mirror of https://github.com/hpcaitech/ColossalAI
93 lines
3.0 KiB
Python
93 lines
3.0 KiB
Python
import torch
|
|
import transformers
|
|
|
|
from ..registry import ModelAttribute, model_zoo
|
|
|
|
# ===============================
|
|
# Register single-sentence OPT
|
|
# ===============================
|
|
BATCH_SIZE = 2
|
|
SEQ_LENGTH = 16
|
|
|
|
|
|
def data_gen():
|
|
input_ids = torch.Tensor([[1, 15043, 29892, 590, 11203, 338, 274, 1082]]).long()
|
|
attention_mask = torch.Tensor([[1, 1, 1, 1, 1, 1, 1, 1]]).long()
|
|
return dict(input_ids=input_ids, attention_mask=attention_mask)
|
|
|
|
|
|
def data_gen_for_causal_lm():
|
|
# LM data gen
|
|
# the `labels` of LM is the token of the output, cause no padding, use `input_ids` as `labels`
|
|
data = data_gen()
|
|
labels = data["input_ids"].clone()
|
|
data["labels"] = labels
|
|
return data
|
|
|
|
|
|
def data_gen_for_sequence_classification():
|
|
# LM data gen
|
|
# the `labels` of LM is the token of the output, cause no padding, use `input_ids` as `labels`
|
|
data = data_gen()
|
|
data["input_ids"].clone()
|
|
data["labels"] = torch.tensor([1])
|
|
return data
|
|
|
|
|
|
def data_gen_for_question_answering():
|
|
# LM data gen
|
|
# the `labels` of LM is the token of the output, cause no padding, use `input_ids` as `labels`
|
|
data = data_gen()
|
|
data["start_positions"] = torch.tensor([0])
|
|
data["end_positions"] = torch.tensor([1])
|
|
return data
|
|
|
|
|
|
output_transform_fn = lambda x: x
|
|
loss_fn_for_opt_model = lambda x: torch.nn.functional.mse_loss(
|
|
x.last_hidden_state, torch.ones_like(x.last_hidden_state)
|
|
)
|
|
loss_fn_for_lm = lambda x: x.loss
|
|
config = transformers.OPTConfig(
|
|
hidden_size=128,
|
|
num_hidden_layers=2,
|
|
num_attention_heads=4,
|
|
dropout=0,
|
|
)
|
|
|
|
# register the following models
|
|
# transformers.OPTModel,
|
|
# transformers.OPTForCausalLM,
|
|
model_zoo.register(
|
|
name="transformers_opt",
|
|
model_fn=lambda: transformers.OPTModel(config),
|
|
data_gen_fn=data_gen,
|
|
output_transform_fn=output_transform_fn,
|
|
loss_fn=loss_fn_for_opt_model,
|
|
model_attribute=ModelAttribute(has_control_flow=True),
|
|
)
|
|
model_zoo.register(
|
|
name="transformers_opt_for_causal_lm",
|
|
model_fn=lambda: transformers.OPTForCausalLM(config),
|
|
data_gen_fn=data_gen_for_causal_lm,
|
|
output_transform_fn=output_transform_fn,
|
|
loss_fn=loss_fn_for_lm,
|
|
model_attribute=ModelAttribute(has_control_flow=True),
|
|
)
|
|
model_zoo.register(
|
|
name="transformers_opt_for_question_answering",
|
|
model_fn=lambda: transformers.OPTForQuestionAnswering(config),
|
|
data_gen_fn=data_gen_for_question_answering,
|
|
output_transform_fn=output_transform_fn,
|
|
loss_fn=loss_fn_for_lm,
|
|
model_attribute=ModelAttribute(has_control_flow=True),
|
|
)
|
|
|
|
# TODO The loss and gradient check in the test are failing, to be fixed.
|
|
# model_zoo.register(name='transformers_opt_for_sequence_classification',
|
|
# model_fn=lambda: transformers.OPTForSequenceClassification(config),
|
|
# data_gen_fn=data_gen_for_sequence_classification,
|
|
# output_transform_fn=output_transform_fn,
|
|
# loss_fn=loss_fn_for_lm,
|
|
# model_attribute=ModelAttribute(has_control_flow=True))
|