# 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 OLMoE model.""" import unittest from transformers import OlmoeConfig, is_torch_available 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 ( OlmoeForCausalLM, OlmoeModel, ) class OlmoeModelTester: 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, 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, num_experts_per_tok=2, num_experts=8, norm_topk_prob=False, output_router_logits=False, router_aux_loss_coef=0.001, intermediate_size=12, ): 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 self.num_experts_per_tok = num_experts_per_tok self.num_experts = num_experts self.norm_topk_prob = norm_topk_prob self.output_router_logits = output_router_logits self.router_aux_loss_coef = router_aux_loss_coef 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 OlmoeConfig( 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, num_experts_per_tok=self.num_experts_per_tok, num_experts=self.num_experts, norm_topk_prob=self.norm_topk_prob, output_router_logits=self.output_router_logits, router_aux_loss_coef=self.router_aux_loss_coef, ) def create_and_check_model( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = OlmoeModel(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 OlmoeModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = (OlmoeModel, OlmoeForCausalLM) if is_torch_available() else () pipeline_model_mapping = ( { "feature-extraction": OlmoeModel, "text-generation": OlmoeForCausalLM, } 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 = OlmoeModelTester(self) self.config_tester = ConfigTester(self, config_class=OlmoeConfig, 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 class OlmoeIntegrationTest(unittest.TestCase): @slow def test_model_7b_logits(self): input_ids = [[1, 306, 4658, 278, 6593, 310, 2834, 338]] model = OlmoeForCausalLM.from_pretrained("allenai/OLMoE-1B-7B-0924", device_map="auto") out = model(torch.tensor(input_ids, device=model.device)).logits.float() # Expected mean on dim = -1 EXPECTED_MEAN = torch.tensor([[-1.3814, -3.4450, -2.2990, -1.9542, -2.4387, -2.7941, -2.9312, -2.8309]]) torch.testing.assert_close(out.mean(-1).cpu(), EXPECTED_MEAN, rtol=1e-2, atol=1e-2) # slicing logits[0, 0, 0:30] EXPECTED_SLICE = torch.tensor([-2.3874, -2.4076, -2.4995, 4.2278, 1.4004, -0.0252, 0.4189, -2.7560, 0.3531, 1.6678, -0.7941, -1.1818, -0.2920, 0.7131, -1.4173, 1.6723, 0.5406, 0.1345, -0.1800, 0.2304, 1.2791, 0.7489, 0.6341, -0.0151, -1.3693, -1.2532, -2.3921, 0.7376, 1.6876, 0.5483]) # fmt: skip torch.testing.assert_close(out[0, 0, :30].cpu(), 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, no matter \nhow fast they are moving. This is a very counter-intuitive \nconcept, and it took Einstein a long time to come up with \nthe theory. The theory of relativity is based on two \npostulates""" prompt = "Simply put, the theory of relativity states that " tokenizer = AutoTokenizer.from_pretrained("allenai/OLMoE-1B-7B-0924", device_map="auto") model = OlmoeForCausalLM.from_pretrained("allenai/OLMoE-1B-7B-0924", 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_fast_special_tokens(self): fast_tokenizer = GPTNeoXTokenizerFast.from_pretrained("allenai/OLMoE-1B-7B-0924") 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/OLMoE-1B-7B-0924") 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=True), "Hi Hello") self.assertEqual(rust_tokenizer.encode("Hi Hello"), [12764, 50275, 12092]) self.assertEqual(rust_tokenizer.decode([12764, 50275, 12092], skip_special_tokens=True), "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])