fit nits in compare

README.md

@@ -129,13 +129,13 @@ hlops are syntactic sugar around mlops. They support most things torch does.
 
 ### mlops
 
-mlops are mid level ops, there's 16 of them. They understand derivatives. They are very simple.
+mlops are mid level ops. They understand derivatives. They are very simple.
 
 ```
-Log, Exp                                      # unary ops
-Sum, Max                                      # reduce ops (with axis argument)
-Maximum, Add, Sub, Mul, Pow, Div              # binary ops (no broadcasting, use expand)
-Expand, Reshape, Permute, Pad, Shrink, Flip   # movement ops
+Log, Exp                                                       # unary ops
+Sum, Max                                                       # reduce ops (with axis argument)
+Maximum, Add, Sub, Mul, Pow, Div, CompareLess, CompareEqual    # binary ops (no broadcasting, use expand)
+Expand, Reshape, Permute, Pad, Shrink, Flip                    # movement ops
 ```
 
 You no longer need to write mlops for a new accelerator

tinygrad/jit.py

@@ -1,6 +1,7 @@
 from typing import Callable, List, Tuple, Any, Dict, cast, Union
 import itertools
 from tinygrad.helpers import DEBUG, colored
+
 from tinygrad.lazy import Device
 from tinygrad.tensor import Tensor
 from tinygrad.ops import GlobalCounters, CompiledBuffer, RawBuffer

tinygrad/mlops.py

@@ -51,10 +51,10 @@ class Max(Function):
     return max_is_amount.binary_op(BinaryOps.MUL, grad_output_expanded)
 
 # ************* binary ops *************
+
 class CompareLess(Function):
   def forward(self, x, y):
-    self.ret = x.binary_op(BinaryOps.CMPLT, y)
-    return self.ret
+    return x.binary_op(BinaryOps.CMPLT, y)
   
 class CompareEqual(Function):
   def forward(self, x, y):

tinygrad/runtime/ops_cpu.py

@@ -24,7 +24,7 @@ def einsum_mulacc(einsum, get_strides, expand):
 
 numpy_fxn_for_op : Dict[Op, Callable] = {**base_fxn_for_op, **{
   UnaryOps.NOOP: lambda x: np.ascontiguousarray(x), UnaryOps.EXP: lambda x: np.exp(x), UnaryOps.LOG: lambda x: np.log(x),
-  BinaryOps.MAX: np.maximum, BinaryOps.CMPEQ: lambda x,y: (x==y).astype(np.float32), BinaryOps.CMPLT: lambda x, y: (x<y).astype(np.float32),
+  BinaryOps.MAX: np.maximum, BinaryOps.CMPEQ: lambda x,y: (x==y).astype(np.float32), BinaryOps.CMPLT: lambda x,y: (x<y).astype(np.float32),
   MovementOps.FLIP: lambda x, axis: np.flip(x, axis), MovementOps.PERMUTE: lambda x, order: x.transpose(order),
   MovementOps.PAD: lambda x, padding: np.pad(x, padding), MovementOps.EXPAND: lambda x, new_shape: np.broadcast_to(x, new_shape),
   FusedOps.MULACC: einsum_mulacc(lambda s,a,b: np.einsum(s, a.copy(), b.copy()), lambda x: x.strides, np.broadcast_to)

tinygrad/runtime/ops_torch.py

@@ -6,7 +6,7 @@ from tinygrad.runtime.ops_cpu import base_fxn_for_op, einsum_mulacc
 
 torch_fxn_for_op : Dict[Op, Callable] = {**base_fxn_for_op, **{
   UnaryOps.NOOP: lambda x: x.contiguous(), UnaryOps.EXP: lambda x: x.exp(), UnaryOps.LOG: lambda x: x.log(),
-  BinaryOps.MAX: torch.maximum, BinaryOps.CMPEQ: lambda x,y: (x==y).float(), BinaryOps.CMPLT: lambda x, y:(x<y).float(),
+  BinaryOps.MAX: torch.maximum, BinaryOps.CMPEQ: lambda x,y: (x==y).float(), BinaryOps.CMPLT: lambda x,y: (x<y).float(),
   MovementOps.PAD: lambda x, padding: torch.nn.functional.pad(x, [item for sublist in padding[::-1] for item in sublist]),
   FusedOps.MULACC: einsum_mulacc(torch.einsum, lambda x: x.stride(), lambda x,s: x.expand(s))
 }}