未验证 提交 897789b1 编写于 作者: Y Yan Chunwei 提交者: GitHub

fix save_inferece_model bug (#15365)

上级 ba02ac46
...@@ -65,6 +65,7 @@ pass_library(conv_elementwise_add2_act_fuse_pass inference) ...@@ -65,6 +65,7 @@ pass_library(conv_elementwise_add2_act_fuse_pass inference)
pass_library(conv_elementwise_add_fuse_pass inference) pass_library(conv_elementwise_add_fuse_pass inference)
pass_library(conv_affine_channel_fuse_pass inference) pass_library(conv_affine_channel_fuse_pass inference)
pass_library(transpose_flatten_concat_fuse_pass inference) pass_library(transpose_flatten_concat_fuse_pass inference)
pass_library(identity_scale_op_clean_pass base)
# There may be many transpose-flatten structures in a model, and the output of # There may be many transpose-flatten structures in a model, and the output of
# these structures will be used as inputs to the concat Op. This pattern will # these structures will be used as inputs to the concat Op. This pattern will
......
// Copyright (c) 2019 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 "paddle/fluid/framework/ir/identity_scale_op_clean_pass.h"
#include <string>
#include "paddle/fluid/framework/ir/graph_pattern_detector.h"
namespace paddle {
namespace framework {
namespace ir {
std::unique_ptr<ir::Graph> IdentityScaleOpCleanPass::ApplyImpl(
std::unique_ptr<ir::Graph> graph) const {
FusePassBase::Init("identity_scale_op_clean", graph.get());
// pre_op -> scale_in -> scale_op -> scale_out
// ->
// pre_op -> scale_out
GraphPatternDetector detector;
auto pre_op = detector.mutable_pattern()->NewNode("pre_op")->assert_is_op();
auto scale_in = detector.mutable_pattern()
->NewNode("scale_in")
->assert_is_op_input("scale")
->AsIntermediate();
auto scale_op = detector.mutable_pattern()
->NewNode("scale_fuse")
->assert_is_op("scale")
->assert_op_attr<float>("scale", 1.)
->assert_op_attr<float>("bias", 0.);
auto scale_out = detector.mutable_pattern()
->NewNode("scale_out")
->assert_is_op_output("scale");
pre_op->LinksTo({scale_in});
scale_op->LinksFrom({scale_in}).LinksTo({scale_out});
GraphPatternDetector::handle_t handler = [&](
const GraphPatternDetector::subgraph_t& subgraph, Graph* graph) {
Node* scale_op_var = subgraph.at(scale_op);
Node* scale_in_var = subgraph.at(scale_in);
Node* scale_out_var = subgraph.at(scale_out);
Node* pre_op_var = subgraph.at(pre_op);
// Link pre_op directly to scale_out
const std::string scale_in_name = scale_in_var->Name();
const std::string scale_out_name = scale_out_var->Name();
// Remove links in graph
GraphSafeRemoveNodes(graph, {scale_in_var, scale_op_var});
// Modify proto message
auto* pre_op_desc = pre_op_var->Op();
for (auto& parameter : *pre_op_desc->Proto()->mutable_outputs()) {
auto* arguments = parameter.mutable_arguments();
auto it = std::find(arguments->begin(), arguments->end(), scale_in_name);
PADDLE_ENFORCE(it != arguments->end());
*it = scale_out_name;
}
IR_NODE_LINK_TO(pre_op_var, scale_out_var);
};
detector(graph.get(), handler);
return graph;
}
} // namespace ir
} // namespace framework
} // namespace paddle
REGISTER_PASS(identity_scale_op_clean_pass,
paddle::framework::ir::IdentityScaleOpCleanPass);
// Copyright (c) 2019 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.
#pragma once
#include "paddle/fluid/framework/ir/fuse_pass_base.h"
namespace paddle {
namespace framework {
namespace ir {
class IdentityScaleOpCleanPass : public FusePassBase {
protected:
std::unique_ptr<ir::Graph> ApplyImpl(std::unique_ptr<ir::Graph> graph) const;
private:
virtual ~IdentityScaleOpCleanPass() = default;
};
} // namespace ir
} // namespace framework
} // namespace paddle
...@@ -117,6 +117,7 @@ class CpuPassStrategy : public PassStrategy { ...@@ -117,6 +117,7 @@ class CpuPassStrategy : public PassStrategy {
"conv_bn_fuse_pass", // "conv_bn_fuse_pass", //
"conv_eltwiseadd_bn_fuse_pass", // "conv_eltwiseadd_bn_fuse_pass", //
"is_test_pass", // "is_test_pass", //
"identity_scale_op_clean_pass", //
}); });
use_gpu_ = false; use_gpu_ = false;
} }
...@@ -155,6 +156,7 @@ class GpuPassStrategy : public PassStrategy { ...@@ -155,6 +156,7 @@ class GpuPassStrategy : public PassStrategy {
GpuPassStrategy() : PassStrategy({}) { GpuPassStrategy() : PassStrategy({}) {
passes_.assign({ passes_.assign({
"infer_clean_graph_pass", // "infer_clean_graph_pass", //
"identity_scale_op_clean_pass", //
"conv_affine_channel_fuse_pass", // "conv_affine_channel_fuse_pass", //
"conv_eltwiseadd_affine_channel_fuse_pass", // "conv_eltwiseadd_affine_channel_fuse_pass", //
"conv_bn_fuse_pass", // "conv_bn_fuse_pass", //
......
...@@ -21,9 +21,10 @@ import shutil ...@@ -21,9 +21,10 @@ import shutil
import six import six
from functools import reduce from functools import reduce
from paddle.fluid import layers
from paddle.fluid.executor import Executor from paddle.fluid.executor import Executor
from paddle.fluid.evaluator import Evaluator from paddle.fluid.evaluator import Evaluator
from paddle.fluid.framework import Program, Parameter, default_main_program, default_startup_program, Variable from paddle.fluid.framework import Program, Parameter, default_main_program, default_startup_program, Variable, program_guard
from . import core from . import core
__all__ = [ __all__ = [
...@@ -931,6 +932,17 @@ def save_inference_model(dirname, ...@@ -931,6 +932,17 @@ def save_inference_model(dirname,
if main_program is None: if main_program is None:
main_program = default_main_program() main_program = default_main_program()
# fix the bug that the activation op's output as target will be pruned.
# will affect the inference performance.
# TODO(Superjomn) add an IR pass to remove 1-scale op.
with program_guard(main_program):
uniq_target_vars = []
for var in target_vars:
if isinstance(var, Variable):
var1 = layers.scale(var, 1.)
uniq_target_vars.append(var1)
target_vars = uniq_target_vars
# when a pserver and a trainer running on the same machine, mkdir may conflict # when a pserver and a trainer running on the same machine, mkdir may conflict
try: try:
os.makedirs(dirname) os.makedirs(dirname)
......
...@@ -82,7 +82,8 @@ class TestBook(unittest.TestCase): ...@@ -82,7 +82,8 @@ class TestBook(unittest.TestCase):
self.assertEqual(feed_var_names, ["x", "y"]) self.assertEqual(feed_var_names, ["x", "y"])
self.assertEqual(len(fetch_vars), 1) self.assertEqual(len(fetch_vars), 1)
self.assertEqual(str(fetch_vars[0]), str(avg_cost)) print("fetch %s" % str(fetch_vars[0]))
self.assertTrue("scale" in str(fetch_vars[0]))
self.assertEqual(expected, actual) self.assertEqual(expected, actual)
......
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