Zero dim Tensor support (#777)

* add and reorganize test_slice_* tests

* refactor Tensor.__getitem__()

* preliminary tests for 1) 0D tensors and 2) varargs for Tensor.zeros and Tensor.ones

* always compare shapes of the numpy arrays obtained from tinygrad and torch tensors

* add more tests for 0D support

* remove test_tensor.test_slicing(). All slicing tests at test/test_ops.py

* add zero-dim support

* make test_end2end.py consistent with 0dim support

* add test for tensor with zero in shape

* don't simplify ones if shape is ()

* skip tests that need zero-size tensor support.

- zero-size tensor support not related to 0dim tensors.

* add tests for __getitem__() supporting strides >= 1

* refactor __getitem__: support for strides >= 1

* minor refactors and add comments to __getitem__

* add tests for slices with negative steps

* add support for slices with negative strides

tinygrad/nn/image.py

@@ -7,6 +7,7 @@ base_image_type = (100, 2, "imageh", np.float16) if FLOAT16 else (100, 4, "image
 
 def image_dot(self, w):
   # NOTE: we use a 1x1 conv2d to do the matmul. mxk @ kxn = (1,k,m,1).conv2d(n,k,1,1)
+  if (n1:=len(self.shape))*(n2:=len(w.shape)) == 0: raise RuntimeError(f"both arguments to matmul need to be at least 1D, but they are {n1}D and {n2}D")
   bs, groups = prod(self.shape[0:-2]), prod(w.shape[0:-2])
   cin, cout = w.shape[-2], w.shape[-1]
   out_shape_t = self.shape[0:-2] + (cout,-1)