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chore: upgrade transformers 4.52.3 (#6575)

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Co-authored-by: Mick <mickjagger19@icloud.com>
This commit is contained in:
Yineng Zhang
2025-05-25 22:49:58 -07:00
committed by GitHub
parent 84147254c9
commit 7eb9d8e594
5 changed files with 152 additions and 125 deletions
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2
python/pyproject.toml
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@@ -41,7 +41,7 @@ runtime_common = [
"soundfile==0.13.1",
"scipy",
"torchao==0.9.0",
"transformers==4.51.1",
"transformers==4.52.3",
"uvicorn",
"uvloop",
"xgrammar==0.1.19",

20
python/sglang/srt/configs/internvl.py
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@@ -7,11 +7,8 @@ import sentencepiece as spm
from transformers import (
TOKENIZER_MAPPING,
LlamaConfig,
Phi3Config,
PretrainedConfig,
PreTrainedTokenizer,
PreTrainedTokenizerFast,
Qwen2Config,
)
from sglang.utils import logger
@@ -302,24 +299,23 @@ class InternVLChatConfig(PretrainedConfig):
)
if llm_config is None:
# TODO: There might still be a bug in transformers version 4.44 and above.
llm_config = {"architectures": [""]}
llm_config = {"architectures": ["InternLM2ForCausalLM"]}
logger.info(
"llm_config is None. Initializing the LlamaConfig config with default values (`LlamaConfig`)."
)
self.vision_config = InternVisionConfig(**vision_config)
if llm_config["architectures"][0] == "LlamaForCausalLM":
if llm_config.get("architectures")[0] == "LlamaForCausalLM":
self.llm_config = LlamaConfig(**llm_config)
elif llm_config["architectures"][0] == "InternLM2ForCausalLM":
elif llm_config.get("architectures")[0] == "InternLM2ForCausalLM":
self.llm_config = InternLM2Config(**llm_config)
elif llm_config["architectures"][0] == "Phi3ForCausalLM":
self.llm_config = Phi3Config(**llm_config)
elif llm_config["architectures"][0] == "Qwen2ForCausalLM":
self.llm_config = Qwen2Config(**llm_config)
else:
raise ValueError(
"Unsupported architecture: {}".format(llm_config["architectures"][0])
"Unsupported architecture: {}".format(
llm_config.get("architectures")[0]
)
)
self.use_backbone_lora = use_backbone_lora
self.use_llm_lora = use_llm_lora
self.pad2square = pad2square

15
python/sglang/srt/configs/model_config.py
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@@ -196,6 +196,21 @@ class ModelConfig:
self.v_head_dim = self.hf_text_config.v_head_dim
self.qk_nope_head_dim = self.hf_text_config.qk_nope_head_dim
else:
if (
"MistralModel" in self.hf_config.architectures
or "MixtralForCausalLM" in self.hf_config.architectures
):
if getattr(self, "head_dim", None) is None:
self.head_dim = (
self.hf_config.hidden_size // self.hf_config.num_attention_heads
)
# In transformers==4.52.3, the head_dim is null in MistralConfig
if (
not hasattr(self.hf_text_config, "head_dim")
or self.hf_text_config.head_dim is None
):
setattr(self.hf_text_config, "head_dim", self.head_dim)
self.attention_arch = AttentionArch.MHA
self.num_attention_heads = self.hf_text_config.num_attention_heads

19
python/sglang/test/runners.py
View File

@@ -26,6 +26,7 @@ from transformers import (
AutoModelForCausalLM,
AutoModelForVision2Seq,
AutoProcessor,
GenerationConfig,
)
from sglang.srt.entrypoints.engine import Engine
@@ -382,13 +383,17 @@ class HFRunner:
model = base_model
outputs = model.generate(
input_ids,
do_sample=False,
temperature=None,
top_p=None,
max_new_tokens=max_new_tokens,
return_dict_in_generate=True,
output_scores=(not output_str_only),
input_ids=input_ids,
generation_config=GenerationConfig(
do_sample=False,
temperature=None,
top_p=None,
max_new_tokens=max_new_tokens,
return_dict_in_generate=True,
output_scores=(not output_str_only),
# make sure to disable compile
disable_compile=True,
),
)
text = tokenizer.decode(

221
test/srt/test_vlm_accuracy.py
View File

@@ -10,8 +10,15 @@ import requests
import torch
import torch.nn.functional as F
from PIL import Image
from transformers import AutoModel, AutoProcessor, AutoTokenizer
from transformers import (
AutoModel,
AutoProcessor,
AutoTokenizer,
Gemma3ForConditionalGeneration,
Qwen2_5_VLForConditionalGeneration,
)
from sglang import Engine
from sglang.srt.configs.model_config import ModelConfig
from sglang.srt.conversation import generate_chat_conv
from sglang.srt.managers.mm_utils import embed_mm_inputs, init_embedding_cache
@@ -34,6 +41,9 @@ class VisionLLMLogitsBase(unittest.IsolatedAsyncioTestCase):
def setUpClass(cls):
cls.image_url = "https://github.com/sgl-project/sglang/blob/main/test/lang/example_image.png?raw=true"
cls.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
cls.model_path = ""
cls.chat_template = ""
cls.processor = ""
response = requests.get(cls.image_url)
cls.main_image = Image.open(BytesIO(response.content))
@@ -160,107 +170,108 @@ class VisionLLMLogitsBase(unittest.IsolatedAsyncioTestCase):
return self.model_runner.model
class TestMiniCPMVLogits(VisionLLMLogitsBase):
@classmethod
def setUpClass(cls):
super().setUpClass()
cls.model_path = "openbmb/MiniCPM-V-2_6"
cls.tokenizer = AutoTokenizer.from_pretrained(
cls.model_path, trust_remote_code=True
)
cls.processor = AutoProcessor.from_pretrained(
cls.model_path, trust_remote_code=True
)
cls.chat_template = "minicpmv"
cls.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
cls.hf_model = (
AutoModel.from_pretrained(
cls.model_path, torch_dtype=torch.bfloat16, trust_remote_code=True
)
.eval()
.to(cls.device)
)
init_embedding_cache(0)
async def test_vlm_embedding_output(self):
"""
Compares the embedding output of vlm
"""
inputs = self.get_processor_output()
with torch.no_grad():
# hf
model_inputs = {
"input_ids": inputs.input_ids,
"image_bound": inputs.image_bound,
"pixel_values": inputs.pixel_values,
"tgt_sizes": inputs.tgt_sizes,
}
(hf_output, _) = self.hf_model.get_vllm_embedding(
model_inputs,
)
hf_output = hf_output.squeeze(0)
# sglang
model = self.get_sglang_model()
input_ids = inputs["input_ids"].to(self.device).flatten()
pixel_values = inputs["pixel_values"]
tgt_sizes = inputs["tgt_sizes"]
pixel_values_flat: List[torch.Tensor] = []
tgt_sizes_flat: List[torch.Tensor] = []
for pixel_b, tgt_b in zip(pixel_values, tgt_sizes):
# per image
if len(pixel_b) != len(tgt_b):
raise ValueError(
"Inconsistent N lengths, found: "
f"{len(pixel_b)} vs {len(tgt_b)}"
)
for pixel_n, tgt_n in zip(pixel_b, tgt_b):
pixel_values_flat += [pixel_n]
tgt_sizes_flat += [tgt_n]
im_start_id, im_end_id = (
self.tokenizer.im_start_id,
self.tokenizer.im_end_id,
)
slice_start_id, slice_end_id = (
self.tokenizer.slice_start_id,
self.tokenizer.slice_end_id,
)
image_offsets = BaseMultimodalProcessor.get_mm_items_offset_by_pair(
input_ids=input_ids, mm_start_id=im_start_id, mm_end_id=im_end_id
)
slice_offsets = BaseMultimodalProcessor.get_mm_items_offset_by_pair(
input_ids=input_ids, mm_start_id=slice_start_id, mm_end_id=slice_end_id
)
image_offsets.extend(slice_offsets)
image_offsets = sorted(image_offsets)
sglang_output = embed_mm_inputs(
mm_inputs_list=[
MultimodalInputs(
mm_items=[
MultimodalDataItem(
pixel_values=pixel_values_flat,
image_offsets=image_offsets,
tgt_size=tgt_sizes_flat,
modality=Modality.IMAGE,
pad_value=self.processor.tokenizer.unk_token_id,
)
]
),
],
extend_prefix_lens=[0],
extend_seq_lens=[input_ids.shape[0]],
input_ids=input_ids,
input_embedding=model.get_input_embeddings(),
image_data_embedding_func=model.get_image_feature,
placeholder_tokens={
Modality.IMAGE: self.processor.tokenizer.unk_token_id,
},
)
self.compare_outputs(sglang_output, hf_output)
# TODO: MiniCPMV is not compatible with transformers==4.52.3, temporarily disabled
# class TestMiniCPMVLogits(VisionLLMLogitsBase):
# @classmethod
# def setUpClass(cls):
# super().setUpClass()
# cls.model_path = "openbmb/MiniCPM-V-2_6"
# cls.tokenizer = AutoTokenizer.from_pretrained(
# cls.model_path, trust_remote_code=True
# )
# cls.processor = AutoProcessor.from_pretrained(
# cls.model_path, trust_remote_code=True
# )
# cls.chat_template = "minicpmv"
#
# cls.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# cls.hf_model = (
# AutoModel.from_pretrained(
# cls.model_path, torch_dtype=torch.bfloat16, trust_remote_code=True
# )
# .eval()
# .to(cls.device)
# )
# init_embedding_cache(0)
#
# async def test_vlm_embedding_output(self):
# """
# Compares the embedding output of vlm
# """
# inputs = self.get_processor_output()
#
# with torch.no_grad():
# # hf
# model_inputs = {
# "input_ids": inputs.input_ids,
# "image_bound": inputs.image_bound,
# "pixel_values": inputs.pixel_values,
# "tgt_sizes": inputs.tgt_sizes,
# }
# (hf_output, _) = self.hf_model.get_vllm_embedding(
# model_inputs,
# )
# hf_output = hf_output.squeeze(0)
#
# # sglang
# model = self.get_sglang_model()
# input_ids = inputs["input_ids"].to(self.device).flatten()
#
# pixel_values = inputs["pixel_values"]
# tgt_sizes = inputs["tgt_sizes"]
# pixel_values_flat: List[torch.Tensor] = []
# tgt_sizes_flat: List[torch.Tensor] = []
# for pixel_b, tgt_b in zip(pixel_values, tgt_sizes):
# # per image
# if len(pixel_b) != len(tgt_b):
# raise ValueError(
# "Inconsistent N lengths, found: "
# f"{len(pixel_b)} vs {len(tgt_b)}"
# )
# for pixel_n, tgt_n in zip(pixel_b, tgt_b):
# pixel_values_flat += [pixel_n]
# tgt_sizes_flat += [tgt_n]
#
# im_start_id, im_end_id = (
# self.tokenizer.im_start_id,
# self.tokenizer.im_end_id,
# )
# slice_start_id, slice_end_id = (
# self.tokenizer.slice_start_id,
# self.tokenizer.slice_end_id,
# )
#
# image_offsets = BaseMultimodalProcessor.get_mm_items_offset_by_pair(
# input_ids=input_ids, mm_start_id=im_start_id, mm_end_id=im_end_id
# )
# slice_offsets = BaseMultimodalProcessor.get_mm_items_offset_by_pair(
# input_ids=input_ids, mm_start_id=slice_start_id, mm_end_id=slice_end_id
# )
# image_offsets.extend(slice_offsets)
# image_offsets = sorted(image_offsets)
#
# sglang_output = embed_mm_inputs(
# mm_inputs_list=[
# MultimodalInputs(
# mm_items=[
# MultimodalDataItem(
# pixel_values=pixel_values_flat,
# image_offsets=image_offsets,
# tgt_size=tgt_sizes_flat,
# modality=Modality.IMAGE,
# pad_value=self.processor.tokenizer.unk_token_id,
# )
# ]
# ),
# ],
# extend_prefix_lens=[0],
# extend_seq_lens=[input_ids.shape[0]],
# input_ids=input_ids,
# input_embedding=model.get_input_embeddings(),
# image_data_embedding_func=model.get_image_feature,
# placeholder_tokens={
# Modality.IMAGE: self.processor.tokenizer.unk_token_id,
# },
# )
#
# self.compare_outputs(sglang_output, hf_output)