[dynamo][itertools] refactor itertools.chain and itertools.chain.from_iterable to use polyfills (#133864)

Pull Request resolved: https://github.com/pytorch/pytorch/pull/133864
Approved by: https://github.com/jansel

torch/_dynamo/decorators.py

@@ -170,6 +170,8 @@ def substitute_in_graph(
     *,
     can_constant_fold_through: bool = False,
     skip_signature_check: bool = False,
+    # type that is embedded in the Python interpreter
+    is_embedded_type: bool = False,  # internal use only
 ) -> Callable[[_F], _F]:
     """
     Register a polyfill handler for a function, usually a C function from the C extension, to be
@@ -219,10 +221,22 @@ def substitute_in_graph(
         >>> torch.compile(operator.indexOf, fullgraph=True)([1, 2, 3, 4, 5], 3)
         2
     """
-    if not is_function(original_fn):
+    if not is_function(original_fn) and not (
+        is_embedded_type and inspect.isclass(original_fn)
+    ):
         raise TypeError(
             f"substitute_in_graph expects a function but got {type(original_fn)!r}"
         )
+    if is_embedded_type:
+        if not inspect.isclass(original_fn):
+            raise TypeError(
+                f"substitute_in_graph expects a class but got {type(original_fn)!r}"
+            )
+
+        from .variables.builder import ITERTOOLS_POLYFILLED_TYPE_IDS, ITERTOOLS_TYPE_IDS
+
+        if id(original_fn) in ITERTOOLS_TYPE_IDS:
+            ITERTOOLS_POLYFILLED_TYPE_IDS.add(id(original_fn))
 
     def wrapper(traceable_fn: _F) -> _F:
         if not is_function(traceable_fn):

torch/_dynamo/polyfills/__init__.py

@@ -4,15 +4,28 @@ Python polyfills for common builtins.
 
 # NOTE: 1. Please do not import any submodule in the directory here to avoid circular imports.
 #       2. While adding a new polyfill module, also add it to POLYFILLED_MODULE_NAMES in loader.py.
+#          Add it in the TYPE_CHECKING block below as well.
 
 # mypy: allow-untyped-defs
 
-import math
+from typing import Any, Callable, Sequence, TYPE_CHECKING
-from typing import Any, Callable, Sequence
 
 import torch
 
 
+if TYPE_CHECKING:
+    # Load by torch._dynamo.polyfills.loader
+    # See also the POLYFILLED_MODULE_NAMES in torch/_dynamo/polyfills/loader.py
+    # Put the submodules here to avoid circular imports
+    from . import (
+        builtins as builtins,
+        functools as functools,
+        itertools as itertools,
+        os as os,
+        sys as sys,
+    )
+
+
 def index(iterator, item, start=0, end=None):
     for i, elem in islice(enumerate(iterator), start, end):
         if item == elem:
@@ -50,6 +63,8 @@ def repeat(item, count):
 
 
 def radians(x):
+    import math
+
     return math.pi / 180.0 * x
 
 

torch/_dynamo/polyfills/itertools.py

@@ -10,12 +10,31 @@ from typing import Iterable, Iterator, TypeVar
 from ..decorators import substitute_in_graph
 
 
-__all__ = ["tee"]
+__all__ = [
+    "chain",
+    "chain_from_iterable",
+    "tee",
+]
 
 
 _T = TypeVar("_T")
 
 
+# Reference: https://docs.python.org/3/library/itertools.html#itertools.chain
+@substitute_in_graph(itertools.chain, is_embedded_type=True)  # type: ignore[arg-type]
+def chain(*iterables: Iterable[_T]) -> Iterator[_T]:
+    for iterable in iterables:
+        yield from iterable
+
+
+@substitute_in_graph(itertools.chain.from_iterable)  # type: ignore[arg-type]
+def chain_from_iterable(iterable: Iterable[Iterable[_T]], /) -> Iterator[_T]:
+    return itertools.chain(*iterable)
+
+
+chain.from_iterable = chain_from_iterable  # type: ignore[method-assign]
+
+
 # Reference: https://docs.python.org/3/library/itertools.html#itertools.tee
 @substitute_in_graph(itertools.tee)
 def tee(iterable: Iterable[_T], n: int = 2, /) -> tuple[Iterator[_T], ...]:

torch/_dynamo/polyfills/loader.py

@@ -11,6 +11,7 @@ if TYPE_CHECKING:
     from types import ModuleType
 
 
+# See also the TYPE_CHECKING block in torch/_dynamo/polyfills/__init__.py
 POLYFILLED_MODULE_NAMES: Tuple[str, ...] = (
     "builtins",
     "functools",

torch/_dynamo/trace_rules.py

@@ -2993,9 +2993,7 @@ def _builtin_function_ids() -> Dict[int, str]:
             if not k.startswith("_") and callable(v)
         }
     )
-    rv.update(
+    rv.update({id(v): f"itertools.{v.__name__}" for v in (itertools.islice,)})
-        {id(v): f"itertools.{v.__name__}" for v in (itertools.chain, itertools.islice)}
-    )
     rv.update(
         {
             id(cast): "typing.cast",
@@ -3474,9 +3472,7 @@ def check_verbose(obj, is_inlined_call=False):
 
     # Consulte the central trace rules defined in torch._dynamo.trace_rules.
     reasons: Set[str] = set()
-    rule = torch._dynamo.trace_rules.lookup_inner(
+    rule = lookup_inner(fi.py_obj, fi.name, fi.filename, is_inlined_call, reasons)
-        fi.py_obj, fi.name, fi.filename, is_inlined_call, reasons
-    )
     if issubclass(rule, (UserFunctionVariable, PolyfilledFunctionVariable)):
         return SkipResult(
             False,

torch/_dynamo/variables/builder.py

@@ -17,7 +17,17 @@ import sys
 import types
 import warnings
 import weakref
-from typing import Any, List, MutableMapping, NamedTuple, Optional, TYPE_CHECKING, Union
+from typing import (
+    Any,
+    FrozenSet,
+    List,
+    MutableMapping,
+    NamedTuple,
+    Optional,
+    Set,
+    TYPE_CHECKING,
+    Union,
+)
 
 import torch
 from torch import SymInt
@@ -320,6 +330,17 @@ class FrameStateSizeEntry:
     stride: Optional[List[int]]
 
 
+# All class-based iterators in itertools
+# NOTE: use id() because some objects are not hashable, it will raise error during lookup
+ITERTOOLS_TYPE_IDS: FrozenSet[int] = frozenset(
+    id(member)
+    for name, member in vars(itertools).items()
+    if not name.startswith("_") and inspect.isclass(member)
+)
+# Will be updated later in substitute_in_graph in torch/_dynamo/polyfills/itertools.py
+ITERTOOLS_POLYFILLED_TYPE_IDS: Set[int] = set()
+
+
 class VariableBuilder:
     """Wrap a python value in a VariableTracker() instance"""
 
@@ -875,7 +896,10 @@ class VariableBuilder:
                 value,
                 source=self.source,
             )
-        elif istype(value, type) and value in itertools.__dict__.values():
+        elif (
+            id(value) in ITERTOOLS_TYPE_IDS
+            and id(value) not in ITERTOOLS_POLYFILLED_TYPE_IDS
+        ):
             self.install_guards(GuardBuilder.FUNCTION_MATCH)
             return ItertoolsVariable(value, source=self.source)
         elif isinstance(value, torch.SymBool):

torch/_dynamo/variables/builtin.py

@@ -994,15 +994,6 @@ class BuiltinVariable(VariableTracker):
             )
         if self.fn is dict and name == "fromkeys":
             return BuiltinVariable.call_custom_dict_fromkeys(tx, dict, *args, **kwargs)
-        if self.fn is itertools.chain and name == "from_iterable":
-            assert len(args) == 1
-            assert len(kwargs) == 0
-            obj = args[0]
-            items = []
-            for item in obj.unpack_var_sequence(tx):
-                items.extend(item.unpack_var_sequence(tx))
-            return variables.TupleVariable(items)
-
         return super().call_method(tx, name, args, kwargs)
 
     def _call_int_float(self, tx: "InstructionTranslator", arg):
@@ -1942,13 +1933,6 @@ class BuiltinVariable(VariableTracker):
                 )
             return variables.ListVariable(items)
 
-    def call_chain(self, tx: "InstructionTranslator", *args):
-        if all(obj.has_unpack_var_sequence(tx) for obj in args):
-            items = []
-            for obj in args:
-                items.extend(obj.unpack_var_sequence(tx))
-            return variables.TupleVariable(items)
-
     def call_islice(self, tx: "InstructionTranslator", iterable, *args):
         if iterable.has_unpack_var_sequence(tx) and all(
             x.is_python_constant() for x in args

torch/_dynamo/variables/functions.py

@@ -18,6 +18,7 @@ from ..utils import (
     check_constant_args,
     check_unspec_or_constant_args,
     identity,
+    is_function,
     is_wrapper_or_member_descriptor,
     istype,
     make_cell,
@@ -992,6 +993,27 @@ class PolyfilledFunctionVariable(VariableTracker):
                     handler,
                 ).call_function(tx, args, kwargs)
 
+        return super().call_function(tx, args, kwargs)
+
+    def call_method(
+        self,
+        tx,
+        name,
+        args: "List[VariableTracker]",
+        kwargs: "Dict[str, VariableTracker]",
+    ) -> "VariableTracker":
+        if name == "__call__":
+            return self.call_function(tx, args, kwargs)
+
+        method = getattr(self.fn, name, None)
+        assert method is not None, f"Member {name} not found in {self.fn}"
+        assert is_function(method), f"Member {name} is not callable in {self.fn}"
+        options = {}
+        if self.source:
+            options["source"] = AttrSource(self.source, name)
+        member_variable = PolyfilledFunctionVariable(method, **options)
+        return member_variable.call_function(tx, args, kwargs)
+
     def as_python_constant(self):
         return self.fn
 

torch/_dynamo/variables/iter.py

@@ -52,14 +52,6 @@ class ItertoolsVariable(VariableTracker):
             for item in itertools.product(*seqs):
                 items.append(variables.TupleVariable(list(item)))
             return variables.ListIteratorVariable(items, mutable_local=MutableLocal())
-        elif (
-            self.value is itertools.chain
-            and not kwargs
-            and all(arg.has_unpack_var_sequence(tx) for arg in args)
-        ):
-            seqs = [arg.unpack_var_sequence(tx) for arg in args]
-            items = list(itertools.chain.from_iterable(seqs))
-            return variables.ListIteratorVariable(items, mutable_local=MutableLocal())
         elif self.value is itertools.accumulate:
             from .builtin import BuiltinVariable