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提交 d79d2f68 编写于 作者: C chengduozh
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Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into fix_shape_api_doc 

test=develop
上级 3ce12b1b 28609b34
隐藏空白更改
内联 并排
Showing with 1952 addition and 403 deletion
paddle/fluid/framework/details/CMakeLists.txt
浏览文件 @ d79d2f68
......@@ -50,7 +50,12 @@ cc_library(data_balance_op_handle SRCS data_balance_op_handle.cc DEPS op_handle_
cc_library(gather_op_handle SRCS gather_op_handle.cc DEPS op_handle_base scope ddim memory variable_visitor)
cc_library(fuse_vars_op_handle SRCS fuse_vars_op_handle.cc DEPS op_handle_base scope)
cc_library(memory_optimize_helper SRCS memory_optimize_helper.cc DEPS graph graph_helper)
if(WITH_GPU)
cc_library(memory_optimize_helper SRCS memory_optimize_helper.cc DEPS graph graph_helper gpu_info)
else()
cc_library(memory_optimize_helper SRCS memory_optimize_helper.cc DEPS graph graph_helper cpu_info)
endif()
cc_library(memory_optimize_pass SRCS memory_optimize_pass.cc DEPS memory_optimize_helper pass)
cc_library(inplace_op_pass SRCS inplace_op_pass.cc DEPS memory_optimize_pass op_info)
cc_library(modify_op_lock_and_record_event_pass SRCS modify_op_lock_and_record_event_pass.cc DEPS computation_op_handle op_graph_view multi_devices_helper)
......
paddle/fluid/framework/details/build_strategy.cc
浏览文件 @ d79d2f68
......@@ -240,7 +240,9 @@ std::unique_ptr<ir::Graph> BuildStrategy::Apply(
continue;
}
}
VLOG(3) << "Start Apply Pass " << pass->Type();
graph = pass->Apply(std::move(graph));
VLOG(3) << "Finish Apply Pass " << pass->Type();
}
return graph;
}
......
paddle/fluid/framework/details/inplace_op_pass.cc
浏览文件 @ d79d2f68
......@@ -49,7 +49,7 @@ DEFINE_bool(
"If this option turns on, only these op in whitelist can be inplaced."
"If it turns off, all of the running op can be candidate of inplaced op."
"Such as scale, elementwise_add"
"By default, it's turned on");
"By default, it's turned off");
DECLARE_string(memory_optimize_debug);
......
paddle/fluid/framework/details/memory_optimize_helper.cc
浏览文件 @ d79d2f68
......@@ -13,13 +13,19 @@
// limitations under the License.
#include "paddle/fluid/framework/details/memory_optimize_helper.h"
#include <algorithm>
#include <deque>
#include <functional>
#include <iostream>
#include <iterator>
#include <numeric>
#include <sstream>
#include <string>
#include "paddle/fluid/framework/var_desc.h"
#include "paddle/fluid/platform/cpu_info.h"
#ifdef PADDLE_WITH_CUDA
#include "paddle/fluid/platform/gpu_info.h"
#endif // PADDLE_WITH_CUDA
namespace paddle {
namespace framework {
......@@ -166,6 +172,11 @@ struct NodeComparator {
bool operator()(ir::Node* lhs, ir::Node* rhs) const {
auto* lhs_desc = FindVarDescInBlock(lhs);
auto* rhs_desc = FindVarDescInBlock(rhs);
// match data type
if (lhs_desc->GetDataType() != rhs_desc->GetDataType()) {
return false;
}
// match shape
auto lhs_shape = lhs_desc->GetShape();
auto rhs_shape = rhs_desc->GetShape();
if ((lhs_shape[0] == -1 && rhs_shape[0] == -1) ||
......@@ -230,6 +241,27 @@ ir::Node* OrderedSet::FindBestFitNode(ir::Node* var) const {
return found_node;
}
ir::Node* OrderedSet::FindNextBestFitNode(ir::Node* var, ir::Node* prev) const {
ir::Node* found_node = nullptr;
NodeComparator functor;
auto it =
std::find_if(nodes_.begin(), nodes_.end(), [&](const NodeVector& v) {
if (v.front() == prev)
return true;
else
return false;
});
PADDLE_ENFORCE(it != nodes_.end(), "Not found previous in node list!");
for (it = std::next(it); it != nodes_.end(); ++it) {
auto& candidate = it->front();
if (functor(var, candidate)) {
found_node = candidate;
break;
}
}
return found_node;
}
bool OrderedSet::Has(ir::Node* var) const {
if (mark_table_.count(var->Name())) {
auto& node_in_samename = mark_table_.at(var->Name());
......@@ -241,10 +273,15 @@ bool OrderedSet::Has(ir::Node* var) const {
return false;
}
void OrderedSet::Erase(const std::string& var) {
PADDLE_ENFORCE(mark_table_.count(var));
nodes_.erase(mark_table_[var]);
mark_table_.erase(var);
}
void OrderedSet::Erase(ir::Node* var) {
PADDLE_ENFORCE(mark_table_.count(var->Name()));
nodes_.erase(mark_table_[var->Name()]);
mark_table_.erase(var->Name());
PADDLE_ENFORCE(var != nullptr);
Erase(var->Name());
}
std::string OrderedSet::ToString() const {
......@@ -274,14 +311,35 @@ bool NodeCanReused(ir::Node* node) {
return flag;
}
int MinChunkSize() {
int size{0};
#ifdef PADDLE_WITH_CUDA
size = platform::GpuMinChunkSize();
#else
size = platform::CpuMinChunkSize();
#endif // PADDLE_WITH_CUDA
return size;
}
bool NodeCanReused(const VarDesc& node) {
auto type = node.GetType();
// only these types holds bulk of gpu memory
if (!(type == proto::VarType::LOD_TENSOR ||
type == proto::VarType::SELECTED_ROWS ||
type == proto::VarType::LOD_TENSOR_ARRAY)) {
return false;
}
if (node.Persistable() || node.GetShape().empty()) {
// persistable variable is parameter
if (node.Persistable()) {
return false;
}
// shape < min_chunk_size is meaningless.
// further more, fetched loss always has size = 1
// which should not be reused.
auto shape = node.GetShape();
int size = std::abs(
std::accumulate(shape.begin(), shape.end(), 1, std::multiplies<int>()));
if (shape.empty() || size < MinChunkSize()) {
return false;
}
// vars can be @EMPTY@, @LR_DECAY_REUSE_ID@. For example, while_grad
......@@ -461,7 +519,9 @@ ir::Node* ControlFlowGraph::GetNodeByName(const std::string& name,
for (auto* node : ops_) {
if (node == op) break;
for (auto& output : node->outputs) {
if (output->Name() == name) {
PADDLE_ENFORCE((output != nullptr && output->IsVar()),
"Output is empty!");
if (output->Var() && output->Name() == name) {
found_node = output;
}
}
......
paddle/fluid/framework/details/memory_optimize_helper.h
浏览文件 @ d79d2f68
......@@ -55,6 +55,7 @@ class OrderedSet {
void Insert(ir::Node* var);
void Erase(ir::Node* var);
void Erase(const std::string& var);
bool Has(ir::Node* var) const;
void Clear() {
mark_table_.clear();
......@@ -62,6 +63,7 @@ class OrderedSet {
}
// find the bestfit shape node block with var.
ir::Node* FindBestFitNode(ir::Node* var) const;
ir::Node* FindNextBestFitNode(ir::Node* var, ir::Node* prev) const;
// map store non-const iterator, can not promise const
int GetNodeIndexInPool(ir::Node* var);
// pool all node to string
......
paddle/fluid/framework/details/memory_optimize_helper_test.cc
浏览文件 @ d79d2f68
......@@ -107,6 +107,52 @@ TEST(OrderedSet, Normal) {
ASSERT_EQ(pool.GetNodeIndexInPool(cache), 5); // match 4:[5,2]
}
}
TEST(OrderedSet, FindBestFitNode) {
OrderedSet pool;
std::vector<std::unique_ptr<ir::Node>> nodes;
ProgramDesc prog;
BlockDesc* block_desc = prog.MutableBlock(0);
auto* op_desc = block_desc->AppendOp();
op_desc->SetType("dummy");
std::unique_ptr<ir::Node> op = ir::CreateNodeForTest(op_desc);
{
auto desc = block_desc->Var("a");
desc->SetShape({128, 128});
std::unique_ptr<ir::Node> node = ir::CreateNodeForTest(desc);
node->inputs.emplace_back(op.get());
nodes.emplace_back(std::move(node));
}
{
auto desc = block_desc->Var("b");
desc->SetShape({128, 129});
std::unique_ptr<ir::Node> node = ir::CreateNodeForTest(desc);
node->inputs.emplace_back(op.get());
nodes.emplace_back(std::move(node));
}
{
auto desc = block_desc->Var("c");
desc->SetShape({128, 128});
std::unique_ptr<ir::Node> node = ir::CreateNodeForTest(desc);
node->inputs.emplace_back(op.get());
nodes.emplace_back(std::move(node));
}
for (auto& node : nodes) {
pool.Insert(node.get());
}
// FindNextBestFitNode
auto* n = nodes[0].get();
auto* cache = pool.FindBestFitNode(n);
PADDLE_ENFORCE(cache->Name() == "a");
cache = pool.FindNextBestFitNode(n, cache);
PADDLE_ENFORCE(cache->Name() == "c");
cache = pool.FindNextBestFitNode(n, cache);
PADDLE_ENFORCE(cache->Name() == "b");
}
} // namespace details
} // namespace framework
} // namespace paddle
......
paddle/fluid/framework/details/memory_optimize_pass.cc
浏览文件 @ d79d2f68
......@@ -69,55 +69,59 @@ std::unique_ptr<ir::Graph> MemoryOptimizePass::ApplyImpl(
}
for (auto& var : op->outputs) {
if (!NodeCanReused(var) || cfg_->Use(op).count(var->Name()) == 0 ||
skip_set_.count(var->Name()))
if (var->IsVar() && !var->IsCtrlVar() && skip_set_.count(var->Name())) {
VLOG(3) << "Skip set contains variable of " << var->Name()
<< "disable reuse on it. skipped";
continue;
ir::Node* cache = pool_.FindBestFitNode(var);
if (var->Name() == FLAGS_memory_optimize_debug) {
VLOG(3) << "start match var " << DebugString(var) << " of op "
<< op->Name();
VLOG(3) << pool_.ToString();
VLOG(3) << "matched in pool : "
<< ((cache == nullptr) ? "False" : "True");
}
if (NodeCanReused(var) && cfg_->Use(op).count(var->Name()) == 0) {
ir::Node* cache = pool_.FindBestFitNode(var);
while (cache != nullptr && var->Name() == cache->Name()) {
VLOG(3) << "The same cache variable is cascade reused. "
<< cache->Name() << " is re-filled to the pool after "
<< "the reused op is finished. Current op can not "
<< "replace it again. Skip this candidate.";
cache = pool_.FindNextBestFitNode(var, cache);
}
if (var->Name() == FLAGS_memory_optimize_debug) {
VLOG(3) << "start match var " << DebugString(var) << " of op "
<< op->Name();
VLOG(3) << pool_.ToString();
VLOG(3) << "matched in pool : "
<< ((cache == nullptr) ? "False" : "True");
}
if (cache == nullptr) continue;
if (var->Name() == cache->Name()) {
VLOG(3) << "The same cache variable is cascade reused." << var->Name()
<< " is re-filled to the pool after"
<< "the reused op is finished. Current op can not "
<< "replace it again. Skip this candidate.";
continue;
int node_idx_in_pool = pool_.GetNodeIndexInPool(cache);
VLOG(3) << string::Sprintf(
"!!! %s, %s => %s, cache idx %d, pool size %d",
std::to_string(reuse_id++), DebugString(var), DebugString(cache),
node_idx_in_pool, static_cast<int>(pool_.size()));
// update CFG Graph on the fly.
// reused var maybe re-fill into the pool
cfg_->RenameVarInCFGGraph(var->Name(), cache->Name(), idx);
// NOTE(dzhwinter): we need to both update the ProgramDesc
// and IR Graph. because op_desc/var_desc is used in CreateOp,
// CreateVar when running happens. But IR Graph
// define the dependence relationship between nodes.
RenameVarInGraphDesc(var->Name(), cache->Name(), idx);
RenameVarInGraphNode(var->Name(), cache->Name(), idx, graph.get());
pool_.Erase(cache);
}
if (cache != nullptr) {
int node_idx_in_pool = pool_.GetNodeIndexInPool(cache);
VLOG(3) << string::Sprintf(
"!!! %s, %s => %s, cache idx %d, pool size %d",
std::to_string(reuse_id++), DebugString(var), DebugString(cache),
node_idx_in_pool, static_cast<int>(pool_.size()));
// NOTE(dzhwinter): update the ProgramDesc/IR Graph
// and the CFG Graph on the fly.
//
// IR Graph define the dependence relationship between nodes.
//
// ProgramDesc defines the input/output vars. Its used in
// CreateOp, CreateVar when running happens.
//
// CFG Graph store the liveness information, when reuse happens
// we also need to update the variable liveness.
const std::string var_name = var->Name();
const std::string cache_name = cache->Name();
// fill the pool
std::unordered_set<std::string> unlived_vars;
for (auto var : cfg_->LiveIn(op)) {
if (cfg_->LiveOut(op).count(var) == 0) {
unlived_vars.emplace(var);
cfg_->RenameVarInCFGGraph(var_name, cache_name, idx);
RenameVarInGraphDesc(var_name, cache_name, idx);
RenameVarInGraphNode(var_name, cache_name, idx, graph.get());
pool_.Erase(cache_name);
}
}
for (auto var : unlived_vars) {
}
// fill the pool
for (auto var : cfg_->LiveIn(op)) {
if (cfg_->LiveOut(op).count(var) == 0) {
ir::Node* var_node = cfg_->GetNodeByName(var, op);
if (var_node == nullptr || var_node->IsCtrlVar()) continue;
if (NodeCanReused(var_node) && !pool_.Has(var_node)) {
pool_.Insert(var_node);
}
......@@ -273,8 +277,7 @@ void MemoryOptimizePass::RenameVarInGraphNode(const std::string& var,
// redirect the input to the latest version of cache_var
for (auto* node : op->inputs) {
if (node->Name() == var) {
ir::Node* cache_node = graph->CreateVarNode(var_desc.get());
var_nodes_[cache_var].emplace_back(cache_node);
ir::Node* cache_node = var_nodes_[cache_var].back();
// swap node to cache_node
cache_node->outputs.insert(cache_node->outputs.end(),
......@@ -283,11 +286,15 @@ void MemoryOptimizePass::RenameVarInGraphNode(const std::string& var,
auto* prev_op = node->inputs[0];
std::replace(prev_op->outputs.begin(), prev_op->outputs.end(), node,
cache_node);
cache_node->inputs.emplace_back(prev_op);
for (auto* next_op : node->outputs) {
std::replace(next_op->inputs.begin(), next_op->inputs.end(), node,
cache_node);
}
// erase unused node
auto& nodes = var_nodes_.at(var);
nodes.erase(std::remove(nodes.begin(), nodes.end(), node), nodes.end());
graph->RemoveNode(node);
}
}
......@@ -307,15 +314,14 @@ void MemoryOptimizePass::RenameVarInGraphNode(const std::string& var,
std::replace(next_op->inputs.begin(), next_op->inputs.end(), node,
cache_node);
}
// erase unused node
auto& nodes = var_nodes_.at(var);
nodes.erase(std::remove(nodes.begin(), nodes.end(), node), nodes.end());
graph->RemoveNode(node);
}
}
}
// release node of unused var in graph
for (auto* node : var_nodes_[var]) {
graph->RemoveNode(node);
}
var_nodes_.at(var).clear();
}
} // namespace details
......
paddle/fluid/framework/inplace_op_inference_test.cc
浏览文件 @ d79d2f68
......@@ -179,11 +179,11 @@ TEST(InferInplace, SingleOpInplaceInToOut) {
op->SetOutput("Out", {"test2_out"});
prog.MutableBlock(0)->Var("test2_a")->SetType(proto::VarType::LOD_TENSOR);
prog.MutableBlock(0)->Var("test2_a")->SetShape({32, 64});
prog.MutableBlock(0)->Var("test2_a")->SetShape({32, 64, 128, 128});
prog.MutableBlock(0)->Var("test2_b")->SetType(proto::VarType::LOD_TENSOR);
prog.MutableBlock(0)->Var("test2_c")->SetType(proto::VarType::LOD_TENSOR);
prog.MutableBlock(0)->Var("test2_out");
prog.MutableBlock(0)->Var("test2_out")->SetShape({32, 16});
prog.MutableBlock(0)->Var("test2_out")->SetShape({32, 16, 128, 128});
auto& infer_inplace = OpInfoMap::Instance().Get(op->Type()).infer_inplace_;
auto in_to_outs = infer_inplace(*op, op->Block());
......@@ -201,11 +201,11 @@ TEST(InferInplace, SingleGradOpInplaceInToOut) {
op->SetOutput(GradVarName("X"), {"test2_a", "test2_b", "test2_c"});
prog.MutableBlock(0)->Var("test2_a")->SetType(proto::VarType::LOD_TENSOR);
prog.MutableBlock(0)->Var("test2_a")->SetShape({32, 16});
prog.MutableBlock(0)->Var("test2_a")->SetShape({32, 16, 1024, 1024});
prog.MutableBlock(0)->Var("test2_b")->SetType(proto::VarType::LOD_TENSOR);
prog.MutableBlock(0)->Var("test2_c")->SetType(proto::VarType::LOD_TENSOR);
prog.MutableBlock(0)->Var("test2_out");
prog.MutableBlock(0)->Var("test2_out")->SetShape({32, 16});
prog.MutableBlock(0)->Var("test2_out")->SetShape({32, 16, 1024, 1024});
auto& infer_inplace = OpInfoMap::Instance().Get(op->Type()).infer_inplace_;
auto in_to_outs = infer_inplace(*op, op->Block());
......@@ -233,12 +233,12 @@ TEST(InferInplace, MultiOutInplaceInToOut) {
prog.MutableBlock(0)->Var("o0");
prog.MutableBlock(0)->Var("y0");
prog.MutableBlock(0)->Var("z0");
prog.MutableBlock(0)->Var("a0")->SetShape({32, 16});
prog.MutableBlock(0)->Var("b0")->SetShape({32, 16});
prog.MutableBlock(0)->Var("c0")->SetShape({32, 16});
prog.MutableBlock(0)->Var("o0")->SetShape({32, 16});
prog.MutableBlock(0)->Var("y0")->SetShape({32, 16});
prog.MutableBlock(0)->Var("z0")->SetShape({32, 16});
prog.MutableBlock(0)->Var("a0")->SetShape({32, 16, 1024, 1024});
prog.MutableBlock(0)->Var("b0")->SetShape({32, 16, 1024, 1024});
prog.MutableBlock(0)->Var("c0")->SetShape({32, 16, 1024, 1024});
prog.MutableBlock(0)->Var("o0")->SetShape({32, 16, 1024, 1024});
prog.MutableBlock(0)->Var("y0")->SetShape({32, 16, 1024, 1024});
prog.MutableBlock(0)->Var("z0")->SetShape({32, 16, 1024, 1024});
auto& infer_inplace = OpInfoMap::Instance().Get(op->Type()).infer_inplace_;
auto in_to_outs = infer_inplace(*op, op->Block());
......@@ -267,12 +267,12 @@ TEST(InferInplace, MultiGradInplaceInToOut) {
prog.MutableBlock(0)->Var("o0");
prog.MutableBlock(0)->Var("y0");
prog.MutableBlock(0)->Var("z0");
prog.MutableBlock(0)->Var("a0")->SetShape({32, 16});
prog.MutableBlock(0)->Var("b0")->SetShape({32, 16});
prog.MutableBlock(0)->Var("c0")->SetShape({32, 16});
prog.MutableBlock(0)->Var("o0")->SetShape({32, 16});
prog.MutableBlock(0)->Var("y0")->SetShape({32, 16});
prog.MutableBlock(0)->Var("z0")->SetShape({32, 16});
prog.MutableBlock(0)->Var("a0")->SetShape({32, 16, 1024, 1024});
prog.MutableBlock(0)->Var("b0")->SetShape({32, 16, 1024, 1024});
prog.MutableBlock(0)->Var("c0")->SetShape({32, 16, 1024, 1024});
prog.MutableBlock(0)->Var("o0")->SetShape({32, 16, 1024, 1024});
prog.MutableBlock(0)->Var("y0")->SetShape({32, 16, 1024, 1024});
prog.MutableBlock(0)->Var("z0")->SetShape({32, 16, 1024, 1024});
auto& infer_inplace = OpInfoMap::Instance().Get(op->Type()).infer_inplace_;
auto in_to_outs = infer_inplace(*op, op->Block());
......
paddle/fluid/framework/ir/identity_scale_op_clean_pass.cc
浏览文件 @ d79d2f68
......@@ -38,9 +38,13 @@ std::unique_ptr<ir::Graph> IdentityScaleOpCleanPass::ApplyImpl(
->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");
auto scale_out =
detector.mutable_pattern()
->NewNode("scale_out")
->assert_is_op_output("scale")
// scale's output var should has only one consumer, or it can't be
// removed.
->assert_more([](Node* x) { return x->outputs.size() == 1UL; });
pre_op->LinksTo({scale_in});
scale_op->LinksFrom({scale_in}).LinksTo({scale_out});
......
paddle/fluid/imperative/layer.cc
浏览文件 @ d79d2f68
......@@ -207,7 +207,7 @@ framework::LoDTensor& VarBase::GradValue() {
std::map<std::string, std::vector<VarBase*>> OpBase::ApplyGrad() {
if (grad_op_descs_.empty() && backward_id_ <= 0) {
LOG(WARNING) << "op with no grad: " << op_desc_->Type();
VLOG(3) << "op with no grad: " << op_desc_->Type();
return {};
}
......
paddle/fluid/inference/analysis/ir_passes/subgraph_detector.cc
浏览文件 @ d79d2f68
......@@ -460,77 +460,6 @@ inline bool CheckNodeIndegreeEquals(const Node &node, size_t n) {
return node.inputs.size() == n;
}
NodesTSIterator::NodesTSIterator(const std::vector<Node *> &source) {
PADDLE_ENFORCE(!source.empty(),
"Start points of topological sorting should not be empty!");
// CHECK all the inputs' in-degree is 0
for (auto *node : source) {
PADDLE_ENFORCE(CheckNodeIndegreeEquals(*node, 0));
}
std::unordered_set<Node *> visited;
std::unordered_set<Node *> to_visit{source.begin(), source.end()};
std::vector<Node *> inlink_visited;
while (!to_visit.empty()) {
std::vector<Node *> queue(to_visit.begin(), to_visit.end());
for (auto *p : queue) {
if (Agent(p).deleted()) {
visited.insert(p);
to_visit.erase(p);
}
inlink_visited.clear();
std::copy_if(p->inputs.begin(), p->inputs.end(),
std::back_inserter(inlink_visited),
[&](Node *x) -> bool { return visited.count(x) != 0; });
if (inlink_visited.size() == p->inputs.size()) {
sorted_.push_back(p);
for (auto *_ : p->outputs) {
if (!visited.count(_)) {
to_visit.insert(_);
}
}
to_visit.erase(p);
visited.insert(p);
}
}
}
}
NodesTSIterator::NodesTSIterator(const NodesTSIterator &other)
: sorted_(other.sorted_), cursor_(other.cursor_) {}
Node &NodesTSIterator::operator*() {
PADDLE_ENFORCE_LT(cursor_, sorted_.size());
return *sorted_[cursor_];
}
NodesTSIterator &NodesTSIterator::operator++() {
if (++cursor_ >= sorted_.size()) {
sorted_.clear();
cursor_ = 0;
}
return *this;
}
NodesTSIterator &NodesTSIterator::operator=(const NodesTSIterator &other) {
cursor_ = other.cursor_;
sorted_ = other.sorted_;
return *this;
}
bool NodesTSIterator::operator==(const NodesTSIterator &other) {
return sorted_ == other.sorted_ && cursor_ == other.cursor_;
}
Node *NodesTSIterator::operator->() {
PADDLE_ENFORCE_LT(cursor_, sorted_.size());
return sorted_[cursor_];
}
} // namespace analysis
} // namespace inference
} // namespace paddle
paddle/fluid/inference/analysis/ir_passes/subgraph_detector.h
浏览文件 @ d79d2f68
......@@ -30,6 +30,7 @@ namespace inference {
namespace analysis {
using framework::ir::Graph;
using framework::ir::NodesTSIterator;
const char kIsFunctionNode[] = "__is_function_node__";
const char kFunctionNodeSubGraph[] = "__function_node_sub_graph__";
......@@ -132,32 +133,6 @@ struct Agent {
framework::ir::Node *x_;
};
// Topological sorting iterator on nodes.
struct NodesTSIterator
: public std::iterator<std::forward_iterator_tag, framework::ir::Node *> {
NodesTSIterator() = default;
explicit NodesTSIterator(const std::vector<framework::ir::Node *> &source);
NodesTSIterator(NodesTSIterator &&other)
: sorted_(std::move(other.sorted_)), cursor_(other.cursor_) {
other.cursor_ = 0;
}
NodesTSIterator(const NodesTSIterator &other);
framework::ir::Node &operator*();
NodesTSIterator &operator++();
// TODO(Superjomn) current implementation just compare the first
// element, need to compare the graph and all the elements in the queue and
// set.
NodesTSIterator &operator=(const NodesTSIterator &other);
bool operator==(const NodesTSIterator &other);
bool operator!=(const NodesTSIterator &other) { return !(*this == other); }
framework::ir::Node *operator->();
private:
std::vector<framework::ir::Node *> sorted_;
size_t cursor_{0};
};
// The nodes those have no input will be treated as start points.
static std::vector<framework::ir::Node *> ExtractStartPoints(const Graph &g) {
std::vector<framework::ir::Node *> result;
......
paddle/fluid/operators/detection/density_prior_box_op.h
浏览文件 @ d79d2f68
......@@ -72,7 +72,7 @@ class DensityPriorBoxOpKernel : public framework::OpKernel<T> {
#ifdef PADDLE_WITH_MKLML
#pragma omp parallel for
#endif
for (int i = 0; i < fixed_ratios.size(); i++) {
for (size_t i = 0; i < fixed_ratios.size(); i++) {
sqrt_fixed_ratios.push_back(sqrt(fixed_ratios[i]));
}
......@@ -115,11 +115,10 @@ class DensityPriorBoxOpKernel : public framework::OpKernel<T> {
}
}
if (clip) {
platform::Transform<platform::CPUDeviceContext> trans;
ClipFunctor<T> clip_func;
trans(ctx.template device_context<platform::CPUDeviceContext>(),
boxes->data<T>(), boxes->data<T>() + boxes->numel(),
boxes->data<T>(), clip_func);
T* dt = boxes->data<T>();
std::transform(dt, dt + boxes->numel(), dt, [](T v) -> T {
return std::min<T>(std::max<T>(v, 0.), 1.);
});
}
framework::Tensor var_t;
var_t.mutable_data<T>(
......@@ -141,7 +140,7 @@ class DensityPriorBoxOpKernel : public framework::OpKernel<T> {
#pragma omp parallel for collapse(2)
#endif
for (int i = 0; i < box_num; ++i) {
for (int j = 0; j < variances.size(); ++j) {
for (size_t j = 0; j < variances.size(); ++j) {
e_vars(i, j) = variances[j];
}
}
......
paddle/fluid/operators/detection/prior_box_op.h
浏览文件 @ d79d2f68
......@@ -46,13 +46,6 @@ inline void ExpandAspectRatios(const std::vector<float>& input_aspect_ratior,
}
}
template <typename T>
struct ClipFunctor {
HOSTDEVICE inline T operator()(T in) const {
return std::min<T>(std::max<T>(in, 0.), 1.);
}
};
template <typename T>
class PriorBoxOpKernel : public framework::OpKernel<T> {
public:
......@@ -101,31 +94,30 @@ class PriorBoxOpKernel : public framework::OpKernel<T> {
boxes->mutable_data<T>(ctx.GetPlace());
vars->mutable_data<T>(ctx.GetPlace());
auto e_boxes = framework::EigenTensor<T, 4>::From(*boxes);
T* b_t = boxes->data<T>();
for (int h = 0; h < feature_height; ++h) {
for (int w = 0; w < feature_width; ++w) {
T center_x = (w + offset) * step_width;
T center_y = (h + offset) * step_height;
T box_width, box_height;
int idx = 0;
for (size_t s = 0; s < min_sizes.size(); ++s) {
auto min_size = min_sizes[s];
if (min_max_aspect_ratios_order) {
box_width = box_height = min_size / 2.;
e_boxes(h, w, idx, 0) = (center_x - box_width) / img_width;
e_boxes(h, w, idx, 1) = (center_y - box_height) / img_height;
e_boxes(h, w, idx, 2) = (center_x + box_width) / img_width;
e_boxes(h, w, idx, 3) = (center_y + box_height) / img_height;
idx++;
b_t[0] = (center_x - box_width) / img_width;
b_t[1] = (center_y - box_height) / img_height;
b_t[2] = (center_x + box_width) / img_width;
b_t[3] = (center_y + box_height) / img_height;
b_t += 4;
if (max_sizes.size() > 0) {
auto max_size = max_sizes[s];
// square prior with size sqrt(minSize * maxSize)
box_width = box_height = sqrt(min_size * max_size) / 2.;
e_boxes(h, w, idx, 0) = (center_x - box_width) / img_width;
e_boxes(h, w, idx, 1) = (center_y - box_height) / img_height;
e_boxes(h, w, idx, 2) = (center_x + box_width) / img_width;
e_boxes(h, w, idx, 3) = (center_y + box_height) / img_height;
idx++;
b_t[0] = (center_x - box_width) / img_width;
b_t[1] = (center_y - box_height) / img_height;
b_t[2] = (center_x + box_width) / img_width;
b_t[3] = (center_y + box_height) / img_height;
b_t += 4;
}
// priors with different aspect ratios
for (size_t r = 0; r < aspect_ratios.size(); ++r) {
......@@ -135,11 +127,11 @@ class PriorBoxOpKernel : public framework::OpKernel<T> {
}
box_width = min_size * sqrt(ar) / 2.;
box_height = min_size / sqrt(ar) / 2.;
e_boxes(h, w, idx, 0) = (center_x - box_width) / img_width;
e_boxes(h, w, idx, 1) = (center_y - box_height) / img_height;
e_boxes(h, w, idx, 2) = (center_x + box_width) / img_width;
e_boxes(h, w, idx, 3) = (center_y + box_height) / img_height;
idx++;
b_t[0] = (center_x - box_width) / img_width;
b_t[1] = (center_y - box_height) / img_height;
b_t[2] = (center_x + box_width) / img_width;
b_t[3] = (center_y + box_height) / img_height;
b_t += 4;
}
} else {
// priors with different aspect ratios
......@@ -147,21 +139,21 @@ class PriorBoxOpKernel : public framework::OpKernel<T> {
float ar = aspect_ratios[r];
box_width = min_size * sqrt(ar) / 2.;
box_height = min_size / sqrt(ar) / 2.;
e_boxes(h, w, idx, 0) = (center_x - box_width) / img_width;
e_boxes(h, w, idx, 1) = (center_y - box_height) / img_height;
e_boxes(h, w, idx, 2) = (center_x + box_width) / img_width;
e_boxes(h, w, idx, 3) = (center_y + box_height) / img_height;
idx++;
b_t[0] = (center_x - box_width) / img_width;
b_t[1] = (center_y - box_height) / img_height;
b_t[2] = (center_x + box_width) / img_width;
b_t[3] = (center_y + box_height) / img_height;
b_t += 4;
}
if (max_sizes.size() > 0) {
auto max_size = max_sizes[s];
// square prior with size sqrt(minSize * maxSize)
box_width = box_height = sqrt(min_size * max_size) / 2.;
e_boxes(h, w, idx, 0) = (center_x - box_width) / img_width;
e_boxes(h, w, idx, 1) = (center_y - box_height) / img_height;
e_boxes(h, w, idx, 2) = (center_x + box_width) / img_width;
e_boxes(h, w, idx, 3) = (center_y + box_height) / img_height;
idx++;
b_t[0] = (center_x - box_width) / img_width;
b_t[1] = (center_y - box_height) / img_height;
b_t[2] = (center_x + box_width) / img_width;
b_t[3] = (center_y + box_height) / img_height;
b_t += 4;
}
}
}
......@@ -169,11 +161,10 @@ class PriorBoxOpKernel : public framework::OpKernel<T> {
}
if (clip) {
platform::Transform<platform::CPUDeviceContext> trans;
ClipFunctor<T> clip_func;
trans(ctx.template device_context<platform::CPUDeviceContext>(),
boxes->data<T>(), boxes->data<T>() + boxes->numel(),
boxes->data<T>(), clip_func);
T* dt = boxes->data<T>();
std::transform(dt, dt + boxes->numel(), dt, [](T v) -> T {
return std::min<T>(std::max<T>(v, 0.), 1.);
});
}
framework::Tensor var_t;
......
paddle/fluid/operators/group_norm_op.cc
浏览文件 @ d79d2f68
......@@ -170,13 +170,48 @@ class GroupNormGradMaker : public framework::SingleGradOpDescMaker {
}
};
class GroupNormInplaceInToOut : public framework::InplaceInToOut {
public:
using InplaceInToOut::InplaceInToOut;
protected:
std::unordered_map<std::string, std::string> Apply(
const framework::OpDesc &op_desc,
framework::BlockDesc *block) const override {
return {{"X", "Y"}};
}
};
class GroupNormGradInplaceInToOut : public framework::InplaceInToOut {
public:
using InplaceInToOut::InplaceInToOut;
protected:
std::unordered_map<std::string, std::string> Apply(
const framework::OpDesc &op_desc,
framework::BlockDesc *block) const override {
return {{framework::GradVarName("Y"), framework::GradVarName("X")}};
}
};
class GroupNormOpInferVarType
: public framework::PassInDtypeAndVarTypeToOutput {
protected:
std::unordered_map<std::string, std::string> GetInputOutputWithSameType()
const override {
return {{"X", /*->*/ "Y"}};
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OPERATOR(group_norm, ops::GroupNormOp, ops::GroupNormOpMaker,
ops::GroupNormGradMaker);
REGISTER_OPERATOR(group_norm_grad, ops::GroupNormGradOp);
ops::GroupNormOpInferVarType, ops::GroupNormGradMaker,
ops::GroupNormInplaceInToOut);
REGISTER_OPERATOR(group_norm_grad, ops::GroupNormGradOp,
ops::GroupNormGradInplaceInToOut);
REGISTER_OP_CPU_KERNEL(
group_norm, ops::GroupNormKernel<paddle::platform::CPUDeviceContext, float>,
ops::GroupNormKernel<paddle::platform::CPUDeviceContext, double>);
......
paddle/fluid/operators/jit/benchmark.cc
浏览文件 @ d79d2f68
......@@ -339,6 +339,71 @@ void BenchSoftmaxKernel() {
}
}
template <jit::KernelType KT, typename T, typename PlaceType>
void BenchLayerNormKernel() {
const T epsilon = 9.99999975e-06;
for (int n : {1, 2, 10}) {
for (int x_dim_0 : {1, 9, 17, 50}) {
int left = n * x_dim_0;
for (int x_dim_1 : TestSizes()) {
int right = x_dim_1;
int sz = left * right;
Tensor x, mean, var, scale, bias, out;
x.Resize({n, x_dim_0, x_dim_1});
out.Resize({n, x_dim_0, x_dim_1});
mean.Resize({n, x_dim_0});
var.Resize({n, x_dim_0});
scale.Resize({x_dim_1});
bias.Resize({x_dim_1});
RandomVec<T>(sz, x.mutable_data<T>(PlaceType()), -2.f, 2.f);
RandomVec<T>(left, mean.mutable_data<T>(PlaceType()), -2.f, 2.f);
RandomVec<T>(left, var.mutable_data<T>(PlaceType()), -2.f, 2.f);
RandomVec<T>(right, scale.mutable_data<T>(PlaceType()), -2.f, 2.f);
RandomVec<T>(right, bias.mutable_data<T>(PlaceType()), -2.f, 2.f);
const T* scale_data = scale.data<T>();
const T* bias_data = bias.data<T>();
T* x_data = x.data<T>();
T* mean_data = mean.data<T>();
T* var_data = var.data<T>();
T* out_data = out.mutable_data<T>(PlaceType());
BenchAllImpls<KT, jit::LayerNormTuples<T>, PlaceType>(
right, x_data, out_data, mean_data, var_data, scale_data, bias_data,
left, epsilon, right);
}
}
}
}
template <jit::KernelType KT, typename T, typename PlaceType>
void BenchCRFDecodingKernel() {
constexpr int state_trans_base_idx = 2;
for (int seq_len : {1, 11, 17, 50}) {
for (int tag_num : TestSizes()) {
int x_sz = seq_len * tag_num;
int w_sz = (tag_num + state_trans_base_idx) * tag_num;
Tensor x, w, alpha, track;
x.Resize({seq_len, tag_num});
w.Resize({tag_num + state_trans_base_idx, tag_num});
alpha.Resize({seq_len, tag_num});
track.Resize({seq_len, tag_num});
RandomVec<T>(x_sz, x.mutable_data<T>(PlaceType()), -2.f, 2.f);
RandomVec<T>(w_sz, w.mutable_data<T>(PlaceType()), -2.f, 2.f);
const T* x_data = x.data<T>();
const T* w_data = w.data<T>();
T* alpha_data = alpha.mutable_data<T>(PlaceType());
int* track_data = track.mutable_data<int>(PlaceType());
BenchAllImpls<KT, jit::CRFDecodingTuples<T>, PlaceType>(
tag_num, seq_len, x_data, w_data, alpha_data, track_data, tag_num);
}
}
}
using T = float;
using CPUPlace = paddle::platform::CPUPlace;
......@@ -382,6 +447,16 @@ BENCH_FP32_CPU(kMatMul) { BenchMatMulKernel<jit::kMatMul, T, CPUPlace>(); }
// softmax
BENCH_FP32_CPU(kSoftmax) { BenchSoftmaxKernel<jit::kSoftmax, T, CPUPlace>(); }
// layernorm
BENCH_FP32_CPU(kLayerNorm) {
BenchLayerNormKernel<jit::kLayerNorm, T, CPUPlace>();
}
// crfdecoding
BENCH_FP32_CPU(kCRFDecoding) {
BenchCRFDecodingKernel<jit::kCRFDecoding, T, CPUPlace>();
}
// Benchmark all jit kernels including jitcode, mkl and refer.
// To use this tool, run command: ./benchmark [options...]
// Options:
......
paddle/fluid/operators/jit/test.cc
浏览文件 @ d79d2f68
......@@ -292,6 +292,63 @@ struct TestFuncWithRefer<jit::MatMulTuples<T>, std::vector<T>, std::vector<T>,
}
};
template <typename T>
struct TestFuncWithRefer<jit::LayerNormTuples<T>, std::vector<T>,
std::vector<T>, std::vector<T>, std::vector<T>,
std::vector<T>, std::vector<T>, int, float, int> {
void operator()(const typename jit::LayerNormTuples<T>::func_type tgt,
std::vector<T>& x, std::vector<T>& outref, // NOLINT
std::vector<T>& mean, std::vector<T>& var, // NOLINT
const std::vector<T>& scale, const std::vector<T>& bias,
int left, const float epsilon, int right) {
EXPECT_TRUE(tgt != nullptr);
EXPECT_EQ(x.size(), static_cast<size_t>(left * right));
EXPECT_EQ(outref.size(), static_cast<size_t>(left * right));
EXPECT_EQ(mean.size(), static_cast<size_t>(left));
EXPECT_EQ(var.size(), static_cast<size_t>(left));
EXPECT_EQ(scale.size(), static_cast<size_t>(right));
EXPECT_EQ(bias.size(), static_cast<size_t>(right));
std::vector<T> outtgt(outref.size());
const T* scale_data = scale.data();
const T* bias_data = bias.data();
T* x_data = x.data();
T* mean_data = mean.data();
T* var_data = var.data();
T* outref_data = outref.data();
T* outtgt_data = outtgt.data();
tgt(x_data, outtgt_data, mean_data, var_data, scale_data, bias_data, left,
epsilon, right);
ExpectEQ<T>(outtgt_data, outref_data, left * right);
}
};
template <typename T>
struct TestFuncWithRefer<jit::CRFDecodingTuples<T>, int, std::vector<T>,
std::vector<T>, std::vector<T>, std::vector<int>,
int> {
void operator()(const typename jit::CRFDecodingTuples<T>::func_type tgt,
const int seq_len, const std::vector<T>& x,
const std::vector<T>& w, std::vector<T>& alpharef, // NOLINT
std::vector<int>& trackref, int tag_num) { // NOLINT
constexpr int state_trans_base_idx = 2;
EXPECT_TRUE(tgt != nullptr);
EXPECT_EQ(x.size(), static_cast<size_t>(seq_len * tag_num));
EXPECT_EQ(w.size(),
static_cast<size_t>((tag_num + state_trans_base_idx) * tag_num));
EXPECT_EQ(alpharef.size(), static_cast<size_t>(seq_len * tag_num));
EXPECT_EQ(trackref.size(), static_cast<size_t>(seq_len * tag_num));
std::vector<T> alphatgt(alpharef.size());
std::vector<int> tracktgt(trackref.size());
memcpy(trackref.data(), tracktgt.data(), tag_num * sizeof(int));
tgt(seq_len, (const T*)x.data(), (const T*)w.data(), alphatgt.data(),
tracktgt.data(), tag_num);
ExpectEQ<T>(alpharef.data(), alphatgt.data(), seq_len * tag_num);
ExpectEQ<int>(trackref.data(), tracktgt.data(), seq_len * tag_num);
}
};
template <jit::KernelType KT, typename KernelTuples, typename PlaceType,
typename... Args>
void TestAllImpls(const typename KernelTuples::attr_type& attr, Args... args) {
......@@ -640,6 +697,71 @@ void TestNCHW16CMulNCKernel() {
}
}
template <paddle::operators::jit::KernelType KT, typename T, typename PlaceType>
void TestLayerNormKernel() {
VLOG(10) << "===== Test JITKernel " << jit::to_string(KT);
const T epsilon = 9.99999975e-06;
for (int n : {1, 2, 10}) {
for (int x_dim_0 : {1, 9, 17, 50}) {
int left = n * x_dim_0;
for (int x_dim_1 : TestSizes()) {
int right = x_dim_1;
auto ref = jit::GetRefer<KT, jit::LayerNormTuples<T>>();
EXPECT_TRUE(ref != nullptr);
int sz = left * right;
std::vector<T> x(sz), mean(left), var(left), scale(right), bias(right),
outref(sz);
RandomVec<T>(sz, x.data(), -2.f, 2.f);
RandomVec<T>(left, mean.data(), -2.f, 2.f);
RandomVec<T>(left, var.data(), -2.f, 2.f);
RandomVec<T>(right, scale.data(), -2.f, 2.f);
RandomVec<T>(right, bias.data(), -2.f, 2.f);
const T* scale_data = scale.data();
const T* bias_data = bias.data();
T* x_data = x.data();
T* mean_data = mean.data();
T* var_data = var.data();
T* outref_data = outref.data();
ref(x_data, outref_data, mean_data, var_data, scale_data, bias_data,
left, epsilon, right);
TestAllImpls<KT, jit::LayerNormTuples<T>, PlaceType, std::vector<T>,
std::vector<T>, std::vector<T>, std::vector<T>,
std::vector<T>, std::vector<T>, int, float>(
right, x, outref, mean, var, scale, bias, left, epsilon, right);
}
}
}
}
template <paddle::operators::jit::KernelType KT, typename T, typename PlaceType>
void TestCRFDecodingKernel() {
VLOG(10) << "===== Test JITKernel " << jit::to_string(KT);
constexpr int state_trans_base_idx = 2;
for (int seq_len : {1, 11, 17, 50}) {
for (int tag_num : TestSizes()) {
auto ref = jit::GetRefer<KT, jit::CRFDecodingTuples<T>>();
EXPECT_TRUE(ref != nullptr);
int x_sz = seq_len * tag_num;
int w_sz = (tag_num + state_trans_base_idx) * tag_num;
std::vector<T> x(x_sz), w(w_sz), alpharef(x_sz);
std::vector<int> trackref(x_sz);
RandomVec<T>(x_sz, x.data(), -2.f, 2.f);
RandomVec<T>(w_sz, w.data(), -2.f, 2.f);
ref(seq_len, (const T*)x.data(), (const T*)w.data(), alpharef.data(),
trackref.data(), tag_num);
TestAllImpls<KT, jit::CRFDecodingTuples<T>, PlaceType, int,
std::vector<T>, std::vector<T>, std::vector<T>,
std::vector<int>, int>(tag_num, seq_len, x, w, alpharef,
trackref, tag_num);
}
}
}
// XYZNTuple
TEST(JITKernel, kVMul) {
TestXYZNKernel<jit::kVMul, float, CPUPlace>();
......@@ -761,7 +883,16 @@ TEST(JITKernel, kNCHW16CMulNC) {
TestNCHW16CMulNCKernel<jit::kNCHW16CMulNC, double, CPUPlace>();
}
// TODO(yihua/TJ): add crf decoding and layer norm unit tests
TEST(JITKernel, kLayerNorm) {
TestLayerNormKernel<jit::kLayerNorm, float, paddle::platform::CPUPlace>();
TestLayerNormKernel<jit::kLayerNorm, double, paddle::platform::CPUPlace>();
}
TEST(JITKernel, kCRFDecoding) {
TestCRFDecodingKernel<jit::kCRFDecoding, float, paddle::platform::CPUPlace>();
TestCRFDecodingKernel<jit::kCRFDecoding, double,
paddle::platform::CPUPlace>();
}
TEST(JITKernel, pool) {
// TODO(TJ): add some test
......
paddle/fluid/operators/load_combine_op.cc
浏览文件 @ d79d2f68
......@@ -64,7 +64,7 @@ class LoadCombineOp : public framework::OperatorBase {
auto *tensor = out_var->GetMutable<framework::LoDTensor>();
// Error checking
PADDLE_ENFORCE(static_cast<bool>(buffer), "Cannot read more");
PADDLE_ENFORCE(static_cast<bool>(*buffer), "Cannot read more");
// Get data from fin to tensor
DeserializeFromStream(*buffer, tensor, dev_ctx);
......@@ -90,6 +90,10 @@ class LoadCombineOp : public framework::OperatorBase {
tensor->ShareDataWith(fp16_tensor);
}
}
buffer->peek();
PADDLE_ENFORCE(buffer->eof(),
"You are not allowed to load partial data via "
"load_combine_op, use load_op instead.");
}
};
......
paddle/fluid/operators/lstm_op.h
浏览文件 @ d79d2f68
......@@ -311,6 +311,10 @@ class LSTMGradKernel : public framework::OpKernel<T> {
lstm_grad.prev_state_grad = c0_g ? ordered_c0_g.data<T>() : nullptr;
}
// lstm_value.output_value not used in bp, set to nullptr
// lstm_grad.state_active_grad not used in bp, set to nullptr
lstm_value.output_value = nullptr;
lstm_grad.state_active_grad = nullptr;
int cur_batch_size = bend - bstart;
math::LstmUnitGradFunctor<DeviceContext, T>::compute(
device_ctx, lstm_value, lstm_grad, frame_size, cur_batch_size,
......
paddle/fluid/operators/lstmp_op.h
浏览文件 @ d79d2f68
......@@ -405,6 +405,11 @@ class LSTMPGradKernel : public framework::OpKernel<T> {
}
int cur_batch_size = bend - bstart;
// lstmp_value.output_value not used in bp, set to null
// lstmp_grad.state_active_grad not used in bp, set to null
lstmp_value.output_value = nullptr;
lstmp_grad.state_active_grad = nullptr;
math::LstmUnitGradFunctor<DeviceContext, T>::compute(
device_ctx, lstmp_value, lstmp_grad, frame_size, cur_batch_size,
gate_act, cell_act, cand_act);
......
paddle/fluid/operators/row_conv_op.cc
浏览文件 @ d79d2f68
......@@ -109,23 +109,23 @@ from future subsequences in a computationally efficient manner to improve
unidirectional recurrent neural networks. The row convolution operator is
different from the 1D sequence convolution, and is computed as follows:
Given an input sequence $in$ of length $t$ and input dimension $d$,
and a filter ($W$) of size $context \times d$,
Given an input sequence $X$ of length $t$ and input dimension $D$,
and a filter ($W$) of size $context \times D$,
the output sequence is convolved as:
$$
out_{i, :} = \\sum_{j=i}^{i + context} in_{j,:} \\cdot W_{i-j, :}
out_{i} = \\sum_{j=i}^{i + context - 1} X_{j} \\cdot W_{j-i}
$$
In the above equation:
* $Out_{i}$: The i-th row of output variable with shape [1, D].
* $\\tau$: Future context size.
* $context$: Future context size.
* $X_{j}$: The j-th row of input variable with shape [1, D].
* $W_{i-j}$: The (i-j)-th row of parameters with shape [1, D].
* $W_{j-i}$: The (j-i)-th row of parameters with shape [1, D].
More details about row_conv please refer to
the design document
......
paddle/fluid/platform/enforce.h
浏览文件 @ d79d2f68
......@@ -233,9 +233,11 @@ inline void throw_on_error(ncclResult_t stat, const std::string& msg) {
#endif // __APPLE__ and windows
#endif // PADDLE_WITH_CUDA
#define PADDLE_THROW(...) \
throw ::paddle::platform::EnforceNotMet( \
::paddle::string::Sprintf(__VA_ARGS__), __FILE__, __LINE__)
#define PADDLE_THROW(...) \
do { \
throw ::paddle::platform::EnforceNotMet( \
::paddle::string::Sprintf(__VA_ARGS__), __FILE__, __LINE__); \
} while (0)
#define PADDLE_ENFORCE(COND, ...) \
do { \
......@@ -270,23 +272,25 @@ inline void throw_on_error(ncclResult_t stat, const std::string& msg) {
* extra messages is also supported, for example:
* PADDLE_ENFORCE(a, b, "some simple enforce failed between %d numbers", 2)
*/
#define PADDLE_ENFORCE_NOT_NULL(__VAL, ...) \
do { \
if (UNLIKELY(nullptr == (__VAL))) { \
PADDLE_THROW(#__VAL " should not be null\n%s", \
paddle::string::Sprintf("" __VA_ARGS__)); \
} \
#define PADDLE_ENFORCE_NOT_NULL(__VAL, ...) \
do { \
if (UNLIKELY(nullptr == (__VAL))) { \
PADDLE_THROW(#__VAL " should not be null\n%s", \
::paddle::string::Sprintf(__VA_ARGS__)); \
} \
} while (0)
#define __PADDLE_BINARY_COMPARE(__VAL0, __VAL1, __CMP, __INV_CMP, ...) \
do { \
if (UNLIKELY(!((__VAL0)__CMP(__VAL1)))) { \
auto __cond1__ = (__VAL0); \
auto __cond2__ = (__VAL1); \
if (UNLIKELY(!((__cond1__)__CMP(__cond2__)))) { \
PADDLE_THROW("Enforce failed. Expected %s " #__CMP \
" %s, but received %s:%s " #__INV_CMP " %s:%s.\n%s", \
#__VAL0, #__VAL1, #__VAL0, \
paddle::string::to_string(__VAL0), #__VAL1, \
paddle::string::to_string(__VAL1), \
paddle::string::Sprintf("" __VA_ARGS__)); \
::paddle::string::to_string(__cond1__), #__VAL1, \
::paddle::string::to_string(__cond2__), \
::paddle::string::Sprintf(__VA_ARGS__)); \
} \
} while (0)
......
paddle/fluid/pybind/ir.cc
浏览文件 @ d79d2f68
......@@ -13,10 +13,12 @@
// limitations under the License.
#include "paddle/fluid/pybind/ir.h"
#include <algorithm>
#include <string>
#include <unordered_map>
#include <unordered_set>
#include "paddle/fluid/framework/ir/graph.h"
#include "paddle/fluid/framework/ir/graph_helper.h"
#include "paddle/fluid/framework/ir/graph_pattern_detector.h"
#include "paddle/fluid/framework/ir/node.h"
#include "paddle/fluid/framework/op_desc.h"
......@@ -27,6 +29,10 @@ namespace py = pybind11;
using paddle::framework::ir::Graph;
using paddle::framework::ir::Node;
using paddle::framework::ir::GraphSafeRemoveNodes;
using paddle::framework::ir::HasCircle;
using paddle::framework::ir::GraphNum;
using paddle::framework::ir::TopologySortOperations;
using paddle::framework::ir::BuildOperationAdjList;
using paddle::framework::OpDesc;
using paddle::framework::ProgramDesc;
using paddle::framework::VarDesc;
......@@ -36,6 +42,12 @@ namespace paddle {
namespace pybind {
void BindGraph(py::module *m) {
m->def("graph_safe_remove_nodes", GraphSafeRemoveNodes);
m->def("has_circle", HasCircle);
m->def("graph_num", GraphNum);
m->def("topology_sort", TopologySortOperations,
return_value_policy::reference);
m->def("build_adjacency_list", BuildOperationAdjList,
return_value_policy::reference);
py::class_<Graph, std::shared_ptr<Graph>>(
*m, "Graph",
"The graph is a Directed Acyclic Single Static Assignment Graph, see "
......@@ -46,7 +58,6 @@ void BindGraph(py::module *m) {
.def("get_float", &Graph::Get<float>)
.def("get_double", &Graph::Get<double>)
.def("get_string", &Graph::Get<std::string>)
.def("get_program", &Graph::Get<ProgramDesc>)
.def("get_marked_nodes", &Graph::Get<std::unordered_set<const Node *>>)
.def("set", [](Graph &self, const std::string &attr_name,
int attr) { return self.Set(attr_name, new int(attr)); })
......@@ -63,11 +74,6 @@ void BindGraph(py::module *m) {
[](Graph &self, const std::string &attr_name, double attr) {
return self.Set(attr_name, new double(attr));
})
.def("set",
[](Graph &self, const std::string &attr_name,
const ProgramDesc &attr) {
return self.Set(attr_name, new ProgramDesc(attr));
})
.def("set",
[](Graph &self, const std::string &attr_name,
const std::unordered_set<const Node *> &attr) {
......@@ -108,42 +114,42 @@ void BindNode(py::module *m) {
.def("is_op", &Node::IsOp)
.def("is_var", &Node::IsVar)
.def("is_ctrl_var", &Node::IsCtrlVar)
.def("clear_inputs", [](Node &self) { self.inputs.clear(); })
.def("inputs_remove",
[](Node &self, int node_id) {
for (auto it = self.inputs.begin(); it != self.inputs.end();
it++) {
if ((*it)->id() == node_id) {
self.inputs.erase(it);
}
auto pos = std::find_if(
self.inputs.begin(), self.inputs.end(),
[&node_id](const Node *n) { return n->id() == node_id; });
if (pos != self.inputs.end()) {
self.inputs.erase(pos);
}
})
.def("inputs_remove",
[](Node &self, Node &node) {
for (auto it = self.inputs.begin(); it != self.inputs.end();
it++) {
if (*it == &node) {
self.inputs.erase(it);
}
auto pos =
std::find(self.inputs.begin(), self.inputs.end(), &node);
if (pos != self.inputs.end()) {
self.inputs.erase(pos);
}
})
.def("inputs_append",
[](Node &self, Node &node) { self.inputs.push_back(&node); })
.def("clear_outputs", [](Node &self) { self.outputs.clear(); })
.def("outputs_remove",
[](Node &self, int node_id) {
for (auto it = self.outputs.begin(); it != self.outputs.end();
it++) {
if ((*it)->id() == node_id) {
self.outputs.erase(it);
}
auto pos = std::find_if(
self.outputs.begin(), self.outputs.end(),
[&node_id](const Node *n) { return n->id() == node_id; });
if (pos != self.outputs.end()) {
self.outputs.erase(pos);
}
})
.def("outputs_remove",
[](Node &self, Node &node) {
for (auto it = self.outputs.begin(); it != self.outputs.end();
it++) {
if (*it == &node) {
self.outputs.erase(it);
}
auto pos =
std::find(self.outputs.begin(), self.outputs.end(), &node);
if (pos != self.outputs.end()) {
self.outputs.erase(pos);
}
})
.def("outputs_append",
......
paddle/fluid/pybind/pybind.cc
浏览文件 @ d79d2f68
......@@ -829,8 +829,7 @@ All parameter, weight, gradient are variables in Paddle.
m.def("disable_profiler", platform::DisableProfiler);
m.def("is_profiler_enabled", platform::IsProfileEnabled);
m.def("reset_profiler", platform::ResetProfiler);
m.def("get_pass", [](const py::bytes &binary_str) {
std::string pass_type(binary_str);
m.def("get_pass", [](const std::string &pass_type) {
auto pass = framework::ir::PassRegistry::Instance().Get(pass_type);
return std::shared_ptr<framework::ir::Pass>(std::move(pass));
});
......@@ -838,10 +837,9 @@ All parameter, weight, gradient are variables in Paddle.
py::class_<ir::Pass, std::shared_ptr<ir::Pass>> pass(m, "Pass");
pass.def(py::init())
.def("has", &ir::Pass::Has)
.def("set",
[](ir::Pass &self, const std::string &attr_name,
const ProgramDesc &attr) {
return self.Set(attr_name, new ProgramDesc(attr));
.def("set_not_owned",
[](ir::Pass &self, const std::string &attr_name, ProgramDesc &attr) {
self.SetNotOwned<ProgramDesc>(attr_name, &attr);
})
.def(
"set",
......@@ -850,7 +848,6 @@ All parameter, weight, gradient are variables in Paddle.
})
.def("set", [](ir::Pass &self, const std::string &name,
int val) { self.Set<const int>(name, new int(val)); })
.def("get_program", &ir::Pass::Get<ProgramDesc>)
.def("type", &ir::Pass::Type)
.def("apply", [](ir::Pass &self, std::shared_ptr<ir::Graph> graph) {
std::unique_ptr<ir::Graph> origin_graph(graph.get());
......
python/CMakeLists.txt
浏览文件 @ d79d2f68
......@@ -64,6 +64,7 @@ if (WITH_TESTING)
add_subdirectory(paddle/dataset/tests)
add_subdirectory(paddle/fluid/tests)
add_subdirectory(paddle/fluid/contrib/tests)
add_subdirectory(paddle/fluid/contrib/slim/tests)
endif()
install(DIRECTORY ${PADDLE_PYTHON_PACKAGE_DIR}
DESTINATION opt/paddle/share/wheels
......
python/paddle/fluid/compiler.py
浏览文件 @ d79d2f68
......@@ -177,7 +177,10 @@ class CompiledProgram(object):
# FIXME(dzhwinter): enable_inplace should be after memory_optimize
# if turn on python memory optimize, turn off the inplace_pass.
self._build_strategy.enable_inplace = False if self._program._is_mem_optimized else True
if self._build_strategy.memory_optimize is None:
self._build_strategy.memory_optimize = False if main._is_mem_optimized else True
if self._build_strategy.enable_inplace is None:
self._build_strategy.enable_inplace = False if main._is_mem_optimized else True
if self._build_strategy.num_trainers > 1 and trainers_endpoints:
assert self._build_strategy.num_trainers == len(
......
python/paddle/fluid/contrib/int8_inference/README.md
浏览文件 @ d79d2f68
......@@ -63,10 +63,10 @@ Notes:
## 4. How to reproduce the results
* Small dataset
```bash
python python/paddle/fluid/contrib/tests/test_calibration.py
FLAGS_use_mkldnn=true python python/paddle/fluid/contrib/tests/test_calibration.py
```
* Full dataset
```bash
DATASET=full python python/paddle/fluid/contrib/tests/test_calibration.py
FLAGS_use_mkldnn=true DATASET=full python python/paddle/fluid/contrib/tests/test_calibration.py
```
python/paddle/fluid/contrib/slim/quantization/quantization_pass.py
浏览文件 @ d79d2f68
......@@ -13,14 +13,19 @@
# limitations under the License.
import collections
import numpy as np
import six
from ..... import compat as cpt
from .... import core
from ....framework import IrGraph
from ....framework import Program
from ....framework import Variable
from ....initializer import Constant
from .... import unique_name
__all__ = ['QuantizationTransformPass']
__all__ = [
'QuantizationTransformPass', 'QuantizationFreezePass', 'ConvertToInt8Pass',
'TransformForMobilePass'
]
class QuantizationTransformPass(object):
......@@ -35,7 +40,13 @@ class QuantizationTransformPass(object):
"""
Convert and rewrite the IrGraph according to weight and
activation quantization type.
Args:
scope(fluid.Scope): When activation use 'range_abs_max' as the quantize
type, this pass will create some new parameters. The scope is used to
initialize these new parameters.
program_exe(fluid.Executor): program_exe is used to initialize new
parameters described above.
weight_bits (int): quantization bit number for weights,
the bias is not quantized.
activation_bits (int): quantization bit number for activation.
......@@ -49,6 +60,7 @@ class QuantizationTransformPass(object):
support 'abs_max'. The 'range_abs_max' usually is not used for
weight, since weights are fixed once the model is well trained.
window_size (int): the window size for 'range_abs_max' quantization.
Examples:
.. code-block:: python
# The original graph will be rewrite.
......@@ -88,31 +100,35 @@ class QuantizationTransformPass(object):
self._quantizable_grad_ops = [
'%s_grad' % (op) for op in self._quantizable_ops
]
self._fake_quant_op_types = [
'fake_quantize_abs_max', 'fake_quantize_range_abs_max'
]
self._fake_dequant_op_types = ['fake_dequantize_max_abs']
self._is_test = None
self._global_step = None
def apply(self, graph):
"""
Quantize the graph for training process. According to weight and
activation quantization type, the graph will be added some fake
quantize operators and fake dequantize operators.
Args:
graph(IrGraph): the applied graph.
"""
assert isinstance(graph,
IrGraph), 'graph must be the instance of IrGraph.'
self._need_initialized.clear()
self._is_test = graph.is_test()
# marked the variable which has been dequantized.
dequantized_vars = collections.OrderedDict()
params = [p.name() for p in graph.all_parameters()]
persistable_vars = [p.name() for p in graph.all_persistable_vars()]
def _transform_forward(graph, op):
for var_node in op.inputs:
if var_node.name() in dequantized_vars:
dequant_var_node = dequantized_vars[var_node.name()]
else:
quant_bits = self._weight_bits if var_node.name() in params \
quant_bits = self._weight_bits if var_node.name() in persistable_vars \
else self._activation_bits
quant_type = self._weight_quantize_type if var_node.name() \
in params else self._activation_quantize_type
in persistable_vars else self._activation_quantize_type
quant_var_node, scale_var_node = self._insert_quant_op(
graph, var_node, quant_bits, quant_type)
dequant_var_node = self._insert_dequant_op(
......@@ -150,9 +166,14 @@ class QuantizationTransformPass(object):
assert self._program_exe is not None, \
'The program_exe cannot be set None when activation_quantize_type equals to range_abs_max.'
init_program = Program()
for var_desc, initializer in self._need_initialized.iteritems():
var = Variable(init_program.global_block())
var._set_desc(var_desc)
for var_desc, initializer in six.iteritems(self._need_initialized):
var = init_program.global_block().create_var(
name=var_desc.name(),
shape=var_desc.shape(),
dtype=var_desc.dtype(),
type=var_desc.type(),
lod_level=var_desc.lod_level(),
persistable=var_desc.persistable())
initializer(var, init_program.global_block())
self._program_exe.run(program=init_program, scope=self._scope)
......@@ -161,7 +182,7 @@ class QuantizationTransformPass(object):
def _create_global_step(self, graph):
if self._weight_quantize_type == 'range_abs_max' or \
self._activation_quantize_type == 'range_abs_max':
counter_name = '@STEP_COUNTER@'
counter_name = cpt.to_text('@STEP_COUNTER@')
for node in graph.all_vars():
if node.name() == counter_name:
self._global_step = node
......@@ -175,9 +196,14 @@ class QuantizationTransformPass(object):
Constant(value=0, force_cpu=True)
global_step_out = graph.create_var_node_from_desc(
global_step_in.var())
# The attribute of `op_role` is needed by ParallelExecutor.
increment_op = graph.create_op_node(
op_type='increment',
attrs={'step': 1.0},
attrs={
'step': 1.0,
'op_role':
core.op_proto_and_checker_maker.OpRole.Forward
},
inputs={'X': global_step_in},
outputs={'Out': global_step_out})
graph.link_to(global_step_in, increment_op)
......@@ -212,7 +238,10 @@ class QuantizationTransformPass(object):
var_dtype=var_node.var().dtype())
quant_op_node = graph.create_op_node(
op_type='fake_quantize_abs_max',
attrs={'bit_length': quant_bits},
attrs={
'bit_length': quant_bits,
'op_role': core.op_proto_and_checker_maker.OpRole.Forward
},
inputs={'X': var_node},
outputs={'Out': quant_var_node,
'OutScale': scale_var_node})
......@@ -257,7 +286,8 @@ class QuantizationTransformPass(object):
attrs = {
'window_size': self._window_size,
'bit_length': quant_bits,
'is_test': self._is_test
'is_test': self._is_test,
'op_role': core.op_proto_and_checker_maker.OpRole.Forward
}
quant_op_node = graph.create_op_node(
op_type='fake_quantize_range_abs_max',
......@@ -290,7 +320,10 @@ class QuantizationTransformPass(object):
max_range = (1 << (quant_bits - 1)) - 1
dequant_op_node = graph.create_op_node(
op_type='fake_dequantize_max_abs',
attrs={'max_range': float(max_range)},
attrs={
'max_range': float(max_range),
'op_role': core.op_proto_and_checker_maker.OpRole.Forward
},
inputs={'X': var_node,
'Scale': scale_var_node},
outputs={'Out': dequant_var_node})
......@@ -316,3 +349,330 @@ class QuantizationTransformPass(object):
Return the scale name of quantized variable for the input `var_name`.
"""
return "%s.scale" % (var_name)
class QuantizationFreezePass(object):
"""
The freeze pass is used to adjust the quantize operator order, for example:
1) `activation -> quant -> dequant -> conv2d` will be freezed into
`activation -> quant -> conv2d -> dequant`
2) `weight -> quant -> dequant -> conv2d` will be freezed into `weight -> conv2d`,
and weight will be sacled offline.
Args:
scope(fluid.Scope): scope is used to get the weight tensor values.
place(fluid.CPUPlace|fluid.CUDAPlace): place is used to restore the weight tensors.
weight_bits (int): quantization bit number for weights.
activation_bits (int): quantization bit number for activation.
weight_quantize_type (str): quantization type for weights, support 'abs_max'.
The 'range_abs_max' usually is not used for weight, since weights are fixed once the
model is well trained.
"""
def __init__(self,
scope,
place,
weight_bits=8,
activation_bits=8,
weight_quantize_type='abs_max'):
assert scope is not None, \
'The scope cannot be set None.'
assert place is not None, \
'The place cannot be set None.'
self._scope = scope
self._place = place
self._weight_bits = weight_bits
self._activation_bits = activation_bits
self._weight_quantize_type = weight_quantize_type
self._quantizable_ops = ['conv2d', 'depthwise_conv2d', 'mul']
self._fake_quant_op_names = [
'fake_quantize_abs_max', 'fake_quantize_range_abs_max'
]
self._fake_dequant_op_names = ['fake_dequantize_max_abs']
self._op_input_rename_map = collections.OrderedDict()
self._op_output_rename_map = collections.OrderedDict()
self._var_scale_map = collections.OrderedDict()
def apply(self, graph):
"""
Adjust quantize/dequantize operators order for the inference process.
Args:
graph(IrGraph): the applied graph.
"""
persistable_vars = [p.name() for p in graph.all_persistable_vars()]
ops = graph.all_ops()
for op_node in ops:
op_name = op_node.name()
if op_name in self._fake_quant_op_names:
input_arg_name = op_node.op().input('X')[0]
if input_arg_name in persistable_vars:
if self._weight_quantize_type == 'abs_max':
param = self._load_var(input_arg_name)
scale_v = np.max(np.abs(param))
else:
scale_v = self._load_var(op_node.op().output('OutScale')
[0])[0]
self._var_scale_map[input_arg_name] = scale_v
else:
scale_v = graph.var_node(op_node.op().output('OutScale')[0])
self._var_scale_map[input_arg_name] = scale_v
if input_arg_name in persistable_vars:
self._remove_fake_quant_and_dequant_op(graph, op_node)
# quantize weight and restore
param_v = self._load_var(input_arg_name)
quantized_param_v = self._quant(param_v, scale_v,
self._weight_bits)
self._restore_var(input_arg_name, quantized_param_v)
ops = graph.all_ops()
for op_node in ops:
op_name = op_node.name()
if op_name in self._fake_dequant_op_names:
self._remove_fake_quant_and_dequant_op(graph, op_node)
ops = graph.all_ops()
for op_node in ops:
op_name = op_node.name()
if op_name in self._quantizable_ops:
self._insert_post_dequant_op(graph, op_node)
for op_node in ops:
# insert dequant_op after fc/conv, need to rename inputs of the followed ops
for var_node in op_node.inputs:
name = var_node.name()
if name in self._op_output_rename_map:
old_in = graph.var_node(name)
new_in = self._op_output_rename_map[name]
graph.update_input_link(old_in, new_in, op_node)
# remove the unused var node in the graph
self._remove_unused_var_nodes(graph)
return graph
def _remove_fake_quant_and_dequant_op(self, graph, op_node):
k = op_node.op().output('Out')[0]
v = op_node.op().input('X')[0]
if v not in self._op_input_rename_map:
self._op_input_rename_map[k] = v
else:
self._op_input_rename_map[k] = self._op_input_rename_map[v]
graph.safe_remove_nodes(op_node)
def _insert_post_dequant_op(self, graph, op_node):
max_range = None
scale_var_node = None
persistable_vars = [p.name() for p in graph.all_persistable_vars()]
for var_node in op_node.inputs:
name = var_node.name()
if name in self._op_input_rename_map:
old_in = graph.var_node(name)
new_in = graph.var_node(self._op_input_rename_map[name])
new_in.clear_outputs()
graph.update_input_link(old_in, new_in, op_node)
original_var_name = self._original_var_name(name)
scale_v = self._var_scale_map[original_var_name]
if original_var_name in persistable_vars:
param_range = (1 << (self._weight_bits - 1)) - 1
act_range = (1 << (self._activation_bits - 1)) - 1
assert self._is_float(
scale_v), 'The scale of parameter %s is not a float.' % (
original_var_name)
max_range = param_range * act_range / scale_v
else:
assert isinstance(scale_v, core.Node)
scale_var_node = self._var_scale_map[original_var_name]
if len(op_node.outputs) != 1:
raise ValueError("Only support one output, but op %s has"
" more than one output." % (op_node.name()))
output_var_node = op_node.outputs[0]
dequant_var_node = graph.create_var_node(
name=self._dequantized_var_name(output_var_node.name()),
var_type=output_var_node.var().type(),
shape=output_var_node.var().shape(),
var_dtype=output_var_node.var().dtype())
dequant_op_node = graph.create_op_node(
op_type='fake_dequantize_max_abs',
attrs={
'max_range': float(max_range),
'op_role': core.op_proto_and_checker_maker.OpRole.Forward
},
inputs={'X': output_var_node,
'Scale': scale_var_node},
outputs={'Out': dequant_var_node})
graph.link_to(output_var_node, dequant_op_node)
graph.link_to(scale_var_node, dequant_op_node)
graph.link_to(dequant_op_node, dequant_var_node)
self._op_output_rename_map[output_var_node.name()] = dequant_var_node
return dequant_var_node
def _load_var(self, name):
return np.array(self._scope.find_var(name).get_tensor())
def _restore_var(self, name, array):
tensor = self._scope.find_var(name).get_tensor()
tensor.set(array, self._place)
def _remove_unused_var_nodes(self, graph):
all_used_vars = set()
ops = graph.all_ops()
for op_node in ops:
for input_node in op_node.inputs:
all_used_vars.add(input_node)
for output_node in op_node.outputs:
all_used_vars.add(output_node)
all_unused_vars = graph.all_vars() - all_used_vars
graph.safe_remove_nodes(all_unused_vars)
def _original_var_name(self, var_name):
"""
Return the original variable name.
"""
if var_name.endswith('.quantized.dequantized'):
return var_name[:-len('.quantized.dequantized')]
if var_name.endswith('.quantized'):
return var_name[:-len('.quantized')]
if var_name.endswith('.dequantized'):
return var_name[:-len('.dequantized')]
if var_name.endswith('.scale'):
return var_name[:-len('.scale')]
else:
return var_name
def _dequantized_var_name(self, var_name):
"""
Return dequantized variable name for the input `var_name`.
"""
return "%s.dequantized" % (var_name)
def _is_float(self, v):
return isinstance(v, float) or isinstance(v, np.float32) \
or isinstance(v, np.float64)
def _quant(self, x, scale, num_bits):
return np.round(x / scale * ((1 << (num_bits - 1)) - 1))
class ConvertToInt8Pass(object):
"""
Convert the weights into int8_t type.
Args:
scope(fluid.Scope): scope is used to get the weight tensor values.
place(fluid.CPUPlace|fluid.CUDAPlace): place is used to restore the
8bits weight tensors.
"""
def __init__(self, scope, place):
assert scope is not None, \
'The scope cannot be set None.'
assert place is not None, \
'The place cannot be set None.'
self._scope = scope
self._place = place
self._quantizable_ops = ['conv2d', 'depthwise_conv2d', 'mul']
def apply(self, graph):
"""
Convert weights' tpye of the graph. After that, the data type of the
graph weigths is int8_t.
Args:
graph(IrGraph): the applied graph.
"""
persistable_vars = [p.name() for p in graph.all_persistable_vars()]
ops = graph.all_ops()
input_map = {}
for op_node in ops:
op_name = op_node.name()
if op_name in self._quantizable_ops:
for var_node in op_node.inputs:
name = var_node.name()
if name in persistable_vars:
if name not in input_map:
int8_var_node = self._convert_to_int8(graph,
var_node)
input_map[name] = int8_var_node
graph.update_input_link(var_node, input_map[name],
op_node)
# remove the unused var node in the graph
self._remove_unused_var_nodes(graph)
return graph
def _convert_to_int8(self, graph, var_node):
int8_var_node_name = var_node.name() + ".int8"
int8_var_node = graph.create_param_node(
name=cpt.to_text(int8_var_node_name),
var_type=var_node.var().type(),
shape=var_node.var().shape(),
var_dtype=core.VarDesc.VarType.INT8)
array = self._load_var(var_node.name())
self._scope.var(int8_var_node_name)
self._store_var(int8_var_node_name, array, np.int8)
return int8_var_node
def _load_var(self, name):
return np.array(self._scope.find_var(name).get_tensor())
def _store_var(self, name, array, dtype):
tensor = self._scope.find_var(name).get_tensor()
tensor.set(array.astype(dtype), self._place)
def _remove_unused_var_nodes(self, graph):
all_used_vars = set()
ops = graph.all_ops()
for op_node in ops:
for input_node in op_node.inputs:
all_used_vars.add(input_node)
for output_node in op_node.outputs:
all_used_vars.add(output_node)
all_unused_vars = graph.all_vars() - all_used_vars
graph.safe_remove_nodes(all_unused_vars)
class TransformForMobilePass(object):
"""
This pass is used to convert the freezed graph for paddle-mobile execution.
"""
def __init__(self):
self._fake_quant_op_names = [
'fake_quantize_abs_max', 'fake_quantize_range_abs_max'
]
self._fake_dequant_op_names = ['fake_dequantize_max_abs']
def apply(self, graph):
"""
Because paddle-mobile use `quantize` an `dequantize` as the names of
quantize operator and dequantize operator, the `apply` function just
realize this logic.
Args:
graph(IrGraph): the graph will be transformed.
"""
ops = graph.all_ops()
for op_node in ops:
name = op_node.name()
if name in self._fake_quant_op_names:
op_node.op().set_type('quantize')
quant_node = graph.create_op_node_from_desc(op_node.op())
for input_node in op_node.inputs:
graph.link_to(input_node, quant_node)
for output_node in op_node.outputs:
graph.link_to(quant_node, output_node)
graph.safe_remove_nodes(op_node)
if name in self._fake_dequant_op_names:
op_node.op().set_type('dequantize')
dequant_node = graph.create_op_node_from_desc(op_node.op())
for input_node in op_node.inputs:
graph.link_to(input_node, dequant_node)
for output_node in op_node.outputs:
graph.link_to(dequant_node, output_node)
graph.safe_remove_nodes(op_node)
return graph
python/paddle/fluid/contrib/slim/tests/CMakeLists.txt 0 → 100644
浏览文件 @ d79d2f68
file(GLOB TEST_OPS RELATIVE "${CMAKE_CURRENT_SOURCE_DIR}" "test_*.py")
string(REPLACE ".py" "" TEST_OPS "${TEST_OPS}")
foreach(src ${TEST_OPS})
py_test(${src} SRCS ${src}.py)
endforeach()
python/paddle/fluid/contrib/slim/unitest/configs/config.yaml → python/paddle/fluid/contrib/slim/tests/configs/config.yaml
浏览文件 @ d79d2f68
version: 1.0
include: ["./unitest/configs/pruners.yaml", "./unitest/configs/pruners_0.yaml"]
include: ["./configs/pruners.yaml", "./configs/pruners_0.yaml"]
pruners:
pruner_1:
class: 'RatioPruner'
......
python/paddle/fluid/contrib/slim/unitest/test_factory.py → python/paddle/fluid/contrib/slim/tests/test_factory.py
浏览文件 @ d79d2f68
......@@ -18,7 +18,7 @@ import unittest
class TestFactory(unittest.TestCase):
def test_parse(self):
factory = ConfigFactory('./unitest/configs/config.yaml')
factory = ConfigFactory('./configs/config.yaml')
pruner = factory.instance('pruner_1')
self.assertEquals(pruner.ratios['conv1_1.w'], 0.3)
......
python/paddle/fluid/contrib/slim/tests/test_graph.py 0 → 100644
浏览文件 @ d79d2f68
# 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.
from __future__ import print_function
import unittest
import paddle.fluid as fluid
import six
from paddle.fluid.framework import IrGraph
from paddle.fluid import core
def residual_block(num):
def conv_bn_layer(input,
ch_out,
filter_size,
stride,
padding,
act='relu',
bias_attr=False):
tmp = fluid.layers.conv2d(
input=input,
filter_size=filter_size,
num_filters=ch_out,
stride=stride,
padding=padding,
act=None,
bias_attr=bias_attr)
return fluid.layers.batch_norm(input=tmp, act=act)
data = fluid.layers.data(name='image', shape=[1, 32, 32], dtype='float32')
label = fluid.layers.data(name='label', shape=[1], dtype='int64')
hidden = data
for _ in six.moves.xrange(num):
conv = conv_bn_layer(hidden, 16, 3, 1, 1, act=None, bias_attr=True)
short = conv_bn_layer(hidden, 16, 1, 1, 0, act=None)
hidden = fluid.layers.elementwise_add(x=conv, y=short, act='relu')
fc = fluid.layers.fc(input=hidden, size=10)
loss = fluid.layers.cross_entropy(input=fc, label=label)
loss = fluid.layers.mean(loss)
return loss
class TestGraph(unittest.TestCase):
def test_graph_functions(self):
main = fluid.Program()
startup = fluid.Program()
with fluid.program_guard(main, startup):
loss = residual_block(2)
opt = fluid.optimizer.Adam(learning_rate=0.001)
opt.minimize(loss)
graph = IrGraph(core.Graph(main.desc), for_test=False)
marked_nodes = set()
for op in graph.all_ops():
if op.name().find('conv2d') > -1:
marked_nodes.add(op)
graph.draw('.', 'residual', marked_nodes)
self.assertFalse(graph.has_circle())
self.assertEqual(graph.graph_num(), 1)
nodes = graph.topology_sort()
self.assertEqual(len(nodes), len(graph.all_ops()))
nodes_map = graph.build_adjacency_list()
self.assertEqual(len(nodes_map), len(graph.all_ops()))
nodes_num = len(graph.all_nodes())
graph.safe_remove_nodes(marked_nodes)
self.assertEqual(len(graph.all_nodes()), nodes_num - len(marked_nodes))
if __name__ == '__main__':
unittest.main()
python/paddle/fluid/contrib/slim/unitest/test_quantization_pass.py → python/paddle/fluid/contrib/slim/tests/test_quantization_pass.py
浏览文件 @ d79d2f68
......@@ -17,9 +17,12 @@ import random
import numpy as np
import paddle.fluid as fluid
import six
from paddle.fluid.framework import Program
import paddle
from paddle.fluid.framework import IrGraph
from paddle.fluid.contrib.slim.quantization import QuantizationTransformPass
from paddle.fluid.contrib.slim.quantization import QuantizationFreezePass
from paddle.fluid.contrib.slim.quantization import ConvertToInt8Pass
from paddle.fluid.contrib.slim.quantization import TransformForMobilePass
from paddle.fluid import core
......@@ -65,6 +68,28 @@ def residual_block(num):
return loss
def conv_net(img, label):
conv_pool_1 = fluid.nets.simple_img_conv_pool(
input=img,
filter_size=5,
num_filters=20,
pool_size=2,
pool_stride=2,
act="relu")
conv_pool_1 = fluid.layers.batch_norm(conv_pool_1)
conv_pool_2 = fluid.nets.simple_img_conv_pool(
input=conv_pool_1,
filter_size=5,
num_filters=50,
pool_size=2,
pool_stride=2,
act="relu")
prediction = fluid.layers.fc(input=conv_pool_2, size=10, act='softmax')
loss = fluid.layers.cross_entropy(input=prediction, label=label)
avg_loss = fluid.layers.mean(loss)
return avg_loss
class TestQuantizationTransformPass(unittest.TestCase):
def setUp(self):
self.quantizable_op_and_inputs = {
......@@ -171,5 +196,177 @@ class TestQuantizationTransformPass(unittest.TestCase):
self.residual_block_quant('range_abs_max')
class TestQuantizationFreezePass(unittest.TestCase):
def freeze_graph(self, use_cuda, seed, quant_type):
def build_program(main, startup, is_test):
main.random_seed = seed
startup.random_seed = seed
with fluid.unique_name.guard():
with fluid.program_guard(main, startup):
img = fluid.layers.data(
name='image', shape=[1, 28, 28], dtype='float32')
label = fluid.layers.data(
name='label', shape=[1], dtype='int64')
loss = conv_net(img, label)
if not is_test:
opt = fluid.optimizer.Adam(learning_rate=0.001)
opt.minimize(loss)
return [img, label], loss
random.seed(0)
np.random.seed(0)
main = fluid.Program()
startup = fluid.Program()
test_program = fluid.Program()
feeds, loss = build_program(main, startup, False)
build_program(test_program, startup, True)
test_program = test_program.clone(for_test=True)
main_graph = IrGraph(core.Graph(main.desc), for_test=False)
test_graph = IrGraph(core.Graph(test_program.desc), for_test=True)
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
exe = fluid.Executor(place)
scope = fluid.Scope()
with fluid.scope_guard(scope):
exe.run(startup)
transform_pass = QuantizationTransformPass(
scope=scope, program_exe=exe, activation_quantize_type=quant_type)
transform_pass.apply(main_graph)
transform_pass.apply(test_graph)
dev_name = '_gpu_' if use_cuda else '_cpu_'
marked_nodes = set()
for op in main_graph.all_ops():
if op.name().find('quantize') > -1:
marked_nodes.add(op)
main_graph.draw('.', 'main' + dev_name + quant_type, marked_nodes)
marked_nodes = set()
for op in test_graph.all_ops():
if op.name().find('quantize') > -1:
marked_nodes.add(op)
test_graph.draw('.', 'test' + dev_name + quant_type, marked_nodes)
quantized_main_program = main_graph.to_program()
quantized_test_program = test_graph.to_program()
iters = 5
batch_size = 8
#train_exe = fluid.ParallelExecutor(
# main_program=quantized_main_program,
# use_cuda=bool(use_cuda),
# loss_name=loss.name,
# scope=scope)
train_reader = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.mnist.train(), buf_size=500),
batch_size=batch_size)
test_reader = paddle.batch(
paddle.dataset.mnist.test(), batch_size=batch_size)
feeder = fluid.DataFeeder(feed_list=feeds, place=place)
with fluid.scope_guard(scope):
for _ in range(iters):
data = next(train_reader())
loss_v = exe.run(program=quantized_main_program,
feed=feeder.feed(data),
fetch_list=[loss])
#loss_v = train_exe.run(feed=feeder.feed(data),
# fetch_list=[loss.name])
#print('{}: {}'.format('loss' + dev_name + quant_type, loss_v))
test_data = next(test_reader())
with fluid.program_guard(quantized_test_program):
w_var = fluid.framework._get_var('conv2d_1.w_0.quantized',
quantized_test_program)
# Testing
with fluid.scope_guard(scope):
test_loss1, w_quant = exe.run(program=quantized_test_program,
feed=feeder.feed(test_data),
fetch_list=[loss, w_var])
# Freeze graph for inference, but the weight of fc/conv is still float type.
freeze_pass = QuantizationFreezePass(scope=scope, place=place)
freeze_pass.apply(test_graph)
marked_nodes = set()
for op in test_graph.all_ops():
if op.name().find('quantize') > -1:
marked_nodes.add(op)
test_graph.draw('.', 'test_freeze' + dev_name + quant_type,
marked_nodes)
server_program = test_graph.to_program()
with fluid.scope_guard(scope):
test_loss2, = exe.run(program=server_program,
feed=feeder.feed(test_data),
fetch_list=[loss])
self.assertAlmostEqual(test_loss1, test_loss2, delta=5e-3)
#print('{}: {}'.format('test_loss1' + dev_name + quant_type, test_loss1))
#print('{}: {}'.format('test_loss2' + dev_name + quant_type, test_loss2))
w_freeze = np.array(scope.find_var('conv2d_1.w_0').get_tensor())
# Maybe failed, this is due to the calculation precision
# self.assertAlmostEqual(np.sum(w_freeze), np.sum(w_quant))
#print('{}: {}'.format('w_freeze' + dev_name + quant_type,
# np.sum(w_freeze)))
#print('{}: {}'.format('w_quant' + dev_name + quant_type,
# np.sum(w_quant)))
# Convert parameter to 8-bit.
convert_int8_pass = ConvertToInt8Pass(scope=scope, place=place)
convert_int8_pass.apply(test_graph)
marked_nodes = set()
for op in test_graph.all_ops():
if op.name().find('quantize') > -1:
marked_nodes.add(op)
test_graph.draw('.', 'test_int8' + dev_name + quant_type, marked_nodes)
server_program_int8 = test_graph.to_program()
# Save the 8-bit parameter and model file.
with fluid.scope_guard(scope):
fluid.io.save_inference_model('server_int8' + dev_name + quant_type,
['image', 'label'], [loss], exe,
server_program_int8)
# Test whether the 8-bit parameter and model file can be loaded successfully.
[infer, feed, fetch] = fluid.io.load_inference_model(
'server_int8' + dev_name + quant_type, exe)
# Check the loaded 8-bit weight.
w_8bit = np.array(scope.find_var('conv2d_1.w_0.int8').get_tensor())
self.assertEqual(w_8bit.dtype, np.int8)
self.assertEqual(np.sum(w_8bit), np.sum(w_freeze))
#print('{}: {}'.format('w_8bit' + dev_name + quant_type, np.sum(w_8bit)))
#print('{}: {}'.format('w_freeze' + dev_name + quant_type,
# np.sum(w_freeze)))
mobile_pass = TransformForMobilePass()
mobile_pass.apply(test_graph)
marked_nodes = set()
for op in test_graph.all_ops():
if op.name().find('quantize') > -1:
marked_nodes.add(op)
test_graph.draw('.', 'test_mobile' + dev_name + quant_type,
marked_nodes)
mobile_program = test_graph.to_program()
with fluid.scope_guard(scope):
fluid.io.save_inference_model('mobile_int8' + dev_name + quant_type,
['image', 'label'], [loss], exe,
mobile_program)
def test_freeze_graph_cuda_dynamic(self):
if fluid.core.is_compiled_with_cuda():
with fluid.unique_name.guard():
self.freeze_graph(True, seed=1, quant_type='abs_max')
def test_freeze_graph_cpu_dynamic(self):
with fluid.unique_name.guard():
self.freeze_graph(False, seed=2, quant_type='abs_max')
def test_freeze_graph_cuda_static(self):
if fluid.core.is_compiled_with_cuda():
with fluid.unique_name.guard():
self.freeze_graph(True, seed=1, quant_type='range_abs_max')
def test_freeze_graph_cpu_static(self):
with fluid.unique_name.guard():
self.freeze_graph(False, seed=2, quant_type='range_abs_max')
if __name__ == '__main__':
unittest.main()
python/paddle/fluid/contrib/tests/CMakeLists.txt
浏览文件 @ d79d2f68
......@@ -6,5 +6,9 @@ if(APPLE OR WIN32 OR NOT WITH_MKL)
endif()
foreach(src ${TEST_OPS})
py_test(${src} SRCS ${src}.py)
if(src MATCHES "test_calibration")
py_test(${src} SRCS ${src}.py ENVS FLAGS_use_mkldnn=true)
else()
py_test(${src} SRCS ${src}.py)
endif()
endforeach()
python/paddle/fluid/contrib/tests/test_calibration.py
浏览文件 @ d79d2f68
......@@ -199,7 +199,6 @@ class TestCalibrationForResnet50(unittest.TestCase):
def run_program(self, model_path, generate_int8=False, algo='direct'):
image_shape = [3, 224, 224]
os.environ['FLAGS_use_mkldnn'] = 'True'
fluid.memory_optimize(fluid.default_main_program())
......@@ -241,9 +240,6 @@ class TestCalibrationForResnet50(unittest.TestCase):
label = label.reshape([-1, 1])
running_program = calibrator.sampling_program.clone(
) if generate_int8 else infer_program.clone()
for op in running_program.current_block().ops:
if op.has_attr("use_mkldnn"):
op._set_attr("use_mkldnn", True)
t1 = time.time()
_, acc1, _ = exe.run(
......
python/paddle/fluid/contrib/tests/test_quantize_transpiler.py
浏览文件 @ d79d2f68
......@@ -204,9 +204,11 @@ class TestQuantizeTranspiler(unittest.TestCase):
build_program(test_program, startup, True)
test_program = test_program.clone(for_test=True)
quant_transpiler = QuantizeTranspiler()
quant_transpiler.training_transpile(main)
quant_transpiler.training_transpile(test_program)
quant_type = 'range_abs_max' # 'range_abs_max' or 'abs_max'
quant_transpiler = QuantizeTranspiler(
activation_quantize_type=quant_type)
quant_transpiler.training_transpile(main, startup)
quant_transpiler.training_transpile(test_program, startup)
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
exe = fluid.Executor(place)
......
python/paddle/fluid/framework.py
浏览文件 @ d79d2f68
......@@ -16,6 +16,8 @@ from __future__ import print_function
import collections
from collections import defaultdict
from collections import Iterable
import contextlib
from .wrapped_decorator import signature_safe_contextmanager
import os
import re
......@@ -1529,12 +1531,16 @@ class Block(object):
class IrGraph(object):
"""
IrGraph uses core.Graph as the delegation to accomplish the manipulation.
Python IrGraph. Beneath it is a core.Graph, which is used for
create a c++ Ir Pass Graph. An IrGraph is just a graph view of
a Program. In an IrGraph, both Variables and Operators are graph
nodes.
"""
def __init__(self, graph, for_test=False):
"""
Construct the IrGraph using core.Graph.
Construct an IrGraph using core.Graph.
Args:
graph(core.Graph): C++ Graph.
for_test(bool): True for the test graph and false for the train graph.
......@@ -1545,23 +1551,81 @@ class IrGraph(object):
self._for_test = for_test
def is_test(self):
"""
If the graph is used for testing, the function returns true. Otherwise, returns false.
"""
return self._for_test
def all_parameters(self):
param_nodes = set()
for node in self.graph.nodes():
if node.is_var() and node.var() is not None and node.var(
).persistable():
param_nodes.add(node)
return param_nodes
def all_nodes(self):
"""
Return all nodes included in the graph as a set.
"""
return {node for node in self.graph.nodes()}
def all_vars(self):
"""
Return all variable nodes included in the graph as a set.
"""
return {node for node in self.graph.nodes() if node.is_var()}
def all_persistable_vars(self):
"""
Return all persistable variable nodes included in the graph as a set.
"""
persistable_nodes = set()
for node in self.graph.nodes():
if node.is_var() and node.var() is not None and node.var(
).persistable():
persistable_nodes.add(node)
return persistable_nodes
def all_ops(self):
"""
Return all operator nodes included in the graph as a set.
"""
return {node for node in self.graph.nodes() if node.is_op()}
def var_node(self, name):
"""
Get a variable node by name from the graph.
Args:
name(str): the name of the variable node.
Raises:
ValueError: The If input's type is not str, or this graph
doesn't have a variable with the giving name.
Returns:
core.Node: the variable node with the giving name.
"""
if not isinstance(name, six.string_types):
raise TypeError(
"var require string as parameter, but get %s instead." %
(type(name)))
target_var_node = None
var_nodes = self.all_vars()
for var_node in var_nodes:
if var_node.name() == name:
target_var_node = var_node
if target_var_node is None:
raise ValueError("var_node %s not in this graph" % name)
return target_var_node
def create_param_node(self, name, var_type, shape, var_dtype):
"""
Create a persistable variable node in the graph. In IrGraph,
it can not distinguish between persistable variables and parameters.
Args:
name(str): the name of the persistable variable node.
vart_type(core.VarDesc.VarType): the type of the persistable variable node.
shape(list): the shape of the persistable variable node.
var_dtype(core.VarDesc.VarType): the data type of the persistable variable node.
Returns:
core.Node: the created persistable variable node.
"""
var_desc = core.VarDesc(name)
var_desc.set_type(var_type)
var_desc.set_shape(shape)
......@@ -1570,6 +1634,20 @@ class IrGraph(object):
return self.graph.create_var_node(var_desc)
def create_var_node(self, name, var_type, shape, var_dtype):
"""
Create a variable node in the graph. The created variable node is
not persistable.
Args:
name(str): the name of the variable node.
vart_type(core.VarDesc.VarType): the type of the variable node.
shape(list): the shape of the variable node.
var_dtype(core.VarDesc.VarType): the data type of the variable node.
Returns:
core.Node: the created variable node.
"""
var_desc = core.VarDesc(name)
var_desc.set_type(var_type)
var_desc.set_shape(shape)
......@@ -1577,19 +1655,41 @@ class IrGraph(object):
return self.graph.create_var_node(var_desc)
def create_var_node_from_desc(self, var_desc):
"""
Create a variable node by using an existing VarDesc in the graph.
Depend on the giving VarDesc, the created variable node may be persistable.
Args:
var_desc(core.VarDesc): the giving variable description.
Returns:
core.Node: the created variable node.
"""
return self.graph.create_var_node(var_desc)
def create_op_node(self, op_type, attrs, inputs, outputs):
"""
Create a operator node in the graph.
Args:
op_type(str): the type of the operator node.
attrs(dict): the attributes of the operator node.
inputs(dict): the inputs of the operator node.
outputs(dict): the outpus of the operator node.
Returns:
core.Node: the created operator node.
"""
op_desc = core.OpDesc()
op_desc.set_type(op_type)
for attr, value in attrs.iteritems():
for attr, value in six.iteritems(attrs):
self._update_desc_attr(op_desc, attr, value)
for input_name, var_nodes in inputs.iteritems():
for input_name, var_nodes in six.iteritems(inputs):
if not isinstance(var_nodes, list):
var_nodes = [var_nodes]
op_desc.set_input(input_name,
[var_node.name() for var_node in var_nodes])
for output_name, var_nodes in outputs.iteritems():
for output_name, var_nodes in six.iteritems(outputs):
if not isinstance(var_nodes, list):
var_nodes = [var_nodes]
op_desc.set_output(output_name,
......@@ -1597,11 +1697,29 @@ class IrGraph(object):
return self.graph.create_op_node(op_desc)
def create_op_node_from_desc(self, op_desc):
"""
Create a operator node by using an existing OpDesc in the graph.
Args:
op_desc(core.VarDesc): the giving operator description.
Returns:
core.Node: the created operator node.
"""
return self.graph.create_op_node(op_desc)
def update_input_link(self, old_input_node, new_input_node, op_node):
assert old_input_node in self.graph.nodes() and new_input_node in self.graph.nodes() and \
op_node in self.graph.nodes(), 'Th three arguments must be in the graph nodes.'
"""
Update the input's link of a operator node.
Args:
old_input_node(core.Node): the old input node of the giving op_node.
new_input_node(core.Node): the new input node of the giving op_node.
op_node(core.Node): the operator node that is needed to update input's link.
"""
assert old_input_node in self.graph.nodes() and new_input_node in \
self.graph.nodes() and op_node in self.graph.nodes(), \
'The three arguments(old_input_node&new_input_node&op_node) must be in the graph nodes.'
old_input_node.outputs_remove(op_node)
op_node.inputs_remove(old_input_node)
new_input_node.outputs_append(op_node)
......@@ -1609,17 +1727,85 @@ class IrGraph(object):
op_node.op()._rename_input(old_input_node.name(), new_input_node.name())
def link_to(self, node_in, node_out):
"""
Connect two nodes.
Args:
node_in(core.Node): the input node.
node_out(core.Node): the output node.
"""
assert node_in in self.graph.nodes() and node_out in self.graph.nodes(), \
'Th two arguments must be in the graph nodes.'
'The two arguments(node_in&node_out) must be in the graph nodes.'
node_in.outputs_append(node_out)
node_out.inputs_append(node_in)
def safe_remove_nodes(self, remove_nodes):
"""
Remove nodes safely since links connected to these removed nodes are
also removed.
Args:
remove_nodes(set): the nodes prepared to be removed.
"""
if not isinstance(remove_nodes, set):
remove_nodes = set(remove_nodes)
if isinstance(remove_nodes, Iterable):
remove_nodes = set(remove_nodes)
else:
remove_nodes = {remove_nodes}
core.graph_safe_remove_nodes(self.graph, remove_nodes)
def draw(self, save_path, name, marked_nodes=None):
def has_circle(self):
"""
Check if the graph has a circle.
Returns:
bool: True if the graph has a circle else False.
"""
return core.has_circle(self.graph)
def graph_num(self):
"""
Count the number of unconnected graphs in this graph.
Returns:
int: the number of unconnected graphs.
"""
return core.graph_num(self.graph)
def topology_sort(self):
"""
Perform the topology sort operation on the graph.
Notes: the `graph` cannot contain a circle.
Returns:
set(core.Node): nodes in topology order.
"""
return core.topology_sort(self.graph)
def build_adjacency_list(self):
"""
Build an adjacency list of operations for the `graph`.
Returns:
dict{core.Node: set(core.Node)}: the adjacency list.
"""
return core.build_adjacency_list(self.graph)
def draw(self, save_path, name, marked_nodes=None, remove_ctr_var=True):
"""
Draw the graph. If `dot` command is installed, the drawn graph
will be saved as pdf file type, otherwise dot file type is used.
Args:
save_path(str): the save path of drawn graph.
name(str): the name of drawn graph.
marked_nodes(set(core.Node)): nodes that are needed to be marked.
Default value is None.
remove_ctr_var(bool): If it is set True, all control variable nodes
in the graph will be removed. Default value is True.
"""
def _convert_to_pdf(dot_file_path):
pdf_save_path = os.path.splitext(dot_file_path)[0] + '.pdf'
exited_code = subprocess.call('dot -Tpdf ' + dot_file_path \
......@@ -1629,15 +1815,17 @@ class IrGraph(object):
print('The {} is saved as the dot filetype.'.format(
dot_file_path))
remove_ctr_vars = set()
if remove_ctr_var:
remove_ctr_vars = set()
for node in self.graph.nodes():
if node.is_ctrl_var():
remove_ctr_vars.add(node)
self.safe_remove_nodes(remove_ctr_vars)
ops_num = 0
for node in self.graph.nodes():
if node.is_ctrl_var():
remove_ctr_vars.add(node)
elif node.is_op():
if node.is_op():
ops_num += 1
print('Total ops num = {}.'.format(ops_num))
self.safe_remove_nodes(remove_ctr_vars)
if marked_nodes is not None:
if not isinstance(marked_nodes, set):
marked_nodes = set(marked_nodes)
......@@ -1652,10 +1840,20 @@ class IrGraph(object):
_convert_to_pdf(viz_dot_path)
def to_program(self):
"""
Convert the graph into a Program.
Notes: When the graph includes backward operator nodes, the
conversion process may be failed. Usually, this function is
only used to convert a test graph.
Returns:
Program: a program converted from the graph.
"""
convert_pass = core.get_pass('graph_to_program_pass')
convert_pass.set('program', Program().desc)
desc = core.ProgramDesc()
convert_pass.set_not_owned('program', desc)
convert_pass.apply(self.graph)
desc = convert_pass.get_program('program')
program = Program._construct_from_desc(desc)
return program
......
python/paddle/fluid/imperative/layers.py
浏览文件 @ d79d2f68
......@@ -12,6 +12,7 @@
# See the License for the specific language governing permissions and
# limitations under the License.
import collections
import contextlib
import sys
import numpy as np
......@@ -30,31 +31,45 @@ class Layer(core.Layer):
def __init__(self, dtype=core.VarDesc.VarType.FP32, name=None):
self._built = False
self._dtype = dtype
self._parameters = collections.OrderedDict()
self._sub_layers = collections.OrderedDict()
def parameters(self, include_sublayers=True):
"""Returns a list of Parameters from current and sub-layers.
Args:
include_sublayers: If true, also include the parameters from
sublayers.
Returns a list of Parameters.
"""
ret = [p for p in self._parameters.values()]
if include_sublayers:
for l in self._sub_layers.values():
for p in l.parameters(include_sublayers):
ret.append(p)
return ret
def sublayers(self, include_sublayers=True):
"""Returns a list of sub layers.
def parameters(self):
params = []
for key in self.__dict__.keys():
value = self.__dict__[key]
if isinstance(value, framework.Parameter):
params.append(value)
elif isinstance(value, core.Layer):
params.extend(value.parameters())
elif isinstance(value, collections.Container):
if len(value) == 0:
continue
if isinstance(value[0], framework.Parameter):
params.extend(value)
elif isinstance(value[0], core.Layer):
for v in value:
params.extend(v.parameters())
return params
Args:
include_sublayers: If true, also include the layers from sublayers.
Returns a list of sub layers.
"""
ret = [l for l in self._sub_layers.values()]
if include_sublayers:
for l in self._sub_layers.values():
for sub_l in l.sublayers(include_sublayers):
ret.append(sub_l)
return ret
def clear_gradients(self):
for p in self.parameters():
p._clear_gradient()
def _build_once(self, inputs):
def _build_once(self, *args):
pass
def __call__(self, *inputs):
......@@ -71,6 +86,66 @@ class Layer(core.Layer):
def backward(self, *inputs):
raise ValueError("Layer shouldn't implement backward")
def add_sublayer(self, name, sublayer):
"""Adds a sub Layer instance.
Added sublayer can be access like self.name.
Args:
name: name of this sublayer.
sublayer: an instance of Layer.
Returns:
the sublayer passed in.
"""
assert isinstance(sublayer, core.Layer)
self._sub_layers[name] = sublayer
return sublayer
def add_parameter(self, name, parameter):
"""Adds a Parameter instance.
Added parameter can be access like self.name.
Args:
name: name of this sublayer.
parameter: an instance of Parameter.
Returns:
the parameter passed in.
"""
assert isinstance(parameter, framework.Parameter)
self._parameters[name] = parameter
return parameter
def __getattr__(self, name):
if name in self._parameters:
return self._parameters[name]
elif name in self._sub_layers:
return self._sub_layers[name]
def __setattr__(self, name, value):
if isinstance(value, framework.Parameter):
params = self.__dict__.get('_parameters', None)
if params is None:
raise ValueError(
"super(YourLayer, self).__init__() should be called first")
params[name] = value
elif isinstance(value, core.Layer):
layers = self.__dict__.get('_sub_layers', None)
if layers is None:
raise ValueError(
"super(YourLayer, self).__init__() should be called first")
layers[name] = value
else:
object.__setattr__(self, name, value)
def __delattr__(self, name):
if name in self._parameters:
del self._parameters[name]
elif name in self._sub_layers:
del self._sub_layers[name]
else:
object.__delattr__(self, name)
class PyLayer(core.PyLayer):
"""Layers composed of user-defined python codes."""
......
python/paddle/fluid/imperative/nn.py
浏览文件 @ d79d2f68
......@@ -225,9 +225,6 @@ class FC(layers.Layer):
act=act,
name=name)
def parameters(self):
return [self._w, self._b]
def _build_once(self, input):
input_shape = input.shape
param_shape = [
......@@ -478,9 +475,6 @@ class Embedding(layers.Layer):
dtype=self._dtype,
is_bias=False)
def parameters(self):
return [self._w]
def forward(self, input):
out = self._helper.create_variable_for_type_inference(self._dtype)
self._helper.append_op(
......
python/paddle/fluid/layers/nn.py
浏览文件 @ d79d2f68
......@@ -3236,7 +3236,7 @@ def group_norm(input,
# create output
mean_out = helper.create_variable(dtype=dtype, stop_gradient=True)
variance_out = helper.create_variable(dtype=dtype, stop_gradient=True)
group_norm_out = helper.create_variable(dtype)
group_norm_out = helper.create_variable(dtype=dtype)
helper.append_op(
type="group_norm",
......@@ -5936,13 +5936,10 @@ def reshape(x, shape, actual_shape=None, act=None, inplace=False, name=None):
than :attr:`shape`.
act (str): The non-linear activation to be applied to the reshaped tensor
variable.
inplace(bool): Must use :attr:`False` if :attr:`x` is used in multiple
operators. If this flag is set :attr:`True`, reuse input
:attr:`x` to reshape, which will change the shape of
tensor variable :attr:`x` and might cause errors when
:attr:`x` is used in multiple operators. If :attr:`False`,
preserve the shape :attr:`x` and create a new output tensor
variable whose data is copied from input x but reshaped.
inplace(bool): If ``inplace`` is `True`, the input and output of ``layers.reshape``
are the same variable, otherwise, the input and output of
``layers.reshape`` are different variables. Note that if :attr:`x`
is more than one layer's input, ``inplace`` must be :attr:`False`.
name (str): The name of this layer. It is optional.
Returns:
......@@ -8335,6 +8332,46 @@ def stack(x, axis=0):
If :code:`axis` < 0, it would be replaced with :code:`axis+rank(x[0])+1`.
If :code:`axis` is None, it would be replaced with 0.
For Example:
.. code-block:: text
Case 1:
Input:
x[0].data = [ [1.0 , 2.0 ] ]
x[0].dims = [1, 2]
x[1].data = [ [3.0 , 4.0 ] ]
x[1].dims = [1, 2]
x[2].data = [ [5.0 , 6.0 ] ]
x[2].dims = [1, 2]
Attrs:
axis = 0
Output:
Out.data =[ [ [1.0, 2.0] ],
[ [3.0, 4.0] ],
[ [5.0, 6.0] ] ]
Out.dims = [3, 1, 2]
Case 2:
Given
x[0].data = [ [1.0 , 2.0 ] ]
x[0].dims = [1, 2]
x[1].data = [ [3.0 , 4.0 ] ]
x[1].dims = [1, 2]
x[2].data = [ [5.0 , 6.0 ] ]
x[2].dims = [1, 2]
Attrs:
axis = 1 or axis = -2
Output:
Out.data =[ [ [1.0, 2.0]
[3.0, 4.0]
[5.0, 6.0] ] ]
Out.dims = [1, 3, 2]
Args:
x (Variable|list(Variable)|tuple(Variable)): Input variables.
axis (int|None): The axis along which all inputs are stacked.
......
python/paddle/fluid/layers/tensor.py
浏览文件 @ d79d2f68
......@@ -567,7 +567,7 @@ def ones(shape, dtype, force_cpu=False):
It also sets *stop_gradient* to True.
Args:
shape(tuple|list|None): Shape of output tensor
shape(tuple|list): Shape of output tensor
dtype(np.dtype|core.VarDesc.VarType|str): Data type of output tensor
Returns:
......@@ -578,6 +578,10 @@ def ones(shape, dtype, force_cpu=False):
data = fluid.layers.ones(shape=[1], dtype='int64')
"""
assert isinstance(shape, list) or isinstance(
shape, tuple), "The shape's type should be list or tuple."
assert reduce(lambda x, y: x * y,
shape) > 0, "The shape is invalid: %s." % (str(shape))
return fill_constant(value=1.0, **locals())
......
python/paddle/fluid/parallel_executor.py
浏览文件 @ d79d2f68
......@@ -148,6 +148,8 @@ class ParallelExecutor(object):
else framework.default_main_program()
# FIXME(dzhwinter): enable_inplace should be after memory_optimize
# if turn on python memory optimize, turn off the inplace_pass.
if build_strategy.memory_optimize is None:
build_strategy.memory_optimize = False if main._is_mem_optimized else True
if build_strategy.enable_inplace is None:
build_strategy.enable_inplace = False if main._is_mem_optimized else True
scope = scope if scope is not None else executor.global_scope()
......
python/paddle/fluid/tests/unittests/CMakeLists.txt
浏览文件 @ d79d2f68
......@@ -77,6 +77,7 @@ list(REMOVE_ITEM TEST_OPS test_bilinear_interp_op)
list(REMOVE_ITEM TEST_OPS test_nearest_interp_op)
list(REMOVE_ITEM TEST_OPS test_imperative_resnet)
list(REMOVE_ITEM TEST_OPS test_imperative_optimizer)
list(REMOVE_ITEM TEST_OPS test_ir_memory_optimize_transformer)
foreach(TEST_OP ${TEST_OPS})
py_test_modules(${TEST_OP} MODULES ${TEST_OP})
endforeach(TEST_OP)
......@@ -107,6 +108,9 @@ py_test_modules(test_parallel_executor_crf MODULES test_parallel_executor_crf SE
py_test_modules(test_parallel_executor_fetch_feed MODULES test_parallel_executor_fetch_feed SERIAL)
set_tests_properties(test_parallel_executor_fetch_feed PROPERTIES TIMEOUT 450)
py_test_modules(test_parallel_executor_transformer MODULES test_parallel_executor_transformer SERIAL)
if(NOT WIN32)
py_test_modules(test_ir_memory_optimize_transformer MODULES test_ir_memory_optimize_transformer SERIAL)
endif()
if(NOT APPLE)
py_test_modules(test_image_classification_resnet MODULES test_image_classification_resnet SERIAL)
if(CMAKE_BUILD_TYPE STREQUAL "Debug")
......
python/paddle/fluid/tests/unittests/parallel_executor_test_base.py
浏览文件 @ d79d2f68
......@@ -79,7 +79,7 @@ class TestParallelExecutorBase(unittest.TestCase):
if use_reduce else fluid.BuildStrategy.ReduceStrategy.AllReduce
build_strategy.fuse_elewise_add_act_ops = fuse_elewise_add_act_ops
build_strategy.fuse_relu_depthwise_conv = fuse_relu_depthwise_conv
build_strategy.memory_optimize = use_ir_memory_optimize
build_strategy.memory_optimize = False if memory_opt else use_ir_memory_optimize
# python memory optimization is conflict with inplace pass.
# Use ir graph memory optimization after inplace pass is the correct way.
build_strategy.enable_inplace = False if memory_opt else enable_inplace
......
python/paddle/fluid/tests/unittests/test_base_layer.py 0 → 100644
浏览文件 @ d79d2f68
# 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.
import unittest
import numpy as np
import paddle.fluid as fluid
from paddle.fluid.layer_helper import LayerHelper
class L1(fluid.imperative.Layer):
def __init__(self):
super(L1, self).__init__()
self._helper = LayerHelper(
'MyLayer',
param_attr=fluid.ParamAttr(
initializer=fluid.initializer.Constant(value=0.1)))
self.w1 = self._helper.create_parameter(
attr=self._helper.param_attr,
shape=[2, 2],
dtype='float32',
is_bias=False)
self.w2 = self._helper.create_parameter(
attr=self._helper.param_attr,
shape=[2, 2],
dtype='float32',
is_bias=False)
def forward(self):
return self.w1 + self.w2
class L2(fluid.imperative.Layer):
def __init__(self):
super(L2, self).__init__()
self.layer1 = L1()
self.layer2 = L1()
def forward(self):
return self.layer1() + self.layer2()
class L3(fluid.imperative.Layer):
def __init__(self):
super(L3, self).__init__()
self.layer1 = L2()
self.layer2 = L2()
def forward(self):
return self.layer1() + self.layer2()
class TestBaseLayer(unittest.TestCase):
def test_one_level(self):
with fluid.imperative.guard():
l = L1()
ret = l()
self.assertEqual(l.w1.name, "MyLayer_0.w_0")
self.assertEqual(l.w2.name, "MyLayer_0.w_1")
self.assertTrue(np.allclose(ret._numpy(), 0.2 * np.ones([2, 2])))
def test_three_level(self):
with fluid.imperative.guard():
l = L3()
ret = l()
self.assertTrue(np.allclose(ret._numpy(), 0.8 * np.ones([2, 2])))
if __name__ == '__main__':
unittest.main()
python/paddle/fluid/tests/unittests/test_dist_transpiler.py
浏览文件 @ d79d2f68
......@@ -22,6 +22,9 @@ import six
import unittest
import numpy as np
import gc
gc.set_debug(gc.DEBUG_COLLECTABLE)
import paddle.fluid as fluid
......@@ -99,6 +102,12 @@ class TranspilerTest(unittest.TestCase):
with fluid.unique_name.guard():
with fluid.program_guard(main, startup):
self.transpiler_test_impl()
# NOTE: run gc.collect to eliminate pybind side objects to
# prevent random double-deallocate when inherited in python.
del self.transpiler
del main
del startup
gc.collect()
class TestBasicModel(TranspilerTest):
......@@ -797,6 +806,7 @@ class TestNCCL2Transpile(TranspilerTest):
print([op.type for op in startup.global_block().ops])
self.assertEqual(startup.global_block().ops[-1].type, "gen_nccl_id")
self.assertIsNotNone(startup.global_block().vars.get("NCCLID"))
gc.collect()
else:
pass
......
python/paddle/fluid/tests/unittests/test_fuse_elewise_add_act_pass.py
浏览文件 @ d79d2f68
......@@ -121,6 +121,8 @@ class TestMNIST(TestParallelExecutorBase):
regularization=fluid.regularizer.L2Decay(1e-6))
return optimizer
# NOTE(dzh):
# need to make it compatible with elewise fuse act
not_fuse_op_first_loss, not_fuse_op_last_loss = self.check_network_convergence(
model,
feed_dict={"image": img,
......@@ -128,6 +130,7 @@ class TestMNIST(TestParallelExecutorBase):
use_cuda=use_cuda,
fuse_elewise_add_act_ops=False,
memory_opt=False,
use_ir_memory_optimize=False,
optimizer=_optimizer)
fuse_op_first_loss, fuse_op_last_loss = self.check_network_convergence(
model,
......@@ -136,6 +139,7 @@ class TestMNIST(TestParallelExecutorBase):
use_cuda=use_cuda,
fuse_elewise_add_act_ops=True,
memory_opt=False,
use_ir_memory_optimize=False,
optimizer=_optimizer)
for loss in zip(not_fuse_op_first_loss, fuse_op_first_loss):
......
python/paddle/fluid/tests/unittests/test_imperative.py
浏览文件 @ d79d2f68
......@@ -333,6 +333,18 @@ class TestImperative(unittest.TestCase):
self.assertTrue(np.allclose(dy_out, static_out))
self.assertTrue(np.allclose(dy_grad, static_grad))
params = mlp.parameters(True)
self.assertEqual("FC_0.w_0", params[0].name)
self.assertEqual("FC_0.b_0", params[1].name)
self.assertEqual("FC_1.w_0", params[2].name)
self.assertEqual("FC_1.b_0", params[3].name)
self.assertEqual(len(params), 4)
sublayers = mlp.sublayers(True)
self.assertEqual(mlp._fc1, sublayers[0])
self.assertEqual(mlp._fc2, sublayers[1])
self.assertEqual(len(sublayers), 2)
def test_rnn(self):
np_inp = np.array([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0], [7.0, 8.0, 9.0],
[10.0, 11.0, 12.0]])
......
python/paddle/fluid/tests/unittests/test_imperative_gan.py
浏览文件 @ d79d2f68
......@@ -33,9 +33,6 @@ class Discriminator(fluid.imperative.Layer):
self._fc1 = FC(size=32, act='elu', name="d_fc1")
self._fc2 = FC(size=1, name="d_fc2")
def parameters(self):
return self._fc1.parameters() + self._fc2.parameters()
def forward(self, inputs):
x = self._fc1(inputs)
return self._fc2(x)
......@@ -48,10 +45,6 @@ class Generator(fluid.imperative.Layer):
self._fc2 = FC(size=64, act='elu', name="g_fc2")
self._fc3 = FC(size=1, name="g_fc3")
def parameters(self):
return self._fc1.parameters() + self._fc2.parameters(
) + self._fc3.parameters()
def forward(self, inputs):
x = self._fc1(inputs)
x = self._fc2(x)
......
python/paddle/fluid/tests/unittests/test_imperative_ptb_rnn.py
浏览文件 @ d79d2f68
......@@ -75,16 +75,6 @@ class SimpleLSTMRNN(fluid.imperative.Layer):
self.hidden_array.append(pre_hidden)
self.cell_array.append(pre_cell)
def parameters(self):
parameters = list()
for param in self.weight_1_arr:
parameters.append(param)
for param in self.weight_2_arr:
parameters.append(param)
for bias in self.bias_arr:
parameters.append(bias)
return parameters
def forward(self, input_embedding, init_hidden=None, init_cell=None):
res = []
for index in range(self._num_steps):
......@@ -177,12 +167,6 @@ class PtbModel(fluid.imperative.Layer):
def _build_once(self, input, label, init_hidden, init_cell):
pass
def parameters(self):
parameters = self.simple_lstm_rnn.parameters() + [
self.softmax_weight, self.softmax_bias
] + self.embedding.parameters()
return parameters
def forward(self, input, label, init_hidden, init_cell):
init_h = fluid.layers.reshape(
......
python/paddle/fluid/tests/unittests/test_imperative_resnet.py
浏览文件 @ d79d2f68
......@@ -21,7 +21,6 @@ import paddle
import paddle.fluid as fluid
from paddle.fluid import core
from paddle.fluid.layer_helper import LayerHelper
from paddle.fluid.optimizer import SGDOptimizer
from paddle.fluid.imperative.nn import Conv2D, Pool2D, BatchNorm, FC
from paddle.fluid.imperative.base import to_variable
from test_imperative_base import new_program_scope
......@@ -173,11 +172,13 @@ class ResNet(fluid.imperative.Layer):
for block in range(len(depth)):
shortcut = False
for i in range(depth[block]):
bottleneck_block = BottleneckBlock(
num_channels=num_channels,
num_filters=num_filters[block],
stride=2 if i == 0 and block != 0 else 1,
shortcut=shortcut)
bottleneck_block = self.add_sublayer(
'bb_%d_%d' % (block, i),
BottleneckBlock(
num_channels=num_channels,
num_filters=num_filters[block],
stride=2 if i == 0 and block != 0 else 1,
shortcut=shortcut))
num_channels = bottleneck_block._num_channels_out
self.bottleneck_block_list.append(bottleneck_block)
shortcut = True
......@@ -223,8 +224,7 @@ class TestImperativeResnet(unittest.TestCase):
batch_size=batch_size)
dy_param_init_value = {}
for param in fluid.default_main_program().global_block(
).all_parameters():
for param in resnet.parameters():
dy_param_init_value[param.name] = param._numpy()
for batch_id, data in enumerate(train_reader()):
......@@ -247,16 +247,14 @@ class TestImperativeResnet(unittest.TestCase):
dy_out = avg_loss._numpy()
if batch_id == 0:
for param in fluid.default_main_program().global_block(
).all_parameters():
for param in resnet.parameters():
if param.name not in dy_param_init_value:
dy_param_init_value[param.name] = param._numpy()
avg_loss._backward()
dy_grad_value = {}
for param in fluid.default_main_program().global_block(
).all_parameters():
for param in resnet.parameters():
if not param.stop_gradient:
np_array = np.array(param._ivar._grad_ivar().value()
.get_tensor())
......@@ -267,8 +265,7 @@ class TestImperativeResnet(unittest.TestCase):
resnet.clear_gradients()
dy_param_value = {}
for param in fluid.default_main_program().global_block(
).all_parameters():
for param in resnet.parameters():
dy_param_value[param.name] = param._numpy()
with new_program_scope():
......@@ -349,6 +346,7 @@ class TestImperativeResnet(unittest.TestCase):
self.assertTrue(np.allclose(static_out, dy_out))
self.assertEqual(len(dy_param_init_value), len(static_param_init_value))
for key, value in six.iteritems(static_param_init_value):
self.assertTrue(np.allclose(value, dy_param_init_value[key]))
self.assertTrue(np.isfinite(value.all()))
......
python/paddle/fluid/tests/unittests/test_ir_memory_optimize_transformer.py 0 → 100644
浏览文件 @ d79d2f68
# 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.
import os
import unittest
import paddle.fluid as fluid
import paddle.fluid.core as core
os.environ['FLAGS_eager_delete_tensor_gb'] = "0.0"
os.environ[
'RECORDIO_FILENAME'] = '/tmp/ir_memory_optimize_transformer.wmt16.recordio'
from test_parallel_executor_transformer import TestTransformer
from test_parallel_executor_transformer import transformer
# NOTE(dzhwinter): test diferent strategy colisions.
# open the eager delete tensor strategy by default.
class TestTransformerWithIR(TestTransformer):
def test_main(self):
if core.is_compiled_with_cuda():
# check python transpiler
self.check_network_convergence(
transformer,
use_cuda=True,
memory_opt=True,
use_ir_memory_optimize=False)
# check IR memory optimize
self.check_network_convergence(
transformer,
use_cuda=True,
memory_opt=False,
use_ir_memory_optimize=True)
if __name__ == '__main__':
unittest.main()
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