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.
51 lines
1.6 KiB
51 lines
1.6 KiB
import torch
|
|
import torch.nn as nn
|
|
from transformers import GPT2Config, 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.model = GPT2LMHeadModel(
|
|
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))
|
|
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]
|
|
|
|
|
|
class GPTLMLoss(nn.Module):
|
|
|
|
def __init__(self):
|
|
super().__init__()
|
|
self.loss_fn = nn.CrossEntropyLoss()
|
|
|
|
def forward(self, logits, labels):
|
|
shift_logits = logits[..., :-1, :].contiguous()
|
|
shift_labels = labels[..., 1:].contiguous()
|
|
# Flatten the tokens
|
|
return self.loss_fn(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1))
|
|
|
|
|
|
def gpt2_medium(checkpoint=False):
|
|
return GPTLMModel(hidden_size=1024, num_layers=24, num_attention_heads=16, checkpoint=checkpoint)
|
|
|
|
|
|
def gpt2_xl(checkpoint=False):
|
|
return GPTLMModel(hidden_size=1600, num_layers=48, num_attention_heads=32, checkpoint=checkpoint)
|