Add shared ERROR_IF statements
* Added WrongInputType and WrongOutputType checks
* Delete and Add extra inputs/outputs when doing op graph checks
* Add incorrect rank checks
* Entire op dictionary is now passed to build functions
* Error if validation now done in separate function
* Duplicate tests are ignored
Change-Id: Ie1117bdbc9bdeb42059123792ce1df4cc56db54e
Signed-off-by: Matthew Haddon <matthew.haddon@arm.com>
diff --git a/verif/tosa_error_if.py b/verif/tosa_error_if.py
index e310804..58595d3 100644
--- a/verif/tosa_error_if.py
+++ b/verif/tosa_error_if.py
@@ -22,4 +22,9 @@
ShiftNotZero = "ShiftNotZero"
ShiftSmallerOne = "ShiftSmallerOne"
ShiftLargerEleven = "ShiftLargerEleven"
+ WrongInputType = "WrongInputType"
+ WrongOutputType = "WrongOutputType"
+ WrongInputList = "WrongInputList"
+ WrongOutputList = "WrongOutputList"
+ WrongRank = "WrongRank"
diff --git a/verif/tosa_test_gen.py b/verif/tosa_test_gen.py
index 2384e03..07dc7e5 100644
--- a/verif/tosa_test_gen.py
+++ b/verif/tosa_test_gen.py
@@ -49,7 +49,6 @@
Op = tosa.Op.Op()
ResizeMode = tosa.ResizeMode.ResizeMode()
-
class TosaQuantGen:
"""QuantizedInfo random generator helper functions. Specify with 'qgen': in the operator defintion"""
@@ -165,7 +164,8 @@
def tgNHWC(testGen, opName, rank, error_name=None):
pl, const = opName["operands"]
- assert rank == 4
+ if error_name != ErrorIf.WrongRank:
+ assert rank == 4
shape = testGen.makeShape(rank)
@@ -897,26 +897,28 @@
arg_list = []
ifm_shape = shapeList[0]
-
- for m in [ResizeMode.NEAREST, ResizeMode.BILINEAR]:
+ for mode in [ResizeMode.NEAREST, ResizeMode.BILINEAR]:
# Exclude illegal {mode, type} configurations. Pick legal output types
- if m == ResizeMode.NEAREST and dtype == DType.INT8:
+ if mode == ResizeMode.NEAREST and dtype == DType.INT8:
outputDTypeList = [DType.INT8]
- elif m == ResizeMode.NEAREST and dtype == DType.INT16:
+ elif mode == ResizeMode.NEAREST and dtype == DType.INT16:
outputDTypeList = [DType.INT16]
- elif m == ResizeMode.BILINEAR and dtype == DType.INT8:
+ elif mode == ResizeMode.BILINEAR and dtype == DType.INT8:
outputDTypeList = [DType.INT32]
- elif m == ResizeMode.BILINEAR and dtype == DType.INT16:
+ elif mode == ResizeMode.BILINEAR and dtype == DType.INT16:
outputDTypeList = [DType.INT48]
elif dtype == DType.FLOAT:
outputDTypeList = [DType.FLOAT]
+ elif error_name == ErrorIf.WrongInputType:
+ # If an incorrect input type is used then we set a 'correct'
+ # output type to avoid other errors
+ outputDTypeList = [DType.INT8, DType.INT16, DType.INT32]
else:
continue
for outputDType in outputDTypeList:
for perm in range(testGen.args.num_rand_permutations):
-
# Randomly generate legal output dimensions and shift
# and then compute the stride and offset based on them
# A output_dim of 1 will cause offset to exceed allowed range
@@ -945,9 +947,11 @@
offset_fp = [fp_offset_y, fp_offset_x]
if error_name is not None:
- shift, stride, stride_fp, offset, offset_fp = TosaErrorIfArgGen.eiResizeErrorIf(
+ shift, stride, stride_fp, offset, offset_fp, outputDTypeNew = TosaErrorIfArgGen.eiResizeErrorIf(
testGen,
error_name,
+ mode,
+ dtype,
shapeList,
outputDType,
shift,
@@ -956,21 +960,23 @@
offset,
offset_fp
)
+ else:
+ outputDTypeNew = outputDType
arg_list.append(
(
"mode{}_odim{}x{}_out{}_st{:.2f}x{:.2f}_off{:.2f}x{:.2f}".format(
- "N" if m == ResizeMode.NEAREST else "B",
+ "N" if mode == ResizeMode.NEAREST else "B",
output_dims[0],
output_dims[1],
- testGen.typeStr(outputDType),
+ testGen.typeStr(outputDTypeNew),
stride_fp[0],
stride_fp[1],
offset_fp[0],
offset_fp[1],
),
[
- m,
+ mode,
stride,
offset,
shift,
@@ -978,7 +984,7 @@
offset_fp,
output_dims,
dtype,
- outputDType,
+ outputDTypeNew,
],
)
)
@@ -1012,9 +1018,11 @@
offset_fp = [0.0, 0.0]
if error_name is not None:
- shift, stride, stride_fp, offset, offset_fp = TosaErrorIfArgGen.eiResizeErrorIf(
+ shift, stride, stride_fp, offset, offset_fp, outputDTypeNew = TosaErrorIfArgGen.eiResizeErrorIf(
testGen,
error_name,
+ mode,
+ dtype,
shapeList,
outputDType,
shift,
@@ -1023,22 +1031,24 @@
offset,
offset_fp
)
+ else:
+ outputDTypeNew = outputDType
arg_list.append(
(
"mode{}_shift{}_odim{}x{}_out{}_st{}x{}_off{}x{}".format(
- "N" if m == ResizeMode.NEAREST else "B",
+ "N" if mode == ResizeMode.NEAREST else "B",
shift,
output_dims[0],
output_dims[1],
- testGen.typeStr(outputDType),
+ testGen.typeStr(outputDTypeNew),
stride[0],
stride[1],
offset[0],
offset[1],
),
[
- m,
+ mode,
stride,
offset,
shift,
@@ -1046,7 +1056,7 @@
offset_fp,
output_dims,
dtype,
- outputDType,
+ outputDTypeNew,
],
)
)
@@ -1076,7 +1086,7 @@
class TosaErrorIfArgGen:
@staticmethod
- def eiResizeErrorIf(testGen, error_name, shapeList, outputDType, shift, stride, stride_fp, offset, offset_fp):
+ def eiResizeErrorIf(testGen, error_name, mode, dtype, shapeList, outputDType, shift, stride, stride_fp, offset, offset_fp):
if outputDType == DType.FLOAT:
if error_name == ErrorIf.StrideSmallerEqualZero:
@@ -1117,16 +1127,202 @@
elif error_name == ErrorIf.OffsetSmallerEqualMin:
offset = [(-16 << shift) - 1, (-16 << shift) - 1]
- return shift, stride, stride_fp, offset, offset_fp
+ if error_name == ErrorIf.WrongOutputType:
+ if mode == ResizeMode.NEAREST and dtype == DType.INT8:
+ incorrect_types = (DType.INT4, DType.INT16, DType.INT32, DType.INT48, DType.FLOAT)
+ elif mode == ResizeMode.NEAREST and dtype == DType.INT16:
+ incorrect_types = (DType.INT4, DType.INT8, DType.INT32, DType.INT48, DType.FLOAT)
+ elif mode == ResizeMode.BILINEAR and dtype == DType.INT8:
+ incorrect_types = (DType.INT4, DType.INT8, DType.INT16, DType.INT48, DType.FLOAT)
+ elif mode == ResizeMode.BILINEAR and dtype == DType.INT16:
+ incorrect_types = (DType.INT4, DType.INT8, DType.INT16, DType.INT32, DType.FLOAT)
+ elif dtype == DType.FLOAT:
+ incorrect_types = (DType.INT4, DType.INT8, DType.INT16, DType.INT32, DType.INT48)
+ outputDType = testGen.rng.choice(a=incorrect_types)
+ return shift, stride, stride_fp, offset, offset_fp, outputDType
+
+ @staticmethod
+ def eiInvalidateInputOutputList(testGen, error_name, input_list, output_list):
+ # Mess up input/output tensors for ERROR_IF checks
+ if error_name == "WrongInputList":
+ add_input = testGen.rng.choice([True, False])
+ if add_input:
+ input_list.append('eiDummyInput')
+ else:
+ input_list = input_list[:-1]
+ if error_name == "WrongOutputList":
+ add_output = testGen.rng.choice([True, False])
+ if add_output:
+ output_list.append('eiDummyOutput')
+ else:
+ output_list = []
+ return input_list, output_list
class TosaErrorValidator:
+ @staticmethod
+ def evValidateErrorIfs(serializer, validator_fcns, error_name, **kwargs):
+ # Check ERROR_IF statements
+
+ for val_fcn in validator_fcns:
+ val_result = val_fcn(True, **kwargs)
+
+ validator_name = val_result['error_name']
+ error_result = val_result['error_result']
+ error_reason = val_result['error_reason']
+
+ if error_result:
+ if error_name == validator_name:
+ serializer.setExpectedReturnCode(2, error_reason)
+ else:
+ print(f"Multiple ERROR_IF checks hit \nError required: {error_name}, Error_produced: {validator_name}")
+ return None # Return None to delete test if wrong ERROR_IF is hit
+ else:
+ if error_name == validator_name:
+ print(f"No ERROR_IF hit for {error_name}")
+ return None
+
+ @staticmethod
+ def evWrongInputType(check=False, **kwargs):
+ all_dtypes = (DType.BOOL, DType.INT4, DType.INT8, DType.INT16, DType.INT32, DType.INT48, DType.FLOAT)
+
+ # Find the unsupported input data types
+ assert 'op' in kwargs
+ op = kwargs['op']
+ input_dtypes = op['types']
+ wrong_input_dtypes = list(set(all_dtypes) - set(input_dtypes))
+
+ error_name = ErrorIf.WrongInputType
+ param_reqs = {"rank": None, "dtype": wrong_input_dtypes, "shape": None}
+ error_result = False
+ error_reason = "Input data type not supported for this operator"
+
+ if check:
+ input_dtype = kwargs['input_dtype']
+ if input_dtype not in input_dtypes:
+ error_result = True
+
+ info_dict = {
+ "error_name": error_name,
+ "error_result": error_result,
+ "error_reason": error_reason,
+ "param_reqs": param_reqs
+ }
+ return info_dict
+
+ @staticmethod
+ def evWrongOutputType(check=False, **kwargs):
+ error_name = ErrorIf.WrongOutputType
+ param_reqs = {"rank": None, "dtype": None, "shape": None}
+ error_result = False
+ error_reason = "Output data type not supported for this configuration of operator"
+
+ if check:
+ input_dtype = kwargs['input_dtype']
+ output_dtype = kwargs['output_dtype']
+ mode = kwargs['mode']
+
+ if (
+ (mode == ResizeMode.NEAREST and input_dtype == DType.INT8 and output_dtype != DType.INT8) or
+ (mode == ResizeMode.NEAREST and input_dtype == DType.INT16 and output_dtype != DType.INT16) or
+ (mode == ResizeMode.BILINEAR and input_dtype == DType.INT8 and output_dtype != DType.INT32) or
+ (mode == ResizeMode.BILINEAR and input_dtype == DType.INT16 and output_dtype != DType.INT48) or
+ (input_dtype == DType.FLOAT and output_dtype != DType.FLOAT)
+ ):
+ error_result = True
+
+ info_dict = {
+ "error_name": error_name,
+ "error_result": error_result,
+ "error_reason": error_reason,
+ "param_reqs": param_reqs
+ }
+ return info_dict
+
+ @staticmethod
+ def evWrongRank(check=False, **kwargs):
+ all_ranks = (1, 2, 3, 4, 5)
+
+ # Make a list of incorrect ranks
+ assert 'op' in kwargs
+ op = kwargs['op']
+ rmin, rmax = op['rank']
+ rank_range = range(rmin, rmax + 1)
+ incorrect_ranks = list(set(all_ranks) - set(rank_range))
+ # Set minimum incorrect rank to 3 to avoid index error
+ if op['op'] == Op.RESIZE:
+ incorrect_ranks = [3, 5]
+
+ error_name = ErrorIf.WrongRank
+ param_reqs = {"rank": incorrect_ranks, "dtype": None, "shape": None}
+ error_result = False
+ error_reason = "Rank not supported for this operator"
+
+ if check:
+ input_shape = kwargs['input_shape']
+ if op['op'] == Op.RESIZE and len(input_shape.shape) != 4:
+ error_result = True
+
+ info_dict = {
+ "error_name": error_name,
+ "error_result": error_result,
+ "error_reason": error_reason,
+ "param_reqs": param_reqs
+ }
+ return info_dict
+
+ @staticmethod
+ def evWrongInputList(check=False, **kwargs):
+ error_name = ErrorIf.WrongInputList
+ param_reqs = {"rank": None, "dtype": None, "shape": None}
+ error_result = False
+ error_reason = "Op input list does not match expected input"
+
+ if check:
+ op = kwargs['op']
+ input_list = kwargs['input_list']
+ num_operands = kwargs['num_operands']
+ if len(input_list) != num_operands:
+ error_result = True
+
+ info_dict = {
+ "error_name": error_name,
+ "error_result": error_result,
+ "error_reason": error_reason,
+ "param_reqs": param_reqs
+ }
+ return info_dict
+
+ @staticmethod
+ def evWrongOutputList(check=False, **kwargs):
+ error_name = ErrorIf.WrongOutputList
+ param_reqs = {"rank": None, "dtype": None, "shape": None}
+ error_result = False
+ error_reason = "Op output list does not match expected output"
+
+ if check:
+ output_list = kwargs['output_list']
+ # Note this will be incorrect if an operator returns more than one output
+ if len(output_list) != 1:
+ error_result = True
+
+ info_dict = {
+ "error_name": error_name,
+ "error_result": error_result,
+ "error_reason": error_reason,
+ "param_reqs": param_reqs
+ }
+ return info_dict
@staticmethod
def evMaxDimExceeded(check=False, **kwargs):
error_name = ErrorIf.MaxDimExceeded
- param_reqs = {"rank": [4,4], "dtype": [DType.INT8], "shape": [[1, 16584, 5, 1]]}
+ param_reqs = {
+ "rank": [4,4],
+ "dtype": [DType.INT8],
+ "shape": [[1, 16584, 5, 1], [1, 2, 16499, 4]]
+ }
error_result = False
error_reason = "At least one maximum dimension is larger than 16384"
@@ -1156,8 +1352,13 @@
if check:
input_dtype = kwargs['input_dtype']
- if input_dtype == DType.FLOAT:
+ output_dtype = kwargs['output_dtype']
+ if input_dtype != DType.FLOAT and output_dtype == DType.FLOAT:
+ stride = kwargs['stride'] # Work around wrong input/output type tests
+ elif output_dtype == DType.FLOAT:
stride = kwargs['stride_fp']
+ elif input_dtype == DType.FLOAT and output_dtype != DType.FLOAT:
+ stride = kwargs['stride_fp'] # Work around wrong input/output type tests
else:
stride = kwargs['stride']
@@ -1231,8 +1432,8 @@
if check:
shift = kwargs['shift']
- input_dtype = kwargs['input_dtype']
- if input_dtype == DType.FLOAT:
+ output_dtype = kwargs['output_dtype']
+ if output_dtype == DType.FLOAT:
offset = kwargs['offset_fp']
else:
offset = kwargs['offset']
@@ -1259,8 +1460,8 @@
if check:
shift = kwargs['shift']
- input_dtype = kwargs['input_dtype']
- if input_dtype == DType.FLOAT:
+ output_dtype = kwargs['output_dtype']
+ if output_dtype == DType.FLOAT:
offset = kwargs['offset_fp']
else:
offset = kwargs['offset']
@@ -1292,7 +1493,8 @@
if check:
shift = kwargs['shift']
input_dtype = kwargs['input_dtype']
- if input_dtype == DType.FLOAT and shift != 0:
+ output_dtype = kwargs['output_dtype']
+ if input_dtype == DType.FLOAT and output_dtype == DType.FLOAT and shift != 0:
error_result = True
info_dict = {
@@ -1314,7 +1516,8 @@
if check:
shift = kwargs['shift']
input_dtype = kwargs['input_dtype']
- if shift < 1 and input_dtype != DType.FLOAT:
+ output_dtype = kwargs['output_dtype']
+ if shift < 1 and input_dtype != DType.FLOAT and output_dtype != DType.FLOAT:
error_result = True
info_dict = {
@@ -1395,8 +1598,6 @@
return False
-
-
@staticmethod
def ivHeightWidthSmallerZero(**kwargs):
opName = kwargs['opName']
@@ -1653,17 +1854,21 @@
def build_unary(self, op, a, qinfo=None):
result_tens = OutputShaper.unaryOp(self.ser, a)
- self.ser.addOperator(op, [a.name], [result_tens.name], None, qinfo)
+ # build_placeholder returns an int, ABS/other ops does not
+ if isinstance(op, int):
+ self.ser.addOperator(op, [a.name], [result_tens.name], None, qinfo)
+ else:
+ self.ser.addOperator(op['op'], [a.name], [result_tens.name], None, qinfo)
return result_tens
def build_binary_broadcast(self, op, a, b):
result_tens = OutputShaper.binaryBroadcastOp(self.ser, a, b)
- self.ser.addOperator(op, [a.name, b.name], [result_tens.name])
+ self.ser.addOperator(op['op'], [a.name, b.name], [result_tens.name])
return result_tens
def build_binary_nonbroadcast(self, op, a, b):
result_tens = OutputShaper.binaryNonBroadcastOp(self.ser, a, b)
- self.ser.addOperator(op, [a.name, b.name], [result_tens.name])
+ self.ser.addOperator(op['op'], [a.name, b.name], [result_tens.name])
return result_tens
def build_arithmetic_right_shift(self, op, a, b, round):
@@ -1672,7 +1877,7 @@
attr = ts.TosaSerializerAttribute()
attr.ArithmeticRightShiftAttribute(round)
- self.ser.addOperator(op, [a.name, b.name], [result_tens.name], attr)
+ self.ser.addOperator(op['op'], [a.name, b.name], [result_tens.name], attr)
return result_tens
def build_mul(self, op, a, b, shift):
@@ -1686,7 +1891,7 @@
attr = ts.TosaSerializerAttribute()
attr.MulAttribute(shift)
- self.ser.addOperator(op, [a.name, b.name], [result_tens.name], attr)
+ self.ser.addOperator(op['op'], [a.name, b.name], [result_tens.name], attr)
return result_tens
def build_table(self, op, a):
@@ -1701,18 +1906,18 @@
table_tens = self.ser.addConst(table_arr.shape, table_dtype, table_arr)
result_tens = OutputShaper.tableOp(self.ser, a, table_dtype)
- self.ser.addOperator(op, [a.name, table_tens.name], [result_tens.name], None)
+ self.ser.addOperator(op['op'], [a.name, table_tens.name], [result_tens.name], None)
return result_tens
def build_select(self, op, cond, a, b):
result_tens = OutputShaper.selectOp(self.ser, cond, a, b)
- self.ser.addOperator(op, [cond.name, a.name, b.name], [result_tens.name])
+ self.ser.addOperator(op['op'], [cond.name, a.name, b.name], [result_tens.name])
return result_tens
def build_comparison(self, op, a, b):
result_tens = OutputShaper.binaryComparisonOp(self.ser, a, b)
- self.ser.addOperator(op, [a.name, b.name], [result_tens.name])
+ self.ser.addOperator(op['op'], [a.name, b.name], [result_tens.name])
return result_tens
def build_argmax(self, op, a, axis):
@@ -1721,7 +1926,7 @@
attr = ts.TosaSerializerAttribute()
attr.AxisAttribute(axis)
- self.ser.addOperator(op, [a.name], [result_tens.name], attr)
+ self.ser.addOperator(op['op'], [a.name], [result_tens.name], attr)
return result_tens
def build_pool2d(self, op, input, stride, pad, kernel, qinfo=None):
@@ -1730,7 +1935,7 @@
attr = ts.TosaSerializerAttribute()
attr.PoolAttribute(kernel, stride, pad)
- self.ser.addOperator(op, [input.name], [result_tens.name], attr, qinfo)
+ self.ser.addOperator(op['op'], [input.name], [result_tens.name], attr, qinfo)
return result_tens
def build_conv2d(self, op, ifm, filter, bias, strides, padding, dilations, qinfo):
@@ -1743,7 +1948,7 @@
attr.ConvAttribute(padding, strides, dilations)
self.ser.addOperator(
- op, [ifm.name, filter.name, bias.name], [result_tens.name], attr, qinfo
+ op['op'], [ifm.name, filter.name, bias.name], [result_tens.name], attr, qinfo
)
return result_tens
@@ -1757,7 +1962,7 @@
attr.ConvAttribute(padding, strides, dilations)
self.ser.addOperator(
- op, [ifm.name, filter.name, bias.name], [result_tens.name], attr, qinfo
+ op['op'], [ifm.name, filter.name, bias.name], [result_tens.name], attr, qinfo
)
return result_tens
@@ -1771,7 +1976,7 @@
attr.TransposeConvAttribute(outpad, stride, dilation, output_shape)
self.ser.addOperator(
- op, [ifm.name, filter.name, bias.name], [result_tens.name], attr, qinfo
+ op['op'], [ifm.name, filter.name, bias.name], [result_tens.name], attr, qinfo
)
return result_tens
@@ -1786,7 +1991,7 @@
attr.ConvAttribute(padding, strides, dilations)
self.ser.addOperator(
- op, [ifm.name, filter.name, bias.name], [result_tens.name], attr, qinfo
+ op['op'], [ifm.name, filter.name, bias.name], [result_tens.name], attr, qinfo
)
return result_tens
@@ -1794,13 +1999,13 @@
result_tens = OutputShaper.fullyConnectedOp(self.ser, ifm, filter)
self.ser.addOperator(
- op, [ifm.name, filter.name, bias.name], [result_tens.name], None, qinfo
+ op['op'], [ifm.name, filter.name, bias.name], [result_tens.name], None, qinfo
)
return result_tens
def build_matmul(self, op, a, b, qinfo):
result_tens = OutputShaper.matmulOp(self.ser, a, b)
- self.ser.addOperator(op, [a.name, b.name], [result_tens.name], None, qinfo)
+ self.ser.addOperator(op['op'], [a.name, b.name], [result_tens.name], None, qinfo)
return result_tens
def build_reduce(self, op, a, axis):
@@ -1809,7 +2014,7 @@
attr = ts.TosaSerializerAttribute()
attr.AxisAttribute(axis)
- self.ser.addOperator(op, [a.name], result_tens.name, attr)
+ self.ser.addOperator(op['op'], [a.name], result_tens.name, attr)
return result_tens
def build_clamp(self, op, a):
@@ -1823,7 +2028,7 @@
else:
attr.ClampAttribute(min(v), max(v), 0, 0)
- self.ser.addOperator(op, [a.name], [result_tens.name], attr)
+ self.ser.addOperator(op['op'], [a.name], [result_tens.name], attr)
return result_tens
def build_leaky_relu(self, op, a):
@@ -1832,24 +2037,24 @@
attr.LeakyReluAttribute(self.getRandNumberDType(DType.FLOAT))
- self.ser.addOperator(op, [a.name], [result_tens.name], attr)
+ self.ser.addOperator(op['op'], [a.name], [result_tens.name], attr)
return result_tens
# Needs an additional type/input
def build_prelu(self, op, a):
result_tens = OutputShaper.unaryOp(self.ser, a)
- self.ser.addOperator(op, [a.name], [result_tens.name])
+ self.ser.addOperator(op['op'], [a.name], [result_tens.name])
return result_tens
def build_sigmoid(self, op, a):
result_tens = OutputShaper.unaryOp(self.ser, a)
- self.ser.addOperator(op, [a.name], [result_tens.name])
+ self.ser.addOperator(op['op'], [a.name], [result_tens.name])
return result_tens
def build_tanh(self, op, a):
result_tens = OutputShaper.unaryOp(self.ser, a)
- self.ser.addOperator(op, [a.name], [result_tens.name])
+ self.ser.addOperator(op['op'], [a.name], [result_tens.name])
return result_tens
def build_concat(self, op, *a):
@@ -1868,7 +2073,8 @@
for tensor in a:
input_tensor_names.append(tensor.name)
- self.ser.addOperator(op, input_tensor_names, [result_tens.name], attr)
+ self.ser.addOperator(op['op'], input_tensor_names, [result_tens.name], attr)
+ return result_tens
def build_pad(self, op, a, padding, qinfo):
result_tens = OutputShaper.padOp(self.ser, a, padding)
@@ -1879,7 +2085,7 @@
padding_tens = self.ser.addConst(padding.shape, DType.INT32, padding)
self.ser.addOperator(
- op, [a.name, padding_tens.name], [result_tens.name], None, qinfo
+ op['op'], [a.name, padding_tens.name], [result_tens.name], None, qinfo
)
return result_tens
@@ -1889,7 +2095,7 @@
attr = ts.TosaSerializerAttribute()
attr.ReshapeAttribute(newShape)
- self.ser.addOperator(op, [a.name], [result_tens.name], attr)
+ self.ser.addOperator(op['op'], [a.name], [result_tens.name], attr)
return result_tens
def build_reverse(self, op, a, axis):
@@ -1898,7 +2104,7 @@
attr = ts.TosaSerializerAttribute()
attr.AxisAttribute(axis)
- self.ser.addOperator(op, [a.name], [result_tens.name], attr)
+ self.ser.addOperator(op['op'], [a.name], [result_tens.name], attr)
return result_tens
def build_transpose(self, op, a, perms):
@@ -1906,7 +2112,7 @@
perms_tens = self.ser.addConst([len(perms)], DType.INT32, np.int32(perms))
- self.ser.addOperator(op, [a.name, perms_tens.name], [result_tens.name])
+ self.ser.addOperator(op['op'], [a.name, perms_tens.name], [result_tens.name])
return result_tens
def build_slice(self, op, a, begin, size):
@@ -1915,7 +2121,7 @@
attr = ts.TosaSerializerAttribute()
attr.SliceAttribute(begin, size)
- self.ser.addOperator(op, [a.name], [result_tens.name], attr)
+ self.ser.addOperator(op['op'], [a.name], [result_tens.name], attr)
return result_tens
def build_tile(self, op, a, multiples):
@@ -1924,7 +2130,7 @@
attr = ts.TosaSerializerAttribute()
attr.TileAttribute(multiples)
- self.ser.addOperator(op, [a.name], [result_tens.name], attr)
+ self.ser.addOperator(op['op'], [a.name], [result_tens.name], attr)
return result_tens
def build_gather(self, op, values):
@@ -1943,7 +2149,7 @@
result_tens = OutputShaper.gatherOp(self.ser, values, indicies)
- self.ser.addOperator(op, [values.name, indicies.name], [result_tens.name])
+ self.ser.addOperator(op['op'], [values.name, indicies.name], [result_tens.name])
return result_tens
@@ -1962,11 +2168,12 @@
result_tens = OutputShaper.scatterOp(self.ser, values_in, indicies, input)
self.ser.addOperator(
- op, [values_in.name, indicies.name, input.name], [result_tens.name]
+ op['op'], [values_in.name, indicies.name, input.name], [result_tens.name]
)
return result_tens
+
def build_resize(
self,
op,
@@ -1998,30 +2205,32 @@
error_name
)
- # Check ERROR_IF statements
- for val_fcn in validator_fcns:
- val_result = val_fcn(
- check=True,
- shift=shift,
- input_dtype=input_dtype,
- input_shape=input,
- output_shape=output_dims,
- offset=offset,
- offset_fp=offset_fp,
- stride=stride,
- stride_fp=stride_fp)
+ # Invalidate Input/Output list for error if checks.
+ input_list = [input.name]
+ output_list = [result_tens.name]
+ pCount, cCount = op["operands"]
+ num_operands = pCount + cCount
+ input_list, output_list = TosaErrorIfArgGen.eiInvalidateInputOutputList(self, error_name, input_list, output_list)
- validator_name = val_result['error_name']
- error_result = val_result['error_result']
- error_reason = val_result['error_reason']
-
- if error_result:
- if error_name == validator_name:
- self.ser.setExpectedReturnCode(2, error_reason)
- else:
- print(f"Multiple ERROR_IF checks hit \nError required: {error_name}, Error_produced: {validator_name}")
- return None # Return None to delete test if wrong ERROR_IF is hit
-
+ TosaErrorValidator.evValidateErrorIfs(
+ self.ser,
+ validator_fcns,
+ error_name,
+ op=op,
+ mode=mode,
+ shift=shift,
+ input_dtype=input_dtype,
+ output_dtype=output_dtype,
+ input_shape=input,
+ output_shape=output_dims,
+ offset=offset,
+ offset_fp=offset_fp,
+ stride=stride,
+ stride_fp=stride_fp,
+ input_list=input_list,
+ output_list=output_list,
+ num_operands=num_operands,
+ )
attr = ts.TosaSerializerAttribute()
@@ -2029,7 +2238,7 @@
output_dims, stride, offset, shift, stride_fp, offset_fp, mode
)
- self.ser.addOperator(op, [input.name], [result_tens.name], attr)
+ self.ser.addOperator(op['op'], input_list, output_list, attr)
return result_tens
def build_identityn(self, op, val, val2):
@@ -2048,7 +2257,7 @@
# Type Conversion
def build_cast(self, op, val, out_dtype):
result_tens = OutputShaper.typeConversionOp(self.ser, val, out_dtype)
- self.ser.addOperator(op, [val.name], [result_tens.name])
+ self.ser.addOperator(op['op'], [val.name], [result_tens.name])
return result_tens
def build_rescale(self, op, val, out_dtype, scale32, double_round, per_channel):
@@ -2117,7 +2326,7 @@
per_channel,
)
- self.ser.addOperator(op, [val.name], [result_tens.name], attr)
+ self.ser.addOperator(op['op'], [val.name], [result_tens.name], attr)
return result_tens
def build_cond_if_const(self, op, then_tens, else_tens, cond):
@@ -2143,7 +2352,7 @@
attr.CondIfAttribute(then_block, else_block)
# Finally, build the op and the two blocks
- self.ser.addOperator(op, [cond_tens.name], [result_tens.name], attr)
+ self.ser.addOperator(op['op'], [cond_tens.name], [result_tens.name], attr)
self.ser.startBasicBlock(then_block)
# Build the actual then/else tensors inside their blocks
@@ -2173,7 +2382,7 @@
# Finally, build the op and the two blocks
self.ser.addOperator(
- op, [cond_tens.name, a.name, b.name], [result_tens.name], attr
+ op['op'], [cond_tens.name, a.name, b.name], [result_tens.name], attr
)
self.ser.startBasicBlock(then_block)
@@ -2211,7 +2420,7 @@
# While_loop operator
self.ser.addOperator(
- op,
+ op['op'],
[iter.name, a.name, acc.name],
[iter_out.name, a_out.name, acc_out.name],
attr,
@@ -2358,14 +2567,13 @@
if testType in ['negative', 'both'] and "error_if_validators" in op:
error_if_validators = op["error_if_validators"]
for validator in error_if_validators:
- validator_info = validator()
+ validator_info = validator(check=False, op=op)
error_name = validator_info['error_name']
error_arguments = validator_info['param_reqs']
#Set parameters as required
if error_arguments['rank'] != None:
- rmin, rmax = error_arguments['rank']
- rankFilter = range(rmin, rmax + 1)
+ rankFilter = error_arguments['rank']
else:
rankFilter = base_rankFilter
if error_arguments['dtype'] != None:
@@ -2377,7 +2585,7 @@
else:
shapes = base_shapeFilter[:2] # Reduce number of shapes to keep test numbers small
- for r in range(rmin, rmax + 1):
+ for r in rankFilter:
for t in dtypeFilter:
# Create the placeholder and const tensors
for shape in shapes:
@@ -2656,14 +2864,14 @@
try:
if error_if_validators is None:
if qinfo is not None:
- resultName = build_fcn(self, op["op"], *tens, *testArgs, qinfo)
+ resultName = build_fcn(self, op, *tens, *testArgs, qinfo)
else:
- resultName = build_fcn(self, op["op"], *tens, *testArgs)
+ resultName = build_fcn(self, op, *tens, *testArgs)
else:
if qinfo is not None:
- resultName = build_fcn(self, op["op"], *tens, *testArgs, qinfo, error_if_validators, error_name)
+ resultName = build_fcn(self, op, *tens, *testArgs, qinfo, error_if_validators, error_name)
else:
- resultName = build_fcn(self, op["op"], *tens, *testArgs, error_if_validators, error_name)
+ resultName = build_fcn(self, op, *tens, *testArgs, error_if_validators, error_name)
except TypeError as e:
print(
"build_fcn: {}\nTensors: {}\nArgs: {}\n".format(
@@ -3238,7 +3446,8 @@
"invalid_test_validators": (TosaInvalidValidator.ivWrongDataTypeOrModeResize, TosaInvalidValidator.ivBadStride),
"error_if_validators": (TosaErrorValidator.evMaxDimExceeded, TosaErrorValidator.evStrideSmallerEqualZero, TosaErrorValidator.evStrideLargerDimension,
TosaErrorValidator.evStrideLargerEqualMax, TosaErrorValidator.evOffsetSmallerEqualMin, TosaErrorValidator.evOffsetLargerEqualMax,
- TosaErrorValidator.evShiftNotZero, TosaErrorValidator.evShiftSmallerOne, TosaErrorValidator.evShiftLargerEleven)
+ TosaErrorValidator.evShiftNotZero, TosaErrorValidator.evShiftSmallerOne, TosaErrorValidator.evShiftLargerEleven, TosaErrorValidator.evWrongInputType,
+ TosaErrorValidator.evWrongOutputType, TosaErrorValidator.evWrongRank, TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList)
},
# Type conversion
"cast": {
@@ -3656,8 +3865,10 @@
output_dtype,
error_name = None
):
-
- output_dims = [input.shape[0], output_dims[0], output_dims[1], input.shape[3]]
+ if error_name == ErrorIf.WrongRank:
+ output_dims = [input.shape[0], output_dims[0], output_dims[0], input.shape[0]]
+ else:
+ output_dims = [input.shape[0], output_dims[0], output_dims[1], input.shape[3]]
return ser.addOutput(output_dims, output_dtype)
diff --git a/verif/tosa_verif_build_tests.py b/verif/tosa_verif_build_tests.py
index 5b84314..040481b 100755
--- a/verif/tosa_verif_build_tests.py
+++ b/verif/tosa_verif_build_tests.py
@@ -235,7 +235,14 @@
print("{} matching tests".format(len(testList)))
results = []
+ testStrings = []
for opName, testStr, dtype, error, shapeList, testArgs in testList:
+ # Check for and skip duplicate tests
+ if testStr in testStrings:
+ continue
+ else:
+ testStrings.append(testStr)
+
if args.verbose:
print(testStr)
results.append(ttg.serializeTest(opName, testStr, dtype, error, shapeList, testArgs))