def _xla_call_translation_rule(ctx,
avals_in,
avals_out,
*in_nodes,
name,
backend=None,
call_jaxpr,
donated_invars,
inline=None,
device=None):
del device, donated_invars, inline # Ignored.
c = ctx.builder
check_backend_matches(backend, ctx.platform)
subc = xc.XlaBuilder(f"jit_{name}")
args = [parameter(subc, i, c.get_shape(n)) for i, n in enumerate(in_nodes)]
sub_ctx = ctx.replace(builder=subc,
name_stack=extend_name_stack(ctx.name_stack,
wrap_name(name, 'jit')))
out_nodes = jaxpr_subcomp(sub_ctx, call_jaxpr, (), *args)
if len(out_nodes) == 1:
subc = subc.Build(out_nodes[0])
return [xops.Call(c, subc, list(in_nodes))]
else:
subc = subc.Build(xops.Tuple(subc, out_nodes))
return xla_destructure(c, xops.Call(c, subc, list(in_nodes)))
def _sharded_jit_translation_rule(ctx, avals_in, avals_out, *in_nodes,
in_parts, out_parts_thunk, nparts, name,
call_jaxpr, local_in_parts,
local_out_parts_thunk, local_nparts):
subc = xc.XlaBuilder(f"sharded_jit_{name}")
# We assume any extra leading in_nodes are constants and replicate them.
num_extra_nodes = len(in_nodes) - len(in_parts)
assert num_extra_nodes >= 0
in_parts = (None, ) * num_extra_nodes + in_parts
args = []
for i, (n, sharding) in enumerate(safe_zip(in_nodes, in_parts)):
# We use xla.set_sharding instead of xla.with_sharding because inlined calls
# shouldn't have shardings set directly on the inputs or outputs.
arg = xla.parameter(subc, i, ctx.builder.GetShape(n))
args.append(xla.set_sharding(subc, arg, sharding))
sub_ctx = ctx.replace(builder=subc,
name_stack=extend_name_stack(
wrap_name(name, "sharded_jit")))
out_nodes = xla.jaxpr_subcomp(sub_ctx, call_jaxpr, (), *args)
out_parts = out_parts_thunk()
assert len(out_parts) == len(out_nodes)
out_nodes = [
xla.set_sharding(subc, out, sharding)
for out, sharding in safe_zip(out_nodes, out_parts)
]
subc = subc.build(xops.Tuple(subc, out_nodes))
return xla.xla_destructure(ctx.builder,
xops.Call(ctx.builder, subc, list(in_nodes)))
def _remat_using_cond(ctx, in_nodes, name, call_jaxpr):
"""Lower remat to a Conditional which always returns true. This:
1. Circumvents common subexpression elimination.
2. In common case of `jax.grad(jax.remat(f))`, ensures the remat blocks
occur after the primal blocks, because cotangent is an input to the
Conditional."""
# Fake condition which always selects True branch.
c = ctx.builder
rng = xops.RngUniform(xops.Constant(c, np.array(0, dtype=np.float32)),
xops.Constant(c, np.array(1, dtype=np.float32)),
xc.Shape.array_shape(xc.PrimitiveType.F32, []))
pred = xops.Lt(rng, xops.Constant(c, np.array(2, dtype=np.float32)))
true_op = xops.Tuple(c, in_nodes)
remat_subc = xc.XlaBuilder("remat_call_subcomputation")
input_op = parameter(remat_subc, 0, c.get_shape(true_op), replicated=[])
args = xla_destructure(remat_subc, input_op)
sub_ctx = ctx.replace(builder=remat_subc,
name_stack=extend_name_stack(
ctx.name_stack, wrap_name(name, 'remat')))
out_nodes = jaxpr_subcomp(sub_ctx, call_jaxpr, (), *args)
out_node_shapes = [remat_subc.get_shape(o) for o in out_nodes]
remat_subc = remat_subc.build(xops.Tuple(remat_subc, out_nodes))
false_op = true_op
dummy_subc = xc.XlaBuilder("remat_call_dummy_subcomputation")
parameter(dummy_subc, 0, c.get_shape(false_op), replicated=[])
out_nodes = [_zeros(dummy_subc, s) for s in out_node_shapes]
dummy_subc = dummy_subc.build(xops.Tuple(dummy_subc, out_nodes))
return xla_destructure(
c, xops.Conditional(pred, true_op, remat_subc, false_op, dummy_subc))
def _remat_using_while(ctx, in_nodes, name, call_jaxpr):
"""Lower remat to a single iteration while loop."""
c = ctx.builder
# Dummy subc for getting subcomp shapes.
dummy_inputs = xops.Tuple(c, in_nodes)
dummy_subc = xc.XlaBuilder("remat_dummy_subcomputation")
dummy_input_op = parameter(dummy_subc,
0,
c.get_shape(dummy_inputs),
replicated=[])
dummy_args = xla_destructure(dummy_subc, dummy_input_op)
dummy_ctx = ctx.replace(builder=dummy_subc,
name_stack=extend_name_stack(
ctx.name_stack, wrap_name(name, 'remat')))
dummy_subcomp_outs = jaxpr_subcomp(dummy_ctx, call_jaxpr, (), *dummy_args)
out_node_shapes = [dummy_subc.get_shape(o) for o in dummy_subcomp_outs]
i_init = xops.Constant(c, np.array(0, dtype=np.int32))
zeros_like_outs = [_zeros(c, s) for s in out_node_shapes]
inputs = xops.Tuple(c, [i_init] + list(in_nodes) + zeros_like_outs)
cond_subc = xc.XlaBuilder("remat_cond_subcomputation")
input_op = parameter(cond_subc, 0, c.get_shape(inputs), replicated=[])
i = xops.GetTupleElement(input_op, 0)
rng = xops.RngUniform(
xops.Constant(cond_subc, np.array(1, dtype=np.int32)),
xops.Constant(cond_subc, np.array(2, dtype=np.int32)),
xc.Shape.array_shape(xc.PrimitiveType.S32, []))
cond_subc = cond_subc.build(xops.Lt(i, rng))
body_subc = xc.XlaBuilder("remat_body_subcomputation")
input_op = parameter(body_subc, 0, c.get_shape(inputs), replicated=[])
i, *args = xla_destructure(body_subc, input_op)[:len(in_nodes) + 1]
i_next = xops.Add(i, xops.Constant(body_subc, np.array(1, dtype=np.int32)))
body_ctx = ctx.replace(builder=body_subc,
name_stack=extend_name_stack(
ctx.name_stack, wrap_name(name, 'remat')))
subcomp_outs = jaxpr_subcomp(body_ctx, call_jaxpr, (), *args)
out_nodes = [i_next] + args + list(subcomp_outs)
body_subc = body_subc.build(xops.Tuple(body_subc, out_nodes))
outs = xops.While(cond_subc, body_subc, inputs)
return xla_destructure(c, outs)[len(in_nodes) + 1:]
def _named_call_translation_rule(ctx, avals_in, avals_out, *in_nodes,
name="core_call", backend=None, call_jaxpr):
check_backend_matches(backend, ctx.platform)
c = ctx.builder
subc = xc.XlaBuilder(name)
args = [parameter(subc, i, c.GetShape(n)) for i, n in enumerate(in_nodes)]
sub_ctx = ctx.replace(builder=subc,
name_stack=extend_name_stack(ctx.name_stack, name))
out_nodes = jaxpr_subcomp(sub_ctx, call_jaxpr, (), *args)
subc = subc.Build(xops.Tuple(subc, out_nodes))
return xla_destructure(c, xops.Call(c, subc, list(in_nodes)))
def _sharded_jit_lowering(ctx, *in_nodes, in_parts, out_parts_thunk, nparts,
name, call_jaxpr, local_in_parts,
local_out_parts_thunk, local_nparts):
# We assume any extra leading in_nodes are constants and replicate them.
num_extra_nodes = len(in_nodes) - len(in_parts)
assert num_extra_nodes >= 0
in_parts = (None, ) * num_extra_nodes + in_parts
args = []
for ns, sharding in safe_zip(
safe_map(mlir.wrap_singleton_ir_values, in_nodes), in_parts):
if sharding is not None:
args.append([
mlir.wrap_with_sharding_op(n, xla.sharding_to_proto(sharding))
for n in ns
])
else:
args.append(ns)
sub_ctx = ctx.module_context.replace(
name_stack=extend_name_stack(wrap_name(name, "sharded_jit")))
fn = mlir.lower_jaxpr_to_fun(sub_ctx, f"sharded_jit_{name}",
core.ClosedJaxpr(call_jaxpr, ()))
output_types = safe_map(mlir.aval_to_ir_types, ctx.avals_out)
flat_output_types = util.flatten(output_types)
call = std.CallOp(flat_output_types,
ir.FlatSymbolRefAttr.get(fn.name.value),
mlir.flatten_lowering_ir_args(args))
out_nodes = util.unflatten(call.results, safe_map(len, output_types))
out_parts = out_parts_thunk()
outputs = []
for ns, sharding in safe_zip(out_nodes, out_parts):
if sharding is not None:
outputs.append([
mlir.wrap_with_sharding_op(n, xla.sharding_to_proto(sharding))
for n in ns
])
else:
outputs.append(ns)
return outputs
def _cond_lowering(ctx, index, *args, branches, linear):
del linear # Unused.
joined_effects = core.join_effects(*(branch.effects
for branch in branches))
ordered_effects = [
eff for eff in joined_effects if eff in core.ordered_effects
]
num_tokens = len(ordered_effects)
tokens_in = ctx.tokens_in.subset(ordered_effects)
output_token_types = [mlir.token_type() for _ in ordered_effects]
output_types = [
*output_token_types, *_map(mlir.aval_to_ir_types, ctx.avals_out)
]
flat_output_types = util.flatten(output_types)
# mhlo.CaseOp takes a single argument 'index' and the corresponding blocks
# have no arguments; the computation within the block uses implicit
# captures.
case_op = mhlo.CaseOp(flat_output_types,
index=index,
num_branches=len(branches))
name_stack = extend_name_stack(ctx.module_context.name_stack, 'cond')
for i, jaxpr in enumerate(branches):
branch = case_op.regions[i].blocks.append()
with ir.InsertionPoint(branch):
sub_ctx = ctx.module_context.replace(
name_stack=xla.extend_name_stack(name_stack,
f'branch_{i}_fun'))
out_vals, tokens_out = mlir.jaxpr_subcomp(
sub_ctx, jaxpr.jaxpr, tokens_in,
_map(mlir.ir_constants, jaxpr.consts),
*_map(mlir.wrap_singleton_ir_values, args))
out_tokens = [tokens_out.get(eff) for eff in ordered_effects]
out_vals = [*out_tokens, *out_vals]
mhlo.ReturnOp(util.flatten(out_vals))
tokens_and_outputs = util.unflatten(case_op.results,
_map(len, output_types))
tokens, outputs = util.split_list(tokens_and_outputs, [num_tokens])
ctx.set_tokens_out(mlir.TokenSet(zip(ordered_effects, tokens)))
return outputs
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
if platform not in ["tpu", "gpu"]:
# TODO(skye): fall back to regular jit?
raise ValueError(f"sharded_jit not supported for {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)
c = xc.XlaBuilder("spjit_{}".format(fun.__name__))
xla_consts = _map(partial(xla.pyval_to_ir_constant, c), consts)
xla_args = _xla_sharded_args(c, global_abstract_args, in_parts)
axis_env = xla.AxisEnv(nrep, (), ())
ctx = xla.TranslationContext(
c, platform, axis_env,
extend_name_stack(wrap_name(name, "sharded_jit")))
out_nodes = xla.jaxpr_subcomp(ctx, jaxpr, xla_consts, *xla_args)
out_tuple = xla.with_sharding(c, out_parts, xops.Tuple, c, out_nodes)
built = c.Build(out_tuple)
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.id 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)
