Correct types of int16 and int8 input tensors for tests
Fixes input tensors for int16 and int8 tests for MUL,
LOGICAL_LEFT/RIGHT_SHIFT, COND_IF, WHILE_LOOP
Change tensor generator functions to use type aware
randTensor() function.
Signed-off-by: Jeremy Johnson <jeremy.johnson@arm.com>
Change-Id: If81a783929c29e1fd5e91477dbcdaf79c738f6b0
diff --git a/verif/generator/tosa_arg_gen.py b/verif/generator/tosa_arg_gen.py
index 26dd6f9..22b23fe 100644
--- a/verif/generator/tosa_arg_gen.py
+++ b/verif/generator/tosa_arg_gen.py
@@ -925,8 +925,8 @@
# range
max_val = (1 << 31) - 1
min_val = -max_val
- arr = np.int32(
- rng.integers(low=min_val, high=(max_val + 1), size=shapeList[0])
+ arr = rng.randTensor(
+ shapeList[0], dtypeList[0], data_range=(min_val, (max_val + 1))
)
tens_ser_list = []
tens_ser_list.append(
@@ -1046,9 +1046,13 @@
tens_ser_list = []
for idx, shape in enumerate(shapeList[:]):
if dtypeList[0] == DType.INT32:
- arr = rng.randTensor(shapeList[idx], DType.INT16)
+ # Limit data range to avoid saturation
+ arr = np.int32(rng.randTensor(shapeList[idx], DType.INT16))
else:
- arr = np.int32(rng.integers(low=0, high=32, size=shapeList[idx]))
+ arr = rng.randTensor(
+ shapeList[idx], dtypeList[0], data_range=(0, 32)
+ )
+
if pRemain > 0:
tens_ser_list.append(
testGen.ser.addPlaceholder(shape, dtypeList[idx], arr)
@@ -1080,13 +1084,13 @@
for idx, shape in enumerate(shapeList[:]):
if idx == 1:
if dtypeList[idx] == DType.INT8:
- arr = np.int8(rng.integers(low=0, high=8, size=shape))
+ arr = rng.randTensor(shape, dtypeList[idx], data_range=(0, 8))
elif dtypeList[idx] == DType.INT16:
- arr = np.int16(rng.integers(low=0, high=16, size=shape))
+ arr = rng.randTensor(shape, dtypeList[idx], data_range=(0, 16))
elif dtypeList[idx] == DType.INT32:
- arr = np.int32(rng.integers(low=0, high=32, size=shape))
+ arr = rng.randTensor(shape, dtypeList[idx], data_range=(0, 32))
elif error_name == ErrorIf.WrongInputType:
- arr = np.int32(rng.integers(low=0, high=8, size=shape))
+ arr = rng.randTensor(shape, DType.INT32, data_range=(0, 8))
else:
raise Exception("OpArithmeticRightShift: invalid input dtype")
else:
@@ -1261,12 +1265,17 @@
shift = 0
else:
shift = argsDict["shift"]
+
+ np_type = np.int32
if dtypeList[0] == DType.INT8:
num_bits = 8
+ np_type = np.int8
elif dtypeList[0] == DType.INT16:
num_bits = 16
+ np_type = np.int16
elif dtypeList[0] in (DType.INT32, DType.SHAPE):
num_bits = 32
+ # np_type is not used for DType.SHAPE so leave as np.int32
elif error_name == ErrorIf.WrongInputType:
num_bits = 8
else:
@@ -1282,8 +1291,12 @@
low = -(2 ** (num_bits - 1))
high = (2 ** (num_bits - 1)) - 1
- a_arr = np.int32(rng.integers(low=low, high=high, size=shapeList[0]))
- b_arr = np.int32(rng.integers(low=low, high=high, size=shapeList[1]))
+ a_arr = rng.randTensor(
+ shapeList[0], DType.INT32, data_range=(low, high)
+ )
+ b_arr = rng.randTensor(
+ shapeList[1], DType.INT32, data_range=(low, high)
+ )
i = 0
while True:
@@ -1318,12 +1331,12 @@
# MUL with 3 inputs (3rd is shift)
tens_ser_list.append(
testGen.ser.addPlaceholder(
- shapeList[0], dtypeList[0], a_arr.astype(np.int32)
+ shapeList[0], dtypeList[0], a_arr.astype(np_type)
)
)
tens_ser_list.append(
testGen.ser.addPlaceholder(
- shapeList[1], dtypeList[1], b_arr.astype(np.int32)
+ shapeList[1], dtypeList[1], b_arr.astype(np_type)
)
)
tens_ser_list.append(
@@ -1369,7 +1382,8 @@
pCount == 2 and cCount == 0
), "Op.LOGICAL_LEFT_SHIFT or Op.LOGICAL_RIGHT_SHIFT must have 2 placeholders, 0 consts"
values_arr = rng.randTensor(shapeList[0], dtypeList[0])
- shift_arr = np.int32(rng.integers(low=0, high=32, size=shapeList[1]))
+ shift_arr = rng.randTensor(shapeList[1], dtypeList[0], data_range=(0, 32))
+
tens_ser_list = []
tens_ser_list.append(
testGen.ser.addPlaceholder(shapeList[0], dtypeList[0], values_arr)
@@ -1439,8 +1453,8 @@
# Limit values so that the sum cannot exceed the range of an int32 during
# summation of any axis
range_val = int((1 << 31) / max(shapeList[0]))
- values_arr = np.int32(
- rng.integers(low=-range_val, high=range_val, size=shapeList[0])
+ values_arr = rng.randTensor(
+ shapeList[0], dtype, data_range=(-range_val, range_val)
)
tens_ser_list = []
tens_ser_list.append(