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未验证 提交 0c9f09b8 编写于 作者: S Sławomir Siwek 提交者： GitHub
浏览文件
操作

Migrate old C++ unit tests to Python framework (#47006) 

* softplus+activation

* fc + elementwise_add test refactored

* rename MKLDNN to OneDNN

* fc+activation tests refactored

* remove softplus ut

* whitespace

* whitespace

* codestyle

* codestyle

* add more cases to fc+act

* remove softplus+hard_sigmoid pass

* remove softplus + hard_sigmoid UT

* add approximate for gelu

* swish beta range

* new codestyle

* reduce number of tests
上级 dc85b393
隐藏空白更改
内联 并排
Showing with 174 addition and 1015 deletion
paddle/fluid/framework/ir/CMakeLists.txt
浏览文件 @ 0c9f09b8
...@@ -399,13 +399,6 @@ if(WITH_MKLDNN) ...@@ -399,13 +399,6 @@ if(WITH_MKLDNN)
test_params_quantization_mkldnn_pass SRCS test_params_quantization_mkldnn_pass SRCS
mkldnn/params_quantization_mkldnn_pass_tester.cc DEPS mkldnn/params_quantization_mkldnn_pass_tester.cc DEPS
params_quantization_mkldnn_pass) params_quantization_mkldnn_pass)
cc_test_old(
test_fc_elementwise_add_mkldnn_fuse_pass SRCS
mkldnn/fc_elementwise_add_mkldnn_fuse_pass_tester.cc DEPS
fc_elementwise_add_mkldnn_fuse_pass pass_test_util)
cc_test_old(
test_fc_act_mkldnn_fuse_pass SRCS mkldnn/fc_act_mkldnn_fuse_pass_tester.cc
DEPS fc_act_mkldnn_fuse_pass pass_test_util)
cc_test_old( cc_test_old(
test_batch_norm_act_fuse_pass SRCS test_batch_norm_act_fuse_pass SRCS
mkldnn/batch_norm_act_fuse_pass_tester.cc DEPS batch_norm_act_fuse_pass mkldnn/batch_norm_act_fuse_pass_tester.cc DEPS batch_norm_act_fuse_pass
......
paddle/fluid/framework/ir/mkldnn/fc_act_mkldnn_fuse_pass_tester.cc 已删除 100644 → 0
浏览文件 @ dc85b393
// Copyright (c) 2020 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.
// See the License for the specific language governing permissions and
// limitations under the License.
#include <gtest/gtest.h>
#include "paddle/fluid/framework/ir/mkldnn/fc_act_mkldnn_fuse_pass.h"
#include "paddle/fluid/framework/ir/pass_test_util.h"
#include "paddle/fluid/framework/op_desc.h"
#include "paddle/fluid/framework/op_version_registry.h"
#include "paddle/fluid/framework/program_desc.h"
#include "paddle/fluid/platform/errors.h"
namespace paddle {
namespace framework {
namespace ir {
// ------------------------------ Test cases -----------------------------------
TEST(FuseFCActOneDNNPass, ThrowUseMkldnn) {
auto prog =
test::BuildProgramDesc({"x", "fc_y", "act_y"}, {"weights", "bias"});
test::CreateOp(&prog,
"fc",
{
{"Input", "x"},
{"W", "weights"},
{"Bias", "bias"},
},
{{"Out", "fc_y"}},
false);
test::CreateOp(&prog, "gelu", {{"X", "fc_y"}}, {{"Out", "act_y"}}, false);
Graph graph(prog);
// No fusion in this attribute configuration
constexpr int removed_nodes_count = 0;
EXPECT_THROW(
test::RunPassAndAssert(
&graph, "fc_act_mkldnn_fuse_pass", "x", "act_y", removed_nodes_count),
paddle::platform::EnforceNotMet);
}
TEST(FuseFCActOneDNNPass, FuseWithGeluTanh) {
auto prog =
test::BuildProgramDesc({"x", "fc_y", "act_y"}, {"weights", "bias"});
test::CreateOp(&prog,
"fc",
{
{"Input", "x"},
{"W", "weights"},
{"Bias", "bias"},
},
{{"Out", "fc_y"}});
auto* act_op =
test::CreateOp(&prog, "gelu", {{"X", "fc_y"}}, {{"Out", "act_y"}}, false);
act_op->SetAttr("approximate", true);
Graph graph(prog);
constexpr int removed_nodes_count = 2;
EXPECT_TRUE(test::RunPassAndAssert(
&graph, "fc_act_mkldnn_fuse_pass", "x", "act_y", removed_nodes_count));
EXPECT_TRUE(test::AssertOpsCount(graph, {{"fc", 1}, {"gelu", 0}}));
for (const auto* node : graph.Nodes()) {
if (node->IsOp() && node->Op()->Type() == "fc") {
const auto* op = node->Op();
ASSERT_TRUE(op->HasAttr("use_mkldnn"));
EXPECT_TRUE(PADDLE_GET_CONST(bool, op->GetAttr("use_mkldnn")));
ASSERT_TRUE(op->HasAttr("fuse_activation"));
auto act_type =
PADDLE_GET_CONST(std::string, op->GetAttr("fuse_activation"));
EXPECT_EQ(act_type.compare("gelu_tanh"), 0);
}
}
}
TEST(FuseFCActOneDNNPass, FuseWithGeluErf) {
auto prog =
test::BuildProgramDesc({"x", "fc_y", "act_y"}, {"weights", "bias"});
test::CreateOp(&prog,
"fc",
{
{"Input", "x"},
{"W", "weights"},
{"Bias", "bias"},
},
{{"Out", "fc_y"}});
auto* act_op =
test::CreateOp(&prog, "gelu", {{"X", "fc_y"}}, {{"Out", "act_y"}}, false);
act_op->SetAttr("approximate", false);
Graph graph(prog);
constexpr int removed_nodes_count = 2;
EXPECT_TRUE(test::RunPassAndAssert(
&graph, "fc_act_mkldnn_fuse_pass", "x", "act_y", removed_nodes_count));
EXPECT_TRUE(test::AssertOpsCount(graph, {{"fc", 1}, {"gelu", 0}}));
for (const auto* node : graph.Nodes()) {
if (node->IsOp() && node->Op()->Type() == "fc") {
const auto* op = node->Op();
ASSERT_TRUE(op->HasAttr("use_mkldnn"));
EXPECT_TRUE(PADDLE_GET_CONST(bool, op->GetAttr("use_mkldnn")));
ASSERT_TRUE(op->HasAttr("fuse_activation"));
auto act_type =
PADDLE_GET_CONST(std::string, op->GetAttr("fuse_activation"));
EXPECT_EQ(act_type.compare("gelu_erf"), 0);
}
}
}
TEST(FuseFCActOneDNNPass, FuseWithGeluAuto) {
auto prog =
test::BuildProgramDesc({"x", "fc_y", "act_y"}, {"weights", "bias"});
test::CreateOp(&prog,
"fc",
{
{"Input", "x"},
{"W", "weights"},
{"Bias", "bias"},
},
{{"Out", "fc_y"}});
test::CreateOp(&prog, "gelu", {{"X", "fc_y"}}, {{"Out", "act_y"}}, false);
Graph graph(prog);
constexpr int removed_nodes_count = 2;
EXPECT_TRUE(test::RunPassAndAssert(
&graph, "fc_act_mkldnn_fuse_pass", "x", "act_y", removed_nodes_count));
EXPECT_TRUE(test::AssertOpsCount(graph, {{"fc", 1}, {"gelu", 0}}));
for (const auto* node : graph.Nodes()) {
if (node->IsOp() && node->Op()->Type() == "fc") {
const auto* op = node->Op();
ASSERT_TRUE(op->HasAttr("use_mkldnn"));
EXPECT_TRUE(PADDLE_GET_CONST(bool, op->GetAttr("use_mkldnn")));
ASSERT_TRUE(op->HasAttr("fuse_activation"));
auto act_type =
PADDLE_GET_CONST(std::string, op->GetAttr("fuse_activation"));
EXPECT_EQ(act_type.compare("gelu"), 0);
}
}
}
TEST(FuseFCActOneDNNPass, FuseWithTanh) {
auto prog =
test::BuildProgramDesc({"x", "fc_y", "act_y"}, {"weights", "bias"});
test::CreateOp(&prog,
"fc",
{
{"Input", "x"},
{"W", "weights"},
{"Bias", "bias"},
},
{{"Out", "fc_y"}});
test::CreateOp(&prog, "tanh", {{"X", "fc_y"}}, {{"Out", "act_y"}}, false);
Graph graph(prog);
constexpr int removed_nodes_count = 2;
EXPECT_TRUE(test::RunPassAndAssert(
&graph, "fc_act_mkldnn_fuse_pass", "x", "act_y", removed_nodes_count));
EXPECT_TRUE(test::AssertOpsCount(graph, {{"fc", 1}, {"tanh", 0}}));
for (const auto* node : graph.Nodes()) {
if (node->IsOp() && node->Op()->Type() == "fc") {
const auto* op = node->Op();
ASSERT_TRUE(op->HasAttr("use_mkldnn"));
EXPECT_TRUE(PADDLE_GET_CONST(bool, op->GetAttr("use_mkldnn")));
ASSERT_TRUE(op->HasAttr("fuse_activation"));
auto act_type =
PADDLE_GET_CONST(std::string, op->GetAttr("fuse_activation"));
EXPECT_EQ(act_type.compare("tanh"), 0);
}
}
}
TEST(FuseFCActOneDNNPass, FuseWithSigmoid) {
auto prog =
test::BuildProgramDesc({"x", "fc_y", "act_y"}, {"weights", "bias"});
test::CreateOp(&prog,
"fc",
{
{"Input", "x"},
{"W", "weights"},
{"Bias", "bias"},
},
{{"Out", "fc_y"}});
test::CreateOp(&prog, "sigmoid", {{"X", "fc_y"}}, {{"Out", "act_y"}}, false);
Graph graph(prog);
constexpr int removed_nodes_count = 2;
EXPECT_TRUE(test::RunPassAndAssert(
&graph, "fc_act_mkldnn_fuse_pass", "x", "act_y", removed_nodes_count));
EXPECT_TRUE(test::AssertOpsCount(graph, {{"fc", 1}, {"sigmoid", 0}}));
for (const auto* node : graph.Nodes()) {
if (node->IsOp() && node->Op()->Type() == "fc") {
const auto* op = node->Op();
ASSERT_TRUE(op->HasAttr("use_mkldnn"));
EXPECT_TRUE(PADDLE_GET_CONST(bool, op->GetAttr("use_mkldnn")));
ASSERT_TRUE(op->HasAttr("fuse_activation"));
auto act_type =
PADDLE_GET_CONST(std::string, op->GetAttr("fuse_activation"));
EXPECT_EQ(act_type.compare("sigmoid"), 0);
}
}
}
TEST(FuseFCActOneDNNPass, FuseWithMish) {
auto prog =
test::BuildProgramDesc({"x", "fc_y", "act_y"}, {"weights", "bias"});
test::CreateOp(&prog,
"fc",
{
{"Input", "x"},
{"W", "weights"},
{"Bias", "bias"},
},
{{"Out", "fc_y"}});
test::CreateOp(&prog, "mish", {{"X", "fc_y"}}, {{"Out", "act_y"}}, false);
Graph graph(prog);
constexpr int removed_nodes_count = 2;
EXPECT_TRUE(test::RunPassAndAssert(
&graph, "fc_act_mkldnn_fuse_pass", "x", "act_y", removed_nodes_count));
EXPECT_TRUE(test::AssertOpsCount(graph, {{"fc", 1}, {"mish", 0}}));
for (const auto* node : graph.Nodes()) {
if (node->IsOp() && node->Op()->Type() == "fc") {
const auto* op = node->Op();
ASSERT_TRUE(op->HasAttr("use_mkldnn"));
EXPECT_TRUE(PADDLE_GET_CONST(bool, op->GetAttr("use_mkldnn")));
ASSERT_TRUE(op->HasAttr("fuse_activation"));
auto act_type =
PADDLE_GET_CONST(std::string, op->GetAttr("fuse_activation"));
EXPECT_EQ(act_type.compare("mish"), 0);
}
}
}
TEST(FuseFCActOneDNNPass, FuseWithHardSwish) {
auto prog =
test::BuildProgramDesc({"x", "fc_y", "act_y"}, {"weights", "bias"});
test::CreateOp(&prog,
"fc",
{
{"Input", "x"},
{"W", "weights"},
{"Bias", "bias"},
},
{{"Out", "fc_y"}});
test::CreateOp(
&prog, "hard_swish", {{"X", "fc_y"}}, {{"Out", "act_y"}}, false);
Graph graph(prog);
constexpr int removed_nodes_count = 2;
EXPECT_TRUE(test::RunPassAndAssert(
&graph, "fc_act_mkldnn_fuse_pass", "x", "act_y", removed_nodes_count));
EXPECT_TRUE(test::AssertOpsCount(graph, {{"fc", 1}, {"hard_swish", 0}}));
for (const auto* node : graph.Nodes()) {
if (node->IsOp() && node->Op()->Type() == "fc") {
const auto* op = node->Op();
ASSERT_TRUE(op->HasAttr("use_mkldnn"));
EXPECT_TRUE(PADDLE_GET_CONST(bool, op->GetAttr("use_mkldnn")));
ASSERT_TRUE(op->HasAttr("fuse_activation"));
auto act_type =
PADDLE_GET_CONST(std::string, op->GetAttr("fuse_activation"));
EXPECT_EQ(act_type.compare("hard_swish"), 0);
}
}
}
TEST(FuseFCActOneDNNPass, pass_op_version_check) {
ASSERT_TRUE(
paddle::framework::compatible::PassVersionCheckerRegistrar::GetInstance()
.IsPassCompatible("fc_act_mkldnn_fuse_pass"));
}
} // namespace ir
} // namespace framework
} // namespace paddle
USE_PASS(fc_act_mkldnn_fuse_pass);
paddle/fluid/framework/ir/mkldnn/fc_elementwise_add_mkldnn_fuse_pass_tester.cc 已删除 100644 → 0
浏览文件 @ dc85b393
// 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.
// See the License for the specific language governing permissions and
// limitations under the License.
#include <gtest/gtest.h>
#include "paddle/fluid/framework/ir/mkldnn/fc_elementwise_add_mkldnn_fuse_pass.h"
#include "paddle/fluid/framework/ir/pass_test_util.h"
#include "paddle/fluid/framework/op_proto_maker.h"
#include "paddle/fluid/framework/op_version_registry.h"
namespace paddle {
namespace framework {
namespace ir {
// Nodes elementwise_add and FC_output are deleted
// FC node is removed and new version with fuse-pass is added
// In general, the graph is 2 vertices smaller (per fuse-pass)
constexpr int nodes_removed = 3;
constexpr int nodes_added = 1;
OpDesc* Create_Op_FC(ProgramDesc* prog,
const std::vector<test::InOutVarNamePair>& inputs,
const std::vector<test::InOutVarNamePair>& outputs) {
auto* op = prog->MutableBlock(0)->AppendOp();
op->SetType("fc");
op->SetAttr("use_mkldnn", true);
op->SetAttr("in_num_col_dims", 1);
for (const auto& input : inputs) {
op->SetInput(input.first, {input.second});
}
for (const auto& output : outputs) {
op->SetOutput(output.first, {output.second});
}
op->SetAttr(OpProtoAndCheckerMaker::OpRoleAttrName(),
static_cast<int>(OpRole::kForward));
return op;
}
OpDesc* Create_Op_elementwise_add(
ProgramDesc* prog,
const std::vector<test::InOutVarNamePair>& inputs,
const std::vector<test::InOutVarNamePair>& outputs,
bool use_mkldnn = true) {
auto* op = prog->MutableBlock(0)->AppendOp();
op->SetType("elementwise_add");
op->SetAttr("use_mkldnn", use_mkldnn);
op->SetAttr("axis", -1);
for (const auto& input : inputs) {
op->SetInput(input.first, {input.second});
}
for (const auto& output : outputs) {
op->SetOutput(output.first, {output.second});
}
op->SetAttr(OpProtoAndCheckerMaker::OpRoleAttrName(),
static_cast<int>(OpRole::kForward));
return op;
}
TEST(FCElementwiseAddMKLDNNFusePass, FCBiasAsY) {
auto prog =
test::BuildProgramDesc({"a", "b", "c", "d", "e"}, {"bias", "weights"});
test::CreateOp(&prog, "sigmoid", {{"X", "a"}}, {{"Out", "b"}});
Create_Op_FC(&prog,
{{"Input", "b"}, {"Bias", "bias"}, {"W", "weights"}},
{{"Out", "c"}});
Create_Op_elementwise_add(&prog, {{"X", "a"}, {"Y", "c"}}, {{"Out", "d"}});
test::CreateOp(&prog, "relu", {{"X", "d"}}, {{"Out", "e"}});
Graph graph(prog);
EXPECT_TRUE(test::RunPassAndAssert(&graph,
"fc_elementwise_add_mkldnn_fuse_pass",
"a",
"e",
nodes_removed,
nodes_added));
EXPECT_TRUE(test::AssertOpsCount(graph, {{"fc", 1}, {"elementwise_add", 0}}));
}
TEST(FCElementwiseAddMKLDNNFusePass, FCBiasAsX) {
auto prog =
test::BuildProgramDesc({"a", "b", "c", "d", "e"}, {"bias", "weights"});
test::CreateOp(&prog, "sigmoid", {{"X", "a"}}, {{"Out", "b"}});
Create_Op_FC(&prog,
{{"Input", "b"}, {"Bias", "bias"}, {"W", "weights"}},
{{"Out", "c"}});
Create_Op_elementwise_add(&prog, {{"X", "c"}, {"Y", "a"}}, {{"Out", "d"}});
test::CreateOp(&prog, "relu", {{"X", "d"}}, {{"Out", "e"}});
Graph graph(prog);
EXPECT_TRUE(test::RunPassAndAssert(&graph,
"fc_elementwise_add_mkldnn_fuse_pass",
"a",
"e",
nodes_removed,
nodes_added));
EXPECT_TRUE(test::AssertOpsCount(graph, {{"fc", 1}, {"elementwise_add", 0}}));
}
TEST(FCElementwiseAddMKLDNNFusePass, NoFusion_NotResidualConnection) {
auto prog = test::BuildProgramDesc({"a", "b", "c", "d", "e", "f", "g"},
{"bias", "weights", "bias2", "weights2"});
test::CreateOp(&prog, "sigmoid", {{"X", "a"}}, {{"Out", "b"}});
Create_Op_FC(&prog,
{{"Input", "b"}, {"Bias", "bias"}, {"W", "weights"}},
{{"Out", "c"}});
Create_Op_FC(&prog,
{{"Input", "d"}, {"Bias", "bias2"}, {"W", "weights2"}},
{{"Out", "e"}});
Create_Op_elementwise_add(&prog, {{"X", "c"}, {"Y", "e"}}, {{"Out", "f"}});
test::CreateOp(&prog, "relu", {{"X", "f"}}, {{"Out", "g"}});
Graph graph(prog);
EXPECT_TRUE(test::RunPassAndAssert(
&graph, "fc_elementwise_add_mkldnn_fuse_pass", "a", "g", 0, 0));
EXPECT_TRUE(test::AssertOpsCount(graph, {{"fc", 2}, {"elementwise_add", 1}}));
}
TEST(FCElementwiseAddMKLDNNFusePass, NoFusion_HasActivationFused) {
auto prog =
test::BuildProgramDesc({"a", "b", "c", "d", "e"}, {"bias", "weights"});
test::CreateOp(&prog, "sigmoid", {{"X", "a"}}, {{"Out", "b"}});
OpDesc* fc =
Create_Op_FC(&prog,
{{"Input", "b"}, {"Bias", "bias"}, {"W", "weights"}},
{{"Out", "c"}});
std::string activation{"relu"};
fc->SetAttr("activation_type", activation);
Create_Op_elementwise_add(&prog, {{"X", "c"}, {"Y", "a"}}, {{"Out", "d"}});
test::CreateOp(&prog, "relu", {{"X", "d"}}, {{"Out", "e"}});
Graph graph(prog);
EXPECT_TRUE(test::RunPassAndAssert(
&graph, "fc_elementwise_add_mkldnn_fuse_pass", "a", "e", 0, 0));
EXPECT_TRUE(test::AssertOpsCount(graph, {{"fc", 1}, {"elementwise_add", 1}}));
}
TEST(FCElementwiseAddMKLDNNFusePass, FC_Residual_VITOCR) {
auto prog = test::BuildProgramDesc(
{"a", "b", "c", "d", "e", "f", "g", "h", "i"},
{"ln_bias", "ln_scale", "bias", "weights", "bias2", "weights2"});
Create_Op_elementwise_add(&prog, {{"X", "a"}, {"Y", "b"}}, {{"Out", "c"}});
test::CreateOp(&prog,
"layer_norm",
{{"X", "c"}, {"Bias", "ln_bias"}, {"Scale", "ln_scale"}},
{{"Y", "d"}});
Create_Op_FC(&prog,
{{"Input", "d"}, {"Bias", "bias"}, {"W", "weights"}},
{{"Out", "e"}});
test::CreateOp(&prog, "gelu", {{"X", "e"}}, {{"Out", "f"}});
Create_Op_FC(&prog,
{{"Input", "f"}, {"Bias", "bias2"}, {"W", "weights2"}},
{{"Out", "g"}});
Create_Op_elementwise_add(&prog, {{"X", "g"}, {"Y", "c"}}, {{"Out", "h"}});
test::CreateOp(&prog, "relu", {{"X", "h"}}, {{"Out", "i"}});
Graph graph(prog);
EXPECT_TRUE(test::RunPassAndAssert(&graph,
"fc_elementwise_add_mkldnn_fuse_pass",
"a",
"i",
nodes_removed,
nodes_added));
EXPECT_TRUE(test::AssertOpsCount(graph, {{"fc", 2}, {"elementwise_add", 1}}));
}
TEST(FCElementwiseAddMKLDNNFusePass, FC_Residual_Sequence) {
auto prog = test::BuildProgramDesc(
{"a", "b", "c", "d", "e", "f", "g", "h", "i", "j", "k", "l", "m"},
{"ln_bias",
"ln_scale",
"bias",
"weights",
"bias2",
"weights2",
"ln_bias2",
"ln_scale2",
"bias3",
"weights3",
"bias4",
"weights4"});
Create_Op_elementwise_add(&prog, {{"X", "a"}, {"Y", "b"}}, {{"Out", "c"}});
test::CreateOp(&prog,
"layer_norm",
{{"X", "c"}, {"Bias", "ln_bias"}, {"Scale", "ln_scale"}},
{{"Y", "d"}});
Create_Op_FC(&prog,
{{"Input", "d"}, {"Bias", "bias"}, {"W", "weights"}},
{{"Out", "e"}});
test::CreateOp(&prog, "gelu", {{"X", "e"}}, {{"Out", "f"}});
Create_Op_FC(&prog,
{{"Input", "f"}, {"Bias", "bias2"}, {"W", "weights2"}},
{{"Out", "g"}});
Create_Op_elementwise_add(&prog, {{"X", "g"}, {"Y", "c"}}, {{"Out", "h"}});
test::CreateOp(&prog,
"layer_norm",
{{"X", "h"}, {"Bias", "ln_bias2"}, {"Scale", "ln_scale2"}},
{{"Y", "i"}});
Create_Op_FC(&prog,
{{"Input", "i"}, {"Bias", "bias3"}, {"W", "weights3"}},
{{"Out", "j"}});
test::CreateOp(&prog, "gelu", {{"X", "j"}}, {{"Out", "k"}});
Create_Op_FC(&prog,
{{"Input", "k"}, {"Bias", "bias4"}, {"W", "weights4"}},
{{"Out", "l"}});
Create_Op_elementwise_add(&prog, {{"X", "h"}, {"Y", "l"}}, {{"Out", "m"}});
Graph graph(prog);
EXPECT_TRUE(test::RunPassAndAssert(&graph,
"fc_elementwise_add_mkldnn_fuse_pass",
"a",
"m",
nodes_removed * 2,
nodes_added * 2));
EXPECT_TRUE(test::AssertOpsCount(graph, {{"fc", 4}, {"elementwise_add", 1}}));
}
TEST(FCElementwiseAddMKLDNNFusePass, pass_op_version_check) {
ASSERT_TRUE(
paddle::framework::compatible::PassVersionCheckerRegistrar::GetInstance()
.IsPassCompatible("fc_elementwise_add_mkldnn_fuse_pass"));
}
} // namespace ir
} // namespace framework
} // namespace paddle
USE_PASS(fc_elementwise_add_mkldnn_fuse_pass);
paddle/fluid/framework/ir/mkldnn/softplus_activation_mkldnn_fuse_pass.cc
浏览文件 @ 0c9f09b8
...@@ -29,6 +29,11 @@ using string::PrettyLogDetail; ...@@ -29,6 +29,11 @@ using string::PrettyLogDetail;
void SoftplusActivationOneDNNPass::ApplyImpl(Graph *graph) const { void SoftplusActivationOneDNNPass::ApplyImpl(Graph *graph) const {
auto act_types = paddle::platform::GetSupportedActivations(); auto act_types = paddle::platform::GetSupportedActivations();
// Currently softplus can't be fused with hard_sigmoid
act_types.erase(
std::remove(act_types.begin(), act_types.end(), "hard_sigmoid"),
act_types.end());
for (const auto &act_type : act_types) { for (const auto &act_type : act_types) {
FuseSoftplusActivation(graph, act_type); FuseSoftplusActivation(graph, act_type);
} }
...@@ -113,7 +118,6 @@ REGISTER_PASS_CAPABILITY(softplus_activation_mkldnn_fuse_pass) ...@@ -113,7 +118,6 @@ REGISTER_PASS_CAPABILITY(softplus_activation_mkldnn_fuse_pass)
.EQ("abs", 0) .EQ("abs", 0)
.LE("clip", 1) .LE("clip", 1)
.EQ("gelu", 0) .EQ("gelu", 0)
.EQ("hard_sigmoid", 0)
.LE("hard_swish", 0) .LE("hard_swish", 0)
.LE("leaky_relu", 1) .LE("leaky_relu", 1)
.LE("mish", 1) .LE("mish", 1)
......
paddle/fluid/framework/ir/mkldnn/softplus_activation_mkldnn_fuse_pass_tester.cc 已删除 100644 → 0
浏览文件 @ dc85b393
// Copyright (c) 2021 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.
// See the License for the specific language governing permissions and
// limitations under the License.
#include <gtest/gtest.h>
#include <vector>
#include "paddle/fluid/framework/ir/mkldnn/softplus_activation_mkldnn_fuse_pass.h"
#include "paddle/fluid/framework/op_proto_maker.h"
namespace paddle {
namespace framework {
namespace ir {
void MainTest(const std::string& activation_type) {
auto prog =
test::BuildProgramDesc({"softplus_x", "softplus_out", "activation_out"});
test::CreateOp(
&prog, "softplus", {{"X", "softplus_x"}}, {{"Out", "softplus_out"}});
test::CreateOp(&prog,
activation_type,
{{"X", "softplus_out"}},
{{"Out", "activation_out"}},
false);
Graph graph(prog);
constexpr int removed_nodes_count = 2;
EXPECT_TRUE(test::RunPassAndAssert(&graph,
"softplus_activation_mkldnn_fuse_pass",
"softplus_x",
"activation_out",
removed_nodes_count));
EXPECT_TRUE(
test::AssertOpsCount(graph, {{"softplus", 1}, {activation_type, 0}}));
for (const auto* node : graph.Nodes()) {
if (node->IsOp() && node->Op()->Type() == "softplus") {
const auto* op = node->Op();
ASSERT_TRUE(op->HasAttr("use_mkldnn"));
EXPECT_TRUE(PADDLE_GET_CONST(bool, op->GetAttr("use_mkldnn")));
ASSERT_TRUE(op->HasAttr("fuse_activation"));
auto activation_type =
PADDLE_GET_CONST(std::string, op->GetAttr("fuse_activation"));
EXPECT_EQ(activation_type.compare(activation_type), 0);
}
}
}
// clang-format off
TEST(FuseSoftplusActivationOneDNNPass, FuseSoftplusWithTanh) {MainTest("tanh")}
TEST(FuseSoftplusActivationOneDNNPass, FuseSoftplusWithRelu) {MainTest("relu")}
TEST(FuseSoftplusActivationOneDNNPass,
FuseSoftplusWithLeakyRelu) {MainTest("leaky_relu")}
TEST(FuseSoftplusActivationOneDNNPass,
FuseSoftplusWithSwish) {MainTest("swish")}
TEST(FuseSoftplusActivationOneDNNPass,
FuseSoftplusWithHardswish) {MainTest("hardswish")}
TEST(FuseSoftplusActivationOneDNNPass, FuseSoftplusWithSqrt) {MainTest("sqrt")}
TEST(FuseSoftplusActivationOneDNNPass, FuseSoftplusWithAbs) {MainTest("abs")}
TEST(FuseSoftplusActivationOneDNNPass, FuseSoftplusWithClip) {MainTest("clip")}
TEST(FuseSoftplusActivationOneDNNPass, FuseSoftplusWithGelu) {MainTest("gelu")}
TEST(FuseSoftplusActivationOneDNNPass,
FuseSoftplusWithRelu6) {MainTest("relu6")}
TEST(FuseSoftplusActivationOneDNNPass, FuseSoftplusWithSigmoid) {
MainTest("sigmoid")
}
// clang-format on
} // namespace ir
} // namespace framework
} // namespace paddle
USE_PASS(softplus_activation_mkldnn_fuse_pass);
python/paddle/fluid/tests/unittests/ir/inference/CMakeLists.txt
浏览文件 @ 0c9f09b8
...@@ -232,7 +232,7 @@ if(WITH_GPU AND TENSORRT_FOUND) ...@@ -232,7 +232,7 @@ if(WITH_GPU AND TENSORRT_FOUND)
set_tests_properties(test_mkldnn_conv_mish_fuse_pass PROPERTIES TIMEOUT 300) set_tests_properties(test_mkldnn_conv_mish_fuse_pass PROPERTIES TIMEOUT 300)
set_tests_properties(test_onednn_fc_activation_fuse_pass PROPERTIES TIMEOUT set_tests_properties(test_onednn_fc_activation_fuse_pass PROPERTIES TIMEOUT
300) 300)
set_tests_properties(test_mkldnn_fc_elementwise_add_fuse_pass set_tests_properties(test_onednn_fc_elementwise_add_fuse_pass
PROPERTIES TIMEOUT 120) PROPERTIES TIMEOUT 120)
set_tests_properties(test_mkldnn_conv_affine_channel_fuse_pass set_tests_properties(test_mkldnn_conv_affine_channel_fuse_pass
PROPERTIES TIMEOUT 60) PROPERTIES TIMEOUT 60)
......
python/paddle/fluid/tests/unittests/ir/inference/test_mkldnn_fc_act_fuse_pass.py 已删除 100644 → 0
浏览文件 @ dc85b393
# Copyright (c) 2020 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.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Test for fusion of fc and activation."""
import unittest
import numpy as np
import paddle.fluid as fluid
from inference_pass_test import InferencePassTest
from paddle import enable_static
from paddle.fluid.core import PassVersionChecker
enable_static()
class FCGeluTanhOneDnnFusePassTest(InferencePassTest):
def setUp(self):
self.set_params()
with fluid.program_guard(self.main_program, self.startup_program):
data = fluid.data(
name="data", shape=[-1, 128, 768], dtype="float32"
)
fc_out = fluid.layers.fc(input=data, size=3072, num_flatten_dims=2)
gelu_out = fluid.layers.gelu(fc_out, approximate=False)
self.feeds = {"data": np.random.random((1, 128, 768)).astype("float32")}
self.fetch_list = [gelu_out]
self.enable_mkldnn = True
def set_params(self):
self.pass_name = "fc_act_mkldnn_fuse_pass"
def test_check_output(self):
self.check_output()
class FCGeluErfOneDnnFusePassTest(InferencePassTest):
def setUp(self):
self.set_params()
with fluid.program_guard(self.main_program, self.startup_program):
data = fluid.data(
name="data", shape=[-1, 128, 768], dtype="float32"
)
fc_out = fluid.layers.fc(input=data, size=3072, num_flatten_dims=2)
gelu_out = fluid.layers.gelu(fc_out, approximate=True)
self.feeds = {"data": np.random.random((1, 128, 768)).astype("float32")}
self.fetch_list = [gelu_out]
self.enable_mkldnn = True
def set_params(self):
self.pass_name = "fc_act_mkldnn_fuse_pass"
def test_check_output(self):
self.check_output()
self.assertTrue(PassVersionChecker.IsCompatible(self.pass_name))
class FCTanhOneDnnFusePassTest(InferencePassTest):
def setUp(self):
self.set_params()
with fluid.program_guard(self.main_program, self.startup_program):
data = fluid.data(
name="data", shape=[-1, 128, 768], dtype="float32"
)
fc_out = fluid.layers.fc(input=data, size=3072, num_flatten_dims=2)
tanh_out = fluid.layers.tanh(fc_out)
self.feeds = {"data": np.random.random((1, 128, 768)).astype("float32")}
self.fetch_list = [tanh_out]
self.enable_mkldnn = True
def set_params(self):
self.pass_name = "fc_act_mkldnn_fuse_pass"
def test_check_output(self):
self.check_output()
self.assertTrue(PassVersionChecker.IsCompatible(self.pass_name))
class FCSigmoidOneDnnFusePassTest(InferencePassTest):
def setUp(self):
self.set_params()
with fluid.program_guard(self.main_program, self.startup_program):
data = fluid.data(
name="data", shape=[-1, 128, 768], dtype="float32"
)
fc_out = fluid.layers.fc(input=data, size=3072, num_flatten_dims=2)
sigmoid_out = fluid.layers.sigmoid(fc_out)
self.feeds = {"data": np.random.random((1, 128, 768)).astype("float32")}
self.fetch_list = [sigmoid_out]
self.enable_mkldnn = True
def set_params(self):
self.pass_name = "fc_act_mkldnn_fuse_pass"
def test_check_output(self):
self.check_output()
self.assertTrue(PassVersionChecker.IsCompatible(self.pass_name))
class FCHardSwishOneDnnFusePassTest(InferencePassTest):
def setUp(self):
self.set_params()
with fluid.program_guard(self.main_program, self.startup_program):
data = fluid.data(
name="data", shape=[-1, 128, 768], dtype="float32"
)
fc_out = fluid.layers.fc(input=data, size=3072, num_flatten_dims=2)
hardswish_out = fluid.layers.hard_swish(fc_out)
self.feeds = {"data": np.random.random((1, 128, 768)).astype("float32")}
self.fetch_list = [hardswish_out]
self.enable_mkldnn = True
def set_params(self):
self.pass_name = "fc_act_mkldnn_fuse_pass"
def test_check_output(self):
self.check_output()
self.assertTrue(PassVersionChecker.IsCompatible(self.pass_name))
class FCMishOneDnnFusePassTest(InferencePassTest):
def setUp(self):
self.set_params()
with fluid.program_guard(self.main_program, self.startup_program):
data = fluid.data(
name="data", shape=[-1, 128, 768], dtype="float32"
)
fc_out = fluid.layers.fc(input=data, size=3072, num_flatten_dims=2)
mish_out = fluid.layers.mish(fc_out)
self.feeds = {"data": np.random.random((1, 128, 768)).astype("float32")}
self.fetch_list = [mish_out]
self.enable_mkldnn = True
def set_params(self):
self.pass_name = "fc_act_mkldnn_fuse_pass"
def test_check_output(self):
self.check_output()
self.assertTrue(PassVersionChecker.IsCompatible(self.pass_name))
if __name__ == "__main__":
unittest.main()
python/paddle/fluid/tests/unittests/ir/inference/test_mkldnn_softplus_activation_fuse_pass.py 已删除 100644 → 0
浏览文件 @ dc85b393
# Copyright (c) 2021 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.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
import numpy as np
from inference_pass_test import InferencePassTest
import paddle
import paddle.fluid as fluid
from paddle.fluid.core import PassVersionChecker
class SoftplusActivationReluOneDNNFusePassTest(InferencePassTest):
fuse_alpha = None
fuse_beta = None
pass_name = 'softplus_activation_mkldnn_fuse_pass'
def setUp(self):
self.set_params()
with fluid.program_guard(self.main_program, self.startup_program):
data = fluid.data(
name="data", shape=[-1, 3, 100, 100], dtype="float32"
)
softplus_out = fluid.layers.softplus(data)
if self.fuse_beta is not None:
activation_out = self.fuse_activation(
softplus_out, self.fuse_alpha, self.fuse_beta
)
elif self.fuse_alpha is not None:
activation_out = self.fuse_activation(
softplus_out, self.fuse_alpha
)
else:
activation_out = self.fuse_activation(softplus_out)
self.feeds = {
"data": np.random.random((1, 3, 100, 100)).astype("float32"),
}
self.fetch_list = [activation_out]
self.enable_mkldnn = True
def set_params(self):
self.fuse_activation = fluid.layers.relu
def test_check_output(self):
use_gpu = False
self.check_output_with_option(use_gpu)
def test_pass_compatible(self):
self.assertTrue(PassVersionChecker.IsCompatible(self.pass_name))
class SoftplusActivationTanhOneDNNFusePassTest(
SoftplusActivationReluOneDNNFusePassTest
):
def set_params(self):
self.fuse_activation = fluid.layers.tanh
class SoftplusActivationLeakyReluOneDNNFusePassTest(
SoftplusActivationReluOneDNNFusePassTest
):
def set_params(self):
self.fuse_activation = fluid.layers.leaky_relu
self.fuse_alpha = 0.3
class SoftplusActivationSwishOneDNNFusePassTest(
SoftplusActivationReluOneDNNFusePassTest
):
def set_params(self):
self.fuse_activation = fluid.layers.swish
self.fuse_alpha = 3
class SoftplusActivationHardSwishOneDNNFusePassTest(
SoftplusActivationReluOneDNNFusePassTest
):
def set_params(self):
self.fuse_activation = fluid.layers.hard_swish
class SoftplusActivationSqrtOneDNNFusePassTest(
SoftplusActivationReluOneDNNFusePassTest
):
def set_params(self):
self.fuse_activation = fluid.layers.hard_swish
class SoftplusActivationAbsOneDNNFusePassTest(
SoftplusActivationReluOneDNNFusePassTest
):
def set_params(self):
self.fuse_activation = fluid.layers.abs
class SoftplusActivationClipOneDNNFusePassTest(
SoftplusActivationReluOneDNNFusePassTest
):
def set_params(self):
self.fuse_activation = fluid.layers.clip
self.fuse_alpha = 1.1
self.fuse_beta = 5.2
class SoftplusActivationGeluErfOneDNNFusePassTest(
SoftplusActivationReluOneDNNFusePassTest
):
def set_params(self):
self.fuse_activation = fluid.layers.gelu
class SoftplusActivationGeluTanhOneDNNFusePassTest(
SoftplusActivationReluOneDNNFusePassTest
):
def set_params(self):
self.fuse_activation = fluid.layers.gelu
self.fuse_alpha = True # simulated "Approximate" attr
class SoftplusActivationRelu6OneDNNFusePassTest(
SoftplusActivationReluOneDNNFusePassTest
):
def set_params(self):
self.fuse_activation = fluid.layers.relu6
class SoftplusActivationSigmoidOneDNNFusePassTest(
SoftplusActivationReluOneDNNFusePassTest
):
def set_params(self):
self.fuse_activation = fluid.layers.sigmoid
if __name__ == "__main__":
paddle.enable_static()
unittest.main()
python/paddle/fluid/tests/unittests/ir/inference/test_mkldnn_fc_elementwise_add_fuse_pass.py → python/paddle/fluid/tests/unittests/ir/inference/test_onednn_fc_elementwise_add_fuse_pass.py
浏览文件 @ 0c9f09b8
...@@ -12,64 +12,57 @@ ...@@ -12,64 +12,57 @@
# See the License for the specific language governing permissions and # See the License for the specific language governing permissions and
# limitations under the License. # limitations under the License.
from auto_scan_test import PassAutoScanTest
from program_config import TensorConfig, ProgramConfig, OpConfig
import numpy as np import numpy as np
from functools import partial
import unittest import unittest
import hypothesis.strategies as st import hypothesis.strategies as st
from auto_scan_test import PassAutoScanTest
from functools import partial
from program_config import TensorConfig, ProgramConfig, OpConfig
class TestFCElementwiseAddMkldnnFusePass(PassAutoScanTest): class TestFCElementwiseAddOneDNNFusePass(PassAutoScanTest):
def sample_program_config(self, draw): def sample_program_config(self, draw):
axis = draw(st.sampled_from([-1, 0, 1])) axis = draw(st.sampled_from([-1, 0, 1]))
fc_as_x = draw(st.sampled_from([True, False])) fc_as_x = draw(st.booleans())
fc_in = draw(st.sampled_from([32, 64])) fc_in = draw(st.sampled_from([32, 64]))
fc_wei = draw(st.sampled_from([32, 64])) fc_wei = draw(st.sampled_from([32, 64]))
def generate_input(): def generate_data(shape):
return np.random.random([fc_in, fc_wei]).astype(np.float32) return np.random.random(shape).astype(np.float32)
def generate_fc_weight():
return np.random.random([fc_wei, fc_wei]).astype(np.float32)
def generate_fc_bias():
return np.random.random([fc_wei]).astype(np.float32)
relu_op = OpConfig( relu_op = OpConfig(
type="relu", type='relu',
inputs={"X": ["input_data"]}, inputs={'X': ['input_data']},
outputs={"Out": ["relu_out"]}, outputs={'Out': ['relu_out']},
attrs={}, attrs={},
) )
fc_op = OpConfig( fc_op = OpConfig(
type="fc", type='fc',
inputs={ inputs={
"Input": ["relu_out"], 'Input': ['relu_out'],
"W": ["fc_weight"], 'W': ['fc_weight'],
"Bias": ["fc_bias"], 'Bias': ['fc_bias'],
}, },
outputs={"Out": ["fc_output"]}, outputs={'Out': ['fc_output']},
attrs={ attrs={
"use_mkldnn": True, 'use_mkldnn': True,
"padding_weights": False, 'padding_weights': False,
"activation_type": "", 'activation_type': '',
"in_num_col_dims": 1, 'in_num_col_dims': 1,
}, },
) )
if fc_as_x: if fc_as_x:
inputs = {"X": ["fc_output"], "Y": ["input_data"]} inputs = {'X': ['fc_output'], 'Y': ['input_data']}
else: else:
inputs = {"X": ["input_data"], "Y": ["fc_output"]} inputs = {'X': ['input_data'], 'Y': ['fc_output']}
elt_add_op = OpConfig( elt_add_op = OpConfig(
type="elementwise_add", type='elementwise_add',
inputs=inputs, inputs=inputs,
outputs={"Out": ["elementwise_output"]}, outputs={'Out': ['elementwise_output']},
attrs={'axis': axis}, attrs={'axis': axis, 'use_mkldnn': True},
) )
model_net = [relu_op, fc_op, elt_add_op] model_net = [relu_op, fc_op, elt_add_op]
...@@ -77,26 +70,34 @@ class TestFCElementwiseAddMkldnnFusePass(PassAutoScanTest): ...@@ -77,26 +70,34 @@ class TestFCElementwiseAddMkldnnFusePass(PassAutoScanTest):
program_config = ProgramConfig( program_config = ProgramConfig(
ops=model_net, ops=model_net,
weights={ weights={
"fc_weight": TensorConfig(data_gen=partial(generate_fc_weight)), 'fc_weight': TensorConfig(
"fc_bias": TensorConfig(data_gen=partial(generate_fc_bias)), data_gen=partial(generate_data, [fc_wei, fc_wei])
),
'fc_bias': TensorConfig(
data_gen=partial(generate_data, [fc_wei])
),
}, },
inputs={ inputs={
"input_data": TensorConfig(data_gen=partial(generate_input)) 'input_data': TensorConfig(
data_gen=partial(generate_data, [fc_in, fc_wei])
)
}, },
outputs=["elementwise_output"], outputs=['elementwise_output'],
) )
return program_config return program_config
def sample_predictor_configs(self, program_config): def sample_predictor_configs(self, program_config):
config = self.create_inference_config(use_mkldnn=True) config = self.create_inference_config(
yield config, ["relu", "fc"], (1e-5, 1e-5) use_mkldnn=True, passes=['fc_elementwise_add_mkldnn_fuse_pass']
)
yield config, ['relu', 'fc'], (1e-5, 1e-5)
def test(self): def test(self):
self.run_and_statis( self.run_and_statis(
quant=False, passes=["fc_elementwise_add_mkldnn_fuse_pass"] quant=False, passes=['fc_elementwise_add_mkldnn_fuse_pass']
) )
if __name__ == "__main__": if __name__ == '__main__':
unittest.main() unittest.main()
python/paddle/fluid/tests/unittests/ir/inference/test_onednn_softplus_activation_fuse_pass.py 0 → 100644
浏览文件 @ 0c9f09b8
# 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.
# See the License for the specific language governing permissions and
# limitations under the License.
import hypothesis.strategies as st
import numpy as np
import unittest
from auto_scan_test import PassAutoScanTest
from functools import partial
from program_config import TensorConfig, ProgramConfig, OpConfig
class TestSoftplusActivationOneDNNFusePass(PassAutoScanTest):
def sample_program_config(self, draw):
activation_type = draw(
st.sampled_from(
[
'relu',
'gelu',
'tanh',
'sigmoid',
'swish',
'mish',
'sqrt',
'hard_swish',
'abs',
'relu6',
'clip',
'leaky_relu',
]
)
)
def generate_input():
return np.random.random([4, 3, 100, 100]).astype(np.float32)
softplus_op = OpConfig(
type='softplus',
inputs={
'X': ['activation_X'],
},
outputs={'Out': ['softplus_out']},
attrs={
'beta': draw(st.floats(min_value=0.5, max_value=2)),
'threshold': draw(st.floats(min_value=15, max_value=30)),
},
)
if activation_type == 'clip':
activation_op = OpConfig(
activation_type,
inputs={'X': ['softplus_out']},
outputs={'Out': ['activation_output']},
min=draw(st.floats(min_value=0.1, max_value=0.49)),
max=draw(st.floats(min_value=0.5, max_value=1.0)),
)
elif activation_type == "gelu":
activation_op = OpConfig(
activation_type,
inputs={"X": ["softplus_out"]},
outputs={"Out": ["activation_output"]},
approximate=draw(st.booleans()),
)
elif activation_type == 'leaky_relu':
activation_op = OpConfig(
activation_type,
inputs={'X': ['softplus_out']},
outputs={'Out': ['activation_output']},
alpha=draw(st.floats(min_value=0.1, max_value=1.0)),
)
elif activation_type == 'relu6':
activation_op = OpConfig(
activation_type,
inputs={'X': ['softplus_out']},
outputs={'Out': ['activation_output']},
threshold=draw(st.floats(min_value=1.0, max_value=10.0)),
)
elif activation_type == 'swish':
activation_op = OpConfig(
activation_type,
inputs={'X': ['softplus_out']},
outputs={'Out': ['activation_output']},
beta=draw(st.floats(min_value=0.1, max_value=10.0)),
)
else:
activation_op = OpConfig(
activation_type,
inputs={'X': ['softplus_out']},
outputs={'Out': ['activation_output']},
)
model_net = [softplus_op, activation_op]
program_config = ProgramConfig(
ops=model_net,
weights={},
inputs={
'activation_X': TensorConfig(data_gen=partial(generate_input))
},
outputs=['activation_output'],
)
return program_config
def sample_predictor_configs(self, program_config):
config = self.create_inference_config(use_mkldnn=True)
yield config, ['softplus'], (1e-5, 1e-5)
def test(self):
self.run_and_statis(
quant=False,
max_examples=40,
passes=['softplus_activation_mkldnn_fuse_pass'],
)
if __name__ == '__main__':
unittest.main()
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