# coding=utf-8 # Copyright 2025 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 Doge model.""" import unittest from transformers import AutoTokenizer, DogeConfig, is_torch_available, set_seed from transformers.testing_utils import ( require_read_token, require_torch, require_torch_accelerator, 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 ( DogeForCausalLM, DogeForSequenceClassification, DogeModel, ) class DogeModelTester: def __init__( self, parent, batch_size=8, seq_length=16, is_training=True, use_input_mask=True, use_token_type_ids=False, use_labels=True, vocab_size=128, hidden_size=32, num_hidden_layers=2, num_attention_heads=4, intermediate_size=64, hidden_act="silu", max_position_embeddings=512, type_vocab_size=16, type_sequence_label_size=2, initializer_range=0.02, num_labels=3, 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.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.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 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.vocab_size) config = self.get_config() return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels def get_config(self): return DogeConfig( 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, 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): model = DogeModel(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 create_and_check_model_as_decoder( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, encoder_hidden_states, encoder_attention_mask, ): config.add_cross_attention = True model = DogeModel(config) model.to(torch_device) model.eval() result = model( input_ids, attention_mask=input_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, ) result = model( input_ids, attention_mask=input_mask, encoder_hidden_states=encoder_hidden_states, ) result = model(input_ids, attention_mask=input_mask) self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size)) def create_and_check_for_causal_lm( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, encoder_hidden_states, encoder_attention_mask, ): model = DogeForCausalLM(config=config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=input_mask, labels=token_labels) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size)) def create_and_check_decoder_model_past_large_inputs( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, encoder_hidden_states, encoder_attention_mask, ): config.is_decoder = True config.add_cross_attention = True model = DogeForCausalLM(config=config) model.to(torch_device) model.eval() # first forward pass outputs = model( input_ids, attention_mask=input_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, use_cache=True, ) past_key_values = outputs.past_key_values # create hypothetical multiple next token and extent to next_input_ids next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size) next_mask = ids_tensor((self.batch_size, 3), vocab_size=2) # append to next input_ids and next_input_ids = torch.cat([input_ids, next_tokens], dim=-1) next_attention_mask = torch.cat([input_mask, next_mask], dim=-1) output_from_no_past = model( next_input_ids, attention_mask=next_attention_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, output_hidden_states=True, )["hidden_states"][0] output_from_past = model( next_tokens, attention_mask=next_attention_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, past_key_values=past_key_values, output_hidden_states=True, )["hidden_states"][0] # select random slice random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item() output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx].detach() output_from_past_slice = output_from_past[:, :, random_slice_idx].detach() self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]) # test that outputs are equal for slice self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3)) 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, ) = config_and_inputs inputs_dict = {"input_ids": input_ids, "attention_mask": input_mask} return config, inputs_dict @require_torch class DogeModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = ( ( DogeModel, DogeForCausalLM, DogeForSequenceClassification, ) if is_torch_available() else () ) all_generative_model_classes = (DogeForCausalLM,) if is_torch_available() else () pipeline_model_mapping = ( { "feature-extraction": DogeModel, "text-classification": DogeForSequenceClassification, "text-generation": DogeForCausalLM, "zero-shot": DogeForSequenceClassification, } if is_torch_available() else {} ) has_attentions = False # 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] # used in `test_torch_compile_for_training` _torch_compile_train_cls = DogeForCausalLM if is_torch_available() else None def setUp(self): self.model_tester = DogeModelTester(self) self.config_tester = ConfigTester(self, config_class=DogeConfig, 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) def test_doge_sequence_classification_model(self): config, input_dict = self.model_tester.prepare_config_and_inputs_for_common() config.num_labels = 3 input_ids = input_dict["input_ids"] attention_mask = input_ids.ne(1).to(torch_device) sequence_labels = ids_tensor([self.model_tester.batch_size], self.model_tester.type_sequence_label_size) model = DogeForSequenceClassification(config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=attention_mask, labels=sequence_labels) self.assertEqual(result.logits.shape, (self.model_tester.batch_size, self.model_tester.num_labels)) def test_doge_sequence_classification_model_for_single_label(self): config, input_dict = self.model_tester.prepare_config_and_inputs_for_common() config.num_labels = 3 config.problem_type = "single_label_classification" input_ids = input_dict["input_ids"] attention_mask = input_ids.ne(1).to(torch_device) sequence_labels = ids_tensor([self.model_tester.batch_size], self.model_tester.type_sequence_label_size) model = DogeForSequenceClassification(config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=attention_mask, labels=sequence_labels) self.assertEqual(result.logits.shape, (self.model_tester.batch_size, self.model_tester.num_labels)) def test_doge_sequence_classification_model_for_multi_label(self): config, input_dict = self.model_tester.prepare_config_and_inputs_for_common() config.num_labels = 3 config.problem_type = "multi_label_classification" input_ids = input_dict["input_ids"] attention_mask = input_ids.ne(1).to(torch_device) sequence_labels = ids_tensor( [self.model_tester.batch_size, config.num_labels], self.model_tester.type_sequence_label_size ).to(torch.float) model = DogeForSequenceClassification(config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=attention_mask, labels=sequence_labels) self.assertEqual(result.logits.shape, (self.model_tester.batch_size, self.model_tester.num_labels)) @unittest.skip(reason="Doge buffers include complex numbers, which breaks this test") def test_save_load_fast_init_from_base(self): pass def test_tp_plan_matches_params(self): """Need to overwrite as the plan contains keys that are valid but depend on some configs flags and cannot be valid all at the same time""" config, _ = self.model_tester.prepare_config_and_inputs_for_common() # They are valid but not always used, depending on config.is_moe flag (the modules are not the same in both cases) problematic_keys = { "layers.*.mlp.router_gate": "colwise_rep", "layers.*.mlp.down_embed": "rowwise_rep", "layers.*.mlp.up_embed": "rowwise_rep", } if not config.is_moe: for key in problematic_keys: config.base_model_tp_plan.pop(key) super().test_tp_plan_matches_params() # Put them back in class attribute config.base_model_tp_plan.update(problematic_keys) @require_torch_accelerator class DogeIntegrationTest(unittest.TestCase): # This variable is used to determine which CUDA device are we using for our runners (A10 or T4) # Depending on the hardware we get different logits / generations cuda_compute_capability_major_version = None @classmethod def setUpClass(cls): if is_torch_available() and torch.cuda.is_available(): # 8 is for A100 / A10 and 7 for T4 cls.cuda_compute_capability_major_version = torch.cuda.get_device_capability()[0] @slow @require_read_token def test_Doge_20M_hard(self): """ An integration test for Doge-20M. It tests against a long output to ensure the subtle numerical differences """ EXPECTED_TEXT = "Here's everything I know about dogs. Dogs is the best animal in the world. It is a very popular and popular dog in the United States. It is a very popular" tokenizer = AutoTokenizer.from_pretrained("SmallDoge/Doge-20M") model = DogeForCausalLM.from_pretrained("SmallDoge/Doge-20M", device_map="auto", dtype=torch.bfloat16) input_text = ["Here's everything I know about dogs. Dogs is the best animal in the"] set_seed(0) model_inputs = tokenizer(input_text, return_tensors="pt").to(model.device) generated_ids = model.generate(**model_inputs, max_new_tokens=20, do_sample=False) generated_text = tokenizer.decode(generated_ids[0], skip_special_tokens=True) self.assertEqual(generated_text, EXPECTED_TEXT)