Merge branch 'liqun/NewSequenceSliceStage2Stage'

Source/CNTKv2LibraryDll/PrimitiveFunction.cpp

@@ -600,24 +600,35 @@ namespace CNTK
                                 VerifyStaticAxis(ax, m_inputs[0].Shape());
 
                                 size_t sliceAxisDim = m_inputs[0].Shape()[ax.StaticAxisIndex()];
-                                int realBeginIndex = (beginIndex[i] >= 0) ? beginIndex[i] : beginIndex[i] + sliceAxisDim;
+                                if (sliceAxisDim == NDShape::FreeDimension && (beginIndex[i] < 0 || endIndex[i] <= 0))
-                                int realEndIndex = (endIndex[i] > 0) ? endIndex[i] : endIndex[i] + sliceAxisDim;
-                                if ((sliceAxisDim < realEndIndex) || (realEndIndex < realBeginIndex) || (realBeginIndex < 0))
-                                    RuntimeError("Function '%S': Slice operation index range [%d,%d), interpreted as [%d,%d), is invalid for input '%S' shape '%S'.",
-                                        AsString().c_str(),
-                                        beginIndex[i],
-                                        endIndex[i],
-                                        realBeginIndex,
-                                        realEndIndex,
-                                        m_inputs[0].AsString().c_str(),
-                                        m_inputs[0].Shape().AsString().c_str());
-                                // propagate as much as we can
-                                // Note: If the sliceAxisDim is a free dimension and the slice size is relative to the sliceAxisDim then the
-                                // corresponding outputDim is also a free dimension
-                                if ((((sliceAxisDim != NDShape::FreeDimension) && (sliceAxisDim != NDShape::InferredDimension)) || (((beginIndex[i] >= 0) && (endIndex[i] > 0)) || ((beginIndex[i] < 0) && (endIndex[i] <= 0)))) &&
-                                    ((ax.StaticAxisIndex() < (int)outputTensorShape.GetRank()) && (0 <= realBeginIndex) && (realBeginIndex <= realEndIndex) && (realEndIndex <= sliceAxisDim)))
                                 {
-                                    outputTensorShape.NarrowTo(ax.StaticAxisIndex(), realBeginIndex, realEndIndex, strides[i]);
+                                    // not able to calculate real indices. do not narrow either.
+                                    // note that endIndex[i] = 0 means to (and include) the last.
+                                    // One case for this condition is to export and import, in ONNX format, a CNTK Sequence.Slice op.
+                                    // In this case, if batch size is larger than 1 and input data are a zigged array (i.e. sequences of various lengths),
+                                    // model evaludation will not march the original CNTK model.
+                                }
+                                else
+                                {
+                                    int realBeginIndex = (beginIndex[i] >= 0) ? beginIndex[i] : beginIndex[i] + sliceAxisDim;
+                                    int realEndIndex = (endIndex[i] > 0) ? endIndex[i] : endIndex[i] + sliceAxisDim;
+                                    if ((sliceAxisDim < realEndIndex) || (realEndIndex < realBeginIndex) || (realBeginIndex < 0))
+                                        RuntimeError("Function '%S': Slice operation index range [%d,%d), interpreted as [%d,%d), is invalid for input '%S' shape '%S'.",
+                                            AsString().c_str(),
+                                            beginIndex[i],
+                                            endIndex[i],
+                                            realBeginIndex,
+                                            realEndIndex,
+                                            m_inputs[0].AsString().c_str(),
+                                            m_inputs[0].Shape().AsString().c_str());
+                                    // propagate as much as we can
+                                    // Note: If the sliceAxisDim is a free dimension and the slice size is relative to the sliceAxisDim then the
+                                    // corresponding outputDim is also a free dimension
+                                    if ((((sliceAxisDim != NDShape::FreeDimension) && (sliceAxisDim != NDShape::InferredDimension)) || (((beginIndex[i] >= 0) && (endIndex[i] > 0)) || ((beginIndex[i] < 0) && (endIndex[i] <= 0)))) &&
+                                        ((ax.StaticAxisIndex() < (int)outputTensorShape.GetRank()) && (0 <= realBeginIndex) && (realBeginIndex <= realEndIndex) && (realEndIndex <= sliceAxisDim)))
+                                    {
+                                        outputTensorShape.NarrowTo(ax.StaticAxisIndex(), realBeginIndex, realEndIndex, strides[i]);
+                                    }
                                 }
                             }
                             outputShape = AsNDShape(outputTensorShape, /*allowNonFlattenableTensorShapes = */ true);

Source/CNTKv2LibraryDll/proto/onnx/CNTKToONNX.cpp

@@ -83,7 +83,7 @@ private:
         std::vector<LotusIR::NodeArg *>& outputs, Graph *graph);
 
     static LotusIR::Node *AddReshapeNode(LotusIR::NodeArg &nodeArg, const std::vector<int> &newShape, const std::string &outArgName,
-        LotusIR::Graph* graph, int dynamicAxisCount);
+        LotusIR::Graph* graph);
     static LotusIR::Node *AddMatMulNode(LotusIR::NodeArg &nodeArg1, LotusIR::NodeArg &nodeArg2, LotusIR::Graph* graph,
         const std::string &out_arg_name);
     static LotusIR::Node *AddArgMaxNode(LotusIR::NodeArg &nodeArg, LotusIR::Graph* graph, int axis);
@@ -1181,10 +1181,13 @@ bool IsUnSupportedLayerNormalization(const FunctionPtr src)
 FunctionPtr SkipBatchAndSequenceAxisOp(const FunctionPtr src)
 {
     if ((src->OpName() == L"ToSequenceOp" && src->Inputs()[0].Owner() &&
-        src->Inputs()[0].Owner()->OpName() == L"ToBatchAxis") || 
+        src->Inputs()[0].Owner()->OpName() == L"ToBatchAxis") ||
         (src->OpName() == L"UnpackBatchAxis" && src->Inputs()[0].Owner() &&
             src->Inputs()[0].Owner()->OpName() == L"UnpackSequenceOp"))
         return src->Inputs()[0].Owner()->Inputs()[0].Owner();
+    else if (src->OpName() == L"UnpackBatchAxis" && src->Inputs()[0].Owner() &&
+        src->Inputs()[0].Owner()->OpName() == L"Sequence::Slice")
+        return src->Inputs()[0].Owner();
     else
         return src;
 }
@@ -1342,11 +1345,12 @@ bool CNTKToONNXHelper::FilterInput(const FunctionPtr& src, const CNTK::Variable&
 }
 
 /*
-CNTK python static axis is zero based. Free/Inferred axis is not static.
+CNTK python static axis is zero based. Batch and Sequence axis is not static axis.
-ONNX batch axis, if exists, is 0. in this case static axes start from 1.
+CNTK cpp get static axis in a sanitized form (e.g. -axis - 1 by sanitize_axis)
-CNTK cpp get static axis in a dis-normalized form (e.g. -axis - 1)
+In general CNTK node attribute contains axis
-In general CNTK node attribute contains axis in this dis-normalized form.
+in a dis-normalized form (e.g. index from the last dimension).
-This function converts dis-normalized form to ONNX form.
+This function converts axis to ONNX form
+(e.g. index from the first dimension of the shape including both static and dynamic axes).
 */
 int64_t CNTKToONNXHelper::ConvertAxisToOnnx(const Axis &axis, const Variable &operand)
 {
@@ -2315,9 +2319,9 @@ LotusIR::Node *CNTKToONNXHelper::AddReshapeNodeAccordingToONNXVersion(Graph *gra
 
 
 LotusIR::Node *CNTKToONNXHelper::AddReshapeNode(LotusIR::NodeArg &nodeArg, const std::vector<int> &newShape, const std::string &outArgName, 
-    LotusIR::Graph *graph, int dynamicAxisCount)
+    LotusIR::Graph *graph)
 {
-    onnx::TypeProto typeProto = ToTypeProto(newShape, dynamicAxisCount);
+    onnx::TypeProto typeProto = ToTypeProto(newShape, false);
     UpdateONNXType(CNTK::DataType::Float, typeProto);
 
     LotusIR::NodeArg &outputArg = graph->GetOrCreateNodeArg(outArgName, &typeProto);
@@ -2386,6 +2390,144 @@ LotusIR::Node *CNTKToONNXHelper::InsertReshapeNodeToCNTKFunction(const FunctionP
     return reshapeNode;
 }
 
+// To parse Sequence.Slice node graph to collect axis/begin index/end index
+// and to build an ONNX Slice op.
+// IMPORTANT NOTE:
+// This function convert a CNTK Sequence::Slice op to ONNX Slice op. 
+// CNTK Sequence::Slice has ability to handle input of zigged arrays (i.e. sequences of various lengths).
+// ONNX Slice does not support zigged arrays data format. 
+// Therefore in case of batch size larger than 1 and input data are a zigged arrays, 
+// we do not expect model evaludation to generate marching numbers between CNTK and ONNX.
+// with this following CNTK example:
+// model = C.sequence.slice(C.sequence.input_variable((1)), -2, -1)
+// model.eval([[0, 1, 2], [0, 1, 2, 3, 4]])
+// CNTK output is:
+// array([[1.], [3.]], dtype = float32)
+// output from exported ONNX model will be:
+// array([[padding_value], [3.]], dtype = float32)
+LotusIR::Node* CNTKToONNXHelper::CreateSequenceSliceNode(const FunctionPtr& src,
+    LotusIR::Graph* graph,
+    std::unordered_map<FunctionPtr, LotusIR::Node*>& functionNodes,
+    std::unordered_map<Variable, LotusIR::Node*>& variableNodes,
+    const std::unordered_map<Variable, Variable>& compositeOutputsMap)
+{
+    auto f = src->BlockRoot();
+    int64_t beginIndex = 0, endIndex = 0;
+
+    auto packedIndex = f->Inputs()[1].Owner();
+    auto whereFunc = packedIndex->Inputs()[1].Owner();
+    auto inputToWhere = whereFunc->Inputs()[0].Owner();
+    // input to Where node can be:
+    // ElementTimes - both indices are non-zero, beginIndex/endIndex are from First/Second inputs 
+    // FutureValue - beginIndex is negative, endIndex is zero
+    // PastValue - endIndex is positive, beginIndex is zero
+    // 1 Minus FutureValue - endIndex is negative, beginIndex is zero
+    // 1 Minus PastValue - beginIndex is positive, endIndex is zero
+    auto reportLogicError = [&src]()
+    {
+        LogicError("Failed to parse Sequence.Slice node %s(%s).", ToLegacyString(ToUTF8(src->Name())).c_str(), ToLegacyString(ToUTF8(src->Uid())).c_str());
+    };
+    if (inputToWhere->OpName() == L"ElementTimes")
+    {
+        {
+            auto beginToWhere = inputToWhere->Inputs()[0].Owner();
+            if (beginToWhere->OpName() == L"Minus")
+            {
+                auto beginToMinusMustBeAPastValueOp = beginToWhere->Inputs()[1].Owner();
+                if (beginToMinusMustBeAPastValueOp->OpName() == L"PastValue")
+                    beginIndex = static_cast<int64_t>(beginToMinusMustBeAPastValueOp->Attributes()[PrimitiveFunction::AttributeNameOffset].Value<size_t>());
+                else
+                    reportLogicError();
+            }
+            else if (beginToWhere->OpName() == L"FutureValue")
+            {
+                beginIndex = -static_cast<int64_t>(beginToWhere->Attributes()[PrimitiveFunction::AttributeNameOffset].Value<size_t>());
+            }
+            else
+                reportLogicError();
+        }
+        {
+            auto endToWhere = inputToWhere->Inputs()[1].Owner();
+            if (endToWhere->OpName() == L"Minus")
+            {
+                auto endToMinusMustBeAFutureValueOp = endToWhere->Inputs()[1].Owner();
+                if (endToMinusMustBeAFutureValueOp->OpName() == L"FutureValue")
+                    endIndex = -static_cast<int64_t>(endToMinusMustBeAFutureValueOp->Attributes()[PrimitiveFunction::AttributeNameOffset].Value<size_t>());
+                else
+                    reportLogicError();
+            }
+            else if (endToWhere->OpName() == L"PastValue")
+            {
+                endIndex = static_cast<int64_t>(endToWhere->Attributes()[PrimitiveFunction::AttributeNameOffset].Value<size_t>());
+            }
+            else
+                reportLogicError();
+        }
+    }
+    else if (inputToWhere->OpName() == L"FutureValue")
+    {
+        beginIndex = -static_cast<int64_t>(inputToWhere->Attributes()[PrimitiveFunction::AttributeNameOffset].Value<size_t>());
+    }
+    else if (inputToWhere->OpName() == L"PastValue")
+    {
+        endIndex = static_cast<int64_t>(inputToWhere->Attributes()[PrimitiveFunction::AttributeNameOffset].Value<size_t>());
+    }
+    else if (inputToWhere->OpName() == L"Minus")
+    {
+        auto inputToMinus = inputToWhere->Inputs()[1].Owner();
+        if (inputToMinus->OpName() == L"FutureValue")
+        {
+            endIndex = -static_cast<int64_t>(inputToMinus->Attributes()[PrimitiveFunction::AttributeNameOffset].Value<size_t>());
+        }
+        else if (inputToMinus->OpName() == L"PastValue")
+        {
+            beginIndex = static_cast<int64_t>(inputToMinus->Attributes()[PrimitiveFunction::AttributeNameOffset].Value<size_t>());
+        }
+    }
+
+    if (endIndex == 0)
+        // this is where CNTK and numpy disagree. numpy will output an empty matrix
+        // where CNTK outputs from beginIndex to (and include) the last.
+        endIndex = INT_MAX;
+
+    std::vector<LotusIR::NodeArg *> inputs;
+    ProcessInputs(src, graph, functionNodes, variableNodes, compositeOutputsMap, inputs);
+
+    //std::vector<LotusIR::NodeArg *> outputs;
+    //ProcessOutputs(src, outputs, graph);
+    auto outputArgType = ToTypeProto(src->Output().Shape(), src->Output().HasBatchAxis(), src->Output().HasSequenceAxis());
+    UpdateONNXType(src->Output().GetDataType(), outputArgType);
+
+    std::string outputName = ToLegacyString(ToUTF8(src->BlockRoot()->Output().Uid()));
+    std::string sliceOutputName = outputName;
+    bool seq_dim_is_1 = endIndex - beginIndex == 1 || (endIndex == INT_MAX && beginIndex == -1);
+    if (seq_dim_is_1)
+    {
+        // it appears that sequence.slice squeezes sequence axis out if slice length is 1
+        sliceOutputName += "_PreReshape";
+    }
+
+    LotusIR::NodeArg &outputNodeArg = graph->GetOrCreateNodeArg(sliceOutputName, &outputArgType);
+
+    const std::string & nodeName = ToLegacyString(ToUTF8(src->Name()));
+    LotusIR::Node *sequenceSliceNode = graph->AddNode(nodeName, "Slice", "", { inputs[inputs.size() - 1] }, { &outputNodeArg });
+    sequenceSliceNode->AddAttribute("axes", std::vector<int64_t>({ int64_t(0) }));
+    sequenceSliceNode->AddAttribute("ends", std::vector<int64_t>({ endIndex }));
+    sequenceSliceNode->AddAttribute("starts", std::vector<int64_t>({ beginIndex }));
+    if (seq_dim_is_1)
+    {
+        // CNTK Sequence.Slice op squeezes the sequence axis if it is of dimension 1.
+        // insert reshape to remove sequence axis
+        std::vector<int> newShape(reverse(Cast<size_t, int>(src->Output().Shape().Dimensions())));
+        // add batch size at end
+        newShape.insert(newShape.begin(), 1);
+        const std::string outArgName = sliceOutputName;
+        return AddReshapeNode(outputNodeArg, newShape, outputName, graph);
+    }
+    else
+        return sequenceSliceNode;
+}
+
 //
 // This is the main horsepower, it navigate CNTK graph recursivley while keep track of all visited nodes and variables,
 // and create the corresponding ONNX graph.
@@ -2417,7 +2559,15 @@ LotusIR::Node* CNTKToONNXHelper::CreateNode(const FunctionPtr& initialSrc,
     //        return CreateLSTMNode(src, graph, functionNodes, variableNodes, compositeOutputsMap);
     //}
     //else
-    if (cntkOpName == "RNNStep")
+    if (cntkOpName == "Sequence::Slice")
+    {
+        return CreateSequenceSliceNode(src,
+            graph,
+            functionNodes,
+            variableNodes,
+            compositeOutputsMap);
+    }
+    else if (cntkOpName == "RNNStep")
     {
         return CreateRNNNode(src, graph, functionNodes, variableNodes, compositeOutputsMap);
     }

Source/CNTKv2LibraryDll/proto/onnx/ONNXToCNTK.cpp

@@ -2678,7 +2678,6 @@ FunctionPtr ONNXToCNTKHelper::CreateFunction(const Node *node, const std::vector
     // { L"", "Split)
     else if (onnxOpName == "Slice")
     {
-        // axes is optional so provide a default
         std::vector<Axis> axes = ConvertONNXAxesToCNTKCppApi(GetNamedAttributeAsInt64Vec(node, "axes"), inputs[0]);
 
         std::vector<int64_t> starts64 = GetNamedAttributeAsInt64Vec(node, "starts");
@@ -2692,7 +2691,14 @@ FunctionPtr ONNXToCNTKHelper::CreateFunction(const Node *node, const std::vector
 
         std::vector<int> starts = VecInt64ToVecInt(starts64);
         std::vector<int> ends = VecInt64ToVecInt(ends64);
+        for (auto &e : ends)
+        {
+            // CNTK treats endIndex of 0 as to (and include) the last.
+            if (e == INT_MAX)
+                e = 0;
+        }
 
+        // axes is optional so provide a default
         if (axes.empty())
         {
             for (int i = 0; i < starts.size(); i++)
@@ -2702,13 +2708,6 @@ FunctionPtr ONNXToCNTKHelper::CreateFunction(const Node *node, const std::vector
             }
         }
 
-        bool workaroundONNXRT = false;
-        if (workaroundONNXRT)
-        {
-            axes.erase(axes.begin());
-            starts.erase(starts.begin());
-            ends.erase(ends.begin());
-        }
         FunctionPtr cntkFunction = Slice(inputs[0], axes, starts, ends, ToFixedWStringFromMultiByte(node->Name()));
         return cntkFunction;
     }

Source/CNTKv2LibraryDll/proto/onnx/RNNHelper.cpp

@@ -463,11 +463,21 @@ Variable ToBatchAndSequence(Variable input)
     return operandWithBatchAndSequenceAxis;
 }
 
-FunctionPtr UnpackBatchAndSequence(FunctionPtr rnnFunction)
+FunctionPtr UnpackBatchAndSequence(FunctionPtr rnnFunction, bool doTranspose)
 {
     FunctionPtr cntkFunctionWithoutSequenceAxis = Sequence::Unpack(rnnFunction, 0, L"");
     FunctionPtr cntkFunctionWithoutDynamicAxis = UnpackBatch(cntkFunctionWithoutSequenceAxis, L"");
-    return cntkFunctionWithoutDynamicAxis;
+    if (doTranspose)
+    {
+        FunctionPtr transpose = TransposeAxes(cntkFunctionWithoutDynamicAxis,
+            Axis(cntkFunctionWithoutDynamicAxis->Output().Shape().Rank() - 2),
+            Axis(cntkFunctionWithoutDynamicAxis->Output().Shape().Rank() - 1), L"");
+        return transpose;
+    }
+    else
+        // in case of RNN ops, transpose is inserted after the op so we do not do transpose again
+        // TODO: do not transpose after RNN ops so we have one code path here.
+        return cntkFunctionWithoutDynamicAxis;
 }
 
 FunctionPtr CreateLSTM(const LotusIR::Node *node, const std::vector<Variable> &inputs, const std::string &direction,
@@ -557,7 +567,7 @@ FunctionPtr CreateLSTM(const LotusIR::Node *node, const std::vector<Variable> &i
         rnnFunction = Splice(operands, Axis(0), ToFixedWStringFromMultiByte(node->Name()));
     }
 
-    FunctionPtr unpackedRnnFunction = UnpackBatchAndSequence(rnnFunction);
+    FunctionPtr unpackedRnnFunction = UnpackBatchAndSequence(rnnFunction, false);
     return unpackedRnnFunction;
 }
 
@@ -622,7 +632,7 @@ FunctionPtr CreateGRU(const LotusIR::Node *node, const std::vector<Variable> &in
         rnnFunction = Splice(operands, Axis(0), ToFixedWStringFromMultiByte(node->Name()));
     }
 
-    FunctionPtr unpackedRnnFunction = UnpackBatchAndSequence(rnnFunction);
+    FunctionPtr unpackedRnnFunction = UnpackBatchAndSequence(rnnFunction, false);
     return unpackedRnnFunction;
 }
 
@@ -678,7 +688,7 @@ FunctionPtr CreateRNN(const LotusIR::Node *node, const std::vector<Variable> &in
         rnnFunction = Splice(operands, Axis(0), ToFixedWStringFromMultiByte(node->Name()));
     }
 
-    FunctionPtr unpackedRnnFunction = UnpackBatchAndSequence(rnnFunction);
+    FunctionPtr unpackedRnnFunction = UnpackBatchAndSequence(rnnFunction, false);
     return unpackedRnnFunction;
 }
 

Source/CNTKv2LibraryDll/proto/onnx/RNNHelper.h

@@ -184,4 +184,4 @@ std::vector<CNTK::FunctionPtr> GetRNNBlocksFromSingleOrBidirectionalRNN(const CN
 
 CNTK::Variable ToBatchAndSequence(CNTK::Variable input);
 
-CNTK::FunctionPtr UnpackBatchAndSequence(CNTK::FunctionPtr rnnFunction);
+CNTK::FunctionPtr UnpackBatchAndSequence(CNTK::FunctionPtr rnnFunction, bool doTranspose = true);

bindings/python/cntk/tests/onnx_op_test.py

@@ -93,20 +93,17 @@ def verify_sequence_model(model, data, tmpdir, name, device=None, loaded_model=N
         perm[0], perm[1] = perm[1], perm[0]
         return np.transpose(data, perm)
 
-    assert model.output.has_sequence_axis() and model.output.has_batch_axis()
-    
     # data here is reference to the outside data object. create deepcopy to avoid changing the outside data since it might get reused.
     data = deepcopy(data)
     opname = model.owner.op_name
 
     loaded_model = try_save_load_resave_onnx_model(model, tmpdir, name, loaded_model)
 
-    model_shape = model.shape
 
-    # When both batch and sequence axes exist, model input will have batch and sequence axes 
+    # in cases like with RNN models where models have both batch and sequence axis
-    # swapped to match the onnx model. In this case, input and output data shall be adjusted accordingly.  
+    # as dynamic axis, imported models will have the dynamic axes as free_dimensions in static shapes. 
-    model_shape = (CNTK_FREEDIM_AXIS_DENOTATION, CNTK_FREEDIM_AXIS_DENOTATION, ) + model_shape
+    if model.output.dynamic_axes == (C.Axis('defaultBatchAxis'), C.Axis('defaultDynamicAxis')):
-    assert model_shape == loaded_model.shape
+        assert (CNTK_FREEDIM_AXIS_DENOTATION, CNTK_FREEDIM_AXIS_DENOTATION, ) + model.shape == loaded_model.shape
         
     dataOnnx = TranposeDynamicAxis(data)
     if device:
@@ -116,13 +113,13 @@ def verify_sequence_model(model, data, tmpdir, name, device=None, loaded_model=N
         o0 = model.eval({model.arguments[0]:data})
         o1 = loaded_model.eval({loaded_model.arguments[0]:dataOnnx})
 
-    if (type(o0) is list):
+    o0 = np.array(o0)
-        o0 = o0[0]
+    o1 = np.array(o1)
-    if (type(o1) is list):
-        o1 = o1[0]
 
-    # squeeze the batch axis (=1) to match original model output
+    # if there is a sequence axis in the output, it must be swapped with batch axis 
-    o1 = np.squeeze(o1, axis=1)
+    # to match the original CNTK model's output 
+    if model.outputs[0].has_sequence_axis(): 
+        o1 = TranposeDynamicAxis(o1)
 
     assert np.allclose(o0, o1)
     return loaded_model
@@ -1352,6 +1349,21 @@ def test_Slice(tmpdir, dtype):
         model = C.slice(x1, [0,1], [1,0], [2,1]);
         verify_one_input(model, data, tmpdir, 'Slice2_1')
 
+#Sequence.Slice 
+@pytest.mark.parametrize("beginIndex, endIndex", (  
+    (-2, -1), (0, -1), (1, -1), (-1, 0), (1, 0), (-4, 2), (0, 1), (1, 2)))
+@pytest.mark.parametrize("dtype", DType_Config)
+def test_SequenceSlice(tmpdir, dtype, beginIndex, endIndex):
+    batch_size = 1
+    sequence_length = 5
+    feature_shape = (3,)
+    shape = (batch_size, sequence_length, *feature_shape)
+    data = np.reshape(range(0, np.prod(shape)), shape).astype(dtype)
+    testName = "test_sequence_slice_{0}.{1}".format(beginIndex, endIndex)
+    print(testName)
+    model = C.sequence.slice(C.sequence.input_variable((feature_shape)), beginIndex, endIndex)
+    verify_sequence_model(model, data, tmpdir, testName)
+
 #Softmax
 @pytest.mark.parametrize("dtype", DType_Config)
 def test_Softmax(tmpdir, dtype):