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未验证 提交 fddea674 编写于 作者: J joanna.wozna.intel 提交者： GitHub
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Fix cpu_bfloat16_pass (#28730) 

* Fix cpu_bfloat16_pass

* Add output_format

* Fix incorrect SetOutput

* Change fromating
上级 2fd16cf6
隐藏空白更改
内联 并排
Showing with 315 addition and 109 deletion
paddle/fluid/framework/ir/graph_pattern_detector.cc
浏览文件 @ fddea674
......@@ -2181,6 +2181,36 @@ PDNode *patterns::FirstBfloat16Ops::operator()() {
return op;
}
PDNode *patterns::DuplicatedInputs::operator()() {
auto op = pattern->NewNode(op_repr())->assert_is_ops({"concat", "sum"});
op->assert_more([&](Node *node) {
return node->Op()->GetAttrIfExists<std::string>("mkldnn_data_type") ==
"bfloat16";
});
return op;
}
PDNode *patterns::UnnecessaryReorders::operator()() {
auto prev_op = pattern->NewNode(prev_op_repr())->assert_is_op();
prev_op->assert_more([&](Node *node) {
return node->Op()->GetAttrIfExists<std::string>("mkldnn_data_type") ==
"bfloat16";
});
auto *quant_in = pattern->NewNode(quant_in_repr())
->assert_is_op_input("quantize", "Input");
auto *quant_op = pattern->NewNode(quant_op_repr())->assert_is_op("quantize");
auto *quant_out = pattern->NewNode(quant_out_repr())
->assert_is_op_output("quantize", "Output");
prev_op->LinksTo({quant_in});
quant_op->LinksFrom({quant_in}).LinksTo({quant_out});
return quant_out;
}
PDNode *patterns::MKLDNNInPlace::operator()() {
const std::unordered_set<std::string> &supported_op_types = {
"abs",
......
paddle/fluid/framework/ir/graph_pattern_detector.h
浏览文件 @ fddea674
......@@ -1273,6 +1273,26 @@ struct FirstBfloat16Ops : public PatternBase {
PATTERN_DECL_NODE(op);
};
struct DuplicatedInputs : public PatternBase {
DuplicatedInputs(PDPattern* pattern, const std::string& name_scope)
: PatternBase(pattern, name_scope, "many_inputs_op") {}
PDNode* operator()();
PATTERN_DECL_NODE(op);
};
struct UnnecessaryReorders : public PatternBase {
UnnecessaryReorders(PDPattern* pattern, const std::string& name_scope)
: PatternBase(pattern, name_scope, "unnecessary_reorders") {}
PDNode* operator()();
PATTERN_DECL_NODE(prev_op);
PATTERN_DECL_NODE(quant_in);
PATTERN_DECL_NODE(quant_op);
PATTERN_DECL_NODE(quant_out);
};
// Pattern used for enforcing inplace computation for in-place computation
// supporting DNNL ops. softmax, batch_norm and layer_norm
struct MKLDNNInPlace : public PatternBase {
......
paddle/fluid/framework/ir/mkldnn/cpu_bfloat16_pass.cc
浏览文件 @ fddea674
......@@ -33,58 +33,157 @@ void UnlinkNodes(ir::Node* a, ir::Node* b) {
b->inputs.end());
}
void CPUBFloat16Pass::SetInputDataType(ir::Graph* graph) const {
void AddQuantize(Graph* g, ir::Node* op, ir::Node* op_in,
int* quantize_counter) {
VarDesc quantize_out_desc(patterns::PDNodeName("quantize", "out"));
auto* quantize_out_node = g->CreateVarNode(&quantize_out_desc);
OpDesc q_desc;
q_desc.SetType("quantize");
q_desc.SetInput("Input", std::vector<std::string>({op_in->Name()}));
q_desc.SetOutput("Output",
std::vector<std::string>({quantize_out_node->Name()}));
q_desc.SetAttr("Scale", 1.f);
q_desc.SetAttr("bfloat16", true);
q_desc.SetAttr("output_format", op->Op()->HasAttr("data_layout")
? op->Op()->GetAttr("data_layout")
: std::string("NCHW"));
auto quantize_op = g->CreateOpNode(&q_desc);
std::vector<std::string> input_names;
for (auto name : op->Op()->InputNames()) {
for (auto input_name : op->Op()->Input(name)) {
if (input_name == op_in->Name()) input_names.push_back(name);
}
}
PADDLE_ENFORCE_NE(
input_names.empty(), true,
platform::errors::NotFound(
"Operator before operator should have input as op output"));
for (auto name = input_names.begin(); name < input_names.end(); name++)
op->Op()->SetInput(*name,
std::vector<std::string>({quantize_out_node->Name()}));
UnlinkNodes(op_in, op);
IR_NODE_LINK_TO(op_in, quantize_op);
IR_NODE_LINK_TO(quantize_op, quantize_out_node);
IR_NODE_LINK_TO(quantize_out_node, op);
(*quantize_counter)++;
}
void AddQuantizes(Graph* g, ir::Node* op, int* quantize_counter) {
auto inputs = op->inputs;
PADDLE_ENFORCE_GE(inputs.size(), 1,
platform::errors::InvalidArgument(
"OP(%s)'s inputs(%d) must be equal or greater than 1.",
op->Name(), inputs.size()));
PADDLE_ENFORCE_EQ(op->outputs.size(), 1,
platform::errors::InvalidArgument(
"OP(%s)'s outputs(%d) must be equal to 1.", op->Name(),
op->outputs.size()));
OpDesc q_desc;
q_desc.SetType("quantize");
std::vector<Node*> quantize_out_nodes(inputs.size());
std::vector<std::string> quantize_out_node_names(inputs.size());
for (size_t i = 0; i < inputs.size(); i++) {
VarDesc quantize_out_desc(patterns::PDNodeName("quantize", "out"));
quantize_out_nodes[i] = g->CreateVarNode(&quantize_out_desc);
quantize_out_node_names[i] = quantize_out_nodes[i]->Name();
q_desc.SetInput("Input", std::vector<std::string>({inputs[i]->Name()}));
q_desc.SetOutput("Output",
std::vector<std::string>({quantize_out_node_names[i]}));
q_desc.SetAttr("Scale", 1.f);
q_desc.SetAttr("bfloat16", true);
q_desc.SetAttr("output_format", op->Op()->HasAttr("data_layout")
? op->Op()->GetAttr("data_layout")
: std::string("NCHW"));
auto quantize_op = g->CreateOpNode(&q_desc);
UnlinkNodes(inputs[i], op);
IR_NODE_LINK_TO(inputs[i], quantize_op);
IR_NODE_LINK_TO(quantize_op, quantize_out_nodes[i]);
IR_NODE_LINK_TO(quantize_out_nodes[i], op);
(*quantize_counter)++;
}
op->Op()->SetInput("X", quantize_out_node_names);
}
void AddReoderBeforeDuplicatedInputs(ir::Graph* graph, int* quantize_counter) {
GraphPatternDetector gpd;
patterns::DuplicatedInputs duplicated_inputs{gpd.mutable_pattern(),
"duplicated_inputs"};
duplicated_inputs();
auto handler = [&](const GraphPatternDetector::subgraph_t& subgraph,
Graph* g) {
GET_IR_NODE_FROM_SUBGRAPH(op, op, duplicated_inputs);
AddQuantizes(g, op, quantize_counter);
};
gpd(graph, handler);
}
void RemoveUnnecessaryReorders(ir::Graph* graph, int* quantize_counter) {
GraphPatternDetector gpd;
patterns::UnnecessaryReorders unnecessary_reorders{gpd.mutable_pattern(),
"unnecessary_reorders"};
unnecessary_reorders();
auto handler = [&](const GraphPatternDetector::subgraph_t& subgraph,
Graph* g) {
GET_IR_NODE_FROM_SUBGRAPH(prev_op, prev_op, unnecessary_reorders);
GET_IR_NODE_FROM_SUBGRAPH(quant_in, quant_in, unnecessary_reorders);
GET_IR_NODE_FROM_SUBGRAPH(quant_op, quant_op, unnecessary_reorders);
GET_IR_NODE_FROM_SUBGRAPH(quant_out, quant_out, unnecessary_reorders);
std::string op_output_name;
for (auto name : prev_op->Op()->OutputNames())
for (auto output_name : prev_op->Op()->Output(name))
if (output_name == quant_in->Name()) op_output_name = name;
PADDLE_ENFORCE_NE(
op_output_name.empty(), true,
platform::errors::NotFound(
"Operator before operator should have input as op output"));
prev_op->Op()->SetOutput(op_output_name,
std::vector<std::string>({quant_out->Name()}));
IR_NODE_LINK_TO(prev_op, quant_out);
GraphSafeRemoveNodes(graph, {quant_in, quant_op});
(*quantize_counter)--;
};
gpd(graph, handler);
}
void AddReoderBeforeSingleInputs(ir::Graph* graph, int* quantize_counter) {
GraphPatternDetector gpd;
patterns::FirstBfloat16Ops bfloat16_ops{gpd.mutable_pattern(),
"first_bfloat16_ops"};
bfloat16_ops();
int quantize_counter = 0;
auto handler = [&](const GraphPatternDetector::subgraph_t& subgraph,
Graph* g) {
GET_IR_NODE_FROM_SUBGRAPH(prev_op, prev_op, bfloat16_ops);
GET_IR_NODE_FROM_SUBGRAPH(op_in, op_in, bfloat16_ops);
GET_IR_NODE_FROM_SUBGRAPH(op, op, bfloat16_ops);
if (op->Op()->Type() != "conv2d" && prev_op->Op()->Type() != "quantize") {
VarDesc quantize_out_desc(patterns::PDNodeName("quantize", "out"));
auto* quantize_out_node = g->CreateVarNode(&quantize_out_desc);
// create a quantize op node
OpDesc q_desc;
q_desc.SetType("quantize");
q_desc.SetInput("Input", std::vector<std::string>({op_in->Name()}));
q_desc.SetOutput("Output",
std::vector<std::string>({quantize_out_node->Name()}));
q_desc.SetAttr("Scale", 1.f);
q_desc.SetAttr("bfloat16", true);
q_desc.SetAttr("output_format", Has("data_layout")
? Get<std::string>("data_layout")
: "NCHW");
auto quantize_op = g->CreateOpNode(&q_desc); // OpDesc will be copied.
std::string op_input_name;
for (auto name : op->Op()->InputNames()) {
for (auto input_name : op->Op()->Input(name)) {
if (input_name == op_in->Name()) op_input_name = name;
}
}
PADDLE_ENFORCE_NE(
op_input_name.empty(), true,
platform::errors::NotFound(
"Operator before operator should have input as op output"));
op->Op()->SetInput(op_input_name,
std::vector<std::string>({quantize_out_node->Name()}));
UnlinkNodes(op_in, op);
IR_NODE_LINK_TO(op_in, quantize_op);
IR_NODE_LINK_TO(quantize_op, quantize_out_node);
IR_NODE_LINK_TO(quantize_out_node, op);
quantize_counter++;
auto prev_op_type = prev_op->Op()->Type();
if (op->Op()->Type() != "conv2d" && prev_op_type != "quantize" &&
prev_op_type != "sum" && prev_op_type != "concat") {
AddQuantize(g, op, op_in, quantize_counter);
}
};
gpd(graph, handler);
}
void CPUBFloat16Pass::SetInputDataType(ir::Graph* graph) const {
int quantize_counter = 0;
AddReoderBeforeDuplicatedInputs(graph, &quantize_counter);
RemoveUnnecessaryReorders(graph, &quantize_counter);
AddReoderBeforeSingleInputs(graph, &quantize_counter);
PrettyLogDetail("--- added %d quantize op before bfloat16 op",
quantize_counter);
}
......@@ -101,45 +200,42 @@ void CPUBFloat16Pass::SetOutputDataType(ir::Graph* graph) const {
GET_IR_NODE_FROM_SUBGRAPH(op, op, bfloat16_ops);
GET_IR_NODE_FROM_SUBGRAPH(op_out, op_out, bfloat16_ops);
GET_IR_NODE_FROM_SUBGRAPH(next_op, next_op, bfloat16_ops);
if ((op->Op()->HasAttr("force_fp32_output") ||
op->Op()->HasProtoAttr("force_fp32_output")) &&
!op->Op()->GetAttrIfExists<bool>("fuse_residual_connection")) {
op->Op()->SetAttr("force_fp32_output", true);
force_fp32_counter++;
} else if (op->Op()->Type() != "prior_box") {
// Create dequantize input variable
VarDesc dequantize_in_desc(patterns::PDNodeName("dequantize", "in"));
auto* dequantize_in_node = g->CreateVarNode(&dequantize_in_desc);
VarDesc dequantize_out_desc(patterns::PDNodeName("dequantize", "out"));
auto* dequantize_out_node = g->CreateVarNode(&dequantize_out_desc);
// create a dequantize op node for output.
OpDesc deq_desc;
deq_desc.SetType("dequantize");
deq_desc.SetInput("Input",
std::vector<std::string>({dequantize_in_node->Name()}));
deq_desc.SetOutput("Output", std::vector<std::string>({op_out->Name()}));
deq_desc.SetInput("Input", std::vector<std::string>({op_out->Name()}));
deq_desc.SetOutput(
"Output", std::vector<std::string>({dequantize_out_node->Name()}));
deq_desc.SetAttr("Scale", 1.0f);
auto dequantize_op = g->CreateOpNode(&deq_desc);
std::string op_output_name;
for (auto name : op->Op()->OutputNames()) {
for (auto output_name : op->Op()->Output(name)) {
if (output_name == op_out->Name()) op_output_name = name;
std::string next_op_input_name;
for (auto name : next_op->Op()->InputNames()) {
for (auto input_name : next_op->Op()->Input(name)) {
if (input_name == op_out->Name()) next_op_input_name = name;
}
}
PADDLE_ENFORCE_NE(
op_output_name.empty(), true,
next_op_input_name.empty(), true,
platform::errors::NotFound(
"Operator after operator should have input as op output"));
op->Op()->SetOutput(op_output_name, std::vector<std::string>(
{dequantize_in_node->Name()}));
"Operator before operator should have input as op output"));
UnlinkNodes(op, op_out);
IR_NODE_LINK_TO(op, dequantize_in_node);
IR_NODE_LINK_TO(dequantize_in_node, dequantize_op);
IR_NODE_LINK_TO(dequantize_op, op_out);
next_op->Op()->SetInput(
next_op_input_name,
std::vector<std::string>({dequantize_out_node->Name()}));
UnlinkNodes(op_out, next_op);
IR_NODE_LINK_TO(op_out, dequantize_op);
IR_NODE_LINK_TO(dequantize_op, dequantize_out_node);
IR_NODE_LINK_TO(dequantize_out_node, next_op);
dequantize_counter++;
}
};
......
paddle/fluid/framework/ir/mkldnn/cpu_bfloat16_pass_tester.cc
浏览文件 @ fddea674
......@@ -42,60 +42,45 @@ void SetOp(ProgramDesc* prog, const std::string& type, const std::string& name,
type == "dropout") {
op->SetInput("X", {inputs[0]});
op->SetOutput("Out", {outputs[0]});
op->SetAttr("mkldnn_data_type", mkldnn_data_type);
if (type != "dropout") op->SetAttr("mkldnn_data_type", mkldnn_data_type);
} else if (type == "fc") {
op->SetInput("Input", {inputs[0]});
op->SetOutput("Out", {outputs[0]});
op->SetAttr("mkldnn_data_type", mkldnn_data_type);
} else if (type == "concat") {
op->SetAttr("force_fp32_output", force_fp32_output);
} else if (type == "concat" || type == "sum") {
op->SetInput("X", inputs);
op->SetOutput("Out", outputs);
op->SetAttr("mkldnn_data_type", mkldnn_data_type);
} else if (type == "matmul" || type == "elementwise_add") {
} else if (type == "matmul" || type == "elementwise_add" ||
type == "elementwise_mul") {
op->SetInput("X", {inputs[0]});
if (inputs.size() > 1) op->SetInput("Y", {inputs[1]});
op->SetOutput("Out", {outputs[0]});
op->SetAttr("mkldnn_data_type", mkldnn_data_type);
if (type == "matmul") op->SetAttr("force_fp32_output", force_fp32_output);
} else if (type == "layer_norm") {
op->SetInput("X", {inputs[0]});
op->SetOutput("Y", {outputs[0]});
op->SetAttr("mkldnn_data_type", mkldnn_data_type);
}
}
static const std::initializer_list<std::string> variable_names{
"z", "a", "b", "c", "d", "e", "f", "g", "h", "i"};
void PreparePass(std::unique_ptr<ir::Graph>* graph, const ProgramDesc& prog,
const std::initializer_list<std::string> variable_names,
int* original_nodes_num, int* current_nodes_num) {
auto pass = PassRegistry::Instance().Get("cpu_bfloat16_pass");
graph->reset(pass->Apply(graph->release()));
*original_nodes_num = (*graph)->Nodes().size();
(*graph).reset(pass->Apply((*graph).release()));
*current_nodes_num = (*graph)->Nodes().size();
}
static const std::initializer_list<std::string> variable_names{
"z", "a", "b", "c", "d", "e", "f", "g", "h", "i"};
ProgramDesc BuildProgramDesc(bool use_mkldnn) {
ProgramDesc prog;
for (auto& v : variable_names) {
prog.MutableBlock(0)->Var(v);
}
SetOp(&prog, "dropout", "Dropout1", {"z"}, {"a"}, use_mkldnn, "float32");
SetOp(&prog, "conv2d", "Conv1", {"a"}, {"b"}, use_mkldnn, "bfloat16");
SetOp(&prog, "pool2d", "Pool1", {"b"}, {"c"}, use_mkldnn, "bfloat16");
SetOp(&prog, "conv2d", "Conv1", {"c"}, {"d"}, use_mkldnn, "bfloat16");
SetOp(&prog, "dropout", "Dropout2", {"d"}, {"e"}, use_mkldnn, "float32");
SetOp(&prog, "transpose2", "Transpose1", {"e"}, {"f"}, use_mkldnn,
"bfloat16");
SetOp(&prog, "reshape2", "Reshape1", {"f"}, {"g"}, use_mkldnn, "bfloat16");
SetOp(&prog, "concat", "Concat1", {"g"}, {"h"}, use_mkldnn, "bfloat16");
SetOp(&prog, "dropout", "Dropout3", {"h"}, {"i"}, use_mkldnn, "float32");
return prog;
}
void MainTest(const ProgramDesc& prog, int conv_count, int pool_count,
int transpose_count, int quant_count, int dequant_count,
int added_nodes_count) {
void MainTest(const ProgramDesc& prog, int quant_count, int dequant_count,
int force_fp32_count, int added_nodes_count) {
std::unique_ptr<ir::Graph> graph(new ir::Graph(prog));
int original_nodes_num, current_nodes_num;
PreparePass(&graph, prog, variable_names, &original_nodes_num,
......@@ -103,39 +88,114 @@ void MainTest(const ProgramDesc& prog, int conv_count, int pool_count,
int quantize_nodes_count = 0;
int dequantize_nodes_count = 0;
int conv2d_nodes_count = 0;
int pool2d_nodes_count = 0;
int transpose2_nodes_count = 0;
int force_fp32_nodes_count = 0;
for (auto* node : graph->Nodes()) {
if (node->IsOp()) {
auto* op = node->Op();
if (op->Type() == "conv2d") {
conv2d_nodes_count++;
} else if (op->Type() == "pool2d") {
pool2d_nodes_count++;
} else if (op->Type() == "transpose2") {
transpose2_nodes_count++;
} else if (op->Type() == "quantize") {
if (op->Type() == "quantize") {
quantize_nodes_count++;
} else if (op->Type() == "dequantize") {
dequantize_nodes_count++;
} else if (op->Type() == "conv2d" || op->Type() == "matmul" ||
op->Type() == "fc") {
if (op->GetAttrIfExists<bool>("force_fp32_output"))
force_fp32_nodes_count++;
}
}
}
EXPECT_EQ(conv2d_nodes_count, conv_count);
EXPECT_EQ(pool2d_nodes_count, pool_count);
EXPECT_EQ(transpose2_nodes_count, transpose_count);
EXPECT_EQ(quantize_nodes_count, quant_count);
EXPECT_EQ(dequantize_nodes_count, dequant_count);
EXPECT_EQ(force_fp32_nodes_count, force_fp32_count);
EXPECT_EQ(original_nodes_num + added_nodes_count, current_nodes_num);
}
TEST(CpuQuantizePass, quantize) {
ProgramDesc BuildProgramDescConv(bool use_mkldnn) {
ProgramDesc prog;
for (auto& v : variable_names) {
prog.MutableBlock(0)->Var(v);
}
SetOp(&prog, "dropout", "Dropout", {"a"}, {"b"}, use_mkldnn, "float32");
SetOp(&prog, "conv2d", "Conv1", {"b"}, {"c"}, use_mkldnn, "bfloat16");
SetOp(&prog, "pool2d", "Pool", {"c"}, {"d"}, use_mkldnn, "bfloat16");
SetOp(&prog, "conv2d", "Conv2", {"d"}, {"e"}, use_mkldnn, "bfloat16");
SetOp(&prog, "transpose2", "Transpose", {"e"}, {"f"}, use_mkldnn, "float32");
return prog;
}
TEST(CpuBfloat16Pass, convolution) {
bool use_mkldnn = true;
// 0 added + 1 force_fp32_output
int added_nodes = 0;
MainTest(BuildProgramDescConv(use_mkldnn), 0, 0, 1, added_nodes);
}
ProgramDesc BuildProgramDescDoubleInput(bool use_mkldnn) {
ProgramDesc prog;
for (auto& v : variable_names) {
prog.MutableBlock(0)->Var(v);
}
SetOp(&prog, "dropout", "Dropout", {"a"}, {"b"}, use_mkldnn, "float32");
SetOp(&prog, "matmul", "Matmul", {"b", "b"}, {"c"}, use_mkldnn, "bfloat16");
SetOp(&prog, "transpose2", "Transpose", {"d"}, {"e"}, use_mkldnn, "float32");
SetOp(&prog, "elementwise_add", "ElemetwiseAdd", {"c", "e"}, {"f"},
use_mkldnn, "bfloat16");
SetOp(&prog, "reshape2", "Reshape", {"f"}, {"g"}, use_mkldnn, "bfloat16");
return prog;
}
TEST(CpuBfloat16Pass, double_input_ops) {
bool use_mkldnn = true;
// 2 quant + 2 quant out
int added_nodes = 4;
MainTest(BuildProgramDescDoubleInput(use_mkldnn), 2, 0, 0, added_nodes);
}
ProgramDesc BuildProgramDescDuplicatedInput(bool use_mkldnn) {
ProgramDesc prog;
for (auto& v : variable_names) {
prog.MutableBlock(0)->Var(v);
}
SetOp(&prog, "dropout", "Dropout1", {"a"}, {"b"}, use_mkldnn, "float32");
SetOp(&prog, "dropout", "Dropout2", {"c"}, {"d"}, use_mkldnn, "float32");
SetOp(&prog, "concat", "Concat", {"b", "d"}, {"e"}, use_mkldnn, "bfloat16");
SetOp(&prog, "transpose2", "Transpose", {"f"}, {"g"}, use_mkldnn, "float32");
SetOp(&prog, "sum", "Sum", {"e", "g"}, {"h"}, use_mkldnn, "bfloat16");
SetOp(&prog, "reshape2", "Reshape", {"h"}, {"i"}, use_mkldnn, "bfloat16");
return prog;
}
TEST(CpuBfloat16Pass, duplicated_input_ops) {
bool use_mkldnn = true;
// 3 quant + 3 quant out
int added_nodes = 6;
MainTest(BuildProgramDescDuplicatedInput(use_mkldnn), 3, 0, 0, added_nodes);
}
ProgramDesc BuildProgramDescDoubleOutputs(bool use_mkldnn) {
ProgramDesc prog;
for (auto& v : variable_names) {
prog.MutableBlock(0)->Var(v);
}
SetOp(&prog, "layer_norm", "LayerNorm1", {"a"}, {"b"}, use_mkldnn,
"bfloat16");
SetOp(&prog, "dropout", "Dropout1", {"b"}, {"c"}, use_mkldnn, "float32");
SetOp(&prog, "transpose2", "Transpose", {"b"}, {"d"}, use_mkldnn, "bfloat16");
SetOp(&prog, "layer_norm", "LayerNorm2", {"d"}, {"e"}, use_mkldnn,
"bfloat16");
SetOp(&prog, "reshape2", "Reshape", {"e"}, {"f"}, use_mkldnn, "float32");
SetOp(&prog, "dropout", "Dropout2", {"e"}, {"g"}, use_mkldnn, "float32");
return prog;
}
TEST(CpuBfloat16Pass, double_outputs_ops) {
bool use_mkldnn = true;
// 1 quantize + 1 dequantize
int added_nodes = 2;
MainTest(BuildProgramDesc(use_mkldnn), 2, 1, 1, 1, 2, added_nodes);
// 3 dequant + 3 dequant out
int added_nodes = 6;
MainTest(BuildProgramDescDoubleOutputs(use_mkldnn), 0, 3, 0, added_nodes);
}
} // namespace ir
......
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