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GPT Neo (#10848)

Browse Source 
* 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>
This commit is contained in:
Suraj Patil
2021-03-30 19:12:30 +05:30
committed by GitHub
parent a04eb8d369
commit 860264379f
14 changed files with 1953 additions and 28 deletions
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tests/test_modeling_gpt_neo.py Normal file
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# coding=utf-8
# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Testing suite for the PyTorch GPT Neo model. """
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from .test_configuration_common import ConfigTester
from .test_generation_utils import GenerationTesterMixin
from .test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_torch_available():
import torch
from transformers import (
GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST,
GPT2Tokenizer,
GPTNeoConfig,
GPTNeoForCausalLM,
GPTNeoModel,
)
class GPTNeoModelTester:
def __init__(
self,
parent,
batch_size=14,
seq_length=7,
is_training=True,
use_token_type_ids=True,
use_input_mask=True,
use_labels=True,
use_mc_token_ids=True,
vocab_size=99,
hidden_size=32,
num_hidden_layers=4,
attention_types=[[["global", "local"], 2]],
num_attention_heads=4,
intermediate_size=37,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=512,
window_size=7,
type_vocab_size=16,
type_sequence_label_size=2,
initializer_range=0.02,
num_labels=3,
num_choices=4,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
self.use_token_type_ids = use_token_type_ids
self.use_input_mask = use_input_mask
self.use_labels = use_labels
self.use_mc_token_ids = use_mc_token_ids
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.window_size = window_size
self.type_vocab_size = type_vocab_size
self.type_sequence_label_size = type_sequence_label_size
self.initializer_range = initializer_range
self.num_labels = num_labels
self.num_choices = num_choices
self.bos_token_id = vocab_size - 1
self.eos_token_id = vocab_size - 1
self.pad_token_id = vocab_size - 1
self.chunk_length = window_size
self.attention_types = attention_types
def get_large_model_config(self):
return GPTNeoConfig.from_pretrained("gpt_neo")
def prepare_config_and_inputs(self, gradient_checkpointing=False):
input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
input_mask = None
if self.use_input_mask:
input_mask = random_attention_mask([self.batch_size, self.seq_length])
token_type_ids = None
if self.use_token_type_ids:
token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
mc_token_ids = None
if self.use_mc_token_ids:
mc_token_ids = ids_tensor([self.batch_size, self.num_choices], self.seq_length)
sequence_labels = None
token_labels = None
choice_labels = None
if self.use_labels:
sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
choice_labels = ids_tensor([self.batch_size], self.num_choices)
config = GPTNeoConfig(
vocab_size=self.vocab_size,
hidden_size=self.hidden_size,
num_layers=self.num_hidden_layers,
num_heads=self.num_attention_heads,
max_position_embeddings=self.max_position_embeddings,
use_cache=not gradient_checkpointing,
bos_token_id=self.bos_token_id,
eos_token_id=self.eos_token_id,
pad_token_id=self.pad_token_id,
gradient_checkpointing=gradient_checkpointing,
window_size=self.window_size,
attention_types=self.attention_types,
)
head_mask = ids_tensor([self.num_hidden_layers, self.num_attention_heads], 2)
return (
config,
input_ids,
input_mask,
head_mask,
token_type_ids,
mc_token_ids,
sequence_labels,
token_labels,
choice_labels,
)
def prepare_config_and_inputs_for_decoder(self):
(
config,
input_ids,
input_mask,
head_mask,
token_type_ids,
mc_token_ids,
sequence_labels,
token_labels,
choice_labels,
) = self.prepare_config_and_inputs()
encoder_hidden_states = floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
encoder_attention_mask = ids_tensor([self.batch_size, self.seq_length], vocab_size=2)
return (
config,
input_ids,
input_mask,
head_mask,
token_type_ids,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
)
def create_and_check_gpt_neo_model(self, config, input_ids, input_mask, head_mask, token_type_ids, *args):
model = GPTNeoModel(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, token_type_ids=token_type_ids, head_mask=head_mask)
result = model(input_ids, token_type_ids=token_type_ids)
result = model(input_ids)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
# past_key_values is not implemented
# self.parent.assertEqual(len(result.past_key_values), config.n_layer)
def create_and_check_gpt_neo_model_past(self, config, input_ids, input_mask, head_mask, token_type_ids, *args):
model = GPTNeoModel(config=config)
model.to(torch_device)
model.eval()
# first forward pass
outputs = model(input_ids, token_type_ids=token_type_ids, use_cache=True)
outputs_use_cache_conf = model(input_ids, token_type_ids=token_type_ids)
outputs_no_past = model(input_ids, token_type_ids=token_type_ids, use_cache=False)
self.parent.assertTrue(len(outputs) == len(outputs_use_cache_conf))
self.parent.assertTrue(len(outputs) == len(outputs_no_past) + 1)
output, past = outputs.to_tuple()
# create hypothetical next token and extent to next_input_ids
next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size)
next_token_types = ids_tensor([self.batch_size, 1], self.type_vocab_size)
# append to next input_ids and token_type_ids
next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
next_token_type_ids = torch.cat([token_type_ids, next_token_types], dim=-1)
output_from_no_past = model(next_input_ids, token_type_ids=next_token_type_ids)["last_hidden_state"]
output_from_past = model(next_tokens, token_type_ids=next_token_types, past_key_values=past)[
"last_hidden_state"
]
# select random slice
random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
output_from_no_past_slice = output_from_no_past[:, -1, random_slice_idx].detach()
output_from_past_slice = output_from_past[:, 0, random_slice_idx].detach()
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3))
def create_and_check_lm_head_model(self, config, input_ids, input_mask, head_mask, token_type_ids, *args):
model = GPTNeoForCausalLM(config)
model.to(torch_device)
model.eval()
result = model(input_ids, token_type_ids=token_type_ids, labels=input_ids)
self.parent.assertEqual(result.loss.shape, ())
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
def create_and_check_forward_and_backwards(self, config, input_ids, input_mask, head_mask, token_type_ids, *args):
model = GPTNeoForCausalLM(config)
model.to(torch_device)
result = model(input_ids, token_type_ids=token_type_ids, labels=input_ids)
self.parent.assertEqual(result.loss.shape, ())
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
result.loss.backward()
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(
config,
input_ids,
input_mask,
head_mask,
token_type_ids,
mc_token_ids,
sequence_labels,
token_labels,
choice_labels,
) = config_and_inputs
inputs_dict = {
"input_ids": input_ids,
"token_type_ids": token_type_ids,
"head_mask": head_mask,
}
return config, inputs_dict
@require_torch
class GPTNeoModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):
all_model_classes = (GPTNeoModel, GPTNeoForCausalLM) if is_torch_available() else ()
all_generative_model_classes = (GPTNeoForCausalLM,) if is_torch_available() else ()
test_missing_keys = False
test_pruning = False
test_model_parallel = False
# special case for DoubleHeads model
def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
inputs_dict = super()._prepare_for_class(inputs_dict, model_class, return_labels=return_labels)
return inputs_dict
def setUp(self):
self.model_tester = GPTNeoModelTester(self)
self.config_tester = ConfigTester(self, config_class=GPTNeoConfig, n_embd=37)
def test_config(self):
self.config_tester.run_common_tests()
def test_gpt_neo_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_gpt_neo_model(*config_and_inputs)
def test_gpt_neo_model_past(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_gpt_neo_model_past(*config_and_inputs)
def test_gpt_neo_lm_head_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_lm_head_model(*config_and_inputs)
def test_gpt_neo_gradient_checkpointing(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs(gradient_checkpointing=True)
self.model_tester.create_and_check_forward_and_backwards(*config_and_inputs)
def _get_local_attn_seq_len_block_len_windows(self, seq_len, window_size):
block_length = window_size
while seq_len % block_length != 0:
block_length -= 1
windows = seq_len // block_length
local_seq_len = window_size + block_length
return local_seq_len, block_length, windows
def test_attention_outputs(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.return_dict = True
seq_len = getattr(self.model_tester, "seq_length", None)
encoder_seq_length = getattr(self.model_tester, "encoder_seq_length", seq_len)
encoder_key_length = getattr(self.model_tester, "key_length", encoder_seq_length)
chunk_length = getattr(self.model_tester, "chunk_length", None)
for model_class in self.all_model_classes:
inputs_dict["output_attentions"] = True
inputs_dict["output_hidden_states"] = False
config.return_dict = True
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
config.output_attentions = True
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
# test global attention shape
self.assertListEqual(
list(attentions[0].shape[-3:]),
[self.model_tester.num_attention_heads, encoder_seq_length, seq_len],
)
# test local attention shape
encoder_key_length = self._get_local_attn_seq_len_block_len_windows(seq_len, chunk_length)[0]
self.assertListEqual(
list(attentions[-1].shape[-3:]),
[self.model_tester.num_attention_heads, seq_len, encoder_key_length],
)
out_len = len(outputs)
# Check attention is always last and order is fine
inputs_dict["output_attentions"] = True
inputs_dict["output_hidden_states"] = True
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
if hasattr(self.model_tester, "num_hidden_states_types"):
added_hidden_states = self.model_tester.num_hidden_states_types
else:
added_hidden_states = 1
self.assertEqual(out_len + added_hidden_states, len(outputs))
self_attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(self_attentions), self.model_tester.num_hidden_layers)
# test global attention shape
self.assertListEqual(
list(self_attentions[0].shape[-3:]),
[self.model_tester.num_attention_heads, encoder_seq_length, seq_len],
)
# test local attention shape
self.assertListEqual(
list(self_attentions[-1].shape[-3:]),
[self.model_tester.num_attention_heads, seq_len, encoder_key_length],
)
def _check_attentions_for_generate(
self, batch_size, attentions, min_length, max_length, config, use_cache=False, num_beam_groups=1
):
self.assertIsInstance(attentions, tuple)
self.assertListEqual(
[isinstance(iter_attentions, tuple) for iter_attentions in attentions], [True] * len(attentions)
)
self.assertEqual(len(attentions), (max_length - min_length) * num_beam_groups)
for idx, iter_attentions in enumerate(attentions):
tgt_len = min_length + idx if not use_cache else 1
src_len = min_length + idx
global_expected_shape = (
batch_size * num_beam_groups,
config.num_attention_heads,
tgt_len,
src_len,
)
local_seq_len, block_len, windows = self._get_local_attn_seq_len_block_len_windows(
src_len, config.window_size
)
block_len = 1 if use_cache else block_len
local_expected_shape = (
batch_size * num_beam_groups,
windows,
config.num_attention_heads,
block_len,
local_seq_len,
)
shapes = [layer_attention.shape for layer_attention in iter_attentions]
# every other layer is local attention layers
# so alternate between expected shapes
expected_shape = [
global_expected_shape if i % 2 == 0 else local_expected_shape for i, _ in enumerate(iter_attentions)
]
# check attn size
self.assertListEqual(shapes, expected_shape)
@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)