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提交 d231e550 编写于 作者: S sneaxiy
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merge develop 

test=develop
上级 cf8d2e67 080740b3
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Showing with 3318 addition and 912 deletion
paddle/fluid/API.spec
浏览文件 @ d231e550
......@@ -118,9 +118,10 @@ paddle.fluid.layers.label_smooth ArgSpec(args=['label', 'prior_dist', 'epsilon',
paddle.fluid.layers.roi_pool ArgSpec(args=['input', 'rois', 'pooled_height', 'pooled_width', 'spatial_scale'], varargs=None, keywords=None, defaults=(1, 1, 1.0))
paddle.fluid.layers.roi_align ArgSpec(args=['input', 'rois', 'pooled_height', 'pooled_width', 'spatial_scale', 'sampling_ratio', 'name'], varargs=None, keywords=None, defaults=(1, 1, 1.0, -1, None))
paddle.fluid.layers.dice_loss ArgSpec(args=['input', 'label', 'epsilon'], varargs=None, keywords=None, defaults=(1e-05,))
paddle.fluid.layers.image_resize ArgSpec(args=['input', 'out_shape', 'scale', 'name', 'resample'], varargs=None, keywords=None, defaults=(None, None, None, 'BILINEAR'))
paddle.fluid.layers.image_resize ArgSpec(args=['input', 'out_shape', 'scale', 'name', 'resample', 'actual_shape'], varargs=None, keywords=None, defaults=(None, None, None, 'BILINEAR', None))
paddle.fluid.layers.image_resize_short ArgSpec(args=['input', 'out_short_len', 'resample'], varargs=None, keywords=None, defaults=('BILINEAR',))
paddle.fluid.layers.resize_bilinear ArgSpec(args=['input', 'out_shape', 'scale', 'name'], varargs=None, keywords=None, defaults=(None, None, None))
paddle.fluid.layers.resize_bilinear ArgSpec(args=['input', 'out_shape', 'scale', 'name', 'actual_shape'], varargs=None, keywords=None, defaults=(None, None, None, None))
paddle.fluid.layers.resize_nearest ArgSpec(args=['input', 'out_shape', 'scale', 'name', 'actual_shape'], varargs=None, keywords=None, defaults=(None, None, None, None))
paddle.fluid.layers.gather ArgSpec(args=['input', 'index'], varargs=None, keywords=None, defaults=None)
paddle.fluid.layers.scatter ArgSpec(args=['input', 'index', 'updates', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.sequence_scatter ArgSpec(args=['input', 'index', 'updates', 'name'], varargs=None, keywords=None, defaults=(None,))
......@@ -178,6 +179,7 @@ paddle.fluid.layers.space_to_depth ArgSpec(args=['x', 'blocksize', 'name'], vara
paddle.fluid.layers.affine_grid ArgSpec(args=['theta', 'out_shape', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.sequence_reverse ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.affine_channel ArgSpec(args=['x', 'scale', 'bias', 'data_layout', 'name'], varargs=None, keywords=None, defaults=(None, None, 'NCHW', None))
paddle.fluid.layers.similarity_focus ArgSpec(args=['input', 'axis', 'indexes', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.hash ArgSpec(args=['input', 'hash_size', 'num_hash', 'name'], varargs=None, keywords=None, defaults=(1, None))
paddle.fluid.layers.grid_sampler ArgSpec(args=['x', 'grid', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.log_loss ArgSpec(args=['input', 'label', 'epsilon', 'name'], varargs=None, keywords=None, defaults=(0.0001, None))
......@@ -200,6 +202,7 @@ paddle.fluid.layers.create_tensor ArgSpec(args=['dtype', 'name', 'persistable'],
paddle.fluid.layers.create_parameter ArgSpec(args=['shape', 'dtype', 'name', 'attr', 'is_bias', 'default_initializer'], varargs=None, keywords=None, defaults=(None, None, False, None))
paddle.fluid.layers.create_global_var ArgSpec(args=['shape', 'value', 'dtype', 'persistable', 'force_cpu', 'name'], varargs=None, keywords=None, defaults=(False, False, None))
paddle.fluid.layers.cast ArgSpec(args=['x', 'dtype'], varargs=None, keywords=None, defaults=None)
paddle.fluid.layers.tensor_array_to_tensor ArgSpec(args=['input', 'axis', 'name'], varargs=None, keywords=None, defaults=(1, None))
paddle.fluid.layers.concat ArgSpec(args=['input', 'axis', 'name'], varargs=None, keywords=None, defaults=(0, None))
paddle.fluid.layers.sums ArgSpec(args=['input', 'out'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.assign ArgSpec(args=['input', 'output'], varargs=None, keywords=None, defaults=(None,))
......
paddle/fluid/framework/data_device_transform.cc
浏览文件 @ d231e550
......@@ -18,8 +18,8 @@ namespace framework {
void TransDataDevice(const Tensor &in, const platform::Place &dst_place,
Tensor *out) {
VLOG(3) << "DeviceTransform in, src_place " << in.place()
<< " dst_place: " << dst_place;
VLOG(30) << "DeviceTransform in, src_place " << in.place()
<< " dst_place: " << dst_place;
PADDLE_ENFORCE_NE(
in.place().which(), dst_place.which(),
......
paddle/fluid/framework/data_device_transform_test.cu
浏览文件 @ d231e550
......@@ -49,10 +49,10 @@ class TestOpWithKernel : public OperatorWithKernel {
OpKernelType GetExpectedKernelType(
const ExecutionContext& ctx) const override {
if (Attr<bool>("use_gpu")) {
VLOG(3) << "force use gpu kernel";
VLOG(30) << "force use gpu kernel";
return OpKernelType(proto::VarType::FP32, platform::CUDAPlace(0));
} else {
VLOG(3) << "use default kernel";
VLOG(30) << "use default kernel";
return OpKernelType(proto::VarType::FP32,
ctx.Input<Tensor>("input")->place());
}
......@@ -148,7 +148,7 @@ TEST(Operator, CPUtoGPU) {
// get output
auto* output2 = scope.Var("OUT2");
gpu_op->Run(scope, cuda_place);
VLOG(3) << "after gpu_op run";
VLOG(30) << "after gpu_op run";
// auto* output2_ptr = output2->Get<LoDTensor>().data<float>();
paddle::platform::DeviceContextPool& pool =
......
paddle/fluid/framework/details/broadcast_op_handle.cc
浏览文件 @ d231e550
......@@ -60,7 +60,7 @@ void BroadcastOpHandle::BroadcastOneVar(
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(30) << "in var " << in_var_handle.name_ << "not inited, return!";
return;
}
......
paddle/fluid/framework/details/modify_op_lock_and_record_event_pass.cc
浏览文件 @ d231e550
......@@ -45,8 +45,8 @@ std::unique_ptr<ir::Graph> ModifyOpLockAndRecordEventPass::ApplyImpl(
IsLockAndRecordEventFreeComputationOpHandle(compute_op, graph_view);
compute_op->SetLockAndRecordEventFree(is_lock_and_record_event_free);
if (is_lock_and_record_event_free) {
VLOG(10) << "Set is_lock_and_record_event_free be true in op "
<< compute_op->DebugString();
VLOG(100) << "Set is_lock_and_record_event_free be true in op "
<< compute_op->DebugString();
}
}
return ir_graph;
......
paddle/fluid/framework/details/multi_devices_graph_pass.cc
浏览文件 @ d231e550
......@@ -399,7 +399,7 @@ std::unique_ptr<ir::Graph> MultiDevSSAGraphBuilder::ApplyImpl(
for (size_t i = 0; i < backward_vars.size(); i += 2) {
auto &p_name = backward_vars[i];
auto &g_name = backward_vars[i + 1];
VLOG(10) << "Bcast " << g_name << " for parameter " << p_name;
VLOG(100) << "Bcast " << g_name << " for parameter " << p_name;
switch (strategy_.reduce_) {
case BuildStrategy::ReduceStrategy::kReduce:
......@@ -809,8 +809,8 @@ int MultiDevSSAGraphBuilder::CreateRPCOp(
node->Op()->GetAttr(OpProtoAndCheckerMaker::OpRoleVarAttrName()));
PADDLE_ENFORCE_EQ(send_param_grad.size(), 2U);
op_dev_id = GetAppropriateDeviceID({send_param_grad[1]});
VLOG(10) << "send grad " << input_var_names[0] << " origin "
<< send_param_grad[1] << " place: " << op_dev_id;
VLOG(100) << "send grad " << input_var_names[0] << " origin "
<< send_param_grad[1] << " place: " << op_dev_id;
for (auto &varname : input_var_names) {
sharded_var_device->emplace(varname, op_dev_id);
}
......@@ -826,9 +826,9 @@ int MultiDevSSAGraphBuilder::CreateRPCOp(
if (recv_param_grad.size() == 2U) {
op_dev_id =
GetVarDeviceID(*result, recv_param_grad[1], *sharded_var_device);
VLOG(10) << "recv param " << recv_param_grad[0]
<< " get grad place: " << recv_param_grad[1]
<< " place: " << op_dev_id;
VLOG(100) << "recv param " << recv_param_grad[0]
<< " get grad place: " << recv_param_grad[1]
<< " place: " << op_dev_id;
} else {
op_dev_id = GetAppropriateDeviceID(output_var_names);
}
......
paddle/fluid/framework/details/reference_count_pass.cc
浏览文件 @ d231e550
......@@ -140,8 +140,8 @@ std::unique_ptr<ir::Graph> ReferenceCountPass::ApplyImpl(
if (next_compute_op != nullptr) {
if (compute_ref_cnt_map.count(next_compute_op)) {
compute_ref_cnt_map[next_compute_op]->AddVar(var_name);
VLOG(5) << "Add reference count of " << var_name << " to Operator "
<< next_compute_op->Name();
VLOG(50) << "Add reference count of " << var_name << " to Operator "
<< next_compute_op->Name();
} else {
// Create new reference_count_op_handle
ir::Node *ref_cnt_node = graph->CreateEmptyNode(
......
paddle/fluid/framework/details/scale_loss_grad_op_handle.cc
浏览文件 @ d231e550
......@@ -51,7 +51,7 @@ void ScaleLossGradOpHandle::RunImpl() {
->stream();
memory::Copy(boost::get<platform::CUDAPlace>(place_), tmp,
platform::CPUPlace(), &coeff_, sizeof(float), stream);
VLOG(10) << place_ << "RUN Scale loss grad op";
VLOG(100) << place_ << "RUN Scale loss grad op";
});
#endif
}
......
paddle/fluid/framework/details/sequential_execution_pass.cc
浏览文件 @ d231e550
......@@ -94,8 +94,8 @@ std::unique_ptr<ir::Graph> SequentialExecutionPass::ApplyImpl(
op_node_list[i - 1]->outputs.push_back(dep_var);
dep_var->outputs.push_back(op_node_list[i]);
dep_var->inputs.push_back(op_node_list[i - 1]);
VLOG(10) << "Add dependencies between " << op_node_list[i - 1]->Name()
<< " and " << op_node_list[i]->Name();
VLOG(100) << "Add dependencies between " << op_node_list[i - 1]->Name()
<< " and " << op_node_list[i]->Name();
}
return graph;
}
......
paddle/fluid/framework/details/threaded_ssa_graph_executor.cc
浏览文件 @ d231e550
......@@ -210,16 +210,16 @@ void ThreadedSSAGraphExecutor::RunOp(
details::OpHandleBase *op) {
auto op_run = [ready_var_q, op, this] {
try {
if (VLOG_IS_ON(10)) {
VLOG(10) << op << " " << op->Name() << " : " << op->DebugString();
if (VLOG_IS_ON(100)) {
VLOG(100) << op << " " << op->Name() << " : " << op->DebugString();
}
if (LIKELY(!strategy_.dry_run_)) {
op->Run(strategy_.use_cuda_);
}
VLOG(10) << op << " " << op->Name() << " Done ";
VLOG(100) << op << " " << op->Name() << " Done ";
running_ops_--;
ready_var_q->Extend(op->Outputs());
VLOG(10) << op << " " << op->Name() << "Signal posted";
VLOG(100) << op << " " << op->Name() << "Signal posted";
} catch (...) {
exception_holder_.Catch(std::current_exception());
}
......
paddle/fluid/framework/executor.cc
浏览文件 @ d231e550
......@@ -43,7 +43,7 @@ ExecutorPrepareContext::ExecutorPrepareContext(
}
ExecutorPrepareContext::~ExecutorPrepareContext() {
VLOG(5) << "destroy ExecutorPrepareContext";
VLOG(50) << "destroy ExecutorPrepareContext";
}
template <typename RefCntMap>
......@@ -60,7 +60,7 @@ static void DeleteUnusedTensors(const Scope& scope, const OperatorBase* op,
if ((it->second)-- == 1) {
auto* var = scope.FindVar(name);
if (var != nullptr) {
VLOG(10) << "Erase tensor \'" << name << "\'";
VLOG(100) << "Erase tensor \'" << name << "\'";
if (var->IsType<LoDTensor>()) {
erase_tensors.insert(var->GetMutable<LoDTensor>());
} else if (var->IsType<SelectedRows>()) {
......@@ -141,21 +141,21 @@ void Executor::CreateVariables(const ProgramDesc& pdesc, Scope* scope,
if (var->Persistable()) {
auto* ptr = const_cast<Scope*>(ancestor_scope)->Var(var->Name());
InitializeVariable(ptr, var->GetType());
VLOG(3) << "Create Variable " << var->Name()
<< " global, which pointer is " << ptr;
VLOG(30) << "Create Variable " << var->Name()
<< " global, which pointer is " << ptr;
} else {
auto* ptr = scope->Var(var->Name());
InitializeVariable(ptr, var->GetType());
VLOG(3) << "Create Variable " << var->Name()
<< " locally, which pointer is " << ptr;
VLOG(30) << "Create Variable " << var->Name()
<< " locally, which pointer is " << ptr;
}
}
} else {
for (auto& var : global_block.AllVars()) {
auto* ptr = scope->Var(var->Name());
InitializeVariable(ptr, var->GetType());
VLOG(3) << "Create variable " << var->Name() << ", which pointer is "
<< ptr;
VLOG(30) << "Create variable " << var->Name() << ", which pointer is "
<< ptr;
}
}
}
......@@ -286,7 +286,7 @@ void Executor::Run(const ProgramDesc& program, Scope* scope,
int i = 0;
for (auto& feed_target : (*feed_targets)) {
std::string var_name = feed_target.first;
VLOG(3) << "feed target's name: " << var_name;
VLOG(30) << "feed target's name: " << var_name;
// prepend feed op
auto* op = global_block->PrependOp();
......@@ -309,7 +309,7 @@ void Executor::Run(const ProgramDesc& program, Scope* scope,
int i = 0;
for (auto& fetch_target : (*fetch_targets)) {
std::string var_name = fetch_target.first;
VLOG(3) << "fetch target's name: " << var_name;
VLOG(30) << "fetch target's name: " << var_name;
// append fetch op
auto* op = global_block->AppendOp();
......@@ -459,7 +459,7 @@ void Executor::RunPreparedContext(
void Executor::EnableMKLDNN(const ProgramDesc& program) {
#ifdef PADDLE_WITH_MKLDNN
VLOG(3) << "use_mkldnn=True";
VLOG(30) << "use_mkldnn=True";
for (size_t bid = 0; bid < program.Size(); ++bid) {
auto* block = const_cast<ProgramDesc&>(program).MutableBlock(bid);
for (auto* op : block->AllOps()) {
......
paddle/fluid/framework/feed_fetch_method.cc
浏览文件 @ d231e550
......@@ -25,7 +25,7 @@ void SetFeedVariable(Scope* scope, const LoDTensor& input,
const std::string& var_name, size_t index) {
// If var_name Variable is not found in GlobalScope, a new variable will
// be created.
VLOG(3) << "SetFeedVariable name=" << var_name << " index=" << index;
VLOG(30) << "SetFeedVariable name=" << var_name << " index=" << index;
Variable* g_feed_value = scope->Var(var_name);
auto& feed_inputs = *(g_feed_value->GetMutable<FeedFetchList>());
if (index >= feed_inputs.size()) {
......@@ -47,8 +47,8 @@ LoDTensor& GetFetchVariable(const Scope& scope, const std::string& var_name,
typeid(FeedFetchList).name());
auto& fetch_outputs = *g_fetch_value->GetMutable<FeedFetchList>();
auto& tensor = fetch_outputs[index];
VLOG(3) << "Fetch " << var_name << " with index " << index
<< " shape= " << tensor.dims();
VLOG(30) << "Fetch " << var_name << " with index " << index
<< " shape= " << tensor.dims();
PADDLE_ENFORCE_LT(index, fetch_outputs.size());
return tensor;
}
......
paddle/fluid/framework/ir/attention_lstm_fuse_pass.cc
浏览文件 @ d231e550
......@@ -147,19 +147,19 @@ void PrepareParameters(Graph* graph, const Param& param) {
scope->Var(param.LSTMX)->GetMutable<LoDTensor>();
scope->Var(param.LSTMOUT)->GetMutable<LoDTensor>();
#define GATE_W(name__) \
auto* W_##name__##_w0 = scope->FindVar(#name__ ".w_0"); \
auto* W_##name__##_w1 = scope->FindVar(#name__ ".w_1"); \
auto* W_##name__##_b0 = scope->FindVar(#name__ ".b_0"); \
CHECK_P3(W_##name__##_w0, W_##name__##_w1, W_##name__##_b0); \
VLOG(4) << #name__ "_w0" \
<< " shape: " << W_##name__##_w0->Get<LoDTensor>().dims(); \
VLOG(4) << #name__ "_w1" \
<< " shape: " << W_##name__##_w1->Get<LoDTensor>().dims(); \
VLOG(4) << #name__ "_b0" \
<< " shape: " << W_##name__##_b0->Get<LoDTensor>().dims(); \
auto& W_##name__##_w0_t = W_##name__##_w0->Get<LoDTensor>(); \
auto& W_##name__##_w1_t = W_##name__##_w1->Get<LoDTensor>(); \
#define GATE_W(name__) \
auto* W_##name__##_w0 = scope->FindVar(#name__ ".w_0"); \
auto* W_##name__##_w1 = scope->FindVar(#name__ ".w_1"); \
auto* W_##name__##_b0 = scope->FindVar(#name__ ".b_0"); \
CHECK_P3(W_##name__##_w0, W_##name__##_w1, W_##name__##_b0); \
VLOG(40) << #name__ "_w0" \
<< " shape: " << W_##name__##_w0->Get<LoDTensor>().dims(); \
VLOG(40) << #name__ "_w1" \
<< " shape: " << W_##name__##_w1->Get<LoDTensor>().dims(); \
VLOG(40) << #name__ "_b0" \
<< " shape: " << W_##name__##_b0->Get<LoDTensor>().dims(); \
auto& W_##name__##_w0_t = W_##name__##_w0->Get<LoDTensor>(); \
auto& W_##name__##_w1_t = W_##name__##_w1->Get<LoDTensor>(); \
auto& W_##name__##_b0_t = W_##name__##_b0->Get<LoDTensor>();
GATE_W(forget);
......@@ -208,7 +208,7 @@ void PrepareLSTMWeight(const LoDTensor& W_forget_w0,
int D = W_forget_w0.dims()[0];
int M = W_forget_w1.dims()[0];
out->Resize(make_ddim({D + M, 4 * D}));
VLOG(3) << "LSTMWeight resized to " << out->dims();
VLOG(30) << "LSTMWeight resized to " << out->dims();
float* out_data = out->mutable_data<float>(platform::CPUPlace());
std::array<const float*, 4> tensors(
......
paddle/fluid/framework/ir/conv_bias_mkldnn_fuse_pass.cc
浏览文件 @ d231e550
......@@ -57,7 +57,7 @@ std::unique_ptr<ir::Graph> ConvBiasFusePass::ApplyImpl(
int found_conv_bias_count = 0;
auto handler = [&](const GraphPatternDetector::subgraph_t& subgraph,
Graph* g) {
VLOG(4) << "handle ConvBias fuse";
VLOG(40) << "handle ConvBias fuse";
GET_IR_NODE_FROM_SUBGRAPH(conv_weight, conv_weight,
conv_bias_pattern); // Filter
GET_IR_NODE_FROM_SUBGRAPH(conv_out, conv_out, conv_bias_pattern); // tmp
......@@ -74,7 +74,7 @@ std::unique_ptr<ir::Graph> ConvBiasFusePass::ApplyImpl(
// check if fuse can be done and if MKL-DNN should be used
FuseOptions fuse_option = FindFuseOption(*conv, *eltwise);
if (fuse_option == DO_NOT_FUSE || fuse_option == FUSE_NATIVE) {
VLOG(3) << "do not perform conv+bias fuse";
VLOG(30) << "do not perform conv+bias fuse";
return;
}
......
paddle/fluid/framework/ir/conv_bn_fuse_pass.cc
浏览文件 @ d231e550
......@@ -121,7 +121,7 @@ std::unique_ptr<ir::Graph> ConvBNFusePass::ApplyImpl(
int found_conv_bn_count = 0;
auto handler = [&](const GraphPatternDetector::subgraph_t& subgraph,
Graph* g) {
VLOG(4) << "handle ConvBN fuse";
VLOG(40) << "handle ConvBN fuse";
// conv, batch_norm,
// conv_weight, conv_out,
......@@ -133,7 +133,7 @@ std::unique_ptr<ir::Graph> ConvBNFusePass::ApplyImpl(
// check if fuse can be done and if MKL-DNN should be used
FuseOptions fuse_option = FindFuseOption(*conv, *batch_norm);
if (fuse_option == DO_NOT_FUSE) {
VLOG(3) << "do not perform conv+bn fuse";
VLOG(30) << "do not perform conv+bn fuse";
return;
}
......@@ -241,7 +241,7 @@ std::unique_ptr<ir::Graph> ConvEltwiseAddBNFusePass::ApplyImpl(
int found_conv_bn_count = 0;
auto handler = [&](const GraphPatternDetector::subgraph_t& subgraph,
Graph* g) {
VLOG(4) << "handle ConvBN fuse";
VLOG(40) << "handle ConvBN fuse";
// conv, batch_norm,
// conv_weight, conv_out,
......
paddle/fluid/framework/ir/conv_relu_mkldnn_fuse_pass.cc
浏览文件 @ d231e550
......@@ -38,7 +38,7 @@ std::unique_ptr<ir::Graph> ConvReLUFusePass::ApplyImpl(
int found_conv_relu_count = 0;
auto handler = [&](const GraphPatternDetector::subgraph_t& subgraph,
Graph* g) {
VLOG(4) << "handle ConvReLU fuse";
VLOG(40) << "handle ConvReLU fuse";
GET_IR_NODE_FROM_SUBGRAPH(conv_weight, conv_weight,
conv_relu_pattern); // Filter
GET_IR_NODE_FROM_SUBGRAPH(conv_out, conv_out, conv_relu_pattern); // tmp
......@@ -48,7 +48,7 @@ std::unique_ptr<ir::Graph> ConvReLUFusePass::ApplyImpl(
FuseOptions fuse_option = FindFuseOption(*conv, *relu);
if (fuse_option == DO_NOT_FUSE) {
VLOG(3) << "do not perform conv+relu fuse";
VLOG(30) << "do not perform conv+relu fuse";
return;
}
......
paddle/fluid/framework/ir/depthwise_conv_mkldnn_pass.cc
浏览文件 @ d231e550
......@@ -39,7 +39,7 @@ std::unique_ptr<ir::Graph> DepthwiseConvMKLDNNPass::ApplyImpl(
int found_depthwise_conv_mkldnn_count = 0;
auto handler = [&](const GraphPatternDetector::subgraph_t& subgraph,
Graph* g) {
VLOG(3) << "handle DepthwiseConvMKLDNN fuse";
VLOG(30) << "handle DepthwiseConvMKLDNN fuse";
GET_NODE(depthwise_conv, (*pattern));
depthwise_conv->Op()->SetType("conv2d");
found_depthwise_conv_mkldnn_count++;
......
paddle/fluid/framework/ir/fc_fuse_pass.cc
浏览文件 @ d231e550
......@@ -39,7 +39,7 @@ std::unique_ptr<ir::Graph> FCFusePass::ApplyImpl(
int found_fc_count = 0;
auto handler = [&](const GraphPatternDetector::subgraph_t& subgraph,
Graph* g) {
VLOG(4) << "handle FC fuse";
VLOG(40) << "handle FC fuse";
GET_IR_NODE_FROM_SUBGRAPH(w, w, fc_pattern);
GET_IR_NODE_FROM_SUBGRAPH(fc_bias, bias, fc_pattern);
GET_IR_NODE_FROM_SUBGRAPH(fc_out, Out, fc_pattern);
......
paddle/fluid/framework/ir/fuse_elewise_add_act_pass.cc
浏览文件 @ d231e550
......@@ -61,7 +61,7 @@ std::unique_ptr<ir::Graph> FuseElewiseAddActPass::FuseElewiseAddAct(
auto handler = [&](const GraphPatternDetector::subgraph_t &subgraph,
Graph *g) {
VLOG(4) << "handle FuseElewiseAddAct fuse";
VLOG(40) << "handle FuseElewiseAddAct fuse";
GET_IR_NODE_FROM_SUBGRAPH(ele_y, ele_y, elewise_add_act_pattern);
GET_IR_NODE_FROM_SUBGRAPH(ele_out, elewise_add_out,
elewise_add_act_pattern);
......@@ -77,10 +77,10 @@ std::unique_ptr<ir::Graph> FuseElewiseAddActPass::FuseElewiseAddAct(
Node *elewise_add_act_node = CreateFuseElewiseAddActNode(
g, act, ele_add, ele_x_n, ele_y_n, ele_out_n, act_out_n);
VLOG(4) << "\n\t " << ele_x_n << " and " << ele_y_n << " -> "
<< ele_add->Name() << " -> " << ele_out_n << "\n"
<< "\t " << ele_out_n << " -> " << act->Name() << " -> "
<< act_out_n;
VLOG(40) << "\n\t " << ele_x_n << " and " << ele_y_n << " -> "
<< ele_add->Name() << " -> " << ele_out_n << "\n"
<< "\t " << ele_out_n << " -> " << act->Name() << " -> "
<< act_out_n;
ReLinkNodes(g, ele_out, ele_add, act, elewise_add_act_node);
found_elewise_add_act_count++;
......@@ -113,7 +113,7 @@ std::unique_ptr<ir::Graph> FuseElewiseAddActPass::FuseActElewiseAdd(
auto handler = [&](const GraphPatternDetector::subgraph_t &subgraph,
Graph *g) {
VLOG(4) << "handle FuseElewiseAddAct fuse";
VLOG(40) << "handle FuseElewiseAddAct fuse";
GET_IR_NODE_FROM_SUBGRAPH(act_out, act_out, act_elewise_add_pattern);
GET_IR_NODE_FROM_SUBGRAPH(ele_x, ele_x, act_elewise_add_pattern);
GET_IR_NODE_FROM_SUBGRAPH(ele_out, elewise_add_out,
......@@ -129,9 +129,9 @@ std::unique_ptr<ir::Graph> FuseElewiseAddActPass::FuseActElewiseAdd(
Node *elewise_add_act_node = CreateFuseElewiseAddActNode(
g, ele_add, act, elewise_add_x_n, act_i_n, act_o_n, elewise_add_out_n);
VLOG(4) << "\n\t " << act_i_n << " -> " << act->Name() << " -> " << act_o_n
<< "\n\t " << act_o_n << " and " << elewise_add_x_n << " -> "
<< ele_add->Name() << " -> " << elewise_add_out_n;
VLOG(40) << "\n\t " << act_i_n << " -> " << act->Name() << " -> " << act_o_n
<< "\n\t " << act_o_n << " and " << elewise_add_x_n << " -> "
<< ele_add->Name() << " -> " << elewise_add_out_n;
ReLinkNodes(g, act_out, act, ele_add, elewise_add_act_node);
found_elewise_add_act_count++;
......@@ -165,7 +165,7 @@ std::unique_ptr<ir::Graph> FuseElewiseAddActPass::FuseElewiseAddActInplaceGrad(
auto handler = [&](const GraphPatternDetector::subgraph_t &subgraph,
Graph *g) {
VLOG(4) << "handle FuseElewiseAddActGrad1 fuse";
VLOG(40) << "handle FuseElewiseAddActGrad1 fuse";
GET_IR_NODE_FROM_SUBGRAPH(act_out, act_out, elewise_add_act_grad_pattern);
GET_IR_NODE_FROM_SUBGRAPH(act_grad, act_grad, elewise_add_act_grad_pattern);
GET_IR_NODE_FROM_SUBGRAPH(d_itermediate_out, d_itermediate_out,
......@@ -208,10 +208,10 @@ std::unique_ptr<ir::Graph> FuseElewiseAddActPass::FuseElewiseAddActInplaceGrad(
auto fused_node = g->CreateOpNode(&desc);
VLOG(4) << "\n\t " << d_act_out_n << " and " << act_out_n << " -> "
<< act_grad->Name() << " -> " << d_itermediate_out_n << "\n\t "
<< d_itermediate_out_n << " and " << act_out_n << " -> "
<< ele_add_grad->Name() << " -> " << d_itermediate_out_n;
VLOG(40) << "\n\t " << d_act_out_n << " and " << act_out_n << " -> "
<< act_grad->Name() << " -> " << d_itermediate_out_n << "\n\t "
<< d_itermediate_out_n << " and " << act_out_n << " -> "
<< ele_add_grad->Name() << " -> " << d_itermediate_out_n;
ReLinkNodes(g, d_itermediate_out, act_grad, ele_add_grad, fused_node);
found_elewise_add_act_count++;
......
paddle/fluid/framework/ir/graph.cc
浏览文件 @ d231e550
......@@ -92,7 +92,7 @@ Graph::Graph(const ProgramDesc &program) : program_(program) {
std::map<std::string, std::vector<ir::Node *>> Graph::InitFromProgram(
const ProgramDesc &program) {
VLOG(3) << "block in program:" << program_.Size();
VLOG(30) << "block in program:" << program_.Size();
std::unordered_map<std::string, VarDesc *> all_vars;
// var nodes for each var name, will have multiple versions in SSA
std::map<std::string, std::vector<ir::Node *>> var_nodes;
......@@ -160,7 +160,7 @@ void Graph::ResolveHazard(
auto it_old = versions.rbegin();
++it_old;
for (; it_old != versions.rend(); it_new = it_old, ++it_old) {
VLOG(3) << "deal with var: " << (*it_new)->Name();
VLOG(30) << "deal with var: " << (*it_new)->Name();
ir::Node *write_op =
(*it_new)->inputs.empty() ? nullptr : (*it_new)->inputs[0];
const auto &read_ops = (*it_old)->outputs;
......
paddle/fluid/framework/ir/graph.h
浏览文件 @ d231e550
......@@ -89,7 +89,7 @@ class Graph {
attr_name);
attrs_[attr_name] = attr;
attr_dels_[attr_name] = [attr, attr_name]() {
VLOG(3) << "deleting " << attr_name;
VLOG(30) << "deleting " << attr_name;
delete attr;
};
}
......
paddle/fluid/framework/ir/graph_helper.cc
浏览文件 @ d231e550
......@@ -33,8 +33,9 @@ void SortHelper(
}
}
VLOG(3) << "topology sort insert: " << node->Name()
<< reinterpret_cast<void *>(node) << " input " << node->inputs.size();
VLOG(30) << "topology sort insert: " << node->Name()
<< reinterpret_cast<void *>(node) << " input "
<< node->inputs.size();
ret->push_back(node);
}
......@@ -103,9 +104,9 @@ std::map<ir::Node *, std::unordered_set<ir::Node *>> BuildOperationAdjList(
for (auto &var : n->inputs) {
for (auto &adj_n : var->inputs) {
PADDLE_ENFORCE(adj_n->NodeType() == ir::Node::Type::kOperation);
VLOG(4) << "adj " << adj_n->Name() << reinterpret_cast<void *>(adj_n)
<< " -> " << n->Name() << reinterpret_cast<void *>(n)
<< " via " << var->Name() << reinterpret_cast<void *>(var);
VLOG(40) << "adj " << adj_n->Name() << reinterpret_cast<void *>(adj_n)
<< " -> " << n->Name() << reinterpret_cast<void *>(n)
<< " via " << var->Name() << reinterpret_cast<void *>(var);
adj_list[n].insert(adj_n);
}
}
......@@ -163,10 +164,10 @@ size_t GraphNum(const Graph &graph) {
graph_nodes.emplace_back(g_nodes);
}
if (VLOG_IS_ON(10)) {
VLOG(10) << "graph_num: " << graph_nodes.size();
if (VLOG_IS_ON(100)) {
VLOG(100) << "graph_num: " << graph_nodes.size();
for (auto &g_n : graph_nodes) {
VLOG(10) << "graph_nodes: " << g_n.size();
VLOG(100) << "graph_nodes: " << g_n.size();
if (g_n.size() < 10) {
std::stringstream out;
for (auto &node : g_n) {
......@@ -180,7 +181,7 @@ size_t GraphNum(const Graph &graph) {
}
out << "]";
}
VLOG(10) << out.str();
VLOG(100) << out.str();
}
}
}
......
paddle/fluid/framework/ir/graph_pattern_detector.cc
浏览文件 @ d231e550
......@@ -12,6 +12,7 @@
// See the License for the specific language governing permissions and
// limitations under the License.
#include <algorithm>
#include <array>
#include <string>
#include <vector>
......@@ -91,19 +92,19 @@ void GraphPatternDetector::operator()(Graph *graph,
PrettyLogEndl(Style::detail(), "--- detect %d subgraphs", subgraphs.size());
int id = 0;
for (auto &g : subgraphs) {
VLOG(3) << "optimizing #" << id++ << " subgraph";
VLOG(30) << "optimizing #" << id++ << " subgraph";
handler(g, graph);
}
}
bool GraphPatternDetector::MarkPDNodesInGraph(const ir::Graph &graph) {
VLOG(3) << "mark pdnodes in graph";
VLOG(30) << "mark pdnodes in graph";
if (graph.Nodes().empty()) return false;
for (auto &node : GraphTraits::DFS(graph)) {
for (const auto &pdnode : pattern_.nodes()) {
if (pdnode->Tell(&node)) {
VLOG(4) << "pdnode " << pdnode->name() << " marked";
VLOG(40) << "pdnode " << pdnode->name() << " marked";
pdnodes2nodes_[pdnode.get()].insert(&node);
}
}
......@@ -111,7 +112,7 @@ bool GraphPatternDetector::MarkPDNodesInGraph(const ir::Graph &graph) {
// Check to early stop if some PDNode can't find matched Node.
for (auto &pdnode : pattern_.nodes()) {
if (!pdnodes2nodes_.count(pdnode.get())) {
VLOG(4) << pdnode->name() << " can't find matched Node, early stop";
VLOG(40) << pdnode->name() << " can't find matched Node, early stop";
// return false;
}
}
......@@ -120,7 +121,7 @@ bool GraphPatternDetector::MarkPDNodesInGraph(const ir::Graph &graph) {
GetMarkedNodes(const_cast<Graph *>(&graph)).insert(n);
}
}
VLOG(3) << pdnodes2nodes_.size() << " nodes marked";
VLOG(30) << pdnodes2nodes_.size() << " nodes marked";
return !pdnodes2nodes_.empty();
}
......@@ -213,7 +214,7 @@ GraphPatternDetector::DetectPatterns() {
// Extend a PDNode to subgraphs by deducing the connection relations defined
// in edges of PDNodes.
for (const auto &edge : pattern_.edges()) {
VLOG(4) << "check " << edge.first->name() << " -> " << edge.second->name();
VLOG(40) << "check " << edge.first->name() << " -> " << edge.second->name();
// TODO(Superjomn) Fix bug here, the groups might be duplicate here.
// Each role has two PDNodes, which indicates two roles.
// Detect two Nodes that can match these two roles and they are connected.
......@@ -224,7 +225,7 @@ GraphPatternDetector::DetectPatterns() {
// source -> target
for (Node *source : pdnodes2nodes_[edge.first]) {
for (Node *target : pdnodes2nodes_[edge.second]) {
VLOG(8) << "check " << source->id() << " -- " << target->id();
VLOG(80) << "check " << source->id() << " -- " << target->id();
// TODO(Superjomn) add some prune strategies.
for (const auto &group : pre_groups) {
HitGroup new_group = group;
......@@ -240,12 +241,13 @@ GraphPatternDetector::DetectPatterns() {
}
}
}
VLOG(3) << "step " << step << " get records: " << cur_groups.size();
VLOG(30) << "step " << step << " get records: " << cur_groups.size();
for (auto &group : cur_groups) {
for (auto &item : group.roles) {
VLOG(4) << "node " << item.second->id() << " as " << item.first->name();
VLOG(40) << "node " << item.second->id() << " as "
<< item.first->name();
}
VLOG(4) << "=========================================================";
VLOG(40) << "=========================================================";
}
}
......
paddle/fluid/framework/ir/graph_viz_pass.cc
浏览文件 @ d231e550
......@@ -41,7 +41,7 @@ std::string FormatName(const Node* node) {
std::unique_ptr<ir::Graph> GraphVizPass::ApplyImpl(
std::unique_ptr<ir::Graph> graph) const {
const std::string graph_viz_path = Get<std::string>(kGraphVizPath);
VLOG(3) << "draw IR graph viz to " << graph_viz_path;
VLOG(30) << "draw IR graph viz to " << graph_viz_path;
std::unique_ptr<std::ostream> fout(new std::ofstream(graph_viz_path));
PADDLE_ENFORCE(fout->good());
std::ostream& sout = *fout;
......
paddle/fluid/framework/ir/mkldnn_placement_pass.cc
浏览文件 @ d231e550
......@@ -20,7 +20,7 @@ namespace ir {
std::unique_ptr<ir::Graph> MKLDNNPlacementPass::ApplyImpl(
std::unique_ptr<ir::Graph> graph) const {
VLOG(3) << "Aplies MKL-DNN placement strategy.";
VLOG(30) << "Aplies MKL-DNN placement strategy.";
for (const Node* n : graph->Nodes()) {
if (n->IsOp() && n->Op()->HasAttr("use_mkldnn")) {
n->Op()->SetAttr("use_mkldnn", true);
......
paddle/fluid/framework/ir/multi_batch_merge_pass.cc
浏览文件 @ d231e550
......@@ -62,7 +62,7 @@ VarDesc UpdateGradVarDesc(
string::Sprintf("%s.repeat.%d", var_desc->Name(), repeat);
VarDesc repeated_var = CopyVarDesc(var_desc);
repeated_var.SetName(new_gname);
VLOG(3) << "update " << var_desc->Name() << " to repeat " << repeat;
VLOG(30) << "update " << var_desc->Name() << " to repeat " << repeat;
return repeated_var;
}
return *var_desc;
......@@ -78,7 +78,7 @@ std::unique_ptr<Graph> BatchMergePass::ApplyImpl(
std::vector<ir::Node*> nodes = TopologySortOperations(*graph);
auto origin_nodes = graph->ReleaseNodes();
VLOG(3) << "origin nodes count: " << origin_nodes.size();
VLOG(30) << "origin nodes count: " << origin_nodes.size();
ir::Graph& result = *graph;
// 1. record op nodes of different roles
......@@ -137,8 +137,8 @@ std::unique_ptr<Graph> BatchMergePass::ApplyImpl(
"%s.repeat.%d", repeated_op.Input("Variance")[0], i);
bn_vars_need_rename.insert(repeated_op.Input("Mean")[0]);
bn_vars_need_rename.insert(repeated_op.Input("Variance")[0]);
VLOG(3) << "renaming " << repeated_op.Input("Mean")[0] << " to "
<< new_mean_name;
VLOG(30) << "renaming " << repeated_op.Input("Mean")[0] << " to "
<< new_mean_name;
repeated_op.RenameInput(repeated_op.Input("Mean")[0], new_mean_name);
repeated_op.RenameInput(repeated_op.Input("Variance")[0], new_var_name);
repeated_op.RenameOutput(repeated_op.Output("MeanOut")[0],
......
paddle/fluid/framework/ir/pass.h
浏览文件 @ d231e550
......@@ -76,7 +76,7 @@ class Pass {
attr_name);
attrs_[attr_name] = attr;
attr_dels_[attr_name] = [attr, attr_name]() {
VLOG(3) << "deleting " << attr_name;
VLOG(30) << "deleting " << attr_name;
delete attr;
};
}
......
paddle/fluid/framework/ir/seq_concat_fc_fuse_pass.cc
浏览文件 @ d231e550
......@@ -12,10 +12,13 @@
// See the License for the specific language governing permissions and
// limitations under the License.
#include "paddle/fluid/framework/ir/seq_concat_fc_fuse_pass.h"
#include <set>
#include <string>
#include "paddle/fluid/framework/ir/fuse_pass_base.h"
#include "paddle/fluid/framework/ir/graph_pattern_detector.h"
#include "paddle/fluid/framework/ir/graph_viz_pass.h"
#include "paddle/fluid/framework/ir/seq_concat_fc_fuse_pass.h"
#include "paddle/fluid/framework/lod_tensor.h"
namespace paddle {
......@@ -159,10 +162,7 @@ PDNode* BuildFCPattern(PDPattern* pattern, PDNode* fc_x) {
std::set<std::string> acts({"sigmoid", "tanh", "relu", "identity"});
PDNode* act = pattern->NewNode(
[=](Node* x) {
return x && x->IsOp() && acts.count(x->Op()->Type());
},
[=](Node* x) { return x && x->IsOp() && acts.count(x->Op()->Type()); },
"act");
PDNode* fc_out = pattern->NewNode(
......@@ -196,7 +196,7 @@ std::unique_ptr<ir::Graph> SeqConcatFcFusePass::ApplyImpl(
detector(graph.get(), [&](const GraphPatternDetector::subgraph_t& subgraph,
Graph* graph) {
VLOG(4) << "get one concat pattern";
VLOG(40) << "get one concat pattern";
// fc
GET_NODE(fc_w, detector.pattern());
GET_NODE(fc_bias, detector.pattern());
......
paddle/fluid/framework/ir/seqconv_eltadd_relu_fuse_pass.cc
浏览文件 @ d231e550
......@@ -60,7 +60,7 @@ int BuildFusion(Graph* graph, const std::string& name_scope, Scope* scope) {
auto handler = [&](const GraphPatternDetector::subgraph_t& subgraph,
Graph* g) {
VLOG(4) << "handle SeqConv EltAdd Relu fuse";
VLOG(40) << "handle SeqConv EltAdd Relu fuse";
GET_IR_NODE_FROM_SUBGRAPH(seqconv, seqconv, fuse_pattern);
GET_IR_NODE_FROM_SUBGRAPH(seqconv_weight, seqconv_weight, fuse_pattern);
GET_IR_NODE_FROM_SUBGRAPH(seqconv_out, seqconv_out, fuse_pattern);
......
paddle/fluid/framework/lod_rank_table.cc
浏览文件 @ d231e550
......@@ -31,7 +31,7 @@ void LoDRankTable::Reset(const LoD& lod, size_t level) {
TableItem item;
item.index = i;
item.length = vec[i + 1] - vec[i];
VLOG(10) << "Add item to rank table " << item.index << " " << item.length;
VLOG(100) << "Add item to rank table " << item.index << " " << item.length;
items_.emplace_back(item);
}
// NOTE(yuyang18):
......
paddle/fluid/framework/mixed_vector_test.cc
浏览文件 @ d231e550
......@@ -51,7 +51,7 @@ TEST(mixed_vector, InitWithCount) {
TEST(mixed_vector, ForEach) {
vec<int> tmp;
for (auto& v : tmp) {
VLOG(3) << v;
VLOG(30) << v;
}
}
......
paddle/fluid/framework/naive_executor.cc
浏览文件 @ d231e550
......@@ -71,7 +71,7 @@ void NaiveExecutor::Prepare(Scope *parent_scope,
void NaiveExecutor::Run() {
for (auto &op : ops_) {
VLOG(4) << "run " << op->Type();
VLOG(40) << "run " << op->Type();
op->Run(*scope_, place_);
}
}
......@@ -95,21 +95,21 @@ void NaiveExecutor::CreateVariables(const ProgramDesc &desc, Scope *scope,
if (var->Persistable()) {
auto *ptr = const_cast<Scope *>(ancestor_scope)->Var(var->Name());
InitializeVariable(ptr, var->GetType());
VLOG(3) << "Create Variable " << var->Name()
<< " global, which pointer is " << ptr;
VLOG(30) << "Create Variable " << var->Name()
<< " global, which pointer is " << ptr;
} else { // Create temporary variables in local scope.
auto *ptr = scope->Var(var->Name());
InitializeVariable(ptr, var->GetType());
VLOG(3) << "Create Variable " << var->Name()
<< " locally, which pointer is " << ptr;
VLOG(30) << "Create Variable " << var->Name()
<< " locally, which pointer is " << ptr;
}
}
} else {
for (auto &var : global_block.AllVars()) {
auto *ptr = scope->Var(var->Name());
InitializeVariable(ptr, var->GetType());
VLOG(3) << "Create variable " << var->Name() << ", which pointer is "
<< ptr;
VLOG(30) << "Create variable " << var->Name() << ", which pointer is "
<< ptr;
}
}
}
......
paddle/fluid/framework/op_desc.cc
浏览文件 @ d231e550
......@@ -82,7 +82,7 @@ class CompileTimeInferShapeContext : public InferShapeContext {
auto *in_var = block_.FindVarRecursive(Inputs(in)[i]);
auto *out_var = block_.FindVarRecursive(Outputs(out)[j]);
if (in_var->GetType() != proto::VarType::LOD_TENSOR) {
VLOG(3) << "input " << in << " is not LodTensor";
VLOG(30) << "input " << in << " is not LodTensor";
return;
}
out_var->SetLoDLevel(in_var->GetLoDLevel());
......@@ -241,32 +241,32 @@ void OpDesc::SetAttr(const std::string &name, const Attribute &v) {
const proto::OpProto::Attr &attr = GetProtoAttr(name);
switch (attr.type()) {
case proto::AttrType::BOOLEANS: {
VLOG(11) << "SetAttr: " << Type() << ", " << name
<< " from INTS to BOOLEANS";
VLOG(110) << "SetAttr: " << Type() << ", " << name
<< " from INTS to BOOLEANS";
this->attrs_[name] = std::vector<bool>();
break;
}
case proto::AttrType::INTS: {
VLOG(11) << "SetAttr: " << Type() << ", " << name
<< " from INTS to INTS";
VLOG(110) << "SetAttr: " << Type() << ", " << name
<< " from INTS to INTS";
this->attrs_[name] = std::vector<int>();
break;
}
case proto::AttrType::FLOATS: {
VLOG(11) << "SetAttr: " << Type() << ", " << name
<< " from INTS to FLOATS";
VLOG(110) << "SetAttr: " << Type() << ", " << name
<< " from INTS to FLOATS";
this->attrs_[name] = std::vector<float>();
break;
}
case proto::AttrType::STRINGS: {
VLOG(11) << "SetAttr: " << Type() << ", " << name
<< " from INTS to STRINGS";
VLOG(110) << "SetAttr: " << Type() << ", " << name
<< " from INTS to STRINGS";
this->attrs_[name] = std::vector<std::string>();
break;
}
case proto::AttrType::BLOCKS: {
VLOG(11) << "SetAttr: " << Type() << ", " << name
<< " from INTS to BLOCKS";
VLOG(110) << "SetAttr: " << Type() << ", " << name
<< " from INTS to BLOCKS";
this->SetBlocksAttr(name, std::vector<BlockDesc *>());
return;
}
......@@ -499,13 +499,13 @@ void OpDesc::CheckAttrs() {
}
void OpDesc::InferShape(const BlockDesc &block) const {
VLOG(3) << "CompileTime infer shape on " << Type();
VLOG(30) << "CompileTime infer shape on " << Type();
InitInferShapeFuncs();
auto &infer_shape = OpInfoMap::Instance().Get(this->Type()).infer_shape_;
PADDLE_ENFORCE(static_cast<bool>(infer_shape),
"%s's infer_shape has not been registered", this->Type());
CompileTimeInferShapeContext ctx(*this, block);
if (VLOG_IS_ON(10)) {
if (VLOG_IS_ON(100)) {
std::ostringstream sout;
auto inames = this->InputArgumentNames();
sout << " From [";
......@@ -516,7 +516,7 @@ void OpDesc::InferShape(const BlockDesc &block) const {
std::copy(onames.begin(), onames.end(),
std::ostream_iterator<std::string>(sout, ", "));
sout << "]";
VLOG(10) << sout.str();
VLOG(100) << sout.str();
}
infer_shape(&ctx);
}
......@@ -607,7 +607,7 @@ DDim CompileTimeInferShapeContext::GetDim(const std::string &name) const {
auto shape = var->GetShape();
res = shape.empty() ? make_ddim({0UL}) : make_ddim(shape);
} catch (...) {
VLOG(5) << "GetDim of variable " << name << " error";
VLOG(50) << "GetDim of variable " << name << " error";
std::rethrow_exception(std::current_exception());
}
return res;
......@@ -624,7 +624,7 @@ std::vector<DDim> CompileTimeInferShapeContext::GetRepeatedDims(
res.push_back(s.empty() ? make_ddim({0UL}) : make_ddim(s));
}
} catch (...) {
VLOG(5) << "GetRepeatedDim of variable " << name << " error.";
VLOG(50) << "GetRepeatedDim of variable " << name << " error.";
std::rethrow_exception(std::current_exception());
}
return res;
......
paddle/fluid/framework/op_registry.cc
浏览文件 @ d231e550
......@@ -46,9 +46,9 @@ static VariableNameMap ConvertOpDescVarsToVarNameMap(
std::unique_ptr<OperatorBase> OpRegistry::CreateOp(
const proto::OpDesc& op_desc) {
VLOG(1) << "CreateOp directly from OpDesc is deprecated. It should only be"
"used in unit tests. Use CreateOp(const OpDesc& op_desc) "
"instead.";
VLOG(10) << "CreateOp directly from OpDesc is deprecated. It should only be"
"used in unit tests. Use CreateOp(const OpDesc& op_desc) "
"instead.";
VariableNameMap inputs = ConvertOpDescVarsToVarNameMap(op_desc.inputs());
VariableNameMap outputs = ConvertOpDescVarsToVarNameMap(op_desc.outputs());
AttributeMap attrs;
......
paddle/fluid/framework/operator.cc
浏览文件 @ d231e550
......@@ -140,7 +140,7 @@ static LoD GetLoD(const Scope& scope, const std::string& name) {
}
void OperatorBase::Run(const Scope& scope, const platform::Place& place) {
VLOG(4) << place << " " << DebugStringEx(&scope);
VLOG(40) << place << " " << DebugStringEx(&scope);
if (platform::is_gpu_place(place)) {
#ifndef PADDLE_WITH_CUDA
PADDLE_THROW("Cannot run operator on place %s", place);
......@@ -160,7 +160,7 @@ void OperatorBase::Run(const Scope& scope, const platform::Place& place) {
} else {
RunImpl(scope, place);
}
VLOG(3) << place << " " << DebugStringEx(&scope);
VLOG(30) << place << " " << DebugStringEx(&scope);
}
bool OperatorBase::HasInputs(const std::string& name) const {
......@@ -259,6 +259,8 @@ std::string OperatorBase::DebugStringEx(const Scope* scope) const {
if (row_size >= 0) {
ss << "[row_size=" << row_size << "]";
}
std::string dtype = GetDtype(*scope, output.second[i]);
ss << ":" << dtype;
ss << "[" << GetDims(*scope, var_name, true) << "]";
ss << "(" << GetLoD(*scope, var_name) << ")";
}
......@@ -715,14 +717,14 @@ void OperatorWithKernel::RunImpl(const Scope& scope,
auto expected_kernel_key =
this->GetExpectedKernelType(ExecutionContext(*this, scope, *dev_ctx));
VLOG(3) << "expected_kernel_key:" << expected_kernel_key;
VLOG(30) << "expected_kernel_key:" << expected_kernel_key;
auto kernel_iter = kernels.find(expected_kernel_key);
#ifdef PADDLE_WITH_MKLDNN
// workaround for missing MKLDNN kernel when FLAGS_use_mkldnn env var is set
if (kernel_iter == kernels.end() &&
expected_kernel_key.library_type_ == LibraryType::kMKLDNN) {
VLOG(3) << "missing MKLDNN kernel: fallbacking to PLAIN one";
VLOG(30) << "missing MKLDNN kernel: fallbacking to PLAIN one";
expected_kernel_key.library_type_ = LibraryType::kPlain;
expected_kernel_key.data_layout_ = DataLayout::kAnyLayout;
kernel_iter = kernels.find(expected_kernel_key);
......@@ -774,7 +776,8 @@ void OperatorWithKernel::TransferInplaceVarsBack(
const Scope& scope, const std::vector<std::string>& inplace_vars,
const Scope& transfer_scope) const {
for (auto& var_name : inplace_vars) {
VLOG(3) << "share inplace var " + var_name + " back to it's original scope";
VLOG(30) << "share inplace var " + var_name +
" back to it's original scope";
auto* original_tensor =
GetMutableLoDTensorOrSelectedRowsValueFromVar(scope.FindVar(var_name));
auto* var = transfer_scope.FindVar(var_name);
......@@ -815,8 +818,8 @@ Scope* OperatorWithKernel::TryTransferData(
transfered_inplace_vars->emplace_back(var_name);
}
VLOG(3) << "Transform Variable " << var_name << " from "
<< kernel_type_for_var << " to " << expected_kernel_key;
VLOG(30) << "Transform Variable " << var_name << " from "
<< kernel_type_for_var << " to " << expected_kernel_key;
if (new_scope == nullptr) {
new_scope = &scope.NewScope();
......
paddle/fluid/framework/parallel_executor.cc
浏览文件 @ d231e550
......@@ -199,7 +199,7 @@ void ParallelExecutor::BCastParamsToDevices(
auto &main_tensor = main_var->Get<LoDTensor>();
if (!main_tensor.IsInitialized()) {
VLOG(3) << "one in var not inited, return!";
VLOG(30) << "one in var not inited, return!";
continue;
}
auto &dims = main_tensor.dims();
......
paddle/fluid/framework/scope.cc
浏览文件 @ d231e550
......@@ -149,7 +149,7 @@ Variable* Scope::VarInternal(const std::string& name) {
v = new Variable();
vars_[name].reset(v);
VLOG(3) << "Create variable " << name;
VLOG(30) << "Create variable " << name;
v->name_ = &(vars_.find(name)->first);
return v;
}
......
paddle/fluid/framework/selected_rows.cc
浏览文件 @ d231e550
......@@ -176,7 +176,7 @@ void SelectedRows::Get(const framework::Tensor& ids, framework::Tensor* value,
PADDLE_ENFORCE(value->IsInitialized(),
"The value tensor should be initialized.");
if (ids.numel() == 0) {
VLOG(3) << "keys is empty, please check data!";
VLOG(30) << "keys is empty, please check data!";
} else {
int64_t value_width = value_->numel() / value_->dims()[0];
PADDLE_ENFORCE_EQ(value_width, value->numel() / value->dims()[0],
......
paddle/fluid/framework/tensor_util.cc
浏览文件 @ d231e550
......@@ -23,8 +23,8 @@ namespace framework {
void TensorCopy(const Tensor& src, const platform::Place& dst_place,
const platform::DeviceContext& ctx, Tensor* dst) {
VLOG(3) << "TensorCopy " << src.dims() << " from " << src.place() << " to "
<< dst_place;
VLOG(30) << "TensorCopy " << src.dims() << " from " << src.place() << " to "
<< dst_place;
src.check_memory_size();
dst->Resize(src.dims());
......@@ -38,8 +38,8 @@ void TensorCopy(const Tensor& src, const platform::Place& dst_place,
if (platform::is_cpu_place(src_place) && platform::is_cpu_place(dst_place)) {
if (src_ptr == dst_ptr) {
VLOG(3) << "Skip copy the same data async from " << src_place << " to "
<< dst_place;
VLOG(30) << "Skip copy the same data async from " << src_place << " to "
<< dst_place;
return;
}
memory::Copy(boost::get<platform::CPUPlace>(dst_place), dst_ptr,
......@@ -78,8 +78,8 @@ void TensorCopy(const Tensor& src, const platform::Place& dst_place,
reinterpret_cast<const platform::CUDADeviceContext&>(ctx).stream();
if (platform::is_same_place(src_place, dst_place)) {
if (src_ptr == dst_ptr) {
VLOG(3) << "Skip copy the same data async from " << src_place << " to "
<< dst_place;
VLOG(30) << "Skip copy the same data async from " << src_place << " to "
<< dst_place;
return;
}
memory::Copy(dst_gpu_place, dst_ptr, src_gpu_place, src_ptr, size,
......@@ -115,8 +115,8 @@ void TensorCopy(const Tensor& src, const platform::Place& dst_place,
void TensorCopySync(const Tensor& src, const platform::Place& dst_place,
Tensor* dst) {
VLOG(3) << "TensorCopySync " << src.dims() << " from " << src.place()
<< " to " << dst_place;
VLOG(30) << "TensorCopySync " << src.dims() << " from " << src.place()
<< " to " << dst_place;
src.check_memory_size();
dst->Resize(src.dims());
dst->set_layout(src.layout());
......@@ -126,8 +126,8 @@ void TensorCopySync(const Tensor& src, const platform::Place& dst_place,
auto size = src.numel() * SizeOfType(src.type());
if (platform::is_cpu_place(src_place) && platform::is_cpu_place(dst_place)) {
if (src_ptr == dst_ptr) {
VLOG(3) << "Skip copy the same data from " << src_place << " to "
<< dst_place;
VLOG(30) << "Skip copy the same data from " << src_place << " to "
<< dst_place;
return;
}
memory::Copy(boost::get<platform::CPUPlace>(dst_place), dst_ptr,
......@@ -147,8 +147,8 @@ void TensorCopySync(const Tensor& src, const platform::Place& dst_place,
} else if (platform::is_gpu_place(src_place) &&
platform::is_gpu_place(dst_place)) {
if (src_ptr == dst_ptr && platform::is_same_place(src_place, dst_place)) {
VLOG(3) << "Skip copy the same data from " << src_place << " to "
<< dst_place;
VLOG(30) << "Skip copy the same data from " << src_place << " to "
<< dst_place;
return;
}
auto src_gpu_place = boost::get<platform::CUDAPlace>(src_place);
......
paddle/fluid/framework/threadpool.cc
浏览文件 @ d231e550
......@@ -39,7 +39,7 @@ void ThreadPool::Init() {
int num_threads = std::thread::hardware_concurrency();
if (FLAGS_dist_threadpool_size > 0) {
num_threads = FLAGS_dist_threadpool_size;
VLOG(1) << "set dist_threadpool_size to " << num_threads;
VLOG(10) << "set dist_threadpool_size to " << num_threads;
}
PADDLE_ENFORCE_GT(num_threads, 0);
threadpool_.reset(new ThreadPool(num_threads));
......
paddle/fluid/framework/var_desc.cc
浏览文件 @ d231e550
......@@ -61,10 +61,10 @@ size_t VarDesc::GetTensorDescNum() const {
void VarDesc::SetShapes(
const std::vector<std::vector<int64_t>> &multiple_dims) {
if (multiple_dims.size() != GetTensorDescNum()) {
VLOG(3) << "WARNING: The number of given shapes(" << multiple_dims.size()
<< ") doesn't match the existing tensor number("
<< GetTensorDescNum()
<< "). The Reader is going to be reinitialized.";
VLOG(30) << "WARNING: The number of given shapes(" << multiple_dims.size()
<< ") doesn't match the existing tensor number("
<< GetTensorDescNum()
<< "). The Reader is going to be reinitialized.";
SetTensorDescNum(multiple_dims.size());
}
std::vector<proto::VarType::TensorDesc *> tensors = mutable_tensor_descs();
......@@ -94,11 +94,11 @@ void VarDesc::SetDataType(proto::VarType::Type data_type) {
void VarDesc::SetDataTypes(
const std::vector<proto::VarType::Type> &multiple_data_type) {
if (multiple_data_type.size() != GetTensorDescNum()) {
VLOG(3) << "WARNING: The number of given data types("
<< multiple_data_type.size()
<< ") doesn't match the existing tensor number("
<< GetTensorDescNum()
<< "). The Reader is going to be reinitialized.";
VLOG(30) << "WARNING: The number of given data types("
<< multiple_data_type.size()
<< ") doesn't match the existing tensor number("
<< GetTensorDescNum()
<< "). The Reader is going to be reinitialized.";
SetTensorDescNum(multiple_data_type.size());
}
std::vector<proto::VarType::TensorDesc *> tensor_descs =
......@@ -139,11 +139,11 @@ void VarDesc::SetLoDLevel(int32_t lod_level) {
void VarDesc::SetLoDLevels(const std::vector<int32_t> &multiple_lod_level) {
if (multiple_lod_level.size() != GetTensorDescNum()) {
VLOG(3) << "WARNING: The number of given lod_levels("
<< multiple_lod_level.size()
<< ") doesn't match the existing tensor number("
<< GetTensorDescNum()
<< "). The Reader is going to be reinitialized.";
VLOG(30) << "WARNING: The number of given lod_levels("
<< multiple_lod_level.size()
<< ") doesn't match the existing tensor number("
<< GetTensorDescNum()
<< "). The Reader is going to be reinitialized.";
SetTensorDescNum(multiple_lod_level.size());
}
switch (desc_.type().type()) {
......
paddle/fluid/framework/var_type_inference.h
浏览文件 @ d231e550
......@@ -13,6 +13,9 @@ See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include <string>
#include "paddle/fluid/framework/block_desc.h"
#include "paddle/fluid/framework/op_desc.h"
#include "paddle/fluid/framework/type_defs.h"
namespace paddle {
......@@ -24,5 +27,27 @@ class VarTypeInference {
virtual void operator()(const OpDesc& op_desc, BlockDesc* block) const = 0;
};
class PassInDtypeAndVarTypeToOutput : public framework::VarTypeInference {
public:
void operator()(const framework::OpDesc& op_desc,
framework::BlockDesc* block) const final {
auto in_out_var_names = this->GetInputOutputWithSameType();
for (auto& i_o_n : in_out_var_names) {
auto& x_name = op_desc.Input(i_o_n.first).at(0);
auto& out_name = op_desc.Output(i_o_n.second).at(0);
auto& x = block->FindRecursiveOrCreateVar(x_name);
auto& out = block->FindRecursiveOrCreateVar(out_name);
out.SetType(x.GetType());
out.SetDataType(x.GetDataType());
}
}
protected:
virtual std::unordered_map<std::string, std::string>
GetInputOutputWithSameType() const = 0;
};
} // namespace framework
} // namespace paddle
paddle/fluid/inference/analysis/analyzer.cc
浏览文件 @ d231e550
......@@ -60,7 +60,7 @@ class DfgPassManagerImpl final : public DfgPassManager {
private:
void AddPass(const std::string& name, AnalysisPass* pass) {
VLOG(3) << "Adding pass " << name;
VLOG(30) << "Adding pass " << name;
Register(name, pass);
AddGraphvizDebugerPass(pass);
}
......@@ -104,7 +104,7 @@ void Analyzer::Run(Argument* argument) {
passes.push_back("graph_viz_pass"); // add graphviz for debug.
#ifdef PADDLE_WITH_MKLDNN
if (use_mkldnn_) {
VLOG(3) << "Adding MKL-DNN placement pass";
VLOG(30) << "Adding MKL-DNN placement pass";
passes.push_back("mkldnn_placement_pass");
}
#endif
......@@ -113,7 +113,9 @@ void Analyzer::Run(Argument* argument) {
passes.push_back("infer_clean_graph_pass");
passes.push_back("graph_viz_pass"); // add graphviz for debug.
for (auto& pass : ir_passes_) {
if (!disabled_ir_passes_.count(pass)) {
// skip mkldnn pass when use_mkldnn_ = false;
bool skip_pass = (!use_mkldnn_) && pass.find("mkldnn") != std::string::npos;
if (!disabled_ir_passes_.count(pass) && !skip_pass) {
passes.push_back(pass);
passes.push_back("graph_viz_pass"); // add graphviz for debug.
}
......
paddle/fluid/inference/analysis/argument.h
浏览文件 @ d231e550
......@@ -68,8 +68,8 @@ struct Argument {
key);
attrs_[key] = data;
attr_deleters_[key] = [data, key]() {
VLOG(3) << "xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx";
VLOG(3) << "argument delete attr: " << key;
VLOG(30) << "xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx";
VLOG(30) << "argument delete attr: " << key;
delete data;
};
}
......
paddle/fluid/inference/analysis/data_flow_graph.cc
浏览文件 @ d231e550
......@@ -132,7 +132,7 @@ void DataFlowGraph::Build(const framework::ir::Graph &graph) {
Node *x{nullptr};
if (ir_node->IsOp()) {
PADDLE_ENFORCE(ir_node->Op());
VLOG(4) << "get op " << ir_node << " " << ir_node->Name();
VLOG(40) << "get op " << ir_node << " " << ir_node->Name();
x = nodes.Create(Node::Type::kFunction);
x->attr("ir_node").Pointer() = ir_node;
PADDLE_ENFORCE(ir_node->Op()->Proto());
......@@ -141,7 +141,7 @@ void DataFlowGraph::Build(const framework::ir::Graph &graph) {
} else if (ir_node->IsVar()) {
// Not create a Node for IR ControlDepVar, considering Inference currently
// just used in single thread scenerio.
VLOG(4) << "get var " << ir_node->Name();
VLOG(40) << "get var " << ir_node->Name();
x = nodes.Create(Node::Type::kValue);
x->attr("ir_node").Pointer() = ir_node;
x->SetName(ir_node->Name());
......@@ -151,9 +151,9 @@ void DataFlowGraph::Build(const framework::ir::Graph &graph) {
}
ir_node_map.emplace(ir_node, x);
}
VLOG(4) << "finish creating Nodes";
VLOG(40) << "finish creating Nodes";
VLOG(4) << "to create edge";
VLOG(40) << "to create edge";
// Create links
for (auto *ir_node : graph.Nodes()) {
auto it = ir_node_map.find(ir_node);
......@@ -175,7 +175,7 @@ void DataFlowGraph::Build(const framework::ir::Graph &graph) {
"Can't deduce any inputs from the graph, Is the graph empty?");
ir_graph = &graph;
VLOG(3) << "finished build from IR";
VLOG(30) << "finished build from IR";
}
void DataFlowGraph::Clean() {
......
paddle/fluid/inference/analysis/data_flow_graph_to_fluid_pass.cc
浏览文件 @ d231e550
......@@ -239,9 +239,10 @@ void DataFlowGraphToFluidPass::AddEngineOp(Node *node) {
framework::BlockDesc block_desc(nullptr, &proto);
block_desc.Proto()->set_parent_idx(-1);
block_desc.Proto()->set_idx(0);
VLOG(4) << "origin variable size: "
<< argument_->origin_program_desc->blocks(0).vars().size();
VLOG(4) << "transformed variable size: " << block_desc.Proto()->vars().size();
VLOG(40) << "origin variable size: "
<< argument_->origin_program_desc->blocks(0).vars().size();
VLOG(40) << "transformed variable size: "
<< block_desc.Proto()->vars().size();
// copy ops.
for (auto *node : block_node->subgraph) {
......
paddle/fluid/inference/analysis/dfg_graphviz_draw_pass.cc
浏览文件 @ d231e550
......@@ -29,7 +29,7 @@ void DFG_GraphvizDrawPass::Run(DataFlowGraph *graph) {
auto png_path = dot_path.substr(0, dot_path.size() - 4) + ".png";
std::string message;
VLOG(3) << "draw to " << png_path;
VLOG(30) << "draw to " << png_path;
ExecShellCommand("dot -Tpng " + dot_path + " -o " + png_path, &message);
}
......
paddle/fluid/inference/analysis/fluid_to_ir_pass.cc
浏览文件 @ d231e550
......@@ -29,7 +29,7 @@ void FluidToIrPass::EnableParamModify(const std::string &model_dir,
PADDLE_ENFORCE(argument_);
argument_->Set(framework::ir::kParamScopeAttr, new framework::Scope);
// Load parameters.
VLOG(3) << "Loading parameters from " << model_dir;
VLOG(30) << "Loading parameters from " << model_dir;
LoadParams(&argument_->Get<framework::Scope>(framework::ir::kParamScopeAttr),
model_dir, prog_file, param_file);
}
......
paddle/fluid/inference/analysis/model_store_pass.cc
浏览文件 @ d231e550
......@@ -35,21 +35,21 @@ void ModelStorePass::Run(DataFlowGraph *x) {
std::stringstream ss;
// NOTE these commands only works on linux.
ss << "mkdir -p " << *argument_->model_output_store_path;
VLOG(3) << "run command: " << ss.str();
VLOG(30) << "run command: " << ss.str();
PADDLE_ENFORCE_EQ(system(ss.str().c_str()), 0);
ss.str("");
ss << "cp " << *argument_->fluid_model_dir << "/*"
<< " " << *argument_->model_output_store_path;
VLOG(3) << "run command: " << ss.str();
VLOG(30) << "run command: " << ss.str();
PADDLE_ENFORCE_EQ(system(ss.str().c_str()), 0);
// Store program
PADDLE_ENFORCE_NOT_NULL(argument_->transformed_program_desc,
"program desc is not transformed, should call "
"DataFlowGraphToFluidPass first.");
VLOG(3) << "store analyzed program to "
<< *argument_->model_output_store_path;
VLOG(30) << "store analyzed program to "
<< *argument_->model_output_store_path;
const std::string program_output_path =
*argument_->model_output_store_path + "/__model__";
std::ofstream file(program_output_path, std::ios::binary);
......
paddle/fluid/inference/analysis/pass_manager.cc
浏览文件 @ d231e550
......@@ -23,7 +23,7 @@ namespace analysis {
bool PassManager::Initialize(Argument* argument) {
argument_ = argument;
for (auto& pass : data_) {
VLOG(3) << "Initializing pass [" << pass->repr() << "]";
VLOG(30) << "Initializing pass [" << pass->repr() << "]";
if (!pass->Initialize(argument)) {
LOG(ERROR) << "Failed to initialize pass [" << pass->repr() << "]";
return false;
......@@ -34,7 +34,7 @@ bool PassManager::Initialize(Argument* argument) {
void DfgPassManager::RunAll() {
PADDLE_ENFORCE(argument_);
VLOG(3) << "Total " << data_.size() << " Analysys passes";
VLOG(30) << "Total " << data_.size() << " Analysys passes";
for (auto& pass : data_) {
string::PrettyLogEndl(string::Style::H1(), "* Running Analysis pass [%s]",
pass->repr());
......
paddle/fluid/inference/analysis/subgraph_splitter.cc
浏览文件 @ d231e550
......@@ -232,7 +232,7 @@ std::vector<std::vector<Node *>> SubGraphSplitter::ExtractSubGraphs() {
BriefNode *brief_node = itr.second;
if (!brief_node->node->attr(kMarkerAttrName).Bool()) {
VLOG(4) << brief_node->node->id() << " node not a trt candicate.";
VLOG(40) << brief_node->node->id() << " node not a trt candicate.";
continue;
}
......
paddle/fluid/inference/analysis/tensorrt_subgraph_pass.cc
浏览文件 @ d231e550
......@@ -25,9 +25,9 @@ TensorRTSubGraphPass::TensorRTSubGraphPass(
void TensorRTSubGraphPass::Run(DataFlowGraph *graph) {
SubGraphFuse(graph, node_inside_subgraph_teller_, argument_)();
VLOG(4) << "debug info "
<< graph->HumanReadableInfo(false /*show_values*/,
true /*show_functions*/);
VLOG(40) << "debug info "
<< graph->HumanReadableInfo(false /*show_values*/,
true /*show_functions*/);
}
} // namespace analysis
......
paddle/fluid/inference/api/analysis_predictor.cc
浏览文件 @ d231e550
......@@ -38,7 +38,7 @@ using contrib::AnalysisConfig;
bool AnalysisPredictor::Init(
const std::shared_ptr<framework::Scope> &parent_scope,
const std::shared_ptr<framework::ProgramDesc> &program) {
VLOG(3) << "Predictor::init()";
VLOG(30) << "Predictor::init()";
#if !defined(_WIN32)
if (FLAGS_profile) {
LOG(WARNING) << "Profiler is actived, might affect the performance";
......@@ -89,7 +89,7 @@ bool AnalysisPredictor::Init(
bool AnalysisPredictor::Run(const std::vector<PaddleTensor> &inputs,
std::vector<PaddleTensor> *output_data,
int batch_size) {
VLOG(3) << "Predictor::predict";
VLOG(30) << "Predictor::predict";
inference::Timer timer;
timer.tic();
// set feed variable
......@@ -109,7 +109,7 @@ bool AnalysisPredictor::Run(const std::vector<PaddleTensor> &inputs,
LOG(ERROR) << "fail to get fetches";
return false;
}
VLOG(3) << "predict cost: " << timer.toc() << "ms";
VLOG(30) << "predict cost: " << timer.toc() << "ms";
// Fix TensorArray reuse not cleaned bug.
tensor_array_batch_cleaner_.CollectTensorArrays(scope_.get());
......@@ -119,7 +119,7 @@ bool AnalysisPredictor::Run(const std::vector<PaddleTensor> &inputs,
bool AnalysisPredictor::SetFeed(const std::vector<PaddleTensor> &inputs,
framework::Scope *scope) {
VLOG(3) << "Predictor::set_feed";
VLOG(30) << "Predictor::set_feed";
if (inputs.size() != feeds_.size()) {
LOG(ERROR) << "wrong feed input size, need " << feeds_.size() << " but get "
<< inputs.size();
......@@ -184,7 +184,7 @@ void AnalysisPredictor::GetFetchOne(const framework::LoDTensor &fetch,
bool AnalysisPredictor::GetFetch(std::vector<PaddleTensor> *outputs,
framework::Scope *scope) {
VLOG(3) << "Predictor::get_fetch";
VLOG(30) << "Predictor::get_fetch";
outputs->resize(fetchs_.size());
for (size_t i = 0; i < fetchs_.size(); ++i) {
int idx = boost::get<int>(fetchs_[i]->GetAttr("col"));
......@@ -246,7 +246,7 @@ void AnalysisPredictor::OptimizeInferenceProgram() {
}
CHECK(argument_.transformed_program_desc);
VLOG(5) << "to prepare executor";
VLOG(50) << "to prepare executor";
inference_program_.reset(
new framework::ProgramDesc(*argument_.transformed_program_desc));
if (argument_.Has(framework::ir::kParamScopeAttr)) {
......@@ -260,7 +260,7 @@ void AnalysisPredictor::OptimizeInferenceProgram() {
template <>
std::unique_ptr<PaddlePredictor> CreatePaddlePredictor<
AnalysisConfig, PaddleEngineKind::kAnalysis>(const AnalysisConfig &config) {
VLOG(3) << "create AnalysisConfig";
VLOG(30) << "create AnalysisConfig";
if (config.use_gpu) {
// 1. GPU memeroy
PADDLE_ENFORCE_GT(
......@@ -274,7 +274,7 @@ std::unique_ptr<PaddlePredictor> CreatePaddlePredictor<
std::string flag = "--fraction_of_gpu_memory_to_use=" +
std::to_string(config.fraction_of_gpu_memory);
flags.push_back(flag);
VLOG(3) << "set flag: " << flag;
VLOG(30) << "set flag: " << flag;
framework::InitGflags(flags);
}
}
......
paddle/fluid/inference/api/analysis_predictor.h
浏览文件 @ d231e550
......@@ -13,6 +13,8 @@
// limitations under the License.
#pragma once
#include <algorithm>
#include <map>
#include <string>
#include <vector>
#include "paddle/fluid/framework/naive_executor.h"
......
paddle/fluid/inference/api/api.cc
浏览文件 @ d231e550
......@@ -16,7 +16,6 @@
#include "paddle/fluid/framework/scope.h"
#include "paddle/fluid/inference/api/paddle_inference_api.h"
#include "paddle/fluid/platform/enforce.h"
#include "paddle_inference_api.h"
namespace paddle {
......
paddle/fluid/inference/api/api_impl.cc
浏览文件 @ d231e550
......@@ -157,7 +157,7 @@ bool NativePaddlePredictor::Run(const std::vector<PaddleTensor> &inputs,
LOG(ERROR) << "fail to get fetches";
return false;
}
VLOG(3) << "predict cost: " << timer.toc() << "ms";
VLOG(30) << "predict cost: " << timer.toc() << "ms";
// Fix TensorArray reuse not cleaned bug.
tensor_array_batch_cleaner_.CollectTensorArrays(scope_.get());
......
paddle/fluid/inference/api/api_tensorrt_subgraph_engine.cc
浏览文件 @ d231e550
......@@ -34,7 +34,7 @@ class TensorRTSubgraphPredictor : public NativePaddlePredictor {
bool Init(const std::shared_ptr<framework::Scope>& parent_scope) {
FLAGS_IA_enable_tensorrt_subgraph_engine = true;
VLOG(3) << "Predictor::init()";
VLOG(30) << "Predictor::init()";
if (config_.use_gpu) {
place_ = paddle::platform::CUDAPlace(config_.device);
} else {
......@@ -70,7 +70,7 @@ class TensorRTSubgraphPredictor : public NativePaddlePredictor {
OptimizeInferenceProgram();
ctx_ = executor_->Prepare(*inference_program_, 0);
VLOG(5) << "to create variables";
VLOG(50) << "to create variables";
executor_->CreateVariables(*inference_program_,
sub_scope_ ? sub_scope_ : scope_.get(), 0);
// Get the feed_target_names and fetch_target_names
......@@ -114,9 +114,9 @@ class TensorRTSubgraphPredictor : public NativePaddlePredictor {
new ProgramDesc(*inference_program_->Proto()));
Singleton<Analyzer>::Global().Run(&argument);
CHECK(argument.transformed_program_desc);
VLOG(5) << "transformed program:\n"
<< argument.transformed_program_desc->SerializeAsString();
VLOG(5) << "to prepare executor";
VLOG(50) << "transformed program:\n"
<< argument.transformed_program_desc->SerializeAsString();
VLOG(50) << "to prepare executor";
inference_program_.reset(
new framework::ProgramDesc(*argument.transformed_program_desc));
}
......@@ -129,7 +129,7 @@ template <>
std::unique_ptr<PaddlePredictor>
CreatePaddlePredictor<MixedRTConfig, PaddleEngineKind::kAutoMixedTensorRT>(
const MixedRTConfig& config) {
VLOG(3) << "create TensorRTSubgraphPredictor";
VLOG(30) << "create TensorRTSubgraphPredictor";
if (config.use_gpu) {
// 1. GPU memeroy
PADDLE_ENFORCE_GT(
......@@ -143,7 +143,7 @@ CreatePaddlePredictor<MixedRTConfig, PaddleEngineKind::kAutoMixedTensorRT>(
std::string flag = "--fraction_of_gpu_memory_to_use=" +
std::to_string(config.fraction_of_gpu_memory);
flags.push_back(flag);
VLOG(3) << "set flag: " << flag;
VLOG(30) << "set flag: " << flag;
framework::InitGflags(flags);
}
}
......
paddle/fluid/inference/api/demo_ci/trt_mobilenet_demo.cc
浏览文件 @ d231e550
......@@ -45,7 +45,7 @@ void Main() {
config.fraction_of_gpu_memory = 0.1; // set by yourself
predictor = CreatePaddlePredictor<paddle::contrib::MixedRTConfig>(config);
VLOG(3) << "begin to process data";
VLOG(30) << "begin to process data";
// Just a single batch of data.
std::string line;
std::ifstream file(FLAGS_data);
......@@ -60,13 +60,13 @@ void Main() {
PaddleBuf(record.data.data(), record.data.size() * sizeof(float));
input.dtype = PaddleDType::FLOAT32;
VLOG(3) << "run executor";
VLOG(30) << "run executor";
std::vector<PaddleTensor> output;
predictor->Run({input}, &output, 1);
VLOG(3) << "output.size " << output.size();
VLOG(30) << "output.size " << output.size();
auto& tensor = output.front();
VLOG(3) << "output: " << SummaryTensor(tensor);
VLOG(30) << "output: " << SummaryTensor(tensor);
// compare with reference result
CheckOutput(FLAGS_refer, tensor);
......
paddle/fluid/inference/api/demo_ci/utils.h
浏览文件 @ d231e550
......@@ -47,7 +47,7 @@ static void split(const std::string& str, char sep,
}
Record ProcessALine(const std::string& line) {
VLOG(3) << "process a line";
VLOG(30) << "process a line";
std::vector<std::string> columns;
split(line, '\t', &columns);
CHECK_EQ(columns.size(), 2UL)
......@@ -65,8 +65,8 @@ Record ProcessALine(const std::string& line) {
for (auto& s : shape_strs) {
record.shape.push_back(std::stoi(s));
}
VLOG(3) << "data size " << record.data.size();
VLOG(3) << "data shape size " << record.shape.size();
VLOG(30) << "data size " << record.data.size();
VLOG(30) << "data shape size " << record.shape.size();
return record;
}
......@@ -78,8 +78,8 @@ void CheckOutput(const std::string& referfile, const PaddleTensor& output) {
file.close();
size_t numel = output.data.length() / PaddleDtypeSize(output.dtype);
VLOG(3) << "predictor output numel " << numel;
VLOG(3) << "reference output numel " << refer.data.size();
VLOG(30) << "predictor output numel " << numel;
VLOG(30) << "reference output numel " << refer.data.size();
CHECK_EQ(numel, refer.data.size());
switch (output.dtype) {
case PaddleDType::INT64: {
......
paddle/fluid/inference/api/demo_ci/vis_demo.cc
浏览文件 @ d231e550
......@@ -49,11 +49,11 @@ void Main(bool use_gpu) {
config.fraction_of_gpu_memory = 0.1; // set by yourself
}
VLOG(3) << "init predictor";
VLOG(30) << "init predictor";
predictor = CreatePaddlePredictor<NativeConfig>(config);
analysis_predictor = CreatePaddlePredictor<AnalysisConfig>(config);
VLOG(3) << "begin to process data";
VLOG(30) << "begin to process data";
// Just a single batch of data.
std::string line;
std::ifstream file(FLAGS_data);
......@@ -68,13 +68,13 @@ void Main(bool use_gpu) {
PaddleBuf(record.data.data(), record.data.size() * sizeof(float));
input.dtype = PaddleDType::FLOAT32;
VLOG(3) << "run executor";
VLOG(30) << "run executor";
std::vector<PaddleTensor> output, analysis_output;
predictor->Run({input}, &output, 1);
VLOG(3) << "output.size " << output.size();
VLOG(30) << "output.size " << output.size();
auto& tensor = output.front();
VLOG(3) << "output: " << SummaryTensor(tensor);
VLOG(30) << "output: " << SummaryTensor(tensor);
// compare with reference result
CheckOutput(FLAGS_refer, tensor);
......
paddle/fluid/inference/api/details/reset_tensor_array.cc
浏览文件 @ d231e550
......@@ -26,7 +26,7 @@ void TensorArrayBatchCleaner::CollectTensorArrays(framework::Scope *scope) {
// parameter.
if (var_name == "feed" || var_name == "fetch") continue;
if (var->Type() == typeid(framework::LoDTensorArray)) {
VLOG(4) << "collect " << var_name;
VLOG(40) << "collect " << var_name;
arrays_.push_back(var->GetMutable<framework::LoDTensorArray>());
}
}
......@@ -34,7 +34,7 @@ void TensorArrayBatchCleaner::CollectTensorArrays(framework::Scope *scope) {
CollectTensorArrays(kid);
}
VLOG(3) << "Collect " << arrays_.size() << " arrays";
VLOG(30) << "Collect " << arrays_.size() << " arrays";
flag_ = false;
}
}
......
paddle/fluid/inference/api/helper.h
浏览文件 @ d231e550
......@@ -16,13 +16,14 @@
#include <glog/logging.h>
#include <sys/time.h>
#include <algorithm>
#include <chrono> // NOLINT
#include <numeric>
#include <sstream>
#include <string>
#include <vector>
#include "paddle/fluid/inference/api/paddle_inference_api.h"
#include "paddle/fluid/string/printf.h"
#include "paddle_inference_api.h"
namespace paddle {
namespace inference {
......
paddle/fluid/inference/io.cc
浏览文件 @ d231e550
......@@ -59,7 +59,8 @@ void ReadBinaryFile(const std::string& filename, std::string* contents) {
bool IsPersistable(const framework::VarDesc* var) {
if (var->Persistable() &&
var->GetType() != framework::proto::VarType::FEED_MINIBATCH &&
var->GetType() != framework::proto::VarType::FETCH_LIST) {
var->GetType() != framework::proto::VarType::FETCH_LIST &&
var->GetType() != framework::proto::VarType::RAW) {
return true;
}
return false;
......@@ -77,7 +78,7 @@ void LoadPersistables(framework::Executor* executor, framework::Scope* scope,
for (auto* var : global_block.AllVars()) {
if (IsPersistable(var)) {
VLOG(3) << "persistable variable's name: " << var->Name();
VLOG(30) << "persistable variable's name: " << var->Name();
framework::VarDesc* new_var = load_block->Var(var->Name());
new_var->SetShape(var->GetShape());
......@@ -120,7 +121,7 @@ std::unique_ptr<framework::ProgramDesc> Load(framework::Executor* executor,
const std::string& dirname) {
std::string model_filename = dirname + "/__model__";
std::string program_desc_str;
VLOG(3) << "loading model from " << model_filename;
VLOG(30) << "loading model from " << model_filename;
ReadBinaryFile(model_filename, &program_desc_str);
std::unique_ptr<framework::ProgramDesc> main_program(
......
paddle/fluid/inference/tensorrt/convert/concat_op.cc
浏览文件 @ d231e550
......@@ -25,7 +25,7 @@ class ConcatOpConverter : public OpConverter {
public:
void operator()(const framework::proto::OpDesc& op,
const framework::Scope& scope, bool test_mode) override {
VLOG(4) << "convert a fluid mul op to tensorrt mul layer without bias";
VLOG(40) << "convert a fluid mul op to tensorrt mul layer without bias";
framework::OpDesc op_desc(op, nullptr);
// Declare inputs
......
paddle/fluid/inference/tensorrt/convert/dropout_op.cc
浏览文件 @ d231e550
......@@ -25,7 +25,7 @@ class DropoutOpConverter : public OpConverter {
public:
void operator()(const framework::proto::OpDesc& op,
const framework::Scope& scope, bool test_mode) override {
VLOG(4) << "convert a fluid dropout op to tensorrt dropout layer";
VLOG(40) << "convert a fluid dropout op to tensorrt dropout layer";
framework::OpDesc op_desc(op, nullptr);
// Declare inputs
auto* input1 = engine_->GetITensor(op_desc.Input("X")[0]);
......
paddle/fluid/inference/tensorrt/convert/fc_op.cc
浏览文件 @ d231e550
......@@ -52,7 +52,7 @@ class FcOpConverter : public OpConverter {
public:
void operator()(const framework::proto::OpDesc& op,
const framework::Scope& scope, bool test_mode) override {
VLOG(4) << "convert a fluid fc op to tensorrt fc layer without bias";
VLOG(40) << "convert a fluid fc op to tensorrt fc layer without bias";
framework::OpDesc op_desc(op, nullptr);
PADDLE_ENFORCE_EQ(op_desc.Input("X").size(), 1);
......
paddle/fluid/inference/tensorrt/convert/mul_op.cc
浏览文件 @ d231e550
......@@ -25,7 +25,7 @@ class MulOpConverter : public OpConverter {
public:
void operator()(const framework::proto::OpDesc& op,
const framework::Scope& scope, bool test_mode) override {
VLOG(4) << "convert a fluid mul op to tensorrt mul layer without bias";
VLOG(40) << "convert a fluid mul op to tensorrt mul layer without bias";
framework::OpDesc op_desc(op, nullptr);
// Declare inputs
......
paddle/fluid/inference/tensorrt/convert/pad_op.cc
浏览文件 @ d231e550
......@@ -25,7 +25,7 @@ class PadOpConverter : public OpConverter {
public:
void operator()(const framework::proto::OpDesc& op,
const framework::Scope& scope, bool test_mode) override {
VLOG(4) << "convert a fluid transpose op to tensorrt tranpose layer";
VLOG(40) << "convert a fluid transpose op to tensorrt tranpose layer";
framework::OpDesc op_desc(op, nullptr);
// Declare inputs
......
paddle/fluid/inference/tensorrt/convert/pool2d_op.cc
浏览文件 @ d231e550
......@@ -25,7 +25,7 @@ class Pool2dOpConverter : public OpConverter {
public:
void operator()(const framework::proto::OpDesc& op,
const framework::Scope& scope, bool test_mode) override {
VLOG(4)
VLOG(40)
<< "convert a fluid pool2d op to tensorrt pool2d layer without bias";
framework::OpDesc op_desc(op, nullptr);
// Declare inputs
......
paddle/fluid/inference/tensorrt/convert/softmax_op.cc
浏览文件 @ d231e550
......@@ -25,7 +25,7 @@ class SoftMaxOpConverter : public OpConverter {
public:
void operator()(const framework::proto::OpDesc& op,
const framework::Scope& scope, bool test_mode) override {
VLOG(4)
VLOG(40)
<< "convert a fluid softmax op to tensorrt softmax layer without bias";
framework::OpDesc op_desc(op, nullptr);
// Declare inputs
......
paddle/fluid/inference/tensorrt/engine.h
浏览文件 @ d231e550
......@@ -134,7 +134,7 @@ class TensorRTEngine : public EngineBase {
std::unordered_map<std::string /*name*/, std::unique_ptr<framework::Tensor>>
weight_map;
// TODO: (NHZLX)
// TODO(NHZLX)
// In the normal case, the paddle-trt exists bug when runing the googlenet.
// When there are more than two convolutions of 1 * 1 with the same input, the
// paddle-tensorrt will do the merging optimization, which fuse those conv
......
paddle/fluid/inference/tests/api/analyzer_vis_tester.cc
浏览文件 @ d231e550
......@@ -27,7 +27,7 @@ struct Record {
};
Record ProcessALine(const std::string &line) {
VLOG(3) << "process a line";
VLOG(30) << "process a line";
std::vector<std::string> columns;
split(line, '\t', &columns);
CHECK_EQ(columns.size(), 2UL)
......@@ -45,8 +45,8 @@ Record ProcessALine(const std::string &line) {
for (auto &s : shape_strs) {
record.shape.push_back(std::stoi(s));
}
VLOG(3) << "data size " << record.data.size();
VLOG(3) << "data shape size " << record.shape.size();
VLOG(30) << "data size " << record.data.size();
VLOG(30) << "data shape size " << record.shape.size();
return record;
}
......
paddle/fluid/memory/detail/buddy_allocator.cc
浏览文件 @ d231e550
......@@ -32,11 +32,11 @@ BuddyAllocator::BuddyAllocator(
system_allocator_(std::move(system_allocator)) {}
BuddyAllocator::~BuddyAllocator() {
VLOG(10) << "BuddyAllocator Disconstructor makes sure that all of these "
"have actually been freed";
VLOG(100) << "BuddyAllocator Disconstructor makes sure that all of these "
"have actually been freed";
while (!pool_.empty()) {
auto block = static_cast<MemoryBlock*>(std::get<2>(*pool_.begin()));
VLOG(10) << "Free from block (" << block << ", " << max_chunk_size_ << ")";
VLOG(100) << "Free from block (" << block << ", " << max_chunk_size_ << ")";
system_allocator_->Free(block, max_chunk_size_, block->index(cache_));
cache_.invalidate(block);
......@@ -57,12 +57,12 @@ void* BuddyAllocator::Alloc(size_t unaligned_size) {
// acquire the allocator lock
std::lock_guard<std::mutex> lock(mutex_);
VLOG(10) << "Allocate " << unaligned_size << " bytes from chunk size "
<< size;
VLOG(100) << "Allocate " << unaligned_size << " bytes from chunk size "
<< size;
// if the allocation is huge, send directly to the system allocator
if (size > max_chunk_size_) {
VLOG(10) << "Allocate from system allocator.";
VLOG(100) << "Allocate from system allocator.";
return SystemAlloc(size);
}
......@@ -77,9 +77,9 @@ void* BuddyAllocator::Alloc(size_t unaligned_size) {
return nullptr;
}
} else {
VLOG(10) << "Allocation from existing memory block " << std::get<2>(*it)
<< " at address "
<< reinterpret_cast<MemoryBlock*>(std::get<2>(*it))->data();
VLOG(100) << "Allocation from existing memory block " << std::get<2>(*it)
<< " at address "
<< reinterpret_cast<MemoryBlock*>(std::get<2>(*it))->data();
}
total_used_ += size;
......@@ -96,10 +96,10 @@ void BuddyAllocator::Free(void* p) {
// Acquire the allocator lock
std::lock_guard<std::mutex> lock(mutex_);
VLOG(10) << "Free from address " << block;
VLOG(100) << "Free from address " << block;
if (block->type(cache_) == MemoryBlock::HUGE_CHUNK) {
VLOG(10) << "Free directly from system allocator";
VLOG(100) << "Free directly from system allocator";
system_allocator_->Free(block, block->total_size(cache_),
block->index(cache_));
......@@ -116,8 +116,8 @@ void BuddyAllocator::Free(void* p) {
// Trying to merge the right buddy
if (block->has_right_buddy(cache_)) {
VLOG(10) << "Merging this block " << block << " with its right buddy "
<< block->right_buddy(cache_);
VLOG(100) << "Merging this block " << block << " with its right buddy "
<< block->right_buddy(cache_);
auto right_buddy = block->right_buddy(cache_);
......@@ -134,8 +134,8 @@ void BuddyAllocator::Free(void* p) {
// Trying to merge the left buddy
if (block->has_left_buddy(cache_)) {
VLOG(10) << "Merging this block " << block << " with its left buddy "
<< block->left_buddy(cache_);
VLOG(100) << "Merging this block " << block << " with its left buddy "
<< block->left_buddy(cache_);
auto left_buddy = block->left_buddy(cache_);
......@@ -151,8 +151,8 @@ void BuddyAllocator::Free(void* p) {
}
// Dumping this block into pool
VLOG(10) << "Inserting free block (" << block << ", "
<< block->total_size(cache_) << ")";
VLOG(100) << "Inserting free block (" << block << ", "
<< block->total_size(cache_) << ")";
pool_.insert(
IndexSizeAddress(block->index(cache_), block->total_size(cache_), block));
......@@ -174,7 +174,7 @@ void* BuddyAllocator::SystemAlloc(size_t size) {
size_t index = 0;
void* p = system_allocator_->Alloc(&index, size);
VLOG(10) << "Allocated " << p << " from system allocator.";
VLOG(100) << "Allocated " << p << " from system allocator.";
if (p == nullptr) return nullptr;
......@@ -200,8 +200,8 @@ BuddyAllocator::PoolSet::iterator BuddyAllocator::RefillPool() {
if (p == nullptr) return pool_.end();
VLOG(10) << "Creating and inserting new block " << p
<< " from system allocator";
VLOG(100) << "Creating and inserting new block " << p
<< " from system allocator";
static_cast<MemoryBlock*>(p)->init(&cache_, MemoryBlock::FREE_CHUNK, index,
max_chunk_size_, nullptr, nullptr);
......@@ -245,19 +245,19 @@ void* BuddyAllocator::SplitToAlloc(BuddyAllocator::PoolSet::iterator it,
auto block = static_cast<MemoryBlock*>(std::get<2>(*it));
pool_.erase(it);
VLOG(10) << "Split block (" << block << ", " << block->total_size(cache_)
<< ") into";
VLOG(100) << "Split block (" << block << ", " << block->total_size(cache_)
<< ") into";
block->split(&cache_, size);
VLOG(10) << "Left block (" << block << ", " << block->total_size(cache_)
<< ")";
VLOG(100) << "Left block (" << block << ", " << block->total_size(cache_)
<< ")";
block->set_type(&cache_, MemoryBlock::ARENA_CHUNK);
// the rest of memory if exist
if (block->has_right_buddy(cache_)) {
if (block->right_buddy(cache_)->type(cache_) == MemoryBlock::FREE_CHUNK) {
VLOG(10) << "Insert right block (" << block->right_buddy(cache_) << ", "
<< block->right_buddy(cache_)->total_size(cache_) << ")";
VLOG(100) << "Insert right block (" << block->right_buddy(cache_) << ", "
<< block->right_buddy(cache_)->total_size(cache_) << ")";
pool_.insert(
IndexSizeAddress(block->right_buddy(cache_)->index(cache_),
......@@ -284,7 +284,7 @@ void BuddyAllocator::CleanIdleFallBackAlloc() {
return;
}
VLOG(10) << "Return block " << block << " to fallback allocator.";
VLOG(100) << "Return block " << block << " to fallback allocator.";
system_allocator_->Free(block, max_chunk_size_, block->index(cache_));
cache_.invalidate(block);
......@@ -320,7 +320,7 @@ void BuddyAllocator::CleanIdleNormalAlloc() {
MemoryBlock* block = static_cast<MemoryBlock*>(std::get<2>(*pool));
VLOG(10) << "Return block " << block << " to base allocator.";
VLOG(100) << "Return block " << block << " to base allocator.";
system_allocator_->Free(block, max_chunk_size_, block->index(cache_));
cache_.invalidate(block);
......
paddle/fluid/memory/detail/meta_cache.cc
浏览文件 @ d231e550
......@@ -29,7 +29,7 @@ MemoryBlock::Desc MetadataCache::load(const MemoryBlock* block) const {
return existing_desc->second;
} else {
auto* desc = reinterpret_cast<const MemoryBlock::Desc*>(block);
VLOG(10) << "Load MemoryBlock::Desc type=" << desc->type;
VLOG(100) << "Load MemoryBlock::Desc type=" << desc->type;
PADDLE_ASSERT(desc->check_guards());
return *reinterpret_cast<const MemoryBlock::Desc*>(block);
}
......
paddle/fluid/memory/malloc.cc
浏览文件 @ d231e550
......@@ -78,7 +78,7 @@ void* Alloc<platform::CPUPlace>(const platform::CPUPlace& place, size_t size) {
if (FLAGS_init_allocated_mem) {
memset(p, 0xEF, size);
}
VLOG(10) << " pointer=" << p;
VLOG(100) << " pointer=" << p;
return p;
}
......@@ -111,12 +111,12 @@ BuddyAllocator* GetGPUBuddyAllocator(int gpu_id) {
std::unique_ptr<detail::SystemAllocator>(new detail::GPUAllocator(i)),
platform::GpuMinChunkSize(), platform::GpuMaxChunkSize());
VLOG(10) << "\n\nNOTE: each GPU device use "
<< FLAGS_fraction_of_gpu_memory_to_use * 100
<< "% of GPU memory.\n"
<< "You can set GFlags environment variable '"
<< "FLAGS_fraction_of_gpu_memory_to_use"
<< "' to change the fraction of GPU usage.\n\n";
VLOG(100) << "\n\nNOTE: each GPU device use "
<< FLAGS_fraction_of_gpu_memory_to_use * 100
<< "% of GPU memory.\n"
<< "You can set GFlags environment variable '"
<< "FLAGS_fraction_of_gpu_memory_to_use"
<< "' to change the fraction of GPU usage.\n\n";
}
});
......
paddle/fluid/operators/CMakeLists.txt
浏览文件 @ d231e550
......@@ -317,6 +317,7 @@ op_library(save_op DEPS lod_tensor)
op_library(load_op DEPS lod_tensor)
op_library(save_combine_op DEPS lod_tensor)
op_library(load_combine_op DEPS lod_tensor)
op_library(tensor_array_to_tensor_op DEPS concat_op)
op_library(concat_op DEPS concat_and_split)
list(REMOVE_ITEM GENERAL_OPS ${DEPS_OPS})
......
paddle/fluid/operators/activation_op.cc
浏览文件 @ d231e550
......@@ -91,16 +91,12 @@ class ActivationOp : public framework::OperatorWithKernel {
}
};
class ActivationOpInferVarType : public framework::VarTypeInference {
public:
void operator()(const framework::OpDesc& op_desc,
framework::BlockDesc* block) const override {
auto x_name = op_desc.Input("X")[0];
auto out_name = op_desc.Output("Out")[0];
auto& x = block->FindRecursiveOrCreateVar(x_name);
auto& out = block->FindRecursiveOrCreateVar(out_name);
out.SetType(x.GetType());
out.SetDataType(x.GetDataType());
class ActivationOpInferVarType
: public framework::PassInDtypeAndVarTypeToOutput {
protected:
std::unordered_map<std::string, std::string> GetInputOutputWithSameType()
const override {
return std::unordered_map<std::string, std::string>{{"X", /*->*/ "Out"}};
}
};
......
paddle/fluid/operators/activation_op.h
浏览文件 @ d231e550
......@@ -95,7 +95,7 @@ class ActivationGradKernel
auto x = framework::EigenVector<T>::Flatten(*X);
functor(*place, x, out, dout, dx);
} else {
VLOG(10) << " Inplace activation ";
VLOG(100) << " Inplace activation ";
auto x = framework::EigenVector<T>::Flatten(*dX);
functor(*place, x, out, dout, dx);
}
......
paddle/fluid/operators/adam_op.h
浏览文件 @ d231e550
......@@ -297,7 +297,7 @@ class AdamOpKernel : public framework::OpKernel<T> {
auto& grad =
Ref(ctx.Input<framework::SelectedRows>("Grad"), "Must set Grad");
if (grad.rows().size() == 0) {
VLOG(3) << "grad row size is 0!!";
VLOG(30) << "grad row size is 0!!";
return;
}
......
paddle/fluid/operators/add_position_encoding_op.h
浏览文件 @ d231e550
......@@ -66,9 +66,10 @@ class AddPositionEncodingKernel : public framework::OpKernel<T> {
x_lod.empty() ? max_seq_len : x_lod[0][i + 1] - x_lod[0][i];
for (int j = 0; j < max_length; ++j) {
for (int k = 0; k < half_size; ++k) {
const double val = (half_size > 1)
? j / pow(10000.0, double(k) / (half_size - 1))
: j / 10000.0;
const double val =
(half_size > 1)
? j / pow(10000.0, static_cast<double>(k) / (half_size - 1))
: j / 10000.0;
dst_ptr[k] = src_ptr[k] * alpha + sin(val) * beta;
dst_ptr[half_size + k] =
src_ptr[half_size + k] * alpha + cos(val) * beta;
......
paddle/fluid/operators/array_operator.h
浏览文件 @ d231e550
......@@ -49,7 +49,7 @@ class ArrayOp : public framework::OperatorBase {
} else {
offset = static_cast<size_t>(*i_tensor.data<int64_t>());
}
VLOG(10) << " Offset = " << offset;
VLOG(100) << " Offset = " << offset;
return offset;
}
};
......
paddle/fluid/operators/array_to_lod_tensor_op.cc
浏览文件 @ d231e550
......@@ -148,8 +148,8 @@ class ArrayToLoDTensorOp : public framework::OperatorBase {
size_t start_offset = lod_and_offset.second.first;
size_t end_offset = lod_and_offset.second.second;
VLOG(10) << "idx=" << idx << " x_idx=" << x_idx << " ["
<< ", " << end_offset << "]";
VLOG(100) << "idx=" << idx << " x_idx=" << x_idx << " ["
<< ", " << end_offset << "]";
// Copy data
PADDLE_ENFORCE_GE(end_offset, start_offset);
size_t len = end_offset - start_offset;
......
paddle/fluid/operators/batch_norm_op.cc
浏览文件 @ d231e550
......@@ -170,6 +170,15 @@ The required data format for this layer is one of the following:
}
};
class BatchNormOpInferVarType
: public framework::PassInDtypeAndVarTypeToOutput {
protected:
std::unordered_map<std::string, std::string> GetInputOutputWithSameType()
const override {
return std::unordered_map<std::string, std::string>{{"X", /*->*/ "Y"}};
}
};
template <typename T>
class BatchNormKernel<platform::CPUDeviceContext, T>
: public framework::OpKernel<T> {
......@@ -525,7 +534,7 @@ class BatchNormGradMaker : public framework::SingleGradOpDescMaker {
namespace ops = paddle::operators;
REGISTER_OPERATOR(batch_norm, ops::BatchNormOp, ops::BatchNormOpMaker,
ops::BatchNormGradMaker);
ops::BatchNormOpInferVarType, ops::BatchNormGradMaker);
REGISTER_OPERATOR(batch_norm_grad, ops::BatchNormGradOp);
REGISTER_OP_CPU_KERNEL(
......
paddle/fluid/operators/batch_norm_op.cu.cc
浏览文件 @ d231e550
......@@ -96,7 +96,7 @@ class BatchNormKernel<platform::CUDADeviceContext, T>
mode_ = CUDNN_BATCHNORM_SPATIAL;
#endif
VLOG(3) << "Setting descriptors.";
VLOG(30) << "Setting descriptors.";
std::vector<int> dims;
std::vector<int> strides;
if (data_layout == DataLayout::kNCHW) {
......
paddle/fluid/operators/beam_search_op.cc
浏览文件 @ d231e550
......@@ -33,11 +33,11 @@ void BeamSearch::operator()(const framework::LoDTensor &pre_ids,
auto items = SelectTopBeamSizeItems(pre_ids, pre_scores);
auto selected_items = ToMap(items, high_level.back());
VLOG(3) << "selected_items:";
VLOG(30) << "selected_items:";
for (size_t i = 0; i < selected_items.size(); ++i) {
VLOG(3) << "offset:" << i;
VLOG(30) << "offset:" << i;
for (auto &item : selected_items[i]) {
VLOG(3) << ItemToString(item);
VLOG(30) << ItemToString(item);
}
}
......@@ -138,11 +138,11 @@ std::vector<std::vector<BeamSearch::Item>> BeamSearch::SelectTopBeamSizeItems(
}
result.emplace_back(items);
}
VLOG(3) << "SelectTopBeamSizeItems result size " << result.size();
VLOG(30) << "SelectTopBeamSizeItems result size " << result.size();
for (auto &items : result) {
VLOG(3) << "item set:";
VLOG(30) << "item set:";
for (auto &item : items) {
VLOG(3) << ItemToString(item);
VLOG(30) << ItemToString(item);
}
}
......
paddle/fluid/operators/bilinear_interp_op.h 已删除 100644 → 0
浏览文件 @ cf8d2e67
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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 "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/math/math_function.h"
namespace paddle {
namespace operators {
using Tensor = framework::Tensor;
template <typename T>
class BilinearInterpKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
auto* input_t = ctx.Input<Tensor>("X"); // float tensor
auto* output_t = ctx.Output<Tensor>("Out"); // float tensor
auto out_dims = output_t->dims();
auto* input = input_t->data<T>();
int out_h = ctx.Attr<int>("out_h");
int out_w = ctx.Attr<int>("out_w");
auto out_size_t = ctx.Input<Tensor>("OutSize");
if (out_size_t != nullptr) {
auto out_size_data = out_size_t->data<int>();
out_h = out_size_data[0];
out_w = out_size_data[1];
}
auto* output = output_t->mutable_data<T>(
{out_dims[0], out_dims[1], out_h, out_w}, ctx.GetPlace());
int batch_size = input_t->dims()[0];
int channels = input_t->dims()[1];
int in_h = input_t->dims()[2];
int in_w = input_t->dims()[3];
int in_hw = in_h * in_w;
int out_hw = out_h * out_w;
int in_chw = channels * in_hw;
int out_chw = channels * out_hw;
float ratio_h =
(out_h > 1) ? static_cast<float>(in_h - 1) / (out_h - 1) : 0.f;
float ratio_w =
(out_w > 1) ? static_cast<float>(in_w - 1) / (out_w - 1) : 0.f;
if (in_h == out_h && in_w == out_w) {
memcpy(output, input, input_t->numel() * sizeof(T));
} else {
for (int k = 0; k < batch_size; ++k) { // loop for batches
for (int i = 0; i < out_h; ++i) { // loop for images
int h = ratio_h * i;
int hid = (h < in_h - 1) ? 1 : 0;
float h1lambda = ratio_h * i - h;
float h2lambda = 1.f - h1lambda;
for (int j = 0; j < out_w; ++j) {
int w = ratio_w * j;
int wid = (w < in_w - 1) ? 1 : 0;
float w1lambda = ratio_w * j - w;
float w2lambda = 1.f - w1lambda;
// calculate four position for bilinear interpolation
const T* in_pos = &input[k * in_chw + h * in_w + w];
T* out_pos = &output[k * out_chw + i * out_w + j];
for (int c = 0; c < channels; ++c) { // loop for channels
// bilinear interpolation
out_pos[0] = static_cast<T>(
h2lambda * (w2lambda * in_pos[0] + w1lambda * in_pos[wid]) +
h1lambda * (w2lambda * in_pos[hid * in_w] +
w1lambda * in_pos[hid * in_w + wid]));
in_pos += in_hw;
out_pos += out_hw;
}
}
}
}
}
}
};
template <typename T>
class BilinearInterpGradKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
auto* d_input_t = ctx.Output<Tensor>(framework::GradVarName("X"));
auto* d_output_t = ctx.Input<Tensor>(framework::GradVarName("Out"));
auto* d_output = d_output_t->data<T>();
auto* d_input = d_input_t->mutable_data<T>(ctx.GetPlace());
auto& device_ctx =
ctx.template device_context<platform::CPUDeviceContext>();
math::SetConstant<platform::CPUDeviceContext, T> zero;
zero(device_ctx, d_input_t, static_cast<T>(0.0));
int out_h = ctx.Attr<int>("out_h");
int out_w = ctx.Attr<int>("out_w");
auto out_size_t = ctx.Input<Tensor>("OutSize");
if (out_size_t != nullptr) {
auto out_size_data = out_size_t->data<int>();
out_h = out_size_data[0];
out_w = out_size_data[1];
}
int batch_size = d_input_t->dims()[0];
int channels = d_input_t->dims()[1];
int in_h = d_input_t->dims()[2];
int in_w = d_input_t->dims()[3];
int in_hw = in_h * in_w;
int out_hw = out_h * out_w;
int in_chw = channels * in_hw;
int out_chw = channels * out_hw;
float ratio_h =
(out_h > 1) ? static_cast<float>(in_h - 1) / (out_h - 1) : 0.f;
float ratio_w =
(out_w > 1) ? static_cast<float>(in_w - 1) / (out_w - 1) : 0.f;
if (in_h == out_h && in_w == out_w) {
memcpy(d_input, d_output, d_input_t->numel() * sizeof(T));
} else {
for (int k = 0; k < batch_size; ++k) { // loop for batches
for (int i = 0; i < out_h; ++i) { // loop for images
int h = ratio_h * i;
int hid = (h < in_h - 1) ? 1 : 0;
float h1lambda = ratio_h * i - h;
float h2lambda = 1 - h1lambda;
for (int j = 0; j < out_w; ++j) {
int w = ratio_w * j;
int wid = (w < in_w - 1) ? 1 : 0;
float w1lambda = ratio_w * j - w;
float w2lambda = 1 - w1lambda;
T* in_pos = &d_input[k * in_chw + h * in_w + w];
const T* out_pos = &d_output[k * out_chw + i * out_w + j];
for (int c = 0; c < channels; ++c) { // loop for channels
in_pos[0] += static_cast<T>(h2lambda * w2lambda * out_pos[0]);
in_pos[wid] += static_cast<T>(h2lambda * w1lambda * out_pos[0]);
in_pos[hid * in_w] +=
static_cast<T>(h1lambda * w2lambda * out_pos[0]);
in_pos[hid * in_w + wid] +=
static_cast<T>(h1lambda * w1lambda * out_pos[0]);
in_pos += in_hw;
out_pos += out_hw;
}
}
}
}
}
}
};
} // namespace operators
} // namespace paddle
paddle/fluid/operators/checkpoint_notify_op.cc
浏览文件 @ d231e550
......@@ -46,8 +46,8 @@ class CheckpointNotifyOp : public framework::OperatorBase {
auto lookup_table_save_dir =
string::Sprintf("%s/%s_%d", dir, lookup_table_name, i);
rpc_client->AsyncCheckpointNotify(epmap[i], lookup_table_save_dir);
VLOG(3) << "checkpoint notify sending lookup table: " << lookup_table_name
<< " and dir:" << dir << " to " << epmap[i];
VLOG(30) << "checkpoint notify sending lookup table: "
<< lookup_table_name << " and dir:" << dir << " to " << epmap[i];
}
PADDLE_ENFORCE(rpc_client->Wait(), "internal error in RPCClient");
}
......
paddle/fluid/operators/concat_op.cc
浏览文件 @ d231e550
......@@ -37,7 +37,7 @@ class ConcatOp : public framework::OperatorWithKernel {
PADDLE_ENFORCE_GT(n, 0, "Input tensors count should > 0.");
if (n == 1) {
VLOG(3) << "Warning: concat op have only one input, may waste memory";
VLOG(30) << "Warning: concat op have only one input, may waste memory";
}
auto out_dims = ins[0];
......
paddle/fluid/operators/conv_cudnn_op.cu.cc
浏览文件 @ d231e550
......@@ -15,15 +15,22 @@ limitations under the License. */
#include "paddle/fluid/framework/eigen.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/memory/memory.h"
#include "paddle/fluid/operators/conv_cudnn_op_cache.h"
#include "paddle/fluid/operators/conv_op.h"
#include "paddle/fluid/platform/assert.h"
#include "paddle/fluid/platform/cudnn_helper.h"
#include "paddle/fluid/platform/float16.h"
#include "paddle/fluid/platform/profiler.h"
DEFINE_bool(cudnn_deterministic, false,
"Whether allow using an autotuning algorithm for convolution "
"operator. The autotuning algorithm may be non-deterministic. If "
"true, the algorithm is deterministic.");
DEFINE_uint64(conv_workspace_size_limit, 4096,
"cuDNN convolution workspace limit in MB unit.");
DEFINE_bool(cudnn_exhaustive_search, false,
"Whether enable exhaustive search for cuDNN convolution or "
"not, defalut is False.");
namespace paddle {
namespace operators {
......@@ -36,13 +43,25 @@ using DataLayout = platform::DataLayout;
template <typename T>
using ScalingParamType = typename platform::CudnnDataType<T>::ScalingParamType;
static constexpr char kCUDNNFwdAlgoCache[] = "kCUDNNFwdAlgoCache";
static constexpr char kCUDNNBwdDataAlgoCache[] = "kCUDNNBwdDataAlgoCache";
static constexpr char kCUDNNBwdFilterAlgoCache[] = "kCUDNNBwdFilterAlgoCache";
static constexpr size_t kCONV_CUDNN_WORKSPACE_LIMIT_BYTES =
static_cast<size_t>(1024) * 1024 * 1024;
static constexpr size_t kNUM_CUDNN_FWD_ALGS =
CUDNN_CONVOLUTION_BWD_FILTER_ALGO_COUNT;
static constexpr size_t kNUM_CUDNN_BWD_FILTER_ALGS =
CUDNN_CONVOLUTION_BWD_FILTER_ALGO_COUNT;
static constexpr size_t kNUM_CUDNN_BWD_DATA_ALGS =
CUDNN_CONVOLUTION_BWD_DATA_ALGO_COUNT;
template <typename T>
class CUDNNConvOpKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
auto& dev_ctx = ctx.template device_context<platform::CUDADeviceContext>();
PADDLE_ENFORCE(platform::is_gpu_place(ctx.GetPlace()),
"It must use CUDAPlace.");
auto* input = ctx.Input<Tensor>("Input");
......@@ -55,6 +74,8 @@ class CUDNNConvOpKernel : public framework::OpKernel<T> {
int groups = ctx.Attr<int>("groups");
int64_t user_workspace_size =
static_cast<size_t>(ctx.Attr<int>("workspace_size_MB"));
bool exhaustive_search =
FLAGS_cudnn_exhaustive_search || ctx.Attr<bool>("exhaustive_search");
const T* input_data = input->data<T>();
const T* filter_data = filter->data<T>();
......@@ -120,19 +141,19 @@ class CUDNNConvOpKernel : public framework::OpKernel<T> {
// ------------------- cudnn conv workspace ---------------------
size_t workspace_size_in_bytes; // final workspace to allocate.
size_t workspace_size_limit = kCONV_CUDNN_WORKSPACE_LIMIT_BYTES;
if (user_workspace_size > 0) {
workspace_size_limit = user_workspace_size * 1024 * 1024;
if (FLAGS_conv_workspace_size_limit > 0 || user_workspace_size > 0) {
int64_t max_user_size =
std::max(static_cast<int64_t>(FLAGS_conv_workspace_size_limit),
user_workspace_size);
workspace_size_limit = max_user_size * 1024 * 1024;
}
// ------------------- cudnn conv algorithm ---------------------
cudnnConvolutionFwdAlgo_t algo;
auto& dev_ctx = ctx.template device_context<platform::CUDADeviceContext>();
auto handle = dev_ctx.cudnn_handle();
auto workspace_handle = dev_ctx.cudnn_workspace_handle();
CUDNN_ENFORCE(platform::dynload::cudnnGetConvolutionForwardAlgorithm(
handle, cudnn_input_desc, cudnn_filter_desc, cudnn_conv_desc,
cudnn_output_desc, CUDNN_CONVOLUTION_FWD_SPECIFY_WORKSPACE_LIMIT,
workspace_size_limit, &algo));
bool half_float = false;
#if CUDA_VERSION >= 9000 && CUDNN_VERSION_MIN(7, 0, 1)
// Tensor core is supported since the volta GPU and
// is only enabled when input and filter data are float16
......@@ -143,14 +164,66 @@ class CUDNNConvOpKernel : public framework::OpKernel<T> {
cudnn_conv_desc, CUDNN_TENSOR_OP_MATH));
// Currently tensor core is only enabled using this algo
algo = CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_PRECOMP_GEMM;
VLOG(5) << "use cudnn_tensor_op_math";
half_float = true;
VLOG(50) << "use cudnn_tensor_op_math";
} else {
CUDNN_ENFORCE(platform::dynload::cudnnSetConvolutionMathType(
cudnn_conv_desc, CUDNN_DEFAULT_MATH));
VLOG(5) << "NOT use cudnn_tensor_op_math";
VLOG(50) << "NOT use cudnn_tensor_op_math";
}
#endif
auto x_dims = framework::vectorize(input->dims());
auto f_dims = framework::vectorize(filter->dims());
if ((!exhaustive_search) && (!half_float)) {
CUDNN_ENFORCE(platform::dynload::cudnnGetConvolutionForwardAlgorithm(
handle, cudnn_input_desc, cudnn_filter_desc, cudnn_conv_desc,
cudnn_output_desc, CUDNN_CONVOLUTION_FWD_SPECIFY_WORKSPACE_LIMIT,
workspace_size_limit, &algo));
VLOG(3) << "cuDNN forward algo " << algo;
} else if (exhaustive_search && (!half_float)) {
AlgorithmsCache<cudnnConvolutionFwdAlgo_t>* algo_cache = nullptr;
if (ctx.scope().FindVar(kCUDNNFwdAlgoCache)) {
algo_cache =
ctx.scope()
.FindVar(kCUDNNFwdAlgoCache)
->GetMutable<AlgorithmsCache<cudnnConvolutionFwdAlgo_t>>();
} else {
algo_cache =
const_cast<framework::Scope&>(ctx.scope())
.Var(kCUDNNFwdAlgoCache)
->GetMutable<AlgorithmsCache<cudnnConvolutionFwdAlgo_t>>();
}
algo = algo_cache->GetAlgorithm(
x_dims, f_dims, strides, paddings, dilations, 0, [&]() {
int returned_algo_count;
std::array<cudnnConvolutionFwdAlgoPerf_t, kNUM_CUDNN_FWD_ALGS>
fwd_perf_stat;
auto cudnn_find_func = [&](void* cudnn_workspace) {
CUDNN_ENFORCE(
platform::dynload::cudnnFindConvolutionForwardAlgorithmEx(
handle, cudnn_input_desc, input_data, cudnn_filter_desc,
filter_data, cudnn_conv_desc, cudnn_output_desc,
output_data, kNUM_CUDNN_FWD_ALGS, &returned_algo_count,
fwd_perf_stat.data(), cudnn_workspace,
workspace_size_limit));
};
workspace_handle.RunFunc(cudnn_find_func, workspace_size_limit);
VLOG(3) << "Perf result: (algo: stat, time, memory)";
for (int i = 0; i < returned_algo_count; ++i) {
const auto& stat = fwd_perf_stat[i];
VLOG(3) << stat.algo << ": " << stat.status << " " << stat.time
<< " " << stat.memory;
}
return fwd_perf_stat[0].algo;
});
VLOG(3) << "choose algo " << algo;
} else {
PADDLE_ENFORCE(half_float,
"cuDNN exhaustive search doesn't support half float.");
}
// get workspace size able to allocate
CUDNN_ENFORCE(platform::dynload::cudnnGetConvolutionForwardWorkspaceSize(
handle, cudnn_input_desc, cudnn_filter_desc, cudnn_conv_desc,
......@@ -162,7 +235,6 @@ class CUDNNConvOpKernel : public framework::OpKernel<T> {
// ------------------- cudnn conv forward ---------------------
ScalingParamType<T> alpha = 1.0f, beta = 0.0f;
auto workspace_handle = dev_ctx.cudnn_workspace_handle();
for (int i = 0; i < groups; i++) {
auto cudnn_func = [&](void* cudnn_workspace) {
CUDNN_ENFORCE(platform::dynload::cudnnConvolutionForward(
......@@ -180,6 +252,7 @@ template <typename T>
class CUDNNConvGradOpKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
auto& dev_ctx = ctx.template device_context<platform::CUDADeviceContext>();
PADDLE_ENFORCE(platform::is_gpu_place(ctx.GetPlace()),
"It must use CUDAPlace.");
auto input = ctx.Input<Tensor>("Input");
......@@ -198,6 +271,13 @@ class CUDNNConvGradOpKernel : public framework::OpKernel<T> {
int groups = ctx.Attr<int>("groups");
int64_t user_workspace_size =
static_cast<size_t>(ctx.Attr<int>("workspace_size_MB"));
bool exhaustive_search =
FLAGS_cudnn_exhaustive_search || ctx.Attr<bool>("exhaustive_search");
if (exhaustive_search && FLAGS_cudnn_deterministic) {
PADDLE_THROW(
"Cann't set exhaustive_search True and "
"FLAGS_cudnn_deterministic True at same time.");
}
// ------------------- cudnn descriptors ---------------------
ScopedTensorDescriptor input_desc;
......@@ -265,14 +345,66 @@ class CUDNNConvGradOpKernel : public framework::OpKernel<T> {
cudnnConvolutionBwdFilterAlgo_t filter_algo;
size_t workspace_size_in_bytes = 0, tmp_size = 0;
size_t workspace_size_limit = kCONV_CUDNN_WORKSPACE_LIMIT_BYTES;
if (user_workspace_size > 0) {
workspace_size_limit = user_workspace_size * 1024 * 1024;
if (FLAGS_conv_workspace_size_limit > 0 || user_workspace_size > 0) {
int64_t max_user_size =
std::max(static_cast<int64_t>(FLAGS_conv_workspace_size_limit),
user_workspace_size);
workspace_size_limit = max_user_size * 1024 * 1024;
}
auto& dev_ctx = ctx.template device_context<platform::CUDADeviceContext>();
auto x_dims = framework::vectorize(input->dims());
auto f_dims = framework::vectorize(filter->dims());
auto handle = dev_ctx.cudnn_handle();
auto workspace_handle = dev_ctx.cudnn_workspace_handle();
if (input_grad) {
if (!FLAGS_cudnn_deterministic) {
T* input_grad_data = input_grad->mutable_data<T>(ctx.GetPlace());
if (exhaustive_search) {
AlgorithmsCache<cudnnConvolutionBwdDataAlgo_t>* data_algo_cache;
if (ctx.scope().FindVar(kCUDNNBwdDataAlgoCache)) {
data_algo_cache =
ctx.scope()
.FindVar(kCUDNNBwdDataAlgoCache)
->GetMutable<
AlgorithmsCache<cudnnConvolutionBwdDataAlgo_t>>();
} else {
data_algo_cache =
const_cast<framework::Scope&>(ctx.scope())
.Var(kCUDNNBwdDataAlgoCache)
->GetMutable<
AlgorithmsCache<cudnnConvolutionBwdDataAlgo_t>>();
}
data_algo = data_algo_cache->GetAlgorithm(
x_dims, f_dims, strides, paddings, dilations, 0, [&]() {
int returned_algo_count;
std::array<cudnnConvolutionBwdDataAlgoPerf_t,
kNUM_CUDNN_BWD_DATA_ALGS>
data_perf_stat;
auto cudnn_find_bd_data_func = [&](void* cudnn_workspace) {
CUDNN_ENFORCE(
platform::dynload::
cudnnFindConvolutionBackwardDataAlgorithmEx(
handle, cudnn_filter_desc, filter_data,
cudnn_output_grad_desc, output_grad_data,
cudnn_conv_desc, cudnn_input_desc, input_grad_data,
kNUM_CUDNN_BWD_DATA_ALGS, &returned_algo_count,
data_perf_stat.data(), cudnn_workspace,
workspace_size_limit));
};
workspace_handle.RunFunc(cudnn_find_bd_data_func,
workspace_size_limit);
VLOG(3) << "Perf result: (algo: stat, time, memory)";
for (int i = 0; i < returned_algo_count; ++i) {
const auto& stat = data_perf_stat[i];
VLOG(3) << stat.algo << ": " << stat.status << " " << stat.time
<< " " << stat.memory;
}
return data_perf_stat[0].algo;
});
VLOG(3) << "cuDNN backward data algo " << data_algo;
} else if (FLAGS_cudnn_deterministic) {
data_algo = CUDNN_CONVOLUTION_BWD_DATA_ALGO_1;
} else {
CUDNN_ENFORCE(
platform::dynload::cudnnGetConvolutionBackwardDataAlgorithm(
handle, cudnn_filter_desc,
......@@ -285,10 +417,7 @@ class CUDNNConvGradOpKernel : public framework::OpKernel<T> {
cudnn_input_desc,
CUDNN_CONVOLUTION_BWD_DATA_SPECIFY_WORKSPACE_LIMIT,
workspace_size_limit, &data_algo));
} else {
data_algo = CUDNN_CONVOLUTION_BWD_DATA_ALGO_1;
}
CUDNN_ENFORCE(
platform::dynload::cudnnGetConvolutionBackwardDataWorkspaceSize(
handle, cudnn_filter_desc, cudnn_output_grad_desc,
......@@ -297,17 +426,54 @@ class CUDNNConvGradOpKernel : public framework::OpKernel<T> {
}
if (filter_grad) {
if (!FLAGS_cudnn_deterministic) {
T* filter_grad_data = filter_grad->mutable_data<T>(ctx.GetPlace());
if (exhaustive_search) {
AlgorithmsCache<cudnnConvolutionBwdFilterAlgo_t>* f_algo_cache;
if (ctx.scope().FindVar(kCUDNNBwdFilterAlgoCache)) {
f_algo_cache =
ctx.scope()
.FindVar(kCUDNNBwdFilterAlgoCache)
->GetMutable<
AlgorithmsCache<cudnnConvolutionBwdFilterAlgo_t>>();
} else {
f_algo_cache =
const_cast<framework::Scope&>(ctx.scope())
.Var(kCUDNNBwdFilterAlgoCache)
->GetMutable<
AlgorithmsCache<cudnnConvolutionBwdFilterAlgo_t>>();
}
filter_algo = f_algo_cache->GetAlgorithm(
x_dims, f_dims, strides, paddings, dilations, 0, [&]() {
int returned_algo_count;
std::array<cudnnConvolutionBwdFilterAlgoPerf_t,
kNUM_CUDNN_BWD_FILTER_ALGS>
filter_perf_stat;
auto cudnn_find_bd_f_func = [&](void* cudnn_workspace) {
CUDNN_ENFORCE(
platform::dynload::
cudnnFindConvolutionBackwardFilterAlgorithmEx(
handle, cudnn_input_desc, input_data,
cudnn_output_grad_desc, output_grad_data,
cudnn_conv_desc, cudnn_filter_desc,
filter_grad_data, kNUM_CUDNN_BWD_FILTER_ALGS,
&returned_algo_count, filter_perf_stat.data(),
cudnn_workspace, workspace_size_limit));
};
workspace_handle.RunFunc(cudnn_find_bd_f_func,
workspace_size_limit);
return filter_perf_stat[0].algo;
});
VLOG(3) << "cuDNN backward filter algo " << filter_algo;
} else if (FLAGS_cudnn_deterministic) {
filter_algo = CUDNN_CONVOLUTION_BWD_FILTER_ALGO_1;
} else {
CUDNN_ENFORCE(
platform::dynload::cudnnGetConvolutionBackwardFilterAlgorithm(
handle, cudnn_input_desc, cudnn_output_grad_desc,
cudnn_conv_desc, cudnn_filter_desc,
CUDNN_CONVOLUTION_BWD_FILTER_SPECIFY_WORKSPACE_LIMIT,
workspace_size_limit, &filter_algo));
} else {
filter_algo = CUDNN_CONVOLUTION_BWD_FILTER_ALGO_1;
}
CUDNN_ENFORCE(
platform::dynload::cudnnGetConvolutionBackwardFilterWorkspaceSize(
handle, cudnn_input_desc, cudnn_output_grad_desc, cudnn_conv_desc,
......@@ -317,7 +483,6 @@ class CUDNNConvGradOpKernel : public framework::OpKernel<T> {
// ------------------- cudnn conv backward data ---------------------
ScalingParamType<T> alpha = 1.0f, beta = 0.0f;
auto workspace_handle = dev_ctx.cudnn_workspace_handle();
if (input_grad) {
T* input_grad_data = input_grad->mutable_data<T>(ctx.GetPlace());
// Because beta is zero, it is unnecessary to reset input_grad.
......
paddle/fluid/operators/conv_cudnn_op_cache.h 0 → 100644
浏览文件 @ d231e550
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include <functional>
#include <unordered_map>
#include <vector>
namespace paddle {
namespace operators {
template <typename TAlgorithm>
class AlgorithmsCache {
public:
// Caches the best algorithm for a given
// combination of tensor dimensions & compute data type.
TAlgorithm GetAlgorithm(
const std::vector<int64_t>& dims1, const std::vector<int64_t>& dims2,
const std::vector<int>& strides, const std::vector<int>& paddings,
const std::vector<int>& dilations,
int algorithmFlags, // can set for different data type
std::function<TAlgorithm()> gen_func);
private:
std::unordered_map<int64_t, TAlgorithm> hash_;
std::mutex mutex_;
};
template <typename TAlgorithm>
TAlgorithm AlgorithmsCache<TAlgorithm>::GetAlgorithm(
const std::vector<int64_t>& dims1, const std::vector<int64_t>& dims2,
const std::vector<int>& strides, const std::vector<int>& paddings,
const std::vector<int>& dilations, int algorithmFlags,
std::function<TAlgorithm()> gen_func) {
std::lock_guard<std::mutex> lock(mutex_);
int64_t seed = 0;
// Hash all of the inputs, use to try and look up a previously
// discovered algorithm, or fall back to generating a new one.
std::hash<int64_t> hashFn;
// do hash like boost
// https://stackoverflow.com/questions/2590677/how-do-i-combine-hash-values-in-c0x
for (const auto num : dims1) {
seed ^= hashFn(num) + 0x9e3779b9 + (seed << 6) + (seed >> 2);
}
for (const auto num : dims2) {
seed ^= hashFn(num) + 0x9e3779b9 + (seed << 6) + (seed >> 2) + 1;
}
for (const auto num : strides) {
seed ^= hashFn(static_cast<int64_t>(num)) + 0x9e3779b9 + (seed << 6) +
(seed >> 2) + 2;
}
for (const auto num : paddings) {
seed ^= hashFn(static_cast<int64_t>(num)) + 0x9e3779b9 + (seed << 6) +
(seed >> 2) + 3;
}
for (const auto num : dilations) {
seed ^= hashFn(static_cast<int64_t>(num)) + 0x9e3779b9 + (seed << 6) +
(seed >> 2) + 4;
}
seed ^= hashFn(static_cast<int64_t>(algorithmFlags)) + 0x9e3779b9 +
(seed << 6) + (seed >> 2) + 5;
if (seed == 0) return gen_func();
if (hash_.find(seed) == hash_.end()) {
TAlgorithm value = gen_func();
hash_[seed] = value;
}
return hash_[seed];
}
} // namespace operators
} // namespace paddle
paddle/fluid/operators/conv_op.cc
浏览文件 @ d231e550
......@@ -189,6 +189,11 @@ void Conv2DOpMaker::Make() {
"workspace size can increase performance but also requires "
"better hardware. This size should be chosen carefully.")
.SetDefault(4096);
AddAttr<bool>("exhaustive_search",
"(bool, default false) cuDNN has many algorithm to calculation "
"convolution, whether enable exhaustive search ",
"for cuDNN convolution or not, defalut is False.")
.SetDefault(false);
AddComment(R"DOC(
Convolution Operator.
......@@ -219,6 +224,15 @@ $$
)DOC");
}
class ConvOpInferVarType : public framework::PassInDtypeAndVarTypeToOutput {
protected:
std::unordered_map<std::string, std::string> GetInputOutputWithSameType()
const override {
return std::unordered_map<std::string, std::string>{
{"Input", /*->*/ "Output"}};
}
};
void Conv3DOpMaker::Make() {
AddInput(
"Input",
......@@ -283,7 +297,11 @@ void Conv3DOpMaker::Make() {
"workspace size can increase performance but also requires "
"better hardware. This size should be chosen carefully.")
.SetDefault(4096);
AddAttr<bool>("exhaustive_search",
"(bool, default false) cuDNN has many algorithm to calculation "
"convolution, whether enable exhaustive search ",
"for cuDNN convolution or not, defalut is False.")
.SetDefault(false);
AddComment(R"DOC(
Convolution3D Operator.
......@@ -356,6 +374,7 @@ framework::OpKernelType ConvOpGrad::GetExpectedKernelType(
namespace ops = paddle::operators;
REGISTER_OPERATOR(conv2d, ops::ConvOp, ops::Conv2DOpMaker,
ops::ConvOpInferVarType,
paddle::framework::DefaultGradOpDescMaker<true>);
REGISTER_OPERATOR(conv2d_grad, ops::ConvOpGrad);
......@@ -363,7 +382,9 @@ REGISTER_OPERATOR(conv2d_grad, ops::ConvOpGrad);
REGISTER_OPERATOR(depthwise_conv2d, ops::ConvOp, ops::Conv2DOpMaker,
paddle::framework::DefaultGradOpDescMaker<true>);
REGISTER_OPERATOR(depthwise_conv2d_grad, ops::ConvOpGrad);
REGISTER_OPERATOR(conv3d, ops::ConvOp, ops::Conv3DOpMaker,
ops::ConvOpInferVarType,
paddle::framework::DefaultGradOpDescMaker<true>);
REGISTER_OPERATOR(conv3d_grad, ops::ConvOpGrad);
......
paddle/fluid/operators/cross_entropy_op.cc
浏览文件 @ d231e550
......@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/operators/cross_entropy_op.h"
#include <string>
namespace paddle {
namespace operators {
......@@ -179,6 +180,15 @@ or not. But the output only shares the LoD information with input X.
)DOC");
}
};
class CrossEntropyOpInferVarType
: public framework::PassInDtypeAndVarTypeToOutput {
protected:
std::unordered_map<std::string, std::string> GetInputOutputWithSameType()
const override {
return std::unordered_map<std::string, std::string>{{"X", /*->*/ "Y"}};
}
};
} // namespace operators
} // namespace paddle
......@@ -186,6 +196,7 @@ namespace ops = paddle::operators;
using CPUCtx = paddle::platform::CPUDeviceContext;
REGISTER_OPERATOR(cross_entropy, ops::CrossEntropyOp, ops::CrossEntropyOpMaker,
ops::CrossEntropyOpInferVarType,
paddle::framework::DefaultGradOpDescMaker<true>);
REGISTER_OPERATOR(cross_entropy_grad, ops::CrossEntropyGradientOp);
REGISTER_OP_CPU_KERNEL(cross_entropy, ops::CrossEntropyOpKernel<CPUCtx, float>,
......
paddle/fluid/operators/distributed/brpc_server.cc
浏览文件 @ d231e550
......@@ -133,10 +133,10 @@ void AsyncBRPCServer::StartServer() {
void AsyncBRPCServer::ShutDownImpl() { server_.Stop(1000); }
void AsyncBRPCServer::WaitServerReady() {
VLOG(3) << "AsyncGRPCServer is wait server ready";
VLOG(30) << "AsyncGRPCServer is wait server ready";
std::unique_lock<std::mutex> lock(this->mutex_ready_);
condition_ready_.wait(lock, [=] { return this->ready_ == 1; });
VLOG(3) << "AsyncGRPCServer WaitSeverReady";
VLOG(30) << "AsyncGRPCServer WaitSeverReady";
}
}; // namespace distributed
......
paddle/fluid/operators/distributed/grpc_client.cc
浏览文件 @ d231e550
......@@ -38,7 +38,7 @@ void GRPCClient::SendComplete() {
std::unique_lock<std::mutex> lk(completed_mutex_);
if (!completed_) {
for (auto& it : channels_) {
VLOG(3) << "send complete message to " << it.first;
VLOG(30) << "send complete message to " << it.first;
this->AsyncSendComplete(it.first);
}
PADDLE_ENFORCE(this->Wait(), "internal grpc error");
......@@ -81,7 +81,7 @@ VarHandlePtr GRPCClient::AsyncSendVar(const std::string& ep,
::grpc::ByteBuffer req;
SerializeToByteBuffer(var_name_val, var, *p_ctx, &req, "", trainer_id_);
VLOG(3) << s->GetVarHandlePtr()->String() << " begin";
VLOG(30) << s->GetVarHandlePtr()->String() << " begin";
// stub context
s->response_call_back_ = nullptr;
......@@ -142,7 +142,7 @@ VarHandlePtr GRPCClient::AsyncGetVar(const std::string& ep,
::grpc::ByteBuffer buf;
RequestToByteBuffer<sendrecv::VariableMessage>(req, &buf);
VLOG(3) << s->GetVarHandlePtr()->String() << " begin";
VLOG(30) << s->GetVarHandlePtr()->String() << " begin";
// stub context
s->response_call_back_ = ProcGetResponse;
......@@ -190,7 +190,7 @@ VarHandlePtr GRPCClient::AsyncPrefetchVar(const std::string& ep,
::grpc::ByteBuffer req;
SerializeToByteBuffer(in_var_name_val, var, *p_ctx, &req, out_var_name_val);
VLOG(3) << s->GetVarHandlePtr()->String() << " begin";
VLOG(30) << s->GetVarHandlePtr()->String() << " begin";
// stub context
s->response_call_back_ = ProcGetResponse;
......@@ -328,14 +328,14 @@ void GRPCClient::Proceed() {
void* tag = nullptr;
bool ok = false;
VLOG(3) << "GRPCClient Proceed begin";
VLOG(30) << "GRPCClient Proceed begin";
while (!stopped_ && cq_.Next(&tag, &ok)) {
BaseProcessor* c = static_cast<BaseProcessor*>(tag);
GPR_ASSERT(ok);
PADDLE_ENFORCE(c);
if (c->status_.ok()) {
VLOG(3) << c->GetVarHandlePtr()->String() << " process";
VLOG(30) << c->GetVarHandlePtr()->String() << " process";
c->Process();
} else if (c->status_.error_code() == grpc::StatusCode::DEADLINE_EXCEEDED) {
// FIXME(gongwb): parse error_details?
......@@ -370,7 +370,7 @@ void GRPCClient::Proceed() {
sync_cond_.notify_all();
}
}
VLOG(3) << "GRPCClient Proceed end";
VLOG(30) << "GRPCClient Proceed end";
}
std::shared_ptr<grpc::Channel> GRPCClient::GetChannel(const std::string& ep) {
......
paddle/fluid/operators/distributed/grpc_server.cc
浏览文件 @ d231e550
......@@ -98,7 +98,7 @@ class RequestSend final : public RequestBase {
void Process() override {
std::string varname = GetReqName();
VLOG(4) << "RequestSend var_name:" << varname;
VLOG(40) << "RequestSend var_name:" << varname;
auto scope = request_->GetMutableLocalScope();
auto invar = request_->GetVar();
......@@ -135,7 +135,7 @@ class RequestGet final : public RequestBase {
// proc request.
std::string varname = request_.varname();
int trainer_id = request_.trainer_id();
VLOG(4) << "RequestGet " << varname;
VLOG(40) << "RequestGet " << varname;
auto scope = request_handler_->scope();
auto invar = scope->FindVar(varname);
......@@ -182,8 +182,8 @@ class RequestPrefetch final : public RequestBase {
std::string in_var_name = request_->Varname();
std::string out_var_name = request_->OutVarname();
int trainer_id = request_->GetTrainerId();
VLOG(4) << "RequestPrefetch, in_var_name: " << in_var_name
<< " out_var_name: " << out_var_name;
VLOG(40) << "RequestPrefetch, in_var_name: " << in_var_name
<< " out_var_name: " << out_var_name;
auto scope = request_->GetMutableLocalScope();
auto invar = scope->FindVar(in_var_name);
......@@ -231,8 +231,8 @@ class RequestCheckpointNotify final : public RequestBase {
std::string checkpoint_dir = request_->OutVarname();
int trainer_id = request_->GetTrainerId();
VLOG(4) << "RequestCheckpointNotify notify: " << checkpoint_notify
<< ", dir: " << checkpoint_dir;
VLOG(40) << "RequestCheckpointNotify notify: " << checkpoint_notify
<< ", dir: " << checkpoint_dir;
request_handler_->Handle(checkpoint_notify, scope, nullptr, nullptr,
trainer_id, checkpoint_dir);
......@@ -246,10 +246,10 @@ class RequestCheckpointNotify final : public RequestBase {
};
void AsyncGRPCServer::WaitServerReady() {
VLOG(4) << "AsyncGRPCServer is wait server ready";
VLOG(40) << "AsyncGRPCServer is wait server ready";
std::unique_lock<std::mutex> lock(this->mutex_ready_);
condition_ready_.wait(lock, [=] { return this->ready_ == 1; });
VLOG(4) << "AsyncGRPCServer WaitSeverReady";
VLOG(40) << "AsyncGRPCServer WaitSeverReady";
}
void AsyncGRPCServer::StartServer() {
......@@ -282,14 +282,15 @@ void AsyncGRPCServer::StartServer() {
reqs.reserve(kRequestBufSize);
for (int i = 0; i < kRequestBufSize; i++) {
VLOG(6) << "TryToRegisterNewOne on RPC NAME: " << rpc_name << " I: " << i;
VLOG(60) << "TryToRegisterNewOne on RPC NAME: " << rpc_name
<< " I: " << i;
TryToRegisterNewOne(rpc_name, i);
}
for (int i = 0; i < threadnum; i++) {
rpc_threads_[rpc_name].emplace_back(new std::thread(std::bind(
&AsyncGRPCServer::HandleRequest, this, cq.get(), rpc_name, f)));
VLOG(4) << t.first << " creates threads!";
VLOG(40) << t.first << " creates threads!";
}
}
......@@ -306,7 +307,7 @@ void AsyncGRPCServer::StartServer() {
auto& threads = t.second;
for (size_t i = 0; i < threads.size(); ++i) {
threads[i]->join();
VLOG(4) << t.first << " threads ends!";
VLOG(40) << t.first << " threads ends!";
}
}
}
......@@ -314,7 +315,7 @@ void AsyncGRPCServer::StartServer() {
void AsyncGRPCServer::ShutdownQueue() {
for (auto& t : rpc_cq_) {
t.second->Shutdown();
VLOG(4) << t.first << " queue shutdown!";
VLOG(40) << t.first << " queue shutdown!";
}
}
......@@ -323,7 +324,7 @@ void AsyncGRPCServer::ShutDownImpl() {
is_shut_down_ = true;
ShutdownQueue();
VLOG(4) << "server_ shutdown!";
VLOG(40) << "server_ shutdown!";
server_->Shutdown();
}
......@@ -331,12 +332,12 @@ void AsyncGRPCServer::TryToRegisterNewOne(const std::string& rpc_name,
int req_id) {
std::unique_lock<std::mutex> lock(cq_mutex_);
if (is_shut_down_) {
VLOG(4) << "shutdown, do not TryToRegisterNewSendOne";
VLOG(40) << "shutdown, do not TryToRegisterNewSendOne";
return;
}
VLOG(4) << "TryToRegisterNewOne on RPC NAME: " << rpc_name
<< " REQ ID: " << req_id;
VLOG(40) << "TryToRegisterNewOne on RPC NAME: " << rpc_name
<< " REQ ID: " << req_id;
auto& reqs = rpc_reqs_[rpc_name];
auto& handler = rpc_call_map_[rpc_name];
......@@ -357,7 +358,7 @@ void AsyncGRPCServer::TryToRegisterNewOne(const std::string& rpc_name,
reqs[req_id] = b;
VLOG(4) << "Create RequestSend status:" << b->Status();
VLOG(40) << "Create RequestSend status:" << b->Status();
}
void AsyncGRPCServer::HandleRequest(
......@@ -367,15 +368,15 @@ void AsyncGRPCServer::HandleRequest(
bool ok = false;
while (true) {
VLOG(4) << "HandleRequest " << rpc_name << " wait next";
VLOG(40) << "HandleRequest " << rpc_name << " wait next";
if (!cq->Next(&tag, &ok)) {
VLOG(3) << "CompletionQueue " << rpc_name << " shutdown!";
VLOG(30) << "CompletionQueue " << rpc_name << " shutdown!";
break;
}
int req_id = static_cast<int>(reinterpret_cast<intptr_t>(tag));
VLOG(4) << "HandleRequest " << rpc_name << ", req_id:" << req_id
<< " get next";
VLOG(40) << "HandleRequest " << rpc_name << ", req_id:" << req_id
<< " get next";
auto& reqs = rpc_reqs_[rpc_name];
RequestBase* base = nullptr;
......@@ -385,7 +386,7 @@ void AsyncGRPCServer::HandleRequest(
base = reqs[req_id];
}
VLOG(3) << base->Status2String(rpc_name);
VLOG(30) << base->Status2String(rpc_name);
// reference:
// https://github.com/tensorflow/tensorflow/issues/5596
......
paddle/fluid/operators/distributed/request_handler.h
浏览文件 @ d231e550
......@@ -75,7 +75,7 @@ class VarHandle {
wait_cond_.wait(lk, [this] { return status_ != kDefaultState; });
ret = status_;
}
VLOG(7) << "VarHandle wait:" << ret;
VLOG(70) << "VarHandle wait:" << ret;
return ret != kErrorState;
}
......@@ -84,7 +84,7 @@ class VarHandle {
std::unique_lock<std::mutex> lk(sync_mutex_);
status_ = ok ? kFinishState : kErrorState;
}
VLOG(7) << "VarHandle finish:" << ok;
VLOG(70) << "VarHandle finish:" << ok;
wait_cond_.notify_all();
}
......
paddle/fluid/operators/distributed/request_handler_impl.cc
浏览文件 @ d231e550
......@@ -38,19 +38,19 @@ bool RequestSendHandler::Handle(const std::string& varname,
framework::Variable** outvar,
const int trainer_id,
const std::string& out_var_name) {
VLOG(4) << "RequestSendHandler:" << varname;
VLOG(40) << "RequestSendHandler:" << varname;
// Sync
if (varname == BATCH_BARRIER_MESSAGE) {
VLOG(3) << "sync: recv BATCH_BARRIER_MESSAGE";
VLOG(30) << "sync: recv BATCH_BARRIER_MESSAGE";
rpc_server_->IncreaseBatchBarrier(kRequestSend);
} else if (varname == COMPLETE_MESSAGE) {
VLOG(3) << "sync: recv complete message";
VLOG(30) << "sync: recv complete message";
rpc_server_->Complete();
} else {
// Async
if (!sync_mode_) {
VLOG(3) << "async process var: " << varname;
VLOG(30) << "async process var: " << varname;
try {
executor_->RunPreparedContext((*grad_to_prepared_ctx_)[varname].get(),
scope);
......@@ -61,7 +61,7 @@ bool RequestSendHandler::Handle(const std::string& varname,
return true;
} else { // sync
rpc_server_->WaitCond(kRequestSend);
VLOG(3) << "sync: processing received var: " << varname;
VLOG(30) << "sync: processing received var: " << varname;
if (invar == nullptr) {
LOG(FATAL) << "sync: Can not find server side var: " << varname;
......@@ -78,10 +78,10 @@ bool RequestGetHandler::Handle(const std::string& varname,
framework::Variable** outvar,
const int trainer_id,
const std::string& out_var_name) {
VLOG(4) << "RequestGetHandler:" << varname;
VLOG(40) << "RequestGetHandler:" << varname;
if (sync_mode_) {
if (varname == FETCH_BARRIER_MESSAGE) {
VLOG(3) << "sync: recv fetch barrier message";
VLOG(30) << "sync: recv fetch barrier message";
rpc_server_->IncreaseBatchBarrier(kRequestGet);
} else {
rpc_server_->WaitCond(kRequestGet);
......@@ -93,13 +93,14 @@ bool RequestGetHandler::Handle(const std::string& varname,
// NOTE: the format is determined by distributed_transpiler.py
std::string param_bak_name =
string::Sprintf("%s.trainer_%d_bak", varname, trainer_id);
VLOG(3) << "getting " << param_bak_name << " trainer_id " << trainer_id;
VLOG(30) << "getting " << param_bak_name << " trainer_id "
<< trainer_id;
auto var = scope_->FindVar(varname);
auto t_orig = var->Get<framework::LoDTensor>();
auto param_bak = scope_->Var(param_bak_name);
auto t = param_bak->GetMutable<framework::LoDTensor>();
t->mutable_data(dev_ctx_->GetPlace(), t_orig.type());
VLOG(3) << "copying " << varname << " to " << param_bak_name;
VLOG(30) << "copying " << varname << " to " << param_bak_name;
framework::TensorCopy(t_orig, dev_ctx_->GetPlace(), t);
}
*outvar = scope_->FindVar(varname);
......@@ -114,7 +115,7 @@ bool RequestPrefetchHandler::Handle(const std::string& varname,
framework::Variable** outvar,
const int trainer_id,
const std::string& out_var_name) {
VLOG(4) << "RequestPrefetchHandler " << varname;
VLOG(40) << "RequestPrefetchHandler " << varname;
auto var_desc = program_->Block(0).FindVar(out_var_name);
InitializeVariable(*outvar, var_desc->GetType());
......@@ -138,8 +139,8 @@ bool RequestCheckpointHandler::Handle(const std::string& varname,
auto* lt_var = scope_->FindVar(LOOKUP_TABLE_PATH)->GetMutable<std::string>();
lt_var->clear();
lt_var->append(out_var_name);
VLOG(4) << "RequestCheckpointHandler update var kLookupTablePath to: "
<< out_var_name;
VLOG(40) << "RequestCheckpointHandler update var kLookupTablePath to: "
<< out_var_name;
executor_->RunPreparedContext(checkpoint_prepared_ctx_.get(), scope_);
return true;
}
......
paddle/fluid/operators/distributed/rpc_server.cc
浏览文件 @ d231e550
......@@ -39,7 +39,7 @@ void RPCServer::SavePort() const {
port_file.open(file_path);
port_file << selected_port_;
port_file.close();
VLOG(4) << "selected port written to " << file_path;
VLOG(40) << "selected port written to " << file_path;
}
void RPCServer::WaitBarrier(const std::string& rpc_name) {
......@@ -49,12 +49,12 @@ void RPCServer::WaitBarrier(const std::string& rpc_name) {
exit_flag_.load());
});
VLOG(3) << "batch_barrier_: " << rpc_name << " "
<< barrier_counter_[rpc_name];
VLOG(30) << "batch_barrier_: " << rpc_name << " "
<< barrier_counter_[rpc_name];
}
void RPCServer::IncreaseBatchBarrier(const std::string rpc_name) {
VLOG(4) << "RPCServer begin IncreaseBatchBarrier " << rpc_name;
VLOG(40) << "RPCServer begin IncreaseBatchBarrier " << rpc_name;
int b = 0;
std::unique_lock<std::mutex> lock(mutex_);
b = ++barrier_counter_[rpc_name];
......@@ -71,7 +71,7 @@ void RPCServer::Complete() {
client_num_--;
need_reset_all_vars_ = true;
VLOG(4) << "decrease client_num to: " << client_num_;
VLOG(40) << "decrease client_num to: " << client_num_;
if (cur_cond_.load() == rpc_cond_map_[kRequestGet]) {
barrier_counter_[kRequestGet]--;
}
......@@ -90,7 +90,7 @@ int RPCServer::GetClientNum() {
}
void RPCServer::ResetBarrierCounter() {
VLOG(3) << "RPCServer ResetBarrierCounter ";
VLOG(30) << "RPCServer ResetBarrierCounter ";
std::unique_lock<std::mutex> lock(mutex_);
for (auto& t : barrier_counter_) {
t.second = 0;
......@@ -105,12 +105,12 @@ void RPCServer::RegisterRPC(const std::string& rpc_name,
static int cond = -1;
rpc_cond_map_[rpc_name] = ++cond;
VLOG(4) << "RegisterRPC rpc_name:" << rpc_name << ", handler:" << handler
<< ", cond:" << rpc_cond_map_[rpc_name];
VLOG(40) << "RegisterRPC rpc_name:" << rpc_name << ", handler:" << handler
<< ", cond:" << rpc_cond_map_[rpc_name];
}
void RPCServer::SetCond(const std::string& rpc_name) {
VLOG(3) << "RPCServer SetCond " << rpc_name;
VLOG(30) << "RPCServer SetCond " << rpc_name;
{
std::unique_lock<std::mutex> lock(mutex_);
cur_cond_ = rpc_cond_map_[rpc_name];
......@@ -120,7 +120,7 @@ void RPCServer::SetCond(const std::string& rpc_name) {
}
void RPCServer::WaitCond(const std::string& rpc_name) {
VLOG(4) << "RPCServer WaitCond " << rpc_name;
VLOG(40) << "RPCServer WaitCond " << rpc_name;
int cond = 0;
{
std::unique_lock<std::mutex> lock(mutex_);
......
paddle/fluid/operators/distributed/variable_response.cc
浏览文件 @ d231e550
......@@ -50,7 +50,7 @@ bool VariableResponse::ReadRaw(::google::protobuf::io::CodedInputStream* input,
size_to_write = length - total_written;
}
// This log is useful to see how long a internal block size is of rpc.
VLOG(7) << "copy " << size_to_write << " data to CUDAPlace";
VLOG(70) << "copy " << size_to_write << " data to CUDAPlace";
memory::Copy(boost::get<platform::CUDAPlace>(place),
reinterpret_cast<void*>(p), cpu, data, size_to_write,
gpu_dev_ctx.stream());
......@@ -79,7 +79,7 @@ bool VariableResponse::ReadRaw(::google::protobuf::io::CodedInputStream* input,
// TODO(gongwb): can we avoid copy?
platform::CPUPlace cpu;
// This log is useful to see how long a internal block size is of rpc.
VLOG(7) << "copy " << size_to_write << " data to CPUPlace";
VLOG(70) << "copy " << size_to_write << " data to CPUPlace";
memory::Copy(cpu, reinterpret_cast<void*>(p), cpu, data, size_to_write);
p += size_to_write;
......@@ -198,8 +198,8 @@ bool VariableResponse::ProcSerializedField(
#endif
}
VLOG(7) << "ProcSerializedField:" << meta_.varname()
<< ", type:" << meta_.type() << std::endl;
VLOG(70) << "ProcSerializedField:" << meta_.varname()
<< ", type:" << meta_.type() << std::endl;
framework::DDim dims = GetDims(meta_.dims());
if (meta_.type() == sendrecv::LOD_TENSOR) {
PADDLE_ENFORCE(meta_.lod_size() >= 0, "lod info should be got first!");
......
paddle/fluid/operators/elementwise_op.h
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......@@ -75,16 +75,12 @@ class ElementwiseOp : public framework::OperatorWithKernel {
}
};
class ElementwiseOpInferVarType : public framework::VarTypeInference {
public:
void operator()(const framework::OpDesc &op_desc,
framework::BlockDesc *block) const override {
auto x_name = op_desc.Input("X")[0];
auto out_name = op_desc.Output("Out")[0];
auto &x = block->FindRecursiveOrCreateVar(x_name);
auto &out = block->FindRecursiveOrCreateVar(out_name);
out.SetType(x.GetType());
out.SetDataType(x.GetDataType());
class ElementwiseOpInferVarType
: public framework::PassInDtypeAndVarTypeToOutput {
protected:
std::unordered_map<std::string, std::string> GetInputOutputWithSameType()
const override {
return std::unordered_map<std::string, std::string>{{"X", /*->*/ "Out"}};
}
};
......
paddle/fluid/operators/feed_op.cc
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paddle/fluid/operators/fetch_op.cc
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paddle/fluid/operators/interpolate_op.h 0 → 100644
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paddle/fluid/operators/mean_op.cc
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paddle/fluid/operators/mul_op.cc
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paddle/fluid/operators/pool_op.cc
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paddle/fluid/operators/recv_op.cc
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paddle/fluid/operators/save_op.cc
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paddle/fluid/operators/send_op.cc
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paddle/fluid/operators/sgd_op.h
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paddle/fluid/operators/similarity_focus_op.cc 0 → 100644
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paddle/fluid/operators/similarity_focus_op.h 0 → 100644
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paddle/fluid/operators/sum_op.cc
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paddle/fluid/operators/tensor_array_to_tensor_op.cc 0 → 100644
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paddle/fluid/operators/while_op.cc
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paddle/fluid/platform/gpu_info.cc
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paddle/fluid/platform/init.cc
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paddle/fluid/platform/nccl_helper.h
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paddle/fluid/pybind/protobuf.cc
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paddle/scripts/paddle_build.sh
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paddle/testing/TestUtil.cpp
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python/paddle/fluid/__init__.py
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python/paddle/fluid/layers/nn.py
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python/paddle/fluid/optimizer.py
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