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Unverified Commit f89d18ff authored by youkaichao's avatar youkaichao Committed by GitHub
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[6/N] pass whole config to inner model (#10205) 


Signed-off-by: default avataryoukaichao <youkaichao@gmail.com>
parent f0f2e563
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Showing with 189 additions and 279 deletions
vllm/model_executor/models/gpt_bigcode.py
View file @ f89d18ff
......@@ -25,7 +25,7 @@ from transformers import GPTBigCodeConfig
from vllm.attention import Attention, AttentionMetadata
from vllm.compilation.decorators import support_torch_compile
from vllm.config import CacheConfig, LoRAConfig, VllmConfig
from vllm.config import CacheConfig, VllmConfig
from vllm.distributed import get_pp_group, get_tensor_model_parallel_world_size
from vllm.model_executor.layers.activation import get_act_fn
from vllm.model_executor.layers.linear import (ColumnParallelLinear,
......@@ -189,15 +189,14 @@ class GPTBigCodeBlock(nn.Module):
@support_torch_compile
class GPTBigCodeModel(nn.Module):
def __init__(
self,
config: GPTBigCodeConfig,
cache_config: Optional[CacheConfig] = None,
quant_config: Optional[QuantizationConfig] = None,
lora_config: Optional[LoRAConfig] = None,
prefix: str = "",
):
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
super().__init__()
config = vllm_config.model_config.hf_config
cache_config = vllm_config.cache_config
quant_config = vllm_config.quant_config
lora_config = vllm_config.lora_config
self.config = config
assert not config.add_cross_attention
......@@ -265,7 +264,6 @@ class GPTBigCodeForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
):
super().__init__()
config = vllm_config.model_config.hf_config
cache_config = vllm_config.cache_config
quant_config = vllm_config.quant_config
lora_config = vllm_config.lora_config
......@@ -273,8 +271,8 @@ class GPTBigCodeForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
self.lora_config = lora_config
self.quant_config = quant_config
self.transformer = GPTBigCodeModel(config, cache_config, quant_config,
lora_config)
self.transformer = GPTBigCodeModel(vllm_config=vllm_config,
prefix=prefix)
if self.config.tie_word_embeddings:
self.lm_head = self.transformer.wte
else:
......
vllm/model_executor/models/gpt_j.py
View file @ f89d18ff
......@@ -42,7 +42,8 @@ from vllm.sequence import IntermediateTensors
from .interfaces import SupportsPP
from .utils import (is_pp_missing_parameter,
make_empty_intermediate_tensors_factory, make_layers)
make_empty_intermediate_tensors_factory, make_layers,
maybe_prefix)
class GPTJAttention(nn.Module):
......@@ -177,14 +178,13 @@ class GPTJBlock(nn.Module):
@support_torch_compile
class GPTJModel(nn.Module):
def __init__(
self,
config: GPTJConfig,
cache_config: Optional[CacheConfig] = None,
quant_config: Optional[QuantizationConfig] = None,
prefix: str = "",
):
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
super().__init__()
config = vllm_config.model_config.hf_config
cache_config = vllm_config.cache_config
quant_config = vllm_config.quant_config
self.config = config
self.embed_dim = config.n_embd
self.wte = VocabParallelEmbedding(
......@@ -236,12 +236,13 @@ class GPTJForCausalLM(nn.Module, SupportsPP):
):
super().__init__()
config = vllm_config.model_config.hf_config
cache_config = vllm_config.cache_config
quant_config = vllm_config.quant_config
self.config = config
self.quant_config = quant_config
assert not config.tie_word_embeddings
self.transformer = GPTJModel(config, cache_config, quant_config)
self.transformer = GPTJModel(vllm_config=vllm_config,
prefix=maybe_prefix(
prefix, "transformer"))
self.lm_head = ParallelLMHead(
config.vocab_size,
config.n_embd,
......
vllm/model_executor/models/gpt_neox.py
View file @ f89d18ff
......@@ -41,7 +41,8 @@ from vllm.sequence import IntermediateTensors
from .interfaces import SupportsPP
from .utils import (is_pp_missing_parameter,
make_empty_intermediate_tensors_factory, make_layers)
make_empty_intermediate_tensors_factory, make_layers,
maybe_prefix)
class GPTNeoXAttention(nn.Module):
......@@ -189,14 +190,13 @@ class GPTNeoXLayer(nn.Module):
@support_torch_compile
class GPTNeoXModel(nn.Module):
def __init__(
self,
config: GPTNeoXConfig,
cache_config: Optional[CacheConfig] = None,
quant_config: Optional[QuantizationConfig] = None,
prefix: str = "",
):
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
super().__init__()
config = vllm_config.model_config.hf_config
cache_config = vllm_config.cache_config
quant_config = vllm_config.quant_config
self.config = config
self.embed_in = VocabParallelEmbedding(
......@@ -249,11 +249,11 @@ class GPTNeoXForCausalLM(nn.Module, SupportsPP):
):
super().__init__()
config = vllm_config.model_config.hf_config
cache_config = vllm_config.cache_config
quant_config = vllm_config.quant_config
self.config = config
self.quant_config = quant_config
self.gpt_neox = GPTNeoXModel(config, cache_config, quant_config)
self.gpt_neox = GPTNeoXModel(vllm_config=vllm_config,
prefix=maybe_prefix(prefix, "gpt_neox"))
self.embed_out = ParallelLMHead(
config.vocab_size,
config.hidden_size,
......
vllm/model_executor/models/granite.py
View file @ f89d18ff
......@@ -28,7 +28,7 @@ from transformers import GraniteConfig
from vllm.attention import Attention, AttentionMetadata
from vllm.compilation.decorators import support_torch_compile
from vllm.config import CacheConfig, LoRAConfig, VllmConfig
from vllm.config import CacheConfig, VllmConfig
from vllm.distributed import (get_pp_group, get_tensor_model_parallel_rank,
get_tensor_model_parallel_world_size)
from vllm.model_executor.layers.activation import SiluAndMul
......@@ -52,7 +52,8 @@ from vllm.platforms import current_platform
from vllm.sequence import IntermediateTensors
from .interfaces import SupportsLoRA, SupportsPP
from .utils import PPMissingLayer, is_pp_missing_parameter, make_layers
from .utils import (PPMissingLayer, is_pp_missing_parameter, make_layers,
maybe_prefix)
class GraniteMLP(nn.Module):
......@@ -257,15 +258,14 @@ class GraniteDecoderLayer(nn.Module):
@support_torch_compile
class GraniteModel(nn.Module):
def __init__(
self,
config: GraniteConfig,
cache_config: Optional[CacheConfig] = None,
quant_config: Optional[QuantizationConfig] = None,
lora_config: Optional[LoRAConfig] = None,
prefix: str = "",
) -> None:
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
super().__init__()
config = vllm_config.model_config.hf_config
cache_config = vllm_config.cache_config
quant_config = vllm_config.quant_config
lora_config = vllm_config.lora_config
self.config = config
self.padding_idx = config.pad_token_id
lora_vocab = (lora_config.lora_extra_vocab_size *
......@@ -370,25 +370,17 @@ class GraniteForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
"up_proj": ("gate_up_proj", 1),
}
def __init__(
self,
vllm_config: VllmConfig,
prefix: str = "",
) -> None:
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
super().__init__()
config = vllm_config.model_config.hf_config
cache_config = vllm_config.cache_config
quant_config = vllm_config.quant_config
lora_config = vllm_config.lora_config
self.config = config
self.lora_config = lora_config
self.model = GraniteModel(config,
cache_config,
quant_config,
lora_config=lora_config,
prefix="model")
self.model = GraniteModel(vllm_config=vllm_config,
prefix=maybe_prefix(prefix, "model"))
if get_pp_group().is_last_rank:
self.unpadded_vocab_size = config.vocab_size
if lora_config:
......
vllm/model_executor/models/granitemoe.py
View file @ f89d18ff
......@@ -28,7 +28,7 @@ from transformers.models.granitemoe import GraniteMoeConfig
from vllm.attention import Attention, AttentionMetadata
from vllm.compilation.decorators import support_torch_compile
from vllm.config import CacheConfig, LoRAConfig, VllmConfig
from vllm.config import CacheConfig, VllmConfig
from vllm.distributed import get_pp_group, get_tensor_model_parallel_world_size
from vllm.model_executor.layers.fused_moe import FusedMoE
from vllm.model_executor.layers.layernorm import RMSNorm
......@@ -47,7 +47,7 @@ from vllm.sequence import IntermediateTensors
from . import mixtral
from .interfaces import SupportsLoRA, SupportsPP
from .utils import make_layers
from .utils import make_layers, maybe_prefix
class GraniteMoeMoE(nn.Module):
......@@ -247,15 +247,14 @@ class GraniteMoeDecoderLayer(nn.Module):
@support_torch_compile
class GraniteMoeModel(nn.Module):
def __init__(
self,
config: GraniteMoeConfig,
cache_config: Optional[CacheConfig] = None,
quant_config: Optional[QuantizationConfig] = None,
lora_config: Optional[LoRAConfig] = None,
prefix: str = "",
) -> None:
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
super().__init__()
config = vllm_config.model_config.hf_config
cache_config = vllm_config.cache_config
quant_config = vllm_config.quant_config
lora_config = vllm_config.lora_config
self.padding_idx = config.pad_token_id
lora_vocab = (lora_config.lora_extra_vocab_size *
(lora_config.max_loras or 1)) if lora_config else 0
......@@ -333,25 +332,17 @@ class GraniteMoeForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
}
embedding_padding_modules = ["lm_head"]
def __init__(
self,
vllm_config: VllmConfig,
prefix: str = "",
) -> None:
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
super().__init__()
config = vllm_config.model_config.hf_config
cache_config = vllm_config.cache_config
quant_config = vllm_config.quant_config
lora_config = vllm_config.lora_config
self.config = config
self.lora_config = lora_config
self.model = GraniteMoeModel(config,
cache_config,
quant_config,
lora_config=lora_config,
prefix="model")
self.model = GraniteMoeModel(vllm_config=vllm_config,
prefix=maybe_prefix(prefix, "model"))
self.unpadded_vocab_size = config.vocab_size
if lora_config:
self.unpadded_vocab_size += lora_config.lora_extra_vocab_size
......
vllm/model_executor/models/idefics3.py
View file @ f89d18ff
......@@ -22,17 +22,15 @@ import torch.utils.checkpoint
from PIL import Image
from torch import nn
# Temporary solution for transformers below 4.46.0.
from transformers import PretrainedConfig as Idefics3Config
from transformers import ProcessorMixin as Idefics3ImageProcessor
from vllm.attention import AttentionMetadata
from vllm.config import CacheConfig, VllmConfig
from vllm.config import VllmConfig
from vllm.inputs import (INPUT_REGISTRY, DecoderOnlyInputs, DummyData,
InputContext, token_inputs)
from vllm.logger import init_logger
from vllm.model_executor.layers.linear import ReplicatedLinear
from vllm.model_executor.layers.logits_processor import LogitsProcessor
from vllm.model_executor.layers.quantization import QuantizationConfig
from vllm.model_executor.layers.sampler import Sampler, SamplerOutput
from vllm.model_executor.layers.vocab_parallel_embedding import ParallelLMHead
from vllm.model_executor.sampling_metadata import SamplingMetadata
......@@ -48,7 +46,8 @@ from .idefics2_vision_model import (
# yapf: enable
from .interfaces import SupportsMultiModal
from .llama import LlamaModel
from .utils import AutoWeightsLoader, flatten_bn, merge_multimodal_embeddings
from .utils import (AutoWeightsLoader, flatten_bn, maybe_prefix,
merge_multimodal_embeddings)
logger = init_logger(__name__)
......@@ -417,13 +416,13 @@ class Idefics3Connector(nn.Module):
class Idefics3Model(nn.Module):
def __init__(
self,
config: Idefics3Config,
cache_config: Optional[CacheConfig] = None,
quant_config: Optional[QuantizationConfig] = None,
):
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
super().__init__()
config = vllm_config.model_config.hf_config
cache_config = vllm_config.cache_config
quant_config = vllm_config.quant_config
self.config = config
self.padding_idx = self.config.text_config.pad_token_id
self.vocab_size = self.config.text_config.vocab_size
......@@ -613,22 +612,18 @@ class Idefics3Model(nn.Module):
@INPUT_REGISTRY.register_input_processor(input_processor_for_idefics3)
class Idefics3ForConditionalGeneration(nn.Module, SupportsMultiModal):
def __init__(
self,
vllm_config: VllmConfig,
prefix: str = "",
) -> None:
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
super().__init__()
config = vllm_config.model_config.hf_config
cache_config = vllm_config.cache_config
quant_config = vllm_config.quant_config
multimodal_config = vllm_config.model_config.multimodal_config
self.config = config
self.multimodal_config = multimodal_config
self.model = Idefics3Model(config, cache_config, quant_config)
self.model = Idefics3Model(vllm_config=vllm_config,
prefix=maybe_prefix(prefix, "model"))
self.image_token_id = self.config.image_token_id
self.lm_head = ParallelLMHead(
......
vllm/model_executor/models/internlm2.py
View file @ f89d18ff
......@@ -250,14 +250,13 @@ class InternLMDecoderLayer(nn.Module):
@support_torch_compile
class InternLM2Model(nn.Module):
def __init__(
self,
config: PretrainedConfig,
cache_config: Optional[CacheConfig] = None,
quant_config: Optional[QuantizationConfig] = None,
prefix: str = "",
) -> None:
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
super().__init__()
config = vllm_config.model_config.hf_config
cache_config = vllm_config.cache_config
quant_config = vllm_config.quant_config
self.config = config
self.padding_idx = config.pad_token_id
self.vocab_size = config.vocab_size
......@@ -317,20 +316,13 @@ class InternLM2Model(nn.Module):
class InternLM2ForCausalLM(nn.Module, SupportsPP):
def __init__(
self,
vllm_config: VllmConfig,
prefix: str = "",
) -> None:
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
super().__init__()
config = vllm_config.model_config.hf_config
cache_config = vllm_config.cache_config
quant_config = vllm_config.quant_config
self.config = config
self.quant_config = quant_config
self.model = InternLM2Model(config,
cache_config,
quant_config,
self.model = InternLM2Model(vllm_config=vllm_config,
prefix=maybe_prefix(prefix, "model"))
self.output = ParallelLMHead(config.vocab_size,
config.hidden_size,
......
vllm/model_executor/models/internlm2_ve.py
View file @ f89d18ff
......@@ -104,14 +104,13 @@ class InternLM2VEDecoderLayer(nn.Module):
class InternLM2VEModel(InternLM2Model):
def __init__(
self,
config: PretrainedConfig,
cache_config: Optional[CacheConfig] = None,
quant_config: Optional[QuantizationConfig] = None,
prefix: str = "",
) -> None:
super().__init__(config, cache_config, quant_config)
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
super().__init__(vllm_config=vllm_config, prefix=prefix)
config = vllm_config.model_config.hf_config
cache_config = vllm_config.cache_config
quant_config = vllm_config.quant_config
self.start_layer, self.end_layer, self.layers = make_layers(
config.num_hidden_layers,
lambda prefix: InternLM2VEDecoderLayer(
......@@ -159,12 +158,8 @@ class InternLM2VEModel(InternLM2Model):
class InternLM2VEForCausalLM(InternLM2ForCausalLM):
def __init__(
self,
vllm_config: VllmConfig,
prefix: str = "",
) -> None:
super().__init__(vllm_config, prefix=prefix)
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
super().__init__(vllm_config=vllm_config, prefix=prefix)
config = vllm_config.model_config.hf_config
cache_config = vllm_config.cache_config
......
vllm/model_executor/models/internvl.py
View file @ f89d18ff
......@@ -35,7 +35,7 @@ from .clip import (dummy_image_for_clip, dummy_seq_data_for_clip,
get_clip_num_patches)
from .interfaces import SupportsMultiModal, SupportsPP
from .utils import (AutoWeightsLoader, flatten_bn, init_vllm_registered_model,
merge_multimodal_embeddings)
maybe_prefix, merge_multimodal_embeddings)
IMG_START = '<img>'
IMG_END = '</img>'
......@@ -435,13 +435,13 @@ class InternVLChatModel(nn.Module, SupportsMultiModal, SupportsPP):
config,
quant_config=quant_config,
is_mono=self.is_mono,
prefix="vision_model",
prefix=maybe_prefix(prefix, "vision_model"),
)
self.language_model = init_vllm_registered_model(
config.text_config,
vllm_config=vllm_config,
prefix="language_model")
prefix=maybe_prefix(prefix, "language_model"))
self.mlp1 = self._init_mlp1(config)
......
vllm/model_executor/models/jais.py
View file @ f89d18ff
......@@ -44,7 +44,8 @@ from vllm.transformers_utils.configs import JAISConfig
from .interfaces import SupportsPP
from .utils import (is_pp_missing_parameter,
make_empty_intermediate_tensors_factory, make_layers)
make_empty_intermediate_tensors_factory, make_layers,
maybe_prefix)
class SwiGLUActivation(nn.Module):
......@@ -215,14 +216,13 @@ class JAISBlock(nn.Module):
@support_torch_compile
class JAISModel(nn.Module):
def __init__(
self,
config: JAISConfig,
cache_config: Optional[CacheConfig] = None,
quant_config: Optional[QuantizationConfig] = None,
prefix: str = "",
):
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
super().__init__()
config = vllm_config.model_config.hf_config
cache_config = vllm_config.cache_config
quant_config = vllm_config.quant_config
self.config = config
assert not config.add_cross_attention
assert not config.scale_attn_by_inverse_layer_idx
......@@ -293,11 +293,12 @@ class JAISLMHeadModel(nn.Module, SupportsPP):
):
super().__init__()
config = vllm_config.model_config.hf_config
cache_config = vllm_config.cache_config
quant_config = vllm_config.quant_config
self.config = config
self.quant_config = quant_config
self.transformer = JAISModel(config, cache_config, quant_config)
self.transformer = JAISModel(vllm_config=vllm_config,
prefix=maybe_prefix(
prefix, "transformer"))
if self.config.tie_word_embeddings:
self.lm_head = self.transformer.wte
else:
......
vllm/model_executor/models/jamba.py
View file @ f89d18ff
......@@ -7,7 +7,7 @@ from transformers import JambaConfig
from vllm.attention.backends.abstract import AttentionMetadata
from vllm.attention.layer import Attention
from vllm.config import CacheConfig, LoRAConfig, VllmConfig
from vllm.config import CacheConfig, VllmConfig
from vllm.distributed import get_tensor_model_parallel_world_size
from vllm.model_executor.layers.fused_moe import FusedMoE
from vllm.model_executor.layers.layernorm import RMSNorm
......@@ -29,6 +29,7 @@ from vllm.worker.model_runner import (_BATCH_SIZES_TO_CAPTURE,
_get_graph_batch_size)
from .interfaces import HasInnerState, SupportsLoRA
from .utils import maybe_prefix
KVCache = Tuple[torch.Tensor, torch.Tensor]
......@@ -258,14 +259,14 @@ ALL_DECODER_LAYER_TYPES = {
class JambaModel(nn.Module):
def __init__(
self,
config: JambaConfig,
quant_config: Optional[QuantizationConfig] = None,
cache_config: Optional[CacheConfig] = None,
lora_config: Optional[LoRAConfig] = None,
) -> None:
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
super().__init__()
config = vllm_config.model_config.hf_config
cache_config = vllm_config.cache_config
quant_config = vllm_config.quant_config
lora_config = vllm_config.lora_config
self.config = config
self.padding_idx = config.pad_token_id
lora_vocab = ((lora_config.lora_extra_vocab_size *
......@@ -348,14 +349,9 @@ class JambaForCausalLM(nn.Module, HasInnerState, SupportsLoRA):
}
embedding_padding_modules = ["lm_head"]
def __init__(
self,
vllm_config: VllmConfig,
prefix: str = "",
) -> None:
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
config = vllm_config.model_config.hf_config
cache_config = vllm_config.cache_config
quant_config = vllm_config.quant_config
lora_config = vllm_config.lora_config
scheduler_config = vllm_config.scheduler_config
assert not cache_config.enable_prefix_caching, \
......@@ -364,10 +360,8 @@ class JambaForCausalLM(nn.Module, HasInnerState, SupportsLoRA):
super().__init__()
self.config = config
self.scheduler_config = scheduler_config
self.model = JambaModel(config,
cache_config=cache_config,
quant_config=quant_config,
lora_config=lora_config)
self.model = JambaModel(vllm_config=vllm_config,
prefix=maybe_prefix(prefix, "model"))
self.unpadded_vocab_size = config.vocab_size
if lora_config:
self.unpadded_vocab_size += lora_config.lora_extra_vocab_size
......
vllm/model_executor/models/llama.py
View file @ f89d18ff
......@@ -28,7 +28,7 @@ from transformers import LlamaConfig
from vllm.attention import Attention, AttentionMetadata
from vllm.compilation.decorators import support_torch_compile
from vllm.config import CacheConfig, LoRAConfig, VllmConfig
from vllm.config import CacheConfig, VllmConfig
from vllm.distributed import (get_pp_group, get_tensor_model_parallel_rank,
get_tensor_model_parallel_world_size)
from vllm.model_executor.layers.activation import SiluAndMul
......@@ -271,15 +271,14 @@ class LlamaDecoderLayer(nn.Module):
@support_torch_compile
class LlamaModel(nn.Module):
def __init__(
self,
config: LlamaConfig,
cache_config: Optional[CacheConfig] = None,
quant_config: Optional[QuantizationConfig] = None,
lora_config: Optional[LoRAConfig] = None,
prefix: str = "",
) -> None:
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
super().__init__()
config = vllm_config.model_config.hf_config
cache_config = vllm_config.cache_config
quant_config = vllm_config.quant_config
lora_config = vllm_config.lora_config
self.config = config
self.padding_idx = config.pad_token_id
lora_vocab = (lora_config.lora_extra_vocab_size *
......@@ -492,24 +491,16 @@ class LlamaForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
"norm": "model.norm"
}
def __init__(
self,
vllm_config: VllmConfig,
prefix: str = "",
) -> None:
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
super().__init__()
config = vllm_config.model_config.hf_config
cache_config = vllm_config.cache_config
quant_config = vllm_config.quant_config
lora_config = vllm_config.lora_config
pooler_config = vllm_config.model_config.pooler_config
self.config = config
self.lora_config = lora_config
self.model = LlamaModel(config,
cache_config,
quant_config,
lora_config=lora_config,
self.model = LlamaModel(vllm_config=vllm_config,
prefix=maybe_prefix(prefix, "model"))
if get_pp_group().is_last_rank:
self.unpadded_vocab_size = config.vocab_size
......@@ -652,23 +643,12 @@ class LlamaEmbeddingModel(nn.Module, SupportsLoRA, SupportsPP):
}
embedding_padding_modules = []
def __init__(
self,
vllm_config: VllmConfig,
prefix: str = "",
) -> None:
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
super().__init__()
config = vllm_config.model_config.hf_config
cache_config = vllm_config.cache_config
quant_config = vllm_config.quant_config
lora_config = vllm_config.lora_config
pooler_config = vllm_config.model_config.pooler_config
self.model = LlamaModel(config,
cache_config,
quant_config,
lora_config,
self.model = LlamaModel(vllm_config=vllm_config,
prefix=maybe_prefix(prefix, "model"))
self._pooler = Pooler.from_config_with_defaults(
pooler_config,
......
vllm/model_executor/models/llava.py
View file @ f89d18ff
......@@ -32,7 +32,7 @@ from .siglip import (SiglipVisionModel, dummy_image_for_siglip,
dummy_seq_data_for_siglip, get_max_siglip_image_tokens,
input_processor_for_siglip)
from .utils import (AutoWeightsLoader, flatten_bn, init_vllm_registered_model,
merge_multimodal_embeddings)
maybe_prefix, merge_multimodal_embeddings)
class LlavaImagePixelInputs(TypedDict):
......@@ -282,7 +282,7 @@ class LlavaForConditionalGeneration(nn.Module, SupportsMultiModal, SupportsPP):
config,
quant_config,
require_post_norm=False,
prefix="vision_tower")
prefix=maybe_prefix(prefix, "vision_tower"))
self.multi_modal_projector = LlavaMultiModalProjector(
vision_hidden_size=config.vision_config.hidden_size,
text_hidden_size=config.text_config.hidden_size,
......@@ -291,7 +291,7 @@ class LlavaForConditionalGeneration(nn.Module, SupportsMultiModal, SupportsPP):
self.language_model = init_vllm_registered_model(
config.text_config,
vllm_config=vllm_config,
prefix="language_model")
prefix=maybe_prefix(prefix, "language_model"))
self.make_empty_intermediate_tensors = (
self.language_model.make_empty_intermediate_tensors)
......
vllm/model_executor/models/llava_next.py
View file @ f89d18ff
......@@ -31,7 +31,7 @@ from .siglip import (SiglipVisionModel, dummy_image_for_siglip,
dummy_seq_data_for_siglip, get_siglip_image_feature_size,
get_siglip_patch_grid_length, input_processor_for_siglip)
from .utils import (AutoWeightsLoader, embed_multimodal, flatten_bn,
init_vllm_registered_model)
init_vllm_registered_model, maybe_prefix)
class LlavaNextImagePixelInputs(TypedDict):
......@@ -296,7 +296,7 @@ class LlavaNextForConditionalGeneration(nn.Module, SupportsMultiModal,
config,
quant_config,
require_post_norm=False,
prefix="vision_tower")
prefix=maybe_prefix(prefix, "vision_tower"))
self.image_newline = nn.Parameter(
torch.empty(config.text_config.hidden_size))
self.multi_modal_projector = LlavaMultiModalProjector(
......@@ -307,7 +307,7 @@ class LlavaNextForConditionalGeneration(nn.Module, SupportsMultiModal,
self.language_model = init_vllm_registered_model(
config.text_config,
vllm_config=vllm_config,
prefix="language_model")
prefix=maybe_prefix(prefix, "language_model"))
# The same model class supports both language generation and embedding
# because the architecture name is the same
......
vllm/model_executor/models/llava_next_video.py
View file @ f89d18ff
......@@ -29,7 +29,7 @@ from .llava import init_vision_tower_for_llava
from .siglip import (SiglipVisionModel, dummy_image_for_siglip,
dummy_seq_data_for_siglip)
from .utils import (AutoWeightsLoader, init_vllm_registered_model,
merge_multimodal_embeddings)
maybe_prefix, merge_multimodal_embeddings)
# For profile run
_MAX_FRAMES_PER_VIDEO = 32
......@@ -267,7 +267,7 @@ class LlavaNextVideoForConditionalGeneration(nn.Module, SupportsMultiModal,
config,
quant_config,
require_post_norm=False,
prefix="vision_tower")
prefix=maybe_prefix(prefix, "vision_tower"))
self.vision_resampler = LlavaNextVideoPooler(config)
self.multi_modal_projector = LlavaNextMultiModalProjector(
vision_hidden_size=config.vision_config.hidden_size,
......@@ -276,7 +276,7 @@ class LlavaNextVideoForConditionalGeneration(nn.Module, SupportsMultiModal,
self.language_model = init_vllm_registered_model(
config.text_config,
vllm_config=vllm_config,
prefix="language_model")
prefix=maybe_prefix(prefix, "language_model"))
self.make_empty_intermediate_tensors = (
self.language_model.model.make_empty_intermediate_tensors)
......
vllm/model_executor/models/llava_onevision.py
View file @ f89d18ff
......@@ -35,7 +35,7 @@ from .siglip import (SiglipVisionModel, dummy_seq_data_for_siglip,
dummy_video_for_siglip, get_siglip_image_feature_size,
get_siglip_patch_grid_length, input_processor_for_siglip)
from .utils import (AutoWeightsLoader, flatten_bn, init_vllm_registered_model,
merge_multimodal_embeddings)
maybe_prefix, merge_multimodal_embeddings)
# Result in the max possible feature size (2x2 grid of 336x336px tiles)
MAX_IMAGE_FEATURE_SIZE_HEIGHT = MAX_IMAGE_FEATURE_SIZE_WIDTH = 448
......@@ -418,12 +418,12 @@ class LlavaOnevisionForConditionalGeneration(nn.Module, SupportsMultiModal,
config,
quant_config,
require_post_norm=False,
prefix="vision_tower")
prefix=maybe_prefix(prefix, "vision_tower"))
self.multi_modal_projector = LlavaOnevisionMultiModalProjector(config)
self.language_model = init_vllm_registered_model(
config.text_config,
vllm_config=vllm_config,
prefix="language_model")
prefix=maybe_prefix(prefix, "language_model"))
self.image_newline = nn.Parameter(
torch.empty(config.text_config.hidden_size))
......
vllm/model_executor/models/mamba.py
View file @ f89d18ff
......@@ -6,7 +6,7 @@ from torch import nn
from transformers import MambaConfig
from vllm.attention.backends.abstract import AttentionMetadata
from vllm.config import CacheConfig, LoRAConfig, VllmConfig
from vllm.config import CacheConfig, VllmConfig
from vllm.distributed import get_tensor_model_parallel_world_size
from vllm.model_executor.layers.layernorm import RMSNorm
from vllm.model_executor.layers.logits_processor import LogitsProcessor
......@@ -26,6 +26,8 @@ from vllm.sequence import IntermediateTensors
from vllm.worker.model_runner import (_BATCH_SIZES_TO_CAPTURE,
_get_graph_batch_size)
from .utils import maybe_prefix
KVCache = Tuple[torch.Tensor, torch.Tensor]
......@@ -73,14 +75,14 @@ class MambaDecoderLayer(nn.Module):
class MambaModel(nn.Module):
def __init__(
self,
config: MambaConfig,
quant_config: Optional[QuantizationConfig] = None,
cache_config: Optional[CacheConfig] = None,
lora_config: Optional[LoRAConfig] = None,
) -> None:
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
super().__init__()
config = vllm_config.model_config.hf_config
cache_config = vllm_config.cache_config
quant_config = vllm_config.quant_config
lora_config = vllm_config.lora_config
self.config = config
self.padding_idx = config.pad_token_id
lora_vocab = ((lora_config.lora_extra_vocab_size *
......@@ -130,14 +132,9 @@ class MambaModel(nn.Module):
class MambaForCausalLM(nn.Module, HasInnerState, IsAttentionFree):
def __init__(
self,
vllm_config: VllmConfig,
prefix: str = "",
) -> None:
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
config = vllm_config.model_config.hf_config
cache_config = vllm_config.cache_config
quant_config = vllm_config.quant_config
lora_config = vllm_config.lora_config
scheduler_config = vllm_config.scheduler_config
assert not cache_config.enable_prefix_caching, \
......@@ -146,10 +143,8 @@ class MambaForCausalLM(nn.Module, HasInnerState, IsAttentionFree):
super().__init__()
self.config = config
self.scheduler_config = scheduler_config
self.backbone = MambaModel(config,
cache_config=cache_config,
quant_config=quant_config,
lora_config=lora_config)
self.backbone = MambaModel(vllm_config=vllm_config,
prefix=maybe_prefix(prefix, "backbone"))
self.unpadded_vocab_size = config.vocab_size
if lora_config:
self.unpadded_vocab_size += lora_config.lora_extra_vocab_size
......
vllm/model_executor/models/minicpm.py
View file @ f89d18ff
......@@ -29,7 +29,7 @@ from transformers import PretrainedConfig
from vllm.attention import Attention, AttentionMetadata
from vllm.compilation.decorators import support_torch_compile
from vllm.config import CacheConfig, LoRAConfig, VllmConfig
from vllm.config import CacheConfig, VllmConfig
from vllm.distributed import (get_pp_group, get_tensor_model_parallel_rank,
get_tensor_model_parallel_world_size,
tensor_model_parallel_all_reduce)
......@@ -53,7 +53,8 @@ from vllm.sequence import IntermediateTensors
from .interfaces import SupportsLoRA, SupportsPP
from .utils import (is_pp_missing_parameter,
make_empty_intermediate_tensors_factory, make_layers)
make_empty_intermediate_tensors_factory, make_layers,
maybe_prefix)
class MiniCPMMoE(nn.Module):
......@@ -351,15 +352,14 @@ class MiniCPMDecoderLayer(nn.Module):
@support_torch_compile
class MiniCPMModel(nn.Module):
def __init__(
self,
config: PretrainedConfig,
cache_config: Optional[CacheConfig] = None,
quant_config: Optional[QuantizationConfig] = None,
lora_config: Optional[LoRAConfig] = None,
prefix: str = "",
) -> None:
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
super().__init__()
config = vllm_config.model_config.hf_config
cache_config = vllm_config.cache_config
quant_config = vllm_config.quant_config
lora_config = vllm_config.lora_config
self.config = config
self.cache_config = cache_config
self.quant_config = quant_config
......@@ -461,24 +461,22 @@ class MiniCPMForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
}
embedding_padding_modules = ["lm_head"]
def __init__(
self,
vllm_config: VllmConfig,
prefix: str = "",
) -> None:
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
super().__init__()
config = vllm_config.model_config.hf_config
cache_config = vllm_config.cache_config
quant_config = vllm_config.quant_config
lora_config = vllm_config.lora_config
self.prefix = prefix
self.vllm_config = vllm_config
self.config = config
self.lora_config = lora_config
self.cache_config = cache_config
self.quant_config = quant_config
self.num_experts = getattr(self.config, "num_experts", 0)
self._init_model()
self._init_model(vllm_config=vllm_config, prefix=prefix)
unpadded_vocab_size = config.vocab_size
if lora_config:
unpadded_vocab_size += lora_config.lora_extra_vocab_size
......@@ -502,11 +500,9 @@ class MiniCPMForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
self.make_empty_intermediate_tensors = (
self.model.make_empty_intermediate_tensors)
def _init_model(self):
self.model = MiniCPMModel(config=self.config,
cache_config=self.cache_config,
quant_config=self.quant_config,
lora_config=self.lora_config)
def _init_model(self, *, vllm_config: VllmConfig, prefix: str = ""):
self.model = MiniCPMModel(vllm_config=vllm_config,
prefix=maybe_prefix(prefix, "model"))
def forward(
self,
......
vllm/model_executor/models/minicpm3.py
View file @ f89d18ff
......@@ -28,7 +28,7 @@ from torch import nn
from transformers import PretrainedConfig
from vllm.attention import Attention, AttentionMetadata
from vllm.config import CacheConfig
from vllm.config import CacheConfig, VllmConfig
from vllm.distributed import get_tensor_model_parallel_world_size
from vllm.model_executor.layers.layernorm import RMSNorm
from vllm.model_executor.layers.linear import (ColumnParallelLinear,
......@@ -40,7 +40,7 @@ from vllm.model_executor.models.minicpm import (MiniCPMDecoderLayer,
MiniCPMForCausalLM,
MiniCPMModel)
from .utils import make_layers
from .utils import make_layers, maybe_prefix
class MiniCPM3Attention(nn.Module):
......@@ -238,8 +238,6 @@ class MiniCPM3ForCausalLM(MiniCPMForCausalLM):
# `embedding_modules` and `embedding_padding_modules`
# are inherited from MiniCPMForCausalLM
def _init_model(self):
self.model = MiniCPM3Model(config=self.config,
cache_config=self.cache_config,
quant_config=self.quant_config,
lora_config=self.lora_config)
def _init_model(self, *, vllm_config: VllmConfig, prefix: str = ""):
self.model = MiniCPM3Model(vllm_config=vllm_config,
prefix=maybe_prefix(prefix, "model"))
vllm/model_executor/models/minicpmv.py
View file @ f89d18ff
......@@ -34,7 +34,7 @@ from transformers import PretrainedConfig
from typing_extensions import NotRequired
from vllm.attention import AttentionMetadata
from vllm.config import CacheConfig, VllmConfig
from vllm.config import VllmConfig
from vllm.inputs import (INPUT_REGISTRY, DecoderOnlyInputs, DummyData,
InputContext, token_inputs)
from vllm.model_executor.layers.logits_processor import LogitsProcessor
......@@ -59,7 +59,7 @@ from vllm.sequence import IntermediateTensors, SequenceData
from .idefics2_vision_model import Idefics2VisionTransformer
from .interfaces import SupportsLoRA, SupportsMultiModal, SupportsPP
from .utils import is_pp_missing_parameter
from .utils import is_pp_missing_parameter, maybe_prefix
_KEYS_TO_MODIFY_MAPPING = {
"llm.lm_head": "lm_head",
......@@ -390,7 +390,6 @@ class MiniCPMVBaseModel(nn.Module, SupportsMultiModal, SupportsPP):
):
config = vllm_config.model_config.hf_config
multimodal_config = vllm_config.model_config.multimodal_config
cache_config = vllm_config.cache_config
quant_config = vllm_config.quant_config
super().__init__()
# All MiniCPM-V models disable `tie_word_embeddings` but
......@@ -401,11 +400,11 @@ class MiniCPMVBaseModel(nn.Module, SupportsMultiModal, SupportsPP):
self.multimodal_config = multimodal_config
self.version = get_version_by_config(self.config)
self.llm = self.init_llm(config,
cache_config,
quant_config,
prefix="llm")
self.vpm = self.init_vision_module(config, quant_config, prefix="vpm")
self.llm = self.init_llm(vllm_config=vllm_config,
prefix=maybe_prefix(prefix, "llm"))
self.vpm = self.init_vision_module(config,
quant_config,
prefix=maybe_prefix(prefix, "vpm"))
param_dtype = torch.get_default_dtype()
self.vpm.to(dtype=param_dtype)
self.vision_dim = (self.vpm.embed_dim if self.version == (2, 0) else
......@@ -414,13 +413,15 @@ class MiniCPMVBaseModel(nn.Module, SupportsMultiModal, SupportsPP):
self.resampler = self.init_resampler(self.embed_dim,
self.vision_dim,
quant_config=quant_config,
prefix="resampler")
prefix=maybe_prefix(
prefix, "resampler"))
self.resampler.to(device="cuda", dtype=param_dtype)
# TODO: why is there _KEYS_TO_MODIFY_MAPPING? lm_head should be in llm
self.lm_head = ParallelLMHead(config.vocab_size,
config.hidden_size,
quant_config=quant_config,
prefix="llm.lm_head")
prefix=maybe_prefix(
prefix, "llm.lm_head"))
self.logits_processor = LogitsProcessor(config.vocab_size)
self.sampler = get_sampler()
......@@ -661,9 +662,7 @@ class MiniCPMVBaseModel(nn.Module, SupportsMultiModal, SupportsPP):
def init_llm(
self,
config: PretrainedConfig,
cache_config: Optional[CacheConfig] = None,
quant_config: Optional[QuantizationConfig] = None,
vllm_config: VllmConfig,
prefix: str = "",
) -> nn.Module:
raise NotImplementedError
......@@ -711,16 +710,10 @@ class MiniCPMV2_0(MiniCPMVBaseModel):
def init_llm(
self,
config: PretrainedConfig,
cache_config: Optional[CacheConfig] = None,
quant_config: Optional[QuantizationConfig] = None,
vllm_config: VllmConfig,
prefix: str = "",
) -> nn.Module:
return LLMWrapper(MiniCPMModel(config,
cache_config=cache_config,
quant_config=quant_config,
prefix=prefix),
return LLMWrapper(MiniCPMModel(vllm_config=vllm_config, prefix=prefix),
name="model")
def init_vision_module(
......@@ -875,15 +868,10 @@ class MiniCPMV2_5(MiniCPMVBaseModel, SupportsLoRA):
def init_llm(
self,
config: PretrainedConfig,
cache_config: Optional[CacheConfig] = None,
quant_config: Optional[QuantizationConfig] = None,
vllm_config: VllmConfig,
prefix: str = "",
) -> nn.Module:
return LLMWrapper(LlamaModel(config,
cache_config=cache_config,
quant_config=quant_config,
prefix=prefix),
return LLMWrapper(LlamaModel(vllm_config=vllm_config, prefix=prefix),
name="model")
def init_vision_module(
......@@ -1022,16 +1010,10 @@ class MiniCPMV2_6(MiniCPMVBaseModel, SupportsLoRA):
def init_llm(
self,
config: PretrainedConfig,
cache_config: Optional[CacheConfig] = None,
quant_config: Optional[QuantizationConfig] = None,
vllm_config: VllmConfig,
prefix: str = "",
) -> nn.Module:
return LLMWrapper(Qwen2Model(config,
cache_config=cache_config,
quant_config=quant_config,
prefix=prefix),
return LLMWrapper(Qwen2Model(vllm_config=vllm_config, prefix=prefix),
name="model")
def init_vision_module(
......@@ -1151,4 +1133,4 @@ class MiniCPMV(MiniCPMVBaseModel, SupportsLoRA):
if instance_class is None:
raise ValueError(
"Currently, MiniCPMV only supports versions 2.0, 2.5, and 2.6")
return instance_class(vllm_config, prefix=prefix)
return instance_class(vllm_config=vllm_config, prefix=prefix)
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