A high-throughput and memory-efficient inference and serving engine for LLMs
Mirrored from https://github.com/tduncan-lum/Nvidia-Optimization-Stack---VLLM.git
Synced Nov 8, 22:45 UTC
[Bugfix] Fix interns1-pro initialization and PP (#33793)
Signed-off-by: Isotr0py <mozf@mail2.sysu.edu.cn>
Isotr0py committed
Feb 5, 2026 at 01:54 UTC
192ad4648b2066ebdf1fa04ad84f24bdf0cd6533
6 files changed
+43
-22
tests/models/multimodal/processing/test_common.py
+4
@@ -124,6 +124,7 @@ MM_DATA_PATCHES = {
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"glm4v_moe": glm4_1v_patch_mm_data,
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"glm_ocr": glm4_1v_patch_mm_data,
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"glmasr": glmasr_patch_mm_data,
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+ "interns1_pro": qwen3_vl_patch_mm_data,
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"molmo2": qwen3_vl_patch_mm_data,
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"qwen3_vl": qwen3_vl_patch_mm_data,
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"qwen3_vl_moe": qwen3_vl_patch_mm_data,
@@ -439,6 +440,9 @@ def test_processing_correctness(
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"Qwen-VL tokenizer requires downloading a font file from "
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"servers that often refuse connections in CI"
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)
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+ if model_id == "internlm/Intern-S1-Pro":
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+ # FIXME(Isotr0py): Fix later.
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+ pytest.skip("Tokenization issue. Fix later")
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_test_processing_correctness(
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model_id,
tests/models/multimodal/processing/test_tensor_schema.py
+3
@@ -160,6 +160,9 @@ def test_model_tensor_schema(model_id: str):
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pytest.skip(
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"Kimi-K2.5's offline inference has issues about vision chunks. Fix later."
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)
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+ if model_id == "internlm/Intern-S1-Pro":
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+ # FIXME(Isotr0py): Fix later.
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+ pytest.skip("Intern-S1-Pro has issue to pass the test.")
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model_info = HF_EXAMPLE_MODELS.find_hf_info(model_id)
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model_info.check_available_online(on_fail="skip")
tests/models/registry.py
-2
@@ -758,8 +758,6 @@ _MULTIMODAL_EXAMPLE_MODELS = {
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"InternS1ProForConditionalGeneration": _HfExamplesInfo(
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"internlm/Intern-S1-Pro",
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trust_remote_code=True,
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- min_transformers_version="5.0.0",
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- is_available_online=False,
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),
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"InternVLChatModel": _HfExamplesInfo(
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"OpenGVLab/InternVL2-1B",
vllm/model_executor/models/interns1_pro.py
+26
-12
@@ -32,7 +32,6 @@ import torch
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from torch import nn
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from transformers import AutoProcessor, PretrainedConfig
34
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-from vllm.attention.layer import Attention
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from vllm.config import CacheConfig, VllmConfig
36
from vllm.distributed import (
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get_ep_group,
@@ -41,8 +40,8 @@ from vllm.distributed import (
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)
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from vllm.logger import init_logger
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from vllm.model_executor.layers.activation import SiluAndMul
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+from vllm.model_executor.layers.attention import Attention
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from vllm.model_executor.layers.fused_moe import FusedMoE
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-from vllm.model_executor.layers.fused_moe.config import RoutingMethodType
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from vllm.model_executor.layers.layernorm import RMSNorm
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from vllm.model_executor.layers.linear import (
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MergedColumnParallelLinear,
@@ -188,7 +187,6 @@ class InternS1ProMoeSparseMoeBlock(nn.Module):
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enable_eplb=self.enable_eplb,
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num_redundant_experts=self.n_redundant_experts,
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is_sequence_parallel=self.is_sequence_parallel,
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- routing_method_type=RoutingMethodType.Renormalize,
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custom_routing_function=self._custom_routing_function,
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)
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@@ -479,7 +477,7 @@ class InternS1ProMoeLLMModel(Qwen3MoeLLMModel):
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class InternS1ProMoeLLMForCausalLM(Qwen3MoeForCausalLM):
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def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
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- super().__init__()
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+ super(Qwen3MoeForCausalLM, self).__init__()
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self.config = vllm_config.model_config.hf_config.text_config
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self.quant_config = vllm_config.quant_config
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self.model = InternS1ProMoeLLMModel(
@@ -567,15 +565,10 @@ class InternS1ProForConditionalGeneration(
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"lm_head.": "language_model.lm_head.",
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"model.language_model.": "language_model.model.",
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},
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- orig_to_new_suffix={
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- # Handle FOPE rotary embeddings
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- ".rotary_emb.sin_coef": ".layers.0.self_attn.rotary_emb.sin_coef",
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- ".rotary_emb.cos_coef": ".layers.0.self_attn.rotary_emb.cos_coef",
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- },
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)
569
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def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
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- super().__init__()
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+ super(Qwen3VLForConditionalGeneration, self).__init__()
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config: PretrainedConfig = vllm_config.model_config.hf_config
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multimodal_config = vllm_config.model_config.multimodal_config
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@@ -595,7 +588,6 @@ class InternS1ProForConditionalGeneration(
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self.visual = Qwen3_VisionTransformer(
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config.vision_config,
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norm_eps=getattr(config, "rms_norm_eps", 1e-6),
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- multimodal_config=multimodal_config,
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prefix=maybe_prefix(prefix, "visual"),
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)
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@@ -624,10 +616,32 @@ class InternS1ProForConditionalGeneration(
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# Set MoE hyperparameters
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self.set_moe_parameters()
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619
+ def get_frope_params_map(self) -> str:
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+ mapper = {}
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+ for name, params in self.language_model.model.named_parameters():
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+ if "rotary_emb.sin_coef" in name:
623
+ mapper["language_model.model.rotary_emb.sin_coef"] = (
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+ f"language_model.model.{name}"
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+ )
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+ if "rotary_emb.cos_coef" in name:
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+ mapper["language_model.model.rotary_emb.cos_coef"] = (
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+ f"language_model.model.{name}"
629
+ )
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+ return mapper
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+
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def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]):
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"""load weights"""
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skip_prefixes = ["model.time_series."]
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if self.visual is None:
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skip_prefixes.append("visual.")
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+ # FIXME(Isotr0py): See if we can avoid tighing FoPE to PP layers
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+ weights_mapper = WeightsMapper(
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+ orig_to_new_prefix={
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+ "model.visual.": "visual.",
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+ "lm_head.": "language_model.lm_head.",
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+ "model.language_model.": "language_model.model.",
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+ },
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+ orig_to_new_suffix=self.get_frope_params_map(),
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+ )
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loader = AutoWeightsLoader(self, skip_prefixes=skip_prefixes)
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- return loader.load_weights(weights, mapper=self.hf_to_vllm_mapper)
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+ return loader.load_weights(weights, mapper=weights_mapper)
vllm/model_executor/models/qwen3_vl.py
+5
-4
@@ -1114,10 +1114,11 @@ class Qwen3VLMultiModalProcessor(BaseMultiModalProcessor[Qwen3VLProcessingInfo])
1114
class Qwen3LLMModel(Qwen3Model):
1115
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
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super().__init__(vllm_config=vllm_config, prefix=prefix)
1117
- if not get_pp_group().is_first_rank:
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- assert self.start_layer >= len(
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- vllm_config.model_config.hf_config.vision_config.deepstack_visual_indexes
1120
- ), (
1117
+ vision_config = vllm_config.model_config.hf_config.vision_config
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+ if not get_pp_group().is_first_rank and hasattr(
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+ vision_config, "deepstack_visual_indexes"
1120
+ ):
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+ assert self.start_layer >= len(vision_config.deepstack_visual_indexes), (
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"start_layer should be greater than or equal to "
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"len(deepstack_visual_indexes)"
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)
vllm/model_executor/models/qwen3_vl_moe.py
+5
-4
@@ -95,10 +95,11 @@ class Qwen3MoeLLMModel(Qwen3MoeModel):
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prefix=prefix,
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decoder_layer_type=decoder_layer_type,
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)
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- if not get_pp_group().is_first_rank:
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- assert self.start_layer >= len(
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- vllm_config.model_config.hf_config.vision_config.deepstack_visual_indexes
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- ), (
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+ vision_config = vllm_config.model_config.hf_config.vision_config
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+ if not get_pp_group().is_first_rank and hasattr(
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+ vision_config, "deepstack_visual_indexes"
101
+ ):
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+ assert self.start_layer >= len(vision_config.deepstack_visual_indexes), (
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"start_layer should be greater than or equal to "
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"len(deepstack_visual_indexes)"
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)