tensor.py

# inspired by https://github.com/karpathy/micrograd/blob/master/micrograd/engine.py
from __future__ import annotations
import time, math
from collections import defaultdict
from functools import partialmethod, reduce
from itertools import accumulate
import numpy as np
from typing import List, Tuple, Callable, Optional, ClassVar, Type, Union, Sequence

from tinygrad.helpers import ImageDType, argfix, make_pair, getenv, IMAGE, DEBUG, flatten, DType, dtypes
from tinygrad.lazy import LazyBuffer
from tinygrad.ops import Device, LoadOps

# An instantiation of the Function is the Context
class Function:
  def __init__(self, device:str, *tensors:Tensor):
    self.device = device
    self.needs_input_grad = [t.requires_grad for t in tensors]
    self.requires_grad = True if any(self.needs_input_grad) else None if None in self.needs_input_grad else False
    if self.requires_grad: self.parents = tensors

  def forward(self, *args, **kwargs): raise NotImplementedError(f"forward not implemented for {type(self)}")
  def backward(self, *args, **kwargs): raise RuntimeError(f"backward not implemented for {type(self)}")

  @classmethod
  def apply(fxn:Type[Function], *x:Tensor, **kwargs) -> Tensor:
    ctx = fxn(x[0].device, *x)
    ret = Tensor(ctx.forward(*[t.lazydata for t in x], **kwargs), device=ctx.device, requires_grad=ctx.requires_grad)
    if ctx.requires_grad and not Tensor.no_grad: ret._ctx = ctx    # used by autograd engine
    return ret

import tinygrad.mlops as mlops

# **** start with two base classes, Tensor and Function ****

class Tensor:
  __slots__ = "lazydata", "requires_grad", "grad", "_ctx"
  __deletable__ = ('_ctx',)
  training: ClassVar[bool] = False
  no_grad: ClassVar[bool] = False
  default_type: ClassVar[DType] = dtypes.float32
  def __init__(self, data:Union[int, float, list, LazyBuffer, np.ndarray], device:Optional[str]=None, dtype:Optional[DType]=None, requires_grad:Optional[bool]=None):
    assert dtype is None or isinstance(dtype, DType), f"invalid dtype {dtype}"
    device = Device.canonicalize(device)
    # tensors have gradients, buffers do not
    self.grad: Optional[Tensor] = None

    # NOTE: this can be in three states. False and None: no gradient, True: gradient
    # None (the default) will be updated to True if it's put in an optimizer
    self.requires_grad: Optional[bool] = requires_grad

    # internal variables used for autograd graph construction
    self._ctx: Optional[Function] = None
    if isinstance(data, LazyBuffer): assert dtype is None or dtype == data.dtype, "dtype doesn't match, and casting isn't supported"
    elif isinstance(data, (int, float)):
      self.lazydata = LazyBuffer.loadop(LoadOps.CONST, tuple(), dtype or Tensor.default_type, device, data)
      return
    elif data.__class__ is list:
      assert dtype is None or dtype.np is not None, f"{dtype} doesn't have a numpy dtype"
      data = LazyBuffer.fromCPU(np.array(data, dtype=(dtype or Tensor.default_type).np))
    elif isinstance(data, np.ndarray):
      data = LazyBuffer.fromCPU(data)
    else: raise RuntimeError(f"can't create Tensor from {data}")

    self.lazydata = data if data.device == device else LazyBuffer.loadop(LoadOps.FROM, data.shape, data.dtype, device, src=data)

  def __repr__(self):
    return f"<Tensor {self.lazydata!r} on {self.device} with grad {(self.grad.lazydata if self.grad else None)!r}>"

  # Python has a non moving GC, so this should be okay
  def __hash__(self): return id(self)

  @property
  def device(self) -> str: return self.lazydata.device

  @property
  def shape(self) -> Tuple[int, ...]: return self.lazydata.shape

  @property
  def dtype(self) -> DType: return self.lazydata.dtype

  # ***** data handlers ****

  def realize(self) -> Tensor:
    self.lazydata.realize()
    return self

  def assign(self, x) -> Tensor:
    # TODO: this is a hack for writing to DISK
    if self.device.startswith("DISK"):
      if x.__class__ is not Tensor: x = Tensor(x, device="CPU", dtype=self.dtype)
      self.lazydata.realize().realized._copyin(x.numpy())  # type: ignore
      return self
    if x.__class__ is not Tensor: x = Tensor(x, device=self.device, dtype=self.dtype)
    assert self.shape == x.shape and self.device == x.device, f"assign shape mismatch {self.shape} != {x.shape} or device mismatch {self.device} != {x.device}"
    assert not x.requires_grad  # self requires_grad is okay?
    if DEBUG >= 4: print(f"assign {self.lazydata} <- {x.lazydata}")
    if self.lazydata.realized is not None and not getenv("DISALLOW_ASSIGN"): x.lazydata.output_buffer = self.lazydata.realized
    self.lazydata = x.lazydata
    return self

  def detach(self): return Tensor(self.lazydata, device=self.device, requires_grad=False)
  def numpy(self) -> np.ndarray: return self.lazydata.toCPU()

  # TODO: if things are realized this won't work
  def to_(self, device:str):
    assert self.lazydata.realized is None
    self.lazydata.device = device
    if self.grad: self.grad.to_(device)

  def to(self, device:str) -> Tensor:
    ret = Tensor(self.lazydata, device)
    if self.grad: ret.grad = self.grad.to(device)
    return ret

  # ***** creation llop entrypoint *****

  @staticmethod
  def _loadop(op, sz, device:Optional[str]=None, dtype:Optional[DType]=None, arg=None, **kwargs):
    return Tensor(LazyBuffer.loadop(op, [sz], Tensor.default_type if dtype is None else dtype, Device.canonicalize(device), arg), dtype=dtype, device=device, **kwargs)

  @staticmethod
  def empty(*shape, **kwargs): return Tensor._loadop(LoadOps.EMPTY, math.prod(shape), **kwargs).reshape(shape)

  _seed: int = int(time.time())
  @staticmethod
  def manual_seed(seed=0): Tensor._seed = seed

  @staticmethod
  def rand(*shape, **kwargs):
    Tensor._seed += 1
    return Tensor._loadop(LoadOps.RAND, math.prod(shape), arg=Tensor._seed, **kwargs).reshape(shape)

  # ***** creation helper functions *****

  @staticmethod
  def full(shape:Tuple[int, ...], fill_value, **kwargs): return Tensor(fill_value, **kwargs).reshape([1]*len(new_shape := argfix(shape))).expand(new_shape)

  @staticmethod
  def zeros(*shape, **kwargs): return Tensor.full(argfix(*shape), 0, **kwargs)

  @staticmethod
  def ones(*shape, **kwargs): return Tensor.full(argfix(*shape), 1, **kwargs)

  @staticmethod
  def arange(start, stop=None, step=1, **kwargs):
    if stop is None: stop, start = start, 0
    return Tensor.full((math.ceil((stop-start)/step),), step, **kwargs).cumsum() + (start - step)

  @staticmethod
  def eye(dim:int, **kwargs): return Tensor.full((dim,1),1,**kwargs).pad(((0,0),(0,dim))).reshape(dim*(dim+1)).shrink(((0,dim*dim),)).reshape(dim, dim)

  def full_like(self, fill_value, **kwargs):
    return Tensor.full(self.shape, fill_value=fill_value, dtype=kwargs.pop("dtype", self.dtype), device=kwargs.pop("device", self.device), **kwargs)
  def zeros_like(self, **kwargs): return self.full_like(0, **kwargs)
  def ones_like(self, **kwargs): return self.full_like(1, **kwargs)

  # ***** rng hlops *****

  @staticmethod
  def randn(*shape, dtype:Optional[DType]=None, **kwargs) -> Tensor:
    # https://en.wikipedia.org/wiki/Box%E2%80%93Muller_transform
    src = Tensor.rand(2, *shape, **kwargs)
    return src[0].mul(2*math.pi).cos().mul((1 - src[1]).log().mul(-2).sqrt()).cast(Tensor.default_type if dtype is None else dtype)

  @staticmethod
  def normal(*shape, mean=0.0, std=1.0, **kwargs) -> Tensor: return (std * Tensor.randn(*shape, **kwargs)) + mean

  @staticmethod
  def uniform(*shape, low=-1.0, high=1.0, **kwargs) -> Tensor:
    dtype = kwargs.pop("dtype", Tensor.default_type)
    return ((high-low) * Tensor.rand(*shape, **kwargs)).cast(dtype) + low

  @staticmethod
  def scaled_uniform(*shape, **kwargs) -> Tensor: return Tensor.uniform(*shape, **kwargs).mul(math.prod(shape)**-0.5)

  # https://www.tensorflow.org/api_docs/python/tf/keras/initializers/GlorotUniform
  @staticmethod
  def glorot_uniform(*shape, **kwargs) -> Tensor: return Tensor.uniform(*shape, **kwargs).mul((6/(shape[0]+math.prod(shape[1:])))**0.5)

  # https://pytorch.org/docs/stable/_modules/torch/nn/init.html#kaiming_uniform_
  @staticmethod
  def kaiming_uniform(*shape, a:float = 0.01, **kwargs) -> Tensor:
    bound = math.sqrt(3.0) * math.sqrt(2.0 / (1 + a ** 2)) / math.sqrt(math.prod(shape[1:]))
    return Tensor.uniform(*shape, low=-bound, high=bound, **kwargs)

  # https://pytorch.org/docs/stable/_modules/torch/nn/init.html#kaiming_normal_
  @staticmethod
  def kaiming_normal(*shape, a:float = 0.01, **kwargs) -> Tensor:
    std = math.sqrt(2.0 / (1 + a ** 2)) / math.sqrt(math.prod(shape[1:]))
    return Tensor.normal(*shape, mean=0.0, std=std, **kwargs)

  # ***** toposort and backward pass *****
  def deepwalk(self):
    def _deepwalk(node, visited, nodes):
      visited.add(node)
      if getattr(node, "_ctx", None):
        for i in node._ctx.parents:
          if i not in visited: _deepwalk(i, visited, nodes)
        nodes.append(node)
      return nodes
    return _deepwalk(self, set(), [])

  def backward(self):
    assert self.shape == tuple(), f"backward can only be called for scalar tensors, but it has shape {self.shape})"

    # fill in the first grad with one. don't use Tensor.ones because we don't need contiguous
    # this is "implicit gradient creation"
    self.grad = Tensor(1, device=self.device, requires_grad=False)

    for t0 in reversed(self.deepwalk()):
      assert (t0.grad is not None)
      grads = t0._ctx.backward(t0.grad.lazydata)
      grads = [Tensor(g, device=self.device, requires_grad=False) if g is not None else None
        for g in ([grads] if len(t0._ctx.parents) == 1 else grads)]
      for t, g in zip(t0._ctx.parents, grads):
        if g is not None and t.requires_grad:
          assert g.shape == t.shape, f"grad shape must match tensor shape, {g.shape!r} != {t.shape!r}"
          t.grad = g if t.grad is None else (t.grad + g)
      del t0._ctx

  # ***** movement mlops *****
  def reshape(self, shape, *args) -> Tensor:
    new_shape = argfix(shape, *args)
    assert 0 not in new_shape, f"zeros not allowed in shape {new_shape}"
    return mlops.Reshape.apply(self, shape=tuple([-math.prod(self.shape) // math.prod(new_shape) if s == -1 else s for s in new_shape]))
  def expand(self, shape, *args) -> Tensor: return mlops.Expand.apply(self, shape=tuple([x if x != -1 else s for s,x in zip(self.shape, argfix(shape, *args))]))
  def permute(self, order, *args) -> Tensor: return mlops.Permute.apply(self, order=argfix(order, *args))
  def flip(self, axis, *args) -> Tensor: return mlops.Flip.apply(self, axis=[x if x >= 0 else x+len(self.shape) for x in argfix(axis, *args)])
  def shrink(self, arg:Tuple[Tuple[int, int], ...]) -> Tensor: return mlops.Shrink.apply(self, arg=arg) if any(x != (0,s) for x,s in zip(arg, self.shape)) else self
  def pad(self, arg: Tuple[Tuple[int, int], ...], value:float=0) -> Tensor:
    ret = mlops.Pad.apply(self, arg=arg) if any(x != (0, 0) for x in arg) else self
    return ret if 0 == value else ret + mlops.Pad.apply(Tensor.ones_like(self), arg=arg).where(0, value)

  # ***** movement hlops *****

  # - Negative indices are taken relative to the end of the sequence, so X[-2] returns the 2nd-to-last element
  # - A slice i:j returns the elements with indices in [i, j)
  #    - If omitted, i and j will default to 0 and N, respectively, where N is the length of the sequence
  #    - Negative values for i and j are taken relative to the end of the sequence
  #    - Both i and j will be clamped to the range (-N, N], where N in the length of the sequence
  # - Indexing with None on a given axis will add a new dimension of size one before that axis
  # - Empty slices are not allowed (tensors with 0s in shape have to be supported first, for all backends).
  # - For a slice [i:j:k] finding the correct indices is delegated to slice.indices(len).
  # - Strides > 1 and < 0 are now allowed!:
  #    - This works by applying Shrink -> [[Flip -> ] Pad -> Reshape -> Shrink] -> Reshape (ops in brackets are optional)
  #    - Idea of stride < 0 support:
  #        - Do the slice first, flip the axes were slice.step is negative, do slice.step -> -slice.step. Go to steps below.
  #    - Idea of stride `s` > 1 support (Pad -> Reshape -> Shrink):
  #        - Instead of doing [::s] on axis [dim_sz], do [:, 0] on axes [dim_sz_padded // s, s].
  #        - So pad dim_sz with as many zeros as needed (dim_sz -> dim_sz_padded) so that reshape to [dim_sz_padded // s, s]
  #          is possible.
  #        - Apply Shrink to do the slice [:, 0] on axes of shapes [dim_sz_padded // s, s].
  # - Fancy indexing and combined indexing is supported
  #    - Combined indexing works by letting regular slicing finish first -> computing the resulting dims w.r.t to Tensors passed in -> fancy indexing
  #    - Any Tensors passed in __getitem__ will perform (CMPEQ with arange -> MUL with self -> SUM_REDUCE) iteratively
  #        - The first iteration will expand the dim of self while consecutive iterations will reduce the dim
  #    - There's a special case where a permute is needed at the end:
  #        - if first Tensor passed in (expand dims) is not at dim 0
  #        - and following Tensors does not follow consecutively to the end of fancy indexing's dims
  def __getitem__(self, val): # val: Union[int, slice, Tensor, None, Ellipsis, Tuple[Union[int, slice, Tensor, None, Ellipsis], ...]]
    def normalize_int(e, i, dim_sz):
      if -dim_sz <= e < dim_sz: return e if e != -1 else dim_sz-1
      raise IndexError(f"index {e} is out of bounds for dimension {i} with size {self.shape[i]}")

    orig_slices = list(val) if isinstance(val, tuple) else [val]
    count = defaultdict(list)
    for i,v in enumerate(orig_slices): count[type(v)].append(i)

    if (num_slices := len(count[int]) + len(count[slice]) + len(count[Tensor])) > len(self.shape): raise IndexError(f"too many indices for tensor of dimension {len(self.shape)}")
    if len(ellipsis_found := count[type(Ellipsis)]) > 1: raise IndexError("an index can only have a single ellipsis ('...')")

    ellipsis_idx = ellipsis_found[0] if ellipsis_found else len(orig_slices)
    orig_slices[ellipsis_idx:ellipsis_idx+1] = [slice(None)] * (len(self.shape) - num_slices)

    valid_slices = [v for v in orig_slices if v is not None]
    valid_slices = [v if isinstance(v, slice) else slice(y_ := normalize_int(v, i, dim_sz), y_+1) if isinstance(v, int) else slice(None) for i, (v, dim_sz) in enumerate(zip(valid_slices, self.shape))]

    start, stop, strides = zip(*y) if (y := [s.indices(dim_sz) for s, dim_sz in zip(valid_slices, self.shape)]) else ((), (), ())
    new_slice = tuple((s, e) if st > 0 else (e+1, s+1) for s, e, st in zip(start, stop, strides))
    sliced_tensor = self.shrink(new_slice).flip(axis=[i for i, s in enumerate(strides) if s < 0])
    new_shape = sliced_tensor.shape
    if any(abs(s) != 1 for s in strides):
      strides = tuple(abs(s) for s in strides)
      # Pad: add pad at the end: [dim_sz] -> [dim_sz_padded]
      padded_tensor = sliced_tensor.pad(tuple((0, s-(dim_sz % s) if dim_sz % s != 0 else 0) for s, dim_sz in zip(strides, sliced_tensor.shape)))
      # Reshape: [dim_sz_padded] -> [dim_sz_padded // s, s]
      reshaped_tensor = padded_tensor.reshape(flatten([sh // s, s] for sh, s in zip(padded_tensor.shape, strides)))
      new_shape = reshaped_tensor.shape[::2]
      # Shrink: do [:, 0]
      sliced_tensor = reshaped_tensor.shrink(tuple(flatten(((0, sh), (0, 1)) for sh in new_shape)))

    final_shape, it_shape, dim, tensors, dim_collapsed = [], iter(new_shape), [], [], 0
    for i,s in enumerate(orig_slices):
      if s is None: final_shape.append(1)
      else: # s is int or slice or Tensor
        dim_shape = next(it_shape)
        if isinstance(s, int):
          dim_collapsed += 1
        else:
          final_shape.append(dim_shape)
          if isinstance(s, Tensor):
            tensors.append(s)
            dim.append(i-dim_collapsed)
    ret = sliced_tensor.reshape(tuple(final_shape))

    if tensors: # Fancy/tensor indexing
      # normalize idx
      # TODO: first contiguous fixes torch+cpu_only CI, but it causes llvm to fail. Second one fixes llvm
      idx = [t.sign().contiguous().__neg__().contiguous().relu() * ret.shape[d] + t for d,t in zip(dim, tensors)]
      max_dim = max(i.ndim for i in idx)
      # compute sum_dim, arange, and idx
      sum_dim = [d if n==0 else d+max_dim-n for n,d in enumerate(dim)]
      arange = [Tensor.arange(ret.shape[d], dtype=dtypes.int32, requires_grad=False, device=self.device).reshape(*[1]*sd, ret.shape[d], *[1]*(ret.ndim + max_dim - n - sd - 1)) for n,(sd,d) in enumerate(zip(sum_dim, dim))]
      first_idx = [idx[0].reshape(*[1]*dim[0], *[1]*(1 + max_dim - idx[0].ndim), *idx[0].shape, *[1]*(ret.ndim - dim[0] - 1))]
      rest_idx = [i.reshape(*[1]*dim[0], *[1]*(max_dim - i.ndim), *i.shape, *[1]*(ret.ndim - dim[0] - n)) for n,i in enumerate(idx[1:], 1)]
      idx = first_idx + rest_idx
      ret = ret.reshape(*ret.shape[:sum_dim[0]+1], *[1]*max_dim, *ret.shape[sum_dim[0]+1:])
      # iteratively fancy index
      for a,i,sd in zip(arange, idx, sum_dim): ret = (a==i).mul(ret).sum(sd)
      # special permute case
      if dim[0] != 0 and len(dim) != 1 and dim != list(range(dim[0], dim[-1]+1)):
        ret_dims = list(range(ret.ndim))
        ret = ret.permute(ret_dims[dim[0]:dim[0]+max_dim] + ret_dims[:dim[0]] + ret_dims[dim[0]+max_dim:])
    return ret

  # NOTE: using slice is discouraged and things should migrate to pad and shrink
  def slice(self, arg:Sequence[Optional[Tuple[int, int]]], value:float=0) -> Tensor:
    arg_ = tuple([a if a is not None else (0,s) for s,a in zip(self.shape, arg)])
    padding = tuple([(max(0, -p[0]), max(0, p[1]-self.shape[i])) for i,p in enumerate(arg_)])
    return self.pad(padding, value=value).shrink(tuple([(p[0] + padding[i][0], p[1] + padding[i][0]) for i,p in enumerate(arg_)]))

  def gather(self: Tensor, idx: Tensor, dim: int):
    assert idx.ndim == self.ndim, "self.ndim must equal idx.ndim"
    assert all(s >= i for s,i in zip(self.shape, idx.shape)), "all dim of idx.shape must be smaller than self.shape"
    if dim < 0: dim += self.ndim
    idx = idx.transpose(ax1=dim, ax2=0).unsqueeze(-1)
    permarg = list(range(self.ndim))
    permarg = permarg[1:dim] + [permarg[0]] + permarg[dim+1:] + [permarg[dim]] if dim != 0 else permarg[1:] + [permarg[0]]
    return ((idx == Tensor.arange(self.shape[dim], dtype=dtypes.int32, requires_grad=False, device=self.device)) * self.permute(*permarg).shrink(tuple([*[(0,sh) for sh in idx.shape[1:-1]], (0,self.shape[dim])])).unsqueeze(0)).sum(-1).transpose(ax1=0, ax2=dim)

  def cat(self, *args, dim=0):
    dim = (dim + len(self.shape)) if dim < 0 else dim
    assert all(len(y.shape) == len(self.shape) and all(y.shape[i] == s for i,s in enumerate(self.shape) if i != dim) for y in args)
    catargs = [self, *args]
    assert all(t.shape for t in catargs), "zero-dimensional tensor cannot be concatenated"
    shapes = [s.shape[dim] for s in catargs]
    shape_cumsum = [0, *accumulate(shapes)]
    slc = [[(0, 0) for _ in self.shape] for _ in catargs]
    for shp,k,s in zip(shapes, shape_cumsum[:-1], slc):
      s[dim] = (k, shape_cumsum[-1] - k - shp)
    return reduce(Tensor.__add__, [arg.pad(tuple(s)) for arg,s in zip(catargs, slc)])

  @staticmethod
  def stack(tensors, dim=0):
    first = tensors[0].unsqueeze(dim)
    unsqueezed_tensors = [tensor.unsqueeze(dim) for tensor in tensors[1:]]
    # checks for shapes and number of dimensions delegated to cat
    return first.cat(*unsqueezed_tensors, dim=dim)

  def repeat(self, repeats):
    base_shape = (1,) * (len(repeats) - self.ndim) + self.shape
    new_shape = [x for b in base_shape for x in [1, b]]
    expand_shape = [x for rs in zip(repeats, base_shape) for x in rs]
    final_shape = [r*s for r,s in zip(repeats, base_shape)]
    return self.reshape(new_shape).expand(expand_shape).reshape(final_shape)

  def chunk(self, num:int, dim:int) -> List[Tensor]:
    dim, step = dim + self.ndim if dim < 0 else dim, math.ceil(self.shape[dim]/num)
    slice_params = [[slice(None)]*dim + [slice(k, k + step)] for k in range(0, self.shape[dim], step)]
    return [self[tuple(sl)] for sl in slice_params]

  def squeeze(self, dim=None):
    if dim is None: return self if 1 not in self.shape else self.reshape(*[size for size in self.shape if size != 1])
    if dim <= 0 and self.ndim == 0: return self # This is to match PyTorch behavior
    if not -self.ndim <= dim < self.ndim: raise IndexError(f"Dimension out of range (expected to be in range of [{-self.ndim if self.ndim > 0 else self.ndim-1}, {self.ndim-1 if self.ndim > 0 else self.ndim}], but got {dim})")
    if dim < 0: dim += self.ndim
    return self if self.shape[dim] != 1 else self.reshape(*[size for idx, size in enumerate(self.shape) if idx != dim])

  def unsqueeze(self, dim):
    if dim < 0: dim = len(self.shape) + dim + 1
    return self.reshape(self.shape[:dim] + (1,) + self.shape[dim:])

  # (padding_left, padding_right, padding_top, padding_bottom)
  def pad2d(self, padding:Union[List[int], Tuple[int, ...]], value:float=0):
    slc = [(-p0, s+p1) for p0,p1,s in zip(padding[::2], padding[1::2], self.shape[::-1])][::-1]
    return self.slice([(0,s) for s in self.shape[:-(len(padding)//2)]] + slc, value=value)

  @property
  def T(self) -> Tensor: return self.transpose()
  def transpose(self, ax1=1, ax2=0) -> Tensor:
    order = list(range(len(self.shape)))
    order[ax1], order[ax2] = order[ax2], order[ax1]
    return self.permute(order)
  def flatten(self, start_dim=0): return self.reshape(shape=self.shape[:start_dim] + (-1,))

  # ***** reduce ops *****

  def _reduce(self, fxn:Type[Function], axis:Optional[Union[int, Tuple[int, ...]]]=None, keepdim=False):
    axis_: List[int] = list(range(len(self.shape))) if axis is None else ([axis] if axis.__class__ is int else list(axis)) # type: ignore
    axis_ = [x if x >= 0 else x+len(self.shape) for x in axis_]
    shape = [s for i,s in enumerate(self.shape) if i not in axis_]
    ret = fxn.apply(self, new_shape=tuple([1 if i in axis_ else s for i,s in enumerate(self.shape)]))
    return ret if keepdim else ret.reshape(shape=shape)

  def sum(self, axis=None, keepdim=False): return self._reduce(mlops.Sum, axis, keepdim)
  def max(self, axis=None, keepdim=False): return self._reduce(mlops.Max, axis, keepdim)
  def min(self, axis=None, keepdim=False): return -((-self).max(axis=axis, keepdim=keepdim))

  def mean(self, axis=None, keepdim=False):
    out = self.sum(axis=axis, keepdim=keepdim)
    return out * (math.prod(out.shape)/math.prod(self.shape))
  def std(self, axis=None, keepdim=False, correction=1):
    square_sum = ((self - self.mean(axis=axis, keepdim=True)).square()).sum(axis=axis, keepdim=keepdim)
    return (square_sum / (math.prod(self.shape)/math.prod(square_sum.shape)-correction)).sqrt()
  def _softmax(self, axis):
    m = self - self.max(axis=axis, keepdim=True)
    e = m.exp()
    return m, e, e.sum(axis=axis, keepdim=True)

  def softmax(self, axis=-1):
    _, e, ss = self._softmax(axis)
    return e.div(ss)

  def log_softmax(self, axis=-1):
    m, _, ss = self._softmax(axis)
    return m - ss.log()

  def argmax(self, axis=None, keepdim=False):
    if axis is None:
      idx = (self == self.max(axis)) * Tensor.arange(math.prod(self.shape)-1,-1,-1, dtype=dtypes.int32, requires_grad=False, device=self.device).reshape(self.shape)
      return math.prod(self.shape) - idx.max() - 1
    axis = axis + len(self.shape) if axis < 0 else axis
    m = self == self.max(axis=axis, keepdim=True)
    idx = m * Tensor.arange(self.shape[axis]-1,-1,-1, dtype=dtypes.int32, requires_grad=False, device=self.device).reshape(self.shape[axis], *[1]*(self.ndim-axis-1))
    return self.shape[axis]-idx.max(axis=axis, keepdim=keepdim)-1
  def argmin(self, axis=None, keepdim=False): return (-self).argmax(axis=axis, keepdim=keepdim)

  # ***** processing ops *****

  def _pool(self, k_:Tuple[int, ...], stride:Union[Tuple[int, ...], int]=1, dilation:Union[Tuple[int, ...], int]=1) -> Tensor:
    assert len(self.shape) >= len(k_), f"can't pool {self.shape} with {k_}"
    s_, d_ = make_pair(stride, len(k_)), make_pair(dilation, len(k_))
    assert len(k_) == len(s_) and len(k_) == len(d_), f"stride/dilation mismatch kernel:{k_} stride:{s_} dilation:{d_}"
    slc_prefix, prefix, i_ = [(0,x) for x in self.shape[0:-len(k_)]], self.shape[0:-len(k_)], self.shape[-len(k_):]
    if any(k > s for k,s in zip(k_, s_)) or any(d != 1 for d in d_):
      o_ = [(i - d * (k-1) - 1)//s + 1 for i,d,k,s in zip(i_, d_, k_, s_)]
      e_ = [math.ceil(k*(i+d) / i) for k,i,d in zip(k_, i_, d_)]  # expands such that we don't need padding
      xup = self.reshape(*prefix, *flatten((1,i) for i in i_)).expand(*prefix, *flatten((e,i) for e,i in zip(e_, i_))).reshape(*prefix, *[e*i for e,i in zip(e_, i_)])
      # slide by dilation
      xup = xup.slice(slc_prefix + [(0,k*(i+d)) for k,i,d in zip(k_, i_, d_)])
      xup = xup.reshape(*prefix, *flatten((k,i+d) for k,i,d in zip(k_, i_, d_)))
      xup = xup.slice(slc_prefix + flatten(((0,k), (0,o*s)) for k,o,s in zip(k_, o_, s_)))
      # handle stride, and permute to move reduce to the end
      xup = xup.reshape(*prefix, *flatten((k,o,s) for k,o,s in zip(k_, o_, s_)))
      xup = xup.slice(slc_prefix + flatten(((0,k), (0,o), (0,1)) for k,o in zip(k_, o_)))
      xup = xup.reshape(*prefix, *flatten((k,o) for k,o in zip(k_, o_)))
      return xup.permute(*range(len(prefix)), *[len(prefix)+i*2+1 for i in range(len(k_))], *[len(prefix)+i*2 for i in range(len(k_))])
    # TODO: once the shapetracker can optimize well, remove this alternative implementation. or not if the CPU implementation doesn't use ShapeTracker
    o_ = [(i+(s-k))//s for i,s,k in zip(i_, s_, k_)]
    xup = self.slice(slc_prefix + [(0,o*s) for o,s in zip(o_, s_)])
    xup = xup.reshape(*prefix, *flatten(((o, s) for o,s in zip(o_, s_))))
    xup = xup.slice(slc_prefix + flatten(((0,o), (0,k)) for o,k in zip(o_, k_)))
    return xup.permute(*range(len(prefix)), *[len(prefix)+i*2 for i in range(len(k_))], *[len(prefix)+i*2+1 for i in range(len(k_))])

  # NOTE: these work for more than 2D
  def avg_pool2d(self, kernel_size=(2,2), stride=None): return self._pool(make_pair(kernel_size), stride if stride is not None else kernel_size).mean(axis=tuple(range(0-len(make_pair(kernel_size)), 0)))
  def max_pool2d(self, kernel_size=(2,2), stride=None, dilation=1): return self._pool(make_pair(kernel_size), stride if stride is not None else kernel_size, dilation).max(axis=tuple(range(0-len(make_pair(kernel_size)), 0)))

  def conv_transpose2d(self, weight:Tensor, bias:Optional[Tensor]=None, groups=1, stride=1, dilation=1, padding=0, output_padding=0) -> Tensor:
    HW, trailing = weight.shape[2:], list(range(3, len(weight.shape)+1))
    x, w = self, weight.reshape(groups, weight.shape[0]//groups, weight.shape[1], *weight.shape[2:]).permute(0,2,1,*trailing).flip(trailing)
    stride = make_pair(stride, len(HW))
    if any(s>1 for s in stride):
      x = x.reshape(*x.shape[:2], *flatten((k,1) for k in x.shape[2:]))
      x = x.pad(((0,0), (0,0), *flatten(((0,0),(0,s-1)) for s in stride)))
      x = x.reshape(*x.shape[:2], *[k*s for k,s in zip(x.shape[2::2], stride)])
      x = x.shrink(((0,x.shape[0]), (0,x.shape[1]), *[(0,k-(s-1)) for k,s in zip(x.shape[2:], stride)]))
    padding = flatten((((k-1)*d-p,(k-1)*d-p+op) for k,d,p,op in reversed(list(zip(HW, make_pair(dilation, len(HW)), make_pair(padding, len(HW)), make_pair(output_padding, len(HW)))))))
    return x.conv2d(w.reshape(w.shape[0]*w.shape[1],*w.shape[2:]), groups=groups, bias=bias, dilation=dilation, padding=padding)

  wino = int(getenv("WINO", "0"))
  def conv2d(self, weight:Tensor, bias:Optional[Tensor]=None, groups=1, stride=1, dilation=1, padding=0) -> Tensor:
    (bs,cin_), (cout,cin), HW = self.shape[:2], weight.shape[:2], weight.shape[2:]
    assert groups*cin == cin_ and len(self.shape) == len(weight.shape), f"Input Tensor shape {self.shape} does not match the shape of the weights {weight.shape}. ({groups*cin} vs. {cin_})"
    if isinstance(padding, (tuple,list)): assert len(padding) == 2*len(HW) or len(padding) == len(HW), f"Expected padding of length {2*len(HW)} or {len(HW)}, but got {len(padding)} for tensor of shape {self.shape}"
    padding_ = [padding]*2*len(HW) if isinstance(padding, int) else (padding if len(padding) == 2*len(HW) else [p for p in padding for _ in range(2)][::-1])

    # conv2d is a pooling op (with padding)
    x = self.pad2d(padding_)._pool(HW, stride, dilation)   # (bs, groups*cin, oy, ox, H, W)
    rcout, oyx = cout//groups, x.shape[2:-len(HW)]
    if not all(x == 3 for x in HW) or stride != 1 or dilation != 1 or not Tensor.wino:
      # normal conv
      x = x.reshape(bs, groups, cin, 1, *oyx, *HW).expand(bs, groups, cin, rcout, *oyx, *HW).permute(0,1,3,*[4+i for i in range(len(oyx))],2,*[4+len(oyx)+i for i in range(len(HW))])

      # conv! broadcasted to (bs, groups, rcout, *oyx, cin, *HW)
      ret = (x * weight.reshape(1, groups, rcout, *[1] * len(oyx), cin, *HW)).sum([-1-i for i in range(1+len(oyx))], keepdim=True).reshape(bs, cout, *oyx)
      return ret if bias is None else ret.add(bias.reshape(1, -1, *[1] * len(HW)))

    # winograd conv 3 kernel f(4x4,3x3) see: http://arxiv.org/abs/1509.09308
    def apply_matrix(mat, t, dim=0): return t if dim == len(HW) else Tensor.stack([apply_matrix(mat, sum(mat[i][j] * t[j] for j in range(len(mat[i])) if mat[i][j]), dim=dim+1) for i in range(len(mat))])
    HWI, HWO = (6,) * len(HW), (4,) * len(HW)  # F(4x4,3x3) winograd tiles
    winograd_Bt = [[4, 0, -5, 0, 1, 0], [0, -4, -4, 1, 1, 0], [0, 4, -4, -1, 1, 0], [0, -2, -1, 2, 1, 0], [0, 2, -1, -2, 1, 0], [0, 4, 0, -5, 0, 1]]
    winograd_G = [[1/4, 0, 0], [-1/6, -1/6, -1/6], [-1/6, 1/6, -1/6], [1/24, 1/12, 1/6], [1/24, -1/12, 1/6], [0, 0, 1]]
    winograd_At = [[1, 1, 1, 1, 1, 0], [0, 1, -1, 2, -2, 0], [0, 1, 1, 4, 4, 0], [0, 1, -1, 8, -8, 1]]  # applying At in pre-order almost doubles compilation time

    # todo: stride == dilation
    # use padding to round up to 4x4 output tiles
    d = self.pad2d(sum([[padding_[i*2], padding_[i*2+1] + (-(dim + sum(padding_[i * 2:(i + 1) * 2]) - 2) % 4)] for i, dim in enumerate(self.shape[-len(HW):])], []))._pool(HWI, HWO)  # (bs, cin_, tyx, HWI)
    d = d.permute(*range(len(d.shape)-len(HW),len(d.shape)), *range(len(d.shape)-len(HW))).contiguous_backward()  # move HW to the front: # (HWI, bs, cin_, tyx)
    tyx = d.shape[-len(HWI):]  # dim of tiling

    g = weight.permute(*range(len(weight.shape)-len(HW),len(weight.shape)), *range(len(weight.shape)-len(HW)))  # move HW to the front

    # compute 6x6 winograd tiles: GgGt, BtdB
    gfactors = apply_matrix(winograd_G, g).contiguous().reshape(*HWI, 1, groups, rcout, cin, *([1]*len(tyx)))  # (HWI, groups * rcout, cin) -> (HWI, bs=1, groups, rcout, cin, tyx=(1,1))
    dfactors = apply_matrix(winograd_Bt, d).contiguous().reshape(*HWI, bs, groups, 1, cin, *tyx)  # (HWI, bs, cin_, tyx) -> (HWI, bs, groups, 1 ,cin, *tyx)

    ret = apply_matrix(winograd_At, (gfactors * dfactors).sum(axis=-1-len(HW)))  # matmul; sum across cin: (HWI, bs, groups, rcout, *tyx); then HWI -> HWO: (HWO, bs, groups, rcout, *tyx)

    ret = ret.permute([*range(len(HW), len(ret.shape)-len(HW)), *[i+o for i in range(len(HW)) for o in [len(ret.shape)-len(HW),0]]])  # interleave tyx and HWO: (bs, groups, rcout, oy, HO, ox, WO)
    ret = ret.reshape(bs, cout, *[c * HWO[i] for i, c in enumerate(tyx)]).shrink(tuple((0, s) for s in [bs, cout, *oyx]))  # merge groups and rcout, tyx and HWO: (bs, groups, cout, *yx), shrink to final

    return (ret if bias is None else ret.add(bias.reshape(1, -1, *[1 for _ in range(len(HW))]))).contiguous().contiguous_backward()

  def dot(self, w:Tensor) -> Tensor:
    n1, n2 = len(self.shape), len(w.shape)
    assert n1 != 0 and n2 != 0, f"both arguments to matmul need to be at least 1D, but they are {n1}D and {n2}D"
    assert self.shape[-1] == w.shape[-min(n2, 2)], f"Input Tensor shapes {self.shape} and {w.shape} cannot be multiplied ({self.shape[-1]} != {w.shape[-min(n2, 2)]})"
    x = self.reshape(*self.shape[0:-1], *[1]*min(n1-1, n2-1, 1), self.shape[-1])
    w = w.reshape(*w.shape[0:-2], *[1]*min(n1-1, n2-1, 1), *w.shape[-min(n2, 2):]).transpose(-1, -min(n2, 2))
    return (x*w).sum(-1)

  def cumsum(self, axis:int=0) -> Tensor: return self.transpose(axis,-1).pad2d((self.shape[axis]-1,0))._pool((self.shape[axis],)).sum(-1).transpose(axis,-1)

  # ***** mlops (unary) *****

  def __neg__(self): return mlops.Neg.apply(self)
  def contiguous(self): return mlops.Contiguous.apply(self)
  def contiguous_backward(self): return mlops.ContiguousBackward.apply(self)
  def log(self): return mlops.Log.apply(self)
  def log2(self): return mlops.Log.apply(self)/math.log(2)
  def exp(self): return mlops.Exp.apply(self)
  def relu(self): return mlops.Relu.apply(self)
  def sigmoid(self): return mlops.Sigmoid.apply(self)
  def sin(self): return mlops.Sin.apply(self)
  def sqrt(self): return mlops.Sqrt.apply(self)
  def rsqrt(self): return (1/self).sqrt()
  def cos(self): return ((math.pi/2)-self).sin()
  def tan(self): return self.sin() / self.cos()

  @staticmethod
  def _tri(r:int, c:int, k:int=0, **kwargs) -> Tensor: return Tensor.arange(r, **kwargs).unsqueeze(1).expand(r,c) <= Tensor.arange(-k, c-k, **kwargs).unsqueeze(0).expand(r,c)
  def triu(self, k:int=0) -> Tensor: return Tensor._tri(self.shape[-2], self.shape[-1], k=k, dtype=self.dtype, device=self.device).where(self, Tensor.zeros_like(self))
  def tril(self, k:int=0) -> Tensor: return Tensor._tri(self.shape[-2], self.shape[-1], k=k+1, dtype=self.dtype, device=self.device).where(Tensor.zeros_like(self), self)

  # ***** math functions (unary) *****
  def trunc(self: Tensor) -> Tensor: return self.cast(dtypes.int32).contiguous().cast(self.dtype)
  def ceil(self: Tensor) -> Tensor: return (self > (b := self.trunc())).where(b+1, b)
  def floor(self: Tensor) -> Tensor: return (self < (b := self.trunc())).where(b-1, b)

  def square(self): return self*self
  def clip(self, min_, max_): return self.maximum(min_).minimum(max_)
  def abs(self): return self.relu() + (-self).relu()
  def sign(self): return self / (self.abs() + 1e-10)
  def reciprocal(self): return 1.0/self

  # ***** activation functions (unary) *****
  def elu(self, alpha=1.0): return self.relu() - alpha*(1-self.exp()).relu()
  def celu(self, alpha=1.0): return self.maximum(0) + (alpha * ((self / alpha).exp() - 1)).minimum(0)
  def swish(self): return self * self.sigmoid()
  def silu(self): return self.swish()   # The SiLU function is also known as the swish function.
  def relu6(self): return self.relu() - (self-6).relu()
  def hardswish(self): return self * (self+3).relu6() * (1/6)
  def tanh(self): return 2.0 * ((2.0 * self).sigmoid()) - 1.0
  def hardtanh(self, min_val=-1, max_val=1): return self.clip(min_val, max_val)
  def gelu(self): return 0.5 * self * (1 + (self * 0.7978845608 * (1 + 0.044715 * self * self)).tanh())
  def quick_gelu(self): return self * (self * 1.702).sigmoid()
  def leakyrelu(self, neg_slope=0.01): return self.relu() - (-neg_slope*self).relu()
  def mish(self): return self * self.softplus().tanh()
  def softplus(self, beta=1): return (1/beta) * (1 + (self*beta).exp()).log()
  def softsign(self): return self / (1 + self.abs())

  # ***** broadcasted binary mlops *****

  def _broadcasted(self, y:Union[Tensor, float], reverse:bool=False) -> Tuple[Tensor, Tensor]:
    x: Tensor = self
    if not isinstance(y, Tensor):
      y = Tensor(y, device=self.device, requires_grad=False, dtype=self.dtype if self.dtype != dtypes.bool and self.dtype.__class__ is not ImageDType else dtypes.float32)
    if reverse: x, y = y, x
    if (xshape:=x.shape) == (yshape:=y.shape): return (x, y)

    shape_delta = len(xshape) - len(yshape)
    if shape_delta > 0: y = y.reshape((1,) * shape_delta + yshape)
    elif shape_delta < 0: x = x.reshape((1,) * -shape_delta + xshape)
    if (xshape:=x.shape) == (yshape:=y.shape): return (x, y)

    shape_ret = tuple([max(x, y) for x, y in zip(xshape, yshape)])
    if xshape != shape_ret: x = x.expand(shape_ret)
    if yshape != shape_ret: y = y.expand(shape_ret)
    return (x, y)

  def add(self, x:Union[Tensor, float], reverse=False) -> Tensor: return mlops.Add.apply(*self._broadcasted(x, reverse)) if x.__class__ is Tensor or x else self
  def sub(self, x:Union[Tensor, float], reverse=False) -> Tensor: return mlops.Sub.apply(*self._broadcasted(x, reverse)) if x.__class__ is Tensor or x else (-self if reverse else self)
  def mul(self, x:Union[Tensor, float], reverse=False) -> Tensor:
    if x.__class__ is not Tensor and x == 0.0: return mlops.Zero.apply(self)
    if x.__class__ is not Tensor and x == -1.0: return -self
    return mlops.Mul.apply(*self._broadcasted(x, reverse)) if x.__class__ is Tensor or x != 1.0 else self
  def div(self, x:Union[Tensor, float], reverse=False) -> Tensor: return mlops.Div.apply(*self._broadcasted(x, reverse)) if x.__class__ is Tensor or reverse or not x or not dtypes.is_float(self.dtype) else self.mul(1/x)
  def pow(self, x:Union[Tensor, float], reverse=False) -> Tensor:
    if x.__class__ is not Tensor and not reverse:
      # simple pow identities
      if x < 0: return (1.0/self).pow(-x)
      if x == 3.0: return self*self*self
      if x == 2.0: return self*self
      if x == 1.0: return self
      if x == 0.5: return self.sqrt()
    if not isinstance(x, Tensor) and reverse and x > 0: return self.mul(math.log(x)).exp()
    ar = self.abs().log().mul(x).exp() if not reverse or isinstance(x, Tensor) else self.mul(math.log(abs(x))).exp()
    # correct sign of negative numbers raised to a power (cos has a period of 2pi so we use it here to get the oddness of the power)
    sign = (x * math.pi).cos() if isinstance(x, Tensor) else math.cos(x * math.pi) if not reverse else (self * math.pi).cos()
    # we only need to correct the sign if the base is negative
    base_sign = ((self.sign() if not reverse else x.sign() if isinstance(x, Tensor) else math.copysign(1, x)) - 1) / -2
    # we need 0 to be positive so we need to correct base_sign when the base is 0
    base_sign = base_sign - (1.5 * (1 - (self.sign().abs() if not reverse else x.sign().abs() if isinstance(x, Tensor) else abs(int(bool(x))))))
    # inject nan if the base is negative and the power is not an integer
    to_nan = (((x - x.trunc()) * 1e10).abs().clip(0, 1) if isinstance(x, Tensor) else int(bool(x - int(x))) if not reverse else ((self - self.trunc()) * 1e10).abs().clip(0, 1)) * base_sign
    inject_nan = ((((-to_nan) * 2) + 1)).log().add(1) if isinstance(to_nan, Tensor) else 1 if not to_nan else float("nan")
    return ar.mul(sign * base_sign + (1 - base_sign)).mul(inject_nan)
  def matmul(self, x:Tensor, reverse=False) -> Tensor: return x.dot(self) if reverse else self.dot(x)

  def maximum(self, x:Union[Tensor, float]) -> Tensor: return (self<x).detach().where(x, (self>x).detach().where(self, (self+x)/2))
  def minimum(self, x:Union[Tensor, float]) -> Tensor: return -((-self).maximum(-x))

  def where(self:Tensor, input_:Union[Tensor, float], other:Union[Tensor, float]):
    x_,y = self._broadcasted(input_)
    x,z = x_._broadcasted(other)
    return mlops.Where.apply(x, *y._broadcasted(z))

  # ***** binary op wrappers (18 wasted lines to make the typechecker happy) *****

  # NOTE: __pow__ and friends are broken in mypyc with the ** operator
  def __add__(self, x) -> Tensor: return self.add(x)
  def __sub__(self, x) -> Tensor: return self.sub(x)
  def __mul__(self, x) -> Tensor: return self.mul(x)
  def __pow__(self, x) -> Tensor: return self.pow(x)
  def __truediv__(self, x) -> Tensor: return self.div(x)
  def __matmul__(self, x) -> Tensor: return self.matmul(x)

  def __radd__(self, x) -> Tensor: return self.add(x, True)
  def __rsub__(self, x) -> Tensor: return self.sub(x, True)
  def __rmul__(self, x) -> Tensor: return self.mul(x, True)
  def __rpow__(self, x) -> Tensor: return self.pow(x, True)
  def __rtruediv__(self, x) -> Tensor: return self.div(x, True)
  def __rmatmul__(self, x) -> Tensor: return self.matmul(x, True)

  def __iadd__(self, x) -> Tensor: return self.assign(self.add(x))
  def __isub__(self, x) -> Tensor: return self.assign(self.sub(x))
  def __imul__(self, x) -> Tensor: return self.assign(self.mul(x))
  def __ipow__(self, x) -> Tensor: return self.assign(self.pow(x))
  def __itruediv__(self, x) -> Tensor: return self.assign(self.div(x))
  def __imatmul__(self, x) -> Tensor: return self.assign(self.matmul(x))

  def __lt__(self, x) -> Tensor: return mlops.Less.apply(*self._broadcasted(x, False))
  def __gt__(self, x) -> Tensor: return mlops.Less.apply(*self._broadcasted(x, True))
  def __ge__(self, x) -> Tensor: return 1.0-(self<x)
  def __le__(self, x) -> Tensor: return 1.0-(self>x)
  def __ne__(self, x) -> Tensor: return (self<x) + (self>x)   # type: ignore
  def __eq__(self, x) -> Tensor: return 1.0-(self != x)       # type: ignore

  # ***** functional nn ops *****

  def linear(self, weight:Tensor, bias:Optional[Tensor]=None):
    x = self.mul(weight) if len(weight.shape) == 1 else self.dot(weight)
    return x.add(bias) if bias is not None else x

  def sequential(self, ll:List[Callable[[Tensor], Tensor]]): return reduce(lambda x,f: f(x), ll, self)

  def layernorm(self, axis=-1, eps:float=1e-5) -> Tensor:
    y = (self - self.mean(axis, keepdim=True))
    return y.mul((y*y).mean(axis, keepdim=True).add(eps).rsqrt())

  def batchnorm(self, weight:Optional[Tensor], bias:Optional[Tensor], mean:Tensor, invstd:Tensor) -> Tensor:
    x = (self - mean.reshape(shape=[1, -1, 1, 1]))
    if weight: x = x * weight.reshape(shape=[1, -1, 1, 1])
    ret = x.mul(invstd.reshape(shape=[1, -1, 1, 1]) if len(invstd.shape) == 1 else invstd)
    return (ret + bias.reshape(shape=[1, -1, 1, 1])) if bias else ret

  def dropout(self, p=0.5) -> Tensor:
    if not Tensor.training or p == 0: return self
    mask = (Tensor.rand(*self.shape, requires_grad=False, device=self.device) >= p).cast(dtypes.bool)
    return self * mask * (1/(1.0 - p))

  def scaled_dot_product_attention(self, key:Tensor, value:Tensor, attn_mask:Optional[Tensor]=None, dropout_p:float=0.0, is_causal:bool=False) -> Tensor:
    if is_causal: attn_mask = Tensor.ones(self.shape[-2], key.shape[-2], requires_grad=False, device=self.device).tril(0).cast(dtypes.bool)
    if attn_mask is not None and attn_mask.dtype == dtypes.bool: attn_mask = (attn_mask == 0).where(-float("inf"), attn_mask)
    return (self @ key.transpose(-2,-1) / math.sqrt(self.shape[-1]) + attn_mask).softmax(-1).dropout(dropout_p) @ value

  def sparse_categorical_crossentropy(self, Y, ignore_index=-1) -> Tensor:
    loss_mask = Y != ignore_index
    y_counter = Tensor.arange(self.shape[-1], dtype=dtypes.int32, requires_grad=False, device=self.device).unsqueeze(0).expand(Y.numel(), self.shape[-1])
    y = ((y_counter == Y.flatten().reshape(-1, 1)).where(-1.0, 0) * loss_mask.reshape(-1, 1)).reshape(*Y.shape, self.shape[-1])
    return self.log_softmax().mul(y).sum() / loss_mask.sum()

  # ***** cast ops *****

  def cast(self, dtype:DType) -> Tensor: return mlops.Cast.apply(self, dtype=dtype) if self.dtype != dtype else self
  def bitcast(self, dtype:DType) -> Tensor:
    assert self.dtype.itemsize == dtype.itemsize, "can't bitcast mismatched dtype itemsizes"
    return mlops.Cast.apply(self, dtype=dtype, bitcast=True) if self.dtype != dtype else self
  def float(self) -> Tensor: return self.cast(dtypes.float32)
  def half(self) -> Tensor: return self.cast(dtypes.float16)

  # ***** convenience stuff *****

  @property
  def ndim(self) -> int: return len(self.shape)
  def numel(self) -> int: return math.prod(self.shape)
  def element_size(self) -> int: return self.dtype.itemsize
  def nbytes(self) -> int: return self.numel() * self.element_size()
  def is_floating_point(self) -> bool: return dtypes.is_float(self.dtype)

# register functions to move between devices
for device in Device._buffers:
  setattr(Tensor, f"{device.lower()}", partialmethod(Tensor.to, device))
  setattr(Tensor, f"{device.lower()}_", partialmethod(Tensor.to_, device))

if IMAGE:
  # if IMAGE>0 we install these replacement functions in Tensor (hack!)
  from tinygrad.nn.image import image_conv2d, image_dot
  setattr(Tensor, "conv2d", image_conv2d)
  setattr(Tensor, "dot", image_dot)