use autoconfig w rala

src/axolotl/cli/integrations/convert_rala.py

@@ -82,6 +82,7 @@ def convert_rala(cfg, cli_args, config_path):
             zero_init=cli_args.zero_init,
         )
         model.to(cfg.device, dtype=cfg.torch_dtype)
+        model.config.model_type = "llama-rala"
     except Exception as exc:
         LOG.error(Fore.RED + "Conversion failed: %s" + Fore.RESET, str(exc))
         raise

src/axolotl/integrations/rala/__init__.py

@@ -2,10 +2,8 @@
 
 import logging
 
-# from transformers.models.llama.modeling_llama import LLAMA_ATTENTION_CLASSES
-
 from axolotl.integrations.base import BasePlugin
-from axolotl.integrations.rala.auto.llama.modeling_rala import LlamaRALAAttention
+from axolotl.integrations.rala.auto.llama.modeling_rala import register_rala_model
 
 LOG = logging.getLogger(__name__)
 
@@ -18,7 +16,6 @@ class RalaPlugin(BasePlugin):
     def get_input_args(self):
         return "axolotl.integrations.rala.args.RalaArgs"
 
-    def set_attn_config(self, cfg, model_kwargs, model_config):
+    def pre_model_load(self, cfg):
-        # if cfg.rala_attention:
+        if cfg.rala_attention:
-        #     model_kwargs["attn_implementation"] = "rala"
+            register_rala_model()
-        ...

src/axolotl/integrations/rala/auto/llama/configuration_rala.py

@@ -9,4 +9,5 @@ class LlamaRalaConfig(LlamaConfig):
     Configuration for LlamaRala model
     """
 
+    model_type = "llama-rala"
     softmax_every: int = 6  # every N-th layer applies softmax

src/axolotl/integrations/rala/auto/llama/modeling_rala.py

@@ -19,11 +19,18 @@ from typing import List, Optional, Tuple, Union, Unpack
 import torch
 import torch.nn.functional as F
 from torch import nn
-from transformers import Cache, GenerationMixin, LlamaModel
+from transformers import (
+    AutoConfig,
+    AutoModel,
+    AutoModelForCausalLM,
+    Cache,
+    GenerationMixin,
+    LlamaModel,
+)
 from transformers.modeling_outputs import CausalLMOutputWithPast
 from transformers.models.llama.modeling_llama import (
+    LLAMA_ATTENTION_CLASSES,
     KwargsForCausalLM,
-    LlamaAttention,
     LlamaDynamicNTKScalingRotaryEmbedding,
     LlamaLinearScalingRotaryEmbedding,
     LlamaMLP,
@@ -329,7 +336,10 @@ class LlamaRalaDecoderLayer(nn.Module):
         if LlamaRalaConfig.is_layer_idx_softmax(
             config.num_hidden_layers, layer_idx, config.softmax_every
         ):
-            self.self_attn = LlamaAttention(config=config, layer_idx=layer_idx)
+            self.self_attn = LLAMA_ATTENTION_CLASSES[config._attn_implementation](
+                config=config, layer_idx=layer_idx
+            )
+            # self.self_attn = LlamaAttention(config=config, layer_idx=layer_idx)
         else:
             self.self_attn = LlamaRALAAttention(config=config, layer_idx=layer_idx)
 
@@ -594,3 +604,20 @@ class LlamaRalaForCausalLM(LlamaPreTrainedModel, GenerationMixin):
             hidden_states=outputs.hidden_states,
             attentions=outputs.attentions,
         )
+
+
+def register_rala_model() -> None:
+    """
+    Register differential attention components with the transformers library.
+    This function registers the differential attention configurations and model classes
+    with the Auto* classes from `transformers`, making them available through the
+    standard model loading pipeline.
+    """
+    # Register configs
+    AutoConfig.register("llama-rala", LlamaRalaConfig)
+
+    # Register models
+    AutoModel.register(LlamaRalaConfig, LlamaRalaModel)
+    AutoModelForCausalLM.register(LlamaRalaConfig, LlamaRalaForCausalLM)
+
+    LLAMA_ATTENTION_CLASSES["rala"] = LlamaRALAAttention

src/axolotl/integrations/rala/convert.py

@@ -7,8 +7,10 @@ from torch import nn
 from transformers import PreTrainedModel
 from transformers.models.llama.modeling_llama import LlamaAttention
 
-from axolotl.integrations.rala import LlamaRALAAttention
+from axolotl.integrations.rala.auto.llama.modeling_rala import (
-from axolotl.integrations.rala.auto.llama.modeling_rala import LlamaRalaDecoderLayer
+    LlamaRALAAttention,
+    LlamaRalaDecoderLayer,
+)
 
 logger = logging.getLogger(__name__)