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[Model] Supporet InternVL2_5 on v0.11.0 (#72)

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Co-authored-by: v_qiaoyijin <v_qiaoyijin@baidu.com>
This commit is contained in:
Joeegin
2026-01-04 16:38:05 +08:00
committed by GitHub
parent 684ce2761e
commit ded24f5026
3 changed files with 97 additions and 46 deletions
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32
vllm_kunlun/models/internlm2.py
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@@ -3,6 +3,7 @@
from collections.abc import Iterable
from functools import partial
from itertools import islice
from typing import Any, Optional, Union
import torch
@@ -30,10 +31,10 @@ from vllm.model_executor.layers.rotary_embedding import get_rope
from vllm.model_executor.layers.vocab_parallel_embedding import (
ParallelLMHead, VocabParallelEmbedding)
from vllm.model_executor.model_loader.weight_utils import default_weight_loader
from vllm.model_executor.sampling_metadata import SamplingMetadata
from vllm.sequence import IntermediateTensors
from vllm.model_executor.models.interfaces import SupportsLoRA, SupportsPP, default_pooling_type
from vllm.model_executor.models.interfaces import SupportsLoRA, SupportsPP
from vllm.model_executor.models.interfaces_base import default_pooling_type
from vllm.model_executor.models.utils import (is_pp_missing_parameter,
make_empty_intermediate_tensors_factory, make_layers,
maybe_prefix)
@@ -298,7 +299,7 @@ class InternLM2Model(nn.Module):
assert intermediate_tensors is not None
hidden_states = intermediate_tensors["hidden_states"]
residual = intermediate_tensors["residual"]
for layer in self.layers[self.start_layer:self.end_layer]:
for layer in islice(self.layers, self.start_layer, self.end_layer):
hidden_states, residual = layer(positions, hidden_states, residual)
if not get_pp_group().is_last_rank:
return IntermediateTensors({
@@ -358,10 +359,8 @@ class InternLM2ForCausalLM(nn.Module, SupportsPP, SupportsLoRA):
def compute_logits(
self,
hidden_states: torch.Tensor,
sampling_metadata: SamplingMetadata,
) -> Optional[torch.Tensor]:
logits = self.logits_processor(self.output, hidden_states,
sampling_metadata)
logits = self.logits_processor(self.output, hidden_states)
return logits
def load_weights(self, weights: Iterable[tuple[str,
@@ -423,13 +422,15 @@ class InternLM2ForRewardModel(InternLM2ForCausalLM):
delattr(self, attr)
config = vllm_config.model_config.hf_config
self.v_head = RowParallelLinear(
config.hidden_size,
1,
bias=False,
input_is_parallel=False,
prefix=maybe_prefix(prefix, "v_head"),
)
self.head_dtype = vllm_config.model_config.head_dtype
self.v_head = RowParallelLinear(config.hidden_size,
1,
bias=False,
input_is_parallel=False,
params_dtype=self.head_dtype,
prefix=maybe_prefix(prefix, "v_head"),
return_bias=False)
pooler_config = vllm_config.model_config.pooler_config
assert pooler_config is not None
@@ -446,5 +447,6 @@ class InternLM2ForRewardModel(InternLM2ForCausalLM):
) -> Union[torch.Tensor, IntermediateTensors]:
hidden_states = self.model(input_ids, positions, intermediate_tensors,
inputs_embeds)
logits, _ = self.v_head(hidden_states)
return logits
hidden_states = hidden_states.to(self.head_dtype)
logits = self.v_head(hidden_states)
return logits