optimum/exporters/executorch/recipes/xnnpack.py

# Copyright 2025 The HuggingFace Team. All rights reserved.
#
# 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
#
#     http://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.

import logging
from typing import Dict, Union

from packaging.version import parse
from tabulate import tabulate
from torch.export import ExportedProgram

from executorch import version as executorch_version
from executorch.backends.xnnpack.partition.xnnpack_partitioner import XnnpackPartitioner
from executorch.devtools.backend_debug import get_delegation_info
from executorch.exir import (
    EdgeCompileConfig,
    ExecutorchBackendConfig,
    ExecutorchProgram,
    to_edge_transform_and_lower,
)
from optimum.executorch.passes.remove_padding_idx_embedding_pass import RemovePaddingIdxEmbeddingPass

from ..integrations import (
    CausalLMExportableModule,
    MaskedLMExportableModule,
    Seq2SeqLMExportableModule,
)
from ..recipe_registry import register_recipe


@register_recipe("xnnpack")
def export_to_executorch_with_xnnpack(
    model: Union[CausalLMExportableModule, MaskedLMExportableModule, Seq2SeqLMExportableModule],
    **kwargs,
):
    """
    Export a PyTorch model to ExecuTorch w/ delegation to XNNPACK backend.

    This function also write metadata required by the ExecuTorch runtime to the model.

    Args:
        model (Union[CausalLMExportableModule, MaskedLMExportableModule, Seq2SeqLMExportableModule]):
            The PyTorch model to be exported to ExecuTorch.
        **kwargs:
            Additional keyword arguments for recipe-specific configurations, e.g. export using different example inputs, or different compile/bechend configs.

    Returns:
        Dict[str, ExecutorchProgram]:
            A map of exported and optimized program for ExecuTorch.
            For encoder-decoder models or multimodal models, it may generate multiple programs.
    """

    def _lower_to_executorch(
        exported_programs: Dict[str, ExportedProgram],
        metadata=None,
    ) -> Dict[str, ExecutorchProgram]:
        et_progs = {}
        backend_config_dict = {
            "extract_delegate_segments": True,
        }
        if parse(executorch_version.__version__).base_version > "0.6.0":
            backend_config_dict["do_quant_fusion_and_const_prop"] = True

        for pte_name, exported_program in exported_programs.items():
            logging.debug(f"\nExported program for {pte_name}.pte: {exported_program}")
            et_progs[pte_name] = to_edge_transform_and_lower(
                exported_program,
                partitioner=[XnnpackPartitioner()],
                compile_config=EdgeCompileConfig(
                    _check_ir_validity=False,
                    _skip_dim_order=True,
                ),
                constant_methods=metadata,
                transform_passes=[RemovePaddingIdxEmbeddingPass()],
            ).to_executorch(
                config=ExecutorchBackendConfig(**backend_config_dict),
            )
            logging.debug(
                f"\nExecuTorch program for {pte_name}.pte: {et_progs[pte_name].exported_program().graph_module}"
            )
            delegation_info = get_delegation_info(et_progs[pte_name].exported_program().graph_module)
            logging.debug(f"\nDelegation info Summary for {pte_name}.pte: {delegation_info.get_summary()}")
            logging.debug(
                f"\nDelegation info for {pte_name}.pte: {tabulate(delegation_info.get_operator_delegation_dataframe(), headers='keys', tablefmt='fancy_grid')}"
            )
        return et_progs

    exported_progs = model.export()

    if (
        model.config._attn_implementation == "custom_sdpa"
        or model.config._attn_implementation == "custom_sdpa_ring_kv_cache"
    ):
        # Sanity check to make sure the exported program contains the custom sdpa operator.
        if not any(
            node.op == "call_function" and "custom_sdpa" in str(node.target)
            for exported_program in exported_progs.values()
            for node in exported_program.graph_module.graph.nodes
        ):
            raise ValueError("'custom_sdpa' not found in the graph.")

    return _lower_to_executorch(exported_progs, model.metadata)