# Copyright 2023 The HuggingFace 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. # import math import unittest from transformers import BitsAndBytesConfig, MptConfig, is_torch_available from transformers.testing_utils import ( Expectations, require_bitsandbytes, require_deterministic_for_xpu, 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, random_attention_mask from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from transformers import ( AutoTokenizer, MptForCausalLM, MptForQuestionAnswering, MptForSequenceClassification, MptForTokenClassification, MptModel, ) @require_torch class MptModelTester: def __init__( self, parent, batch_size=14, seq_length=7, is_training=True, use_token_type_ids=False, use_input_mask=True, use_labels=True, use_mc_token_ids=True, vocab_size=99, hidden_size=48, num_hidden_layers=2, num_attention_heads=4, intermediate_size=37, hidden_act="gelu", hidden_dropout_prob=0.1, attention_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, ): self.parent = parent self.batch_size = batch_size self.seq_length = seq_length self.is_training = is_training self.use_token_type_ids = use_token_type_ids self.use_input_mask = use_input_mask self.use_labels = use_labels self.use_mc_token_ids = use_mc_token_ids 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_dropout_prob = attention_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.scope = None self.bos_token_id = vocab_size - 1 self.eos_token_id = vocab_size - 1 self.pad_token_id = vocab_size - 1 def prepare_config_and_inputs(self, gradient_checkpointing=False): 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]) sequence_labels = None if self.use_labels: sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size) config = self.get_config(gradient_checkpointing=gradient_checkpointing) return (config, input_ids, input_mask, sequence_labels) def get_config(self, gradient_checkpointing=False): return MptConfig( vocab_size=self.vocab_size, seq_length=self.seq_length, hidden_size=self.hidden_size, n_layers=self.num_hidden_layers, n_heads=self.num_attention_heads, hidden_dropout=self.hidden_dropout_prob, attention_dropout=self.attention_dropout_prob, n_positions=self.max_position_embeddings, type_vocab_size=self.type_vocab_size, initializer_range=self.initializer_range, use_cache=True, bos_token_id=self.bos_token_id, eos_token_id=self.eos_token_id, pad_token_id=self.pad_token_id, num_labels=self.num_labels, gradient_checkpointing=gradient_checkpointing, dtype="float32", ) def create_and_check_mpt_model(self, config, input_ids, input_mask, *args): model = MptModel(config=config) model.to(torch_device) model.eval() result = model(input_ids) self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size)) self.parent.assertEqual(len(result.past_key_values), config.n_layers) def create_and_check_mpt_model_past(self, config, input_ids, input_mask, *args): model = MptModel(config=config) model.to(torch_device) model.eval() # first forward pass outputs = model(input_ids, attention_mask=torch.ones_like(input_ids), use_cache=True) outputs_use_cache_conf = model(input_ids, attention_mask=torch.ones_like(input_ids)) outputs_no_past = model(input_ids, use_cache=False, attention_mask=torch.ones_like(input_ids)) self.parent.assertTrue(len(outputs) == len(outputs_use_cache_conf)) self.parent.assertTrue(len(outputs) == len(outputs_no_past) + 1) past = outputs["past_key_values"] # create hypothetical next token and extent to next_input_ids next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size) # append to next input_ids and token_type_ids next_input_ids = torch.cat([input_ids, next_tokens], dim=-1) output_from_no_past = model(next_input_ids)["last_hidden_state"] output_from_past = model(next_tokens, past_key_values=past)["last_hidden_state"] # select random slice random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item() output_from_no_past_slice = output_from_no_past[:, -1, random_slice_idx].detach() output_from_past_slice = output_from_past[:, 0, random_slice_idx].detach() # 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 create_and_check_mpt_model_attention_mask_past(self, config, input_ids, input_mask, *args): model = MptModel(config=config) model.to(torch_device) model.eval() # create attention mask attn_mask = torch.ones(input_ids.shape, dtype=torch.long, device=torch_device) half_seq_length = self.seq_length // 2 attn_mask[:, half_seq_length:] = 0 # first forward pass output, past = model(input_ids, attention_mask=attn_mask).to_tuple() # create hypothetical next token and extent to next_input_ids next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size) # change a random masked slice from input_ids random_seq_idx_to_change = ids_tensor((1,), half_seq_length).item() + 1 random_other_next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size).squeeze(-1) input_ids[:, -random_seq_idx_to_change] = random_other_next_tokens # append to next input_ids and attn_mask next_input_ids = torch.cat([input_ids, next_tokens], dim=-1) attn_mask = torch.cat( [attn_mask, torch.ones((attn_mask.shape[0], 1), dtype=torch.long, device=torch_device)], dim=1, ) # get two different outputs output_from_no_past = model(next_input_ids, attention_mask=attn_mask)["last_hidden_state"] output_from_past = model(next_tokens, past_key_values=past, attention_mask=attn_mask)["last_hidden_state"] # select random slice random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item() output_from_no_past_slice = output_from_no_past[:, -1, random_slice_idx].detach() output_from_past_slice = output_from_past[:, 0, random_slice_idx].detach() # 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 create_and_check_mpt_model_past_large_inputs(self, config, input_ids, input_mask, *args): model = MptModel(config=config) model.to(torch_device) model.eval() # first forward pass outputs = model( input_ids, attention_mask=input_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, output_hidden_states=True, ) hidden_states_from_no_past = output_from_no_past["hidden_states"][0] output_from_past = model( next_tokens, attention_mask=next_attention_mask, past_key_values=past_key_values, output_hidden_states=True, ) hidden_states_from_past = output_from_past["hidden_states"][0] # select random slice random_slice_idx = ids_tensor((1,), hidden_states_from_past.shape[-1]).item() output_from_no_past_slice = hidden_states_from_no_past[:, -3:, random_slice_idx].detach() output_from_past_slice = hidden_states_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 create_and_check_lm_head_model(self, config, input_ids, input_mask, *args): model = MptForCausalLM(config) model.to(torch_device) model.eval() result = model(input_ids, labels=input_ids) self.parent.assertEqual(result.loss.shape, ()) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size)) def create_and_check_sequence_classification_model(self, config, input_ids, input_mask, *args): config.num_labels = self.num_labels model = MptForSequenceClassification(config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=input_mask) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels)) def create_and_check_token_classification_model(self, config, input_ids, input_mask, *args): model = MptForTokenClassification(config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=input_mask) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels)) def create_and_check_forward_and_backwards( self, config, input_ids, input_mask, *args, gradient_checkpointing=False ): model = MptForCausalLM(config) model.to(torch_device) if gradient_checkpointing: model.gradient_checkpointing_enable() result = model(input_ids, labels=input_ids) self.parent.assertEqual(result.loss.shape, ()) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size)) result.loss.backward() def create_and_check_mpt_weight_initialization(self, config, *args): model = MptModel(config) model_std = model.config.initializer_range / math.sqrt(2 * model.config.n_layers) for key in model.state_dict(): if "c_proj" in key and "weight" in key: self.parent.assertLessEqual(abs(torch.std(model.state_dict()[key]) - model_std), 0.001) self.parent.assertLessEqual(abs(torch.mean(model.state_dict()[key]) - 0.0), 0.01) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() config, input_ids, input_mask, sequence_labels = config_and_inputs inputs_dict = {"input_ids": input_ids} return config, inputs_dict class MptConfigTester(ConfigTester): def __init__(self, parent, config_class=None, has_text_modality=True, common_properties=None, **kwargs): super().__init__(parent, config_class, has_text_modality, common_properties, **kwargs) def test_attn_config_as_dict(self): config = self.config_class(**self.inputs_dict, attn_config={"attn_impl": "flash", "softmax_scale": None}) self.parent.assertTrue(config.attn_config.attn_impl == "flash") self.parent.assertTrue(config.attn_config.softmax_scale is None) def run_common_tests(self): self.test_attn_config_as_dict() return super().run_common_tests() @require_torch class MptModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = ( ( MptModel, MptForCausalLM, MptForSequenceClassification, MptForTokenClassification, MptForQuestionAnswering, ) if is_torch_available() else () ) test_missing_keys = False pipeline_model_mapping = ( { "feature-extraction": MptModel, "question-answering": MptForQuestionAnswering, "text-classification": MptForSequenceClassification, "text-generation": MptForCausalLM, "token-classification": MptForTokenClassification, "zero-shot": MptForSequenceClassification, } if is_torch_available() else {} ) def setUp(self): self.model_tester = MptModelTester(self) self.config_tester = MptConfigTester(self, config_class=MptConfig, n_embd=37) def test_config(self): self.config_tester.run_common_tests() def test_mpt_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_mpt_model(*config_and_inputs) def test_mpt_model_alibi_tensor(self): # test creation of alibi tensor when num heads is not a power of two config_and_inputs = self.model_tester.prepare_config_and_inputs() config_and_inputs[0].n_heads = 6 self.model_tester.create_and_check_mpt_model(*config_and_inputs) def test_mpt_model_past(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_mpt_model_past(*config_and_inputs) def test_mpt_model_att_mask_past(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_mpt_model_attention_mask_past(*config_and_inputs) def test_mpt_model_past_large_inputs(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_mpt_model_past_large_inputs(*config_and_inputs) def test_mpt_lm_head_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_lm_head_model(*config_and_inputs) def test_mpt_sequence_classification_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_sequence_classification_model(*config_and_inputs) def test_mpt_token_classification_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_token_classification_model(*config_and_inputs) def test_mpt_gradient_checkpointing(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_forward_and_backwards(*config_and_inputs, gradient_checkpointing=True) def test_mpt_weight_initialization(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_mpt_weight_initialization(*config_and_inputs) @unittest.skip(reason="For backward compatibility the lm_head is not in the model's state dict on the Hub.") def test_model_weights_reload_no_missing_tied_weights(self): pass @slow def test_model_from_pretrained(self): model_name = "mosaicml/mpt-7b" model = MptModel.from_pretrained(model_name) self.assertIsNotNone(model) @slow @require_torch_accelerator @require_bitsandbytes class MptIntegrationTests(unittest.TestCase): def test_generation_8k(self): model_id = "mosaicml/mpt-7b-8k" tokenizer = AutoTokenizer.from_pretrained(model_id) # Load in 4bit to fit the daily CI runner GPU RAM model = MptForCausalLM.from_pretrained( model_id, dtype=torch.bfloat16, device_map={"": 0}, quantization_config=BitsAndBytesConfig(load_in_4bit=True), ) input_text = "Hello" expected_outputs = Expectations({ (None, None): "Hello, I'm a new user of the forum. I have a question about the \"Solaris", ("cuda", 8): "Hello, I'm a new user of the forum. I have a question. I have a problem with", ("rocm", (9, 5)): "Hello, I'm a newbie to the forum. I have a question about the \"B\" in", }) # fmt: off expected_output = expected_outputs.get_expectation() inputs = tokenizer(input_text, return_tensors="pt").to(torch_device) outputs = model.generate(**inputs, max_new_tokens=20) decoded_output = tokenizer.decode(outputs[0], skip_special_tokens=True) self.assertEqual(decoded_output, expected_output) def test_generation(self): model_id = "mosaicml/mpt-7b" tokenizer = AutoTokenizer.from_pretrained(model_id) # Load in 4bit to fit the daily CI runner GPU RAM model = MptForCausalLM.from_pretrained( model_id, dtype=torch.bfloat16, device_map={"": 0}, quantization_config=BitsAndBytesConfig(load_in_4bit=True), ) input_text = "Hello" expected_outputs = Expectations({ (None, None): "Hello and welcome to the first episode of the new podcast, The Frugal Feminist.\n", ("rocm", (9, 5)): "Hello and welcome to the first day of the new release at The Stamp Man!\nToday we are", ("xpu", 3): "Hello and welcome to the first ever episode of the new and improved, and hopefully improved, podcast.\n", ("cuda", 8): "Hello and welcome to the first ever episode of the new and improved, and hopefully improved, podcast.\n", }) # fmt: off expected_output = expected_outputs.get_expectation() inputs = tokenizer(input_text, return_tensors="pt").to(torch_device) outputs = model.generate(**inputs, max_new_tokens=20) decoded_output = tokenizer.decode(outputs[0], skip_special_tokens=True) self.assertEqual(decoded_output, expected_output) @require_deterministic_for_xpu def test_generation_batched(self): model_id = "mosaicml/mpt-7b" tokenizer = AutoTokenizer.from_pretrained(model_id) # Load in 4bit to fit the daily CI runner GPU RAM model = MptForCausalLM.from_pretrained( model_id, dtype=torch.bfloat16, device_map={"": 0}, quantization_config=BitsAndBytesConfig(load_in_4bit=True), ) input_texts = ["Hello my name is", "Today I am going at the gym and"] tokenizer.pad_token_id = tokenizer.eos_token_id tokenizer.padding_side = "left" inputs = tokenizer(input_texts, return_tensors="pt", padding=True).to(torch_device) expected_outputs = Expectations( { (None, None): [ "Hello my name is Tiffany and I am a mother of two beautiful children. I have been a nanny for the", "Today I am going at the gym and then I am going to go to the grocery store. I am going to buy some food and some", ], ("xpu", 3): [ "Hello my name is Tiffany. I am a mother of two beautiful children. I have been a nanny for over", "Today I am going at the gym and then I am going to go to the mall with my mom. I am going to go to the", ], ("cuda", 8): [ "Hello my name is Tiffany and I am a mother of two beautiful children. I have been a nanny for over", "Today I am going at the gym and then I am going to go to the grocery store. I am going to make a list of things", ], ("rocm", (9, 5)): [ "Hello my name is Jasmine and I am a very sweet and loving dog. I am a very playful dog and I", "Today I am going at the gym and then I am going to go to the mall. I am going to buy a new pair of jeans", ], } ) expected_output = expected_outputs.get_expectation() outputs = model.generate(**inputs, max_new_tokens=20) decoded_outputs = tokenizer.batch_decode(outputs, skip_special_tokens=True) for i, predicted_output in enumerate(decoded_outputs): self.assertEqual(predicted_output, expected_output[i]) @require_deterministic_for_xpu def test_model_logits(self): model_id = "mosaicml/mpt-7b" # Load in 4bit to fit the daily CI runner GPU RAM model = MptForCausalLM.from_pretrained( model_id, dtype=torch.bfloat16, device_map={"": 0}, quantization_config=BitsAndBytesConfig(load_in_4bit=True), ) dummy_input = torch.LongTensor([[1, 2, 3, 4, 5]]).to(torch_device) outputs = model(dummy_input, output_hidden_states=True) expected_slices = Expectations( { (None, None): torch.Tensor([-0.2520, -0.2178, -0.1953]), ("xpu", 3): torch.Tensor([-0.2656, -0.2246, -0.2637]), ("cuda", 8): torch.Tensor([-0.2559, -0.2227, -0.2217]), # TODO: This is quite a bit off, check BnB ("rocm", (9, 5)): torch.Tensor([-0.3008, -0.1309, -0.1562]), } ) expected_slice = expected_slices.get_expectation().to(torch_device, torch.bfloat16) predicted_slice = outputs.hidden_states[-1][0, 0, :3] torch.testing.assert_close(expected_slice, predicted_slice, rtol=1e-3, atol=1e-3)