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PaddleDetection

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PaddleDetection
提交 54f9d44e 编写于 作者: L liuwei1031
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Merge remote-tracking branch 'upstream/develop' into develop

上级 a4dc3d2b 62d36ce0
隐藏空白更改
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Showing with 1593 addition and 214 deletion
cmake/generic.cmake
浏览文件 @ 54f9d44e
......@@ -748,7 +748,7 @@ function(grpc_library TARGET_NAME)
#FIXME(putcn): the follwoing line is supposed to generate *.pb.h and cc, but
# somehow it didn't. line 602 to 604 is to patching this. Leaving this here
# for now to enable dist CI.
protobuf_generate_cpp(grpc_proto_srcs grpc_proto_hdrs "${ABS_PROTO}")
paddle_protobuf_generate_cpp(grpc_proto_srcs grpc_proto_hdrs "${ABS_PROTO}")
set(grpc_grpc_srcs "${CMAKE_CURRENT_BINARY_DIR}/${PROTO_WE}.grpc.pb.cc")
set(grpc_grpc_hdrs "${CMAKE_CURRENT_BINARY_DIR}/${PROTO_WE}.grpc.pb.h")
cc_library("${TARGET_NAME}_proto" SRCS "${grpc_proto_srcs}")
......@@ -791,7 +791,7 @@ function(brpc_library TARGET_NAME)
get_filename_component(PROTO_WE ${brpc_library_PROTO} NAME_WE)
get_filename_component(PROTO_PATH ${ABS_PROTO} PATH)
protobuf_generate_cpp(brpc_proto_srcs brpc_proto_hdrs "${ABS_PROTO}")
paddle_protobuf_generate_cpp(brpc_proto_srcs brpc_proto_hdrs "${ABS_PROTO}")
cc_library("${TARGET_NAME}_proto" SRCS "${brpc_proto_srcs}")
cc_library("${TARGET_NAME}" SRCS "${brpc_library_SRCS}" DEPS "${TARGET_NAME}_proto" "${brpc_library_DEPS}")
endfunction()
paddle/fluid/API.spec
浏览文件 @ 54f9d44e
......@@ -70,8 +70,8 @@ paddle.fluid.layers.fc ArgSpec(args=['input', 'size', 'num_flatten_dims', 'param
paddle.fluid.layers.embedding ArgSpec(args=['input', 'size', 'is_sparse', 'is_distributed', 'padding_idx', 'param_attr', 'dtype'], varargs=None, keywords=None, defaults=(False, False, None, None, 'float32'))
paddle.fluid.layers.dynamic_lstm ArgSpec(args=['input', 'size', 'h_0', 'c_0', 'param_attr', 'bias_attr', 'use_peepholes', 'is_reverse', 'gate_activation', 'cell_activation', 'candidate_activation', 'dtype', 'name'], varargs=None, keywords=None, defaults=(None, None, None, None, True, False, 'sigmoid', 'tanh', 'tanh', 'float32', None))
paddle.fluid.layers.dynamic_lstmp ArgSpec(args=['input', 'size', 'proj_size', 'param_attr', 'bias_attr', 'use_peepholes', 'is_reverse', 'gate_activation', 'cell_activation', 'candidate_activation', 'proj_activation', 'dtype', 'name'], varargs=None, keywords=None, defaults=(None, None, True, False, 'sigmoid', 'tanh', 'tanh', 'tanh', 'float32', None))
paddle.fluid.layers.dynamic_gru ArgSpec(args=['input', 'size', 'param_attr', 'bias_attr', 'is_reverse', 'gate_activation', 'candidate_activation', 'h_0'], varargs=None, keywords=None, defaults=(None, None, False, 'sigmoid', 'tanh', None))
paddle.fluid.layers.gru_unit ArgSpec(args=['input', 'hidden', 'size', 'param_attr', 'bias_attr', 'activation', 'gate_activation'], varargs=None, keywords=None, defaults=(None, None, 'tanh', 'sigmoid'))
paddle.fluid.layers.dynamic_gru ArgSpec(args=['input', 'size', 'param_attr', 'bias_attr', 'is_reverse', 'gate_activation', 'candidate_activation', 'h_0', 'origin_mode'], varargs=None, keywords=None, defaults=(None, None, False, 'sigmoid', 'tanh', None, False))
paddle.fluid.layers.gru_unit ArgSpec(args=['input', 'hidden', 'size', 'param_attr', 'bias_attr', 'activation', 'gate_activation', 'origin_mode'], varargs=None, keywords=None, defaults=(None, None, 'tanh', 'sigmoid', False))
paddle.fluid.layers.linear_chain_crf ArgSpec(args=['input', 'label', 'param_attr'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.crf_decoding ArgSpec(args=['input', 'param_attr', 'label'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.cos_sim ArgSpec(args=['X', 'Y'], varargs=None, keywords=None, defaults=None)
......@@ -215,6 +215,7 @@ paddle.fluid.layers.py_func ArgSpec(args=['func', 'x', 'out', 'backward_func', '
paddle.fluid.layers.psroi_pool ArgSpec(args=['input', 'rois', 'output_channels', 'spatial_scale', 'pooled_height', 'pooled_width', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.teacher_student_sigmoid_loss ArgSpec(args=['input', 'label', 'soft_max_up_bound', 'soft_max_lower_bound'], varargs=None, keywords=None, defaults=(15.0, -15.0))
paddle.fluid.layers.huber_loss ArgSpec(args=['input', 'label', 'delta'], varargs=None, keywords=None, defaults=None)
paddle.fluid.layers.tree_conv ArgSpec(args=['nodes_vector', 'edge_set', 'output_size', 'num_filters', 'max_depth', 'act', 'param_attr', 'bias_attr', 'name'], varargs=None, keywords=None, defaults=(1, 2, 'tanh', None, None, None))
paddle.fluid.layers.data ArgSpec(args=['name', 'shape', 'append_batch_size', 'dtype', 'lod_level', 'type', 'stop_gradient'], varargs=None, keywords=None, defaults=(True, 'float32', 0, VarType.LOD_TENSOR, True))
paddle.fluid.layers.open_files ArgSpec(args=['filenames', 'shapes', 'lod_levels', 'dtypes', 'thread_num', 'buffer_size', 'pass_num', 'is_test'], varargs=None, keywords=None, defaults=(None, None, 1, None))
paddle.fluid.layers.read_file ArgSpec(args=['reader'], varargs=None, keywords=None, defaults=None)
......
paddle/fluid/framework/details/all_reduce_deps_pass.cc
浏览文件 @ 54f9d44e
......@@ -82,13 +82,13 @@ std::unique_ptr<ir::Graph> AllReduceDepsPass::ApplyImpl(
PADDLE_ENFORCE(i0 != nullptr && i1 != nullptr, "%s convert to %s error",
op1->DebugString(), op2->DebugString());
auto l_it = vars.find(i0->name_);
auto r_it = vars.find(i1->name_);
auto l_it = vars.find(i0->name());
auto r_it = vars.find(i1->name());
if (l_it->second < r_it->second) return true;
if (l_it->second == r_it->second) {
return i0->name_ < i1->name_;
return i0->name() < i1->name();
}
return false;
......
paddle/fluid/framework/details/all_reduce_op_handle.cc
浏览文件 @ 54f9d44e
......@@ -70,9 +70,9 @@ void AllReduceOpHandle::RunImpl() {
auto *s = local_scopes_[i];
auto &local_scope = *s->FindVar(kLocalExecScopeName)->Get<Scope *>();
auto &lod_tensor =
local_scope.FindVar(in_var_handles[i]->name_)->Get<LoDTensor>();
local_scope.FindVar(in_var_handles[i]->name())->Get<LoDTensor>();
lod_tensors.emplace_back(&lod_tensor);
PADDLE_ENFORCE_EQ(in_var_handles[i]->name_, out_var_handles[i]->name_,
PADDLE_ENFORCE_EQ(in_var_handles[i]->name(), out_var_handles[i]->name(),
"The name of input and output should be equal.");
}
......@@ -134,7 +134,7 @@ void AllReduceOpHandle::RunImpl() {
auto &trg = *this->local_scopes_[0]
->FindVar(kLocalExecScopeName)
->Get<Scope *>()
->FindVar(out_var_handles[0]->name_)
->FindVar(out_var_handles[0]->name())
->GetMutable<framework::LoDTensor>();
// Reduce All Tensor to trg in CPU
......@@ -145,7 +145,7 @@ void AllReduceOpHandle::RunImpl() {
auto &scope =
*local_scopes_[i]->FindVar(kLocalExecScopeName)->Get<Scope *>();
auto &p = places_[i];
auto *var = scope.FindVar(out_var_handles[i]->name_);
auto *var = scope.FindVar(out_var_handles[i]->name());
auto *dev_ctx = dev_ctxes_.at(p);
RunAndRecordEvent(p, [&trg, var, dev_ctx, p] {
......
paddle/fluid/framework/details/broadcast_op_handle.cc
浏览文件 @ 54f9d44e
......@@ -56,11 +56,11 @@ void BroadcastOpHandle::BroadcastOneVar(
const std::vector<VarHandle *> &out_var_handles,
const std::vector<const Scope *> &var_scopes) {
auto *in_var =
var_scopes.at(in_var_handle.scope_idx_)->FindVar(in_var_handle.name_);
var_scopes.at(in_var_handle.scope_idx())->FindVar(in_var_handle.name());
PADDLE_ENFORCE_NOT_NULL(in_var);
Tensor &in_tensor = VariableVisitor::GetMutableTensor(in_var);
if (UNLIKELY(!in_tensor.IsInitialized())) {
VLOG(3) << "in var " << in_var_handle.name_ << "not inited, return!";
VLOG(3) << "in var " << in_var_handle.name() << "not inited, return!";
return;
}
......@@ -71,9 +71,9 @@ void BroadcastOpHandle::BroadcastOneVar(
if (out_var_handle->IsTheSameVar(in_var_handle)) {
continue;
}
auto &out_p = out_var_handle->place_;
auto *out_var = var_scopes.at(out_var_handle->scope_idx_)
->FindVar(out_var_handle->name_);
auto &out_p = out_var_handle->place();
auto *out_var = var_scopes.at(out_var_handle->scope_idx())
->FindVar(out_var_handle->name());
RunAndRecordEvent(out_p, [in_tensor, out_var] {
paddle::framework::TensorCopy(
......@@ -91,11 +91,11 @@ void BroadcastOpHandle::BroadcastOneVar(
size_t numel = static_cast<size_t>(in_tensor.numel());
for (auto out_var_handle : out_var_handles) {
Variable *out_var = var_scopes.at(out_var_handle->scope_idx_)
->FindVar(out_var_handle->name_);
Variable *out_var = var_scopes.at(out_var_handle->scope_idx())
->FindVar(out_var_handle->name());
int dst_id =
boost::get<platform::CUDAPlace>(out_var_handle->place_).device;
boost::get<platform::CUDAPlace>(out_var_handle->place()).device;
auto &nccl_ctx = nccl_ctxs_->at(dst_id);
......@@ -106,7 +106,7 @@ void BroadcastOpHandle::BroadcastOneVar(
} else {
send_recv_buffer = VariableVisitor::GetMutableTensor(out_var)
.Resize(in_tensor.dims())
.mutable_data(out_var_handle->place_);
.mutable_data(out_var_handle->place());
}
broadcast_calls.emplace_back(
......@@ -126,11 +126,11 @@ void BroadcastOpHandle::BroadcastOneVar(
}
if (!out_handle->IsTheSameVar(in_var_handle)) {
auto out_var = var_scopes.at(in_var_handle.scope_idx_)
->FindVar(out_var_handles[0]->name_);
auto out_var = var_scopes.at(in_var_handle.scope_idx())
->FindVar(out_var_handles[0]->name());
paddle::framework::TensorCopy(
in_tensor, in_var_handle.place_,
*(dev_ctxes_.at(in_var_handle.place_)),
in_tensor, in_var_handle.place(),
*(dev_ctxes_.at(in_var_handle.place())),
&VariableVisitor::GetMutableTensor(out_var));
}
});
......@@ -148,7 +148,7 @@ void BroadcastOpHandle::InitOutputValue(
var_scopes.emplace_back(s->FindVar(kLocalExecScopeName)->Get<Scope *>());
}
auto *in_var =
var_scopes.at(in_var_handle.scope_idx_)->FindVar(in_var_handle.name_);
var_scopes.at(in_var_handle.scope_idx())->FindVar(in_var_handle.name());
Tensor &in_tensor = VariableVisitor::GetMutableTensor(in_var);
......@@ -158,9 +158,9 @@ void BroadcastOpHandle::InitOutputValue(
if (out_var_handle->IsTheSameVar(in_var_handle)) {
continue;
}
auto t_out_p = out_var_handle->place_;
auto *out_var = var_scopes.at(out_var_handle->scope_idx_)
->FindVar(out_var_handle->name_);
auto t_out_p = out_var_handle->place();
auto *out_var = var_scopes.at(out_var_handle->scope_idx())
->FindVar(out_var_handle->name());
PADDLE_ENFORCE_NOT_NULL(out_var);
if (is_gpu_place(in_tensor.place())) {
PADDLE_ENFORCE(platform::is_gpu_place(t_out_p),
......
paddle/fluid/framework/details/data_balance_op_handle.cc
浏览文件 @ 54f9d44e
......@@ -100,13 +100,13 @@ void DataBalanceOpHandle::RunImpl() {
std::vector<std::vector<LoDTensor *>> lod_tensors(data_num);
std::vector<int> device_sizes;
for (int i = 0; i < static_cast<int>(in_var_handles.size()); ++i) {
PADDLE_ENFORCE_EQ(in_var_handles[i]->name_, out_var_handles[i]->name_,
PADDLE_ENFORCE_EQ(in_var_handles[i]->name(), out_var_handles[i]->name(),
"The name of input and output should be equal.");
int place_idx = i / data_num;
int data_idx = i % data_num;
auto *local_scope =
local_scopes_[place_idx]->FindVar(kLocalExecScopeName)->Get<Scope *>();
auto *tensor_var = local_scope->FindVar(in_var_handles[i]->name_);
auto *tensor_var = local_scope->FindVar(in_var_handles[i]->name());
PADDLE_ENFORCE(tensor_var->IsType<LoDTensor>());
auto *tensor = tensor_var->GetMutable<LoDTensor>();
lod_tensors[data_idx].push_back(tensor);
......
paddle/fluid/framework/details/fetch_op_handle.cc
浏览文件 @ 54f9d44e
......@@ -52,12 +52,12 @@ void FetchOpHandle::RunImpl() {
for (size_t i = 0; i < inputs_.size(); ++i) {
auto *var_handle = static_cast<VarHandle *>(inputs_[i]);
auto &scope = scopes.at(var_handle->scope_idx_);
auto &scope = scopes.at(var_handle->scope_idx());
auto *var = scope->FindVar(kLocalExecScopeName)
->Get<Scope *>()
->FindVar(var_handle->name_);
->FindVar(var_handle->name());
PADDLE_ENFORCE_NOT_NULL(var, "Cannot find variable %s in execution scope",
var_handle->name_);
var_handle->name());
auto &t = var->Get<framework::LoDTensor>();
if (platform::is_gpu_place(t.place())) {
......
paddle/fluid/framework/details/fuse_vars_op_handle.cc
浏览文件 @ 54f9d44e
......@@ -29,14 +29,14 @@ void FuseVarsOpHandle::RunImpl() {
auto scope = local_scope_->FindVar(kLocalExecScopeName)->Get<Scope *>();
auto out_var_handle = out_var_handles[0];
auto out_var = scope->Var(out_var_handle->name_);
auto out_var = scope->Var(out_var_handle->name());
auto out_tensor = out_var->GetMutable<LoDTensor>();
out_tensor->Resize({total_numel_}).mutable_data(this->place_, type_);
int64_t s = 0;
for (size_t i = 1; i < out_var_handles.size(); ++i) {
auto out_name = out_var_handles[i]->name_;
auto out_name = out_var_handles[i]->name();
auto out_t = scope->Var(out_name)->GetMutable<LoDTensor>();
auto numel = this->inputs_numel_.at(out_name);
out_t->ShareDataWith(out_tensor->Slice(s, s + numel));
......
paddle/fluid/framework/details/gather_op_handle.cc
浏览文件 @ 54f9d44e
......@@ -49,7 +49,7 @@ void GatherOpHandle::RunImpl() {
auto in_0_handle = in_var_handles[0];
auto pre_in_var =
var_scopes.at(in_0_handle->scope_idx_)->FindVar(in_0_handle->name_);
var_scopes.at(in_0_handle->scope_idx())->FindVar(in_0_handle->name());
PADDLE_ENFORCE_NOT_NULL(pre_in_var);
PADDLE_ENFORCE(pre_in_var->IsType<framework::SelectedRows>(),
......@@ -65,7 +65,7 @@ void GatherOpHandle::RunImpl() {
// Gather the inputs
for (auto *in_handle : in_var_handles) {
auto *in_var =
var_scopes.at(in_handle->scope_idx_)->FindVar(in_handle->name_);
var_scopes.at(in_handle->scope_idx())->FindVar(in_handle->name());
PADDLE_ENFORCE_NOT_NULL(in_var);
VariableVisitor::EnforceShapeAndDTypeEQ(*in_var, *pre_in_var);
......@@ -77,7 +77,7 @@ void GatherOpHandle::RunImpl() {
}
// NOTE: The Places of all input tensor must be all on CPU or all on GPU.
platform::Place t_out_p = out_var_handle->place_;
platform::Place t_out_p = out_var_handle->place();
if (platform::is_gpu_place(pre_in_value.place())) {
PADDLE_ENFORCE(platform::is_gpu_place(t_out_p),
"Places of input and output must be all on GPU.");
......@@ -85,8 +85,8 @@ void GatherOpHandle::RunImpl() {
t_out_p = platform::CPUPlace();
}
auto out_var =
var_scopes.at(out_var_handle->scope_idx_)->FindVar(out_var_handle->name_);
auto out_var = var_scopes.at(out_var_handle->scope_idx())
->FindVar(out_var_handle->name());
PADDLE_ENFORCE_NOT_NULL(out_var);
auto out_value = out_var->GetMutable<framework::SelectedRows>();
out_value->set_height(pre_in_value.height());
......@@ -99,9 +99,9 @@ void GatherOpHandle::RunImpl() {
Tensor *out_tensor = out_value->mutable_value();
// copy
auto dev_ctx = dev_ctxes_.at(out_var_handle->place_);
RunAndRecordEvent(out_var_handle->place_, [in_tensors, out_tensor, &dev_ctx,
t_out_p] {
auto dev_ctx = dev_ctxes_.at(out_var_handle->place());
RunAndRecordEvent(out_var_handle->place(), [in_tensors, out_tensor, &dev_ctx,
t_out_p] {
int s = 0, e = 0;
for (size_t j = 0; j < in_tensors.size(); ++j) {
e += in_tensors[j].dims()[0];
......
paddle/fluid/framework/details/memory_early_delete_pass.cc
浏览文件 @ 54f9d44e
......@@ -33,7 +33,7 @@ static ComputationOpHandle* FindNextComputationOpHandle(VarHandle* var_in) {
queue.pop();
for (auto* op : var->PendingOps()) {
auto* compute_op = dynamic_cast<ComputationOpHandle*>(op);
if (compute_op != nullptr && compute_op->GetPlace() == var_in->place_) {
if (compute_op != nullptr && compute_op->GetPlace() == var_in->place()) {
return compute_op;
}
for (auto* out_var : op->Outputs()) {
......@@ -64,7 +64,7 @@ std::unique_ptr<ir::Graph> MemoryEarlyDeletePass::ApplyImpl(
for (auto& var : vars) {
auto* var_handle = dynamic_cast<VarHandle*>(var);
auto var_name = var->Node()->Name();
auto& var_place = var_handle->place_;
auto& var_place = var_handle->place();
if (unlived_vars.count(var_name) == 0) continue;
if (!unlived_vars[var_name].empty()) {
if (compute_op != nullptr &&
......
paddle/fluid/framework/details/multi_devices_graph_print_pass.cc
浏览文件 @ 54f9d44e
......@@ -52,11 +52,11 @@ void GraphvizSSAGraphPrinter::Print(const ir::Graph &graph,
vars[var_ptr] = cur_var_id;
if (var_handle_ptr) {
sout << "var_" << cur_var_id << " [label=\"" << var_handle_ptr->name_
sout << "var_" << cur_var_id << " [label=\"" << var_handle_ptr->name()
<< "\\n"
<< var_handle_ptr->place_ << "\\n"
<< "scope: " << var_handle_ptr->scope_idx_ << "\\n"
<< "v" << var_handle_ptr->version_ << "\"]" << std::endl;
<< var_handle_ptr->place() << "\\n"
<< "scope: " << var_handle_ptr->scope_idx() << "\\n"
<< "v" << var_handle_ptr->version() << "\"]" << std::endl;
} else if (dummy_ptr) {
sout << "var_" << cur_var_id << " [label=\"dummy\"]" << std::endl;
}
......
paddle/fluid/framework/details/reduce_op_handle.cc
浏览文件 @ 54f9d44e
......@@ -60,8 +60,8 @@ void ReduceOpHandle::GatherSelectedRows(
*CollectiveContext::GetInstance();
// 1. gather local selected rows, merge them
std::string gathered_var_name = out_var_handle->name_ + "_gathered_tmp";
auto scope = local_scopes_.at(out_var_handle->scope_idx_);
std::string gathered_var_name = out_var_handle->name() + "_gathered_tmp";
auto scope = local_scopes_.at(out_var_handle->scope_idx());
auto gathered_var_mid = scope->Var(gathered_var_name);
auto gathered_select_rows =
gathered_var_mid->GetMutable<framework::SelectedRows>();
......@@ -73,7 +73,7 @@ void ReduceOpHandle::GatherSelectedRows(
// merge them
auto merged_dev_ctx = dynamic_cast<DevCtx *>(dev_ctxes.at(out_place));
std::string merged_var_name =
GetRemoteVarName(out_var_handle->name_, collective_context.trainer_id_);
GetRemoteVarName(out_var_handle->name(), collective_context.trainer_id_);
auto merged_select_rows =
scope->Var(merged_var_name)->GetMutable<SelectedRows>();
operators::math::scatter::MergeAdd<DevCtx, DataType> merge_func;
......@@ -101,7 +101,7 @@ void ReduceOpHandle::GatherSelectedRows(
operators::distributed::RemoteVar var;
var.trainer_id_ = i;
var.var_name_ = GetRemoteVarName(out_var_handle->name_, i);
var.var_name_ = GetRemoteVarName(out_var_handle->name(), i);
var.ep_ = collective_context.endpoints_[i];
vars.push_back(var);
......@@ -166,7 +166,7 @@ void ReduceOpHandle::RunImpl() {
}
auto pre_in_var =
var_scopes.at(in_0_handle->scope_idx_)->FindVar(in_0_handle->name_);
var_scopes.at(in_0_handle->scope_idx())->FindVar(in_0_handle->name());
PADDLE_ENFORCE_NOT_NULL(pre_in_var);
// Wait input done, this Wait is asynchronous operation
......@@ -175,15 +175,15 @@ void ReduceOpHandle::RunImpl() {
// NOTE: The Places of all input tensor must be all on CPU or all on GPU.
std::vector<platform::Place> in_places; // used to get dev_ctx
for (auto *in_handle : in_var_handles) {
in_places.emplace_back(in_handle->place_);
in_places.emplace_back(in_handle->place());
auto in_var =
var_scopes.at(in_handle->scope_idx_)->FindVar(in_handle->name_);
var_scopes.at(in_handle->scope_idx())->FindVar(in_handle->name());
PADDLE_ENFORCE_NOT_NULL(in_var);
VariableVisitor::EnforceShapeAndDTypeEQ(*pre_in_var, *in_var);
}
auto out_var =
var_scopes.at(out_var_handle->scope_idx_)->FindVar(out_var_handle->name_);
auto out_var = var_scopes.at(out_var_handle->scope_idx())
->FindVar(out_var_handle->name());
PADDLE_ENFORCE_NOT_NULL(out_var);
// NOTE: The tensors' Place of input and output must be all on GPU or all on
......@@ -191,9 +191,9 @@ void ReduceOpHandle::RunImpl() {
auto in_p = VariableVisitor::GetMutableTensor(pre_in_var).place();
platform::Place t_out_p;
if (platform::is_gpu_place(in_p)) {
PADDLE_ENFORCE(platform::is_gpu_place(out_var_handle->place_),
PADDLE_ENFORCE(platform::is_gpu_place(out_var_handle->place()),
"Places of input and output must be all on GPU.");
t_out_p = out_var_handle->place_;
t_out_p = out_var_handle->place();
} else {
t_out_p = platform::CPUPlace();
}
......@@ -253,7 +253,7 @@ void ReduceOpHandle::RunImpl() {
auto &reduce_sum_trg = *this->local_scopes_[0]
->FindVar(kLocalExecScopeName)
->Get<Scope *>()
->FindVar(out_var_handle->name_)
->FindVar(out_var_handle->name())
->GetMutable<framework::LoDTensor>();
ReduceLoDTensor func(lod_tensors, &reduce_sum_trg);
VisitDataType(lod_tensors[0]->type(), func);
......@@ -269,9 +269,9 @@ void ReduceOpHandle::RunImpl() {
auto pre_in = pre_in_var->Get<framework::LoDTensor>();
VariableVisitor::ShareDimsAndLoD(*pre_in_var, out_var);
VariableVisitor::GetMutableTensor(out_var).mutable_data(
out_var_handle->place_, pre_in.type());
out_var_handle->place(), pre_in.type());
auto out_p = out_var_handle->place_;
auto out_p = out_var_handle->place();
int root_id = boost::get<platform::CUDAPlace>(out_p).device;
std::vector<std::function<void()>> all_reduce_calls;
for (size_t i = 0; i < var_scopes.size(); ++i) {
......@@ -286,7 +286,7 @@ void ReduceOpHandle::RunImpl() {
if (root_id == dev_id) {
recvbuffer =
out_var->GetMutable<framework::LoDTensor>()->mutable_data(
out_var_handle->place_);
out_var_handle->place());
}
int type = platform::ToNCCLDataType(lod_tensor.type());
......@@ -320,8 +320,8 @@ std::vector<const T *> ReduceOpHandle::GetInputValues(
const std::vector<const Scope *> &var_scopes) const {
std::vector<const T *> in_selected_rows;
for (auto *in_handle : in_var_handles) {
auto &in_sr = var_scopes.at(in_handle->scope_idx_)
->FindVar(in_handle->name_)
auto &in_sr = var_scopes.at(in_handle->scope_idx())
->FindVar(in_handle->name())
->Get<T>();
in_selected_rows.emplace_back(&in_sr);
}
......
paddle/fluid/framework/details/rpc_op_handle.cc
浏览文件 @ 54f9d44e
......@@ -30,7 +30,7 @@ RPCOpHandle::RPCOpHandle(ir::Node *node, const framework::OpDesc &op_desc,
void RPCOpHandle::RunImpl() {
for (auto *in : inputs_) {
auto &p = static_cast<VarHandle *>(in)->place_;
auto &p = static_cast<VarHandle *>(in)->place();
if (ir::IsControlDepVar(*in->Node())) {
continue;
}
......
paddle/fluid/framework/details/scale_loss_grad_op_handle.cc
浏览文件 @ 54f9d44e
......@@ -68,7 +68,7 @@ struct ScaleLossGradFunctor {
void ScaleLossGradOpHandle::RunImpl() {
// Doesn't wait any event
std::string var_name = static_cast<VarHandle *>(this->outputs_[0])->name_;
std::string var_name = static_cast<VarHandle *>(this->outputs_[0])->name();
auto &local_scope = *scope_->FindVar(kLocalExecScopeName)->Get<Scope *>();
auto *tensor = local_scope.FindVar(var_name)->GetMutable<LoDTensor>();
......
paddle/fluid/framework/details/var_handle.h
浏览文件 @ 54f9d44e
......@@ -111,15 +111,22 @@ struct VarHandle : public VarHandleBase {
// version field currently is not used, however, just store the version to
// debug easily.
private:
size_t version_;
size_t scope_idx_;
std::string name_;
platform::Place place_;
public:
bool IsTheSameVar(const VarHandle& o) const {
return o.generated_op_ == generated_op_ && o.name_ == name_ &&
o.scope_idx_ == scope_idx_;
}
size_t version() const { return version_; }
size_t scope_idx() const { return scope_idx_; }
const std::string& name() const { return name_; }
const platform::Place& place() const { return place_; }
};
// Dummy Variable. It is used to represent dependencies between operators
......
paddle/fluid/inference/api/analysis_config.cc
浏览文件 @ 54f9d44e
......@@ -127,6 +127,7 @@ void contrib::AnalysisConfig::EnableTensorRtEngine(int workspace_size,
use_tensorrt_ = true;
tensorrt_workspace_size_ = workspace_size;
tensorrt_max_batchsize_ = max_batch_size;
tensorrt_min_subgraph_size_ = min_subgraph_size;
Update();
}
......@@ -145,8 +146,8 @@ void contrib::AnalysisConfig::Update() {
LOG(ERROR)
<< "TensorRT engine is not available when EnableGpu() not actived.";
} else {
// Append after the infer_clean pass.
pass_builder()->InsertPass(1, "tensorrt_subgraph_pass");
// Append after the Affine_channel_conv_fuse pass.
pass_builder()->InsertPass(3, "tensorrt_subgraph_pass");
}
}
......
paddle/fluid/inference/api/analysis_predictor.cc
浏览文件 @ 54f9d44e
......@@ -561,6 +561,7 @@ AnalysisPredictor::~AnalysisPredictor() {
}
std::unique_ptr<PaddlePredictor> AnalysisPredictor::Clone() {
std::lock_guard<std::mutex> lk(clone_mutex_);
auto *x = new AnalysisPredictor(config_);
x->Init(scope_, inference_program_);
return std::unique_ptr<PaddlePredictor>(x);
......
paddle/fluid/inference/api/analysis_predictor.h
浏览文件 @ 54f9d44e
......@@ -115,6 +115,8 @@ class AnalysisPredictor : public PaddlePredictor {
// concurrency problems, wrong results and memory leak, so cache them.
std::vector<framework::LoDTensor> feed_tensors_;
details::TensorArrayBatchCleaner tensor_array_batch_cleaner_;
// A mutex help to make Clone thread safe.
std::mutex clone_mutex_;
private:
// Some status here that help to determine the status inside the predictor.
......
paddle/fluid/inference/api/analysis_predictor_tester.cc
浏览文件 @ 54f9d44e
......@@ -179,8 +179,9 @@ TEST(AnalysisPredictor, Clone) {
threads.emplace_back([&predictors, &inputs, i] {
LOG(INFO) << "thread #" << i << " running";
std::vector<PaddleTensor> outputs;
auto predictor = predictors.front()->Clone();
for (int j = 0; j < 10; j++) {
ASSERT_TRUE(predictors[i]->Run(inputs, &outputs));
ASSERT_TRUE(predictor->Run(inputs, &outputs));
}
});
}
......
paddle/fluid/inference/api/api_impl.cc
浏览文件 @ 54f9d44e
......@@ -161,13 +161,16 @@ bool NativePaddlePredictor::Run(const std::vector<PaddleTensor> &inputs,
}
std::unique_ptr<PaddlePredictor> NativePaddlePredictor::Clone() {
std::lock_guard<std::mutex> lk(clone_mutex_);
VLOG(3) << "Predictor::clone";
std::unique_ptr<PaddlePredictor> cls(new NativePaddlePredictor(config_));
if (!dynamic_cast<NativePaddlePredictor *>(cls.get())->Init(scope_)) {
// Hot fix the bug that result diff in multi-thread.
// TODO(Superjomn) re-implement a real clone here.
if (!dynamic_cast<NativePaddlePredictor *>(cls.get())->Init(nullptr)) {
LOG(ERROR) << "fail to call Init";
return nullptr;
}
#ifdef __clang__
// fix clang compile error
return cls;
......
paddle/fluid/inference/api/api_impl.h
浏览文件 @ 54f9d44e
......@@ -74,6 +74,8 @@ class NativePaddlePredictor : public PaddlePredictor {
// Do not use unique_ptr, use parent scope to delete
framework::Scope *sub_scope_{nullptr};
details::TensorArrayBatchCleaner tensor_array_batch_cleaner_;
// A mutex to make Clone thread safe.
std::mutex clone_mutex_;
};
} // namespace paddle
paddle/fluid/inference/api/details/zero_copy_tensor.cc
浏览文件 @ 54f9d44e
......@@ -33,9 +33,15 @@ void ZeroCopyTensor::Reshape(const std::vector<int> &shape) {
tensor->Resize(framework::make_ddim(shape));
}
#define EAGER_GET_TENSOR \
if (!tensor_) { \
tensor_ = FindTensor(); \
} \
auto *tensor = static_cast<framework::LoDTensor *>(tensor_);
template <typename T>
T *ZeroCopyTensor::mutable_data(PaddlePlace place) {
auto *tensor = static_cast<framework::LoDTensor *>(FindTensor());
EAGER_GET_TENSOR;
switch (static_cast<int>(place)) {
case static_cast<int>(PaddlePlace::kCPU): {
return tensor->mutable_data<T>(platform::CPUPlace());
......@@ -52,7 +58,7 @@ T *ZeroCopyTensor::mutable_data(PaddlePlace place) {
template <typename T>
T *ZeroCopyTensor::data(PaddlePlace *place, int *size) const {
auto *tensor = static_cast<framework::LoDTensor *>(FindTensor());
EAGER_GET_TENSOR;
auto *res = tensor->data<T>();
if (platform::is_cpu_place(tensor->place())) {
......@@ -87,13 +93,13 @@ void *ZeroCopyTensor::FindTensor() const {
}
std::vector<int64_t> ZeroCopyTensor::shape() const {
auto *tensor = static_cast<framework::LoDTensor *>(FindTensor());
PADDLE_ENFORCE(tensor, "not found tensor called %s in the scope", name_);
EAGER_GET_TENSOR;
PADDLE_ENFORCE(tensor_, "not found tensor called %s in the scope", name_);
return framework::vectorize(tensor->dims());
}
void ZeroCopyTensor::SetLoD(const std::vector<std::vector<size_t>> &x) {
auto *tensor = static_cast<framework::LoDTensor *>(FindTensor());
EAGER_GET_TENSOR;
framework::LoD lod;
for (auto &level : x) {
lod.emplace_back(level);
......@@ -102,8 +108,8 @@ void ZeroCopyTensor::SetLoD(const std::vector<std::vector<size_t>> &x) {
}
std::vector<std::vector<size_t>> ZeroCopyTensor::lod() const {
EAGER_GET_TENSOR;
std::vector<std::vector<size_t>> res;
auto *tensor = static_cast<framework::LoDTensor *>(FindTensor());
for (auto &level : tensor->lod()) {
res.emplace_back(level);
}
......
paddle/fluid/inference/api/paddle_api.h
浏览文件 @ 54f9d44e
......@@ -146,6 +146,9 @@ class ZeroCopyTensor {
bool input_or_output_;
friend class AnalysisPredictor;
void* scope_{nullptr};
// The corresponding tensor pointer inside Paddle workspace is cached for
// performance.
mutable void* tensor_{nullptr};
};
/** A simple Inference API for Paddle.
......@@ -167,18 +170,40 @@ class PaddlePredictor {
std::vector<PaddleTensor>* output_data,
int batch_size = -1) = 0;
/** Zero copy input and output optimization.
* Get the input or output tensors, and operate on their memory directly,
* without copy.
/** \brief Get a mutable tensor directly.
*
* NOTE Only works in AnalysisPredictor.
*
* One can also use this to modify any temporary variable related tensors in
* the predictor.
*
*/
virtual std::unique_ptr<ZeroCopyTensor> GetInputTensor(
const std::string& name) {
return nullptr;
}
/**
* \brief Get an immutable tensor without copy.
*
* NOTE Only works in AnalysisPredictor.
* One can use this API to get any temporary tensors in the predictor and
* read it.
*/
virtual std::unique_ptr<ZeroCopyTensor> GetOutputTensor(
const std::string& name) {
return nullptr;
}
/**
* \brief Run the predictor with zero-copied inputs and outputs.
*
* NOTE Only works in AnalysisPredictor.
*
* This will save the IO copy for transfering inputs and outputs to predictor
* workspace and get some performance improvement.
* To use it, one should call the `AnalysisConfig.SwitchUseFeedFetchOp(true)`
* and then use the `GetInputTensor` and `GetOutputTensor` to directly write
* or read the input/output tensors.
*/
virtual bool ZeroCopyRun() { return false; }
/** Clone a predictor that share the model weights, the Cloned predictor
......
paddle/fluid/inference/tests/api/CMakeLists.txt
浏览文件 @ 54f9d44e
......@@ -84,7 +84,12 @@ inference_analysis_api_test(test_analyzer_lac ${LAC_INSTALL_DIR} analyzer_lac_te
# MM DNN
set(MM_DNN_INSTALL_DIR "${INFERENCE_DEMO_INSTALL_DIR}/mm_dnn")
download_model_and_data(${MM_DNN_INSTALL_DIR} "MM_DNN_model.tar.gz" "MM_DNN_data.txt.tar.gz")
inference_analysis_api_test(test_analyzer_mm_dnn ${MM_DNN_INSTALL_DIR} analyzer_mm_dnn_tester.cc)
inference_analysis_api_test(test_analyzer_mm_dnn ${MM_DNN_INSTALL_DIR} analyzer_mm_dnn_tester.cc SERIAL)
# Pyramid DNN
set(PYRAMID_DNN_INSTALL_DIR "${INFERENCE_DEMO_INSTALL_DIR}/pyramid_dnn")
download_model_and_data(${PYRAMID_DNN_INSTALL_DIR} "PyramidDNN_model.tar.gz" "PyramidDNN_data.txt.tar.gz")
inference_analysis_api_test(test_analyzer_pyramid_dnn ${PYRAMID_DNN_INSTALL_DIR} analyzer_pyramid_dnn_tester.cc)
# text_classification
set(TEXT_CLASSIFICATION_INSTALL_DIR "${INFERENCE_DEMO_INSTALL_DIR}/text_classification")
......
paddle/fluid/inference/tests/api/analyzer_pyramid_dnn_tester.cc 0 → 100644
浏览文件 @ 54f9d44e
// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include "paddle/fluid/inference/tests/api/tester_helper.h"
namespace paddle {
namespace inference {
using contrib::AnalysisConfig;
struct DataRecord {
std::vector<std::vector<int64_t>> query_basic, query_phrase, title_basic,
title_phrase;
std::vector<size_t> lod1, lod2, lod3, lod4;
size_t batch_iter{0}, batch_size{1}, num_samples; // total number of samples
DataRecord() = default;
explicit DataRecord(const std::string &path, int batch_size = 1)
: batch_size(batch_size) {
Load(path);
}
DataRecord NextBatch() {
DataRecord data;
size_t batch_end = batch_iter + batch_size;
// NOTE skip the final batch, if no enough data is provided.
if (batch_end <= query_basic.size()) {
GetInputPerBatch(query_basic, &data.query_basic, &data.lod1, batch_iter,
batch_end);
GetInputPerBatch(query_phrase, &data.query_phrase, &data.lod2, batch_iter,
batch_end);
GetInputPerBatch(title_basic, &data.title_basic, &data.lod3, batch_iter,
batch_end);
GetInputPerBatch(title_phrase, &data.title_phrase, &data.lod4, batch_iter,
batch_end);
}
batch_iter += batch_size;
return data;
}
void Load(const std::string &path) {
std::ifstream file(path);
std::string line;
int num_lines = 0;
while (std::getline(file, line)) {
std::vector<std::string> data;
split(line, ';', &data);
// load query data
std::vector<int64_t> query_basic_data;
split_to_int64(data[1], ' ', &query_basic_data);
std::vector<int64_t> query_phrase_data;
split_to_int64(data[2], ' ', &query_phrase_data);
// load title data
std::vector<int64_t> title_basic_data;
split_to_int64(data[3], ' ', &title_basic_data);
std::vector<int64_t> title_phrase_data;
split_to_int64(data[4], ' ', &title_phrase_data);
// filter the empty data
bool flag =
data[1].size() && data[2].size() && data[3].size() && data[4].size();
if (flag) {
query_basic.push_back(std::move(query_basic_data));
query_phrase.push_back(std::move(query_phrase_data));
title_basic.push_back(std::move(title_basic_data));
title_phrase.push_back(std::move(title_phrase_data));
num_lines++;
}
}
num_samples = num_lines;
}
};
void PrepareInputs(std::vector<PaddleTensor> *input_slots, DataRecord *data,
int batch_size) {
PaddleTensor query_basic_tensor, query_phrase_tensor, title_basic_tensor,
title_phrase_tensor;
query_basic_tensor.name = "query_basic";
query_phrase_tensor.name = "query_phrase";
title_basic_tensor.name = "pos_title_basic";
title_phrase_tensor.name = "pos_title_phrase";
auto one_batch = data->NextBatch();
// assign data
TensorAssignData<int64_t>(&query_basic_tensor, one_batch.query_basic,
one_batch.lod1);
TensorAssignData<int64_t>(&query_phrase_tensor, one_batch.query_phrase,
one_batch.lod2);
TensorAssignData<int64_t>(&title_basic_tensor, one_batch.title_basic,
one_batch.lod3);
TensorAssignData<int64_t>(&title_phrase_tensor, one_batch.title_phrase,
one_batch.lod4);
// Set inputs.
input_slots->assign({query_basic_tensor, query_phrase_tensor,
title_basic_tensor, title_phrase_tensor});
for (auto &tensor : *input_slots) {
tensor.dtype = PaddleDType::INT64;
}
}
void SetConfig(contrib::AnalysisConfig *cfg) {
cfg->SetModel(FLAGS_infer_model);
cfg->DisableGpu();
cfg->SwitchSpecifyInputNames();
cfg->SwitchIrOptim();
}
void SetInput(std::vector<std::vector<PaddleTensor>> *inputs) {
DataRecord data(FLAGS_infer_data, FLAGS_batch_size);
std::vector<PaddleTensor> input_slots;
int epoch = FLAGS_test_all_data ? data.num_samples / FLAGS_batch_size : 1;
LOG(INFO) << "number of samples: " << epoch * FLAGS_batch_size;
for (int bid = 0; bid < epoch; ++bid) {
PrepareInputs(&input_slots, &data, FLAGS_batch_size);
(*inputs).emplace_back(input_slots);
}
}
// Easy for profiling independently.
TEST(Analyzer_Pyramid_DNN, profile) {
contrib::AnalysisConfig cfg;
SetConfig(&cfg);
std::vector<PaddleTensor> outputs;
std::vector<std::vector<PaddleTensor>> input_slots_all;
SetInput(&input_slots_all);
TestPrediction(reinterpret_cast<const PaddlePredictor::Config *>(&cfg),
input_slots_all, &outputs, FLAGS_num_threads);
if (FLAGS_num_threads == 1 && !FLAGS_test_all_data) {
PADDLE_ENFORCE_EQ(outputs.size(), 1UL);
size_t size = GetSize(outputs[0]);
PADDLE_ENFORCE_GT(size, 0);
float *result = static_cast<float *>(outputs[0].data.data());
// output is probability, which is in (0, 1).
for (size_t i = 0; i < size; i++) {
EXPECT_GT(result[i], 0);
EXPECT_LT(result[i], 1);
}
}
}
// Check the fuse status
TEST(Analyzer_Pyramid_DNN, fuse_statis) {
contrib::AnalysisConfig cfg;
SetConfig(&cfg);
int num_ops;
auto predictor = CreatePaddlePredictor<AnalysisConfig>(cfg);
auto fuse_statis = GetFuseStatis(
static_cast<AnalysisPredictor *>(predictor.get()), &num_ops);
}
// Compare result of NativeConfig and AnalysisConfig
TEST(Analyzer_Pyramid_DNN, compare) {
contrib::AnalysisConfig cfg;
SetConfig(&cfg);
std::vector<std::vector<PaddleTensor>> input_slots_all;
SetInput(&input_slots_all);
CompareNativeAndAnalysis(
reinterpret_cast<const PaddlePredictor::Config *>(&cfg), input_slots_all);
}
// Compare Deterministic result
TEST(Analyzer_Pyramid_DNN, compare_determine) {
AnalysisConfig cfg;
SetConfig(&cfg);
std::vector<std::vector<PaddleTensor>> input_slots_all;
SetInput(&input_slots_all);
CompareDeterministic(reinterpret_cast<const PaddlePredictor::Config *>(&cfg),
input_slots_all);
}
} // namespace inference
} // namespace paddle
paddle/fluid/operators/CMakeLists.txt
浏览文件 @ 54f9d44e
......@@ -65,7 +65,7 @@ set(COMMON_OP_DEPS ${OP_HEADER_DEPS})
set(COMMON_OP_DEPS ${COMMON_OP_DEPS} selected_rows_functor selected_rows lod_tensor maxouting unpooling pooling lod_rank_table context_project sequence_pooling executor)
set(COMMON_OP_DEPS ${COMMON_OP_DEPS} dynload_warpctc)
set(COMMON_OP_DEPS ${COMMON_OP_DEPS} sequence_padding sequence_scale cos_sim_functor memory jit_kernel_helper concat_and_split cross_entropy softmax vol2col im2col sampler)
set(COMMON_OP_DEPS ${COMMON_OP_DEPS} sequence_padding sequence_scale cos_sim_functor memory jit_kernel_helper concat_and_split cross_entropy softmax vol2col im2col sampler tree2col)
set(COMMON_OP_DEPS ${COMMON_OP_DEPS} sequence2batch lstm_compute matrix_bit_code gru_compute activation_functions)
if (WITH_GPU)
set(COMMON_OP_DEPS ${COMMON_OP_DEPS} depthwise_conv prelu)
......
paddle/fluid/operators/controlflow/while_op.cc
浏览文件 @ 54f9d44e
......@@ -58,7 +58,6 @@ class WhileOp : public framework::OperatorBase {
void RunImpl(const framework::Scope &scope,
const platform::Place &dev_place) const override {
PADDLE_ENFORCE_NOT_NULL(scope.FindVar(Input(kCondition)));
auto &cond = scope.FindVar(Input(kCondition))->Get<LoDTensor>();
PADDLE_ENFORCE_EQ(cond.dims(), paddle::framework::make_ddim({1}));
......@@ -73,27 +72,18 @@ class WhileOp : public framework::OperatorBase {
PADDLE_ENFORCE(platform::is_cpu_place(cond.place()),
"Condition of while op must in CPU memory.");
bool is_test = Attr<bool>("is_test");
auto &skip_vars = Attr<std::vector<std::string>>(kSkipEagerDeletionVars);
VLOG(2) << GetSkipEagerDeletionVarsDebugString(skip_vars);
bool is_test = Attr<bool>("is_test");
auto ctx = executor.Prepare(*program, block->ID(), skip_vars);
if (!is_test) {
while (cond.data<bool>()[0]) {
auto &current_scope = scope.NewScope();
step_scopes->push_back(&current_scope);
executor.RunPreparedContext(ctx.get(), &current_scope, false, true,
true);
}
} else {
while (cond.data<bool>()[0]) {
auto &current_scope = scope.NewScope();
executor.CreateVariables(*program, &current_scope, block->ID());
while (cond.data<bool>()[0]) {
executor.RunPreparedContext(ctx.get(), &current_scope, false, false,
false);
step_scopes->push_back(&current_scope);
executor.RunPreparedContext(ctx.get(), &current_scope, false, true, true);
if (is_test) {
scope.DeleteScope(&current_scope);
}
scope.DeleteScope(&current_scope);
}
}
};
......
paddle/fluid/operators/distributed/CMakeLists.txt
浏览文件 @ 54f9d44e
......@@ -7,7 +7,7 @@ if(WITH_GRPC)
else()
set(cc_generic_services "true")
endif()
configure_file(send_recv.proto.in ${CMAKE_CURRENT_BINARY_DIR}/send_recv.proto @ONLY)
configure_file(send_recv.proto.in ${CMAKE_CURRENT_SOURCE_DIR}/send_recv.proto @ONLY)
# FIXME(typhoonzero): use add_subdirectory once we clean the dependency of these files
set(DISTRIBUTE_COMPILE_FLAGS "-Wno-non-virtual-dtor -Wno-error=non-virtual-dtor -Wno-error=delete-non-virtual-dtor")
......@@ -19,8 +19,8 @@ if(WITH_GRPC)
variable_response.cc
collective_client.cc collective_server.cc
${GRPC_SRCS}
PROTO ${CMAKE_CURRENT_BINARY_DIR}/send_recv.proto
DEPS lod_tensor selected_rows_functor memory ${GRPC_DEPS})
PROTO send_recv.proto
DEPS lod_tensor selected_rows_functor memory)
set_source_files_properties(grpc_serde_test.cc rpc_server_test.cc PROPERTIES COMPILE_FLAGS ${DISTRIBUTE_COMPILE_FLAGS})
set(RPC_DEPS sendrecvop_rpc ${GRPC_DEPS})
......
paddle/fluid/operators/gru_op.cc
浏览文件 @ 54f9d44e
......@@ -137,6 +137,10 @@ class GRUOpMaker : public framework::OpProtoAndCheckerMaker {
"(bool, defalut: False) "
"whether to compute reversed GRU.")
.SetDefault(false);
AddAttr<bool>("origin_mode",
"bool"
"use origin mode in article https://arxiv.org/abs/1412.3555")
.SetDefault(false);
AddComment(R"DOC(
GRU Operator implements part calculations of the complete GRU as following:
......@@ -221,6 +225,7 @@ class GRUCPUKernel : public framework::OpKernel<T> {
public:
void BatchCompute(const framework::ExecutionContext& context) const {
using DeviceContext = paddle::platform::CPUDeviceContext;
bool origin_mode = context.Attr<bool>("origin_mode");
auto* input = context.Input<LoDTensor>("Input");
auto* h0 = context.Input<Tensor>("H0");
auto* weight = context.Input<Tensor>("Weight");
......@@ -327,7 +332,7 @@ class GRUCPUKernel : public framework::OpKernel<T> {
math::detail::forward_final_output(
math::detail::forward::gru_finalOutput<T>(), gru_value, frame_size,
cur_batch_size, active_node);
cur_batch_size, active_node, origin_mode);
gru_value.prev_out_value = gru_value.output_value;
}
......@@ -351,7 +356,7 @@ class GRUCPUKernel : public framework::OpKernel<T> {
math::GRUUnitFunctor<DeviceContext, T>::compute(
dev_ctx, gru_value, frame_size, cur_batch_size, active_node,
active_gate);
active_gate, origin_mode);
gru_value.prev_out_value = gru_value.output_value;
}
......
paddle/fluid/operators/gru_op.cu.cc
浏览文件 @ 54f9d44e
......@@ -21,6 +21,7 @@ template <typename DeviceContext, typename T>
class GRUKernel : public framework::OpKernel<T> {
public:
void BatchCompute(const framework::ExecutionContext& context) const {
bool origin_mode = context.Attr<bool>("origin_mode");
auto* input = context.Input<LoDTensor>("Input");
auto* h0 = context.Input<Tensor>("H0");
auto* weight = context.Input<Tensor>("Weight");
......@@ -87,7 +88,7 @@ class GRUKernel : public framework::OpKernel<T> {
gru_value.reset_output_value = reset_hidden_prev_t.data<T>();
math::GRUUnitFunctor<DeviceContext, T>::compute(
dev_ctx, gru_value, frame_size, cur_batch_size, active_node,
active_gate);
active_gate, origin_mode);
gru_value.prev_out_value = gru_value.output_value;
}
......
paddle/fluid/operators/gru_op.h
浏览文件 @ 54f9d44e
......@@ -41,6 +41,7 @@ template <typename DeviceContext, typename T>
class GRUGradKernel : public framework::OpKernel<T> {
public:
void BatchCompute(const framework::ExecutionContext& context) const {
bool origin_mode = context.Attr<bool>("origin_mode");
auto* h0 = context.Input<Tensor>("H0");
auto* weight = context.Input<Tensor>("Weight");
const T* weight_data = weight->data<T>();
......@@ -146,7 +147,7 @@ class GRUGradKernel : public framework::OpKernel<T> {
math::GRUUnitGradFunctor<DeviceContext, T>::compute(
dev_ctx, gru_value, gru_grad, frame_size, cur_batch_size, active_node,
active_gate);
active_gate, origin_mode);
}
if (input_grad) {
input_grad->mutable_data<T>(context.GetPlace());
......
paddle/fluid/operators/gru_unit_op.cc
浏览文件 @ 54f9d44e
......@@ -111,6 +111,13 @@ class GRUUnitOpMaker : public framework::OpProtoAndCheckerMaker {
"The activation type used in update gate and reset gate.")
.SetDefault(sigmoid)
.InEnum({identity, sigmoid, tanh, relu});
AddAttr<bool>("origin_mode",
"bool"
"use origin mode in article <Learning Phrase Representations "
"using RNN Encoder–Decoder\n"
"for Statistical Machine "
"Translation>(https://arxiv.org/pdf/1406.1078.pdf)")
.SetDefault(false);
AddComment(R"DOC(
GRUUnit Operator implements partial calculations of the GRU unit as following:
......
paddle/fluid/operators/gru_unit_op.h
浏览文件 @ 54f9d44e
......@@ -113,7 +113,11 @@ class GRUUnitKernel : public framework::OpKernel<T> {
auto c = g.slice(c_offsets, extents); // output candidate
// calculate final output
h.device(place) = u * (c - h_p) + h_p;
if (context.Attr<bool>("origin_mode")) {
h.device(place) = c + u * (h_p - c); // (1 - u) * c + u * h_p
} else {
h.device(place) = u * (c - h_p) + h_p; // u * c + (1 - u) * h_p
}
}
};
......@@ -180,11 +184,19 @@ class GRUUnitGradKernel : public framework::OpKernel<T> {
auto c = g.slice(c_offsets, extents); // output candidate
// backward for unactivated update gate
ActGradCompute(context.Attr<int>("gate_activation"), place, u, u,
d_g.slice(u_offsets, extents), d_h * (c - h_p));
// backward for unactivated output candidate
ActGradCompute(context.Attr<int>("activation"), place, c, c,
d_g.slice(c_offsets, extents), d_h * u);
if (context.Attr<bool>("origin_mode")) {
ActGradCompute(context.Attr<int>("gate_activation"), place, u, u,
d_g.slice(u_offsets, extents), d_h * (h_p - c));
// backward for unactivated output candidate
ActGradCompute(context.Attr<int>("activation"), place, c, c,
d_g.slice(c_offsets, extents), d_h * (1 - u));
} else {
ActGradCompute(context.Attr<int>("gate_activation"), place, u, u,
d_g.slice(u_offsets, extents), d_h * (c - h_p));
// backward for unactivated output candidate
ActGradCompute(context.Attr<int>("activation"), place, c, c,
d_g.slice(c_offsets, extents), d_h * u);
}
// backward for reset_hidden_prev
auto blas = math::GetBlas<DeviceContext, T>(context);
blas.GEMM(false, true, batch_size, frame_size, frame_size, 1,
......@@ -213,7 +225,11 @@ class GRUUnitGradKernel : public framework::OpKernel<T> {
T* hidden_prev_grad_data =
hidden_prev_grad->mutable_data<T>(context.GetPlace());
auto d_h_p = EigenMatrix<T>::From(*hidden_prev_grad);
d_h_p.device(place) = d_r_h_p * r + d_h * (u.constant(T(1)) - u);
if (context.Attr<bool>("origin_mode")) {
d_h_p.device(place) = d_r_h_p * r + d_h * u;
} else {
d_h_p.device(place) = d_r_h_p * r + d_h * (1 - u);
}
blas.GEMM(false, true, batch_size, frame_size, frame_size * 2, 1,
gate_grad_data, frame_size * 3, weight_data, frame_size * 2, 1,
hidden_prev_grad_data, frame_size);
......
paddle/fluid/operators/math/CMakeLists.txt
浏览文件 @ 54f9d44e
......@@ -60,6 +60,7 @@ math_library(matrix_bit_code)
math_library(unpooling)
math_library(vol2col)
math_library(prelu)
math_library(tree2col DEPS math_function)
cc_test(math_function_test SRCS math_function_test.cc DEPS math_function)
cc_test(selected_rows_functor_test SRCS selected_rows_functor_test.cc DEPS selected_rows_functor)
......
paddle/fluid/operators/math/detail/gru_cpu_kernel.h
浏览文件 @ 54f9d44e
......@@ -56,7 +56,8 @@ template <class OpFinalOutput, typename T>
void hl_naive_gru_forward_final_output(OpFinalOutput op_final_output,
T *gate_value, T *prev_output_value,
T *output_value, int frame_size,
ActivationType active_node) {
ActivationType active_node,
bool origin_mode) {
T r_value_update_gate;
T r_value_frame_state;
T r_prev_out = 0;
......@@ -72,7 +73,7 @@ void hl_naive_gru_forward_final_output(OpFinalOutput op_final_output,
}
op_final_output(&r_value_update_gate, &r_value_frame_state, &r_prev_out,
&r_output, active_node);
&r_output, active_node, origin_mode);
frame_state[i] = r_value_frame_state;
output_value[i] = r_output;
......@@ -146,7 +147,8 @@ template <class OpFinalOutput, typename T>
void hl_avx_gru_forward_final_output(OpFinalOutput op_final_output,
T *gate_value, T *prev_output_value,
T *output_value, int frame_size,
ActivationType active_node) {
ActivationType active_node,
bool origin_mode) {
#ifdef __AVX__
__m256 r_value_update_gate, r_value_update_gate_last = _mm256_set1_ps(0.0f);
__m256 r_value_frame_state, r_value_frame_state_last = _mm256_set1_ps(0.0f);
......@@ -180,7 +182,7 @@ void hl_avx_gru_forward_final_output(OpFinalOutput op_final_output,
}
op_final_output(&r_value_update_gate, &r_value_frame_state, &r_prev_out,
&r_output, active_node);
&r_output, active_node, origin_mode);
_mm256_storeu_ps(reinterpret_cast<float *>(frame_state + i),
r_value_frame_state);
......@@ -190,7 +192,7 @@ void hl_avx_gru_forward_final_output(OpFinalOutput op_final_output,
if (rest > 0) {
i = n - block;
op_final_output(&r_value_update_gate_last, &r_value_frame_state_last,
&r_prev_out_last, &r_output, active_node);
&r_prev_out_last, &r_output, active_node, origin_mode);
_mm256_storeu_ps(reinterpret_cast<float *>(frame_state + i),
r_value_frame_state_last);
......@@ -227,17 +229,18 @@ inline void forward_reset_output(OpResetOutput op_reset_output,
template <class OpFinalOutput, typename T>
inline void forward_final_output(OpFinalOutput op_final_output,
GRUMetaValue<T> value, int frame_size,
int batch_size, ActivationType active_node) {
int batch_size, ActivationType active_node,
bool origin_mode) {
for (int b = 0; b < batch_size; b++) {
if (OpFinalOutput::avx && (frame_size > static_cast<int>(8 - 1)) &&
(sizeof(T) == 4)) {
hl_avx_gru_forward_final_output(op_final_output, value.gate_value,
value.prev_out_value, value.output_value,
frame_size, active_node);
frame_size, active_node, origin_mode);
} else {
hl_naive_gru_forward_final_output(
op_final_output, value.gate_value, value.prev_out_value,
value.output_value, frame_size, active_node);
value.output_value, frame_size, active_node, origin_mode);
}
value.gate_value += frame_size * 3;
......@@ -253,7 +256,8 @@ void hl_naive_gru_backward_state_grad(OpStateGrad op_state_grad, T *gate_value,
T *gate_grad, T *prev_out_value,
T *prev_out_grad, T *output_grad,
int frame_size,
ActivationType active_node) {
ActivationType active_node,
bool origin_mode) {
T r_update_gate_value;
T r_update_gate_grad;
T r_frame_state_value;
......@@ -279,7 +283,7 @@ void hl_naive_gru_backward_state_grad(OpStateGrad op_state_grad, T *gate_value,
op_state_grad(&r_update_gate_value, &r_update_gate_grad,
&r_frame_state_value, &r_frame_state_grad, &r_prev_out_value,
&r_prev_out_grad, &r_out_grad, active_node);
&r_prev_out_grad, &r_out_grad, active_node, origin_mode);
update_gate_grad[i] = r_update_gate_grad;
frame_state_grad[i] = r_frame_state_grad;
......@@ -338,8 +342,8 @@ template <class OpStateGrad, typename T>
void hl_avx_gru_backward_state_grad(OpStateGrad op_state_grad, T *gate_value,
T *gate_grad, T *prev_out_value,
T *prev_out_grad, T *output_grad,
int frame_size,
ActivationType active_node) {
int frame_size, ActivationType active_node,
bool origin_mode) {
#ifdef __AVX__
__m256 r_update_gate_value;
__m256 r_update_gate_grad;
......@@ -368,7 +372,7 @@ void hl_avx_gru_backward_state_grad(OpStateGrad op_state_grad, T *gate_value,
op_state_grad(&r_update_gate_value, &r_update_gate_grad,
&r_frame_state_value, &r_frame_state_grad, &r_prev_out_value,
&r_prev_out_grad, &r_out_grad, active_node);
&r_prev_out_grad, &r_out_grad, active_node, origin_mode);
update_gate_grad[i] = r_update_gate_grad;
frame_state_grad[i] = r_frame_state_grad;
......@@ -431,16 +435,18 @@ template <class OpStateGrad, typename T>
inline void backward_state_grad(OpStateGrad op_state_grad,
GRUMetaValue<T> value, GRUMetaGrad<T> grad,
int frame_size, int batch_size,
ActivationType active_node) {
ActivationType active_node, bool origin_mode) {
for (int b = 0; b < batch_size; b++) {
if (OpStateGrad::avx && !(frame_size & (8 - 1)) && (sizeof(T) == 4)) {
hl_avx_gru_backward_state_grad(
op_state_grad, value.gate_value, grad.gate_grad, value.prev_out_value,
grad.prev_out_grad, grad.output_grad, frame_size, active_node);
hl_avx_gru_backward_state_grad(op_state_grad, value.gate_value,
grad.gate_grad, value.prev_out_value,
grad.prev_out_grad, grad.output_grad,
frame_size, active_node, origin_mode);
} else {
hl_naive_gru_backward_state_grad(
op_state_grad, value.gate_value, grad.gate_grad, value.prev_out_value,
grad.prev_out_grad, grad.output_grad, frame_size, active_node);
hl_naive_gru_backward_state_grad(op_state_grad, value.gate_value,
grad.gate_grad, value.prev_out_value,
grad.prev_out_grad, grad.output_grad,
frame_size, active_node, origin_mode);
}
value.gate_value += frame_size * 3;
......
paddle/fluid/operators/math/detail/gru_gpu_kernel.h
浏览文件 @ 54f9d44e
......@@ -72,7 +72,8 @@ __global__ void KeGruForwardFinalOutput(OpFinalOutput op_final_output,
T *gate_value, T *prev_output_value,
T *output_value, int frame_size,
int batch_size,
ActivationType active_node) {
ActivationType active_node,
bool origin_mode) {
const int frame_idx = blockIdx.x * blockDim.x + threadIdx.x;
if (frame_idx >= frame_size) return;
int batch_idx = 0;
......@@ -94,7 +95,7 @@ __global__ void KeGruForwardFinalOutput(OpFinalOutput op_final_output,
}
op_final_output(&r_value_update_gate, &r_value_frame_state, &r_prev_out,
&r_output, active_node);
&r_output, active_node, origin_mode);
gate_value[frame_idx + frame_size * 2] = r_value_frame_state;
output_value[frame_idx] = r_output;
......@@ -109,7 +110,8 @@ __global__ void KeGruBackwardStateGrad(OpStateGrad op_state_grad, T *gate_value,
T *gate_grad, T *prev_out_value,
T *prev_out_grad, T *output_grad,
int frame_size, int batch_size,
ActivationType active_node) {
ActivationType active_node,
bool origin_mode) {
const int frame_idx = blockIdx.x * blockDim.x + threadIdx.x;
if (frame_idx >= frame_size) return;
int batch_idx = 0;
......@@ -139,7 +141,7 @@ __global__ void KeGruBackwardStateGrad(OpStateGrad op_state_grad, T *gate_value,
op_state_grad(&r_update_gate_value, &r_update_gate_grad, &r_frame_state_value,
&r_frame_state_grad, &r_prev_out_value, &r_prev_out_grad,
&r_out_grad, active_node);
&r_out_grad, active_node, origin_mode);
gate_grad[frame_idx + frame_size * 0] = r_update_gate_grad;
gate_grad[frame_idx + frame_size * 2] = r_frame_state_grad;
......
paddle/fluid/operators/math/detail/gru_kernel.h
浏览文件 @ 54f9d44e
......@@ -57,10 +57,16 @@ class gru_finalOutput {
public:
HOSTDEVICE void operator()(T *value_update_gate, T *value_frame_state,
T *prev_out, T *value_output,
ActivationType act_input) {
ActivationType act_input, bool origin_mode) {
*value_frame_state = activation(*value_frame_state, act_input);
*value_output = *prev_out - ((*value_update_gate) * (*prev_out)) +
((*value_update_gate) * (*value_frame_state));
if (origin_mode) {
*value_output = ((*value_update_gate) * (*prev_out)) +
*value_frame_state -
((*value_update_gate) * (*value_frame_state));
} else {
*value_output = *prev_out - ((*value_update_gate) * (*prev_out)) +
((*value_update_gate) * (*value_frame_state));
}
}
#ifndef __NVCC__
#ifndef __AVX__
......@@ -69,11 +75,20 @@ class gru_finalOutput {
static const bool avx = true;
HOSTDEVICE void operator()(__m256 *value_update_gate,
__m256 *value_frame_state, __m256 *prev_out,
__m256 *value_output, ActivationType act_input) {
__m256 *value_output, ActivationType act_input,
bool origin_mode) {
*value_frame_state = activation(*value_frame_state, act_input);
*value_output = _mm256_add_ps(
_mm256_sub_ps(*prev_out, _mm256_mul_ps(*value_update_gate, *prev_out)),
_mm256_mul_ps(*value_update_gate, *value_frame_state));
if (origin_mode) {
*value_output = _mm256_sub_ps(
_mm256_add_ps(_mm256_mul_ps(*value_update_gate, *prev_out),
*value_frame_state),
_mm256_mul_ps(*value_update_gate, *value_frame_state));
} else {
*value_output = _mm256_add_ps(
_mm256_sub_ps(*prev_out,
_mm256_mul_ps(*value_update_gate, *prev_out)),
_mm256_mul_ps(*value_update_gate, *value_frame_state));
}
}
#endif
#endif
......@@ -88,13 +103,23 @@ class gru_stateGrad {
HOSTDEVICE void operator()(T *value_update_gate, T *grad_update_gate,
T *value_frame_state, T *grad_frame_state,
T *value_prev_out, T *grad_prev_out,
T *grad_output, ActivationType act_input) {
*grad_update_gate = (*grad_output * (*value_frame_state));
*grad_update_gate -= (*grad_output * (*value_prev_out));
*grad_prev_out -= (*grad_output * (*value_update_gate));
*grad_prev_out += *grad_output;
*grad_frame_state = activation(*grad_output * (*value_update_gate),
*value_frame_state, act_input);
T *grad_output, ActivationType act_input,
bool origin_mode) {
if (origin_mode) {
*grad_update_gate =
(*grad_output) * ((*value_prev_out) - (*value_frame_state));
*grad_prev_out += (*grad_output * (*value_update_gate));
*grad_frame_state = activation(
*grad_output * (static_cast<T>(1.0) - (*value_update_gate)),
*value_frame_state, act_input);
} else {
*grad_update_gate =
(*grad_output) * ((*value_frame_state) - (*value_prev_out));
*grad_prev_out +=
(*grad_output * (static_cast<T>(1.0) - *value_update_gate));
*grad_frame_state = activation(*grad_output * (*value_update_gate),
*value_frame_state, act_input);
}
}
#ifndef __NVCC__
#ifndef __AVX__
......@@ -106,17 +131,27 @@ class gru_stateGrad {
__m256 *value_frame_state,
__m256 *grad_frame_state, __m256 *value_prev_out,
__m256 *grad_prev_out, __m256 *grad_output,
ActivationType act_input) {
*grad_update_gate = _mm256_mul_ps(*grad_output, *value_frame_state);
*grad_update_gate = _mm256_sub_ps(
*grad_update_gate, _mm256_mul_ps(*grad_output, *value_prev_out));
*grad_prev_out = _mm256_add_ps(
_mm256_sub_ps(*grad_prev_out,
_mm256_mul_ps(*grad_output, *value_update_gate)),
*grad_output);
*grad_frame_state =
activation(_mm256_mul_ps(*grad_output, *value_update_gate),
*value_frame_state, act_input);
ActivationType act_input, bool origin_mode) {
if (origin_mode) {
*grad_update_gate = _mm256_mul_ps(
*grad_output, _mm256_sub_ps(*value_prev_out, *value_frame_state));
*grad_prev_out = _mm256_add_ps(
*grad_prev_out, _mm256_mul_ps(*grad_output, *value_update_gate));
*grad_frame_state = activation(
_mm256_mul_ps(*grad_output, _mm256_sub_ps(_mm256_set1_ps(1.0f),
*value_update_gate)),
*value_frame_state, act_input);
} else {
*grad_update_gate = _mm256_mul_ps(
*grad_output, _mm256_sub_ps(*value_frame_state, *value_prev_out));
*grad_prev_out = _mm256_add_ps(
*grad_prev_out,
_mm256_mul_ps(*grad_output, _mm256_sub_ps(_mm256_set1_ps(1.0f),
*value_update_gate)));
*grad_frame_state =
activation(_mm256_mul_ps(*grad_output, *value_update_gate),
*value_frame_state, act_input);
}
}
#endif
#endif
......
paddle/fluid/operators/math/gru_compute.cc
浏览文件 @ 54f9d44e
......@@ -23,7 +23,8 @@ struct GRUUnitFunctor<platform::CPUDeviceContext, T> {
static void compute(const platform::CPUDeviceContext &context,
GRUMetaValue<T> value, int frame_size, int batch_size,
const detail::ActivationType active_node,
const detail::ActivationType active_gate) {
const detail::ActivationType active_gate,
bool origin_mode) {
#ifndef __NVCC__
auto blas = math::GetBlas<platform::CPUDeviceContext, T>(context);
if (value.prev_out_value) {
......@@ -43,7 +44,8 @@ struct GRUUnitFunctor<platform::CPUDeviceContext, T> {
}
detail::forward_final_output(detail::forward::gru_finalOutput<T>(), value,
frame_size, batch_size, active_node);
frame_size, batch_size, active_node,
origin_mode);
#endif
}
};
......@@ -54,10 +56,12 @@ struct GRUUnitGradFunctor<platform::CPUDeviceContext, T> {
GRUMetaValue<T> value, GRUMetaGrad<T> grad,
int frame_size, int batch_size,
const detail::ActivationType active_node,
const detail::ActivationType active_gate) {
const detail::ActivationType active_gate,
bool origin_mode) {
#ifndef __NVCC__
detail::backward_state_grad(detail::backward::gru_stateGrad<T>(), value,
grad, frame_size, batch_size, active_node);
grad, frame_size, batch_size, active_node,
origin_mode);
auto blas = math::GetBlas<platform::CPUDeviceContext, T>(context);
if (value.prev_out_value && grad.prev_out_grad) {
blas.GEMM(false, true, batch_size, frame_size, frame_size, 1,
......
paddle/fluid/operators/math/gru_compute.cu
浏览文件 @ 54f9d44e
......@@ -24,7 +24,8 @@ struct GRUUnitFunctor<platform::CUDADeviceContext, T> {
static void compute(const platform::CUDADeviceContext &context,
GRUMetaValue<T> value, int frame_size, int batch_size,
const detail::ActivationType active_node,
const detail::ActivationType active_gate) {
const detail::ActivationType active_gate,
bool origin_mode) {
auto stream = context.stream();
dim3 threads;
dim3 grid;
......@@ -73,14 +74,14 @@ struct GRUUnitFunctor<platform::CUDADeviceContext, T> {
T><<<grid, threads, 0, stream>>>(
detail::forward::gru_finalOutput<T>(), value.gate_value,
value.prev_out_value, value.output_value, frame_size, batch_size,
active_node);
active_node, origin_mode);
} else {
detail::KeGruForwardFinalOutput<detail::forward::gru_finalOutput<T>,
/* is_batch= */ true,
T><<<grid, threads, 0, stream>>>(
detail::forward::gru_finalOutput<T>(), value.gate_value,
value.prev_out_value, value.output_value, frame_size, batch_size,
active_node);
active_node, origin_mode);
}
}
};
......@@ -91,7 +92,8 @@ struct GRUUnitGradFunctor<platform::CUDADeviceContext, T> {
GRUMetaValue<T> value, GRUMetaGrad<T> grad,
int frame_size, int batch_size,
const detail::ActivationType active_node,
const detail::ActivationType active_gate) {
const detail::ActivationType active_gate,
bool origin_mode) {
auto stream = context.stream();
dim3 threads;
dim3 grid;
......@@ -111,14 +113,14 @@ struct GRUUnitGradFunctor<platform::CUDADeviceContext, T> {
/* is_batch= */ false><<<grid, threads, 0, stream>>>(
detail::backward::gru_stateGrad<T>(), value.gate_value,
grad.gate_grad, value.prev_out_value, grad.prev_out_grad,
grad.output_grad, frame_size, batch_size, active_node);
grad.output_grad, frame_size, batch_size, active_node, origin_mode);
} else {
detail::KeGruBackwardStateGrad<
detail::backward::gru_stateGrad<T>,
/* is_batch= */ true><<<grid, threads, 0, stream>>>(
detail::backward::gru_stateGrad<T>(), value.gate_value,
grad.gate_grad, value.prev_out_value, grad.prev_out_grad,
grad.output_grad, frame_size, batch_size, active_node);
grad.output_grad, frame_size, batch_size, active_node, origin_mode);
}
auto blas = math::GetBlas<platform::CUDADeviceContext, T>(context);
......
paddle/fluid/operators/math/gru_compute.h
浏览文件 @ 54f9d44e
......@@ -44,7 +44,8 @@ struct GRUUnitFunctor {
static void compute(const DeviceContext &context, GRUMetaValue<T> value,
int frame_size, int batch_size,
const detail::ActivationType active_node,
const detail::ActivationType active_gate);
const detail::ActivationType active_gate,
bool origin_mode);
};
template <typename DeviceContext, typename T>
......@@ -52,7 +53,8 @@ struct GRUUnitGradFunctor {
static void compute(const DeviceContext &context, GRUMetaValue<T> value,
GRUMetaGrad<T> grad, int frame_size, int batch_size,
const detail::ActivationType active_node,
const detail::ActivationType active_gate);
const detail::ActivationType active_gate,
bool origin_mode);
};
} // namespace math
......
paddle/fluid/operators/math/tree2col.cc 0 → 100644
浏览文件 @ 54f9d44e
// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include "paddle/fluid/operators/math/tree2col.h"
#include <deque>
#include <stack>
namespace paddle {
namespace operators {
namespace math {
using Tensor = framework::Tensor;
std::vector<TreeNode> Tree2ColUtil::construct_patch(
size_t root, int max_depth, const std::vector<std::vector<int>> &tr) {
std::stack<TreeNode, std::deque<TreeNode>> stack;
std::unordered_map<int, bool> visited;
std::vector<TreeNode> patch;
stack.push(TreeNode(root, 1, 1, 0));
patch.emplace_back(TreeNode(root, 1, 1, 0));
visited[root] = true;
while (!stack.empty()) {
TreeNode &u = stack.top();
bool end = true;
size_t node = u.get_node(), sz = tr[node].size();
visited[node] = true;
for (size_t i = 0; i < sz; i++) {
size_t v = tr[node][i];
if (!visited[v] && static_cast<int>(u.get_depth()) + 1 < max_depth) {
visited[v] = true;
stack.push(TreeNode(v, i, sz, u.get_depth() + 1));
patch.push_back(TreeNode(v, i + 1, sz, u.get_depth() + 1));
end = false;
}
}
if (end) {
stack.pop();
}
}
return patch;
}
void Tree2ColUtil::construct_tree(const paddle::Tensor &EdgeSet,
std::vector<std::vector<int>> *tr,
size_t *node_count) {
auto edge_set_dims = EdgeSet.dims();
PADDLE_ENFORCE_EQ(edge_set_dims[1], 2);
int64_t edge_count = EdgeSet.numel();
const int *edge_data = EdgeSet.data<int>();
for (int64_t i = 0; i < edge_count; i += 2) {
int u = edge_data[i], v = edge_data[i + 1];
if (u != 0 && v != 0) (*node_count)++;
}
(*node_count)++;
tr->resize(static_cast<size_t>(*node_count + 1));
for (int64_t i = 0; i < edge_count; i += 2) {
int u = edge_data[i], v = edge_data[i + 1];
if (u != 0 && v != 0) {
tr->at(u).push_back(v);
} else {
break;
}
}
}
template <typename T>
class Tree2ColFunctor<platform::CPUDeviceContext, T> {
public:
void operator()(const platform::CPUDeviceContext &context,
const framework::Tensor &EdgeSet,
const framework::Tensor &node_features,
framework::Tensor *patch, int max_depth) {
std::vector<std::vector<int>> tr;
auto feature_dims = node_features.dims();
auto cpu_place = boost::get<platform::CPUPlace>(context.GetPlace());
math::SetConstant<platform::CPUDeviceContext, T> constant;
int64_t feature_size = feature_dims[1];
size_t patch_elem_size = 3 * static_cast<size_t>(feature_size);
size_t node_count = 0, patch_count = 0, patch_size;
Tree2ColUtil::construct_tree(EdgeSet, &tr, &node_count);
std::vector<std::vector<TreeNode>> processing_list;
for (size_t u = 1; u <= node_count; u++) {
std::vector<TreeNode> temp_patch =
Tree2ColUtil::construct_patch(u, max_depth, tr);
if (!temp_patch.empty()) {
processing_list.emplace_back(temp_patch);
}
}
patch_size = processing_list.size();
T *patch_data =
patch->mutable_data<T>({static_cast<int64_t>(patch_size),
static_cast<int64_t>(patch_elem_size)},
cpu_place);
constant(context, patch, 0);
const T *features = node_features.data<T>();
for (auto &patch_item : processing_list) {
size_t pointer_base = patch_count * patch_elem_size;
for (auto &v : patch_item) {
T eta_l = v.eta_l<T>(max_depth), eta_r = v.eta_r<T>(max_depth),
eta_t = v.eta_t<T>(max_depth);
size_t id = v.get_node() - 1;
for (int i = 0; i < feature_size; i++) {
patch_data[pointer_base + i * 3] +=
eta_l * features[id * feature_size + i];
patch_data[pointer_base + i * 3 + 1] +=
eta_r * features[id * feature_size + i];
patch_data[pointer_base + i * 3 + 2] +=
eta_t * features[id * feature_size + i];
}
}
patch_count++;
}
patch->Resize({static_cast<int64_t>(patch_count),
static_cast<int64_t>(patch_elem_size)});
}
};
template <typename T>
class Col2TreeFunctor<platform::CPUDeviceContext, T> {
public:
void operator()(const platform::CPUDeviceContext &context,
const framework::Tensor &EdgeSet,
const framework::Tensor &out_grad, framework::Tensor *in_grad,
int max_depth) {
std::vector<std::vector<int>> tr;
auto output_dims = out_grad.dims();
auto cpu_place = boost::get<platform::CPUPlace>(context.GetPlace());
math::SetConstant<platform::CPUDeviceContext, T> constant;
int64_t output_size = output_dims[1];
size_t grad_elem_size = 3 * static_cast<size_t>(output_size);
size_t node_count = 0, grad_count = 0;
Tree2ColUtil::construct_tree(EdgeSet, &tr, &node_count);
std::vector<std::vector<TreeNode>> processing_list;
std::vector<std::vector<TreeNode>> grad_list;
grad_list.resize(node_count);
for (size_t u = 1; u <= node_count; u++) {
std::vector<TreeNode> tmp =
Tree2ColUtil::construct_patch(u, max_depth, tr);
if (!tmp.empty()) {
processing_list.push_back(tmp);
}
}
for (size_t patch_id = 0; patch_id < processing_list.size(); patch_id++) {
for (auto v : processing_list[patch_id]) {
grad_list[v.get_node() - 1].push_back(v.change_node(patch_id + 1));
}
}
T *grad_data =
in_grad->mutable_data<T>({static_cast<int64_t>(node_count),
static_cast<int64_t>(grad_elem_size)},
cpu_place);
constant(context, in_grad, 0);
const T *out_g = out_grad.data<T>();
for (auto &patch_item : grad_list) {
size_t pointer_base = grad_count * grad_elem_size;
for (auto &v : patch_item) {
T eta_l = v.eta_l<T>(max_depth), eta_r = v.eta_r<T>(max_depth),
eta_t = v.eta_t<T>(max_depth);
size_t id = v.get_node() - 1;
for (int i = 0; i < output_size; i++) {
grad_data[pointer_base + i * 3] +=
eta_l * out_g[id * output_size + i];
grad_data[pointer_base + i * 3 + 1] +=
eta_r * out_g[id * output_size + i];
grad_data[pointer_base + i * 3 + 2] +=
eta_t * out_g[id * output_size + i];
}
}
grad_count++;
}
}
};
template class Tree2ColFunctor<platform::CPUDeviceContext, float>;
template class Tree2ColFunctor<platform::CPUDeviceContext, double>;
template class Col2TreeFunctor<platform::CPUDeviceContext, float>;
template class Col2TreeFunctor<platform::CPUDeviceContext, double>;
} // namespace math
} // namespace operators
} // namespace paddle
paddle/fluid/operators/math/tree2col.cu 0 → 100644
浏览文件 @ 54f9d44e
// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include <stack>
#include "paddle/fluid/operators/math/math_function.h"
#include "paddle/fluid/operators/math/tree2col.h"
namespace paddle {
namespace operators {
namespace math {
using Tensor = framework::Tensor;
using Node = paddle::operators::math::TreeNode;
template <typename T>
__global__ void tree2col(const T* eta, const int* node, const int* index,
const T* vectors, T* result, int feature_size, int n) {
const int thread_id =
(blockIdx.x * gridDim.y + blockIdx.y) * blockDim.x + threadIdx.x;
const int patch_id = thread_id / feature_size;
const int j = thread_id % feature_size;
if (patch_id < n) {
const int begin_o = patch_id * 3 * feature_size;
const int begin = index[patch_id * 2], end = index[patch_id * 2 + 1];
T res_l = 0, res_r = 0, res_t = 0;
for (int i = begin; i < end; i++) {
const int id = node[i];
const T vec = vectors[id * feature_size + j];
res_l += eta[i * 3] * vec;
res_r += eta[i * 3 + 1] * vec;
res_t += eta[i * 3 + 2] * vec;
}
result[begin_o + j * 3] = res_l;
result[begin_o + j * 3 + 1] = res_r;
result[begin_o + j * 3 + 2] = res_t;
}
}
template <typename T>
class Tree2ColFunctor<platform::CUDADeviceContext, T> {
public:
void operator()(const paddle::platform::CUDADeviceContext& context,
const framework::Tensor& EdgeSet,
const framework::Tensor& node_features,
framework::Tensor* patch, int max_depth) {
std::vector<std::vector<int>> tr;
auto gpu_place = boost::get<platform::CUDAPlace>(context.GetPlace());
auto cpu_place = platform::CPUPlace();
auto stream = context.stream();
auto feature_dims = node_features.dims();
math::SetConstant<platform::CUDADeviceContext, T> constant;
Tensor EdgeSet_cpu;
framework::TensorCopy(EdgeSet, cpu_place, &EdgeSet_cpu);
int64_t feature_size = feature_dims[1];
size_t patch_elem_size = 3 * static_cast<size_t>(feature_size);
size_t node_count = 0, patch_count = 0, total_size = 0;
size_t max_size = feature_dims[0];
Tree2ColUtil::construct_tree(EdgeSet_cpu, &tr, &node_count);
std::vector<std::vector<Node>> processing_list;
for (size_t u = 1; u <= node_count; u++) {
std::vector<Node> tmp = Tree2ColUtil::construct_patch(u, max_depth, tr);
if (!tmp.empty()) {
processing_list.push_back(tmp);
total_size += tmp.size();
}
}
size_t patch_size = processing_list.size();
Tensor node_cpu, node_gpu, eta_cpu, eta_gpu, index_cpu, index_gpu;
int* node = node_cpu.mutable_data<int>({static_cast<int64_t>(total_size)},
cpu_place);
T* eta = eta_cpu.mutable_data<T>({static_cast<int64_t>(total_size * 3)},
cpu_place);
int* index = index_cpu.mutable_data<int>(
{static_cast<int64_t>(patch_size * 2)}, cpu_place);
int idx = 0, index_idx = 0;
for (auto& tmp : processing_list) {
index[index_idx++] = idx;
for (auto& v : tmp) {
node[idx] = static_cast<int>(v.node - 1);
eta[idx * 3] = v.eta_l<T>(max_depth);
eta[idx * 3 + 1] = v.eta_r<T>(max_depth);
eta[idx * 3 + 2] = v.eta_t<T>(max_depth);
idx++;
}
index[index_idx++] = idx;
}
framework::TensorCopy(node_cpu, gpu_place, context, &node_gpu);
framework::TensorCopy(eta_cpu, gpu_place, context, &eta_gpu);
framework::TensorCopy(index_cpu, gpu_place, context, &index_gpu);
int elem_size = patch_size * feature_size;
int blocks = (elem_size + 1024 - 1) / 1024;
int block_x = 512;
int block_y = (blocks + 512 - 1) / 512;
dim3 threads(1024, 1);
dim3 grid(block_x, block_y);
patch->mutable_data<T>(
{static_cast<int64_t>(max_size), static_cast<int64_t>(patch_elem_size)},
gpu_place);
constant(context, patch, 0);
tree2col<T><<<grid, threads, 0, stream>>>(
eta_gpu.data<T>(), node_gpu.data<int>(), index_gpu.data<int>(),
node_features.data<T>(), patch->data<T>(), feature_size, patch_size);
}
};
template <typename T>
class Col2TreeFunctor<platform::CUDADeviceContext, T> {
public:
void operator()(const platform::CUDADeviceContext& context,
const framework::Tensor& EdgeSet,
const framework::Tensor& patch_grad,
framework::Tensor* embedding_grad, int max_depth) {
std::vector<std::vector<int>> tr;
auto gpu_place = boost::get<platform::CUDAPlace>(context.GetPlace());
auto cpu_place = platform::CPUPlace();
auto stream = context.stream();
auto output_dims = patch_grad.dims();
math::SetConstant<platform::CUDADeviceContext, T> constant;
Tensor EdgeSet_cpu;
framework::TensorCopy(EdgeSet, cpu_place, &EdgeSet_cpu);
int64_t output_size = output_dims[1];
size_t patch_elem_size = 3 * static_cast<size_t>(output_size);
size_t node_count = 0, patch_count = 0;
size_t max_size = output_dims[0];
Tree2ColUtil::construct_tree(EdgeSet_cpu, &tr, &node_count);
std::vector<std::vector<Node>> processing_list;
std::vector<std::vector<Node>> grad_list;
grad_list.resize(node_count);
size_t total_size = 0, grad_size = node_count;
for (size_t u = 1; u <= node_count; u++) {
std::vector<Node> tmp = Tree2ColUtil::construct_patch(u, max_depth, tr);
if (!tmp.empty()) {
processing_list.push_back(tmp);
}
}
for (size_t patch_id = 0; patch_id < processing_list.size(); patch_id++) {
for (auto v : processing_list[patch_id]) {
grad_list[v.get_node() - 1].push_back(v.change_node(patch_id + 1));
}
}
for (auto& tmp : grad_list) {
total_size += tmp.size();
}
Tensor node_cpu, node_gpu, eta_cpu, eta_gpu, index_cpu, index_gpu;
int* node = node_cpu.mutable_data<int>({static_cast<int64_t>(total_size)},
cpu_place);
T* eta = eta_cpu.mutable_data<T>({static_cast<int64_t>(total_size * 3)},
cpu_place);
int* index = index_cpu.mutable_data<int>(
{static_cast<int64_t>(grad_size * 2)}, cpu_place);
size_t idx = 0, index_idx = 0;
for (auto& tmp : grad_list) {
index[index_idx++] = idx;
for (auto& v : tmp) {
node[idx] = static_cast<int>(v.node - 1);
eta[idx * 3] = v.eta_l<T>(max_depth);
eta[idx * 3 + 1] = v.eta_r<T>(max_depth);
eta[idx * 3 + 2] = v.eta_t<T>(max_depth);
idx++;
}
index[index_idx++] = idx;
}
framework::TensorCopy(node_cpu, gpu_place, &node_gpu);
framework::TensorCopy(eta_cpu, gpu_place, &eta_gpu);
framework::TensorCopy(index_cpu, gpu_place, &index_gpu);
int elem_size = output_size * grad_size;
int blocks = (elem_size + 1024 - 1) / 1024;
int block_x = 512;
int block_y = (blocks + 512 - 1) / 512;
dim3 threads(1024, 1);
dim3 grid(block_x, block_y);
embedding_grad->mutable_data<T>(
{static_cast<int64_t>(max_size), static_cast<int64_t>(patch_elem_size)},
gpu_place);
constant(context, embedding_grad, 0);
tree2col<T><<<grid, threads, 0, stream>>>(
eta_gpu.data<T>(), node_gpu.data<int>(), index_gpu.data<int>(),
patch_grad.data<T>(), embedding_grad->data<T>(), output_size,
grad_size);
}
};
template class Tree2ColFunctor<platform::CUDADeviceContext, float>;
template class Tree2ColFunctor<platform::CUDADeviceContext, double>;
template class Col2TreeFunctor<platform::CUDADeviceContext, float>;
template class Col2TreeFunctor<platform::CUDADeviceContext, double>;
} // namespace math
} // namespace operators
} // namespace paddle
paddle/fluid/operators/math/tree2col.h 0 → 100644
浏览文件 @ 54f9d44e
// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#pragma once
#include <array>
#include <unordered_map>
#include <vector>
#include "paddle/fluid/framework/tensor.h"
#include "paddle/fluid/operators/math/math_function.h"
namespace paddle {
using Tensor = framework::Tensor;
using DDim = framework::DDim;
namespace operators {
namespace math {
class TreeNode {
public:
size_t node;
explicit TreeNode(size_t node = 0, size_t index = 0, size_t pclen = 0,
size_t depth = 0)
: node(node), index(index), pclen(pclen), depth(depth) {}
template <typename T>
T eta_t(T filter_depth) {
return ((filter_depth - this->depth) / filter_depth);
}
template <typename T>
T eta_l(T filter_depth) {
T temp;
if (this->pclen == 1) {
temp = 0.5;
} else {
temp = (this->index - 1.0) / (this->pclen - 1.0);
}
return (1.0 - this->eta_t<T>(filter_depth)) * temp;
}
template <typename T>
T eta_r(T filter_depth) {
return (1.0 - this->eta_t<T>(filter_depth)) *
(1.0 - this->eta_l<T>(filter_depth));
}
TreeNode change_node(size_t v) {
return TreeNode(v, this->index, this->pclen, this->depth);
}
size_t get_node() { return this->node; }
size_t get_depth() { return this->depth; }
private:
size_t index, pclen, depth;
};
class Tree2ColUtil {
public:
static std::vector<TreeNode> construct_patch(
size_t root, int max_depth, const std::vector<std::vector<int>> &tr);
static void construct_tree(const Tensor &EdgeSet,
std::vector<std::vector<int>> *tr,
size_t *node_count);
};
template <typename DeviceContext, typename T>
class Tree2ColFunctor {
public:
void operator()(const DeviceContext &context,
const framework::Tensor &EdgeSet,
const framework::Tensor &node_features,
framework::Tensor *patch, int max_depth);
};
template <typename DeviceContext, typename T>
class Col2TreeFunctor {
public:
void operator()(const DeviceContext &context,
const framework::Tensor &EdgeSet,
const framework::Tensor &out_grad, framework::Tensor *in_grad,
int max_depth);
};
} // namespace math
} // namespace operators
} // namespace paddle
paddle/fluid/operators/tree_conv_op.cc 0 → 100644
浏览文件 @ 54f9d44e
// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include "paddle/fluid/operators/tree_conv_op.h"
#include <string>
namespace paddle {
namespace operators {
class TreeConvOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("NodesVector",
"(Tensor) The feature vector of every node on the tree. "
"The shape of the feature vector must be "
"[max_tree_node_size, feature_size].");
AddInput("EdgeSet",
"(Tensor) The Edges of Tree. The edge must be directional. "
"The shape of the edge set must be [max_tree_node_size, 2].");
AddInput("Filter",
"(Tensor) The feature detector. "
"The shape of the filter is "
"[feature_size, 3, output_size, num_filters].");
AddOutput("Out",
"(Tensor) The feature vector of subtrees. "
"The shape of the output tensor is [max_tree_node_size, "
"output_size, num_filters]. "
"The output tensor could be a new feature "
"vector for next tree convolution layers.");
AddAttr<int>("max_depth",
"(int, default: 2) The depth of feature detector.")
.SetDefault(2)
.GreaterThan(1);
AddComment(R"DOC(
**Tree-Based Convolution Operator**
Tree-Based Convolution is a kind of convolution based on tree structure.
Tree-Based Convolution is a part of Tree-Based Convolution Neural Network(TBCNN),
which is used to classify tree structures, such as Abstract Syntax Tree.
Tree-Based Convolution proposed a kind of data structure called continuous binary tree,
which regards multiway tree as binary tree.
The paper of Tree-Based Convolution Operator is here:
https://arxiv.org/abs/1409.5718v1
)DOC");
}
};
class TreeConvOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext *ctx) const override {
PADDLE_ENFORCE(ctx->HasOutput("Out"));
auto edge_dims = ctx->GetInputDim("EdgeSet");
auto vector_dims = ctx->GetInputDim("NodesVector");
auto filter_dims = ctx->GetInputDim("Filter");
PADDLE_ENFORCE_EQ(edge_dims[2], 2, "Input(EdgeSet) dim[2] should be 2");
PADDLE_ENFORCE_EQ(edge_dims.size(), 3,
"The dimension of EdgeSet Tensor should be 3");
PADDLE_ENFORCE_EQ(vector_dims.size(), 3,
"The dimension of NodesVector Tensor should be 3");
PADDLE_ENFORCE_EQ(filter_dims.size(), 4,
"The dimension of Filter Tensor should be 4");
PADDLE_ENFORCE_EQ(filter_dims[1], 3, "Input(Filter) dim[1] should be 3");
PADDLE_ENFORCE_EQ(
filter_dims[0], vector_dims[2],
"Input(Filter) dim[0] must equal to Input(NodesVector) dim[2]");
auto output_dims = framework::make_ddim(
{vector_dims[0], vector_dims[1], filter_dims[2], filter_dims[3]});
ctx->SetOutputDim("Out", output_dims);
}
protected:
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext &ctx) const override {
return framework::OpKernelType(ctx.Input<Tensor>("NodesVector")->type(),
ctx.device_context());
}
};
class TreeConvGradOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext *ctx) const override {
auto vectors_dims = ctx->GetInputDim("NodesVector");
auto filter_dims = ctx->GetInputDim("Filter");
PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")),
"the gradient of output(Out) must not be null");
if (ctx->HasOutput(framework::GradVarName("Filter"))) {
ctx->SetOutputDim(framework::GradVarName("Filter"), filter_dims);
}
if (ctx->HasOutput(framework::GradVarName("NodesVector"))) {
ctx->SetOutputDim(framework::GradVarName("NodesVector"), vectors_dims);
}
}
protected:
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext &ctx) const override {
return framework::OpKernelType(ctx.Input<Tensor>("NodesVector")->type(),
ctx.device_context());
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OPERATOR(tree_conv, ops::TreeConvOp, ops::TreeConvOpMaker,
paddle::framework::DefaultGradOpDescMaker<true>);
REGISTER_OPERATOR(tree_conv_grad, ops::TreeConvGradOp);
REGISTER_OP_CPU_KERNEL(
tree_conv, ops::TreeConvKernel<paddle::platform::CPUDeviceContext, float>,
ops::TreeConvKernel<paddle::platform::CPUDeviceContext, double>);
REGISTER_OP_CPU_KERNEL(
tree_conv_grad,
ops::TreeConvGradKernel<paddle::platform::CPUDeviceContext, float>,
ops::TreeConvGradKernel<paddle::platform::CPUDeviceContext, double>);
paddle/fluid/operators/tree_conv_op.cu 0 → 100644
浏览文件 @ 54f9d44e
// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include "paddle/fluid/operators/tree_conv_op.h"
namespace ops = paddle::operators;
REGISTER_OP_CUDA_KERNEL(
tree_conv, ops::TreeConvKernel<paddle::platform::CUDADeviceContext, float>,
ops::TreeConvKernel<paddle::platform::CUDADeviceContext, double>);
REGISTER_OP_CUDA_KERNEL(
tree_conv_grad,
ops::TreeConvGradKernel<paddle::platform::CUDADeviceContext, float>,
ops::TreeConvGradKernel<paddle::platform::CUDADeviceContext, double>);
paddle/fluid/operators/tree_conv_op.h 0 → 100644
浏览文件 @ 54f9d44e
// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#pragma once
#include <iostream>
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/math/blas.h"
#include "paddle/fluid/operators/math/tree2col.h"
namespace paddle {
namespace operators {
using Tensor = framework::Tensor;
using DDim = framework::DDim;
template <typename DeviceContext, typename T>
class TreeConvKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext &ctx) const override {
math::Tree2ColFunctor<DeviceContext, T> tree2col;
math::SetConstant<DeviceContext, T> constant;
auto *Edges = ctx.Input<Tensor>("EdgeSet");
auto *Embeddings = ctx.Input<Tensor>("NodesVector");
auto *Filter = ctx.Input<Tensor>("Filter");
auto *output_emb = ctx.Output<Tensor>("Out");
int max_depth = ctx.Attr<int>("max_depth");
auto &dev_ctx = ctx.template device_context<DeviceContext>();
auto blas = math::GetBlas<DeviceContext, T>(dev_ctx);
Tensor W;
W.ShareDataWith(*Filter);
W.Resize(framework::flatten_to_2d(Filter->dims(), 2));
int batch_size = static_cast<int>(Edges->dims()[0]);
int n = static_cast<int>(Embeddings->dims()[1]);
int out_size = static_cast<int>(Filter->dims()[2]);
int num_filters = static_cast<int>(Filter->dims()[3]);
output_emb->mutable_data<T>({batch_size, n, out_size, num_filters},
ctx.GetPlace());
auto edge_set_slicedim = framework::slice_ddim(
Edges->dims(), 1, static_cast<int>(Edges->dims().size()));
auto embedding_slicedim = framework::slice_ddim(
Embeddings->dims(), 1, static_cast<int>(Embeddings->dims().size()));
auto output_slicedim = framework::slice_ddim(
output_emb->dims(), 1, static_cast<int>(output_emb->dims().size()));
output_slicedim = framework::flatten_to_2d(output_slicedim, 1);
for (int idx = 0; idx < batch_size; idx++) {
auto edge_set = Edges->Slice(idx, idx + 1).Resize(edge_set_slicedim);
auto embeddings =
Embeddings->Slice(idx, idx + 1).Resize(embedding_slicedim);
auto out_vec = output_emb->Slice(idx, idx + 1).Resize(output_slicedim);
Tensor patch;
tree2col(dev_ctx, edge_set, embeddings, &patch, max_depth);
constant(dev_ctx, &out_vec, 0);
blas.MatMul(patch, W, &out_vec);
}
}
};
template <typename DeviceContext, typename T>
class TreeConvGradKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext &ctx) const override {
auto *out_g = ctx.Input<Tensor>(framework::GradVarName("Out"));
auto *in_g = ctx.Output<Tensor>(framework::GradVarName("NodesVector"));
auto *filter_g = ctx.Output<Tensor>(framework::GradVarName("Filter"));
int max_depth = ctx.Attr<int>("max_depth");
auto *Embeddings = ctx.Input<Tensor>("NodesVector");
auto *edges = ctx.Input<Tensor>("EdgeSet");
auto *Filter = ctx.Input<Tensor>("Filter");
math::Tree2ColFunctor<DeviceContext, T> tree2col;
math::Col2TreeFunctor<DeviceContext, T> col2tree;
math::SetConstant<DeviceContext, T> constant;
auto &dev_ctx = ctx.template device_context<DeviceContext>();
auto blas = math::GetBlas<DeviceContext, T>(dev_ctx);
Tensor W;
W.ShareDataWith(*Filter);
W.Resize(framework::flatten_to_2d(Filter->dims(), 1));
int batch_size = static_cast<int>(Embeddings->dims()[0]);
auto edge_set_slicedim = framework::slice_ddim(
edges->dims(), 1, static_cast<int>(edges->dims().size()));
auto embedding_slicedim = framework::slice_ddim(
Embeddings->dims(), 1, static_cast<int>(Embeddings->dims().size()));
auto out_grad_dims = framework::slice_ddim(
out_g->dims(), 1, static_cast<int>(out_g->dims().size()));
out_grad_dims = framework::flatten_to_2d(out_grad_dims, 1);
if (filter_g) {
filter_g->mutable_data<T>(Filter->dims(), ctx.GetPlace());
Tensor f_g;
f_g.ShareDataWith(*filter_g);
f_g.Resize(framework::flatten_to_2d(Filter->dims(), 2));
constant(dev_ctx, filter_g, 0);
for (int batch_id = 0; batch_id < batch_size; batch_id++) {
auto edge_set =
edges->Slice(batch_id, batch_id + 1).Resize(edge_set_slicedim);
auto embeddings = Embeddings->Slice(batch_id, batch_id + 1)
.Resize(embedding_slicedim);
auto out_grad =
out_g->Slice(batch_id, batch_id + 1).Resize(out_grad_dims);
Tensor patch;
tree2col(dev_ctx, edge_set, embeddings, &patch, max_depth);
blas.MatMul(patch, true, out_grad, false, T(1.0), &f_g, T(1.0));
}
}
if (in_g) {
auto input_grad_dims = framework::slice_ddim(
in_g->dims(), 1, static_cast<int>(in_g->dims().size()));
in_g->mutable_data<T>(Embeddings->dims(), ctx.GetPlace());
constant(dev_ctx, in_g, 0);
for (int batch_id = 0; batch_id < batch_size; batch_id++) {
auto edge_set =
edges->Slice(batch_id, batch_id + 1).Resize(edge_set_slicedim);
auto out_grad =
out_g->Slice(batch_id, batch_id + 1).Resize(out_grad_dims);
auto in_grad =
in_g->Slice(batch_id, batch_id + 1).Resize(input_grad_dims);
Tensor in_grad_temp;
col2tree(dev_ctx, edge_set, out_grad, &in_grad_temp, max_depth);
blas.MatMul(in_grad_temp, false, W, true, &in_grad);
}
}
}
};
} // namespace operators
} // namespace paddle
python/paddle/fluid/async_executor.py
浏览文件 @ 54f9d44e
......@@ -200,7 +200,6 @@ class AsyncExecutor(object):
local_path,
self.instance.get_worker_index(),
self.instance.get_node_cnt() / 2,
file_cnt,
multi_processes=process_num)
self.instance.barrier_worker() #wait for download_data
......
python/paddle/fluid/layers/nn.py
浏览文件 @ 54f9d44e
......@@ -183,6 +183,7 @@ __all__ = [
'psroi_pool',
'teacher_student_sigmoid_loss',
'huber_loss',
'tree_conv',
]
kIgnoreIndex = -100
......@@ -864,12 +865,14 @@ def dynamic_gru(input,
is_reverse=False,
gate_activation='sigmoid',
candidate_activation='tanh',
h_0=None):
h_0=None,
origin_mode=False):
"""
**Gated Recurrent Unit (GRU) Layer**
Refer to `Empirical Evaluation of Gated Recurrent Neural Networks on
Sequence Modeling <https://arxiv.org/abs/1412.3555>`_ .
if origin_mode is False, then the equation of a gru step is from paper
`Empirical Evaluation of Gated Recurrent Neural Networks on Sequence
Modeling <https://arxiv.org/pdf/1412.3555.pdf>`_ .
The formula is as follows:
......@@ -883,6 +886,21 @@ def dynamic_gru(input,
h_t & = (1-u_t) \odot h_{t-1} + u_t \odot \\tilde{h_t}
if origin_mode is True then the equation is from paper
Learning Phrase Representations using RNN Encoder-Decoder for Statistical
Machine Translation <https://arxiv.org/pdf/1406.1078.pdf>`_
.. math::
u_t & = act_g(W_{ux}x_{t} + W_{uh}h_{t-1} + b_u)
r_t & = act_g(W_{rx}x_{t} + W_{rh}h_{t-1} + b_r)
\\tilde{h_t} & = act_c(W_{cx}x_{t} + W_{ch}(r_t \odot h_{t-1}) + b_c)
h_t & = u_t \odot h_{t-1} + (1-u_t) \odot \\tilde{h_t}
The :math:`\odot` is the element-wise product of the vectors. :math:`act_g`
is the update gate and reset gate activation function and :math:`sigmoid`
is usually used for it. :math:`act_c` is the activation function for
......@@ -980,7 +998,8 @@ def dynamic_gru(input,
attrs={
'is_reverse': is_reverse,
'gate_activation': gate_activation,
'activation': candidate_activation
'activation': candidate_activation,
'origin_mode': origin_mode
})
return hidden
......@@ -991,9 +1010,14 @@ def gru_unit(input,
param_attr=None,
bias_attr=None,
activation='tanh',
gate_activation='sigmoid'):
gate_activation='sigmoid',
origin_mode=False):
"""
GRU unit layer. The equation of a gru step is:
**GRU unit layer**
if origin_mode is True, then the equation of a gru step is from paper
`Learning Phrase Representations using RNN Encoder-Decoder for Statistical
Machine Translation <https://arxiv.org/pdf/1406.1078.pdf>`_
.. math::
u_t & = actGate(xu_{t} + W_u h_{t-1} + b_u)
......@@ -1002,7 +1026,21 @@ def gru_unit(input,
m_t & = actNode(xm_t + W_c dot(r_t, h_{t-1}) + b_m)
h_t & = dot((1-u_t), m_t) + dot(u_t, h_{t-1})
h_t & = dot(u_t, h_{t-1}) + dot((1-u_t), m_t)
if origin_mode is False, then the equation of a gru step is from paper
`Empirical Evaluation of Gated Recurrent Neural Networks on Sequence
Modeling <https://arxiv.org/pdf/1412.3555.pdf>`_
.. math::
u_t & = actGate(xu_{t} + W_u h_{t-1} + b_u)
r_t & = actGate(xr_{t} + W_r h_{t-1} + b_r)
m_t & = actNode(xm_t + W_c dot(r_t, h_{t-1}) + b_m)
h_t & = dot((1-u_t), h_{t-1}) + dot(u_t, m_t)
The inputs of gru unit includes :math:`z_t`, :math:`h_{t-1}`. In terms
of the equation above, the :math:`z_t` is split into 3 parts -
......@@ -9893,3 +9931,73 @@ def huber_loss(input, label, delta):
'Residual': residual},
attrs={'delta': delta})
return out
@templatedoc()
def tree_conv(nodes_vector,
edge_set,
output_size,
num_filters=1,
max_depth=2,
act='tanh',
param_attr=None,
bias_attr=None,
name=None):
"""
${comment}
Args:
nodes_vector(${nodes_vector_type}): ${nodes_vector_comment}
edge_set(${edge_set_type}): ${edge_set_comment}
output_size(int): output feature width
num_filters(int): number of filters, Default 1
max_depth(int): max depth of filters, Default 2
act(str): activation function, Default tanh
param_attr(ParamAttr): the parameter attribute for the filters, Default None
bias_attr(ParamAttr): the parameter attribute for the bias of this layer, Default None
name(str): a name of this layer(optional). If set None, the layer will be named automatically, Default None
Returns:
out(${out_type}): ${out_comment}
Examples:
.. code-block:: python
nodes_vector = layers.data(name='vectors', shape=[None, 10, 5], dtype='float32)
# None for batch size, 10 for max_node_size of dataset, 5 for vector width
edge_set = layers.data(name='edge_set', shape=[None, 10, 2], dtype='float32')
# None for batch size, 10 for max_node_size of dataset, 2 for every edge has two nodes
# edges must be directional
out_vector = layers.tree_conv(nodes_vector, edge_set, 6, 1, 2, 'tanh',
ParamAttr(initializer=Constant(1.0), ParamAttr(initializer=Constant(1.0))
# the shape of output will be [None, 10, 6, 1],
# None for batch size, 10 for max_node_size of dataset, 6 for output size, 1 for 1 filter
out_vector = layers.reshape(out_vector, shape=[None, 10, 6])
# After reshape, output tensor could be nodes_vector for next tree convolution
out_vector_2 = layers.tree_conv(out_vector, edge_set, 3, 4, 2, 'tanh',
ParamAttr(initializer=Constant(1.0), ParamAttr(initializer=Constant(1.0))
# also output tensor could be pooling(the pooling in paper called global pooling)
pooled = layers.reduce_max(out_vector, dims=2) # global pooling
"""
helper = LayerHelper("tree_conv", **locals())
dtype = helper.input_dtype('nodes_vector')
feature_size = nodes_vector.shape[2]
W_shape = [feature_size, 3, output_size, num_filters]
W = helper.create_parameter(
attr=param_attr, shape=W_shape, dtype=dtype, is_bias=False)
if name == None:
out = helper.create_variable_for_type_inference(dtype=dtype)
else:
out = helper.create_variable(name=name, dtype=dtype, persistable=False)
helper.append_op(
type='tree_conv',
inputs={'NodesVector': nodes_vector,
'EdgeSet': edge_set,
'Filter': W},
outputs={'Out': out, },
attrs={'max_depth': max_depth})
if helper.bias_attr:
pre_activation = helper.append_bias_op(out)
else:
pre_activation = out
return helper.append_activation(pre_activation)
python/paddle/fluid/tests/book/high-level-api/recommender_system/test_recommender_system_newapi.py
浏览文件 @ 54f9d44e
......@@ -231,14 +231,17 @@ def infer(use_cuda, inference_program, params_dirname):
# Correspondingly, recursive_sequence_lengths = [[3, 2]] contains one
# level of detail info, indicating that `data` consists of two sequences
# of length 3 and 2, respectively.
user_id = fluid.create_lod_tensor([[1]], [[1]], place)
gender_id = fluid.create_lod_tensor([[1]], [[1]], place)
age_id = fluid.create_lod_tensor([[0]], [[1]], place)
job_id = fluid.create_lod_tensor([[10]], [[1]], place)
movie_id = fluid.create_lod_tensor([[783]], [[1]], place)
category_id = fluid.create_lod_tensor([[10, 8, 9]], [[3]], place)
movie_title = fluid.create_lod_tensor([[1069, 4140, 2923, 710, 988]], [[5]],
place)
user_id = fluid.create_lod_tensor([[np.int64(1)]], [[1]], place)
gender_id = fluid.create_lod_tensor([[np.int64(1)]], [[1]], place)
age_id = fluid.create_lod_tensor([[np.int64(0)]], [[1]], place)
job_id = fluid.create_lod_tensor([[np.int64(10)]], [[1]], place)
movie_id = fluid.create_lod_tensor([[np.int64(783)]], [[1]], place)
category_id = fluid.create_lod_tensor(
[np.array(
[10, 8, 9], dtype='int64')], [[3]], place)
movie_title = fluid.create_lod_tensor(
[np.array(
[1069, 4140, 2923, 710, 988], dtype='int64')], [[5]], place)
results = inferencer.infer(
{
......
python/paddle/fluid/tests/book/test_recommender_system.py
浏览文件 @ 54f9d44e
......@@ -271,26 +271,30 @@ def infer(use_cuda, save_dirname=None):
# Correspondingly, recursive_sequence_lengths = [[3, 2]] contains one
# level of detail info, indicating that `data` consists of two sequences
# of length 3 and 2, respectively.
user_id = fluid.create_lod_tensor([[1]], [[1]], place)
user_id = fluid.create_lod_tensor([[np.int64(1)]], [[1]], place)
assert feed_target_names[1] == "gender_id"
gender_id = fluid.create_lod_tensor([[1]], [[1]], place)
gender_id = fluid.create_lod_tensor([[np.int64(1)]], [[1]], place)
assert feed_target_names[2] == "age_id"
age_id = fluid.create_lod_tensor([[0]], [[1]], place)
age_id = fluid.create_lod_tensor([[np.int64(0)]], [[1]], place)
assert feed_target_names[3] == "job_id"
job_id = fluid.create_lod_tensor([[10]], [[1]], place)
job_id = fluid.create_lod_tensor([[np.int64(10)]], [[1]], place)
assert feed_target_names[4] == "movie_id"
movie_id = fluid.create_lod_tensor([[783]], [[1]], place)
movie_id = fluid.create_lod_tensor([[np.int64(783)]], [[1]], place)
assert feed_target_names[5] == "category_id"
category_id = fluid.create_lod_tensor([[10, 8, 9]], [[3]], place)
category_id = fluid.create_lod_tensor(
[np.array(
[10, 8, 9], dtype='int64')], [[3]], place)
assert feed_target_names[6] == "movie_title"
movie_title = fluid.create_lod_tensor([[1069, 4140, 2923, 710, 988]],
[[5]], place)
movie_title = fluid.create_lod_tensor(
[np.array(
[1069, 4140, 2923, 710, 988], dtype='int64')], [[5]],
place)
# Construct feed as a dictionary of {feed_target_name: feed_target_data}
# and results will contain a list of data corresponding to fetch_targets.
......
python/paddle/fluid/tests/unittests/test_auc_op.py
浏览文件 @ 54f9d44e
......@@ -24,7 +24,7 @@ class TestAucOp(OpTest):
def setUp(self):
self.op_type = "auc"
pred = np.random.random((128, 2)).astype("float32")
labels = np.random.randint(0, 2, (128, 1))
labels = np.random.randint(0, 2, (128, 1)).astype("int64")
num_thresholds = 200
stat_pos = np.zeros((num_thresholds + 1, )).astype("int64")
......
python/paddle/fluid/tests/unittests/test_gru_op.py
浏览文件 @ 54f9d44e
......@@ -31,7 +31,8 @@ def gru(
is_reverse,
act_state,
act_gate,
dtype='float32'):
dtype='float32',
origin_mode=False):
def _seq_to_batch(lod, is_reverse):
idx_in_seq_list = []
seq_lens = lod[0]
......@@ -66,7 +67,10 @@ def gru(
w_c = w.flatten()[D * D * 2:].reshape((D, D))
c = act_state(np.dot(r_h_p, w_c) + g[:, D * 2:])
g = np.hstack((u_r, c))
h = u * c + (1 - u) * h_p
if origin_mode:
h = (1 - u) * c + u * h_p
else:
h = u * c + (1 - u) * h_p
return g, r_h_p, h
T = sum(lod[0])
......@@ -110,6 +114,7 @@ class TestGRUOp(OpTest):
self.act_state = 'tanh'
self.act_gate = 'sigmoid'
self.dtype = 'float64'
self.origin_mode = False
self.set_confs()
T = sum(self.lod[0])
......@@ -126,7 +131,8 @@ class TestGRUOp(OpTest):
batch_gate, batch_reset_hidden_prev, batch_hidden, hidden = gru(
input, self.lod, h0, weight, bias, self.is_reverse,
ACTIVATION[self.act_state], ACTIVATION[self.act_gate], self.dtype)
ACTIVATION[self.act_state], ACTIVATION[self.act_gate], self.dtype,
self.origin_mode)
self.inputs = {'Input': (input, self.lod), 'Weight': weight}
if self.with_bias:
......@@ -145,7 +151,8 @@ class TestGRUOp(OpTest):
self.attrs = {
'activation': self.act_state,
'gate_activation': self.act_gate,
'is_reverse': self.is_reverse
'is_reverse': self.is_reverse,
'origin_mode': self.origin_mode
}
def test_check_output(self):
......@@ -155,12 +162,24 @@ class TestGRUOp(OpTest):
self.check_grad(['Input', 'H0', 'Weight', 'Bias'], ['Hidden'])
class TestGRUOriginMode(TestGRUOp):
def set_confs(self):
self.origin_mode = True
class TestGRUOp2(TestGRUOp):
def set_confs(self):
self.D = 19
self.dtype = 'float32'
class TestGRUOp2OriginMode(TestGRUOp):
def set_confs(self):
self.D = 19
self.dtype = 'float32'
self.origin_mode = True
class TestGRUOpNoInitial(TestGRUOp):
def set_confs(self):
self.with_h0 = False
......@@ -182,5 +201,11 @@ class TestGRUOpReverse(TestGRUOp):
self.is_reverse = True
class TestGRUOpReverseOriginMode(TestGRUOp):
def set_confs(self):
self.is_reverse = True
self.origin_mode = True
if __name__ == "__main__":
unittest.main()
python/paddle/fluid/tests/unittests/test_gru_unit_op.py
浏览文件 @ 54f9d44e
......@@ -53,7 +53,7 @@ class TestGRUUnitOp(OpTest):
GRUActivationType.relu: relu,
}
def set_inputs(self):
def set_inputs(self, origin_mode=False):
batch_size = self.batch_size
frame_size = self.frame_size
self.op_type = 'gru_unit'
......@@ -68,10 +68,11 @@ class TestGRUUnitOp(OpTest):
}
self.attrs = {
'activation': GRUActivationType.tanh,
'gate_activation': GRUActivationType.sigmoid
'gate_activation': GRUActivationType.sigmoid,
'origin_mode': origin_mode
}
def set_outputs(self):
def set_outputs(self, origin_mode=False):
# GRU calculations
batch_size = self.batch_size
frame_size = self.frame_size
......@@ -93,7 +94,10 @@ class TestGRUUnitOp(OpTest):
c = self.activate[self.attrs['activation']](np.dot(r_h_p, w_c) +
g[:, frame_size * 2:])
g = np.hstack((u_r, c))
h = u * c + (1 - u) * h_p
if origin_mode:
h = (1 - u) * c + u * h_p
else:
h = u * c + (1 - u) * h_p
self.outputs = {
'Gate': g.astype('float64'),
'ResetHiddenPrev': r_h_p.astype('float64'),
......@@ -111,8 +115,14 @@ class TestGRUUnitOp(OpTest):
self.check_grad(['Input', 'HiddenPrev', 'Weight'], ['Hidden'])
class TestGRUUnitOpOriginMode(TestGRUUnitOp):
def setUp(self):
self.set_inputs(origin_mode=True)
self.set_outputs(origin_mode=True)
class TestGRUUnitOpWithBias(TestGRUUnitOp):
def set_inputs(self):
def set_inputs(self, origin_mode=False):
batch_size = self.batch_size
frame_size = self.frame_size
super(TestGRUUnitOpWithBias, self).set_inputs()
......@@ -120,7 +130,8 @@ class TestGRUUnitOpWithBias(TestGRUUnitOp):
-0.1, 0.1, (1, frame_size * 3)).astype('float64')
self.attrs = {
'activation': GRUActivationType.identity,
'gate_activation': GRUActivationType.sigmoid
'gate_activation': GRUActivationType.sigmoid,
'origin_mode': origin_mode
}
def test_check_grad(self):
......@@ -132,5 +143,11 @@ class TestGRUUnitOpWithBias(TestGRUUnitOp):
no_grad_set=set('Input'))
class TestGRUUnitOpWithBiasOriginMode(TestGRUUnitOpWithBias):
def setUp(self):
self.set_inputs(origin_mode=True)
self.set_outputs(origin_mode=True)
if __name__ == '__main__':
unittest.main()
python/paddle/fluid/tests/unittests/test_nce.py
浏览文件 @ 54f9d44e
......@@ -68,7 +68,8 @@ class TestNCE(OpTest):
weight = np.random.randn(num_classes, dim).astype(np.float32)
bias = np.random.randn(num_classes).astype(np.float32)
sample_weight = np.random.randn(batch_size).astype(np.float32)
labels = np.random.randint(0, num_classes, (batch_size, num_true_class))
labels = np.random.randint(0, num_classes,
(batch_size, num_true_class)).astype("int64")
self.attrs = {
'num_total_classes': num_classes,
'num_neg_samples': num_neg_samples,
......
python/paddle/fluid/tests/unittests/test_parallel_executor_fetch_feed.py
浏览文件 @ 54f9d44e
......@@ -24,14 +24,14 @@ import os
def Lenet(data, class_dim):
conv1 = fluid.layers.conv2d(data, 32, 5, 1, act=None)
conv1 = fluid.layers.conv2d(data, 4, 5, 1, act=None)
bn1 = fluid.layers.batch_norm(conv1, act='relu')
pool1 = fluid.layers.pool2d(bn1, 2, 'max', 2)
conv2 = fluid.layers.conv2d(pool1, 50, 5, 1, act=None)
conv2 = fluid.layers.conv2d(pool1, 16, 5, 1, act=None)
bn2 = fluid.layers.batch_norm(conv2, act='relu')
pool2 = fluid.layers.pool2d(bn2, 2, 'max', 2)
fc1 = fluid.layers.fc(pool2, size=500, act='relu')
fc1 = fluid.layers.fc(pool2, size=50, act='relu')
fc2 = fluid.layers.fc(fc1, size=class_dim, act='softmax')
return fc2
......
python/paddle/fluid/tests/unittests/test_tree_conv_op.py 0 → 100644
浏览文件 @ 54f9d44e
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import numpy as np
from op_test import OpTest
def collect_node_patch(og, max_depth):
"""
The naive method to construct patches
:param og: original graph
:param max_depth: the depth of convolution filters
:return: convolution patches
"""
def gen(node, max_depth):
collected = [(node, 1, 1, 0, max_depth)]
def recurse_helper(node, depth):
if depth > max_depth:
return
l = len(og[node])
for idx, c in enumerate(og[node], 1):
if depth + 1 < max_depth:
collected.append((c, idx, l, depth + 1, max_depth))
recurse_helper(c, depth + 1)
recurse_helper(node, 0)
return collected
res = []
for u in range(1, len(og)):
lis = gen(u, max_depth)
if len(lis) > 0:
res.append(lis)
return res
class TestTreeConvOp(OpTest):
def setUp(self):
self.n = 17
self.fea_size = 3
self.output_size = 1
self.max_depth = 2
self.batch_size = 1
self.num_filters = 1
adj_array = [
1, 2, 1, 3, 1, 4, 1, 5, 2, 6, 2, 7, 2, 8, 4, 9, 4, 10, 5, 11, 6, 12,
6, 13, 9, 14, 9, 15, 9, 16, 9, 17
]
adj = np.array(adj_array).reshape((1, self.n - 1, 2)).astype('int32')
adj = np.tile(adj, (self.batch_size, 1, 1))
self.op_type = 'tree_conv'
vectors = np.random.random(
(self.batch_size, self.n, self.fea_size)).astype('float32')
self.inputs = {
'EdgeSet': adj,
'NodesVector': vectors,
'Filter': np.random.random((self.fea_size, 3, self.output_size,
self.num_filters)).astype('float32')
}
self.attrs = {'max_depth': self.max_depth}
vectors = []
for i in range(self.batch_size):
vector = self.get_output_naive(i)
vectors.append(vector)
self.outputs = {'Out': np.array(vectors).astype('float32'), }
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(
['NodesVector', 'Filter'], 'Out', max_relative_error=0.5)
def get_output_naive(self, batch_id):
og = [[] for i in range(1, self.n + 2)]
st = np.array(self.inputs['EdgeSet'][batch_id]).tolist()
for e in st:
og[e[0]].append(e[1])
patches = collect_node_patch(og, self.max_depth)
W = np.array(self.inputs['Filter']).astype('float32')
W = np.transpose(W, axes=[1, 0, 2, 3])
vec = []
for i, patch in enumerate(patches, 1):
result = np.zeros((1, W.shape[2], W.shape[3]))
for v in patch:
eta_t = float(v[4] - v[3]) / float(v[4])
eta_l = (1.0 - eta_t) * (0.5 if v[2] == 1 else
float(v[1] - 1.0) / float(v[2] - 1.0))
eta_r = (1.0 - eta_t) * (1.0 - eta_l)
x = self.inputs['NodesVector'][batch_id][v[0] - 1]
eta = np.array([eta_l, eta_r, eta_t]).reshape(
(3, 1)).astype('float32')
Wconvi = np.tensordot(eta, W, axes=([0], [0]))
x = np.array(x).reshape((1, 1, self.fea_size))
res = np.tensordot(x, Wconvi, axes=2)
result = result + res
vec.append(result)
vec = np.concatenate(vec, axis=0)
vec = np.concatenate(
[
vec, np.zeros(
(self.n - vec.shape[0], W.shape[2], W.shape[3]),
dtype='float32')
],
axis=0)
return vec
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