diff --git a/examples/so_vits_svc.py b/examples/so_vits_svc.py
index 2d443cde2c..95e90fa696 100644
--- a/examples/so_vits_svc.py
+++ b/examples/so_vits_svc.py
@@ -504,7 +504,7 @@ def load_checkpoint_enc(checkpoint_path, model: ContentVec, optimizer=None, skip
         obj, v = getattr(parent, "weight"), weight_norm(weight_v, weight_g, 0)
         weight_g, weight_v, parent, skip = None, None, None, False
       if not skip and obj.shape == v.shape:
-        if "feature_extractor" in key and (isinstance(parent, nn.GroupNorm) or isinstance(parent, nn.LayerNorm)):  # cast
+        if "feature_extractor" in key and (isinstance(parent, (nn.GroupNorm, nn.LayerNorm))):  # cast
           obj.assign(v.to(obj.device).float())
         else:
           obj.assign(v.to(obj.device))
diff --git a/extra/export_model.py b/extra/export_model.py
index 8e60373f29..4f326432e9 100644
--- a/extra/export_model.py
+++ b/extra/export_model.py
@@ -38,7 +38,7 @@ def jit_model(model, *args) -> Tuple[TinyJit,Dict[int,str]]:
   @TinyJit
   def run(*x):
     out = model.forward(*x) if hasattr(model, "forward") else model(*x)
-    assert isinstance(out, tuple) or isinstance(out, list) or isinstance(out, Tensor), "model output must be a Tensor, tuple, or a list of Tensors for export"
+    assert isinstance(out, (tuple, list, Tensor)), "model output must be a Tensor, tuple, or a list of Tensors for export"
     out = [out] if isinstance(out, Tensor) else out
     return [o.realize() for o in out]
 
diff --git a/extra/sqtt/rgptool.py b/extra/sqtt/rgptool.py
index 3244cc153b..a0d499e62e 100755
--- a/extra/sqtt/rgptool.py
+++ b/extra/sqtt/rgptool.py
@@ -22,7 +22,7 @@ CHUNK_CLASSES = {
 }
 
 def pretty(val, pad=0) -> str:
-  if isinstance(val, ctypes.Structure) or isinstance(val, ctypes.Union):
+  if isinstance(val, (ctypes.Structure, ctypes.Union)):
     nl = '\n' # old python versions don't support \ in f-strings
     return f"{val.__class__.__name__}({nl}{' '*(pad+2)}{(f', {nl}'+' '*(pad+2)).join([f'{field[0]}={pretty(getattr(val, field[0]), pad=pad+2)}' for field in val._fields_])}{nl}{' '*pad})"
   if isinstance(val, ctypes.Array):
diff --git a/extra/torch_hook/hook_torch.py b/extra/torch_hook/hook_torch.py
index 24aa780ed1..f3b8113b4a 100644
--- a/extra/torch_hook/hook_torch.py
+++ b/extra/torch_hook/hook_torch.py
@@ -39,7 +39,7 @@ class DispatchLog(TorchDispatchMode):
     should_call_tiny = kwargs.get('device') is not None and kwargs['device'].type == "cuda"
 
     def can_print_arg(arg):
-      return args is None or isinstance(arg, str) or isinstance(arg, int) or isinstance(arg, float) or isinstance(arg, bool)
+      return args is None or isinstance(arg, (str, int, float, bool))
 
     def create_tiny_mapping(arg):
       if WRAP_TINY:
diff --git a/test/test_graph.py b/test/test_graph.py
index d37c4c50ba..ffc04844ed 100644
--- a/test/test_graph.py
+++ b/test/test_graph.py
@@ -38,6 +38,10 @@ def helper_alloc_rawbuffer(device, fill=False):
       rawbuf.copyin(Tensor(data).realize().lazydata.base.realized.as_buffer())
   return rawbuf
 
+def helper_create_offset_rawbuffer(base, offset=0):
+  x = Buffer(base.device, base.size-offset, base.dtype, base=base, offset=offset)
+  return x.ensure_allocated()
+
 def helper_run_jit(jis, bufs, out_buffers):
   for rawbuf in out_buffers:
     mv = memoryview(bytearray(rawbuf.size * rawbuf.dtype.itemsize))
@@ -229,5 +233,18 @@ class TestGraph(unittest.TestCase):
 
     helper_test_graphs(Device[d0].graph, graphs)
 
+  def test_graph_offset_bufs(self):
+    d0 = Device.DEFAULT
+    if not hasattr(Device[d0].allocator, "_offset"): self.skipTest("device does not support _offset")
+
+    b0 = [helper_alloc_rawbuffer(d0, fill=True) for _ in range(1)]
+    b0 += [helper_create_offset_rawbuffer(b0[0]), helper_create_offset_rawbuffer(b0[0])]
+
+    graphs = [
+      [helper_copy_op(d0, b0[0], b0[2]), helper_exec_op(d0, b0[1], [b0[0], b0[2]])],
+    ]
+
+    helper_test_graphs(Device[d0].graph, graphs)
+
 if __name__ == '__main__':
   unittest.main()
diff --git a/tinygrad/codegen/devectorizer.py b/tinygrad/codegen/devectorizer.py
index de691dd443..0450624886 100644
--- a/tinygrad/codegen/devectorizer.py
+++ b/tinygrad/codegen/devectorizer.py
@@ -4,7 +4,7 @@ from collections import defaultdict
 from tinygrad.dtype import dtypes, ImageDType, PtrDType
 from tinygrad.ops import UOp, Ops, UPat, PatternMatcher, resolve
 from tinygrad.ops import graph_rewrite, GroupOp
-from tinygrad.codegen.symbolic import symbolic_simple, split_uop, uop_given_valid, parse_valid, simplify_valid, sym
+from tinygrad.codegen.symbolic import symbolic_simple, split_uop, uop_given_valid, parse_valid, simplify_valid, sym, symbolic
 from tinygrad.helpers import getenv, flatten, TRANSCENDENTAL, AMX, prod, DEVECTORIZE
 from tinygrad.codegen.transcendental import xexp2, xlog2, xsin, xpow, TRANSCENDENTAL_SUPPORTED_DTYPES
 from tinygrad.renderer import Renderer
@@ -15,14 +15,16 @@ def expand_index(buf:UOp, vec:UOp, mask:UOp|None=None):
   if getenv("UNSAFE_DISABLE_MASK", 0): mask = None
   # first, extract all the relevant offsets
   offsets_rootsrc: defaultdict[Any, dict[int, list[int]]] = defaultdict(dict)
+  midx, mmask = graph_rewrite(UOp.sink(UOp.sink(*[vec.gep(i) for i in range(vec.dtype.count)]),
+                                       UOp.sink(*[mask.gep(i) for i in range(vec.dtype.count)]) if mask is not None else UOp(Ops.NOOP)),
+                                       symbolic, name=f"index_buf_{buf.arg}").src
   for i in range(vec.dtype.count):
-    idx = vec.gep(i).simplify()
-    #graph_rewrite(idx, PatternMatcher([]))
+    idx: Any = midx.src[i]
     if idx.op is Ops.ADD and idx.src[1].op is Ops.CONST: root_src, arg = idx.src[0], idx.src[1].arg
     elif idx.op is Ops.ADD and idx.src[0].op is Ops.CONST: root_src, arg = idx.src[1], idx.src[0].arg
     elif idx.op is Ops.CONST: root_src, arg = "CONST", idx.arg
     else: root_src, arg = idx, 0
-    if mask is not None: root_src = (mask.gep(i).simplify(), root_src)
+    if mask is not None: root_src = (mmask.src[i], root_src)
     offsets_rootsrc[root_src].setdefault(arg, []).append(i)
 
   # the buf.dtype is always a pointer
diff --git a/tinygrad/engine/jit.py b/tinygrad/engine/jit.py
index 98e9d43e2d..1400d8d772 100644
--- a/tinygrad/engine/jit.py
+++ b/tinygrad/engine/jit.py
@@ -120,7 +120,9 @@ class GraphRunner(Runner):
       if id(rawbuf.base._buf) in self.w_dependency_map: wait_nodes.append(self.w_dependency_map[id(rawbuf.base._buf)])
       if i in write:
         if id(rawbuf.base._buf) in self.r_dependency_map: wait_nodes.extend(self.r_dependency_map.pop(id(rawbuf.base._buf)))
-        self.w_dependency_map[id(rawbuf.base._buf)] = new_dependency
+
+    for i,rawbuf in enumerate(rawbufs):
+      if i in write: self.w_dependency_map[id(rawbuf.base._buf)] = new_dependency
       else: self.r_dependency_map[id(rawbuf.base._buf)].append(new_dependency)
 
     return list({id(x):x for x in wait_nodes}.values())
diff --git a/tinygrad/ops.py b/tinygrad/ops.py
index 45c747cca9..578794f844 100644
--- a/tinygrad/ops.py
+++ b/tinygrad/ops.py
@@ -714,7 +714,7 @@ class UPat(MathTrait):
   def __init__(self, op:Optional[Union[Ops, tuple[Ops, ...], set[Ops]]]=None, dtype:Optional[Union[DType, tuple[DType, ...]]]=None,
                src:Optional[Union[tuple[UPat, ...], list[UPat], UPat]]=None, arg:Any=None,
                name:Optional[str]=None, allow_any_len:bool=False, location=None, custom_early_reject:Optional[set[Ops]]=None):
-    assert op is None or isinstance(op, Ops) or isinstance(op, tuple) or isinstance(op, set), "op must be Ops or tuple of Ops"
+    assert op is None or isinstance(op, (Ops, tuple, set)), "op must be Ops or tuple of Ops"
     self.op: Optional[tuple[Ops, ...]] = (op,) if isinstance(op, Ops) else (tuple(op) if isinstance(op, set) else op)
     self.dtype: Optional[tuple[DType, ...]] = (dtype,) if isinstance(dtype, DType) else dtype
     self.arg, self.name, self._in_src, self.custom_early_reject = arg, name, src, custom_early_reject