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[PT2][Optimus] Add missing example value for introduced nodes #132297

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[PT2][Optimus] Add missing example value for introduced nodes #132297

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36 changes: 0 additions & 36 deletions test/inductor/test_split_cat_fx_passes.py
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Original file line number Diff line number Diff line change
Expand Up @@ -16,7 +16,6 @@ def patch(f):
"normalization_pass": {},
"remove_split_with_size_one_pass": {},
"merge_getitem_cat_pass": {},
"merge_stack_tahn_unbind_pass": {},
"merge_splits_pass": {},
"mutate_cat_pass": {},
"split_cat_pass": {},
Expand Down Expand Up @@ -1269,41 +1268,6 @@ def unbind_cat_to_view(x):
)
counters.clear()

@patch
def test_stack_tahn_unbind_merge(self):
def stack_tahn_unbind(x):
l1_out = torch.split(x, [20, 20, 20, 10, 10, 20, 20], 1)
item0 = l1_out[0]
item1 = l1_out[1]
item2 = l1_out[2]
item3 = l1_out[3]
item4 = l1_out[4]
item5 = l1_out[5]
item6 = l1_out[6]
stack = torch.stack(tensors=(item0, item1, item2), dim=0)
cat_1 = torch.cat((item3, item4), 1)
cat_2 = torch.cat((item5, item6), 1)
tanh = torch.tanh(stack)
unbind = torch.unbind(tanh, 0)
return torch.cat((unbind[0], unbind[1], torch.cat((cat_1, cat_2), 1)), 1)

args = [
torch.randn(50, 120),
]
for fn, expected_stack_tahn_unbind_merged in [
(stack_tahn_unbind, 1),
]:
expected = fn(*args)
actual = torch.compile(fn)(*args)

torch.testing.assert_close(actual, expected)
self.assertEqual(
counters["inductor"]["merge_stack_tahn_unbind_pass"],
expected_stack_tahn_unbind_merged,
)
self.assertIn("merge_getitem_cat_pass_pre_grad", optimus_scuba_log)
counters.clear()

def test_numpy_compat_normalization(self):
def fn(x, y):
a = torch.stack([x, y], axis=1)
Expand Down
6 changes: 6 additions & 0 deletions torch/_inductor/fx_passes/group_batch_fusion.py
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Original file line number Diff line number Diff line change
Expand Up @@ -921,9 +921,15 @@ def fuse(self, graph: torch.fx.GraphModule, subset: List[torch.fx.Node]):
self.op,
args=(stack_inputs,),
)
if is_node_meta_valid(batch_op):
batch_op.meta["example_value"] = self.op(batch_op.meta["example_value"])
unbind_op = graph.call_function(
torch.unbind, args=(batch_op,), kwargs={"dim": 0}
)
if is_node_meta_valid(unbind_op):
unbind_op.meta["example_value"] = torch.unbind(
unbind_op.meta["example_value"], dim=0
)
for i, node in enumerate(batch_nodes):
with graph.inserting_after(unbind_op):
getitem = graph.call_function(operator.getitem, args=(unbind_op, i))
Expand Down
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