def compile_or_get_cached(backend, computation, compile_options):
# Avoid import cycle between jax and jax.experimental
from jax.experimental.compilation_cache import compilation_cache as cc
if isinstance(computation, ir.Module):
sym_name = computation.operation.attributes['sym_name']
module_name = ir.StringAttr(sym_name).value
computation = mlir.module_to_string(computation)
else:
module_name = computation.name()
# Persistent compilation cache only implemented on TPU.
# TODO(skye): add warning when initializing cache on unsupported default platform
if cc.is_initialized() and backend.platform == 'tpu':
cached_executable = cc.get_executable(computation, compile_options, backend)
if cached_executable is not None:
logging.info('Persistent compilation cache hit for %s.', module_name)
return cached_executable
else:
compiled = backend_compile(backend, computation, compile_options)
cc.put_executable(module_name, computation, compile_options, compiled,
backend)
return compiled
if FLAGS.jax_dump_ir_to:
ir_str = (computation if isinstance(computation, str)
else computation.as_hlo_text())
_dump_ir_to_file(module_name, ir_str)
return backend_compile(backend, computation, compile_options)
def hlo(self) -> xc.XlaComputation:
if self.is_trivial():
raise ValueError("A trivial computation has no HLO")
if isinstance(self._hlo, xc.XlaComputation):
return self._hlo
return xe.mlir.mlir_module_to_xla_computation(
mlir.module_to_string(self._hlo),
use_tuple_args=self.compile_args["tuple_args"])
def compile_or_get_cached(backend, computation, compile_options):
# Avoid import cycle between jax and jax.experimental
from jax.experimental.compilation_cache import compilation_cache as cc
if isinstance(computation, ir.Module):
sym_name = computation.operation.attributes['sym_name']
module_name = ir.StringAttr(sym_name).value
# Convert ir.Module to str representation (the default), unless the
# back-end expliclity flags the ability to handle a module directly
# (avoiding the overhead of back and forth conversions)
if getattr(backend, "needs_str_ir", True):
computation = mlir.module_to_string(computation)
else:
module_name = computation.name()
# Persistent compilation cache only implemented on TPU.
# TODO(skye): add warning when initializing cache on unsupported default platform
if cc.is_initialized() and backend.platform == 'tpu':
cached_executable = cc.get_executable(computation, compile_options,
backend)
if cached_executable is not None:
logging.info('Persistent compilation cache hit for %s.',
module_name)
return cached_executable
else:
compiled = backend_compile(backend, computation, compile_options)
cc.put_executable(module_name, computation, compile_options,
compiled, backend)
return compiled
if FLAGS.jax_dump_ir_to:
if isinstance(computation, xc.XlaComputation):
ir_str = computation.as_hlo_text()
elif isinstance(computation, ir.Module):
ir_str = mlir.module_to_string(computation)
else:
assert isinstance(computation, str)
ir_str = computation
_dump_ir_to_file(module_name, ir_str)
return backend_compile(backend, computation, compile_options)
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 mhlo(self) -> str:
if self.is_trivial():
raise ValueError("A trivial computation has no MHLO")
if isinstance(self._hlo, xc.XlaComputation):
return xe.mlir.xla_computation_to_mlir_module(self._hlo)
return mlir.module_to_string(self._hlo)
