Configurable embeddings upcast (#2621)

* fsdp embeddings should be float32 per comment

* patch peft to not upcast everything

* add tabs back to code check

* fix import

* add configurable option and fix check

* add check for dtypes

* move embeddings test to patch dir

* fix test

* fix comment and logic

tests/e2e/patched/test_peft_embeddings.py

@@ -0,0 +1,63 @@
+"""
+Test case for handling embeddings when using peft
+"""
+
+import torch
+
+from axolotl.train import setup_model_and_tokenizer
+from axolotl.utils.config import normalize_config, validate_config
+from axolotl.utils.dict import DictDefault
+
+
+class TestLlamaPeftEmbeddings:
+    """
+    test class for handling embeddings when using peft
+    """
+
+    def test_peft_embeddings_upcast(self, temp_dir):
+        # pylint: disable=duplicate-code
+        cfg = DictDefault(
+            {
+                "base_model": "HuggingFaceTB/SmolLM2-135M",
+                "load_in_4bit": True,
+                "adapter": "qlora",
+                "lora_r": 8,
+                "lora_alpha": 16,
+                "lora_target_linear": True,
+                "trust_remote_code": True,
+                "sequence_len": 512,
+                "val_set_size": 0.01,
+                "special_tokens": {
+                    "pad_token": "<|endoftext|>",
+                },
+                "datasets": [
+                    {
+                        "path": "mhenrichsen/alpaca_2k_test",
+                        "type": "alpaca",
+                    },
+                ],
+                "num_epochs": 1,
+                "max_steps": 2,
+                "micro_batch_size": 1,
+                "gradient_accumulation_steps": 1,
+                "output_dir": temp_dir,
+                "learning_rate": 0.00001,
+                "optimizer": "adamw_8bit",
+                "lr_scheduler": "cosine",
+                "flash_attention": True,
+                "sample_packing": False,
+                "bf16": "auto",
+                "save_safetensors": True,
+                "embeddings_skip_upcast": True,
+            }
+        )
+
+        cfg = validate_config(cfg)
+        normalize_config(cfg)
+
+        model, _, _, _ = setup_model_and_tokenizer(cfg)
+
+        # Check if the embeddings are upcast correctly
+        # only embed_tokens is a parameter that may be upcast
+        assert model.base_model.model.model.embed_tokens.weight.dtype == torch.bfloat16
+        assert model.base_model.model.lm_head.weight.dtype == torch.bfloat16