8K level: rank 6 tests support using generator groups
Split generation of conformance tests into groups to avoid a change
in existing tests selection
* A standard group is used for the normal/default conformance tests
* An 8k level group covers the extension of testing for 8k level
* Other arbitrary groups can be created and the group names will be
used to tag the tests, so that in the future they could be filtered
Group selection criteria is in a new dictionary
* A default selection config is used for generation groups
* But a different config can be used for a particular generation group
Added parameter sparsity for pad of rank6 and above, and further
parameter sparsity options to test_select to limit number of tests
selected.
Signed-off-by: Jeremy Johnson <jeremy.johnson@arm.com>
Change-Id: Ib9f1185c0ae6d0957df2116411c17e958157c6d3
diff --git a/verif/conformance/test_select.py b/verif/conformance/test_select.py
index 72a4e84..66b2e56 100644
--- a/verif/conformance/test_select.py
+++ b/verif/conformance/test_select.py
@@ -130,6 +130,7 @@
test_dir: Path,
config: Dict[str, Dict[str, List[Any]]],
negative=False,
+ ignore_missing=False,
):
"""Initialise the selection parameters for an operator.
@@ -144,6 +145,8 @@
parameter names and pre-chosen values
"sparsity" - a dictionary of parameter names with a
sparsity value
+ "full_sparsity" - "true"/"false" to use the sparsity
+ value on permutes/params/preselected
"exclude_patterns" - a list of regex's whereby each
match will not be considered for selection.
Exclusion happens BEFORE test selection (i.e.
@@ -153,6 +156,7 @@
negative: bool indicating if negative testing is being selected
which filters for ERRORIF in the test name and only selects
the first test found (ERRORIF tests)
+ ignore_missing: bool indicating if missing tests should be ignored
EXAMPLE CONFIG (with non-json comments):
"params": {
@@ -190,6 +194,7 @@
), f"{self.__class__.__name__}: {self.name} is not a valid operator name"
self.negative = negative
+ self.ignore_missing = ignore_missing
self.wks_param_names = self.param_names.copy()
if self.negative:
# need to override positive set up - use "errorifs" config if set
@@ -207,6 +212,9 @@
self.params = config["params"] if "params" in config else {}
self.permutes = config["permutes"] if "permutes" in config else []
self.sparsity = config["sparsity"] if "sparsity" in config else {}
+ self.full_sparsity = (
+ (config["full_sparsity"] == "true") if "full_sparsity" in config else False
+ )
self.preselected = config["preselected"] if "preselected" in config else {}
self.exclude_patterns = (
config["exclude_patterns"] if "exclude_patterns" in config else []
@@ -308,7 +316,7 @@
unused_values = {k: set(v) for k, v in self.params.items()}
# select tests matching permuted, or preselected, parameter combinations
- for path in self.test_paths:
+ for n, path in enumerate(self.test_paths):
path_params = self.path_params(path)
if path_params in unused_permuted or path_params in unused_preselected:
unused_paths.remove(path)
@@ -321,10 +329,20 @@
for p in list(unused_permuted):
if p["case"] == path_params["case"]:
unused_permuted.remove(p)
+ if self.full_sparsity:
+ # Test for sparsity
+ skip = False
+ for k in path_params:
+ if k in self.sparsity and n % self.sparsity[k] != 0:
+ logger.debug(f"Skipping due to {k} sparsity - {path.name}")
+ skip = True
+ break
+ if skip:
+ continue
# remove the param values used by this path
for k in path_params:
unused_values[k].discard(path_params[k])
- logger.debug(f"FOUND: {path.name}")
+ logger.debug(f"FOUND wanted: {path.name}")
yield path
# search for tests that match any unused parameter values
@@ -339,26 +357,30 @@
# remove the param values used by this path
for p in path_params:
unused_values[p].discard(path_params[p])
- logger.debug(f"FOUND: {path.name}")
+ sparsity = self.sparsity[k] if k in self.sparsity else 0
+ logger.debug(f"FOUND unused [{k}/{n}/{sparsity}]: {path.name}")
yield path
break
- # report any preselected combinations that were not found
- for params in unused_preselected:
- logger.warning(f"MISSING preselected: {params}")
- # report any permuted combinations that were not found
- for params in unused_permuted:
- logger.debug(f"MISSING permutation: {params}")
- # report any param values that were not found
- for k, values in unused_values.items():
- if values:
- if k not in self.sparsity:
- logger.warning(f"MISSING {len(values)} values for {k}: {values}")
- else:
- logger.info(
- f"Skipped {len(values)} values for {k} due to sparsity setting"
- )
- logger.debug(f"Values skipped: {values}")
+ if not self.ignore_missing:
+ # report any preselected combinations that were not found
+ for params in unused_preselected:
+ logger.warning(f"MISSING preselected: {params}")
+ # report any permuted combinations that were not found
+ for params in unused_permuted:
+ logger.debug(f"MISSING permutation: {params}")
+ # report any param values that were not found
+ for k, values in unused_values.items():
+ if values:
+ if k not in self.sparsity:
+ logger.warning(
+ f"MISSING {len(values)} values for {k}: {values}"
+ )
+ else:
+ logger.info(
+ f"Skipped {len(values)} values for {k} due to sparsity setting"
+ )
+ logger.debug(f"Values skipped: {values}")
class AbsOperator(Operator):
@@ -858,6 +880,12 @@
help="A JSON file defining the parameters to use for each operator",
)
parser.add_argument(
+ "--selector",
+ default="default",
+ type=str,
+ help="The selector in the selection dictionary to use for each operator",
+ )
+ parser.add_argument(
"--full-path", action="store_true", help="output the full path for each test"
)
parser.add_argument(
@@ -907,7 +935,14 @@
for op_name in Operator.registry:
if not args.operators or op_name in args.operators:
op_params = config[op_name] if op_name in config else {}
- op = Operator.registry[op_name](args.test_dir, op_params, negative)
+ if "selection" in op_params and args.selector in op_params["selection"]:
+ selection_config = op_params["selection"][args.selector]
+ else:
+ logger.warning(
+ f"Could not find selection config {args.selector} for {op_name}"
+ )
+ selection_config = {}
+ op = Operator.registry[op_name](args.test_dir, selection_config, negative)
for test_path in op.select_tests():
print(test_path.resolve() if args.full_path else test_path.name)
diff --git a/verif/conformance/tosa_base_profile_ops_info.json b/verif/conformance/tosa_base_profile_ops_info.json
index 24e01c5..5825719 100644
--- a/verif/conformance/tosa_base_profile_ops_info.json
+++ b/verif/conformance/tosa_base_profile_ops_info.json
@@ -1,2598 +1,3634 @@
{
"abs": {
"group": "ew_unary",
- "generator_args": [
- [
- "--target-dtype",
- "int32",
- "--tensor-dim-range",
- "16,64",
- "--target-rank",
- "1",
- "--target-rank",
- "2",
- "--target-rank",
- "3"
- ],
- [
- "--target-dtype",
- "int32",
- "--tensor-dim-range",
- "1,16",
- "--target-rank",
- "4",
- "--target-rank",
- "5"
- ],
- [
- "--target-dtype",
- "int32",
- "--target-shape",
- "1,2,65533,1",
- "--target-shape",
- "1,65539,1,1,1"
- ]
- ],
- "params": {},
- "permutes": [
- "shape",
- "type"
- ],
"profile": [
"tosa-bi",
"tosa-mi"
- ]
+ ],
+ "generation": {
+ "standard": {
+ "generator_args": [
+ [
+ "--target-dtype",
+ "int32",
+ "--tensor-dim-range",
+ "16,64",
+ "--target-rank",
+ "1",
+ "--target-rank",
+ "2",
+ "--target-rank",
+ "3"
+ ],
+ [
+ "--target-dtype",
+ "int32",
+ "--tensor-dim-range",
+ "1,16",
+ "--target-rank",
+ "4",
+ "--target-rank",
+ "5"
+ ],
+ [
+ "--target-dtype",
+ "int32",
+ "--target-shape",
+ "1,2,65533,1",
+ "--target-shape",
+ "1,65539,1,1,1"
+ ]
+ ]
+ },
+ "8k_level": {
+ "no_negative_tests": "true",
+ "generator_args": [
+ [
+ "--target-dtype",
+ "int32",
+ "--tensor-dim-range",
+ "1,10",
+ "--target-rank",
+ "6"
+ ]
+ ]
+ }
+ },
+ "selection": {
+ "default": {
+ "params": {},
+ "permutes": [
+ "shape",
+ "type"
+ ]
+ }
+ }
},
"add": {
"group": "ew_binary",
- "generator_args": [
- [
- "--target-dtype",
- "int32",
- "--tensor-dim-range",
- "16,64",
- "--target-rank",
- "1",
- "--target-rank",
- "2",
- "--target-rank",
- "3"
- ],
- [
- "--target-dtype",
- "int32",
- "--tensor-dim-range",
- "1,16",
- "--target-rank",
- "4",
- "--target-rank",
- "5"
- ],
- [
- "--target-dtype",
- "int32",
- "--target-shape",
- "1,2,65533,1",
- "--target-shape",
- "1,65539,1,1,1"
- ]
- ],
- "params": {},
- "permutes": [
- "shape",
- "type"
- ],
"profile": [
"tosa-bi",
"tosa-mi"
- ]
+ ],
+ "generation": {
+ "standard": {
+ "generator_args": [
+ [
+ "--target-dtype",
+ "int32",
+ "--tensor-dim-range",
+ "16,64",
+ "--target-rank",
+ "1",
+ "--target-rank",
+ "2",
+ "--target-rank",
+ "3"
+ ],
+ [
+ "--target-dtype",
+ "int32",
+ "--tensor-dim-range",
+ "1,16",
+ "--target-rank",
+ "4",
+ "--target-rank",
+ "5"
+ ],
+ [
+ "--target-dtype",
+ "int32",
+ "--target-shape",
+ "1,2,65533,1",
+ "--target-shape",
+ "1,65539,1,1,1"
+ ]
+ ]
+ },
+ "8k_level": {
+ "no_negative_tests": "true",
+ "generator_args": [
+ [
+ "--target-dtype",
+ "int32",
+ "--tensor-dim-range",
+ "1,10",
+ "--target-rank",
+ "6"
+ ]
+ ]
+ }
+ },
+ "selection": {
+ "default": {
+ "params": {},
+ "permutes": [
+ "shape",
+ "type"
+ ]
+ }
+ }
},
"argmax": {
"group": "tensor",
- "generator_args": [
- [
- "--target-dtype",
- "int8",
- "--target-dtype",
- "int16",
- "--tensor-dim-range",
- "32,64",
- "--target-rank",
- "1",
- "--target-rank",
- "2",
- "--target-rank",
- "3",
- "--num-rand-permutations",
- "2"
- ],
- [
- "--target-dtype",
- "int8",
- "--target-dtype",
- "int16",
- "--tensor-dim-range",
- "1,32",
- "--target-rank",
- "4",
- "--num-rand-permutations",
- "2"
- ],
- [
- "--target-dtype",
- "int8",
- "--target-shape",
- "1,3,1,65535",
- "--target-shape",
- "1,65538,1,2",
- "--num-rand-permutations",
- "2"
- ]
- ],
- "params": {},
- "permutes": [
- "shape",
- "type"
- ],
"profile": [
"tosa-bi",
"tosa-mi"
- ]
+ ],
+ "generation": {
+ "standard": {
+ "generator_args": [
+ [
+ "--target-dtype",
+ "int8",
+ "--target-dtype",
+ "int16",
+ "--tensor-dim-range",
+ "32,64",
+ "--target-rank",
+ "1",
+ "--target-rank",
+ "2",
+ "--target-rank",
+ "3",
+ "--num-rand-permutations",
+ "2"
+ ],
+ [
+ "--target-dtype",
+ "int8",
+ "--target-dtype",
+ "int16",
+ "--tensor-dim-range",
+ "1,32",
+ "--target-rank",
+ "4",
+ "--num-rand-permutations",
+ "2"
+ ],
+ [
+ "--target-dtype",
+ "int8",
+ "--target-shape",
+ "1,3,1,65535",
+ "--target-shape",
+ "1,65538,1,2",
+ "--num-rand-permutations",
+ "2"
+ ]
+ ]
+ }
+ },
+ "selection": {
+ "default": {
+ "params": {},
+ "permutes": [
+ "shape",
+ "type"
+ ]
+ }
+ }
},
"arithmetic_right_shift": {
"group": "ew_binary",
- "generator_args": [
- [
- "--target-dtype",
- "int8",
- "--target-dtype",
- "int16",
- "--target-dtype",
- "int32",
- "--tensor-dim-range",
- "16,64",
- "--target-rank",
- "1",
- "--target-rank",
- "2",
- "--target-rank",
- "3"
- ],
- [
- "--target-dtype",
- "int8",
- "--target-dtype",
- "int16",
- "--target-dtype",
- "int32",
- "--tensor-dim-range",
- "1,16",
- "--target-rank",
- "4",
- "--target-rank",
- "5"
- ],
- [
- "--target-dtype",
- "int8",
- "--target-shape",
- "1,2,65533,1",
- "--target-shape",
- "1,65539,1,1,1"
- ]
- ],
- "params": {},
- "permutes": [
- "shape",
- "type",
- "rounding"
- ],
"profile": [
"tosa-bi",
"tosa-mi"
- ]
+ ],
+ "generation": {
+ "standard": {
+ "generator_args": [
+ [
+ "--target-dtype",
+ "int8",
+ "--target-dtype",
+ "int16",
+ "--target-dtype",
+ "int32",
+ "--tensor-dim-range",
+ "16,64",
+ "--target-rank",
+ "1",
+ "--target-rank",
+ "2",
+ "--target-rank",
+ "3"
+ ],
+ [
+ "--target-dtype",
+ "int8",
+ "--target-dtype",
+ "int16",
+ "--target-dtype",
+ "int32",
+ "--tensor-dim-range",
+ "1,16",
+ "--target-rank",
+ "4",
+ "--target-rank",
+ "5"
+ ],
+ [
+ "--target-dtype",
+ "int8",
+ "--target-shape",
+ "1,2,65533,1",
+ "--target-shape",
+ "1,65539,1,1,1"
+ ]
+ ]
+ },
+ "8k_level": {
+ "no_negative_tests": "true",
+ "generator_args": [
+ [
+ "--target-dtype",
+ "int8",
+ "--target-dtype",
+ "int16",
+ "--target-dtype",
+ "int32",
+ "--tensor-dim-range",
+ "1,10",
+ "--target-rank",
+ "6"
+ ]
+ ]
+ }
+ },
+ "selection": {
+ "default": {
+ "params": {},
+ "permutes": [
+ "shape",
+ "type",
+ "rounding"
+ ]
+ }
+ }
},
"avg_pool2d": {
"group": "tensor",
- "generator_args": [
- [
- "--target-dtype",
- "int8",
- "--target-dtype",
- "int16",
- "--target-shape",
- "1,49,42,28",
- "--target-shape",
- "1,11,44,3",
- "--allow-pooling-and-conv-oversizes"
- ],
- [
- "--target-dtype",
- "int8",
- "--target-shape",
- "1,65535,5,1",
- "--target-shape",
- "1,3,65537,1",
- "--allow-pooling-and-conv-oversizes"
- ]
- ],
- "params": {
- "shape": [],
- "type": [
- "i8",
- "i16"
- ],
- "kernel": [],
- "stride": [],
- "pad": []
- },
- "permutes": [
- "shape",
- "type",
- "pad"
- ],
"profile": [
"tosa-bi",
"tosa-mi"
- ]
+ ],
+ "generation": {
+ "standard": {
+ "generator_args": [
+ [
+ "--target-dtype",
+ "int8",
+ "--target-dtype",
+ "int16",
+ "--target-shape",
+ "1,49,42,28",
+ "--target-shape",
+ "1,11,44,3",
+ "--allow-pooling-and-conv-oversizes"
+ ],
+ [
+ "--target-dtype",
+ "int8",
+ "--target-shape",
+ "1,65535,5,1",
+ "--target-shape",
+ "1,3,65537,1",
+ "--allow-pooling-and-conv-oversizes"
+ ]
+ ]
+ }
+ },
+ "selection": {
+ "default": {
+ "params": {
+ "shape": [],
+ "type": [
+ "i8",
+ "i16"
+ ],
+ "kernel": [],
+ "stride": [],
+ "pad": []
+ },
+ "permutes": [
+ "shape",
+ "type",
+ "pad"
+ ]
+ }
+ }
},
"bitwise_and": {
"group": "ew_binary",
- "generator_args": [
- [
- "--target-dtype",
- "int8",
- "--target-dtype",
- "int16",
- "--target-dtype",
- "int32",
- "--tensor-dim-range",
- "1,64",
- "--target-rank",
- "1",
- "--target-rank",
- "2",
- "--target-rank",
- "3"
- ],
- [
- "--target-dtype",
- "int8",
- "--tensor-dim-range",
- "1,16",
- "--target-rank",
- "4",
- "--target-rank",
- "5"
- ],
- [
- "--target-dtype",
- "int8",
- "--target-shape",
- "1,2,65536,1",
- "--target-shape",
- "1,65539,1,1,1"
- ]
- ],
- "params": {},
- "permutes": [
- "shape",
- "type"
- ],
"profile": [
"tosa-bi",
"tosa-mi"
- ]
+ ],
+ "generation": {
+ "standard": {
+ "generator_args": [
+ [
+ "--target-dtype",
+ "int8",
+ "--target-dtype",
+ "int16",
+ "--target-dtype",
+ "int32",
+ "--tensor-dim-range",
+ "1,64",
+ "--target-rank",
+ "1",
+ "--target-rank",
+ "2",
+ "--target-rank",
+ "3"
+ ],
+ [
+ "--target-dtype",
+ "int8",
+ "--tensor-dim-range",
+ "1,16",
+ "--target-rank",
+ "4",
+ "--target-rank",
+ "5"
+ ],
+ [
+ "--target-dtype",
+ "int8",
+ "--target-shape",
+ "1,2,65536,1",
+ "--target-shape",
+ "1,65539,1,1,1"
+ ]
+ ]
+ },
+ "8k_level": {
+ "no_negative_tests": "true",
+ "generator_args": [
+ [
+ "--target-dtype",
+ "int8",
+ "--target-dtype",
+ "int16",
+ "--tensor-dim-range",
+ "1,10",
+ "--target-rank",
+ "6"
+ ]
+ ]
+ }
+ },
+ "selection": {
+ "default": {
+ "params": {},
+ "permutes": [
+ "shape",
+ "type"
+ ]
+ }
+ }
},
"bitwise_not": {
"group": "ew_unary",
- "generator_args": [
- [
- "--target-dtype",
- "int8",
- "--target-dtype",
- "int16",
- "--target-dtype",
- "int32",
- "--tensor-dim-range",
- "2,64",
- "--target-rank",
- "1",
- "--target-rank",
- "2",
- "--target-rank",
- "3"
- ],
- [
- "--target-dtype",
- "int8",
- "--tensor-dim-range",
- "1,16",
- "--target-rank",
- "4",
- "--target-rank",
- "5"
- ],
- [
- "--target-dtype",
- "int8",
- "--target-shape",
- "1,2,65537,1",
- "--target-shape",
- "1,65538,1,1,1"
- ]
- ],
- "params": {},
- "permutes": [
- "shape",
- "type"
- ],
"profile": [
"tosa-bi",
"tosa-mi"
- ]
+ ],
+ "generation": {
+ "standard": {
+ "generator_args": [
+ [
+ "--target-dtype",
+ "int8",
+ "--target-dtype",
+ "int16",
+ "--target-dtype",
+ "int32",
+ "--tensor-dim-range",
+ "2,64",
+ "--target-rank",
+ "1",
+ "--target-rank",
+ "2",
+ "--target-rank",
+ "3"
+ ],
+ [
+ "--target-dtype",
+ "int8",
+ "--tensor-dim-range",
+ "1,16",
+ "--target-rank",
+ "4",
+ "--target-rank",
+ "5"
+ ],
+ [
+ "--target-dtype",
+ "int8",
+ "--target-shape",
+ "1,2,65537,1",
+ "--target-shape",
+ "1,65538,1,1,1"
+ ]
+ ]
+ },
+ "8k_level": {
+ "no_negative_tests": "true",
+ "generator_args": [
+ [
+ "--target-dtype",
+ "int16",
+ "--target-dtype",
+ "int32",
+ "--tensor-dim-range",
+ "1,16",
+ "--target-rank",
+ "4",
+ "--target-rank",
+ "5"
+ ],
+ [
+ "--target-dtype",
+ "int8",
+ "--target-dtype",
+ "int16",
+ "--target-dtype",
+ "int32",
+ "--tensor-dim-range",
+ "1,10",
+ "--target-rank",
+ "6"
+ ]
+ ]
+ }
+ },
+ "selection": {
+ "default": {
+ "params": {},
+ "permutes": [
+ "shape",
+ "type"
+ ]
+ }
+ }
},
"bitwise_or": {
"group": "ew_binary",
- "generator_args": [
- [
- "--target-dtype",
- "int8",
- "--target-dtype",
- "int16",
- "--target-dtype",
- "int32",
- "--tensor-dim-range",
- "3,64",
- "--target-rank",
- "1",
- "--target-rank",
- "2",
- "--target-rank",
- "3"
- ],
- [
- "--target-dtype",
- "int8",
- "--tensor-dim-range",
- "1,16",
- "--target-rank",
- "4",
- "--target-rank",
- "5"
- ],
- [
- "--target-dtype",
- "int8",
- "--target-shape",
- "1,2,65538,1",
- "--target-shape",
- "1,65537,1,1,1"
- ]
- ],
- "params": {},
- "permutes": [
- "shape",
- "type"
- ],
"profile": [
"tosa-bi",
"tosa-mi"
- ]
+ ],
+ "generation": {
+ "standard": {
+ "generator_args": [
+ [
+ "--target-dtype",
+ "int8",
+ "--target-dtype",
+ "int16",
+ "--target-dtype",
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}
}
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diff --git a/verif/conformance/tosa_main_profile_ops_info.json b/verif/conformance/tosa_main_profile_ops_info.json
index 1a9306f..5df89d6 100644
--- a/verif/conformance/tosa_main_profile_ops_info.json
+++ b/verif/conformance/tosa_main_profile_ops_info.json
@@ -1,2536 +1,2929 @@
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+ "1,1,65533,3",
+ "--target-shape",
+ "1,1,2,1,65545"
+ ]
+ ]
+ }
+ },
+ "selection": {
+ "default": {
+ "params": {},
+ "permutes": [
+ "shape",
+ "type"
+ ]
+ }
+ }
},
"slice": {
"group": "data_layout",
- "generator_args": [
- [
- "--target-dtype",
- "fp32",
- "--target-dtype",
- "fp16",
- "--target-dtype",
- "bf16",
- "--fp-values-range",
- "-2.0,2.0",
- "--tensor-dim-range",
- "16,64",
- "--target-rank",
- "1",
- "--target-rank",
- "2",
- "--target-rank",
- "3",
- "--num-rand-permutations",
- "2"
- ],
- [
- "--target-dtype",
- "fp32",
- "--target-dtype",
- "fp16",
- "--target-dtype",
- "bf16",
- "--fp-values-range",
- "-2.0,2.0",
- "--tensor-dim-range",
- "1,16",
- "--target-rank",
- "4",
- "--num-rand-permutations",
- "2"
- ],
- [
- "--target-dtype",
- "bf16",
- "--fp-values-range",
- "-2.0,2.0",
- "--target-shape",
- "1,1,655341,1",
- "--target-shape",
- "1,65539,1,1",
- "--num-rand-permutations",
- "2"
- ]
- ],
- "params": {},
- "permutes": [
- "shape",
- "type",
- "perm"
- ],
"profile": [
"tosa-bi",
"tosa-mi"
- ]
+ ],
+ "generation": {
+ "standard": {
+ "generator_args": [
+ [
+ "--target-dtype",
+ "fp32",
+ "--target-dtype",
+ "fp16",
+ "--target-dtype",
+ "bf16",
+ "--fp-values-range",
+ "-2.0,2.0",
+ "--tensor-dim-range",
+ "16,64",
+ "--target-rank",
+ "1",
+ "--target-rank",
+ "2",
+ "--target-rank",
+ "3",
+ "--num-rand-permutations",
+ "2"
+ ],
+ [
+ "--target-dtype",
+ "fp32",
+ "--target-dtype",
+ "fp16",
+ "--target-dtype",
+ "bf16",
+ "--fp-values-range",
+ "-2.0,2.0",
+ "--tensor-dim-range",
+ "1,16",
+ "--target-rank",
+ "4",
+ "--num-rand-permutations",
+ "2"
+ ],
+ [
+ "--target-dtype",
+ "bf16",
+ "--fp-values-range",
+ "-2.0,2.0",
+ "--target-shape",
+ "1,1,655341,1",
+ "--target-shape",
+ "1,65539,1,1",
+ "--num-rand-permutations",
+ "2"
+ ]
+ ]
+ }
+ },
+ "selection": {
+ "default": {
+ "params": {},
+ "permutes": [
+ "shape",
+ "type",
+ "perm"
+ ]
+ }
+ }
},
"tanh": {
"group": "activation",
- "generator_args": [
- [
- "--target-dtype",
- "fp32",
- "--target-dtype",
- "fp16",
- "--target-dtype",
- "bf16",
- "--fp-values-range",
- "-2.0,2.0",
- "--tensor-dim-range",
- "18,60",
- "--target-rank",
- "1",
- "--target-rank",
- "2",
- "--target-rank",
- "3"
- ],
- [
- "--target-dtype",
- "fp32",
- "--target-dtype",
- "fp16",
- "--target-dtype",
- "bf16",
- "--fp-values-range",
- "-2.0,2.0",
- "--tensor-dim-range",
- "1,24",
- "--target-rank",
- "4",
- "--target-rank",
- "5"
- ],
- [
- "--target-dtype",
- "fp32",
- "--fp-values-range",
- "-2.0,2.0",
- "--target-shape",
- "1,65535,2,1,1",
- "--target-shape",
- "1,65540,1,2"
- ]
- ],
- "params": {},
- "permutes": [
- "shape",
- "type"
- ],
"profile": [
"tosa-mi"
- ]
+ ],
+ "generation": {
+ "standard": {
+ "generator_args": [
+ [
+ "--target-dtype",
+ "fp32",
+ "--target-dtype",
+ "fp16",
+ "--target-dtype",
+ "bf16",
+ "--fp-values-range",
+ "-2.0,2.0",
+ "--tensor-dim-range",
+ "18,60",
+ "--target-rank",
+ "1",
+ "--target-rank",
+ "2",
+ "--target-rank",
+ "3"
+ ],
+ [
+ "--target-dtype",
+ "fp32",
+ "--target-dtype",
+ "fp16",
+ "--target-dtype",
+ "bf16",
+ "--fp-values-range",
+ "-2.0,2.0",
+ "--tensor-dim-range",
+ "1,24",
+ "--target-rank",
+ "4",
+ "--target-rank",
+ "5"
+ ],
+ [
+ "--target-dtype",
+ "fp32",
+ "--fp-values-range",
+ "-2.0,2.0",
+ "--target-shape",
+ "1,65535,2,1,1",
+ "--target-shape",
+ "1,65540,1,2"
+ ]
+ ]
+ }
+ },
+ "selection": {
+ "default": {
+ "params": {},
+ "permutes": [
+ "shape",
+ "type"
+ ]
+ }
+ }
}
}
\ No newline at end of file
diff --git a/verif/conformance/tosa_verif_conformance_generator.py b/verif/conformance/tosa_verif_conformance_generator.py
index 4971fb0..2d9dad3 100644
--- a/verif/conformance/tosa_verif_conformance_generator.py
+++ b/verif/conformance/tosa_verif_conformance_generator.py
@@ -11,6 +11,7 @@
- Tests are converted into JSON format and saved to desired output directory.
"""
import argparse
+import copy
import json
import logging
import multiprocessing as mp
@@ -47,6 +48,10 @@
DEFAULT_SEED = 42
+# When there is a dictionary of generator argument lists (groups) only the
+# standard group will have negative tests generated for it
+STANDARD_GENERATOR_GROUP = "standard"
+
class GenConformanceError(Exception):
"""Generation error reporting exception."""
@@ -78,17 +83,17 @@
return (rc.stdout, rc.stderr)
-def build_op_tests(args, profile, operator, test_params):
+def build_op_tests(
+ args, test_type, profile, operator, group, gen_args_list, gen_neg_dim_range
+):
"""Build tests for a given operator.
- Builds a set of tests based on the parameters defined in test_params
+ Builds a set of tests based on the given generator arguments list
Returns operator output directory
"""
- assert operator in test_params
-
build_tests_cmd = "tosa_verif_build_tests"
- op_build_dir = args.build_dir / profile
+ op_build_dir = args.build_dir / profile / group
build_cmd_base = [
build_tests_cmd,
@@ -102,19 +107,19 @@
build_cmds_list = []
- if args.test_type in ["positive", "both"]:
+ if test_type in ["positive", "both"]:
# Append extra parameters and run test generator for each set of parameters.
- for arglist in test_params[operator]["generator_args"]:
+ for arglist in gen_args_list:
build_cmd_pos_test = build_cmd_base.copy()
build_cmd_pos_test.extend(["--test-type", "positive"])
build_cmd_pos_test.extend(arglist)
build_cmds_list.append(build_cmd_pos_test)
- if args.test_type in ["negative", "both"]:
+ if test_type in ["negative", "both"]:
# Get target-dtypes options and any filter string to limit tests
target_dtypes_args = []
filter_str = None
- for arglist in test_params[operator]["generator_args"]:
+ for arglist in gen_args_list:
idx = 0
while idx < len(arglist):
if arglist[idx] == "--target-dtype":
@@ -130,11 +135,8 @@
build_cmd_neg_test.extend(["--filter", filter_str])
build_cmd_neg_test.extend(["--test-type", "negative"])
# Limit sizes of negative tests
- dim_range = (
- test_params[operator]["generator_negative_dim_range"]
- if "generator_negative_dim_range" in test_params[operator]
- else "1,16"
- )
+ dim_range = gen_neg_dim_range if gen_neg_dim_range is not None else "1,16"
+
build_cmd_neg_test.extend(["--tensor-dim-range", dim_range])
build_cmd_neg_test.extend(target_dtypes_args)
build_cmds_list.append(build_cmd_neg_test)
@@ -255,6 +257,7 @@
tests=None,
group=None,
trim_op_subdir=False,
+ tags=None,
):
"""Convert tests to JSON and save to output directory."""
ref_model_dir = args.ref_model_dir
@@ -267,6 +270,9 @@
# even if we are only producing tests for tosa_mi
for op_profile in op_profiles_list:
c2c_args_base.extend(["--profile", op_profile])
+ if tags is not None:
+ for tag in tags:
+ c2c_args_base.extend(["--tag", tag])
if args.framework_schema:
c2c_args_base.extend(["--framework-schema", str(args.framework_schema)])
c2c_args_base.append("--output-directory")
@@ -290,8 +296,10 @@
c2c_args_list.append(c2c_args)
if len(c2c_args_list) == 0:
- logger.warning("No tests found. Nothing to convert")
- return
+ logger.error(
+ f"No tests found for {operator}. Nothing to convert in {op_build_dir}"
+ )
+ raise (GenConformanceError())
job_pool = mp.Pool(args.num_cores)
@@ -324,17 +332,21 @@
def get_op_tests_selection(
- args, profile, operator, op_build_dir, test_params, negative=False
+ args,
+ profile,
+ operator,
+ op_build_dir,
+ selection_config,
+ negative=False,
+ ignore_missing=False,
):
"""Use test picker to get subsection of tests generated."""
- assert operator in test_params
+ # Need a full copy of the config as the selector updates it
+ config = copy.deepcopy(selection_config)
logger.info("Choosing {} tests".format(("negative" if negative else "positive")))
try:
- op_params = test_params[operator]
op = Operator.registry[operator](
- op_build_dir,
- op_params,
- negative,
+ op_build_dir, config, negative=negative, ignore_missing=ignore_missing
)
except KeyError:
logger.error(f"{operator} operator is not supported by test_select")
@@ -692,14 +704,6 @@
)
continue
- if (
- args.test_type == "negative"
- and "no_negative_tests" in test_params[op]
- and test_params[op]["no_negative_tests"]
- ):
- logger.warning(f"No negative tests for {op}")
- continue
-
op_profiles_list = test_params[op]["profile"]
if (
args.profile != PROFILES_ALL
@@ -709,52 +713,117 @@
logger.debug(f"Skipping {op} as not part of {args.profile}")
continue
- op_build_dir = build_op_tests(args, profile, op, test_params)
-
operator_group = test_params[op]["group"]
root_output_dir = args.output_dir / "operators"
- if args.convert_all_tests:
- logger.debug(f"Running and converting all {op} tests")
- generate_results(args, profile, op, op_build_dir)
- operator_test_list = None
- else:
- logger.debug(f"Running and converting selection of {op} tests")
- if args.test_type in ["positive", "both"]:
- tests_gen, tests_gen2 = tee(
- get_op_tests_selection(
- args, profile, op, op_build_dir, test_params
+
+ # Iterate through the generation groups selecting tests from each
+ for gen_name, gen_dict in test_params[op]["generation"].items():
+ no_neg_tests = (
+ "no_negative_tests" in gen_dict
+ and gen_dict["no_negative_tests"] == "true"
+ )
+
+ if no_neg_tests:
+ if args.test_type == "negative":
+ logger.info(
+ f"No negative tests for {op} / generation group {gen_name}"
)
- )
- generate_results(args, profile, op, op_build_dir, tests_gen)
- operator_test_list = list(tests_gen2)
+ continue
+ # Only produce positive tests
+ test_type = "positive"
else:
- operator_test_list = []
- if args.test_type in ["negative", "both"] and (
- "no_negative_tests" not in test_params[op]
- or not test_params[op]["no_negative_tests"]
- ):
- operator_test_list.extend(
- get_op_tests_selection(
- args,
- profile,
- op,
- op_build_dir,
- test_params,
- negative=True,
- )
+ test_type = args.test_type
+
+ gen_neg_dim_range = (
+ gen_dict["negative_dim_range"]
+ if "negative_dim_range" in gen_dict
+ else None
+ )
+
+ ignore_missing = gen_name != STANDARD_GENERATOR_GROUP
+ tags = (
+ [gen_name] if gen_name != STANDARD_GENERATOR_GROUP else None
+ )
+
+ op_build_dir = build_op_tests(
+ args,
+ test_type,
+ profile,
+ op,
+ gen_name,
+ gen_dict["generator_args"],
+ gen_neg_dim_range,
+ )
+
+ if args.convert_all_tests:
+ logger.debug(f"Running and converting all {op} tests")
+ generate_results(args, profile, op, op_build_dir)
+ operator_test_list = None
+ else:
+ logger.debug(
+ f"Running and converting selection of {op} tests"
)
- output_dir = convert_tests(
- args,
- profile,
- op,
- op_build_dir,
- root_output_dir,
- op_profiles_list,
- tests=operator_test_list,
- group=operator_group,
- )
- if not args.keep_large_files:
- check_op_tests(args, profile, op, output_dir)
+ # Work out which selection criteria we are using
+ if "selector" in gen_dict:
+ selector_name = gen_dict["selector"]
+ if selector_name not in test_params[op]["selection"]:
+ logger.warn(
+ f"Could not find {selector_name} in selection dict for {op} - using default"
+ )
+ selector_name = "default"
+ else:
+ selector_name = "default"
+ if selector_name not in test_params[op]["selection"]:
+ logger.error(
+ f"Could not find {selector_name} in selection dict for {op}"
+ )
+ raise (GenConformanceError())
+
+ # Selection criteria
+ selection_config = test_params[op]["selection"][
+ selector_name
+ ]
+ if test_type in ["positive", "both"]:
+ tests_gen, tests_gen2 = tee(
+ get_op_tests_selection(
+ args,
+ profile,
+ op,
+ op_build_dir,
+ selection_config,
+ ignore_missing=ignore_missing,
+ )
+ )
+ generate_results(
+ args, profile, op, op_build_dir, tests_gen
+ )
+ operator_test_list = list(tests_gen2)
+ else:
+ operator_test_list = []
+ if test_type in ["negative", "both"]:
+ operator_test_list.extend(
+ get_op_tests_selection(
+ args,
+ profile,
+ op,
+ op_build_dir,
+ selection_config,
+ negative=True,
+ )
+ )
+ output_dir = convert_tests(
+ args,
+ profile,
+ op,
+ op_build_dir,
+ root_output_dir,
+ op_profiles_list,
+ tests=operator_test_list,
+ group=operator_group,
+ tags=tags,
+ )
+ if not args.keep_large_files:
+ check_op_tests(args, profile, op, output_dir)
except GenConformanceError:
return 1
diff --git a/verif/generator/tosa_arg_gen.py b/verif/generator/tosa_arg_gen.py
index 9209d9c..e2a69f1 100644
--- a/verif/generator/tosa_arg_gen.py
+++ b/verif/generator/tosa_arg_gen.py
@@ -1043,6 +1043,18 @@
return axes
@staticmethod
+ def _calculate_sparsity(num_tests, sparsity_factor):
+ sparsity = num_tests // sparsity_factor + 1
+ # If there are only a small number of tests, just select them all
+ if sparsity < 13:
+ sparsity = 1
+ # To get a variety of parameter combinations sparsity should not be a
+ # multiple of 2, 3 or 5
+ while sparsity % 2 == 0 or sparsity % 3 == 0 or sparsity % 5 == 0:
+ sparsity += 1
+ return sparsity
+
+ @staticmethod
def agConv(testGen, opName, shapeList, dtypes, error_name=None):
arg_list = []
@@ -1101,14 +1113,9 @@
# There are too many parameter combinations, so generate them sparsely,
# very sparse for negative tests
sparsity_factor = 2 if error_name else 120
- sparsity = len(paddings) * len(strides) * len(dilations) // sparsity_factor + 1
- # If there are only a small number of tests, just select them all
- if sparsity < 13:
- sparsity = 1
- # To get a variety of parameter combinations sparsity should not be a
- # multiple of 2, 3 or 5
- while sparsity % 2 == 0 or sparsity % 3 == 0 or sparsity % 5 == 0:
- sparsity += 1
+ sparsity = TosaArgGen._calculate_sparsity(
+ len(paddings) * len(strides) * len(dilations), sparsity_factor
+ )
n = 0
for s in sorted(list(strides)):
@@ -1311,7 +1318,17 @@
else:
return []
- for paddings in shape_pad_values:
+ list_shape_pad_values = list(shape_pad_values)
+ # If we are producing tests for rank 6 or greater use sparsity
+ if len(list_shape_pad_values) > 1024:
+ sparsity_factor = 2 if error_name else 120
+ sparsity = TosaArgGen._calculate_sparsity(
+ len(list_shape_pad_values), sparsity_factor
+ )
+ else:
+ sparsity = 1
+
+ for n, paddings in enumerate(list_shape_pad_values):
paddings = list(paddings)
args_valid = True
@@ -1325,8 +1342,7 @@
paddings[i] = (0, 0)
if all([p > -1 for p in paddings[i]]):
args_valid = False
-
- if args_valid:
+ if args_valid and n % sparsity == 0:
name = "pad"
for r in range(rank):
before, after = paddings[r]
@@ -1670,7 +1686,8 @@
factors = TosaArgGen.getFactors(totalElements)
for p in range(testGen.args.num_rand_permutations):
- newRank = testGen.randInt(1, 7)
+ # Rank from 1 to TOSA_TENSOR_MAX_RANK
+ newRank = testGen.randInt(1, (testGen.TOSA_TENSOR_MAX_RANK + 1))
if len(factors) < newRank:
continue
diff --git a/verif/generator/tosa_test_gen.py b/verif/generator/tosa_test_gen.py
index 779f59e..c816c6b 100644
--- a/verif/generator/tosa_test_gen.py
+++ b/verif/generator/tosa_test_gen.py
@@ -25,6 +25,7 @@
class TosaTestGen:
# Maximum rank of tensor supported by test generator.
+ # This currently matches the 8K level defined in the specification.
TOSA_TENSOR_MAX_RANK = 6
def __init__(self, args):
@@ -3647,7 +3648,6 @@
"pad": {
"op": Op.PAD,
"operands": (1, 0),
- "rank": (1, 5),
"build_fcn": (
build_pad,
TosaTensorGen.tgBasic,