shuffle and split dataset after save/load

FAQS.md

@@ -1,4 +1,4 @@
 # FAQs
 
 - Can you train StableLM with this? Yes, but only with a single GPU atm. Multi GPU support is coming soon! Just waiting on this [PR](https://github.com/huggingface/transformers/pull/22874)
--
+- Will this work with Deepspeed? That's still a WIP, but setting `export ACCELERATE_USE_DEEPSPEED=true` should work in some cases

ds_config.json

@@ -11,11 +11,10 @@
     "min_loss_scale": 1
   },
   "scheduler": {
-    "type": "WarmupLR",
+    "type": "OneCycle",
     "params": {
-      "warmup_min_lr": "auto",
+      "cycle_min_lr": 1e-7,
-      "warmup_max_lr": "auto",
+      "cycle_max_lr": 1e-4
-      "warmup_num_steps": "auto"
     }
   },
   "zero_optimization": {
@@ -25,7 +24,8 @@
     "allgather_bucket_size": 5e8,
     "contiguous_gradients": true,
     "reduce_bucket_size": "auto",
-    "reduce_scatter": true
+    "reduce_scatter": true,
+    "stage3_gather_16bit_weights_on_model_save": true
   },
   "gradient_accumulation_steps": "auto",
   "gradient_clipping": "auto",

src/axolotl/utils/data.py

@@ -119,16 +119,15 @@ def load_prepare_datasets(tokenizer, cfg, default_dataset_prepared_path):
             seq_length=max_packed_sequence_len,
         )
         logging.info("merging, packing, shuffling, and splitting master dataset")
-        # TODO don't split dataset here, shuffle and save first, then split, that way we can
+        dataset = Dataset.from_list([_ for _ in constant_len_dataset]).shuffle(seed=42)
-        #  re-split when loading again
-        dataset = Dataset.from_list([_ for _ in constant_len_dataset]).train_test_split(
-            test_size=cfg.val_set_size, shuffle=True, seed=42
-        )
 
         if cfg.local_rank == 0:
             logging.info(f"Saving prepared dataset to disk... {prepared_ds_path}")
             dataset.save_to_disk(prepared_ds_path)
 
+    dataset = dataset.train_test_split(
+        test_size=cfg.val_set_size, shuffle=False
+    )
     train_dataset = dataset["train"]
     eval_dataset = dataset["test"]
 

src/axolotl/utils/models.py

@@ -75,7 +75,7 @@ def load_model(
                 snapshot_download_kwargs = {}
                 if cfg.base_model_ignore_patterns:
                     snapshot_download_kwargs["ignore_patterns"] = cfg.base_model_ignore_patterns
-                cache_model_path = Path(snapshot_download(base_model, ** snapshot_download_kwargs))
+                cache_model_path = Path(snapshot_download(base_model, **snapshot_download_kwargs))
                 files = (
                     list(cache_model_path.glob("*.pt"))
                     + list(cache_model_path.glob("*.safetensors"))