hf_text-generation-inference/server/text_generation_server/adapters/config.py

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Enable multiple LoRa adapters (#2010) * 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>
2024-06-25 12:46:27 -06:00
# Origin: https://github.com/predibase/lorax
# Path: lorax/server/lorax_server/adapters/config.py
# License: Apache License Version 2.0, January 2004
from abc import ABC, abstractmethod
from dataclasses import dataclass
from typing import TYPE_CHECKING, Dict, Optional, Set, Tuple
import torch
from text_generation_server.adapters.weights import AdapterWeights
if TYPE_CHECKING:
from text_generation_server.models.model import Model
@dataclass
class ModuleMap:
module_name: str
module_weights: Dict[str, Tuple[torch.Tensor, str]]
@dataclass
class AdapterConfig(ABC):
base_model_name_or_path: str
@abstractmethod
def map_weights_for_model(
self,
adapter_weights: Dict[int, AdapterWeights],
weight_names: Tuple[str],
) -> Tuple[ModuleMap, Set[str]]:
pass
@abstractmethod
def load_batched_adapter_weights(
self,
model: "Model",
module_map: ModuleMap,
layer_type: str,
unused_weight_names: Set[str],
dynamic: bool,
) -> Optional[AdapterWeights]:
pass