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62c7ea0201
* tmp commit * move tests to the right class * remove ALL all_generative_model_classes = ... * skip tf roberta * skip InstructBlipForConditionalGenerationDecoderOnlyTest * videollava * reduce diff * reduce diff * remove on vlms * fix a few more * manual rebase bits * more manual rebase * remove all manual generative model class test entries * fix up to ernie * a few more removals * handle remaining cases * recurrent gemma * it's better here * make fixup * tf idefics is broken * tf bert + generate is broken * don't touch tf :() * don't touch tf :( * make fixup * better comments for test skips * revert tf changes * remove empty line removal * one more * missing one
433 lines
17 KiB
Python
433 lines
17 KiB
Python
# coding=utf-8
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# Copyright 2024 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 OLMoE model."""
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import unittest
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from parameterized import parameterized
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from transformers import OlmoeConfig, is_torch_available, set_seed
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from transformers.models.auto.tokenization_auto import AutoTokenizer
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from transformers.models.gpt_neox.tokenization_gpt_neox_fast import GPTNeoXTokenizerFast
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from transformers.testing_utils import (
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require_tokenizers,
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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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OlmoeForCausalLM,
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OlmoeModel,
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)
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class OlmoeModelTester:
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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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hidden_act="silu",
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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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num_experts_per_tok=2,
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num_experts=8,
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norm_topk_prob=False,
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output_router_logits=False,
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router_aux_loss_coef=0.001,
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intermediate_size=12,
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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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self.num_experts_per_tok = num_experts_per_tok
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self.num_experts = num_experts
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self.norm_topk_prob = norm_topk_prob
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self.output_router_logits = output_router_logits
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self.router_aux_loss_coef = router_aux_loss_coef
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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_like(input_ids).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 OlmoeConfig(
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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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num_experts_per_tok=self.num_experts_per_tok,
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num_experts=self.num_experts,
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norm_topk_prob=self.norm_topk_prob,
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output_router_logits=self.output_router_logits,
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router_aux_loss_coef=self.router_aux_loss_coef,
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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 = OlmoeModel(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 = OlmoeModel(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 = OlmoeForCausalLM(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 = OlmoeForCausalLM(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 OlmoeModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
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all_model_classes = (OlmoeModel, OlmoeForCausalLM) if is_torch_available() else ()
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pipeline_model_mapping = (
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{
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"feature-extraction": OlmoeModel,
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"text-generation": OlmoeForCausalLM,
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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_pruning = False
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fx_compatible = False
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# Need to use `0.8` instead of `0.9` for `test_cpu_offload`
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# This is because we are hitting edge cases with the causal_mask buffer
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model_split_percents = [0.5, 0.7, 0.8]
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def setUp(self):
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self.model_tester = OlmoeModelTester(self)
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self.config_tester = ConfigTester(self, config_class=OlmoeConfig, 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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@unittest.skip(reason="OLMoE does not support head pruning.")
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def test_headmasking(self):
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pass
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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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@unittest.skip(reason="OLMoE 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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@parameterized.expand([("linear",), ("dynamic",)])
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def test_model_rope_scaling(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 = OlmoeModel(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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config.rope_scaling = {"type": scaling_type, "factor": 10.0}
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scaled_model = OlmoeModel(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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# Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
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# maximum sequence length, so the outputs for the short input should match.
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if scaling_type == "dynamic":
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torch.testing.assert_close(original_short_output, scaled_short_output, rtol=1e-5, atol=1e-5)
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else:
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self.assertFalse(torch.allclose(original_short_output, scaled_short_output, atol=1e-5))
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# The output should be different for long inputs
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self.assertFalse(torch.allclose(original_long_output, scaled_long_output, atol=1e-5))
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@require_torch
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class OlmoeIntegrationTest(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 = OlmoeForCausalLM.from_pretrained("allenai/OLMoE-1B-7B-0924", device_map="auto")
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out = model(torch.tensor(input_ids)).logits.float()
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# Expected mean on dim = -1
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EXPECTED_MEAN = torch.tensor([[-1.3814, -3.4450, -2.2990, -1.9542, -2.4387, -2.7941, -2.9312, -2.8309]])
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torch.testing.assert_close(out.mean(-1), EXPECTED_MEAN, rtol=1e-2, atol=1e-2)
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# slicing logits[0, 0, 0:30]
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EXPECTED_SLICE = torch.tensor([-2.3874, -2.4076, -2.4995, 4.2278, 1.4004, -0.0252, 0.4189, -2.7560, 0.3531, 1.6678, -0.7941, -1.1818, -0.2920, 0.7131, -1.4173, 1.6723, 0.5406, 0.1345, -0.1800, 0.2304, 1.2791, 0.7489, 0.6341, -0.0151, -1.3693, -1.2532, -2.3921, 0.7376, 1.6876, 0.5483]) # fmt: skip
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torch.testing.assert_close(out[0, 0, :30], EXPECTED_SLICE, rtol=1e-2, atol=1e-2)
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@slow
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def test_model_7b_greedy_generation(self):
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EXPECTED_TEXT_COMPLETION = """Simply put, the theory of relativity states that \nthe speed of light is the same for all observers, no matter \nhow fast they are moving. This is a very counter-intuitive \nconcept, and it took Einstein a long time to come up with \nthe theory. The theory of relativity is based on two \npostulates"""
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prompt = "Simply put, the theory of relativity states that "
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tokenizer = AutoTokenizer.from_pretrained("allenai/OLMoE-1B-7B-0924", device_map="auto")
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input_ids = tokenizer.encode(prompt, return_tensors="pt")
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model = OlmoeForCausalLM.from_pretrained("allenai/OLMoE-1B-7B-0924", device_map="auto")
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# greedy generation outputs
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generated_ids = model.generate(input_ids, max_new_tokens=64, top_p=None, temperature=1, do_sample=False)
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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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@require_tokenizers
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def test_fast_special_tokens(self):
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fast_tokenizer = GPTNeoXTokenizerFast.from_pretrained("allenai/OLMoE-1B-7B-0924")
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original_add_eos_token = fast_tokenizer.add_eos_token
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fast_tokenizer.add_eos_token = False
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fast = fast_tokenizer.encode("A sample test")
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self.assertEqual(fast, [34, 3410, 1071])
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fast_tokenizer.add_eos_token = True
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fast = fast_tokenizer.encode("A sample test")
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self.assertEqual(fast, [34, 3410, 1071, 50279])
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fast_tokenizer.add_eos_token = original_add_eos_token
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@require_tokenizers
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def test_simple_encode_decode(self):
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rust_tokenizer = GPTNeoXTokenizerFast.from_pretrained("allenai/OLMoE-1B-7B-0924")
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self.assertEqual(rust_tokenizer.encode("This is a test"), [1552, 310, 247, 1071])
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self.assertEqual(rust_tokenizer.decode([1552, 310, 247, 1071], skip_special_tokens=True), "This is a test")
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# bytefallback showcase
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self.assertEqual(rust_tokenizer.encode("生活的真谛是"), [20025, 46549, 5225, 48561, 33656, 238, 12105]) # fmt: skip
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self.assertEqual(
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rust_tokenizer.decode([20025, 46549, 5225, 48561, 33656, 238, 12105], skip_special_tokens=True),
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"生活的真谛是",
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)
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# Inner spaces showcase
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self.assertEqual(rust_tokenizer.encode("Hi Hello"), [12764, 50276, 12092])
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self.assertEqual(rust_tokenizer.decode([12764, 50276, 12092], skip_special_tokens=True), "Hi Hello")
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self.assertEqual(rust_tokenizer.encode("Hi Hello"), [12764, 50275, 12092])
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self.assertEqual(rust_tokenizer.decode([12764, 50275, 12092], skip_special_tokens=True), "Hi Hello")
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self.assertEqual(rust_tokenizer.encode(""), [])
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|
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self.assertEqual(rust_tokenizer.encode(" "), [209])
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|
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self.assertEqual(rust_tokenizer.encode(" "), [50276])
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|
|
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self.assertEqual(rust_tokenizer.encode(" Hello"), [24387])
|