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Merge pull request #22735 from dfm:custom-vjp-remat-opt
PiperOrigin-RevId: 658043956
This commit is contained in:
jax authors
commit
c7eb023746
@ -16,7 +16,7 @@ from __future__ import annotations
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from collections.abc import Callable, Sequence
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import dataclasses
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from functools import update_wrapper, reduce, partial
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from functools import update_wrapper, reduce, partial, wraps
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import inspect
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from typing import Any, Generic, TypeVar
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@ -497,6 +497,7 @@ class custom_vjp(Generic[ReturnValue]):
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self.fwd: Callable[..., tuple[ReturnValue, Any]] | None = None
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self.bwd: Callable[..., tuple[Any, ...]] | None = None
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self.symbolic_zeros = False
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self.optimize_remat = False
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__getattr__ = custom_api_util.forward_attr
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@ -504,6 +505,7 @@ class custom_vjp(Generic[ReturnValue]):
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fwd: Callable[..., tuple[ReturnValue, Any]],
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bwd: Callable[..., tuple[Any, ...]],
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symbolic_zeros: bool = False,
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optimize_remat: bool = False,
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) -> None:
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"""Define a custom VJP rule for the function represented by this instance.
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@ -560,6 +562,10 @@ class custom_vjp(Generic[ReturnValue]):
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objects that are given as input leaves to the ``fwd`` rule.
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Default ``False``.
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optimize_remat: boolean, an experimental flag to enable an automatic
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optimization when this function is used under :func:`jax.remat`. This
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will be most useful when the ``fwd`` rule is an opaque call such as a
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Pallas kernel or a custom call. Default ``False``.
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Returns:
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None.
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@ -582,6 +588,10 @@ class custom_vjp(Generic[ReturnValue]):
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self.fwd = fwd
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self.bwd = bwd
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self.symbolic_zeros = symbolic_zeros
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self.optimize_remat = optimize_remat
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if self.symbolic_zeros and self.optimize_remat:
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raise NotImplementedError(
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"remat optimization for custom_vjp does not support symbolic zeros")
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@traceback_util.api_boundary
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def __call__(self, *args: Any, **kwargs: Any) -> ReturnValue: # pytype: disable=invalid-annotation
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@ -591,6 +601,12 @@ class custom_vjp(Generic[ReturnValue]):
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raise AttributeError(msg)
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fwd_name = getattr(self.fwd, '__name__', str(self.fwd))
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args = _resolve_kwargs(self.fun, args, kwargs)
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if self.optimize_remat:
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fwd = optimize_remat_of_custom_vjp_fwd(
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self.fun, self.fwd, nondiff_argnums=self.nondiff_argnums,
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symbolic_zeros=self.symbolic_zeros)
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else:
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fwd = self.fwd
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if config.enable_custom_vjp_by_custom_transpose.value:
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if self.nondiff_argnums:
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raise NotImplementedError(
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@ -604,16 +620,16 @@ class custom_vjp(Generic[ReturnValue]):
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f_, dyn_args = argnums_partial(lu.wrap_init(self.fun), dyn_argnums,
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args, require_static_args_hashable=False)
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static_args = [args[i] for i in self.nondiff_argnums]
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fwd, _ = argnums_partial(lu.wrap_init(self.fwd), dyn_argnums, args,
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fwd_, _ = argnums_partial(lu.wrap_init(fwd), dyn_argnums, args,
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require_static_args_hashable=False)
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bwd = _add_args(lu.wrap_init(self.bwd), static_args)
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else:
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f_, dyn_args = lu.wrap_init(self.fun), args
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fwd, bwd = lu.wrap_init(self.fwd), lu.wrap_init(self.bwd)
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fwd_, bwd = lu.wrap_init(fwd), lu.wrap_init(self.bwd)
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args_flat, in_tree = tree_flatten(dyn_args)
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in_avals = [core.raise_to_shaped(core.get_aval(x)) for x in args_flat]
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flat_fun, out_type = _flatten_fun_nokwargs(f_, in_tree)
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flat_fwd, out_trees = _flatten_fwd(fwd, self.symbolic_zeros, primal_name,
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flat_fwd, out_trees = _flatten_fwd(fwd_, self.symbolic_zeros, primal_name,
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fwd_name, in_tree, out_type)
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flat_bwd = _flatten_bwd(bwd, in_tree, in_avals, out_trees).call_wrapped
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out_flat = custom_vjp_call_p.bind(flat_fun, flat_fwd, flat_bwd,
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@ -1407,3 +1423,226 @@ def custom_vjp_by_custom_transpose(fun, fwd, bwd):
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# TODO(mattjj): remove these stubs, which exist to avoid breaking internal users
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custom_jvp_call_jaxpr_p = core.Primitive("custom_jvp_call_jaxpr")
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# The following is a helper for optimizing the behavior of custom_vjp when used
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# under remat. This is really only useful when the `fwd` function to custom_vjp
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# executes a black box kernel. Otherwise, DCE will perform this optimization
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# automatically.
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#
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# TODO(dfm): Eventually this should probably be the default behavior for
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# custom_vjp, if we can make it so that it is a no-op for most cases. Right now,
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# it is written in "initial-style" so it doesn't support eager mode. This was
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# a reasonable compromise when written because it made the implementation
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# simpler, but it would be worth revisiting this.
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def optimize_remat_of_custom_vjp_fwd(
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fun: Callable[..., ReturnValue],
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fwd: Callable[..., tuple[ReturnValue, Any]],
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nondiff_argnums: tuple[int, ...] = (),
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symbolic_zeros: bool = False,
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) -> Callable[..., tuple[ReturnValue, Any]]:
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if symbolic_zeros:
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# TODO(dfm): This probably shouldn't be too hard to support.
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raise NotImplementedError(
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"remat optimization for custom_vjp does not support symbolic zeros")
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@wraps(fwd)
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def wrapped_fwd(*args, **kwargs) -> tuple[ReturnValue, Any]:
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# TODO(dfm): This initial logic is duplicated from custom_vjp.__call__
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# above and it would be good to consolidate it.
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primal_name = getattr(fun, "__name__", str(fun))
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fwd_name = getattr(fwd, "__name__", str(fwd))
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args = _resolve_kwargs(fwd, args, kwargs)
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if nondiff_argnums:
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for i in nondiff_argnums: _check_for_tracers(args[i])
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nondiff_argnums_ = set(nondiff_argnums)
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dyn_argnums = [i for i in range(len(args)) if i not in nondiff_argnums_]
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f_, dyn_args = argnums_partial(lu.wrap_init(fun), dyn_argnums,
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args, require_static_args_hashable=False)
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fwd_, _ = argnums_partial(lu.wrap_init(fwd), dyn_argnums, args,
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require_static_args_hashable=False)
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else:
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f_, dyn_args = lu.wrap_init(fun), args
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fwd_ = lu.wrap_init(fwd)
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args_flat, in_tree = tree_flatten(dyn_args)
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flat_fun, out_type = _flatten_fun_nokwargs(f_, in_tree)
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flat_fwd, out_trees = _flatten_fwd(fwd_, False, primal_name, fwd_name,
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in_tree, out_type)
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in_avals = [core.raise_to_shaped(core.get_aval(x)) for x in args_flat]
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fwd_jaxpr, _, consts, () = pe.trace_to_jaxpr_dynamic(flat_fwd, in_avals)
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fwd_jaxpr = pe.close_jaxpr(pe.convert_constvars_jaxpr(fwd_jaxpr))
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prim_tree, res_tree = out_trees()
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num_res = res_tree.num_leaves
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if fwd_jaxpr.effects:
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raise NotImplementedError(
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"remat optimization for custom_vjp does not support forward "
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f"functions with side effects, but {fwd_name} has the following "
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f"effects: {fwd_jaxpr.effects}")
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@pe._memoize
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def fun_jaxpr_thunk():
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jaxpr, _, consts, () = pe.trace_to_jaxpr_dynamic(flat_fun, in_avals)
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return jaxpr, consts
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out_flat = remat_opt_p.bind(*consts, *args_flat,
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num_consts=len(consts),
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num_res=num_res,
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fwd_jaxpr=fwd_jaxpr,
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fun_jaxpr_thunk=fun_jaxpr_thunk)
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res, out_flat = split_list(out_flat, [num_res])
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out_tree = treedef_tuple((prim_tree, res_tree))
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return tree_unflatten(out_tree, (*out_flat, *res))
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return wrapped_fwd
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def _remat_opt_impl(
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*args,
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num_consts: int,
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num_res: int,
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fwd_jaxpr: core.ClosedJaxpr,
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fun_jaxpr_thunk: Callable[[], core.ClosedJaxpr],
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):
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del num_consts, num_res, fun_jaxpr_thunk # unused
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return core.jaxpr_as_fun(fwd_jaxpr)(*args)
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def _remat_opt_abstract_eval(*args, fwd_jaxpr: core.ClosedJaxpr, **_):
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del args
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return fwd_jaxpr.out_avals, fwd_jaxpr.effects
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def _remat_opt_vmap(
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spmd_axis_name, axis_size, axis_name, main_type, args, in_dims,
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*,
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num_consts: int,
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num_res: int,
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fwd_jaxpr: core.ClosedJaxpr,
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fun_jaxpr_thunk: Callable[[], core.ClosedJaxpr],
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):
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args = [batching.moveaxis(x, d, 0) if d is not not_mapped and d != 0
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else x for x, d in zip(args, in_dims)]
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in_batched = [d is not not_mapped for d in in_dims]
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batched_fwd_jaxpr, out_batched = batching.batch_jaxpr(
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fwd_jaxpr, axis_size, in_batched, False,
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axis_name, spmd_axis_name, main_type)
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out_dims = [0 if b else not_mapped for b in out_batched]
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_, prim_batched = split_list(in_batched, [num_consts])
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@pe._memoize
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def batched_fun_jaxpr_thunk():
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fun_jaxpr = core.ClosedJaxpr(*fun_jaxpr_thunk())
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batched_fun_jaxpr, out_batched = batching.batch_jaxpr(
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fun_jaxpr, axis_size, prim_batched, False, axis_name, spmd_axis_name,
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main_type)
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return batched_fun_jaxpr.jaxpr, batched_fun_jaxpr.consts
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batched_outs = remat_opt_p.bind(*args, num_consts=num_consts,
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num_res=num_res,
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fwd_jaxpr=batched_fwd_jaxpr,
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fun_jaxpr_thunk=batched_fun_jaxpr_thunk)
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return batched_outs, out_dims
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def _remat_opt_jvp(
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primals,
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tangents,
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*,
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num_consts: int,
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num_res: int,
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fwd_jaxpr: core.ClosedJaxpr,
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fun_jaxpr_thunk: Callable[[], core.ClosedJaxpr],
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):
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consts, primals = split_list(primals, [num_consts])
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consts_dot, tangents = split_list(tangents, [num_consts])
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# Tangents must be instantated in case we end up DCEing later.
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tangents = map(ad.instantiate_zeros, tangents)
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consts_nz = [not isinstance(t, Zero) for t in consts_dot]
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consts_dot = [c for nz, c in zip(consts_nz, consts_dot) if nz]
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in_nz = consts_nz + [True] * len(tangents)
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fwd_jaxpr_jvp_, out_nz = ad.jvp_jaxpr(fwd_jaxpr, in_nz, True)
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num_out = len(out_nz) - num_res
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fwd_jaxpr_jvp_ = ad.rearrange_binders(
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fwd_jaxpr_jvp_, [num_consts, len(primals)],
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[len(consts_dot), len(tangents)], [num_res, num_out], [num_res, num_out])
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fwd_jaxpr_jvp = pe.close_jaxpr(pe.convert_constvars_jaxpr(fwd_jaxpr_jvp_.jaxpr))
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@pe._memoize
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def fun_jvp_jaxpr_thunk():
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fun_jaxpr = core.ClosedJaxpr(*fun_jaxpr_thunk())
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in_nz = [True] * len(primals)
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fun_jvp_jaxpr, _ = ad.jvp_jaxpr(fun_jaxpr, in_nz, True)
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return fun_jvp_jaxpr.jaxpr, fun_jvp_jaxpr.consts
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new_num_consts = len(fwd_jaxpr_jvp_.consts) + num_consts + len(consts_dot)
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outs = remat_opt_p.bind(*fwd_jaxpr_jvp_.consts, *consts, *consts_dot,
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*primals, *tangents, num_consts=new_num_consts,
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num_res=2 * num_res, fwd_jaxpr=fwd_jaxpr_jvp,
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fun_jaxpr_thunk=fun_jvp_jaxpr_thunk)
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res, res_dot, outs, outs_dot = split_list(outs, [num_res, num_res, num_out])
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return (*res, *outs), (*res_dot, *outs_dot)
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def _remat_opt_transpose(
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cts, *args,
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num_consts: int,
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num_res: int,
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fwd_jaxpr: core.ClosedJaxpr,
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fun_jaxpr_thunk: Callable[[], core.ClosedJaxpr],
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):
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# TODO(dfm): It shouldn't be too hard to implement this as needed in the
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# future.
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raise NotImplementedError(
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"remat optimization for custom_vjp does not support higher-order AD")
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def _remat_opt_dce(used_outs: list[bool], eqn: core.JaxprEqn):
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used_res, used_prims = split_list(used_outs, [eqn.params["num_res"]])
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outvars = [v for used, v in zip(used_outs, eqn.outvars) if used]
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if any(used_res):
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# If any of the residuals are used, we still need to run fwd at this point,
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# but we may end up DCEing again in the future, so we must instantiate all
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# the input primals.
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instantiate = [False] * eqn.params["num_consts"]
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instantiate += [True] * (len(eqn.invars) - eqn.params["num_consts"])
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new_jaxpr, used_ins = pe.dce_jaxpr(eqn.params["fwd_jaxpr"].jaxpr, used_outs,
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instantiate=instantiate)
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closed_jaxpr = pe.close_jaxpr(new_jaxpr)
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invars = [v for used, v in zip(used_ins, eqn.invars) if used]
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new_params = dict(eqn.params)
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new_num_consts = sum(split_list(used_ins, [eqn.params["num_consts"]])[0])
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new_params["num_consts"] = new_num_consts
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new_params["fwd_jaxpr"] = closed_jaxpr
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new_params["num_res"] = sum(used_res)
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new_eqn = pe.new_jaxpr_eqn(
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invars, outvars, remat_opt_p, new_params, closed_jaxpr.effects,
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eqn.source_info, eqn.ctx)
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return used_ins, new_eqn
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else:
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# If none of the residuals are used, we run the primal computation instead.
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# At this point we drop this custom DCE behavior, but since the primal might
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# have different consts than fwd, we build a new JaxprEqn with a closed_call
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# primitive.
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fun_jaxpr, consts = eqn.params["fun_jaxpr_thunk"]()
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new_jaxpr, used_consts, used_ins = pe.dce_jaxpr_consts(fun_jaxpr, used_prims)
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consts = [c for used, c in zip(used_consts, consts) if used]
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closed_jaxpr = core.ClosedJaxpr(new_jaxpr, consts)
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_, invars = split_list(eqn.invars, [eqn.params["num_consts"]])
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invars = [v for used, v in zip(used_ins, invars) if used]
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new_eqn = pe.new_jaxpr_eqn(
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invars, outvars, core.closed_call_p, dict(call_jaxpr=closed_jaxpr),
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closed_jaxpr.effects, eqn.source_info, eqn.ctx)
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used_ins = [False] * eqn.params["num_consts"] + used_ins
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return used_ins, new_eqn
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remat_opt_p = core.Primitive("remat_opt")
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remat_opt_p.multiple_results = True
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remat_opt_p.def_impl(_remat_opt_impl)
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remat_opt_p.def_effectful_abstract_eval(_remat_opt_abstract_eval)
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xla.register_initial_style_primitive(remat_opt_p)
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mlir.register_lowering(remat_opt_p, mlir.lower_fun(
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_remat_opt_impl, multiple_results=True))
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batching.spmd_axis_primitive_batchers[remat_opt_p] = _remat_opt_vmap
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batching.axis_primitive_batchers[remat_opt_p] = partial(_remat_opt_vmap, None)
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ad.primitive_jvps[remat_opt_p] = _remat_opt_jvp
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ad.primitive_transposes[remat_opt_p] = _remat_opt_transpose
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pe.dce_rules[remat_opt_p] = _remat_opt_dce
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@ -30,6 +30,7 @@ from jax._src.custom_derivatives import (
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custom_vjp_primal_tree_values as custom_vjp_primal_tree_values,
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CustomVJPPrimal as CustomVJPPrimal,
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linear_call as linear_call,
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remat_opt_p as remat_opt_p,
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)
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from jax._src.ad_util import (
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@ -3448,6 +3448,17 @@ def _custom_lin(*args: TfVal, **_) -> Sequence[TfVal]:
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tf_impl[ad.custom_lin_p] = _custom_lin
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def _remat_opt(*args: TfVal, num_consts: int, num_res: int,
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fwd_jaxpr: core.ClosedJaxpr,
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fun_jaxpr_thunk: Callable) -> Sequence[TfVal]:
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del num_consts, num_res, fun_jaxpr_thunk
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return _interpret_jaxpr(fwd_jaxpr, *args, extra_name_stack="remat_opt",
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fresh_constant_cache=False)
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tf_impl[custom_derivatives.remat_opt_p] = _remat_opt
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PartitionsOrReplicated = Union[tuple[int, ...], None]
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def split_to_logical_devices(tensor: TfVal,
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@ -9653,6 +9653,83 @@ class CustomVJPTest(jtu.JaxTestCase):
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jax.grad(f)(1., 2.) # don't crash
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def test_optimize_remat(self):
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def fun(x):
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# This array is included to make sure that we handle consts appropriately
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return np.array([1.0])*x
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def fwd(x):
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return np.array([2.0])*x*x/np.array([1.0]), (x,)
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fwd = custom_derivatives.optimize_remat_of_custom_vjp_fwd(fun, fwd)
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x = jnp.linspace(0, 5.0, 10)
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self.assertAllClose(jax.jit(fwd)(x)[0], 2*x*x) # Shouldn't hit custom DCE
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self.assertAllClose(jax.jit(lambda x: fwd(x)[0])(x), x) # Should be DCEed
|
||||
|
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def test_optimize_remat_vmap(self):
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def fun(x):
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||||
return (np.array([1.0])*x)[0]
|
||||
def fwd(x):
|
||||
return (np.array([2.0])*x*x/np.array([1.0]))[0], (x,)
|
||||
fwd = custom_derivatives.optimize_remat_of_custom_vjp_fwd(fun, fwd)
|
||||
x = jnp.linspace(0, 5.0, 10)
|
||||
self.assertAllClose(jax.jit(jax.vmap(fwd))(x)[0], 2*x*x)
|
||||
self.assertAllClose(jax.jit(lambda x: jax.vmap(fwd)(x)[0])(x), x)
|
||||
|
||||
def test_optimize_remat_cond(self):
|
||||
def fun(x):
|
||||
return x
|
||||
def fwd(x):
|
||||
return x*x, (x,)
|
||||
fwd = custom_derivatives.optimize_remat_of_custom_vjp_fwd(fun, fwd)
|
||||
|
||||
def g(x):
|
||||
return jax.lax.cond(True, fwd, lambda x: (2.0 * x, (x,)), x)
|
||||
x = jnp.linspace(0, 5.0, 10)
|
||||
self.assertAllClose(jax.jit(g)(x)[0], x*x)
|
||||
self.assertAllClose(jax.jit(lambda x: g(x)[0])(x), x)
|
||||
|
||||
def test_optimize_remat_jvp(self):
|
||||
def fun(x):
|
||||
return x**2
|
||||
def fwd_(x):
|
||||
return x*x, (x,)
|
||||
fwd = custom_derivatives.optimize_remat_of_custom_vjp_fwd(fun, fwd_)
|
||||
calc = jax.jvp(fwd, (3.2,), (1.0,))
|
||||
expected = jax.jvp(fwd_, (3.2,), (1.0,))
|
||||
self.assertAllClose(calc, expected)
|
||||
|
||||
@jax.jit
|
||||
def g(x, t):
|
||||
(y, r), (y_dot, r_dot) = jax.jvp(fwd, (x,), (t,))
|
||||
return y, y_dot
|
||||
calc = g(3.2, 1.0)
|
||||
expected = jax.jvp(fun, (3.2,), (1.0,))
|
||||
self.assertAllClose(calc, expected)
|
||||
|
||||
def test_optimize_remat_gh21303(self):
|
||||
@jax.custom_vjp
|
||||
def f(x):
|
||||
return jnp.tan(x)
|
||||
|
||||
def f_fwd(x):
|
||||
return jnp.sin(x), (x,)
|
||||
|
||||
def f_bwd(res, g):
|
||||
x, = res
|
||||
cos_x = jnp.cos(x)
|
||||
return (cos_x * g,)
|
||||
|
||||
f.defvjp(f_fwd, f_bwd, optimize_remat=True)
|
||||
|
||||
def temp(x):
|
||||
out = jax.remat(f)(x)
|
||||
out = out ** 2
|
||||
return out
|
||||
|
||||
v, g = jax.value_and_grad(temp)(3.2)
|
||||
self.assertAllClose(v, jnp.tan(3.2)**2)
|
||||
|
||||
|
||||
def transpose_unary(f, x_example):
|
||||
def transposed(y):
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user