Support multiple inputs in FCOp.

paddle/operators/fc_op.cc

@@ -24,30 +24,49 @@ class FCOp : public NetOp {
        const framework::VariableNameMap &outputs,
        const framework::AttributeMap &attrs)
       : NetOp(type, inputs, outputs, attrs) {
-    // mul_out = X * W
+    auto x = Inputs("X");
+    auto w = Inputs("W");
+    PADDLE_ENFORCE_EQ(
+        x.size(), w.size(),
+        "The size of inputs X(%d) should be the same as that of weights W(%d).",
+        x.size(), w.size());
+
+    int n = x.size();
+    PADDLE_ENFORCE_GE(n, 1,
+                      "The size of inputs X(%d) should be no less than 1.", n);
+
+    // mul_out = X[0] * W[0] + ... + X[n-1] * W[n-1]
+    AppendOp(
+        framework::OpRegistry::CreateOp("mul", {{"X", {x[0]}}, {"W", {w[0]}}},
+                                        {{"Out", {Output("mul_out")}}}, {}));
+
+    for (int i = 1; i < n; i++) {
+      // mul_out = mul_out + X[i] * W[i]
+      AppendOp(
+          framework::OpRegistry::CreateOp("mul", {{"X", {x[i]}}, {"Y", {w[i]}}},
+                                          {{"Out", {Output("add_out")}}}, {}));
       AppendOp(framework::OpRegistry::CreateOp(
-        "mul", {{"X", {Input("X")}}, {"Y", {Input("W")}}},
+          "add", {{"X", {Output("mul_out")}}, {"Y", {Output("add_out")}}},
           {{"Out", {Output("mul_out")}}}, {}));
+    }
 
-    std::string add_out_name = "mul_out";
     auto b = Input("b");
+    std::string add_out = "mul_out";
     if (b != framework::kEmptyVarName) {
       // add_out = mul_out + b
       AppendOp(framework::OpRegistry::CreateOp(
           "rowwise_add", {{"X", {Output("mul_out")}}, {"b", {Input("b")}}},
           {{"Out", {Output("add_out")}}}, {}));
-      add_out_name = "add_out";
+      add_out = "add_out";
     } else {
-      auto add_out = Output("add_out");
-      if (add_out != framework::kEmptyVarName) {
-        this->Rename(add_out, framework::kEmptyVarName);
+      if (Output("add_out") != framework::kEmptyVarName) {
+        this->Rename(Output("add_out"), framework::kEmptyVarName);
       }
     }
 
-    auto activation = GetAttr<std::string>("activation");
-    AppendOp(framework::OpRegistry::CreateOp(activation,
-                                             {{"X", {Output(add_out_name)}}},
-                                             {{"Y", {Output("Out")}}}, {}));
+    auto activation = Attr<std::string>("activation");
+    AppendOp(framework::OpRegistry::CreateOp(
+        activation, {{"X", {Output(add_out)}}}, {{"Y", {Output("Y")}}}, {}));
     CompleteAddOp(false);
   }
 };
@@ -56,11 +75,11 @@ class FCOpMaker : public framework::OpProtoAndCheckerMaker {
  public:
   FCOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
       : OpProtoAndCheckerMaker(proto, op_checker) {
-    AddInput("X", "The 2D input matrix of FC operator.");
-    AddInput("W", "The 2D weight matrix of FC operator.");
-    AddInput("b", "The 1D bias vector of FC operator");
+    AddInput("X", "The 2-D input matrix of FC operator.").AsDuplicable();
+    AddInput("W", "The 2-D weight matrix of FC operator.").AsDuplicable();
+    AddInput("b", "The 1-D bias vector of FC operator");
 
-    AddOutput("Out", "The activated output matrix of FC operator");
+    AddOutput("Y", "The activated output matrix of FC operator");
     AddOutput("mul_out", "The non-actived output of FC operator, X * W")
         .AsIntermediate();
     AddOutput("add_out", "The non-actived output of FC operator, X * W + b")
@@ -78,7 +97,7 @@ learned weights with a matrix multiplication followed by a bias offset
 (optionally).
 
 Equation:
-  Out = Act(sum_n{X_i * W_i} + b)
+  Y = Act(sum_n{X_i * W_i} + b)
 
 where X_i is a 2D matrix of size (M x K), usually M is the minibatch size and
 K is the number of features. W_i is also a 2D matrix of size (K x N),

python/paddle/v2/framework/tests/test_fc_op.py

 import unittest
 import numpy as np
-from gradient_checker import GradientChecker, create_op
-from op_test_util import OpTestMeta
+from op_test import OpTest
+import paddle.v2.framework.core as core
 from paddle.v2.framework.op import Operator
 
 
-class TestFCOp(unittest.TestCase):
-    __metaclass__ = OpTestMeta
-
+class TestFCOp(OpTest):
     def setUp(self):
-        self.type = "fc"
+        print "Run"
+        self.op_type = "fc"
+        x0 = np.random.random((32, 256)).astype("float32")
+        x1 = np.random.random((32, 256)).astype("float32")
+        w0 = np.random.random((256, 100)).astype("float32")
+        w1 = np.random.random((256, 100)).astype("float32")
+        b = np.random.random(100).astype("float32")
         self.inputs = {
-            "X": np.random.random((32, 784)).astype("float32"),
-            "W": np.random.random((784, 1000)).astype("float32"),
-            "b": np.random.random(1000).astype("float32")
+            "X": {
+                "X0": x0,
+                "X1": x1
+            },
+            "W": {
+                "W0": w0,
+                "W1": w1
+            },
+            "b": b
         }
-        self.attrs = {"activation": "sigmoid"}
-        mul_out = np.dot(self.inputs["X"], self.inputs["W"])
-        add_out = np.add(mul_out, self.inputs["b"])
-        sigmoid_out = 1 / (1 + np.exp(-add_out))
+        #self.attrs = {"activation": "sigmoid"}
+        mul_out = np.dot(x0, w0) + np.dot(x1, w1)
+        add_out = np.add(mul_out, b)
+        #sigmoid_out = 1 / (1 + np.exp(-add_out))
+        sigmoid_out = add_out
         self.outputs = {
             "mul_out": mul_out,
             "add_out": add_out,
-            "Out": sigmoid_out
+            "Y": sigmoid_out
         }
 
+    def test_check_output(self):
+        self.check_output(core.CPUPlace())
+        self.check_output(core.GPUPlace(0))
 
-class TestFCGradOp(GradientChecker):
-    def test_normal(self):
-        self.inputs = {
-            "X": np.random.random((32, 256)).astype("float32"),
-            "W": np.random.random((256, 100)).astype("float32"),
-            "b": np.random.random(100).astype("float32")
-        }
-        op = Operator(
-            "fc",
-            X="X",
-            W="W",
-            b="b",
-            Out="Out",
-            mul_out="mul_out",
-            add_out="add_out",
-            activation="sigmoid")
-        self.check_grad(op, self.inputs, ["X", "W", "b"], "Out")
+    #def test_check_grad(self):
+    #    self.check_grad(["X0", "X1", "W0", "W1", "b"], "Y")
 
 
 if __name__ == '__main__':