Main Compliance testing for SCATTER and GATHER
Added indices shuffling and random INT32 support to generate lib
with testing of these new random generator modes
Signed-off-by: Jeremy Johnson <jeremy.johnson@arm.com>
Change-Id: I058d8b092470228075e8fe69c2ededa639163003
diff --git a/verif/generator/tosa_test_gen.py b/verif/generator/tosa_test_gen.py
index 53b0b75..ee935d4 100644
--- a/verif/generator/tosa_test_gen.py
+++ b/verif/generator/tosa_test_gen.py
@@ -66,6 +66,12 @@
v = maxFP
elif v == "-max":
v = -maxFP
+ elif v < 0:
+ # Trim to minimum data type value
+ v = max(v, -maxFP)
+ elif v > 0:
+ # Trim to maximum data type value
+ v = min(v, maxFP)
vals.append(v)
return tuple(sorted(vals))
@@ -1722,27 +1728,19 @@
self.ser.addOperator(op["op"], input_list, output_list, attr)
return result_tens
- def build_gather(self, op, values, validator_fcns=None, error_name=None):
+ def build_gather(
+ self, op, inputs, args_dict, validator_fcns=None, error_name=None, qinfo=None
+ ):
+ assert len(inputs) == 2
+ values, indices = inputs
- # Create a new indicies tensor
- # here with data that doesn't exceed the dimensions of the values tensor
-
- K = values.shape[1] # K
- W = self.randInt(
- self.args.tensor_shape_range[0], self.args.tensor_shape_range[1]
- ) # W
- indicies_arr = np.int32(
- self.rng.integers(low=0, high=K, size=[values.shape[0], W])
- ) # (N, W)
- indicies = self.ser.addConst(indicies_arr.shape, DType.INT32, indicies_arr)
-
- result_tens = OutputShaper.gatherOp(
- self.ser, self.rng, values, indicies, error_name
+ result_tensor = OutputShaper.gatherOp(
+ self.ser, self.rng, values, indices, error_name
)
# Invalidate Input/Output list for error if checks.
- input_list = [values.name, indicies.name]
- output_list = [result_tens.name]
+ input_list = [values.name, indices.name]
+ output_list = [result_tensor.name]
pCount, cCount = op["operands"]
num_operands = pCount + cCount
input_list, output_list = TosaErrorIfArgGen.eiInvalidateInputOutputList(
@@ -1755,10 +1753,10 @@
error_name,
op=op,
input_shape=values.shape,
- output_shape=result_tens.shape,
+ output_shape=result_tensor.shape,
input_dtype=values.dtype,
- output_dtype=result_tens.dtype,
- result_tensors=[result_tens],
+ output_dtype=result_tensor.dtype,
+ result_tensors=[result_tensor],
input_list=input_list,
output_list=output_list,
num_operands=num_operands,
@@ -1767,33 +1765,24 @@
self.ser.addOperator(op["op"], input_list, output_list)
- return result_tens
+ compliance = self.tensorComplianceMetaData(
+ op, values.dtype, args_dict, result_tensor, error_name
+ )
- def build_scatter(self, op, values_in, input, validator_fcns=None, error_name=None):
+ return TosaTestGen.BuildInfo(result_tensor, compliance)
- K = values_in.shape[1] # K
- W = input.shape[1] # W
-
- # Create an indices tensor here with data that doesn't exceed the
- # dimension K of the values_in tensor and does NOT repeat the same K
- # location as needed by the spec:
- # "It is not permitted to repeat the same output index within a single
- # SCATTER operation and so each output index occurs at most once."
- assert K >= W
- arr = []
- for n in range(values_in.shape[0]):
- # Get a shuffled list of output indices and limit it to size W
- arr.append(self.rng.permutation(K)[:W])
- indices_arr = np.array(arr, dtype=np.int32) # (N, W)
- indices = self.ser.addConst(indices_arr.shape, DType.INT32, indices_arr)
-
- result_tens = OutputShaper.scatterOp(
+ def build_scatter(
+ self, op, inputs, args_dict, validator_fcns=None, error_name=None, qinfo=None
+ ):
+ assert len(inputs) == 3
+ values_in, indices, input = inputs
+ result_tensor = OutputShaper.scatterOp(
self.ser, self.rng, values_in, indices, input, error_name
)
# Invalidate Input/Output list for error if checks.
input_list = [values_in.name, indices.name, input.name]
- output_list = [result_tens.name]
+ output_list = [result_tensor.name]
pCount, cCount = op["operands"]
num_operands = pCount + cCount
input_list, output_list = TosaErrorIfArgGen.eiInvalidateInputOutputList(
@@ -1806,10 +1795,10 @@
error_name,
op=op,
input_shape=values_in.shape,
- output_shape=result_tens.shape,
+ output_shape=result_tensor.shape,
input_dtype=values_in.dtype,
- output_dtype=result_tens.dtype,
- result_tensors=[result_tens],
+ output_dtype=result_tensor.dtype,
+ result_tensors=[result_tensor],
input_list=input_list,
output_list=output_list,
num_operands=num_operands,
@@ -1818,7 +1807,11 @@
self.ser.addOperator(op["op"], input_list, output_list)
- return result_tens
+ compliance = self.tensorComplianceMetaData(
+ op, values_in.dtype, args_dict, result_tensor, error_name
+ )
+
+ return TosaTestGen.BuildInfo(result_tensor, compliance)
def build_resize(
self,
@@ -4310,14 +4303,13 @@
# Scatter/Gather
"gather": {
"op": Op.GATHER,
- # Only specify 'values' tensor here. 'indices' is generated in op building stage
- "operands": (1, 0),
+ "operands": (2, 0),
"rank": (3, 3),
"build_fcn": (
build_gather,
- TosaTensorGen.tgBasic,
- TosaTensorValuesGen.tvgDefault,
- None,
+ TosaTensorGen.tgGather,
+ TosaTensorValuesGen.tvgGather,
+ TosaArgGen.agNone,
),
"types": (
DType.INT8,
@@ -4334,18 +4326,19 @@
TosaErrorValidator.evWrongOutputList,
TosaErrorValidator.evWrongRank,
),
+ "data_gen": {
+ "fp": (gtu.DataGenType.PSEUDO_RANDOM,),
+ },
},
"scatter": {
"op": Op.SCATTER,
- # Only specify 'values_in' tensor here.
- # 'indices' and 'input' are generated in op building stage
- "operands": (2, 0),
+ "operands": (3, 0),
"rank": (3, 3),
"build_fcn": (
build_scatter,
TosaTensorGen.tgScatter,
- TosaTensorValuesGen.tvgDefault,
- None,
+ TosaTensorValuesGen.tvgScatter,
+ TosaArgGen.agNone,
),
"types": TYPE_INT_FP,
"error_if_validators": (
@@ -4355,6 +4348,9 @@
TosaErrorValidator.evWrongOutputList,
TosaErrorValidator.evWrongRank,
),
+ "data_gen": {
+ "fp": (gtu.DataGenType.PSEUDO_RANDOM,),
+ },
},
# Image operations
"resize": {