- Added EPS for softmax MKLDNN op

- EPS added to softmax mkldnn primitive outcome is limited to training
phase

Fixes after review

clang format fixes

clang format fixes

Author: jczaja

paddle/fluid/operators/softmax_mkldnn_op.cc

@@ -73,6 +73,15 @@ class SoftmaxMKLDNNKernel : public paddle::framework::OpKernel<T> {
                                    softmax_dst_memory);
     std::vector<primitive> pipeline{softmax};
     stream(stream::kind::eager).submit(pipeline).wait();
+
+    const bool is_test = ctx.Attr<bool>("is_test");
+    if (!is_test) {
+      T threshold = exp(-64);
+      for (size_t i = 0; i < dst_tz[0] * dst_tz[1]; ++i) {
+        output_data[i] =
+            output_data[i] < threshold ? threshold : output_data[i];
+      }
+    }
   }
 };
 

paddle/fluid/operators/softmax_op.cc

@@ -97,6 +97,9 @@ class SoftmaxOpMaker : public framework::OpProtoAndCheckerMaker {
     AddAttr<bool>("use_mkldnn",
                   "(bool, default false) Only used in mkldnn kernel")
         .SetDefault(false);
+    AddAttr<bool>("is_test",
+                  "Disable epsilon adding to softmax results. Used by MKLDNN.")
+        .SetDefault(false);
     AddComment(R"DOC(
 Softmax Operator.
 

python/paddle/fluid/layers/nn.py

@@ -87,6 +87,7 @@ def fc(input,
        bias_attr=None,
        use_mkldnn=False,
        act=None,
+       is_test=False,
        name=None):
     """
     **Fully Connected Layer**
@@ -133,6 +134,7 @@ def fc(input,
         bias_attr (ParamAttr|list of ParamAttr, default None): The parameter attribute for the bias
             of this layer. If it is set to None, no bias will be added to the output units.
         act (str, default None): Activation to be applied to the output of this layer.
+        is_test(bool): A flag indicating whether execution is in test phase.
         use_mkldnn(bool): Use mkldnn kernel or not, it is valid only when the mkldnn
             library is installed. Default: False
         name (str, default None): The name of this layer.
@@ -177,7 +179,9 @@ def fc(input,
                     "W": w},
             outputs={"Out": tmp},
             attrs={"use_mkldnn": use_mkldnn,
-                   "bias_attr": bias_attr})
+                   "is_test": is_test,
+                   "bias_attr": bias_attr
+                  })
         return helper.append_activation(tmp)
     else:
         for input_var, param_attr in helper.iter_inputs_and_params():