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未验证 提交 10188e8f 编写于 作者: X xiaoguoguo626807 提交者： GitHub
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【prim】delete multiply_triple_grad dygraph path (#54558) 

* mutiply_triple delete

* add case

* add timeout
上级 60e3e350
隐藏空白更改
内联 并排
Showing with 17 addition and 449 deletion
paddle/fluid/eager/api/manual/eager_manual/nodes/multiply_node.cc
浏览文件 @ 10188e8f
......@@ -349,35 +349,22 @@ MultiplyDoubleGradNode::operator()(
// Call grad_api function
if (paddle::prim::PrimCommonUtils::IsEagerPrimEnabled()) {
bool original_global_grad = egr::Controller::Instance().HasGrad();
if (!create_graph) {
egr::Controller::Instance().SetHasGrad(create_graph);
}
paddle::prim::multiply_double_grad<paddle::Tensor>(x,
y,
fwd_grad_out,
fwd_grad_grad_x_optional,
fwd_grad_grad_y_optional,
axis,
api_output_0,
api_output_1,
api_output_2);
VLOG(4) << "Composite api multiply_double_grad is called ";
if (!create_graph) {
egr::Controller::Instance().SetHasGrad(original_global_grad);
}
} else {
paddle::experimental::multiply_double_grad(x,
y,
fwd_grad_out,
fwd_grad_grad_x_optional,
fwd_grad_grad_y_optional,
axis,
api_output_0,
api_output_1,
api_output_2);
VLOG(4) << "Fused api multiply_double_grad is called ";
bool original_global_grad = egr::Controller::Instance().HasGrad();
if (!create_graph) {
egr::Controller::Instance().SetHasGrad(create_graph);
}
paddle::prim::multiply_double_grad<paddle::Tensor>(x,
y,
fwd_grad_out,
fwd_grad_grad_x_optional,
fwd_grad_grad_y_optional,
axis,
api_output_0,
api_output_1,
api_output_2);
VLOG(4) << "Composite api multiply_double_grad is called ";
if (!create_graph) {
egr::Controller::Instance().SetHasGrad(original_global_grad);
}
// Check NaN and Inf id needed
......@@ -417,56 +404,6 @@ MultiplyDoubleGradNode::operator()(
// Create Grad Node
if (!paddle::prim::PrimCommonUtils::IsEagerPrimEnabled()) {
if (trace_backward) {
paddle::platform::RecordEvent node_creation_record_event(
"multiply_double_grad node_creation",
paddle::platform::TracerEventType::OperatorInner,
1);
// Node Construction
auto grad_node = std::shared_ptr<MultiplyTripleGradNode>(
new MultiplyTripleGradNode(3, 5));
// SetAttributes if needed
grad_node->SetAttributeaxis(-1);
// Set TensorWrappers for Forward Inputs if needed
grad_node->SetTensorWrapperx(x);
grad_node->SetTensorWrappery(y);
grad_node->SetTensorWrapperfwd_grad_out(fwd_grad_out);
grad_node->SetTensorWrapperfwd_grad_grad_x(fwd_grad_grad_x);
grad_node->SetTensorWrapperfwd_grad_grad_y(fwd_grad_grad_y);
// SetGradOutMeta & SetEdges
grad_node->SetGradOutMeta(x, 0);
grad_node->SetGradOutMeta(y, 1);
grad_node->SetGradOutMeta(fwd_grad_out, 2);
grad_node->SetGradOutMeta(fwd_grad_grad_x, 3);
grad_node->SetGradOutMeta(fwd_grad_grad_y, 4);
// SetOutRank & SetHistory & SetGradInMeta
if (grad_x_autograd_meta) {
egr::EagerUtils::SetOutRankWithSlot(grad_x_autograd_meta, 0);
}
if (grad_y_autograd_meta) {
egr::EagerUtils::SetOutRankWithSlot(grad_y_autograd_meta, 1);
}
if (grad_grad_out_autograd_meta) {
egr::EagerUtils::SetOutRankWithSlot(grad_grad_out_autograd_meta, 2);
}
if (grad_x_autograd_meta) {
egr::EagerUtils::SetHistory(grad_x_autograd_meta, grad_node);
}
if (grad_y_autograd_meta) {
egr::EagerUtils::SetHistory(grad_y_autograd_meta, grad_node);
}
if (grad_grad_out_autograd_meta) {
egr::EagerUtils::SetHistory(grad_grad_out_autograd_meta, grad_node);
}
grad_node->SetGradInMeta(grad_x, 0);
grad_node->SetGradInMeta(grad_y, 1);
grad_node->SetGradInMeta(grad_grad_out, 2);
// Set TensorWrappers for Forward Outputs if needed
}
}
VLOG(4) << "Finish AD API GRAD: multiply_double_grad";
// LOG IF DEBUG
......@@ -521,309 +458,6 @@ MultiplyDoubleGradNode::operator()(
return returns;
}
paddle::small_vector<std::vector<paddle::Tensor>, egr::kSlotSmallVectorSize>
MultiplyTripleGradNode::operator()(
paddle::small_vector<std::vector<paddle::Tensor>,
egr::kSlotSmallVectorSize>& grads,
bool create_graph,
bool is_new_grad) {
VLOG(3) << "Running AD API GRAD: "
<< "multiply_triple_grad";
// Fill Zero For GradIn Tensors
const auto& input_metas = this->InputMeta();
egr::EagerUtils::FillZeroForEmptyOptionalGradInput(&grads[0][0],
input_metas[0][0]);
egr::EagerUtils::FillZeroForEmptyOptionalGradInput(&grads[1][0],
input_metas[1][0]);
egr::EagerUtils::FillZeroForEmptyOptionalGradInput(&grads[2][0],
input_metas[2][0]);
// Apply Gradient Hooks
auto hooked_grads = ApplyGradientHooks(grads);
// Collect GradIn Tensors, Attrs and Recovered TensorWrappers
auto x = egr::EagerUtils::RecoverTensorWrapper(&this->x_);
auto y = egr::EagerUtils::RecoverTensorWrapper(&this->y_);
auto fwd_grad_out =
egr::EagerUtils::RecoverTensorWrapper(&this->fwd_grad_out_);
auto fwd_grad_grad_x =
egr::EagerUtils::RecoverTensorWrapper(&this->fwd_grad_grad_x_);
paddle::optional<paddle::Tensor> fwd_grad_grad_x_optional;
if (fwd_grad_grad_x.impl())
fwd_grad_grad_x_optional =
paddle::make_optional<paddle::Tensor>(fwd_grad_grad_x);
auto fwd_grad_grad_y =
egr::EagerUtils::RecoverTensorWrapper(&this->fwd_grad_grad_y_);
paddle::optional<paddle::Tensor> fwd_grad_grad_y_optional;
if (fwd_grad_grad_y.impl())
fwd_grad_grad_y_optional =
paddle::make_optional<paddle::Tensor>(fwd_grad_grad_y);
auto& grad_x_grad = hooked_grads[0][0];
paddle::optional<paddle::Tensor> grad_x_grad_optional;
if (grad_x_grad.initialized())
grad_x_grad_optional = paddle::make_optional<paddle::Tensor>(grad_x_grad);
auto& grad_y_grad = hooked_grads[1][0];
paddle::optional<paddle::Tensor> grad_y_grad_optional;
if (grad_y_grad.initialized())
grad_y_grad_optional = paddle::make_optional<paddle::Tensor>(grad_y_grad);
auto& grad_grad_out_grad = hooked_grads[2][0];
paddle::optional<paddle::Tensor> grad_grad_out_grad_optional;
if (grad_grad_out_grad.initialized())
grad_grad_out_grad_optional =
paddle::make_optional<paddle::Tensor>(grad_grad_out_grad);
auto& axis = this->axis_;
// Prepare Grad function call
const auto& out_metas = OutputMeta();
paddle::small_vector<std::vector<paddle::Tensor>, egr::kSlotSmallVectorSize>
returns(5);
for (int i = 0; i < 5; ++i) {
out_metas[i].size() == 0 ? returns[i].resize(1)
: returns[i].resize(out_metas[i].size());
}
auto* api_output_0 =
(out_metas[0].empty() || out_metas[0][0].IsStopGradient())
? nullptr
: &returns[0][0];
auto* api_output_1 =
(out_metas[1].empty() || out_metas[1][0].IsStopGradient())
? nullptr
: &returns[1][0];
auto* api_output_2 =
(out_metas[2].empty() || out_metas[2][0].IsStopGradient())
? nullptr
: &returns[2][0];
auto* api_output_3 =
(out_metas[3].empty() || out_metas[3][0].IsStopGradient())
? nullptr
: &returns[3][0];
auto* api_output_4 =
(out_metas[4].empty() || out_metas[4][0].IsStopGradient())
? nullptr
: &returns[4][0];
// Runtime check if we need next grad
bool trace_backward = egr::Controller::Instance().HasGrad() && create_graph;
// Inplace Check
// Inplace Strategy
VLOG(5) << "Running C++ API: "
<< "multiply_triple_grad";
// Before log info
if (VLOG_IS_ON(3)) {
const char* INPUT_PRINT_TEMPLATE = "{ Input: [%s]} ";
std::string input_str = "";
std::string output_str = "";
const char* TENSOR_GRAD_X_GRAD_TEMPLATE = " \n( grad_x_grad , [%s]), ";
std::string input_grad_x_grad_str = paddle::string::Sprintf(
TENSOR_GRAD_X_GRAD_TEMPLATE, egr::EagerUtils::TensorStr(grad_x_grad));
input_str += input_grad_x_grad_str;
const char* TENSOR_GRAD_Y_GRAD_TEMPLATE = " \n( grad_y_grad , [%s]), ";
std::string input_grad_y_grad_str = paddle::string::Sprintf(
TENSOR_GRAD_Y_GRAD_TEMPLATE, egr::EagerUtils::TensorStr(grad_y_grad));
input_str += input_grad_y_grad_str;
const char* TENSOR_GRAD_GRAD_OUT_GRAD_TEMPLATE =
" \n( grad_grad_out_grad , [%s]), ";
std::string input_grad_grad_out_grad_str =
paddle::string::Sprintf(TENSOR_GRAD_GRAD_OUT_GRAD_TEMPLATE,
egr::EagerUtils::TensorStr(grad_grad_out_grad));
input_str += input_grad_grad_out_grad_str;
const char* TENSOR_X_TEMPLATE = " \n( x , [%s]), ";
std::string input_x_str = paddle::string::Sprintf(
TENSOR_X_TEMPLATE, egr::EagerUtils::TensorStr(x));
input_str += input_x_str;
const char* TENSOR_Y_TEMPLATE = " \n( y , [%s]), ";
std::string input_y_str = paddle::string::Sprintf(
TENSOR_Y_TEMPLATE, egr::EagerUtils::TensorStr(y));
input_str += input_y_str;
const char* TENSOR_FWD_GRAD_OUT_TEMPLATE = " \n( fwd_grad_out , [%s]), ";
std::string input_fwd_grad_out_str = paddle::string::Sprintf(
TENSOR_FWD_GRAD_OUT_TEMPLATE, egr::EagerUtils::TensorStr(fwd_grad_out));
input_str += input_fwd_grad_out_str;
const char* TENSOR_FWD_GRAD_GRAD_X_TEMPLATE =
" \n( fwd_grad_grad_x , [%s]), ";
std::string input_fwd_grad_grad_x_str =
paddle::string::Sprintf(TENSOR_FWD_GRAD_GRAD_X_TEMPLATE,
egr::EagerUtils::TensorStr(fwd_grad_grad_x));
input_str += input_fwd_grad_grad_x_str;
const char* TENSOR_FWD_GRAD_GRAD_Y_TEMPLATE =
" \n( fwd_grad_grad_y , [%s]), ";
std::string input_fwd_grad_grad_y_str =
paddle::string::Sprintf(TENSOR_FWD_GRAD_GRAD_Y_TEMPLATE,
egr::EagerUtils::TensorStr(fwd_grad_grad_y));
input_str += input_fwd_grad_grad_y_str;
VLOG(3) << paddle::string::Sprintf(INPUT_PRINT_TEMPLATE, input_str);
}
// Call grad_api function
paddle::experimental::multiply_triple_grad(x,
y,
fwd_grad_out,
fwd_grad_grad_x_optional,
fwd_grad_grad_y_optional,
grad_x_grad_optional,
grad_y_grad_optional,
grad_grad_out_grad_optional,
axis,
api_output_0,
api_output_1,
api_output_2,
api_output_3,
api_output_4);
// Check NaN and Inf id needed
if (FLAGS_check_nan_inf) {
try {
egr::CheckTensorHasNanOrInf("multiply_triple_grad", returns);
} catch (...) {
LOG(WARNING) << "There are nan/inf in (multiply_triple_grad)";
auto forward_trace = GetForwardTrace();
std::cout << forward_trace << std::endl;
std::rethrow_exception(std::current_exception());
}
}
// Get GradOut autograd_meta
auto& x_grad = returns[0][0];
egr::AutogradMeta* x_grad_autograd_meta =
returns[0][0].initialized() ? egr::EagerUtils::autograd_meta(&x_grad)
: nullptr;
if (x_grad_autograd_meta) x_grad_autograd_meta->SetStopGradient(false);
auto& y_grad = returns[1][0];
egr::AutogradMeta* y_grad_autograd_meta =
returns[1][0].initialized() ? egr::EagerUtils::autograd_meta(&y_grad)
: nullptr;
if (y_grad_autograd_meta) y_grad_autograd_meta->SetStopGradient(false);
auto& fwd_grad_out_grad = returns[2][0];
egr::AutogradMeta* fwd_grad_out_grad_autograd_meta =
returns[2][0].initialized()
? egr::EagerUtils::autograd_meta(&fwd_grad_out_grad)
: nullptr;
if (fwd_grad_out_grad_autograd_meta)
fwd_grad_out_grad_autograd_meta->SetStopGradient(false);
auto& fwd_grad_grad_x_grad = returns[3][0];
egr::AutogradMeta* fwd_grad_grad_x_grad_autograd_meta =
returns[3][0].initialized()
? egr::EagerUtils::autograd_meta(&fwd_grad_grad_x_grad)
: nullptr;
if (fwd_grad_grad_x_grad_autograd_meta)
fwd_grad_grad_x_grad_autograd_meta->SetStopGradient(false);
auto& fwd_grad_grad_y_grad = returns[4][0];
egr::AutogradMeta* fwd_grad_grad_y_grad_autograd_meta =
returns[4][0].initialized()
? egr::EagerUtils::autograd_meta(&fwd_grad_grad_y_grad)
: nullptr;
if (fwd_grad_grad_y_grad_autograd_meta)
fwd_grad_grad_y_grad_autograd_meta->SetStopGradient(false);
// Create Grad Node
if (trace_backward) {
PADDLE_THROW(phi::errors::Unavailable(
"The Op multiply_triple_grad doesn't have any grad"
"op. If you don't intend calculating higher order"
"derivatives, please set `create_graph`to False."));
}
VLOG(4) << "Finish AD API GRAD: multiply_triple_grad";
// LOG IF DEBUG
if (VLOG_IS_ON(4)) {
const char* INPUT_PRINT_TEMPLATE = "{ Input: [%s], \n Output: [%s] } ";
std::string input_str = "";
std::string output_str = "";
const char* TENSOR_GRAD_X_GRAD_TEMPLATE = " \n( grad_x_grad , [%s]), ";
std::string input_grad_x_grad_str = paddle::string::Sprintf(
TENSOR_GRAD_X_GRAD_TEMPLATE, egr::EagerUtils::TensorStr(grad_x_grad));
input_str += input_grad_x_grad_str;
const char* TENSOR_GRAD_Y_GRAD_TEMPLATE = " \n( grad_y_grad , [%s]), ";
std::string input_grad_y_grad_str = paddle::string::Sprintf(
TENSOR_GRAD_Y_GRAD_TEMPLATE, egr::EagerUtils::TensorStr(grad_y_grad));
input_str += input_grad_y_grad_str;
const char* TENSOR_GRAD_GRAD_OUT_GRAD_TEMPLATE =
" \n( grad_grad_out_grad , [%s]), ";
std::string input_grad_grad_out_grad_str =
paddle::string::Sprintf(TENSOR_GRAD_GRAD_OUT_GRAD_TEMPLATE,
egr::EagerUtils::TensorStr(grad_grad_out_grad));
input_str += input_grad_grad_out_grad_str;
const char* TENSOR_X_TEMPLATE = " \n( x , [%s]), ";
std::string input_x_str = paddle::string::Sprintf(
TENSOR_X_TEMPLATE, egr::EagerUtils::TensorStr(x));
input_str += input_x_str;
const char* TENSOR_Y_TEMPLATE = " \n( y , [%s]), ";
std::string input_y_str = paddle::string::Sprintf(
TENSOR_Y_TEMPLATE, egr::EagerUtils::TensorStr(y));
input_str += input_y_str;
const char* TENSOR_FWD_GRAD_OUT_TEMPLATE = " \n( fwd_grad_out , [%s]), ";
std::string input_fwd_grad_out_str = paddle::string::Sprintf(
TENSOR_FWD_GRAD_OUT_TEMPLATE, egr::EagerUtils::TensorStr(fwd_grad_out));
input_str += input_fwd_grad_out_str;
const char* TENSOR_FWD_GRAD_GRAD_X_TEMPLATE =
" \n( fwd_grad_grad_x , [%s]), ";
std::string input_fwd_grad_grad_x_str =
paddle::string::Sprintf(TENSOR_FWD_GRAD_GRAD_X_TEMPLATE,
egr::EagerUtils::TensorStr(fwd_grad_grad_x));
input_str += input_fwd_grad_grad_x_str;
const char* TENSOR_FWD_GRAD_GRAD_Y_TEMPLATE =
" \n( fwd_grad_grad_y , [%s]), ";
std::string input_fwd_grad_grad_y_str =
paddle::string::Sprintf(TENSOR_FWD_GRAD_GRAD_Y_TEMPLATE,
egr::EagerUtils::TensorStr(fwd_grad_grad_y));
input_str += input_fwd_grad_grad_y_str;
const char* TENSOR_X_GRAD_TEMPLATE = " \n ( x_grad , [%s]), ";
std::string output_x_grad_str = paddle::string::Sprintf(
TENSOR_X_GRAD_TEMPLATE, egr::EagerUtils::TensorStr(x_grad));
output_str += output_x_grad_str;
const char* TENSOR_Y_GRAD_TEMPLATE = " \n ( y_grad , [%s]), ";
std::string output_y_grad_str = paddle::string::Sprintf(
TENSOR_Y_GRAD_TEMPLATE, egr::EagerUtils::TensorStr(y_grad));
output_str += output_y_grad_str;
const char* TENSOR_FWD_GRAD_OUT_GRAD_TEMPLATE =
" \n ( fwd_grad_out_grad , [%s]), ";
std::string output_fwd_grad_out_grad_str =
paddle::string::Sprintf(TENSOR_FWD_GRAD_OUT_GRAD_TEMPLATE,
egr::EagerUtils::TensorStr(fwd_grad_out_grad));
output_str += output_fwd_grad_out_grad_str;
const char* TENSOR_FWD_GRAD_GRAD_X_GRAD_TEMPLATE =
" \n ( fwd_grad_grad_x_grad , [%s]), ";
std::string output_fwd_grad_grad_x_grad_str = paddle::string::Sprintf(
TENSOR_FWD_GRAD_GRAD_X_GRAD_TEMPLATE,
egr::EagerUtils::TensorStr(fwd_grad_grad_x_grad));
output_str += output_fwd_grad_grad_x_grad_str;
const char* TENSOR_FWD_GRAD_GRAD_Y_GRAD_TEMPLATE =
" \n ( fwd_grad_grad_y_grad , [%s]), ";
std::string output_fwd_grad_grad_y_grad_str = paddle::string::Sprintf(
TENSOR_FWD_GRAD_GRAD_Y_GRAD_TEMPLATE,
egr::EagerUtils::TensorStr(fwd_grad_grad_y_grad));
output_str += output_fwd_grad_grad_y_grad_str;
VLOG(4) << paddle::string::Sprintf(
INPUT_PRINT_TEMPLATE, input_str, output_str);
}
// Return
if (NeedComplexToRealConversion()) HandleComplexGradToRealGrad(&returns);
return returns;
}
namespace sparse {
paddle::small_vector<std::vector<paddle::Tensor>, egr::kSlotSmallVectorSize>
MultiplyGradNode::operator()(
......
paddle/fluid/eager/api/manual/eager_manual/nodes/nodes.h
浏览文件 @ 10188e8f
......@@ -312,69 +312,6 @@ class MultiplyDoubleGradNode : public egr::GradNodeBase {
int axis_ = -1;
};
class MultiplyTripleGradNode : public egr::GradNodeBase {
public:
MultiplyTripleGradNode() : egr::GradNodeBase() {}
MultiplyTripleGradNode(size_t bwd_in_slot_num, size_t bwd_out_slot_num)
: egr::GradNodeBase(bwd_in_slot_num, bwd_out_slot_num) {}
~MultiplyTripleGradNode() override = default;
virtual paddle::small_vector<std::vector<paddle::Tensor>,
egr::kSlotSmallVectorSize>
operator()(paddle::small_vector<std::vector<paddle::Tensor>, // NOLINT
egr::kSlotSmallVectorSize>& grads, // NOLINT
bool create_graph = false,
bool is_new_grad = false) override;
std::string name() override { return "MultiplyTripleGradNode"; }
void ClearTensorWrappers() override {
x_.clear();
y_.clear();
fwd_grad_out_.clear();
fwd_grad_grad_x_.clear();
fwd_grad_grad_y_.clear();
SetIsTensorWrappersCleared(true);
}
std::shared_ptr<GradNodeBase> Copy() const override {
auto copied_node = std::shared_ptr<MultiplyTripleGradNode>(
new MultiplyTripleGradNode(*this));
return copied_node;
}
// SetTensorWrapperX, SetTensorWrapperY, ...
void SetTensorWrapperx(const paddle::Tensor& x) {
x_ = egr::TensorWrapper(x, false);
}
void SetTensorWrappery(const paddle::Tensor& y) {
y_ = egr::TensorWrapper(y, false);
}
void SetTensorWrapperfwd_grad_out(const paddle::Tensor& fwd_grad_out) {
fwd_grad_out_ = egr::TensorWrapper(fwd_grad_out, false);
}
void SetTensorWrapperfwd_grad_grad_x(const paddle::Tensor& fwd_grad_grad_x) {
fwd_grad_grad_x_ = egr::TensorWrapper(fwd_grad_grad_x, false);
}
void SetTensorWrapperfwd_grad_grad_y(const paddle::Tensor& fwd_grad_grad_y) {
fwd_grad_grad_y_ = egr::TensorWrapper(fwd_grad_grad_y, false);
}
// SetAttributes
void SetAttributeaxis(const int& axis) { axis_ = axis; }
private:
// TensorWrappers
egr::TensorWrapper x_;
egr::TensorWrapper y_;
egr::TensorWrapper fwd_grad_out_;
egr::TensorWrapper fwd_grad_grad_x_;
egr::TensorWrapper fwd_grad_grad_y_;
// Attributes
int axis_ = -1;
};
class SyncBatchNormGradNode : public egr::GradNodeBase {
public:
SyncBatchNormGradNode() : egr::GradNodeBase() {}
......
test/prim/prim/vjp/CMakeLists.txt
浏览文件 @ 10188e8f
......@@ -8,7 +8,7 @@ foreach(TEST_OP ${TEST_OPS})
py_test_modules(${TEST_OP} MODULES ${TEST_OP} ENVS ${GC_ENVS})
endforeach()
set_tests_properties(test_comp_high_grad PROPERTIES TIMEOUT 50)
set_tests_properties(test_comp_high_grad PROPERTIES TIMEOUT 100)
add_subdirectory(eager)
add_subdirectory(static)
test/prim/prim/vjp/test_comp_high_grad.py
浏览文件 @ 10188e8f
......@@ -226,7 +226,6 @@ class TestSubtractHighGradCheck(unittest.TestCase):
self.func_triple(p)
'''
@param.parameterized_class(
('shape1', 'shape2'),
[
......@@ -330,8 +329,6 @@ class TestMultiplyHighGradCheck(unittest.TestCase):
self.func_double(p)
self.func_triple(p)
'''
@param.parameterized_class(
('shape1'),
......
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