# Copyright 2022 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 Cohere model.""" import unittest from transformers import CohereConfig, is_torch_available from transformers.testing_utils import ( require_bitsandbytes, require_torch, require_torch_multi_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 AutoTokenizer, CohereForCausalLM, CohereModel # Copied from transformers.tests.models.llama.LlamaModelTester with Llama->Cohere class CohereModelTester: config_class = CohereConfig if is_torch_available(): model_class = CohereModel for_causal_lm_class = CohereForCausalLM 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, 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, 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.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 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 # Ignore copy def get_config(self): return self.config_class( 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, ) def create_and_check_model( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = self.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 CohereModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = (CohereModel, CohereForCausalLM) if is_torch_available() else () pipeline_model_mapping = ( { "feature-extraction": CohereModel, "text-generation": CohereForCausalLM, } 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 = CohereModelTester(self) self.config_tester = ConfigTester(self, config_class=CohereConfig, 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 @slow class CohereIntegrationTest(unittest.TestCase): @require_torch_multi_accelerator @require_bitsandbytes def test_batched_4bit(self): model_id = "CohereForAI/c4ai-command-r-v01-4bit" EXPECTED_TEXT = [ 'Hello today I am going to show you how to make a simple and easy card using the new stamp set called "Hello" from the Occasions catalog. This set is so versatile and can be used for many occasions. I used the new In', "Hi there, here we are again with another great collection of free fonts for your next project. This time we have gathered 10 free fonts that you can download and use in your designs. These fonts are perfect for any kind", ] model = CohereForCausalLM.from_pretrained(model_id, device_map="auto") tokenizer = AutoTokenizer.from_pretrained(model_id) tokenizer.pad_token = tokenizer.eos_token text = ["Hello today I am going to show you how to", "Hi there, here we are"] inputs = tokenizer(text, return_tensors="pt", padding=True).to(torch_device) output = model.generate(**inputs, max_new_tokens=40, do_sample=False) self.assertEqual(tokenizer.batch_decode(output, skip_special_tokens=True), EXPECTED_TEXT) def test_batched_small_model_logits(self): # Since the model is very large, we created a random cohere model so that we can do a simple # logits check on it. model_id = "hf-internal-testing/cohere-random" EXPECTED_LOGITS = torch.Tensor( [ [[0.0000, 0.0285, 0.0322], [0.0000, 0.0011, 0.1105], [0.0000, -0.0018, -0.1019]], [[0.0000, 0.1080, 0.0454], [0.0000, -0.1808, -0.1553], [0.0000, 0.0452, 0.0369]], ] ).to(device=torch_device, dtype=torch.float16) tokenizer = AutoTokenizer.from_pretrained(model_id) model = CohereForCausalLM.from_pretrained(model_id, dtype=torch.float16).to(torch_device) tokenizer.pad_token = tokenizer.eos_token text = ["Hello today I am going to show you how to", "Hi there, here we are"] inputs = tokenizer(text, return_tensors="pt", padding=True).to(torch_device) with torch.no_grad(): output = model(**inputs) logits = output.logits torch.testing.assert_close(EXPECTED_LOGITS, logits[:, -3:, :3], rtol=1e-3, atol=1e-3)