# coding=utf-8 # Copyright 2024 JetMoe AI and 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 JetMoe model.""" import gc import unittest import pytest from transformers import AutoTokenizer, JetMoeConfig, is_torch_available from transformers.testing_utils import ( backend_empty_cache, require_flash_attn, require_torch, require_torch_gpu, 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 ( JetMoeForCausalLM, JetMoeForSequenceClassification, JetMoeModel, ) class JetMoeModelTester: 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_key_value_heads=2, kv_channels=8, intermediate_size=37, hidden_act="silu", num_local_experts=4, num_experts_per_tok=2, 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.kv_channels = kv_channels self.num_attention_heads = num_key_value_heads * num_experts_per_tok self.num_key_value_heads = num_key_value_heads self.intermediate_size = intermediate_size self.hidden_act = hidden_act self.num_local_experts = num_local_experts self.num_experts_per_tok = num_experts_per_tok 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.ones(self.batch_size, self.seq_length).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 def get_config(self): return JetMoeConfig( vocab_size=self.vocab_size, hidden_size=self.hidden_size, num_hidden_layers=self.num_hidden_layers, num_key_value_heads=self.num_key_value_heads, kv_channels=self.kv_channels, intermediate_size=self.intermediate_size, activation_function=self.hidden_act, num_local_experts=self.num_local_experts, num_experts_per_tok=self.num_experts_per_tok, 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 = JetMoeModel(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, choice_labels, encoder_hidden_states, encoder_attention_mask, ): config.add_cross_attention = True model = JetMoeModel(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, choice_labels, encoder_hidden_states, encoder_attention_mask, ): model = JetMoeForCausalLM(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 = JetMoeForCausalLM(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, choice_labels, ) = config_and_inputs inputs_dict = {"input_ids": input_ids, "attention_mask": input_mask} return config, inputs_dict @require_torch class JetMoeModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = ( (JetMoeModel, JetMoeForCausalLM, JetMoeForSequenceClassification) if is_torch_available() else () ) pipeline_model_mapping = ( { "feature-extraction": JetMoeModel, "text-classification": JetMoeForSequenceClassification, "text-generation": JetMoeForCausalLM, "zero-shot": JetMoeForSequenceClassification, } if is_torch_available() else {} ) test_headmasking = False test_pruning = False test_mismatched_shapes = False test_cpu_offload = False test_disk_offload_bin = False test_disk_offload_safetensors = False def setUp(self): self.model_tester = JetMoeModelTester(self) self.config_tester = ConfigTester( self, config_class=JetMoeConfig, common_properties=["hidden_size", "num_hidden_layers"] ) # Copied from tests.models.llama.test_modeling_llama.LlamaModelTest.test_config def test_config(self): self.config_tester.run_common_tests() # Copied from tests.models.llama.test_modeling_llama.LlamaModelTest.test_model def test_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*config_and_inputs) # Copied from tests.models.llama.test_modeling_llama.LlamaModelTest.test_model_various_embeddings 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) # Copied from tests.models.llama.test_modeling_llama.LlamaModelTest.test_llama_sequence_classification_model with llama->jetmoe, Llama->JetMoe def test_jetmoe_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 = JetMoeForSequenceClassification(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)) # Copied from tests.models.llama.test_modeling_llama.LlamaModelTest.test_llama_sequence_classification_model_for_single_label with llama->jetmoe, Llama->JetMoe def test_jetmoe_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 = JetMoeForSequenceClassification(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)) # Copied from tests.models.llama.test_modeling_llama.LlamaModelTest.test_llama_sequence_classification_model_for_multi_label with llama->jetmoe, Llama->JetMoe def test_jetmoe_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 = JetMoeForSequenceClassification(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="JetMoe buffers include complex numbers, which breaks this test") def test_save_load_fast_init_from_base(self): pass @unittest.skip(reason="JetMoe uses MoA on all models so the KV cache is a non standard format") def test_past_key_values_format(self): pass @require_flash_attn @require_torch_gpu @pytest.mark.flash_attn_test @slow def test_flash_attn_2_inference_equivalence_right_padding(self): self.skipTest(reason="JetMoe flash attention does not support right padding") @require_torch class JetMoeIntegrationTest(unittest.TestCase): @slow def test_model_8b_logits(self): input_ids = [1, 306, 4658, 278, 6593, 310, 2834, 338] model = JetMoeForCausalLM.from_pretrained("jetmoe/jetmoe-8b") input_ids = torch.tensor([input_ids]).to(model.model.embed_tokens.weight.device) with torch.no_grad(): out = model(input_ids).logits.float().cpu() # Expected mean on dim = -1 EXPECTED_MEAN = torch.tensor([[0.2507, -2.7073, -1.3445, -1.9363, -1.7216, -1.7370, -1.9054, -1.9792]]) torch.testing.assert_close(out.mean(-1), EXPECTED_MEAN, rtol=1e-2, atol=1e-2) # slicing logits[0, 0, 0:30] EXPECTED_SLICE = torch.tensor([-3.3689, 5.9006, 5.7450, -1.7012, -4.7072, -4.7071, -4.7071, -4.7071, -4.7072, -4.7072, -4.7072, -4.7071, 3.8321, 9.1746, -4.7071, -4.7072, -4.7071, -4.7072, -4.7071, -4.7072, -4.7071, -4.7071, -4.7071, -4.7071, -4.7071, -4.7071, -4.7071, -4.7071, -4.7071, -4.7071]) # fmt: skip torch.testing.assert_close(out[0, 0, :30], EXPECTED_SLICE, rtol=1e-4, atol=1e-4) del model backend_empty_cache(torch_device) gc.collect() @slow def test_model_8b_generation(self): EXPECTED_TEXT_COMPLETION = """My favourite condiment is ....\nI love ketchup. I love""" prompt = "My favourite condiment is " tokenizer = AutoTokenizer.from_pretrained("jetmoe/jetmoe-8b", use_fast=False) model = JetMoeForCausalLM.from_pretrained("jetmoe/jetmoe-8b") input_ids = tokenizer.encode(prompt, return_tensors="pt").to(model.model.embed_tokens.weight.device) # greedy generation outputs generated_ids = model.generate(input_ids, max_new_tokens=10, temperature=0) text = tokenizer.decode(generated_ids[0], skip_special_tokens=True) self.assertEqual(EXPECTED_TEXT_COMPLETION, text) del model backend_empty_cache(torch_device) gc.collect() @slow def test_model_8b_batched_generation(self): EXPECTED_TEXT_COMPLETION = [ """My favourite condiment is ....\nI love ketchup. I love""", """My favourite 2018 Christmas present was a new pair""", ] prompt = [ "My favourite condiment is ", "My favourite ", ] tokenizer = AutoTokenizer.from_pretrained("jetmoe/jetmoe-8b", use_fast=False) model = JetMoeForCausalLM.from_pretrained("jetmoe/jetmoe-8b") input_ids = tokenizer(prompt, return_tensors="pt", padding=True).to(model.model.embed_tokens.weight.device) print(input_ids) # greedy generation outputs generated_ids = model.generate(**input_ids, max_new_tokens=10, temperature=0) text = tokenizer.batch_decode(generated_ids, skip_special_tokens=True) print(text) self.assertEqual(EXPECTED_TEXT_COMPLETION, text) del model backend_empty_cache(torch_device) gc.collect()