474 lines
21 KiB
Python
474 lines
21 KiB
Python
# coding=utf-8
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# Copyright 2024 Microsoft and the HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Testing suite for the PyTorch Phi-3 model."""
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import unittest
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from parameterized import parameterized
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from transformers import Phi3Config, is_torch_available, set_seed
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from transformers.testing_utils import (
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require_torch,
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slow,
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torch_device,
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)
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from ...generation.test_utils import GenerationTesterMixin
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from ...test_configuration_common import ConfigTester
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from ...test_modeling_common import ModelTesterMixin, ids_tensor
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from ...test_pipeline_mixin import PipelineTesterMixin
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if is_torch_available():
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import torch
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from transformers import (
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AutoTokenizer,
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Phi3ForCausalLM,
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Phi3ForSequenceClassification,
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Phi3ForTokenClassification,
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Phi3Model,
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)
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class Phi3ModelTester:
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def __init__(
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self,
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parent,
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batch_size=13,
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seq_length=7,
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is_training=True,
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use_input_mask=True,
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use_token_type_ids=False,
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use_labels=True,
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vocab_size=99,
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hidden_size=32,
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num_hidden_layers=2,
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num_attention_heads=4,
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intermediate_size=37,
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hidden_act="gelu",
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hidden_dropout_prob=0.1,
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attention_probs_dropout_prob=0.1,
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max_position_embeddings=512,
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type_vocab_size=16,
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type_sequence_label_size=2,
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initializer_range=0.02,
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num_labels=3,
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num_choices=4,
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pad_token_id=0,
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scope=None,
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):
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self.parent = parent
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self.batch_size = batch_size
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self.seq_length = seq_length
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self.is_training = is_training
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self.use_input_mask = use_input_mask
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self.use_token_type_ids = use_token_type_ids
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self.use_labels = use_labels
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self.vocab_size = vocab_size
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self.hidden_size = hidden_size
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self.num_hidden_layers = num_hidden_layers
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self.num_attention_heads = num_attention_heads
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self.intermediate_size = intermediate_size
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self.hidden_act = hidden_act
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self.hidden_dropout_prob = hidden_dropout_prob
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self.attention_probs_dropout_prob = attention_probs_dropout_prob
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self.max_position_embeddings = max_position_embeddings
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self.type_vocab_size = type_vocab_size
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self.type_sequence_label_size = type_sequence_label_size
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self.initializer_range = initializer_range
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self.num_labels = num_labels
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self.num_choices = num_choices
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self.pad_token_id = pad_token_id
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self.scope = scope
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# Copied from tests.models.llama.test_modeling_llama.LlamaModelTester.prepare_config_and_inputs
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def prepare_config_and_inputs(self):
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input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
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input_mask = None
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if self.use_input_mask:
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input_mask = torch.tril(torch.ones(self.batch_size, self.seq_length)).to(torch_device)
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token_type_ids = None
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if self.use_token_type_ids:
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token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
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sequence_labels = None
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token_labels = None
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choice_labels = None
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if self.use_labels:
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sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
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token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
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choice_labels = ids_tensor([self.batch_size], self.num_choices)
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config = self.get_config()
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return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
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def get_config(self):
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return Phi3Config(
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vocab_size=self.vocab_size,
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hidden_size=self.hidden_size,
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num_hidden_layers=self.num_hidden_layers,
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num_attention_heads=self.num_attention_heads,
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intermediate_size=self.intermediate_size,
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hidden_act=self.hidden_act,
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hidden_dropout_prob=self.hidden_dropout_prob,
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attention_probs_dropout_prob=self.attention_probs_dropout_prob,
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max_position_embeddings=self.max_position_embeddings,
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type_vocab_size=self.type_vocab_size,
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is_decoder=False,
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initializer_range=self.initializer_range,
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pad_token_id=self.pad_token_id,
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)
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# Copied from tests.models.llama.test_modeling_llama.LlamaModelTester.create_and_check_model with Llama->Phi3
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def create_and_check_model(
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self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
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):
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model = Phi3Model(config=config)
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model.to(torch_device)
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model.eval()
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result = model(input_ids, attention_mask=input_mask)
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result = model(input_ids)
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self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
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# Copied from tests.models.llama.test_modeling_llama.LlamaModelTester.create_and_check_model_as_decoder with Llama->Phi3
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def create_and_check_model_as_decoder(
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self,
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config,
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input_ids,
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token_type_ids,
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input_mask,
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sequence_labels,
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token_labels,
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choice_labels,
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encoder_hidden_states,
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encoder_attention_mask,
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):
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config.add_cross_attention = True
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model = Phi3Model(config)
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model.to(torch_device)
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model.eval()
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result = model(
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input_ids,
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attention_mask=input_mask,
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encoder_hidden_states=encoder_hidden_states,
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encoder_attention_mask=encoder_attention_mask,
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)
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result = model(
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input_ids,
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attention_mask=input_mask,
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encoder_hidden_states=encoder_hidden_states,
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)
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result = model(input_ids, attention_mask=input_mask)
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self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
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# Copied from tests.models.llama.test_modeling_llama.LlamaModelTester.create_and_check_for_causal_lm with Llama->Phi3
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def create_and_check_for_causal_lm(
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self,
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config,
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input_ids,
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token_type_ids,
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input_mask,
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sequence_labels,
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token_labels,
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choice_labels,
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encoder_hidden_states,
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encoder_attention_mask,
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):
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model = Phi3ForCausalLM(config=config)
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model.to(torch_device)
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model.eval()
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result = model(input_ids, attention_mask=input_mask, labels=token_labels)
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self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
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# Copied from tests.models.llama.test_modeling_llama.LlamaModelTester.create_and_check_decoder_model_past_large_inputs with Llama->Phi3
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def create_and_check_decoder_model_past_large_inputs(
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self,
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config,
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input_ids,
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token_type_ids,
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input_mask,
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sequence_labels,
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token_labels,
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choice_labels,
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encoder_hidden_states,
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encoder_attention_mask,
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):
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config.is_decoder = True
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config.add_cross_attention = True
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model = Phi3ForCausalLM(config=config)
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model.to(torch_device)
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model.eval()
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# first forward pass
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outputs = model(
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input_ids,
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attention_mask=input_mask,
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encoder_hidden_states=encoder_hidden_states,
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encoder_attention_mask=encoder_attention_mask,
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use_cache=True,
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)
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past_key_values = outputs.past_key_values
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# create hypothetical multiple next token and extent to next_input_ids
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next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size)
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next_mask = ids_tensor((self.batch_size, 3), vocab_size=2)
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# append to next input_ids and
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next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
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next_attention_mask = torch.cat([input_mask, next_mask], dim=-1)
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output_from_no_past = model(
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next_input_ids,
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attention_mask=next_attention_mask,
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encoder_hidden_states=encoder_hidden_states,
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encoder_attention_mask=encoder_attention_mask,
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output_hidden_states=True,
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)["hidden_states"][0]
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output_from_past = model(
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next_tokens,
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attention_mask=next_attention_mask,
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encoder_hidden_states=encoder_hidden_states,
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encoder_attention_mask=encoder_attention_mask,
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past_key_values=past_key_values,
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output_hidden_states=True,
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)["hidden_states"][0]
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# select random slice
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random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
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output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx].detach()
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output_from_past_slice = output_from_past[:, :, random_slice_idx].detach()
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self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1])
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# test that outputs are equal for slice
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self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3))
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# Copied from tests.models.llama.test_modeling_llama.LlamaModelTester.prepare_config_and_inputs_for_common
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def prepare_config_and_inputs_for_common(self):
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config_and_inputs = self.prepare_config_and_inputs()
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(
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config,
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input_ids,
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token_type_ids,
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input_mask,
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sequence_labels,
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token_labels,
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choice_labels,
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) = config_and_inputs
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inputs_dict = {"input_ids": input_ids, "attention_mask": input_mask}
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return config, inputs_dict
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@require_torch
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class Phi3ModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
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all_model_classes = (
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(Phi3Model, Phi3ForCausalLM, Phi3ForSequenceClassification, Phi3ForTokenClassification)
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if is_torch_available()
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else ()
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)
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all_generative_model_classes = (Phi3ForCausalLM,) if is_torch_available() else ()
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pipeline_model_mapping = (
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{
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"feature-extraction": Phi3Model,
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"text-classification": Phi3ForSequenceClassification,
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"text-generation": Phi3ForCausalLM,
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"token-classification": Phi3ForTokenClassification,
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"zero-shot": Phi3ForSequenceClassification,
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}
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if is_torch_available()
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else {}
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)
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test_headmasking = False
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test_pruning = False
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# TODO (ydshieh): Check this. See https://app.circleci.com/pipelines/github/huggingface/transformers/79292/workflows/fa2ba644-8953-44a6-8f67-ccd69ca6a476/jobs/1012905
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def is_pipeline_test_to_skip(
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self, pipeline_test_casse_name, config_class, model_architecture, tokenizer_name, processor_name
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):
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return True
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# Copied from tests.models.llama.test_modeling_llama.LlamaModelTest.setUp with Llama->Phi3
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def setUp(self):
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self.model_tester = Phi3ModelTester(self)
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self.config_tester = ConfigTester(self, config_class=Phi3Config, hidden_size=37)
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# Copied from tests.models.llama.test_modeling_llama.LlamaModelTest.test_config
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def test_config(self):
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self.config_tester.run_common_tests()
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# Copied from tests.models.llama.test_modeling_llama.LlamaModelTest.test_model
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def test_model(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_model(*config_and_inputs)
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# Copied from tests.models.llama.test_modeling_llama.LlamaModelTest.test_llama_sequence_classification_model with Llama->Phi3,llama->phi3
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def test_phi3_sequence_classification_model(self):
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config, input_dict = self.model_tester.prepare_config_and_inputs_for_common()
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config.num_labels = 3
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input_ids = input_dict["input_ids"]
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attention_mask = input_ids.ne(1).to(torch_device)
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sequence_labels = ids_tensor([self.model_tester.batch_size], self.model_tester.type_sequence_label_size)
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model = Phi3ForSequenceClassification(config)
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model.to(torch_device)
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model.eval()
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result = model(input_ids, attention_mask=attention_mask, labels=sequence_labels)
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self.assertEqual(result.logits.shape, (self.model_tester.batch_size, self.model_tester.num_labels))
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# Copied from tests.models.llama.test_modeling_llama.LlamaModelTest.test_llama_sequence_classification_model_for_single_label with Llama->Phi3,llama->phi3
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def test_phi3_sequence_classification_model_for_single_label(self):
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config, input_dict = self.model_tester.prepare_config_and_inputs_for_common()
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config.num_labels = 3
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config.problem_type = "single_label_classification"
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input_ids = input_dict["input_ids"]
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attention_mask = input_ids.ne(1).to(torch_device)
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sequence_labels = ids_tensor([self.model_tester.batch_size], self.model_tester.type_sequence_label_size)
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model = Phi3ForSequenceClassification(config)
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model.to(torch_device)
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model.eval()
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result = model(input_ids, attention_mask=attention_mask, labels=sequence_labels)
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self.assertEqual(result.logits.shape, (self.model_tester.batch_size, self.model_tester.num_labels))
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# Copied from tests.models.llama.test_modeling_llama.LlamaModelTest.test_llama_sequence_classification_model_for_multi_label with Llama->Phi3,llama->phi3
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def test_phi3_sequence_classification_model_for_multi_label(self):
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config, input_dict = self.model_tester.prepare_config_and_inputs_for_common()
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config.num_labels = 3
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config.problem_type = "multi_label_classification"
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input_ids = input_dict["input_ids"]
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attention_mask = input_ids.ne(1).to(torch_device)
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sequence_labels = ids_tensor(
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[self.model_tester.batch_size, config.num_labels], self.model_tester.type_sequence_label_size
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).to(torch.float)
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model = Phi3ForSequenceClassification(config)
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model.to(torch_device)
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model.eval()
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result = model(input_ids, attention_mask=attention_mask, labels=sequence_labels)
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self.assertEqual(result.logits.shape, (self.model_tester.batch_size, self.model_tester.num_labels))
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@parameterized.expand([("su",), ("yarn",)])
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def test_model_rope_scaling_from_config(self, scaling_type):
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config, _ = self.model_tester.prepare_config_and_inputs_for_common()
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short_input = ids_tensor([1, 10], config.vocab_size)
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long_input = ids_tensor([1, int(config.max_position_embeddings * 1.5)], config.vocab_size)
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set_seed(42) # Fixed seed at init time so the two models get the same random weights
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original_model = Phi3Model(config)
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original_model.to(torch_device)
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original_model.eval()
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original_short_output = original_model(short_input).last_hidden_state
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original_long_output = original_model(long_input).last_hidden_state
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set_seed(42) # Fixed seed at init time so the two models get the same random weights
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n_factors = config.hidden_size // config.num_attention_heads // 2
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config.rope_scaling = {
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"type": scaling_type,
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"short_factor": [5.0 for _ in range(n_factors)],
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"long_factor": [5.0 for _ in range(n_factors)],
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}
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scaled_model = Phi3Model(config)
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scaled_model.to(torch_device)
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scaled_model.eval()
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scaled_short_output = scaled_model(short_input).last_hidden_state
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scaled_long_output = scaled_model(long_input).last_hidden_state
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# Scaling changes the RoPE embeddings, both for the short and long outputs
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self.assertFalse(torch.allclose(original_short_output, scaled_short_output, atol=1e-5))
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self.assertFalse(torch.allclose(original_long_output, scaled_long_output, atol=1e-5))
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@slow
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@require_torch
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class Phi3IntegrationTest(unittest.TestCase):
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def test_model_phi3_mini_4k_instruct_logits(self):
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input_ids = {
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"input_ids": torch.tensor(
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[[1212, 318, 281, 1672, 2643, 290, 428, 318, 257, 1332]], dtype=torch.long, device=torch_device
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)
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}
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model = Phi3ForCausalLM.from_pretrained("microsoft/phi-3-mini-4k-instruct").to(torch_device)
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model.eval()
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output = model(**input_ids).logits
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EXPECTED_OUTPUT = torch.tensor([[ 0.9979, -1.9449, -2.5613, -2.2110, -0.9323, -2.2726, -3.2468, -2.0122,-1.0021, -1.2764, -1.0876, -1.2358, 3.9385, 6.2152, -0.3695, -2.3285,-1.2907, -1.8238, -1.9941, -2.2098, -0.6923, -1.6793, -1.1660, -2.0469,-0.7369, -1.4101, -1.4091, -3.1694, -1.8383, -1.1952],[ 3.0525, 1.9178, 3.7016, 0.9263, 0.3397, 1.9584, 2.1347, 0.3482, 1.3773, 0.2153, 0.2798, 0.8360, 9.0936, 11.4944, -0.3575, -0.9442,-0.1246, 1.3869, 0.9846, 1.7243, 0.9150, 1.0823, 0.4313, 1.5742, 0.2566, -0.1401, -1.3019, 0.4967, 0.6941, 0.7214]]).to(torch_device) # fmt: skip
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self.assertTrue(torch.allclose(EXPECTED_OUTPUT, output[0, :2, :30], atol=1e-4, rtol=1e-4))
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def test_phi3_mini_4k_instruct_generation(self):
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model = Phi3ForCausalLM.from_pretrained("microsoft/phi-3-mini-4k-instruct")
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tokenizer = AutoTokenizer.from_pretrained("microsoft/phi-3-mini-4k-instruct")
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|
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|
messages = [
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{
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"role": "system",
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"content": "You are a helpful digital assistant. Please provide safe, ethical and accurate information to the user.",
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},
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{"role": "user", "content": "Can you provide ways to eat combinations of bananas and dragonfruits?"},
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|
]
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inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt")
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|
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outputs = model.generate(inputs, max_new_tokens=32)
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|
output_text = tokenizer.batch_decode(outputs)
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|
|
|
EXPECTED_OUTPUT = [
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|
"<s><|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|> Absolutely! Bananas and dragonfruits are both delicious fruits that can be combined in various ways to create tasty and nutrit"
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|
]
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|
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self.assertListEqual(output_text, EXPECTED_OUTPUT)
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|
|
|
def test_model_phi3_mini_128k_instruct_logits(self):
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|
input_ids = {
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|
"input_ids": torch.tensor(
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|
[[1212, 318, 281, 1672, 2643, 290, 428, 318, 257, 1332]], dtype=torch.long, device=torch_device
|
|
)
|
|
}
|
|
|
|
model = Phi3ForCausalLM.from_pretrained("microsoft/phi-3-mini-128k-instruct").to(torch_device)
|
|
model.eval()
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|
|
|
output = model(**input_ids).logits
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|
|
|
EXPECTED_OUTPUT = torch.tensor([[ 1.8478, -0.5709, -1.6792, -1.2133, -0.7809, -0.8817, -2.0969, -1.1191,-0.7731, -1.0483, -0.5961, -1.3067, 3.1325, 6.9442, -0.4803, -0.9154,-1.3085, -1.0822, -1.1433, -0.7660, -0.8531, -0.9150, -0.6179, -1.6153,-0.2239, -1.3207, -1.1187, -2.4795, -1.4733, -0.4931],[ 3.5839, 2.4722, 3.7130, 1.2032, 0.7356, 2.7777, 2.5256, 0.9157, 1.6431, 0.3533, 0.5100, 1.3512, 8.9873, 10.9815, 0.3530, 0.1473, 0.2051, 1.8553, 1.5988, 2.2268, 1.1897, 1.2829, 0.7894, 1.8895, 0.7666, 0.4122, -0.9316, 0.9936, 1.2722, 0.8263]]).to(torch_device) # fmt: skip
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|
|
|
self.assertTrue(torch.allclose(EXPECTED_OUTPUT, output[0, :2, :30], atol=1e-4, rtol=1e-4))
|
|
|
|
def test_phi3_mini_128k_instruct_generation(self):
|
|
model = Phi3ForCausalLM.from_pretrained("microsoft/phi-3-mini-128k-instruct")
|
|
tokenizer = AutoTokenizer.from_pretrained("microsoft/phi-3-mini-128k-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 = [
|
|
"<s><|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 can be combined in various delicious and healthy ways. Here are some ideas:\n\n1."
|
|
]
|
|
|
|
self.assertListEqual(output_text, EXPECTED_OUTPUT)
|