mirror of
https://github.com/huggingface/transformers.git
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64845307b3
* remove unnecessary unsqueeze-squeeze in llama * correct other models * fix * revert gpt_neox_japanese * fix copie * fix test
459 lines
18 KiB
Python
459 lines
18 KiB
Python
# coding=utf-8
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# Copyright 2023 Mistral AI 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 Mistral model. """
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import tempfile
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import unittest
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from pytest import mark
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from transformers import AutoTokenizer, MistralConfig, is_torch_available
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from transformers.testing_utils import require_flash_attn, require_torch, require_torch_gpu, slow, torch_device
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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, random_attention_mask
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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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MistralForCausalLM,
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MistralForSequenceClassification,
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MistralModel,
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)
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# Copied from transformers.tests.mistral.test_modelling_mistral.MistralModelTest with Llama->Mistral
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class MistralModelTester:
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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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num_key_value_heads=2,
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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.num_key_value_heads = num_key_value_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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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 = random_attention_mask([self.batch_size, self.seq_length])
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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 MistralConfig(
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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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num_key_value_heads=self.num_key_value_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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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 = MistralModel(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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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 = MistralModel(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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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 = MistralForCausalLM(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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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 = MistralForCausalLM(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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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 MistralModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
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all_model_classes = (
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(MistralModel, MistralForCausalLM, MistralForSequenceClassification) if is_torch_available() else ()
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)
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all_generative_model_classes = (MistralForCausalLM,) if is_torch_available() else ()
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pipeline_model_mapping = (
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{
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"feature-extraction": MistralModel,
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"text-classification": MistralForSequenceClassification,
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"text-generation": MistralForCausalLM,
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"zero-shot": MistralForSequenceClassification,
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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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def setUp(self):
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self.model_tester = MistralModelTester(self)
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self.config_tester = ConfigTester(self, config_class=MistralConfig, hidden_size=37)
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def test_config(self):
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self.config_tester.run_common_tests()
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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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def test_model_various_embeddings(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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for type in ["absolute", "relative_key", "relative_key_query"]:
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config_and_inputs[0].position_embedding_type = type
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self.model_tester.create_and_check_model(*config_and_inputs)
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def test_Mistral_sequence_classification_model(self):
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config, input_dict = self.model_tester.prepare_config_and_inputs_for_common()
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print(config)
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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 = MistralForSequenceClassification(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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def test_Mistral_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 = MistralForSequenceClassification(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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def test_Mistral_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 = MistralForSequenceClassification(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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@unittest.skip("Mistral buffers include complex numbers, which breaks this test")
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def test_save_load_fast_init_from_base(self):
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pass
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@unittest.skip("Mistral uses GQA on all models so the KV cache is a non standard format")
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def test_past_key_values_format(self):
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pass
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@require_flash_attn
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@require_torch_gpu
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@mark.flash_attn_test
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@slow
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def test_flash_attn_2_generate_padding_right(self):
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import torch
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for model_class in self.all_generative_model_classes:
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if not model_class._supports_flash_attn_2:
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return
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config, _ = self.model_tester.prepare_config_and_inputs_for_common()
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model = model_class(config)
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with tempfile.TemporaryDirectory() as tmpdirname:
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model.save_pretrained(tmpdirname)
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model = model_class.from_pretrained(
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tmpdirname, torch_dtype=torch.float16, use_flash_attention_2=False, low_cpu_mem_usage=True
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).to(torch_device)
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dummy_input = torch.LongTensor([[0, 2, 3, 4], [0, 2, 3, 4]]).to(torch_device)
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dummy_attention_mask = torch.LongTensor([[1, 1, 1, 1], [1, 1, 1, 0]]).to(torch_device)
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model.generate(dummy_input, attention_mask=dummy_attention_mask, max_new_tokens=1, do_sample=False)
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model = model_class.from_pretrained(
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tmpdirname, torch_dtype=torch.float16, use_flash_attention_2=True, low_cpu_mem_usage=True
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).to(torch_device)
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with self.assertRaises(ValueError):
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_ = model.generate(
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dummy_input, attention_mask=dummy_attention_mask, max_new_tokens=1, do_sample=False
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)
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@require_flash_attn
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@require_torch_gpu
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@mark.flash_attn_test
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@slow
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def test_flash_attn_2_inference_padding_right(self):
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import torch
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for model_class in self.all_model_classes:
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if not model_class._supports_flash_attn_2:
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return
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config, _ = self.model_tester.prepare_config_and_inputs_for_common()
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model = model_class(config)
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with tempfile.TemporaryDirectory() as tmpdirname:
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model.save_pretrained(tmpdirname)
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model_fa = model_class.from_pretrained(
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tmpdirname, torch_dtype=torch.bfloat16, use_flash_attention_2=True
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)
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model_fa.to(torch_device)
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model = model_class.from_pretrained(
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tmpdirname, torch_dtype=torch.bfloat16, use_flash_attention_2=False
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)
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model.to(torch_device)
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dummy_input = torch.LongTensor([[1, 2, 3, 4, 5]]).to(torch_device)
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dummy_attention_mask = torch.LongTensor([[1, 1, 1, 1, 0]]).to(torch_device)
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_ = model(dummy_input, output_hidden_states=True).hidden_states[-1]
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with self.assertRaises(ValueError):
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_ = model_fa(
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dummy_input, attention_mask=dummy_attention_mask, output_hidden_states=True
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).hidden_states[-1]
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@require_torch
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class MistralIntegrationTest(unittest.TestCase):
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@slow
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def test_model_7b_logits(self):
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input_ids = [1, 306, 4658, 278, 6593, 310, 2834, 338]
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model = MistralForCausalLM.from_pretrained("mistralai/Mistral-7B-v0.1", device_map="auto")
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input_ids = torch.tensor([input_ids]).to(model.model.embed_tokens.weight.device)
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out = model(input_ids).logits.cpu()
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# Expected mean on dim = -1
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EXPECTED_MEAN = torch.tensor([[-2.5548, -2.5737, -3.0600, -2.5906, -2.8478, -2.8118, -2.9325, -2.7694]])
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torch.testing.assert_close(out.mean(-1), EXPECTED_MEAN, atol=1e-2, rtol=1e-2)
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# slicing logits[0, 0, 0:30]
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# fmt: off
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EXPECTED_SLICE = torch.tensor([-5.8781, -5.8616, -0.1052, -4.7200, -5.8781, -5.8774, -5.8773, -5.8777, -5.8781, -5.8780, -5.8781, -5.8779, -1.0787, 1.7583, -5.8779, -5.8780, -5.8783, -5.8778, -5.8776, -5.8781, -5.8784, -5.8778, -5.8778, -5.8777, -5.8779, -5.8778, -5.8776, -5.8780, -5.8779, -5.8781])
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# fmt: on
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print(out[0, 0, :30])
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torch.testing.assert_close(out[0, 0, :30], EXPECTED_SLICE, atol=1e-4, rtol=1e-4)
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@slow
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def test_model_7b_generation(self):
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EXPECTED_TEXT_COMPLETION = (
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"""My favourite condiment is mayonnaise. I love it on sandwiches, in salads, on burgers"""
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)
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prompt = "My favourite condiment is "
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tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-v0.1", use_fast=False)
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input_ids = tokenizer.encode(prompt, return_tensors="pt").to(torch_device)
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model = MistralForCausalLM.from_pretrained("mistralai/Mistral-7B-v0.1").to(torch_device)
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# greedy generation outputs
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generated_ids = model.generate(input_ids, max_new_tokens=20, temperature=0)
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text = tokenizer.decode(generated_ids[0], skip_special_tokens=True)
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self.assertEqual(EXPECTED_TEXT_COMPLETION, text)
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