diff options
Diffstat (limited to 'verif/generator/tosa_arg_gen.py')
| -rw-r--r-- | verif/generator/tosa_arg_gen.py | 63 |
1 files changed, 63 insertions, 0 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, }, ) ) |