fix types w lora (#478)

src/axolotl/utils/models.py

@@ -11,7 +11,6 @@ import bitsandbytes as bnb
 import torch
 import transformers
 from optimum.bettertransformer import BetterTransformer
-from peft.tuners.lora import LoraLayer
 from transformers import (  # noqa: F401
     AutoConfig,
     AutoModelForCausalLM,
@@ -348,6 +347,14 @@ def load_model(
     if model.device.type == "cuda":
         log_gpu_memory_usage(LOG, "after model load", model.device)
 
+    # make sure these are fp32 per Ramesh et al. (2021)
+    for name, module in model.named_modules():
+        if "norm" in name:
+            module.to(torch.float32)
+        if "lm_head" in name or "embed_tokens" in name:
+            if hasattr(module, "weight"):
+                module.to(torch.float32)
+
     if not cfg.gptq and (
         (cfg.adapter == "lora" and load_in_8bit)
         or (cfg.adapter == "qlora" and cfg.load_in_4bit)
@@ -357,6 +364,16 @@ def load_model(
             model, use_gradient_checkpointing=cfg.gradient_checkpointing
         )
 
+        # LlamaRMSNorm layers are in fp32 after kbit_training, so we need to
+        # convert them back to fp16/bf16 for flash-attn compatibility.
+        if cfg.flash_attention and cfg.is_llama_derived_model:
+            for name, module in model.named_modules():
+                if "norm" in name:
+                    module.to(cfg.torch_dtype)
+                if "lm_head" in name or "embed_tokens" in name:
+                    if hasattr(module, "weight"):
+                        module.to(cfg.torch_dtype)
+
     model, lora_config = load_adapter(model, cfg, cfg.adapter)
 
     if cfg.ddp and not load_in_8bit:
@@ -500,22 +517,6 @@ def load_lora(model, cfg):
     else:
         model = get_peft_model(model, lora_config)
 
-    for name, module in model.named_modules():
-        if isinstance(module, LoraLayer):
-            module = module.to(cfg.torch_dtype)
-        if "norm" in name:
-            module = module.to(torch.float32)
-        if "lm_head" in name or "embed_tokens" in name:
-            if hasattr(module, "weight"):
-                module = module.to(cfg.torch_dtype)
-
-    # LlamaRMSNorm layers are in fp32 after kbit_training, so we need to
-    # convert them back to fp16/bf16 for flash-attn compatibility.
-    if cfg.flash_attention and cfg.is_llama_derived_model:
-        for name, module in model.named_modules():
-            if "norm" in name:
-                module = module.to(cfg.torch_dtype)
-
     model.print_trainable_parameters()
 
     return model, lora_config