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refactor: use processor in infer backup #368

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refactor: use processor in infer backup #368

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47 changes: 10 additions & 37 deletions examples/infer_backup.py
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Original file line number Diff line number Diff line change
Expand Up @@ -12,8 +12,6 @@
# =============================================================================

import infinicore
import transformers
from transformers import AutoTokenizer
from tokenizers import decoders as _dec
from infinilm.modeling_utils import load_model_state_dict_by_file
from infinilm.distributed import DistConfig
Expand All @@ -26,6 +24,7 @@
from infinilm.cache import StaticKVCacheConfig, PagedKVCacheConfig
from packaging import version
from infinilm.base_config import BaseConfig
from infinilm.processors import AutoInfinilmProcessor

from PIL import Image
import torch
Expand Down Expand Up @@ -72,7 +71,8 @@ def test(
# ---------------------------------------------------------------------------- #
# create tokenizer
# ---------------------------------------------------------------------------- #
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
text_processor = AutoInfinilmProcessor.from_pretrained(model_path)
tokenizer = text_processor.get_tokenizer()

processor = None
if image_path is not None:
Expand All @@ -84,24 +84,6 @@ def test(
)
tokenizer = processor.tokenizer

if "llama" == model.model_type:
backend = getattr(tokenizer, "backend_tokenizer", None)
target = getattr(backend, "_tokenizer", backend)
norm = getattr(target, "normalizer", None)
dec = getattr(target, "decoder", None)
sn = repr(norm)[:800] if norm is not None else ""
sd = repr(dec)[:800] if dec is not None else ""
has_prepend = "Prepend" in sn
has_strip = "Strip" in sd
if has_prepend and has_strip:
target.decoder = _dec.Sequence(
[
_dec.Replace("▁", " "),
_dec.ByteFallback(),
_dec.Fuse(),
]
)

# ---------------------------------------------------------------------------- #
# tokenize
# ---------------------------------------------------------------------------- #
Expand All @@ -119,7 +101,7 @@ def test(

if hasattr(tokenizer, "chat_template") and tokenizer.chat_template is not None:
input_contents = [
tokenizer.apply_chat_template(
text_processor.apply_chat_template(
conversation=[{"role": "user", "content": prompt}],
add_generation_prompt=True,
tokenize=False,
Expand Down Expand Up @@ -152,21 +134,12 @@ def test(
else:
raise ValueError(f"Unsupported multimodal model_type: {model.model_type}")
else:
if version.parse(transformers.__version__) < version.parse("5.0.0"):
# Ideally this is solved by upgrading transformers. However, doing so causes version mismatch between transformers and mlu pytorch on devices with Phytium CPU. So a branch is temporarily used.
input_ids_list = [
tokenizer.encode_plus(
text, truncation=True, max_length=2048, add_special_tokens=True
)["input_ids"]
for text in input_contents
]
else:
input_ids_list = [
tokenizer._encode_plus(
text, truncation=True, max_length=2048, add_special_tokens=True
)["input_ids"]
for text in input_contents
]
input_ids_list = [
tokenizer.encode(
text, truncation=True, max_length=2048, add_special_tokens=True
)
for text in input_contents
]

# ---------------------------------------------------------------------------- #
# Create KVCache
Expand Down