mirror of https://github.com/hpcaitech/ColossalAI
You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
79 lines
2.7 KiB
79 lines
2.7 KiB
import torch
|
|
import transformers
|
|
from transformers import MistralConfig
|
|
|
|
from ..registry import ModelAttribute, model_zoo
|
|
|
|
# ===============================
|
|
# Register single-sentence Mistral
|
|
# ===============================
|
|
|
|
|
|
def data_gen():
|
|
# Generated from following code snippet
|
|
#
|
|
# from transformers import AutoModelForCausalLM, AutoTokenizer
|
|
# tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-v0.1")
|
|
# input = 'My favourite condiment is vinegar' (last two words repeated to satisfy length requirement)
|
|
# tokenized_input = tokenizer([input], return_tensors="pt")
|
|
# input_ids = tokenized_input['input_ids']
|
|
# attention_mask = tokenized_input['attention_mask']
|
|
input_ids = torch.tensor([[1, 1984, 16020, 2076, 2487, 349, 21375, 4749]], 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_sequence_classification():
|
|
# sequence classification data gen
|
|
data = data_gen()
|
|
data["labels"] = torch.tensor([1], dtype=torch.int64)
|
|
return data
|
|
|
|
|
|
# define output transform function
|
|
output_transform_fn = lambda x: x
|
|
|
|
# define loss function
|
|
loss_fn_for_mistral_model = lambda x: torch.nn.functional.mse_loss(
|
|
x.last_hidden_state, torch.ones_like(x.last_hidden_state)
|
|
)
|
|
loss_fn = lambda x: x.loss
|
|
loss_fn_for_seq_classification = lambda output: output.logits.mean()
|
|
|
|
config = MistralConfig(
|
|
hidden_size=256, intermediate_size=256, num_attention_heads=64, num_hidden_layers=2, vocab_size=50258
|
|
)
|
|
|
|
model_zoo.register(
|
|
name="transformers_mistral",
|
|
model_fn=lambda: transformers.MistralModel(config),
|
|
data_gen_fn=data_gen,
|
|
output_transform_fn=output_transform_fn,
|
|
loss_fn=loss_fn_for_mistral_model,
|
|
model_attribute=ModelAttribute(has_control_flow=True),
|
|
)
|
|
model_zoo.register(
|
|
name="transformers_mistral_for_casual_lm",
|
|
model_fn=lambda: transformers.MistralForCausalLM(config),
|
|
data_gen_fn=data_gen_for_lm,
|
|
output_transform_fn=output_transform_fn,
|
|
loss_fn=loss_fn,
|
|
model_attribute=ModelAttribute(has_control_flow=True),
|
|
)
|
|
model_zoo.register(
|
|
name="transformers_mistral_for_sequence_classification",
|
|
model_fn=lambda: transformers.MistralForSequenceClassification(config),
|
|
data_gen_fn=data_gen_for_sequence_classification,
|
|
output_transform_fn=output_transform_fn,
|
|
loss_fn=loss_fn_for_seq_classification,
|
|
model_attribute=ModelAttribute(has_control_flow=True),
|
|
)
|