biondizzle/vllm
1
0
Fork 0
Code Issues Pull Requests Actions 2 Packages Projects Releases Wiki Activity

[Bugfix] Fix interns1-pro initialization and PP (#33793)

Browse Source 
Signed-off-by: Isotr0py <mozf@mail2.sysu.edu.cn>
This commit is contained in:
Isotr0py
2026-02-05 01:54:45 +08:00
committed by GitHub
parent 0e92298622
commit 192ad4648b
6 changed files with 43 additions and 22 deletions
Download Patch File Download Diff File Expand all files Collapse all files

38
vllm/model_executor/models/interns1_pro.py
View File

@@ -32,7 +32,6 @@ import torch
from torch import nn
from transformers import AutoProcessor, PretrainedConfig
from vllm.attention.layer import Attention
from vllm.config import CacheConfig, VllmConfig
from vllm.distributed import (
get_ep_group,
@@ -41,8 +40,8 @@ from vllm.distributed import (
)
from vllm.logger import init_logger
from vllm.model_executor.layers.activation import SiluAndMul
from vllm.model_executor.layers.attention import Attention
from vllm.model_executor.layers.fused_moe import FusedMoE
from vllm.model_executor.layers.fused_moe.config import RoutingMethodType
from vllm.model_executor.layers.layernorm import RMSNorm
from vllm.model_executor.layers.linear import (
MergedColumnParallelLinear,
@@ -188,7 +187,6 @@ class InternS1ProMoeSparseMoeBlock(nn.Module):
enable_eplb=self.enable_eplb,
num_redundant_experts=self.n_redundant_experts,
is_sequence_parallel=self.is_sequence_parallel,
routing_method_type=RoutingMethodType.Renormalize,
custom_routing_function=self._custom_routing_function,
)
@@ -479,7 +477,7 @@ class InternS1ProMoeLLMModel(Qwen3MoeLLMModel):
class InternS1ProMoeLLMForCausalLM(Qwen3MoeForCausalLM):
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
super().__init__()
super(Qwen3MoeForCausalLM, self).__init__()
self.config = vllm_config.model_config.hf_config.text_config
self.quant_config = vllm_config.quant_config
self.model = InternS1ProMoeLLMModel(
@@ -567,15 +565,10 @@ class InternS1ProForConditionalGeneration(
"lm_head.": "language_model.lm_head.",
"model.language_model.": "language_model.model.",
},
orig_to_new_suffix={
# Handle FOPE rotary embeddings
".rotary_emb.sin_coef": ".layers.0.self_attn.rotary_emb.sin_coef",
".rotary_emb.cos_coef": ".layers.0.self_attn.rotary_emb.cos_coef",
},
)
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
super().__init__()
super(Qwen3VLForConditionalGeneration, self).__init__()
config: PretrainedConfig = vllm_config.model_config.hf_config
multimodal_config = vllm_config.model_config.multimodal_config
@@ -595,7 +588,6 @@ class InternS1ProForConditionalGeneration(
self.visual = Qwen3_VisionTransformer(
config.vision_config,
norm_eps=getattr(config, "rms_norm_eps", 1e-6),
multimodal_config=multimodal_config,
prefix=maybe_prefix(prefix, "visual"),
)
@@ -624,10 +616,32 @@ class InternS1ProForConditionalGeneration(
# Set MoE hyperparameters
self.set_moe_parameters()
def get_frope_params_map(self) -> str:
mapper = {}
for name, params in self.language_model.model.named_parameters():
if "rotary_emb.sin_coef" in name:
mapper["language_model.model.rotary_emb.sin_coef"] = (
f"language_model.model.{name}"
)
if "rotary_emb.cos_coef" in name:
mapper["language_model.model.rotary_emb.cos_coef"] = (
f"language_model.model.{name}"
)
return mapper
def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]):
"""load weights"""
skip_prefixes = ["model.time_series."]
if self.visual is None:
skip_prefixes.append("visual.")
# FIXME(Isotr0py): See if we can avoid tighing FoPE to PP layers
weights_mapper = WeightsMapper(
orig_to_new_prefix={
"model.visual.": "visual.",
"lm_head.": "language_model.lm_head.",
"model.language_model.": "language_model.model.",
},
orig_to_new_suffix=self.get_frope_params_map(),
)
loader = AutoWeightsLoader(self, skip_prefixes=skip_prefixes)
return loader.load_weights(weights, mapper=self.hf_to_vllm_mapper)
return loader.load_weights(weights, mapper=weights_mapper)