/
githubmirror
/
transformers
Обзор
Документация
Войти
/
githubmirror
/
transformers
Код
Запросы
0
Пакеты
0
Релизы
0
Аналитика
Безопасность
main
tests/models/olmo/test_modeling_olmo.py
352 строки
15 KB
E. Ariño de la Rubia
Add ForSequenceClassification heads for the OLMo family (#45551)
22 апр 2026, 17:24
Не верифицирован
22 апр 2026, 17:24
864db66
Код
Авторство
О чём код?
# 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 OLMo model.""" import unittest import pytest from transformers import OlmoConfig, is_torch_available from transformers.generation.configuration_utils import GenerationConfig from transformers.models.auto.tokenization_auto import AutoTokenizer from transformers.models.gpt_neox.tokenization_gpt_neox import GPTNeoXTokenizer as GPTNeoXTokenizerFast from transformers.testing_utils import ( require_tokenizers, require_torch, slow, torch_device, ) from ...generation.test_utils import GenerationTesterMixin from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, ids_tensor from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from transformers import ( OlmoForCausalLM, OlmoForSequenceClassification, OlmoModel, ) class OlmoModelTester: def __init__( self, parent, batch_size=13, seq_length=7, is_training=True, use_input_mask=True, use_token_type_ids=False, use_labels=True, vocab_size=99, hidden_size=32, num_hidden_layers=2, num_attention_heads=4, intermediate_size=37, hidden_act="silu", hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_position_embeddings=512, type_vocab_size=16, type_sequence_label_size=2, initializer_range=0.02, num_labels=3, num_choices=4, pad_token_id=0, scope=None, ): self.parent = parent self.batch_size = batch_size self.seq_length = seq_length self.is_training = is_training self.use_input_mask = use_input_mask self.use_token_type_ids = use_token_type_ids self.use_labels = use_labels self.vocab_size = vocab_size self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.intermediate_size = intermediate_size self.hidden_act = hidden_act self.hidden_dropout_prob = hidden_dropout_prob self.attention_probs_dropout_prob = attention_probs_dropout_prob self.max_position_embeddings = max_position_embeddings self.type_vocab_size = type_vocab_size self.type_sequence_label_size = type_sequence_label_size self.initializer_range = initializer_range self.num_labels = num_labels self.num_choices = num_choices self.pad_token_id = pad_token_id self.scope = scope def prepare_config_and_inputs(self): input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size) input_mask = None if self.use_input_mask: input_mask = torch.tril(torch.ones_like(input_ids).to(torch_device)) token_type_ids = None if self.use_token_type_ids: token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size) sequence_labels = None token_labels = None choice_labels = None if self.use_labels: sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size) token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels) choice_labels = ids_tensor([self.batch_size], self.num_choices) config = self.get_config() return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels def get_config(self): return OlmoConfig( vocab_size=self.vocab_size, hidden_size=self.hidden_size, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, intermediate_size=self.intermediate_size, hidden_act=self.hidden_act, hidden_dropout_prob=self.hidden_dropout_prob, attention_probs_dropout_prob=self.attention_probs_dropout_prob, max_position_embeddings=self.max_position_embeddings, type_vocab_size=self.type_vocab_size, is_decoder=False, initializer_range=self.initializer_range, pad_token_id=self.pad_token_id, ) def create_and_check_model( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = OlmoModel(config=config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=input_mask) result = model(input_ids) self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size)) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() ( config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, ) = config_and_inputs inputs_dict = {"input_ids": input_ids, "attention_mask": input_mask} return config, inputs_dict @require_torch class OlmoModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = (OlmoModel, OlmoForCausalLM, OlmoForSequenceClassification) if is_torch_available() else () pipeline_model_mapping = ( { "feature-extraction": OlmoModel, "text-generation": OlmoForCausalLM, "text-classification": OlmoForSequenceClassification, "zero-shot": OlmoForSequenceClassification, } if is_torch_available() else {} ) # Need to use `0.8` instead of `0.9` for `test_cpu_offload` # This is because we are hitting edge cases with the causal_mask buffer model_split_percents = [0.5, 0.7, 0.8] def setUp(self): self.model_tester = OlmoModelTester(self) self.config_tester = ConfigTester(self, config_class=OlmoConfig, hidden_size=32) def test_config(self): self.config_tester.run_common_tests() def test_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*config_and_inputs) @require_torch class OlmoIntegrationTest(unittest.TestCase): @slow def test_model_1b_logits(self): input_ids = [[1, 306, 4658, 278, 6593, 310, 2834, 338]] model = OlmoForCausalLM.from_pretrained("allenai/OLMo-1B-hf", device_map="auto") out = model(torch.tensor(input_ids)).logits.float() # Expected mean on dim = -1 EXPECTED_MEAN = torch.tensor([[2.2869, 0.3315, 0.9876, 1.4146, 1.8804, 2.0430, 1.7055, 1.2065]]) torch.testing.assert_close(out.mean(-1), EXPECTED_MEAN, rtol=1e-2, atol=1e-2) # slicing logits[0, 0, 0:30] EXPECTED_SLICE = torch.tensor([2.5551, -1.1230, 11.0510, 12.4977, 7.9651, 7.2342, 6.1885, 7.8340, 9.9847, 12.6695, 12.2345, 10.7970, 8.4749, 14.2483, 12.9588, 13.9233, 11.0496, 5.5749, 7.4466, 7.7914, 6.8440, 5.8951, 4.8180, 4.1935, 4.5216, 4.7256, 3.9553, 12.2870, 12.4990, 8.1591]) # fmt: skip torch.testing.assert_close(out[0, 0, :30], EXPECTED_SLICE, rtol=1e-2, atol=1e-2) @slow def test_model_7b_logits(self): input_ids = [[1, 306, 4658, 278, 6593, 310, 2834, 338]] model = OlmoForCausalLM.from_pretrained("allenai/OLMo-7B-hf", device_map="auto") out = model(torch.tensor(input_ids)).logits.float() # Expected mean on dim = -1 EXPECTED_MEAN = torch.tensor([[0.0271, 0.0249, -0.0578, -0.0870, 0.0167, 0.0710, 0.1002, 0.0677]]) torch.testing.assert_close(out.mean(-1), EXPECTED_MEAN, rtol=1e-2, atol=1e-2) # slicing logits[0, 0, 0:30] EXPECTED_SLICE = torch.tensor([-1.7433, -1.6685, 7.4941, 6.1506, 0.1364, -0.1127, 1.3224, 4.5458, 4.2068, 5.8296, 7.4723, 2.7925, 3.1245, 10.8872, 10.0758, 10.6717, 7.0945, 1.2398, 3.6766, 4.2365, 2.5655, 2.2222, 1.7418, 0.5223, 0.7753, 1.0938, 0.6723, 6.2522, 6.2264, 1.8105]) # fmt: skip torch.testing.assert_close(out[0, 0, :30], EXPECTED_SLICE, rtol=1e-2, atol=1e-2) @slow def test_model_7b_twin_2t_logits(self): input_ids = [[1, 306, 4658, 278, 6593, 310, 2834, 338]] model = OlmoForCausalLM.from_pretrained("allenai/OLMo-7B-Twin-2T-hf", device_map="auto") out = model(torch.tensor(input_ids)).logits.float() # Expected mean on dim = -1 EXPECTED_MEAN = torch.tensor([[-0.3636, -0.3825, -0.4800, -0.3696, -0.8388, -0.9737, -0.9849, -0.8356]]) torch.testing.assert_close(out.mean(-1), EXPECTED_MEAN, rtol=1e-2, atol=1e-2) # slicing logits[0, 0, 0:30] EXPECTED_SLICE = torch.tensor([-2.0833, -1.9234, 8.7312, 7.8049, 1.0372, 0.8941, 3.1548, 1.8502, 5.5511, 5.5793, 8.1166, 4.5906, 1.8691, 11.6377, 8.9858, 11.6447, 7.4549, 1.4725, 2.8399, 2.7568, 1.4011, 1.6958, 0.5572, 0.5231, 0.3068, 0.5364, 0.6769, 7.9636, 8.2379, 1.7950]) # fmt: skip torch.testing.assert_close(out[0, 0, :30], EXPECTED_SLICE, rtol=1e-2, atol=1e-2) @slow def test_model_7b_greedy_generation(self): EXPECTED_TEXT_COMPLETION = """Simply put, the theory of relativity states that \nthe speed of light is the same for all observers.\n\nThe theory of relativity is a theory of physics that describes the \nmovement of objects in space and time.\n\nThe theory of relativity is a theory of physics that describes the \nmovement of objects in space and time.\n\n""" prompt = "Simply put, the theory of relativity states that " tokenizer = AutoTokenizer.from_pretrained("allenai/OLMo-7B-hf", device_map="auto") input_ids = tokenizer.encode(prompt, return_tensors="pt") model = OlmoForCausalLM.from_pretrained("allenai/OLMo-7B-hf", device_map="auto") # greedy generation outputs generated_ids = model.generate(input_ids, max_new_tokens=64, top_p=None, temperature=1, do_sample=False) text = tokenizer.decode(generated_ids[0], skip_special_tokens=True) self.assertEqual(EXPECTED_TEXT_COMPLETION, text) @require_tokenizers def test_fast_special_tokens(self): fast_tokenizer = GPTNeoXTokenizerFast.from_pretrained("allenai/OLMo-1B-hf") original_add_eos_token = fast_tokenizer.add_eos_token fast_tokenizer.add_eos_token = False fast = fast_tokenizer.encode("A sample test") self.assertEqual(fast, [34, 3410, 1071]) fast_tokenizer.add_eos_token = True fast = fast_tokenizer.encode("A sample test") self.assertEqual(fast, [34, 3410, 1071, 50279]) fast_tokenizer.add_eos_token = original_add_eos_token @require_tokenizers def test_simple_encode_decode(self): rust_tokenizer = GPTNeoXTokenizerFast.from_pretrained("allenai/OLMo-1B-hf", add_eos_token=False) self.assertEqual(rust_tokenizer.encode("This is a test"), [1552, 310, 247, 1071]) self.assertEqual(rust_tokenizer.decode([1552, 310, 247, 1071], skip_special_tokens=True), "This is a test") # bytefallback showcase self.assertEqual(rust_tokenizer.encode("生活的真谛是"), [20025, 46549, 5225, 48561, 33656, 238, 12105]) # fmt: skip self.assertEqual( rust_tokenizer.decode([20025, 46549, 5225, 48561, 33656, 238, 12105], skip_special_tokens=True), "生活的真谛是", ) # Inner spaces showcase self.assertEqual(rust_tokenizer.encode("Hi Hello"), [12764, 50276, 12092]) self.assertEqual(rust_tokenizer.decode([12764, 50276, 12092], skip_special_tokens=False), "Hi Hello") self.assertEqual(rust_tokenizer.encode("Hi Hello"), [12764, 50275, 12092]) self.assertEqual(rust_tokenizer.decode([12764, 50275, 12092], skip_special_tokens=False), "Hi Hello") self.assertEqual(rust_tokenizer.encode(""), []) self.assertEqual(rust_tokenizer.encode(" "), [209]) self.assertEqual(rust_tokenizer.encode(" "), [50276]) self.assertEqual(rust_tokenizer.encode(" Hello"), [24387]) @pytest.mark.torch_export_test @slow def test_export_static_cache(self): from transformers.integrations.executorch import ( TorchExportableModuleWithStaticCache, ) olmo_model = "allenai/OLMo-1B-hf" tokenizer = AutoTokenizer.from_pretrained(olmo_model, pad_token="</s>", padding_side="right") EXPECTED_TEXT_COMPLETION = [ "Simply put, the theory of relativity states that \nthe speed of light is the same in all reference frames.\n\nThe speed of light", ] max_generation_length = tokenizer(EXPECTED_TEXT_COMPLETION, return_tensors="pt", padding=True)[ "input_ids" ].shape[-1] # Load model device = "cpu" # TODO (joao / export experts): should be on `torch_device`, but causes GPU OOM dtype = torch.bfloat16 cache_implementation = "static" attn_implementation = "sdpa" batch_size = 1 model = OlmoForCausalLM.from_pretrained( olmo_model, device_map=device, dtype=dtype, attn_implementation=attn_implementation, generation_config=GenerationConfig( use_cache=True, cache_implementation=cache_implementation, max_length=max_generation_length, cache_config={ "batch_size": batch_size, "max_cache_len": max_generation_length, }, ), ) prompts = ["Simply put, the theory of relativity states that "] prompt_tokens = tokenizer(prompts, return_tensors="pt", padding=True).to(model.device) prompt_token_ids = prompt_tokens["input_ids"] max_new_tokens = max_generation_length - prompt_token_ids.shape[-1] # Static Cache + eager eager_generated_ids = model.generate( **prompt_tokens, max_new_tokens=max_new_tokens, do_sample=False, cache_implementation=cache_implementation ) eager_generated_text = tokenizer.batch_decode(eager_generated_ids, skip_special_tokens=True) self.assertEqual(EXPECTED_TEXT_COMPLETION, eager_generated_text) # Static Cache + export from transformers.integrations.executorch import TorchExportableModuleForDecoderOnlyLM exportable_module = TorchExportableModuleForDecoderOnlyLM(model) exported_program = exportable_module.export( input_ids=torch.tensor([[1]], dtype=torch.long, device=model.device), cache_position=torch.tensor([0], dtype=torch.long, device=model.device), ) ep_generated_ids = TorchExportableModuleWithStaticCache.generate( exported_program=exported_program, prompt_token_ids=prompt_token_ids, max_new_tokens=max_new_tokens ) ep_generated_text = tokenizer.batch_decode(ep_generated_ids, skip_special_tokens=True) self.assertEqual(EXPECTED_TEXT_COMPLETION, ep_generated_text)