mirror of
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860264379f
* lets begin * boom boom * fix out proj in attn * fix attention * fix local attention * add tokenizer * fix imports * autotokenizer * fix checkpoint name * cleanup * more clean-up * more cleanup * output attentions * fix attn mask creation * fix imports * config doc * add tests * add slow tests * quality * add conversion script * copyright * typo * another bites the dust * fix attention tests * doc * add embed init in convert function * fix copies * remove tokenizer * enable caching * address review comments * improve config and create attn layer list internally * more consistent naming * init hf config from mesh-tf config json file * remove neo tokenizer from doc * handle attention_mask in local attn layer * attn_layers => attention_layers * add tokenizer_class in config * fix docstring * raise if len of attention_layers is not same as num_layers * remove tokenizer_class from config * more consistent naming * fix doc * fix checkpoint names * fp16 compat * Apply suggestions from code review Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> Co-authored-by: Lysandre Debut <lysandre@huggingface.co> Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
512 lines
21 KiB
Python
512 lines
21 KiB
Python
# coding=utf-8
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# Copyright 2021 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 GPT Neo model. """
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import unittest
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from transformers import is_torch_available
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from transformers.testing_utils import require_torch, slow, torch_device
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from .test_configuration_common import ConfigTester
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from .test_generation_utils import GenerationTesterMixin
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from .test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
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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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GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST,
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GPT2Tokenizer,
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GPTNeoConfig,
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GPTNeoForCausalLM,
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GPTNeoModel,
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)
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class GPTNeoModelTester:
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def __init__(
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self,
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parent,
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batch_size=14,
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seq_length=7,
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is_training=True,
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use_token_type_ids=True,
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use_input_mask=True,
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use_labels=True,
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use_mc_token_ids=True,
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vocab_size=99,
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hidden_size=32,
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num_hidden_layers=4,
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attention_types=[[["global", "local"], 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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window_size=7,
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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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):
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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_token_type_ids = use_token_type_ids
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self.use_input_mask = use_input_mask
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self.use_labels = use_labels
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self.use_mc_token_ids = use_mc_token_ids
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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.window_size = window_size
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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.bos_token_id = vocab_size - 1
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self.eos_token_id = vocab_size - 1
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self.pad_token_id = vocab_size - 1
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self.chunk_length = window_size
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self.attention_types = attention_types
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def get_large_model_config(self):
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return GPTNeoConfig.from_pretrained("gpt_neo")
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def prepare_config_and_inputs(self, gradient_checkpointing=False):
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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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mc_token_ids = None
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if self.use_mc_token_ids:
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mc_token_ids = ids_tensor([self.batch_size, self.num_choices], self.seq_length)
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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 = GPTNeoConfig(
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vocab_size=self.vocab_size,
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hidden_size=self.hidden_size,
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num_layers=self.num_hidden_layers,
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num_heads=self.num_attention_heads,
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max_position_embeddings=self.max_position_embeddings,
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use_cache=not gradient_checkpointing,
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bos_token_id=self.bos_token_id,
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eos_token_id=self.eos_token_id,
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pad_token_id=self.pad_token_id,
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gradient_checkpointing=gradient_checkpointing,
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window_size=self.window_size,
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attention_types=self.attention_types,
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)
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head_mask = ids_tensor([self.num_hidden_layers, self.num_attention_heads], 2)
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return (
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config,
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input_ids,
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input_mask,
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head_mask,
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token_type_ids,
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mc_token_ids,
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sequence_labels,
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token_labels,
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choice_labels,
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)
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def prepare_config_and_inputs_for_decoder(self):
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(
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config,
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input_ids,
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input_mask,
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head_mask,
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token_type_ids,
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mc_token_ids,
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sequence_labels,
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token_labels,
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choice_labels,
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) = self.prepare_config_and_inputs()
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encoder_hidden_states = floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
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encoder_attention_mask = ids_tensor([self.batch_size, self.seq_length], vocab_size=2)
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return (
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config,
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input_ids,
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input_mask,
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head_mask,
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token_type_ids,
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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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def create_and_check_gpt_neo_model(self, config, input_ids, input_mask, head_mask, token_type_ids, *args):
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model = GPTNeoModel(config=config)
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model.to(torch_device)
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model.eval()
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result = model(input_ids, token_type_ids=token_type_ids, head_mask=head_mask)
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result = model(input_ids, token_type_ids=token_type_ids)
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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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# past_key_values is not implemented
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# self.parent.assertEqual(len(result.past_key_values), config.n_layer)
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def create_and_check_gpt_neo_model_past(self, config, input_ids, input_mask, head_mask, token_type_ids, *args):
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model = GPTNeoModel(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(input_ids, token_type_ids=token_type_ids, use_cache=True)
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outputs_use_cache_conf = model(input_ids, token_type_ids=token_type_ids)
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outputs_no_past = model(input_ids, token_type_ids=token_type_ids, use_cache=False)
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self.parent.assertTrue(len(outputs) == len(outputs_use_cache_conf))
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self.parent.assertTrue(len(outputs) == len(outputs_no_past) + 1)
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output, past = outputs.to_tuple()
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# create hypothetical next token and extent to next_input_ids
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next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size)
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next_token_types = ids_tensor([self.batch_size, 1], self.type_vocab_size)
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# append to next input_ids and token_type_ids
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next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
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next_token_type_ids = torch.cat([token_type_ids, next_token_types], dim=-1)
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output_from_no_past = model(next_input_ids, token_type_ids=next_token_type_ids)["last_hidden_state"]
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output_from_past = model(next_tokens, token_type_ids=next_token_types, past_key_values=past)[
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"last_hidden_state"
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]
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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[:, -1, random_slice_idx].detach()
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output_from_past_slice = output_from_past[:, 0, random_slice_idx].detach()
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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 create_and_check_lm_head_model(self, config, input_ids, input_mask, head_mask, token_type_ids, *args):
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model = GPTNeoForCausalLM(config)
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model.to(torch_device)
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model.eval()
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result = model(input_ids, token_type_ids=token_type_ids, labels=input_ids)
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self.parent.assertEqual(result.loss.shape, ())
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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_forward_and_backwards(self, config, input_ids, input_mask, head_mask, token_type_ids, *args):
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model = GPTNeoForCausalLM(config)
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model.to(torch_device)
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result = model(input_ids, token_type_ids=token_type_ids, labels=input_ids)
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self.parent.assertEqual(result.loss.shape, ())
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self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
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result.loss.backward()
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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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input_mask,
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head_mask,
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token_type_ids,
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mc_token_ids,
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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 = {
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"input_ids": input_ids,
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"token_type_ids": token_type_ids,
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"head_mask": head_mask,
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}
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return config, inputs_dict
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@require_torch
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class GPTNeoModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):
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all_model_classes = (GPTNeoModel, GPTNeoForCausalLM) if is_torch_available() else ()
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all_generative_model_classes = (GPTNeoForCausalLM,) if is_torch_available() else ()
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test_missing_keys = False
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test_pruning = False
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test_model_parallel = False
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# special case for DoubleHeads model
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def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
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inputs_dict = super()._prepare_for_class(inputs_dict, model_class, return_labels=return_labels)
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return inputs_dict
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def setUp(self):
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self.model_tester = GPTNeoModelTester(self)
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self.config_tester = ConfigTester(self, config_class=GPTNeoConfig, n_embd=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_gpt_neo_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_gpt_neo_model(*config_and_inputs)
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def test_gpt_neo_model_past(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_gpt_neo_model_past(*config_and_inputs)
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def test_gpt_neo_lm_head_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_lm_head_model(*config_and_inputs)
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def test_gpt_neo_gradient_checkpointing(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs(gradient_checkpointing=True)
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self.model_tester.create_and_check_forward_and_backwards(*config_and_inputs)
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def _get_local_attn_seq_len_block_len_windows(self, seq_len, window_size):
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block_length = window_size
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while seq_len % block_length != 0:
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block_length -= 1
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windows = seq_len // block_length
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local_seq_len = window_size + block_length
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return local_seq_len, block_length, windows
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def test_attention_outputs(self):
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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config.return_dict = True
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seq_len = getattr(self.model_tester, "seq_length", None)
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encoder_seq_length = getattr(self.model_tester, "encoder_seq_length", seq_len)
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encoder_key_length = getattr(self.model_tester, "key_length", encoder_seq_length)
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chunk_length = getattr(self.model_tester, "chunk_length", None)
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for model_class in self.all_model_classes:
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inputs_dict["output_attentions"] = True
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inputs_dict["output_hidden_states"] = False
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config.return_dict = True
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model = model_class(config)
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model.to(torch_device)
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model.eval()
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with torch.no_grad():
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outputs = model(**self._prepare_for_class(inputs_dict, model_class))
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attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
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self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
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# check that output_attentions also work using config
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del inputs_dict["output_attentions"]
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config.output_attentions = True
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model = model_class(config)
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model.to(torch_device)
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model.eval()
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with torch.no_grad():
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outputs = model(**self._prepare_for_class(inputs_dict, model_class))
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attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
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self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
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# test global attention shape
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self.assertListEqual(
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list(attentions[0].shape[-3:]),
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[self.model_tester.num_attention_heads, encoder_seq_length, seq_len],
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)
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# test local attention shape
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encoder_key_length = self._get_local_attn_seq_len_block_len_windows(seq_len, chunk_length)[0]
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self.assertListEqual(
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list(attentions[-1].shape[-3:]),
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[self.model_tester.num_attention_heads, seq_len, encoder_key_length],
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)
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out_len = len(outputs)
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# Check attention is always last and order is fine
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inputs_dict["output_attentions"] = True
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inputs_dict["output_hidden_states"] = True
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model = model_class(config)
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model.to(torch_device)
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model.eval()
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with torch.no_grad():
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outputs = model(**self._prepare_for_class(inputs_dict, model_class))
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if hasattr(self.model_tester, "num_hidden_states_types"):
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added_hidden_states = self.model_tester.num_hidden_states_types
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else:
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added_hidden_states = 1
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self.assertEqual(out_len + added_hidden_states, len(outputs))
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self_attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
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self.assertEqual(len(self_attentions), self.model_tester.num_hidden_layers)
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# test global attention shape
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self.assertListEqual(
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list(self_attentions[0].shape[-3:]),
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[self.model_tester.num_attention_heads, encoder_seq_length, seq_len],
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)
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# test local attention shape
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self.assertListEqual(
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list(self_attentions[-1].shape[-3:]),
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[self.model_tester.num_attention_heads, seq_len, encoder_key_length],
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)
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def _check_attentions_for_generate(
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self, batch_size, attentions, min_length, max_length, config, use_cache=False, num_beam_groups=1
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):
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self.assertIsInstance(attentions, tuple)
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self.assertListEqual(
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[isinstance(iter_attentions, tuple) for iter_attentions in attentions], [True] * len(attentions)
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)
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self.assertEqual(len(attentions), (max_length - min_length) * num_beam_groups)
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for idx, iter_attentions in enumerate(attentions):
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tgt_len = min_length + idx if not use_cache else 1
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src_len = min_length + idx
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global_expected_shape = (
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batch_size * num_beam_groups,
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config.num_attention_heads,
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tgt_len,
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src_len,
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)
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local_seq_len, block_len, windows = self._get_local_attn_seq_len_block_len_windows(
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src_len, config.window_size
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)
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block_len = 1 if use_cache else block_len
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local_expected_shape = (
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batch_size * num_beam_groups,
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windows,
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config.num_attention_heads,
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block_len,
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local_seq_len,
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)
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shapes = [layer_attention.shape for layer_attention in iter_attentions]
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# every other layer is local attention layers
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# so alternate between expected shapes
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expected_shape = [
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global_expected_shape if i % 2 == 0 else local_expected_shape for i, _ in enumerate(iter_attentions)
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]
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# check attn size
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self.assertListEqual(shapes, expected_shape)
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||
@slow
|
||
def test_batch_generation(self):
|
||
model = GPTNeoForCausalLM.from_pretrained("EleutherAI/gpt_neo_xl")
|
||
model.to(torch_device)
|
||
tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
|
||
|
||
tokenizer.padding_side = "left"
|
||
|
||
# Define PAD Token = EOS Token = 50256
|
||
tokenizer.pad_token = tokenizer.eos_token
|
||
model.config.pad_token_id = model.config.eos_token_id
|
||
|
||
# use different length sentences to test batching
|
||
sentences = [
|
||
"Hello, my dog is a little",
|
||
"Today, I am",
|
||
]
|
||
|
||
inputs = tokenizer(sentences, return_tensors="pt", padding=True)
|
||
input_ids = inputs["input_ids"].to(torch_device)
|
||
|
||
outputs = model.generate(
|
||
input_ids=input_ids,
|
||
attention_mask=inputs["attention_mask"].to(torch_device),
|
||
)
|
||
|
||
inputs_non_padded = tokenizer(sentences[0], return_tensors="pt").input_ids.to(torch_device)
|
||
output_non_padded = model.generate(input_ids=inputs_non_padded)
|
||
|
||
num_paddings = inputs_non_padded.shape[-1] - inputs["attention_mask"][-1].long().sum().cpu().item()
|
||
inputs_padded = tokenizer(sentences[1], return_tensors="pt").input_ids.to(torch_device)
|
||
output_padded = model.generate(input_ids=inputs_padded, max_length=model.config.max_length - num_paddings)
|
||
|
||
batch_out_sentence = tokenizer.batch_decode(outputs, skip_special_tokens=True)
|
||
non_padded_sentence = tokenizer.decode(output_non_padded[0], skip_special_tokens=True)
|
||
padded_sentence = tokenizer.decode(output_padded[0], skip_special_tokens=True)
|
||
|
||
expected_output_sentence = [
|
||
"Hello, my dog is a little bit of a kitty. She is a very sweet and loving",
|
||
"Today, I am going to talk about the best way to get a job in the",
|
||
]
|
||
self.assertListEqual(expected_output_sentence, batch_out_sentence)
|
||
self.assertListEqual(expected_output_sentence, [non_padded_sentence, padded_sentence])
|
||
|
||
@slow
|
||
def test_model_from_pretrained(self):
|
||
for model_name in GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
|
||
model = GPTNeoModel.from_pretrained(model_name)
|
||
self.assertIsNotNone(model)
|
||
|
||
|
||
@require_torch
|
||
class GPTNeoModelLanguageGenerationTest(unittest.TestCase):
|
||
@slow
|
||
def test_lm_generate_gpt_neo(self):
|
||
for checkpointing in [True, False]:
|
||
model = GPTNeoForCausalLM.from_pretrained("EleutherAI/gpt_neo_xl", gradient_checkpointing=checkpointing)
|
||
model.to(torch_device)
|
||
input_ids = torch.tensor([[464, 3290]], dtype=torch.long, device=torch_device) # The dog
|
||
# fmt: off
|
||
expected_output_ids = [464, 3290, 12, 3380, 4866, 286, 262, 1492, 11, 543, 318, 257, 4947, 286, 27126, 416, 262, 2739, 1772, 11] # The dog-eared copy of the book, which is a collection of essays by the late author,
|
||
# fmt: on
|
||
output_ids = model.generate(input_ids, do_sample=False)
|
||
self.assertListEqual(output_ids[0].tolist(), expected_output_ids)
|
||
|
||
@slow
|
||
def test_gpt_neo_sample(self):
|
||
tokenizer = GPT2Tokenizer.from_pretrained("EleutherAI/gpt_neo_xl")
|
||
model = GPTNeoForCausalLM.from_pretrained("EleutherAI/gpt_neo_xl")
|
||
model.to(torch_device)
|
||
|
||
torch.manual_seed(0)
|
||
tokenized = tokenizer("Today is a nice day and", return_tensors="pt", return_token_type_ids=True)
|
||
input_ids = tokenized.input_ids.to(torch_device)
|
||
output_ids = model.generate(input_ids, do_sample=True)
|
||
output_str = tokenizer.decode(output_ids[0], skip_special_tokens=True)
|
||
|
||
EXPECTED_OUTPUT_STR = "Today is a nice day and if you don’t get the memo here is what you can"
|
||
self.assertEqual(output_str, EXPECTED_OUTPUT_STR)
|