Source code for tvm_ffi._convert

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"""Conversion utilities to convert Python objects into TVM FFI values."""

from __future__ import annotations

from numbers import Number
from types import ModuleType
from typing import Any

from . import container, core

torch: ModuleType | None = None
try:
    import torch  # type: ignore[no-redef]
except ImportError:
    pass

numpy: ModuleType | None = None
try:
    import numpy
except ImportError:
    pass


[docs] def convert(value: Any) -> Any: # noqa: PLR0911,PLR0912 """Convert a Python object into TVM FFI values. This helper mirrors the automatic argument conversion that happens when calling FFI functions. It is primarily useful in tests or places where an explicit conversion is desired. Parameters ---------- value The Python object to be converted. Returns ------- ffi_obj The converted TVM FFI object. Examples -------- .. code-block:: python import tvm_ffi # Lists and tuples become tvm_ffi.Array a = tvm_ffi.convert([1, 2, 3]) assert isinstance(a, tvm_ffi.Array) # Dicts become tvm_ffi.Map m = tvm_ffi.convert({"a": 1, "b": 2}) assert isinstance(m, tvm_ffi.Map) # Strings and bytes become zero-copy FFI-aware types s = tvm_ffi.convert("hello") b = tvm_ffi.convert(b"bytes") assert isinstance(s, tvm_ffi.core.String) assert isinstance(b, tvm_ffi.core.Bytes) # Callables are wrapped as tvm_ffi.Function f = tvm_ffi.convert(lambda x: x + 1) assert isinstance(f, tvm_ffi.Function) # Array libraries that support DLPack export can be converted to Tensor import numpy as np x = tvm_ffi.convert(np.arange(4, dtype="int32")) assert isinstance(x, tvm_ffi.Tensor) Note ---- Function arguments to ffi function calls are automatically converted. So this function is mainly only used in internal or testing scenarios. """ if isinstance(value, (core.Object, core.PyNativeObject, bool, Number)): return value elif isinstance(value, (tuple, list)): return container.Array(value) elif isinstance(value, dict): return container.Map(value) elif isinstance(value, str): return core.String(value) elif isinstance(value, (bytes, bytearray)): return core.Bytes(value) elif isinstance(value, core.ObjectConvertible): return value.asobject() elif callable(value): return core._convert_to_ffi_func(value) elif value is None: return None elif hasattr(value, "__dlpack__"): return core.from_dlpack(value) elif torch is not None and isinstance(value, torch.dtype): return core._convert_torch_dtype_to_ffi_dtype(value) elif numpy is not None and isinstance(value, numpy.dtype): return core._convert_numpy_dtype_to_ffi_dtype(value) elif isinstance(value, Exception): return core._convert_to_ffi_error(value) else: # in this case, it is an opaque python object return core._convert_to_opaque_object(value)