Cleanup root objects

pytensor/__init__.py

@@ -1,168 +1,32 @@
-"""
-PyTensor is an optimizing compiler in Python, built to evaluate
-complicated expressions (especially matrix-valued ones) as quickly as
-possible.  PyTensor compiles expression graphs (see :doc:`graph` ) that
-are built by Python code. The expressions in these graphs are called
-`Apply` nodes and the variables in these graphs are called `Variable`
-nodes.
-
-You compile a graph by calling `function`, which takes a graph, and
-returns a callable object.  One of pytensor's most important features is
-that `function` can transform your graph before compiling it.  It can
-replace simple expressions with faster or more numerically stable
-implementations.
-
-To learn more, check out:
-
-- Op List (:doc:`oplist`)
-
-"""
-
 __docformat__ = "restructuredtext en"
 
-# Set a default logger. It is important to do this before importing some other
-# pytensor code, since this code may want to log some messages.
-import logging
-import sys
-import warnings
-from functools import singledispatch
-from pathlib import Path
-from typing import Any, NoReturn, Optional
 
 from pytensor import _version
 
 
 __version__: str = _version.get_versions()["version"]
 
+del _version
 
-pytensor_logger = logging.getLogger("pytensor")
-logging_default_handler = logging.StreamHandler()
-logging_default_formatter = logging.Formatter(
-    fmt="%(levelname)s (%(name)s): %(message)s"
-)
-logging_default_handler.setFormatter(logging_default_formatter)
-pytensor_logger.setLevel(logging.WARNING)
-
-if not pytensor_logger.hasHandlers():
-    pytensor_logger.addHandler(logging_default_handler)
-
-
-# Disable default log handler added to pytensor_logger when the module
-# is imported.
-def disable_log_handler(logger=pytensor_logger, handler=logging_default_handler):
-    if logger.hasHandlers():
-        logger.removeHandler(handler)
-
-
-# Raise a meaningful warning/error if the pytensor directory is in the Python
-# path.
-rpath = Path(__file__).parent.resolve()
-if any(rpath == Path(p).resolve() for p in sys.path):
-    raise RuntimeError("You have the pytensor directory in your Python path.")
 
 from pytensor.configdefaults import config
 
 
-# This is the api version for ops that generate C code.  External ops
-# might need manual changes if this number goes up.  An undefined
-# __api_version__ can be understood to mean api version 0.
-#
-# This number is not tied to the release version and should change
-# very rarely.
-__api_version__ = 1
-
-# isort: off
-from pytensor.graph.basic import Variable
-from pytensor.graph.replace import clone_replace, graph_replace
-
-# isort: on
-
-
-def as_symbolic(x: Any, name: str | None = None, **kwargs) -> Variable:
-    """Convert `x` into an equivalent PyTensor `Variable`.
-
-    Parameters
-    ----------
-    x
-        The object to be converted into a ``Variable`` type. A
-        ``numpy.ndarray`` argument will not be copied, but a list of numbers
-        will be copied to make an ``numpy.ndarray``.
-    name
-        If a new ``Variable`` instance is created, it will be named with this
-        string.
-    kwargs
-        Options passed to the appropriate sub-dispatch functions.  For example,
-        `ndim` and `dtype` can be passed when `x` is an `numpy.ndarray` or
-        `Number` type.
-
-    Raises
-    ------
-    TypeError
-        If `x` cannot be converted to a `Variable`.
-
-    """
-    if isinstance(x, Variable):
-        return x
-
-    res = _as_symbolic(x, **kwargs)
-    res.name = name
-    return res
-
-
-@singledispatch
-def _as_symbolic(x: Any, **kwargs) -> Variable:
-    from pytensor.tensor import as_tensor_variable
-
-    return as_tensor_variable(x, **kwargs)
-
-
 # isort: off
-from pytensor import scalar, tensor
+from pytensor import tensor
+from pytensor import sparse
 from pytensor.compile import (
     In,
     Mode,
     Out,
-    ProfileStats,
-    predefined_linkers,
-    predefined_modes,
-    predefined_optimizers,
     shared,
+    wrap_py,
+    function,
 )
-from pytensor.compile.function import function, function_dump
-from pytensor.compile.function.types import FunctionMaker
-from pytensor.gradient import Lop, Rop, grad, subgraph_grad
+from pytensor.gradient import Lop, Rop, grad
 from pytensor.printing import debugprint as dprint
-from pytensor.printing import pp, pprint
-from pytensor.updates import OrderedUpdates
-
-# isort: on
-
-
-def get_underlying_scalar_constant(v):
-    """Return the constant scalar (i.e. 0-D) value underlying variable `v`.
-
-    If `v` is the output of dim-shuffles, fills, allocs, cast, etc.
-    this function digs through them.
-
-    If ``pytensor.sparse`` is also there, we will look over CSM `Op`.
 
-    If `v` is not some view of constant data, then raise a
-    `NotScalarConstantError`.
-    """
-    warnings.warn(
-        "get_underlying_scalar_constant is deprecated. Use tensor.get_underlying_scalar_constant_value instead.",
-        FutureWarning,
-    )
-    from pytensor.tensor.basic import get_underlying_scalar_constant_value
-
-    return get_underlying_scalar_constant_value(v)
-
-
-# isort: off
-import pytensor.tensor.random.var
-import pytensor.sparse
 from pytensor.ifelse import ifelse
-from pytensor.scan import checkpoints
 from pytensor.scan.basic import scan
 from pytensor.scan.views import foldl, foldr, map, reduce
 from pytensor.compile.builders import OpFromGraph

pytensor/basic.py

@@ -0,0 +1,42 @@
+from functools import singledispatch
+from typing import Any
+
+from pytensor.graph import Variable
+
+
+def as_symbolic(x: Any, name: str | None = None, **kwargs) -> Variable:
+    """Convert `x` into an equivalent PyTensor `Variable`.
+
+    Parameters
+    ----------
+    x
+        The object to be converted into a ``Variable`` type. A
+        ``numpy.ndarray`` argument will not be copied, but a list of numbers
+        will be copied to make an ``numpy.ndarray``.
+    name
+        If a new ``Variable`` instance is created, it will be named with this
+        string.
+    kwargs
+        Options passed to the appropriate sub-dispatch functions.  For example,
+        `ndim` and `dtype` can be passed when `x` is an `numpy.ndarray` or
+        `Number` type.
+
+    Raises
+    ------
+    TypeError
+        If `x` cannot be converted to a `Variable`.
+
+    """
+    if isinstance(x, Variable):
+        return x
+
+    res = _as_symbolic(x, **kwargs)
+    res.name = name
+    return res
+
+
+@singledispatch
+def _as_symbolic(x: Any, **kwargs) -> Variable:
+    from pytensor.tensor import as_tensor_variable
+
+    return as_tensor_variable(x, **kwargs)

pytensor/compile/mode.py

@@ -5,7 +5,7 @@
 
 import logging
 import warnings
-from typing import Any, Literal
+from typing import Any
 
 from pytensor.compile.function.types import Supervisor
 from pytensor.configdefaults import config
@@ -512,7 +512,7 @@ def get_mode(orig_string):
     if upper_string == "FAST_RUN":
         linker = config.linker
         if linker == "auto":
-            return CVM if config.cxx else VM
+            return NUMBA
         return fast_run_linkers_to_mode[linker]
 
     global _CACHED_RUNTIME_MODES
@@ -565,26 +565,3 @@ def register_mode(name, mode):
     if name in predefined_modes:
         raise ValueError(f"Mode name already taken: {name}")
     predefined_modes[name] = mode
-
-
-def get_target_language(mode=None) -> tuple[Literal["py", "c", "numba", "jax"], ...]:
-    """Get the compilation target language."""
-
-    if mode is None:
-        mode = get_default_mode()
-
-    linker = mode.linker
-
-    if isinstance(linker, NumbaLinker):
-        return ("numba",)
-    if isinstance(linker, JAXLinker):
-        return ("jax",)
-    if isinstance(linker, PerformLinker):
-        return ("py",)
-    if isinstance(linker, CLinker):
-        return ("c",)
-
-    if isinstance(linker, VMLinker | OpWiseCLinker):
-        return ("c", "py") if config.cxx else ("py",)
-
-    raise Exception(f"Unsupported Linker: {linker}")