def from_torch(model: TorchBertModel,
device: Optional[torch.device] = None):
if device is not None and 'cuda' in device.type and torch.cuda.is_available(
):
model.to(device)
embeddings = BertEmbeddings.from_torch(model.embeddings)
encoder = BertEncoder.from_torch(model.encoder)
return BertModelNoPooler(embeddings, encoder)
def from_torch(
model: TorchBertModel, # from_torch函数实现
device: Optional[torch.device] = None):
if device is not None and 'cuda' in device.type and torch.cuda.is_available(
):
model.to(device)
bertmodel = turbo_transformers.BertModel.from_torch(model.bert)
# We can copy the following code and do not change it
# Notice: classifier is the class member of BertForSequenceClassification. If user define the other class member,
# they need modify it here.
return BertForSequenceClassification(bertmodel, model.classifier)
class BertMultiTask:
def __init__(self, job_config, use_pretrain, tokenizer, cache_dir, device, write_log, summary_writer):
self.job_config = job_config
if not use_pretrain:
model_config = self.job_config.get_model_config()
bert_config = BertConfig(**model_config)
bert_config.vocab_size = len(tokenizer.vocab)
self.bert_encoder = BertModel(bert_config)
# Use pretrained bert weights
else:
self.bert_encoder = BertModel.from_pretrained(self.job_config.get_model_file_type())
bert_config = self.bert_encoder.config
self.bert_encoder.to(device)
self.network=MTLRouting(self.bert_encoder, write_log = write_log, summary_writer = summary_writer)
#config_data=self.config['data']
loss_calculation = BertPretrainingLoss(self.bert_encoder, bert_config)
loss_calculation.to(device)
# Pretrain Dataset
self.network.register_batch(BatchType.PRETRAIN_BATCH, "pretrain_dataset", loss_calculation=loss_calculation)
self.device=device
# self.network = self.network.float()
# print(f"Bert ID: {id(self.bert_encoder)} from GPU: {dist.get_rank()}")
def save(self, filename: str):
network=self.network.module
return torch.save(network.state_dict(), filename)
def load(self, model_state_dict: str):
return self.network.module.load_state_dict(torch.load(model_state_dict, map_location=lambda storage, loc: storage))
def move_batch(self, batch, non_blocking=False):
return batch.to(self.device, non_blocking)
def eval(self):
self.network.eval()
def train(self):
self.network.train()
def save_bert(self, filename: str):
return torch.save(self.bert_encoder.state_dict(), filename)
def to(self, device):
assert isinstance(device, torch.device)
self.network.to(device)
def half(self):
self.network.half()
class TestBertModel(unittest.TestCase):
def init_data(self, use_cuda) -> None:
torch.set_grad_enabled(False)
torch.set_num_threads(4)
turbo_transformers.set_num_threads(4)
self.test_device = torch.device('cuda:0') if use_cuda else \
torch.device('cpu:0')
self.cfg = BertConfig()
self.torch_model = BertModel(self.cfg)
self.torch_model.eval()
if torch.cuda.is_available():
self.torch_model.to(self.test_device)
self.turbo_model = turbo_transformers.BertModel.from_torch(
self.torch_model, self.test_device)
def check_torch_and_turbo(self, use_cuda):
self.init_data(use_cuda)
num_iter = 1
device_name = "GPU" if use_cuda else "CPU"
input_ids = torch.randint(low=0,
high=self.cfg.vocab_size - 1,
size=(1, 10),
dtype=torch.long,
device=self.test_device)
torch_model = lambda: self.torch_model(input_ids)
torch_result, torch_qps, torch_time = \
test_helper.run_model(torch_model, use_cuda, num_iter)
print(f'BertModel PyTorch({device_name}) QPS {torch_qps}')
turbo_model = (lambda: self.turbo_model(input_ids))
with turbo_transformers.pref_guard("bert_perf") as perf:
turbo_result, turbo_qps, turbo_time = \
test_helper.run_model(turbo_model, use_cuda, num_iter)
print(f'BertModel TurboTransformer({device_name}) QPS {turbo_qps}')
self.assertTrue(
numpy.allclose(torch_result[0][:, 0].cpu(),
turbo_result[0].cpu(),
atol=1e-3,
rtol=1e-3))
def test_bert_model(self):
if torch.cuda.is_available() and \
turbo_transformers.config.is_compiled_with_cuda():
self.check_torch_and_turbo(use_cuda=True)
self.check_torch_and_turbo(use_cuda=False)
class BertEmbed:
def __init__(self):
config = BertConfig.from_json_file(join(BERT_PATH, 'bert_config.json'))
self.tokenizer = BertTokenizer(vocab_file=join(BERT_PATH, 'vocab.txt'))
self.model = BertModel(config, add_pooling_layer=False)
load_tf_weights_in_bert(self.model,
tf_checkpoint_path=join(
BERT_PATH, 'bert_model.ckpt'),
strip_bert=True)
self.model.to(PT_DEVICE)
self.model.eval()
def get_embedding(self, sentences):
x = self.tokenizer(sentences, return_tensors='pt',
padding=True).to(PT_DEVICE)
with torch.no_grad():
output = self.model(**x)[0]
return output.cpu().numpy()
def from_torch(model: TorchBertModel,
device: Optional[torch.device] = None,
backend: Optional[str] = None,
use_memory_opt=False):
"""
Args:
model : a PyTorch Bert Model
device : cpu or GPU
backend : a string to indicates kernel provides
Four options. [onnxrt-cpu, onnxrt-gpu, turbo-cpu, turbo-gpu]
use_memory_opt [bool] whether or not use memory opt for variable length inputs.
"""
use_gpu = False
if device is None:
device = model.device
# we may need to move to GPU explicitly
if 'cuda' in device.type and torch.cuda.is_available():
model.to(device)
if backend is None:
backend = "turbo" # On GPU turbo is faster
use_gpu = True
else:
if backend is None:
backend = "onnxrt" # On CPU onnxrt is faster
if backend == "turbo":
embeddings = BertEmbeddings.from_torch(model.embeddings)
encoder = BertEncoder.from_torch(model.encoder)
bertmodel_nopooler = BertModelNoPooler(embeddings, encoder)
pooler = BertPooler.from_torch(model.pooler)
return BertModel(bertmodel_nopooler, pooler, "turbo", model.config)
elif backend == "onnxrt":
import onnx
import onnxruntime
import onnxruntime.backend
inputs = {
'input_ids':
torch.randint(32, [2, 32], dtype=torch.long).to(
device), # list of numerical ids for the tokenised text
'attention_mask':
torch.ones([2, 32],
dtype=torch.long).to(device), # dummy list of ones
'token_type_ids':
torch.ones([2, 32],
dtype=torch.long).to(device), # dummy list of ones
}
onnx_model_path = "/tmp/temp_turbo_onnx.model"
with open(onnx_model_path, 'wb') as outf:
torch.onnx.export(
model=model,
args=(inputs['input_ids'], inputs['attention_mask'],
inputs['token_type_ids']
), # model input (or a tuple for multiple inputs)
f=outf,
input_names=[
'input_ids', 'attention_mask', 'token_type_ids'
],
opset_version=11, # the ONNX version to export the model to
do_constant_folding=
True, # whether to execute constant folding for optimization
output_names=['output'],
dynamic_axes={
'input_ids': [0, 1],
'attention_mask': [0, 1],
'token_type_ids': [0, 1]
})
# num_threads = "8"
# os.environ['OMP_NUM_THREADS'] = str(num_threads)
# os.environ['MKL_NUM_THREADS'] = str(num_threads)
onnx_model = onnx.load_model(f=onnx_model_path)
onnx_model = onnxruntime.backend.prepare(
model=onnx_model,
device='GPU' if use_gpu else "CPU",
graph_optimization_level=onnxruntime.GraphOptimizationLevel.
ORT_ENABLE_ALL)
return BertModel(onnx_model, None, "onnxrt")
class TestBertModel(unittest.TestCase):
def init_data(self, use_cuda) -> None:
torch.set_grad_enabled(False)
torch.set_num_threads(4)
turbo_transformers.set_num_threads(4)
self.test_device = torch.device('cuda:0') if use_cuda else \
torch.device('cpu:0')
self.cfg = BertConfig()
self.torch_model = BertModel(self.cfg)
self.torch_model.eval()
if torch.cuda.is_available():
self.torch_model.to(self.test_device)
self.turbo_model = turbo_transformers.BertModel.from_torch(
self.torch_model, self.test_device, "turbo")
def check_torch_and_turbo(self,
use_cuda,
batch_size,
seq_len,
use_memory_opt=True):
self.init_data(use_cuda)
num_iter = 1
device_name = "GPU" if use_cuda else "CPU"
input_ids = torch.randint(low=0,
high=self.cfg.vocab_size - 1,
size=(batch_size, seq_len),
dtype=torch.long,
device=self.test_device)
torch_model = lambda: self.torch_model(input_ids)
torch_result, torch_qps, torch_time = \
test_helper.run_model(torch_model, use_cuda, num_iter)
print(f'BertModel PyTorch({device_name}) QPS {torch_qps}')
turbo_model = (lambda: self.turbo_model(input_ids))
if use_memory_opt:
turbo_transformers.bert_opt_mem_allocate_api(
input_ids.size()[0], # batch
input_ids.size()[1], # seq_len
self.cfg.num_attention_heads,
self.cfg.hidden_size,
self.cfg.num_hidden_layers,
"GPU" if 'cuda' in input_ids.device.type else "CPU")
with turbo_transformers.pref_guard("bert_perf") as perf:
turbo_result, turbo_qps, turbo_time = \
test_helper.run_model(turbo_model, use_cuda, num_iter)
print(f'BertModel TurboTransformer({device_name}) QPS {turbo_qps}')
print(f"batch {batch_size} seq_len {seq_len}")
print(torch.max(torch_result[0].cpu() - turbo_result[0].cpu()))
self.assertTrue(
numpy.allclose(torch_result[0].cpu(),
turbo_result[0].cpu(),
atol=1e-2,
rtol=1e-3))
def bert_model_test_helper(self, use_memory_opt=False):
if use_memory_opt:
turbo_transformers.reset_allocator_schema("model-aware")
for batch_size in [2, 4, 1]:
for seq_len in [50, 4, 16]:
if torch.cuda.is_available() and \
turbo_transformers.config.is_compiled_with_cuda():
self.check_torch_and_turbo(use_cuda=True,
batch_size=batch_size,
seq_len=seq_len,
use_memory_opt=use_memory_opt)
self.check_torch_and_turbo(use_cuda=False,
batch_size=batch_size,
seq_len=seq_len,
use_memory_opt=use_memory_opt)
if use_memory_opt:
turbo_transformers.reset_allocator_schema("naive")
def test_bert_model(self):
# self.bert_model_test_helper(True)
self.bert_model_test_helper(False)
class TestBertModel(unittest.TestCase):
def init_data(self, use_cuda) -> None:
torch.set_grad_enabled(False)
torch.set_num_threads(1)
self.test_device = torch.device('cuda:0') if use_cuda else \
torch.device('cpu:0')
self.cfg = BertConfig()
self.torch_model = BertModel(self.cfg)
self.torch_model.eval()
if torch.cuda.is_available():
self.torch_model.to(self.test_device)
self.turbo_model = turbo_transformers.BertModel.from_torch(
self.torch_model, self.test_device)
self.turbo_pooler_model = turbo_transformers.BertModelWithPooler.from_torch(
self.torch_model, self.test_device)
def check_torch_and_turbo(self, use_cuda, use_pooler):
self.init_data(use_cuda)
num_iter = 2
device_name = "GPU" if use_cuda else "CPU"
input_ids = torch.randint(low=0,
high=self.cfg.vocab_size - 1,
size=(2, 32),
dtype=torch.long,
device=self.test_device)
torch_model = lambda: self.torch_model(input_ids)
torch_result, torch_qps, torch_time = \
test_helper.run_model(torch_model, use_cuda, num_iter)
print(f'BertModel Plain PyTorch({device_name}) QPS {torch_qps}')
turbo_model = (
lambda: self.turbo_pooler_model(input_ids)) if use_pooler else (
lambda: self.turbo_model(input_ids))
turbo_result, turbo_qps, turbo_time = \
test_helper.run_model(turbo_model, use_cuda, num_iter)
print(f'BertModel TurboTransformer({device_name}) QPS {turbo_qps}')
torch_result_final = (torch_result[1]).cpu().numpy(
) if use_pooler else torch_result[0][:, 0].cpu().numpy()
turbo_result_final = turbo_result[0].cpu().numpy()
#TODO(jiaruifang, v_cshi) check why pooler introduce more difference
if use_pooler:
print(
"encode output diff: ",
numpy.max((torch_result[0][:, 0]).cpu().numpy() -
turbo_result[1].cpu().numpy()).reshape(-1))
print(
"pooler output diff: ",
numpy.max(
(turbo_result_final - torch_result_final).reshape(-1)))
(atol, rtol) = (1e-2, 1e-2) if use_pooler else (5e-3, 1e-4)
self.assertTrue(
numpy.allclose(torch_result_final,
turbo_result_final,
atol=atol,
rtol=rtol))
def test_bert_model(self):
if torch.cuda.is_available() and \
turbo_transformers.config.is_compiled_with_cuda():
self.check_torch_and_turbo(use_cuda=True, use_pooler=False)
self.check_torch_and_turbo(use_cuda=True, use_pooler=True)
self.check_torch_and_turbo(use_cuda=False, use_pooler=False)
self.check_torch_and_turbo(use_cuda=False, use_pooler=True)
