no long

test/test_rangeify.py

@@ -76,6 +76,18 @@ class TestRangeifyEdgeCase(unittest.TestCase):
     res = Tensor.cat(a, c, dim=0)
     self.assertEqual(res.numpy()[-1, :16].tolist(), [512] * 16)
 
+  def test_pcontig_multi_gather(self):
+    # regression test: local bufferize must have device set for const_like to work
+    with Context(PCONTIG=2):
+      # NOTE: with uint type, this will become a long and fail on WEBGPU
+      forest = Tensor(list(range(8)), dtype='int')
+      idx = Tensor([0, 0], dtype='int')
+      node_val = forest.gather(0, idx)
+      idx2 = idx * 2 + 1
+      node_val2 = forest.gather(0, idx2)
+      result = (node_val + node_val2).numpy()
+    self.assertEqual(result.tolist(), [1, 1])
+
 if getenv("BIG") > 2:
   # llama 8B (8192)
   BS, HEADS, SEQLEN, EMB = 4, 32, 8192, 128

tinygrad/schedule/indexing.py

@@ -74,7 +74,7 @@ def create_bufferize_and_index_based_on_ranges(ctx:IndexingContext, x:UOp):
         removable = x.op is not Ops.COPY and s.op not in ALWAYS_CONTIGUOUS
         # None in the device assigns it a number later
         opts = BufferizeOpts(device=s.device, removable=removable) if len(ctx.range_map[s][1]) == len(realized_ranges) else \
-               BufferizeOpts(None, AddrSpace.LOCAL, removable=removable)
+               BufferizeOpts(device=s.device, addrspace=AddrSpace.LOCAL, removable=removable)
         new_src = UOp(Ops.BUFFERIZE, s.dtype, src=(new_src,)+closed_ranges, arg=opts, tag=s.tag if opts.addrspace == AddrSpace.GLOBAL else None)
         if x in ctx.range_map: new_src = new_src.index(*[r for i,r in enumerate(ctx.range_map[x][0]) if i in realized_ranges])
     new_srcs.append(new_src)

tinygrad/schedule/rangeify.py

@@ -1,4 +1,4 @@
-from dataclasses import dataclass, field
+from dataclasses import dataclass, field, replace
 import itertools
 from tinygrad.dtype import dtypes, PtrDType, ImageDType, AddrSpace
 from tinygrad.uop.ops import PatternMatcher, UPat, Ops, UOp, resolve, GroupOp, _substitute, KernelInfo
@@ -429,6 +429,9 @@ to_define_global = PatternMatcher([
   (UPat(Ops.BIND, name="b"), unbind_kernel),
   (UPat((Ops.MSTACK, Ops.MSELECT, Ops.AFTER), name="after"), handle_after),
 
+  # remove device from local BUFFERIZE
+  (UPat(Ops.BUFFERIZE, name="b"), lambda b: b.replace(arg=replace(b.arg, device=None))),
+
   # HACK in case any CONSTs were replaced
   # this is only needed if you are using symbolic
   (UPat((Ops.CONST, Ops.DEFINE_VAR), name="c"), lambda c: c.replace(src=()) if len(c.src) else None),

tinygrad/viz/serve.py

@@ -120,7 +120,7 @@ def uop_to_json(x:UOp) -> dict[int, dict]:
       if u._shape is not None:
         label += f"\n{shape_to_str(u.shape)}"
       if u.op in {Ops.INDEX, Ops.BUFFERIZE}:
-        label += f"\n{u.render()}"
+        if len(u.toposort()) < 30: label += f"\n{u.render()}"
         ranges: list[UOp] = []
         for us in u.src[1:]: ranges += [s for s in us.toposort() if s.op in {Ops.RANGE, Ops.SPECIAL}]
         if ranges: label += "\n"+' '.join([f"{s.render()}={s.vmax+1}" for s in ranges])