support lora (#1)

- feat: support lora (5ed05aac24feb06378f0e19b6ae1ad4b26fe613d)
- Update modeling_lora.py (c380b5a69a5f279bad937ce1d5de87248bf0adf2)
- feat: initialize models with or without adapters (79c3c9397232303f34ba232f60e5cb6856aaf3f0)
- chore: change num lora def value (f960115170389c74fe144f688be72b8821b8e35e)
- small change (6060bad367d8fb677124ffbb8ee3ee2d7849e352)
- feat: merge stuff (841b70fc561e8292098ed7e890a75ff352ab987b)

configuration_xlm_roberta.py

@@ -22,6 +22,8 @@ class XLMRobertaFlashConfig(PretrainedConfig):
             position_embedding_type="absolute",
             use_cache=True,
             classifier_dropout=None,
+            num_loras=1,
+            load_trained_adapters=False,
             use_flash_attn=True,
             torch_dtype=None,
             emb_pooler=None,
@@ -29,6 +31,7 @@ class XLMRobertaFlashConfig(PretrainedConfig):
     ):
         super().__init__(pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)
 
+
         self.vocab_size = vocab_size
         self.hidden_size = hidden_size
         self.num_hidden_layers = num_hidden_layers
@@ -44,10 +47,11 @@ class XLMRobertaFlashConfig(PretrainedConfig):
         self.position_embedding_type = position_embedding_type
         self.use_cache = use_cache
         self.classifier_dropout = classifier_dropout
+        self.num_loras = num_loras
+        self.load_trained_adapters = load_trained_adapters
         self.use_flash_attn = use_flash_attn
         self.emb_pooler = emb_pooler
         if torch_dtype and hasattr(torch, torch_dtype) and type(getattr(torch, torch_dtype)) is torch.dtype:
             self.torch_dtype = getattr(torch, torch_dtype)
         else:
             self.torch_dtype = torch_dtype
-

modeling_lora.py

@@ -0,0 +1,325 @@
+import math
+import os
+from functools import partial
+from typing import Iterator, Optional, Tuple, Union
+
+import torch
+import torch.nn.utils.parametrize as parametrize
+from torch import nn
+from torch.nn import Parameter
+from transformers import PretrainedConfig
+
+from .modeling_xlm_roberta import XLMRobertaModel, XLMRobertaPreTrainedModel, XLMRobertaFlashConfig
+
+
+def initialized_weights(
+    shape: Tuple[int], num_adaptions: int, init: str = "kaiming"
+) -> torch.Tensor:
+    weight_data = []
+    for _ in range(num_adaptions):
+        new_adaption = torch.zeros(shape)
+        if init == "kaiming":
+            nn.init.kaiming_uniform_(new_adaption, a=math.sqrt(5))
+        elif init == "normal":
+            nn.init.normal_(new_adaption)
+        else:
+            raise NotImplementedError
+        weight_data.append(new_adaption)
+    return torch.stack(weight_data, dim=0)
+
+
+class LoRAParametrization(nn.Module):
+    """
+    This LoRA implementation was inspired by  https://github.com/cccntu/minLoRA
+    The MIT License (MIT) Copyright (c) 2020 Andrej Karpathy
+    Permission is hereby granted, free of charge, to any person obtaining a copy of this software
+    and associated documentation files (the "Software"), to deal in the Software without restriction,
+    including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense,
+    and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so,
+    subject to the following conditions:
+    The above copyright notice and this permission notice shall be included in all copies or substantial
+    portions of the Software.
+    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT
+    LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
+    IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,
+    WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE
+    SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+    """
+    def __init__(
+        self,
+        fan_in: int,
+        fan_out: int,
+        layer_type: str = "linear",
+        num_adaptions: int = 1,
+        rank: int = 4,
+        lora_dropout_p: float = 0.0,
+        lora_alpha: float = 1,
+    ):
+        super().__init__()
+        # if weight is stored as (fan_out, fan_in), the memory layout of A & B follows (W + BA)x
+        # otherwise, it's x(W + AB). This allows us to tie the weights between linear layers and embeddings
+        fan_in_fan_out = layer_type == "embedding"
+        self.swap = (lambda x: (x[1], x[0])) if fan_in_fan_out else (lambda x: x)
+
+        if layer_type == "linear":
+            self.lora_A = nn.Parameter(
+                initialized_weights((rank, fan_in), num_adaptions, init="kaiming")
+            )
+            self.lora_B = nn.Parameter(torch.zeros((num_adaptions, fan_out, rank)))
+        elif layer_type == "embedding":
+            self.lora_A = nn.Parameter(torch.zeros((num_adaptions, fan_in, rank)))
+            self.lora_B = nn.Parameter(
+                initialized_weights(
+                    (rank, fan_out), num_adaptions=num_adaptions, init="normal"
+                )
+            )
+        else:
+            raise NotImplementedError
+
+        self.lora_alpha, self.rank = lora_alpha, rank
+        self.scaling = lora_alpha / rank
+        self.lora_dropout = (
+            nn.Dropout(p=lora_dropout_p) if lora_dropout_p > 0 else lambda x: x
+        )
+        self.dropout_fn = self._dropout if lora_dropout_p > 0 else lambda x: x
+        self.register_buffer(
+            "lora_dropout_mask",
+            torch.ones(self.swap((1, fan_in)), dtype=self.lora_A.dtype),
+            persistent=False,
+        )
+        self.forward_fn = lambda x: x
+        self.current_task = None
+
+    def _dropout(self, A):
+        # to mimic the original implementation: A @ dropout(x), we do (A * dropout(ones)) @ x
+        return A * self.lora_dropout(self.lora_dropout_mask)
+
+    def lora_forward(self, X):
+        assert self.current_task is not None
+        return (
+            X
+            + torch.matmul(
+                *self.swap(
+                    (
+                        self.lora_B[self.current_task],
+                        self.dropout_fn(self.lora_A[self.current_task]),
+                    )
+                )
+            ).view(X.shape)
+            * self.scaling
+        )
+
+    def forward(self, X):
+        return self.forward_fn(X)
+
+    @property
+    def current_task(self):
+        return self._current_task
+
+    @current_task.setter
+    def current_task(self, task: Union[None, int]):
+        self._current_task = task
+        if task is None:
+            self.forward_fn = lambda x: x
+        else:
+            self.forward_fn = self.lora_forward
+
+    @classmethod
+    def from_linear(
+        cls,
+        layer: nn.Module,
+        num_adaptions: int = 1,
+        rank: int = 4,
+        lora_dropout_p: float = 0.0,
+        lora_alpha: int = 1,
+    ):
+        assert isinstance(layer, nn.Linear)
+        fan_out, fan_in = layer.weight.shape
+        return cls(
+            fan_in,
+            fan_out,
+            num_adaptions=num_adaptions,
+            layer_type="linear",
+            rank=rank,
+            lora_dropout_p=lora_dropout_p,
+            lora_alpha=lora_alpha,
+        )
+
+    @classmethod
+    def from_embedding(
+        cls, layer, num_adaptions=1, rank=4, lora_dropout_p=0.0, lora_alpha=1
+    ):
+        assert isinstance(layer, nn.Embedding)
+        fan_in, fan_out = layer.weight.shape
+        return cls(
+            fan_in,
+            fan_out,
+            num_adaptions=num_adaptions,
+            layer_type="embedding",
+            rank=rank,
+            lora_dropout_p=lora_dropout_p,
+            lora_alpha=lora_alpha,
+        )
+
+    @classmethod
+    def add_to_layer(
+        cls, layer, num_adaptions=1, rank=4, lora_dropout_p=0.0, lora_alpha=1
+    ):
+        if isinstance(layer, nn.Linear):
+            parametrize.register_parametrization(
+                layer,
+                "weight",
+                cls.from_linear(
+                    layer,
+                    num_adaptions=num_adaptions,
+                    rank=rank,
+                    lora_dropout_p=lora_dropout_p,
+                    lora_alpha=lora_alpha,
+                ),
+            )
+        elif isinstance(layer, nn.Embedding):
+            parametrize.register_parametrization(
+                layer,
+                "weight",
+                cls.from_embedding(
+                    layer,
+                    num_adaptions=num_adaptions,
+                    rank=rank,
+                    lora_dropout_p=lora_dropout_p,
+                    lora_alpha=lora_alpha,
+                ),
+            )
+
+    @staticmethod
+    def select_task_for_layer(layer: nn.Module, task_idx: Optional[int] = None):
+        if isinstance(layer, LoRAParametrization):
+            layer.current_task = task_idx
+
+    @staticmethod
+    def merge_lora_into_layer(layer: nn.Module):
+        if hasattr(layer, "parametrizations"):
+            for attr_name in layer.parametrizations.keys():
+                parametrize.remove_parametrizations(layer, attr_name, leave_parametrized=True)
+
+
+class XLMRobertaLoRA(XLMRobertaPreTrainedModel):
+    def __init__(self, config: XLMRobertaFlashConfig, roberta: Optional[XLMRobertaModel] = None, add_pooling_layer=True):
+        super().__init__(config)
+
+        if roberta is None:
+            self.roberta = XLMRobertaModel(config, add_pooling_layer=add_pooling_layer)
+        else:
+            self.roberta = roberta
+
+        self._is_merged = False
+        self._num_adaptions = config.num_loras
+        self._register_lora(self._num_adaptions)
+
+        self.main_params_trainable = False
+        self._task_idx = None
+        # By default, we select the first LoRA
+        self.current_task = 0
+
+    @property
+    def main_params_trainable(self):
+        return self._main_params_trainable
+
+    @main_params_trainable.setter
+    def main_params_trainable(self, val: bool):
+        """Whether the main parameters (i.e. those that are not LoRA) should be trainable.
+        This method sets the `requires_grad_` attribute of the main weights
+        and controls which parameters are returned in `self.parameters()`.
+        :param val: Whether or not to make the parameters trainable.
+        :return: None
+        """
+        self._main_params_trainable = val
+        for name, param in super().named_parameters():
+            if "lora" not in name:
+                param.requires_grad_(val)
+
+    def merge_lora(self):
+        """Merges currently selected LoRA into main weights."""
+        if self._is_merged:
+            raise Exception('LoRA has already been merged, cannot merge again')
+        self._is_merged = True
+        self.apply(LoRAParametrization.merge_lora_into_layer)
+
+    @classmethod
+    def from_pretrained(
+        cls,
+        pretrained_model_name_or_path: Optional[Union[str, os.PathLike]],
+        *model_args,
+        config: Optional[Union[PretrainedConfig, str, os.PathLike]] = None,
+        cache_dir: Optional[Union[str, os.PathLike]] = None,
+        ignore_mismatched_sizes: bool = False,
+        force_download: bool = False,
+        local_files_only: bool = False,
+        token: Optional[Union[str, bool]] = None,
+        revision: str = "main",
+        use_safetensors: bool = None,
+        **kwargs,
+    ):
+        config = XLMRobertaFlashConfig.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
+        if config.load_trained_adapters:
+            return super().from_pretrained(
+                pretrained_model_name_or_path,
+                *model_args,
+                **kwargs
+            )
+        else:
+            roberta = XLMRobertaModel.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
+            return cls(config, roberta=roberta)
+
+    def _register_lora(self, num_adaptions=1, rank=4, lora_dropout_p=0.0, lora_alpha=1):
+        self.apply(
+            partial(
+                LoRAParametrization.add_to_layer,
+                num_adaptions=num_adaptions,
+                rank=rank,
+                lora_dropout_p=lora_dropout_p,
+                lora_alpha=lora_alpha,
+            )
+        )
+
+    @property
+    def current_task(self):
+        """ Which LoRA is currently selected
+        :return: Integer or None (when LoRA is disabled)
+        """
+        return self._task_idx
+
+    @current_task.setter
+    def current_task(self, task_idx: Union[None, int]):
+        """Set the LoRA that is to be used.
+        The LoRA is specified by `task_idx`, which may be an integer >= 0,
+        indexing the available LoRAs. If it is None, no LoRA is used.
+        :param task_idx: Which LoRA to use
+        :return:
+        """
+        if self._is_merged:
+            raise Exception('LoRA has been merged, cannot select new task')
+        assert task_idx is None or 0 <= task_idx < self._num_adaptions
+        if self._task_idx != task_idx:
+            # In this case, we need to update the LoRAs everywhere
+            self._task_idx = task_idx
+            self.apply(
+                partial(LoRAParametrization.select_task_for_layer, task_idx=task_idx)
+            )
+
+    def forward(self, *args, current_task: Union[None, int] = -1, **kwargs):
+        if current_task is None or current_task >= 0:
+            self.current_task = current_task
+        return self.roberta(*args, **kwargs)
+
+    def parameters(self, recurse: bool = True) -> Iterator[Parameter]:
+        for _, param in self.named_parameters(recurse=recurse):
+            yield param
+
+    def named_parameters(
+        self, prefix: str = "", recurse: bool = True, remove_duplicate: bool = True
+    ) -> Iterator[Tuple[str, Parameter]]:
+        for name, param in super().named_parameters(
+            prefix=prefix, recurse=recurse, remove_duplicate=remove_duplicate
+        ):
+            if "lora" in name or self.main_params_trainable:
+                yield name, param