88 lines
3.2 KiB
Python
88 lines
3.2 KiB
Python
from typing import Iterable, Tuple
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import torch
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from torch import nn
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from transformers import LlamaConfig
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from sglang.srt.layers.pooler import EmbeddingPoolerOutput, Pooler, PoolingType
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from sglang.srt.model_executor.model_runner import ForwardBatch
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from sglang.srt.model_loader.weight_utils import default_weight_loader
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from sglang.srt.models.llama import LlamaModel
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from sglang.srt.utils import add_prefix
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class LlamaEmbeddingModel(nn.Module):
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def __init__(
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self,
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config: LlamaConfig,
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quant_config=None,
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prefix: str = "",
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) -> None:
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super().__init__()
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self.model = LlamaModel(
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config, quant_config=quant_config, prefix=add_prefix("model", prefix)
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)
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self.pooler = Pooler(pooling_type=PoolingType.LAST, normalize=True)
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@torch.no_grad()
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def forward(
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self,
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input_ids: torch.Tensor,
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positions: torch.Tensor,
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forward_batch: ForwardBatch,
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input_embeds: torch.Tensor = None,
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get_embedding: bool = True,
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) -> EmbeddingPoolerOutput:
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assert (
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get_embedding
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), "LlamaEmbeddingModel / MistralModel is only used for embedding"
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hidden_states = self.model(input_ids, positions, forward_batch, input_embeds)
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return self.pooler(hidden_states, forward_batch)
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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 or "projector" in name:
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return
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if "rotary_emb.cos_cached" in name 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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return
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if name.startswith("model.vision_tower") and name not in params_dict:
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return
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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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return
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param = params_dict[name]
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weight_loader = getattr(param, "weight_loader", default_weight_loader)
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weight_loader(param, loaded_weight)
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class MistralModel(LlamaEmbeddingModel):
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pass
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EntryClass = [LlamaEmbeddingModel, MistralModel]
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