From 22e7289fe0db8bbf105250c5d71fd8ea863484d9 Mon Sep 17 00:00:00 2001
From: chenyu <chenyu@fastmail.com>
Date: Mon, 29 Jul 2024 18:23:42 -0400
Subject: [PATCH] s/self.shape_len - self.upcasted/self.first_upcast (#5802)

missed the one with spaces.
[run_process_replay]
---
 tinygrad/codegen/kernel.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/tinygrad/codegen/kernel.py b/tinygrad/codegen/kernel.py
index 6a2cef7083..a7b01624fe 100644
--- a/tinygrad/codegen/kernel.py
+++ b/tinygrad/codegen/kernel.py
@@ -468,7 +468,7 @@ class Kernel:
       self.reshape_and_permute(None, tuple(permute))
     elif opt.op is OptOps.PADTO:
       check(not self.vars, "does not work with symbolic shape")
-      check(axis < self.shape_len - self.upcasted, "cannot pad upcasted")
+      check(axis < self.first_upcast, "cannot pad upcasted")
       # ok to pad SUM if all parent ops have f(0) = 0
       if self.first_reduce <= axis:
         check((r:=cast(LazyOp, self.reduceop)).op is ReduceOps.SUM and \
@@ -551,7 +551,7 @@ class Kernel:
     # **** below this line need to be optional and benchmarked ****
 
     # TODO: doing extra upcasts with images doesn't work for some reason (maybe has to do with to_image_idx)
-    # to trigger the above bug, remove prod(self.full_shape[self.shape_len - self.upcasted:]) from the below
+    # to trigger the above bug, remove prod(self.full_shape[self.first_upcast:]) from the below
     # expression and run test/test_ops.py with IMAGE=2
     # if there are small dims with lots of valid masks, upcast them (they might be from Tensor.stack)
     # this can be made much smarter
@@ -561,7 +561,7 @@ class Kernel:
       # we might want to be able to split axes that are masked, or refuse to merge them in simplify_merge_adjacent
       # for now skip upcasting here if there is a symbolic axis
       if isinstance(self.full_shape[axis], int) and self.full_shape[axis] <= 7 and any(st.axis_is_masked(axis) for st in self.sts) and \
-        prod(self.full_shape[self.shape_len - self.upcasted:]) * prod(self.full_shape[j] for j in to_upcast) * self.full_shape[axis] <= 7 * 7:
+        prod(self.full_shape[self.first_upcast:]) * prod(self.full_shape[j] for j in to_upcast) * self.full_shape[axis] <= 7 * 7:
         if DEBUG >= 4: print(f"upcasting masked axis : {axis}")
         to_upcast.append(axis)
     for axis in to_upcast[::-1]: self.apply_opt(Opt(OptOps.UPCAST, axis, 0))
@@ -712,7 +712,7 @@ class Kernel:
         if self.group_for_reduces:
           start = LazyOp(op.op, tuple(fixup_ast(x, apply_to_st) for x in op.src), arg)
           local_shape = (1,) * self.global_dims + self.full_shape[self.global_dims:self.global_dims+self.local_dims+self.group_for_reduces] + \
-            (1,) * (self.shape_len - self.upcasted - self.group_for_reduces - self.first_reduce) + tuple([x[0] for x in self.upcasted_axis(0)])
+            (1,) * (self.first_upcast - self.group_for_reduces - self.first_reduce) + tuple([x[0] for x in self.upcasted_axis(0)])
           local_buffer = MemBuffer(-1, start.dtype, ShapeTracker.from_shape(local_shape))
           local_store = LazyOp(BufferOps.STORE, (start,), local_buffer)
           local_load = LazyOp(BufferOps.LOAD, (local_store,), local_buffer)