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Add support for encoding pretokenized sequences #42

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ea1a031
support for encoding pretokenized content
kabachuha Jun 16, 2024
9a31ac8
Merge remote-tracking branch 'upstream/main' into from-pretokenized
kabachuha Jun 16, 2024
04f1288
correct slicing for tokenizer output batches
kabachuha Jun 16, 2024
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45 changes: 28 additions & 17 deletions gritlm/gritlm.py
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Original file line number Diff line number Diff line change
Expand Up @@ -3,7 +3,7 @@
import numpy as np
import torch
from tqdm import tqdm
from transformers import AutoModel, AutoModelForCausalLM, AutoTokenizer
from transformers import AutoModel, AutoModelForCausalLM, AutoTokenizer, BatchEncoding


class GritLM(torch.nn.Module):
Expand Down Expand Up @@ -92,7 +92,7 @@ def encode_corpus(self, corpus: Union[List[str], str, List[Dict[str, str]]], **k
@torch.no_grad()
def encode(
self,
sentences: Union[List[str], str],
sentences: Union[Union[BatchEncoding, dict], Union[List[str], str]],
batch_size: int = 256,
max_length: int = 512,
instruction: str = "",
Expand All @@ -106,25 +106,36 @@ def encode(
if self.num_gpus > 1:
batch_size *= self.num_gpus

input_was_string = False
if isinstance(sentences, str):
sentences = [sentences]
input_was_string = True
input_type = "string"
elif isinstance(sentences, dict):
input_type = "dict"
elif isinstance(sentences, BatchEncoding):
sentences = dict(sentences)
input_type = "dict"
else:
input_type = "list"

all_embeddings, all_kv_caches = [], []
for start_index in tqdm(range(0, len(sentences), batch_size), desc="Batches", disable=len(sentences)<256):
sentences_batch = [
instruction + s + self.embed_eos for s in sentences[start_index:start_index + batch_size]
]
# This will prepend the bos token if the tokenizer has `add_bos_token=True`
inputs = self.tokenizer(
sentences_batch,
padding=True,
truncation=True,
return_tensors='pt',
max_length=max_length,
add_special_tokens=add_special_tokens,
).to(self.device)
if input_type == "list" or input_type == "string":
sentences_batch = sentences[start_index:start_index + batch_size]
sentences_batch = [
instruction + s + self.embed_eos for s in sentences_batch
]
# This will prepend the bos token if the tokenizer has `add_bos_token=True`
inputs = self.tokenizer(
sentences_batch,
padding=True,
truncation=True,
return_tensors='pt',
max_length=max_length,
add_special_tokens=add_special_tokens,
).to(self.device)
elif input_type == "dict":
inputs = {k: v[start_index:start_index + batch_size] for k,v in sentences.items() if isinstance(v, torch.Tensor)}
inputs = {k: v.to(self.device) for k,v in inputs.items() if isinstance(v, torch.Tensor)}

if (self.attn is not None) and (self.attn[:2] == 'bb'):
inputs["is_causal"] = False
Expand Down Expand Up @@ -166,7 +177,7 @@ def encode(
all_embeddings = (
torch.cat(all_embeddings, dim=0) if convert_to_tensor else np.concatenate(all_embeddings, axis=0)
)
if input_was_string:
if input_type == "string":
all_embeddings = all_embeddings[0]
if get_cache:
# all_kv_caches = (
Expand Down