def init_data(self, use_cuda):
test_device = torch.device('cuda:0') if use_cuda else \
torch.device('cpu:0')
if not use_cuda:
torch.set_num_threads(4)
turbo_transformers.set_num_threads(4)
torch.set_grad_enabled(False)
self.cfg = BertConfig(attention_probs_dropout_prob=0.0,
hidden_dropout_prob=0.0)
self.cfg.output_attentions = True
torch_attention = BertAttention(self.cfg)
torch_attention.eval()
if use_cuda:
torch_attention.to(test_device)
# Get FT Attention
turbo_attention = turbo_transformers.BertAttention.from_torch(
torch_attention)
turbo_decoder_attention = turbo_transformers.MultiHeadedAttention.from_torch(
torch_attention, is_trans_weight=False)
hidden_size = self.cfg.hidden_size
input_tensor = torch.rand(size=(batch_size, seq_length,
hidden_size),
dtype=torch.float32,
device=test_device)
attention_mask = torch.ones((batch_size, seq_length),
dtype=torch.float32,
device=test_device)
attention_mask = attention_mask[:, None, None, :]
attention_mask = (1.0 - attention_mask) * -10000.0
return torch_attention, turbo_attention, turbo_decoder_attention, input_tensor, attention_mask
def init_data(self, use_cuda):
self.test_device = torch.device('cuda:0') if use_cuda else \
torch.device('cpu:0')
if not use_cuda:
torch.set_num_threads(4)
turbo_transformers.set_num_threads(4)
self.cfg = DistilBertConfig(attention_probs_dropout_prob=0.0,
hidden_dropout_prob=0.0)
torch.set_grad_enabled(False)
self.torch_ffn = DistilFFN(self.cfg)
self.torch_ffn.eval()
self.output_layer_norm = torch.nn.LayerNorm(
normalized_shape=self.cfg.dim, eps=1e-12)
if use_cuda:
self.torch_ffn.to(self.test_device)
self.output_layer_norm.to(self.test_device)
self.turbo_ffn = turbo_transformers.DistrillFFN.from_torch(
self.torch_ffn, self.output_layer_norm)
# (batch_size, input_len, model_dim)
self.inputs = torch.rand(size=(batch_size, input_len,
self.cfg.dim),
dtype=torch.float32,
device=self.test_device)
print(self.cfg.activation)
def benchmark_turbo_transformers(model_name: str, seq_len: int,
batch_size: int, n: int, num_threads: int):
import torch
import transformers
import contexttimer
import turbo_transformers
import cProfile
import benchmark_helper
turbo_transformers.set_num_threads(num_threads)
if model_name == "bert":
cfg = transformers.BertConfig()
model = transformers.BertModel(cfg)
model.eval()
model = turbo_transformers.BertModel.from_torch(model)
elif model_name == "albert":
cfg = transformers.AlbertConfig()
model = transformers.AlbertModel(cfg)
model.eval()
model = turbo_transformers.AlbertModel.from_torch(model)
elif model_name == "roberta":
cfg = transformers.RobertaConfig()
model = transformers.RobertaModel(cfg)
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)
benchmark_helper.run_model(lambda: model(input_ids), False, n, batch_size,
seq_len, "turbo", num_threads)
def init_data(self, use_cuda):
self.test_device = torch.device('cuda:0') if use_cuda else \
torch.device('cpu:0')
if not use_cuda:
torch.set_num_threads(4)
turbo_transformers.set_num_threads(4)
self.cfg = DistilBertConfig(attention_probs_dropout_prob=0.0,
hidden_dropout_prob=0.0)
torch.set_grad_enabled(False)
self.torch_transformer_block = DistilTransformerBlock(self.cfg)
self.torch_transformer_block.eval()
if use_cuda:
self.torch_transformer_block.to(self.test_device)
self.turbo_transformer_block = turbo_transformers.DistrillTransformerBlock.from_torch(
self.torch_transformer_block)
# (batch_size, input_len, model_dim)
self.attention_mask = torch.ones((batch_size, input_len),
dtype=torch.float32,
device=self.test_device)
self.inputs = torch.rand(size=(batch_size, input_len,
self.cfg.dim),
dtype=torch.float32,
device=self.test_device)
def init_data(self, use_cuda: bool) -> None:
self.test_device = torch.device('cuda:0') if use_cuda else \
torch.device('cpu:0')
if not use_cuda:
torch.set_num_threads(4)
turbo_transformers.set_num_threads(4)
torch.set_grad_enabled(False)
self.cfg = AlbertConfig()
self.torch_layer = AlbertLayer(self.cfg)
if torch.cuda.is_available():
self.torch_layer.to(self.test_device)
self.torch_layer.eval()
self.hidden_size = self.cfg.hidden_size
self.input_tensor = torch.rand(size=(batch_size, seq_length,
self.hidden_size),
dtype=torch.float32,
device=self.test_device)
self.attention_mask = torch.ones((batch_size, seq_length),
dtype=torch.float32,
device=self.test_device)
self.attention_mask = self.attention_mask[:, None, None, :]
self.attention_mask = (1.0 - self.attention_mask) * -10000.0
self.turbo_layer = turbo_transformers.AlbertLayer.from_torch(
self.torch_layer)
def init_data(self, use_cuda):
test_device = torch.device('cuda:0') if use_cuda else \
torch.device('cpu:0')
if not use_cuda:
torch.set_num_threads(4)
turbo_transformers.set_num_threads(4)
torch.set_grad_enabled(False)
self.cfg = DistilBertConfig(attention_probs_dropout_prob=0.0,
hidden_dropout_prob=0.0)
self.cfg.output_attentions = True
self.torch_attention = DistilAttention(self.cfg)
self.torch_sa_layer_norm = nn.LayerNorm(
normalized_shape=self.cfg.dim, eps=1e-12)
self.torch_attention.eval()
self.torch_sa_layer_norm.eval()
if use_cuda:
self.torch_attention.to(test_device)
self.torch_sa_layer_norm.to(test_device)
# Get FT Attention
self.turbo_attention = turbo_transformers.DistillBertAttention.from_torch(
self.torch_attention, self.torch_sa_layer_norm)
hidden_size = self.cfg.hidden_size
self.input_tensor = torch.rand(size=(batch_size, seq_length,
hidden_size),
dtype=torch.float32,
device=test_device)
# NOTE, the mask of distilled attention is different from huggingface bert attention.
self.attention_mask = torch.ones((batch_size, seq_length),
dtype=torch.float32,
device=test_device)
def init_data(self, use_cuda):
self.test_device = torch.device('cuda:0') if use_cuda else \
torch.device('cpu:0')
if not use_cuda:
torch.set_num_threads(4)
turbo_transformers.set_num_threads(4)
self.model_dim = 1024
self.d_ff = 4096
torch.set_grad_enabled(False)
onmt_ffn = PositionwiseFeedForward(self.model_dim, self.d_ff)
onmt_ffn.eval()
if use_cuda:
onmt_ffn.to(self.test_device)
turbo_ffn_trans = turbo_transformers.PositionwiseFeedForward.from_onmt(
onmt_ffn, is_trans_weight=True)
turbo_ffn_notrans = turbo_transformers.PositionwiseFeedForward.from_onmt(
onmt_ffn, is_trans_weight=False)
# (batch_size, input_len, model_dim)
inputs = torch.rand(size=(batch_size, input_len, self.model_dim),
dtype=torch.float32,
device=self.test_device)
return onmt_ffn, turbo_ffn_trans, turbo_ffn_notrans, inputs
def init_data(self, use_cuda):
self.test_device = torch.device('cuda:0') if use_cuda else \
torch.device('cpu:0')
if not use_cuda:
torch.set_num_threads(4)
turbo_transformers.set_num_threads(4)
self.cfg = DistilBertConfig(attention_probs_dropout_prob=0.0,
hidden_dropout_prob=0.0)
torch.set_grad_enabled(False)
self.torch_model = DistilBertModel(self.cfg)
self.torch_model.eval()
if use_cuda:
self.torch_model.to(self.test_device)
self.turbo_transformer = turbo_transformers.DistilBertModel.from_torch(
self.torch_model)
# (batch_size, input_len, model_dim)
self.inputs = torch.randint(low=0,
high=self.cfg.vocab_size - 1,
size=(batch_size, input_len),
dtype=torch.long,
device=self.test_device)
self.attention_mask = torch.ones((batch_size, input_len),
dtype=torch.long,
device=self.test_device)
self.head_mask = [None] * self.cfg.num_hidden_layers
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 init_data(self, use_cuda):
self.test_device = torch.device('cuda:0') if use_cuda else \
torch.device('cpu:0')
if not use_cuda:
torch.set_num_threads(4)
turbo_transformers.set_num_threads(4)
torch.set_grad_enabled(False)
self.head_count = 16
self.model_dim = 1024 #self.model_dim should % self.head_count = 0
self.size_per_head = int(self.model_dim / self.head_count)
self.query_seq_len_list = query_seq_len_list
self.key_seq_len_list = key_seq_len_list
# build the torch model
self.model = MultiHeadedAttention(self.head_count, self.model_dim)
self.model.eval()
if use_cuda:
self.model.to(self.test_device)
# prepare torch input data
self.Q_list = []
for query_seq_len in query_seq_len_list:
Q = torch.rand(
size=(
1,
query_seq_len, #from_seq
self.model_dim),
dtype=torch.float32,
device=self.test_device)
self.Q_list.append(Q)
self.K_list = []
self.V_list = []
for key_seq_len in key_seq_len_list:
K = torch.rand(
size=(
1,
key_seq_len, #from_seq
self.model_dim),
dtype=torch.float32,
device=self.test_device)
V = torch.rand(
size=(
1,
key_seq_len, #to_seq
self.model_dim),
dtype=torch.float32,
device=self.test_device)
self.K_list.append(K)
self.V_list.append(V)
# prepare turbo smart batch model
self.turbo_smart_pad = turbo_transformers.MultiHeadedAttentionSmartBatch.from_onmt(
self.model)
def init_data(self, use_cuda):
self.test_device = torch.device('cuda:0') if use_cuda else \
torch.device('cpu:0')
if not use_cuda:
torch.set_num_threads(4)
turbo_transformers.set_num_threads(4)
torch.set_grad_enabled(False)
self.head_count = 16
self.model_dim = 1024 #self.model_dim should % self.head_count = 0
self.size_per_head = int(self.model_dim / self.head_count)
onmt_multi_headed_attention = MultiHeadedAttention(
self.head_count, self.model_dim)
onmt_multi_headed_attention.eval()
torch_layernorm = torch.nn.LayerNorm(self.model_dim, eps=1e-6)
torch_layernorm.eval()
if use_cuda:
onmt_multi_headed_attention.to(self.test_device)
torch_layernorm.to(self.test_device)
K = torch.rand(
size=(
batch_size,
key_seq_len, #from_seq
self.model_dim),
dtype=torch.float32,
device=self.test_device)
V = torch.rand(size=(batch_size, key_seq_len, self.model_dim),
dtype=torch.float32,
device=self.test_device)
Q = torch.rand(
size=(
batch_size,
query_seq_len, #to_seq
self.model_dim),
dtype=torch.float32,
device=self.test_device)
turbo_attn_trans = turbo_transformers.MultiHeadedAttention.from_onmt(
onmt_multi_headed_attention,
torch_layernorm,
is_trans_weight=True)
turbo_attn_notrans = turbo_transformers.MultiHeadedAttention.from_onmt(
onmt_multi_headed_attention,
torch_layernorm,
is_trans_weight=False)
if with_quantize_dynamic and not use_cuda:
self.q_onmt_multi_headed_attention = torch.quantization.quantize_dynamic(
onmt_multi_headed_attention)
return onmt_multi_headed_attention, torch_layernorm, turbo_attn_trans, turbo_attn_notrans, Q, K, V
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 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_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 test(loadtype: LoadType):
# use 4 threads for computing
turbo_transformers.set_num_threads(4)
model_id = "bert-base-uncased"
model = transformers.BertModel.from_pretrained(model_id)
model.eval()
cfg = model.config
input_ids = torch.tensor(
([12166, 10699, 16752, 4454], [5342, 16471, 817, 16022]),
dtype=torch.long)
position_ids = torch.tensor(([1, 0, 0, 0], [1, 1, 1, 0]), dtype=torch.long)
segment_ids = torch.tensor(([1, 1, 1, 0], [1, 0, 0, 0]), dtype=torch.long)
torch.set_grad_enabled(False)
torch_res = model(
input_ids, position_ids=position_ids, token_type_ids=segment_ids
) # sequence_output, pooled_output, (hidden_states), (attentions)
print("torch bert sequence output: ",
torch_res[0][:, 0, :]) #get the first sequence
print("torch bert pooler output: ", torch_res[1]) # pooled_output
# there are three ways to load pretrained model.
if loadtype is LoadType.PYTORCH:
# 1, from a PyTorch model, which has loaded a pretrained model
tt_model = turbo_transformers.BertModel.from_torch(model)
elif loadtype is LoadType.PRETRAINED:
# 2. directly load from checkpoint (torch saved model)
tt_model = turbo_transformers.BertModel.from_pretrained(model_id)
elif loadtype is LoadType.NPZ:
# 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 = "/workspace/bert_torch.npz"
tt_model = turbo_transformers.BertModel.from_npz(in_file, cfg)
else:
raise ("LoadType is not supported")
res = tt_model(
input_ids, position_ids=position_ids,
token_type_ids=segment_ids) # sequence_output, pooled_output
print("turbo bert sequence output:", res[0], res[0].size())
print("turbo bert pooler output: ", res[1]) # pooled_output
def init_data(self, use_cuda: bool) -> None:
self.test_device = torch.device('cuda:0') if use_cuda else \
torch.device('cpu:0')
if not use_cuda:
torch.set_num_threads(4)
turbo_transformers.set_num_threads(4)
torch.set_grad_enabled(False)
self.cfg = BertConfig()
self.torch_intermediate = BertIntermediate(self.cfg)
if torch.cuda.is_available():
self.torch_intermediate.to(self.test_device)
self.torch_intermediate.eval()
self.turbo_intermediate = turbo_transformers.BertIntermediate.from_torch(
self.torch_intermediate)
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 init_data(self, use_cuda: bool) -> None:
self.test_device = torch.device('cuda:0') if use_cuda else \
torch.device('cpu:0')
if not use_cuda:
torch.set_num_threads(4)
turbo_transformers.set_num_threads(4)
torch.set_grad_enabled(False)
self.cfg = AlbertConfig(hidden_size=768,
num_attention_heads=12,
intermediate_size=3072)
self.torch_model = AlbertModel(self.cfg)
if torch.cuda.is_available():
self.torch_model.to(self.test_device)
self.torch_model.eval()
self.hidden_size = self.cfg.hidden_size
self.turbo_model = turbo_transformers.AlbertModel.from_torch(
self.torch_model)
def test(loadtype: LoadType, use_cuda: bool):
cfg = transformers.AlbertConfig(hidden_size=768,
num_attention_heads=12,
intermediate_size=3072)
model = transformers.AlbertModel(cfg)
model.eval()
torch.set_grad_enabled(False)
test_device = torch.device('cuda:0') if use_cuda else \
torch.device('cpu:0')
cfg = model.config
# use 4 threads for computing
turbo_transformers.set_num_threads(4)
input_ids = torch.tensor(
([12166, 10699, 16752, 4454], [5342, 16471, 817, 16022]),
dtype=torch.long)
model.to(test_device)
start_time = time.time()
for _ in range(10):
torch_res = model(input_ids)
end_time = time.time()
print("\ntorch time consum: {}".format(end_time - start_time))
# there are three ways to load pretrained model.
if loadtype is LoadType.PYTORCH:
# 1, from a PyTorch model, which has loaded a pretrained model
tt_model = turbo_transformers.AlbertModel.from_torch(model)
else:
raise ("LoadType is not supported")
start_time = time.time()
for _ in range(10):
res = tt_model(input_ids) # sequence_output, pooled_output
end_time = time.time()
print("\nturbo time consum: {}".format(end_time - start_time))
assert (numpy.max(
numpy.abs(res[0].cpu().numpy() - torch_res[0].cpu().numpy())) < 0.1)
def init_data(self, use_cuda: bool) -> None:
self.test_device = torch.device('cuda:0') if use_cuda else \
torch.device('cpu:0')
if not use_cuda:
torch.set_num_threads(4)
turbo_transformers.set_num_threads(4)
torch.set_grad_enabled(False)
self.cfg = AlbertConfig()
self.torch_model = AlbertModel(self.cfg)
if torch.cuda.is_available():
self.torch_model.to(self.test_device)
self.torch_model.eval()
self.hidden_size = self.cfg.hidden_size
self.input_tensor = torch.randint(low=0,
high=self.cfg.vocab_size - 1,
size=(batch_size, seq_length),
device=self.test_device)
self.turbo_model = turbo_transformers.AlbertModel.from_torch(
self.torch_model)
def benchmark_turbo_transformers(model: str, seq_len: int, batch_size: int,
n: int, num_threads: int):
import torch
import transformers
import contexttimer
import turbo_transformers
import cProfile
import benchmark_helper
turbo_transformers.set_num_threads(num_threads)
model_id = "bert-base-uncased"
model = transformers.BertModel.from_pretrained(
model_id) # type: transformers.BertModel
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)
model = turbo_transformers.BertModel.from_torch(model)
benchmark_helper.run_model(lambda: model(input_ids), False, n, batch_size,
seq_len, "turbo", num_threads)
def test(use_cuda):
torch.set_grad_enabled(False)
torch.set_num_threads(4)
turbo_transformers.set_num_threads(4)
test_device = torch.device('cuda:0') if use_cuda else \
torch.device('cpu:0')
cfg = RobertaConfig()
torch_model = RobertaModel(cfg)
torch_model.eval()
if torch.cuda.is_available():
torch_model.to(test_device)
turbo_model = turbo_transformers.RobertaModel.from_torch(
torch_model, test_device)
input_ids = torch.randint(low=0,
high=cfg.vocab_size - 1,
size=(1, 10),
dtype=torch.long,
device=test_device)
torch_result = torch_model(input_ids)
torch_result_final = torch_result[0][:, 0].cpu().numpy()
turbo_result = turbo_model(input_ids)
turbo_result_final = turbo_result[0].cpu().numpy()
# See the differences
# print(numpy.size(torch_result_final), numpy.size(turbo_result_final))
# print(torch_result_final - turbo_result_final)
assert (numpy.allclose(torch_result_final,
turbo_result_final,
atol=1e-3,
rtol=1e-3))
def init_data(self, use_cuda):
self.test_device = torch.device('cuda:0') if use_cuda else \
torch.device('cpu:0')
if not use_cuda:
torch.set_num_threads(4)
turbo_transformers.set_num_threads(4)
torch.set_grad_enabled(False)
self.model_dim = 1024
self.onmt_encoder = TransformerEncoderLayer(d_model=self.model_dim,
heads=8,
d_ff=1024,
dropout=0.,
attention_dropout=0.)
self.onmt_encoder.eval()
if use_cuda:
self.onmt_encoder.to(self.test_device)
self.turbo_encoder = turbo_transformers.TransformerEncoderLayer.from_onmt(
self.onmt_encoder)
# https://pytorch.org/docs/stable/quantization.html
if with_quantize_dynamic and not use_cuda:
self.quantized_onmt_encoder = torch.quantization.quantize_dynamic(
self.onmt_encoder)
def __init__(self,
model,
fields,
src_reader,
tgt_reader,
gpu=-1,
n_best=1,
min_length=0,
max_length=100,
ratio=0.,
beam_size=30,
random_sampling_topk=1,
random_sampling_temp=1,
stepwise_penalty=None,
dump_beam=False,
block_ngram_repeat=0,
ignore_when_blocking=frozenset(),
replace_unk=False,
phrase_table="",
data_type="text",
verbose=False,
report_time=False,
copy_attn=False,
global_scorer=None,
out_file=None,
report_align=False,
report_score=True,
logger=None,
seed=-1):
self.model = model
# fjr add turbo
turbo_transformers.set_num_threads(4)
self.turbo_decoder = turbo_transformers.TransformerDecoder.from_onmt(
self.model.decoder)
self.fields = fields
tgt_field = dict(self.fields)["tgt"].base_field
self._tgt_vocab = tgt_field.vocab
self._tgt_eos_idx = self._tgt_vocab.stoi[tgt_field.eos_token]
self._tgt_pad_idx = self._tgt_vocab.stoi[tgt_field.pad_token]
self._tgt_bos_idx = self._tgt_vocab.stoi[tgt_field.init_token]
self._tgt_unk_idx = self._tgt_vocab.stoi[tgt_field.unk_token]
self._tgt_vocab_len = len(self._tgt_vocab)
self._gpu = gpu
self._use_cuda = gpu > -1
self._dev = torch.device("cuda", self._gpu) \
if self._use_cuda else torch.device("cpu")
self.n_best = n_best
self.max_length = max_length
self.beam_size = beam_size
self.random_sampling_temp = random_sampling_temp
self.sample_from_topk = random_sampling_topk
self.min_length = min_length
self.ratio = ratio
self.stepwise_penalty = stepwise_penalty
self.dump_beam = dump_beam
self.block_ngram_repeat = block_ngram_repeat
self.ignore_when_blocking = ignore_when_blocking
self._exclusion_idxs = {
self._tgt_vocab.stoi[t]
for t in self.ignore_when_blocking
}
self.src_reader = src_reader
self.tgt_reader = tgt_reader
self.replace_unk = replace_unk
if self.replace_unk and not self.model.decoder.attentional:
raise ValueError("replace_unk requires an attentional decoder.")
self.phrase_table = phrase_table
self.data_type = data_type
self.verbose = verbose
self.report_time = report_time
self.copy_attn = copy_attn
self.global_scorer = global_scorer
if self.global_scorer.has_cov_pen and \
not self.model.decoder.attentional:
raise ValueError(
"Coverage penalty requires an attentional decoder.")
self.out_file = out_file
self.report_align = report_align
self.report_score = report_score
self.logger = logger
self.use_filter_pred = False
self._filter_pred = None
# for debugging
self.beam_trace = self.dump_beam != ""
self.beam_accum = None
if self.beam_trace:
self.beam_accum = {
"predicted_ids": [],
"beam_parent_ids": [],
"scores": [],
"log_probs": []
}
set_random_seed(seed, self._use_cuda)
def test(loadtype: LoadType, use_cuda: bool):
test_device = torch.device('cuda:0') if use_cuda else \
torch.device('cpu:0')
model_id = "bert-base-uncased"
model = transformers.BertModel.from_pretrained(model_id)
model.eval()
model.to(test_device)
torch.set_grad_enabled(False)
cfg = model.config
# use 4 threads for computing
turbo_transformers.set_num_threads(4)
input_ids = torch.tensor(
([12166, 10699, 16752, 4454], [5342, 16471, 817, 16022]),
dtype=torch.long,
device=test_device)
# position_ids = torch.tensor(([1, 0, 0, 0], [1, 1, 1, 0]), dtype=torch.long, device = test_device)
segment_ids = torch.tensor(([1, 1, 1, 0], [1, 0, 0, 0]),
dtype=torch.long,
device=test_device)
start_time = time.time()
for _ in range(10):
torch_res = model(
input_ids, token_type_ids=segment_ids
) # sequence_output, pooled_output, (hidden_states), (attentions)
end_time = time.time()
print("\ntorch time consum: {}".format(end_time - start_time))
print("torch bert sequence output: ",
torch_res[0][:, 0, :]) #get the first sequence
print("torch bert pooler output: ", torch_res[1]) # pooled_output
# there are three ways to load pretrained model.
if loadtype is LoadType.PYTORCH:
# 1, from a PyTorch model, which has loaded a pretrained model
# note that you can choose "turbo" or "onnxrt" as backend
# "turbo" is a hand-crafted implementation and optimized with OMP.
tt_model = turbo_transformers.BertModel.from_torch(
model, test_device, "turbo")
elif loadtype is LoadType.PRETRAINED:
# 2. directly load from checkpoint (torch saved model)
tt_model = turbo_transformers.BertModel.from_pretrained(
model_id, test_device)
elif loadtype is LoadType.NPZ:
# 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 = "/workspace/bert_torch.npz"
tt_model = turbo_transformers.BertModel.from_npz(
in_file, cfg, test_device)
else:
raise ("LoadType is not supported")
start_time = time.time()
for _ in range(10):
res = tt_model(
input_ids,
token_type_ids=segment_ids) # 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))
assert (torch.max(torch.abs(res[0] - torch_res[0])) < 0.2)
return BertForSequenceClassification(bertmodel, model.classifier)
@staticmethod
def from_pretrained(model_id_or_path: str,
device: Optional[torch.device] = None):
# First, Use the function of from_pretrained to load the model you trained.
torch_model = TorchBertForSequenceClassification.from_pretrained(
model_id_or_path)
# Then, Use the init function of the acceleration model to get it.
model = BertForSequenceClassification.from_torch(torch_model, device)
model._torch_model = torch_model # prevent destroy torch model.
return model
# use 4 threads for BERT inference
turbo_transformers.set_num_threads(4)
model_id = os.path.join(
os.path.dirname(__file__),
'test-seq-classification-model') # the model of huggingface's path
tokenizer = BertTokenizer.from_pretrained(
model_id) # the initialization of tokenizer
turbo_model = BertForSequenceClassification.from_pretrained(
model_id,
torch.device('cpu:0')) # the initialization of the acceleration model
# predict after loading the model
input_ids = torch.tensor(
tokenizer.encode('测试一下bert模型的性能和精度是不是符合要求?',
add_special_tokens=True)).unsqueeze(0)
torch_result = turbo_model(input_ids)
