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Update 4 MptIntegrationTests expected outputs (#30989)

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* fix

* fix

* fix

* fix

* fix

* [run-slow] mpt

---------

Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
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Yih-Dar 2024-05-23 18:27:54 +02:00 committed by GitHub
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commit 2a89673fe5
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1 changed files with 5 additions and 7 deletions
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12
tests/models/mpt/test_modeling_mpt.py
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@ -447,7 +447,7 @@ class MptIntegrationTests(unittest.TestCase):
)
input_text = "Hello"
expected_output = 'Hello, I\'m a new user of the forum. I have a question about the "Safety"'
expected_output = "Hello, I'm a new user of the forum. I have a question about the \"Solaris"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=20)
@ -465,9 +465,7 @@ class MptIntegrationTests(unittest.TestCase):
)
input_text = "Hello"
expected_output = (
"Hello and welcome to the first day of the new release countdown for the month of May!\nToday"
)
expected_output = "Hello and welcome to the first episode of the new podcast, The Frugal Feminist.\n"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=20)
@ -491,8 +489,8 @@ class MptIntegrationTests(unittest.TestCase):
inputs = tokenizer(input_texts, return_tensors="pt", padding=True).to(torch_device)
expected_output = [
"Hello my name is Tiffany and I am a mother of two beautiful children. I have been a nanny for over",
"Today I am going at the gym and then I am going to go to the grocery store and get some food. I am going to make",
"Hello my name is Tiffany and I am a mother of two beautiful children. I have been a nanny for the",
"Today I am going at the gym and then I am going to go to the grocery store. I am going to buy some food and some",
]
outputs = model.generate(**inputs, max_new_tokens=20)
@ -512,7 +510,7 @@ class MptIntegrationTests(unittest.TestCase):
outputs = model(dummy_input, output_hidden_states=True)
expected_slice = torch.Tensor([-0.2539, -0.2178, -0.1953]).to(torch_device, torch.bfloat16)
expected_slice = torch.Tensor([-0.2520, -0.2178, -0.1953]).to(torch_device, torch.bfloat16)
predicted_slice = outputs.hidden_states[-1][0, 0, :3]
self.assertTrue(torch.allclose(expected_slice, predicted_slice, atol=1e-3, rtol=1e-3))