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test(ci): skip the group_norm samples torch 2.14 rejects [7/n] #2844

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test(ci): skip the group_norm samples torch 2.14 rejects [7/n] #2844

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1 change: 1 addition & 0 deletions thunder/constants.py
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Original file line number Diff line number Diff line change
Expand Up @@ -5,3 +5,4 @@
# PyTorch version constants
# `use_base_version` so that dev builds like "2.13.0a0+gitabc123" compare as "2.13.0"
_TORCH_GREATER_EQUAL_2_13 = compare_version("torch", operator.ge, "2.13.0", use_base_version=True)
_TORCH_GREATER_EQUAL_2_14 = compare_version("torch", operator.ge, "2.14.0", use_base_version=True)
5 changes: 5 additions & 0 deletions thunder/tests/opinfos.py
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Expand Up @@ -23,6 +23,7 @@
import thunder.core.prims as prims
from thunder.core.pytree import tree_map
from thunder.core.symbol import Symbol
from thunder.constants import _TORCH_GREATER_EQUAL_2_14
import thunder.executors as executors
from thunder.tests.framework import _all_devicetypes, custom_comparator, IS_WINDOWS
from thunder.tests.make_tensor import make_tensor, make_tensor_like
Expand Down Expand Up @@ -8574,6 +8575,10 @@ def group_norm_sample_generator(op, device, dtype, requires_grad, **kwargs):
if torch.device(device).type == "cuda" and ndim >= 3 and num_channels == 0:
continue

# torch 2.14 rejects empty channel or spatial dims (an empty batch is still fine)
if _TORCH_GREATER_EQUAL_2_14 and (num_channels == 0 or math.prod(inner_dims) == 0):
continue

a = make(shape)

for weight, bias in itertools.product((False, True), repeat=2):
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