rename reduce_op (#1900)

* rename reduce_op

* more design v2

docs/DESIGNv2.md

@@ -0,0 +1,17 @@
+tinygrad is a bit bloated now, and there's several places where concerns should be seperated and they aren't.
+
+tensor.py and mlops.py are great code. The interface going backward here is:
+
+LazyBuffer.const (this creates a matching size buffer)
+LazyBuffer.contiguous (tbis is not exactly elementwise)
+LazyBuffer.e (elementwise)
+LazyBuffer.r (reduce)
+reshape/permute/expand/stride/shrink/pad (movement)
+
+The lazy.py reordering engine has a lot of junk to deal with movementops that should be removed.
+
+view.py is mostly great code, except it shouldn't have the rendering logic, and the int type should be parameterized to not import from symbolic.
+
+LazyOp shouldn't have LazyBuffers as sources, just LazyOp LoadOps with a tuple of Views. Then the LazyOp uniquely determines the kernel and we don't have to do any replacement.
+
+ShapeTracker probably shouldn't exist and just be a part of LazyBuffer. Most of the stuff in ShapeTracker should move to symbolic_view, which combines view and symbolic.

tinygrad/lazy.py

@@ -243,7 +243,7 @@ class LazyBuffer:
     srcs = _push_movement_ops((self,)) if SHUFFLE_MOVEMENT_OPS else (self,)
     return create_lazybuffer(self.device, ShapeTracker(new_shape), ReduceOps, LazyOp(op, srcs, new_shape), self.dtype, self.var_vals)
 
-  def reduce_op(self:LazyBuffer, op:ReduceOps, new_shape:Tuple[sint, ...]) -> LazyBuffer:
+  def r(self:LazyBuffer, op:ReduceOps, new_shape:Tuple[sint, ...]) -> LazyBuffer:
     if any(not isinstance(s, int) for s in self.shape) or prod(self.shape) // prod(new_shape) < 32768: return self._reduce_op(op, new_shape) # The amount of work should be big enough to take the benefit of "2 kernels" approach.
     heuristic, divisor, dim_to_split = max(((divisor := math.gcd(256, old))/(stride or math.inf), divisor, i) for i, (old, new, stride) in enumerate(zip(self.shape, new_shape, self.st.real_strides())) if old != new) # type: ignore
     if divisor < 16 or heuristic < 0.1: return self._reduce_op(op, new_shape) # Choose largest divisor (>=16) to split on, penalize large strides.
@@ -289,7 +289,7 @@ class LazyBuffer:
         src, rop = self.op.src[0], self.op.op
         src.children.discard(self)
         del self  # TODO: why doesn't this delete remove it from the children
-        return src.permute(arg).reduce_op(cast(ReduceOps, rop), narg)
+        return src.permute(arg).r(cast(ReduceOps, rop), narg)
 
       # move permutes before expands (always, this is safe)
       if self.op.op == MovementOps.EXPAND:

tinygrad/mlops.py

@@ -89,20 +89,20 @@ class Sigmoid(Function):
 class Sum(Function):
   def forward(self, x:LazyBuffer, new_shape:Tuple[int, ...]) -> LazyBuffer:
     self.input_shape = x.shape
-    return x.reduce_op(ReduceOps.SUM, new_shape)
+    return x.r(ReduceOps.SUM, new_shape)
 
   def backward(self, grad_output:LazyBuffer) -> LazyBuffer:
     return grad_output.expand(self.input_shape)
 
 class Max(Function):
   def forward(self, x:LazyBuffer, new_shape:Tuple[int, ...]) -> LazyBuffer:
-    self.x, self.ret = x, x.reduce_op(ReduceOps.MAX, new_shape)
+    self.x, self.ret = x, x.r(ReduceOps.MAX, new_shape)
     return self.ret
 
   def backward(self, grad_output:LazyBuffer) -> LazyBuffer:
     # 1s in locations where the max was chosen (can be two locations)
     max_is_1s = self.x.const(1.0).e(BinaryOps.SUB, self.x.e(BinaryOps.CMPLT, self.ret.expand(self.x.shape)))
-    div = max_is_1s.reduce_op(ReduceOps.SUM, grad_output.shape).expand(self.x.shape)
+    div = max_is_1s.r(ReduceOps.SUM, grad_output.shape).expand(self.x.shape)
     return max_is_1s.e(BinaryOps.DIV, div).e(BinaryOps.MUL, grad_output.expand(self.x.shape))
 
 # ************* binary ops *************
@@ -166,7 +166,7 @@ class Expand(Function):
     return x.expand(shape)
 
   def backward(self, grad_output:LazyBuffer) -> LazyBuffer:
-    return grad_output.reduce_op(ReduceOps.SUM, self.input_shape)
+    return grad_output.r(ReduceOps.SUM, self.input_shape)
 
 class Reshape(Function):
   def forward(self, x:LazyBuffer, shape:Tuple[int, ...]) -> LazyBuffer: