remove tuplize

extra/assembly/amd/emu.py

@@ -1098,7 +1098,7 @@ def _get_runner(inst_bytes: bytes, arch: str = "rdna3"):
   canonical_name = f"{_op_name(inst).lower()}_{base.to_bytes(size, 'little').hex()}"
   sink = sink.replace(arg=KernelInfo(name=canonical_name)).rtag(1)
 
-  with Context(NOOPT=1, CHECK_OOB=0, TUPLE_ORDER=0, EMULATED_DTYPES=""):
+  with Context(NOOPT=1, CHECK_OOB=0, EMULATED_DTYPES=""):
     runner = get_runner('CPU', sink)
   _canonical_runner_cache.append((base, mask, size, runner))
   return runner, True

tinygrad/codegen/late/linearizer.py

@@ -2,7 +2,7 @@ import heapq
 from typing import Any
 from collections import defaultdict
 from tinygrad.uop.ops import PatternMatcher, UOp, Ops, UPat, multirange_str
-from tinygrad.helpers import prod, getenv, TUPLE_ORDER
+from tinygrad.helpers import prod, getenv
 
 def linearize(sink:UOp) -> list[UOp]:
   # this is a toposort with priority
@@ -34,7 +34,7 @@ def linearize(sink:UOp) -> list[UOp]:
     priorities[u] = (run_count, priority, extra)
 
   # number the uops in "ideal" order
-  nkey = {u:i for i,u in enumerate(sorted(lst, key=lambda x: priorities[x]+(x.tuplize if TUPLE_ORDER else ())))}
+  nkey = {u:i for i,u in enumerate(sorted(lst, key=lambda x: priorities[x]+((x.op.value, x.arg, x.dtype),)))}
 
   # then force them to be toposorted in as close to the ideal order as possible
   heap = [(-nkey[sink], sink)]

tinygrad/helpers.py

@@ -199,8 +199,6 @@ SPEC = ContextVar("SPEC", 1)
 CHECK_OOB = ContextVar("CHECK_OOB", 0)
 PCONTIG = ContextVar("PCONTIG", 0)  # partial contiguous in rangeify
 DEBUG_RANGEIFY = ContextVar("DEBUG_RANGEIFY", 0)
-# set to 1, this uses tuplize in the linearizer sort order
-TUPLE_ORDER = ContextVar("TUPLE_ORDER", 1)
 # set to 0 to disable the compiler cache
 CCACHE = ContextVar("CCACHE", 1)
 # allow tf32 to be used on NVIDIA GPUs

tinygrad/uop/ops.py

@@ -191,10 +191,6 @@ class UOp(OpMixin, metaclass=UOpMetaClass):
   # returns map of UOps to their consumers in the graph rooted by self
   def get_consumer_map(self) -> dict[UOp, dict[UOp, None]]: return consumer_map_from_toposort(self.toposort())
 
-  @functools.cached_property
-  def tuplize(self:UOp) -> tuple:
-    return (self.op.value, self.arg, self.dtype,)+tuple([x.tuplize for x in self.src])
-
   @property
   def ptrdtype(self) -> PtrDType:
     if not isinstance(self.dtype, PtrDType): raise RuntimeError(f"ptrdtype called on UOp with type {self.dtype}")

tinygrad/uop/symbolic.py

@@ -179,7 +179,10 @@ gep_pushing = PatternMatcher([
 commutative = PatternMatcher([
   # ** COMMUTATIVE flipping (only for index) **
   # NOTE: this can break merging vector math by only flipping some of them
-  (UPat(GroupOp.Commutative, dtype=dtypes.index, name='x'), lambda x: x.replace(src=x.src[::-1]) if x.src[1].tuplize < x.src[0].tuplize else None),
+  (UPat(GroupOp.Commutative, dtype=dtypes.index, name='x'),
+   lambda x: x.replace(src=x.src[::-1]) if (x.src[1].op.value, x.src[1].arg, x.src[1].dtype,
+     tuple((s.op.value, s.arg, s.dtype) for s in x.src[1].src)) < (x.src[0].op.value, x.src[0].arg, x.src[0].dtype,
+     tuple((s.op.value, s.arg, s.dtype) for s in x.src[0].src)) else None),
 ])
 
 symbolic = symbolic_simple+commutative+PatternMatcher([