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Fix missing initializations for models created in 2024 (#38987)

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* fix GroundingDino

* fix SuperGlue

* fix GroundingDino

* fix MambaModel

* fix OmDetTurbo

* fix SegGpt

* fix Qwen2Audio

* fix Mamba2

* fix DabDetr

* fix Dac

* fix FalconMamba

* skip timm initialization

* fix Encodec and MusicgenMelody

* fix Musicgen

* skip timm initialization test

* fix OmDetTurbo

* clean the code

Co-authored-by: Cyril Vallez <cyril.vallez@gmail.com>

* add reviewed changes

* add back timm

* style

* better check for parametrizations

---------

Co-authored-by: Cyril Vallez <cyril.vallez@gmail.com>
This commit is contained in:
BUI Van Tuan
2025-07-02 15:03:57 +02:00
committed by GitHub
parent 1125513a8d
commit e355c0a11c
21 changed files with 229 additions and 98 deletions
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30
tests/models/mamba2/test_modeling_mamba2.py
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@@ -13,6 +13,7 @@
# limitations under the License.
import math
import unittest
from transformers import AutoTokenizer, Mamba2Config, is_torch_available
@@ -28,7 +29,7 @@ from transformers.utils.import_utils import is_causal_conv1d_available, is_mamba
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
@@ -276,14 +277,37 @@ class Mamba2ModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMix
def test_initialization(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
config.rescale_prenorm_residual = True
configs_no_init = _config_zero_init(config)
for model_class in self.all_model_classes:
model = model_class(config=config)
model = model_class(config=configs_no_init)
for name, param in model.named_parameters():
if "D" in name:
if "dt_proj.bias" in name:
dt = torch.exp(
torch.tensor([0, 1]) * (math.log(config.time_step_max) - math.log(config.time_step_min))
+ math.log(config.time_step_min)
).clamp(min=config.time_step_floor)
inv_dt = dt + torch.log(-torch.expm1(-dt))
if param.requires_grad:
self.assertTrue(param.data.max().item() <= inv_dt[1])
self.assertTrue(param.data.min().item() >= inv_dt[0])
elif "A_log" in name:
A = torch.arange(1, config.num_heads + 1)
torch.testing.assert_close(param.data, torch.log(A), rtol=1e-5, atol=1e-5)
elif "D" in name:
if param.requires_grad:
# check if it's a ones like
torch.testing.assert_close(param.data, torch.ones_like(param.data), rtol=1e-5, atol=1e-5)
else:
if param.requires_grad:
if "mixer.conv1d.weight" in name or "mixer.dt_bias" in name or "mixer.out_proj.weight" in name:
continue
self.assertIn(
((param.data.mean() * 1e9).round() / 1e9).item(),
[0.0, 1.0],
msg=f"Parameter {name} of model {model_class} seems not properly initialized",
)
@unittest.skip(reason="Mamba 2 weights are not tied")
def test_tied_weights_keys(self):