Changes for 0.23.0 release

- update serialization_lib hash
- PAD:
    1. make padding as an attribute instead of tensor.
    2. add pad_const_int (for non-float type) / pad_const_fp (for float type)
- TRANSPOSE: make perm as an attribute instead of tensor
- TABLE: make table as attribute instead of tensor
- update examples/ tests

Signed-off-by: Kevin Cheng <kevin.cheng@arm.com>
Change-Id: Iddc446db4b356ba2f36ea4a79b7220b9cfc2aa4e

15 files changed, 127 insertions, 100 deletions

diff --git a/examples/test_add_1x4x4x4_f32/flatbuffer-tf/test_add_1x4x4x4_f32.tosa b/examples/test_add_1x4x4x4_f32/flatbuffer-tf/test_add_1x4x4x4_f32.tosa
index 8d004e8..5a9457d 100644
--- a/examples/test_add_1x4x4x4_f32/flatbuffer-tf/test_add_1x4x4x4_f32.tosa
+++ b/examples/test_add_1x4x4x4_f32/flatbuffer-tf/test_add_1x4x4x4_f32.tosa
diff --git a/examples/test_add_1x4x4x4_f32/flatbuffer-tflite/test_add_1x4x4x4_f32.tosa b/examples/test_add_1x4x4x4_f32/flatbuffer-tflite/test_add_1x4x4x4_f32.tosa
index 8d004e8..5a9457d 100644
--- a/examples/test_add_1x4x4x4_f32/flatbuffer-tflite/test_add_1x4x4x4_f32.tosa
+++ b/examples/test_add_1x4x4x4_f32/flatbuffer-tflite/test_add_1x4x4x4_f32.tosa
diff --git a/examples/test_add_1x4x4x4_f32/model.pb b/examples/test_add_1x4x4x4_f32/model.pb
index 59c6aec..a3c5cae 100644
--- a/examples/test_add_1x4x4x4_f32/model.pb
+++ b/examples/test_add_1x4x4x4_f32/model.pb
@@ -92,5 +92,5 @@ node {
   }
 }
 versions {
-  producer: 810
+  producer: 925
 }
diff --git a/examples/test_add_1x4x4x4_f32/model.tflite b/examples/test_add_1x4x4x4_f32/model.tflite
index 36a6799..5a88e98 100644
--- a/examples/test_add_1x4x4x4_f32/model.tflite
+++ b/examples/test_add_1x4x4x4_f32/model.tflite
diff --git a/examples/test_conv2d_1x1_1x32x32x8_f32_st11_padSAME_dilat11/flatbuffer-tf/test_conv2d_1x1_1x32x32x8_f32_st11_padSAME_dilat11.tosa b/examples/test_conv2d_1x1_1x32x32x8_f32_st11_padSAME_dilat11/flatbuffer-tf/test_conv2d_1x1_1x32x32x8_f32_st11_padSAME_dilat11.tosa
index 71564f2..36168d7 100644
--- a/examples/test_conv2d_1x1_1x32x32x8_f32_st11_padSAME_dilat11/flatbuffer-tf/test_conv2d_1x1_1x32x32x8_f32_st11_padSAME_dilat11.tosa
+++ b/examples/test_conv2d_1x1_1x32x32x8_f32_st11_padSAME_dilat11/flatbuffer-tf/test_conv2d_1x1_1x32x32x8_f32_st11_padSAME_dilat11.tosa
diff --git a/examples/test_conv2d_1x1_1x32x32x8_f32_st11_padSAME_dilat11/flatbuffer-tflite/test_conv2d_1x1_1x32x32x8_f32_st11_padSAME_dilat11.tosa b/examples/test_conv2d_1x1_1x32x32x8_f32_st11_padSAME_dilat11/flatbuffer-tflite/test_conv2d_1x1_1x32x32x8_f32_st11_padSAME_dilat11.tosa
index 0dc565a..36168d7 100644
--- a/examples/test_conv2d_1x1_1x32x32x8_f32_st11_padSAME_dilat11/flatbuffer-tflite/test_conv2d_1x1_1x32x32x8_f32_st11_padSAME_dilat11.tosa
+++ b/examples/test_conv2d_1x1_1x32x32x8_f32_st11_padSAME_dilat11/flatbuffer-tflite/test_conv2d_1x1_1x32x32x8_f32_st11_padSAME_dilat11.tosa
diff --git a/examples/test_conv2d_1x1_1x32x32x8_f32_st11_padSAME_dilat11/model.pb b/examples/test_conv2d_1x1_1x32x32x8_f32_st11_padSAME_dilat11/model.pb
index b21dff3..9d199e4 100644
--- a/examples/test_conv2d_1x1_1x32x32x8_f32_st11_padSAME_dilat11/model.pb
+++ b/examples/test_conv2d_1x1_1x32x32x8_f32_st11_padSAME_dilat11/model.pb
@@ -137,5 +137,5 @@ node {
   }
 }
 versions {
-  producer: 810
+  producer: 925
 }
diff --git a/examples/test_conv2d_1x1_1x32x32x8_f32_st11_padSAME_dilat11/model.tflite b/examples/test_conv2d_1x1_1x32x32x8_f32_st11_padSAME_dilat11/model.tflite
index 8b5bdf6..b2a60f8 100644
--- a/examples/test_conv2d_1x1_1x32x32x8_f32_st11_padSAME_dilat11/model.tflite
+++ b/examples/test_conv2d_1x1_1x32x32x8_f32_st11_padSAME_dilat11/model.tflite
diff --git a/examples/test_conv2d_1x1_1x32x32x8_qi8_st11_padSAME_dilat11/flatbuffer-tflite/test_conv2d_1x1_1x32x32x8_qi8_st11_padSAME_dilat11.tosa b/examples/test_conv2d_1x1_1x32x32x8_qi8_st11_padSAME_dilat11/flatbuffer-tflite/test_conv2d_1x1_1x32x32x8_qi8_st11_padSAME_dilat11.tosa
index 600590c..05a78b6 100644
--- a/examples/test_conv2d_1x1_1x32x32x8_qi8_st11_padSAME_dilat11/flatbuffer-tflite/test_conv2d_1x1_1x32x32x8_qi8_st11_padSAME_dilat11.tosa
+++ b/examples/test_conv2d_1x1_1x32x32x8_qi8_st11_padSAME_dilat11/flatbuffer-tflite/test_conv2d_1x1_1x32x32x8_qi8_st11_padSAME_dilat11.tosa
diff --git a/reference_model/src/ops/data_layout.cc b/reference_model/src/ops/data_layout.cc
index 674227b..05a11e0 100644
--- a/reference_model/src/ops/data_layout.cc
+++ b/reference_model/src/ops/data_layout.cc
@@ -128,10 +128,11 @@ OpPad<Rank, Dtype>::OpPad(SubgraphTraverser* sgt_,
                           uint64_t id_)
     : GraphNode(sgt_, Op_PAD, id_)
 {
-    setRequiredOperands(2, 1);
+    setRequiredOperands(1, 1);
     setRequiredRank(0, 6);
 
     INIT_QINFO(Pad);
+    INIT_ATTRIBUTE(Pad);
 }
 
 template <int Rank, DType Dtype>
@@ -159,9 +160,22 @@ int OpPad<Rank, Dtype>::checkTensorAttributes()
         return 1;
     }
 
-    in       = dynamic_cast<TosaReference::TensorTemplate<TIn>*>(inputs[0]);
-    out      = dynamic_cast<TosaReference::TensorTemplate<TOut>*>(outputs[0]);
-    paddings = dynamic_cast<TosaReference::TensorTemplate<ETensor2<int32_t>>*>(inputs[1]);
+    in  = dynamic_cast<TosaReference::TensorTemplate<TIn>*>(inputs[0]);
+    out = dynamic_cast<TosaReference::TensorTemplate<TOut>*>(outputs[0]);
+    ASSERT_MEM(in && out);
+
+    // padding in spec is 2D array in shape of [Rank, 2]
+    // Reference model implement this as 1D array of [Rank * 2], with ordering:
+    // [Rank0_front, Rank0_back, Rank1_front, Rank1_back, ..., Rank(N-1)_front, Rank(N-1)_back]
+    ERROR_IF(attribute->padding().size() != (Rank * 2), "OpPad: padding length needs to be (rank(input1) * 2)");
+
+    for (int i = 0; i < Rank; i++)
+    {
+        int32_t pad_front = attribute->padding()[2 * i];
+        int32_t pad_back  = attribute->padding()[2 * i + 1];
+        ERROR_IF((pad_front < 0) || (pad_back < 0), "OpPad: padding can't be smaller than 0");
+        paddings_array[i] = std::make_pair(pad_front, pad_back);
+    }
 
     if (this->qinfo && Dtype != DType_INT8)
     {
@@ -174,18 +188,24 @@ int OpPad<Rank, Dtype>::checkTensorAttributes()
 template <int Rank, DType Dtype>
 int OpPad<Rank, Dtype>::eval()
 {
-    // Move this to
-    for (int i = 0; i < Rank; i++)
+    InEigenType pad_value = 0;
+
+    switch (Dtype)
     {
-        ERROR_IF((paddings->getTensor()(i, 0) < 0) || (paddings->getTensor()(i, 1) < 0),
-                 "OpPad: padding can't be smaller than 0");
-        paddings_array[i] = std::make_pair(paddings->getTensor()(i, 0), paddings->getTensor()(i, 1));
+        case DType_BOOL:
+        case DType_INT8:
+        case DType_INT16:
+        case DType_INT32:
+            pad_value = (InEigenType)attribute->pad_const_int();
+            break;
+        case DType_FLOAT:
+            pad_value = (InEigenType)attribute->pad_const_fp();
+            break;
     }
 
-    InEigenType pad_value = 0;
-    if (this->qinfo)
+    if (this->qinfo && Dtype == DType_INT8)
     {
-        pad_value = (InEigenType)this->qinfo->input_zp();
+        pad_value += (InEigenType)this->qinfo->input_zp();
     }
 
     this->out->getTensor() = this->in->getTensor().pad(this->paddings_array, pad_value);
@@ -602,8 +622,10 @@ OpTranspose<Rank, Dtype>::OpTranspose(SubgraphTraverser* sgt_,
                                       uint64_t id_)
     : GraphNode(sgt_, Op_TRANSPOSE, id_)
 {
-    setRequiredOperands(2, 1);
+    setRequiredOperands(1, 1);
     setRequiredRank(0, 6);
+
+    INIT_ATTRIBUTE(Transpose);
 }
 
 template <int Rank, DType Dtype>
@@ -634,9 +656,10 @@ int OpTranspose<Rank, Dtype>::checkTensorAttributes()
         return 1;
     }
 
-    in          = dynamic_cast<TosaReference::TensorTemplate<TIn>*>(inputs[0]);
-    out         = dynamic_cast<TosaReference::TensorTemplate<TOut>*>(outputs[0]);
-    perm_tensor = dynamic_cast<TosaReference::TensorTemplate<ETensor1<int32_t>>*>(inputs[1]);
+    in  = dynamic_cast<TosaReference::TensorTemplate<TIn>*>(inputs[0]);
+    out = dynamic_cast<TosaReference::TensorTemplate<TOut>*>(outputs[0]);
+
+    ASSERT_MEM(in && out);
 
     return 0;
 }
@@ -646,7 +669,7 @@ int OpTranspose<Rank, Dtype>::eval()
 {
     for (int32_t d = 0; d < Rank; d++)
     {
-        perm_array[d] = this->perm_tensor->getTensor().data()[d];
+        perm_array[d] = attribute->perm()[d];
         ERROR_IF(perm_array[d] < 0 or perm_array[d] >= Rank, "OpTranspose: index out of boundary");
     }
 
diff --git a/reference_model/src/ops/data_layout.h b/reference_model/src/ops/data_layout.h
index 9f44fc7..bad88e4 100644
--- a/reference_model/src/ops/data_layout.h
+++ b/reference_model/src/ops/data_layout.h
@@ -63,8 +63,8 @@ protected:
     Eigen::array<std::pair<ptrdiff_t, ptrdiff_t>, Rank> paddings_array;
     TosaReference::TensorTemplate<TIn>* in;
     TosaReference::TensorTemplate<TOut>* out;
-    TosaReference::TensorTemplate<Eigen::Tensor<int32_t, 2>>* paddings;
     TosaPadQuantInfo* qinfo;
+    TosaPadAttribute* attribute;
 };
 
 template <int InRank, int OutRank, DType Dtype>
@@ -207,8 +207,8 @@ public:
 
 protected:
     Eigen::array<int, Rank> perm_array;
+    TosaTransposeAttribute* attribute;
     TosaReference::TensorTemplate<TIn>* in;
-    TosaReference::TensorTemplate<ETensor1<int32_t>>* perm_tensor;
     TosaReference::TensorTemplate<TOut>* out;
 };
 };    // namespace TosaReference
diff --git a/reference_model/src/ops/ewise_binary.cc b/reference_model/src/ops/ewise_binary.cc
index 6808604..415cd1c 100644
--- a/reference_model/src/ops/ewise_binary.cc
+++ b/reference_model/src/ops/ewise_binary.cc
@@ -490,8 +490,10 @@ OpTable<Rank, InDtype>::OpTable(SubgraphTraverser* sgt_,
                                 uint64_t id_)
     : GraphNode(sgt_, Op_TABLE, id_)
 {
-    setRequiredOperands(2, 1);
+    setRequiredOperands(1, 1);
     setRequiredRank(0, 6);
+
+    INIT_ATTRIBUTE(Table);
 }
 
 template <int Rank, DType InDtype>
@@ -509,36 +511,18 @@ int OpTable<Rank, InDtype>::checkTensorAttributes()
         return 1;
     }
 
-    if (inputs[1]->getRank() != 1)
-    {
-        printNodeValidationError("OpTable: Table must be rank 1 tensor");
-        return 1;
-    }
+    ERROR_IF(inputs[0]->getDtype() != InDtype, "OpTable: Unexpected input type");
+    ERROR_IF(attribute->table().size() != TableNumEntries, "OpTable: table attribute size must be %u", TableNumEntries);
 
-    if (inputs[0]->getDtype() == DType_INT8)
+    for (uint32_t i = 0; i < TableNumEntries; i++)
     {
-        if (inputs[1]->getElementCount() != 256 || inputs[1]->getDtype() != DType_INT8)
-        {
-            printNodeValidationError("OpTable: Table must be INT8[256] if input is INT8");
-            return 1;
-        }
-        ERROR_IF(outputs[0]->getDtype() != DType_INT8, "OpTable: output tensor must be INT8");
-    }
-    else if (inputs[0]->getDtype() == DType_INT16)
-    {
-        if (inputs[1]->getElementCount() != 513 || inputs[1]->getDtype() != DType_INT16)
-        {
-            printNodeValidationError("OpTable: Table must be INT16[513] if input is INT16");
-            return 1;
-        }
-        ERROR_IF(outputs[0]->getDtype() != DType_INT32, "OpTable: output tensor must be INT32");
+        table[i] = (TableEigenType)attribute->table()[i];
     }
 
-    in    = dynamic_cast<TosaReference::TensorTemplate<TIn>*>(inputs[0]);
-    table = dynamic_cast<TosaReference::TensorTemplate<TTable>*>(inputs[1]);
-    out   = dynamic_cast<TosaReference::TensorTemplate<TOut>*>(outputs[0]);
+    in  = dynamic_cast<TosaReference::TensorTemplate<TIn>*>(inputs[0]);
+    out = dynamic_cast<TosaReference::TensorTemplate<TOut>*>(outputs[0]);
 
-    ASSERT_MEM(in && table && out);
+    ASSERT_MEM(in && out);
 
     return 0;
 }
@@ -552,7 +536,7 @@ int OpTable<Rank, InDtype>::eval()
             this->out->getTensor() = this->in->getTensor().unaryExpr([this](InEigenType in) -> OutEigenType {
                 int32_t input_truncated = std::min<int32_t>(std::max<int32_t>(in, QInMin), QInMax);
                 int32_t index           = input_truncated - QInMin;
-                int32_t value           = this->table->getTensor()(index);
+                int32_t value           = table[index];
 
                 return value;
             });
@@ -568,8 +552,8 @@ int OpTable<Rank, InDtype>::eval()
                 int32_t frac  = (input_truncated)&0x7F;    // 7-bit fraction
 
                 // 3. interpolate, generate 16.7 (23-bit) output
-                int32_t base  = this->table->getTensor()(index);
-                int32_t next  = this->table->getTensor()(index + 1);
+                int32_t base  = table[index];
+                int32_t next  = table[index + 1];
                 int32_t value = (base << 7) + (next - base) * frac;
 
                 return value;
diff --git a/reference_model/src/ops/ewise_binary.h b/reference_model/src/ops/ewise_binary.h
index fd4d408..373dfb8 100644
--- a/reference_model/src/ops/ewise_binary.h
+++ b/reference_model/src/ops/ewise_binary.h
@@ -184,26 +184,28 @@ public:
     virtual int checkTensorAttributes();
     virtual int eval();
 
-    static constexpr DType TableDtype        = (InDtype == DType_INT8) ? DType_INT8 : DType_INT16;
-    static constexpr DType OutDtype          = (InDtype == DType_INT8) ? DType_INT8 : DType_INT32;
-    using InEigenType                        = typename GetEigenType<InDtype>::type;
-    using TableEigenType                     = typename GetEigenType<TableDtype>::type;
-    using OutEigenType                       = typename GetEigenType<OutDtype>::type;
-    using TIn                                = Eigen::Tensor<InEigenType, Rank>;
-    using TTable                             = Eigen::Tensor<TableEigenType, 1>;
-    using TOut                               = Eigen::Tensor<OutEigenType, Rank>;
-    static constexpr int32_t IntegerBits     = 9;
-    static constexpr int32_t FractionBits    = 7;
-    static constexpr int32_t NumTableEntries = (1 << IntegerBits);
-    static constexpr int32_t QInMin          = GetQMin<InDtype>::value;
-    static constexpr int32_t QInMax          = GetQMax<InDtype>::value;
-    static constexpr int32_t QOutMin         = GetQMin<OutDtype>::value;
-    static constexpr int32_t QOutMax         = GetQMax<OutDtype>::value;
+    static constexpr DType TableDtype         = (InDtype == DType_INT8) ? DType_INT8 : DType_INT16;
+    static constexpr DType OutDtype           = (InDtype == DType_INT8) ? DType_INT8 : DType_INT32;
+    static constexpr uint32_t TableNumEntries = (InDtype == DType_INT8) ? 256 : 513;
+    using InEigenType                         = typename GetEigenType<InDtype>::type;
+    using TableEigenType                      = typename GetEigenType<TableDtype>::type;
+    using OutEigenType                        = typename GetEigenType<OutDtype>::type;
+    using TIn                                 = Eigen::Tensor<InEigenType, Rank>;
+    using TTable                              = Eigen::Tensor<TableEigenType, 1>;
+    using TOut                                = Eigen::Tensor<OutEigenType, Rank>;
+    static constexpr int32_t IntegerBits      = 9;
+    static constexpr int32_t FractionBits     = 7;
+    static constexpr int32_t NumTableEntries  = (1 << IntegerBits);
+    static constexpr int32_t QInMin           = GetQMin<InDtype>::value;
+    static constexpr int32_t QInMax           = GetQMax<InDtype>::value;
+    static constexpr int32_t QOutMin          = GetQMin<OutDtype>::value;
+    static constexpr int32_t QOutMax          = GetQMax<OutDtype>::value;
 
 protected:
     TosaReference::TensorTemplate<TIn>* in;
-    TosaReference::TensorTemplate<TTable>* table;
     TosaReference::TensorTemplate<TOut>* out;
+    TosaTableAttribute* attribute;
+    std::array<TableEigenType, TableNumEntries> table;
 };
 
 };    // namespace TosaReference
diff --git a/thirdparty/serialization_lib b/thirdparty/serialization_lib
-Subproject e6563f52231c603b409638b22530d016757542c
+Subproject 38d214cfa4491f1956f28d7eff428a8ed07d824
diff --git a/verif/tosa_test_gen.py b/verif/tosa_test_gen.py
index 105f016..b9cca18 100644
--- a/verif/tosa_test_gen.py
+++ b/verif/tosa_test_gen.py
@@ -676,12 +676,21 @@ class TosaArgGen:
         axis_pad_values = [x for x in itertools.product(pad_values, pad_values)]
         shape_pad_values = itertools.product(*([axis_pad_values] * rank))
 
+        if dtype in [DType.BOOL, DType.INT8, DType.INT16, DType.INT32]:
+            pad_const_int = testGen.getRandNumberDType(dtype)
+            pad_const_fp = 0
+        elif dtype == DType.FLOAT:
+            pad_const_int = 0
+            pad_const_fp = testGen.getRandNumberDType(dtype)
+        else:
+            return []
+
         for paddings in shape_pad_values:
             name = "pad"
             for r in range(rank):
                 before, after = paddings[r]
                 name = f"{name}{before}{after}"
-            arg_list.append((name, [np.array(paddings)]))
+            arg_list.append((name, [np.array(paddings), pad_const_int, pad_const_fp]))
 
         return arg_list
 
@@ -1176,6 +1185,27 @@ class TosaArgGen:
 
         return arg_list
 
+    @staticmethod
+    def agTable(testGen, opName, shapeList, dtype, error_name=None):
+        arg_list = []
+
+        if dtype == DType.INT8:
+            table = np.int32(
+                testGen.rng.integers(low=-128, high=128, size=[256])
+            ).tolist()
+        else:  # INT16
+            table = np.int32(
+                testGen.rng.integers(low=-32768, high=32768, size=[513])
+            ).tolist()
+
+        arg_list.append(
+            (
+                "",
+                [table],
+            )
+        )
+        return arg_list
+
     def agCondIf(testGen, opName, shapeList, dtype, error_name=None):
         # CondIf generates the condition values here.
         # Convert to tensors in the build function, along with the
@@ -1527,13 +1557,8 @@ class TosaErrorValidator:
             op = kwargs['op']
             input_list = kwargs['input_list']
             num_operands = kwargs['num_operands']
-            # both PAD, TRANSPOSE add an extra const layer in the build function
-            if op['op'] in [Op.PAD, Op.TRANSPOSE]:
-                if len(input_list) != num_operands + 1:
-                    error_result = True
-            else:
-                if len(input_list) != num_operands:
-                    error_result = True
+            if len(input_list) != num_operands:
+                error_result = True
 
         info_dict = {
             "error_name": error_name,
@@ -2907,19 +2932,13 @@ class TosaTestGen:
         self.ser.addOperator(op['op'], [a.name, b.name], [result_tens.name], attr)
         return result_tens
 
-    def build_table(self, op, a):
-        # Constant size depending on type, random values
-        if a.dtype == DType.INT16:
-            table_dtype = DType.INT16
-            table_arr = self.getRandTensor([513], table_dtype)
-        else:
-            assert a.dtype == DType.INT8
-            table_dtype = DType.INT8
-            table_arr = self.getRandTensor([256], table_dtype)
+    def build_table(self, op, a, table):
+        result_tens = OutputShaper.tableOp(self.ser, a)
 
-        table_tens = self.ser.addConst(table_arr.shape, table_dtype, table_arr)
-        result_tens = OutputShaper.tableOp(self.ser, a, table_dtype)
-        self.ser.addOperator(op['op'], [a.name, table_tens.name], [result_tens.name], None)
+        attr = ts.TosaSerializerAttribute()
+        attr.TableAttribute(table)
+
+        self.ser.addOperator(op['op'], [a.name], [result_tens.name], attr)
 
         return result_tens
 
@@ -3219,16 +3238,14 @@ class TosaTestGen:
         self.ser.addOperator(op['op'], input_tensor_names, [result_tens.name], attr)
         return result_tens
 
-    def build_pad(self, op, a, padding, validator_fcns=None, error_name=None, qinfo=None):
+    def build_pad(self, op, a, padding, pad_const_int, pad_const_float, validator_fcns=None, error_name=None, qinfo=None):
         result_tens = OutputShaper.padOp(self.ser, self.rng, a, padding, error_name)
 
-        # Need to turn the padding array into a TOSA tensor here.
-        # This is one of the few tensor operands that does not get
-        # randomly generated
-        padding_tens = self.ser.addConst(padding.shape, DType.INT32, padding)
+        attr = ts.TosaSerializerAttribute()
+        attr.PadAttribute(padding.flatten(), pad_const_int, pad_const_float)
 
         # Invalidate Input/Output list for error if checks.
-        input_list = [a.name, padding_tens.name]
+        input_list = [a.name]
         output_list = [result_tens.name]
         pCount, cCount = op["operands"]
         num_operands = pCount + cCount
@@ -3252,7 +3269,7 @@ class TosaTestGen:
         )
 
         self.ser.addOperator(
-            op['op'], input_list, output_list, None, qinfo
+            op['op'], input_list, output_list, attr, qinfo
         )
         return result_tens
 
@@ -3299,10 +3316,11 @@ class TosaTestGen:
     def build_transpose(self, op, a, perms, validator_fcns=None, error_name=None):
         result_tens = OutputShaper.transposeOp(self.ser, self.rng, a, perms, error_name)
 
-        perms_tens = self.ser.addConst([len(perms)], DType.INT32, np.int32(perms))
+        attr = ts.TosaSerializerAttribute()
+        attr.TransposeAttribute(perms)
 
         # Invalidate Input/Output list for error if checks.
-        input_list = [a.name, perms_tens.name]
+        input_list = [a.name]
         output_list = [result_tens.name]
         pCount, cCount = op["operands"]
         num_operands = pCount + cCount
@@ -3325,7 +3343,7 @@ class TosaTestGen:
         )
 
 
-        self.ser.addOperator(op['op'], input_list, output_list)
+        self.ser.addOperator(op['op'], input_list, output_list, attr)
         return result_tens
 
     def build_slice(self, op, a, start, size, validator_fcns=None, error_name=None):
@@ -4564,7 +4582,7 @@ class TosaTestGen:
             # but create the table tensor in the build function, as it may be
             # a different type from the input
             "operands": (1, 0),
-            "build_fcn": (build_table, TosaTensorGen.tgBasic, None),
+            "build_fcn": (build_table, TosaTensorGen.tgBasic, TosaArgGen.agTable),
             "types": [DType.INT8, DType.INT16],
         },
         # Elementwise Unary operators
@@ -5376,10 +5394,10 @@ class OutputShaper:
         return ser.addOutput(output_shape, values_in.dtype)
 
     @staticmethod
-    def tableOp(ser, input, table_dtype):
+    def tableOp(ser, input):
         # Same shape as the input, but dtype dependent on table dtype
-        assert table_dtype == DType.INT16 or table_dtype == DType.INT8
-        output_dtype = DType.INT32 if table_dtype == DType.INT16 else DType.INT8
+        assert input.dtype == DType.INT16 or input.dtype == DType.INT8
+        output_dtype = DType.INT32 if input.dtype == DType.INT16 else DType.INT8
         return ser.addOutput(input.shape, output_dtype)
 
     @staticmethod