# coding=utf-8 # Copyright 2024 Microsoft 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 PhiMoE model.""" import unittest from typing import List from parameterized import parameterized from transformers import PhimoeConfig, StaticCache, is_torch_available, set_seed 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 from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from transformers import ( AutoTokenizer, PhimoeForCausalLM, PhimoeForSequenceClassification, PhimoeModel, ) end_of_text_token = 32000 class PhimoeMiniWithStaticCache(torch.nn.Module): def __init__(self, model: PhimoeForCausalLM, batch_size: int, max_seq_len: int): super().__init__() self.model = model self.cache = StaticCache( config=model.config, batch_size=batch_size, max_cache_len=max_seq_len, device=self.model.device, dtype=self.model.dtype, ) def forward( self, input_ids: torch.LongTensor = None, ) -> torch.FloatTensor: return self.model.forward( input_ids=input_ids, use_cache=True, return_dict=True, past_key_values=self.cache, ).logits @staticmethod def generate(model: PhimoeForCausalLM, prompt_tokens: torch.LongTensor, max_seq_len: int) -> List[int]: model = PhimoeMiniWithStaticCache(model, 1, max_seq_len + prompt_tokens.shape[-1]) response_tokens = [] for input_pos in range(prompt_tokens.shape[-1]): result = model.forward( input_ids=prompt_tokens[:, input_pos : input_pos + 1], ) response_tokens.append(prompt_tokens[0][input_pos].item()) current_token = torch.argmax(result[:, -1, :], dim=-1).item() response_tokens.append(current_token) while current_token != end_of_text_token and len(response_tokens) < max_seq_len: result = model.forward( input_ids=torch.tensor([[current_token]], dtype=torch.long), ) current_token = torch.argmax(result[:, -1, :], dim=-1).item() response_tokens.append(current_token) return response_tokens class PhimoeModelTester: 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, num_key_value_heads=4, intermediate_size=37, hidden_act="gelu", hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_position_embeddings=131072, 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, original_max_position_embeddings=4096, ): 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.scope = scope self.original_max_position_embeddings = original_max_position_embeddings # Copied from tests.models.llama.test_modeling_llama.LlamaModelTester.prepare_config_and_inputs 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 def get_config(self): return PhimoeConfig( vocab_size=self.vocab_size, hidden_size=self.hidden_size, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, num_key_value_heads=self.num_key_value_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, num_experts_per_tok=2, num_local_experts=2, original_max_position_embeddings=self.original_max_position_embeddings, ) # Copied from tests.models.llama.test_modeling_llama.LlamaModelTester.create_and_check_model with Llama->Phimoe def create_and_check_model( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = PhimoeModel(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->Phimoe 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 = PhimoeModel(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->Phimoe 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 = PhimoeForCausalLM(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)) # Copied from tests.models.llama.test_modeling_llama.LlamaModelTester.create_and_check_decoder_model_past_large_inputs with Llama->Phimoe 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 = PhimoeForCausalLM(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 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 PhimoeModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = ( (PhimoeModel, PhimoeForCausalLM, PhimoeForSequenceClassification) if is_torch_available() else () ) all_generative_model_classes = (PhimoeForCausalLM,) if is_torch_available() else () pipeline_model_mapping = ( { "feature-extraction": PhimoeModel, "text-classification": PhimoeForSequenceClassification, "text-generation": PhimoeForCausalLM, "zero-shot": PhimoeForSequenceClassification, } if is_torch_available() else {} ) test_headmasking = False test_pruning = False # TODO (ydshieh): Check this. See https://app.circleci.com/pipelines/github/huggingface/transformers/79292/workflows/fa2ba644-8953-44a6-8f67-ccd69ca6a476/jobs/1012905 def is_pipeline_test_to_skip( self, pipeline_test_casse_name, config_class, model_architecture, tokenizer_name, processor_name ): return True # Copied from tests.models.llama.test_modeling_llama.LlamaModelTest.setUp with Llama->Phimoe def setUp(self): self.model_tester = PhimoeModelTester(self) self.config_tester = ConfigTester(self, config_class=PhimoeConfig, hidden_size=37) # 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_llama_sequence_classification_model with Llama->Phimoe,llama->phimoe def test_phimoe_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 = PhimoeForSequenceClassification(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->Phimoe,llama->phimoe def test_phimoe_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 = PhimoeForSequenceClassification(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->Phimoe,llama->phimoe def test_phimoe_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 = PhimoeForSequenceClassification(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)) @parameterized.expand([("longrope",)]) def test_model_rope_scaling_from_config(self, scaling_type): 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.original_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 = PhimoeModel(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 n_factors = config.hidden_size // config.num_attention_heads // 2 config.rope_scaling = { "type": scaling_type, "short_factor": [3.0 for _ in range(n_factors)], "long_factor": [5.0 for _ in range(n_factors)], "short_mscale": 1.243163121016122, "long_mscale": 1.243163121016122, "original_max_position_embeddings": 4096, } scaled_model = PhimoeModel(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 # Scaling changes the RoPE embeddings, both for the short and long outputs self.assertFalse(torch.allclose(original_short_output, scaled_short_output, atol=1e-5)) self.assertFalse(torch.allclose(original_long_output, scaled_long_output, atol=1e-5)) @parameterized.expand([("longrope",)]) def test_model_rope_scaling_short_long_factor(self, scaling_type): config, _ = self.model_tester.prepare_config_and_inputs_for_common() n_factors = config.hidden_size // config.num_key_value_heads // 2 config.rope_scaling = { "type": scaling_type, "short_factor": [3.0 for _ in range(n_factors)], "long_factor": [5.0 for _ in range(n_factors)], "short_mscale": 1.243163121016122, "long_mscale": 1.243163121016122, "original_max_position_embeddings": 4096, } input_tensor = ids_tensor([1, 4090], config.vocab_size) model = PhimoeForCausalLM(config) model.to(torch_device) model.eval() generation_args_short = { "max_length": config.original_max_position_embeddings, "temperature": 0.0, "use_cache": True, "do_sample": False, "return_dict_in_generate": True, } output_with_short_factor = model.generate(input_tensor, **generation_args_short) keys_with_short_factor = output_with_short_factor.past_key_values[0][0] generation_args_long = { "max_length": config.original_max_position_embeddings + 5, "temperature": 0.0, "use_cache": True, "do_sample": False, "return_dict_in_generate": True, "output_logits": True, } output_with_long_factor = model.generate(input_tensor, **generation_args_long) keys_with_long_factor = output_with_long_factor.past_key_values[0][0] last_token_logits = output_with_long_factor.logits[-1][-1] regenerated_last_token_logits = model(output_with_long_factor.sequences[:, :-1]).logits[0][-1] keys_with_long_factor = keys_with_long_factor[:, :, : config.original_max_position_embeddings - 1, :] # KV cache is re-computed after reaching the (`config.original_max_position_embeddings`+1)th token position self.assertFalse(torch.allclose(keys_with_short_factor, keys_with_long_factor, atol=1e-3, rtol=1e-3)) # Last token generated using long factor self.assertTrue(torch.allclose(last_token_logits, regenerated_last_token_logits, atol=1e-2, rtol=1e-2)) @slow @require_torch class PhimoeIntegrationTest(unittest.TestCase): def test_model_phimoe_instruct_logits(self): input_ids = { "input_ids": torch.tensor( [[1212, 318, 281, 1672, 2643, 290, 428, 318, 257, 1332]], dtype=torch.long, device=torch_device ) } model = PhimoeForCausalLM.from_pretrained("microsoft/Phi-3.5-MoE-instruct").to(torch_device) model.eval() output = model(**input_ids).logits EXPECTED_OUTPUT = torch.tensor([[-3.5312, -2.5000, -1.2734, 0.3555, -0.7578, -0.4727, 0.5977, -0.4316, 0.2256, -1.2188, -1.6797, 0.9961, 3.7656, 11.3125, -1.3828, -4.8438, -5.7500, -1.9375, 0.7227, -0.3438, -0.2100, -0.4277, -0.0444, -0.5352, -0.6406, -0.1016, -0.4258, -1.0234, 0.4297, -0.6250], [-0.9883, 0.1455, -0.4902, 2.3594, 0.7031, 3.1406, 0.4375, 0.2559, 0.6172, -2.1094, -1.3359, 2.5938, 4.9062, 10.8125, -0.1094, 1.5781, -4.9375, 0.7148, -0.0972, 1.7656, -0.0801, 0.2217, 0.1875, -0.4629, 1.5781, 0.3535, 0.0874, 0.6836, -0.0518, -1.2969]]).to(torch_device) # fmt: skip self.assertTrue(torch.allclose(EXPECTED_OUTPUT, output[0, :2, :30], atol=1e-4, rtol=1e-4)) def test_phimoe_instruct_generation(self): model = PhimoeForCausalLM.from_pretrained("microsoft/Phi-3.5-MoE-instruct") tokenizer = AutoTokenizer.from_pretrained("microsoft/Phi-3.5-MoE-instruct") messages = [ { "role": "system", "content": "You are a helpful digital assistant. Please provide safe, ethical and accurate information to the user.", }, {"role": "user", "content": "Can you provide ways to eat combinations of bananas and dragonfruits?"}, ] inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt") outputs = model.generate(inputs, max_new_tokens=32) output_text = tokenizer.batch_decode(outputs) EXPECTED_OUTPUT = [ "<|system|> You are a helpful digital assistant. Please provide safe, ethical and accurate information to the user.<|end|><|user|> Can you provide ways to eat combinations of bananas and dragonfruits?<|end|><|assistant|> Certainly! Bananas and dragonfruits are both delicious and nutritious fruits that can be combined in various ways to create tast" ] self.assertListEqual(output_text, EXPECTED_OUTPUT) def test_phimoe_instruct_with_static_cache(self): model = PhimoeForCausalLM.from_pretrained("microsoft/Phi-3.5-MoE-instruct") tokenizer = AutoTokenizer.from_pretrained("microsoft/Phi-3.5-MoE-instruct") messages = [ { "role": "system", "content": "You are a helpful digital assistant. Please provide safe, ethical and accurate information to the user.", }, {"role": "user", "content": "Can you provide ways to eat combinations of bananas and dragonfruits?"}, ] inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt") response_tokens = PhimoeMiniWithStaticCache.generate(model, inputs, 64) output_text = tokenizer.batch_decode(torch.tensor([response_tokens], dtype=torch.long, device=torch_device)) EXPECTED_OUTPUT = [ "<|system|> You are a helpful digital assistant. Please provide safe, ethical and accurate information to the user.<|end|><|user|> Can you provide ways to eat combinations of bananas and dragonfruits?<|end|><|assistant|> Certainly! Bananas and dragonfruits are both delicious and nutritious fruits that can" ] self.assertListEqual(output_text, EXPECTED_OUTPUT)