Merge branch 'master' into fork2onnx

docs/Changelog.md

@@ -1326,8 +1326,6 @@ This version of the operator has been available since version 1 of the default O
 <dd>Specify if the RNN is forward, reverse, or bidirectional. Must be one of forward (default), reverse, or bidirectional.</dd>
 <dt><tt>hidden_size</tt> : int</dt>
 <dd>Number of neurons in the hidden layer</dd>
-<dt><tt>linear_before_reset</tt> : int</dt>
-<dd>When computing the output of the hidden gate, apply the linear transformation before multiplying by the output of the reset gate.</dd>
 <dt><tt>output_sequence</tt> : int</dt>
 <dd>The sequence output for the hidden is optional if 0. Default 0.</dd>
 </dl>

onnx/defs/math/defs.cc

@@ -784,7 +784,28 @@ Given two equivalent values, this operator uses the indices along the axis  as
         "Dimension on which to do the sort. Default -1, which indicates the last"
         " axis",
         AttributeProto::INT,
-        static_cast<int64_t>(-1));
+        static_cast<int64_t>(-1))
+	.TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+		// Type inference:
+		propagateElemTypeFromInputToOutput(ctx, 0, 0);
+		updateOutputElemType(ctx, 1, TensorProto::INT64);
+
+		// Shape inference:
+		if (!hasInputShape(ctx, 0))
+			return;
+		auto& input_shape = getInputShape(ctx, 0);
+		int64_t rank = input_shape.dim_size();
+		int64_t axis = getAttribute(ctx, "axis", -1);
+		if (axis < 0) axis += rank;
+		if (axis < 0 || axis >= rank) return; // erroneous attribute value
+		int64_t k = getAttribute(ctx, "k", -1);
+		if (k <= 0) return; // erroneous attribute value
+		// TODO: unclear what results should be if axis has less than k elements.
+		TensorShapeProto result_shape = input_shape;
+		result_shape.mutable_dim(static_cast<int>(axis))->set_dim_value(k);
+		updateOutputShape(ctx, 0, result_shape);
+		updateOutputShape(ctx, 1, result_shape);
+	});
 
 ONNX_OPERATOR_SCHEMA(Sin)
     .SinceVersion(7)
@@ -801,7 +822,8 @@ Calculates the sine of the given input tensor, element-wise.
     .TypeConstraint(
         "T",
         {"tensor(float16)", "tensor(float)", "tensor(double)"},
-        "Constrain input and output types to float tensors.");
+        "Constrain input and output types to float tensors.")
+	.TypeAndShapeInferenceFunction(propagateShapeAndTypeFromFirstInput);
 
 ONNX_OPERATOR_SCHEMA(Cos)
     .SinceVersion(7)
@@ -818,7 +840,8 @@ Calculates the cosine of the given input tensor, element-wise.
     .TypeConstraint(
         "T",
         {"tensor(float16)", "tensor(float)", "tensor(double)"},
-        "Constrain input and output types to float tensors.");
+        "Constrain input and output types to float tensors.")
+	.TypeAndShapeInferenceFunction(propagateShapeAndTypeFromFirstInput);
 
 
 ONNX_OPERATOR_SCHEMA(Tan)
@@ -836,7 +859,8 @@ Calculates the tangent of the given input tensor, element-wise.
     .TypeConstraint(
         "T",
         {"tensor(float16)", "tensor(float)", "tensor(double)"},
-        "Constrain input and output types to float tensors.");
+        "Constrain input and output types to float tensors.")
+	.TypeAndShapeInferenceFunction(propagateShapeAndTypeFromFirstInput);
 
 ONNX_OPERATOR_SCHEMA(Asin)
     .SinceVersion(7)
@@ -853,7 +877,8 @@ Calculates the arcsine (inverse of sine) of the given input tensor, element-wise
     .TypeConstraint(
         "T",
         {"tensor(float16)", "tensor(float)", "tensor(double)"},
-        "Constrain input and output types to float tensors.");
+        "Constrain input and output types to float tensors.")
+	.TypeAndShapeInferenceFunction(propagateShapeAndTypeFromFirstInput);
 
 ONNX_OPERATOR_SCHEMA(Acos)
     .SinceVersion(7)
@@ -870,7 +895,8 @@ Calculates the arccosine (inverse of cosine) of the given input tensor, element-
     .TypeConstraint(
         "T",
         {"tensor(float16)", "tensor(float)", "tensor(double)"},
-        "Constrain input and output types to float tensors.");
+        "Constrain input and output types to float tensors.")
+	.TypeAndShapeInferenceFunction(propagateShapeAndTypeFromFirstInput);
 
 
 ONNX_OPERATOR_SCHEMA(Atan)
@@ -888,4 +914,5 @@ Calculates the arctangent (inverse of tangent) of the given input tensor, elemen
     .TypeConstraint(
         "T",
         {"tensor(float16)", "tensor(float)", "tensor(double)"},
-        "Constrain input and output types to float tensors.");
+        "Constrain input and output types to float tensors.")
+	.TypeAndShapeInferenceFunction(propagateShapeAndTypeFromFirstInput);

onnx/defs/rnn/old.cc

@@ -171,13 +171,6 @@ Equations (Default: f=Sigmoid, g=Tanh):
         "for default if not specified.",
         AttributeProto::STRINGS,
         OPTIONAL)
-    .Attr(
-        "linear_before_reset",
-        "When computing the output of the hidden gate, "
-        "apply the linear transformation before multiplying by the output of the "
-        "reset gate.",
-        AttributeProto::INT,
-        OPTIONAL)
     .Input(
         1,
         "W",

onnx/defs/tensor/defs.cc

@@ -848,7 +848,13 @@ For example A = [[1, 2], [3, 4]], B = [1, 2], tile(A, B) = [[1, 2, 1, 2], [3, 4,
     .TypeConstraint(
         "T1",
         {"tensor(int64)"},
-        "Constrain repeat's type to int64 tensors.");
+        "Constrain repeat's type to int64 tensors.")
+	.TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
+	    propagateElemTypeFromInputToOutput(ctx, 0, 0);
+		// Only rank of output can be inferred. We can do better if second input is
+		// a constant, but this requires extending InferenceContext interface to
+		// get values of constant inputs.
+     });
 
 ONNX_OPERATOR_SCHEMA(Upsample)
 	.SinceVersion(7)

onnx/test/shape_inference_test.py

@@ -497,6 +497,24 @@ def test_depth_to_space(self):
             [])
         self._assert_inferred(graph, [make_tensor_value_info('z', TensorProto.FLOAT, (2, 3, 100, 100))])
 
+    def test_topk_default_axis(self):
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, (3, 4, 5, 10))],
+            [make_node('TopK', ['x'], ['y', 'z'], k=2)],
+            [])
+        self._assert_inferred(graph,
+                              [make_tensor_value_info('y', TensorProto.FLOAT, (3, 4, 5, 2)),
+                               make_tensor_value_info('z', TensorProto.INT64, (3, 4, 5, 2))])
+
+    def test_topk(self):
+        graph = self._make_graph(
+            [('x', TensorProto.FLOAT, (3, 4, 5, 10))],
+            [make_node('TopK', ['x'], ['y', 'z'], k=2, axis=2)],
+            [])
+        self._assert_inferred(graph,
+                              [make_tensor_value_info('y', TensorProto.FLOAT, (3, 4, 2, 10)),
+                               make_tensor_value_info('z', TensorProto.INT64, (3, 4, 2, 10))])
+
     def test_gemm(self):
         graph = self._make_graph(
             [('x', TensorProto.FLOAT, (7, 5)),