def lower_xla_callable(fun: lu.WrappedFun, device, backend, name,
donated_invars, *arg_specs):
if device is not None and backend is not None:
raise ValueError("can't specify both a device and a backend for jit, "
"got device={} and backend={}".format(device, backend))
abstract_args, arg_devices = util.unzip2(arg_specs)
with log_elapsed_time(f"Finished tracing + transforming {fun.__name__} "
"for jit in {elapsed_time} sec"):
jaxpr, out_avals, consts = pe.trace_to_jaxpr_final(
fun, abstract_args, pe.debug_info_final(fun, "jit"))
if any(isinstance(c, core.Tracer) for c in consts):
raise UnexpectedTracerError("Encountered an unexpected tracer.")
jaxpr, kept_const_idx, kept_var_idx = _prune_unused_inputs(jaxpr)
consts = [c for i, c in enumerate(consts) if i in kept_const_idx]
pruned_arg_specs = (a for i, a in enumerate(arg_specs) if i in kept_var_idx)
abstract_args, arg_devices = util.unzip2(pruned_arg_specs)
donated_invars = [
x for i, x in enumerate(donated_invars) if i in kept_var_idx
]
map(prefetch, itertools.chain(consts, jaxpr_literals(jaxpr)))
jaxpr = apply_outfeed_rewriter(jaxpr)
nreps = jaxpr_replicas(jaxpr)
device = _xla_callable_device(nreps, backend, device, arg_devices)
backend = xb.get_device_backend(device) if device else xb.get_backend(backend)
# Computations that only produce constants and/or only rearrange their inputs,
# which are often produced from partial evaluation, don't need compilation,
# and don't need to evaluate their arguments.
if not jaxpr.eqns:
return XlaComputation(
name, None, True, None, jaxpr=jaxpr, consts=consts, device=device,
in_avals=abstract_args, out_avals=out_avals, kept_var_idx=kept_var_idx)
if not _on_exit:
log_priority = logging.WARNING if config.jax_log_compiles else logging.DEBUG
if len(abstract_args) > 10:
msg = f"Compiling {fun.__name__} ({id(fun)}) for {len(abstract_args)} args."
else:
msg = f"Compiling {fun.__name__} ({id(fun)} for args {abstract_args}."
logging.log(log_priority, msg)
if nreps > 1:
warnings.warn(
f"The jitted function {name} includes a pmap. Using "
"jit-of-pmap can lead to inefficient data movement, as the outer jit "
"does not preserve sharded data representations and instead collects "
"input and output arrays onto a single device. "
"Consider removing the outer jit unless you know what you're doing. "
"See https://github.com/google/jax/issues/2926.")
if nreps > xb.device_count(backend):
raise ValueError(
f"compiling computation `{name}` that requires {nreps} replicas, but "
f"only {xb.device_count(backend)} XLA devices are available.")
if xb.process_count() > 1 and (nreps > 1 or jaxpr_has_pmap(jaxpr)):
raise NotImplementedError(
"jit of multi-host pmap not implemented (and jit-of-pmap can cause "
"extra data movement anyway, so maybe you don't want it after all).")
# pass long arg lists as tuple for TPU
tuple_args = len(abstract_args) > 100
axis_env = xla.AxisEnv(nreps, (), ())
name_stack = xla.new_name_stack(xla.wrap_name(name, 'jit'))
closed_jaxpr = core.ClosedJaxpr(jaxpr, consts)
module: Union[str, xc.XlaComputation]
module_name = f"jit_{fun.__name__}"
if config.jax_enable_mlir:
module = mlir.lower_jaxpr_to_module(
module_name, closed_jaxpr, backend.platform,
mlir.ReplicaAxisContext(axis_env), name_stack, donated_invars)
else:
module = xla.lower_jaxpr_to_xla_module(
module_name, closed_jaxpr, backend.platform, axis_env,
name_stack, tuple_args, donated_invars, replicated_args=None,
arg_partitions=None, out_partitions=None)
return XlaComputation(
name, module, False, donated_invars, nreps=nreps, device=device,
backend=backend, tuple_args=tuple_args, in_avals=abstract_args,
out_avals=out_avals, kept_var_idx=kept_var_idx)
def _sharded_callable(
fun: lu.WrappedFun, nparts: Optional[int],
in_parts: Tuple[pxla.PartitionsOrReplicated, ...],
out_parts_thunk: Callable[[], Tuple[pxla.PartitionsOrReplicated, ...]],
local_in_parts: Optional[Tuple[pxla.PartitionsOrReplicated, ...]],
local_out_parts_thunk: Callable[[], Optional[Tuple[
pxla.PartitionsOrReplicated,
...]]], local_nparts: Optional[int], name: str, *abstract_args):
nrep = 1
if local_in_parts is None:
local_in_parts = in_parts
global_abstract_args = [
pxla.get_global_aval(arg, parts,
lparts) for arg, parts, lparts in safe_zip(
abstract_args, in_parts, local_in_parts)
]
if logging.vlog_is_on(2):
logging.vlog(2, "abstract_args: %s", abstract_args)
logging.vlog(2, "global_abstract_args: %s", global_abstract_args)
logging.vlog(2, "in_parts: %s", in_parts)
logging.vlog(2, "local_in_parts: %s", local_in_parts)
jaxpr, global_out_avals, consts = pe.trace_to_jaxpr_final(
fun, global_abstract_args)
platform = xb.get_backend().platform
nparts = pxla.reconcile_num_partitions(jaxpr, nparts)
assert nparts is not None
if nparts > xb.device_count():
raise ValueError(
f"sharded_jit computation requires {nparts} devices, "
f"but only {xb.device_count()} devices are available.")
if xb.local_device_count() < nparts < xb.device_count():
raise NotImplementedError(
f"sharded_jit across multiple hosts must use all available devices. "
f"Got {nparts} out of {xb.device_count()} requested devices "
f"(local device count: {xb.local_device_count()})")
if local_nparts is None:
if nparts > xb.local_device_count():
raise ValueError(
"Specify 'local_nparts' when using cross-process sharded_jit "
"and all inputs and outputs are replicated.")
else:
local_nparts = nparts
if local_nparts > xb.local_device_count():
raise ValueError(
f"sharded_jit computation requires {local_nparts} local devices, "
f"but only {xb.local_device_count()} local devices are available.")
if logging.vlog_is_on(2):
logging.vlog(2, "nparts: %d local_nparts: %d", nparts, local_nparts)
out_parts = out_parts_thunk()
local_out_parts = local_out_parts_thunk()
if local_out_parts is None:
local_out_parts = out_parts
if logging.vlog_is_on(2):
logging.vlog(2, "out_parts: %s", out_parts)
logging.vlog(2, "local_out_parts: %s", local_out_parts)
local_out_avals = [
pxla.get_local_aval(out, parts,
lparts) for out, parts, lparts in safe_zip(
global_out_avals, out_parts, local_out_parts)
]
log_priority = logging.WARNING if config.jax_log_compiles else logging.DEBUG
logging.log(log_priority, "Compiling %s for %d devices with args %s.",
fun.__name__, nparts, global_abstract_args)
axis_env = xla.AxisEnv(nrep, (), ())
unordered_effects = [
eff for eff in jaxpr.effects if eff not in core.ordered_effects
]
ordered_effects = [
eff for eff in jaxpr.effects if eff in core.ordered_effects
]
module, _ = mlir.lower_jaxpr_to_module(
f"spjit_{fun.__name__}",
core.ClosedJaxpr(jaxpr, consts),
unordered_effects,
ordered_effects,
platform=platform,
axis_context=mlir.ReplicaAxisContext(axis_env),
name_stack=new_name_stack(wrap_name(name, "sharded_jit")),
donated_args=[False] * len(in_parts),
arg_shardings=safe_map(xla.sharding_to_proto, in_parts),
result_shardings=safe_map(xla.sharding_to_proto, out_parts))
built = xc._xla.mlir.mlir_module_to_xla_computation(
mlir.module_to_string(module), use_tuple_args=False, return_tuple=True)
if nparts <= xb.local_device_count():
devices = xb.local_devices()[:nparts]
else:
assert nparts == xb.device_count()
devices = xb.devices()
device_assignment = np.array([[d for d in devices]])
device_assignment = np.reshape(device_assignment, (-1, nparts))
# device_assignment = None # TODO(skye): replace with default device assignment?
compiled = dispatch.backend_compile(
xb.get_backend(), built,
xb.get_compile_options(nrep, nparts, device_assignment))
input_specs = [
pxla.partitioned_sharding_spec(local_nparts, parts, aval)
for parts, aval in zip(local_in_parts, abstract_args)
]
input_indices = [
pxla.spec_to_indices(aval.shape, spec) if spec is not None else None
for aval, spec in zip(abstract_args, input_specs)
]
handle_args = partial(pxla.shard_args, compiled.local_devices(),
input_indices)
handle_outs = _avals_to_results_handler(
nrep,
local_nparts, # type: ignore
local_out_parts,
local_out_avals)
return partial(_execute_spatially_partitioned, compiled, handle_args,
handle_outs)
def lower_xla_callable(fun: lu.WrappedFun, device, backend, name,
donated_invars, always_lower: bool, keep_unused: bool,
*arg_specs):
"""Lower into XLA.
Args:
always_lower: If `True`, even trivial programs (not doing any computation
such as lambda x: x) will be lowered into an XLA program.
keep_unused: If `False` (the default), arguments that JAX determines to be
unused by `fun` *may* be dropped from resulting compiled XLA executables.
Such arguments will not be transferred to the device nor provided to the
underlying executable. If `True`, unused arguments will not be pruned.
"""
if device is not None and backend is not None:
raise ValueError("can't specify both a device and a backend for jit, "
"got device={} and backend={}".format(
device, backend))
abstract_args, arg_devices = util.unzip2(arg_specs)
if fun.in_type is not None:
abstract_args, which_explicit = util.unzip2(fun.in_type)
else:
which_explicit = None
with log_elapsed_time(f"Finished tracing + transforming {fun.__name__} "
"for jit in {elapsed_time} sec"):
jaxpr, out_avals, consts = pe.trace_to_jaxpr_final(
fun, abstract_args, pe.debug_info_final(fun, "jit"),
which_explicit)
if any(isinstance(c, core.Tracer) for c in consts):
raise UnexpectedTracerError("Encountered an unexpected tracer.")
# TODO(mattjj): handle argument pruning w/ dynamic shapes
if fun.in_type is None and not keep_unused:
jaxpr, kept_const_idx, kept_var_idx = _prune_unused_inputs(jaxpr)
consts = [c for i, c in enumerate(consts) if i in kept_const_idx]
abstract_args, arg_devices = util.unzip2(
[a for i, a in enumerate(arg_specs) if i in kept_var_idx])
donated_invars = [
x for i, x in enumerate(donated_invars) if i in kept_var_idx
]
del kept_const_idx
else:
kept_var_idx = set(range(len(abstract_args)))
map(prefetch, itertools.chain(consts, jaxpr_literals(jaxpr)))
jaxpr = apply_outfeed_rewriter(jaxpr)
nreps = jaxpr_replicas(jaxpr)
device = _xla_callable_device(nreps, backend, device, arg_devices)
backend = xb.get_device_backend(device) if device else xb.get_backend(
backend)
if (config.jax_dynamic_shapes and jaxpr_has_bints(jaxpr)
and not _backend_supports_unbounded_dynamic_shapes(backend)):
jaxpr, consts = pe.pad_jaxpr(jaxpr, consts)
# Computations that only produce constants and/or only rearrange their inputs,
# which are often produced from partial evaluation, don't need compilation,
# and don't need to evaluate their arguments.
if not jaxpr.eqns and not always_lower:
return XlaComputation(name,
None,
True,
None,
None,
jaxpr=jaxpr,
consts=consts,
device=device,
in_avals=abstract_args,
out_avals=out_avals,
has_unordered_effects=False,
ordered_effects=[],
kept_var_idx=kept_var_idx,
keepalive=None)
if not _on_exit:
log_priority = logging.WARNING if config.jax_log_compiles else logging.DEBUG
if len(abstract_args) > 10:
msg = f"Compiling {fun.__name__} ({id(fun)}) for {len(abstract_args)} args."
else:
msg = f"Compiling {fun.__name__} ({id(fun)} for args {abstract_args}."
logging.log(log_priority, msg)
if nreps > 1:
warnings.warn(
f"The jitted function {name} includes a pmap. Using "
"jit-of-pmap can lead to inefficient data movement, as the outer jit "
"does not preserve sharded data representations and instead collects "
"input and output arrays onto a single device. "
"Consider removing the outer jit unless you know what you're doing. "
"See https://github.com/google/jax/issues/2926.")
if nreps > xb.device_count(backend):
raise ValueError(
f"compiling computation `{name}` that requires {nreps} replicas, but "
f"only {xb.device_count(backend)} XLA devices are available.")
if xb.process_count() > 1 and (nreps > 1 or jaxpr_has_pmap(jaxpr)):
raise NotImplementedError(
"jit of multi-host pmap not implemented (and jit-of-pmap can cause "
"extra data movement anyway, so maybe you don't want it after all)."
)
# pass long arg lists as tuple for TPU
tuple_args = len(abstract_args) > 100
axis_env = xla.AxisEnv(nreps, (), ())
name_stack = util.new_name_stack(util.wrap_name(name, 'jit'))
closed_jaxpr = core.ClosedJaxpr(jaxpr, consts)
module_name = f"jit_{fun.__name__}"
unordered_effects = [
eff for eff in closed_jaxpr.effects if eff not in core.ordered_effects
]
ordered_effects = [
eff for eff in closed_jaxpr.effects if eff in core.ordered_effects
]
module, keepalive = mlir.lower_jaxpr_to_module(
module_name, closed_jaxpr,
unordered_effects, ordered_effects, backend.platform,
mlir.ReplicaAxisContext(axis_env), name_stack, donated_invars)
return XlaComputation(name,
module,
False,
donated_invars,
which_explicit,
nreps=nreps,
device=device,
backend=backend,
tuple_args=tuple_args,
in_avals=abstract_args,
out_avals=out_avals,
has_unordered_effects=bool(unordered_effects),
ordered_effects=ordered_effects,
kept_var_idx=kept_var_idx,
keepalive=keepalive)