practice training models
this time i thought a little about separating the inputs from the
outputs, as well as how to do things in general when emotionally very
hard a litle
this doesn't seem out of the box, but you could concatenate the inputs
and outputs and mask the inputs as -100 when passing to cross entropy
i've shut down the lax server :D
import transformers, torch, torch.nn
pipe = transformers.pipeline('text-generation',
'meta-llama/Meta-Llama-3.1-8B', device_map='auto')
model = pipe.model
tokenizer = pipe.tokenizer
lines = open(__file__).read().split('\n')
# if we were separating input from output we might want to paste the
tokens after tokenizing, and left-pad the input while right-padding
the output?
tokenizer.pad_token = tokenizer.eos_token
inputs = tokenizer(lines, return_tensors='pt', padding=True)#,
padding_side='left')
# outputs = tokenizer(lines[1:] + lines[:1], return_tensors='pt',
padding=True, padding_side='right')
# inputs['labels'] = torch.cat( ... concatenate inputs and outputs
# but set inputs to -100
inputs['labels'] = inputs['input_ids'].detach().clone()
inputs['labels'][inputs['attention_mask']==0] = -100
print(inputs['input_ids'].min(), inputs['input_ids'].max())
model.train()
loss = 10
last_loss = 0
lr=1e-5
optim = torch.optim.Adam(model.parameters(),lr=lr)
while loss > 0.1:
optim.zero_grad()
model.zero_grad()
# this passes the epoch as a batch, which may enforce less
generality than minibatches, but also needs different code for larger
data, but is faster
print('forward')
last_loss = loss
loss = model(**inputs).loss
print(loss.item())
if loss > last_loss:
lr /= 2
optim = torch.optim.Adam(model.parameters(),lr=lr)
print('backward')
loss.backward()
optim.step()
# don't forget to save the model
