Rewrote Tensor.cat to be shorter and (hopefully) clearer (#372)

* Rewrote Tensor.cat to be shorter and (hopefully) clearer

* Use cumsum[-1] instead of separate sum

tinygrad/tensor.py

@@ -1,6 +1,6 @@
 # inspired by https://github.com/karpathy/micrograd/blob/master/micrograd/engine.py
 from __future__ import annotations
-import inspect, functools, importlib
+import inspect, functools, importlib, itertools
 import numpy as np
 from tinygrad.helpers import prod
 from typing import List, Tuple, Callable, Optional
@@ -139,28 +139,17 @@ class Tensor:
       assert s.step is None or s.step == 1
     return self.slice(arg = arg + [(0,self.shape[i]) for i in range(len(arg), len(self.shape))])
 
-  # TODO: there has to be a cleaner way to write this
   def cat(self, *args, dim=0):
     dim = (dim + len(self.shape)) if dim < 0 else dim
-    for y in args: assert len(self.shape) == len(y.shape)
+    for y in args:
+      assert len(y.shape) == len(self.shape)
+      assert all(y.shape[i] == s for i,s in enumerate(self.shape) if i != dim)
     args = [self] + list(args)
-    s = [[] for _ in range(len(args))]
-    for i in range(len(self.shape)):
-      if i != dim:
-        for y in args: assert self.shape[i] == y.shape[i]
-        for j in range(len(args)):
-          s[j].append((0, self.shape[i]))
-      else:
-        shape_sum = 0
-        for y in args: shape_sum += y.shape[i]
-        k = 0
-        for j,y in enumerate(args):
-          s[j].append((-k, shape_sum-k))
-          k += y.shape[i]
-    ret = self.slice(arg=s[0])
-    for ts,y in zip(s[1:], args[1:]):
-      ret += y.slice(arg=ts)
-    return ret
+    shape_cumsum = [0, *itertools.accumulate(y.shape[dim] for y in args)]
+    slc = [[(0, s) for s in self.shape] for _ in args]
+    for s,k in zip(slc, shape_cumsum): s[dim] = (-k, shape_cumsum[-1]-k)
+    slices = [arg.slice(arg=s) for arg,s in zip(args, slc)]
+    return functools.reduce(Tensor.__iadd__, slices)
 
   def matmul(self:Tensor, w:Tensor):
     # NOTE: we use a 1x1 conv2d to do the matmul. mxk @ kxn = (1,k,m,1).conv2d(n,k,1,1)