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stackless fixes #37

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stackless fixes #37

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163 changes: 72 additions & 91 deletions quax/_core.py
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Original file line number Diff line number Diff line change
Expand Up @@ -141,76 +141,51 @@ def _wrap_if_array(x: Union[ArrayLike, "Value"]) -> "Value":


class _QuaxTrace(core.Trace[_QuaxTracer]):
def pure(self, val: ArrayLike) -> _QuaxTracer:
if _is_value(val):
raise TypeError(
f"Encountered Quax value of type {type(val)}. These must be "
"transformed by passing them across a `quax.quaxify` boundary before "
"being used.\n"
"For example, the following is incorrect, as `SomeValue()` is not "
"explicitly passed across the API boundary:\n"
"```\n"
"def f(x):\n"
" return x + SomeValue()\n"
"\n"
"quax.quaxify(f)(AnotherValue())\n"
"```\n"
"This should instead be written as the following:\n"
"explicitly passed across the API boundary:\n"
"```\n"
"def f(x, y):\n"
" return x + y\n"
"\n"
"quax.quaxify(f)(AnotherValue(), SomeValue())\n"
"```\n"
"To better understand this, remember that the purpose of Quax is "
"take a JAX program (given as a function) that acts on arrays, and to "
"instead run it with array-ish types. But in the first example above, "
"the original program already has an array-ish type, even before the "
"`quaxify` is introduced."
)
if not eqx.is_array_like(val):
raise TypeError(f"{type(val)} is not a JAX type.")
return _QuaxTracer(self, _DenseArrayValue(val)) # pyright: ignore

def lift(self, tracer: core.Tracer) -> _QuaxTracer:
return _QuaxTracer(self, _DenseArrayValue(tracer))
def __init__(self, parent_trace, tag):
self.tag = tag
self.parent_trace = parent_trace

def sublift(self, tracer: _QuaxTracer) -> _QuaxTracer:
return tracer
def to_value(self, val):
if isinstance(val, _QuaxTracer) and val._trace.tag is self.tag:
return val.value
else:
return _DenseArrayValue(val)

def process_primitive(self, primitive, tracers, params):
values = [t.value for t in tracers]
# params = dict(params); params.pop('sharding', None)
values = [self.to_value(t) for t in tracers]
values = tuple(
x.array if isinstance(x, _DenseArrayValue) else x for x in values
)
try:
rule = _rules[primitive]
except KeyError:
out = _default_process(primitive, values, params)
with core.set_current_trace(self.parent_trace):
out = _default_process(primitive, values, params)
else:
try:
method, _ = rule.resolve_method(values)
except plum.NotFoundLookupError:
out = _default_process(primitive, values, params)
with core.set_current_trace(self.parent_trace):
out = _default_process(primitive, values, params)
else:
out = method(*values, **params)
with core.set_current_trace(self.parent_trace):
out = method(*values, **params)
if primitive.multiple_results:
return [_QuaxTracer(self, _wrap_if_array(x)) for x in out] # pyright: ignore
else:
return _QuaxTracer(self, _wrap_if_array(out)) # pyright: ignore

def process_custom_jvp_call(self, primitive, fun, jvp, tracers, *, symbolic_zeros):
in_values = [t.value for t in tracers]
in_values = [self.to_value(t) for t in tracers]
# Each `t.value` will be some `Value`, and thus a PyTree. Here we flatten the
# `Value`-ness away.
in_leaves, in_treedef = jtu.tree_flatten(in_values)
fun, out_treedef1 = _custom_jvp_fun_wrap(fun, self.main, in_treedef) # pyright: ignore
jvp, out_treedef2 = _custom_jvp_jvp_wrap(jvp, self.main, in_treedef) # pyright: ignore
with jax.ensure_compile_time_eval():
out_leaves = primitive.bind(
fun, jvp, *in_leaves, symbolic_zeros=symbolic_zeros
)
fun, out_treedef1 = _custom_jvp_fun_wrap(fun, self.tag, in_treedef) # pyright: ignore
jvp, out_treedef2 = _custom_jvp_jvp_wrap(jvp, self.tag, in_treedef) # pyright: ignore
out_leaves = primitive.bind_with_trace(
self.parent_trace, (fun, jvp, *in_leaves), dict(symbolic_zeros=symbolic_zeros),
)
_, out_treedef = lu.merge_linear_aux(out_treedef1, out_treedef2)
out_values = jtu.tree_unflatten(out_treedef, out_leaves)
return [_QuaxTracer(self, x) for x in out_values]
Expand All @@ -219,51 +194,59 @@ def process_custom_jvp_call(self, primitive, fun, jvp, tracers, *, symbolic_zero


@lu.transformation_with_aux # pyright: ignore
def _custom_jvp_fun_wrap(main, in_treedef, *in_leaves):
trace = main.with_cur_sublevel()
def _custom_jvp_fun_wrap(tag, in_treedef, *in_leaves):
in_values = jtu.tree_unflatten(in_treedef, in_leaves)
in_tracers = [x if type(x) is SZ else _QuaxTracer(trace, x) for x in in_values]
out_tracers = yield in_tracers, {}
# The symbolic zero branch here will actually create a `quax.zero.Zero`!
out_tracers = [
jnp.zeros(t.aval.shape, t.aval.dtype) if type(t) is SZ else t # pyright: ignore
for t in out_tracers
]
out_values = [trace.full_raise(t).value for t in out_tracers]
with core.take_current_trace() as parent_trace:
trace = _QuaxTrace(parent_trace, tag)
in_tracers = [x if type(x) is SZ else _QuaxTracer(trace, x) for x in in_values]
with core.set_current_trace(trace):
out_tracers = yield in_tracers, {}
# The symbolic zero branch here will actually create a `quax.zero.Zero`!
out_tracers = [
jnp.zeros(t.aval.shape, t.aval.dtype) if type(t) is SZ else t # pyright: ignore
for t in out_tracers
]
out_values = [trace.to_value(t) for t in out_tracers]
del out_tracers
del trace, in_tracers
out_leaves, out_treedef = jtu.tree_flatten(out_values)
yield out_leaves, out_treedef


@lu.transformation_with_aux # pyright: ignore
def _custom_jvp_jvp_wrap(main, in_treedef, *in_primals_and_tangents):
trace = main.with_cur_sublevel()
def _custom_jvp_jvp_wrap(tag, in_treedef, *in_primals_and_tangents):
in_primals = in_primals_and_tangents[: len(in_primals_and_tangents) // 2]
in_tangents = in_primals_and_tangents[len(in_primals_and_tangents) // 2 :]
in_primal_values = jtu.tree_unflatten(in_treedef, in_primals)
in_tangent_values = jtu.tree_unflatten(in_treedef, in_tangents)
# Calling `_QuaxTracer` directly here, not using `trace.{pure,lift}` as each `x` is
# a `Value`, not an array (=> pure) or tracer (=> lift).
in_tracers = [
_QuaxTracer(trace, x) for x in it.chain(in_primal_values, in_tangent_values)
]
out_tracers = yield in_tracers, {}
# The symbolic zero branch here will actually create a `quax.zero.Zero`!
out_tracers = [
jnp.zeros(t.aval.shape, t.aval.dtype) if type(t) is SZ else t # pyright: ignore
for t in out_tracers
]
out_values = [trace.full_raise(t).value for t in out_tracers]
out_primal_values = out_values[: len(out_values) // 2]
out_tangent_values = out_values[len(out_values) // 2 :]
out_primal_values2 = []
out_tangent_values2 = []
assert len(out_primal_values) == len(out_tangent_values)
for primal, tangent in zip(out_primal_values, out_tangent_values):
if primal.__class__ != tangent.__class__:
primal = primal.materialise()
tangent = tangent.materialise()
out_primal_values2.append(primal)
out_tangent_values2.append(tangent)
with core.take_current_trace() as parent_trace:
trace = _QuaxTrace(parent_trace, tag)
in_tracers = [
_QuaxTracer(trace, x) for x in it.chain(in_primal_values, in_tangent_values)
]
with core.set_current_trace(trace):
out_tracers = yield in_tracers, {}
# The symbolic zero branch here will actually create a `quax.zero.Zero`!
out_tracers = [
jnp.zeros(t.aval.shape, t.aval.dtype) if type(t) is SZ else t # pyright: ignore
for t in out_tracers
]
out_values = [trace.to_value(t) for t in out_tracers]
out_primal_values = out_values[: len(out_values) // 2]
out_tangent_values = out_values[len(out_values) // 2 :]
out_primal_values2 = []
out_tangent_values2 = []
assert len(out_primal_values) == len(out_tangent_values)
for primal, tangent in zip(out_primal_values, out_tangent_values):
if primal.__class__ != tangent.__class__:
primal = primal.materialise()
tangent = tangent.materialise()
out_primal_values2.append(primal)
out_tangent_values2.append(tangent)
del out_tracers
del trace, in_tracers
out_primals, out_primal_treedef = jtu.tree_flatten(out_primal_values2)
out_tangents, out_tangent_treedef = jtu.tree_flatten(out_tangent_values2)
if out_primal_treedef != out_tangent_treedef:
Expand Down Expand Up @@ -307,21 +290,20 @@ def __wrapped__(self) -> CT:
return self.fn

def __call__(self, *args, **kwargs):
with core.new_main(_QuaxTrace, dynamic=self.dynamic) as main:
trace = _QuaxTrace(main, core.cur_sublevel())
# Note that we do *not* wrap arraylikes here. We let that happen in
# `_QuaxTrace.{pure,lift}` as necessary. This means that we can do e.g.
# quaxify(jnp.moveaxis)(array, source=0, destination=-1).
dynamic, static = eqx.partition(
(self.fn, args, kwargs), self.filter_spec, is_leaf=_is_value
)
dynamic, static = eqx.partition(
(self.fn, args, kwargs), self.filter_spec, is_leaf=_is_value
)
tag = core.TraceTag()
with core.take_current_trace() as parent_trace:
trace = _QuaxTrace(parent_trace, tag)
dynamic = jtu.tree_map(
ft.partial(_wrap_tracer, trace),
dynamic,
is_leaf=_is_value,
)
fn, args, kwargs = eqx.combine(dynamic, static)
out = fn(*args, **kwargs)
with core.set_current_trace(trace):
out = fn(*args, **kwargs)
out = jtu.tree_map(ft.partial(_unwrap_tracer, trace), out)
return out

Expand Down Expand Up @@ -543,8 +525,7 @@ def _(*args: Union[ArrayLike, ArrayValue], jaxpr, inline, **kwargs):
else:
leaves, treedef = jtu.tree_flatten(args) # remove all Values
flat_fun = lambda x: fun(*jtu.tree_unflatten(treedef, x))
with jax.ensure_compile_time_eval(): # replace the dynamic QuaxTrace
return jax.jit(flat_fun)(leaves) # now we can call without Quax.
return jax.jit(flat_fun)(leaves) # now we can call without Quax.


@register(jax.lax.while_p)
Expand Down
2 changes: 1 addition & 1 deletion quax/examples/sparse/_core.py
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Expand Up @@ -78,7 +78,7 @@ def _op_sparse_to_dense(x, y, op):


@quax.register(lax.broadcast_in_dim_p)
def _(value: BCOO, *, broadcast_dimensions, shape) -> BCOO:
def _(value: BCOO, *, broadcast_dimensions, shape, sharding=None) -> BCOO:
n_extra_batch_dims = len(shape) - value.ndim
if broadcast_dimensions != tuple(range(n_extra_batch_dims, len(shape))):
raise NotImplementedError(
Expand Down
9 changes: 5 additions & 4 deletions quax/examples/zero/_core.py
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Expand Up @@ -42,23 +42,24 @@ def materialise(self):

@quax.register(lax.broadcast_in_dim_p)
def _(
value: ArrayLike, *, broadcast_dimensions, shape
value: ArrayLike, *, broadcast_dimensions, shape, sharding=None
) -> Union[ArrayLike, quax.ArrayValue]:
aval = jax.core.get_aval(value)
if isinstance(aval, jax.core.ConcreteArray) and aval.shape == () and aval.val == 0:
if False and aval.shape == () and aval.val == 0:
return Zero(shape, np.result_type(value))
else:
# Avoid an infinite loop, by pushing a new interpreter to the dynamic
# interpreter stack.
with jax.ensure_compile_time_eval():
out = lax.broadcast_in_dim_p.bind(
value, broadcast_dimensions=broadcast_dimensions, shape=shape
value, broadcast_dimensions=broadcast_dimensions, shape=shape,
sharding=sharding
)
return out # pyright: ignore


@quax.register(lax.broadcast_in_dim_p)
def _(value: Zero, *, broadcast_dimensions, shape) -> Zero:
def _(value: Zero, *, broadcast_dimensions, shape, sharding=None) -> Zero:
del broadcast_dimensions
return Zero(shape, value.dtype)

Expand Down