tests/kit/model_zoo/transformers/t5.py - main - githubmirror/ColossalAI

tests/kit/model_zoo/transformers/t5.py
98 строк4 KB
Guangyao Zhang
[shardformer] Support the T5ForTokenClassification model (#5816)
27 июн 2024, 11:40
27 июн 2024, 11:40d9d5e7e
import torch
import transformers

from ..registry import ModelAttribute, model_zoo

# ===============================
# Register single-sentence T5
# ===============================


# define data gen function
def data_gen_for_encoder_only():
    # Generated from following code snippet
    #
    # from transformers import T5Config, T5Tokenizer
    # config = T5Config(decoder_start_token_id=0)
    # tokenizer = T5Tokenizer.from_pretrained("t5-small")
    # input_ids = tokenizer("translate English to German: The house is wonderful.", return_tensors="pt").input_ids
    input_ids = torch.Tensor([[13959, 1566, 12, 2968, 10, 37, 629, 19, 1627, 5, 1, 12, 1627, 5, 1, 12]]).long()
    attention_mask = torch.Tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0]]).long()
    return dict(input_ids=input_ids, attention_mask=attention_mask)


def data_gen_for_conditional_generation():
    # labels is generated with the following code
    #
    # labels = tokenizer("Das Haus ist wunderbar.", return_tensors="pt").input_ids
    data = data_gen_for_encoder_only()
    labels = torch.Tensor([[644, 4598, 229, 19250, 5, 1, 644, 4598, 229, 19250, 5, 1, 229, 19250, 5, 1]]).long()
    data["labels"] = labels
    return data


def data_gen_for_t5_model():
    # decoder_inputs_ids is obtained with the following code
    # decoder_input_ids = model._shift_right(input_ids)
    data = data_gen_for_encoder_only()
    decoder_input_ids = torch.Tensor([[0, 13959, 1566, 12, 2968, 10, 37, 629, 19, 1627, 5, 5, 19, 1627, 5, 5]]).long()
    data["decoder_input_ids"] = decoder_input_ids
    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_for_encoder_only()
    data["labels"] = torch.tensor([[1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=torch.int64)
    return data


# output transform function
output_transform_fn = lambda x: x

# define loss function
loss_fn_for_t5_model = lambda x: x["last_hidden_state"].mean()
loss_fn_for_encoder_only = lambda x: x["last_hidden_state"].mean()
loss_fn_for_conditional_generation = lambda x: x["loss"]
loss_fn_for_token_classification = lambda x: x["loss"]

# define model config
config = transformers.T5Config(d_model=128, num_layers=2, dropout_rate=0, decoder_start_token_id=0)

# register the following models
# transformers.T5Model,
# transformers.T5ForConditionalGeneration,
# transformers.T5EncoderModel,
model_zoo.register(
    name="transformers_t5",
    model_fn=lambda: transformers.T5Model(config),
    data_gen_fn=data_gen_for_t5_model,
    output_transform_fn=output_transform_fn,
    loss_fn=loss_fn_for_t5_model,
    model_attribute=ModelAttribute(has_control_flow=True),
)
model_zoo.register(
    name="transformers_t5_for_conditional_generation",
    model_fn=lambda: transformers.T5ForConditionalGeneration(config),
    data_gen_fn=data_gen_for_conditional_generation,
    output_transform_fn=output_transform_fn,
    loss_fn=loss_fn_for_conditional_generation,
    model_attribute=ModelAttribute(has_control_flow=True),
)
model_zoo.register(
    name="transformers_t5_encoder_model",
    model_fn=lambda: transformers.T5EncoderModel(config),
    data_gen_fn=data_gen_for_encoder_only,
    output_transform_fn=output_transform_fn,
    loss_fn=loss_fn_for_encoder_only,
    model_attribute=ModelAttribute(has_control_flow=True),
)
model_zoo.register(
    name="transformers_t5_for_token_classification",
    model_fn=lambda: transformers.T5ForTokenClassification(config),
    data_gen_fn=data_gen_for_token_classification,
    output_transform_fn=output_transform_fn,
    loss_fn=loss_fn_for_token_classification,
    model_attribute=ModelAttribute(has_control_flow=True),
)