Skip to content

N
ngraph
Commit f820195b authored by Scott Cyphers's avatar Scott Cyphers
Browse files
Options

Merge branch 'r0.25' into cyphers/25tomaster

parents 32b54cc4 9937f8b5
Hide whitespace changes
Inline Side-by-side
Showing with 333 additions and 12 deletions
CMakeLists.txt
View file @ f820195b
......@@ -398,6 +398,10 @@ if (NGRAPH_MLIR_ENABLE)
set(NGRAPH_MLIR_SOURCE_DIR ${CMAKE_SOURCE_DIR}/src/contrib/mlir)
endif()
if (NGRAPH_STATIC_LIB_ENABLE)
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -DNGRAPH_STATIC_LIB_ENABLE")
endif()
if (NGRAPH_PLAIDML_ENABLE)
find_package(PlaidML CONFIG)
if (NOT PLAIDML_FOUND)
......
CODEOWNERS
View file @ f820195b
......@@ -55,7 +55,7 @@ project/doc-contributor-README.rst @indie
/src/ngraph/runtime/hybrid/ @rkimballn1
/src/ngraph/runtime/intelgpu/ @dmyershov
/src/ngraph/runtime/interpreter/ @rkimballn1
/src/ngraph/runtime/plaidml/ @earhart
/src/ngraph/runtime/plaidml/ @earhart @dgkutnic
/src/ngraph/runtime/reference/ @aprocter
/src/ngraph/runtime/reference/allreduce.*pp @wenzhe-nrv @aprocter
/src/ngraph/type/ @diyessi
......
src/ngraph/CMakeLists.txt
View file @ f820195b
......@@ -519,6 +519,14 @@ if(NOT NGRAPH_JSON_ENABLE)
target_compile_definitions(ngraph PUBLIC NGRAPH_JSON_DISABLE)
endif()
if(NGRAPH_INTERPRETER_STATIC_LIB_ENABLE)
target_compile_definitions(ngraph PUBLIC NGRAPH_INTERPRETER_STATIC_LIB_ENABLE)
endif()
if(NGRAPH_CPU_STATIC_LIB_ENABLE)
target_compile_definitions(ngraph PUBLIC NGRAPH_CPU_STATIC_LIB_ENABLE)
endif()
if(NGRAPH_DISTRIBUTED_ENABLE)
if(NGRAPH_DISTRIBUTED_MLSL_ENABLE)
target_include_directories(ngraph SYSTEM PRIVATE libmlsl)
......
src/ngraph/runtime/backend.cpp
View file @ f820195b
......@@ -37,6 +37,9 @@ std::string runtime::Backend::s_backend_shared_library_search_directory;
// This finds the full path of the containing shared library
static string find_my_pathname()
{
#ifdef NGRAPH_STATIC_LIB_ENABLE
return "";
#else
#ifdef _WIN32
HMODULE hModule = GetModuleHandleW(L"ngraph.dll");
WCHAR wpath[MAX_PATH];
......@@ -52,6 +55,7 @@ static string find_my_pathname()
dladdr(reinterpret_cast<void*>(find_my_pathname), &dl_info);
return dl_info.dli_fname;
#endif
#endif
}
runtime::Backend::~Backend()
......
src/ngraph/runtime/backend_manager.cpp
View file @ f820195b
......@@ -32,12 +32,18 @@
using namespace std;
using namespace ngraph;
#ifdef NGRAPH_STATIC_LIB_ENABLE
#define DLERROR() ""
#else
#ifdef _WIN32
#define CLOSE_LIBRARY(a) FreeLibrary(a)
#define DLSYM(a, b) GetProcAddress(a, b)
#define DLERROR() ""
#else
#define CLOSE_LIBRARY(a) dlclose(a)
#define DLSYM(a, b) dlsym(a, b)
#define DLERROR() dlerror()
#endif
#endif
unordered_map<string, runtime::BackendConstructor*>& runtime::BackendManager::get_registry()
......@@ -101,19 +107,19 @@ shared_ptr<runtime::Backend> runtime::BackendManager::create_backend(const std::
}
else
{
#ifndef NGRAPH_STATIC_LIB_ENABLE
DL_HANDLE handle = open_shared_library(type);
if (!handle)
{
stringstream ss;
ss << "Backend '" << type << "' not registered. Error:";
#ifndef _WIN32
ss << dlerror();
ss << DLERROR();
#endif
throw runtime_error(ss.str());
}
#ifndef _WIN32
dlerror(); // Clear any pending errors
DLERROR(); // Clear any pending errors
#endif
function<runtime::BackendConstructor*()> get_backend_constructor_pointer =
reinterpret_cast<runtime::BackendConstructor* (*)()>(
......@@ -127,7 +133,7 @@ shared_ptr<runtime::Backend> runtime::BackendManager::create_backend(const std::
{
string error;
#ifndef _WIN32
const char* err = dlerror();
const char* err = DLERROR();
error = (err ? err : "");
#endif
CLOSE_LIBRARY(handle);
......@@ -136,6 +142,7 @@ shared_ptr<runtime::Backend> runtime::BackendManager::create_backend(const std::
"library.\nError='" +
error + "'");
}
#endif
}
return backend;
}
......@@ -146,6 +153,7 @@ DL_HANDLE runtime::BackendManager::open_shared_library(string type)
string lib_suffix = SHARED_LIB_SUFFIX;
DL_HANDLE handle = nullptr;
#ifndef NGRAPH_STATIC_LIB_ENABLE
// strip off attributes, IE:CPU becomes IE
auto colon = type.find(":");
......@@ -163,9 +171,9 @@ DL_HANDLE runtime::BackendManager::open_shared_library(string type)
SetDllDirectory((LPCSTR)my_directory.c_str());
handle = LoadLibrary(library_path.c_str());
#else
dlerror(); // Clear any pending errors
DLERROR(); // Clear any pending errors
handle = dlopen(library_path.c_str(), RTLD_NOW | RTLD_GLOBAL);
const char* err = dlerror();
const char* err = DLERROR();
error = (err ? err : "");
#endif
if (!handle)
......@@ -175,12 +183,14 @@ DL_HANDLE runtime::BackendManager::open_shared_library(string type)
ss << "\nOpen error message '" << error << "'";
throw runtime_error(ss.str());
}
#endif
return handle;
}
map<string, string> runtime::BackendManager::get_registered_device_map()
{
map<string, string> rc;
#ifndef NGRAPH_STATIC_LIB_ENABLE
string my_directory =
file_util::get_directory(Backend::get_backend_shared_library_search_directory());
vector<string> backend_list;
......@@ -197,6 +207,7 @@ map<string, string> runtime::BackendManager::get_registered_device_map()
}
};
file_util::iterate_files(my_directory, f, false, true);
#endif
return rc;
}
......
src/ngraph/runtime/cpu/builder/quantized_concat.cpp
View file @ f820195b
......@@ -95,6 +95,9 @@ namespace ngraph
}
}
REGISTER_OP_BUILDER(QuantizedConcat);
#ifdef NGRAPH_CPU_STATIC_LIB_ENABLE
void register_builders_quantized_concat_cpp() {}
#endif
}
}
}
src/ngraph/runtime/cpu/builder/quantized_max_pool.cpp
View file @ f820195b
......@@ -69,6 +69,9 @@ namespace ngraph
}
}
REGISTER_OP_BUILDER(QuantizedMaxPool);
#ifdef NGRAPH_CPU_STATIC_LIB_ENABLE
void register_builders_quantized_max_pool_cpp() {}
#endif
}
}
}
src/ngraph/runtime/cpu/cpu_builder_registry.cpp
View file @ f820195b
......@@ -77,6 +77,8 @@ namespace ngraph
register_builders_tile_cpp();
register_builders_topk_cpp();
register_builders_update_slice_cpp();
register_builders_quantized_concat_cpp();
register_builders_quantized_max_pool_cpp();
}
}
}
......
src/ngraph/runtime/cpu/cpu_builder_registry.hpp
View file @ f820195b
......@@ -76,6 +76,8 @@ namespace ngraph
void register_builders_tile_cpp();
void register_builders_topk_cpp();
void register_builders_update_slice_cpp();
void register_builders_quantized_concat_cpp();
void register_builders_quantized_max_pool_cpp();
}
}
}
......
src/ngraph/runtime/cpu/unit_test.manifest
View file @ f820195b
......@@ -10,3 +10,6 @@ max_3d_to_scalar_int32
# Not implemented
send_recv
send_recv_ring
# param not supported in CPU backend
group_conv_data_dilation
src/ngraph/runtime/plaidml/CMakeLists.txt
View file @ f820195b
......@@ -33,6 +33,7 @@ set(SRC
plaidml_ops_convolution.cpp
plaidml_ops_dot.cpp
plaidml_ops_general.cpp
plaidml_ops_group_convolution.cpp
plaidml_ops_implicit_broadcast.cpp
plaidml_ops_index_reduction.cpp
plaidml_ops_io.cpp
......
src/ngraph/runtime/plaidml/plaidml_compiler.cpp
View file @ f820195b
......@@ -17,6 +17,7 @@
#include "ngraph/runtime/plaidml/plaidml_compiler.hpp"
#include "ngraph/graph_util.hpp"
#include "ngraph/log.hpp"
#include "ngraph/op/fused/group_conv.hpp"
#include "ngraph/pass/algebraic_simplification.hpp"
#include "ngraph/pass/core_fusion.hpp"
#include "ngraph/pass/cse.hpp"
......@@ -66,7 +67,7 @@ namespace
PLAIDML_DEBUG << "Retire tensor: " << t;
}
}
}
} // namespace
ngraph::runtime::plaidml::Compiler::Compiler(Config* config)
: m_config{config}
......@@ -87,7 +88,11 @@ std::shared_ptr<ngraph::runtime::plaidml::PlaidML_Executable>
pass_manager.set_per_pass_validation(false);
// We apply the same general-purposes passes as the CPU backend.
pass_manager.register_pass<ngraph::pass::FusedOpDecomposition>();
pass_manager.register_pass<ngraph::pass::FusedOpDecomposition>([](const Node& node) -> bool {
if (node.description() == ngraph::op::GroupConvolution().description())
return true;
return false;
});
pass_manager.register_pass<ngraph::pass::LikeReplacement>();
pass_manager.register_pass<ngraph::pass::NopElimination>();
pass_manager.register_pass<ngraph::pass::ZeroDimTensorElimination>();
......
src/ngraph/runtime/plaidml/plaidml_ops_group_convolution.cpp 0 → 100644
View file @ f820195b
//*****************************************************************************
// 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 "ngraph/except.hpp"
#include "ngraph/log.hpp"
#include "ngraph/op/fused/group_conv.hpp"
#include "ngraph/op/slice.hpp"
#include "ngraph/runtime/plaidml/plaidml_impl.hpp"
namespace ngraph
{
namespace runtime
{
namespace plaidml
{
NGRAPH_PLAIDML_OP_CLASS(ImplGroupConvolution, OpImpl<::ngraph::op::GroupConvolution>);
}
} // namespace runtime
} // namespace ngraph
// GroupConvolution implements a grouped convolution, with optional striding, padding, and dilation.
void ngraph::runtime::plaidml::ImplGroupConvolution::Apply()
{
this->check_inputs(2);
this->check_outputs(1);
const auto& image = op_input(0);
const auto& filter = op_input(1);
auto rank = op().get_input_shape(0).size() - 2;
const auto& groups = op().get_groups();
const auto& padding_above = op().get_padding_above();
const auto& padding_below = op().get_padding_below();
const auto& strides = op().get_window_movement_strides();
const auto& filter_dilation = op().get_window_dilation_strides();
const auto& data_dilation = op().get_data_dilation_strides();
const auto& grps =
static_cast<::vertexai::plaidml::variable>(static_cast<const int64_t>(groups));
const auto& dd0 =
static_cast<::vertexai::plaidml::variable>(static_cast<const int64_t>(data_dilation[0]));
const auto& dd1 =
static_cast<::vertexai::plaidml::variable>(static_cast<const int64_t>(data_dilation[1]));
const auto& fd0 =
static_cast<::vertexai::plaidml::variable>(static_cast<const int64_t>(filter_dilation[0]));
const auto& fd1 =
static_cast<::vertexai::plaidml::variable>(static_cast<const int64_t>(filter_dilation[1]));
const auto& pxb =
static_cast<::vertexai::plaidml::variable>(static_cast<const int64_t>(padding_below[0]));
const auto& pyb =
static_cast<::vertexai::plaidml::variable>(static_cast<const int64_t>(padding_below[1]));
const auto& pxa =
static_cast<::vertexai::plaidml::variable>(static_cast<const int64_t>(padding_above[0]));
const auto& pya =
static_cast<::vertexai::plaidml::variable>(static_cast<const int64_t>(padding_above[1]));
const auto& sx =
static_cast<::vertexai::plaidml::variable>(static_cast<const int64_t>(strides[0]));
const auto& sy =
static_cast<::vertexai::plaidml::variable>(static_cast<const int64_t>(strides[1]));
this->set_output(::vertexai::plaidml::function{R"(
function (I[N, CI, XI0, XI1], F[CO, FCI, XF0, XF1], DD0, DD1, FD0, FD1, G, PXB, PYB, PXA, PYA, SX, SY) -> (O) {
O[n, (CO/G) * g + co, x, y: N, CO, ((DD0 * (XI0 - 1) + PXA + PXB) - (FD0 * (XF0 - 1)) + SX) / SX, ((DD1 * (XI1 - 1) + PYA + PYB) - (FD1 * (XF1 - 1)) + SY) / SY] =
+(I[n, (CI/G) * g + ci, (x + FD0 * xf0 - PXB)/DD0, (y + FD1 * xf1 - PYB)/DD1] * F[(CO/G) * g + co, ci, xf0, xf1]), co < CO/G;
})"}(image, filter, dd0, dd1, fd0, fd1, grps, pxb, pyb, pxa, pya, sx, sy));
}
src/ngraph/runtime/plaidml/unit_test.manifest
View file @ f820195b
......@@ -178,7 +178,6 @@ conv_bias_2d
conv_bias_3d
conv_bias_bprop_2d
conv_bias_add_2d
group_conv
space_to_depth
depth_to_space
normalize_across_chw_scalar_scale_4d
......@@ -278,8 +277,6 @@ lstm_cell_no_bias_no_peepholes
lstm_cell_bias_peepholes
lstm_cell_bias_peepholes_clip_input_forget
lstm_cell_activaction_functions
group_conv_transpose
group_conv_transpose_output_shape
divide_python_rounding_int32
backwards_batchmatmul_tensor2_tensor2
......
test/backend/fused_op.in.cpp
View file @ f820195b
......@@ -344,6 +344,205 @@ NGRAPH_TEST(${BACKEND_NAME}, group_conv)
EXPECT_EQ(expected, read_vector<float>(result0));
}
NGRAPH_TEST(${BACKEND_NAME}, group_conv_striding)
{
auto data = make_shared<op::Parameter>(element::f32, Shape{1, 4, 2, 2});
auto filters = make_shared<op::Parameter>(element::f32, Shape{2, 2, 1, 1});
auto group_conv = make_shared<op::GroupConvolution>(data,
filters,
Strides{2, 2},
Strides{1, 1},
CoordinateDiff{0, 0},
CoordinateDiff{0, 0},
Strides{1, 1},
2);
auto f0 = make_shared<Function>(NodeVector{group_conv}, ParameterVector{data, filters});
auto backend = runtime::Backend::create("${BACKEND_NAME}");
// Create some tensors for input/output
auto a = backend->create_tensor(element::f32, Shape{1, 4, 2, 2});
copy_data(a, vector<float>{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16});
auto b = backend->create_tensor(element::f32, Shape{2, 2, 1, 1});
copy_data(b, vector<float>{1, 2, 3, 4});
auto result0 = backend->create_tensor(element::f32, Shape{1, 2, 1, 1});
auto handle = backend->compile(f0);
handle->call_with_validate({result0}, {a, b});
vector<float> expected{11, 79};
EXPECT_EQ(expected, read_vector<float>(result0));
}
NGRAPH_TEST(${BACKEND_NAME}, group_conv_window_dilation)
{
auto data = make_shared<op::Parameter>(element::f32, Shape{1, 4, 2, 2});
auto filters = make_shared<op::Parameter>(element::f32, Shape{2, 2, 1, 1});
auto group_conv = make_shared<op::GroupConvolution>(data,
filters,
Strides{1, 1},
Strides{2, 2},
CoordinateDiff{0, 0},
CoordinateDiff{0, 0},
Strides{1, 1},
2);
auto f0 = make_shared<Function>(NodeVector{group_conv}, ParameterVector{data, filters});
auto backend = runtime::Backend::create("${BACKEND_NAME}");
// Create some tensors for input/output
auto a = backend->create_tensor(element::f32, Shape{1, 4, 2, 2});
copy_data(a, vector<float>{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16});
auto b = backend->create_tensor(element::f32, Shape{2, 2, 1, 1});
copy_data(b, vector<float>{1, 2, 3, 4});
auto result0 = backend->create_tensor(element::f32, Shape{1, 2, 2, 2});
auto handle = backend->compile(f0);
handle->call_with_validate({result0}, {a, b});
vector<float> expected{11, 14, 17, 20, 79, 86, 93, 100};
EXPECT_EQ(expected, read_vector<float>(result0));
}
NGRAPH_TEST(${BACKEND_NAME}, group_conv_data_dilation)
{
auto data = make_shared<op::Parameter>(element::f32, Shape{1, 4, 2, 2});
auto filters = make_shared<op::Parameter>(element::f32, Shape{2, 2, 1, 1});
auto group_conv = make_shared<op::GroupConvolution>(data,
filters,
Strides{1, 1},
Strides{1, 1},
CoordinateDiff{0, 0},
CoordinateDiff{0, 0},
Strides{2, 2},
2);
auto f0 = make_shared<Function>(NodeVector{group_conv}, ParameterVector{data, filters});
auto backend = runtime::Backend::create("${BACKEND_NAME}");
// Create some tensors for input/output
auto a = backend->create_tensor(element::f32, Shape{1, 4, 2, 2});
copy_data(a, vector<float>{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16});
auto b = backend->create_tensor(element::f32, Shape{2, 2, 1, 1});
copy_data(b, vector<float>{1, 2, 3, 4});
auto result0 = backend->create_tensor(element::f32, Shape{1, 2, 3, 3});
auto handle = backend->compile(f0);
handle->call_with_validate({result0}, {a, b});
vector<float> expected{11, 0, 14, 0, 0, 0, 17, 0, 20, 79, 0, 86, 0, 0, 0, 93, 0, 100};
EXPECT_EQ(expected, read_vector<float>(result0));
}
NGRAPH_TEST(${BACKEND_NAME}, group_conv_padding)
{
auto data = make_shared<op::Parameter>(element::f32, Shape{1, 4, 2, 2});
auto filters = make_shared<op::Parameter>(element::f32, Shape{2, 2, 1, 1});
auto group_conv = make_shared<op::GroupConvolution>(data,
filters,
Strides{1, 1},
Strides{1, 1},
CoordinateDiff{1, 0},
CoordinateDiff{0, 1},
Strides{1, 1},
2);
auto f0 = make_shared<Function>(NodeVector{group_conv}, ParameterVector{data, filters});
auto backend = runtime::Backend::create("${BACKEND_NAME}");
// Create some tensors for input/output
auto a = backend->create_tensor(element::f32, Shape{1, 4, 2, 2});
copy_data(a, vector<float>{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16});
auto b = backend->create_tensor(element::f32, Shape{2, 2, 1, 1});
copy_data(b, vector<float>{1, 2, 3, 4});
auto result0 = backend->create_tensor(element::f32, Shape{1, 2, 3, 3});
auto handle = backend->compile(f0);
handle->call_with_validate({result0}, {a, b});
vector<float> expected{0, 0, 0, 11, 14, 0, 17, 20, 0, 0, 0, 0, 79, 86, 0, 93, 100, 0};
EXPECT_EQ(expected, read_vector<float>(result0));
}
NGRAPH_TEST(${BACKEND_NAME}, group_conv_padding_and_window_dilation)
{
auto data = make_shared<op::Parameter>(element::f32, Shape{1, 4, 2, 2});
auto filters = make_shared<op::Parameter>(element::f32, Shape{2, 2, 1, 1});
auto group_conv = make_shared<op::GroupConvolution>(data,
filters,
Strides{1, 1},
Strides{2, 2},
CoordinateDiff{1, 0},
CoordinateDiff{0, 1},
Strides{1, 1},
2);
auto f0 = make_shared<Function>(NodeVector{group_conv}, ParameterVector{data, filters});
auto backend = runtime::Backend::create("${BACKEND_NAME}");
// Create some tensors for input/output
auto a = backend->create_tensor(element::f32, Shape{1, 4, 2, 2});
copy_data(a, vector<float>{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16});
auto b = backend->create_tensor(element::f32, Shape{2, 2, 1, 1});
copy_data(b, vector<float>{1, 2, 3, 4});
auto result0 = backend->create_tensor(element::f32, Shape{1, 2, 3, 3});
auto handle = backend->compile(f0);
handle->call_with_validate({result0}, {a, b});
vector<float> expected{0, 0, 0, 11, 14, 0, 17, 20, 0, 0, 0, 0, 79, 86, 0, 93, 100, 0};
EXPECT_EQ(expected, read_vector<float>(result0));
}
NGRAPH_TEST(${BACKEND_NAME}, group_conv_input_shape_variation)
{
auto data = make_shared<op::Parameter>(element::f32, Shape{1, 4, 4, 1});
auto filters = make_shared<op::Parameter>(element::f32, Shape{2, 2, 1, 1});
auto group_conv = make_shared<op::GroupConvolution>(data,
filters,
Strides{1, 1},
Strides{2, 2},
CoordinateDiff{1, 0},
CoordinateDiff{0, 1},
Strides{1, 1},
2);
auto f0 = make_shared<Function>(NodeVector{group_conv}, ParameterVector{data, filters});
auto backend = runtime::Backend::create("${BACKEND_NAME}");
// Create some tensors for input/output
auto a = backend->create_tensor(element::f32, Shape{1, 4, 4, 1});
copy_data(a, vector<float>{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16});
auto b = backend->create_tensor(element::f32, Shape{2, 2, 1, 1});
copy_data(b, vector<float>{1, 2, 3, 4});
auto result0 = backend->create_tensor(element::f32, Shape{1, 2, 5, 2});
auto handle = backend->compile(f0);
handle->call_with_validate({result0}, {a, b});
vector<float> expected{0, 0, 11, 0, 14, 0, 17, 0, 20, 0, 0, 0, 79, 0, 86, 0, 93, 0, 100, 0};
EXPECT_EQ(expected, read_vector<float>(result0));
}
NGRAPH_TEST(${BACKEND_NAME}, group_conv_input_data_variation)
{
auto data = make_shared<op::Parameter>(element::f32, Shape{1, 4, 3, 3});
auto filters = make_shared<op::Parameter>(element::f32, Shape{2, 2, 1, 1});
auto group_conv = make_shared<op::GroupConvolution>(data,
filters,
Strides{1, 1},
Strides{2, 2},
CoordinateDiff{1, 0},
CoordinateDiff{0, 1},
Strides{1, 1},
2);
auto f0 = make_shared<Function>(NodeVector{group_conv}, ParameterVector{data, filters});
auto backend = runtime::Backend::create("${BACKEND_NAME}");
// Create some tensors for input/output
auto a = backend->create_tensor(element::f32, Shape{1, 4, 3, 3});
copy_data(a, vector<float>{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,
13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24,
25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36});
auto b = backend->create_tensor(element::f32, Shape{2, 2, 1, 1});
copy_data(b, vector<float>{1, 2, 3, 4});
auto result0 = backend->create_tensor(element::f32, Shape{1, 2, 4, 4});
auto handle = backend->compile(f0);
handle->call_with_validate({result0}, {a, b});
vector<float> expected{0, 0, 0, 0, 21, 24, 27, 0, 30, 33, 36, 0, 39, 42, 45, 0,
0, 0, 0, 0, 169, 176, 183, 0, 190, 197, 204, 0, 211, 218, 225, 0};
EXPECT_EQ(expected, read_vector<float>(result0));
}
NGRAPH_TEST(${BACKEND_NAME}, space_to_depth)
{
auto A = make_shared<op::Parameter>(element::f32, Shape{1, 2, 4, 4});
......
Markdown is supported
0% or
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment