exo/tinychat/test/test_schedule.py

# this will be the new test_ops for the next level
# schedule confirms the right things are capable of fusing
# NOTE: this has overlap with external_test_opt.py

import unittest
import numpy as np
from typing import List, Optional, Union
from tinygrad import nn, dtypes
from tinygrad.device import Device
from tinygrad.tensor import Tensor
from tinygrad.ops import BinaryOps, MetaOps, ReduceOps, UnaryOps
from tinygrad.helpers import DEBUG, flatten, getenv
from tinygrad.codegen.kernel import Kernel
from tinygrad.engine.graph import print_tree
from tinygrad.engine.schedule import create_schedule
from tinygrad.engine.realize import run_schedule
from test.helpers import is_dtype_supported
from tinygrad.function import Function
from tinygrad.lazy import LazyBuffer, view_supported_devices

class KernelCountException(Exception): pass
def check_schedule(t:Union[Tensor, List[Tensor]], allowed:int, to_prerealize:Optional[List[Tensor]]=None, filter_sink=True):
  if isinstance(t, Tensor): t = [t]
  seen = set()
  if to_prerealize:
    for pre in to_prerealize:
      for s in pre.schedule(seen=seen.copy()):
        for i,out in enumerate(s.outputs):
          seen.add(out)
  sched = create_schedule(flatten([r.lazydata.lbs for r in t]), seen)
  if filter_sink: sched = [s for s in sched if s.ast.op is MetaOps.KERNEL]
  if len(sched) != allowed: print(f"SCHEDULE ISSUE, expecting {allowed} got {len(sched)}")
  if len(sched) != allowed or DEBUG >= 3:
    for i, s in enumerate(sched):
      print("kernel", i+1)
      print_tree(s.ast)
  if len(sched) != allowed: raise KernelCountException(f"{len(sched)=} != {allowed}")
  # test the (sink) ops linearize
  for s in sched:
    if s.ast.op is not MetaOps.KERNEL: continue
    l = Kernel(s.ast)
    l.hand_coded_optimizations()
    l.linearize()
  return sched

class TestSchedule(unittest.TestCase):
  def test_basic_binop_fusion(self):
    a = Tensor.empty(10)
    b = Tensor.empty(10)
    c = Tensor.empty(10)
    d = a+b+c
    check_schedule(d, 1)

  def test_basic_binop_fusion_deep(self):
    a = Tensor.empty(10)
    b = Tensor.empty(10)
    c = Tensor.empty(10)
    d = Tensor.empty(10)
    e = a+b+c+d
    check_schedule(e, 1)

  def test_mulacc_fusion(self):
    a = Tensor.empty(10)
    b = Tensor.empty(10)
    c = (a*b).sum()
    check_schedule(c, 1)

  def test_mulacc_relu_fusion(self):
    a = Tensor.empty(10)
    b = Tensor.empty(10)
    c = (a*b).sum().relu()
    check_schedule(c, 1)

  def test_binop_reshape_fusion(self):
    a = Tensor.empty(10)
    b = Tensor.empty(10)
    c = Tensor.empty(5,2)
    d = (a+b).reshape(5,2)+c
    check_schedule(d, 1)

  def test_binop_permute_fusion(self):
    a = Tensor.empty(2,5)
    b = Tensor.empty(2,5)
    c = Tensor.empty(5,2)
    d = (a+b).permute(1,0)+c
    check_schedule(d, 1)

  def test_constants_are_embedded(self):
    a = Tensor.empty(3,3) * 2
    check_schedule(a, 2, filter_sink=False)

  def test_binop_elu_fusion(self):
    a = Tensor.empty(10)
    b = a.elu()
    check_schedule(b, 1)

  def test_binop_reshape_reduce_fusion(self):
    a = Tensor.empty(100)
    b = Tensor.empty(100)
    c = (a+b).reshape(10, 10).sum(axis=0, keepdim=True)
    check_schedule(c, 1)

  def test_reduce_reshape_binop_fusion(self):
    a = Tensor.empty(10,10)
    b = Tensor.empty(10)
    c = a.sum(axis=0) + b
    check_schedule(c, 1)

  # not pushing permutes through reduces
  def test_reduce_permute_binop_fusion(self):
    a = Tensor.empty(10,10,10)
    b = Tensor.empty(10,10,1)
    c = a.sum(axis=0, keepdim=True).permute(2,1,0) + b
    with self.assertRaises(KernelCountException): check_schedule(c, 1)

  def test_binop_early_reshape_reduce_fusion(self):
    a = Tensor.empty(100)
    b = Tensor.empty(100)
    c = Tensor.empty(10,10)
    d = ((a+b).reshape(10,10) + c).sum(axis=0)
    check_schedule(d, 1)

  def test_diamond_folded(self):
    a = Tensor.empty(10)
    b = Tensor.empty(10)
    c = Tensor.empty(10)
    d = Tensor.empty(10)
    ab = a+b
    e = (ab+c) + (ab+d)
    check_schedule(e, 1)

  def test_cache_binaryop(self):
    a = Tensor.empty(10)
    b = Tensor.empty(10)
    c = a+b
    d = a+b
    check_schedule(d, 0, [c])

  # failing in new lazy
  def test_cache_binaryop_reshaped(self):
    a = Tensor.empty(10)
    b = Tensor.empty(10)
    c = a+b
    d = a.reshape(10,1)+b.reshape(10,1)
    with self.assertRaises(KernelCountException): check_schedule(d, 0, [c])

  # failing in new lazy
  def test_cache_binaryop_transpose(self):
    a = Tensor.empty(10,10)
    b = Tensor.empty(10,10)
    c = (a.T*b.T).T #.contiguous()
    d = a*b
    with self.assertRaises(KernelCountException): check_schedule(d, 0, [c])

  def test_cache_two_reduceops(self):
    a = Tensor.empty(10)
    b = a.sum()
    c = a.sum()
    bc = b+c
    check_schedule(bc, 1)

  def test_cache_reduce_parent(self):
    x = Tensor.empty(32)
    r0 = x.mean(axis=0, keepdim=True)
    r1 = (x - r0).sum(axis=0).div(2)
    out = r0 + r1
    schedule = check_schedule(out, 2)
    reduceops = [x for si in schedule for x in si.ast.lazyops if x.op in ReduceOps]
    assert len(reduceops) == 2

  def test_cache_reduce_multiple_children(self):
    x = Tensor.empty(32)
    y = Tensor.empty(4, 4)
    r0 = x.mean(axis=0, keepdim=True)
    r1 = (x - r0).sum(axis=0).div(2)
    out0 = r0 + y
    out1 = r1 + y
    schedule = check_schedule([out0, out1], 4)
    reduceops = [x for si in schedule for x in si.ast.lazyops if x.op in ReduceOps]
    assert len(reduceops) == 2

  def test_fold_double_unary(self):
    y = Tensor.empty(2)
    out = y.sum(keepdim=True).sqrt().__neg__()
    check_schedule(out, 1)

  #@unittest.skip("may want to reconsider this")
  def test_fold_batchnorm(self):
    with Tensor.train():
      img = Tensor.empty(1,32,4,4)
      bn = nn.BatchNorm2d(32, track_running_stats=False)
      out = bn(img)
      check_schedule(out, 3)

  def test_fold_conv_batchnorm_notrain(self):
    with Tensor.train(False):
      img = Tensor.empty(1,3,8,8)
      c1 = nn.Conv2d(3,32,3)
      bn = nn.BatchNorm2d(32, track_running_stats=False)
      out = bn(c1(img)).relu()
      check_schedule(out, 1, [c1.weight, c1.bias])

  def test_fold_conv_batchnorm(self):
    with Tensor.train():
      img = Tensor.empty(1,3,8,8)
      c1 = nn.Conv2d(3,32,3)
      bn = nn.BatchNorm2d(32, track_running_stats=False)
      out = bn(c1(img)).relu()
      check_schedule(out, 4, [c1.weight, c1.bias])

  def test_fold_conv_batchnorm_optim(self):
    # this is too high
    for optim, cnt in [(nn.optim.Adam, 19), (nn.optim.SGD, 17)]:
      with self.subTest(optim=optim.__name__):
        with Tensor.train():
          img = Tensor.ones(1,3,4,4)
          c1 = nn.Conv2d(3,32,3)
          bn = nn.BatchNorm2d(32, track_running_stats=False)
          opt = optim(nn.state.get_parameters([c1, bn]))
          img_bn = bn(c1(img)).elu().sum()
          opt.zero_grad()
          img_bn.backward()
          check_schedule(opt.schedule_step(), cnt)

  def test_fold_conv_relu_backward(self):
    c1 = nn.Conv2d(3,16,3, bias=False)
    c1.weight.requires_grad = True

    # run
    img = Tensor.rand(2,3,64,64, requires_grad=True)
    c1(img).relu().mean().backward()
    # TODO: this should be 4, not 5
    # img.grad is requiring two reduces
    check_schedule([img.grad, c1.weight.grad], 5)

  def test_fold_batchnorm_backward(self):
    with Tensor.train():
      x = Tensor.empty((2, 16, 8, 8)).contiguous()
      bn = nn.BatchNorm2d(16)
      bn.weight.requires_grad = bn.bias.requires_grad = x.requires_grad = True
      fw = bn(x).contiguous_backward().relu().contiguous()
      fw.sum().backward()
      # TODO: this is too many
      check_schedule([x.grad, bn.weight.grad, bn.bias.grad, fw], 10)

  def test_fold_conv_relu(self):
    c1 = nn.Conv2d(3,16,3)

    # run
    img = Tensor.ones(2,3,64,64)
    out = c1(img).relu()
    check_schedule(out, 1, [c1.weight, c1.bias])

  def test_fold_conv_relu_alt(self):
    img = Tensor.ones(1,4,8,8)
    c1 = nn.Conv2d(4, 4, kernel_size=3)
    c2 = nn.Conv2d(4, 4, kernel_size=3)
    img_conv = img.sequential([c1, Tensor.relu, c2, Tensor.relu])
    check_schedule(img_conv, 2, [*nn.state.get_parameters(c1), *nn.state.get_parameters(c2), img])

  def test_fold_conv_relu_nobias(self):
    img = Tensor.ones(1,4,8,8)
    c1 = nn.Conv2d(4, 4, kernel_size=3, bias=False)
    c2 = nn.Conv2d(4, 4, kernel_size=3, bias=False)
    out = img.sequential([c1, Tensor.relu, c2, Tensor.relu])
    check_schedule(out, 2, [c1.weight, c2.weight, img])

  def test_fold_conv_elu(self):
    c1 = nn.Conv2d(3,16,3)

    # run
    img = Tensor.rand(2,3,64,64)
    out = c1(img).elu()
    check_schedule(out, 1, [c1.weight, c1.bias, img])

  def test_fold_conv_elu_alt(self):
    img = Tensor.ones(1,4,8,8).contiguous()
    c1 = nn.Conv2d(4, 4, kernel_size=3)
    c2 = nn.Conv2d(4, 4, kernel_size=3)
    img_conv = img.sequential([c1, Tensor.elu, c2, Tensor.elu])
    check_schedule(img_conv, 2, [*nn.state.get_parameters(c1), *nn.state.get_parameters(c2), img])

  def test_two_sum(self):
    img = Tensor.empty(64,64)
    x = (img.sum(0) + img.sum(1))
    out = x.relu()
    del x    # is 3 without this
    check_schedule(out, 2)

  #@unittest.skip("failing in old lazy")
  def test_push_permute_through_reshape(self):
    a = Tensor.empty(16,16)
    b = Tensor.empty(16,16)
    c = (a+b).reshape(4,4,4,4).permute(2,3,0,1).contiguous()
    check_schedule(c, 1)

  #@unittest.skip("failing in old lazy")
  def test_push_permute_through_reshape_alt(self):
    a = Tensor.empty(4,4,4,4)
    b = Tensor.empty(4,4,4,4)
    c = (a+b).reshape(16,16).permute(1,0).contiguous()
    check_schedule(c, 1)

  def test_no_binop_rerun(self):
    a = Tensor.empty(16)
    b = Tensor.empty(16)
    c = a+b
    d = (a+b).reshape(16,1)
    check_schedule(d, 0, [c])

  def test_multi_permute_should_collapse(self):
    a = Tensor.empty(4,4,4,4)
    b = Tensor.empty(16)
    c = a.sum((0,1)).cast(dtypes.float16).permute(1,0).reshape(4,4,1).permute(1,0,2).reshape(16) + b
    check_schedule(c, 1)

  def test_fancy_reshape_fusion(self):
    a = Tensor.empty(10)
    b = Tensor.empty(10)
    c = a+b
    d = a.reshape(10,1)+b.reshape(10,1)
    out = c.sum() + d.sum()
    with self.assertRaises(KernelCountException): check_schedule(out, 1)

  def test_children_dont_push(self):
    a = Tensor.empty(10, 10, 1)
    b = Tensor.empty(10, 10, 1)
    d = (a+b).expand(10, 10, 10)
    e = (a+b).permute(2,1,0)
    f = d+e
    check_schedule(f, 2)

  # failing in new lazy
  def test_dont_fuse_binops_with_children(self):
    a = Tensor.empty(10)
    b = Tensor.empty(10)
    c = Tensor.empty(10)
    keep_me = a+b
    e = keep_me.sum() # noqa: F841 give keep_me a child (NOTE: BinaryOps won't be a child since it will instant fuse)
    d = keep_me+c
    with self.assertRaises(KernelCountException): check_schedule(d, 2)
    with self.assertRaises(KernelCountException): check_schedule(keep_me, 0, [d])

  #@unittest.skip("failing in old lazy")
  def test_permute_breaks_fusion(self):
    a = Tensor.empty(10, 10, 10)
    b = Tensor.empty(10, 10)
    c = (a.sum(axis=2) + b).permute(1,0)
    d = c.permute(1,0)
    check_schedule(d, 1)

  def test_some_permute_fusion(self):
    a = Tensor.empty(8192, 16)
    b = Tensor.empty(1, 16)
    d = (a.T + b.expand(8192, 16).T)
    c = a + b.expand(8192, 16)
    e = d.T
    check_schedule(c, 1)
    check_schedule(e, 1)

  def test_shrink_fuse(self):
    a = Tensor.empty(8192, 16)
    b = Tensor.empty(8192, 16)
    c = a * b
    d = Tensor.empty(1, 16)
    e = c[0] * d
    check_schedule(e, 1)

  def test_expand_nofuse(self):
    a = Tensor.empty(1, 16)
    b = Tensor.empty(1, 16)
    c = a * b
    d = Tensor.empty(8192, 16)
    e = c * d
    check_schedule(e, 2)

  # this is the failing case in openpilot...it's very simple like this
  def test_image_conv_fusion(self):
    w1 = Tensor.empty(16, 16, 1, 1)
    b1 = Tensor.empty(16)
    w2 = Tensor.empty(16, 16, 1, 1)
    b2 = Tensor.empty(16)
    w3 = Tensor.empty(16, 16, 1, 1)
    b3 = Tensor.empty(16)

    x = Tensor.empty(1, 16, 32, 32)
    x = base = x.image_conv2d(w1, b1)
    x = x.image_conv2d(w2, b2) + base
    x = x.image_conv2d(w3, b3)

    # NOOP, 3 convs, contiguous
    with self.assertRaises(KernelCountException): check_schedule(x, 5)

  def test_image_conv_fusion_minimal(self):
    b1 = Tensor.empty(16)
    b2 = Tensor.empty(16)
    def p(x): return x.permute(1,0).contiguous().reshape(32,16,1).expand(32,16,16).sum(axis=2).permute(1,0)

    x = Tensor.empty(16, 32)
    x = base = p(x) + b1.reshape(16,1)
    x = p(x)
    x = x + b2.reshape(16,1)
    x = x + base
    del base
    x = p(x)
    check_schedule(x, 4)

  def test_image_conv_fusion_more_minimal(self):
    b1 = Tensor.empty(16)
    def p(x): return x.permute(1,0).contiguous().reshape(32,16,1).expand(32,16,16).sum(axis=2).permute(1,0)

    x = Tensor.empty(16, 32)
    x = base = p(x) + b1.reshape(16,1)
    x = p(x)
    del base
    check_schedule(x, 3)

  def test_resnet_block(self):
    old_training = Tensor.training
    Tensor.training = False

    in_planes, planes = 64, 64
    conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, stride=1, padding=1, bias=False)
    bn1 = nn.BatchNorm2d(planes)
    conv2 = nn.Conv2d(planes, planes, kernel_size=3, padding=1, stride=1, bias=False)
    bn2 = nn.BatchNorm2d(planes)

    x = Tensor.empty(1, 64, 32, 32)
    out = bn1(conv1(x)).relu()
    out = bn2(conv2(out))
    out = (out + x).relu()
    check_schedule(out, 2, [conv1.weight, conv2.weight])
    Tensor.training = old_training

  def test_contiguous_while_contiguous(self):
    x = Tensor.empty(1, 64, 32, 32)
    out = x.contiguous()
    check_schedule(out, 1, filter_sink=False)

  def test_contiguous_while_not_contiguous(self):
    x = Tensor.empty(1, 64, 32, 32)
    out = x.permute(0,2,3,1).contiguous()
    check_schedule(out, 2, filter_sink=False)

  def test_fold_with_contiguous(self):
    a = Tensor.randn(16, 16, 16).realize()
    b = Tensor.randn(16, 16).realize()
    c = (a.sum(2).contiguous() + b).contiguous()
    check_schedule(c, 2)

  def test_double_from(self):
    x = Tensor([1,2,3,4])
    out = x.to('npy')
    check_schedule(out, 0, filter_sink=False)

  def test_pow_const_tensor_simplified(self):
    x = Tensor([1,2,3,4])
    # NOTE: this does not test ** Tensor(2) is simpler in ast than ** Tensor(2.5)
    out = x ** Tensor(2)
    check_schedule(out, 1)

  def test_pow_const_tensor_to_zero(self):
    x = Tensor([1,2,3,4])
    out = x ** Tensor(0)
    # NOTE: this is ConstBuffer 0 + ConstBuffer 1
    check_schedule(out, 0)

  def test_zero_size(self):
    x = Tensor.empty(2, 3, 0)
    out = x + 1
    check_schedule(out, 0, filter_sink=False)

  def test_reduce_permute_nofuse(self):
    x = Tensor.empty(32, 32, 32)
    y = Tensor.empty(32, 32)
    out = x.sum(axis=2).T+y
    check_schedule(out, 2)

  def test_two_elus_sum(self):
    x = Tensor.empty(32, 32)
    y = Tensor.empty(32, 32)
    out = x.sum(1).relu().elu() + y.sum(1).relu().elu()
    check_schedule(out, 2)

  # multireduce spec
  @unittest.skipUnless(getenv("SPLIT_REDUCEOP", 1), "Testing split reducop requires SPLIT_REDUCEOP")
  def test_preserve_multistage_reduce(self):
    big_enough = getenv("REDUCEOP_SPLIT_THRESHOLD", 32768)
    x = Tensor.randn(big_enough).realize()
    out = (x - x.max(keepdim=True)).max()
    run_schedule(check_schedule(out, 4))
    np.testing.assert_allclose(out.numpy(), (x.numpy() - x.numpy().max(keepdims=True)).max())

  def test_multistage_reduce(self):
    x = Tensor.empty(32, 32, 32)
    out = x.sum(2).relu().sum(1)
    check_schedule(out, 2)

  def test_multistage_reduce_fork(self):
    x = Tensor.empty(32, 32, 32)
    x = x.sum(2)
    out2 = x + 1
    out = x.relu().sum(1) + out2[0]
    check_schedule(out, 2)

  # multireduce spec
  def test_example_matmul(self):
    x = Tensor.eye(64, requires_grad=True)
    y = Tensor.eye(64, requires_grad=True)
    z = y.matmul(x).sum()
    z.backward()
    out = x.grad.contiguous()
    run_schedule(check_schedule(out, 2))
    np.testing.assert_allclose(out.numpy(), np.ones((64,64)))

  def test_contiguous_add(self):
    x = Tensor.empty(32)
    y = Tensor.empty(32)
    z = Tensor.empty(32)
    out = (x+y).contiguous()+z
    check_schedule(out, 2)

  def test_double_sum_ref(self):
    x = Tensor.empty(32, 32, 32)
    x = x.sum(2)
    out = x + x[:, 4]
    check_schedule(out, 2)

  def test_reduce_shrink(self):
    x = Tensor.empty(32, 32)
    y = Tensor.empty(16)
    x = x.sum(1)
    x = x[:16]
    out = x + y
    check_schedule(out, 2)  # TODO: this should be 1

  # multireduce spec
  def test_multireduce_shrink(self):
    Tensor.manual_seed(0)
    a = Tensor.randn(32, 32).realize()
    b = Tensor.randn(32, 32).realize()
    c = Tensor.randn(16).realize()
    a_out = a.sum(1)
    a_out = a_out[:16]
    b_out = b.sum(1)
    b_out = b_out[:16]
    out = a_out + b_out + c
    # run_schedule(check_schedule(out, 2))  # TODO: this should be 1 (can we make it 1 with the new linearizer?)
    run_schedule(check_schedule(out, 3))
    np.testing.assert_allclose(out.numpy(), a.numpy().sum(axis=1)[:16] + b.numpy().sum(axis=1)[:16] + c.numpy(), atol=1e-4, rtol=1e-4)

  # broken due to const folding and two contiguous are different kernels
  def test_const_no_recompute(self):
    x = Tensor(2) + Tensor(2)
    y = Tensor(2) + Tensor(2)
    out = x.contiguous() + y.contiguous()
    with self.assertRaises(KernelCountException): check_schedule(out, 2, filter_sink=False)

  # multireduce spec
  def test_reduce_same_size(self):
    Tensor.manual_seed(0)
    a = Tensor.randn(4, 4).realize()
    out0 = a.sum() + 2
    out1 = a.sum() + 4
    out2 = out0 * out1
    run_schedule(check_schedule([out0, out1, out2], 1))
    np.testing.assert_allclose(out0.numpy(), out0_np:=a.numpy().sum()+2, atol=1e-4, rtol=1e-6)
    np.testing.assert_allclose(out1.numpy(), out1_np:=a.numpy().sum()+4, atol=1e-4, rtol=1e-6)
    np.testing.assert_allclose(out2.numpy(), out0_np*out1_np, atol=1e-4, rtol=1e-6)

  # multireduce spec
  def test_reduce_multiple_paths(self):
    Tensor.manual_seed(0)
    a = Tensor.randn(4, 4).realize()
    out0 = a.sum().exp2()
    # out1 has two paths to a.sum()
    out1 = a.sum() + out0
    run_schedule(check_schedule([out0, out1], 1))
    np.testing.assert_allclose(out0.numpy(), out0_np:=np.exp2(a.numpy().sum()), atol=1e-4, rtol=1e-4)
    np.testing.assert_allclose(out1.numpy(), a.numpy().sum()+out0_np, atol=1e-4, rtol=1e-6)

  # multireduce spec
  def test_multireduce_reduce_multiple_paths(self):
    Tensor.manual_seed(0)
    a = Tensor.randn(4, 4).realize()
    out0 = a.sum().exp2()
    out1 = a.sum() + out0
    b = (a + out0 + out1)
    out2 = b.sum().exp2()
    out3 = b.sum() + out2
    # run_schedule(check_schedule([out0, out1, out2, out3], 1))
    run_schedule(check_schedule([out0, out1, out2, out3], 2))
    np.testing.assert_allclose(out0.numpy(), np_out0:=np.exp2(a.numpy().sum()), atol=1e-4, rtol=1e-4)
    np.testing.assert_allclose(out1.numpy(), np_out1:=a.numpy().sum()+np_out0, atol=1e-4, rtol=1e-4)
    np_b = (a.numpy() + np_out0 + np_out1)
    np.testing.assert_allclose(out2.numpy(), np_out2:=np.exp2(np_b.sum()), atol=1e-4, rtol=1e-4)
    np.testing.assert_allclose(out3.numpy(), np_b.sum()+np_out2, atol=1e-4, rtol=1e-4)

  # multireduce spec
  def test_reduce_ext_reduce_child(self):
    Tensor.manual_seed(0)
    a = Tensor.randn(4, 4).realize()
    b = Tensor.randn(4, 4).realize()
    # b.sum() is not a descendant of the fused nodes
    out0 = a.sum() + b.sum() + 2
    out1 = a.sum() + b.sum() + 4
    # run_schedule(check_schedule([out0, out1], 1))
    run_schedule(check_schedule([out0, out1], 4))
    np.testing.assert_allclose(out0.numpy(), a.numpy().sum()+b.numpy().sum()+2, atol=1e-4, rtol=1e-4)
    np.testing.assert_allclose(out1.numpy(), a.numpy().sum()+b.numpy().sum()+4, atol=1e-4, rtol=1e-4)

  # multireduce spec
  def test_reduce_multiple_paths_midreduce(self):
    Tensor.manual_seed(0)
    a = Tensor.randn(4, 4).realize()
    r = a.sum()
    out0 = r.exp2()
    # reduce node in the indirect path from r to out2
    out1 = (a - out0).max()
    out2 = r + out1
    # run_schedule(check_schedule([r, out0, out1, out2], 1))
    run_schedule(check_schedule([r, out0, out1, out2], 4))
    np.testing.assert_allclose(r.numpy(), r_np:=a.numpy().sum(), atol=1e-4, rtol=1e-4)
    np.testing.assert_allclose(out0.numpy(), out0_np:=np.exp2(r_np), atol=1e-4, rtol=1e-4)
    np.testing.assert_allclose(out1.numpy(), out1_np:=(a.numpy() - out0_np).max(), atol=1e-4, rtol=1e-4)
    np.testing.assert_allclose(out2.numpy(), r_np + out1_np, atol=1e-4, rtol=1e-4)

  # multireduce spec
  def test_reduce_multiple_paths_midreduce_fused(self):
    Tensor.manual_seed(0)
    a = Tensor.randn(4, 4).realize()
    b = Tensor.randn(4, 4).realize()
    out0 = a.sum() + 4
    out1 = b.max() + out0*2
    out2 = a.sum() + out1
    # run_schedule(check_schedule([out0, out1, out2], 1))
    run_schedule(check_schedule([out0, out1, out2], 4))
    np.testing.assert_allclose(out0.numpy(), out0_np:=a.numpy().sum()+4, atol=1e-4, rtol=1e-6)
    np.testing.assert_allclose(out1.numpy(), out1_np:=b.numpy().max() + out0_np*2, atol=1e-4, rtol=1e-6)
    np.testing.assert_allclose(out2.numpy(), a.numpy().sum() + out1_np, atol=1e-4, rtol=1e-6)

  # multireduce spec
  def test_reduce_multiple_paths_midexpand(self):
    Tensor.manual_seed(0)
    a = Tensor.randn(4, 4).realize()
    b = Tensor.randn(4, 4, 4).realize()
    r = a.sum()
    out0 = r.exp2()
    # e1 is in the indirect path from a.sum() to out1
    e = b + out0
    out1 = r + e[0][0][0]
    # run_schedule(check_schedule([r, out0, out1, e], 3)) # 1 or 2 or 3? should be 1 (one reduce) but the different outputs might make it 3
    run_schedule(check_schedule([r, out0, out1, e], 4))
    np.testing.assert_allclose(r.numpy(), r_np:=a.numpy().sum(), atol=1e-4, rtol=1e-4)
    np.testing.assert_allclose(out0.numpy(), out0_np:=np.exp2(r_np), atol=1e-4, rtol=1e-4)
    np.testing.assert_allclose(e.numpy(), e_np:=b.numpy() + out0_np, atol=1e-4, rtol=1e-4)
    np.testing.assert_allclose(out1.numpy(), r_np + e_np[0][0][0], atol=1e-4, rtol=1e-4)

  # changed by multireduce
  def test_reduce_expand_child(self):
    Tensor.manual_seed(0)
    a = Tensor.randn((32, 32, 32)).realize()
    b = Tensor.randn((1, 16)).realize()
    out0 = a.sum() + 2
    out1 = a.sum() + b
    # run_schedule(check_schedule([out0, out1], 2))
    run_schedule(check_schedule([out0, out1], 4))
    np.testing.assert_allclose(out0.numpy(), a.numpy().sum()+2, atol=1e-4, rtol=1e-4)
    np.testing.assert_allclose(out1.numpy(), a.numpy().sum()+b.numpy(), atol=1e-4, rtol=1e-4)

  def test_reduce_shrink_child(self):
    a = Tensor.empty(100, 100)
    b = Tensor.empty(10,)
    c = a.sum() + b[0]
    d = a.sum() + 2
    check_schedule([c, d], 1)

  def test_reduce_multiple_paths_midshrink(self):
    a = Tensor.empty(4, 4)
    r = a.sum(axis=1)
    out0 = r.exp2()
    out1 = out0[0] + out0
    check_schedule([r, out0, out1], 3)

  def test_reduce_shrink_output(self):
    a = Tensor.empty(4, 4)
    r = a.sum(keepdim=True)
    out0 = r.exp2()
    out1 = out0[0] + Tensor.empty(1, )
    check_schedule([r, out0, out1], 3)

  # multireduce spec
  def test_std_multireduce_fusion(self):
    Tensor.manual_seed(0)
    x = Tensor.randn(4, 32).realize()
    out = x.std(-1)
    run_schedule(check_schedule(out, 2))
    np.testing.assert_allclose(out.numpy(), x.numpy().std(axis=-1, ddof=1), atol=1e-4, rtol=1e-4)

  # multireduce spec
  def test_argmin_multireduce_fusion(self):
    Tensor.manual_seed(0)
    x = Tensor.randn(4, 32).realize()
    out = x.argmin(-1)
    run_schedule(check_schedule(out, 3))
    np.testing.assert_equal(out.numpy(), x.numpy().argmin(axis=-1))

  # multireduce spec
  def test_argmax_multireduce_fusion(self):
    Tensor.manual_seed(0)
    x = Tensor.randn(4, 32).realize()
    out = x.argmax(-1)
    run_schedule(check_schedule(out, 3))
    np.testing.assert_equal(out.numpy(), x.numpy().argmax(axis=-1))

  # multireduce spec
  def test_scaled_dot_product_attention_multireduce_fusion(self):
    Tensor.manual_seed(0)
    q = Tensor.randn(32,8,16,64).realize()
    k = Tensor.randn(32,8,16,64).realize()
    v = Tensor.randn(32,8,16,64).realize()
    out = Tensor.scaled_dot_product_attention(q,k,v)
    check_schedule(out, 5) # correctness checked in test_ops

  # multireduce spec
  def test_ugly_reduceop_pairing(self):
    Tensor.manual_seed(0)
    a = Tensor.randn(4, 32).realize()
    b = Tensor.randn(4, 32).realize()
    c = Tensor.randn(4, 32).realize()
    out = (c * a.sum(-1, keepdim=True)).sum(-1) + (b * a.sum(-1, keepdim=True)).sum(-1) # a.sum has >1 children but should still fuse
    # run_schedule(check_schedule(out, 1))
    run_schedule(check_schedule(out, 3))
    np.testing.assert_allclose(out.numpy(), \
      (c.numpy()*a.numpy().sum(axis=-1,keepdims=True)).sum(-1) + (b.numpy()*a.numpy().sum(axis=-1,keepdims=True)).sum(-1), atol=1e-4, rtol=1e-4)

  # multireduce spec
  def test_reduce_expand_reduce_fusion(self):
    Tensor.manual_seed(0)
    a = Tensor.randn(4, 32).realize()
    out = (a+a.sum(-1, keepdim=True)).sum(-1)
    # run_schedule(check_schedule(out, 1))
    run_schedule(check_schedule(out, 2))
    np.testing.assert_allclose(out.numpy(), (a.numpy()+a.numpy().sum(axis=-1,keepdims=True)).sum(axis=-1), atol=1e-4, rtol=1e-4)

  # multireduce spec
  def test_reduce_expand_reduce_expand_fusion(self):
    Tensor.manual_seed(0)
    a = Tensor.randn(4, 32).realize()
    out = a+(a+a.sum(-1,keepdim=True)).sum(-1, keepdim=True)
    # run_schedule(check_schedule(out, 2))
    run_schedule(check_schedule(out, 3))
    np.testing.assert_allclose(out.numpy(), \
      a.numpy()+(a.numpy()+a.numpy().sum(axis=-1,keepdims=True)).sum(axis=-1,keepdims=True), atol=1e-4, rtol=1e-4)

  # multireduce spec
  def test_branching_reduces_and_expands_fusion(self):
    Tensor.manual_seed(0)
    a = Tensor.randn(4, 32).realize()
    out0 = a+a.sum(-1, keepdim=True)
    out1 = out0.sum(-1)
    # run_schedule(check_schedule(out, 2))
    run_schedule(check_schedule([out0, out1], 3))
    np.testing.assert_allclose(out0.numpy(), a.numpy()+a.numpy().sum(axis=-1,keepdims=True), atol=1e-4, rtol=1e-4)
    np.testing.assert_allclose(out1.numpy(), (a.numpy()+a.numpy().sum(axis=-1,keepdims=True)).sum(axis=-1), atol=1e-4, rtol=1e-4)

  # multireduce spec
  def test_multireduce_fusion_simple_sequential(self):
    Tensor.manual_seed(0)
    x = Tensor.randn(4, 32).realize()
    y = Tensor.randn(4, 32).realize()
    out = (y + x.sum(axis=-1, keepdim=True)).sum(axis=-1)
    # run_schedule(check_schedule(out, 1))
    run_schedule(check_schedule(out, 2))
    np.testing.assert_allclose(out.numpy(), (y.numpy() + x.numpy().sum(axis=-1, keepdims=True)).sum(axis=-1), atol=1e-4, rtol=1e-4)

  # multireduce spec
  def test_multireduce_fusion_simple_parallel(self):
    Tensor.manual_seed(0)
    x = Tensor.randn(4, 32).realize()
    y = Tensor.randn(4, 32).realize()
    out = y.sum(axis=-1) + x.sum(axis=-1)
    # run_schedule(check_schedule(out, 1))
    run_schedule(check_schedule(out, 2))
    np.testing.assert_allclose(out.numpy(), y.numpy().sum(axis=-1) + x.numpy().sum(axis=-1), atol=1e-4, rtol=1e-4)

  # multireduce spec
  def test_multireduce_fusion_sequential(self):
    Tensor.manual_seed(0)
    x = Tensor.randn(4, 32).realize()
    out = x.std(-1)
    # run_schedule(check_schedule(out, 1))
    run_schedule(check_schedule(out, 2))
    np.testing.assert_allclose(out.numpy(), x.numpy().std(axis=-1, ddof=1), atol=1e-4, rtol=1e-4)

  # multireduce spec
  def test_multireduce_fusion_parallel(self):
    Tensor.manual_seed(0)
    x = Tensor.randn(4, 32).realize()
    y = Tensor.randn(4, 32).realize()
    out = x.std(-1) + y.std(-1)
    # run_schedule(check_schedule(out, 1))
    run_schedule(check_schedule(out, 4))
    np.testing.assert_allclose(out.numpy(), x.numpy().std(axis=-1, ddof=1) + y.numpy().std(axis=-1, ddof=1), atol=1e-4, rtol=1e-4)

  # multireduce spec
  def test_multireduce_diffops_sequential(self):
    Tensor.manual_seed(0)
    x = Tensor.randn(4, 32).realize()
    out = (x - x.max(-1, keepdim=True)).sum(-1)
    # run_schedule(check_schedule(out, 1))
    run_schedule(check_schedule(out, 2))
    np.testing.assert_allclose(out.numpy(), (x.numpy() - x.numpy().max(axis=-1, keepdims=True)).sum(axis=-1), atol=1e-4, rtol=1e-4)

  # multireduce spec
  def test_multireduce_fusion_diffops_parallel(self):
    Tensor.manual_seed(0)
    x = Tensor.randn(4, 32).realize()
    y = Tensor.randn(4, 32).realize()
    out = x.sum(-1) + y.max(-1)
    # run_schedule(check_schedule(out, 1))
    run_schedule(check_schedule(out, 2))
    np.testing.assert_allclose(out.numpy(), x.numpy().sum(axis=-1) + y.numpy().max(axis=-1), atol=1e-4, rtol=1e-4)

  # multireduce spec
  def test_multireduce_fusion_sequential_and_parallel(self):
    Tensor.manual_seed(0)
    x = Tensor.randn(4, 32).realize()
    y = Tensor.randn(4, 32).realize()
    mu = (x - x.max(axis=-1, keepdim=True)).mean(axis=-1, keepdim=True) + (y - y.max(axis=-1, keepdim=True)).mean(axis=-1, keepdim=True)
    out = [((x - mu).square().sum(-1)/x.shape[-1]).sqrt(), ((y - mu).square().sum(-1)/y.shape[-1]).sqrt()]
    np_mu = (x.numpy() - x.numpy().max(axis=-1, keepdims=True)).mean(axis=-1, keepdims=True) + \
      (y.numpy() - y.numpy().max(axis=-1, keepdims=True)).mean(axis=-1, keepdims=True)
    # run_schedule(check_schedule(out, 1))
    run_schedule(check_schedule(out, 6))
    np.testing.assert_allclose(out[0].numpy(), np.sqrt(np.square(x.numpy() - np_mu).sum(-1)/x.shape[-1]), atol=1e-4, rtol=1e-4)
    np.testing.assert_allclose(out[1].numpy(), np.sqrt(np.square(y.numpy() - np_mu).sum(-1)/y.shape[-1]), atol=1e-4, rtol=1e-4)

  # multireduce spec
  def test_multimatmul_fusion(self):
    Tensor.manual_seed(0)
    a,b = Tensor.randn(4, 64).realize(), Tensor.rand(64,8).realize()
    c,d = Tensor.randn(4, 64).realize(), Tensor.rand(64,8).realize()
    out = a@b + c@d
    # run_schedule(check_schedule(out, 1))
    run_schedule(check_schedule(out, 2))
    np.testing.assert_allclose(out.numpy(), a.numpy()@b.numpy() + c.numpy()@d.numpy(), atol=1e-4, rtol=1e-4)

  def test_softmax_fusion(self):
    Tensor.manual_seed(0)
    x = Tensor.randn(4, 12, 64, 64).realize()
    out = x.softmax()
    # run_schedule(check_schedule(out, 2))
    run_schedule(check_schedule(out, 3))
    expected = (x_exp:=np.exp(x.numpy()-x.numpy().max(-1, keepdims=True)))/x_exp.sum(-1, keepdims=True)
    np.testing.assert_allclose(out.numpy(), expected, atol=1e-4, rtol=1e-4)

  # changed by: multireduce spec
  def test_layernorm_onelayer_fusion(self):
    Tensor.manual_seed(0)
    layer = nn.LayerNorm([10, 10])
    layer.weight = Tensor.randn(10,10).realize()
    layer.bias = Tensor.randn(10,10).realize()
    x = Tensor.randn(20, 5, 10, 10).realize()
    out = layer(x)
    # run_schedule(check_schedule(out, 2))
    run_schedule(check_schedule(out, 3))
    y = (x.numpy() - x.numpy().mean(layer.axis, keepdims=True))
    expected = y / np.sqrt((y*y).mean(layer.axis, keepdims=True) + layer.eps)
    np.testing.assert_allclose(out.numpy(), expected * layer.weight.numpy() + layer.bias.numpy(), atol=1e-4, rtol=1e-4)

  def test_scaled_dot_product_attention_fusion(self):
    x, y, z, m = (Tensor.empty(32, 8, 16, 16) for _ in range(4))
    out = Tensor.scaled_dot_product_attention(x, y, z, attn_mask=m)
    check_schedule(out, 5)

  def test_scaled_dot_product_attention_causal_fusion(self):
    x, y, z, m = (Tensor.empty(32, 8, 16, 16) for _ in range(4))
    out = Tensor.scaled_dot_product_attention(x, y, z, attn_mask=m, is_causal=True)
    check_schedule(out, 6)

  def test_adam_step_fusion(self):
    with Tensor.train():
      x = Tensor.empty(4, 64, 768)
      layer = nn.Linear(768, 768*4)
      opt = nn.optim.Adam(nn.state.get_parameters(layer), lr=1e-4)
      layer(x).relu().sum().backward()
      check_schedule(opt.schedule_step(), 11)

  def test_adam_conv_fuse(self):
    with Tensor.train():
      img = Tensor.empty(2,3,4,4)
      c1 = nn.Conv2d(3,32,3)
      opt = nn.optim.Adam(nn.state.get_parameters(c1), lr=1e-4)
      opt.zero_grad()
      c1(img).relu().sum().backward()
      check_schedule(opt.schedule_step(), 11)

  def test_adam_2convs_fuse(self):
    with Tensor.train():
      img = Tensor.empty(2,3,4,4)
      c1 = nn.Conv2d(3,16,3,bias=False)
      c2 = nn.Conv2d(16,32,3,bias=False)
      opt = nn.optim.Adam(nn.state.get_parameters([c1, c2]), lr=1e-4)
      opt.zero_grad()
      c2(c1(img).relu()).relu().sum().backward()
      check_schedule(opt.schedule_step(), 13)

  def test_sgd_conv_fuse(self):
    with Tensor.train():
      img = Tensor.empty(2,3,4,4)
      c1 = nn.Conv2d(3,32,3)
      opt = nn.optim.SGD(nn.state.get_parameters(c1))
      opt.zero_grad()
      c1(img).relu().sum().backward()
      check_schedule(opt.schedule_step(), 7)

  def test_sgd_2convs_fuse(self):
    with Tensor.train():
      img = Tensor.empty(2,3,4,4)
      c1 = nn.Conv2d(3,16,3,bias=False)
      c2 = nn.Conv2d(16,32,3,bias=False)
      opt = nn.optim.SGD(nn.state.get_parameters([c1, c2]))
      opt.zero_grad()
      c2(c1(img).relu()).relu().sum().backward()
      check_schedule(opt.schedule_step(), 7)

  def test_fold_2convs_sgd_nesterov_momentum_wd(self):
    with Tensor.train():
      img = Tensor.empty(2,3,4,4)
      c1 = nn.Conv2d(3,16,3,bias=False)
      c2 = nn.Conv2d(16,32,3,bias=False)
      opt = nn.optim.SGD(nn.state.get_parameters([c1, c2]), nesterov=True, momentum=0.9, weight_decay=0.1)
      opt.zero_grad()
      c2(c1(img).relu()).relu().sum().backward()
      check_schedule(opt.schedule_step(), 9)

  def test_sgd_4convs_fuse(self):
    with Tensor.train():
      img = Tensor.empty(2,3,64,64)
      c1 = nn.Conv2d(3,4,3,bias=False)
      c2 = nn.Conv2d(4,8,3,bias=False)
      c3 = nn.Conv2d(8,16,3,bias=False)
      c4 = nn.Conv2d(16,32,3,bias=False)
      opt = nn.optim.SGD(nn.state.get_parameters([c1, c2, c3, c4]))
      opt.zero_grad()
      c4(c3(c2(c1(img).relu()).relu()).relu()).relu().sum().backward()
      check_schedule(opt.schedule_step(), 22)

  @unittest.skipUnless(is_dtype_supported(dtypes.half), "need half")
  def test_prefer_half_buffer(self):
    x = Tensor.ones(4).contiguous().realize()
    # y = Tensor.ones(4).contiguous().realize()
    z = Tensor.ones(4, 4).contiguous().realize()

    # should not create extra kernel if output will be realized anyways
    dummy = x.sum().half().float()
    check_schedule(dummy, 1)
    dummy = x.sum().half().float().contiguous() + 1
    check_schedule(dummy, 2)

    # shared between two outputs
    shared = x.sum().half().float()
    a = shared * 2
    b = shared * 3
    sched = check_schedule([a, b], 1)
    for si in sched[:-2]: assert all(out.dtype == dtypes.half for out in si.outputs)

    # reduce
    a = z.sum(axis=0).half().float().sum(axis=0)
    sched = check_schedule(a, 2)
    for si in sched[:-1]: assert all(out.dtype == dtypes.half for out in si.outputs)

    # expand
    # expand will realize just after the .float(), so requires change to realize-before-expand
    # normal = (x.sum().half().float().reshape(1) * y).sum()
    # sched = check_schedule(normal, 2)
    # for si in sched[:-1]: assert all(out.dtype == dtypes.half for out in si.outputs[:-1])

    # parallel reduce
    # a = x.sum().half().float() * y.sum().half().float()
    # b = a + 1
    # c = a + 2
    # sched = check_schedule([b, c], 4)
    # doesn't store either in half because it doesn't chase

  def test_reduce_simple_chase(self):
    a = Tensor.empty(4, 4, 4)
    r = a.sum(0) + 6
    b = r.sum(0) * 4
    c = r.sum(1) * 2
    schedule = check_schedule([b, c], 3)
    assert schedule[0].ast.src[0].src[0].op is BinaryOps.ADD

  # multireduce spec
  def test_multireduce_simple_chase(self):
    Tensor.manual_seed(0)
    a = Tensor.randn(4, 4, 4).realize()
    r = (a + (a.sum(0, keepdim=True) + 6)).sum(0) * 2
    b = r.sum(0) + 8
    c = r.sum(1) + 12
    np_r = (a.numpy() + (a.numpy().sum(0) + 6)).sum(0) * 2
    # schedule = check_schedule([b,c], 3)
    # assert schedule[0].ast[0].src[0].op is BinaryOps.MUL
    schedule = check_schedule([b,c], 4)
    run_schedule(schedule)
    np.testing.assert_allclose(b.numpy(), np_r.sum(0) + 8, atol=1e-4, rtol=1e-4)
    np.testing.assert_allclose(c.numpy(), np_r.sum(1) + 12, atol=1e-4, rtol=1e-4)

  def test_push_permute_chase(self):
    a = Tensor.empty(4, 4, 4)
    b = Tensor.empty(4, 4)
    r = a.sum(2) + b
    d = r.T * 4
    e = r * d
    schedule = check_schedule([d, e], 3)
    assert schedule[0].ast.src[0].src[0].op is BinaryOps.ADD

  # multireduce spec
  def test_multireduce_push_permute_chase(self):
    Tensor.manual_seed(0)
    a = Tensor.randn(4, 4, 4).realize()
    b = Tensor.randn(4, 4).realize()
    r = a.sum(2) + b
    d = r.T * 4
    e = r * (d + a).sum(2)
    schedule = check_schedule([d, e], 3) # make sure it doesn't fuse
    assert schedule[0].ast.src[0].src[0].op is BinaryOps.ADD
    run_schedule(schedule)
    np.testing.assert_allclose(d.numpy(), (a.numpy().sum(2) + b.numpy()).T * 4, atol=1e-4, rtol=1e-4)
    np.testing.assert_allclose(e.numpy(), (a.numpy().sum(2) + b.numpy()) * (d.numpy() + a.numpy()).sum(2), atol=1e-4, rtol=1e-4)

  def test_push_shrink_chase(self):
    a = Tensor.empty(16, 16)
    b = Tensor.empty(4)
    c = Tensor.empty(16, )
    r = a.sum(1) + c
    d = r[:4] * b
    schedule = check_schedule(d, 2)
    assert schedule[0].ast.src[0].src[0].op is BinaryOps.ADD

  # multireduce spec
  def test_multireduce_push_shrink_chase(self):
    Tensor.manual_seed(0)
    a = Tensor.randn(16, 16).realize()
    b = Tensor.randn(4).realize()
    c = Tensor.randn(16, ).realize()
    d = Tensor.randn(16, 16).realize()
    r = a.sum(1) + c
    out = r[:4] * b + d.sum(1)[:4]
    # schedule = check_schedule(out, 2)
    schedule = check_schedule(out, 3)
    assert schedule[0].ast.src[0].src[0].op is BinaryOps.ADD
    run_schedule(schedule)
    np.testing.assert_allclose(out.numpy(), (a.numpy().sum(1) + c.numpy())[:4] * b.numpy() + d.numpy().sum(1)[:4], atol=1e-4, rtol=1e-4)

  def test_midreduce_nochase(self):
    a = Tensor.empty(16, 16)
    b = (a.sum(0) + a.max(1)) + 2
    schedule = check_schedule(b, 2)
    assert schedule[0].ast.src[0].src[0].op is ReduceOps.MAX

  # multireduce spec
  def test_multireduce_midreduce_nochase(self):
    Tensor.manual_seed(0)
    a = Tensor.randn(16, 16).realize()
    b = (a.sum(0)+a.max(0) + a.max(1)+a.sum(1)) + 2
    # schedule = check_schedule(b, 2)
    schedule = check_schedule(b, 4)
    assert schedule[0].ast.src[0].src[0].op is ReduceOps.MAX
    run_schedule(schedule)
    np.testing.assert_allclose(b.numpy(), a.numpy().sum(0)+a.numpy().max(0) + a.numpy().max(1)+a.numpy().sum(1)+2, atol=1e-4, rtol=1e-4)

  # changed by: multireduce spec
  # pattern in test_transformer
  def test_partial_fuse1(self):
    Tensor.manual_seed(0)
    a = Tensor.randn(16, 16).realize()
    b = Tensor.randn(16, 16).realize()
    c = a.sum() + 2
    d = (a.sum() - b.sum()) * 4
    # run_schedule(check_schedule([c, d], 1))
    run_schedule(check_schedule([c, d], 3))
    np.testing.assert_allclose(c.numpy(), a.numpy().sum()+2, atol=1e-4, rtol=1e-4)
    np.testing.assert_allclose(d.numpy(), (a.numpy().sum() - b.numpy().sum()) * 4, atol=1e-4, rtol=1e-4)

  # changed by: multireduce spec
  # pattern in conv
  def test_partial_fuse2(self):
    Tensor.manual_seed(0)
    a = Tensor.randn(16, 16).realize()
    b = Tensor.randn(16, 16).realize()
    c = a.sum() + 2
    d = b.sum() - c
    # run_schedule(check_schedule([c, d], 1))
    run_schedule(check_schedule([c, d], 2))
    np.testing.assert_allclose(c.numpy(), a.numpy().sum()+2, atol=1e-4, rtol=1e-4)
    np.testing.assert_allclose(d.numpy(), b.numpy().sum()-(a.numpy().sum()+2), atol=1e-4, rtol=1e-4)

  # changed by: multireduce spec
  # pattern in adam
  def test_partial_fuse3(self):
    Tensor.manual_seed(0)
    a = Tensor.randn(16, 16).realize()
    b = Tensor.randn(16, 16).realize()
    c = a.sum() + 2
    d = a.sum() * 2
    e = c * d
    f = b.sum() - e
    # run_schedule(check_schedule([c, d, e, f], 1))
    run_schedule(check_schedule([c, d, e, f], 2))
    np.testing.assert_allclose(c.numpy(), c_np:=a.numpy().sum()+2, atol=1e-4, rtol=1e-4)
    np.testing.assert_allclose(d.numpy(), d_np:=a.numpy().sum()*2, atol=1e-4, rtol=1e-4)
    np.testing.assert_allclose(e.numpy(), e_np:=c_np*d_np, atol=1e-4, rtol=1e-4)
    np.testing.assert_allclose(f.numpy(), b.numpy().sum() - e_np, atol=1e-4, rtol=1e-4)

  # changed by: multireduce spec
  def test_partial_fuse4(self):
    Tensor.manual_seed(0)
    a = Tensor.randn(16, 16).realize()
    b = Tensor.randn(16, 16).realize()
    c = a.sum() + 2
    d = a.sum() * 2
    e = c * d
    f = (b - d).sum() - e
    # run_schedule(check_schedule([c, d, e, f], 1))
    run_schedule(check_schedule([c, d, e, f], 3))
    np.testing.assert_allclose(c.numpy(), c_np:=a.numpy().sum()+2, atol=1e-4, rtol=1e-4)
    np.testing.assert_allclose(d.numpy(), d_np:=a.numpy().sum()*2, atol=1e-4, rtol=1e-4)
    np.testing.assert_allclose(e.numpy(), e_np:=c_np*d_np, atol=1e-4, rtol=1e-4)
    np.testing.assert_allclose(f.numpy(), (b.numpy()-d_np).sum()-e_np, atol=1e-4, rtol=1e-4)

  def test_pad_reduce_safe(self):
    Tensor.manual_seed(0)
    a = Tensor.rand(3, 4, 5).realize()
    b = Tensor.rand(3, 4, 5).realize()
    out = (a + b).pad(((0, 1), (0, 1), (0, 1)), 1.0).sum().contiguous()
    run_schedule(check_schedule(out, 1))
    np.testing.assert_allclose(out.numpy(), np.pad(a.numpy()+b.numpy(), ((0, 1), (0, 1), (0, 1)), constant_values=1.0).sum())

  # multireduce spec
  def test_multireduce_pad_reduce_safe(self):
    Tensor.manual_seed(0)
    a = Tensor.randn(3, 4, 5).realize()
    b = Tensor.randn(3, 4, 5).realize()
    out = (a.pad(((0, 1), (0, 1), (0, 1)), 1.0).sum(keepdim=True)+b.pad(((0, 1), (0, 1), (0, 1)), 1.0).sum()).contiguous()
    # run_schedule(check_schedule(out, 1))
    run_schedule(check_schedule(out, 2))
    np.testing.assert_allclose(out.numpy(), np.pad(a.numpy(), ((0, 1), (0, 1), (0, 1)), constant_values=1.0).sum(keepdims=True) + \
                                                   np.pad(b.numpy(), ((0, 1), (0, 1), (0, 1)), constant_values=1.0).sum(), atol=1e-4, rtol=1e-4)

  def test_pad_reduce_unsafe(self):
    Tensor.manual_seed(0)
    a = Tensor.rand(3, 4, 5).realize()
    out = a.log2().pad(((0, 1), (0, 1), (0, 1)), 1.0).sum().contiguous()
    run_schedule(check_schedule(out, 2))
    np.testing.assert_allclose(out.numpy(), np.pad(np.log2(a.numpy()), ((0, 1), (0, 1), (0, 1)), constant_values=1.0).sum(), rtol=1e-6)

  # multireduce spec
  def test_multireduce_pad_reduce_unsafe(self):
    Tensor.manual_seed(0)
    a = Tensor.randn(3, 4, 5).abs().realize()
    b = Tensor.randn(3, 4, 5).abs().realize()
    out = (a.log2().pad(((0, 1), (0, 1), (0, 1)), 1.0).sum()+b).abs().log2().pad(((0, 1), (0, 1), (0, 1)), 1.0).sum().contiguous()
    # run_schedule(check_schedule(out, 1))
    run_schedule(check_schedule(out, 4))
    np.testing.assert_allclose(out.numpy(), np.pad(np.log2(np.abs(np.pad(np.log2(a.numpy()), ((0, 1), (0, 1), (0, 1)), constant_values=1.0).sum() + \
                                                   b.numpy())), ((0, 1), (0, 1), (0, 1)), constant_values=1.0).sum(), atol=1e-4, rtol=1e-6)

  def test_shrink_pad_safe(self):
    a = Tensor.ones((3, )).contiguous().realize()
    b = Tensor.ones((3, )).contiguous().realize()
    out = (a + b).shrink(((0, 1),)).pad(((0, 1),)).contiguous()
    run_schedule(check_schedule(out, 1))
    np.testing.assert_equal(out.numpy(), [2, 0])

  def test_shrink_pad_unsafe(self):
    a = Tensor.ones((3, )).contiguous().realize()
    out = a.exp2().shrink(((0, 1),)).pad(((0, 1),)).contiguous()
    run_schedule(check_schedule(out, 2))
    np.testing.assert_equal(out.numpy(), [2, 0])

  def test_base_change_shrink_pad(self):
    a = Tensor.ones(3, 3).contiguous().realize()
    b = a.exp2()
    c = b[:-1, :-1]
    d = c.pad(((0, 1), (0, 1))) * 2
    run_schedule(check_schedule(d, 2))
    np.testing.assert_equal(d.numpy(), np.pad(np.exp2(a.numpy())[:-1, :-1], ((0, 1), (0, 1)))*2)

  def test_base_change_expand_pad(self):
    a = Tensor.ones(3, 3).contiguous().realize()
    b = a.exp2()
    c = b[:, None, :]
    d = c.pad(((0, 0), (1, 1), (0, 0))) * 2
    run_schedule(check_schedule(d, 2))
    np.testing.assert_equal(d.numpy(), np.pad(np.exp2(a.numpy())[:, None, :], ((0, 0), (1, 1), (0, 0)))*2)

  # TODO like openpilot with imagef
  @unittest.skipUnless(is_dtype_supported(dtypes.half), "need half")
  def test_base_change_expand_expand(self):
    a = Tensor.ones(4, 4).contiguous().realize()
    b = a.cast(dtypes.half).expand(2, 4, 4)
    c = b.cast(dtypes.int).expand(2, 2, 4, 4)
    run_schedule(check_schedule(c, 2))
    np.testing.assert_equal(c.numpy(), np.ones(((2, 2, 4, 4)), dtype=np.int32))

  def test_base_change_pad_expand(self):
    a = Tensor.full((4, 4), 1.).contiguous().realize()
    b = Tensor.full((4, 4), 2.).contiguous().realize()
    c = (a + b).pad(((1, 1), (1, 1)))
    d = c.cast(dtypes.int).expand((2, 6, 6)) * 4
    run_schedule(check_schedule(d, 2))
    c_np = np.pad((np.full((4, 4), 2., dtype=np.float32) + np.full((4, 4), 1., dtype=np.float32)), ((1, 1), (1, 1)), constant_values=0.0)
    np.testing.assert_equal(d.numpy(), np.broadcast_to(c_np.astype(np.half), (2, *c_np.shape)) * 4)

  def test_pad_reduce_unsafe_multiview_st(self):
    P = Tensor.ones(3, 3).contiguous()
    sums = P.sum(axis=1, keepdim=True)
    P /= sums
    p = P[0]
    p = p.pad(((1, 0), ))
    p = p.repeat([2])
    run_schedule(check_schedule(p, 3))
    tiny_ret = p.numpy()

    P = np.ones((3, 3), dtype=np.float32)
    sums = P.sum(axis=1, keepdims=True)
    P /= sums
    p = P[0]
    p = np.pad(p, (1, 0), 'constant')
    p = np.tile(p, 2)
    np.testing.assert_allclose(tiny_ret, p)

  @unittest.skipIf(Device.DEFAULT not in view_supported_devices, "subbuffer not supported")
  def test_bitcast_subbufer(self):
    a = Tensor.empty(1, dtype=dtypes.float32).realize()
    b = CycleBitcast.apply(a)
    check_schedule(b, 2) # this should fuse when it makes sense

  def test_bitcast_disable_subbufer(self):
    a = Tensor.empty(1, dtype=dtypes.float32).realize()
    b = CycleBitcast.apply(a, allow_buffer_view=False)
    check_schedule(b, 1)

  def test_reduceop_reshape_dont_push(self):
    Tensor.manual_seed(0)
    x = Tensor.randn(10, 20).realize()
    out = x.argmax(1)
    run_schedule(check_schedule(out, 3)) # TODO: push a reduceop through a reshape

class CycleBitcast(Function):
  def forward(self, x: LazyBuffer, allow_buffer_view=True):
    a = x.e(UnaryOps.NEG).cast(dtypes.int32, True, allow_buffer_view)
    b = x.cast(dtypes.int32, True, allow_buffer_view)
    return a.e(BinaryOps.ADD, b)

if __name__ == '__main__':
  unittest.main(verbosity=2)