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fix mpt test of different outputs from cuda (#37691)

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* fix mpt test

Signed-off-by: jiqing-feng <jiqing.feng@intel.com>

* fix mpt tests with Expectations

Signed-off-by: jiqing-feng <jiqing.feng@intel.com>

* fix typo

Signed-off-by: jiqing-feng <jiqing.feng@intel.com>

* fix output

Signed-off-by: jiqing-feng <jiqing.feng@intel.com>

* fix format

Signed-off-by: jiqing-feng <jiqing.feng@intel.com>

---------

Signed-off-by: jiqing-feng <jiqing.feng@intel.com>
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jiqing-feng 2025-04-26 00:04:56 +08:00 committed by GitHub
parent 0cfbf9c95b
commit 555693fbfa
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1 changed files with 15 additions and 4 deletions
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19
tests/models/mpt/test_modeling_mpt.py
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@ -20,6 +20,7 @@ from transformers import MptConfig, is_torch_available
from transformers.testing_utils import (
Expectations,
require_bitsandbytes,
require_deterministic_for_xpu,
require_torch,
require_torch_accelerator,
slow,
@ -483,6 +484,7 @@ class MptIntegrationTests(unittest.TestCase):
decoded_output = tokenizer.decode(outputs[0], skip_special_tokens=True)
self.assertEqual(decoded_output, expected_output)
@require_deterministic_for_xpu
def test_generation_batched(self):
model_id = "mosaicml/mpt-7b"
tokenizer = AutoTokenizer.from_pretrained(model_id)
@ -498,10 +500,19 @@ 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 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",
]
expected_outputs = Expectations(
{
("xpu", 3): [
"Hello my name is Tiffany. 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 mall with my mom. I am going to go to the",
],
("cuda", 7): [
"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",
],
}
)
expected_output = expected_outputs.get_expectation()
outputs = model.generate(**inputs, max_new_tokens=20)
decoded_outputs = tokenizer.batch_decode(outputs, skip_special_tokens=True)