mirror of https://github.com/hpcaitech/ColossalAI
123 lines
4.4 KiB
Python
123 lines
4.4 KiB
Python
import torch
|
|
import transformers
|
|
|
|
from ..registry import ModelAttribute, model_zoo
|
|
|
|
# ===============================
|
|
# Register Bloom
|
|
# ===============================
|
|
|
|
|
|
def data_gen():
|
|
# Generated from following code snippet
|
|
#
|
|
# from transformers import BloomTokenizer
|
|
# input = 'Hello, my dog is cute'
|
|
# tokenized_input = tokenizer(input, return_tensors='pt')
|
|
# input_ids = tokenized_input['input_ids']
|
|
# attention_mask = tokenized_input['attention_mask']
|
|
input_ids = torch.tensor([[59414, 15, 2670, 35433, 632, 207595, 632, 207595]], dtype=torch.int64)
|
|
attention_mask = torch.tensor([[1, 1, 1, 1, 1, 1, 1, 1]], dtype=torch.int64)
|
|
return dict(input_ids=input_ids, attention_mask=attention_mask)
|
|
|
|
|
|
def data_gen_for_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()
|
|
data["labels"] = data["input_ids"].clone()
|
|
return data
|
|
|
|
|
|
def data_gen_for_token_classification():
|
|
# token classification data gen
|
|
# `labels` is the type not the token id for token classification, 0 or 1
|
|
data = data_gen()
|
|
data["labels"] = torch.tensor([[0, 0, 0, 0, 0, 0, 0, 0]], dtype=torch.int64)
|
|
return data
|
|
|
|
|
|
def data_gen_for_sequence_classification():
|
|
# sequence classification data gen
|
|
data = data_gen()
|
|
data["labels"] = torch.tensor([0], dtype=torch.int64)
|
|
return data
|
|
|
|
|
|
def data_gen_for_question_answering():
|
|
# obtained with the following code
|
|
#
|
|
# from transformers import AutoTokenizer
|
|
# tokenizer = AutoTokenizer.from_pretrained("bigscience/bloom-560m")
|
|
# question, text = "Who was Jim Henson?", "Jim Henson was a nice puppet"
|
|
# inputs = tokenizer(question, text, return_tensors="pt")
|
|
|
|
input_ids = torch.tensor(
|
|
[[57647, 1620, 23967, 620, 107373, 34, 91514, 620, 107373, 1620, 267, 35378, 48946, 18161, 48946, 18161]],
|
|
dtype=torch.int64,
|
|
)
|
|
attention_mask = torch.tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], dtype=torch.int64)
|
|
start_positions = torch.tensor([1], dtype=torch.int64)
|
|
end_positions = torch.tensor([10], dtype=torch.int64)
|
|
return dict(
|
|
input_ids=input_ids, attention_mask=attention_mask, start_positions=start_positions, end_positions=end_positions
|
|
)
|
|
|
|
|
|
# define output transform function
|
|
output_transform_fn = lambda x: x
|
|
|
|
# define loss function
|
|
loss_fn_for_bloom_model = lambda x: torch.nn.functional.mse_loss(
|
|
x.last_hidden_state, torch.ones_like(x.last_hidden_state)
|
|
)
|
|
loss_fn_for_causal_lm = lambda x: x.loss
|
|
loss_fn_for_classification = lambda x: x.loss
|
|
loss_fn_for_question_answering = lambda x: x.loss
|
|
|
|
config = transformers.BloomConfig(
|
|
n_layer=2, n_head=4, vocab_size=250880, hidden_dropout=0, attention_dropout=0, hidden_size=64, pad_token_id=50256
|
|
)
|
|
|
|
# register the following models
|
|
model_zoo.register(
|
|
name="transformers_bloom",
|
|
model_fn=lambda: transformers.BloomModel(config),
|
|
data_gen_fn=data_gen,
|
|
output_transform_fn=output_transform_fn,
|
|
loss_fn=loss_fn_for_bloom_model,
|
|
model_attribute=ModelAttribute(has_control_flow=True),
|
|
)
|
|
model_zoo.register(
|
|
name="transformers_bloom_for_causal_lm",
|
|
model_fn=lambda: transformers.BloomForCausalLM(config),
|
|
data_gen_fn=data_gen_for_lm,
|
|
output_transform_fn=output_transform_fn,
|
|
loss_fn=loss_fn_for_causal_lm,
|
|
model_attribute=ModelAttribute(has_control_flow=True),
|
|
)
|
|
model_zoo.register(
|
|
name="transformers_bloom_for_sequence_classification",
|
|
model_fn=lambda: transformers.BloomForSequenceClassification(config),
|
|
data_gen_fn=data_gen_for_sequence_classification,
|
|
output_transform_fn=output_transform_fn,
|
|
loss_fn=loss_fn_for_classification,
|
|
model_attribute=ModelAttribute(has_control_flow=True),
|
|
)
|
|
model_zoo.register(
|
|
name="transformers_bloom_for_token_classification",
|
|
model_fn=lambda: transformers.BloomForTokenClassification(config),
|
|
data_gen_fn=data_gen_for_token_classification,
|
|
output_transform_fn=output_transform_fn,
|
|
loss_fn=loss_fn_for_classification,
|
|
model_attribute=ModelAttribute(has_control_flow=True),
|
|
)
|
|
model_zoo.register(
|
|
name="transformers_bloom_for_question_answering",
|
|
model_fn=lambda: transformers.BloomForQuestionAnswering(config),
|
|
data_gen_fn=data_gen_for_question_answering,
|
|
output_transform_fn=output_transform_fn,
|
|
loss_fn=loss_fn_for_question_answering,
|
|
model_attribute=ModelAttribute(has_control_flow=True),
|
|
)
|