Function _frompy

tinygrad/tensor.py:53–62

(x:list|tuple|bytes, dtype:DType, device:str|tuple[str,...])

  return (len(subs),) + (subs[0] if subs else ())

def _frompy(x:list|tuple|bytes, dtype:DType, device:str|tuple[str,...]) -> UOp:
  if isinstance(x, bytes): ret, data = UOp.new_buffer("PYTHON", len(x)//dtype.itemsize, dtype), x
  else:
    ret = UOp.empty(shape:=get_shape(x), dtype, "PYTHON")
    assert dtype.fmt is not None, f"{dtype=} has None fmt"
    truncate_function = truncate[dtype]
    data = struct.pack(f"{prod(shape)}{dtype.fmt}", *[truncate_function(dtype.const(xi)) for xi in fully_flatten(x)])
  # fake realize. if target device is PYTHON it needs bytearray to be writable
  ret.buffer.allocate(memoryview(data if device != "PYTHON" else bytearray(data)))
  return ret

def _get_winograd_matcols(mat, dims:int, shp:tuple[sint, ...], device:str|tuple[str, ...]|None, dtype:DType) -> list[list[Tensor]]:
  return [[Tensor.cat(*[Tensor.full(shp[:dim] + (1,) + shp[dim+1:], float(m[k]), device=device, dtype=dtype, buffer=False) for m in mat], dim=dim)