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Shard

shard

ShardAxis: TypeAlias = I16 module-attribute

The type used to represent numbers on a grid axis.

See the MLIR definition.

ShardAxesAttr: TypeAlias = DenseArrayBase[ShardAxis] module-attribute

The type used to represent a list of grid axes.

See the MLIR definition.

Shard = Dialect('shard', [BroadcastOp, GatherOp, RecvOp, SendOp, ScatterOp, ShiftOp, GridOp, ShardingOp, ShardOp], [ReductionKindAttr, ShardingType, ShardAxesArrayAttr]) module-attribute

ShardAxesArrayAttr dataclass

Bases: ParametrizedAttribute, OpaqueSyntaxAttribute

ShardAxesArrayAttr attribute for representing multiple grid axes.

Reflects the MLIR attribute.

Source code in xdsl/dialects/shard.py 
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@irdl_attr_definition
class ShardAxesArrayAttr(ParametrizedAttribute, OpaqueSyntaxAttribute):
    """
    ShardAxesArrayAttr attribute for representing multiple grid axes.

    Reflects [the MLIR attribute](https://github.com/llvm/llvm-project/blob/fef02d48c08db859ef83f84232ed78bd9d1c323a/mlir/include/mlir/Dialect/Shard/IR/ShardBase.td#L83).
    """

    name = "shard.axisarray"

    axes: ArrayAttr[ShardAxesAttr]

    @classmethod
    def parse_parameters(cls, parser: AttrParser) -> Sequence[Attribute]:
        """
        Parses a ShardAxesArrayAttr, which has the syntax of a list
        of lists, e.g.:

        [[1, 2, 3], [], [4, 5]]
        """
        axes = parser.parse_comma_separated_list(
            parser.Delimiter.SQUARE,
            lambda: _parse_grid_axes_attr(parser),
        )

        return (ArrayAttr(axes),)

    def print_parameters(self, printer: Printer) -> None:
        """
        Prints a ShardAxesArrayAttr, which has the syntax of a list
        of lists, e.g.:

        [[1, 2, 3], [], [4, 5]]
        """
        with printer.in_square_brackets():
            printer.print_list(
                self.axes.data,
                lambda x: _print_sublist(printer, x),
            )

name = 'shard.axisarray' class-attribute instance-attribute

axes: ArrayAttr[ShardAxesAttr] instance-attribute

parse_parameters(parser: AttrParser) -> Sequence[Attribute] classmethod

Parses a ShardAxesArrayAttr, which has the syntax of a list of lists, e.g.:

[[1, 2, 3], [], [4, 5]]

Source code in xdsl/dialects/shard.py 
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@classmethod
def parse_parameters(cls, parser: AttrParser) -> Sequence[Attribute]:
    """
    Parses a ShardAxesArrayAttr, which has the syntax of a list
    of lists, e.g.:

    [[1, 2, 3], [], [4, 5]]
    """
    axes = parser.parse_comma_separated_list(
        parser.Delimiter.SQUARE,
        lambda: _parse_grid_axes_attr(parser),
    )

    return (ArrayAttr(axes),)

print_parameters(printer: Printer) -> None

Prints a ShardAxesArrayAttr, which has the syntax of a list of lists, e.g.:

[[1, 2, 3], [], [4, 5]]

Source code in xdsl/dialects/shard.py 
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def print_parameters(self, printer: Printer) -> None:
    """
    Prints a ShardAxesArrayAttr, which has the syntax of a list
    of lists, e.g.:

    [[1, 2, 3], [], [4, 5]]
    """
    with printer.in_square_brackets():
        printer.print_list(
            self.axes.data,
            lambda x: _print_sublist(printer, x),
        )

ReductionKind

Bases: StrEnum

Reduction kind for grid dialect

Source code in xdsl/dialects/shard.py 
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class ReductionKind(StrEnum):
    "Reduction kind for grid dialect"

    SUM = auto()
    MAX = auto()
    MIN = auto()
    PRODUCT = auto()
    AVERAGE = auto()
    BITWISE_AND = auto()
    BITWISE_OR = auto()
    BITWISE_XOR = auto()
    GENERIC = auto()

SUM = auto() class-attribute instance-attribute

MAX = auto() class-attribute instance-attribute

MIN = auto() class-attribute instance-attribute

PRODUCT = auto() class-attribute instance-attribute

AVERAGE = auto() class-attribute instance-attribute

BITWISE_AND = auto() class-attribute instance-attribute

BITWISE_OR = auto() class-attribute instance-attribute

BITWISE_XOR = auto() class-attribute instance-attribute

GENERIC = auto() class-attribute instance-attribute

ReductionKindAttr dataclass

Bases: EnumAttribute[ReductionKind], SpacedOpaqueSyntaxAttribute

Source code in xdsl/dialects/shard.py 
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@irdl_attr_definition
class ReductionKindAttr(EnumAttribute[ReductionKind], SpacedOpaqueSyntaxAttribute):
    name = "shard.partial"

    assembly_format = "$value"

name = 'shard.partial' class-attribute instance-attribute

assembly_format = '$value' class-attribute instance-attribute

ShardingType dataclass

Bases: ParametrizedAttribute, TypeAttribute

Source code in xdsl/dialects/shard.py 
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@irdl_attr_definition
class ShardingType(ParametrizedAttribute, TypeAttribute):
    name = "shard.sharding"

name = 'shard.sharding' class-attribute instance-attribute

CollectiveCommunicationOp dataclass

Bases: IRDLOperation, ABC

Base class for collective communication ops.

Source code in xdsl/dialects/shard.py 
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class CollectiveCommunicationOp(IRDLOperation, ABC):
    """
    Base class for collective communication ops.
    """

    grid = prop_def(FlatSymbolRefAttr)
    grid_axes = prop_def(
        ShardAxesAttr, default_value=ShardAxesAttr(i16, BytesAttr(b""))
    )

grid = prop_def(FlatSymbolRefAttr) class-attribute instance-attribute

grid_axes = prop_def(ShardAxesAttr, default_value=(ShardAxesAttr(i16, BytesAttr(b'')))) class-attribute instance-attribute

BroadcastOp dataclass

Bases: CollectiveCommunicationOp

Broadcast tensor from one device to many devices.

See external documentation.

Source code in xdsl/dialects/shard.py 
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@irdl_op_definition
class BroadcastOp(CollectiveCommunicationOp):
    """
    Broadcast tensor from one device to many devices.

    See [external documentation](https://mlir.llvm.org/docs/Dialects/Shard/#shardbroadcast-shardbroadcastop).
    """

    name = "shard.broadcast"

    input = operand_def(TensorType)
    root = prop_def(DenseArrayBase[I64])
    root_dynamic = var_operand_def(IndexType)

    result = result_def(TensorType)

    traits = traits_def(Pure())

    assembly_format = (
        "$input `on` $grid (`grid_axes` `=` $grid_axes^)? "
        + "`root` `=` custom<DynamicIndexList>($root_dynamic, $root) "
        + "attr-dict `:` functional-type(operands, results)"
    )

    custom_directives = (DynamicIndexList,)

name = 'shard.broadcast' class-attribute instance-attribute

input = operand_def(TensorType) class-attribute instance-attribute

root = prop_def(DenseArrayBase[I64]) class-attribute instance-attribute

root_dynamic = var_operand_def(IndexType) class-attribute instance-attribute

result = result_def(TensorType) class-attribute instance-attribute

traits = traits_def(Pure()) class-attribute instance-attribute

assembly_format = '$input `on` $grid (`grid_axes` `=` $grid_axes^)? ' + '`root` `=` custom<DynamicIndexList>($root_dynamic, $root) ' + 'attr-dict `:` functional-type(operands, results)' class-attribute instance-attribute

custom_directives = (DynamicIndexList,) class-attribute instance-attribute

GatherOp dataclass

Bases: CollectiveCommunicationOp

Gather tensor shards from many devices to a single device.

See external documentation.

Source code in xdsl/dialects/shard.py 
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@irdl_op_definition
class GatherOp(CollectiveCommunicationOp):
    """
    Gather tensor shards from many devices to a single device.

    See [external documentation](https://mlir.llvm.org/docs/Dialects/Shard/#shardgather-shardgatherop).
    """

    name = "shard.gather"

    input = operand_def(TensorType)
    gather_axis = prop_def(IntegerAttr.constr(IndexTypeConstr))
    root = prop_def(DenseArrayBase[I64])
    root_dynamic = var_operand_def(IndexType)

    result = result_def(TensorType)

    traits = traits_def(Pure())

    assembly_format = (
        "$input `on` $grid (`grid_axes` `=` $grid_axes^)? "
        + "`gather_axis` `=` $gather_axis "
        + "`root` `=` custom<DynamicIndexList>($root_dynamic, $root) "
        + "attr-dict `:` functional-type(operands, results)"
    )

    custom_directives = (DynamicIndexList,)

name = 'shard.gather' class-attribute instance-attribute

input = operand_def(TensorType) class-attribute instance-attribute

gather_axis = prop_def(IntegerAttr.constr(IndexTypeConstr)) class-attribute instance-attribute

root = prop_def(DenseArrayBase[I64]) class-attribute instance-attribute

root_dynamic = var_operand_def(IndexType) class-attribute instance-attribute

result = result_def(TensorType) class-attribute instance-attribute

traits = traits_def(Pure()) class-attribute instance-attribute

assembly_format = '$input `on` $grid (`grid_axes` `=` $grid_axes^)? ' + '`gather_axis` `=` $gather_axis ' + '`root` `=` custom<DynamicIndexList>($root_dynamic, $root) ' + 'attr-dict `:` functional-type(operands, results)' class-attribute instance-attribute

custom_directives = (DynamicIndexList,) class-attribute instance-attribute

ScatterOp dataclass

Bases: CollectiveCommunicationOp

Scatter tensor over a device shard.

For each device group split the input tensor on the root device along axis scatter_axis and scatter the parts across the group devices.

See external documentation.

Source code in xdsl/dialects/shard.py 
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@irdl_op_definition
class ScatterOp(CollectiveCommunicationOp):
    """
    Scatter tensor over a device shard.

    For each device group split the input tensor on the `root` device along
    axis `scatter_axis` and scatter the parts across the group devices.

    See [external documentation](https://mlir.llvm.org/docs/Dialects/Shard/#shardscatter-shardscatterop).
    """

    name = "shard.scatter"

    input = operand_def(TensorType)
    scatter_axis = prop_def(IntegerAttr.constr(IndexTypeConstr))

    root = prop_def(DenseArrayBase[I64])
    root_dynamic = var_operand_def(IndexType)

    result = result_def(TensorType)

    traits = traits_def(
        Pure(),
    )

    assembly_format = (
        "$input `on` $grid (`grid_axes` `=` $grid_axes^)? "
        + "`scatter_axis` `=` $scatter_axis "
        + "`root` `=` custom<DynamicIndexList>($root_dynamic, $root) "
        + "attr-dict `:` functional-type(operands, results)"
    )

    custom_directives = (DynamicIndexList,)

name = 'shard.scatter' class-attribute instance-attribute

input = operand_def(TensorType) class-attribute instance-attribute

scatter_axis = prop_def(IntegerAttr.constr(IndexTypeConstr)) class-attribute instance-attribute

root = prop_def(DenseArrayBase[I64]) class-attribute instance-attribute

root_dynamic = var_operand_def(IndexType) class-attribute instance-attribute

result = result_def(TensorType) class-attribute instance-attribute

traits = traits_def(Pure()) class-attribute instance-attribute

assembly_format = '$input `on` $grid (`grid_axes` `=` $grid_axes^)? ' + '`scatter_axis` `=` $scatter_axis ' + '`root` `=` custom<DynamicIndexList>($root_dynamic, $root) ' + 'attr-dict `:` functional-type(operands, results)' class-attribute instance-attribute

custom_directives = (DynamicIndexList,) class-attribute instance-attribute

RecvOp dataclass

Bases: CollectiveCommunicationOp

Receive from a device within a device group.

Source code in xdsl/dialects/shard.py 
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@irdl_op_definition
class RecvOp(CollectiveCommunicationOp):
    """
    Receive from a device within a device group.
    """

    name = "shard.recv"

    input = operand_def(TensorType)
    source = opt_prop_def(DenseArrayBase[I64])
    source_dynamic = var_operand_def(IndexType)

    result = result_def(TensorType)

    assembly_format = (
        "$input `on` $grid (`grid_axes` `=` $grid_axes^)? "
        + "(`source` `=` custom<DynamicIndexList>($source_dynamic, $source)^)? "
        + "attr-dict `:` functional-type(operands, results)"
    )

    custom_directives = (DynamicIndexList,)

name = 'shard.recv' class-attribute instance-attribute

input = operand_def(TensorType) class-attribute instance-attribute

source = opt_prop_def(DenseArrayBase[I64]) class-attribute instance-attribute

source_dynamic = var_operand_def(IndexType) class-attribute instance-attribute

result = result_def(TensorType) class-attribute instance-attribute

assembly_format = '$input `on` $grid (`grid_axes` `=` $grid_axes^)? ' + '(`source` `=` custom<DynamicIndexList>($source_dynamic, $source)^)? ' + 'attr-dict `:` functional-type(operands, results)' class-attribute instance-attribute

custom_directives = (DynamicIndexList,) class-attribute instance-attribute

SendOp dataclass

Bases: CollectiveCommunicationOp

Send from one device to another within a device group.

Source code in xdsl/dialects/shard.py 
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@irdl_op_definition
class SendOp(CollectiveCommunicationOp):
    """
    Send from one device to another within a device group.
    """

    name = "shard.send"

    input = operand_def(TensorType)

    destination = prop_def(DenseArrayBase[I64])
    destination_dynamic = var_operand_def(IndexType)

    result = result_def(TensorType)

    assembly_format = (
        "$input `on` $grid (`grid_axes` `=` $grid_axes^)? "
        + "`destination` `=` custom<DynamicIndexList>($destination_dynamic, $destination) "
        + "attr-dict `:` functional-type(operands, results)"
    )

    custom_directives = (DynamicIndexList,)

name = 'shard.send' class-attribute instance-attribute

input = operand_def(TensorType) class-attribute instance-attribute

destination = prop_def(DenseArrayBase[I64]) class-attribute instance-attribute

destination_dynamic = var_operand_def(IndexType) class-attribute instance-attribute

result = result_def(TensorType) class-attribute instance-attribute

assembly_format = '$input `on` $grid (`grid_axes` `=` $grid_axes^)? ' + '`destination` `=` custom<DynamicIndexList>($destination_dynamic, $destination) ' + 'attr-dict `:` functional-type(operands, results)' class-attribute instance-attribute

custom_directives = (DynamicIndexList,) class-attribute instance-attribute

ShiftOp dataclass

Bases: CollectiveCommunicationOp

Shift over a device shard.

Within each device group shift along shift_axis by offset. If the rotate flag is present a rotation is performed instead of a shift.

Source code in xdsl/dialects/shard.py 
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@irdl_op_definition
class ShiftOp(CollectiveCommunicationOp):
    """
    Shift over a device shard.

    Within each device group shift along `shift_axis` by `offset`. If the
    `rotate` flag is present a rotation is performed instead of a shift.
    """

    name = "shard.shift"

    input = operand_def(TensorType)

    shift_axis = prop_def(IntegerAttr.constr(IndexTypeConstr))
    offset = prop_def(IntegerAttr[I64])
    rotate = prop_def(UnitAttr)

    result = result_def(TensorType)

    traits = traits_def(
        Pure(),
    )

    assembly_format = (
        "$input `on` $grid (`grid_axes` `=` $grid_axes^)? "
        + "`shift_axis` `=` $shift_axis "
        + "`offset` `=` $offset "
        + "(`rotate` $rotate^)? "
        + "attr-dict `:` type($input) `->` type($result)"
    )

name = 'shard.shift' class-attribute instance-attribute

input = operand_def(TensorType) class-attribute instance-attribute

shift_axis = prop_def(IntegerAttr.constr(IndexTypeConstr)) class-attribute instance-attribute

offset = prop_def(IntegerAttr[I64]) class-attribute instance-attribute

rotate = prop_def(UnitAttr) class-attribute instance-attribute

result = result_def(TensorType) class-attribute instance-attribute

traits = traits_def(Pure()) class-attribute instance-attribute

assembly_format = '$input `on` $grid (`grid_axes` `=` $grid_axes^)? ' + '`shift_axis` `=` $shift_axis ' + '`offset` `=` $offset ' + '(`rotate` $rotate^)? ' + 'attr-dict `:` type($input) `->` type($result)' class-attribute instance-attribute

GridOp dataclass

Bases: IRDLOperation

Source code in xdsl/dialects/shard.py 
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@irdl_op_definition
class GridOp(IRDLOperation):
    name = "shard.grid"

    sym_name = prop_def(SymbolNameConstraint())
    shape = prop_def(DenseArrayBase[I64])

    traits = traits_def(SymbolOpInterface(), Pure())

    assembly_format = (
        "$sym_name `(` `shape` `=` custom<DimensionList>($shape) `)` attr-dict"
    )

    custom_directives = (DimensionList,)

    def verify_(self):
        if not self.shape.get_values():
            raise VerifyException(
                "'shard.grid' op rank of grid is expected to be a positive integer"
            )

name = 'shard.grid' class-attribute instance-attribute

sym_name = prop_def(SymbolNameConstraint()) class-attribute instance-attribute

shape = prop_def(DenseArrayBase[I64]) class-attribute instance-attribute

traits = traits_def(SymbolOpInterface(), Pure()) class-attribute instance-attribute

assembly_format = '$sym_name `(` `shape` `=` custom<DimensionList>($shape) `)` attr-dict' class-attribute instance-attribute

custom_directives = (DimensionList,) class-attribute instance-attribute

verify_()

Source code in xdsl/dialects/shard.py 
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def verify_(self):
    if not self.shape.get_values():
        raise VerifyException(
            "'shard.grid' op rank of grid is expected to be a positive integer"
        )

ShardingOp dataclass

Bases: IRDLOperation

Define the sharding of a tensor.

Note: halo_sizes and sharded_dims_offsets are mutually exclusive.

See external documentation

Source code in xdsl/dialects/shard.py 
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@irdl_op_definition
class ShardingOp(IRDLOperation):
    """
    Define the sharding of a tensor.

    Note: `halo_sizes` and `sharded_dims_offsets` are mutually exclusive.

    See [external documentation](https://mlir.llvm.org/docs/Dialects/Shard/#shardsharding-shardshardingop)
    """

    name = "shard.sharding"

    grid = prop_def(FlatSymbolRefAttr)
    split_axes = prop_def(ShardAxesArrayAttr)
    static_sharded_dims_offsets = prop_def(
        DenseArrayBase[I64], default_value=DenseArrayBase[I64](i64, BytesAttr(b""))
    )
    dynamic_sharded_dims_offsets = var_operand_def(I64)
    static_halo_sizes = prop_def(
        DenseArrayBase[I64], default_value=DenseArrayBase[I64](i64, BytesAttr(b""))
    )
    dynamic_halo_sizes = var_operand_def(I64)

    result = result_def(ShardingType)

    irdl_options = (AttrSizedOperandSegments(as_property=True),)

    traits = traits_def(
        Pure(),
    )

    assembly_format = (
        "$grid "
        + "`split_axes` `=` $split_axes "
        + "(`halo_sizes` `=` custom<DynamicIndexList>($dynamic_halo_sizes, $static_halo_sizes)^)? "
        + "(`sharded_dims_offsets` `=` "
        + "custom<DynamicIndexList>($dynamic_sharded_dims_offsets, $static_sharded_dims_offsets)^)? "
        + "attr-dict `:` type($result)"
    )

    custom_directives = (DynamicIndexList,)

    def verify_(self) -> None:
        dims_offsets = (
            self.static_sharded_dims_offsets or self.dynamic_sharded_dims_offsets
        )
        halo_sizes = self.static_halo_sizes or self.dynamic_halo_sizes

        if dims_offsets and halo_sizes:
            raise VerifyException(
                "'shard.sharding' op halo sizes and shard offsets are mutually exclusive"
            )

name = 'shard.sharding' class-attribute instance-attribute

grid = prop_def(FlatSymbolRefAttr) class-attribute instance-attribute

split_axes = prop_def(ShardAxesArrayAttr) class-attribute instance-attribute

static_sharded_dims_offsets = prop_def(DenseArrayBase[I64], default_value=(DenseArrayBase[I64](i64, BytesAttr(b'')))) class-attribute instance-attribute

dynamic_sharded_dims_offsets = var_operand_def(I64) class-attribute instance-attribute

static_halo_sizes = prop_def(DenseArrayBase[I64], default_value=(DenseArrayBase[I64](i64, BytesAttr(b'')))) class-attribute instance-attribute

dynamic_halo_sizes = var_operand_def(I64) class-attribute instance-attribute

result = result_def(ShardingType) class-attribute instance-attribute

irdl_options = (AttrSizedOperandSegments(as_property=True),) class-attribute instance-attribute

traits = traits_def(Pure()) class-attribute instance-attribute

assembly_format = '$grid ' + '`split_axes` `=` $split_axes ' + '(`halo_sizes` `=` custom<DynamicIndexList>($dynamic_halo_sizes, $static_halo_sizes)^)? ' + '(`sharded_dims_offsets` `=` ' + 'custom<DynamicIndexList>($dynamic_sharded_dims_offsets, $static_sharded_dims_offsets)^)? ' + 'attr-dict `:` type($result)' class-attribute instance-attribute

custom_directives = (DynamicIndexList,) class-attribute instance-attribute

verify_() -> None

Source code in xdsl/dialects/shard.py 
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def verify_(self) -> None:
    dims_offsets = (
        self.static_sharded_dims_offsets or self.dynamic_sharded_dims_offsets
    )
    halo_sizes = self.static_halo_sizes or self.dynamic_halo_sizes

    if dims_offsets and halo_sizes:
        raise VerifyException(
            "'shard.sharding' op halo sizes and shard offsets are mutually exclusive"
        )

ShardOp

Bases: IRDLOperation

Annotate on how a tensor is sharded across a shard.

See external documentation.

Source code in xdsl/dialects/shard.py 
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@irdl_op_definition
class ShardOp(IRDLOperation):
    """
    Annotate on how a tensor is sharded across a shard.

    See [external documentation](https://mlir.llvm.org/docs/Dialects/Shard/#shardshard-shardshardop).
    """

    name = "shard.shard"

    T: ClassVar = VarConstraint("T", TensorType.constr())

    src = operand_def(T)
    sharding = operand_def(ShardingType)
    annotate_for_users = opt_prop_def(UnitAttr)

    result = result_def(T)

    traits = traits_def(
        Pure(),
    )

    assembly_format = "$src `to` $sharding (`annotate_for_users` $annotate_for_users^)? attr-dict `:` type($result)"

    def __init__(
        self,
        src: SSAValue,
        sharding: SSAValue,
        annotate_for_users: UnitAttr | None,
    ):
        return super().__init__(
            operands=[src, sharding],
            result_types=[src.type],
            properties={
                "annotate_for_users": annotate_for_users,
            },
        )

name = 'shard.shard' class-attribute instance-attribute

T: ClassVar = VarConstraint('T', TensorType.constr()) class-attribute instance-attribute

src = operand_def(T) class-attribute instance-attribute

sharding = operand_def(ShardingType) class-attribute instance-attribute

annotate_for_users = opt_prop_def(UnitAttr) class-attribute instance-attribute

result = result_def(T) class-attribute instance-attribute

traits = traits_def(Pure()) class-attribute instance-attribute

assembly_format = '$src `to` $sharding (`annotate_for_users` $annotate_for_users^)? attr-dict `:` type($result)' class-attribute instance-attribute

__init__(src: SSAValue, sharding: SSAValue, annotate_for_users: UnitAttr | None)

Source code in xdsl/dialects/shard.py 
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def __init__(
    self,
    src: SSAValue,
    sharding: SSAValue,
    annotate_for_users: UnitAttr | None,
):
    return super().__init__(
        operands=[src, sharding],
        result_types=[src.type],
        properties={
            "annotate_for_users": annotate_for_users,
        },
    )