autodidax.md
---
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---
```{raw-cell}
---
Copyright 2021 The JAX Authors.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
https://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
---
```
[](https://colab.research.google.com/github/google/jax/blob/main/docs/autodidax.ipynb)
+++
# Autodidax: JAX core from scratch
Ever want to learn how JAX works, but the implementation seemed impenetrable?
Well, you're in luck! By reading this tutorial, you'll learn every big idea in
JAX's core system. You'll even get clued into our weird jargon!
**This is a work-in-progress draft.** There are some important ingredients
missing, still to come in parts 5 and 6 (and more?). There are also some
simplifications here that we haven't yet applied to the main system, but we
will.
+++
## Part 1: Transformations as interpreters: standard evaluation, `jvp`, and `vmap`
We want to transform functions that look like this:
```python
def f(x):
y = sin(x) * 2.
z = - y + x
return z
```
Think of functions like `sin` and the arithmetic operations underlying the
infix operators (`mul`, `add`, and `neg`) as primitive operations, meaning
atomic units of processing rather than compositions.
"Transform" means "interpret differently." Instead of standard interpretation
where we apply primitive operations to numerical inputs to produce numerical
outputs, we want to override primitive application and let different values
flow through our program. For example, we might want to replace the
application of every primitive with an application of [its JVP
rule](https://jax.readthedocs.io/en/latest/notebooks/autodiff_cookbook.html),
and let primal-tangent pairs flow through our program. Moreover, we want to be
able to compose multiple transformations, leading to stacks of interpreters.
+++
### JAX core machinery
We can implement stacks of interpreters and even have them all discharge on
the fly as we execute the Python function to be transformed. To start, let's
define these primitives so that we can intercept their application:
```{code-cell}
from typing import NamedTuple
class Primitive(NamedTuple):
name: str
add_p = Primitive('add')
mul_p = Primitive('mul')
neg_p = Primitive("neg")
sin_p = Primitive("sin")
cos_p = Primitive("cos")
reduce_sum_p = Primitive("reduce_sum")
greater_p = Primitive("greater")
less_p = Primitive("less")
transpose_p = Primitive("transpose")
broadcast_p = Primitive("broadcast")
def add(x, y): return bind1(add_p, x, y)
def mul(x, y): return bind1(mul_p, x, y)
def neg(x): return bind1(neg_p, x)
def sin(x): return bind1(sin_p, x)
def cos(x): return bind1(cos_p, x)
def greater(x, y): return bind1(greater_p, x, y)
def less(x, y): return bind1(less_p, x, y)
def transpose(x, perm): return bind1(transpose_p, x, perm=perm)
def broadcast(x, shape, axes): return bind1(broadcast_p, x, shape=shape, axes=axes)
def reduce_sum(x, axis=None):
if axis is None:
axis = tuple(range(np.ndim(x)))
if type(axis) is int:
axis = (axis,)
return bind1(reduce_sum_p, x, axis=axis)
def bind1(prim, *args, **params):
out, = bind(prim, *args, **params)
return out
```
We'll set up array data types and infix operator methods in a moment.
A `Primitive` is just an object with a name, to which we attach our
interpretation rules (one for each transformation). The `bind` function is our
interception point: it'll figure out which transformation rule to apply, based
on how the arguments are boxed in tracers and what interpreters are active.
The functions that user code calls, like `add` and `sin`, are just wrappers
around calls to `bind`. These wrappers let us control how arguments are passed
to `bind`, and in particular we follow a handy internal convention: when we
call `bind`, we pass values representing array data as positional arguments,
and we pass metadata like the `axis` argument to `sum_p` via keyword. This
calling convention simplifies some core logic (since e.g. instances of the
`Tracer` class to be defined below can only occur in positional arguments to
`bind`). The wrappers can also provide docstrings!
We represent active interpreters as a stack. The stack is just a simple
`list`, and each element is a container with an integer level (corresponding
to the element's height in the stack), an interpreter type (which we'll call a
`trace_type`), and an optional field for any global data the interpreter
needs. We call each element a `MainTrace`, though maybe "Interpreter" would be
more descriptive.
```{code-cell}
from collections.abc import Sequence
from contextlib import contextmanager
from typing import Optional, Any
class MainTrace(NamedTuple):
level: int
trace_type: type['Trace']
global_data: Optional[Any]
trace_stack: list[MainTrace] = []
dynamic_trace: Optional[MainTrace] = None # to be employed in Part 3
@contextmanager
def new_main(trace_type: type['Trace'], global_data=None):
level = len(trace_stack)
main = MainTrace(level, trace_type, global_data)
trace_stack.append(main)
try:
yield main
finally:
trace_stack.pop()
```
When we're about to apply a transformation, we'll push another interpreter
onto the stack using `new_main`. Then, as we apply primitives in the function,
we can think of the `bind` first being interpreted by the trace at the top of
the stack (i.e. with the highest level). If that first interpreter itself
binds other primitives in its interpretation rule for the primitive, like how
the JVP rule of `sin_p` might bind `cos_p` and `mul_p`, then those `bind`
calls will be handled by the interpreter at the next level down.
What goes at the bottom of the interpreter stack? At the bottom, we know all
the transformation interpreters are finished, and we just want to do standard
evaluation. So at the bottom we'll put an evaluation interpreter.
Let's sketch out the interface for interpreters, which is based on the `Trace`
and `Tracer` base classes. A `Tracer` represents a boxed-up value, perhaps
carrying some extra context data used by the interpreter. A `Trace` handles
boxing up values into `Tracers` and also handles primitive application.
```{code-cell}
class Trace:
main: MainTrace
def __init__(self, main: MainTrace) -> None:
self.main = main
def pure(self, val): assert False # must override
def lift(self, val): assert False # must override
def process_primitive(self, primitive, tracers, params):
assert False # must override
```
The first two methods are about boxing up values in `Tracer`s, which are the
objects that flow through the Python programs we transform. The last method is
the callback we'll use to interpret primitive application.
The `Trace` itself doesn't contain any data, other than a reference to its
corresponding `MainTrace` instance. In fact, multiple instances of a `Trace`
might be created and discarded during an application of a transformation,
whereas only a single `MainTrace` instance is created per application of a
transformation.
As for `Tracer`s themselves, each one carries an abstract value (and forwards
infix operators to it), and the rest is up to the transformation. (The
relationship between `Tracer`s and `AbstractValue`s is that there's one
`Tracer` per transformation, and at least one `AbstractValue` per base type,
like arrays.)
```{code-cell}
import numpy as np
class Tracer:
_trace: Trace
__array_priority__ = 1000
@property
def aval(self):
assert False # must override
def full_lower(self):
return self # default implementation
def __neg__(self): return self.aval._neg(self)
def __add__(self, other): return self.aval._add(self, other)
def __radd__(self, other): return self.aval._radd(self, other)
def __mul__(self, other): return self.aval._mul(self, other)
def __rmul__(self, other): return self.aval._rmul(self, other)
def __gt__(self, other): return self.aval._gt(self, other)
def __lt__(self, other): return self.aval._lt(self, other)
def __bool__(self): return self.aval._bool(self)
def __nonzero__(self): return self.aval._nonzero(self)
def __getattr__(self, name):
try:
return getattr(self.aval, name)
except AttributeError:
raise AttributeError(f"{self.__class__.__name__} has no attribute {name}")
def swap(f): return lambda x, y: f(y, x)
```
```{code-cell}
class ShapedArray:
array_abstraction_level = 1
shape: tuple[int, ...]
dtype: np.dtype
def __init__(self, shape, dtype):
self.shape = shape
self.dtype = dtype
@property
def ndim(self):
return len(self.shape)
_neg = staticmethod(neg)
_add = staticmethod(add)
_radd = staticmethod(swap(add))
_mul = staticmethod(mul)
_rmul = staticmethod(swap(mul))
_gt = staticmethod(greater)
_lt = staticmethod(less)
@staticmethod
def _bool(tracer):
raise Exception("ShapedArray can't be unambiguously converted to bool")
@staticmethod
def _nonzero(tracer):
raise Exception("ShapedArray can't be unambiguously converted to bool")
def str_short(self):
return f'{self.dtype.name}[{",".join(str(d) for d in self.shape)}]'
def __hash__(self):
return hash((self.shape, self.dtype))
def __eq__(self, other):
return (type(self) is type(other) and
self.shape == other.shape and self.dtype == other.dtype)
def __repr__(self):
return f"ShapedArray(shape={self.shape}, dtype={self.dtype})"
class ConcreteArray(ShapedArray):
array_abstraction_level = 2
val: np.ndarray
def __init__(self, val):
self.val = val
self.shape = val.shape
self.dtype = val.dtype
@staticmethod
def _bool(tracer):
return bool(tracer.aval.val)
@staticmethod
def _nonzero(tracer):
return bool(tracer.aval.val)
def get_aval(x):
if isinstance(x, Tracer):
return x.aval
elif type(x) in jax_types:
return ConcreteArray(np.asarray(x))
else:
raise TypeError(x)
jax_types = {bool, int, float,
np.bool_, np.int32, np.int64, np.float32, np.float64, np.ndarray}
```
Notice that we actually have two `AbstractValue`s for arrays, representing
different levels of abstraction. A `ShapedArray` represents the set of all
possible arrays with a given shape and dtype. A `ConcreteArray` represents a
singleton set consisting of a single array value.
Now that we've set up the interpreter stack, the Trace/Tracer API for
interpreters, and abstract values, we can come back to implement `bind`:
```{code-cell}
def bind(prim, *args, **params):
top_trace = find_top_trace(args)
tracers = [full_raise(top_trace, arg) for arg in args]
outs = top_trace.process_primitive(prim, tracers, params)
return [full_lower(out) for out in outs]
```
The main action is that we call `find_top_trace` to figure out which
interpreter should handle this primitive application. We then call that top
trace's `process_primitive` so that the trace can apply its interpretation
rule. The calls to `full_raise` just ensure that the inputs are boxed in the
top trace's `Tracer` instances, and the call to `full_lower` is an optional
optimization so that we unbox values out of `Tracer`s as much as possible.
```{code-cell}
import operator as op
def find_top_trace(xs) -> Trace:
top_main = max((x._trace.main for x in xs if isinstance(x, Tracer)),
default=trace_stack[0], key=op.attrgetter('level'))
if dynamic_trace and dynamic_trace.level > top_main.level:
top_main = dynamic_trace
return top_main.trace_type(top_main)
```
In words, ignoring the `dynamic_trace` step until Part 3, `find_top_trace`
returns the highest-level interpreter associated with the `Tracer`s on its
inputs, and otherwise returns the interpreter at the bottom of the stack
(which is always an evaluation trace, at least for now). This is a deviation
from the description above, where we always start by running the interpreter
at the top of the stack and then work our way down, applying every interpreter
in the stack. Instead, we're only applying an interpreter when the input
arguments to a primitive bind are boxed in a `Tracer` corresponding to that
interpreter. This optimization lets us skip irrelevant transformations, but
bakes in an assumption that transformations mostly follow data dependence
(except for the special bottom-of-the-stack interpreter, which interprets
everything).
An alternative would be to have every interpreter in the stack interpret every
operation. That's worth exploring! JAX is designed around data dependence in
large part because that's so natural for automatic differentiation, and JAX's
roots are in autodiff. But it may be over-fit.
```{code-cell}
def full_lower(val: Any):
if isinstance(val, Tracer):
return val.full_lower()
else:
return val
def full_raise(trace: Trace, val: Any) -> Tracer:
if not isinstance(val, Tracer):
assert type(val) in jax_types
return trace.pure(val)
level = trace.main.level
if val._trace.main is trace.main:
return val
elif val._trace.main.level < level:
return trace.lift(val)
elif val._trace.main.level > level:
raise Exception(f"Can't lift level {val._trace.main.level} to {level}.")
else: # val._trace.level == level
raise Exception(f"Different traces at same level: {val._trace}, {trace}.")
```
The logic in `full_raise` serves to box values into `Tracer`s for a particular
`Trace`, calling different methods on the `Trace` based on context:
`Trace.pure` is called on non-`Tracer` constants, and `Trace.lift` is called
for values that are already `Tracer`s from a lower-level interpreter. These
two methods could share the same implementation, but by distinguishing them in
the core logic we can provide more information to the `Trace` subclass.
That's it for the JAX core! Now we can start adding interpreters.
+++
### Evaluation interpreter
We'll start with the simplest interpreter: the evaluation interpreter that
will sit at the bottom of the interpreter stack.
```{code-cell}
class EvalTrace(Trace):
pure = lift = lambda self, x: x # no boxing in Tracers needed
def process_primitive(self, primitive, tracers, params):
return impl_rules[primitive](*tracers, **params)
trace_stack.append(MainTrace(0, EvalTrace, None)) # special bottom of the stack
# NB: in JAX, instead of a dict we attach impl rules to the Primitive instance
impl_rules = {}
impl_rules[add_p] = lambda x, y: [np.add(x, y)]
impl_rules[mul_p] = lambda x, y: [np.multiply(x, y)]
impl_rules[neg_p] = lambda x: [np.negative(x)]
impl_rules[sin_p] = lambda x: [np.sin(x)]
impl_rules[cos_p] = lambda x: [np.cos(x)]
impl_rules[reduce_sum_p] = lambda x, *, axis: [np.sum(x, axis)]
impl_rules[greater_p] = lambda x, y: [np.greater(x, y)]
impl_rules[less_p] = lambda x, y: [np.less(x, y)]
impl_rules[transpose_p] = lambda x, *, perm: [np.transpose(x, perm)]
def broadcast_impl(x, *, shape, axes):
for axis in sorted(axes):
x = np.expand_dims(x, axis)
return [np.broadcast_to(x, shape)]
impl_rules[broadcast_p] = broadcast_impl
```
With this interpreter, we can evaluate user functions:
```{code-cell}
def f(x):
y = sin(x) * 2.
z = - y + x
return z
print(f(3.0))
```
Woo! Like going around in a big circle. But the point of this indirection is
that now we can add some real transformations.
+++
### Forward-mode autodiff with `jvp`
First, a few helper functions:
```{code-cell}
import builtins
def zeros_like(val):
aval = get_aval(val)
return np.zeros(aval.shape, aval.dtype)
def unzip2(pairs):
lst1, lst2 = [], []
for x1, x2 in pairs:
lst1.append(x1)
lst2.append(x2)
return lst1, lst2
def map(f, *xs):
return list(builtins.map(f, *xs))
def zip(*args):
fst, *rest = args = map(list, args)
n = len(fst)
for arg in rest:
assert len(arg) == n
return list(builtins.zip(*args))
```
The `Tracer` for forward-mode autodiff carries a primal-tangent pair. The
`Trace` applies JVP rules.
```{code-cell}
class JVPTracer(Tracer):
def __init__(self, trace, primal, tangent):
self._trace = trace
self.primal = primal
self.tangent = tangent
@property
def aval(self):
return get_aval(self.primal)
class JVPTrace(Trace):
pure = lift = lambda self, val: JVPTracer(self, val, zeros_like(val))
def process_primitive(self, primitive, tracers, params):
primals_in, tangents_in = unzip2((t.primal, t.tangent) for t in tracers)
jvp_rule = jvp_rules[primitive]
primal_outs, tangent_outs = jvp_rule(primals_in, tangents_in, **params)
return [JVPTracer(self, x, t) for x, t in zip(primal_outs, tangent_outs)]
jvp_rules = {}
```
Notice both `pure` and `lift` package a value into a `JVPTracer` with the
minimal amount of context, which is a zero tangent value.
Let's add some JVP rules for primitives:
```{code-cell}
def add_jvp(primals, tangents):
(x, y), (x_dot, y_dot) = primals, tangents
return [x + y], [x_dot + y_dot]
jvp_rules[add_p] = add_jvp
def mul_jvp(primals, tangents):
(x, y), (x_dot, y_dot) = primals, tangents
return [x * y], [x_dot * y + x * y_dot]
jvp_rules[mul_p] = mul_jvp
def sin_jvp(primals, tangents):
(x,), (x_dot,) = primals, tangents
return [sin(x)], [cos(x) * x_dot]
jvp_rules[sin_p] = sin_jvp
def cos_jvp(primals, tangents):
(x,), (x_dot,) = primals, tangents
return [cos(x)], [-sin(x) * x_dot]
jvp_rules[cos_p] = cos_jvp
def neg_jvp(primals, tangents):
(x,), (x_dot,) = primals, tangents
return [neg(x)], [neg(x_dot)]
jvp_rules[neg_p] = neg_jvp
def reduce_sum_jvp(primals, tangents, *, axis):
(x,), (x_dot,) = primals, tangents
return [reduce_sum(x, axis)], [reduce_sum(x_dot, axis)]
jvp_rules[reduce_sum_p] = reduce_sum_jvp
def greater_jvp(primals, tangents):
(x, y), _ = primals, tangents
out_primal = greater(x, y)
return [out_primal], [zeros_like(out_primal)]
jvp_rules[greater_p] = greater_jvp
def less_jvp(primals, tangents):
(x, y), _ = primals, tangents
out_primal = less(x, y)
return [out_primal], [zeros_like(out_primal)]
jvp_rules[less_p] = less_jvp
```
Finally, we add a transformation API to kick off the trace:
```{code-cell}
def jvp_v1(f, primals, tangents):
with new_main(JVPTrace) as main:
trace = JVPTrace(main)
tracers_in = [JVPTracer(trace, x, t) for x, t in zip(primals, tangents)]
out = f(*tracers_in)
tracer_out = full_raise(trace, out)
primal_out, tangent_out = tracer_out.primal, tracer_out.tangent
return primal_out, tangent_out
```
And with that, we can differentiate!
```{code-cell}
x = 3.0
y, sin_deriv_at_3 = jvp_v1(sin, (x,), (1.0,))
print(sin_deriv_at_3)
print(cos(3.0))
```
```{code-cell}
def f(x):
y = sin(x) * 2.
z = - y + x
return z
x, xdot = 3., 1.
y, ydot = jvp_v1(f, (x,), (xdot,))
print(y)
print(ydot)
```
```{code-cell}
def deriv(f):
return lambda x: jvp_v1(f, (x,), (1.,))[1]
print(deriv(sin)(3.))
print(deriv(deriv(sin))(3.))
print(deriv(deriv(deriv(sin)))(3.))
print(deriv(deriv(deriv(deriv(sin))))(3.))
```
```{code-cell}
def f(x):
if x > 0.: # Python control flow
return 2. * x
else:
return x
print(deriv(f)(3.))
print(deriv(f)(-3.))
```
## Pytrees and flattening user functions' inputs and outputs
+++
A limitation with `jvp_v1` is that it assumes the user function accepts arrays
as positional arguments and produces a single array as output. What if it
produced a list as output? Or accepted nested containers as inputs? It would
be a pain to deal with all the possible containers in inputs and outputs at
every layer of the stack. Instead, we can wrap the user function so that the
wrapped version accepts arrays as inputs and returns a flat list of arrays as
output. The wrapper just needs to unflatten its input, call the user function,
and flatten the output.
Here's how we'd like to write `jvp`, assuming the user always gives us
functions that take arrays as inputs and produces a flat list of arrays as
outputs:
```{code-cell}
def jvp_flat(f, primals, tangents):
with new_main(JVPTrace) as main:
trace = JVPTrace(main)
tracers_in = [JVPTracer(trace, x, t) for x, t in zip(primals, tangents)]
outs = f(*tracers_in)
tracers_out = [full_raise(trace, out) for out in outs]
primals_out, tangents_out = unzip2((t.primal, t.tangent) for t in tracers_out)
return primals_out, tangents_out
```
To support user functions that have arbitrary containers in the inputs and
outputs, here's how we'd write the user-facing `jvp` wrapper:
```{code-cell}
def jvp(f, primals, tangents):
primals_flat, in_tree = tree_flatten(primals)
tangents_flat, in_tree2 = tree_flatten(tangents)
if in_tree != in_tree2: raise TypeError
f, out_tree = flatten_fun(f, in_tree)
primals_out_flat, tangents_out_flat = jvp_flat(f, primals_flat, tangents_flat)
primals_out = tree_unflatten(out_tree(), primals_out_flat)
tangents_out = tree_unflatten(out_tree(), tangents_out_flat)
return primals_out, tangents_out
```
Notice that we had to plumb the tree structure of the user function output
back to the caller of `flatten_fun`. That information isn't available until we
actually run the user function, so `flatten_fun` just returns a reference to a
mutable cell, represented as a thunk. These side-effects are safe because we
always run the user function exactly once. (This safe regime is the reason for
the "linear" name in `linear_util.py`, in the sense of [linear
types](https://en.wikipedia.org/wiki/Substructural_type_system).)
All that remains is to write `tree_flatten`, `tree_unflatten`, and
`flatten_fun`.
```{code-cell}
:tags: [hide-input]
def flatten_fun(f, in_tree):
store = Store()
def flat_fun(*args_flat):
pytree_args = tree_unflatten(in_tree, args_flat)
out = f(*pytree_args)
out_flat, out_tree = tree_flatten(out)
store.set_value(out_tree)
return out_flat
return flat_fun, store
class Empty: pass
empty = Empty()
class Store:
val = empty
def set_value(self, val):
assert self.val is empty
self.val = val
def __call__(self):
return self.val
```
```{code-cell}
:tags: [hide-input]
from collections.abc import Hashable, Iterable, Iterator
import itertools as it
from typing import Callable
class NodeType(NamedTuple):
name: str
to_iterable: Callable
from_iterable: Callable
def register_pytree_node(ty: type, to_iter: Callable, from_iter: Callable
) -> None:
node_types[ty] = NodeType(str(ty), to_iter, from_iter)
node_types: dict[type, NodeType] = {}
register_pytree_node(tuple, lambda t: (None, t), lambda _, xs: tuple(xs))
register_pytree_node(list, lambda l: (None, l), lambda _, xs: list(xs))
register_pytree_node(dict,
lambda d: map(tuple, unzip2(sorted(d.items()))),
lambda keys, vals: dict(zip(keys, vals)))
class PyTreeDef(NamedTuple):
node_type: NodeType
node_metadata: Hashable
child_treedefs: tuple['PyTreeDef', ...]
class Leaf: pass
leaf = Leaf()
def tree_flatten(x: Any) -> tuple[list[Any], PyTreeDef]:
children_iter, treedef = _tree_flatten(x)
return list(children_iter), treedef
def _tree_flatten(x: Any) -> tuple[Iterable, PyTreeDef]:
node_type = node_types.get(type(x))
if node_type:
node_metadata, children = node_type.to_iterable(x)
children_flat, child_trees = unzip2(map(_tree_flatten, children))
flattened = it.chain.from_iterable(children_flat)
return flattened, PyTreeDef(node_type, node_metadata, tuple(child_trees))
else:
return [x], leaf
def tree_unflatten(treedef: PyTreeDef, xs: list[Any]) -> Any:
return _tree_unflatten(treedef, iter(xs))
def _tree_unflatten(treedef: PyTreeDef, xs: Iterator) -> Any:
if treedef is leaf:
return next(xs)
else:
children = (_tree_unflatten(t, xs) for t in treedef.child_treedefs)
return treedef.node_type.from_iterable(treedef.node_metadata, children)
```
With this pytree-handling `jvp` implementation, we can now handle arbitrary
input and output containers. That'll come in handy with future transformations
too!
```{code-cell}
def f(x):
y = sin(x) * 2.
z = - y + x
return {'hi': z, 'there': [x, y]}
x, xdot = 3., 1.
y, ydot = jvp(f, (x,), (xdot,))
print(y)
print(ydot)
```
### Vectorized batching with `vmap`
First, a couple helper functions, one for producing mapped abstract values
from unmapped ones (by removing an axis), and one for moving batch dimensions
around:
```{code-cell}
def mapped_aval(batch_dim, aval):
shape = list(aval.shape)
del shape[batch_dim]
return ShapedArray(tuple(shape), aval.dtype)
def move_batch_axis(axis_size, src, dst, x):
if src is not_mapped:
target_shape = list(np.shape(x))
target_shape.insert(dst, axis_size)
return broadcast(x, target_shape, [dst])
elif src == dst:
return x
else:
return moveaxis(x, src, dst)
def moveaxis(x, src: int, dst: int):
perm = [i for i in range(np.ndim(x)) if i != src]
perm.insert(dst, src)
return transpose(x, perm)
```
The `Tracer` for vectorized batching carries a batched value and an optional
integer indicating which axis (if any) is the batch axis.
```{code-cell}
from typing import Union
class NotMapped: pass
not_mapped = NotMapped()
BatchAxis = Union[NotMapped, int]
class BatchTracer(Tracer):
def __init__(self, trace, val, batch_dim: BatchAxis):
self._trace = trace
self.val = val
self.batch_dim = batch_dim
@property
def aval(self):
if self.batch_dim is not_mapped:
return get_aval(self.val)
else:
return mapped_aval(self.batch_dim, get_aval(self.val))
def full_lower(self):
if self.batch_dim is not_mapped:
return full_lower(self.val)
else:
return self
class BatchTrace(Trace):
pure = lift = lambda self, val: BatchTracer(self, val, not_mapped)
def process_primitive(self, primitive, tracers, params):
vals_in, bdims_in = unzip2((t.val, t.batch_dim) for t in tracers)
vmap_rule = vmap_rules[primitive]
val_outs, bdim_outs = vmap_rule(self.axis_size, vals_in, bdims_in, **params)
return [BatchTracer(self, x, bd) for x, bd in zip(val_outs, bdim_outs)]
@property
def axis_size(self):
return self.main.global_data
vmap_rules = {}
```
Here we've implemented the optional `Tracer.full_lower` method, which lets us
peel off a batching tracer if it's not needed because it doesn't represent a
batched value.
For `BatchTrace`, analogous to `JVPTrace`, the methods `pure` and `lift` just
box a value in a `BatchTracer` with the minimal amount of context, which in
this case is a `batch_dim` taking the sentinel value `not_mapped`. Notice we
use the `MainTrace`'s interpreter-global data field to store the batch axis
size.
Next we can define batching interpreter rules for each primitive:
```{code-cell}
from functools import partial
def binop_batching_rule(op, axis_size, vals_in, dims_in):
(x, y), (x_bdim, y_bdim) = vals_in, dims_in
if x_bdim != y_bdim:
if x_bdim is not_mapped:
x = move_batch_axis(axis_size, x_bdim, y_bdim, x)
x_bdim = y_bdim
else:
y = move_batch_axis(axis_size, y_bdim, x_bdim, y)
return [op(x, y)], [x_bdim]
vmap_rules[add_p] = partial(binop_batching_rule, add)
vmap_rules[mul_p] = partial(binop_batching_rule, mul)
def vectorized_unop_batching_rule(op, axis_size, vals_in, dims_in):
(x,), (x_bdim,) = vals_in, dims_in
return [op(x)], [x_bdim]
vmap_rules[sin_p] = partial(vectorized_unop_batching_rule, sin)
vmap_rules[cos_p] = partial(vectorized_unop_batching_rule, cos)
vmap_rules[neg_p] = partial(vectorized_unop_batching_rule, neg)
def reduce_sum_batching_rule(axis_size, vals_in, dims_in, *, axis):
(x,), (x_bdim,) = vals_in, dims_in
new_axis = tuple(ax + (x_bdim <= ax) for ax in axis)
out_bdim = x_bdim - sum(ax < x_bdim for ax in axis)
return [reduce_sum(x, new_axis)], [out_bdim]
vmap_rules[reduce_sum_p] = reduce_sum_batching_rule
```
Finally, we add a transformation API to kick off the trace:
```{code-cell}
def vmap_flat(f, in_axes, *args):
axis_size, = {x.shape[ax] for x, ax in zip(args, in_axes)
if ax is not not_mapped}
with new_main(BatchTrace, axis_size) as main:
trace = BatchTrace(main)
tracers_in = [BatchTracer(trace, x, ax) if ax is not None else x
for x, ax in zip(args, in_axes)]
outs = f(*tracers_in)
tracers_out = [full_raise(trace, out) for out in outs]
vals_out, bdims_out = unzip2((t.val, t.batch_dim) for t in tracers_out)
outs_transposed = [move_batch_axis(axis_size, bdim, 0, val_out)
for val_out, bdim in zip(vals_out, bdims_out)]
return outs_transposed
def vmap(f, in_axes):
def batched_f(*args):
args_flat, in_tree = tree_flatten(args)
in_axes_flat, in_tree2 = tree_flatten(in_axes)
if in_tree != in_tree2: raise TypeError
f_flat, out_tree = flatten_fun(f, in_tree)
outs_flat = vmap_flat(f_flat, in_axes_flat, *args_flat)
return tree_unflatten(out_tree(), outs_flat)
return batched_f
```
```{code-cell}
def add_one_to_a_scalar(scalar):
assert np.ndim(scalar) == 0
return 1 + scalar
vector_in = np.arange(3.)
vector_out = vmap(add_one_to_a_scalar, (0,))(vector_in)
print(vector_in)
print(vector_out)
```
```{code-cell}
def jacfwd(f, x):
pushfwd = lambda v: jvp(f, (x,), (v,))[1]
vecs_in = np.eye(np.size(x)).reshape(np.shape(x) * 2)
return vmap(pushfwd, (0,))(vecs_in)
def f(x):
return sin(x)
jacfwd(f, np.arange(3.))
```
That's it for `jvp` and `vmap`!
+++
## Part 2: Jaxprs
The next transformations on the horizon are `jit` for just-in-time
compilation and `vjp` for reverse-mode autodiff. (`grad` is just a small
wrapper around `vjp`.) Whereas `jvp` and `vmap` only needed each `Tracer` to
carry a little bit of extra context, for both `jit` and `vjp` we need much
richer context: we need to represent _programs_. That is, we need jaxprs!
Jaxprs are JAX's internal intermediate representation of programs. They are
explicitly typed, functional, first-order, and in ANF form. We need a
program representation for `jit` because the purpose of `jit` is to stage
computation out of Python. For any computation we want to stage out, we need
to be able to represent it as data, and build it up as we trace a Python
function. Similarly, `vjp` needs a way to represent the computation for the
backward pass of reverse-mode autodiff. We use the same jaxpr program
representation for both needs.
(Building a program representation is the most
[free](https://en.wikipedia.org/wiki/Free_object) kind of
trace-transformation, and so except for issues around handling native Python
control flow, any transformation could be implemented by first tracing to a
jaxpr and then interpreting the jaxpr.)
+++
### Jaxpr data structures
The jaxpr term syntax is roughly:
```
jaxpr ::=
{ lambda <binder> , ... .
let <eqn>
...
in ( <atom> , ... ) }
binder ::= <var>:<array_type>
var ::= a | b | c | ...
atom ::= <var> | <literal>
literal ::= <int32> | <int64> | <float32> | <float64>
eqn ::= <binder> , ... = <primitive> [ <params> ] <atom> , ...
```
The syntax of types is:
```
jaxpr_type ::= [ <array_type> , ... ] -> [ <array_type> , ... ]
array_type ::= <dtype>[<shape>]
dtype ::= f32 | f64 | i32 | i64
shape ::= <int> , ...
```
How do we represent these as Python data structures? We reuse ShapedArrays to
represent types, and we can represent the term syntax with a few Python
structs:
```{code-cell}
class Var:
aval: ShapedArray
def __init__(self, aval): self.aval = aval
class Lit:
val: Any
aval: ShapedArray
def __init__(self, val):
self.aval = aval = raise_to_shaped(get_aval(val))
self.val = np.array(val, aval.dtype)
Atom = Union[Var, Lit]
class JaxprEqn(NamedTuple):
primitive: Primitive
inputs: list[Atom]
params: dict[str, Any]
out_binders: list[Var]
class Jaxpr(NamedTuple):
in_binders: list[Var]
eqns: list[JaxprEqn]
outs: list[Atom]
def __hash__(self): return id(self)
__eq__ = op.is_
def raise_to_shaped(aval):
return ShapedArray(aval.shape, aval.dtype)
```
Type-checking a jaxpr involves checking that there are no unbound variables,
that variables are only bound once, and that for each equation the type of
the primitive application matches the type of the output binders.
```{code-cell}
class JaxprType(NamedTuple):
in_types: list[ShapedArray]
out_types: list[ShapedArray]
def __repr__(self):
in_types = ', '.join(aval.str_short() for aval in self.in_types)
out_types = ', '.join(aval.str_short() for aval in self.out_types)
return f'({in_types}) -> ({out_types})'
def typecheck_jaxpr(jaxpr: Jaxpr) -> JaxprType:
env: set[Var] = set()
for v in jaxpr.in_binders:
if v in env: raise TypeError
env.add(v)
for eqn in jaxpr.eqns:
in_types = [typecheck_atom(env, x) for x in eqn.inputs]
out_types = abstract_eval_rules[eqn.primitive](*in_types, **eqn.params)
for out_binder, out_type in zip(eqn.out_binders, out_types):
if not out_type == out_binder.aval: raise TypeError
for out_binder in eqn.out_binders:
if out_binder in env: raise TypeError
env.add(out_binder)
in_types = [v.aval for v in jaxpr.in_binders]
out_types = [typecheck_atom(env, x) for x in jaxpr.outs]
return JaxprType(in_types, out_types)
def typecheck_atom(env: set[Var], x: Atom) -> ShapedArray:
if isinstance(x, Var):
if x not in env: raise TypeError("unbound variable")
return x.aval
elif isinstance(x, Lit):
return raise_to_shaped(get_aval(x.val))
else:
assert False
```
We can apply the function represented by a jaxpr to arguments with a simple
interpreter.
```{code-cell}
def eval_jaxpr(jaxpr: Jaxpr, args: list[Any]) -> list[Any]:
env: dict[Var, Any] = {}
def read(x: Atom) -> Any:
return env[x] if type(x) is Var else x.val
def write(v: Var, val: Any) -> None:
assert v not in env # single-assignment
env[v] = val
map(write, jaxpr.in_binders, args)
for eqn in jaxpr.eqns:
in_vals = map(read, eqn.inputs)
outs = bind(eqn.primitive, *in_vals, **eqn.params)
map(write, eqn.out_binders, outs)
return map(read, jaxpr.outs)
def jaxpr_as_fun(jaxpr: Jaxpr):
return lambda *args: eval_jaxpr(jaxpr, args)
```
By using `bind` in the interpreter, this interpreter itself is traceable.
+++
### Building jaxprs with tracing
Now that we have jaxprs as a data structure, we need ways to produce these
from tracing Python code. In general there are two variants of how we trace to
a jaxpr; `jit` uses one and `vjp` uses the other. We'll start with the one
used by `jit`, which is also used by control flow primitives like `lax.cond`,
`lax.while_loop`, and `lax.scan`.
```{code-cell}
def split_list(lst: list[Any], n: int) -> tuple[list[Any], list[Any]]:
assert 0 <= n <= len(lst)
return lst[:n], lst[n:]
def partition_list(bs: list[bool], l: list[Any]) -> tuple[list[Any], list[Any]]:
assert len(bs) == len(l)
lists = lst1, lst2 = [], []
for b, x in zip(bs, l):
lists[b].append(x)
return lst1, lst2
```
```{code-cell}
# NB: the analogous class in JAX is called 'DynamicJaxprTracer'
class JaxprTracer(Tracer):
__slots__ = ['aval']
aval: ShapedArray
def __init__(self, trace, aval):
self._trace = trace
self.aval = aval
# NB: the analogous class in JAX is called 'DynamicJaxprTrace'
class JaxprTrace(Trace):
def new_arg(self, aval: ShapedArray) -> JaxprTracer:
aval = raise_to_shaped(aval)
tracer = self.builder.new_tracer(self, aval)
self.builder.tracer_to_var[id(tracer)] = Var(aval)
return tracer
def get_or_make_const_tracer(self, val: Any) -> JaxprTracer:
tracer = self.builder.const_tracers.get(id(val))
if tracer is None:
tracer = self.builder.new_tracer(self, raise_to_shaped(get_aval(val)))
self.builder.add_const(tracer, val)
return tracer
pure = lift = get_or_make_const_tracer
def process_primitive(self, primitive, tracers, params):
avals_in = [t.aval for t in tracers]
avals_out = abstract_eval_rules[primitive](*avals_in, **params)
out_tracers = [self.builder.new_tracer(self, a) for a in avals_out]
inputs = [self.builder.getvar(t) for t in tracers]
outvars = [self.builder.add_var(t) for t in out_tracers]
self.builder.add_eqn(JaxprEqn(primitive, inputs, params, outvars))
return out_tracers
@property
def builder(self):
return self.main.global_data
# NB: in JAX, we instead attach abstract eval rules to Primitive instances
abstract_eval_rules = {}
```
Notice that we keep as interpreter-global data a builder object, which keeps
track of variables, constants, and eqns as we build up the jaxpr.
```{code-cell}
class JaxprBuilder:
eqns: list[JaxprEqn]
tracer_to_var: dict[int, Var]
const_tracers: dict[int, JaxprTracer]
constvals: dict[Var, Any]
tracers: list[JaxprTracer]
def __init__(self):
self.eqns = []
self.tracer_to_var = {}
self.const_tracers = {}
self.constvals = {}
self.tracers = []
def new_tracer(self, trace: JaxprTrace, aval: ShapedArray) -> JaxprTracer:
tracer = JaxprTracer(trace, aval)
self.tracers.append(tracer)
return tracer
def add_eqn(self, eqn: JaxprEqn) -> None:
self.eqns.append(eqn)
def add_var(self, tracer: JaxprTracer) -> Var:
assert id(tracer) not in self.tracer_to_var
var = self.tracer_to_var[id(tracer)] = Var(tracer.aval)
return var
def getvar(self, tracer: JaxprTracer) -> Var:
var = self.tracer_to_var.get(id(tracer))
assert var is not None
return var
def add_const(self, tracer: JaxprTracer, val: Any) -> Var:
var = self.add_var(tracer)
self.const_tracers[id(val)] = tracer
self.constvals[var] = val
return var
def build(self, in_tracers: list[JaxprTracer], out_tracers: list[JaxprTracer]
) -> tuple[Jaxpr, list[Any]]:
constvars, constvals = unzip2(self.constvals.items())
t2v = lambda t: self.tracer_to_var[id(t)]
in_binders = constvars + [t2v(t) for t in in_tracers]
out_vars = [t2v(t) for t in out_tracers]
jaxpr = Jaxpr(in_binders, self.eqns, out_vars)
typecheck_jaxpr(jaxpr)
jaxpr, constvals = _inline_literals(jaxpr, constvals)
return jaxpr, constvals
```
```{code-cell}
def _inline_literals(jaxpr: Jaxpr, consts: list[Any]) -> tuple[Jaxpr, list[Any]]:
const_binders, other_binders = split_list(jaxpr.in_binders, len(consts))
scalars = [type(x) in jax_types and not get_aval(x).shape for x in consts]
new_const_binders, lit_binders = partition_list(scalars, const_binders)
new_consts, lit_vals = partition_list(scalars, consts)
literals = dict(zip(lit_binders, map(Lit, lit_vals)))
new_eqns = [JaxprEqn(eqn.primitive, [literals.get(x, x) for x in eqn.inputs],
eqn.params, eqn.out_binders) for eqn in jaxpr.eqns]
new_outs = [literals.get(x, x) for x in jaxpr.outs]
new_jaxpr = Jaxpr(new_const_binders + other_binders, new_eqns, new_outs)
typecheck_jaxpr(new_jaxpr)
return new_jaxpr, new_consts
```
The rules we need for `JaxprTrace.process_primitive` are essentially typing
rules for primitive applications: given the primitive, its parameters, and
types for the inputs, the rule must produce a type for the output, which is
then packaged with the output `JaxprTracer`. We can use abstract evaluation
rules for this same purpose, even though they can be more general (since
abstract evaluation rules must accept ConcreteArray inputs, and since they
need only return an upper bound on the set of possible outputs, they can
produce ConcreteArray outputs as well). We'll reuse these abstract evaluation
rules for the other jaxpr-producing trace machinery, where the potential extra
generality is useful.
```{code-cell}
def binop_abstract_eval(x: ShapedArray, y: ShapedArray) -> list[ShapedArray]:
if not isinstance(x, ShapedArray) or not isinstance(y, ShapedArray):
raise TypeError
if raise_to_shaped(x) != raise_to_shaped(y): raise TypeError
return [ShapedArray(x.shape, x.dtype)]
abstract_eval_rules[add_p] = binop_abstract_eval
abstract_eval_rules[mul_p] = binop_abstract_eval
def compare_abstract_eval(x: ShapedArray, y: ShapedArray) -> list[ShapedArray]:
if not isinstance(x, ShapedArray) or not isinstance(y, ShapedArray):
raise TypeError
if x.shape != y.shape: raise TypeError
return [ShapedArray(x.shape, np.dtype('bool'))]
abstract_eval_rules[greater_p] = compare_abstract_eval
abstract_eval_rules[less_p] = compare_abstract_eval
def vectorized_unop_abstract_eval(x: ShapedArray) -> list[ShapedArray]:
return [ShapedArray(x.shape, x.dtype)]
abstract_eval_rules[sin_p] = vectorized_unop_abstract_eval
abstract_eval_rules[cos_p] = vectorized_unop_abstract_eval
abstract_eval_rules[neg_p] = vectorized_unop_abstract_eval
def reduce_sum_abstract_eval(x: ShapedArray, *, axis: tuple[int, ...]
) -> list[ShapedArray]:
axis_ = set(axis)
new_shape = [d for i, d in enumerate(x.shape) if i not in axis_]
return [ShapedArray(tuple(new_shape), x.dtype)]
abstract_eval_rules[reduce_sum_p] = reduce_sum_abstract_eval
def broadcast_abstract_eval(x: ShapedArray, *, shape: Sequence[int],
axes: Sequence[int]) -> list[ShapedArray]:
return [ShapedArray(tuple(shape), x.dtype)]
abstract_eval_rules[broadcast_p] = broadcast_abstract_eval
```
To check our implementation of jaxprs, we can add a `make_jaxpr`
transformation and a pretty-printer:
```{code-cell}
from functools import lru_cache
@lru_cache() # ShapedArrays are hashable
def make_jaxpr_v1(f, *avals_in):
avals_in, in_tree = tree_flatten(avals_in)
f, out_tree = flatten_fun(f, in_tree)
builder = JaxprBuilder()
with new_main(JaxprTrace, builder) as main:
trace = JaxprTrace(main)
tracers_in = [trace.new_arg(aval) for aval in avals_in]
outs = f(*tracers_in)
tracers_out = [full_raise(trace, out) for out in outs]
jaxpr, consts = builder.build(tracers_in, tracers_out)
return jaxpr, consts, out_tree()
```
```{code-cell}
:tags: [hide-input]
from collections import defaultdict
import string
class PPrint:
lines: list[tuple[int, str]]
def __init__(self, lines):
self.lines = lines
def indent(self, indent: int) -> 'PPrint':
return PPrint([(indent + orig_indent, s) for orig_indent, s in self.lines])
def __add__(self, rhs: 'PPrint') -> 'PPrint':
return PPrint(self.lines + rhs.lines)
def __rshift__(self, rhs: 'PPrint') -> 'PPrint':
if not rhs.lines: return self
if not self.lines: return rhs
indent, s = self.lines[-1]
indented_block = rhs.indent(indent + len(s))
common_line = s + ' ' * rhs.lines[0][0] + rhs.lines[0][1]
return PPrint(self.lines[:-1]
+ [(indent, common_line)]
+ indented_block.lines[1:])
def __str__(self) -> str:
return '\n'.join(' ' * indent + s for indent, s in self.lines)
def pp(s: Any) -> PPrint:
return PPrint([(0, line) for line in str(s).splitlines()])
def vcat(ps: list[PPrint]) -> PPrint:
return sum(ps, pp(''))
def pp_jaxpr(jaxpr: Jaxpr) -> PPrint:
namegen = (''.join(s) for r in it.count(1)
for s in it.permutations(string.ascii_lowercase, r))
names = defaultdict(lambda: next(namegen))
in_binders = ', '.join(var_str(names, x) for x in jaxpr.in_binders)
eqns = vcat([pp_eqn(names, e) for e in jaxpr.eqns])
outs = ', '.join(names[v] if isinstance(v, Var) else str(v.val)
for v in jaxpr.outs)
return (pp(f'{{ lambda {in_binders} .') +
((pp('let ') >> eqns) + pp(f'in ( {outs} ) }}')).indent(2))
def var_str(names: defaultdict[Var, str], v: Var) -> str:
return f'{names[v]}:{v.aval.str_short()}'
def pp_eqn(names: defaultdict[Var, str], eqn: JaxprEqn) -> PPrint:
rule = pp_rules.get(eqn.primitive)
if rule:
return rule(names, eqn)
else:
lhs = pp(' '.join(var_str(names, v) for v in eqn.out_binders))
rhs = (pp(eqn.primitive.name) >> pp_params(eqn.params) >>
pp(' '.join(names[x] if isinstance(x, Var) else str(x.val)
for x in eqn.inputs)))
return lhs >> pp(' = ') >> rhs
def pp_params(params: dict[str, Any]) -> PPrint:
items = sorted(params.items())
if items:
return pp(' [ ') >> vcat([pp(f'{k}={v}') for k, v in items]) >> pp(' ] ')
else:
return pp(' ')
Jaxpr.__repr__ = lambda self: str(pp_jaxpr(self))
pp_rules: dict[Primitive, Callable[..., PPrint]] = {}
```
```{code-cell}
jaxpr, consts, _ = make_jaxpr_v1(lambda x: 2. * x, raise_to_shaped(get_aval(3.)))
print(jaxpr)
print(typecheck_jaxpr(jaxpr))
```
But there's a limitation here: because of how `find_top_trace` operates by
data dependence, `make_jaxpr_v1` can't stage out all the primitive operations
performed by the Python callable it's given. For example:
```{code-cell}
jaxpr, consts, _ = make_jaxpr_v1(lambda: mul(2., 2.))
print(jaxpr)
```
This is precisely the issue that
[omnistaging](https://github.com/google/jax/pull/3370) fixed.
We want to ensure that the `JaxprTrace` started by `make_jaxpr` is always
applied, regardless of whether any inputs to `bind` are boxed in corresponding
`JaxprTracer` instances. We can achieve this by employing the `dynamic_trace`
global defined in Part 1:
```{code-cell}
@contextmanager
def new_dynamic(main: MainTrace):
global dynamic_trace
prev_dynamic_trace, dynamic_trace = dynamic_trace, main
try:
yield
finally:
dynamic_trace = prev_dynamic_trace
@lru_cache()
def make_jaxpr(f: Callable, *avals_in: ShapedArray,
) -> tuple[Jaxpr, list[Any], PyTreeDef]:
avals_in, in_tree = tree_flatten(avals_in)
f, out_tree = flatten_fun(f, in_tree)
builder = JaxprBuilder()
with new_main(JaxprTrace, builder) as main:
with new_dynamic(main):
trace = JaxprTrace(main)
tracers_in = [trace.new_arg(aval) for aval in avals_in]
outs = f(*tracers_in)
tracers_out = [full_raise(trace, out) for out in outs]
jaxpr, consts = builder.build(tracers_in, tracers_out)
return jaxpr, consts, out_tree()
jaxpr, consts, _ = make_jaxpr(lambda: mul(2., 2.))
print(jaxpr)
```
Using `dynamic_trace` this way is conceptually the same as stashing the
current interpreter stack and starting a new one with the `JaxprTrace` at the
bottom. That is, no interpreters lower in the stack than the `dynamic_trace`
are applied (since `JaxprTrace.process_primitive` doesn't call `bind`), though
if the Python callable being traced to a jaxpr itself uses transformations
then those can be pushed onto the interpreter stack above the `JaxprTrace`.
But temporarily stashing the interpreter stack would break up the system
state. The `dynamic_trace` tag achieves the same goals while keeping the
system state simpler.
+++
That's it for jaxprs! With jaxprs in hand, we can implement the remaining
major JAX features.
+++
## Part 3: `jit`, simplified
While `jit` has a transformation-like API in that it accepts a Python callable
as an argument, under the hood it's really a higher-order primitive rather
than a transformation. A primitive is _higher-order_ when it's parameterized
by a function.
+++
### On-the-fly ("final style") and staged ("initial style") processing
There are two options for how to handle higher-order primitives. Each requires
a different approach to tracing and engenders different tradeoffs:
1. **On-the-fly processing, where `bind` takes a Python callable as an
argument.** We defer forming a jaxpr until as late as possible, namely
until we're running the final interpreter at the bottom of the interpreter
stack. That way we can swap a `JaxprTrace` in at the bottom of the
interpreter stack and thus stage out rather than execute all primitive
operations. With this approach, transformations in the stack get applied as
we execute the Python callable as usual. This approach can be very tricky
to implement, but it's as general as possible because it allows
higher-order primitives not to raise the abstraction level of their
arguments and thus allows data-dependent Python control flow. We refer to
this approach as using a "final-style higher-order primitive" employing the
discharge-at-tracing-time "final-style transformations" we've used so far.
2. **Staged processing, where `bind` takes a jaxpr as an argument.** Before we
call `bind`, in the primitive wrapper we can just use `make_jaxpr` to form
a jaxpr up-front and be done with the Python callable entirely. In this
case, `make_jaxpr` puts its `JaxprTrace` at the top of the interpreter
stack, and no transformations lower in the stack, which might enter via
closed-over Tracers, are applied to the Python callable as we trace it.
(Transformations applied within the Python callable are applied as usual,
being added to the stack above the JaxprTrace.) Instead, the
transformations lower in the stack are later applied to the call primitive,
and the call primitive's rules must then transform the jaxpr itself.
Because we trace to a jaxpr up-front, this approach can't support
data-dependent Python control flow, but it is more straightforward to
implement. We refer to this kind of higher-order primitive as an
"initial-style higher-order primitive", and say that its jaxpr-processing
transformation rules are "initial-style transformation rules."
The latter approach fits for `jit` because we don't need to support
data-dependent Python control flow in the user-provided Python callable, as
the whole purpose of `jit` is to stage computation out of Python to be
executed by XLA. (In contrast, `custom_jvp` is a higher-order primitive in
which we want to support data-dependent Python control flow.)
Historically, we started using the "initial-style" and "final-style"
terminology after reading the [typed tagless final
interpreters](http://okmij.org/ftp/tagless-final/index.html) paper, and
jokingly referring to JAX as an implementation of "untyped tagful final
interpreters." We don't claim to carry over (or understand) any deep meaning
behind these terms; we loosely use "initial style" to mean "build an AST and
then transform it", and we use "final style" to mean "transform as we trace."
But it's just imprecise yet sticky jargon.
+++
With the initial-style approach, here's the user-facing `jit` wrapper:
```{code-cell}
def jit(f):
def f_jitted(*args):
avals_in = [raise_to_shaped(get_aval(x)) for x in args]
jaxpr, consts, out_tree = make_jaxpr(f, *avals_in)
outs = bind(xla_call_p, *consts, *args, jaxpr=jaxpr, num_consts=len(consts))
return tree_unflatten(out_tree, outs)
return f_jitted
xla_call_p = Primitive('xla_call')
```
With any new primitive, we need to give it transformation rules, starting with
its evaluation rule. When we evaluate an application of the `xla_call`
primitive, we want to stage out the computation to XLA. That involves
translating the jaxpr to an XLA HLO program, transferring the argument values
to the XLA device, executing the XLA program, and transferring back the
results. We'll cache the XLA HLO compilation so that for each `jit`ted
function it only needs to be performed once per argument shape and dtype
signature.
First, some utilities.
```{code-cell}
class IDHashable:
val: Any
def __init__(self, val):
self.val = val
def __hash__(self) -> int:
return id(self.val)
def __eq__(self, other):
return type(other) is IDHashable and id(self.val) == id(other.val)
```
Next, we'll define the evaluation rule for `xla_call`:
```{code-cell}
from jax._src import xla_bridge as xb
from jax._src.lib import xla_client as xc
xe = xc._xla
xops = xc._xla.ops
def xla_call_impl(*args, jaxpr: Jaxpr, num_consts: int):
consts, args = args[:num_consts], args[num_consts:]
hashable_consts = tuple(map(IDHashable, consts))
execute = xla_callable(IDHashable(jaxpr), hashable_consts)
return execute(*args)
impl_rules[xla_call_p] = xla_call_impl
@lru_cache()
def xla_callable(hashable_jaxpr: IDHashable,
hashable_consts: tuple[IDHashable, ...]):
jaxpr: Jaxpr = hashable_jaxpr.val
typecheck_jaxpr(jaxpr)
consts = [x.val for x in hashable_consts]
in_avals = [v.aval for v in jaxpr.in_binders[len(consts):]]
c = xc.XlaBuilder('xla_call')
xla_consts = _xla_consts(c, consts)
xla_params = _xla_params(c, in_avals)
outs = jaxpr_subcomp(c, jaxpr, xla_consts + xla_params)
out = xops.Tuple(c, outs)
compiled = xb.get_backend(None).compile(
xc._xla.mlir.xla_computation_to_mlir_module(c.build(out)))
return partial(execute_compiled, compiled, [v.aval for v in jaxpr.outs])
def _xla_consts(c: xe.XlaBuilder, consts: list[Any]) -> list[xe.XlaOp]:
unique_consts = {id(cnst): cnst for cnst in consts}
xla_consts = {
id_: xops.ConstantLiteral(c, cnst) for id_, cnst in unique_consts.items()}
return [xla_consts[id(cnst)] for cnst in consts]
def _xla_params(c: xe.XlaBuilder, avals_in: list[ShapedArray]) -> list[xe.XlaOp]:
return [xops.Parameter(c, i, _xla_shape(a)) for i, a in enumerate(avals_in)]
def _xla_shape(aval: ShapedArray) -> xe.Shape:
return xc.Shape.array_shape(xc.dtype_to_etype(aval.dtype), aval.shape)
```
The main action is in `xla_callable`, which compiles a jaxpr into an XLA HLO
program using `jaxpr_subcomp`, then returns a callable which executes the
compiled program:
```{code-cell}
def jaxpr_subcomp(c: xe.XlaBuilder, jaxpr: Jaxpr, args: list[xe.XlaOp]
) -> list[xe.XlaOp]:
env: dict[Var, xe.XlaOp] = {}
def read(x: Atom) -> xe.XlaOp:
return env[x] if type(x) is Var else xops.Constant(c, np.asarray(x.val))
def write(v: Var, val: xe.XlaOp) -> None:
env[v] = val
map(write, jaxpr.in_binders, args)
for eqn in jaxpr.eqns:
in_avals = [x.aval for x in eqn.inputs]
in_vals = map(read, eqn.inputs)
rule = xla_translations[eqn.primitive]
out_vals = rule(c, in_avals, in_vals, **eqn.params)
map(write, eqn.out_binders, out_vals)
return map(read, jaxpr.outs)
def execute_compiled(compiled, out_avals, *args):
input_bufs = [input_handlers[type(x)](x) for x in args]
out_bufs = compiled.execute(input_bufs)
return [handle_result(aval, buf) for aval, buf in zip(out_avals, out_bufs)]
default_input_handler = xb.get_backend(None).buffer_from_pyval
input_handlers = {ty: default_input_handler for ty in
[bool, int, float, np.ndarray, np.float64, np.float32]}
def handle_result(aval: ShapedArray, buf):
del aval # Unused for now
return np.asarray(buf)
xla_translations = {}
```
Notice that `jaxpr_subcomp` has the structure of a simple interpreter. That's
a common pattern: the way we process jaxprs is usually with an interpreter.
And as with any interpreter, we need an interpretation rule for each
primitive:
```{code-cell}
def direct_translation(op, c, in_avals, in_vals):
del c, in_avals
return [op(*in_vals)]
xla_translations[add_p] = partial(direct_translation, xops.Add)
xla_translations[mul_p] = partial(direct_translation, xops.Mul)
xla_translations[neg_p] = partial(direct_translation, xops.Neg)
xla_translations[sin_p] = partial(direct_translation, xops.Sin)
xla_translations[cos_p] = partial(direct_translation, xops.Cos)
xla_translations[greater_p] = partial(direct_translation, xops.Gt)
xla_translations[less_p] = partial(direct_translation, xops.Lt)
def reduce_sum_translation(c, in_avals, in_vals, *, axis):
(x_aval,), (x,) = in_avals, in_vals
zero = xops.ConstantLiteral(c, np.array(0, x_aval.dtype))
subc = xc.XlaBuilder('add')
shape = _xla_shape(ShapedArray((), x_aval.dtype))
xops.Add(xops.Parameter(subc, 0, shape), xops.Parameter(subc, 1, shape))
return [xops.Reduce(c, [x], [zero], subc.build(), axis)]
xla_translations[reduce_sum_p] = reduce_sum_translation
def broadcast_translation(c, in_avals, in_vals, *, shape, axes):
x, = in_vals
dims_complement = [i for i in range(len(shape)) if i not in axes]
return [xops.BroadcastInDim(x, shape, dims_complement)]
xla_translations[broadcast_p] = broadcast_translation
```
With that, we can now use `jit` to stage out, compile, and execute programs
with XLA!
```{code-cell}
@jit
def f(x, y):
print('tracing!')
return sin(x) * cos(y)
```
```{code-cell}
z = f(3., 4.) # 'tracing!' prints the first time
print(z)
```
```{code-cell}
z = f(4., 5.) # 'tracing!' doesn't print, compilation cache hit!
print(z)
```
```{code-cell}
@jit
def f(x):
return reduce_sum(x, axis=0)
print(f(np.array([1., 2., 3.])))
```
```{code-cell}
def f(x):
y = sin(x) * 2.
z = - y + x
return z
def deriv(f):
return lambda x: jvp(f, (x,), (1.,))[1]
print( deriv(deriv(f))(3.))
print(jit(deriv(deriv(f)))(3.))
```
Instead of implementing `jit` to first trace to a jaxpr and then to lower the
jaxpr to XLA HLO, it might appear that we could have skipped the jaxpr step
and just lowered to HLO while tracing. That is, perhaps we could have instead
implemented `jit` with a `Trace` and `Tracer` that appended to the XLA HLO
graph incrementally on each primitive bind. That's correct for now, but won't
be possible when we introduce compiled SPMD computations because there we must
know the number of replicas needed before compiling the program.
+++
We haven't yet defined any transformation rules for `xla_call_p` other than
its evaluation rule. That is, we can't yet do `vmap`-of-`jit` or
`jvp`-of-`jit` or even `jit`-of`-jit`. Instead `jit` has to be at the "top
level." Let's fix that!
```{code-cell}
def xla_call_jvp_rule(primals, tangents, *, jaxpr, num_consts):
del num_consts # Unused
new_jaxpr, new_consts = jvp_jaxpr(jaxpr)
outs = bind(xla_call_p, *new_consts, *primals, *tangents, jaxpr=new_jaxpr,
num_consts=len(new_consts))
n = len(outs) // 2
primals_out, tangents_out = outs[:n], outs[n:]
return primals_out, tangents_out
jvp_rules[xla_call_p] = xla_call_jvp_rule
@lru_cache()
def jvp_jaxpr(jaxpr: Jaxpr) -> tuple[Jaxpr, list[Any]]:
def jvp_traceable(*primals_and_tangents):
n = len(primals_and_tangents) // 2
primals, tangents = primals_and_tangents[:n], primals_and_tangents[n:]
return jvp(jaxpr_as_fun(jaxpr), primals, tangents)
in_avals = [v.aval for v in jaxpr.in_binders]
new_jaxpr, new_consts, _ = make_jaxpr(jvp_traceable, *in_avals, *in_avals)
return new_jaxpr, new_consts
```
```{code-cell}
def xla_call_vmap_rule(axis_size, vals_in, dims_in, *, jaxpr, num_consts):
del num_consts # Unused
new_jaxpr, new_consts = vmap_jaxpr(jaxpr, axis_size, tuple(dims_in))
outs = bind(xla_call_p, *new_consts, *vals_in, jaxpr=new_jaxpr,
num_consts=len(new_consts))
return outs, [0] * len(outs)
vmap_rules[xla_call_p] = xla_call_vmap_rule
@lru_cache()
def vmap_jaxpr(jaxpr: Jaxpr, axis_size: int, bdims_in: tuple[BatchAxis, ...]
) -> tuple[Jaxpr, list[Any]]:
vmap_traceable = vmap(jaxpr_as_fun(jaxpr), tuple(bdims_in))
in_avals = [unmapped_aval(axis_size, d, v.aval)
for v, d in zip(jaxpr.in_binders, bdims_in)]
new_jaxpr, new_consts, _ = make_jaxpr(vmap_traceable, *in_avals)
return new_jaxpr, new_consts
def unmapped_aval(axis_size: int, batch_dim: BatchAxis, aval: ShapedArray
) -> ShapedArray:
if batch_dim is not_mapped:
return aval
else:
shape = list(aval.shape)
shape.insert(batch_dim, axis_size)
return ShapedArray(tuple(shape), aval.dtype)
```
```{code-cell}
def xla_call_abstract_eval_rule(*in_types, jaxpr, num_consts):
del num_consts # Unused
jaxpr_type = typecheck_jaxpr(jaxpr)
if not all(t1 == t2 for t1, t2 in zip(jaxpr_type.in_types, in_types)):
raise TypeError
return jaxpr_type.out_types
abstract_eval_rules[xla_call_p] = xla_call_abstract_eval_rule
def xla_call_translation(c, in_avals, in_vals, *, jaxpr, num_consts):
del num_consts # Only used at top-level.
# Calling jaxpr_subcomp directly would inline. We generate a Call HLO instead.
subc = xc.XlaBuilder('inner xla_call')
xla_params = _xla_params(subc, in_avals)
outs = jaxpr_subcomp(subc, jaxpr, xla_params)
subc = subc.build(xops.Tuple(subc, outs))
return destructure_tuple(c, xops.Call(c, subc, in_vals))
xla_translations[xla_call_p] = xla_call_translation
def destructure_tuple(c, tup):
num_elements = len(c.get_shape(tup).tuple_shapes())
return [xops.GetTupleElement(tup, i) for i in range(num_elements)]
```
```{code-cell}
@jit
def f(x):
print('tracing!')
y = sin(x) * 2.
z = - y + x
return z
x, xdot = 3., 1.
y, ydot = jvp(f, (x,), (xdot,))
print(y)
print(ydot)
```
```{code-cell}
y, ydot = jvp(f, (x,), (xdot,)) # 'tracing!' not printed
```
```{code-cell}
ys = vmap(f, (0,))(np.arange(3.))
print(ys)
```
One piece missing is device memory persistence for arrays. That is, we've
defined `handle_result` to transfer results back to CPU memory as NumPy
arrays, but it's often preferable to avoid transferring results just to
transfer them back for the next operation. We can do that by introducing an
`Array` class, which can wrap XLA buffers and otherwise duck-type
`numpy.ndarray`s:
```{code-cell}
def handle_result(aval: ShapedArray, buf): # noqa: F811
return Array(aval, buf)
class Array:
buf: Any
aval: ShapedArray
def __init__(self, aval, buf):
self.aval = aval
self.buf = buf
dtype = property(lambda self: self.aval.dtype)
shape = property(lambda self: self.aval.shape)
ndim = property(lambda self: self.aval.ndim)
def __array__(self): return np.asarray(self.buf)
def __repr__(self): return repr(np.asarray(self.buf))
def __str__(self): return str(np.asarray(self.buf))
_neg = staticmethod(neg)
_add = staticmethod(add)
_radd = staticmethod(add)
_mul = staticmethod(mul)
_rmul = staticmethod(mul)
_gt = staticmethod(greater)
_lt = staticmethod(less)
input_handlers[Array] = lambda x: x.buf
jax_types.add(Array)
```
```{code-cell}
@jit
def f(x):
y = sin(x) * 2.
z = - y + x
return z
x, xdot = 3., 1.
y, ydot = jvp(f, (x,), (xdot,))
print(y)
print(ydot)
```
```{code-cell}
:tags: [hide-input]
def pprint_xla_call(names: defaultdict[Var, str], eqn: JaxprEqn) -> PPrint:
lhs = pp(' '.join(var_str(names, v) for v in eqn.out_binders))
params_without_jaxpr = {k:v for k, v in eqn.params.items() if k != 'jaxpr'}
rhs = (pp(eqn.primitive.name) >> pp_params(params_without_jaxpr) >>
pp(' '.join(names[x] if isinstance(x, Var) else str(x.val)
for x in eqn.inputs)))
return vcat([lhs >> pp(' = ') >> rhs,
pp_jaxpr(eqn.params['jaxpr']).indent(2)])
pp_rules[xla_call_p] = pprint_xla_call
```
## Part 4: `linearize` and `vjp` (and `grad`!)
The `linearize` and `vjp` autodiff functions are built on `jvp`, but involve
jaxprs as well. That's because both involve staging out, or delaying,
computation.
+++
### `linearize`
In the case of `linearize`, we want to stage out the linear part of a `jvp`
computation. That is, in terms of
[Haskell-like type signatures](https://wiki.haskell.org/Type_signature),
if we have `jvp : (a -> b) -> (a, T a) -> (b, T b)`,
then we write `linearize : (a -> b) -> a -> (b, T a -o T b)`, using `T a` to
mean "the tangent type of `a`" and using the "lollipop" `-o` rather than the
arrow `->` to indicate a _linear_ function. We define the semantics of
`linearize` in terms of `jvp` too:
```python
y, f_lin = linearize(f, x)
y_dot = f_lin(x_dot)
```
gives the same result for `(y, y_dot)` as
```
y, y_dot = jvp(f, (x,), (x_dot,))
```
where the application of `f_lin` does not redo any of the linearization work.
We'll represent the delayed linear part `f_lin : T a -o T b` as a jaxpr.
Tangentially, now that we have linear arrows `-o`, we can provide a slightly
more informative type for `jvp`:
```
jvp : (a -> b) -> (UnrestrictedUse a, T a) -o (UnrestrictedUse b, T b)
```
Here we're writing `UnrestrictedUse` just to indicate that we have a special
pair where the first element can be used in an unrestricted (nonlinear) way.
In conjunction with the linear arrow, this notation is just meant to express
that the function `jvp f` uses its first input in a nonlinear way but its
second input in a linear way, producing a corresponding nonlinear output
(which can be used in a nonlinear way) paired with a linear output. This more
refined type signature encodes the data dependencies in `jvp f`, which are
useful for partial evaluation.
To build the `f_lin` jaxpr from a JVP, we need to perform partial evaluation:
we evaluate all the primal values as we trace, but stage the tangent
computations into a jaxpr. This is our second way to build jaxprs. But where
`make_jaxpr` and its underlying `JaxprTrace`/`JaxprTracer` interpreters aim
to stage out every primitive bind, this second approach stages out only those
primitive binds with a data dependence on tangent inputs.
First, some utilities:
```{code-cell}
def split_half(lst: list[Any]) -> tuple[list[Any], list[Any]]:
assert not len(lst) % 2
return split_list(lst, len(lst) // 2)
def merge_lists(which: list[bool], l1: list[Any], l2: list[Any]) -> list[Any]:
l1, l2 = iter(l1), iter(l2)
out = [next(l2) if b else next(l1) for b in which]
assert next(l1, None) is next(l2, None) is None
return out
```
Next, we'll write `linearize` by combining `jvp` together with a general
partial evaluation transformation, to be added next:
```{code-cell}
def linearize_flat(f, *primals_in):
pvals_in = ([PartialVal.known(x) for x in primals_in] +
[PartialVal.unknown(vspace(get_aval(x))) for x in primals_in])
def f_jvp(*primals_tangents_in):
primals_out, tangents_out = jvp(f, *split_half(primals_tangents_in))
return [*primals_out, *tangents_out]
jaxpr, pvals_out, consts = partial_eval_flat(f_jvp, pvals_in)
primal_pvals, _ = split_half(pvals_out)
assert all(pval.is_known for pval in primal_pvals)
primals_out = [pval.const for pval in primal_pvals]
f_lin = lambda *tangents: eval_jaxpr(jaxpr, [*consts, *tangents])
return primals_out, f_lin
def linearize(f, *primals_in):
primals_in_flat, in_tree = tree_flatten(primals_in)
f, out_tree = flatten_fun(f, in_tree)
primals_out_flat, f_lin_flat = linearize_flat(f, *primals_in_flat)
primals_out = tree_unflatten(out_tree(), primals_out_flat)
def f_lin(*tangents_in):
tangents_in_flat, in_tree2 = tree_flatten(tangents_in)
if in_tree != in_tree2: raise TypeError
tangents_out_flat = f_lin_flat(*tangents_in_flat)
return tree_unflatten(out_tree(), tangents_out_flat)
return primals_out, f_lin
def vspace(aval: ShapedArray) -> ShapedArray:
return raise_to_shaped(aval) # TODO handle integers?
```
Now we turn to the general partial evaluation transformation. The goal is to
accept a Python callable and a list of inputs, some known and some unknown,
and to produce (1) all the outputs which can be computed from the known
inputs, together with (2) a jaxpr representing the part of the Python
callable's computation which can only be performed after the remaining inputs
are known.
This transformation is tricky to summarize in a type signature. If we
assume the input function's type signature is `(a1, a2) -> (b1, b2)`, where
`a1` and `a2` represent the known and unknown inputs, respectively, and where
`b1` only has a data dependency on `a1` while `b2` has some data dependency on
`a2`, then we might write
```
partial_eval : ((a1, a2) -> (b1, b2)) -> a1 -> exists r. (b1, r, (r, a2) -> b2)
```
In words, given values for the inputs of type `a1`, `partial_eval` produces
the outputs of type `b1` along with "residual" values of
existentially-quantified type `r` representing the intermediates required to
complete the computation in the second stage. It also produces a function of
type `(r, a2) -> b2` which accepts the residual values as well as the
remaining inputs and produces the remaining outputs.
We like to think of partial evaluation as "unzipping" one computation into
two. For example, consider this jaxpr:
```
{ lambda a:float64[] .
let b:float64[] = sin a
c:float64[] = neg b
in ( c ) }
```
A jaxpr for the JVP would look like:
```
{ lambda a:float64[] b:float64[] .
let c:float64[] = sin a
d:float64[] = cos a
e:float64[] = mul d b
f:float64[] = neg c
g:float64[] = neg e
in ( f, g ) }
```
If we imagine applying partial evaluation to this jaxpr with the first input
known and the second unknown, we end up 'unzipping' the JVP jaxpr into primal
and tangent jaxprs:
```
{ lambda a:float64[] .
let c:float64[] = sin a
d:float64[] = cos a
f:float64[] = neg c
in ( f, d ) }
```
```
{ lambda d:float64[] b:float64[] .
let e:float64[] = mul d b
g:float64[] = neg e
in ( g ) }
```
This second jaxpr represents the linear computation that we want from
`linearize`.
However, unlike in this jaxpr example, we want the computation on known values
to occur while evaluating the input Python callable. That is, rather than
forming a jaxpr for the entire function `(a1, a2) -> (b1, b2)`, staging all
operations out of Python first before sorting out what can be evaluated now
and what must be delayed, we want only to form a jaxpr for those operations
that _must_ be delayed due to a dependence on unknown inputs. In the context
of automatic differentiation, this is the feature that ultimately enables us
to handle functions like `grad(lambda x: x**2 if x > 0 else 0.)`. Python
control flow works because partial evaluation keeps the primal computation in
Python. As a consequence, our `Trace` and `Tracer` subclasses must on the fly
sort out what can be evaluated and what must be staged out into a jaxpr.
First, we start with a `PartialVal` class, which represents a value that can
be either known or unknown:
```{code-cell}
class PartialVal(NamedTuple):
aval: ShapedArray
const: Optional[Any]
@classmethod
def known(cls, val: Any):
return PartialVal(get_aval(val), val)
@classmethod
def unknown(cls, aval: ShapedArray):
return PartialVal(aval, None)
is_known = property(lambda self: self.const is not None)
is_unknown = property(lambda self: self.const is None)
```
Partial evaluation will take a list of `PartialVal`s representing inputs, and
return a list of `PartialVal` outputs along with a jaxpr representing the
delayed computation:
```{code-cell}
def partial_eval_flat(f: Callable, pvals_in: list[PartialVal]
) -> tuple[Jaxpr, list[PartialVal], list[Any]]:
with new_main(PartialEvalTrace) as main:
trace = PartialEvalTrace(main)
tracers_in = [trace.new_arg(pval) for pval in pvals_in]
outs = f(*tracers_in)
tracers_out = [full_raise(trace, out) for out in outs]
pvals_out = [t.pval for t in tracers_out]
unk_tracers_in = [t for t in tracers_in if t.pval.is_unknown]
unk_tracers_out = [t for t in tracers_out if t.pval.is_unknown]
jaxpr, consts = tracers_to_jaxpr(unk_tracers_in, unk_tracers_out)
return jaxpr, pvals_out, consts
```
Next we need to implement `PartialEvalTrace` and its `PartialEvalTracer`. This
interpreter will build a jaxpr on the fly while tracking data dependencies. To
do so, it builds a bipartite directed acyclic graph (DAG) between
`PartialEvalTracer` nodes, representing staged-out values, and `JaxprRecipe`
nodes, representing formulas for how to compute some values from others. One
kind of recipe is a `JaxprEqnRecipe`, corresponding to a `JaxprEqn`'s
primitive application, but we also have recipe types for constants and lambda
binders:
```{code-cell}
from weakref import ref, ReferenceType
class LambdaBindingRecipe(NamedTuple):
pass
class ConstRecipe(NamedTuple):
val: Any
class JaxprEqnRecipe(NamedTuple):
prim: Primitive
tracers_in: list['PartialEvalTracer']
params: dict[str, Any]
avals_out: list[ShapedArray]
tracer_refs_out: list['ReferenceType[PartialEvalTracer]']
JaxprRecipe = Union[LambdaBindingRecipe, ConstRecipe, JaxprEqnRecipe]
```
```{code-cell}
class PartialEvalTracer(Tracer):
pval: PartialVal
recipe: Optional[JaxprRecipe]
def __init__(self, trace, pval, recipe):
self._trace = trace
self.pval = pval
self.recipe = recipe
aval = property(lambda self: self.pval.aval)
def full_lower(self):
if self.pval.is_known:
return full_lower(self.pval.const)
return self
```
The `PartialEvalTrace` contains the logic for constructing the graph of
`JaxprRecipe`s and `PartialEvalTracer`s. Each argument corresponds to a
`LambdaBindingRecipe` leaf node, and each constant is a `ConstRecipe` leaf
node holding a reference to the constant. All other tracers and recipes come
from `process_primitive`, which forms tracers with `JaxprEqnRecipe`s.
For most primitives, the `process_primitive` logic is straightforward: if all
inputs are known then we can bind the primitive on the known values
(evaluating it in Python) and avoid forming tracers corresponding to the
output. If instead any input is unknown then we instead stage out into a
`JaxprEqnRecipe` representing the primitive application. To build the tracers
representing unknown outputs, we need avals, which we get from the abstract
eval rules. (Notice that tracers reference `JaxprEqnRecipe`s, and
`JaxprEqnRecipe`s reference tracers; we avoid circular garbage by using
weakrefs.)
That `process_primitive` logic applies to most primitives, but `xla_call_p`
requires recursive treatment. So we special-case its rule in a
`partial_eval_rules` dict.
```{code-cell}
class PartialEvalTrace(Trace):
def new_arg(self, pval: PartialVal) -> Any:
return PartialEvalTracer(self, pval, LambdaBindingRecipe())
def lift(self, val: Any) -> PartialEvalTracer:
return PartialEvalTracer(self, PartialVal.known(val), None)
pure = lift
def instantiate_const(self, tracer: PartialEvalTracer) -> PartialEvalTracer:
if tracer.pval.is_unknown:
return tracer
else:
pval = PartialVal.unknown(raise_to_shaped(tracer.aval))
return PartialEvalTracer(self, pval, ConstRecipe(tracer.pval.const))
def process_primitive(self, primitive, tracers, params):
if all(t.pval.is_known for t in tracers):
return bind(primitive, *map(full_lower, tracers), **params)
rule = partial_eval_rules.get(primitive)
if rule: return rule(self, tracers, **params)
tracers_in = [self.instantiate_const(t) for t in tracers]
avals_in = [t.aval for t in tracers_in]
avals_out = abstract_eval_rules[primitive](*avals_in, **params)
tracers_out = [PartialEvalTracer(self, PartialVal.unknown(aval), None)
for aval in avals_out]
eqn = JaxprEqnRecipe(primitive, tracers_in, params, avals_out,
map(ref, tracers_out))
for t in tracers_out: t.recipe = eqn
return tracers_out
partial_eval_rules = {}
```
Now that we can build graph representations of jaxprs with `PartialEvalTrace`,
we need a mechanism to convert the graph representation to a standard jaxpr.
The jaxpr corresponds to a topological sort of the graph.
```{code-cell}
def tracers_to_jaxpr(tracers_in: list[PartialEvalTracer],
tracers_out: list[PartialEvalTracer]):
tracer_to_var: dict[int, Var] = {id(t): Var(raise_to_shaped(t.aval))
for t in tracers_in}
constvar_to_val: dict[int, Any] = {}
constid_to_var: dict[int, Var] = {}
processed_eqns: set[int] = set()
eqns: list[JaxprEqn] = []
for t in toposort(tracers_out, tracer_parents):
if isinstance(t.recipe, LambdaBindingRecipe):
assert id(t) in set(map(id, tracers_in))
elif isinstance(t.recipe, ConstRecipe):
val = t.recipe.val
var = constid_to_var.get(id(val))
if var is None:
aval = raise_to_shaped(get_aval(val))
var = constid_to_var[id(val)] = Var(aval)
constvar_to_val[var] = val
tracer_to_var[id(t)] = var
elif isinstance(t.recipe, JaxprEqnRecipe):
if id(t.recipe) not in processed_eqns:
eqns.append(recipe_to_eqn(tracer_to_var, t.recipe))
processed_eqns.add(id(t.recipe))
else:
raise TypeError(t.recipe)
constvars, constvals = unzip2(constvar_to_val.items())
in_binders = constvars + [tracer_to_var[id(t)] for t in tracers_in]
out_vars = [tracer_to_var[id(t)] for t in tracers_out]
jaxpr = Jaxpr(in_binders, eqns, out_vars)
typecheck_jaxpr(jaxpr)
return jaxpr, constvals
def recipe_to_eqn(tracer_to_var: dict[int, Var], recipe: JaxprEqnRecipe
) -> JaxprEqn:
inputs = [tracer_to_var[id(t)] for t in recipe.tracers_in]
out_binders = [Var(aval) for aval in recipe.avals_out]
for t_ref, var in zip(recipe.tracer_refs_out, out_binders):
if t_ref() is not None: tracer_to_var[id(t_ref())] = var
return JaxprEqn(recipe.prim, inputs, recipe.params, out_binders)
def tracer_parents(t: PartialEvalTracer) -> list[PartialEvalTracer]:
return t.recipe.tracers_in if isinstance(t.recipe, JaxprEqnRecipe) else []
```
```{code-cell}
:tags: [hide-input]
def toposort(out_nodes: list[Any], parents: Callable[[Any], list[Any]]):
if not out_nodes: return []
out_nodes = remove_duplicates(out_nodes)
child_counts = {}
stack = list(out_nodes)
while stack:
node = stack.pop()
if id(node) in child_counts:
child_counts[id(node)] += 1
else:
child_counts[id(node)] = 1
stack.extend(parents(node))
for node in out_nodes:
child_counts[id(node)] -= 1
sorted_nodes = []
childless_nodes = [node for node in out_nodes if not child_counts[id(node)]]
while childless_nodes:
node = childless_nodes.pop()
sorted_nodes.append(node)
for parent in parents(node):
if child_counts[id(parent)] == 1:
childless_nodes.append(parent)
else:
child_counts[id(parent)] -= 1
sorted_nodes = sorted_nodes[::-1]
check_toposort(sorted_nodes, parents)
return sorted_nodes
def remove_duplicates(lst):
seen = set()
return [x for x in lst if id(x) not in seen and not seen.add(id(x))]
def check_toposort(nodes: list[Any], parents: Callable[[Any], list[Any]]):
seen = set()
for node in nodes:
assert all(id(parent) in seen for parent in parents(node))
seen.add(id(node))
```
Now we can linearize!
```{code-cell}
y, sin_lin = linearize(sin, 3.)
print(y, sin(3.))
print(sin_lin(1.), cos(3.))
```
To handle `linearize`-of-`jit`, we still need to write a partial evaluation
rule for `xla_call_p`. Other than tracer bookkeeping, the main task is to
perform partial evaluation of a jaxpr, 'unzipping' it into two jaxprs.
There are actually two rules to write: one for trace-time partial evaluation,
which we'll call `xla_call_partial_eval`, and one for partial evaluation of
jaxprs, which we'll call `xla_call_peval_eqn`.
```{code-cell}
def xla_call_partial_eval(trace, tracers, *, jaxpr, num_consts):
del num_consts # Unused
in_unknowns = [not t.pval.is_known for t in tracers]
jaxpr1, jaxpr2, out_unknowns, num_res = partial_eval_jaxpr(jaxpr, in_unknowns)
known_tracers, unknown_tracers = partition_list(in_unknowns, tracers)
known_vals = [t.pval.const for t in known_tracers]
outs1_res = bind(xla_call_p, *known_vals, jaxpr=jaxpr1, num_consts=0)
outs1, res = split_list(outs1_res, len(jaxpr1.outs) - num_res)
res_tracers = [trace.instantiate_const(full_raise(trace, x)) for x in res]
outs2 = [PartialEvalTracer(trace, PartialVal.unknown(v.aval), None)
for v in jaxpr2.outs]
eqn = JaxprEqnRecipe(xla_call_p, res_tracers + unknown_tracers,
dict(jaxpr=jaxpr2, num_consts=0),
[v.aval for v in jaxpr2.outs], map(ref, outs2))
for t in outs2: t.recipe = eqn
return merge_lists(out_unknowns, outs1, outs2)
partial_eval_rules[xla_call_p] = xla_call_partial_eval
def partial_eval_jaxpr(jaxpr: Jaxpr, in_unknowns: list[bool],
instantiate: Optional[list[bool]] = None,
) -> tuple[Jaxpr, Jaxpr, list[bool], int]:
env: dict[Var, bool] = {}
residuals: set[Var] = set()
def read(x: Atom) -> bool:
return type(x) is Var and env[x]
def write(unk: bool, v: Var) -> None:
env[v] = unk
def new_res(x: Atom) -> Atom:
if type(x) is Var: residuals.add(x)
return x
eqns1, eqns2 = [], []
map(write, in_unknowns, jaxpr.in_binders)
for eqn in jaxpr.eqns:
unks_in = map(read, eqn.inputs)
rule = partial_eval_jaxpr_rules.get(eqn.primitive)
if rule:
eqn1, eqn2, unks_out, res = rule(unks_in, eqn)
eqns1.append(eqn1); eqns2.append(eqn2); residuals.update(res)
map(write, unks_out, eqn.out_binders)
elif any(unks_in):
inputs = [v if unk else new_res(v) for unk, v in zip(unks_in, eqn.inputs)]
eqns2.append(JaxprEqn(eqn.primitive, inputs, eqn.params, eqn.out_binders))
map(partial(write, True), eqn.out_binders)
else:
eqns1.append(eqn)
map(partial(write, False), eqn.out_binders)
out_unknowns = map(read, jaxpr.outs)
if instantiate is not None:
for v, uk, inst in zip(jaxpr.outs, out_unknowns, instantiate):
if inst and not uk: new_res(v)
out_unknowns = map(op.or_, out_unknowns, instantiate)
residuals, num_res = list(residuals), len(residuals)
assert all(type(v) is Var for v in residuals), residuals
ins1, ins2 = partition_list(in_unknowns, jaxpr.in_binders)
outs1, outs2 = partition_list(out_unknowns, jaxpr.outs)
jaxpr1 = Jaxpr(ins1, eqns1, outs1 + residuals)
jaxpr2 = Jaxpr(residuals + ins2, eqns2, outs2)
typecheck_partial_eval_jaxpr(jaxpr, in_unknowns, out_unknowns, jaxpr1, jaxpr2)
return jaxpr1, jaxpr2, out_unknowns, num_res
def typecheck_partial_eval_jaxpr(jaxpr, unks_in, unks_out, jaxpr1, jaxpr2):
jaxprty = typecheck_jaxpr(jaxpr) # (a1, a2) -> (b1, b2 )
jaxpr1ty = typecheck_jaxpr(jaxpr1) # a1 -> (b1, res)
jaxpr2ty = typecheck_jaxpr(jaxpr2) # (res, a2) -> b2
a1, a2 = partition_list(unks_in, jaxprty.in_types)
b1, b2 = partition_list(unks_out, jaxprty.out_types)
b1_, res = split_list(jaxpr1ty.out_types, len(b1))
res_, a2_ = split_list(jaxpr2ty.in_types, len(res))
b2_ = jaxpr2ty.out_types
if jaxpr1ty.in_types != a1: raise TypeError
if jaxpr2ty.out_types != b2: raise TypeError
if b1 != b1_: raise TypeError
if res != res_: raise TypeError
if a2 != a2_: raise TypeError
if b2 != b2_: raise TypeError
partial_eval_jaxpr_rules = {}
def xla_call_peval_eqn(unks_in: list[bool], eqn: JaxprEqn,
) -> tuple[JaxprEqn, JaxprEqn, list[bool], list[Var]]:
jaxpr = eqn.params['jaxpr']
jaxpr1, jaxpr2, unks_out, num_res = partial_eval_jaxpr(jaxpr, unks_in)
ins1, ins2 = partition_list(unks_in, eqn.inputs)
out_binders1, out_binders2 = partition_list(unks_out, eqn.out_binders)
residuals = [Var(v.aval) for v in jaxpr2.in_binders[:num_res]]
eqn1 = JaxprEqn(xla_call_p, ins1, dict(jaxpr=jaxpr1, num_consts=0),
out_binders1 + residuals)
eqn2 = JaxprEqn(xla_call_p, residuals + ins2,
dict(jaxpr=jaxpr2, num_consts=0), out_binders2)
return eqn1, eqn2, unks_out, residuals
partial_eval_jaxpr_rules[xla_call_p] = xla_call_peval_eqn
```
With that, we can compose `linearize` and `jit` however we like:
```{code-cell}
@jit
def f(x):
y = sin(x) * 2.
z = - y + x
return z
y, f_lin = linearize(f, 3.)
y_dot = f_lin(1.)
print(y, y_dot)
```
```{code-cell}
@jit
def f(x):
y = sin(x) * 2.
z = g(x, y)
return z
@jit
def g(x, y):
return cos(x) + y
y, f_lin = linearize(f, 3.)
y_dot = f_lin(1.)
print(y, y_dot)
```
### `vjp` and `grad`
The `vjp` transformation works a lot like linearize. Its type signature is
analogous:
```
linearize : (a -> b) -> a -> (b, T a -o T b)
vjp : (a -> b) -> a -> (b, T b -o T a)
```
The only difference is that we transpose the linear part of the computation
before returning it, so that it goes from type `T a -o T b` to type `T b -o T
a`. That is, we'll implement `vjp` as, essentially,
```
def vjp(f, x):
y, f_lin = linearize(f, x)
f_vjp = lambda y_bar: transpose(f_lin)(y_bar)
return y, f_vjp
```
Since we have the linear computation as a jaxpr, not just a Python callable,
we can implement the transpose transformation as a jaxpr interpreter.
```{code-cell}
def vjp_flat(f, *primals_in):
pvals_in = ([PartialVal.known(x) for x in primals_in] +
[PartialVal.unknown(vspace(get_aval(x))) for x in primals_in])
primal_pvals_in, tangent_pvals_in = split_half(pvals_in)
def f_jvp(*primals_tangents_in):
primals_out, tangents_out = jvp(f, *split_half(primals_tangents_in))
return [*primals_out, *tangents_out]
jaxpr, pvals_out, consts = partial_eval_flat(f_jvp, pvals_in) # linearize
primal_pvals, _ = split_half(pvals_out)
assert all(pval.is_known for pval in primal_pvals)
primals_out = [pval.const for pval in primal_pvals]
transpose_inputs = consts + [UndefPrimal(p.aval) for p in tangent_pvals_in]
f_vjp = lambda *cts: eval_jaxpr_transposed(jaxpr, transpose_inputs, cts)
return primals_out, f_vjp
def vjp(f, *primals_in):
primals_in_flat, in_tree = tree_flatten(primals_in)
f, out_tree = flatten_fun(f, in_tree)
primals_out_flat, f_vjp_flat = vjp_flat(f, *primals_in_flat)
primals_out = tree_unflatten(out_tree(), primals_out_flat)
def f_vjp(*cotangents_out):
cotangents_out_flat, _ = tree_flatten(cotangents_out)
cotangents_in_flat = f_vjp_flat(*cotangents_out_flat)
return tree_unflatten(in_tree, cotangents_in_flat)
return primals_out, f_vjp
class UndefPrimal(NamedTuple):
aval: ShapedArray
register_pytree_node(UndefPrimal,
lambda u: (u.aval, ()),
lambda aval, _: UndefPrimal(aval))
```
We use `UndefPrimal` instances to indicate which arguments with respect to
which we want to transpose. These arise because in general, being explicit
about closed-over values, we want to transpose functions of type
`a -> b -o c` to functions of type `a -> c -o b`. Even more generally, the
inputs with respect to which the function is linear could be scattered through
the argument list. So we indicate the linear positions using `UndefPrimal`.
We register `UndefPrimal` as a pytree node because the pytree mechanism gives
a handy way to prune these placeholders out of argument lists.
Next, we can write `eval_jaxpr_transposed`, along with transpose rules for
all primitives which can be linear in at least one argument:
```{code-cell}
# NB: the analogous function in JAX is called 'backward_pass'
def eval_jaxpr_transposed(jaxpr: Jaxpr, args: list[Any], cotangents: list[Any]
) -> list[Any]:
primal_env: dict[Var, Any] = {}
ct_env: dict[Var, Any] = {}
def read_primal(x: Atom) -> Any:
return primal_env.get(x, UndefPrimal(x.aval)) if type(x) is Var else x.val
def write_primal(v: Var, val: Any) -> None:
if type(val) is not UndefPrimal:
primal_env[v] = val
def read_cotangent(v: Var) -> Any:
return ct_env.pop(v, np.zeros(v.aval.shape, v.aval.dtype))
def write_cotangent(x: Atom, val: Any):
if type(x) is Var and val is not None:
ct_env[x] = add(ct_env[x], val) if x in ct_env else val
map(write_primal, jaxpr.in_binders, args)
map(write_cotangent, jaxpr.outs, cotangents)
for eqn in jaxpr.eqns[::-1]:
primals_in = map(read_primal, eqn.inputs)
cts_in = map(read_cotangent, eqn.out_binders)
rule = transpose_rules[eqn.primitive]
cts_out = rule(cts_in, *primals_in, **eqn.params)
map(write_cotangent, eqn.inputs, cts_out)
return [read_cotangent(v) for v, x in zip(jaxpr.in_binders, args)
if type(x) is UndefPrimal]
transpose_rules = {}
```
```{code-cell}
def mul_transpose_rule(cts, x, y):
z_bar, = cts
assert (type(x) is UndefPrimal) ^ (type(y) is UndefPrimal)
return [mul(z_bar, y), None] if type(x) is UndefPrimal else [None, mul(x, z_bar)]
transpose_rules[mul_p] = mul_transpose_rule
def neg_transpose_rule(cts, x):
ybar, = cts
assert type(x) is UndefPrimal
return [neg(ybar)]
transpose_rules[neg_p] = neg_transpose_rule
def add_transpose_rule(cts, x, y):
z_bar, = cts
return [z_bar, z_bar]
transpose_rules[add_p] = add_transpose_rule
def reduce_sum_transpose_rule(cts, x, *, axis):
y_bar, = cts
return [broadcast(y_bar, x.aval.shape, axis)]
transpose_rules[reduce_sum_p] = reduce_sum_transpose_rule
def xla_call_transpose_rule(cts, *invals, jaxpr, num_consts):
del num_consts # Unused
undef_primals = [type(x) is UndefPrimal for x in invals]
transposed_jaxpr, new_consts = transpose_jaxpr(jaxpr, tuple(undef_primals))
residuals, _ = partition_list(undef_primals, invals)
outs = bind(xla_call_p, *new_consts, *residuals, *cts,
jaxpr=transposed_jaxpr, num_consts=len(new_consts))
outs = iter(outs)
return [next(outs) if undef else None for undef in undef_primals]
transpose_rules[xla_call_p] = xla_call_transpose_rule
@lru_cache()
def transpose_jaxpr(jaxpr: Jaxpr, undef_primals: tuple[bool, ...]
) -> tuple[Jaxpr, list[Any]]:
avals_in, avals_out = typecheck_jaxpr(jaxpr)
traceable = partial(eval_jaxpr_transposed, jaxpr)
args = [UndefPrimal(a) if u else a for a, u in zip(avals_in, undef_primals)]
trans_jaxpr, consts, _ = make_jaxpr(traceable, tuple(args), tuple(avals_out))
typecheck_jaxpr(trans_jaxpr)
return trans_jaxpr, consts
```
Now that we can linearize and transpose, we can finally write `grad`:
```{code-cell}
def grad(f):
def gradfun(x, *xs):
y, f_vjp = vjp(f, x, *xs)
if np.shape(y) != (): raise TypeError
x_bar, *_ = f_vjp(np.ones(np.shape(y), np.result_type(y)))
return x_bar
return gradfun
```
```{code-cell}
y, f_vjp = vjp(sin, 3.)
print(f_vjp(1.), cos(3.))
```
```{code-cell}
def f(x):
y = sin(x) * 2.
z = - y + x
return z
print(grad(f)(3.))
```
```{code-cell}
@jit
def f(x):
y = x * 2.
z = g(y)
return z
@jit
def g(x):
return cos(x) * 2.
print(grad(f)(3.))
```
Here's something of a compositionality stress test:
```{code-cell}
# from core_test.py fun_with_nested_calls_2
def foo(x):
@jit
def bar(y):
def baz(w):
q = jit(lambda x: y)(x)
q = q + jit(lambda: y)()
q = q + jit(lambda y: w + y)(y)
q = jit(lambda w: jit(sin)(x) * y)(1.0) + q
return q
p, t = jvp(baz, (x + 1.0,), (y,))
return t + (x * p)
return bar(x)
def assert_allclose(*vals):
for v1, v2 in zip(vals[:-1], vals[1:]):
np.testing.assert_allclose(v1, v2)
ans1 = f(3.)
ans2 = jit(f)(3.)
ans3, _ = jvp(f, (3.,), (5.,))
ans4, _ = jvp(jit(f), (3.,), (5.,))
assert_allclose(ans1, ans2, ans3, ans4)
deriv1 = grad(f)(3.)
deriv2 = grad(jit(f))(3.)
deriv3 = jit(grad(jit(f)))(3.)
_, deriv4 = jvp(f, (3.,), (1.,))
_, deriv5 = jvp(jit(f), (3.,), (1.,))
assert_allclose(deriv1, deriv2, deriv3, deriv4, deriv5)
hess1 = grad(grad(f))(3.)
hess2 = grad(grad(jit(f)))(3.)
hess3 = grad(jit(grad(f)))(3.)
hess4 = jit(grad(grad(f)))(3.)
_, hess5 = jvp(grad(f), (3.,), (1.,))
_, hess6 = jvp(jit(grad(f)), (3.,), (1.,))
_, hess7 = jvp(jit(grad(f)), (3.,), (1.,))
assert_allclose(hess1, hess2, hess3, hess4, hess5, hess6, hess7)
```
## Part 5: the control flow primitives `cond`
Next we'll add higher-order primitives for staged-out control flow. These
resemble `jit` from Part 3, another higher-order primitive, but differ in that
they are parameterized by multiple callables rather than just one.
+++
### Adding `cond`
We introduce a `cond` primitive to represent conditional application of one
function or another inside a jaxpr. We write the type of `cond` as
`Bool -> (a -> b) -> (a -> b) -> a -> b`. In words, `cond` takes a boolean
representing the predicate and two functions of equal types. Depending on the
value of the predicate, it applies one function or the other to its final
argument.
In Python, we represent it as a function which itself takes two functions as
arguments. As with `jit`, the first step is to call `make_jaxpr` on its
callable arguments to turn them into jaxprs:
```{code-cell}
def cond(pred, true_fn, false_fn, *operands):
avals_in = [raise_to_shaped(get_aval(x)) for x in operands]
true_jaxpr, true_consts, out_tree = make_jaxpr(true_fn, *avals_in)
false_jaxpr, false_consts, out_tree_ = make_jaxpr(false_fn, *avals_in)
if out_tree != out_tree_: raise TypeError
true_jaxpr, false_jaxpr = _join_jaxpr_consts(
true_jaxpr, false_jaxpr, len(true_consts), len(false_consts))
if typecheck_jaxpr(true_jaxpr) != typecheck_jaxpr(false_jaxpr):
raise TypeError
outs = bind_cond(pred, *true_consts, *false_consts, *operands,
true_jaxpr=true_jaxpr, false_jaxpr=false_jaxpr)
return tree_unflatten(out_tree, outs)
cond_p = Primitive('cond')
def _join_jaxpr_consts(jaxpr1: Jaxpr, jaxpr2: Jaxpr, n1: int, n2: int
) -> tuple[Jaxpr, Jaxpr]:
jaxpr1_type, jaxpr2_type = typecheck_jaxpr(jaxpr1), typecheck_jaxpr(jaxpr2)
assert jaxpr1_type.in_types[n1:] == jaxpr2_type.in_types[n2:]
consts1, rest1 = split_list(jaxpr1.in_binders, n1)
consts2, rest2 = split_list(jaxpr2.in_binders, n2)
new_jaxpr1 = Jaxpr(consts1 + consts2 + rest1, jaxpr1.eqns, jaxpr1.outs)
new_jaxpr2 = Jaxpr(consts1 + consts2 + rest2, jaxpr2.eqns, jaxpr2.outs)
return new_jaxpr1, new_jaxpr2
def bind_cond(pred, *args, true_jaxpr, false_jaxpr):
assert len(args) == len(true_jaxpr.in_binders) == len(false_jaxpr.in_binders)
return bind(cond_p, pred, *args, true_jaxpr=true_jaxpr, false_jaxpr=false_jaxpr)
```
We require `true_jaxpr` and `false_jaxpr` to have the same type, but because
they might close over different constants (and because jaxprs can only
represent closed terms, i.e. can't have free variables and are instead
closure-converted) we need to use the helper `_join_jaxpr_consts` to make
consistent the input binder lists of the two jaxprs. (To be more economical we
could try to identify pairs of constants with the same shapes, but instead we
just concatenate the lists of constants.)
Next we can turn to adding interpreter rules for `cond`. Its evaluation rule
is simple:
```{code-cell}
def cond_impl(pred, *operands, true_jaxpr, false_jaxpr):
if pred:
return eval_jaxpr(true_jaxpr, operands)
else:
return eval_jaxpr(false_jaxpr, operands)
impl_rules[cond_p] = cond_impl
```
```{code-cell}
out = cond(True, lambda: 3, lambda: 4)
print(out)
```
For its JVP and vmap rules, we only need to call the same `jvp_jaxpr` and
`vmap_jaxpr` utilities we created for `jit`, followed by another pass of
`_join_jaxpr_consts`:
```{code-cell}
def cond_jvp_rule(primals, tangents, *, true_jaxpr, false_jaxpr):
pred, *primals = primals
_ , *tangents = tangents
true_jaxpr , true_consts = jvp_jaxpr(true_jaxpr)
false_jaxpr, false_consts = jvp_jaxpr(false_jaxpr)
true_jaxpr, false_jaxpr = _join_jaxpr_consts(
true_jaxpr, false_jaxpr, len(true_consts), len(false_consts))
assert typecheck_jaxpr(true_jaxpr) == typecheck_jaxpr(false_jaxpr)
outs = bind_cond(pred, *true_consts, *false_consts, *primals, *tangents,
true_jaxpr=true_jaxpr, false_jaxpr=false_jaxpr)
primals_out, tangents_out = split_half(outs)
return primals_out, tangents_out
jvp_rules[cond_p] = cond_jvp_rule
```
```{code-cell}
out, out_tan = jvp(lambda x: cond(True, lambda: x * x, lambda: 0.), (1.,), (1.,))
print(out_tan)
```
```{code-cell}
def cond_vmap_rule(axis_size, vals_in, dims_in, *, true_jaxpr, false_jaxpr):
pred , *vals_in = vals_in
pred_dim, *dims_in = dims_in
if pred_dim is not not_mapped: raise NotImplementedError # TODO
true_jaxpr, true_consts = vmap_jaxpr(true_jaxpr, axis_size, tuple(dims_in))
false_jaxpr, false_consts = vmap_jaxpr(false_jaxpr, axis_size, tuple(dims_in))
true_jaxpr, false_jaxpr = _join_jaxpr_consts(
true_jaxpr, false_jaxpr, len(true_consts), len(false_consts))
assert typecheck_jaxpr(true_jaxpr) == typecheck_jaxpr(false_jaxpr)
outs = bind_cond(pred, *true_consts, *false_consts, *vals_in,
true_jaxpr=true_jaxpr, false_jaxpr=false_jaxpr)
return outs, [0] * len(outs)
vmap_rules[cond_p] = cond_vmap_rule
```
```{code-cell}
xs = np.array([1., 2., 3])
out = vmap(lambda x: cond(True, lambda: x + 1., lambda: 0.), (0,))(xs)
print(out)
```
Notice that we're not currently supporting the case where the predicate value
itself is batched. In mainline JAX, we handle this case by transforming the
conditional to a [select primitive](https://jax.readthedocs.io/en/latest/_autosummary/jax.lax.select.html).
That transformation is semantically correct so long as `true_fun` and
`false_fun` do not involve any side-effecting primitives.
Another thing not represented here, but present in the mainline JAX, is that
applying transformations to two jaxprs of equal type might result in jaxprs of
different types. For example, applying the mainline JAX version of
`vmap_jaxpr` to the identity-function jaxpr
```
{ lambda a:float32[] .
let
in ( a ) }
```
would result in a jaxpr with a batched output, of type
`[float32[10]] -> [float32[10]]` if the batch size were 10, while applying it
to the zero-function jaxpr
```
{ lambda a:float32[] .
let
in ( 0. ) }
```
would result in a jaxpr with an unbatched output, of type
`[float32[10]] -> [float32[]]`. This is an optimization, aimed at not batching
values unnecessarily. But it means that in `cond` we'd need an extra step of
joining the two transformed jaxprs to have consistent output types. We don't
need this step here because we chose `vmap_jaxpr` always to batch all outputs
over the leading axis.
+++
Next we can turn to abstract evaluation and XLA lowering rules:
```{code-cell}
def cond_abstract_eval(pred_type, *in_types, true_jaxpr, false_jaxpr):
if pred_type != ShapedArray((), np.dtype('bool')): raise TypeError
jaxpr_type = typecheck_jaxpr(true_jaxpr)
if jaxpr_type != typecheck_jaxpr(false_jaxpr):
raise TypeError
if not all(t1 == t2 for t1, t2 in zip(jaxpr_type.in_types, in_types)):
raise TypeError
return jaxpr_type.out_types
abstract_eval_rules[cond_p] = cond_abstract_eval
def cond_translation(c, in_avals, in_vals, *, true_jaxpr, false_jaxpr):
del in_avals # Unused
pred, *in_vals = in_vals
flat_vals, in_tree = tree_flatten(in_vals)
operand = xops.Tuple(c, flat_vals)
operand_shape = c.get_shape(operand)
def make_comp(name: str, jaxpr: Jaxpr) -> xe.XlaComputation:
c = xc.XlaBuilder(name)
operand = xops.Parameter(c, 0, operand_shape)
operands = tree_unflatten(in_tree, destructure_tuple(c, operand))
outs = jaxpr_subcomp(c, jaxpr, operands)
return c.build(xops.Tuple(c, outs))
true_comp = make_comp('true_fn', true_jaxpr)
false_comp = make_comp('false_fn', false_jaxpr)
int_etype = xc.dtype_to_etype(np.dtype('int32'))
out = xops.Conditional(xops.ConvertElementType(pred, int_etype),
[false_comp, true_comp], [operand] * 2)
return destructure_tuple(c, out)
xla_translations[cond_p] = cond_translation
```
```{code-cell}
out = jit(lambda: cond(False, lambda: 1, lambda: 2))()
print(out)
```
Finally, to support reverse-mode automatic differentiation, we need partial
evaluation and transposition rules. For partial evaluation, we need to
introduce another jaxpr-munging utility, `_join_jaxpr_res`, to handle the fact
that applying partial evaluation to `true_fun` and `false_fun` will in general
result in distinct residuals. We use `_join_jaxpr_res` to make the output
types of the transformed jaxprs consistent (while `_join_jaxpr_consts` dealt
with input types).
```{code-cell}
def cond_partial_eval(trace, tracers, *, true_jaxpr, false_jaxpr):
pred_tracer, *tracers = tracers
assert pred_tracer.pval.is_known
pred = pred_tracer.pval.const
in_uks = [not t.pval.is_known for t in tracers]
*jaxprs, out_uks, num_res = _cond_partial_eval(true_jaxpr, false_jaxpr, in_uks)
t_jaxpr1, f_jaxpr1, t_jaxpr2, f_jaxpr2 = jaxprs
known_tracers, unknown_tracers = partition_list(in_uks, tracers)
known_vals = [t.pval.const for t in known_tracers]
outs1_res = bind_cond(pred, *known_vals,
true_jaxpr=t_jaxpr1, false_jaxpr=f_jaxpr1)
outs1, res = split_list(outs1_res, len(outs1_res) - num_res)
pred_tracer_ = trace.instantiate_const(full_raise(trace, pred_tracer))
res_tracers = [trace.instantiate_const(full_raise(trace, x)) for x in res]
outs2 = [PartialEvalTracer(trace, PartialVal.unknown(v.aval), None)
for v in t_jaxpr2.outs]
eqn = JaxprEqnRecipe(cond_p, [pred_tracer_, *res_tracers, *unknown_tracers],
dict(true_jaxpr=t_jaxpr2, false_jaxpr=f_jaxpr2),
[v.aval for v in t_jaxpr2.outs], map(ref, outs2))
for t in outs2: t.recipe = eqn
return merge_lists(out_uks, outs1, outs2)
partial_eval_rules[cond_p] = cond_partial_eval
def _cond_partial_eval(true_jaxpr: Jaxpr, false_jaxpr: Jaxpr, in_uks: list[bool]
) -> tuple[Jaxpr, Jaxpr, Jaxpr, Jaxpr, list[bool], int]:
_, _, t_out_uks, _ = partial_eval_jaxpr(true_jaxpr , in_uks)
_, _, f_out_uks, _ = partial_eval_jaxpr(false_jaxpr, in_uks)
out_uks = map(op.or_, t_out_uks, f_out_uks)
t_jaxpr1, t_jaxpr2, _, t_nres = partial_eval_jaxpr(true_jaxpr , in_uks, out_uks)
f_jaxpr1, f_jaxpr2, _, f_nres = partial_eval_jaxpr(false_jaxpr, in_uks, out_uks)
t_jaxpr1, f_jaxpr1 = _join_jaxpr_res(t_jaxpr1, f_jaxpr1, t_nres, f_nres)
t_jaxpr2, f_jaxpr2 = _join_jaxpr_consts(t_jaxpr2, f_jaxpr2, t_nres, f_nres)
assert typecheck_jaxpr(t_jaxpr1) == typecheck_jaxpr(f_jaxpr1)
assert typecheck_jaxpr(t_jaxpr2) == typecheck_jaxpr(f_jaxpr2)
num_res = t_nres + f_nres
return t_jaxpr1, f_jaxpr1, t_jaxpr2, f_jaxpr2, out_uks, num_res
def _join_jaxpr_res(jaxpr1: Jaxpr, jaxpr2: Jaxpr, n1: int, n2: int
) -> tuple[Jaxpr, Jaxpr]:
jaxpr1_type, jaxpr2_type = typecheck_jaxpr(jaxpr1), typecheck_jaxpr(jaxpr2)
out_types1, _ = split_list(jaxpr1_type.out_types, len(jaxpr1.outs) - n1)
out_types2, _ = split_list(jaxpr2_type.out_types, len(jaxpr2.outs) - n2)
assert out_types1 == out_types2
outs1, res1 = split_list(jaxpr1.outs, len(jaxpr1.outs) - n1)
outs2, res2 = split_list(jaxpr2.outs, len(jaxpr2.outs) - n2)
zeros_like1 = [Lit(np.zeros(v.aval.shape, v.aval.dtype)) for v in res1]
zeros_like2 = [Lit(np.zeros(v.aval.shape, v.aval.dtype)) for v in res2]
new_jaxpr1 = Jaxpr(jaxpr1.in_binders, jaxpr1.eqns, outs1 + res1 + zeros_like2)
new_jaxpr2 = Jaxpr(jaxpr2.in_binders, jaxpr2.eqns, outs2 + zeros_like1 + res2)
return new_jaxpr1, new_jaxpr2
```
```{code-cell}
_, f_lin = linearize(lambda x: cond(True, lambda: x, lambda: 0.), 1.)
out = f_lin(3.14)
print(out)
```
```{code-cell}
def cond_peval_eqn(unks_in: list[bool], eqn: JaxprEqn,
) -> tuple[JaxprEqn, JaxprEqn, list[bool], list[Atom]]:
pred_unk, *unks_in = unks_in
assert not pred_unk
true_jaxpr, false_jaxpr = eqn.params['true_jaxpr'], eqn.params['false_jaxpr']
*jaxprs, unks_out, num_res = _cond_partial_eval(true_jaxpr, false_jaxpr, unks_in)
t_jaxpr1, f_jaxpr1, t_jaxpr2, f_jaxpr2 = jaxprs
ins1, ins2 = partition_list(unks_in, eqn.inputs[1:])
outs1, outs2 = partition_list(unks_out, eqn.out_binders)
residuals, _ = split_list(t_jaxpr2.in_binders, num_res)
eqn1 = JaxprEqn(cond_p, [eqn.inputs[0], *ins1],
dict(true_jaxpr=t_jaxpr1, false_jaxpr=f_jaxpr1),
outs1 + residuals)
eqn2 = JaxprEqn(cond_p, [eqn.inputs[0], *residuals, *ins2],
dict(true_jaxpr=t_jaxpr2, false_jaxpr=f_jaxpr2),
outs2)
res = [eqn.inputs[0], *residuals] if type(eqn.inputs[0]) is Var else residuals
return eqn1, eqn2, unks_out, res
partial_eval_jaxpr_rules[cond_p] = cond_peval_eqn
```
```{code-cell}
_, f_lin = linearize(jit(lambda x: cond(True, lambda: x, lambda: 0.)), 1.)
out = f_lin(3.14)
print(out)
```
Transposition is a fairly straightforward application of `transpose_jaxpr`:
```{code-cell}
def cond_transpose_rule(cts, pred, *invals, true_jaxpr, false_jaxpr):
undef_primals = tuple(type(x) is UndefPrimal for x in invals)
true_jaxpr, true_consts = transpose_jaxpr(true_jaxpr, undef_primals)
false_jaxpr, false_consts = transpose_jaxpr(false_jaxpr, undef_primals)
true_jaxpr, false_jaxpr = _join_jaxpr_consts(
true_jaxpr, false_jaxpr, len(true_consts), len(false_consts))
res = [x for x in invals if type(x) is not UndefPrimal]
outs = bind_cond(pred, *true_consts, *false_consts, *res, *cts,
true_jaxpr=true_jaxpr, false_jaxpr=false_jaxpr)
outs = iter(outs)
return [None] + [next(outs) if type(x) is UndefPrimal else None for x in invals]
transpose_rules[cond_p] = cond_transpose_rule
```
```{code-cell}
out = grad(lambda x: cond(True, lambda: x * x, lambda: 0.))(1.)
print(out)
```
```{code-cell}
:tags: [hide-input]
def pprint_cond(names: defaultdict[Var, str], eqn: JaxprEqn) -> PPrint:
true_jaxpr, false_jaxpr = eqn.params['true_jaxpr'], eqn.params['false_jaxpr']
new_params = {k:v for k, v in eqn.params.items() if not k.endswith('jaxpr')}
lhs = pp(' '.join(var_str(names, v) for v in eqn.out_binders))
rhs = (pp(eqn.primitive.name) >> pp_params(new_params) >>
pp(' '.join(names[x] if isinstance(x, Var) else str(x.val)
for x in eqn.inputs)))
return vcat([lhs >> pp(' = ') >> rhs,
pp_jaxpr(true_jaxpr).indent(2),
pp_jaxpr(false_jaxpr).indent(2)])
pp_rules[cond_p] = pprint_cond
```
