mirror of https://github.com/hpcaitech/ColossalAI
51 lines
1.6 KiB
Python
51 lines
1.6 KiB
Python
import torch
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import torch.nn as nn
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from transformers import GPT2Config, GPT2LMHeadModel
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class GPTLMModel(nn.Module):
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def __init__(self,
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hidden_size=768,
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num_layers=12,
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num_attention_heads=12,
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max_seq_len=1024,
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vocab_size=50257,
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checkpoint=False):
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super().__init__()
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self.checkpoint = checkpoint
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self.model = GPT2LMHeadModel(
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GPT2Config(n_embd=hidden_size,
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n_layer=num_layers,
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n_head=num_attention_heads,
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n_positions=max_seq_len,
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n_ctx=max_seq_len,
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vocab_size=vocab_size))
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if checkpoint:
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self.model.gradient_checkpointing_enable()
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def forward(self, input_ids, attention_mask):
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# Only return lm_logits
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return self.model(input_ids=input_ids, attention_mask=attention_mask, use_cache=not self.checkpoint)[0]
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class GPTLMLoss(nn.Module):
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def __init__(self):
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super().__init__()
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self.loss_fn = nn.CrossEntropyLoss()
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def forward(self, logits, labels):
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shift_logits = logits[..., :-1, :].contiguous()
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shift_labels = labels[..., 1:].contiguous()
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# Flatten the tokens
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return self.loss_fn(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1))
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def gpt2_medium(checkpoint=False):
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return GPTLMModel(hidden_size=1024, num_layers=24, num_attention_heads=16, checkpoint=checkpoint)
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def gpt2_xl(checkpoint=False):
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return GPTLMModel(hidden_size=1600, num_layers=48, num_attention_heads=32, checkpoint=checkpoint)
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