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feature/add print op (#6799)
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‎paddle/operators/CMakeLists.txt

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@@ -135,6 +135,7 @@ op_library(detection_output_op DEPS softmax)
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op_library(sequence_softmax_op DEPS softmax)
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op_library(sum_op DEPS selected_rows_functor)
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op_library(sgd_op DEPS selected_rows_functor)
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op_library(print_op DEPS lod_tensor)
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op_library(adagrad_op DEPS selected_rows_functor)
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op_library(conv_op DEPS vol2col)
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op_library(pool_op DEPS pooling)

‎paddle/operators/print_op.cc

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/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License. */
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#include <algorithm>
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#include <ctime>
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#include "paddle/framework/op_registry.h"
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namespace paddle {
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namespace operators {
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#define CLOG std::cout
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struct Formater {
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std::string message;
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std::string name;
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std::vector<int> dims;
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std::type_index dtype{typeid(char)};
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framework::LoD lod;
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int summarize;
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void* data{nullptr};
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void operator()(size_t size) {
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PrintMessage();
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PrintName();
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PrintDims();
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PrintDtype();
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PrintLod();
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PrintData(size);
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}
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private:
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void PrintMessage() { CLOG << std::time(nullptr) << "\t" << message; }
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void PrintName() {
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if (!name.empty()) {
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CLOG << "Tensor[" << name << "]" << std::endl;
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}
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}
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void PrintDims() {
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if (!dims.empty()) {
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CLOG << "\tshape: [";
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for (auto i : dims) {
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CLOG << i << ",";
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}
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CLOG << "]" << std::endl;
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}
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}
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void PrintDtype() {
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if (dtype.hash_code() != typeid(char).hash_code()) {
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CLOG << "\tdtype: " << dtype.name() << std::endl;
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}
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}
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void PrintLod() {
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if (!lod.empty()) {
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CLOG << "\tLoD: [";
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for (auto level : lod) {
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CLOG << "[ ";
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for (auto i : level) {
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CLOG << i << ",";
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}
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CLOG << " ]";
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}
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CLOG << "]" << std::endl;
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}
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}
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void PrintData(size_t size) {
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PADDLE_ENFORCE_NOT_NULL(data);
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// print float
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if (dtype.hash_code() == typeid(float).hash_code()) {
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Display<float>(size);
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}
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if (dtype.hash_code() == typeid(double).hash_code()) {
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Display<double>(size);
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}
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if (dtype.hash_code() == typeid(int).hash_code()) {
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Display<int>(size);
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}
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if (dtype.hash_code() == typeid(int64_t).hash_code()) {
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Display<int64_t>(size);
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}
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}
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template <typename T>
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void Display(size_t size) {
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auto* d = (T*)data;
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CLOG << "\tdata: ";
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if (summarize != -1) {
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summarize = std::min(size, (size_t)summarize);
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for (int i = 0; i < summarize; i++) {
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CLOG << d[i] << ",";
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}
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} else {
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for (size_t i = 0; i < size; i++) {
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CLOG << d[i] << ",";
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}
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}
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CLOG << std::endl;
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}
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};
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// TODO(ChunweiYan) there should be some other printers for TensorArray
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class TensorPrintOp : public framework::OperatorBase {
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public:
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TensorPrintOp(const std::string& type,
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const framework::VariableNameMap& inputs,
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const framework::VariableNameMap& outputs,
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const framework::AttributeMap& attrs)
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: OperatorBase(type, inputs, outputs, attrs) {}
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TensorPrintOp(const TensorPrintOp& o)
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: framework::OperatorBase(
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static_cast<const framework::OperatorBase&>(o)) {
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PADDLE_THROW("Not implemented");
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}
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void Run(const framework::Scope& scope,
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const platform::Place& place) const override {
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// Only run the `first_n` times.
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int first_n = Attr<int>("first_n");
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if (first_n > 0 && ++times_ > first_n) return;
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PADDLE_ENFORCE(!Inputs("input").empty(), "input should be set");
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auto* input_var = scope.FindVar(Input("input"));
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PADDLE_ENFORCE_NOT_NULL(input_var);
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auto& tensor = input_var->Get<framework::LoDTensor>();
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// TODO(ChunweiYan) support GPU
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PADDLE_ENFORCE(platform::is_cpu_place(tensor.place()));
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Formater formater;
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if (Attr<bool>("print_tensor_name")) {
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formater.name = Inputs("input").front();
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}
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if (Attr<bool>("print_tensor_type")) {
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formater.dtype = tensor.type();
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}
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if (Attr<bool>("print_tensor_shape")) {
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formater.dims.assign(tensor.dims()[0],
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tensor.dims()[tensor.dims().size() - 1]);
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}
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if (Attr<bool>("print_tensor_lod")) {
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formater.lod = tensor.lod();
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}
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formater.summarize = Attr<int>("summarize");
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formater.data = (void*)tensor.data<void>();
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formater(tensor.numel());
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}
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private:
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mutable int times_{0};
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};
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class PrintOpProtoAndCheckMaker : public framework::OpProtoAndCheckerMaker {
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public:
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PrintOpProtoAndCheckMaker(OpProto* proto, OpAttrChecker* op_checker)
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: OpProtoAndCheckerMaker(proto, op_checker) {
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AddInput("input", "the tensor that will be displayed.");
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AddAttr<int>("first_n", "Only log `first_n` number of times.");
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AddAttr<std::string>("message", "A string message to print as a prefix.");
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AddAttr<int>("summarize", "Print this number of elements in the tensor.");
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AddAttr<bool>("print_tensor_name", "Whether to print the tensor name.");
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AddAttr<bool>("print_tensor_type", "Whether to print the tensor's dtype.");
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AddAttr<bool>("print_tensor_shape", "Whether to print the tensor's shape.");
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AddAttr<bool>("print_tensor_lod", "Whether to print the tensor's lod.");
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AddComment(R"DOC(
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Creates a print op that will print when a tensor is accessed.
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Wraps the tensor passed in so that whenever that a tensor is accessed,
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the message `message` is printed, along with the current value of the
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tensor `t`.)DOC");
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}
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};
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class InferShape : public framework::InferShapeBase {
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public:
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void operator()(framework::InferShapeContext* context) const override {
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PADDLE_ENFORCE(context->HasInput("input"), "input should be set");
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}
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};
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class InferVarType : public framework::VarTypeInference {
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public:
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void operator()(const framework::OpDesc& op_desc,
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framework::BlockDesc* block) const override {}
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};
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} // namespace operators
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} // namespace paddle
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REGISTER_OPERATOR(print, paddle::operators::TensorPrintOp,
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paddle::operators::PrintOpProtoAndCheckMaker,
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paddle::operators::InferShape,
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paddle::operators::InferVarType,
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paddle::framework::EmptyGradOpMaker);

‎python/paddle/v2/fluid/layers/control_flow.py

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@@ -12,7 +12,7 @@
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'array_to_lod_tensor', 'increment', 'array_write', 'create_array',
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'less_than', 'array_read', 'shrink_memory', 'array_length', 'IfElse',
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'DynamicRNN', 'ConditionalBlock', 'StaticRNN', 'reorder_lod_tensor_by_rank',
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'ParallelDo'
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'ParallelDo', 'Print'
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]
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@@ -110,6 +110,61 @@ def merge_lod_tensor(in_true, in_false, x, mask, level=0):
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return out
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def Print(input,
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first_n=-1,
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message=None,
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summarize=-1,
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print_tensor_name=True,
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print_tensor_type=True,
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print_tensor_shape=True,
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print_tensor_lod=True):
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'''
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**Print operator**
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This creates a print op that will print when a tensor is accessed.
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Wraps the tensor passed in so that whenever that a tensor is accessed,
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the message `message` is printed, along with the current value of the
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tensor `t`.
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Args:
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input(Variable): A Tensor to print.
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summarize(int): Print this number of elements in the tensor, will print all
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if left negative.
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message(str): A string message to print as a prefix.
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first_n(int): Only log `first_n` number of times.
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print_tensor_name(bool): Print the tensor name.
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print_tensor_type(bool): Print the tensor type.
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print_tensor_shape(bool): Print the tensor shape.
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print_tensor_lod(bool): Print the tensor lod.
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Returns:
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None
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Examples:
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.. code-block:: python
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value = some_layer(...)
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Print(value, summarize=10,
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message="The content of some_layer: ")
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'''
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helper = LayerHelper('print', **locals())
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out = helper.create_tmp_variable(dtype='int32')
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helper.append_op(
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type='print',
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inputs={'input': input},
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attrs={
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'first_n': first_n,
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'summarize': summarize,
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'message': message or "",
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'print_tensor_name': print_tensor_name,
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'print_tensor_type': print_tensor_type,
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'print_tensor_shape': print_tensor_shape,
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'print_tensor_lod': print_tensor_lod,
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})
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return out
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class BlockGuard(object):
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"""
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BlockGuard class.

‎python/paddle/v2/fluid/tests/test_print_op.py

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import unittest
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import numpy as np
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from paddle.v2.fluid.executor import Executor
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import paddle.v2.fluid.core as core
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import paddle.v2.fluid.layers as pd
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class TestSumOp(unittest.TestCase):
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def test_tensor(self):
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i = pd.zeros(shape=[2, 10], dtype='float32')
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pd.Print(i, message="I am a message", summarize=10)
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cpu = core.CPUPlace()
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exe = Executor(cpu)
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exe.run()
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if __name__ == '__main__':
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unittest.main()

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