InputGen: introduce random manager (#7)

Summary:
Pull Request resolved: #7

Introduces a random manager in InputGen. This allows to generate reproducible data, by seeding the random manager.
```
from inputgen.utils.random_manager import random_manager
random_manager.seed(1729)
```

Differential Revision: D59668295

.github/workflows/python-app.yml

@@ -30,4 +30,4 @@ jobs:
 
     - name: Run tests
       run: |
-        python -m unittest discover -s test -p "*.py"
+        python -m unittest discover -s test/inputgen -p "*.py"

examples/random_seed.py

@@ -0,0 +1,35 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+import torch
+from inputgen.argtuple.gen import ArgumentTupleGenerator
+from inputgen.utils.random_manager import random_manager
+from specdb.db import SpecDictDB
+
+
+def main():
+    # example to seed all random number generators
+    random_manager.seed(1729)
+
+    spec = SpecDictDB["add.Tensor"]
+    op = torch.ops.aten.add.Tensor
+    for ix, (posargs, inkwargs, outargs) in enumerate(
+        ArgumentTupleGenerator(spec).gen()
+    ):
+        op(*posargs, **inkwargs, **outargs)
+        print(
+            posargs[0].shape,
+            posargs[0].dtype,
+            posargs[1].shape,
+            posargs[1].dtype,
+            inkwargs["alpha"],
+        )
+        if ix == 1:
+            print(posargs[0])
+
+
+if __name__ == "__main__":
+    main()

inputgen/argument/engine.py

@@ -4,7 +4,6 @@
 # This source code is licensed under the license found in the
 # LICENSE file in the root directory of this source tree.
 
-import random
 from typing import Any, List, Optional, Tuple, Union
 
 import torch
@@ -13,6 +12,7 @@
 from inputgen.attribute.model import Attribute
 from inputgen.attribute.solve import AttributeSolver
 from inputgen.specs.model import Constraint, ConstraintSuffix
+from inputgen.utils.random_manager import random_manager as rm
 from inputgen.variable.type import ScalarDtype
 
 
@@ -60,7 +60,9 @@ def gen_structure_with_depth_and_length(
             yield from self.gen_structure_with_depth(depth, focus, length)
             return
 
-        focus_ixs = range(length) if focus == attr else (random.choice(range(length)),)
+        focus_ixs = (
+            range(length) if focus == attr else (rm.get_random().choice(range(length)),)
+        )
         for focus_ix in focus_ixs:
             values = [()]
             for ix in range(length):
@@ -241,7 +243,7 @@ def gen_value_spaces(self, focus, dtype, struct):
         if focus == Attribute.VALUE:
             return [v.space for v in variables]
         else:
-            return [random.choice(variables).space]
+            return [rm.get_random().choice(variables).space]
 
     def gen(self, focus):
         # TODO(mcandales): Enable Tensor List generation

inputgen/argument/gen.py

@@ -9,6 +9,7 @@
 
 import torch
 from inputgen.argument.engine import MetaArg
+from inputgen.utils.random_manager import random_manager
 from inputgen.variable.gen import VariableGenerator
 from inputgen.variable.space import VariableSpace
 from torch.testing._internal.common_dtype import floating_types, integral_types
@@ -41,6 +42,8 @@ def gen(self):
         )
 
     def get_random_tensor(self, size, dtype, high=None, low=None):
+        torch_rng = random_manager.get_torch()
+
         if low is None and high is None:
             low = -100
             high = 100
@@ -55,7 +58,9 @@ def get_random_tensor(self, size, dtype, high=None, low=None):
             elif not self.space.contains(1):
                 return torch.full(size, False, dtype=dtype)
             else:
-                return torch.randint(low=0, high=2, size=size, dtype=dtype)
+                return torch.randint(
+                    low=0, high=2, size=size, dtype=dtype, generator=torch_rng
+                )
 
         if dtype in integral_types():
             low = math.ceil(low)
@@ -68,16 +73,38 @@ def get_random_tensor(self, size, dtype, high=None, low=None):
 
         if dtype == torch.uint8:
             if not self.space.contains(0):
-                return torch.randint(low=max(1, low), high=high, size=size, dtype=dtype)
+                return torch.randint(
+                    low=max(1, low),
+                    high=high,
+                    size=size,
+                    dtype=dtype,
+                    generator=torch_rng,
+                )
             else:
-                return torch.randint(low=max(0, low), high=high, size=size, dtype=dtype)
+                return torch.randint(
+                    low=max(0, low),
+                    high=high,
+                    size=size,
+                    dtype=dtype,
+                    generator=torch_rng,
+                )
 
-        t = torch.randint(low=low, high=high, size=size, dtype=dtype)
+        t = torch.randint(
+            low=low, high=high, size=size, dtype=dtype, generator=torch_rng
+        )
         if not self.space.contains(0):
             if high > 0:
-                pos = torch.randint(low=max(1, low), high=high, size=size, dtype=dtype)
+                pos = torch.randint(
+                    low=max(1, low),
+                    high=high,
+                    size=size,
+                    dtype=dtype,
+                    generator=torch_rng,
+                )
             else:
-                pos = torch.randint(low=low, high=0, size=size, dtype=dtype)
+                pos = torch.randint(
+                    low=low, high=0, size=size, dtype=dtype, generator=torch_rng
+                )
             t = torch.where(t == 0, pos, t)
 
         if dtype in integral_types():

inputgen/attribute/engine.py

@@ -12,7 +12,7 @@
 from inputgen.attribute.solve import AttributeSolver
 from inputgen.specs.model import Constraint
 from inputgen.variable.gen import VariableGenerator
-from inputgen.variable.type import ScalarDtype
+from inputgen.variable.type import ScalarDtype, sort_values_of_type
 
 
 class AttributeEngine(AttributeSolver):
@@ -51,4 +51,4 @@ def gen(self, focus: Attribute, *args):
             if len(vals) == 0:
                 vals = VariableGenerator(variable.space).gen(num)
             gen_vals.update(vals)
-        return gen_vals
+        return sort_values_of_type(self.vtype, gen_vals)

inputgen/utils/random_manager.py

@@ -0,0 +1,33 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+import random
+
+import torch
+
+
+class RandomManager:
+    def __init__(self):
+        self._rng = random.Random()
+        self._torch_rng = torch.Generator()
+
+    def seed(self, seed):
+        """
+        Seeds the random number generators for random and torch.
+        """
+        self._rng.seed(seed)
+        self._torch_rng.manual_seed(seed)
+
+    def get_random(self):
+        # self._rng.seed(42)
+        return self._rng
+
+    def get_torch(self):
+        # self._torch_rng.manual_seed(42)
+        return self._torch_rng
+
+
+random_manager = RandomManager()

inputgen/variable/gen.py

@@ -5,11 +5,12 @@
 # LICENSE file in the root directory of this source tree.
 
 import math
-import random
 from typing import Any, List, Optional, Set, Union
 
+from inputgen.utils.random_manager import random_manager as rm
 from inputgen.variable.constants import BOUND_ON_INF, INT64_MAX, INT64_MIN
 from inputgen.variable.space import Interval, Intervals, VariableSpace
+from inputgen.variable.type import sort_values_of_type
 from inputgen.variable.utils import nextdown, nextup
 
 
@@ -51,7 +52,7 @@ def gen_float_from_interval(r: Interval) -> Optional[float]:
     elif lower > upper:
         return None
     else:
-        return random.uniform(lower, upper)
+        return rm.get_random().uniform(lower, upper)
 
 
 def gen_min_float_from_intervals(rs: Intervals) -> Optional[float]:
@@ -69,7 +70,7 @@ def gen_max_float_from_intervals(rs: Intervals) -> Optional[float]:
 def gen_float_from_intervals(rs: Intervals) -> Optional[float]:
     if rs.empty():
         return None
-    r = random.choice(rs.intervals)
+    r = rm.get_random().choice(rs.intervals)
     return gen_float_from_interval(r)
 
 
@@ -112,7 +113,7 @@ def gen_int_from_interval(r: Interval) -> Optional[int]:
     elif upper is None:
         upper = max(lower, 0) + BOUND_ON_INF
     assert lower is not None and upper is not None
-    return random.randint(lower, upper)
+    return rm.get_random().randint(lower, upper)
 
 
 def gen_min_int_from_intervals(rs: Intervals) -> Optional[int]:
@@ -133,7 +134,7 @@ def gen_int_from_intervals(rs: Intervals) -> Optional[int]:
     intervals_with_ints = [r for r in rs.intervals if r.contains_int()]
     if len(intervals_with_ints) == 0:
         return None
-    r = random.choice(intervals_with_ints)
+    r = rm.get_random().choice(intervals_with_ints)
     return gen_int_from_interval(r)
 
 
@@ -147,6 +148,12 @@ def __init__(self, space: VariableSpace):
         self.vtype = space.vtype
         self.space = space
 
+    def _sorted(self, values: Set[Any]) -> List[Any]:
+        return sort_values_of_type(self.vtype, values)
+
+    def _sample(self, values: Set[Any], num: int) -> List[Any]:
+        return rm.get_random().sample(self._sorted(values), num)
+
     def gen_min(self) -> Any:
         """Returns the minimum value of the space."""
         if self.space.empty() or self.vtype not in [bool, int, float]:
@@ -221,7 +228,7 @@ def gen_edges_non_extreme(self, num: int = 2) -> Set[Any]:
         edges_not_extreme = self.gen_edges() - self.gen_extremes()
         if num >= len(edges_not_extreme):
             return edges_not_extreme
-        return set(random.sample(list(edges_not_extreme), num))
+        return set(self._sample(edges_not_extreme, num))
 
     def gen_non_edges(self, num: int = 2) -> Set[Any]:
         """Generates non-edge (or interior) values of the space."""
@@ -232,7 +239,7 @@ def gen_non_edges(self, num: int = 2) -> Set[Any]:
         if self.space.discrete.initialized:
             vals = self.space.discrete.values - edge_or_extreme_vals
             if num < len(vals):
-                vals = set(random.sample(list(vals), num))
+                vals = set(self._sample(vals, num))
         else:
             for _ in range(100):
                 v: Optional[Union[int, float]] = None
@@ -269,11 +276,8 @@ def gen_balanced(self, num: int = 6) -> Set[Any]:
 
         if num >= len(balanced):
             return balanced
-        return set(random.sample(list(balanced), num))
+        return set(self._sample(balanced, num))
 
     def gen(self, num: int = 6) -> List[Any]:
         """Generates a sorted (if applicable), balanced sample of the space."""
-        vals = list(self.gen_balanced(num))
-        if self.vtype in [bool, int, float, str]:
-            return sorted(vals)
-        return vals
+        return sort_values_of_type(self.vtype, self.gen_balanced(num))

inputgen/variable/type.py

@@ -6,7 +6,7 @@
 
 import math
 from enum import Enum
-from typing import Any
+from typing import Any, List, Set
 
 import torch
 
@@ -93,3 +93,13 @@ def convert_to_vtype(vtype: type, v: Any) -> Any:
     if vtype == float:
         return float(v)
     return v
+
+
+def sort_values_of_type(vtype: type, values: Set[Any]) -> List[Any]:
+    if vtype in [bool, int, float, str, tuple]:
+        return sorted(values)
+    if vtype == torch.dtype:
+        return [v for v in SUPPORTED_TENSOR_DTYPES if v in values]
+    if vtype == ScalarDtype:
+        return [v for v in ScalarDtype if v in values]
+    return list(values)