lines from helpers

extra/helpers.py

@@ -0,0 +1,5 @@
+import time
+
+class Timing(object):
+  def __enter__(self): self.st = time.monotonic_ns()
+  def __exit__(self, exc_type, exc_val, exc_tb): print(f"{(time.monotonic_ns()-self.st)*1e-6:.2f} ms")

tinygrad/helpers.py

@@ -1,4 +1,4 @@
-import os, math, functools, time
+import os, math, functools
 from typing import Tuple, Union, List
 
 def dedup(x): return list(dict.fromkeys(x))   # retains list order
@@ -11,15 +11,8 @@ def partition(lst, fxn): return [x for x in lst if fxn(x)], [x for x in lst if n
 def make_pair(x:Union[int, Tuple[int, ...]], cnt=2) -> Tuple[int, ...]: return (x,)*cnt if isinstance(x, int) else x
 def flatten(l): return [item for sublist in l for item in sublist]
 
-class Timing(object):
-  def __enter__(self): self.st = time.monotonic_ns()
-  def __exit__(self, exc_type, exc_val, exc_tb): print(f"{(time.monotonic_ns()-self.st)*1e-6:.2f} ms")
-
 @functools.lru_cache(maxsize=None)
 def getenv(key, default=0): return type(default)(os.getenv(key, default))
+
 DEBUG = getenv("DEBUG", 0)
 IMAGE = getenv("IMAGE", 0)
-
-def shape_to_axis(old_shape, new_shape):
-  assert len(old_shape) == len(new_shape), "reduce shapes must have same dimensions"
-  return tuple([i for i,(a,b) in enumerate(zip(old_shape, new_shape)) if a != b])

tinygrad/runtime/ops_cpu.py

@@ -1,8 +1,11 @@
 import numpy as np
 import operator
-from typing import ClassVar, Callable, Dict
+from typing import ClassVar, Callable, Dict, Tuple
 from tinygrad.ops import UnaryOps, BinaryOps, MovementOps, ReduceOps, FusedOps, InterpretedBuffer, Op
-from tinygrad.helpers import shape_to_axis
+
+def shape_to_axis(old_shape:Tuple[int, ...], new_shape:Tuple[int, ...]) -> Tuple[int, ...]:
+  assert len(old_shape) == len(new_shape), "reduce shapes must have same dimensions"
+  return tuple(i for i,(a,b) in enumerate(zip(old_shape, new_shape)) if a != b)
 
 base_fxn_for_op : Dict[Op, Callable] = {
   UnaryOps.NEG: lambda x: -x, UnaryOps.NOT: lambda x: (1.0 - x),