[ref model] Change RescaleOp attrs to inputs
This patch implements changes required for RescaleOp's
multiplier and shift changing from attributes to inputs
Signed-off-by: Tai Ly <tai.ly@arm.com>
Change-Id: I178919727e3220c749dad0ebce141e695868fee0
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 @@
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 @@
)
@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 @@
# 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 @@
"scale": scale32,
"double_round": double_round,
"per_channel": per_channel,
+ "multiplier": multiplier_arr,
+ "shift": shift_arr,
},
)
)