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checkhand/venv/lib/python3.11/site-packages/jax/_src/interpreters/mlir.py
51hhh 7903ed308f 提交
2025-08-11 12:24:21 +08:00

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# Copyright 2021 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.
# Lowering and execution path that converts jaxprs into MLIR.
from __future__ import annotations
import collections
from collections.abc import Callable, Iterable, Iterator, Sequence
import contextlib
import dataclasses
import functools
from functools import partial
import heapq
import io
import itertools
import operator
import os
import re
import types
import typing
from typing import Any, NamedTuple, Protocol, Union, cast as type_cast
import warnings
from jax._src import ad_util
from jax._src import api_util
from jax._src import config
from jax._src import core
from jax._src import dtypes
from jax._src import effects as effects_lib
from jax._src import hashable_array
from jax._src import jaxpr_util
from jax._src import linear_util as lu
from jax._src import path
from jax._src import sharding_impls
from jax._src import source_info_util
from jax._src import util
from jax._src import xla_bridge as xb
from jax._src.interpreters import partial_eval as pe
from jax._src.interpreters import xla
from jax._src.layout import AutoLayout, DeviceLocalLayout
from jax._src.lib import _jax
from jax._src.lib import xla_client as xc
from jax._src.lib.mlir import dialects, ir, passmanager
from jax._src.lib.mlir import register_jax_dialects
from jax._src.lib.mlir.dialects import func as func_dialect, hlo
from jax._src.mesh import AxisType
from jax._src.partition_spec import PartitionSpec
from jax._src.sharding import Sharding as JSharding
from jax._src.sharding_impls import ( AUTO, NamedSharding,
SdyArray, SdyArrayList,
modify_sdy_sharding_wrt_axis_types)
from jax._src.state.types import AbstractRef
from jax._src.util import foreach
import numpy as np
# mypy: ignore-errors
map, unsafe_map = util.safe_map, map
zip, unsafe_zip = util.safe_zip, zip
T = typing.TypeVar("T")
Value = Any # = ir.Value
# mypy implicitly sets this variable to true when type checking.
MYPY = False
_JAX_DUMP_IR_TO = config.string_flag(
'jax_dump_ir_to', os.getenv('JAX_DUMP_IR_TO', ''),
help="Path to which the IR that is emitted by JAX should be dumped as "
"text files. If omitted, JAX will not dump IR. "
"Supports the special value 'sponge' to pick the path from the "
"environment variable TEST_UNDECLARED_OUTPUTS_DIR.")
_JAX_INCLUDE_DEBUG_INFO_IN_DUMPS = config.string_flag(
'jax_include_debug_info_in_dumps',
os.getenv('JAX_INCLUDE_DEBUG_INFO_IN_DUMPS', "True"),
help="Determine whether or not to keep debug symbols and location information "
"when dumping IR code. By default, debug information will be preserved in "
"the IR dump. To avoid exposing source code and potentially sensitive "
"information, set to false")
lowerable_effects: effects_lib.EffectTypeSet = effects_lib.lowerable_effects
# IR Helpers
IrValues = Union[ir.Value, tuple[ir.Value, ...]]
def _is_not_block_argument(x: IrValues) -> bool:
return not isinstance(x, ir.BlockArgument)
def dense_int_elements(xs) -> ir.DenseIntElementsAttr:
return ir.DenseIntElementsAttr.get(np.asarray(xs, np.int64))
dense_int_array = ir.DenseI64ArrayAttr.get
def dense_bool_elements(xs: Sequence[bool]) -> ir.DenseElementsAttr:
a = np.packbits(np.array(xs, np.bool_), bitorder='little')
# TODO(b/209005197): Work around for MLIR crash for non-splat single element
# buffers.
if len(xs) == 1:
a = np.array(0 if a.item() == 0 else 0xff, np.uint8)
return ir.DenseElementsAttr.get(
a, type=ir.IntegerType.get_signless(1), shape=[len(xs)])
def dense_bool_array(xs: Sequence[bool]) -> ir.DenseBoolArrayAttr:
return ir.DenseBoolArrayAttr.get(xs)
def i32_attr(i): return ir.IntegerAttr.get(ir.IntegerType.get_signless(32), i)
def i64_attr(i): return ir.IntegerAttr.get(ir.IntegerType.get_signless(64), i)
def shape_tensor(sizes: Sequence[int | ir.RankedTensorType]
) -> ir.RankedTensorType:
int1d = aval_to_ir_type(core.ShapedArray((1,), np.int32))
i32_type = aval_to_ir_type(core.ShapedArray((), np.int32))
def lower_dim(d):
if type(d) is int:
return ir_constant(np.array([d], np.int32))
else:
if d.type != i32_type:
d = hlo.convert(i32_type, d)
return hlo.reshape(int1d, d)
ds = map(lower_dim, sizes)
if not ds:
return type_cast(ir.RankedTensorType, ir_constant(np.array([], np.int32)))
elif len(ds) == 1:
return ds[0]
else:
return hlo.concatenate(ds, i64_attr(0))
def delegate_lowering(ctx, lowering_fun, *args, **ctx_override_kwargs):
"""Side-effects on `ctx`"""
ctx_new = ctx.replace(**ctx_override_kwargs)
out = lowering_fun(ctx_new, *args)
ctx.set_tokens_out(ctx_new.tokens_out)
return out
# IR Types
IrTypes = Union[ir.Type, tuple[ir.Type, ...]]
def _is_ir_values(x: IrValues) -> bool:
"""Returns true if `x` is an ir.Value or tuple of ir.Values"""
if isinstance(x, ir.Value):
return True
return (isinstance(x, tuple) and len(x) != 1
and all(isinstance(v, ir.Value) for v in x))
# Non-canonicalized dtype to IR type mapping.
_dtype_to_ir_type : dict[np.dtype, Callable[[], ir.Type]] = {
np.dtype(dtypes.float0): partial(ir.IntegerType.get_signless, 1),
np.dtype(np.bool_): partial(ir.IntegerType.get_signless, 1),
np.dtype(dtypes.int4): partial(ir.IntegerType.get_signless, 4),
np.dtype(np.int8): partial(ir.IntegerType.get_signless, 8),
np.dtype(np.int16): partial(ir.IntegerType.get_signless, 16),
np.dtype(np.int32): partial(ir.IntegerType.get_signless, 32),
np.dtype(np.int64): partial(ir.IntegerType.get_signless, 64),
np.dtype(dtypes.uint4): partial(ir.IntegerType.get_unsigned, 4),
np.dtype(np.uint8): partial(ir.IntegerType.get_unsigned, 8),
np.dtype(np.uint16): partial(ir.IntegerType.get_unsigned, 16),
np.dtype(np.uint32): partial(ir.IntegerType.get_unsigned, 32),
np.dtype(np.uint64): partial(ir.IntegerType.get_unsigned, 64),
np.dtype(dtypes.float8_e4m3b11fnuz): ir.Float8E4M3B11FNUZType.get,
np.dtype(dtypes.float8_e4m3fn): ir.Float8E4M3FNType.get,
np.dtype(dtypes.float8_e4m3fnuz): ir.Float8E4M3FNUZType.get,
np.dtype(dtypes.float8_e5m2): ir.Float8E5M2Type.get,
np.dtype(dtypes.float8_e5m2fnuz): ir.Float8E5M2FNUZType.get,
np.dtype(dtypes.bfloat16): ir.BF16Type.get,
np.dtype(np.float16): ir.F16Type.get,
np.dtype(np.float32): ir.F32Type.get,
np.dtype(np.float64): ir.F64Type.get,
np.dtype(np.complex64): lambda: ir.ComplexType.get(ir.F32Type.get()),
np.dtype(np.complex128): lambda: ir.ComplexType.get(ir.F64Type.get()),
np.dtype(dtypes.int2): partial(ir.IntegerType.get_signless, 2),
np.dtype(dtypes.uint2): partial(ir.IntegerType.get_unsigned, 2),
np.dtype(dtypes.float8_e3m4): ir.Float8E3M4Type.get,
np.dtype(dtypes.float8_e4m3): ir.Float8E4M3Type.get,
np.dtype(dtypes.float8_e8m0fnu): ir.Float8E8M0FNUType.get,
np.dtype(dtypes.float4_e2m1fn): ir.Float4E2M1FNType.get,
}
def dtype_to_ir_type(dtype: core.bint | np.dtype | np.generic) -> ir.Type:
if isinstance(dtype, core.bint):
# TODO Support different-size underlying dtypes to take advantage of the
# bound for packing?
dtype = np.dtype(np.int32)
assert isinstance(dtype, (np.dtype, np.generic)), type(dtype)
dtype = np.dtype(dtype)
try:
ir_type_factory = _dtype_to_ir_type[dtype]
except KeyError as err:
raise TypeError(
f"No dtype_to_ir_type handler for dtype: {dtype}") from err
return ir_type_factory()
def _array_ir_types(aval: core.ShapedArray | core.DShapedArray) -> ir.Type:
aval = core.physical_aval(aval) # type: ignore
if not core.is_constant_shape(aval.shape):
return _dynamic_array_ir_types(aval) # type: ignore
return ir.RankedTensorType.get(aval.shape, dtype_to_ir_type(aval.dtype)) # type: ignore
def _dynamic_array_ir_types(aval: core.ShapedArray) -> ir.Type:
dyn_size = ir.ShapedType.get_dynamic_size()
shape = [d if type(d) is int else dyn_size for d in aval.shape]
return ir.RankedTensorType.get(shape, dtype_to_ir_type(aval.dtype))
ir_type_handlers: dict[type[core.AbstractValue], Callable[[Any], IrTypes]] = {}
def aval_to_ir_type(aval: core.AbstractValue) -> IrTypes:
"""Converts a JAX aval to zero or more MLIR IR types.
In general, a JAX value may be represented by multiple IR values, so this
function may return a tuple of types."""
try:
return ir_type_handlers[type(aval)](aval)
except KeyError as err:
raise TypeError(f"No ir_type_handler for aval type: {type(aval)}") from err
ir_type_handlers[core.ShapedArray] = _array_ir_types
ir_type_handlers[core.AbstractToken] = lambda _: hlo.TokenType.get()
ir_type_handlers[core.DShapedArray] = _dynamic_array_ir_types
# This is a backwards compatibility shim for external users of jax.mlir apis.
def aval_to_ir_types(aval: core.AbstractValue) -> tuple[ir.Type, ...]:
typ = aval_to_ir_type(aval)
return (typ,) if isinstance(typ, ir.Type) else typ
# Constants
class ConstantHandler(Protocol):
def __call__(self, val: Any) -> IrValues:
"""Builds an IR representation for a constant `val`.
A JAX value is represented by zero or more IR values."""
_constant_handlers : dict[type, ConstantHandler] = {}
def register_constant_handler(type_: type, handler_fun: ConstantHandler):
_constant_handlers[type_] = handler_fun
def get_constant_handler(type_: type) -> ConstantHandler:
return _constant_handlers[type_]
def ir_constant(val: Any) -> IrValues:
"""Translate a Python `val` to an IR constant, canonicalizing its dtype.
Args:
val: a Python value to be translated to a constant.
Returns:
A representation of the constant as an IR value or sequence of IR values.
"""
for t in type(val).__mro__:
handler = _constant_handlers.get(t)
if handler:
out = handler(val)
assert _is_ir_values(out), (type(val), out)
return out
if hasattr(val, '__jax_array__'):
return ir_constant(val.__jax_array__())
raise TypeError(f"No constant handler for type: {type(val)}")
def _numpy_array_constant(x: np.ndarray | np.generic) -> IrValues:
element_type = dtype_to_ir_type(x.dtype)
shape = x.shape
if x.dtype == np.bool_:
x = np.packbits(x, bitorder='little') # type: ignore
x = np.ascontiguousarray(x)
attr = ir.DenseElementsAttr.get(x, type=element_type, shape=shape) # type: ignore
return hlo.constant(attr)
def _masked_array_constant_handler(*args, **kwargs):
raise ValueError("numpy masked arrays are not supported as direct inputs to JAX functions. "
"Use arr.filled() to convert the value to a standard numpy array.")
register_constant_handler(np.ma.MaskedArray, _masked_array_constant_handler)
def _ndarray_constant_handler(val: np.ndarray | np.generic) -> IrValues:
"""Constant handler for ndarray literals, handling zero-size strides.
In most cases this function calls _numpy_array_constant(val) except it has
special handling of arrays with any strides of size zero: for those, it
generates appropriate calls to NumpyArrayConstant, Broadcast, and Transpose
to avoid staging in large literals that might arise from np.zeros or np.ones
or the output of lax.broadcast (which uses np.broadcast_to which in turn
uses size-zero strides).
Args:
val: an ndarray.
Returns:
An XLA ComputationDataHandle / XlaOp representing the constant ndarray
staged into the XLA Computation.
"""
if val.dtype == dtypes.float0:
return _numpy_array_constant(np.zeros(val.shape, dtype=np.bool_))
elif 0 in val.strides and val.size > 0:
zero_stride_axes, = np.where(np.equal(0, val.strides))
other_axes, = np.where(np.not_equal(0, val.strides))
collapsed_val = val[tuple(0 if ax in zero_stride_axes else slice(None) # type: ignore
for ax in range(val.ndim))]
out = hlo.broadcast_in_dim(
ir.RankedTensorType.get(
val.shape, dtype_to_ir_type(collapsed_val.dtype)), # type: ignore
_numpy_array_constant(collapsed_val),
dense_int_array(other_axes)) # type: ignore
return out
else:
return _numpy_array_constant(val)
register_constant_handler(np.ndarray, _ndarray_constant_handler)
for _scalar_type in [np.int8, np.int16, np.int32, np.int64,
np.uint8, np.uint16, np.uint32, np.uint64,
np.float16, np.float32, np.float64,
np.complex64, np.complex128,
np.bool_, np.longlong, dtypes.bfloat16]:
register_constant_handler(_scalar_type, _ndarray_constant_handler) # type: ignore
def _python_scalar_handler(dtype, val):
return _numpy_array_constant(np.array(val, dtype))
for ptype, dtype in dtypes.python_scalar_dtypes.items():
register_constant_handler(ptype, partial(_python_scalar_handler, dtype))
def _token_constant_handler(val):
return hlo.create_token()
register_constant_handler(core.Token, _token_constant_handler)
# Attributes
AttributeHandler = Callable[[Any], ir.Attribute]
_attribute_handlers: dict[type[Any], AttributeHandler] = {}
def register_attribute_handler(type_: type[Any], handler_fun: AttributeHandler):
_attribute_handlers[type_] = handler_fun
def get_attribute_handler(type_: type[Any]) -> AttributeHandler:
return _attribute_handlers[type_]
def _numpy_scalar_attribute(val: Any) -> ir.Attribute:
mlir_type = dtype_to_ir_type(val.dtype)
if isinstance(mlir_type, ir.IntegerType):
return ir.IntegerAttr.get(mlir_type, val)
elif isinstance(mlir_type, ir.FloatType):
return ir.FloatAttr.get(mlir_type, val)
else:
raise TypeError(f"Unsupported scalar attribute type: {type(val)}")
def _numpy_array_attribute(x: np.ndarray | np.generic) -> ir.Attribute:
shape = x.shape
if x.dtype == np.bool_:
x = np.packbits(x, bitorder='little') # type: ignore
x = np.ascontiguousarray(x)
element_type = dtype_to_ir_type(x.dtype)
return ir.DenseElementsAttr.get(x, type=element_type, shape=shape) # type: ignore
def _numpy_array_attribute_handler(val: np.ndarray | np.generic) -> ir.Attribute:
if 0 in val.strides and val.size > 0:
raise ValueError(
"NumPy arrays with zero strides are not supported as MLIR attributes")
if val.dtype == dtypes.float0:
val = np.zeros(val.shape, dtype=np.bool_)
if dtypes.is_python_scalar(val) or np.isscalar(val):
return _numpy_scalar_attribute(val)
else:
return _numpy_array_attribute(val)
register_attribute_handler(np.ndarray, _numpy_array_attribute_handler)
register_attribute_handler(hashable_array.HashableArray,
lambda x: _numpy_array_attribute_handler(x.val))
for _scalar_type in [np.int8, np.int16, np.int32, np.int64,
np.uint8, np.uint16, np.uint32, np.uint64,
np.float16, np.float32, np.float64,
np.complex64, np.complex128,
np.bool_, np.longlong, dtypes.bfloat16]:
register_attribute_handler(_scalar_type, _numpy_array_attribute_handler) # type: ignore
def _dtype_attribute_handler(dtype: np.dtype | np.generic) -> ir.Attribute:
return ir.TypeAttr.get(dtype_to_ir_type(dtype))
register_attribute_handler(np.dtype, _dtype_attribute_handler)
register_attribute_handler(np.generic, _dtype_attribute_handler)
def _python_scalar_attribute_handler(dtype, val):
return _numpy_scalar_attribute(np.array(val, dtype))
for ptype, dtype in dtypes.python_scalar_dtypes.items():
register_attribute_handler(
ptype, partial(_python_scalar_attribute_handler, dtype))
register_attribute_handler(str, ir.StringAttr.get)
register_attribute_handler(bytes, ir.StringAttr.get)
def _dict_attribute_handler(val: dict[str, Any]) -> ir.Attribute:
return ir.DictAttr.get({k: ir_attribute(v) for k, v in val.items()})
register_attribute_handler(dict, _dict_attribute_handler)
def _sequence_attribute_handler(val: Sequence[Any]) -> ir.Attribute:
return ir.ArrayAttr.get([ir_attribute(v) for v in val])
register_attribute_handler(list, _sequence_attribute_handler)
register_attribute_handler(tuple, _sequence_attribute_handler)
register_attribute_handler(ir.Attribute, lambda x: x)
register_attribute_handler(ir.Type, lambda x: x)
def ir_attribute(val: Any) -> ir.Attribute:
"""Convert a Python value to an MLIR attribute."""
for t in type(val).__mro__:
handler = _attribute_handlers.get(t)
if handler:
out = handler(val)
assert isinstance(out, ir.Attribute), (type(val), out)
return out
if hasattr(val, '__jax_array__'):
return ir_attribute(val.__jax_array__())
raise TypeError(f"No attribute handler defined for type: {type(val)}")
# Source locations
def get_canonical_source_file(file_name: str, caches: TracebackCaches) -> str:
canonical_file_name = caches.canonical_name_cache.get(file_name, None)
if canonical_file_name is not None:
return canonical_file_name
pattern = config.hlo_source_file_canonicalization_regex.value
if pattern:
file_name = re.sub(pattern, '', file_name)
caches.canonical_name_cache[file_name] = file_name
return file_name
def _is_user_file(ctx: ModuleContext, file_name: str) -> bool:
is_user = ctx.traceback_caches.is_user_file_cache.get(file_name, None)
if is_user is not None:
return is_user
out = source_info_util.is_user_filename(file_name)
ctx.traceback_caches.is_user_file_cache[file_name] = out
return out
def _traceback_to_location(ctx: ModuleContext, tb: xc.Traceback) -> ir.Location:
"""Converts a full traceback to a callsite() MLIR location."""
loc = ctx.traceback_caches.traceback_cache.get(tb, None)
if loc is not None:
return loc
frame_locs = []
frames_limit = config.traceback_in_locations_limit.value
frames_limit = frames_limit if frames_limit >= 0 else 1000
codes, lastis = tb.raw_frames()
for i, code in enumerate(codes):
if not _is_user_file(ctx, code.co_filename):
continue
lasti = lastis[i]
code_lasti = code, lasti
loc = ctx.traceback_caches.location_cache.get(code_lasti, None)
if loc is None:
frame = source_info_util.raw_frame_to_frame(code, lasti)
file_loc = ir.Location.file(
get_canonical_source_file(frame.file_name, ctx.traceback_caches),
frame.start_line,
frame.start_column,
frame.end_line,
frame.end_column,
)
loc = ir.Location.name(frame.function_name, childLoc=file_loc)
ctx.traceback_caches.location_cache[code_lasti] = loc
frame_locs.append(loc)
if len(frame_locs) >= frames_limit:
break
n = len(frame_locs)
if n == 0:
loc = ir.Location.unknown()
elif n == 1:
loc = frame_locs[0]
else:
loc = ir.Location.callsite(frame_locs[0], frame_locs[1:])
ctx.traceback_caches.traceback_cache[tb] = loc
return loc
def _source_info_to_location(
ctx: ModuleContext, primitive: core.Primitive,
source_info: source_info_util.SourceInfo) -> ir.Location:
eqn_str = f'{source_info.name_stack}/{primitive.name}'
if config.include_full_tracebacks_in_locations.value:
if source_info.traceback is None:
loc = ir.Location.unknown()
else:
loc = _traceback_to_location(ctx, source_info.traceback)
else:
frame = source_info_util.user_frame(source_info)
if frame is None:
loc = ir.Location.unknown()
else:
loc = ir.Location.file(get_canonical_source_file(frame.file_name,
ctx.traceback_caches),
frame.start_line, frame.start_column)
loc = ir.Location.name(eqn_str, childLoc=loc)
# TODO(phawkins): also include primitive.name as the operator type.
return loc
upstream_dialects = ir.DialectRegistry()
if register_jax_dialects:
register_jax_dialects.register_dialects(upstream_dialects)
# Dumping MLIR modules
_ir_dump_counter = itertools.count()
def dump_module_to_file(module: ir.Module, stage_name: str) -> str | None:
"""Dumps the `module` IR to a file.
Dumps the module if JAX_DUMP_IR_TO is defined.
Args:
module: The module to dump
stage_name: A name to distinguish different stages of a module, will be
appended to the `module.name`.
Returns:
The name of the file containing the dump if JAX_DUMP_IR_TO is defined and
the module was dumped, `None` otherwise.
"""
out_dir_name = _JAX_DUMP_IR_TO.value
if not out_dir_name:
return None
if out_dir_name == "sponge":
out_dir_name = os.environ.get("TEST_UNDECLARED_OUTPUTS_DIR", "")
if not out_dir_name:
raise ValueError("JAX_DUMP_IR_TO='sponge' but "
"TEST_UNDECLARED_OUTPUTS_DIR is not defined")
id = next(_ir_dump_counter)
sym_name = module.operation.attributes['sym_name']
module_name = ir.StringAttr(sym_name).value
name = f"jax_ir{id:04d}_{_make_string_safe_for_filename(module_name)}_{stage_name}.mlir"
out_dir = path.Path(out_dir_name)
out_dir.mkdir(parents=True, exist_ok=True)
full_path = out_dir / name
full_path.write_text(module_to_string(module))
return name
def dump_module_message(module: ir.Module, stage_name: str) -> str:
dumped_to = dump_module_to_file(module, stage_name)
if dumped_to:
return f"The module was dumped to {dumped_to}."
else:
return "Define JAX_DUMP_IR_TO to dump the module."
def _make_string_safe_for_filename(s: str) -> str:
return re.sub(r'[^\w.)( -]', '', s)
def module_to_string(module: ir.Module, enable_debug_info=None) -> str:
output = io.StringIO()
if enable_debug_info is None:
enable_debug_flag = str.lower(_JAX_INCLUDE_DEBUG_INFO_IN_DUMPS.value)
enable_debug_info = enable_debug_flag not in ('false', '0')
module.operation.print(file=output, enable_debug_info=enable_debug_info)
return output.getvalue()
def module_to_bytecode(module: ir.Module) -> bytes:
output = io.BytesIO()
module.operation.write_bytecode(file=output)
return output.getvalue()
# Translation rules
# Create one global thread pool that can be shared between multiple ir.Contexts
# and enabling multi-threading
global_thread_pool = ir.ThreadPool()
class JaxIrContext(ir.Context):
def __init__(self, *args, **kwargs):
# Note: we're very intentionally *not* calling the __init__() of our
# immediate superclass ir.Context, whose __init__() has the unfortunate side
# effect of loading all the dialects linked into the binary into the
# context. We want to ensure that only the dialects we need are loaded.
super(ir.Context, self).__init__(*args, **kwargs)
def make_ir_context() -> ir.Context:
"""Creates an MLIR context suitable for JAX IR."""
context = JaxIrContext()
context.append_dialect_registry(upstream_dialects)
context.load_all_available_dialects()
context.set_thread_pool(global_thread_pool)
dialects.sdy.register_dialect(context)
dialects.mhlo.register_mhlo_dialect(context)
dialects.chlo.register_dialect(context)
dialects.hlo.register_dialect(context)
return context
AxisContext = Union[
sharding_impls.SPMDAxisContext,
sharding_impls.ReplicaAxisContext,
sharding_impls.ShardingContext,
]
class ShapePolyLoweringState:
# The names of the dimension variables, sorted by name. This is the order in
# which they are passed to the IR functions that need them. This is only
# used for native serialization with polymorphic shapes when
# --jax_dynamic_shapes is off.
# TODO: for multi-platform lowering we prepend to the regular dimension
# variables a fake dimension variable "platform_index_". This is a
# temporary abuse, taking advantage that for platform index we need the
# same lowering strategy as for dimension variables: add it as argument to
# inner functions, and pass the values along at the call sites.
dim_vars: tuple[str, ...]
# Whether the module uses dimension variables, either in its inputs or
# from an inner call to Exported modules that uses dimension variables.
# This includes the case when the called Exported module uses a platform
# index argument.
uses_dim_vars: bool
# If the first dimension variable is a platform index argument
has_platform_index_argument: bool
def __init__(self,
dim_vars: tuple[str, ...],
lowering_platforms: tuple[str, ...] | None):
if lowering_platforms is not None and len(lowering_platforms) > 1:
dim_vars = ("_platform_index",) + tuple(dim_vars)
self.has_platform_index_argument = True
else:
self.has_platform_index_argument = False
self.uses_dim_vars = (len(dim_vars) > 0)
self.dim_vars = dim_vars
@dataclasses.dataclass(frozen=True)
class LoweringParameters:
# A mapping between primitives and user-defined LoweringRules.
# When lowering a primitive, give priority to the rule in this map over
# existing Jax rules.
override_lowering_rules: tuple[tuple[core.Primitive, LoweringRule]] | None = None
# Signals that the entire computation being lowered operates on global
# constants. This will result in adding jax.global_constant attributes
# to the arguments of all functions that are created, e.g., floor_divide.
# This is used only in export and jax2tf in presence of shape polymorphism
# or multi-platform lowering.
global_constant_computation: bool = False
# Signals that we are lowering for exporting.
for_export: bool = False
# See usage in https://docs.jax.dev/en/latest/export/export.html#ensuring-forward-and-backward-compatibility
# We have this here to ensure it is reflected in the cache keys
export_ignore_forward_compatibility: bool = False
@dataclasses.dataclass
class TracebackCaches:
traceback_cache: dict[xc.Traceback, ir.Location]
location_cache: dict[tuple[types.CodeType, int], ir.Location]
canonical_name_cache: dict[str, str]
is_user_file_cache: dict[str, bool]
def __init__(self):
self.traceback_cache = {}
self.location_cache = {}
self.canonical_name_cache = {}
self.is_user_file_cache = {}
@dataclasses.dataclass
class ModuleContext:
"""Module-wide context information for MLIR lowering."""
context: ir.Context
module: ir.Module
ip: ir.InsertionPoint
symbol_table: ir.SymbolTable
# The lowering platforms for the module. Can be more than one only when
# exporting.
platforms: Sequence[str]
# See ModuleContext.get_backend() for backend and platforms usage.
backend: xb.XlaBackend | None
axis_context: AxisContext
keepalives: list[Any]
channel_iterator: Iterator[int]
host_callbacks: list[Any]
# Keep state for the lowering of shape polymorphism
shape_poly_state: ShapePolyLoweringState
all_default_mem_kind: bool
# Cached primitive lowerings.
cached_primitive_lowerings: dict[Any, func_dialect.FuncOp]
# Cached traceback information.
traceback_caches: TracebackCaches
lowering_parameters: LoweringParameters
@property
def axis_env(self) -> sharding_impls.AxisEnv:
return self.axis_context.axis_env
def __init__(
self,
*,
platforms: Sequence[str],
backend: xb.XlaBackend | None,
axis_context: AxisContext,
keepalives: list[Any],
channel_iterator: Iterator[int],
host_callbacks: list[Any],
lowering_parameters: LoweringParameters,
context: ir.Context | None = None,
module: ir.Module | None = None,
ip: ir.InsertionPoint | None = None,
symbol_table: ir.SymbolTable | None = None,
cached_primitive_lowerings: None | (dict[Any, func_dialect.FuncOp]) = None,
traceback_caches: None | TracebackCaches = None,
shape_poly_state = None,
all_default_mem_kind: bool = True):
self.context = context or make_ir_context()
self.module = module or ir.Module.create(loc=ir.Location.unknown(self.context))
self.ip = ip or ir.InsertionPoint(self.module.body)
self.symbol_table = symbol_table or ir.SymbolTable(self.module.operation)
self.backend = backend
self.platforms = platforms
self.axis_context = axis_context
self.cached_primitive_lowerings = ({} if cached_primitive_lowerings is None
else cached_primitive_lowerings)
self.traceback_caches = (TracebackCaches() if traceback_caches is None
else traceback_caches)
self.channel_iterator = channel_iterator
self.keepalives = keepalives
self.host_callbacks = host_callbacks
self.shape_poly_state = (
shape_poly_state or ShapePolyLoweringState((), tuple(platforms)))
self.all_default_mem_kind = all_default_mem_kind
self.lowering_parameters = lowering_parameters
def get_backend(self) -> xb.XlaBackend:
if len(self.platforms) > 1:
raise NotImplementedError(
"accessing .backend in multi-lowering setting. This can occur when "
"lowering a primitive that has not been adapted to multi-platform "
"lowering")
if self.backend is not None:
if xb.canonicalize_platform(self.backend.platform) != self.platforms[0]:
raise ValueError(
"the platform for the specified backend "
f"{xb.canonicalize_platform(self.backend.platform)} is different "
f"from the lowering platform {self.platforms[0]}")
return self.backend
return xb.get_backend(self.platforms[0])
def new_channel(self) -> int:
channel = next(self.channel_iterator)
# `xla::HostCallback` requires a 16-bit channel ID.
if channel >= (1 << 16):
raise RuntimeError(
"Host callback lowering created too many channels. PjRt does not"
" support more than 65535 channels")
return channel
# Adds an IFRT host callback object to the context. A reference to these
# callbacks will be provided to IFRT during compilation so it can do things
# like serialize them and keep them alive.
def add_host_callback(self, host_callback: Any) -> None:
self.host_callbacks.append(host_callback)
# Keeps a value alive as long as the Python executable is alive.
# TODO(phawkins): this feature is problematic, because you almost certainly
# want to keep alive values as long as the underlying runtime executable is
# still alive/executing. The Python executable object may have a shorter
# lifetime, so it's highly likely any caller of this method is buggy.
def add_keepalive(self, keepalive: Any) -> None:
self.keepalives.append(keepalive)
def replace(self, **kw): return dataclasses.replace(self, **kw)
@dataclasses.dataclass
class LoweringRuleContext:
"""Per-rule context information for MLIR lowering."""
module_context: ModuleContext
name_stack: source_info_util.NameStack
primitive: core.Primitive | None
avals_in: Sequence[core.AbstractValue]
avals_out: Any # Usually Sequence[core.AbstractValue], but sometimes None.
tokens_in: TokenSet
tokens_out: TokenSet | None # Mutable store for output containers
axis_size_env: dict[core.Var, ir.Value] | None = None # Dynamic axis sizes
# The values for the dimension variables in same order as
# module_context.shape_poly_state.dim_vars
dim_var_values: Sequence[ir.Value] = ()
jaxpr_eqn_ctx: core.JaxprEqnContext | None = None
# Override module_context.platforms if not None. Used during multi-platform
# lowering, when in a scope with a subset of the module_context.platforms.
platforms: Sequence[str] | None = None
def set_tokens_out(self, tokens_out: TokenSet):
assert self.tokens_out is None, 'Should only set `tokens_out` once.'
self.tokens_out = tokens_out
def replace(self, **kw): return dataclasses.replace(self, **kw) # pytype: disable=wrong-arg-types # dataclasses-replace-types
def is_forward_compat(self) -> bool:
"""Returns true if the lowering parameters are in forward compatibility mode.
"""
lowering_parameters = self.module_context.lowering_parameters
check_platforms: Sequence[str] = (
self.platforms or self.module_context.platforms
)
force_forward_compat = any(
p in xb.FORCE_FORWARD_COMPAT_LOWERING_PLATFORMS for p in check_platforms
)
return (
lowering_parameters.for_export or force_forward_compat
) and not lowering_parameters.export_ignore_forward_compatibility
if not MYPY:
class LoweringRule(Protocol):
def __call__(self, ctx: LoweringRuleContext,
*args: ir.Value | Sequence[ir.Value],
**kw) -> Sequence[ir.Value | Sequence[ir.Value]]:
"""Converts a JAX primitive invocation into MLIR."""
else:
LoweringRule = Any
_lowerings: dict[core.Primitive, LoweringRule] = {}
_platform_specific_lowerings: dict[str, dict[core.Primitive, LoweringRule]]
_platform_specific_lowerings = collections.defaultdict(dict)
def register_lowering(prim: core.Primitive, rule: LoweringRule,
platform: str | None = None):
if platform is None:
_lowerings[prim] = rule
else:
if not xb.is_known_platform(platform):
known_platforms = sorted(xb.known_platforms())
raise NotImplementedError(
f"Registering an MLIR lowering rule for primitive {prim}"
f" for an unknown platform {platform}. Known platforms are:"
f" {', '.join(known_platforms)}.")
# For backward compatibility reasons, we allow rules to be registered
# under "gpu" even though the platforms are now called "cuda" and "rocm".
# TODO(phawkins): fix up users to specify either "cuda" or "rocm" and remove
# this expansion.
for p in xb.expand_platform_alias(platform):
_platform_specific_lowerings[p][prim] = rule
return rule
def flatten_ir_values(xs: Iterable[IrValues]) -> list[ir.Value]:
"""Concatenates/flattens a list of ir.Values or ir.Value sequences."""
out = []
for x in xs:
if isinstance(x, ir.Value):
out.append(x)
else:
out.extend(x)
return out
def flatten_ir_types(xs: Iterable[IrTypes]) -> list[ir.Type]:
"""Concatenates/flattens a list of ir.Types or ir.Type sequences."""
out = []
for x in xs:
if isinstance(x, ir.Type):
out.append(x)
else:
out.extend(x)
return out
_unflatten_done = object()
def unflatten_ir_types(xs: Iterable[ir.Type], ns: Sequence[int]) -> list[IrTypes]:
"""Splits `xs` into subsequences of lengths `ns`.
Unlike `split_list`, the `sum(ns)` must be equal to `len(xs)`, and if n == 1
then types are not wrapped in a singleton list."""
xs_iter = iter(xs)
unflattened: list[IrTypes]
unflattened = [next(xs_iter) if n == 1 else tuple(next(xs_iter)
for _ in range(n)) for n in ns]
assert next(xs_iter, _unflatten_done) is _unflatten_done
return unflattened
def len_ir_types(x: IrTypes) -> int:
return 1 if isinstance(x, ir.Type) else len(x)
def unflatten_ir_values_like_types(xs: Iterable[ir.Value],
ys: Sequence[IrTypes]) -> list[IrValues]:
"""Splits `xs` into subsequences of lengths `ns`.
Unlike `split_list`, the `sum(ns)` must be equal to `len(xs)`, and if n == 1
then values are not wrapped in a singleton list."""
xs_iter = iter(xs)
unflattened: list[IrValues]
unflattened = [next(xs_iter) if isinstance(y, ir.Type) else
tuple(next(xs_iter) for _ in range(len(y)))
for y in ys]
assert next(xs_iter, _unflatten_done) is _unflatten_done
return unflattened
_module_name_regex = re.compile(r"[^\w.-]")
def sanitize_name(name: str) -> str:
"""Ensure a name is usable as module or function name."""
return _module_name_regex.sub("_", name)
def sharded_aval(aval: core.AbstractValue,
sharding: JSharding | AUTO | None) -> core.AbstractValue:
"""Returns the new aval sharded based on sharding proto."""
if sharding is None:
return aval
if isinstance(sharding, AUTO):
return aval
if isinstance(aval, core.AbstractToken):
return aval
if not isinstance(aval, (core.ShapedArray, core.DShapedArray)):
raise NotImplementedError
return aval.update(sharding.shard_shape(aval.shape), sharding=None) # type: ignore
def eval_dynamic_shape(ctx: LoweringRuleContext,
shape: core.Shape) -> tuple[int | Value, ...]:
if config.dynamic_shapes.value:
assert ctx.axis_size_env is not None
return tuple(ctx.axis_size_env.get(d, d) for d in shape) # type: ignore
else:
ctx = ctx.replace(
primitive="eval_dynamic_shape",
avals_in=[core.dim_value_aval()] * len(ctx.module_context.shape_poly_state.dim_vars),
tokens_out=None)
res = lower_fun(
partial(core.evaluate_shape, shape, ctx.module_context.shape_poly_state.dim_vars),
multiple_results=True)(ctx, *ctx.dim_var_values)
return tuple(operator.index(d) if core.is_constant_dim(d) else d_ir
for d, d_ir in zip(shape, flatten_ir_values(res)))
# TODO: replace usage of eval_dynamic_shape_as_vals with eval_dynamic_shape_as_ivals
def eval_dynamic_shape_as_vals(ctx: LoweringRuleContext,
shape: core.Shape) -> tuple[Value, ...]:
"""Evaluates the dynamic shapes as int32 values."""
def convert_dim(d: int | Value):
if type(d) is int:
return ir_constant(np.array(d, dtype=np.int32))
else:
i32_type = aval_to_ir_type(core.ShapedArray((), np.int32))
if d.type != i32_type: # type: ignore
return hlo.convert(i32_type, d)
else:
return d
return tuple(convert_dim(v) for v in eval_dynamic_shape(ctx, shape))
def eval_dynamic_shape_as_ivals(
ctx: LoweringRuleContext, shape: core.Shape
) -> tuple[int | Value, ...]:
"""Evaluates the dynamic shapes as int or ir.int32 values."""
def convert_dim(d: int | Value) -> int | ir.Value:
if type(d) is int:
return d
else:
i32_type = aval_to_ir_type(core.ShapedArray((), np.int32))
if d.type != i32_type: # type: ignore
return hlo.convert(i32_type, d)
else:
return d
return tuple(convert_dim(v) for v in eval_dynamic_shape(ctx, shape))
def eval_dynamic_shape_as_tensor(ctx: LoweringRuleContext,
shape: core.Shape) -> Value:
"""Evaluates the dynamic shapes as one 1d int32 tensor."""
return shape_tensor(eval_dynamic_shape(ctx, shape))
class LoweringResult(NamedTuple):
module: ir.Module
keepalive: Any | None
host_callbacks: list[Any]
shape_poly_state: ShapePolyLoweringState
_platforms_with_donation = ["cpu", "cuda", "rocm", "tpu", "neuron"]
def add_manual_axes(axis_ctx: sharding_impls.SPMDAxisContext, sharding, ndim):
mesh = axis_ctx.mesh.abstract_mesh
sharding_mesh = sharding.mesh.abstract_mesh
if (isinstance(sharding, sharding_impls.NamedSharding) and
sharding_mesh.shape == mesh.shape):
out_mesh, spec = sharding_mesh, sharding.spec
else:
out_mesh, spec = mesh, sharding_impls.parse_flatten_op_sharding(
sharding._to_xla_hlo_sharding(ndim), mesh)[0]
out_mesh = out_mesh.update_axis_types(
{a: AxisType.Manual for a in axis_ctx.manual_axes})
out = sharding_impls.NamedSharding(out_mesh, spec,
memory_kind=sharding.memory_kind)
manual_axes = out.mesh.manual_axes
if any(p in manual_axes for s in out.spec
if s is not None and s is not PartitionSpec.UNCONSTRAINED
for p in (s if isinstance(s, tuple) else (s,))):
raise ValueError(
f'pspec {out.spec} contains a manual axes {manual_axes} of mesh'
f' which is not allowed. If you are using a'
' with_sharding_constraint under a shard_map, only use the'
' mesh axis in PartitionSpec which are not manual.')
return out
def _to_physical_op_sharding(
ctx: ModuleContext,
aval: core.AbstractValue, sharding: JSharding | AUTO | None,
) -> xc.OpSharding | SdyArray | None:
if sharding is None:
return None
if all_unconstrained(sharding, aval):
return None
if isinstance(sharding, AUTO):
if config.use_shardy_partitioner.value:
return sharding._to_sdy_sharding(aval.ndim) # type: ignore
return None
assert isinstance(sharding, JSharding)
if isinstance(aval, AbstractRef):
return _to_physical_op_sharding(ctx, aval.inner_aval, sharding)
assert isinstance(aval, (core.ShapedArray, core.DShapedArray))
if dtypes.issubdtype(aval.dtype, dtypes.extended):
sharding = sharding_impls.physical_sharding(aval, sharding)
aval = core.physical_aval(aval)
axis_ctx = ctx.axis_context
if (isinstance(axis_ctx, sharding_impls.SPMDAxisContext) and
axis_ctx.manual_axes):
sharding = add_manual_axes(axis_ctx, sharding, aval.ndim)
if config.use_shardy_partitioner.value:
return sharding._to_sdy_sharding(aval.ndim) # type: ignore
return sharding._to_xla_hlo_sharding(aval.ndim).to_proto() # type: ignore
def _to_xla_layout(layout: DeviceLocalLayout | None | AutoLayout,
aval: core.AbstractValue) -> str | None:
if layout is None:
return None
if isinstance(layout, AutoLayout):
return "auto"
if aval is core.abstract_token:
return None
return str(layout._to_xla_layout(aval.dtype)) # type: ignore
def _get_mem_kind(s: JSharding | AUTO | None) -> str | None:
if s is None:
return None
if isinstance(s, AUTO):
return None
assert isinstance(s, JSharding)
return s.memory_kind
def contains_unconstrained(s):
return (isinstance(s, NamedSharding)
and PartitionSpec.UNCONSTRAINED in s.spec)
def all_unconstrained(s, aval):
if isinstance(s, NamedSharding):
if aval.ndim != len(s.spec):
return False
return all(p is PartitionSpec.UNCONSTRAINED for p in s.spec)
return False
class UnconstrainedVariants(NamedTuple):
contains_unconstrained: bool
all_unconstrained: bool
unconstrained_dims: set[int] | None
def _get_unconstrained_variants(s, aval) -> UnconstrainedVariants:
us = contains_unconstrained(s)
unconstrained_dims = ({i for i, p in enumerate(s.spec) # pytype: disable=attribute-error
if p is PartitionSpec.UNCONSTRAINED} if us else None)
return UnconstrainedVariants(
contains_unconstrained=us, all_unconstrained=all_unconstrained(s, aval),
unconstrained_dims=unconstrained_dims)
def check_jaxpr_constants(closed_jaxpr: core.ClosedJaxpr):
"""Check if a JAXPR contains an excessive amount of constants, if so, report where they were captured"""
if (threshold := config.captured_constants_warn_bytes.value) == -1:
return
# need the unaesthetic getter here as some of the consts in the test suite are arbitrary objects
total_iter, nbytes_iter = itertools.tee(
map(lambda c: getattr(c, "nbytes", 0), closed_jaxpr.consts)
)
if (total_bytes := sum(total_iter)) < threshold:
return
message = (
"A large amount of constants were captured during lowering"
f" ({util.pprint_bytes(total_bytes)} total). If this is intentional,"
" disable this warning by setting JAX_CAPTURED_CONSTANTS_WARN_BYTES=-1. "
)
if not (num_frames := config.captured_constants_report_frames.value):
message += (
"To obtain a report of where these constants were encountered, "
"set JAX_CAPTURED_CONSTANTS_REPORT_FRAMES=-1."
)
warnings.warn(message)
return
message += (
"The subsequent report may be disabled by setting JAX_CAPTURED_CONSTANTS_REPORT_FRAMES=0.\n\n"
f"Largest {min(num_frames, len(closed_jaxpr.consts))} allocation(s):\n"
)
try:
nbytes_var_const = zip(nbytes_iter, closed_jaxpr.jaxpr.constvars, closed_jaxpr.consts)
for nbytes, var, const in heapq.nlargest(5, nbytes_var_const, key=operator.itemgetter(0)):
message += f" Constant {type(const)}, {var.aval.str_short()}, {util.pprint_bytes(nbytes)} captured at:\n"
for eqn in jaxpr_util.eqns_using_var(closed_jaxpr.jaxpr, var):
call_frame_source_info = source_info_util.summarize(eqn.source_info, num_frames)
message += " " * 2 + call_frame_source_info.replace("\n", "\n" + " " * 2) + "\n\n"
warnings.warn(message)
except Exception as exc:
warnings.warn(message + f" Exception raised while generating report: {exc}")
def lower_jaxpr_to_module(
module_name: str,
jaxpr: core.ClosedJaxpr,
*,
ordered_effects: list[core.Effect],
# See ModuleContext.get_backend() for backend and platforms usage.
platforms: Sequence[str],
backend: xb.XlaBackend | None,
axis_context: AxisContext,
name_stack: source_info_util.NameStack,
donated_args: Sequence[bool],
replicated_args: Sequence[bool] | None = None,
arg_shardings: Sequence[JSharding | AUTO | None] | None = None,
result_shardings: Sequence[JSharding | AUTO | None] | None = None,
in_layouts: Sequence[DeviceLocalLayout | None | AutoLayout] | None = None,
out_layouts: Sequence[DeviceLocalLayout | None | AutoLayout] | None = None,
arg_names: Sequence[str] | None = None,
result_names: Sequence[str] | None = None,
num_replicas: int = 1,
num_partitions: int = 1,
all_default_mem_kind: bool = True,
input_output_aliases: None | tuple[int | None, ...] = None,
propagated_out_mem_kinds: tuple[None | str, ...] | None = None,
lowering_parameters: LoweringParameters,
) -> LoweringResult:
"""Lowers a top-level jaxpr to an MLIR module.
Handles the quirks of the argument/return value passing conventions of the
runtime.
"""
util.test_event("lower_jaxpr_to_module")
platforms = tuple(map(xb.canonicalize_platform, platforms))
in_avals = (jaxpr.in_avals if arg_shardings is None else
map(sharded_aval, jaxpr.in_avals, arg_shardings))
out_avals = (jaxpr.out_avals if result_shardings is None else
map(sharded_aval, jaxpr.out_avals, result_shardings))
if all_default_mem_kind:
arg_memory_kinds = None
result_memory_kinds = None
else:
arg_memory_kinds = (map(_get_mem_kind, arg_shardings)
if arg_shardings is not None else None)
result_memory_kinds = (map(_get_mem_kind, result_shardings)
if result_shardings is not None else None)
# TODO(yashkatariya): Simplify the donation logic.
xla_donated_args = None
platforms_with_donation = [p for p in platforms
if p in _platforms_with_donation]
if platforms_with_donation:
if len(platforms_with_donation) != len(platforms) and (
xla_donated_args or any(donated_args)):
raise NotImplementedError(
"In multi-platform lowering either all or no lowering platforms "
f"should support donation. Lowering for {platforms} of which "
f"only {platforms_with_donation} support donation")
input_output_aliases, donated_args, xla_donated_args = _set_up_aliases(
input_output_aliases, in_avals, out_avals, donated_args,
arg_memory_kinds, result_memory_kinds, in_layouts, out_layouts,
result_shardings if num_partitions > 1 else None)
if (num_partitions > 1 and
(result_shardings is None or
any(s is None or isinstance(s, AUTO) or contains_unconstrained(s)
for s in result_shardings))):
if xla_donated_args is None:
xla_donated_args = [False] * len(donated_args)
for input_id in range(len(donated_args)):
if donated_args[input_id]:
xla_donated_args[input_id] = True
donated_args[input_id] = False
if any(donated_args):
unused_donations = [str(a) for a, d in zip(in_avals, donated_args) if d]
msg = "See an explanation at https://docs.jax.dev/en/latest/faq.html#buffer-donation."
if not platforms_with_donation:
msg = f"Donation is not implemented for {platforms}.\n{msg}"
if unused_donations:
warnings.warn("Some donated buffers were not usable:"
f" {', '.join(unused_donations)}.\n{msg}")
# Delete donated_args by default here, since it's not needed beyond this point
del donated_args
unlowerable_effects = lowerable_effects.filter_not_in(jaxpr.effects)
if unlowerable_effects:
raise ValueError(f'Cannot lower jaxpr with effects: {jaxpr.effects}')
# HLO channels need to start at 1
channel_iter = itertools.count(1)
# Create a keepalives list that will be mutated during the lowering.
keepalives: list[Any] = []
host_callbacks: list[Any] = []
dim_vars: Sequence[str]
if not config.dynamic_shapes.value:
# Find the dimension variables
all_dim_poly = [d for aval in jaxpr.in_avals if hasattr(aval, "shape")
for d in aval.shape if not core.is_constant_dim(d)]
dim_vars = tuple(sorted(functools.reduce(lambda acc, new: acc.union(new._get_vars()),
all_dim_poly, set())))
else:
dim_vars = ()
ctx = ModuleContext(backend=backend,
platforms=platforms, axis_context=axis_context,
keepalives=keepalives,
channel_iterator=channel_iter,
host_callbacks=host_callbacks,
lowering_parameters=lowering_parameters,
shape_poly_state=ShapePolyLoweringState(dim_vars, platforms),
all_default_mem_kind=all_default_mem_kind)
with ctx.context, ir.Location.unknown(ctx.context):
# Remove module name characters that XLA would alter. This ensures that
# XLA computation preserves the module name.
attrs = ctx.module.operation.attributes
module_name = sanitize_name(module_name)
attrs["sym_name"] = ir.StringAttr.get(module_name)
attrs["mhlo.num_replicas"] = i32_attr(num_replicas)
attrs["mhlo.num_partitions"] = i32_attr(num_partitions)
lower_jaxpr_to_fun(
ctx, "main", jaxpr, ordered_effects,
name_stack=name_stack,
public=True,
replicated_args=replicated_args,
arg_shardings=arg_shardings,
result_shardings=result_shardings,
input_output_aliases=input_output_aliases,
xla_donated_args=xla_donated_args,
arg_names=arg_names,
result_names=result_names,
arg_memory_kinds=arg_memory_kinds,
result_memory_kinds=result_memory_kinds,
arg_layouts=in_layouts,
result_layouts=out_layouts,
propagated_out_mem_kinds=propagated_out_mem_kinds)
try:
if not ctx.module.operation.verify():
raise ValueError(
"Cannot lower jaxpr with verifier errors. " +
dump_module_message(ctx.module, "verification"))
except ir.MLIRError as e:
msg_lines = ["Cannot lower jaxpr with verifier errors:"]
def emit_diagnostic_info(d):
msg_lines.append(f"\t{d.message}")
msg_lines.append(f"\t\tat {d.location}")
for n in d.notes:
emit_diagnostic_info(n)
for d in e.error_diagnostics:
emit_diagnostic_info(d)
raise ValueError("\n".join(msg_lines) + "\n" +
dump_module_message(ctx.module, "verification")) from e
if config.use_shardy_partitioner.value:
with ctx.context:
pipeline = passmanager.PassManager.parse(
'builtin.module(sdy-lift-inlined-meshes)')
pipeline.run(ctx.module.operation)
util.test_event("mlir.collect_lowered_jaxprs", jaxpr, ctx.module)
return LoweringResult(ctx.module, ctx.keepalives, ctx.host_callbacks,
ctx.shape_poly_state)
def _set_up_aliases(input_output_aliases, avals_in, avals_out,
donated_args, arg_memory_kinds, result_memory_kinds,
in_layouts, out_layouts, result_shardings):
if input_output_aliases is None:
input_output_aliases = [None] * len(avals_in)
else:
input_output_aliases = list(input_output_aliases)
# To match-up in-avals to out-avals we only care about the number of
# bytes, so we strip off unrelated aval metadata (eg. the named shape)
strip_metadata = lambda a: (a if a is core.abstract_token else
core.ShapedArray(a.shape, a.dtype))
avals_in = map(strip_metadata, avals_in)
avals_out = map(strip_metadata, avals_out)
# Both arg and result memory kinds need to be specified to donate based on
# the memory kind. For jit's where out_shardings is not specified, we don't
# know the memory kind so don't condition the logic based on the memory kind.
# TODO(yashkatariya): Note that this logic should be in C++ where we make
# donation decisions are made after SPMD propagation passes and memory
# placement passes so that we have all the information.
if (arg_memory_kinds is None or result_memory_kinds is None or
any(a is None for a in arg_memory_kinds) or
any(r is None for r in result_memory_kinds)):
arg_memory_kinds = [None] * len(avals_in)
result_memory_kinds = [None] * len(avals_out)
donations = collections.defaultdict(collections.deque)
for i, (aval, am, donated, aliased) in enumerate(
zip(avals_in, arg_memory_kinds, donated_args, input_output_aliases)):
if donated and aliased is None:
donations[(aval, am)].append(i)
xla_donated_args = None
out_donated_args = list(donated_args)
in_out_layout_not_none = in_layouts is not None and out_layouts is not None
for i, (aval, rm) in enumerate(zip(avals_out, result_memory_kinds)):
# Only donate if memory kinds match. Relax this when the compiler can
# donate across memories.
key = (aval, rm)
if donations.get(key, ()):
input_id = donations[key].popleft()
out_donated_args[input_id] = False
if (in_out_layout_not_none and
isinstance(in_layouts[input_id], AutoLayout) and
not isinstance(out_layouts[i], AutoLayout)):
raise ValueError(
f"Input layout being donated was {in_layouts[input_id]} while"
f" output layout was {out_layouts[i]}. Did you mean to set the"
" **output layout** to **DeviceLocalLayout.AUTO**?\nThis will"
" allow for the input and output layout to be chosen by XLA and"
" not the layout of the output which might not be optimal.")
if (in_out_layout_not_none and
not isinstance(in_layouts[input_id], AutoLayout) and
isinstance(out_layouts[i], AutoLayout)):
raise ValueError(
f"Input layout being donated was {in_layouts[input_id]} while"
f" output layout was {out_layouts[i]}. Did you mean to set the"
" **input layout** to **DeviceLocalLayout.AUTO**?\nThis will allow"
" for the input and output layout to be chosen by XLA and not the"
" layout of the input which might not be optimal.")
if (in_layouts is None or out_layouts is None or
in_layouts[input_id] == out_layouts[i]) and (
result_shardings is None or not (
(s := result_shardings[i]) is None or
isinstance(s, AUTO) or contains_unconstrained(s))):
input_output_aliases[input_id] = i
else:
# Fallback to xla donation if layouts don't match.
if xla_donated_args is None:
xla_donated_args = [False] * len(avals_in)
xla_donated_args[input_id] = True
return input_output_aliases, out_donated_args, xla_donated_args
Token = ir.Value
token_type = hlo.TokenType.get
create_token = hlo.create_token
class TokenSet:
"""An immutable container of tokens to be used to lower effectful jaxprs. When lowering
effectful jaxprs, we need to thread HLO tokens to sequence them. Each effect
will need its own token that will be threaded in and out of the effectful
primitives. A `TokenSet` encapsulates a set of HLO tokens that will be
used by the lowering rules.
"""
_tokens: collections.OrderedDict[core.Effect, Token]
def __init__(self, *args, **kwargs):
self._tokens = collections.OrderedDict(*args, **kwargs)
def __len__(self):
return len(self._tokens)
def get(self, effect: core.Effect) -> Token:
return self._tokens[effect]
@classmethod
def create(cls, effects: Sequence[core.Effect]) -> TokenSet:
"""Creates a `TokenSet` corresponding to a list of `core.Effect`s."""
tokens = [create_token() for _ in effects]
return TokenSet(zip(effects, tokens))
def items(self) -> Sequence[tuple[core.Effect, Token]]:
return tuple(self._tokens.items())
def effects(self) -> set[core.Effect]:
return set(self._tokens.keys())
def subset(self, effects: Sequence[core.Effect]) -> TokenSet:
"""Return a subset of the `TokenSet` restricted to a set of `core.Effect`s."""
return TokenSet((eff, self._tokens[eff]) for eff in effects)
def update_tokens(self, tokens: TokenSet) -> TokenSet:
"""Returns a new `TokenSet` with tokens replaced with ones from the input `TokenSet`."""
new_tokens = []
for eff in self.effects():
if eff in tokens._tokens:
new_tokens.append((eff, tokens._tokens[eff]))
else:
new_tokens.append((eff, self._tokens[eff]))
return TokenSet(new_tokens)
def lower_jaxpr_to_fun(
ctx: ModuleContext,
name: str,
jaxpr: core.ClosedJaxpr,
effects: Sequence[core.Effect],
name_stack: source_info_util.NameStack,
*,
public: bool = False,
replicated_args: Sequence[bool] | None = None,
arg_shardings: Sequence[JSharding | AUTO | None] | None = None,
result_shardings: Sequence[JSharding | AUTO | None] | None = None,
use_sharding_annotations: bool = True,
input_output_aliases: Sequence[int | None] | None = None,
xla_donated_args: Sequence[bool] | None = None,
api_name: str = "jit",
arg_names: Sequence[str | None] | None = None,
result_names: Sequence[str] | None = None,
arg_memory_kinds: Sequence[str | None] | None = None,
result_memory_kinds: Sequence[str | None] | None = None,
arg_layouts: Sequence[DeviceLocalLayout | None | AutoLayout] | None = None,
result_layouts: Sequence[DeviceLocalLayout | None | AutoLayout] | None = None,
propagated_out_mem_kinds: tuple[None | str, ...] | None = None,
) -> func_dialect.FuncOp:
"""Lowers jaxpr and its callees to an IR function.
Assumes that an MLIR context, location, and insertion point are set.
Args:
ctx: the lowering context.
name: the function name. The name will be uniquified by the symbol table,
so it is ok to use the same name multiple times.
jaxpr: the jaxpr to lower.
effects: a sequence of `core.Effect`s corresponding to an ordering of tokens
that will be created in or used by the lowered function.
public: if true, the function's visibility is set to "public".
replicated_args: if present, annotates arguments as replicated.
arg_shardings: sharding annotations for each argument (optional).
result_shardings: sharding annotations for each result (optional).
use_sharding_annotations: if True, use "mhlo.sharding" annotations on
parameters and return values to express sharding. If False, use
hlo.custom_call operators with sharding annotations.
TODO(b/228598865): remove this option when "mhlo.sharding" annotations are
propagated on non-entry functions during MLIR->HLO conversion.
input_output_aliases: optional sequence that maps argument numbers to the
corresponding output that should alias them.
xla_donated_args: optional sequence of args to set donation annotations.
api_name: The name of the higher level primitive which should show up in the
name stack.
Returns:
MLIR func op
"""
util.test_event("lower_jaxpr_to_fun", name)
check_jaxpr_constants(jaxpr)
# The first dimension variable may be the platform index
num_dim_vars = len(ctx.shape_poly_state.dim_vars)
dim_var_avals = [core.ShapedArray((), dtypes.canonicalize_dtype(np.int64))] * num_dim_vars
dim_var_types = map(aval_to_ir_type, dim_var_avals)
# Function inputs: *dim_var_values, *tokens, *actual_inputs
input_types = map(aval_to_ir_type, jaxpr.in_avals)
output_types = map(aval_to_ir_type, jaxpr.out_avals)
num_tokens = len(effects)
token_types = [token_type() for _ in effects]
token_avals = [core.abstract_token] * num_tokens
# Order of arguments: dim vars, tokens, array inputs
input_avals = dim_var_avals + token_avals + jaxpr.in_avals
input_types = [*dim_var_types, *token_types, *input_types]
output_avals = [core.abstract_token] * num_tokens + jaxpr.out_avals
output_types = [*token_types, *output_types]
if input_output_aliases is not None:
token_input_output_aliases = [None] * (num_dim_vars + num_tokens)
input_output_aliases = [*token_input_output_aliases, *input_output_aliases]
# Update the existing aliases to account for the new output values
input_output_aliases = [None if a is None
else a + num_tokens
for a in input_output_aliases]
if arg_shardings is not None:
token_shardings = [None] * (num_dim_vars + num_tokens)
arg_shardings = [*token_shardings, *arg_shardings]
if result_shardings is not None:
token_shardings = [None] * num_tokens
result_shardings = [*token_shardings, *result_shardings]
if replicated_args is not None:
token_replicated_args = [False] * (num_dim_vars + num_tokens)
replicated_args = [*token_replicated_args, *replicated_args]
if arg_memory_kinds is not None:
token_memory_kinds = [None] * (num_dim_vars + num_tokens)
arg_memory_kinds = [*token_memory_kinds, *arg_memory_kinds]
if result_memory_kinds is not None:
token_memory_kinds = [None] * num_tokens
result_memory_kinds = [*token_memory_kinds, *result_memory_kinds]
if arg_layouts is not None:
token_layouts = [None] * (num_dim_vars + num_tokens)
arg_layouts = [*token_layouts, *arg_layouts]
if result_layouts is not None:
token_layouts = [None] * num_tokens
result_layouts = [*token_layouts, *result_layouts]
if xla_donated_args is not None:
xla_donated_args = [*([False] * (num_dim_vars + num_tokens)), *xla_donated_args]
flat_input_types = flatten_ir_types(input_types)
flat_output_types = flatten_ir_types(output_types)
ftype = ir.FunctionType.get(flat_input_types, flat_output_types)
func_op = func_dialect.FuncOp(name, ftype, ip=ctx.ip)
func_op.attributes["sym_visibility"] = ir.StringAttr.get(
"public" if public else "private")
ctx.symbol_table.insert(func_op)
ir_arg_shardings = None
if arg_shardings is not None:
ir_arg_shardings = util.flatten(
[[_to_physical_op_sharding(ctx, a, s)] * len_ir_types(types)
for a, s, types in zip(input_avals, arg_shardings, input_types)])
ir_arg_memory_kinds = None
if arg_memory_kinds is not None:
ir_arg_memory_kinds = util.flatten(
[[mk] * len_ir_types(types)
for mk, types in zip(arg_memory_kinds, input_types)])
ir_arg_layouts = None
if arg_layouts is not None:
ir_arg_layouts = util.flatten(
[[_to_xla_layout(l, a)] * len_ir_types(types)
for l, a, types in zip(arg_layouts, input_avals, input_types)])
ir_donated_args = None
if xla_donated_args is not None:
ir_donated_args = util.flatten(
[[is_donated] * len_ir_types(types)
for is_donated, types in zip(xla_donated_args, input_types)])
ir_result_shardings = None
unconstrained_variants = None
if result_shardings is not None:
ir_result_shardings = util.flatten(
[[_to_physical_op_sharding(ctx, a, s)] * len_ir_types(types)
for a, s, types in zip(output_avals, result_shardings, output_types)])
unconstrained_variants = util.flatten(
[[_get_unconstrained_variants(s, a)] * len_ir_types(types)
for a, s, types in zip(output_avals, result_shardings, output_types)])
ir_result_memory_kinds = None
custom_call_ir_result_memory_kinds = None
if result_memory_kinds is not None:
if propagated_out_mem_kinds is None:
propagated_out_mem_kinds = (None,) * len(result_memory_kinds)
res, custom_call_res = [], []
for pom, mk, types in zip(propagated_out_mem_kinds, result_memory_kinds,
output_types):
if pom is not None and mk is None:
res.append([pom] * len_ir_types(types))
else:
res.append([mk] * len_ir_types(types)) # type: ignore
# To add the custom call on the output to signal a transfer, only do it
# if memory kind comes from out_shardings on `jit` and result_memory_kinds
# comes from out_shardings on `jit`.
custom_call_res.append([mk] * len_ir_types(types))
ir_result_memory_kinds = util.flatten(res)
custom_call_ir_result_memory_kinds = util.flatten(custom_call_res)
ir_result_layouts = None
if result_layouts is not None:
ir_result_layouts = util.flatten(
[[_to_xla_layout(l, a)] * len_ir_types(types)
for l, a, types in zip(result_layouts, output_avals, output_types)])
if (
replicated_args is not None
or ir_arg_shardings is not None
or ir_arg_memory_kinds is not None
or ir_arg_layouts is not None
or input_output_aliases is not None
or ir_donated_args is not None
or arg_names is not None
or num_tokens > 0
or num_dim_vars > 0
):
arg_attrs: list[dict[str, ir.Attribute]] = [
{} for _ in range(len(flat_input_types))]
if replicated_args is not None:
replicated_ir_args = [[replicated] * len_ir_types(types) for replicated, types
in zip(replicated_args, input_types)]
for attrs, replicated in zip(arg_attrs, util.flatten(replicated_ir_args)):
if replicated:
attrs["mhlo.is_same_data_across_replicas"] = ir.BoolAttr.get(True)
if use_sharding_annotations and ir_arg_shardings is not None:
for attrs, sharding in zip(arg_attrs, ir_arg_shardings):
if sharding is not None:
if config.use_shardy_partitioner.value:
attrs["sdy.sharding"] = get_sharding_attr(sharding)
else:
attrs["mhlo.sharding"] = get_sharding_attr(sharding)
if ir_arg_memory_kinds is not None:
for attrs, memory_kind in zip(arg_attrs, ir_arg_memory_kinds):
if memory_kind is not None:
attrs["mhlo.memory_kind"] = ir.StringAttr.get(memory_kind)
if ir_arg_layouts is not None:
for attrs, layout in zip(arg_attrs, ir_arg_layouts):
if layout is not None:
attrs["mhlo.layout_mode"] = ir.StringAttr.get(layout)
if ir_donated_args is not None:
for attrs, is_donated in zip(arg_attrs, ir_donated_args):
if is_donated:
attrs["jax.buffer_donor"] = ir.BoolAttr.get(True)
if input_output_aliases is not None:
output_ids = util.unflatten(
list(range(len(flat_output_types))), map(len_ir_types, output_types))
aliases: list[int | None] = []
for itypes, alias in zip(input_types, input_output_aliases):
if alias is None:
aliases.extend([None] * len_ir_types(itypes))
else:
aliases.extend(output_ids[alias])
for attrs, alias in zip(arg_attrs, aliases):
if alias is not None:
attrs["tf.aliasing_output"] = i32_attr(alias)
if num_dim_vars > 0:
for var_name, attrs in zip(ctx.shape_poly_state.dim_vars,
arg_attrs[:num_dim_vars]):
attrs["jax.global_constant"] = ir.StringAttr.get(var_name)
elif ctx.lowering_parameters.global_constant_computation:
for attrs in arg_attrs:
attrs["jax.global_constant"] = ir.StringAttr.get("")
if num_tokens > 0:
token_arg_attrs = arg_attrs[num_dim_vars:num_dim_vars + num_tokens]
for attrs in token_arg_attrs:
attrs["jax.token"] = ir.BoolAttr.get(True)
func_op.arg_attrs = ir.ArrayAttr.get(
[ir.DictAttr.get(attrs) for attrs in arg_attrs])
result_attrs: list[dict[str, ir.Attribute]] = [
{} for _ in range(len(flat_output_types))]
if num_tokens > 0:
token_result_attrs = result_attrs[:num_tokens]
for attrs in token_result_attrs:
attrs["jax.token"] = ir.BoolAttr.get(True)
if result_names:
named_result_attrs = result_attrs[num_tokens:]
if len(named_result_attrs) == len(result_names):
for attrs, name_ in zip(named_result_attrs, result_names):
attrs['jax.result_info'] = ir.StringAttr.get(name_)
if use_sharding_annotations and ir_result_shardings is not None:
for attrs, sharding, uv in zip(result_attrs, ir_result_shardings,
unconstrained_variants): # type: ignore
if sharding is not None and not uv.contains_unconstrained:
if config.use_shardy_partitioner.value:
attrs["sdy.sharding"] = get_sharding_attr(sharding)
else:
attrs["mhlo.sharding"] = get_sharding_attr(sharding)
if ir_result_memory_kinds is not None:
for attrs, mem_kind in zip(result_attrs, ir_result_memory_kinds):
if mem_kind is not None:
attrs['mhlo.memory_kind'] = ir.StringAttr.get(mem_kind)
if ir_result_layouts is not None:
for attrs, layout in zip(result_attrs, ir_result_layouts):
if layout is not None:
attrs['mhlo.layout_mode'] = ir.StringAttr.get(layout)
func_op.result_attrs = ir.ArrayAttr.get(
[ir.DictAttr.get(attrs) for attrs in result_attrs])
if arg_names:
arg_locs = [ir.Location.unknown()] * (num_dim_vars + num_tokens)
for n in arg_names:
arg_locs.append(ir.Location.name(n) if n else ir.Location.unknown())
entry_block = func_op.add_entry_block(arg_locs)
else:
entry_block = func_op.add_entry_block()
with ir.InsertionPoint(entry_block):
flat_args = entry_block.arguments
# We separate out the dimension variable inputs, the token inputs and
# the regular inputs. The dimension variables and token inputs
# will be passed to `jaxpr_subcomp` separately from the `args`.
dim_var_values, _, _ = util.split_list(flat_args, [num_dim_vars, num_tokens])
# A lowering context just for function body entry/exit code.
entry_lowering_ctx = LoweringRuleContext(
module_context=ctx, name_stack=name_stack, primitive=None,
avals_in=[], avals_out=None,
tokens_in=TokenSet.create([]), tokens_out=None,
axis_size_env=None, dim_var_values=dim_var_values)
if not use_sharding_annotations and ir_arg_shardings is not None:
flat_args = [
a if s is None else wrap_with_sharding_op(entry_lowering_ctx, a, a_aval, s)
for a, s, a_aval in zip(flat_args, ir_arg_shardings, input_avals)]
if ir_arg_shardings is not None and name == "main":
flat_args = [
replicate_trailing_dims(entry_lowering_ctx, o, a)
if (a is not core.abstract_token and
dtypes.issubdtype(a.dtype, dtypes.extended) and
(s is None or all_unconstrained(rs, a))) else o # pytype: disable=attribute-error
for o, s, a, rs in zip(flat_args, ir_arg_shardings, input_avals,
arg_shardings) # type: ignore
]
_, token_args, unflattened_args = util.split_list(
unflatten_ir_values_like_types(flat_args, input_types),
[num_dim_vars, num_tokens])
tokens_in = TokenSet(zip(effects, token_args))
args: list[IrValues] = unflattened_args
if name is not None:
callee_name_stack = name_stack.extend(util.wrap_name(name, api_name))
else:
callee_name_stack = name_stack
consts = [ir_constant(xla.canonicalize_dtype(x)) for x in jaxpr.consts]
out_vals, tokens_out = jaxpr_subcomp(
ctx, jaxpr.jaxpr, callee_name_stack, tokens_in,
consts, *args, dim_var_values=dim_var_values)
outs: list[IrValues] = []
for eff in effects:
outs.append(tokens_out.get(eff))
outs.extend(out_vals)
flat_outputs = flatten_ir_values(outs)
if not use_sharding_annotations and ir_result_shardings is not None:
flat_outputs = [
o if s is None else wrap_with_sharding_op(entry_lowering_ctx, o, o_aval, s)
for o, s, o_aval in zip(flat_outputs, ir_result_shardings, output_avals)]
if ir_result_shardings is not None:
temp_flat_outputs = []
for o, s, o_aval, uv in zip(flat_outputs, ir_result_shardings,
output_avals, unconstrained_variants): # type: ignore
if (s is not None and uv.contains_unconstrained and
not uv.all_unconstrained):
if config.use_shardy_partitioner.value:
s = modify_sdy_sharding_wrt_axis_types(s, o_aval.sharding.mesh)
temp_flat_outputs.append(wrap_with_sharding_op(
entry_lowering_ctx, o, o_aval, s,
unspecified_dims=uv.unconstrained_dims))
else:
temp_flat_outputs.append(o)
flat_outputs = temp_flat_outputs
# Insert a custom call if output is on host because XLA needs that to do the
# transfer.
if custom_call_ir_result_memory_kinds is not None and name == "main":
flat_outputs = [
o if mk is None else wrap_with_memory_kind(o, mk, o_aval)
for o, mk, o_aval in zip(
flat_outputs, custom_call_ir_result_memory_kinds, output_avals)]
if ir_result_shardings is not None and name == "main":
flat_outputs = [
replicate_trailing_dims(entry_lowering_ctx, o, a)
if (a is not core.abstract_token and
dtypes.issubdtype(a.dtype, dtypes.extended) and
(s is None or all_unconstrained(rs, a))) else o # pytype: disable=attribute-error
for o, s, a, rs in zip(flat_outputs, ir_result_shardings, output_avals,
result_shardings) # type: ignore
]
func_dialect.return_(flat_outputs)
return func_op
def wrap_with_memory_kind(
x: ir.Value, memory_kind: str, aval_out: core.AbstractValue) -> ir.Value:
if aval_out is None:
result_type = x.type
else:
typ = aval_to_ir_type(aval_out)
assert isinstance(typ, ir.Type), typ
result_type = typ
op = custom_call("annotate_device_placement", result_types=[result_type],
operands=[x], has_side_effect=True, api_version=1)
dict_attr = {"_xla_buffer_placement": ir.StringAttr.get(memory_kind)}
op.attributes["mhlo.frontend_attributes"] = ir.DictAttr.get(dict_attr)
return op.result
def replicate_trailing_dims(ctx, val: ir.Value, aval) -> ir.Value:
# Set the sharding of extended dtypes to be UNCONSTRAINED
# (i.e. XLA will choose) on aval.shape.
# For the trailing dims i.e. the dimension of key_shape on the base_array,
# the sharding is set to be REPLICATED always.
# For example: if the key.shape is (8, 2) and key_data(key).shape is (8, 2, 2),
# then the sharding will be P(P.UNCONSTRAINED, P.UNCONSTRAINED, None).
# The below custom call achieves the sharding like above example.
assert isinstance(aval, (core.ShapedArray, core.DShapedArray))
if config.use_shardy_partitioner.value:
physical_ndim = core.physical_aval(aval).ndim
s = SdyArray(
mesh_shape=None,
dim_shardings=[
sharding_impls.SdyDim(axes=[], is_open=i < aval.ndim)
for i in range(physical_ndim)
])
return wrap_with_sharding_op(ctx, val, aval, s)
else:
return wrap_with_sharding_op(
ctx, val, aval, xc.HloSharding.replicate().to_proto(),
unspecified_dims=set(range(aval.ndim)))
def _emit_lowering_rule_as_fun(lowering_rule,
ctx: LoweringRuleContext) -> func_dialect.FuncOp:
"""Emits the contents of a lowering rule as a private function."""
num_dim_vars = len(ctx.module_context.shape_poly_state.dim_vars)
# TODO(necula) maybe only pass the dim_vars if they are needed?
dim_var_types = [
aval_to_ir_type(core.ShapedArray((), dtypes.canonicalize_dtype(np.int64)))
] * num_dim_vars
input_types = map(aval_to_ir_type, ctx.avals_in)
output_types = map(aval_to_ir_type, ctx.avals_out)
effs = list(ctx.tokens_in.effects())
token_types = [token_type() for _ in effs]
input_types = [*dim_var_types, *token_types, *input_types]
output_types = [*token_types, *output_types]
flat_input_types = flatten_ir_types(input_types)
flat_output_types = flatten_ir_types(output_types)
ftype = ir.FunctionType.get(flat_input_types, flat_output_types)
assert ctx.primitive is not None
func_op = func_dialect.FuncOp(ctx.primitive.name, ftype,
ip=ctx.module_context.ip)
func_op.attributes["sym_visibility"] = ir.StringAttr.get("private")
ctx.module_context.symbol_table.insert(func_op)
entry_block = func_op.add_entry_block()
with ir.InsertionPoint(entry_block):
unflattened_args = unflatten_ir_values_like_types(
entry_block.arguments, input_types)
dim_var_values, token_args, unflattened_args = util.split_list(unflattened_args, [num_dim_vars, len(ctx.tokens_in)])
sub_ctx = ctx.replace(tokens_in=TokenSet(zip(effs, token_args)),
dim_var_values=dim_var_values)
outs = lowering_rule(sub_ctx, *unflattened_args)
if sub_ctx.tokens_out:
outs = [*[sub_ctx.tokens_out.get(eff) for eff in effs], outs]
func_dialect.return_(flatten_ir_values(outs))
return func_op
class HashableLiteral:
"""Hashable wrapper of core.Literal, used for deduplicating IR constants."""
__slots__ = ["value", "data"]
value: core.Literal
# Copy of the value suitable for an equality comparison. We are careful to
# avoid floating point comparisons here, because in particular we don't want
# 0.0 and -0.0 to be considered equal, but we are fine with NaNs being equal.
data: bytes | int | bool | None
def __init__(self, value):
self.value = value
if isinstance(value.val, (np.generic, np.ndarray)):
self.data = value.val.tobytes()
elif isinstance(value.val, (bool, int)):
self.data = value.val
elif isinstance(value.val, float):
self.data = np.float64(value.val).tobytes()
elif isinstance(value.val, complex):
self.data = np.complex128(value.val).tobytes()
else:
self.data = None # Unhandled case.
def __hash__(self):
return hash(self.data)
def __eq__(self, other):
if type(self.value.val) != type(other.value.val):
return False
if self.value.aval != other.value.aval:
return False
if self.data is None:
return self is other
return self.data == other.data
def jaxpr_subcomp(ctx: ModuleContext, jaxpr: core.Jaxpr,
name_stack: source_info_util.NameStack,
tokens: TokenSet,
consts: Sequence[IrValues],
*args: IrValues,
dim_var_values: Sequence[ir.Value]
) -> tuple[Sequence[IrValues], TokenSet]:
"""Lowers a jaxpr into MLIR, inlined into an existing function.
Assumes that an MLIR context, location, and insertion point are set.
dim_var_values: the list of dimension variables values in the current
IR function, in the order of ctx.shape_poly_state.dim_vars.
"""
assert "gpu" not in ctx.platforms
cached_ir_consts: dict[HashableLiteral, IrValues] = {}
def read(v: core.Atom) -> IrValues:
if type(v) is core.Literal:
h = HashableLiteral(v)
c = cached_ir_consts.get(h)
if c is None:
c = ir_constant(xla.canonicalize_dtype(v.val))
cached_ir_consts[h] = c
return c
else:
assert isinstance(v, core.Var)
return env[v]
def aval(v: core.Atom) -> core.AbstractValue:
if type(v) is core.Literal:
return core.abstractify(v.val)
else:
return v.aval
def write(v: core.Var, node: IrValues):
assert node is not None
w: IrValues
if isinstance(node, ir.Value):
w = node
else:
if len(node) == 1:
warnings.warn(
"JAX lowering rules should not wrap singleton values in tuples. "
"It will be an error to wrap a singleton value in a tuple in a "
"future version of JAX.",
DeprecationWarning, stacklevel=2)
w = node[0]
else:
w = tuple(node)
env[v] = w
def get_override_lowering_rule(primitive: core.Primitive) -> LoweringRule | None:
if ctx.lowering_parameters.override_lowering_rules is None:
return None
for p, rule in ctx.lowering_parameters.override_lowering_rules:
if primitive is p:
return rule
return None
env: dict[core.Var, IrValues] = {}
assert all(_is_ir_values(v) for v in args), args
assert all(_is_ir_values(v) for v in consts), consts
assert isinstance(name_stack, source_info_util.NameStack), type(name_stack)
assert len(args) == len(jaxpr.invars), (jaxpr, args)
assert len(consts) == len(jaxpr.constvars), (jaxpr, consts)
assert len(ctx.shape_poly_state.dim_vars) == len(dim_var_values), (ctx.shape_poly_state.dim_vars, dim_var_values)
foreach(write, jaxpr.constvars, consts)
foreach(write, jaxpr.invars, args)
last_used = core.last_used(jaxpr)
for eqn in jaxpr.eqns:
in_nodes = map(read, eqn.invars)
source_info = eqn.source_info.replace(
name_stack=name_stack + eqn.source_info.name_stack)
loc = _source_info_to_location(ctx, eqn.primitive, source_info)
with (source_info_util.user_context(eqn.source_info.traceback), loc,
eqn.ctx.manager):
override_rule = get_override_lowering_rule(eqn.primitive)
platform_rules: dict[str, LoweringRule] = {}
default_rule: LoweringRule | None = None
# See mlir.lower_per_platform for meaning of `platform_rules` and `default_rule`
if override_rule is not None:
default_rule = override_rule
else:
# First the platform-specific rules
for p in _platforms_for_eqn_ctx(eqn.ctx) or ctx.platforms:
if eqn.primitive in _platform_specific_lowerings[p]:
platform_rules[p] = _platform_specific_lowerings[p][eqn.primitive]
# Now the default rule
if eqn.primitive in _lowerings:
default_rule = _lowerings[eqn.primitive]
effects = list(effects_lib.ordered_effects.filter_in(eqn.effects))
tokens_in = tokens.subset(effects)
avals_in = map(aval, eqn.invars)
rule_ctx = LoweringRuleContext(
module_context=ctx, primitive=eqn.primitive,
name_stack=source_info.name_stack,
avals_in=avals_in,
avals_out=map(aval, eqn.outvars), tokens_in=tokens_in,
tokens_out=None, jaxpr_eqn_ctx=eqn.ctx, dim_var_values=dim_var_values)
if config.dynamic_shapes.value:
axis_size_env = {d: read(d)
for a in avals_in if type(a) is core.DShapedArray
for d in a.shape if type(d) is core.Var}
rule_ctx = rule_ctx.replace(axis_size_env=axis_size_env)
assert all(_is_ir_values(v) for v in in_nodes), (eqn, in_nodes)
ans = lower_per_platform(rule_ctx, str(eqn.primitive),
platform_rules, default_rule,
eqn.effects,
*in_nodes, **eqn.params)
if effects:
# If there were ordered effects in the primitive, there should be output
# tokens we need for subsequent ordered effects.
tokens_out = rule_ctx.tokens_out
if tokens_out is None:
raise ValueError(
f'Lowering rule for `{eqn.primitive}` needs to set `tokens_out` '
f'because it has effects: {eqn.effects}.')
if tokens_out.effects() != tokens_in.effects():
raise ValueError(
f'Lowering rule for `{eqn.primitive}` '
'returns incorrect set of output tokens. '
f'Expected: {tuple(tokens_in.effects())} vs. Actual: {tuple(tokens_out.effects())}')
tokens = tokens.update_tokens(tokens_out)
try:
out_nodes = tuple(ans)
except TypeError as e:
raise ValueError("Output of translation rule must be iterable: "
f"{eqn}, got output {ans}") from e
assert len(ans) == len(eqn.outvars), (ans, eqn)
foreach(write, eqn.outvars, out_nodes)
core.clean_up_dead_vars(eqn, env, last_used)
return tuple(read(v) for v in jaxpr.outvars), tokens
def _platforms_for_eqn_ctx(eqn_ctx: core.JaxprEqnContext | None
) -> tuple[str, ...]:
"""Returns platforms to override based on compute type of jaxpr equation."""
if eqn_ctx is None:
return ()
if eqn_ctx.compute_type == 'device_host':
return ('cpu',)
if eqn_ctx.compute_type == 'tpu_sparsecore':
return ('tpu',)
return ()
def _platforms_for_eqn(ctx: LoweringRuleContext) -> tuple[str, ...]:
"""The lowering platforms for the current eqn"""
return tuple((_platforms_for_eqn_ctx(ctx.jaxpr_eqn_ctx) or
ctx.platforms or ctx.module_context.platforms))
def lower_per_platform(ctx: LoweringRuleContext,
description: str,
platform_rules: dict[str, LoweringRule],
default_rule: LoweringRule | None,
effects: effects_lib.Effects,
*rule_args: ir.Value | tuple[ir.Value, ...],
**rule_kwargs) -> Sequence[ir.Value]:
"""Emits code for a primitive for the current lowering platform(s).
For example, given
platform_rules = dict(tpu=rule0, cpu=rule0)
default_rule = rule1
and
ctx.module_context.lowering_parameters.platforms = ("cpu",)
emits:
rule0(ctx, *rule_args, **rule_kwargs)
In case of multi-platform lowering, e.g., if
ctx.module_context.lowering_parameters.platforms = ("cpu", "cuda", "tpu")
emits:
rule_idx = case current_platform_idx:
0: return 0 # cpu rule index
1: return 1 # cuda rule index
2: return 0 # tpu rule index
output = case rule_idx
0: return rule0(*rule_args, **rule_kwargs)
1: return rule1(*rule_args, **rule_kwargs)
Args:
ctx: lowering context.
description: a string to include in error messages.
platform_rules: map platform names, e.g., "cpu", "cuda", to
`LoweringRule`s, for the platforms that have non-default lowering.
default_rule: an optional rule to use for platforms not in `platform_rules`.
effects: the set of effects for the current primitive.
rule_args: the args of the lowering rules.
rule_kwargs: the kwargs of the lowering rules.
"""
platforms: Sequence[str] = _platforms_for_eqn(ctx)
# Special case the common case (single-platform lowering)
if len(platforms) == 1:
rule = platform_rules.get(platforms[0], default_rule)
if rule is None:
raise NotImplementedError(
f"MLIR translation rule for primitive '{description}' not "
f"found for platform {platforms[0]}")
# Multi-platform lowering
kept_rules: list[LoweringRule] = [] # Only the rules for the platforms of interest
platform_to_kept_rules_idx: dict[str, int] = {}
for p, prule in platform_rules.items():
if p not in platforms:
continue
platform_to_kept_rules_idx[p] = len(kept_rules)
kept_rules.append(prule)
platforms_without_specific_rule = [p for p in platforms
if p not in platform_to_kept_rules_idx]
if platforms_without_specific_rule:
if default_rule is None:
raise NotImplementedError(
f"MLIR translation rule for primitive '{description}' not "
f"found for platforms {platforms_without_specific_rule}")
for p in platforms_without_specific_rule:
platform_to_kept_rules_idx[p] = len(kept_rules)
kept_rules.append(default_rule)
assert kept_rules
# If there is a single rule left just apply the rule, without conditionals.
if len(kept_rules) == 1:
output = kept_rules[0](ctx, *rule_args, **rule_kwargs)
foreach(
lambda o: wrap_compute_type_in_place(ctx, o.owner),
filter(_is_not_block_argument, flatten_ir_values(output)),
)
foreach(
lambda o: wrap_xla_metadata_in_place(ctx, o.owner),
flatten_ir_values(output),
)
return output
assert len(platforms) > 1 and len(kept_rules) >= 2, (platforms, kept_rules)
assert len(ctx.dim_var_values) >= 1, "Must have a platform_index variable"
# The first dim_var_values is the platform index
current_platform_idx = ctx.dim_var_values[0]
# Compute the rule index based on the current platform
i32_type = aval_to_ir_type(core.ShapedArray((), dtype=np.int32))
if current_platform_idx.type != i32_type:
current_platform_idx = hlo.convert(i32_type, current_platform_idx)
rule_idx_op = hlo.CaseOp([i32_type],
index=current_platform_idx,
num_branches=len(platforms))
for i, p in enumerate(platforms):
branch = rule_idx_op.regions[i].blocks.append()
with ir.InsertionPoint(branch):
hlo.return_([ir_constant(np.int32(platform_to_kept_rules_idx[p]))])
ordered_effects = effects_lib.ordered_effects.filter_in(effects)
rule_out_avals = [core.abstract_token] * len(ordered_effects) + ctx.avals_out
output_types = map(aval_to_ir_type, rule_out_avals)
case_op = hlo.CaseOp(flatten_ir_types(output_types),
index=rule_idx_op,
num_branches=len(kept_rules))
for i, rule in enumerate(kept_rules):
platforms_for_this_rule = [p
for p, rule_idx in platform_to_kept_rules_idx.items()
if rule_idx == i]
inner_ctx = ctx.replace(platforms=platforms_for_this_rule)
branch = case_op.regions[i].blocks.append()
with ir.InsertionPoint(branch):
output = rule(inner_ctx, *rule_args, **rule_kwargs)
try:
out_nodes = flatten_ir_values(output)
except TypeError as e:
raise ValueError("Output of translation rule must be iterable: "
f"{description}, got output {output}") from e
foreach(
lambda o: wrap_compute_type_in_place(ctx, o.owner),
filter(_is_not_block_argument, out_nodes),
)
foreach(
lambda o: wrap_xla_metadata_in_place(ctx, o.owner),
out_nodes,
)
if inner_ctx.tokens_out is not None:
assert len(ordered_effects) == len(inner_ctx.tokens_out)
out_nodes = [inner_ctx.tokens_out.get(eff)
for eff in ordered_effects] + out_nodes
hlo.return_(out_nodes)
results = case_op.results
if ordered_effects:
tokens, results = util.split_list(
unflatten_ir_values_like_types(results, output_types),
[len(ordered_effects)])
tokens_out = ctx.tokens_in.update_tokens(TokenSet(zip(ordered_effects,
tokens)))
ctx.set_tokens_out(tokens_out)
return results
def _ir_consts(consts) -> list[IrValues]:
unique_consts = {id(const): const for const in consts}
ir_consts = {
id_: ir_constant(xla.canonicalize_dtype(const))
for id_, const in unique_consts.items()
}
return [ir_consts[id(const)] for const in consts]
def lower_fun(fun: Callable, multiple_results: bool = True) -> Callable:
"""Converts a traceable JAX function `fun` into a lowering rule.
The returned function does not use `avals_out`, so callers may pass any value
as `avals_out`."""
def f_lowered(ctx: LoweringRuleContext, *args, **params):
f = fun if multiple_results else lambda *args, **kw: (fun(*args, **kw),)
wrapped_fun = lu.wrap_init(f, params,
debug_info=api_util.debug_info("lower_fun", fun, args, params))
manager = (contextlib.nullcontext() if ctx.jaxpr_eqn_ctx is None else
ctx.jaxpr_eqn_ctx.manager)
with manager:
if config.dynamic_shapes.value:
# We might be applying this function to arguments with dynamic shapes,
# i.e. there might be Vars in the shape tuples of ctx.avals_in. In that
# case, we need to form a jaxpr with leading binders for those axis size
# arguments (by computing an InputType and using trace_to_jaxpr_dynamic2),
# and we need to call jaxpr_subcomp with these arguments made explicit.
assert ctx.axis_size_env is not None
args = (*ctx.axis_size_env.values(), *args)
idx = {d: core.DBIdx(i) for i, d in enumerate(ctx.axis_size_env)}
i32_aval = core.ShapedArray((), np.dtype('int32'))
implicit_args = [(i32_aval, False)] * len(ctx.axis_size_env)
explicit_args = [(a.update(shape=tuple(idx.get(d, d) for d in a.shape)) # type: ignore
if type(a) is core.DShapedArray else a, True)
for a in ctx.avals_in]
wrapped_fun = lu.annotate(wrapped_fun, (*implicit_args, *explicit_args))
jaxpr, _, consts = pe.trace_to_jaxpr_dynamic2(wrapped_fun)
else:
jaxpr, _, consts, () = pe.trace_to_jaxpr_dynamic(wrapped_fun, ctx.avals_in)
# TODO(frostig,mattjj): check ctx.avals_out against jaxpr avals out?
if ctx.platforms is not None:
sub_context = ctx.module_context.replace(platforms=ctx.platforms)
else:
sub_context = ctx.module_context
out, tokens = jaxpr_subcomp(
sub_context, jaxpr, ctx.name_stack, ctx.tokens_in,
_ir_consts(consts), *args,
dim_var_values=ctx.dim_var_values)
ctx.set_tokens_out(tokens)
return out
return f_lowered
def _lower_jaxpr_to_fun_cached(ctx, fn_name, call_jaxpr, effects, name_stack,
arg_names=None, result_names=None):
if not call_jaxpr.consts and arg_names is result_names is None:
# Cacheable.
key = (fn_name, call_jaxpr.jaxpr, tuple(effects))
try:
func_op = ctx.cached_primitive_lowerings[key]
except KeyError:
num_callbacks = len(ctx.host_callbacks)
func_op = lower_jaxpr_to_fun(
ctx, fn_name, call_jaxpr, effects, name_stack, arg_names=arg_names,
result_names=result_names)
# If this Jaxpr includes callbacks, we can't cache the lowering because
# on TPU every callback must have a globally unique channel, but the
# channel gets assigned during lowering.
has_callbacks = len(ctx.host_callbacks) > num_callbacks
if not has_callbacks or "tpu" not in ctx.platforms:
ctx.cached_primitive_lowerings[key] = func_op
else:
func_op = lower_jaxpr_to_fun(
ctx, fn_name, call_jaxpr, effects, name_stack, arg_names=arg_names,
result_names=result_names)
return func_op
def check_backend_matches(inner_backend: str | None,
lowering_platforms: Sequence[str]):
# For nested calls, the outermost call sets the backend for all inner calls;
# it's an error if the inner call has a conflicting explicit backend spec.
if inner_backend is None:
return
outer_backend, *more_lowering_platforms = lowering_platforms
if more_lowering_platforms:
raise NotImplementedError(
"Multi-platform lowering when a backend= parameter is specified")
if (inner_backend != outer_backend and
outer_backend not in xb.expand_platform_alias(inner_backend)):
raise ValueError(
f"Outer-jit backend specification {outer_backend} must match explicit "
f"inner-jit backend specification {inner_backend}.")
def lower_called_computation(
fn_name,
name_stack,
call_jaxpr,
ctx: ModuleContext,
avals_out,
tokens_in,
backend=None,
arg_names=None,
result_names=None,
):
if isinstance(call_jaxpr, core.Jaxpr):
call_jaxpr = pe.close_jaxpr(call_jaxpr)
check_backend_matches(backend, ctx.platforms)
effects = list(tokens_in.effects())
output_types = map(aval_to_ir_type, avals_out)
output_types = [token_type()] * len(effects) + output_types
func_op = _lower_jaxpr_to_fun_cached(
ctx,
fn_name,
call_jaxpr,
effects,
name_stack,
arg_names=arg_names,
result_names=result_names,
)
return func_op, output_types, effects
def call_lowering(fn_name, name_stack, call_jaxpr, backend,
ctx: ModuleContext, avals_in,
avals_out, tokens_in, *args,
dim_var_values: Sequence[ir.Value],
arg_names=None, result_names=None):
del avals_in
func_op, output_types, effects = lower_called_computation(
fn_name, name_stack, call_jaxpr, ctx, avals_out, tokens_in,
backend=backend, arg_names=arg_names, result_names=result_names)
symbol_name = func_op.name.value
flat_output_types = flatten_ir_types(output_types)
tokens = [tokens_in.get(eff) for eff in effects]
args = (*dim_var_values, *tokens, *args)
call = func_dialect.CallOp(flat_output_types,
ir.FlatSymbolRefAttr.get(symbol_name),
flatten_ir_values(args))
out_nodes = unflatten_ir_values_like_types(call.results, output_types)
tokens, out_nodes = util.split_list(out_nodes, [len(effects)])
tokens_out = tokens_in.update_tokens(TokenSet(zip(effects, tokens)))
return out_nodes, tokens_out
def core_call_lowering(ctx: LoweringRuleContext,
*args, name, backend=None, call_jaxpr):
out_nodes, tokens = call_lowering(
name, ctx.name_stack, call_jaxpr, backend, ctx.module_context,
ctx.avals_in, ctx.avals_out, ctx.tokens_in, *args,
dim_var_values=ctx.dim_var_values)
ctx.set_tokens_out(tokens)
return out_nodes
register_lowering(core.call_p, partial(core_call_lowering, name="core_call"))
register_lowering(core.closed_call_p,
partial(core_call_lowering, name=None))
def map_compute_type(c_type: str) -> str:
if c_type == "device_host":
return "host"
elif c_type == "device":
return "dense"
elif c_type == "tpu_sparsecore":
return "sparse"
raise ValueError(f"Invalid compute type {c_type}. Current supported values "
"are `device_host`, `device` and `tpu_sparsecore`")
def wrap_compute_type_in_place(ctx: LoweringRuleContext, op: ir.Operation) -> None:
if ctx.jaxpr_eqn_ctx is not None and ctx.jaxpr_eqn_ctx.compute_type is not None:
if ctx.jaxpr_eqn_ctx.compute_type.startswith("gpu_stream:"):
stream = ctx.jaxpr_eqn_ctx.compute_type.split(":")[1]
dict_attr = {
"_xla_stream_annotation": ir.StringAttr.get(stream),
"inlineable": ir.StringAttr.get("false"),
}
op.operation.attributes["mhlo.frontend_attributes"] = ir.DictAttr.get(dict_attr)
else:
dict_attr = {"_xla_compute_type": ir.StringAttr.get(
map_compute_type(ctx.jaxpr_eqn_ctx.compute_type))}
op.operation.attributes["mhlo.frontend_attributes"] = ir.DictAttr.get(dict_attr)
def wrap_xla_metadata_in_place(ctx: LoweringRuleContext, op: ir.Operation) -> None:
ctx_attributes = {}
existing_attributes = {}
if ctx.jaxpr_eqn_ctx is not None and ctx.jaxpr_eqn_ctx.xla_metadata:
for k, v in ctx.jaxpr_eqn_ctx.xla_metadata.items():
ctx_attributes[k] = ir.StringAttr.get(str(v).lower())
if isinstance(op, ir.Operation):
# combine with existing mhlo.frontend_attributes
op_attributes_dict = {attr.name: attr.attr for attr in op.attributes}
for k, attributes in op_attributes_dict.items():
if k == "mhlo.frontend_attributes":
v_dict = {attr.name: attr.attr for attr in attributes}
for fa_key, fa_val in v_dict.items():
existing_attributes[fa_key] = fa_val
op.attributes["mhlo.frontend_attributes"] = ir.DictAttr.get(
ctx_attributes | existing_attributes
)
def broadcast_in_dim(ctx: LoweringRuleContext, op, aval_out: core.AbstractValue, *,
broadcast_dimensions) -> ir.Value:
# broadcast_dimension[i] is the axis of the result where the axis i of
# op is broadcast.
# Lower a possibly-dynamic broadcast_in_dim
if dtypes.issubdtype(aval_out.dtype, dtypes.extended): # type: ignore
elt_shape = core.physical_element_aval(aval_out.dtype).shape # type: ignore
trailing_dims = [aval_out.ndim + i for i in range(len(elt_shape))] # type: ignore
broadcast_dimensions = [*broadcast_dimensions, *trailing_dims]
physical_aval_out = core.physical_aval(aval_out)
return broadcast_in_dim(
ctx, op, physical_aval_out, broadcast_dimensions=broadcast_dimensions)
else:
if not core.is_constant_shape(aval_out.shape): # type: ignore
shape = eval_dynamic_shape_as_tensor(ctx, aval_out.shape) # type: ignore
out = hlo.dynamic_broadcast_in_dim(
aval_to_ir_type(aval_out), op,
shape,
dense_int_array(broadcast_dimensions),
)
else:
assert all(d != ir.ShapedType.get_dynamic_size()
for d in aval_out.shape), aval_out # type: ignore
out = hlo.broadcast_in_dim(
aval_to_ir_type(aval_out), op,
dense_int_array(broadcast_dimensions))
wrap_compute_type_in_place(ctx, out.owner)
return out
def multi_broadcast_in_dim(ctx: LoweringRuleContext,
ops: Sequence[ir.Value],
ops_avals: Sequence[core.AbstractValue],
out_shape: core.Shape) -> Sequence[ir.Value]:
"""Broadcasts multiple ops to the out_shape."""
out = []
for op, op_aval in zip(ops, ops_avals):
op_aval_shape = op_aval.shape # type: ignore
if core.definitely_equal_shape(op_aval_shape, out_shape):
out.append(op)
else:
assert len(op_aval_shape) <= len(out_shape), (op_aval_shape, out_shape)
broadcast_dimensions = list(range(len(out_shape) - len(op_aval_shape), len(out_shape)))
out.append(broadcast_in_dim(ctx, op,
core.ShapedArray(out_shape, op_aval.dtype), # type: ignore
broadcast_dimensions=broadcast_dimensions))
return out
def reshape(ctx: LoweringRuleContext, op, aval_out: core.AbstractValue) -> ir.Value:
aval_out = core.physical_aval(aval_out)
if not core.is_constant_shape(aval_out.shape): # type: ignore
shape = eval_dynamic_shape_as_tensor(ctx, aval_out.shape) # type: ignore
return hlo.dynamic_reshape(
aval_to_ir_type(aval_out), op, shape,
)
else:
return hlo.reshape(aval_to_ir_type(aval_out), op)
def slice_op(ctx: LoweringRuleContext, x, aval_out, *,
start_indices, limit_indices, strides) -> ir.Value:
if dtypes.issubdtype(aval_out.dtype, dtypes.extended):
elt_shape = core.physical_element_aval(aval_out.dtype).shape
trailing_zeros = [0] * len(elt_shape)
trailing_ones = [1] * len(elt_shape)
start_indices = (*start_indices, *trailing_zeros)
limit_indices = (*limit_indices, *elt_shape)
strides = (*strides, *trailing_ones)
physical_aval_out = core.physical_aval(aval_out)
return slice_op(ctx, x, physical_aval_out, start_indices=start_indices,
limit_indices=limit_indices, strides=strides)
else:
if any(not core.is_constant_shape(s) for s in (start_indices, limit_indices, strides)):
start_indices = eval_dynamic_shape_as_tensor(ctx, start_indices)
limit_indices = eval_dynamic_shape_as_tensor(ctx, limit_indices)
strides = eval_dynamic_shape_as_tensor(ctx, strides)
return hlo.real_dynamic_slice(
aval_to_ir_type(aval_out),
x, start_indices, limit_indices, strides)
else:
return hlo.slice(x,
dense_int_array(start_indices),
dense_int_array(limit_indices),
dense_int_array(strides))
def dynamic_slice(ctx: LoweringRuleContext, aval_out, x, *,
start_indices) -> ir.Value:
x_aval = ctx.avals_in[0]
if dtypes.issubdtype(aval_out.dtype, dtypes.extended):
elt_shape = core.physical_element_aval(aval_out.dtype).shape
index_avals = ctx.avals_in[1:]
dtype = dtypes.canonicalize_dtype(
index_avals[0].dtype if index_avals else 'int64') # type: ignore
trailing_zeros = [ir_constant(np.array(0, dtype))] * len(elt_shape)
start_indices = (*start_indices, *trailing_zeros)
aval_out = core.physical_aval(aval_out)
x_aval = core.physical_aval(x_aval)
slice_sizes = aval_out.shape
if not core.is_constant_shape(slice_sizes):
# lax.dynamic_slice clamps the start indices, but we are going to
# lower to RealDynamicSliceOp, which is a version of SliceOp, and does
# not have the clamping behavior. We clamp start ourselves.
slice_sizes = eval_dynamic_shape_as_tensor(ctx, slice_sizes)
clamped_start = hlo.clamp(
shape_tensor([0] * len(start_indices)),
shape_tensor(start_indices),
hlo.subtract(
eval_dynamic_shape_as_tensor(ctx, x_aval.shape), # type: ignore
slice_sizes))
return hlo.real_dynamic_slice(
aval_to_ir_type(aval_out), x,
clamped_start,
hlo.add(clamped_start, slice_sizes),
shape_tensor([1] * len(start_indices))
)
else:
return hlo.dynamic_slice(x, start_indices, dense_int_array(slice_sizes))
def dynamic_update_slice(ctx: LoweringRuleContext, aval_out, x, update, *,
start_indices) -> ir.Value:
if dtypes.issubdtype(aval_out.dtype, dtypes.extended):
elt_shape = core.physical_element_aval(aval_out.dtype).shape
index_avals = ctx.avals_in[2:]
dtype = dtypes.canonicalize_dtype(
index_avals[0].dtype if index_avals else 'int64') # type: ignore
zeros = [ir_constant(np.array(0, dtype=dtype))] * len(elt_shape)
start_indices = (*start_indices, *zeros)
physical_aval_out = core.physical_aval(aval_out)
return dynamic_update_slice(ctx, physical_aval_out, x, update,
start_indices=start_indices)
else:
# TODO(necula): handle dynamic shapes
return hlo.dynamic_update_slice(x, update, start_indices)
def pad(ctx: LoweringRuleContext, aval_out,
x, padding_value,
padding_low, padding_high, padding_interior) -> ir.Value:
if all(core.is_constant_shape(s) for s in (padding_low,
padding_high, padding_interior)):
return hlo.pad(x, padding_value,
dense_int_array(padding_low),
dense_int_array(padding_high),
dense_int_array(padding_interior))
else:
padding_low = eval_dynamic_shape_as_tensor(ctx, padding_low)
padding_high = eval_dynamic_shape_as_tensor(ctx, padding_high)
padding_interior = eval_dynamic_shape_as_tensor(ctx, padding_interior)
return hlo.dynamic_pad(
aval_to_ir_type(aval_out),
x, padding_value, padding_low, padding_high, padding_interior)
def iota(ctx: LoweringRuleContext, aval_out, *, dimension: int):
if not core.is_constant_shape(aval_out.shape):
shape = eval_dynamic_shape_as_tensor(ctx, aval_out.shape)
return hlo.dynamic_iota(
aval_to_ir_type(aval_out),
shape,
i64_attr(dimension),
)
else:
return hlo.iota(aval_to_ir_type(aval_out), i64_attr(dimension))
def full_like_aval(ctx: LoweringRuleContext, value, aval: core.ShapedArray) -> ir.Value:
"""Returns an IR constant shaped full of `value` shaped like `aval`."""
zero = ir_constant(np.array(value, dtypes.canonicalize_dtype(aval.dtype)))
return broadcast_in_dim(ctx, zero, aval, broadcast_dimensions=())
def add_jaxvals_lowering(ctx, x, y):
if (isinstance(a := ctx.avals_in[0], core.ShapedArray) and
dtypes.issubdtype(a.dtype, dtypes.extended)):
return lower_fun(lambda x, y: [a.dtype._rules.add(a.dtype, x, y)])(ctx, x, y)
return [hlo.add(x, y)]
register_lowering(ad_util.add_jaxvals_p, add_jaxvals_lowering)
register_lowering(ad_util.stop_gradient_p, lambda ctx, x: [x])
def compare_hlo(x, y, direction: str, comparison_type: str | None = None):
"""Creates CompareOp."""
if comparison_type is None:
elem_type = ir.RankedTensorType(x.type).element_type
if isinstance(elem_type, ir.IntegerType):
comparison_type = "UNSIGNED" if elem_type.is_unsigned else "SIGNED"
else:
comparison_type = "FLOAT"
return hlo.compare(
x,
y,
hlo.ComparisonDirectionAttr.get(direction),
compare_type=hlo.ComparisonTypeAttr.get(comparison_type))
def _minmax_hlo(op, cmp, x, y):
"""Min/max that compares complex values lexicographically as pairs."""
tensor_type = ir.RankedTensorType(x.type)
if ir.ComplexType.isinstance(tensor_type.element_type):
rx = hlo.real(x)
ry = hlo.real(y)
real_eq = compare_hlo(rx, ry, "EQ", "FLOAT")
real_cmp = compare_hlo(rx, ry, cmp, "FLOAT")
imag_cmp = compare_hlo(hlo.imag(x), hlo.imag(y), cmp, "FLOAT")
which = hlo.select(real_eq, imag_cmp, real_cmp)
return hlo.select(which, x, y)
else:
return op(x, y)
min_hlo = partial(_minmax_hlo, hlo.minimum, "LT")
max_hlo = partial(_minmax_hlo, hlo.maximum, "GT")
def convert_hlo(ctx: LoweringRuleContext, x, aval_in, aval_out):
"""Variant of convert that has HLO semantics.
In particular, treat casts to boolean as x != 0, rather than truncating
integer values (b/209440332)."""
if (not dtypes.issubdtype(aval_out.dtype, dtypes.extended) and
aval_out.dtype == np.dtype(np.bool_)):
if dtypes.issubdtype(aval_in.dtype, np.inexact):
compare_type = "FLOAT"
elif dtypes.issubdtype(aval_in.dtype, np.signedinteger):
compare_type = "SIGNED"
else:
compare_type = "UNSIGNED"
x = compare_hlo(x, full_like_aval(ctx, 0, aval_in), "NE", compare_type)
# continue, to adjust the shape if needed
return hlo.convert(aval_to_ir_type(aval_out), x)
def _wrap_with_spmd_op(name: str,
ctx: LoweringRuleContext,
x: ir.Value,
aval_out: core.AbstractValue,
sharding: xc.OpSharding | SdyArray,
unspecified_dims: set[int] | None = None,
has_side_effect: bool = False,
allow_shardy_lowering: bool = False):
if config.use_shardy_partitioner.value and allow_shardy_lowering:
return dialects.sdy.ShardingConstraintOp(x, sharding.build()).result # type: ignore
# unspecified_dims indicate dimensions whose shardings are not specified and
# XLA sharding propagation can change them.
if unspecified_dims:
backend_config = "unspecified_dims=[" + ",".join(
[str(i) for i in sorted(unspecified_dims)]) + "]"
else:
backend_config = ""
result_type = aval_to_ir_type(aval_out)
assert isinstance(result_type, ir.Type), result_type
out_shape = core.physical_aval(aval_out).shape # type: ignore
if core.is_constant_shape(out_shape):
result_shapes = None
else:
result_shapes = [eval_dynamic_shape_as_tensor(ctx, out_shape)]
op = custom_call(name, result_types=[result_type], operands=[x],
backend_config=backend_config,
api_version=1,
result_shapes=result_shapes,
has_side_effect=has_side_effect)
set_sharding(op, sharding)
return op.result
wrap_with_sharding_op = partial(_wrap_with_spmd_op, "Sharding",
allow_shardy_lowering=True)
wrap_with_full_to_shard_op = partial(_wrap_with_spmd_op, "SPMDFullToShardShape")
wrap_with_shard_to_full_op = partial(_wrap_with_spmd_op, "SPMDShardToFullShape")
def lower_with_sharding_in_types(ctx, op, aval, sharding_proto=None):
if aval.sharding.mesh.empty:
return op
# Don't emit a wsc under full manual mode to avoid increasing HLO size.
if aval.sharding.mesh._are_all_axes_manual:
return op
if aval.sharding.mesh._are_all_axes_auto:
return op
# TODO(yashkatariya): If all the axes in pspec are AUTO or collective,
# `return op` early and avoid bloating HLO size.
if dtypes.issubdtype(aval.dtype, dtypes.extended):
aval = core.physical_aval(aval)
if config.use_shardy_partitioner.value:
proto = (aval.sharding._to_sdy_sharding(aval.ndim)
if sharding_proto is None else sharding_proto)
proto = modify_sdy_sharding_wrt_axis_types(proto, aval.sharding.mesh)
return wrap_with_sharding_op(ctx, op, aval, proto)
else:
proto = (aval.sharding._to_xla_hlo_sharding(aval.ndim).to_proto()
if sharding_proto is None else sharding_proto)
unspecified_dims = None
if aval.sharding.mesh._any_axis_auto:
# TODO(yashkatariya): Maybe if any mesh axis is auto, mark all axes
# as unspecified?
unspecified_dims = {i for i, s in enumerate(aval.sharding.spec) if s is None}
return wrap_with_sharding_op(ctx, op, aval, proto, unspecified_dims)
def set_sharding(op, sharding: xc.OpSharding | SdyArray | SdyArrayList):
if config.use_shardy_partitioner.value:
op.attributes["sdy.sharding"] = get_sharding_attr(sharding)
else:
op.attributes["mhlo.sharding"] = get_sharding_attr(sharding)
def get_sharding_attr(
sharding: xc.OpSharding | SdyArray | SdyArrayList
) -> ir.Attribute:
if config.use_shardy_partitioner.value:
return sharding.build() # type: ignore
else:
# If there are very large numbers of devices, use the proto representation.
# The MHLO to HLO conversion supports both, and the proto representation is
# more compact.
if len(sharding.tile_assignment_devices) > 100: # type: ignore
return ir.StringAttr.get(sharding.SerializeToString()) # type: ignore
else:
return ir.StringAttr.get(repr(xc.HloSharding.from_proto(sharding))) # type: ignore[arg-type]
def wrap_with_layout_op(ctx: LoweringRuleContext,
x: ir.Value,
aval_out: core.AbstractValue,
layout: DeviceLocalLayout,
aval_in: core.AbstractValue):
result_type = aval_to_ir_type(aval_out)
assert isinstance(result_type, ir.Type), result_type
out_shape = core.physical_aval(aval_out).shape # type: ignore
if core.is_constant_shape(out_shape):
result_shapes = None
else:
result_shapes = [eval_dynamic_shape_as_tensor(ctx, out_shape)]
op = custom_call('LayoutConstraint', result_types=[result_type], operands=[x],
api_version=1,
result_shapes=result_shapes,
# Set operand layouts to anything. XLA will ignore it.
operand_layouts=[list(range(aval_in.ndim))], # type: ignore
# TODO(yashkatariya): Figure out how to pass tiling to the
# custom call.
result_layouts=[layout.major_to_minor[::-1]])
return op.result
# MLIR lowerings for lax primitives
def cache_lowering(f):
"""Decorator that causes the contents of a lowering rule to be reused.
The lowering will be emitted out-of-line in a separate function, together with
a call to that function. If the same primitive is called with the same shapes
and parameters, a new call to the original function will be added, without
emitting a new function. We allow for different lowering for the same
primitive for different platforms in the same module.
"""
@functools.wraps(f)
def cached_lowering(ctx, *args, **params):
assert ctx.primitive is not None
key = (f, ctx.primitive,
tuple(ctx.avals_in), tuple(ctx.avals_out),
tuple(params.items()))
try:
func = ctx.module_context.cached_primitive_lowerings.get(key)
except TypeError:
# If the parameters aren't hashable, give up on caching.
# TODO(phawkins): switch to requiring hashability, when XLA fallback
# computations have been ported to MLIR.
return f(ctx, *args, **params)
if func is None:
func = _emit_lowering_rule_as_fun(partial(f, **params), ctx)
ctx.module_context.cached_primitive_lowerings[key] = func
output_types = map(aval_to_ir_type, ctx.avals_out)
args = tuple(ctx.dim_var_values) + args
flat_output_types = flatten_ir_types(output_types)
call = func_dialect.CallOp(flat_output_types,
ir.FlatSymbolRefAttr.get(func.name.value),
flatten_ir_values(args))
return unflatten_ir_values_like_types(call.results, output_types)
return cached_lowering
def merge_mlir_modules(dst_module: ir.Module,
sym_name: str,
src_module: ir.Module,
dst_symtab: ir.SymbolTable | None = None) -> str:
"""
Args:
dst_module: the module into which the contents of src_module should be
moved. Nothing in dst_module will be renamed.
sym_name: the desired name for the "main" function of src_module after
merging. This is a hint: the true name may be different because of symbol
uniquification, and the true name is returned by this function.
src_module: the module whose contents are to be alpha-renamed, set to
private visibility, and merged into dst_module. src_module must contain
exactly one symbol named "main".
dst_symtab: the symbol table of `dst_module`
Functions in src_module will be renamed such that they do not collide with
functions in dst_module.
This function mutates `src_module`. On return, `src_module` is left in an
undefined state.
Returns:
the name of src_module's main() function, after renaming.
"""
assert dst_module.context == src_module.context
src_symtab = ir.SymbolTable(src_module.operation)
dst_symtab = dst_symtab or ir.SymbolTable(dst_module.operation)
used_names = set()
# Rename all symbols in src_module that clash with names in dst_module, or
# are the "main" symbol.
renamings = {}
for op in src_module.body.operations:
name = op.sym_name.value
should_rename = name in dst_symtab or name == "main"
if should_rename:
base_name = sym_name if name == "main" else name
new_name = base_name
i = 0
# Replacements are chosen such that the new names are present in neither
# src_module, dst_module, or the set of fresh names we've already used.
# Since we rename names one at a time, if new names were in src_module,
# they might themselves collide with a later renaming.
while (new_name in src_symtab or new_name in dst_symtab or
new_name in used_names):
new_name = f"{base_name}_{i}"
i += 1
renamings[name] = new_name
used_names.add(new_name)
# Apply the symbol renamings to symbol definitions.
private = ir.StringAttr.get("private")
for op in src_module.body.operations:
name = op.sym_name.value
if name in renamings:
src_symtab.set_symbol_name(op, renamings[name])
op.attributes["sym_visibility"] = private
# Apply the symbol renamings to symbol uses.
for old_name, new_name in renamings.items():
for op in src_module.body.operations:
src_symtab.replace_all_symbol_uses(old_name, new_name, op)
for op in src_module.body.operations:
dst_module.body.append(op)
dst_symtab.insert(op)
return renamings["main"]
DEVICE_TO_DEVICE_TYPE = 1
SEND_TO_HOST_TYPE = 2
RECV_FROM_HOST_TYPE = 3
def build_mlir_module_helper(
closed_jaxpr: core.ClosedJaxpr, *, name: str,
platforms: Sequence[str],
backend: xb.XlaBackend | None,
axis_context: AxisContext) -> ir.Module:
"""Helper to generate pmap-style XLA computations for custom partitioners."""
unlowerable_effects = lowerable_effects.filter_not_in(closed_jaxpr.effects)
if unlowerable_effects:
raise ValueError(f'Cannot lower jaxpr with effects: {closed_jaxpr.effects}')
lowering_result = lower_jaxpr_to_module(name, closed_jaxpr,
backend=backend, ordered_effects=[],
name_stack=source_info_util.NameStack(),
donated_args=[False] * len(closed_jaxpr.jaxpr.invars),
axis_context=axis_context, platforms=platforms,
lowering_parameters=LoweringParameters())
return lowering_result.module
def custom_call(
call_target_name: str,
*,
result_types: Sequence[ir.Type],
operands: Sequence[ir.Value],
backend_config: str | bytes | dict[str, ir.Attribute] = "",
has_side_effect: bool = False,
result_shapes: Sequence[ir.Value] | None = None,
called_computations: Sequence[str] = (),
api_version: int = 2,
operand_output_aliases: dict[int, int] | None = None,
operand_layouts: Sequence[Sequence[int]] | None = None,
result_layouts: Sequence[Sequence[int]] | None = None,
extra_attributes: dict[str, ir.Attribute] | None = None,
) -> ir.Operation:
"""Helper function for building an hlo.CustomCall.
Args:
call_target_name: the name of the custom call target
result_types: the MLIR types of the results of the custom call
operands: the MLIR IR values that are arguments to the custom call
backend_config: an opaque string passed to the custom call kernel
has_side_effect: if True, marks the custom call as effectful
result_shapes: tensors that represent the result shapes, to be used when
the results have dynamic shapes. If not-None, its length must match the
number of the results.
called_computations: the list of function names called by the custom call.
api_version: the ABI contract version of the custom call
operand_output_aliases: a dict mapping operand numbers to outputs they alias
operand_layouts: a sequence of layouts (dimension orders) for each operand
result_layouts: a sequence of layouts (dimension orders) for each result
extra_attributes: additional IR attributes to apply to the custom_call.
"""
operands = list(operands)
if backend_config is None:
backend_config_attr = ir.StringAttr.get("")
elif isinstance(backend_config, (str, bytes)):
backend_config_attr = ir.StringAttr.get(backend_config)
elif isinstance(backend_config, dict):
# TODO(necula): it seems that the CustomCallOp constructor requires that
# backend_config_attr be a string attribute, even though in some cases we
# need it to be a DictAttr, e.g., for ApproxTopK on TPU.
# "Verification failed: 'stablehlo.custom_call' op attribute 'backend_config' failed to satisfy constraint: string attribute"
# To workaround this limitation we first set it to the empty string and we
# use an unregistered attribute mhlo.backend_config to hold the DictAttr.
# We must also use api_version=1 to ensure that mhlo.backend_config is
# handled properly.
backend_config_attr = ir.StringAttr.get("")
api_version = 1
else:
raise ValueError("custom_call backend_config unexpected type: " + str(backend_config))
attributes = dict(
call_target_name=ir.StringAttr.get(call_target_name),
has_side_effect=ir.BoolAttr.get(has_side_effect),
backend_config=backend_config_attr,
api_version=i32_attr(api_version),
called_computations=ir.ArrayAttr.get(
[ir.FlatSymbolRefAttr.get(name) for name in called_computations]
),
)
if operand_output_aliases is not None:
attributes["output_operand_aliases"] = ir.ArrayAttr.get([
hlo.OutputOperandAlias.get(
# if len(result_types) == 1 then the aliasing refers implicitly to
# the only output.
output_tuple_indices=[output_idx] if len(result_types) > 1 else [],
operand_index=input_idx,
operand_tuple_indices=[],
)
for input_idx, output_idx in (operand_output_aliases.items() or ())
])
if extra_attributes is not None:
attributes.update(extra_attributes)
if result_shapes is not None:
# We add the result_shapes at the end of the operands, and must pass
# the indices_of_output_operands attribute. This attribute is not yet
# accepted by the CustomCall constructor, so we use build_generic
attributes["indices_of_shape_operands"] = ir.DenseIntElementsAttr.get(
np.asarray(list(range(len(operands), len(operands) + len(result_shapes))),
dtype=np.int64))
if operand_layouts is not None:
assert len(operand_layouts) == len(operands), (operand_layouts, operands)
operand_layouts = list(operand_layouts) + [(0,)] * len(result_shapes)
operands = list(operands) + list(result_shapes)
if operand_layouts is not None:
attributes["operand_layouts"] = ir.ArrayAttr.get([
ir.DenseIntElementsAttr.get(
np.atleast_1d(np.asarray(l, dtype=np.int64)),
type=ir.IndexType.get()) for l in operand_layouts
])
if result_layouts is not None:
assert result_layouts is not None
assert len(result_layouts) == len(result_types), (
result_layouts, result_types)
attributes["result_layouts"] = ir.ArrayAttr.get([
ir.DenseIntElementsAttr.get(
np.atleast_1d(np.asarray(l, dtype=np.int64)),
type=ir.IndexType.get()) for l in result_layouts
])
op = hlo.CustomCallOp.build_generic(results=result_types, operands=operands,
attributes=attributes)
if isinstance(backend_config, dict):
op.operation.attributes["mhlo.backend_config"] = ir.DictAttr.get(
backend_config)
return op
def reduce_window(
ctx: LoweringRuleContext,
*,
# Base name to be used for the reducer function
reducer_name: str,
# Compute the reducer body given the reducer.
reducer_body: Callable[[ir.Block], Sequence[ir.Value]],
operands: Sequence[ir.Value],
init_values: Sequence[ir.Value],
init_values_avals: Sequence[core.AbstractValue],
out_avals: Sequence[core.AbstractValue],
window_dimensions, window_strides, padding, base_dilation, window_dilation):
"""Builds a ReduceWindowOp, with support for dynamic shapes."""
scalar_types = flatten_ir_types([aval_to_ir_type(aval) for aval in init_values_avals])
if any(not core.is_constant_shape(s)
for s in [window_dimensions, window_dilation, window_strides, base_dilation, *padding]):
# d_padding will be an array i32[N, 2] with pad_lo and pad_hi for each
# spatial dimension.
int2d = aval_to_ir_type(core.ShapedArray((1, 2), np.int32))
def prep_one_pad(pad_lo_hi: tuple[core.DimSize, core.DimSize]):
pads = eval_dynamic_shape_as_tensor(ctx, pad_lo_hi) # i32[2]
return hlo.reshape(int2d, pads)
d_padding = hlo.concatenate(list(map(prep_one_pad, padding)), i64_attr(0))
# Build the reducer
reducer_type = ir.FunctionType.get(
scalar_types + scalar_types, scalar_types)
with ir.InsertionPoint.at_block_begin(ctx.module_context.module.body):
reducer = func_dialect.FuncOp(reducer_name, reducer_type)
ctx.module_context.symbol_table.insert(reducer)
entry_block = reducer.add_entry_block()
with ir.InsertionPoint(entry_block):
hlo.return_(reducer_body(entry_block))
rw = custom_call(
"stablehlo.dynamic_reduce_window",
result_types=flatten_ir_types(map(aval_to_ir_type, out_avals)),
operands=[
*operands, *init_values,
eval_dynamic_shape_as_tensor(ctx, window_dimensions),
eval_dynamic_shape_as_tensor(ctx, window_strides),
eval_dynamic_shape_as_tensor(ctx, base_dilation),
eval_dynamic_shape_as_tensor(ctx, window_dilation),
d_padding],
called_computations=[reducer.name.value],
)
else: # Static shapes
rw = hlo.ReduceWindowOp(
list(map(aval_to_ir_type, out_avals)),
operands, init_values,
dense_int_array(window_dimensions),
window_strides=dense_int_array(window_strides),
base_dilations=dense_int_array(base_dilation),
window_dilations=dense_int_array(window_dilation),
padding=ir.DenseIntElementsAttr.get(np.asarray(padding, np.int64),
shape=[len(padding), 2]))
reducer = rw.regions[0].blocks.append(*(scalar_types + scalar_types))
with ir.InsertionPoint(reducer):
hlo.return_(reducer_body(reducer))
return [lower_with_sharding_in_types(ctx, r, aval)
for r, aval in zip(rw.results, ctx.avals_out)]
def refine_polymorphic_shapes(module: ir.Module) -> ir.Module:
"""Refines the polymorphic shapes inside a module.
Given a module with static input shapes, but using dynamic shapes due to
shape polymorphism, runs shape refinement to resolve all the dynamic shapes.
Then verifies that there are no more dynamic shapes in the module.
"""
try:
refine_polymorphic_shapes = partial(_jax.mlir.refine_polymorphic_shapes,
mlir_module=module_to_bytecode(module),
enable_shape_assertions=True,
validate_static_shapes=True)
refined_module_str = refine_polymorphic_shapes(
enable_shardy=config.use_shardy_partitioner.value)
except Exception as e:
raise ValueError(
"Error refining shapes. " +
dump_module_message(module, "before_refine_polymorphic_shapes")) from e
context = make_ir_context()
with context:
return ir.Module.parse(refined_module_str)
########################### pvary ##################################
register_lowering(core.pvary_p, lambda ctx, *x, axes, axis_index_groups: x)
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