Merge pull request #5499 from reyoung/feature/increase_cpu

Feature/increase cpu

Author: reyoung

paddle/framework/backward.cc

@@ -408,6 +408,11 @@ std::vector<std::unique_ptr<OpDescBind>> MakeBlockBackward(
 
     for (const auto& desc : op_grads) {
       for (const std::string& out_name : desc->OutputArgumentNames()) {
+        if (out_name.find("@GRAD") == std::string::npos) {
+          // Not all outputs of a backward operator is a gradient. Only gradient
+          // need to be sum. Skip variables are not gradient.
+          continue;
+        }
         dup_out_ops[out_name].emplace_back(grad_desc_idx);
       }
       ++grad_desc_idx;

paddle/operators/increment_op.cc

@@ -12,22 +12,57 @@
    See the License for the specific language governing permissions and
    limitations under the License. */
 
-#include "paddle/operators/increment_op.h"
+#include "paddle/framework/op_registry.h"
 
 namespace paddle {
 namespace operators {
 
-class IncrementOp : public framework::OperatorWithKernel {
+class IncrementInferShape : public framework::InferShapeBase {
  public:
-  using framework::OperatorWithKernel::OperatorWithKernel;
-
-  void InferShape(framework::InferShapeContext *ctx) const override {
+  void operator()(framework::InferShapeContext *ctx) const override {
     PADDLE_ENFORCE(ctx->HasInput("X"),
                    "Input(X) of IncrementOp should not be null.");
     PADDLE_ENFORCE(ctx->HasOutput("Out"),
                    "Output(Out) of IncrementOp should not be null.");
+    PADDLE_ENFORCE_EQ(1, framework::product(ctx->GetInputDim("X")));
     ctx->SetOutputDim("Out", ctx->GetInputDim("X"));
-    ctx->ShareLoD("X", /*->*/ "Out");
+  }
+};
+
+struct IncrementFunctor {
+  IncrementFunctor(const framework::LoDTensor &x, framework::LoDTensor *out,
+                   float value)
+      : x_(x), out_(out), value_(value) {}
+
+  template <typename T>
+  void operator()() const {
+    *out_->data<T>() = *x_.data<T>() + static_cast<T>(value_);
+  }
+
+  const framework::LoDTensor &x_;
+  framework::LoDTensor *out_;
+  float value_;
+};
+
+class IncrementOp : public framework::OperatorBase {
+ public:
+  IncrementOp(const std::string &type, const framework::VariableNameMap &inputs,
+              const framework::VariableNameMap &outputs,
+              const framework::AttributeMap &attrs)
+      : OperatorBase(type, inputs, outputs, attrs) {}
+
+  void Run(const framework::Scope &scope,
+           const platform::DeviceContext &dev_ctx) const override {
+    auto &x = scope.FindVar(Input("X"))->Get<framework::LoDTensor>();
+    auto &out =
+        *scope.FindVar(Output("Out"))->GetMutable<framework::LoDTensor>();
+
+    PADDLE_ENFORCE(platform::is_cpu_place(x.place()));
+    out.Resize(x.dims());
+    out.mutable_data(x.place(), x.type());
+    float value = Attr<float>("step");
+    framework::VisitDataType(framework::ToDataType(out.type()),
+                             IncrementFunctor(x, &out, value));
   }
 };
 
@@ -59,10 +94,10 @@ class IncrementGradOpMaker : public framework::SingleGradOpDescMaker {
 
   std::unique_ptr<framework::OpDescBind> Apply() const override {
     auto *grad_op = new framework::OpDescBind();
-    grad_op->SetType("scale");
-    grad_op->SetInput("X", OutputGrad("Out"));
-    grad_op->SetOutput("Out", InputGrad("X"));
-    grad_op->SetAttr("scale", 1.0f);
+    grad_op->SetType("increment");
+    grad_op->SetInput("X", Output("Out"));
+    grad_op->SetOutput("Out", Input("X"));
+    grad_op->SetAttr("step", -boost::get<float>(GetAttr("step")));
     return std::unique_ptr<framework::OpDescBind>(grad_op);
   }
 };
@@ -71,11 +106,5 @@ class IncrementGradOpMaker : public framework::SingleGradOpDescMaker {
 }  // namespace paddle
 
 namespace ops = paddle::operators;
-
-REGISTER_OPERATOR(increment, ops::IncrementOp, ops::IncrementOpMaker,
-                  ops::IncrementGradOpMaker);
-REGISTER_OP_CPU_KERNEL(
-    increment, ops::IncrementKernel<paddle::platform::CPUPlace, float>,
-    ops::IncrementKernel<paddle::platform::CPUPlace, double>,
-    ops::IncrementKernel<paddle::platform::CPUPlace, int>,
-    ops::IncrementKernel<paddle::platform::CPUPlace, int64_t>);
+REGISTER_OPERATOR(increment, ops::IncrementOp, ops::IncrementInferShape,
+                  ops::IncrementOpMaker, ops::IncrementGradOpMaker);

paddle/operators/increment_op.cu

@@ -872,7 +872,7 @@ def array_to_lod_tensor(x, table, main_program=None):
 
 
 def fill_constant(shape, dtype, value, main_program=None):
-    helper = LayerHelper("ones", **locals())
+    helper = LayerHelper("fill_constant", **locals())
     out = helper.create_tmp_variable(dtype=dtype)
     helper.append_op(
         type='fill_constant',
@@ -895,9 +895,12 @@ def zeros(shape, dtype, main_program=None):
     return fill_constant(value=0.0, **locals())
 
 
-def increment(x, value=1.0, main_program=None):
+def increment(x, value=1.0, in_place=True, main_program=None):
     helper = LayerHelper("increment", **locals())
-    out = helper.create_tmp_variable(dtype=x.data_type)
+    if in_place:
+        out = x
+    else:
+        out = helper.create_tmp_variable(dtype=x.data_type)
     helper.append_op(
         type='increment',
         inputs={'X': [x]},

paddle/operators/increment_op.h

@@ -20,21 +20,19 @@ def test_read_write(self):
             each_x.stop_gradient = False
 
         i = layers.zeros(shape=[1], dtype='int64')
+        i.stop_gradient = False
         arr = layers.array_write(x=x[0], i=i)
         i = layers.increment(x=i)
-        i.stop_gradient = True
         arr = layers.array_write(x=x[1], i=i, array=arr)
         i = layers.increment(x=i)
-        i.stop_gradient = True
         arr = layers.array_write(x=x[2], i=i, array=arr)
 
         i = layers.zeros(shape=[1], dtype='int64')
+        i.stop_gradient = False
         a0 = layers.array_read(array=arr, i=i)
         i = layers.increment(x=i)
-        i.stop_gradient = True  # index should not calculate gradient
         a1 = layers.array_read(array=arr, i=i)
         i = layers.increment(x=i)
-        i.stop_gradient = True
         a2 = layers.array_read(array=arr, i=i)
 
         mean_a0 = layers.mean(x=a0)