def numba_funcify_QRFull(op, node, **kwargs):
mode = op.mode
if mode != "reduced":
warnings.warn(
("Numba will use object mode to allow the "
"`mode` argument to `numpy.linalg.qr`."),
UserWarning,
)
if len(node.outputs) > 1:
ret_sig = numba.types.Tuple(
[get_numba_type(o.type) for o in node.outputs])
else:
ret_sig = get_numba_type(node.outputs[0].type)
@numba.njit
def qr_full(x):
with numba.objmode(ret=ret_sig):
ret = np.linalg.qr(x, mode=mode)
return ret
else:
out_dtype = node.outputs[0].type.numpy_dtype
inputs_cast = int_to_float_fn(node.inputs, out_dtype)
@numba.njit(inline="always")
def qr_full(x):
return np.linalg.qr(inputs_cast(x))
return qr_full
def numba_funcify_SVD(op, node, **kwargs):
full_matrices = op.full_matrices
compute_uv = op.compute_uv
if not compute_uv:
warnings.warn(
("Numba will use object mode to allow the "
"`compute_uv` argument to `numpy.linalg.svd`."),
UserWarning,
)
ret_sig = get_numba_type(node.outputs[0].type)
@numba.njit
def svd(x):
with numba.objmode(ret=ret_sig):
ret = np.linalg.svd(x, full_matrices, compute_uv)
return ret
else:
out_dtype = node.outputs[0].type.numpy_dtype
inputs_cast = int_to_float_fn(node.inputs, out_dtype)
@numba.njit(inline="always")
def svd(x):
return np.linalg.svd(inputs_cast(x), full_matrices)
return svd
def numba_funcify_MatrixPinv(op, node, **kwargs):
out_dtype = node.outputs[0].type.numpy_dtype
inputs_cast = int_to_float_fn(node.inputs, out_dtype)
@numba.njit(inline="always")
def matrixpinv(x):
return np.linalg.pinv(inputs_cast(x)).astype(out_dtype)
return matrixpinv
def numba_funcify_Det(op, node, **kwargs):
out_dtype = node.outputs[0].type.numpy_dtype
inputs_cast = int_to_float_fn(node.inputs, out_dtype)
@numba.njit(inline="always")
def det(x):
return numba_basic.direct_cast(np.linalg.det(inputs_cast(x)),
out_dtype)
return det
def numba_funcify_Eig(op, node, **kwargs):
out_dtype_1 = node.outputs[0].type.numpy_dtype
out_dtype_2 = node.outputs[1].type.numpy_dtype
inputs_cast = int_to_float_fn(node.inputs, out_dtype_1)
@numba.njit
def eig(x):
out = np.linalg.eig(inputs_cast(x))
return (out[0].astype(out_dtype_1), out[1].astype(out_dtype_2))
return eig