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PaddleDetection
提交 c1f7e54f 编写于 作者: S sneaxiy
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merge develop 

test=develop
上级 13429c3e 3babc801
隐藏空白更改
内联 并排
Showing with 1245 addition and 763 deletion
paddle/fluid/API.spec
浏览文件 @ c1f7e54f
......@@ -350,6 +350,22 @@ paddle.fluid.contrib.QuantizeTranspiler.__init__ ArgSpec(args=['self', 'weight_b
paddle.fluid.contrib.QuantizeTranspiler.convert_to_int8 ArgSpec(args=['self', 'program', 'place', 'scope'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.contrib.QuantizeTranspiler.freeze_program ArgSpec(args=['self', 'program', 'place', 'fuse_bn', 'scope'], varargs=None, keywords=None, defaults=(False, None))
paddle.fluid.contrib.QuantizeTranspiler.training_transpile ArgSpec(args=['self', 'program', 'startup_program'], varargs=None, keywords=None, defaults=(None, None))
paddle.fluid.contrib.load_persistables_for_increment ArgSpec(args=['dirname', 'executor', 'program', 'lookup_table_var', 'lookup_table_var_path'], varargs=None, keywords=None, defaults=None)
paddle.fluid.contrib.load_persistables_for_inference ArgSpec(args=['dirname', 'executor', 'program', 'lookup_table_var_name'], varargs=None, keywords=None, defaults=None)
paddle.fluid.contrib.convert_dist_to_sparse_program ArgSpec(args=['program'], varargs=None, keywords=None, defaults=None)
paddle.fluid.contrib.HDFSClient.__init__ ArgSpec(args=['self', 'hadoop_home', 'configs'], varargs=None, keywords=None, defaults=None)
paddle.fluid.contrib.HDFSClient.delete ArgSpec(args=['self', 'hdfs_path'], varargs=None, keywords=None, defaults=None)
paddle.fluid.contrib.HDFSClient.download ArgSpec(args=['self', 'hdfs_path', 'local_path', 'overwrite', 'unzip'], varargs=None, keywords=None, defaults=(False, False))
paddle.fluid.contrib.HDFSClient.is_dir ArgSpec(args=['self', 'hdfs_path'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.contrib.HDFSClient.is_exist ArgSpec(args=['self', 'hdfs_path'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.contrib.HDFSClient.ls ArgSpec(args=['self', 'hdfs_path'], varargs=None, keywords=None, defaults=None)
paddle.fluid.contrib.HDFSClient.lsr ArgSpec(args=['self', 'hdfs_path', 'only_file', 'sort'], varargs=None, keywords=None, defaults=(True, True))
paddle.fluid.contrib.HDFSClient.make_local_dirs ArgSpec(args=['local_path'], varargs=None, keywords=None, defaults=None)
paddle.fluid.contrib.HDFSClient.makedirs ArgSpec(args=['self', 'hdfs_path'], varargs=None, keywords=None, defaults=None)
paddle.fluid.contrib.HDFSClient.rename ArgSpec(args=['self', 'hdfs_src_path', 'hdfs_dst_path', 'overwrite'], varargs=None, keywords=None, defaults=(False,))
paddle.fluid.contrib.HDFSClient.upload ArgSpec(args=['self', 'hdfs_path', 'local_path', 'overwrite', 'retry_times'], varargs=None, keywords=None, defaults=(False, 5))
paddle.fluid.contrib.multi_download ArgSpec(args=['client', 'hdfs_path', 'local_path', 'trainer_id', 'trainers', 'multi_processes'], varargs=None, keywords=None, defaults=(5,))
paddle.fluid.contrib.multi_upload ArgSpec(args=['client', 'hdfs_path', 'local_path', 'multi_processes', 'overwrite', 'sync'], varargs=None, keywords=None, defaults=(5, False, True))
paddle.fluid.transpiler.DistributeTranspiler.__init__ ArgSpec(args=['self', 'config'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.transpiler.DistributeTranspiler.get_pserver_program ArgSpec(args=['self', 'endpoint'], varargs=None, keywords=None, defaults=None)
paddle.fluid.transpiler.DistributeTranspiler.get_pserver_programs ArgSpec(args=['self', 'endpoint'], varargs=None, keywords=None, defaults=None)
......
paddle/fluid/framework/details/build_strategy.cc
浏览文件 @ c1f7e54f
......@@ -131,9 +131,7 @@ std::shared_ptr<ir::PassBuilder> BuildStrategy::CreatePassesFromStrategy(
std::unique_ptr<ir::Graph> BuildStrategy::Apply(
const ProgramDesc &main_program, const std::vector<platform::Place> &places,
const std::string &loss_var_name,
const std::unordered_set<std::string> &param_names,
const std::vector<Scope *> &local_scopes,
const std::string &loss_var_name, const std::vector<Scope *> &local_scopes,
#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
const bool use_cuda, platform::NCCLContextMap *nccl_ctxs) const {
#else
......@@ -149,9 +147,6 @@ std::unique_ptr<ir::Graph> BuildStrategy::Apply(
pass->SetNotOwned<const std::vector<platform::Place>>("places", &places);
pass->Erase("loss_var_name");
pass->SetNotOwned<const std::string>("loss_var_name", &loss_var_name);
pass->Erase("params");
pass->SetNotOwned<const std::unordered_set<std::string>>("params",
&param_names);
pass->Erase("local_scopes");
pass->SetNotOwned<const std::vector<Scope *>>("local_scopes",
&local_scopes);
......
paddle/fluid/framework/details/build_strategy.h
浏览文件 @ c1f7e54f
......@@ -106,16 +106,15 @@ struct BuildStrategy {
// Apply the passes built by the pass_builder_. The passes will be
// applied to the Program and output an ir::Graph.
std::unique_ptr<ir::Graph> Apply(
const ProgramDesc &main_program,
const std::vector<platform::Place> &places,
const std::string &loss_var_name,
const std::unordered_set<std::string> &param_names,
const std::vector<Scope *> &local_scopes,
std::unique_ptr<ir::Graph> Apply(const ProgramDesc &main_program,
const std::vector<platform::Place> &places,
const std::string &loss_var_name,
const std::vector<Scope *> &local_scopes,
#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
const bool use_cuda, platform::NCCLContextMap *nccl_ctxs) const;
const bool use_cuda,
platform::NCCLContextMap *nccl_ctxs) const;
#else
const bool use_cuda) const;
const bool use_cuda) const;
#endif
private:
......
paddle/fluid/framework/details/multi_devices_graph_pass.cc
浏览文件 @ c1f7e54f
......@@ -130,7 +130,6 @@ void AddOutputToLeafOps(ir::Graph *graph) {
static const char kLossVarName[] = "loss_var_name";
static const char kPlaces[] = "places";
static const char kParams[] = "params";
static const char kLocalScopes[] = "local_scopes";
static const char kStrategy[] = "strategy";
static const char kNumTrainers[] = "num_trainers";
......@@ -147,9 +146,6 @@ void MultiDevSSAGraphBuilder::Init() const {
nccl_ctxs_ = &Get<platform::NCCLContextMap>("nccl_ctxs");
#endif
for (auto &p : Get<const std::unordered_set<std::string>>(kParams)) {
grad_names_.insert(GradVarName(p));
}
balance_vars_.resize(places_.size(), 0);
if (strategy_.enable_data_balance_ && places_.size() == 1) {
LOG(WARNING) << "It is no need to enable data balance when there is only "
......@@ -359,7 +355,9 @@ std::unique_ptr<ir::Graph> MultiDevSSAGraphBuilder::ApplyImpl(
BuildStrategy::GradientScaleStrategy::kCustomized) {
// TODO(paddle-dev): Why is there no input for this op_handle?
auto loss_grad_name = node->Op()->OutputArgumentNames()[0];
CreateScaleLossGradOp(&result, loss_grad_name, node->outputs[0]);
auto out_dtype = all_vars_.at(loss_grad_name)->GetDataType();
CreateScaleLossGradOp(&result, loss_grad_name, node->outputs[0],
out_dtype);
}
// This assumes the backward generating code will ensure IsScaleLossOp
// is true only for the op that scale the final scalar loss.
......@@ -662,13 +660,13 @@ int MultiDevSSAGraphBuilder::GetVarDeviceID(
void MultiDevSSAGraphBuilder::CreateScaleLossGradOp(
ir::Graph *result, const std::string &loss_grad_name,
ir::Node *out_var_node) const {
ir::Node *out_var_node, proto::VarType::Type dtype) const {
for (size_t i = 0; i < places_.size(); ++i) {
// Insert ScaleCost OpHandle
auto *dev_ctx = platform::DeviceContextPool::Instance().Get(places_[i]);
auto *op_handle = new ScaleLossGradOpHandle(
result->CreateEmptyNode("scale_loss_grad", ir::Node::Type::kOperation),
local_scopes_.size(), local_scopes_[i], places_[i], dev_ctx);
local_scopes_.size(), local_scopes_[i], places_[i], dev_ctx, dtype);
result->Get<GraphOps>(kGraphOps).emplace_back(op_handle);
// FIXME: Currently ScaleLossGradOp only use device_count as scale
......@@ -896,7 +894,6 @@ REGISTER_PASS(multi_devices_pass,
paddle::framework::details::MultiDevSSAGraphBuilder)
.RequirePassAttr(paddle::framework::details::kLossVarName)
.RequirePassAttr(paddle::framework::details::kPlaces)
.RequirePassAttr(paddle::framework::details::kParams)
.RequirePassAttr(paddle::framework::details::kLocalScopes)
.RequirePassAttr(paddle::framework::details::kStrategy)
.RequirePassAttr(paddle::framework::details::kNumTrainers);
paddle/fluid/framework/details/multi_devices_graph_pass.h
浏览文件 @ c1f7e54f
......@@ -68,7 +68,8 @@ class MultiDevSSAGraphBuilder : public ir::Pass {
void CreateScaleLossGradOp(ir::Graph *result,
const std::string &loss_grad_name,
ir::Node *out_var_node) const;
ir::Node *out_var_node,
proto::VarType::Type dtype) const;
VarHandle *CreateReduceOp(ir::Graph *result, const std::string &og,
int dst_dev_id) const;
......@@ -102,7 +103,6 @@ class MultiDevSSAGraphBuilder : public ir::Pass {
mutable std::string loss_var_name_;
mutable std::vector<platform::Place> places_;
mutable std::vector<Scope *> local_scopes_;
mutable std::unordered_set<std::string> grad_names_;
mutable BuildStrategy strategy_;
mutable std::unordered_map<std::string, VarDesc *> all_vars_;
......
paddle/fluid/framework/details/scale_loss_grad_op_handle.cc
浏览文件 @ c1f7e54f
......@@ -22,39 +22,66 @@ namespace details {
ScaleLossGradOpHandle::ScaleLossGradOpHandle(ir::Node *node, size_t num_dev,
Scope *scope,
platform::Place place,
platform::DeviceContext *dev_ctx)
platform::DeviceContext *dev_ctx,
proto::VarType::Type dtype)
: OpHandleBase(node),
coeff_(static_cast<float>(1.0 / num_dev)),
scope_(scope),
place_(place) {
place_(place),
out_dtype_(dtype) {
this->SetDeviceContext(place_, dev_ctx);
}
ScaleLossGradOpHandle::~ScaleLossGradOpHandle() {}
struct ScaleLossGradFunctor {
float coeff_;
Tensor *out_;
platform::Place place_;
OpHandleBase *op_handle_;
proto::VarType::Type out_dtype_;
platform::DeviceContext *ctx_;
ScaleLossGradFunctor(float coeff, Tensor *out, platform::Place place,
OpHandleBase *op_handle, proto::VarType::Type dtype,
platform::DeviceContext *ctx)
: coeff_(coeff), out_(out), place_(place), out_dtype_(dtype), ctx_(ctx) {}
template <typename OutT>
void apply() const {
auto *out_data = out_->mutable_data<OutT>(place_);
if (platform::is_cpu_place(place_)) {
*out_data = static_cast<OutT>(coeff_);
} else {
#ifdef PADDLE_WITH_CUDA
OutT cast_coeff = static_cast<OutT>(coeff_);
auto stream = static_cast<platform::CUDADeviceContext *>(ctx_)->stream();
memory::Copy(boost::get<platform::CUDAPlace>(place_), out_data,
platform::CPUPlace(), &cast_coeff, SizeOfType(out_dtype_),
stream);
VLOG(10) << place_ << "RUN Scale loss grad op";
#endif
}
}
};
void ScaleLossGradOpHandle::RunImpl() {
// Doesn't wait any event
std::string var_name = static_cast<VarHandle *>(this->outputs_[0])->name_;
auto &local_scope = *scope_->FindVar(kLocalExecScopeName)->Get<Scope *>();
float *tmp = local_scope.FindVar(var_name)
->GetMutable<LoDTensor>()
->mutable_data<float>(make_ddim({1}), place_);
auto *tensor = local_scope.FindVar(var_name)->GetMutable<LoDTensor>();
tensor->Resize(make_ddim({1}));
if (platform::is_cpu_place(place_)) {
*tmp = coeff_;
} else {
#ifdef PADDLE_WITH_CUDA
this->RunAndRecordEvent([&] {
auto stream = static_cast<platform::CUDADeviceContext *>(
this->dev_ctxes_.at(place_))
->stream();
memory::Copy(boost::get<platform::CUDAPlace>(place_), tmp,
platform::CPUPlace(), &coeff_, sizeof(float), stream);
VLOG(10) << place_ << "RUN Scale loss grad op";
});
ScaleLossGradFunctor func(coeff_, tensor, place_, this, out_dtype_,
this->dev_ctxes_.at(place_));
this->RunAndRecordEvent([&] { framework::VisitDataType(out_dtype_, func); });
#else
ScaleLossGradFunctor func(coeff_, tensor, place_, this, out_dtype_, nullptr);
framework::VisitDataType(out_dtype_, func);
#endif
}
}
std::string ScaleLossGradOpHandle::Name() const { return "Scale LossGrad"; }
......
paddle/fluid/framework/details/scale_loss_grad_op_handle.h
浏览文件 @ c1f7e54f
......@@ -26,8 +26,8 @@ namespace details {
struct ScaleLossGradOpHandle : public OpHandleBase {
ScaleLossGradOpHandle(ir::Node *node, size_t num_dev, Scope *scope,
platform::Place place,
platform::DeviceContext *context);
platform::Place place, platform::DeviceContext *context,
proto::VarType::Type dtype);
~ScaleLossGradOpHandle() final;
......@@ -40,6 +40,7 @@ struct ScaleLossGradOpHandle : public OpHandleBase {
float coeff_;
Scope *scope_;
platform::Place place_;
proto::VarType::Type out_dtype_;
};
} // namespace details
......
paddle/fluid/framework/ir/conv_elementwise_add_mkldnn_fuse_pass.cc
浏览文件 @ c1f7e54f
......@@ -24,35 +24,6 @@ namespace paddle {
namespace framework {
namespace ir {
// The function keeps the graph consistent by replacing
// a node 'from' in the set of inputs nodes
// of the visited node by a node 'to'.
void CorrectGraphEdges(Graph* graph, Node* from, Node* to) {
for (auto& node : GraphTraits::DFS(*graph)) {
auto from_in_inputs =
std::find(std::begin(node.inputs), std::end(node.inputs), from);
if (from_in_inputs != std::end(node.inputs)) {
IR_NODE_LINK_TO(to, (&node));
auto inputs = node.Op()->Inputs();
using input_type = VariableNameMap::value_type;
std::for_each(std::begin(inputs), std::end(inputs),
[from, to, &node](const input_type& i) -> void {
auto param_names = i.second;
auto pi = std::find(std::begin(param_names),
std::end(param_names), from->Name());
if (pi != std::end(param_names)) {
node.Op()->SetInput(i.first, {to->Name()});
}
});
}
}
}
bool IsReachable(ir::Graph* graph, Node* from, Node* to) {
auto find_node = [](ir::Graph* graph, const Node* node) -> Node* {
for (auto n : graph->Nodes()) {
......@@ -99,25 +70,12 @@ bool IsReachable(ir::Graph* graph, Node* from, Node* to) {
return false;
}
boost::optional<Node*> HasBias(const Node& op, const std::string& bias_name) {
auto bias_input_names = op.Op()->Inputs();
auto bias_it = bias_input_names.find(bias_name);
if (bias_it != std::end(bias_input_names)) {
bool has_bias = !bias_it->second.empty();
if (has_bias) {
auto bias_names = bias_it->second;
auto bias_names_it =
std::find_if(std::begin(op.inputs), std::end(op.inputs),
[&bias_names](Node* n) -> bool {
return n->Name() == bias_names[0];
});
return *bias_names_it;
}
}
return boost::none;
template <typename T>
boost::optional<T> HasAttribute(const Node& op, const std::string& attr) {
if (op.Op()->HasAttr(attr))
return boost::get<T>(op.Op()->GetAttr(attr));
else
return boost::none;
}
ResidualConnectionMKLDNNFusePass::IdentityFuseHandle::IdentityFuseHandle(
......@@ -151,40 +109,18 @@ void ResidualConnectionMKLDNNFusePass::IdentityFuseHandle::operator()(
if (!IsReachable(graph, elementwise_add_identity, conv_output)) return;
OpDesc op_desc;
op_desc.SetType("conv2d");
op_desc.SetInput("Input", {conv_input->Name()});
op_desc.SetInput("Filter", {conv_filter->Name()});
op_desc.SetInput("ResidualData", {elementwise_add_identity->Name()});
op_desc.SetOutput("Output", {conv_output->Name()});
auto fuse_relu = HasAttribute<bool>(*conv_op, "fuse_relu");
if (fuse_relu && *fuse_relu) return;
auto conv_bias = HasBias(*conv_op, "Bias");
conv_op->Op()->SetInput("ResidualData", {elementwise_add_identity->Name()});
conv_op->Op()->SetOutput("Output", {elementwise_add_out->Name()});
conv_op->Op()->SetAttr("fuse_residual_connection", true);
if (conv_bias) {
op_desc.SetInput("Bias", {(*conv_bias)->Name()});
}
for (const auto& attr : conv_op->Op()->GetAttrMap()) {
op_desc.SetAttr(attr.first, attr.second);
}
op_desc.SetAttr("fuse_residual_connection", true);
GraphSafeRemoveNodes(graph, {conv_output, elementwise_add_op});
auto fused_conv_op = graph->CreateOpNode(&op_desc);
IR_NODE_LINK_TO(conv_input, fused_conv_op);
IR_NODE_LINK_TO(conv_filter, fused_conv_op);
IR_NODE_LINK_TO(elementwise_add_identity, fused_conv_op);
IR_NODE_LINK_TO(fused_conv_op, conv_output);
if (conv_bias) {
IR_NODE_LINK_TO((*conv_bias), fused_conv_op);
}
IR_NODE_LINK_TO(elementwise_add_identity, conv_op);
IR_NODE_LINK_TO(conv_op, elementwise_add_out);
CorrectGraphEdges(graph, elementwise_add_out, conv_output);
GraphSafeRemoveNodes(graph,
{elementwise_add_out, conv_op, elementwise_add_op});
(*fusion_stats)++;
}
......@@ -229,60 +165,33 @@ void ResidualConnectionMKLDNNFusePass::ProjectionFuseHandle::operator()(
Node* projection_node;
Node* residual_conv_op;
Node* residual_conv_input;
Node* residual_conv_filter;
Node* residual_conv_output;
if (IsReachable(graph, conv_x_input, conv_y_output)) {
projection_node = conv_x_output;
residual_conv_op = conv_y_op;
residual_conv_input = conv_y_input;
residual_conv_filter = conv_y_filter;
residual_conv_output = conv_y_output;
} else if (IsReachable(graph, conv_y_input, conv_x_output)) {
projection_node = conv_y_output;
residual_conv_op = conv_x_op;
residual_conv_input = conv_x_input;
residual_conv_filter = conv_x_filter;
residual_conv_output = conv_x_output;
} else {
return;
}
OpDesc op_desc;
op_desc.SetType("conv2d");
auto fuse_relu = HasAttribute<bool>(*residual_conv_op, "fuse_relu");
if (fuse_relu && *fuse_relu) return;
op_desc.SetInput("Input", {residual_conv_input->Name()});
op_desc.SetInput("Filter", {residual_conv_filter->Name()});
op_desc.SetInput("ResidualData", {projection_node->Name()});
op_desc.SetOutput("Output", {residual_conv_output->Name()});
residual_conv_op->Op()->SetInput("ResidualData", {projection_node->Name()});
residual_conv_op->Op()->SetOutput("Output", {elementwise_add_out->Name()});
auto residual_conv_bias = HasBias(*residual_conv_op, "Bias");
residual_conv_op->Op()->SetAttr("fuse_residual_connection", true);
if (residual_conv_bias) {
op_desc.SetInput("Bias", {(*residual_conv_bias)->Name()});
}
for (const auto& attr : residual_conv_op->Op()->GetAttrMap()) {
op_desc.SetAttr(attr.first, attr.second);
}
op_desc.SetAttr("fuse_residual_connection", true);
GraphSafeRemoveNodes(graph, {residual_conv_output, elementwise_add_op});
auto fused_conv_op = graph->CreateOpNode(&op_desc);
IR_NODE_LINK_TO(residual_conv_input, fused_conv_op);
IR_NODE_LINK_TO(residual_conv_filter, fused_conv_op);
IR_NODE_LINK_TO(projection_node, fused_conv_op);
IR_NODE_LINK_TO(fused_conv_op, residual_conv_output);
if (residual_conv_bias) {
IR_NODE_LINK_TO((*residual_conv_bias), fused_conv_op);
}
IR_NODE_LINK_TO(projection_node, residual_conv_op);
IR_NODE_LINK_TO(residual_conv_op, elementwise_add_out);
CorrectGraphEdges(graph, elementwise_add_out, residual_conv_output);
GraphSafeRemoveNodes(
graph, {elementwise_add_out, residual_conv_op, elementwise_add_op});
(*fusion_stats)++;
}
......
paddle/fluid/framework/op_desc.cc
浏览文件 @ c1f7e54f
......@@ -110,22 +110,125 @@ class CompileTimeInferShapeContext : public InferShapeContext {
}
}
std::vector<InferShapeVarPtr> GetInputVarPtrs(
const std::string &name) override {
const std::vector<std::string> arg_names = Inputs(name);
std::vector<InferShapeVarPtr> res;
res.reserve(arg_names.size());
std::transform(arg_names.begin(), arg_names.end(), std::back_inserter(res),
[this](const std::string &name) {
return block_.FindVarRecursive(name);
});
return res;
}
std::vector<InferShapeVarPtr> GetOutputVarPtrs(
const std::string &name) override {
const std::vector<std::string> arg_names = Outputs(name);
std::vector<InferShapeVarPtr> res;
res.reserve(arg_names.size());
std::transform(arg_names.begin(), arg_names.end(), std::back_inserter(res),
[this](const std::string &name) {
return block_.FindVarRecursive(name);
});
return res;
}
DDim GetInputDim(const std::string &name) const override {
const std::vector<std::string> &arg_names = Inputs(name);
PADDLE_ENFORCE_EQ(arg_names.size(), 1UL,
"Input(%s) should hold one element, but now it holds %d",
name, arg_names.size());
return this->GetDim(arg_names[0]);
}
std::vector<DDim> GetInputsDim(const std::string &name) const override {
const std::vector<std::string> &arg_names = Inputs(name);
return GetDims(arg_names);
}
bool IsRuntime() const override;
std::vector<proto::VarType::Type> GetInputsVarType(
const std::string &name) const override {
return GetVarTypes(Inputs(name));
}
std::vector<proto::VarType::Type> GetOutputsVarType(
const std::string &name) const override {
return GetVarTypes(Outputs(name));
}
void SetOutputDim(const std::string &name, const DDim &dim) override {
auto &arg_names = Outputs(name);
PADDLE_ENFORCE_EQ(arg_names.size(), 1UL,
"Output(%s) should hold one element, but now it holds %d",
name, arg_names.size());
SetDim(arg_names[0], dim);
}
void SetOutputsDim(const std::string &name,
const std::vector<DDim> &dims) override {
auto &names = Outputs(name);
SetDims(names, dims);
}
protected:
proto::VarType::Type GetVarType(const std::string &name) const override;
std::vector<proto::VarType::Type> GetVarTypes(
const std::vector<std::string> &names) const {
std::vector<proto::VarType::Type> retv;
retv.resize(names.size());
std::transform(
names.begin(), names.end(), retv.begin(),
std::bind(std::mem_fn(&CompileTimeInferShapeContext::GetVarType), this,
std::placeholders::_1));
return retv;
}
proto::VarType::Type GetVarType(const std::string &name) const;
DDim GetDim(const std::string &name) const {
auto var = block_.FindVarRecursive(name);
PADDLE_ENFORCE(var != nullptr, "Cannot find variable %s", name);
DDim res;
try {
auto shape = var->GetShape();
res = shape.empty() ? make_ddim({0UL}) : make_ddim(shape);
} catch (...) {
VLOG(5) << "GetDim of variable " << name << " error";
std::rethrow_exception(std::current_exception());
}
return res;
}
DDim GetDim(const std::string &name) const override;
std::vector<DDim> GetDims(const std::vector<std::string> &names) const {
std::vector<DDim> ret;
ret.reserve(names.size());
std::transform(
names.begin(), names.end(), std::back_inserter(ret),
[this](const std::string &name) { return this->GetDim(name); });
return ret;
}
void SetDim(const std::string &name, const DDim &dim);
void SetDim(const std::string &name, const DDim &dim) override;
void SetDims(const std::vector<std::string> &names,
const std::vector<DDim> &dims) {
size_t length = names.size();
PADDLE_ENFORCE_EQ(length, dims.size());
for (size_t i = 0; i < length; ++i) {
if (names[i] == framework::kEmptyVarName) {
continue;
}
SetDim(names[i], dims[i]);
}
}
std::vector<DDim> GetRepeatedDims(const std::string &name) const override;
void SetRepeatedDims(const std::string &name,
const std::vector<DDim> &dims) override;
InferShapeVarPtr GetVarPtr(const std::string &name) override;
const OpDesc &op_;
const BlockDesc &block_;
};
......@@ -644,20 +747,6 @@ const std::vector<std::string> &CompileTimeInferShapeContext::Outputs(
return op_.Output(name);
}
DDim CompileTimeInferShapeContext::GetDim(const std::string &name) const {
auto var = block_.FindVarRecursive(name);
PADDLE_ENFORCE(var != nullptr, "Cannot find variable %s", name);
DDim res;
try {
auto shape = var->GetShape();
res = shape.empty() ? make_ddim({0UL}) : make_ddim(shape);
} catch (...) {
VLOG(5) << "GetDim of variable " << name << " error";
std::rethrow_exception(std::current_exception());
}
return res;
}
std::vector<DDim> CompileTimeInferShapeContext::GetRepeatedDims(
const std::string &name) const {
auto var = block_.FindVarRecursive(name);
......@@ -696,10 +785,5 @@ proto::VarType::Type CompileTimeInferShapeContext::GetVarType(
return block_.FindVarRecursive(name)->GetType();
}
InferShapeVarPtr CompileTimeInferShapeContext::GetVarPtr(
const std::string &name) {
return block_.FindVarRecursive(name);
}
} // namespace framework
} // namespace paddle
paddle/fluid/framework/operator.cc
浏览文件 @ c1f7e54f
......@@ -142,12 +142,14 @@ RuntimeContext::RuntimeContext(const VariableNameMap& innames,
const Scope& scope) {
for (auto& var_name_item : innames) {
std::vector<Variable*>& input_vars = inputs[var_name_item.first];
input_vars.reserve(var_name_item.second.size());
for (auto& var_name : var_name_item.second) {
input_vars.push_back(scope.FindVar(var_name));
}
}
for (auto& var_name_item : outnames) {
std::vector<Variable*>& output_vars = outputs[var_name_item.first];
output_vars.reserve(var_name_item.second.size());
for (auto& var_name : var_name_item.second) {
output_vars.push_back(scope.FindVar(var_name));
}
......@@ -556,30 +558,28 @@ class RuntimeInferShapeContext : public InferShapeContext {
bool HasOutput(const std::string& name) const override {
// has only one output
const auto& outs = op_.Outputs();
const auto& outs = ctx_.outputs;
auto it = outs.find(name);
if (it == outs.end()) {
return false;
}
const auto& out = it->second;
if (out.size() == 0 || out[0] == kEmptyVarName) {
if (out.size() == 0) {
return false;
}
PADDLE_ENFORCE_EQ(out.size(), 1UL,
"Output %s should not have more than one outputs", name);
return scope_.FindVar(out[0]) != nullptr;
return out[0] != nullptr;
}
bool HasInputs(const std::string& name) const override {
if (!op_.HasInputs(name)) {
return false;
}
auto inputs = op_.Inputs(name);
if (inputs.empty()) {
const auto& ins = ctx_.inputs;
auto it = ins.find(name);
if (it == ins.end() || it->second.empty()) {
return false;
}
for (auto& input : inputs) {
if (scope_.FindVar(input) == nullptr) {
for (auto& input : it->second) {
if (input == nullptr) {
return false;
}
}
......@@ -587,15 +587,13 @@ class RuntimeInferShapeContext : public InferShapeContext {
}
bool HasOutputs(const std::string& name) const override {
if (!op_.HasOutputs(name)) {
return false;
}
auto outputs = op_.Outputs(name);
if (outputs.empty()) {
const auto& outs = ctx_.outputs;
auto it = outs.find(name);
if (it == outs.end() || it->second.empty()) {
return false;
}
for (auto& output : outputs) {
if (scope_.FindVar(output) == nullptr) {
for (auto& output : it->second) {
if (output == nullptr) {
return false;
}
}
......@@ -616,16 +614,18 @@ class RuntimeInferShapeContext : public InferShapeContext {
void ShareDim(const std::string& in, const std::string& out, size_t i = 0,
size_t j = 0) override {
PADDLE_ENFORCE_LT(i, Inputs(in).size());
PADDLE_ENFORCE_LT(j, Outputs(out).size());
const std::string& input_n = Inputs(in)[i];
const std::string& output_n = Outputs(out)[j];
auto in_it = ctx_.inputs.find(in);
auto out_it = ctx_.outputs.find(out);
PADDLE_ENFORCE(in_it != ctx_.inputs.end() && in_it->second.size() > i,
"Inputs %s should have %llu argument", in, i);
PADDLE_ENFORCE(out_it != ctx_.outputs.end() && out_it->second.size() > j,
"Outputs %s should have %llu argument", out, j);
Variable* in_var = in_it->second[i];
Variable* out_var = out_it->second[j];
Variable* in_var = scope_.FindVar(input_n);
Variable* out_var = scope_.FindVar(output_n);
PADDLE_ENFORCE(in_var->Type() == out_var->Type(),
"The type of %s and %s is not the same.", output_n,
GetDim(input_n));
"The type of %s and %s is not the same.", in, out);
if (in_var->IsType<framework::SelectedRows>()) {
auto& in_sele_rows = in_var->Get<framework::SelectedRows>();
......@@ -646,13 +646,16 @@ class RuntimeInferShapeContext : public InferShapeContext {
void ShareLoD(const std::string& in, const std::string& out, size_t i = 0,
size_t j = 0) const override {
const std::vector<std::string>& inputs = Inputs(in);
const std::vector<std::string>& outputs = Outputs(out);
PADDLE_ENFORCE_LT(i, inputs.size());
PADDLE_ENFORCE_LT(j, outputs.size());
Variable* in_var = scope_.FindVar(inputs.at(i));
auto in_it = ctx_.inputs.find(in);
auto out_it = ctx_.outputs.find(out);
PADDLE_ENFORCE(in_it != ctx_.inputs.end() && in_it->second.size() > i,
"Inputs %s should have %llu argument", in, i);
PADDLE_ENFORCE(out_it != ctx_.outputs.end() && out_it->second.size() > j,
"Outputs %s should have %llu argument", out, j);
Variable* in_var = in_it->second.at(i);
if (!in_var->IsType<LoDTensor>()) return;
Variable* out_var = scope_.FindVar(outputs.at(j));
Variable* out_var = out_it->second.at(j);
PADDLE_ENFORCE(out_var->IsType<LoDTensor>(),
"The %d-th output of Output(%s) must be LoDTensor.", j, out);
auto in_tensor = in_var->Get<LoDTensor>();
......@@ -687,9 +690,64 @@ class RuntimeInferShapeContext : public InferShapeContext {
bool IsRuntime() const override { return true; }
// TODO(paddle-dev): Can this be template?
std::vector<InferShapeVarPtr> GetInputVarPtrs(
const std::string& name) override {
const std::vector<Variable*>& vars = InputVars(name);
std::vector<InferShapeVarPtr> res;
res.reserve(vars.size());
res.insert(res.begin(), vars.begin(), vars.end());
return res;
}
std::vector<InferShapeVarPtr> GetOutputVarPtrs(
const std::string& name) override {
const std::vector<Variable*>& vars = OutputVars(name);
std::vector<InferShapeVarPtr> res;
res.reserve(vars.size());
res.insert(res.begin(), vars.begin(), vars.end());
return res;
}
DDim GetInputDim(const std::string& name) const override {
const std::vector<Variable*>& vars = InputVars(name);
PADDLE_ENFORCE_EQ(vars.size(), 1UL,
"Input(%s) should hold one element, but now it holds %d",
name, vars.size());
return this->GetDim(vars[0]);
}
std::vector<DDim> GetInputsDim(const std::string& name) const override {
const std::vector<Variable*>& vars = InputVars(name);
return GetDims(vars);
}
std::vector<proto::VarType::Type> GetInputsVarType(
const std::string& name) const override {
return GetVarTypes(InputVars(name));
}
std::vector<proto::VarType::Type> GetOutputsVarType(
const std::string& name) const override {
return GetVarTypes(OutputVars(name));
}
void SetOutputDim(const std::string& name, const DDim& dim) override {
auto& vars = OutputVars(name);
PADDLE_ENFORCE_EQ(vars.size(), 1UL,
"Output(%s) should hold one element, but now it holds %d",
name, vars.size());
SetDim(vars[0], dim);
}
void SetOutputsDim(const std::string& name,
const std::vector<DDim>& dims) override {
auto& vars = OutputVars(name);
SetDims(vars, dims);
}
protected:
DDim GetDim(const std::string& name) const override {
Variable* var = scope_.FindVar(name);
DDim GetDim(Variable* var) const {
PADDLE_ENFORCE_NOT_NULL(var);
if (var->IsType<LoDTensor>()) {
return var->Get<LoDTensor>().dims();
......@@ -697,25 +755,44 @@ class RuntimeInferShapeContext : public InferShapeContext {
return var->Get<SelectedRows>().GetCompleteDims();
} else {
PADDLE_THROW(
"Only LoDTensor/SelectedRows support 'GetDim', but Variable %s's "
"Only LoDTensor/SelectedRows support 'GetDim', but Variables "
"type_id is %s.",
name, ToTypeName(var->Type()));
ToTypeName(var->Type()));
}
}
std::vector<DDim> GetDims(const std::vector<Variable*>& vars) const {
std::vector<DDim> ret;
ret.reserve(vars.size());
std::transform(vars.begin(), vars.end(), std::back_inserter(ret),
[this](Variable* var) { return this->GetDim(var); });
return ret;
}
std::vector<DDim> GetRepeatedDims(const std::string& name) const override {
PADDLE_THROW("Only compile time support this method");
}
void SetDim(const std::string& name, const DDim& dim) override {
Variable* var = scope_.FindVar(name);
void SetDim(Variable* var, const DDim& dim) {
if (var->IsType<LoDTensor>()) {
var->GetMutable<LoDTensor>()->Resize(dim);
} else if (var->IsType<SelectedRows>()) {
var->GetMutable<SelectedRows>()->set_height(dim[0]);
} else {
PADDLE_THROW("Variable %s type_id %s, expect LoDTensor/SelectedRows.",
name, ToTypeName(var->Type()));
PADDLE_THROW("Variable type_id %s, expect LoDTensor/SelectedRows.",
ToTypeName(var->Type()));
}
}
void SetDims(const std::vector<Variable*>& vars,
const std::vector<DDim>& dims) {
size_t length = vars.size();
PADDLE_ENFORCE_EQ(length, dims.size());
for (size_t i = 0; i < length; ++i) {
if (vars[i] == nullptr) {
continue;
}
SetDim(vars[i], dims[i]);
}
}
......@@ -724,16 +801,36 @@ class RuntimeInferShapeContext : public InferShapeContext {
PADDLE_THROW("Only compile time support this method");
}
proto::VarType::Type GetVarType(const std::string& name) const override {
auto* var = scope_.FindVar(name);
return ToVarType(var->Type());
std::vector<proto::VarType::Type> GetVarTypes(
const std::vector<Variable*>& vars) const {
std::vector<proto::VarType::Type> retv;
retv.resize(vars.size());
std::transform(vars.begin(), vars.end(), retv.begin(),
std::bind(std::mem_fn(&RuntimeInferShapeContext::GetVarType),
this, std::placeholders::_1));
return retv;
}
InferShapeVarPtr GetVarPtr(const std::string& name) override {
return scope_.FindVar(name);
proto::VarType::Type GetVarType(Variable* var) const {
return ToVarType(var->Type());
}
private:
const std::vector<Variable*>& InputVars(const std::string& name) const {
auto it = ctx_.inputs.find(name);
PADDLE_ENFORCE(it != ctx_.inputs.end(),
"Operator %s does not have the input %s.", op_.Type(), name);
return it->second;
}
const std::vector<Variable*>& OutputVars(const std::string& name) const {
auto it = ctx_.outputs.find(name);
PADDLE_ENFORCE(it != ctx_.outputs.end(),
"Operator %s does not have the outputs %s.", op_.Type(),
name);
return it->second;
}
const OperatorBase& op_;
const Scope& scope_;
const RuntimeContext& ctx_;
......@@ -864,8 +961,7 @@ Scope* OperatorWithKernel::PrepareData(
for (size_t i = 0; i < var_name_item.second.size(); ++i) {
auto& var_name = var_name_item.second[i];
auto* var = scope.FindVar(var_name);
input_vars[i] = var;
auto* var = input_vars[i];
// Only tensor can be tranfer to another device.
if (var == nullptr || !VarIsTensor(*var)) {
......
paddle/fluid/framework/parallel_executor.cc
浏览文件 @ c1f7e54f
......@@ -190,7 +190,6 @@ std::vector<Scope *> &ParallelExecutor::GetLocalScopes() {
ParallelExecutor::ParallelExecutor(
const std::vector<platform::Place> &places,
const std::unordered_set<std::string> &params,
const std::unordered_set<std::string> &bcast_vars,
const ProgramDesc &main_program, const std::string &loss_var_name,
Scope *scope, const std::vector<Scope *> &local_scopes,
......@@ -209,7 +208,7 @@ ParallelExecutor::ParallelExecutor(
"the number of places must be greater than 1.");
}
// Step 1. Bcast the params to devs.
// Step 1. Bcast the bcast_vars to devs.
// Create local scopes
if (local_scopes.empty()) {
member_->own_local_scope_ = true;
......@@ -249,12 +248,12 @@ ParallelExecutor::ParallelExecutor(
// ncclOp
#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
std::unique_ptr<ir::Graph> graph = build_strategy.Apply(
main_program, member_->places_, loss_var_name, params,
member_->local_scopes_, member_->use_cuda_, member_->nccl_ctxs_.get());
main_program, member_->places_, loss_var_name, member_->local_scopes_,
member_->use_cuda_, member_->nccl_ctxs_.get());
#else
std::unique_ptr<ir::Graph> graph =
build_strategy.Apply(main_program, member_->places_, loss_var_name,
params, member_->local_scopes_, member_->use_cuda_);
member_->local_scopes_, member_->use_cuda_);
#endif
auto max_memory_size = GetEagerDeletionThreshold();
if (max_memory_size >= 0) {
......
paddle/fluid/framework/parallel_executor.h
浏览文件 @ c1f7e54f
......@@ -41,7 +41,6 @@ class ParallelExecutor {
public:
explicit ParallelExecutor(const std::vector<platform::Place> &places,
const std::unordered_set<std::string> &params,
const std::unordered_set<std::string> &bcast_vars,
const ProgramDesc &main_program,
const std::string &loss_var_name, Scope *scope,
......
paddle/fluid/framework/shape_inference.cc
浏览文件 @ c1f7e54f
......@@ -22,20 +22,6 @@ limitations under the License. */
namespace paddle {
namespace framework {
DDim InferShapeContext::GetInputDim(const std::string &name) const {
const std::vector<std::string> &arg_names = Inputs(name);
PADDLE_ENFORCE_EQ(arg_names.size(), 1UL,
"Input(%s) should hold one element, but now it holds %d",
name, arg_names.size());
return this->GetDim(arg_names[0]);
}
std::vector<DDim> InferShapeContext::GetInputsDim(
const std::string &name) const {
const std::vector<std::string> &arg_names = Inputs(name);
return GetDims(arg_names);
}
std::vector<DDim> InferShapeContext::GetReaderDims(
const std::string &name) const {
const std::vector<std::string> &arg_names = Inputs(name);
......@@ -46,26 +32,6 @@ std::vector<DDim> InferShapeContext::GetReaderDims(
return this->GetRepeatedDims(arg_names[0]);
}
DDim InferShapeContext::GetInputsElementDim(const std::string &name,
int idx) const {
const std::vector<std::string> &names = Inputs(name);
return this->GetDim(names[idx]);
}
void InferShapeContext::SetOutputDim(const std::string &name, const DDim &dim) {
auto &arg_names = Outputs(name);
PADDLE_ENFORCE_EQ(arg_names.size(), 1UL,
"Output(%s) should hold one element, but now it holds %d",
name, arg_names.size());
SetDim(arg_names[0], dim);
}
void InferShapeContext::SetOutputsDim(const std::string &name,
const std::vector<DDim> &dims) {
auto &names = Outputs(name);
SetDims(names, dims);
}
void InferShapeContext::SetReaderDims(const std::string &name,
const std::vector<DDim> &dims) {
const std::vector<std::string> &arg_names = Outputs(name);
......@@ -76,69 +42,5 @@ void InferShapeContext::SetReaderDims(const std::string &name,
return this->SetRepeatedDims(arg_names[0], dims);
}
std::vector<InferShapeVarPtr> InferShapeContext::GetInputVarPtrs(
const std::string &name) {
const std::vector<std::string> arg_names = Inputs(name);
std::vector<InferShapeVarPtr> res;
res.reserve(arg_names.size());
std::transform(
arg_names.begin(), arg_names.end(), std::back_inserter(res),
[this](const std::string &name) { return this->GetVarPtr(name); });
return res;
}
std::vector<InferShapeVarPtr> InferShapeContext::GetOutputVarPtrs(
const std::string &name) {
const std::vector<std::string> arg_names = Outputs(name);
std::vector<InferShapeVarPtr> res;
res.reserve(arg_names.size());
std::transform(
arg_names.begin(), arg_names.end(), std::back_inserter(res),
[this](const std::string &name) { return this->GetVarPtr(name); });
return res;
}
std::vector<DDim> InferShapeContext::GetDims(
const std::vector<std::string> &names) const {
std::vector<DDim> ret;
ret.reserve(names.size());
std::transform(
names.begin(), names.end(), std::back_inserter(ret),
[this](const std::string &name) { return this->GetDim(name); });
return ret;
}
void InferShapeContext::SetDims(const std::vector<std::string> &names,
const std::vector<DDim> &dims) {
size_t length = names.size();
PADDLE_ENFORCE_EQ(length, dims.size());
for (size_t i = 0; i < length; ++i) {
if (names[i] == framework::kEmptyVarName) {
continue;
}
SetDim(names[i], dims[i]);
}
}
std::vector<proto::VarType::Type> InferShapeContext::GetInputsVarType(
const std::string &name) const {
return GetVarTypes(Inputs(name));
}
std::vector<proto::VarType::Type> InferShapeContext::GetOutputsVarType(
const std::string &name) const {
return GetVarTypes(Outputs(name));
}
std::vector<proto::VarType::Type> InferShapeContext::GetVarTypes(
const std::vector<std::string> &names) const {
std::vector<proto::VarType::Type> retv;
retv.resize(names.size());
std::transform(names.begin(), names.end(), retv.begin(),
std::bind(std::mem_fn(&InferShapeContext::GetVarType), this,
std::placeholders::_1));
return retv;
}
} // namespace framework
} // namespace paddle
paddle/fluid/framework/shape_inference.h
浏览文件 @ c1f7e54f
......@@ -33,22 +33,23 @@ class InferShapeContext {
virtual bool HasInput(const std::string &name) const = 0;
virtual bool HasOutput(const std::string &name) const = 0;
std::vector<proto::VarType::Type> GetInputsVarType(
const std::string &name) const;
std::vector<proto::VarType::Type> GetOutputsVarType(
const std::string &name) const;
virtual std::vector<proto::VarType::Type> GetInputsVarType(
const std::string &name) const = 0;
virtual std::vector<proto::VarType::Type> GetOutputsVarType(
const std::string &name) const = 0;
virtual bool HasInputs(const std::string &name) const = 0;
virtual bool HasOutputs(const std::string &name) const = 0;
DDim GetInputDim(const std::string &name) const;
std::vector<DDim> GetInputsDim(const std::string &name) const;
std::vector<DDim> GetReaderDims(const std::string &name) const;
DDim GetInputsElementDim(const std::string &name, int idx) const;
virtual DDim GetInputDim(const std::string &name) const = 0;
virtual std::vector<DDim> GetInputsDim(const std::string &name) const = 0;
virtual std::vector<DDim> GetReaderDims(const std::string &name) const;
void SetOutputDim(const std::string &name, const DDim &dim);
void SetOutputsDim(const std::string &name, const std::vector<DDim> &dims);
void SetReaderDims(const std::string &name, const std::vector<DDim> &dims);
virtual void SetOutputDim(const std::string &name, const DDim &dim) = 0;
virtual void SetOutputsDim(const std::string &name,
const std::vector<DDim> &dims) = 0;
virtual void SetReaderDims(const std::string &name,
const std::vector<DDim> &dims);
virtual AttrReader Attrs() const = 0;
virtual const std::vector<std::string> &Inputs(
......@@ -67,27 +68,15 @@ class InferShapeContext {
virtual bool IsRuntime() const = 0;
std::vector<InferShapeVarPtr> GetInputVarPtrs(const std::string &name);
std::vector<InferShapeVarPtr> GetOutputVarPtrs(const std::string &name);
virtual InferShapeVarPtr GetVarPtr(const std::string &name) = 0;
// Note: In while op, we need this to be public
void SetDims(const std::vector<std::string> &names,
const std::vector<DDim> &dims);
virtual std::vector<InferShapeVarPtr> GetInputVarPtrs(
const std::string &name) = 0;
virtual std::vector<InferShapeVarPtr> GetOutputVarPtrs(
const std::string &name) = 0;
protected:
virtual DDim GetDim(const std::string &name) const = 0;
virtual void SetDim(const std::string &name, const DDim &dim) = 0;
virtual std::vector<DDim> GetRepeatedDims(const std::string &name) const = 0;
virtual void SetRepeatedDims(const std::string &name,
const std::vector<DDim> &dims) = 0;
std::vector<DDim> GetDims(const std::vector<std::string> &names) const;
std::vector<proto::VarType::Type> GetVarTypes(
const std::vector<std::string> &names) const;
virtual proto::VarType::Type GetVarType(const std::string &name) const = 0;
};
} // namespace framework
......
paddle/fluid/imperative/layer.cc
浏览文件 @ c1f7e54f
......@@ -188,11 +188,13 @@ std::vector<Variable*> OpBase::ApplyGrad(framework::Scope* scope) {
std::vector<Variable*> ret;
for (size_t i = 0; i < input_vars_->size(); ++i) {
bool found = false;
VarBase* origin_var = (*input_vars_)[i];
for (const std::string& outvar : grad_op_desc_->OutputArgumentNames()) {
Variable* var = scope->FindVar(outvar);
VarBase* origin_var = (*input_vars_)[i];
std::string orig_var = grad_to_var_->at(outvar);
PADDLE_ENFORCE(origin_var->var_desc_->Name() == orig_var);
if (origin_var->var_desc_->Name() != orig_var) {
continue;
}
VLOG(3) << "apply grad " << outvar << " with origin " << orig_var;
origin_var->ApplyGrad(scope, var);
found = true;
......
paddle/fluid/imperative/tracer.h
浏览文件 @ c1f7e54f
......@@ -43,9 +43,12 @@ void CreateGradOp(const framework::OpDesc& op_desc,
class Tracer {
public:
explicit Tracer(framework::BlockDesc* root_block) : root_block_(root_block) {
explicit Tracer(framework::BlockDesc* root_block,
framework::BlockDesc* startup_block)
: root_block_(root_block), startup_block_(startup_block) {
root_scope_ = new framework::Scope();
scopes_[root_block_] = root_scope_;
scopes_[startup_block_] = root_scope_;
}
virtual ~Tracer() { delete root_scope_; }
......@@ -80,6 +83,8 @@ class Tracer {
} else {
op->pre_ops_->push_back(nullptr);
}
VLOG(3) << "input vname " << vname << " "
<< var->Get<framework::LoDTensor>().dims().size();
}
*op->output_vars_ = outputs;
......@@ -98,12 +103,19 @@ class Tracer {
outputs[i]->pre_op_ = op;
outputs[i]->pre_op_out_idx_ = i;
}
VLOG(3) << "tracer running " << op_desc->Type();
op_base->Run(*scope, platform::CPUPlace());
framework::OpDesc* grad_op_desc;
auto grad_to_var = new std::unordered_map<std::string, std::string>();
CreateGradOp(*op_desc, {}, {block}, &grad_op_desc, grad_to_var);
op->grad_op_desc_ = grad_op_desc;
op->grad_to_var_ = grad_to_var;
if (block == startup_block_) {
op->grad_op_desc_ = nullptr;
op->grad_to_var_ = nullptr;
} else {
framework::OpDesc* grad_op_desc;
auto grad_to_var = new std::unordered_map<std::string, std::string>();
CreateGradOp(*op_desc, {}, {block}, &grad_op_desc, grad_to_var);
op->grad_op_desc_ = grad_op_desc;
op->grad_to_var_ = grad_to_var;
}
op->block_ = block;
}
......@@ -121,6 +133,7 @@ class Tracer {
private:
std::map<framework::BlockDesc*, framework::Scope*> scopes_;
framework::BlockDesc* root_block_;
framework::BlockDesc* startup_block_;
framework::Scope* root_scope_;
};
......
paddle/fluid/operators/controlflow/while_op.cc
浏览文件 @ c1f7e54f
......@@ -398,26 +398,41 @@ class WhileGradOpShapeInference : public framework::InferShapeBase {
ctx->HasInputs(kOutputs);
ctx->HasInputs(framework::GradVarName(kOutputs));
auto p_names = ctx->Inputs(kX);
auto pg_ig_names = ctx->Outputs(kXGRAD);
auto var_types = ctx->GetInputsVarType(kX);
std::vector<std::string> names_to_set;
std::vector<framework::DDim> dims_to_set;
for (size_t i = 0; i < p_names.size(); ++i) {
std::vector<framework::InferShapeVarPtr> in_var_ptrs =
ctx->GetInputVarPtrs(kX);
std::vector<framework::InferShapeVarPtr> out_var_ptrs =
ctx->GetOutputVarPtrs(kXGRAD);
PADDLE_ENFORCE(in_var_ptrs.size() == out_var_ptrs.size());
for (size_t i = 0; i < in_var_ptrs.size(); ++i) {
if (pg_ig_names[i] == framework::kEmptyVarName) {
continue;
}
auto dims = ctx->GetInputsElementDim(kX, i);
if (var_types[i] == framework::proto::VarType::LOD_TENSOR) {
names_to_set.push_back(pg_ig_names[i]);
dims_to_set.push_back(dims);
} else if (var_types[i] == framework::proto::VarType::LOD_TENSOR_ARRAY) {
// not sure how to set the dim of LOD_TENSOR_ARRAY
names_to_set.push_back(pg_ig_names[i]);
dims_to_set.push_back(dims);
if (ctx->IsRuntime()) {
framework::Variable *in_var =
boost::get<framework::Variable *>(in_var_ptrs[i]);
framework::Variable *out_var =
boost::get<framework::Variable *>(out_var_ptrs[i]);
auto type = framework::ToVarType(in_var->Type());
if (type == framework::proto::VarType::LOD_TENSOR) {
out_var->GetMutable<LoDTensor>()->Resize(
in_var->Get<framework::LoDTensor>().dims());
} else if (type == framework::proto::VarType::SELECTED_ROWS) {
out_var->GetMutable<framework::SelectedRows>()->set_height(
in_var->Get<framework::SelectedRows>().GetCompleteDims()[0]);
} else if (type == framework::proto::VarType::LOD_TENSOR_ARRAY) {
PADDLE_THROW("WhileGradOp doesn't support type %d",
static_cast<int>(type));
}
} else {
framework::VarDesc *in_var =
boost::get<framework::VarDesc *>(in_var_ptrs[i]);
boost::get<framework::VarDesc *>(out_var_ptrs[i])
->SetShape(in_var->GetShape());
}
}
ctx->SetDims(names_to_set, dims_to_set);
}
};
......
paddle/fluid/operators/conv_mkldnn_op.cc
浏览文件 @ c1f7e54f
......@@ -155,11 +155,14 @@ class ConvMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
auto chosen_memory_format =
platform::data_format_to_memory_format(data_format);
if (is_conv3d) {
chosen_memory_format =
platform::MKLDNNFormatForSize(src_tz.size(), chosen_memory_format);
weights_format = mkldnn::memory::format::any;
// Check the format for user's special output
if (chosen_memory_format != mkldnn::memory::format::any) {
if (is_conv3d) {
chosen_memory_format =
platform::MKLDNNFormatForSize(src_tz.size(), chosen_memory_format);
}
}
weights_format = GetWeightsFormat(chosen_memory_format, g, is_conv3d);
auto src_md = platform::MKLDNNMemDesc(
src_tz, platform::MKLDNNGetDataType<T>(), chosen_memory_format);
......@@ -435,11 +438,14 @@ class ConvMKLDNNGradOpKernel : public paddle::framework::OpKernel<T> {
auto chosen_memory_format =
platform::data_format_to_memory_format(data_format);
if (is_conv3d) {
chosen_memory_format =
platform::MKLDNNFormatForSize(src_tz.size(), chosen_memory_format);
weights_format = mkldnn::memory::format::any;
// Check the format for user's special output
if (chosen_memory_format != mkldnn::memory::format::any) {
if (is_conv3d) {
chosen_memory_format =
platform::MKLDNNFormatForSize(src_tz.size(), chosen_memory_format);
}
}
weights_format = GetWeightsFormat(chosen_memory_format, g, is_conv3d);
auto src_md = platform::MKLDNNMemDesc(
src_tz, platform::MKLDNNGetDataType<T>(), chosen_memory_format);
......
paddle/fluid/operators/distributed/grpc_client.cc
浏览文件 @ c1f7e54f
......@@ -12,6 +12,7 @@ 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 <stdlib.h>
#include <limits>
#include "glog/logging.h" // For VLOG
......@@ -420,7 +421,15 @@ void GRPCClient::Proceed() {
sync_cond_.notify_all();
}
}
VLOG(3) << "GRPCClient Proceed end";
// Last log message
// Avoid using VLOG() and LOG(): in the destructor of google::LogMessage() a
// static Mutex log_mutex is used for synchronization, which might have been
// destructed at this moment.
if (FLAGS_v >= 3) {
std::string msg("GRPCClient Proceed end");
fwrite(msg.c_str(), msg.length(), 1, stdout);
}
}
std::shared_ptr<grpc::Channel> GRPCClient::GetChannel(const std::string& ep) {
......
paddle/fluid/operators/elementwise/elementwise_div_op.cu
浏览文件 @ c1f7e54f
......@@ -12,18 +12,23 @@ 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/operators/elementwise/elementwise_div_op.h"
#include "paddle/fluid/platform/float16.h"
namespace ops = paddle::operators;
REGISTER_OP_CUDA_KERNEL(
elementwise_div,
ops::ElementwiseDivKernel<paddle::platform::CUDADeviceContext, float>,
ops::ElementwiseDivKernel<paddle::platform::CUDADeviceContext,
paddle::platform::float16>,
ops::ElementwiseDivKernel<paddle::platform::CUDADeviceContext, double>,
ops::ElementwiseDivKernel<paddle::platform::CUDADeviceContext, int>,
ops::ElementwiseDivKernel<paddle::platform::CUDADeviceContext, int64_t>);
REGISTER_OP_CUDA_KERNEL(
elementwise_div_grad,
ops::ElementwiseDivGradKernel<paddle::platform::CUDADeviceContext, float>,
ops::ElementwiseDivGradKernel<paddle::platform::CUDADeviceContext,
paddle::platform::float16>,
ops::ElementwiseDivGradKernel<paddle::platform::CUDADeviceContext, double>,
ops::ElementwiseDivGradKernel<paddle::platform::CUDADeviceContext, int>,
ops::ElementwiseDivGradKernel<paddle::platform::CUDADeviceContext,
......
paddle/fluid/operators/elementwise/elementwise_mul_op.cu
浏览文件 @ c1f7e54f
......@@ -12,19 +12,21 @@ 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/operators/elementwise/elementwise_mul_op.h"
#include "paddle/fluid/platform/float16.h"
namespace ops = paddle::operators;
namespace plat = paddle::platform;
REGISTER_OP_CUDA_KERNEL(
elementwise_mul,
ops::ElementwiseMulKernel<paddle::platform::CUDADeviceContext, float>,
ops::ElementwiseMulKernel<paddle::platform::CUDADeviceContext, double>,
ops::ElementwiseMulKernel<paddle::platform::CUDADeviceContext, int>,
ops::ElementwiseMulKernel<paddle::platform::CUDADeviceContext, int64_t>);
elementwise_mul, ops::ElementwiseMulKernel<plat::CUDADeviceContext, float>,
ops::ElementwiseMulKernel<plat::CUDADeviceContext, double>,
ops::ElementwiseMulKernel<plat::CUDADeviceContext, int>,
ops::ElementwiseMulKernel<plat::CUDADeviceContext, int64_t>,
ops::ElementwiseMulKernel<plat::CUDADeviceContext, plat::float16>);
REGISTER_OP_CUDA_KERNEL(
elementwise_mul_grad,
ops::ElementwiseMulGradKernel<paddle::platform::CUDADeviceContext, float>,
ops::ElementwiseMulGradKernel<paddle::platform::CUDADeviceContext, double>,
ops::ElementwiseMulGradKernel<paddle::platform::CUDADeviceContext, int>,
ops::ElementwiseMulGradKernel<paddle::platform::CUDADeviceContext,
int64_t>);
ops::ElementwiseMulGradKernel<plat::CUDADeviceContext, float>,
ops::ElementwiseMulGradKernel<plat::CUDADeviceContext, double>,
ops::ElementwiseMulGradKernel<plat::CUDADeviceContext, int>,
ops::ElementwiseMulGradKernel<plat::CUDADeviceContext, int64_t>,
ops::ElementwiseMulGradKernel<plat::CUDADeviceContext, plat::float16>);
paddle/fluid/operators/fill_zeros_like_op.cu.cc
浏览文件 @ c1f7e54f
......@@ -14,6 +14,7 @@ limitations under the License. */
#include "paddle/fluid/operators/fill_zeros_like_op.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/platform/float16.h"
namespace ops = paddle::operators;
REGISTER_OP_CUDA_KERNEL(
......@@ -22,4 +23,6 @@ REGISTER_OP_CUDA_KERNEL(
ops::FillZerosLikeKernel<paddle::platform::CUDADeviceContext, int64_t>,
ops::FillZerosLikeKernel<paddle::platform::CUDADeviceContext, float>,
ops::FillZerosLikeKernel<paddle::platform::CUDADeviceContext, double>,
ops::FillZerosLikeKernel<paddle::platform::CUDADeviceContext,
paddle::platform::float16>,
ops::FillZerosLikeKernel<paddle::platform::CUDADeviceContext, bool>);
paddle/fluid/operators/metrics/accuracy_op.cu
浏览文件 @ c1f7e54f
......@@ -16,6 +16,7 @@ limitations under the License. */
#include <thrust/reduce.h>
#include "paddle/fluid/operators/metrics/accuracy_op.h"
#include "paddle/fluid/platform/cuda_primitives.h"
#include "paddle/fluid/platform/float16.h"
#include "paddle/fluid/platform/gpu_info.h"
namespace paddle {
......@@ -94,6 +95,7 @@ class AccuracyOpCUDAKernel : public framework::OpKernel<T> {
// FIXME(typhoonzero): types of T is for inference data.
// label data is always int64
REGISTER_OP_CUDA_KERNEL(accuracy,
paddle::operators::AccuracyOpCUDAKernel<float>,
paddle::operators::AccuracyOpCUDAKernel<double>);
REGISTER_OP_CUDA_KERNEL(
accuracy, paddle::operators::AccuracyOpCUDAKernel<float>,
paddle::operators::AccuracyOpCUDAKernel<double>,
paddle::operators::AccuracyOpCUDAKernel<paddle::platform::float16>);
paddle/fluid/operators/mul_op.cc
浏览文件 @ c1f7e54f
......@@ -49,7 +49,8 @@ class MulOp : public framework::OperatorWithKernel {
PADDLE_ENFORCE_GT(
y_dims.size(), y_num_col_dims,
"The input tensor Y's rank of MulOp should be larger than "
"y_num_col_dims.");
"y_num_col_dims: %ld vs %ld",
y_dims.size(), y_num_col_dims);
auto x_mat_dims = framework::flatten_to_2d(x_dims, x_num_col_dims);
auto y_mat_dims = framework::flatten_to_2d(y_dims, y_num_col_dims);
......
paddle/fluid/operators/optimizers/momentum_op.cu
浏览文件 @ c1f7e54f
......@@ -14,8 +14,11 @@ limitations under the License. */
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/optimizers/momentum_op.h"
#include "paddle/fluid/platform/float16.h"
namespace ops = paddle::operators;
REGISTER_OP_CUDA_KERNEL(
momentum, ops::MomentumOpKernel<paddle::platform::CUDADeviceContext, float>,
ops::MomentumOpKernel<paddle::platform::CUDADeviceContext, double>);
ops::MomentumOpKernel<paddle::platform::CUDADeviceContext, double>,
ops::MomentumOpKernel<paddle::platform::CUDADeviceContext,
paddle::platform::float16>);
paddle/fluid/operators/optimizers/momentum_op.h
浏览文件 @ c1f7e54f
......@@ -237,7 +237,8 @@ class SparseMomentumFunctor<T, UseNesterov> {
inline HOSTDEVICE void operator()(size_t i) {
auto row_idx =
math::BinarySearch<int64_t>(rows_, row_height_, i / row_numel_);
T g = row_idx >= 0 ? g_[row_idx * row_numel_ + i % row_numel_] : 0;
T g = row_idx >= 0 ? g_[row_idx * row_numel_ + i % row_numel_]
: static_cast<T>(0);
// put memory access in register
const T p = p_[i];
const T lr = lr_[0];
......@@ -282,7 +283,8 @@ class SparseMomentumFunctor<T, NoNesterov> {
inline HOSTDEVICE void operator()(size_t i) {
auto row_idx =
math::BinarySearch<int64_t>(rows_, row_height_, i / row_numel_);
T g = row_idx >= 0 ? g_[row_idx * row_numel_ + i % row_numel_] : 0;
T g = row_idx >= 0 ? g_[row_idx * row_numel_ + i % row_numel_]
: static_cast<T>(0);
// put memory access in register
const T p = p_[i];
const T lr = lr_[0];
......
paddle/fluid/operators/top_k_op.cu
浏览文件 @ c1f7e54f
......@@ -16,6 +16,7 @@ limitations under the License. */
#include "paddle/fluid/operators/top_k_op.h"
#include "paddle/fluid/platform/assert.h"
#include "paddle/fluid/platform/cuda_device_function.h"
#include "paddle/fluid/platform/float16.h"
namespace paddle {
namespace operators {
......@@ -150,7 +151,7 @@ __device__ __forceinline__ void ThreadGetTopK(Pair<T> topk[], int* beam,
if (k < MaxLength - (*beam)) {
topk[k] = topk[k + *beam];
} else {
topk[k].set(-INFINITY, -1);
topk[k].set(-static_cast<T>(INFINITY), -1);
}
}
if (!(*is_empty)) {
......@@ -160,7 +161,7 @@ __device__ __forceinline__ void ThreadGetTopK(Pair<T> topk[], int* beam,
}
*max = topk[MaxLength - 1];
if ((*max).v == -1) *is_empty = true;
if ((*max).v == -static_cast<T>(1)) *is_empty = true;
*beam = 0;
}
}
......@@ -181,7 +182,7 @@ __device__ __forceinline__ void ThreadGetTopK(Pair<T> topk[], int* beam,
if (k < MaxLength - *beam) {
topk[k] = topk[k + *beam];
} else {
topk[k].set(-INFINITY, -1);
topk[k].set(-static_cast<T>(INFINITY), -1);
}
}
if (!(*is_empty)) {
......@@ -278,7 +279,7 @@ __global__ void KeMatrixTopK(T* output, int output_stride, int64_t* indices,
bool firststep = true;
for (int j = 0; j < MaxLength; j++) {
topk[j].set(-INFINITY, -1);
topk[j].set(-static_cast<T>(INFINITY), -1);
}
while (top_num) {
ThreadGetTopK<T, MaxLength, BlockSize>(
......@@ -362,5 +363,7 @@ class TopkOpCUDAKernel : public framework::OpKernel<T> {
} // namespace operators
} // namespace paddle
REGISTER_OP_CUDA_KERNEL(top_k, paddle::operators::TopkOpCUDAKernel<float>,
paddle::operators::TopkOpCUDAKernel<double>);
REGISTER_OP_CUDA_KERNEL(
top_k, paddle::operators::TopkOpCUDAKernel<float>,
paddle::operators::TopkOpCUDAKernel<double>,
paddle::operators::TopkOpCUDAKernel<paddle::platform::float16>);
paddle/fluid/platform/nccl_helper.h
浏览文件 @ c1f7e54f
......@@ -23,6 +23,7 @@
#include "paddle/fluid/framework/data_type.h"
#include "paddle/fluid/platform/dynload/nccl.h"
#include "paddle/fluid/platform/enforce.h"
#include "paddle/fluid/platform/float16.h"
#define NCCL_ID_VARNAME "NCCLID"
......@@ -38,6 +39,8 @@ inline ncclDataType_t ToNCCLDataType(framework::proto::VarType::Type type) {
return ncclInt;
} else if (type == framework::proto::VarType::INT64) {
return ncclInt64;
} else if (type == framework::proto::VarType::FP16) {
return ncclFloat16;
} else {
PADDLE_THROW("Not supported");
}
......
paddle/fluid/pybind/imperative.cc
浏览文件 @ c1f7e54f
......@@ -24,8 +24,9 @@ namespace pybind {
void BindTracer(pybind11::module *m) {
pybind11::class_<imperative::Tracer>(*m, "Tracer", "")
.def("__init__",
[](imperative::Tracer &self, framework::BlockDesc *root_block) {
new (&self) imperative::Tracer(root_block);
[](imperative::Tracer &self, framework::BlockDesc *root_block,
framework::BlockDesc *startup_block) {
new (&self) imperative::Tracer(root_block, startup_block);
})
.def("trace", &imperative::Tracer::Trace)
.def("get_scope", &imperative::Tracer::GetScope,
......
paddle/fluid/pybind/pybind.cc
浏览文件 @ c1f7e54f
......@@ -977,7 +977,6 @@ All parameter, weight, gradient are variables in Paddle.
cannot be updated after being finalized.)DOC");
pe.def(py::init<const std::vector<platform::Place> &,
const std::unordered_set<std::string> &,
const std::unordered_set<std::string> &, const ProgramDesc &,
const std::string &, Scope *, std::vector<Scope *> &,
const ExecutionStrategy &, const BuildStrategy &, size_t,
......
python/paddle/fluid/backward.py
浏览文件 @ c1f7e54f
......@@ -489,8 +489,11 @@ def append_backward(loss, parameter_list=None, no_grad_set=None,
grad_to_var = dict()
op_desc = _create_op_desc_(
"fill_constant", {}, {"Out": [_append_grad_suffix_(loss.name)]}, {
"shape": [1],
"fill_constant",
{},
{"Out": [_append_grad_suffix_(loss.name)]},
{
"shape": [1], # TODO(panyx0718): This can be loss.shape.
"value": 1.0,
"dtype": loss.dtype,
"force_cpu": False,
......
python/paddle/fluid/contrib/__init__.py
浏览文件 @ c1f7e54f
......@@ -22,9 +22,12 @@ from . import op_frequence
from .op_frequence import *
from . import quantize
from .quantize import *
from . import utils
from .utils import *
__all__ = []
__all__ += decoder.__all__
__all__ += memory_usage_calc.__all__
__all__ += op_frequence.__all__
__all__ += quantize.__all__
__all__ += utils.__all__
python/paddle/fluid/contrib/utils/__init__.py
浏览文件 @ c1f7e54f
......@@ -13,10 +13,11 @@
# limitations under the License.
from __future__ import print_function
#from . import lookup_table_utils
#from .lookup_table_utils import *
from . import lookup_table_utils
from .lookup_table_utils import *
from . import hdfs_utils
from .hdfs_utils import *
#__all__ = lookup_table_utils.__all__
__all__ = hdfs_utils.__all__
__all__ = []
__all__ += lookup_table_utils.__all__
__all__ += hdfs_utils.__all__
python/paddle/fluid/contrib/utils/hdfs_utils.py
浏览文件 @ c1f7e54f
......@@ -14,6 +14,7 @@
"""HDFS Utils"""
import os
import sys
import subprocess
import multiprocessing
from datetime import datetime
......@@ -24,7 +25,7 @@ import errno
import logging
__all__ = ["HDFSClient", "multi_download"]
__all__ = ["HDFSClient", "multi_download", "multi_upload"]
logging.basicConfig(format='%(asctime)s - %(levelname)s - %(message)s')
_logger = logging.getLogger("hdfs_utils")
......@@ -93,13 +94,15 @@ class HDFSClient(object):
def upload(self, hdfs_path, local_path, overwrite=False, retry_times=5):
"""
upload the local file to hdfs
Args:
hdfs_path: hdfs path, target path
local_path: local file path, source path
overwrite: will overwrite the original file
retry_times: max times retry to upload
Returns:
upload the local file to hdfs
Args:
hdfs_path(str): the hdfs file path
local_path(str): the local file path
overwrite(bool|None): will overwrite the file on HDFS or not
retry_times(int|5): retry times
Returns:
True or False
"""
assert hdfs_path is not None
......@@ -109,7 +112,7 @@ class HDFSClient(object):
_logger.warn(
"The Local path: {} is dir and I will support it later, return".
format(local_path))
return
return False
base = os.path.basename(local_path)
if not self.is_exist(hdfs_path):
......@@ -141,14 +144,16 @@ class HDFSClient(object):
def download(self, hdfs_path, local_path, overwrite=False, unzip=False):
"""
download from hdfs
Args:
hdfs_path: hdfs path, target path
local_path: local file path, source path
overwrite: will remove original file and overwrite it.
unzip: ignore this param
Returns
True or False
download file from HDFS
Args:
hdfs_path(str): the hdfs file path
local_path(str): the local file path
overwrite(bool|None): will overwrite the file on HDFS or not
unzip(bool|False): if the download file is compressed by zip, unzip it or not.
Returns:
True or False
"""
_logger.info('Downloading %r to %r.', hdfs_path, local_path)
_logger.info('Download of %s to %r complete.', hdfs_path, local_path)
......@@ -188,13 +193,13 @@ class HDFSClient(object):
def is_exist(self, hdfs_path=None):
"""
whether the remote hdfs path exists?
Args:
hdfs_path: default value(${OUTPUT_PATH}/${SYS_USER_ID}/${SYS_JOB_ID}/tmp)
fs_name: The default values are the same as in the job configuration
fs_ugi: The default values are the same as in the job configuration
Returns:
True or False
whether the remote HDFS path exists
Args:
hdfs_path(str): the hdfs file path
Returns:
True or False
"""
exist_cmd = ['-test', '-e', hdfs_path]
returncode, output, errors = self.__run_hdfs_cmd(
......@@ -211,13 +216,13 @@ class HDFSClient(object):
def is_dir(self, hdfs_path=None):
"""
whether the remote hdfs path exists?
Args:
remote_file_path: default value(${OUTPUT_PATH}/${SYS_USER_ID}/${SYS_JOB_ID}/tmp)
fs_name: The default values are the same as in the job configuration
fs_ugi: The default values are the same as in the job configuration
Returns:
True or False
whether the remote HDFS path is directory
Args:
hdfs_path(str): the hdfs file path
Returns:
True or False
"""
if not self.is_exist(hdfs_path):
......@@ -237,17 +242,17 @@ class HDFSClient(object):
def delete(self, hdfs_path):
"""
Remove a file or directory from HDFS.
Remove a file or directory from HDFS.
whether the remote HDFS path exists
Args:
param hdfs_path: HDFS path.
param recursive: Recursively delete files and directories. By default,
this method will raise an :class:`HdfsError` if trying to delete a
non-empty directory.
hdfs_path: HDFS path.
Returns:
True or False
This function returns `True` if the deletion was successful and `False` if
no file or directory previously existed at `hdfs_path`.
"""
_logger.info('Deleting %r.', hdfs_path)
......@@ -273,16 +278,14 @@ class HDFSClient(object):
def rename(self, hdfs_src_path, hdfs_dst_path, overwrite=False):
"""
Rename a file or folder.
Args:
:param hdfs_src_path: Source path.
:param hdfs_dst_path: Destination path. If the path already exists and is
a directory, the source will be moved into it. If the path exists and is
a file, or if a parent destination directory is missing, this method will
raise an :class:`HdfsError`.
Move a file or folder on HDFS.
Args:
hdfs_path(str): HDFS path.
overwrite(bool|False): If the path already exists and overwrite is False, will return False.
Returns:
This function returns `True` if the rename was successful and `False` if
rename was faild.
True or False
"""
assert hdfs_src_path is not None
assert hdfs_dst_path is not None
......@@ -320,17 +323,20 @@ class HDFSClient(object):
raise
def makedirs(self, hdfs_path):
"""Create a remote directory, recursively if necessary.
"""
Create a remote directory, recursively if necessary.
Args:
:param hdfs_path: Remote path. Intermediate directories will be created
appropriately.
hdfs_path(str): Remote path. Intermediate directories will be created appropriately.
Returns:
True if make a directories was successful, False when make a directiries was failed.
True or False
"""
_logger.info('Creating directories to %r.', hdfs_path)
assert hdfs_path is not None
if self.is_exist(hdfs_path):
_logger.error("HDFS path is exist: {}".format(hdfs_path))
return
mkdirs_commands = ['-mkdir', hdfs_path]
......@@ -346,11 +352,13 @@ class HDFSClient(object):
def ls(self, hdfs_path):
"""
ls a hdfs_path.
Args:
:param hdfs_path: hdfs_path will be ls.
ls directory contents about HDFS hdfs_path
Args:
hdfs_path(str): Remote HDFS path will be ls.
Returns:
This function returns a `list` that contaion all files in the hdfs_path.
List: a contents list about hdfs_path.
"""
assert hdfs_path is not None
......@@ -378,11 +386,15 @@ class HDFSClient(object):
def lsr(self, hdfs_path, only_file=True, sort=True):
"""
ls a hdfs_path sort by time.
Args:
:param hdfs_path: hdfs_path will be ls.
list directory contents about HDFS hdfs_path recursively
Args:
hdfs_path(str): Remote HDFS path.
only_file(bool|True): will discard folders.
sort(bool|True): will be sorted by create time.
Returns:
This function returns a `list` that contaion all files sorted by time in the hdfs_path.
List: a contents list about hdfs_path.
"""
def sort_by_time(v1, v2):
......@@ -422,21 +434,106 @@ class HDFSClient(object):
return ret_lines
def multi_download(client,
hdfs_path,
local_path,
trainer_id,
trainers,
multi_processes=5):
"""
Download files from HDFS using multi process.
Args:
client(HDFSClient): instance of HDFSClient
hdfs_path(str): path on hdfs
local_path(str): path on local
trainer_id(int): current trainer id
trainers(int): all trainers number
multi_processes(int|5): the download data process at the same time, default=5
Returns:
List:
Download files in local folder.
"""
def __subprocess_download(datas):
for data in datas:
re_path = os.path.relpath(os.path.dirname(data), hdfs_path)
if re_path == os.curdir:
sub_local_re_path = local_path
else:
sub_local_re_path = os.path.join(local_path, re_path)
client.download(data, sub_local_re_path)
assert isinstance(client, HDFSClient)
client.make_local_dirs(local_path)
_logger.info("Make local dir {} successfully".format(local_path))
all_need_download = client.lsr(hdfs_path, sort=True)
need_download = all_need_download[trainer_id::trainers]
_logger.info("Get {} files From all {} files need to be download from {}".
format(len(need_download), len(all_need_download), hdfs_path))
_logger.info("Start {} multi process to download datas".format(
multi_processes))
procs = []
for i in range(multi_processes):
process_datas = need_download[i::multi_processes]
p = multiprocessing.Process(
target=__subprocess_download, args=(process_datas, ))
procs.append(p)
p.start()
# complete the processes
for proc in procs:
proc.join()
_logger.info("Finish {} multi process to download datas".format(
multi_processes))
local_downloads = []
for data in need_download:
data_name = os.path.basename(data)
re_path = os.path.relpath(os.path.dirname(data), hdfs_path)
if re_path == os.curdir:
local_re_path = os.path.join(local_path, data_name)
else:
local_re_path = os.path.join(local_path, re_path, data_name)
local_downloads.append(local_re_path)
return local_downloads
def getfilelist(path):
rlist = []
for dir, folder, file in os.walk(path):
for i in file:
t = os.path.join(dir, i)
rlist.append(t)
for r in rlist:
print(r)
def multi_upload(client,
hdfs_path,
local_path,
multi_processes=5,
overwrite=False):
overwrite=False,
sync=True):
"""
Upload file to hdfs.
Upload files to HDFS using multi process.
Args:
:param overwrite: will overwrite hdfs file or not
:param multi_processes: the upload data process at the same time, default=5
:param client: instance of HDFSClient
:param hdfs_path: path on hdfs
:param local_path: path on local
client(HDFSClient): instance of HDFSClient
hdfs_path(str): path on hdfs
local_path(str): path on local
multi_processes(int|5): the upload data process at the same time, default=5
overwrite(bool|False): will overwrite file on HDFS or not
sync(bool|True): upload files sync or not.
Returns:
None
"""
def __subprocess_upload(datas):
......@@ -446,13 +543,6 @@ def multi_upload(client,
client.upload(hdfs_re_path, data, overwrite, retry_times=5)
def get_local_files(path):
"""
Get all local files
Args:
path: local file path
Returns:
A list that contation all files in the path.
"""
rlist = []
if not os.path.isdir(path):
......@@ -488,71 +578,6 @@ def multi_upload(client,
multi_processes))
def multi_download(client,
hdfs_path,
local_path,
trainer_id,
trainers,
file_cnt,
multi_processes=5):
"""
multi_download
Args:
:param client: instance of HDFSClient
:param hdfs_path: path on hdfs
:param local_path: path on local
:param trainer_id: current trainer id
:param trainers: all trainers number
:param file_cnt: all file number
:param multi_processes: the download data process at the same time, default=5
:return: None
Returns:
A list that be downloaded.
"""
def __subprocess_download(datas):
for data in datas:
re_path = os.path.relpath(os.path.dirname(data), hdfs_path)
local_re_path = os.path.join(local_path, re_path)
client.download(data, local_re_path)
assert isinstance(client, HDFSClient)
client.make_local_dirs(local_path)
_logger.info("Make local dir {} successfully".format(local_path))
all_need_download = client.lsr(hdfs_path, sort=True)[:file_cnt]
need_download = all_need_download[trainer_id::trainers]
_logger.info("Get {} files From all {} files need to be download from {}".
format(len(need_download), len(all_need_download), hdfs_path))
_logger.info("Start {} multi process to download datas".format(
multi_processes))
procs = []
for i in range(multi_processes):
process_datas = need_download[i::multi_processes]
p = multiprocessing.Process(
target=__subprocess_download, args=(process_datas, ))
procs.append(p)
p.start()
# complete the processes
for proc in procs:
proc.join()
_logger.info("Finish {} multi process to download datas".format(
multi_processes))
local_downloads = []
for data in need_download:
data_name = os.path.basename(data)
re_path = os.path.relpath(os.path.dirname(data), hdfs_path)
local_re_path = os.path.join(local_path, re_path, data_name)
local_downloads.append(local_re_path)
return local_downloads
if __name__ == "__main__":
hadoop_home = "/home/client/hadoop-client/hadoop/"
......
python/paddle/fluid/contrib/utils/lookup_table_utils.py
浏览文件 @ c1f7e54f
......@@ -18,14 +18,12 @@ import os
import time
import logging
import paddle
import paddle.fluid as fluid
from paddle.fluid import core
from paddle.fluid import io
from paddle.fluid import Program
__all__ = [
"load_inference_model", "load_persistable_vars",
"load_persistables_for_increment", "load_persistables_for_inference",
"convert_dist_to_sparse_program"
]
......@@ -80,19 +78,28 @@ def __get_prefetch_op_tuples(main_program):
return prefetch_op_tuples
def convert_dist_to_sparse_program(main_program):
if not main_program._distributed_lookup_table:
def convert_dist_to_sparse_program(program):
"""
WARNING: this function will only be used for distributed training with distributed lookup table.
when we train model with distributed lookup table but want to do the local inference, we can use
this function to convert the train program with distributed lookup table to sparse lookup table.
:param program(Program): the program must be the trainer program, which will be get by the distribute transpiler.
:return:
program: The `program` is a Program, it's the program replace distributed lookup table to sparse lookup table.
"""
if not program._distributed_lookup_table:
_logger.warn(
"There are no distributed lookup tables need to be converted")
return
# create table param and grad var in pserver program
origin_emb_var = "{}.origin".format(main_program._distributed_lookup_table)
emb_var = main_program._distributed_lookup_table
main_program.global_block()._rename_var(emb_var, origin_emb_var)
origin_param_var = main_program.global_block().vars[origin_emb_var]
origin_emb_var = "{}.origin".format(program._distributed_lookup_table)
emb_var = program._distributed_lookup_table
program.global_block()._rename_var(emb_var, origin_emb_var)
origin_param_var = program.global_block().vars[origin_emb_var]
param_var = main_program.global_block().create_var(
param_var = program.global_block().create_var(
name=emb_var,
shape=origin_param_var.shape,
dtype=origin_param_var.dtype,
......@@ -100,28 +107,28 @@ def convert_dist_to_sparse_program(main_program):
persistable=True)
# parameter must be selected rows
param_var.desc.set_type(core.VarDesc.VarType.SELECTED_ROWS)
main_program._sync_with_cpp()
program._sync_with_cpp()
prefetch_op_tuples = __get_prefetch_op_tuples(main_program)
prefetch_op_tuples = __get_prefetch_op_tuples(program)
split_ids_id = prefetch_op_tuples[0]
for idx in range(split_ids_id + 2, split_ids_id - 1, -1):
main_program.global_block()._remove_op(idx)
main_program.desc.flush()
program.global_block()._remove_op(idx)
program.desc.flush()
in_out_pairs = zip(prefetch_op_tuples[1], prefetch_op_tuples[2])
for in_out_pair in in_out_pairs:
idx = split_ids_id
ids = main_program.global_block().vars[in_out_pair[0]]
out = main_program.global_block().vars[in_out_pair[1]]
__insert_lookup_sparse_table_op(main_program, idx, ids, param_var, out)
main_program.desc.flush()
return main_program
ids = program.global_block().vars[in_out_pair[0]]
out = program.global_block().vars[in_out_pair[1]]
__insert_lookup_sparse_table_op(program, idx, ids, param_var, out)
program.desc.flush()
return program
def load_persistable_vars(executor, dirname, program, lookup_table_var):
def _load_persistable_vars(executor, dirname, program, lookup_table_vars):
def _is_checkpoint_var(exclude_fluid_vars=None):
"""
the checkpoint will not save or load all the variables.
......@@ -159,8 +166,82 @@ def load_persistable_vars(executor, dirname, program, lookup_table_var):
return is_valid
def _load_lookup_table_vars(executor, dirname, main_program,
lookup_table_vars):
io.load_vars(
executor,
dirname=dirname,
main_program=program,
predicate=_is_checkpoint_var(lookup_table_vars),
filename=None)
def load_persistables_for_increment(dirname, executor, program,
lookup_table_var, lookup_table_var_path):
"""
WARNING: this function will only be used for distributed training with distributed lookup table.
for increment trainning, the pserver will not only load dense variables,
but also load the suitable lookup table var. Because of slice lookup table
var with HASH, we must load the correct slice var.
:param dirname(str): The directory path
:param executor(Executor): The executor to run for loading inference model.
:param program(Program): The parameter server program, which will run on Pserver.
:param lookup_table_var: the distributed lookup tables var name.
:param lookup_table_var_path: the the distributed lookup tables var location.
:return: None
"""
def __load_lookup_table_vars(executor, main_program, lookup_table_var,
lookup_table_var_path):
emb_var = main_program.global_block().var(lookup_table_var)
load_program = Program()
load_block = load_program.global_block()
load_block.append_op(
type='load',
inputs={},
outputs={'Out': [emb_var]},
attrs={'file_path': lookup_table_var_path})
executor.run(load_program)
if not os.path.isdir(dirname):
raise ValueError("There is no directory named '%s'", dirname)
if not os.path.exists(lookup_table_var_path):
raise ValueError("There is no file named '%s'", lookup_table_var_path)
if not isinstance(program, Program):
raise ValueError("program must be an instance of fluid.Program")
_logger.info("Start Load Sparse Program With "
"Distributed Lookup Table Vars from {}, time = {}".format(
dirname, time.ctime()))
_load_persistable_vars(executor, dirname, program, [lookup_table_var])
__load_lookup_table_vars(executor, program, lookup_table_var,
lookup_table_var_path)
_logger.info("Finish Load Sparse Program With "
"Distributed Lookup Table Vars from {}, time = {}".format(
dirname, time.ctime()))
def load_persistables_for_inference(dirname, executor, program,
lookup_table_var_name):
"""
WARNING: this function will only be used for inference with distributed lookup table.
Inference with distributed lookup table is a little funky, this function will load distributed
lookup table vars into sparse var, can be used in local inference mode.
:param dirname(str): The directory path
:param executor(Executor): The executor to run for loading inference model.
:param program(Program): The parameter server program, which will run on Pserver.
:param lookup_table_var_name: the distributed lookup tables var name.
:return: None
"""
def __load_lookup_table_vars(executor, dirname, main_program,
lookup_table_vars):
if not os.path.isdir(dirname):
raise ValueError("There is no directory named '%s'", dirname)
......@@ -209,48 +290,34 @@ def load_persistable_vars(executor, dirname, program, lookup_table_var):
global_block.append_op(type='delete_var', inputs={'X': sums})
executor.run(convert_program)
_logger.info("Start Load Sparse Program With "
"Distributed Lookup Table Vars from {}, time = {}".format(
dirname, time.ctime()))
lookup_table_vars = [lookup_table_var]
io.load_vars(
executor,
dirname=dirname,
main_program=program,
predicate=_is_checkpoint_var(lookup_table_vars),
filename=None)
_load_lookup_table_vars(executor, dirname, program, lookup_table_vars)
_logger.info("Finish Load Sparse Program With "
"Distributed Lookup Table Vars from {}, time = {}".format(
dirname, time.ctime()))
def load_inference_model(dirname, executor, lookup_table_var_name):
if not os.path.isdir(dirname):
raise ValueError("There is no directory named '%s'", dirname)
local_model = os.path.join(dirname, model_filename)
if program:
if not isinstance(program, Program):
raise ValueError("program must be an instance of fluid.Program")
else:
local_model = os.path.join(dirname, model_filename)
with open(local_model, "rb") as f:
program_desc_str = f.read()
with open(local_model, "rb") as f:
program_desc_str = f.read()
program = Program.parse_from_string(program_desc_str)
program = Program.parse_from_string(program_desc_str)
if not core._is_program_version_supported(program._version()):
raise ValueError("Unsupported program version: %d\n" %
program._version())
if not core._is_program_version_supported(program._version()):
raise ValueError("Unsupported program version: %d\n" %
program._version())
# Binary data also need version.
load_persistable_vars(executor, dirname, program, lookup_table_var_name)
_logger.info("Start Load Sparse Program With "
"Distributed Lookup Table Vars from {}, time = {}".format(
dirname, time.ctime()))
_load_persistable_vars(executor, dirname, program, [lookup_table_var_name])
__load_lookup_table_vars(executor, dirname, program,
[lookup_table_var_name])
feed_target_names = program.desc.get_feed_target_names()
fetch_target_names = program.desc.get_fetch_target_names()
fetch_targets = [
program.global_block().var(name) for name in fetch_target_names
]
_logger.info("Finish Load Sparse Program With "
"Distributed Lookup Table Vars from {}, time = {}".format(
dirname, time.ctime()))
return [program, feed_target_names, fetch_targets]
return program
python/paddle/fluid/data_feeder.py
浏览文件 @ c1f7e54f
......@@ -44,6 +44,8 @@ class DataToLoDTensorConverter(object):
self.dtype = 'int64'
elif dtype == core.VarDesc.VarType.FP64:
self.dtype = 'float64'
elif dtype == core.VarDesc.VarType.FP16:
self.dtype = 'float16'
elif dtype == core.VarDesc.VarType.INT32:
self.dtype = 'int32'
elif dtype == core.VarDesc.VarType.UINT8:
......
python/paddle/fluid/framework.py
浏览文件 @ c1f7e54f
......@@ -1324,6 +1324,9 @@ class Block(object):
def _prepend_op(self, *args, **kwargs):
op_desc = self.desc._prepend_op()
op = Operator(self, op_desc, *args, **kwargs)
if _in_imperative_mode():
_imperative_tracer().trace(op.iop, [v._ivar for v in op.inputs],
[v._ivar for v in op.outputs], self.desc)
self.ops.insert(0, op)
return op
......
python/paddle/fluid/imperative/base.py
浏览文件 @ c1f7e54f
......@@ -28,7 +28,8 @@ def enabled():
def guard():
train = framework.Program()
startup = framework.Program()
tracer = core.Tracer(train.current_block().desc)
tracer = core.Tracer(train.current_block().desc,
startup.current_block().desc)
with framework.program_guard(train, startup):
with framework.unique_name.guard():
with framework._imperative_guard(tracer):
......
python/paddle/fluid/imperative/layers.py
浏览文件 @ c1f7e54f
......@@ -25,11 +25,9 @@ __all__ = ['PyLayer']
class PyLayer(core.Layer):
def __init__(self):
pass
self._built = False
def __call__(self, inputs):
# TODO(panyx0718): Support declarative mode as well.
assert base.enabled()
if not isinstance(inputs, list) and not isinstance(inputs, tuple):
inputs = [inputs]
......@@ -37,8 +35,15 @@ class PyLayer(core.Layer):
for x in inputs:
py_var = base.to_variable(x)
var_inputs.append(py_var)
if not self._built:
self._build_once(inputs)
self._built = True
outputs = self.forward(var_inputs)
return outputs
def _build_once(self, inputs):
pass
def forward(self, inputs):
return []
python/paddle/fluid/initializer.py
浏览文件 @ c1f7e54f
......@@ -18,6 +18,7 @@ from . import framework
import numpy as np
import contextlib
from .core import VarDesc
from . import unique_name
__all__ = [
'Constant', 'Uniform', 'Normal', 'TruncatedNormal', 'Xavier', 'Bilinear',
......@@ -207,16 +208,39 @@ class UniformInitializer(Initializer):
# Initialization Ops should be prepended and not appended
if self._seed == 0:
self._seed = block.program.random_seed
# to be compatible of fp16 initalizers
if var.dtype == VarDesc.VarType.FP16:
out_dtype = VarDesc.VarType.FP32
out_var = block.create_var(
name=unique_name.generate(".".join(['gaussian_random', 'tmp'])),
shape=var.shape,
dtype=out_dtype,
type=VarDesc.VarType.LOD_TENSOR,
persistable=False)
else:
out_dtype = var.dtype
out_var = var
op = block._prepend_op(
type="uniform_random",
outputs={"Out": var},
outputs={"Out": out_var},
attrs={
"shape": var.shape,
"dtype": int(var.dtype),
"dtype": out_dtype,
"min": self._low,
"max": self._high,
"seed": self._seed
})
if var.dtype == VarDesc.VarType.FP16:
block.append_op(
type="cast",
inputs={"X": out_var},
outputs={"Out": var},
attrs={"in_dtype": out_var.dtype,
"out_dtype": var.dtype})
var.op = op
return op
......@@ -261,17 +285,39 @@ class NormalInitializer(Initializer):
# Initialization Ops should be prepended and not appended
if self._seed == 0:
self._seed = block.program.random_seed
# to be compatible of fp16 initalizers
if var.dtype == VarDesc.VarType.FP16:
out_dtype = VarDesc.VarType.FP32
out_var = block.create_var(
name=unique_name.generate(".".join(['gaussian_random', 'tmp'])),
shape=var.shape,
dtype=out_dtype,
type=VarDesc.VarType.LOD_TENSOR,
persistable=False)
else:
out_dtype = var.dtype
out_var = var
op = block._prepend_op(
type="gaussian_random",
outputs={"Out": var},
outputs={"Out": out_var},
attrs={
"shape": var.shape,
"dtype": int(var.dtype),
"dtype": out_dtype,
"mean": self._mean,
"std": self._std_dev,
"seed": self._seed,
"use_mkldnn": False
})
if var.dtype == VarDesc.VarType.FP16:
block.append_op(
type="cast",
inputs={"X": out_var},
outputs={"Out": var},
attrs={"in_dtype": out_var.dtype,
"out_dtype": var.dtype})
var.op = op
return op
......
python/paddle/fluid/layers/learning_rate_scheduler.py
浏览文件 @ c1f7e54f
......@@ -63,14 +63,18 @@ def noam_decay(d_model, warmup_steps):
Returns:
The decayed learning rate.
"""
with default_main_program()._lr_schedule_guard():
global_step = _decay_step_counter(1)
a = global_step**-0.5
b = (warmup_steps**-1.5) * global_step
lr_value = (d_model**-0.5) * nn.elementwise_min(a, b)
def _lr_schedule(dtype):
with default_main_program()._lr_schedule_guard():
global_step = _decay_step_counter(1)
return lr_value
a = global_step**-0.5
b = (warmup_steps**-1.5) * global_step
lr_value = (d_model**-0.5) * nn.elementwise_min(a, b)
return lr_value
return _lr_schedule
def exponential_decay(learning_rate, decay_steps, decay_rate, staircase=False):
......@@ -109,15 +113,19 @@ def exponential_decay(learning_rate, decay_steps, decay_rate, staircase=False):
sgd_optimizer.minimize(avg_cost)
"""
with default_main_program()._lr_schedule_guard():
global_step = _decay_step_counter()
div_res = global_step / decay_steps
if staircase:
div_res = ops.floor(div_res)
decayed_lr = learning_rate * (decay_rate**div_res)
def _lr_schedule(dtype):
with default_main_program()._lr_schedule_guard():
global_step = _decay_step_counter()
div_res = global_step / decay_steps
if staircase:
div_res = ops.floor(div_res)
decayed_lr = learning_rate * (decay_rate**div_res)
return decayed_lr
return decayed_lr
return _lr_schedule
def natural_exp_decay(learning_rate, decay_steps, decay_rate, staircase=False):
......@@ -138,15 +146,19 @@ def natural_exp_decay(learning_rate, decay_steps, decay_rate, staircase=False):
Returns:
The decayed learning rate
"""
with default_main_program()._lr_schedule_guard():
global_step = _decay_step_counter()
div_res = global_step / decay_steps
if staircase:
div_res = ops.floor(div_res)
decayed_lr = learning_rate * ops.exp(-1 * decay_rate * div_res)
def _lr_schedule(dtype):
with default_main_program()._lr_schedule_guard():
global_step = _decay_step_counter()
div_res = global_step / decay_steps
if staircase:
div_res = ops.floor(div_res)
decayed_lr = learning_rate * ops.exp(-1 * decay_rate * div_res)
return decayed_lr
return decayed_lr
return _lr_schedule
def inverse_time_decay(learning_rate, decay_steps, decay_rate, staircase=False):
......@@ -184,16 +196,20 @@ def inverse_time_decay(learning_rate, decay_steps, decay_rate, staircase=False):
staircase=True))
sgd_optimizer.minimize(avg_cost)
"""
with default_main_program()._lr_schedule_guard():
global_step = _decay_step_counter()
div_res = global_step / decay_steps
if staircase:
div_res = ops.floor(div_res)
def _lr_schedule(dtype):
with default_main_program()._lr_schedule_guard():
global_step = _decay_step_counter()
decayed_lr = learning_rate / (1 + decay_rate * div_res)
div_res = global_step / decay_steps
if staircase:
div_res = ops.floor(div_res)
return decayed_lr
decayed_lr = learning_rate / (1 + decay_rate * div_res)
return decayed_lr
return _lr_schedule
def polynomial_decay(learning_rate,
......@@ -224,28 +240,33 @@ def polynomial_decay(learning_rate,
Returns:
Variable: The decayed learning rate
"""
with default_main_program()._lr_schedule_guard():
global_step = _decay_step_counter()
if cycle:
div_res = ops.ceil(global_step / decay_steps)
zero_var = tensor.fill_constant(
shape=[1], dtype='float32', value=0.0)
one_var = tensor.fill_constant(
shape=[1], dtype='float32', value=1.0)
def _lr_schedule(dtype, decay_steps=decay_steps):
with default_main_program()._lr_schedule_guard():
global_step = _decay_step_counter()
if cycle:
div_res = ops.ceil(global_step / decay_steps)
zero_var = tensor.fill_constant(
shape=[1], dtype=dtype, value=0.0)
one_var = tensor.fill_constant(
shape=[1], dtype=dtype, value=1.0)
with control_flow.Switch() as switch:
with switch.case(global_step == zero_var):
tensor.assign(input=one_var, output=div_res)
decay_steps = decay_steps * div_res
else:
decay_steps_var = tensor.fill_constant(
shape=[1], dtype=dtype, value=float(decay_steps))
global_step = nn.elementwise_min(
x=global_step, y=decay_steps_var)
with control_flow.Switch() as switch:
with switch.case(global_step == zero_var):
tensor.assign(input=one_var, output=div_res)
decay_steps = decay_steps * div_res
else:
decay_steps_var = tensor.fill_constant(
shape=[1], dtype='float32', value=float(decay_steps))
global_step = nn.elementwise_min(x=global_step, y=decay_steps_var)
decayed_lr = (learning_rate - end_learning_rate) * \
((1 - global_step / decay_steps) ** power) + end_learning_rate
return decayed_lr
decayed_lr = (learning_rate - end_learning_rate) * \
((1 - global_step / decay_steps) ** power) + end_learning_rate
return decayed_lr
return _lr_schedule
def piecewise_decay(boundaries, values):
......@@ -273,38 +294,42 @@ def piecewise_decay(boundaries, values):
"""
with default_main_program()._lr_schedule_guard():
if len(values) - len(boundaries) != 1:
raise ValueError("len(values) - len(boundaries) should be 1")
global_step = _decay_step_counter()
lr = tensor.create_global_var(
shape=[1],
value=0.0,
dtype='float32',
persistable=True,
name="learning_rate")
with control_flow.Switch() as switch:
for i in range(len(boundaries)):
boundary_val = tensor.fill_constant(
shape=[1],
dtype='float32',
value=float(boundaries[i]),
force_cpu=True)
value_var = tensor.fill_constant(
shape=[1], dtype='float32', value=float(values[i]))
with switch.case(global_step < boundary_val):
tensor.assign(value_var, lr)
last_value_var = tensor.fill_constant(
def _lr_schedule(dtype):
with default_main_program()._lr_schedule_guard():
if len(values) - len(boundaries) != 1:
raise ValueError("len(values) - len(boundaries) should be 1")
global_step = _decay_step_counter()
lr = tensor.create_global_var(
shape=[1],
value=0.0,
dtype='float32',
value=float(values[len(values) - 1]))
with switch.default():
tensor.assign(last_value_var, lr)
persistable=True,
name="learning_rate")
with control_flow.Switch() as switch:
for i in range(len(boundaries)):
boundary_val = tensor.fill_constant(
shape=[1],
dtype='float32',
value=float(boundaries[i]),
force_cpu=True)
value_var = tensor.fill_constant(
shape=[1], dtype='float32', value=float(values[i]))
with switch.case(global_step < boundary_val):
tensor.assign(value_var, lr)
last_value_var = tensor.fill_constant(
shape=[1],
dtype='float32',
value=float(values[len(values) - 1]))
with switch.default():
tensor.assign(last_value_var, lr)
return lr
return lr
return _lr_schedule
def append_LARS(params_grads, learning_rate, weight_decay):
......
python/paddle/fluid/layers/nn.py
浏览文件 @ c1f7e54f
......@@ -29,6 +29,7 @@ from . import utils
from .. import unique_name
from functools import reduce
from .. import core
from ..imperative import layers
__all__ = [
'fc',
......@@ -2797,6 +2798,10 @@ def batch_norm(input,
helper = LayerHelper('batch_norm', **locals())
dtype = helper.input_dtype()
# use fp32 for bn parameter
if dtype == core.VarDesc.VarType.FP16:
dtype = core.VarDesc.VarType.FP32
input_shape = input.shape
if data_layout == 'NCHW':
channel_num = input_shape[1]
......@@ -2831,7 +2836,7 @@ def batch_norm(input,
trainable=False,
do_model_average=do_model_average_for_mean_and_var),
shape=param_shape,
dtype=input.dtype)
dtype=dtype)
mean.stop_gradient = True
variance = helper.create_parameter(
......@@ -2841,7 +2846,7 @@ def batch_norm(input,
trainable=False,
do_model_average=do_model_average_for_mean_and_var),
shape=param_shape,
dtype=input.dtype)
dtype=dtype)
variance.stop_gradient = True
# create output
......@@ -9426,3 +9431,47 @@ def huber_loss(input, label, delta):
'Residual': residual},
attrs={'delta': delta})
return out
class FC(layers.PyLayer):
def __init__(self,
size,
param_attr=None,
num_flatten_dims=1,
dtype=core.VarDesc.VarType.FP32):
super(FC, self).__init__()
self._size = size
self._num_flatten_dims = num_flatten_dims
self._dtype = dtype
self._helper = LayerHelper('FC', param_attr=param_attr)
def _build_once(self, inputs):
input_shape = inputs[0].shape
param_shape = [
reduce(lambda a, b: a * b, input_shape[self._num_flatten_dims:], 1)
] + [self._size]
self._w = self._helper.create_parameter(
attr=self._helper.param_attr,
shape=param_shape,
dtype=self._dtype,
is_bias=False)
def forward(self, inputs):
tmp = self._helper.create_variable_for_type_inference(self._dtype)
self._helper.append_op(
type="mul",
inputs={"X": inputs[0],
"Y": self._w},
outputs={"Out": tmp},
attrs={
"x_num_col_dims": self._num_flatten_dims,
"y_num_col_dims": 1
})
out = self._helper.create_variable_for_type_inference(self._dtype)
self._helper.append_op(
type="sum",
inputs={"X": [tmp]},
outputs={"Out": out},
attrs={"use_mkldnn": False})
return out
python/paddle/fluid/optimizer.py
浏览文件 @ c1f7e54f
......@@ -50,17 +50,21 @@ class Optimizer(object):
def __init__(self, learning_rate, regularization=None, name=None):
if not isinstance(learning_rate, float) and \
not isinstance(learning_rate, framework.Variable):
raise TypeError("learning rate should be float or Variable")
not isinstance(learning_rate, framework.Variable) and \
not callable(learning_rate):
raise TypeError(
"learning rate should be float or Variable or callable(dtype)")
self._name = name
self.regularization = regularization
self._learning_rate = learning_rate
# the learning rate type should be inferenced from loss
self._dtype = None
# each program should have a independent learning rate
# program -> Variable(learning_rate)
# program -> Variable(learning_rate) or:
# program -> callable(return learning_rate Variable)
self._learning_rate_map = dict()
if isinstance(self._learning_rate, framework.Variable):
if isinstance(self._learning_rate, framework.Variable) or \
callable(self._learning_rate):
self._learning_rate_map[framework.default_main_program(
)] = self._learning_rate
# Dictionary of accumulators. Some optimizer subclasses need to
......@@ -75,6 +79,11 @@ class Optimizer(object):
if isinstance(lr, framework.Variable):
return
elif callable(lr):
dtype = 'float32' if self._dtype is None else self._dtype
self._learning_rate_map[framework.default_main_program()] = lr(
dtype)
return
else:
if not isinstance(self._learning_rate, float):
raise TypeError(
......
python/paddle/fluid/parallel_executor.py
浏览文件 @ c1f7e54f
......@@ -92,35 +92,27 @@ class ParallelExecutor(object):
num_trainers=1,
trainer_id=0,
scope=None):
# step1: get places, the places are used in run too.
self._places = []
self._act_places = []
if use_cuda:
gpus = []
gpus_env = os.getenv("FLAGS_selected_gpus")
if gpus_env:
gpus = [int(s) for s in gpus_env.split(",")]
else:
for i in six.moves.range(core.get_cuda_device_count()):
gpus.append(i)
for i in gpus:
p = core.Place()
self._act_places.append(core.CUDAPlace(i))
p.set_place(self._act_places[-1])
self._places.append(p)
gpus = [
i for i in six.moves.range(core.get_cuda_device_count())
]
self._places = [core.CUDAPlace(i) for i in gpus]
else:
cpu_num = int(
os.environ.get('CPU_NUM', multiprocessing.cpu_count()))
for i in six.moves.range(cpu_num):
p = core.Place()
self._act_places.append(core.CPUPlace())
p.set_place(self._act_places[-1])
self._places.append(p)
self._places = [core.CPUPlace() for _ in six.moves.range(cpu_num)]
assert self._places, "no place for execution"
# step2: init exec_strategy
if exec_strategy is None:
exec_strategy = ExecutionStrategy()
exec_strategy.use_cuda = use_cuda
if exec_strategy.num_threads == 0:
if use_cuda:
# Experiments on se-resnext shows that too many threads hurt
......@@ -131,49 +123,54 @@ class ParallelExecutor(object):
os.environ.get('CPU_NUM', multiprocessing.cpu_count()))
exec_strategy.num_threads = cpu_num * 2
# step3: init build_strategy
if build_strategy is None:
build_strategy = BuildStrategy()
build_strategy.num_trainers = num_trainers
build_strategy.trainer_id = trainer_id
main = main_program
main = main if main else framework.default_main_program()
# step4: get main_program, scope, local_scopes
main = main_program if main_program \
else framework.default_main_program()
scope = scope if scope is not None else executor.global_scope()
if share_vars_from and not isinstance(share_vars_from,
ParallelExecutor):
raise TypeError("share_vars_from must be ParallelExecutor.")
local_scopes = share_vars_from.executor.local_scopes()\
if share_vars_from else []
# step5: check trainers_endpoints, it is used for distribution.
trainers_endpoints = main._trainers_endpoints
if num_trainers > 1 and trainers_endpoints:
assert num_trainers == len(
trainers_endpoints), "num_trainers == len(end_points)"
build_strategy.trainers_endpoints = trainers_endpoints
if scope == None:
scope = executor.global_scope()
if share_vars_from and not isinstance(share_vars_from,
ParallelExecutor):
raise TypeError("share_vars_from must be ParallelExecutor.")
local_scopes = share_vars_from.executor.local_scopes(
) if share_vars_from else []
self.persistable_vars = [
v.name for v in [
# step5: get persistable_vars, parameter_vars, places. persistable_vars
# need be broadcast to other local_scope.
persistable_vars = set([
cpt.to_text(v.name) for v in [
var for var in main.list_vars()
if var.persistable and var.type != core.VarDesc.VarType.RAW
]
]
])
def place_obj(place):
p = core.Place()
p.set_place(place)
return p
places = list(map(place_obj, self._places))
# step6: init ParallelExecutor
self.executor = core.ParallelExecutor(
self._places,
set([
cpt.to_text(p.name)
for p in main.global_block().iter_parameters()
if not p.stop_gradient
]),
set(cpt.to_text(var) for var in self.persistable_vars), main.desc,
places, persistable_vars, main.desc,
cpt.to_text(loss_name)
if loss_name else six.u(''), scope, local_scopes, exec_strategy,
build_strategy, num_trainers, trainer_id)
self.scope = scope
def run(self, fetch_list, feed=None, feed_dict=None, return_numpy=True):
......@@ -261,7 +258,7 @@ class ParallelExecutor(object):
self.executor.feed_and_split_tensor_into_local_scopes(
feed_tensor_dict)
elif isinstance(feed, list) or isinstance(feed, tuple):
if len(feed) != len(self._act_places):
if len(feed) != len(self._places):
raise ValueError(
"Feed a list of tensor, the list should be the same size as places"
)
......@@ -277,7 +274,7 @@ class ParallelExecutor(object):
tensor = each[feed_name]
if not isinstance(tensor, core.LoDTensor):
tmp = core.LoDTensor()
tmp.set(tensor, self._act_places[i])
tmp.set(tensor, self._places[i])
tensor = tmp
res_dict[feed_name] = tensor
res.append(res_dict)
......@@ -294,4 +291,4 @@ class ParallelExecutor(object):
@property
def device_count(self):
return len(self._act_places)
return len(self._places)
python/paddle/fluid/tests/unittests/op_test.py
浏览文件 @ c1f7e54f
......@@ -368,6 +368,8 @@ class OpTest(unittest.TestCase):
place = core.CUDAPlace(0)
if core.is_float16_supported(place):
return [place]
else:
return []
else:
return []
places = [fluid.CPUPlace()]
......
python/paddle/fluid/tests/unittests/test_accuracy_op.py
浏览文件 @ c1f7e54f
......@@ -22,8 +22,10 @@ from op_test import OpTest
class TestAccuracyOp(OpTest):
def setUp(self):
self.op_type = "accuracy"
self.dtype = np.float32
self.init_dtype()
n = 8192
infer = np.random.random((n, 1)).astype("float32")
infer = np.random.random((n, 1)).astype(self.dtype)
indices = np.random.randint(0, 2, (n, 1))
label = np.random.randint(0, 2, (n, 1))
self.inputs = {'Out': infer, 'Indices': indices, "Label": label}
......@@ -34,14 +36,25 @@ class TestAccuracyOp(OpTest):
num_correct += 1
break
self.outputs = {
'Accuracy': np.array([num_correct / float(n)]).astype("float32"),
'Accuracy': np.array([num_correct / float(n)]).astype(self.dtype),
'Correct': np.array([num_correct]).astype("int32"),
'Total': np.array([n]).astype("int32")
}
def init_dtype(self):
pass
def test_check_output(self):
self.check_output()
class TestAccuracyOpFp16(TestAccuracyOp):
def init_dtype(self):
self.dtype = np.float16
def test_check_output(self):
self.check_output(atol=1e-3)
if __name__ == '__main__':
unittest.main()
python/paddle/fluid/tests/unittests/test_conv2d_mkldnn_op.py
浏览文件 @ c1f7e54f
......@@ -16,7 +16,7 @@ from __future__ import print_function
import unittest
from test_conv2d_op import TestConv2dOp, TestWithPad, TestWithStride
from test_conv2d_op import TestConv2dOp, TestWithPad, TestWithStride, TestWithGroup, TestWith1x1, TestWithInput1x1Filter1x1
class TestMKLDNN(TestConv2dOp):
......@@ -37,5 +37,23 @@ class TestMKLDNNWithStride(TestWithStride):
self.data_format = "NCHW"
class TestMKLDNNWithGroup(TestWithGroup):
def init_kernel_type(self):
self.use_mkldnn = True
self.data_format = "NCHW"
class TestMKLDNNWith1x1(TestWith1x1):
def init_kernel_type(self):
self.use_mkldnn = True
self.data_format = "NCHW"
class TestMKLDNNWithInput1x1Filter1x1(TestWithInput1x1Filter1x1):
def init_kernel_type(self):
self.use_mkldnn = True
self.data_format = "NCHW"
if __name__ == '__main__':
unittest.main()
python/paddle/fluid/tests/unittests/test_elementwise_div_op.py
浏览文件 @ c1f7e54f
......@@ -21,14 +21,16 @@ from op_test import OpTest
class ElementwiseDivOp(OpTest):
def setUp(self):
self.op_type = "elementwise_div"
self.dtype = np.float32
self.init_dtype()
""" Warning
CPU gradient check error!
'X': np.random.random((32,84)).astype("float32"),
'Y': np.random.random((32,84)).astype("float32")
"""
self.inputs = {
'X': np.random.uniform(0.1, 1, [13, 17]).astype("float32"),
'Y': np.random.uniform(0.1, 1, [13, 17]).astype("float32")
'X': np.random.uniform(0.1, 1, [13, 17]).astype(self.dtype),
'Y': np.random.uniform(0.1, 1, [13, 17]).astype(self.dtype)
}
self.outputs = {'Out': np.divide(self.inputs['X'], self.inputs['Y'])}
......@@ -46,6 +48,9 @@ class ElementwiseDivOp(OpTest):
self.check_grad(
['X'], 'Out', max_relative_error=0.05, no_grad_set=set('Y'))
def init_dtype(self):
pass
class TestElementwiseDivOp_scalar(ElementwiseDivOp):
def setUp(self):
......@@ -126,5 +131,21 @@ class TestElementwiseDivOp_broadcast_3(ElementwiseDivOp):
}
class TestElementwiseDivOpFp16(ElementwiseDivOp):
def init_dtype(self):
self.dtype = np.float16
def test_check_grad_normal(self):
self.check_grad(['X', 'Y'], 'Out', max_relative_error=1)
def test_check_grad_ingore_x(self):
self.check_grad(
['Y'], 'Out', max_relative_error=1, no_grad_set=set("X"))
def test_check_grad_ingore_y(self):
self.check_grad(
['X'], 'Out', max_relative_error=1, no_grad_set=set('Y'))
if __name__ == '__main__':
unittest.main()
python/paddle/fluid/tests/unittests/test_elementwise_mul_op.py
浏览文件 @ c1f7e54f
......@@ -135,5 +135,10 @@ class TestElementwiseMulOp_broadcast_3(ElementwiseMulOp):
}
class TestElementwiseMulOpFp16(ElementwiseMulOp):
def init_dtype(self):
self.dtype = np.float16
if __name__ == '__main__':
unittest.main()
python/paddle/fluid/tests/unittests/test_fill_zeros_like_op.py
浏览文件 @ c1f7e54f
......@@ -22,12 +22,22 @@ from op_test import OpTest
class TestFillZerosLikeOp(OpTest):
def setUp(self):
self.op_type = "fill_zeros_like"
self.inputs = {'X': np.random.random((219, 232)).astype("float32")}
self.dtype = np.float32
self.init_dtype()
self.inputs = {'X': np.random.random((219, 232)).astype(self.dtype)}
self.outputs = {'Out': np.zeros_like(self.inputs["X"])}
def init_dtype(self):
pass
def test_check_output(self):
self.check_output()
class TestFillZerosLikeOpFp16(TestFillZerosLikeOp):
def init_dtype(self):
self.dtype = np.float16
if __name__ == "__main__":
unittest.main()
python/paddle/fluid/tests/unittests/test_imperative.py
浏览文件 @ c1f7e54f
......@@ -12,12 +12,23 @@
# See the License for the specific language governing permissions and
# limitations under the License.
import contextlib
import unittest
import sys
import numpy as np
import paddle.fluid as fluid
from paddle.fluid import core
from paddle.fluid.layers.nn import FC
@contextlib.contextmanager
def new_program_scope():
prog = fluid.Program()
startup_prog = fluid.Program()
scope = fluid.core.Scope()
with fluid.scope_guard(scope):
with fluid.program_guard(prog, startup_prog):
yield
class MyLayer(fluid.imperative.PyLayer):
......@@ -30,6 +41,23 @@ class MyLayer(fluid.imperative.PyLayer):
return [fluid.layers.elementwise_mul(x, x)]
class MLP(fluid.imperative.PyLayer):
def __init__(self):
super(MLP, self).__init__()
self._fc1 = FC(3,
fluid.ParamAttr(
initializer=fluid.initializer.Constant(value=0.1)))
self._fc2 = FC(4,
fluid.ParamAttr(
initializer=fluid.initializer.Constant(value=0.1)))
def forward(self, inputs):
x = self._fc1(inputs[0])
x = self._fc2(x)
x = fluid.layers.reduce_sum(x)
return x
class TestImperative(unittest.TestCase):
def test_layer(self):
with fluid.imperative.guard():
......@@ -39,13 +67,56 @@ class TestImperative(unittest.TestCase):
l.forward([])
def test_layer_in_out(self):
np_inp = np.array([1.0, 2.0, -1.0], dtype=np.float32)
with fluid.imperative.guard():
l = MyLayer()
x = l(np.array([1.0, 2.0, -1.0], dtype=np.float32))[0]
x = l(np_inp)[0]
self.assertIsNotNone(x)
sys.stderr.write("%s output: %s\n" % (x, x._numpy()))
dy_out = x._numpy()
x._backward()
sys.stderr.write("grad %s\n" % l._x_for_debug._gradient())
dy_grad = l._x_for_debug._gradient()
with new_program_scope():
inp = fluid.layers.data(
name="inp", shape=[3], append_batch_size=False)
l = MyLayer()
x = l(inp)[0]
param_grads = fluid.backward.append_backward(
x, parameter_list=[l._x_for_debug.name])[0]
exe = fluid.Executor(fluid.CPUPlace())
static_out, static_grad = exe.run(
feed={inp.name: np_inp},
fetch_list=[x.name, param_grads[1].name])
self.assertTrue(np.allclose(dy_out, static_out))
self.assertTrue(np.allclose(dy_grad, static_grad))
def test_mlp(self):
np_inp = np.array([[1.0, 2.0], [3.0, 4.0]], dtype=np.float32)
with fluid.imperative.guard():
mlp = MLP()
out = mlp(np_inp)
dy_out = out._numpy()
out._backward()
dy_grad = mlp._fc1._w._gradient()
with new_program_scope():
inp = fluid.layers.data(
name="inp", shape=[2, 2], append_batch_size=False)
mlp = MLP()
out = mlp(inp)
param_grads = fluid.backward.append_backward(
out, parameter_list=[mlp._fc1._w.name])[0]
exe = fluid.Executor(fluid.CPUPlace())
exe.run(fluid.default_startup_program())
static_out, static_grad = exe.run(
feed={inp.name: np_inp},
fetch_list=[out.name, param_grads[1].name])
self.assertTrue(np.allclose(dy_out, static_out))
self.assertTrue(np.allclose(dy_grad, static_grad))
if __name__ == '__main__':
......
python/paddle/fluid/tests/unittests/test_learning_rate_scheduler.py
浏览文件 @ c1f7e54f
......@@ -97,7 +97,7 @@ class TestLearningRateDecay(unittest.TestCase):
startup_prog = fluid.Program()
with fluid.program_guard(main_prog, startup_prog):
decayed_lr = fluid_decay_fn(**kwargs)
decayed_lr = fluid_decay_fn(**kwargs)("float32")
place = fluid.CPUPlace()
exe = fluid.Executor(place)
......
python/paddle/fluid/tests/unittests/test_momentum_op.py
浏览文件 @ c1f7e54f
......@@ -24,11 +24,13 @@ from op_test import OpTest
class TestMomentumOp1(OpTest):
def setUp(self):
self.op_type = "momentum"
self.dtype = np.float32
self.init_dtype()
param = np.random.random((123, 321)).astype("float32")
grad = np.random.random((123, 321)).astype("float32")
velocity = np.zeros((123, 321)).astype("float32")
learning_rate = np.array([0.001]).astype("float32")
param = np.random.random((123, 321)).astype(self.dtype)
grad = np.random.random((123, 321)).astype(self.dtype)
velocity = np.zeros((123, 321)).astype(self.dtype)
learning_rate = np.array([0.001]).astype(self.dtype)
mu = 0.0001
use_nesterov = False
......@@ -50,10 +52,21 @@ class TestMomentumOp1(OpTest):
self.outputs = {'ParamOut': param_out, 'VelocityOut': velocity_out}
def init_dtype(self):
pass
def test_check_output(self):
self.check_output()
class TestMomentumOpFp16(TestMomentumOp1):
def init_dtype(self):
self.dtype = np.float16
def test_check_output(self):
self.check_output(atol=1e-3)
class TestMomentumOp2(OpTest):
'''Test Momentum with default values for attributes
'''
......
python/paddle/fluid/tests/unittests/test_top_k_op.py
浏览文件 @ c1f7e54f
......@@ -23,8 +23,11 @@ class TestTopkOp(OpTest):
def setUp(self):
self.set_args()
self.op_type = "top_k"
self.dtype = np.float32
self.init_dtype()
k = self.top_k
input = np.random.random((self.row, k)).astype("float32")
input = np.random.random((self.row, k)).astype(self.dtype)
output = np.ndarray((self.row, k))
indices = np.ndarray((self.row, k)).astype("int64")
......@@ -38,6 +41,9 @@ class TestTopkOp(OpTest):
self.outputs = {'Out': output, 'Indices': indices}
def init_dtype(self):
pass
def set_args(self):
self.row = 32
self.top_k = 1
......@@ -46,6 +52,11 @@ class TestTopkOp(OpTest):
self.check_output()
class TestTopkOpFp16(TestTopkOp):
def init_dtype(self):
self.dtype = np.float16
class TestTopkOp3d(OpTest):
def setUp(self):
self.op_type = "top_k"
......
python/setup.py.in
浏览文件 @ c1f7e54f
......@@ -107,9 +107,9 @@ packages=['paddle',
'paddle.fluid.distributed',
'paddle.fluid.layers',
'paddle.fluid.contrib',
'paddle.fluid.contrib.utils',
'paddle.fluid.contrib.decoder',
'paddle.fluid.contrib.quantize',
'paddle.fluid.contrib.utils',
'paddle.fluid.transpiler',
'paddle.fluid.transpiler.details']
......
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