fix model save / load logic

src/axolotl/cli/integrations/convert_diff_transformer.py

@@ -75,7 +75,7 @@ def convert_diff_transformer(cfg, cli_args, config_path):
         LOG.info("Converting to differential attention...")
         try:
             model = convert_to_diff_attention(model, cli_args.zero_init)
-            model.to(model.device)
+            model.to(cfg.device, dtype=cfg.torch_dtype)
         except Exception as exc:
             LOG.error(Fore.RED + "Conversion failed: %s" + Fore.RESET, str(exc))
             raise

src/axolotl/common/cli.py

@@ -4,7 +4,7 @@ shared module for cli specific things
 
 import logging
 from dataclasses import dataclass, field
-from typing import Optional
+from typing import Optional, Union
 
 import axolotl.monkeypatch.data.batch_dataset_fetcher  # pylint: disable=unused-import  # noqa: F401
 from axolotl.logging_config import configure_logging
@@ -67,7 +67,7 @@ class ConvertDiffTransformerCliArgs:
 def load_model_and_tokenizer(
     *,
     cfg: DictDefault,
-    cli_args: TrainerCliArgs,
+    cli_args: Union[TrainerCliArgs, EvaluateCliArgs, ConvertDiffTransformerCliArgs],
 ):
     LOG.info(f"loading tokenizer... {cfg.tokenizer_config or cfg.base_model_config}")
     tokenizer = load_tokenizer(cfg)

src/axolotl/evaluate.py

@@ -9,13 +9,13 @@ from typing import Dict, Optional
 import torch
 from accelerate.logging import get_logger
 
-from axolotl.common.cli import TrainerCliArgs
+from axolotl.common.cli import EvaluateCliArgs, load_model_and_tokenizer
 from axolotl.logging_config import configure_logging
 from axolotl.train import TrainDatasetMeta
 from axolotl.utils import set_pytorch_cuda_alloc_conf
 from axolotl.utils.dict import DictDefault
-from axolotl.utils.models import load_model, load_processor, load_tokenizer
+from axolotl.utils.models import load_processor
-from axolotl.utils.trainer import setup_trainer
+from axolotl.utils.trainer import set_pytorch_cuda_alloc_conf, setup_trainer
 
 project_root = os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))
 src_dir = os.path.join(project_root, "src")
@@ -63,7 +63,7 @@ def evaluate_dataset(
 
 
 def evaluate(
-    *, cfg: DictDefault, cli_args: TrainerCliArgs, dataset_meta: TrainDatasetMeta
+    *, cfg: DictDefault, cli_args: EvaluateCliArgs, dataset_meta: TrainDatasetMeta
 ) -> Dict[str, float]:
     """
     Evaluate a model on training and validation datasets
@@ -83,12 +83,11 @@ def evaluate(
     # Enable expandable segments for cuda allocation to improve VRAM usage
     set_pytorch_cuda_alloc_conf()
 
-    # Load tokenizer
+    # Load model
-    LOG.debug(
+    LOG.debug("loading model for evaluation...")
-        f"loading tokenizer... {cfg.tokenizer_config or cfg.base_model_config}",
+
-        main_process_only=True,
+    model, tokenizer = load_model_and_tokenizer(cfg=cfg, cli_args=cli_args)
-    )
+    model = model.to(cfg.device, dtype=cfg.torch_dtype)
-    tokenizer = load_tokenizer(cfg)
 
     # Load processor for multimodal models if needed
     processor = None
@@ -100,12 +99,6 @@ def evaluate(
     eval_dataset = dataset_meta.eval_dataset
     total_num_steps = dataset_meta.total_num_steps
 
-    # Load model
-    LOG.debug("loading model for evaluation...")
-    model, _ = load_model(
-        cfg, tokenizer, processor=processor, inference=cli_args.inference
-    )
-
     # Set up trainer
     trainer = setup_trainer(
         cfg,

src/axolotl/integrations/diff_transformer/convert.py

@@ -59,7 +59,7 @@ def copy_attention_weights(
         nn.init.zeros_(new_attn.lambda_k1)
         nn.init.zeros_(new_attn.lambda_q2)
         nn.init.zeros_(new_attn.lambda_k2)
-        new_attn.lambda_init = 0.0
+        nn.init.zeros_(new_attn.lambda_init)
 
     logger.debug(
         "Copied positive attention weights from %s to %s",
@@ -105,6 +105,7 @@ def convert_to_diff_attention(
                 )
 
                 # Copy weights from old attention to new attention
+                new_attention.to(child.q_proj.weight.device)
                 copy_attention_weights(child, new_attention, zero_init=zero_init)
 
                 # Replace the layer

src/axolotl/integrations/diff_transformer/multihead_diffattn.py

@@ -70,13 +70,12 @@ class LlamaDifferentialAttention(nn.Module):
         self.base_num_kv_heads = config.num_key_value_heads
         self.head_dim = config.hidden_size // config.num_attention_heads
 
-        self.scaling = self.head_dim**-0.5
         self.layer_idx = layer_idx
         self.max_position_embeddings = config.max_position_embeddings
         self.rope_theta = config.rope_theta
         self.is_causal = True
 
-        dtype = getattr(config, "torch_dtype", torch.float32)
+        dtype = torch.float32
 
         # For Q1 and Q2
         self.q_proj = nn.Linear(
@@ -111,7 +110,10 @@ class LlamaDifferentialAttention(nn.Module):
         )
 
         # Initialize differential attention parameters
-        self.lambda_init = lambda_init_fn(self.layer_idx)
+        self.lambda_init = nn.Parameter(
+            torch.full((), lambda_init_fn(self.layer_idx), dtype=dtype),
+            requires_grad=False,
+        )
         self.lambda_q1 = nn.Parameter(
             torch.zeros(self.head_dim, dtype=dtype).normal_(mean=0, std=0.1)
         )
@@ -197,6 +199,14 @@ class LlamaDifferentialAttention(nn.Module):
             self.head_dim
         )
 
+        # Add this debug step right after computing attention weights in the forward pass
+        attn_weights1 = torch.matmul(q1, k1.transpose(-1, -2)) / math.sqrt(
+            self.head_dim
+        )
+        attn_weights2 = torch.matmul(q2, k2.transpose(-1, -2)) / math.sqrt(
+            self.head_dim
+        )
+
         if attention_mask is not None:
             causal_mask = attention_mask[:, :, :, : k1.shape[-2]]
             attn_weights1 = attn_weights1 + causal_mask

src/axolotl/integrations/diff_transformer/patches.py

@@ -9,16 +9,13 @@ from .multihead_diffattn import (
 )
 
 
-def patch_transformers():
+def patch_llama_attention_classes():
     """Patch transformers to support differential attention"""
 
     # Add our attention class to the registry
     LLAMA_ATTENTION_CLASSES["differential_eager"] = LlamaDifferentialAttention
     LLAMA_ATTENTION_CLASSES["differential_sdpa"] = LlamaDifferentialSdpaAttention
 
-    # Store original method for use in our patch
-    # original_autoset = PreTrainedModel._autoset_attn_implementation
-
     @classmethod
     def new_autoset(_, config, **kwargs):  # pylint: disable=unused-argument
         config._attn_implementation_autoset = True  # pylint: disable=protected-access

src/axolotl/utils/models.py

@@ -444,6 +444,13 @@ class ModelLoader:
 
             patch_mistral_cross_entropy()
 
+        if self.cfg.diff_attention:
+            from axolotl.integrations.diff_transformer.patches import (
+                patch_llama_attention_classes,
+            )
+
+            patch_llama_attention_classes()
+
     def patch_attention(self) -> None:
         if hasattr(self.model_config, "model_type"):
             if self.model_config.model_type == "mllama" and self.cfg.flash_attention:
@@ -1058,10 +1065,6 @@ class ModelLoader:
             integrate_rope_embeddings()
 
     def load_model(self) -> Tuple[PreTrainedModel, Optional[PeftConfig]]:
-        from axolotl.integrations.diff_transformer.patches import patch_transformers
-
-        patch_transformers()
-
         self.apply_patches()
         self.set_auto_model_loader()
         self.set_device_map_config()