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c23a1c1932
* Add the helium model. * Add a missing helium. * And add another missing helium. * Use float for the rmsnorm mul. * Add the Helium tokenizer converter. * Add the pad token as suggested by Arthur. * Update the RMSNorm + some other tweaks. * Fix more rebase issues. * fix copies and style * fixes and add helium.md * add missing tests * udpate the backlink * oups * style * update init, and expected results * small fixes * match test outputs * style fixup, fix doc builder * add dummies and we should be good to go!z * update sdpa and fa2 documentation --------- Co-authored-by: laurent <laurent.mazare@gmail.com>
111 lines
3.9 KiB
Python
111 lines
3.9 KiB
Python
# coding=utf-8
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# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Testing suite for the PyTorch Helium model."""
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import unittest
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from transformers import AutoModelForCausalLM, AutoTokenizer, HeliumConfig, is_torch_available
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from transformers.testing_utils import (
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require_read_token,
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require_torch,
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slow,
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torch_device,
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)
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from ...test_configuration_common import ConfigTester
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from ..gemma.test_modeling_gemma import GemmaModelTest, GemmaModelTester
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if is_torch_available():
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import torch
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from transformers import (
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HeliumForCausalLM,
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HeliumForSequenceClassification,
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HeliumForTokenClassification,
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HeliumModel,
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)
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class HeliumModelTester(GemmaModelTester):
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if is_torch_available():
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config_class = HeliumConfig
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model_class = HeliumModel
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for_causal_lm_class = HeliumForCausalLM
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for_sequence_class = HeliumForSequenceClassification
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for_token_class = HeliumForTokenClassification
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@require_torch
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class HeliumModelTest(GemmaModelTest, unittest.TestCase):
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all_model_classes = (
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(HeliumModel, HeliumForCausalLM, HeliumForSequenceClassification, HeliumForTokenClassification)
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if is_torch_available()
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else ()
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)
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all_generative_model_classes = (HeliumForCausalLM,) if is_torch_available() else ()
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pipeline_model_mapping = (
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{
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"feature-extraction": HeliumModel,
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"text-classification": HeliumForSequenceClassification,
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"token-classification": HeliumForTokenClassification,
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"text-generation": HeliumForCausalLM,
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"zero-shot": HeliumForSequenceClassification,
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}
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if is_torch_available()
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else {}
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)
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test_headmasking = False
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test_pruning = False
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_is_stateful = True
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model_split_percents = [0.5, 0.6]
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def setUp(self):
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self.model_tester = HeliumModelTester(self)
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self.config_tester = ConfigTester(self, config_class=HeliumConfig, hidden_size=37)
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@slow
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# @require_torch_gpu
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class HeliumIntegrationTest(unittest.TestCase):
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input_text = ["Hello, today is a great day to"]
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# This variable is used to determine which CUDA device are we using for our runners (A10 or T4)
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# Depending on the hardware we get different logits / generations
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cuda_compute_capability_major_version = None
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@classmethod
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def setUpClass(cls):
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if is_torch_available() and torch.cuda.is_available():
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# 8 is for A100 / A10 and 7 for T4
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cls.cuda_compute_capability_major_version = torch.cuda.get_device_capability()[0]
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@require_read_token
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def test_model_2b(self):
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model_id = "kyutai/helium-1-preview"
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EXPECTED_TEXTS = [
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"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"
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]
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model = AutoModelForCausalLM.from_pretrained(
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model_id, low_cpu_mem_usage=True, torch_dtype=torch.bfloat16, revision="refs/pr/1"
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).to(torch_device)
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tokenizer = AutoTokenizer.from_pretrained(model_id, revision="refs/pr/1")
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inputs = tokenizer(self.input_text, return_tensors="pt", padding=True).to(torch_device)
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output = model.generate(**inputs, max_new_tokens=20, do_sample=False)
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output_text = tokenizer.batch_decode(output, skip_special_tokens=True)
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self.assertEqual(output_text, EXPECTED_TEXTS)
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