def test_shape_on_random_data(self):
set_seed(42)
bs = 3
src_len = 5
tgt_len = 7
encoder_config = transformers.BertConfig(
hidden_size=11,
intermediate_size=44,
vocab_size=17,
num_hidden_layers=1,
num_attention_heads=1,
)
encoder = transformers.BertModel(encoder_config)
# decoder accepts vocabulary of schema vocab + pointer embeddings
decoder_config = transformers.BertConfig(
hidden_size=11,
intermediate_size=44,
vocab_size=23,
is_decoder=True,
num_hidden_layers=1,
num_attention_heads=1,
)
decoder = transformers.BertModel(decoder_config)
# logits are projected into schema vocab and combined with pointer scores
max_pointer = src_len + 3
model = EncoderDecoderWPointerModel(encoder=encoder,
decoder=decoder,
max_src_len=max_pointer)
x_enc = torch.randint(0, encoder_config.vocab_size, size=(bs, src_len))
x_dec = torch.randint(0, decoder_config.vocab_size, size=(bs, tgt_len))
out = model(input_ids=x_enc, decoder_input_ids=x_dec)
# different encoders return different number of outputs
# e.g. BERT returns two, but DistillBERT only one
self.assertGreaterEqual(len(out), 4)
schema_vocab = decoder_config.vocab_size - max_pointer
combined_logits = out[0]
expected_shape = (bs, tgt_len, schema_vocab + src_len)
self.assertEqual(combined_logits.shape, expected_shape)
decoder_hidden = out[1]
expected_shape = (bs, tgt_len, decoder_config.hidden_size)
self.assertEqual(decoder_hidden.shape, expected_shape)
combined_logits = out[2]
expected_shape = (bs, decoder_config.hidden_size)
self.assertEqual(combined_logits.shape, expected_shape)
encoder_hidden = out[3]
expected_shape = (bs, src_len, encoder_config.hidden_size)
self.assertEqual(encoder_hidden.shape, expected_shape)
def __init__(self, code_token_counter, query_token_counter):
self.code_token_counter = code_token_counter
get_counter_map(code_token_counter)
self.code_config = transformers.BertConfig(
vocab_size=len(code_token_counter),
pad_token_id=get_counter_map(code_token_counter)["[PAD]"])
self.code_model = transformers.BertModel(self.code_config)
self.query_token_counter = query_token_counter
self.query_config = transformers.BertConfig(
vocab_size=len(query_token_counter),
pad_token_id=get_counter_map(query_token_counter)["[PAD]"])
self.query_model = transformers.BertModel(self.query_config)
def test_loss_computation(self):
torch.manual_seed(42)
src_vocab_size = 17
tgt_vocab_size = 23
encoder_config = transformers.BertConfig(
hidden_size=11,
intermediate_size=44,
vocab_size=src_vocab_size,
num_hidden_layers=1,
num_attention_heads=1,
)
encoder = transformers.BertModel(encoder_config)
max_position = 7
decoder_config = transformers.BertConfig(
hidden_size=11,
intermediate_size=44,
vocab_size=tgt_vocab_size + max_position,
is_decoder=True,
num_hidden_layers=1,
num_attention_heads=1,
)
decoder = transformers.BertModel(decoder_config)
model = EncoderDecoderWPointerModel(encoder=encoder,
decoder=decoder,
max_src_len=7)
# similar to real data
src_seq = torch.LongTensor([[1, 6, 12, 15, 2, 0, 0],
[1, 6, 12, 15, 5, 3, 2]])
tgt_seq = torch.LongTensor([
[8, 6, 4, 10, 11, 8, 5, 1, 12, 7, 7, 0, 0],
[8, 6, 4, 10, 11, 8, 5, 1, 12, 13, 14, 7, 7],
])
mask = torch.FloatTensor([[0, 1, 1, 1, 0, 0, 0], [0, 1, 1, 1, 1, 1,
0]])
loss = model(
input_ids=src_seq,
decoder_input_ids=tgt_seq,
pointer_mask=mask,
labels=tgt_seq,
)[0]
self.assertEqual(loss.shape, torch.Size([]))
self.assertEqual(loss.dtype, torch.float32)
self.assertGreater(loss, 0)
def generate_onnx_model(model_name: str, filename: str, seq_len: int,
batch_size: int, backend: str):
import transformers
import torch
import os
test_device = torch.device('cuda:0') if backend == "GPU" else torch.device(
'cpu:0')
torch.set_grad_enabled(False)
if model_name == "bert":
cfg = transformers.BertConfig()
model = transformers.BertModel(cfg)
elif model_name == "albert":
cfg = transformers.AlbertConfig()
model = transformers.AlbertModel(cfg)
elif model_name == "roberta":
cfg = transformers.RobertaConfig()
model = transformers.RobertaModel(cfg)
else:
raise (f"benchmark does not support {model_name}")
model.eval()
model.to(test_device)
cfg = model.config # type: transformers.BertConfig
input_ids = torch.randint(low=0,
high=cfg.vocab_size - 1,
size=(batch_size, seq_len),
dtype=torch.long,
device=test_device)
with open(filename, 'wb') as outf:
torch.onnx.export(model=model, args=(input_ids, ), f=outf)
outf.flush()
return cfg.vocab_size
def __init__(self,
vocab_size,
hidden_size,
dropout,
n_layers=1,
vocab_file='./data/vocab.txt'):
super(UntrainedEncoderBERT, self).__init__()
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.dropout = dropout
self.dropout_layer = nn.Dropout(dropout)
self.embedding = nn.Embedding(vocab_size,
hidden_size,
padding_idx=PAD_token)
self.embedding.weight.data.normal_(0, 0.1)
self.config = transformers.BertConfig(vocab_size=self.vocab_size,
hidden_size=self.hidden_size,
num_hidden_layers=n_layers,
hidden_dropout_prob=dropout,
attention_probs_dropout=dropout,
num_attention_heads=16,
output_hidden_states=True,
max_position_embeddings=1024)
self.tokenizer = transformers.BertTokenizer(vocab_file,
pad_token='PAD',
unk_token='UNK',
sep_token='EOS')
self.BERT = transformers.BertModel(self.config)
self.training = True
def dataset():
"""
Check if the data in two instances is different
"""
args = "--config demo_tiny_128".split()
config = transformers.BertConfig(**(vars(parse_bert_args(args))))
opts = get_options(config)
loader = TFRecordPretrainingDataset(config.input_files)
loader = get_dataloader(config, opts)
# Save part of the data as list
loader_list = list(loader)[0][0][0].numpy()
# MPI to broadcast data in root=1 to root=0
from mpi4py import MPI
comm = MPI.COMM_WORLD
rank = comm.Get_rank()
loader_list_copy = np.copy(loader_list)
comm.Bcast(loader_list, root=1)
# Assert if data broadcast to root=0 is different
if comm.Get_rank() == 0 and not np.all(loader_list_copy == loader_list):
print('Passed test: instances have different data')
# Wait until both roots are finished
time.sleep(2)
def benchmark_torch(model_name: str, seq_len: int, batch_size: int, n: int,
num_threads: int):
import torch
import transformers
import contexttimer
import benchmark_helper
torch.set_num_threads(num_threads)
torch.set_grad_enabled(False)
if model_name == "bert":
cfg = transformers.BertConfig()
model = transformers.BertModel(cfg)
elif model_name == "albert":
cfg = transformers.AlbertConfig()
model = transformers.AlbertModel(cfg)
elif model_name == "roberta":
cfg = transformers.RobertaConfig()
model = transformers.RobertaModel(cfg)
else:
raise (f"benchmark does not support {model_name}")
model.eval()
cfg = model.config # type: transformers.BertConfig
input_ids = torch.randint(low=0,
high=cfg.vocab_size - 1,
size=(batch_size, seq_len),
dtype=torch.long)
benchmark_helper.run_model(lambda: model(input_ids), False, n, batch_size,
seq_len, "torch", num_threads)
def __init__(self, hp: Optional[ModelParams] = ModelParams(), max_seq_len=1024):
super().__init__()
self.hp = hp
config = transformers.BertConfig(
hidden_size=self.hp.dim,
num_hidden_layers=0,
num_attention_heads=1,
intermediate_size=0,
max_position_embeddings=max_seq_len,
output_attentions=False,
output_hidden_states=False,
return_dict=True,
)
self.e = transformers.BertModel(config, add_pooling_layer=False)
for name, param in self.e.named_parameters():
# param names
# embeddings.word_embeddings.weight
# embeddings.position_embeddings.weight
# embeddings.token_type_embeddings.weight
# embeddings.LayerNorm.weight
# embeddings.LayerNorm.bias
if 'position_embeddings' in name:
requires_grad = self.hp.position_embedding_requires_grad
else:
requires_grad = self.hp.requires_grad
param.requires_grad = requires_grad
def __init__(
self,
train_dataloader,
val_dataloader,
args,
fold,
baselines=None,
gamma=0.99,
beta=0.01,
lr=1e-5,
device="cpu",
):
if args.decoder == "lstm":
self.policy = PolicyNet(args=args)
else:
config = transformers.BertConfig()
config.hidden_size = 2048
config.num_attention_heads = 8
config.num_hidden_layers = 4
config.max_position_embeddings = 1500
self.policy = Transformer(config, device)
self.policy.to(device)
self.optimizer = torch.optim.Adam(self.policy.parameters(), lr=lr)
self.baselines = baselines
self.train_dataloader = train_dataloader
self.val_dataloader = val_dataloader
self.device = device
self.args = args
self.fold = fold
self.beta = beta
def get_torch_model(
model_name: str,
input_shape: Tuple[int, ...],
output_shape: Tuple[int, int], # pylint: disable=unused-argument
dtype: str = "float32",
) -> Tuple[IRModule, Dict[str, NDArray]]:
"""Load model from torch model zoo
Parameters
----------
model_name : str
The name of the model to load
input_shape: Tuple[int, ...]
Tuple for input shape
output_shape: Tuple[int, int]
Tuple for output shape
dtype: str
Tensor data type
"""
assert dtype == "float32"
import torch # type: ignore # pylint: disable=import-error,import-outside-toplevel
from torchvision import models # type: ignore # pylint: disable=import-error,import-outside-toplevel
import transformers # type: ignore # pylint: disable=import-error,import-outside-toplevel
import os # type: ignore # pylint: disable=import-error,import-outside-toplevel
def do_trace(model, inp):
model.eval()
model_trace = torch.jit.trace(model, inp)
model_trace.eval()
return model_trace
# Load model from torchvision
if MODEL_TYPES[model_name] == MODEL_TYPE.TEXT_CLASSIFICATION:
os.environ["TOKENIZERS_PARALLELISM"] = "false"
model = transformers.BertModel(
transformers.BertConfig(
num_hidden_layers=12,
hidden_size=768,
intermediate_size=3072,
num_attention_heads=12,
return_dict=False,
))
model.eval()
input_data = torch.randint(10000, input_shape)
shape_list = [("input_ids", input_shape)]
scripted_model = torch.jit.trace(model, [input_data], strict=False)
elif MODEL_TYPES[model_name] == MODEL_TYPE.IMAGE_CLASSIFICATION:
model = getattr(models, model_name)()
# Setup input
input_data = torch.randn(input_shape).type(torch.float32)
shape_list = [("input0", input_shape)]
# Get trace. Depending on the model type, wrapper may be necessary.
scripted_model = do_trace(model, input_data)
else:
raise ValueError("Unsupported model in Torch model zoo.")
# Convert torch model to relay module
mod, params = relay.frontend.from_pytorch(scripted_model, shape_list)
return mod, params
def generate_onnx_model(model_name: str,
use_gpu: bool,
filename: str,
seq_len: int,
batch_size: int,
backend: str,
use_dynamic_axes: bool = False):
import transformers
import torch
import os
test_device = torch.device(
'cuda:0') if backend == "GPU" and use_gpu else torch.device('cpu:0')
torch.set_grad_enabled(False)
if model_name == "bert":
# use a real model to check the correctness
if checkonnxrest:
model = transformers.BertModel.from_pretrained("bert-base-uncased")
else:
cfg = transformers.BertConfig()
model = transformers.BertModel(cfg)
elif model_name == "albert":
cfg = transformers.AlbertConfig()
model = transformers.AlbertModel(cfg)
elif model_name == "roberta":
cfg = transformers.RobertaConfig()
model = transformers.RobertaModel(cfg)
else:
raise (f"benchmark does not support {model_name}")
model.eval()
model.to(test_device)
cfg = model.config # type: transformers.BertConfig
input_ids = torch.randint(low=0,
high=cfg.vocab_size - 1,
size=(batch_size, seq_len),
dtype=torch.long,
device=test_device)
with open(filename, 'wb') as outf:
if not use_dynamic_axes:
torch.onnx.export(model=model, args=(input_ids, ), f=outf)
else:
torch.onnx.export(model=model,
args=(input_ids, ),
f=outf,
input_names=['input'],
output_names=['output'],
dynamic_axes={
'input': [0, 1],
'output': [0, 1]
})
# If not intended to make onnxruntime support variable batch size and sequence length,
# you can unset the parameter `dynamic_axes`.
# For some model, you have to try `opset_version=12`
outf.flush()
return cfg.vocab_size, cfg
def benchmark_turbo_transformers(model_name: str, seq_len: int,
batch_size: int, n: int, enable_random: bool,
max_seq_len: int, min_seq_len: int,
num_threads: int, use_gpu: bool,
enable_mem_opt: bool):
import torch
import transformers
import turbo_transformers
import benchmark_helper
test_device = torch.device('cuda:0') if use_gpu else torch.device('cpu:0')
cfg = None
torch.set_grad_enabled(False)
if model_name == "bert":
cfg = transformers.BertConfig()
model = transformers.BertModel(cfg)
model.to(test_device)
model.eval()
model = turbo_transformers.BertModel.from_torch(model, backend="turbo")
elif model_name == "albert":
cfg = transformers.AlbertConfig(hidden_size=768,
num_attention_heads=12,
intermediate_size=3072)
model = transformers.AlbertModel(cfg)
model.to(test_device)
model.eval()
model = turbo_transformers.AlbertModel.from_torch(model)
elif model_name == "roberta":
cfg = transformers.RobertaConfig()
model = transformers.RobertaModel(cfg)
model.to(test_device)
model.eval()
model = turbo_transformers.RobertaModel.from_torch(model)
elif model_name == "distilbert":
cfg = transformers.DistilBertConfig()
model = transformers.DistilBertModel(cfg)
model.to(test_device)
model.eval()
model = turbo_transformers.DistilBertModel.from_torch(model)
else:
raise (f"benchmark does not support {model_name}")
turbo_transformers.set_num_threads(num_threads)
if enable_random:
if enable_mem_opt:
turbo_transformers.reset_allocator_schema("model-aware")
benchmark_helper.run_variable_model(model, use_gpu, n, max_seq_len,
min_seq_len, "turbo", num_threads,
cfg, enable_mem_opt, model_name)
if enable_mem_opt:
turbo_transformers.reset_allocator_schema("naive")
else:
input_ids = torch.randint(low=0,
high=cfg.vocab_size - 1,
size=(batch_size, seq_len),
dtype=torch.long,
device=test_device)
benchmark_helper.run_model(lambda: model(input_ids), use_gpu, n,
batch_size, seq_len, "turbo", num_threads,
enable_mem_opt, model_name)
def test_shape_on_real_data_batched(self):
set_seed(42)
src_vocab_size = 17
tgt_vocab_size = 23
max_position = 7
encoder_config = transformers.BertConfig(
hidden_size=11,
intermediate_size=44,
vocab_size=src_vocab_size,
num_hidden_layers=1,
num_attention_heads=1,
)
encoder = transformers.BertModel(encoder_config)
decoder_config = transformers.BertConfig(
hidden_size=11,
intermediate_size=44,
vocab_size=tgt_vocab_size + max_position,
is_decoder=True,
num_hidden_layers=1,
num_attention_heads=1,
)
decoder = transformers.BertModel(decoder_config)
model = EncoderDecoderWPointerModel(encoder=encoder,
decoder=decoder,
max_src_len=max_position)
# similar to real data
src_seq = torch.LongTensor([[1, 6, 12, 15, 2, 0, 0],
[1, 6, 12, 15, 5, 3, 2]])
tgt_seq = torch.LongTensor([
[8, 6, 4, 10, 11, 8, 5, 1, 12, 7, 7, 0, 0],
[8, 6, 4, 10, 11, 8, 5, 1, 12, 13, 14, 7, 7],
])
mask = torch.FloatTensor([[0, 1, 1, 1, 0, 0, 0], [0, 1, 1, 1, 1, 1,
0]])
combined_logits = model(input_ids=src_seq,
decoder_input_ids=tgt_seq,
pointer_mask=mask)[0]
expected_shape = (2, tgt_seq.shape[1],
tgt_vocab_size + src_seq.shape[1])
self.assertEqual(combined_logits.shape, expected_shape)
def test_shape_on_real_data(self):
set_seed(42)
src_vocab_size = 17
tgt_vocab_size = 23
max_position = 5
encoder_config = transformers.BertConfig(
hidden_size=11,
intermediate_size=44,
vocab_size=src_vocab_size,
num_hidden_layers=1,
num_attention_heads=1,
)
encoder = transformers.BertModel(encoder_config)
decoder_config = transformers.BertConfig(
hidden_size=11,
intermediate_size=44,
vocab_size=tgt_vocab_size + max_position,
is_decoder=True,
num_hidden_layers=1,
num_attention_heads=1,
)
decoder = transformers.BertModel(decoder_config)
model = EncoderDecoderWPointerModel(encoder=encoder,
decoder=decoder,
max_src_len=max_position)
# similar to real data
# e.g. '[CLS] Directions to Lowell [SEP]'
src_seq = torch.LongTensor([[1, 6, 12, 15, 2]])
# e.g. '[IN:GET_DIRECTIONS Directions to [SL:DESTINATION Lowell]]'
tgt_seq = torch.LongTensor([[8, 6, 4, 10, 11, 8, 5, 1, 12, 7, 7]])
mask = torch.FloatTensor([[0, 1, 1, 1, 0]])
combined_logits = model(input_ids=src_seq,
decoder_input_ids=tgt_seq,
pointer_mask=mask)[0]
expected_shape = (1, tgt_seq.shape[1],
tgt_vocab_size + src_seq.shape[1])
self.assertEqual(combined_logits.shape, expected_shape)
def test_run_with_no_exception(self):
# Arrange
train_data_file = os.path.join(os.path.dirname(__file__),
"sample_sst2.csv")
tempdir = tempfile.mkdtemp()
batch = 3
# Bert Config
vocab_size = 20000
sequence_len = 20
num_classes = 14
bert_config = transformers.BertConfig(vocab_size=vocab_size,
hidden_size=10,
num_hidden_layers=1,
num_attention_heads=1,
num_labels=num_classes)
# Mock tokenisor
mock_tokenisor = MagicMock()
mock_tokenisor.tokenize.side_effect = lambda x: x.split(" ")
mock_tokenisor.convert_tokens_to_ids = lambda x: [
i for i, _ in enumerate(x)
]
# Builder
b = Builder(train_data=train_data_file,
val_data=train_data_file,
dataset_factory_name=
"datasets.sst2_dataset_factory.SST2DatasetFactory",
checkpoint_dir=tempdir,
epochs=2,
grad_accumulation_steps=1,
early_stopping_patience=2,
batch_size=batch,
max_seq_len=sequence_len,
model_dir=tempdir)
b.set_tokensior(mock_tokenisor)
b.set_bert_config(bert_config)
trainer = b.get_trainer()
# Get data loaders
train_dataloader, val_dataloader = b.get_train_val_dataloader()
# Act
# Run training
trainer.run_train(train_iter=train_dataloader,
validation_iter=val_dataloader,
model_network=b.get_network(),
loss_function=b.get_loss_function(),
optimizer=b.get_optimiser(),
pos_label=b.get_pos_label_index())
def test_smart_batch(use_cuda: bool):
test_device = torch.device('cuda:0') if use_cuda else \
torch.device('cpu:0')
cfg = transformers.BertConfig(attention_probs_dropout_prob=0.0,
hidden_dropout_prob=0.0)
torch_model = transformers.BertModel(cfg)
# model_id = "bert-base-uncased"
# torch_model = transformers.BertModel.from_pretrained(model_id)
torch_model.eval()
torch_model.to(test_device)
torch.set_grad_enabled(False)
cfg = torch_model.config
# use 4 threads for computing
if not use_cuda:
turbo_transformers.set_num_threads(4)
# Initialize a turbo BertModel with smart batching from torch model.
turbo_model = turbo_transformers.BertModelSmartBatch.from_torch(
torch_model)
# a batch of queries with different lengths.
query_seq_len_list = [18, 2, 3, 51]
input_list = []
# generate random inputs. Of course you can use real data.
for query_seq_len in query_seq_len_list:
input_seq = torch.randint(low=0,
high=cfg.vocab_size - 1,
size=(1, query_seq_len),
dtype=torch.long,
device=test_device)
input_list.append(input_seq)
# start inference
s_res = serial_bert_inference(torch_model, input_list)
b_res = batch_bert_inference(turbo_model, input_list, query_seq_len_list)
print(torch.max(torch.abs(b_res - s_res)))
assert (torch.max(torch.abs(b_res - s_res)) < 1e-2)
start_time = time.time()
for i in range(10):
serial_bert_inference(torch_model, input_list)
end_time = time.time()
print("\ntorch time consum: {}".format(end_time - start_time))
start_time = time.time()
for i in range(10):
batch_bert_inference(turbo_model, input_list, query_seq_len_list)
end_time = time.time()
print("\nturbo time consum: {}".format(end_time - start_time))
def init_model(num):
configuration = transformers.BertConfig("./model/config.json")
model_class = transformers.BertForSequenceClassification
tokenizer_class = transformers.BertTokenizer
if num == 1:
pretrained_weights = './model/model1/'
idx_to_token = {
0: "[no connective]",
1: "and",
2: "by contrast",
3: "by then",
4: "finally",
5: "for example",
6: "however",
7: "in other words",
8: "in particular",
9: "indeed",
10: "instead",
11: "meanwhile",
12: "moreover",
13: "nevertheless",
14: "on the other hand",
15: "otherwise",
16: "overall",
17: "rather",
18: "then",
19: "therefore"
}
else:
pretrained_weights = './model/model2/'
idx_to_token = {
0: "[no connective]",
1: "although",
2: "and",
3: "because",
4: "but",
5: "for example",
6: "however",
7: "or",
8: "so",
9: "so that",
10: "unless",
11: "while"
}
pretrained_tokenizer = './tokenizer'
# load weights for tokenizer and model
tokenizer = tokenizer_class.from_pretrained(pretrained_tokenizer)
model = model_class.from_pretrained(pretrained_weights, )
model.eval()
return model, tokenizer, idx_to_token
def __post_init__(self):
assert self.size in ["small", "large"]
if self.size == "small":
self.hidden_size = 256
self.num_attention_heads = 4
self.num_hidden_layers = 2
self.model_config = transformers.BertConfig(
hidden_size=self.hidden_size,
num_attention_heads=self.num_attention_heads,
num_hidden_layers=self.num_hidden_layers,
max_position_embeddings=self.max_position_embeddings,
)
def benchmark_turbo_transformers(model_name: str, seq_len: int,
batch_size: int, n: int, enable_random: bool,
max_seq_len: int, min_seq_len: int,
num_threads: int, use_gpu: bool):
import torch
import transformers
import contexttimer
import turbo_transformers
import benchmark_helper
test_device = torch.device('cuda:0') if use_gpu else torch.device('cpu:0')
if use_gpu:
print("using GPU")
else:
print("using CPU")
cfg = None
torch.set_grad_enabled(False)
if model_name == "bert":
cfg = transformers.BertConfig()
model = transformers.BertModel(cfg)
model.to(test_device)
model.eval()
model = turbo_transformers.BertModel.from_torch(model)
elif model_name == "albert":
cfg = transformers.AlbertConfig()
model = transformers.AlbertModel(cfg)
model.to(test_device)
model.eval()
model = turbo_transformers.AlbertModel.from_torch(model)
elif model_name == "roberta":
cfg = transformers.RobertaConfig()
model = transformers.RobertaModel(cfg)
model.to(test_device)
model.eval()
model = turbo_transformers.RobertaModel.from_torch(model)
else:
raise (f"benchmark does not support {model_name}")
turbo_transformers.set_num_threads(num_threads)
if enable_random:
benchmark_helper.run_variable_model(model, use_gpu, n, max_seq_len,
min_seq_len, "turbo", num_threads,
cfg)
else:
input_ids = torch.randint(low=0,
high=cfg.vocab_size - 1,
size=(batch_size, seq_len),
dtype=torch.long,
device=test_device)
benchmark_helper.run_model(lambda: model(input_ids), use_gpu, n,
batch_size, seq_len, "turbo", num_threads)
def __init__(self, decode_fn):
super(BleurtEmbDiscriminator, self).__init__()
self.decode_fn = decode_fn
from BleurtTorch import BleurtModel
import transformers
checkpoint = "bleurt/bleurt-base-128-torch.pb"
config = transformers.BertConfig()
self.bleurt_model = BleurtModel(config)
self.bleurt_model.load_state_dict(torch.load(checkpoint))
self.bleurt_model.eval()
self.tokenizer = transformers.BertTokenizerFast.from_pretrained(
"bert-base-uncased")
from BleurtTorch import encode_batch
self.encode_batch = encode_batch
def test_wikipedia_dataset():
args = "--config demo_tiny_128".split()
config = transformers.BertConfig(**(vars(parse_bert_args(args))))
config.vocab_size = 30522
config.input_files = ["data/wikipedia/128/wiki_000.tfrecord"]
num_tokens = 0
replacement_counts = Counter({"103": 0, "same": 0, "random": 0})
dataset = get_dataset(config)
opts = get_options(config)
loader = DataLoader(opts,
dataset,
batch_size=config.batch_size,
num_workers=config.dataloader_workers)
for datum in tqdm(loader):
tokens, attn_mask, types, mask_lm_pos, labels, nsp = datum
tokens = tokens.numpy()
attn_mask = attn_mask.numpy()
types = types.numpy()
mask_lm_pos = mask_lm_pos.numpy()
labels = labels.numpy()
nsp = nsp.numpy()
for b in range(config.batch_size):
check_dimensions(config, tokens[b], attn_mask[b], types[b],
mask_lm_pos[b], labels[b], nsp[b])
check_tokens(config, tokens[b], mask_lm_pos[b], labels[b])
check_attention_mask(attn_mask[b], tokens[b])
check_mask_lm_positions(config, mask_lm_pos[b])
check_labels(config, tokens[b], mask_lm_pos[b], labels[b])
check_token_type(types[b])
check_nsp(nsp[b])
replacement_counts += mask_type_count(tokens[b], mask_lm_pos[b],
labels[b])
# Number of tokens, not including padding
num_tokens += attn_mask[b, attn_mask[b] == 1].shape[0]
# Test masked token proportions
total = sum(replacement_counts.values())
for k in replacement_counts:
replacement_counts[k] /= total
assert (0.79 < replacement_counts["103"] < 0.81)
assert (0.09 < replacement_counts["same"] < 0.11)
assert (0.09 < replacement_counts["random"] < 0.11)
assert (0.14 < total / num_tokens < 0.16) # should be ~0.15
def benchmark_torch_jit(model_name: str, seq_len: int, batch_size: int, n: int,
enable_random: bool, max_seq_len: int,
min_seq_len: int, num_threads: int, use_gpu: bool,
enable_mem_opt: bool):
import transformers
import contexttimer
import torch.jit
torch.set_num_threads(num_threads)
torch.set_grad_enabled(False)
if model_name == "bert":
cfg = transformers.BertConfig()
model = transformers.BertModel(cfg)
elif model_name == "albert":
cfg = transformers.AlbertConfig()
model = transformers.AlbertModel(cfg)
elif model_name == "roberta":
cfg = transformers.RobertaConfig()
model = transformers.RobertaModel(cfg)
else:
raise (f"benchmark does not support {model_name}")
model.eval()
input_ids = torch.randint(low=0,
high=cfg.vocab_size - 1,
size=(batch_size, seq_len),
dtype=torch.long)
model = torch.jit.trace(model, (input_ids, ))
with torch.jit.optimized_execution(True):
model(input_ids)
with contexttimer.Timer() as t:
for _ in range(n):
model(input_ids)
print(
json.dumps({
"QPS": n / t.elapsed,
"elapsed": t.elapsed,
"n": n,
"batch_size": batch_size,
"seq_len": seq_len,
"framework": "torch_jit",
"n_threads": num_threads,
"model_name": model_name
}))
def __init__(self,
vocab: nnlp.Vocab,
n_embd: int = 256,
n_layer: int = 4,
n_head: int = 4,
n_position: int = 128,
n_ctx: int = 128):
super(BERTLMModel, self).__init__()
config = transformers.BertConfig(vocab_size=len(vocab),
hidden_size=n_embd,
num_hidden_layers=n_layer,
num_attention_heads=n_head,
output_hidden_states=True)
self.bert_model = transformers.BertForMaskedLM(config)
self.vocab = vocab
self.mlm_probability = 0.15
def test(use_cuda: bool):
test_device_name = "GPU" if use_cuda else "CPU"
test_device = torch.device('cuda:0') if use_cuda else \
torch.device('cpu:0')
cfg = transformers.BertConfig()
# use 4 threads for computing
turbo_transformers.set_num_threads(4)
input_ids = np.array(
([12166, 10699, 16752, 4454], [5342, 16471, 817, 16022]),
dtype=np.int64)
segment_ids = np.array(([1, 1, 1, 0], [1, 0, 0, 0]), dtype=np.int64)
input_ids_tensor = turbo_transformers.nparray2tensor(
input_ids, test_device_name)
segment_ids_tensor = turbo_transformers.nparray2tensor(
segment_ids, test_device_name)
# 3. load model from npz
if len(sys.argv) == 2:
try:
print(sys.argv[1])
in_file = sys.argv[1]
except:
sys.exit("ERROR. can not open ", sys.argv[1])
else:
in_file = "/home/jiaruifang/codes/TurboTransformers/bert.npz"
# 255 MiB
tt_model = turbo_transformers.BertModel.from_npz(in_file, cfg, test_device)
# 1169 MiB
start_time = time.time()
for _ in range(10):
res = tt_model(input_ids_tensor,
token_type_ids=segment_ids_tensor,
return_type=turbo_transformers.ReturnType.NUMPY
) # sequence_output, pooled_output
end_time = time.time()
print("turbo bert sequence output:", res[0][:, 0, :])
print("turbo bert pooler output: ", res[1]) # pooled_output
print("\nturbo time consum: {}".format(end_time - start_time))
def benchmark_torch(model_name: str, seq_len: int, batch_size: int, n: int,
enable_random: bool, max_seq_len: int, min_seq_len: int,
num_threads: int, use_gpu: bool, enable_mem_opt: bool):
import torch
import transformers
import benchmark_helper
test_device = torch.device('cuda:0') if use_gpu else torch.device('cpu:0')
torch.set_grad_enabled(False)
torch.set_num_threads(num_threads)
cfg = None
if model_name == "bert":
cfg = transformers.BertConfig()
model = transformers.BertModel(cfg)
elif model_name == "albert":
cfg = transformers.AlbertConfig()
model = transformers.AlbertModel(cfg)
elif model_name == "roberta":
cfg = transformers.RobertaConfig()
model = transformers.RobertaModel(cfg)
elif model_name == "distilbert":
cfg = transformers.DistilBertConfig()
model = transformers.DistilBertModel(cfg)
else:
raise (f"benchmark does not support {model_name}")
model.eval()
model.to(test_device)
# cfg = model.config # type: transformers.BertConfig
if enable_random:
benchmark_helper.run_variable_model(model, use_gpu, n, max_seq_len,
min_seq_len, "torch", num_threads,
cfg, enable_mem_opt, model_name)
else:
input_ids = torch.randint(low=0,
high=cfg.vocab_size - 1,
size=(batch_size, seq_len),
dtype=torch.long,
device=test_device)
benchmark_helper.run_model(lambda: model(input_ids), use_gpu, n,
batch_size, seq_len, "torch", num_threads,
enable_mem_opt, model_name)
def benchmark_turbo_transformers(model_name: str, seq_len: int,
batch_size: int, n: int):
import torch
import transformers
import contexttimer
import turbo_transformers
import benchmark_helper
if not torch.cuda.is_available():
print("cuda is not available for torch")
return
test_device = torch.device('cuda:0')
if model_name == "bert":
cfg = transformers.BertConfig()
model = transformers.BertModel(cfg)
model.to(test_device)
model.eval()
model = turbo_transformers.BertModel.from_torch(model)
elif model_name == "albert":
cfg = transformers.AlbertConfig()
model = transformers.AlbertModel(cfg)
model.to(test_device)
model.eval()
model = turbo_transformers.AlbertModel.from_torch(model)
elif model_name == "roberta":
cfg = transformers.RobertaConfig()
model = transformers.RobertaModel(cfg)
model.to(test_device)
model.eval()
model = turbo_transformers.RobertaModel.from_torch(model)
else:
raise (f"benchmark does not support {model_name}")
cfg = model.config # type: transformers.BertConfig
input_ids = torch.randint(low=0,
high=cfg.vocab_size - 1,
size=(batch_size, seq_len),
dtype=torch.long,
device=test_device)
benchmark_helper.run_model(lambda: model(input_ids), True, n, batch_size,
seq_len, "turbo")
def build_ner_biobert_model(hparams):
# data_dir = pathlib.Path(DATA_DIR)
exp_dirpath = pathlib.Path(dirname)
train_filename = "./ner/%s/%s.train"%(hparams.get("dataset"), hparams.get("dataset"))
dev_filename = "./ner/%s/%s.dev"%(hparams.get("dataset"), hparams.get("dataset"))
test_filename = "./ner/%s/%s.test"%(hparams.get("dataset"), hparams.get("dataset"))
data_manager = BioNERDatasetManager(
train_filename=train_filename,
dev_filename=dev_filename,
test_filename=test_filename,
column_names=["NER"],
train_only="ner",
)
config = transformers.BertConfig(output_hidden_states=True, vocab_size=28996)
model = AutoModelWithLMHead.from_pretrained("monologg/biobert_v1.1_pubmed", config=config)
tokenizer = AutoTokenizer.from_pretrained("monologg/biobert_v1.1_pubmed")
# TODO: Specifying the max length
biobert2seqencoder = Biobert2SeqEncoder(
tokenizer=tokenizer,
model=model,
device=torch.device(hparams.get("device")),
)
model = RnnSeqCrfTagger(
rnn2seqencoder=biobert2seqencoder,
encoding_dim=768,
device=torch.device(hparams.get("device")),
datasets_manager=data_manager,
)
infer = SequenceLabellingInference(
model=model,
model_filepath=str(exp_dirpath.joinpath("checkpoints", "best_model.pt")),
datasets_manager=data_manager,
)
return infer
def __init__(self, p):
super().__init__()
self.bert_config = transformers.BertConfig(
vocab_size=1,
hidden_size=p.input_dim,
num_hidden_layers=p.n_layers,
num_attention_heads=p.n_heads,
intermediate_size=p.ffn_dim,
hidden_dropout_prob=p.dropout_p,
attention_probs_dropout_prob=p.dropout_p,
max_position_embeddings=p.seq_length,
position_embedding_type='absolute'
)
self.bert = BertModel(self.bert_config)
self.conv = nn.Conv2d(1, 1, kernel_size=(1,p.input_dim))
self.fc = nn.Linear(in_features=p.input_dim, out_features=1)
self.relu = nn.ReLU()
self.last_layer = p.last_layer
def __init__(self, hp: Optional[ModelParams] = ModelParams()):
super().__init__()
self.hp = hp
config = transformers.BertConfig(
hidden_size=self.hp.dim,
num_hidden_layers=0,
num_attention_heads=1,
intermediate_size=0,
max_position_embeddings=self.hp.max_seq_len,
output_attentions=False,
output_hidden_states=False,
return_dict=True,
)
self.e = transformers.BertModel(config, add_pooling_layer=False)
for name, param in self.e.named_parameters():
if name == 'position_embeddings':
requires_grad = False
else:
requires_grad = self.hp.requires_grad
param.requires_grad = requires_grad
def test_constant_lrschedule():
"""
Test that lr schedule "constant" results in unchanging LR
"""
import warnings
warnings.filterwarnings("ignore", category=torch.jit.TracerWarning)
args = """
--config unit_test
--lr-schedule constant
""".split()
config = transformers.BertConfig(**(vars(parse_bert_args(args))))
opts = get_options(config)
# IPU Model and Optimizer
model = PipelinedBertWithLoss(config).half().train()
optimizer = get_optimizer(config, model)
scheduler = get_lr_scheduler(optimizer, "constant")
poptorch_model = poptorch.trainingModel(model, opts, optimizer=optimizer)
def mock_data():
return get_generated_datum(config)
# Compile the model
poptorch_model.compile(*mock_data())
# Starting lr should be 1.0
assert poptorch_model._dict_optimizer["groups"][0]["learningRate"][
0] == config.learning_rate
# Run for some steps
for _ in range(5):
outputs = poptorch_model(*mock_data())
scheduler.step()
poptorch_model.setOptimizer(optimizer)
# LR should be unchanged
assert poptorch_model._dict_new_optimizer["groups"][0]["learningRate"][
0] == config.learning_rate
