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compiler.py
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compiler.py
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import functools
from .autoray import (
do,
infer_backend,
backend_like,
tree_map,
tree_iter,
tree_flatten,
tree_unflatten,
is_array,
)
from . import lazy
class CompilePython:
"""A simple compiler that unravels all autoray calls, optionally sharing
intermediates and folding constants, converts this to a code object using
``compile``, then executes this using ``exec``.
Parameters
----------
fn : callable
Function to compile - should have signature
``fn(*args, **kwargs) -> array``, with ``args`` and ``kwargs`` any
nested combination of ``tuple``, ``list`` and ``dict`` objects
containing arrays (or other constant arguments), and perform array
operations on these using ``autoray.do``.
fold_constants : bool, optional
Whether to fold all constant array operations into the graph, which
might increase memory usage.
share_intermediates : bool, optional
Whether to cache all computational nodes during the trace, so that any
shared intermediate results can be identified.
"""
def __init__(self, fn, fold_constants=True, share_intermediates=True):
self._fn = fn
self._fold_constants = fold_constants
self._share_intermediates = share_intermediates
self._jit_fn = None
def setup(self, args, kwargs):
"""Convert the example arrays to lazy variables and trace them through
the function.
"""
variables = tree_map(lazy.array, (args, kwargs))
if self._share_intermediates:
with backend_like("autoray.lazy"), lazy.shared_intermediates():
outs = self._fn(*variables[0], **variables[1])
else:
with backend_like("autoray.lazy"):
outs = self._fn(*variables[0], **variables[1])
return lazy.Function(
variables, outs, fold_constants=self._fold_constants
)
def __call__(self, *args, array_backend=None, **kwargs):
"""If necessary, build, then call the compiled function."""
if self._jit_fn is None:
self._jit_fn = self.setup(args, kwargs)
return self._jit_fn(args, kwargs)
class CompileJax:
""" """
def __init__(self, fn, enable_x64=None, platform_name=None, **kwargs):
self._fn = fn
self._enable_x64 = enable_x64
self._platform_name = platform_name
self._jit_fn = None
self._jit_kwargs = kwargs
def setup(self):
import jax
if self._enable_x64 is not None:
import jax
jax.config.update("jax_enable_x64", self._enable_x64)
if self._platform_name is not None:
import jax
jax.config.update("jax_platform_name", self._platform_name)
self._jit_fn = jax.jit(self._fn, **self._jit_kwargs)
self._fn = None
def __call__(self, *args, array_backend=None, **kwargs):
if self._jit_fn is None:
self.setup()
out = self._jit_fn(*args, **kwargs)
if array_backend != "jax":
out = do("asarray", out, like=array_backend)
return out
class CompileTensorFlow:
""" """
def __init__(self, fn, **kwargs):
self._fn = fn
kwargs.setdefault("autograph", False)
self._jit_fn = None
self._jit_kwargs = kwargs
def setup(self):
import tensorflow as tf
self._jit_fn = tf.function(**self._jit_kwargs)(self._fn)
self._fn = None
def __call__(self, *args, array_backend=None, **kwargs):
if self._jit_fn is None:
self.setup()
out = self._jit_fn(*args, **kwargs)
if array_backend != "tensorflow":
out = do("asarray", out, like=array_backend)
return out
class CompileTorch:
""" """
def __init__(self, fn, **kwargs):
import torch
self.torch = torch
if not hasattr(fn, "__name__") and isinstance(fn, functools.partial):
# torch jit.trace requires fn.__name__ and others
functools.update_wrapper(fn, fn.func)
self._fn = fn
self._jit_fn = None
kwargs.setdefault("check_trace", False)
self._jit_kwargs = kwargs
def setup(self, *args, **kwargs):
flat_tensors, ref_tree = tree_flatten((args, kwargs), get_ref=True)
def flat_fn(flat_tensors):
args, kwargs = tree_unflatten(flat_tensors, ref_tree)
return self._fn(*args, **kwargs)
self._jit_fn = self.torch.jit.trace(
flat_fn, [flat_tensors], **self._jit_kwargs
)
def __call__(self, *args, array_backend=None, **kwargs):
if array_backend != "torch":
# torch doesn't handle numpy arrays itself
args = tree_map(self.torch.as_tensor, args, is_array)
if self._jit_fn is None:
self.setup(*args, **kwargs)
out = self._jit_fn(tree_flatten((args, kwargs)))
if array_backend != "torch":
out = do("asarray", out, like=array_backend)
return out
_backend_lookup = {}
_compiler_lookup = {
"jax": CompileJax,
"tensorflow": CompileTensorFlow,
"torch": CompileTorch,
}
class AutoCompiled:
"""Just in time compile a ``autoray.do`` using function. See the main
wrapper ``autojit``.
"""
def __init__(self, fn, backend=None, compiler_opts=None):
self._fn = fn
self._backend = backend
self._compiled_fns = {}
if compiler_opts is None:
self._compiler_kwargs = {}
else:
self._compiler_kwargs = compiler_opts
def __call__(self, *args, backend=None, **kwargs):
array_backend = infer_backend(
next(tree_iter((args, kwargs), is_array))
)
if backend is None:
if self._backend is None:
# no backend specified anywhere, use the array backend
backend = array_backend
else:
# use the backend specified at init
backend = self._backend
# work out which compiler to use for combo of backend and array backend
try:
key = _backend_lookup[backend, array_backend]
except KeyError:
if backend in _compiler_lookup:
key = backend
else:
key = f"python-{array_backend}"
_backend_lookup[backend, array_backend] = key
try:
fn_compiled = self._compiled_fns[key]
except KeyError:
if "python" in key:
backend = "python"
backend_compiler = _compiler_lookup.get(backend, CompilePython)
compiler_kwargs = self._compiler_kwargs.get(backend, {})
fn_compiled = backend_compiler(self._fn, **compiler_kwargs)
self._compiled_fns[key] = fn_compiled
return fn_compiled(*args, array_backend=array_backend, **kwargs)
def autojit(fn=None, *, backend=None, compiler_opts=None):
"""Just-in-time compile an ``autoray`` function, automatically choosing
the backend based on the input arrays, or via keyword argument.
The backend used to do the compilation can be set in three ways:
1. Automatically based on the arrays the function is called with,
i.e. ``cfn(*torch_arrays)`` will use ``torch.jit.trace``.
2. In this wrapper, ``@autojit(backend='jax')``, to provide a
specific default instead.
3. When you call the function ``cfn(*arrays, backend='torch')`` to
override on a per-call basis.
If the arrays supplied are of a different backend type to the compiler,
then the returned array will also be converted back, i.e.
``cfn(*numpy_arrays, backend='tensorflow')`` will return a ``numpy`` array.
The ``'python'`` backend simply extracts and unravels all the ``do`` calls
into a code object using ``compile`` which is then run with ``exec``.
This makes use of shared intermediates and constant folding, strips
away any python scaffoliding, and is compatible with any library, but the
resulting function is not 'low-level' in the same way as the other
backends.
Parameters
----------
fn : callable
The autoray function to compile.
backend : {None, 'python', 'jax', 'torch', 'tensorflow'}, optional
If set, use this as the default backend.
compiler_opts : dict[dict], optional
Dict of dicts when you can supply options for each compiler backend
separately, e.g.:
``@autojit(compiler_opts={'tensorflow': {'jit_compile': True}})``.
Returns
-------
cfn : callable
The function with auto compilation.
"""
kws = dict(backend=backend, compiler_opts=compiler_opts)
if fn is None:
return functools.partial(autojit, **kws)
return functools.wraps(fn)(AutoCompiled(fn, **kws))