907 lines
39 KiB
Python
907 lines
39 KiB
Python
# coding=utf-8
|
|
# Copyright 2020 The HuggingFace 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.
|
|
|
|
|
|
import datetime
|
|
import gc
|
|
import math
|
|
import unittest
|
|
|
|
import pytest
|
|
|
|
from transformers import GPT2Config, is_torch_available
|
|
from transformers.testing_utils import (
|
|
backend_empty_cache,
|
|
require_flash_attn,
|
|
require_torch,
|
|
require_torch_gpu,
|
|
slow,
|
|
torch_device,
|
|
)
|
|
|
|
from ...generation.test_utils import GenerationTesterMixin
|
|
from ...test_configuration_common import ConfigTester
|
|
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
|
|
from ...test_pipeline_mixin import PipelineTesterMixin
|
|
|
|
|
|
if is_torch_available():
|
|
import torch
|
|
|
|
from transformers import (
|
|
GPT2DoubleHeadsModel,
|
|
GPT2ForQuestionAnswering,
|
|
GPT2ForSequenceClassification,
|
|
GPT2ForTokenClassification,
|
|
GPT2LMHeadModel,
|
|
GPT2Model,
|
|
GPT2Tokenizer,
|
|
)
|
|
|
|
|
|
class GPT2ModelTester:
|
|
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=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,
|
|
type_vocab_size=16,
|
|
type_sequence_label_size=2,
|
|
initializer_range=0.02,
|
|
num_labels=3,
|
|
num_choices=4,
|
|
scope=None,
|
|
):
|
|
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.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.scope = None
|
|
self.bos_token_id = vocab_size - 1
|
|
self.eos_token_id = vocab_size - 1
|
|
self.pad_token_id = vocab_size - 1
|
|
|
|
def get_large_model_config(self):
|
|
return GPT2Config.from_pretrained("openai-community/gpt2")
|
|
|
|
def prepare_config_and_inputs(
|
|
self, gradient_checkpointing=False, scale_attn_by_inverse_layer_idx=False, reorder_and_upcast_attn=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 = self.get_config(
|
|
gradient_checkpointing=gradient_checkpointing,
|
|
scale_attn_by_inverse_layer_idx=scale_attn_by_inverse_layer_idx,
|
|
reorder_and_upcast_attn=reorder_and_upcast_attn,
|
|
)
|
|
|
|
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 get_config(
|
|
self, gradient_checkpointing=False, scale_attn_by_inverse_layer_idx=False, reorder_and_upcast_attn=False
|
|
):
|
|
return GPT2Config(
|
|
vocab_size=self.vocab_size,
|
|
n_embd=self.hidden_size,
|
|
n_layer=self.num_hidden_layers,
|
|
n_head=self.num_attention_heads,
|
|
n_inner=self.intermediate_size,
|
|
activation_function=self.hidden_act,
|
|
resid_pdrop=self.hidden_dropout_prob,
|
|
attn_pdrop=self.attention_probs_dropout_prob,
|
|
n_positions=self.max_position_embeddings,
|
|
type_vocab_size=self.type_vocab_size,
|
|
initializer_range=self.initializer_range,
|
|
use_cache=True,
|
|
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,
|
|
scale_attn_by_inverse_layer_idx=scale_attn_by_inverse_layer_idx,
|
|
reorder_and_upcast_attn=reorder_and_upcast_attn,
|
|
)
|
|
|
|
def get_pipeline_config(self):
|
|
config = self.get_config()
|
|
config.vocab_size = 300
|
|
return config
|
|
|
|
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_gpt2_model(self, config, input_ids, input_mask, head_mask, token_type_ids, *args):
|
|
model = GPT2Model(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))
|
|
self.parent.assertEqual(len(result.past_key_values), config.n_layer)
|
|
|
|
def create_and_check_gpt2_model_past(self, config, input_ids, input_mask, head_mask, token_type_ids, *args):
|
|
model = GPT2Model(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_gpt2_model_attention_mask_past(
|
|
self, config, input_ids, input_mask, head_mask, token_type_ids, *args
|
|
):
|
|
model = GPT2Model(config=config)
|
|
model.to(torch_device)
|
|
model.eval()
|
|
|
|
# create attention mask
|
|
attn_mask = torch.ones(input_ids.shape, dtype=torch.long, device=torch_device)
|
|
half_seq_length = self.seq_length // 2
|
|
attn_mask[:, half_seq_length:] = 0
|
|
|
|
# first forward pass
|
|
output, past = model(input_ids, attention_mask=attn_mask).to_tuple()
|
|
|
|
# create hypothetical next token and extent to next_input_ids
|
|
next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size)
|
|
|
|
# change a random masked slice from input_ids
|
|
random_seq_idx_to_change = ids_tensor((1,), half_seq_length).item() + 1
|
|
random_other_next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size).squeeze(-1)
|
|
input_ids[:, -random_seq_idx_to_change] = random_other_next_tokens
|
|
|
|
# append to next input_ids and attn_mask
|
|
next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
|
|
attn_mask = torch.cat(
|
|
[attn_mask, torch.ones((attn_mask.shape[0], 1), dtype=torch.long, device=torch_device)],
|
|
dim=1,
|
|
)
|
|
|
|
# get two different outputs
|
|
output_from_no_past = model(next_input_ids, attention_mask=attn_mask)["last_hidden_state"]
|
|
output_from_past = model(next_tokens, past_key_values=past, attention_mask=attn_mask)["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_gpt2_model_past_large_inputs(
|
|
self, config, input_ids, input_mask, head_mask, token_type_ids, *args
|
|
):
|
|
model = GPT2Model(config=config)
|
|
model.to(torch_device)
|
|
model.eval()
|
|
|
|
# first forward pass
|
|
outputs = model(input_ids, token_type_ids=token_type_ids, attention_mask=input_mask, use_cache=True)
|
|
|
|
output, past = outputs.to_tuple()
|
|
|
|
# create hypothetical next token and extent to next_input_ids
|
|
next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size)
|
|
next_token_types = ids_tensor([self.batch_size, 3], self.type_vocab_size)
|
|
next_mask = ids_tensor((self.batch_size, 3), vocab_size=2)
|
|
|
|
# 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)
|
|
next_attention_mask = torch.cat([input_mask, next_mask], dim=-1)
|
|
|
|
output_from_no_past = model(
|
|
next_input_ids, token_type_ids=next_token_type_ids, attention_mask=next_attention_mask
|
|
)["last_hidden_state"]
|
|
output_from_past = model(
|
|
next_tokens, token_type_ids=next_token_types, attention_mask=next_attention_mask, past_key_values=past
|
|
)["last_hidden_state"]
|
|
self.parent.assertTrue(output_from_past.shape[1] == next_tokens.shape[1])
|
|
|
|
# select random slice
|
|
random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
|
|
output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx].detach()
|
|
output_from_past_slice = output_from_past[:, :, 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 = GPT2LMHeadModel(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, gradient_checkpointing=False
|
|
):
|
|
model = GPT2LMHeadModel(config)
|
|
model.to(torch_device)
|
|
if gradient_checkpointing:
|
|
model.gradient_checkpointing_enable()
|
|
|
|
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 create_and_check_double_lm_head_model(
|
|
self, config, input_ids, input_mask, head_mask, token_type_ids, mc_token_ids, *args
|
|
):
|
|
model = GPT2DoubleHeadsModel(config)
|
|
model.to(torch_device)
|
|
model.eval()
|
|
|
|
multiple_choice_inputs_ids = input_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
|
|
multiple_choice_input_mask = input_mask.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
|
|
multiple_choice_token_type_ids = token_type_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
|
|
|
|
inputs = {
|
|
"input_ids": multiple_choice_inputs_ids,
|
|
"mc_token_ids": mc_token_ids,
|
|
"attention_mask": multiple_choice_input_mask,
|
|
"token_type_ids": multiple_choice_token_type_ids,
|
|
"labels": multiple_choice_inputs_ids,
|
|
}
|
|
|
|
result = model(**inputs)
|
|
self.parent.assertEqual(result.loss.shape, ())
|
|
self.parent.assertEqual(
|
|
result.logits.shape, (self.batch_size, self.num_choices, self.seq_length, self.vocab_size)
|
|
)
|
|
self.parent.assertEqual(result.mc_logits.shape, (self.batch_size, self.num_choices))
|
|
|
|
def create_and_check_gpt2_for_question_answering(
|
|
self, config, input_ids, input_mask, head_mask, token_type_ids, mc_token_ids, sequence_labels, *args
|
|
):
|
|
config.num_labels = self.num_labels
|
|
model = GPT2ForQuestionAnswering(config)
|
|
model.to(torch_device)
|
|
model.eval()
|
|
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids)
|
|
self.parent.assertEqual(result.start_logits.shape, (self.batch_size, self.seq_length))
|
|
self.parent.assertEqual(result.end_logits.shape, (self.batch_size, self.seq_length))
|
|
|
|
def create_and_check_gpt2_for_sequence_classification(
|
|
self, config, input_ids, input_mask, head_mask, token_type_ids, mc_token_ids, sequence_labels, *args
|
|
):
|
|
config.num_labels = self.num_labels
|
|
model = GPT2ForSequenceClassification(config)
|
|
model.to(torch_device)
|
|
model.eval()
|
|
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=sequence_labels)
|
|
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
|
|
|
|
def create_and_check_gpt2_for_token_classification(
|
|
self, config, input_ids, input_mask, head_mask, token_type_ids, mc_token_ids, sequence_labels, *args
|
|
):
|
|
config.num_labels = self.num_labels
|
|
model = GPT2ForTokenClassification(config)
|
|
model.to(torch_device)
|
|
model.eval()
|
|
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids)
|
|
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels))
|
|
|
|
def create_and_check_gpt2_weight_initialization(self, config, *args):
|
|
model = GPT2Model(config)
|
|
model_std = model.config.initializer_range / math.sqrt(2 * model.config.n_layer)
|
|
for key in model.state_dict().keys():
|
|
if "c_proj" in key and "weight" in key:
|
|
self.parent.assertLessEqual(abs(torch.std(model.state_dict()[key]) - model_std), 0.001)
|
|
self.parent.assertLessEqual(abs(torch.mean(model.state_dict()[key]) - 0.0), 0.01)
|
|
|
|
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 GPT2ModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
|
|
all_model_classes = (
|
|
(
|
|
GPT2Model,
|
|
GPT2LMHeadModel,
|
|
GPT2DoubleHeadsModel,
|
|
GPT2ForQuestionAnswering,
|
|
GPT2ForSequenceClassification,
|
|
GPT2ForTokenClassification,
|
|
)
|
|
if is_torch_available()
|
|
else ()
|
|
)
|
|
all_generative_model_classes = (GPT2LMHeadModel, GPT2DoubleHeadsModel) if is_torch_available() else ()
|
|
pipeline_model_mapping = (
|
|
{
|
|
"feature-extraction": GPT2Model,
|
|
"question-answering": GPT2ForQuestionAnswering,
|
|
"text-classification": GPT2ForSequenceClassification,
|
|
"text-generation": GPT2LMHeadModel,
|
|
"token-classification": GPT2ForTokenClassification,
|
|
"zero-shot": GPT2ForSequenceClassification,
|
|
}
|
|
if is_torch_available()
|
|
else {}
|
|
)
|
|
all_parallelizable_model_classes = (GPT2LMHeadModel, GPT2DoubleHeadsModel) if is_torch_available() else ()
|
|
fx_compatible = True
|
|
test_missing_keys = False
|
|
test_model_parallel = True
|
|
|
|
# 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)
|
|
|
|
if return_labels:
|
|
if model_class.__name__ == "GPT2DoubleHeadsModel":
|
|
inputs_dict["labels"] = torch.zeros(
|
|
(self.model_tester.batch_size, self.model_tester.num_choices, self.model_tester.seq_length),
|
|
dtype=torch.long,
|
|
device=torch_device,
|
|
)
|
|
inputs_dict["input_ids"] = inputs_dict["labels"]
|
|
inputs_dict["token_type_ids"] = inputs_dict["labels"]
|
|
inputs_dict["mc_token_ids"] = torch.zeros(
|
|
(self.model_tester.batch_size, self.model_tester.num_choices),
|
|
dtype=torch.long,
|
|
device=torch_device,
|
|
)
|
|
inputs_dict["mc_labels"] = torch.zeros(
|
|
self.model_tester.batch_size, dtype=torch.long, device=torch_device
|
|
)
|
|
return inputs_dict
|
|
|
|
def setUp(self):
|
|
self.model_tester = GPT2ModelTester(self)
|
|
self.config_tester = ConfigTester(self, config_class=GPT2Config, n_embd=37)
|
|
|
|
def tearDown(self):
|
|
super().tearDown()
|
|
# clean-up as much as possible GPU memory occupied by PyTorch
|
|
gc.collect()
|
|
backend_empty_cache(torch_device)
|
|
|
|
def test_config(self):
|
|
self.config_tester.run_common_tests()
|
|
|
|
def test_gpt2_model(self):
|
|
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
|
self.model_tester.create_and_check_gpt2_model(*config_and_inputs)
|
|
|
|
def test_gpt2_model_past(self):
|
|
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
|
self.model_tester.create_and_check_gpt2_model_past(*config_and_inputs)
|
|
|
|
def test_gpt2_model_att_mask_past(self):
|
|
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
|
self.model_tester.create_and_check_gpt2_model_attention_mask_past(*config_and_inputs)
|
|
|
|
def test_gpt2_model_past_large_inputs(self):
|
|
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
|
self.model_tester.create_and_check_gpt2_model_past_large_inputs(*config_and_inputs)
|
|
|
|
def test_gpt2_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_gpt2_double_lm_head_model(self):
|
|
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
|
self.model_tester.create_and_check_double_lm_head_model(*config_and_inputs)
|
|
|
|
def test_gpt2_question_answering_model(self):
|
|
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
|
self.model_tester.create_and_check_gpt2_for_question_answering(*config_and_inputs)
|
|
|
|
def test_gpt2_sequence_classification_model(self):
|
|
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
|
self.model_tester.create_and_check_gpt2_for_sequence_classification(*config_and_inputs)
|
|
|
|
def test_gpt2_token_classification_model(self):
|
|
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
|
self.model_tester.create_and_check_gpt2_for_token_classification(*config_and_inputs)
|
|
|
|
def test_gpt2_gradient_checkpointing(self):
|
|
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
|
self.model_tester.create_and_check_forward_and_backwards(*config_and_inputs, gradient_checkpointing=True)
|
|
|
|
def test_gpt2_scale_attn_by_inverse_layer_idx(self):
|
|
config_and_inputs = self.model_tester.prepare_config_and_inputs(scale_attn_by_inverse_layer_idx=True)
|
|
self.model_tester.create_and_check_forward_and_backwards(*config_and_inputs)
|
|
|
|
def test_gpt2_reorder_and_upcast_attn(self):
|
|
config_and_inputs = self.model_tester.prepare_config_and_inputs(reorder_and_upcast_attn=True)
|
|
self.model_tester.create_and_check_forward_and_backwards(*config_and_inputs)
|
|
|
|
def test_gpt2_weight_initialization(self):
|
|
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
|
self.model_tester.create_and_check_gpt2_weight_initialization(*config_and_inputs)
|
|
|
|
@unittest.skip(
|
|
reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124"
|
|
)
|
|
def test_training_gradient_checkpointing(self):
|
|
pass
|
|
|
|
@unittest.skip(
|
|
reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124"
|
|
)
|
|
def test_training_gradient_checkpointing_use_reentrant(self):
|
|
pass
|
|
|
|
@unittest.skip(
|
|
reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124"
|
|
)
|
|
def test_training_gradient_checkpointing_use_reentrant_false(self):
|
|
pass
|
|
|
|
@slow
|
|
def test_batch_generation(self):
|
|
model = GPT2LMHeadModel.from_pretrained("openai-community/gpt2")
|
|
model.to(torch_device)
|
|
tokenizer = GPT2Tokenizer.from_pretrained("openai-community/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",
|
|
]
|
|
|
|
inputs = tokenizer(sentences, return_tensors="pt", padding=True)
|
|
input_ids = inputs["input_ids"].to(torch_device)
|
|
token_type_ids = torch.cat(
|
|
[
|
|
input_ids.new_full((input_ids.shape[0], input_ids.shape[1] - 1), 0),
|
|
input_ids.new_full((input_ids.shape[0], 1), 500),
|
|
],
|
|
dim=-1,
|
|
)
|
|
|
|
outputs = model.generate(
|
|
input_ids=input_ids,
|
|
attention_mask=inputs["attention_mask"].to(torch_device),
|
|
)
|
|
|
|
outputs_tt = model.generate(
|
|
input_ids=input_ids,
|
|
attention_mask=inputs["attention_mask"].to(torch_device),
|
|
token_type_ids=token_type_ids,
|
|
)
|
|
|
|
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)
|
|
batch_out_sentence_tt = tokenizer.batch_decode(outputs_tt, 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 mess. I'm not sure if he's going",
|
|
"Today, I'm going to be doing a lot of research on this. I",
|
|
]
|
|
self.assertListEqual(expected_output_sentence, batch_out_sentence)
|
|
self.assertTrue(batch_out_sentence_tt != batch_out_sentence) # token_type_ids should change output
|
|
self.assertListEqual(expected_output_sentence, [non_padded_sentence, padded_sentence])
|
|
|
|
@slow
|
|
def test_batch_generation_2heads(self):
|
|
model = GPT2DoubleHeadsModel.from_pretrained("openai-community/gpt2")
|
|
model.to(torch_device)
|
|
tokenizer = GPT2Tokenizer.from_pretrained("openai-community/gpt2")
|
|
|
|
tokenizer.padding_side = "left"
|
|
|
|
# This tokenizer has no pad token, so we have to set it in some way
|
|
# 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",
|
|
]
|
|
|
|
inputs = tokenizer(sentences, return_tensors="pt", padding=True)
|
|
input_ids = inputs["input_ids"].to(torch_device)
|
|
token_type_ids = torch.cat(
|
|
[
|
|
input_ids.new_full((input_ids.shape[0], input_ids.shape[1] - 1), 0),
|
|
input_ids.new_full((input_ids.shape[0], 1), 500),
|
|
],
|
|
dim=-1,
|
|
)
|
|
|
|
outputs = model.generate(
|
|
input_ids=input_ids,
|
|
attention_mask=inputs["attention_mask"].to(torch_device),
|
|
)
|
|
|
|
outputs_tt = model.generate(
|
|
input_ids=input_ids,
|
|
attention_mask=inputs["attention_mask"].to(torch_device),
|
|
token_type_ids=token_type_ids,
|
|
)
|
|
|
|
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)
|
|
batch_out_sentence_tt = tokenizer.batch_decode(outputs_tt, 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 mess. I'm not sure if he's going",
|
|
"Today, I'm going to be doing a lot of research on this. I",
|
|
]
|
|
self.assertListEqual(expected_output_sentence, batch_out_sentence)
|
|
self.assertTrue(batch_out_sentence_tt != batch_out_sentence) # token_type_ids should change output
|
|
self.assertListEqual(expected_output_sentence, [non_padded_sentence, padded_sentence])
|
|
|
|
@slow
|
|
def test_model_from_pretrained(self):
|
|
model_name = "openai-community/gpt2"
|
|
model = GPT2Model.from_pretrained(model_name)
|
|
self.assertIsNotNone(model)
|
|
|
|
|
|
@require_torch
|
|
class GPT2ModelLanguageGenerationTest(unittest.TestCase):
|
|
def tearDown(self):
|
|
super().tearDown()
|
|
# clean-up as much as possible GPU memory occupied by PyTorch
|
|
gc.collect()
|
|
backend_empty_cache(torch_device)
|
|
|
|
def _test_lm_generate_gpt2_helper(
|
|
self,
|
|
gradient_checkpointing=False,
|
|
reorder_and_upcast_attn=False,
|
|
scale_attn_by_inverse_layer_idx=False,
|
|
verify_outputs=True,
|
|
):
|
|
model = GPT2LMHeadModel.from_pretrained(
|
|
"openai-community/gpt2",
|
|
reorder_and_upcast_attn=reorder_and_upcast_attn,
|
|
scale_attn_by_inverse_layer_idx=scale_attn_by_inverse_layer_idx,
|
|
)
|
|
if gradient_checkpointing:
|
|
model.gradient_checkpointing_enable()
|
|
else:
|
|
model.gradient_checkpointing_disable()
|
|
model.to(torch_device)
|
|
|
|
# The dog
|
|
input_ids = torch.tensor([[464, 3290]], dtype=torch.long, device=torch_device)
|
|
|
|
# The dog was found in a field near the intersection of West and West Streets.\n\nThe dog
|
|
expected_output_ids = [464, 3290, 373, 1043, 287, 257, 2214, 1474, 262, 16246, 286, 2688, 290, 2688, 27262, 13, 198, 198, 464, 3290,] # fmt: skip
|
|
output_ids = model.generate(input_ids, do_sample=False)
|
|
if verify_outputs:
|
|
self.assertListEqual(output_ids[0].tolist(), expected_output_ids)
|
|
|
|
@slow
|
|
def test_lm_generate_gpt2(self):
|
|
self._test_lm_generate_gpt2_helper()
|
|
|
|
@slow
|
|
def test_lm_generate_gpt2_with_gradient_checkpointing(self):
|
|
self._test_lm_generate_gpt2_helper(gradient_checkpointing=True)
|
|
|
|
@slow
|
|
def test_lm_generate_gpt2_with_reorder_and_upcast_attn(self):
|
|
self._test_lm_generate_gpt2_helper(reorder_and_upcast_attn=True)
|
|
|
|
@slow
|
|
def test_lm_generate_gpt2_with_scale_attn_by_inverse_layer_idx(self):
|
|
self._test_lm_generate_gpt2_helper(scale_attn_by_inverse_layer_idx=True, verify_outputs=False)
|
|
|
|
@slow
|
|
def test_gpt2_sample(self):
|
|
tokenizer = GPT2Tokenizer.from_pretrained("openai-community/gpt2")
|
|
model = GPT2LMHeadModel.from_pretrained("openai-community/gpt2")
|
|
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)
|
|
|
|
token_type_ids = tokenized.token_type_ids.to(torch_device)
|
|
output_seq = model.generate(input_ids=input_ids, do_sample=True, num_return_sequences=5)
|
|
output_seq_tt = model.generate(
|
|
input_ids=input_ids, token_type_ids=token_type_ids, do_sample=True, num_return_sequences=5
|
|
)
|
|
output_seq_strs = tokenizer.batch_decode(output_seq, skip_special_tokens=True)
|
|
output_seq_tt_strs = tokenizer.batch_decode(output_seq_tt, skip_special_tokens=True)
|
|
|
|
EXPECTED_OUTPUT_STR = (
|
|
"Today is a nice day and if you don't know anything about the state of play during your holiday"
|
|
)
|
|
self.assertEqual(output_str, EXPECTED_OUTPUT_STR)
|
|
self.assertTrue(
|
|
all(output_seq_strs[idx] != output_seq_tt_strs[idx] for idx in range(len(output_seq_tt_strs)))
|
|
) # token_type_ids should change output
|
|
|
|
@slow
|
|
def test_gpt2_sample_max_time(self):
|
|
tokenizer = GPT2Tokenizer.from_pretrained("openai-community/gpt2")
|
|
model = GPT2LMHeadModel.from_pretrained("openai-community/gpt2")
|
|
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)
|
|
|
|
MAX_TIME = 0.5
|
|
|
|
start = datetime.datetime.now()
|
|
model.generate(input_ids, do_sample=True, max_time=MAX_TIME, max_length=256)
|
|
duration = datetime.datetime.now() - start
|
|
self.assertGreater(duration, datetime.timedelta(seconds=MAX_TIME))
|
|
self.assertLess(duration, datetime.timedelta(seconds=1.5 * MAX_TIME))
|
|
|
|
start = datetime.datetime.now()
|
|
model.generate(input_ids, do_sample=False, max_time=MAX_TIME, max_length=256)
|
|
duration = datetime.datetime.now() - start
|
|
self.assertGreater(duration, datetime.timedelta(seconds=MAX_TIME))
|
|
self.assertLess(duration, datetime.timedelta(seconds=1.5 * MAX_TIME))
|
|
|
|
start = datetime.datetime.now()
|
|
model.generate(input_ids, do_sample=False, num_beams=2, max_time=MAX_TIME, max_length=256)
|
|
duration = datetime.datetime.now() - start
|
|
self.assertGreater(duration, datetime.timedelta(seconds=MAX_TIME))
|
|
self.assertLess(duration, datetime.timedelta(seconds=1.5 * MAX_TIME))
|
|
|
|
start = datetime.datetime.now()
|
|
model.generate(input_ids, do_sample=True, num_beams=2, max_time=MAX_TIME, max_length=256)
|
|
duration = datetime.datetime.now() - start
|
|
self.assertGreater(duration, datetime.timedelta(seconds=MAX_TIME))
|
|
self.assertLess(duration, datetime.timedelta(seconds=1.5 * MAX_TIME))
|
|
|
|
start = datetime.datetime.now()
|
|
model.generate(input_ids, do_sample=False, max_time=None, max_length=256)
|
|
duration = datetime.datetime.now() - start
|
|
self.assertGreater(duration, datetime.timedelta(seconds=1.5 * MAX_TIME))
|
|
|
|
@slow
|
|
def test_contrastive_search_gpt2(self):
|
|
article = (
|
|
"DeepMind Technologies is a British artificial intelligence subsidiary of Alphabet Inc. and research "
|
|
"laboratory founded in 2010. DeepMind was acquired by Google in 2014. The company is based"
|
|
)
|
|
|
|
gpt2_tokenizer = GPT2Tokenizer.from_pretrained("openai-community/gpt2-large")
|
|
gpt2_model = GPT2LMHeadModel.from_pretrained("openai-community/gpt2-large").to(torch_device)
|
|
input_ids = gpt2_tokenizer(article, return_tensors="pt").input_ids.to(torch_device)
|
|
|
|
outputs = gpt2_model.generate(input_ids, penalty_alpha=0.6, top_k=4, max_length=256)
|
|
|
|
generated_text = gpt2_tokenizer.batch_decode(outputs, skip_special_tokens=True)
|
|
|
|
self.assertListEqual(
|
|
generated_text,
|
|
[
|
|
"DeepMind Technologies is a British artificial intelligence subsidiary of Alphabet Inc. and research "
|
|
"laboratory founded in 2010. DeepMind was acquired by Google in 2014. The company is based in London, "
|
|
"United Kingdom\n\nGoogle has a lot of data on its users and uses it to improve its products, such as "
|
|
"Google Now, which helps users find the information they're looking for on the web. But the company "
|
|
"is not the only one to collect data on its users. Facebook, for example, has its own facial "
|
|
"recognition technology, as well as a database of millions of photos that it uses to personalize its "
|
|
"News Feed.\n\nFacebook's use of data is a hot topic in the tech industry, with privacy advocates "
|
|
"concerned about the company's ability to keep users' information private. In a blog post last "
|
|
'year, Facebook CEO Mark Zuckerberg said his company would "do our best to be transparent about our '
|
|
'data use and how we use it."\n\n"We have made it clear that we do not sell or share your data with '
|
|
'third parties," Zuckerberg wrote. "If you have questions or concerns, please reach out to us at '
|
|
'privacy@facebook.com."\n\nGoogle declined to comment on the privacy implications of its use of data, '
|
|
"but said in a statement to The Associated Press that"
|
|
],
|
|
)
|
|
|
|
@require_flash_attn
|
|
@require_torch_gpu
|
|
@pytest.mark.flash_attn_test
|
|
@slow
|
|
def test_flash_attn_2_generate_padding_left(self):
|
|
"""
|
|
Overwritting the common test as the test is flaky on tiny models
|
|
"""
|
|
model = GPT2LMHeadModel.from_pretrained("gpt2", torch_dtype=torch.float16).to(0)
|
|
|
|
tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
|
|
|
|
texts = ["hi", "Hello this is a very long sentence"]
|
|
|
|
tokenizer.padding_side = "left"
|
|
tokenizer.pad_token = tokenizer.eos_token
|
|
|
|
inputs = tokenizer(texts, return_tensors="pt", padding=True).to(0)
|
|
|
|
output_native = model.generate(**inputs, max_new_tokens=20, do_sample=False)
|
|
output_native = tokenizer.batch_decode(output_native)
|
|
|
|
model = GPT2LMHeadModel.from_pretrained(
|
|
"gpt2", device_map={"": 0}, attn_implementation="flash_attention_2", torch_dtype=torch.float16
|
|
)
|
|
|
|
output_fa_2 = model.generate(**inputs, max_new_tokens=20, do_sample=False)
|
|
output_fa_2 = tokenizer.batch_decode(output_fa_2)
|
|
|
|
expected_output = [
|
|
"<|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|>hi, who was born in the city of Kolkata, was a member of the Kolkata",
|
|
"Hello this is a very long sentence. I'm sorry. I'm sorry. I'm sorry. I'm sorry. I'm sorry",
|
|
]
|
|
|
|
self.assertListEqual(output_native, output_fa_2)
|
|
self.assertListEqual(output_native, expected_output)
|