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[Cherry-pick]Update layout autotune for module with no modified (#46541) (#46515) #46880

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Oct 12, 2022
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[Cherry-pick]Update layout autotune for module with no modified (#46541) (#46515) #46880

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f305805
Update for SetOutDataLayout when VarType is EagerVariable (#46515)
AnnaTrainingG Sep 27, 2022
303cb69
Update layout autotune for module with no modified (#46541)
AnnaTrainingG Oct 11, 2022
4485f86
delte notes
AnnaTrainingG Oct 11, 2022
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194 changes: 69 additions & 125 deletions paddle/fluid/eager/eager_layout_auto_tune.h
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Original file line number Diff line number Diff line change
Expand Up @@ -32,99 +32,74 @@ inline bool NeedTransLayout(
}
return false;
}
inline std::shared_ptr<EagerLayoutTransformer> BaseTransformer(
const std::string& op_name,
const paddle::small_vector<std::vector<paddle::experimental::Tensor>,
kSlotSmallVectorSize>& tensors_vector) {
std::shared_ptr<EagerLayoutTransformer> transposer = nullptr;
bool unstart =
(paddle::imperative::LayoutAutoTune::Instance().GetDesiredLayout() ==
paddle::experimental::DataLayout::UNDEFINED);
auto first_layout = tensors_vector[0][0].layout();
VLOG(3) << "Layout autotune was is start ? " << (!unstart) << op_name
<< "'s layout is " << first_layout;

transposer = std::make_shared<EagerLayoutTransformer>(
op_name, tensors_vector, first_layout);
return transposer;
}

// For agnostic op like add, relu, exp
inline std::shared_ptr<EagerLayoutTransformer> EagerLayoutAutotune(
const std::string& op_name,
const paddle::small_vector<std::vector<paddle::experimental::Tensor>,
kSlotSmallVectorSize>& tensors_vector) {
auto desired_layout =
paddle::imperative::LayoutAutoTune::Instance().GetDesiredLayout();
auto default_layout =
paddle::imperative::LayoutAutoTune::Instance().GetDefaultLayout();
// For agnostic op like add, relu, exp
auto first_layout = tensors_vector[0][0].layout();
if (NeedTransLayout(tensors_vector, first_layout)) {
auto desired_layout = DesiredLayout();
bool is_started =
!(desired_layout == paddle::experimental::DataLayout::UNDEFINED);
if (is_started && NeedTransLayout(tensors_vector, first_layout)) {
bool need_trans_back = false;
for (size_t i = 0; i < tensors_vector.size(); i++) {
for (size_t idx = 0; idx < tensors_vector[0].size(); idx++) {
if (4 != tensors_vector[i][idx].shape().size()) {
need_trans_back = true;
VLOG(3) << "Agnostic op " << op_name << " shape is "
<< tensors_vector[i][idx].shape().size() << " and layout is "
<< tensors_vector[i][idx].layout();
}
}
}
auto final_layout = need_trans_back ? default_layout : desired_layout;
auto final_layout = need_trans_back ? DefaultLayout() : desired_layout;
VLOG(4) << op_name << "'s has different layout, need trans to "
<< final_layout;
return std::make_shared<EagerLayoutTransformer>(
op_name, tensors_vector, final_layout);
}
return BaseTransformer(op_name, tensors_vector);
return std::make_shared<EagerLayoutTransformer>(
op_name, tensors_vector, first_layout);
}

// For lightly op like reduce
template <typename T>
inline std::shared_ptr<EagerLayoutTransformer> EagerLayoutAutotune(
const std::string& op_name,
const paddle::small_vector<std::vector<paddle::experimental::Tensor>,
kSlotSmallVectorSize>& tensors_vector,
T* attr) {
VLOG(3) << "Lightly op " << op_name << "'s shape is "
<< tensors_vector[0][0].shape().size() << " and layout is "
<< tensors_vector[0][0].layout();

std::shared_ptr<EagerLayoutTransformer> transposer = nullptr;
transposer =
std::make_shared<EagerLightlyLayoutSensitiveOpTransformer>(op_name);
return transposer;
// For lightly op like reduce
if (!(DesiredLayout() == paddle::experimental::DataLayout::UNDEFINED)) {
VLOG(4) << "LayoutAutotune was unstarted. Current op :" << op_name;
return std::make_shared<EagerLayoutTransformer>(
op_name, tensors_vector, tensors_vector[0][0].layout());
}
return std::make_shared<EagerLightlyLayoutSensitiveOpTransformer>(op_name);
}

// For lightly op like argmax
template <typename T1, typename T2>
inline std::shared_ptr<EagerLayoutTransformer> EagerLayoutAutotune(
const std::string& op_name,
const paddle::small_vector<std::vector<paddle::experimental::Tensor>,
kSlotSmallVectorSize>& tensors_vector,
T1* axis,
T2* keep_dim) {
VLOG(3) << "Lightly op " << op_name << "'s shape is "
<< tensors_vector[0][0].shape().size() << " and layout is "
<< tensors_vector[0][0].layout();

// For lightly op like argmax
return EagerLayoutAutotune<T1>(op_name, tensors_vector, axis);
}

// heavily string data_format, data_layout
template <>
inline std::shared_ptr<EagerLayoutTransformer> EagerLayoutAutotune(
const std::string& op_name,
const paddle::small_vector<std::vector<paddle::experimental::Tensor>,
kSlotSmallVectorSize>& tensors_vector,
std::string* attr) {
auto first_layout = tensors_vector[0][0].layout();
// Heavily op with (string) data_format, data_layout
auto transposer = std::make_shared<EagerLayoutTransformer>(
op_name, tensors_vector, first_layout);
if (paddle::imperative::LayoutAutoTune::Instance().GetDesiredLayout() ==
paddle::experimental::DataLayout::UNDEFINED) {
op_name, tensors_vector, tensors_vector[0][0].layout());
if (DesiredLayout() == paddle::experimental::DataLayout::UNDEFINED) {
// Layout autotune only supports model with convolutional layers
VLOG(3) << "Optimze Layout was not started " << op_name;
if (op_name != "conv2d") {
VLOG(4) << "LayoutAutotune was unstarted. Current op :" << op_name;
return transposer;
} else {
auto data_type = tensors_vector[0][0].dtype();
Expand All @@ -134,7 +109,8 @@ inline std::shared_ptr<EagerLayoutTransformer> EagerLayoutAutotune(
bool is_tune_fp16 =
(data_type == paddle::experimental::DataType::FLOAT16) &&
(*attr == "NCHW");
VLOG(3) << "Conv2d_dy's dtype " << data_type << " format" << (*attr);
VLOG(4) << "LayoutAutoTune assert with dtype and layout, Current op : "
<< op_name;
if (is_tune_fp32) {
paddle::imperative::LayoutAutoTune::Instance().SetDesiredLayout(
paddle::experimental::DataLayout::NCHW);
Expand All @@ -147,58 +123,45 @@ inline std::shared_ptr<EagerLayoutTransformer> EagerLayoutAutotune(
paddle::imperative::LayoutAutoTune::Instance().SetDefaultLayout(
paddle::experimental::DataLayout::NCHW);
} else {
VLOG(4) << "DisableLayoutAutoTune accoding to Conv op"
<< " dtype : " << data_type << " format : " << (*attr);
egr::Controller::Instance().DisableLayoutAutoTune();
return transposer;
}
VLOG(3)
<< "Tune the layout from " << *attr << " to "
<< paddle::imperative::LayoutAutoTune::Instance().GetDesiredLayout();
VLOG(4) << "LayoutAutoTune from " << *attr << " to " << DesiredLayout();
}
}

if (paddle::imperative::LayoutAutoTune::Instance().IsHeavilyLayoutSensitive(
op_name)) {
VLOG(3)
<< op_name
<< "'s LayoutTransformer is EagerHeavilyLayoutSensitiveOpTransformer";
auto heavily_transposer =
std::make_shared<EagerHeavilyLayoutSensitiveOpTransformer>(op_name,
attr);
return heavily_transposer;
return std::make_shared<EagerHeavilyLayoutSensitiveOpTransformer>(op_name,
attr);
}

VLOG(3) << op_name << "'s LayoutTransformer is unimplemented. Use default.";
return transposer;
return std::make_shared<EagerLightlyLayoutSensitiveOpTransformer>(op_name);
}

// lightly transpose
template <>
inline std::shared_ptr<EagerLayoutTransformer> EagerLayoutAutotune(
const std::string& op_name,
const paddle::small_vector<std::vector<paddle::experimental::Tensor>,
kSlotSmallVectorSize>& tensors_vector,
std::vector<int>* attr) {
auto first_layout = tensors_vector[0][0].layout();
std::shared_ptr<EagerLayoutTransformer> transposer = nullptr;
if (paddle::imperative::LayoutAutoTune::Instance().GetDesiredLayout() ==
paddle::experimental::DataLayout::UNDEFINED) {
VLOG(3) << "Optimze Layout was not started" << op_name;
transposer = std::make_shared<EagerLayoutTransformer>(
op_name, tensors_vector, first_layout);
return transposer;
// lightly transpose
if (DesiredLayout() == paddle::experimental::DataLayout::UNDEFINED) {
VLOG(4) << "LayoutAutotune was unstarted. Current op :" << op_name;
return std::make_shared<EagerLayoutTransformer>(
op_name, tensors_vector, tensors_vector[0][0].layout());
}

if (op_name == "transpose2" &&
(tensors_vector[0][0].layout() ==
paddle::imperative::LayoutAutoTune::Instance().GetDesiredLayout())) {
(tensors_vector[0][0].layout() == DesiredLayout())) {
auto trans = std::make_shared<EagerTransposeOpTransformer>(op_name);
trans->SetAttr(attr,
tensors_vector[0][0].layout() ==
paddle::experimental::DataLayout::NHWC);
return trans;
}
transposer =
std::make_shared<EagerLightlyLayoutSensitiveOpTransformer>(op_name);
return transposer;
return std::make_shared<EagerLightlyLayoutSensitiveOpTransformer>(op_name);
}

// lightly int argmax
Expand All @@ -210,93 +173,74 @@ EagerLayoutAutotune<paddle::experimental::Scalar, bool>(
kSlotSmallVectorSize>& tensors_vector,
paddle::experimental::Scalar* axis,
bool* keep_dim) {
auto first_layout = tensors_vector[0][0].layout();
std::shared_ptr<EagerLayoutTransformer> transposer = nullptr;
if (paddle::imperative::LayoutAutoTune::Instance().GetDesiredLayout() ==
paddle::experimental::DataLayout::UNDEFINED) {
VLOG(3) << "Optimze Layout was not started" << op_name;
transposer = std::make_shared<EagerLayoutTransformer>(
op_name, tensors_vector, first_layout);
return transposer;
if (DesiredLayout() == paddle::experimental::DataLayout::UNDEFINED) {
VLOG(4) << "LayoutAutotune was unstarted. Current op :" << op_name;
return std::make_shared<EagerLayoutTransformer>(
op_name, tensors_vector, tensors_vector[0][0].layout());
}
auto desired_layout =
paddle::imperative::LayoutAutoTune::Instance().GetDesiredLayout();

if (op_name == "argmax" &&
(tensors_vector[0][0].layout() == desired_layout) && (*keep_dim)) {
(tensors_vector[0][0].layout() == DesiredLayout()) && (*keep_dim)) {
std::shared_ptr<EagerArgmaxOpTransformer> argmax_transform = nullptr;
argmax_transform = std::make_shared<EagerArgmaxOpTransformer>(op_name);
argmax_transform->SetAttr(axis,
tensors_vector[0][0].layout() ==
paddle::experimental::DataLayout::NHWC);
return argmax_transform;
}
transposer =
std::make_shared<EagerLightlyLayoutSensitiveOpTransformer>(op_name);
return transposer;
return std::make_shared<EagerLightlyLayoutSensitiveOpTransformer>(op_name);
}

// lightly for flatten
template <>
inline std::shared_ptr<EagerLayoutTransformer> EagerLayoutAutotune<int, int>(
const std::string& op_name,
const paddle::small_vector<std::vector<paddle::experimental::Tensor>,
kSlotSmallVectorSize>& tensors_vector,
int* start_axis,
int* stop_axis) {
auto first_layout = tensors_vector[0][0].layout();
std::shared_ptr<EagerLayoutTransformer> transposer = nullptr;
auto desired_layout =
paddle::imperative::LayoutAutoTune::Instance().GetDesiredLayout();
if (desired_layout == paddle::experimental::DataLayout::UNDEFINED) {
VLOG(3) << "Optimze Layout was not started" << op_name;
transposer = std::make_shared<EagerLayoutTransformer>(
op_name, tensors_vector, first_layout);
return transposer;
if (DesiredLayout() == paddle::experimental::DataLayout::UNDEFINED) {
VLOG(4) << "Optimze Layout was not started" << op_name;
return std::make_shared<EagerLayoutTransformer>(
op_name, tensors_vector, tensors_vector[0][0].layout());
}
bool no_tranpose = tensors_vector[0][0].layout() == desired_layout;

bool no_tranpose = tensors_vector[0][0].layout() == DesiredLayout();
bool is_valid = ((*start_axis) == 1 && (*stop_axis) == 3);
if (op_name == "flatten" || op_name == "flatten_contiguous_range") {
if (no_tranpose && is_valid) {
std::shared_ptr<EagerFlattenOpTransformer> flatten_transform = nullptr;
flatten_transform = std::make_shared<EagerFlattenOpTransformer>(op_name);
return flatten_transform;
return std::make_shared<EagerFlattenOpTransformer>(op_name);
}
}

transposer =
std::make_shared<EagerLightlyLayoutSensitiveOpTransformer>(op_name);
return transposer;
return std::make_shared<EagerLightlyLayoutSensitiveOpTransformer>(op_name);
}

// lightly int Concat
template <>
inline std::shared_ptr<EagerLayoutTransformer>
EagerLayoutAutotune<paddle::experimental::Scalar>(
const std::string& op_name,
const paddle::small_vector<std::vector<paddle::experimental::Tensor>,
kSlotSmallVectorSize>& tensors_vector,
paddle::experimental::Scalar* axis) {
auto desired_layout =
paddle::imperative::LayoutAutoTune::Instance().GetDesiredLayout();
auto first_layout = tensors_vector[0][0].layout();
std::shared_ptr<EagerLayoutTransformer> transposer = nullptr;
if (desired_layout == paddle::experimental::DataLayout::UNDEFINED) {
VLOG(3) << "Optimze Layout was not started" << op_name;
transposer = std::make_shared<EagerLayoutTransformer>(
op_name, tensors_vector, first_layout);
return transposer;
if (DesiredLayout() == paddle::experimental::DataLayout::UNDEFINED) {
VLOG(4) << "Optimze Layout was not started" << op_name;
return std::make_shared<EagerLayoutTransformer>(
op_name, tensors_vector, tensors_vector[0][0].layout());
}

auto desired_layout = DesiredLayout();
if (NeedTransLayout(tensors_vector, desired_layout)) {
VLOG(3) << op_name << " need transpose to default layout";
transposer =
std::make_shared<EagerLightlyLayoutSensitiveOpTransformer>(op_name);
return transposer;
} else {
auto trans = std::make_shared<EagerConcatOpTransformer>(op_name);
trans->SetAttr(axis, desired_layout);
return trans;
VLOG(4) << op_name << "'s has different layout";
return std::make_shared<EagerLightlyLayoutSensitiveOpTransformer>(op_name);
}
if (op_name == "Concat") {
if (desired_layout == tensors_vector[0][0].layout() &&
tensors_vector[0][0].shape().size() == 4) {
auto trans = std::make_shared<EagerConcatOpTransformer>(op_name);
trans->SetAttr(axis, desired_layout);
return trans;
}
}
return std::make_shared<EagerLightlyLayoutSensitiveOpTransformer>(op_name);
}

} // namespace egr
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