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[VLM] Support PP for Qwen2.5-VL (#13075)

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Co-authored-by: luoyuan.luo <luoyuan.luo@antgroup.com>
Co-authored-by: Tianyu Guo <guoty9@mail2.sysu.edu.cn>
This commit is contained in:
Yuan Luo
2025-11-12 23:18:44 +08:00
committed by GitHub
parent c2e56dadb2
commit 706502ff6c
3 changed files with 88 additions and 54 deletions
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75
python/sglang/srt/managers/mm_utils.py
View File

@@ -660,43 +660,46 @@ def general_mm_embed_routine(
"""
assert hasattr(language_model, "get_input_embeddings")
embed_tokens = language_model.get_input_embeddings()
if (
not forward_batch.forward_mode.is_decode()
and not forward_batch.forward_mode.is_target_verify()
and forward_batch.contains_mm_inputs()
):
mm_inputs_list = [
mm_input for mm_input in forward_batch.mm_inputs if mm_input is not None
]
extend_prefix_lens = [
prefix_len
for i, prefix_len in enumerate(forward_batch.extend_prefix_lens_cpu)
if forward_batch.mm_inputs[i] is not None
]
extend_seq_lens = [
seq_len
for i, seq_len in enumerate(forward_batch.extend_seq_lens_cpu)
if forward_batch.mm_inputs[i] is not None
]
inputs_embeds, other_info = embed_mm_inputs(
mm_inputs_list=mm_inputs_list,
extend_prefix_lens=extend_prefix_lens,
extend_seq_lens=extend_seq_lens,
input_ids=input_ids,
multimodal_model=multimodal_model,
input_embedding=embed_tokens,
data_embedding_func_mapping=data_embedding_funcs,
placeholder_tokens=placeholder_tokens,
use_deepstack=use_deepstack,
)
# add for qwen3_vl deepstack
if use_deepstack:
kwargs["input_deepstack_embeds"] = other_info["input_deepstack_embeds"]
# once used, mm_inputs is useless, considering chunked-prefill is disabled for multimodal models
# just being defensive here
forward_batch.mm_inputs = None
if not hasattr(language_model, "pp_group") or language_model.pp_group.is_first_rank:
if (
not forward_batch.forward_mode.is_decode()
and not forward_batch.forward_mode.is_target_verify()
and forward_batch.contains_mm_inputs()
):
mm_inputs_list = [
mm_input for mm_input in forward_batch.mm_inputs if mm_input is not None
]
extend_prefix_lens = [
prefix_len
for i, prefix_len in enumerate(forward_batch.extend_prefix_lens_cpu)
if forward_batch.mm_inputs[i] is not None
]
extend_seq_lens = [
seq_len
for i, seq_len in enumerate(forward_batch.extend_seq_lens_cpu)
if forward_batch.mm_inputs[i] is not None
]
inputs_embeds, other_info = embed_mm_inputs(
mm_inputs_list=mm_inputs_list,
extend_prefix_lens=extend_prefix_lens,
extend_seq_lens=extend_seq_lens,
input_ids=input_ids,
multimodal_model=multimodal_model,
input_embedding=embed_tokens,
data_embedding_func_mapping=data_embedding_funcs,
placeholder_tokens=placeholder_tokens,
use_deepstack=use_deepstack,
)
# add for qwen3_vl deepstack
if use_deepstack:
kwargs["input_deepstack_embeds"] = other_info["input_deepstack_embeds"]
# once used, mm_inputs is useless, considering chunked-prefill is disabled for multimodal models
# just being defensive here
forward_batch.mm_inputs = None
else:
inputs_embeds = embed_tokens(input_ids)
else:
inputs_embeds = embed_tokens(input_ids)
inputs_embeds = None
hidden_states = language_model(
input_ids=None,

8
python/sglang/srt/managers/scheduler.py
View File

@@ -382,10 +382,12 @@ class Scheduler(
# avoiding any coupling with CUDA streams/devices.
if self.server_args.enable_dp_attention:
self.cpu_group = self.attn_tp_cpu_group
self.entry_rank = self.attn_tp_group.first_rank
self.is_entry_rank = self.attn_tp_rank == 0
else:
self.cpu_group = self.tp_cpu_group
self.is_entry_rank = self.tp_group.rank == 0
self.entry_rank = self.tp_group.first_rank
self.is_entry_rank = self.tp_group.rank_in_group == 0
self.pad_input_ids_func = self.tp_worker.get_pad_input_ids_func()
set_random_seed(self.random_seed)
@@ -1221,7 +1223,7 @@ class Scheduler(
if group_world_size > 1:
obj_list = [image_inputs]
torch.distributed.broadcast_object_list(
obj_list, src=0, group=self.cpu_group
obj_list, src=self.entry_rank, group=self.cpu_group
)
image_inputs = obj_list[0]
else:
@@ -1229,7 +1231,7 @@ class Scheduler(
if group_world_size > 1:
obj_list = [None]
torch.distributed.broadcast_object_list(
obj_list, src=0, group=self.cpu_group
obj_list, src=self.entry_rank, group=self.cpu_group
)
image_inputs = obj_list[0]
else:

59
python/sglang/srt/models/qwen2_5_vl.py
View File

@@ -40,6 +40,7 @@ from transformers.models.qwen2_5_vl.modeling_qwen2_5_vl import (
Qwen2_5_VisionRotaryEmbedding,
)
from sglang.srt.distributed.parallel_state import get_pp_group
from sglang.srt.layers.attention.vision import VisionAttention
from sglang.srt.layers.layernorm import RMSNorm
from sglang.srt.layers.linear import (
@@ -50,13 +51,14 @@ from sglang.srt.layers.linear import (
from sglang.srt.layers.logits_processor import LogitsProcessor
from sglang.srt.layers.pooler import Pooler, PoolingType
from sglang.srt.layers.quantization.base_config import QuantizationConfig
from sglang.srt.layers.utils import PPMissingLayer, get_layer_id
from sglang.srt.layers.vocab_parallel_embedding import ParallelLMHead
from sglang.srt.managers.mm_utils import (
MultiModalityDataPaddingPatternMultimodalTokens,
general_mm_embed_routine,
)
from sglang.srt.managers.schedule_batch import MultimodalDataItem, MultimodalInputs
from sglang.srt.model_executor.forward_batch_info import ForwardBatch
from sglang.srt.model_executor.forward_batch_info import ForwardBatch, PPProxyTensors
from sglang.srt.model_loader.weight_utils import default_weight_loader
from sglang.srt.models.qwen2 import Qwen2Model
from sglang.srt.models.utils import permute_inv
@@ -482,6 +484,7 @@ class Qwen2_5_VLForConditionalGeneration(nn.Module):
) -> None:
super().__init__()
self.pp_group = get_pp_group()
self.config = config
self.visual = Qwen2_5_VisionTransformer(
config.vision_config,
@@ -498,15 +501,20 @@ class Qwen2_5_VLForConditionalGeneration(nn.Module):
prefix=add_prefix("model", prefix),
)
if config.tie_word_embeddings:
self.lm_head = self.model.embed_tokens
if self.pp_group.is_last_rank:
if self.pp_group.world_size == 1 and self.config.tie_word_embeddings:
self.lm_head = self.model.embed_tokens
else:
self.lm_head = ParallelLMHead(
self.config.vocab_size,
self.config.hidden_size,
quant_config=quant_config,
prefix=add_prefix("lm_head", prefix),
)
else:
self.lm_head = ParallelLMHead(
config.vocab_size,
config.hidden_size,
quant_config=quant_config,
prefix=add_prefix("lm_head", prefix),
)
# ranks other than the last rank will have a placeholder layer
self.lm_head = PPMissingLayer()
self.is_mrope_enabled = "mrope_section" in self.config.rope_scaling
self.logits_processor = LogitsProcessor(config)
@@ -551,6 +559,7 @@ class Qwen2_5_VLForConditionalGeneration(nn.Module):
positions: torch.Tensor,
forward_batch: ForwardBatch,
get_embedding: bool = False,
pp_proxy_tensors: Optional[PPProxyTensors] = None,
):
"""Run forward pass for Qwen2_5-VL.
@@ -583,18 +592,25 @@ class Qwen2_5_VLForConditionalGeneration(nn.Module):
language_model=self.model,
multimodal_model=self,
positions=positions,
pp_proxy_tensors=pp_proxy_tensors,
)
aux_hidden_states = None
if self.capture_aux_hidden_states:
hidden_states, aux_hidden_states = hidden_states
if not get_embedding:
return self.logits_processor(
input_ids, hidden_states, self.lm_head, forward_batch, aux_hidden_states
)
if self.pp_group.is_last_rank:
if not get_embedding:
return self.logits_processor(
input_ids,
hidden_states,
self.lm_head,
forward_batch,
)
else:
return self.pooler(hidden_states, forward_batch)
else:
return self.pooler(hidden_states, forward_batch)
return hidden_states
def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
stacked_params_mapping = [
@@ -620,6 +636,16 @@ class Qwen2_5_VLForConditionalGeneration(nn.Module):
):
continue
name = name.replace(weight_name, param_name)
layer_id = get_layer_id(name)
if (
layer_id is not None
and hasattr(self.model, "start_layer")
and (
layer_id < self.model.start_layer
or layer_id >= self.model.end_layer
)
):
continue
# Skip loading extra bias for GPTQ models.
if name.endswith(".bias") and name not in params_dict:
@@ -637,7 +663,10 @@ class Qwen2_5_VLForConditionalGeneration(nn.Module):
# Skip loading extra bias for GPTQ models.
if name.endswith(".bias") and name not in params_dict:
continue
param = params_dict[name]
if name in params_dict.keys():
param = params_dict[name]
else:
continue
except KeyError:
print(params_dict.keys())
raise