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primitives.py
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# Copyright 2023 The JAX Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Module for Pallas:TPU-specific JAX primitives and functions."""
from __future__ import annotations
import dataclasses
import enum
from typing import Any, Callable
import jax
from jax._src import api_util
from jax._src import core as jax_core
from jax._src import dtypes
from jax._src import effects
from jax._src import linear_util as lu
from jax._src import pretty_printer as pp
from jax._src import state
from jax._src import tree_util
from jax._src import util
from jax._src.state import indexing
from jax._src.state import primitives as sp
from jax._src.interpreters import mlir
from jax._src.interpreters import partial_eval as pe
from jax._src.pallas import core as pl_core
from jax._src.pallas.mosaic import core as tpu_core
from jax._src.typing import DTypeLike
import jax.numpy as jnp
map, unsafe_map = util.safe_map, map
zip, unsafe_zip = util.safe_zip, zip
repeat_p = jax_core.Primitive('repeat')
def repeat(x, repeats, axis):
return repeat_p.bind(x, repeats=repeats, axis=axis)
@repeat_p.def_abstract_eval
def _repeat_abstract_eval(x, *, repeats, axis):
shape = list(x.shape)
shape[axis] *= repeats
return jax_core.ShapedArray(shape, x.dtype)
def _repeat_lowering_rule(ctx: mlir.LoweringRuleContext, x, *, repeats, axis):
def _repeat(x):
return jnp.repeat(x, repeats, axis)
return mlir.lower_fun(_repeat, multiple_results=False)(ctx, x)
mlir.register_lowering(repeat_p, _repeat_lowering_rule)
bitcast_p = jax_core.Primitive("bitcast")
def bitcast(x, ty: DTypeLike):
ty = dtypes.canonicalize_dtype(ty)
if len(x.shape) < 2:
raise ValueError("Not implemented: bitcast 1D")
if x.shape[-2] * x.dtype.itemsize % ty.itemsize:
raise ValueError(
"Not implemented: the 2nd minor dim can not be perfectly packed or"
" unpacked"
)
return bitcast_p.bind(x, ty=ty)
@bitcast_p.def_abstract_eval
def _bitcast_abstract_eval(x, *, ty):
shape = list(x.shape)
shape[-2] = shape[-2] * x.dtype.itemsize // ty.itemsize
return jax_core.ShapedArray(shape, ty)
def _bitcast_lowering_rule(ctx: mlir.LoweringRuleContext, x, *, ty):
def _bitcast(x):
if x.dtype.itemsize < ty.itemsize:
*leading, m, n = x.shape
packing = ty.itemsize // x.dtype.itemsize
x = x.reshape(*leading, m // packing, packing, n)
x = jnp.swapaxes(x, -1, -2)
return jax.lax.bitcast_convert_type(x, ty)
if x.dtype.itemsize > ty.itemsize:
y = jax.lax.bitcast_convert_type(x, ty)
*leading, m, n, packing = y.shape
return jnp.swapaxes(y, -1, -2).reshape(*leading, m * packing, n)
return jax.lax.bitcast_convert_type(x, ty)
return mlir.lower_fun(_bitcast, multiple_results=False)(ctx, x)
mlir.register_lowering(bitcast_p, _bitcast_lowering_rule)
roll_p = jax_core.Primitive("roll")
def roll(
x,
shift: int,
axis: int,
*,
stride: int | None = None,
stride_axis: int | None = None,
):
if shift < 0:
raise ValueError("shift must be non-negative.")
if axis < 0 or axis >= len(x.shape):
raise ValueError("axis is out of range.")
if (stride is None) != (stride_axis is None):
raise ValueError("stride and stride_axis must be both specified or not.")
if stride is not None and stride_axis is not None:
if stride < 0:
raise ValueError("stride must be non-negative.")
if stride_axis < 0 or stride_axis >= len(x.shape):
raise ValueError("stride_axis is out of range")
if axis == stride_axis:
raise ValueError("expected axis and stride_axis are different.")
return roll_p.bind(
x, shift=shift, axis=axis, stride=stride, stride_axis=stride_axis
)
@roll_p.def_abstract_eval
def _roll_abstract_eval(x, **_):
return jax_core.raise_to_shaped(x)
def _roll_lowering_rule(
ctx: mlir.LoweringRuleContext, x, *, shift, axis, stride, stride_axis
):
def _roll(x):
if stride is None:
return jnp.roll(x, shift, axis)
outputs = [
jnp.roll(xs, shift + i * stride, axis)
for i, xs in enumerate(jnp.split(x, x.shape[stride_axis], stride_axis))
]
return jnp.concatenate(outputs, stride_axis)
return mlir.lower_fun(_roll, multiple_results=False)(ctx, x)
mlir.register_lowering(roll_p, _roll_lowering_rule)
run_scoped_p = jax_core.Primitive('run_scoped')
run_scoped_p.multiple_results = True
def run_scoped(f: Callable[..., None], *types, **kw_types) -> None:
flat_types, in_tree = tree_util.tree_flatten((types, kw_types))
flat_fun, _ = api_util.flatten_fun(lu.wrap_init(f), in_tree)
avals = map(lambda t: t.get_aval(), flat_types)
jaxpr, _, consts, () = pe.trace_to_jaxpr_dynamic(flat_fun, avals)
run_scoped_p.bind(*consts, jaxpr=jaxpr)
@run_scoped_p.def_effectful_abstract_eval
def _run_scoped_abstract_eval(*args, jaxpr):
# jaxpr will have effects for its inputs (Refs that are allocated) and for
# constvars (closed over Refs). The effects for the allocated Refs are local
# to the jaxpr and shouldn't propagate out.
nonlocal_effects = {
eff for eff in jaxpr.effects
if not (
isinstance(eff, effects.JaxprInputEffect)
and eff.input_index >= len(jaxpr.constvars)
)
}
return [], nonlocal_effects
class DeviceIdType(enum.Enum):
MESH = "mesh"
LOGICAL = "logical"
def check_sem_avals(sem_aval, sem_indexers_avals, name):
if not isinstance(sem_aval, state.AbstractRef):
raise ValueError(f"Cannot {name} on a non-semaphore Ref: {sem_aval}")
sem_shape = sem_aval.shape
if sem_indexers_avals:
sem_shape = sem_indexers_avals[-1].get_indexer_shape()
if sem_shape:
raise ValueError(f"Cannot {name} on a non-()-shaped semaphore: {sem_shape}")
sem_dtype = sem_aval.dtype
if not (
jnp.issubdtype(sem_dtype, tpu_core.semaphore)
or jnp.issubdtype(sem_dtype, tpu_core.barrier_semaphore)
):
raise ValueError(f"Must {name} a REGULAR or BARRIER semaphore: {sem_dtype}")
semaphore_read_p = jax_core.Primitive("semaphore_read")
semaphore_read_p.multiple_results = False
def semaphore_read(sem_or_view):
ref, indexers = _get_ref_and_indexers(sem_or_view)
args = [ref, indexers]
flat_args, args_tree = tree_util.tree_flatten(args)
return semaphore_read_p.bind(*flat_args, args_tree=args_tree)
@semaphore_read_p.def_abstract_eval
def _semaphore_read_abstract_eval(
*avals,
args_tree,
):
sem_aval, sem_indexers_avals = tree_util.tree_unflatten(args_tree, avals)
check_sem_avals(sem_aval, sem_indexers_avals, "read")
return jax_core.ShapedArray((), jnp.dtype("int32"))
semaphore_signal_p = jax_core.Primitive('semaphore_signal')
semaphore_signal_p.multiple_results = True
def semaphore_signal(
sem_or_view,
inc: int | jax.Array = 1,
*,
device_id: int | jax.Array | None | tuple[int | jax.Array, ...] = None,
device_id_type: DeviceIdType = DeviceIdType.MESH,
core_index: int | jax.Array | None = None,
):
ref, indexers = _get_ref_and_indexers(sem_or_view)
inc = jnp.asarray(inc, dtype=jnp.int32)
args = [ref, indexers, inc, device_id, core_index]
flat_args, args_tree = tree_util.tree_flatten(args)
semaphore_signal_p.bind(
*flat_args,
args_tree=args_tree,
device_id_type=device_id_type,
)
@semaphore_signal_p.def_abstract_eval
def _semaphore_signal_abstract_eval(
*avals,
args_tree,
device_id_type: DeviceIdType,
):
del device_id_type
sem_aval, sem_indexers_avals, value_aval, device_id_avals, core_index_aval = (
tree_util.tree_unflatten(args_tree, avals)
)
check_sem_avals(sem_aval, sem_indexers_avals, "signal")
if value_aval.dtype != jnp.dtype("int32"):
raise ValueError("Must signal an int32 value.")
if device_id_avals is not None:
device_id_flat_avals = tree_util.tree_leaves(device_id_avals)
for aval in device_id_flat_avals:
if aval.dtype != jnp.dtype("int32"):
raise ValueError("`device_id`s must be an int32 value.")
return []
def _semaphore_signal_pp_eqn(eqn: jax_core.JaxprEqn,
context: jax_core.JaxprPpContext,
settings: jax_core.JaxprPpSettings):
del settings
invars = eqn.invars
tree = eqn.params["args_tree"]
(
sem,
sem_indexers,
value,
device_ids,
_,
) = tree_util.tree_unflatten(tree, invars)
out = pp.concat([
pp.text('semaphore_signal'),
pp.text(' '),
sp.pp_ref_indexers(context, sem, sem_indexers),
pp.text(' '),
pp.text(jax_core.pp_var(value, context)),
])
if device_ids is not None:
flat_device_ids = tree_util.tree_leaves(device_ids)
if not flat_device_ids:
return out
device_ids_pp = [pp.text(jax_core.pp_var(flat_device_ids[0], context))]
for device_id in flat_device_ids[1:]:
device_ids_pp.append(pp.text(" "))
device_ids_pp.append(pp.text(jax_core.pp_var(device_id, context)))
out = pp.concat([out, pp.concat(device_ids_pp)])
return out
jax_core.pp_eqn_rules[semaphore_signal_p] = _semaphore_signal_pp_eqn
semaphore_wait_p = jax_core.Primitive('semaphore_wait')
semaphore_wait_p.multiple_results = True
def semaphore_wait(sem_or_view, dec: int | jax.Array = 1):
ref, indexers = _get_ref_and_indexers(sem_or_view)
dec = jnp.asarray(dec, dtype=jnp.int32)
args = [ref, indexers, dec]
flat_args, args_tree = tree_util.tree_flatten(args)
semaphore_wait_p.bind(*flat_args, args_tree=args_tree)
@semaphore_wait_p.def_abstract_eval
def _semaphore_wait_abstract_eval(*avals, args_tree):
sem_aval, sem_indexers_avals, value_aval = tree_util.tree_unflatten(args_tree, avals)
check_sem_avals(sem_aval, sem_indexers_avals, "wait")
if value_aval.dtype != jnp.dtype("int32"):
raise ValueError("Must wait an int32 value.")
return []
def _semaphore_wait_pp_eqn(eqn: jax_core.JaxprEqn,
context: jax_core.JaxprPpContext,
settings: jax_core.JaxprPpSettings):
del settings
invars = eqn.invars
tree = eqn.params["args_tree"]
(
sem,
sem_indexers,
value,
) = tree_util.tree_unflatten(tree, invars)
return pp.concat([
pp.text('semaphore_wait'),
pp.text(' '),
sp.pp_ref_indexers(context, sem, sem_indexers),
pp.text(' '),
pp.text(jax_core.pp_var(value, context)),
])
jax_core.pp_eqn_rules[semaphore_wait_p] = _semaphore_wait_pp_eqn
@dataclasses.dataclass
class AsyncCopyDescriptor:
src_ref: Any
src_indexers: tuple[indexing.NDIndexer, ...]
dst_ref: Any
dst_indexers: tuple[indexing.NDIndexer, ...]
dst_sem: int | jax.Array
dst_sem_indexers: tuple[indexing.NDIndexer, ...]
src_sem: int | jax.Array | None
src_sem_indexers: tuple[indexing.NDIndexer, ...] | None
device_id: int | jax.Array | None
device_id_type: DeviceIdType = DeviceIdType.MESH
def __post_init__(self):
if (self.src_sem is None) ^ (self.device_id is None):
raise ValueError("Either both or neither `src_sem` and `device_id` "
"can be set.")
@property
def is_remote(self):
return self.src_sem is not None
def start(self):
flat_args, tree = tree_util.tree_flatten((
self.src_ref,
self.src_indexers,
self.dst_ref,
self.dst_indexers,
self.dst_sem,
self.dst_sem_indexers,
self.src_sem,
self.src_sem_indexers,
self.device_id,
))
dma_start_p.bind(*flat_args, tree=tree, device_id_type=self.device_id_type)
def wait(self):
if self.is_remote:
self.wait_send()
self.wait_recv()
def wait_recv(self):
wait_args, tree = tree_util.tree_flatten(
(self.dst_sem, self.dst_sem_indexers, self.dst_ref, self.dst_indexers)
)
dma_wait_p.bind(
*wait_args, tree=tree, device_id_type=self.device_id_type
)
def wait_send(self):
if not self.is_remote:
raise ValueError("Cannot `wait_send` on a local copy.")
wait_args, tree = tree_util.tree_flatten(
(self.src_sem, self.src_sem_indexers, self.src_ref, self.src_indexers)
)
dma_wait_p.bind(
*wait_args, tree=tree, device_id_type=self.device_id_type
)
dma_start_p = jax_core.Primitive('dma_start')
dma_start_p.multiple_results = True
@dma_start_p.def_abstract_eval
def _dma_start_abstract_eval(*args, tree, device_id_type):
(
src_ref_aval,
src_indexers_avals,
dst_ref_aval,
dst_indexers_avals,
dst_sem_aval,
dst_sem_indexers_avals,
src_sem_aval,
src_sem_indexers_avals,
device_id_aval,
) = tree_util.tree_unflatten(tree, args)
dst_sem_shape = dst_sem_aval.shape
if dst_sem_indexers_avals:
dst_sem_shape = dst_sem_indexers_avals[-1].get_indexer_shape()
if dst_sem_shape:
raise ValueError(
f"Cannot signal on a non-()-shaped semaphore: {dst_sem_shape}"
)
if src_sem_aval is not None:
src_sem_shape = src_sem_aval.shape
if src_sem_indexers_avals:
src_sem_shape = src_sem_indexers_avals[-1].get_indexer_shape()
if src_sem_shape:
raise ValueError(
f"Cannot signal on a non-()-shaped semaphore: {src_sem_shape}"
)
return []
def _dma_start_pp_eqn(eqn: jax_core.JaxprEqn,
context: jax_core.JaxprPpContext,
settings: jax_core.JaxprPpSettings):
invars = eqn.invars
tree = eqn.params["tree"]
(
src_ref,
src_indexers,
dst_ref,
dst_indexers,
dst_sem,
dst_sem_indexers,
src_sem,
src_sem_indexers,
device_id,
) = tree_util.tree_unflatten(tree, invars)
del src_sem_indexers
# TODO(sharadmv): pretty print source semaphores and device id
if src_sem or device_id:
return jax_core._pp_eqn(eqn, context, settings)
return pp.concat([
pp.text('dma_start'),
pp.text(' '),
sp.pp_ref_indexers(context, src_ref, src_indexers),
pp.text(' -> '),
sp.pp_ref_indexers(context, dst_ref, dst_indexers),
pp.text(' '),
sp.pp_ref_indexers(context, dst_sem, dst_sem_indexers),
])
jax_core.pp_eqn_rules[dma_start_p] = _dma_start_pp_eqn
dma_wait_p = jax_core.Primitive('dma_wait')
dma_wait_p.multiple_results = True
@dma_wait_p.def_abstract_eval
def _dma_wait_abstract_eval(*args, tree, device_id_type):
del args, tree, device_id_type
return []
def _dma_wait_pp_eqn(eqn: jax_core.JaxprEqn,
context: jax_core.JaxprPpContext,
settings: jax_core.JaxprPpSettings):
del settings
invars = eqn.invars
tree = eqn.params["tree"]
sem, sem_indexers, ref, indexers = tree_util.tree_unflatten(tree, invars)
return pp.concat([
pp.text('dma_wait'),
pp.text(' '),
sp.pp_ref_indexers(context, ref, indexers),
pp.text(' '),
sp.pp_ref_indexers(context, sem, sem_indexers),
])
jax_core.pp_eqn_rules[dma_wait_p] = _dma_wait_pp_eqn
def _get_ref_and_indexers(ref):
if isinstance(ref, state.RefView):
return ref.ref, ref.indexers
return ref, ()
def make_async_copy(src_ref, dst_ref, sem):
"""Issues a DMA copying from src_ref to dst_ref."""
src_ref, src_indexers = _get_ref_and_indexers(src_ref)
dst_ref, dst_indexers = _get_ref_and_indexers(dst_ref)
sem, sem_indexers = _get_ref_and_indexers(sem)
return AsyncCopyDescriptor(src_ref, src_indexers, dst_ref, dst_indexers,
sem, sem_indexers, None, None, None,
DeviceIdType.MESH)
def async_copy(src_ref, dst_ref, sem):
"""Issues a DMA copying from src_ref to dst_ref."""
copy_descriptor = make_async_copy(src_ref, dst_ref, sem)
copy_descriptor.start()
return copy_descriptor
def make_async_remote_copy(src_ref, dst_ref, send_sem, recv_sem, device_id,
device_id_type: DeviceIdType = DeviceIdType.MESH):
"""Creates a description of a remote copy operation.
Copies data from src_ref on the current device to dst_ref on the device
specified by device_id. Both semaphores should be waited on using the
descriptor on both source and target devices.
Note that device_id can also refer to the current device.
Args:
src_ref: The source Reference.
dst_ref: The destination Reference.
send_sem: The semaphore on the source device.
recv_sem: The semaphore on the destination device.
device_id: The device id of the destination device.
device_id_type: The type of the device id.
Returns:
An AsyncCopyDescriptor.
"""
src_ref, src_indexers = _get_ref_and_indexers(src_ref)
send_sem, send_sem_indexers = _get_ref_and_indexers(send_sem)
dst_ref, dst_indexers = _get_ref_and_indexers(dst_ref)
recv_sem, recv_sem_indexers = _get_ref_and_indexers(recv_sem)
return AsyncCopyDescriptor(
src_ref, src_indexers, dst_ref, dst_indexers, recv_sem, recv_sem_indexers,
send_sem, send_sem_indexers, device_id, device_id_type=device_id_type)
def async_remote_copy(src_ref, dst_ref, send_sem, recv_sem, device_id,
device_id_type: DeviceIdType = DeviceIdType.MESH):
copy_descriptor = make_async_remote_copy(src_ref, dst_ref, send_sem, recv_sem,
device_id, device_id_type)
copy_descriptor.start()
return copy_descriptor
device_id_p = jax_core.Primitive('device_id')
@device_id_p.def_abstract_eval
def _device_id_abstract_eval():
return jax_core.ShapedArray((), jnp.dtype("int32"))
device_id = device_id_p.bind
get_barrier_semaphore_p = jax_core.Primitive('get_barrier_semaphore')
@get_barrier_semaphore_p.def_abstract_eval
def _get_barrier_semaphore_abstract_eval():
return pl_core.AbstractMemoryRef(
jax_core.ShapedArray((), tpu_core.BarrierSemaphoreTy()),
tpu_core.TPUMemorySpace.SEMAPHORE,
)
def get_barrier_semaphore():
"""Returns a barrier semaphore.
This function returns a barrier semaphore based on the collective_id of the
current pallas kernel.
It's very important that the semaphore is wait-ed back down to 0, or else the
semaphores will become corrupted.
It's also very important that the collective_id is different for each pallas
kernel with communication. E.g. if you have two pallas kernels, one that syncs
across the X axis of the device mesh and the second that syncs across the Y
axis, they must have different collective_ids.
However it is legal for two kernels that perform the same synchronization
pattern (e.g. only communicating with neighbours on the same mesh axis)
to share a collective_id. However, if in doubt, prefer not sharing
collective_ids, as doing so incorrectly can lead to silent data corruption or
crashes.
Note that re-using the same collective_id doesn't guarantee that the same
semaphore is provided by XLA.
"""
return get_barrier_semaphore_p.bind()