autodidax.py

# ---
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# [![Open in
# Colab](https://colab.research.google.com/assets/colab-badge.svg)](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:

# +
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.

# +
from contextlib import contextmanager
from typing import Type, List, Tuple, Sequence, 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.

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.)

# +
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)


# +
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`:

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.

# +
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.

# +
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.

# +
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:

# +
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:

# +
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

map_ = map
def map(f, *xs):
  return list(map_(f, *xs))

zip_ = zip
def zip(*args):
  fst, *rest = args = map(list, args)
  n = len(fst)
  for arg in rest:
    assert len(arg) == n
  return list(zip_(*args))


# -

# The `Tracer` for forward-mode autodiff carries a primal-tangent pair. The
# `Trace` applies JVP rules.

# +
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:

# +
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:

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!

x = 3.0
y, sin_deriv_at_3 = jvp_v1(sin, (x,), (1.0,))
print(sin_deriv_at_3)
print(cos(3.0))


# +
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)


# +
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.))


# +
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:

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:

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`.

# + 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


# + tags=["hide-input"]
import itertools as it
from typing import Callable, Type, Hashable, Dict, Iterable, Iterator

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!

# +
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:

# +
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.

# +
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:

# +
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:

# +
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


# +
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)


# +
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:

# +
from typing import Set

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.

# +
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.

# +
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`.

# +
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


# +
# 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.

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


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.

# +
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:

# +
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()


# + tags=["hide-input"]
from typing import DefaultDict
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]] = {}
# -

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:

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:

# +
@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:

# +
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 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.

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`:

# +
from jax._src.lib 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(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:

# +
def jaxpr_subcomp(c: xe.XlaBuilder, jaxpr: Jaxpr, args: List[xe.XlaOp]
                  ) -> 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 buf.to_py()

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:

# +
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!

@jit
def f(x, y):
  print('tracing!')
  return sin(x) * cos(y)


z = f(3., 4.)  # 'tracing!' prints the first time
print(z)

z = f(4., 5.)  # 'tracing!' doesn't print, compilation cache hit!
print(z)


# +
@jit
def f(x):
  return reduce_sum(x, axis=0)

print(f(np.array([1., 2., 3.])))


# +
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!

# +
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


# +
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)


# +
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)]


# +
@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)
# -

y, ydot = jvp(f, (x,), (xdot,))  # 'tracing!' not printed

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 a
# `DeviceArray` class, which can wrap XLA buffers and otherwise duck-type
# `numpy.ndarray`s:

# +
def handle_result(aval: ShapedArray, buf):  # noqa: F811
  return DeviceArray(aval, buf)

class DeviceArray:
  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 self.buf.to_py()
  def __repr__(self):  return repr(self.buf.to_py())
  def __str__(self):   return str(self.buf.to_py())

  _neg = staticmethod(neg)
  _add = staticmethod(add)
  _radd = staticmethod(add)
  _mul = staticmethod(mul)
  _rmul = staticmethod(mul)
  _gt = staticmethod(greater)
  _lt = staticmethod(less)
input_handlers[DeviceArray] = lambda x: x.buf

jax_types.add(DeviceArray)


# +
@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)


# + 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, 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:

# +
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:

# +
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:

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:

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:

# +
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]


# -

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.

# +
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.

# +
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 []


# + 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!

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`.

# +
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:

# +
@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)


# +
@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.

# +
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:

# +
# 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 = {}


# +
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`:

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


y, f_vjp = vjp(sin, 3.)
print(f_vjp(1.), cos(3.))


# +
def f(x):
  y = sin(x) * 2.
  z = - y + x
  return z

print(grad(f)(3.))


# +
@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:

# +
# 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:

# +
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:

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

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`:

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

out, out_tan = jvp(lambda x: cond(True, lambda: x * x, lambda: 0.), (1.,), (1.,))
print(out_tan)


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

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:

# +
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
# -

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).

# +
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


# -

_, f_lin = linearize(lambda x: cond(True, lambda: x, lambda: 0.), 1.)
out = f_lin(3.14)
print(out)


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

_, 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`:

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

out = grad(lambda x: cond(True, lambda: x * x, lambda: 0.))(1.)
print(out)


# + 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