diff options
Diffstat (limited to 'verif/generator/tosa_test_gen.py')
| -rw-r--r-- | verif/generator/tosa_test_gen.py | 47 |
1 files changed, 11 insertions, 36 deletions
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": [ |