Remove additional message

paddle/fluid/operators/fc_mkldnn_op.cc

@@ -23,67 +23,56 @@ namespace operators {
 using paddle::framework::Tensor;
 using paddle::platform::MKLDNNDeviceContext;
 
-struct MKLDNNMatrixSize final {
-  explicit MKLDNNMatrixSize(const std::vector<int>& in,
-                            const std::vector<int>& w)
-      : mb{in[0]}, ic{in[1]}, oc{w[1]}, h{in[2]}, w{in[3]} {}
-
-  bool is_spatial() const { return h > 2 && w > 2; }
-
-  const int mb;
-  const int ic;
-  const int oc;
-  const int h, w;
-};
-
 template <typename T>
 class MKLDNNMD {
  public:
   explicit MKLDNNMD(const T* in, const T* w, bool bias)
-      : sz_(std::unique_ptr<MKLDNNMatrixSize>(new MKLDNNMatrixSize(
-            paddle::framework::vectorize2int(in->dims()),
-            paddle::framework::vectorize2int(w->dims())))) {
+      : in{paddle::framework::vectorize2int(in->dims())},
+        w{paddle::framework::vectorize2int(w->dims())} {
     with_bias_ = bias;
   }
 
   mkldnn::memory::desc dst() const {
-    return platform::MKLDNNMemDesc({sz_->mb, sz_->oc},
+    return platform::MKLDNNMemDesc({in[0], w[1]},
                                    mkldnn::memory::data_type::f32,
                                    mkldnn::memory::format::nc);
   }
 
   mkldnn::memory::desc src() const {
-    return sz_->is_spatial()
-               ? platform::MKLDNNMemDesc({sz_->mb, sz_->ic, sz_->h, sz_->w},
+    return is_spatial()
+               ? platform::MKLDNNMemDesc({in[0], in[1], in[2], in[3]},
                                          mkldnn::memory::data_type::f32,
                                          mkldnn::memory::format::nchw)
-               : platform::MKLDNNMemDesc({sz_->mb, sz_->ic},
+               : platform::MKLDNNMemDesc({in[0], in[1]},
                                          mkldnn::memory::data_type::f32,
                                          mkldnn::memory::format::nc);
   }
 
   mkldnn::memory::desc weights() const {
-    return sz_->is_spatial()
-               ? platform::MKLDNNMemDesc({sz_->oc, sz_->ic, sz_->h, sz_->w},
+    return is_spatial()
+               ? platform::MKLDNNMemDesc({w[1], in[1], in[2], in[3]},
                                          mkldnn::memory::data_type::f32,
                                          mkldnn::memory::format::oihw)
-               : platform::MKLDNNMemDesc({sz_->oc, sz_->ic},
+               : platform::MKLDNNMemDesc({w[1], in[1]},
                                          mkldnn::memory::data_type::f32,
                                          mkldnn::memory::format::oi);
   }
 
   mkldnn::memory::desc bias() const {
     return with_bias_
-               ? platform::MKLDNNMemDesc({sz_->oc},
-                                         mkldnn::memory::data_type::f32,
+               ? platform::MKLDNNMemDesc({w[1]}, mkldnn::memory::data_type::f32,
                                          mkldnn::memory::format::format_undef)
                : platform::MKLDNNMemDesc({}, mkldnn::memory::data_type::f32,
                                          mkldnn::memory::format::format_undef);
   }
 
  private:
-  std::unique_ptr<MKLDNNMatrixSize> sz_;
+  bool is_spatial() const { return in.size() > 1 && w.size() > 1; }
+
+  std::vector<int> in;
+  std::vector<int> w;
   bool with_bias_;
+  bool is_spatial_;
 };
 
 class MKLDNNMemory {

paddle/fluid/operators/fc_op.cc

@@ -29,8 +29,8 @@ void FCOp::InferShape(framework::InferShapeContext* ctx) const {
   auto w_dims = ctx->GetInputDim("W");
   std::vector<int64_t> output_shape({in_dims[0], w_dims[1]});
 
-  PADDLE_ENFORCE(in_dims.size() == 4,
-                 "Fully Connected input should be 4-D tensor.");
+  PADDLE_ENFORCE(in_dims.size() == 4 || in_dims.size() == 2,
+                 "Fully Connected input should be 2-D or 4-D tensor.");
 
   PADDLE_ENFORCE(w_dims.size() == 2,
                  "Fully Connected input should be 2-D tensor.");
@@ -96,22 +96,11 @@ FCOpMaker::FCOpMaker(OpProto* proto, OpAttrChecker* op_checker)
 
   The fully connected operation calculates the output based on the input, weights and bias attribute.
   The size of each dimension of the parameters checked in the infer-shape.
-  Input(Input) is NCHW or NC format. Where N is batch size, C is the number of channels, 
-               H is the height of the feature, and W is the width of the feature.
-  Weights(W) is OIHW or OI format. Where H is the height of the feature, W is the width of the feature, 
-             O is the height of output, and I is the number of channels.
-  Output(Out) is NC format. Where N is batch size, and C is the number of channels.
   The matrix of bias is generated by the mkldnn framework, when the bias_attr is True.
   Additional parametrs are use_mkldnn and bias_attr.
   The input(X) size and output(Out) size may be diffrent.
 
-Example:
-  Input:
-       Input shape:  $(N, C_{in}, H_{in}, W_{in})$
-       Weight shape: $(O_{out}, I_{in}, H_{in}, W_{in})$
-       Bias shape:   $(O_{out})$
-  Output:
-       Output shape: $(N, C_{out})$
+  The fully connected layer only supports MKLDNN version
 )DOC");
 }
 

python/paddle/fluid/layers/nn.py

@@ -167,8 +167,9 @@ def fc(input,
             shape=param_shape,
             dtype=dtype,
             is_bias=False)
+        if bias_attr is None or bias_attr is False:
             bias_attr = False
-        if bias_attr is not None:
+        else:
             bias_attr = True
         helper.append_op(
             type="fc",