ColossalAI/tests/kit/model_zoo/transformers/chatglm.py

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import torch
import transformers
from colossalai.shardformer.modeling.chatglm2_6b.configuration_chatglm import ChatGLMConfig
from colossalai.shardformer.modeling.chatglm2_6b.modeling_chatglm import ChatGLMForConditionalGeneration, ChatGLMModel
from ..registry import ModelAttribute, model_zoo
# ================================
# Register single-sentence ChatGLM
# ================================
def data_gen():
input_ids = torch.tensor([[5941, 15, 2670, 3543, 632, 2075]], dtype=torch.int64)
attention_mask = torch.tensor([[1, 1, 1, 1, 1, 1]])
return dict(input_ids=input_ids, attention_mask=attention_mask)
def data_gen_for_conditional_generation():
# token classification data gen
# `labels` is the type not the token id for token classification, 0 or 1
data = data_gen()
labels = data['input_ids'].clone()
data['labels'] = labels
return data
# define output transform function
output_transform_fn = lambda x: x
# define loss function
loss_fn_for_chatglm_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
config = ChatGLMConfig(num_layers=2,
padded_vocab_size=65024,
hidden_size=64,
num_attention_heads=8,
rmsnorm=True,
original_rope=True,
use_cache=True,
torch_dtype=torch.float32)
model_zoo.register(name='transformers_chatglm',
model_fn=lambda: ChatGLMModel(config, empty_init=False),
data_gen_fn=data_gen,
output_transform_fn=output_transform_fn,
loss_fn=loss_fn_for_chatglm_model,
model_attribute=ModelAttribute(has_control_flow=True))
model_zoo.register(name="transformers_chatglm_for_conditional_generation",
model_fn=lambda: ChatGLMForConditionalGeneration(config, empty_init=False),
data_gen_fn=data_gen_for_conditional_generation,
output_transform_fn=output_transform_fn,
loss_fn=loss_fn,
model_attribute=ModelAttribute(has_control_flow=True))