ColossalAI/examples/language/gpt/experiments/pipeline_parallel/model_zoo.py

74 lines
2.4 KiB
Python

from torch import nn
from transformers import GPT2Config, GPT2LMHeadModel
## Define the Model and Loss Based on Huggingface transformers GPT2LMHeadModel
class GPTLMModel(nn.Module):
def __init__(self,
hidden_size=768,
num_layers=12,
num_attention_heads=12,
max_seq_len=1024,
vocab_size=50257,
checkpoint=False):
super().__init__()
self.checkpoint = checkpoint
self.config = GPT2Config(n_embd=hidden_size,
n_layer=num_layers,
n_head=num_attention_heads,
n_positions=max_seq_len,
n_ctx=max_seq_len,
vocab_size=vocab_size)
self.model = GPT2LMHeadModel(self.config)
if checkpoint:
self.model.gradient_checkpointing_enable()
def forward(self, input_ids, attention_mask):
# Only return lm_logits
return self.model(input_ids=input_ids, attention_mask=attention_mask, use_cache=not self.checkpoint)[0]
def gpt2_medium(checkpoint=False):
return GPTLMModel(hidden_size=1024, num_layers=24, num_attention_heads=16, checkpoint=checkpoint)
def gpt2_xl(checkpoint=True):
return GPTLMModel(hidden_size=1600, num_layers=48, num_attention_heads=32, checkpoint=checkpoint)
def gpt2_10b(checkpoint=True):
return GPTLMModel(hidden_size=4096, num_layers=50, num_attention_heads=16, checkpoint=checkpoint)
def gpt2_14b(checkpoint=True):
return GPTLMModel(hidden_size=4096, num_layers=70, num_attention_heads=16, checkpoint=checkpoint)
def gpt2_20b(checkpoint=True):
return GPTLMModel(hidden_size=8192, num_layers=25, num_attention_heads=16, checkpoint=checkpoint)
def gpt2_24b(checkpoint=True):
return GPTLMModel(hidden_size=8192, num_layers=30, num_attention_heads=16, checkpoint=checkpoint)
def model_builder(model_size: str) -> callable:
if model_size == "gpt2_medium":
return gpt2_medium
elif model_size == "gpt2_xl":
return gpt2_xl
elif model_size == "gpt2_10b":
return gpt2_10b
elif model_size == "gpt2_14b":
return gpt2_14b
elif model_size == "gpt2_20b":
return gpt2_20b
elif model_size == "gpt2_24b":
return gpt2_24b
else:
raise TypeError(f"model_builder {model_size}")
__all__ = ['model_builder']