Fix Deepspeed loading

deepspeed/zero3_bf16.json

@@ -0,0 +1,39 @@
+{
+  "zero_optimization": {
+    "stage": 3,
+    "overlap_comm": true,
+    "contiguous_gradients": true,
+    "sub_group_size": 0,
+    "reduce_bucket_size": "auto",
+    "stage3_prefetch_bucket_size": "auto",
+    "stage3_param_persistence_threshold": "auto",
+    "stage3_max_live_parameters": 0,
+    "stage3_max_reuse_distance": 0,
+    "stage3_gather_16bit_weights_on_model_save": true
+  },
+  "bf16": {
+    "enabled": true
+  },
+  "fp16": {
+    "enabled": "auto",
+    "auto_cast": false,
+    "loss_scale": 0,
+    "initial_scale_power": 32,
+    "loss_scale_window": 1000,
+    "hysteresis": 2,
+    "min_loss_scale": 1
+  },
+  "optimizer": {
+    "type": "AdamW",
+    "params": {
+      "lr": "auto",
+      "betas": "auto",
+      "eps": "auto",
+      "weight_decay": "auto"
+    }
+  },
+  "gradient_accumulation_steps": "auto",
+  "train_batch_size": "auto",
+  "train_micro_batch_size_per_gpu": "auto",
+  "wall_clock_breakdown": false
+}

src/axolotl/cli/train.py

@@ -22,8 +22,8 @@
 
 def do_cli(config: Path = Path("examples/"), **kwargs):
     # pylint: disable=duplicate-code
-    print_axolotl_text_art()
     parsed_cfg = load_cfg(config, **kwargs)
+    print_axolotl_text_art()
     check_accelerate_default_config()
     check_user_token()
     parser = transformers.HfArgumentParser((TrainerCliArgs))

src/axolotl/train.py

@@ -18,6 +18,7 @@
 from axolotl.logging_config import configure_logging
 from axolotl.monkeypatch import neft_embeddings
 from axolotl.utils.dict import DictDefault
+from axolotl.utils.freeze import freeze_parameters_except
 from axolotl.utils.models import load_model, load_tokenizer
 from axolotl.utils.trainer import setup_trainer
 
@@ -78,6 +79,9 @@ def train(
             )
     resume_from_checkpoint = cfg.resume_from_checkpoint
 
+    if cfg.unfrozen_parameters:
+        freeze_parameters_except(model, cfg.unfrozen_parameters)
+
     trainer = setup_trainer(
         cfg, train_dataset, eval_dataset, model, tokenizer, total_num_steps
     )

src/axolotl/utils/freeze.py

@@ -0,0 +1,32 @@
+"""
+module to freeze/unfreeze parameters by name
+"""
+import re
+
+
+def freeze_parameters_except(model, regex_patterns):
+    """
+    Freezes all layers of the given model except for the layers that match given regex patterns.
+    Periods in the patterns are treated as literal periods, not as wildcard characters.
+
+    Parameters:
+    - model (nn.Module): The PyTorch model to be modified.
+    - regex_patterns (list of str): List of regex patterns to match layer names to keep unfrozen.
+
+    Returns:
+    None; the model is modified in place.
+    """
+    # Escape periods and compile the regex patterns
+    compiled_patterns = [
+        re.compile(pattern.replace(".", "\\.")) for pattern in regex_patterns
+    ]
+
+    # First, freeze all parameters in the model
+    for param in model.parameters():
+        param.requires_grad = False
+
+    # Unfreeze layers that match the regex patterns
+    for name, child in model.named_children():
+        if any(pattern.match(name) for pattern in compiled_patterns):
+            for param in child.parameters():
+                param.requires_grad = True

src/axolotl/utils/models.py

@@ -21,6 +21,7 @@
     PreTrainedModel,
     PreTrainedTokenizerBase,
 )
+from transformers.deepspeed import is_deepspeed_zero3_enabled
 
 from axolotl.models.mamba import fix_mamba_attn_for_loss
 from axolotl.prompt_tokenizers import LLAMA_DEFAULT_EOS_TOKEN
@@ -285,6 +286,9 @@ def load_model(
     model_kwargs["max_memory"] = cfg.max_memory
     model_kwargs["torch_dtype"] = cfg.torch_dtype
 
+    if is_deepspeed_zero3_enabled():
+        del model_kwargs["device_map"]
+
     if cfg.model_revision:
         model_kwargs["revision"] = cfg.model_revision
     if cfg.gptq:

src/axolotl/utils/trainer.py

@@ -276,6 +276,7 @@ def prepare_optim_env(cfg):
         setup_fsdp_envs(cfg)
     elif cfg.deepspeed:
         os.environ["ACCELERATE_USE_DEEPSPEED"] = "true"
+        os.environ["ACCELERATE_DEEPSPEED_CONFIG_FILE"] = cfg.deepspeed
 
 
 def setup_trainer(cfg, train_dataset, eval_dataset, model, tokenizer, total_num_steps):