto dq_input1 = make_shared<op::Dequantize>(input1,
input1_scale,
input1_zero_point,
input1_scale->get_element_type(),
input1_scale.get_element_type(),
axes);
auto dot = make_shared<op::Dot>(dq_input0, dq_input1, 1);
......@@ -76,24 +76,23 @@ namespace ngraph
dot,
output_scale,
output_zero_point,
output_zero_point->get_element_type(),
output_zero_point.get_element_type(),
axes,
op::Quantize::RoundMode::ROUND_NEAREST_TOWARD_EVEN);
}
}
shared_ptr<Node> QuantizedLinearMatmulInteger(const shared_ptr<Node>& input0,
const shared_ptr<Node>& input1)
shared_ptr<Node> QuantizedLinearMatmulInteger(const Output<Node>& input0,
const Output<Node>& input1)
{
auto output_scale = make_constant(element::f32, Shape{}, 1);
return make_shared<op::QuantizedDot>(input0, input1, output_scale, false, false);
}
shared_ptr<Node>
QuantizedLinearMatmulInteger(const std::shared_ptr<Node>& input0,
const std::shared_ptr<Node>& input1,
const std::shared_ptr<Node>& input0_zero_point,
const std::shared_ptr<Node>& input1_zero_point)
shared_ptr<Node> QuantizedLinearMatmulInteger(const Output<Node>& input0,
const Output<Node>& input1,
const Output<Node>& input0_zero_point,
const Output<Node>& input1_zero_point)
{
// Check if zero points are constant and zero
if (ngraph::is_zero(input0_zero_point) && ngraph::is_zero(input1_zero_point))
......
src/ngraph/builder/quantization/quantized_linear_matmul.hpp
View file @ 956e8b3a
......@@ -25,24 +25,23 @@ namespace ngraph
{
namespace quantization
{
std::shared_ptr<Node>
QuantizedLinearMatmul(const std::shared_ptr<Node>& input0,
const std::shared_ptr<Node>& input1,
const std::shared_ptr<Node>& input0_scale,
const std::shared_ptr<Node>& input0_zero_point,
const std::shared_ptr<Node>& input1_scale,
const std::shared_ptr<Node>& input1_zero_point,
const std::shared_ptr<Node>& output_scale,
const std::shared_ptr<Node>& output_zero_point);
std::shared_ptr<Node> QuantizedLinearMatmul(const Output<Node>& input0,
const Output<Node>& input1,
const Output<Node>& input0_scale,
const Output<Node>& input0_zero_point,
const Output<Node>& input1_scale,
const Output<Node>& input1_zero_point,
const Output<Node>& output_scale,
const Output<Node>& output_zero_point);
std::shared_ptr<Node> QuantizedLinearMatmulInteger(const std::shared_ptr<Node>& input0,
const std::shared_ptr<Node>& input1);
std::shared_ptr<Node> QuantizedLinearMatmulInteger(const Output<Node>& input0,
const Output<Node>& input1);
std::shared_ptr<Node>
QuantizedLinearMatmulInteger(const std::shared_ptr<Node>& input0,
const std::shared_ptr<Node>& input1,
const std::shared_ptr<Node>& input0_zero_point,
const std::shared_ptr<Node>& input1_zero_point);
QuantizedLinearMatmulInteger(const Output<Node>& input0,
const Output<Node>& input1,
const Output<Node>& input0_zero_point,
const Output<Node>& input1_zero_point);
}
}
}
src/ngraph/builder/quantization_util.hpp
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This diff is collapsed.
src/ngraph/builder/quantization_utils.cpp
View file @ 956e8b3a
......@@ -22,26 +22,26 @@ namespace ngraph
{
namespace quantization_utils
{
std::shared_ptr<Node> max_abs(std::shared_ptr<Node> a, std::shared_ptr<Node> b)
std::shared_ptr<Node> max_abs(const Output<Node>& a, const Output<Node>& b)
{
auto abs_a = std::make_shared<op::Abs>(a);
auto abs_b = std::make_shared<op::Abs>(b);
return std::make_shared<op::Maximum>(abs_a, abs_b);
}
std::shared_ptr<Node> get_scale(std::shared_ptr<Node> input_min_range,
std::shared_ptr<Node> input_max_range,
std::shared_ptr<Node> get_scale(const Output<Node>& input_min_range,
const Output<Node>& input_max_range,
const ngraph::element::Type& quant_type,
bool bump_by_eps)
{
auto type = input_min_range->get_element_type();
if (type != input_max_range->get_element_type())
auto type = input_min_range.get_element_type();
if (type != input_max_range.get_element_type())
{
throw ngraph_error("get_scale: min and max must have same type");
}
auto shape = input_min_range->get_shape();
if (shape != input_max_range->get_shape())
auto shape = input_min_range.get_shape();
if (shape != input_max_range.get_shape())
{
throw ngraph_error("get_scale: min and max must have same shape");
}
......
src/ngraph/builder/quantization_utils.hpp
View file @ 956e8b3a
......@@ -37,10 +37,10 @@ namespace ngraph
{
namespace quantization_utils
{
std::shared_ptr<Node> max_abs(std::shared_ptr<Node> a, std::shared_ptr<Node> b);
std::shared_ptr<Node> max_abs(const Output<Node>& a, const Output<Node>& b);
std::shared_ptr<Node> get_scale(std::shared_ptr<Node> input_min_range,
std::shared_ptr<Node> input_max_range,
std::shared_ptr<Node> get_scale(const Output<Node>& input_min_range,
const Output<Node>& input_max_range,
const ngraph::element::Type& quant_type,
bool bump_by_eps = false);
}
......
src/ngraph/builder/quantized_conv_builder.cpp
View file @ 956e8b3a
......@@ -26,35 +26,34 @@ namespace ngraph
{
namespace builder
{
shared_ptr<Node> QuantizedConvolutionBuilder(const shared_ptr<Node>& input,
const shared_ptr<Node>& filters,
shared_ptr<Node> QuantizedConvolutionBuilder(const Output<Node>& input,
const Output<Node>& filters,
const Strides& window_movement_strides,
const Strides& window_dilation_strides,
const CoordinateDiff& padding_below,
const CoordinateDiff& padding_above,
const Strides& data_dilation_strides,
const shared_ptr<Node>& min_input,
const shared_ptr<Node>& max_input,
const shared_ptr<Node>& min_filter,
const shared_ptr<Node>& max_filter,
const shared_ptr<Node>& min_output,
const shared_ptr<Node>& max_output,
const Output<Node>& min_input,
const Output<Node>& max_input,
const Output<Node>& min_filter,
const Output<Node>& max_filter,
const Output<Node>& min_output,
const Output<Node>& max_output,
const ngraph::element::Type& output_type,
const ngraph::AxisSet& input_axes,
const ngraph::AxisSet& filter_axes,
const ngraph::AxisSet& output_axes)
{
auto input_scale =
quantization_utils::get_scale(min_input, max_input, input->get_element_type());
quantization_utils::get_scale(min_input, max_input, input.get_element_type());
auto filter_scale =
quantization_utils::get_scale(min_filter, max_filter, filters->get_element_type());
quantization_utils::get_scale(min_filter, max_filter, filters.get_element_type());
auto output_scale = quantization_utils::get_scale(min_output, max_output, output_type);
// TODO: Check for this later
// For Builders the zero point is assumed to be zero (for now)
auto input_zero_point = op::Constant::create(input->get_element_type(), Shape{}, {0});
auto filter_zero_point =
op::Constant::create(filters->get_element_type(), Shape{}, {0});
auto input_zero_point = op::Constant::create(input.get_element_type(), Shape{}, {0});
auto filter_zero_point = op::Constant::create(filters.get_element_type(), Shape{}, {0});
return make_shared<op::QuantizedConvolution>(
input,
......
src/ngraph/builder/quantized_conv_builder.hpp
View file @ 956e8b3a
......@@ -26,19 +26,19 @@ namespace ngraph
namespace builder
{
std::shared_ptr<Node>
QuantizedConvolutionBuilder(const std::shared_ptr<Node>& input,
const std::shared_ptr<Node>& filters,
QuantizedConvolutionBuilder(const Output<Node>& input,
const Output<Node>& filters,
const Strides& window_movement_strides,
const Strides& window_dilation_strides,
const CoordinateDiff& padding_below,
const CoordinateDiff& padding_above,
const Strides& data_dilation_strides,
const std::shared_ptr<Node>& min_input,
const std::shared_ptr<Node>& max_input,
const std::shared_ptr<Node>& min_filter,
const std::shared_ptr<Node>& max_filter,
const std::shared_ptr<Node>& min_output,
const std::shared_ptr<Node>& max_output,
const Output<Node>& min_input,
const Output<Node>& max_input,
const Output<Node>& min_filter,
const Output<Node>& max_filter,
const Output<Node>& min_output,
const Output<Node>& max_output,
const ngraph::element::Type& output_type,
const ngraph::AxisSet& input_axes = ngraph::AxisSet{},
const ngraph::AxisSet& filter_axes = ngraph::AxisSet{},
......
src/ngraph/graph_util.cpp
View file @ 956e8b3a
......@@ -293,9 +293,9 @@ std::shared_ptr<ngraph::Function> ngraph::clone_function(const ngraph::Function&
return std::make_shared<ngraph::Function>(cloned_results, cloned_params);
}
bool ngraph::is_equal_to_const_value(std::string const_value, std::shared_ptr<Node> reduce_constant)
bool ngraph::is_equal_to_const_value(std::string const_value, const Output<Node>& reduce_constant)
{
if (auto rc = dynamic_pointer_cast<ngraph::op::Constant>(reduce_constant))
if (auto rc = dynamic_pointer_cast<ngraph::op::Constant>(reduce_constant.get_node_shared_ptr()))
{
auto cshape = rc->get_shape();
size_t n = shape_size(cshape);
......@@ -454,7 +454,7 @@ std::shared_ptr<Node> ngraph::make_constant_from_string(std::string val,
return std::make_shared<op::Constant>(element_type, shape, cvals);
}
bool ngraph::is_zero(std::shared_ptr<Node> reduce_constant)
bool ngraph::is_zero(const Output<Node>& reduce_constant)
{
auto result_bool = is_equal_to_const_value("0", reduce_constant);
return result_bool;
......
src/ngraph/graph_util.hpp
View file @ 956e8b3a
......@@ -349,7 +349,7 @@ namespace ngraph
// Check if all paths from X to a result go through Y
bool is_post_dominated(Node* X, Node* Y);
bool is_equal_to_const_value(std::string const_value, std::shared_ptr<Node> reduce_constant);
bool is_equal_to_const_value(std::string const_value, const Output<Node>& reduce_constant);
// input nodes are cloned and returned
// NodeMap input may contain default node mapping i.e. pre-cloned nodes
......@@ -383,7 +383,7 @@ namespace ngraph
const element::Type& element_type,
const Shape& shape);
bool is_zero(std::shared_ptr<Node> reduce_constant);
bool is_zero(const Output<Node>& reduce_constant);
NodeVector get_subgraph_outputs(const NodeVector& nodes,
const NodeVector& exclusions,
......
src/ngraph/node.cpp
View file @ 956e8b3a
......@@ -86,6 +86,18 @@ std::shared_ptr<Node> Node::copy_with_new_inputs(const OutputVector& inputs) con
return copy_with_new_inputs(inputs, get_control_dependencies());
}
std::shared_ptr<Node> Node::get_output_as_single_output_node(size_t i)
{
for (auto in : output(i).get_target_inputs())
{
if (in.get_node()->description() == op::GetOutputElement::type_name)
{
return in.get_node()->shared_from_this();
}
}
return get_output_element(output(i), true);
}
std::shared_ptr<Node>
Node::copy_with_new_inputs(const OutputVector& inputs,
const std::vector<std::shared_ptr<Node>>& control_dependencies) const
......
src/ngraph/node.hpp
View file @ 956e8b3a
......@@ -279,6 +279,8 @@ namespace ngraph
/// Returns the partial shape for output i
const PartialShape& get_output_partial_shape(size_t i) const;
std::shared_ptr<Node> get_output_as_single_output_node(size_t i);
/// Checks that there is exactly one output and returns its shape
// TODO: deprecate in favor of node->output(0).get_shape() with a suitable check in the
// calling code, or updates to the calling code if it is making an invalid assumption of
......@@ -554,6 +556,13 @@ namespace ngraph
///
/// TODO: Make a plan to deprecate this.
std::shared_ptr<NodeType> get_node_shared_ptr() const { return m_node; }
/// \return A useable shared pointer to this output. If index 0, the node,
/// otherwise find or create a GOE.
std::shared_ptr<Node> as_single_output_node() const NGRAPH_DEPRECATED("Transitional.")
{
return m_node->get_output_as_single_output_node(m_index);
}
/// \return The index of the output referred to by this output handle.
size_t get_index() const { return m_index; }
/// \return A reference to the tensor descriptor for this output.
......
src/ngraph/op/experimental/quantized_avg_pool.cpp
View file @ 956e8b3a
......@@ -21,13 +21,15 @@
using namespace std;
using namespace ngraph;
op::QuantizedAvgPool::QuantizedAvgPool(const shared_ptr<Node>& arg,
const string op::QuantizedAvgPool::type_name{"QuantizedAvgPool"};
op::QuantizedAvgPool::QuantizedAvgPool(const Output<Node>& arg,
const Shape& window_shape,
const Strides& window_movement_strides,
const Shape& padding_below,
const Shape& padding_above,
bool include_padding_in_avg_computation)
: Op("QuantizedAvgPool", check_single_output_args({arg}))
: Op({arg})
, m_window_shape(window_shape)
, m_window_movement_strides(window_movement_strides)
, m_padding_below(padding_below)
......@@ -35,15 +37,16 @@ op::QuantizedAvgPool::QuantizedAvgPool(const shared_ptr<Node>& arg,
, m_include_padding_in_avg_computation(include_padding_in_avg_computation)
{
constructor_validate_and_infer_types();
}
if (arg->get_element_type() != element::u8 && arg->get_element_type() != element::i8)
void op::QuantizedAvgPool::validate_and_infer_types()
{
auto arg(input(0).get_source_output());
if (arg.get_element_type() != element::u8 && arg.get_element_type() != element::i8)
{
throw ngraph_error("QuantizedAvgPool supported only for i8/u8!");
}
}
void op::QuantizedAvgPool::validate_and_infer_types()
{
auto& arg_shape = get_input_shape(0);
if (0 == m_window_movement_strides.size() && arg_shape.size() > 2)
......
src/ngraph/op/experimental/quantized_avg_pool.hpp
View file @ 956e8b3a
......@@ -28,6 +28,11 @@ namespace ngraph
class QuantizedAvgPool : public Op
{
public:
NGRAPH_API
static const std::string type_name;
const std::string& description() const override { return type_name; }
/// \brief Constructs a batched average pooling operation.
QuantizedAvgPool() = default;
/// \brief Constructs a batched average pooling operation.
///
/// \param arg The node producing the input data batch tensor.<br>
......@@ -43,7 +48,7 @@ namespace ngraph
/// \param include_padding_in_avg_computation If true then averages include padding
/// elements, each treated as the number zero. If false, padding elements are entirely
/// ignored when computing averages.
QuantizedAvgPool(const std::shared_ptr<Node>& arg,
QuantizedAvgPool(const Output<Node>& arg,
const Shape& window_shape,
const Strides& window_movement_strides,
const Shape& padding_below,
......
src/ngraph/op/experimental/quantized_conv_bias.cpp
View file @ 956e8b3a
......@@ -27,17 +27,19 @@
using namespace std;
using namespace ngraph;
op::QuantizedConvolutionBias::QuantizedConvolutionBias(const shared_ptr<Node>& data_batch,
const shared_ptr<Node>& filters,
const shared_ptr<Node>& bias,
const string op::QuantizedConvolutionBias::type_name{"QuantizedConvolutionBias"};
op::QuantizedConvolutionBias::QuantizedConvolutionBias(const Output<Node>& data_batch,
const Output<Node>& filters,
const Output<Node>& bias,
const Strides& window_movement_strides,
const Strides& window_dilation_strides,
const CoordinateDiff& padding_below,
const CoordinateDiff& padding_above,
const Strides& data_dilation_strides,
const shared_ptr<Node>& scale,
const Output<Node>& scale,
const bool with_relu)
: Op("QuantizedConvolutionBias", check_single_output_args({data_batch, filters, bias, scale}))
: Op({data_batch, filters, bias, scale})
, m_window_movement_strides(window_movement_strides)
, m_window_dilation_strides(window_dilation_strides)
, m_padding_below(padding_below)
......@@ -47,8 +49,8 @@ op::QuantizedConvolutionBias::QuantizedConvolutionBias(const shared_ptr<Node>& d
{
constructor_validate_and_infer_types();
auto& data_batch_shape = data_batch->get_shape();
auto& filters_shape = filters->get_shape();
auto& data_batch_shape = data_batch.get_shape();
auto& filters_shape = filters.get_shape();
// TODO: call ngraph util
// util::validate_convbias_shapes(data_batch_shape, filters_shape, bias->get_shape());
......@@ -92,20 +94,21 @@ shared_ptr<Node> op::QuantizedConvolutionBias::copy_with_new_args(const NodeVect
m_with_relu));
}
op::QuantizedConvolutionBiasAdd::QuantizedConvolutionBiasAdd(const shared_ptr<Node>& data_batch,
const shared_ptr<Node>& filters,
const shared_ptr<Node>& bias,
const shared_ptr<Node>& sum_input,
const string op::QuantizedConvolutionBiasAdd::type_name{"QuantizedConvolutionBiasAdd"};
op::QuantizedConvolutionBiasAdd::QuantizedConvolutionBiasAdd(const Output<Node>& data_batch,
const Output<Node>& filters,
const Output<Node>& bias,
const Output<Node>& sum_input,
const Strides& window_movement_strides,
const Strides& window_dilation_strides,
const CoordinateDiff& padding_below,
const CoordinateDiff& padding_above,
const Strides& data_dilation_strides,
const shared_ptr<Node>& scale,
const shared_ptr<Node>& sum_scale,
const Output<Node>& scale,
const Output<Node>& sum_scale,
const bool with_relu)
: Op("QuantizedConvolutionBiasAdd",
check_single_output_args({data_batch, filters, bias, sum_input, scale, sum_scale}))
: Op({data_batch, filters, bias, sum_input, scale, sum_scale})
, m_window_movement_strides(window_movement_strides)
, m_window_dilation_strides(window_dilation_strides)
, m_padding_below(padding_below)
......@@ -115,8 +118,8 @@ op::QuantizedConvolutionBiasAdd::QuantizedConvolutionBiasAdd(const shared_ptr<No
{
constructor_validate_and_infer_types();
auto& data_batch_shape = data_batch->get_shape();
auto& filters_shape = filters->get_shape();
auto& data_batch_shape = data_batch.get_shape();
auto& filters_shape = filters.get_shape();
// TODO: call ngraph util
// util::validate_convbias_shapes(data_batch_shape, filters_shape, bias->get_shape());
......@@ -163,21 +166,22 @@ shared_ptr<Node>
m_with_relu));
}
const string op::QuantizedConvolutionBiasSignedAdd::type_name{"QuantizedConvolutionBiasSignedAdd"};
op::QuantizedConvolutionBiasSignedAdd::QuantizedConvolutionBiasSignedAdd(
const shared_ptr<Node>& data_batch,
const shared_ptr<Node>& filters,
const shared_ptr<Node>& bias,
const shared_ptr<Node>& sum_input,
const Output<Node>& data_batch,
const Output<Node>& filters,
const Output<Node>& bias,
const Output<Node>& sum_input,
const Strides& window_movement_strides,
const Strides& window_dilation_strides,
const CoordinateDiff& padding_below,
const CoordinateDiff& padding_above,
const Strides& data_dilation_strides,
const shared_ptr<Node>& scale,
const shared_ptr<Node>& sum_scale,
const Output<Node>& scale,
const Output<Node>& sum_scale,
const bool with_relu)
: Op("QuantizedConvolutionBiasSignedAdd",
check_single_output_args({data_batch, filters, bias, sum_input, scale, sum_scale}))
: Op({data_batch, filters, bias, sum_input, scale, sum_scale})
, m_window_movement_strides(window_movement_strides)
, m_window_dilation_strides(window_dilation_strides)
, m_padding_below(padding_below)
......@@ -187,8 +191,8 @@ op::QuantizedConvolutionBiasSignedAdd::QuantizedConvolutionBiasSignedAdd(
{
constructor_validate_and_infer_types();
auto& data_batch_shape = data_batch->get_shape();
auto& filters_shape = filters->get_shape();
auto& data_batch_shape = data_batch.get_shape();
auto& filters_shape = filters.get_shape();
// TODO: call ngraph util
// util::validate_convbias_shapes(data_batch_shape, filters_shape, bias->get_shape());
......
src/ngraph/op/experimental/quantized_conv_bias.hpp
View file @ 956e8b3a
......@@ -27,15 +27,18 @@ namespace ngraph
class QuantizedConvolutionBias : public Op
{
public:
QuantizedConvolutionBias(const std::shared_ptr<Node>& data_batch,
const std::shared_ptr<Node>& filters,
const std::shared_ptr<Node>& bias,
NGRAPH_API
static const std::string type_name;
const std::string& description() const override { return type_name; }
QuantizedConvolutionBias(const Output<Node>& data_batch,
const Output<Node>& filters,
const Output<Node>& bias,
const Strides& window_movement_strides,
const Strides& window_dilation_strides,
const CoordinateDiff& padding_below,
const CoordinateDiff& padding_above,
const Strides& data_dilation_strides,
const std::shared_ptr<Node>& scale,
const Output<Node>& scale,
const bool with_relu = false);
const Strides& get_window_movement_strides() const { return m_window_movement_strides; }
......@@ -43,9 +46,9 @@ namespace ngraph
const CoordinateDiff& get_padding_below() const { return m_padding_below; }
const CoordinateDiff& get_padding_above() const { return m_padding_above; }
const Strides& get_data_dilation_strides() const { return m_data_dilation_strides; }
std::shared_ptr<Node> get_bias() { return get_argument(2); }
std::shared_ptr<Node> get_filters() { return get_argument(1); }
std::shared_ptr<Node> get_data_batch() { return get_argument(0); }
Output<Node> get_bias() { return input(2).get_source_output(); }
Output<Node> get_filters() { return input(1).get_source_output(); }
Output<Node> get_data_batch() { return input(0).get_source_output(); }
bool with_relu() const { return m_with_relu; }
virtual std::shared_ptr<Node>
copy_with_new_args(const NodeVector& new_args) const override;
......@@ -62,17 +65,20 @@ namespace ngraph
class QuantizedConvolutionBiasAdd : public Op
{
public:
QuantizedConvolutionBiasAdd(const std::shared_ptr<Node>& data_batch,
const std::shared_ptr<Node>& filters,
const std::shared_ptr<Node>& bias,
const std::shared_ptr<Node>& sum_input,
NGRAPH_API
static const std::string type_name;
const std::string& description() const override { return type_name; }
QuantizedConvolutionBiasAdd(const Output<Node>& data_batch,
const Output<Node>& filters,
const Output<Node>& bias,
const Output<Node>& sum_input,
const Strides& window_movement_strides,
const Strides& window_dilation_strides,
const CoordinateDiff& padding_below,
const CoordinateDiff& padding_above,
const Strides& data_dilation_strides,
const std::shared_ptr<Node>& scale,
const std::shared_ptr<Node>& sum_scale,
const Output<Node>& scale,
const Output<Node>& sum_scale,
const bool with_relu = false);
const Strides& get_window_movement_strides() const { return m_window_movement_strides; }
......@@ -80,9 +86,9 @@ namespace ngraph
const CoordinateDiff& get_padding_below() const { return m_padding_below; }
const CoordinateDiff& get_padding_above() const { return m_padding_above; }
const Strides& get_data_dilation_strides() const { return m_data_dilation_strides; }
std::shared_ptr<Node> get_bias() { return get_argument(2); }
std::shared_ptr<Node> get_filters() { return get_argument(1); }
std::shared_ptr<Node> get_data_batch() { return get_argument(0); }
Output<Node> get_bias() { return input(2).get_source_output(); }
Output<Node> get_filters() { return input(1).get_source_output(); }
Output<Node> get_data_batch() { return input(0).get_source_output(); }
bool with_relu() const { return m_with_relu; }
virtual std::shared_ptr<Node>
copy_with_new_args(const NodeVector& new_args) const override;
......@@ -99,17 +105,20 @@ namespace ngraph
class QuantizedConvolutionBiasSignedAdd : public Op
{
public:
QuantizedConvolutionBiasSignedAdd(const std::shared_ptr<Node>& data_batch,
const std::shared_ptr<Node>& filters,
const std::shared_ptr<Node>& bias,
const std::shared_ptr<Node>& sum_input,
NGRAPH_API
static const std::string type_name;
const std::string& description() const override { return type_name; }
QuantizedConvolutionBiasSignedAdd(const Output<Node>& data_batch,
const Output<Node>& filters,
const Output<Node>& bias,
const Output<Node>& sum_input,
const Strides& window_movement_strides,
const Strides& window_dilation_strides,
const CoordinateDiff& padding_below,
const CoordinateDiff& padding_above,
const Strides& data_dilation_strides,
const std::shared_ptr<Node>& scale,
const std::shared_ptr<Node>& sum_scale,
const Output<Node>& scale,
const Output<Node>& sum_scale,
const bool with_relu = false);
const Strides& get_window_movement_strides() const { return m_window_movement_strides; }
......@@ -117,9 +126,9 @@ namespace ngraph
const CoordinateDiff& get_padding_below() const { return m_padding_below; }
const CoordinateDiff& get_padding_above() const { return m_padding_above; }
const Strides& get_data_dilation_strides() const { return m_data_dilation_strides; }
std::shared_ptr<Node> get_bias() { return get_argument(2); }
std::shared_ptr<Node> get_filters() { return get_argument(1); }
std::shared_ptr<Node> get_data_batch() { return get_argument(0); }
Output<Node> get_bias() { return input(2).get_source_output(); }
Output<Node> get_filters() { return input(1).get_source_output(); }
Output<Node> get_data_batch() { return input(0).get_source_output(); }
bool with_relu() const { return m_with_relu; }
virtual std::shared_ptr<Node>
copy_with_new_args(const NodeVector& new_args) const override;
......
src/ngraph/op/experimental/quantized_conv_relu.cpp
View file @ 956e8b3a
......@@ -26,15 +26,17 @@
using namespace std;
using namespace ngraph;
op::QuantizedConvolutionRelu::QuantizedConvolutionRelu(const shared_ptr<Node>& data_batch,
const shared_ptr<Node>& filters,
const string op::QuantizedConvolutionRelu::type_name{"QuantizedConvolutionRelu"};
op::QuantizedConvolutionRelu::QuantizedConvolutionRelu(const Output<Node>& data_batch,
const Output<Node>& filters,
const Strides& window_movement_strides,
const Strides& window_dilation_strides,
const CoordinateDiff& padding_below,
const CoordinateDiff& padding_above,
const Strides& data_dilation_strides,
const shared_ptr<Node>& scale)
: Op("QuantizedConvolutionRelu", check_single_output_args({data_batch, filters, scale}))
const Output<Node>& scale)
: Op({data_batch, filters, scale})
, m_window_movement_strides(window_movement_strides)
, m_window_dilation_strides(window_dilation_strides)
, m_padding_below(padding_below)
......@@ -43,8 +45,8 @@ op::QuantizedConvolutionRelu::QuantizedConvolutionRelu(const shared_ptr<Node>& d
{
constructor_validate_and_infer_types();
auto& data_batch_shape = data_batch->get_shape();
auto& filters_shape = filters->get_shape();
auto& data_batch_shape = data_batch.get_shape();
auto& filters_shape = filters.get_shape();
set_output_type(0,
element::u8,
......
src/ngraph/op/experimental/quantized_conv_relu.hpp
View file @ 956e8b3a
......@@ -27,22 +27,25 @@ namespace ngraph
class QuantizedConvolutionRelu : public Op
{
public:
QuantizedConvolutionRelu(const std::shared_ptr<Node>& data_batch,
const std::shared_ptr<Node>& filters,
NGRAPH_API
static const std::string type_name;
const std::string& description() const override { return type_name; }
QuantizedConvolutionRelu(const Output<Node>& data_batch,
const Output<Node>& filters,
const Strides& window_movement_strides,
const Strides& window_dilation_strides,
const CoordinateDiff& padding_below,
const CoordinateDiff& padding_above,
const Strides& data_dilation_strides,
const std::shared_ptr<Node>& scale);
const Output<Node>& scale);
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; }
const CoordinateDiff& get_padding_above() const { return m_padding_above; }
const Strides& get_data_dilation_strides() const { return m_data_dilation_strides; }
std::shared_ptr<Node> get_filters() { return get_argument(1); }
std::shared_ptr<Node> get_data_batch() { return get_argument(0); }
Output<Node> get_filters() { return input(1).get_source_output(); }
Output<Node> get_data_batch() { return input(0).get_source_output(); }
bool with_relu() const { return true; }
virtual std::shared_ptr<Node>
copy_with_new_args(const NodeVector& new_args) const override;
......
src/ngraph/op/experimental/quantized_dot.cpp
View file @ 956e8b3a
......@@ -24,19 +24,21 @@
using namespace std;
using namespace ngraph;
op::QuantizedDot::QuantizedDot(const shared_ptr<Node>& data,
const shared_ptr<Node>& weights,
const shared_ptr<Node>& scale,
const string op::QuantizedDot::type_name{"QuantizedDot"};
op::QuantizedDot::QuantizedDot(const Output<Node>& data,
const Output<Node>& weights,
const Output<Node>& scale,
bool requantize,
bool with_relu)
: Op("QuantizedDot", check_single_output_args({data, weights, scale}))
: Op({data, weights, scale})
, m_requantize(requantize)
, m_with_relu(with_relu)
{
constructor_validate_and_infer_types();
auto& data_shape = data->get_shape();
auto& weights_shape = weights->get_shape();
auto& data_shape = data.get_shape();
auto& weights_shape = weights.get_shape();
// QuantizedDot does [m ,n] * [n, k] = [m, k]
NODE_VALIDATION_CHECK(this,
data_shape.size() == 2 && weights_shape.size() == 2 &&
......@@ -47,7 +49,7 @@ op::QuantizedDot::QuantizedDot(const shared_ptr<Node>& data,
weights_shape);
auto output_et = requantize ? (with_relu ? element::u8 : element::i8) : element::i32;
if (data->get_element_type() == element::u8 && weights->get_element_type() == element::u8)
if (data.get_element_type() == element::u8 && weights.get_element_type() == element::u8)
{
output_et = element::u8;
}
......
src/ngraph/op/experimental/quantized_dot.hpp
View file @ 956e8b3a
......@@ -27,9 +27,12 @@ namespace ngraph
class QuantizedDot : public Op
{
public:
QuantizedDot(const std::shared_ptr<Node>& data,
const std::shared_ptr<Node>& weights,
const std::shared_ptr<Node>& scale,
NGRAPH_API
static const std::string type_name;
const std::string& description() const override { return type_name; }
QuantizedDot(const Output<Node>& data,
const Output<Node>& weights,
const Output<Node>& scale,
bool requantize = true,
bool with_relu = false);
......
src/ngraph/op/experimental/quantized_dot_bias.cpp
View file @ 956e8b3a
......@@ -24,21 +24,23 @@
using namespace std;
using namespace ngraph;
op::QuantizedDotBias::QuantizedDotBias(const shared_ptr<Node>& data,
const shared_ptr<Node>& weights,
const shared_ptr<Node>& bias,
const shared_ptr<Node>& scale,
const string op::QuantizedDotBias::type_name{"QuantizedDotBias"};
op::QuantizedDotBias::QuantizedDotBias(const Output<Node>& data,
const Output<Node>& weights,
const Output<Node>& bias,
const Output<Node>& scale,
bool requantize,
bool with_relu)
: Op("QuantizedDotBias", check_single_output_args({data, weights, bias, scale}))
: Op({data, weights, bias, scale})
, m_requantize(requantize)
, m_with_relu(with_relu)
{
constructor_validate_and_infer_types();
auto& data_shape = data->get_shape();
auto& weights_shape = weights->get_shape();
auto& bias_shape = bias->get_shape();
auto& data_shape = data.get_shape();
auto& weights_shape = weights.get_shape();
auto& bias_shape = bias.get_shape();
NODE_VALIDATION_CHECK(this,
data_shape.size() == 2 && weights_shape.size() == 2 &&
data_shape[1] == weights_shape[1],
......
src/ngraph/op/experimental/quantized_dot_bias.hpp
View file @ 956e8b3a
......@@ -27,10 +27,13 @@ namespace ngraph
class QuantizedDotBias : public Op
{
public:
QuantizedDotBias(const std::shared_ptr<Node>& data,
const std::shared_ptr<Node>& weights,
const std::shared_ptr<Node>& bias,
const std::shared_ptr<Node>& scale,
NGRAPH_API
static const std::string type_name;
const std::string& description() const override { return type_name; }
QuantizedDotBias(const Output<Node>& data,
const Output<Node>& weights,
const Output<Node>& bias,
const Output<Node>& scale,
bool requantize = true,
bool with_relu = false);
......
src/ngraph/op/experimental/quantized_max_pool.cpp
View file @ 956e8b3a
......@@ -22,12 +22,14 @@
using namespace std;
using namespace ngraph;
op::QuantizedMaxPool::QuantizedMaxPool(const shared_ptr<Node>& arg,
const string op::QuantizedMaxPool::type_name{"QuantizedMaxPool"};
op::QuantizedMaxPool::QuantizedMaxPool(const Output<Node>& arg,
const Shape& window_shape,
const Strides& window_movement_strides,
const Shape& padding_below,
const Shape& padding_above)
: Op("QuantizedMaxPool", check_single_output_args({arg}))
: Op({arg})
, m_window_shape(window_shape)
, m_window_movement_strides(window_movement_strides)
, m_padding_below(padding_below)
......@@ -35,7 +37,7 @@ op::QuantizedMaxPool::QuantizedMaxPool(const shared_ptr<Node>& arg,
{
constructor_validate_and_infer_types();
if (arg->get_element_type() != element::u8 && arg->get_element_type() != element::i8)
if (arg.get_element_type() != element::u8 && arg.get_element_type() != element::i8)
{
throw ngraph_error("QuantizedMaxPool supported only for i8/u8!");
}
......
src/ngraph/op/experimental/quantized_max_pool.hpp
View file @ 956e8b3a
......@@ -26,6 +26,9 @@ namespace ngraph
class QuantizedMaxPool : public Op
{
public:
NGRAPH_API
static const std::string type_name;
const std::string& description() const override { return type_name; }
/// \brief Constructs a batched max pooling operation.
///
/// \param arg The node producing the input data batch tensor.
......@@ -33,7 +36,7 @@ namespace ngraph
/// \param window_movement_strides The window movement strides.
/// \param padding_below The below-padding shape.
/// \param padding_above The above-padding shape.
QuantizedMaxPool(const std::shared_ptr<Node>& arg,
QuantizedMaxPool(const Output<Node>& arg,
const Shape& window_shape,
const Strides& window_movement_strides,
const Shape& padding_below,
......
src/ngraph/op/get_output_element.cpp
View file @ 956e8b3a
......@@ -71,20 +71,7 @@ NodeVector op::get_output_elements(const shared_ptr<Node>& mon)
NodeVector goes(mon->get_output_size());
for (auto o : mon->outputs())
{
shared_ptr<Node> goe;
for (auto in : o.get_target_inputs())
{
if (in.get_node()->description() == op::GetOutputElement::type_name)
{
goe = in.get_node()->shared_from_this();
break;
}
}
if (goe == nullptr)
{
goe = get_output_element(o, true);
}
goes.at(o.get_index()) = goe;
goes.at(o.get_index()) = o.as_single_output_node();
}
return goes;
}
src/ngraph/op/quantize.cpp
View file @ 956e8b3a
......@@ -20,14 +20,16 @@
using namespace std;
using namespace ngraph;
op::Quantize::Quantize(const shared_ptr<Node>& input,
const shared_ptr<Node>& scale,
const shared_ptr<Node>& zero_point,
const string op::Quantize::type_name{"Quantize"};
op::Quantize::Quantize(const Output<Node>& input,
const Output<Node>& scale,
const Output<Node>& zero_point,
const element::Type& type,
const AxisSet& axes,
RoundMode round_mode)
: Op("Quantize", check_single_output_args({input, scale, zero_point}))
: Op({input, scale, zero_point})
, m_type(type)
, m_axes(axes)
, m_round_mode(round_mode)
......
src/ngraph/op/quantize.hpp
View file @ 956e8b3a
......@@ -30,6 +30,9 @@ namespace ngraph
class Quantize : public ngraph::op::Op
{
public:
NGRAPH_API
static const std::string type_name;
const std::string& description() const override { return type_name; }
enum class RoundMode
{
// round to nearest integer
......@@ -82,9 +85,9 @@ namespace ngraph
/// \param type output element type
/// \param axes axis positions on which `scale` and `zero_point` are specified
/// \param round_mode describes how to perform ROUND function (see above)
Quantize(const std::shared_ptr<Node>& input,
const std::shared_ptr<Node>& scale,
const std::shared_ptr<Node>& zero_point,
Quantize(const Output<Node>& input,
const Output<Node>& scale,
const Output<Node>& zero_point,
const ngraph::element::Type& type,
const ngraph::AxisSet& axes,
RoundMode round_mode);
......
src/ngraph/op/quantized_convolution.cpp
View file @ 956e8b3a
......@@ -24,32 +24,33 @@
using namespace std;
using namespace ngraph;
op::QuantizedConvolution::QuantizedConvolution(const shared_ptr<Node>& input,
const shared_ptr<Node>& filters,
const string op::QuantizedConvolution::type_name{"QuantizedConvolution"};
op::QuantizedConvolution::QuantizedConvolution(const Output<Node>& input,
const Output<Node>& filters,
const Strides& window_movement_strides,
const Strides& window_dilation_strides,
const CoordinateDiff& padding_below,
const CoordinateDiff& padding_above,
const Strides& data_dilation_strides,
const shared_ptr<Node>& input_scale,
const shared_ptr<Node>& input_zero_point,
const shared_ptr<Node>& filter_scale,
const shared_ptr<Node>& filter_zero_point,
const shared_ptr<Node>& output_scale,
const shared_ptr<Node>& output_zero_point,
const Output<Node>& input_scale,
const Output<Node>& input_zero_point,
const Output<Node>& filter_scale,
const Output<Node>& filter_zero_point,
const Output<Node>& output_scale,
const Output<Node>& output_zero_point,
const element::Type& output_type,
const AxisSet& input_axes,
const AxisSet& filter_axes,
const AxisSet& output_axes)
: Op("QuantizedConvolution",
check_single_output_args({input,
filters,
input_scale,
input_zero_point,
filter_scale,
filter_zero_point,
output_scale,
output_zero_point}))
: Op({input,
filters,
input_scale,
input_zero_point,
filter_scale,
filter_zero_point,
output_scale,
output_zero_point})
, m_window_movement_strides(window_movement_strides)
, m_window_dilation_strides(window_dilation_strides)
, m_padding_below(padding_below)
......
src/ngraph/op/quantized_convolution.hpp
View file @ 956e8b3a
......@@ -26,6 +26,9 @@ namespace ngraph
class QuantizedConvolution : public Op
{
public:
NGRAPH_API
static const std::string type_name;
const std::string& description() const override { return type_name; }
/// \brief Constructs a quantized convolution operation.
///
/// \param input The node producing the input data batch tensor.
......@@ -45,19 +48,19 @@ namespace ngraph
/// \param input_axes Input axes set for channel wise quantization
/// \param filter_axes Filter axes set for channel wise quantization
/// \param output_axes Output axes set for channel wise quantization
QuantizedConvolution(const std::shared_ptr<Node>& input,
const std::shared_ptr<Node>& filters,
QuantizedConvolution(const Output<Node>& input,
const Output<Node>& filters,
const Strides& window_movement_strides,
const Strides& window_dilation_strides,
const CoordinateDiff& padding_below,
const CoordinateDiff& padding_above,
const Strides& data_dilation_strides,
const std::shared_ptr<Node>& input_scale,
const std::shared_ptr<Node>& input_zero_point,
const std::shared_ptr<Node>& filter_scale,
const std::shared_ptr<Node>& filter_zero_point,
const std::shared_ptr<Node>& output_scale,
const std::shared_ptr<Node>& output_zero_point,
const Output<Node>& input_scale,
const Output<Node>& input_zero_point,
const Output<Node>& filter_scale,
const Output<Node>& filter_zero_point,
const Output<Node>& output_scale,
const Output<Node>& output_zero_point,
const ngraph::element::Type& output_type,
const ngraph::AxisSet& input_axes = ngraph::AxisSet{},
const ngraph::AxisSet& filter_axes = ngraph::AxisSet{},
......
src/ngraph/op/select.cpp
View file @ 956e8b3a
......@@ -26,10 +26,10 @@
using namespace std;
using namespace ngraph;
op::Select::Select(const shared_ptr<Node>& arg0,
const shared_ptr<Node>& arg1,
const shared_ptr<Node>& arg2)
: Op("Select", check_single_output_args({arg0, arg1, arg2}))
const string op::Select::type_name{"Select"};
op::Select::Select(const Output<Node>& arg0, const Output<Node>& arg1, const Output<Node>& arg2)
: Op({arg0, arg1, arg2})
{
constructor_validate_and_infer_types();
}
......
src/ngraph/op/select.hpp
View file @ 956e8b3a
......@@ -40,14 +40,17 @@ namespace ngraph
class Select : public Op
{
public:
NGRAPH_API
static const std::string type_name;
const std::string& description() const override { return type_name; }
/// \brief Constructs a selection operation.
Select() = default;
/// \brief Constructs a selection operation.
///
/// \param arg0 Node that produces the first input tensor.
/// \param arg1 Node that produces the second input tensor.
/// \param arg2 Node that produces the third input tensor.
Select(const std::shared_ptr<Node>& arg0,
const std::shared_ptr<Node>& arg1,
const std::shared_ptr<Node>& arg2);
Select(const Output<Node>& arg0, const Output<Node>& arg1, const Output<Node>& arg2);
virtual std::shared_ptr<Node>
copy_with_new_args(const NodeVector& new_args) const override;
......
src/ngraph/op/subtract.cpp
View file @ 956e8b3a
......@@ -20,10 +20,12 @@
using namespace std;
using namespace ngraph;
op::Subtract::Subtract(const shared_ptr<Node>& arg0,
const shared_ptr<Node>& arg1,
const string op::Subtract::type_name{"Subtract"};
op::Subtract::Subtract(const Output<Node>& arg0,
const Output<Node>& arg1,
const AutoBroadcastSpec& autob)
: BinaryElementwiseArithmetic("Subtract", arg0, arg1, autob)
: BinaryElementwiseArithmetic(arg0, arg1, autob)
{
constructor_validate_and_infer_types();
}
......@@ -50,8 +52,7 @@ void op::Subtract::generate_adjoints(autodiff::Adjoints& adjoints, const NodeVec
adjoints.add_delta(y, -delta);
}
shared_ptr<ngraph::Node> ngraph::operator-(const shared_ptr<ngraph::Node> arg0,
const shared_ptr<ngraph::Node> arg1)
shared_ptr<ngraph::Node> ngraph::operator-(const Output<Node> arg0, const Output<Node> arg1)
{
return make_shared<ngraph::op::Subtract>(arg0, arg1);
}
src/ngraph/op/subtract.hpp
View file @ 956e8b3a
......@@ -26,13 +26,17 @@ namespace ngraph
class Subtract : public util::BinaryElementwiseArithmetic
{
public:
NGRAPH_API
static const std::string type_name;
const std::string& description() const override { return type_name; }
Subtract() = default;
/// \brief Constructs an subtraction operation.
///
/// \param arg0 Node that produces the first input tensor.
/// \param arg1 Node that produces the second input tensor.
/// \param autob Auto broadcast specification
Subtract(const std::shared_ptr<Node>& arg0,
const std::shared_ptr<Node>& arg1,
Subtract(const Output<Node>& arg0,
const Output<Node>& arg1,
const AutoBroadcastSpec& autob = AutoBroadcastSpec());
virtual std::shared_ptr<Node>
......@@ -42,6 +46,6 @@ namespace ngraph
const NodeVector& deltas) override;
};
}
std::shared_ptr<ngraph::Node> operator-(const std::shared_ptr<ngraph::Node> arg0,
const std::shared_ptr<ngraph::Node> arg1);
std::shared_ptr<ngraph::Node> operator-(const Output<ngraph::Node> arg0,
const Output<ngraph::Node> arg1);
}
src/ngraph/pass/constant_folding.cpp
View file @ 956e8b3a
This diff is collapsed.
src/ngraph/pass/constant_folding.hpp
View file @ 956e8b3a
......@@ -42,7 +42,9 @@ public:
CONVERT,
SHAPE_OF,
REVERSE,
PRODUCT
PRODUCT,
SUM,
CONCAT
};
ConstantFolding(const ngraph::BuildNodeExecutorMap& cfmap = ngraph::BuildNodeExecutorMap())
......@@ -60,6 +62,8 @@ public:
construct_constant_shape_of();
construct_constant_reverse();
construct_constant_product();
construct_constant_sum();
construct_constant_concat();
}
//this allows to specify the order in which matchers will be run
......@@ -84,6 +88,8 @@ public:
case CFTransformations::SHAPE_OF: construct_constant_shape_of(); break;
case CFTransformations::REVERSE: construct_constant_reverse(); break;
case CFTransformations::PRODUCT: construct_constant_product(); break;
case CFTransformations::SUM: construct_constant_sum(); break;
case CFTransformations::CONCAT: construct_constant_concat(); break;
}
}
}
......@@ -100,6 +106,8 @@ private:
void construct_constant_shape_of();
void construct_constant_reverse();
void construct_constant_product();
void construct_constant_sum();
void construct_constant_concat();
ngraph::BuildNodeExecutorMap m_cfmap;
};
src/ngraph/pass/reshape_sinking.cpp
View file @ 956e8b3a
This diff is collapsed.
src/ngraph/runtime/allocator.cpp
View file @ 956e8b3a
......@@ -51,6 +51,6 @@ public:
ngraph::runtime::Allocator* ngraph::runtime::get_default_allocator()
{
static std::unique_ptr<DefaultAllocator> allocator(new DefaultAllocator());
return allocator.get();
static DefaultAllocator* allocator = new DefaultAllocator();
return allocator;
}
src/ngraph/runtime/backend.hpp
View file @ 956e8b3a
......@@ -146,7 +146,7 @@ public:
virtual Allocator* get_host_memory_allocator() { return nullptr; }
/// \brief Set the host memory allocator to be used by the backend
/// \param allocator is pointer to host memory allocator object
virtual void set_host_memory_allocator(std::unique_ptr<Allocator> allocator) {}
virtual void set_host_memory_allocator(Allocator* allocator) {}
/// \brief Returns memory allocator used by backend for device allocations
virtual Allocator* get_device_memory_allocator()
{
......
src/ngraph/runtime/cpu/cpu_backend.cpp
View file @ 956e8b3a
......@@ -187,11 +187,10 @@ runtime::Allocator* runtime::cpu::CPU_Backend::get_host_memory_allocator()
{
return runtime::get_default_allocator();
}
return m_allocator.get();
return m_allocator;
}
void runtime::cpu::CPU_Backend::set_host_memory_allocator(
std::unique_ptr<runtime::Allocator> allocator)
void runtime::cpu::CPU_Backend::set_host_memory_allocator(Allocator* allocator)
{
if (m_allocator)
{
......@@ -200,7 +199,7 @@ void runtime::cpu::CPU_Backend::set_host_memory_allocator(
throw ngraph_error(
"Allocator already exists. Changing allocators mid-execution is not permitted.");
}
m_allocator = std::move(allocator);
m_allocator = allocator;
}
vector<runtime::PerformanceCounter> runtime::cpu::CPU_Executable::get_performance_data() const
......
src/ngraph/runtime/cpu/cpu_backend.hpp
View file @ 956e8b3a
......@@ -65,7 +65,7 @@ namespace ngraph
void remove_compiled_function(std::shared_ptr<Executable> exec) override;
Allocator* get_host_memory_allocator() override;
void set_host_memory_allocator(std::unique_ptr<Allocator> allocator) override;
void set_host_memory_allocator(Allocator* allocator) override;
bool is_supported(const Node& node) const override;
bool is_supported_property(const Property prop) const override;
......@@ -76,7 +76,7 @@ namespace ngraph
std::mutex m_exec_map_mutex;
std::unordered_map<std::shared_ptr<Function>, std::shared_ptr<Executable>>
m_exec_map;
std::unique_ptr<Allocator> m_allocator;
Allocator* m_allocator;
};
class CPU_BACKEND_API CPU_Executable : public runtime::Executable
......
src/ngraph/runtime/cpu/cpu_builder.cpp
View file @ 956e8b3a
......@@ -470,6 +470,12 @@ namespace ngraph
BUILD_UNARY_ELEMWISE_CF_FUNCTOR(runtime::cpu::kernel::sqrt);
}
template <>
NodeExecutorTy Builder::BUILDER_CF_DECL(ngraph::op::Sign)
{
BUILD_UNARY_ELEMWISE_CF_FUNCTOR(runtime::cpu::kernel::sign);
}
#define TI(x) type_index(typeid(x))
BuildOpMap& GetGlobalBuildDispatcher()
......@@ -536,6 +542,7 @@ namespace ngraph
REGISTER_CF_BUILDER(Negative);
REGISTER_CF_BUILDER(Relu);
REGISTER_CF_BUILDER(Sqrt);
REGISTER_CF_BUILDER(Sign);
}
}
}
src/ngraph/runtime/gpu/unit_test.manifest
View file @ 956e8b3a
......@@ -98,7 +98,7 @@ all_2x2x3_eliminate_dims_0_1
all_2x2x3_eliminate_dims_0_2
all_2x2x3_eliminate_dims_1_2
all_2x2x3_eliminate_dims_0_1_2
dynamic_GPU.all
all_dynamic
# GPU backend uses floats to implement these ops for int32
floor_int32
......
src/ngraph/runtime/reference/abs.hpp
View file @ 956e8b3a
......@@ -30,7 +30,7 @@ namespace ngraph
for (size_t i = 0; i < count; i++)
{
// TODO: generic "abs" doesn't work here for some reason.
out[i] = (arg[i] < 0 ? -arg[i] : arg[i]);
out[i] = (arg[i] < T(0) ? T(-arg[i]) : arg[i]);
}
}
}
......
src/ngraph/runtime/reference/sign.hpp
View file @ 956e8b3a
......@@ -29,7 +29,7 @@ namespace ngraph
{
for (size_t i = 0; i < count; i++)
{
out[i] = (arg[i] < 0 ? -1 : (arg[i] > 0 ? 1 : 0));
out[i] = (arg[i] < T(0) ? T(-1) : (arg[i] > T(0) ? T(1) : T(0)));
}
}
}
......
src/tools/nbench/benchmark_pipelined.cpp
View file @ 956e8b3a
......@@ -96,6 +96,10 @@ static void
result->read(data->get_data_ptr(),
data->get_element_count() * data->get_element_type().size());
}
if (current_iteration == (s_iterations + s_warmup_iterations - 1))
{
s_timer.stop();
}
}
}
}
......@@ -177,7 +181,6 @@ vector<runtime::PerformanceCounter> run_benchmark_pipelined(shared_ptr<Function>
{
threads[i].join();
}
s_timer.stop();
float time = s_timer.get_milliseconds();
cout << time / iterations << "ms per iteration" << endl;
......
test/CMakeLists.txt
View file @ 956e8b3a
......@@ -207,20 +207,20 @@ add_subdirectory(util)
# backend specific test files must meet the following requirements:
# 1) The must be named <name>.in.cpp
# 2) They must be in the test directory
# 2) They must be in the `test/backend` directory
# 3) Include "util/test_control.hpp" in your cpp file
# 4) add the line `static string s_manifest = "${MANIFEST}";` to your cpp file
# 5) Use the `NGRAPH_TEST` macro in place of `TEST`.
# All such files are configured via cmake which replaces all instances of cmake variables
# such as ${BACKEND_NAME} with their values, such as CPU, GPU, or INTERPRETER.
set(MULTI_TEST_SRC
autodiff.in.cpp
backend/abc.in.cpp
backend/aliased_output.in.cpp
backend/all.in.cpp
backend/any.in.cpp
backend/api.in.cpp
backend/arg_reduce.in.cpp
backend/autodiff.in.cpp
backend/batch_mat_mul.in.cpp
backend/batch_norm.in.cpp
backend/binary_elementwise.in.cpp
......@@ -231,7 +231,13 @@ set(MULTI_TEST_SRC
backend/constant.in.cpp
backend/convert.in.cpp
backend/convolution.in.cpp
backend/convolution_reference.in.cpp
backend/dot.in.cpp
backend/dyn_broadcast.in.cpp
backend/dyn_replace_slice_reference.in.cpp
backend/dyn_reshape.in.cpp
backend/dyn_slice_reference.in.cpp
backend/dynamic.in.cpp
backend/embedding_lookup.in.cpp
backend/function_name.in.cpp
backend/fused_op.in.cpp
......@@ -253,6 +259,7 @@ set(MULTI_TEST_SRC
backend/product.in.cpp
backend/quantize_dequantize.in.cpp
backend/quantized_convolution.in.cpp
backend/range.in.cpp
backend/relu.in.cpp
backend/replace_slice.in.cpp
backend/reshape.in.cpp
......@@ -267,14 +274,11 @@ set(MULTI_TEST_SRC
backend/sum.in.cpp
backend/tensorview_custom_mem.in.cpp
backend/topk.in.cpp
backend/transpose.in.cpp
backend/unhandled_op.in.cpp
backend/unary_elementwise.in.cpp
backend/validate_call.in.cpp
backend/zero_sized.in.cpp
convolution_test.in.cpp
dyn_replace_slice_test.in.cpp
dyn_slice_test.in.cpp
dynamic.in.cpp
)
if (NGRAPH_MLIR_ENABLE)
......@@ -282,11 +286,11 @@ if (NGRAPH_MLIR_ENABLE)
endif()
if(NGRAPH_DISTRIBUTED_ENABLE)
list(APPEND MULTI_TEST_SRC distributed.in.cpp)
list(APPEND MULTI_TEST_SRC backend/distributed.in.cpp)
endif()
if (NGRAPH_CPU_ENABLE)
list(APPEND MULTI_TEST_SRC backend_graph_comparison.in.cpp)
list(APPEND MULTI_TEST_SRC backend/graph_comparison.in.cpp)
endif()
if (NGRAPH_ONNX_IMPORT_ENABLE)
......
test/backend/all.in.cpp
View file @ 956e8b3a
......@@ -316,3 +316,53 @@ NGRAPH_TEST(${BACKEND_NAME}, all_change_axis)
handle->call_with_validate({result}, {a});
EXPECT_EQ((vector<char>{1, 0, 1}), read_vector<char>(result));
}
NGRAPH_TEST(${BACKEND_NAME}, all_dynamic)
{
// Create a graph for f(x,axes:int32) = All(x,Convert<int64>(axes)).
auto x = make_shared<op::Parameter>(element::boolean, PartialShape::dynamic());
auto axes = make_shared<op::Parameter>(element::i32, PartialShape{Dimension::dynamic()});
auto axes_i64 = make_shared<op::Convert>(axes, element::i64);
auto all = make_shared<op::All>(x, axes_i64);
ASSERT_TRUE(all->get_output_partial_shape(0).rank().is_dynamic());
auto f = make_shared<Function>(NodeVector{all}, ParameterVector{x, axes});
auto backend = runtime::Backend::create("${BACKEND_NAME}", true);
auto ex = backend->compile(f);
auto t_r = backend->create_dynamic_tensor(element::boolean, PartialShape::dynamic());
std::vector<Shape> x_shapes{
Shape{2, 3}, Shape{2, 3}, Shape{2, 3}, Shape{2, 3}, Shape{5}, Shape{5}};
std::vector<std::vector<int32_t>> axeses{{}, {0}, {1}, {0, 1}, {}, {0}};
std::vector<std::vector<char>> inputs{{1, 0, 1, 0, 1, 0},
{1, 0, 1, 0, 0, 1},
{1, 0, 1, 1, 1, 1},
{1, 0, 1, 0, 1, 0},
{1, 0, 1, 0, 1},
{1, 0, 1, 0, 1}};
std::vector<Shape> expected_result_shapes{
Shape{2, 3}, Shape{3}, Shape{2}, Shape{}, Shape{5}, Shape{}};
std::vector<std::vector<char>> expected_results{
{1, 0, 1, 0, 1, 0}, {0, 0, 1}, {0, 1}, {0}, {1, 0, 1, 0, 1}, {0}};
for (size_t i = 0; i < x_shapes.size(); i++)
{
auto t_x = backend->create_tensor(element::boolean, x_shapes[i]);
auto t_axes = backend->create_tensor(element::i32, Shape{axeses[i].size()});
copy_data(t_x, inputs[i]);
copy_data(t_axes, axeses[i]);
ex->call_with_validate({t_r}, {t_x, t_axes});
ASSERT_EQ(t_r->get_shape(), expected_result_shapes[i]);
auto results = read_vector<char>(t_r);
ASSERT_EQ(results, expected_results[i]);
}
}
test/backend/batch_mat_mul.in.cpp
View file @ 956e8b3a
......@@ -89,7 +89,8 @@ NGRAPH_TEST(${BACKEND_NAME}, batch_mat_mul_forward)
auto backend_results = execute(backend_f, batchmatmul_args, "${BACKEND_NAME}");
for (size_t i = 0; i < ref_results.size(); i++)
{
EXPECT_TRUE(test::all_close(ref_results.at(i), backend_results.at(i), 1.0e-4f, 1.0e-4f));
EXPECT_TRUE(test::all_close_f(
ref_results.at(i), backend_results.at(i), DEFAULT_FLOAT_TOLERANCE_BITS + 3));
}
}
#endif
test/backend/computation_reuse.in.cpp
View file @ 956e8b3a
......@@ -55,16 +55,19 @@ NGRAPH_TEST(${BACKEND_NAME}, computation_reuse)
vector<float> weights(512, 0.5f);
vector<float> rv(128);
auto a = backend->create_tensor(element::f32, shape_a, input.data());
auto b = backend->create_tensor(element::f32, shape_b, weights.data());
auto result = backend->create_tensor(element::f32, shape_r, rv.data());
auto a = backend->create_tensor(element::f32, shape_a);
auto b = backend->create_tensor(element::f32, shape_b);
auto result = backend->create_tensor(element::f32, shape_r);
auto handle = backend->compile(f);
handle->call_with_validate({result}, {a, b});
copy_data(a, input);
copy_data(b, weights);
vector<float> rv_saved(rv);
auto exec = backend->compile(f);
exec->call_with_validate({result}, {a, b});
vector<float> rv_saved(read_vector<float>(result));
b->set_stale(false);
handle->call_with_validate({result}, {a, b});
EXPECT_TRUE(test::all_close_f(rv_saved, rv));
exec->call_with_validate({result}, {a, b});
EXPECT_TRUE(test::all_close_f(rv_saved, read_vector<float>(result)));
}
test/backend/dyn_broadcast.in.cpp 0 → 100644
View file @ 956e8b3a
//*****************************************************************************
// 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.
//*****************************************************************************
#include "gtest/gtest.h"
#include "ngraph/ngraph.hpp"
#include "util/all_close_f.hpp"
#include "util/test_control.hpp"
#include "util/test_tools.hpp"
using namespace std;
using namespace ngraph;
static string s_manifest = "${MANIFEST}";
NGRAPH_TEST(${BACKEND_NAME}, dyn_broadcast)
{
// Create a graph for
// f(x,shape:i32,axes:32) = Broadcast(x,Convert<i64>(shape),Convert<i64>(axes)).
auto x = make_shared<op::Parameter>(element::f32, PartialShape::dynamic());
auto shape = make_shared<op::Parameter>(element::i32, PartialShape{Dimension::dynamic()});
auto axes = make_shared<op::Parameter>(element::i32, PartialShape{Dimension::dynamic()});
auto shape_i64 = make_shared<op::Convert>(shape, element::i64);
auto axes_i64 = make_shared<op::Convert>(axes, element::i64);
auto bc = make_shared<op::DynBroadcast>(x, shape_i64, axes_i64);
auto f = make_shared<Function>(NodeVector{bc}, ParameterVector{x, shape, axes});
auto backend = runtime::Backend::create("${BACKEND_NAME}", true);
auto ex = backend->compile(f);
auto t_r = backend->create_dynamic_tensor(element::f32, PartialShape::dynamic());
std::vector<Shape> x_shapes{Shape{}, Shape{}, Shape{2}, Shape{2}};
std::vector<std::vector<int32_t>> shapes{{2, 2}, {2, 2, 2}, {3, 2}, {2, 3}};
std::vector<std::vector<int32_t>> axeses{{0, 1}, {0, 1, 2}, {0}, {1}};
std::vector<std::vector<float>> inputs{{6}, {7}, {10, 11}, {10, 11}};
std::vector<Shape> expected_result_shapes{
Shape{2, 2}, Shape{2, 2, 2}, Shape{3, 2}, Shape{2, 3}};
std::vector<std::vector<float>> expected_results{
{6, 6, 6, 6}, {7, 7, 7, 7, 7, 7, 7, 7}, {10, 11, 10, 11, 10, 11}, {10, 10, 10, 11, 11, 11}};
for (size_t i = 0; i < x_shapes.size(); i++)
{
auto t_x = backend->create_tensor(element::f32, x_shapes[i]);
auto t_shape = backend->create_tensor(element::i32, Shape{shapes[i].size()});
auto t_axes = backend->create_tensor(element::i32, Shape{axeses[i].size()});
copy_data(t_x, inputs[i]);
copy_data(t_shape, shapes[i]);
copy_data(t_axes, axeses[i]);
ex->call_with_validate({t_r}, {t_x, t_shape, t_axes});
ASSERT_EQ(t_r->get_shape(), expected_result_shapes[i]);
auto results = read_vector<float>(t_r);
ASSERT_TRUE(test::all_close_f(results, expected_results[i], MIN_FLOAT_TOLERANCE_BITS));
}
}
test/backend/dyn_reshape.in.cpp 0 → 100644
View file @ 956e8b3a
//*****************************************************************************
// 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.
//*****************************************************************************
#include "gtest/gtest.h"
#include "ngraph/ngraph.hpp"
#include "util/all_close_f.hpp"
#include "util/test_control.hpp"
#include "util/test_tools.hpp"
using namespace std;
using namespace ngraph;
static string s_manifest = "${MANIFEST}";
NGRAPH_TEST(${BACKEND_NAME}, dyn_reshape)
{
auto backend = runtime::Backend::create("${BACKEND_NAME}", true);
auto build_graph = [&backend](bool zero_flag) {
// Create a graph for f(x,shape) = DynReshape(x,shape,zero_flag=zero_flag).
auto x = make_shared<op::Parameter>(element::i32, PartialShape::dynamic());
auto shape = make_shared<op::Parameter>(element::i64, PartialShape::dynamic(1));
auto dyn_reshape = make_shared<op::DynReshape>(x, shape, zero_flag);
EXPECT_TRUE(dyn_reshape->get_output_partial_shape(0).same_scheme(PartialShape::dynamic()));
auto f = make_shared<Function>(NodeVector{dyn_reshape}, ParameterVector{x, shape});
auto ex = backend->compile(f);
return ex;
};
auto t_r = backend->create_dynamic_tensor(element::i32, PartialShape::dynamic());
auto ex_flag_off = build_graph(false);
auto ex_flag_on = build_graph(true);
std::vector<std::tuple<bool, Shape, std::vector<int32_t>, std::vector<int64_t>, Shape>> tests;
tests.emplace_back(make_tuple(
false, Shape{2, 3}, vector<int32_t>{1, 2, 3, 4, 5, 6}, vector<int64_t>{6}, Shape{6}));
tests.emplace_back(make_tuple(
true, Shape{2, 3}, vector<int32_t>{1, 2, 3, 4, 5, 6}, vector<int64_t>{6}, Shape{6}));
tests.emplace_back(make_tuple(
false, Shape{2, 3}, vector<int32_t>{1, 2, 3, 4, 5, 6}, vector<int64_t>{-1}, Shape{6}));
tests.emplace_back(make_tuple(false,
Shape{2, 3},
vector<int32_t>{1, 2, 3, 4, 5, 6},
vector<int64_t>{2, -1},
Shape{2, 3}));
tests.emplace_back(make_tuple(false,
Shape{2, 3},
vector<int32_t>{1, 2, 3, 4, 5, 6},
vector<int64_t>{3, -1},
Shape{3, 2}));
tests.emplace_back(make_tuple(false,
Shape{2, 3},
vector<int32_t>{1, 2, 3, 4, 5, 6},
vector<int64_t>{3, 2, -1},
Shape{3, 2, 1}));
tests.emplace_back(make_tuple(true,
Shape{2, 3},
vector<int32_t>{1, 2, 3, 4, 5, 6},
vector<int64_t>{3, 2, -1},
Shape{3, 2, 1}));
tests.emplace_back(make_tuple(true,
Shape{2, 3},
vector<int32_t>{1, 2, 3, 4, 5, 6},
vector<int64_t>{0, 0, -1},
Shape{2, 3, 1}));
tests.emplace_back(make_tuple(true,
Shape{2, 3},
vector<int32_t>{1, 2, 3, 4, 5, 6},
vector<int64_t>{2, 0, -1},
Shape{2, 3, 1}));
tests.emplace_back(make_tuple(
true, Shape{0, 3, 4}, vector<int32_t>{}, vector<int64_t>{3, -1, 2}, Shape{3, 0, 2}));
for (auto& test : tests)
{
bool zero_flag = get<0>(test);
const Shape& in_shape = get<1>(test);
const std::vector<int32_t>& data = get<2>(test);
const std::vector<int64_t>& dims = get<3>(test);
const Shape& out_shape = get<4>(test);
auto t_x = backend->create_tensor(element::i32, in_shape);
auto t_shape = backend->create_tensor(element::i64, Shape{dims.size()});
copy_data(t_x, data);
copy_data(t_shape, dims);
auto ex = zero_flag ? ex_flag_on : ex_flag_off;
ex->call_with_validate({t_r}, {t_x, t_shape});
ASSERT_EQ(t_r->get_element_type(), element::i32);
ASSERT_EQ(t_r->get_shape(), out_shape);
auto results = read_vector<int32_t>(t_r);
ASSERT_EQ(results, data);
}
}
test/backend/range.in.cpp 0 → 100644
View file @ 956e8b3a
//*****************************************************************************
// 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.
//*****************************************************************************
#include "gtest/gtest.h"
#include "ngraph/ngraph.hpp"
#include "util/all_close_f.hpp"
#include "util/test_control.hpp"
#include "util/test_tools.hpp"
using namespace std;
using namespace ngraph;
static string s_manifest = "${MANIFEST}";
template <typename T>
struct RangeTest
{
T start;
T stop;
T step;
Shape expected_result_shape;
std::vector<T> expected_result;
};
// TODO(amprocte): We should test this with more than just int32, but there is a bug in the
// handling of element type-changing that is currently blocking doing that easily.
NGRAPH_TEST(${BACKEND_NAME}, range)
{
// Create a graph for f(start,stop,step) = Range(start,stop,step).
auto start = make_shared<op::Parameter>(element::i32, Shape{});
auto stop = make_shared<op::Parameter>(element::i32, Shape{});
auto step = make_shared<op::Parameter>(element::i32, Shape{});
auto range = make_shared<op::Range>(start, stop, step);
ASSERT_TRUE(range->get_output_partial_shape(0).same_scheme(PartialShape::dynamic(1)));
auto f = make_shared<Function>(NodeVector{range}, ParameterVector{start, stop, step});
auto backend = runtime::Backend::create("${BACKEND_NAME}", true);
auto ex = backend->compile(f);
auto t_r = backend->create_dynamic_tensor(element::i32, PartialShape::dynamic());
std::vector<RangeTest<int32_t>> int32_tests = {
RangeTest<int32_t>{0, 10, 1, Shape{10}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}},
RangeTest<int32_t>{-5, 6, 3, Shape{4}, {-5, -2, 1, 4}},
RangeTest<int32_t>{10, 0, 1, Shape{0}, {}},
RangeTest<int32_t>{10, 5, -3, Shape{2}, {10, 7}}};
for (auto& test : int32_tests)
{
auto t_start = backend->create_tensor(element::i32, Shape{});
auto t_stop = backend->create_tensor(element::i32, Shape{});
auto t_step = backend->create_tensor(element::i32, Shape{});
copy_data(t_start, std::vector<int32_t>{test.start});
copy_data(t_stop, std::vector<int32_t>{test.stop});
copy_data(t_step, std::vector<int32_t>{test.step});
ex->call_with_validate({t_r}, {t_start, t_stop, t_step});
ASSERT_EQ(t_r->get_element_type(), element::i32);
ASSERT_EQ(t_r->get_shape(), test.expected_result_shape);
auto results = read_vector<int32_t>(t_r);
ASSERT_EQ(results, test.expected_result);
}
}
test/backend/sum.in.cpp
View file @ 956e8b3a
......@@ -690,3 +690,53 @@ NGRAPH_TEST(${BACKEND_NAME}, sum_stable_simple_double)
#endif
#endif
NGRAPH_TEST(${BACKEND_NAME}, sum_dynamic)
{
// Create a graph for f(x,axes:int32) = Sum(x,Convert<int64>(axes)).
auto x = make_shared<op::Parameter>(element::f32, PartialShape::dynamic());
auto axes = make_shared<op::Parameter>(element::i32, PartialShape{Dimension::dynamic()});
auto axes_i64 = make_shared<op::Convert>(axes, element::i64);
auto sum = make_shared<op::Sum>(x, axes_i64);
ASSERT_TRUE(sum->get_output_partial_shape(0).rank().is_dynamic());
auto f = make_shared<Function>(NodeVector{sum}, ParameterVector{x, axes});
auto backend = runtime::Backend::create("${BACKEND_NAME}", true);
auto ex = backend->compile(f);
auto t_r = backend->create_dynamic_tensor(element::f32, PartialShape::dynamic());
std::vector<Shape> x_shapes{
Shape{2, 3}, Shape{2, 3}, Shape{2, 3}, Shape{2, 3}, Shape{5}, Shape{5}};
std::vector<std::vector<int32_t>> axeses{{}, {0}, {1}, {0, 1}, {}, {0}};
std::vector<std::vector<float>> inputs{{1, 2, 3, 4, 5, 6},
{1, 2, 3, 4, 5, 6},
{1, 2, 3, 4, 5, 6},
{1, 2, 3, 4, 5, 6},
{1, 2, 3, 4, 5},
{1, 2, 3, 4, 5}};
std::vector<Shape> expected_result_shapes{
Shape{2, 3}, Shape{3}, Shape{2}, Shape{}, Shape{5}, Shape{}};
std::vector<std::vector<float>> expected_results{
{1, 2, 3, 4, 5, 6}, {5, 7, 9}, {6, 15}, {21}, {1, 2, 3, 4, 5}, {15}};
for (size_t i = 0; i < x_shapes.size(); i++)
{
auto t_x = backend->create_tensor(element::f32, x_shapes[i]);
auto t_axes = backend->create_tensor(element::i32, Shape{axeses[i].size()});
copy_data(t_x, inputs[i]);
copy_data(t_axes, axeses[i]);
ex->call_with_validate({t_r}, {t_x, t_axes});
ASSERT_EQ(t_r->get_shape(), expected_result_shapes[i]);
auto results = read_vector<float>(t_r);
ASSERT_TRUE(test::all_close_f(results, expected_results[i], MIN_FLOAT_TOLERANCE_BITS));
}
}
test/backend/transpose.in.cpp 0 → 100644
View file @ 956e8b3a
//*****************************************************************************
// 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.
//*****************************************************************************
#include "gtest/gtest.h"
#include "ngraph/ngraph.hpp"
#include "util/all_close_f.hpp"
#include "util/test_control.hpp"
#include "util/test_tools.hpp"
using namespace std;
using namespace ngraph;
static string s_manifest = "${MANIFEST}";
NGRAPH_TEST(${BACKEND_NAME}, transpose)
{
//
// Create a graph for f(x,perm) = Transpose(x,Convert<i64>(perm)). We'll do the permutation in
// i32 and cast it to i64, just for fun (and to mirror the TensorFlow test I am porting here).
//
auto x = make_shared<op::Parameter>(element::f32, PartialShape::dynamic());
auto perm = make_shared<op::Parameter>(element::i32, PartialShape{Dimension::dynamic()});
auto perm_i64 = make_shared<op::Convert>(perm, element::i64);
auto x_transpose = make_shared<op::Transpose>(x, perm_i64);
auto f = make_shared<Function>(NodeVector{x_transpose}, ParameterVector{x, perm});
auto backend = runtime::Backend::create("${BACKEND_NAME}", true);
auto ex = backend->compile(f);
auto t_r = backend->create_dynamic_tensor(element::f32, PartialShape::dynamic());
std::vector<Shape> x_shapes{Shape{2, 3}, Shape{2, 3}, Shape{2, 2, 3}};
std::vector<std::vector<int32_t>> perms{{0, 1}, {1, 0}, {2, 1, 0}};
std::vector<std::vector<float>> inputs{
{1, 2, 3, 4, 5, 6}, {1, 2, 3, 4, 5, 6}, {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}};
std::vector<Shape> expected_result_shapes{Shape{2, 3}, Shape{3, 2}, {3, 2, 2}};
// Generated with numpy, so don't worry. :)
std::vector<std::vector<float>> expected_results{
{1, 2, 3, 4, 5, 6}, {1, 4, 2, 5, 3, 6}, {1, 7, 4, 10, 2, 8, 5, 11, 3, 9, 6, 12}};
for (size_t i = 0; i < x_shapes.size(); i++)
{
auto t_x = backend->create_tensor(element::f32, x_shapes[i]);
auto t_perm = backend->create_tensor(element::i32, Shape{perms[i].size()});
copy_data(t_x, inputs[i]);
copy_data(t_perm, perms[i]);
ex->call_with_validate({t_r}, {t_x, t_perm});
ASSERT_EQ(t_r->get_shape(), expected_result_shapes[i]);
auto results = read_vector<float>(t_r);
ASSERT_TRUE(test::all_close_f(results, expected_results[i], MIN_FLOAT_TOLERANCE_BITS));
}
}
test/constant_folding.cpp
View file @ 956e8b3a
......@@ -408,6 +408,57 @@ TEST(constant_folding, const_product)
ASSERT_EQ(values_expected, values_out);
}
TEST(constant_folding, const_sum)
{
Shape input_shape{3, 3};
vector<int32_t> values_in{1, 2, 3, 4, 5, 6, 7, 8, 9};
auto constant = op::Constant::create(element::i32, input_shape, values_in);
auto convert = make_shared<op::Sum>(constant, AxisSet{1});
auto f = make_shared<Function>(convert, ParameterVector{});
pass::Manager pass_manager;
pass_manager.register_pass<pass::ConstantFolding>();
pass_manager.run_passes(f);
ASSERT_EQ(count_ops_of_type<op::Sum>(f), 0);
ASSERT_EQ(count_ops_of_type<op::Constant>(f), 1);
auto new_const =
std::dynamic_pointer_cast<op::Constant>(f->get_results().at(0)->get_argument(0));
ASSERT_TRUE(new_const);
auto values_out = new_const->get_vector<int32_t>();
vector<int32_t> values_expected{6, 15, 24};
ASSERT_EQ(values_expected, values_out);
}
TEST(constant_folding, const_concat)
{
auto constant0 =
op::Constant::create(element::i32, Shape{2, 3}, vector<int32_t>{1, 2, 3, 4, 5, 6});
auto constant1 = op::Constant::create(element::i32, Shape{2, 1}, vector<int32_t>{7, 8});
auto concat = make_shared<op::Concat>(NodeVector{constant0, constant1}, 1);
auto f = make_shared<Function>(concat, ParameterVector{});
pass::Manager pass_manager;
pass_manager.register_pass<pass::ConstantFolding>();
pass_manager.run_passes(f);
ASSERT_EQ(count_ops_of_type<op::Concat>(f), 0);
ASSERT_EQ(count_ops_of_type<op::Constant>(f), 1);
auto new_const =
std::dynamic_pointer_cast<op::Constant>(f->get_results().at(0)->get_argument(0));
ASSERT_TRUE(new_const);
auto values_out = new_const->get_vector<int32_t>();
vector<int32_t> values_expected{1, 2, 3, 7, 4, 5, 6, 8};
ASSERT_EQ(values_expected, values_out);
}
TEST(constant_folding, pass_property)
{
auto pass = std::make_shared<ngraph::pass::ConstantFolding>();
......
test/update_convolution_reference.sh
View file @ 956e8b3a
......@@ -15,4 +15,4 @@
# limitations under the License.
# ******************************************************************************
declare THIS_SCRIPT_DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )"
python ${THIS_SCRIPT_DIR}/ref_generators/generate_convolution_ref.py ${THIS_SCRIPT_DIR}/convolution_test.in.cpp
python ${THIS_SCRIPT_DIR}/ref_generators/generate_convolution_ref.py ${THIS_SCRIPT_DIR}/backend/convolution_reference.in.cpp
test/update_dyn_replace_slice_reference.sh
View file @ 956e8b3a
......@@ -15,4 +15,4 @@
# limitations under the License.
# ******************************************************************************
declare THIS_SCRIPT_DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )"
python ${THIS_SCRIPT_DIR}/ref_generators/generate_dyn_replace_slice_ref.py ${THIS_SCRIPT_DIR}/dyn_replace_slice_test.in.cpp
python ${THIS_SCRIPT_DIR}/ref_generators/generate_dyn_replace_slice_ref.py ${THIS_SCRIPT_DIR}/backend/dyn_replace_slice_reference.in.cpp
test/update_dyn_slice_reference.sh
View file @ 956e8b3a
......@@ -15,4 +15,4 @@
# limitations under the License.
# ******************************************************************************
declare THIS_SCRIPT_DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )"
python ${THIS_SCRIPT_DIR}/ref_generators/generate_dyn_slice_ref.py ${THIS_SCRIPT_DIR}/dyn_slice_test.in.cpp
python ${THIS_SCRIPT_DIR}/ref_generators/generate_dyn_slice_ref.py ${THIS_SCRIPT_DIR}/backend/dyn_slice_reference.in.cpp
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