# coding=utf-8 # 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 DBRX model.""" import unittest from transformers import DbrxConfig, is_torch_available from transformers.testing_utils import 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, random_attention_mask from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from transformers import DbrxForCausalLM, DbrxModel class DbrxModelTester: def __init__( self, parent, hidden_size=32, ffn_hidden_size=32, num_attention_heads=4, kv_n_heads=4, num_hidden_layers=5, max_position_embeddings=512, type_vocab_size=16, batch_size=13, seq_length=7, is_training=True, use_input_mask=True, use_token_type_ids=False, use_labels=True, use_cache=True, type_sequence_label_size=2, num_labels=3, num_choices=4, scope=None, clip_qkv=8, rope_theta=500000, attn_config_model_type="", emb_pdrop=0.0, moe_jitter_eps=0, moe_loss_weight=0.05, moe_num_experts=16, moe_top_k=4, ffn_config_model_type="", ffn_act_fn_name="gelu", initializer_range=0.02, output_router_logits=False, resid_pdrop=0.0, tie_word_embeddings=False, torch_dtype="bfloat16", vocab_size=99, is_decoder=True, pad_token_id=0, ): # Parameters unique to testing self.batch_size = batch_size self.seq_length = seq_length self.type_vocab_size = type_vocab_size self.type_sequence_label_size = type_sequence_label_size self.num_labels = num_labels self.num_choices = num_choices self.scope = scope self.parent = parent 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 # attn_config params self.clip_qkv = clip_qkv self.kv_n_heads = kv_n_heads self.rope_theta = rope_theta self.attn_config_model_type = attn_config_model_type # ffn_config params self.ffn_hidden_size = ffn_hidden_size self.moe_jitter_eps = moe_jitter_eps self.moe_loss_weight = moe_loss_weight self.moe_num_experts = moe_num_experts self.moe_top_k = moe_top_k self.ffn_config_model_type = ffn_config_model_type self.ffn_act_fn_name = ffn_act_fn_name # Other model params self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.max_position_embeddings = max_position_embeddings self.vocab_size = vocab_size self.use_cache = use_cache self.initializer_range = initializer_range self.emb_pdrop = emb_pdrop self.output_router_logits = output_router_logits self.resid_pdrop = resid_pdrop self.tie_word_embeddings = tie_word_embeddings self.torch_dtype = torch_dtype self.is_decoder = is_decoder self.pad_token_id = pad_token_id # Make the dictionaries self.ffn_config = { "ffn_hidden_size": self.ffn_hidden_size, "moe_jitter_eps": self.moe_jitter_eps, "moe_loss_weight": self.moe_loss_weight, "moe_num_experts": self.moe_num_experts, "moe_top_k": self.moe_top_k, "model_type": self.ffn_config_model_type, "ffn_act_fn": {"name": self.ffn_act_fn_name}, } self.attn_config = { "clip_qkv": self.clip_qkv, "kv_n_heads": self.kv_n_heads, "model_type": self.attn_config_model_type, "rope_theta": self.rope_theta, } 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 = random_attention_mask([self.batch_size, self.seq_length]) 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): # Behind the scenes, `DbrxConfig` maps the parameters `hidden_size`, `num_hidden_layers`, # `num_attention_heads`, `max_position_embeddings` to the parameters `d_model`, `n_layers`, # `n_heads`, `max_seq_len` respectively. We use the first group of parameters because # other tests expect every model to have these parameters with these specific names. config = DbrxConfig( vocab_size=self.vocab_size, hidden_size=self.hidden_size, # mapped to `d_model` num_hidden_layers=self.num_hidden_layers, # mapped to `n_layers` num_attention_heads=self.num_attention_heads, # mapped to `n_heads` max_position_embeddings=self.max_position_embeddings, # mapped to `max_seq_len` attn_config=self.attn_config, ffn_config=self.ffn_config, resid_pdrop=self.resid_pdrop, emb_pdrop=self.emb_pdrop, use_cache=self.use_cache, initializer_range=self.initializer_range, output_router_logits=self.output_router_logits, is_decoder=self.is_decoder, pad_token_id=self.pad_token_id, ) return config # Copied from tests.models.llama.test_modeling_llama.LlamaModelTester.create_and_check_model with Llama->Dbrx def create_and_check_model( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = DbrxModel(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)) # Copied from tests.models.llama.test_modeling_llama.LlamaModelTester.create_and_check_model_as_decoder with Llama->Dbrx def create_and_check_model_as_decoder( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, encoder_hidden_states, encoder_attention_mask, ): config.add_cross_attention = True model = DbrxModel(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)) # Copied from tests.models.llama.test_modeling_llama.LlamaModelTester.create_and_check_for_causal_lm with Llama->Dbrx def create_and_check_for_causal_lm( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, encoder_hidden_states, encoder_attention_mask, ): model = DbrxForCausalLM(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, choice_labels, encoder_hidden_states, encoder_attention_mask, ): config.is_decoder = True config.add_cross_attention = True model = DbrxForCausalLM(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)) # Copied from tests.models.llama.test_modeling_llama.LlamaModelTester.prepare_config_and_inputs_for_common with Llama->Dbrx 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 DbrxModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = (DbrxModel, DbrxForCausalLM) if is_torch_available() else () all_generative_model_classes = (DbrxForCausalLM,) if is_torch_available() else () pipeline_model_mapping = {"text-generation": DbrxForCausalLM} if is_torch_available() else {} test_headmasking = False test_pruning = False def setUp(self): self.model_tester = DbrxModelTester(self) self.config_tester = ConfigTester(self, config_class=DbrxConfig, d_model=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) def test_model_various_embeddings(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() for type in ["absolute", "relative_key", "relative_key_query"]: config_and_inputs[0].position_embedding_type = type self.model_tester.create_and_check_model(*config_and_inputs) @slow def test_model_from_pretrained(self): model_name = "eitanturok/dbrx-tiny" model = DbrxModel.from_pretrained(model_name) self.assertIsNotNone(model) @unittest.skip("Dbrx models have weight tying disabled.") def test_tied_weights_keys(self): pass # Offload does not work with Dbrx models because of the forward of DbrxExperts where we chunk the experts. # The issue is that the offloaded weights of the mlp layer are still on meta device (w1_chunked, v1_chunked, w2_chunked) @unittest.skip("Dbrx models do not work with offload") def test_cpu_offload(self): pass @unittest.skip("Dbrx models do not work with offload") def test_disk_offload_safetensors(self): pass @unittest.skip("Dbrx models do not work with offload") def test_disk_offload_bin(self): pass @require_torch class DbrxModelIntegrationTest(unittest.TestCase): @slow def test_tiny_model_logits(self): model = DbrxForCausalLM.from_pretrained("Rocketknight1/dbrx-tiny-random") input_ids = torch.tensor([[0, 1, 2, 3, 4, 5]]) output = model(input_ids)[0] vocab_size = model.vocab_size expected_shape = torch.Size((1, 6, vocab_size)) self.assertEqual(output.shape, expected_shape) expected_slice = torch.tensor( [ [ [-1.6300e-04, 5.0118e-04, 2.5437e-04], [2.0422e-05, 2.7210e-04, -1.5125e-04], [-1.5105e-04, 4.6879e-04, 3.3309e-04], ] ] ) self.assertTrue(torch.allclose(output[:, :3, :3], expected_slice, atol=1e-4))