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陈赣
2026-06-05 16:53:03 +08:00
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# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Testing suite for the PyTorch Helium model."""
import unittest
from transformers import AutoModelForCausalLM, AutoTokenizer, is_torch_available
from transformers.testing_utils import (
Expectations,
require_torch,
slow,
torch_device,
)
from ...causal_lm_tester import CausalLMModelTest, CausalLMModelTester
if is_torch_available():
import torch
from transformers import (
HeliumModel,
)
class HeliumModelTester(CausalLMModelTester):
if is_torch_available():
base_model_class = HeliumModel
@require_torch
class HeliumModelTest(CausalLMModelTest, unittest.TestCase):
_is_stateful = True
model_split_percents = [0.5, 0.6]
model_tester_class = HeliumModelTester
@slow
class HeliumIntegrationTest(unittest.TestCase):
input_text = ["Hello, today is a great day to"]
def test_model_2b(self):
model_id = "kyutai/helium-1-preview"
expected_texts = Expectations(
{
("rocm", (9, 5)): ["Hello, today is a great day to start a new project. I have been working on a new project for a while now, and I have"],
(None, None): ["Hello, today is a great day to start a new project. I have been working on a new project for a while now and I have"],
("cuda", 8): ['Hello, today is a great day to start a new project. I have been working on a new project for a while now, and I'],
}
) # fmt: skip
EXPECTED_TEXTS = expected_texts.get_expectation()
model = AutoModelForCausalLM.from_pretrained(model_id, dtype=torch.bfloat16, revision="refs/pr/1").to(
torch_device
)
tokenizer = AutoTokenizer.from_pretrained(model_id, revision="refs/pr/1")
inputs = tokenizer(self.input_text, return_tensors="pt", padding=True).to(torch_device)
output = model.generate(**inputs, max_new_tokens=20, do_sample=False)
output_text = tokenizer.batch_decode(output, skip_special_tokens=True)
self.assertEqual(output_text, EXPECTED_TEXTS)