Function const

python/tvm/relax/expr.py:1158–1204

Create a constant value. Parameters ---------- value: bool | int | float | numpy.ndarray | tvm.runtime.Tensor The constant value. dtype: Optional[str] The data type of the resulting constant. Note ---- When dtype is None, we use the following rule:

(
    value: bool | int | float | _np.ndarray | tvm.runtime.Tensor, dtype: str | None = None
)

def const(
    value: bool | int | float | _np.ndarray | tvm.runtime.Tensor, dtype: str | None = None
) -> Constant:
    """Create a constant value.

    Parameters
    ----------
    value: bool | int | float | numpy.ndarray | tvm.runtime.Tensor
        The constant value.

    dtype: Optional[str]
        The data type of the resulting constant.

    Note
    ----
    When dtype is None, we use the following rule:

    - int maps to "int32"
    - float maps to "float32"
    - bool maps to "bool"
    - other using the same default rule as numpy.
    """
    # Needed for bf16 and fp8 support (does not come with numpy)
    import ml_dtypes  # pylint: disable=unused-import,import-outside-toplevel

    if isinstance(value, Number | (bool | list)):
        value = _np.array(value, dtype=dtype)

    if not dtype:
        # when dtype is None: int maps to "int32", float maps to "float32"
        dtype = {  # type: ignore
            _np.dtype("int64"): _np.int32,  # type: ignore
            _np.dtype("float64"): _np.float32,  # type: ignore
        }.get(
            value.dtype,
            None,  # type: ignore
        )

    if isinstance(value, _np.ndarray | _np.generic):
        if dtype is not None:
            value = value.astype(dtype)
        value = tvm.runtime.tensor(value)

    if not isinstance(value, tvm.runtime.Tensor):
        raise ValueError("value has to be scalar or Tensor")

    return Constant(value)


@tvm_ffi.register_object("relax.TEPlaceholderOp")