fix generator type specification and set default values for some opti…

…onal attributes. (onnx#419)

* set default value for optional attributes and set value of Constant as required.

* fix generator op type specification bug

* doc updates

docs/Changelog.md

@@ -650,7 +650,7 @@ opset_import {
 #### Attributes
 
 <dl>
-<dt><tt>value</tt> : tensor</dt>
+<dt><tt>value</tt> : tensor (required)</dt>
 <dd>The value for the elements of the output tensor.</dd>
 </dl>
 
@@ -3492,7 +3492,7 @@ opset_import {
 <dd>The standard deviation of the normal distribution.</dd>
 <dt><tt>seed</tt> : float</dt>
 <dd>(Optional) Seed to the random generator, if not specified we will auto generate one.</dd>
-<dt><tt>shape</tt> : list of ints</dt>
+<dt><tt>shape</tt> : list of ints (required)</dt>
 <dd>The shape of the output tensor.</dd>
 </dl>
 
@@ -3549,7 +3549,7 @@ opset_import {
 #### Inputs
 
 <dl>
-<dt><tt>input</tt> : T</dt>
+<dt><tt>input</tt> : tensor(int32)</dt>
 <dd>Input tensor to provide shape information.</dd>
 </dl>
 
@@ -3597,7 +3597,7 @@ opset_import {
 <dd>Lower boundary of the output values.</dd>
 <dt><tt>seed</tt> : float</dt>
 <dd>(Optional) Seed to the random generator, if not specified we will auto generate one.</dd>
-<dt><tt>shape</tt> : list of ints</dt>
+<dt><tt>shape</tt> : list of ints (required)</dt>
 <dd>The shape of the output tensor.</dd>
 </dl>
 
@@ -3653,7 +3653,7 @@ opset_import {
 #### Inputs
 
 <dl>
-<dt><tt>input</tt> : T</dt>
+<dt><tt>input</tt> : tensor(int32)</dt>
 <dd>Input tensor to provide shape information.</dd>
 </dl>
 

docs/Operators.md

@@ -744,7 +744,7 @@ opset_import {
 #### Attributes
 
 <dl>
-<dt><tt>value</tt> : tensor</dt>
+<dt><tt>value</tt> : tensor (required)</dt>
 <dd>The value for the elements of the output tensor.</dd>
 </dl>
 
@@ -3261,7 +3261,7 @@ opset_import {
 <dd>The standard deviation of the normal distribution.</dd>
 <dt><tt>seed</tt> : float</dt>
 <dd>(Optional) Seed to the random generator, if not specified we will auto generate one.</dd>
-<dt><tt>shape</tt> : list of ints</dt>
+<dt><tt>shape</tt> : list of ints (required)</dt>
 <dd>The shape of the output tensor.</dd>
 </dl>
 
@@ -3319,7 +3319,7 @@ opset_import {
 #### Inputs
 
 <dl>
-<dt><tt>input</tt> : T</dt>
+<dt><tt>input</tt> : tensor(int32)</dt>
 <dd>Input tensor to provide shape information.</dd>
 </dl>
 
@@ -3368,7 +3368,7 @@ opset_import {
 <dd>Lower boundary of the output values.</dd>
 <dt><tt>seed</tt> : float</dt>
 <dd>(Optional) Seed to the random generator, if not specified we will auto generate one.</dd>
-<dt><tt>shape</tt> : list of ints</dt>
+<dt><tt>shape</tt> : list of ints (required)</dt>
 <dd>The shape of the output tensor.</dd>
 </dl>
 
@@ -3425,7 +3425,7 @@ opset_import {
 #### Inputs
 
 <dl>
-<dt><tt>input</tt> : T</dt>
+<dt><tt>input</tt> : tensor(int32)</dt>
 <dd>Input tensor to provide shape information.</dd>
 </dl>
 

onnx/defs/experiments/defs.cc

@@ -236,7 +236,7 @@ OPERATOR_SCHEMA(Scale)
 Scale takes one input data (Tensor<float>) and produces one output data
 (Tensor<float>) whose value is the input data tensor scaled element-wise.
 )DOC")
-    .Attr("scale", "(float, default 1.0) the scale to apply.", AttributeProto::FLOAT, OPTIONAL);
+    .Attr("scale", "(float, default 1.0) the scale to apply.", AttributeProto::FLOAT, 1.0f);
 
 OPERATOR_SCHEMA(GRUUnit)
     .SetSupportLevel(SupportType::EXPERIMENTAL)

onnx/defs/generator/defs.cc

@@ -9,8 +9,7 @@ OPERATOR_SCHEMA(Constant)
     .Attr(
           "value",
           "The value for the elements of the output tensor.",
-          AttributeProto::TENSOR,
-          OPTIONAL)
+          AttributeProto::TENSOR)
     .Output(
             0,
             "output",
@@ -50,8 +49,7 @@ TensorProto message.
     .Attr(
           "shape",
           "The shape of the output tensor.",
-          AttributeProto::INTS,
-          OPTIONAL)
+          AttributeProto::INTS)
     .Output(
             0,
             "output",
@@ -92,8 +90,7 @@ TensorProto message.
     .Attr(
           "shape",
           "The shape of the output tensor.",
-          AttributeProto::INTS,
-          OPTIONAL)
+          AttributeProto::INTS)
     .Output(
             0,
             "output",
@@ -134,7 +131,7 @@ TensorProto message.
     .Input(
            0,
            "input",
-           "Input tensor to provide shape information.", "T")
+           "Input tensor to provide shape information.", "tensor(int32)")
     .Output(
             0,
             "output",
@@ -176,7 +173,7 @@ TensorProto message.
     .Input(
            0,
            "input",
-           "Input tensor to provide shape information.", "T")
+           "Input tensor to provide shape information.", "tensor(int32)")
     .Output(
             0,
             "output",

onnx/defs/logical/defs.cc

@@ -19,7 +19,7 @@ detailed description of the broadcasting rules.
 )DOC";
         ReplaceAll(doc, "{name}", name);
         schema.SetDoc(doc);
-        schema.Attr("broadcast", "Enable broadcasting", AttributeProto::INT, OPTIONAL);
+        schema.Attr("broadcast", "Enable broadcasting", AttributeProto::INT, static_cast<int64_t>(0));
         schema.Attr("axis", "If set, defines the broadcast dimensions.",
                     AttributeProto::INT,
                     OPTIONAL);

onnx/defs/nn/defs.cc

@@ -101,7 +101,7 @@ namespace onnx {
                         AttributeProto::INTS, OPTIONAL);
             schema.Attr("p",
                         "p value of the Lp norm used to pool over the input data, default is 2.",
-                        AttributeProto::INT, OPTIONAL);
+                        AttributeProto::INT, static_cast<int64_t>(2));
             schema.Input(0,
                          "X",
                          "Input data tensor from the previous operator; "
@@ -345,7 +345,7 @@ namespace onnx {
             schema.Attr("p",
                         "p value of the Lp norm used to pool over the input data, default is 2.",
                         AttributeProto::INT,
-                        OPTIONAL);
+                        static_cast<int64_t>(2));
             schema.Input(0,
                          "X",
                          "Input data tensor from the previous operator; "

onnx/defs/rnn/defs.cc

@@ -126,7 +126,7 @@ Equations (Default: f=Tanh):
           "input gate. The activation function must be one of the activation "
           "functions specified above. Optional: Default `Tanh` if not specified.",
           AttributeProto::STRINGS,
-          OPTIONAL)
+          std::vector<std::string>{"Tanh", "Tanh"})
     .Input(1, "W",
 	   "The weight tensor for input gate. Concatenation of `Wi` and `WBi` "
            "(if bidirectional). The tensor has shape "