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Rename OLMo November to OLMo2 (#34864)

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* Rename/move OLMo Nov files to OLMo2

* Rename Olmo1124 and its variants to Olmo2
This commit is contained in:
Shane A
2024-11-25 07:31:22 -08:00
committed by GitHub
parent 1de3598d30
commit 9121ab8fe8
17 changed files with 221 additions and 221 deletions
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0
tests/models/olmo_1124/__init__.py → tests/models/olmo2/__init__.py
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60
tests/models/olmo_1124/test_modeling_olmo_1124.py → tests/models/olmo2/test_modeling_olmo2.py
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@@ -12,14 +12,14 @@
# 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 November 2024 model."""
"""Testing suite for the PyTorch OLMo2 model."""
import unittest
from packaging import version
from parameterized import parameterized
from transformers import Olmo1124Config, is_torch_available, set_seed
from transformers import Olmo2Config, is_torch_available, set_seed
from transformers.generation.configuration_utils import GenerationConfig
from transformers.models.auto.tokenization_auto import AutoTokenizer
from transformers.testing_utils import (
@@ -39,12 +39,12 @@ if is_torch_available():
import torch
from transformers import (
Olmo1124ForCausalLM,
Olmo1124Model,
Olmo2ForCausalLM,
Olmo2Model,
)
class Olmo1124ModelTester:
class Olmo2ModelTester:
def __init__(
self,
parent,
@@ -119,7 +119,7 @@ class Olmo1124ModelTester:
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def get_config(self):
return Olmo1124Config(
return Olmo2Config(
vocab_size=self.vocab_size,
hidden_size=self.hidden_size,
num_hidden_layers=self.num_hidden_layers,
@@ -138,7 +138,7 @@ class Olmo1124ModelTester:
def create_and_check_model(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = Olmo1124Model(config=config)
model = Olmo2Model(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask)
@@ -158,7 +158,7 @@ class Olmo1124ModelTester:
encoder_attention_mask,
):
config.add_cross_attention = True
model = Olmo1124Model(config)
model = Olmo2Model(config)
model.to(torch_device)
model.eval()
result = model(
@@ -187,7 +187,7 @@ class Olmo1124ModelTester:
encoder_hidden_states,
encoder_attention_mask,
):
model = Olmo1124ForCausalLM(config=config)
model = Olmo2ForCausalLM(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, labels=token_labels)
@@ -207,7 +207,7 @@ class Olmo1124ModelTester:
):
config.is_decoder = True
config.add_cross_attention = True
model = Olmo1124ForCausalLM(config=config)
model = Olmo2ForCausalLM(config=config)
model.to(torch_device)
model.eval()
@@ -271,13 +271,13 @@ class Olmo1124ModelTester:
@require_torch
class Olmo1124ModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (Olmo1124Model, Olmo1124ForCausalLM) if is_torch_available() else ()
all_generative_model_classes = (Olmo1124ForCausalLM,) if is_torch_available() else ()
class Olmo2ModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (Olmo2Model, Olmo2ForCausalLM) if is_torch_available() else ()
all_generative_model_classes = (Olmo2ForCausalLM,) if is_torch_available() else ()
pipeline_model_mapping = (
{
"feature-extraction": Olmo1124Model,
"text-generation": Olmo1124ForCausalLM,
"feature-extraction": Olmo2Model,
"text-generation": Olmo2ForCausalLM,
}
if is_torch_available()
else {}
@@ -290,8 +290,8 @@ class Olmo1124ModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterM
model_split_percents = [0.5, 0.7, 0.8]
def setUp(self):
self.model_tester = Olmo1124ModelTester(self)
self.config_tester = ConfigTester(self, config_class=Olmo1124Config, hidden_size=37)
self.model_tester = Olmo2ModelTester(self)
self.config_tester = ConfigTester(self, config_class=Olmo2Config, hidden_size=37)
def test_config(self):
self.config_tester.run_common_tests()
@@ -300,7 +300,7 @@ class Olmo1124ModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterM
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*config_and_inputs)
@unittest.skip(reason="OLMo November 2024 does not support head pruning.")
@unittest.skip(reason="OLMo2 does not support head pruning.")
def test_headmasking(self):
pass
@@ -310,7 +310,7 @@ class Olmo1124ModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterM
config_and_inputs[0].position_embedding_type = type
self.model_tester.create_and_check_model(*config_and_inputs)
@unittest.skip(reason="OLMo November 2024 buffers include complex numbers, which breaks this test")
@unittest.skip(reason="OLMo2 buffers include complex numbers, which breaks this test")
def test_save_load_fast_init_from_base(self):
pass
@@ -321,7 +321,7 @@ class Olmo1124ModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterM
long_input = ids_tensor([1, int(config.max_position_embeddings * 1.5)], config.vocab_size)
set_seed(42) # Fixed seed at init time so the two models get the same random weights
original_model = Olmo1124Model(config)
original_model = Olmo2Model(config)
original_model.to(torch_device)
original_model.eval()
original_short_output = original_model(short_input).last_hidden_state
@@ -329,7 +329,7 @@ class Olmo1124ModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterM
set_seed(42) # Fixed seed at init time so the two models get the same random weights
config.rope_scaling = {"type": scaling_type, "factor": 10.0}
scaled_model = Olmo1124Model(config)
scaled_model = Olmo2Model(config)
scaled_model.to(torch_device)
scaled_model.eval()
scaled_short_output = scaled_model(short_input).last_hidden_state
@@ -347,11 +347,11 @@ class Olmo1124ModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterM
@require_torch
class Olmo1124IntegrationTest(unittest.TestCase):
class Olmo2IntegrationTest(unittest.TestCase):
@slow
def test_model_7b_logits(self):
input_ids = [[1, 306, 4658, 278, 6593, 310, 2834, 338]]
model = Olmo1124ForCausalLM.from_pretrained("shanearora/OLMo-7B-1124-hf", device_map="auto")
model = Olmo2ForCausalLM.from_pretrained("shanearora/OLMo2-7B-1124-hf", device_map="auto")
out = model(torch.tensor(input_ids)).logits.float()
# Expected mean on dim = -1
EXPECTED_MEAN = torch.tensor(
@@ -366,8 +366,8 @@ class Olmo1124IntegrationTest(unittest.TestCase):
def test_model_7b_greedy_generation(self):
EXPECTED_TEXT_COMPLETION = """Simply put, the theory of relativity states that 1) the speed of light is constant, 2) the speed of light is the fastest speed possible, and 3) the speed of light is the same for all observers, regardless of their relative motion. The theory of relativity is based on the idea that the speed of light is constant. This means that"""
prompt = "Simply put, the theory of relativity states that "
tokenizer = AutoTokenizer.from_pretrained("shanearora/OLMo-7B-1124-hf", device_map="auto")
model = Olmo1124ForCausalLM.from_pretrained("shanearora/OLMo-7B-1124-hf", device_map="auto")
tokenizer = AutoTokenizer.from_pretrained("shanearora/OLMo2-7B-1124-hf", device_map="auto")
model = Olmo2ForCausalLM.from_pretrained("shanearora/OLMo2-7B-1124-hf", device_map="auto")
input_ids = tokenizer.encode(prompt, return_tensors="pt").to(model.device)
# greedy generation outputs
@@ -377,7 +377,7 @@ class Olmo1124IntegrationTest(unittest.TestCase):
@require_tokenizers
def test_simple_encode_decode(self):
rust_tokenizer = AutoTokenizer.from_pretrained("shanearora/OLMo-7B-1124-hf")
rust_tokenizer = AutoTokenizer.from_pretrained("shanearora/OLMo2-7B-1124-hf")
self.assertEqual(rust_tokenizer.encode("This is a test"), [2028, 374, 264, 1296])
self.assertEqual(rust_tokenizer.decode([2028, 374, 264, 1296], skip_special_tokens=True), "This is a test")
@@ -414,9 +414,9 @@ class Olmo1124IntegrationTest(unittest.TestCase):
convert_and_export_with_cache,
)
olmo_1124_model = "shanearora/OLMo-7B-1124-hf"
olmo2_model = "shanearora/OLMo2-7B-1124-hf"
tokenizer = AutoTokenizer.from_pretrained(olmo_1124_model, pad_token="</s>", padding_side="right")
tokenizer = AutoTokenizer.from_pretrained(olmo2_model, pad_token="</s>", padding_side="right")
EXPECTED_TEXT_COMPLETION = [
"Simply put, the theory of relativity states that 1) the speed of light is constant, 2) the speed of light",
]
@@ -439,8 +439,8 @@ class Olmo1124IntegrationTest(unittest.TestCase):
"max_cache_len": max_generation_length,
},
)
model = Olmo1124ForCausalLM.from_pretrained(
olmo_1124_model,
model = Olmo2ForCausalLM.from_pretrained(
olmo2_model,
device_map=device,
torch_dtype=dtype,
attn_implementation=attn_implementation,