Convert stencil to ll mlir

convert_stencil_to_ll_mlir

CastOpToMemRef dataclass

Bases: RewritePattern

Source code in xdsl/transforms/experimental/convert_stencil_to_ll_mlir.py

@dataclass
class CastOpToMemRef(RewritePattern):
    @op_type_rewrite_pattern
    def match_and_rewrite(self, op: CastOp, rewriter: PatternRewriter, /):
        assert isa(op.result.type, FieldType[Attribute])
        assert isinstance(op.result.type.bounds, StencilBoundsAttr)

        result_type = StencilToMemRefType(op.result.type)

        cast = memref.CastOp.get(op.field, result_type)

        rewriter.replace_op(op, cast)

__init__() -> None

match_and_rewrite(op: CastOp, rewriter: PatternRewriter)

Source code in xdsl/transforms/experimental/convert_stencil_to_ll_mlir.py

@op_type_rewrite_pattern
def match_and_rewrite(self, op: CastOp, rewriter: PatternRewriter, /):
    assert isa(op.result.type, FieldType[Attribute])
    assert isinstance(op.result.type.bounds, StencilBoundsAttr)

    result_type = StencilToMemRefType(op.result.type)

    cast = memref.CastOp.get(op.field, result_type)

    rewriter.replace_op(op, cast)

ReturnOpToMemRef dataclass

Bases: RewritePattern

Source code in xdsl/transforms/experimental/convert_stencil_to_ll_mlir.py

@dataclass
class ReturnOpToMemRef(RewritePattern):
    return_target: dict[ApplyOp, list[SSAValue | None]]

    @op_type_rewrite_pattern
    def match_and_rewrite(self, op: ReturnOp, rewriter: PatternRewriter, /):
        unroll_factor = op.unroll_factor
        n_res = len(op.arg) // unroll_factor

        store_list: list[Operation] = []
        apply = op.parent_op()
        assert isinstance(apply, ApplyOp)
        if op.unroll is not None:
            apply.attributes["__unroll__"] = op.unroll

        n_dims = apply.get_rank()

        for j in range(n_res):
            if len(apply.res) > 0:
                target = self.return_target[apply][j]
            else:
                target = apply.dest[j]
                rewriter.insert_op(
                    subview := field_subview(target), InsertPoint.before(apply)
                )
                target = subview

            unroll = op.unroll
            if unroll is None:
                unroll = IndexAttr.get(*([1] * n_dims))

            for k, offset in enumerate(product(*(range(u) for u in unroll))):
                arg = op.arg[j * unroll_factor + k]
                index_ops: list[Operation] = list(
                    IndexOp(
                        attributes={
                            "dim": builtin.IntegerAttr.from_index_int_value(i),
                            "offset": IndexAttr.get(*([0] * n_dims)),
                        },
                        result_types=[builtin.IndexType()],
                    )
                    for i in range(n_dims)
                )
                store_list += index_ops

                for i in range(n_dims):
                    if offset[i] != 0:
                        constant_op = arith.ConstantOp.from_int_and_width(
                            offset[i], builtin.IndexType()
                        )
                        add_op = arith.AddiOp(index_ops[i], constant_op)
                        index_ops[i] = add_op
                        store_list.append(constant_op)
                        store_list.append(add_op)

                if isinstance(arg.type, ResultType):
                    result_owner = _find_result_store(arg)
                    for owner in result_owner:
                        if owner.arg:
                            if target is not None:
                                store = memref.StoreOp.get(owner.arg, target, index_ops)
                            else:
                                store = list[Operation]()
                            rewriter.replace_op(
                                owner,
                                store,
                                new_results=[owner.arg],
                            )
                        else:
                            dummy = UnrealizedConversionCastOp.get([], [arg.type.elem])
                            rewriter.replace_op(owner, dummy)

                else:
                    if target is not None:
                        store = memref.StoreOp.get(arg, target, index_ops)
                        store_list.append(store)

        rewriter.insert_op(store_list)
        rewriter.erase_op(op)

return_target: dict[ApplyOp, list[SSAValue | None]] instance-attribute

__init__(return_target: dict[ApplyOp, list[SSAValue | None]]) -> None

match_and_rewrite(op: ReturnOp, rewriter: PatternRewriter)

Source code in xdsl/transforms/experimental/convert_stencil_to_ll_mlir.py

@op_type_rewrite_pattern
def match_and_rewrite(self, op: ReturnOp, rewriter: PatternRewriter, /):
    unroll_factor = op.unroll_factor
    n_res = len(op.arg) // unroll_factor

    store_list: list[Operation] = []
    apply = op.parent_op()
    assert isinstance(apply, ApplyOp)
    if op.unroll is not None:
        apply.attributes["__unroll__"] = op.unroll

    n_dims = apply.get_rank()

    for j in range(n_res):
        if len(apply.res) > 0:
            target = self.return_target[apply][j]
        else:
            target = apply.dest[j]
            rewriter.insert_op(
                subview := field_subview(target), InsertPoint.before(apply)
            )
            target = subview

        unroll = op.unroll
        if unroll is None:
            unroll = IndexAttr.get(*([1] * n_dims))

        for k, offset in enumerate(product(*(range(u) for u in unroll))):
            arg = op.arg[j * unroll_factor + k]
            index_ops: list[Operation] = list(
                IndexOp(
                    attributes={
                        "dim": builtin.IntegerAttr.from_index_int_value(i),
                        "offset": IndexAttr.get(*([0] * n_dims)),
                    },
                    result_types=[builtin.IndexType()],
                )
                for i in range(n_dims)
            )
            store_list += index_ops

            for i in range(n_dims):
                if offset[i] != 0:
                    constant_op = arith.ConstantOp.from_int_and_width(
                        offset[i], builtin.IndexType()
                    )
                    add_op = arith.AddiOp(index_ops[i], constant_op)
                    index_ops[i] = add_op
                    store_list.append(constant_op)
                    store_list.append(add_op)

            if isinstance(arg.type, ResultType):
                result_owner = _find_result_store(arg)
                for owner in result_owner:
                    if owner.arg:
                        if target is not None:
                            store = memref.StoreOp.get(owner.arg, target, index_ops)
                        else:
                            store = list[Operation]()
                        rewriter.replace_op(
                            owner,
                            store,
                            new_results=[owner.arg],
                        )
                    else:
                        dummy = UnrealizedConversionCastOp.get([], [arg.type.elem])
                        rewriter.replace_op(owner, dummy)

            else:
                if target is not None:
                    store = memref.StoreOp.get(arg, target, index_ops)
                    store_list.append(store)

    rewriter.insert_op(store_list)
    rewriter.erase_op(op)

LoadOpToMemRef

Bases: RewritePattern

Source code in xdsl/transforms/experimental/convert_stencil_to_ll_mlir.py

class LoadOpToMemRef(RewritePattern):
    @op_type_rewrite_pattern
    def match_and_rewrite(self, op: LoadOp, rewriter: PatternRewriter, /):
        for use in op.field.uses:
            if isa(use.operation, StoreOp):
                raise VerifyException(
                    "Cannot lower directly if loading and storing the same field! Try running `stencil-bufferize` before."
                )
        field = op.field.type
        assert isa(field, FieldType[Attribute])
        assert isa(field.bounds, StencilBoundsAttr)
        temp = op.res.type
        assert isa(temp, TempType[Attribute])
        assert isa(temp.bounds, StencilBoundsAttr)

        assert_subset(field, temp)

        offsets = [i for i in -field.bounds.lb]
        sizes = [i for i in temp.get_shape()]
        strides = [1] * len(sizes)

        subview = memref.SubviewOp.from_static_parameters(
            op.field, StencilToMemRefType(field), offsets, sizes, strides
        )

        rewriter.replace_op(op, subview)
        name = None
        if subview.source.name_hint:
            name = subview.source.name_hint + "_loadview"
        subview.result.name_hint = name

match_and_rewrite(op: LoadOp, rewriter: PatternRewriter)

Source code in xdsl/transforms/experimental/convert_stencil_to_ll_mlir.py

@op_type_rewrite_pattern
def match_and_rewrite(self, op: LoadOp, rewriter: PatternRewriter, /):
    for use in op.field.uses:
        if isa(use.operation, StoreOp):
            raise VerifyException(
                "Cannot lower directly if loading and storing the same field! Try running `stencil-bufferize` before."
            )
    field = op.field.type
    assert isa(field, FieldType[Attribute])
    assert isa(field.bounds, StencilBoundsAttr)
    temp = op.res.type
    assert isa(temp, TempType[Attribute])
    assert isa(temp.bounds, StencilBoundsAttr)

    assert_subset(field, temp)

    offsets = [i for i in -field.bounds.lb]
    sizes = [i for i in temp.get_shape()]
    strides = [1] * len(sizes)

    subview = memref.SubviewOp.from_static_parameters(
        op.field, StencilToMemRefType(field), offsets, sizes, strides
    )

    rewriter.replace_op(op, subview)
    name = None
    if subview.source.name_hint:
        name = subview.source.name_hint + "_loadview"
    subview.result.name_hint = name

BufferOpToMemRef dataclass

Bases: RewritePattern

Source code in xdsl/transforms/experimental/convert_stencil_to_ll_mlir.py

@dataclass
class BufferOpToMemRef(RewritePattern):
    return_targets: dict[ApplyOp, list[SSAValue | None]]

    @op_type_rewrite_pattern
    def match_and_rewrite(self, op: BufferOp, rewriter: PatternRewriter, /):
        # The current lowering simply allocate at block entry and deallocates at block
        # exit.
        # One could be smarter, e.g., aim precisly at where the first and last need is
        # But first, this requires more code with stencil.combine in the mix.
        # And second, do we want to be that smart? We use it iterated either way, so
        # probably always hoping to hoist (de)allocations out of the loop anyway?
        temp_t = op.temp.type
        assert isa(temp_t, TempType[Attribute])
        temp_bounds = temp_t.bounds
        assert isa(temp_bounds, StencilBoundsAttr)

        block = op.parent_block()
        assert block is not None
        first_op = block.first_op
        assert first_op is not None
        last_op = block.last_op
        assert last_op is not None

        shape = temp_t.get_shape()
        strides = [prod(shape[i + 1 :]) for i in range(len(shape))]
        offset = -sum(o * s for o, s in zip(temp_bounds.lb, strides, strict=True))

        layout = memref.StridedLayoutAttr(strides, offset)
        alloc = memref.AllocOp.get(
            temp_t.get_element_type(), shape=temp_t.get_shape(), layout=layout
        )
        alloc_type = alloc.memref.type
        assert isa(alloc_type, MemRefType)

        rewriter.insert_op(alloc, InsertPoint.before(first_op))

        update_return_target(self.return_targets, op.temp, alloc.memref)

        dealloc = memref.DeallocOp.get(alloc.memref)

        if not op.res.uses:
            rewriter.insert_op(dealloc, InsertPoint.after(op))
            rewriter.erase_op(op)
            return

        rewriter.insert_op(dealloc, InsertPoint.before(last_op))
        rewriter.replace_op(op, [], [alloc.memref])

return_targets: dict[ApplyOp, list[SSAValue | None]] instance-attribute

__init__(return_targets: dict[ApplyOp, list[SSAValue | None]]) -> None

match_and_rewrite(op: BufferOp, rewriter: PatternRewriter)

Source code in xdsl/transforms/experimental/convert_stencil_to_ll_mlir.py

@op_type_rewrite_pattern
def match_and_rewrite(self, op: BufferOp, rewriter: PatternRewriter, /):
    # The current lowering simply allocate at block entry and deallocates at block
    # exit.
    # One could be smarter, e.g., aim precisly at where the first and last need is
    # But first, this requires more code with stencil.combine in the mix.
    # And second, do we want to be that smart? We use it iterated either way, so
    # probably always hoping to hoist (de)allocations out of the loop anyway?
    temp_t = op.temp.type
    assert isa(temp_t, TempType[Attribute])
    temp_bounds = temp_t.bounds
    assert isa(temp_bounds, StencilBoundsAttr)

    block = op.parent_block()
    assert block is not None
    first_op = block.first_op
    assert first_op is not None
    last_op = block.last_op
    assert last_op is not None

    shape = temp_t.get_shape()
    strides = [prod(shape[i + 1 :]) for i in range(len(shape))]
    offset = -sum(o * s for o, s in zip(temp_bounds.lb, strides, strict=True))

    layout = memref.StridedLayoutAttr(strides, offset)
    alloc = memref.AllocOp.get(
        temp_t.get_element_type(), shape=temp_t.get_shape(), layout=layout
    )
    alloc_type = alloc.memref.type
    assert isa(alloc_type, MemRefType)

    rewriter.insert_op(alloc, InsertPoint.before(first_op))

    update_return_target(self.return_targets, op.temp, alloc.memref)

    dealloc = memref.DeallocOp.get(alloc.memref)

    if not op.res.uses:
        rewriter.insert_op(dealloc, InsertPoint.after(op))
        rewriter.erase_op(op)
        return

    rewriter.insert_op(dealloc, InsertPoint.before(last_op))
    rewriter.replace_op(op, [], [alloc.memref])

AllocOpToMemRef

Bases: RewritePattern

Source code in xdsl/transforms/experimental/convert_stencil_to_ll_mlir.py

class AllocOpToMemRef(RewritePattern):
    @op_type_rewrite_pattern
    def match_and_rewrite(self, op: AllocOp, rewriter: PatternRewriter, /):
        alloc = memref.AllocOp(
            [], [], StencilToMemRefType(cast(StencilType[Attribute], op.field.type))
        )
        rewriter.replace_op(op, alloc)

match_and_rewrite(op: AllocOp, rewriter: PatternRewriter)

Source code in xdsl/transforms/experimental/convert_stencil_to_ll_mlir.py

@op_type_rewrite_pattern
def match_and_rewrite(self, op: AllocOp, rewriter: PatternRewriter, /):
    alloc = memref.AllocOp(
        [], [], StencilToMemRefType(cast(StencilType[Attribute], op.field.type))
    )
    rewriter.replace_op(op, alloc)

ApplyOpFieldSubviews dataclass

Bases: RewritePattern

Source code in xdsl/transforms/experimental/convert_stencil_to_ll_mlir.py

@dataclass
class ApplyOpFieldSubviews(RewritePattern):
    @op_type_rewrite_pattern
    def match_and_rewrite(self, op: ApplyOp, rewriter: PatternRewriter, /):
        args = [
            field_subview(arg) if isinstance(arg.type, FieldType) else arg
            for arg in op.args
        ]
        if args == list(op.args):
            return

        new_apply = ApplyOp.create(
            operands=[SSAValue.get(arg) for arg in args] + list(op.dest),
            result_types=[r.type for r in op.res],
            regions=[op.detach_region(0)],
            attributes=op.attributes,
            properties=op.properties,
        )
        rewriter.replace_op(
            op, [*(arg for arg in args if isinstance(arg, Operation)), new_apply]
        )

__init__() -> None

match_and_rewrite(op: ApplyOp, rewriter: PatternRewriter)

Source code in xdsl/transforms/experimental/convert_stencil_to_ll_mlir.py

@op_type_rewrite_pattern
def match_and_rewrite(self, op: ApplyOp, rewriter: PatternRewriter, /):
    args = [
        field_subview(arg) if isinstance(arg.type, FieldType) else arg
        for arg in op.args
    ]
    if args == list(op.args):
        return

    new_apply = ApplyOp.create(
        operands=[SSAValue.get(arg) for arg in args] + list(op.dest),
        result_types=[r.type for r in op.res],
        regions=[op.detach_region(0)],
        attributes=op.attributes,
        properties=op.properties,
    )
    rewriter.replace_op(
        op, [*(arg for arg in args if isinstance(arg, Operation)), new_apply]
    )

ApplyOpToParallel dataclass

Bases: RewritePattern

Source code in xdsl/transforms/experimental/convert_stencil_to_ll_mlir.py

@dataclass
class ApplyOpToParallel(RewritePattern):
    return_targets: dict[ApplyOp, list[SSAValue | None]]

    @op_type_rewrite_pattern
    def match_and_rewrite(self, op: ApplyOp, rewriter: PatternRewriter, /):
        if len(op.res) > 0:
            res_type = op.res[0].type
            assert isa(res_type, TempType[Attribute])
            assert isinstance(res_type.bounds, StencilBoundsAttr)
            lb = res_type.bounds.lb
            ub = res_type.bounds.ub
        else:
            assert op.bounds is not None
            lb = op.bounds.lb
            ub = op.bounds.ub

        # Get this apply's ReturnOp
        unroll = op.attributes.get("__unroll__", None)
        if unroll is not None:
            assert isinstance(unroll, IndexAttr)
            unroll = list(u for u in unroll)

        rank = op.get_rank()
        body = prepare_apply_body(op)
        if unroll is None:
            unroll = [1] * rank
        else:
            unroll = [i for i in unroll]

        # Then create the corresponding scf.parallel
        boilerplate_ops = [
            *(
                lowerBounds := [
                    arith.ConstantOp.from_int_and_width(x, builtin.IndexType())
                    for x in lb
                ]
            ),
            *(
                steps := [
                    arith.ConstantOp.from_int_and_width(x, builtin.IndexType())
                    for x in unroll
                ]
            ),
            *(
                upperBounds := [
                    arith.ConstantOp.from_int_and_width(x, builtin.IndexType())
                    for x in ub
                ]
            ),
        ]

        # Generate an outer parallel loop as well as two inner sequential
        # loops. The inner sequential loops ensure that the computational
        # kernel itself is not slowed down by the OpenMP runtime.
        tiled_steps = steps
        p = scf.ParallelOp(
            lower_bounds=lowerBounds,
            upper_bounds=upperBounds,
            steps=tiled_steps,
            body=Region(body),
        )
        for index in body.walk():
            if isinstance(index, IndexOp):
                offset = list(index.offset)
                ops: list[Operation] = []
                res: list[SSAValue] = [body.args[index.dim.value.data]]
                if offset[index.dim.value.data] != 0:
                    ops = [
                        cst := arith.ConstantOp.from_int_and_width(
                            offset[index.dim.value.data], builtin.IndexType()
                        ),
                        add := arith.AddiOp(body.args[index.dim.value.data], cst),
                    ]
                    res = [add.result]
                rewriter.replace_op(index, ops, res)

        # Get the maybe updated results
        new_results = self.return_targets[op] if op in self.return_targets else []
        # Replace with the loop and necessary constants.
        assert isa(boilerplate_ops, list[Operation])
        rewriter.insert_op([*boilerplate_ops, p])
        rewriter.replace_op(op, [], new_results)

return_targets: dict[ApplyOp, list[SSAValue | None]] instance-attribute

__init__(return_targets: dict[ApplyOp, list[SSAValue | None]]) -> None

match_and_rewrite(op: ApplyOp, rewriter: PatternRewriter)

Source code in xdsl/transforms/experimental/convert_stencil_to_ll_mlir.py

@op_type_rewrite_pattern
def match_and_rewrite(self, op: ApplyOp, rewriter: PatternRewriter, /):
    if len(op.res) > 0:
        res_type = op.res[0].type
        assert isa(res_type, TempType[Attribute])
        assert isinstance(res_type.bounds, StencilBoundsAttr)
        lb = res_type.bounds.lb
        ub = res_type.bounds.ub
    else:
        assert op.bounds is not None
        lb = op.bounds.lb
        ub = op.bounds.ub

    # Get this apply's ReturnOp
    unroll = op.attributes.get("__unroll__", None)
    if unroll is not None:
        assert isinstance(unroll, IndexAttr)
        unroll = list(u for u in unroll)

    rank = op.get_rank()
    body = prepare_apply_body(op)
    if unroll is None:
        unroll = [1] * rank
    else:
        unroll = [i for i in unroll]

    # Then create the corresponding scf.parallel
    boilerplate_ops = [
        *(
            lowerBounds := [
                arith.ConstantOp.from_int_and_width(x, builtin.IndexType())
                for x in lb
            ]
        ),
        *(
            steps := [
                arith.ConstantOp.from_int_and_width(x, builtin.IndexType())
                for x in unroll
            ]
        ),
        *(
            upperBounds := [
                arith.ConstantOp.from_int_and_width(x, builtin.IndexType())
                for x in ub
            ]
        ),
    ]

    # Generate an outer parallel loop as well as two inner sequential
    # loops. The inner sequential loops ensure that the computational
    # kernel itself is not slowed down by the OpenMP runtime.
    tiled_steps = steps
    p = scf.ParallelOp(
        lower_bounds=lowerBounds,
        upper_bounds=upperBounds,
        steps=tiled_steps,
        body=Region(body),
    )
    for index in body.walk():
        if isinstance(index, IndexOp):
            offset = list(index.offset)
            ops: list[Operation] = []
            res: list[SSAValue] = [body.args[index.dim.value.data]]
            if offset[index.dim.value.data] != 0:
                ops = [
                    cst := arith.ConstantOp.from_int_and_width(
                        offset[index.dim.value.data], builtin.IndexType()
                    ),
                    add := arith.AddiOp(body.args[index.dim.value.data], cst),
                ]
                res = [add.result]
            rewriter.replace_op(index, ops, res)

    # Get the maybe updated results
    new_results = self.return_targets[op] if op in self.return_targets else []
    # Replace with the loop and necessary constants.
    assert isa(boilerplate_ops, list[Operation])
    rewriter.insert_op([*boilerplate_ops, p])
    rewriter.replace_op(op, [], new_results)

AccessOpToMemRef dataclass

Bases: RewritePattern

Source code in xdsl/transforms/experimental/convert_stencil_to_ll_mlir.py

@dataclass
class AccessOpToMemRef(RewritePattern):
    @op_type_rewrite_pattern
    def match_and_rewrite(self, op: AccessOp, rewriter: PatternRewriter, /):
        temp = op.temp.type
        assert StencilTypeConstr.verifies(temp)
        assert isinstance(temp.bounds, StencilBoundsAttr)

        memref_offset = op.offset

        mapping = (
            op.offset_mapping
            if op.offset_mapping is not None
            else range(len(memref_offset))
        )

        args = [
            IndexOp(
                attributes={
                    "dim": builtin.IntegerAttr.from_index_int_value(i),
                    "offset": IndexAttr.get(*([0] * op.get_apply().get_rank())),
                },
                result_types=[builtin.IndexType()],
            )
            for i in mapping
        ]

        off_const_ops: list[Operation] = []
        memref_load_args: list[BlockArgument | OpResult] = []

        # This will apply an offset to the index if one is required
        # (e.g the offset is not zero), otherwise will use the index value directly
        for arg, x in zip(args, memref_offset):
            if x != 0:
                constant_op = arith.ConstantOp.from_int_and_width(
                    x, builtin.IndexType()
                )
                add_op = arith.AddiOp(arg, constant_op)
                memref_load_args.append(add_op.results[0])
                off_const_ops += [constant_op, add_op]
            else:
                memref_load_args.append(arg.idx)

        load = memref.LoadOp(
            operands=[op.temp, memref_load_args], result_types=[temp.element_type]
        )

        rewriter.insert_op(args)
        rewriter.replace_op(op, [*off_const_ops, load], [load.res])

__init__() -> None

match_and_rewrite(op: AccessOp, rewriter: PatternRewriter)

Source code in xdsl/transforms/experimental/convert_stencil_to_ll_mlir.py

@op_type_rewrite_pattern
def match_and_rewrite(self, op: AccessOp, rewriter: PatternRewriter, /):
    temp = op.temp.type
    assert StencilTypeConstr.verifies(temp)
    assert isinstance(temp.bounds, StencilBoundsAttr)

    memref_offset = op.offset

    mapping = (
        op.offset_mapping
        if op.offset_mapping is not None
        else range(len(memref_offset))
    )

    args = [
        IndexOp(
            attributes={
                "dim": builtin.IntegerAttr.from_index_int_value(i),
                "offset": IndexAttr.get(*([0] * op.get_apply().get_rank())),
            },
            result_types=[builtin.IndexType()],
        )
        for i in mapping
    ]

    off_const_ops: list[Operation] = []
    memref_load_args: list[BlockArgument | OpResult] = []

    # This will apply an offset to the index if one is required
    # (e.g the offset is not zero), otherwise will use the index value directly
    for arg, x in zip(args, memref_offset):
        if x != 0:
            constant_op = arith.ConstantOp.from_int_and_width(
                x, builtin.IndexType()
            )
            add_op = arith.AddiOp(arg, constant_op)
            memref_load_args.append(add_op.results[0])
            off_const_ops += [constant_op, add_op]
        else:
            memref_load_args.append(arg.idx)

    load = memref.LoadOp(
        operands=[op.temp, memref_load_args], result_types=[temp.element_type]
    )

    rewriter.insert_op(args)
    rewriter.replace_op(op, [*off_const_ops, load], [load.res])

StencilStoreToSubview dataclass

Bases: RewritePattern

Source code in xdsl/transforms/experimental/convert_stencil_to_ll_mlir.py

@dataclass
class StencilStoreToSubview(RewritePattern):
    return_targets: dict[ApplyOp, list[SSAValue | None]]

    @op_type_rewrite_pattern
    def match_and_rewrite(self, op: StoreOp, rewriter: PatternRewriter, /):
        for use in op.field.uses:
            if isa(use.operation, LoadOp):
                raise VerifyException(
                    "Cannot lower directly if loading and storing the same field! "
                    "Try running `stencil-bufferize` before."
                )
            if isa(use.operation, StoreOp) and use.operation is not op:
                raise VerifyException(
                    "Cannot lower directly if storing to the same field multiple "
                    "times! Try running `stencil-bufferize` before."
                )
        field = op.field
        assert isa(field.type, FieldType[Attribute])
        assert isa(field.type.bounds, StencilBoundsAttr)
        temp = op.temp
        assert isa(temp.type, TempType[Attribute])
        offsets = [i for i in -field.type.bounds.lb]
        sizes = [i for i in temp.type.get_shape()]
        subview = memref.SubviewOp.from_static_parameters(
            field,
            StencilToMemRefType(field.type),
            offsets,
            sizes,
            [1] * len(sizes),
        )
        name = None
        if subview.source.name_hint:
            name = subview.source.name_hint + "_storeview"
        subview.result.name_hint = name
        if isinstance(field.owner, Operation):
            rewriter.insert_op(subview, InsertPoint.after(field.owner))
        else:
            rewriter.insert_op(subview, InsertPoint.at_start(field.owner))

        rewriter.erase_op(op)

        update_return_target(self.return_targets, field, subview.result)

return_targets: dict[ApplyOp, list[SSAValue | None]] instance-attribute

__init__(return_targets: dict[ApplyOp, list[SSAValue | None]]) -> None

match_and_rewrite(op: StoreOp, rewriter: PatternRewriter)

Source code in xdsl/transforms/experimental/convert_stencil_to_ll_mlir.py

@op_type_rewrite_pattern
def match_and_rewrite(self, op: StoreOp, rewriter: PatternRewriter, /):
    for use in op.field.uses:
        if isa(use.operation, LoadOp):
            raise VerifyException(
                "Cannot lower directly if loading and storing the same field! "
                "Try running `stencil-bufferize` before."
            )
        if isa(use.operation, StoreOp) and use.operation is not op:
            raise VerifyException(
                "Cannot lower directly if storing to the same field multiple "
                "times! Try running `stencil-bufferize` before."
            )
    field = op.field
    assert isa(field.type, FieldType[Attribute])
    assert isa(field.type.bounds, StencilBoundsAttr)
    temp = op.temp
    assert isa(temp.type, TempType[Attribute])
    offsets = [i for i in -field.type.bounds.lb]
    sizes = [i for i in temp.type.get_shape()]
    subview = memref.SubviewOp.from_static_parameters(
        field,
        StencilToMemRefType(field.type),
        offsets,
        sizes,
        [1] * len(sizes),
    )
    name = None
    if subview.source.name_hint:
        name = subview.source.name_hint + "_storeview"
    subview.result.name_hint = name
    if isinstance(field.owner, Operation):
        rewriter.insert_op(subview, InsertPoint.after(field.owner))
    else:
        rewriter.insert_op(subview, InsertPoint.at_start(field.owner))

    rewriter.erase_op(op)

    update_return_target(self.return_targets, field, subview.result)

TrivialExternalLoadOpCleanup

Bases: RewritePattern

Source code in xdsl/transforms/experimental/convert_stencil_to_ll_mlir.py

class TrivialExternalLoadOpCleanup(RewritePattern):
    @op_type_rewrite_pattern
    def match_and_rewrite(self, op: ExternalLoadOp, rewriter: PatternRewriter, /):
        assert isa(op.result.type, FieldType[Attribute])
        rewriter.replace_value_with_new_type(
            op.result, StencilToMemRefType(op.result.type)
        )

        if op.field.type == op.result.type:
            rewriter.replace_op(op, [], [op.field])
        pass

match_and_rewrite(op: ExternalLoadOp, rewriter: PatternRewriter)

Source code in xdsl/transforms/experimental/convert_stencil_to_ll_mlir.py

@op_type_rewrite_pattern
def match_and_rewrite(self, op: ExternalLoadOp, rewriter: PatternRewriter, /):
    assert isa(op.result.type, FieldType[Attribute])
    rewriter.replace_value_with_new_type(
        op.result, StencilToMemRefType(op.result.type)
    )

    if op.field.type == op.result.type:
        rewriter.replace_op(op, [], [op.field])
    pass

TrivialExternalStoreOpCleanup

Bases: RewritePattern

Source code in xdsl/transforms/experimental/convert_stencil_to_ll_mlir.py

class TrivialExternalStoreOpCleanup(RewritePattern):
    @op_type_rewrite_pattern
    def match_and_rewrite(self, op: ExternalStoreOp, rewriter: PatternRewriter, /):
        rewriter.erase_op(op)

match_and_rewrite(op: ExternalStoreOp, rewriter: PatternRewriter)

Source code in xdsl/transforms/experimental/convert_stencil_to_ll_mlir.py

@op_type_rewrite_pattern
def match_and_rewrite(self, op: ExternalStoreOp, rewriter: PatternRewriter, /):
    rewriter.erase_op(op)

CombineOpCleanup

Bases: RewritePattern

Just remove stencil.combines as they are just used for return target analysis.

Source code in xdsl/transforms/experimental/convert_stencil_to_ll_mlir.py

class CombineOpCleanup(RewritePattern):
    """
    Just remove `stencil.combine`s as they are just used for return target analysis.
    """

    @op_type_rewrite_pattern
    def match_and_rewrite(self, op: CombineOp, rewriter: PatternRewriter, /):
        rewriter.erase_op(op)

match_and_rewrite(op: CombineOp, rewriter: PatternRewriter)

Source code in xdsl/transforms/experimental/convert_stencil_to_ll_mlir.py

@op_type_rewrite_pattern
def match_and_rewrite(self, op: CombineOp, rewriter: PatternRewriter, /):
    rewriter.erase_op(op)

StencilTypeConversion dataclass

Bases: TypeConversionPattern

Source code in xdsl/transforms/experimental/convert_stencil_to_ll_mlir.py

class StencilTypeConversion(TypeConversionPattern):
    @attr_constr_rewrite_pattern(StencilTypeConstr)
    def convert_type(self, typ: StencilType[Attribute]) -> MemRefType:
        return StencilToMemRefType(typ)

convert_type(typ: StencilType[Attribute]) -> MemRefType

Source code in xdsl/transforms/experimental/convert_stencil_to_ll_mlir.py

@attr_constr_rewrite_pattern(StencilTypeConstr)
def convert_type(self, typ: StencilType[Attribute]) -> MemRefType:
    return StencilToMemRefType(typ)

ResultTypeConversion dataclass

Bases: TypeConversionPattern

Source code in xdsl/transforms/experimental/convert_stencil_to_ll_mlir.py

class ResultTypeConversion(TypeConversionPattern):
    @attr_type_rewrite_pattern
    def convert_type(self, typ: ResultType) -> Attribute:
        return typ.elem

convert_type(typ: ResultType) -> Attribute

Source code in xdsl/transforms/experimental/convert_stencil_to_ll_mlir.py

@attr_type_rewrite_pattern
def convert_type(self, typ: ResultType) -> Attribute:
    return typ.elem

ConvertStencilToLLMLIRPass dataclass

Bases: ModulePass

Source code in xdsl/transforms/experimental/convert_stencil_to_ll_mlir.py

@dataclass(frozen=True)
class ConvertStencilToLLMLIRPass(ModulePass):
    name = "convert-stencil-to-ll-mlir"

    def apply(self, ctx: Context, op: builtin.ModuleOp) -> None:
        return_targets: dict[ApplyOp, list[SSAValue | None]] = return_target_analysis(
            op
        )

        the_one_pass = PatternRewriteWalker(
            GreedyRewritePatternApplier(
                [
                    ApplyOpFieldSubviews(),
                    ApplyOpToParallel(return_targets),
                    BufferOpToMemRef(return_targets),
                    StencilStoreToSubview(return_targets),
                    CastOpToMemRef(),
                    LoadOpToMemRef(),
                    AccessOpToMemRef(),
                    ReturnOpToMemRef(return_targets),
                    TrivialExternalLoadOpCleanup(),
                    TrivialExternalStoreOpCleanup(),
                    AllocOpToMemRef(),
                ]
            ),
            apply_recursively=True,
            walk_reverse=True,
            walk_regions_first=True,
        )
        the_one_pass.rewrite_module(op)
        type_pass = PatternRewriteWalker(
            GreedyRewritePatternApplier(
                [
                    CombineOpCleanup(),
                    StencilTypeConversion(recursive=True),
                    ResultTypeConversion(recursive=True),
                ]
            ),
            walk_reverse=True,
        )
        type_pass.rewrite_module(op)

name = 'convert-stencil-to-ll-mlir' class-attribute instance-attribute

__init__() -> None

apply(ctx: Context, op: builtin.ModuleOp) -> None

Source code in xdsl/transforms/experimental/convert_stencil_to_ll_mlir.py

def apply(self, ctx: Context, op: builtin.ModuleOp) -> None:
    return_targets: dict[ApplyOp, list[SSAValue | None]] = return_target_analysis(
        op
    )

    the_one_pass = PatternRewriteWalker(
        GreedyRewritePatternApplier(
            [
                ApplyOpFieldSubviews(),
                ApplyOpToParallel(return_targets),
                BufferOpToMemRef(return_targets),
                StencilStoreToSubview(return_targets),
                CastOpToMemRef(),
                LoadOpToMemRef(),
                AccessOpToMemRef(),
                ReturnOpToMemRef(return_targets),
                TrivialExternalLoadOpCleanup(),
                TrivialExternalStoreOpCleanup(),
                AllocOpToMemRef(),
            ]
        ),
        apply_recursively=True,
        walk_reverse=True,
        walk_regions_first=True,
    )
    the_one_pass.rewrite_module(op)
    type_pass = PatternRewriteWalker(
        GreedyRewritePatternApplier(
            [
                CombineOpCleanup(),
                StencilTypeConversion(recursive=True),
                ResultTypeConversion(recursive=True),
            ]
        ),
        walk_reverse=True,
    )
    type_pass.rewrite_module(op)

StencilToMemRefType(input_type: StencilType[_TypeElement]) -> MemRefType[_TypeElement]

Source code in xdsl/transforms/experimental/convert_stencil_to_ll_mlir.py

def StencilToMemRefType(
    input_type: StencilType[_TypeElement],
) -> MemRefType[_TypeElement]:
    return MemRefType(input_type.element_type, input_type.get_shape())

collectBlockArguments(number: int, block: Block)

Source code in xdsl/transforms/experimental/convert_stencil_to_ll_mlir.py

def collectBlockArguments(number: int, block: Block):
    args = []

    while len(args) < number:
        args = list(block.args[0 : number - len(args)]) + args

        parent = block.parent_block()
        if parent is None:
            break

        block = parent

    return args

update_return_target(return_targets: dict[ApplyOp, list[SSAValue | None]], old_target: SSAValue, new_target: SSAValue | None)

Source code in xdsl/transforms/experimental/convert_stencil_to_ll_mlir.py

def update_return_target(
    return_targets: dict[ApplyOp, list[SSAValue | None]],
    old_target: SSAValue,
    new_target: SSAValue | None,
):
    for targets in return_targets.values():
        for i, target in enumerate(targets):
            if target == old_target:
                targets[i] = new_target

assert_subset(field: FieldType[Attribute], temp: TempType[Attribute])

Source code in xdsl/transforms/experimental/convert_stencil_to_ll_mlir.py

def assert_subset(field: FieldType[Attribute], temp: TempType[Attribute]):
    assert isinstance(field.bounds, StencilBoundsAttr)
    assert isinstance(temp.bounds, StencilBoundsAttr)
    if temp.bounds.lb < field.bounds.lb:
        raise VerifyException(
            "The stencil computation requires a field with lower bound at least "
            f"{temp.bounds.lb}, got {field.bounds.lb}, min: {min(field.bounds.lb, temp.bounds.lb)}"
        )
    if temp.bounds.ub > field.bounds.ub:
        raise VerifyException(
            "The stencil computation requires a field with upper bound at least "
            f"{temp.bounds.ub}, got {field.bounds.ub}, max: {max(field.bounds.ub, temp.bounds.ub)}"
        )

prepare_apply_body(op: ApplyOp)

Source code in xdsl/transforms/experimental/convert_stencil_to_ll_mlir.py

def prepare_apply_body(op: ApplyOp):
    # First replace all current arguments by their definition
    # and erase them from the block. (We are changing the op
    # to a loop, which has access to them either way)
    entry = op.region.block

    for operand, arg in zip(op.operands, entry.args):
        arg.replace_by(operand)
        entry.erase_arg(arg)
    entry.add_op(scf.ReduceOp())
    for _ in range(op.get_rank()):
        entry.insert_arg(builtin.IndexType(), 0)

    return op.region.detach_block(entry)

field_subview(field: SSAValue)

Source code in xdsl/transforms/experimental/convert_stencil_to_ll_mlir.py

def field_subview(field: SSAValue):
    assert isa(field_type := field.type, FieldType[Attribute])
    assert isinstance(bounds := field_type.bounds, StencilBoundsAttr)
    offsets = [i for i in -bounds.lb]
    sizes = [i for i in field_type.get_shape()]
    strides = [1] * len(sizes)

    return memref.SubviewOp.from_static_parameters(
        field, StencilToMemRefType(field_type), offsets, sizes, strides
    )

return_target_analysis(module: builtin.ModuleOp)

Source code in xdsl/transforms/experimental/convert_stencil_to_ll_mlir.py

def return_target_analysis(module: builtin.ModuleOp):
    return_targets: dict[ApplyOp, list[SSAValue | None]] = {}

    for op in module.walk():
        if not isinstance(op, ReturnOp):
            continue

        apply = op.parent_op()
        assert isinstance(apply, ApplyOp)

        return_targets[apply] = []
        for res in list(apply.res):
            store = [
                use
                for use in list(res.uses)
                if isinstance(use.operation, StoreOp | BufferOp | CombineOp)
            ]

            if len(store) > 1:
                warn("Each stencil result should be stored only once.")
                continue

            elif len(store) == 0:
                field = None
            else:
                field = _get_use_target(store[0])

            return_targets[apply].append(field)

    return return_targets