dropout contiguous after >= p (#6892)

make it a bool buffer

tinygrad/tensor.py

@@ -454,7 +454,7 @@ class Tensor:
     return counts0.cat(counts1)
 
   @staticmethod
-  def rand(*shape, device:Optional[str]=None, dtype:Optional[DTypeLike]=None, **kwargs) -> Tensor:
+  def rand(*shape, device:Optional[str]=None, dtype:Optional[DTypeLike]=None, contiguous:bool=True, **kwargs) -> Tensor:
     """
     Creates a tensor with the given shape, filled with random values from a uniform distribution over the interval `[0, 1)`.
 
@@ -509,7 +509,7 @@ class Tensor:
     if getenv("MOCKGPU") and _device: out = out.to(_device)
 
     out.requires_grad = kwargs.get("requires_grad")
-    return out.contiguous()
+    return out.contiguous() if contiguous else out
 
   # ***** creation helper functions *****
 
@@ -673,13 +673,15 @@ class Tensor:
     """
     dtype = kwargs.pop("dtype", self.dtype)
     device = kwargs.pop("device", self.device)
+    contiguous = kwargs.pop("contiguous", True)
     if isinstance(self.device, tuple):
       assert isinstance(self.lazydata, MultiLazyBuffer)
       if self.lazydata.axis is not None:
-        rands = [cast(LazyBuffer, Tensor.rand(*lb.shape, device=lb.device, dtype=dtype).lazydata) for lb in self.lazydata.lbs]
+        rands = [cast(LazyBuffer, Tensor.rand(*lb.shape, device=lb.device, dtype=dtype, contiguous=contiguous, **kwargs).lazydata) \
+                 for lb in self.lazydata.lbs]
         return Tensor(MultiLazyBuffer(rands, self.lazydata.axis), device=self.device, dtype=dtype, **kwargs)
-      return Tensor.rand(*self.shape, dtype=dtype, **kwargs).shard(self.device)
-    return Tensor.rand(*self.shape, device=device, dtype=dtype, **kwargs)
+      return Tensor.rand(*self.shape, dtype=dtype, contiguous=contiguous, **kwargs).shard(self.device)
+    return Tensor.rand(*self.shape, device=device, dtype=dtype, contiguous=contiguous, **kwargs)
 
   # ***** rng hlops *****
 
@@ -3131,7 +3133,7 @@ class Tensor:
     ```
     """
     if not Tensor.training or p == 0: return self
-    return (Tensor.rand_like(self, requires_grad=False, dtype=dtypes.default_float) >= p).where(self, 0) * (1/(1.0 - p))
+    return (Tensor.rand_like(self, requires_grad=False, dtype=dtypes.default_float, contiguous=False) >= p).contiguous().where(self, 0) / (1.0 - p)
 
   def one_hot(self, num_classes:int=-1) -> Tensor:
     """