60 lines
2.0 KiB
Python
60 lines
2.0 KiB
Python
from typing import Iterable, List, Optional, Tuple, Union
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import torch
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from torch import nn
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from vllm.attention import AttentionMetadata
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from vllm.model_executor.layers.pooler import Pooler, PoolingType
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from vllm.model_executor.pooling_metadata import PoolingMetadata
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from vllm.sequence import IntermediateTensors, PoolerOutput
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from .interfaces import SupportsPP
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from .llama import LlamaModel
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class LlamaEmbeddingModel(nn.Module, SupportsPP):
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"""A model that uses Llama with additional embedding functionalities.
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This class encapsulates the LlamaModel and provides an interface for
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embedding operations and customized pooling functions.
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Attributes:
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model: An instance of LlamaModel used for forward operations.
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_pooler: An instance of Pooler used for pooling operations.
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"""
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def __init__(
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self,
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**kwargs,
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) -> None:
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super().__init__()
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self.model = LlamaModel(**kwargs)
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self._pooler = Pooler(pooling_type=PoolingType.LAST, normalize=True)
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self.make_empty_intermediate_tensors = (
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self.model.make_empty_intermediate_tensors)
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def forward(
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self,
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input_ids: Optional[torch.Tensor],
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positions: torch.Tensor,
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kv_caches: List[torch.Tensor],
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attn_metadata: AttentionMetadata,
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intermediate_tensors: Optional[IntermediateTensors] = None,
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inputs_embeds: Optional[torch.Tensor] = None,
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) -> Union[torch.Tensor, IntermediateTensors]:
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return self.model(input_ids, positions, kv_caches, attn_metadata,
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intermediate_tensors, inputs_embeds)
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def pooler(
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self,
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hidden_states: torch.Tensor,
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pooling_metadata: PoolingMetadata,
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) -> Optional[PoolerOutput]:
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return self._pooler(hidden_states, pooling_metadata)
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def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
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self.model.load_weights(weights)
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def load_kv_cache_scales(self, quantization_param_path: str) -> None:
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self.model.load_kv_cache_scales(quantization_param_path)
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