diff options
Diffstat (limited to 'verif/generator/tosa_arg_gen.py')
| -rw-r--r-- | verif/generator/tosa_arg_gen.py | 21 |
1 files changed, 13 insertions, 8 deletions
diff --git a/verif/generator/tosa_arg_gen.py b/verif/generator/tosa_arg_gen.py index 6675025..9147605 100644 --- a/verif/generator/tosa_arg_gen.py +++ b/verif/generator/tosa_arg_gen.py @@ -1119,14 +1119,18 @@ class TosaTensorValuesGen: return TosaTensorValuesGen.TVGInfo(tens_ser_list, None) @staticmethod - def tvgEqual(testGen, op, dtypeList, shapeList, testArgs, error_name=None): - if error_name is None: + def tvgEqual(testGen, opName, dtypeList, shapeList, argsDict, error_name=None): + if error_name is None and not gtu.dtypeIsSupportedByCompliance(dtypeList[0]): + # Integer + op = testGen.TOSA_OP_LIST[opName] pCount, cCount = op["operands"] assert ( pCount == 2 and cCount == 0 ), "Op.EQUAL must have 2 placeholders, 0 consts" + a_arr = testGen.getRandTensor(shapeList[0], dtypeList[0]) b_arr = testGen.getRandTensor(shapeList[1], dtypeList[1]) + # Using random numbers means that it will be very unlikely that # there are any matching (equal) values, therefore force that # there are twice the number of matching values as the tensor rank @@ -1147,17 +1151,18 @@ class TosaTensorValuesGen: a_arr[tuple(a_index)] = b_arr[tuple(b_index)] - placeholders = [] - placeholders.append( + tens_ser_list = [] + tens_ser_list.append( testGen.ser.addPlaceholder(shapeList[0], dtypeList[0], a_arr) ) - placeholders.append( + tens_ser_list.append( testGen.ser.addPlaceholder(shapeList[1], dtypeList[1], b_arr) ) - return placeholders + return TosaTensorValuesGen.TVGInfo(tens_ser_list, None) else: - return TosaTensorValuesGen.tvgDefault( - testGen, op, dtypeList, shapeList, testArgs, error_name + # ERROR_IF or floating point test + return TosaTensorValuesGen.tvgLazyGenDefault( + testGen, opName, dtypeList, shapeList, argsDict, error_name ) @staticmethod |