make phi training work with Loras (#588)

* valdiation for phi loras

* fix model config class check

* update readme for phi traiing

src/axolotl/utils/config.py

@@ -75,6 +75,7 @@ def normalize_config(cfg):
         cfg.torch_dtype = torch.float32
 
     model_config = load_model_config(cfg)
+    cfg.model_config_type = model_config.model_type
 
     # figure out if the model is llama
     cfg.is_llama_derived_model = (
@@ -237,6 +238,21 @@ def validate_config(cfg):
             raise ValueError(
                 "`early_stopping_patience` requires that eval_steps should evenly divide save_steps."
             )
+
+    if cfg.model_type == "MixFormerSequentialForCausalLM" and cfg.adapter is not None:
+        LOG.warning("Use AutoModelForCausalLM for phi/MixFormer models with qLoRA")
+
+    if cfg.model_config_type == "mixformer-sequential":
+        if cfg.sample_packing:
+            if cfg.adapter is not None:
+                LOG.warning(
+                    "phi/MixFormer models are not currently compatible with LoRA and sample_packing"
+                )
+            if cfg.model_type == "AutoModelForCausalLM":
+                raise ValueError(
+                    "`model_type: MixFormerSequentialForCausalLM` required for sample_packing"
+                )
+
     # TODO
     # MPT 7b
     # https://github.com/facebookresearch/bitsandbytes/issues/25

src/axolotl/utils/models.py

@@ -1,6 +1,5 @@
 """Module for models and model loading"""
-
-
+import importlib
 import logging
 import math
 import os
@@ -155,11 +154,26 @@ def load_model(
         LOG.info("patching _expand_mask")
         hijack_expand_mask()
 
+    model_config = load_model_config(cfg)
+
+    # special handling b/c remote MixFormers code doesn't have _no_split_modules set
+    if (
+        "MixFormerSequentialConfig" in model_config.__class__.__name__
+        and cfg.model_type == "AutoModelForCausalLM"
+    ):
+        module_name = model_config.__class__.__module__.replace(
+            ".configuration_mixformer_sequential", ".modeling_mixformer_sequential"
+        )
+        modeling_phi = importlib.import_module(module_name)
+        # pylint:disable=protected-access
+        modeling_phi.MixFormerSequentialForCausalLM._no_split_modules = [
+            "ParallelBlock"
+        ]
+
     model_kwargs = {}
     if cfg.model_revision:
         model_kwargs["revision"] = cfg.model_revision
     if cfg.gptq:
-        model_config = load_model_config(cfg)
         if not hasattr(model_config, "quantization_config"):
             LOG.warning("model config does not contain quantization_config information")
         else: