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Unverified Commit 60252edd authored by Scott Cyphers's avatar Scott Cyphers Committed by GitHub
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Merge branch 'master' into ayzhuang/batch_norm_infer_relu_fusion

parents 341205cf 47342339
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Showing with 185 additions and 229 deletions
cmake/external_mkldnn.cmake
View file @ 60252edd
......@@ -18,10 +18,10 @@ include(ExternalProject)
# Includes blas 3.8.0 in mkldnn
set(NGRAPH_MKLDNN_SHORT_VERSION 0)
set(NGRAPH_MKLDNN_FULL_VERSION 0.19.0.0)
set(NGRAPH_MKLDNN_VERSION "v0.19")
set(NGRAPH_MKLDNN_FULL_VERSION 0.20.0.0)
set(NGRAPH_MKLDNN_VERSION "v0.20")
set(NGRAPH_MKLDNN_SUB_VERSION "2019.0.5.20190502")
set(NGRAPH_MKLDNN_GIT_TAG "027de76")
set(NGRAPH_MKLDNN_GIT_TAG "v0.20")
#------------------------------------------------------------------------------
# Fetch and install MKL-DNN
......
cmake/mkldnn.patch
View file @ 60252edd
......@@ -28,16 +28,3 @@ index f10feb20..05f47961 100644
set_property(TARGET ${LIB_NAME} PROPERTY PUBLIC_HEADER ${HEADERS})
target_include_directories(${LIB_NAME} PUBLIC
diff --git a/src/cpu/jit_avx512_common_conv_kernel.cpp b/src/cpu/jit_avx512_common_conv_kernel.cpp
index 1bb98fa43..b8b54401f 100644
--- a/src/cpu/jit_avx512_common_conv_kernel.cpp
+++ b/src/cpu/jit_avx512_common_conv_kernel.cpp
@@ -3055,7 +3055,7 @@ void jit_avx512_common_conv_bwd_weights_kernel_f32::bias_kernel_3d() {
void jit_avx512_common_conv_bwd_weights_kernel_f32
::compute_oh_loop_common()
{
- assert(jcp.harness == harness_mb_reduction);
+ assert(one_of(jcp.harness, harness_mb_reduction, harness_3d_reduction));
int b_pad = jcp.b_pad;
int t_pad = jcp.t_pad;
bool is_dilated = jcp.dilate_h != 0;
doc/sphinx/conf.py
View file @ 60252edd
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
#
......@@ -73,11 +71,11 @@ author = 'Intel Corporation'
# built documents.
#
# The short X.Y version.
version = '0.22'
version = '0.23'
# The Documentation full version, including alpha/beta/rc tags. Some features
# available in the latest code will not necessarily be documented first
release = '0.22.0'
release = '0.23.0'
# The language for content autogenerated by Sphinx. Refer to documentation
# for a list of supported languages.
......
doc/sphinx/ngraph_theme/ngversions.html
View file @ 60252edd
......@@ -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/v0.22.0">0.22</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>
<li><a href="https://github.com/NervanaSystems/ngraph/releases/tag/v0.18.1">0.18.1</a></li>
</ul></dd>
</dl>
<dl>
......
doc/sphinx/source/project/release-notes.rst
View file @ 60252edd
......@@ -6,28 +6,30 @@ Release Notes
nGraph is provided as source code, APIs, build scripts, and some binary formats
for various Compiler stack configurations and use cases.
For downloads formatted as ``.zip`` and ``tar.gz``, see
https://github.com/NervanaSystems/ngraph/releases.
This page includes additional documentation updates.
We are pleased to announce the release of version |version|-doc.
==============================
Core updates for |version|
~~~~~~~~~~~~~~~~~~~~~~~~~~~
+ PlaidML support
+ More ONNX ops
+ Optimizations
+ Don't reseed RNG on each use
+ Elementwise divide defaults to Python semantics
+ GenerateMask seed optional
0.22-doc
--------
+ Initial doc and API for IntelGPU backend.
+ DynamicBackend API.
+ Note deprecation of support of MXNet's ``ngraph-mxnet`` PyPI.
+ Noted changes on graph inspection options resultant from PR 3016.
+ Added better tips and details to doc-contributor-README.
Latest doc updates
~~~~~~~~~~~~~~~~~~
+ Document new debug tool
+ Note deprecation of MXNet's ``ngraph-mxnet`` PyPI
+ Note default change to `svg` files for graphs and visualization
+ Add more prominent tips for contributors who find the doc-contributor-README
.. important:: Pre-releases (``-rc-0.*``) have newer features, and are less stable.
......@@ -36,8 +38,15 @@ Core updates for |version|
Changelog on Previous Releases
==============================
For downloads formatted as ``.zip`` and ``tar.gz``, see
https://github.com/NervanaSystems/ngraph/releases.
0.22
----
+ More ONNX ops
+ Optimizations
+ Don't reseed RNG on each use
+ Initial doc and API for IntelGPU backend
+ DynamicBackend API
0.21
----
......@@ -51,12 +60,6 @@ https://github.com/NervanaSystems/ngraph/releases.
+ offset arg for tensor creation is deprecated
+ static linking support
+ Initial test of 0.21-doc
0.21-doc
--------
Summary of documentation-related changes:
+ Updated :doc:`doc-contributor-README` for new community-based contributions.
+ Added instructions on how to test or display the installed nGraph version.
+ Added instructions on building nGraph bridge (ngraph-bridge).
......@@ -82,8 +85,6 @@ Summary of documentation-related changes:
0.19
----
**Download** `0.19.0-rc.2`_
+ More dynamic shape preparation
+ Distributed interface factored out
+ fp16 and bfloat16 types
......@@ -103,9 +104,6 @@ Summary of documentation-related changes:
0.18
----
**Download** `0.18.1`_
+ Python formatting issue
+ mkl-dnn work-around
+ Event tracing improvements
......@@ -118,8 +116,6 @@ Summary of documentation-related changes:
0.17
----
**Download** `0.17.0-rc.1`_
+ Allow negative padding in more places
+ Add code generation for some quantized ops
+ Preliminary dynamic shape support
......@@ -131,11 +127,6 @@ Summary of documentation-related changes:
0.16
----
* **Download**: `0.16.0-rc.3`_
* **Download** `0.16.0-rc.2`_
* **Download** `0.16.0-rc.1`_
+ NodeInput and NodeOutput classes prepare for simplifications of Node
+ Test improvements
+ Additional quantization ops
......@@ -143,11 +134,3 @@ Summary of documentation-related changes:
+ Fix memory leak
+ Concat optimization
+ Doc updates
.. _0.20.0-rc.0: https://github.com/NervanaSystems/ngraph/releases/tag/v0.20.0-rc.0_
.. _0.19.0-rc.2: https://github.com/NervanaSystems/ngraph/releases/tag/v0.19.0-rc.2_
.. _0.18.1: https://github.com/NervanaSystems/ngraph/releases/tag/v0.18.1_
.. _0.17.0-rc.1: `https://github.com/NervanaSystems/ngraph/releases/tag/v0.17.0-rc.1
.. _0.16.0-rc.3: https://github.com/NervanaSystems/ngraph/releases/tag/v0.16.0-rc.3
.. _0.16.0-rc.2: https://github.com/NervanaSystems/ngraph/releases/tag/v0.16.0-rc.2
.. _0.16.0-rc.1: https://github.com/NervanaSystems/ngraph/releases/tag/v0.16.0-rc.1
python/test_requirements.txt
View file @ 60252edd
pytest
tox
pydocstyle==3.0.0
flake8
flake8-commas
flake8-comprehensions
......
src/ngraph/CMakeLists.txt
View file @ 60252edd
......@@ -370,7 +370,6 @@ set (SRC
op/util/index_reduction.hpp
op/util/logical_reduction.cpp
op/util/logical_reduction.hpp
op/util/reshape.hpp
op/util/rnn_cell_base.cpp
op/util/rnn_cell_base.hpp
op/util/unary_elementwise_arithmetic.cpp
......
src/ngraph/op/util/reshape.hpp deleted 100644 → 0
View file @ 341205cf
//*****************************************************************************
// 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 <memory>
#include <vector>
#include "ngraph/builder/reshape.hpp"
#include "ngraph/node.hpp"
#include "ngraph/shape.hpp"
namespace ngraph
{
namespace op
{
namespace util
{
/// \brief Change shape of input tensor.
///
/// \param[in] node The node producing the tensor to be reshaped.
/// \param[in] shape The new shape for input tensor.
///
/// \return The node representing a Reshape operation.
///
std::shared_ptr<ngraph::Node> reshape(const std::shared_ptr<ngraph::Node>& node,
const Shape& shape)
{
return builder::reshape(node, shape);
}
/// \brief Permute axes according to specified axes_order parameter.
///
/// \param node The node which axes we want to permute.
/// \param axes_order The permutation of node tensor axes.
///
/// \return: New node with permuted axes.
std::shared_ptr<ngraph::Node> reorder_axes(const std::shared_ptr<ngraph::Node>& node,
std::vector<std::size_t> axes_order)
{
return builder::reorder_axes(node, axes_order);
}
/// \brief Return transposed tensor (with axes in reversed order).
///
/// \param node Input tensor we want to transpose
///
/// \return: New node with reversed dimensions.
std::shared_ptr<ngraph::Node> transpose(const std::shared_ptr<ngraph::Node>& node)
{
return builder::transpose(node);
}
/// \brief Flatten the input tensor into a 2D matrix.
///
/// \param node The tensor to be flattened.
/// \param axis The axis dividing shape.
///
/// \return The new node will be a 2D matrix representing the flattened input node.
std::shared_ptr<ngraph::Node> flatten(const std::shared_ptr<ngraph::Node>& node,
int axis)
{
return builder::flatten(node, axis);
}
} // namespace util
} // namespace op
} // namespace ngraph
src/ngraph/runtime/allocator.cpp
View file @ 60252edd
......@@ -49,7 +49,8 @@ public:
}
};
std::unique_ptr<ngraph::runtime::Allocator> ngraph::runtime::create_default_allocator()
ngraph::runtime::Allocator* ngraph::runtime::get_default_allocator()
{
return std::unique_ptr<DefaultAllocator>(new DefaultAllocator());
static std::unique_ptr<DefaultAllocator> allocator(new DefaultAllocator());
return allocator.get();
}
src/ngraph/runtime/allocator.hpp
View file @ 60252edd
......@@ -30,7 +30,7 @@ namespace ngraph
class DefaultAllocator;
/// \brief Create a default allocator that calls into system
/// allocation libraries
std::unique_ptr<Allocator> create_default_allocator();
ngraph::runtime::Allocator* get_default_allocator();
}
}
......
src/ngraph/runtime/cpu/cpu_backend.cpp
View file @ 60252edd
......@@ -185,7 +185,7 @@ runtime::Allocator* runtime::cpu::CPU_Backend::get_host_memory_allocator()
{
if (!m_allocator)
{
m_allocator = create_default_allocator();
return runtime::get_default_allocator();
}
return m_allocator.get();
}
......
src/ngraph/runtime/generic_cpu/CMakeLists.txt
View file @ 60252edd
......@@ -15,10 +15,10 @@
# ******************************************************************************
if (NGRAPH_GENERIC_CPU_ENABLE)
find_package(OpenMP)
if (OPENMP_FOUND)
add_compile_options(${OpenMP_CXX_FLAGS})
endif()
# find_package(OpenMP)
# if (OPENMP_FOUND)
# add_compile_options(${OpenMP_CXX_FLAGS})
# endif()
add_library(gcpu_backend SHARED gcpu_backend.cpp gcpu_executable.cpp node_wrapper.cpp)
if(NGRAPH_LIB_VERSIONING_ENABLE)
set_target_properties(gcpu_backend PROPERTIES
......
src/ngraph/runtime/generic_cpu/gcpu_backend.cpp
View file @ 60252edd
......@@ -52,14 +52,14 @@ runtime::gcpu::GCPUBackend::GCPUBackend(const vector<string>& unsupported_op_nam
shared_ptr<runtime::Tensor> runtime::gcpu::GCPUBackend::create_tensor(const element::Type& type,
const Shape& shape)
{
return make_shared<runtime::HostTensor>(type, shape, this);
return make_shared<runtime::HostTensor>(type, shape);
}
shared_ptr<runtime::Tensor> runtime::gcpu::GCPUBackend::create_tensor(const element::Type& type,
const Shape& shape,
void* memory_pointer)
{
return make_shared<runtime::HostTensor>(type, shape, memory_pointer, this);
return make_shared<runtime::HostTensor>(type, shape, memory_pointer);
}
shared_ptr<runtime::Executable>
......
src/ngraph/runtime/generic_cpu/gcpu_executable.cpp
View file @ 60252edd
......@@ -15,17 +15,22 @@
//*****************************************************************************
#include "ngraph/runtime/generic_cpu/gcpu_executable.hpp"
#include "ngraph/cpio.hpp"
#include "ngraph/descriptor/layout/dense_tensor_layout.hpp"
#include "ngraph/except.hpp"
#include "ngraph/op/convert.hpp"
#include "ngraph/op/select.hpp"
#include "ngraph/op/util/binary_elementwise_comparison.hpp"
#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"
#include "ngraph/pass/memory_layout.hpp"
#include "ngraph/runtime/backend_manager.hpp"
#include "ngraph/serializer.hpp"
#include "ngraph/util.hpp"
using namespace std;
......@@ -35,21 +40,35 @@ using descriptor::layout::DenseTensorLayout;
runtime::gcpu::GCPUExecutable::GCPUExecutable(const shared_ptr<Function>& function,
bool enable_performance_collection)
: m_is_compiled{true}
, m_performance_counters_enabled{enable_performance_collection}
{
{
m_is_compiled = true;
m_function = clone_function(*function);
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(function);
pass_manager.run_passes(m_function);
for (const shared_ptr<Node>& node : function->get_ordered_ops())
for (const shared_ptr<Node>& node : m_function->get_ordered_ops())
{
m_wrapped_nodes.emplace_back(node);
}
set_parameters_and_results(*m_function);
}
runtime::gcpu::GCPUExecutable::GCPUExecutable(const std::string& model_string)
: m_is_compiled{true}
, m_performance_counters_enabled{false}
{
m_function = deserialize(model_string);
for (const shared_ptr<Node>& node : m_function->get_ordered_ops())
{
m_wrapped_nodes.emplace_back(node);
}
set_parameters_and_results(*function);
set_parameters_and_results(*m_function);
}
bool runtime::gcpu::GCPUExecutable::call(const vector<shared_ptr<runtime::Tensor>>& outputs,
......@@ -82,7 +101,7 @@ bool runtime::gcpu::GCPUExecutable::call(const vector<shared_ptr<runtime::Tensor
{
for (size_t i = 0; i < param->get_output_size(); ++i)
{
descriptor::Tensor* tensor = param->get_output_tensor_ptr(i).get();
descriptor::Tensor* tensor = &param->output(i).get_tensor();
tensor_map.insert({tensor, func_inputs[input_count++]});
}
}
......@@ -95,14 +114,14 @@ bool runtime::gcpu::GCPUExecutable::call(const vector<shared_ptr<runtime::Tensor
{
throw ngraph_error("One of function's outputs isn't op::Result");
}
descriptor::Tensor* tensor = output->get_output_tensor_ptr(0).get();
descriptor::Tensor* tensor = &output->output(0).get_tensor();
tensor_map.insert({tensor, func_outputs[output_count]});
}
// for each ordered op in the graph
for (const NodeWrapper& wrapped : m_wrapped_nodes)
{
const Node* op = &wrapped.get_node();
auto op = wrapped.get_node();
auto type_id = wrapped.get_typeid();
if (type_id == OP_TYPEID::Parameter)
{
......@@ -111,9 +130,9 @@ bool runtime::gcpu::GCPUExecutable::call(const vector<shared_ptr<runtime::Tensor
// get op inputs from map
vector<shared_ptr<HostTensor>> op_inputs;
for (const descriptor::Input& input : op->get_inputs())
for (auto input : op->inputs())
{
descriptor::Tensor* tensor = input.get_output().get_tensor_ptr().get();
descriptor::Tensor* tensor = &input.get_tensor();
op_inputs.push_back(tensor_map.at(tensor));
}
......@@ -121,14 +140,14 @@ bool runtime::gcpu::GCPUExecutable::call(const vector<shared_ptr<runtime::Tensor
vector<shared_ptr<HostTensor>> op_outputs;
for (size_t i = 0; i < op->get_output_size(); ++i)
{
descriptor::Tensor* tensor = op->get_output_tensor_ptr(i).get();
descriptor::Tensor* tensor = &op->output(i).get_tensor();
shared_ptr<HostTensor> host_tensor;
auto it = tensor_map.find(tensor);
if (it == tensor_map.end())
{
const Shape& shape = op->get_output_shape(i);
const element::Type& type = op->get_output_element_type(i);
string name = op->get_output_tensor(i).get_name();
string name = op->output(i).get_tensor().get_name();
host_tensor = make_shared<runtime::HostTensor>(type, shape, name);
tensor_map.insert({tensor, host_tensor});
}
......@@ -177,7 +196,7 @@ bool runtime::gcpu::GCPUExecutable::call(const vector<shared_ptr<runtime::Tensor
}
if (m_nan_check_enabled)
{
perform_nan_check(op_outputs, op);
perform_nan_check(op_outputs, op.get());
}
}
......@@ -186,19 +205,9 @@ bool runtime::gcpu::GCPUExecutable::call(const vector<shared_ptr<runtime::Tensor
void runtime::gcpu::GCPUExecutable::generate_calls(const element::Type& type,
const NodeWrapper& op,
const vector<shared_ptr<HostTensor>>& outputs,
const vector<shared_ptr<HostTensor>>& inputs)
const vector<shared_ptr<HostTensor>>& out,
const vector<shared_ptr<HostTensor>>& in)
{
vector<void*> out;
vector<const void*> in;
for (auto t : outputs)
{
out.push_back(t->get_data_ptr());
}
for (auto t : inputs)
{
in.push_back(t->get_data_ptr());
}
stringstream ss;
switch (type.get_type_enum())
{
......@@ -216,7 +225,8 @@ void runtime::gcpu::GCPUExecutable::generate_calls(const element::Type& type,
case element::Type_t::undefined:
case element::Type_t::dynamic:
case element::Type_t::bf16:
ss << "unsupported element type " << type << " op " << op.get_node().get_name();
case element::Type_t::f16:
ss << "unsupported element type " << type << " op " << op.get_node()->get_name();
throw ngraph_error(ss.str());
}
}
......@@ -229,11 +239,9 @@ void runtime::gcpu::GCPUExecutable::set_nan_check(bool enable)
vector<runtime::PerformanceCounter> runtime::gcpu::GCPUExecutable::get_performance_data() const
{
vector<runtime::PerformanceCounter> rc;
for (const pair<const Node*, stopwatch> p : m_timer_map)
for (const pair<shared_ptr<const Node>, stopwatch> p : m_timer_map)
{
rc.emplace_back(p.first->get_name().c_str(),
p.second.get_total_microseconds(),
p.second.get_call_count());
rc.emplace_back(p.first, p.second.get_total_microseconds(), p.second.get_call_count());
}
return rc;
}
......@@ -286,3 +294,12 @@ void runtime::gcpu::GCPUExecutable::perform_nan_check(const vector<shared_ptr<Ho
arg_number++;
}
}
void runtime::gcpu::GCPUExecutable::save(ostream& out)
{
cpio::Writer writer(out);
string si = "INTERPRETER Save File 1.0";
writer.write("save_info", si.data(), si.size());
string model = serialize(m_function, 0);
writer.write("model", model.data(), model.size());
}
src/ngraph/runtime/generic_cpu/kernel/broadcast.hpp
View file @ 60252edd
......@@ -140,6 +140,91 @@ namespace ngraph
}
}
template <typename T>
void broadcast_5d(const T* in,
T* out,
const Shape& in_shape,
const Shape& out_shape,
const AxisSet& broadcast_axes)
{
size_t index[5];
size_t* out_index = 0;
for (size_t i = 0; i < 5; i++)
{
if (broadcast_axes.count(i) == 0)
{
out_index = &index[i];
break;
}
}
for (index[0] = 0; index[0] < out_shape[0]; ++index[0])
{
for (index[1] = 0; index[1] < out_shape[1]; ++index[1])
{
for (index[2] = 0; index[2] < out_shape[2]; ++index[2])
{
for (index[3] = 0; index[3] < out_shape[3]; ++index[3])
{
for (index[4] = 0; index[4] < out_shape[4]; ++index[4])
{
out[index[0] * out_shape[1] * out_shape[2] * out_shape[3] *
out_shape[4] +
index[1] * out_shape[2] * out_shape[3] * out_shape[4] +
index[2] * out_shape[3] * out_shape[4] +
index[3] * out_shape[4] + index[4]] = in[*out_index];
}
}
}
}
}
}
template <typename T>
void broadcast_6d(const T* in,
T* out,
const Shape& in_shape,
const Shape& out_shape,
const AxisSet& broadcast_axes)
{
size_t index[6];
size_t* out_index = 0;
for (size_t i = 0; i < 6; i++)
{
if (broadcast_axes.count(i) == 0)
{
out_index = &index[i];
break;
}
}
for (index[0] = 0; index[0] < out_shape[0]; ++index[0])
{
for (index[1] = 0; index[1] < out_shape[1]; ++index[1])
{
for (index[2] = 0; index[2] < out_shape[2]; ++index[2])
{
for (index[3] = 0; index[3] < out_shape[3]; ++index[3])
{
for (index[4] = 0; index[4] < out_shape[4]; ++index[4])
{
for (index[5] = 0; index[5] < out_shape[5]; ++index[5])
{
out[index[0] * out_shape[1] * out_shape[2] *
out_shape[3] * out_shape[4] * out_shape[5] +
index[1] * out_shape[2] * out_shape[3] *
out_shape[4] * out_shape[5] +
index[2] * out_shape[3] * out_shape[4] *
out_shape[5] +
index[3] * out_shape[4] * out_shape[5] +
index[4] * out_shape[5] + index[5]] =
in[*out_index];
}
}
}
}
}
}
}
template <typename T>
void broadcast(const T* in,
T* out,
......@@ -167,6 +252,16 @@ namespace ngraph
case 4:
broadcast_4d<T>(in, out, in_shape, out_shape, broadcast_axes);
break;
case 5:
broadcast_5d<T>(in, out, in_shape, out_shape, broadcast_axes);
break;
case 6:
broadcast_6d<T>(in, out, in_shape, out_shape, broadcast_axes);
break;
default:
runtime::reference::broadcast<T>(
in, out, in_shape, out_shape, broadcast_axes);
break;
}
}
else
......
src/ngraph/runtime/generic_cpu/kernel/reshape.hpp
View file @ 60252edd
......@@ -244,10 +244,7 @@ namespace ngraph
case 4: reshape_in4<T>(in, out, in_shape, in_axis_order, out_shape); break;
case 5: reshape_in5<T>(in, out, in_shape, in_axis_order, out_shape); break;
case 6: reshape_in6<T>(in, out, in_shape, in_axis_order, out_shape); break;
default:
NGRAPH_INFO << "reference::reshape";
reference::reshape(in, out, in_shape, in_axis_order, out_shape);
break;
default: reference::reshape(in, out, in_shape, in_axis_order, out_shape); break;
}
}
}
......
src/ngraph/runtime/generic_cpu/kernel/result.hpp deleted 100644 → 0
View file @ 341205cf
//*****************************************************************************
// 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 <algorithm>
#include <cmath>
#include <numeric>
#include <vector>
#include "ngraph/shape.hpp"
namespace ngraph
{
namespace runtime
{
namespace gcpu
{
namespace kernel
{
template <typename T>
void result(const T* arg, T* out, size_t count)
{
memcpy(out, arg, sizeof(T) * count);
}
}
}
}
}
src/ngraph/runtime/generic_cpu/node_wrapper.hpp
View file @ 60252edd
......@@ -51,7 +51,7 @@ class ngraph::runtime::gcpu::NodeWrapper
public:
NodeWrapper(const std::shared_ptr<const ngraph::Node>& node);
const Node& get_node() const { return *m_node; }
std::shared_ptr<const Node> get_node() const { return m_node; }
ngraph::runtime::gcpu::OP_TYPEID get_typeid() const { return m_typeid; }
private:
std::shared_ptr<const ngraph::Node> m_node;
......
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