Updated build_tests to support different random generators
All generator functions now take RNG argument to allow different
random number generators, rather than relying on global RNG
Default behaviour is the same as before using global RNG
Added stable random generation mode
* shape rng based on operator, rank and datatype
* arguments rng based on operator, shape and datatype
* build operands and data rng based on op, shape, datatype and args
Add optional stable RNG test generation to conformance_generator
Signed-off-by: Jeremy Johnson <jeremy.johnson@arm.com>
Change-Id: I5ee4ff85575a81177fd74ed1617e946bfa3a0769
diff --git a/verif/generator/tosa_test_gen.py b/verif/generator/tosa_test_gen.py
index 3173906..7702753 100644
--- a/verif/generator/tosa_test_gen.py
+++ b/verif/generator/tosa_test_gen.py
@@ -20,6 +20,8 @@
from generator.tosa_error_if import TosaErrorIfArgGen
from generator.tosa_error_if import TosaErrorValidator
from generator.tosa_error_if import TosaInvalidValidator
+from generator.tosa_random_gen import TosaHashRandomGenerator
+from generator.tosa_random_gen import TosaRandomGenerator
from schemavalidation.schemavalidation import TestDescSchemaValidator
from tosa.DType import DType
from tosa.Op import Op
@@ -50,10 +52,10 @@
self.basePath = args.output_dir
self.random_seed = args.random_seed
self.ser = None
- self.rng = np.random.default_rng(self.random_seed)
self.createDynamicOpLists()
self.initOpListDefaults()
self.quantGen = TosaQuantGen()
+ self.global_rng = None
# Force makeShape to do a specific starting shape
self.targetted_shape = None
# JSON schema validation
@@ -80,12 +82,18 @@
vals.append(v)
return tuple(sorted(vals))
- self.random_float_range = {}
+ self.random_dtype_range = {
+ DType.SHAPE: tuple(self.args.tensor_shape_range[0:2])
+ }
for dtype in (DType.FP32, DType.FP16, DType.BF16, DType.FP8E4M3, DType.FP8E5M2):
- self.random_float_range[dtype] = convertFPRange(
+ self.random_dtype_range[dtype] = convertFPRange(
args.tensor_fp_value_range,
TosaTensorValuesGen.TVG_FLOAT_HIGH_VALUE[dtype],
)
+ self.resetGlobalRNG()
+
+ def resetGlobalRNG(self):
+ self.global_rng = TosaRandomGenerator(self.random_seed, self.random_dtype_range)
def createSerializer(self, opName, testPath):
self.testPath = os.path.join(opName, testPath)
@@ -148,93 +156,7 @@
with path_desc.open("w") as fd:
json.dump(desc, fd, indent=1)
- def resetRNG(self, seed=None):
- if seed is None:
- seed = self.random_seed + 1
- self.rng = np.random.default_rng(seed)
-
- def getDTypeRange(self, dtype, high_inclusive=False):
- # Returns dtype value range boundaries (low, high)
- # The high boundary is excluded in the range
- # unless high_inclusive is True
- if dtype in (DType.FP32, DType.FP16, DType.BF16, DType.FP8E4M3, DType.FP8E5M2):
- return self.random_float_range[dtype]
- elif dtype == DType.BOOL:
- rng = (0, 2)
- elif dtype == DType.UINT8:
- rng = (0, 256)
- elif dtype == DType.UINT16:
- rng = (0, 65536)
- elif dtype == DType.INT4:
- # TOSA specific INT4 weight range from -7 to 7
- rng = (-7, 8)
- elif dtype == DType.INT8:
- rng = (-128, 128)
- elif dtype == DType.INT16:
- rng = (-32768, 32768)
- elif dtype == DType.INT32:
- rng = (-(1 << 31), (1 << 31))
- elif dtype == DType.SHAPE:
- rng = tuple(self.args.tensor_shape_range[0:2])
- elif dtype == DType.INT48:
- rng = (-(1 << 47), (1 << 47))
- else:
- raise Exception("Unknown dtype: {}".format(dtype))
-
- if not high_inclusive:
- # Exclusive high: low <= range < high
- return rng
- else:
- # Inclusive range: low <= range <= high
- return (rng[0], rng[1] - 1)
-
- def getRandTensor(self, shape, dtype, data_range=None):
- if data_range is None:
- low, high = self.getDTypeRange(dtype)
- else:
- low, high = data_range
-
- if dtype == DType.BOOL:
- return np.bool_(self.rng.choice(a=[False, True], size=shape))
- elif dtype == DType.INT4:
- return np.int8(self.rng.integers(low=low, high=high, size=shape))
- elif dtype == DType.INT8:
- return np.int8(self.rng.integers(low=low, high=high, size=shape))
- elif dtype == DType.UINT8:
- return np.uint8(self.rng.integers(low=low, high=high, size=shape))
- elif dtype == DType.INT16:
- return np.int16(self.rng.integers(low=low, high=high, size=shape))
- elif dtype == DType.UINT16:
- return np.uint16(self.rng.integers(low=low, high=high, size=shape))
- elif dtype in (DType.INT48, DType.SHAPE):
- return np.int64(self.rng.integers(low=low, high=high, size=shape))
- elif dtype in (
- DType.FP16,
- DType.BF16,
- DType.FP32,
- DType.FP8E4M3,
- DType.FP8E5M2,
- ):
- f_tensor = self.rng.uniform(low=low, high=high, size=shape)
-
- if dtype == DType.FP16:
- return np.float16(f_tensor)
- else:
- f32_tensor = np.float32(f_tensor)
- if dtype == DType.BF16:
- # Floor the last 16 bits of each f32 value
- return np.float32(gtu.vect_f32_to_bf16(f32_tensor))
- elif dtype == DType.FP8E4M3:
- return np.float32(gtu.vect_f32_to_fp8e4m3(f32_tensor))
- elif dtype == DType.FP8E5M2:
- return np.float32(gtu.vect_f32_to_fp8e5m2(f32_tensor))
- else:
- return f32_tensor
- else:
- # All other integer types
- return np.int32(self.rng.integers(low=low, high=high, size=shape))
-
- def buildPlaceholderTensors(self, shape_list, dtype_list):
+ def buildPlaceholderTensors(self, rng, shape_list, dtype_list):
placeholders = []
assert len(shape_list) == len(dtype_list)
@@ -242,12 +164,12 @@
arr = None
for idx, shape in enumerate(shape_list):
if not self.args.lazy_data_gen:
- arr = self.getRandTensor(shape, dtype_list[idx])
+ arr = rng.randTensor(shape, dtype_list[idx])
placeholders.append(self.ser.addPlaceholder(shape, dtype_list[idx], arr))
return placeholders
- def buildConstTensors(self, shape_list, dtype_list):
+ def buildConstTensors(self, rng, shape_list, dtype_list):
consts = []
assert len(shape_list) == len(dtype_list)
@@ -255,16 +177,16 @@
arr = None
for idx, shape in enumerate(shape_list):
if not self.args.lazy_data_gen:
- arr = self.getRandTensor(shape, dtype_list[idx])
+ arr = rng.randTensor(shape, dtype_list[idx])
consts.append(self.ser.addConst(shape, dtype_list[idx], arr))
return consts
- def makeShape(self, rank):
+ def makeShape(self, rng, rank):
if self.targetted_shape:
return np.int32(self.targetted_shape)
return np.int32(
- self.rng.integers(
+ rng.integers(
low=self.args.tensor_shape_range[0],
high=self.args.tensor_shape_range[1],
size=rank,
@@ -274,33 +196,6 @@
def setTargetShape(self, shape):
self.targetted_shape = shape
- def randInt(self, low=0, high=256):
- return np.int32(self.rng.integers(low=low, high=high, size=1))[0]
-
- def getRandNumberDType(self, dtype):
- low, high = self.getDTypeRange(dtype)
-
- if dtype == DType.FP32:
- return np.float32(self.rng.uniform(low=low, high=high))
- elif dtype == DType.FP16:
- return np.float16(self.rng.uniform(low=low, high=high))
- elif dtype == DType.BF16:
- rand_f32 = np.float32(self.rng.uniform(low=low, high=high))
- return gtu.vect_f32_to_bf16(rand_f32)
- elif dtype == DType.FP8E4M3:
- rand_f32 = np.float32(self.rng.uniform(low=low, high=high))
- return gtu.vect_f32_to_fp8e4m3(rand_f32)
- elif dtype == DType.FP8E5M2:
- rand_f32 = np.float32(self.rng.uniform(low=low, high=high))
- return gtu.vect_f32_to_fp8e5m2(rand_f32)
- elif dtype == DType.BOOL:
- return self.rng.choice([False, True])
- elif dtype == DType.INT48 or dtype == DType.SHAPE:
- # Special size
- return np.int64(self.rng.integers(low, high, size=1))[0]
-
- return np.int32(self.rng.integers(low, high, size=1))[0]
-
def shapeStr(self, shape):
sStr = []
@@ -330,8 +225,8 @@
shape[0] = min(shape[0], self.args.max_batch_size)
return shape
- def makeDimension(self):
- return self.randInt(
+ def makeDimension(self, rng):
+ return rng.randInt(
low=self.args.tensor_shape_range[0], high=self.args.tensor_shape_range[1]
)
@@ -445,11 +340,18 @@
return compliance
def build_unary(
- self, op, inputs, args_dict, validator_fcns=None, error_name=None, qinfo=None
+ self,
+ rng,
+ op,
+ inputs,
+ args_dict,
+ validator_fcns=None,
+ error_name=None,
+ qinfo=None,
):
assert len(inputs) == 1
a = inputs[0]
- result_tensor = OutputShaper.unaryOp(self.ser, self.rng, a, error_name)
+ result_tensor = OutputShaper.unaryOp(self.ser, rng, a, error_name)
assert not isinstance(op, int)
@@ -457,8 +359,10 @@
if error_name == ErrorIf.WrongOutputType:
if result_tensor.dtype not in [DType.INT8, DType.UINT8]:
qinfo = [
- TosaQuantGen.getZeroPoint(self, a.dtype),
- TosaQuantGen.getZeroPoint(self, result_tensor.dtype),
+ TosaQuantGen.getZeroPoint(rng, self.args.zeropoint, a.dtype),
+ TosaQuantGen.getZeroPoint(
+ rng, self.args.zeropoint, result_tensor.dtype
+ ),
]
# Invalidate Input/Output list for error if checks.
@@ -467,7 +371,7 @@
pCount, cCount = op["operands"]
num_operands = pCount + cCount
input_list, output_list = TosaErrorIfArgGen.eiInvalidateInputOutputList(
- self, error_name, input_list, output_list
+ rng, error_name, input_list, output_list
)
if not TosaErrorValidator.evValidateErrorIfs(
@@ -498,13 +402,11 @@
return TosaTestGen.BuildInfo(result_tensor, compliance)
def build_binary_broadcast(
- self, op, inputs, args_dict, validator_fcns, error_name=None, qinfo=None
+ self, rng, op, inputs, args_dict, validator_fcns, error_name=None, qinfo=None
):
assert len(inputs) == 2
a, b = inputs
- result_tensor = OutputShaper.binaryBroadcastOp(
- self.ser, self.rng, a, b, error_name
- )
+ result_tensor = OutputShaper.binaryBroadcastOp(self.ser, rng, a, b, error_name)
# Invalidate Input/Output list for error if checks.
input_list = [a.name, b.name]
@@ -512,7 +414,7 @@
pCount, cCount = op["operands"]
num_operands = pCount + cCount
input_list, output_list = TosaErrorIfArgGen.eiInvalidateInputOutputList(
- self, error_name, input_list, output_list
+ rng, error_name, input_list, output_list
)
if not TosaErrorValidator.evValidateErrorIfs(
@@ -539,20 +441,20 @@
return TosaTestGen.BuildInfo(result_tensor, compliance)
- def build_binary_nonbroadcast(self, op, a, b, validator_fcns=None, error_name=None):
- result_tens = OutputShaper.binaryNonBroadcastOp(self.ser, a, b)
- self.ser.addOperator(op["op"], [a.name, b.name], [result_tens.name])
- return result_tens
-
def build_arithmetic_right_shift(
- self, op, inputs, args_dict, validator_fcns=None, error_name=None, qinfo=None
+ self,
+ rng,
+ op,
+ inputs,
+ args_dict,
+ validator_fcns=None,
+ error_name=None,
+ qinfo=None,
):
assert len(inputs) == 2
a, b = inputs
round = args_dict["round"]
- result_tensor = OutputShaper.binaryBroadcastOp(
- self.ser, self.rng, a, b, error_name
- )
+ result_tensor = OutputShaper.binaryBroadcastOp(self.ser, rng, a, b, error_name)
# Invalidate Input/Output list for error if checks.
input_list = [a.name, b.name]
@@ -560,7 +462,7 @@
pCount, cCount = op["operands"]
num_operands = pCount + cCount
input_list, output_list = TosaErrorIfArgGen.eiInvalidateInputOutputList(
- self, error_name, input_list, output_list
+ rng, error_name, input_list, output_list
)
if not TosaErrorValidator.evValidateErrorIfs(
@@ -591,15 +493,20 @@
return TosaTestGen.BuildInfo(result_tensor, compliance)
def build_mul(
- self, op, inputs, args_dict, validator_fcns=None, error_name=None, qinfo=None
+ self,
+ rng,
+ op,
+ inputs,
+ args_dict,
+ validator_fcns=None,
+ error_name=None,
+ qinfo=None,
):
# Note that mul is binary operator but it has a shift value tensor
assert len(inputs) == 3
a, b, s = inputs
- result_tensor = OutputShaper.binaryBroadcastOp(
- self.ser, self.rng, a, b, error_name
- )
+ result_tensor = OutputShaper.binaryBroadcastOp(self.ser, rng, a, b, error_name)
# Special for multiply: Force the result to INT32 for INT types
if a.dtype not in (DType.FP16, DType.BF16, DType.FP32):
@@ -607,7 +514,7 @@
if error_name == ErrorIf.WrongOutputType:
all_dtypes = [DType.INT8, DType.INT16, DType.INT48]
- outputDType = self.rng.choice(all_dtypes)
+ outputDType = rng.choice(all_dtypes)
result_tensor.setDtype(outputDType)
# Invalidate Input/Output list for error if checks.
@@ -616,7 +523,7 @@
pCount, cCount = op["operands"]
num_operands = pCount + cCount
input_list, output_list = TosaErrorIfArgGen.eiInvalidateInputOutputList(
- self, error_name, input_list, output_list
+ rng, error_name, input_list, output_list
)
if not TosaErrorValidator.evValidateErrorIfs(
@@ -644,12 +551,19 @@
return TosaTestGen.BuildInfo(result_tensor, compliance)
def build_table(
- self, op, inputs, args_dict, validator_fcns=None, error_name=None, qinfo=None
+ self,
+ rng,
+ op,
+ inputs,
+ args_dict,
+ validator_fcns=None,
+ error_name=None,
+ qinfo=None,
):
assert len(inputs) == 1
a = inputs[0]
table = args_dict["table"]
- result_tensor = OutputShaper.tableOp(self.ser, self.rng, a, error_name)
+ result_tensor = OutputShaper.tableOp(self.ser, rng, a, error_name)
attr = ts.TosaSerializerAttribute()
attr.TableAttribute(table)
@@ -660,7 +574,7 @@
pCount, cCount = op["operands"]
num_operands = pCount + cCount
input_list, output_list = TosaErrorIfArgGen.eiInvalidateInputOutputList(
- self, error_name, input_list, output_list
+ rng, error_name, input_list, output_list
)
if not TosaErrorValidator.evValidateErrorIfs(
@@ -687,14 +601,19 @@
return TosaTestGen.BuildInfo(result_tensor, compliance)
def build_select(
- self, op, inputs, args_dict, validator_fcns=None, error_name=None, qinfo=None
+ self,
+ rng,
+ op,
+ inputs,
+ args_dict,
+ validator_fcns=None,
+ error_name=None,
+ qinfo=None,
):
assert len(inputs) == 3
cond, a, b = inputs
- result_tensor = OutputShaper.selectOp(
- self.ser, self.rng, cond, a, b, error_name
- )
+ result_tensor = OutputShaper.selectOp(self.ser, rng, cond, a, b, error_name)
# Invalidate Input/Output list for error if checks.
input_list = [cond.name, a.name, b.name]
@@ -702,7 +621,7 @@
pCount, cCount = op["operands"]
num_operands = pCount + cCount
input_list, output_list = TosaErrorIfArgGen.eiInvalidateInputOutputList(
- self, error_name, input_list, output_list
+ rng, error_name, input_list, output_list
)
if not TosaErrorValidator.evValidateErrorIfs(
@@ -735,14 +654,19 @@
return TosaTestGen.BuildInfo(result_tensor, compliance)
def build_comparison(
- self, op, inputs, args_dict, validator_fcns=None, error_name=None, qinfo=None
+ self,
+ rng,
+ op,
+ inputs,
+ args_dict,
+ validator_fcns=None,
+ error_name=None,
+ qinfo=None,
):
assert len(inputs) == 2
a, b = inputs
- result_tensor = OutputShaper.binaryComparisonOp(
- self.ser, self.rng, a, b, error_name
- )
+ result_tensor = OutputShaper.binaryComparisonOp(self.ser, rng, a, b, error_name)
# Invalidate Input/Output list for error if checks.
input_list = [a.name, b.name]
@@ -750,7 +674,7 @@
pCount, cCount = op["operands"]
num_operands = pCount + cCount
input_list, output_list = TosaErrorIfArgGen.eiInvalidateInputOutputList(
- self, error_name, input_list, output_list
+ rng, error_name, input_list, output_list
)
if not TosaErrorValidator.evValidateErrorIfs(
@@ -783,12 +707,12 @@
return TosaTestGen.BuildInfo(result_tensor, compliance)
def build_argmax(
- self, op, inputs, args_dict, validator_fcns, error_name, qinfo=None
+ self, rng, op, inputs, args_dict, validator_fcns, error_name, qinfo=None
):
assert len(inputs) == 1
a = inputs[0]
axis = args_dict["axis"]
- result_tensor = OutputShaper.argmaxOp(self.ser, self.rng, a, axis, error_name)
+ result_tensor = OutputShaper.argmaxOp(self.ser, rng, a, axis, error_name)
# Invalidate Input/Output list for error if checks.
input_list = [a.name]
@@ -796,7 +720,7 @@
pCount, cCount = op["operands"]
num_operands = pCount + cCount
input_list, output_list = TosaErrorIfArgGen.eiInvalidateInputOutputList(
- self, error_name, input_list, output_list
+ rng, error_name, input_list, output_list
)
if not TosaErrorValidator.evValidateErrorIfs(
@@ -828,6 +752,7 @@
def build_pool2d(
self,
+ rng,
op,
inputs,
args_dict,
@@ -846,15 +771,17 @@
kernel = args_dict["kernel"]
result_tensor = OutputShaper.pool2dOp(
- self.ser, self.rng, input, kernel, stride, pad, error_name
+ self.ser, rng, input, kernel, stride, pad, error_name
)
# Ensure new output type has correct qinfo
if error_name == ErrorIf.WrongInputType:
if input.dtype not in [DType.INT8, DType.UINT8]:
qinfo = [
- TosaQuantGen.getZeroPoint(self, input.dtype),
- TosaQuantGen.getZeroPoint(self, result_tensor.dtype),
+ TosaQuantGen.getZeroPoint(rng, self.args.zeropoint, input.dtype),
+ TosaQuantGen.getZeroPoint(
+ rng, self.args.zeropoint, result_tensor.dtype
+ ),
]
# Invalidate Input/Output list for error if checks.
@@ -863,7 +790,7 @@
pCount, cCount = op["operands"]
num_operands = pCount + cCount
input_list, output_list = TosaErrorIfArgGen.eiInvalidateInputOutputList(
- self, error_name, input_list, output_list
+ rng, error_name, input_list, output_list
)
if not TosaErrorValidator.evValidateErrorIfs(
@@ -903,6 +830,7 @@
def build_conv2d(
self,
+ rng,
op,
inputs,
args_dict,
@@ -920,7 +848,7 @@
assert len(padding) == 4
result_tensor = OutputShaper.conv2dOp(
self.ser,
- self.rng,
+ rng,
ifm,
filter,
accum_dtype,
@@ -936,8 +864,10 @@
DType.UINT8,
):
qinfo = [
- TosaQuantGen.getZeroPoint(self, ifm.dtype),
- TosaQuantGen.getZeroPoint(self, result_tensor.dtype),
+ TosaQuantGen.getZeroPoint(rng, self.args.zeropoint, ifm.dtype),
+ TosaQuantGen.getZeroPoint(
+ rng, self.args.zeropoint, result_tensor.dtype
+ ),
]
# Invalidate Input/Output list for error_if checks.
@@ -945,7 +875,7 @@
output_list = [result_tensor.name]
num_operands = sum(op["operands"])
input_list, output_list = TosaErrorIfArgGen.eiInvalidateInputOutputList(
- self, error_name, input_list, output_list
+ rng, error_name, input_list, output_list
)
if not TosaErrorValidator.evValidateErrorIfs(
@@ -985,6 +915,7 @@
def build_conv3d(
self,
+ rng,
op,
inputs,
args_dict,
@@ -1002,7 +933,7 @@
assert len(padding) == 6
result_tensor = OutputShaper.conv3dOp(
self.ser,
- self.rng,
+ rng,
ifm,
filter,
accum_dtype,
@@ -1018,8 +949,10 @@
DType.UINT8,
):
qinfo = [
- TosaQuantGen.getZeroPoint(self, ifm.dtype),
- TosaQuantGen.getZeroPoint(self, result_tensor.dtype),
+ TosaQuantGen.getZeroPoint(rng, self.args.zeropoint, ifm.dtype),
+ TosaQuantGen.getZeroPoint(
+ rng, self.args.zeropoint, result_tensor.dtype
+ ),
]
# Invalidate Input/Output list for error_if checks.
@@ -1027,7 +960,7 @@
output_list = [result_tensor.name]
num_operands = sum(op["operands"])
input_list, output_list = TosaErrorIfArgGen.eiInvalidateInputOutputList(
- self, error_name, input_list, output_list
+ rng, error_name, input_list, output_list
)
if not TosaErrorValidator.evValidateErrorIfs(
@@ -1067,6 +1000,7 @@
def build_transpose_conv2d(
self,
+ rng,
op,
inputs,
args_dict,
@@ -1083,7 +1017,7 @@
assert len(out_pad) == 4
result_tensor = OutputShaper.transposeConv2DOp(
- self.ser, self.rng, ifm, output_shape, accum_dtype, error_name
+ self.ser, rng, ifm, output_shape, accum_dtype, error_name
)
# Ensure new output type has correct qinfo
@@ -1092,8 +1026,10 @@
DType.UINT8,
):
qinfo = [
- TosaQuantGen.getZeroPoint(self, ifm.dtype),
- TosaQuantGen.getZeroPoint(self, result_tensor.dtype),
+ TosaQuantGen.getZeroPoint(rng, self.args.zeropoint, ifm.dtype),
+ TosaQuantGen.getZeroPoint(
+ rng, self.args.zeropoint, result_tensor.dtype
+ ),
]
# Invalidate Input/Output list for error_if checks.
@@ -1101,7 +1037,7 @@
output_list = [result_tensor.name]
num_operands = sum(op["operands"])
input_list, output_list = TosaErrorIfArgGen.eiInvalidateInputOutputList(
- self, error_name, input_list, output_list
+ rng, error_name, input_list, output_list
)
if not TosaErrorValidator.evValidateErrorIfs(
@@ -1142,6 +1078,7 @@
def build_depthwise_conv2d(
self,
+ rng,
op,
inputs,
args_dict,
@@ -1158,7 +1095,7 @@
result_tensor = OutputShaper.depthwiseConv2dOp(
self.ser,
- self.rng,
+ rng,
ifm,
filter,
accum_dtype,
@@ -1174,8 +1111,10 @@
DType.UINT8,
):
qinfo = [
- TosaQuantGen.getZeroPoint(self, ifm.dtype),
- TosaQuantGen.getZeroPoint(self, result_tensor.dtype),
+ TosaQuantGen.getZeroPoint(rng, self.args.zeropoint, ifm.dtype),
+ TosaQuantGen.getZeroPoint(
+ rng, self.args.zeropoint, result_tensor.dtype
+ ),
]
# Invalidate Input/Output list for error_if checks.
@@ -1183,7 +1122,7 @@
output_list = [result_tensor.name]
num_operands = sum(op["operands"])
input_list, output_list = TosaErrorIfArgGen.eiInvalidateInputOutputList(
- self, error_name, input_list, output_list
+ rng, error_name, input_list, output_list
)
if not TosaErrorValidator.evValidateErrorIfs(
@@ -1223,6 +1162,7 @@
def build_fully_connected(
self,
+ rng,
op,
inputs,
args_dict,
@@ -1235,7 +1175,7 @@
accum_dtype = args_dict["acc_type"]
result_tensor = OutputShaper.fullyConnectedOp(
- self.ser, self.rng, ifm, filter, accum_dtype, error_name
+ self.ser, rng, ifm, filter, accum_dtype, error_name
)
# Invalidate Input/Output list for error if checks.
@@ -1244,7 +1184,7 @@
pCount, cCount = op["operands"]
num_operands = pCount + cCount
input_list, output_list = TosaErrorIfArgGen.eiInvalidateInputOutputList(
- self, error_name, input_list, output_list
+ rng, error_name, input_list, output_list
)
if not TosaErrorValidator.evValidateErrorIfs(
@@ -1278,13 +1218,20 @@
return TosaTestGen.BuildInfo(result_tensor, compliance)
def build_matmul(
- self, op, inputs, args_dict, validator_fcns=None, error_name=None, qinfo=None
+ self,
+ rng,
+ op,
+ inputs,
+ args_dict,
+ validator_fcns=None,
+ error_name=None,
+ qinfo=None,
):
assert len(inputs) == 2
a, b = inputs
accum_dtype = args_dict["acc_type"]
result_tensor = OutputShaper.matmulOp(
- self.ser, self.rng, a, b, accum_dtype, error_name
+ self.ser, rng, a, b, accum_dtype, error_name
)
# Invalidate Input/Output list for error if checks.
@@ -1293,7 +1240,7 @@
pCount, cCount = op["operands"]
num_operands = pCount + cCount
input_list, output_list = TosaErrorIfArgGen.eiInvalidateInputOutputList(
- self, error_name, input_list, output_list
+ rng, error_name, input_list, output_list
)
if not TosaErrorValidator.evValidateErrorIfs(
@@ -1328,12 +1275,12 @@
return TosaTestGen.BuildInfo(result_tensor, compliance)
def build_reduce(
- self, op, inputs, args_dict, validator_fcns, error_name=None, qinfo=None
+ self, rng, op, inputs, args_dict, validator_fcns, error_name=None, qinfo=None
):
assert len(inputs) == 1
a = inputs[0]
axis = args_dict["axis"]
- result_tensor = OutputShaper.reduceOp(self.ser, self.rng, a, axis, error_name)
+ result_tensor = OutputShaper.reduceOp(self.ser, rng, a, axis, error_name)
# Invalidate Input/Output list for error if checks.
input_list = [a.name]
@@ -1341,7 +1288,7 @@
pCount, cCount = op["operands"]
num_operands = pCount + cCount
input_list, output_list = TosaErrorIfArgGen.eiInvalidateInputOutputList(
- self, error_name, input_list, output_list
+ rng, error_name, input_list, output_list
)
if not TosaErrorValidator.evValidateErrorIfs(
@@ -1377,19 +1324,26 @@
return TosaTestGen.BuildInfo(result_tensor, compliance)
def build_clamp(
- self, op, inputs, args_dict, validator_fcns=None, error_name=None, qinfo=None
+ self,
+ rng,
+ op,
+ inputs,
+ args_dict,
+ validator_fcns=None,
+ error_name=None,
+ qinfo=None,
):
assert len(inputs) == 1
a = inputs[0]
- result_tensor = OutputShaper.unaryOp(self.ser, self.rng, a, error_name)
+ result_tensor = OutputShaper.unaryOp(self.ser, rng, a, error_name)
- v = [self.getRandNumberDType(a.dtype), self.getRandNumberDType(a.dtype)]
+ v = [rng.randNumberDType(a.dtype), rng.randNumberDType(a.dtype)]
if error_name == ErrorIf.MaxSmallerMin:
# Make sure the numbers are different to invoke this error
while v[0] == v[1]:
- v = [self.getRandNumberDType(a.dtype), self.getRandNumberDType(a.dtype)]
+ v = [rng.randNumberDType(a.dtype), rng.randNumberDType(a.dtype)]
max_val = min(v)
min_val = max(v)
else:
@@ -1402,7 +1356,7 @@
pCount, cCount = op["operands"]
num_operands = pCount + cCount
input_list, output_list = TosaErrorIfArgGen.eiInvalidateInputOutputList(
- self, error_name, input_list, output_list
+ rng, error_name, input_list, output_list
)
if not TosaErrorValidator.evValidateErrorIfs(
@@ -1449,29 +1403,20 @@
return TosaTestGen.BuildInfo(result_tensor, compliance)
- def build_leaky_relu(self, op, a, validator_fcns=None, error_name=None):
- result_tens = OutputShaper.unaryOp(self.ser, self.rng, a, error_name)
- attr = ts.TosaSerializerAttribute()
-
- attr.LeakyReluAttribute(self.getRandNumberDType(DType.FP32))
-
- 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, validator_fcns=None, error_name=None):
- result_tens = OutputShaper.unaryOp(self.ser, self.rng, a, error_name)
-
- self.ser.addOperator(op["op"], [a.name], [result_tens.name])
- return result_tens
-
def build_activation(
- self, op, inputs, args_dict, validator_fcns=None, error_name=None, qinfo=None
+ self,
+ rng,
+ op,
+ inputs,
+ args_dict,
+ validator_fcns=None,
+ error_name=None,
+ qinfo=None,
):
assert len(inputs) == 1
a = inputs[0]
- result_tensor = OutputShaper.unaryOp(self.ser, self.rng, a, error_name)
+ result_tensor = OutputShaper.unaryOp(self.ser, rng, a, error_name)
# Invalidate Input/Output list for error if checks.
input_list = [a.name]
@@ -1479,7 +1424,7 @@
pCount, cCount = op["operands"]
num_operands = pCount + cCount
input_list, output_list = TosaErrorIfArgGen.eiInvalidateInputOutputList(
- self, error_name, input_list, output_list
+ rng, error_name, input_list, output_list
)
if not TosaErrorValidator.evValidateErrorIfs(
@@ -1507,7 +1452,14 @@
return TosaTestGen.BuildInfo(result_tensor, compliance)
def build_concat(
- self, op, inputs, args_dict, validator_fcns=None, error_name=None, qinfo=None
+ self,
+ rng,
+ op,
+ inputs,
+ args_dict,
+ validator_fcns=None,
+ error_name=None,
+ qinfo=None,
):
if op["op"] == Op.CONCAT_SHAPE:
axis = 0
@@ -1517,7 +1469,7 @@
assert type(axis) == int
result_tensor = OutputShaper.concatOp(
- self.ser, self.rng, axis, inputs, error_name=error_name
+ self.ser, rng, axis, inputs, error_name=error_name
)
input_tensor_names = []
@@ -1530,7 +1482,7 @@
pCount, cCount = op["operands"]
num_operands = pCount + cCount
input_list, output_list = TosaErrorIfArgGen.eiInvalidateInputOutputList(
- self, error_name, input_list, output_list
+ rng, error_name, input_list, output_list
)
if not TosaErrorValidator.evValidateErrorIfs(
@@ -1567,6 +1519,7 @@
def build_pad(
self,
+ rng,
op,
inputs,
args_dict,
@@ -1581,7 +1534,7 @@
pad_const_int = args_dict["pad_const_int"]
pad_const_float = args_dict["pad_const_fp"]
- result_tensor = OutputShaper.padOp(self.ser, self.rng, a, padding, error_name)
+ result_tensor = OutputShaper.padOp(self.ser, rng, a, padding, error_name)
# get pad_const_val_as_bytes from either pad_const_float or pad_const_int
if gtu.dtypeIsFloat(a.dtype):
@@ -1598,7 +1551,7 @@
pCount, cCount = op["operands"]
num_operands = pCount + cCount
input_list, output_list = TosaErrorIfArgGen.eiInvalidateInputOutputList(
- self, error_name, input_list, output_list
+ rng, error_name, input_list, output_list
)
if not TosaErrorValidator.evValidateErrorIfs(
@@ -1630,6 +1583,7 @@
def build_dim(
self,
+ rng,
op,
inputs,
args_dict,
@@ -1640,7 +1594,7 @@
assert len(inputs) == 1
a = inputs[0]
axis = args_dict["axis"]
- result_tensor = OutputShaper.dimOp(self.ser, self.rng, a, axis, error_name)
+ result_tensor = OutputShaper.dimOp(self.ser, rng, a, axis, error_name)
# Invalidate Input/Output list for error if checks.
input_list = [a.name]
@@ -1648,7 +1602,7 @@
pCount, cCount = op["operands"]
num_operands = pCount + cCount
input_list, output_list = TosaErrorIfArgGen.eiInvalidateInputOutputList(
- self, error_name, input_list, output_list
+ rng, error_name, input_list, output_list
)
if not TosaErrorValidator.evValidateErrorIfs(
@@ -1675,15 +1629,20 @@
return TosaTestGen.BuildInfo(result_tensor, None)
def build_reshape(
- self, op, inputs, args_dict, validator_fcns=None, error_name=None, qinfo=None
+ self,
+ rng,
+ op,
+ inputs,
+ args_dict,
+ validator_fcns=None,
+ error_name=None,
+ qinfo=None,
):
assert len(inputs) == 2
a = inputs[0]
shape = inputs[1]
shape_attr = args_dict["new_shape"]
- result_tensor = OutputShaper.reshapeOp(
- self.ser, self.rng, a, shape_attr, error_name
- )
+ result_tensor = OutputShaper.reshapeOp(self.ser, rng, a, shape_attr, error_name)
# Invalidate Input/Output list for error if checks.
input_list = [a.name, shape.name]
@@ -1691,7 +1650,7 @@
pCount, cCount = op["operands"]
num_operands = pCount + cCount
input_list, output_list = TosaErrorIfArgGen.eiInvalidateInputOutputList(
- self, error_name, input_list, output_list
+ rng, error_name, input_list, output_list
)
if not TosaErrorValidator.evValidateErrorIfs(
@@ -1719,12 +1678,19 @@
return TosaTestGen.BuildInfo(result_tensor, compliance)
def build_reverse(
- self, op, inputs, args_dict, validator_fcns=None, error_name=None, qinfo=None
+ self,
+ rng,
+ op,
+ inputs,
+ args_dict,
+ validator_fcns=None,
+ error_name=None,
+ qinfo=None,
):
assert len(inputs) == 1
a = inputs[0]
axis = args_dict["axis"]
- result_tensor = OutputShaper.unaryOp(self.ser, self.rng, a, error_name)
+ result_tensor = OutputShaper.unaryOp(self.ser, rng, a, error_name)
# Invalidate Input/Output list for error if checks.
input_list = [a.name]
@@ -1732,7 +1698,7 @@
pCount, cCount = op["operands"]
num_operands = pCount + cCount
input_list, output_list = TosaErrorIfArgGen.eiInvalidateInputOutputList(
- self, error_name, input_list, output_list
+ rng, error_name, input_list, output_list
)
if not TosaErrorValidator.evValidateErrorIfs(
@@ -1759,15 +1725,20 @@
return TosaTestGen.BuildInfo(result_tensor, None)
def build_transpose(
- self, op, inputs, args_dict, validator_fcns=None, error_name=None, qinfo=None
+ self,
+ rng,
+ op,
+ inputs,
+ args_dict,
+ validator_fcns=None,
+ error_name=None,
+ qinfo=None,
):
assert len(inputs) == 1
a = inputs[0]
perms = args_dict["perms"]
- result_tensor = OutputShaper.transposeOp(
- self.ser, self.rng, a, perms, error_name
- )
+ result_tensor = OutputShaper.transposeOp(self.ser, rng, a, perms, error_name)
attr = ts.TosaSerializerAttribute()
attr.TransposeAttribute(perms)
@@ -1778,7 +1749,7 @@
pCount, cCount = op["operands"]
num_operands = pCount + cCount
input_list, output_list = TosaErrorIfArgGen.eiInvalidateInputOutputList(
- self, error_name, input_list, output_list
+ rng, error_name, input_list, output_list
)
if not TosaErrorValidator.evValidateErrorIfs(
@@ -1808,7 +1779,14 @@
return TosaTestGen.BuildInfo(result_tensor, compliance)
def build_slice(
- self, op, inputs, args_dict, validator_fcns=None, error_name=None, qinfo=None
+ self,
+ rng,
+ op,
+ inputs,
+ args_dict,
+ validator_fcns=None,
+ error_name=None,
+ qinfo=None,
):
assert len(inputs) == 3
a, start_var, size_var = inputs
@@ -1816,7 +1794,7 @@
size_const = args_dict["size"]
result_tensor = OutputShaper.sliceOp(
- self.ser, self.rng, a, start_const, size_const, error_name
+ self.ser, rng, a, start_const, size_const, error_name
)
# Invalidate Input/Output list for error if checks.
@@ -1825,7 +1803,7 @@
pCount, cCount = op["operands"]
num_operands = pCount + cCount
input_list, output_list = TosaErrorIfArgGen.eiInvalidateInputOutputList(
- self, error_name, input_list, output_list
+ rng, error_name, input_list, output_list
)
if not TosaErrorValidator.evValidateErrorIfs(
@@ -1856,14 +1834,21 @@
return TosaTestGen.BuildInfo(result_tensor, compliance)
def build_tile(
- self, op, inputs, args_dict, validator_fcns=None, error_name=None, qinfo=None
+ self,
+ rng,
+ op,
+ inputs,
+ args_dict,
+ validator_fcns=None,
+ error_name=None,
+ qinfo=None,
):
assert len(inputs) == 2
a = inputs[0]
multiples = inputs[1]
multiples_attr = args_dict["multiples"]
result_tensor = OutputShaper.tileOp(
- self.ser, self.rng, a, multiples_attr, error_name
+ self.ser, rng, a, multiples_attr, error_name
)
# Invalidate Input/Output list for error if checks.
@@ -1872,7 +1857,7 @@
pCount, cCount = op["operands"]
num_operands = pCount + cCount
input_list, output_list = TosaErrorIfArgGen.eiInvalidateInputOutputList(
- self, error_name, input_list, output_list
+ rng, error_name, input_list, output_list
)
if not TosaErrorValidator.evValidateErrorIfs(
@@ -1901,13 +1886,20 @@
return TosaTestGen.BuildInfo(result_tensor, compliance)
def build_gather(
- self, op, inputs, args_dict, validator_fcns=None, error_name=None, qinfo=None
+ self,
+ rng,
+ op,
+ inputs,
+ args_dict,
+ validator_fcns=None,
+ error_name=None,
+ qinfo=None,
):
assert len(inputs) == 2
values, indices = inputs
result_tensor = OutputShaper.gatherOp(
- self.ser, self.rng, values, indices, error_name
+ self.ser, rng, values, indices, error_name
)
# Invalidate Input/Output list for error if checks.
@@ -1916,7 +1908,7 @@
pCount, cCount = op["operands"]
num_operands = pCount + cCount
input_list, output_list = TosaErrorIfArgGen.eiInvalidateInputOutputList(
- self, error_name, input_list, output_list
+ rng, error_name, input_list, output_list
)
if not TosaErrorValidator.evValidateErrorIfs(
@@ -1944,12 +1936,19 @@
return TosaTestGen.BuildInfo(result_tensor, compliance)
def build_scatter(
- self, op, inputs, args_dict, validator_fcns=None, error_name=None, qinfo=None
+ self,
+ rng,
+ 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
+ self.ser, rng, values_in, indices, input, error_name
)
# Invalidate Input/Output list for error if checks.
@@ -1958,7 +1957,7 @@
pCount, cCount = op["operands"]
num_operands = pCount + cCount
input_list, output_list = TosaErrorIfArgGen.eiInvalidateInputOutputList(
- self, error_name, input_list, output_list
+ rng, error_name, input_list, output_list
)
if not TosaErrorValidator.evValidateErrorIfs(
@@ -1987,6 +1986,7 @@
def build_resize(
self,
+ rng,
op,
inputs,
args_dict,
@@ -2008,7 +2008,7 @@
result_tensor = OutputShaper.resizeOp(
self.ser,
- self.rng,
+ rng,
input,
mode,
scale,
@@ -2030,7 +2030,7 @@
pCount, cCount = op["operands"]
num_operands = pCount + cCount
input_list, output_list = TosaErrorIfArgGen.eiInvalidateInputOutputList(
- self, error_name, input_list, output_list
+ rng, error_name, input_list, output_list
)
if not TosaErrorValidator.evValidateErrorIfs(
@@ -2064,16 +2064,15 @@
return TosaTestGen.BuildInfo(result_tensor, compliance)
- def build_identityn(self, op, val, val2, validator_fcns=None, error_name=None):
- result_tens = OutputShaper.unaryOp(self.ser, self.rng, val, error_name)
- result_tens2 = OutputShaper.unaryOp(self.ser, self.rng, val2, error_name)
- self.ser.addOperator(
- op, [val.name, val2.name], [result_tens.name, result_tens2.name]
- )
- return result_tens
-
def build_const(
- self, op, inputs, args_dict, validator_fcns=None, error_name=None, qinfo=None
+ self,
+ rng,
+ op,
+ inputs,
+ args_dict,
+ validator_fcns=None,
+ error_name=None,
+ qinfo=None,
):
assert len(inputs) == 1
val = inputs[0]
@@ -2087,14 +2086,21 @@
# Type Conversion
def build_cast(
- self, op, inputs, args_dict, validator_fcns=None, error_name=None, qinfo=None
+ self,
+ rng,
+ op,
+ inputs,
+ args_dict,
+ validator_fcns=None,
+ error_name=None,
+ qinfo=None,
):
assert len(inputs) == 1
val = inputs[0]
out_dtype = args_dict["out_type"]
result_tensor = OutputShaper.typeConversionOp(
- self.ser, self.rng, val, out_dtype, error_name
+ self.ser, rng, val, out_dtype, error_name
)
# Invalidate Input/Output list for error if checks.
@@ -2103,7 +2109,7 @@
pCount, cCount = op["operands"]
num_operands = pCount + cCount
input_list, output_list = TosaErrorIfArgGen.eiInvalidateInputOutputList(
- self, error_name, input_list, output_list
+ rng, error_name, input_list, output_list
)
if not TosaErrorValidator.evValidateErrorIfs(
@@ -2132,6 +2138,7 @@
def build_rescale(
self,
+ rng,
op,
inputs,
args_dict,
@@ -2151,7 +2158,7 @@
multiplier_arr = args_dict["multiplier"]
result_tensor = OutputShaper.typeConversionOp(
- self.ser, self.rng, val, out_dtype, error_name
+ self.ser, rng, val, out_dtype, error_name
)
if per_channel:
@@ -2166,46 +2173,46 @@
output_unsigned = False
if val.dtype == DType.INT8:
- input_zp = self.randInt(-128, 128)
+ input_zp = rng.randInt(-128, 128)
in_type_width += 1
elif val.dtype == DType.UINT8:
- input_zp = self.randInt(0, 256)
+ input_zp = rng.randInt(0, 256)
in_type_width += 1
input_unsigned = True
elif error_name in [
ErrorIf.InputZeroPointNotZero,
ErrorIf.U16InputZeroPointNotValid,
]:
- input_zp = self.randInt(-128, 128)
+ input_zp = rng.randInt(-128, 128)
if input_zp == 0:
- input_zp = input_zp + self.rng.integers(1, 10)
+ input_zp = input_zp + rng.integers(1, 10)
in_type_width += 1
elif val.dtype == DType.UINT16:
# Must come after ErrorIf.U16InputZeroPointNotValid check
- input_zp = self.rng.choice([0, 32768])
+ input_zp = rng.choice([0, 32768])
in_type_width += 1
input_unsigned = True
else:
input_zp = 0
if out_dtype == DType.INT8:
- output_zp = self.randInt(-128, 128)
+ output_zp = rng.randInt(-128, 128)
out_type_width += 1
elif out_dtype == DType.UINT8:
- output_zp = self.randInt(0, 256)
+ output_zp = rng.randInt(0, 256)
out_type_width += 1
output_unsigned = True
elif error_name in [
ErrorIf.OutputZeroPointNotZero,
ErrorIf.U16OutputZeroPointNotValid,
]:
- output_zp = self.randInt(-128, 128)
+ output_zp = rng.randInt(-128, 128)
if output_zp == 0:
- output_zp = output_zp + self.rng.integers(1, 10)
+ output_zp = output_zp + rng.integers(1, 10)
out_type_width += 1
elif out_dtype == DType.UINT16:
# Must come after ErrorIf.U16OutputZeroPointNotValid check
- output_zp = self.rng.choice([0, 32768])
+ output_zp = rng.choice([0, 32768])
out_type_width += 1
output_unsigned = True
else:
@@ -2255,7 +2262,7 @@
pCount, cCount = op["operands"]
num_operands = pCount + cCount
input_list, output_list = TosaErrorIfArgGen.eiInvalidateInputOutputList(
- self, error_name, input_list, output_list
+ rng, error_name, input_list, output_list
)
qinfo = (input_zp, output_zp)
@@ -2296,13 +2303,13 @@
return TosaTestGen.BuildInfo(result_tensor, compliance)
- def _get_condition_tensor(self, op, cond, error_name):
+ def _get_condition_tensor(self, rng, op, cond, error_name):
if error_name == ErrorIf.CondIfCondNotMatchingBool:
- cond_type = gtu.get_wrong_output_type(op, self.rng, DType.BOOL)
+ cond_type = gtu.get_wrong_output_type(op, rng, DType.BOOL)
else:
cond_type = DType.BOOL
if error_name == ErrorIf.CondIfCondShapeNotSizeOne:
- choice = self.rng.choice([1, 2])
+ choice = rng.choice([1, 2])
if choice == 1:
cond_shape = [2]
else:
@@ -2315,6 +2322,7 @@
def build_cond_if_const(
self,
+ rng,
op,
inputs,
args_dict,
@@ -2331,7 +2339,7 @@
cond = args_dict["condition"]
# Condition tensor
- cond_tens = self._get_condition_tensor(op, cond, error_name)
+ cond_tens = self._get_condition_tensor(rng, op, cond, error_name)
# Make then/else tensors
out_shape = then_tens.shape
@@ -2346,14 +2354,14 @@
incorrect_shape = deepcopy(then_tens.shape)
for i in range(len(incorrect_shape)):
incorrect_shape[i] += (
- self.rng.choice([-3, -2, 2, 3])
+ rng.choice([-3, -2, 2, 3])
if incorrect_shape[i] > 3
- else self.rng.choice([1, 2, 4])
+ else rng.choice([1, 2, 4])
)
- incorrect_arr = np.int32(self.rng.integers(0, 256, size=incorrect_shape))
+ incorrect_arr = np.int32(rng.integers(0, 256, size=incorrect_shape))
- then_arr = np.int32(self.rng.integers(0, 256, size=out_shape))
- else_arr = np.int32(self.rng.integers(0, 256, size=out_shape))
+ then_arr = np.int32(rng.integers(0, 256, size=out_shape))
+ else_arr = np.int32(rng.integers(0, 256, size=out_shape))
# And the result tensor based on any of the outputs
result_tensor = self.ser.addOutput(out_shape, dtype)
@@ -2400,6 +2408,7 @@
def build_cond_if_binary(
self,
+ rng,
op,
inputs,
args_dict,
@@ -2415,7 +2424,7 @@
cond = args_dict["condition"]
# Condition tensor
- cond_tens = self._get_condition_tensor(op, cond, error_name)
+ cond_tens = self._get_condition_tensor(rng, op, cond, error_name)
result_tensor = self.ser.addOutput(a.shape, a.dtype)
@@ -2433,7 +2442,7 @@
]:
incorrect_shape = a.shape.copy()
for i in range(len(incorrect_shape)):
- incorrect_shape[i] += self.rng.choice([-3, -2, 2, 3])
+ incorrect_shape[i] += rng.choice([-3, -2, 2, 3])
incorrect_block_input = deepcopy(a)
incorrect_block_input.shape = incorrect_shape
@@ -2503,7 +2512,14 @@
return TosaTestGen.BuildInfo(result_tensor, compliance)
def build_while_loop(
- self, op, inputs, args_dict, validator_fcns=None, error_name=None, qinfo=None
+ self,
+ rng,
+ op,
+ inputs,
+ args_dict,
+ validator_fcns=None,
+ error_name=None,
+ qinfo=None,
):
assert len(inputs) == 1
a = inputs[0]
@@ -2528,7 +2544,7 @@
if error_name == ErrorIf.InputListOutputListMismatch:
incorrect_acc = deepcopy(acc)
for i in range(len(incorrect_acc.shape)):
- incorrect_acc.shape[i] += self.rng.choice([-3, -2, 2, 3])
+ incorrect_acc.shape[i] += rng.choice([-3, -2, 2, 3])
acc_out = self.ser.addIntermediate(incorrect_acc.shape, acc.dtype)
else:
acc_out = self.ser.addIntermediate(acc.shape, acc.dtype)
@@ -2549,13 +2565,13 @@
]:
incorrect_iter = deepcopy(iter)
for i in range(len(incorrect_iter.shape)):
- incorrect_iter.shape[i] += self.rng.choice([-3, -2, 2, 3])
+ incorrect_iter.shape[i] += rng.choice([-3, -2, 2, 3])
if len(incorrect_iter.shape) == 0:
- incorrect_iter.shape.append(self.rng.choice([-3, -2, 2, 3]))
+ incorrect_iter.shape.append(rng.choice([-3, -2, 2, 3]))
incorrect_acc = deepcopy(acc)
for i in range(len(incorrect_acc.shape)):
- incorrect_acc.shape[i] += self.rng.choice([-3, -2, 2, 3])
+ incorrect_acc.shape[i] += rng.choice([-3, -2, 2, 3])
# COND block (input: iter, output: cond_tens )
self.ser.addBasicBlock(cond_block)
@@ -2571,11 +2587,11 @@
zero_tens = self.ser.addConst([], DType.INT32, [np.int32(0)])
if error_name == ErrorIf.CondGraphOutputNotMatchingBool:
- cond_type = self.rng.choice([DType.INT8, DType.INT32, DType.FP32])
+ cond_type = rng.choice([DType.INT8, DType.INT32, DType.FP32])
else:
cond_type = DType.BOOL
if error_name == ErrorIf.CondGraphOutputShapeNotSizeOne:
- choice = self.rng.choice([1, 2])
+ choice = rng.choice([1, 2])
if choice == 1:
cond_shape = [3]
else:
@@ -2635,6 +2651,7 @@
def build_fft2d(
self,
+ rng,
op,
inputs,
args_dict,
@@ -2646,7 +2663,7 @@
val1, val2 = inputs
inverse = args_dict["inverse"]
- results = OutputShaper.fft2dOp(self.ser, self.rng, val1, val2, error_name)
+ results = OutputShaper.fft2dOp(self.ser, rng, val1, val2, error_name)
input_names = [val1.name, val2.name]
pCount, cCount = op["operands"]
@@ -2657,7 +2674,7 @@
output_dtypes = [res.dtype for res in results]
input_names, output_names = TosaErrorIfArgGen.eiInvalidateInputOutputList(
- self, error_name, input_names, output_names
+ rng, error_name, input_names, output_names
)
if not TosaErrorValidator.evValidateErrorIfs(
@@ -2699,6 +2716,7 @@
def build_rfft2d(
self,
+ rng,
op,
inputs,
args_dict,
@@ -2708,7 +2726,7 @@
):
assert len(inputs) == 1
val = inputs[0]
- results = OutputShaper.rfft2dOp(self.ser, self.rng, val, error_name)
+ results = OutputShaper.rfft2dOp(self.ser, rng, val, error_name)
input_names = [val.name]
pCount, cCount = op["operands"]
@@ -2719,7 +2737,7 @@
output_dtypes = [res.dtype for res in results]
input_names, output_names = TosaErrorIfArgGen.eiInvalidateInputOutputList(
- self, error_name, input_names, output_names
+ rng, error_name, input_names, output_names
)
if not TosaErrorValidator.evValidateErrorIfs(
@@ -2755,12 +2773,19 @@
return TosaTestGen.BuildInfo(results, compliance)
def build_shape_op(
- self, op, inputs, args_dict, validator_fcns=None, error_name=None, qinfo=None
+ self,
+ rng,
+ op,
+ inputs,
+ args_dict,
+ validator_fcns=None,
+ error_name=None,
+ qinfo=None,
):
assert len(inputs) == 2
a, b = inputs
- result_tensor = OutputShaper.addShapeOp(self.ser, self.rng, a, b, error_name)
+ result_tensor = OutputShaper.addShapeOp(self.ser, rng, a, b, error_name)
# Invalidate Input/Output list for error if checks.
input_list = [a.name, b.name]
@@ -2895,8 +2920,9 @@
except KeyError:
raise Exception("Cannot find op with name {}".format(opName))
- # Initialize a new random number generator
- self.rng = np.random.default_rng(self.random_seed)
+ if not self.args.stable_rng:
+ # Initialize a new random number generator per op
+ self.resetGlobalRNG()
_, tgen_fcn, _, agen_fcn = op["build_fcn"]
@@ -2933,37 +2959,53 @@
if shape is not None and len(shape) != r:
continue
self.setTargetShape(shape)
- shapeList = tgen_fcn(self, op, r, error_name)
+ typeStr = self.typeStr(t)
+ if self.args.stable_rng:
+ shape_rng = TosaHashRandomGenerator(
+ self.random_seed,
+ [opName, r, typeStr],
+ self.random_dtype_range,
+ )
+ else:
+ shape_rng = self.global_rng
+ shapeList = tgen_fcn(self, shape_rng, op, r, error_name)
shapeStr = self.shapeStr(shapeList[0])
- typeStr = self.typeStr(t)
# Argument lists consists of tuples of the (str, []) string representation and the build function argument list
argList = []
if agen_fcn:
- argList = agen_fcn(self, opName, shapeList, t, error_name)
+ if self.args.stable_rng:
+ arg_rng = TosaHashRandomGenerator(
+ self.random_seed,
+ [opName, shapeStr, typeStr],
+ self.random_dtype_range,
+ )
+ else:
+ arg_rng = self.global_rng
+
+ argList = agen_fcn(
+ self, arg_rng, opName, shapeList, t, error_name
+ )
else:
argList = [("", [])]
for argStr, args in argList:
+ # Create the test name string - for example: add_1x2x3_i32
if testType == "positive":
- if argStr:
- testStr = "{}_{}_{}_{}".format(
- opName, shapeStr, typeStr, argStr
- )
- else:
- testStr = "{}_{}_{}".format(
- opName, shapeStr, typeStr
- )
- elif testType == "negative":
- if argStr:
- testStr = "{}_ERRORIF_{}_{}_{}_{}".format(
- opName, error_name, shapeStr, typeStr, argStr
- )
- else:
- testStr = "{}_ERRORIF_{}_{}_{}".format(
- opName, error_name, shapeStr, typeStr
- )
+ name_parts = [opName, shapeStr, typeStr]
+ else:
+ assert testType == "negative"
+ name_parts = [
+ opName,
+ "ERRORIF",
+ error_name,
+ shapeStr,
+ typeStr,
+ ]
+ if argStr:
+ name_parts.append(argStr)
+ testStr = "_".join(name_parts)
testList.append(
(opName, testStr, t, error_name, shapeList, args)
@@ -3038,8 +3080,18 @@
# Build the random tensor operands and the test
+ # Set the random number generator
+ if self.args.stable_rng:
+ build_rng = TosaHashRandomGenerator(
+ self.random_seed, [testStr], self.random_dtype_range
+ )
+ else:
+ build_rng = self.global_rng
+
if qgen is not None:
- qinfo = qgen(self, op, dtype_or_dtypeList, error_name)
+ qinfo = qgen(
+ build_rng, self.args.zeropoint, op, dtype_or_dtypeList, error_name
+ )
else:
qinfo = None
@@ -3053,13 +3105,16 @@
# New interface with args info in dictionary
assert "dg_type" in argsDict
- tvgInfo = tvgen_fcn(self, opName, dtypeList, shapeList, argsDict, error_name)
+ tvgInfo = tvgen_fcn(
+ self, build_rng, opName, dtypeList, shapeList, argsDict, error_name
+ )
if tvgInfo.dataGenDict:
tensMeta["data_gen"] = tvgInfo.dataGenDict
tens = tvgInfo.tensorList
result = build_fcn(
self,
+ build_rng,
op,
tens,
argsDict,