# Copyright 2025 HuggingFace Inc. # # 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. import tempfile from inspect import signature import pytest from parameterized import parameterized from transformers import set_seed from transformers.testing_utils import ( is_flaky, require_flash_attn, require_torch_gpu, slow, ) from .test_configuration_common import ConfigTester from .test_modeling_common import ( GenerationTesterMixin, ModelTesterMixin, ids_tensor, is_torch_available, require_torch, torch_device, ) from .test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch class CausalLMModelTester: _required_attributes = ("base_model_class", "config_class", "causal_lm_class") forced_config_args = [ "pad_token_id" ] # Arguments that should be passed to the config class even if not in its signature config_class = None base_model_class = None causal_lm_class = None sequence_classification_class = None token_classification_class = None question_answering_class = None def _verify_model_attributes(self): for required_attribute in self._required_attributes: if getattr(self, required_attribute) is None: raise ValueError( f"You have inherited from CausalLMModelTester but did not set the {required_attribute} attribute." ) @property def all_model_classes(self): return [ model_class for model_class in ( self.base_model_class, self.causal_lm_class, self.sequence_classification_class, self.token_classification_class, ) if model_class is not None ] 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=2, num_key_value_heads=2, intermediate_size=37, hidden_act="gelu", 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, bos_token_id=1, eos_token_id=2, is_decoder=False, scope=None, expert_interval=1, moe_intermediate_size=12, shared_expert_intermediate_size=36, shared_expert_gate=True, num_experts_per_tok=2, num_experts=8, mamba_n_groups=1, mamba_n_heads=16, mamba_d_state=16, mamba_d_conv=4, mamba_expand=2, mamba_chunk_size=16, ): self._verify_model_attributes() 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.num_key_value_heads = num_key_value_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.bos_token_id = bos_token_id self.eos_token_id = eos_token_id self.scope = scope self.head_dim = self.hidden_size // self.num_attention_heads self.is_decoder = is_decoder self.expert_interval = expert_interval self.moe_intermediate_size = moe_intermediate_size self.shared_expert_intermediate_size = shared_expert_intermediate_size self.shared_expert_gate = shared_expert_gate self.num_experts_per_tok = num_experts_per_tok self.num_experts = num_experts self.mamba_n_groups = mamba_n_groups self.mamba_n_heads = mamba_n_heads self.mamba_d_state = mamba_d_state self.mamba_d_conv = mamba_d_conv self.mamba_expand = mamba_expand self.mamba_chunk_size = mamba_chunk_size 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 @property def config_args(self): return list(signature(self.config_class.__init__).parameters.keys()) def get_config(self): kwargs = {} model_name_to_common_name = {v: k for k, v in self.config_class.attribute_map.items()} for k in self.config_args + self.forced_config_args: if hasattr(self, k) and k != "self": kwargs[k] = getattr(self, k) elif k in model_name_to_common_name and hasattr(self, model_name_to_common_name[k]): kwargs[k] = getattr(self, model_name_to_common_name[k]) return self.config_class(**kwargs) def create_and_check_model( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = self.base_model_class(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 CausalLMModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin): test_headmasking = False test_pruning = False model_tester_class = None all_model_classes = None rotary_embedding_layer = None # Enables RoPE tests if set pipeline_model_mapping = None def setUp(self): if self.model_tester_class is None: raise ValueError( "You have inherited from CausalLMModelTest but did not set the model_tester_class attribute." ) self.model_tester = self.model_tester_class(self) self.config_tester = ConfigTester(self, config_class=self.model_tester.config_class) if self.all_model_classes is None: self.all_model_classes = self.model_tester.all_model_classes if self.pipeline_model_mapping is None: raise ValueError( "You have inherited from CausalLMModelTest but did not set the pipeline_model_mapping attribute." ) 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_sequence_classification_model(self): if self.model_tester.sequence_classification_class is None: self.skipTest("Model does not support sequence classification") 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 = self.model_tester.sequence_classification_class(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_sequence_classification_model_for_single_label(self): if self.model_tester.sequence_classification_class is None: self.skipTest("Model does not support sequence classification") 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 = self.model_tester.sequence_classification_class(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_sequence_classification_model_for_multi_label(self): if self.model_tester.sequence_classification_class is None: self.skipTest("Model does not support sequence classification") 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 = self.model_tester.sequence_classification_class(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_token_classification_model(self): if self.model_tester.token_classification_class is None: self.skipTest("Model does not support token classification") 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) token_labels = ids_tensor([self.model_tester.batch_size, self.model_tester.seq_length], config.num_labels) model = self.model_tester.token_classification_class(config=config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=attention_mask, labels=token_labels) self.assertEqual( result.logits.shape, (self.model_tester.batch_size, self.model_tester.seq_length, self.model_tester.num_labels), ) @parameterized.expand([("linear",), ("dynamic",), ("yarn",)]) def test_model_rope_scaling_from_config(self, scaling_type): if self.rotary_embedding_layer is None: self.skipTest("Rotary embedding layer not set") config, _ = self.model_tester.prepare_config_and_inputs_for_common() short_input = ids_tensor([1, 10], config.vocab_size) 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 = self.model_tester_class.base_model_class(config) original_model.to(torch_device) original_model.eval() original_short_output = original_model(short_input).last_hidden_state original_long_output = original_model(long_input).last_hidden_state 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 = self.model_tester_class.base_model_class(config) scaled_model.to(torch_device) scaled_model.eval() scaled_short_output = scaled_model(short_input).last_hidden_state scaled_long_output = scaled_model(long_input).last_hidden_state # Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original # maximum sequence length, so the outputs for the short input should match. if scaling_type == "dynamic": torch.testing.assert_close(original_short_output, scaled_short_output, rtol=1e-5, atol=1e-5) else: self.assertFalse(torch.allclose(original_short_output, scaled_short_output, atol=1e-5)) # The output should be different for long inputs self.assertFalse(torch.allclose(original_long_output, scaled_long_output, atol=1e-5)) def test_model_rope_scaling(self): if self.rotary_embedding_layer is None: self.skipTest("Rotary embedding layer not set") config, _ = self.model_tester.prepare_config_and_inputs_for_common() scaling_factor = 10 short_input_length = 10 long_input_length = int(config.max_position_embeddings * 1.5) # Inputs x = torch.randn( 1, dtype=torch.float32, device=torch_device ) # used exclusively to get the dtype and the device position_ids_short = torch.arange(short_input_length, dtype=torch.long, device=torch_device) position_ids_short = position_ids_short.unsqueeze(0) position_ids_long = torch.arange(long_input_length, dtype=torch.long, device=torch_device) position_ids_long = position_ids_long.unsqueeze(0) # Sanity check original RoPE original_rope = self.rotary_embedding_layer(config=config).to(torch_device) original_cos_short, original_sin_short = original_rope(x, position_ids_short) original_cos_long, original_sin_long = original_rope(x, position_ids_long) torch.testing.assert_close(original_cos_short, original_cos_long[:, :short_input_length, :]) torch.testing.assert_close(original_sin_short, original_sin_long[:, :short_input_length, :]) # Sanity check linear RoPE scaling # New position "x" should match original position with index "x/scaling_factor" config.rope_scaling = {"type": "linear", "factor": scaling_factor} linear_scaling_rope = self.rotary_embedding_layer(config=config).to(torch_device) linear_cos_short, linear_sin_short = linear_scaling_rope(x, position_ids_short) linear_cos_long, linear_sin_long = linear_scaling_rope(x, position_ids_long) torch.testing.assert_close(linear_cos_short, linear_cos_long[:, :short_input_length, :]) torch.testing.assert_close(linear_sin_short, linear_sin_long[:, :short_input_length, :]) for new_position in range(0, long_input_length, scaling_factor): original_position = int(new_position // scaling_factor) torch.testing.assert_close(linear_cos_long[:, new_position, :], original_cos_long[:, original_position, :]) torch.testing.assert_close(linear_sin_long[:, new_position, :], original_sin_long[:, original_position, :]) # Sanity check Dynamic NTK RoPE scaling # Scaling should only be observed after a long input is fed. We can observe that the frequencies increase # with scaling_factor (or that `inv_freq` decreases) config.rope_scaling = {"type": "dynamic", "factor": scaling_factor} ntk_scaling_rope = self.rotary_embedding_layer(config=config).to(torch_device) ntk_cos_short, ntk_sin_short = ntk_scaling_rope(x, position_ids_short) ntk_cos_long, ntk_sin_long = ntk_scaling_rope(x, position_ids_long) torch.testing.assert_close(ntk_cos_short, original_cos_short) torch.testing.assert_close(ntk_sin_short, original_sin_short) with self.assertRaises(AssertionError): torch.testing.assert_close(ntk_cos_long, original_cos_long) with self.assertRaises(AssertionError): torch.testing.assert_close(ntk_sin_long, original_sin_long) self.assertTrue((ntk_scaling_rope.inv_freq <= original_rope.inv_freq).all()) # Sanity check Yarn RoPE scaling # Scaling should be over the entire input config.rope_scaling = {"type": "yarn", "factor": scaling_factor} yarn_scaling_rope = self.rotary_embedding_layer(config=config).to(torch_device) yarn_cos_short, yarn_sin_short = yarn_scaling_rope(x, position_ids_short) yarn_cos_long, yarn_sin_long = yarn_scaling_rope(x, position_ids_long) torch.testing.assert_close(yarn_cos_short, yarn_cos_long[:, :short_input_length, :]) torch.testing.assert_close(yarn_sin_short, yarn_sin_long[:, :short_input_length, :]) with self.assertRaises(AssertionError): torch.testing.assert_close(yarn_cos_short, original_cos_short) with self.assertRaises(AssertionError): torch.testing.assert_close(yarn_sin_short, original_sin_short) with self.assertRaises(AssertionError): torch.testing.assert_close(yarn_cos_long, original_cos_long) with self.assertRaises(AssertionError): torch.testing.assert_close(yarn_sin_long, original_sin_long) @require_flash_attn @require_torch_gpu @pytest.mark.flash_attn_test @is_flaky() @slow def test_flash_attn_2_equivalence(self): for model_class in self.all_model_classes: if not model_class._supports_flash_attn_2: self.skipTest(reason="Model does not support Flash Attention 2") config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() model = model_class(config) with tempfile.TemporaryDirectory() as tmpdirname: model.save_pretrained(tmpdirname) model_fa = model_class.from_pretrained( tmpdirname, torch_dtype=torch.bfloat16, attn_implementation="flash_attention_2" ) model_fa.to(torch_device) model = model_class.from_pretrained( tmpdirname, torch_dtype=torch.bfloat16, attn_implementation="eager" ) model.to(torch_device) dummy_input = inputs_dict[model_class.main_input_name] dummy_input = dummy_input.to(torch_device) outputs = model(dummy_input, output_hidden_states=True) outputs_fa = model_fa(dummy_input, output_hidden_states=True) logits = outputs.hidden_states[-1] logits_fa = outputs_fa.hidden_states[-1] assert torch.allclose(logits_fa, logits, atol=2e-3)