Revert workaround for TF safetensors loading (#30128)
* 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
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@@ -111,7 +111,7 @@ class GenerationIntegrationTestsMixin:
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article = """Justin Timberlake."""
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article = """Justin Timberlake."""
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gpt2_tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2")
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gpt2_tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2")
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gpt2_model = model_cls.from_pretrained("hf-internal-testing/tiny-random-gpt2", use_safetensors=is_pt)
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gpt2_model = model_cls.from_pretrained("hf-internal-testing/tiny-random-gpt2")
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input_ids = gpt2_tokenizer(article, return_tensors=return_tensors).input_ids
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input_ids = gpt2_tokenizer(article, return_tensors=return_tensors).input_ids
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if is_pt:
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if is_pt:
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gpt2_model = gpt2_model.to(torch_device)
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gpt2_model = gpt2_model.to(torch_device)
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@@ -582,7 +582,7 @@ class GenerationIntegrationTestsMixin:
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tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2")
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tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2")
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text = """Hello, my dog is cute and"""
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text = """Hello, my dog is cute and"""
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tokens = tokenizer(text, return_tensors=return_tensors)
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tokens = tokenizer(text, return_tensors=return_tensors)
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model = model_cls.from_pretrained("hf-internal-testing/tiny-random-gpt2", use_safetensors=is_pt)
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model = model_cls.from_pretrained("hf-internal-testing/tiny-random-gpt2")
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if is_pt:
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if is_pt:
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model = model.to(torch_device)
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model = model.to(torch_device)
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tokens = tokens.to(torch_device)
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tokens = tokens.to(torch_device)
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@@ -611,7 +611,7 @@ class GenerationIntegrationTestsMixin:
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tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2")
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tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2")
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text = """Hello, my dog is cute and"""
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text = """Hello, my dog is cute and"""
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tokens = tokenizer(text, return_tensors=return_tensors)
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tokens = tokenizer(text, return_tensors=return_tensors)
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model = model_cls.from_pretrained("hf-internal-testing/tiny-random-gpt2", use_safetensors=is_pt)
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model = model_cls.from_pretrained("hf-internal-testing/tiny-random-gpt2")
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if is_pt:
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if is_pt:
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model = model.to(torch_device)
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model = model.to(torch_device)
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tokens = tokens.to(torch_device)
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tokens = tokens.to(torch_device)
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@@ -638,7 +638,7 @@ class GenerationIntegrationTestsMixin:
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tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2")
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tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2")
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text = """Hello, my dog is cute and"""
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text = """Hello, my dog is cute and"""
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tokens = tokenizer(text, return_tensors=return_tensors)
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tokens = tokenizer(text, return_tensors=return_tensors)
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model = model_cls.from_pretrained("hf-internal-testing/tiny-random-gpt2", use_safetensors=is_pt)
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model = model_cls.from_pretrained("hf-internal-testing/tiny-random-gpt2")
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if is_pt:
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if is_pt:
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model = model.to(torch_device)
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model = model.to(torch_device)
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tokens = tokens.to(torch_device)
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tokens = tokens.to(torch_device)
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@@ -194,7 +194,7 @@ class TFGenerationIntegrationTests(unittest.TestCase, GenerationIntegrationTests
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tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2")
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tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2")
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text = """Hello, my dog is cute and"""
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text = """Hello, my dog is cute and"""
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tokens = tokenizer(text, return_tensors="tf")
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tokens = tokenizer(text, return_tensors="tf")
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model = TFAutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2", use_safetensors=False)
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model = TFAutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2")
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eos_token_id = 638
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eos_token_id = 638
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# forces the generation to happen on CPU, to avoid GPU-related quirks
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# forces the generation to happen on CPU, to avoid GPU-related quirks
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@@ -268,7 +268,6 @@ class TextGenerationPipelineTests(unittest.TestCase):
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text_generator = TextGenerationPipeline(model=model, tokenizer=tokenizer)
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text_generator = TextGenerationPipeline(model=model, tokenizer=tokenizer)
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return text_generator, ["This is a test", "Another test"]
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return text_generator, ["This is a test", "Another test"]
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@require_torch # See https://github.com/huggingface/transformers/issues/30117
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def test_stop_sequence_stopping_criteria(self):
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def test_stop_sequence_stopping_criteria(self):
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prompt = """Hello I believe in"""
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prompt = """Hello I believe in"""
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text_generator = pipeline("text-generation", model="hf-internal-testing/tiny-random-gpt2")
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text_generator = pipeline("text-generation", model="hf-internal-testing/tiny-random-gpt2")
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