mirror of
https://github.com/huggingface/text-generation-inference.git
synced 2025-04-21 23:12:07 +00:00
04e1af94d7
* feat: first draft load multiple lora * feat: load weights within layer and refactor lora pass * fix: refactor and reduce lora math * feat: baseline impl single request multi lora support * feat: prefer lorax implementation and port loading logic * fix: prefer adapter_data and refactors * feat: perfer loraxs custom punica kernels and add mlp loras * fix: adjust batch for bgmv * fix: adjust adapter_segments logic when in batch * fix: refactor and move changes to v3 proto * fix: pass model_id for all flash causal lms * fix: pass model_id for all causal and seq2seq lms * fix: add model_id to model test * feat: add lora support to mistral and refactors * feat: prefer model id in request * fix: include rust code for adapter id * feat: bump launcher and add new lora docs * feat: support base model generation and refactors * fix: rename doc to retry ci build * feat: support if vlm models * fix: add adapter_data param and avoid missing layers * fix: add adapter_data param to phi and neox * fix: update all models forwards to include adapter_data * fix: add model_id to IdeficsCausalLM * Update lora.md Fixed a typo * Update lora.md Fixing spam image * fix: add lora kernel to dockerfile, support running without kernels and refactors * fix: avoid dockerfile conflict * fix: refactors and adjust flash llama lora logic * fix: skip llama test due to CI issue (temp) * fix: skip llama test CI (temp) 2 * fix: revert skips and prefer updated ci token for tests * fix: refactors and helpful comments * fix: add noop in TensorParallelAdapterRowLinear too * fix: refactor and move shard_lora_weights logic * fix: exit early if no adapter_data --------- Co-authored-by: Derek <datavistics@gmail.com>
45 lines
1.1 KiB
Python
45 lines
1.1 KiB
Python
# Origin: https://github.com/predibase/lorax
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# Path: lorax/server/lorax_server/adapters/config.py
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# License: Apache License Version 2.0, January 2004
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from abc import ABC, abstractmethod
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from dataclasses import dataclass
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from typing import TYPE_CHECKING, Dict, Optional, Set, Tuple
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import torch
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from text_generation_server.adapters.weights import AdapterWeights
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if TYPE_CHECKING:
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from text_generation_server.models.model import Model
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@dataclass
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class ModuleMap:
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module_name: str
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module_weights: Dict[str, Tuple[torch.Tensor, str]]
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@dataclass
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class AdapterConfig(ABC):
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base_model_name_or_path: str
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@abstractmethod
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def map_weights_for_model(
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self,
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adapter_weights: Dict[int, AdapterWeights],
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weight_names: Tuple[str],
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) -> Tuple[ModuleMap, Set[str]]:
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pass
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@abstractmethod
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def load_batched_adapter_weights(
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self,
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model: "Model",
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module_map: ModuleMap,
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layer_type: str,
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unused_weight_names: Set[str],
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dynamic: bool,
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) -> Optional[AdapterWeights]:
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pass
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