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Jax use donated arguments

jax_use_donated_arguments

SubstituteDonatedTensors dataclass

Bases: RewritePattern

Looks at returned tensors and if they match donated argument tensors ask bufferization to use them as buffers.

Source code in xdsl/transforms/jax_use_donated_arguments.py 
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@dataclass
class SubstituteDonatedTensors(RewritePattern):
    """
    Looks at returned tensors and if they match donated argument tensors ask bufferization to use them as buffers.
    """

    remove_matched_outputs: bool = False

    @op_type_rewrite_pattern
    def match_and_rewrite(self, op: ReturnOp, rewriter: PatternRewriter, /):
        func_op = op.parent_op()
        assert isinstance(func_op, FuncOp)

        if func_op.arg_attrs is None or len(func_op.body.blocks) > 1:
            return

        donated_input_mask = tuple(
            isinstance(inp.type, TensorType) and "tf.aliasing_output" in attr.data
            for inp, attr in zip(func_op.args, func_op.arg_attrs, strict=True)
        )
        donated_inputs = tuple(itertools.compress(func_op.args, donated_input_mask))

        if not donated_inputs:
            return

        donated_input_by_output = map_donated_input_by_output(
            donated_inputs, op.arguments
        )
        if not donated_input_by_output:
            return

        ordered_buffered_outputs = tuple(
            arg for arg in op.arguments if arg in donated_input_by_output
        )
        new_ops: list[Operation] = [
            MaterializeInDestinationOp(
                operands=[out, donated_input_by_output[out]], result_types=[out.type]
            )
            for out in ordered_buffered_outputs
        ]
        new_output_mapping = {
            out: mater_ops.results[0]
            for out, mater_ops in zip(ordered_buffered_outputs, new_ops, strict=True)
        }
        new_outputs = tuple(new_output_mapping.get(out, out) for out in op.arguments)

        return_types = tuple(func_op.function_type.outputs.data)
        if self.remove_matched_outputs:
            kept_outputs_mask = tuple(
                out not in donated_input_by_output for out in op.arguments
            )
            return_types = list(itertools.compress(return_types, kept_outputs_mask))
            new_outputs = list(itertools.compress(new_outputs, kept_outputs_mask))

        new_ops.append(ReturnOp(*new_outputs))
        func_op.function_type = FunctionType.from_lists(
            func_op.function_type.inputs.data, return_types
        )
        rewriter.replace_op(op, new_ops)

        # remove the donation attribute to avoid their reuse if we run the pass multiple times on the same function
        used_donated_arguments = set(donated_input_by_output.values())
        new_input_attrs = [dict(attr.data) for attr in func_op.arg_attrs]

        for inp, new_attr in zip(func_op.args, new_input_attrs, strict=True):
            if inp in used_donated_arguments:
                del new_attr["tf.aliasing_output"]

        func_op.arg_attrs = ArrayAttr(
            [DictionaryAttr(attr) for attr in new_input_attrs]
        )

remove_matched_outputs: bool = False class-attribute instance-attribute

__init__(remove_matched_outputs: bool = False) -> None

match_and_rewrite(op: ReturnOp, rewriter: PatternRewriter)

Source code in xdsl/transforms/jax_use_donated_arguments.py 
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@op_type_rewrite_pattern
def match_and_rewrite(self, op: ReturnOp, rewriter: PatternRewriter, /):
    func_op = op.parent_op()
    assert isinstance(func_op, FuncOp)

    if func_op.arg_attrs is None or len(func_op.body.blocks) > 1:
        return

    donated_input_mask = tuple(
        isinstance(inp.type, TensorType) and "tf.aliasing_output" in attr.data
        for inp, attr in zip(func_op.args, func_op.arg_attrs, strict=True)
    )
    donated_inputs = tuple(itertools.compress(func_op.args, donated_input_mask))

    if not donated_inputs:
        return

    donated_input_by_output = map_donated_input_by_output(
        donated_inputs, op.arguments
    )
    if not donated_input_by_output:
        return

    ordered_buffered_outputs = tuple(
        arg for arg in op.arguments if arg in donated_input_by_output
    )
    new_ops: list[Operation] = [
        MaterializeInDestinationOp(
            operands=[out, donated_input_by_output[out]], result_types=[out.type]
        )
        for out in ordered_buffered_outputs
    ]
    new_output_mapping = {
        out: mater_ops.results[0]
        for out, mater_ops in zip(ordered_buffered_outputs, new_ops, strict=True)
    }
    new_outputs = tuple(new_output_mapping.get(out, out) for out in op.arguments)

    return_types = tuple(func_op.function_type.outputs.data)
    if self.remove_matched_outputs:
        kept_outputs_mask = tuple(
            out not in donated_input_by_output for out in op.arguments
        )
        return_types = list(itertools.compress(return_types, kept_outputs_mask))
        new_outputs = list(itertools.compress(new_outputs, kept_outputs_mask))

    new_ops.append(ReturnOp(*new_outputs))
    func_op.function_type = FunctionType.from_lists(
        func_op.function_type.inputs.data, return_types
    )
    rewriter.replace_op(op, new_ops)

    # remove the donation attribute to avoid their reuse if we run the pass multiple times on the same function
    used_donated_arguments = set(donated_input_by_output.values())
    new_input_attrs = [dict(attr.data) for attr in func_op.arg_attrs]

    for inp, new_attr in zip(func_op.args, new_input_attrs, strict=True):
        if inp in used_donated_arguments:
            del new_attr["tf.aliasing_output"]

    func_op.arg_attrs = ArrayAttr(
        [DictionaryAttr(attr) for attr in new_input_attrs]
    )

JaxUseDonatedArguments dataclass

Bases: ModulePass

Source code in xdsl/transforms/jax_use_donated_arguments.py 
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@dataclass(frozen=True)
class JaxUseDonatedArguments(ModulePass):
    name = "jax-use-donated-arguments"

    remove_matched_outputs: bool = False

    def apply(self, ctx: Context, op: builtin.ModuleOp) -> None:
        PatternRewriteWalker(
            SubstituteDonatedTensors(self.remove_matched_outputs),
            apply_recursively=False,
            walk_reverse=True,
            walk_regions_first=True,
        ).rewrite_module(op)

name = 'jax-use-donated-arguments' class-attribute instance-attribute

remove_matched_outputs: bool = False class-attribute instance-attribute

__init__(remove_matched_outputs: bool = False) -> None

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

Source code in xdsl/transforms/jax_use_donated_arguments.py 
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def apply(self, ctx: Context, op: builtin.ModuleOp) -> None:
    PatternRewriteWalker(
        SubstituteDonatedTensors(self.remove_matched_outputs),
        apply_recursively=False,
        walk_reverse=True,
        walk_regions_first=True,
    ).rewrite_module(op)

map_donated_input_by_output(donatable_inputs: Sequence[BlockArgument], outputs: Sequence[SSAValue]) -> dict[SSAValue, BlockArgument]

Find suitable donated buffers for each of returned variables. Each buffer can be used only once. Types of the buffer and the variable should match.

Source code in xdsl/transforms/jax_use_donated_arguments.py 
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def map_donated_input_by_output(
    donatable_inputs: Sequence[BlockArgument], outputs: Sequence[SSAValue]
) -> dict[SSAValue, BlockArgument]:
    """
    Find suitable donated buffers for each of returned variables.
    Each buffer can be used only once.
    Types of the buffer and the variable should match.
    """

    donatable_inputs_by_type: dict[Attribute, list[BlockArgument]] = defaultdict(list)
    for inp in donatable_inputs:
        donatable_inputs_by_type[inp.type].append(inp)

    outputs_by_type: dict[Attribute, list[SSAValue]] = defaultdict(list)
    for out in outputs:
        if isinstance(out.owner, MaterializeInDestinationOp) and isinstance(
            out.owner.dest, BlockArgument
        ):
            # output has already been buffered
            continue
        outputs_by_type[out.type].append(out)

    mapping_by_type = {
        k: tuple(zip(donatable_inputs_by_type[k], outputs_by_type[k]))
        for k in donatable_inputs_by_type.keys() & outputs_by_type.keys()
    }

    return {o: i for mapping in mapping_by_type.values() for (i, o) in mapping}