提交 a32ce8c4 编写于 作者: M minqiyang

Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into port_pybind11

......@@ -4,7 +4,7 @@ Paddle 预测 API
为了更简单方便的预测部署,Fluid 提供了一套高层 API
用来隐藏底层不同的优化实现。
`预测库相关代码 <https://github.com/PaddlePaddle/Paddle/tree/develop/paddle/contrib/inference>`__
`预测库相关代码 <https://github.com/PaddlePaddle/Paddle/tree/develop/paddle/fluid/inference/api>`__
包括
- 头文件 ``paddle_inference_api.h`` 定义了所有的接口
......@@ -104,5 +104,5 @@ engine
------------
- `inference
demos <https://github.com/PaddlePaddle/Paddle/tree/develop/paddle/contrib/inference/demo>`__
- `复杂单线程/多线程例子 <https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/contrib/inference/test_paddle_inference_api_impl.cc>`__
demos <https://github.com/PaddlePaddle/Paddle/tree/develop/paddle/fluid/inference/api/demo_ci>`__
- `复杂单线程/多线程例子 <https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/fluid/inference/api/api_impl_tester.cc>`__
......@@ -3,7 +3,10 @@ cc_library(graph SRCS graph.cc DEPS node)
cc_library(graph_helper SRCS graph_helper.cc DEPS graph)
cc_library(pass SRCS pass.cc DEPS graph node graph_helper)
cc_library(graph_viz_pass SRCS graph_viz_pass.cc DEPS graph pass graph_helper)
cc_library(graph_traits SRCS graph_traits.cc DEPS graph)
cc_library(graph_pattern_detecter SRCS graph_pattern_detecter.cc DEPS graph graph_helper graph_traits)
cc_test(pass_test SRCS pass_test.cc DEPS graph pass graph_helper)
cc_test(graph_test SRCS graph_test.cc DEPS graph graph_helper op_registry)
cc_test(graph_helper_test SRCS graph_helper_test.cc DEPS graph graph_helper op_registry)
cc_test(test_graph_pattern_detecter SRCS graph_pattern_detecter_tester.cc DEPS graph_pattern_detecter)
// Copyright (c) 2018 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 <string>
#include <vector>
#include "paddle/fluid/framework/ir/graph_helper.h"
#include "paddle/fluid/framework/ir/graph_pattern_detecter.h"
#include "paddle/fluid/framework/ir/graph_traits.h"
#include "paddle/fluid/platform/enforce.h"
namespace paddle {
namespace framework {
namespace ir {
PDNode* PDPattern::NewNode(PDNode::teller_t&& teller, const std::string& name) {
nodes_.emplace_back(new PDNode(std::move(teller), name));
auto* cur = nodes_.back().get();
return cur;
}
void PDPattern::AddEdge(PDNode* a, PDNode* b) {
PADDLE_ENFORCE(a);
PADDLE_ENFORCE(b);
PADDLE_ENFORCE(a != b, "can't connect to the same nodes.");
edges_.emplace_back(a, b);
}
void GraphPatternDetecter::operator()(Graph* graph,
GraphPatternDetecter::handle_t handler) {
if (!MarkPDNodesInGraph(*graph)) return;
auto subgraphs = DetectPatterns();
UniquePatterns(&subgraphs);
RemoveOverlappedMatch(&subgraphs);
for (auto& g : subgraphs) {
handler(g, graph);
}
}
bool GraphPatternDetecter::MarkPDNodesInGraph(const ir::Graph& graph) {
if (graph.Nodes().empty()) return false;
for (auto& node : GraphTraits::DFS(graph)) {
for (const auto& pdnode : pattern_.nodes()) {
if (pdnode->Tell(&node)) {
pdnodes2nodes_[pdnode.get()].insert(&node);
}
}
}
return !pdnodes2nodes_.empty();
}
struct HitGroup {
std::unordered_map<PDNode*, Node*> roles;
bool Match(Node* node, PDNode* pat) {
return !roles.count(pat) || roles.at(pat) == node;
}
void Register(Node* node, PDNode* pat) { roles[pat] = node; }
};
// Tell whether Node a links to b.
bool IsNodesLink(Node* a, Node* b) {
for (auto* node : a->outputs) {
if (b == node) {
return true;
}
}
return false;
}
std::vector<GraphPatternDetecter::subgraph_t>
GraphPatternDetecter::DetectPatterns() {
// Init empty subgraphs.
std::vector<GraphPatternDetecter::subgraph_t> result;
std::vector<HitGroup> init_groups;
PADDLE_ENFORCE(!pattern_.edges().empty(), "At least one edge is needed");
auto* first_pnode = pattern_.edges().front().first;
if (!pdnodes2nodes_.count(first_pnode)) return result;
for (auto* node : pdnodes2nodes_[first_pnode]) {
HitGroup group;
group.roles[first_pnode] = node;
init_groups.emplace_back(group);
}
int step = 0;
std::array<std::vector<HitGroup>, 2> bi_records;
bi_records[0] = std::move(init_groups);
// Extend a PDNode to subgraphs by deducing the connection relations defined
// in edges of PDNodes.
for (const auto& edge : pattern_.edges()) {
// Each role has two PDNodes, which indicates two roles.
// Detect two Nodes that can match these two roles and they are connected.
auto& pre_groups = bi_records[step % 2];
auto& cur_groups = bi_records[1 - (step++ % 2)];
cur_groups.clear();
// source -> target
for (Node* source : pdnodes2nodes_[edge.first]) {
for (Node* target : pdnodes2nodes_[edge.second]) {
// TODO(Superjomn) add some prune strategies.
for (const auto& group : pre_groups) {
HitGroup new_group = group;
if (IsNodesLink(source, target) &&
new_group.Match(source, edge.first)) {
new_group.Register(source, edge.first);
if (new_group.Match(target, edge.second)) {
new_group.Register(target, edge.second);
cur_groups.push_back(new_group);
// TODO(Superjomn) need to unique
}
}
}
}
}
}
for (auto& group : bi_records[step % 2]) {
GraphPatternDetecter::subgraph_t subgraph;
for (auto& role : group.roles) {
subgraph.emplace(role.first, role.second);
}
result.emplace_back(subgraph);
}
return result;
}
void GraphPatternDetecter::UniquePatterns(
std::vector<GraphPatternDetecter::subgraph_t>* subgraphs) {
if (subgraphs->empty()) return;
std::vector<GraphPatternDetecter::subgraph_t> result;
std::unordered_set<size_t> set;
for (auto& g : *subgraphs) {
size_t key = 0;
for (auto& item : g) {
key ^= std::hash<void*>{}(item.first);
key ^= std::hash<void*>{}(item.second);
}
if (!set.count(key)) {
result.emplace_back(g);
set.insert(key);
}
}
*subgraphs = result;
}
void GraphPatternDetecter::RemoveOverlappedMatch(
std::vector<subgraph_t>* subgraphs) {
std::vector<subgraph_t> result;
std::unordered_set<Node*> node_set;
for (const auto& subgraph : *subgraphs) {
bool valid = true;
for (auto& item : subgraph) {
if (node_set.count(item.second)) {
valid = false;
break;
}
}
if (valid) {
for (auto& item : subgraph) {
node_set.insert(item.second);
}
result.push_back(subgraph);
}
}
*subgraphs = result;
}
} // namespace ir
} // namespace framework
} // namespace paddle
// Copyright (c) 2018 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
#ifdef PADDLE_WITH_TESTING
#include <gtest/gtest_prod.h>
#endif
#include <numeric>
#include "paddle/fluid/framework/ir/graph.h"
#include "paddle/fluid/framework/ir/node.h"
namespace paddle {
namespace framework {
namespace ir {
// Some basic torminolygies:
// - PDPattern: a pattern defined as a data flow graph.
// - PDNode: the node in the pattern, each PDNode represents an `ir::Node`
// that meets some conditions defined in `PDNode.teller`.
// - A pattern is defined with PDNodes with edges.
// Pattern detector node. This node helps to build a pattern.
struct PDNode {
// tell whether an ir::Node* is a candidation for a PDNode.
using teller_t = std::function<bool(Node*)>;
PDNode(teller_t&& teller, const std::string& name = "")
: teller_(teller), name_(name) {
PADDLE_ENFORCE(teller_ != nullptr, "invalid teller functer is set.");
}
PDNode(PDNode&& other) = default;
std::vector<PDNode*> inlinks;
std::vector<PDNode*> outlinks;
bool Tell(Node* node) const {
PADDLE_ENFORCE(teller_ != nullptr, "teller should be set for a PDNode");
return teller_(node);
}
const std::string& name() const { return name_; }
PDNode(const PDNode&) = delete;
PDNode& operator=(const PDNode&) = delete;
private:
teller_t teller_;
std::string name_;
};
/*
* A pattern in a graph, which defined with PDNode and edges. Most graph
* patterns can be divided into PDNodes and link relations between them.
*
* For example, the FC fusion need to filter the MUL and ELEMENTWISE_ADD
* operators from the computation graph, the MUL's output should have only one
* consumer which is the ELEMENTWISE_ADD.
* This pattern can be defined as with the following pseudo codes
*
* // Create two operator PDNodes.
* MUL = PDPattern.NewNode()
* ELE = PDPattern.NewNode()
* // Create the variable PDNodes.
* MUL_out = PDPattern.NewNode()
* // Add teller to define some rules that help to filter the target Nodes.
* MUL.teller = lambda(node): node->IsOp() && node->Op()->Type == "mul";
* ELE.teller = lambda(node): \
* node->IsOp() && node->Op()->Type == "elementwise_add";
* MUL_out.teller = lambda(node): node->IsVar() && (MUL in node->inputs)
* && (ELE in node->outputs)
*
* One can add more specific tellers for PDNodes or edges, both the Operator
* and Variable Nodes can be ruled in PDNode.teller.
*
* PDPattern can record the general patterns, such as the pattern represents
* - Op in CPU -> Op in GPU -> Op in CPU, to findout the IO abnormal place.
* - Ops whose inputs and outputs share the same variables
*/
class PDPattern {
public:
using edge_t = std::pair<PDNode*, PDNode*>;
void AddEdge(PDNode* a, PDNode* b);
PDNode* NewNode(PDNode::teller_t&& teller, const std::string& name = "");
const std::vector<std::unique_ptr<PDNode>>& nodes() const { return nodes_; }
const std::vector<edge_t>& edges() const { return edges_; }
private:
#ifdef PADDLE_WITH_TESTING
FRIEND_TEST(PDPattern, AddEdge);
FRIEND_TEST(PDPattern, NewNode);
#endif
std::vector<std::unique_ptr<PDNode>> nodes_;
std::vector<edge_t> edges_;
};
/*
* GraphPatternDetecter helps to detect the specific patterns in the graph.
* Input a pattern, output a list of the matched subgraphs/nodes.
* This helper can be used to support fuse(conv+batchnorm => batchnorm e.g.).
*
* The algorithm has three phases:
* 1. Mark the nodes that match the defined PDNodes in a PDPattern,
* 2. Extend a PDNode to subgraphs by deducing the connection relation defined
* in PAPattern(the edges),
* 3. Get the filtered subgraphs and treat them with a pre-defined handler.
*
* Usage:
* // Create a detector
* GraphPatternDetecter detector;
* // Define the detector's pattern, by adding PDNode and define the edges.
* auto* node0 = detector.mutable_pattern().AddNode(...)
* auto* node1 = detector.mutable_pattern().AddNode(...)
* node0->teller = some lambda.
* node1->teller = some lambda.
* detector.mutable_pattern().AddEdge(node0, node1);
* // Create an handler, to define the behavior of treating the filtered
* // subgraphs that comply with the patterns.
* GraphPatternDetecter::handle_t handler = some labmda
* // Execute the detector.
* detector(&graph, handler);
*/
class GraphPatternDetecter {
public:
using subgraph_t = std::unordered_map<PDNode*, Node*>;
// Operate on the detected pattern.
using handle_t =
std::function<void(const subgraph_t& /*hitted pattern*/, Graph*)>;
void operator()(Graph* graph, handle_t handler);
const PDPattern& pattern() const { return pattern_; }
PDPattern* mutable_pattern() { return &pattern_; }
private:
// Mark the nodes that fits the pattern.
bool MarkPDNodesInGraph(const ir::Graph& graph);
// Detect all the pattern and output the hit records.
std::vector<subgraph_t> DetectPatterns();
// Remove duplicate patterns.
void UniquePatterns(std::vector<subgraph_t>* subgraphs);
// Remove overlapped match subgraphs, when overlapped, keep the previous one.
void RemoveOverlappedMatch(std::vector<subgraph_t>* subgraphs);
#ifdef PADDLE_WITH_TESTING
FRIEND_TEST(GraphPatternDetecter, MarkPDNodesInGraph);
FRIEND_TEST(GraphPatternDetecter, DetectPatterns);
#endif
private:
using hit_rcd_t =
std::pair<Node* /*node in graph*/, PDNode* /*node in pattern*/>;
PDPattern pattern_;
std::vector<hit_rcd_t> marked_records_;
std::unordered_map<const PDNode*, std::unordered_set<Node*>> pdnodes2nodes_;
};
} // namespace ir
} // namespace framework
} // namespace paddle
// Copyright (c) 2018 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/graph_pattern_detecter.h"
#include <gtest/gtest.h>
namespace paddle {
namespace framework {
namespace ir {
void BuildGraph(Graph* g) {
ir::Node* o1 = g->CreateEmptyNode("op1", Node::Type::kOperation);
ir::Node* o2 = g->CreateEmptyNode("op2", Node::Type::kOperation);
ir::Node* o3 = g->CreateEmptyNode("op3", Node::Type::kOperation);
ir::Node* o4 = g->CreateEmptyNode("op4", Node::Type::kOperation);
ir::Node* o5 = g->CreateEmptyNode("op5", Node::Type::kOperation);
ir::Node* v1 = g->CreateEmptyNode("var1", Node::Type::kVariable);
ir::Node* v2 = g->CreateEmptyNode("var2", Node::Type::kVariable);
ir::Node* v3 = g->CreateEmptyNode("var3", Node::Type::kVariable);
ir::Node* v4 = g->CreateEmptyNode("var4", Node::Type::kVariable);
// o1->v1->o2
o1->outputs.push_back(v1);
o2->inputs.push_back(v1);
v1->inputs.push_back(o1);
v1->outputs.push_back(o2);
// o2->v2->o3
// o2->v2->o4
o2->outputs.push_back(v2);
o3->inputs.push_back(v2);
o4->inputs.push_back(v2);
v2->inputs.push_back(o2);
v2->outputs.push_back(o3);
v2->outputs.push_back(o4);
// o2->v3->o5
o2->outputs.push_back(v3);
o5->inputs.push_back(v3);
v3->inputs.push_back(o2);
v3->outputs.push_back(o5);
// o3-v4->o5
o3->outputs.push_back(v4);
o5->inputs.push_back(v4);
v4->inputs.push_back(o3);
v4->outputs.push_back(o5);
}
TEST(PDPattern, NewNode) {
PDPattern x;
auto* n = x.NewNode([](Node* x) { return true; });
ASSERT_TRUE(n);
ASSERT_EQ(x.nodes_.size(), 1UL);
}
TEST(PDPattern, AddEdge) {
PDPattern x;
auto* a = x.NewNode([](Node* x) { return true; });
auto* b = x.NewNode([](Node* x) { return true; });
ASSERT_TRUE(a);
ASSERT_TRUE(b);
x.AddEdge(a, b);
ASSERT_EQ(x.nodes_.size(), 2UL);
ASSERT_EQ(x.edges_.size(), 1UL);
ASSERT_EQ(x.edges_.front().first, a);
ASSERT_EQ(x.edges_.front().second, b);
ASSERT_EQ(x.nodes().size(), 2UL);
ASSERT_EQ(x.edges().size(), 1UL);
ASSERT_EQ(x.edges().front().first, a);
ASSERT_EQ(x.edges().front().second, b);
}
TEST(GraphPatternDetecter, MarkPDNodesInGraph) {
GraphPatternDetecter x;
// mark o2, o3, v2
// The pattern is a graph:
// o2(a node named o2) -> v2(a node named v2)
// v2 -> o3(a node named o3)
auto* o2 = x.pattern_.NewNode([](Node* node) {
// The teller can be any condition, such as op type, or variable's shape.
return node && node->Name() == "op2" && node->IsOp();
});
auto* o3 = x.pattern_.NewNode([](Node* node) {
// The teller can be any condition, such as op type, or variable's shape.
return node && node->Name() == "op3" && node->IsOp();
});
auto* v2 = x.pattern_.NewNode([](Node* node) {
// The teller can be any condition, such as op type, or variable's shape.
return node && node->Name() == "var2" && node->IsVar();
});
ASSERT_FALSE(o2->Tell(nullptr));
ASSERT_FALSE(o3->Tell(nullptr));
ASSERT_FALSE(v2->Tell(nullptr));
x.pattern_.AddEdge(o2, v2);
x.pattern_.AddEdge(v2, o3);
ASSERT_EQ(x.pattern_.edges().size(), 2UL);
ASSERT_EQ(x.pattern_.edges()[0].first, o2);
ASSERT_EQ(x.pattern_.edges()[0].second, v2);
ASSERT_EQ(x.pattern_.edges()[1].first, v2);
ASSERT_EQ(x.pattern_.edges()[1].second, o3);
ProgramDesc program;
Graph graph(program);
BuildGraph(&graph);
x.MarkPDNodesInGraph(graph);
ASSERT_EQ(x.pdnodes2nodes_.size(), 3UL);
auto subgraphs = x.DetectPatterns();
ASSERT_EQ(subgraphs.size(), 1UL);
}
TEST(GraphPatternDetecter, MultiSubgraph) {
ProgramDesc program;
Graph graph(program);
BuildGraph(&graph);
GraphPatternDetecter x;
// The pattern is a graph:
// op -> var
auto* any_op = x.mutable_pattern()->NewNode(
[](Node* node) {
return node->IsOp() && (node->Name() == "op2" || node->Name() == "op3");
},
"OP0");
auto* any_var = x.mutable_pattern()->NewNode(
[](Node* node) { return node->IsVar(); }, "VAR");
auto* any_op1 = x.mutable_pattern()->NewNode(
[](Node* node) { return node->IsOp(); }, "OP1");
x.mutable_pattern()->AddEdge(any_op, any_var);
x.mutable_pattern()->AddEdge(any_var, any_op1);
int count = 0;
GraphPatternDetecter::handle_t handle = [&](
const GraphPatternDetecter::subgraph_t& s, Graph* g) {
LOG(INFO) << "Detect " << s.at(any_op)->Name() << " -> "
<< s.at(any_var)->Name() << " -> " << s.at(any_op1)->Name();
count++;
};
x(&graph, handle);
// 1. Detect op3 -> var4 -> op5
// 2. Detect op2 -> var2 -> op3
// 3. Detect op2 -> var2 -> op4
// 4. Detect op2 -> var3 -> op5
// But 2 and 3 and 4 overlapped, so keep 2, so the final choices are 1 and 2
ASSERT_GE(count, 1UL);
ASSERT_LE(count, 2UL);
}
} // namespace ir
} // namespace framework
} // namespace paddle
// Copyright (c) 2018 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/graph_traits.h"
namespace paddle {
namespace framework {
namespace ir {
//
// NodesDFSIterator
//
NodesDFSIterator::NodesDFSIterator(const std::vector<Node *> &source) {
for (auto *x : source) stack_.push(x);
}
NodesDFSIterator::NodesDFSIterator(NodesDFSIterator &&other) noexcept
: stack_(std::move(other.stack_)),
visited_(std::move(other.visited_)) {}
NodesDFSIterator::NodesDFSIterator(const NodesDFSIterator &other)
: stack_(other.stack_), visited_(other.visited_) {}
Node &NodesDFSIterator::operator*() {
PADDLE_ENFORCE(!stack_.empty());
return *stack_.top();
}
NodesDFSIterator &NodesDFSIterator::operator++() {
PADDLE_ENFORCE(!stack_.empty(), "the iterator exceeds range");
visited_.insert(stack_.top());
auto *cur = stack_.top();
stack_.pop();
for (auto *x : cur->outputs) {
if (!visited_.count(x)) {
stack_.push(x);
}
}
return *this;
}
bool NodesDFSIterator::operator==(const NodesDFSIterator &other) {
if (stack_.empty()) return other.stack_.empty();
if ((!stack_.empty()) && (!other.stack_.empty())) {
return stack_.top() == other.stack_.top();
}
return false;
}
NodesDFSIterator &NodesDFSIterator::operator=(const NodesDFSIterator &other) {
stack_ = other.stack_;
visited_ = other.visited_;
return *this;
}
Node *NodesDFSIterator::operator->() { return stack_.top(); }
} // namespace ir
} // namespace framework
} // namespace paddle
// Copyright (c) 2018 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 <stack>
#include "paddle/fluid/framework/ir/graph.h"
#include "paddle/fluid/framework/ir/node.h"
namespace paddle {
namespace framework {
namespace ir {
template <typename IteratorT>
class iterator_range {
IteratorT begin_, end_;
public:
template <typename Container>
explicit iterator_range(Container &&c) : begin_(c.begin()), end_(c.end()) {}
iterator_range(const IteratorT &begin, const IteratorT &end)
: begin_(begin), end_(end) {}
const IteratorT &begin() const { return begin_; }
const IteratorT &end() const { return end_; }
};
// DFS iterator on nodes.
struct NodesDFSIterator
: public std::iterator<std::forward_iterator_tag, Node *> {
NodesDFSIterator() = default;
explicit NodesDFSIterator(const std::vector<Node *> &source);
NodesDFSIterator(NodesDFSIterator &&other) noexcept;
NodesDFSIterator(const NodesDFSIterator &other);
Node &operator*();
NodesDFSIterator &operator++();
// TODO(Superjomn) current implementation just compare the first
// element, need to compare the graph and all the elements in the queue and
// set.
NodesDFSIterator &operator=(const NodesDFSIterator &other);
bool operator==(const NodesDFSIterator &other);
bool operator!=(const NodesDFSIterator &other) { return !(*this == other); }
Node *operator->();
private:
std::stack<Node *> stack_;
std::unordered_set<Node *> visited_;
};
/*
* GraphTraits contains some graph traversal algorithms.
*
* Usage:
*
*/
struct GraphTraits {
static iterator_range<NodesDFSIterator> DFS(const Graph &g) {
auto start_points = ExtractStartPoints(g);
NodesDFSIterator x(start_points);
return iterator_range<NodesDFSIterator>(NodesDFSIterator(start_points),
NodesDFSIterator());
}
private:
// The nodes those have no input will be treated as start points.
static std::vector<Node *> ExtractStartPoints(const Graph &g) {
std::vector<Node *> result;
for (auto *node : g.Nodes()) {
if (node->inputs.empty()) {
result.push_back(node);
}
}
return result;
}
};
} // namespace ir
} // namespace framework
} // namespace paddle
......@@ -58,6 +58,9 @@ class Node {
return op_desc_;
}
bool IsOp() const { return type_ == Type::kOperation; }
bool IsVar() const { return type_ == Type::kVariable; }
std::vector<Node*> inputs;
std::vector<Node*> outputs;
......
......@@ -12,6 +12,8 @@
# See the License for the specific language governing permissions and
# limitations under the License.
import math
import unittest
import paddle.fluid as fluid
from paddle.fluid.transpiler.distribute_transpiler import delete_ops
......@@ -363,12 +365,13 @@ class TestL2DecayWithPiecewise(TranspilerTest):
class TestDistLookupTableBase(TranspilerTest):
def network_with_table(self, is_sparse, is_distributed):
self.table_size = 1000
self.emb_size = 64
def emb_pool(ids):
table_size = 1000
emb_size = 64
emb = fluid.layers.embedding(
input=ids,
size=[table_size, emb_size],
size=[self.table_size, self.emb_size],
dtype='float32',
param_attr='shared_w', # share parameter
is_sparse=is_sparse,
......@@ -537,6 +540,22 @@ class TestAsyncDistLookupTable(TestDistLookupTableBase):
self.assertEqual([op.type for op in trainer.blocks[0].ops], ops)
class TestDistLookupTableSliceSize(TestDistLookupTableBase):
def net_conf(self):
self.network_with_table(is_sparse=True, is_distributed=True)
def transpiler_test_impl(self):
config = fluid.DistributeTranspilerConfig()
pserver1, startup1 = self.get_pserver(self.pserver1_ep, config)
self.assertTrue(self.transpiler.has_distributed_lookup_table)
lookup_table_var = pserver1.global_block().vars[
self.transpiler.table_name]
row_size = lookup_table_var.shape[0]
calc_row_size = int(math.ceil(self.table_size / self.pservers))
self.assertEqual(row_size, calc_row_size)
class TestRMSPropOptimizer(TranspilerTest):
def net_conf(self):
x = fluid.layers.data(name='x', shape=[1000], dtype='float32')
......
......@@ -887,9 +887,15 @@ class DistributeTranspiler(object):
# create table param and grad var in pserver program
origin_param_var = self.origin_program.global_block().vars[
self.table_name]
zero_dim = int(
math.ceil(origin_param_var.shape[0] / len(self.pserver_endpoints)))
table_shape = list(origin_param_var.shape)
table_shape[0] = zero_dim
param_var = pserver_program.global_block().create_var(
name=origin_param_var.name,
shape=origin_param_var.shape,
shape=table_shape,
dtype=origin_param_var.dtype,
type=core.VarDesc.VarType.SELECTED_ROWS,
persistable=True)
......
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