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Revert transformers to 4.57.1 (#14801)

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This commit is contained in:
Yuhao Yang
2025-12-11 03:04:36 +08:00
committed by GitHub
parent ef1ab2302a
commit c1bd5ee8c5
7 changed files with 10 additions and 26 deletions
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2
python/pyproject.toml
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@@ -69,7 +69,7 @@ dependencies = [
"torchvision",
"torchao==0.9.0",
"tqdm",
"transformers==5.0.0rc0",
"transformers==4.57.1",
"uvicorn",
"uvloop",
"xgrammar==0.1.27",

2
python/pyproject_cpu.toml
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@@ -59,7 +59,7 @@ dependencies = [
"timm==1.0.16",
"torchao==0.9.0",
"tqdm",
"transformers==5.0.0rc0",
"transformers==4.57.1",
"uvicorn",
"uvloop",
"xgrammar==0.1.27",

2
python/pyproject_other.toml
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@@ -59,7 +59,7 @@ runtime_common = [
"timm==1.0.16",
"torchao==0.9.0",
"tqdm",
"transformers==5.0.0rc0",
"transformers==4.57.1",
"uvicorn",
"uvloop",
"xgrammar==0.1.27",

2
python/pyproject_xpu.toml
View File

@@ -63,7 +63,7 @@ dependencies = [
"timm==1.0.16",
"torchao==0.9.0",
"tqdm",
"transformers==5.0.0rc0",
"transformers==4.57.1",
"uvicorn",
"uvloop",
# "xgrammar==0.1.24", , xgrammar depends on CUDA PyTorch and Triton only

19
python/sglang/srt/configs/model_config.py
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@@ -80,16 +80,6 @@ def get_nsa_index_n_heads(config: PretrainedConfig) -> int:
return config.index_n_heads
def handle_rope_parameters(config: PretrainedConfig):
if hasattr(config, "rope_scaling"):
rope_scaling = config.rope_scaling
if isinstance(rope_scaling, dict):
for k, v in rope_scaling.items():
if not hasattr(config, k):
setattr(config, k, v)
return
class ModelConfig:
def __init__(
self,
@@ -137,8 +127,6 @@ class ModelConfig:
**kwargs,
)
self.hf_text_config = get_hf_text_config(self.hf_config)
handle_rope_parameters(self.hf_text_config)
handle_rope_parameters(self.hf_config)
self.hf_generation_config = get_generation_config(
self.model_path,
trust_remote_code=trust_remote_code,
@@ -370,10 +358,9 @@ class ModelConfig:
mscale_all_dim = self.hf_config.rope_scaling.get(
"mscale_all_dim", False
)
scaling_factor = self.hf_config.rope_scaling.get("factor")
if scaling_factor is not None:
mscale = yarn_get_mscale(scaling_factor, float(mscale_all_dim))
self.scaling = self.scaling * mscale * mscale
scaling_factor = self.hf_config.rope_scaling["factor"]
mscale = yarn_get_mscale(scaling_factor, float(mscale_all_dim))
self.scaling = self.scaling * mscale * mscale
elif "MiniCPM3ForCausalLM" in self.hf_config.architectures:
self.head_dim = 128

6
python/sglang/srt/models/deepseek_v2.py
View File

@@ -2704,12 +2704,6 @@ class DeepseekV2DecoderLayer(nn.Module):
self.config = config
rope_theta = getattr(config, "rope_theta", 10000)
rope_scaling = getattr(config, "rope_scaling", None)
if rope_scaling is not None:
# In transformers 5.0.0rc0+, rope_theta and rope_type are also included in rope_scaling.
# Therefore, if rope_scaling contains only these two keys,
# it effectively means there are no special rope_scaling parameters.
if set(rope_scaling.keys()) <= {"rope_theta", "rope_type"}:
rope_scaling = None
max_position_embeddings = getattr(config, "max_position_embeddings", 8192)
self.speculative_algorithm = SpeculativeAlgorithm.from_string(
get_global_server_args().speculative_algorithm