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98 lines
3.5 KiB
Python
98 lines
3.5 KiB
Python
"""Compiler submodule"""
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from typing import List, Union
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from mlir._mlir_libs._zamalang._compiler import JitCompilerEngine as _JitCompilerEngine
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from mlir._mlir_libs._zamalang._compiler import LambdaArgument as _LambdaArgument
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from mlir._mlir_libs._zamalang._compiler import round_trip as _round_trip
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import numpy as np
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def round_trip(mlir_str: str) -> str:
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"""Parse the MLIR input, then return it back.
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Args:
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mlir_str (str): MLIR code to parse.
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Raises:
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TypeError: if the argument is not an str.
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Returns:
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str: parsed MLIR input.
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"""
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if not isinstance(mlir_str, str):
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raise TypeError("input must be an `str`")
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return _round_trip(mlir_str)
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def create_execution_argument(value: Union[int, np.ndarray]) -> "_LambdaArgument":
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"""Create an execution argument holding either an int or tensor value.
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Args:
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value (Union[int, numpy.array]): value of the argument, either an int, or a numpy array
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Raises:
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TypeError: if the values aren't in the expected range, or using a wrong type
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Returns:
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_LambdaArgument: lambda argument holding the appropriate value
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"""
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if not isinstance(value, (int, np.ndarray)):
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raise TypeError("value of execution argument must be either int or numpy.array")
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if isinstance(value, int):
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if not (0 <= value < (2 ** 64 - 1)):
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raise TypeError("single integer must be in the range [0, 2**64 - 1] (uint64)")
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return _LambdaArgument.from_scalar(value)
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else:
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assert isinstance(value, np.ndarray)
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if value.dtype != np.uint8:
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raise TypeError("numpy.array must be of dtype uint8")
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return _LambdaArgument.from_tensor(value.flatten().tolist(), value.shape)
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class CompilerEngine:
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def __init__(self, mlir_str: str = None):
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self._engine = _JitCompilerEngine()
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self._lambda = None
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if mlir_str is not None:
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self.compile_fhe(mlir_str)
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def compile_fhe(self, mlir_str: str, func_name: str = "main"):
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"""Compile the MLIR input.
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Args:
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mlir_str (str): MLIR to compile.
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func_name (str): name of the function to set as entrypoint.
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Raises:
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TypeError: if the argument is not an str.
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"""
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if not isinstance(mlir_str, str):
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raise TypeError("input must be an `str`")
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self._lambda = self._engine.build_lambda(mlir_str, func_name)
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def run(self, *args: List[Union[int, np.ndarray]]) -> Union[int, np.ndarray]:
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"""Run the compiled code.
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Args:
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*args: list of arguments for execution. Each argument can be an int, or a numpy.array
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Raises:
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TypeError: if execution arguments can't be constructed
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RuntimeError: if the engine has not compiled any code yet
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RuntimeError: if the return type is unknown
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Returns:
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int or numpy.array: result of execution.
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"""
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if self._lambda is None:
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raise RuntimeError("need to compile an MLIR code first")
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execution_arguments = [create_execution_argument(arg) for arg in args]
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lambda_arg = self._lambda.invoke(execution_arguments)
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if lambda_arg.is_scalar():
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return lambda_arg.get_scalar()
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elif lambda_arg.is_tensor():
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shape = lambda_arg.get_tensor_shape()
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tensor = np.array(lambda_arg.get_tensor_data()).reshape(shape)
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return tensor
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else:
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raise RuntimeError("unknown return type")
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