Added fc + activation fuse pass (currently only gelu, sigmoid and tanh are supported) (#29772)
jakpiase
5 years ago
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GitHub
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// Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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#include "paddle/fluid/framework/ir/mkldnn/fc_act_mkldnn_fuse_pass.h"
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#include "paddle/fluid/framework/ir/graph_pattern_detector.h"
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#include "paddle/fluid/framework/op_version_registry.h"
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#include "paddle/fluid/platform/enforce.h"
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#include "paddle/fluid/string/pretty_log.h"
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namespace paddle {
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namespace framework {
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namespace ir {
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using string::PrettyLogDetail;
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void FuseFCActOneDNNPass::ApplyImpl(Graph *graph) const {
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std::vector<std::string> act_types = {"gelu", "tanh", "sigmoid"};
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for (std::string act_type : act_types) FuseFCAct(graph, act_type);
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}
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void FuseFCActOneDNNPass::FuseFCAct(Graph *graph,
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const std::string &act_type) const {
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PADDLE_ENFORCE_NOT_NULL(
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graph, platform::errors::InvalidArgument("Graph cannot be nullptr."));
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FusePassBase::Init("fc_act", graph);
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GraphPatternDetector gpd;
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patterns::FCActOneDNN fc_act_pattern(gpd.mutable_pattern(), "fc_act");
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fc_act_pattern(act_type);
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int found_fc_act_count = 0;
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auto handler = [&](const GraphPatternDetector::subgraph_t &subgraph,
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Graph *g) {
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VLOG(4) << "Fuse fc with activation op.";
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// FC output
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GET_IR_NODE_FROM_SUBGRAPH(fc_out, fc_out, fc_act_pattern);
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// ACT output
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GET_IR_NODE_FROM_SUBGRAPH(act_out, act_out, fc_act_pattern);
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// ops
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GET_IR_NODE_FROM_SUBGRAPH(fc, fc, fc_act_pattern);
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GET_IR_NODE_FROM_SUBGRAPH(act, act, fc_act_pattern);
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auto *fc_op = fc->Op();
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auto *act_op = act->Op();
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if (fc_op->HasAttr("use_mkldnn")) {
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PADDLE_ENFORCE(
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BOOST_GET_CONST(bool, fc_op->GetAttr("use_mkldnn")),
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platform::errors::PreconditionNotMet(
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"The FC+Act fusion may happen only when oneDNN library "
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"is used."));
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}
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if (act_type == "gelu" && act_op->HasAttr("approximate")) {
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bool approximate = BOOST_GET_CONST(bool, act_op->GetAttr("approximate"));
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std::string type = approximate ? "_tanh" : "_erf";
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fc_op->SetAttr("activation_type", act_type + type);
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} else
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fc_op->SetAttr("activation_type", act_type);
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fc_op->SetAttr("use_mkldnn", true);
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fc_op->SetOutput("Out", {act_out->Name()});
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IR_OP_VAR_LINK(fc, act_out);
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GraphSafeRemoveNodes(g, {act, fc_out});
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found_fc_act_count++;
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};
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gpd(graph, handler);
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AddStatis(found_fc_act_count);
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PrettyLogDetail("--- fused %d fc with %s activation", found_fc_act_count,
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act_type);
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}
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} // namespace ir
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} // namespace framework
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} // namespace paddle
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REGISTER_PASS(fc_act_mkldnn_fuse_pass,
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paddle::framework::ir::FuseFCActOneDNNPass);
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REGISTER_PASS_CAPABILITY(fc_act_mkldnn_fuse_pass)
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.AddCombination(
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paddle::framework::compatible::OpVersionComparatorCombination()
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.LE("fc", 0)
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.LE("gelu", 0)
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.LE("sigmoid", 0)
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.LE("tanh", 0));
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@ -0,0 +1,45 @@
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// Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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#pragma once
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#include <string>
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#include "paddle/fluid/framework/ir/fuse_pass_base.h"
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#include "paddle/fluid/framework/ir/graph.h"
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namespace paddle {
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namespace framework {
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namespace ir {
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/*
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* \brief Fuse the FC and activation operators into single OneDNN's
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* FC with post-op.
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*
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* \note Currently only GeLU, sigmoid and tanh are supported as an activation
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* function.
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*/
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class FuseFCActOneDNNPass : public FusePassBase {
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public:
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virtual ~FuseFCActOneDNNPass() {}
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protected:
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void ApplyImpl(ir::Graph *graph) const override;
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void FuseFCAct(ir::Graph *graph, const std::string &act_types) const;
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};
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} // namespace ir
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} // namespace framework
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} // namespace paddlea
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# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Test for fusion of fc and activation."""
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from __future__ import print_function
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import unittest
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import numpy as np
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import paddle.fluid as fluid
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from inference_pass_test import InferencePassTest
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from paddle import enable_static
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from paddle.fluid.core import PassVersionChecker
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enable_static()
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class FCGeluTanhOneDnnFusePassTest(InferencePassTest):
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def setUp(self):
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self.set_params()
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with fluid.program_guard(self.main_program, self.startup_program):
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data = fluid.data(
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name="data", shape=[-1, 128, 768], dtype="float32")
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fc_out = fluid.layers.fc(input=data, size=3072, num_flatten_dims=2)
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gelu_out = fluid.layers.gelu(fc_out, approximate=False)
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self.feeds = {"data": np.random.random((1, 128, 768)).astype("float32")}
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self.fetch_list = [gelu_out]
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self.enable_mkldnn = True
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def set_params(self):
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self.pass_name = "fc_act_mkldnn_fuse_pass"
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def test_check_output(self):
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self.check_output()
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class FCGeluErfOneDnnFusePassTest(InferencePassTest):
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def setUp(self):
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self.set_params()
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with fluid.program_guard(self.main_program, self.startup_program):
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data = fluid.data(
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name="data", shape=[-1, 128, 768], dtype="float32")
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fc_out = fluid.layers.fc(input=data, size=3072, num_flatten_dims=2)
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gelu_out = fluid.layers.gelu(fc_out, approximate=True)
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self.feeds = {"data": np.random.random((1, 128, 768)).astype("float32")}
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self.fetch_list = [gelu_out]
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self.enable_mkldnn = True
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def set_params(self):
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self.pass_name = "fc_act_mkldnn_fuse_pass"
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def test_check_output(self):
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self.check_output()
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self.assertTrue(PassVersionChecker.IsCompatible(self.pass_name))
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class FCTanhOneDnnFusePassTest(InferencePassTest):
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def setUp(self):
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self.set_params()
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with fluid.program_guard(self.main_program, self.startup_program):
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data = fluid.data(
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name="data", shape=[-1, 128, 768], dtype="float32")
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fc_out = fluid.layers.fc(input=data, size=3072, num_flatten_dims=2)
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tanh_out = fluid.layers.tanh(fc_out)
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self.feeds = {"data": np.random.random((1, 128, 768)).astype("float32")}
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self.fetch_list = [tanh_out]
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self.enable_mkldnn = True
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def set_params(self):
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self.pass_name = "fc_act_mkldnn_fuse_pass"
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def test_check_output(self):
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self.check_output()
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self.assertTrue(PassVersionChecker.IsCompatible(self.pass_name))
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class FCSigmoidOneDnnFusePassTest(InferencePassTest):
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def setUp(self):
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self.set_params()
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with fluid.program_guard(self.main_program, self.startup_program):
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data = fluid.data(
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name="data", shape=[-1, 128, 768], dtype="float32")
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fc_out = fluid.layers.fc(input=data, size=3072, num_flatten_dims=2)
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sigmoid_out = fluid.layers.sigmoid(fc_out)
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self.feeds = {"data": np.random.random((1, 128, 768)).astype("float32")}
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self.fetch_list = [sigmoid_out]
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self.enable_mkldnn = True
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def set_params(self):
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self.pass_name = "fc_act_mkldnn_fuse_pass"
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def test_check_output(self):
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self.check_output()
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self.assertTrue(PassVersionChecker.IsCompatible(self.pass_name))
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if __name__ == "__main__":
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unittest.main()
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