Sequence length limited

[Henrykwokkk]

I tried this model, but the sequence length that the Routing Transformer can process seemed limited. I set the batch size as 16 and the sequence length as 1024, but it was out of GPU memory.

[lucidrains]

Hmm, that doesn't seem right, have you tried running the colab? Want to share your script?

[lucidrains]

How deep is your network? Try turning on reversibility?

[Henrykwokkk]

How deep is your network? Try turning on reversibility?

The depths of both the encoder and decoder are 3. Let me give you the feedback later.

[lucidrains]

how much memory are you working with? can you show me your full settings?

[Henrykwokkk]

The setting is shown as follows: NUM_BATCHES = int(1e5) BATCH_SIZE = 32 LEARNING_RATE = 1e-4 GENERATE_EVERY = 100 NUM_TOKENS = 256 + 2 ENC_SEQ_LEN = 1024 DEC_SEQ_LEN = 2048 model = RoutingTransformerEncDec( dim=512, enc_num_tokens=NUM_TOKENS, enc_depth=3, enc_heads=8, enc_max_seq_len=ENC_SEQ_LEN, enc_window_size=32, dec_num_tokens = NUM_TOKENS, dec_depth = 3, dec_heads = 8, dec_max_seq_len=DEC_SEQ_LEN, dec_window_size=32, ).cuda() RuntimeError happened: Tried to allocate 64.00 MiB (GPU 0; 10.76 GiB total capacity; 9.58 GiB already allocated; 20.94 MiB free; 9.89 GiB reserved in total by PyTorch)

Similarly, I also had this problem in implementing the reformer model, When I implemented about 500 batches, it was out of memory.

[lucidrains]

So firstly, turn on reversibility, and second, you can decrease your batch size and do gradient accumulation instead

[Henrykwokkk]

So firstly, turn on reversibility, and second, you can decrease your batch size and do gradient accumulation instead

I turned on reversibility and set the batch size 8. But the training stopped at batch 172 and RuntimeError happened because CUDA out of memory.

[lucidrains]

@guohanyang1994 make your batch size even smaller and increase your gradient accumulation

[Henrykwokkk]

Could I ask why CUDA out of memory occurs during training (batch 172) rather than at the beginning of training?

[tomweingarten]

Hard to say without seeing the code, but are your batches different sizes? It's possible it takes that long to hit the longest combination of sequence lengths.

[Henrykwokkk]

The batch size is fixed at 4. The sequence length is set to 2048 at most, but it still stops at about the 1200th batch. I am still confused :( But thanks for your reply.

[lucidrains]

@guohanyang1994 are you sure you don't have a memory leak? Routing Transformer has been trained on GPT3 sized datasets successfully by others, so I doubt there's any problems with the framework

[Henrykwokkk]

Oh yeah, exactly it is the memory leak problem. I have fixed it and thank you so much. Sorry to bother you as an NLP beginner.

[lucidrains]

ok np :D