ia3 keeps casting to float32, handle it here for now

src/axolotl/monkeypatch/llama_attn_hijack_flash.py

@@ -116,6 +116,8 @@ def flashattn_forward(
     attention_mask: [bsz, q_len]
     """
     # pylint: disable=duplicate-code
+    original_dtype = hidden_states.dtype
+
     bsz, q_len, _ = hidden_states.size()
 
     if not hasattr(self, "pretraining_tp"):
@@ -151,6 +153,13 @@ def flashattn_forward(
         key_states = self.k_proj(hidden_states)
         value_states = self.v_proj(hidden_states)
 
+    if query_states.dtype == torch.float32:
+        query_states = query_states.to(dtype=original_dtype)
+    if key_states.dtype == torch.float32:
+        key_states = key_states.to(dtype=original_dtype)
+    if value_states.dtype == torch.float32:
+        value_states = value_states.to(dtype=original_dtype)
+
     query_states = query_states.view(
         bsz, q_len, self.num_heads, self.head_dim
     ).transpose(1, 2)
@@ -309,6 +318,10 @@ def flashattn_forward(
     else:
         attn_output = self.o_proj(attn_output)
 
+    # handle conversion back for IA3
+    if attn_output.dtype == torch.float32:
+        attn_output = attn_output.to(dtype=original_dtype)
+
     return attn_output, None, past_key_value
 
 
@@ -502,6 +515,7 @@ def llama_model_forward(
     )
 
     hidden_states = inputs_embeds
+    original_dtype = hidden_states.dtype
 
     if self.gradient_checkpointing and self.training:
         if use_cache:
@@ -559,6 +573,10 @@ def llama_model_forward(
 
         hidden_states = layer_outputs[0]
 
+        # handle conversion back for IA3
+        if hidden_states.dtype == torch.float32:
+            hidden_states = hidden_states.to(dtype=original_dtype)
+
         if use_cache:
             next_decoder_cache += (layer_outputs[2 if output_attentions else 1],)
 

src/axolotl/utils/models.py

@@ -431,7 +431,7 @@ def load_model(
                 if hasattr(module, "weight"):
                     module.to(cfg.torch_dtype)
 
-    model, lora_config = load_adapter(model, cfg, cfg.adapter)
+    model, peft_config = load_adapter(model, cfg, cfg.adapter)
 
     if cfg.ddp and not load_in_8bit:
         model.to(f"cuda:{cfg.local_rank}")
@@ -462,7 +462,7 @@ def load_model(
         log_gpu_memory_usage(LOG, "after adapters", model.device)
 
     # TODO resume_from_checkpoint handling
-    return model, lora_config
+    return model, peft_config
 
 
 def load_adapter(model, cfg, adapter, inference=False):