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未验证 提交 c2f0e9c4 编写于 作者: G gouzil 提交者： GitHub
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[clang-tidy] NO.8 enable `cppcoreguidelines-narrowing-conversions`. step:2 (#56895) 

* [clang-tidy] replenish cppcoreguidelines-narrowing-conversions

* fix

* fix
上级 e9e07a19
隐藏空白更改
内联 并排
Showing with 603 addition and 537 deletion
paddle/fluid/distributed/auto_parallel/dist_attr.cc
浏览文件 @ c2f0e9c4
......@@ -378,13 +378,13 @@ std::string OperatorDistAttr::to_string() const {
}
void OperatorDistAttr::from_proto(const OperatorDistAttrProto& proto) {
for (int64_t i = 0; i < proto.input_dist_attrs_size(); ++i) {
for (int i = 0; i < proto.input_dist_attrs_size(); ++i) {
TensorDistAttr dist_attr;
std::string name = proto.input_dist_attrs(i).name();
dist_attr.from_proto(proto.input_dist_attrs(i).tensor_dist_attr());
input_dist_attrs_[name] = dist_attr;
}
for (int64_t i = 0; i < proto.output_dist_attrs_size(); ++i) {
for (int i = 0; i < proto.output_dist_attrs_size(); ++i) {
TensorDistAttr dist_attr;
std::string name = proto.output_dist_attrs(i).name();
dist_attr.from_proto(proto.output_dist_attrs(i).tensor_dist_attr());
......
paddle/fluid/distributed/auto_parallel/spmd_rules/common.cc
浏览文件 @ c2f0e9c4
......@@ -223,7 +223,7 @@ std::vector<int64_t> GetDimsMappingForAxes(
const std::unordered_map<std::string, int64_t>& axis_to_dim_map,
const bool unsharded_miss_axis) {
std::vector<int64_t> dims_mapping;
for (int64_t i = 0, n = axes.size(); i < n; i++) {
for (int64_t i = 0, n = static_cast<int64_t>(axes.size()); i < n; i++) {
std::string axis = axes.substr(i, 1);
if (axis == "1") {
dims_mapping.emplace_back(-1);
......
paddle/fluid/distributed/auto_parallel/spmd_rules/cross_entropy_with_softmax_spmd_rule.cc
浏览文件 @ c2f0e9c4
......@@ -34,7 +34,7 @@ CrossEntropyWithSoftmaxSPMDRule::InferForward(
input_specs_size));
auto x_shape = input_specs[0].shape();
int x_ndim = x_shape.size();
int x_ndim = static_cast<int>(x_shape.size());
auto x_dist_attr_src = input_specs[0].dist_attr();
std::vector<int64_t> x_dims_mapping_src = x_dist_attr_src.dims_mapping();
......
paddle/fluid/distributed/auto_parallel/spmd_rules/dim_trans.cc
浏览文件 @ c2f0e9c4
......@@ -85,7 +85,7 @@ void Flatten::set_inputs(const std::vector<DimTrans*>& dims) {
std::string Flatten::to_string() {
std::string ret_str("Flatten(");
for (int64_t i = 0, n = input_dims_.size(); i < n; ++i) {
for (int i = 0, n = static_cast<int>(input_dims_.size()); i < n; ++i) {
ret_str += input_dims_[i]->to_string();
if (i < n - 1) {
ret_str += ",";
......@@ -125,7 +125,7 @@ int64_t Split::local_splitted_shape_value() {
std::string Split::to_string() {
std::string ret_str("Split(");
ret_str += input_dim_trans_->to_string() + ", (";
for (int64_t i = 0, n = splitted_shape_.size(); i < n; ++i) {
for (int i = 0, n = static_cast<int>(splitted_shape_.size()); i < n; ++i) {
ret_str += std::to_string(splitted_shape_[i]);
if (i < n - 1) {
ret_str += ",";
......@@ -161,9 +161,9 @@ DimTrans* make_split(DimTrans* dim,
std::vector<int64_t> new_shape;
// map between from idx in shape to new_shape
std::vector<int64_t> idx_map(shape.size(), -1);
for (int64_t i = 0, n = shape.size(); i < n; ++i) {
for (int i = 0, n = static_cast<int>(shape.size()); i < n; ++i) {
if (shape[id] != 1) {
idx_map[i] = new_shape.size();
idx_map[i] = static_cast<int64_t>(new_shape.size());
new_shape.emplace_back(shape[i]);
}
}
......@@ -173,7 +173,8 @@ DimTrans* make_split(DimTrans* dim,
}
void CleanUp() {
for (int64_t i = 0, n = all_dim_trans.size(); i < n; i++) {
int n = static_cast<int>(all_dim_trans.size());
for (int i = 0; i < n; i++) {
if (all_dim_trans[i]) {
delete all_dim_trans[i];
all_dim_trans[i] = nullptr;
......@@ -210,8 +211,8 @@ DimTrans* GetDimTrans(DimTrans* dim_trans,
} else if (type == DimTrans::Type::FLATTEN) {
Flatten* flatten = dynamic_cast<Flatten*>(dim_trans);
const std::vector<DimTrans*>& inputs = flatten->inputs();
int64_t nmesh = (*shardable)[0].size();
for (int64_t i = 1, n = inputs.size(); i < n; i++) {
int64_t nmesh = (*shardable)[0].size(); // NOLINT
for (int i = 1, n = static_cast<int>(inputs.size()); i < n; i++) {
DimTrans* input = inputs[i];
if (input->type() == DimTrans::Type::INPUTDIM) {
InputDim* inputdim = dynamic_cast<InputDim*>(input);
......@@ -252,7 +253,7 @@ DimTrans* GetDimTrans(DimTrans* dim_trans,
phi::errors::InvalidArgument(
"The returned dim_trans must be INPUTDIM."));
InputDim* inputdim = dynamic_cast<InputDim*>(dim);
int64_t nmesh = mesh_shape.size();
int64_t nmesh = static_cast<int64_t>(mesh_shape.size());
int64_t input_axis = inputdim->input_dim();
// Check whether the sharded dim can be sharded on
......@@ -295,13 +296,15 @@ std::vector<std::vector<int64_t>> InferFromDimTrans(
const std::vector<int64_t>& mesh_shape = mesh.shape();
std::set<int64_t> sharded_input_dims;
for (int64_t i = 0, n = input_dims_mapping.size(); i < n; ++i) {
for (int64_t i = 0, n = static_cast<int64_t>(input_dims_mapping.size());
i < n;
++i) {
if (input_dims_mapping[i] > -1) {
sharded_input_dims.insert(i);
}
}
int64_t ndim = input_shape.size();
int64_t nmesh = mesh_shape.size();
int64_t ndim = static_cast<int64_t>(input_shape.size());
int64_t nmesh = static_cast<int64_t>(mesh_shape.size());
std::vector<std::vector<bool>> shardable(ndim,
std::vector<bool>(nmesh, true));
......@@ -319,7 +322,7 @@ std::vector<std::vector<int64_t>> InferFromDimTrans(
// get the map from sharded input dimensions to output dimensions.
std::vector<int64_t> dim_map_src2tgt(ndim, -1);
for (int64_t i = 0, n = dim_trans.size(); i < n; i++) {
for (int64_t i = 0, n = static_cast<int64_t>(dim_trans.size()); i < n; i++) {
DimTrans* dim = GetDimTrans(dim_trans[i],
&shardable,
&seen_input_dims,
......
paddle/fluid/distributed/auto_parallel/spmd_rules/elementwise_spmd_rule.cc
浏览文件 @ c2f0e9c4
......@@ -25,7 +25,7 @@ ElementwiseSPMDRule::InferForward(
const std::vector<DistTensorSpec>& input_specs,
const paddle::framework::AttributeMap& attrs) {
// step0: Verify Input Args Based on Elementwise Logic
int64_t ninputs = input_specs.size();
int64_t ninputs = static_cast<int64_t>(input_specs.size());
PADDLE_ENFORCE_GT(
ninputs,
0,
......@@ -39,7 +39,7 @@ ElementwiseSPMDRule::InferForward(
std::vector<std::string> input_axes_vec;
int64_t max_ndim = 0;
for (int64_t i = 0; i < ninputs; ++i) {
int64_t ndim = input_specs[i].shape().size();
int64_t ndim = static_cast<int64_t>(input_specs[i].shape().size());
if (ndim > max_ndim) {
max_ndim = ndim;
}
......@@ -49,7 +49,7 @@ ElementwiseSPMDRule::InferForward(
std::vector<int64_t> broadcast_axis_count(max_ndim, 0);
for (int64_t i = 0; i < ninputs; ++i) {
std::vector<int64_t> shape = input_specs[i].shape();
int64_t ndim = shape.size();
int64_t ndim = static_cast<int64_t>(shape.size());
int64_t start_dim = max_ndim - ndim;
std::string axes_notation = GetBroadcastAxes(ndim, max_ndim, alphabet);
if (ninputs > 1) {
......
paddle/fluid/distributed/auto_parallel/spmd_rules/embedding_spmd_rule.cc
浏览文件 @ c2f0e9c4
......@@ -33,8 +33,8 @@ EmbeddingSPMDRule::InferForward(const std::vector<DistTensorSpec>& input_specs,
input_specs_size));
auto x_shape = input_specs[0].shape();
auto weight_shape = input_specs[1].shape();
int x_ndim = x_shape.size();
int weight_ndim = weight_shape.size();
int x_ndim = static_cast<int>(x_shape.size());
int weight_ndim = static_cast<int>(weight_shape.size());
auto x_dist_attr_src = input_specs[0].dist_attr();
auto weight_dist_attr_src = input_specs[1].dist_attr();
std::vector<int64_t> x_dims_mapping = x_dist_attr_src.dims_mapping();
......@@ -170,9 +170,9 @@ EmbeddingSPMDRule::InferBackward(
output_specs_size));
auto x_shape = input_specs[0].shape();
int x_ndim = x_shape.size();
int x_ndim = static_cast<int>(x_shape.size());
auto out_shape = output_specs[0].shape();
int out_ndim = out_shape.size();
int out_ndim = static_cast<int>(out_shape.size());
PADDLE_ENFORCE_EQ(x_ndim,
out_ndim - 1,
......
paddle/fluid/distributed/auto_parallel/spmd_rules/layer_norm_spmd_rule.cc
浏览文件 @ c2f0e9c4
......@@ -34,9 +34,9 @@ LayerNormSPMDRule::InferForward(const std::vector<DistTensorSpec>& input_specs,
auto x_shape = input_specs[0].shape();
auto scale_shape = input_specs[1].shape();
auto bias_shape = input_specs[2].shape();
int x_ndim = x_shape.size();
int scale_ndim = scale_shape.size();
int bias_ndim = bias_shape.size();
int x_ndim = static_cast<int>(x_shape.size());
int scale_ndim = static_cast<int>(scale_shape.size());
int bias_ndim = static_cast<int>(bias_shape.size());
PADDLE_ENFORCE_EQ(
scale_ndim,
......
paddle/fluid/distributed/auto_parallel/spmd_rules/reduction_spmd_rule.cc
浏览文件 @ c2f0e9c4
......@@ -26,7 +26,7 @@ std::pair<std::vector<TensorDistAttr>, std::vector<TensorDistAttr>>
ReductionSPMDRule::InferForward(const std::vector<DistTensorSpec>& input_specs,
const paddle::framework::AttributeMap& attrs) {
// step0: Verify Input Args Based on Elementwise Logic
int64_t ninputs = input_specs.size();
int64_t ninputs = static_cast<int64_t>(input_specs.size());
PADDLE_ENFORCE_EQ(
ninputs,
1,
......@@ -42,7 +42,7 @@ ReductionSPMDRule::InferForward(const std::vector<DistTensorSpec>& input_specs,
std::string alphabet = "abcdefghijklmnopqrstuvwxyz";
// get einsum notation for input
int64_t ndim = input_specs[0].shape().size();
int64_t ndim = static_cast<int64_t>(input_specs[0].shape().size());
std::vector<std::string> input_axes_vec;
std::string input_axes = alphabet.substr(0, ndim);
input_axes_vec.emplace_back(input_axes);
......
paddle/fluid/distributed/auto_parallel/spmd_rules/reshape_spmd_rule.cc
浏览文件 @ c2f0e9c4
......@@ -28,7 +28,7 @@ using phi::distributed::auto_parallel::str_join;
std::vector<int64_t> InferTargetShape(const std::vector<int64_t>& shape,
int64_t len) {
int64_t infer_idx = -1;
for (int64_t i = 0, n = shape.size(); i < n; i++) {
for (int64_t i = 0, n = static_cast<int64_t>(shape.size()); i < n; i++) {
if (shape[i] == -1) {
PADDLE_ENFORCE_EQ(
infer_idx,
......@@ -74,8 +74,8 @@ std::vector<DimTrans*> MakeReshapeDimTrans(
int64_t src_idx = 0, tgt_idx = 0;
int64_t s, t;
int64_t src_len, tgt_len;
src_len = src_shape.size();
tgt_len = inferred_tgt_shape.size();
src_len = static_cast<int64_t>(src_shape.size());
tgt_len = static_cast<int64_t>(inferred_tgt_shape.size());
while (src_idx < src_len || tgt_idx < tgt_len) {
std::vector<int64_t> src_dims, tgt_splitted_shape;
if (src_idx >= src_len) {
......@@ -125,7 +125,9 @@ std::vector<DimTrans*> MakeReshapeDimTrans(
}
DimTrans* flatten = make_flatten(input_dims);
for (int64_t i = 0, n = tgt_splitted_shape.size(); i < n; i++) {
for (int64_t i = 0, n = static_cast<int64_t>(tgt_splitted_shape.size());
i < n;
i++) {
ret.emplace_back(make_split(flatten, tgt_splitted_shape, i));
}
}
......@@ -155,7 +157,7 @@ paddle::distributed::auto_parallel::ReshapeSPMDRule::InferForward(
// handle the '0' values in target shape, '0' indicates
// that the target shape is equal to the source shape
for (int64_t i = 0, n = tgt_shape.size(); i < n; i++) {
for (int64_t i = 0, n = static_cast<int64_t>(tgt_shape.size()); i < n; i++) {
if (tgt_shape[i] == 0) {
tgt_shape[i] = src_shape[i];
}
......@@ -178,7 +180,7 @@ paddle::distributed::auto_parallel::ReshapeSPMDRule::InferForward(
VLOG(4) << "Reshape: input_shape: [" << str_join(src_shape)
<< "] output_shape: [" << str_join(tgt_shape) << "]";
VLOG(4) << "Transformation from input to output:";
for (int64_t i = 0, n = trans.size(); i < n; i++) {
for (int64_t i = 0, n = static_cast<int64_t>(trans.size()); i < n; i++) {
DimTrans* t = trans[i];
VLOG(4) << "\tOutput axis " << i << ": " << t->to_string();
}
......
paddle/fluid/distributed/auto_parallel/spmd_rules/softmax_spmd_rule.cc
浏览文件 @ c2f0e9c4
......@@ -33,7 +33,7 @@ SoftmaxSPMDRule::InferForward(const std::vector<DistTensorSpec>& input_specs,
input_specs_size));
auto x_shape = input_specs[0].shape();
int x_ndim = x_shape.size();
int x_ndim = static_cast<int>(x_shape.size());
auto x_dist_attr_src = input_specs[0].dist_attr();
std::vector<int64_t> x_dims_mapping = x_dist_attr_src.dims_mapping();
......
paddle/fluid/distributed/auto_parallel/spmd_rules/split_spmd_rule.cc
浏览文件 @ c2f0e9c4
......@@ -27,7 +27,7 @@ std::pair<std::vector<TensorDistAttr>, std::vector<TensorDistAttr>>
SplitSPMDRule::InferForward(const std::vector<DistTensorSpec>& input_specs,
const paddle::framework::AttributeMap& attrs) {
// step0: Verify Input Args Based on Elementwise Logic
int64_t ninputs = input_specs.size();
int64_t ninputs = static_cast<int64_t>(input_specs.size());
PADDLE_ENFORCE_EQ(
ninputs,
1,
......@@ -37,7 +37,7 @@ SplitSPMDRule::InferForward(const std::vector<DistTensorSpec>& input_specs,
VerifySpecs(input_specs, "split");
// step1: Build Einsum Notation
int64_t ndim = input_specs[0].shape().size();
int64_t ndim = static_cast<int64_t>(input_specs[0].shape().size());
int64_t noutput = 0;
// split api uses num or sections as attribute
if (attrs.find("num") != attrs.end()) {
......@@ -45,7 +45,7 @@ SplitSPMDRule::InferForward(const std::vector<DistTensorSpec>& input_specs,
} else if (attrs.find("sections") != attrs.end()) {
std::vector<int64_t> sections =
ExtractAttr<std::vector<int64_t>>("sections", attrs);
noutput = sections.size();
noutput = static_cast<int64_t>(sections.size());
}
int64_t axis = ExtractAttr<int>("axis", attrs);
if (axis < 0) {
......
paddle/fluid/distributed/auto_parallel/spmd_rules/transpose_spmd_rule.cc
浏览文件 @ c2f0e9c4
......@@ -23,7 +23,7 @@ std::pair<std::vector<TensorDistAttr>, std::vector<TensorDistAttr>>
TransposeSPMDRule::InferForward(const std::vector<DistTensorSpec>& input_specs,
const paddle::framework::AttributeMap& attrs) {
// step0: Verify Input Args Based on Transpose Logic
int64_t ninputs = input_specs.size();
int64_t ninputs = static_cast<int64_t>(input_specs.size());
PADDLE_ENFORCE_EQ(
ninputs,
1,
......@@ -38,13 +38,13 @@ TransposeSPMDRule::InferForward(const std::vector<DistTensorSpec>& input_specs,
std::string alphabet = "abcdefghijklmnopqrstuvwxyz";
// get einsum notation for input
int64_t ndim = input_specs[0].shape().size();
int64_t ndim = static_cast<int64_t>(input_specs[0].shape().size());
std::vector<std::string> input_axes_vec;
std::string input_axes = alphabet.substr(0, ndim);
input_axes_vec.emplace_back(input_axes);
// get einsum notation for output
for (int64_t i = 0, n = perm_dims.size(); i < n; ++i) {
for (int64_t i = 0, n = static_cast<int64_t>(perm_dims.size()); i < n; ++i) {
// convert the negative dim value to normal dim value
if (perm_dims[i] < 0) {
perm_dims[i] = ndim + perm_dims[i];
......
paddle/fluid/distributed/collective/reducer.cc
浏览文件 @ c2f0e9c4
......@@ -953,9 +953,9 @@ void EagerReducer::MarkGroupReady(size_t group_index) {
++next_group_) {
UNUSED auto &group = groups_[next_group_];
if (group.is_sparse_) {
AllReduceSparse(&group, next_group_);
AllReduceSparse(&group, static_cast<int>(next_group_));
} else {
FusedAllReduceSchedule(&group, next_group_);
FusedAllReduceSchedule(&group, static_cast<int>(next_group_));
}
}
}
......@@ -1078,7 +1078,7 @@ void EagerReducer::FusedAllReduceSchedule(EagerGroup *group,
// div nranks
paddle::experimental::scale_(
group->dense_contents_, 1.0 / nranks_, 0.0, false);
group->dense_contents_, 1.0 / nranks_, 0.0, false); // NOLINT
// all_reduce
std::vector<Tensor> reduce_tensors = {group->dense_contents_};
......@@ -1104,7 +1104,8 @@ void EagerReducer::AllReduceSparse(EagerGroup *group,
const int curr_group_index) {
// div nranks
Tensor sparse_tensor(group->sparse_contents_);
paddle::experimental::scale_(sparse_tensor, 1.0 / nranks_, 0.0, false);
paddle::experimental::scale_(
sparse_tensor, 1.0 / nranks_, 0.0, false); // NOLINT
VLOG(3) << "sparse_group [" << curr_group_index << "] start allreduce.";
......
paddle/fluid/distributed/fleet_executor/carrier.cc
浏览文件 @ c2f0e9c4
......@@ -260,7 +260,8 @@ Interceptor* Carrier::SetInterceptor(int64_t interceptor_id,
interceptor->RegisterCarrier(this);
// TODO(fleet_exe dev): get loop
auto* loop = thread_pool_.GetLoop(interceptor_id % thread_num_);
auto* loop =
thread_pool_.GetLoop(static_cast<int>(interceptor_id % thread_num_));
PADDLE_ENFORCE_NOT_NULL(
loop, platform::errors::Fatal("thread task loop must not null"));
interceptor->RegisterTaskLoop(loop);
......@@ -296,7 +297,7 @@ void Carrier::CreateInterceptors(
auto gc = GetGC(place_);
// create source and sink task node
auto max_run_times = microbatch_scopes_.size();
int64_t max_run_times = static_cast<int64_t>(microbatch_scopes_.size());
TaskNode* source = new TaskNode(
rank_, SOURCE_ID, max_run_times); // rank, task_id, max_run_times
TaskNode* sink = new TaskNode(rank_, SINK_ID, max_run_times);
......
paddle/fluid/distributed/fleet_executor/dist_model.cc
浏览文件 @ c2f0e9c4
......@@ -242,7 +242,8 @@ bool DistModel::CommInit() {
std::string var_name_base = "comm_init_";
for (int64_t ring_id : ring_ids) {
VLOG(3) << "Init comm for ring id: " << ring_id;
int64_t ranks_in_group = config_.ring_id_to_ranks_[ring_id].size();
int64_t ranks_in_group =
static_cast<int64_t>(config_.ring_id_to_ranks_[ring_id].size());
int64_t rank_in_group = 0;
std::vector<int64_t> &ranks = config_.ring_id_to_ranks_[ring_id];
for (int64_t rank : ranks) {
......@@ -259,11 +260,11 @@ bool DistModel::CommInit() {
peer_endpoints.emplace_back(config_.trainer_endpoints[rank]);
}
InsertCommOp(var_name_base + std::to_string(order),
ranks_in_group,
rank_in_group,
static_cast<int>(ranks_in_group),
static_cast<int>(rank_in_group),
peer_endpoints,
comm_init_block,
ring_id);
static_cast<int>(ring_id));
order += 1;
}
framework::NaiveExecutor e(place_);
......@@ -408,7 +409,7 @@ bool DistModel::LoadProgram() {
fin.seekg(0, std::ios::end);
pb_content.resize(fin.tellg());
fin.seekg(0, std::ios::beg);
fin.read(&(pb_content.at(0)), pb_content.size());
fin.read(&(pb_content.at(0)), pb_content.size()); // NOLINT
fin.close();
program_proto.ParseFromString(pb_content);
VLOG(5) << pb_content;
......@@ -582,7 +583,7 @@ bool DistModel::FeedData(const std::vector<DistModelTensor> &input_data,
<< DistModelDTypeToString(input_data[i].dtype) << ".";
return false;
}
int feed_idx = feed_names_[target_name];
int feed_idx = static_cast<int>(feed_names_[target_name]);
framework::SetFeedVariable(scope, *input_tensor, "feed", feed_idx);
}
return true;
......
paddle/fluid/distributed/fleet_executor/dist_model.h
浏览文件 @ c2f0e9c4
......@@ -43,7 +43,7 @@ struct DistModelConfig {
framework::ProgramDesc* program_desc{nullptr};
framework::Scope* scope{nullptr};
std::string place{};
int64_t device_id{0};
int device_id{0};
std::string device_type{};
std::vector<std::string> trainer_endpoints{};
std::string current_endpoint{};
......
paddle/fluid/framework/version.cc
浏览文件 @ c2f0e9c4
......@@ -39,10 +39,11 @@ bool IsTensorVersionSupported(uint32_t version) {
std::string DumpVersion(const int64_t version) {
std::stringstream buffer;
const int major = version / MAJOR_COEFF;
const int minor = (version - major * MAJOR_COEFF) / MINOR_COEFF;
const int patch =
(version - major * MAJOR_COEFF - minor * MINOR_COEFF) / PATCH_COEFF;
const int major = static_cast<int>(version / MAJOR_COEFF);
const int minor =
static_cast<int>((version - major * MAJOR_COEFF) / MINOR_COEFF);
const int patch = static_cast<int>(
(version - major * MAJOR_COEFF - minor * MINOR_COEFF) / PATCH_COEFF);
buffer << major << "." << minor << "." << patch;
return buffer.str();
}
......
paddle/fluid/memory/allocation/auto_growth_best_fit_allocator.cc
浏览文件 @ c2f0e9c4
......@@ -199,12 +199,15 @@ void AutoGrowthBestFitAllocator::Trace() const {
cur_idle_bytes += it->second->size_;
}
VLOG(1) << "alloc:" << total_alloc_size_ / static_cast<double>(1024 * 1024)
<< "m free:" << total_free_size_ / static_cast<double>(1024 * 1024)
VLOG(1) << "alloc:"
<< total_alloc_size_ / static_cast<double>(1024 * 1024) // NOLINT
<< "m free:"
<< total_free_size_ / static_cast<double>(1024 * 1024) // NOLINT
<< "m busy:"
<< (total_alloc_size_ - total_free_size_) /
<< (total_alloc_size_ - total_free_size_) / // NOLINT
static_cast<double>(1024 * 1024)
<< "m idle:" << cur_idle_bytes / static_cast<double>(1024 * 1024)
<< "m idle:"
<< cur_idle_bytes / static_cast<double>(1024 * 1024) // NOLINT
<< "m alloc_times:" << total_alloc_times_
<< " free_times:" << total_free_times_
<< " free_blocks_num:" << free_blocks_.size()
......
paddle/fluid/memory/allocation/mmap_allocator.cc
浏览文件 @ c2f0e9c4
......@@ -345,7 +345,8 @@ int MemoryMapAllocationPool::FindFromCache(const int &flag,
const std::string &file_name,
bool check_refcount) {
std::lock_guard<std::mutex> guard(mtx_);
for (size_t idx = 0; idx < memory_map_allocations_.size(); idx++) {
for (int idx = 0; idx < static_cast<int>(memory_map_allocations_.size());
idx++) {
if (memory_map_allocations_.at(idx).flags_ == flag &&
memory_map_allocations_.at(idx).data_size_ == data_size) {
if (file_name.empty() ||
......
paddle/fluid/platform/init.cc
浏览文件 @ c2f0e9c4
......@@ -92,7 +92,7 @@ bool InitGflags(std::vector<std::string> args) {
args.insert(args.begin(), "dummy");
std::vector<char *> argv;
std::string line;
int argc = args.size();
int argc = static_cast<int>(args.size());
for (auto &arg : args) {
argv.push_back(const_cast<char *>(arg.data()));
line += arg;
......
paddle/fluid/platform/profiler.cc
浏览文件 @ c2f0e9c4
......@@ -859,7 +859,8 @@ std::string PrintHostEvents() {
oss << thr_evt_sec.thread_id << std::endl;
for (const auto &evt : thr_evt_sec.events) {
oss << "{ " << evt.name << " | " << evt.start_ns << "ns | " << evt.end_ns
<< "ns | " << (evt.end_ns - evt.start_ns) / 1000.000 << "us }"
<< "ns | " << (evt.end_ns - evt.start_ns) / 1000.000
<< "us }" // NOLINT
<< std::endl;
}
}
......
paddle/fluid/platform/profiler/chrometracing_logger.cc
浏览文件 @ c2f0e9c4
......@@ -450,7 +450,8 @@ void ChromeTracingLogger::HandleTypeMemcpy(
MemcpyEventInfo memcpy_info = device_node.MemcpyInfo();
float memory_bandwidth = 0;
if (device_node.Duration() > 0) {
memory_bandwidth = memcpy_info.num_bytes * 1.0 / device_node.Duration();
memory_bandwidth =
memcpy_info.num_bytes * 1.0 / device_node.Duration(); // NOLINT
}
float dur = nsToMsFloat(device_node.Duration());
std::string dur_display;
......
paddle/fluid/platform/profiler/dump/deserialization_reader.cc
浏览文件 @ c2f0e9c4
......@@ -287,7 +287,7 @@ DeserializationReader::RestoreOperatorSupplementEventNode(
auto shape_vector_proto = shape_vectors_proto.shapes(j);
std::vector<int64_t> shape;
for (int k = 0; k < shape_vector_proto.size_size(); k++) {
shape.push_back(shape_vector_proto.size(k));
shape.push_back(shape_vector_proto.size(k)); // NOLINT
}
input_shape_vec.push_back(shape);
}
......
paddle/fluid/platform/timer.cc
浏览文件 @ c2f0e9c4
......@@ -49,9 +49,9 @@ int Timer::Count() { return _count; }
double Timer::ElapsedUS() { return static_cast<double>(_elapsed); }
double Timer::ElapsedMS() { return _elapsed / 1000.0; }
double Timer::ElapsedMS() { return _elapsed / 1000.0; } // NOLINT
double Timer::ElapsedSec() { return _elapsed / 1000000.0; }
double Timer::ElapsedSec() { return _elapsed / 1000000.0; } // NOLINT
int64_t Timer::Tickus() {
gettimeofday(&_now, nullptr);
......
paddle/phi/core/distributed/auto_parallel/r_to_s_reshard_function.cc
浏览文件 @ c2f0e9c4
......@@ -68,7 +68,7 @@ void RToSReshardFunction::Eval(phi::DeviceContext* dev_ctx,
<< " process participate in.";
std::vector<int64_t> split_num_vec =
BalancedSplit(in.dims()[split_axis], num_of_process);
BalancedSplit(in.dims()[static_cast<int>(split_axis)], num_of_process);
IntArray sections(split_num_vec);
std::vector<DenseTensor> split_out_vec;
......
paddle/phi/core/distributed/auto_parallel/s_to_r_reshard_function.cc
浏览文件 @ c2f0e9c4
......@@ -50,8 +50,8 @@ bool SToRReshardFunction::IsSuitable(const DistTensor& in,
GetSplitAxisWithDimsMapping(in_dims_mapping);
int64_t split_axis = split_axis_to_mesh_axis.begin()->first;
int64_t num_of_process = in_process_mesh.size();
flag &=
(in.local_dims()[split_axis] * num_of_process == in.dims()[split_axis]);
flag &= (in.local_dims()[static_cast<int>(split_axis)] * num_of_process ==
in.dims()[static_cast<int>(split_axis)]);
return flag;
}
......@@ -89,10 +89,11 @@ void SToRReshardFunction::Eval(DeviceContext* dev_ctx,
// first we need to split the result on axis 0,
// then we need to concat the split result on input split axis.
int64_t default_split_axis = 0;
int64_t num_of_process = in_process_ids.size();
int64_t num_of_process = static_cast<int64_t>(in_process_ids.size());
IntArray sections(std::vector<int64_t>(
num_of_process, in.value().dims()[default_split_axis]));
num_of_process,
in.value().dims()[static_cast<int>(default_split_axis)]));
std::vector<DenseTensor> split_out_vec;
RESHARD_FUNCTOR(dev_ctx,
Split,
......
paddle/phi/infermeta/backward.cc
浏览文件 @ c2f0e9c4
......@@ -376,7 +376,7 @@ void FFTC2RGradInferMeta(const MetaTensor& x,
out->set_dtype(ToComplexType(x.dtype()));
phi::DDim out_dim = x.dims();
const int64_t last_fft_axis = axes.back();
const int last_fft_axis = static_cast<int>(axes.back());
if (last_dim_size > 0) {
out_dim.at(last_fft_axis) = last_dim_size / 2 + 1;
} else if (config.is_runtime) {
......@@ -534,7 +534,7 @@ void InstanceNormGradInferMeta(const MetaTensor& x,
phi::errors::InvalidArgument(
"The X@GRAD in InstanceNormGradInferMeta can't be nullptr."));
const auto x_dims = x.dims();
const int C = x_dims[1];
const int C = static_cast<int>(x_dims[1]);
x_grad->set_dims(x_dims);
x_grad->set_dtype(x.dtype());
x_grad->set_layout(x.layout());
......@@ -563,7 +563,7 @@ void InstanceNormDoubleGradInferMeta(const MetaTensor& x,
phi::errors::InvalidArgument(
"The DX in InstanceNormDoubleGradInferMeta can't be nullptr."));
const auto x_dims = x.dims();
const int C = x_dims[1];
const int C = static_cast<int>(x_dims[1]);
dx->set_dims(x_dims);
dx->set_dtype(x.dtype());
dx->set_layout(x.layout());
......@@ -1076,12 +1076,12 @@ void TransposeGradInferMeta(const MetaTensor& x,
std::vector<int> formated_axis = axis;
for (size_t i = 0; i < axis.size(); i++) {
if (axis[i] < 0) {
formated_axis[i] = axis[i] + x_rank;
formated_axis[i] = static_cast<int>(axis[i] + x_rank);
}
}
std::vector<int> reversed_axis(axis);
for (size_t i = 0; i < formated_axis.size(); i++) {
for (int i = 0; i < static_cast<int>(formated_axis.size()); i++) {
reversed_axis[formated_axis[i]] = i;
}
......@@ -1151,7 +1151,7 @@ void UnStackGradInferMeta(const std::vector<const MetaTensor*>& out_grad,
if (axis < 0) axis += (rank + 1);
auto vec = phi::vectorize<int>(input_dims[0]);
vec.insert(vec.begin() + axis, input_dims.size());
vec.insert(vec.begin() + axis, static_cast<int>(input_dims.size()));
x_grad->set_dims(phi::make_ddim(vec));
x_grad->set_dtype(out_grad[0]->dtype());
}
......
paddle/phi/infermeta/binary.cc
浏览文件 @ c2f0e9c4
......@@ -450,7 +450,7 @@ void ConvInferMeta(const MetaTensor& input,
std::vector<int> dilations = dilations_t;
auto in_dims = input.dims();
auto filter_dims = filter.dims();
int dilation_size = dilations.size();
int dilation_size = static_cast<int>(dilations.size());
for (int i = 0; i < dilation_size; ++i) {
PADDLE_ENFORCE_GT(
dilations[i],
......@@ -492,7 +492,7 @@ void ConvInferMeta(const MetaTensor& input,
filter_dims,
filter_dims.size()));
int stride_size = strides.size();
int stride_size = static_cast<int>(strides.size());
for (int i = 0; i < stride_size; ++i) {
PADDLE_ENFORCE_GT(
strides[i],
......@@ -579,11 +579,12 @@ void ConvInferMeta(const MetaTensor& input,
(in_data_dims[i] <= 0 || filter_dims[i + 2] <= 0)) {
output_shape.push_back(-1);
} else {
const int dkernel = dilations[i] * (filter_data_dims[i] - 1) + 1;
int output_size =
const int dkernel =
static_cast<int>(dilations[i] * (filter_data_dims[i] - 1) + 1);
int output_size = static_cast<int>(
(in_data_dims[i] + paddings[2 * i] + paddings[2 * i + 1] - dkernel) /
strides[i] +
1;
1);
output_shape.push_back(output_size);
}
}
......@@ -660,7 +661,7 @@ void ConvTransposeInferMeta(const MetaTensor& x,
filter_dims,
filter_dims.size()));
int stride_size = strides.size();
int stride_size = static_cast<int>(strides.size());
for (int i = 0; i < stride_size; ++i) {
PADDLE_ENFORCE_GT(
strides[i],
......@@ -734,7 +735,7 @@ void ConvTransposeInferMeta(const MetaTensor& x,
output_shape.push_back(filter_dims[1] * groups);
}
const int offset = (data_layout != DataLayout::kNHWC ? 2 : 1);
for (size_t i = 0; i < strides.size(); ++i) {
for (int i = 0; i < static_cast<int>(strides.size()); ++i) {
auto filter_extent = dilations_[i] * (filter_dims[i + 2] - 1) + 1;
auto infer_shape = (config.is_runtime || x_dims[i + offset] > 0)
? (x_dims[i + offset] - 1) * strides[i] -
......@@ -1115,7 +1116,7 @@ void DropoutNdInferMeta(const MetaTensor& x,
std::for_each(
axis.begin(), axis.end(), [&mask_dims, &x_dims](const int64_t& t) {
mask_dims[t] = x_dims[t];
mask_dims[t] = x_dims[static_cast<int>(t)];
});
mask->set_dims(make_ddim(mask_dims));
......@@ -1125,7 +1126,7 @@ void DropoutNdInferMeta(const MetaTensor& x,
void DotInferMeta(const MetaTensor& x, const MetaTensor& y, MetaTensor* out) {
auto x_dims = x.dims();
auto x_rank = static_cast<size_t>(x_dims.size());
int x_rank = static_cast<int>(x_dims.size());
PADDLE_ENFORCE_EQ(true,
1 == x_rank || 2 == x_rank,
phi::errors::PreconditionNotMet(
......@@ -1136,14 +1137,14 @@ void DotInferMeta(const MetaTensor& x, const MetaTensor& y, MetaTensor* out) {
auto y_dims = y.dims();
PADDLE_ENFORCE_EQ(
true,
x_rank == static_cast<size_t>(y_dims.size()),
x_rank == static_cast<int>(y_dims.size()),
phi::errors::PreconditionNotMet(
"ShapeError: The shape of input tensor Y: %s should match with "
"input tenosr X: %s",
y_dims.to_str(),
x_dims.to_str()));
bool shape_match = true;
for (size_t i = 0; i < x_rank; ++i) {
for (int i = 0; i < x_rank; ++i) {
if (x_dims[i] != y_dims[i]) {
shape_match = false;
break;
......@@ -1458,10 +1459,10 @@ void FusedMatmulInferMeta(const MetaTensor& x,
}
}
if (!x_broadcasted) {
new_dims.push_back(M);
new_dims.push_back(M); // NOLINT
}
if (!y_broadcasted) {
new_dims.push_back(N);
new_dims.push_back(N); // NOLINT
}
if (x_broadcasted && y_broadcasted) {
new_dims.push_back(1);
......@@ -1558,10 +1559,10 @@ void GatherInferMeta(const MetaTensor& x,
} else {
std::vector<int> out_dim_vec;
for (int i = 0; i < axis_v; i++) {
out_dim_vec.push_back(input_dim[i]);
out_dim_vec.push_back(input_dim[i]); // NOLINT
}
for (int i = axis_v + 1; i < input_dim.size(); i++) {
out_dim_vec.push_back(input_dim[i]);
out_dim_vec.push_back(input_dim[i]); // NOLINT
}
auto output_dims = phi::make_ddim(out_dim_vec);
out->set_dims(output_dims);
......@@ -1572,21 +1573,21 @@ void GatherInferMeta(const MetaTensor& x,
} else {
if (axis.FromTensor() || axis_v == 0) {
// if axis.FromTensor(), we can not obtain correct shape of output
int batch_size = index_dims[0];
int batch_size = static_cast<int>(index_dims[0]);
phi::DDim output_dims(input_dim);
output_dims[0] = batch_size;
out->set_dims(output_dims);
out->set_dtype(x.dtype());
out->share_lod(x);
} else {
int index_size = index_dims[0];
int index_size = static_cast<int>(index_dims[0]);
std::vector<int> out_dim_vec;
for (int i = 0; i < axis_v; i++) {
out_dim_vec.push_back(input_dim[i]);
out_dim_vec.push_back(input_dim[i]); // NOLINT
}
out_dim_vec.push_back(index_size);
for (int i = axis_v + 1; i < input_dim.size(); i++) {
out_dim_vec.push_back(input_dim[i]);
out_dim_vec.push_back(input_dim[i]); // NOLINT
}
auto output_dims = phi::make_ddim(out_dim_vec);
out->set_dims(output_dims);
......@@ -1602,7 +1603,7 @@ void GatherNdInferMeta(const MetaTensor& x,
auto x_dims = x.dims();
auto x_dims_size = x_dims.size();
auto index_dims = index.dims();
auto index_dims_size = index_dims.size();
int index_dims_size = index_dims.size();
PADDLE_ENFORCE_LE(
index_dims[index_dims_size - 1],
......@@ -1620,7 +1621,9 @@ void GatherNdInferMeta(const MetaTensor& x,
for (int i = 0; i < index_dims_size - 1; ++i) {
result_dims.emplace_back(index_dims[i]);
}
for (int i = index_dims[index_dims_size - 1]; i < x_dims_size; ++i) {
for (int i = static_cast<int>(index_dims[index_dims_size - 1]);
i < x_dims_size;
++i) {
result_dims.emplace_back(x_dims[i]);
}
......@@ -2010,8 +2013,8 @@ void LUUnpackInferMeta(const MetaTensor& x,
2,
phi::errors::InvalidArgument("The rank of input must greater than 2."));
int m = x_dims[x_rank - 1];
int n = x_dims[x_rank - 2];
int m = static_cast<int>(x_dims[x_rank - 1]);
int n = static_cast<int>(x_dims[x_rank - 2]);
int min_mn = std::min(m, n);
if (unpack_ludata) {
auto ldims = x_dims;
......@@ -2157,10 +2160,10 @@ void MatmulInferMeta(const MetaTensor& x,
}
}
if (!x_broadcasted) {
new_dims.push_back(M);
new_dims.push_back(M); // NOLINT
}
if (!y_broadcasted) {
new_dims.push_back(N);
new_dims.push_back(N); // NOLINT
}
auto ddim_out = phi::make_ddim(new_dims);
......@@ -2227,10 +2230,10 @@ void MatmulInt8InferMeta(const MetaTensor& x,
}
}
if (!x_broadcasted) {
new_dims.push_back(M);
new_dims.push_back(M); // NOLINT
}
if (!y_broadcasted) {
new_dims.push_back(N);
new_dims.push_back(N); // NOLINT
}
auto ddim_out = phi::make_ddim(new_dims);
......@@ -2397,8 +2400,8 @@ void MatrixRankTolInferMeta(const MetaTensor& x,
phi::errors::InvalidArgument("The dims of input must be greater than 2"));
if (hermitian) {
int rows = dim_x[dim_x.size() - 2];
int cols = dim_x[dim_x.size() - 1];
int rows = static_cast<int>(dim_x[dim_x.size() - 2]);
int cols = static_cast<int>(dim_x[dim_x.size() - 1]);
PADDLE_ENFORCE_EQ(rows,
cols,
phi::errors::InvalidArgument(
......@@ -2508,7 +2511,7 @@ void PReluInferMeta(const MetaTensor& x,
} else if (mode == "element") {
auto alpha_dim = alpha.dims();
auto alpha_rank = alpha_dim.size();
auto x_rank = x_dim.size();
int x_rank = x_dim.size();
PADDLE_ENFORCE_GE(x_rank,
1,
phi::errors::InvalidArgument(
......@@ -2527,7 +2530,7 @@ void PReluInferMeta(const MetaTensor& x,
x_rank));
size_t x_product = 1;
size_t alpha_product = 1;
for (int64_t i = x_rank - 1; i > 0; i--) {
for (int i = x_rank - 1; i > 0; i--) {
x_product *= x_dim[i];
alpha_product *= alpha_dim[i];
}
......@@ -2647,7 +2650,7 @@ void PriorBoxInferMeta(const MetaTensor& input,
std::vector<int64_t> dim_vec(4);
dim_vec[0] = input_dims[2];
dim_vec[1] = input_dims[3];
dim_vec[2] = num_priors;
dim_vec[2] = static_cast<int64_t>(num_priors);
dim_vec[3] = 4;
out->set_dtype(input.dtype());
......@@ -2720,8 +2723,8 @@ void SearchsortedInferMeta(const MetaTensor& sorted_sequence,
if (sequences_dims.size() != values_dims.size()) {
flag = false;
}
const auto& sequences_dims_size = sequences_dims.size();
for (int64_t dim = 0; dim < sequences_dims_size - 1; ++dim) {
const int& sequences_dims_size = sequences_dims.size();
for (int dim = 0; dim < sequences_dims_size - 1; ++dim) {
if (sequences_dims[dim] != values_dims[dim]) {
flag = false;
break;
......@@ -2918,9 +2921,9 @@ void LstsqInferMeta(const MetaTensor& x,
int x_rank = x_dims.size();
int y_rank = y_dims.size();
int m = x_dims[x_rank - 2];
int n = x_dims[x_rank - 1];
int nrhs = y_dims[x_rank - 1];
int m = static_cast<int>(x_dims[x_rank - 2]);
int n = static_cast<int>(x_dims[x_rank - 1]);
int nrhs = static_cast<int>(y_dims[x_rank - 1]);
PADDLE_ENFORCE_GE(
x_rank,
......@@ -3004,7 +3007,7 @@ void YoloBoxInferMeta(const MetaTensor& x,
MetaConfig config) {
auto dim_x = x.dims();
auto dim_imgsize = img_size.dims();
int anchor_num = anchors.size() / 2;
int anchor_num = static_cast<int>(anchors.size() / 2);
PADDLE_ENFORCE_EQ(
dim_x.size(),
......@@ -3089,7 +3092,7 @@ void YoloBoxInferMeta(const MetaTensor& x,
int box_num;
if ((dim_x[2] > 0 && dim_x[3] > 0) || config.is_runtime) {
box_num = dim_x[2] * dim_x[3] * anchor_num;
box_num = static_cast<int>(dim_x[2] * dim_x[3] * anchor_num);
} else {
box_num = -1;
}
......@@ -3181,10 +3184,10 @@ void SolveInferMeta(const MetaTensor& x, const MetaTensor& y, MetaTensor* out) {
new_dims.assign(y_dims_vec.begin(), y_dims_vec.end() - 2);
}
if (!x_broadcasted) {
new_dims.push_back(M);
new_dims.push_back(M); // NOLINT
}
if (!y_broadcasted) {
new_dims.push_back(N);
new_dims.push_back(N); // NOLINT
}
if (x_broadcasted && y_broadcasted) {
new_dims.push_back(1);
......@@ -3232,7 +3235,7 @@ void UnpoolInferMeta(const MetaTensor& x,
if (config.is_runtime || !output_size.FromTensor()) {
output_size_val = output_size.GetData();
}
for (size_t i = 0; i < ksize.size(); ++i) {
for (int i = 0; i < static_cast<int>(ksize.size()); ++i) {
if (!config.is_runtime && in_x_dims[i + 2] <= 0) {
output_shape.push_back(-1);
} else {
......@@ -3273,7 +3276,7 @@ void Unpool3dInferMeta(const MetaTensor& x,
in_y_dims));
std::vector<int64_t> output_shape({in_x_dims[0], in_x_dims[1]});
for (size_t i = 0; i < ksize.size(); ++i) {
for (int i = 0; i < static_cast<int>(ksize.size()); ++i) {
if (!config.is_runtime && in_x_dims[i + 2] <= 0) {
output_shape.push_back(-1);
} else {
......
paddle/phi/infermeta/fusion.cc
浏览文件 @ c2f0e9c4
......@@ -207,14 +207,14 @@ void Conv1dXPUInferMeta(const MetaTensor& x,
groups));
std::vector<int64_t> out_shape({in_dims[0], filter_dims[0]});
out_shape.push_back(ConvOutSize(in_dims[2],
filter_dims[2],
out_shape.push_back(ConvOutSize(static_cast<int>(in_dims[2]),
static_cast<int>(filter_dims[2]),
dilations,
paddings[0],
paddings[1],
strides));
// set output and output max dims
out->set_dims(DDim(out_shape.data(), out_shape.size()));
out->set_dims(DDim(out_shape.data(), static_cast<int>(out_shape.size())));
out->set_dtype(x.dtype());
out->set_layout(x.layout());
out_max->set_dims(phi::make_ddim({6}));
......@@ -264,9 +264,9 @@ void Conv2dXPUInferMeta(const MetaTensor& x,
filter_dims.size()));
const auto input_channels = in_dims[1];
int stride_size = strides.size();
int stride_size = static_cast<int>(strides.size());
int in_sub_stride_size = in_dims.size() - stride_size;
int dilation_size = dilations.size();
int dilation_size = static_cast<int>(dilations.size());
PADDLE_ENFORCE_EQ(
in_dims.size(),
strides.size() + 2U,
......@@ -333,16 +333,16 @@ void Conv2dXPUInferMeta(const MetaTensor& x,
ksize);
std::vector<int64_t> out_shape({in_dims[0], filter_dims[0]});
for (size_t i = 0; i < strides.size(); ++i) {
out_shape.push_back(ConvOutSize(in_dims[i + 2],
filter_dims[i + 2],
for (int i = 0; i < static_cast<int>(strides.size()); ++i) {
out_shape.push_back(ConvOutSize(static_cast<int>(in_dims[i + 2]),
static_cast<int>(filter_dims[i + 2]),
dilations[i],
paddings_vec[i * 2],
paddings_vec[i * 2 + 1],
strides[i]));
}
// set output and output max dims
out->set_dims(DDim(out_shape.data(), out_shape.size()));
out->set_dims(DDim(out_shape.data(), static_cast<int>(out_shape.size())));
out_max->set_dims(phi::make_ddim({6}));
out->set_dtype(out_dtype);
}
......@@ -388,10 +388,10 @@ void FcXPUInferMeta(const MetaTensor& x,
MetaTensor* out_max) {
std::vector<int> out_shape(in_num_col_dims + 1);
for (int i = 0; i < in_num_col_dims; i++) {
out_shape[i] = x.dims()[i];
out_shape[i] = static_cast<int>(x.dims()[i]);
}
out_shape[in_num_col_dims] = w.dims()[0];
out->set_dims(DDim(out_shape.data(), out_shape.size()));
out_shape[in_num_col_dims] = static_cast<int>(w.dims()[0]);
out->set_dims(DDim(out_shape.data(), static_cast<int>(out_shape.size())));
out->set_dtype(out_dtype);
out->set_layout(x.layout());
out_max->set_dims(phi::make_ddim({6}));
......@@ -671,7 +671,7 @@ void ConvTransposeXPUInferMeta(const MetaTensor& x,
filter_dims,
filter_dims.size()));
int stride_size = strides.size();
int stride_size = static_cast<int>(strides.size());
for (int i = 0; i < stride_size; ++i) {
PADDLE_ENFORCE_GT(
strides[i],
......@@ -744,7 +744,7 @@ void ConvTransposeXPUInferMeta(const MetaTensor& x,
output_shape.push_back(filter_dims[1] * groups);
}
const int offset = (data_format != "NHWC" ? 2 : 1);
for (size_t i = 0; i < strides.size(); ++i) {
for (int i = 0; i < static_cast<int>(strides.size()); ++i) {
auto filter_extent = dilations_[i] * (filter_dims[i + 2] - 1) + 1;
auto infer_shape = (x_dims[i + offset] > 0)
? (x_dims[i + offset] - 1) * strides[i] -
......@@ -901,7 +901,7 @@ void FusedScaleBiasReluConvBnstatsInferMeta(
phi::errors::InvalidArgument("Expect group to be 1, got %d.", groups));
const auto input_channels = in_dims[in_dims.size() - 1];
int dilation_size = dilations.size();
int dilation_size = static_cast<int>(dilations.size());
for (int i = 0; i < dilation_size; ++i) {
PADDLE_ENFORCE_GT(
dilations[i],
......@@ -943,9 +943,9 @@ void FusedScaleBiasReluConvBnstatsInferMeta(
ksize);
std::vector<int64_t> out_shape({in_dims[0]});
for (size_t i = 0; i < strides.size(); ++i) {
out_shape.push_back(ConvOutSize(in_dims[i + 1],
filter_dims[i + 2],
for (int i = 0; i < static_cast<int>(strides.size()); ++i) {
out_shape.push_back(ConvOutSize(static_cast<int>(in_dims[i + 1]),
static_cast<int>(filter_dims[i + 2]),
dilations[i],
paddings_vec[i * 2],
paddings_vec[i * 2 + 1],
......@@ -955,7 +955,7 @@ void FusedScaleBiasReluConvBnstatsInferMeta(
// make shape for other outputs
auto c_dims = phi::make_ddim({filter_dims[0]});
// set output and output max dims
out->set_dims(DDim(out_shape.data(), out_shape.size()));
out->set_dims(DDim(out_shape.data(), static_cast<int>(out_shape.size())));
out_running_mean->set_dims(c_dims);
out_running_var->set_dims(c_dims);
saved_mean->set_dims(c_dims);
......
paddle/phi/infermeta/multiary.cc
浏览文件 @ c2f0e9c4
......@@ -795,7 +795,7 @@ void BroadcastTensorsInferMeta(const std::vector<const MetaTensor*>& x,
int axis = static_cast<int>(input_ddim.size()) - index - 1;
int dim_size = 1;
if (axis >= 0) {
dim_size = input_ddim[axis];
dim_size = static_cast<int>(input_ddim[axis]);
}
if (target_dim_size != 1 && dim_size != 1 &&
......@@ -857,7 +857,7 @@ void CoalesceTensorInferMeta(const std::vector<const MetaTensor*>& input,
MetaTensor* fused_output,
MetaConfig config) {
if (size_of_dtype == -1) {
size_of_dtype = phi::SizeOf(dtype);
size_of_dtype = static_cast<int>(phi::SizeOf(dtype));
}
if (config.is_runtime) {
#if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP)
......@@ -902,7 +902,7 @@ void CoalesceTensorInferMeta(const std::vector<const MetaTensor*>& input,
align_size) /
size_of_dtype
: static_cast<size_t>(size);
numel += len;
numel += static_cast<int64_t>(len);
}
if (fused_output) {
fused_output->set_dims(phi::make_ddim({numel}));
......@@ -925,7 +925,7 @@ void CheckMemoryContinueInferMeta(const std::vector<const MetaTensor*>& input,
const auto& dim = item->dims();
auto size = phi::product(dim);
auto len = size * phi::SizeOf(item->dtype());
numel += len;
numel += static_cast<int64_t>(len);
}
output->set_dims(phi::make_ddim({numel}));
output->set_dtype(phi::DataType::INT8);
......@@ -1215,16 +1215,17 @@ void DeformableConvInferMeta(const MetaTensor& x,
}
std::vector<int64_t> output_shape({in_dims[0], filter_dims[0]});
for (size_t i = 0; i < strides.size(); ++i) {
for (int i = 0; i < static_cast<int>(strides.size()); ++i) {
if (!config.is_runtime &&
(in_dims[i + 2] <= 0 || filter_dims[i + 2] <= 0)) {
output_shape.push_back(-1);
} else {
output_shape.push_back(ConvOutputSize(in_dims[i + 2],
filter_dims[i + 2],
dilations[i],
paddings[i],
strides[i]));
output_shape.push_back(
ConvOutputSize(static_cast<int>(in_dims[i + 2]),
static_cast<int>(filter_dims[i + 2]),
dilations[i],
paddings[i],
strides[i]));
}
}
......@@ -1628,7 +1629,7 @@ void FusedLayerNormInferMeta(const MetaTensor& x,
int32_t rows = 1;
for (int i = 0; i < begin_norm_axis; i++) {
rows *= x.dims()[i];
rows *= static_cast<int32_t>(x.dims()[i]);
}
if (norm_weight) {
......@@ -2025,9 +2026,10 @@ static void Interpolate1DInferShapeCheck(
scale_w));
if (scale_w > 0.) {
// round down
out_w_tmp = (data_layout == DataLayout::kNCHW
? static_cast<int>(dim_x[2] * scale_w)
: static_cast<int>(dim_x[1] * scale_w));
out_w_tmp =
static_cast<int>(data_layout == DataLayout::kNCHW
? static_cast<float>(dim_x[2]) * scale_w
: static_cast<float>(dim_x[1]) * scale_w);
// protect when input shape is -1
out_w_tmp = out_w_tmp > 0 ? out_w_tmp : -1;
}
......@@ -2171,12 +2173,14 @@ static void Interpolate2DInferShapeCheck(
scale_h));
if (scale_h > 0. && scale_w > 0.) {
// round down
out_h_tmp = (data_layout == DataLayout::kNCHW
? static_cast<int>(dim_x[2] * scale_h)
: static_cast<int>(dim_x[1] * scale_h));
out_w_tmp = (data_layout == DataLayout::kNCHW
? static_cast<int>(dim_x[3] * scale_w)
: static_cast<int>(dim_x[2] * scale_w));
out_h_tmp =
static_cast<int>(data_layout == DataLayout::kNCHW
? static_cast<float>(dim_x[2]) * scale_h
: static_cast<float>(dim_x[1]) * scale_h);
out_w_tmp =
static_cast<int>(data_layout == DataLayout::kNCHW
? static_cast<float>(dim_x[3]) * scale_w
: static_cast<float>(dim_x[2]) * scale_w);
// protect when input shape is -1
out_h_tmp = out_h_tmp > 0 ? out_h_tmp : -1;
out_w_tmp = out_w_tmp > 0 ? out_w_tmp : -1;
......@@ -2325,15 +2329,18 @@ static void Interpolate3DInferShapeCheck(
scale_d));
if (scale_d > 0. && scale_h > 0. && scale_w > 0.) {
// round down
out_d_tmp = (data_layout == DataLayout::kNCHW
? static_cast<int>(dim_x[2] * scale_d)
: static_cast<int>(dim_x[1] * scale_d));
out_h_tmp = (data_layout == DataLayout::kNCHW
? static_cast<int>(dim_x[3] * scale_h)
: static_cast<int>(dim_x[2] * scale_h));
out_w_tmp = (data_layout == DataLayout::kNCHW
? static_cast<int>(dim_x[4] * scale_w)
: static_cast<int>(dim_x[3] * scale_w));
out_d_tmp =
static_cast<int>(data_layout == DataLayout::kNCHW
? static_cast<float>(dim_x[2]) * scale_d
: static_cast<float>(dim_x[1]) * scale_d);
out_h_tmp =
static_cast<int>(data_layout == DataLayout::kNCHW
? static_cast<float>(dim_x[3]) * scale_h
: static_cast<float>(dim_x[2]) * scale_h);
out_w_tmp =
static_cast<int>(data_layout == DataLayout::kNCHW
? static_cast<float>(dim_x[4]) * scale_w
: static_cast<float>(dim_x[3]) * scale_w);
// protect when input shape is -1
out_d_tmp = out_d_tmp > 0 ? out_d_tmp : -1;
out_h_tmp = out_h_tmp > 0 ? out_h_tmp : -1;
......@@ -2860,13 +2867,13 @@ void MeshgridInferMeta(const std::vector<const MetaTensor*>& inputs,
std::vector<MetaTensor*> outputs) {
const size_t inputs_num = inputs.size();
auto out_shape = std::vector<int>(inputs_num);
std::vector<int> out_shape = std::vector<int>(inputs_num);
for (size_t i = 0; i < inputs.size(); i++) {
if (inputs[i]->dims().size() == 0) {
out_shape[i] = 1;
} else {
out_shape[i] = inputs[i]->dims()[0];
out_shape[i] = static_cast<int>(inputs[i]->dims()[0]);
}
}
auto out_dims = phi::make_ddim(std::vector<int>(out_shape));
......@@ -3317,7 +3324,7 @@ void RnnInferMeta(const MetaTensor& x,
out->set_dims(out_dims);
out->set_dtype(x.dtype());
int state_num = pre_state.size();
int state_num = static_cast<int>(pre_state.size());
for (int i = 0; i < state_num; ++i) {
state[i]->set_dims(pre_state[i]->dims());
state[i]->set_dtype(x.dtype());
......@@ -3482,7 +3489,7 @@ void SendUERecvInferMeta(const MetaTensor& x,
std::vector<int> x_dims2(x_dims1.begin() + 1, x_dims1.end());
std::vector<int> y_dims2(y_dims1.begin() + 1, y_dims1.end());
int max_dim = std::max(x_dims2.size(), y_dims2.size());
int max_dim = static_cast<int>(std::max(x_dims2.size(), y_dims2.size()));
int axis = std::abs(static_cast<int>(x_dims2.size() - y_dims2.size()));
std::vector<int> x_dims_array(max_dim);
std::vector<int> y_dims_array(max_dim);
......@@ -3552,7 +3559,7 @@ void SendUVInferMeta(const MetaTensor& x,
auto y_dims1 = phi::vectorize<int>(y_dims);
std::vector<int> x_dims2(x_dims1.begin() + 1, x_dims1.end());
std::vector<int> y_dims2(y_dims1.begin() + 1, y_dims1.end());
int max_dim = std::max(x_dims2.size(), y_dims2.size());
int max_dim = static_cast<int>(std::max(x_dims2.size(), y_dims2.size()));
int axis = std::abs(static_cast<int>(x_dims2.size() - y_dims2.size()));
std::vector<int> x_dims_array(max_dim);
std::vector<int> y_dims_array(max_dim);
......@@ -3565,7 +3572,7 @@ void SendUVInferMeta(const MetaTensor& x,
out_dims_array.data(),
max_dim,
axis);
out_dims_array.insert(out_dims_array.begin(), src_index_dims[0]);
out_dims_array.insert(out_dims_array.begin(), src_index_dims[0]); // NOLINT
out->set_dims(phi::make_ddim(out_dims_array));
}
......@@ -3603,7 +3610,7 @@ void StackInferMeta(const std::vector<const MetaTensor*>& x,
axis));
if (axis < 0) axis += (rank + 1);
auto vec = phi::vectorize<int64_t>(out_dim);
vec.insert(vec.begin() + axis, input_dims.size());
vec.insert(vec.begin() + axis, input_dims.size()); // NOLINT
out->set_dims(phi::make_ddim(vec));
out->set_dtype(x.at(0)->dtype());
out->share_lod(*x.at(0));
......@@ -3692,7 +3699,7 @@ void WarpctcInferMeta(const MetaTensor& logits,
int sequence_width = 0;
if (logits_length) {
sequence_width = logits_dims[2];
sequence_width = static_cast<int>(logits_dims[2]);
} else {
sequence_width =
static_cast<int>(phi::product(logits_dims) / logits_dims[0]);
......@@ -3726,7 +3733,7 @@ void WarprnntInferMeta(const MetaTensor& input,
MetaTensor* loss,
MetaTensor* warpctcgrad) {
auto acts_dims = input.dims();
int D = acts_dims[3];
int D = static_cast<int>(acts_dims[3]);
PADDLE_ENFORCE_GE(
blank,
......@@ -3835,8 +3842,8 @@ void YoloLossInferMeta(const MetaTensor& x,
auto dim_x = x.dims();
auto dim_gtbox = gt_box.dims();
auto dim_gtlabel = gt_label.dims();
int anchor_num = anchors.size() / 2;
int mask_num = anchor_mask.size();
int anchor_num = static_cast<int>(anchors.size() / 2);
int mask_num = static_cast<int>(anchor_mask.size());
PADDLE_ENFORCE_EQ(dim_x.size(),
4,
......@@ -4183,10 +4190,10 @@ void MaskedMultiheadAttentionInferMeta(const MetaTensor& x,
MetaTensor* out,
MetaTensor* cache_kv_out,
MetaTensor* beam_cache_offset_out) {
int bsz = x.dims()[0];
int bsz = static_cast<int>(x.dims()[0]);
auto cache_kv_dims = cache_kv.dims();
int num_head = cache_kv.dims()[2];
int dim_head = cache_kv.dims()[4];
int num_head = static_cast<int>(cache_kv.dims()[2]);
int dim_head = static_cast<int>(cache_kv.dims()[4]);
PADDLE_ENFORCE_EQ(
cache_kv_dims.size(),
......
paddle/phi/infermeta/sparse/binary.cc
浏览文件 @ c2f0e9c4
......@@ -69,7 +69,7 @@ inline void ResetSubmKernelSizeAndStrides(const DDim& kernel_dims,
std::vector<int>* paddings,
std::vector<int>* strides) {
for (uint64_t i = 0; i < paddings->size(); i++) {
(*paddings)[i] = kernel_dims[i] / 2;
(*paddings)[i] = kernel_dims[i] / 2; // NOLINT
(*strides)[i] = 1;
}
}
......@@ -95,7 +95,7 @@ void Conv3dInferMeta(const MetaTensor& x,
std::vector<int> kernel_sizes(kernel_dims.size());
for (int i = 0; i < kernel_dims.size(); i++) {
kernel_sizes[i] = kernel_dims[i];
kernel_sizes[i] = static_cast<int>(kernel_dims[i]);
}
std::vector<int> subm_paddings(paddings), subm_strides(strides);
......@@ -143,8 +143,8 @@ void Pool3dInferMeta(const MetaTensor& x,
const auto& x_dims = x.dims();
DDim out_dims = {1, 1, 1, 1, 1};
const std::vector<int>& real_kernel_sizes =
PoolResetKernel(kernel_sizes, x_dims[4], x_dims[4]);
const std::vector<int>& real_kernel_sizes = PoolResetKernel(
kernel_sizes, static_cast<int>(x_dims[4]), static_cast<int>(x_dims[4]));
GetOutShape(
x_dims, real_kernel_sizes, paddings, dilations, strides, &out_dims);
out->set_dtype(x.dtype());
......
paddle/phi/infermeta/ternary.cc
浏览文件 @ c2f0e9c4
......@@ -1099,7 +1099,7 @@ void ScatterNdAddInferMeta(const MetaTensor& x,
const auto& ref_dims = x.dims();
auto ref_dims_size = ref_dims.size();
const auto& index_dims = index.dims();
auto index_dims_size = index_dims.size();
int index_dims_size = static_cast<int>(index_dims.size());
const auto& updates_dims = updates.dims();
auto updates_dims_size = updates_dims.size();
......@@ -1135,10 +1135,12 @@ void ScatterNdAddInferMeta(const MetaTensor& x,
// update.shape = index.shape[:-1] + output.shape[index.shape[-1]:]
std::vector<int64_t> r_updates_dims;
for (int64_t i = 0; i < index_dims_size - 1; ++i) {
for (int i = 0; i < index_dims_size - 1; ++i) {
r_updates_dims.emplace_back(index_dims[i]);
}
for (int64_t i = index_dims[index_dims_size - 1]; i < ref_dims_size; ++i) {
for (int i = static_cast<int>(index_dims[index_dims_size - 1]);
i < ref_dims_size;
++i) {
r_updates_dims.emplace_back(ref_dims[i]);
}
// check for non-0d updates
......@@ -1265,11 +1267,11 @@ void SpectralNormInferMeta(const MetaTensor& weight,
"Attr(power_iters) should be greater equal then 0, but received %d",
power_iters));
int h = dim_weight[dim];
int h = static_cast<int>(dim_weight[dim]);
int w = 1;
for (int i = 0; i < rank_weight; i++) {
if (i != dim) {
w *= dim_weight[i];
w *= static_cast<int>(dim_weight[i]);
}
}
auto dim_u = u.dims();
......
paddle/phi/infermeta/unary.cc
浏览文件 @ c2f0e9c4
......@@ -221,7 +221,7 @@ void ArgMinMaxInferMeta(const MetaTensor& x,
if (flatten) {
all_element_num = phi::product(x_dims);
} else {
all_element_num = x_dims[int_axis];
all_element_num = x_dims[static_cast<int>(int_axis)];
}
PADDLE_ENFORCE_LE(
all_element_num,
......@@ -243,11 +243,13 @@ void ArgMinMaxInferMeta(const MetaTensor& x,
vec = {};
}
} else {
for (int64_t i = 0; i < int_axis; i++) vec.emplace_back(x_dims[i]);
for (int64_t i = 0; i < int_axis; i++)
vec.emplace_back(x_dims[static_cast<int>(i)]);
if (keepdims) {
vec.emplace_back(static_cast<int64_t>(1));
}
for (int64_t i = int_axis + 1; i < x_rank; i++) vec.emplace_back(x_dims[i]);
for (int64_t i = int_axis + 1; i < x_rank; i++)
vec.emplace_back(x_dims[static_cast<int>(i)]);
}
out->set_dims(phi::make_ddim(vec));
......@@ -314,7 +316,7 @@ void AsComplexInferMeta(const MetaTensor& input, MetaTensor* output) {
"Expected the rank of input(X) to be equal to or greater than 1."
"But received rank of input(X) = %d",
input_rank));
const int last_dim_size = in_dims[input_rank - 1];
const int last_dim_size = static_cast<int>(in_dims[input_rank - 1]);
PADDLE_ENFORCE_EQ(
last_dim_size,
2,
......@@ -675,7 +677,7 @@ void CropInferMeta(const MetaTensor& x,
}
auto out_dims = std::vector<int64_t>(shape.size(), -1);
for (size_t i = 0; i < shape_dims.size(); ++i) {
for (int i = 0; i < static_cast<int>(shape_dims.size()); ++i) {
if (shape_dims[i] > 0) {
out_dims[i] = static_cast<int64_t>(shape_dims[i]);
} else {
......@@ -763,7 +765,7 @@ void DiagEmbedInferMeta(
dim1,
dim2));
int new_dim_len = offset_ + x_dims[x_dims.size() - 1];
int new_dim_len = static_cast<int>(offset_ + x_dims[x_dims.size() - 1]);
auto sizes = vectorize(x_dims);
sizes.pop_back();
sizes.insert(sizes.begin() + std::min(dim1_, dim2_), new_dim_len);
......@@ -1171,11 +1173,11 @@ void ExpandInferMeta(const MetaTensor& x,
auto out_rank =
std::max(static_cast<size_t>(x_dims.size()), expand_shape.size());
std::vector<int64_t> out_shape(out_rank);
for (size_t i = 0; i < expand_shape.size(); ++i) {
for (int i = 0; i < static_cast<int>(expand_shape.size()); ++i) {
if (x_dims[i] == -1) {
out_shape[i] = -1;
} else if (expand_shape[i] == -1) {
if (static_cast<size_t>(x_dims.size()) > i) {
if (static_cast<int>(x_dims.size()) > i) {
out_shape[i] = x_dims[i];
} else {
out_shape[i] = -1;
......@@ -1257,7 +1259,7 @@ void FFTC2RInferMeta(const MetaTensor& x,
out,
phi::errors::InvalidArgument("Output of fft_c2r should not be null."));
const phi::DDim x_dim = x.dims();
const int64_t last_fft_axis = axes.back();
const int last_fft_axis = static_cast<int>(axes.back());
// only ensure that fft axes' size greater than zero at runtime
// they might be -1 to indicate unknown size ar compile time
......@@ -1322,7 +1324,7 @@ void FFTR2CInferMeta(const MetaTensor& x,
out->share_dims(x);
} else {
phi::DDim out_dim = x.dims();
const int64_t last_fft_axis = axes.back();
const int last_fft_axis = static_cast<int>(axes.back());
const int64_t last_fft_dim_size = x_dim[last_fft_axis];
out_dim.at(last_fft_axis) = last_fft_dim_size / 2 + 1;
out->set_dims(out_dim);
......@@ -1379,7 +1381,7 @@ void FlattenWithXShapeInferMeta(const MetaTensor& x,
out_shape.reserve(in_dims_size - stop_axis + start_axis + 1);
for (int i = 0; i < start_axis; ++i) {
out_shape.push_back(x_dims[i]);
out_shape.push_back(x_dims[i]); // NOLINT
}
for (int i = start_axis; i <= stop_axis; i++) {
if (x_dims[i] == -1 || outer == -1) {
......@@ -1388,9 +1390,9 @@ void FlattenWithXShapeInferMeta(const MetaTensor& x,
outer *= x_dims[i];
}
}
out_shape.push_back(outer);
out_shape.push_back(outer); // NOLINT
for (int i = stop_axis + 1; i < in_dims_size; i++) {
out_shape.push_back(x_dims[i]);
out_shape.push_back(x_dims[i]); // NOLINT
}
const auto& out_dims = phi::make_ddim(out_shape);
out->set_dims(out_dims);
......@@ -1600,9 +1602,10 @@ void FoldInferMeta(const MetaTensor& x,
std::vector<int> out_dims;
// batch_size
out_dims.push_back(in_dims[0]);
out_dims.push_back(in_dims[0]); // NOLINT
// output_plane
int output_channels = in_dims[1] / (kernel_width * kernel_height);
int output_channels =
static_cast<int>(in_dims[1] / (kernel_width * kernel_height));
out_dims.push_back(output_channels);
int blocks_height = (output_sizes[0] + 2 * paddings[0] -
......@@ -1730,11 +1733,11 @@ void FrameInferMeta(const MetaTensor& x,
int end_axis;
if (axis == 0) {
seq_length = x_dims[0];
seq_length = static_cast<int>(x_dims[0]);
start_axis = 1;
end_axis = x_rank - 1;
} else {
seq_length = x_dims[x_rank - 1];
seq_length = static_cast<int>(x_dims[x_rank - 1]);
start_axis = 0;
end_axis = x_rank - 2;
}
......@@ -1859,11 +1862,11 @@ static phi::DDim ValidateShape(const std::vector<int64_t> shape,
"be -1. But received shape = [%s], shape[%d] is also -1.",
phi::make_ddim(shape),
i));
unk_dim_idx = i;
unk_dim_idx = static_cast<int>(i);
output_shape[i] = shape[i];
} else if (shape[i] == 0) {
if (static_cast<int>(i) < in_dims.size()) {
output_shape[i] = in_dims[i];
output_shape[i] = in_dims[static_cast<int>(i)];
} else {
PADDLE_ENFORCE_EQ(
in_size,
......@@ -1966,7 +1969,7 @@ void InferMetaFromVecValue(const MetaTensor& x,
void InverseInferMeta(const MetaTensor& x, MetaTensor* out) {
auto input_dims = x.dims();
int64_t input_rank = input_dims.size();
int input_rank = static_cast<int>(input_dims.size());
PADDLE_ENFORCE_GE(
input_rank,
2,
......@@ -1975,7 +1978,7 @@ void InverseInferMeta(const MetaTensor& x, MetaTensor* out) {
"But received: Input(Input)'s dimension = %d, shape = [%s].",
input_rank,
input_dims));
for (int64_t i = 0; i < input_rank; ++i) {
for (int i = 0; i < input_rank; ++i) {
PADDLE_ENFORCE_EQ(
(input_dims[i] == -1) || (input_dims[i] > 0),
true,
......@@ -2065,13 +2068,13 @@ void KthvalueInferMeta(const MetaTensor& x,
axis));
}
std::vector<int64_t> dimvec;
for (int64_t i = 0; i < axis; i++) {
for (int i = 0; i < axis; i++) {
dimvec.emplace_back(input_dims[i]);
}
if (keepdim && dim_size > 0) {
dimvec.emplace_back(static_cast<int64_t>(1));
}
for (int64_t i = axis + 1; i < dim_size; i++) {
for (int i = axis + 1; i < dim_size; i++) {
dimvec.emplace_back(input_dims[i]);
}
DDim dims = phi::make_ddim(dimvec);
......@@ -2148,8 +2151,8 @@ void LUInferMeta(const MetaTensor& x,
phi::errors::InvalidArgument("The rank of input must greater than 2."));
out->set_dims(x_dims);
out->set_dtype(x.dtype());
int m = x_dims[x_rank - 1];
int n = x_dims[x_rank - 2];
int m = static_cast<int>(x_dims[x_rank - 1]);
int n = static_cast<int>(x_dims[x_rank - 2]);
int min_mn = std::min(m, n);
auto dims_vec = phi::vectorize(x_dims);
PADDLE_ENFORCE_NOT_NULL(
......@@ -2187,8 +2190,8 @@ void MatrixRankInferMeta(const MetaTensor& x,
"The dims of input must be greater than 2."));
if (hermitian) {
int rows = dim_x[dim_x.size() - 2];
int cols = dim_x[dim_x.size() - 1];
int rows = static_cast<int>(dim_x[dim_x.size() - 2]);
int cols = static_cast<int>(dim_x[dim_x.size() - 1]);
PADDLE_ENFORCE_EQ(rows,
cols,
phi::errors::InvalidArgument(
......@@ -2268,7 +2271,7 @@ void MaxPoolWithIndexInferMeta(const MetaTensor& x,
if (global_pooling) {
kernel_size_.resize(static_cast<size_t>(x_dims.size()) - 2);
for (size_t i = 0; i < kernel_size_.size(); ++i) {
for (int i = 0; i < static_cast<int>(kernel_size_.size()); ++i) {
paddings_[i] = 0;
kernel_size_[i] = static_cast<int>(x_dims[i + 2]);
}
......@@ -2301,12 +2304,15 @@ void MaxPoolWithIndexInferMeta(const MetaTensor& x,
output_shape.insert(
output_shape.end(), kernel_size_.begin(), kernel_size_.end());
} else {
for (size_t i = 0; i < kernel_size_.size(); ++i) {
for (int i = 0; i < static_cast<int>(kernel_size_.size()); ++i) {
if ((!config.is_runtime) && (x_dims[i + 2] < 0)) {
output_shape.push_back(x_dims[i + 2]);
} else {
output_shape.push_back(funcs::MaxPoolOutputSize(
x_dims[i + 2], kernel_size_[i], paddings_[i], strides[i]));
output_shape.push_back(
funcs::MaxPoolOutputSize(static_cast<int>(x_dims[i + 2]),
kernel_size_[i],
paddings_[i],
strides[i]));
}
}
}
......@@ -2365,13 +2371,13 @@ void ModeInferMeta(const MetaTensor& x,
}
std::vector<int64_t> dimvec;
for (int64_t i = 0; i < axis; i++) {
for (int i = 0; i < axis; i++) {
dimvec.emplace_back(input_dims[i]);
}
if (keepdim && dim_size > 0) {
dimvec.emplace_back(static_cast<int64_t>(1));
}
for (int64_t i = axis + 1; i < dim_size; i++) {
for (int i = axis + 1; i < dim_size; i++) {
dimvec.emplace_back(input_dims[i]);
}
DDim dims = phi::make_ddim(dimvec);
......@@ -2391,7 +2397,7 @@ void MultinomialInferMeta(const MetaTensor& x,
MetaConfig config) {
auto int_num_samples = num_samples.to<int>();
auto x_dim = x.dims();
int64_t x_rank = x_dim.size();
int x_rank = static_cast<int>(x_dim.size());
PADDLE_ENFORCE_GT(x_rank,
0,
errors::InvalidArgument(
......@@ -2406,7 +2412,7 @@ void MultinomialInferMeta(const MetaTensor& x,
x_rank));
std::vector<int64_t> out_dims(x_rank);
for (int64_t i = 0; i < x_rank - 1; i++) {
for (int i = 0; i < x_rank - 1; i++) {
out_dims[i] = x_dim[i];
}
......@@ -2484,7 +2490,7 @@ void NanmedianInferMeta(const MetaTensor& x,
for (int64_t i = 0; i < x_rank; i++) {
if (std::find(formated_axis.begin(), formated_axis.end(), i) ==
formated_axis.end()) {
out_dim.push_back(x_dim[i]);
out_dim.push_back(x_dim[i]); // NOLINT
} else if (keep_dim) {
out_dim.push_back(1);
}
......@@ -2611,13 +2617,13 @@ void OverlapAddInferMeta(const MetaTensor& x,
int start_axis;
int end_axis;
if (axis == 0) {
n_frames = x_dims[0];
frame_length = x_dims[1];
n_frames = static_cast<int>(x_dims[0]);
frame_length = static_cast<int>(x_dims[1]);
start_axis = 2;
end_axis = x_rank - 1;
} else {
n_frames = x_dims[x_rank - 1];
frame_length = x_dims[x_rank - 2];
n_frames = static_cast<int>(x_dims[x_rank - 1]);
frame_length = static_cast<int>(x_dims[x_rank - 2]);
start_axis = 0;
end_axis = x_rank - 3;
}
......@@ -3127,12 +3133,13 @@ void PoolInferMeta(const MetaTensor& x,
if ((!config.is_runtime) && (data_dims[i] < 0)) {
output_shape.push_back(data_dims[i]);
} else {
output_shape.push_back(funcs::PoolOutputSize(data_dims[i],
kernel_size_[i],
paddings_[2 * i],
paddings_[2 * i + 1],
strides[i],
ceil_mode));
output_shape.push_back(
funcs::PoolOutputSize(static_cast<int>(data_dims[i]),
kernel_size_[i],
paddings_[2 * i],
paddings_[2 * i + 1],
strides[i],
ceil_mode));
}
}
}
......@@ -3169,8 +3176,8 @@ void QrInferMeta(const MetaTensor& x,
phi::errors::InvalidArgument("the rank of input must greater than 2"));
bool compute_q;
bool reduced_mode;
int m = x_dims[x_rank - 2];
int n = x_dims[x_rank - 1];
int m = static_cast<int>(x_dims[x_rank - 2]);
int n = static_cast<int>(x_dims[x_rank - 1]);
int min_mn = std::min(m, n);
std::tie(compute_q, reduced_mode) = phi::funcs::ParseQrMode(mode);
......@@ -3199,7 +3206,7 @@ DDim ReduceInferDim(const MetaTensor& x,
const std::vector<int64_t>& axis,
bool keep_dim,
bool reduce_all) {
auto x_rank = x.dims().size();
int x_rank = x.dims().size();
std::vector<int64_t> formated_axis = axis;
for (size_t i = 0; i < axis.size(); ++i) {
......@@ -3246,7 +3253,7 @@ DDim ReduceInferDim(const MetaTensor& x,
reduce_all = reduce_all || full_dim;
std::vector<int64_t> out_dim_vector;
for (int64_t i = 0; i < x_rank; ++i) {
for (int i = 0; i < x_rank; ++i) {
if (reduce_all || dims_set.find(i) != dims_set.end()) {
if (keep_dim) {
out_dim_vector.push_back(1);
......@@ -3452,7 +3459,7 @@ void ReverseInferMeta(const MetaTensor& x,
true,
phi::errors::InvalidArgument("'axis' can not be empty."));
const auto& x_dims = x.dims();
for (int a : axis_data) {
for (int64_t a : axis_data) {
PADDLE_ENFORCE_LT(a,
x_dims.size(),
phi::errors::OutOfRange(
......@@ -3827,14 +3834,14 @@ void SplitInferMeta(const MetaTensor& x,
int num_of_unknow = 0;
int sum_of_section = 0;
for (size_t i = 0; i < sections_data.size(); ++i) {
for (int i = 0; i < static_cast<int>(sections_data.size()); ++i) {
sections_vec.push_back(sections_data[i]);
if (sections_data[i] == unknow_dim_val) {
num_of_unknow++;
unknow_dim_idx = i;
} else {
sum_of_section += sections_data[i];
sum_of_section += static_cast<int>(sections_data[i]);
}
}
......@@ -3958,7 +3965,7 @@ void SqueezeInferMeta(const MetaTensor& x,
if (!config.is_runtime && axes.FromTensor()) {
// compile time infershape, set all elements to -1.
int output_size = x.dims().size() - axes.GetData().size();
int output_size = static_cast<int>(x.dims().size() - axes.GetData().size());
if (x.dims().size() == 0 && output_size == -1) {
output_size = 0;
}
......@@ -3970,7 +3977,7 @@ void SqueezeInferMeta(const MetaTensor& x,
tmp.reserve(axes.GetData().size());
std::for_each(axes.GetData().begin(),
axes.GetData().end(),
[&tmp](const int64_t& t) { tmp.push_back(t); });
[&tmp](const int64_t& t) { tmp.push_back(t); }); // NOLINT
auto out_dims = funcs::GetOutputSqueezeShape(tmp, x_dims, false);
out->set_dims(out_dims);
if (x_dims[0] == out_dims[0]) {
......@@ -4221,8 +4228,8 @@ void SvdInferMeta(const MetaTensor& x,
in_dims.size(),
2,
phi::errors::InvalidArgument("the rank of input must greater than 2"));
int m = in_dims[x_rank - 2];
int n = in_dims[x_rank - 1];
int m = static_cast<int>(in_dims[x_rank - 2]);
int n = static_cast<int>(in_dims[x_rank - 1]);
int k = std::min(m, n);
u->set_dims(!full_matrices ? UDDim(in_dims, k) : UDDim(in_dims, m));
vh->set_dims(!full_matrices ? VHDDim(in_dims, k) : VHDDim(in_dims, n));
......@@ -4495,7 +4502,7 @@ void TransposeInferMeta(const MetaTensor& x,
MetaTensor* out) {
auto x_dims = x.dims();
int x_rank = x_dims.size();
int axis_size = axis.size();
int axis_size = static_cast<int>(axis.size());
// Note: x_rank > axis_size when fuse squeeze2 + transpose2, else x_rank ==
// axis_size
......@@ -4572,7 +4579,7 @@ void UnbindInferMeta(const MetaTensor& x,
axis = axis < 0 ? in_dims.size() + axis : axis;
for (int i = 0; i < in_dims.size(); ++i) {
if (i != axis) out_dim.push_back(in_dims[i]);
if (i != axis) out_dim.push_back(in_dims[i]); // NOLINT
}
auto out_dims = phi::make_ddim(out_dim);
......@@ -4746,17 +4753,18 @@ void UnfoldInferMeta(const MetaTensor& x,
dilations[1]));
std::vector<int> out_dims;
out_dims.push_back(in_dims[0]);
int output_channels = in_dims[1] * kernel_sizes[0] * kernel_sizes[1];
out_dims.push_back(in_dims[0]); // NOLINT
int output_channels =
static_cast<int>(in_dims[1] * kernel_sizes[0] * kernel_sizes[1]);
out_dims.push_back(output_channels);
int output_height = phi::funcs::CalcOutputSize(in_dims[2],
int output_height = phi::funcs::CalcOutputSize(static_cast<int>(in_dims[2]),
kernel_sizes[0],
dilations[0],
paddings[0],
paddings[2],
strides[0]);
int output_width = phi::funcs::CalcOutputSize(in_dims[3],
int output_width = phi::funcs::CalcOutputSize(static_cast<int>(in_dims[3]),
kernel_sizes[1],
dilations[1],
paddings[1],
......@@ -5028,7 +5036,7 @@ void UnsqueezeInferMeta(const MetaTensor& x,
"should be in the range of [1, 6] (Eigen limit)"));
if (!config.is_runtime && axes.FromTensor()) {
// compile time infershape. set all elements to -1.
int output_size = x.dims().size() + axes.GetData().size();
int output_size = static_cast<int>(x.dims().size() + axes.GetData().size());
std::vector<int64_t> vec_out_dims(output_size, -1);
out->set_dtype(x.dtype());
out->set_dims(phi::make_ddim(vec_out_dims));
......
paddle/phi/kernels/cpu/lars_momentum_kernel.cc
浏览文件 @ c2f0e9c4
......@@ -36,7 +36,7 @@ void LarsMomentumKernel(
std::vector<DenseTensor*> param_out,
std::vector<DenseTensor*> velocity_out,
std::vector<DenseTensor*> master_param_out) {
int op_num = param.size();
int op_num = static_cast<int>(param.size());
T mu_ = static_cast<T>(mu);
for (int i = 0; i < op_num; ++i) {
auto* lr = learning_rate[i]->data<T>();
......
paddle/phi/kernels/cpu/prelu_kernel.cc
浏览文件 @ c2f0e9c4
......@@ -30,7 +30,7 @@ void PReluKernel(const Context& dev_ctx,
const T* alpha_ptr = alpha.data<T>();
T* o_ptr = dev_ctx.template Alloc<T>(out);
int numel = x.numel();
int numel = static_cast<int>(x.numel());
auto dim = x.dims();
int index = 0;
int i = 0;
......@@ -38,22 +38,22 @@ void PReluKernel(const Context& dev_ctx,
if (data_format == "NCHW") {
int temp = 1;
for (int j = 2; j < dim.size(); j++) {
temp *= dim[j];
temp *= static_cast<int>(dim[j]);
}
for (i = 0; i < numel; i++) {
index = (i / temp) % dim[1];
index = static_cast<int>((i / temp) % dim[1]);
o_ptr[i] = x_ptr[i] > 0 ? x_ptr[i] : alpha_ptr[index] * x_ptr[i];
}
} else {
for (i = 0; i < numel; i++) {
index = i % dim[dim.size() - 1];
index = static_cast<int>(i % dim[dim.size() - 1]);
o_ptr[i] = x_ptr[i] > 0 ? x_ptr[i] : alpha_ptr[index] * x_ptr[i];
}
}
} else if (mode == "element") {
int temp = 1;
for (int j = 1; j < dim.size(); j++) {
temp *= dim[j];
temp *= static_cast<int>(dim[j]);
}
for (i = 0; i < numel; i++) {
index = i % temp;
......
paddle/phi/kernels/cpu/prior_box_kernel.cc
浏览文件 @ c2f0e9c4
......@@ -57,9 +57,10 @@ void PriorBoxKernel(const Context& ctx,
step_height = new_step_h;
}
int num_priors = new_aspect_ratios.size() * min_sizes.size();
int num_priors =
static_cast<int>(new_aspect_ratios.size() * min_sizes.size());
if (!max_sizes.empty()) {
num_priors += max_sizes.size();
num_priors += static_cast<int>(max_sizes.size());
}
ctx.template Alloc<T>(out);
......@@ -148,7 +149,7 @@ void PriorBoxKernel(const Context& ctx,
var_et(0, i) = variances[i];
}
int box_num = feature_height * feature_width * num_priors;
int box_num = static_cast<int>(feature_height * feature_width * num_priors);
auto var_dim = var->dims();
var->Resize({box_num, static_cast<int>(variances.size())});
......
paddle/phi/kernels/cpu/psroi_pool_grad_kernel.cc
浏览文件 @ c2f0e9c4
......@@ -34,10 +34,10 @@ void PsroiPoolGradKernel(const Context& ctx,
DenseTensor* dx) {
if (dx) {
const auto& in_dims = x.dims();
int input_channels = in_dims[1];
int height = in_dims[2];
int width = in_dims[3];
int rois_num_t = rois.dims()[0];
int input_channels = static_cast<int>(in_dims[1]);
int height = static_cast<int>(in_dims[2]);
int width = static_cast<int>(in_dims[3]);
int rois_num_t = static_cast<int>(rois.dims()[0]);
// set roi batch id
DenseTensor rois_batch_id_list;
......@@ -45,7 +45,7 @@ void PsroiPoolGradKernel(const Context& ctx,
int* rois_batch_id_data = ctx.template Alloc<int>(&rois_batch_id_list);
int rois_batch_size;
if (rois_num.get_ptr()) {
rois_batch_size = rois_num->numel();
rois_batch_size = static_cast<int>(rois_num->numel());
auto* rois_num_t_data = rois_num->data<int>();
int start = 0;
for (int n = 0; n < rois_batch_size; ++n) {
......@@ -56,7 +56,7 @@ void PsroiPoolGradKernel(const Context& ctx,
}
} else {
auto rois_lod = rois.lod().back();
rois_batch_size = rois_lod.size() - 1;
rois_batch_size = static_cast<int>(rois_lod.size()) - 1;
// calculate batch id index for each roi according to LoD
for (int n = 0; n < rois_batch_size; ++n) {
for (size_t i = rois_lod[n]; i < rois_lod[n + 1]; ++i) {
......@@ -73,7 +73,7 @@ void PsroiPoolGradKernel(const Context& ctx,
set_zero(ctx, dx, static_cast<T>(0));
// backpropagate gradient per output pixel
int dout_size = dout.numel();
int dout_size = static_cast<int>(dout.numel());
for (int i = 0; i < dout_size; ++i) {
// The output is in order (n, c, ph, pw)
int pw = i % pooled_width;
......
paddle/phi/kernels/cpu/psroi_pool_kernel.cc
浏览文件 @ c2f0e9c4
......@@ -31,11 +31,11 @@ void PsroiPoolKernel(const Context& ctx,
float spatial_scale,
DenseTensor* out) {
auto in_dims = x.dims();
int batch_size = in_dims[0];
int input_channels = in_dims[1];
int height = in_dims[2];
int width = in_dims[3];
int rois_num_t = rois.dims()[0];
int batch_size = static_cast<int>(in_dims[0]);
int input_channels = static_cast<int>(in_dims[1]);
int height = static_cast<int>(in_dims[2]);
int width = static_cast<int>(in_dims[3]);
int rois_num_t = static_cast<int>(rois.dims()[0]);
PADDLE_ENFORCE_EQ(input_channels,
output_channels * pooled_height * pooled_width,
......@@ -55,7 +55,7 @@ void PsroiPoolKernel(const Context& ctx,
int rois_batch_size;
if (rois_num.get_ptr()) {
rois_batch_size = rois_num->numel();
rois_batch_size = static_cast<int>(rois_num->numel());
auto* rois_num_data = rois_num->data<int>();
PADDLE_ENFORCE_EQ(
rois_batch_size,
......@@ -84,13 +84,13 @@ void PsroiPoolKernel(const Context& ctx,
}
} else {
auto rois_lod = rois.lod().back();
rois_batch_size = rois_lod.size() - 1;
rois_batch_size = static_cast<int>(rois_lod.size()) - 1;
PADDLE_ENFORCE_EQ(
rois_batch_size,
batch_size,
errors::InvalidArgument("the rois_batch_size and input(X) "
"batch_size should be the same."));
int rois_num_with_lod = rois_lod[rois_batch_size];
int rois_num_with_lod = static_cast<int>(rois_lod[rois_batch_size]);
PADDLE_ENFORCE_EQ(rois_num_with_lod,
rois_num_t,
errors::InvalidArgument(
......@@ -127,12 +127,14 @@ void PsroiPoolKernel(const Context& ctx,
T bin_size_w = roi_width / static_cast<T>(pooled_width);
// calculate each pixel of the output feature map.
int out_roi_offset = n * out_stride[0];
int out_roi_offset = static_cast<int>(n * out_stride[0]);
for (int c = 0; c < output_channels; ++c) {
// per category
int out_plane_offset = out_roi_offset + c * out_stride[1];
int out_plane_offset =
static_cast<int>(out_roi_offset + c * out_stride[1]);
for (int ph = 0; ph < pooled_height; ++ph) {
int out_row_offset = out_plane_offset + ph * out_stride[2];
int out_row_offset =
static_cast<int>(out_plane_offset + ph * out_stride[2]);
for (int pw = 0; pw < pooled_width; ++pw) {
// calculate w and h at input feature map
int hstart = floor(static_cast<T>(ph) * bin_size_h + roi_start_h);
......@@ -147,14 +149,14 @@ void PsroiPoolKernel(const Context& ctx,
int output_index = out_row_offset + pw;
int input_channel = (c * pooled_height + ph) * pooled_width + pw;
int input_plane_offset =
roi_batch_id * in_stride[0] + input_channel * in_stride[1];
int input_plane_offset = static_cast<int>(
roi_batch_id * in_stride[0] + input_channel * in_stride[1]);
const T* offset_input_data = input_data + input_plane_offset;
T out_sum = 0.;
bool is_empty = (hend <= hstart) || (wend <= wstart);
for (int ih = hstart; ih < hend; ++ih) {
for (int iw = wstart; iw < wend; ++iw) {
int input_index = ih * in_stride[2] + iw;
int input_index = static_cast<int>(ih * in_stride[2] + iw);
out_sum += offset_input_data[input_index];
}
}
......
paddle/phi/kernels/cpu/qr_kernel.cc
浏览文件 @ c2f0e9c4
......@@ -37,11 +37,11 @@ void QrKernel(const Context& ctx,
numel, 0, errors::PreconditionNotMet("The input of QR is empty."));
auto x_dims = x.dims();
int x_rank = x_dims.size();
int m = x_dims[x_rank - 2];
int n = x_dims[x_rank - 1];
int m = static_cast<int>(x_dims[x_rank - 2]);
int n = static_cast<int>(x_dims[x_rank - 1]);
int min_mn = std::min(m, n);
int k = reduced_mode ? min_mn : m;
int batch_size = numel / (m * n);
int batch_size = static_cast<int>(numel / (m * n));
int x_stride = m * n;
int q_stride = m * k;
int r_stride = k * n;
......
paddle/phi/kernels/cpu/rnn_grad_kernel.cc
浏览文件 @ c2f0e9c4
......@@ -83,8 +83,8 @@ struct GradCell {
auto& place = *dev_ctx.eigen_device();
auto mask = EigenMatrix<T>::From(
mask_tensor, phi::make_ddim({mask_tensor.dims()[1], 1}));
auto mask_broadcast =
mask.broadcast(Eigen::DSizes<int, 2>(1, grad_pre_hidden->dims()[2]));
auto mask_broadcast = mask.broadcast(Eigen::DSizes<int, 2>(
1, static_cast<int>(grad_pre_hidden->dims()[2])));
auto pre_hidden_grad = EigenMatrix<T>::Reshape(
*grad_pre_hidden, grad_pre_hidden->dims().size() - 1);
auto pre_hidden_bak_grad = EigenMatrix<T>::Reshape(
......@@ -599,8 +599,8 @@ struct GradLayer {
auto& place = *dev_ctx.eigen_device();
auto mask = EigenMatrix<T>::From(
mask_tensor, phi::make_ddim({mask_tensor.dims()[1], 1}));
auto mask_broadcast =
mask.broadcast(Eigen::DSizes<int, 2>(1, grad_output->dims()[2]));
auto mask_broadcast = mask.broadcast(
Eigen::DSizes<int, 2>(1, static_cast<int>(grad_output->dims()[2])));
auto last_h_grad =
EigenMatrix<T>::Reshape(*grad_last_h, grad_last_h->dims().size() - 1);
......@@ -716,8 +716,8 @@ struct SingleGradLayer : GradLayer<T, GradCellType> {
phi::funcs::SetConstant<CPUContext, T> zero;
zero(dev_ctx, input_grad, static_cast<T>(0.0));
int time_step = input->dims()[0];
int batch_size = input->dims()[1];
int time_step = static_cast<int>(input->dims()[0]);
int batch_size = static_cast<int>(input->dims()[1]);
int direction_num = is_bidirec ? 2 : 1;
// in this section, create the gate_state_grad for the postprocess calculate
......@@ -825,8 +825,8 @@ struct BidirGradLayer : GradLayer<T, GradCellType> {
int hidden_size,
const std::string& mode,
int gate_num) {
int time_step = input->dims()[0];
int batch_size = input->dims()[1];
int time_step = static_cast<int>(input->dims()[0]);
int batch_size = static_cast<int>(input->dims()[1]);
int direction_num = is_bidirec ? 2 : 1;
// split the output two tensor to output_forward, output_backward
phi::funcs::SetConstant<CPUContext, T> zero;
......@@ -1009,8 +1009,8 @@ void RnnGradFunc(const CPUContext& dev_ctx,
}
// get the input_size, batch_size, time_step
const int time_step = x.dims()[0];
const int batch_size = x.dims()[1];
const int time_step = static_cast<int>(x.dims()[0]);
const int batch_size = static_cast<int>(x.dims()[1]);
const int direction_num = is_bidirec ? 2 : 1;
// allocate the memory and initization the x_grad
......
paddle/phi/kernels/cpu/rnn_kernel.cc
浏览文件 @ c2f0e9c4
......@@ -218,7 +218,7 @@ struct Layer {
bool is_test,
DenseTensor* cache_input) {
// crate the temp input for the X * W_ih^T + Bias_ih
const int& hidden_size = weight.dims()[0];
const int& hidden_size = weight.dims()[0]; // NOLINT
cache_input->Resize(
phi::make_ddim({input.dims()[0], input.dims()[1], hidden_size}));
if (is_test) {
......@@ -242,8 +242,8 @@ struct Layer {
EigenMatrix<T>::Reshape(*cache_input, cache_input->dims().size() - 1);
auto bias_ih_tmp =
EigenMatrix<T>::From(bias_ih, phi::make_ddim({1, bias_ih.dims()[0]}));
const int row_num =
phi::product(cache_input->dims()) / cache_input->dims()[2];
const int row_num = static_cast<int>(phi::product(cache_input->dims()) /
cache_input->dims()[2]);
in = in + bias_ih_tmp.broadcast(Eigen::DSizes<int, 2>(row_num, 1));
if (is_gru(mode)) {
// reset_gate update_gate cell_gate = [1, 1, 0]
......@@ -279,8 +279,8 @@ struct Layer {
mask_tensor, phi::make_ddim({mask_tensor.dims()[1], 1}));
auto pre_h = EigenMatrix<T>::Reshape(*init_h, init_h->dims().size() - 1);
auto curr_h = EigenMatrix<T>::Reshape(*last_h, last_h->dims().size() - 1);
auto mask_broadcast =
mask.broadcast(Eigen::DSizes<int, 2>(1, output->dims()[2]));
auto mask_broadcast = mask.broadcast(
Eigen::DSizes<int, 2>(1, static_cast<int>(output->dims()[2])));
curr_h.device(place) = out * mask_broadcast + pre_h * (1 - mask_broadcast);
out.device(place) = out * mask_broadcast;
......@@ -332,7 +332,7 @@ struct Layer {
is_reverse = true;
}
}
const int time_step = input->dims()[0];
const int time_step = static_cast<int>(input->dims()[0]);
this->preprocess(dev_ctx,
*input,
vec[0 + offset * 4],
......@@ -532,7 +532,7 @@ struct Layer {
is_reverse = true;
}
}
const int time_step = input->dims()[0];
const int time_step = static_cast<int>(input->dims()[0]);
this->preprocess(dev_ctx,
*input,
vec[0 + offset * 4],
......@@ -749,9 +749,9 @@ struct BidirLayer : public Layer<T, CellType> {
std::vector<DenseTensor> output_vec(2);
DenseTensor forward_input_w, forward_cell_value, forward_cell_act_value;
DenseTensor backward_input_w, backward_cell_value, backward_cell_act_value;
int time_step = input->dims()[0];
int batch_size = input->dims()[1];
int hidden_size = output->dims()[2];
int time_step = static_cast<int>(input->dims()[0]);
int batch_size = static_cast<int>(input->dims()[1]);
int hidden_size = static_cast<int>(output->dims()[2]);
for (int i = 0; i < 2; ++i) {
output_vec[i].Resize({time_step, batch_size, hidden_size / 2});
dev_ctx.Alloc<T>(&output_vec[i]);
......
paddle/phi/kernels/cpu/roi_align_grad_kernel.cc
浏览文件 @ c2f0e9c4
......@@ -85,7 +85,7 @@ void RoiAlignGradKernel(const Context& dev_ctx,
int channels = in_dims[1];
int height = in_dims[2];
int width = in_dims[3];
int rois_num = boxes.dims()[0];
int rois_num = static_cast<int>(boxes.dims()[0]);
if (!dx) {
return;
......@@ -96,7 +96,7 @@ void RoiAlignGradKernel(const Context& dev_ctx,
int boxes_batch_size;
if (boxes_num) {
boxes_batch_size = boxes_num->numel();
boxes_batch_size = static_cast<int>(boxes_num->numel());
auto* boxes_num_data = boxes_num->data<int>();
int start = 0;
for (int n = 0; n < boxes_batch_size; ++n) {
......@@ -107,7 +107,7 @@ void RoiAlignGradKernel(const Context& dev_ctx,
}
} else {
auto boxes_lod = boxes.lod().back();
boxes_batch_size = boxes_lod.size() - 1;
boxes_batch_size = static_cast<int>(boxes_lod.size() - 1);
for (int n = 0; n < boxes_batch_size; ++n) {
for (std::size_t i = boxes_lod[n]; i < boxes_lod[n + 1]; ++i) {
box_batch_id_data[i] = n;
......@@ -119,7 +119,7 @@ void RoiAlignGradKernel(const Context& dev_ctx,
phi::funcs::SetConstant<Context, T> set_zero;
set_zero(dev_ctx, dx, static_cast<T>(0));
int output_grad_size = out_grad.numel();
int output_grad_size = static_cast<int>(out_grad.numel());
if ((!out_grad.IsInitialized()) || (output_grad_size <= 0)) {
return;
......
paddle/phi/kernels/cpu/roi_align_kernel.cc
浏览文件 @ c2f0e9c4
......@@ -187,11 +187,11 @@ void RoiAlignKernel(const Context& dev_ctx,
bool aligned,
DenseTensor* out) {
auto in_dims = x.dims();
int batch_size = in_dims[0];
int channels = in_dims[1];
int height = in_dims[2];
int width = in_dims[3];
int rois_num = boxes.dims()[0];
int batch_size = static_cast<int>(in_dims[0]);
int channels = static_cast<int>(in_dims[1]);
int height = static_cast<int>(in_dims[2]);
int width = static_cast<int>(in_dims[3]);
int rois_num = static_cast<int>(boxes.dims()[0]);
if (rois_num == 0) {
dev_ctx.template Alloc<T>(out);
......@@ -207,7 +207,7 @@ void RoiAlignKernel(const Context& dev_ctx,
int* roi_batch_id_data = roi_batch_id_list.data<int>();
int boxes_batch_size;
if (boxes_num) {
boxes_batch_size = boxes_num->numel();
boxes_batch_size = static_cast<int>(boxes_num->numel());
PADDLE_ENFORCE_EQ(
boxes_batch_size,
batch_size,
......@@ -233,7 +233,7 @@ void RoiAlignKernel(const Context& dev_ctx,
errors::InvalidArgument("Input(ROIs) Tensor of ROIAlignOp "
"does not contain LoD information."));
auto boxes_lod = lod.back();
int boxes_batch_size = boxes_lod.size() - 1;
int boxes_batch_size = static_cast<int>(boxes_lod.size() - 1);
PADDLE_ENFORCE_EQ(
boxes_batch_size,
batch_size,
......@@ -243,7 +243,7 @@ void RoiAlignKernel(const Context& dev_ctx,
"batch_size = %d",
boxes_batch_size,
batch_size));
int boxes_num_with_lod = boxes_lod[boxes_batch_size];
int boxes_num_with_lod = static_cast<int>(boxes_lod[boxes_batch_size]);
PADDLE_ENFORCE_EQ(
rois_num,
boxes_num_with_lod,
......
paddle/phi/kernels/cpu/roi_pool_grad_kernel.cc
浏览文件 @ c2f0e9c4
......@@ -33,13 +33,13 @@ void RoiPoolGradKernel(const Context& dev_ctx,
float spatial_scale,
DenseTensor* dx) {
if (dx) {
int rois_num = boxes.dims()[0];
int rois_num = static_cast<int>(boxes.dims()[0]);
DenseTensor box_batch_id_list = Empty<int>(dev_ctx, {rois_num});
int* box_batch_id_data = box_batch_id_list.data<int>();
int boxes_batch_size;
if (boxes_num) {
boxes_batch_size = boxes_num->numel();
boxes_batch_size = static_cast<int>(boxes_num->numel());
auto* boxes_num_data = boxes_num->data<int>();
int start = 0;
for (int n = 0; n < boxes_batch_size; ++n) {
......@@ -50,7 +50,7 @@ void RoiPoolGradKernel(const Context& dev_ctx,
}
} else {
auto boxes_lod = boxes.lod().back();
boxes_batch_size = boxes_lod.size() - 1;
boxes_batch_size = static_cast<int>(boxes_lod.size() - 1);
for (int n = 0; n < boxes_batch_size; ++n) {
for (size_t i = boxes_lod[n]; i < boxes_lod[n + 1]; ++i) {
box_batch_id_data[i] = n;
......@@ -71,7 +71,7 @@ void RoiPoolGradKernel(const Context& dev_ctx,
auto roi_stride = phi::stride(boxes.dims());
auto out_stride = phi::stride(out_grad.dims());
int channels = x.dims()[1];
int channels = static_cast<int>(x.dims()[1]);
for (int n = 0; n < rois_num; ++n) {
int roi_batch_idx = box_batch_id_data[n];
......
paddle/phi/kernels/cpu/roi_pool_kernel.cc
浏览文件 @ c2f0e9c4
......@@ -31,11 +31,11 @@ void RoiPoolKernel(const Context& dev_ctx,
DenseTensor* out,
DenseTensor* arg_max) {
auto x_dims = x.dims();
int batch_size = x_dims[0];
int channels = x_dims[1];
int height = x_dims[2];
int width = x_dims[3];
int rois_num = boxes.dims()[0];
int batch_size = static_cast<int>(x_dims[0]);
int channels = static_cast<int>(x_dims[1]);
int height = static_cast<int>(x_dims[2]);
int width = static_cast<int>(x_dims[3]);
int rois_num = static_cast<int>(boxes.dims()[0]);
if (rois_num == 0) {
dev_ctx.template Alloc<T>(out);
......@@ -54,7 +54,7 @@ void RoiPoolKernel(const Context& dev_ctx,
int boxes_batch_size;
if (boxes_num) {
boxes_batch_size = boxes_num->numel();
boxes_batch_size = static_cast<int>(boxes_num->numel());
PADDLE_ENFORCE_EQ(
boxes_batch_size,
batch_size,
......@@ -70,13 +70,13 @@ void RoiPoolKernel(const Context& dev_ctx,
}
} else {
auto boxes_lod = boxes.lod().back();
boxes_batch_size = boxes_lod.size() - 1;
boxes_batch_size = static_cast<int>(boxes_lod.size() - 1);
PADDLE_ENFORCE_EQ(
boxes_batch_size,
batch_size,
phi::errors::InvalidArgument("The boxes_batch_size and imgs "
"batch_size must be the same."));
int rois_num_with_lod = boxes_lod[boxes_batch_size];
int rois_num_with_lod = static_cast<int>(boxes_lod[boxes_batch_size]);
PADDLE_ENFORCE_EQ(
rois_num,
rois_num_with_lod,
......
paddle/phi/kernels/cpu/rrelu_grad_kernel.cc
浏览文件 @ c2f0e9c4
......@@ -28,7 +28,7 @@ void RReluGradKernel(const Context& dev_ctx,
const T* n_ptr = noise.data<T>();
const T* x_ptr = x.data<T>();
const T* out_grad_ptr = out_grad.data<T>();
int numel = x.numel();
int numel = static_cast<int>(x.numel());
if (!x_grad) return;
int i = 0;
......
paddle/phi/kernels/cpu/rrelu_kernel.cc
浏览文件 @ c2f0e9c4
......@@ -32,7 +32,7 @@ void RReluKernel(const Context& dev_ctx,
T* o_ptr = dev_ctx.template Alloc<T>(out);
T* n_ptr = dev_ctx.template Alloc<T>(noise);
T zero = static_cast<T>(0);
int numel = x.numel();
int numel = static_cast<int>(x.numel());
int i = 0;
if (is_test) {
......
paddle/phi/kernels/cpu/send_u_recv_grad_kernel.cc
浏览文件 @ c2f0e9c4
......@@ -79,7 +79,7 @@ void GraphSendRecvGradOpKernelLaunchHelper(
DenseTensor* x_grad,
const DenseTensor* dst_count = nullptr,
const DenseTensor* out = nullptr) {
const int& index_size = dst_index.dims()[0];
const int& index_size = dst_index.dims()[0]; // NOLINT
ctx.template Alloc<T>(x_grad);
T* p_output = x_grad->data<T>();
......
paddle/phi/kernels/cpu/send_u_recv_kernel.cc
浏览文件 @ c2f0e9c4
......@@ -86,7 +86,7 @@ void GraphSendRecvOpKernelLaunchHelper(const Context& ctx,
int64_t out_size,
DenseTensor* out,
DenseTensor* dst_count = nullptr) {
const int& index_size = src_index.dims()[0];
const int& index_size = src_index.dims()[0]; // NOLINT
const auto& src_dims = x.dims();
int64_t memset_size = 1;
......
paddle/phi/kernels/cpu/send_ue_recv_grad_kernel.cc
浏览文件 @ c2f0e9c4
......@@ -365,7 +365,7 @@ void GraphSendUERecvGradOpKernelLaunchHelper(
DenseTensor* y_grad,
const DenseTensor* dst_count = nullptr,
const DenseTensor* out = nullptr) {
const int& index_size = dst_index.dims()[0];
const int& index_size = dst_index.dims()[0]; // NOLINT
ctx.template Alloc<T>(x_grad);
T* x_grad_data = x_grad->data<T>();
......
paddle/phi/kernels/cpu/send_ue_recv_kernel.cc
浏览文件 @ c2f0e9c4
......@@ -115,7 +115,7 @@ void GraphSendUERecvOpKernelLaunchHelper(const Context& ctx,
int64_t out_size,
DenseTensor* out,
DenseTensor* dst_count = nullptr) {
const int& index_size = src_index.dims()[0];
const int& index_size = src_index.dims()[0]; // NOLINT
auto out_dims = out->dims();
int64_t memset_size = 1;
std::vector<int64_t> dims_ = phi::vectorize(out_dims);
......
paddle/phi/kernels/cpu/send_uv_kernel.cc
浏览文件 @ c2f0e9c4
......@@ -57,7 +57,7 @@ void GraphSendUVOpKernelLaunchHelper(const Context& ctx,
const DenseTensor& dst_index,
const std::string& message_op,
DenseTensor* out) {
const int& index_size = src_index.dims()[0];
const int& index_size = src_index.dims()[0]; // NOLINT
PADDLE_ENFORCE_GT(
index_size,
0,
......
paddle/phi/kernels/cpu/sgd_kernel.cc
浏览文件 @ c2f0e9c4
......@@ -70,9 +70,10 @@ void sgd_dense_param_sparse_grad_impl(const DenseTensor& param,
phi::jit::sgd_attr_t attr;
attr.param_height = param_out->dims()[0];
attr.param_width = param_out->numel() / attr.param_height;
attr.grad_height = grad_rows.size(); // note: it is not grad->height()
attr.grad_height =
static_cast<int>(grad_rows.size()); // note: it is not grad->height()
attr.grad_width = grad_value.numel() / attr.grad_height;
attr.selected_rows_size = grad_rows.size();
attr.selected_rows_size = static_cast<int>(grad_rows.size());
auto sgd =
phi::jit::KernelFuncs<phi::jit::SgdTuple<T>, phi::CPUPlace>::Cache().At(
......
paddle/phi/kernels/cpu/shuffle_batch_grad_kernel.cc
浏览文件 @ c2f0e9c4
......@@ -29,11 +29,11 @@ void ShuffleBatchGradKernel(const Context& dev_ctx,
auto embed_size = out_grad.dims()[out_grad.dims().size() - 1];
auto elem_size = 1;
for (auto i = 0; i < out_grad.dims().size() - 1; i++)
elem_size *= out_grad.dims()[i];
elem_size *= static_cast<int>(out_grad.dims()[i]);
std::vector<int> idx_vec_grad(elem_size);
auto* shuffleidx_data = shuffleidx.data<int64_t>();
for (size_t i = 0; i < idx_vec_grad.size(); i++) {
for (int i = 0; i < static_cast<int>(idx_vec_grad.size()); i++) {
idx_vec_grad[shuffleidx_data[i]] = i;
}
......
paddle/phi/kernels/cpu/shuffle_batch_kernel.cc
浏览文件 @ c2f0e9c4
......@@ -27,12 +27,13 @@ void ShuffleBatchKernel(const Context& dev_ctx,
DenseTensor* shuffleidx,
DenseTensor* seed_out) {
auto x_embed_size = x.dims()[x.dims().size() - 1];
auto elem_size = 1;
for (auto i = 0; i < x.dims().size() - 1; i++) elem_size *= x.dims()[i];
int elem_size = 1;
for (auto i = 0; i < x.dims().size() - 1; i++)
elem_size *= static_cast<int>(x.dims()[i]);
std::vector<int64_t> idx_vec; // record shuffled order
idx_vec.reserve(elem_size);
for (auto i = 0; i < elem_size; i++) {
for (int i = 0; i < elem_size; i++) {
idx_vec.push_back(i);
}
int64_t seed_int = 0;
......@@ -48,7 +49,7 @@ void ShuffleBatchKernel(const Context& dev_ctx,
std::random_device rnd;
int64_t seed_tmp = rnd();
std::default_random_engine rng(seed_tmp);
const int n = idx_vec.size();
const int n = static_cast<int>(idx_vec.size());
std::vector<int> v(n);
std::iota(v.begin(), v.end(), 0);
std::vector<bool> visit(n, false);
......@@ -73,7 +74,7 @@ void ShuffleBatchKernel(const Context& dev_ctx,
std::shuffle(v.begin(), v.end(), rng);
idx_vec[curr] = v.back();
v.pop_back();
curr = idx_vec[curr];
curr = static_cast<int>(idx_vec[curr]);
}
}
};
......
paddle/phi/kernels/cpu/sigmoid_cross_entropy_with_logits_grad_kernel.cc
浏览文件 @ c2f0e9c4
......@@ -31,7 +31,7 @@ void SigmoidCrossEntropyWithLogitsGradKernel(
DenseTensor* in_grad) {
auto dx_data = dev_ctx.template Alloc<T>(in_grad);
int limit = in_grad->numel();
int limit = static_cast<int>(in_grad->numel());
auto x_data = x.data<T>();
auto label_data = label.data<T>();
auto dout_data = out_grad.data<T>();
......
paddle/phi/kernels/cpu/sigmoid_cross_entropy_with_logits_kernel.cc
浏览文件 @ c2f0e9c4
......@@ -32,7 +32,7 @@ void SigmoidCrossEntropyWithLogitsKernel(
int ignore_index,
DenseTensor* out) {
auto out_data = dev_ctx.template Alloc<T>(out);
int limit = out->numel();
int limit = static_cast<int>(out->numel());
auto x_data = x.data<T>();
auto label_data = label.data<T>();
auto pos_weight_data =
......
paddle/phi/kernels/cpu/stack_grad_kernel.cc
浏览文件 @ c2f0e9c4
......@@ -26,7 +26,7 @@ void StackGradKernel(const Context& dev_ctx,
int axis,
std::vector<DenseTensor*> x_grad) {
if (axis < 0) axis += out.dims().size();
int n = out.dims()[axis];
int n = static_cast<int>(out.dims()[axis]);
std::vector<T*> dx_datas(n); // NOLINT
for (int i = 0; i < n; i++) {
......@@ -38,8 +38,8 @@ void StackGradKernel(const Context& dev_ctx,
}
auto dy_data = out.data<T>();
int pre = 1;
for (int i = 0; i < axis; ++i) pre *= out.dims()[i];
int total_num = out.numel();
for (int i = 0; i < axis; ++i) pre *= static_cast<int>(out.dims()[i]);
int total_num = static_cast<int>(out.numel());
int post = total_num / (n * pre);
auto dx_data_arr = dx_datas.data();
phi::funcs::StackGradFunctorForRange(
......
paddle/phi/kernels/cpu/stack_kernel.cc
浏览文件 @ c2f0e9c4
......@@ -41,8 +41,8 @@ void StackKernel(const Context& dev_ctx,
int pre = 1, post = 1;
auto& dim = x[0]->dims();
for (auto i = 0; i < axis; ++i) pre *= dim[i];
for (auto i = axis; i < dim.size(); ++i) post *= dim[i];
for (auto i = 0; i < axis; ++i) pre *= static_cast<int>(dim[i]);
for (auto i = axis; i < dim.size(); ++i) post *= static_cast<int>(dim[i]);
auto x_data_arr = x_datas.data();
......
paddle/phi/kernels/cpu/svd_kernel.cc
浏览文件 @ c2f0e9c4
......@@ -100,8 +100,8 @@ void SvdKernel(const Context& dev_ctx,
DenseTensor trans_x = ::phi::TransposeLast2Dim<T>(dev_ctx, X);
auto* x_data = trans_x.data<T>();
auto x_dims = X.dims();
int rows = x_dims[x_dims.size() - 2];
int cols = x_dims[x_dims.size() - 1];
int rows = static_cast<int>(x_dims[x_dims.size() - 2]);
int cols = static_cast<int>(x_dims[x_dims.size() - 1]);
// int k = std::min(rows, cols);
// int col_u = full ? rows : k;
// int col_v = full ? cols : k;
......@@ -113,7 +113,7 @@ void SvdKernel(const Context& dev_ctx,
0,
cols,
errors::InvalidArgument("The col of Input(X) should be greater than 0."));
int batches = numel / (rows * cols);
int batches = static_cast<int>(numel / (rows * cols));
auto* U_out = dev_ctx.template Alloc<phi::dtype::Real<T>>(U);
auto* VH_out = dev_ctx.template Alloc<phi::dtype::Real<T>>(VH);
auto* S_out = dev_ctx.template Alloc<phi::dtype::Real<T>>(S);
......
paddle/phi/kernels/cpu/temporal_shift_grad_kernel.cc
浏览文件 @ c2f0e9c4
......@@ -94,21 +94,24 @@ void TemporalShiftGradKernel(const Context& dev_ctx,
int t = seg_num;
const DataLayout data_layout = phi::StringToDataLayout(data_format_str);
const int nt = output_grad->dims()[0];
const int c = (data_layout == DataLayout::kNCHW ? output_grad->dims()[1]
: output_grad->dims()[3]);
const int h = (data_layout == DataLayout::kNCHW ? output_grad->dims()[2]
: output_grad->dims()[1]);
const int w = (data_layout == DataLayout::kNCHW ? output_grad->dims()[3]
: output_grad->dims()[2]);
const int nt = static_cast<int>(output_grad->dims()[0]);
const int c = static_cast<int>(data_layout == DataLayout::kNCHW
? output_grad->dims()[1]
: output_grad->dims()[3]);
const int h = static_cast<int>(data_layout == DataLayout::kNCHW
? output_grad->dims()[2]
: output_grad->dims()[1]);
const int w = static_cast<int>(data_layout == DataLayout::kNCHW
? output_grad->dims()[3]
: output_grad->dims()[2]);
const int hw = h * w;
const int chw = c * hw;
const int tchw = t * chw;
const int ntchw = nt * chw;
const int c1 = static_cast<int>(c * shift_ratio);
const int c2 = static_cast<int>(c * 2 * shift_ratio);
const int c1 = static_cast<int>(static_cast<float>(c) * shift_ratio);
const int c2 = static_cast<int>(static_cast<float>(c) * 2.f * shift_ratio);
DDim in_grad_dims =
(data_layout == DataLayout::kNCHW ? phi::make_ddim({nt, c, h, w})
......
paddle/phi/kernels/cpu/temporal_shift_kernel.cc
浏览文件 @ c2f0e9c4
......@@ -94,21 +94,21 @@ void TemporalShiftKernel(const Context& dev_ctx,
int t = seg_num;
const DataLayout data_layout = phi::StringToDataLayout(data_format_str);
const int nt = input->dims()[0];
const int c =
(data_layout == DataLayout::kNCHW ? input->dims()[1] : input->dims()[3]);
const int h =
(data_layout == DataLayout::kNCHW ? input->dims()[2] : input->dims()[1]);
const int w =
(data_layout == DataLayout::kNCHW ? input->dims()[3] : input->dims()[2]);
const int nt = static_cast<int>(input->dims()[0]);
const int c = static_cast<int>(
data_layout == DataLayout::kNCHW ? input->dims()[1] : input->dims()[3]);
const int h = static_cast<int>(
data_layout == DataLayout::kNCHW ? input->dims()[2] : input->dims()[1]);
const int w = static_cast<int>(
data_layout == DataLayout::kNCHW ? input->dims()[3] : input->dims()[2]);
const int hw = h * w;
const int chw = c * hw;
const int tchw = t * chw;
const int ntchw = nt * chw;
const int c1 = static_cast<int>(c * shift_ratio);
const int c2 = static_cast<int>(c * 2 * shift_ratio);
const int c1 = static_cast<int>(static_cast<float>(c) * shift_ratio);
const int c2 = static_cast<int>(static_cast<float>(c) * 2.f * shift_ratio);
DDim out_dims =
(data_layout == DataLayout::kNCHW ? phi::make_ddim({nt, c, h, w})
......
paddle/phi/kernels/cpu/top_k_grad_kernel.cc
浏览文件 @ c2f0e9c4
......@@ -102,7 +102,7 @@ void TopkGradKernel(const Context& dev_ctx,
trans.emplace_back(axis);
phi::DDim trans_dims(out_dims);
phi::DDim trans_in_dims(in_dims);
for (size_t i = 0; i < trans.size(); i++) {
for (int i = 0; i < static_cast<int>(trans.size()); i++) {
trans_dims[i] = out_dims[trans[i]];
trans_in_dims[i] = in_dims[trans[i]];
}
......@@ -113,7 +113,7 @@ void TopkGradKernel(const Context& dev_ctx,
trans_ind.Resize(trans_dims);
dev_ctx.template Alloc<T>(&trans_dO);
dev_ctx.template Alloc<int64_t>(&trans_ind);
int ndims = trans.size();
int ndims = static_cast<int>(trans.size());
// Do transpose
funcs::TransCompute<phi::CPUContext, T>(
......
paddle/phi/kernels/cpu/top_k_kernel.cc
浏览文件 @ c2f0e9c4
......@@ -198,17 +198,17 @@ void TopkKernel(const Context& dev_ctx,
// get the trans input_dims, out_dims
phi::DDim trans_dims(in_dims);
phi::DDim trans_out_dims(out->dims());
for (size_t i = 0; i < trans.size(); i++) {
for (int i = 0; i < static_cast<int>(trans.size()); i++) {
trans_dims[i] = in_dims[trans[i]];
}
for (size_t i = 0; i < trans.size(); i++) {
for (int i = 0; i < static_cast<int>(trans.size()); i++) {
trans_out_dims[i] = out_dims[trans[i]];
}
DenseTensor trans_inp;
trans_inp.Resize(trans_dims);
dev_ctx.template Alloc<T>(&trans_inp);
int ndims = trans.size();
int ndims = static_cast<int>(trans.size());
// transpose the input value
funcs::TransCompute<phi::CPUContext, T>(
......
paddle/phi/kernels/cpu/transpose_kernel.cc
浏览文件 @ c2f0e9c4
......@@ -32,7 +32,7 @@ void TransposeKernel(const Context& ctx,
std::vector<int> formated_axis = axis;
for (size_t i = 0; i < axis.size(); i++) {
if (axis[i] < 0) {
formated_axis[i] = axis[i] + x_rank;
formated_axis[i] = static_cast<int>(axis[i] + x_rank);
}
}
......@@ -40,7 +40,7 @@ void TransposeKernel(const Context& ctx,
if (out->numel() == 0) {
return;
}
int rank = formated_axis.size();
int rank = static_cast<int>(formated_axis.size());
switch (rank) {
case 0:
phi::Copy<Context>(ctx, x, ctx.GetPlace(), false, out);
......
paddle/phi/kernels/cpu/triangular_solve_kernel.cc
浏览文件 @ c2f0e9c4
......@@ -37,8 +37,8 @@ void TriangularSolveKernel(const Context& dev_ctx,
std::vector<int64_t> y_bst_dims_vec;
std::tie(x_bst_dims_vec, y_bst_dims_vec) =
funcs::MatrixGetBroadcastDims(x, y);
int x_bst_ndim = x_bst_dims_vec.size();
int y_bst_ndim = y_bst_dims_vec.size();
int x_bst_ndim = static_cast<int>(x_bst_dims_vec.size());
int y_bst_ndim = static_cast<int>(y_bst_dims_vec.size());
// Tensor broadcast to 'out' and temp 'x_bst'
IntArray x_bst_dims(x_bst_dims_vec);
......@@ -56,7 +56,7 @@ void TriangularSolveKernel(const Context& dev_ctx,
int N = static_cast<int>(y_bst_dims_vec[y_bst_ndim - 1]);
int batch_size = 1;
for (int i = 0; i < x_bst_ndim - 2; i++) {
batch_size *= x_bst_dims_vec[i];
batch_size *= static_cast<int>(x_bst_dims_vec[i]);
}
auto blas = phi::funcs::GetBlas<CPUContext, T>(dev_ctx);
......
paddle/phi/kernels/cpu/trunc_grad_kernel.cc
浏览文件 @ c2f0e9c4
......@@ -25,7 +25,7 @@ void TruncGradKernel(const Context& dev_ctx,
DenseTensor* in_grad) {
T* dx_data = dev_ctx.template Alloc<T>(in_grad);
int numel = in_grad->numel();
int numel = static_cast<int>(in_grad->numel());
memset(dx_data, 0.0, numel * sizeof(T));
}
......
paddle/phi/kernels/cpu/unpool_grad_kernel.cc
浏览文件 @ c2f0e9c4
......@@ -40,12 +40,12 @@ void UnpoolGradKernel(const Context& dev_ctx,
const T* output_grad_data = out_grad.data<T>();
phi::funcs::SetConstant<Context, T> zero;
zero(dev_ctx, x_grad, static_cast<T>(0));
const int batch_size = x.dims()[0];
const int input_height = x.dims()[2];
const int input_width = x.dims()[3];
const int output_channels = out.dims()[1];
const int output_height = out.dims()[2];
const int output_width = out.dims()[3];
const int batch_size = static_cast<int>(x.dims()[0]);
const int input_height = static_cast<int>(x.dims()[2]);
const int input_width = static_cast<int>(x.dims()[3]);
const int output_channels = static_cast<int>(out.dims()[1]);
const int output_height = static_cast<int>(out.dims()[2]);
const int output_width = static_cast<int>(out.dims()[3]);
int input_feasize = input_height * input_width;
int output_feasize = output_height * output_width;
const int* indices_data = indices.data<int>();
......@@ -91,14 +91,14 @@ void Unpool3dGradKernel(const Context& dev_ctx,
phi::funcs::SetConstant<Context, T> zero;
zero(dev_ctx, x_grad, static_cast<T>(0));
const int batch_size = x.dims()[0];
const int input_depth = x.dims()[2];
const int input_height = x.dims()[3];
const int input_width = x.dims()[4];
const int output_channels = out.dims()[1];
const int output_depth = out.dims()[2];
const int output_height = out.dims()[3];
const int output_width = out.dims()[4];
const int batch_size = static_cast<int>(x.dims()[0]);
const int input_depth = static_cast<int>(x.dims()[2]);
const int input_height = static_cast<int>(x.dims()[3]);
const int input_width = static_cast<int>(x.dims()[4]);
const int output_channels = static_cast<int>(out.dims()[1]);
const int output_depth = static_cast<int>(out.dims()[2]);
const int output_height = static_cast<int>(out.dims()[3]);
const int output_width = static_cast<int>(out.dims()[4]);
int input_feasize = input_depth * input_height * input_width;
int output_feasize = output_depth * output_height * output_width;
const int* indices_data = indices.data<int>();
......
paddle/phi/kernels/cpu/unpool_kernel.cc
浏览文件 @ c2f0e9c4
......@@ -38,12 +38,12 @@ void UnpoolKernel(const Context& dev_ctx,
phi::funcs::SetConstant<Context, T> set_zero;
set_zero(dev_ctx, out, static_cast<T>(0));
}
const int batch_size = x.dims()[0];
const int input_height = x.dims()[2];
const int input_width = x.dims()[3];
const int output_channels = out->dims()[1];
const int output_height = out->dims()[2];
const int output_width = out->dims()[3];
const int batch_size = static_cast<int>(x.dims()[0]);
const int input_height = static_cast<int>(x.dims()[2]);
const int input_width = static_cast<int>(x.dims()[3]);
const int output_channels = static_cast<int>(out->dims()[1]);
const int output_height = static_cast<int>(out->dims()[2]);
const int output_width = static_cast<int>(out->dims()[3]);
int input_feasize = input_height * input_width;
int output_feasize = output_height * output_width;
const T* input_data = x.data<T>();
......@@ -87,14 +87,14 @@ void Unpool3dKernel(const Context& dev_ctx,
phi::funcs::SetConstant<Context, T> set_zero;
set_zero(dev_ctx, out, static_cast<T>(0));
}
const int batch_size = x.dims()[0];
const int input_depth = x.dims()[2];
const int input_height = x.dims()[3];
const int input_width = x.dims()[4];
const int output_channels = out->dims()[1];
const int output_depth = out->dims()[2];
const int output_height = out->dims()[3];
const int output_width = out->dims()[4];
const int batch_size = static_cast<int>(x.dims()[0]);
const int input_depth = static_cast<int>(x.dims()[2]);
const int input_height = static_cast<int>(x.dims()[3]);
const int input_width = static_cast<int>(x.dims()[4]);
const int output_channels = static_cast<int>(out->dims()[1]);
const int output_depth = static_cast<int>(out->dims()[2]);
const int output_height = static_cast<int>(out->dims()[3]);
const int output_width = static_cast<int>(out->dims()[4]);
int input_feasize = input_depth * input_height * input_width;
int output_feasize = output_depth * output_height * output_width;
const T* input_data = x.data<T>();
......
paddle/phi/kernels/cpu/weighted_sample_neighbors_kernel.cc
浏览文件 @ c2f0e9c4
......@@ -219,7 +219,7 @@ void WeightedSampleNeighborsKernel(const Context& dev_ctx,
const T* x_data = x.data<T>();
const T* eids_data =
(eids.get_ptr() == nullptr ? nullptr : eids.get_ptr()->data<T>());
int bs = x.dims()[0];
int bs = static_cast<int>(x.dims()[0]);
std::vector<T> output;
std::vector<int> output_count;
......
paddle/phi/kernels/cpu/yolo_box_kernel.cc
浏览文件 @ c2f0e9c4
......@@ -38,13 +38,13 @@ void YoloBoxKernel(const Context& dev_ctx,
auto* input = &x;
auto* imgsize = &img_size;
float scale = scale_x_y;
float bias = -0.5 * (scale - 1.);
float bias = -0.5f * (scale - 1.f);
const int n = input->dims()[0];
const int h = input->dims()[2];
const int w = input->dims()[3];
const int box_num = boxes->dims()[1];
const int an_num = anchors.size() / 2;
const int n = static_cast<int>(input->dims()[0]);
const int h = static_cast<int>(input->dims()[2]);
const int w = static_cast<int>(input->dims()[3]);
const int box_num = static_cast<int>(boxes->dims()[1]);
const int an_num = static_cast<int>(anchors.size() / 2);
int input_size_h = downsample_ratio * h;
int input_size_w = downsample_ratio * w;
......
paddle/phi/kernels/cpu/yolo_loss_grad_kernel.cc
浏览文件 @ c2f0e9c4
......@@ -140,12 +140,12 @@ void YoloLossGradKernel(const Context& dev_ctx,
auto input_grad = x_grad;
auto* objness_mask = &objectness_mask;
const int n = input_grad->dims()[0];
const int c = input_grad->dims()[1];
const int h = input_grad->dims()[2];
const int w = input_grad->dims()[3];
const int mask_num = anchor_mask.size();
const int b = gt_match_mask.dims()[1];
const int n = static_cast<int>(input_grad->dims()[0]);
const int c = static_cast<int>(input_grad->dims()[1]);
const int h = static_cast<int>(input_grad->dims()[2]);
const int w = static_cast<int>(input_grad->dims()[3]);
const int mask_num = static_cast<int>(anchor_mask.size());
const int b = static_cast<int>(gt_match_mask.dims()[1]);
int input_size = downsample_ratio * h;
const int stride = h * w;
......
paddle/phi/kernels/cpu/yolo_loss_kernel.cc
浏览文件 @ c2f0e9c4
......@@ -40,7 +40,7 @@ static T L1Loss(T x, T y) {
}
static int GetMaskIndex(std::vector<int> mask, int val) {
for (size_t i = 0; i < mask.size(); i++) {
for (int i = 0; i < static_cast<int>(mask.size()); i++) {
if (mask[i] == val) {
return i;
}
......@@ -196,14 +196,14 @@ void YoloLossKernel(const Context& dev_ctx,
auto* input = &x;
auto objness_mask = objectness_mask;
float scale = scale_x_y;
float bias = -0.5 * (scale - 1.);
float bias = -0.5f * (scale - 1.f);
const int n = input->dims()[0];
const int h = input->dims()[2];
const int w = input->dims()[3];
const int an_num = anchors.size() / 2;
const int mask_num = anchor_mask.size();
const int b = gt_box.dims()[1];
const int n = static_cast<int>(input->dims()[0]);
const int h = static_cast<int>(input->dims()[2]);
const int w = static_cast<int>(input->dims()[3]);
const int an_num = static_cast<int>(anchors.size() / 2);
const int mask_num = static_cast<int>(anchor_mask.size());
const int b = static_cast<int>(gt_box.dims()[1]);
int input_size = downsample_ratio * h;
const int stride = h * w;
......
paddle/phi/kernels/funcs/jit/benchmark.cc
浏览文件 @ c2f0e9c4
......@@ -97,11 +97,11 @@ struct BenchFunc {
for (int i = 0; i < FLAGS_burning; ++i) {
tgt(args...);
}
auto start = phi::PosixInNsec() * 1e-3;
double start = static_cast<double>(phi::PosixInNsec()) * 1e-3;
for (int i = 0; i < FLAGS_repeat; ++i) {
tgt(args...);
}
auto end = phi::PosixInNsec() * 1e-3;
double end = static_cast<double>(phi::PosixInNsec()) * 1e-3;
return static_cast<double>(end - start) / FLAGS_repeat;
}
};
......
paddle/phi/kernels/selected_rows/cpu/adam_kernel.cc
浏览文件 @ c2f0e9c4
......@@ -192,12 +192,12 @@ void AdamDenseParamSparseGradKernel(
"multi thread, currently "
<< param_row_count;
}
for (size_t i = 0; i < grad_rows.size(); ++i) {
for (int i = 0; i < static_cast<int>(grad_rows.size()); ++i) {
row_id_to_grad_row_offset[grad_rows[i]] = i;
}
std::vector<std::future<void>> fs;
int64_t line_in_each_thread =
param_row_count / FLAGS_inner_op_parallelism + 1;
int64_t line_in_each_thread = static_cast<int64_t>(
param_row_count / FLAGS_inner_op_parallelism + static_cast<int64_t>(1));
for (int i = 0; i < FLAGS_inner_op_parallelism; ++i) {
int64_t start = i * line_in_each_thread;
int64_t end = (i + 1) * line_in_each_thread;
......
paddle/phi/kernels/selected_rows/hsigmoid_loss_grad_kernel.cc
浏览文件 @ c2f0e9c4
......@@ -61,7 +61,7 @@ void HSigmoidLossGradKernel(const Context& ctx,
w_grad->set_height(w.dims()[0]);
auto* w_grad_value = w_grad->mutable_value();
phi::DDim temp_dim(w.dims());
temp_dim[0] = real_rows.size();
temp_dim[0] = static_cast<int>(real_rows.size());
w_grad_value->Resize(temp_dim);
phi::HSigmoidLossGradKernelImpl<T>(ctx,
x,
......
paddle/phi/kernels/sparse/cpu/coalesce_kernel.cc
浏览文件 @ c2f0e9c4
......@@ -52,7 +52,7 @@ void CoalesceCooCPUKernel(const CPUContext& dev_ctx,
IntT index = x_indexs[i];
if (indices_to_index.find(index) == indices_to_index.end()) {
std::vector<int64_t> indexs;
indexs.push_back(i);
indexs.push_back(static_cast<int>(i));
indices_to_index[index] = indexs;
} else {
indices_to_index[index].push_back(i);
......
paddle/phi/kernels/sparse/cpu/conv_grad_kernel.cc
浏览文件 @ c2f0e9c4
......@@ -49,10 +49,12 @@ void Conv3dCooGradCPUKernel(const CPUContext& dev_ctx,
const auto& kernel_dims = kernel.dims();
const bool is2D = kernel_dims.size() == 4 ? true : false;
const int kernel_size =
is2D ? kernel_dims[0] * kernel_dims[1]
: kernel_dims[0] * kernel_dims[1] * kernel_dims[2];
const int in_channels = is2D ? kernel_dims[2] : kernel_dims[3];
const int out_channels = is2D ? kernel_dims[3] : kernel_dims[4];
static_cast<int>(is2D ? kernel_dims[0] * kernel_dims[1]
: kernel_dims[0] * kernel_dims[1] * kernel_dims[2]);
const int in_channels =
static_cast<int>(is2D ? kernel_dims[2] : kernel_dims[3]);
const int out_channels =
static_cast<int>(is2D ? kernel_dims[3] : kernel_dims[4]);
int rulebook_len = 0;
const IntT* rulebook_ptr = phi::funcs::sparse::GetRulebookPtr<IntT>(
......
paddle/phi/kernels/sparse/cpu/conv_kernel.cc
浏览文件 @ c2f0e9c4
......@@ -47,8 +47,9 @@ void Conv3dCooCPUKernel(const CPUContext& dev_ctx,
const auto& x_dims = x.dims();
const bool is2D = x_dims.size() == 4 ? true : false;
const auto& kernel_dims = kernel.dims();
int kernel_size = is2D ? kernel_dims[0] * kernel_dims[1]
: kernel_dims[0] * kernel_dims[1] * kernel_dims[2];
int kernel_size =
static_cast<int>(is2D ? kernel_dims[0] * kernel_dims[1]
: kernel_dims[0] * kernel_dims[1] * kernel_dims[2]);
int count_tmp = is2D ? 4 : 5;
std::vector<int> out_dims_vec(count_tmp, 1);
......@@ -56,7 +57,7 @@ void Conv3dCooCPUKernel(const CPUContext& dev_ctx,
std::vector<int> kernel_sizes(kernel_dims.size());
for (int i = 0; i < kernel_dims.size(); i++) {
kernel_sizes[i] = kernel_dims[i];
kernel_sizes[i] = static_cast<int>(kernel_dims[i]);
}
std::vector<int> subm_paddings(paddings), subm_strides(strides);
......@@ -69,8 +70,10 @@ void Conv3dCooCPUKernel(const CPUContext& dev_ctx,
phi::funcs::sparse::GetOutShape(
x_dims, kernel_sizes, subm_paddings, dilations, subm_strides, &out_dims);
const int in_channels = is2D ? kernel_dims[2] : kernel_dims[3];
const int out_channels = is2D ? kernel_dims[3] : kernel_dims[4];
const int in_channels =
static_cast<int>(is2D ? kernel_dims[2] : kernel_dims[3]);
const int out_channels =
static_cast<int>(is2D ? kernel_dims[3] : kernel_dims[4]);
// Second algorithm:
// https://pdfs.semanticscholar.org/5125/a16039cabc6320c908a4764f32596e018ad3.pdf
......@@ -112,7 +115,7 @@ void Conv3dCooCPUKernel(const CPUContext& dev_ctx,
UpdateRulebookAndOutIndex<T, CPUContext, IntT>(
dev_ctx, x, kernel_size, out_channels, out_dims, &tmp_rulebook, out);
n = tmp_rulebook.dims()[1];
n = static_cast<int>(tmp_rulebook.dims()[1]);
rulebook_ptr = tmp_rulebook.data<IntT>();
phi::funcs::sparse::SaveToTable(
......
paddle/phi/kernels/sparse/cpu/elementwise_grad_kernel.cc
浏览文件 @ c2f0e9c4
......@@ -122,8 +122,8 @@ void CopyCsrValues(const Context& dev_ctx,
Copy(dev_ctx, x.cols(), dev_ctx.GetPlace(), false, dx->mutable_cols());
const auto& x_dims = x.dims();
int batch = x_dims.size() == 2 ? 1 : x_dims[0];
int rows = x_dims.size() == 2 ? x_dims[0] : x_dims[1];
int batch = static_cast<int>(x_dims.size() == 2 ? 1 : x_dims[0]);
int rows = static_cast<int>(x_dims.size() == 2 ? x_dims[0] : x_dims[1]);
const IntT* x_crows_ptr = x.crows().data<IntT>();
const IntT* x_cols_ptr = x.cols().data<IntT>();
......
paddle/phi/kernels/sparse/cpu/mask_kernel.cc
浏览文件 @ c2f0e9c4
......@@ -52,7 +52,7 @@ void MaskCooCPUKernel(const CPUContext& dev_ctx,
const int64_t non_zero_num = mask.nnz();
auto dims_2d = flatten_to_2d(dims, sparse_dim);
const int cols = dims_2d[1];
const int cols = static_cast<int>(dims_2d[1]);
const IntT* indices_ptr = indices.data<IntT>();
std::vector<IntT> out_indexs(non_zero_num), sparse_offsets(sparse_dim);
......
paddle/phi/kernels/sparse/cpu/pool_grad_kernel.cc
浏览文件 @ c2f0e9c4
......@@ -34,8 +34,8 @@ void MaxPoolCooGradCPUKernel(const CPUContext& dev_ctx,
const std::vector<int>& kernel_sizes,
SparseCooTensor* x_grad) {
int kernel_size = kernel_sizes[0] * kernel_sizes[1] * kernel_sizes[2];
const int channels = x.dims()[4];
int rulebook_len = rulebook.dims()[1];
const int channels = static_cast<int>(x.dims()[4]);
int rulebook_len = static_cast<int>(rulebook.dims()[1]);
const IntT* rulebook_ptr = rulebook.data<IntT>();
std::vector<int> offsets(kernel_size + 1);
const int* counter_ptr = counter.data<int>();
......
paddle/phi/kernels/sparse/cpu/pool_kernel.cc
浏览文件 @ c2f0e9c4
......@@ -42,7 +42,9 @@ void MaxPoolCooCPUKernel(const CPUContext& dev_ctx,
const auto& x_dims = x.dims();
int kernel_size = kernel_sizes[0] * kernel_sizes[1] * kernel_sizes[2];
const std::vector<int>& real_kernel_sizes =
phi::funcs::sparse::PoolResetKernel(kernel_sizes, x_dims[4], x_dims[4]);
phi::funcs::sparse::PoolResetKernel(kernel_sizes,
static_cast<int>(x_dims[4]),
static_cast<int>(x_dims[4]));
DDim out_dims = {1, 1, 1, 1, 1};
phi::funcs::sparse::GetOutShape(
x_dims, real_kernel_sizes, paddings, dilations, strides, &out_dims);
......@@ -66,7 +68,7 @@ void MaxPoolCooCPUKernel(const CPUContext& dev_ctx,
UpdateRulebookAndOutIndex<T, CPUContext, IntT>(
dev_ctx, x, kernel_size, in_channels, out_dims, rulebook, out);
int rulebook_len = rulebook->dims()[1];
int rulebook_len = static_cast<int>(rulebook->dims()[1]);
const IntT* rulebook_ptr = rulebook->data<IntT>();
counter->Resize({kernel_size});
......
paddle/phi/kernels/sparse/cpu/reshape_kernel.cc
浏览文件 @ c2f0e9c4
......@@ -68,7 +68,7 @@ void ReshapeCooCPUKernel(const Context& dev_ctx,
for (int i = 0; i < x.sparse_dim(); ++i) {
location += x_indices_data[i * x_nnz + j] * x_sparse_part_strides[i];
}
for (size_t i = 0; i < out_sparse_part_dims.size(); ++i) {
for (int i = 0; i < static_cast<int>(out_sparse_part_dims.size()); ++i) {
out_indices_data[i * x_nnz + j] = location / out_sparse_part_strides[i];
location %= out_sparse_part_strides[i];
}
......
paddle/phi/kernels/sparse/cpu/softmax_grad_kernel.cc
浏览文件 @ c2f0e9c4
......@@ -48,9 +48,9 @@ void SoftmaxCsrGradKernel(const Context& dev_ctx,
int row_number = 1;
for (int i = 0; i < out_rank - 1; ++i) {
if (i < out_rank - 2) {
batch_size *= out_dim[i];
batch_size *= static_cast<int>(out_dim[i]);
} else if (i == out_rank - 2) {
row_number = out_dim[i];
row_number = static_cast<int>(out_dim[i]);
}
}
......
paddle/phi/kernels/sparse/cpu/softmax_kernel.cc
浏览文件 @ c2f0e9c4
......@@ -45,9 +45,9 @@ void SoftmaxCsrKernel(const Context& dev_ctx,
int row_number = 1;
for (int i = 0; i < x_rank - 1; ++i) {
if (i < x_rank - 2) {
batch_size *= x_dim[i];
batch_size *= static_cast<int>(x_dim[i]);
} else if (i == x_rank - 2) {
row_number = x_dim[i];
row_number = static_cast<int>(x_dim[i]);
}
}
......
paddle/phi/kernels/sparse/cpu/sparse_utils_kernel.cc
浏览文件 @ c2f0e9c4
......@@ -48,9 +48,9 @@ inline int64_t GetNonZeroNum(const DenseTensor& dense,
sparse_dim,
dims.size()));
auto dims_2d = flatten_to_2d(dims, sparse_dim);
const int rows = dims_2d[0];
const int cols = dims_2d[1];
auto dims_2d = flatten_to_2d(dims, static_cast<int>(sparse_dim));
const int rows = static_cast<int>(dims_2d[0]);
const int cols = static_cast<int>(dims_2d[1]);
const T* data = dense.data<T>();
int64_t non_zero_num = 0;
......@@ -87,15 +87,15 @@ void DenseToCooKernel(const Context& dev_ctx,
int64_t* indices_data = indices.data<int64_t>();
T* values_data = values.data<T>();
auto dims_2d = flatten_to_2d(x_dims, sparse_dim);
const int rows = dims_2d[0];
const int cols = dims_2d[1];
auto dims_2d = flatten_to_2d(x_dims, static_cast<int>(sparse_dim));
const int rows = static_cast<int>(dims_2d[0]);
const int cols = static_cast<int>(dims_2d[1]);
int index = 0;
for (int i = 0; i < rows; i++) {
if (!IsZero(x_data + i * cols, cols)) {
int64_t sparse_index = i;
for (int64_t j = sparse_dim - 1; j >= 0; j--) {
for (int j = static_cast<int>(sparse_dim - 1); j >= 0; j--) {
indices_data[j * non_zero_num + index] = sparse_index % x_dims[j];
sparse_index /= x_dims[j];
}
......@@ -138,8 +138,8 @@ void CsrToCooCPUKernel(const CPUContext& dev_ctx,
IntT* coo_cols_data = coo_rows_data + non_zero_num;
T* coo_values_data = values.data<T>();
int batch = x_dims.size() == 2 ? 1 : x_dims[0];
int rows = x_dims.size() == 2 ? x_dims[0] : x_dims[1];
int batch = static_cast<int>(x_dims.size() == 2 ? 1 : x_dims[0]);
int rows = static_cast<int>(x_dims.size() == 2 ? x_dims[0] : x_dims[1]);
int index = 0;
for (int b = 0; b < batch; b++) {
......@@ -182,8 +182,8 @@ void CooToCsrCPUKernel(const CPUContext& dev_ctx,
"SparseCsrTensor only support 2-D or 3-D matrix"));
const int64_t non_zero_num = x.nnz();
int batchs = x_dims.size() == 2 ? 1 : x_dims[0];
int rows = x_dims.size() == 2 ? x_dims[0] : x_dims[1];
int batchs = static_cast<int>(x_dims.size() == 2 ? 1 : x_dims[0]);
int rows = static_cast<int>(x_dims.size() == 2 ? x_dims[0] : x_dims[1]);
phi::DenseTensor crows = phi::Empty<IntT>(dev_ctx, {batchs * (rows + 1)});
phi::DenseTensor cols = phi::Empty<IntT>(dev_ctx, {non_zero_num});
......@@ -221,9 +221,9 @@ void CooToCsrCPUKernel(const CPUContext& dev_ctx,
for (int b = 0; b < batchs; b++) {
int batch_start = 0;
int batch_non_zero_num = offsets[b];
int batch_non_zero_num = static_cast<int>(offsets[b]);
if (b > 0) {
batch_start = offsets[b - 1];
batch_start = static_cast<int>(offsets[b - 1]);
batch_non_zero_num -= batch_start;
}
auto* coo_rows_ptr = coo_rows_data + batch_start;
......@@ -283,11 +283,11 @@ void CooToDenseCPUKernel(const CPUContext& dev_ctx,
int64_t base_offset = 1;
for (int64_t i = 0; i < dense_dim; i++) {
base_offset *= dense_dims[sparse_dim + i];
base_offset *= dense_dims[static_cast<int>(sparse_dim + i)];
}
std::vector<int64_t> sparse_offsets(sparse_dim);
int64_t offset = 1;
for (int i = sparse_dim - 1; i >= 0; i--) {
for (int i = static_cast<int>(sparse_dim - 1); i >= 0; i--) {
sparse_offsets[i] = offset;
offset *= dense_dims[i];
}
......
paddle/phi/kernels/sparse/cpu/transpose_grad_kernel.cc
浏览文件 @ c2f0e9c4
......@@ -26,7 +26,7 @@ namespace sparse {
std::vector<int> get_cpu_grad_perm(std::vector<int> perm) {
std::vector<int> grad_perm(perm.size());
for (unsigned int i = 0; i < perm.size(); ++i) {
grad_perm[perm[i]] = i;
grad_perm[perm[i]] = static_cast<int>(i);
}
return grad_perm;
}
......
paddle/phi/kernels/sparse/cpu/transpose_kernel.cc
浏览文件 @ c2f0e9c4
......@@ -179,7 +179,7 @@ void TransposeCsrKernel(const Context& dev_ctx,
int64_t x_cols_offset = 0;
int out_cols_index = 0;
for (int i = 0; i < x.dims()[0]; ++i) {
int x_crows_index = i * (x_n_rows + 1);
int x_crows_index = static_cast<int>(i * (x_n_rows + 1));
int64_t start = x_crows_data[x_crows_index + k];
int64_t end = x_crows_data[x_crows_index + 1 + k];
out_crows_data[i + 1] = end - start;
......
paddle/phi/kernels/stride/as_strided_kernel.cc
浏览文件 @ c2f0e9c4
......@@ -24,8 +24,8 @@ void AsStridedKernel(const Context& dev_ctx,
const std::vector<int64_t>& stride,
int64_t offset,
DenseTensor* out) {
out->Resize(DDim(dims.data(), dims.size()));
out->set_strides(DDim(stride.data(), stride.size()));
out->Resize(DDim(dims.data(), static_cast<int>(dims.size())));
out->set_strides(DDim(stride.data(), static_cast<int>(stride.size())));
out->set_offset(offset);
out->ResetHolder(input.Holder());
}
......
paddle/phi/kernels/stride/diagonal_kernel.cc
浏览文件 @ c2f0e9c4
......@@ -30,25 +30,27 @@ void DiagonalStridedKernel(const Context& dev_ctx,
DenseTensor* out) {
size_t x_rank = x.dims().size();
if (axis1 < 0) {
axis1 += x_rank;
axis1 += static_cast<int>(x_rank);
}
if (axis2 < 0) {
axis2 += x_rank;
axis2 += static_cast<int>(x_rank);
}
int64_t diag_size;
int64_t x_offset = x.offset();
int64_t x_offset = static_cast<int64_t>(x.offset());
if (offset >= 0) {
diag_size = std::max<int64_t>(
std::min(x.dims()[axis1], x.dims()[axis2] - offset), 0);
if (diag_size != 0) {
x_offset += offset * x.strides()[axis2] * SizeOf(x.dtype());
x_offset +=
static_cast<int64_t>(offset * x.strides()[axis2] * SizeOf(x.dtype()));
}
} else {
diag_size = std::max<int64_t>(
std::min(x.dims()[axis1] + offset, x.dims()[axis2]), 0);
if (diag_size != 0) {
x_offset -= offset * x.strides()[axis1] * SizeOf(x.dtype());
x_offset -=
static_cast<int64_t>(offset * x.strides()[axis1] * SizeOf(x.dtype()));
}
}
......@@ -62,7 +64,7 @@ void DiagonalStridedKernel(const Context& dev_ctx,
stride.push_back(x.strides()[axis1] + x.strides()[axis2]);
auto meta = out->meta();
auto tmp_dim = DDim(shape.data(), shape.size());
auto tmp_dim = DDim(shape.data(), static_cast<int>(shape.size()));
// if (product(meta.dims) > 0 && meta.dims != tmp_dim) {
// PADDLE_THROW(
// phi::errors::Fatal("Diagonal kernel stride compute diff, infer shape
......@@ -72,7 +74,7 @@ void DiagonalStridedKernel(const Context& dev_ctx,
// tmp_dim));
// }
meta.dims = tmp_dim;
meta.strides = DDim(stride.data(), stride.size());
meta.strides = DDim(stride.data(), static_cast<int>(stride.size()));
meta.offset = x_offset;
out->set_meta(meta);
out->ResetHolder(x.Holder());
......
paddle/phi/kernels/stride/index_select_kernel.cc
浏览文件 @ c2f0e9c4
......@@ -32,15 +32,16 @@ void IndexSelectStridedKernel(const Context& ctx,
std::vector<int64_t> shape = phi::vectorize<int64_t>(x.dims());
std::vector<int64_t> stride = phi::vectorize<int64_t>(x.strides());
int64_t offset = x.offset();
int64_t offset = static_cast<int64_t>(x.offset());
offset = offset + index * stride[dim] * SizeOf(output->dtype());
offset = static_cast<int64_t>(offset +
index * stride[dim] * SizeOf(output->dtype()));
shape.erase(shape.begin() + dim);
stride.erase(stride.begin() + dim);
auto meta = output->meta();
meta.offset = offset;
auto tmp_dim = DDim(shape.data(), shape.size());
auto tmp_dim = DDim(shape.data(), static_cast<int>(shape.size()));
// if (product(meta.dims) > 0 && meta.dims != tmp_dim) {
// PADDLE_THROW(
// phi::errors::Fatal("Index_select kernel stride compute diff, infer "
......@@ -49,7 +50,7 @@ void IndexSelectStridedKernel(const Context& ctx,
// tmp_dim));
// }
meta.dims = tmp_dim;
meta.strides = DDim(stride.data(), stride.size());
meta.strides = DDim(stride.data(), static_cast<int>(stride.size()));
output->set_meta(meta);
output->ResetHolder(x.Holder());
}
......
paddle/phi/kernels/stride/slice_kernel.cc
浏览文件 @ c2f0e9c4
......@@ -49,17 +49,18 @@ void SliceStridedKernel(const Context& ctx,
std::vector<int64_t> output_dims = phi::vectorize<int64_t>(input.dims());
std::vector<int64_t> output_stride = phi::vectorize<int64_t>(input.strides());
int64_t output_offset = input.offset();
int64_t output_offset = static_cast<int64_t>(input.offset());
for (size_t i = 0; i < new_axes.size(); ++i) {
output_offset = output_offset + starts[i] * output_stride[new_axes[i]] *
SizeOf(out->dtype());
output_offset = static_cast<int64_t>(
output_offset +
starts[i] * output_stride[new_axes[i]] * SizeOf(out->dtype()));
output_dims[new_axes[i]] = ends[i] - starts[i];
}
std::vector<uint8_t> decrease_flag(output_dims.size(), 0);
if (decrease_axis.size() > 0) {
for (size_t i = 0; i < decrease_axis.size(); ++i) {
for (int i = 0; i < static_cast<int>(decrease_axis.size()); ++i) {
int64_t axis = decrease_axis[i];
decrease_flag[axis] = 1;
}
......@@ -84,7 +85,7 @@ void SliceStridedKernel(const Context& ctx,
auto meta = out->meta();
meta.offset = output_offset;
auto tmp_dim = DDim(output_dims.data(), output_dims.size());
auto tmp_dim = DDim(output_dims.data(), static_cast<int>(output_dims.size()));
// if (product(meta.dims) > 0 && meta.dims != tmp_dim) {
// PADDLE_THROW(
// phi::errors::Fatal("Slice kernel stride compute diff, infer shape is
......@@ -94,7 +95,8 @@ void SliceStridedKernel(const Context& ctx,
// tmp_dim));
// }
meta.dims = tmp_dim;
meta.strides = DDim(output_stride.data(), output_stride.size());
meta.strides =
DDim(output_stride.data(), static_cast<int>(output_stride.size()));
out->set_meta(meta);
out->ResetHolder(input.Holder());
}
......
paddle/phi/kernels/stride/split_kernel.cc
浏览文件 @ c2f0e9c4
......@@ -28,7 +28,7 @@ void SplitStridedKernel(const Context& dev_ctx,
const IntArray& sections UNUSED,
const Scalar& axis_scalar,
std::vector<DenseTensor*> outs) {
int64_t num = outs.size();
int64_t num = static_cast<int64_t>(outs.size());
int64_t start = 0;
int axis = axis_scalar.to<int>();
......
paddle/phi/kernels/stride/squeeze_kernel.cc
浏览文件 @ c2f0e9c4
......@@ -49,7 +49,8 @@ void SqueezeInferStridedKernel(const Context& dev_ctx,
} else {
for (auto& item : axes) {
item = item < 0 ? item + input_stride.size() : item;
if (item != 0 && input_stride[item] == input_stride[item - 1]) {
if (item != 0 && input_stride[static_cast<int>(item)] ==
input_stride[static_cast<int>(item) - 1]) {
axes_set.insert(item);
}
}
......@@ -65,7 +66,8 @@ void SqueezeInferStridedKernel(const Context& dev_ctx,
auto meta = out->meta();
meta.offset = input.offset();
meta.strides = DDim(output_stride.data(), output_stride.size());
meta.strides =
DDim(output_stride.data(), static_cast<int>(output_stride.size()));
out->set_meta(meta);
return;
}
......@@ -80,7 +82,7 @@ void SqueezeInferStridedKernel(const Context& dev_ctx,
} else {
for (auto item : axes) {
auto axis = item < 0 ? item + input_dims.size() : item;
if (input_dims[axis] == 1) {
if (input_dims[static_cast<int>(axis)] == 1) {
axes_set.insert(axis);
}
}
......@@ -94,7 +96,7 @@ void SqueezeInferStridedKernel(const Context& dev_ctx,
}
auto meta = out->meta();
auto tmp_dim = DDim(output_dims.data(), output_dims.size());
auto tmp_dim = DDim(output_dims.data(), static_cast<int>(output_dims.size()));
// if (product(meta.dims) > 0 && meta.dims != tmp_dim) {
// PADDLE_THROW(
// phi::errors::Fatal("Unsqueeze kernel stride compute diff, infer
......@@ -104,7 +106,8 @@ void SqueezeInferStridedKernel(const Context& dev_ctx,
// tmp_dim));
// }
meta.dims = tmp_dim;
meta.strides = DDim(output_stride.data(), output_stride.size());
meta.strides =
DDim(output_stride.data(), static_cast<int>(output_stride.size()));
meta.offset = input.offset();
out->set_meta(meta);
out->ResetHolder(input.Holder());
......
paddle/phi/kernels/stride/strided_slice_kernel.cc
浏览文件 @ c2f0e9c4
......@@ -37,7 +37,7 @@ void StridedSliceRawStridedKernel(const Context& dev_ctx,
std::vector<int64_t> output_dims = phi::vectorize<int64_t>(input.dims());
std::vector<int64_t> output_stride = phi::vectorize<int64_t>(input.strides());
int64_t output_offset = input.offset();
int64_t output_offset = static_cast<int64_t>(input.offset());
for (size_t i = 0; i < axes.size(); ++i) {
int64_t axis_size = input.dims()[axes[i]];
......@@ -82,7 +82,8 @@ void StridedSliceRawStridedKernel(const Context& dev_ctx,
starts[i] = (strides[i] < 0) ? axis_size - 1 : axis_size;
}
output_offset += starts[i] * output_stride[axes[i]] * SizeOf(out->dtype());
output_offset += static_cast<int>(starts[i] * output_stride[axes[i]] *
SizeOf(out->dtype()));
output_dims[axes[i]] = dim;
output_stride[axes[i]] *= strides[i];
}
......@@ -107,7 +108,7 @@ void StridedSliceRawStridedKernel(const Context& dev_ctx,
auto meta = out->meta();
meta.offset = output_offset;
auto tmp_dim = DDim(output_dims.data(), output_dims.size());
auto tmp_dim = DDim(output_dims.data(), static_cast<int>(output_dims.size()));
// if (product(meta.dims) > 0 && meta.dims != tmp_dim) {
// PADDLE_THROW(
// phi::errors::Fatal("Striede_slice kernel stride compute diff, infer "
......@@ -116,7 +117,8 @@ void StridedSliceRawStridedKernel(const Context& dev_ctx,
// tmp_dim));
// }
meta.dims = tmp_dim;
meta.strides = DDim(output_stride.data(), output_stride.size());
meta.strides =
DDim(output_stride.data(), static_cast<int>(output_stride.size()));
out->set_meta(meta);
out->ResetHolder(input.Holder());
}
......
paddle/phi/kernels/stride/tensor_unfold_kernel.cc
浏览文件 @ c2f0e9c4
......@@ -30,7 +30,8 @@ void TensorUnfoldKernel(const Context& dev_ctx,
const DDim& input_dims = input.dims();
const DDim& input_stride = input.strides();
int64_t max_size = input_dims.size() == 0 ? 1 : input_dims[axis];
int64_t max_size =
input_dims.size() == 0 ? 1 : input_dims[static_cast<int>(axis)];
PADDLE_ENFORCE_LE(
size,
......@@ -48,7 +49,8 @@ void TensorUnfoldKernel(const Context& dev_ctx,
std::vector<int64_t> stride(input_dims.size() + 1);
shape[input_dims.size()] = size;
stride[input_dims.size()] = input_dims.size() == 0 ? 1 : input_stride[axis];
stride[input_dims.size()] =
input_dims.size() == 0 ? 1 : input_stride[static_cast<int>(axis)];
for (int i = 0; i < input_dims.size(); ++i) {
if (i == axis) {
shape[i] = (input_dims[i] - size) / step + 1;
......@@ -59,8 +61,8 @@ void TensorUnfoldKernel(const Context& dev_ctx,
}
}
out->Resize(DDim(shape.data(), shape.size()));
out->set_strides(DDim(stride.data(), stride.size()));
out->Resize(DDim(shape.data(), static_cast<int>(shape.size())));
out->set_strides(DDim(stride.data(), static_cast<int>(stride.size())));
out->set_offset(input.offset());
out->ResetHolder(input.Holder());
}
......
paddle/phi/kernels/stride/transpose_grad_kernel.cc
浏览文件 @ c2f0e9c4
......@@ -28,12 +28,12 @@ void TransposeGradStridedKernel(const Context& dev_ctx,
std::vector<int> formated_axis = axis;
for (size_t i = 0; i < axis_size; i++) {
if (axis[i] < 0) {
formated_axis[i] = axis[i] + axis_size;
formated_axis[i] = static_cast<int>(axis[i] + axis_size);
}
}
std::vector<int> reversed_axis(axis);
for (size_t i = 0; i < axis_size; i++) {
for (int i = 0; i < static_cast<int>(axis_size); i++) {
reversed_axis[formated_axis[i]] = i;
}
......
paddle/phi/kernels/stride/transpose_kernel.cc
浏览文件 @ c2f0e9c4
......@@ -27,7 +27,7 @@ void TransposeStridedKernel(const Context& ctx,
std::vector<int> formated_axis = axis;
for (size_t i = 0; i < axis.size(); i++) {
if (axis[i] < 0) {
formated_axis[i] = axis[i] + x_rank;
formated_axis[i] = static_cast<int>(axis[i] + x_rank);
}
}
......@@ -35,7 +35,7 @@ void TransposeStridedKernel(const Context& ctx,
auto in_stride = x.strides();
auto in_dims = x.dims();
meta.strides = in_stride;
for (size_t i = 0; i < formated_axis.size(); i++) {
for (int i = 0; i < static_cast<int>(formated_axis.size()); i++) {
meta.strides[i] = in_stride[formated_axis[i]];
meta.dims[i] = in_dims[formated_axis[i]];
}
......
paddle/phi/kernels/stride/unbind_kernel.cc
浏览文件 @ c2f0e9c4
......@@ -24,7 +24,7 @@ void UnbindStridedKernel(const Context& dev_ctx,
const DenseTensor& x,
int axis,
std::vector<DenseTensor*> outs) {
int64_t num = outs.size();
int64_t num = static_cast<int64_t>(outs.size());
int64_t start = 0;
axis = axis < 0 ? axis + x.dims().size() : axis;
......
paddle/phi/kernels/stride/unsqueeze_kernel.cc
浏览文件 @ c2f0e9c4
......@@ -34,7 +34,8 @@ void UnsqueezeInferStridedKernel(const Context& dev_ctx,
if (input.Holder() == out->Holder() && input.meta() == out->meta()) {
input_dims = phi::vectorize<int64_t>(out->dims());
for (int64_t i = static_cast<int64_t>(axes.size() - 1); i >= 0; --i) {
axes[i] = axes[i] < 0 ? axes[i] + input_dims.size() : axes[i];
axes[i] = static_cast<int64_t>(axes[i] < 0 ? axes[i] + input_dims.size()
: axes[i]);
axes[i] = axes[i] < 0 ? 0 : axes[i];
input_dims.erase(input_dims.begin() + axes[i]);
}
......@@ -43,8 +44,9 @@ void UnsqueezeInferStridedKernel(const Context& dev_ctx,
std::vector<int64_t> output_dims = input_dims;
std::vector<int64_t> output_stride = input_stride;
for (auto item : axes) {
item = item < 0 ? item + output_dims.size() + 1 : item;
for (int64_t item : axes) {
item =
static_cast<int64_t>(item < 0 ? item + output_dims.size() + 1 : item);
item = item < 0 ? 0 : item;
int64_t stride = static_cast<size_t>(item) >= output_dims.size()
? 1
......@@ -54,7 +56,7 @@ void UnsqueezeInferStridedKernel(const Context& dev_ctx,
}
auto meta = out->meta();
auto tmp_dim = DDim(output_dims.data(), output_dims.size());
auto tmp_dim = DDim(output_dims.data(), static_cast<int>(output_dims.size()));
// if (product(meta.dims) > 0 && meta.dims != tmp_dim) {
// PADDLE_THROW(
// phi::errors::Fatal("Unsqueeze kernel stride compute diff, infer
......@@ -64,7 +66,8 @@ void UnsqueezeInferStridedKernel(const Context& dev_ctx,
// tmp_dim));
// }
meta.dims = tmp_dim;
meta.strides = DDim(output_stride.data(), output_stride.size());
meta.strides =
DDim(output_stride.data(), static_cast<int>(output_stride.size()));
meta.offset = input.offset();
out->set_meta(meta);
out->ResetHolder(input.Holder());
......
paddle/phi/kernels/stride/view_kernel.cc
浏览文件 @ c2f0e9c4
......@@ -23,7 +23,7 @@ void ViewShapeKernel(const Context& dev_ctx,
const DenseTensor& input,
const std::vector<int64_t>& dims,
DenseTensor* out) {
DDim new_dims = DDim(dims.data(), dims.size());
DDim new_dims = DDim(dims.data(), static_cast<int>(dims.size()));
DDim stride;
if (ReshapeStride(input.dims(), input.strides(), new_dims, stride)) {
auto meta = input.meta();
......@@ -67,11 +67,11 @@ void ViewDtypeKernel(const Context& dev_ctx,
DDim output_dims = input.dims();
output_dims[output_dims.size() - 1] =
output_dims[output_dims.size() - 1] * times;
output_dims[output_dims.size() - 1] * times; // NOLINT
DDim output_stride = input.strides();
for (int i = 0; i < output_stride.size(); i++) {
output_stride[i] = output_stride[i] * times;
output_stride[i] = output_stride[i] * times; // NOLINT
}
output_stride[output_stride.size() - 1] = 1;
......@@ -115,7 +115,7 @@ void ViewDtypeKernel(const Context& dev_ctx,
DDim output_dims = input.dims();
output_dims[output_dims.size() - 1] =
output_dims[output_dims.size() - 1] / times;
output_dims[output_dims.size() - 1] / times; // NOLINT
DDim output_stride = input.strides();
for (int i = 0; i < output_stride.size(); i++) {
......@@ -129,7 +129,7 @@ void ViewDtypeKernel(const Context& dev_ctx,
times,
input.dtype(),
dtype));
output_stride[i] = output_stride[i] / times;
output_stride[i] = output_stride[i] / times; // NOLINT
}
output_stride[output_stride.size() - 1] = 1;
......
paddle/phi/kernels/strings/unicode.cc
浏览文件 @ c2f0e9c4
......@@ -35,10 +35,10 @@ const uint8_t* GetUniFlagMap() {
const uint16_t* GetCharcasesMap() {
if (utils_map[0] == nullptr) {
for (uint32_t i = 0; i < 65536; ++i) {
if (utf8proc_islower(i)) {
CHARCASES_MAP[i] = utf8proc_toupper(i);
} else if (utf8proc_isupper(i)) {
CHARCASES_MAP[i] = utf8proc_tolower(i);
if (utf8proc_islower(static_cast<int32_t>(i))) {
CHARCASES_MAP[i] = utf8proc_toupper(static_cast<int32_t>(i));
} else if (utf8proc_isupper(static_cast<int32_t>(i))) {
CHARCASES_MAP[i] = utf8proc_tolower(static_cast<int32_t>(i));
}
}
utils_map[0] = CHARCASES_MAP;
......
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