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88 lines
3.3 KiB
Python
88 lines
3.3 KiB
Python
from typing import Iterable, List, Optional, Tuple
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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.model_loader.weight_utils import default_weight_loader
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from vllm.model_executor.models.llama import LlamaModel
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from vllm.model_executor.pooling_metadata import PoolingMetadata
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from vllm.sequence import PoolerOutput
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class LlamaEmbeddingModel(nn.Module):
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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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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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inputs_embeds: Optional[torch.Tensor] = None,
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) -> torch.Tensor:
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return self.model.forward(input_ids, positions, kv_caches,
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attn_metadata, 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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stacked_params_mapping = [
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# (param_name, shard_name, shard_id)
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("qkv_proj", "q_proj", "q"),
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("qkv_proj", "k_proj", "k"),
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("qkv_proj", "v_proj", "v"),
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("gate_up_proj", "gate_proj", 0),
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("gate_up_proj", "up_proj", 1),
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]
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params_dict = dict(self.model.named_parameters())
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for name, loaded_weight in weights:
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if "rotary_emb.inv_freq" in name:
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continue
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if ("rotary_emb.cos_cached" in name
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or "rotary_emb.sin_cached" in name):
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# Models trained using ColossalAI may include these tensors in
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# the checkpoint. Skip them.
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continue
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for (param_name, weight_name, shard_id) in stacked_params_mapping:
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if weight_name not in name:
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continue
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name = name.replace(weight_name, param_name)
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# Skip loading extra bias for GPTQ models.
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if name.endswith(".bias") and name not in params_dict:
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continue
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param = params_dict[name]
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weight_loader = param.weight_loader
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weight_loader(param, loaded_weight, shard_id)
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break
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else:
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# Skip loading extra bias for GPTQ models.
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if name.endswith(".bias") and name not in params_dict:
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continue
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param = params_dict[name]
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weight_loader = getattr(param, "weight_loader",
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default_weight_loader)
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weight_loader(param, loaded_weight)
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