Comments (4)
It's included in our nvidia docker containers
but you can also get it from here
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Okay, I have installed apex and all of the other dependencies but now I get this:
Generate Samples
WARNING: No training data specified
using world size: 1 and model-parallel size: 1
> using dynamic loss scaling
> initializing model parallel with size 1
> initializing model parallel cuda seeds on global rank 0, model parallel rank 0, and data parallel rank 0 with model parallel seed: 3952 and data parallel seed: 1234
100% 1042301/1042301 [00:00<00:00, 5937749.24B/s]
100% 456318/456318 [00:00<00:00, 3423447.30B/s]
prepare tokenizer done
building GPT2 model ...
> number of parameters on model parallel rank 0: 124475904
WARNING: could not find the metadata file checkpoints/gpt2_345m/latest_checkpointed_iteration.txt
will not load any checkpoints and will start from random
Traceback (most recent call last):
File "generate_samples.py", line 496, in <module>
main()
File "generate_samples.py", line 492, in main
write_and_generate_samples_unconditional(model, tokenizer, args)
File "generate_samples.py", line 319, in write_and_generate_samples_unconditional
for datum in generate_samples_unconditional(model, tokenizer, args):
File "generate_samples.py", line 295, in generate_samples_unconditional
for token_stream in get_token_stream(model, copy.deepcopy(context_tokens), tokenizer, args):
File "generate_samples.py", line 347, in get_token_stream
tokens, attention_mask, position_ids=get_batch(context_tokens_tensor, args)
File "generate_samples.py", line 99, in get_batch
args.reset_attention_mask)
TypeError: get_masks_and_position_ids() missing 1 required positional argument: 'eod_mask_loss'
Do I need to train first? I'm just playing around with this but I'm not sure exactly what it's doing even after reading the documentation. Is this for training GPT-2 and BERT or is it style control? Does it somehow blend the two?
I'm running this in a Colab doc I made by the way.
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Looks like you resolved the apex dependency but are now running into the same issue as: #23
Iād close this in favor of the above.
Not sure if this project is actively maintained but the examples do not work due to Issue 23.
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Closing as the remaining question is not the original topic and a duplicate of #23
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