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Add TF implementation of GPT-J (#15623)

Browse Source 
* Initial commit

* Add TFGPTJModel

* Fix a forward pass

* Add TFGPTJCausalLM

* Add TFGPTJForSequenceClassification

* Add TFGPTJForQuestionAnswering

* Fix docs

* Deal with TF dynamic shapes

* Add Loss parents to models

* Adjust split and merge heads to handle 4 and 5-dim tensors

* Update outputs for @tooslow tests
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Daniel Stancl 2022-03-25 20:27:19 +01:00 committed by GitHub
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8 changed files with 1742 additions and 2 deletions
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2
docs/source/index.mdx
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@ -205,7 +205,7 @@ Flax), PyTorch, and/or TensorFlow.
| Funnel Transformer | ✅ | ✅ | ✅ | ✅ | ❌ |
| GLPN | ❌ | ❌ | ✅ | ❌ | ❌ |
| GPT Neo | ❌ | ❌ | ✅ | ❌ | ✅ |
| GPT-J | ❌ | ❌ | ✅ | ❌ | ✅ |
| GPT-J | ❌ | ❌ | ✅ | ✅ | ✅ |
| Hubert | ❌ | ❌ | ✅ | ✅ | ❌ |
| I-BERT | ❌ | ❌ | ✅ | ❌ | ❌ |
| ImageGPT | ❌ | ❌ | ✅ | ❌ | ❌ |

20
docs/source/model_doc/gptj.mdx
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@ -130,6 +130,26 @@ model.
[[autodoc]] GPTJForQuestionAnswering
- forward
## TFGPTJModel
[[autodoc]] TFGPTJModel
- call
## TFGPTJForCausalLM
[[autodoc]] TFGPTJForCausalLM
- call
## TFGPTJForSequenceClassification
[[autodoc]] TFGPTJForSequenceClassification
- call
## TFGPTJForQuestionAnswering
[[autodoc]] TFGPTJForQuestionAnswering
- call
## FlaxGPTJModel
[[autodoc]] FlaxGPTJModel

16
src/transformers/__init__.py
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@ -1929,6 +1929,15 @@ if is_tf_available():
"TFGPT2PreTrainedModel",
]
)
_import_structure["models.gptj"].extend(
[
"TFGPTJForCausalLM",
"TFGPTJForQuestionAnswering",
"TFGPTJForSequenceClassification",
"TFGPTJModel",
"TFGPTJPreTrainedModel",
]
)
_import_structure["models.hubert"].extend(
[
"TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
@ -4003,6 +4012,13 @@ if TYPE_CHECKING:
TFGPT2Model,
TFGPT2PreTrainedModel,
)
from .models.gptj import (
TFGPTJForCausalLM,
TFGPTJForQuestionAnswering,
TFGPTJForSequenceClassification,
TFGPTJModel,
TFGPTJPreTrainedModel,
)
from .models.hubert import (
TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFHubertForCTC,

5
src/transformers/models/auto/modeling_tf_auto.py
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@ -52,6 +52,7 @@ TF_MODEL_MAPPING_NAMES = OrderedDict(
("bert", "TFBertModel"),
("openai-gpt", "TFOpenAIGPTModel"),
("gpt2", "TFGPT2Model"),
("gptj", "TFGPTJModel"),
("mobilebert", "TFMobileBertModel"),
("transfo-xl", "TFTransfoXLModel"),
("xlnet", "TFXLNetModel"),
@ -123,6 +124,7 @@ TF_MODEL_WITH_LM_HEAD_MAPPING_NAMES = OrderedDict(
("bert", "TFBertForMaskedLM"),
("openai-gpt", "TFOpenAIGPTLMHeadModel"),
("gpt2", "TFGPT2LMHeadModel"),
("gptj", "TFGPTJForCausalLM"),
("mobilebert", "TFMobileBertForMaskedLM"),
("transfo-xl", "TFTransfoXLLMHeadModel"),
("xlnet", "TFXLNetLMHeadModel"),
@ -146,6 +148,7 @@ TF_MODEL_FOR_CAUSAL_LM_MAPPING_NAMES = OrderedDict(
("bert", "TFBertLMHeadModel"),
("openai-gpt", "TFOpenAIGPTLMHeadModel"),
("gpt2", "TFGPT2LMHeadModel"),
("gptj", "TFGPTJForCausalLM"),
("transfo-xl", "TFTransfoXLLMHeadModel"),
("xlnet", "TFXLNetLMHeadModel"),
("xlm", "TFXLMWithLMHeadModel"),
@ -239,6 +242,7 @@ TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES = OrderedDict(
("tapas", "TFTapasForSequenceClassification"),
("funnel", "TFFunnelForSequenceClassification"),
("gpt2", "TFGPT2ForSequenceClassification"),
("gptj", "TFGPTJForSequenceClassification"),
("mpnet", "TFMPNetForSequenceClassification"),
("openai-gpt", "TFOpenAIGPTForSequenceClassification"),
("transfo-xl", "TFTransfoXLForSequenceClassification"),
@ -267,6 +271,7 @@ TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES = OrderedDict(
("xlm", "TFXLMForQuestionAnsweringSimple"),
("electra", "TFElectraForQuestionAnswering"),
("funnel", "TFFunnelForQuestionAnswering"),
("gptj", "TFGPTJForQuestionAnswering"),
("mpnet", "TFMPNetForQuestionAnswering"),
]
)

20
src/transformers/models/gptj/__init__.py
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@ -17,7 +17,7 @@
# limitations under the License.
from typing import TYPE_CHECKING
from ...utils import _LazyModule, is_flax_available, is_torch_available
from ...utils import _LazyModule, is_flax_available, is_tf_available, is_torch_available
_import_structure = {
@ -34,6 +34,15 @@ if is_torch_available():
"GPTJPreTrainedModel",
]
if is_tf_available():
_import_structure["modeling_tf_gptj"] = [
"TFGPTJForCausalLM",
"TFGPTJForQuestionAnswering",
"TFGPTJForSequenceClassification",
"TFGPTJModel",
"TFGPTJPreTrainedModel",
]
if is_flax_available():
_import_structure["modeling_flax_gptj"] = [
"FlaxGPTJForCausalLM",
@ -55,6 +64,15 @@ if TYPE_CHECKING:
GPTJPreTrainedModel,
)
if is_tf_available():
from .modeling_tf_gptj import (
TFGPTJForCausalLM,
TFGPTJForQuestionAnswering,
TFGPTJForSequenceClassification,
TFGPTJModel,
TFGPTJPreTrainedModel,
)
if is_flax_available():
from .modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel, FlaxGPTJPreTrainedModel

1156
src/transformers/models/gptj/modeling_tf_gptj.py Normal file
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35
src/transformers/utils/dummy_tf_objects.py
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@ -1157,6 +1157,41 @@ class TFGPT2PreTrainedModel(metaclass=DummyObject):
requires_backends(self, ["tf"])
class TFGPTJForCausalLM(metaclass=DummyObject):
_backends = ["tf"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["tf"])
class TFGPTJForQuestionAnswering(metaclass=DummyObject):
_backends = ["tf"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["tf"])
class TFGPTJForSequenceClassification(metaclass=DummyObject):
_backends = ["tf"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["tf"])
class TFGPTJModel(metaclass=DummyObject):
_backends = ["tf"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["tf"])
class TFGPTJPreTrainedModel(metaclass=DummyObject):
_backends = ["tf"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["tf"])
TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST = None

490
tests/gptj/test_modeling_tf_gptj.py Normal file
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@ -0,0 +1,490 @@
# 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 unittest
from transformers import AutoTokenizer, GPTJConfig, is_tf_available
from transformers.testing_utils import require_tf, slow, tooslow
from ..test_configuration_common import ConfigTester
from ..test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ..utils.test_modeling_tf_core import TFCoreModelTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers.models.gptj.modeling_tf_gptj import (
TFGPTJForCausalLM,
TFGPTJForQuestionAnswering,
TFGPTJForSequenceClassification,
TFGPTJModel,
shape_list,
)
class TFGPTJModelTester:
def __init__(self, parent):
self.parent = parent
self.batch_size = 13
self.seq_length = 7
self.is_training = True
self.use_token_type_ids = True
self.use_input_mask = True
self.use_labels = True
self.use_mc_token_ids = True
self.vocab_size = 99
self.hidden_size = 32
self.num_hidden_layers = 5
self.num_attention_heads = 4
self.intermediate_size = 37
self.hidden_act = "gelu"
self.hidden_dropout_prob = 0.1
self.attention_probs_dropout_prob = 0.1
self.max_position_embeddings = 512
self.type_vocab_size = 16
self.type_sequence_label_size = 2
self.initializer_range = 0.02
self.num_labels = 3
self.num_choices = 4
self.scope = None
self.bos_token_id = self.vocab_size - 1
self.eos_token_id = self.vocab_size - 1
self.pad_token_id = self.vocab_size - 1
def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
input_mask = None
if self.use_input_mask:
input_mask = ids_tensor([self.batch_size, self.seq_length], vocab_size=2)
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 = GPTJConfig(
vocab_size=self.vocab_size,
n_embd=self.hidden_size,
n_layer=self.num_hidden_layers,
n_head=self.num_attention_heads,
intermediate_size=self.intermediate_size,
hidden_act=self.hidden_act,
hidden_dropout_prob=self.hidden_dropout_prob,
attention_probs_dropout_prob=self.attention_probs_dropout_prob,
n_positions=self.max_position_embeddings,
type_vocab_size=self.type_vocab_size,
initializer_range=self.initializer_range,
bos_token_id=self.bos_token_id,
eos_token_id=self.eos_token_id,
pad_token_id=self.pad_token_id,
return_dict=True,
)
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 create_and_check_gptj_model(self, config, input_ids, input_mask, head_mask, token_type_ids, *args):
model = TFGPTJModel(config=config)
inputs = {
"input_ids": input_ids,
"attention_mask": input_mask,
"token_type_ids": token_type_ids,
}
result = model(inputs)
inputs = [input_ids, None, input_mask] # None is the input for 'past'
result = model(inputs)
result = model(input_ids)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
def create_and_check_gptj_model_past(self, config, input_ids, input_mask, head_mask, token_type_ids, *args):
model = TFGPTJModel(config=config)
# 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 = tf.concat([input_ids, next_tokens], axis=-1)
next_token_type_ids = tf.concat([token_type_ids, next_token_types], axis=-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=past)["last_hidden_state"]
# select random slice
random_slice_idx = int(ids_tensor((1,), shape_list(output_from_past)[-1]))
output_from_no_past_slice = output_from_no_past[:, -1, random_slice_idx]
output_from_past_slice = output_from_past[:, 0, random_slice_idx]
# test that outputs are equal for slice
tf.debugging.assert_near(output_from_past_slice, output_from_no_past_slice, rtol=1e-6)
def create_and_check_gptj_model_attention_mask_past(
self, config, input_ids, input_mask, head_mask, token_type_ids, *args
):
model = TFGPTJModel(config=config)
# create attention mask
half_seq_length = self.seq_length // 2
attn_mask_begin = tf.ones((self.batch_size, half_seq_length), dtype=tf.int32)
attn_mask_end = tf.zeros((self.batch_size, self.seq_length - half_seq_length), dtype=tf.int32)
attn_mask = tf.concat([attn_mask_begin, attn_mask_end], axis=1)
# 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).numpy() + 1
random_other_next_tokens = ids_tensor((self.batch_size, self.seq_length), config.vocab_size)
vector_condition = tf.range(self.seq_length) == (self.seq_length - random_seq_idx_to_change)
condition = tf.transpose(
tf.broadcast_to(tf.expand_dims(vector_condition, -1), (self.seq_length, self.batch_size))
)
input_ids = tf.where(condition, random_other_next_tokens, input_ids)
# append to next input_ids and attn_mask
next_input_ids = tf.concat([input_ids, next_tokens], axis=-1)
attn_mask = tf.concat([attn_mask, tf.ones((shape_list(attn_mask)[0], 1), dtype=tf.int32)], axis=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=past, attention_mask=attn_mask)["last_hidden_state"]
# select random slice
random_slice_idx = int(ids_tensor((1,), shape_list(output_from_past)[-1]))
output_from_no_past_slice = output_from_no_past[:, -1, random_slice_idx]
output_from_past_slice = output_from_past[:, 0, random_slice_idx]
# test that outputs are equal for slice
tf.debugging.assert_near(output_from_past_slice, output_from_no_past_slice, rtol=1e-12)
def create_and_check_gptj_model_past_large_inputs(
self, config, input_ids, input_mask, head_mask, token_type_ids, *args
):
model = TFGPTJModel(config=config)
input_ids = input_ids[:1, :]
input_mask = input_mask[:1, :]
token_type_ids = token_type_ids[:1, :]
self.batch_size = 1
# first forward pass
outputs = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, 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_attn_mask = ids_tensor((self.batch_size, 3), 2)
next_token_types = ids_tensor((self.batch_size, 3), self.type_vocab_size)
# append to next input_ids and token_type_ids
next_input_ids = tf.concat([input_ids, next_tokens], axis=-1)
next_attention_mask = tf.concat([input_mask, next_attn_mask], axis=-1)
next_token_type_ids = tf.concat([token_type_ids, next_token_types], axis=-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=past
)["last_hidden_state"]
self.parent.assertTrue(output_from_past.shape[1] == next_tokens.shape[1])
# select random slice
random_slice_idx = int(ids_tensor((1,), shape_list(output_from_past)[-1]))
output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx]
output_from_past_slice = output_from_past[:, :, random_slice_idx]
# test that outputs are equal for slice
tf.debugging.assert_near(output_from_past_slice, output_from_no_past_slice, rtol=1e-3)
def create_and_check_gptj_lm_head_model(self, config, input_ids, input_mask, head_mask, token_type_ids, *args):
model = TFGPTJForCausalLM(config=config)
inputs = {
"input_ids": input_ids,
"attention_mask": input_mask,
"token_type_ids": token_type_ids,
}
result = model(inputs)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
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,
"attention_mask": input_mask,
}
return config, inputs_dict
@require_tf
class TFGPTJModelTest(TFModelTesterMixin, TFCoreModelTesterMixin, unittest.TestCase):
all_model_classes = (
(TFGPTJForCausalLM, TFGPTJForSequenceClassification, TFGPTJForQuestionAnswering, TFGPTJModel)
if is_tf_available()
else ()
)
all_generative_model_classes = (TFGPTJForCausalLM,) if is_tf_available() else ()
test_onnx = False
test_pruning = False
test_missing_keys = False
test_head_masking = False
def setUp(self):
self.model_tester = TFGPTJModelTester(self)
self.config_tester = ConfigTester(self, config_class=GPTJConfig, n_embd=37)
def test_config(self):
self.config_tester.run_common_tests()
def test_gptj_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_gptj_model(*config_and_inputs)
def test_gptj_model_past(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_gptj_model_past(*config_and_inputs)
def test_gptj_model_att_mask_past(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_gptj_model_attention_mask_past(*config_and_inputs)
def test_gptj_model_past_large_inputs(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_gptj_model_past_large_inputs(*config_and_inputs)
def test_gptj_lm_head_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_gptj_lm_head_model(*config_and_inputs)
def test_model_common_attributes(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
assert isinstance(model.get_input_embeddings(), tf.keras.layers.Layer)
if model_class in self.all_generative_model_classes:
x = model.get_output_embeddings()
assert isinstance(x, tf.keras.layers.Layer)
name = model.get_bias()
assert name is None
else:
x = model.get_output_embeddings()
assert x is None
name = model.get_bias()
assert name is None
@slow
def test_model_from_pretrained(self):
model = TFGPTJModel.from_pretrained("EleutherAI/gpt-j-6B", from_pt=True)
self.assertIsNotNone(model)
@unittest.skip(reason="Currently, model embeddings are going to undergo a major refactor.")
def test_resize_token_embeddings(self):
super().test_resize_token_embeddings()
@require_tf
class TFGPTJModelLanguageGenerationTest(unittest.TestCase):
@tooslow
def test_lm_generate_gptj(self):
# Marked as @tooslow due to GPU OOM
model = TFGPTJForCausalLM.from_pretrained("EleutherAI/gpt-j-6B", from_pt=True)
input_ids = tf.convert_to_tensor([[464, 3290]], dtype=tf.int32) # The dog
# fmt: off
# The dog is a man's best friend. It is a loyal companion, and it is a friend
expected_output_ids = [464, 3290, 318, 257, 582, 338, 1266, 1545, 13, 632, 318, 257, 9112, 15185, 11, 290, 340, 318, 257, 1545]
# fmt: on
output_ids = model.generate(input_ids, do_sample=False)
self.assertListEqual(output_ids[0].numpy().tolist(), expected_output_ids)
@tooslow
def test_gptj_sample(self):
# Marked as @tooslow due to GPU OOM (issue #13676)
tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-j-6B", revision="float16")
model = TFGPTJForCausalLM.from_pretrained("EleutherAI/gpt-j-6B", revision="float16", from_pt=True)
tf.random.set_seed(0)
tokenized = tokenizer("Today is a nice day and", return_tensors="tf", return_token_type_ids=True)
input_ids, token_type_ids = tokenized.input_ids, tokenized.token_type_ids
output_ids = model.generate(input_ids, do_sample=True)
output_str = tokenizer.decode(output_ids[0], skip_special_tokens=True)
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 I am taking an hour to sit in the hammock and just enjoy"
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_gptj_sample_max_time(self):
tokenizer = AutoTokenizer.from_pretrained("anton-l/gpt-j-tiny-random")
model = TFGPTJForCausalLM.from_pretrained("anton-l/gpt-j-tiny-random", from_pt=True)
input_ids = tokenizer("Today is a nice day and", return_tensors="tf", return_token_type_ids=True).input_ids
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))
@tooslow
def test_batch_generation(self):
# Marked as @tooslow due to GPU OOM
model = TFGPTJForCausalLM.from_pretrained("EleutherAI/gpt-j-6B", revision="float16", from_pt=True)
tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-j-6B", revision="float16")
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="tf", padding=True)
input_ids = inputs["input_ids"]
token_type_ids = tf.concat(
[
tf.zeros((input_ids.shape[0], input_ids.shape[1] - 1), dtype=tf.int64),
500 * tf.ones((input_ids.shape[0], 1), dtype=tf.int64),
],
axis=-1,
)
outputs = model.generate(input_ids=input_ids, attention_mask=inputs["attention_mask"])
outputs_tt = model.generate(
input_ids=input_ids,
attention_mask=inputs["attention_mask"],
token_type_ids=token_type_ids,
)
inputs_non_padded = tokenizer(sentences[0], return_tensors="tf").input_ids
output_non_padded = model.generate(input_ids=inputs_non_padded)
num_paddings = (
shape_list(inputs_non_padded)[-1] - tf.reduce_sum(tf.cast(inputs["attention_mask"][-1], tf.int64)).numpy()
)
inputs_padded = tokenizer(sentences[1], return_tensors="tf").input_ids
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 over a year old and has been diagnosed with a heart murmur",
"Today, I’m going to share with you a few of my favorite",
]
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])