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Revert workaround for TF safetensors loading (#30128)

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* See if we can get tests to pass with the fixed weights

* See if we can get tests to pass with the fixed weights

* Replace the revisions now that we don't need them anymore
This commit is contained in:
Matt
2024-04-09 11:04:18 +01:00
committed by GitHub
parent 841e87ef4f
commit ec59a42192
3 changed files with 5 additions and 6 deletions
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8
tests/generation/test_framework_agnostic.py
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@@ -111,7 +111,7 @@ class GenerationIntegrationTestsMixin:
article = """Justin Timberlake."""
gpt2_tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2")
gpt2_model = model_cls.from_pretrained("hf-internal-testing/tiny-random-gpt2", use_safetensors=is_pt)
gpt2_model = model_cls.from_pretrained("hf-internal-testing/tiny-random-gpt2")
input_ids = gpt2_tokenizer(article, return_tensors=return_tensors).input_ids
if is_pt:
gpt2_model = gpt2_model.to(torch_device)
@@ -582,7 +582,7 @@ class GenerationIntegrationTestsMixin:
tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2")
text = """Hello, my dog is cute and"""
tokens = tokenizer(text, return_tensors=return_tensors)
model = model_cls.from_pretrained("hf-internal-testing/tiny-random-gpt2", use_safetensors=is_pt)
model = model_cls.from_pretrained("hf-internal-testing/tiny-random-gpt2")
if is_pt:
model = model.to(torch_device)
tokens = tokens.to(torch_device)
@@ -611,7 +611,7 @@ class GenerationIntegrationTestsMixin:
tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2")
text = """Hello, my dog is cute and"""
tokens = tokenizer(text, return_tensors=return_tensors)
model = model_cls.from_pretrained("hf-internal-testing/tiny-random-gpt2", use_safetensors=is_pt)
model = model_cls.from_pretrained("hf-internal-testing/tiny-random-gpt2")
if is_pt:
model = model.to(torch_device)
tokens = tokens.to(torch_device)
@@ -638,7 +638,7 @@ class GenerationIntegrationTestsMixin:
tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2")
text = """Hello, my dog is cute and"""
tokens = tokenizer(text, return_tensors=return_tensors)
model = model_cls.from_pretrained("hf-internal-testing/tiny-random-gpt2", use_safetensors=is_pt)
model = model_cls.from_pretrained("hf-internal-testing/tiny-random-gpt2")
if is_pt:
model = model.to(torch_device)
tokens = tokens.to(torch_device)

2
tests/generation/test_tf_utils.py
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@@ -194,7 +194,7 @@ class TFGenerationIntegrationTests(unittest.TestCase, GenerationIntegrationTests
tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2")
text = """Hello, my dog is cute and"""
tokens = tokenizer(text, return_tensors="tf")
model = TFAutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2", use_safetensors=False)
model = TFAutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2")
eos_token_id = 638
# forces the generation to happen on CPU, to avoid GPU-related quirks

1
tests/pipelines/test_pipelines_text_generation.py
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@@ -268,7 +268,6 @@ class TextGenerationPipelineTests(unittest.TestCase):
text_generator = TextGenerationPipeline(model=model, tokenizer=tokenizer)
return text_generator, ["This is a test", "Another test"]
@require_torch # See https://github.com/huggingface/transformers/issues/30117
def test_stop_sequence_stopping_criteria(self):
prompt = """Hello I believe in"""
text_generator = pipeline("text-generation", model="hf-internal-testing/tiny-random-gpt2")