prune pass dummy test

paddle/framework/CMakeLists.txt

@@ -49,5 +49,8 @@ cc_library(executor SRCS executor.cc DEPS op_registry device_context scope frame
 #    cc_test(executor_test SRCS executor_test.cc DEPS executor)
 #endif()
 
+cc_library(prune SRCS prune.cc)
+cc_test(prune_test SRCS prune_test.cc DEPS prune recurrent_op device_context)
+
 cc_library(tensor_array SRCS tensor_array.cc DEPS lod_tensor)
 cc_test(tensor_array_test SRCS tensor_array_test.cc DEPS tensor_array place)

paddle/framework/framework.proto

@@ -55,6 +55,7 @@ message OpDesc {
   repeated Var inputs = 1;
   repeated Var outputs = 2;
   repeated Attr attrs = 4;
+  required bool is_target = 5 [ default = false ];
 };
 
 // OpProto describes a C++ framework::OperatorBase derived class.

paddle/framework/prune.cc

@@ -0,0 +1,107 @@
+/* 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. */
+
+#include "paddle/framework/prune.h"
+
+#include <algorithm>
+#include <set>
+#include <string>
+#include <vector>
+
+#include <glog/logging.h>
+
+namespace paddle {
+namespace framework {
+
+const std::string kFeedOpType = "feed";
+const std::string kFetchOpType = "fetch";
+
+bool HasDependentVar(const OpDesc& op_desc,
+                     const std::set<std::string>& dependent_vars) {
+  for (auto& var : op_desc.outputs()) {
+    for (auto& argu : var.arguments()) {
+      if (dependent_vars.count(argu) != 0) {
+        return true;
+      }
+    }
+  }
+  return false;
+}
+
+void Prune(const ProgramDesc& input, ProgramDesc& output, int id) {
+  // TODO(tonyyang-svail):
+  //    - will change to use multiple blocks for RNN op and Cond Op
+
+  auto& block = input.blocks(0);
+  auto& ops = block.ops();
+
+  bool expect_feed = true;
+  for (auto& op_desc : ops) {
+    PADDLE_ENFORCE(op_desc.type() != kFeedOpType || expect_feed,
+                   "All FeedOps are at the beginning of the ProgramDesc");
+    expect_feed = (op_desc.type() == kFeedOpType);
+  }
+
+  bool expect_fetch = true;
+  for (auto op_iter = ops.rbegin(); op_iter != ops.rend(); ++op_iter) {
+    auto& op_desc = *op_iter;
+    PADDLE_ENFORCE(op_desc.type() != kFetchOpType || expect_fetch,
+                   "All FetchOps must at the end of the ProgramDesc");
+    expect_fetch = (op_desc.type() == kFetchOpType);
+  }
+
+  std::set<std::string> dependent_vars;
+  std::vector<bool> should_run;
+  for (auto op_iter = ops.rbegin(); op_iter != ops.rend(); ++op_iter) {
+    auto& op_desc = *op_iter;
+
+    if (op_desc.is_target() || HasDependentVar(op_desc, dependent_vars)) {
+      // erase its output to the dependency graph
+      for (auto& var : op_desc.outputs()) {
+        for (auto& argu : var.arguments()) {
+          dependent_vars.erase(argu);
+        }
+      }
+
+      // insert its input to the dependency graph
+      for (auto& var : op_desc.inputs()) {
+        for (auto& argu : var.arguments()) {
+          dependent_vars.insert(argu);
+        }
+      }
+
+      should_run.push_back(true);
+    } else {
+      should_run.push_back(false);
+    }
+  }
+
+  // since we are traversing the ProgramDesc in reverse order
+  // we reverse the should_run vector
+  std::reverse(should_run.begin(), should_run.end());
+
+  output = input;
+  auto* op_field = output.mutable_blocks(id)->mutable_ops();
+  op_field->Clear();
+  for (size_t i = 0; i < should_run.size(); ++i) {
+    if (should_run[i]) {
+      *op_field->Add() = input.blocks(id).ops(i);
+    }
+  }
+
+  // return should_run;
+}
+
+}  // namespace framework
+}  // namespace paddle

paddle/framework/prune.h

@@ -0,0 +1,26 @@
+/* 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/framework/framework.pb.h"
+#include "paddle/platform/enforce.h"
+
+namespace paddle {
+namespace framework {
+
+void Prune(const ProgramDesc& input, ProgramDesc& output, int id);
+
+}  // namespace framework
+}  // namespace paddle

paddle/framework/prune_test.cc

@@ -0,0 +1,200 @@
+/* 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. */
+
+#include "paddle/framework/prune.h"
+
+#include <gtest/gtest.h>
+#include "paddle/framework/attribute.h"
+#include "paddle/framework/block_desc.h"
+#include "paddle/framework/op_desc.h"
+#include "paddle/framework/op_registry.h"
+#include "paddle/framework/operator.h"
+#include "paddle/framework/program_desc.h"
+#include "paddle/operators/net_op.h"
+
+namespace paddle {
+namespace framework {
+
+using DeviceContext = platform::DeviceContext;
+
+class RowWiseAddOpMaker : public OpProtoAndCheckerMaker {
+ public:
+  RowWiseAddOpMaker(OpProto *proto, OpAttrChecker *op_checker)
+      : OpProtoAndCheckerMaker(proto, op_checker) {
+    AddInput("X", "Input X of Add");
+    AddInput("b", "Bias of Add");
+    AddOutput("Out", "Out of Add");
+    AddComment("Add Op");
+  }
+};
+
+class RowWiseAddGradMaker : public SingleGradOpDescMaker {
+ public:
+  using SingleGradOpDescMaker::SingleGradOpDescMaker;
+
+ protected:
+  std::unique_ptr<OpDescBind> Apply() const override {
+    auto grad_op = new OpDescBind();
+    grad_op->SetInput(GradVarName("Out"), OutputGrad("Out"));
+    grad_op->SetOutput(GradVarName("X"), InputGrad("X"));
+    grad_op->SetOutput(GradVarName("b"), InputGrad("b"));
+    grad_op->SetType("rowwise_add_grad");
+    return std::unique_ptr<OpDescBind>(grad_op);
+  }
+};
+
+class MulOpMaker : public OpProtoAndCheckerMaker {
+ public:
+  MulOpMaker(OpProto *proto, OpAttrChecker *op_checker)
+      : OpProtoAndCheckerMaker(proto, op_checker) {
+    AddInput("X", "A");
+    AddInput("Y", "B");
+    AddOutput("Out", "Out");
+    AddAttr<int>("x_num_col_dims", "").SetDefault(1).EqualGreaterThan(1);
+    AddAttr<int>("y_num_col_dims", "").SetDefault(1).EqualGreaterThan(1);
+    AddComment("Mul");
+  }
+};
+
+class SigmoidOpMaker : public OpProtoAndCheckerMaker {
+ public:
+  SigmoidOpMaker(OpProto *proto, OpAttrChecker *op_checker)
+      : OpProtoAndCheckerMaker(proto, op_checker) {
+    AddInput("X", "X");
+    AddOutput("Out", "Y");
+    AddComment("Sigmoid");
+  }
+};
+
+class NoGradOpMaker : public OpProtoAndCheckerMaker {
+ public:
+  NoGradOpMaker(OpProto *proto, OpAttrChecker *op_checker)
+      : OpProtoAndCheckerMaker(proto, op_checker) {
+    AddInput("X", "X input");
+    AddOutput("Out", "Y output");
+    AddComment("NoGradOp, same input output. no Grad");
+  }
+};
+
+class ManyOutputOpMaker : public OpProtoAndCheckerMaker {
+ public:
+  ManyOutputOpMaker(OpProto *proto, OpAttrChecker *op_checker)
+      : OpProtoAndCheckerMaker(proto, op_checker) {
+    AddInput("x", "x");
+    AddOutput("y", "y");
+    AddOutput("z", "z");
+    AddComment("");
+  }
+};
+
+class FillZeroOpMaker : public OpProtoAndCheckerMaker {
+ public:
+  FillZeroOpMaker(OpProto *proto, OpAttrChecker *op_checker)
+      : OpProtoAndCheckerMaker(proto, op_checker) {
+    AddInput("X", "x");
+    AddOutput("Y", "out");
+    AddComment("");
+  }
+};
+
+class SumOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  SumOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
+      : OpProtoAndCheckerMaker(proto, op_checker) {
+    AddInput("X", "the input tensors of sum operator.").AsDuplicable();
+    AddOutput("Out", "the output tensor of sum operator.");
+    AddComment("");
+  }
+};
+
+class MultInOutOpMaker : public OpProtoAndCheckerMaker {
+ public:
+  MultInOutOpMaker(OpProto *proto, OpAttrChecker *op_checker)
+      : OpProtoAndCheckerMaker(proto, op_checker) {
+    AddInput("X", "x");
+    AddInput("H", "h");
+    AddOutput("Y", "y");
+    AddOutput("Z", "z");
+    AddComment("");
+  }
+};
+
+}  // namespace framework
+}  // namespace paddle
+
+namespace f = paddle::framework;
+namespace ops = paddle::operators;
+using EnforceNotMet = paddle::platform::EnforceNotMet;
+REGISTER_OPERATOR(rowwise_add, f::NOP, f::RowWiseAddOpMaker,
+                  f::RowWiseAddGradMaker);
+REGISTER_OPERATOR(rowwise_add_grad, f::NOP);
+REGISTER_OP(mul, f::NOP, f::MulOpMaker, mul_grad, f::NOP);
+REGISTER_OP(sigmoid, f::NOP, f::SigmoidOpMaker, sigmoid_grad, f::NOP);
+REGISTER_OP_WITHOUT_GRADIENT(nograd, f::NOP, f::NoGradOpMaker);
+REGISTER_OP_WITHOUT_GRADIENT(fill_zeros_like, f::NOP, f::FillZeroOpMaker);
+REGISTER_OP(sum, f::NOP, f::SumOpMaker, sum_grad, f::NOP);
+REGISTER_OP(many_output_op, f::NOP, f::ManyOutputOpMaker, many_output_op_grad,
+            f::NOP);
+REGISTER_OP(mult_in_out, f::NOP, f::MultInOutOpMaker, mult_in_out_grad, f::NOP);
+
+void AddOp(const std::string &type, const f::VariableNameMap &inputs,
+           const f::VariableNameMap &outputs, f::AttributeMap attrs,
+           paddle::framework::BlockDescBind *block) {
+  // insert output
+  for (auto kv : outputs) {
+    for (auto v : kv.second) {
+      auto var = block->NewVar(v);
+      var->SetDataType(paddle::framework::DataType::FP32);
+    }
+  }
+
+  // insert op
+  auto op = block->AppendOp();
+  op->SetType(type);
+  for (auto &kv : inputs) {
+    op->SetInput(kv.first, kv.second);
+  }
+  for (auto &kv : outputs) {
+    op->SetOutput(kv.first, kv.second);
+  }
+  op->SetAttrMap(attrs);
+}
+
+f::ProgramDesc *GetNewProgramDesc() {
+  auto *program_desc = new f::ProgramDesc();
+  auto *root_block = program_desc->add_blocks();
+  root_block->set_idx(0);
+  root_block->set_parent_idx(-1);
+  return program_desc;
+}
+
+TEST(Prune, one_operator) {
+  f::ProgramDesc *program_desc = GetNewProgramDesc();
+  f::ProgramDescBind &program = f::ProgramDescBind::Instance(program_desc);
+  f::BlockDescBind *block = program.Block(0);
+
+  AddOp("mul", {{"X", {"a"}}, {"Y", {"w1"}}}, {{"Out", {"b"}}}, {}, block);
+
+  f::ProgramDesc *pdesc = program.Proto();
+  f::ProgramDesc pruned;
+
+  Prune(*pdesc, pruned, 0);
+  PADDLE_ENFORCE_EQ(pruned.blocks(0).ops_size(), 0);
+
+  pdesc->mutable_blocks(0)->mutable_ops(0)->set_is_target(true);
+  Prune(*pdesc, pruned, 0);
+  PADDLE_ENFORCE_EQ(pruned.blocks(0).ops_size(), 1);
+}
+
+TEST(Prune, simple_optimize) {}