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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 213 additions and 272 deletions
vllm/model_executor/models/arctic.py
View file @ f89d18ff
......@@ -34,7 +34,8 @@ from vllm.transformers_utils.configs.arctic import ArcticConfig
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)
logger = init_logger(__name__)
......@@ -364,14 +365,13 @@ class ArcticDecoderLayer(nn.Module):
@support_torch_compile
class ArcticModel(nn.Module):
def __init__(
self,
config: ArcticConfig,
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.padding_idx = config.pad_token_id
self.vocab_size = config.vocab_size
self.embed_tokens = VocabParallelEmbedding(
......@@ -418,13 +418,10 @@ class ArcticForCausalLM(nn.Module, SupportsPP):
def __init__(self, vllm_config: VllmConfig, prefix: str = "") -> None:
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.model = ArcticModel(config,
cache_config,
quant_config,
prefix=prefix)
self.model = ArcticModel(vllm_config=vllm_config,
prefix=maybe_prefix(prefix, "model"))
self.vocab_size = config.vocab_size
self.lm_head = ParallelLMHead(
self.vocab_size,
......
vllm/model_executor/models/baichuan.py
View file @ f89d18ff
......@@ -253,13 +253,18 @@ class BaiChuanDecoderLayer(nn.Module):
@support_torch_compile
class BaiChuanModel(nn.Module):
def __init__(self,
config: PretrainedConfig,
position_embedding: str,
cache_config: Optional[CacheConfig] = None,
quant_config: Optional[QuantizationConfig] = None,
prefix: str = ""):
def __init__(
self,
vllm_config: VllmConfig,
prefix: str = "",
position_embedding: str = "ROPE",
) -> None:
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
......@@ -332,21 +337,22 @@ class BaiChuanBaseForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
def __init__(
self,
*,
vllm_config: VllmConfig,
prefix: str = "",
position_embedding: str = "ROPE",
):
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.quant_config = quant_config
self.model = BaiChuanModel(config, position_embedding, cache_config,
quant_config)
self.model = BaiChuanModel(vllm_config=vllm_config,
prefix=prefix,
position_embedding=position_embedding)
self.lm_head = ParallelLMHead(config.vocab_size,
config.hidden_size,
quant_config=quant_config)
......@@ -438,16 +444,16 @@ class BaichuanForCausalLM(BaiChuanBaseForCausalLM):
NOTE: the class name has a lower case 'c'.
"""
def __init__(
self,
vllm_config: VllmConfig,
prefix: str = "",
):
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
config = vllm_config.model_config.hf_config
if config.hidden_size == 4096: # baichuan2 7b
super().__init__(vllm_config, prefix, "ROPE")
super().__init__(vllm_config=vllm_config,
prefix=prefix,
position_embedding="ROPE")
else: # baichuan 13b, baichuan2 13b
super().__init__(vllm_config, prefix, "ALIBI")
super().__init__(vllm_config=vllm_config,
prefix=prefix,
position_embedding="ALIBI")
class BaiChuanForCausalLM(BaiChuanBaseForCausalLM):
......@@ -455,9 +461,7 @@ class BaiChuanForCausalLM(BaiChuanBaseForCausalLM):
NOTE: the class name has an upper case 'C'.
"""
def __init__(
self,
vllm_config: VllmConfig,
prefix: str = "",
):
super().__init__(vllm_config, prefix, "ROPE")
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
super().__init__(vllm_config=vllm_config,
prefix=prefix,
position_embedding="ROPE")
vllm/model_executor/models/bart.py
View file @ f89d18ff
......@@ -41,6 +41,8 @@ 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 .utils import maybe_prefix
logger = logging.get_logger(__name__)
......@@ -739,13 +741,14 @@ class BartModel(nn.Module):
"encoder.embed_tokens.weight", "decoder.embed_tokens.weight"
]
def __init__(self,
config: BartConfig,
cache_config: Optional[CacheConfig] = None,
quant_config: Optional[QuantizationConfig] = None,
lora_config: Optional[LoRAConfig] = 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
......@@ -810,20 +813,16 @@ class BartModel(nn.Module):
class BartForConditionalGeneration(nn.Module):
base_model_prefix = "model"
def __init__(self, vllm_config: VllmConfig, 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
# currently all existing BART models have `tie_word_embeddings` enabled
assert config.tie_word_embeddings
self.config = config
self.model = BartModel(config,
cache_config,
quant_config,
lora_config=lora_config)
self.model = BartModel(vllm_config=vllm_config,
prefix=maybe_prefix(prefix, "model"))
self.unpadded_vocab_size = config.vocab_size
if lora_config:
......
vllm/model_executor/models/bert.py
View file @ f89d18ff
......@@ -21,6 +21,8 @@ from vllm.model_executor.model_loader.weight_utils import default_weight_loader
from vllm.model_executor.pooling_metadata import PoolingMetadata
from vllm.sequence import IntermediateTensors, PoolerOutput
from .utils import maybe_prefix
class BertEmbedding(nn.Module):
......@@ -309,12 +311,13 @@ class BertOutput(nn.Module):
class BertModel(nn.Module):
def __init__(self,
config: BertConfig,
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.embeddings = BertEmbedding(config)
self.encoder = BertEncoder(config,
cache_config,
......@@ -382,17 +385,11 @@ class BertEmbeddingModel(nn.Module):
_pooler: An instance of Pooler used for pooling operations.
"""
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
pooler_config = vllm_config.model_config.pooler_config
self.model = BertModel(config, cache_config, quant_config)
self.model = BertModel(vllm_config=vllm_config,
prefix=maybe_prefix(prefix, "model"))
self._pooler = Pooler.from_config_with_defaults(
pooler_config,
pooling_type=PoolingType.CLS,
......
vllm/model_executor/models/blip2.py
View file @ f89d18ff
......@@ -23,7 +23,7 @@ from .blip import (BlipVisionModel, dummy_image_for_blip,
get_max_blip_image_tokens)
from .interfaces import SupportsMultiModal, SupportsPP
from .utils import (AutoWeightsLoader, init_vllm_registered_model,
merge_multimodal_embeddings)
maybe_prefix, merge_multimodal_embeddings)
# We use this internally as placeholders since there is no image token
# defined on the HuggingFace repo
......@@ -483,11 +483,7 @@ def input_processor_for_blip2(ctx: InputContext, inputs: DecoderOnlyInputs):
@INPUT_REGISTRY.register_input_processor(input_processor_for_blip2)
class Blip2ForConditionalGeneration(nn.Module, SupportsMultiModal, 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
......@@ -517,7 +513,7 @@ class Blip2ForConditionalGeneration(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/bloom.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)
def _get_alibi_slopes(total_num_heads: int) -> torch.Tensor:
......@@ -221,14 +222,13 @@ class BloomBlock(nn.Module):
@support_torch_compile
class BloomModel(nn.Module):
def __init__(
self,
config: BloomConfig,
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.embed_dim = config.hidden_size
# Embedding + LN Embedding
......@@ -288,11 +288,12 @@ class BloomForCausalLM(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 = BloomModel(config, cache_config, quant_config)
self.transformer = BloomModel(vllm_config=vllm_config,
prefix=maybe_prefix(
prefix, "transformer"))
if self.config.tie_word_embeddings:
self.lm_head = self.transformer.word_embeddings
else:
......
vllm/model_executor/models/chameleon.py
View file @ f89d18ff
......@@ -37,7 +37,8 @@ from vllm.utils import print_warning_once
from .interfaces import SupportsMultiModal, SupportsPP
from .utils import (is_pp_missing_parameter,
make_empty_intermediate_tensors_factory, make_layers)
make_empty_intermediate_tensors_factory, make_layers,
maybe_prefix)
# These configs are not part of the model config but the preprocessor
# and processor files, so we hardcode them in the model file for now.
......@@ -831,14 +832,13 @@ class ChameleonImageVocabularyMapping:
class ChameleonModel(nn.Module):
def __init__(
self,
config: ChameleonConfig,
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
......@@ -924,19 +924,14 @@ class ChameleonModel(nn.Module):
class ChameleonForConditionalGeneration(nn.Module, SupportsMultiModal,
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
multimodal_config = vllm_config.model_config.multimodal_config
self.config = config
self.multimodal_config = multimodal_config
self.model = ChameleonModel(config, cache_config, quant_config)
self.model = ChameleonModel(vllm_config=vllm_config,
prefix=maybe_prefix(prefix, "model"))
self.unpadded_vocab_size = config.vocab_size
self.lm_head = ParallelLMHead(
self.unpadded_vocab_size,
......
vllm/model_executor/models/chatglm.py
View file @ f89d18ff
......@@ -39,7 +39,8 @@ from vllm.transformers_utils.configs import ChatGLMConfig
from .interfaces import SupportsLoRA, SupportsMultiModal, SupportsPP
from .utils import (is_pp_missing_parameter,
make_empty_intermediate_tensors_factory, make_layers)
make_empty_intermediate_tensors_factory, make_layers,
maybe_prefix)
logger = init_logger(__name__)
......@@ -481,14 +482,13 @@ class GLMTransformer(nn.Module):
class ChatGLMModel(nn.Module):
def __init__(
self,
config: ChatGLMConfig,
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.embedding = VocabParallelEmbedding(config.padded_vocab_size,
......@@ -600,7 +600,6 @@ class ChatGLMForCausalLM(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
multimodal_config = vllm_config.model_config.multimodal_config
......@@ -611,7 +610,9 @@ class ChatGLMForCausalLM(nn.Module, SupportsLoRA, SupportsPP,
self.quant_config = quant_config
self.max_position_embeddings = getattr(config, "max_sequence_length",
8192)
self.transformer = ChatGLMModel(config, cache_config, quant_config)
self.transformer = ChatGLMModel(vllm_config=vllm_config,
prefix=maybe_prefix(
prefix, "transformer"))
if self.config.tie_word_embeddings:
self.transformer.output_layer.weight = (
self.transformer.embedding.weight)
......
vllm/model_executor/models/commandr.py
View file @ f89d18ff
......@@ -28,7 +28,7 @@ from transformers import CohereConfig
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 SiluAndMul
from vllm.model_executor.layers.linear import (MergedColumnParallelLinear,
......@@ -49,7 +49,8 @@ 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)
@torch.compile
......@@ -253,15 +254,14 @@ class CohereDecoderLayer(nn.Module):
@support_torch_compile
class CohereModel(nn.Module):
def __init__(
self,
config: CohereConfig,
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
lora_vocab = (lora_config.lora_extra_vocab_size *
(lora_config.max_loras or 1)) if lora_config else 0
......@@ -332,14 +332,9 @@ class CohereForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
embedding_modules = {"embed_tokens": "input_embeddings"}
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
self.config = config
......@@ -353,10 +348,8 @@ class CohereForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
self.logits_processor = LogitsProcessor(self.unpadded_vocab_size,
config.vocab_size,
scale=config.logit_scale)
self.model = CohereModel(config,
cache_config,
quant_config,
lora_config=lora_config)
self.model = CohereModel(vllm_config=vllm_config,
prefix=maybe_prefix(prefix, "model"))
self.sampler = get_sampler()
self.make_empty_intermediate_tensors = (
self.model.make_empty_intermediate_tensors)
......
vllm/model_executor/models/dbrx.py
View file @ f89d18ff
......@@ -25,7 +25,8 @@ from vllm.transformers_utils.configs.dbrx import DbrxConfig
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 DbrxRouter(nn.Module):
......@@ -294,14 +295,13 @@ class DbrxBlock(nn.Module):
class DbrxModel(nn.Module):
def __init__(
self,
config: DbrxConfig,
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.wte = VocabParallelEmbedding(
config.vocab_size,
config.d_model,
......@@ -357,7 +357,6 @@ class DbrxForCausalLM(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
if config.tie_word_embeddings:
......@@ -365,7 +364,9 @@ class DbrxForCausalLM(nn.Module, SupportsPP):
"tie_word_embeddings is not supported for Dbrx models.")
self.quant_config = quant_config
self.unpadded_vocab_size = config.vocab_size
self.transformer = DbrxModel(config, cache_config, quant_config)
self.transformer = DbrxModel(vllm_config=vllm_config,
prefix=maybe_prefix(
prefix, "transformer"))
self.lm_head = ParallelLMHead(
config.vocab_size,
config.d_model,
......
vllm/model_executor/models/decilm.py
View file @ f89d18ff
......@@ -51,11 +51,7 @@ class DeciLMForCausalLM(LlamaForCausalLM):
instead.
"""
def __init__(
self,
vllm_config: VllmConfig,
prefix: str = "",
) -> None:
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
config = vllm_config.model_config.hf_config
config.num_key_value_heads = max(config.num_key_value_heads_per_layer)
delattr(config, "num_key_value_heads_per_layer")
......
vllm/model_executor/models/deepseek.py
View file @ f89d18ff
......@@ -50,7 +50,8 @@ 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 DeepseekMLP(nn.Module):
......@@ -326,14 +327,13 @@ class DeepseekModel(nn.Module):
fall_back_to_pt_during_load = False
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.padding_idx = config.pad_token_id
self.vocab_size = config.vocab_size
......@@ -383,18 +383,14 @@ class DeepseekModel(nn.Module):
class DeepseekForCausalLM(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 = DeepseekModel(config, cache_config, quant_config)
self.model = DeepseekModel(vllm_config=vllm_config,
prefix=maybe_prefix(prefix, "model"))
self.lm_head = ParallelLMHead(config.vocab_size,
config.hidden_size,
quant_config=quant_config)
......
vllm/model_executor/models/deepseek_v2.py
View file @ f89d18ff
......@@ -51,7 +51,8 @@ from vllm.sequence import IntermediateTensors
from .interfaces import SupportsPP
from .utils import (PPMissingLayer, is_pp_missing_parameter,
make_empty_intermediate_tensors_factory, make_layers)
make_empty_intermediate_tensors_factory, make_layers,
maybe_prefix)
class DeepseekV2MLP(nn.Module):
......@@ -408,14 +409,13 @@ class DeepseekV2Model(nn.Module):
fall_back_to_pt_during_load = False
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.padding_idx = config.pad_token_id
self.vocab_size = config.vocab_size
......@@ -479,21 +479,14 @@ class DeepseekV2Model(nn.Module):
class DeepseekV2ForCausalLM(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 = DeepseekV2Model(config,
cache_config,
quant_config,
prefix="model")
self.model = DeepseekV2Model(vllm_config=vllm_config,
prefix=maybe_prefix(prefix, "model"))
self.lm_head = ParallelLMHead(config.vocab_size,
config.hidden_size,
quant_config=quant_config)
......
vllm/model_executor/models/eagle.py
View file @ f89d18ff
......@@ -14,6 +14,8 @@ from vllm.model_executor.models import ModelRegistry
from vllm.model_executor.sampling_metadata import SamplingMetadata
from vllm.sequence import IntermediateTensors
from .utils import maybe_prefix
class EAGLE(nn.Module):
"""This class implements the EAGLE draft model from the paper: https://arxiv.org/pdf/2401.15077
......@@ -42,7 +44,8 @@ class EAGLE(nn.Module):
architectures = getattr(self.config.model, "architectures", [])
model_cls, _ = ModelRegistry.resolve_model_cls(architectures)
self.model = model_cls(vllm_config, prefix)
self.model = model_cls(vllm_config=vllm_config,
prefix=maybe_prefix(prefix, "model"))
self.fc = nn.Linear(config.model.hidden_size * 2,
config.model.hidden_size,
bias=getattr(self.config, "eagle_fc_bias", False))
......
vllm/model_executor/models/exaone.py
View file @ f89d18ff
......@@ -29,7 +29,7 @@ from torch import nn
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
......@@ -54,7 +54,8 @@ from vllm.transformers_utils.configs.exaone import ExaoneConfig
from .interfaces import SupportsLoRA, SupportsPP
from .utils import (PPMissingLayer, is_pp_missing_parameter,
make_empty_intermediate_tensors_factory, make_layers)
make_empty_intermediate_tensors_factory, make_layers,
maybe_prefix)
class ExaoneGatedMLP(nn.Module):
......@@ -314,15 +315,14 @@ class ExaoneDecoderLayer(nn.Module):
@support_torch_compile
class ExaoneModel(nn.Module):
def __init__(
self,
config: ExaoneConfig,
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 *
......@@ -438,14 +438,9 @@ class ExaoneForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
"c_fc_1": ("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
......@@ -453,11 +448,8 @@ class ExaoneForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
self.lora_config = lora_config
self.transformer = ExaoneModel(
config,
cache_config,
quant_config,
lora_config=lora_config,
prefix="model",
vllm_config=vllm_config,
prefix=maybe_prefix(prefix, "model"),
)
if get_pp_group().is_last_rank:
self.unpadded_vocab_size = config.vocab_size
......
vllm/model_executor/models/falcon.py
View file @ f89d18ff
......@@ -48,7 +48,8 @@ from vllm.transformers_utils.configs import RWConfig
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)
FalconConfig = Union[HF_FalconConfig, RWConfig]
......@@ -332,14 +333,13 @@ class FalconDecoderLayer(nn.Module):
@support_torch_compile
class FalconModel(nn.Module):
def __init__(
self,
config: FalconConfig,
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.hidden_size
self.num_heads = config.num_attention_heads
......@@ -408,11 +408,12 @@ class FalconForCausalLM(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 = FalconModel(config, cache_config, quant_config)
self.transformer = FalconModel(vllm_config=vllm_config,
prefix=maybe_prefix(
prefix, "transformer"))
# only Falcon-11B doesn't share lm_head weight with word embeddings
# and previous Falcon model doesn't have tie_word_embeddings config
# so we set tie_word_embeddings to True by default
......
vllm/model_executor/models/florence2.py
View file @ f89d18ff
......@@ -3,13 +3,10 @@ from typing import Iterable, List, Optional, Tuple
import torch
import torch.nn as nn
from transformers import PretrainedConfig
from vllm.attention import AttentionMetadata
from vllm.config import CacheConfig, VllmConfig
from vllm.config import VllmConfig
from vllm.model_executor.layers.logits_processor import LogitsProcessor
from vllm.model_executor.layers.quantization.base_config import (
QuantizationConfig)
from vllm.model_executor.layers.sampler import SamplerOutput, get_sampler
from vllm.model_executor.model_loader.weight_utils import default_weight_loader
from vllm.model_executor.models.bart import (BartDecoder, BartEncoder,
......@@ -23,11 +20,13 @@ from .utils import AutoWeightsLoader
class Florence2LanguageModel(nn.Module):
def __init__(self,
config: PretrainedConfig,
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 = config.pad_token_id
......@@ -93,15 +92,14 @@ class Florence2LanguageModel(nn.Module):
class Florence2LanguageForConditionalGeneration(nn.Module):
def __init__(self,
config: PretrainedConfig,
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
self.config = config
self.model = Florence2LanguageModel(config,
cache_config=cache_config,
quant_config=quant_config)
self.model = Florence2LanguageModel(vllm_config=vllm_config,
prefix=prefix)
embed_scale = math.sqrt(
config.d_model) if config.scale_embedding else 1.0
......@@ -189,17 +187,15 @@ class Florence2LanguageForConditionalGeneration(nn.Module):
class Florence2ForConditionalGeneration(nn.Module):
def __init__(self, vllm_config: VllmConfig, 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
# TODO(Isotr0py): Add vision backbone
self.language_model = Florence2LanguageForConditionalGeneration(
config=config.text_config,
cache_config=cache_config,
quant_config=quant_config)
vllm_config=vllm_config.with_hf_config(config.text_config),
prefix=prefix,
)
@property
def sampler(self):
......
vllm/model_executor/models/gemma.py
View file @ f89d18ff
......@@ -258,14 +258,13 @@ class GemmaDecoderLayer(nn.Module):
@support_torch_compile
class GemmaModel(nn.Module):
def __init__(
self,
config: GemmaConfig,
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.embed_tokens = VocabParallelEmbedding(
......@@ -372,14 +371,9 @@ class GemmaForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
embedding_modules = {}
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
......@@ -389,9 +383,7 @@ class GemmaForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
self.lora_config = lora_config
self.quant_config = quant_config
self.model = GemmaModel(config,
cache_config,
quant_config,
self.model = GemmaModel(vllm_config=vllm_config,
prefix=maybe_prefix(prefix, "model"))
self.logits_processor = LogitsProcessor(config.vocab_size)
self.sampler = get_sampler()
......
vllm/model_executor/models/gemma2.py
View file @ f89d18ff
......@@ -43,7 +43,8 @@ from vllm.sequence import IntermediateTensors, PoolerOutput
from .interfaces import SupportsLoRA, SupportsPP
from .utils import (AutoWeightsLoader, is_pp_missing_parameter,
make_empty_intermediate_tensors_factory, make_layers)
make_empty_intermediate_tensors_factory, make_layers,
maybe_prefix)
logger = init_logger(__name__)
......@@ -243,11 +244,7 @@ class Gemma2DecoderLayer(nn.Module):
@support_torch_compile
class Gemma2Model(nn.Module):
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
......@@ -399,13 +396,8 @@ class Gemma2ForCausalLM(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 = ""):
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
del lora_config # Unused.
......@@ -414,7 +406,8 @@ class Gemma2ForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
# currently all existing Gemma models have `tie_word_embeddings` enabled
assert config.tie_word_embeddings
self.quant_config = quant_config
self.model = Gemma2Model(config, cache_config, quant_config)
self.model = Gemma2Model(vllm_config=vllm_config,
prefix=maybe_prefix(prefix, "model"))
self.logits_processor = LogitsProcessor(
config.vocab_size, soft_cap=config.final_logit_softcapping)
self.sampler = get_sampler()
......@@ -471,14 +464,11 @@ class Gemma2EmbeddingModel(nn.Module, SupportsPP):
_pooler: An instance of Pooler used for pooling operations.
"""
def __init__(
self,
vllm_config: VllmConfig,
prefix: str = "",
) -> None:
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
super().__init__()
self.model = Gemma2Model(vllm_config, prefix)
self.model = Gemma2Model(vllm_config=vllm_config,
prefix=maybe_prefix(prefix, "model"))
self._pooler = Pooler.from_config_with_defaults(
vllm_config.model_config.pooler_config,
pooling_type=PoolingType.LAST,
......
vllm/model_executor/models/gpt2.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 GPT2Attention(nn.Module):
......@@ -184,14 +185,13 @@ class GPT2Block(nn.Module):
@support_torch_compile
class GPT2Model(nn.Module):
def __init__(
self,
config: GPT2Config,
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
......@@ -247,14 +247,12 @@ class GPT2LMHeadModel(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 = GPT2Model(config,
cache_config,
quant_config,
prefix="transformer")
self.transformer = GPT2Model(vllm_config=vllm_config,
prefix=maybe_prefix(
prefix, "transformer"))
if self.config.tie_word_embeddings:
self.lm_head = self.transformer.wte
else:
......
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