[ref model] Change RescaleOp attrs to inputs

This patch implements changes required for RescaleOp's
multiplier and shift changing from attributes to inputs

Signed-off-by: Tai Ly <tai.ly@arm.com>
Change-Id: I178919727e3220c749dad0ebce141e695868fee0

3 files changed, 82 insertions, 36 deletions

diff --git a/verif/generator/tosa_arg_gen.py b/verif/generator/tosa_arg_gen.py
index 592c491..cbfffae 100644
--- a/verif/generator/tosa_arg_gen.py
+++ b/verif/generator/tosa_arg_gen.py
@@ -744,6 +744,10 @@ class TosaTensorValuesGen:
                         arr = np.int64(argsDict["fixed_data"][idx])
                     elif dtype == DType.INT8:
                         arr = np.int8(argsDict["fixed_data"][idx])
+                    elif dtype == DType.INT16:
+                        arr = np.int16(argsDict["fixed_data"][idx])
+                    elif dtype == DType.INT32:
+                        arr = np.int32(argsDict["fixed_data"][idx])
                     else:
                         assert False, "Unsupported fixed_data type"
                 else:
@@ -1060,6 +1064,26 @@ class TosaTensorValuesGen:
         )
 
     @staticmethod
+    def tvgRescale(testGen, opName, dtypeList, shapeList, argsDict, error_name=None):
+        scale32 = argsDict["scale"]
+        multiplier_arr = argsDict["multiplier"]
+        shift_arr = argsDict["shift"]
+
+        if scale32:
+            dtypeList[1] = DType.INT32
+        else:
+            dtypeList[1] = DType.INT16
+        shapeList[1] = [len(multiplier_arr)]
+        dtypeList[2] = DType.INT8
+        shapeList[2] = [len(shift_arr)]
+        # Create a new list for the pre-generated data in argsDict["fixed_data"]
+        argsDict["fixed_data"] = [None, multiplier_arr, shift_arr]
+
+        return TosaTensorValuesGen.tvgLazyGenDefault(
+            testGen, opName, dtypeList, shapeList, argsDict, error_name
+        )
+
+    @staticmethod
     def tvgPad(testGen, opName, dtypeList, shapeList, argsDict, error_name=None):
         # argsDict["pad"] is 2D array, need to flatten it to get list of values
         pad_values = argsDict["pad"].flatten()
@@ -2842,6 +2866,43 @@ class TosaArgGen:
                             # Illegal condition.  ERROR_IF(!scale32 && double_round)
                             continue
 
+                        if per_channel:
+                            nc = shapeList[0][-1]
+                        else:
+                            nc = 1
+
+                        in_type_width = gtu.dtypeWidth(inDtype)
+                        out_type_width = gtu.dtypeWidth(outDtype)
+
+                        # Calculate scale based on:
+                        # scale = a *(2^output_width)/(2^input_width))
+
+                        a = np.float32(testGen.rng.random(size=[nc]))
+                        scale_arr = a * np.float32(
+                            (1 << out_type_width) / (1 << in_type_width)
+                        )
+
+                        if scale32:
+                            # Cap the scaling at 2^31 - 1 for scale32
+                            scale_arr = np.clip(
+                                scale_arr, 1.0 / (1 << 31), (1 << 31) - 1
+                            )
+                        else:
+                            # Cap the scaling at 2^15 - 1 for scale16
+                            scale_arr = np.clip(scale_arr, 1.0 / (1 << 31), 32767.0)
+
+                        # print('{} {} -> {}'.format(out_type_width, in_type_width, scale_arr))
+
+                        multiplier_arr = np.int32(np.zeros(shape=[nc]))
+                        shift_arr = np.int32(np.zeros(shape=[nc]))
+                        for i in range(nc):
+                            (
+                                multiplier_arr[i],
+                                shift_arr[i],
+                            ) = TosaQuantGen.computeMultiplierAndShift(
+                                scale_arr[i], scale32
+                            )
+
                         arg_list.append(
                             (
                                 "out{}_sc{}_dr{}_pc{}".format(
@@ -2855,6 +2916,8 @@ class TosaArgGen:
                                     "scale": scale32,
                                     "double_round": double_round,
                                     "per_channel": per_channel,
+                                    "multiplier": multiplier_arr,
+                                    "shift": shift_arr,
                                 },
                             )
                         )
diff --git a/verif/generator/tosa_test_gen.py b/verif/generator/tosa_test_gen.py
index 978e735..415858c 100644
--- a/verif/generator/tosa_test_gen.py
+++ b/verif/generator/tosa_test_gen.py
@@ -317,13 +317,6 @@ class TosaTestGen:
                     "Unknown dtype, cannot convert to string: {}".format(dtype)
                 )
 
-    def typeWidth(self, dtype):
-        """Get the datatype width for data types"""
-        if dtype in gtu.DTYPE_ATTRIBUTES:
-            return gtu.DTYPE_ATTRIBUTES[dtype]["width"]
-        else:
-            raise Exception(f"Unknown dtype, cannot determine width: {dtype}")
-
     def constrictBatchSize(self, shape):
         # Limit the batch size unless an explicit target shape set
         if self.args.max_batch_size and not self.args.target_shapes:
@@ -2130,12 +2123,15 @@ class TosaTestGen:
         error_name=None,
         qinfo=None,
     ):
-        assert len(inputs) == 1
+        assert len(inputs) == 3
         val = inputs[0]
+        multiplier_val = inputs[1]
+        shift_val = inputs[2]
         out_dtype = args_dict["output_dtype"]
         scale32 = args_dict["scale"]
         double_round = args_dict["double_round"]
         per_channel = args_dict["per_channel"]
+        shift_arr = args_dict["shift"]
 
         result_tensor = OutputShaper.typeConversionOp(
             self.ser, self.rng, val, out_dtype, error_name
@@ -2146,8 +2142,8 @@ class TosaTestGen:
         else:
             nc = 1
 
-        in_type_width = self.typeWidth(val.dtype)
-        out_type_width = self.typeWidth(out_dtype)
+        in_type_width = gtu.dtypeWidth(val.dtype)
+        out_type_width = gtu.dtypeWidth(out_dtype)
 
         input_unsigned = False
         output_unsigned = False
@@ -2198,31 +2194,10 @@ class TosaTestGen:
         else:
             output_zp = 0
 
-        # Calculate scale based on:
-        # scale = a *(2^output_width)/(2^input_width))
-
-        a = np.float32(self.rng.random(size=[nc]))
-        scale_arr = a * np.float32((1 << out_type_width) / (1 << in_type_width))
-
-        if scale32:
-            pass
-            # Cap the scaling at 2^31 - 1 for scale32
-            scale_arr = np.clip(scale_arr, 1.0 / (1 << 31), (1 << 31) - 1)
-        else:
-            # Cap the scaling at 2^15 - 1 for scale16
-            scale_arr = np.clip(scale_arr, 1.0 / (1 << 31), 32767.0)
-
-        # print('{} {} -> {}'.format(out_type_width, in_type_width, scale_arr))
-
-        multiplier_arr = np.int32(np.zeros(shape=[nc]))
-        shift_arr = np.int32(np.zeros(shape=[nc]))
         min_shift_value_arr = np.int64(np.zeros(shape=[nc]))
         max_shift_value_arr = np.int64(np.zeros(shape=[nc]))
 
         for i in range(nc):
-            multiplier_arr[i], shift_arr[i] = TosaQuantGen.computeMultiplierAndShift(
-                scale_arr[i], scale32
-            )
             min_shift_value_arr[i] = -1 << (shift_arr[i] - 1)
             max_shift_value_arr[i] = (1 << (shift_arr[i] - 1)) - 1
 
@@ -2256,7 +2231,7 @@ class TosaTestGen:
                 )
 
         # Invalidate Input/Output list for error if checks.
-        input_list = [val.name]
+        input_list = [val.name, multiplier_val.name, shift_val.name]
         output_list = [result_tensor.name]
         pCount, cCount = op["operands"]
         num_operands = pCount + cCount
@@ -2287,8 +2262,8 @@ class TosaTestGen:
         attr.RescaleAttribute(
             input_zp,
             output_zp,
-            multiplier_arr,
-            shift_arr,
+            [],
+            [],
             scale32,
             double_round,
             per_channel,
@@ -4809,11 +4784,11 @@ class TosaTestGen:
         },
         "rescale": {
             "op": Op.RESCALE,
-            "operands": (1, 0),
+            "operands": (3, 0),
             "build_fcn": (
                 build_rescale,
                 TosaTensorGen.tgBasic,
-                TosaTensorValuesGen.tvgLazyGenDefault,
+                TosaTensorValuesGen.tvgRescale,
                 TosaArgGen.agRescale,
             ),
             "types": [
diff --git a/verif/generator/tosa_utils.py b/verif/generator/tosa_utils.py
index 31a0ff0..384463f 100644
--- a/verif/generator/tosa_utils.py
+++ b/verif/generator/tosa_utils.py
@@ -55,6 +55,14 @@ class DataGenType(IntEnum):
     FIXED_DATA = 5
 
 
+def dtypeWidth(dtype):
+    """Get the datatype width for data types"""
+    if dtype in DTYPE_ATTRIBUTES:
+        return DTYPE_ATTRIBUTES[dtype]["width"]
+    else:
+        raise Exception(f"Unknown dtype, cannot determine width: {dtype}")
+
+
 def dtypeIsSupportedByCompliance(dtype):
     """Types supported by the new data generation and compliance flow."""
     if isinstance(dtype, list) or isinstance(dtype, tuple):