AMD-SHARK-Studio/shark/shark_importer.py

# Lint as: python3
"""SHARK Importer"""

import sys

# List of the supported frontends.
supported_frontends = {
    "tensorflow",
    "tf",
    "pytorch",
    "torch",
    "tf-lite",
    "tflite",
}


class SharkImporter:
    """
    SharkImporter converts frontend modules into a
    mlir_module. The supported frameworks are tensorflow,
    pytorch, and tf-lite.

    ...

    Attributes
    ----------
    module :
        torch, tensorflow or tf-lite module.
    inputs :
        inputs to the module, may be required for the shape
        information.
    frontend: str
        frontend to which the module belongs.

    Methods
    -------
    import_mlir(is_dynamic, tracing_required, func_name):
        is_dynamic: input shapes to be totally dynamic (pytorch specific).
        tracing_required: whether tracing is required (pytorch specific.
        func_name: The function to be traced out or imported to mlir.

    import_debug(is_dynamic, tracing_required, func_name):
        returns the converted (mlir_module,func_name) with inputs and golden
        outputs.
        The inputs and outputs are converted into np array.
    """

    def __init__(
        self,
        module,
        inputs: tuple = (),
        frontend: str = "torch",
    ):
        self.module = module
        self.inputs = None if len(inputs) == 0 else inputs
        self.frontend = frontend
        if not self.frontend in supported_frontends:
            print(
                f"The frontend is not in the supported_frontends: {supported_frontends}"
            )
            sys.exit(1)

    # NOTE: The default function for torch is "forward" and tf-lite is "main".

    def _torch_mlir(self, is_dynamic, tracing_required):
        from shark.torch_mlir_utils import get_torch_mlir_module

        return get_torch_mlir_module(
            self.module, self.inputs, is_dynamic, tracing_required
        )

    def _tf_mlir(self, func_name):
        from iree.compiler import tf as tfc

        return tfc.compile_module(
            self.module, exported_names=[func_name], import_only=True
        )

    def _tflite_mlir(self, func_name):
        from iree.compiler import tflite as tflitec

        # TODO(Chi): Just add the conversion of tflite model here.
        return tflitec.compile_module(
            self.module, exported_names=[func_name], import_only=True
        )

    # Adds the conversion of the frontend with the private function.
    def import_mlir(
        self,
        is_dynamic=False,
        tracing_required=False,
        func_name="forward",
    ):
        if self.frontend in ["torch", "pytorch"]:
            if self.inputs == None:
                print(
                    "Please pass in the inputs, the inputs are required to determine the shape of the mlir_module"
                )
                sys.exit(1)
            return self._torch_mlir(is_dynamic, tracing_required), func_name
        if self.frontend in ["tf", "tensorflow"]:
            return self._tf_mlir(func_name), func_name
        if self.frontend in ["tflite", "tf-lite"]:
            func_name = "main"
            return self._tflite_mlir(func_name), func_name

    # Converts the frontend specific tensors into np array.
    def convert_to_numpy(self, array_tuple: tuple):
        if self.frontend in ["torch", "pytorch"]:
            return [x.detach().numpy() for x in array_tuple]
        if self.frontend in ["tf", "tensorflow"]:
            return [x.numpy() for x in array_tuple]
        if self.frontend in ["tf-lite", "tflite"]:
            # TODO(Chi): Validate for tf-lite tensors.
            return [x.numpy() for x in array_tuple]

    def import_debug(
        self,
        is_dynamic=False,
        tracing_required=False,
        func_name="forward",
    ):
        if self.inputs == None:
            print(
                f"There is no input provided: {self.inputs}, please provide inputs or simply run import_mlir."
            )
            sys.exit(1)

        imported_mlir = self.import_mlir(
            is_dynamic, tracing_required, func_name
        )
        # TODO: Make sure that any generic function name is accepted. Currently takes in the default function names.
        # TODO: Check for multiple outputs.
        if self.frontend in ["torch", "pytorch"]:
            golden_out = self.module(*self.inputs)
            return (
                imported_mlir,
                self.convert_to_numpy(self.inputs),
                golden_out.detach().numpy(),
            )
        if self.frontend in ["tf", "tensorflow"]:
            golden_out = self.module.forward(*self.inputs)
            return (
                imported_mlir,
                self.convert_to_numpy(self.inputs),
                golden_out.numpy(),
            )
        if self.frontend in ["tflite", "tf-lite"]:
            # TODO(Chi): Validate it for tflite models.
            golden_out = self.module.main(*self.inputs)
            return (
                imported_mlir,
                self.convert_to_numpy(self.inputs),
                golden_out.numpy(),
            )