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fix typos in the tests directory (#36932)

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* chore: fix typos in test codes

* chore: fix typos in test codes

* chore: fix typos in test codes

* chore: fix typos in test codes

* chore: fix typos in test codes

* chore: fix typos in test codes

* chore: fix typos in test codes

* chore: fix typos in test codes

* chore: format codes
This commit is contained in:
Afanti
2025-03-25 17:49:24 +08:00
committed by GitHub
parent 69bcb86c58
commit 19085c28da
9 changed files with 19 additions and 17 deletions
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2
tests/models/chameleon/test_modeling_chameleon.py
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@@ -130,7 +130,7 @@ class ChameleonModelTester:
def get_config(self):
# create dummy vocab map for image2bpe mapping if it needs remapping
# we assume that vocab size is big enough to accoun for image tokens somewhere in the beginning
# we assume that vocab size is big enough to account for image tokens somewhere in the beginning
# same way as in real ckpt, when img tokens are in first half of embeds
# we will need "vq_num_embeds" amount of tokens

6
tests/models/dab_detr/test_modeling_dab_detr.py
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@@ -699,10 +699,10 @@ class DabDetrModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase)
self.model_tester.num_labels,
)
self.assertEqual(outputs.logits.shape, expected_shape)
# Confirm out_indices was propogated to backbone
# Confirm out_indices was propagated to backbone
self.assertEqual(len(model.model.backbone.conv_encoder.intermediate_channel_sizes), 3)
else:
# Confirm out_indices was propogated to backbone
# Confirm out_indices was propagated to backbone
self.assertEqual(len(model.backbone.conv_encoder.intermediate_channel_sizes), 3)
self.assertTrue(outputs)
@@ -726,7 +726,7 @@ class DabDetrModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase)
abs(param.data.max().item()),
msg=f"Parameter {name} of model {model_class} seems not properly initialized",
)
# Modifed from RT-DETR
# Modified from RT-DETR
elif "class_embed" in name and "bias" in name:
bias_tensor = torch.full_like(param.data, bias_value)
torch.testing.assert_close(

4
tests/models/granitemoeshared/test_modeling_granitemoeshared.py
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@@ -358,7 +358,9 @@ class GraniteMoeSharedModelTest(ModelTesterMixin, GenerationTesterMixin, unittes
long_input_length = int(config.max_position_embeddings * 1.5)
# Inputs
x = torch.randn(1, dtype=torch.float32, device=torch_device) # used exlusively to get the dtype and the device
x = torch.randn(
1, dtype=torch.float32, device=torch_device
) # used exclusively to get the dtype and the device
position_ids_short = torch.arange(short_input_length, dtype=torch.long, device=torch_device)
position_ids_short = position_ids_short.unsqueeze(0)
position_ids_long = torch.arange(long_input_length, dtype=torch.long, device=torch_device)

4
tests/models/idefics/test_modeling_idefics.py
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@@ -521,7 +521,7 @@ class IdeficsModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase)
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
attentions = outputs.attentions
# IDEFICS does not support outputting attention score becuase it uses SDPA under the hood
# IDEFICS does not support outputting attention score because it uses SDPA under the hood
self.assertTrue(attentions[0] is None)
out_len = len(outputs)
@@ -539,7 +539,7 @@ class IdeficsModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase)
self_attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(self_attentions), self.model_tester.num_hidden_layers)
# IDEFICS does not support outputting attention score becuase it uses SDPA under the hood
# IDEFICS does not support outputting attention score because it uses SDPA under the hood
self.assertTrue(self_attentions[0] is None)
def test_hidden_states_output(self):

4
tests/models/idefics/test_modeling_tf_idefics.py
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@@ -372,7 +372,7 @@ class TFIdeficsModelTest(TFModelTesterMixin, PipelineTesterMixin, unittest.TestC
model = model_class(config)
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
attentions = outputs.attentions
# IDEFICS does not support outputting attention score becuase it uses SDPA under the hood
# IDEFICS does not support outputting attention score because it uses SDPA under the hood
self.assertTrue(attentions[0] is None)
out_len = len(outputs)
@@ -386,7 +386,7 @@ class TFIdeficsModelTest(TFModelTesterMixin, PipelineTesterMixin, unittest.TestC
self_attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(self_attentions), self.model_tester.num_hidden_layers)
# IDEFICS does not support outputting attention score becuase it uses SDPA under the hood
# IDEFICS does not support outputting attention score because it uses SDPA under the hood
self.assertTrue(self_attentions[0] is None)
def test_hidden_states_output(self):

4
tests/models/mimi/test_modeling_mimi.py
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@@ -485,7 +485,7 @@ class MimiIntegrationTest(unittest.TestCase):
for num_codebooks, expected_rmse in expected_rmse.items():
with torch.no_grad():
# use max bandwith for best possible reconstruction
# use max bandwidth for best possible reconstruction
encoder_outputs = model.encode(inputs["input_values"], num_quantizers=int(num_codebooks))
audio_codes = encoder_outputs[0]
@@ -537,7 +537,7 @@ class MimiIntegrationTest(unittest.TestCase):
model = MimiModel.from_pretrained(model_id, use_cache=use_cache).to(torch_device)
for num_codebooks, expected_rmse in expected_rmses.items():
with torch.no_grad():
# use max bandwith for best possible reconstruction
# use max bandwidth for best possible reconstruction
encoder_outputs = model.encode(inputs["input_values"], num_quantizers=int(num_codebooks))
audio_code_sums = encoder_outputs[0].sum().cpu().item()

8
tests/models/rt_detr/test_modeling_rt_detr.py
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@@ -547,10 +547,10 @@ class RTDetrModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
self.model_tester.num_labels,
)
self.assertEqual(outputs.logits.shape, expected_shape)
# Confirm out_indices was propogated to backbone
# Confirm out_indices was propagated to backbone
self.assertEqual(len(model.model.backbone.intermediate_channel_sizes), 3)
else:
# Confirm out_indices was propogated to backbone
# Confirm out_indices was propagated to backbone
self.assertEqual(len(model.backbone.intermediate_channel_sizes), 3)
self.assertTrue(outputs)
@@ -579,10 +579,10 @@ class RTDetrModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
self.model_tester.num_labels,
)
self.assertEqual(outputs.logits.shape, expected_shape)
# Confirm out_indices was propogated to backbone
# Confirm out_indices was propagated to backbone
self.assertEqual(len(model.model.backbone.intermediate_channel_sizes), 3)
else:
# Confirm out_indices was propogated to backbone
# Confirm out_indices was propagated to backbone
self.assertEqual(len(model.backbone.intermediate_channel_sizes), 3)
self.assertTrue(outputs)

2
tests/models/shieldgemma2/test_processing_shieldgemma2.py
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@@ -140,7 +140,7 @@ class ShieldGemma2ProcessorTest(ProcessorTesterMixin, unittest.TestCase):
if processor.chat_template is None:
self.skipTest("Processor has no chat template")
# Test policies adapated from https://ailuminate.mlcommons.org/benchmarks/ hazard categories
# Test policies adapted from https://ailuminate.mlcommons.org/benchmarks/ hazard categories
custom_policies = {
"cbrne": "Test policy related to indiscriminate weapons.",
"ip": "Test policy related to intellectual property.",

2
tests/models/video_llava/test_modeling_video_llava.py
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@@ -391,7 +391,7 @@ class VideoLlavaForConditionalGenerationModelTest(ModelTesterMixin, GenerationTe
config, input_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config).to(torch_device)
_ = model(**input_dict) # successfull forward with no modifications
_ = model(**input_dict) # successful forward with no modifications
# remove one image but leave the image token in text
input_dict["pixel_values_images"] = input_dict["pixel_values_images"][-1:, ...]