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提交 062556f9 编写于 作者: L Luo Tao
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Merge branch 'develop' into unify

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AUTHORS.md
浏览文件 @ 062556f9
......@@ -46,6 +46,7 @@
| tianbingsz | Tian-Bing Xu |
| tpatejko | Tomasz Patejko |
| typhoonzero | Yi Wu |
| velconia | Qi-Yang Min |
| wanghaoshuang | Hao-Shuang Wang |
| wangyang59 | Yang Wang |
| wangzhen-nlp | Zhen Wang |
......
doc/fluid/design/ir/draft.md
浏览文件 @ 062556f9
## Motivation
There is a ```gap``` between the ```Program``` defined by
user and the ```Executable``` that can be scheduled
There is a `gap` between the `Program` defined by
user and the `Executable` that can be scheduled
efficiently on heterogeneous hardware, either locally
or distributedly.
Usually, the ```gap``` is bridged by
Usually, the `gap` is bridged by
* A serious transformations with defined order.
* These transformations usually involve
```insert, delete, clustering, split, dependency analysis```.
`insert, delete, clustering, split, dependency analysis`.
* Has a simple way to verify and debug each transformation.
......@@ -38,44 +38,44 @@ design below.
#### Node
```Node``` represents an operation that performs some computation or
`Node` represents an operation that performs some computation or
a variable that is input or output of operation.
```Node```s are connected to other ```Node```s via inputs and outputs.
`Node`s are connected to other `Node`s via inputs and outputs.
Other properties (maybe device placement information) can be added
to ```Node``` in the future if it's a
common requirement of many other ```Pass```es. Otherwise, it should live
in a ```Node``` wrapper class that is private to some ```Pass``` or be
a local member of a ```Pass```.
to `Node` in the future if it's a
common requirement of many other `Pass`es. Otherwise, it should live
in a `Node` wrapper class that is private to some `Pass` or be
a local member of a `Pass`.
#### Graph
```Graph``` contains a list of ```Node```s, which are connected to
`Graph` contains a list of `Node`s, which are connected to
each other via inputs and outputs.
TODO: Better definitions for the graph.
```Graph``` can also contain ```Attribute```s. ```Attribute```s
can be ``any`` thing. For example, it can be a list of "wraper"
nodes. The ```wrapper``` nodes compose ```Node```s and provide
helper method for execution or transformation. ```Attribute```
`Graph` can also contain `Attribute`s. `Attribute`s
can be `any` thing. For example, it can be a list of "wraper"
nodes. The `wrapper` nodes compose `Node`s and provide
helper method for execution or transformation. `Attribute`
can also contain other things that describe some properties of
the ```Graph``` or ```Graph``` nodes. ```Attribute``` can be passed
across ```Pass```. However, it should be used with care.
the `Graph` or `Graph` nodes. `Attribute` can be passed
across `Pass`. However, it should be used with care.
#### Pass
```Pass``` represents a transformation of ```Graph```. Its input
is a ```Graph``` and its output is also a ```Graph```. For example,
a ```Pass``` can simply print out the ```Graph```. A ```Pass```
can also fuse some ```Graph```'s ```Node```s.
`Pass` represents a transformation of `Graph`. Its input
is a `Graph` and its output is also a `Graph`. For example,
a `Pass` can simply print out the `Graph`. A `Pass`
can also fuse some `Graph`'s `Node`s.
#### Optimize
```Optimize``` contains a series of ```Pass``` with defined order.
```Optimize``` transforms a ```Graph``` that only contains raw
modeling logic to a ```Graph``` that can be run efficiently while
`Optimize` contains a series of `Pass` with defined order.
`Optimize` transforms a `Graph` that only contains raw
modeling logic to a `Graph` that can be run efficiently while
maintaining the original modeling logic.
......
paddle/fluid/framework/CMakeLists.txt
浏览文件 @ 062556f9
......@@ -22,7 +22,12 @@ endif()
cc_test(eigen_test SRCS eigen_test.cc DEPS tensor)
nv_test(mixed_vector_test SRCS mixed_vector_test.cu DEPS place memory device_context tensor)
if(WITH_GPU)
nv_test(mixed_vector_test SRCS mixed_vector_test.cc mixed_vector_test.cu DEPS place memory device_context tensor)
else()
cc_test(mixed_vector_test SRCS mixed_vector_test.cc DEPS place memory device_context tensor)
endif()
cc_library(lod_tensor SRCS lod_tensor.cc DEPS ddim place tensor framework_proto recordio)
cc_test(lod_tensor_test SRCS lod_tensor_test.cc DEPS lod_tensor memory)
nv_test(lod_tensor_gpu_test SRCS lod_tensor_test.cu DEPS lod_tensor)
......
paddle/fluid/framework/block_desc.h
浏览文件 @ 062556f9
......@@ -88,9 +88,8 @@ class BlockDesc {
OpDesc *InsertOp(size_t index);
/*
* Remove Op and its input/output variables.
* Note that for either input or output variable, if it is also an input or
* output variable of other ops, we should remain it.
* Only remove op itself,
* do nothing to its input and output variables
*/
void RemoveOp(size_t s, size_t e);
......
paddle/fluid/framework/details/CMakeLists.txt
浏览文件 @ 062556f9
cc_library(var_handle SRCS var_handle.cc DEPS place framework_proto)
cc_library(var_handle SRCS var_handle.cc DEPS place framework_proto node)
cc_library(op_handle_base SRCS op_handle_base.cc DEPS var_handle device_context lod_tensor)
cc_library(scale_loss_grad_op_handle SRCS scale_loss_grad_op_handle.cc DEPS op_handle_base scope lod_tensor ddim memory)
cc_library(fetch_op_handle SRCS fetch_op_handle.cc DEPS op_handle_base scope lod_tensor ddim memory)
cc_library(computation_op_handle SRCS computation_op_handle.cc DEPS framework_proto scope place operator op_registry)
cc_library(rpc_op_handle SRCS rpc_op_handle.cc DEPS framework_proto scope place operator op_registry)
cc_library(ssa_graph_builder SRCS ssa_graph_builder.cc DEPS graph)
cc_library(ssa_graph_builder SRCS ssa_graph_builder.cc DEPS graph graph_helper)
cc_library(ssa_graph_printer SRCS ssa_graph_printer.cc DEPS ssa_graph_builder)
cc_library(ssa_graph_checker SRCS ssa_graph_checker.cc DEPS ssa_graph_builder)
......
paddle/fluid/framework/details/multi_devices_graph_builder.cc
浏览文件 @ 062556f9
......@@ -25,6 +25,7 @@
#include "paddle/fluid/framework/details/reduce_op_handle.h"
#include "paddle/fluid/framework/details/rpc_op_handle.h"
#include "paddle/fluid/framework/details/scale_loss_grad_op_handle.h"
#include "paddle/fluid/framework/ir/graph_helper.h"
#include "paddle/fluid/framework/ir/node.h"
#include "paddle/fluid/framework/op_info.h"
#include "paddle/fluid/framework/scope.h"
......@@ -67,7 +68,8 @@ MultiDevSSAGraphBuilder::MultiDevSSAGraphBuilder(
}
}
void MultiDevSSAGraphBuilder::CreateOpHandleIOs(Graph *result, ir::Node *node,
void MultiDevSSAGraphBuilder::CreateOpHandleIOs(ir::Graph *result,
ir::Node *node,
size_t place_id) const {
auto p = places_[place_id];
auto *op_handle = result->Get<GraphOps>("ops").back().get();
......@@ -92,12 +94,11 @@ void MultiDevSSAGraphBuilder::CreateOpHandleIOs(Graph *result, ir::Node *node,
}
std::vector<std::string> MultiDevSSAGraphBuilder::FindDistTrainSendVars(
const std::vector<std::unique_ptr<ir::Node>> &nodes) const {
const std::vector<ir::Node *> &nodes) const {
std::vector<std::string> send_vars;
// since parameters are all in block 0,
// it's enough to only scan send ops in block 0
for (auto &node : nodes) {
if (node->NodeType() != ir::Node::Type::kOperation) continue;
OpDesc *op = node->Op();
// TODO(Yancey1989): use a graceful method to find send op,
// instead of the the hard code string
......@@ -112,10 +113,9 @@ std::vector<std::string> MultiDevSSAGraphBuilder::FindDistTrainSendVars(
}
std::vector<std::string> MultiDevSSAGraphBuilder::FindDistTrainRecvVars(
const std::vector<std::unique_ptr<ir::Node>> &nodes) const {
const std::vector<ir::Node *> &nodes) const {
std::vector<std::string> recv_vars;
for (auto &node : nodes) {
if (node->NodeType() != ir::Node::Type::kOperation) continue;
OpDesc *op = node->Op();
// TODO(Yancey1989): use a graceful method to find recv op,
// instead of the hard code string
......@@ -170,6 +170,7 @@ size_t MultiDevSSAGraphBuilder::GetAppropriateDeviceID(
const std::vector<std::string> &var_names) const {
int64_t numel_sum = 0;
for (auto var_name : var_names) {
if (all_vars_.find(var_name) == all_vars_.end()) continue;
auto var_desc = all_vars_.at(var_name);
PADDLE_ENFORCE_NOT_NULL(var_desc);
auto dim = framework::make_ddim(var_desc->GetShape());
......@@ -186,19 +187,70 @@ size_t MultiDevSSAGraphBuilder::GetAppropriateDeviceID(
return dev_id;
}
std::unique_ptr<Graph> MultiDevSSAGraphBuilder::Apply(
std::unique_ptr<Graph> graph) const {
// Rebuild the graph structure.
auto nodes = std::move(graph->nodes);
graph->nodes.clear();
// Topology sort the graph nodes from inputs to outputs.
// Since SSAGraphBuilder depends on forward/backward nodes to assign devices
// to parameter/gradients before optimizer ops, topo sort is insufficient. (
// some optimizer ops might not depend on any nodes), we manually move all
// optimizer nodes after last backward nodes.
// However, the assumption by SSAGraphBuilder should be relaxed in the future.
std::vector<ir::Node *> SortOpsAndDelayOptimizeOp(const ir::Graph &graph) {
std::vector<ir::Node *> ret = ir::TopologySortOperations(graph);
size_t last_backward = 0;
for (size_t i = 0; i < ret.size(); ++i) {
if (boost::get<int>(
ret[i]->Op()->GetAttr(OpProtoAndCheckerMaker::OpRoleAttrName())) ==
static_cast<int>(OpRole::kBackward)) {
last_backward = i;
}
}
std::vector<ir::Node *> optimize_ops;
std::vector<ir::Node *> sorted_ret;
for (size_t i = 0; i < ret.size(); ++i) {
if (i < last_backward) {
if (boost::get<int>(ret[i]->Op()->GetAttr(
OpProtoAndCheckerMaker::OpRoleAttrName())) ==
static_cast<int>(OpRole::kOptimize)) {
optimize_ops.push_back(ret[i]);
} else {
sorted_ret.push_back(ret[i]);
}
} else if (i == last_backward) {
sorted_ret.push_back(ret[i]);
// Verify that no operations before optimize ops depends on optimize ops.
std::unordered_set<ir::Node *> optimize_set(optimize_ops.begin(),
optimize_ops.end());
for (ir::Node *n : sorted_ret) {
for (ir::Node *in : n->inputs) {
for (ir::Node *pre_n : in->inputs) {
PADDLE_ENFORCE(optimize_set.find(pre_n) == optimize_set.end(),
"optimize operations cannot be depended by forward "
"or backward node %s -> %s",
pre_n->Name(), n->Name());
}
}
}
sorted_ret.insert(sorted_ret.end(), optimize_ops.begin(),
optimize_ops.end());
} else {
sorted_ret.push_back(ret[i]);
}
}
return sorted_ret;
}
std::unique_ptr<ir::Graph> MultiDevSSAGraphBuilder::Apply(
std::unique_ptr<ir::Graph> graph) const {
// Give the topology sort order and rebuild the graph structure.
std::vector<ir::Node *> sorted_ops = SortOpsAndDelayOptimizeOp(*graph);
auto nodes = graph->ReleaseNodes();
ir::Graph &result = *graph;
for (auto &node : nodes) {
if (node->NodeType() == ir::Node::Type::kVariable) {
if (node->NodeType() == ir::Node::Type::kVariable && node->Var()) {
all_vars_.emplace(node->Name(), node->Var());
}
}
Graph &result = *graph;
std::unordered_set<std::string> og_has_been_broadcast;
// We cannot invoke resize. It is a bug of GCC 4.8
......@@ -207,9 +259,9 @@ std::unique_ptr<Graph> MultiDevSSAGraphBuilder::Apply(
result.Set("ops", new GraphOps);
// find send/recv vars so that we can place the distributed training
// realted op in the place 0
auto send_vars = FindDistTrainSendVars(nodes);
auto recv_vars = FindDistTrainRecvVars(nodes);
// related op in the place 0
auto send_vars = FindDistTrainSendVars(sorted_ops);
auto recv_vars = FindDistTrainRecvVars(sorted_ops);
std::vector<std::unordered_set<std::string>> bcast_var_name_set;
bcast_var_name_set.resize(places_.size());
......@@ -217,22 +269,18 @@ std::unique_ptr<Graph> MultiDevSSAGraphBuilder::Apply(
size_t cur_device_id = 0;
bool is_forwarding = true;
// NOTE: Currently, passes before SSAGraphBuilder cannot reorder
// forward, backward nodes. E.g. you can't append an forward node
// at the end of the node list.
// TODO(panyx0718): FIXME: Needs to sort by forward->backward order.
for (auto &node : nodes) {
if (node->NodeType() != ir::Node::Type::kOperation) continue;
for (ir::Node *node : sorted_ops) {
if (boost::get<int>(
node->Op()->GetAttr(OpProtoAndCheckerMaker::OpRoleAttrName())) ==
static_cast<int>(OpRole::kRPC)) {
CreateRPCOp(&result, node.get());
} else if (IsDistTrainOp(node.get(), send_vars, recv_vars)) {
CreateDistTrainOp(&result, node.get());
} else if (IsScaleLossOp(node.get())) {
CreateRPCOp(&result, node);
} else if (IsDistTrainOp(node, send_vars, recv_vars)) {
CreateDistTrainOp(&result, node);
} else if (IsScaleLossOp(node)) {
// user can customize loss@grad if not use_default_grad_scale_
if (strategy_.gradient_scale_ !=
BuildStrategy::GradientScaleStrategy::kCustomized) {
// TODO(paddle-dev): Why is there no input for this op_handle?
CreateScaleLossGradOp(&result);
}
// This assumes the backward generating code will ensure IsScaleLossOp
......@@ -241,24 +289,23 @@ std::unique_ptr<Graph> MultiDevSSAGraphBuilder::Apply(
// the block.
is_forwarding = false;
} else {
int op_dev_id = GetOpDeviceID(node.get());
int op_dev_id = GetOpDeviceID(node);
if (op_dev_id != -1) { // This op only runs on one specific device.
CreateComputationalOp(&result, node.get(), op_dev_id);
CreateComputationalOp(&result, node, op_dev_id);
for (ir::Node *n : node->outputs) {
var_name_on_devices_.emplace(n->Name(), op_dev_id);
}
} else {
// This op runs on all devices, and its output may have parameter's
// gradients.
// TODO(paddle-dev): Why is so special about "read" op?
if (node->Op()->Type() == "read" && strategy_.enable_data_balance_) {
node->Op()->SetAttr("throw_eof_exp", false);
CreateComputationalOps(&result, node.get(), places_.size());
// TODO(paddle-dev): builder shouldn't depend on the out logic of
// a specific op.
CreateComputationalOps(&result, node, places_.size());
const auto &data_var_names = node->Op()->Output("Out");
InsertDataBalanceOp(&result, data_var_names);
} else {
CreateComputationalOps(&result, node.get(), places_.size());
CreateComputationalOps(&result, node, places_.size());
}
if (!is_forwarding && places_.size() > 1) {
......@@ -322,17 +369,17 @@ std::unique_ptr<Graph> MultiDevSSAGraphBuilder::Apply(
}
}
}
/*
Dependency graph has been constructed. However, there are still data
hazards need to be handled.
*/
Dependency graph has been constructed. However, there are still data
hazards need to be handled.
*/
PolishGraphToSupportDataHazards(&result);
/*
* Only variables should be the leaves of graph.
*/
AddOutputToLeafOps(&result);
PADDLE_ENFORCE(!ir::HasCircle(result));
return graph;
}
......@@ -357,7 +404,7 @@ void MultiDevSSAGraphBuilder::SetCommunicationContext(
#endif
}
void MultiDevSSAGraphBuilder::CreateBroadcastOp(Graph *result,
void MultiDevSSAGraphBuilder::CreateBroadcastOp(ir::Graph *result,
const std::string &p_name,
size_t src_dev_id) const {
#ifdef PADDLE_WITH_CUDA
......@@ -387,7 +434,7 @@ void MultiDevSSAGraphBuilder::CreateBroadcastOp(Graph *result,
}
}
void MultiDevSSAGraphBuilder::CreateComputationalOp(Graph *result,
void MultiDevSSAGraphBuilder::CreateComputationalOp(ir::Graph *result,
ir::Node *node,
int dev_id) const {
result->Get<GraphOps>("ops").emplace_back(
......@@ -396,7 +443,7 @@ void MultiDevSSAGraphBuilder::CreateComputationalOp(Graph *result,
CreateOpHandleIOs(result, node, dev_id);
}
void MultiDevSSAGraphBuilder::InsertAllReduceOp(Graph *result,
void MultiDevSSAGraphBuilder::InsertAllReduceOp(ir::Graph *result,
const std::string &og) const {
#ifdef PADDLE_WITH_CUDA
result->Get<GraphOps>("ops").emplace_back(new AllReduceOpHandle(
......@@ -426,7 +473,7 @@ void MultiDevSSAGraphBuilder::InsertAllReduceOp(Graph *result,
}
void MultiDevSSAGraphBuilder::InsertDataBalanceOp(
Graph *result, const std::vector<std::string> &datas) const {
ir::Graph *result, const std::vector<std::string> &datas) const {
#ifdef PADDLE_WITH_CUDA
result->Get<GraphOps>("ops").emplace_back(new DataBalanceOpHandle(
result->CreateEmptyNode("data_balance", ir::Node::Type::kOperation),
......@@ -479,8 +526,8 @@ int MultiDevSSAGraphBuilder::GetOpDeviceID(ir::Node *node) const {
PADDLE_ENFORCE_EQ(param_grad.size(), 2U);
int dev_id = GetVarDeviceID(param_grad[1]);
PADDLE_ENFORCE_NE(dev_id, -1, "dev_id should not be -1.[%s, %s]",
node->Op()->Type(), param_grad[0]);
PADDLE_ENFORCE_NE(dev_id, -1, "dev_id should not be -1.[%s, %s, %s]",
node->Op()->Type(), param_grad[0], param_grad[1]);
return dev_id;
}
......@@ -489,7 +536,7 @@ int MultiDevSSAGraphBuilder::GetVarDeviceID(const std::string &varname) const {
return got == var_name_on_devices_.end() ? -1 : got->second;
}
void MultiDevSSAGraphBuilder::CreateScaleLossGradOp(Graph *result) const {
void MultiDevSSAGraphBuilder::CreateScaleLossGradOp(ir::Graph *result) const {
for (size_t i = 0; i < places_.size(); ++i) {
// Insert ScaleCost OpHandle
#ifdef PADDLE_WITH_CUDA
......@@ -519,7 +566,7 @@ void MultiDevSSAGraphBuilder::CreateScaleLossGradOp(Graph *result) const {
}
}
void MultiDevSSAGraphBuilder::CreateComputationalOps(Graph *result,
void MultiDevSSAGraphBuilder::CreateComputationalOps(ir::Graph *result,
ir::Node *node,
size_t num_places) const {
for (size_t scope_idx = 0; scope_idx < num_places; ++scope_idx) {
......@@ -531,7 +578,7 @@ void MultiDevSSAGraphBuilder::CreateComputationalOps(Graph *result,
}
}
VarHandle *MultiDevSSAGraphBuilder::CreateReduceOp(Graph *result,
VarHandle *MultiDevSSAGraphBuilder::CreateReduceOp(ir::Graph *result,
const std::string &og,
int dst_dev_id) const {
#ifdef PADDLE_WITH_CUDA
......@@ -564,12 +611,11 @@ VarHandle *MultiDevSSAGraphBuilder::CreateReduceOp(Graph *result,
// Find the first occurence of `prev_op_name` and make current `op` depend
// on it.
void MultiDevSSAGraphBuilder::ConnectOp(Graph *result, OpHandleBase *op,
void MultiDevSSAGraphBuilder::ConnectOp(ir::Graph *result, OpHandleBase *op,
const std::string &prev_op_name) const {
for (auto &prev_op : result->Get<GraphOps>("ops")) {
if (prev_op->Name() == prev_op_name) {
auto *dep_var = new DummyVarHandle(
result->CreateEmptyNode("dummy", ir::Node::Type::kVariable));
auto *dep_var = new DummyVarHandle(result->CreateControlDepVar());
prev_op->AddOutput(dep_var);
result->Get<GraphDepVars>("dep_vars").emplace(dep_var);
op->AddInput(dep_var);
......@@ -577,7 +623,7 @@ void MultiDevSSAGraphBuilder::ConnectOp(Graph *result, OpHandleBase *op,
}
}
void MultiDevSSAGraphBuilder::CreateDistTrainOp(Graph *result,
void MultiDevSSAGraphBuilder::CreateDistTrainOp(ir::Graph *result,
ir::Node *node) const {
int op_dev_id = -1;
std::vector<std::string> input_var_names;
......@@ -591,6 +637,7 @@ void MultiDevSSAGraphBuilder::CreateDistTrainOp(Graph *result,
if (node->Op()->Type() == "split_byref" ||
node->Op()->Type() == "split_selected_rows") {
// TODO(paddle-dev): getting the first var is not safe.
op_dev_id = GetVarDeviceID(input_var_names[0]);
if (strategy_.reduce_ == BuildStrategy::ReduceStrategy::kAllReduce) {
op_dev_id = GetAppropriateDeviceID(input_var_names);
......@@ -624,10 +671,14 @@ void MultiDevSSAGraphBuilder::CreateDistTrainOp(Graph *result,
}
// Create RPC related op handles that connects its in ops and out ops.
void MultiDevSSAGraphBuilder::CreateRPCOp(Graph *result, ir::Node *node) const {
void MultiDevSSAGraphBuilder::CreateRPCOp(ir::Graph *result,
ir::Node *node) const {
int op_dev_id = -1;
if (node->Op()->Type() == "send") {
// TODO(paddle-dev): getting the first var is not safe.
op_dev_id = GetVarDeviceID(node->inputs[0]->Name());
PADDLE_ENFORCE(!ir::IsControlDepVar(*node->inputs[0]),
"This hack no longer holds, please fix.");
// the variable name which contains .block means it was splited by
// split_byref op
// so that we can balance the variable blocks to all the pserver
......
paddle/fluid/framework/details/multi_devices_graph_builder.h
浏览文件 @ 062556f9
......@@ -46,11 +46,13 @@ class MultiDevSSAGraphBuilder : public SSAGraphBuilder {
const std::vector<Scope *> &local_scopes,
const BuildStrategy &strategy);
#endif
std::unique_ptr<Graph> Apply(std::unique_ptr<Graph> graph) const override;
std::unique_ptr<ir::Graph> Apply(
std::unique_ptr<ir::Graph> graph) const override;
int GetVarDeviceID(const std::string &varname) const override;
private:
void CreateOpHandleIOs(Graph *result, ir::Node *node, size_t device_id) const;
void CreateOpHandleIOs(ir::Graph *result, ir::Node *node,
size_t device_id) const;
private:
std::string loss_var_name_;
......@@ -64,8 +66,8 @@ class MultiDevSSAGraphBuilder : public SSAGraphBuilder {
bool IsScaleLossOp(ir::Node *node) const;
void CreateRPCOp(Graph *result, ir::Node *node) const;
void CreateDistTrainOp(Graph *result, ir::Node *node) const;
void CreateRPCOp(ir::Graph *result, ir::Node *node) const;
void CreateDistTrainOp(ir::Graph *result, ir::Node *node) const;
/**
* Is this operator as the end-point operator before/after send operator.
......@@ -74,21 +76,22 @@ class MultiDevSSAGraphBuilder : public SSAGraphBuilder {
const std::vector<std::string> &recv_vars) const;
std::vector<std::string> FindDistTrainSendVars(
const std::vector<std::unique_ptr<ir::Node>> &nodes) const;
const std::vector<ir::Node *> &nodes) const;
std::vector<std::string> FindDistTrainRecvVars(
const std::vector<std::unique_ptr<ir::Node>> &nodes) const;
const std::vector<ir::Node *> &nodes) const;
void ConnectOp(Graph *result, OpHandleBase *op,
void ConnectOp(ir::Graph *result, OpHandleBase *op,
const std::string &prev_op_name) const;
void CreateComputationalOps(Graph *result, ir::Node *node,
void CreateComputationalOps(ir::Graph *result, ir::Node *node,
size_t num_places) const;
void CreateScaleLossGradOp(Graph *result) const;
VarHandle *CreateReduceOp(Graph *result, const std::string &og,
void CreateScaleLossGradOp(ir::Graph *result) const;
VarHandle *CreateReduceOp(ir::Graph *result, const std::string &og,
int dst_dev_id) const;
void CreateComputationalOp(Graph *result, ir::Node *node, int dev_id) const;
void CreateComputationalOp(ir::Graph *result, ir::Node *node,
int dev_id) const;
bool IsParameterGradientOnce(
const std::string &og,
......@@ -96,12 +99,12 @@ class MultiDevSSAGraphBuilder : public SSAGraphBuilder {
int GetOpDeviceID(ir::Node *node) const;
void InsertAllReduceOp(Graph *result, const std::string &og) const;
void InsertAllReduceOp(ir::Graph *result, const std::string &og) const;
void InsertDataBalanceOp(Graph *result,
void InsertDataBalanceOp(ir::Graph *result,
const std::vector<std::string> &datas) const;
void CreateBroadcastOp(Graph *result, const std::string &p_name,
void CreateBroadcastOp(ir::Graph *result, const std::string &p_name,
size_t src_dev_id) const;
bool IsSparseGradient(const std::string &og) const;
......
paddle/fluid/framework/details/rpc_op_handle.cc
浏览文件 @ 062556f9
......@@ -13,6 +13,7 @@
// limitations under the License.
#include "paddle/fluid/framework/details/rpc_op_handle.h"
#include "paddle/fluid/framework/ir/graph.h"
namespace paddle {
namespace framework {
......@@ -33,7 +34,7 @@ void RPCOpHandle::RunImpl() {
for (auto *in : inputs_) {
auto &p = static_cast<VarHandle *>(in)->place_;
// FIXME(Yancey1989): need a better solution instead of use DebugString()
if (in->DebugString() == "dummy") { // HACK
if (ir::IsControlDepVar(*in->Node())) { // HACK
continue;
}
if (in->GeneratedOp()) {
......
paddle/fluid/framework/details/ssa_graph_builder.cc
浏览文件 @ 062556f9
......@@ -17,7 +17,7 @@
namespace paddle {
namespace framework {
namespace details {
void SSAGraphBuilder::PolishGraphToSupportDataHazards(Graph *graph) {
void SSAGraphBuilder::PolishGraphToSupportDataHazards(ir::Graph *graph) {
for (auto &var_map : graph->Get<GraphVars>("vars")) {
for (auto &name_pair : var_map) {
if (name_pair.second.size() <= 1) {
......@@ -36,9 +36,18 @@ void SSAGraphBuilder::PolishGraphToSupportDataHazards(Graph *graph) {
// Read Write is the same op.
continue;
}
bool has_dep = false;
for (auto *r_out : read_op->Outputs()) {
for (auto *w_in : write_op->Inputs()) {
if (r_out->Node() == w_in->Node()) {
has_dep = true;
break;
}
}
}
if (has_dep) continue;
auto *dep_var = new DummyVarHandle(
graph->CreateEmptyNode("dummy", ir::Node::Type::kVariable));
auto *dep_var = new DummyVarHandle(graph->CreateControlDepVar());
read_op->AddOutput(dep_var);
write_op->AddInput(dep_var);
graph->Get<GraphDepVars>("dep_vars").emplace(dep_var);
......@@ -49,7 +58,7 @@ void SSAGraphBuilder::PolishGraphToSupportDataHazards(Graph *graph) {
}
VarHandle *SSAGraphBuilder::CreateOrGetLatestVarHandle(
Graph *graph, ir::Node *node, const platform::Place &place,
ir::Graph *graph, ir::Node *node, const platform::Place &place,
size_t place_offset) {
auto &var_holders = graph->Get<GraphVars>("vars")[place_offset];
auto &var_holder = var_holders[node->Name()];
......@@ -70,7 +79,7 @@ VarHandle *SSAGraphBuilder::CreateOrGetLatestVarHandle(
return var;
}
void SSAGraphBuilder::CreateOpOutput(Graph *graph, OpHandleBase *op_handle,
void SSAGraphBuilder::CreateOpOutput(ir::Graph *graph, OpHandleBase *op_handle,
ir::Node *new_node,
const platform::Place &place,
size_t place_offset) {
......@@ -82,13 +91,12 @@ void SSAGraphBuilder::CreateOpOutput(Graph *graph, OpHandleBase *op_handle,
op_handle->AddOutput(var);
}
void SSAGraphBuilder::AddOutputToLeafOps(Graph *graph) {
void SSAGraphBuilder::AddOutputToLeafOps(ir::Graph *graph) {
for (auto &op : graph->Get<GraphOps>("ops")) {
if (!op->Outputs().empty()) {
continue;
}
auto *dummy_leaf = new DummyVarHandle(
graph->CreateEmptyNode("dummy", ir::Node::Type::kVariable));
auto *dummy_leaf = new DummyVarHandle(graph->CreateControlDepVar());
graph->Get<GraphDepVars>("dep_vars").emplace(dummy_leaf);
op->AddOutput(dummy_leaf);
}
......
paddle/fluid/framework/details/ssa_graph_builder.h
浏览文件 @ 062556f9
......@@ -57,26 +57,23 @@ class SSAGraphBuilder : public ir::Pass {
DISABLE_COPY_AND_ASSIGN(SSAGraphBuilder);
protected:
/**
* We only handle write after read(WAR), since it should not have a write
* after write in program. If there are write after write operators, we need
* prune them.
*
* https://en.wikipedia.org/wiki/Hazard_(computer_architecture)#Write_after_read_(WAR)
*/
static void PolishGraphToSupportDataHazards(Graph *graph);
static VarHandle *CreateOrGetLatestVarHandle(Graph *graph, ir::Node *node,
/*
Dependency graph has been constructed. However, there are still data
hazards need to be handled.
*/
static void PolishGraphToSupportDataHazards(ir::Graph *graph);
static VarHandle *CreateOrGetLatestVarHandle(ir::Graph *graph, ir::Node *node,
const platform::Place &place,
size_t place_offset);
// Add an output variable (each_var_name, place, place_offset) to op_handle,
// which belongs to graph
static void CreateOpOutput(Graph *graph, OpHandleBase *op_handle,
static void CreateOpOutput(ir::Graph *graph, OpHandleBase *op_handle,
ir::Node *new_node, const platform::Place &place,
size_t place_offset);
static void AddOutputToLeafOps(Graph *graph);
static void AddOutputToLeafOps(ir::Graph *graph);
};
} // namespace details
} // namespace framework
......
paddle/fluid/framework/details/ssa_graph_checker.cc
浏览文件 @ 062556f9
......@@ -20,7 +20,7 @@ namespace paddle {
namespace framework {
namespace details {
bool SSAGraghBuilderWithChecker::IsValidGraph(const Graph *graph) const {
bool SSAGraghBuilderWithChecker::IsValidGraph(const ir::Graph *graph) const {
std::unordered_map<OpHandleBase *, size_t> pending_ops;
std::unordered_set<VarHandleBase *> pending_vars;
std::unordered_set<VarHandleBase *> ready_vars;
......
paddle/fluid/framework/details/ssa_graph_checker.h
浏览文件 @ 062556f9
......@@ -28,7 +28,8 @@ class SSAGraghBuilderWithChecker : public SSAGraphBuilder {
std::unique_ptr<SSAGraphBuilder>&& builder)
: builder_(std::move(builder)) {}
std::unique_ptr<Graph> Apply(std::unique_ptr<Graph> graph) const override {
std::unique_ptr<ir::Graph> Apply(
std::unique_ptr<ir::Graph> graph) const override {
auto new_graph = builder_->Apply(std::move(graph));
PADDLE_ENFORCE(IsValidGraph(new_graph.get()));
return new_graph;
......@@ -38,7 +39,7 @@ class SSAGraghBuilderWithChecker : public SSAGraphBuilder {
return builder_->GetVarDeviceID(var_name);
}
bool IsValidGraph(const Graph* graph) const;
bool IsValidGraph(const ir::Graph* graph) const;
private:
std::unique_ptr<SSAGraphBuilder> builder_;
......
paddle/fluid/framework/details/ssa_graph_printer.cc
浏览文件 @ 062556f9
......@@ -21,7 +21,7 @@ namespace framework {
namespace details {
template <typename Callback>
static inline void IterAllVar(const Graph &graph, Callback callback) {
static inline void IterAllVar(const ir::Graph &graph, Callback callback) {
for (auto &each : graph.Get<GraphVars>("vars")) {
for (auto &pair1 : each) {
for (auto &pair2 : pair1.second) {
......@@ -35,7 +35,7 @@ static inline void IterAllVar(const Graph &graph, Callback callback) {
}
}
void GraphvizSSAGraphPrinter::Print(const Graph &graph,
void GraphvizSSAGraphPrinter::Print(const ir::Graph &graph,
std::ostream &sout) const {
size_t var_id = 0;
std::unordered_map<const VarHandleBase *, size_t> vars;
......
paddle/fluid/framework/details/ssa_graph_printer.h
浏览文件 @ 062556f9
......@@ -25,12 +25,12 @@ namespace details {
class SSAGraphPrinter {
public:
virtual ~SSAGraphPrinter() {}
virtual void Print(const Graph& graph, std::ostream& sout) const = 0;
virtual void Print(const ir::Graph& graph, std::ostream& sout) const = 0;
};
class GraphvizSSAGraphPrinter : public SSAGraphPrinter {
public:
void Print(const Graph& graph, std::ostream& sout) const override;
void Print(const ir::Graph& graph, std::ostream& sout) const override;
};
class SSAGraghBuilderWithPrinter : public SSAGraphBuilder {
......@@ -50,7 +50,8 @@ class SSAGraghBuilderWithPrinter : public SSAGraphBuilder {
stream_ptr_(std::move(sout)),
stream_ref_(*stream_ptr_) {}
std::unique_ptr<Graph> Apply(std::unique_ptr<Graph> graph) const override {
std::unique_ptr<ir::Graph> Apply(
std::unique_ptr<ir::Graph> graph) const override {
auto new_graph = builder_->Apply(std::move(graph));
printer_->Print(*new_graph, stream_ref_);
return new_graph;
......
paddle/fluid/framework/details/threaded_ssa_graph_executor.cc
浏览文件 @ 062556f9
......@@ -21,7 +21,8 @@ namespace framework {
namespace details {
ThreadedSSAGraphExecutor::ThreadedSSAGraphExecutor(
const ExecutionStrategy &strategy, const std::vector<Scope *> &local_scopes,
const std::vector<platform::Place> &places, std::unique_ptr<Graph> &&graph)
const std::vector<platform::Place> &places,
std::unique_ptr<ir::Graph> &&graph)
: graph_(std::move(graph)),
pool_(strategy.num_threads_ >= 2 ? new ::ThreadPool(strategy.num_threads_)
: nullptr),
......
paddle/fluid/framework/details/threaded_ssa_graph_executor.h
浏览文件 @ 062556f9
......@@ -40,7 +40,7 @@ class ThreadedSSAGraphExecutor : public SSAGraphExecutor {
ThreadedSSAGraphExecutor(const ExecutionStrategy &strategy,
const std::vector<Scope *> &local_scopes,
const std::vector<platform::Place> &places,
std::unique_ptr<Graph> &&graph);
std::unique_ptr<ir::Graph> &&graph);
// Run a SSAGraph by a thread pool
// Use topological sort algorithm
......@@ -53,7 +53,7 @@ class ThreadedSSAGraphExecutor : public SSAGraphExecutor {
details::OpHandleBase *op);
private:
std::unique_ptr<Graph> graph_;
std::unique_ptr<ir::Graph> graph_;
std::unique_ptr<::ThreadPool> pool_;
std::vector<Scope *> local_scopes_;
std::vector<platform::Place> places_;
......
paddle/fluid/framework/details/var_handle.cc
浏览文件 @ 062556f9
......@@ -26,7 +26,7 @@ std::string VarHandle::DebugString() const {
return ss.str();
}
std::string DummyVarHandle::DebugString() const { return "dummy"; }
std::string DummyVarHandle::DebugString() const { return node_->Name(); }
} // namespace details
} // namespace framework
} // namespace paddle
paddle/fluid/framework/ir/CMakeLists.txt
浏览文件 @ 062556f9
cc_library(node SRCS node.cc DEPS proto_desc)
cc_library(graph SRCS graph.cc DEPS node)
cc_library(graph_helper SRCS graph_helper.cc DEPS graph)
cc_library(pass SRCS pass.cc DEPS graph node)
cc_test(graph_test SRCS graph_test.cc DEPS graph proto_desc op_registry)
cc_test(graph_test SRCS graph_test.cc DEPS graph op_registry)
cc_test(graph_helper_test SRCS graph_helper_test.cc DEPS graph_helper op_registry)
paddle/fluid/framework/ir/graph.cc
浏览文件 @ 062556f9
......@@ -12,14 +12,18 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include <algorithm>
#include <unordered_set>
#include "paddle/fluid/framework/ir/graph.h"
#include "paddle/fluid/framework/op_proto_maker.h"
#include "paddle/fluid/framework/program_desc.h"
#include "paddle/fluid/framework/var_desc.h"
namespace paddle {
namespace framework {
namespace ir {
// NOTE(paddle-dev): This graph contains circle.
Graph::Graph(const ProgramDesc &program) : program_(program) {
VLOG(3) << "block in program:" << program_.Size();
std::unordered_map<std::string, VarDesc *> all_vars;
......@@ -27,40 +31,87 @@ Graph::Graph(const ProgramDesc &program) : program_(program) {
all_vars.emplace(var->Name(), var);
}
std::map<std::string, ir::Node *> var_nodes;
std::map<std::string, std::vector<ir::Node *>> var_nodes;
for (auto *op : program.Block(0).AllOps()) {
ir::Node *node = CreateOpNode(op);
// For input args, reuse the same var name if it was created before.
// Otherwise, create a new one.
for (auto &each_var_name : op->InputArgumentNames()) {
ir::Node *var = nullptr;
if (var_nodes.find(each_var_name) != var_nodes.end()) {
var = var_nodes.at(each_var_name);
var = var_nodes.at(each_var_name).back();
} else if (all_vars.count(each_var_name) != 0) {
var = CreateVarNode(all_vars.at(each_var_name));
var_nodes[each_var_name] = var;
var_nodes[each_var_name].push_back(var);
} else {
// TODO(paddle-dev): Seems some assumption doesn't hold?
VLOG(3) << op->Type()
<< " input var not in all_var list: " << each_var_name;
// Operation input var can be optional (dispensable). Which means
// the operation doesn't really need the var at runtime. In this
// case, the no-existed var is ready at the beginning.
var = CreateEmptyNode(each_var_name, ir::Node::Type::kVariable);
var_nodes[each_var_name] = var;
var_nodes[each_var_name].push_back(var);
}
node->inputs.push_back(var);
var->outputs.push_back(node);
}
// For output args, always create a new var.
for (auto &each_var_name : op->OutputArgumentNames()) {
ir::Node *var = nullptr;
if (var_nodes.find(each_var_name) != var_nodes.end()) {
var = var_nodes.at(each_var_name);
} else {
var = CreateVarNode(all_vars.at(each_var_name));
var_nodes[each_var_name] = var;
}
ir::Node *var = CreateVarNode(all_vars.at(each_var_name));
var_nodes[each_var_name].push_back(var);
node->outputs.push_back(var);
var->inputs.push_back(node);
}
}
/**
* We only handle write after read(WAR), since it should not have a write
* after write in program. If there are write after write operators, we need
* prune them.
*
* https://en.wikipedia.org/wiki/Hazard_(computer_architecture)#Write_after_read_(WAR)
*/
for (auto &var : var_nodes) {
auto &versions = var.second;
if (versions.size() <= 1) continue;
auto it_new = versions.rbegin();
auto it_old = versions.rbegin();
++it_old;
for (; it_old != versions.rend(); it_new = it_old, ++it_old) {
ir::Node *write_op =
(*it_new)->inputs.empty() ? nullptr : (*it_new)->inputs[0];
const auto &read_ops = (*it_old)->outputs;
for (auto *read_op : read_ops) {
// Manually add a dependency var from read_op to write_op;
if (read_op == write_op) {
// Read Write is the same op.
continue;
}
// 2 ops might have been connected via other vars.
bool has_dep = false;
for (ir::Node *r_out : read_op->outputs) {
for (ir::Node *w_in : write_op->inputs) {
if (r_out == w_in) {
has_dep = true;
break;
}
}
}
if (has_dep) continue;
ir::Node *dep_var = CreateControlDepVar();
read_op->outputs.push_back(dep_var);
dep_var->inputs.push_back(read_op);
write_op->inputs.push_back(dep_var);
dep_var->outputs.push_back(write_op);
}
}
}
}
bool IsControlDepVar(const ir::Node &var) {
return var.Name().find(ir::Node::kControlDepVarName) != std::string::npos;
}
} // namespace ir
} // namespace framework
} // namespace paddle
paddle/fluid/framework/ir/graph.h
浏览文件 @ 062556f9
......@@ -26,13 +26,14 @@ limitations under the License. */
namespace paddle {
namespace framework {
namespace ir {
class Graph {
public:
explicit Graph(const ProgramDesc& program);
explicit Graph(const ProgramDesc &program);
virtual ~Graph() {
for (auto& attr : attrs_) {
for (auto &attr : attrs_) {
attr_dels_[attr.first]();
}
attrs_.clear();
......@@ -40,12 +41,12 @@ class Graph {
}
template <typename AttrType>
AttrType& Get(const std::string& attr_name) const {
return *boost::any_cast<AttrType*>(attrs_.at(attr_name));
AttrType &Get(const std::string &attr_name) const {
return *boost::any_cast<AttrType *>(attrs_.at(attr_name));
}
template <typename AttrType>
void Set(const std::string& attr_name, AttrType* attr) {
void Set(const std::string &attr_name, AttrType *attr) {
PADDLE_ENFORCE(attrs_.count(attr_name) == 0);
attrs_[attr_name] = attr;
attr_dels_[attr_name] = [attr, attr_name]() {
......@@ -54,29 +55,70 @@ class Graph {
};
}
ir::Node* CreateVarNode(VarDesc* var_desc) {
nodes.emplace_back(new ir::Node(var_desc));
return nodes.back().get();
const std::unordered_set<ir::Node *> &Nodes() const { return node_set_; }
// Create a normal variable with non-null VarDesc.
ir::Node *CreateVarNode(VarDesc *var_desc) {
return AddNode(new ir::Node(var_desc));
}
// Create a normal runnable operator with OpDesc.
ir::Node *CreateOpNode(OpDesc *op_desc) {
return AddNode(new ir::Node(op_desc));
}
ir::Node* CreateOpNode(OpDesc* op_desc) {
nodes.emplace_back(new ir::Node(op_desc));
return nodes.back().get();
// Create a control dependency var that connects 2 operations. The
// var doesn't hold any data. Other than that, it's no different from
// other var, considering dependency analysis.
ir::Node *CreateControlDepVar() {
// TODO(panyx0718): control var name should be really unique.
const std::string name = string::Sprintf(
"%s@%llu", ir::Node::kControlDepVarName, node_set_.size());
return AddNode(new ir::Node(name, ir::Node::Type::kVariable));
}
ir::Node* CreateEmptyNode(const std::string& name, ir::Node::Type type) {
nodes.emplace_back(new ir::Node(name, type));
return nodes.back().get();
// A more free style way of creating a graph node. Mostly use for test
// or "copy" from another node. Avoid using it if possible.
ir::Node *CreateEmptyNode(const std::string &name, ir::Node::Type type) {
return AddNode(new ir::Node(name, type));
}
std::vector<std::unique_ptr<ir::Node>> nodes;
// Clear all node information of the graph and return the ownership of the
// nodes.
std::vector<std::unique_ptr<ir::Node>> ReleaseNodes() {
std::vector<std::unique_ptr<ir::Node>> ret;
for (auto &n : nodes_) {
ret.emplace_back(n.second.release());
}
nodes_.clear();
node_set_.clear();
return ret;
}
private:
// This method takes ownership of `node`.
ir::Node *AddNode(ir::Node *node) {
PADDLE_ENFORCE(node_set_.find(node) == node_set_.end());
nodes_[node].reset(node);
node_set_.insert(node);
return node;
}
void RemoveNode(ir::Node *node) {
PADDLE_ENFORCE(node_set_.find(node) != node_set_.end());
node_set_.erase(node);
nodes_.erase(node);
}
// NOTE: program_ shouldn't be exposed to user.
const ProgramDesc& program_;
const ProgramDesc &program_;
std::map<std::string, boost::any> attrs_;
std::map<std::string, std::function<void(void)>> attr_dels_;
std::map<ir::Node *, std::unique_ptr<ir::Node>> nodes_;
std::unordered_set<ir::Node *> node_set_;
};
bool IsControlDepVar(const ir::Node &var);
} // namespace ir
} // namespace framework
} // namespace paddle
paddle/fluid/framework/ir/graph_helper.cc 0 → 100644
浏览文件 @ 062556f9
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include <algorithm>
#include <unordered_set>
#include "paddle/fluid/framework/ir/graph_helper.h"
namespace paddle {
namespace framework {
namespace ir {
namespace {
void SortHelper(
const std::map<ir::Node *, std::unordered_set<ir::Node *>> &adj_list,
ir::Node *node, std::unordered_set<ir::Node *> *visited,
std::vector<ir::Node *> *ret) {
visited->insert(node);
for (auto adj : adj_list.at(node)) {
if (visited->find(adj) == visited->end()) {
SortHelper(adj_list, adj, visited, ret);
}
}
VLOG(3) << "topology sort insert: " << node->Name()
<< reinterpret_cast<void *>(node) << " input " << node->inputs.size();
ret->push_back(node);
}
bool HasCircleHelper(
ir::Node *node,
const std::map<ir::Node *, std::unordered_set<ir::Node *>> &adj_list,
std::unordered_set<ir::Node *> *visited,
std::unordered_set<ir::Node *> *in_trace) {
if (visited->find(node) == visited->end()) {
visited->insert(node);
in_trace->insert(node);
for (ir::Node *in : adj_list.at(node)) {
if (visited->find(in) == visited->end() &&
HasCircleHelper(in, adj_list, visited, in_trace)) {
return true;
} else if (in_trace->find(in) != in_trace->end()) {
return true;
}
}
}
in_trace->erase(node);
return false;
}
bool HasCircleInternal(
const std::map<ir::Node *, std::unordered_set<ir::Node *>> &adj_list) {
std::unordered_set<ir::Node *> visited;
std::unordered_set<ir::Node *> in_trace;
for (auto &adj : adj_list) {
if (HasCircleHelper(adj.first, adj_list, &visited, &in_trace)) {
return true;
}
}
return false;
}
} // namespace
bool HasCircle(const Graph &graph) {
return HasCircleInternal(BuildOperationAdjList(graph));
}
std::vector<ir::Node *> TopologySortOperations(const Graph &graph) {
std::map<ir::Node *, std::unordered_set<ir::Node *>> adj_list =
BuildOperationAdjList(graph);
PADDLE_ENFORCE(!HasCircleInternal(adj_list));
std::unordered_set<ir::Node *> visited;
std::vector<ir::Node *> ret;
for (auto adj : adj_list) {
if (visited.find(adj.first) == visited.end()) {
SortHelper(adj_list, adj.first, &visited, &ret);
}
}
return ret;
}
std::map<ir::Node *, std::unordered_set<ir::Node *>> BuildOperationAdjList(
const Graph &graph) {
std::map<ir::Node *, std::unordered_set<ir::Node *>> adj_list;
for (auto &n : graph.Nodes()) {
if (n->NodeType() != ir::Node::Type::kOperation) continue;
if (adj_list.find(n) == adj_list.end()) {
adj_list[n] = std::unordered_set<ir::Node *>();
}
for (auto &var : n->inputs) {
for (auto &adj_n : var->inputs) {
PADDLE_ENFORCE(adj_n->NodeType() == ir::Node::Type::kOperation);
adj_list[n].insert(adj_n);
VLOG(3) << "adj " << adj_n->Name() << reinterpret_cast<void *>(adj_n)
<< " -> " << n->Name() << reinterpret_cast<void *>(n)
<< " via " << var->Name() << reinterpret_cast<void *>(var);
}
}
}
return adj_list;
}
} // namespace ir
} // namespace framework
} // namespace paddle
paddle/fluid/framework/ir/graph_helper.h 0 → 100644
浏览文件 @ 062556f9
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include <map>
#include <memory>
#include <vector>
#include "paddle/fluid/framework/ir/graph.h"
#include "paddle/fluid/framework/ir/node.h"
namespace paddle {
namespace framework {
namespace ir {
// Test if the graph contains circle.
bool HasCircle(const Graph &graph);
// Topology Sort the operations in the graph from inputs to outputs.
// `graph` cannot contain circle.
std::vector<ir::Node *> TopologySortOperations(const Graph &graph);
// Build an adjacency list of operations for the `graph`.
std::map<ir::Node *, std::unordered_set<ir::Node *>> BuildOperationAdjList(
const Graph &graph);
} // namespace ir
} // namespace framework
} // namespace paddle
paddle/fluid/framework/ir/graph_helper_test.cc 0 → 100644
浏览文件 @ 062556f9
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/framework/ir/graph.h"
#include <string>
#include "gtest/gtest.h"
#include "paddle/fluid/framework/ir/graph_helper.h"
#include "paddle/fluid/framework/program_desc.h"
namespace paddle {
namespace framework {
namespace ir {
void BuildCircleGraph(Graph* g) {
ir::Node* o1 = g->CreateEmptyNode("op1", Node::Type::kOperation);
ir::Node* v1 = g->CreateEmptyNode("var1", Node::Type::kVariable);
o1->outputs.push_back(v1);
o1->inputs.push_back(v1);
v1->inputs.push_back(o1);
v1->outputs.push_back(o1);
}
void BuildCircleGraph2(Graph* g) {
ir::Node* o1 = g->CreateEmptyNode("op1", Node::Type::kOperation);
ir::Node* o2 = g->CreateEmptyNode("op2", Node::Type::kOperation);
ir::Node* v1 = g->CreateEmptyNode("var1", Node::Type::kVariable);
ir::Node* v2 = g->CreateEmptyNode("var2", Node::Type::kVariable);
o1->outputs.push_back(v1);
o2->inputs.push_back(v1);
v1->inputs.push_back(o1);
v1->outputs.push_back(o2);
o2->outputs.push_back(v2);
o1->inputs.push_back(v2);
v2->inputs.push_back(o2);
v2->outputs.push_back(o1);
}
void BuildNoCircleGraph(Graph* g) {
ir::Node* o1 = g->CreateEmptyNode("op1", Node::Type::kOperation);
ir::Node* o2 = g->CreateEmptyNode("op2", Node::Type::kOperation);
ir::Node* o3 = g->CreateEmptyNode("op3", Node::Type::kOperation);
ir::Node* o4 = g->CreateEmptyNode("op4", Node::Type::kOperation);
ir::Node* o5 = g->CreateEmptyNode("op5", Node::Type::kOperation);
ir::Node* v1 = g->CreateEmptyNode("var1", Node::Type::kVariable);
ir::Node* v2 = g->CreateEmptyNode("var2", Node::Type::kVariable);
ir::Node* v3 = g->CreateEmptyNode("var3", Node::Type::kVariable);
ir::Node* v4 = g->CreateEmptyNode("var4", Node::Type::kVariable);
// o1->v1->o2
o1->outputs.push_back(v1);
o2->inputs.push_back(v1);
v1->inputs.push_back(o1);
v1->outputs.push_back(o2);
// o2->v2->o3
// o2->v2->o4
o2->outputs.push_back(v2);
o3->inputs.push_back(v2);
o4->inputs.push_back(v2);
v2->inputs.push_back(o2);
v2->outputs.push_back(o3);
v2->outputs.push_back(o4);
// o2->v3->o5
o2->outputs.push_back(v3);
o5->inputs.push_back(v3);
v3->inputs.push_back(o2);
v3->outputs.push_back(o5);
// o3-v4->o5
o3->outputs.push_back(v4);
o5->inputs.push_back(v4);
v4->inputs.push_back(o3);
v4->outputs.push_back(o5);
}
TEST(GraphHelperTest, Basic) {
ProgramDesc prog;
Graph g(prog);
BuildCircleGraph(&g);
ASSERT_TRUE(HasCircle(g));
Graph g2(prog);
BuildCircleGraph2(&g2);
ASSERT_TRUE(HasCircle(g2));
auto adj_list = BuildOperationAdjList(g2);
for (auto& adj : adj_list) {
auto& adj_set = adj.second;
if (adj.first->Name() == "op1") {
ASSERT_EQ((*adj_set.begin())->Name(), "op2");
} else if (adj.first->Name() == "op2") {
ASSERT_EQ((*adj_set.begin())->Name(), "op1");
} else {
ASSERT_TRUE(false);
}
}
Graph g3(prog);
BuildNoCircleGraph(&g3);
ASSERT_FALSE(HasCircle(g3));
auto sorted = TopologySortOperations(g3);
std::map<std::string, size_t> node_map;
for (size_t i = 0; i < sorted.size(); ++i) {
node_map[sorted[i]->Name()] = i;
}
ASSERT_EQ(node_map.at("op1"), 0);
ASSERT_EQ(node_map.at("op2"), 1);
ASSERT_TRUE(node_map.at("op3") < node_map.at("op5"));
}
} // namespace ir
} // namespace framework
} // namespace paddle
paddle/fluid/framework/ir/graph_test.cc
浏览文件 @ 062556f9
......@@ -76,6 +76,7 @@ TEST(GraphTest, Basic) {
op->SetType("sum");
op->SetInput("X", {"test_a", "test_b", "test_c"});
op->SetOutput("Out", {"test_out"});
op->SetAttr("op_role", 1);
prog.MutableBlock(0)->Var("test_a")->SetType(proto::VarType::SELECTED_ROWS);
prog.MutableBlock(0)->Var("test_b")->SetType(proto::VarType::SELECTED_ROWS);
......@@ -92,21 +93,22 @@ TEST(GraphTest, Basic) {
ASSERT_EQ(proto::VarType::LOD_TENSOR,
prog.MutableBlock(0)->Var("test_out")->GetType());
std::unique_ptr<Graph> g(new Graph(prog));
ASSERT_EQ(g->nodes[0]->Name(), "sum");
ASSERT_EQ(g->nodes[0]->inputs[0]->Name(), "test_a");
ASSERT_EQ(g->nodes[0]->inputs[1]->Name(), "test_b");
ASSERT_EQ(g->nodes[0]->inputs[2]->Name(), "test_c");
ASSERT_EQ(g->nodes[0]->outputs[0]->Name(), "test_out");
ASSERT_EQ(g->nodes[1]->Name(), "test_a");
ASSERT_EQ(g->nodes[1]->outputs[0]->Name(), "sum");
ASSERT_EQ(g->nodes[2]->Name(), "test_b");
ASSERT_EQ(g->nodes[2]->outputs[0]->Name(), "sum");
ASSERT_EQ(g->nodes[3]->Name(), "test_c");
ASSERT_EQ(g->nodes[3]->outputs[0]->Name(), "sum");
ASSERT_EQ(g->nodes[4]->Name(), "test_out");
ASSERT_EQ(g->nodes[4]->inputs[0]->Name(), "sum");
ASSERT_EQ(g->nodes.size(), 5);
std::unique_ptr<ir::Graph> g(new ir::Graph(prog));
std::vector<ir::Node *> nodes(g->Nodes().begin(), g->Nodes().end());
for (ir::Node *n : nodes) {
if (n->Name() == "sum") {
ASSERT_EQ(n->inputs.size(), 3);
ASSERT_EQ(n->outputs.size(), 1);
} else if (n->Name() == "test_a" || n->Name() == "test_b" ||
n->Name() == "test_c") {
ASSERT_EQ(n->inputs.size(), 0);
ASSERT_EQ(n->outputs.size(), 1);
} else if (n->Name() == "test_out") {
ASSERT_EQ(n->inputs.size(), 1);
ASSERT_EQ(n->outputs.size(), 0);
}
}
ASSERT_EQ(nodes.size(), 5);
}
} // namespace framework
} // namespace paddle
paddle/fluid/framework/ir/node.cc
浏览文件 @ 062556f9
......@@ -15,5 +15,9 @@ limitations under the License. */
#include "paddle/fluid/framework/ir/node.h"
namespace paddle {
namespace framework {} // namespace framework
namespace framework {
namespace ir {
const char Node::kControlDepVarName[] = "__control_var";
} // namespace ir
} // namespace framework
} // namespace paddle
paddle/fluid/framework/ir/node.h
浏览文件 @ 062556f9
......@@ -27,6 +27,8 @@ namespace ir {
class Node {
public:
enum class Type { kOperation, kVariable };
static const char kControlDepVarName[];
explicit Node(const std::string& name, Type type)
: name_(name), var_desc_(nullptr), op_desc_(nullptr), type_(type) {}
......@@ -50,6 +52,7 @@ class Node {
PADDLE_ENFORCE(type_ == Type::kVariable);
return var_desc_;
}
OpDesc* Op() {
PADDLE_ENFORCE(type_ == Type::kOperation);
return op_desc_;
......
paddle/fluid/framework/mixed_vector.h
浏览文件 @ 062556f9
......@@ -16,6 +16,7 @@
#include <algorithm>
#include <initializer_list>
#include <memory>
#include <vector>
#include "paddle/fluid/framework/tensor.h"
......@@ -386,13 +387,14 @@ template <typename T>
class CPUVector : public std::vector<T, std::allocator<T>> {
public:
CPUVector() : std::vector<T>() {}
CPUVector(size_t count, const T &value = T())
CPUVector(size_t count, const T &value = T()) // NOLINT
: std::vector<T>(count, value) {}
CPUVector(std::initializer_list<T> init) : std::vector<T>(init) {}
CPUVector(const std::vector<T> &other) : std::vector<T>(other) {}
explicit CPUVector(const CPUVector<T> &other) : std::vector<T>(other) {}
CPUVector(const std::vector<T> &other) : std::vector<T>(other) {} // NOLINT
CPUVector(const CPUVector<T> &other) : std::vector<T>(other) {}
CPUVector(CPUVector<T> &&other) : std::vector<T>(std::move(other)) {}
CPUVector(std::vector<T> &&other) : std::vector<T>(std::move(other)) {}
CPUVector(std::vector<T> &&other) // NOLINT
: std::vector<T>(std::move(other)) {}
CPUVector &operator=(const CPUVector &other) {
this->assign(other.begin(), other.end());
return *this;
......@@ -410,8 +412,6 @@ class CPUVector : public std::vector<T, std::allocator<T>> {
return os;
}
void resize(size_t size) { this->resize(size); }
T &operator[](size_t id) { return this->at(id); }
const T &operator[](size_t id) const { return this->at(id); }
......
paddle/fluid/framework/mixed_vector_test.cc 0 → 100644
浏览文件 @ 062556f9
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include <memory>
#include "glog/logging.h"
#include "gtest/gtest.h"
#include "paddle/fluid/framework/mixed_vector.h"
template <typename T>
using vec = paddle::framework::Vector<T>;
TEST(mixed_vector, CPU_VECTOR) {
vec<int> tmp;
for (int i = 0; i < 10; ++i) {
tmp.push_back(i);
}
ASSERT_EQ(tmp.size(), 10UL);
vec<int> tmp2;
tmp2 = tmp;
ASSERT_EQ(tmp2.size(), 10UL);
for (int i = 0; i < 10; ++i) {
ASSERT_EQ(tmp2[i], i);
ASSERT_EQ(tmp2[i], tmp[i]);
}
int cnt = 0;
for (auto& t : tmp2) {
ASSERT_EQ(t, cnt);
++cnt;
}
}
TEST(mixed_vector, InitWithCount) {
paddle::framework::Vector<int> vec(10, 10);
for (int i = 0; i < 10; ++i) {
ASSERT_EQ(vec[i], 10);
}
}
TEST(mixed_vector, ForEach) {
vec<int> tmp;
for (auto& v : tmp) {
VLOG(3) << v;
}
}
TEST(mixed_vector, Reserve) {
paddle::framework::Vector<int> vec;
vec.reserve(1);
vec.push_back(0);
vec.push_back(0);
vec.push_back(0);
}
TEST(mixed_vector, Resize) {
paddle::framework::Vector<int> vec;
vec.resize(1);
vec.push_back(0);
vec.push_back(0);
vec.push_back(0);
}
paddle/fluid/framework/mixed_vector_test.cu
浏览文件 @ 062556f9
......@@ -11,7 +11,9 @@
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include <cuda_runtime.h>
#include <memory>
#include "glog/logging.h"
#include "gtest/gtest.h"
......@@ -21,26 +23,6 @@
template <typename T>
using vec = paddle::framework::Vector<T>;
TEST(mixed_vector, CPU_VECTOR) {
vec<int> tmp;
for (int i = 0; i < 10; ++i) {
tmp.push_back(i);
}
ASSERT_EQ(tmp.size(), 10UL);
vec<int> tmp2;
tmp2 = tmp;
ASSERT_EQ(tmp2.size(), 10UL);
for (int i = 0; i < 10; ++i) {
ASSERT_EQ(tmp2[i], i);
ASSERT_EQ(tmp2[i], tmp[i]);
}
int cnt = 0;
for (auto& t : tmp2) {
ASSERT_EQ(t, cnt);
++cnt;
}
}
static __global__ void multiply_10(int* ptr) {
for (int i = 0; i < 10; ++i) {
ptr[i] *= 10;
......@@ -91,24 +73,3 @@ TEST(mixed_vector, MultiGPU) {
ASSERT_EQ(tmp[i], i * 100);
}
}
TEST(mixed_vector, InitWithCount) {
paddle::framework::Vector<int> vec(10, 10);
for (int i = 0; i < 10; ++i) {
ASSERT_EQ(vec[i], 10);
}
}
TEST(mixed_vector, ForEach) {
vec<int> tmp;
for (auto& v : tmp) {
}
}
TEST(mixed_vector, Reserve) {
paddle::framework::Vector<int> vec;
vec.reserve(1);
vec.push_back(0);
vec.push_back(0);
vec.push_back(0);
}
paddle/fluid/framework/parallel_executor.cc
浏览文件 @ 062556f9
......@@ -132,7 +132,7 @@ ParallelExecutor::ParallelExecutor(
#endif
}
builder_ = builder_factory.Create();
std::unique_ptr<Graph> graph(new Graph(main_program));
std::unique_ptr<ir::Graph> graph(new ir::Graph(main_program));
graph = builder_->Apply(std::move(graph));
member_->executor_.reset(new details::ThreadedSSAGraphExecutor(
exec_strategy, member_->local_scopes_, places, std::move(graph)));
......
paddle/fluid/inference/api/demo_ci/clean.sh 0 → 100755
浏览文件 @ 062556f9
set -x
cd `dirname $0`
rm -rf build/ data/
set +x
paddle/fluid/inference/tensorrt/convert/CMakeLists.txt
浏览文件 @ 062556f9
# Add TRT tests
nv_library(tensorrt_converter
SRCS mul_op.cc conv2d_op.cc fc_op.cc
SRCS mul_op.cc conv2d_op.cc fc_op.cc pool2d_op.cc
DEPS tensorrt_engine operator scope framework_proto op_registry)
nv_test(test_op_converter SRCS test_op_converter.cc DEPS
......@@ -13,3 +13,6 @@ nv_test(test_trt_fc_op SRCS test_fc_op.cc fc_op.cc
DEPS ${FLUID_CORE_MODULES} tensorrt_engine mul_op SERIAL)
nv_test(test_trt_activation_op SRCS test_activation_op.cc activation_op.cc
DEPS ${FLUID_CORE_MODULES} tensorrt_engine activation_op SERIAL)
nv_test(test_trt_pool2d_op SRCS test_pool2d_op.cc pool2d_op.cc
DEPS ${FLUID_CORE_MODULES} tensorrt_engine pool_op SERIAL)
paddle/fluid/inference/tensorrt/convert/pool2d_op.cc 0 → 100644
浏览文件 @ 062556f9
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/inference/tensorrt/convert/op_converter.h"
namespace paddle {
namespace inference {
namespace tensorrt {
/*
* Pool2dOp, IPoolingLayer in TRT. This Layer doesn't has weights.
*/
class Pool2dOpConverter : public OpConverter {
public:
void operator()(const framework::proto::OpDesc& op,
const framework::Scope& scope, bool test_mode) override {
VLOG(4)
<< "convert a fluid pool2d op to tensorrt pool2d layer without bias";
framework::OpDesc op_desc(op, nullptr);
// Declare inputs
PADDLE_ENFORCE_EQ(op_desc.Input("X").size(), 1);
PADDLE_ENFORCE_EQ(op_desc.Output("Out").size(), 1);
auto* input1 = engine_->GetITensor(op_desc.Input("X")[0]);
std::string pool_type =
boost::get<std::string>(op_desc.GetAttr("pooling_type"));
std::vector<int> ksize =
boost::get<std::vector<int>>(op_desc.GetAttr("ksize"));
std::vector<int> strides =
boost::get<std::vector<int>>(op_desc.GetAttr("strides"));
std::vector<int> paddings =
boost::get<std::vector<int>>(op_desc.GetAttr("paddings"));
const nvinfer1::DimsHW nv_ksize(ksize[0], ksize[1]);
const nvinfer1::DimsHW nv_strides(strides[0], strides[1]);
const nvinfer1::DimsHW nv_paddings(paddings[0], paddings[1]);
PADDLE_ENFORCE_EQ(input1->getDimensions().nbDims, 3UL);
nvinfer1::PoolingType nv_pool_type = nvinfer1::PoolingType::kMAX;
if (pool_type == "max") {
nv_pool_type = nvinfer1::PoolingType::kMAX;
} else if (pool_type == "avg") {
nv_pool_type = nvinfer1::PoolingType::kAVERAGE;
} else {
PADDLE_THROW("TensorRT unsupported pooling type!");
}
auto* layer = TRT_ENGINE_ADD_LAYER(engine_, Pooling,
*const_cast<nvinfer1::ITensor*>(input1),
nv_pool_type, nv_ksize);
PADDLE_ENFORCE_NOT_NULL(layer, "pool layer could not be created.");
layer->setStride(nv_strides);
layer->setPadding(nv_paddings);
auto output_name = op_desc.Output("Out")[0];
engine_->SetITensor(output_name, layer->getOutput(0));
if (test_mode) {
engine_->DeclareOutput(output_name);
}
}
};
} // namespace tensorrt
} // namespace inference
} // namespace paddle
USE_OP(pool2d);
REGISTER_TRT_OP_CONVERTER(pool2d, Pool2dOpConverter);
paddle/fluid/inference/tensorrt/convert/test_activation_op.cc
浏览文件 @ 062556f9
......@@ -37,7 +37,7 @@ TEST(ReluOpConverter, main) {
validator.SetOp(*desc.Proto());
LOG(INFO) << "execute";
validator.Execute(1);
validator.Execute(5);
}
} // namespace tensorrt
......
paddle/fluid/inference/tensorrt/convert/test_fc_op.cc
浏览文件 @ 062556f9
......@@ -24,9 +24,8 @@ TEST(fc_op, test) {
std::unordered_set<std::string> parameters({"mul-Y"});
framework::Scope scope;
TRTConvertValidation validator(10, parameters, scope, 1000);
validator.DeclInputVar("mul-X", nvinfer1::Dims4(1, 10, 1, 1));
validator.DeclInputVar("mul-X", nvinfer1::Dims3(10, 1, 1));
validator.DeclParamVar("mul-Y", nvinfer1::Dims2(10, 2));
// validator.DeclParamVar("mul-Y", nvinfer1::Dims2(8, 2));
validator.DeclOutputVar("mul-Out", nvinfer1::Dims2(1, 2));
// Prepare Op description
......@@ -38,7 +37,7 @@ TEST(fc_op, test) {
validator.SetOp(*desc.Proto());
validator.Execute(1);
validator.Execute(10);
}
} // namespace tensorrt
......
paddle/fluid/inference/tensorrt/convert/test_mul_op.cc
浏览文件 @ 062556f9
......@@ -23,7 +23,7 @@ namespace tensorrt {
TEST(MulOpConverter, main) {
framework::Scope scope;
std::unordered_set<std::string> parameters;
TRTConvertValidation validator(10, parameters, scope, 1000);
TRTConvertValidation validator(10, parameters, scope, 1000, false);
validator.DeclInputVar("mul-X", nvinfer1::Dims2(10, 6));
validator.DeclInputVar("mul-Y", nvinfer1::Dims2(6, 10));
validator.DeclOutputVar("mul-Out", nvinfer1::Dims2(10, 10));
......@@ -39,7 +39,7 @@ TEST(MulOpConverter, main) {
validator.SetOp(*desc.Proto());
LOG(INFO) << "execute";
validator.Execute(1);
validator.Execute(2);
}
} // namespace tensorrt
......
paddle/fluid/inference/tensorrt/convert/test_pool2d_op.cc 0 → 100644
浏览文件 @ 062556f9
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include <gtest/gtest.h>
#include <fstream>
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/inference/tensorrt/convert/ut_helper.h"
namespace paddle {
namespace inference {
namespace tensorrt {
TEST(Pool2dOpConverter, main) {
framework::Scope scope;
std::unordered_set<std::string> parameters;
TRTConvertValidation validator(5, parameters, scope, 1 << 15);
// The ITensor's Dims should not contain the batch size.
// So, the ITensor's Dims of input and output should be C * H * W.
validator.DeclInputVar("pool2d-X", nvinfer1::Dims3(3, 4, 4));
validator.DeclOutputVar("pool2d-Out", nvinfer1::Dims3(3, 2, 2));
// Prepare Op description
framework::OpDesc desc;
desc.SetType("pool2d");
desc.SetInput("X", {"pool2d-X"});
desc.SetOutput("Out", {"pool2d-Out"});
std::vector<int> ksize({2, 2});
std::vector<int> strides({2, 2});
std::vector<int> paddings({0, 0});
std::string pooling_t = "max";
desc.SetAttr("pooling_type", pooling_t);
desc.SetAttr("ksize", ksize);
desc.SetAttr("strides", strides);
desc.SetAttr("paddings", paddings);
LOG(INFO) << "set OP";
validator.SetOp(*desc.Proto());
LOG(INFO) << "execute";
validator.Execute(3);
}
} // namespace tensorrt
} // namespace inference
} // namespace paddle
USE_OP(pool2d);
paddle/fluid/inference/tensorrt/convert/ut_helper.h
浏览文件 @ 062556f9
......@@ -63,13 +63,16 @@ class TRTConvertValidation {
public:
TRTConvertValidation() = delete;
TRTConvertValidation(int batch_size,
TRTConvertValidation(int max_batch_size,
const std::unordered_set<std::string>& parameters,
framework::Scope& scope, // NOLINT
int workspace_size = 1 << 10)
: parameters_(parameters), scope_(scope) {
int workspace_size = 1 << 10, bool if_add_batch = true)
: parameters_(parameters),
scope_(scope),
if_add_batch_(if_add_batch),
max_batch_size_(max_batch_size) {
// create engine.
engine_.reset(new TensorRTEngine(batch_size, workspace_size, &stream_));
engine_.reset(new TensorRTEngine(max_batch_size, workspace_size, &stream_));
engine_->InitNetwork();
PADDLE_ENFORCE_EQ(cudaStreamCreate(&stream_), 0);
......@@ -84,7 +87,7 @@ class TRTConvertValidation {
// Declare a parameter varaible in the scope.
void DeclParamVar(const std::string& name, const nvinfer1::Dims& dims) {
DeclVar(name, dims);
DeclVar(name, dims, true);
}
void DeclOutputVar(const std::string& name, const nvinfer1::Dims& dims) {
......@@ -92,12 +95,18 @@ class TRTConvertValidation {
}
// Declare a variable in a fluid Scope.
void DeclVar(const std::string& name, const nvinfer1::Dims& dims) {
void DeclVar(const std::string& name, const nvinfer1::Dims& dims,
bool is_param = false) {
platform::CPUPlace place;
platform::CPUDeviceContext ctx(place);
// Init Fluid tensor.
std::vector<int> dim_vec(dims.d, dims.d + dims.nbDims);
// There is no batchsize in ITensor's shape, but We should add it to
// tensor's shape of fluid. If the variable is not parameter and the
// if_add_batch_ flag is true, add the max batchsize to dim_vec.
if (is_param != true && if_add_batch_ == true)
dim_vec.insert(dim_vec.begin(), max_batch_size_);
auto* x = scope_.Var(name);
auto* x_tensor = x->GetMutable<framework::LoDTensor>();
x_tensor->Resize(framework::make_ddim(dim_vec));
......@@ -131,6 +140,7 @@ class TRTConvertValidation {
void Execute(int batch_size) {
// Execute Fluid Op
PADDLE_ENFORCE_LE(batch_size, max_batch_size_);
platform::CPUPlace place;
platform::CPUDeviceContext ctx(place);
op_->Run(scope_, place);
......@@ -149,9 +159,15 @@ class TRTConvertValidation {
auto* var = scope_.FindVar(output);
auto tensor = var->GetMutable<framework::LoDTensor>();
framework::TensorToVector(*tensor, ctx, &fluid_out);
size_t fluid_out_size = fluid_out.size();
if (if_add_batch_ == true) {
fluid_out_size =
batch_size * (framework::product(tensor->dims()) / max_batch_size_);
}
// Compare two output
ASSERT_FALSE(fluid_out.empty());
for (size_t i = 0; i < fluid_out.size(); i++) {
for (size_t i = 0; i < fluid_out_size; i++) {
// Loose the threshold for CI in different machine model.
EXPECT_LT(std::abs(fluid_out[i] - trt_out[i]), 2e-5);
}
......@@ -167,6 +183,12 @@ class TRTConvertValidation {
std::unique_ptr<framework::OpDesc> op_desc_;
const std::unordered_set<std::string>& parameters_;
framework::Scope& scope_;
// The ITensor of trt does not cotain the batch size,
// bug, in most cases, we need to set batch size for
// fluid's tensor shape. This variable indicates
// whether to add batch size to tensor shape of fluid.
bool if_add_batch_;
int max_batch_size_;
};
} // namespace tensorrt
......
paddle/fluid/inference/tensorrt/test_engine.cc
浏览文件 @ 062556f9
......@@ -113,7 +113,7 @@ TEST_F(TensorRTEngineTest, add_layer_multi_dim) {
ASSERT_EQ(y_cpu[1], 14.5);
}
TEST_F(TensorRTEngineTest, test_conv2d_temp) {
TEST_F(TensorRTEngineTest, test_conv2d) {
// Weight in CPU memory.
float raw_weight[9] = {1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0};
float raw_bias[1] = {0};
......@@ -146,6 +146,37 @@ TEST_F(TensorRTEngineTest, test_conv2d_temp) {
ASSERT_EQ(y_cpu[1], 6.0);
}
TEST_F(TensorRTEngineTest, test_pool2d) {
// Weight in CPU memory.
auto* x = engine_->DeclareInput("x", nvinfer1::DataType::kFLOAT,
nvinfer1::Dims3{1, 2, 2});
nvinfer1::PoolingType pool_t = nvinfer1::PoolingType::kAVERAGE;
auto* pool_layer =
TRT_ENGINE_ADD_LAYER(engine_, Pooling, *const_cast<nvinfer1::ITensor*>(x),
pool_t, nvinfer1::DimsHW{2, 2});
PADDLE_ENFORCE(pool_layer != nullptr);
pool_layer->setStride(nvinfer1::DimsHW{1, 1});
pool_layer->setPadding(nvinfer1::DimsHW{0, 0});
engine_->DeclareOutput(pool_layer, 0, "y");
engine_->FreezeNetwork();
ASSERT_EQ(engine_->engine()->getNbBindings(), 2);
float x_v[8] = {1.0, 2.0, 5.0, 0.0, 2.0, 3.0, 5.0, 10.0};
engine_->SetInputFromCPU("x", reinterpret_cast<void*>(&x_v),
8 * sizeof(float));
engine_->Execute(2);
LOG(INFO) << "to get output";
float* y_cpu = new float[2];
engine_->GetOutputInCPU("y", &y_cpu[0], 2 * sizeof(float));
ASSERT_EQ(y_cpu[0], 2.0);
ASSERT_EQ(y_cpu[1], 5.0);
}
} // namespace tensorrt
} // namespace inference
} // namespace paddle
paddle/fluid/inference/tests/book/test_inference_nlp.cc
浏览文件 @ 062556f9
......@@ -20,9 +20,6 @@ limitations under the License. */
#include "gtest/gtest.h"
#include "paddle/fluid/inference/tests/test_helper.h"
#include "paddle/fluid/platform/cpu_helper.h"
#ifdef PADDLE_WITH_MKLML
#include <omp.h>
#endif
DEFINE_string(model_path, "", "Directory of the inference model.");
DEFINE_string(data_file, "", "File of input index data.");
......@@ -30,6 +27,7 @@ DEFINE_int32(repeat, 100, "Running the inference program repeat times");
DEFINE_bool(prepare_vars, true, "Prepare variables before executor");
DEFINE_int32(num_threads, 1, "Number of threads should be used");
DECLARE_bool(use_mkldnn);
DECLARE_int32(paddle_num_threads);
inline double GetCurrentMs() {
struct timeval time;
......@@ -160,12 +158,7 @@ TEST(inference, nlp) {
std::unique_ptr<paddle::framework::Scope> scope(
new paddle::framework::Scope());
#ifdef PADDLE_WITH_MKLML
// only use 1 thread number per std::thread
omp_set_dynamic(0);
omp_set_num_threads(1);
paddle::platform::SetNumThreads(1);
#endif
paddle::platform::SetNumThreads(FLAGS_paddle_num_threads);
double start_ms = 0, stop_ms = 0;
if (FLAGS_num_threads > 1) {
......
paddle/fluid/memory/detail/buddy_allocator.cc
浏览文件 @ 062556f9
......@@ -15,6 +15,10 @@ limitations under the License. */
#include "paddle/fluid/memory/detail/buddy_allocator.h"
#include "glog/logging.h"
DEFINE_bool(free_idle_memory, false,
"If it is true, Paddle will try to free idle memory trunks during "
"running time.");
namespace paddle {
namespace memory {
namespace detail {
......@@ -152,13 +156,14 @@ void BuddyAllocator::Free(void* p) {
pool_.insert(
IndexSizeAddress(block->index(cache_), block->total_size(cache_), block));
// Clean up if existing too much free memory
// Prefer freeing fallback allocation first
CleanIdleFallBackAlloc();
if (FLAGS_free_idle_memory) {
// Clean up if existing too much free memory
// Prefer freeing fallback allocation first
CleanIdleFallBackAlloc();
// Free normal allocation
CleanIdleNormalAlloc();
// Free normal allocation
CleanIdleNormalAlloc();
}
}
size_t BuddyAllocator::Used() { return total_used_; }
......
paddle/fluid/operators/CMakeLists.txt
浏览文件 @ 062556f9
......@@ -192,9 +192,9 @@ if(WITH_DISTRIBUTE)
set(DISTRIBUTE_DEPS "")
if(WITH_GRPC)
set(DISTRIBUTE_DEPS sendrecvop_grpc grpc++_unsecure grpc_unsecure gpr cares zlib protobuf)
set(DISTRIBUTE_DEPS sendrecvop_grpc grpc++_unsecure grpc_unsecure gpr cares zlib protobuf node)
else()
set(DISTRIBUTE_DEPS sendrecvop_brpc brpc leveldb snappystream snappy protobuf ssl crypto zlib)
set(DISTRIBUTE_DEPS sendrecvop_brpc brpc leveldb snappystream snappy protobuf ssl crypto zlib node)
if(WITH_BRPC_RDMA)
find_library(IBVERBS_LIBRARY NAMES ibverbs)
ADD_LIBRARY(ibverbs SHARED IMPORTED GLOBAL)
......@@ -270,6 +270,7 @@ op_library(cos_sim_op DEPS cos_sim_functor)
op_library(parallel_do_op DEPS executor)
op_library(unsqueeze_op DEPS reshape_op)
op_library(squeeze_op DEPS reshape_op)
op_library(extract_rows_op DEPS memory)
if (WITH_GPU)
op_library(conv_op DEPS vol2col depthwise_conv im2col)
......
paddle/fluid/operators/conv_cudnn_op.cu.cc
浏览文件 @ 062556f9
......@@ -77,7 +77,7 @@ class CUDNNConvOpKernel : public framework::OpKernel<T> {
// cudnn 7 can support groups, no need to do it mannually
// FIXME(typhoonzero): find a better way to disable groups
// rather than setting it to 1.
PADDLE_ENFORCE(platform::dynload::cudnnSetConvolutionGroupCount(
CUDNN_ENFORCE(platform::dynload::cudnnSetConvolutionGroupCount(
cudnn_conv_desc, groups));
groups = 1;
#endif
......@@ -129,7 +129,7 @@ class CUDNNConvOpKernel : public framework::OpKernel<T> {
auto& dev_ctx = ctx.template device_context<platform::CUDADeviceContext>();
auto handle = dev_ctx.cudnn_handle();
PADDLE_ENFORCE(platform::dynload::cudnnGetConvolutionForwardAlgorithm(
CUDNN_ENFORCE(platform::dynload::cudnnGetConvolutionForwardAlgorithm(
handle, cudnn_input_desc, cudnn_filter_desc, cudnn_conv_desc,
cudnn_output_desc, CUDNN_CONVOLUTION_FWD_SPECIFY_WORKSPACE_LIMIT,
workspace_size_limit, &algo));
......@@ -140,18 +140,18 @@ class CUDNNConvOpKernel : public framework::OpKernel<T> {
if (dev_ctx.GetComputeCapability() >= 70 &&
std::type_index(typeid(T)) ==
std::type_index(typeid(platform::float16))) {
PADDLE_ENFORCE(platform::dynload::cudnnSetConvolutionMathType(
CUDNN_ENFORCE(platform::dynload::cudnnSetConvolutionMathType(
cudnn_conv_desc, CUDNN_TENSOR_OP_MATH));
// Currently tensor core is only enabled using this algo
algo = CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_PRECOMP_GEMM;
} else {
PADDLE_ENFORCE(platform::dynload::cudnnSetConvolutionMathType(
CUDNN_ENFORCE(platform::dynload::cudnnSetConvolutionMathType(
cudnn_conv_desc, CUDNN_DEFAULT_MATH));
}
#endif
// get workspace size able to allocate
PADDLE_ENFORCE(platform::dynload::cudnnGetConvolutionForwardWorkspaceSize(
CUDNN_ENFORCE(platform::dynload::cudnnGetConvolutionForwardWorkspaceSize(
handle, cudnn_input_desc, cudnn_filter_desc, cudnn_conv_desc,
cudnn_output_desc, algo, &workspace_size_in_bytes));
// It is possible for float16 on Volta GPU to allocate more memory than
......@@ -165,7 +165,7 @@ class CUDNNConvOpKernel : public framework::OpKernel<T> {
// ------------------- cudnn conv forward ---------------------
ScalingParamType<T> alpha = 1.0f, beta = 0.0f;
for (int i = 0; i < groups; i++) {
PADDLE_ENFORCE(platform::dynload::cudnnConvolutionForward(
CUDNN_ENFORCE(platform::dynload::cudnnConvolutionForward(
handle, &alpha, cudnn_input_desc, input_data + i * group_offset_in,
cudnn_filter_desc, filter_data + i * group_offset_filter,
cudnn_conv_desc, algo, cudnn_workspace, workspace_size_in_bytes,
......@@ -218,7 +218,7 @@ class CUDNNConvGradOpKernel : public framework::OpKernel<T> {
// cudnn 7 can support groups, no need to do it mannually
// FIXME(typhoonzero): find a better way to disable groups
// rather than setting it to 1.
PADDLE_ENFORCE(platform::dynload::cudnnSetConvolutionGroupCount(
CUDNN_ENFORCE(platform::dynload::cudnnSetConvolutionGroupCount(
cudnn_conv_desc, groups));
groups = 1;
#endif
......@@ -273,7 +273,7 @@ class CUDNNConvGradOpKernel : public framework::OpKernel<T> {
auto handle = dev_ctx.cudnn_handle();
if (input_grad) {
if (FLAGS_cudnn_deterministic) {
PADDLE_ENFORCE(
CUDNN_ENFORCE(
platform::dynload::cudnnGetConvolutionBackwardDataAlgorithm(
handle, cudnn_filter_desc,
// dyDesc: Handle to the previously initialized input
......@@ -289,7 +289,7 @@ class CUDNNConvGradOpKernel : public framework::OpKernel<T> {
data_algo = CUDNN_CONVOLUTION_BWD_DATA_ALGO_1;
}
PADDLE_ENFORCE(
CUDNN_ENFORCE(
platform::dynload::cudnnGetConvolutionBackwardDataWorkspaceSize(
handle, cudnn_filter_desc, cudnn_output_grad_desc,
cudnn_conv_desc, cudnn_input_desc, data_algo, &tmp_size));
......@@ -298,7 +298,7 @@ class CUDNNConvGradOpKernel : public framework::OpKernel<T> {
if (filter_grad) {
if (FLAGS_cudnn_deterministic) {
PADDLE_ENFORCE(
CUDNN_ENFORCE(
platform::dynload::cudnnGetConvolutionBackwardFilterAlgorithm(
handle, cudnn_input_desc, cudnn_output_grad_desc,
cudnn_conv_desc, cudnn_filter_desc,
......@@ -308,7 +308,7 @@ class CUDNNConvGradOpKernel : public framework::OpKernel<T> {
filter_algo = CUDNN_CONVOLUTION_BWD_FILTER_ALGO_1;
}
PADDLE_ENFORCE(
CUDNN_ENFORCE(
platform::dynload::cudnnGetConvolutionBackwardFilterWorkspaceSize(
handle, cudnn_input_desc, cudnn_output_grad_desc, cudnn_conv_desc,
cudnn_filter_desc, filter_algo, &tmp_size));
......@@ -326,7 +326,7 @@ class CUDNNConvGradOpKernel : public framework::OpKernel<T> {
// Because beta is zero, it is unnecessary to reset input_grad.
for (int i = 0; i < groups; i++) {
PADDLE_ENFORCE(platform::dynload::cudnnConvolutionBackwardData(
CUDNN_ENFORCE(platform::dynload::cudnnConvolutionBackwardData(
handle, &alpha, cudnn_filter_desc,
filter_data + i * group_offset_filter, cudnn_output_grad_desc,
output_grad_data + i * group_offset_out, cudnn_conv_desc, data_algo,
......@@ -339,7 +339,7 @@ class CUDNNConvGradOpKernel : public framework::OpKernel<T> {
T* filter_grad_data = filter_grad->mutable_data<T>(ctx.GetPlace());
// Because beta is zero, it is unnecessary to reset filter_grad.
for (int i = 0; i < groups; i++) {
PADDLE_ENFORCE(platform::dynload::cudnnConvolutionBackwardFilter(
CUDNN_ENFORCE(platform::dynload::cudnnConvolutionBackwardFilter(
handle, &alpha, cudnn_input_desc, input_data + i * group_offset_in,
cudnn_output_grad_desc, output_grad_data + i * group_offset_out,
cudnn_conv_desc, filter_algo, cudnn_workspace,
......
paddle/fluid/operators/conv_transpose_cudnn_op.cu.cc
浏览文件 @ 062556f9
......@@ -87,7 +87,7 @@ class CUDNNConvTransposeOpKernel : public framework::OpKernel<T> {
auto& dev_ctx = ctx.template device_context<platform::CUDADeviceContext>();
auto handle = dev_ctx.cudnn_handle();
// Get the algorithm
PADDLE_ENFORCE(platform::dynload::cudnnGetConvolutionBackwardDataAlgorithm(
CUDNN_ENFORCE(platform::dynload::cudnnGetConvolutionBackwardDataAlgorithm(
handle, cudnn_filter_desc, cudnn_input_desc, cudnn_conv_desc,
// dxDesc: Handle to the previously initialized output tensor
// descriptor.
......@@ -95,7 +95,7 @@ class CUDNNConvTransposeOpKernel : public framework::OpKernel<T> {
workspace_size_limit, &algo));
// get workspace size able to allocate
PADDLE_ENFORCE(
CUDNN_ENFORCE(
platform::dynload::cudnnGetConvolutionBackwardDataWorkspaceSize(
handle, cudnn_filter_desc, cudnn_input_desc, cudnn_conv_desc,
cudnn_output_desc, algo, &workspace_size_in_bytes));
......@@ -110,7 +110,7 @@ class CUDNNConvTransposeOpKernel : public framework::OpKernel<T> {
int filter_offset = filter->numel() / groups;
T alpha = 1.0f, beta = 0.0f;
for (int g = 0; g < groups; g++) {
PADDLE_ENFORCE(platform::dynload::cudnnConvolutionBackwardData(
CUDNN_ENFORCE(platform::dynload::cudnnConvolutionBackwardData(
handle, &alpha, cudnn_filter_desc, filter_data + filter_offset * g,
cudnn_input_desc, input_data + input_offset * g, cudnn_conv_desc,
algo, cudnn_workspace, workspace_size_in_bytes, &beta,
......@@ -178,11 +178,11 @@ class CUDNNConvTransposeGradOpKernel : public framework::OpKernel<T> {
auto handle = dev_ctx.cudnn_handle();
if (input_grad) {
// choose backward algorithm for data
PADDLE_ENFORCE(platform::dynload::cudnnGetConvolutionForwardAlgorithm(
CUDNN_ENFORCE(platform::dynload::cudnnGetConvolutionForwardAlgorithm(
handle, cudnn_output_desc, cudnn_filter_desc, cudnn_conv_desc,
cudnn_input_desc, CUDNN_CONVOLUTION_FWD_SPECIFY_WORKSPACE_LIMIT,
workspace_size_limit, &data_algo));
PADDLE_ENFORCE(platform::dynload::cudnnGetConvolutionForwardWorkspaceSize(
CUDNN_ENFORCE(platform::dynload::cudnnGetConvolutionForwardWorkspaceSize(
handle, cudnn_output_desc, cudnn_filter_desc, cudnn_conv_desc,
cudnn_input_desc, data_algo, &fwd_ws_size));
workspace_size_in_bytes = std::max(workspace_size_in_bytes, fwd_ws_size);
......@@ -190,7 +190,7 @@ class CUDNNConvTransposeGradOpKernel : public framework::OpKernel<T> {
if (filter_grad) {
// choose backward algorithm for filter
PADDLE_ENFORCE(
CUDNN_ENFORCE(
platform::dynload::cudnnGetConvolutionBackwardFilterAlgorithm(
handle, cudnn_output_desc, cudnn_input_desc, cudnn_conv_desc,
cudnn_filter_desc,
......@@ -198,7 +198,7 @@ class CUDNNConvTransposeGradOpKernel : public framework::OpKernel<T> {
workspace_size_limit, &filter_algo));
// get workspace for backwards filter algorithm
PADDLE_ENFORCE(
CUDNN_ENFORCE(
platform::dynload::cudnnGetConvolutionBackwardFilterWorkspaceSize(
handle, cudnn_output_desc, cudnn_input_desc, cudnn_conv_desc,
cudnn_filter_desc, filter_algo, &bwd_filter_ws_size));
......@@ -222,7 +222,7 @@ class CUDNNConvTransposeGradOpKernel : public framework::OpKernel<T> {
T* input_grad_data = input_grad->mutable_data<T>(ctx.GetPlace());
// Because beta is zero, it is unnecessary to reset input_grad.
for (int g = 0; g < groups; g++) {
PADDLE_ENFORCE(platform::dynload::cudnnConvolutionForward(
CUDNN_ENFORCE(platform::dynload::cudnnConvolutionForward(
handle, &alpha, cudnn_output_desc,
output_grad_data + output_grad_offset * g, cudnn_filter_desc,
filter_data + filter_offset * g, cudnn_conv_desc, data_algo,
......@@ -237,7 +237,7 @@ class CUDNNConvTransposeGradOpKernel : public framework::OpKernel<T> {
// Because beta is zero, it is unnecessary to reset filter_grad.
// Gradient with respect to the filter
for (int g = 0; g < groups; g++) {
PADDLE_ENFORCE(platform::dynload::cudnnConvolutionBackwardFilter(
CUDNN_ENFORCE(platform::dynload::cudnnConvolutionBackwardFilter(
handle, &alpha, cudnn_output_desc,
output_grad_data + output_grad_offset * g, cudnn_input_desc,
input_data + input_offset * g, cudnn_conv_desc, filter_algo,
......
paddle/fluid/operators/distributed/CMakeLists.txt
浏览文件 @ 062556f9
......@@ -17,9 +17,9 @@ if(WITH_GRPC)
set(DISTRIBUTE_COMPILE_FLAGS "-Wno-non-virtual-dtor -Wno-error=non-virtual-dtor -Wno-error=delete-non-virtual-dtor")
set_source_files_properties(grpc_serde_test.cc rpc_server_test.cc PROPERTIES COMPILE_FLAGS ${DISTRIBUTE_COMPILE_FLAGS})
cc_test(grpc_serde_test SRCS grpc_serde_test.cc
DEPS grpc++_unsecure grpc_unsecure gpr cares zlib protobuf sendrecvop_grpc scope profiler math_function SERIAL)
cc_test(rpc_server_test SRCS rpc_server_test.cc
DEPS sendrecvop_grpc grpc++_unsecure grpc_unsecure gpr cares zlib protobuf executor proto_desc lookup_table_op SERIAL)
DEPS grpc++_unsecure grpc_unsecure gpr cares zlib protobuf sendrecvop_grpc scope profiler math_function SERIAL)
cc_test(rpc_server_test SRCS rpc_server_test.cc
DEPS sendrecvop_grpc grpc++_unsecure grpc_unsecure gpr cares zlib protobuf executor proto_desc lookup_sparse_table_op SERIAL)
return()
endif()
......
paddle/fluid/operators/distributed/rpc_server_test.cc
浏览文件 @ 062556f9
......@@ -30,7 +30,7 @@ namespace framework = paddle::framework;
namespace platform = paddle::platform;
namespace distributed = paddle::operators::distributed;
USE_OP(lookup_table);
USE_NO_KERNEL_OP(lookup_sparse_table);
std::unique_ptr<distributed::RPCServer> g_rpc_service;
std::unique_ptr<distributed::RequestHandler> g_req_handler;
......@@ -42,13 +42,13 @@ framework::BlockDesc* AppendPrefetchBlcok(framework::ProgramDesc* program) {
framework::VariableNameMap input({{"W", {"w"}}, {"Ids", {"ids"}}});
framework::VariableNameMap output({{"Output", {"out"}}});
auto op = block->AppendOp();
op->SetType("lookup_table");
op->SetType("lookup_sparse_table");
op->SetInput("W", {"w"});
op->SetInput("Ids", {"ids"});
op->SetOutput("Out", {"out"});
auto& out = *root_block->Var("out");
out.SetType(framework::proto::VarType::SELECTED_ROWS);
out.SetType(framework::proto::VarType::LOD_TENSOR);
out.SetShape({10, 10});
return block;
......@@ -59,20 +59,19 @@ void CreateVarsOnScope(framework::Scope* scope, platform::CPUPlace* place) {
w_var->GetMutable<framework::SelectedRows>();
auto out_var = scope->Var("out");
out_var->GetMutable<framework::SelectedRows>();
out_var->GetMutable<framework::LoDTensor>();
auto ids_var = scope->Var("ids");
ids_var->GetMutable<framework::SelectedRows>();
ids_var->GetMutable<framework::LoDTensor>();
}
void InitTensorsOnClient(framework::Scope* scope, platform::CPUPlace* place,
int64_t rows_numel) {
CreateVarsOnScope(scope, place);
auto ids_var = scope->Var("ids")->GetMutable<framework::SelectedRows>();
auto rows = ids_var->mutable_rows();
for (int64_t i = 0; i < rows_numel; ++i) rows->push_back(i * 2);
ids_var->mutable_value()->Resize({rows_numel, 1});
ids_var->mutable_value()->mutable_data<float>(*place);
auto ids_var = scope->Var("ids")->GetMutable<framework::LoDTensor>();
int64_t* ids_ptr =
ids_var->mutable_data<int64_t>(framework::DDim({rows_numel, 1}), *place);
for (int64_t i = 0; i < rows_numel; ++i) ids_ptr[i] = i * 2;
}
void InitTensorsOnServer(framework::Scope* scope, platform::CPUPlace* place,
......@@ -148,11 +147,11 @@ TEST(PREFETCH, CPU) {
client->AsyncPrefetchVar(ep, ctx, scope, in_var_name, out_var_name);
client->Wait();
auto var = scope.Var(out_var_name);
auto value = var->GetMutable<framework::SelectedRows>()->value();
auto ptr = value.mutable_data<float>(place);
auto value = var->GetMutable<framework::LoDTensor>();
auto ptr = value->mutable_data<float>(place);
for (int64_t i = 0; i < rows_numel; ++i) {
EXPECT_EQ(ptr[0 + i * value.dims()[1]], static_cast<float>(i * 2));
EXPECT_EQ(ptr[0 + i * value->dims()[1]], static_cast<float>(i * 2));
}
}
......
paddle/fluid/operators/extract_rows_op.cc 0 → 100644
浏览文件 @ 062556f9
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include <string>
#include <vector>
#include "paddle/fluid/framework/op_registry.h"
namespace paddle {
namespace operators {
class ExtractRowsOpInferShape : public framework::InferShapeBase {
public:
void operator()(framework::InferShapeContext *ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"),
"Input(X) of ExtractRowsOp should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("Out"),
"Output(Out) of ExtractRowsOp should not be null.");
PADDLE_ENFORCE_EQ(ctx->GetInputsVarType("X")[0],
framework::proto::VarType::SELECTED_ROWS,
"The type of input(X) must be SelectedRows.");
auto in_dims = ctx->GetInputDim("X");
ctx->SetOutputDim(
"Out", framework::make_ddim(std::vector<int64_t>{in_dims[0], 1}));
}
};
class ExtractRowsOp : public framework::OperatorBase {
public:
ExtractRowsOp(const std::string &type,
const framework::VariableNameMap &inputs,
const framework::VariableNameMap &outputs,
const framework::AttributeMap &attrs)
: framework::OperatorBase(type, inputs, outputs, attrs) {}
private:
void RunImpl(const framework::Scope &scope,
const platform::Place &place) const override {
auto &in = scope.FindVar(Input("X"))->Get<framework::SelectedRows>();
auto out = scope.FindVar(Output("Out"))->GetMutable<framework::LoDTensor>();
auto in_rows = in.rows();
auto out_dim = framework::make_ddim(
std::vector<int64_t>{static_cast<int64_t>(in_rows.size()), 1});
auto dst_ptr = out->mutable_data<int64_t>(out_dim, in.place());
if (paddle::platform::is_gpu_place(in.place())) {
#ifdef PADDLE_WITH_CUDA
platform::DeviceContextPool &pool =
platform::DeviceContextPool::Instance();
auto *dev_ctx = pool.Get(in.place());
auto src_ptr = in_rows.Data(in.place());
auto stream =
reinterpret_cast<const platform::CUDADeviceContext &>(*dev_ctx)
.stream();
memory::Copy(boost::get<platform::CUDAPlace>(out->place()), dst_ptr,
boost::get<platform::CUDAPlace>(in.place()), src_ptr,
in_rows.size() * sizeof(int64_t), stream);
#else
PADDLE_THROW("Not compiled with CUDA.");
#endif
} else {
memory::Copy(platform::CPUPlace(), dst_ptr, platform::CPUPlace(),
in_rows.data(), in_rows.size() * sizeof(int64_t));
}
}
};
class ExtractRowsOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("X",
"(SelectedRows). The input tensor of extract_rows operator,"
" and its type is SelectedRows.");
AddOutput("Out", "(Tensor). The the rows of input(X).");
AddComment(R"DOC(
ExtractRows Operator.
The function of extract_rows_op is extracting the rows from the input(X)
whose type is SelectedRows.
)DOC");
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OPERATOR(extract_rows, ops::ExtractRowsOp, ops::ExtractRowsOpMaker,
ops::ExtractRowsOpInferShape);
paddle/fluid/operators/lookup_table_op.cc
浏览文件 @ 062556f9
......@@ -33,19 +33,15 @@ class LookupTableOp : public framework::OperatorWithKernel {
auto table_dims = ctx->GetInputDim("W");
auto ids_dims = ctx->GetInputDim("Ids");
auto ids_var_type = ctx->GetInputsVarType("Ids").front();
// The type of Ids(Input) is SelectedRows or LoDTensor, when Ids's type
// is LoDTensor, this tensor contains the ids to be looked up in W
// and it must be a column vector with rank = 2 while the 2nd dimension
// size must be 1, when Ids's type is SelectedRows, the rows of Ids
// contains the ids to be looked up in W;
if (ids_var_type == framework::proto::VarType::LOD_TENSOR) {
PADDLE_ENFORCE_EQ(ids_dims.size(), 2);
PADDLE_ENFORCE_EQ(ids_dims[1], 1);
}
PADDLE_ENFORCE_EQ(ids_dims.size(), 2);
PADDLE_ENFORCE_EQ(ids_dims[1], 1);
ctx->SetOutputDim("Out", {ids_dims[0], table_dims[1]});
ctx->ShareLoD("Ids", /*->*/ "Out");
if (ctx->GetOutputsVarType("Out")[0] ==
framework::proto::VarType::LOD_TENSOR) {
ctx->ShareLoD("Ids", /*->*/ "Out");
}
}
protected:
......@@ -62,17 +58,12 @@ class LookupTableOpMaker : public framework::OpProtoAndCheckerMaker {
AddInput("W",
"(Tensor) The input represents embedding tensors, "
"which is a learnable parameter.");
AddInput(
"Ids",
"(Tensor or SelectedRows) Ids's type can be Tensor or "
"SelectedRows, when Ids's type is Tensor, this tensor contains "
"the ids to be looked up in W and it must be a column vector with "
"rank = 2 while the 2nd dimension size must be 1; when Ids's type is "
"SelectedRows, the rows of Ids contains the ids to be looked up "
"in W.");
AddOutput("Out",
"(Tensor or SelectedRows) The lookup results, which have the "
"same type as W.");
AddInput("Ids",
"An input with type int32 or int64 "
"contains the ids to be looked up in W. "
"Ids must be a column vector with rank = 2. "
"The 2nd dimension size must be 1.");
AddOutput("Out", "The lookup results, which have the same type as W.");
AddAttr<bool>("is_sparse",
"(boolean, default false) "
"Sparse update.")
......@@ -90,15 +81,10 @@ class LookupTableOpMaker : public framework::OpProtoAndCheckerMaker {
Lookup Table Operator.
This operator is used to perform lookups on the parameter W,
then concatenated into a dense or sparse tensor.
The type of Ids(Input) is SelectedRows, Tensor or LoDTensor, when Ids's
type is SelectedRows, the rows of Ids contains the ids to be looked up in W;
when Ids's type is Tensor, this tensor contains the ids to be looked up in W
and it must be a column vector with rank = 2 while the 2nd dimension size must be 1,
at this time, Ids can carry the LoD (Level of Details) information, or not, and
the output only shares the LoD information with input Ids.
then concatenated into a dense tensor.
The input Ids can carry the LoD (Level of Details) information,
or not. And the output only shares the LoD information with input Ids.
)DOC");
}
......
paddle/fluid/operators/lookup_table_op.cu
浏览文件 @ 062556f9
......@@ -23,7 +23,7 @@ namespace operators {
template <typename T, int BlockDimX, int BlockDimY, int GridDimX,
bool PaddingFlag>
__global__ void LookupTable(T* output, const T* table, const int64_t* ids,
__global__ void LookupTable(T *output, const T *table, const int64_t *ids,
const int64_t N, const int64_t K, const int64_t D,
const int64_t padding_idx) {
int idx = threadIdx.x;
......@@ -33,8 +33,8 @@ __global__ void LookupTable(T* output, const T* table, const int64_t* ids,
int64_t id = ids[idy];
PADDLE_ASSERT(id >= 0);
PADDLE_ASSERT(id < N);
T* out = output + idy * D;
const T* tab = table + id * D;
T *out = output + idy * D;
const T *tab = table + id * D;
for (int i = idx; i < D; i += BlockDimX) {
if (PaddingFlag) {
if (id == padding_idx)
......@@ -50,7 +50,7 @@ __global__ void LookupTable(T* output, const T* table, const int64_t* ids,
}
template <typename T, int BlockDimX, int BlockDimY, int GridDimX>
__global__ void LookupTableGrad(T* table, const T* output, const int64_t* ids,
__global__ void LookupTableGrad(T *table, const T *output, const int64_t *ids,
const int64_t N, const int64_t K,
const int64_t D) {
int idx = threadIdx.x;
......@@ -60,8 +60,8 @@ __global__ void LookupTableGrad(T* table, const T* output, const int64_t* ids,
int id = ids[idy];
PADDLE_ASSERT(id >= 0);
PADDLE_ASSERT(id < N);
const T* out = output + idy * D;
T* tab = table + id * D;
const T *out = output + idy * D;
T *tab = table + id * D;
for (int i = idx; i < D; i += BlockDimX) {
paddle::platform::CudaAtomicAdd(&tab[i], out[i]);
}
......@@ -72,36 +72,19 @@ __global__ void LookupTableGrad(T* table, const T* output, const int64_t* ids,
template <typename T>
class LookupTableCUDAKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
auto* table_t = context.Input<LoDTensor>("W");
void Compute(const framework::ExecutionContext &context) const override {
auto *table_t = context.Input<LoDTensor>("W");
auto *ids_t = context.Input<LoDTensor>("Ids");
auto *output_t = context.Output<LoDTensor>("Out");
int64_t padding_idx = context.Attr<int64_t>("padding_idx");
auto* ids_var = context.InputVar("Ids");
Tensor* output_t = context.Output<Tensor>("Out");
int64_t* ids;
int64_t K;
// The type of Ids(Input) is SelectedRows or LoDTensor, when Ids's type
// is LoDTensor, this tensor contains the ids to be looked up in W;
// when Ids's type is SelectedRows, the rows of Ids contains the
// ids to be looked up in W.
if (ids_var->IsType<framework::LoDTensor>()) {
auto* ids_t = context.Input<LoDTensor>("Ids");
ids = const_cast<int64_t*>(ids_t->data<int64_t>());
K = ids_t->numel();
} else if (ids_var->IsType<framework::SelectedRows>()) {
auto* ids_t = context.Input<framework::SelectedRows>("Ids");
ids = const_cast<int64_t*>(ids_t->rows().CUDAData(context.GetPlace()));
K = ids_t->rows().size();
output_t->Resize({K, table_t->dims()[1]});
} else {
PADDLE_THROW("Unsupported Variable Type of Ids");
}
size_t N = table_t->dims()[0];
size_t D = table_t->dims()[1];
auto* table = table_t->data<T>();
auto* output = output_t->mutable_data<T>(context.GetPlace());
size_t K = ids_t->numel();
auto *ids = ids_t->data<int64_t>();
auto *table = table_t->data<T>();
auto *output = output_t->mutable_data<T>(context.GetPlace());
dim3 threads(128, 8);
dim3 grids(8, 1);
......@@ -122,19 +105,19 @@ class LookupTableCUDAKernel : public framework::OpKernel<T> {
template <typename T>
class LookupTableGradCUDAKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
auto& dev_ctx =
void Compute(const framework::ExecutionContext &context) const override {
auto &dev_ctx =
context.template device_context<platform::CUDADeviceContext>();
bool is_sparse = context.Attr<bool>("is_sparse");
// Since paddings are not trainable and fixed in forward, the gradient of
// paddings makes no sense and we don't deal with it in backward.
if (is_sparse) {
auto* ids = context.Input<LoDTensor>("Ids");
auto* table = context.Input<LoDTensor>("W");
auto* d_output = context.Input<LoDTensor>(framework::GradVarName("Out"));
auto* d_table = context.Output<SelectedRows>(framework::GradVarName("W"));
auto *ids = context.Input<LoDTensor>("Ids");
auto *table = context.Input<LoDTensor>("W");
auto *d_output = context.Input<LoDTensor>(framework::GradVarName("Out"));
auto *d_table = context.Output<SelectedRows>(framework::GradVarName("W"));
auto* ids_data = ids->data<int64_t>();
auto *ids_data = ids->data<int64_t>();
auto ids_dim = ids->dims();
auto stream = dev_ctx.stream();
......@@ -150,12 +133,12 @@ class LookupTableGradCUDAKernel : public framework::OpKernel<T> {
d_table->set_rows(new_rows);
auto* d_table_value = d_table->mutable_value();
auto *d_table_value = d_table->mutable_value();
d_table_value->Resize({ids_dim[0], table->dims()[1]});
d_table_value->mutable_data<T>(context.GetPlace());
auto* d_table_data = d_table_value->data<T>();
auto* d_output_data = d_output->data<T>();
auto *d_table_data = d_table_value->data<T>();
auto *d_output_data = d_output->data<T>();
PADDLE_ENFORCE_EQ(d_table_value->dims(), d_output->dims());
memory::Copy(gpu_place, d_table_data, gpu_place, d_output_data,
d_output->numel() * sizeof(T), stream);
......@@ -168,9 +151,9 @@ class LookupTableGradCUDAKernel : public framework::OpKernel<T> {
int N = d_table_t->dims()[0];
int D = d_table_t->dims()[1];
int K = ids_t->numel();
const int64_t* ids = ids_t->data<int64_t>();
const T* d_output = d_output_t->data<T>();
T* d_table = d_table_t->mutable_data<T>(context.GetPlace());
const int64_t *ids = ids_t->data<int64_t>();
const T *d_output = d_output_t->data<T>();
T *d_table = d_table_t->mutable_data<T>(context.GetPlace());
auto t = framework::EigenVector<T>::Flatten(*d_table_t);
t.device(*dev_ctx.eigen_device()) = t.constant(static_cast<T>(0));
......
paddle/fluid/operators/lookup_table_op.h
浏览文件 @ 062556f9
......@@ -36,43 +36,13 @@ template <typename T>
class LookupTableKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext &context) const override {
auto *ids_t = context.Input<LoDTensor>("Ids"); // int tensor
auto *output_t = context.Output<LoDTensor>("Out"); // float tensor
auto *table_var = context.InputVar("W");
auto *ids_var = context.InputVar("Ids");
Tensor *output_t = context.Output<Tensor>("Out");
int64_t padding_idx = context.Attr<int64_t>("padding_idx");
DDim table_dim;
if (table_var->IsType<LoDTensor>()) {
table_dim = context.Input<LoDTensor>("W")->dims();
} else if (table_var->IsType<SelectedRows>()) {
auto *table_t = context.Input<SelectedRows>("W");
table_dim = table_t->value().dims();
} else {
PADDLE_THROW(
"The parameter W of a LookupTable "
"must be either LoDTensor or SelectedRows");
}
int64_t *ids;
int64_t ids_numel;
// The type of Ids(Input) is SelectedRows or LoDTensor, when Ids's type
// is LoDTensor, this tensor contains the ids to be looked up in W;
// when Ids's type is SelectedRows, the rows of Ids contains the
// ids to be looked up in W.
if (ids_var->IsType<LoDTensor>()) {
auto *ids_t = context.Input<LoDTensor>("Ids");
ids = const_cast<int64_t *>(ids_t->data<int64_t>());
ids_numel = ids_t->numel();
} else if (ids_var->IsType<SelectedRows>()) {
auto *ids_t = context.Input<SelectedRows>("Ids");
ids = const_cast<int64_t *>(ids_t->rows().data());
ids_numel = ids_t->rows().size();
output_t->Resize({ids_numel, table_dim[1]});
} else {
PADDLE_THROW("Unsupported Variable Type of Ids");
}
int64_t padding_idx = context.Attr<int64_t>("padding_idx");
int64_t *ids = const_cast<int64_t *>(ids_t->data<int64_t>());
int64_t ids_numel = ids_t->numel();
if (table_var->IsType<LoDTensor>()) {
auto *table_t = context.Input<LoDTensor>("W");
......
paddle/fluid/operators/math/im2col.cc
浏览文件 @ 062556f9
......@@ -40,22 +40,47 @@ class Im2ColFunctor<paddle::operators::math::ColFormat::kCFO,
int im_width = im.dims()[2];
int filter_height = col->dims()[1];
int filter_width = col->dims()[2];
int col_height = col->dims()[3];
int col_width = col->dims()[4];
int output_height = col->dims()[3];
int output_width = col->dims()[4];
int channels_col = im_channels * filter_height * filter_width;
const T* im_data = im.data<T>();
T* col_data = col->data<T>();
// TODO(TJ): change me to template
// further optimaze:
// 1. padding != 1
// 2. could also support stride_h != 1
if (stride[0] == 1 && stride[1] == 1 && dilation[0] == 1 &&
dilation[1] == 1 && padding[0] == 0 && padding[1] == 0) {
int col_matrix_width = output_width * output_height;
size_t copy_size = sizeof(T) * output_width;
for (int oh = 0; oh < output_height; ++oh) {
const T* im_data_start = im_data + oh * im_width;
T* dst_data = col_data + oh * output_width;
for (int ic = 0; ic < im_channels; ++ic) {
const T* src_data = im_data_start + ic * im_height * im_width;
for (int kh = 0; kh < filter_height; ++kh) {
for (int kw = 0; kw < filter_width; ++kw) {
std::memcpy(dst_data, src_data + kw, copy_size);
dst_data = dst_data + col_matrix_width;
}
src_data = src_data + im_width;
}
}
}
return;
}
for (int c = 0; c < channels_col; ++c) {
int w_offset = c % filter_width;
int h_offset = (c / filter_width) % filter_height;
int c_im = c / (filter_width * filter_height);
for (int h = 0; h < col_height; ++h) {
for (int h = 0; h < output_height; ++h) {
int im_row_idx = h * stride[0] - padding[0] + h_offset * dilation[0];
for (int w = 0; w < col_width; ++w) {
for (int w = 0; w < output_width; ++w) {
int im_col_idx = w * stride[1] - padding[1] + w_offset * dilation[1];
int col_idx = (c * col_height + h) * col_width + w;
int col_idx = (c * output_height + h) * output_width + w;
int im_idx = (im_row_idx + c_im * im_height) * im_width + im_col_idx;
col_data[col_idx] = (im_row_idx < 0 || im_row_idx >= im_height ||
......
paddle/fluid/operators/math/im2col_test.cc
浏览文件 @ 062556f9
......@@ -160,8 +160,80 @@ void testIm2col() {
delete context;
}
void testIm2colCPU(int ic, int ih, int iw, int fh, int fw, int ph, int pw) {
paddle::framework::Tensor input;
paddle::framework::Tensor output;
paddle::framework::Tensor ref_output;
std::vector<int> padding({ph, pw});
std::vector<int> stride({1, 1}); // stride_y, stride_x
std::vector<int> dilation({1, 1}); // dilation_y, dilation_x
int output_height = (ih - fh + padding[0] * 2) / stride[0] + 1;
int output_width = (iw - fw + padding[1] * 2) / stride[1] + 1;
float* input_ptr =
input.mutable_data<float>({ic, ih, iw}, paddle::platform::CPUPlace());
for (int i = 0; i < input.numel(); ++i) {
input_ptr[i] = static_cast<float>(i + 1);
}
paddle::platform::CPUPlace place;
paddle::platform::CPUDeviceContext context(place);
output.mutable_data<float>({ic, fh, fw, output_height, output_width}, place);
ref_output.mutable_data<float>({ic, fh, fw, output_height, output_width},
place);
paddle::operators::math::Im2ColFunctor<
paddle::operators::math::ColFormat::kCFO,
paddle::platform::CPUDeviceContext, float>
im2col;
im2col(context, input, dilation, stride, padding, &output);
auto ref_im2col = [&](
const paddle::framework::Tensor& im, const std::vector<int>& dilation,
const std::vector<int>& stride, const std::vector<int>& padding,
paddle::framework::Tensor* col) {
int im_channels = im.dims()[0];
int im_height = im.dims()[1];
int im_width = im.dims()[2];
int filter_height = col->dims()[1];
int filter_width = col->dims()[2];
int output_height = col->dims()[3];
int output_width = col->dims()[4];
int channels_col = im_channels * filter_height * filter_width;
const float* im_data = im.data<float>();
float* col_data = col->data<float>();
for (int c = 0; c < channels_col; ++c) {
int w_offset = c % filter_width;
int h_offset = (c / filter_width) % filter_height;
int c_im = c / (filter_width * filter_height);
for (int h = 0; h < output_height; ++h) {
int im_row_idx = h * stride[0] - padding[0] + h_offset * dilation[0];
for (int w = 0; w < output_width; ++w) {
int im_col_idx = w * stride[1] - padding[1] + w_offset * dilation[1];
int col_idx = (c * output_height + h) * output_width + w;
int im_idx = (im_row_idx + c_im * im_height) * im_width + im_col_idx;
col_data[col_idx] = (im_row_idx < 0 || im_row_idx >= im_height ||
im_col_idx < 0 || im_col_idx >= im_width)
? 0.f
: im_data[im_idx];
}
}
}
};
ref_im2col(input, dilation, stride, padding, &ref_output);
float* out_cfo_ptr = output.data<float>();
float* out_ref_ptr = ref_output.data<float>();
for (int i = 0; i < output.numel(); ++i) {
EXPECT_EQ(out_cfo_ptr[i], out_ref_ptr[i]);
}
}
TEST(math, im2col) {
testIm2col<paddle::platform::CPUDeviceContext, paddle::platform::CPUPlace>();
testIm2colCPU(/*ic*/ 3, /*ih*/ 5, /*iw*/ 5, /*fh*/ 3, /*fw*/ 2, /*ph*/ 0,
/*pw*/ 0);
testIm2colCPU(/*ic*/ 2, /*ih*/ 5, /*iw*/ 4, /*fh*/ 3, /*fw*/ 3, /*ph*/ 1,
/*pw*/ 1);
#ifdef PADDLE_WITH_CUDA
testIm2col<paddle::platform::CUDADeviceContext,
paddle::platform::CUDAPlace>();
......
paddle/fluid/operators/math/softmax.cu
浏览文件 @ 062556f9
......@@ -52,7 +52,7 @@ void SoftmaxCUDNNFunctor<T>::operator()(
xDesc.descriptor<T>(layout, cudnn_tensor_dims);
cudnnTensorDescriptor_t cudnn_y_desc =
xDesc.descriptor<T>(layout, cudnn_tensor_dims);
PADDLE_ENFORCE(platform::dynload::cudnnSoftmaxForward(
CUDNN_ENFORCE(platform::dynload::cudnnSoftmaxForward(
context.cudnn_handle(), CUDNN_SOFTMAX_ACCURATE,
CUDNN_SOFTMAX_MODE_INSTANCE, CudnnDataType<T>::kOne(), cudnn_x_desc,
X->data<T>(), CudnnDataType<T>::kZero(), cudnn_y_desc,
......@@ -83,7 +83,7 @@ void SoftmaxGradCUDNNFunctor<T>::operator()(
dxDesc.descriptor<T>(layout, cudnn_tensor_dims);
cudnnTensorDescriptor_t cudnn_ygrad_desc =
dyDesc.descriptor<T>(layout, cudnn_tensor_dims);
PADDLE_ENFORCE(platform::dynload::cudnnSoftmaxBackward(
CUDNN_ENFORCE(platform::dynload::cudnnSoftmaxBackward(
context.cudnn_handle(), CUDNN_SOFTMAX_ACCURATE,
CUDNN_SOFTMAX_MODE_INSTANCE, CudnnDataType<T>::kOne(), cudnn_y_desc,
Y->data<T>(), cudnn_ygrad_desc, YGrad->data<T>(),
......
paddle/fluid/operators/pool_cudnn_op.cu.cc
浏览文件 @ 062556f9
......@@ -81,7 +81,7 @@ class PoolCUDNNOpKernel : public framework::OpKernel<T> {
// ------------------- cudnn pool algorithm ---------------------
auto handle = ctx.cuda_device_context().cudnn_handle();
ScalingParamType<T> alpha = 1.0f, beta = 0.0f;
PADDLE_ENFORCE(platform::dynload::cudnnPoolingForward(
CUDNN_ENFORCE(platform::dynload::cudnnPoolingForward(
handle, cudnn_pool_desc, &alpha, cudnn_input_desc, input_data, &beta,
cudnn_output_desc, output_data));
}
......@@ -154,7 +154,7 @@ class PoolCUDNNGradOpKernel : public framework::OpKernel<T> {
T *input_grad_data = input_grad->mutable_data<T>(ctx.GetPlace());
// Because beta is zero, it is unnecessary to reset input_grad.
PADDLE_ENFORCE(platform::dynload::cudnnPoolingBackward(
CUDNN_ENFORCE(platform::dynload::cudnnPoolingBackward(
handle, cudnn_pool_desc, &alpha, cudnn_output_desc, output_data,
cudnn_output_desc, output_grad_data, cudnn_input_desc, input_data,
&beta, cudnn_input_desc, input_grad_data));
......
paddle/fluid/operators/send_recv_util.h
浏览文件 @ 062556f9
......@@ -14,6 +14,7 @@ limitations under the License. */
#pragma once
#include <string>
#include "paddle/fluid/framework/ir/node.h"
namespace paddle {
namespace operators {
......@@ -22,7 +23,10 @@ inline bool NeedSend(const framework::Scope& scope,
const std::string& varname) {
// dummy variable is only used in parallel executor to represent
// some dependency relationship, we don't need to send/recv it.
if (varname == "dummy") return false;
// TODO(paddle-dev): Why would parallel executor logic leaked into here?
if (varname.find(framework::ir::Node::kControlDepVarName) !=
std::string::npos)
return false;
auto* var = scope.FindVar(varname);
PADDLE_ENFORCE_NOT_NULL(var, "Can not find variable '%s' in the send side.",
varname);
......
paddle/fluid/platform/cpu_helper.cc
浏览文件 @ 062556f9
......@@ -16,6 +16,7 @@ limitations under the License. */
#include "paddle/fluid/platform/enforce.h"
#ifdef PADDLE_WITH_MKLML
#include <omp.h>
#include "paddle/fluid/platform/dynload/mklml.h"
#endif
......@@ -33,6 +34,7 @@ void SetNumThreads(int num_threads) {
#elif defined(PADDLE_WITH_MKLML)
int real_num_threads = num_threads > 1 ? num_threads : 1;
platform::dynload::MKL_Set_Num_Threads(real_num_threads);
omp_set_num_threads(num_threads);
#else
PADDLE_ENFORCE(false, "To be implemented.");
#endif
......
paddle/fluid/platform/cudnn_helper.h
浏览文件 @ 062556f9
......@@ -59,13 +59,12 @@ inline const char* cudnnGetErrorString(cudnnStatus_t status) {
#define CUDNN_VERSION_MIN(major, minor, patch) \
(CUDNN_VERSION >= ((major)*1000 + (minor)*100 + (patch)))
#define CUDNN_ENFORCE(condition) \
do { \
cudnnStatus_t status = condition; \
if (status != CUDNN_STATUS_SUCCESS) { \
VLOG(1) << ::paddle::platform::cudnnGetErrorString(status); \
PADDLE_THROW("cuDNN call failed"); \
} \
#define CUDNN_ENFORCE(condition) \
do { \
cudnnStatus_t status = condition; \
if (UNLIKELY(status != CUDNN_STATUS_SUCCESS)) { \
PADDLE_THROW(::paddle::platform::cudnnGetErrorString(status)); \
} \
} while (false)
enum class DataLayout { // Not use
......
paddle/fluid/platform/init.cc
浏览文件 @ 062556f9
......@@ -23,6 +23,9 @@ limitations under the License. */
#include "paddle/fluid/platform/place.h"
#include "paddle/fluid/string/piece.h"
DEFINE_int32(paddle_num_threads, 1,
"Number of threads for each paddle instance.");
namespace paddle {
namespace framework {
......@@ -115,7 +118,7 @@ void InitDevices(bool init_p2p, const std::vector<int> devices) {
places.emplace_back(platform::CPUPlace());
platform::DeviceContextPool::Init(places);
#ifndef PADDLE_WITH_MKLDNN
platform::SetNumThreads(1);
platform::SetNumThreads(FLAGS_paddle_num_threads);
#endif
}
......
paddle/scripts/paddle_build.sh
浏览文件 @ 062556f9
......@@ -547,6 +547,7 @@ function test_fluid_inference_lib() {
EOF
cd ${PADDLE_ROOT}/paddle/fluid/inference/api/demo_ci
./run.sh ${PADDLE_ROOT} ${WITH_MKL:-ON} ${WITH_GPU:-OFF}
./clean.sh
fi
}
......
python/paddle/fluid/__init__.py
浏览文件 @ 062556f9
......@@ -62,33 +62,33 @@ from paddle.fluid.layers.math_op_patch import monkey_patch_variable
Tensor = LoDTensor
__all__ = framework.__all__ + executor.__all__ + concurrency.__all__ + \
trainer.__all__ + inferencer.__all__ + transpiler.__all__ + \
parallel_executor.__all__ + lod_tensor.__all__ + [
'io',
'initializer',
'layers',
'contrib',
'transpiler',
'nets',
'optimizer',
'learning_rate_decay',
'backward',
'regularizer',
'LoDTensor',
'LoDTensorArray',
'CPUPlace',
'CUDAPlace',
'CUDAPinnedPlace',
'Tensor',
'ParamAttr',
'WeightNormParamAttr',
'DataFeeder',
'clip',
'profiler',
'unique_name',
'recordio_writer',
'Scope',
]
trainer.__all__ + inferencer.__all__ + transpiler.__all__ + \
parallel_executor.__all__ + lod_tensor.__all__ + [
'io',
'initializer',
'layers',
'contrib',
'transpiler',
'nets',
'optimizer',
'learning_rate_decay',
'backward',
'regularizer',
'LoDTensor',
'LoDTensorArray',
'CPUPlace',
'CUDAPlace',
'CUDAPinnedPlace',
'Tensor',
'ParamAttr',
'WeightNormParamAttr',
'DataFeeder',
'clip',
'profiler',
'unique_name',
'recordio_writer',
'Scope',
]
def __bootstrap__():
......@@ -123,7 +123,7 @@ def __bootstrap__():
read_env_flags = [
'use_pinned_memory', 'check_nan_inf', 'benchmark', 'warpctc_dir',
'eager_delete_scope', 'use_mkldnn', 'initial_cpu_memory_in_mb',
'init_allocated_mem'
'init_allocated_mem', 'free_idle_memory', 'paddle_num_threads'
]
if core.is_compiled_with_dist():
read_env_flags.append('rpc_deadline')
......
python/paddle/fluid/framework.py
浏览文件 @ 062556f9
......@@ -1540,7 +1540,12 @@ class Program(object):
def inference_optimize(self):
"""
This method will create a new program and change the :code:`is_test`
This method will create a new program and do following adjustments on it:
1. Remove all reader variables and their creator ops if exist.
2. Remove the :code:`read_op` if exists.
3. change the :code:`is_test`
attribute of operators to :code:`True`. All the :code:`Parameter`
information will be lost.
......@@ -1554,6 +1559,22 @@ class Program(object):
# core.inference_optimize being fixed.
res = Program()
res.desc = core.ProgramDesc(self.desc)
# remove all readers and the read_op if exist
read_op_idx = 0
root_block = res.desc.block(0)
while True:
if read_op_idx >= root_block.op_size() or root_block.op(
read_op_idx).type() == 'read':
break
read_op_idx += 1
if read_op_idx < root_block.op_size():
root_block._remove_op(0, read_op_idx + 1)
for var in root_block.all_vars():
if var.type() == core.VarDesc.VarType.READER:
root_block._remove_var(var.name())
# change all `is_test` attributes to True
for i in xrange(res.desc.num_blocks()):
block = res.desc.block(i)
for j in xrange(block.op_size()):
......
python/paddle/fluid/io.py
浏览文件 @ 062556f9
......@@ -790,101 +790,3 @@ def get_parameter_value_by_name(name, executor, program=None):
program = default_main_program()
var = program.global_block().var(name)
return get_parameter_value(var, executor)
def get_test_program(filelist, program=None, startup_program=None):
"""
Transpile current train program to a program to read test dataset
if the program is using reader ops like "open_files_op".
"""
def _copy_reader_var_(block, var, new_name=None):
if new_name == None:
new_name = var.name
new_var = block.create_var(
name=str(new_name), type=core.VarDesc.VarType.READER)
new_var.desc.set_shapes(var.desc.shapes())
new_var.desc.set_dtypes(var.desc.dtypes())
new_var.persistable = True
return new_var
def _get_test_reader_name(train_reader_name):
return train_reader_name + "_test"
def _is_reader_op(op):
block = op.block
if "Out" in op.output_names:
reader_out = block.vars[op.output("Out")[0]]
if reader_out.type == core.VarDesc.VarType.READER:
return True
return False
if program == None:
program = default_main_program()
if startup_program == None:
startup_program = default_startup_program()
startup_block = startup_program.global_block()
# 1. find out the orignal reader var name
startup_reader_op_list = []
for op in startup_block.ops:
if _is_reader_op(op):
startup_reader_op_list.append(op)
if len(startup_reader_op_list) == 0:
return program
root_reader_op = startup_reader_op_list[0]
train_test_reader_map = {}
# 2. add operators to startup to read open and read test data files
for op in startup_reader_op_list:
assert (len(op.output("Out")) == 1)
train_reader_name = op.output("Out")[0]
train_reader = startup_block.vars[train_reader_name]
test_reader = _copy_reader_var_(
startup_block,
train_reader,
new_name=_get_test_reader_name(train_reader_name))
train_test_reader_map[train_reader.name] = test_reader
test_op_inputs = {}
for name in op.input_names:
train_arg_names = op.input(name)
test_arg_vars = []
for arg_name in train_arg_names:
arg_var = train_test_reader_map[
arg_name] if name == "UnderlyingReader" else startup_block.vars[
arg_name]
test_arg_vars.append(arg_var)
test_op_inputs[name] = test_arg_vars
test_op = startup_block.append_op(
type=op.type,
inputs=test_op_inputs,
outputs={'Out': [test_reader]},
attrs=op.attrs)
# root reader op's filelist attr for read test files
if op.type == root_reader_op.type:
test_op.set_attr("file_names", filelist)
if op.type == "create_multi_pass_reader":
test_op.set_attr("pass_num", 1)
# 3. rename reader vars in inference program to different name
# to avoid read from train data.
main_block = program.global_block()
for var in main_block.vars.values():
if var.type == core.VarDesc.VarType.READER:
main_block._rename_var(
str(var.name), str(_get_test_reader_name(var.name)))
for op in main_block.ops:
if op.type == root_reader_op.type:
test_op.set_attr("file_names", filelist)
if op.type == "create_multi_pass_reader":
test_op.set_attr("pass_num", 1)
startup_program._sync_with_cpp()
program._sync_with_cpp()
return program
python/paddle/fluid/layers/io.py
浏览文件 @ 062556f9
......@@ -443,9 +443,6 @@ def random_data_generator(low, high, shapes, lod_levels, for_parallel=True):
main_prog_var = _copy_reader_var_(default_main_program().current_block(),
startup_var)
if for_parallel:
main_prog_var = parallel(reader=main_prog_var)
return monkey_patch_reader_methods(main_prog_var)
......
python/paddle/fluid/regularizer.py
浏览文件 @ 062556f9
......@@ -142,14 +142,20 @@ class L2DecayRegularizer(WeightDecayRegularizer):
dtype="float32", shape=param.shape, lod_level=param.lod_level)
if grad.type == core.VarDesc.VarType.SELECTED_ROWS:
idx = block.create_var(
dtype="int64",
shape=param.shape,
type=core.VarDesc.VarType.LOD_TENSOR)
decay = block.create_var(
dtype="float32",
shape=param.shape,
type=core.VarDesc.VarType.SELECTED_ROWS)
block.append_op(
type='extract_rows', inputs={'X': grad}, outputs={'Out': idx})
block.append_op(
type='lookup_table',
inputs={'W': param,
'Ids': grad},
'Ids': idx},
outputs={'Out': decay},
attrs={'is_sparse': True})
param = decay
......@@ -216,14 +222,20 @@ class L1DecayRegularizer(WeightDecayRegularizer):
dtype="float32", shape=param.shape, lod_level=param.lod_level)
if grad.type == core.VarDesc.VarType.SELECTED_ROWS:
idx = block.create_var(
dtype="int64",
shape=param.shape,
type=core.VarDesc.VarType.LOD_TENSOR)
decay = block.create_var(
dtype="float32",
shape=param.shape,
type=core.VarDesc.VarType.SELECTED_ROWS)
block.append_op(
type='extract_rows', inputs={'X': grad}, outputs={'Out': idx})
block.append_op(
type='lookup_table',
inputs={'W': param,
'Ids': grad},
'Ids': idx},
outputs={'Out': decay},
attrs={'is_sparse': True})
......
python/paddle/fluid/tests/demo/text_classification/convert_data_to_recordio.py → python/paddle/fluid/tests/demo/file_reader/convert_data_to_recordio.py
浏览文件 @ 062556f9
......@@ -35,7 +35,7 @@ if len(sys.argv) == 1:
word_dict = paddle.dataset.imdb.word_dict()
else:
word_dict = load_vocab(sys.argv[1])
word_dict["<unk>"] = len(word_dict)
word_dict["<unk>"] = len(word_dict)
print "Dict dim = ", len(word_dict)
# input text data
......@@ -50,7 +50,7 @@ feeder = fluid.DataFeeder(feed_list=[data, label], place=fluid.CPUPlace())
BATCH_SIZE = 128
train_reader = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.imdb.train(word_dict), buf_size=10000),
paddle.dataset.imdb.train(word_dict), buf_size=25000),
batch_size=BATCH_SIZE)
test_reader = paddle.batch(
......
python/paddle/fluid/tests/demo/text_classification/train.py → python/paddle/fluid/tests/demo/file_reader/train.py
浏览文件 @ 062556f9
......@@ -19,7 +19,7 @@ import sys
TRAIN_FILES = ['train.recordio']
TEST_FILES = ['test.recordio']
DICT_DIM = 89528
DICT_DIM = 5147
# embedding dim
emb_dim = 128
......@@ -27,58 +27,46 @@ emb_dim = 128
# hidden dim
hid_dim = 128
# hidden dim2
hid_dim2 = 96
# class num
class_dim = 2
# epoch num
epoch_num = 10
def network_cfg(is_train, pass_num=100):
with fluid.unique_name.guard():
train_file_obj = fluid.layers.open_files(
filenames=TRAIN_FILES,
pass_num=pass_num,
shapes=[[-1, 1], [-1, 1]],
lod_levels=[1, 0],
dtypes=['int64', 'int64'])
test_file_obj = fluid.layers.open_files(
filenames=TEST_FILES,
pass_num=1,
shapes=[[-1, 1], [-1, 1]],
lod_levels=[1, 0],
dtypes=['int64', 'int64'])
if is_train:
file_obj = fluid.layers.shuffle(train_file_obj, buffer_size=1000)
else:
file_obj = test_file_obj
def build_program(is_train):
file_obj_handle = fluid.layers.io.open_files(
filenames=TRAIN_FILES if is_train else TEST_FILES,
shapes=[[-1, 1], [-1, 1]],
lod_levels=[1, 0],
dtypes=['int64', 'int64'])
file_obj = fluid.layers.double_buffer(
file_obj,
name="train_double_buffer" if is_train else 'test_double_buffer')
file_obj = fluid.layers.io.double_buffer(file_obj_handle)
with fluid.unique_name.guard():
data, label = fluid.layers.read_file(file_obj)
emb = fluid.layers.embedding(input=data, size=[DICT_DIM, emb_dim])
# sequence conv with window size = 3
win_size = 3
conv_3 = fluid.nets.sequence_conv_pool(
input=emb,
num_filters=hid_dim,
filter_size=win_size,
filter_size=3,
act="tanh",
pool_type="max")
pool_type="sqrt")
# fc layer after conv
fc_1 = fluid.layers.fc(input=[conv_3], size=hid_dim2)
conv_4 = fluid.nets.sequence_conv_pool(
input=emb,
num_filters=hid_dim,
filter_size=4,
act="tanh",
pool_type="sqrt")
# probability of each class
prediction = fluid.layers.fc(input=[fc_1],
prediction = fluid.layers.fc(input=[conv_3, conv_4],
size=class_dim,
act="softmax")
# cross entropy loss
cost = fluid.layers.cross_entropy(input=prediction, label=label)
......@@ -88,58 +76,62 @@ def network_cfg(is_train, pass_num=100):
if is_train:
# SGD optimizer
sgd_optimizer = fluid.optimizer.Adagrad(learning_rate=0.01)
sgd_optimizer = fluid.optimizer.Adagrad(learning_rate=0.001)
sgd_optimizer.minimize(avg_cost)
return {
'loss': avg_cost,
'log': [avg_cost, acc],
'file': train_file_obj if is_train else test_file_obj
}
return {'loss': avg_cost, 'log': [avg_cost, acc], 'file': file_obj_handle}
def main():
train = fluid.Program()
startup = fluid.Program()
test = fluid.Program()
with fluid.program_guard(train, startup):
train_args = network_cfg(is_train=True)
test = fluid.Program()
train_args = build_program(is_train=True)
with fluid.program_guard(test, fluid.Program()):
test_args = network_cfg(is_train=False)
with fluid.program_guard(test, startup):
test_args = build_program(is_train=False)
use_cuda = fluid.core.is_compiled_with_cuda()
# startup
place = fluid.CUDAPlace(0)
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
exe = fluid.Executor(place=place)
exe.run(startup)
train_exe = fluid.ParallelExecutor(
use_cuda=True, loss_name=train_args['loss'].name, main_program=train)
use_cuda=use_cuda,
loss_name=train_args['loss'].name,
main_program=train)
test_exe = fluid.ParallelExecutor(
use_cuda=use_cuda, main_program=test, share_vars_from=train_exe)
fetch_var_list = [var.name for var in train_args['log']]
for i in xrange(sys.maxint):
result = map(numpy.array,
train_exe.run(fetch_list=fetch_var_list
if i % 1000 == 0 else []))
if len(result) != 0:
print 'Train: ', result
if i % 1000 == 0:
test_exe = fluid.ParallelExecutor(
use_cuda=True, main_program=test, share_vars_from=train_exe)
loss = []
acc = []
try:
while True:
loss_np, acc_np = map(
numpy.array, test_exe.run(fetch_list=fetch_var_list))
loss.append(loss_np[0])
acc.append(acc_np[0])
except:
test_args['file'].reset()
print 'TEST: ', numpy.mean(loss), numpy.mean(acc)
for epoch_id in range(epoch_num):
# train
try:
batch_id = 0
while True:
loss, acc = map(numpy.array,
train_exe.run(fetch_list=fetch_var_list))
print 'Train epoch', epoch_id, 'batch', batch_id, 'loss:', loss, 'acc:', acc
batch_id += 1
except fluid.core.EOFException:
print 'End of epoch', epoch_id
train_args['file'].reset()
# test
loss = []
acc = []
try:
while True:
loss_np, acc_np = map(numpy.array,
test_exe.run(fetch_list=fetch_var_list))
loss.append(loss_np[0])
acc.append(acc_np[0])
except:
test_args['file'].reset()
print 'Test loss:', numpy.mean(loss), 'acc:', numpy.mean(acc)
if __name__ == '__main__':
......
python/paddle/fluid/tests/unittests/dist_se_resnext.py
浏览文件 @ 062556f9
......@@ -278,7 +278,7 @@ class DistSeResneXt2x2:
def run_trainer(self, place, endpoints, trainer_id, trainers, is_dist=True):
test_program, avg_cost, train_reader, test_reader, batch_acc, predict = get_model(
batch_size=20)
batch_size=2)
if is_dist:
t = get_transpiler(trainer_id,
fluid.default_main_program(), endpoints,
......@@ -294,11 +294,7 @@ class DistSeResneXt2x2:
strategy.num_threads = 1
strategy.allow_op_delay = False
exe = fluid.ParallelExecutor(
True,
loss_name=avg_cost.name,
exec_strategy=strategy,
num_trainers=trainers,
trainer_id=trainer_id)
True, loss_name=avg_cost.name, exec_strategy=strategy)
feed_var_list = [
var for var in trainer_prog.global_block().vars.itervalues()
......
python/paddle/fluid/tests/unittests/test_dist_se_resnext.py
浏览文件 @ 062556f9
......@@ -56,7 +56,7 @@ class TestDistSeResneXt2x2(unittest.TestCase):
except os.error:
retry_times -= 1
def non_test_with_place(self):
def test_with_place(self):
# *ATTENTION* THIS TEST NEEDS AT LEAST 2GPUS TO RUN
required_envs = {
"PATH": os.getenv("PATH"),
......
python/paddle/fluid/tests/unittests/test_extract_rows_op.py 0 → 100644
浏览文件 @ 062556f9
# 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.core as core
from paddle.fluid.op import Operator
from op_test import OpTest
class TestExtractRows(OpTest):
def check_with_place(self, place):
scope = core.Scope()
# create and initialize Variable
feature_len = 12
rows = [0, 4, 4, 7]
np_array = np.ones((len(rows), feature_len)).astype("float32")
in_x = scope.var('X').get_selected_rows()
in_x.set_height(len(rows))
in_x.set_rows(rows)
in_x_tensor = in_x.get_tensor()
in_x_tensor.set(np_array, place)
# create Out Variable
out_tensor = scope.var('Out').get_tensor()
# create and run lookup_table operator
extract_rows_op = Operator("extract_rows", X='X', Out='Out')
extract_rows_op.run(scope, place)
# get result from Out
result_array = np.array(out_tensor)
result_array = [ele[0] for ele in result_array]
assert result_array == rows
def test_concat_rows(self):
places = [core.CPUPlace()]
if core.is_compiled_with_cuda():
places.append(core.CUDAPlace(0))
for place in places:
self.check_with_place(place)
if __name__ == '__main__':
unittest.main()
python/paddle/fluid/tests/unittests/test_lookup_table_op.py
浏览文件 @ 062556f9
......@@ -49,53 +49,6 @@ class TestLookupTableOpWithPadding(TestLookupTableOp):
pass
class TestLookupTableIdsIsSelectedRows(OpTest):
def check_with_place(self, place):
scope = core.Scope()
# create and initialize Variable
height = 10
rows = [0, 4, 4, 7]
row_numel = 12
# create and initialize W Variable
W = scope.var('W').get_tensor()
W_array = np.full((height, row_numel), 1.0).astype("float32")
for i in range(height):
W_array[i] *= i
W.set(W_array, place)
# create and initialize Ids Variable
ids_selected_rows = scope.var('Ids').get_selected_rows()
ids_selected_rows.set_height(len(rows))
ids_selected_rows.set_rows(rows)
np_array = np.ones((len(rows), row_numel)).astype("float32")
ids_tensor = ids_selected_rows.get_tensor()
ids_tensor.set(np_array, place)
# create Out Variable
Out = scope.var('Out').get_selected_rows()
# create and run lookup_table operator
concat_rows_op = Operator("lookup_table", W='W', Ids='Ids', Out='Out')
concat_rows_op.run(scope, place)
# get result from Out
Out_tensor = Out.get_tensor()
result_array = np.array(Out_tensor)
# all(): return True if all elements of the iterable are true (or if the iterable is empty)
for idx, row in enumerate(rows):
assert (row == result_array[idx]).all()
def test_concat_rows(self):
places = [core.CPUPlace()]
if core.is_compiled_with_cuda():
places.append(core.CUDAPlace(0))
for place in places:
self.check_with_place(place)
class TestLookupTableWIsSelectedRows(OpTest):
def check_with_place(self, place):
scope = core.Scope()
......
python/paddle/fluid/tests/unittests/test_parallel_executor_mnist.py
浏览文件 @ 062556f9
......@@ -107,44 +107,24 @@ class TestMNIST(TestParallelExecutorBase):
label = np.ones(shape=[32, 1], dtype='int64')
return img, label
# simple_fc
def check_simple_fc_convergence(self, use_cuda, use_reduce=False):
def _compare_reduce_and_allreduce(self, model, use_cuda, random_data=True):
if use_cuda and not core.is_compiled_with_cuda():
return
self.check_network_convergence(simple_fc_net, use_cuda=use_cuda)
self.check_network_convergence(
simple_fc_net, use_cuda=use_cuda, allow_op_delay=True)
img, label = self._init_data()
model, use_cuda=use_cuda, use_reduce=True)
self.check_network_convergence(
simple_fc_net,
feed_dict={"image": img,
"label": label},
use_cuda=use_cuda,
use_reduce=use_reduce)
model, use_cuda=use_cuda, allow_op_delay=True, use_reduce=True)
def check_simple_fc_convergence_with_Reduce(self, use_cuda):
if use_cuda and not core.is_compiled_with_cuda():
return
self.check_network_convergence(
simple_fc_net, use_cuda=use_cuda, use_reduce=True)
self.check_network_convergence(
simple_fc_net,
use_cuda=use_cuda,
allow_op_delay=True,
use_reduce=True)
img, label = self._init_data()
img, label = self._init_data(random_data)
all_reduce_first_loss, all_reduce_last_loss = self.check_network_convergence(
simple_fc_net,
model,
feed_dict={"image": img,
"label": label},
use_cuda=use_cuda,
use_reduce=False)
reduce_first_loss, reduce_last_loss = self.check_network_convergence(
simple_fc_net,
model,
feed_dict={"image": img,
"label": label},
use_cuda=use_cuda,
......@@ -153,7 +133,24 @@ class TestMNIST(TestParallelExecutorBase):
for loss in zip(all_reduce_first_loss, reduce_first_loss):
self.assertAlmostEquals(loss[0], loss[1], delta=1e-6)
for loss in zip(all_reduce_last_loss, reduce_last_loss):
self.assertAlmostEquals(loss[0], loss[1], delta=1e-6)
self.assertAlmostEquals(loss[0], loss[1], delta=1e-4)
# simple_fc
def check_simple_fc_convergence(self, use_cuda, use_reduce=False):
if use_cuda and not core.is_compiled_with_cuda():
return
self.check_network_convergence(simple_fc_net, use_cuda=use_cuda)
self.check_network_convergence(
simple_fc_net, use_cuda=use_cuda, allow_op_delay=True)
img, label = self._init_data()
self.check_network_convergence(
simple_fc_net,
feed_dict={"image": img,
"label": label},
use_cuda=use_cuda,
use_reduce=use_reduce)
def test_simple_fc(self):
# use_cuda
......@@ -162,8 +159,8 @@ class TestMNIST(TestParallelExecutorBase):
def test_simple_fc_with_new_strategy(self):
# use_cuda, use_reduce
self.check_simple_fc_convergence_with_Reduce(True)
self.check_simple_fc_convergence_with_Reduce(False)
self._compare_reduce_and_allreduce(simple_fc_net, True)
self._compare_reduce_and_allreduce(simple_fc_net, False)
def check_simple_fc_parallel_accuracy(self, use_cuda):
if use_cuda and not core.is_compiled_with_cuda():
......@@ -209,39 +206,13 @@ class TestMNIST(TestParallelExecutorBase):
"label": label},
use_cuda=use_cuda)
def check_batchnorm_fc_convergence_use_reduce(self, use_cuda):
if use_cuda and not core.is_compiled_with_cuda():
return
self.check_network_convergence(
fc_with_batchnorm, use_cuda=use_cuda, use_reduce=True)
img, label = self._init_data()
all_reduce_first_loss, all_reduce_last_loss = self.check_network_convergence(
fc_with_batchnorm,
feed_dict={"image": img,
"label": label},
use_cuda=use_cuda,
use_reduce=False)
reduce_first_loss, reduce_last_loss = self.check_network_convergence(
fc_with_batchnorm,
feed_dict={"image": img,
"label": label},
use_cuda=use_cuda,
use_reduce=True)
for loss in zip(all_reduce_first_loss, reduce_first_loss):
self.assertAlmostEquals(loss[0], loss[1], delta=1e-6)
for loss in zip(all_reduce_last_loss, reduce_last_loss):
self.assertAlmostEquals(loss[0], loss[1], delta=1e-4)
def test_batchnorm_fc(self):
self.check_batchnorm_fc_convergence(True)
self.check_batchnorm_fc_convergence(False)
def test_batchnorm_fc_with_new_strategy(self):
self.check_batchnorm_fc_convergence_use_reduce(True)
self.check_batchnorm_fc_convergence_use_reduce(False)
self._compare_reduce_and_allreduce(fc_with_batchnorm, True)
self._compare_reduce_and_allreduce(fc_with_batchnorm, False)
if __name__ == '__main__':
......
python/paddle/fluid/transpiler/distribute_transpiler.py
浏览文件 @ 062556f9
......@@ -779,7 +779,9 @@ class DistributeTranspiler(object):
outputs={"Out": prefetch_output_vars},
attrs={
"epmap": pserver_endpoints,
RPC_OP_ROLE_ATTR_NAME: RPC_OP_ROLE_ATTR_VALUE
# FIXME(qiao) temporarily disable this config because prefetch
# is not act as other rpc op, it's more like a forward op
# RPC_OP_ROLE_ATTR_NAME: RPC_OP_ROLE_ATTR_VALUE
})
# insert concat_op
......@@ -887,7 +889,8 @@ class DistributeTranspiler(object):
# create table optimize block in pserver program
table_opt_op = [
op for op in self.optimize_ops
if op.input("Param")[0] == self.table_name
if 'Param' in op.input_names and op.input("Param")[0] ==
self.table_name
][0]
table_opt_block = pserver_program.create_block(pre_block_idx)
# only support sgd now
......
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