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未验证 提交 5ada98b8 编写于 作者: G gouzil 提交者： GitHub
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[clang-tidy] NO.6 enable `modernize-avoid-c-arrays` step: 2 (#55954)

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.clang-tidy
浏览文件 @ 5ada98b8
......@@ -166,7 +166,7 @@ bugprone-unused-raii,
-misc-unused-alias-decls,
-misc-unused-using-decls,
modernize-avoid-bind,
-modernize-avoid-c-arrays,
modernize-avoid-c-arrays,
-modernize-deprecated-headers,
-modernize-deprecated-ios-base-aliases,
modernize-loop-convert,
......
paddle/fluid/framework/data_type.cc
浏览文件 @ 5ada98b8
......@@ -150,6 +150,7 @@ proto::VarType::Type PromoteTypesIfComplexExists(
// Here is a complete rules table, but some rules are not used.
// It is still written this way because array accessing is still
// more efficient than if-else
// NOLINTBEGIN(*-avoid-c-arrays)
static constexpr proto::VarType::Type promote_types_table[4][4] = {
/* f4 f8 c4 c8*/
/* f4 */ {f4, f8, c4, c8},
......@@ -157,6 +158,7 @@ proto::VarType::Type PromoteTypesIfComplexExists(
/* c4 */ {c4, c8, c4, c8},
/* c8 */ {c8, c8, c8, c8},
};
// NOLINTEND(*-avoid-c-arrays)
return promote_types_table[type_an][type_bn];
}
......
paddle/fluid/framework/details/fused_all_reduce_op_handle.cc
浏览文件 @ 5ada98b8
......@@ -21,7 +21,7 @@
#include "paddle/phi/backends/device_memory_aligment.h"
#include "paddle/phi/core/flags.h"
DEFINE_bool(skip_fused_all_reduce_check, false, "");
DEFINE_bool(skip_fused_all_reduce_check, false, ""); // NOLINT
PHI_DECLARE_bool(allreduce_record_one_event);
namespace paddle {
......
paddle/fluid/framework/device_worker.cc
浏览文件 @ 5ada98b8
......@@ -14,6 +14,7 @@ limitations under the License. */
#include "paddle/fluid/framework/device_worker.h"
#include <array>
#include <chrono>
#include "paddle/fluid/framework/convert_utils.h"
namespace phi {
......@@ -90,7 +91,7 @@ void PrintLodTensorType<float>(phi::DenseTensor* tensor,
std::string& out_val, // NOLINT
char separator,
bool need_leading_separator) {
char buf[MAX_FLOAT_BUFF_SIZE];
std::array<char, MAX_FLOAT_BUFF_SIZE> buf;
auto count = tensor->numel();
if (start < 0 || end > count) {
VLOG(3) << "access violation";
......@@ -104,8 +105,8 @@ void PrintLodTensorType<float>(phi::DenseTensor* tensor,
tensor->data<float>()[i] < FLOAT_EPS) {
out_val += "0";
} else {
sprintf(buf, "%.9f", tensor->data<float>()[i]); // NOLINT
out_val += buf;
sprintf(buf.data(), "%.9f", tensor->data<float>()[i]); // NOLINT
out_val += buf.data();
}
}
}
......
paddle/fluid/framework/hogwild_worker.cc
浏览文件 @ 5ada98b8
......@@ -12,6 +12,7 @@ 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 <array>
#include <ctime>
#include "paddle/fluid/framework/barrier.h"
......@@ -425,12 +426,14 @@ void HogwildWorker::PrintFetchVars() {
if (thread_id_ == 0 && batch_num_ % batch_per_print == 0) {
time_t curtime;
time(&curtime);
char mbstr[80];
std::strftime(
mbstr, sizeof(mbstr), "%Y-%m-%d %H:%M:%S", std::localtime(&curtime));
std::array<char, 80> mbstr;
std::strftime(mbstr.data(),
sizeof(mbstr),
"%Y-%m-%d %H:%M:%S",
std::localtime(&curtime));
std::stringstream ss;
ss << "time: [" << mbstr << "], ";
ss << "time: [" << mbstr.data() << "], ";
ss << "batch: [" << batch_num_ << "], ";
for (int i = 0; i < fetch_var_num; ++i) {
......
paddle/fluid/framework/io/shell.cc
浏览文件 @ 5ada98b8
......@@ -12,6 +12,7 @@
// See the License for the specific language governing permissions and
// limitations under the License.
#include <array>
#define GLOG_NO_ABBREVIATED_SEVERITIES // msvc conflict logging with windows.h
#include "paddle/fluid/framework/io/shell.h"
......@@ -150,14 +151,14 @@ static int shell_popen_fork_internal(const char* real_cmd,
}
static int read_from_pipe(FILE* fp, std::string* output) {
char buf[4096];
std::array<char, 4096> buf;
while (1) {
int n = fread(buf, 1, 4096, fp);
int n = fread(buf.data(), 1, 4096, fp);
if (n <= 0) {
break;
}
output->append(buf, n);
output->append(buf.data(), n);
}
if (!feof(fp)) {
......@@ -249,8 +250,8 @@ std::shared_ptr<FILE> shell_popen(const std::string& cmd,
}
static int shell_p2open_fork_internal(const char* real_cmd,
int pipein_fds[2],
int pipeout_fds[2]) {
int pipein_fds[2], // NOLINT
int pipeout_fds[2]) { // NOLINT
#if defined(_WIN32) || defined(__APPLE__) || defined(PADDLE_ARM)
return 0;
#else
......
paddle/fluid/framework/ir/lock_free_optimize_pass.cc
浏览文件 @ 5ada98b8
......@@ -25,10 +25,10 @@ namespace paddle {
namespace framework {
namespace ir {
const char kSumGradOpName[] = "sum";
const char kSumGradOpName[] = "sum"; // NOLINT
// TODO(minqiyang): only support sgd at current time, please add
// other optimizers later.
const char kOptimizerType[] = "sgd";
const char kOptimizerType[] = "sgd"; // NOLINT
void LockFreeOptimizePass::ApplyImpl(ir::Graph* graph) const {
PADDLE_ENFORCE_NOT_NULL(
......
paddle/fluid/framework/ir/multi_batch_merge_pass.cc
浏览文件 @ 5ada98b8
......@@ -23,7 +23,7 @@ namespace paddle {
namespace framework {
namespace ir {
static const char kNumRepeats[] = "num_repeats";
static const char kNumRepeats[] = "num_repeats"; // NOLINT
typedef std::unordered_map<std::string, std::vector<ir::Node*>> SSAVarList;
ir::Node* SameNameVar(std::unordered_set<ir::Node*> all, ir::Node* target) {
......
paddle/fluid/framework/ir/multi_devices_graph_pass/multi_devices_graph_pass.cc
浏览文件 @ 5ada98b8
......@@ -50,7 +50,7 @@ namespace {
// all operators. NOTE that even we use a vector here, the operators is
// unordered.
typedef std::vector<details::OpHandleBase *> GraphOps;
const char kGraphOps[] = "ops";
const char kGraphOps[] = "ops"; // NOLINT
bool OpHaveRole(const ir::Node &node, const framework::OpRole &role) {
return PADDLE_GET_CONST(
......
paddle/fluid/framework/ir/node.cc
浏览文件 @ 5ada98b8
......@@ -23,7 +23,7 @@ namespace ir {
#if !defined(_WIN32) && (__cplusplus < 201703L)
constexpr char Node::kControlDepVarName[];
#else
const char Node::kControlDepVarName[] = "__control_var";
const char Node::kControlDepVarName[] = "__control_var"; // NOLINT
#endif
std::unique_ptr<Node> CreateNodeForTest(const std::string &name,
......
paddle/fluid/framework/ir/pass.cc 100755 → 100644
浏览文件 @ 5ada98b8
......@@ -36,7 +36,7 @@ namespace paddle {
namespace framework {
namespace ir {
static const char kParamScopeAttr[] = "__param_scope__";
static const char kParamScopeAttr[] = "__param_scope__"; // NOLINT
static const std::vector<std::string> support_subgraph_passes = {
"simplify_with_basic_ops_pass",
......
paddle/fluid/framework/ir/runtime_context_cache_pass.cc
浏览文件 @ 5ada98b8
......@@ -22,7 +22,7 @@ namespace framework {
namespace ir {
void RuntimeContextCachePass::ApplyImpl(ir::Graph* graph) const {
static constexpr char kNotAllowInferShapeCahce[] =
static constexpr char kNotAllowInferShapeCahce[] = // NOLINT
"@NOT_ALLOW_INFERSHAPE_CACHE@";
VLOG(3) << "Applies Runtime Context Cache strategy.";
for (const Node* n : graph->Nodes()) {
......
paddle/fluid/framework/operator.cc
浏览文件 @ 5ada98b8
......@@ -1227,7 +1227,7 @@ bool OpSupportGPU(const std::string& op_type) {
}
struct OperatorWithKernel::CacheImpl {
static const char kNotAllowInferShapeCahce[];
static const char kNotAllowInferShapeCahce[]; // NOLINT
explicit CacheImpl(phi::KernelContext* kernel_ctx,
RuntimeInferShapeContext* infer_shape_ctx,
const std::vector<phi::DenseTensor*>& tensors,
......@@ -1273,7 +1273,8 @@ struct OperatorWithKernel::CacheImpl {
bool not_allow_infer_shape_cache_;
std::vector<phi::DDim> last_ddims_;
};
const char OperatorWithKernel::CacheImpl::kNotAllowInferShapeCahce[] =
const char // NOLINT
OperatorWithKernel::CacheImpl::kNotAllowInferShapeCahce[] =
"@NOT_ALLOW_INFERSHAPE_CACHE@";
static void CheckTensorNANOrInf(const std::string& op_type,
......
paddle/fluid/framework/prune.cc
浏览文件 @ 5ada98b8
......@@ -23,12 +23,12 @@ limitations under the License. */
namespace paddle {
namespace framework {
const char kFeedOpType[] = "feed";
const char kFetchOpType[] = "fetch";
const char kFeedOpType[] = "feed"; // NOLINT
const char kFetchOpType[] = "fetch"; // NOLINT
const char kRecurrent[] = "recurrent";
const char kStates[] = "states";
const char kExStates[] = "ex_states";
const char kRecurrent[] = "recurrent"; // NOLINT
const char kStates[] = "states"; // NOLINT
const char kExStates[] = "ex_states"; // NOLINT
bool HasDependentInputVar(
const proto::OpDesc& op_desc,
......
paddle/fluid/framework/tensor_util.cc
浏览文件 @ 5ada98b8
......@@ -532,7 +532,7 @@ void TensorToStream(std::ostream& os,
} else if (platform::is_custom_place(tensor.place())) {
#ifdef PADDLE_WITH_CUSTOM_DEVICE
constexpr size_t kBufSize = 1024 * 1024 * 64; // 64MB
std::unique_ptr<char[]> buf(new char[kBufSize]);
std::unique_ptr<char[]> buf(new char[kBufSize]); // NOLINT
auto& custom_device_context =
static_cast<const platform::CustomDeviceContext&>(dev_ctx);
platform::CPUPlace cpu;
......@@ -598,7 +598,7 @@ void TensorFromStream(std::istream& is,
// proto buffer
int32_t size;
is.read(reinterpret_cast<char*>(&size), sizeof(size));
std::unique_ptr<char[]> buf(new char[size]);
std::unique_ptr<char[]> buf(new char[size]); // NOLINT
is.read(reinterpret_cast<char*>(buf.get()), size);
PADDLE_ENFORCE_EQ(
desc.ParseFromArray(buf.get(), size),
......@@ -671,7 +671,7 @@ void TensorFromStream(std::istream& is,
0,
platform::errors::InvalidArgument(
"phi::DenseTensor desc size should >= 0"));
std::unique_ptr<char[]> buf(new char[size]);
std::unique_ptr<char[]> buf(new char[size]); // NOLINT
is.read(reinterpret_cast<char*>(buf.get()), size);
PADDLE_ENFORCE_EQ(
desc.ParseFromArray(buf.get(), size),
......
paddle/fluid/framework/tensor_util_test.cc
浏览文件 @ 5ada98b8
......@@ -13,6 +13,7 @@
// limitations under the License.
#include <gtest/gtest.h>
#include <array>
#include <cmath>
#include "paddle/fluid/framework/tensor_util.h"
......@@ -28,8 +29,8 @@ TEST(TensorCopy, Tensor) {
int* src_ptr = src_tensor.mutable_data<int>(phi::make_ddim({3, 3}),
platform::CPUPlace());
int arr[9] = {1, 2, 3, 4, 5, 6, 7, 8, 9};
memcpy(src_ptr, arr, 9 * sizeof(int));
std::array<int, 9> arr = {1, 2, 3, 4, 5, 6, 7, 8, 9};
memcpy(src_ptr, arr.data(), 9 * sizeof(int));
src_tensor.set_layout(DataLayout::kAnyLayout);
auto cpu_place = new platform::CPUPlace();
......@@ -467,7 +468,7 @@ TEST(TensorIsfinite, CPU) {
TEST(Tensor, FromAndToStream) {
phi::DenseTensor src_tensor;
int array[6] = {1, 2, 3, 4, 5, 6};
std::array<int, 6> array = {1, 2, 3, 4, 5, 6};
src_tensor.Resize({2, 3});
int* src_ptr = src_tensor.mutable_data<int>(platform::CPUPlace());
for (int i = 0; i < 6; ++i) {
......
paddle/fluid/inference/analysis/passes/ir_params_sync_among_devices_pass.cc
浏览文件 @ 5ada98b8
......@@ -27,7 +27,7 @@
#include "paddle/fluid/platform/place.h"
#include "paddle/phi/core/dense_tensor.h"
DEFINE_bool(
DEFINE_bool( // NOLINT
custom_model_save_cpu,
false,
"Keep old mode for developers, the model is saved on cpu not device.");
......
paddle/fluid/inference/api/api_impl.cc
浏览文件 @ 5ada98b8
......@@ -26,7 +26,7 @@ limitations under the License. */
#include "paddle/fluid/platform/place.h"
#include "paddle/fluid/platform/profiler.h"
DEFINE_bool(profile, false, "Turn on profiler for fluid");
DEFINE_bool(profile, false, "Turn on profiler for fluid"); // NOLINT
namespace paddle {
namespace {
......
paddle/fluid/inference/io.cc
浏览文件 @ 5ada98b8
......@@ -29,7 +29,9 @@ limitations under the License. */
// phi
#include "paddle/phi/kernels/declarations.h"
DEFINE_string(devices, "", "The devices to be used which is joined by comma.");
DEFINE_string(devices, // NOLINT
"",
"The devices to be used which is joined by comma.");
DEFINE_int32(math_num_threads,
1,
"Number of threads used to run math functions.");
......
paddle/fluid/ir/dialect/kernel_op.cc
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......@@ -20,8 +20,10 @@
namespace paddle {
namespace dialect {
const char* PhiKernelOp::attributes_name[attributes_num] = {
"op_name", "kernel_name", "kernel_key"};
const char* PhiKernelOp::attributes_name[attributes_num] = { // NOLINT
"op_name",
"kernel_name",
"kernel_key"};
void PhiKernelOp::Verify() {
VLOG(4) << "Verifying inputs, outputs and attributes for: PhiKernelOp.";
......
paddle/fluid/ir_adaptor/translator/op_translator.cc
浏览文件 @ 5ada98b8
......@@ -69,8 +69,8 @@ using InputHandlerFn = std::function<ir::OpResult(ir::IrContext*,
ir::Program*)>;
using AttributeHandlerFn = std::function<ir::Attribute(
ir::IrContext*, const OpDesc&, const OpAttributeInfo&)>;
constexpr char kTargetDialectPrefix[] = "pd.";
constexpr char kEmptyVarName[] = "@EMPTY@";
constexpr char kTargetDialectPrefix[] = "pd."; // NOLINT
constexpr char kEmptyVarName[] = "@EMPTY@"; // NOLINT
static const std::unordered_set<std::string> special_non_inplace_ops = {};
......
paddle/fluid/operators/assert_op.cc
浏览文件 @ 5ada98b8
......@@ -12,6 +12,7 @@
See the License for the specific language governing permissions and
limitations under the License. */
#include <array>
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/controlflow/while_op_helper.h"
#include "paddle/phi/kernels/funcs/tensor_formatter.h"
......@@ -34,9 +35,10 @@ class OpBase;
} // namespace imperative
} // namespace paddle
const char kCond[] = "Cond";
const char kData[] = "Data";
const char kSummarize[] = "summarize";
// const char kCond[] = "Cond";
std::array<const char, 5> kCond = {"Cond"};
std::array<const char, 5> kData = {"Data"};
std::array<const char, 10> kSummarize = {"summarize"};
namespace paddle {
namespace operators {
......@@ -52,7 +54,8 @@ class AssertOp : public framework::OperatorBase {
private:
void RunImpl(const framework::Scope &scope,
const platform::Place &dev_place) const override {
const framework::Variable *cond_var_ptr = scope.FindVar(Input(kCond));
const framework::Variable *cond_var_ptr =
scope.FindVar(Input(kCond.data()));
PADDLE_ENFORCE_NOT_NULL(cond_var_ptr,
platform::errors::NotFound(
"Input(Condition) of AssertOp is not found."));
......@@ -71,9 +74,9 @@ class AssertOp : public framework::OperatorBase {
}
funcs::TensorFormatter formatter;
formatter.SetSummarize(Attr<int64_t>(kSummarize));
formatter.SetSummarize(Attr<int64_t>(kSummarize.data()));
const std::vector<std::string> &x_names = Inputs(kData);
const std::vector<std::string> &x_names = Inputs(kData.data());
for (const std::string &name : x_names) {
const framework::Variable *x_var_ptr = scope.FindVar(name);
const phi::DenseTensor &x_tensor = x_var_ptr->Get<phi::DenseTensor>();
......@@ -83,7 +86,7 @@ class AssertOp : public framework::OperatorBase {
PADDLE_THROW(platform::errors::InvalidArgument(
"The condition variable '%s' of AssertOp must be "
"true, but received false",
Input(kCond)));
Input(kCond.data())));
}
};
......@@ -91,13 +94,13 @@ class AssertOpProtoMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput(
kCond,
kCond.data(),
"The boolean scalar condition tensor which is asserted to be true.");
AddInput(kData,
AddInput(kData.data(),
"The tensors to print when the assert condition is not true.")
.AsDuplicable();
AddAttr<int64_t>(
kSummarize,
kSummarize.data(),
"The number of entries of each tensor to print when the "
"assert condition is not true. -1 means print all entries. If "
"the number of entries of a tensor is less then "
......@@ -111,7 +114,8 @@ class AssertOpProtoMaker : public framework::OpProtoAndCheckerMaker {
class AssertOpInferShape : public framework::InferShapeBase {
public:
void operator()(framework::InferShapeContext *context) const override {
OP_INOUT_CHECK(context->HasInputs(kCond), "Input", "Condition", "AssertOp");
OP_INOUT_CHECK(
context->HasInputs(kCond.data()), "Input", "Condition", "AssertOp");
}
};
......
paddle/fluid/operators/controlflow/conditional_block_op.cc
浏览文件 @ 5ada98b8
......@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/operators/controlflow/conditional_block_op.h"
#include <array>
#include "paddle/fluid/framework/new_executor/standalone_executor.h"
#include "paddle/fluid/operators/assign_op.h"
......@@ -29,11 +30,12 @@ PHI_DECLARE_bool(use_mkldnn);
namespace paddle {
namespace operators {
const char ConditionalOp::kInputs[] = "Input";
const char ConditionalOp::kOutputs[] = "Out";
const char ConditionalOp::kCondition[] = "Cond";
const char ConditionalOp::kScope[] = "Scope";
const char ConditionalOp::kSkipEagerDeletionVars[] = "skip_eager_deletion_vars";
const char ConditionalOp::kInputs[] = "Input"; // NOLINT
const char ConditionalOp::kOutputs[] = "Out"; // NOLINT
const char ConditionalOp::kCondition[] = "Cond"; // NOLINT
const char ConditionalOp::kScope[] = "Scope"; // NOLINT
const char ConditionalOp::kSkipEagerDeletionVars[] =
"skip_eager_deletion_vars"; // NOLINT
using Executor = framework::Executor;
using ExecutorPrepareContext = framework::ExecutorPrepareContext;
......
paddle/fluid/operators/detection/roi_perspective_transform_op.cc
浏览文件 @ 5ada98b8
......@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include <algorithm>
#include <array>
#include <memory>
#include <vector>
......@@ -41,7 +42,7 @@ bool GT(T a, T b) {
*check if (x, y) is in the boundary of roi
*/
template <typename T>
bool in_quad(T x, T y, T roi_x[], T roi_y[]) {
bool in_quad(T x, T y, T roi_x[], T roi_y[]) { // NOLINT
for (int i = 0; i < 4; i++) {
T xs = roi_x[i];
T ys = roi_y[i];
......@@ -107,9 +108,9 @@ bool in_quad(T x, T y, T roi_x[], T roi_y[]) {
template <typename T>
void get_transform_matrix(const int transformed_width,
const int transformed_height,
T roi_x[],
T roi_y[],
T matrix[]) {
T roi_x[], // NOLINT
T roi_y[], // NOLINT
T matrix[]) { // NOLINT
T x0 = roi_x[0];
T x1 = roi_x[1];
T x2 = roi_x[2];
......@@ -170,7 +171,8 @@ void get_transform_matrix(const int transformed_width,
*
*/
template <typename T>
void get_source_coords(T matrix[], int out_w, int out_h, T* in_w, T* in_h) {
void get_source_coords(
T matrix[], int out_w, int out_h, T* in_w, T* in_h) { // NOLINT
T u = matrix[0] * out_w + matrix[1] * out_h + matrix[2];
T v = matrix[3] * out_w + matrix[4] * out_h + matrix[5];
T w = matrix[6] * out_w + matrix[7] * out_h + matrix[8];
......@@ -283,17 +285,20 @@ class CPUROIPerspectiveTransformOpKernel : public framework::OpKernel<T> {
for (int n = 0; n < rois_num; ++n) {
const T* n_rois = rois_data + n * 8;
T roi_x[4];
T roi_y[4];
std::array<T, 4> roi_x;
std::array<T, 4> roi_y;
for (int k = 0; k < 4; ++k) {
roi_x[k] = n_rois[2 * k] * spatial_scale;
roi_y[k] = n_rois[2 * k + 1] * spatial_scale;
}
int image_id = roi2image_data[n];
// Get transform matrix
T matrix[9];
get_transform_matrix<T>(
transformed_width, transformed_height, roi_x, roi_y, matrix);
std::array<T, 9> matrix;
get_transform_matrix<T>(transformed_width,
transformed_height,
roi_x.data(),
roi_y.data(),
matrix.data());
for (int i = 0; i < 9; i++) {
transform_matrix[n * 9 + i] = matrix[i];
}
......@@ -305,8 +310,8 @@ class CPUROIPerspectiveTransformOpKernel : public framework::OpKernel<T> {
c * transformed_height * transformed_width +
out_h * transformed_width + out_w;
T in_w, in_h;
get_source_coords<T>(matrix, out_w, out_h, &in_w, &in_h);
if (in_quad<T>(in_w, in_h, roi_x, roi_y)) {
get_source_coords<T>(matrix.data(), out_w, out_h, &in_w, &in_h);
if (in_quad<T>(in_w, in_h, roi_x.data(), roi_y.data())) {
if (GT_E<T>(-0.5, in_w) ||
GT_E<T>(in_w, static_cast<T>(in_width - 0.5)) ||
GT_E<T>(-0.5, in_h) ||
......@@ -431,17 +436,20 @@ class CPUROIPerspectiveTransformGradOpKernel : public framework::OpKernel<T> {
T gradient = 0.0;
for (size_t roi_idx = lod[n]; roi_idx < lod[n + 1]; ++roi_idx) {
const T* rois = rois_data + roi_idx * 8;
T roi_x[4];
T roi_y[4];
std::array<T, 4> roi_x;
std::array<T, 4> roi_y;
for (int k = 0; k < 4; ++k) {
roi_x[k] = rois[2 * k] * spatial_scale;
roi_y[k] = rois[2 * k + 1] * spatial_scale;
}
// Get transform matrix
T matrix[9];
get_transform_matrix<T>(
transformed_width, transformed_height, roi_x, roi_y, matrix);
std::array<T, 9> matrix;
get_transform_matrix<T>(transformed_width,
transformed_height,
roi_x.data(),
roi_y.data(),
matrix.data());
const T* out_grad_ptr = out_grad_data + (roi_idx * channels + c) *
transformed_height *
transformed_width;
......@@ -449,8 +457,9 @@ class CPUROIPerspectiveTransformGradOpKernel : public framework::OpKernel<T> {
for (int out_w = 0; out_w < transformed_width; ++out_w) {
T src_w;
T src_h;
get_source_coords<T>(matrix, out_w, out_h, &src_w, &src_h);
if (in_quad<T>(src_w, src_h, roi_x, roi_y)) {
get_source_coords<T>(
matrix.data(), out_w, out_h, &src_w, &src_h);
if (in_quad<T>(src_w, src_h, roi_x.data(), roi_y.data())) {
if (GT_E<T>(-0.5, src_w) ||
GT_E<T>(src_w, static_cast<T>(in_width - 0.5)) ||
GT_E<T>(-0.5, src_h) ||
......
paddle/fluid/operators/pad2d_op.cc
浏览文件 @ 5ada98b8
......@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include <algorithm>
#include <array>
#include <memory>
#include <string>
#include <vector>
......@@ -406,8 +407,8 @@ template <typename T, typename DeviceContext>
class Pad2dCPUKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
int pads[4];
GetPaddings(pads, context);
std::array<int, 4> pads;
GetPaddings(pads.data(), context);
auto mode = context.Attr<std::string>("mode");
auto data_format = context.Attr<std::string>("data_format");
T value = static_cast<T>(context.Attr<float>("pad_value"));
......@@ -524,8 +525,8 @@ template <typename T, typename DeviceContext>
class Pad2dGradCPUKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
int pads[4];
GetPaddings(pads, context);
std::array<int, 4> pads;
GetPaddings(pads.data(), context);
auto mode = context.Attr<std::string>("mode");
auto data_format = context.Attr<std::string>("data_format");
auto* d_out =
......
paddle/fluid/operators/print_op.cc
浏览文件 @ 5ada98b8
......@@ -12,6 +12,7 @@
See the License for the specific language governing permissions and
limitations under the License. */
#include <array>
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/framework/op_version_registry.h"
#include "paddle/phi/kernels/funcs/tensor_formatter.h"
......@@ -37,9 +38,9 @@ using framework::GradVarName;
#define CLOG std::cout
const char kForward[] = "FORWARD";
const char kBackward[] = "BACKWARD";
const char kBoth[] = "BOTH";
std::array<const char, 8> kForward = {"FORWARD"};
std::array<const char, 9> kBackward = {"BACKWARD"};
std::array<const char, 5> kBoth = {"BOTH"};
// TODO(ChunweiYan) there should be some other printers for TensorArray
class PrintOp : public framework::OperatorBase {
......@@ -79,8 +80,8 @@ class PrintOp : public framework::OperatorBase {
std::string print_phase = Attr<std::string>("print_phase");
bool is_forward = Attr<bool>("is_forward");
if ((is_forward && print_phase == kBackward) ||
(!is_forward && print_phase == kForward)) {
if ((is_forward && print_phase == kBackward.data()) ||
(!is_forward && print_phase == kForward.data())) {
return;
}
......@@ -125,10 +126,10 @@ class PrintOpProtoAndCheckMaker : public framework::OpProtoAndCheckerMaker {
"(string, default 'FORWARD') Which phase to display "
"including 'FORWARD' "
"'BACKWARD' and 'BOTH'.")
.SetDefault(std::string(kBoth))
.InEnum({std::string(kForward),
std::string(kBackward),
std::string(kBoth)});
.SetDefault(std::string(kBoth.data()))
.InEnum({std::string(kForward.data()),
std::string(kBackward.data()),
std::string(kBoth.data())});
AddAttr<bool>("is_forward", "Whether is forward or not").SetDefault(true);
AddComment(R"DOC(
Creates a print op that will print when a tensor is accessed.
......
paddle/fluid/operators/py_func_op.cc
浏览文件 @ 5ada98b8
......@@ -14,6 +14,7 @@
#include "paddle/fluid/operators/py_func_op.h"
#include <array>
#include <memory>
#include <set>
#include <string>
......@@ -30,9 +31,9 @@ namespace py = ::pybind11;
static std::vector<py::object> g_py_callables;
const char kForwardPythonCallableId[] = "forward_callable_id";
const char kBackwardPythonCallableId[] = "backward_callable_id";
const char kPyFuncBackwardSkipVars[] = "backward_skip_vars";
std::array<const char, 20> kForwardPythonCallableId = {"forward_callable_id"};
std::array<const char, 21> kBackwardPythonCallableId = {"backward_callable_id"};
std::array<const char, 19> kPyFuncBackwardSkipVars = {"backward_skip_vars"};
size_t AppendPythonCallableObjectAndReturnId(const py::object &py_obj) {
g_py_callables.emplace_back(py_obj);
......@@ -144,11 +145,12 @@ class PyFuncOpVarTypeInference : public framework::StaticGraphVarTypeInference {
has_out));
PADDLE_ENFORCE_GE(
PADDLE_GET_CONST(int, ctx->GetAttr(kForwardPythonCallableId)),
PADDLE_GET_CONST(int, ctx->GetAttr(kForwardPythonCallableId.data())),
0,
platform::errors::InvalidArgument(
"Function id cannot be less than 0, but received value is %d.",
PADDLE_GET_CONST(int, ctx->GetAttr(kForwardPythonCallableId))));
PADDLE_GET_CONST(int,
ctx->GetAttr(kForwardPythonCallableId.data()))));
if (!has_out) return;
......@@ -200,13 +202,13 @@ class PyFuncOpMaker : public framework::OpProtoAndCheckerMaker {
void Make() override {
AddInput("X", "Inputs of py_func op.").AsDuplicable();
AddOutput("Out", "Outputs of py_func op").AsDuplicable();
AddAttr<int>(kForwardPythonCallableId,
AddAttr<int>(kForwardPythonCallableId.data(),
"Index of registered forward Python function.")
.SetDefault(0);
AddAttr<int>(kBackwardPythonCallableId,
AddAttr<int>(kBackwardPythonCallableId.data(),
"Index of registered backward Python function.")
.SetDefault(-1);
AddAttr<std::vector<std::string>>(kPyFuncBackwardSkipVars,
AddAttr<std::vector<std::string>>(kPyFuncBackwardSkipVars.data(),
"Unused forward in/out in backward op")
.SetDefault(std::vector<std::string>());
AddComment(R"DOC("PyFunc Op")DOC");
......@@ -241,7 +243,8 @@ class PyFuncOpGradDescMaker : public framework::GradOpDescMakerBase {
std::vector<std::unique_ptr<framework::OpDesc>> operator()() const override {
auto &fwd_attrs = Attrs();
// no backward op when backward_id is less than 0
if (PADDLE_GET_CONST(int, fwd_attrs.at(kBackwardPythonCallableId)) < 0) {
if (PADDLE_GET_CONST(int, fwd_attrs.at(kBackwardPythonCallableId.data())) <
0) {
return {};
}
......@@ -249,9 +252,9 @@ class PyFuncOpGradDescMaker : public framework::GradOpDescMakerBase {
grad_op->SetType("py_func");
framework::AttributeMap bwd_attrs;
bwd_attrs[kForwardPythonCallableId] =
fwd_attrs.at(kBackwardPythonCallableId);
bwd_attrs[kBackwardPythonCallableId] = -1;
bwd_attrs[kForwardPythonCallableId.data()] =
fwd_attrs.at(kBackwardPythonCallableId.data());
bwd_attrs[kBackwardPythonCallableId.data()] = -1;
grad_op->SetAttrMap(bwd_attrs);
// All forward inputs
......@@ -262,7 +265,7 @@ class PyFuncOpGradDescMaker : public framework::GradOpDescMakerBase {
// For memory reused, some inputs/output in forward part may be not needed
// in backward part. Skipping these vars helps to save memory
auto &backward_skip_var_list = PADDLE_GET_CONST(
std::vector<std::string>, fwd_attrs.at(kPyFuncBackwardSkipVars));
std::vector<std::string>, fwd_attrs.at(kPyFuncBackwardSkipVars.data()));
std::unordered_set<std::string> backward_skip_var_set(
backward_skip_var_list.begin(), backward_skip_var_list.end());
std::vector<std::string> bwd_ins;
......@@ -336,7 +339,8 @@ class PyFuncOp : public framework::OperatorBase {
outputs[i] = out_var ? out_var->GetMutable<phi::DenseTensor>() : nullptr;
}
auto callable_id = static_cast<size_t>(Attr<int>(kForwardPythonCallableId));
auto callable_id =
static_cast<size_t>(Attr<int>(kForwardPythonCallableId.data()));
auto *py_callable = GetPythonCallableObject(callable_id);
VLOG(10) << "Call Python function with id " << callable_id << ": "
<< PythonFuncDebugString(*py_callable);
......
paddle/fluid/operators/recurrent_op.cc
浏览文件 @ 5ada98b8
......@@ -30,23 +30,25 @@ namespace operators {
using StepScopeVar = std::vector<framework::Scope *>;
const char RecurrentBase::kInputs[] = "inputs";
const char RecurrentBase::kInitialStates[] = "initial_states";
const char RecurrentBase::kParameters[] = "parameters";
const char RecurrentBase::kOutputs[] = "outputs";
const char RecurrentBase::kStepScopes[] = "step_scopes";
const char RecurrentBase::kHasStates[] = "has_states";
const char RecurrentBase::kExStates[] = "ex_states";
const char RecurrentBase::kStates[] = "states";
const char RecurrentBase::kStepBlock[] = "sub_block";
const char RecurrentBase::kReverse[] = "reverse";
const char RecurrentBase::kIsTrain[] = "is_train";
const char RecurrentBase::kSkipEagerDeletionVars[] = "skip_eager_deletion_vars";
const char RecurrentBase::kInputs[] = "inputs"; // NOLINT
const char RecurrentBase::kInitialStates[] = "initial_states"; // NOLINT
const char RecurrentBase::kParameters[] = "parameters"; // NOLINT
const char RecurrentBase::kOutputs[] = "outputs"; // NOLINT
const char RecurrentBase::kStepScopes[] = "step_scopes"; // NOLINT
const char RecurrentBase::kHasStates[] = "has_states"; // NOLINT
const char RecurrentBase::kExStates[] = "ex_states"; // NOLINT
const char RecurrentBase::kStates[] = "states"; // NOLINT
const char RecurrentBase::kStepBlock[] = "sub_block"; // NOLINT
const char RecurrentBase::kReverse[] = "reverse"; // NOLINT
const char RecurrentBase::kIsTrain[] = "is_train"; // NOLINT
const char RecurrentBase::kSkipEagerDeletionVars[] =
"skip_eager_deletion_vars"; // NOLINT
#define GRAD_SUFFIX "@GRAD"
const char RecurrentBase::kInputGrads[] = "inputs" GRAD_SUFFIX;
const char RecurrentBase::kOutputGrads[] = "outputs" GRAD_SUFFIX;
const char RecurrentBase::kParamGrads[] = "parameters" GRAD_SUFFIX;
const char RecurrentBase::kInitStateGrads[] = "initial_states" GRAD_SUFFIX;
const char RecurrentBase::kInputGrads[] = "inputs" GRAD_SUFFIX; // NOLINT
const char RecurrentBase::kOutputGrads[] = "outputs" GRAD_SUFFIX; // NOLINT
const char RecurrentBase::kParamGrads[] = "parameters" GRAD_SUFFIX; // NOLINT
const char RecurrentBase::kInitStateGrads[] =
"initial_states" GRAD_SUFFIX; // NOLINT
static void ClearStepScopes(const platform::DeviceContext &dev_ctx,
framework::Scope *parent_scope,
......
paddle/fluid/platform/collective_helper.cc
浏览文件 @ 5ada98b8
......@@ -15,6 +15,7 @@
#include "paddle/fluid/platform/collective_helper.h"
#include <utility>
#include <vector>
#include "paddle/fluid/memory/allocation/allocator_facade.h"
#include "paddle/fluid/platform/device/device_wrapper.h"
......@@ -537,7 +538,8 @@ void XCCLCommContext::CreateXCCLCommMultiTrainer(
VLOG(1) << "Begin CreateXCCLCommMultiTrainer. device number: " << kDevices
<< ", ntrainers: " << ntrainers << ", train_id: " << train_id
<< ", rind_id: " << ring_id;
phi::ccl::CCLComm comms[kDevices];
std::vector<phi::ccl::CCLComm> comms;
comms.resize(kDevices);
{
for (int i = 0; i < kDevices; i++) {
phi::DeviceManager::SetDevice(device_type_, i);
......@@ -545,7 +547,7 @@ void XCCLCommContext::CreateXCCLCommMultiTrainer(
kDevices * ntrainers,
xccl_id,
train_id * kDevices + i,
comms + i);
comms.data() + i);
VLOG(1) << "CCLCommInitRank: " << i;
}
}
......
paddle/fluid/platform/device/custom/custom_device_test.cc
浏览文件 @ 5ada98b8
......@@ -14,6 +14,7 @@
#include <gtest/gtest.h>
#include <array>
#include <string>
#include "paddle/fluid/framework/tensor_util.h"
......@@ -120,8 +121,8 @@ void TestTensorUtils(const paddle::platform::Place& place) {
int* src_ptr = src_tensor.mutable_data<int>(phi::make_ddim({3, 3}),
paddle::platform::CPUPlace());
int arr[9] = {1, 2, 3, 4, 5, 6, 7, 8, 9};
memcpy(src_ptr, arr, 9 * sizeof(int));
std::array<int, 9> arr = {1, 2, 3, 4, 5, 6, 7, 8, 9};
memcpy(src_ptr, arr.data(), 9 * sizeof(int));
// CPU Tensor to GPU Tensor
paddle::platform::CustomDeviceContext gpu_ctx(place);
......
paddle/fluid/platform/device_event_base.cc
浏览文件 @ 5ada98b8
......@@ -20,13 +20,15 @@
namespace paddle {
namespace platform {
EventCreateFunction DeviceEvent::event_creator_[MaxDeviceTypes];
EventRecordFunction DeviceEvent::event_recorder_[MaxDeviceTypes];
EventQueryFunction DeviceEvent::event_querier_[MaxDeviceTypes];
EventFinishFunction DeviceEvent::event_finisher_[MaxDeviceTypes];
EventSetFinishedFunction DeviceEvent::event_finished_setter_[MaxDeviceTypes];
EventWaitFunction DeviceEvent::event_waiter_[MaxDeviceTypes][MaxDeviceTypes];
EventResetFunction DeviceEvent::event_resetter_[MaxDeviceTypes];
EventCreateFunction DeviceEvent::event_creator_[MaxDeviceTypes]; // NOLINT
EventRecordFunction DeviceEvent::event_recorder_[MaxDeviceTypes]; // NOLINT
EventQueryFunction DeviceEvent::event_querier_[MaxDeviceTypes]; // NOLINT
EventFinishFunction DeviceEvent::event_finisher_[MaxDeviceTypes]; // NOLINT
EventSetFinishedFunction // NOLINT
DeviceEvent::event_finished_setter_[MaxDeviceTypes];
EventWaitFunction DeviceEvent::event_waiter_[MaxDeviceTypes] // NOLINT
[MaxDeviceTypes];
EventResetFunction DeviceEvent::event_resetter_[MaxDeviceTypes]; // NOLINT
/*
* Generate flag used to create event on all sorts of equipment.
......
paddle/fluid/platform/enforce_test.cc
浏览文件 @ 5ada98b8
......@@ -11,6 +11,7 @@ limitations under the License. */
#include "paddle/fluid/platform/enforce.h"
#include <array>
#include <list>
#include "gtest/gtest.h"
......@@ -286,7 +287,7 @@ TEST(ENFORCE_NOT_NULL, FAIL) {
}
struct Dims {
size_t dims_[4];
std::array<size_t, 4> dims_;
bool operator==(const Dims& o) const {
for (size_t i = 0; i < 4; ++i) {
......
paddle/fluid/platform/flags.h
浏览文件 @ 5ada98b8
......@@ -23,7 +23,7 @@
#define __PADDLE_DEFINE_EXPORTED_FLAG( \
__name, __is_writable, __cpp_type, __gflag_type, __default_value, __doc) \
DEFINE_##__gflag_type(__name, __default_value, __doc); \
DEFINE_##__gflag_type(__name, __default_value, __doc); /* NOLINT */ \
struct __PaddleRegisterFlag_##__name { \
__PaddleRegisterFlag_##__name() { \
using FlagDeclaredType = \
......
paddle/fluid/platform/gen_comm_id_helper.cc
浏览文件 @ 5ada98b8
......@@ -23,6 +23,7 @@ limitations under the License. */
#include <sys/socket.h>
#include <algorithm>
#include <array>
#include <string>
#include <thread> // NOLINT
......@@ -220,7 +221,7 @@ static int SocketAccept(int server_fd, const CommHead head) {
struct sockaddr_in client_addr;
socklen_t addr_length = sizeof(client_addr);
char buffer[1024] = {0};
std::array<char, 1024> buffer{0};
int conn = -1;
const char* phead = reinterpret_cast<const char*>(&head);
......@@ -231,8 +232,8 @@ static int SocketAccept(int server_fd, const CommHead head) {
"accept",
conn);
int ret_val = SocketRecv(conn, buffer, sizeof(head));
if (ret_val > 0 && memcmp(buffer, phead, sizeof(head)) == 0) {
int ret_val = SocketRecv(conn, buffer.data(), sizeof(head));
if (ret_val > 0 && memcmp(buffer.data(), phead, sizeof(head)) == 0) {
// send a message to the sender, indicating that the link is correct
CHECK_SYS_CALL(SocketSend(conn, phead, sizeof(head)), "send");
break; // accept client
......@@ -289,7 +290,7 @@ static int ConnectAddr(const std::string& ep, const CommHead head) {
static_assert(sizeof(CommHead) <= 1024,
"sizeof(CommHead) must <= buffer size");
char buffer[1024] = {0};
std::array<char, 1024> buffer{0};
const char* phead = reinterpret_cast<const char*>(&head);
// TODO(wangxi) Set from env, default 900s=15min
......@@ -312,8 +313,8 @@ static int ConnectAddr(const std::string& ep, const CommHead head) {
}
CHECK_SYS_CALL(SocketSend(sock, phead, sizeof(head)), "send");
ret_val = SocketRecv(sock, buffer, sizeof(head));
if (ret_val > 0 && memcmp(buffer, phead, sizeof(head)) == 0) {
ret_val = SocketRecv(sock, buffer.data(), sizeof(head));
if (ret_val > 0 && memcmp(buffer.data(), phead, sizeof(head)) == 0) {
// recv same message from receiver, indicating that the link is correct
break; // accept client
} else {
......@@ -333,71 +334,77 @@ static int ConnectAddr(const std::string& ep, const CommHead head) {
template <typename CommUniqueId>
static void RecvCommID(int conn, CommUniqueId* nccl_id) {
char buffer[MAX_COMMUNIQUEID_LEN] = {0};
std::array<char, MAX_COMMUNIQUEID_LEN> buffer{0};
static_assert(sizeof(CommUniqueId) <= MAX_COMMUNIQUEID_LEN,
"nccl id bytes must <= buffer size");
CHECK_SYS_CALL(SocketRecv(conn, buffer, sizeof(CommUniqueId)),
CHECK_SYS_CALL(SocketRecv(conn, buffer.data(), sizeof(CommUniqueId)),
"recv comm unique id");
memcpy(nccl_id, buffer, sizeof(CommUniqueId));
memcpy(nccl_id, buffer.data(), sizeof(CommUniqueId));
}
template <typename CommUniqueId>
static void SendCommID(int conn, CommUniqueId* nccl_id) {
char buffer[MAX_COMMUNIQUEID_LEN] = {0};
memcpy(buffer, nccl_id, sizeof(CommUniqueId));
std::array<char, MAX_COMMUNIQUEID_LEN> buffer{0};
memcpy(buffer.data(), nccl_id, sizeof(CommUniqueId));
CHECK_SYS_CALL(SocketSend(conn, buffer, sizeof(CommUniqueId)),
CHECK_SYS_CALL(SocketSend(conn, buffer.data(), sizeof(CommUniqueId)),
"send comm unique id");
}
#ifdef PADDLE_WITH_CUSTOM_DEVICE
template <>
void RecvCommID<phi::ccl::CCLRootId>(int conn, phi::ccl::CCLRootId* nccl_id) {
char buffer[MAX_COMMUNIQUEID_LEN] = {0};
CHECK_SYS_CALL(SocketRecv(conn, buffer, sizeof(size_t)),
std::array<char, MAX_COMMUNIQUEID_LEN> buffer{0};
CHECK_SYS_CALL(SocketRecv(conn, buffer.data(), sizeof(size_t)),
"recv comm unique id size");
size_t unique_id_size = *reinterpret_cast<size_t*>(buffer);
size_t unique_id_size = *reinterpret_cast<size_t*>(buffer.data());
VLOG(6) << "RecvCommID size: " << unique_id_size;
nccl_id->resize(unique_id_size);
size_t n_repeat = unique_id_size / MAX_COMMUNIQUEID_LEN;
size_t n_remain = unique_id_size % MAX_COMMUNIQUEID_LEN;
for (size_t i = 0; i < n_repeat; ++i) {
CHECK_SYS_CALL(SocketRecv(conn, buffer, MAX_COMMUNIQUEID_LEN),
CHECK_SYS_CALL(SocketRecv(conn, buffer.data(), MAX_COMMUNIQUEID_LEN),
"recv comm unique id");
memcpy(nccl_id->data() + i * MAX_COMMUNIQUEID_LEN,
buffer,
buffer.data(),
MAX_COMMUNIQUEID_LEN);
}
if (n_remain) {
CHECK_SYS_CALL(SocketRecv(conn, buffer, n_remain), "recv comm unique id");
memcpy(nccl_id->data() + n_repeat * MAX_COMMUNIQUEID_LEN, buffer, n_remain);
CHECK_SYS_CALL(SocketRecv(conn, buffer.data(), n_remain),
"recv comm unique id");
memcpy(nccl_id->data() + n_repeat * MAX_COMMUNIQUEID_LEN,
buffer.data(),
n_remain);
}
VLOG(6) << "RecvCommID done";
}
template <>
void SendCommID<phi::ccl::CCLRootId>(int conn, phi::ccl::CCLRootId* nccl_id) {
char buffer[MAX_COMMUNIQUEID_LEN] = {0};
std::array<char, MAX_COMMUNIQUEID_LEN> buffer{0};
size_t unique_id_size = nccl_id->size();
VLOG(6) << "SendCommID size: " << unique_id_size;
memcpy(buffer, &unique_id_size, sizeof(size_t));
CHECK_SYS_CALL(SocketSend(conn, buffer, sizeof(size_t)),
memcpy(buffer.data(), &unique_id_size, sizeof(size_t));
CHECK_SYS_CALL(SocketSend(conn, buffer.data(), sizeof(size_t)),
"send comm unique id size");
size_t n_repeat = unique_id_size / MAX_COMMUNIQUEID_LEN;
size_t n_remain = unique_id_size % MAX_COMMUNIQUEID_LEN;
for (size_t i = 0; i < n_repeat; ++i) {
memcpy(buffer,
memcpy(buffer.data(),
nccl_id->data() + i * MAX_COMMUNIQUEID_LEN,
MAX_COMMUNIQUEID_LEN);
CHECK_SYS_CALL(SocketSend(conn, buffer, MAX_COMMUNIQUEID_LEN),
CHECK_SYS_CALL(SocketSend(conn, buffer.data(), MAX_COMMUNIQUEID_LEN),
"send comm unique id");
}
if (n_remain) {
memcpy(buffer, nccl_id->data() + n_repeat * MAX_COMMUNIQUEID_LEN, n_remain);
CHECK_SYS_CALL(SocketSend(conn, buffer, n_remain), "send comm unique id");
memcpy(buffer.data(),
nccl_id->data() + n_repeat * MAX_COMMUNIQUEID_LEN,
n_remain);
CHECK_SYS_CALL(SocketSend(conn, buffer.data(), n_remain),
"send comm unique id");
}
VLOG(6) << "SendCommID done";
}
......
paddle/fluid/platform/init.cc
浏览文件 @ 5ada98b8
......@@ -263,7 +263,7 @@ void InitDevices(const std::vector<int> devices) {
#ifndef _WIN32
// Description Quoted from
// https://pubs.opengroup.org/onlinepubs/9699919799/basedefs/signal.h.html
const struct {
const struct { // NOLINT
int signal_number;
const char *name;
const char *error_string;
......
paddle/fluid/platform/profiler.cc
浏览文件 @ 5ada98b8
......@@ -38,7 +38,7 @@ PADDLE_DEFINE_EXPORTED_bool(enable_rpc_profiler,
false,
"Enable rpc profiler or not.");
DEFINE_bool(enable_record_memory, false, "enable memory recorder");
DEFINE_bool(enable_record_memory, false, "enable memory recorder"); // NOLINT
#if defined(_WIN32) && defined(PHI_SHARED)
phi::ProfilerState phi::ProfilerHelper::g_state = phi::ProfilerState::kDisabled;
......
paddle/fluid/platform/profiler/utils.cc
浏览文件 @ 5ada98b8
......@@ -145,13 +145,16 @@ float CalculateEstOccupancy(uint32_t DeviceId,
#endif // PADDLE_WITH_CUPTI
const char* StringTracerMemEventType(TracerMemEventType type) {
static const char* categary_name_[] = {
"Allocate", "Free", "ReservedAllocate", "ReservedFree"};
static const char* categary_name_[] = {// NOLINT
"Allocate",
"Free",
"ReservedAllocate",
"ReservedFree"};
return categary_name_[static_cast<int>(type)];
}
const char* StringTracerEventType(TracerEventType type) {
static const char* categary_name_[] = {"Operator",
static const char* categary_name_[] = {"Operator", // NOLINT
"Dataloader",
"ProfileStep",
"CudaRuntime",
......
paddle/fluid/pybind/eager.cc
浏览文件 @ 5ada98b8
......@@ -777,7 +777,7 @@ int TensorInit(PyObject* self, PyObject* args, PyObject* kwargs) {
PyObject* kw_dist_attr = nullptr;
// the keywords argument
static char* kwlist[] = {const_cast<char*>("value"),
static char* kwlist[] = {const_cast<char*>("value"), // NOLINT
const_cast<char*>("place"),
const_cast<char*>("persistable"),
const_cast<char*>("zero_copy"),
......@@ -1129,7 +1129,7 @@ int StringTensorInit(PyObject* self, PyObject* args, PyObject* kwargs) {
PyObject* kw_dims = nullptr;
// the keywords argument
static char* kwlist[] = {const_cast<char*>("value"),
static char* kwlist[] = {const_cast<char*>("value"), // NOLINT
const_cast<char*>("zero_copy"),
const_cast<char*>("name"),
const_cast<char*>("dims"),
......@@ -1317,12 +1317,12 @@ static void TensorDealloc(TensorObject* self) {
Py_TYPE(self)->tp_free(reinterpret_cast<PyObject*>(self));
}
extern struct PyGetSetDef variable_properties[];
extern struct PyGetSetDef string_tensor_variable_properties[];
extern struct PyGetSetDef variable_properties[]; // NOLINT
extern struct PyGetSetDef string_tensor_variable_properties[]; // NOLINT
extern PyMethodDef variable_methods[];
extern PyMethodDef math_op_patch_methods[];
extern PyMethodDef string_tensor_variable_methods[];
extern PyMethodDef variable_methods[]; // NOLINT
extern PyMethodDef math_op_patch_methods[]; // NOLINT
extern PyMethodDef string_tensor_variable_methods[]; // NOLINT
PyNumberMethods number_methods;
PySequenceMethods sequence_methods;
......
paddle/fluid/pybind/eager_method.cc
浏览文件 @ 5ada98b8
......@@ -455,7 +455,8 @@ static PyObject* tensor_method_numpy_for_string_tensor(TensorObject* self,
longest_pstring->data(), longest_pstring->size());
max_unicode_length = (max_unicode_length == 0) ? 1 : max_unicode_length;
VLOG(6) << "The max unicode length is " << max_unicode_length;
auto sp = std::make_unique<uint32_t[]>(max_unicode_length * numel);
auto sp =
std::make_unique<uint32_t[]>(max_unicode_length * numel); // NOLINT
auto py_array_data = sp.get();
memset(py_array_data, 0, max_unicode_length * numel * sizeof(uint32_t));
for (int64_t i = 0; i < numel; ++i) {
......
paddle/fluid/pybind/eager_properties.cc
浏览文件 @ 5ada98b8
......@@ -445,7 +445,7 @@ PyObject* tensor_properties_get_grad_fn(TensorObject* self, void* closure) {
EAGER_CATCH_AND_THROW_RETURN_NULL
}
struct PyGetSetDef variable_properties[] = {
struct PyGetSetDef variable_properties[] = { // NOLINT
{"data",
(getter)tensor_properties_get_data,
(setter)tensor_properties_set_data,
......@@ -510,7 +510,7 @@ struct PyGetSetDef variable_properties[] = {
{nullptr, nullptr, nullptr, nullptr, nullptr}};
// variable_properties for core.eager.StringTensor
struct PyGetSetDef string_tensor_variable_properties[] = {
struct PyGetSetDef string_tensor_variable_properties[] = { // NOLINT
{"name",
(getter)tensor_properties_get_name,
(setter)tensor_properties_set_name,
......
paddle/fluid/pybind/eager_py_layer.cc
浏览文件 @ 5ada98b8
......@@ -662,7 +662,7 @@ int tensor_properties_set_materialize_grads(PyLayerObject* self,
EAGER_CATCH_AND_THROW_RETURN_NEG
}
PyMethodDef pylayer_methods[] = {{"name",
PyMethodDef pylayer_methods[] = {{"name", // NOLINT
(PyCFunction)(void (*)())pylayer_method_name,
METH_NOARGS,
nullptr},
......@@ -672,7 +672,7 @@ PyMethodDef pylayer_methods[] = {{"name",
nullptr},
{nullptr, nullptr, 0, nullptr}};
struct PyGetSetDef pylayer_properties[] {
struct PyGetSetDef pylayer_properties[] { // NOLINT
{"container",
(getter)tensor_properties_get_container,
(setter)tensor_properties_set_container,
......
paddle/phi/backends/custom/custom_device.cc
浏览文件 @ 5ada98b8
......@@ -356,7 +356,7 @@ class CustomDevice : public DeviceInterface {
}
} else {
if (!pimpl_->memory_copy_p2p) {
std::unique_ptr<uint8_t[]> tmp(new uint8_t[size]);
std::unique_ptr<uint8_t[]> tmp(new uint8_t[size]); // NOLINT
MemoryCopyD2H(src_dev_id, tmp.get(), src, size);
MemoryCopyH2D(dst_dev_id, dst, tmp.get(), size);
} else {
......@@ -447,7 +447,7 @@ class CustomDevice : public DeviceInterface {
PADDLE_ENFORCE_CUSTOM_DEVICE_SUCCESS(
pimpl_->device_memory_set(device, ptr, value, size));
} else {
std::unique_ptr<uint8_t[]> tmp(new uint8_t[size]);
std::unique_ptr<uint8_t[]> tmp(new uint8_t[size]); // NOLINT
memset(tmp.get(), value, size);
MemoryCopyH2D(dev_id, ptr, tmp.get(), size);
}
......
paddle/phi/kernels/cpu/box_coder_kernel.cc
浏览文件 @ 5ada98b8
......@@ -13,6 +13,7 @@
// limitations under the License.
#include "paddle/phi/kernels/box_coder_kernel.h"
#include <array>
#include "paddle/phi/backends/cpu/cpu_context.h"
#include "paddle/phi/core/kernel_registry.h"
......@@ -116,8 +117,8 @@ void DecodeCenterSize(const DenseTensor *target_box,
auto *target_box_data = target_box->data<T>();
auto *prior_box_data = prior_box->data<T>();
T var_data[4] = {1., 1., 1., 1.};
T *var_ptr = var_data;
std::array<T, 4> var_data{1., 1., 1., 1.};
T *var_ptr = var_data.data();
size_t offset = i * col * len + j * len;
int prior_box_offset = axis == 0 ? j * len : i * len;
......
paddle/phi/kernels/cpu/fill_diagonal_tensor_grad_kernel.cc
浏览文件 @ 5ada98b8
......@@ -13,6 +13,7 @@
// limitations under the License.
#include "paddle/phi/kernels/fill_diagonal_tensor_grad_kernel.h"
#include <array>
#include "paddle/phi/backends/cpu/cpu_context.h"
#include "paddle/phi/core/kernel_registry.h"
......@@ -38,10 +39,17 @@ void FillDiagonalTensorGradKernel(const Context& ctx,
}
}
int64_t new_dims[2], strides[2];
std::array<int64_t, 2> new_dims;
std::array<int64_t, 2> strides;
std::vector<int64_t> matdim;
matdim.resize(matrows);
CalMatDims(dx_dims, dim1, dim2, &offset, new_dims, strides, matdim.data());
CalMatDims(dx_dims,
dim1,
dim2,
&offset,
new_dims.data(),
strides.data(),
matdim.data());
auto size = x_grad->numel();
phi::Copy(ctx, out_grad, ctx.GetPlace(), false, x_grad);
......
paddle/phi/kernels/cpu/fill_diagonal_tensor_kernel.cc
浏览文件 @ 5ada98b8
......@@ -13,6 +13,7 @@
// limitations under the License.
#include "paddle/phi/kernels/fill_diagonal_tensor_kernel.h"
#include <array>
#include "paddle/phi/core/kernel_registry.h"
#include "paddle/phi/core/tensor_utils.h"
......@@ -87,10 +88,17 @@ void FillDiagonalTensorKernel(const Context &ctx,
auto matdims = y.dims();
auto fill_dims = phi::flatten_to_2d(matdims, matdims.size() - 1);
int64_t new_dims[2], strides[2];
std::array<int64_t, 2> new_dims;
std::array<int64_t, 2> strides;
std::vector<int64_t> matdim;
matdim.resize(fill_dims[0]);
CalMatDims(out_dims, dim1, dim2, &offset, new_dims, strides, matdim.data());
CalMatDims(out_dims,
dim1,
dim2,
&offset,
new_dims.data(),
strides.data(),
matdim.data());
PADDLE_ENFORCE_EQ(
new_dims[0],
fill_dims[0],
......
paddle/phi/kernels/cpu/index_put_grad_kernel.cc
浏览文件 @ 5ada98b8
......@@ -13,6 +13,7 @@
// limitations under the License.
#include "paddle/phi/kernels/index_put_grad_kernel.h"
#include <array>
#include <numeric>
#include "paddle/phi/core/kernel_registry.h"
#include "paddle/phi/kernels/cast_kernel.h"
......@@ -78,7 +79,7 @@ void LaunchIndexPutGradKernel(const Context& dev_ctx,
bool accumulate,
DenseTensor* value_grad,
DenseTensor* x_grad) {
const int64_t* pd_indices[7];
std::array<const int64_t*, 7> pd_indices;
for (size_t i = 0; i < indices.size(); ++i) {
pd_indices[i] = indices[i]->data<int64_t>();
}
......@@ -93,7 +94,7 @@ void LaunchIndexPutGradKernel(const Context& dev_ctx,
auto x_grad_stride = phi::stride(x_grad_dims);
set_zero_kernel<T>(
numel, pd_indices, x_grad_stride, x_grad_dims, x_grad_data);
numel, pd_indices.data(), x_grad_stride, x_grad_dims, x_grad_data);
}
}
......@@ -111,7 +112,7 @@ void LaunchIndexPutGradKernel(const Context& dev_ctx,
index_put_grad_kernel<T>(numel,
out_grad_data,
pd_indices,
pd_indices.data(),
out_grad_stride,
out_grad_dims,
tmp_value_grad_data);
......@@ -131,7 +132,7 @@ void LaunchIndexPutGradKernel(const Context& dev_ctx,
index_put_grad_kernel<T>(numel,
out_grad_data,
pd_indices,
pd_indices.data(),
out_grad_stride,
out_grad_dims,
value_grad_data);
......@@ -144,7 +145,7 @@ void LaunchIndexPutGradKernel(const Context& dev_ctx,
index_put_grad_kernel<T>(numel,
out_grad_data,
pd_indices,
pd_indices.data(),
out_grad_stride,
out_grad_dims,
tmp_value_grad_data);
......
paddle/phi/kernels/cpu/index_put_kernel.cc
浏览文件 @ 5ada98b8
......@@ -13,6 +13,7 @@
// limitations under the License.
#include "paddle/phi/kernels/index_put_kernel.h"
#include <array>
#include "paddle/phi/backends/cpu/cpu_context.h"
#include "paddle/phi/core/kernel_registry.h"
#include "paddle/phi/kernels/cast_kernel.h"
......@@ -75,7 +76,7 @@ void LaunchIndexPutKernel(const Context& dev_ctx,
int64_t is_single_val_tensor = (value.numel() == 1) ? 0 : INT64_MAX;
const int64_t* pd_indices[7];
std::array<const int64_t*, 7> pd_indices;
for (size_t i = 0; i < indices.size(); ++i) {
pd_indices[i] = indices[i]->data<int64_t>();
}
......@@ -83,7 +84,7 @@ void LaunchIndexPutKernel(const Context& dev_ctx,
index_put_kernel<T>(numel,
x_data,
val_data,
pd_indices,
pd_indices.data(),
x_stride,
x_dims,
is_single_val_tensor,
......
paddle/phi/kernels/cpu/interpolate_grad_kernel.cc
浏览文件 @ 5ada98b8
......@@ -13,6 +13,7 @@
// limitations under the License.
#include "paddle/phi/kernels/interpolate_grad_kernel.h"
#include <array>
#include "paddle/phi/backends/cpu/cpu_context.h"
#include "paddle/phi/common/amp_type_traits.h"
......@@ -190,11 +191,11 @@ static void BicubicInterpolationGrad(const DenseTensor& output_grad,
int input_x = floorf(x_n);
MT x_t = x_n - input_x;
MT x_coeffs[4];
MT y_coeffs[4];
std::array<MT, 4> x_coeffs;
std::array<MT, 4> y_coeffs;
funcs::get_cubic_upsample_coefficients<MT>(x_coeffs, x_t);
funcs::get_cubic_upsample_coefficients<MT>(y_coeffs, y_t);
funcs::get_cubic_upsample_coefficients<MT>(x_coeffs.data(), x_t);
funcs::get_cubic_upsample_coefficients<MT>(y_coeffs.data(), y_t);
for (int i = 0; i < n; i++) { // loop for batches
for (int j = 0; j < c; j++) { // loop for channels
......
paddle/phi/kernels/cpu/interpolate_kernel.cc
浏览文件 @ 5ada98b8
......@@ -13,6 +13,7 @@
// limitations under the License.
#include "paddle/phi/kernels/interpolate_kernel.h"
#include <array>
#include "paddle/phi/backends/cpu/cpu_context.h"
#include "paddle/phi/common/amp_type_traits.h"
......@@ -24,8 +25,8 @@ namespace phi {
template <typename T>
static inline T cubic_interp(T x0, T x1, T x2, T x3, T t) {
T coeffs[4];
funcs::get_cubic_upsample_coefficients<T>(coeffs, t);
std::array<T, 4> coeffs;
funcs::get_cubic_upsample_coefficients<T>(coeffs.data(), t);
return x0 * coeffs[0] + x1 * coeffs[1] + x2 * coeffs[2] + x3 * coeffs[3];
}
......@@ -274,7 +275,7 @@ static void BicubicInterpolation(const DenseTensor& input,
for (int i = 0; i < n; i++) { // loop for batches
for (int j = 0; j < c; j++) { // loop for channels
MT coefficients[4];
std::array<MT, 4> coefficients;
// interp 4 times in x direction
for (int ii = 0; ii < 4; ii++) {
int access_y = std::max(std::min(input_y - 1 + ii, in_h - 1),
......
paddle/phi/kernels/cpu/nms_kernel.cc
浏览文件 @ 5ada98b8
......@@ -13,6 +13,7 @@
// limitations under the License.
#include "paddle/phi/kernels/nms_kernel.h"
#include <array>
#include "paddle/phi/backends/cpu/cpu_context.h"
#include "paddle/phi/core/kernel_registry.h"
......@@ -32,17 +33,17 @@ static int64_t NMS(const T* boxes_data,
for (int64_t i = 0; i < num_boxes; ++i) {
if (masks[i / 64] & 1ULL << (i % 64)) continue;
T box_1[4];
std::array<T, 4> box_1;
for (int k = 0; k < 4; ++k) {
box_1[k] = boxes_data[i * 4 + k];
}
for (int64_t j = i + 1; j < num_boxes; ++j) {
if (masks[j / 64] & 1ULL << (j % 64)) continue;
T box_2[4];
std::array<T, 4> box_2;
for (int k = 0; k < 4; ++k) {
box_2[k] = boxes_data[j * 4 + k];
}
bool is_overlap = CalculateIoU<T>(box_1, box_2, threshold);
bool is_overlap = CalculateIoU<T>(box_1.data(), box_2.data(), threshold);
if (is_overlap) {
masks[j / 64] |= 1ULL << (j % 64);
}
......
paddle/phi/kernels/cpu/yolo_box_kernel.cc
浏览文件 @ 5ada98b8
......@@ -13,6 +13,7 @@
// limitations under the License.
#include "paddle/phi/kernels/yolo_box_kernel.h"
#include <array>
#include "paddle/phi/backends/cpu/cpu_context.h"
#include "paddle/phi/core/kernel_registry.h"
......@@ -66,7 +67,7 @@ void YoloBoxKernel(const Context& dev_ctx,
memset(scores_data, 0, scores->numel() * sizeof(T));
T box[4];
std::array<T, 4> box;
for (int i = 0; i < n; i++) {
int img_height = imgsize_data[2 * i];
int img_width = imgsize_data[2 * i + 1];
......@@ -90,7 +91,7 @@ void YoloBoxKernel(const Context& dev_ctx,
int box_idx = funcs::GetEntryIndex(
i, j, k * w + l, an_num, an_stride, stride, 0, iou_aware);
funcs::GetYoloBox<T>(box,
funcs::GetYoloBox<T>(box.data(),
input_data,
anchors_data,
l,
......@@ -107,8 +108,12 @@ void YoloBoxKernel(const Context& dev_ctx,
scale,
bias);
box_idx = (i * box_num + j * stride + k * w + l) * 4;
funcs::CalcDetectionBox<T>(
boxes_data, box, box_idx, img_height, img_width, clip_bbox);
funcs::CalcDetectionBox<T>(boxes_data,
box.data(),
box_idx,
img_height,
img_width,
clip_bbox);
int label_idx = funcs::GetEntryIndex(
i, j, k * w + l, an_num, an_stride, stride, 5, iou_aware);
......
paddle/phi/kernels/funcs/jit/benchmark.cc
浏览文件 @ 5ada98b8
......@@ -26,7 +26,7 @@
DEFINE_int32(burning, 10, "Burning times.");
DEFINE_int32(repeat, 3000, "Repeat times.");
DEFINE_int32(max_size, 1000, "The Max size would be tested.");
DEFINE_string(filter, "", "The Benchmark name would be run.");
DEFINE_string(filter, "", "The Benchmark name would be run."); // NOLINT
class BenchJITKernel {
public:
......
paddle/phi/kernels/funcs/jit/gen/act.cc
浏览文件 @ 5ada98b8
......@@ -41,7 +41,7 @@ const float ALIGN32_BEG exp_float_consts[] ALIGN32_END = { // NOLINT
REPEAT_8TIMES(SIGMOID_THRESHOLD_MAX),
REPEAT_8TIMES(SIGMOID_THRESHOLD_MIN)};
const int ALIGN32_BEG exp_int_0x7f[] ALIGN32_END = {
const int ALIGN32_BEG exp_int_0x7f[] ALIGN32_END = { // NOLINT
REPEAT_8TIMES(0x7f)}; // NOLINT
int ALIGN32_BEG g_tmp_mem[16] ALIGN32_END = {0}; // NOLINT
......
paddle/phi/kernels/funcs/jit/test.cc
浏览文件 @ 5ada98b8
......@@ -12,6 +12,7 @@ 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 <array>
#include <iostream>
#include <random>
......@@ -1171,7 +1172,9 @@ TEST(JITKernel_helper, GetAllCandidateFuncs) {
TEST(JITKernel_helper, pack_weights) {
const int N = 8 * 60, K = 2;
float src[K][N], yref[K][N], y[K * N];
std::array<std::array<float, N>, K> src;
std::array<std::array<float, N>, K> yref;
std::array<float, N * K> y;
float* x = &(src[0][0]);
float* ref = &(yref[0][0]);
for (int i = 0; i < N * K; ++i) {
......@@ -1200,8 +1203,8 @@ TEST(JITKernel_helper, pack_weights) {
acc += g;
}
jit::pack_weights<float>(x, y, N, K);
ExpectEQ<float>(y, ref, N * K);
jit::pack_weights<float>(x, y.data(), N, K);
ExpectEQ<float>(y.data(), ref, N * K);
}
TEST(JITKernel_helper, attr) {
......
paddle/phi/kernels/strings/unicode.cc
浏览文件 @ 5ada98b8
......@@ -22,8 +22,8 @@ limitations under the License. */
namespace phi {
namespace strings {
static const void* utils_map[4] = {nullptr};
static uint16_t CHARCASES_MAP[65536] = {0};
static const void* utils_map[4] = {nullptr}; // NOLINT
static uint16_t CHARCASES_MAP[65536] = {0}; // NOLINT
const uint8_t* GetUniFlagMap() {
if (utils_map[1] == nullptr) {
......
paddle/utils/array_ref_test.cc
浏览文件 @ 5ada98b8
......@@ -14,6 +14,7 @@
#include "paddle/utils/array_ref.h"
#include <array>
#include <cstdlib>
#include <ctime>
......@@ -35,15 +36,15 @@ TEST(array_ref, array_ref) {
CHECK_EQ(c.data(), &v);
CHECK_EQ(c.equals(paddle::make_array_ref(v)), true);
int v1[5] = {1, 2, 3, 4, 5};
paddle::array_ref<int> d(v1, 5);
std::array<int, 5> v1 = {1, 2, 3, 4, 5};
paddle::array_ref<int> d(v1.data(), 5);
CHECK_EQ(d.size(), size_t(5));
CHECK_EQ(d.data(), v1);
CHECK_EQ(d.equals(paddle::make_array_ref(v1, 5)), true);
CHECK_EQ(d.data(), v1.data());
CHECK_EQ(d.equals(paddle::make_array_ref(v1.data(), 5)), true);
paddle::array_ref<int> e(&v1[0], &v1[4]);
CHECK_EQ(e.size(), size_t(4));
CHECK_EQ(e.data(), v1);
CHECK_EQ(e.data(), v1.data());
CHECK_EQ(e.equals(paddle::make_array_ref(&v1[0], &v1[4])), true);
paddle::small_vector<int, 3> small_vector{1, 2, 3};
......
paddle/utils/span_test.cc
浏览文件 @ 5ada98b8
......@@ -69,7 +69,7 @@ TEST(pointer_length_ctor, span) {
// dynamic size
{
int arr[] = {1, 2, 3};
int arr[] = {1, 2, 3}; // NOLINT
span<int> s(arr, 3);
CHECK_EQ(s.size(), 3UL);
......@@ -80,7 +80,7 @@ TEST(pointer_length_ctor, span) {
// fixed size
{
int arr[] = {1, 2, 3};
int arr[] = {1, 2, 3}; // NOLINT
span<int, 3> s(arr, 3);
CHECK_EQ(s.size(), 3UL);
......@@ -100,7 +100,7 @@ TEST(pointer_pointer_ctor, span) {
// dynamic size
{
int arr[] = {1, 2, 3};
int arr[] = {1, 2, 3}; // NOLINT
span<int> s{arr, arr + 3};
CHECK_EQ(s.size(), 3UL);
......@@ -111,7 +111,7 @@ TEST(pointer_pointer_ctor, span) {
// fixed size
{
int arr[] = {1, 2, 3};
int arr[] = {1, 2, 3}; // NOLINT
span<int, 3> s{arr, arr + 3};
CHECK_EQ(s.size(), 3UL);
......@@ -122,8 +122,8 @@ TEST(pointer_pointer_ctor, span) {
}
TEST(c_array_ctor, span) {
using int_array_t = int[3];
using float_array_t = float[3];
using int_array_t = int[3]; // NOLINT
using float_array_t = float[3]; // NOLINT
static_assert(std::is_nothrow_constructible<span<int>, int_array_t&>::value,
"");
......@@ -171,7 +171,7 @@ TEST(c_array_ctor, span) {
// non-const, dynamic size
{
int arr[] = {1, 2, 3};
int arr[] = {1, 2, 3}; // NOLINT
span<int> s{arr};
CHECK_EQ(s.size(), 3UL);
CHECK_EQ(s.data(), arr);
......@@ -181,7 +181,7 @@ TEST(c_array_ctor, span) {
// const, dynamic size
{
int arr[] = {1, 2, 3};
int arr[] = {1, 2, 3}; // NOLINT
span<int const> s{arr};
CHECK_EQ(s.size(), 3UL);
CHECK_EQ(s.data(), arr);
......@@ -191,7 +191,7 @@ TEST(c_array_ctor, span) {
// non-const, static size
{
int arr[] = {1, 2, 3};
int arr[] = {1, 2, 3}; // NOLINT
span<int, 3> s{arr};
CHECK_EQ(s.size(), 3UL);
CHECK_EQ(s.data(), arr);
......@@ -201,7 +201,7 @@ TEST(c_array_ctor, span) {
// const, dynamic size
{
int arr[] = {1, 2, 3};
int arr[] = {1, 2, 3}; // NOLINT
span<int const, 3> s{arr};
CHECK_EQ(s.size(), 3UL);
CHECK_EQ(s.data(), arr);
......@@ -492,7 +492,7 @@ TEST(ctor_from_spans, span) {
TEST(subview, span) {
// first<N>
{
int arr[] = {1, 2, 3, 4, 5};
int arr[] = {1, 2, 3, 4, 5}; // NOLINT
span<int, 5> s{arr};
auto f = s.first<3>();
......@@ -505,7 +505,7 @@ TEST(subview, span) {
// last<N>
{
int arr[] = {1, 2, 3, 4, 5};
int arr[] = {1, 2, 3, 4, 5}; // NOLINT
span<int, 5> s{arr};
auto l = s.last<3>();
......@@ -518,7 +518,7 @@ TEST(subview, span) {
// subspan<N>
{
int arr[] = {1, 2, 3, 4, 5};
int arr[] = {1, 2, 3, 4, 5}; // NOLINT
span<int, 5> s{arr};
auto ss = s.subspan<1, 2>();
......@@ -531,7 +531,7 @@ TEST(subview, span) {
// first(n)
{
int arr[] = {1, 2, 3, 4, 5};
int arr[] = {1, 2, 3, 4, 5}; // NOLINT
span<int, 5> s{arr};
auto f = s.first(3);
......@@ -544,7 +544,7 @@ TEST(subview, span) {
// last(n)
{
int arr[] = {1, 2, 3, 4, 5};
int arr[] = {1, 2, 3, 4, 5}; // NOLINT
span<int, 5> s{arr};
auto l = s.last(3);
......@@ -557,7 +557,7 @@ TEST(subview, span) {
// subspan(n)
{
int arr[] = {1, 2, 3, 4, 5};
int arr[] = {1, 2, 3, 4, 5}; // NOLINT
span<int, 5> s{arr};
auto ss = s.subspan(1, 2);
......@@ -577,13 +577,13 @@ TEST(observers, span) {
static_assert(empty.size() == 0, ""); // NOLINT
static_assert(empty.empty(), "");
constexpr int arr[] = {1, 2, 3};
constexpr int arr[] = {1, 2, 3}; // NOLINT
static_assert(span<const int>{arr}.size() == 3, "");
static_assert(!span<const int>{arr}.empty(), "");
}
TEST(element_access, span) {
constexpr int arr[] = {1, 2, 3};
constexpr int arr[] = {1, 2, 3}; // NOLINT
span<const int> s{arr};
CHECK_EQ(s[0], arr[0]);
......
paddle/utils/string/string_helper_test.cc
浏览文件 @ 5ada98b8
......@@ -33,7 +33,7 @@ TEST(StringHelper, EndsWith) {
TEST(StringHelper, FormatStringAppend) {
std::string str("hello");
char fmt[] = "%d";
char fmt[] = "%d"; // NOLINT
paddle::string::format_string_append(str, fmt, 10);
EXPECT_EQ(str, "hello10");
......
paddle/utils/string/to_string_test.cc
浏览文件 @ 5ada98b8
......@@ -16,7 +16,7 @@ limitations under the License. */
#include <gtest/gtest.h>
constexpr char kOutputString[] = "User Defined Output";
constexpr char kOutputString[] = "User Defined Output"; // NOLINT
class UserDefinedClass {
public:
};
......
test/cpp/fluid/benchmark/op_tester.cc
浏览文件 @ 5ada98b8
......@@ -33,7 +33,7 @@ namespace paddle {
namespace operators {
namespace benchmark {
DEFINE_string(op_config_list, "", "Path of op config file.");
DEFINE_string(op_config_list, "", "Path of op config file."); // NOLINT
DEFINE_int32(specified_config_id, -1, "Test the specified op config.");
void OpTester::Init(const std::string &filename) {
......
test/cpp/fluid/detection/mask_util_test.cc
浏览文件 @ 5ada98b8
......@@ -29,12 +29,21 @@ void Compare(const T* a, const T* b, const int n) {
}
TEST(MaskUtil, Poly2MaskTest) {
float polys[] = {
1.97f, 1.88f, 5.81f, 1.88f, 1.69f, 6.53f, 5.94f, 6.38f, 1.97f, 1.88f};
float polys[] = {// NOLINT
1.97f,
1.88f,
5.81f,
1.88f,
1.69f,
6.53f,
5.94f,
6.38f,
1.97f,
1.88f};
int h = 8, w = 8;
int k = 5; // length(polys) / 2
// clang-format off
uint8_t expect_mask[] = {
uint8_t expect_mask[] = { // NOLINT
0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 1, 1, 0, 0, 0,
......@@ -67,7 +76,7 @@ TEST(MaskUtil, Poly2BoxesTest) {
{{1.97f, 1.88f, 5.81f, 1.88f, 1.69f, 6.53f, 5.94f, 6.38f, 1.97f, 1.88f}},
{{2.97f, 1.88f, 3.81f, 1.68f, 1.69f, 6.63f, 6.94f, 6.58f, 2.97f, 0.88f}}
};
float expect_boxes[] = {
float expect_boxes[] = { // NOLINT
1.69f, 1.88f, 5.94f, 6.53f,
1.69f, 0.88f, 6.94f, 6.63f
};
......@@ -85,10 +94,10 @@ TEST(MaskUtil, Polys2MaskWrtBoxTest) {
std::vector<std::vector<std::vector<float>>> polys = {{
{1.97f, 1.88f, 5.81f, 1.88f, 1.69f, 6.53f, 5.94f, 6.38f, 1.97f, 1.88f},
{2.97f, 1.88f, 3.81f, 1.68f, 1.69f, 6.63f, 6.94f, 6.58f, 2.97f, 0.88f}}};
float expect_boxes[] = {
float expect_boxes[] = { // NOLINT
1.69f, 0.88f, 6.94f, 6.63f
};
uint8_t expect_mask[] = {
uint8_t expect_mask[] = { // NOLINT
0, 0, 0, 0, 0, 0, 0, 0,
0, 1, 1, 1, 1, 1, 0, 0,
0, 0, 1, 1, 1, 0, 0, 0,
......
test/cpp/fluid/math/im2col_test.cc
浏览文件 @ 5ada98b8
......@@ -15,6 +15,7 @@ limitations under the License. */
#include "paddle/phi/kernels/funcs/im2col.h"
#include <gtest/gtest.h>
#include <array>
#include "paddle/fluid/framework/tensor_util.h"
#include "paddle/fluid/platform/device_context.h"
......@@ -60,8 +61,8 @@ void testIm2col() {
(input_width - filter_size + padding[2] + padding[3]) / stride[1] + 1;
float* input_ptr = input_tmp.mutable_data<float>(
{1, input_height, input_width}, paddle::platform::CPUPlace());
float arr[6] = {0, 1, 2, 3, 4, 5};
memcpy(input_ptr, arr, 6 * sizeof(float));
std::array<float, 6> arr = {0, 1, 2, 3, 4, 5};
memcpy(input_ptr, arr.data(), 6 * sizeof(float));
auto* place = new Place();
DeviceContext* context = new DeviceContext(*place);
......@@ -85,8 +86,8 @@ void testIm2col() {
im2col(*context, input, dilation, stride, padding, &output_cfo);
im2col_ocf(*context, input, dilation, stride, padding, &output_ocf);
float out_cfo_data[] = {0, 1, 1, 2, 3, 4, 4, 5};
float out_ocf_data[] = {0, 1, 3, 4, 1, 2, 4, 5};
std::array<float, 8> out_cfo_data = {0, 1, 1, 2, 3, 4, 4, 5};
std::array<float, 8> out_ocf_data = {0, 1, 3, 4, 1, 2, 4, 5};
float* out_cfo_ptr;
if (paddle::platform::is_cpu_place(*place)) {
......@@ -118,7 +119,7 @@ void testIm2col() {
col2im;
phi::funcs::Col2ImFunctor<phi::funcs::ColFormat::kOCF, DeviceContext, float>
col2im_ocf;
float col2im_data[] = {0, 2, 2, 3, 8, 5};
std::array<float, 6> col2im_data = {0, 2, 2, 3, 8, 5};
memset(input_ptr, 0, 6 * sizeof(float));
if (paddle::platform::is_cpu_place(*place)) {
......
test/cpp/fluid/math/vol2col_test.cc
浏览文件 @ 5ada98b8
......@@ -15,6 +15,7 @@ limitations under the License. */
#include "paddle/phi/kernels/funcs/vol2col.h"
#include <gtest/gtest.h>
#include <array>
#include "paddle/fluid/framework/tensor_util.h"
#include "paddle/fluid/platform/device_context.h"
......@@ -68,8 +69,8 @@ void testVol2col() {
float* input_ptr =
input_tmp.mutable_data<float>({1, input_depth, input_height, input_width},
paddle::platform::CPUPlace());
float arr[12] = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11};
memcpy(input_ptr, arr, 12 * sizeof(float));
std::array<float, 12> arr = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11};
memcpy(input_ptr, arr.data(), 12 * sizeof(float));
if (paddle::platform::is_cpu_place(*place)) {
input = input_tmp;
......@@ -88,7 +89,8 @@ void testVol2col() {
phi::funcs::Vol2ColFunctor<DeviceContext, float> vol2col;
vol2col(*context, input, dilations, strides, paddings, &output);
float vol_2_col[] = {0, 1, 1, 2, 3, 4, 4, 5, 6, 7, 7, 8, 9, 10, 10, 11};
std::array<float, 16> vol_2_col = {
0, 1, 1, 2, 3, 4, 4, 5, 6, 7, 7, 8, 9, 10, 10, 11};
float* out_cfo_ptr;
if (paddle::platform::is_cpu_place(*place)) {
out_cfo_ptr = output.data<float>();
......@@ -103,7 +105,7 @@ void testVol2col() {
}
// Col2Vol test
float col_2_vol[] = {0, 2, 2, 3, 8, 5, 6, 14, 8, 9, 20, 11};
std::array<float, 12> col_2_vol = {0, 2, 2, 3, 8, 5, 6, 14, 8, 9, 20, 11};
memset(input_ptr, 0, 12 * sizeof(float));
if (paddle::platform::is_cpu_place(*place)) {
input = input_tmp;
......
test/cpp/inference/analysis/analyzer_tester.cc
浏览文件 @ 5ada98b8
......@@ -14,6 +14,7 @@
#include <google/protobuf/text_format.h>
#include <gtest/gtest.h>
#include <array>
#include "paddle/fluid/inference/analysis/analyzer.h"
#include "paddle/fluid/inference/analysis/ut_helper.h"
......@@ -66,10 +67,10 @@ void TestWord2vecPrediction(const std::string& model_path) {
// One single batch
int64_t data[4] = {1, 2, 3, 4};
std::array<int64_t, 4> data = {1, 2, 3, 4};
PaddleTensor tensor;
tensor.shape = std::vector<int>({4, 1});
tensor.data = PaddleBuf(data, sizeof(data));
tensor.data = PaddleBuf(data.data(), sizeof(data));
tensor.dtype = PaddleDType::INT64;
// For simplicity, we set all the slots with the same data.
......@@ -87,7 +88,7 @@ void TestWord2vecPrediction(const std::string& model_path) {
platform::errors::PreconditionNotMet(
"Output's data length should be 33168 but got %d",
outputs.front().data.length()));
float result[5] = {
std::array<float, 5> result = {
0.00129761, 0.00151112, 0.000423564, 0.00108815, 0.000932706};
const size_t num_elements = outputs.front().data.length() / sizeof(float);
// The outputs' buffers are in CPU memory.
......
test/cpp/ir/core/ir_infershape_test.cc
浏览文件 @ 5ada98b8
......@@ -44,7 +44,7 @@ class OperationTest
using Op::Op;
static const char *name() { return "test.operation2"; }
static constexpr uint32_t attributes_num = 2;
static const char *attributes_name[attributes_num];
static const char *attributes_name[attributes_num]; // NOLINT
static void Verify() {}
static void InferMeta(phi::InferMetaContext *infer_meta) {
auto fn = PD_INFER_META(phi::CreateInferMeta);
......@@ -54,7 +54,8 @@ class OperationTest
IR_DECLARE_EXPLICIT_TYPE_ID(OperationTest)
IR_DEFINE_EXPLICIT_TYPE_ID(OperationTest)
const char *OperationTest::attributes_name[attributes_num] = {"op2_attr1",
const char *OperationTest::attributes_name[attributes_num] = { // NOLINT
"op2_attr1",
"op2_attr2"};
// Define a dialect, op1 and op2 will be registered by this dialect.
......
test/cpp/ir/core/ir_op_test.cc
浏览文件 @ 5ada98b8
......@@ -89,7 +89,7 @@ class Operation1 : public ir::Op<Operation1> {
using Op::Op;
static const char *name() { return "test.operation1"; }
static constexpr uint32_t attributes_num = 2;
static const char *attributes_name[attributes_num];
static const char *attributes_name[attributes_num]; // NOLINT
void Verify() {
auto &attributes = this->attributes();
if (attributes.count("op1_attr1") == 0 ||
......@@ -114,7 +114,8 @@ class Operation1 : public ir::Op<Operation1> {
argument.AddAttributes(attributes.begin(), attributes.end());
}
};
const char *Operation1::attributes_name[attributes_num] = {"op1_attr1",
const char *Operation1::attributes_name[attributes_num] = { // NOLINT
"op1_attr1",
"op1_attr2"};
IR_DECLARE_EXPLICIT_TYPE_ID(Operation1)
......@@ -127,7 +128,7 @@ class Operation2
using Op::Op;
static const char *name() { return "test.operation2"; }
static constexpr uint32_t attributes_num = 2;
static const char *attributes_name[attributes_num];
static const char *attributes_name[attributes_num]; // NOLINT
void Verify() {
auto &attributes = this->attributes();
if (attributes.count("op2_attr1") == 0 ||
......@@ -141,7 +142,8 @@ class Operation2
}
static void InferShape() { VLOG(2) << "This is op2's InferShape interface."; }
};
const char *Operation2::attributes_name[attributes_num] = {"op2_attr1",
const char *Operation2::attributes_name[attributes_num] = { // NOLINT
"op2_attr1",
"op2_attr2"};
IR_DECLARE_EXPLICIT_TYPE_ID(Operation2)
IR_DEFINE_EXPLICIT_TYPE_ID(Operation2)
......
test/cpp/ir/pattern_rewrite/pattern_rewrite_test.cc
浏览文件 @ 5ada98b8
......@@ -77,7 +77,7 @@ class Operation1 : public ir::Op<Operation1> {
using Op::Op;
static const char *name() { return "test.Operation1"; }
static constexpr uint32_t attributes_num = 2;
static const char *attributes_name[attributes_num];
static const char *attributes_name[attributes_num]; // NOLINT
void Verify();
static void InferShape() { VLOG(2) << "This is op2's InferShape interface."; }
};
......@@ -93,7 +93,8 @@ void Operation1::Verify() {
throw("Type of attribute: parameter_name is not right.");
}
}
const char *Operation1::attributes_name[attributes_num] = {"op2_attr1",
const char *Operation1::attributes_name[attributes_num] = { // NOLINT
"op2_attr1",
"op2_attr2"};
IR_DECLARE_EXPLICIT_TYPE_ID(Operation1)
IR_DEFINE_EXPLICIT_TYPE_ID(Operation1)
......@@ -358,7 +359,7 @@ class Conv2dFusionOpTest : public ir::Op<Conv2dFusionOpTest,
public:
using Op::Op;
static const char *name() { return "pd.conv2d_fusion_test"; }
static const char *attributes_name[10];
static const char *attributes_name[10]; // NOLINT
static constexpr uint32_t attributes_num = 10;
static OpInfoTuple GetOpInfo();
static void Build(ir::Builder &builder, // NOLINT
......@@ -413,7 +414,8 @@ class Conv2dFusionOpTest : public ir::Op<Conv2dFusionOpTest,
static void InferMeta(phi::InferMetaContext *infer_meta);
};
const char *Conv2dFusionOpTest::attributes_name[10] = {"strides",
const char *Conv2dFusionOpTest::attributes_name[10] = { // NOLINT
"strides",
"paddings_t",
"padding_algorithm",
"dilations_t",
......
test/cpp/phi/api/test_from_blob.cc
浏览文件 @ 5ada98b8
......@@ -40,7 +40,7 @@ phi::Place GetPlaceFromPtr(void* data);
TEST(from_blob, CPU) {
// 1. create data
int64_t data[] = {4, 3, 2, 1};
int64_t data[] = {4, 3, 2, 1}; // NOLINT
ASSERT_EQ(paddle::GetPlaceFromPtr(data), phi::CPUPlace());
......
test/cpp/phi/api/test_strings_lower_upper_api.cc
浏览文件 @ 5ada98b8
......@@ -47,13 +47,14 @@ TEST(API, case_convert) {
pstring* cpu_strings_x_data =
dev_ctx->template Alloc<pstring>(cpu_strings_x.get());
std::string strs[] = {"A Short Pstring.",
std::string strs[] = {"A Short Pstring.", // NOLINT
"A Large Pstring Whose Length Is Longer Than 22."};
for (int i = 0; i < 2; ++i) {
cpu_strings_x_data[i] = strs[i];
}
// 2. get expected results
std::string expected_results[] = {strs[0], strs[0], strs[1], strs[1]};
std::string expected_results[] = {
strs[0], strs[0], strs[1], strs[1]}; // NOLINT
std::transform(
strs[0].begin(), strs[0].end(), expected_results[0].begin(), ::tolower);
std::transform(
......@@ -77,7 +78,8 @@ TEST(API, case_convert) {
auto lower_tensor_ptr = lower_tensor->data();
auto upper_tensor_ptr = upper_tensor->data();
const std::string cpu_results[] = {lower_tensor_ptr[0].data(),
const std::string cpu_results[] = {// NOLINT
lower_tensor_ptr[0].data(),
upper_tensor_ptr[0].data(),
lower_tensor_ptr[1].data(),
upper_tensor_ptr[1].data()};
......@@ -101,12 +103,14 @@ TEST(API, case_convert_utf8) {
pstring* cpu_strings_x_data =
dev_ctx->template Alloc<pstring>(cpu_strings_x.get());
std::string strs[] = {"óÓsscHloëË", "óÓsscHloëËóÓsscHloëËóÓsscHloëË"};
std::string strs[] = {"óÓsscHloëË",
"óÓsscHloëËóÓsscHloëËóÓsscHloëË"}; // NOLINT
for (int i = 0; i < 2; ++i) {
cpu_strings_x_data[i] = strs[i];
}
// 2. get expected results
std::string expected_results[] = {"óósschloëë",
std::string expected_results[] = {// NOLINT
"óósschloëë",
"ÓÓSSCHLOËË",
"óósschloëëóósschloëëóósschloëë",
"ÓÓSSCHLOËËÓÓSSCHLOËËÓÓSSCHLOËË"};
......@@ -125,7 +129,8 @@ TEST(API, case_convert_utf8) {
auto lower_tensor_ptr = lower_tensor->data();
auto upper_tensor_ptr = upper_tensor->data();
const char* cpu_results[] = {lower_tensor_ptr[0].data(),
const char* cpu_results[] = {// NOLINT
lower_tensor_ptr[0].data(),
upper_tensor_ptr[0].data(),
lower_tensor_ptr[1].data(),
upper_tensor_ptr[1].data()};
......
test/cpp/phi/core/unroll_array_ops_test.cc
浏览文件 @ 5ada98b8
......@@ -45,8 +45,8 @@ TEST(unroll_ops, fill_constant) {
}
TEST(unroll_ops, assign) {
const int a[] = {1, 2, 3, 4, 5};
int b[] = {0, 0, 0, 0, 0};
const int a[] = {1, 2, 3, 4, 5}; // NOLINT
int b[] = {0, 0, 0, 0, 0}; // NOLINT
UnrollAssign<3>::Run(a, b);
EXPECT_EQ(b[0], 1);
EXPECT_EQ(b[1], 2);
......@@ -56,7 +56,7 @@ TEST(unroll_ops, assign) {
}
TEST(unroll_ops, var_args_assign) {
int a[] = {0, 0, 0};
int a[] = {0, 0, 0}; // NOLINT
UnrollVarArgsAssign<int>::Run(a, 1, 2);
EXPECT_EQ(a[0], 1);
EXPECT_EQ(a[1], 2);
......@@ -64,8 +64,8 @@ TEST(unroll_ops, var_args_assign) {
}
TEST(unroll_ops, compare) {
int a[] = {1, 2, 3};
int b[] = {1, 2, 4};
int a[] = {1, 2, 3}; // NOLINT
int b[] = {1, 2, 4}; // NOLINT
EXPECT_TRUE(UnrollCompare<2>::Run(a, b));
EXPECT_FALSE(UnrollCompare<3>::Run(a, b));
......@@ -75,7 +75,7 @@ TEST(unroll_ops, compare) {
}
TEST(unroll_ops, product) {
int a[] = {2, 3, 4};
int a[] = {2, 3, 4}; // NOLINT
EXPECT_EQ(UnrollProduct<3>::Run(a), a[0] * a[1] * a[2]);
}
......
test/cpp/phi/kernels/strided_memcpy_test.cc
浏览文件 @ 5ada98b8
......@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/phi/kernels/funcs/strided_memcpy.h"
#include <array>
#include "gtest/gtest.h"
#include "paddle/phi/backends/context_pool.h"
......@@ -22,7 +23,7 @@ namespace tests {
TEST(StridedMemcpy, CPUCrop) {
// clang-format off
int src[] = {
int src[] = {// NOLINT
0, 1, 2, 0, 0,
0, 3, 4, 0, 0,
0, 0, 0, 0, 0,
......@@ -31,13 +32,13 @@ TEST(StridedMemcpy, CPUCrop) {
phi::DDim src_stride({5, 1});
int dst[4];
std::array<int, 4> dst;
phi::DDim dst_dim({2, 2});
phi::DDim dst_stride({2, 1});
phi::CPUContext ctx;
phi::funcs::StridedMemcpy<int>(
ctx, src + 1, src_stride, dst_dim, dst_stride, dst);
ctx, src + 1, src_stride, dst_dim, dst_stride, dst.data());
ASSERT_EQ(1, dst[0]);
ASSERT_EQ(2, dst[1]);
......@@ -47,26 +48,25 @@ TEST(StridedMemcpy, CPUCrop) {
TEST(StridedMemcpy, CPUConcat) {
// clang-format off
int src[] = {
int src[] = { // NOLINT
1, 2,
3, 4
};
// clang-format on
int dst[8];
std::array<int, 8> dst;
phi::DDim src_stride({2, 1});
phi::DDim dst_dim({2, 2});
phi::DDim dst_stride({4, 1});
phi::CPUContext ctx;
phi::funcs::StridedMemcpy<int>(
ctx, src, src_stride, dst_dim, dst_stride, dst);
ctx, src, src_stride, dst_dim, dst_stride, dst.data());
phi::funcs::StridedMemcpy<int>(
ctx, src, src_stride, dst_dim, dst_stride, dst + 2);
ctx, src, src_stride, dst_dim, dst_stride, dst.data() + 2);
// clang-format off
int expect_dst[] = {
int expect_dst[] = { // NOLINT
1, 2, 1, 2,
3, 4, 3, 4
};
......
test/cpp/phi/kernels/test_math_function.cc
浏览文件 @ 5ada98b8
......@@ -12,6 +12,7 @@
// See the License for the specific language governing permissions and
// limitations under the License.
#include <array>
#include <set>
#include "gtest/gtest.h"
......@@ -41,16 +42,16 @@ TEST(math_function, gemm_notrans_cblas) {
input1.Resize({2, 3});
float* input1_ptr = dev_ctx->template Alloc<float>(&input1);
float arr1[6] = {0, 1, 2, 3, 4, 5};
memcpy(input1_ptr, arr1, 6 * sizeof(float));
std::array<float, 6> arr1 = {0, 1, 2, 3, 4, 5};
memcpy(input1_ptr, arr1.data(), 6 * sizeof(float));
input2.Resize({3, 4});
float* input2_ptr = dev_ctx->template Alloc<float>(&input2);
float arr2[12] = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11};
memcpy(input2_ptr, arr2, 12 * sizeof(float));
std::array<float, 12> arr2 = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11};
memcpy(input2_ptr, arr2.data(), 12 * sizeof(float));
input3.Resize({2, 4});
float* input3_ptr = dev_ctx->template Alloc<float>(&input3);
float arr3[8] = {0, 1, 2, 3, 4, 5, 6, 7};
memcpy(input3_ptr, arr3, 8 * sizeof(float));
std::array<float, 8> arr3 = {0, 1, 2, 3, 4, 5, 6, 7};
memcpy(input3_ptr, arr3.data(), 8 * sizeof(float));
GetBlas<float>(*dev_ctx).GEMM(false,
false,
......@@ -172,16 +173,16 @@ TEST(math_function, gemm_trans_cblas) {
input1.Resize({2, 3});
float* input1_ptr = dev_ctx->template Alloc<float>(&input1);
float arr1[6] = {0, 1, 2, 3, 4, 5};
memcpy(input1_ptr, arr1, 6 * sizeof(float));
std::array<float, 6> arr1 = {0, 1, 2, 3, 4, 5};
memcpy(input1_ptr, arr1.data(), 6 * sizeof(float));
input2.Resize({4, 3});
float* input2_ptr = dev_ctx->template Alloc<float>(&input2);
float arr2[12] = {0, 4, 8, 1, 5, 9, 2, 6, 10, 3, 7, 11};
memcpy(input2_ptr, arr2, 12 * sizeof(float));
std::array<float, 12> arr2 = {0, 4, 8, 1, 5, 9, 2, 6, 10, 3, 7, 11};
memcpy(input2_ptr, arr2.data(), 12 * sizeof(float));
input3.Resize({2, 4});
float* input3_ptr = dev_ctx->template Alloc<float>(&input3);
float arr3[8] = {0, 1, 2, 3, 4, 5, 6, 7};
memcpy(input3_ptr, arr3, 8 * sizeof(float));
std::array<float, 8> arr3 = {0, 1, 2, 3, 4, 5, 6, 7};
memcpy(input3_ptr, arr3.data(), 8 * sizeof(float));
GetBlas<float>(*dev_ctx).GEMM(false,
true,
......
test/cpp/phi/kernels/test_strings_copy_dev_api.cc
浏览文件 @ 5ada98b8
......@@ -46,7 +46,7 @@ TEST(DEV_API, strings_copy) {
StringTensor string_dst(alloc, meta);
// 2. Assign input text
const char* input[] = {"A Short Pstring.",
const char* input[] = {"A Short Pstring.", // NOLINT
"A Large Pstring Whose Length Is Longer Than 22.",
"abc",
"defg",
......
test/cpp/phi/kernels/test_strings_lower_upper_dev_api.cc
浏览文件 @ 5ada98b8
......@@ -56,7 +56,8 @@ TEST(DEV_API, strings_cast_convert) {
dense_x_data[1] = long_str;
// 2. get expected results
std::string expected_results[] = {short_str, short_str, long_str, long_str};
std::string expected_results[] = {
short_str, short_str, long_str, long_str}; // NOLINT
std::transform(short_str.begin(),
short_str.end(),
expected_results[0].begin(),
......@@ -107,7 +108,8 @@ TEST(DEV_API, strings_cast_convert_utf8) {
dense_x_data[0] = utf8_str;
// 2. get expected results
std::string expected_results[] = {"óósschloëëóósschloëëóósschloëë",
std::string expected_results[] = {// NOLINT
"óósschloëëóósschloëëóósschloëë",
"ÓÓSSCHLOËËÓÓSSCHLOËËÓÓSSCHLOËË"};
// 3. test API, ascii encoding
......
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