Why is the vocab size 64?
#1
by
ekiefl
- opened
The logits output shape is described as (batch_size, seq_len, vocab_size), which in the below example is (2, 11, 64). Why is the vocab size 64 though, when the tokenizer has a vocab of 33?
Example in question:
from transformers import AutoModelForMaskedLM # AutoModel also works
model = AutoModelForMaskedLM.from_pretrained("Synthyra/ESMplusplus_small", trust_remote_code=True)
tokenizer = model.tokenizer
print(len(tokenizer.vocab))
sequences = ["MPRTEIN", "MSEQWENCE"]
tokenized = tokenizer(sequences, padding=True, return_tensors="pt")
# tokenized['labels'] = tokenized['input_ids'].clone() # correctly mask input_ids and set unmasked instances of labels to -100 for MLM training
output = model(**tokenized) # get all hidden states with output_hidden_states=True
print(
output.logits.shape
) # language modeling logits, (batch_size, seq_len, vocab_size), (2, 11, 64)
print(
output.last_hidden_state.shape
) # last hidden state of the model, (batch_size, seq_len, hidden_size), (2, 11, 960)
print(output.loss) # language modeling loss if you passed labels
# print(output.hidden_states) # all hidden states if you passed output_hidden_states=True (in tuple)
Thanks in advance.
I'm not sure why EvoScale chose 64 in the embedding layer / LM head. From pure speculation it may have to do with the other types of tokens in ESM3, or could be because cuda based operations that are divisible by 8 are faster. However, in regular inference, the additional tokens are not used and should not harm performance.
lhallee
changed discussion status to
closed
Gonna leave this open in case other people have the same question.
lhallee
changed discussion status to
open
Thanks @lhallee . You're totally right. Here's a small script I used to convince myself.
from transformers import AutoModelForMaskedLM
model = AutoModelForMaskedLM.from_pretrained("Synthyra/ESMplusplus_small", trust_remote_code=True)
tokenizer = model.tokenizer
mask_pos = 100
sequences = ["L" * 200]
tokenized = tokenizer(sequences, padding=True, return_tensors="pt")
tokenized["input_ids"][0, mask_pos] = tokenizer.mask_token_id
output = model(**tokenized)
# Logit corresponding to L should be the argmax
assert output.logits[0, mask_pos].argmax().item() == tokenizer.vocab["L"]