1 files changed, 160 insertions, 50 deletions

diff --git a/reference_model/src/ops/scatter_gather.cc b/reference_model/src/ops/scatter_gather.cc
index c54204a..2d1026f 100644
--- a/reference_model/src/ops/scatter_gather.cc
+++ b/reference_model/src/ops/scatter_gather.cc
@@ -20,31 +20,61 @@ using namespace TosaReference;
 using namespace Eigen;
 using namespace tosa;
 
-template <int InRank, int IndexRank, DType Dtype>
-OpGather<InRank, IndexRank, Dtype>::OpGather(TosaAttributeBase* attribute_, TosaQuantInfoBase* qinfo_, uint64_t id_)
+template <DType Dtype>
+OpGather<Dtype>::OpGather(TosaAttributeBase* attribute_, TosaQuantInfoBase* qinfo_, uint64_t id_)
     : GraphNode(Op_GATHER, id_)
 {
     setRequiredOperands(2, 1);
-    setRequiredRank(1, 6);
-
-    INIT_ATTRIBUTE(Axis);
 }
 
-template <int InRank, int IndexRank, DType Dtype>
-OpGather<InRank, IndexRank, Dtype>::~OpGather()
-{
-    if (attribute)
-        delete attribute;
-}
+template <DType Dtype>
+OpGather<Dtype>::~OpGather()
+{}
 
-template <int InRank, int IndexRank, DType Dtype>
-int OpGather<InRank, IndexRank, Dtype>::checkTensorAttributes()
+template <DType Dtype>
+int OpGather<Dtype>::checkTensorAttributes()
 {
     if (validateRequiredOperands())
         return 1;
 
-    if (validateRequiredRank(inputs[0]) || validateRequiredRank(inputs[1]) || validateRequiredRank(outputs[0]))
+    if (inputs[0]->getRank() != 3)
+    {
+        printNodeValidationError("OpGather: values needs to be rank 3 tensor");
+        return 1;
+    }
+
+    if (inputs[1]->getRank() != 2)
     {
+        printNodeValidationError("OpGather: indices needs to be rank 2 tensor");
+        return 1;
+    }
+
+    if (outputs[0]->getRank() != 3)
+    {
+        printNodeValidationError("OpGather: output needs to be rank 3 tensor");
+        return 1;
+    }
+
+    K = inputs[0]->getShape()[1];
+    N = outputs[0]->getShape()[0];
+    W = outputs[0]->getShape()[1];
+    C = outputs[0]->getShape()[2];
+
+    if (N != inputs[0]->getShape()[0] || N != inputs[1]->getShape()[0])
+    {
+        printNodeValidationError("OpGather: dimension N mismatch");
+        return 1;
+    }
+
+    if (W != inputs[1]->getShape()[1])
+    {
+        printNodeValidationError("OpGather: dimension W mismatch");
+        return 1;
+    }
+
+    if (C != inputs[0]->getShape()[2])
+    {
+        printNodeValidationError("OpGather: dimension C mismatch");
         return 1;
     }
 
@@ -55,59 +85,133 @@ int OpGather<InRank, IndexRank, Dtype>::checkTensorAttributes()
         return 1;
     }
 
-    in    = dynamic_cast<TosaReference::TensorTemplate<TIn>*>(inputs[0]);
-    index = dynamic_cast<TosaReference::TensorTemplate<TIndex>*>(inputs[1]);
-    out   = dynamic_cast<TosaReference::TensorTemplate<TOut>*>(outputs[0]);
+    values  = dynamic_cast<TosaReference::TensorTemplate<TValue>*>(inputs[0]);
+    indices = dynamic_cast<TosaReference::TensorTemplate<TIndex>*>(inputs[1]);
+    output  = dynamic_cast<TosaReference::TensorTemplate<TOutput>*>(outputs[0]);
 
-    ASSERT_MEM(in && index && out);
+    ASSERT_MEM(values && indices && output);
 
     return 0;
 }
 
-template <int InRank, int IndexRank, DType Dtype>
-int OpGather<InRank, IndexRank, Dtype>::eval()
+template <DType Dtype>
+int OpGather<Dtype>::eval()
+{
+    for (int32_t n = 0; n < N; n++)
+    {
+        for (int32_t w = 0; w < W; w++)
+        {
+            int32_t k = this->indices->getTensor()(n, w);
+            ASSERT_MSG_NODE(k >= 0 && k < K, "OpGather: index(%d, %d)=%d exceed valid range [0, %d]", n, w, k, K);
+            for (int32_t c = 0; c < C; c++)
+            {
+                EigenType value                    = this->values->getTensor()(n, k, c);
+                this->output->getTensor()(n, w, c) = value;
+            }
+        }
+    }
+
+    return GraphNode::eval();
+}
+
+template <DType Dtype>
+OpScatter<Dtype>::OpScatter(TosaAttributeBase* attribute_, TosaQuantInfoBase* qinfo_, uint64_t id_)
+    : GraphNode(Op_SCATTER, id_)
 {
-    int axis = attribute->axis();
+    setRequiredOperands(3, 1);
+}
+
+template <DType Dtype>
+OpScatter<Dtype>::~OpScatter()
+{}
 
-    // calculate size left and right to axis
-    int left_size = 1;
-    for (int i = 0; i < axis; ++i)
+template <DType Dtype>
+int OpScatter<Dtype>::checkTensorAttributes()
+{
+    if (validateRequiredOperands())
+        return 1;
+
+    if (inputs[0]->getRank() != 3)
+    {
+        printNodeValidationError("OpGather: values_in needs to be rank 3 tensor");
+        return 1;
+    }
+
+    if (inputs[1]->getRank() != 2)
+    {
+        printNodeValidationError("OpGather: indices needs to be rank 2 tensor");
+        return 1;
+    }
+
+    if (inputs[2]->getRank() != 3)
     {
-        left_size *= in->getShape()[i];
+        printNodeValidationError("OpGather: input needs to be rank 3 tensor");
+        return 1;
     }
 
-    int right_size = 1;
-    for (int i = axis + 1; i < in->getRank(); ++i)
+    if (outputs[0]->getRank() != 3)
     {
-        right_size *= in->getShape()[i];
+        printNodeValidationError("OpGather: values_out needs to be rank 3 tensor");
+        return 1;
     }
 
-    InEigenType* input_data   = in->getTensor().data();
-    int32_t* index_data       = index->getTensor().data();
-    OutEigenType* output_data = out->getTensor().data();
+    W = inputs[2]->getShape()[1];
+    N = outputs[0]->getShape()[0];
+    K = outputs[0]->getShape()[1];
+    C = outputs[0]->getShape()[2];
 
-    int32_t axis_size   = in->getShape()[axis];
-    int32_t index_count = index->getElementCount();
+    if (N != inputs[0]->getShape()[0] || N != inputs[1]->getShape()[0] || N != inputs[2]->getShape()[0])
+    {
+        printNodeValidationError("OpScatter: dimension N mismatch");
+        return 1;
+    }
 
-    // sanity check if index is valid
-    // need to check until this point since index is not known until runtime
-    for (size_t i = 0; i < index->getElementCount(); i++)
+    if (W != inputs[1]->getShape()[1])
     {
-        if (index_data[i] >= axis_size)
-        {
-            FATAL_ERROR_NODE("OpGather: index[%lu]=%i can't exceed axis_size=%i", i, index_data[i], axis_size);
-        }
+        printNodeValidationError("OpGather: dimension W mismatch");
+        return 1;
     }
 
-    // Eigen stores tensor in column-major
-    // so we iterate through dimension right to axis and the index array
-    // do memory copy with size of left size each time
-    for (int right = 0; right < right_size; ++right)
+    if (C != inputs[0]->getShape()[2] || C != inputs[2]->getShape()[2])
+    {
+        printNodeValidationError("OpGather: dimension C mismatch");
+        return 1;
+    }
+
+    // output and input must be the same types
+    if (inputs[0]->matchType(*outputs[0]))
+    {
+        printNodeValidationError("Failure to match input and output type");
+        return 1;
+    }
+
+    values_in  = dynamic_cast<TosaReference::TensorTemplate<TValue>*>(inputs[0]);
+    indices    = dynamic_cast<TosaReference::TensorTemplate<TIndex>*>(inputs[1]);
+    input      = dynamic_cast<TosaReference::TensorTemplate<TValue>*>(inputs[2]);
+    values_out = dynamic_cast<TosaReference::TensorTemplate<TOutput>*>(outputs[0]);
+
+    ASSERT_MEM(values_in && indices && input && values_out);
+
+    return 0;
+}
+
+template <DType Dtype>
+int OpScatter<Dtype>::eval()
+{
+    // Initializes the output tensor with the input value for values that are unchanged by the scatter operation.
+    this->values_out->getTensor() = this->values_in->getTensor();
+
+    for (int n = 0; n < N; n++)
     {
-        for (int i = 0; i < index_count; ++i)
+        for (int w = 0; w < W; w++)
         {
-            std::memcpy(output_data + (right * index_count + i) * left_size,
-                        input_data + (right * axis_size + index_data[i]) * left_size, sizeof(InEigenType) * left_size);
+            int32_t k = this->indices->getTensor()(n, w);
+            ASSERT_MSG_NODE(k >= 0 && k < K, "OpScatter: index(%d, %d)=%d exceed valid range [0, %d]", n, w, k, K);
+            for (int c = 0; c < C; c++)
+            {
+                EigenType value                        = this->input->getTensor()(n, w, c);
+                this->values_out->getTensor()(n, k, c) = value;
+            }
         }
     }
 
@@ -115,6 +219,12 @@ int OpGather<InRank, IndexRank, Dtype>::eval()
 }
 
 // template explicit instantiation
-DEF_INSTANTIATE_GATHER(OpGather, AINT8);
-DEF_INSTANTIATE_GATHER(OpGather, INT16);
-DEF_INSTANTIATE_GATHER(OpGather, INT32);
+DEF_INSTANTIATE_ONE_TYPE(OpGather, AINT8);
+DEF_INSTANTIATE_ONE_TYPE(OpGather, INT16);
+DEF_INSTANTIATE_ONE_TYPE(OpGather, INT32);
+DEF_INSTANTIATE_ONE_TYPE(OpGather, FLOAT);
+
+DEF_INSTANTIATE_ONE_TYPE(OpScatter, AINT8);
+DEF_INSTANTIATE_ONE_TYPE(OpScatter, INT16);
+DEF_INSTANTIATE_ONE_TYPE(OpScatter, INT32);
+DEF_INSTANTIATE_ONE_TYPE(OpScatter, FLOAT);