diff --git a/test/models/test_real_world.py b/test/models/test_real_world.py
index 0f24f6a975..0e53f88de0 100644
--- a/test/models/test_real_world.py
+++ b/test/models/test_real_world.py
@@ -16,7 +16,7 @@ from examples.stable_diffusion import UNetModel
 
 def kopt_search_hook(k, create_k, to_prg, baseline):
   import nevergrad as ng
-  wanna_output = k.bufs[0].toCPU()
+  wanna_output = k.bufs[0].toCPU().copy()
   def check_opt(x):
     try:
       k = create_k()
diff --git a/test/test_linearizer.py b/test/test_linearizer.py
index 0a117b472b..199c2a821c 100644
--- a/test/test_linearizer.py
+++ b/test/test_linearizer.py
@@ -2,7 +2,7 @@ import numpy as np
 import unittest
 
 from tinygrad.codegen.linearizer import Linearizer, UOps
-from tinygrad.ops import Compiled, Device
+from tinygrad.ops import Compiled, Device, MovementOps, LazyOp
 from tinygrad.tensor import Tensor
 from tinygrad.jit import CacheCollector
 
@@ -85,5 +85,124 @@ class TestLinearizer(unittest.TestCase):
     num_ops = len([uop for uop in k.uops if uop.uop in [UOps.LOAD, UOps.ALU]])
     assert num_ops <= 0, "more load or alu uops than needed"
 
+def helper_linearizer_opt(r:Tensor, opts=[]):
+  wanna_output = None
+  realized_ast = None
+
+  # HACK to get real ast.
+  real_dev_exec_ast = Device[Device.DEFAULT].exec_ast
+  def fake_exec_ast(ast, output=None, **kwargs):
+    nonlocal realized_ast
+    x = real_dev_exec_ast(ast, output, **kwargs)
+    if not(ast.op in MovementOps and ast.src[0].__class__ is not LazyOp and ast.src[0].realized): realized_ast = ast # get last executed
+    return x
+  Device[Device.DEFAULT].exec_ast = fake_exec_ast
+  r = r.realize()  # realize an output buffer
+  assert realized_ast is not None
+  Device[Device.DEFAULT].exec_ast = real_dev_exec_ast
+
+  def check_opt(x, create_k, to_prg):
+    k = create_k()
+    k.process()
+    k.apply_auto_opt(x)
+    prg = to_prg(k)
+    k.bufs[0].realized = k.bufs[0].realized.fromCPU(np.zeros(k.bufs[0].shape, dtype=k.bufs[0].dtype.np)) # Zero to check that all values are filled
+    prg.exec(k.bufs, force_wait=True)
+    np.testing.assert_allclose(wanna_output, k.bufs[0].toCPU(), atol=1e-4, rtol=1e-4)
+
+  # Get baseline, which is not optimized at all.
+  k = Linearizer(realized_ast, r.lazydata, Device[Device.DEFAULT].linearizer_opts)
+  k.process()
+  prg = Device[Device.DEFAULT].to_program(k)
+  prg.exec(k.bufs, force_wait=True)
+  wanna_output = k.bufs[0].toCPU().copy()
+
+  # Check correctness of handcoded optimiztions.
+  k = Linearizer(realized_ast, r.lazydata, Device[Device.DEFAULT].linearizer_opts)
+  k.hand_coded_optimizations()
+  prg = Device[Device.DEFAULT].to_program(k)
+  k.bufs[0].realized = k.bufs[0].realized.fromCPU(np.zeros(k.bufs[0].shape, dtype=k.bufs[0].dtype.np)) # Zero to check that all values are filled
+  prg.exec(k.bufs, force_wait=True)
+  np.testing.assert_allclose(wanna_output, k.bufs[0].toCPU(), atol=1e-4, rtol=1e-4)
+  for x in opts: # Check custom transformations if any.
+    check_opt(x, lambda: Linearizer(realized_ast, r.lazydata, Device[Device.DEFAULT].linearizer_opts), Device[Device.DEFAULT].to_program)
+
+class TestLinearizerOpts(unittest.TestCase):
+  def test_local_and_grouped_reduce(self):
+    if not isinstance(Device[Device.DEFAULT], Compiled) or not Device[Device.DEFAULT].linearizer_opts.has_local:
+      self.skipTest("Only Compiled uses linearizer with locals")
+
+    N = 128
+    Tensor.manual_seed(1882)
+    a = Tensor.rand(4, 4, N, N)
+    b = Tensor.rand(4, 4, N)
+    r = (b.sqrt() + ((a+1).sum(axis=3).exp()))
+    helper_linearizer_opt(r, [
+      [(0, 2, 'L')], [(0, 8, 'L')], [(0, 16, 'L')], # Checking how it works with locals
+      [(0, 2, 'G')], [(0, 32, 'G')], [(0, 64, 'G')], # Checking how it works with grouped reduce
+      [(0, 2, 'L'), (0, 2, 'G')], [(0, 16, 'L'), (0, 16, 'G')], [(0, 32, 'L'), (0, 2, 'G')], [(0, 2, 'L'), (0, 64, 'G')], # Checking how it works with locals + grouped reduce
+      [(0, 2, 'L'), (0, 2, 'G'), (0, 8, 'U'), (0, 4, 'R')], # Checking how it works with locals + grouped reduce + upcasts
+    ])
+
+  def test_upcasts(self):
+    if not isinstance(Device[Device.DEFAULT], Compiled):
+      self.skipTest("Only Compiled uses linearizer")
+
+    N = 16
+    Tensor.manual_seed(1772)
+    a = Tensor.rand(N, N)
+    b = Tensor.rand(N, N)
+    r = (a+b).sqrt() * ((a+1).exp())
+    helper_linearizer_opt(r, [
+      [(0, 2, 'U')], [(0, 4, 'U')], [(0, 8, 'U')], # Checking how it works with upcasts
+    ])
+
+  def test_full_upcast(self):
+    if not isinstance(Device[Device.DEFAULT], Compiled):
+      self.skipTest("Only Compiled uses linearizer")
+
+    Tensor.manual_seed(1772)
+    a = Tensor.rand(4)
+    b = Tensor.rand(4)
+    r = (a+b).sqrt() * ((a+1).exp())
+    helper_linearizer_opt(r, [
+      [(0, 4, 'U')], # Checking how it works with upcasts
+    ])
+
+  def test_matmul(self):
+    if not isinstance(Device[Device.DEFAULT], Compiled) or not Device[Device.DEFAULT].linearizer_opts.has_local:
+      self.skipTest("Only Compiled uses linearizer with locals")
+
+    N = 128
+    Tensor.manual_seed(1552)
+    a = Tensor.rand(N, N)
+    b = Tensor.rand(N, N)
+    r = a@b
+    helper_linearizer_opt(r, [
+      [(0, 2, 'U')], [(0, 4, 'U'), (1, 4, 'U')], # Checking how it works with upcasts
+      [(0, 2, 'L')], [(1, 32, 'L')], [(0, 4, 'L'), (1, 4, 'L')], [(0, 4, 'L'), (1, 32, 'L')], [(0, 16, 'L'), (1, 8, 'L')], # Checking how it works with locals
+      [(0, 2, 'G')], [(0, 32, 'G')], [(0, 32, 'G'), (0, 4, 'R')], # Checking how it works with grouped_reduce
+      [(0, 2, 'L'), (1, 2, 'L'), (0, 32, 'G')], [(0, 16, 'L'), (0, 32, 'G')], [(0, 16, 'L'), (0, 8, 'L'), (0, 4, 'G')], # Checking how it works with local+grouped_reduce
+      [(0, 4, 'L'), (0, 4, 'L'), (0, 16, 'G'), (0, 4, 'R'), (0, 4, 'U'), (1, 2, 'U')], # Checking all together
+      [(0, 4, 'L'), (0, 4, 'L'), (0, 16, 'G'), (0, 4, 'R'), (0, 8, 'U')], # Full global upcast + local
+    ])
+
+  def test_double_reduce(self):
+    if not isinstance(Device[Device.DEFAULT], Compiled) or not Device[Device.DEFAULT].linearizer_opts.has_local:
+      self.skipTest("Only Compiled uses linearizer with locals")
+
+    N = 128
+    Tensor.manual_seed(1552)
+    a = Tensor.rand(8, N, 8, N)
+    r = a.sum(axis=(1,3))
+    helper_linearizer_opt(r, [
+      [(0, 2, 'G')], [(0, 32, 'G')], [(1, 2, 'G')], [(1, 32, 'G')], # Checking how it works with 1 grouped_reduce.
+      [(0, 2, 'G'), (1, 2, 'G')], [(0, 16, 'G'), (1, 2, 'G')], [(0, 4, 'G'), (1, 64, 'G')], # Checking how it works with 2 grouped_reduces.
+      [(0, 16, 'G'), (1, 2, 'G'), (1, 4, 'R')], [(0, 2, 'G'), (1, 32, 'G'), (1, 4, 'R')], # Checking how it works with 2 grouped_reduces + upcasts.
+      [(0, 4, 'L'), (1, 4, 'L'), (0, 8, 'G'), (1, 4, 'G')], [(0, 4, 'L'), (1, 4, 'L'), (0, 2, 'G'), (1, 32, 'G'), (1, 4, 'R')], # Checking how it works with 2 grouped_reduces + upcasts + locals.
+      [(0, 2, 'L'), (1, 2, 'L'), (0, 8, 'G'), (1, 4, 'G'), (0, 2, 'U')], [(0, 2, 'L'), (1, 2, 'L'), (0, 8, 'G'), (1, 4, 'G'), (0, 2, 'U'), (0, 4, 'R'), (1, 4, 'R')], # Checking how it works with 2 grouped_reduces + upcasts + locals.
+      [(0, 4, 'L'), (1, 4, 'L'), (0, 8, 'G'), (1, 4, 'G'), (0, 2, 'U'), (1, 2, 'U')], # No globals
+    ])
+
 if __name__ == '__main__':
   unittest.main()
diff --git a/tinygrad/codegen/optimizer.py b/tinygrad/codegen/optimizer.py
index a651da579c..89bf2875f0 100644
--- a/tinygrad/codegen/optimizer.py
+++ b/tinygrad/codegen/optimizer.py
@@ -131,17 +131,17 @@ class OptimizedKernel(Kernel):
     for axis, amt, typ in x:
       if axis is None or amt == 1: continue
       if typ == "G":
-        assert self.full_shape[self.first_reduce+axis] % amt == 0, "no longer valid shift"
-        self.shift_to(self.first_reduce+axis, amt, top=True, insert_before=self.first_reduce+axis+len(self.group_for_reduce))
+        assert self.full_shape[self.first_reduce+axis+len(self.group_for_reduce)] % amt == 0, "no longer valid shift"
+        self.shift_to(self.first_reduce+axis+len(self.group_for_reduce), amt, top=True, insert_before=self.first_reduce+len(self.group_for_reduce))
         self.group_for_reduce.append(amt)
       if typ == "R":
         typ = "U"
-        axis += self.first_reduce
-      if typ == "U" and (len(self.group_for_reduce) == 0 or self.first_reduce != axis):
+        axis += self.first_reduce + len(self.group_for_reduce)
+      if typ == "U":
         assert self.full_shape[axis] % amt == 0, "no longer valid shift"
         self.shift_to(axis, amt)
         self.upcast()
-      elif typ == "L" and len(self.group_for_reduce) == 0: # TODO: Cannot mix local+group_for_reduce, codegen need to be fixed.
+      elif typ == "L":
         assert self.full_shape[axis] % amt == 0, "no longer valid shift"
         self.shift_to(axis, amt, insert_before=self.first_reduce)
         self.local_dims += 1
diff --git a/tinygrad/codegen/search.py b/tinygrad/codegen/search.py
index 1311a0bc85..9f6299bafe 100644
--- a/tinygrad/codegen/search.py
+++ b/tinygrad/codegen/search.py
@@ -11,15 +11,14 @@ def get_divisors(n, min_div = 1, max_div = 512):
 def kernel_optimize_opts(k:Linearizer):
   import nevergrad as ng
   opts = []
-  if k.first_reduce < k.shape_len: # TODO: Grouped reduces do not work with other locals. More chances to mutate to 1, so locals can be used.
-    opts.append(ng.p.TransitionChoice([(0,s,"G") for s in get_divisors(k.full_shape[k.first_reduce], min_div=16) if all(st.shape[k.first_reduce] % s == 0 or st.shape[k.first_reduce] == 1 for st in k.sts)], transitions=(0.8, 0.2)))
   for i in range(k.first_reduce):
     # TODO: the upcast always happen first, you might want to reverse this?
     # TODO: the order of the locals might improve things too
-    opts.append(ng.p.TransitionChoice([(i,s,"U") for s in get_divisors(k.full_shape[i], max_div=32)]))
-    opts.append(ng.p.TransitionChoice([(i,s,"L") for s in get_divisors(k.full_shape[i])]))
+    opts.append(ng.p.TransitionChoice([(i,s,"U") for s in get_divisors(k.full_shape[i], max_div=8)]))
+    opts.append(ng.p.TransitionChoice([(i,s,"L") for s in get_divisors(k.full_shape[i], min_div=4)]))
   for i in range(k.shape_len-k.first_reduce):
-    opts.append(ng.p.TransitionChoice([(i,s,"R") for s in get_divisors(k.full_shape[k.first_reduce+i], max_div=32)]))
+    opts.append(ng.p.TransitionChoice([(i,s,"R") for s in get_divisors(k.full_shape[k.first_reduce+i], max_div=8)]))
+    opts.append(ng.p.TransitionChoice([(i,s,"G") for s in get_divisors(k.full_shape[k.first_reduce+i], min_div=4) if all(st.shape[k.first_reduce+i] % s == 0 or st.shape[k.first_reduce+i] == 1 for st in k.sts)]))
   return opts
 
 def kernel_optimize_search(k:Linearizer, create_k:Callable[[], Linearizer], to_prg, baseline):