# 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 OLMo2 model.""" import unittest import pytest from packaging import version from transformers import Olmo2Config, is_torch_available from transformers.generation.configuration_utils import GenerationConfig from transformers.models.auto.tokenization_auto import AutoTokenizer from transformers.testing_utils import ( Expectations, cleanup, 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 ( Olmo2ForCausalLM, Olmo2Model, ) class Olmo2ModelTester: 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 Olmo2Config( 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 = Olmo2Model(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 Olmo2ModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = (Olmo2Model, Olmo2ForCausalLM) if is_torch_available() else () pipeline_model_mapping = ( { "feature-extraction": Olmo2Model, "text-generation": Olmo2ForCausalLM, } 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 = Olmo2ModelTester(self) self.config_tester = ConfigTester(self, config_class=Olmo2Config, hidden_size=37) 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 @slow class Olmo2IntegrationTest(unittest.TestCase): def setUp(self): cleanup(torch_device, gc_collect=True) def tearDown(self): cleanup(torch_device, gc_collect=True) def test_model_1b_logits_bfloat16(self): input_ids = [[1, 306, 4658, 278, 6593, 310, 2834, 338]] model = Olmo2ForCausalLM.from_pretrained("allenai/OLMo-2-0425-1B").to(torch_device, torch.bfloat16) out = model(torch.tensor(input_ids, device=torch_device)).logits.float() # Expected mean on dim = -1 expectations = Expectations( { ("cuda", 8): [[-5.6700, -6.5557, -3.1545, -2.7418, -5.5887, -4.5179, -4.9077, -4.6530]], } ) EXPECTED_MEAN = torch.tensor(expectations.get_expectation(), device=torch_device) torch.testing.assert_close(out.mean(-1), EXPECTED_MEAN, rtol=1e-2, atol=1e-2) # slicing logits[0, 0, 0:30] expectations = Expectations( { ("cuda", 8): [2.65625, -5.25, -4.9375, -4.53125, -6.5, -3.828125, -4.15625, -4.1875, -7.0625, -4.71875, -3.609375, -3.09375, -4.59375, -2.640625, -5.25, 0.39453125, 1.3828125, 1.2265625, 1.0078125, 0.57421875, 0.330078125, -0.287109375, -0.3671875, 0.1943359375, -0.0732421875, -6.6875, -4.75, -6.4375, -0.625, -2.625], } ) # fmt: skip EXPECTED_SLICE = torch.tensor(expectations.get_expectation(), device=torch_device) torch.testing.assert_close(out[0, 0, :30], EXPECTED_SLICE, rtol=1e-2, atol=1e-2) def test_model_7b_logits(self): input_ids = [[1, 306, 4658, 278, 6593, 310, 2834, 338]] model = Olmo2ForCausalLM.from_pretrained("shanearora/OLMo2-7B-1124-hf").to(torch_device, dtype=torch.bfloat16) out = model(torch.tensor(input_ids, device=torch_device)).logits.float() # Expected mean on dim = -1 expectations = Expectations( { ("cuda", 8): [[-13.0518, -13.8897, -11.7999, -11.3222, -12.3441, -12.3884, -15.4874, -12.7365]], } ) EXPECTED_MEAN = torch.tensor(expectations.get_expectation(), device=torch_device) torch.testing.assert_close(out.mean(-1), EXPECTED_MEAN, rtol=1e-2, atol=1e-2) # slicing logits[0, 0, 0:30] expectations = Expectations( { ("cuda", 8): [-5.5, -14.4375, -13.8125, -14.875, -14.125, -13.4375, -13.8125, -12.25, -9.5, -12.9375, -11.6875, -6.09375, -12.1875, -6.5, -11.3125, -7.34375, -6.5625, -6.71875, -7.375, -7.96875, -8.0625, -8.1875, -8.75, -8.75, -8.875, -9.9375, -8.1875, -12.875, -7.84375, -11.625], } ) # fmt: skip EXPECTED_SLICE = torch.tensor(expectations.get_expectation(), device=torch_device) torch.testing.assert_close(out[0, 0, :30], EXPECTED_SLICE, rtol=1e-2, atol=1e-2) 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/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 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_simple_encode_decode(self): 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") # bytefallback showcase self.assertEqual(rust_tokenizer.encode("生活的真谛是"), [21990, 76706, 9554, 89151, 39013, 249, 21043]) # fmt: skip self.assertEqual( rust_tokenizer.decode([21990, 76706, 9554, 89151, 39013, 249, 21043], skip_special_tokens=True), "生活的真谛是", ) # Inner spaces showcase self.assertEqual(rust_tokenizer.encode("Hi Hello"), [13347, 220, 22691]) self.assertEqual(rust_tokenizer.decode([13347, 220, 22691], skip_special_tokens=True), "Hi Hello") self.assertEqual(rust_tokenizer.encode("Hi Hello"), [13347, 256, 22691]) self.assertEqual(rust_tokenizer.decode([13347, 256, 22691], skip_special_tokens=True), "Hi Hello") self.assertEqual(rust_tokenizer.encode(""), []) self.assertEqual(rust_tokenizer.encode(" "), [220]) self.assertEqual(rust_tokenizer.encode(" "), [256]) self.assertEqual(rust_tokenizer.encode(" Hello"), [22691]) @pytest.mark.torch_export_test def test_export_static_cache(self): if version.parse(torch.__version__) < version.parse("2.4.0"): self.skipTest(reason="This test requires torch >= 2.4 to run.") from transformers.integrations.executorch import ( TorchExportableModuleWithStaticCache, convert_and_export_with_cache, ) olmo2_model = "shanearora/OLMo2-7B-1124-hf" tokenizer = AutoTokenizer.from_pretrained(olmo2_model, pad_token="", 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", ] 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 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, }, ) model = Olmo2ForCausalLM.from_pretrained( olmo2_model, device_map=device, dtype=dtype, attn_implementation=attn_implementation, generation_config=generation_config, ) 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 exported_program = convert_and_export_with_cache(model) 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)