diff --git a/tinygrad/codegen/kernel.py b/tinygrad/codegen/kernel.py
index 53c91c1a04..2f2242862c 100644
--- a/tinygrad/codegen/kernel.py
+++ b/tinygrad/codegen/kernel.py
@@ -2,7 +2,7 @@ from __future__ import annotations
 import os, math, itertools
 from typing import NamedTuple, Optional, List, Tuple, cast, Dict, Union
 from tinygrad.ops import LazyOp, FlopCounter, get_lazyop_info, UnaryOps, BinaryOps, ReduceOps, MemBuffer, ConstBuffer, BufferOps, Device, Compiled
-from tinygrad.helpers import dedup, dtypes, colored, ImageDType, DType, all_int, ansilen, getenv, prod, DEBUG
+from tinygrad.helpers import dedup, dtypes, colored, ImageDType, DType, ansilen, getenv, prod, DEBUG
 from tinygrad.shape.shapetracker import ShapeTracker, get_contraction
 from tinygrad.shape.symbolic import sint
 from tinygrad.shape.view import View, strides_for_shape
@@ -129,11 +129,6 @@ class Kernel:
   @property
   def membufs(self) -> List[MemBuffer]: return [x for x in self.bufs if isinstance(x, MemBuffer)]
 
-  def has_variable_shape(self) -> bool:
-    for b in self.bufs:
-      if not isinstance(b, LocalBuffer) and not all_int(b.st.views[-1].shape): return True
-    return False
-
   def shape_offsets(self, i): return itertools.product(*[list(range(s)) for s in self.sts[i].shape[self.shape_len-self.upcasted:][::-1]]) if self.upcasted > 0 else [tuple()]
   def float4_axis(self, i): return [x-(self.shape_len-self.upcasted) for x in self.sts[i].unit_stride_axes() if x >= self.shape_len-self.upcasted and self.sts[i].shape[x]%4 == 0]
 
diff --git a/tinygrad/helpers.py b/tinygrad/helpers.py
index fa99c5edd1..0b010cd307 100644
--- a/tinygrad/helpers.py
+++ b/tinygrad/helpers.py
@@ -1,7 +1,7 @@
 from __future__ import annotations
 import os, functools, platform, time, re, contextlib, operator, hashlib, pickle, sqlite3, cProfile, pstats
 import numpy as np
-from typing import Dict, Tuple, Union, List, NamedTuple, Final, Iterator, ClassVar, Optional, Iterable, Any, TypeVar, TYPE_CHECKING
+from typing import Dict, Tuple, Union, List, NamedTuple, Final, Iterator, ClassVar, Optional, Iterable, Any, TypeVar, TYPE_CHECKING, Callable
 if TYPE_CHECKING:  # TODO: remove this and import TypeGuard from typing once minimum python supported version is 3.10
   from typing_extensions import TypeGuard
 
@@ -29,9 +29,9 @@ def round_up(num, amt): return num if num%amt == 0 else num+(amt-(num%amt))
 def merge_dicts(ds:Iterable[Dict]) -> Dict:
   assert len(kvs:=set([(k,v) for d in ds for k,v in d.items()])) == len(set(kv[0] for kv in kvs)), f"cannot merge, {kvs} contains different values for the same key"
   return {k:v for d in ds for k,v in d.items()}
-def partition(lst, fxn):
-  a: list[Any] = []
-  b: list[Any] = []
+def partition(lst:List[T], fxn:Callable[[T],bool]):
+  a:List[T] = []
+  b:List[T] = []
   for s in lst: (a if fxn(s) else b).append(s)
   return a,b
 
@@ -200,8 +200,7 @@ def diskcache_get(table:str, key:Union[Dict, str, int]) -> Any:
     res = cur.execute(f"SELECT val FROM {table} WHERE {' AND '.join([f'{x}=?' for x in key.keys()])}", tuple(key.values()))
   except sqlite3.OperationalError:
     return None  # table doesn't exist
-  if (val:=res.fetchone()) is not None:
-    return pickle.loads(val[0])
+  if (val:=res.fetchone()) is not None: return pickle.loads(val[0])
   return None
 
 _db_tables = set()
diff --git a/tinygrad/shape/symbolic.py b/tinygrad/shape/symbolic.py
index 7661f447a6..c61a362467 100644
--- a/tinygrad/shape/symbolic.py
+++ b/tinygrad/shape/symbolic.py
@@ -1,5 +1,4 @@
 from __future__ import annotations
-from abc import abstractmethod
 import functools
 from math import gcd
 from itertools import product
@@ -187,8 +186,7 @@ class OpNode(Node):
     self.a, self.b = a, b
     self.min, self.max = self.get_bounds()
   def vars(self): return self.a.vars() + (self.b.vars() if isinstance(self.b, Node) else [])
-  @abstractmethod
-  def get_bounds(self) -> Tuple[int, int]: pass
+  def get_bounds(self) -> Tuple[int, int]: raise NotImplementedError("must be implemented")
 
 class LtNode(OpNode):
   def __floordiv__(self, b: Union[Node, int], _=False): return (self.a//b) < (self.b//b)
@@ -211,8 +209,7 @@ class MulNode(OpNode):
   def __mod__(self, b: Union[Node, int]):
     a = (self.a * (self.b%b))
     return Node.__mod__(a, b)
-  def get_bounds(self) -> Tuple[int, int]:
-    return (self.a.min*self.b, self.a.max*self.b) if self.b >= 0 else (self.a.max*self.b, self.a.min*self.b)
+  def get_bounds(self) -> Tuple[int, int]: return (self.a.min*self.b, self.a.max*self.b) if self.b >= 0 else (self.a.max*self.b, self.a.min*self.b)
   def substitute(self, var_vals: Dict[VariableOrNum, Node]) -> Node: return self.a.substitute(var_vals) * (self.b if isinstance(self.b, int) else self.b.substitute(var_vals))
 
 class DivNode(OpNode):
diff --git a/tinygrad/tensor.py b/tinygrad/tensor.py
index 0dc1104496..45bb7f9a73 100644
--- a/tinygrad/tensor.py
+++ b/tinygrad/tensor.py
@@ -41,9 +41,7 @@ class Tensor:
   training: ClassVar[bool] = False
   class train:
     def __init__(self, val=True): self.val = val
-    def __enter__(self):
-      self.prev = Tensor.training
-      Tensor.training = self.val
+    def __enter__(self): self.prev, Tensor.training = Tensor.training, self.val
     def __exit__(self, exc_type: Any, exc_value: Any, traceback: Any): Tensor.training = self.prev
 
   no_grad: ClassVar[bool] = False
@@ -171,8 +169,7 @@ class Tensor:
   @staticmethod
   def eye(dim:int, **kwargs): return Tensor.full((dim,1),1,**kwargs).pad(((0,0),(0,dim))).reshape(dim*(dim+1)).shrink(((0,dim*dim),)).reshape(dim, dim)
 
-  def full_like(self, fill_value, **kwargs):
-    return Tensor.full(self.shape, fill_value=fill_value, dtype=kwargs.pop("dtype", self.dtype), device=kwargs.pop("device", self.device), **kwargs)
+  def full_like(self, fill_value, **kwargs): return Tensor.full(self.shape, fill_value=fill_value, dtype=kwargs.pop("dtype", self.dtype), device=kwargs.pop("device", self.device), **kwargs)
   def zeros_like(self, **kwargs): return self.full_like(0, **kwargs)
   def ones_like(self, **kwargs): return self.full_like(1, **kwargs)
 
@@ -382,8 +379,7 @@ class Tensor:
     shapes = [s.shape[dim] for s in catargs]
     shape_cumsum = [0, *accumulate(shapes)]
     slc = [[(0, 0) for _ in self.shape] for _ in catargs]
-    for shp,k,s in zip(shapes, shape_cumsum[:-1], slc):
-      s[dim] = (k, shape_cumsum[-1] - k - shp)
+    for shp,k,s in zip(shapes, shape_cumsum[:-1], slc): s[dim] = (k, shape_cumsum[-1] - k - shp)
     return reduce(Tensor.__add__, [arg.pad(tuple(s)) for arg,s in zip(catargs, slc)])
 
   @staticmethod