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未验证 提交 6de20581 编写于 作者: C chenjian 提交者： GitHub
浏览文件
操作

Fix operator type record in profiler [cherry-pick PR44582] (#44654) 

* fix record event for operator type in new dygraph (#44582)

* fix new dygraph record event for op

* update unit test

* fix file mode
上级 b71833ea
隐藏空白更改
内联 并排
Showing with 536 addition and 432 deletion
paddle/fluid/eager/auto_code_generator/eager_generator.cc
浏览文件 @ 6de20581
......@@ -476,7 +476,8 @@ static void SlotNameMatching(
PADDLE_THROW(platform::errors::Fatal(
"Detected mismatched slot names."
"grad_slot_name %s matches both %s and %s fwd_slot_name",
grad_slot_name, grad_fwd_slotname_map[grad_slot_name],
grad_slot_name,
grad_fwd_slotname_map[grad_slot_name],
fwd_slot_name));
}
grad_fwd_slotname_map[grad_slot_name] = fwd_slot_name;
......@@ -489,7 +490,8 @@ static void SlotNameMatching(
PADDLE_THROW(platform::errors::Fatal(
"Detected mismatched slot names."
"grad_slot_name %s matches both %s and %s fwd_slot_name",
grad_slot_name, grad_grad_slotname_map[grad_slot_name],
grad_slot_name,
grad_grad_slotname_map[grad_slot_name],
fwd_slot_name));
}
grad_grad_slotname_map[grad_slot_name] = fwd_slot_name;
......@@ -509,7 +511,8 @@ static void SlotNameMatching(
PADDLE_THROW(platform::errors::Fatal(
"Detected mismatched slot names"
"grad_slot_name %s matches both %s and %s fwd_slot_name",
grad_slot_name, grad_fwd_slotname_map[grad_slot_name],
grad_slot_name,
grad_fwd_slotname_map[grad_slot_name],
fwd_slot_name));
}
grad_fwd_slotname_map[grad_slot_name] = fwd_slot_name;
......@@ -522,7 +525,8 @@ static void SlotNameMatching(
PADDLE_THROW(platform::errors::Fatal(
"Detected mismatched slot names."
"grad_slot_name %s matches both %s and %s fwd_slot_name",
grad_slot_name, grad_grad_slotname_map[grad_slot_name],
grad_slot_name,
grad_grad_slotname_map[grad_slot_name],
fwd_slot_name));
}
grad_grad_slotname_map[grad_slot_name] = fwd_slot_name;
......@@ -900,8 +904,8 @@ static bool CollectGradInformationFromOpInfo(
}
std::shared_ptr<paddle::imperative::GradOpNode> grad_node =
op_info.dygraph_grad_op_maker_(op_type, ins, outs, attrs, default_attrs,
{});
op_info.dygraph_grad_op_maker_(
op_type, ins, outs, attrs, default_attrs, {});
if (!grad_node) {
VLOG(6) << "Got nullptr GradOpNode for " << op_type
......@@ -977,12 +981,16 @@ static bool CollectGradInformationFromOpInfo(
/* ------ Slot Name Matching ---- */
for (auto& iter : *op_base_infos) {
// grad_ins -> fwd_ins, fwd_outs
SlotNameMatching(iter.GetGradIns(), fwd_ins, fwd_outs,
SlotNameMatching(iter.GetGradIns(),
fwd_ins,
fwd_outs,
iter.GetMutableGradInsFwdSlotnameMap(),
iter.GetMutableGradInsGradSlotnameMap());
// grad_outs -> fwd_ins, fwd_outs
SlotNameMatching(iter.GetGradOuts(), fwd_ins, fwd_outs,
SlotNameMatching(iter.GetGradOuts(),
fwd_ins,
fwd_outs,
iter.GetMutableGradOutsSlotnameMap(),
iter.GetMutableGradOutsSlotnameMap());
}
......@@ -1042,16 +1050,18 @@ static std::string GenerateGradNodeCreationContent(
"p_autograd_" + inplace_input_name;
const char* GET_SINGLE_AUTOGRAD_META_TEMPLATE =
" %s = egr::EagerUtils::autograd_meta(&%s);\n";
get_output_autograd_meta_str += paddle::string::Sprintf(
GET_SINGLE_AUTOGRAD_META_TEMPLATE, inplace_input_autograd_name,
inplace_input_name);
get_output_autograd_meta_str +=
paddle::string::Sprintf(GET_SINGLE_AUTOGRAD_META_TEMPLATE,
inplace_input_autograd_name,
inplace_input_name);
} else {
const char* GET_SINGLE_AUTOGRAD_META_TEMPLATE =
" egr::AutogradMeta* %s = "
"egr::EagerUtils::autograd_meta(&%s);\n";
get_output_autograd_meta_str +=
paddle::string::Sprintf(GET_SINGLE_AUTOGRAD_META_TEMPLATE,
output_autograd_name, output_name);
output_autograd_name,
output_name);
}
}
}
......@@ -1097,8 +1107,8 @@ static std::string GenerateGradNodeCreationContent(
"require_any_grad);\n";
for (auto& inplace_pair : inplace_map) {
std::string inplace_name = inplace_pair.second;
check_inplace_str += paddle::string::Sprintf(CHECKING_INPLACE_TEMPLATE,
inplace_name, inplace_name);
check_inplace_str += paddle::string::Sprintf(
CHECKING_INPLACE_TEMPLATE, inplace_name, inplace_name);
}
VLOG(6) << "Check Inplace Input";
}
......@@ -1124,9 +1134,11 @@ static std::string GenerateGradNodeCreationContent(
" auto grad_node = std::shared_ptr<GradNode%s>(new GradNode%s(%d, "
"%d));\n";
grad_node_creation_str += " // Create GradOpNode\n";
grad_node_creation_str +=
paddle::string::Sprintf(GRAD_OP_NODE_TEMPLATE, op_type, op_type,
bwd_in_slot_num, bwd_out_slot_num);
grad_node_creation_str += paddle::string::Sprintf(GRAD_OP_NODE_TEMPLATE,
op_type,
op_type,
bwd_in_slot_num,
bwd_out_slot_num);
grad_node_creation_str += "\n";
VLOG(6) << "Generated GradOpNode construction";
......@@ -1158,13 +1170,17 @@ static std::string GenerateGradNodeCreationContent(
// Replace output directly with input in inplace op.
if (!inplace_map.empty() && inplace_map.count(tensor_wrapper_name)) {
auto inplace_input_name = inplace_map[tensor_wrapper_name];
grad_node_creation_str += paddle::string::Sprintf(
SET_TENSOR_WRAPPER_TEMPLATE, tensor_wrapper_name,
inplace_input_name, full_reserved);
grad_node_creation_str +=
paddle::string::Sprintf(SET_TENSOR_WRAPPER_TEMPLATE,
tensor_wrapper_name,
inplace_input_name,
full_reserved);
} else {
grad_node_creation_str += paddle::string::Sprintf(
SET_TENSOR_WRAPPER_TEMPLATE, tensor_wrapper_name,
tensor_wrapper_name, full_reserved);
grad_node_creation_str +=
paddle::string::Sprintf(SET_TENSOR_WRAPPER_TEMPLATE,
tensor_wrapper_name,
tensor_wrapper_name,
full_reserved);
}
}
}
......@@ -1189,9 +1205,10 @@ static std::string GenerateGradNodeCreationContent(
const char* ADD_EDGES_TEMPLATE =
" if(%s) grad_node->AddEdges(%s, %d);\n";
grad_node_creation_str +=
paddle::string::Sprintf(ADD_EDGES_TEMPLATE, input_autograd_name,
input_autograd_name, input_position);
grad_node_creation_str += paddle::string::Sprintf(ADD_EDGES_TEMPLATE,
input_autograd_name,
input_autograd_name,
input_position);
} else {
compute_require_grad_args += ", &" + input_autograd_name;
size_t input_position = fwd_inputs_name_pos_map.at(input_name);
......@@ -1319,7 +1336,7 @@ static std::string GenerateGradNodeCreationContent(
"%s"
" {\n"
" paddle::platform::RecordEvent node_creation_record_event(\"%s\", "
"paddle::platform::TracerEventType::Operator, 1);\n"
"paddle::platform::TracerEventType::OperatorInner, 1);\n"
"%s"
" if(require_any_grad) {\n"
" VLOG(6) << \" Construct Grad for %s \"; \n"
......@@ -1327,11 +1344,17 @@ static std::string GenerateGradNodeCreationContent(
" %s\n"
" }\n"
" }";
std::string grad_node_creation_body_str = paddle::string::Sprintf(
GRAD_NODE_CREATION_TEMPLATE, prepare_autograd_meta_str,
compute_require_grad_args, check_inplace_str, trace_op_body_str,
event_name, get_output_autograd_meta_str, op_type,
pass_stop_gradient_args, grad_node_creation_str);
std::string grad_node_creation_body_str =
paddle::string::Sprintf(GRAD_NODE_CREATION_TEMPLATE,
prepare_autograd_meta_str,
compute_require_grad_args,
check_inplace_str,
trace_op_body_str,
event_name,
get_output_autograd_meta_str,
op_type,
pass_stop_gradient_args,
grad_node_creation_str);
return grad_node_creation_body_str;
}
......@@ -1454,8 +1477,8 @@ static std::pair<std::string, std::string> GenerateForwardFunctionContents(
const char* FWD_INS_CONTENT_TEMPLATE =
"{ \"%s\", egr::EagerUtils::TrySyncToVars(%s) },";
ins_contents_str += paddle::string::Sprintf(FWD_INS_CONTENT_TEMPLATE,
input_name, input_name);
ins_contents_str += paddle::string::Sprintf(
FWD_INS_CONTENT_TEMPLATE, input_name, input_name);
if (input.duplicable()) {
const char* AMP_TENSORS_VECTOR_TEMPLATE = "%s,";
amp_tensors_vector_str +=
......@@ -1518,9 +1541,14 @@ static std::pair<std::string, std::string> GenerateForwardFunctionContents(
const char* DISPENSABLE_AMP_AUTO_CAST_TEMPLATE =
" auto NEW_%s = ((%s.size() > 0) ? egr::AmpAutoCasts(\"%s\", "
"%s, amp_dst_dtype, \"%s\") : %s);\n";
dispensable_amp_auto_cast_str += paddle::string::Sprintf(
DISPENSABLE_AMP_AUTO_CAST_TEMPLATE, input_name, input_name,
input_name, input_name, op_type, input_name);
dispensable_amp_auto_cast_str +=
paddle::string::Sprintf(DISPENSABLE_AMP_AUTO_CAST_TEMPLATE,
input_name,
input_name,
input_name,
input_name,
op_type,
input_name);
} else {
const char* FWD_INS_CONTENT_TEMPLATE =
" if(%s.initialized()) "
......@@ -1535,9 +1563,14 @@ static std::pair<std::string, std::string> GenerateForwardFunctionContents(
const char* DISPENSABLE_AMP_AUTO_CAST_TEMPLATE =
" auto NEW_%s = ((%s.initialized()) ? egr::AmpAutoCast(\"%s\", "
"%s, amp_dst_dtype, \"%s\") : %s);\n";
dispensable_amp_auto_cast_str += paddle::string::Sprintf(
DISPENSABLE_AMP_AUTO_CAST_TEMPLATE, input_name, input_name,
input_name, input_name, op_type, input_name);
dispensable_amp_auto_cast_str +=
paddle::string::Sprintf(DISPENSABLE_AMP_AUTO_CAST_TEMPLATE,
input_name,
input_name,
input_name,
input_name,
op_type,
input_name);
}
}
}
......@@ -1594,9 +1627,11 @@ static std::pair<std::string, std::string> GenerateForwardFunctionContents(
} else if (!inplace_map.empty() && inplace_map.count(output_name)) {
// In inplace op, replace the output with the input directly.
PADDLE_ENFORCE_NE(
inplace_map[output_name], "",
inplace_map[output_name],
"",
paddle::platform::errors::InvalidArgument(
"Inplace op %s has no input corresponding to output %s.", op_type,
"Inplace op %s has no input corresponding to output %s.",
op_type,
output_name));
const char* FWD_OUTS_CONTENT_TEMPLATE = "{ \"%s\", ins[\"%s\"] },";
auto inplace_input_name = inplace_map[output_name];
......@@ -1618,8 +1653,8 @@ static std::pair<std::string, std::string> GenerateForwardFunctionContents(
amp_function_call_args_str += (", " + outnum);
const char* FWD_OUTS_CONTENT_TEMPLATE =
"{ \"%s\", egr::EagerUtils::CreateVars(%s) },";
outs_contents_str += paddle::string::Sprintf(FWD_OUTS_CONTENT_TEMPLATE,
output_name, outnum);
outs_contents_str += paddle::string::Sprintf(
FWD_OUTS_CONTENT_TEMPLATE, output_name, outnum);
core_ops_args_info[op_type].push_back(outnum);
core_ops_args_type_info[op_type].push_back("int");
} else {
......@@ -1738,9 +1773,12 @@ static std::pair<std::string, std::string> GenerateForwardFunctionContents(
std::string view_strategy_str = "";
std::string viwe_input_name = view_op_map[op_type].first;
std::string viwe_output_name = view_op_map[op_type].second;
view_strategy_str += paddle::string::Sprintf(
HANDLE_VIEW_BETWEEN_INPUT_AND_OUTPUT, viwe_input_name, viwe_output_name,
viwe_input_name, viwe_output_name);
view_strategy_str +=
paddle::string::Sprintf(HANDLE_VIEW_BETWEEN_INPUT_AND_OUTPUT,
viwe_input_name,
viwe_output_name,
viwe_input_name,
viwe_output_name);
generated_function_body += view_strategy_str;
generated_function_body += "\n";
......@@ -1794,26 +1832,33 @@ static std::pair<std::string, std::string> GenerateForwardFunctionContents(
" if (outs.count(\"%s\")) "
"egr::EagerUtils::GetOutputs(outs[\"%s\"], %s);\n"
" egr::EagerUtils::Output2Result(%s, &%s);\n";
out_tensor_str = paddle::string::Sprintf(
FWD_OUT_TENSORS_TEMPLATE, output_varname, output_name,
output_name, output_var_args_name, output_var_args_name,
output_varname);
out_tensor_str = paddle::string::Sprintf(FWD_OUT_TENSORS_TEMPLATE,
output_varname,
output_name,
output_name,
output_var_args_name,
output_var_args_name,
output_varname);
} else {
const char* FWD_OUT_TENSORS_TEMPLATE =
" std::vector<paddle::experimental::Tensor> %s;\n"
" egr::EagerUtils::GetOutputs(outs[\"%s\"], %s);\n"
" egr::EagerUtils::Output2Result(%s, &%s);\n";
out_tensor_str = paddle::string::Sprintf(
FWD_OUT_TENSORS_TEMPLATE, output_varname, output_name,
output_var_args_name, output_var_args_name, output_varname);
out_tensor_str = paddle::string::Sprintf(FWD_OUT_TENSORS_TEMPLATE,
output_varname,
output_name,
output_var_args_name,
output_var_args_name,
output_varname);
}
} else {
const char* FWD_OUT_TENSORS_TEMPLATE =
" std::vector<paddle::experimental::Tensor> %s;\n"
" egr::EagerUtils::GetOutputs(outs[\"%s\"], &%s);\n";
out_tensor_str =
paddle::string::Sprintf(FWD_OUT_TENSORS_TEMPLATE, output_varname,
output_name, output_varname);
out_tensor_str = paddle::string::Sprintf(FWD_OUT_TENSORS_TEMPLATE,
output_varname,
output_name,
output_varname);
}
return_types[return_position] =
"std::vector<paddle::experimental::Tensor>";
......@@ -1824,16 +1869,21 @@ static std::pair<std::string, std::string> GenerateForwardFunctionContents(
" if (outs.count(\"%s\")) "
"egr::EagerUtils::GetOutput(outs[\"%s\"][0], %s);\n"
" paddle::experimental::Tensor& %s = *%s;\n";
out_tensor_str = paddle::string::Sprintf(
FWD_OUT_TENSOR_TEMPLATE, output_name, output_name,
output_var_args_name, output_varname, output_var_args_name);
out_tensor_str = paddle::string::Sprintf(FWD_OUT_TENSOR_TEMPLATE,
output_name,
output_name,
output_var_args_name,
output_varname,
output_var_args_name);
} else {
const char* FWD_OUT_TENSOR_TEMPLATE =
" egr::EagerUtils::GetOutput(outs[\"%s\"][0], %s);\n"
" paddle::experimental::Tensor& %s = *%s;\n";
out_tensor_str = paddle::string::Sprintf(
FWD_OUT_TENSOR_TEMPLATE, output_name, output_var_args_name,
output_varname, output_var_args_name);
out_tensor_str = paddle::string::Sprintf(FWD_OUT_TENSOR_TEMPLATE,
output_name,
output_var_args_name,
output_varname,
output_var_args_name);
}
} else {
if (!inplace_map.empty() && inplace_map.count(output_name)) {
......@@ -1845,16 +1895,19 @@ static std::pair<std::string, std::string> GenerateForwardFunctionContents(
" %s.bump_inplace_version();\n"
" VLOG(3) << \"Tensor(\" << %s.name() << \") uses Inplace "
"Strategy.\";\n";
out_tensor_str = paddle::string::Sprintf(
FWD_OUT_TENSOR_TEMPLATE, output_name, inplace_input_name,
inplace_input_name, inplace_input_name);
out_tensor_str = paddle::string::Sprintf(FWD_OUT_TENSOR_TEMPLATE,
output_name,
inplace_input_name,
inplace_input_name,
inplace_input_name);
} else {
const char* FWD_OUT_TENSOR_TEMPLATE =
" paddle::experimental::Tensor %s;\n"
" egr::EagerUtils::GetOutput(outs[\"%s\"][0], &%s);\n";
out_tensor_str =
paddle::string::Sprintf(FWD_OUT_TENSOR_TEMPLATE, output_varname,
output_name, output_varname);
out_tensor_str = paddle::string::Sprintf(FWD_OUT_TENSOR_TEMPLATE,
output_varname,
output_name,
output_varname);
}
}
return_types[return_position] = "paddle::experimental::Tensor";
......@@ -1964,21 +2017,28 @@ static std::pair<std::string, std::string> GenerateForwardFunctionContents(
"%s\n"
"%s\n"
"}\n\n";
std::string fwd_function_str = paddle::string::Sprintf(
FWD_FUNCTION_TEMPLATE, function_proto_return_type_str, function_name,
dygraph_function_args_str, fwd_record_event_str, generated_function_body);
std::string fwd_function_str =
paddle::string::Sprintf(FWD_FUNCTION_TEMPLATE,
function_proto_return_type_str,
function_name,
dygraph_function_args_str,
fwd_record_event_str,
generated_function_body);
// [Generation] Generate forward functions header
const char* FWD_HEADER_TEMPLATE = "%s %s(%s);\n";
std::string dygraph_function_declaration_str = paddle::string::Sprintf(
FWD_HEADER_TEMPLATE, function_proto_return_type_str, function_name,
dygraph_function_args_str);
std::string dygraph_function_declaration_str =
paddle::string::Sprintf(FWD_HEADER_TEMPLATE,
function_proto_return_type_str,
function_name,
dygraph_function_args_str);
return {fwd_function_str, dygraph_function_declaration_str};
}
static std::string GenerateSingleOpBase(
const std::string& fwd_op_type, const std::string& op_base_type,
const std::string& fwd_op_type,
const std::string& op_base_type,
const std::unordered_map<std::string, size_t>& fwd_inputs_name_pos_map,
const std::unordered_map<std::string, size_t>& fwd_outputs_name_pos_map,
const std::vector<proto::OpProto::Var>& in_vars,
......@@ -1994,7 +2054,8 @@ static std::string GenerateSingleOpBase(
std::vector<std::shared_ptr<paddle::imperative::VariableWrapper>>>&
grad_outs,
const paddle::framework::AttributeMap& grad_attrs,
bool is_op_base_per_duplicable_input, size_t* outs_size) {
bool is_op_base_per_duplicable_input,
size_t* outs_size) {
std::string generated_grad_function_body = "";
const std::string& ins_name = "ins" + std::to_string(*outs_size);
......@@ -2029,9 +2090,9 @@ static std::string GenerateSingleOpBase(
"RecoverTensorWrapper("
"&"
"this->%s)) },";
ins_contents_str +=
paddle::string::Sprintf(GRAD_INS_FWD_CONTENT_TEMPLATE,
grad_input_name, struct_fwd_input_name);
ins_contents_str += paddle::string::Sprintf(GRAD_INS_FWD_CONTENT_TEMPLATE,
grad_input_name,
struct_fwd_input_name);
} else if (grad_ins_grad_slotname_map.count(grad_input_name)) {
// Fwd Tensor's Grad
......@@ -2075,18 +2136,25 @@ static std::string GenerateSingleOpBase(
" if(this->%s.size() > 0) %s[\"%s\"] = "
"egr::EagerUtils::TrySyncToVars(egr::EagerUtils::"
"RecoverTensorWrapper(&this->%s));\n";
generated_grad_function_body += paddle::string::Sprintf(
DISPENSABLE_GRAD_INS_FWD_CONTENT_TEMPLATE, struct_fwd_input_name,
ins_name, grad_input_name, struct_fwd_input_name);
generated_grad_function_body +=
paddle::string::Sprintf(DISPENSABLE_GRAD_INS_FWD_CONTENT_TEMPLATE,
struct_fwd_input_name,
ins_name,
grad_input_name,
struct_fwd_input_name);
} else {
const char* DISPENSABLE_GRAD_INS_FWD_CONTENT_TEMPLATE =
" auto %s = egr::EagerUtils::RecoverTensorWrapper(&this->%s);\n"
" if(%s.defined()) %s[\"%s\"] = "
" egr::EagerUtils::TrySyncToVars(%s);\n";
generated_grad_function_body += paddle::string::Sprintf(
DISPENSABLE_GRAD_INS_FWD_CONTENT_TEMPLATE, grad_input_name,
struct_fwd_input_name, grad_input_name, ins_name, grad_input_name,
grad_input_name);
generated_grad_function_body +=
paddle::string::Sprintf(DISPENSABLE_GRAD_INS_FWD_CONTENT_TEMPLATE,
grad_input_name,
struct_fwd_input_name,
grad_input_name,
ins_name,
grad_input_name,
grad_input_name);
}
}
}
......@@ -2203,15 +2271,20 @@ static std::string GenerateSingleOpBase(
" if(%s.size() > 0) %s[\"%s\"] = egr::EagerUtils::CreateVars( "
"this->OutputMeta()[%d].size() );\n";
generated_grad_function_body += paddle::string::Sprintf(
DISPENSABLE_GRAD_OUTS_FWD_CONTENT_TEMPLATE, fwd_name, outs_name,
grad_output_name, fwd_input_position);
DISPENSABLE_GRAD_OUTS_FWD_CONTENT_TEMPLATE,
fwd_name,
outs_name,
grad_output_name,
fwd_input_position);
} else {
const char* DISPENSABLE_GRAD_OUTS_FWD_CONTENT_TEMPLATE =
" if(%s.defined()) %s[\"%s\"] = "
"{std::make_shared<egr::EagerVariable>(egr::Controller::"
"Instance().GenerateUniqueName())};\n";
generated_grad_function_body += paddle::string::Sprintf(
DISPENSABLE_GRAD_OUTS_FWD_CONTENT_TEMPLATE, fwd_name, outs_name,
DISPENSABLE_GRAD_OUTS_FWD_CONTENT_TEMPLATE,
fwd_name,
outs_name,
grad_output_name);
}
}
......@@ -2236,8 +2309,8 @@ static std::string GenerateSingleOpBase(
" auto temp_type = %s[\"in_dtype\"];\n"
" %s[\"in_dtype\"] = %s[\"out_dtype\"];\n"
" %s[\"out_dtype\"] = temp_type;\n";
grad_attrs_str += paddle::string::Sprintf(CAST_GRAD, attrs_name, attrs_name,
attrs_name, attrs_name);
grad_attrs_str += paddle::string::Sprintf(
CAST_GRAD, attrs_name, attrs_name, attrs_name, attrs_name);
}
// Handle dynamic grad attributes
......@@ -2278,8 +2351,8 @@ static std::string GenerateSingleOpBase(
" "
"outputs[0].emplace_back(egr::EagerUtils::GetOutputs(%s[\"%s\"])[0]"
");\n";
outputs_str += paddle::string::Sprintf(BWD_OUTPUT_TEMPLATE, outs_name,
grad_out_name);
outputs_str += paddle::string::Sprintf(
BWD_OUTPUT_TEMPLATE, outs_name, grad_out_name);
}
num_appended_outputs++;
} else {
......@@ -2411,11 +2484,20 @@ static std::string GenerateGradNodeCCContents(
const auto& grad_attrs = op_base_info.GetGradAttrs();
const std::string& op_base_type = op_base_info.GetOpBaseType();
generated_grad_function_body += GenerateSingleOpBase(
fwd_op_type, op_base_type, fwd_inputs_name_pos_map,
fwd_outputs_name_pos_map, in_vars, grad_ins_fwd_slotname_map,
grad_ins_grad_slotname_map, grad_outs_slotname_map, grad_ins, grad_outs,
grad_attrs, is_op_base_per_duplicable_input, &outs_size);
generated_grad_function_body +=
GenerateSingleOpBase(fwd_op_type,
op_base_type,
fwd_inputs_name_pos_map,
fwd_outputs_name_pos_map,
in_vars,
grad_ins_fwd_slotname_map,
grad_ins_grad_slotname_map,
grad_outs_slotname_map,
grad_ins,
grad_outs,
grad_attrs,
is_op_base_per_duplicable_input,
&outs_size);
}
if (is_op_base_per_duplicable_input) {
......@@ -2436,7 +2518,9 @@ static std::string GenerateGradNodeCCContents(
"HandleComplexGradToRealGrad(&outputs);\n"
" return outputs;\n";
generated_grad_function_body =
paddle::string::Sprintf(BWD_RETURN_TEMPLATE, fwd_op_type, in_vars.size(),
paddle::string::Sprintf(BWD_RETURN_TEMPLATE,
fwd_op_type,
in_vars.size(),
generated_grad_function_body);
// [Generation] Get Full Grad Function
......@@ -2455,8 +2539,10 @@ static std::string GenerateGradNodeCCContents(
"this->InputMeta());\n";
}
std::string grad_function_str =
paddle::string::Sprintf(GRAD_FUNCTION_TEMPLATE, fwd_op_type,
fill_zero_str, generated_grad_function_body);
paddle::string::Sprintf(GRAD_FUNCTION_TEMPLATE,
fwd_op_type,
fill_zero_str,
generated_grad_function_body);
VLOG(6) << "Generated returns";
......@@ -2579,9 +2665,12 @@ static std::string GenerateGradNodeHeaderContents(
" %s.emplace_back( egr::TensorWrapper(eager_tensor, %s "
"/*full_reserved*/, %s) );\n"
" }\n";
tensor_wrapper_body_str = paddle::string::Sprintf(
SET_TENSOR_WRAPPER_BODY_TEMPLATE, tensor_wrapper_name,
struct_tensor_wrapper_name, full_reserved_str, no_need_buffer_str);
tensor_wrapper_body_str =
paddle::string::Sprintf(SET_TENSOR_WRAPPER_BODY_TEMPLATE,
tensor_wrapper_name,
struct_tensor_wrapper_name,
full_reserved_str,
no_need_buffer_str);
const char* CLEAR_TENSOR_WRAPPER_TEMPLATE =
"for (auto tw: %s) {\n"
......@@ -2603,9 +2692,12 @@ static std::string GenerateGradNodeHeaderContents(
const char* SET_TENSOR_WRAPPER_BODY_TEMPLATE =
"%s = egr::TensorWrapper(%s, %s /*full_reserved*/, %s);\n";
tensor_wrapper_body_str = paddle::string::Sprintf(
SET_TENSOR_WRAPPER_BODY_TEMPLATE, struct_tensor_wrapper_name,
tensor_wrapper_name, full_reserved_str, no_need_buffer_str);
tensor_wrapper_body_str =
paddle::string::Sprintf(SET_TENSOR_WRAPPER_BODY_TEMPLATE,
struct_tensor_wrapper_name,
tensor_wrapper_name,
full_reserved_str,
no_need_buffer_str);
const char* CLEAR_TENSOR_WRAPPER_TEMPLATE = " %s.clear();\n";
clear_tensor_wrappers_str += paddle::string::Sprintf(
......@@ -2614,19 +2706,33 @@ static std::string GenerateGradNodeHeaderContents(
std::string full_reserved_signature_str = "bool full_reserved";
const char* SET_TENSOR_WRAPPER_TEMPLATE =
" void SetTensorWrapper%s(%s, %s) {\n %s\n }\n";
set_tensor_wrappers_str += paddle::string::Sprintf(
SET_TENSOR_WRAPPER_TEMPLATE, tensor_wrapper_name,
tensor_wrapper_arg_str, full_reserved_signature_str,
tensor_wrapper_body_str);
set_tensor_wrappers_str +=
paddle::string::Sprintf(SET_TENSOR_WRAPPER_TEMPLATE,
tensor_wrapper_name,
tensor_wrapper_arg_str,
full_reserved_signature_str,
tensor_wrapper_body_str);
}
}
VLOG(6) << "Generated TensorWrapper";
std::string grad_node_str = paddle::string::Sprintf(
GRAD_NODE_TEMPLATE, op_type, op_type, op_type, op_type, op_type, op_type,
op_type, clear_tensor_wrappers_str, op_type, op_type, op_type,
set_tensor_wrappers_str, set_attr_map_str, tensor_wrapper_members_str,
attr_members_str);
std::string grad_node_str =
paddle::string::Sprintf(GRAD_NODE_TEMPLATE,
op_type,
op_type,
op_type,
op_type,
op_type,
op_type,
op_type,
clear_tensor_wrappers_str,
op_type,
op_type,
op_type,
set_tensor_wrappers_str,
set_attr_map_str,
tensor_wrapper_members_str,
attr_members_str);
return grad_node_str;
}
......@@ -2760,9 +2866,11 @@ static std::string GenerateCoreOpsReturnsInfo() {
std::string core_ops_returns_info_init_str =
ConvertCoreOpsInfosToString(core_ops_returns_info);
std::string core_ops_info_str = paddle::string::Sprintf(
Core_Ops_Returns_MAP_TEMPLATE, core_ops_args_info_init_str,
core_ops_args_type_info_init_str, core_ops_returns_info_init_str);
std::string core_ops_info_str =
paddle::string::Sprintf(Core_Ops_Returns_MAP_TEMPLATE,
core_ops_args_info_init_str,
core_ops_args_type_info_init_str,
core_ops_returns_info_init_str);
return core_ops_info_str;
}
......
paddle/fluid/eager/auto_code_generator/final_state_generator/python_c_gen.py
浏览文件 @ 6de20581
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
#
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#
# http://www.apache.org/licenses/LICENSE-2.0
#
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
......@@ -71,7 +71,7 @@ PARSE_PYTHON_C_ARGS_TEMPLATE = \
RECORD_EVENT_TEMPLATE = \
" paddle::platform::RecordEvent {}(\"{} {}\", paddle::platform::TracerEventType::Operator, 1);"
"paddle::platform::RecordEvent {}(\"{} {}\", paddle::platform::TracerEventType::UserDefined, 1);"
RETURN_INPLACE_PYOBJECT_TEMPLATE = \
......@@ -253,6 +253,7 @@ NAMESPACE_WRAPPER_TEMPLATE = \
## Generator Classes ##
#######################
class PythonCSingleFunctionGenerator(FunctionGeneratorBase):
def __init__(self, forward_api_contents, namespace):
# Members from Parent:
#self.namespace
......@@ -265,7 +266,7 @@ class PythonCSingleFunctionGenerator(FunctionGeneratorBase):
#self.forward_outputs_position_map
#self.optional_inputs
#self.no_need_buffers
#self.intermediate_outputs
#self.intermediate_outputs
#self.inplace_map
FunctionGeneratorBase.__init__(self, forward_api_contents, namespace)
......@@ -327,8 +328,8 @@ class PythonCSingleFunctionGenerator(FunctionGeneratorBase):
set_device_str = FUNCTION_SET_DEVICE_TEMPLATE.format(expected_place_str)
# Generate Dygraph Function Call Logic
num_args = len(forward_inputs_position_map.keys()) + len(
orig_forward_attrs_list)
num_args = len(
forward_inputs_position_map.keys()) + len(orig_forward_attrs_list)
dygraph_function_call_list = ["" for i in range(num_args)]
for name, (_, pos) in forward_inputs_position_map.items():
dygraph_function_call_list[pos] = f"{name}"
......@@ -336,7 +337,7 @@ class PythonCSingleFunctionGenerator(FunctionGeneratorBase):
dygraph_function_call_list[pos] = f"{name}"
dygraph_function_call_str = ",".join(dygraph_function_call_list)
# Generate Python-C Function Definitions
# Generate Python-C Function Definitions
if is_forward_only:
fwd_function_name = FUNCTION_NAME_TEMPLATE.format(
"paddle::experimental::", namespace, forward_api_name)
......@@ -441,8 +442,9 @@ class PythonCSingleFunctionGenerator(FunctionGeneratorBase):
class PythonCYamlGenerator(YamlGeneratorBase):
def __init__(self, path):
# Parent members:
# Parent members:
# self.namespace
# self.api_yaml_path
# self.forward_api_list
......@@ -457,8 +459,8 @@ class PythonCYamlGenerator(YamlGeneratorBase):
forward_api_list = self.forward_api_list
for forward_api_content in forward_api_list:
f_generator = PythonCSingleFunctionGenerator(forward_api_content,
namespace)
f_generator = PythonCSingleFunctionGenerator(
forward_api_content, namespace)
status = f_generator.run()
if status == True:
......
paddle/fluid/eager/backward.cc
浏览文件 @ 6de20581
......@@ -30,10 +30,10 @@
namespace egr {
/*
* GeneralGrad is Helpper class to implement custom grad operation between
* outputs and inputs.
*
* **/
* GeneralGrad is Helpper class to implement custom grad operation between
* outputs and inputs.
*
* **/
class GeneralGrad {
public:
static GeneralGrad& Instance() { return *general_grad_; }
......@@ -64,7 +64,8 @@ class GeneralGrad {
paddle::platform::errors::Fatal(
"There is no grad op for %s:[%d] or it's"
"stop_gradient=True.",
msg, i));
msg,
i));
if (is_no_grad_vars) {
(no_grad_var_nodes_inputmeta_map)[target_node] = auto_grad_meta;
} else { // normal input
......@@ -248,7 +249,8 @@ class GeneralGrad {
std::vector<paddle::experimental::Tensor> GetResults(
const std::vector<paddle::experimental::Tensor>& inputs,
bool allow_unused, bool create_graph) {
bool allow_unused,
bool create_graph) {
VLOG(6) << "Running in GetResults";
if (inputs.empty()) return {};
......@@ -276,7 +278,8 @@ class GeneralGrad {
tensor_auto_grad_meta->SetStopGradient(!create_graph);
results.emplace_back(iter->second);
} else {
PADDLE_ENFORCE_EQ(allow_unused, true,
PADDLE_ENFORCE_EQ(allow_unused,
true,
paddle::platform::errors::InvalidArgument(
"The %d-th input does not appear in the backward "
"graph. Please check the input tensor or set "
......@@ -493,7 +496,8 @@ std::unordered_map<GradNodeBase*, int> getInDegreeMap(
void EnforceGradNodeHasInput(GradNodeBase* node) {
VLOG(6) << "Running in EnforceGradNodeHasInput";
PADDLE_ENFORCE_NE(
node->IsTensorWrappersCleared(), true,
node->IsTensorWrappersCleared(),
true,
paddle::platform::errors::Fatal(
"The TensorWrappers of %s do not exist. This may be because:\n"
"You calculate backward twice for the same subgraph without "
......@@ -509,10 +513,13 @@ void DuplicateCheck(const std::vector<paddle::experimental::Tensor>& inputs,
for (auto in : inputs) {
AutogradMeta* auto_grad_meta = EagerUtils::unsafe_autograd_meta(in);
PADDLE_ENFORCE_EQ(
visisted_ins.count(auto_grad_meta), 0,
visisted_ins.count(auto_grad_meta),
0,
paddle::platform::errors::AlreadyExists(
"%s contain duplicate tensor %s, please check %s carefully.", msg,
in.name(), msg));
"%s contain duplicate tensor %s, please check %s carefully.",
msg,
in.name(),
msg));
visisted_ins.insert(auto_grad_meta);
}
}
......@@ -522,7 +529,8 @@ GeneralGrad* GeneralGrad::general_grad_ = new GeneralGrad();
std::vector<paddle::experimental::Tensor> RunBackward(
const std::vector<paddle::experimental::Tensor>& tensors, // output
const std::vector<paddle::experimental::Tensor>& grad_tensors,
bool retain_graph, bool create_graph = false,
bool retain_graph,
bool create_graph = false,
const std::vector<paddle::experimental::Tensor>& inputs = {},
bool allow_unused = false,
const std::vector<paddle::experimental::Tensor>& no_grad_vars = {}) {
......@@ -631,8 +639,8 @@ std::vector<paddle::experimental::Tensor> RunBackward(
if (is_general_grad) {
// Prepare several vital preprocess for GeneralGrad
GeneralGrad::Instance().PreparedForGeneralGrad(inputs, no_grad_vars, &queue,
node_input_buffers_dict);
GeneralGrad::Instance().PreparedForGeneralGrad(
inputs, no_grad_vars, &queue, node_input_buffers_dict);
}
VLOG(6) << " startup_ops' size is :" << queue.size();
......@@ -651,7 +659,8 @@ std::vector<paddle::experimental::Tensor> RunBackward(
paddle::platform::RecordEvent node_record_event(
std::string((*node).name()) + " grad_node",
paddle::platform::TracerEventType::Operator, 1);
paddle::platform::TracerEventType::Operator,
1);
if (queue.size() > 1 && node_in_degree_map[node] != 0) {
queue.pop();
......@@ -716,7 +725,8 @@ std::vector<paddle::experimental::Tensor> RunBackward(
"Number of edges should be either empty ( for leaf node "
") or the same as number of output grad tensors, but we "
"got edges size is: %d, grad_output size is: %d",
edges.size(), grad_output_tensors.size()));
edges.size(),
grad_output_tensors.size()));
for (size_t i = 0; i < edges.size(); i++) {
for (size_t j = 0; j < edges[i].size(); j++) {
......@@ -739,7 +749,8 @@ std::vector<paddle::experimental::Tensor> RunBackward(
}
PADDLE_ENFORCE_LT(
j, grad_output_tensors[i].size(),
j,
grad_output_tensors[i].size(),
paddle::platform::errors::Fatal(
"Rank of grad_output_tensors should be less than "
"grad_output_tensors[i].size(), which is: %d. This error may "
......@@ -771,9 +782,10 @@ std::vector<paddle::experimental::Tensor> RunBackward(
VLOG(6) << "Sum grad inputs for edge slot: " << edge_rank.first
<< ", rank: " << edge_rank.second;
node_input_buffers_dict[next_node]->add(
edge_rank.first, edge_rank.second, grad_output_tensor,
create_graph);
node_input_buffers_dict[next_node]->add(edge_rank.first,
edge_rank.second,
grad_output_tensor,
create_graph);
// Update queue
node_in_degree_map[next_node]--;
......@@ -810,7 +822,7 @@ void Backward(
bool retain_graph) {
VLOG(6) << "Run in Backward";
paddle::platform::RecordEvent backward_record_event(
"backward", paddle::platform::TracerEventType::Operator, 1);
"backward", paddle::platform::TracerEventType::UserDefined, 1);
RunBackward(tensors, grad_tensors, retain_graph);
phi::autotune::AutoTuneStatus::Instance().Update();
}
......@@ -819,14 +831,22 @@ std::vector<paddle::experimental::Tensor> Grad(
const std::vector<paddle::experimental::Tensor>& tensors, // outputs
const std::vector<paddle::experimental::Tensor>& inputs,
const std::vector<paddle::experimental::Tensor>& grad_tensors,
bool retain_graph, bool create_graph, bool only_inputs, bool allow_unused,
bool retain_graph,
bool create_graph,
bool only_inputs,
bool allow_unused,
const std::vector<paddle::experimental::Tensor>& no_grad_vars) {
VLOG(6) << "Run in Grad";
DuplicateCheck(inputs, true /* is_input */);
DuplicateCheck(tensors, false /* is_input */);
return RunBackward(tensors, grad_tensors, retain_graph, create_graph, inputs,
allow_unused, no_grad_vars);
return RunBackward(tensors,
grad_tensors,
retain_graph,
create_graph,
inputs,
allow_unused,
no_grad_vars);
}
} // namespace egr
paddle/fluid/platform/profiler/chrometracing_logger.cc
浏览文件 @ 6de20581
......@@ -588,7 +588,7 @@ void ChromeTracingLogger::StartLog() {
std::string(
R"JSON(
{
"id": %d, "name": "%s", "totalGlobalMem": %u,
"id": %d, "name": "%s", "totalGlobalMem": %llu,
"computeMajor": %d, "computeMinor": %d,
"maxThreadsPerBlock": %d, "maxThreadsPerMultiprocessor": %d,
"regsPerBlock": %d, "regsPerMultiprocessor": %d, "warpSize": %d,
......@@ -618,7 +618,7 @@ void ChromeTracingLogger::StartLog() {
std::string(
R"JSON(
{
"id": %d, "name": "%s", "totalGlobalMem": %u,
"id": %d, "name": "%s", "totalGlobalMem": %llu,
"computeMajor": %d, "computeMinor": %d,
"maxThreadsPerBlock": %d, "maxThreadsPerMultiprocessor": %d,
"regsPerBlock": %d, "regsPerMultiprocessor": %d, "warpSize": %d,
......
python/paddle/fluid/tests/unittests/test_profiler_statistic.py
浏览文件 @ 6de20581
......@@ -19,6 +19,7 @@ import paddle.profiler as profiler
class HostPythonNode:
def __init__(self, name, type, start_ns, end_ns, process_id, thread_id):
self.name = name
self.type = type
......@@ -32,6 +33,7 @@ class HostPythonNode:
class DevicePythonNode:
def __init__(self, name, type, start_ns, end_ns, device_id, context_id,
stream_id):
self.name = name
......@@ -44,6 +46,7 @@ class DevicePythonNode:
class TestProfilerStatistic(unittest.TestCase):
def test_statistic_case1(self):
root_node = HostPythonNode('Root Node',
profiler.TracerEventType.UserDefined, 0,
......@@ -54,14 +57,16 @@ class TestProfilerStatistic(unittest.TestCase):
dataloader_node = HostPythonNode('Dataloader',
profiler.TracerEventType.Dataloader, 5,
15, 1000, 1001)
mobilenet_node = HostPythonNode(
'MobileNet', profiler.TracerEventType.Forward, 20, 50, 1000, 1001)
yolonet_node = HostPythonNode(
'Yolov3Net', profiler.TracerEventType.Forward, 50, 110, 1000, 1001)
mobilenet_node = HostPythonNode('MobileNet',
profiler.TracerEventType.Forward, 20,
50, 1000, 1001)
yolonet_node = HostPythonNode('Yolov3Net',
profiler.TracerEventType.Forward, 50, 110,
1000, 1001)
userdefined_node = HostPythonNode('Communication Time',
profiler.TracerEventType.UserDefined,
100, 110, 1000, 1001)
userdefined_node = HostPythonNode(
'Communication Time', profiler.TracerEventType.PythonUserDefined,
100, 110, 1000, 1001)
communication_node = HostPythonNode(
'Communication', profiler.TracerEventType.Communication, 105, 110,
......@@ -72,8 +77,9 @@ class TestProfilerStatistic(unittest.TestCase):
optimization_node = HostPythonNode(
'Optimization', profiler.TracerEventType.Optimization, 220, 300,
1000, 1001)
conv2d_node = HostPythonNode(
'conv2d', profiler.TracerEventType.Operator, 25, 40, 1000, 1001)
conv2d_node = HostPythonNode('conv2d',
profiler.TracerEventType.Operator, 25, 40,
1000, 1001)
sync_batch_norm_node = HostPythonNode('sync_batch_norm',
profiler.TracerEventType.Operator,
60, 100, 1000, 1001)
......@@ -92,10 +98,12 @@ class TestProfilerStatistic(unittest.TestCase):
conv2d_cudaMemCpy = HostPythonNode('cudaMemcpy',
profiler.TracerEventType.CudaRuntime,
35, 40, 1000, 1001)
conv2d_kernel = DevicePythonNode(
'conv2d_kernel', profiler.TracerEventType.Kernel, 35, 50, 0, 0, 0)
conv2d_memcpy = DevicePythonNode(
'conv2d_memcpy', profiler.TracerEventType.Memcpy, 50, 60, 0, 0, 0)
conv2d_kernel = DevicePythonNode('conv2d_kernel',
profiler.TracerEventType.Kernel, 35,
50, 0, 0, 0)
conv2d_memcpy = DevicePythonNode('conv2d_memcpy',
profiler.TracerEventType.Memcpy, 50,
60, 0, 0, 0)
sync_batch_norm_infer_shape = HostPythonNode(
'sync_batch_norm::infer_shape',
profiler.TracerEventType.OperatorInner, 60, 70, 1000, 1001)
......@@ -146,8 +154,8 @@ class TestProfilerStatistic(unittest.TestCase):
'Process Cpu Utilization': '1.02',
'System Cpu Utilization': '0.68'
}
statistic_data = profiler.profiler_statistic.StatisticData(thread_tree,
extra_info)
statistic_data = profiler.profiler_statistic.StatisticData(
thread_tree, extra_info)
time_range_summary = statistic_data.time_range_summary
event_summary = statistic_data.event_summary
......@@ -180,7 +188,7 @@ class TestProfilerStatistic(unittest.TestCase):
0, profiler.TracerEventType.Memcpy), 60)
self.assertEqual(
time_range_summary.get_cpu_range_sum(
profiler.TracerEventType.UserDefined), 25)
profiler.TracerEventType.UserDefined), 15)
self.assertEqual(
time_range_summary.get_cpu_range_sum(
profiler.TracerEventType.Communication), 5)
......@@ -200,8 +208,9 @@ class TestProfilerStatistic(unittest.TestCase):
0)
self.assertEqual(
event_summary.memory_manipulation_items['AsyncMemcpy'].cpu_time, 15)
self.assertEqual(event_summary.memory_manipulation_items['AsyncMemcpy']
.general_gpu_time, 60)
self.assertEqual(
event_summary.memory_manipulation_items['AsyncMemcpy'].
general_gpu_time, 60)
print(
profiler.profiler_statistic._build_table(
statistic_data,
......@@ -222,14 +231,16 @@ class TestProfilerStatistic(unittest.TestCase):
profiler.TracerEventType.Dataloader, 5,
15, 1000, 1001)
mobilenet_node = HostPythonNode(
'MobileNet', profiler.TracerEventType.Forward, 20, 50, 1000, 1001)
yolonet_node = HostPythonNode(
'Yolov3Net', profiler.TracerEventType.Forward, 50, 110, 1000, 1001)
mobilenet_node = HostPythonNode('MobileNet',
profiler.TracerEventType.Forward, 20,
50, 1000, 1001)
yolonet_node = HostPythonNode('Yolov3Net',
profiler.TracerEventType.Forward, 50, 110,
1000, 1001)
userdefined_node = HostPythonNode('Communication Time',
profiler.TracerEventType.UserDefined,
100, 110, 1000, 1001)
userdefined_node = HostPythonNode(
'Communication Time', profiler.TracerEventType.PythonUserDefined,
100, 110, 1000, 1001)
allreduce_launchkernel0 = HostPythonNode(
'cudalaunchkernel', profiler.TracerEventType.CudaRuntime, 102, 104,
1000, 1001)
......@@ -263,8 +274,9 @@ class TestProfilerStatistic(unittest.TestCase):
optimization_node = HostPythonNode(
'Optimization', profiler.TracerEventType.Optimization, 220, 300,
1000, 1001)
conv2d_node = HostPythonNode(
'conv2d', profiler.TracerEventType.Operator, 25, 40, 1000, 1001)
conv2d_node = HostPythonNode('conv2d',
profiler.TracerEventType.Operator, 25, 40,
1000, 1001)
sync_batch_norm_node = HostPythonNode('sync_batch_norm',
profiler.TracerEventType.Operator,
60, 100, 1000, 1001)
......@@ -283,10 +295,12 @@ class TestProfilerStatistic(unittest.TestCase):
conv2d_cudaMemCpy = HostPythonNode('cudaMemcpy',
profiler.TracerEventType.CudaRuntime,
35, 40, 1000, 1001)
conv2d_kernel = DevicePythonNode(
'conv2d_kernel', profiler.TracerEventType.Kernel, 35, 50, 0, 0, 0)
conv2d_memcpy = DevicePythonNode(
'conv2d_memcpy', profiler.TracerEventType.Memcpy, 50, 60, 0, 0, 0)
conv2d_kernel = DevicePythonNode('conv2d_kernel',
profiler.TracerEventType.Kernel, 35,
50, 0, 0, 0)
conv2d_memcpy = DevicePythonNode('conv2d_memcpy',
profiler.TracerEventType.Memcpy, 50,
60, 0, 0, 0)
sync_batch_norm_infer_shape = HostPythonNode(
'sync_batch_norm::infer_shape',
profiler.TracerEventType.OperatorInner, 60, 70, 1000, 1001)
......@@ -363,8 +377,8 @@ class TestProfilerStatistic(unittest.TestCase):
'Process Cpu Utilization': '1.02',
'System Cpu Utilization': '0.68'
}
statistic_data = profiler.profiler_statistic.StatisticData(thread_tree,
extra_info)
statistic_data = profiler.profiler_statistic.StatisticData(
thread_tree, extra_info)
time_range_summary = statistic_data.time_range_summary
event_summary = statistic_data.event_summary
distributed_summary = statistic_data.distributed_summary
......@@ -398,7 +412,7 @@ class TestProfilerStatistic(unittest.TestCase):
0, profiler.TracerEventType.Memcpy), 60)
self.assertEqual(
time_range_summary.get_cpu_range_sum(
profiler.TracerEventType.UserDefined), 25)
profiler.TracerEventType.UserDefined), 15)
self.assertEqual(
time_range_summary.get_cpu_range_sum(
profiler.TracerEventType.Communication), 5)
......@@ -433,8 +447,9 @@ class TestProfilerStatistic(unittest.TestCase):
0)
self.assertEqual(
event_summary.memory_manipulation_items['AsyncMemcpy'].cpu_time, 15)
self.assertEqual(event_summary.memory_manipulation_items['AsyncMemcpy']
.general_gpu_time, 60)
self.assertEqual(
event_summary.memory_manipulation_items['AsyncMemcpy'].
general_gpu_time, 60)
print(
profiler.profiler_statistic._build_table(
statistic_data,
......@@ -454,8 +469,9 @@ class TestProfilerStatistic(unittest.TestCase):
dataloader_node = HostPythonNode('Dataloader',
profiler.TracerEventType.Dataloader, 5,
15, 1000, 1001)
mobilenet_node = HostPythonNode(
'MobileNet', profiler.TracerEventType.Forward, 20, 50, 1000, 1001)
mobilenet_node = HostPythonNode('MobileNet',
profiler.TracerEventType.Forward, 20,
50, 1000, 1001)
backward_node = HostPythonNode('Gradient Backward',
profiler.TracerEventType.Backward, 120,
......@@ -463,12 +479,13 @@ class TestProfilerStatistic(unittest.TestCase):
optimization_node = HostPythonNode(
'Optimization', profiler.TracerEventType.Optimization, 220, 300,
1000, 1001)
userdefined_node = HostPythonNode('Communication Time',
profiler.TracerEventType.UserDefined,
60, 70, 1000, 1001)
userdefined_node = HostPythonNode(
'Communication Time', profiler.TracerEventType.PythonUserDefined,
60, 70, 1000, 1001)
conv2d_node = HostPythonNode(
'conv2d', profiler.TracerEventType.Operator, 25, 25, 1000, 1001)
conv2d_node = HostPythonNode('conv2d',
profiler.TracerEventType.Operator, 25, 25,
1000, 1001)
conv2d_infer_shape = HostPythonNode(
'conv2d::infer_shape', profiler.TracerEventType.OperatorInner, 25,
......@@ -480,8 +497,9 @@ class TestProfilerStatistic(unittest.TestCase):
'cudalaunchkernel', profiler.TracerEventType.CudaRuntime, 25, 25,
1000, 1001)
conv2d_kernel = DevicePythonNode(
'conv2d_kernel', profiler.TracerEventType.Kernel, 35, 35, 0, 0, 0)
conv2d_kernel = DevicePythonNode('conv2d_kernel',
profiler.TracerEventType.Kernel, 35,
35, 0, 0, 0)
another_kernel = DevicePythonNode(
'void phi::funcs::VectorizedBroadcastKernel<float, float, phi::funcs::AddFunctor<float>, phi::funcs::AddFunctor<float>>()',
profiler.TracerEventType.Kernel, 35, 35, 0, 0, 0)
......@@ -500,15 +518,16 @@ class TestProfilerStatistic(unittest.TestCase):
'Process Cpu Utilization': '1.02',
'System Cpu Utilization': '0.68'
}
statistic_data = profiler.profiler_statistic.StatisticData(thread_tree,
extra_info)
statistic_data = profiler.profiler_statistic.StatisticData(
thread_tree, extra_info)
time_range_summary = statistic_data.time_range_summary
event_summary = statistic_data.event_summary
self.assertEqual(event_summary.items['conv2d'].cpu_time, 0)
self.assertEqual(event_summary.items['conv2d'].general_gpu_time, 0)
self.assertEqual(event_summary.userdefined_items['Communication Time']
.general_gpu_time, 0)
self.assertEqual(
event_summary.userdefined_items['Communication Time'].
general_gpu_time, 0)
for sort_key in [
profiler.SortedKeys.CPUTotal, profiler.SortedKeys.CPUMax,
profiler.SortedKeys.CPUMin, profiler.SortedKeys.CPUAvg,
......@@ -516,12 +535,11 @@ class TestProfilerStatistic(unittest.TestCase):
profiler.SortedKeys.GPUMin, profiler.SortedKeys.GPUAvg
]:
print(
profiler.profiler_statistic._build_table(
statistic_data,
sorted_by=sort_key,
op_detail=True,
thread_sep=False,
time_unit='ms'))
profiler.profiler_statistic._build_table(statistic_data,
sorted_by=sort_key,
op_detail=True,
thread_sep=False,
time_unit='ms'))
if __name__ == '__main__':
......
python/paddle/profiler/profiler_statistic.py
浏览文件 @ 6de20581
......@@ -197,8 +197,8 @@ class TimeRangeSummary:
def __init__(self):
self.CPUTimeRange = collections.defaultdict(list)
self.GPUTimeRange = collections.defaultdict(
lambda: collections.defaultdict(list)
) # GPU events should be divided into different devices
lambda: collections.defaultdict(
list)) # GPU events should be divided into different devices
self.CPUTimeRangeSum = collections.defaultdict(int)
self.GPUTimeRangeSum = collections.defaultdict(
lambda: collections.defaultdict(int))
......@@ -212,8 +212,8 @@ class TimeRangeSummary:
for threadid, hostnodes in thread2hostnodes.items():
CPUTimeRange = collections.defaultdict(list)
GPUTimeRange = collections.defaultdict(
lambda: collections.defaultdict(lambda: collections.defaultdict(list))
) # device_id/type/stream_id
lambda: collections.defaultdict(lambda: collections.defaultdict(
list))) # device_id/type/stream_id
for hostnode in hostnodes[1:]: #skip root node
CPUTimeRange[hostnode.type].append(
(hostnode.start_ns, hostnode.end_ns))
......@@ -235,8 +235,8 @@ class TimeRangeSummary:
for device_id, device_time_ranges in GPUTimeRange.items():
for event_type, event_time_ranges in device_time_ranges.items():
for stream_id, time_ranges in event_time_ranges.items():
time_ranges = merge_self_ranges(
time_ranges, is_sorted=False)
time_ranges = merge_self_ranges(time_ranges,
is_sorted=False)
self.GPUTimeRange[device_id][event_type] = merge_ranges(
self.GPUTimeRange[device_id][event_type],
time_ranges,
......@@ -310,25 +310,27 @@ class DistributedSummary:
for devicenode in runtimenode.device_node:
if devicenode.type == TracerEventType.Kernel:
if 'nccl' in devicenode.name.lower():
self.gpu_communication_range.append((
devicenode.start_ns, devicenode.end_ns))
self.gpu_communication_range.append(
(devicenode.start_ns,
devicenode.end_ns))
else:
self.computation_range.append((
devicenode.start_ns, devicenode.end_ns))
self.computation_range.append(
(devicenode.start_ns,
devicenode.end_ns))
self.cpu_calls = len(set(self.cpu_communication_range))
self.gpu_calls = len(set(self.gpu_communication_range))
self.cpu_communication_range = merge_self_ranges(
self.cpu_communication_range, is_sorted=False)
self.gpu_communication_range = merge_self_ranges(
self.gpu_communication_range, is_sorted=False)
self.communication_range = merge_ranges(
self.cpu_communication_range,
self.gpu_communication_range,
is_sorted=True)
self.computation_range = merge_self_ranges(
self.computation_range, is_sorted=False)
self.overlap_range = intersection_ranges(
self.communication_range, self.computation_range, is_sorted=True)
self.communication_range = merge_ranges(self.cpu_communication_range,
self.gpu_communication_range,
is_sorted=True)
self.computation_range = merge_self_ranges(self.computation_range,
is_sorted=False)
self.overlap_range = intersection_ranges(self.communication_range,
self.computation_range,
is_sorted=True)
class EventSummary:
......@@ -337,6 +339,7 @@ class EventSummary:
"""
class DeviceItem:
def __init__(self, name):
self.name = name
self.call = 0
......@@ -360,6 +363,7 @@ class EventSummary:
self.add_gpu_time(node.end_ns - node.start_ns)
class OperatorItem:
def __init__(self, name):
self.name = name
self.call = 0
......@@ -430,6 +434,7 @@ class EventSummary:
self.devices[name].add_item(devicenode)
class GeneralItem:
def __init__(self, name):
self.name = name
self.call = 0
......@@ -513,7 +518,8 @@ class EventSummary:
or 'memset' in host_statistic_node.name.lower():
self.add_memory_manipulation_item(host_statistic_node)
else:
self.add_userdefined_item(host_statistic_node)
if host_statistic_node.type == TracerEventType.PythonUserDefined:
self.add_userdefined_item(host_statistic_node)
self.add_kernel_item(host_statistic_nodes[0])
for threadid, root_statistic_node in node_statistic_trees.items():
......@@ -688,13 +694,14 @@ def _build_table(statistic_data,
append(row_format.format(*headers))
append(header_sep)
row_values = [
'CPU(Process)', format_ratio(
float(statistic_data.extra_info['Process Cpu Utilization']))
'CPU(Process)',
format_ratio(float(
statistic_data.extra_info['Process Cpu Utilization']))
]
append(row_format.format(*row_values))
row_values = [
'CPU(System)', format_ratio(
float(statistic_data.extra_info['System Cpu Utilization']))
'CPU(System)',
format_ratio(float(statistic_data.extra_info['System Cpu Utilization']))
]
append(row_format.format(*row_values))
for gpu_name in statistic_data.time_range_summary.get_gpu_devices():
......@@ -783,20 +790,22 @@ def _build_table(statistic_data,
TracerEventType.
Communication] = statistic_data.distributed_summary.gpu_calls
sorted_items = sorted(
cpu_type_time.items(), key=lambda x: x[1], reverse=True)
sorted_items = sorted(cpu_type_time.items(),
key=lambda x: x[1],
reverse=True)
event_type, time = sorted_items[0]
row_values = [
'{}'.format(str(event_type).split('.')[1]), cpu_call_times[event_type],
format_time(
time, unit=time_unit), format_ratio(float(time) / total_time)
format_time(time, unit=time_unit),
format_ratio(float(time) / total_time)
]
append(row_format.format(*row_values))
for event_type, time in sorted_items[1:]:
row_values = [
' {}'.format(str(event_type).split('.')[1]),
cpu_call_times[event_type], format_time(
time, unit=time_unit), format_ratio(float(time) / total_time)
cpu_call_times[event_type],
format_time(time, unit=time_unit),
format_ratio(float(time) / total_time)
]
append(row_format.format(*row_values))
append(header_sep)
......@@ -806,8 +815,9 @@ def _build_table(statistic_data,
for event_type, time in gpu_type_time.items():
row_values = [
' {}'.format(str(event_type).split('.')[1]),
gpu_call_times[event_type], format_time(
time, unit=time_unit), format_ratio(float(time) / total_time)
gpu_call_times[event_type],
format_time(time, unit=time_unit),
format_ratio(float(time) / total_time)
]
append(row_format.format(*row_values))
......@@ -851,24 +861,16 @@ def _build_table(statistic_data,
row_values = [
'{}'.format(name), item.call,
'{} / {} / {} / {} / {}'.format(
format_time(
item.cpu_time, unit=time_unit),
format_time(
item.avg_cpu_time, unit=time_unit),
format_time(
item.max_cpu_time, unit=time_unit),
format_time(
item.min_cpu_time, unit=time_unit),
format_time(item.cpu_time, unit=time_unit),
format_time(item.avg_cpu_time, unit=time_unit),
format_time(item.max_cpu_time, unit=time_unit),
format_time(item.min_cpu_time, unit=time_unit),
format_ratio(float(item.cpu_time) / total_time)),
'{} / {} / {} / {} / {}'.format(
format_time(
item.gpu_time, unit=time_unit),
format_time(
item.avg_gpu_time, unit=time_unit),
format_time(
item.max_gpu_time, unit=time_unit),
format_time(
item.min_gpu_time, unit=time_unit),
format_time(item.gpu_time, unit=time_unit),
format_time(item.avg_gpu_time, unit=time_unit),
format_time(item.max_gpu_time, unit=time_unit),
format_time(item.min_gpu_time, unit=time_unit),
format_ratio(gpu_ratio))
]
all_row_values.append(row_values)
......@@ -884,12 +886,10 @@ def _build_table(statistic_data,
gpu_ratio = float(other_gpu_time) / gpu_total_time
row_values = [
' Others', '-', '{} / - / - / - / {}'.format(
format_time(
other_time, unit=time_unit),
format_time(other_time, unit=time_unit),
format_ratio(float(other_time) / total_time)),
'{} / - / - / - / {}'.format(
format_time(
other_gpu_time, unit=time_unit),
format_time(other_gpu_time, unit=time_unit),
format_ratio(gpu_ratio))
]
all_row_values.append(row_values)
......@@ -971,28 +971,28 @@ def _build_table(statistic_data,
overlap_time = sum_ranges(
statistic_data.distributed_summary.overlap_range)
row_values = [
'ProfileStep', format_time(
total_time, unit=time_unit),
'ProfileStep',
format_time(total_time, unit=time_unit),
format_ratio(float(total_time) / total_time)
]
append(row_format.format(*row_values))
row_values = [
' Communication', format_time(
communication_time, unit=time_unit),
' Communication',
format_time(communication_time, unit=time_unit),
format_ratio(float(communication_time) / total_time)
]
append(row_format.format(*row_values))
row_values = [
' Computation', format_time(
computation_time, unit=time_unit),
' Computation',
format_time(computation_time, unit=time_unit),
format_ratio(float(computation_time) / total_time)
]
append(row_format.format(*row_values))
row_values = [
' Overlap', format_time(
overlap_time, unit=time_unit),
' Overlap',
format_time(overlap_time, unit=time_unit),
format_ratio(float(overlap_time) / total_time)
]
append(row_format.format(*row_values))
......@@ -1026,39 +1026,35 @@ def _build_table(statistic_data,
for thread_id, items in thread_items.items():
all_row_values.append("Thread: {}".format(thread_id))
if sorted_by == SortedKeys.CPUTotal:
sorted_items = sorted(
items.items(), key=lambda x: x[1].cpu_time, reverse=True)
sorted_items = sorted(items.items(),
key=lambda x: x[1].cpu_time,
reverse=True)
elif sorted_by == SortedKeys.CPUAvg:
sorted_items = sorted(
items.items(),
key=lambda x: x[1].avg_cpu_time,
reverse=True)
sorted_items = sorted(items.items(),
key=lambda x: x[1].avg_cpu_time,
reverse=True)
elif sorted_by == SortedKeys.CPUMax:
sorted_items = sorted(
items.items(),
key=lambda x: x[1].max_cpu_time,
reverse=True)
sorted_items = sorted(items.items(),
key=lambda x: x[1].max_cpu_time,
reverse=True)
elif sorted_by == SortedKeys.CPUMin:
sorted_items = sorted(
items.items(), key=lambda x: x[1].min_cpu_time)
sorted_items = sorted(items.items(),
key=lambda x: x[1].min_cpu_time)
elif sorted_by == SortedKeys.GPUTotal:
sorted_items = sorted(
items.items(),
key=lambda x: x[1].general_gpu_time,
reverse=True)
sorted_items = sorted(items.items(),
key=lambda x: x[1].general_gpu_time,
reverse=True)
elif sorted_by == SortedKeys.GPUAvg:
sorted_items = sorted(
items.items(),
key=lambda x: x[1].avg_general_gpu_time,
reverse=True)
sorted_items = sorted(items.items(),
key=lambda x: x[1].avg_general_gpu_time,
reverse=True)
elif sorted_by == SortedKeys.GPUMax:
sorted_items = sorted(
items.items(),
key=lambda x: x[1].max_general_gpu_time,
reverse=True)
sorted_items = sorted(items.items(),
key=lambda x: x[1].max_general_gpu_time,
reverse=True)
elif sorted_by == SortedKeys.GPUMin:
sorted_items = sorted(
items.items(), key=lambda x: x[1].min_general_gpu_time)
sorted_items = sorted(items.items(),
key=lambda x: x[1].min_general_gpu_time)
total_op_cpu_time = 0
total_op_gpu_time = 0
......@@ -1077,24 +1073,16 @@ def _build_table(statistic_data,
gpu_ratio = float(item.general_gpu_time) / total_op_gpu_time
row_values = [
name, item.call, '{} / {} / {} / {} / {}'.format(
format_time(
item.cpu_time, unit=time_unit),
format_time(
item.avg_cpu_time, unit=time_unit),
format_time(
item.max_cpu_time, unit=time_unit),
format_time(
item.min_cpu_time, unit=time_unit),
format_time(item.cpu_time, unit=time_unit),
format_time(item.avg_cpu_time, unit=time_unit),
format_time(item.max_cpu_time, unit=time_unit),
format_time(item.min_cpu_time, unit=time_unit),
format_ratio(cpu_ratio)),
'{} / {} / {} / {} / {}'.format(
format_time(
item.general_gpu_time, unit=time_unit),
format_time(
item.avg_general_gpu_time, unit=time_unit),
format_time(
item.max_general_gpu_time, unit=time_unit),
format_time(
item.min_general_gpu_time, unit=time_unit),
format_time(item.general_gpu_time, unit=time_unit),
format_time(item.avg_general_gpu_time, unit=time_unit),
format_time(item.max_general_gpu_time, unit=time_unit),
format_time(item.min_general_gpu_time, unit=time_unit),
format_ratio(gpu_ratio))
]
all_row_values.append(row_values)
......@@ -1117,28 +1105,24 @@ def _build_table(statistic_data,
row_values = [
' {}'.format(innerop_name), innerop_node.call,
'{} / {} / {} / {} / {}'.format(
format_time(
innerop_node.cpu_time, unit=time_unit),
format_time(
innerop_node.avg_cpu_time, unit=time_unit),
format_time(
innerop_node.max_cpu_time, unit=time_unit),
format_time(
innerop_node.min_cpu_time, unit=time_unit),
format_time(innerop_node.cpu_time,
unit=time_unit),
format_time(innerop_node.avg_cpu_time,
unit=time_unit),
format_time(innerop_node.max_cpu_time,
unit=time_unit),
format_time(innerop_node.min_cpu_time,
unit=time_unit),
format_ratio(cpu_ratio)),
'{} / {} / {} / {} / {}'.format(
format_time(
innerop_node.general_gpu_time,
unit=time_unit),
format_time(
innerop_node.avg_general_gpu_time,
unit=time_unit),
format_time(
innerop_node.max_general_gpu_time,
unit=time_unit),
format_time(
innerop_node.min_general_gpu_time,
unit=time_unit),
format_time(innerop_node.general_gpu_time,
unit=time_unit),
format_time(innerop_node.avg_general_gpu_time,
unit=time_unit),
format_time(innerop_node.max_general_gpu_time,
unit=time_unit),
format_time(innerop_node.min_general_gpu_time,
unit=time_unit),
format_ratio(gpu_ratio))
]
all_row_values.append(row_values)
......@@ -1148,8 +1132,8 @@ def _build_table(statistic_data,
gpu_ratio = 0
else:
gpu_ratio = float(
device_node.
gpu_time) / innerop_node.general_gpu_time
device_node.gpu_time
) / innerop_node.general_gpu_time
if len(device_node_name) + 4 > name_column_width:
device_node_name = device_node_name[:
name_column_width
......@@ -1159,17 +1143,14 @@ def _build_table(statistic_data,
' {}'.format(device_node_name),
device_node.call, '- / - / - / - / -',
'{} / {} / {} / {} / {}'.format(
format_time(
device_node.gpu_time, unit=time_unit),
format_time(
device_node.avg_gpu_time,
unit=time_unit),
format_time(
device_node.max_gpu_time,
unit=time_unit),
format_time(
device_node.min_gpu_time,
unit=time_unit),
format_time(device_node.gpu_time,
unit=time_unit),
format_time(device_node.avg_gpu_time,
unit=time_unit),
format_time(device_node.max_gpu_time,
unit=time_unit),
format_time(device_node.min_gpu_time,
unit=time_unit),
format_ratio(gpu_ratio))
]
all_row_values.append(row_values)
......@@ -1188,14 +1169,14 @@ def _build_table(statistic_data,
' {}'.format(device_node_name), device_node.call,
'- / - / - / - / -',
'{} / {} / {} / {} / {}'.format(
format_time(
device_node.gpu_time, unit=time_unit),
format_time(
device_node.avg_gpu_time, unit=time_unit),
format_time(
device_node.max_gpu_time, unit=time_unit),
format_time(
device_node.min_gpu_time, unit=time_unit),
format_time(device_node.gpu_time,
unit=time_unit),
format_time(device_node.avg_gpu_time,
unit=time_unit),
format_time(device_node.max_gpu_time,
unit=time_unit),
format_time(device_node.min_gpu_time,
unit=time_unit),
format_ratio(gpu_ratio))
]
all_row_values.append(row_values)
......@@ -1249,21 +1230,20 @@ def _build_table(statistic_data,
all_row_values = []
kernel_items = statistic_data.event_summary.kernel_items
if sorted_by == SortedKeys.GPUAvg:
sorted_items = sorted(
kernel_items.items(),
key=lambda x: x[1].avg_gpu_time,
reverse=True)
sorted_items = sorted(kernel_items.items(),
key=lambda x: x[1].avg_gpu_time,
reverse=True)
elif sorted_by == SortedKeys.GPUMax:
sorted_items = sorted(
kernel_items.items(),
key=lambda x: x[1].max_gpu_time,
reverse=True)
sorted_items = sorted(kernel_items.items(),
key=lambda x: x[1].max_gpu_time,
reverse=True)
elif sorted_by == SortedKeys.GPUMin:
sorted_items = sorted(
kernel_items.items(), key=lambda x: x[1].min_gpu_time)
sorted_items = sorted(kernel_items.items(),
key=lambda x: x[1].min_gpu_time)
else:
sorted_items = sorted(
kernel_items.items(), key=lambda x: x[1].gpu_time, reverse=True)
sorted_items = sorted(kernel_items.items(),
key=lambda x: x[1].gpu_time,
reverse=True)
total_kernel_gpu_time = 0
for name, item in sorted_items:
......@@ -1277,14 +1257,10 @@ def _build_table(statistic_data,
name,
item.call,
'{} / {} / {} / {} / {}'.format(
format_time(
item.gpu_time, unit=time_unit),
format_time(
item.avg_gpu_time, unit=time_unit),
format_time(
item.max_gpu_time, unit=time_unit),
format_time(
item.min_gpu_time, unit=time_unit),
format_time(item.gpu_time, unit=time_unit),
format_time(item.avg_gpu_time, unit=time_unit),
format_time(item.max_gpu_time, unit=time_unit),
format_time(item.min_gpu_time, unit=time_unit),
format_ratio(gpu_ratio)),
]
all_row_values.append(row_values)
......@@ -1349,24 +1325,16 @@ def _build_table(statistic_data,
name,
item.call,
'{} / {} / {} / {} / {}'.format(
format_time(
item.cpu_time, unit=time_unit),
format_time(
item.avg_cpu_time, unit=time_unit),
format_time(
item.max_cpu_time, unit=time_unit),
format_time(
item.min_cpu_time, unit=time_unit),
format_time(item.cpu_time, unit=time_unit),
format_time(item.avg_cpu_time, unit=time_unit),
format_time(item.max_cpu_time, unit=time_unit),
format_time(item.min_cpu_time, unit=time_unit),
format_ratio(float(item.cpu_time) / total_time)),
'{} / {} / {} / {} / {}'.format(
format_time(
item.general_gpu_time, unit=time_unit),
format_time(
item.avg_general_gpu_time, unit=time_unit),
format_time(
item.max_general_gpu_time, unit=time_unit),
format_time(
item.min_general_gpu_time, unit=time_unit),
format_time(item.general_gpu_time, unit=time_unit),
format_time(item.avg_general_gpu_time, unit=time_unit),
format_time(item.max_general_gpu_time, unit=time_unit),
format_time(item.min_general_gpu_time, unit=time_unit),
format_ratio(gpu_ratio)),
]
all_row_values.append(row_values)
......@@ -1429,39 +1397,35 @@ def _build_table(statistic_data,
for thread_id, items in userdefined_thread_items.items():
all_row_values.append("Thread: {}".format(thread_id))
if sorted_by == SortedKeys.CPUTotal:
sorted_items = sorted(
items.items(), key=lambda x: x[1].cpu_time, reverse=True)
sorted_items = sorted(items.items(),
key=lambda x: x[1].cpu_time,
reverse=True)
elif sorted_by == SortedKeys.CPUAvg:
sorted_items = sorted(
items.items(),
key=lambda x: x[1].avg_cpu_time,
reverse=True)
sorted_items = sorted(items.items(),
key=lambda x: x[1].avg_cpu_time,
reverse=True)
elif sorted_by == SortedKeys.CPUMax:
sorted_items = sorted(
items.items(),
key=lambda x: x[1].max_cpu_time,
reverse=True)
sorted_items = sorted(items.items(),
key=lambda x: x[1].max_cpu_time,
reverse=True)
elif sorted_by == SortedKeys.CPUMin:
sorted_items = sorted(
items.items(), key=lambda x: x[1].min_cpu_time)
sorted_items = sorted(items.items(),
key=lambda x: x[1].min_cpu_time)
elif sorted_by == SortedKeys.GPUTotal:
sorted_items = sorted(
items.items(),
key=lambda x: x[1].general_gpu_time,
reverse=True)
sorted_items = sorted(items.items(),
key=lambda x: x[1].general_gpu_time,
reverse=True)
elif sorted_by == SortedKeys.GPUAvg:
sorted_items = sorted(
items.items(),
key=lambda x: x[1].avg_general_gpu_time,
reverse=True)
sorted_items = sorted(items.items(),
key=lambda x: x[1].avg_general_gpu_time,
reverse=True)
elif sorted_by == SortedKeys.GPUMax:
sorted_items = sorted(
items.items(),
key=lambda x: x[1].max_general_gpu_time,
reverse=True)
sorted_items = sorted(items.items(),
key=lambda x: x[1].max_general_gpu_time,
reverse=True)
elif sorted_by == SortedKeys.GPUMin:
sorted_items = sorted(
items.items(), key=lambda x: x[1].min_general_gpu_time)
sorted_items = sorted(items.items(),
key=lambda x: x[1].min_general_gpu_time)
for name, item in sorted_items:
if gpu_total_time == 0:
......@@ -1472,24 +1436,16 @@ def _build_table(statistic_data,
name,
item.call,
'{} / {} / {} / {} / {}'.format(
format_time(
item.cpu_time, unit=time_unit),
format_time(
item.avg_cpu_time, unit=time_unit),
format_time(
item.max_cpu_time, unit=time_unit),
format_time(
item.min_cpu_time, unit=time_unit),
format_time(item.cpu_time, unit=time_unit),
format_time(item.avg_cpu_time, unit=time_unit),
format_time(item.max_cpu_time, unit=time_unit),
format_time(item.min_cpu_time, unit=time_unit),
format_ratio(float(item.cpu_time) / total_time)),
'{} / {} / {} / {} / {}'.format(
format_time(
item.general_gpu_time, unit=time_unit),
format_time(
item.avg_general_gpu_time, unit=time_unit),
format_time(
item.max_general_gpu_time, unit=time_unit),
format_time(
item.min_general_gpu_time, unit=time_unit),
format_time(item.general_gpu_time, unit=time_unit),
format_time(item.avg_general_gpu_time, unit=time_unit),
format_time(item.max_general_gpu_time, unit=time_unit),
format_time(item.min_general_gpu_time, unit=time_unit),
format_ratio(gpu_ratio)),
]
all_row_values.append(row_values)
......
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