Update test builder internal interfaces

Move remaining ops from using testArgs to argsDict.
All tvg/build_fcn function interfaces updated.

Signed-off-by: Jeremy Johnson <jeremy.johnson@arm.com>
Change-Id: Ie886fd931bd74608bda621363100bf8bfd7385e6

1 files changed, 79 insertions, 38 deletions

diff --git a/verif/generator/tosa_arg_gen.py b/verif/generator/tosa_arg_gen.py
index 33e74b5..7ec0cfe 100644
--- a/verif/generator/tosa_arg_gen.py
+++ b/verif/generator/tosa_arg_gen.py
@@ -636,18 +636,6 @@ class TosaTensorValuesGen:
             self.tensorList = tensorList
             self.dataGenDict = dataGenDict
 
-    @staticmethod
-    def tvgDefault(testGen, op, dtypeList, shapeList, testArgs, error_name=None):
-        pCount, cCount = op["operands"]
-
-        tens = []
-        tens.extend(
-            testGen.buildPlaceholderTensors(shapeList[0:pCount], dtypeList[0:pCount])
-        )
-        tens.extend(testGen.buildConstTensors(shapeList[pCount:], dtypeList[pCount:]))
-
-        return tens
-
     # Default high value for random numbers
     TVG_FLOAT_HIGH_VALUE = {
         DType.FP32: (1 << 128) - (1 << (127 - 23)),
@@ -969,7 +957,7 @@ class TosaTensorValuesGen:
 
     @staticmethod
     def tvgCondIfWhileLoop(
-        testGen, op, dtypeList, shapeList, testArgs, error_name=None
+        testGen, opName, dtypeList, shapeList, argsDict, error_name=None
     ):
         if dtypeList[0] in (
             DType.INT32,
@@ -979,9 +967,10 @@ class TosaTensorValuesGen:
             # Limit input tensors with cond_if_binary or while_loop to stop
             # saturation of add/sub ops with int32 and keep all logical shift
             # values between 0 to 31 for int16 or int8
+            op = testGen.TOSA_OP_LIST[opName]
             pCount, cCount = op["operands"]
             pRemain = pCount
-            placeholders = []
+            tens_ser_list = []
             for idx, shape in enumerate(shapeList[:]):
                 if dtypeList[0] == DType.INT32:
                     arr = testGen.getRandTensor(shapeList[idx], DType.INT16)
@@ -990,32 +979,33 @@ class TosaTensorValuesGen:
                         testGen.rng.integers(low=0, high=32, size=shapeList[idx])
                     )
                 if pRemain > 0:
-                    placeholders.append(
+                    tens_ser_list.append(
                         testGen.ser.addPlaceholder(shape, dtypeList[idx], arr)
                     )
                     pRemain -= 1
                 else:
-                    placeholders.append(
+                    tens_ser_list.append(
                         testGen.ser.addConst(shape, dtypeList[idx], arr)
                     )
 
-            return placeholders
+            return TosaTensorValuesGen.TVGInfo(tens_ser_list, None)
         else:
-            return TosaTensorValuesGen.tvgDefault(
-                testGen, op, dtypeList, shapeList, testArgs, error_name
+            return TosaTensorValuesGen.tvgLazyGenDefault(
+                testGen, opName, dtypeList, shapeList, argsDict, error_name
             )
 
     @staticmethod
     def tvgArithmeticRightShift(
-        testGen, op, dtypeList, shapeList, testArgs, error_name=None
+        testGen, opName, dtypeList, shapeList, argsDict, error_name=None
     ):
+        op = testGen.TOSA_OP_LIST[opName]
         pCount, cCount = op["operands"]
         # Force value of operand[1] to be within [0, num_bits]
         assert (
             pCount == 2 and cCount == 0
         ), "Op.ArithmeticRightShift must have 2 placeholders, 0 consts"
 
-        placeholders = []
+        tens_ser_list = []
         for idx, shape in enumerate(shapeList[:]):
             if idx == 1:
                 if dtypeList[idx] == DType.INT8:
@@ -1030,23 +1020,23 @@ class TosaTensorValuesGen:
                     raise Exception("OpArithmeticRightShift: invalid input dtype")
             else:
                 arr = testGen.getRandTensor(shape, dtypeList[idx])
-            placeholders.append(testGen.ser.addPlaceholder(shape, dtypeList[idx], arr))
+            tens_ser_list.append(testGen.ser.addPlaceholder(shape, dtypeList[idx], arr))
 
-        return placeholders
+        return TosaTensorValuesGen.TVGInfo(tens_ser_list, None)
 
     @staticmethod
-    def tvgReshape(testGen, op, dtypeList, shapeList, argsDict, error_name=None):
+    def tvgReshape(testGen, opName, dtypeList, shapeList, argsDict, error_name=None):
         dtypeList[1] = DType.SHAPE
         shapeList[1] = [len(argsDict["new_shape"])]
         # Create a new list for the pre-generated data in argsDict["fixed_data"]
         argsDict["fixed_data"] = [None, argsDict["new_shape"]]
 
         return TosaTensorValuesGen.tvgLazyGenDefault(
-            testGen, op, dtypeList, shapeList, argsDict, error_name
+            testGen, opName, dtypeList, shapeList, argsDict, error_name
         )
 
     @staticmethod
-    def tvgPad(testGen, op, dtypeList, shapeList, argsDict, error_name=None):
+    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()
         dtypeList[1] = DType.SHAPE
@@ -1055,11 +1045,11 @@ class TosaTensorValuesGen:
         argsDict["fixed_data"] = [None, pad_values]
 
         return TosaTensorValuesGen.tvgLazyGenDefault(
-            testGen, op, dtypeList, shapeList, argsDict, error_name
+            testGen, opName, dtypeList, shapeList, argsDict, error_name
         )
 
     @staticmethod
-    def tvgSlice(testGen, op, dtypeList, shapeList, argsDict, error_name=None):
+    def tvgSlice(testGen, opName, dtypeList, shapeList, argsDict, error_name=None):
         dtypeList[1] = DType.SHAPE
         shapeList[1] = [len(argsDict["start"])]
         dtypeList[2] = DType.SHAPE
@@ -1068,17 +1058,17 @@ class TosaTensorValuesGen:
         argsDict["fixed_data"] = [None, argsDict["start"], argsDict["size"]]
 
         return TosaTensorValuesGen.tvgLazyGenDefault(
-            testGen, op, dtypeList, shapeList, argsDict, error_name
+            testGen, opName, dtypeList, shapeList, argsDict, error_name
         )
 
     @staticmethod
-    def tvgTile(testGen, op, dtypeList, shapeList, argsDict, error_name=None):
+    def tvgTile(testGen, opName, dtypeList, shapeList, argsDict, error_name=None):
         dtypeList[1] = DType.SHAPE
         shapeList[1] = [len(argsDict["multiples"])]
         argsDict["fixed_data"] = [None, argsDict["multiples"]]
 
         return TosaTensorValuesGen.tvgLazyGenDefault(
-            testGen, op, dtypeList, shapeList, argsDict, error_name
+            testGen, opName, dtypeList, shapeList, argsDict, error_name
         )
 
     @staticmethod
@@ -2776,10 +2766,23 @@ class TosaArgGen:
                                     int(double_round),
                                     int(per_channel),
                                 ),
-                                [outDtype, scale32, double_round, per_channel],
+                                {
+                                    "output_dtype": outDtype,
+                                    "scale": scale32,
+                                    "double_round": double_round,
+                                    "per_channel": per_channel,
+                                },
                             )
                         )
 
+        arg_list = TosaArgGen._add_data_generators(
+            testGen,
+            opName,
+            inDtype,
+            arg_list,
+            error_name,
+        )
+        # Return list of tuples: (arg_str, args_dict)
         return arg_list
 
     @staticmethod
@@ -2808,9 +2811,20 @@ class TosaArgGen:
     def agArithmeticRightShift(testGen, opName, shapeList, dtype, error_name=None):
         arg_list = []
 
-        arg_list.append(("roundTrue", [True]))
-        arg_list.append(("roundFalse", [False]))
+        for round in (True, False):
+            args_dict = {
+                "round": round,
+            }
+            arg_list.append((f"round{round}", args_dict))
 
+        arg_list = TosaArgGen._add_data_generators(
+            testGen,
+            opName,
+            dtype,
+            arg_list,
+            error_name,
+        )
+        # Return list of tuples: (arg_str, args_dict)
         return arg_list
 
     @staticmethod
@@ -3414,9 +3428,18 @@ class TosaArgGen:
         arg_list.append(
             (
                 "",
-                [table],
+                {"table": table},
             )
         )
+        # Now add data generator types
+        arg_list = TosaArgGen._add_data_generators(
+            testGen,
+            opName,
+            dtype,
+            arg_list,
+            error_name,
+        )
+        # Return list of tuples: (arg_str, args_dict)
         return arg_list
 
     def agCondIf(testGen, opName, shapeList, dtype, error_name=None):
@@ -3426,15 +3449,33 @@ class TosaArgGen:
         arg_list = []
 
         for c in [False, True]:
-            arg_list.append(("cond{}".format(int(c)), [c]))
+            arg_list.append(("cond{}".format(int(c)), {"condition": c}))
 
+        # Now add data generator types
+        arg_list = TosaArgGen._add_data_generators(
+            testGen,
+            opName,
+            dtype,
+            arg_list,
+            error_name,
+        )
+        # Return list of tuples: (arg_str, args_dict)
         return arg_list
 
     def agWhileLoop(testGen, opName, shapeList, dtype, error_name=None):
         # While loop: 0 iterations, 1, more than 1
         arg_list = []
 
-        for iter in [0, 1, 4]:
-            arg_list.append(("iter{}".format(iter), [iter]))
+        for iterations in [0, 1, 4]:
+            arg_list.append(("iter{}".format(iterations), {"iterations": iterations}))
 
+        # Now add data generator types
+        arg_list = TosaArgGen._add_data_generators(
+            testGen,
+            opName,
+            dtype,
+            arg_list,
+            error_name,
+        )
+        # Return list of tuples: (arg_str, args_dict)
         return arg_list