Merge branch 'main' into pixtral_integration2

deepspeed_configs/zero1_torch_compile.json

@@ -0,0 +1,27 @@
+{
+  "zero_optimization": {
+    "stage": 1,
+    "overlap_comm": true
+  },
+  "bf16": {
+    "enabled": "auto"
+  },
+  "fp16": {
+    "enabled": "auto",
+    "auto_cast": false,
+    "loss_scale": 0,
+    "initial_scale_power": 32,
+    "loss_scale_window": 1000,
+    "hysteresis": 2,
+    "min_loss_scale": 1
+  },
+  "compile": {
+    "disable": false,
+    "backend": "inductor"
+  },
+  "gradient_accumulation_steps": "auto",
+  "gradient_clipping": "auto",
+  "train_batch_size": "auto",
+  "train_micro_batch_size_per_gpu": "auto",
+  "wall_clock_breakdown": false
+}

requirements.txt

@@ -61,4 +61,4 @@ antlr4-python3-runtime==4.13.2
 torchao==0.7.0
 schedulefree==1.3.0
 
-axolotl-contribs-lgpl==0.0.1b2
+axolotl-contribs-lgpl==0.0.2

src/axolotl/cli/main.py

@@ -93,7 +93,7 @@ def evaluate(config: str, accelerate: bool, **kwargs):
 @click.argument("config", type=click.Path(exists=True, path_type=str))
 @click.option(
     "--accelerate/--no-accelerate",
-    default=True,
+    default=False,
     help="Use accelerate launch for multi-GPU inference",
 )
 @click.option(
@@ -124,7 +124,7 @@ def inference(
     if lora_model_dir:
         kwargs["lora_model_dir"] = lora_model_dir
     if base_model:
-        kwargs["output_dir"] = base_model
+        kwargs["base_model"] = base_model
 
     if accelerate:
         base_cmd = ["accelerate", "launch", "-m", "axolotl.cli.inference"]

src/axolotl/core/trainer_builder.py

@@ -57,6 +57,7 @@
 from axolotl.utils import is_comet_available, is_mlflow_available
 from axolotl.utils.callbacks import (
     EvalFirstStepCallback,
+    GCCallback,
     GPUStatsCallback,
     LossWatchDogCallback,
     SaveAxolotlConfigtoWandBCallback,
@@ -68,7 +69,7 @@
 )
 from axolotl.utils.callbacks.lisa import lisa_callback_factory
 from axolotl.utils.callbacks.profiler import PytorchProfilerCallback
-from axolotl.utils.chat_templates import get_chat_template
+from axolotl.utils.chat_templates import get_chat_template_from_config
 from axolotl.utils.collators import (
     BatchSamplerDataCollatorForSeq2Seq,
     DataCollatorForSeq2Seq,
@@ -1453,6 +1454,8 @@ def get_callbacks(self):
         if self.cfg.loss_watchdog_threshold is not None:
             callbacks.append(LossWatchDogCallback(self.cfg))
 
+        if self.cfg.gc_steps:
+            callbacks.append(GCCallback(gc_steps=self.cfg.gc_steps))
         callbacks.append(SaveModelCallback())
 
         return callbacks
@@ -1832,8 +1835,8 @@ def build(self, total_num_steps):
         training_arguments_kwargs["model_type"] = self.cfg.model_config_type
         training_arguments_kwargs["pretraining"] = bool(self.cfg.pretraining_dataset)
         if self.cfg.chat_template:
-            training_arguments_kwargs["chat_template"] = get_chat_template(
-                self.cfg.chat_template,
+            training_arguments_kwargs["chat_template"] = get_chat_template_from_config(
+                cfg=self.cfg,
                 tokenizer=self.tokenizer,
             )
 

src/axolotl/train.py

@@ -1,5 +1,6 @@
 """Prepare and train a model on a dataset. Can also infer from a model or merge lora"""
 
+import inspect
 import os
 import signal
 import sys
@@ -126,7 +127,20 @@ def train(
     )
 
     if cfg.fix_untrained_tokens:
-        fix_untrained_tokens(model, tokenizer, train_dataset)
+        # check if the `token_ids_to_fix` kwarg exists in the fix_untrained_tokens args
+        sig = inspect.signature(fix_untrained_tokens)
+        # if the function has the `token_ids_to_fix` arg, and fix_untrained_tokens is a list
+        if "token_ids_to_fix" in sig.parameters and isinstance(
+            cfg.fix_untrained_tokens, list
+        ):
+            fix_untrained_tokens(
+                model,
+                tokenizer,
+                train_dataset,
+                token_ids_to_fix=cfg.fix_untrained_tokens,
+            )
+        else:
+            fix_untrained_tokens(model, tokenizer, train_dataset)
         if cfg.local_rank == 0:
             model.save_pretrained(
                 str(Path(cfg.output_dir)), safe_serialization=safe_serialization

src/axolotl/utils/callbacks/__init__.py

@@ -2,6 +2,7 @@
 
 from __future__ import annotations
 
+import gc
 import logging
 import math
 import os
@@ -842,3 +843,17 @@ def on_train_end(  # pylint: disable=unused-argument
     ):
         control.should_save = True
         return control
+
+
+class GCCallback(TrainerCallback):
+    """Callback to garbage collect torch cache"""
+
+    def __init__(self, gc_steps=None):
+        self.gc_steps = gc_steps
+
+    def on_step_end(
+        self, args, state, control, **kwargs  # pylint: disable=unused-argument
+    ):
+        if state.global_step % self.gc_steps == 0:
+            torch.cuda.empty_cache()
+            gc.collect()

src/axolotl/utils/config/models/input/v0_4_1/__init__.py

@@ -669,6 +669,8 @@ class Config:
     loss_watchdog_threshold: Optional[float] = None
     loss_watchdog_patience: Optional[int] = None
 
+    gc_steps: Optional[int] = None
+
     bf16: Optional[Union[Literal["auto"], bool]] = "auto"
     fp16: Optional[bool] = None
     bfloat16: Optional[bool] = None  # for non-AMP cases
@@ -795,7 +797,7 @@ class Config:
     chat_template_jinja: Optional[str] = None
     default_system_message: Optional[str] = None
 
-    fix_untrained_tokens: Optional[bool] = None
+    fix_untrained_tokens: Optional[Union[int, List[int]]] = None
 
     # INTERNALS - document for now, generally not set externally
     is_preprocess: Optional[bool] = None