tweaks to data loading, 8 bit adam, accelerate and deepspeed

configs/llama_13B_alpaca.yml

@@ -0,0 +1,39 @@
+base_model: huggyllama/llama-13b
+model_type: LlamaForCausalLM
+tokenizer_type: LlamaTokenizer
+load_in_8bit: true
+datasets:
+  - path: anon8231489123/ShareGPT_Vicuna_unfiltered
+    data_files: ShareGPT_V3_unfiltered_cleaned_split_no_imsorry.json
+    type: sharegpt
+dataset_prepared_path: last_run_prepared
+val_set_size: 0.002
+adapter:
+lora_model_dir:
+sequence_len: 2048
+lora_r: 8
+lora_alpha: 16
+lora_dropout: 0.05
+lora_target_modules:
+  - q_proj
+  - v_proj
+lora_fan_in_fan_out: false
+wandb_project:
+wandb_watch:
+wandb_run_id:
+wandb_log_model: checkpoint
+output_dir: ./llama-13b-sharegpt
+batch_size: 64
+micro_batch_size: 2
+warmup_steps: 1000
+save_steps:
+eval_steps:
+num_epochs: 5
+learning_rate: 0.00003
+train_on_inputs: false
+group_by_length: false
+bf16: true
+tf32: true
+early_stopping_patience: 5
+resume_from_checkpoint:
+local_rank:

src/axolotl/utils/data.py

@@ -2,7 +2,7 @@ import logging
 from hashlib import md5
 from pathlib import Path
 
-from datasets import load_from_disk, load_dataset, IterableDataset, Dataset
+from datasets import load_from_disk, load_dataset, IterableDataset, Dataset, concatenate_datasets
 from huggingface_hub import hf_hub_download
 
 from axolotl.datasets import TokenizedPromptDataset, ConstantLengthDataset
@@ -44,10 +44,11 @@ def load_prepare_datasets(tokenizer, cfg, default_dataset_prepared_path):
     )
 
     if any(prepared_ds_path.glob("*")):
-        logging.info("Loading prepared dataset from disk...")
+        logging.info(f"Loading prepared dataset from disk ay {prepared_ds_path}...")
         dataset = load_from_disk(str(prepared_ds_path))
         logging.info("Prepared dataset loaded from disk...")
     else:
+        logging.info(f"Unable to find prepared dataset in {prepared_ds_path}")
         logging.info("Loading raw datasets...")
         datasets = []
         for d in cfg.datasets:
@@ -113,18 +114,26 @@ def load_prepare_datasets(tokenizer, cfg, default_dataset_prepared_path):
                 datasets.append(ds_wrapper)
             else:
                 logging.error(f"unhandled prompt tokenization strategy: {d.type}")
-        constant_len_dataset = ConstantLengthDataset(
-            tokenizer,
-            datasets,
-            seq_length=max_packed_sequence_len,
-        )
-        logging.info("merging, packing, shuffling, and splitting master dataset")
-        dataset = Dataset.from_list([_ for _ in constant_len_dataset]).shuffle(seed=42)
+        logging.info("merging and shuffling master dataset")
 
+        dataset = concatenate_datasets(datasets).shuffle(seed=42)
         if cfg.local_rank == 0:
-            logging.info(f"Saving prepared dataset to disk... {prepared_ds_path}")
+            logging.info(f"Saving merged prepared dataset to disk... {prepared_ds_path}")
             dataset.save_to_disk(prepared_ds_path)
 
+        if cfg.max_packed_sequence_len is not None:
+            constant_len_dataset = ConstantLengthDataset(
+                tokenizer,
+                [dataset],
+                seq_length=max_packed_sequence_len,
+            )
+            logging.info("packing master dataset")
+            dataset = Dataset.from_list([_ for _ in constant_len_dataset])
+
+    if cfg.dataset_shard_num and cfg.dataset_shard_idx is not None:
+        logging.info(f"Using index #{cfg.dataset_shard_idx} of {cfg.dataset_shard_num} shards")
+        dataset = dataset.shard(num_shards=cfg.dataset_shard_num, index=cfg.dataset_shard_idx)
+
     dataset = dataset.train_test_split(
         test_size=cfg.val_set_size, shuffle=False
     )

src/axolotl/utils/models.py

@@ -101,12 +101,19 @@ def load_model(
             )
             load_in_8bit = False
         elif is_llama_derived_model and "LlamaForCausalLM" in globals():
-            model = LlamaForCausalLM.from_pretrained(
-                base_model,
-                load_in_8bit=cfg.load_in_8bit,
-                torch_dtype=torch_dtype,
-                device_map=cfg.device_map,
-            )
+            if not cfg.load_in_8bit:
+                model = LlamaForCausalLM.from_pretrained(
+                    base_model,
+                    device_map=cfg.device_map,
+                )
+            else:
+                model = LlamaForCausalLM.from_pretrained(
+                    base_model,
+                    load_in_8bit=cfg.load_in_8bit,
+                    torch_dtype=torch_dtype,
+                    device_map=cfg.device_map,
+                )
+
         elif model_type:
             model = getattr(transformers, model_type).from_pretrained(
                 base_model,

src/axolotl/utils/trainer.py

@@ -1,5 +1,9 @@
 import math
+import os
+from pathlib import Path
+
 import bitsandbytes as bnb
+import torch.cuda
 import transformers
 from torch import nn
 from torch.optim.lr_scheduler import OneCycleLR
@@ -12,7 +16,7 @@ def setup_trainer(cfg, train_dataset, eval_dataset, model, tokenizer):
         math.ceil(len(train_dataset) * cfg.num_epochs / cfg.batch_size)
     )
     warmup_steps = cfg.warmup_steps if cfg.warmup_steps else min(int(0.03 * total_num_steps), 100)
-    logging_steps = max(min(int(0.005 * total_num_steps), 10), 1)
+    logging_steps = cfg.logging_steps if cfg.logging_steps else max(min(int(0.005 * total_num_steps), 10), 1)
     save_steps = eval_steps = cfg.save_steps if cfg.save_steps else min(int(0.05 * total_num_steps), 200)
 
     training_arguments_kwargs = {}
@@ -26,6 +30,15 @@ def setup_trainer(cfg, train_dataset, eval_dataset, model, tokenizer):
     if cfg.gradient_checkpointing is not None:
         training_arguments_kwargs["gradient_checkpointing"] = cfg.gradient_checkpointing
 
+    # deepspeed
+    if os.environ.get("ACCELERATE_USE_DEEPSPEED") == "true" and torch.cuda.device_count() > 1:
+        if cfg.deepspeed:
+            training_arguments_kwargs["deepspeed"] = cfg.deepspeed
+        else:
+            # make a guess here
+            # TODO search Path("./") for one
+            training_arguments_kwargs["deepspeed"] = "./ds_config.json"
+
     training_args = transformers.TrainingArguments(
         per_device_train_batch_size=cfg.micro_batch_size,
         gradient_accumulation_steps=cfg.gradient_accumulation_steps,
@@ -37,7 +50,7 @@ def setup_trainer(cfg, train_dataset, eval_dataset, model, tokenizer):
         save_steps=save_steps,
         output_dir=cfg.output_dir,
         save_total_limit=3,
-        load_best_model_at_end=True if cfg.val_set_size > 0 else False,
+        load_best_model_at_end=True if cfg.val_set_size > 0 and save_steps % eval_steps == 0 else False,
         ddp_find_unused_parameters=False if cfg.ddp else None,
         group_by_length=cfg.group_by_length,
         report_to="wandb" if cfg.use_wandb else None,
@@ -47,7 +60,7 @@ def setup_trainer(cfg, train_dataset, eval_dataset, model, tokenizer):
 
     trainer_kwargs = {}
 
-    if cfg.load_in_8bit and not cfg.load_4bit:
+    if cfg.optimizer == "adam8bit" and not cfg.load_4bit and not "deepspeed" in training_arguments_kwargs:
         decay_parameters = get_parameter_names(model, [nn.LayerNorm])
         decay_parameters = [name for name in decay_parameters if "bias" not in name]
         optimizer_grouped_parameters = [