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未验证 提交 54a43981 编写于 作者: J Jiabin Yang 提交者： GitHub
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[Eager] Optimize log (#45783) 

* make eager log readable

* fix compile error

* recover test

* invoke ci again
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paddle/fluid/eager/accumulation/accumulation_node.cc
浏览文件 @ 54a43981
......@@ -16,6 +16,7 @@
#include "glog/logging.h"
#include "paddle/fluid/eager/eager_tensor.h"
#include "paddle/fluid/eager/utils.h"
#include "paddle/fluid/imperative/gradient_accumulator.h"
#include "paddle/fluid/platform/device_context.h"
#include "paddle/fluid/platform/enforce.h"
......@@ -89,7 +90,7 @@ GradNodeAccumulation::operator()(
kSlotSmallVectorSize>& grads, // NOLINT
bool create_graph,
bool is_new_grad) {
VLOG(3) << "Running Eager Backward Node: GradNodeAccumulation";
VLOG(3) << "Running AD API Grad: GradNodeAccumulation";
PADDLE_ENFORCE(grads.size() == 1,
paddle::platform::errors::Fatal(
"GradNodeAccumulation should take exactly 1 grad tensor"
......@@ -122,7 +123,22 @@ GradNodeAccumulation::operator()(
if (ReduceHooksRegistered()) {
ApplyReduceHooks();
}
VLOG(3) << "Finish AD API Grad: GradNodeAccumulation";
if (VLOG_IS_ON(4)) {
const char* INPUT_PRINT_TEMPLATE = "{ Input: [%s], Output: [%s] } ";
std::string input_str = "";
std::string output_str = "";
const char* TENSOR_OUT_GRAD_TEMPLATE = "(grads[0][0], [%s]), ";
std::string input_out_grad_str = paddle::string::Sprintf(
TENSOR_OUT_GRAD_TEMPLATE, egr::EagerUtils::TensorStr(grads[0][0]));
const char* TENSOR_X_GRAD_TEMPLATE = "(grad_out, [%s]), ";
std::string output_x_grad_str = paddle::string::Sprintf(
TENSOR_X_GRAD_TEMPLATE, egr::EagerUtils::TensorStr(grad_out));
output_str += output_x_grad_str;
VLOG(4) << paddle::string::Sprintf(
INPUT_PRINT_TEMPLATE, input_str, output_str);
}
return {{grad_out}};
}
......
paddle/fluid/eager/accumulation/accumulation_node.h
浏览文件 @ 54a43981
......@@ -24,7 +24,7 @@ class GradNodeAccumulation : public GradNodeBase {
public:
// Constructor: configure fwd input tensors to grad node
explicit GradNodeAccumulation(AutogradMeta* meta) : GradNodeBase(1, 1) {
VLOG(6) << "Construct GradNodeAccumulation";
VLOG(5) << "Construct GradNodeAccumulation";
if (meta) {
weak_grad_ = meta->WeakGrad();
}
......@@ -33,7 +33,7 @@ class GradNodeAccumulation : public GradNodeBase {
}
~GradNodeAccumulation() override {
VLOG(6) << "Destruct GradNodeAccumulation";
VLOG(5) << "Destruct GradNodeAccumulation";
}
// Functor: perform backward computations
......@@ -44,7 +44,7 @@ class GradNodeAccumulation : public GradNodeBase {
bool create_graph = false,
bool is_new_grad = false) override;
void ClearTensorWrappers() override { VLOG(6) << "Do nothing here now"; }
void ClearTensorWrappers() override { VLOG(5) << "Do nothing here now"; }
std::string name() { return "GradNodeAccumulation"; }
......
paddle/fluid/eager/api/manual/eager_manual/dygraph_forward_api.h
浏览文件 @ 54a43981
......@@ -16,10 +16,10 @@
#include "paddle/phi/api/include/tensor.h"
paddle::experimental::Tensor add_n_dygraph_function(
paddle::experimental::Tensor add_n_ad_func(
const std::vector<paddle::experimental::Tensor>& x);
paddle::experimental::Tensor conv2d_dygraph_function(
paddle::experimental::Tensor conv2d_ad_func(
const paddle::experimental::Tensor& input,
const paddle::experimental::Tensor& filter,
std::vector<int> strides,
......
paddle/fluid/eager/api/manual/eager_manual/forwards/add_n_fwd_func.cc
浏览文件 @ 54a43981
......@@ -23,7 +23,7 @@
#pragma GCC diagnostic ignored "-Wunused-variable"
DECLARE_bool(check_nan_inf);
paddle::experimental::Tensor add_n_dygraph_function(
paddle::experimental::Tensor add_n_ad_func(
const std::vector<paddle::experimental::Tensor>& x) {
// Dygraph Record Event
paddle::platform::RecordEvent dygraph_entrance_record_event(
......@@ -46,7 +46,7 @@ paddle::experimental::Tensor add_n_dygraph_function(
paddle::imperative::AutoCastGuard guard(
egr::Controller::Instance().GetCurrentTracer(),
paddle::imperative::AmpLevel::O0);
return add_n_dygraph_function(NEW_x);
return add_n_ad_func(NEW_x);
}
}
......@@ -56,7 +56,7 @@ paddle::experimental::Tensor add_n_dygraph_function(
std::vector<egr::AutogradMeta*>* x_autograd_meta = &x_autograd_meta_vec;
// Forward API Call
VLOG(3) << "Final State Running: "
<< "add_n_dygraph_function";
<< "add_n_ad_func";
auto api_result = paddle::experimental::add_n(x);
// Check NaN and Inf if needed
if (FLAGS_check_nan_inf) {
......
paddle/fluid/eager/api/manual/eager_manual/forwards/conv2d_fwd_function.cc
浏览文件 @ 54a43981
......@@ -24,7 +24,7 @@
#pragma GCC diagnostic ignored "-Wunused-variable"
DECLARE_bool(check_nan_inf);
paddle::experimental::Tensor conv2d_dygraph_function(
paddle::experimental::Tensor conv2d_ad_func(
const paddle::experimental::Tensor& input,
const paddle::experimental::Tensor& filter,
std::vector<int> strides,
......@@ -60,7 +60,7 @@ paddle::experimental::Tensor conv2d_dygraph_function(
paddle::imperative::AutoCastGuard guard(
egr::Controller::Instance().GetCurrentTracer(),
paddle::imperative::AmpLevel::O0);
return conv2d_dygraph_function(NEW_input,
return conv2d_ad_func(NEW_input,
NEW_filter,
strides,
paddings,
......@@ -89,7 +89,7 @@ paddle::experimental::Tensor conv2d_dygraph_function(
bool is_enable_tune =
paddle::imperative::LayoutAutoTune::Instance().UseLayoutAutoTune();
paddle::imperative::LayoutAutoTune::Instance().DisableLayoutAutoTune();
auto out = conv2d_dygraph_function(NEW_input,
auto out = conv2d_ad_func(NEW_input,
filter,
strides,
paddings,
......@@ -115,7 +115,7 @@ paddle::experimental::Tensor conv2d_dygraph_function(
egr::EagerUtils::nullable_autograd_meta(filter);
// Forward API Call
VLOG(3) << "Final State Running: "
<< "conv2d_dygraph_function";
<< "conv2d_ad_func";
auto api_result = paddle::experimental::conv2d(input,
filter,
strides,
......
paddle/fluid/eager/api/manual/eager_manual/nodes/add_n_node.cc
浏览文件 @ 54a43981
......@@ -64,8 +64,7 @@ AddNGradNodeFinal::operator()(
// dygraph function
for (size_t i = 0; i < returns[0].size(); i++) {
returns[0][i] =
::scale_dygraph_function(out_grad, phi::Scalar(1.0), 0.0, true);
returns[0][i] = ::scale_ad_func(out_grad, phi::Scalar(1.0), 0.0, true);
}
// Check NaN and Inf id needed
......
paddle/fluid/eager/api/manual/fluid_manual/forwards/fused_attention_fwd_func.cc
浏览文件 @ 54a43981
......@@ -531,7 +531,6 @@ fused_attention_dygraph_function(
egr::EagerUtils::SetHistory(p_autograd_Y, grad_node);
grad_node->SetGradInMeta(Y, 19);
egr::EagerUtils::CheckAndRetainGrad(Y);
auto QKVOut_accumulation_node =
std::make_shared<egr::GradNodeAccumulation>(p_autograd_QKVOut);
egr::EagerUtils::SetOutRankWithSlot(p_autograd_QKVOut, 0);
......
paddle/fluid/eager/auto_code_generator/generator/codegen_utils.py
浏览文件 @ 54a43981
......@@ -161,11 +161,24 @@ def GetGradNodeName(string):
string = str2Hump(string)
if string.rfind("Grad") == (len(string) - 4):
string = string[:-4]
return f"{string}GradNodeFinal"
return f"{string}GradNode"
def GetDygraphForwardFunctionName(string):
return f"{string}_dygraph_function"
return f"{string}_ad_func"
def GetDygraphLogName(string):
def str2Hump(text):
arr = filter(None, text.split('_'))
res = ''
for i in arr:
res = res + i[0].upper() + i[1:]
return res
string = str2Hump(string)
return string
def GetIntermediateAPIFunctionName(string):
......@@ -198,7 +211,7 @@ def GetInplacedFunctionName(function_name):
def GetForwardFunctionName(string):
return f"{string}_dygraph_function"
return f"{string}_ad_func"
def GetIndent(num):
......
paddle/fluid/eager/auto_code_generator/generator/eager_gen.py
浏览文件 @ 54a43981
......@@ -23,7 +23,7 @@ from codegen_utils import ReadFwdFile, ReadBwdFile
from codegen_utils import FindGradName, FindForwardName, GetSavedName, GetGradNodeName
from codegen_utils import IsPlainTensorType, IsVectorTensorType
from codegen_utils import GetConstReference, RemoveConstAndReference
from codegen_utils import GetDygraphForwardFunctionName, GetIntermediateAPIFunctionName
from codegen_utils import GetDygraphForwardFunctionName, GetIntermediateAPIFunctionName, GetDygraphLogName
from codegen_utils import GetAutoGradMetaName, GetAutoGradMetaVectorName
from codegen_utils import RemoveSpecialSymbolsInName, RecoverBaseNameOfInplaceFunction
from codegen_utils import GetInplacedFunctionName
......@@ -150,6 +150,7 @@ class {} : public egr::GradNodeBase {{
GRAD_FUNCTION_TEMPLATE = \
"""
paddle::small_vector<std::vector<paddle::experimental::Tensor>, egr::kSlotSmallVectorSize> {}::operator()(paddle::small_vector<std::vector<paddle::experimental::Tensor>, egr::kSlotSmallVectorSize>& grads, bool create_graph, bool is_new_grad) {{
VLOG(3) << \"Running AD API GRAD: \" << \"{}\";
// Fill Zero For GradIn Tensors
{}
// Apply Gradient Hooks
......@@ -166,7 +167,7 @@ paddle::small_vector<std::vector<paddle::experimental::Tensor>, egr::kSlotSmallV
// Inplace Strategy
{}
// Call grad_api function
VLOG(3) << \"Final State Running: {}\";
VLOG(5) << \"Running C++ API: \" << \"{}\";
{}
// Check NaN and Inf id needed
{}
......@@ -174,6 +175,9 @@ paddle::small_vector<std::vector<paddle::experimental::Tensor>, egr::kSlotSmallV
{}
// Create Grad Node
{}
VLOG(4) << \"Finish AD API GRAD: {}";
// LOG IF DEBUG
{}
// Return
{}
}}
......@@ -182,6 +186,7 @@ paddle::small_vector<std::vector<paddle::experimental::Tensor>, egr::kSlotSmallV
FORWARD_FUNCTION_TEMPLATE = \
"""
{} {}({}) {{
VLOG(3) << \"Running AD API: \" << \"{}\";
// Dygraph Record Event
{}
// AMP Logic
......@@ -191,7 +196,7 @@ FORWARD_FUNCTION_TEMPLATE = \
// Get Input AutoGradMeta
{}
// Forward API Call
VLOG(3) << \"Final State Running: \" << \"{}\";
VLOG(5) << \"Running C++ API: \" << \"{}\";
{}
// Check NaN and Inf if needed
{}
......@@ -206,15 +211,29 @@ FORWARD_FUNCTION_TEMPLATE = \
{}{}
// Node Creation
{}
VLOG(4) << \"Finish AD API: {}";
// LOG IF DEBUG
{}
// Returns
return {};
}}
"""
LOG_PRINT_TEMPLATE = \
"""
if(VLOG_IS_ON(4)){{
const char* INPUT_PRINT_TEMPLATE = \"{{ Input: [%s], Output: [%s] }} \";
{}
VLOG(4) << paddle::string::Sprintf(INPUT_PRINT_TEMPLATE, input_str, output_str);
}}
"""
FORWARD_ONLY_FUNCTION_TEMPLATE = \
"""
{} {}({}) {{
VLOG(3) << \"Running AD API: \" << \"{}\";
// Dygraph Record Event
{}
// AMP Logic
......@@ -222,11 +241,13 @@ FORWARD_ONLY_FUNCTION_TEMPLATE = \
// Layout autotune
{}
// Forward API Call
VLOG(3) << \"Final State Running: \" << \"{}\";
VLOG(5) << \"Running C++ API: \" << \"{}\";
{}
// Get Outputs
{}
VLOG(4) << \"Finish AD API: {}";
// LOG IF DEBUG
{}
// Returns
return {};
}}
......@@ -867,7 +888,7 @@ class DygraphFunctionGeneratorBase(FunctionGeneratorBase):
set_grad_out_meta_list.append(set_grad_out_meta)
set_grad_out_meta_str = "\n".join(set_grad_out_meta_list)
# SetOutRank & SetHistory & SetGradInMeta & CheckAndRetainGrad
# SetOutRank & SetHistory & SetGradInMeta
set_out_rank_list = []
set_history_list = []
set_grad_in_meta_list = []
......@@ -885,7 +906,6 @@ class DygraphFunctionGeneratorBase(FunctionGeneratorBase):
set_grad_in_meta = f"{indent}grad_node->SetGradInMeta({name}, {pos});"
set_retain_grad = f"{indent}egr::EagerUtils::CheckAndRetainGrad({name});"
set_out_rank_list.append(set_out_rank)
set_history_list.append(set_history)
set_grad_in_meta_list.append(set_grad_in_meta)
......@@ -1294,7 +1314,8 @@ class DygraphForwardFunctionGenerator(DygraphFunctionGeneratorBase):
node_creation_str = self.node_creation_str
dygraph_event_str = f"{indent}paddle::platform::RecordEvent dygraph_entrance_record_event(\"{forward_api_name} dygraph\", paddle::platform::TracerEventType::Operator, 1);\n"
forward_function_name = GetDygraphForwardFunctionName(forward_api_name)
forward_ad_function_name = GetDygraphForwardFunctionName(
forward_api_name)
# Forward amp logic
kernel_trans2_op_name_str = f"auto op_name = phi::TransToFluidOpName(\"{forward_api_name}\");"
......@@ -1307,9 +1328,10 @@ class DygraphForwardFunctionGenerator(DygraphFunctionGeneratorBase):
amp_autocast_list) + " " + " ".join(
amp_autocast_optional_list)
amp_inputs_call_args_str = ", ".join(amp_inputs_call_list)
amp_call_str = f"return {forward_function_name}({amp_inputs_call_args_str});"
amp_call_str = f"return {forward_ad_function_name}({amp_inputs_call_args_str});"
if is_inplaced or (forward_api_name == "cast"):
amp_logic_str = ""
amp_logic_str = "\n VLOG(5) << \" No AMP for {} because it is a inplace or cast api. \"; ".format(
forward_ad_function_name)
else:
amp_logic_str = AMP_LOGIC_TEMPLATE.format(
kernel_trans2_op_name_str, amp_tensors_vector_list_str,
......@@ -1335,8 +1357,8 @@ class DygraphForwardFunctionGenerator(DygraphFunctionGeneratorBase):
layout_autotune_attr) == 0:
layout_logic_str = ""
else:
# after_call_str = f"return {forward_function_name}({layout_inputs_call_args_str});\n"
after_call_str = f"auto api_result = {forward_function_name}({layout_inputs_call_args_str});\n"
# after_call_str = f"return {forward_ad_function_name}({layout_inputs_call_args_str});\n"
after_call_str = f"auto api_result = {forward_ad_function_name}({layout_inputs_call_args_str});\n"
layout_logic_str = LAYOUT_LOGIC_TEMPLATE.format(
amp_tensors_vector_list_str,
" ".join(layout_tensors_vector_optional_list),
......@@ -1345,26 +1367,45 @@ class DygraphForwardFunctionGenerator(DygraphFunctionGeneratorBase):
" ".join(layout_autotune_optional_list), after_call_str,
layout_autotune_outs_list, returns_str)
# For inputs outputs prepare for logging
var_str = f"\n{indent} std::string input_str = \"\";"
var_str += f"\n{indent} std::string output_str = \"\";"
for name, (ttype, pos) in forward_inputs_position_map.items():
var_str += f"\n{indent} const char* TENSOR_{name.upper()}_TEMPLATE = \"({name}, [%s]), \";"
var_str += f"\n{indent} std::string input_{name}_str = paddle::string::Sprintf(TENSOR_{name.upper()}_TEMPLATE, egr::EagerUtils::TensorStr({name}));"
var_str += f"\n{indent} input_str += input_{name}_str; "
for name, (ttype, pos) in forward_outputs_position_map.items():
var_str += f"\n{indent} const char* TENSOR_{name.upper()}_TEMPLATE = \"({name}, [%s]), \";"
var_str += f"\n{indent} std::string output_{name}_str = paddle::string::Sprintf(TENSOR_{name.upper()}_TEMPLATE, egr::EagerUtils::TensorStr({name}));"
var_str += f"\n{indent} output_str += output_{name}_str; "
log_str = LOG_PRINT_TEMPLATE.format(var_str)
# Generate forward_definition_str and forward_declaration_str
if self.is_forward_only:
if len(amp_tensors_vector_list) == 0:
amp_logic_str = ""
amp_logic_str = "\n VLOG(7) << \" No AMP for {} because it has no input. \"; ".format(
forward_ad_function_name)
self.forward_definition_str += FORWARD_ONLY_FUNCTION_TEMPLATE.format(
returns_type_str, forward_function_name,
inputs_args_definition_str, dygraph_event_str, amp_logic_str,
layout_logic_str, forward_function_name, forward_call_str,
get_outputs_str, returns_str)
returns_type_str,
forward_ad_function_name, inputs_args_definition_str,
GetDygraphLogName(forward_api_name), dygraph_event_str,
amp_logic_str, layout_logic_str, forward_api_name,
forward_call_str, get_outputs_str, forward_ad_function_name,
log_str, returns_str)
else:
self.forward_definition_str += FORWARD_FUNCTION_TEMPLATE.format(
returns_type_str, forward_function_name,
inputs_args_definition_str, dygraph_event_str, amp_logic_str,
layout_logic_str, inputs_autograd_meta_str,
forward_function_name, forward_call_str, check_nan_inf_str,
returns_type_str,
forward_ad_function_name, inputs_args_definition_str,
GetDygraphLogName(forward_api_name), dygraph_event_str,
amp_logic_str, layout_logic_str, inputs_autograd_meta_str,
forward_api_name, forward_call_str, check_nan_inf_str,
get_outputs_str, outputs_autograd_meta_str,
compute_require_grad_args_str, check_inplace_str,
bump_inplace_version_str, node_creation_str, returns_str)
bump_inplace_version_str, node_creation_str,
forward_ad_function_name, log_str, returns_str)
self.forward_declaration_str += f"{returns_type_str} {forward_function_name}({inputs_args_declaration_str});\n"
self.forward_declaration_str += f"{returns_type_str} {forward_ad_function_name}({inputs_args_declaration_str});\n"
def GenerateInplacedForwardDygraphFunctions(self):
# Inplaced Version Dygraph Function Generation
......@@ -1770,7 +1811,8 @@ class DygraphNodeGenerator(DygraphFunctionGeneratorBase):
forward_api_name = self.grad_api_contents['invoke'].split(
'(')[0].strip()
autograd_api = self.grad_api_contents['invoke'].replace(
forward_api_name, forward_api_name + '_dygraph_function', 1)
forward_api_name,
GetDygraphForwardFunctionName(forward_api_name), 1)
grad_function_call_str = f"""
if (trace_backward) {{
{indent}{autograd_api_out} api_output = {autograd_api};
......@@ -1839,13 +1881,40 @@ class DygraphNodeGenerator(DygraphFunctionGeneratorBase):
returns_str += f"{indent}return returns;\n"
grad_node_name = GetGradNodeName(self.backward_api_name)
# For inputs outputs prepare for logging
var_str = f"\n{indent} std::string input_str = \"\";"
var_str += f"\n{indent} std::string output_str = \"\";"
for name, (ttype, fwd_position,
grad_api_position) in backward_grad_inputs_map.items():
new_name = self.TransformToNextGradName(name)
var_str += f"\n{indent} const char* TENSOR_{new_name.upper()}_TEMPLATE = \"({new_name}, [%s]), \";"
var_str += f"\n{indent} std::string input_{new_name}_str = paddle::string::Sprintf(TENSOR_{new_name.upper()}_TEMPLATE, egr::EagerUtils::TensorStr({new_name}));"
var_str += f"\n{indent} input_str += input_{new_name}_str; "
for name, (backward_input_type, is_fwd_input,
grad_api_position), in backward_forward_inputs_map.items():
new_name = self.TransformToNextGradName(name)
var_str += f"\n{indent} const char* TENSOR_{new_name.upper()}_TEMPLATE = \"({new_name}, [%s]), \";"
var_str += f"\n{indent} std::string input_{new_name}_str = paddle::string::Sprintf(TENSOR_{new_name.upper()}_TEMPLATE, egr::EagerUtils::TensorStr({new_name}));"
var_str += f"\n{indent} input_str += input_{new_name}_str; "
for name, (ttype, fwd_position,
grad_api_position) in backward_grad_outputs_map.items():
new_name = self.TransformToNextGradName(name)
var_str += f"\n{indent} const char* TENSOR_{new_name.upper()}_TEMPLATE = \"({new_name}, [%s]), \";"
var_str += f"\n{indent} std::string output_{new_name}_str = paddle::string::Sprintf(TENSOR_{new_name.upper()}_TEMPLATE, egr::EagerUtils::TensorStr({new_name}));"
var_str += f"\n{indent} output_str += output_{new_name}_str; "
log_str = LOG_PRINT_TEMPLATE.format(var_str)
self.node_definition_str = GRAD_FUNCTION_TEMPLATE.format(
grad_node_name, fill_zero_str, get_grad_in_args_str,
grad_function_prepare_str, compute_require_next_grad_str,
inplace_check_str, inplace_for_grad_outs_str, grad_node_name,
grad_node_name, GetDygraphLogName(self.backward_api_name),
fill_zero_str, get_grad_in_args_str, grad_function_prepare_str,
compute_require_next_grad_str, inplace_check_str,
inplace_for_grad_outs_str, self.backward_api_name,
grad_function_call_str, check_nan_inf_str,
outputs_autograd_meta_str, next_grad_node_creation_str, returns_str)
outputs_autograd_meta_str, next_grad_node_creation_str,
GetDygraphLogName(self.backward_api_name), log_str, returns_str)
def run(self):
super().run()
......
paddle/fluid/eager/backward.cc
浏览文件 @ 54a43981
......@@ -133,7 +133,7 @@ std::vector<paddle::experimental::Tensor> RunBackward(
AutogradMeta* auto_grad_meta = EagerUtils::nullable_autograd_meta(tensor);
if (auto_grad_meta == nullptr) {
VLOG(3) << "Skip auto grad since there is no grad op for var or loss is "
VLOG(5) << "Skip auto grad since there is no grad op for var or loss is "
"stop_gradient=True: "
<< tensor.name();
continue;
......@@ -141,14 +141,14 @@ std::vector<paddle::experimental::Tensor> RunBackward(
// Get grad input info from target tensors
auto input_info = auto_grad_meta->OutRankInfo();
VLOG(2) << "Out Rank of Tensor is slot: " << input_info.first
VLOG(5) << "Out Rank of Tensor is slot: " << input_info.first
<< ", rank: " << input_info.second;
// Get target GradNodeBase from target tensors
auto shared_grad_node = auto_grad_meta->GetMutableGradNode();
if (shared_grad_node == nullptr || shared_grad_node.get() == nullptr ||
auto_grad_meta->StopGradient()) {
VLOG(3) << "Skip auto grad since there is no grad op for var or loss is "
VLOG(5) << "Skip auto grad since there is no grad op for var or loss is "
"stop_gradient=True: "
<< tensor.name();
continue;
......@@ -169,7 +169,7 @@ std::vector<paddle::experimental::Tensor> RunBackward(
// Prepare GradTensorHolder
if (!node_input_buffers_dict.count(grad_node)) {
VLOG(6) << "Create Value for grad input tensor " << i
VLOG(5) << "Create Value for grad input tensor " << i
<< " of grad node: " << grad_node->name();
node_input_buffers_dict[grad_node] =
std::make_unique<GradTensorHolder>(grad_node->InputMeta());
......@@ -184,13 +184,13 @@ std::vector<paddle::experimental::Tensor> RunBackward(
"grad_tensors should either have "
"size = 0 or same size as tensors."));
// Feed given tensor if it's provided
VLOG(6) << "Fill grad input tensor " << i << "with give grad tensor";
VLOG(3) << "Fill grad input tensor " << i << "with give grad tensor";
// Deep copy
node_input_buffers_dict[grad_node]->CopyValueFromTensor(
input_info.first, input_info.second, grad_tensors[i]);
} else {
VLOG(6) << "Fill grad input tensor " << i << " with 1.0";
VLOG(3) << "Fill grad input tensor " << i << " with 1.0";
// Initialize tensor with 1.0
// Forward Tensor "tensor" is passed to indicate tensortype, datatype and
// dims
......@@ -210,12 +210,12 @@ std::vector<paddle::experimental::Tensor> RunBackward(
inputs, no_grad_vars, orig_queue, &queue, node_input_buffers_dict);
}
VLOG(6) << "Update In degree Map for backward";
VLOG(5) << "Update In degree Map for backward";
// 3. Compute in_degree for each node
std::unordered_map<GradNodeBase*, int> node_in_degree_map =
getInDegreeMap(queue);
VLOG(3) << "Startup_ops's size is " << queue.size();
VLOG(5) << "Startup_ops's size is " << queue.size();
/* --- Topological Visit --- */
// 1. Pop queue
......@@ -224,11 +224,10 @@ std::vector<paddle::experimental::Tensor> RunBackward(
// |- node(grads)
// |- Prepare for next node
// 3. Update queue
VLOG(3) << "Run Backward";
while (!queue.empty()) {
GradNodeBase* node = queue.front();
VLOG(3) << "Running GradNode:" << node->name() << " addr:" << node;
VLOG(3) << "Preparing GradNode:" << node->name() << " addr:" << node;
VLOG(4) << EagerUtils::GradNodeStr(*node);
paddle::platform::RecordEvent node_record_event(
std::string((*node).name()),
paddle::platform::TracerEventType::Operator,
......@@ -255,7 +254,7 @@ std::vector<paddle::experimental::Tensor> RunBackward(
// Check input
EnforceGradNodeHasInput(node);
VLOG(6) << "Run Backward Kernel with GradTensorHolder.";
VLOG(7) << "Run Backward Kernel with GradTensorHolder.";
// Run Pre Backward Node and get outputs
paddle::small_vector<std::vector<paddle::experimental::Tensor>,
kSlotSmallVectorSize>
......@@ -269,7 +268,7 @@ std::vector<paddle::experimental::Tensor> RunBackward(
// retain_grad or not
if (!retain_graph) {
VLOG(6)
VLOG(3)
<< "retain_graph is false, need to clear the TensorWrapper of nodes.";
node->ClearTensorWrappers();
}
......@@ -322,11 +321,11 @@ std::vector<paddle::experimental::Tensor> RunBackward(
if ((!grad_output_tensor.defined() ||
!grad_output_tensor.initialized())) {
VLOG(6) << "We get grad_output_tensor with slot: " << i
VLOG(7) << "We get grad_output_tensor with slot: " << i
<< ", rank: " << j << " as uninitialized or undefined tensor";
}
VLOG(6) << "Get Edge and grad_output_tensor with slot: " << i
VLOG(7) << "Get Edge and grad_output_tensor with slot: " << i
<< ", rank: " << j
<< " 's name is: " << grad_output_tensor.name();
......@@ -335,12 +334,12 @@ std::vector<paddle::experimental::Tensor> RunBackward(
const auto& input_meta = next_node->InputMeta();
auto grad_tensor_holder =
std::make_unique<GradTensorHolder>(input_meta);
VLOG(6) << "Construct GradTensorHolder for grad node: "
VLOG(7) << "Construct GradTensorHolder for grad node: "
<< next_node->name();
node_input_buffers_dict[next_node] = std::move(grad_tensor_holder);
}
VLOG(6) << "Sum grad inputs for edge slot: " << edge_rank.first
VLOG(3) << "Sum grad inputs for edge slot: " << edge_rank.first
<< ", rank: " << edge_rank.second;
node_input_buffers_dict[next_node]->add(edge_rank.first,
......@@ -350,7 +349,7 @@ std::vector<paddle::experimental::Tensor> RunBackward(
// Update queue
node_in_degree_map[next_node]--;
VLOG(6) << next_node->name()
VLOG(7) << next_node->name()
<< " ref_cnt is: " << node_in_degree_map[next_node];
PADDLE_ENFORCE(
......@@ -382,7 +381,7 @@ std::vector<paddle::experimental::Tensor> RunBackward(
}
}
VLOG(6) << "Run Backward Final hook size: "
VLOG(7) << "Run Backward Final hook size: "
<< egr::Controller::Instance().FinalBackwardHooks().size();
for (auto& hook : egr::Controller::Instance().FinalBackwardHooks()) {
(*hook)();
......@@ -390,6 +389,7 @@ std::vector<paddle::experimental::Tensor> RunBackward(
egr::Controller::Instance().ClearFinalBackwardHooks();
if (!is_general_grad) return {};
return GeneralGrad::Instance().GetResults(inputs, allow_unused, create_graph);
VLOG(3) << "Finish Backward";
}
void Backward(
......
paddle/fluid/eager/eager_amp_auto_cast.h
浏览文件 @ 54a43981
......@@ -45,7 +45,7 @@ inline paddle::experimental::Tensor Cast(
const bool trace_backward = true) {
if (input.is_sparse_coo_tensor() || input.is_sparse_csr_tensor()) {
if (trace_backward) {
return sparse::cast_dygraph_function(
return sparse::cast_ad_func(
input, paddle::experimental::DataType::UNDEFINED, dst_dtype);
} else {
return paddle::experimental::sparse::cast(
......@@ -53,7 +53,7 @@ inline paddle::experimental::Tensor Cast(
}
} else {
if (trace_backward) {
return cast_dygraph_function(input, dst_dtype);
return cast_ad_func(input, dst_dtype);
} else {
return paddle::experimental::cast(input, dst_dtype);
}
......
paddle/fluid/eager/eager_layout_transformer.h
浏览文件 @ 54a43981
......@@ -35,7 +35,7 @@ inline paddle::experimental::Tensor EagerTraceTransposeOp(
} else {
axis = {0, 1, 2, 3};
}
auto out_tensor = transpose_dygraph_function(in, axis);
auto out_tensor = transpose_ad_func(in, axis);
VLOG(4) << "AutoTune Transpose from "
<< paddle::framework::DataLayoutToString(in.layout()) << " to "
<< paddle::framework::DataLayoutToString(layout);
......
paddle/fluid/eager/grad_node_info.cc
浏览文件 @ 54a43981
......@@ -41,7 +41,7 @@ static void CheckTensor(const paddle::experimental::Tensor& pre,
"The tensor in before and after hook are not consistent"));
}
if (pre.initialized() && post.initialized()) {
VLOG(4) << paddle::framework::DataType2String(pre.dtype()) << " "
VLOG(7) << paddle::framework::DataType2String(pre.dtype()) << " "
<< paddle::framework::DataType2String(post.dtype());
PADDLE_ENFORCE_EQ(
pre.dtype(),
......@@ -62,7 +62,7 @@ static void CheckTensor(const paddle::experimental::Tensor& pre,
}
GradNodeBase::GradNodeBase(size_t bwd_in_slot_num, size_t bwd_out_slot_num) {
VLOG(6) << "Construct GradNodeBase";
VLOG(7) << "Construct GradNodeBase";
bwd_in_meta_.resize(bwd_in_slot_num);
bwd_out_meta_.resize(bwd_out_slot_num);
}
......@@ -84,7 +84,7 @@ GradNodeBase::MutableOutputMeta() {
void GradNodeBase::SetGradInMeta(const paddle::experimental::Tensor& fwd_out,
size_t slot_rank) {
VLOG(6) << "Set GradSlotMeta for Grad Inputs";
VLOG(7) << "Set GradSlotMeta for Grad Inputs";
auto* fwd_out_meta = egr::EagerUtils::nullable_autograd_meta(fwd_out);
PADDLE_ENFORCE_LE(
slot_rank,
......@@ -104,7 +104,7 @@ void GradNodeBase::SetGradInMeta(const paddle::experimental::Tensor& fwd_out,
}
if (!fwd_out.initialized()) {
VLOG(6)
VLOG(7)
<< "Skip Configuring GradSlotMeta for uninitialized GradInput Tensor";
return;
}
......@@ -123,7 +123,7 @@ void GradNodeBase::SetGradInMeta(const paddle::experimental::Tensor& fwd_out,
static_cast<phi::SparseCsrTensor*>(fwd_out.impl().get());
dense_tensor = csr_tensor->mutable_non_zero_elements();
} else {
VLOG(6) << "Unable to initialize the DenseTensorMeta of GradSlotMeta with "
VLOG(7) << "Unable to initialize the DenseTensorMeta of GradSlotMeta with "
"non-DenseTensor argument.";
}
PADDLE_ENFORCE_NE(
......@@ -145,7 +145,7 @@ void GradNodeBase::SetGradInMeta(const paddle::experimental::Tensor& fwd_out,
void GradNodeBase::SetGradInMeta(
const std::vector<paddle::experimental::Tensor>& fwd_out,
size_t slot_rank) {
VLOG(6) << "Set GradSlotMeta for Grad Inputs";
VLOG(7) << "Set GradSlotMeta for Grad Inputs";
size_t slot_size = fwd_out.size();
PADDLE_ENFORCE_LE(
slot_rank,
......@@ -177,7 +177,7 @@ void GradNodeBase::SetGradInMeta(
}
if (!fwd_out_tensor.initialized()) {
VLOG(6)
VLOG(7)
<< "Skip Configuring GradSlotMeta for uninitialized GradInput Tensor";
return;
}
......@@ -202,7 +202,7 @@ void GradNodeBase::SetGradInMeta(
need_complex_to_real_ = true;
}
} else {
VLOG(6) << "Unable to initialize the DenseTensorMeta of GradSlotMeta "
VLOG(7) << "Unable to initialize the DenseTensorMeta of GradSlotMeta "
"with non-DenseTensor argument.";
}
}
......@@ -260,7 +260,7 @@ void GradNodeBase::SetGradOutMeta(const paddle::experimental::Tensor& fwd_in,
meta.SetPlace(fwd_in.place());
}
} else {
VLOG(6) << "Unable to initialize the DenseTensorMeta of GradSlotMeta with "
VLOG(7) << "Unable to initialize the DenseTensorMeta of GradSlotMeta with "
"non-DenseTensor argument.";
}
}
......@@ -319,7 +319,7 @@ void GradNodeBase::SetGradOutMeta(
meta.SetPlace(fwd_in_tensor.place());
}
} else {
VLOG(6)
VLOG(7)
<< "Unable to initialize the DenseTensorMeta of GradSlotMeta with "
"non-DenseTensor argument.";
}
......
paddle/fluid/eager/grad_node_info.h
浏览文件 @ 54a43981
......@@ -74,7 +74,7 @@ class Edge {
}
void SetGradNode(const std::shared_ptr<GradNodeBase>& node) {
VLOG(6) << "Reseting Edge's Grad Node";
VLOG(7) << "Reseting Edge's Grad Node";
grad_node_ = node;
}
......@@ -167,10 +167,10 @@ class GradSlotMeta {
class GradNodeBase {
public:
GradNodeBase() { VLOG(6) << "Construct GradNodeBase"; }
GradNodeBase() { VLOG(7) << "Construct GradNodeBase"; }
GradNodeBase(size_t bwd_in_slot_num, size_t bwd_out_slot_num);
// TODO(jiabin): Should we have other constructor here?
virtual ~GradNodeBase() { VLOG(6) << "Destruct GradNodeBase"; }
virtual ~GradNodeBase() { VLOG(7) << "Destruct GradNodeBase"; }
/**
* operator() designed to contian the real backward execution logic, it should
......@@ -255,14 +255,14 @@ class GradNodeBase {
std::map<int64_t, std::tuple<size_t, size_t, std::shared_ptr<TensorHook>>>
GetGradientHookFuntions() {
VLOG(6) << "GetGradientHookFuntions ";
VLOG(7) << "GetGradientHookFuntions ";
return gradient_hooks_;
}
void SetGradientHookFuntions(
std::map<int64_t, std::tuple<size_t, size_t, std::shared_ptr<TensorHook>>>
hooks) {
VLOG(6) << "SetGradientHookFuntions ";
VLOG(7) << "SetGradientHookFuntions ";
gradient_hooks_ = hooks;
}
......
paddle/fluid/eager/grad_tensor_holder.cc
浏览文件 @ 54a43981
......@@ -143,7 +143,7 @@ void GradTensorHolder::add(size_t slot_id,
if (t.is_dense_tensor()) {
if (buffer_tensor.is_dense_tensor()) {
if (create_graph || t.is_custom_device()) {
buffer_tensor = add_dygraph_function(t, buffer_tensor);
buffer_tensor = add_ad_func(t, buffer_tensor);
} else {
paddle::imperative::TensorAdd<paddle::experimental::Tensor>(
t, &buffer_tensor);
......@@ -170,7 +170,7 @@ void GradTensorHolder::add(size_t slot_id,
std::make_shared<phi::DenseTensor>(
buffer_sparse->non_zero_elements()));
if (create_graph || t.is_custom_device()) {
buffer_values = add_dygraph_function(t_values, buffer_values);
buffer_values = add_ad_func(t_values, buffer_values);
} else {
paddle::imperative::TensorAdd<paddle::experimental::Tensor>(
t_values, &buffer_values);
......
paddle/fluid/eager/tensor_wrapper.h
浏览文件 @ 54a43981
......@@ -100,10 +100,10 @@ class TensorWrapper {
std::shared_ptr<GradNodeBase> new_grad_node = weak_grad_node_.lock();
if (new_grad_node) {
VLOG(3) << "Recovered TensorWrapper with GradNode "
VLOG(7) << "Recovered TensorWrapper with GradNode "
<< new_grad_node->name() << " addr: " << new_grad_node.get();
} else {
VLOG(3) << "Recovered TensorWrapper with Empty GradNode";
VLOG(7) << "Recovered TensorWrapper with Empty GradNode";
}
auto* intermediate_autograd_meta =
EagerUtils::nullable_autograd_meta(intermidiate_tensor_);
......@@ -129,7 +129,7 @@ class TensorWrapper {
private:
void check_inplace_version() {
if (no_need_buffer_) {
VLOG(6) << "There's no need to check inplace_version because "
VLOG(7) << "There's no need to check inplace_version because "
"no_need_buffer_ is true.";
return;
}
......@@ -154,10 +154,10 @@ class TensorWrapper {
intermidiate_tensor_.name(),
tensor_version,
wrapper_version_snapshot));
VLOG(6) << " The wrapper_version_snapshot of Tensor '"
VLOG(7) << " The wrapper_version_snapshot of Tensor '"
<< intermidiate_tensor_.name() << "' is [ "
<< wrapper_version_snapshot << " ]";
VLOG(6) << " The tensor_version of Tensor '"
VLOG(7) << " The tensor_version of Tensor '"
<< intermidiate_tensor_.name() << "' is [ " << tensor_version
<< " ]";
}
......
paddle/fluid/eager/tests/performance_tests/benchmark_utils.cc
浏览文件 @ 54a43981
......@@ -77,7 +77,7 @@ void benchmark_eager_matmul(const paddle::experimental::Tensor& X,
size_t max_num_runs = accuracy_check ? 2 : max_num_benchmark_runs;
for (size_t i = 0; i < max_num_runs; i++) {
input_tensor0 = matmul_dygraph_function(input_tensor0, Y, false, false);
input_tensor0 = matmul_ad_func(input_tensor0, Y, false, false);
}
std::vector<paddle::experimental::Tensor> target_tensors = {input_tensor0};
......
paddle/fluid/eager/to_static/run_program_op_func.h
浏览文件 @ 54a43981
......@@ -54,7 +54,7 @@ static void clear_no_grad_edges_with_partial_block(
}
}
inline void run_program_dygraph_function(
inline void run_program_ad_func(
const std::vector<paddle::experimental::Tensor>& x,
const std::vector<paddle::experimental::Tensor>& params,
std::vector<paddle::experimental::Tensor*>& out, // NOLINT
......
paddle/fluid/eager/utils.cc
浏览文件 @ 54a43981
......@@ -296,7 +296,7 @@ void EagerUtils::HandleViewBetweenInputAndOutput(
view_output_dense_tensor->ShareInplaceVersionCounterWith(
*input_dense_tensor);
VLOG(3) << "Perform View between Output Tensor("
VLOG(4) << "Perform View between Output Tensor("
<< view_output_tensor->name() << ") and Input Tensor("
<< input_tensor.name()
<< "), share allocation and inplace version.";
......@@ -409,7 +409,7 @@ std::vector<paddle::experimental::Tensor> EagerUtils::RecoverTensorWrapper(
}
return ret;
}
// TODO(jiabin): remove all this when we fix all test using tmp grad
void EagerUtils::CheckAndRetainGrad(
const paddle::experimental::Tensor& tensor) {
VLOG(6) << "Check RetainGradForTensor: " << tensor.name();
......
paddle/fluid/eager/utils.h
浏览文件 @ 54a43981
......@@ -230,6 +230,7 @@ class EagerUtils {
const std::vector<paddle::experimental::Tensor>& tensors);
static void CheckAndRetainGrad(
const std::vector<paddle::experimental::Tensor*>& tensors);
static std::shared_ptr<egr::GradNodeBase> GetGradAccumulationNode(
const paddle::experimental::Tensor& tensor);
......@@ -246,6 +247,184 @@ class EagerUtils {
static void FillZeroForEmptyGradInput(
std::vector<paddle::experimental::Tensor>* in_grads,
const std::vector<GradSlotMeta>& grad_in_metas);
/**
* Print Input Output (level 0 means least info, level 2 means most info)
* **/
static const std::string TensorStr(const paddle::experimental::Tensor& t) {
std::string tensor_name_str = "";
if (t.name() == "") {
tensor_name_str = "None";
} else {
tensor_name_str = t.name();
}
const char* TENSOR_INFO_TEMPLATE =
"{ Type: [ \"%s\" ], Dtype:[ \"%s\" ], Place:[ \"%s\" ] }";
std::string tensor_info_str = "";
if (t.defined()) {
if (t.initialized()) {
tensor_info_str += paddle::string::Sprintf(TENSOR_INFO_TEMPLATE,
t.impl()->type_info().name(),
t.dtype(),
t.place().DebugString());
} else {
tensor_info_str += paddle::string::Sprintf(TENSOR_INFO_TEMPLATE,
t.impl()->type_info().name(),
"Unknown",
"Unknown");
}
} else {
tensor_info_str += "Unknown";
}
if (VLOG_IS_ON(6)) {
const char* TENSOR_PRINT_TEMPLATE =
"{ Name:[ \"%s\" ], Initialized: [ \"%d\" ], Ptr: [ \"%d\" ] "
"TensorInfo: [ \"%s\" ], ADInfo:[ \"%s\" ] }";
auto* ad_meta = nullable_autograd_meta(t);
if (!ad_meta && !(ad_meta->WeakGrad().lock().get())) {
std::string ad_info_str = "";
const char* AD_INFO_TEMPLATE =
"{ Grad: [ \"%s\" ], GradNode: [ %s ], StopGradient: [ %d ] }";
ad_info_str += paddle::string::Sprintf(AD_INFO_TEMPLATE,
TensorStr(ad_meta->Grad()),
GradNodeStr(t),
ad_meta->StopGradient());
return paddle::string::Sprintf(TENSOR_PRINT_TEMPLATE,
tensor_name_str,
t.initialized(),
t.impl(),
tensor_info_str,
ad_info_str);
} else {
return paddle::string::Sprintf(TENSOR_PRINT_TEMPLATE,
tensor_name_str,
t.initialized(),
t.impl(),
tensor_info_str,
"None");
}
} else if (VLOG_IS_ON(5)) {
const char* TENSOR_PRINT_TEMPLATE =
"{ Name:[ \"%s\" ], Initialized: [ \"%d\" ], Ptr: [ \"%d\" ] "
"TensorInfo: [ \"%s\" ] }";
return paddle::string::Sprintf(TENSOR_PRINT_TEMPLATE,
tensor_name_str,
t.initialized(),
t.impl(),
tensor_info_str);
} else if (VLOG_IS_ON(4)) {
const char* TENSOR_PRINT_TEMPLATE =
"{ Name:[ \"%s\" ], Initialized: [ \"%d\" ], Ptr: [ \"%d\" ] }";
return paddle::string::Sprintf(
TENSOR_PRINT_TEMPLATE, tensor_name_str, t.initialized(), t.impl());
} else {
return "[ Not specified tensor log level ]";
}
}
static const std::string GradNodeStr(const egr::GradNodeBase& node) {
if (VLOG_IS_ON(6)) {
const char* GRAD_NODE_TEMPLATE =
" { BackwardOutMeta: [ %s ], BackwardInMeta: [ %s ] }";
const char* GRAD_SLOT_META_TEMPLATE = " {SlotSize: [%d]: %s} ";
const char* SLOT_INFO_TEMPLATE =
" {SlotID: [\"%s\"], StopGradients: [ %s ], Edges[ %s ] }";
auto out_metas = node.OutputMeta();
auto in_metas = node.InputMeta();
std::string out_slot_str = "";
std::string in_slot_str = "";
const char* EDGE_INFO_TEMPLATE = " { [%d, %d]: [%s, %s] }, ";
std::string slot_str = "";
for (size_t i = 0; i < out_metas.size(); i++) {
std::string edges_str = "";
std::string sg_str = "";
for (const GradSlotMeta& meta : out_metas[i]) {
const egr::Edge& edge = meta.GetEdge();
if (edge.IsInitialized()) {
edges_str += paddle::string::Sprintf(EDGE_INFO_TEMPLATE,
edge.GetEdgeRankInfo().first,
edge.GetEdgeRankInfo().second,
edge.GetGradNode(),
edge.GetGradNode()->name());
} else {
edges_str += paddle::string::Sprintf("{ NULL Edge }");
}
sg_str += meta.IsStopGradient() ? "1, " : "0, ";
}
out_slot_str +=
paddle::string::Sprintf(SLOT_INFO_TEMPLATE, i, sg_str, edges_str);
}
std::string out_meta_str = paddle::string::Sprintf(
GRAD_SLOT_META_TEMPLATE, out_metas.size(), out_slot_str);
for (size_t i = 0; i < in_metas.size(); i++) {
std::string edges_str = "";
std::string sg_str = "";
for (const GradSlotMeta& meta : in_metas[i]) {
edges_str += paddle::string::Sprintf("{ NULL Edge }");
sg_str += meta.IsStopGradient() ? "1, " : "0, ";
}
in_slot_str +=
paddle::string::Sprintf(SLOT_INFO_TEMPLATE, i, sg_str, edges_str);
}
std::string in_meta_str =
paddle::string::Sprintf(GRAD_SLOT_META_TEMPLATE, in_slot_str);
return paddle::string::Sprintf(
GRAD_NODE_TEMPLATE, out_meta_str, in_meta_str);
} else if (VLOG_IS_ON(5)) {
const char* GRAD_NODE_TEMPLATE =
" { BackwardOutMeta: [ %s ], BackwardInMeta: [ %s ] }";
const char* GRAD_SLOT_META_TEMPLATE = "SlotSize: [\"%d\"]";
std::string out_meta_str = paddle::string::Sprintf(
GRAD_SLOT_META_TEMPLATE, node.OutputMeta().size());
std::string in_meta_str = paddle::string::Sprintf(
GRAD_SLOT_META_TEMPLATE, node.InputMeta().size());
return paddle::string::Sprintf(
GRAD_NODE_TEMPLATE, out_meta_str, in_meta_str);
} else {
return "[ Not specified grad node log level. ] ";
}
}
static const std::string GradNodeStr(const paddle::experimental::Tensor& t) {
auto* ad_meta = nullable_autograd_meta(t);
if (ad_meta && !(ad_meta->GetMutableGradNode().get())) {
return GradNodeStr((*ad_meta->GetMutableGradNode().get()));
} else {
return "None";
}
}
static const std::string TensorStr(
const std::vector<paddle::experimental::Tensor>& tensors) {
std::string tensors_str = "";
for (const auto& tensor : tensors) {
tensors_str += TensorStr(tensor) + ", ";
}
return "[ " + tensors_str + " ]";
}
static const std::string TensorStr(
const paddle::optional<paddle::experimental::Tensor>& t) {
if (!t.is_initialized()) {
return "{ UnDefinedTensor }";
} else {
return TensorStr((*t.get_ptr()));
}
}
static const std::string TensorStr(
const paddle::optional<std::vector<paddle::experimental::Tensor>>&
tensors) {
std::string tensors_str = "";
if (!tensors.is_initialized()) {
return "[ UnDefinedTensor List ]";
} else {
for (const auto& tensor : (*tensors.get_ptr())) {
tensors_str += TensorStr(tensor) + ", ";
}
return "[ " + tensors_str + " ]";
}
}
};
} // namespace egr
paddle/fluid/pybind/eager_custom_python_api.h
浏览文件 @ 54a43981
......@@ -30,13 +30,13 @@ static PyObject *eager_api_linear(PyObject *self,
auto bias = GetTensorFromArgs("linear", "Bias", args, 2, true);
tstate = PyEval_SaveThread();
if (bias.initialized()) {
auto mm_out = matmul_dygraph_function(x, weight, false, false);
auto out = add_dygraph_function(mm_out, bias);
auto mm_out = matmul_ad_func(x, weight, false, false);
auto out = add_ad_func(mm_out, bias);
PyEval_RestoreThread(tstate);
tstate = nullptr;
return ToPyObject(out);
} else {
auto mm_out = matmul_dygraph_function(x, weight, false, false);
auto mm_out = matmul_ad_func(x, weight, false, false);
PyEval_RestoreThread(tstate);
tstate = nullptr;
return ToPyObject(mm_out);
......
paddle/fluid/pybind/eager_legacy_custom_python_api.h
浏览文件 @ 54a43981
......@@ -38,7 +38,7 @@ static PyObject *eager_api_run_program(PyObject *self,
"run_program", args, 6, PyTuple_GET_SIZE(args), attrs);
tstate = PyEval_SaveThread();
run_program_dygraph_function(X, Params, Out, OutScope, DOut, attrs);
run_program_ad_func(X, Params, Out, OutScope, DOut, attrs);
PyEval_RestoreThread(tstate);
tstate = nullptr;
Py_RETURN_NONE;
......
paddle/fluid/pybind/eager_method.cc
浏览文件 @ 54a43981
......@@ -808,14 +808,14 @@ static PyObject* tensor__getitem_index_not_tensor(TensorObject* self,
decrease_axis.end());
if (op_type == "slice") {
out = slice_dygraph_function(self->tensor,
out = slice_ad_func(self->tensor,
slice_axes_tmp,
slice_starts,
slice_ends,
infer_flags_tmp,
decrease_axis_tmp);
} else if (op_type == "strided_slice") {
out = strided_slice_dygraph_function(
out = strided_slice_ad_func(
self->tensor, slice_axes, slice_starts, slice_ends, slice_strides);
} else {
PADDLE_THROW(platform::errors::InvalidArgument(
......@@ -854,7 +854,7 @@ static PyObject* tensor__getitem_index_not_tensor(TensorObject* self,
}
paddle::experimental::Tensor new_out;
new_out = unsqueeze_dygraph_function(out, none_axes);
new_out = unsqueeze_ad_func(out, none_axes);
return ToPyObject(new_out);
}
}
......@@ -870,7 +870,7 @@ static PyObject* tensor__getitem_index_not_tensor(TensorObject* self,
paddle::framework::TensorFromVector(
list_select_idxs, *dev_ctx, idx_tensor.get());
framework::AttributeMap attrs = {{"dim", 0}};
out = index_select_dygraph_function(self->tensor, select_index, 0);
out = index_select_ad_func(self->tensor, select_index, 0);
}
return ToPyObject(out);
......
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