diff options
Diffstat (limited to 'verif/generator/tosa_arg_gen.py')
| -rw-r--r-- | verif/generator/tosa_arg_gen.py | 47 |
1 files changed, 37 insertions, 10 deletions
diff --git a/verif/generator/tosa_arg_gen.py b/verif/generator/tosa_arg_gen.py index 0851aca..592c491 100644 --- a/verif/generator/tosa_arg_gen.py +++ b/verif/generator/tosa_arg_gen.py @@ -254,19 +254,16 @@ class TosaTensorGen: return shape_list @staticmethod - def tgBroadcastFuzz(testGen, op, rank, error_name=None): + def _get_broadcast_shapes(testGen, num_shapes, rank, error_name=None): shape = testGen.makeShape(rank) - - pl, const = op["operands"] - shape_list = [] # Choose one of the inputs to broadcast # Note: Simplifies OutputShaper code if we don't change first shape for errors - bcast_idx = testGen.randInt(0 if error_name is None else 1, pl + const) + bcast_idx = testGen.randInt(0 if error_name is None else 1, num_shapes) fuzz_idx = testGen.randInt(0, rank) - for i in range(pl + const): + for i in range(num_shapes): shape_bcast = shape.copy() # To test broadcasting, the chosen fuzz index dimension should not be 1 @@ -295,6 +292,22 @@ class TosaTensorGen: return shape_list @staticmethod + def tgBroadcastFuzz(testGen, op, rank, error_name=None): + pl, const = op["operands"] + num_shapes = pl + const + return TosaTensorGen._get_broadcast_shapes( + testGen, num_shapes, rank, error_name + ) + + @staticmethod + def tgMul(testGen, op, rank, error_name=None): + # Get broadcast shapes for the first 2 inputs as the 3rd is shift + shape_list = TosaTensorGen._get_broadcast_shapes(testGen, 2, rank, error_name) + # Add a single dimension tensor for shift + shape_list.append([1]) + return shape_list + + @staticmethod def tgConv2D(testGen, op, rank, error_name=None): pl, const = op["operands"] @@ -727,7 +740,12 @@ class TosaTensorValuesGen: # Ignore lazy data gen option and create data array using any range limits if "fixed_data" in argsDict and argsDict["fixed_data"][idx] is not None: - arr = np.int64(argsDict["fixed_data"][idx]) + if dtype == DType.SHAPE: + arr = np.int64(argsDict["fixed_data"][idx]) + elif dtype == DType.INT8: + arr = np.int8(argsDict["fixed_data"][idx]) + else: + assert False, "Unsupported fixed_data type" else: arr = testGen.getRandTensor(shape, dtype, data_range) if roundMode: @@ -1147,6 +1165,13 @@ class TosaTensorValuesGen: if data_range: argsDict["data_range"] = data_range + if dtypeList[0] != DType.SHAPE: + # Need to supply shift tensor for MUL (not needed for MUL_SHAPE) + dtypeList[2] = DType.INT8 + shapeList[2] = [1] + # Create a new list for the pre-generated data in argsDict["fixed_data"] + argsDict["fixed_data"] = [None, None, [argsDict["shift"]]] + return TosaTensorValuesGen.tvgLazyGenDefault( testGen, opName, dtypeList, shapeList, argsDict, error_name ) @@ -1154,9 +1179,6 @@ class TosaTensorValuesGen: # Integer test op = testGen.TOSA_OP_LIST[opName] pCount, cCount = op["operands"] - assert ( - pCount == 2 and cCount == 0 - ), "Op.MUL must have 2 placeholders, 0 consts" tens_ser_list = [] @@ -1213,6 +1235,7 @@ class TosaTensorValuesGen: b_arr = b_arr // 2 if dtypeList[0] == DType.SHAPE: + # MUL_SHAPE with 2 inputs tens_ser_list.append( testGen.ser.addPlaceholder(shapeList[0], dtypeList[0], a_arr_64) ) @@ -1220,12 +1243,16 @@ class TosaTensorValuesGen: testGen.ser.addPlaceholder(shapeList[1], dtypeList[1], b_arr_64) ) else: + # MUL with 3 inputs (3rd is shift) tens_ser_list.append( testGen.ser.addPlaceholder(shapeList[0], dtypeList[0], a_arr) ) tens_ser_list.append( testGen.ser.addPlaceholder(shapeList[1], dtypeList[1], b_arr) ) + tens_ser_list.append( + testGen.ser.addPlaceholder([1], DType.INT8, np.int8([shift])) + ) return TosaTensorValuesGen.TVGInfo(tens_ser_list, None) |