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authorJeremy Johnson <jeremy.johnson@arm.com>2023-09-27 14:59:43 +0100
committerEric Kunze <eric.kunze@arm.com>2024-03-19 20:28:57 +0000
commit0a6d1deef02f2bd76b3068d615565f20c46075a5 (patch)
treea90e8a17bb167e83419733d20c5e23f2c9c50af2
parent60dc48c4ddf30f2a76d4cfcf1b40ca57b6f3bf95 (diff)
downloadreference_model-0a6d1deef02f2bd76b3068d615565f20c46075a5.tar.gz
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
Diffstat
-rw-r--r--verif/conformance/tosa_main_profile_ops_info.json6
-rw-r--r--verif/conformance/tosa_verif_conformance_generator.py7
-rw-r--r--verif/generator/tosa_arg_gen.py580
-rw-r--r--verif/generator/tosa_error_if.py78
-rw-r--r--verif/generator/tosa_random_gen.py174
-rw-r--r--verif/generator/tosa_test_gen.py703
-rw-r--r--verif/generator/tosa_verif_build_tests.py9
7 files changed, 901 insertions, 656 deletions
diff --git a/verif/conformance/tosa_main_profile_ops_info.json b/verif/conformance/tosa_main_profile_ops_info.json
index 63a2a9c..fbf5a82 100644
--- a/verif/conformance/tosa_main_profile_ops_info.json
+++ b/verif/conformance/tosa_main_profile_ops_info.json
@@ -723,7 +723,7 @@
"profile": [
"tosa-mi"
],
- "support_for": [ "lazy_data_gen", "generator_select" ],
+ "support_for": [ "lazy_data_gen", "generator_select", "stable_random_gen" ],
"gen_filter": "^conv2d",
"generation": {
"standard": {
@@ -754,7 +754,7 @@
"--target-shape",
"1,65537,1,3",
"--target-shape",
- "1,2,65531,2",
+ "1,2,65530,2",
"--tensor-dim-range",
"1,16",
"--max-conv-dilation",
@@ -1881,7 +1881,7 @@
"profile": [
"tosa-mi"
],
- "support_for": [ "lazy_data_gen", "generator_select" ],
+ "support_for": [ "lazy_data_gen", "generator_select", "stable_random_gen" ],
"generation": {
"standard": {
"generator_args": [
diff --git a/verif/conformance/tosa_verif_conformance_generator.py b/verif/conformance/tosa_verif_conformance_generator.py
index 7c82f31..5402c21 100644
--- a/verif/conformance/tosa_verif_conformance_generator.py
+++ b/verif/conformance/tosa_verif_conformance_generator.py
@@ -138,6 +138,8 @@ def build_op_tests(
if "lazy_data_gen" in supports and args.lazy_data_generation:
build_cmd_base.append("--lazy-data-generation")
+ if "stable_random_gen" in supports and not args.global_random_generation:
+ build_cmd_base.append("--stable-random-generation")
if "generator_select" in supports:
if selector_info is None:
@@ -545,6 +547,11 @@ def parse_args(argv=None):
help="Type of tests produced (default is both)",
)
parser.add_argument(
+ "--global-random-generation",
+ action="store_true",
+ help="Disable stable random generation of tests that support this mode",
+ )
+ parser.add_argument(
"--lazy-data-generation",
action="store_true",
help="Enable lazy data generation (only for tosa-mi)",
diff --git a/verif/generator/tosa_arg_gen.py b/verif/generator/tosa_arg_gen.py
index a2ef5bf..83487a1 100644
--- a/verif/generator/tosa_arg_gen.py
+++ b/verif/generator/tosa_arg_gen.py
@@ -30,48 +30,48 @@ class TosaQuantGen:
pass
@staticmethod
- def getZeroPoint(testGen, dtype, error_name=None):
+ def getZeroPoint(rng, zeropoint, dtype, error_name=None):
if dtype == DType.INT8:
- if testGen.args.zeropoint is not None:
- return min(127, max(-128, testGen.args.zeropoint))
- return testGen.randInt(-128, 128)
+ if zeropoint is not None:
+ return min(127, max(-128, zeropoint))
+ return rng.randInt(-128, 128)
elif dtype == DType.UINT8:
- if testGen.args.zeropoint is not None:
- return min(255, max(0, testGen.args.zeropoint))
- return testGen.randInt(0, 256)
+ if zeropoint is not None:
+ return min(255, max(0, zeropoint))
+ return rng.randInt(0, 256)
elif error_name in [
ErrorIf.InputZeroPointNotZero,
ErrorIf.WeightZeroPointNotZero,
ErrorIf.OutputZeroPointNotZero,
]:
- zero_point = testGen.randInt(-128, 128)
+ zero_point = rng.randInt(-128, 128)
if zero_point == 0:
zero_point = 1
return zero_point
return 0
@staticmethod
- def qgUnary(testGen, op, dtype, error_name=None):
+ def qgUnary(rng, zeropoint, op, dtype, error_name=None):
if error_name == ErrorIf.InputZeroPointNotZero:
qinfo = [
- TosaQuantGen.getZeroPoint(testGen, dtype, error_name),
- TosaQuantGen.getZeroPoint(testGen, dtype),
+ TosaQuantGen.getZeroPoint(rng, zeropoint, dtype, error_name),
+ TosaQuantGen.getZeroPoint(rng, zeropoint, dtype),
]
elif error_name == ErrorIf.OutputZeroPointNotZero:
qinfo = [
- TosaQuantGen.getZeroPoint(testGen, dtype),
- TosaQuantGen.getZeroPoint(testGen, dtype, error_name),
+ TosaQuantGen.getZeroPoint(rng, zeropoint, dtype),
+ TosaQuantGen.getZeroPoint(rng, zeropoint, dtype, error_name),
]
else:
qinfo = [
- TosaQuantGen.getZeroPoint(testGen, dtype),
- TosaQuantGen.getZeroPoint(testGen, dtype),
+ TosaQuantGen.getZeroPoint(rng, zeropoint, dtype),
+ TosaQuantGen.getZeroPoint(rng, zeropoint, dtype),
]
return qinfo
@staticmethod
- def qgConv(testGen, op, dtype_or_dtypeList, error_name=None):
+ def qgConv(rng, zeropoint, op, dtype_or_dtypeList, error_name=None):
if isinstance(dtype_or_dtypeList, list):
# a list of [input, weights, accumulator] dtypes
dtypeList = dtype_or_dtypeList
@@ -81,32 +81,32 @@ class TosaQuantGen:
if error_name == ErrorIf.InputZeroPointNotZero:
qinfo = [
- TosaQuantGen.getZeroPoint(testGen, dtypeList[0], error_name),
- TosaQuantGen.getZeroPoint(testGen, dtypeList[1]),
+ TosaQuantGen.getZeroPoint(rng, zeropoint, dtypeList[0], error_name),
+ TosaQuantGen.getZeroPoint(rng, zeropoint, dtypeList[1]),
]
elif error_name == ErrorIf.WeightZeroPointNotZero:
qinfo = [
- TosaQuantGen.getZeroPoint(testGen, dtypeList[0]),
- TosaQuantGen.getZeroPoint(testGen, dtypeList[1], error_name),
+ TosaQuantGen.getZeroPoint(rng, zeropoint, dtypeList[0]),
+ TosaQuantGen.getZeroPoint(rng, zeropoint, dtypeList[1], error_name),
]
else:
qinfo = [
- TosaQuantGen.getZeroPoint(testGen, dtypeList[0]),
- TosaQuantGen.getZeroPoint(testGen, dtypeList[1]),
+ TosaQuantGen.getZeroPoint(rng, zeropoint, dtypeList[0]),
+ TosaQuantGen.getZeroPoint(rng, zeropoint, dtypeList[1]),
]
return qinfo
@staticmethod
- def qgMatmul(testGen, op, dtype, error_name=None):
+ def qgMatmul(rng, zeropoint, op, dtype, error_name=None):
if error_name == ErrorIf.InputZeroPointNotZero:
qinfo = [
- TosaQuantGen.getZeroPoint(testGen, dtype, error_name),
- TosaQuantGen.getZeroPoint(testGen, dtype, error_name),
+ TosaQuantGen.getZeroPoint(rng, zeropoint, dtype, error_name),
+ TosaQuantGen.getZeroPoint(rng, zeropoint, dtype, error_name),
]
else:
qinfo = [
- TosaQuantGen.getZeroPoint(testGen, dtype),
- TosaQuantGen.getZeroPoint(testGen, dtype),
+ TosaQuantGen.getZeroPoint(rng, zeropoint, dtype),
+ TosaQuantGen.getZeroPoint(rng, zeropoint, dtype),
]
return qinfo
@@ -166,9 +166,9 @@ class TosaTensorGen:
pass
@staticmethod
- def tgBasic(testGen, opName, rank, error_name=None):
- pl, const = opName["operands"]
- shape = testGen.makeShape(rank)
+ def tgBasic(testGen, rng, op, rank, error_name=None):
+ pl, const = op["operands"]
+ shape = testGen.makeShape(rng, rank)
# Constrict the overall size of the shape when creating ERROR_IF tests
if error_name:
@@ -181,20 +181,20 @@ class TosaTensorGen:
# Generates an input rank mismatch for operators with more than one input
if error_name == ErrorIf.RankMismatch:
if rank == 1 and i != 1:
- shape = testGen.makeShape(rank + testGen.rng.choice([1, 2, 3]))
+ shape = testGen.makeShape(rng, rank + rng.choice([1, 2, 3]))
elif i != 1:
- shape = testGen.makeShape(rank + testGen.rng.choice([-1, 1]))
+ shape = testGen.makeShape(rng, rank + rng.choice([-1, 1]))
return shape_list
@staticmethod
- def tgNHWC(testGen, opName, rank, error_name=None):
- pl, const = opName["operands"]
+ def tgNHWC(testGen, rng, op, rank, error_name=None):
+ pl, const = op["operands"]
if error_name != ErrorIf.WrongRank:
assert rank == 4
- shape = testGen.makeShape(rank)
+ shape = testGen.makeShape(rng, rank)
shape = testGen.constrictBatchSize(shape)
# Constrict the overall size of the shape when creating ERROR_IF tests
@@ -208,7 +208,7 @@ class TosaTensorGen:
return shape_list
@staticmethod
- def tgGather(testGen, opName, rank, error_name=None):
+ def tgGather(testGen, rng, opName, rank, error_name=None):
pl, const = opName["operands"]
assert pl == 2
@@ -216,18 +216,18 @@ class TosaTensorGen:
if error_name != ErrorIf.WrongRank:
assert rank == 3
- values_shape = testGen.makeShape(rank)
+ values_shape = testGen.makeShape(rng, rank)
values_shape = testGen.constrictBatchSize(values_shape)
N = values_shape[0]
- W = testGen.makeDimension()
+ W = testGen.makeDimension(rng)
indices_shape = [N, W]
shape_list = [values_shape, indices_shape]
return shape_list
@staticmethod
- def tgScatter(testGen, opName, rank, error_name=None):
+ def tgScatter(testGen, rng, opName, rank, error_name=None):
pl, const = opName["operands"]
assert pl == 3
@@ -235,7 +235,7 @@ class TosaTensorGen:
if error_name != ErrorIf.WrongRank:
assert rank == 3
- values_in_shape = testGen.makeShape(rank)
+ values_in_shape = testGen.makeShape(rng, rank)
values_in_shape = testGen.constrictBatchSize(values_in_shape)
N = values_in_shape[0]
@@ -246,7 +246,7 @@ class TosaTensorGen:
# once (having a W greater than K means that you have to repeat a K index)
W_min = min(testGen.args.tensor_shape_range[0], K)
W_max = min(testGen.args.tensor_shape_range[1], K)
- W = testGen.randInt(W_min, W_max) if W_min < W_max else W_min
+ W = rng.randInt(W_min, W_max) if W_min < W_max else W_min
input_shape = [N, W, C]
@@ -258,14 +258,14 @@ class TosaTensorGen:
return shape_list
@staticmethod
- def _get_broadcast_shapes(testGen, num_shapes, rank, error_name=None):
- shape = testGen.makeShape(rank)
+ def _get_broadcast_shapes(testGen, rng, num_shapes, rank, error_name=None):
+ shape = testGen.makeShape(rng, rank)
shape_list = []
# Choose one of the inputs to broadcast
# Note: Simplifies OutputShaper code if we don't change first shape for errors
- bcast_idx = testGen.randInt(0 if error_name is None else 1, num_shapes)
- fuzz_idx = testGen.randInt(0, rank)
+ bcast_idx = rng.randInt(0 if error_name is None else 1, num_shapes)
+ fuzz_idx = rng.randInt(0, rank)
for i in range(num_shapes):
shape_bcast = shape.copy()
@@ -278,13 +278,13 @@ class TosaTensorGen:
if i == bcast_idx:
if error_name == ErrorIf.RankMismatch:
# Add one rank to the shape (or more for rank of 1)
- extra_ranks = testGen.rng.choice([1, 2, 3]) if rank == 1 else 1
+ extra_ranks = rng.choice([1, 2, 3]) if rank == 1 else 1
shape_bcast = np.concatenate(
- (shape_bcast, testGen.makeShape(extra_ranks))
+ (shape_bcast, testGen.makeShape(rng, extra_ranks))
)
if rank != 1:
# Either keep the extra rank, or remove it
- new_len = testGen.rng.choice([-2, len(shape_bcast)])
+ new_len = rng.choice([-2, len(shape_bcast)])
shape_bcast = shape_bcast[:new_len]
elif error_name == ErrorIf.BroadcastShapesMismatch:
shape_bcast[fuzz_idx] += 2
@@ -296,30 +296,32 @@ class TosaTensorGen:
return shape_list
@staticmethod
- def tgBroadcastFuzz(testGen, op, rank, error_name=None):
+ def tgBroadcastFuzz(testGen, rng, op, rank, error_name=None):
pl, const = op["operands"]
num_shapes = pl + const
return TosaTensorGen._get_broadcast_shapes(
- testGen, num_shapes, rank, error_name
+ testGen, rng, num_shapes, rank, error_name
)
@staticmethod
- def tgMul(testGen, op, rank, error_name=None):
+ def tgMul(testGen, rng, op, rank, error_name=None):
# Get broadcast shapes for the first 2 inputs as the 3rd is shift
- shape_list = TosaTensorGen._get_broadcast_shapes(testGen, 2, rank, error_name)
+ shape_list = TosaTensorGen._get_broadcast_shapes(
+ testGen, rng, 2, rank, error_name
+ )
# Add a single dimension tensor for shift
shape_list.append([1])
return shape_list
@staticmethod
- def tgConv2D(testGen, op, rank, error_name=None):
+ def tgConv2D(testGen, rng, op, rank, error_name=None):
pl, const = op["operands"]
if error_name != ErrorIf.WrongRank:
assert rank == 4
# IFM dimensions are NHWC
- ifm_shape = testGen.makeShape(rank)
+ ifm_shape = testGen.makeShape(rng, rank)
ifm_shape = testGen.constrictBatchSize(ifm_shape)
# Constrict the overall size of the shape when creating ERROR_IF tests
@@ -332,7 +334,7 @@ class TosaTensorGen:
filter_hw = op["filter"]
# Generate a random OFM depth
- ofm_depth = testGen.makeDimension()
+ ofm_depth = testGen.makeDimension(rng)
# The filter dimensions are OHWI
filter_shape = np.asarray([ofm_depth, filter_hw[0], filter_hw[1], ifm_shape[3]])
@@ -343,14 +345,14 @@ class TosaTensorGen:
return [ifm_shape, filter_shape, bias_shape]
@staticmethod
- def tgConv3D(testGen, op, rank, error_name=None):
+ def tgConv3D(testGen, rng, op, rank, error_name=None):
pl, const = op["operands"]
if error_name != ErrorIf.WrongRank:
assert rank == 5
# IFM dimensions are NDHWC
- ifm_shape = testGen.makeShape(rank)
+ ifm_shape = testGen.makeShape(rng, rank)
ifm_shape = testGen.constrictBatchSize(ifm_shape)
# Constrict the overall size of the shape when creating ERROR_IF tests
@@ -363,7 +365,7 @@ class TosaTensorGen:
filter_dhw = op["filter"]
# Generate a random OFM channel
- ofm_channel = testGen.makeDimension()
+ ofm_channel = testGen.makeDimension(rng)
# The filter dimensions are ODHWI
filter_shape = np.asarray(
@@ -376,14 +378,14 @@ class TosaTensorGen:
return [ifm_shape, filter_shape, bias_shape]
@staticmethod
- def tgTransposeConv2D(testGen, op, rank, error_name=None):
+ def tgTransposeConv2D(testGen, rng, op, rank, error_name=None):
pl, const = op["operands"]
if error_name != ErrorIf.WrongRank:
assert rank == 4
# IFM dimensions are NHWC
- ifm_shape = testGen.makeShape(rank)
+ ifm_shape = testGen.makeShape(rng, rank)
ifm_shape = testGen.constrictBatchSize(ifm_shape)
# Constrict the overall size of the shape when creating ERROR_IF tests
@@ -396,7 +398,7 @@ class TosaTensorGen:
filter_hw = op["filter"]
# Generate a random OFM depth
- ofm_depth = testGen.makeDimension()
+ ofm_depth = testGen.makeDimension(rng)
# The filter dimensions are OHWI
filter_shape = np.asarray([ofm_depth, filter_hw[0], filter_hw[1], ifm_shape[3]])
@@ -407,7 +409,7 @@ class TosaTensorGen:
return [ifm_shape, filter_shape, bias_shape]
@staticmethod
- def tgDepthwiseConv2D(testGen, op, rank, error_name=None):
+ def tgDepthwiseConv2D(testGen, rng, op, rank, error_name=None):
pl, const = op["operands"]
if error_name != ErrorIf.WrongRank:
@@ -415,7 +417,7 @@ class TosaTensorGen:
assert pl == 1 and const == 2
# IFM dimensions are NHWC
- ifm_shape = testGen.makeShape(rank)
+ ifm_shape = testGen.makeShape(rng, rank)
ifm_shape = testGen.constrictBatchSize(ifm_shape)
# Constrict the overall size of the shape when creating ERROR_IF tests
@@ -431,7 +433,7 @@ class TosaTensorGen:
# Generate a random OFM depth, but don't let it get too big because
# the output depth is M * C
filter_m = (
- testGen.makeDimension() % (testGen.args.tensor_shape_range[1] // 4)
+ testGen.makeDimension(rng) % (testGen.args.tensor_shape_range[1] // 4)
) + 1
# The filter dimensions are HWCM
@@ -443,7 +445,7 @@ class TosaTensorGen:
return [ifm_shape, filter_shape, bias_shape]
@staticmethod
- def tgFFT2d(testGen, op, rank, error_name=None):
+ def tgFFT2d(testGen, rng, op, rank, error_name=None):
pl, const = op["operands"]
if error_name != ErrorIf.WrongRank:
@@ -451,7 +453,7 @@ class TosaTensorGen:
assert pl == 2 and const == 0
# IFM dimensions are NHW
- ifm_shape = testGen.makeShape(rank)
+ ifm_shape = testGen.makeShape(rng, rank)
# Select nearest lower power of two from input height and width
ifm_shape[1] = 2 ** int(math.log(ifm_shape[1], 2))
@@ -466,7 +468,7 @@ class TosaTensorGen:
inc_h = 2 if ifm_shape[1] == 1 else 1
inc_w = 2 if ifm_shape[2] == 1 else 1
inc_choices = [(inc_h, 0), (0, inc_w), (inc_h, inc_w)]
- selected_inc = testGen.rng.choice(inc_choices)
+ selected_inc = rng.choice(inc_choices)
ifm_shape[1] += selected_inc[0]
ifm_shape[2] += selected_inc[1]
@@ -474,15 +476,15 @@ class TosaTensorGen:
ifm_shapes = [ifm_shape.copy(), ifm_shape.copy()]
if error_name == ErrorIf.FFTInputShapeMismatch:
- modify_shape = testGen.rng.choice([0, 1])
+ modify_shape = rng.choice([0, 1])
# Only modify kernel (H, W)
- modify_dim = testGen.rng.choice([1, 2])
+ modify_dim = rng.choice([1, 2])
ifm_shapes[modify_shape][modify_dim] *= 2
return [ifm_shapes[0], ifm_shapes[1]]
@staticmethod
- def tgRFFT2d(testGen, op, rank, error_name=None):
+ def tgRFFT2d(testGen, rng, op, rank, error_name=None):
pl, const = op["operands"]
if error_name != ErrorIf.WrongRank:
@@ -490,7 +492,7 @@ class TosaTensorGen:
assert pl == 1 and const == 0
# IFM dimensions are NHW
- ifm_shape = testGen.makeShape(rank)
+ ifm_shape = testGen.makeShape(rng, rank)
# Select nearest lower power of two from input height and width
ifm_shape[1] = 2 ** int(math.log(ifm_shape[1], 2))
@@ -506,7 +508,7 @@ class TosaTensorGen:
inc_h = 2 if ifm_shape[1] == 1 else 1
inc_w = 2 if ifm_shape[2] == 1 else 1
inc_choices = [(inc_h, 0), (0, inc_w), (inc_h, inc_w)]
- selected_inc = testGen.rng.choice(inc_choices)
+ selected_inc = rng.choice(inc_choices)
ifm_shape[1] += selected_inc[0]
ifm_shape[2] += selected_inc[1]
@@ -515,19 +517,19 @@ class TosaTensorGen:
return [ifm_shape]
@staticmethod
- def tgFullyConnected(testGen, op, rank, error_name=None):
+ def tgFullyConnected(testGen, rng, op, rank, error_name=None):
pl, const = op["operands"]
if error_name != ErrorIf.WrongRank:
assert rank == 2
- input_shape = testGen.makeShape(rank)
+ input_shape = testGen.makeShape(rng, rank)
# Constrict the overall size of the shape when creating ERROR_IF tests
if error_name:
input_shape = TosaErrorIfArgGen.eiRestrictDimensions(input_shape)
- filter_oc = testGen.rng.integers(
+ filter_oc = rng.integers(
low=testGen.args.tensor_shape_range[0],
high=testGen.args.tensor_shape_range[1],
size=1,
@@ -539,14 +541,14 @@ class TosaTensorGen:
return [input_shape, filter_shape, bias_shape]
@staticmethod
- def tgMatmul(testGen, op, rank, error_name=None):
+ def tgMatmul(testGen, rng, op, rank, error_name=None):
pl, const = op["operands"]
if error_name != ErrorIf.WrongRank:
assert rank == 3
assert pl == 2 and const == 0
- a_shape = testGen.makeShape(rank)
+ a_shape = testGen.makeShape(rng, rank)
# Constrict the overall size of the shape when creating ERROR_IF tests
if error_name:
@@ -554,7 +556,7 @@ class TosaTensorGen:
# Get a random number for b_oc even if target shape is defined
b_oc = np.int32(
- testGen.rng.integers(
+ rng.integers(
low=testGen.args.tensor_shape_range[0],
high=testGen.args.tensor_shape_range[1],
size=1,
@@ -568,24 +570,24 @@ class TosaTensorGen:
return [a_shape, b_shape]
@staticmethod
- def tgConcat(testGen, opName, rank, error_name=None):
- pl, const = opName["operands"]
- shape = testGen.makeShape(rank)
+ def tgConcat(testGen, rng, op, rank, error_name=None):
+ pl, const = op["operands"]
+ shape = testGen.makeShape(rng, rank)
# Create extra tensors to concat.
# Take into account value of pl when getting maximum number of concats
- num_tensors = testGen.randInt(0, 4)
+ num_tensors = rng.randInt(0, 4)
shape_list = []
for i in range(pl + const + num_tensors):
if error_name == ErrorIf.ConcatInputRankMismatch and i != 0:
- remove = testGen.rng.choice([True, False])
+ remove = rng.choice([True, False])
wrongShape = shape.copy()
if remove and len(shape) > 1:
wrongShape = wrongShape[1:]
else:
wrongShape = list(wrongShape)
- wrongShape.append(testGen.rng.integers(1, 10))
+ wrongShape.append(rng.integers(1, 10))
shape_list.append(wrongShape)
else:
@@ -594,7 +596,7 @@ class TosaTensorGen:
return shape_list
@staticmethod
- def tgConcatConstInput(testGen, shapeList, axis, error_name=None):
+ def tgConcatConstInput(rng, shapeList, axis, error_name=None):
if error_name in [
ErrorIf.AxisSmallerZero,
ErrorIf.AxisLargerRank,
@@ -610,7 +612,7 @@ class TosaTensorGen:
for shape in shapeList[1:]:
# Negative test shapeLists are created individually for each test,
# so no need to copy the shape before altering it.
- shape[(axis + 1) % len(shape)] += testGen.rng.integers(5, 10)
+ shape[(axis + 1) % len(shape)] += rng.integers(5, 10)
return shapeList
# Create copy of shape we are going to split (so we don't alter shapeList)
@@ -630,7 +632,7 @@ class TosaTensorGen:
# invalidate dimensions
if error_name == ErrorIf.ConcatInputDimMismatch:
- shape[(axis + 1) % len(shape)] += testGen.rng.integers(5, 10)
+ shape[(axis + 1) % len(shape)] += rng.integers(5, 10)
else:
shape[axis] = remaining_length
@@ -672,12 +674,12 @@ class TosaTensorValuesGen:
}
@staticmethod
- def _get_data_range(testGen, dtype, highValueLookup, lowValueLookup=None):
+ def _get_data_range(rng, dtype, highValueLookup, lowValueLookup=None):
# Return a tuple of (low,high) data range values for the given data
# type using a combination of per operator table limits, data limits
# and user supplied ranges for FP numbers
if dtype in highValueLookup:
- type_range = testGen.getDTypeRange(dtype, high_inclusive=True)
+ type_range = rng.dTypeRange(dtype, high_inclusive=True)
high_val = highValueLookup[dtype]
if lowValueLookup is not None and dtype in lowValueLookup:
low_val = lowValueLookup[dtype]
@@ -703,7 +705,7 @@ class TosaTensorValuesGen:
@staticmethod
def tvgLazyGenDefault(
- testGen, opName, dtypeList, shapeList, argsDict, error_name=None
+ testGen, rng, opName, dtypeList, shapeList, argsDict, error_name=None
):
# Variable inputs versus constants
pCount, cCount = testGen.TOSA_OP_LIST[opName]["operands"]
@@ -742,8 +744,8 @@ class TosaTensorValuesGen:
):
# Change from inclusive to exclusive range
data_range = (data_range[0], data_range[1] + 1)
- # Ignore lazy data gen option and create data array using any range limits
+ # Ignore lazy data gen option and create data array using any range limits
if "fixed_data" in argsDict and argsDict["fixed_data"][idx] is not None:
if dtype == DType.SHAPE:
arr = np.int64(argsDict["fixed_data"][idx])
@@ -756,7 +758,7 @@ class TosaTensorValuesGen:
else:
assert False, "Unsupported fixed_data type"
else:
- arr = testGen.getRandTensor(shape, dtype, data_range)
+ arr = rng.randTensor(shape, dtype, data_range)
if roundMode:
arr = np.round(arr)
if idx < pCount:
@@ -802,8 +804,7 @@ class TosaTensorValuesGen:
info["data"] = [int(i) for i in argsDict["fixed_data"][idx]]
tens_meta["fixed_data_info"] = info
else:
- # TODO - generate seed for this generator based on test
- info["rng_seed"] = 42
+ info["rng_seed"] = rng.seed
data_range = None
if "data_range_list" in argsDict:
@@ -814,9 +815,7 @@ class TosaTensorValuesGen:
data_range = argsDict["data_range"]
if data_range is None:
- data_range = testGen.getDTypeRange(
- dtypeList[idx], high_inclusive=True
- )
+ data_range = rng.dTypeRange(dtypeList[idx], high_inclusive=True)
info["range"] = [str(v) for v in data_range]
tens_meta["pseudo_random_info"] = info
elif dg_type == gtu.DataGenType.DOT_PRODUCT:
@@ -836,7 +835,7 @@ class TosaTensorValuesGen:
elif dg_type == gtu.DataGenType.FULL_RANGE:
info = {}
info["start_val"] = int(
- testGen.randInt(0, gtu.DTYPE_ATTRIBUTES[dtypeList[idx]]["fullset"])
+ rng.randInt(0, gtu.DTYPE_ATTRIBUTES[dtypeList[idx]]["fullset"])
)
tens_meta["full_range_info"] = info
else:
@@ -883,7 +882,9 @@ class TosaTensorValuesGen:
return TosaTensorValuesGen.TVGInfo(tens_ser_list, tens_data)
@staticmethod
- def tvgNegate(testGen, opName, dtypeList, shapeList, argsDict, error_name=None):
+ def tvgNegate(
+ testGen, rng, opName, dtypeList, shapeList, argsDict, error_name=None
+ ):
if dtypeList[0] == DType.INT32 and error_name is None:
# Integer test
op = testGen.TOSA_OP_LIST[opName]
@@ -896,7 +897,7 @@ class TosaTensorValuesGen:
max_val = (1 << 31) - 1
min_val = -max_val
arr = np.int32(
- testGen.rng.integers(low=min_val, high=(max_val + 1), size=shapeList[0])
+ rng.integers(low=min_val, high=(max_val + 1), size=shapeList[0])
)
tens_ser_list = []
tens_ser_list.append(
@@ -906,7 +907,7 @@ class TosaTensorValuesGen:
else:
# ERROR_IF or floating point test
return TosaTensorValuesGen.tvgLazyGenDefault(
- testGen, opName, dtypeList, shapeList, argsDict, error_name
+ testGen, rng, opName, dtypeList, shapeList, argsDict, error_name
)
# Set the ADD/SUB data range to half the largest value to avoid infinities
@@ -917,7 +918,9 @@ class TosaTensorValuesGen:
}
@staticmethod
- def tvgAddSub(testGen, opName, dtypeList, shapeList, argsDict, error_name=None):
+ def tvgAddSub(
+ testGen, rng, opName, dtypeList, shapeList, argsDict, error_name=None
+ ):
if dtypeList[0] in (DType.INT32, DType.SHAPE) and error_name is None:
# Make sure the integer operation does not cause value saturation - where
# the number wraps due to limited number of bits to store the answer
@@ -929,8 +932,8 @@ class TosaTensorValuesGen:
tens_ser_list = []
add = op["op"] in (Op.ADD, Op.ADD_SHAPE)
data_range = testGen.args.tensor_shape_range
- a_arr = testGen.getRandTensor(shapeList[0], dtypeList[0], data_range)
- b_arr = testGen.getRandTensor(shapeList[1], dtypeList[1], data_range)
+ a_arr = rng.randTensor(shapeList[0], dtypeList[0], data_range)
+ b_arr = rng.randTensor(shapeList[1], dtypeList[1], data_range)
if add:
res_arr = np.add(a_arr, b_arr, dtype=np.int64)
else:
@@ -985,18 +988,18 @@ class TosaTensorValuesGen:
else:
# ERROR_IF or floating point test
data_range = TosaTensorValuesGen._get_data_range(
- testGen, dtypeList[0], TosaTensorValuesGen.TVG_FLOAT_HIGH_VALUE_ADDSUB
+ rng, dtypeList[0], TosaTensorValuesGen.TVG_FLOAT_HIGH_VALUE_ADDSUB
)
if data_range:
argsDict["data_range"] = data_range
return TosaTensorValuesGen.tvgLazyGenDefault(
- testGen, opName, dtypeList, shapeList, argsDict, error_name
+ testGen, rng, opName, dtypeList, shapeList, argsDict, error_name
)
@staticmethod
def tvgCondIfWhileLoop(
- testGen, opName, dtypeList, shapeList, argsDict, error_name=None
+ testGen, rng, opName, dtypeList, shapeList, argsDict, error_name=None
):
if dtypeList[0] in (
DType.INT32,
@@ -1012,11 +1015,9 @@ class TosaTensorValuesGen:
tens_ser_list = []
for idx, shape in enumerate(shapeList[:]):
if dtypeList[0] == DType.INT32:
- arr = testGen.getRandTensor(shapeList[idx], DType.INT16)
+ arr = rng.randTensor(shapeList[idx], DType.INT16)
else:
- arr = np.int32(
- testGen.rng.integers(low=0, high=32, size=shapeList[idx])
- )
+ arr = np.int32(rng.integers(low=0, high=32, size=shapeList[idx]))
if pRemain > 0:
tens_ser_list.append(
testGen.ser.addPlaceholder(shape, dtypeList[idx], arr)
@@ -1030,12 +1031,12 @@ class TosaTensorValuesGen:
return TosaTensorValuesGen.TVGInfo(tens_ser_list, None)
else:
return TosaTensorValuesGen.tvgLazyGenDefault(
- testGen, opName, dtypeList, shapeList, argsDict, error_name
+ testGen, rng, opName, dtypeList, shapeList, argsDict, error_name
)
@staticmethod
def tvgArithmeticRightShift(
- testGen, opName, dtypeList, shapeList, argsDict, error_name=None
+ testGen, rng, opName, dtypeList, shapeList, argsDict, error_name=None
):
op = testGen.TOSA_OP_LIST[opName]
pCount, cCount = op["operands"]
@@ -1048,34 +1049,38 @@ class TosaTensorValuesGen:
for idx, shape in enumerate(shapeList[:]):
if idx == 1:
if dtypeList[idx] == DType.INT8:
- arr = np.int32(testGen.rng.integers(low=0, high=8, size=shape))
+ arr = np.int32(rng.integers(low=0, high=8, size=shape))
elif dtypeList[idx] == DType.INT16:
- arr = np.int32(testGen.rng.integers(low=0, high=16, size=shape))
+ arr = np.int32(rng.integers(low=0, high=16, size=shape))
elif dtypeList[idx] == DType.INT32:
- arr = np.int32(testGen.rng.integers(low=0, high=32, size=shape))
+ arr = np.int32(rng.integers(low=0, high=32, size=shape))
elif error_name == ErrorIf.WrongInputType:
- arr = np.int32(testGen.rng.integers(low=0, high=8, size=shape))
+ arr = np.int32(rng.integers(low=0, high=8, size=shape))
else:
raise Exception("OpArithmeticRightShift: invalid input dtype")
else:
- arr = testGen.getRandTensor(shape, dtypeList[idx])
+ arr = rng.randTensor(shape, dtypeList[idx])
tens_ser_list.append(testGen.ser.addPlaceholder(shape, dtypeList[idx], arr))
return TosaTensorValuesGen.TVGInfo(tens_ser_list, None)
@staticmethod
- def tvgReshape(testGen, opName, dtypeList, shapeList, argsDict, error_name=None):
+ def tvgReshape(
+ testGen, rng, opName, dtypeList, shapeList, argsDict, error_name=None
+ ):
dtypeList[1] = DType.SHAPE
shapeList[1] = [len(argsDict["new_shape"])]
# Create a new list for the pre-generated data in argsDict["fixed_data"]
argsDict["fixed_data"] = [None, argsDict["new_shape"]]
return TosaTensorValuesGen.tvgLazyGenDefault(
- testGen, opName, dtypeList, shapeList, argsDict, error_name
+ testGen, rng, opName, dtypeList, shapeList, argsDict, error_name
)
@staticmethod
- def tvgRescale(testGen, opName, dtypeList, shapeList, argsDict, error_name=None):
+ def tvgRescale(
+ testGen, rng, opName, dtypeList, shapeList, argsDict, error_name=None
+ ):
scale32 = argsDict["scale"]
multiplier_arr = argsDict["multiplier"]
shift_arr = argsDict["shift"]
@@ -1091,11 +1096,11 @@ class TosaTensorValuesGen:
argsDict["fixed_data"] = [None, multiplier_arr, shift_arr]
return TosaTensorValuesGen.tvgLazyGenDefault(
- testGen, opName, dtypeList, shapeList, argsDict, error_name
+ testGen, rng, opName, dtypeList, shapeList, argsDict, error_name
)
@staticmethod
- def tvgPad(testGen, opName, dtypeList, shapeList, argsDict, error_name=None):
+ def tvgPad(testGen, rng, 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()
dtypeList[1] = DType.SHAPE
@@ -1104,11 +1109,11 @@ class TosaTensorValuesGen:
argsDict["fixed_data"] = [None, pad_values]
return TosaTensorValuesGen.tvgLazyGenDefault(
- testGen, opName, dtypeList, shapeList, argsDict, error_name
+ testGen, rng, opName, dtypeList, shapeList, argsDict, error_name
)
@staticmethod
- def tvgSlice(testGen, opName, dtypeList, shapeList, argsDict, error_name=None):
+ def tvgSlice(testGen, rng, opName, dtypeList, shapeList, argsDict, error_name=None):
dtypeList[1] = DType.SHAPE
shapeList[1] = [len(argsDict["start"])]
dtypeList[2] = DType.SHAPE
@@ -1117,30 +1122,34 @@ class TosaTensorValuesGen:
argsDict["fixed_data"] = [None, argsDict["start"], argsDict["size"]]
return TosaTensorValuesGen.tvgLazyGenDefault(
- testGen, opName, dtypeList, shapeList, argsDict, error_name
+ testGen, rng, opName, dtypeList, shapeList, argsDict, error_name
)
@staticmethod
- def tvgTile(testGen, opName, dtypeList, shapeList, argsDict, error_name=None):
+ def tvgTile(testGen, rng, opName, dtypeList, shapeList, argsDict, error_name=None):
dtypeList[1] = DType.SHAPE
shapeList[1] = [len(argsDict["multiples"])]
argsDict["fixed_data"] = [None, argsDict["multiples"]]
return TosaTensorValuesGen.tvgLazyGenDefault(
- testGen, opName, dtypeList, shapeList, argsDict, error_name
+ testGen, rng, opName, dtypeList, shapeList, argsDict, error_name
)
@staticmethod
- def tvgSelect(testGen, opName, dtypeList, shapeList, argsDict, error_name=None):
+ def tvgSelect(
+ testGen, rng, opName, dtypeList, shapeList, argsDict, error_name=None
+ ):
# Set datatype of condition tensor to boolean
dtypeList[0] = DType.BOOL
return TosaTensorValuesGen.tvgLazyGenDefault(
- testGen, opName, dtypeList, shapeList, argsDict, error_name
+ testGen, rng, opName, dtypeList, shapeList, argsDict, error_name
)
@staticmethod
- def tvgIntDiv(testGen, opName, dtypeList, shapeList, argsDict, error_name=None):
+ def tvgIntDiv(
+ testGen, rng, opName, dtypeList, shapeList, argsDict, error_name=None
+ ):
if error_name is None:
op = testGen.TOSA_OP_LIST[opName]
pCount, cCount = op["operands"]
@@ -1154,8 +1163,8 @@ class TosaTensorValuesGen:
# 1. divisor == 0
# 2. dividend == -(1<<31) and divisor == -1
while True:
- dividend_arr = testGen.getRandTensor(shapeList[0], dtypeList[0])
- divisor_arr = testGen.getRandTensor(shapeList[1], dtypeList[1])
+ dividend_arr = rng.randTensor(shapeList[0], dtypeList[0])
+ divisor_arr = rng.randTensor(shapeList[1], dtypeList[1])
if (divisor_arr == 0).any():
continue
@@ -1175,7 +1184,7 @@ class TosaTensorValuesGen:
return TosaTensorValuesGen.TVGInfo(tens_ser_list, None)
else:
return TosaTensorValuesGen.tvgLazyGenDefault(
- testGen, opName, dtypeList, shapeList, argsDict, error_name
+ testGen, rng, opName, dtypeList, shapeList, argsDict, error_name
)
# Set the MUL data range to the square root of the largest value
@@ -1187,7 +1196,7 @@ class TosaTensorValuesGen:
}
@staticmethod
- def tvgMul(testGen, opName, dtypeList, shapeList, argsDict, error_name=None):
+ def tvgMul(testGen, rng, opName, dtypeList, shapeList, argsDict, error_name=None):
if error_name is not None or dtypeList[0] in (
DType.FP16,
DType.BF16,
@@ -1195,7 +1204,7 @@ class TosaTensorValuesGen:
):
# ERROR_IF or floating point test
data_range = TosaTensorValuesGen._get_data_range(
- testGen, dtypeList[0], TosaTensorValuesGen.TVG_FLOAT_HIGH_VALUE_MUL
+ rng, dtypeList[0], TosaTensorValuesGen.TVG_FLOAT_HIGH_VALUE_MUL
)
if data_range:
argsDict["data_range"] = data_range
@@ -1208,10 +1217,9 @@ class TosaTensorValuesGen:
argsDict["fixed_data"] = [None, None, [argsDict["shift"]]]
return TosaTensorValuesGen.tvgLazyGenDefault(
- testGen, opName, dtypeList, shapeList, argsDict, error_name
+ testGen, rng, opName, dtypeList, shapeList, argsDict, error_name
)
else:
- # Integer test
op = testGen.TOSA_OP_LIST[opName]
pCount, cCount = op["operands"]
@@ -1231,7 +1239,9 @@ class TosaTensorValuesGen:
elif error_name == ErrorIf.WrongInputType:
num_bits = 8
else:
- raise Exception("OpMul: invalid input dtype")
+ raise Exception(
+ f"OpMul: invalid input dtype {gtu.DTYPE_ATTRIBUTES[dtypeList[0]]['str']}"
+ )
for idx, shape in enumerate(shapeList[:]):
if dtypeList[idx] == DType.SHAPE:
@@ -1241,12 +1251,8 @@ class TosaTensorValuesGen:
low = -(2 ** (num_bits - 1))
high = (2 ** (num_bits - 1)) - 1
- a_arr = np.int32(
- testGen.rng.integers(low=low, high=high, size=shapeList[0])
- )
- b_arr = np.int32(
- testGen.rng.integers(low=low, high=high, size=shapeList[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]))
i = 0
while True:
@@ -1292,7 +1298,9 @@ class TosaTensorValuesGen:
return TosaTensorValuesGen.TVGInfo(tens_ser_list, None)
@staticmethod
- def tvgConcat(testGen, opName, dtypeList, shapeList, argsDict, error_name=None):
+ def tvgConcat(
+ testGen, rng, opName, dtypeList, shapeList, argsDict, error_name=None
+ ):
count = len(shapeList) - testGen.args.num_const_inputs_concat
if count < 1:
count = 1
@@ -1302,12 +1310,10 @@ class TosaTensorValuesGen:
op = testGen.TOSA_OP_LIST[opName]
if op["op"] == Op.CONCAT_SHAPE:
# Set the axis to 0
- shapeList = TosaTensorGen.tgConcatConstInput(
- testGen, shapeList, 0, error_name
- )
+ shapeList = TosaTensorGen.tgConcatConstInput(rng, shapeList, 0, error_name)
else:
shapeList = TosaTensorGen.tgConcatConstInput(
- testGen, shapeList, argsDict["axis"], error_name
+ rng, shapeList, argsDict["axis"], error_name
)
# Override default pCount/cCount for operator
@@ -1315,20 +1321,20 @@ class TosaTensorValuesGen:
argsDict["c_count"] = len(shapeList) - count
return TosaTensorValuesGen.tvgLazyGenDefault(
- testGen, opName, dtypeList, shapeList, argsDict, error_name
+ testGen, rng, opName, dtypeList, shapeList, argsDict, error_name
)
@staticmethod
def tvgLogicalShift(
- testGen, opName, dtypeList, shapeList, argsDict, error_name=None
+ testGen, rng, opName, dtypeList, shapeList, argsDict, error_name=None
):
op = testGen.TOSA_OP_LIST[opName]
pCount, cCount = op["operands"]
assert (
pCount == 2 and cCount == 0
), "Op.LOGICAL_LEFT_SHIFT or Op.LOGICAL_RIGHT_SHIFT must have 2 placeholders, 0 consts"
- values_arr = testGen.getRandTensor(shapeList[0], dtypeList[0])
- shift_arr = np.int32(testGen.rng.integers(low=0, high=32, size=shapeList[1]))
+ values_arr = rng.randTensor(shapeList[0], dtypeList[0])
+ shift_arr = np.int32(rng.integers(low=0, high=32, size=shapeList[1]))
tens_ser_list = []
tens_ser_list.append(
testGen.ser.addPlaceholder(shapeList[0], dtypeList[0], values_arr)
@@ -1340,7 +1346,7 @@ class TosaTensorValuesGen:
return TosaTensorValuesGen.TVGInfo(tens_ser_list, None)
@staticmethod
- def tvgEqual(testGen, opName, dtypeList, shapeList, argsDict, error_name=None):
+ def tvgEqual(testGen, rng, opName, dtypeList, shapeList, argsDict, error_name=None):
if error_name is None and not gtu.dtypeIsSupportedByCompliance(dtypeList[0]):
# Integer
op = testGen.TOSA_OP_LIST[opName]
@@ -1349,8 +1355,8 @@ class TosaTensorValuesGen:
pCount == 2 and cCount == 0
), "Op.EQUAL must have 2 placeholders, 0 consts"
- a_arr = testGen.getRandTensor(shapeList[0], dtypeList[0])
- b_arr = testGen.getRandTensor(shapeList[1], dtypeList[1])
+ a_arr = rng.randTensor(shapeList[0], dtypeList[0])
+ b_arr = rng.randTensor(shapeList[1], dtypeList[1])
# Using random numbers means that it will be very unlikely that
# there are any matching (equal) values, therefore force that
@@ -1362,9 +1368,7 @@ class TosaTensorValuesGen:
for axis in range(0, len(shapeList[0])):
# Index can be up to the largest dimension in both shapes
index = np.int32(
- testGen.rng.integers(
- 0, max(shapeList[0][axis], shapeList[1][axis])
- )
+ rng.integers(0, max(shapeList[0][axis], shapeList[1][axis]))
)
# Reduce the index down to a shape's dim for broadcasting
a_index.append(min(shapeList[0][axis] - 1, index))
@@ -1383,11 +1387,13 @@ class TosaTensorValuesGen:
else:
# ERROR_IF or floating point test
return TosaTensorValuesGen.tvgLazyGenDefault(
- testGen, opName, dtypeList, shapeList, argsDict, error_name
+ testGen, rng, opName, dtypeList, shapeList, argsDict, error_name
)
@staticmethod
- def tvgReduceSum(testGen, opName, dtypeList, shapeList, argsDict, error_name=None):
+ def tvgReduceSum(
+ testGen, rng, opName, dtypeList, shapeList, argsDict, error_name=None
+ ):
dtype = dtypeList[0]
if dtype == DType.INT32:
op = testGen.TOSA_OP_LIST[opName]
@@ -1399,7 +1405,7 @@ class TosaTensorValuesGen:
# summation of any axis
range_val = int((1 << 31) / max(shapeList[0]))
values_arr = np.int32(
- testGen.rng.integers(low=-range_val, high=range_val, size=shapeList[0])
+ rng.integers(low=-range_val, high=range_val, size=shapeList[0])
)
tens_ser_list = []
tens_ser_list.append(
@@ -1419,18 +1425,18 @@ class TosaTensorValuesGen:
/ max(shapeList[0])
}
data_range = TosaTensorValuesGen._get_data_range(
- testGen, dtype, highval_lookup
+ rng, dtype, highval_lookup
)
assert data_range is not None
argsDict["data_range"] = data_range
return TosaTensorValuesGen.tvgLazyGenDefault(
- testGen, opName, dtypeList, shapeList, argsDict, error_name
+ testGen, rng, opName, dtypeList, shapeList, argsDict, error_name
)
@staticmethod
def tvgReduceProduct(
- testGen, opName, dtypeList, shapeList, argsDict, error_name=None
+ testGen, rng, opName, dtypeList, shapeList, argsDict, error_name=None
):
dtype = dtypeList[0]
if error_name is None:
@@ -1442,20 +1448,20 @@ class TosaTensorValuesGen:
1 / max(shapeList[0]),
)
}
- data_range = TosaTensorValuesGen._get_data_range(
- testGen, dtype, highval_lookup
- )
+ data_range = TosaTensorValuesGen._get_data_range(rng, dtype, highval_lookup)
assert data_range is not None
argsDict["data_range"] = data_range
return TosaTensorValuesGen.tvgLazyGenDefault(
- testGen, opName, dtypeList, shapeList, argsDict, error_name
+ testGen, rng, opName, dtypeList, shapeList, argsDict, error_name
)
@staticmethod
- def tvgResize(testGen, opName, dtypeList, shapeList, argsDict, error_name=None):
+ def tvgResize(
+ testGen, rng, opName, dtypeList, shapeList, argsDict, error_name=None
+ ):
data_range = TosaTensorValuesGen._get_data_range(
- testGen,
+ rng,
dtypeList[0],
TosaTensorValuesGen.TVG_FLOAT_HIGH_VALUE,
)
@@ -1476,7 +1482,7 @@ class TosaTensorValuesGen:
argsDict["fixed_data"] = [None, scale_values, offset_values, border_values]
return TosaTensorValuesGen.tvgLazyGenDefault(
- testGen, opName, dtypeList, shapeList, argsDict, error_name
+ testGen, rng, opName, dtypeList, shapeList, argsDict, error_name
)
# Set the POW exponent high data range
@@ -1537,10 +1543,10 @@ class TosaTensorValuesGen:
}
@staticmethod
- def tvgPow(testGen, opName, dtypeList, shapeList, argsDict, error_name=None):
+ def tvgPow(testGen, rng, opName, dtypeList, shapeList, argsDict, error_name=None):
if error_name is not None:
return TosaTensorValuesGen.tvgLazyGenDefault(
- testGen, opName, dtypeList, shapeList, argsDict, error_name
+ testGen, rng, opName, dtypeList, shapeList, argsDict, error_name
)
dtype = dtypeList[0]
# Different ranges for POW
@@ -1548,25 +1554,25 @@ class TosaTensorValuesGen:
if test_set == 0:
# Positive base with fractional exponent
base_range = TosaTensorValuesGen._get_data_range(
- testGen,
+ rng,
dtype,
TosaTensorValuesGen.TVG_FLOAT_HIGH_VALUE_POW_BASE,
TosaTensorValuesGen.TVG_FLOAT_LOW_VALUE_POW_BASE,
)
exp_range = TosaTensorValuesGen._get_data_range(
- testGen, dtype, TosaTensorValuesGen.TVG_FLOAT_HIGH_VALUE_POW_EXP
+ rng, dtype, TosaTensorValuesGen.TVG_FLOAT_HIGH_VALUE_POW_EXP
)
exp_round = False
else:
# Integer exponent
exp_range = TosaTensorValuesGen._get_data_range(
- testGen, dtype, TosaTensorValuesGen.TVG_FLOAT_HIGH_VALUE_POW_EXP
+ rng, dtype, TosaTensorValuesGen.TVG_FLOAT_HIGH_VALUE_POW_EXP
)
exp_round = True
if test_set == 1:
# Positive base
base_range = TosaTensorValuesGen._get_data_range(
- testGen,
+ rng,
dtype,
TosaTensorValuesGen.TVG_FLOAT_HIGH_VALUE_POW_BASE,
TosaTensorValuesGen.TVG_FLOAT_LOW_VALUE_POW_BASE,
@@ -1576,7 +1582,7 @@ class TosaTensorValuesGen:
# Negative base
# Supply new look up tables with negative values
base_range = TosaTensorValuesGen._get_data_range(
- testGen,
+ rng,
dtype,
{dtype: -TosaTensorValuesGen.TVG_FLOAT_LOW_VALUE_POW_BASE[dtype]},
{dtype: -TosaTensorValuesGen.TVG_FLOAT_HIGH_VALUE_POW_BASE[dtype]},
@@ -1593,15 +1599,17 @@ class TosaTensorValuesGen:
)
argsDict["data_range_list"] = data_range_list
return TosaTensorValuesGen.tvgLazyGenDefault(
- testGen, opName, dtypeList, shapeList, argsDict, error_name
+ testGen, rng, opName, dtypeList, shapeList, argsDict, error_name
)
@staticmethod
- def tvgLogRsqrt(testGen, opName, dtypeList, shapeList, argsDict, error_name=None):
+ def tvgLogRsqrt(
+ testGen, rng, opName, dtypeList, shapeList, argsDict, error_name=None
+ ):
# LOG & RSQRT data range from lowest expressible positive number to
# largest to avoid NaNs
data_range = TosaTensorValuesGen._get_data_range(
- testGen,
+ rng,
dtypeList[0],
TosaTensorValuesGen.TVG_FLOAT_HIGH_VALUE,
TosaTensorValuesGen.TVG_FLOAT_LOW_VALUE,
@@ -1610,7 +1618,7 @@ class TosaTensorValuesGen:
argsDict["data_range"] = data_range
return TosaTensorValuesGen.tvgLazyGenDefault(
- testGen, opName, dtypeList, shapeList, argsDict, error_name
+ testGen, rng, opName, dtypeList, shapeList, argsDict, error_name
)
# Set the EXP data range to the log of the largest to smallest values
@@ -1627,9 +1635,9 @@ class TosaTensorValuesGen:
}
@staticmethod
- def tvgExp(testGen, opName, dtypeList, shapeList, argsDict, error_name=None):
+ def tvgExp(testGen, rng, opName, dtypeList, shapeList, argsDict, error_name=None):
data_range = TosaTensorValuesGen._get_data_range(
- testGen,
+ rng,
dtypeList[0],
TosaTensorValuesGen.TVG_FLOAT_HIGH_VALUE_EXP,
TosaTensorValuesGen.TVG_FLOAT_LOW_VALUE_EXP,
@@ -1638,12 +1646,12 @@ class TosaTensorValuesGen:
argsDict["data_range"] = data_range
return TosaTensorValuesGen.tvgLazyGenDefault(
- testGen, opName, dtypeList, shapeList, argsDict, error_name
+ testGen, rng, opName, dtypeList, shapeList, argsDict, error_name
)
@staticmethod
def tvgFullyConnected(
- testGen, opName, dtypeList, shapeList, argsDict, error_name=None
+ testGen, rng, opName, dtypeList, shapeList, argsDict, error_name=None
):
dtype = dtypeList[0]
if (
@@ -1658,26 +1666,24 @@ class TosaTensorValuesGen:
highval_lookup = {
dtype: math.pow(TosaTensorValuesGen.TVG_FLOAT_HIGH_VALUE[dtype], 1 / IC)
}
- data_range = TosaTensorValuesGen._get_data_range(
- testGen, dtype, highval_lookup
- )
+ data_range = TosaTensorValuesGen._get_data_range(rng, dtype, highval_lookup)
assert data_range is not None
argsDict["data_range"] = data_range
return TosaTensorValuesGen.tvgLazyGenDefault(
- testGen, opName, dtypeList, shapeList, argsDict, error_name
+ testGen, rng, opName, dtypeList, shapeList, argsDict, error_name
)
@staticmethod
- def tvgCast(testGen, opName, dtypeList, shapeList, argsDict, error_name=None):
+ def tvgCast(testGen, rng, opName, dtypeList, shapeList, argsDict, error_name=None):
in_dtype = dtypeList[0]
out_dtype = argsDict["out_type"]
# Create look up to limit input tensor to output type maximums to avoid
# FP infinities and saturation of integers
- out_range = testGen.getDTypeRange(out_dtype, high_inclusive=True)
+ out_range = rng.dTypeRange(out_dtype, high_inclusive=True)
highval_lookup = {in_dtype: out_range[1]}
data_range = TosaTensorValuesGen._get_data_range(
- testGen,
+ rng,
in_dtype,
highval_lookup,
)
@@ -1686,11 +1692,13 @@ class TosaTensorValuesGen:
argsDict["data_range"] = data_range
return TosaTensorValuesGen.tvgLazyGenDefault(
- testGen, opName, dtypeList, shapeList, argsDict, error_name
+ testGen, rng, opName, dtypeList, shapeList, argsDict, error_name
)
@staticmethod
- def tvgGather(testGen, opName, dtypeList, shapeList, argsDict, error_name=None):
+ def tvgGather(
+ testGen, rng, opName, dtypeList, shapeList, argsDict, error_name=None
+ ):
K = shapeList[0][1]
# Fix the type of the indices tensor
@@ -1709,11 +1717,11 @@ class TosaTensorValuesGen:
for idx, shape in enumerate(shapeList):
dtype = dtypeList[idx]
if idx != 1:
- arr = testGen.getRandTensor(shape, dtype)
+ arr = rng.randTensor(shape, dtype)
tens_ser_list.append(testGen.ser.addPlaceholder(shape, dtype, arr))
else:
# Limit data range of indices tensor upto K (exclusive)
- arr = testGen.getRandTensor(shape, dtype, (0, K))
+ arr = rng.randTensor(shape, dtype, (0, K))
# To match old functionality - create indices as CONST
tens_ser_list.append(testGen.ser.addConst(shape, dtype, arr))
@@ -1729,11 +1737,13 @@ class TosaTensorValuesGen:
argsDict["data_range_list"] = data_range_list
return TosaTensorValuesGen.tvgLazyGenDefault(
- testGen, opName, dtypeList, shapeList, argsDict, error_name
+ testGen, rng, opName, dtypeList, shapeList, argsDict, error_name
)
@staticmethod
- def tvgScatter(testGen, opName, dtypeList, shapeList, argsDict, error_name=None):
+ def tvgScatter(
+ testGen, rng, opName, dtypeList, shapeList, argsDict, error_name=None
+ ):
K = shapeList[0][1]
W = shapeList[2][1]
@@ -1760,7 +1770,7 @@ class TosaTensorValuesGen:
for idx, shape in enumerate(shapeList):
dtype = dtypeList[idx]
if idx != 1:
- arr = testGen.getRandTensor(shape, dtype)
+ arr = rng.randTensor(shape, dtype)
tens_ser_list.append(testGen.ser.addPlaceholder(shape, dtype, arr))
else:
# Create the indices array
@@ -1769,7 +1779,7 @@ class TosaTensorValuesGen:
for n in range(shape[0]):
# Get a shuffled list of output indices (0 to K-1) and
# limit length to W
- arr.append(testGen.rng.permutation(K)[:W])
+ arr.append(rng.permutation(K)[:W])
indices_arr = np.array(arr, dtype=np.int32) # (N, W)
# To match old functionality - create indices as CONST
tens_ser_list.append(
@@ -1789,7 +1799,7 @@ class TosaTensorValuesGen:
argsDict["data_range_list"] = data_range_list
return TosaTensorValuesGen.tvgLazyGenDefault(
- testGen, opName, dtypeList, shapeList, argsDict, error_name
+ testGen, rng, opName, dtypeList, shapeList, argsDict, error_name
)
@@ -1881,7 +1891,7 @@ class TosaArgGen:
return new_arg_list
@staticmethod
- def agNone(testGen, opName, shapeList, dtype, error_name=None):
+ def agNone(testGen, rng, opName, shapeList, dtype, error_name=None):
"""A trivial argument generator for operators that don't take any
non-tensor arguments"""
arg_list = TosaArgGen._add_data_generators(
@@ -1896,7 +1906,7 @@ class TosaArgGen:
return arg_list
@staticmethod
- def agPow(testGen, opName, shapeList, dtype, error_name=None):
+ def agPow(testGen, rng, opName, shapeList, dtype, error_name=None):
"""Pow operator needs different test sets to cover random numbers
without creating NaNs or Infs"""
arg_list = TosaArgGen._add_data_generators(
@@ -1911,17 +1921,17 @@ class TosaArgGen:
return arg_list
@staticmethod
- def agAxis(testGen, opName, shapeList, dtype, error_name=None):
+ def agAxis(testGen, rng, opName, shapeList, dtype, error_name=None):
"""Build the axis argument for operators that take a single axis"""
arg_list = []
shape = shapeList[0]
if error_name == ErrorIf.AxisSmallerZero:
# Set too small axis
- axes = [testGen.rng.integers(-5, 0)]
+ axes = [rng.integers(-5, 0)]
elif error_name == ErrorIf.AxisLargerRank:
# Set too large axis
- axes = [testGen.rng.integers(len(shape) + 1, len(shape) + 10)]
+ axes = [rng.integers(len(shape) + 1, len(shape) + 10)]
else:
# Create tests for each dimension
axes = range(0, len(shape))
@@ -1967,7 +1977,7 @@ class TosaArgGen:
return sparsity
@staticmethod
- def agConv(testGen, opName, shapeList, dtypes, error_name=None):
+ def agConv(testGen, rng, opName, shapeList, dtypes, error_name=None):
# Used by CONV2D, CONV3D and DEPTHWISE_CONV2D
arg_list = []
@@ -2005,13 +2015,13 @@ class TosaArgGen:
# Generate comprehensive argument lists
# - except for named errors, which use specific invalid value(s)
if error_name == ErrorIf.PadSmallerZero:
- p_vals = [testGen.rng.choice(range(-5, 0))]
+ p_vals = [rng.choice(range(-5, 0))]
else:
p_vals = [x for x in range(0, testGen.args.max_conv_padding + 1)]
paddings = {x for x in itertools.product(*([p_vals] * k_rank * 2))}
if error_name == ErrorIf.StrideSmallerOne:
# Can't use stride=0, as it is used to derive output shape, as a divisor
- s_vals = [testGen.rng.choice(range(-5, 0))]
+ s_vals = [rng.choice(range(-5, 0))]
else:
# Stride must be greater than 1 to force non-integer error
startStride = (
@@ -2022,7 +2032,7 @@ class TosaArgGen:
]
strides = {x for x in itertools.product(*([s_vals] * k_rank))}
if error_name == ErrorIf.DilationSmallerOne:
- d_vals = [testGen.rng.choice(range(-5, 1))]
+ d_vals = [rng.choice(range(-5, 1))]
else:
d_vals = [x for x in range(1, testGen.args.max_conv_dilation + 1)]
dilations = {x for x in itertools.product(*([d_vals] * k_rank))}
@@ -2195,13 +2205,13 @@ class TosaArgGen:
return arg_list
@staticmethod
- def agFullyConnected(testGen, opName, shapeList, dtypes, error_name=None):
+ def agFullyConnected(testGen, rng, opName, shapeList, dtypes, error_name=None):
assert isinstance(dtypes, (list, tuple)), f"{dtypes} unexpected"
input_dtype = dtypes[0]
if error_name == ErrorIf.WrongOutputType:
- accum_dtype = gtu.get_wrong_output_type(opName, testGen.rng, input_dtype)
+ accum_dtype = gtu.get_wrong_output_type(opName, rng, input_dtype)
elif error_name == ErrorIf.WrongInputType:
# Pick some potentially correct output dtype if input type is incorrect
accum_dtype = DType.INT32
@@ -2230,7 +2240,7 @@ class TosaArgGen:
return arg_list
@staticmethod
- def agMatMul(testGen, opName, shapeList, dtype, error_name=None):
+ def agMatMul(testGen, rng, opName, shapeList, dtype, error_name=None):
# Get valid accumulate type(s)
if dtype == DType.INT8:
accum_dtypes = [DType.INT32]
@@ -2249,7 +2259,7 @@ class TosaArgGen:
if error_name == ErrorIf.WrongOutputType:
# Get incorrect output dtype for ErrorIf case
- accum_dtypes = [gtu.get_wrong_output_type(opName, testGen.rng, dtype)]
+ accum_dtypes = [gtu.get_wrong_output_type(opName, rng, dtype)]
elif error_name == ErrorIf.WrongInputType:
# Pick some potentially correct output dtype if input type is incorrect
accum_dtypes = [DType.INT32]
@@ -2283,7 +2293,7 @@ class TosaArgGen:
return arg_list
@staticmethod
- def agTransposeConv2D(testGen, opName, shapeList, dtypes, error_name=None):
+ def agTransposeConv2D(testGen, rng, opName, shapeList, dtypes, error_name=None):
arg_list = []
if testGen.args.level8k and error_name is not None:
@@ -2310,9 +2320,7 @@ class TosaArgGen:
smallest_padding_size = -min(k_shape[0], k_shape[1]) + 1
if error_name == ErrorIf.PadLargerEqualKernel:
max_filter_size = -max(k_shape[0], k_shape[1])
- p_vals = [
- testGen.rng.choice(range(max_filter_size - 10, max_filter_size))
- ]
+ p_vals = [rng.choice(range(max_filter_size - 10, max_filter_size))]
else:
p_vals = [
x
@@ -2323,7 +2331,7 @@ class TosaArgGen:
paddings = {x for x in itertools.product(*([p_vals] * 4))}
if error_name == ErrorIf.StrideSmallerOne:
# Can't use stride=0, as it is used to derive output shape, as a divisor
- s_vals = [testGen.rng.choice(range(-5, 0))]
+ s_vals = [rng.choice(range(-5, 0))]
else:
s_vals = [x for x in range(1, testGen.args.max_conv_stride + 1)]
strides = {x for x in itertools.product(*([s_vals] * 2))}
@@ -2440,7 +2448,7 @@ class TosaArgGen:
return arg_list
@staticmethod
- def agPad(testGen, opName, shapeList, dtype, error_name=None):
+ def agPad(testGen, rng, opName, shapeList, dtype, error_name=None):
rank = len(shapeList[0])
# Exhaustively test combinations of padding on each side of each dimension
@@ -2454,11 +2462,11 @@ class TosaArgGen:
shape_pad_values = itertools.product(*([axis_pad_values] * rank))
if dtype in [DType.BOOL, DType.INT8, DType.INT16, DType.INT32]:
- pad_const_int = testGen.getRandNumberDType(dtype)
+ pad_const_int = rng.randNumberDType(dtype)
pad_const_fp = 0
elif gtu.dtypeIsFloat(dtype):
pad_const_int = 0
- pad_const_fp = testGen.getRandNumberDType(dtype)
+ pad_const_fp = rng.randNumberDType(dtype)
else:
return []
@@ -2516,7 +2524,7 @@ class TosaArgGen:
return arg_list
@staticmethod
- def agPooling(testGen, opName, shapeList, dtype, error_name=None):
+ def agPooling(testGen, rng, opName, shapeList, dtype, error_name=None):
arg_list = []
shape = shapeList[0]
@@ -2658,7 +2666,7 @@ class TosaArgGen:
ErrorIf.PadLargerEqualKernel,
]:
sNew, pNew, kNew = TosaErrorIfArgGen.eiPoolingErrorIf(
- testGen, error_name, s, p, k
+ rng, error_name, s, p, k
)
if None not in [sNew, pNew, kNew] and n % sparsity == 0:
arg_list.append(
@@ -2722,12 +2730,12 @@ class TosaArgGen:
return arg_list
@staticmethod
- def agCast(testGen, opName, shapeList, inDtype, error_name=None):
+ def agCast(testGen, rng, opName, shapeList, inDtype, error_name=None):
arg_list = []
# Enumerate the output types here
if error_name == ErrorIf.WrongOutputType:
- dtypeList = TosaErrorIfArgGen.eiCastErrorIf(testGen, inDtype)
+ dtypeList = TosaErrorIfArgGen.eiCastErrorIf(inDtype)
elif inDtype == DType.INT8:
dtypeList = [
DType.BOOL,
@@ -2811,7 +2819,7 @@ class TosaArgGen:
return arg_list
@staticmethod
- def agRescale(testGen, opName, shapeList, inDtype, error_name=None):
+ def agRescale(testGen, rng, opName, shapeList, inDtype, error_name=None):
arg_list = []
# Enumerate the output types here
@@ -2906,7 +2914,7 @@ class TosaArgGen:
# Calculate scale based on:
# scale = a *(2^output_width)/(2^input_width))
- a = np.float32(testGen.rng.random(size=[nc]))
+ a = np.float32(rng.random(size=[nc]))
scale_arr = a * np.float32(
(1 << out_type_width) / (1 << in_type_width)
)
@@ -2965,13 +2973,13 @@ class TosaArgGen:
return arg_list
@staticmethod
- def agMul(testGen, opName, shapeList, dtype, error_name=None):
+ def agMul(testGen, rng, opName, shapeList, dtype, error_name=None):
arg_list = []
if dtype is DType.INT32:
for p in range(testGen.args.num_rand_permutations):
- shift = testGen.randInt(0, 32)
+ shift = rng.randInt(0, 32)
arg_list.append(("perm{}_shift{}".format(p, shift), {"shift": shift}))
else:
arg_list.append(("perm0_shift0", {"shift": 0}))
@@ -2988,7 +2996,7 @@ class TosaArgGen:
return arg_list
@staticmethod
- def agArithmeticRightShift(testGen, opName, shapeList, dtype, error_name=None):
+ def agArithmeticRightShift(testGen, rng, opName, shapeList, dtype, error_name=None):
arg_list = []
for round in (True, False):
@@ -3009,7 +3017,7 @@ class TosaArgGen:
return arg_list
@staticmethod
- def agFFT2d(testGen, opName, shapeList, dtype, error_name=None):
+ def agFFT2d(testGen, rng, opName, shapeList, dtype, error_name=None):
arg_list = []
shape = shapeList[0]
@@ -3037,7 +3045,7 @@ class TosaArgGen:
return arg_list
@staticmethod
- def agRFFT2d(testGen, opName, shapeList, dtype, error_name=None):
+ def agRFFT2d(testGen, rng, opName, shapeList, dtype, error_name=None):
arg_list = []
shape = shapeList[0]
@@ -3074,7 +3082,7 @@ class TosaArgGen:
return factors
@staticmethod
- def agReshape(testGen, opName, shapeList, dtype, error_name=None):
+ def agReshape(testGen, rng, opName, shapeList, dtype, error_name=None):
arg_list = []
origShape = shapeList[0]
@@ -3085,7 +3093,7 @@ class TosaArgGen:
# This code is NOT fast. Fortunately, the numbers are fairly small.
for p in range(testGen.args.num_rand_permutations):
# Rank from 1 to TOSA_TENSOR_MAX_RANK
- newRank = testGen.randInt(1, (testGen.TOSA_TENSOR_MAX_RANK + 1))
+ newRank = rng.randInt(1, (testGen.TOSA_TENSOR_MAX_RANK + 1))
if len(factors) < newRank:
continue
@@ -3095,12 +3103,12 @@ class TosaArgGen:
# Generate the new shape of the chosen new rank
newShape = []
remainingElements = totalElements
- shuffledFactors = testGen.rng.permutation(factors)
+ shuffledFactors = rng.permutation(factors)
for i in range(1, newRank):
# pick rank-1 factors
newShape.append(shuffledFactors[0])
remainingElements = remainingElements // shuffledFactors[0]
- shuffledFactors = testGen.rng.permutation(
+ shuffledFactors = rng.permutation(
TosaArgGen.getFactors(remainingElements)
)
newShape.append(remainingElements)
@@ -3136,7 +3144,7 @@ class TosaArgGen:
return arg_list
@staticmethod
- def agTranspose(testGen, opName, shapeList, dtype, error_name=None):
+ def agTranspose(testGen, rng, opName, shapeList, dtype, error_name=None):
arg_list = []
ifm_shape = shapeList[0]
@@ -3151,7 +3159,7 @@ class TosaArgGen:
elif error_name == ErrorIf.IndexUsedTwice:
# Create list with a duplicated index
perm_range = list(range(len(ifm_shape)))
- index_choice = testGen.rng.choice(range(len(perm_range)))
+ index_choice = rng.choice(range(len(perm_range)))
perm_range[(index_choice + 1) % len(perm_range)] = perm_range[index_choice]
permutations = [p for p in itertools.permutations(perm_range)]
@@ -3163,7 +3171,7 @@ class TosaArgGen:
limit = min(len(permutations), testGen.args.num_rand_permutations)
# Get random permutation generator that uses all permutations
- random_permutations = testGen.rng.permutation(permutations)
+ random_permutations = rng.permutation(permutations)
# Create list of required amount of permutations
arg_list = [
@@ -3183,7 +3191,7 @@ class TosaArgGen:
return arg_list
@staticmethod
- def agSlice(testGen, opName, shapeList, dtype, error_name=None):
+ def agSlice(testGen, rng, opName, shapeList, dtype, error_name=None):
arg_list = []
ifm_shape = shapeList[0]
@@ -3197,8 +3205,8 @@ class TosaArgGen:
for i in range(rank):
if ifm_shape[i] > 1:
- start.append(testGen.randInt(0, ifm_shape[i]))
- size.append(testGen.randInt(0, ifm_shape[i] - start[i]))
+ start.append(rng.randInt(0, ifm_shape[i]))
+ size.append(rng.randInt(0, ifm_shape[i] - start[i]))
# Invalid slice size?
if size[i] == 0:
@@ -3210,7 +3218,7 @@ class TosaArgGen:
if valid:
# If ERROR_IF test required then incorrect start, size will be returned
start, size = TosaErrorIfArgGen.eiSliceErrorIf(
- testGen, error_name, ifm_shape, start, size
+ rng, error_name, ifm_shape, start, size
)
arg_list.append(("perm{}".format(p), {"start": start, "size": size}))
# Now add data generator types
@@ -3226,7 +3234,7 @@ class TosaArgGen:
return arg_list
@staticmethod
- def agTile(testGen, opName, shapeList, dtype, error_name=None):
+ def agTile(testGen, rng, opName, shapeList, dtype, error_name=None):
arg_list = []
ifm_shape = shapeList[0]
@@ -3246,7 +3254,7 @@ class TosaArgGen:
elif max(ifm_shape) > 1000:
multiples.append(2)
else:
- multiples.append(testGen.randInt(1, 4))
+ multiples.append(rng.randInt(1, 4))
arg_list.append(("perm{}".format(p), {"multiples": multiples}))
# Now add data generator types
@@ -3262,15 +3270,15 @@ class TosaArgGen:
return arg_list
@staticmethod
- def agResize(testGen, opName, shapeList, dtype, error_name=None):
+ def agResize(testGen, rng, opName, shapeList, dtype, error_name=None):
arg_list = []
ifm_shape = shapeList[0]
def get_aspect_ratio_resize_params():
common_aspect_ratios = ((3, 2), (16, 9), (4, 3))
- aspect_ratio = testGen.rng.choice(common_aspect_ratios)
- invert = testGen.rng.choice((False, True))
- letterbox = testGen.rng.choice((False, True))
+ aspect_ratio = rng.choice(common_aspect_ratios)
+ invert = rng.choice((False, True))
+ letterbox = rng.choice((False, True))
scale_y_n = aspect_ratio[0] if invert else aspect_ratio[1]
scale_x_n = aspect_ratio[1] if invert else aspect_ratio[0]
@@ -3279,13 +3287,13 @@ class TosaArgGen:
if letterbox:
max_border = scale_y_n
- border_y = testGen.randInt(low=0, high=max_border)
+ border_y = rng.randInt(low=0, high=max_border)
border_x = 0
else:
# Pillarboxing
border_y = 0
max_border = scale_x_n
- border_x = testGen.randInt(low=0, high=max_border)
+ border_x = rng.randInt(low=0, high=max_border)
scale = (scale_y_n, scale_y_d, scale_x_n, scale_x_d)
offset = (offset_y, offset_x)
@@ -3296,13 +3304,13 @@ class TosaArgGen:
def get_upscale_downscale_params():
valid_params = False
while not valid_params:
- upscale = testGen.rng.choice((False, True))
+ upscale = rng.choice((False, True))
# True if sampling begins from (0,0). Otherwise (-0.5,-0.5)
- origin_sampling = testGen.rng.choice((False, True))
+ origin_sampling = rng.choice((False, True))
if upscale:
- shift = testGen.randInt(low=1, high=4)
+ shift = rng.randInt(low=1, high=4)
scale_x_d = scale_y_d = 1
scale_x_n = scale_y_n = (
1 << shift if origin_sampling else 2 << shift
@@ -3328,16 +3336,16 @@ class TosaArgGen:
if not valid_scale_y_ds:
scale_y_d = 1
else:
- scale_y_d = testGen.rng.choice(valid_scale_y_ds)
+ scale_y_d = rng.choice(valid_scale_y_ds)
if not valid_scale_x_ds:
scale_x_d = 1
else:
- scale_x_d = testGen.rng.choice(valid_scale_x_ds)
+ scale_x_d = rng.choice(valid_scale_x_ds)
border_x = border_y = 0
- offset_y = testGen.randInt(0, 16 * scale_y_n)
- offset_x = testGen.randInt(0, 16 * scale_x_n)
+ offset_y = rng.randInt(0, 16 * scale_y_n)
+ offset_x = rng.randInt(0, 16 * scale_x_n)
valid_params = True
scale = (scale_y_n, scale_y_d, scale_x_n, scale_x_d)
@@ -3356,11 +3364,11 @@ class TosaArgGen:
return scale_d
# Scale
- scale_y_n = testGen.randInt(low=1, high=(1 << 11))
- scale_x_n = testGen.randInt(low=1, high=(1 << 11))
+ scale_y_n = rng.randInt(low=1, high=(1 << 11))
+ scale_x_n = rng.randInt(low=1, high=(1 << 11))
- scale_y_d = testGen.randInt(low=1, high=(16 * scale_y_n))
- scale_x_d = testGen.randInt(low=1, high=(16 * scale_x_n))
+ scale_y_d = rng.randInt(low=1, high=(16 * scale_y_n))
+ scale_x_d = rng.randInt(low=1, high=(16 * scale_x_n))
scale_y_d = fix_scale_to_max_scale(
scale_y_n, scale_y_d, testGen.TOSA_8K_LEVEL_MAX_SCALE
@@ -3370,10 +3378,10 @@ class TosaArgGen:
)
# Offsets and border within the scale
- offset_y = testGen.randInt(low=-scale_y_n, high=(16 * scale_y_n))
- offset_x = testGen.randInt(low=-scale_x_n, high=(16 * scale_x_n))
- border_y = testGen.randInt(low=(-16 * scale_y_n), high=scale_y_n)
- border_x = testGen.randInt(low=(-16 * scale_x_n), high=scale_x_n)
+ offset_y = rng.randInt(low=-scale_y_n, high=(16 * scale_y_n))
+ offset_x = rng.randInt(low=-scale_x_n, high=(16 * scale_x_n))
+ border_y = rng.randInt(low=(-16 * scale_y_n), high=scale_y_n)
+ border_x = rng.randInt(low=(-16 * scale_x_n), high=scale_x_n)
scale = (scale_y_n, scale_y_d, scale_x_n, scale_x_d)
offset = (offset_y, offset_x)
@@ -3382,24 +3390,24 @@ class TosaArgGen:
def get_level_8k_params():
# Create 64x scale - 64/1 to 2048/32
- scale_d = testGen.randInt(
+ scale_d = rng.randInt(
low=1, high=(1 << 11) / testGen.TOSA_8K_LEVEL_MAX_SCALE
)
scale_n = scale_d * testGen.TOSA_8K_LEVEL_MAX_SCALE
# Create half to fifth scaling
- scale_d_alt = testGen.randInt(low=2, high=6)
+ scale_d_alt = rng.randInt(low=2, high=6)
scale_n_alt = 1
- switch = testGen.rng.choice((False, True))
+ switch = rng.choice((False, True))
if switch:
scale = (scale_n_alt, scale_d_alt, scale_n, scale_d)
else:
scale = (scale_n, scale_d, scale_n_alt, scale_d_alt)
- offset_y = testGen.rng.choice((-scale[0], 0, (16 * scale[0]) - 1))
- offset_x = testGen.rng.choice((-scale[2], 0, (16 * scale[2]) - 1))
+ offset_y = rng.choice((-scale[0], 0, (16 * scale[0]) - 1))
+ offset_x = rng.choice((-scale[2], 0, (16 * scale[2]) - 1))
offset = (offset_y, offset_x)
- border_y = testGen.rng.choice((-16 * scale[0], 0, scale[0] - 1))
- border_x = testGen.rng.choice((-16 * scale[2], 0, scale[2] - 1))
+ border_y = rng.choice((-16 * scale[0], 0, scale[0] - 1))
+ border_x = rng.choice((-16 * scale[2], 0, scale[2] - 1))
border = (border_y, border_x)
return scale, offset, border
@@ -3437,7 +3445,7 @@ class TosaArgGen:
while perm < testGen.args.num_rand_permutations:
# Random choice of type of params we are testing
if not testGen.args.level8k:
- _rnd_param_fn = testGen.rng.choice(
+ _rnd_param_fn = rng.choice(
(
get_rand_params,
get_upscale_downscale_params,
@@ -3541,7 +3549,7 @@ class TosaArgGen:
border,
outputDTypeNew,
) = TosaErrorIfArgGen.eiResizeErrorIf(
- testGen,
+ rng,
error_name,
mode,
dtype,
@@ -3596,17 +3604,13 @@ class TosaArgGen:
return arg_list
@staticmethod
- def agTable(testGen, opName, shapeList, dtype, error_name=None):
+ def agTable(testGen, rng, opName, shapeList, dtype, error_name=None):
arg_list = []
if dtype == DType.INT8:
- table = np.int32(
- testGen.rng.integers(low=-128, high=128, size=[256])
- ).tolist()
+ table = np.int32(rng.integers(low=-128, high=128, size=[256])).tolist()
else: # INT16
- table = np.int32(
- testGen.rng.integers(low=-32768, high=32768, size=[513])
- ).tolist()
+ table = np.int32(rng.integers(low=-32768, high=32768, size=[513])).tolist()
# Make sure all slopes are within REQUIRE min/max 16-bit int
for idx in range(len(table) - 1):
slope = table[idx + 1] - table[idx]
@@ -3635,7 +3639,7 @@ class TosaArgGen:
# Return list of tuples: (arg_str, args_dict)
return arg_list
- def agCondIf(testGen, opName, shapeList, dtype, error_name=None):
+ def agCondIf(testGen, rng, opName, shapeList, dtype, error_name=None):
# CondIf generates the condition values here.
# Convert to tensors in the build function, along with the
# then and else blocks
@@ -3656,7 +3660,7 @@ class TosaArgGen:
# Return list of tuples: (arg_str, args_dict)
return arg_list
- def agWhileLoop(testGen, opName, shapeList, dtype, error_name=None):
+ def agWhileLoop(testGen, rng, opName, shapeList, dtype, error_name=None):
# While loop: 0 iterations, 1, more than 1
arg_list = []
diff --git a/verif/generator/tosa_error_if.py b/verif/generator/tosa_error_if.py
index 3972edd..e557f06 100644
--- a/verif/generator/tosa_error_if.py
+++ b/verif/generator/tosa_error_if.py
@@ -94,7 +94,7 @@ class ErrorIf(object):
class TosaErrorIfArgGen:
@staticmethod
def eiResizeErrorIf(
- testGen,
+ rng,
error_name,
mode,
dtype,
@@ -105,28 +105,28 @@ class TosaErrorIfArgGen:
border,
):
if error_name == ErrorIf.ScaleSmallerEqualZero:
- index = testGen.randInt(low=0, high=4)
- scale[index] = testGen.rng.choice([-2, -1, 0])
+ index = rng.randInt(low=0, high=4)
+ scale[index] = rng.choice([-2, -1, 0])
elif error_name == ErrorIf.ScaleNLargerMax:
- index = testGen.rng.choice([0, 2])
- scale[index] = (1 << 11) + testGen.rng.choice([1, 2, 3])
+ index = rng.choice([0, 2])
+ scale[index] = (1 << 11) + rng.choice([1, 2, 3])
elif error_name == ErrorIf.ScaleDLargerMax:
- index = testGen.rng.choice([1, 3])
- scale[index] = 16 * scale[index - 1] + testGen.rng.choice([0, 1, 2])
+ index = rng.choice([1, 3])
+ scale[index] = 16 * scale[index - 1] + rng.choice([0, 1, 2])
if error_name == ErrorIf.OffsetLargerEqualMax:
- index = testGen.rng.choice([0, 1])
- offset[index] = 16 * scale[index * 2] + testGen.rng.choice([0, 1, 2])
+ index = rng.choice([0, 1])
+ offset[index] = 16 * scale[index * 2] + rng.choice([0, 1, 2])
elif error_name == ErrorIf.OffsetSmallerMin:
- index = testGen.rng.choice([0, 1])
- offset[index] = -scale[index * 2] - testGen.rng.choice([1, 2, 3])
+ index = rng.choice([0, 1])
+ offset[index] = -scale[index * 2] - rng.choice([1, 2, 3])
if error_name == ErrorIf.BorderLargerEqualMax:
- index = testGen.rng.choice([0, 1])
- border[index] = scale[index * 2] + testGen.rng.choice([0, 1, 2])
+ index = rng.choice([0, 1])
+ border[index] = scale[index * 2] + rng.choice([0, 1, 2])
elif error_name == ErrorIf.BorderSmallerMin:
- index = testGen.rng.choice([0, 1])
- border[index] = -16 * scale[index * 2] - testGen.rng.choice([1, 2, 3])
+ index = rng.choice([0, 1])
+ border[index] = -16 * scale[index * 2] - rng.choice([1, 2, 3])
if error_name == ErrorIf.WrongOutputType:
if mode == ResizeMode.NEAREST and dtype == DType.INT8:
@@ -192,12 +192,12 @@ class TosaErrorIfArgGen:
DType.INT48,
DType.FP16,
)
- outputDType = testGen.rng.choice(a=incorrect_types)
+ outputDType = rng.choice(a=incorrect_types)
return scale, offset, border, outputDType
@staticmethod
- def eiPoolingErrorIf(testGen, error_name, stride, pad, kernel):
+ def eiPoolingErrorIf(rng, error_name, stride, pad, kernel):
if (
error_name == ErrorIf.StrideSmallerOne
# padding must not exceed the kernel size
@@ -207,30 +207,30 @@ class TosaErrorIfArgGen:
and pad[3] < kernel[1]
):
wrongStride = (
- testGen.rng.choice([0, -1, -2, -3]),
- testGen.rng.choice([0, -1, -2, -3]),
+ rng.choice([0, -1, -2, -3]),
+ rng.choice([0, -1, -2, -3]),
)
return wrongStride, pad, kernel
elif error_name == ErrorIf.PadSmallerZero:
wrongPad = (
- testGen.rng.choice([-1, -2, -3]),
- testGen.rng.choice([-1, -2, -3]),
- testGen.rng.choice([-1, -2, -3]),
- testGen.rng.choice([-1, -2, -3]),
+ rng.choice([-1, -2, -3]),
+ rng.choice([-1, -2, -3]),
+ rng.choice([-1, -2, -3]),
+ rng.choice([-1, -2, -3]),
)
return stride, wrongPad, kernel
elif error_name == ErrorIf.KernelSmallerOne:
wrongKernel = (
- testGen.rng.choice([0, -1, -2, -3]),
- testGen.rng.choice([0, -1, -2, -3]),
+ rng.choice([0, -1, -2, -3]),
+ rng.choice([0, -1, -2, -3]),
)
return stride, pad, wrongKernel
elif error_name == ErrorIf.PadLargerEqualKernel:
wrongPad = (
- testGen.rng.choice([kernel[0], kernel[0] + 1, kernel[0] + 2]),
- testGen.rng.choice([kernel[0], kernel[0] + 1, kernel[0] + 2]),
- testGen.rng.choice([kernel[1], kernel[1] + 1, kernel[1] + 2]),
- testGen.rng.choice([kernel[1], kernel[1] + 1, kernel[1] + 2]),
+ rng.choice([kernel[0], kernel[0] + 1, kernel[0] + 2]),
+ rng.choice([kernel[0], kernel[0] + 1, kernel[0] + 2]),
+ rng.choice([kernel[1], kernel[1] + 1, kernel[1] + 2]),
+ rng.choice([kernel[1], kernel[1] + 1, kernel[1] + 2]),
)
return stride, wrongPad, kernel
else:
@@ -265,16 +265,16 @@ class TosaErrorIfArgGen:
return False
@staticmethod
- def eiInvalidateInputOutputList(testGen, error_name, input_list, output_list):
+ def eiInvalidateInputOutputList(rng, 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])
+ add_input = rng.choice([True, False])
if add_input:
input_list.append("eiDummyInput")
else:
input_list = input_list[:-1]
elif error_name == "WrongOutputList":
- add_output = testGen.rng.choice([True, False])
+ add_output = rng.choice([True, False])
if add_output:
output_list.append("eiDummyOutput")
else:
@@ -291,25 +291,25 @@ class TosaErrorIfArgGen:
new_shape = [max(d - 1, 1) for d in new_shape]
return new_shape
- def eiSliceErrorIf(testGen, error_name, input_shape, start, size):
+ def eiSliceErrorIf(rng, error_name, input_shape, start, size):
if error_name == ErrorIf.StartSmallerZero:
newStart = []
for i in range(len(input_shape)):
- newStart.append(testGen.rng.choice([-3, -2, -1]))
+ newStart.append(rng.choice([-3, -2, -1]))
return newStart, size
elif error_name == ErrorIf.SizeSmallerEqualZero:
newSize = []
for i in range(len(input_shape)):
- newSize.append(testGen.rng.choice([-3, -2, -1, 0]))
+ newSize.append(rng.choice([-3, -2, -1, 0]))
return start, newSize
elif error_name == ErrorIf.StartSizeOutsideBounds:
newStart, newSize = [], []
for i in range(len(input_shape)):
newStart.append(input_shape[i] - 1)
- newSize.append(testGen.rng.choice([2, 3, 4]))
+ newSize.append(rng.choice([2, 3, 4]))
return newStart, newSize
elif error_name == ErrorIf.InputSizeStartLengthMismatch:
- remove = testGen.rng.choice([True, False])
+ remove = rng.choice([True, False])
# Get an empty tensor when diminishing dimension on 1-d tensor.
if len(start) == 1 or len(size) == 1:
@@ -328,9 +328,7 @@ class TosaErrorIfArgGen:
return start, size
@staticmethod
- def eiCastErrorIf(testGen, input_dtype):
- # if input_dtype in [DType.BOOL, DType.FP32]:
- # outputDType = [DType.BOOL, DType.INT48, DType.FP32]
+ def eiCastErrorIf(input_dtype):
if input_dtype in [DType.BOOL]:
outputDType = [
DType.BOOL,
diff --git a/verif/generator/tosa_random_gen.py b/verif/generator/tosa_random_gen.py
new file mode 100644
index 0000000..ae8ae5c
--- /dev/null
+++ b/verif/generator/tosa_random_gen.py
@@ -0,0 +1,174 @@
+# Copyright (c) 2024, ARM Limited.
+# SPDX-License-Identifier: Apache-2.0
+import hashlib
+import logging
+
+import generator.tosa_utils as gtu
+import numpy as np
+from tosa.DType import DType
+
+logging.basicConfig()
+logger = logging.getLogger("tosa_verif_build_tests")
+
+
+class TosaRandomGenerator(np.random.Generator):
+ """Equivalent to numpy.default_rng, with support for TOSA data types"""
+
+ def __init__(self, seed, restrict_range_by_type={}):
+ """Create random generator with TOSA type support.
+
+ seed: integer seed
+ restrict_range_by_type: see TosaHashRandomGenerator.__init__()
+ """
+ self._restrict_range_by_type = restrict_range_by_type
+ self._seed = int(seed)
+ self._bitgen = np.random.PCG64(self._seed)
+ super().__init__(self._bitgen)
+
+ @property
+ def seed(self):
+ return self._seed
+
+ @property
+ def hexSeed(self):
+ return hex(self._seed)
+
+ def dTypeRange(self, dtype, high_inclusive=False):
+ """Returns range tuple for given dtype.
+
+ dtype: DType
+ high_inclusive: True for inclusive high values
+ Returns: dtype value range boundaries tuple (low, high)
+ The high boundary is excluded in the range unless high_inclusive is True
+ """
+ if dtype in self._restrict_range_by_type:
+ rng = self._restrict_range_by_type[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.INT48:
+ rng = (-(1 << 47), (1 << 47))
+ else:
+ # Float types and SHAPE should be in _restrict_range_by_type dict
+ raise Exception("Unknown supported dtype: {}".format(dtype))
+
+ if dtype in (DType.FP16, DType.BF16, DType.FP32, DType.FP8E4M3, DType.FP8E5M2):
+ # Floating point - range is always inclusive
+ return rng
+ else:
+ # Integer
+ if not high_inclusive:
+ # Exclusive high: low <= range < high
+ return rng
+ else:
+ # Inclusive range: low <= range <= high
+ return (rng[0], rng[1] - 1)
+
+ def randInt(self, low=0, high=256):
+ return np.int32(self.integers(low=low, high=high, size=1))[0]
+
+ def randNumberDType(self, dtype):
+ low, high = self.dTypeRange(dtype)
+
+ if dtype == DType.FP32:
+ return np.float32(self.uniform(low=low, high=high))
+ elif dtype == DType.FP16:
+ return np.float16(self.uniform(low=low, high=high))
+ elif dtype == DType.BF16:
+ rand_f32 = np.float32(self.uniform(low=low, high=high))
+ return gtu.vect_f32_to_bf16(rand_f32)
+ elif dtype == DType.FP8E4M3:
+ rand_f32 = np.float32(self.uniform(low=low, high=high))
+ return gtu.vect_f32_to_fp8e4m3(rand_f32)
+ elif dtype == DType.FP8E5M2:
+ rand_f32 = np.float32(self.uniform(low=low, high=high))
+ return gtu.vect_f32_to_fp8e5m2(rand_f32)
+ elif dtype == DType.BOOL:
+ return self.choice([False, True])
+ elif dtype == DType.INT48 or dtype == DType.SHAPE:
+ # Special size
+ return np.int64(self.integers(low, high, size=1))[0]
+
+ return np.int32(self.integers(low, high, size=1))[0]
+
+ def randTensor(self, shape, dtype, data_range=None):
+ if data_range is None:
+ low, high = self.dTypeRange(dtype)
+ else:
+ low, high = data_range
+
+ if dtype == DType.BOOL:
+ return np.bool_(self.choice(a=[False, True], size=shape))
+ elif dtype == DType.INT4:
+ return np.int8(self.integers(low=low, high=high, size=shape))
+ elif dtype == DType.INT8:
+ return np.int8(self.integers(low=low, high=high, size=shape))
+ elif dtype == DType.UINT8:
+ return np.uint8(self.integers(low=low, high=high, size=shape))
+ elif dtype == DType.INT16:
+ return np.int16(self.integers(low=low, high=high, size=shape))
+ elif dtype == DType.UINT16:
+ return np.uint16(self.integers(low=low, high=high, size=shape))
+ elif dtype in (DType.INT48, DType.SHAPE):
+ return np.int64(self.integers(low=low, high=high, size=shape))
+ elif dtype in (
+ DType.FP16,
+ DType.BF16,
+ DType.FP32,
+ DType.FP8E4M3,
+ DType.FP8E5M2,
+ ):
+ f_tensor = self.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.integers(low=low, high=high, size=shape))
+
+
+class TosaHashRandomGenerator(TosaRandomGenerator):
+ """Hash seeded TOSA random number generator."""
+
+ def __init__(self, seed, seed_list, restrict_range_by_type={}):
+ """Create TOSA random generator seeding it with a hashable list.
+
+ seed: integer starting seed
+ seed_list: list of hashable items to add to starting seed
+ restrict_range_by_type: dictionary of DTypes with (low, high) range tuples
+ This must contain entries for SHAPE and all Floating Point data types.
+ NOTE: For integers, the high value must be the exclusive value
+ """
+ # Convert seed_list to strings
+ seed_strings_list = [str(s) for s in seed_list]
+ # Create a single string and create hash
+ self._seed_string = "__".join(seed_strings_list)
+ self._hash = hashlib.md5(bytes(self._seed_string, "utf-8"))
+ # Add the hash value to the given seed
+ seed += int(self._hash.hexdigest(), 16)
+
+ logger.debug(f"Seed={seed} Seed string={self._seed_string}")
+ super().__init__(seed, restrict_range_by_type)
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 ErrorIf
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 @@ class TosaTestGen:
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 @@ class TosaTestGen:
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 @@ class TosaTestGen:
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 @@ class TosaTestGen:
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 @@ class TosaTestGen:
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 @@ class TosaTestGen:
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 @@ class TosaTestGen:
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 @@ class TosaTestGen:
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 @@ class TosaTestGen:
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 @@ class TosaTestGen:
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 @@ class TosaTestGen:
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 @@ class TosaTestGen:
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 @@ class TosaTestGen:
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 @@ class TosaTestGen:
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 @@ class TosaTestGen:
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 @@ class TosaTestGen:
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 @@ class TosaTestGen:
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 @@ class TosaTestGen:
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 @@ class TosaTestGen:
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 @@ class TosaTestGen:
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 @@ class TosaTestGen:
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 @@ class TosaTestGen:
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 @@ class TosaTestGen:
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 @@ class TosaTestGen:
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 @@ class TosaTestGen:
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 @@ class TosaTestGen:
def build_pool2d(
self,
+ rng,
op,
inputs,
args_dict,
@@ -846,15 +771,17 @@ class TosaTestGen:
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 @@ class TosaTestGen:
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 @@ class TosaTestGen:
def build_conv2d(
self,
+ rng,
op,
inputs,
args_dict,
@@ -920,7 +848,7 @@ class TosaTestGen:
assert len(padding) == 4
result_tensor = OutputShaper.conv2dOp(
self.ser,
- self.rng,
+ rng,
ifm,
filter,
accum_dtype,
@@ -936,8 +864,10 @@ class TosaTestGen:
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 @@ class TosaTestGen:
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 @@ class TosaTestGen:
def build_conv3d(
self,
+ rng,
op,
inputs,
args_dict,
@@ -1002,7 +933,7 @@ class TosaTestGen:
assert len(padding) == 6
result_tensor = OutputShaper.conv3dOp(
self.ser,
- self.rng,
+ rng,
ifm,
filter,
accum_dtype,
@@ -1018,8 +949,10 @@ class TosaTestGen:
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 @@ class TosaTestGen:
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 @@ class TosaTestGen:
def build_transpose_conv2d(
self,
+ rng,
op,
inputs,
args_dict,
@@ -1083,7 +1017,7 @@ class TosaTestGen:
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 @@ class TosaTestGen:
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 @@ class TosaTestGen:
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 @@ class TosaTestGen:
def build_depthwise_conv2d(
self,
+ rng,
op,
inputs,
args_dict,
@@ -1158,7 +1095,7 @@ class TosaTestGen:
result_tensor = OutputShaper.depthwiseConv2dOp(
self.ser,
- self.rng,
+ rng,
ifm,
filter,
accum_dtype,
@@ -1174,8 +1111,10 @@ class TosaTestGen:
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 @@ class TosaTestGen:
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 @@ class TosaTestGen:
def build_fully_connected(
self,
+ rng,
op,
inputs,
args_dict,
@@ -1235,7 +1175,7 @@ class TosaTestGen:
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 @@ class TosaTestGen:
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 @@ class TosaTestGen:
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 @@ class TosaTestGen:
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 @@ class TosaTestGen:
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 @@ class TosaTestGen:
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 @@ class TosaTestGen:
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 @@ class TosaTestGen:
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 @@ class TosaTestGen:
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 @@ class TosaTestGen:
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 @@ class TosaTestGen:
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 @@ class TosaTestGen:
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 @@ class TosaTestGen:
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 @@ class TosaTestGen:
def build_pad(
self,
+ rng,
op,
inputs,
args_dict,
@@ -1581,7 +1534,7 @@ class TosaTestGen:
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 @@ class TosaTestGen:
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 @@ class TosaTestGen:
def build_dim(
self,
+ rng,
op,
inputs,
args_dict,
@@ -1640,7 +1594,7 @@ class TosaTestGen:
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 @@ class TosaTestGen:
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 @@ class TosaTestGen:
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 @@ class TosaTestGen:
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 @@ class TosaTestGen:
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 @@ class TosaTestGen:
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 @@ class TosaTestGen:
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 @@ class TosaTestGen:
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 @@ class TosaTestGen:
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 @@ class TosaTestGen:
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 @@ class TosaTestGen:
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 @@ class TosaTestGen:
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 @@ class TosaTestGen:
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 @@ class TosaTestGen:
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 @@ class TosaTestGen:
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 @@ class TosaTestGen:
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 @@ class TosaTestGen:
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 @@ class TosaTestGen:
def build_resize(
self,
+ rng,
op,
inputs,
args_dict,
@@ -2008,7 +2008,7 @@ class TosaTestGen:
result_tensor = OutputShaper.resizeOp(
self.ser,
- self.rng,
+ rng,
input,
mode,
scale,
@@ -2030,7 +2030,7 @@ class TosaTestGen:
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 @@ class TosaTestGen:
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 @@ class TosaTestGen:
# 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 @@ class TosaTestGen:
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 @@ class TosaTestGen:
def build_rescale(
self,
+ rng,
op,
inputs,
args_dict,
@@ -2151,7 +2158,7 @@ class TosaTestGen:
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 @@ class TosaTestGen:
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 @@ class TosaTestGen:
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 @@ class TosaTestGen:
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 @@ class TosaTestGen:
def build_cond_if_const(
self,
+ rng,
op,
inputs,
args_dict,
@@ -2331,7 +2339,7 @@ class TosaTestGen:
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 @@ class TosaTestGen:
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 @@ class TosaTestGen:
def build_cond_if_binary(
self,
+ rng,
op,
inputs,
args_dict,
@@ -2415,7 +2424,7 @@ class TosaTestGen:
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 @@ class TosaTestGen:
]:
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 @@ class TosaTestGen:
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 @@ class TosaTestGen:
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 @@ class TosaTestGen:
]:
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 @@ class TosaTestGen:
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 @@ class TosaTestGen:
def build_fft2d(
self,
+ rng,
op,
inputs,
args_dict,
@@ -2646,7 +2663,7 @@ class TosaTestGen:
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 @@ class TosaTestGen:
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 @@ class TosaTestGen:
def build_rfft2d(
self,
+ rng,
op,
inputs,
args_dict,
@@ -2708,7 +2726,7 @@ class TosaTestGen:
):
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 @@ class TosaTestGen:
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 @@ class TosaTestGen:
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 @@ class TosaTestGen:
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 @@ class TosaTestGen:
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 @@ class TosaTestGen:
# 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 @@ class TosaTestGen:
# 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,
diff --git a/verif/generator/tosa_verif_build_tests.py b/verif/generator/tosa_verif_build_tests.py
index 47c351a..83c06d7 100644
--- a/verif/generator/tosa_verif_build_tests.py
+++ b/verif/generator/tosa_verif_build_tests.py
@@ -80,6 +80,13 @@ def parseArgs(argv):
help="Random seed for test generation",
)
+ parser.add_argument(
+ "--stable-random-generation",
+ dest="stable_rng",
+ action="store_true",
+ help="Produces less variation (when the test-generator changes) in the test output using the same options",
+ )
+
filter_group.add_argument(
"--filter",
dest="filter",
@@ -395,7 +402,7 @@ def main(argv=None):
else:
# Use the random number generator to shuffle the test list
# and select the per op tests from it
- tests = testList.select(ttg.rng)
+ tests = testList.select(ttg.global_rng)
if args.list_tests:
for test in tests:
generated by cgit v1.2.1 (git 2.23.0) at 2024-04-28 15:32:14 +0000