bump transformers and set roundup_power2_divisions for more VRAM impr…

…ovements, low bit ao optimizers (#1769)

* bump transformers and set roundup_power2_divisions for more VRAM improvements

* support for low bit optimizers from torch ao

* fix check for alternate optimizers and use nous models on hf for llama3

* add missing check for ao_adamw_fp8

* fix check when using custom optimizers w adamw

docs/torchao.qmd

@@ -0,0 +1,19 @@
+---
+title: "PyTorch ao"
+description: "Custom data types and layouts for training and inference"
+---
+
+### Installation
+
+Stable Release from the PyTorch index
+
+```bash
+pip install torchao --extra-index-url https://download.pytorch.org/whl/cu121 # full options are cpu/cu118/cu121/cu124
+```
+
+
+Nightly release
+
+```bash
+pip install --pre torchao-nightly --index-url https://download.pytorch.org/whl/nightly/cu121 # full options are cpu/cu118/cu121/cu124
+```

examples/llama-3/fft-8b.yaml

@@ -1,4 +1,4 @@
-base_model: meta-llama/Meta-Llama-3-8B
+base_model: NousResearch/Meta-Llama-3-8B
 model_type: LlamaForCausalLM
 tokenizer_type: AutoTokenizer
 

examples/llama-3/instruct-lora-8b.yml

@@ -1,4 +1,4 @@
-base_model: meta-llama/Meta-Llama-3-8B-Instruct
+base_model: NousResearch/Meta-Llama-3-8B-Instruct
 model_type: LlamaForCausalLM
 tokenizer_type: AutoTokenizer
 

examples/llama-3/lora-8b.yml

@@ -1,4 +1,4 @@
-base_model: meta-llama/Meta-Llama-3-8B
+base_model: NousResearch/Meta-Llama-3-8B
 model_type: LlamaForCausalLM
 tokenizer_type: AutoTokenizer
 

examples/llama-3/qlora.yml

@@ -1,4 +1,4 @@
-base_model: meta-llama/Meta-Llama-3-8B
+base_model: NousResearch/Meta-Llama-3-8B
 model_type: AutoModelForCausalLM
 tokenizer_type: AutoTokenizer
 

requirements.txt

@@ -1,7 +1,7 @@
 --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/
 packaging==23.2
 peft==0.11.1
-transformers==4.42.3
+transformers==4.42.4
 tokenizers==0.19.1
 bitsandbytes==0.43.1
 accelerate==0.32.0

src/axolotl/core/trainer_builder.py

@@ -305,7 +305,8 @@ def _wrap_model(self, model, training=True, dataloader=None):
     def create_optimizer(self):
         if (
             self.args.loraplus_lr_ratio is None
-            and self.args.alternate_optimizer != "optimi_adamw"
+            and self.args.alternate_optimizer
+            not in ["optimi_adamw", "ao_adamw_8bit", "ao_adamw_4bit", "ao_adamw_fp8"]
         ):
             return super().create_optimizer()
 
@@ -356,6 +357,24 @@ def create_optimizer(self):
                         optimizer_grouped_parameters, foreach=False, **optimizer_kwargs
                     )
                 )
+            elif self.args.alternate_optimizer == "ao_adamw_4bit":
+                from torchao.prototype.low_bit_optim import AdamW4bit
+
+                self.optimizer = (  # pylint: disable=attribute-defined-outside-init
+                    AdamW4bit(optimizer_grouped_parameters, **optimizer_kwargs)
+                )
+            elif self.args.alternate_optimizer == "ao_adamw_8bit":
+                from torchao.prototype.low_bit_optim import AdamW8bit
+
+                self.optimizer = (  # pylint: disable=attribute-defined-outside-init
+                    AdamW8bit(optimizer_grouped_parameters, **optimizer_kwargs)
+                )
+            elif self.args.alternate_optimizer == "ao_adamw_fp8":
+                from torchao.prototype.low_bit_optim import AdamWFp8
+
+                self.optimizer = (  # pylint: disable=attribute-defined-outside-init
+                    AdamWFp8(optimizer_grouped_parameters, **optimizer_kwargs)
+                )
 
         if is_sagemaker_mp_enabled():
             self.optimizer = smp.DistributedOptimizer(  # pylint: disable=attribute-defined-outside-init
@@ -1452,7 +1471,12 @@ def build(self, total_num_steps):
 
         trainer_kwargs = {}
 
-        if self.cfg.optimizer == "optimi_adamw":
+        if self.cfg.optimizer in [
+            "optimi_adamw",
+            "ao_adamw_4bit",
+            "ao_adamw_8bit",
+            "ao_adamw_fp8",
+        ]:
             # Set default so transformers doesn't throw
             training_arguments_kwargs["optim"] = "adamw_hf"
             training_arguments_kwargs["alternate_optimizer"] = self.cfg.optimizer

src/axolotl/train.py

@@ -57,7 +57,9 @@ def train(
     torch_major, torch_minor = int(torch_version[0]), int(torch_version[1])
     if torch_major == 2 and torch_minor >= 2:
         if os.getenv("PYTORCH_CUDA_ALLOC_CONF") is None:
-            os.environ["PYTORCH_CUDA_ALLOC_CONF"] = "expandable_segments:True"
+            os.environ[
+                "PYTORCH_CUDA_ALLOC_CONF"
+            ] = "expandable_segments:True,roundup_power2_divisions:16"
 
     # load the tokenizer first
     LOG.debug(

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

@@ -346,7 +346,16 @@ class HyperparametersConfig(BaseModel):
     learning_rate: Union[str, float]
     weight_decay: Optional[float] = 0.0
     optimizer: Optional[
-        Union[OptimizerNames, Literal["lion_pytorch", "optimi_adamw"]]
+        Union[
+            OptimizerNames,
+            Literal[
+                "lion_pytorch",
+                "optimi_adamw",
+                "ao_adamw_4bit",
+                "ao_adamw_8bit",
+                "ao_adamw_fp8",
+            ],
+        ]
     ] = OptimizerNames.ADAMW_HF.value
     optim_args: Optional[Union[str, Dict[str, Any]]] = Field(
         default=None, metadata={"help": "Optional arguments to supply to optimizer."}
@@ -850,7 +859,7 @@ def check_better_transformers(self):
     @model_validator(mode="after")
     def check_adamw_optimizer_params(self):
         if any([self.adam_beta1, self.adam_beta2, self.adam_epsilon]) and (
-            not self.optimizer or "adamw" not in self.optimizer.value
+            not self.optimizer or "adamw" not in str(self.optimizer).lower()
         ):
             LOG.warning("adamw hyperparameters found, but no adamw optimizer set")
         return self