fix the context manager call

.github/workflows/main.yml

@@ -23,6 +23,11 @@ jobs:
             python_version: "3.10"
             pytorch: 2.0.1
             axolotl_extras:
+          - cuda: 118
+            cuda_version: 11.8.0
+            python_version: "3.9"
+            pytorch: 2.0.1
+            axolotl_extras: gptq
     runs-on: self-hosted
     steps:
       - name: Checkout
@@ -68,6 +73,11 @@ jobs:
             pytorch: 2.0.1
             axolotl_extras:
             is_latest: true
+          - cuda: 118
+            cuda_version: 11.8.0
+            python_version: "3.9"
+            pytorch: 2.0.1
+            axolotl_extras: gptq
     runs-on: self-hosted
     steps:
       - name: Checkout

.github/workflows/tests.yml

@@ -24,7 +24,7 @@ jobs:
 
       - name: Install dependencies
         run: |
-          pip install -e .
+          pip install -e .[peft]
           pip install -r requirements-tests.txt
 
       - name: Run tests

docker/Dockerfile

@@ -11,13 +11,14 @@ RUN apt-get update && \
 
 WORKDIR /workspace
 
+RUN pip3 install "peft @ git+https://github.com/huggingface/peft.git@main"
 RUN git clone --depth=1 https://github.com/OpenAccess-AI-Collective/axolotl.git
 # If AXOLOTL_EXTRAS is set, append it in brackets
 RUN cd axolotl && \
     if [ "$AXOLOTL_EXTRAS" != "" ] ; then \
-        pip install -e .[flash-attn,gptq,$AXOLOTL_EXTRAS]; \
+        pip install -e .[flash-attn,$AXOLOTL_EXTRAS]; \
     else \
-        pip install -e .[flash-attn,gptq]; \
+        pip install -e .[flash-attn]; \
     fi
 
 # fix so that git fetch/pull from remote works

examples/gptq-lora-7b/README.md

@@ -0,0 +1,8 @@
+# LLaMa 7B using LoRA
+
+This is a good place to start for beginners. This will run on an NVIDIA RTX4090 with no other changes needed.
+
+```shell
+accelerate launch scripts/finetune.py examples/gptq-lora-7b/config.yml
+
+```

examples/gptq-lora-7b/config.yml

@@ -0,0 +1,63 @@
+base_model: Neko-Institute-of-Science/LLaMA-7B-4bit-128g
+base_model_config: Neko-Institute-of-Science/LLaMA-7B-4bit-128g
+model_type: LlamaForCausalLM
+tokenizer_type: LlamaTokenizer
+trust_remote_code:
+load_in_8bit: true
+gptq: true
+datasets:
+  - path: vicgalle/alpaca-gpt4
+    type: alpaca
+dataset_prepared_path: last_run_prepared
+val_set_size: 0.02
+adapter:
+lora_model_dir:
+sequence_len: 2048
+max_packed_sequence_len:
+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: llama-7b-lora-int4
+wandb_entity:
+wandb_watch:
+wandb_run_id:
+wandb_log_model:
+output_dir: ./llama-7b-lora-int4
+gradient_accumulation_steps: 1
+micro_batch_size: 1
+num_epochs: 3
+optimizer: adamw_bnb_8bit
+torchdistx_path:
+lr_scheduler: cosine
+learning_rate: 0.0000002
+train_on_inputs: false
+group_by_length: false
+fp16: true
+bf16: false
+tf32: true
+early_stopping_patience:
+resume_from_checkpoint:
+local_rank:
+logging_steps: 5
+xformers_attention:
+flash_attention:
+gradient_checkpointing: true
+gptq_groupsize: 128
+gptq_model_v1: false
+warmup_steps: 20
+eval_steps: 110
+save_steps: 660
+debug:
+deepspeed:
+weight_decay: 0.0001
+fsdp:
+fsdp_config:
+tokens:
+  pad_token: "<pad>"
+  bos_token: "<s>"
+  eos_token: "</s>"
+  unk_token: "<unk>"

examples/llama-2/gptq-lora.yml

@@ -1,76 +0,0 @@
-base_model: TheBloke/Llama-2-7B-GPTQ
-base_model_config: TheBloke/Llama-2-7B-GPTQ
-is_llama_derived_model: false
-gptq: true
-gptq_bits: 4
-model_type: AutoModelForCausalLM
-tokenizer_type: LlamaTokenizer
-tokenizer_use_fast: true
-tokenizer_legacy: true
-load_in_8bit: false
-load_in_4bit: false
-strict: false
-push_dataset_to_hub:
-hf_use_auth_token: true
-datasets:
-  - path: mhenrichsen/alpaca_2k_test
-    type: alpaca
-dataset_prepared_path: last_run_prepared
-val_set_size: 0.01
-adapter: lora
-lora_model_dir:
-sequence_len: 4096
-sample_packing:
-lora_r: 8
-lora_alpha: 32
-lora_dropout: 0.05
-lora_target_modules:
-  - k_proj
-  - o_proj
-  - q_proj
-  - v_proj
-lora_target_linear:
-lora_fan_in_fan_out:
-wandb_project:
-wandb_watch:
-wandb_run_id:
-wandb_log_model:
-output_dir: ./model-out
-gradient_accumulation_steps: 1
-micro_batch_size: 1
-num_epochs: 3
-optimizer: adamw_torch
-adam_beta2: 0.95
-adam_eps: 0.00001
-max_grad_norm: 1.0
-torchdistx_path:
-lr_scheduler: cosine
-lr_quadratic_warmup: true
-learning_rate: 0.000017
-train_on_inputs: false
-group_by_length: false
-bf16: false
-fp16: false
-float16: true
-tf32: true
-gradient_checkpointing: true
-early_stopping_patience:
-resume_from_checkpoint:
-local_rank:
-logging_steps: 1
-xformers_attention:
-flash_attention:
-sdp_attention:
-flash_optimum:
-gptq_groupsize:
-gptq_model_v1:
-warmup_steps: 100
-eval_steps:
-save_steps:
-debug:
-deepspeed:
-weight_decay: 0.1
-special_tokens:
-  bos_token: "<s>"
-  eos_token: "</s>"
-  unk_token: "<unk>"

requirements.txt

@@ -1,7 +1,3 @@
---extra-index-url https://download.pytorch.org/whl/cu118
---extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/
-torch==2.0.1
-auto-gptq
 packaging
 peft @ git+https://github.com/huggingface/peft.git
 transformers @ git+https://github.com/huggingface/transformers.git

scripts/finetune.py

@@ -24,7 +24,7 @@ from axolotl.utils.config import normalize_config, validate_config
 from axolotl.utils.data import prepare_dataset
 from axolotl.utils.dict import DictDefault
 from axolotl.utils.distributed import is_main_process
-from axolotl.utils.models import load_tokenizer
+from axolotl.utils.models import load_model_config, load_tokenizer
 from axolotl.utils.tokenization import check_dataset_labels
 from axolotl.utils.wandb import setup_wandb_env_vars
 
@@ -216,6 +216,15 @@ def load_cfg(config: Path = Path("examples/"), **kwargs):
             else:
                 cfg[k] = kwargs[k]
 
+    model_config = load_model_config(cfg)
+
+    # figure out if the model is llama
+    cfg.is_llama_derived_model = (
+        (hasattr(model_config, "model_type") and model_config.model_type == "llama")
+        or cfg.is_llama_derived_model
+        or "llama" in cfg.base_model
+        or (cfg.model_type and "llama" in cfg.model_type.lower())
+    )
     validate_config(cfg)
 
     normalize_config(cfg)

setup.py

@@ -2,27 +2,15 @@
 
 from setuptools import find_packages, setup
 
-
-def parse_requirements():
-    _install_requires = []
-    _dependency_links = []
-    with open("./requirements.txt", encoding="utf-8") as requirements_file:
-        lines = [
-            r.strip() for r in requirements_file.readlines() if "auto-gptq" not in r
-        ]
-        for line in lines:
-            if line.startswith("--extra-index-url"):
-                # Handle custom index URLs
-                _, url = line.split()
-                _dependency_links.append(url)
-            elif "flash-attn" not in line and line and line[0] != "#":
-                # Handle standard packages
-                _install_requires.append(line)
-    return _install_requires, _dependency_links
-
-
-install_requires, dependency_links = parse_requirements()
-
+install_requires = []
+with open("./requirements.txt", encoding="utf-8") as requirements_file:
+    # don't include peft yet until we check the int4
+    # need to manually install peft for now...
+    reqs = [r.strip() for r in requirements_file.readlines() if "peft" not in r]
+    reqs = [r for r in reqs if "flash-attn" not in r]
+    reqs = [r for r in reqs if r and r[0] != "#"]
+    for r in reqs:
+        install_requires.append(r)
 
 setup(
     name="axolotl",
@@ -31,10 +19,12 @@ setup(
     package_dir={"": "src"},
     packages=find_packages(),
     install_requires=install_requires,
-    dependency_links=dependency_links,
     extras_require={
         "gptq": [
-            "auto-gptq",
+            "alpaca_lora_4bit @ git+https://github.com/winglian/alpaca_lora_4bit.git@setup_pip",
+        ],
+        "gptq_triton": [
+            "alpaca_lora_4bit[triton] @ git+https://github.com/winglian/alpaca_lora_4bit.git@setup_pip",
         ],
         "flash-attn": [
             "flash-attn==2.0.8",
@@ -42,5 +32,8 @@ setup(
         "extras": [
             "deepspeed",
         ],
+        "peft": [
+            "peft @ git+https://github.com/huggingface/peft.git",
+        ],
     },
 )

src/axolotl/core/datasets.py

@@ -1,144 +0,0 @@
-import logging
-from dataclasses import dataclass, field
-from enum import Enum
-from pathlib import Path
-from typing import Any, Dict, Generator, List, Optional, Union
-
-from datasets import Dataset as Dataset_ds
-from datasets import DatasetDict, IterableDataset, load_dataset, load_from_disk
-from huggingface_hub import hf_hub_download
-
-logger = logging.getLogger("axolotl")
-
-
-class DsType(Enum):
-    JSON = "json"
-    ARROW = "arrow"
-    PARQUET = "parquet"
-
-
-@dataclass
-class DatasetConfiguration:
-    path: str
-    type: str
-    name: Optional[str] = field(
-        default=None,
-        metadata={"help": "the name of the dataset configuration to load."},
-    )
-    ds_type: Optional[DsType] = None
-    data_files: Optional[Union[str, List[str]]] = None
-    shards: Optional[int] = None
-    test_size: Optional[float] = None
-
-    @staticmethod
-    def from_dict(d: Dict[str, Any]) -> Generator["DatasetConfiguration", None, None]:
-        if "name" in d and isinstance(d["name"], list):
-            name = d.pop("name")
-            for n in name:
-                yield DatasetConfiguration(
-                    **d,
-                    name=n,
-                )
-
-
-def load_dataset_from_local(config: DatasetConfiguration) -> Optional[Dataset_ds]:
-    local_path = Path(config.path)
-    if not local_path.exists():
-        return None
-    ds = None
-    if local_path.is_dir():
-        if config.ds_type:
-            # TODO dirs with arrow or parquet files could be loaded with `load_from_disk`
-            ds = load_from_disk(config.path)
-        else:
-            ds = load_dataset(
-                config.path,
-                name=config.name,
-                data_files=config.data_files,
-                streaming=False,
-                split=None,
-            )
-    elif local_path.is_file():
-        ds_type = "json"
-        if config.ds_type:
-            ds_type = config.ds_type.value
-        elif "parquet" in config.path:
-            ds_type = "parquet"
-        elif "arrow" in config.path:
-            ds_type = "arrow"
-        ds = load_dataset(
-            ds_type,
-            name=config.name,
-            data_files=config.path,
-            streaming=False,
-            split=None,  # is this correct?
-        )
-    if not ds:
-        raise ValueError(
-            "unhandled dataset load: local path exists, but is neither a directory or a file"
-        )
-    return ds
-
-
-# TODO should this be a DatasetDict?
-class Dataset(Dataset_ds):
-    _config: DatasetConfiguration
-
-    def __init__(self, *args, config: DatasetConfiguration = None, **kwargs):
-        self._config = config
-        super().__init__(*args, **kwargs)
-
-    @staticmethod
-    def from_config(
-        config: DatasetConfiguration,
-        token: bool = False,
-        default_test_size: float = 0.1,
-    ):
-        ds = load_dataset_from_local(config)
-        if not ds:
-            try:
-                ds = load_dataset(
-                    config.path,
-                    name=config.name,
-                    data_files=config.data_files,
-                    token=token,
-                )
-            except FileNotFoundError:
-                pass
-        if not ds:
-            fp = hf_hub_download(
-                repo_id=config.path,
-                repo_type="dataset",
-                filename=config.data_files,
-                token=token,
-            )
-            ds = load_dataset(
-                "json", name=config.name, data_files=fp, streaming=False, split=None
-            )
-        if not ds:
-            raise ValueError("unhandled dataset load")
-        test_size = config.test_size if config.test_size else default_test_size
-        # determine if the dataset is pre-tokenized
-        check_ds = ds["train"] if isinstance(ds, DatasetDict) and "train" in ds else ds
-        is_ds_tokenized = False
-        if "input_ids" in check_ds.features:
-            is_ds_tokenized = True
-            if "attention_mask" not in check_ds.features:
-                logger.warning("`attention_mask` missing from pre-tokenized dataset")
-            if "labels" not in check_ds.features:
-                logger.warning("`labels` missing from pre-tokenized dataset")
-        if test_size and (not isinstance(ds, DatasetDict) or "test" not in ds):
-            ds.train_test_split(test_size=test_size, shuffle=False)
-            pass
-
-
-class DatasetCollection:
-    datasets: List[Dataset] = []
-
-    def __init__(self, datasets: Union[Dataset, List[Dataset]]):
-        self.datasets = datasets if isinstance(datasets, list) else [datasets]
-
-    def __iter__(self):
-        for ds in self.datasets:
-            for d in ds:
-                yield d

src/axolotl/monkeypatch/llama_attn_hijack_flash.py

@@ -2,9 +2,7 @@
 
 # copied from https://github.com/lm-sys/FastChat/blob/main/fastchat/train/llama_flash_attn_monkey_patch.py
 
-import logging
 import warnings
-from functools import partial
 from typing import List, Optional, Tuple, Union
 
 import torch
@@ -35,9 +33,6 @@ except ImportError:
     )
 
 
-LOG = logging.getLogger("axolotl")
-
-
 def replace_llama_attn_with_flash_attn(packed: Optional[bool] = False):
     transformers.models.llama.modeling_llama.LlamaModel._prepare_decoder_attention_mask = (  # pylint: disable=protected-access
         _prepare_decoder_attention_mask
@@ -49,34 +44,6 @@ def replace_llama_attn_with_flash_attn(packed: Optional[bool] = False):
             llama_model_forward
         )
 
-    try:
-        from flash_attn.losses.cross_entropy import CrossEntropyLoss
-
-        LOG.info("patching with flash_attn.losses.cross_entropy")
-        transformers.models.llama.modeling_llama.CrossEntropyLoss = partial(
-            CrossEntropyLoss, inplace_backward=True
-        )
-    except ImportError:
-        LOG.info(
-            "optimized flash-attention CrossEntropyLoss not found (run `pip install 'git+https://github.com/Dao-AILab/flash-attention.git#egg=xentropy_cuda_lib&subdirectory=csrc/xentropy'`)"
-        )
-
-    try:
-        from flash_attn.ops.rms_norm import RMSNorm
-
-        class LlamaRMSNorm(RMSNorm):
-            """Patched LLamaRMSNorm"""
-
-            def __init__(self, hidden_size, eps=1e-6):
-                super().__init__(hidden_size, eps=eps)
-
-        LOG.info("patching with flash_attn.ops.rms_norm")
-        transformers.models.llama.modeling_llama.LlamaRMSNorm = LlamaRMSNorm
-    except ImportError:
-        LOG.info(
-            "optimized flash-attention RMSNorm not found (run `pip install 'git+https://github.com/Dao-AILab/flash-attention.git#egg=dropout_layer_norm&subdirectory=csrc/layer_norm'`)"
-        )
-
 
 # Disable the transformation of the attention mask in LlamaModel as the flash attention
 # requires the attention mask to be the same as the key_padding_mask

src/axolotl/prompters.py

@@ -309,6 +309,10 @@ class ShareGPTPrompter:  # pylint: disable=too-few-public-methods
         )
 
     def build_prompt(self, source) -> Generator[str, None, None]:
+        # ignore the system prompt if provided
+        if source[0]["from"] == "system":
+            source.pop(0)
+
         if len(source) < 2:
             # If there isn't a back and forth conversation, ignore it
             # also happens on the data splitting leaving empty conversations
@@ -317,12 +321,6 @@ class ShareGPTPrompter:  # pylint: disable=too-few-public-methods
             )
 
         conv = self._conversation.copy()
-
-        # Add the conversation system prompt if provided, otherwise use the default one
-        if source[0]["from"] == "system":
-            conv.system = source[0]["value"]
-            source.pop(0)
-
         roles = {"human": conv.roles[0], "gpt": conv.roles[1]}
 
         try:

src/axolotl/utils/callbacks.py

@@ -11,6 +11,7 @@ import numpy as np
 import pandas as pd
 import torch
 import torch.distributed as dist
+from accelerate.state import PartialState
 from datasets import load_dataset
 from optimum.bettertransformer import BetterTransformer
 from tqdm import tqdm
@@ -24,12 +25,9 @@ from transformers.trainer_utils import PREFIX_CHECKPOINT_DIR, IntervalStrategy
 
 from axolotl.utils.bench import log_gpu_memory_usage
 from axolotl.utils.distributed import (
-    barrier,
     gather_scalar_from_all_ranks,
     get_world_size,
-    is_distributed,
     is_main_process,
-    zero_first,
 )
 
 if TYPE_CHECKING:
@@ -37,6 +35,7 @@ if TYPE_CHECKING:
 
 LOG = logging.getLogger("axolotl.callbacks")
 IGNORE_INDEX = -100
+dist_state = PartialState()
 
 
 class SavePeftModelCallback(TrainerCallback):  # pylint: disable=too-few-public-methods
@@ -211,7 +210,7 @@ def bench_eval_callback_factory(trainer, tokenizer):
             "subject": example["subject"],
         }
 
-    with zero_first(is_main_process()):
+    with dist_state.main_process_first():
         bench_dataset = bench_dataset.map(tokenize_evals)
         bench_dataset = bench_dataset.filter(lambda x: x["labels"][-2] in abcd_idx)
 
@@ -259,7 +258,7 @@ def bench_eval_callback_factory(trainer, tokenizer):
             for s, p, r in zip(bench_name, preds, refs):  # pylint: disable=invalid-name
                 bench_names[s]["preds"].append(p)
                 bench_names[s]["refs"].append(r)
-            barrier()
+            dist_state.wait_for_everyone()
             local_bench_names = bench_names
             gathered_bench_names: List[Dict] = [{} for _ in range(get_world_size())]
             # Gather results from all GPUs to GPU 0
@@ -271,13 +270,10 @@ def bench_eval_callback_factory(trainer, tokenizer):
                 lambda: len(data_loader), get_world_size()
             )
 
-            if is_distributed() and not is_main_process():
+            if not is_main_process():
                 dist.gather_object(local_bench_names, dst=0)
             else:
-                if is_distributed():
-                    dist.gather_object(local_bench_names, gathered_bench_names, dst=0)
-                else:
-                    gathered_bench_names = [local_bench_names]
+                dist.gather_object(local_bench_names, gathered_bench_names, dst=0)
                 bench_loss = sum(loss_bench_ranks) / sum(len_data_loader_ranks)
                 results = {f"{bench_split}_bench_loss": bench_loss}
 

src/axolotl/utils/config.py

@@ -6,7 +6,6 @@ import os
 import torch
 
 from axolotl.utils.bench import log_gpu_memory_usage
-from axolotl.utils.models import load_model_config
 
 LOG = logging.getLogger("axolotl")
 
@@ -70,16 +69,6 @@ def normalize_config(cfg):
     else:
         cfg.torch_dtype = torch.float32
 
-    model_config = load_model_config(cfg)
-
-    # figure out if the model is llama
-    cfg.is_llama_derived_model = (
-        (hasattr(model_config, "model_type") and model_config.model_type == "llama")
-        or cfg.is_llama_derived_model
-        or "llama" in cfg.base_model
-        or (cfg.model_type and "llama" in cfg.model_type.lower())
-    )
-
     log_gpu_memory_usage(LOG, "baseline", cfg.device)
 
 
@@ -108,7 +97,9 @@ def validate_config(cfg):
             "To calculate the equivalent gradient_accumulation_steps, divide batch_size / micro_batch_size / number of gpus.",
         )
     if cfg.load_4bit:
-        raise ValueError("cfg.load_4bit parameter has been deprecated")
+        raise ValueError(
+            "cfg.load_4bit parameter has been deprecated and replaced by cfg.gptq"
+        )
 
     if cfg.adapter == "qlora":
         if cfg.merge_lora:

src/axolotl/utils/data.py

@@ -7,6 +7,7 @@ from pathlib import Path
 from typing import Tuple, Union
 
 import torch
+from accelerate.state import PartialState
 from datasets import (
     Dataset,
     DatasetDict,
@@ -42,7 +43,6 @@ from axolotl.prompters import (
     SummarizeTLDRPrompter,
 )
 from axolotl.utils.dict import DictDefault
-from axolotl.utils.distributed import is_main_process, zero_first
 from axolotl.utils.trainer import (
     calculate_total_num_steps,
     process_datasets_for_packing,
@@ -50,11 +50,12 @@ from axolotl.utils.trainer import (
 
 LOG = logging.getLogger("axolotl")
 DEFAULT_DATASET_PREPARED_PATH = "last_run_prepared"
+state = PartialState()
 
 
 def prepare_dataset(cfg, tokenizer):
     if not cfg.pretraining_dataset:
-        with zero_first(is_main_process()):
+        with state.main_process_first():
             train_dataset, eval_dataset = load_prepare_datasets(
                 tokenizer, cfg, DEFAULT_DATASET_PREPARED_PATH
             )
@@ -69,7 +70,7 @@ def prepare_dataset(cfg, tokenizer):
         train_dataset = train_dataset.with_format("torch")
         eval_dataset = None
 
-    with zero_first(is_main_process()):
+    with state.main_process_first():
         train_dataset, eval_dataset = process_datasets_for_packing(
             cfg, train_dataset, eval_dataset
         )
@@ -507,7 +508,7 @@ def load_prepare_datasets(
             to_hash_test.encode(), usedforsecurity=False
         ).hexdigest()
 
-        with zero_first(is_main_process()):
+        with state.main_process_first():
             dataset = dataset.train_test_split(
                 test_size=cfg.val_set_size,
                 shuffle=False,

src/axolotl/utils/distributed.py

@@ -1,29 +1,27 @@
 """
 utility helpers for distributed checks
 """
-import os
-from contextlib import contextmanager
-
 import torch
 import torch.distributed as dist
-from accelerate import Accelerator
+from accelerate import DistributedType
+from accelerate.state import PartialState
+from accelerate.utils import wait_for_everyone
 
 accelerate = None  # pylint: disable=invalid-name
 
-
-def load_accelerate():
-    global accelerate  # pylint: disable=global-statement
-    accelerate = Accelerator()
+state = PartialState()
 
 
 def is_distributed():
     """
     Check if distributed training is initialized.
     """
-    global accelerate  # pylint: disable=global-statement
-    if not accelerate:
-        accelerate = Accelerator()
-    return dist.is_available() and dist.is_initialized()
+    return state.distributed_type in (
+        DistributedType.MULTI_GPU,
+        DistributedType.MULTI_CPU,
+        DistributedType.DEEPSPEED,
+        DistributedType.FSDP,
+    )
 
 
 def barrier():
@@ -31,34 +29,19 @@ def barrier():
     Acts as a barrier to wait for all processes. This ensures that all processes
     reach the barrier before proceeding further.
     """
-    if is_distributed():
-        dist.barrier()
+    wait_for_everyone()
 
 
-def is_main_process():
+def is_main_process() -> bool:
     """
     Check if the current process is the main process.
     If not in distributed mode, always return True.
     """
-    if not is_distributed():
-        return True
-    return dist.get_rank() == 0
+    return state.is_main_process
 
 
-def get_world_size():
-    return int(os.getenv("WORLD_SIZE", "1"))
-
-
-@contextmanager
-def zero_first(is_main):
-    """
-    runs the wrapped context so that rank 0 runs first before other ranks
-    """
-    if not is_main:  # other ranks wait first
-        barrier()
-    yield
-    if is_main:  # then rank 0 waits after it has run the context
-        barrier()
+def get_world_size() -> int:
+    return state.num_processes
 
 
 def gather_scalar_from_all_ranks(fn, world_size=1):  # pylint: disable=invalid-name
@@ -74,11 +57,9 @@ def gather_scalar_from_all_ranks(fn, world_size=1):  # pylint: disable=invalid-n
     - A list of computed values from all ranks if on the gathering rank, otherwise None.
     """
     value_scalar = fn()
-    if not is_distributed():
-        return [value_scalar]
     value_tensor = torch.tensor(value_scalar, device=dist.get_rank()).float()
 
-    if not is_main_process():
+    if not state.is_main_process:
         dist.gather(value_tensor, dst=0)
     else:
         gathered_tensors = [torch.zeros_like(value_tensor) for _ in range(world_size)]

src/axolotl/utils/models.py

@@ -4,19 +4,19 @@
 import logging
 import math
 import os
+from pathlib import Path
 from typing import Optional, Tuple  # noqa: F401
 
 import bitsandbytes as bnb
 import torch
 import transformers
 from optimum.bettertransformer import BetterTransformer
-from peft import PeftConfig, prepare_model_for_kbit_training
+from peft import PeftConfig
 from transformers import (  # noqa: F401
     AutoConfig,
     AutoModelForCausalLM,
     AutoTokenizer,
     BitsAndBytesConfig,
-    GPTQConfig,
     LlamaConfig,
     PreTrainedModel,
     PreTrainedTokenizerBase,
@@ -155,17 +155,32 @@ def load_model(
         LOG.info("patching _expand_mask")
         hijack_expand_mask()
 
+    try:
+        if cfg.gptq:
+            from alpaca_lora_4bit.monkeypatch.peft_tuners_lora_monkey_patch import (
+                replace_peft_model_with_int4_lora_model,
+            )
+
+            replace_peft_model_with_int4_lora_model()
+    except Exception as err:
+        LOG.exception(err)
+        raise err
+
+    if not cfg.gptq and (
+        (cfg.adapter == "lora" and load_in_8bit)
+        or (cfg.adapter == "qlora" and cfg.load_in_4bit)
+    ):
+        try:
+            from peft import prepare_model_for_kbit_training
+        except ImportError:
+            # For backward compatibility
+            from peft import (
+                prepare_model_for_int8_training as prepare_model_for_kbit_training,
+            )
+
     model_kwargs = {}
     if cfg.model_revision:
         model_kwargs["revision"] = cfg.model_revision
-    if cfg.gptq:
-        model_config = load_model_config(cfg)
-        if hasattr(model_config, "quantization_config"):
-            LOG.warning("model config does not contain quantization_config information")
-        else:
-            model_kwargs["quantization_config"] = GPTQConfig(
-                **model_config.quantization_config
-            )
     if cfg.adapter == "qlora" and cfg.load_in_4bit:
         model_kwargs["quantization_config"] = BitsAndBytesConfig(
             load_in_4bit=True,
@@ -176,7 +191,45 @@ def load_model(
             bnb_4bit_quant_type="nf4",
         )
     try:
-        if cfg.is_llama_derived_model and not cfg.trust_remote_code and not cfg.gptq:
+        if cfg.gptq and cfg.is_llama_derived_model:
+            from alpaca_lora_4bit.autograd_4bit import load_llama_model_4bit_low_ram
+            from huggingface_hub import snapshot_download
+
+            try:
+                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)
+                )
+                files = (
+                    list(cache_model_path.glob("*.pt"))
+                    + list(cache_model_path.glob("*.safetensors"))
+                    + list(cache_model_path.glob("*.bin"))
+                )
+                if len(files) > 0:
+                    model_path = str(files[0])
+                else:
+                    LOG.warning(
+                        "unable to find a cached model file, this will likely fail..."
+                    )
+                    model_path = str(cache_model_path)
+            except Exception:  # pylint: disable=broad-exception-caught
+                model_path = cfg.base_model
+            model, _ = load_llama_model_4bit_low_ram(
+                base_model_config if base_model_config else base_model,
+                model_path,
+                device_map=cfg.device_map,
+                half=cfg.fp16,
+                groupsize=cfg.gptq_groupsize if cfg.gptq_groupsize else -1,
+                is_v1_model=cfg.gptq_model_v1
+                if cfg.gptq_model_v1 is not None
+                else True,
+            )
+            load_in_8bit = False
+        elif cfg.is_llama_derived_model and not cfg.trust_remote_code:
             from transformers import LlamaForCausalLM
 
             config_kwargs = {}
@@ -222,24 +275,15 @@ def load_model(
         #     )
         #     model.train() # sets to train instead of eval mode
         elif model_type and not cfg.trust_remote_code:
-            if cfg.gptq:
-                model = AutoModelForCausalLM.from_pretrained(
-                    base_model,
-                    device_map=cfg.device_map,
-                    torch_dtype=cfg.torch_dtype,
-                    trust_remote_code=cfg.trust_remote_code or False,
-                    **model_kwargs,
-                )
-            else:
-                model = getattr(transformers, model_type).from_pretrained(
-                    base_model,
-                    device_map=cfg.device_map,
-                    load_in_8bit=cfg.load_in_8bit and cfg.adapter is not None,
-                    load_in_4bit=cfg.load_in_4bit and cfg.adapter is not None,
-                    torch_dtype=cfg.torch_dtype,
-                    trust_remote_code=cfg.trust_remote_code or False,
-                    **model_kwargs,
-                )
+            model = getattr(transformers, model_type).from_pretrained(
+                base_model,
+                device_map=cfg.device_map,
+                load_in_8bit=cfg.load_in_8bit and cfg.adapter is not None,
+                load_in_4bit=cfg.load_in_4bit and cfg.adapter is not None,
+                torch_dtype=cfg.torch_dtype,
+                trust_remote_code=cfg.trust_remote_code or False,
+                **model_kwargs,
+            )
         else:
             config = AutoConfig.from_pretrained(
                 base_model,
@@ -315,12 +359,11 @@ def load_model(
                 module.to(torch.float32)
 
     needs_fa2_dtype = cfg.adapter or cfg.fsdp
-    if (cfg.adapter == "lora" and load_in_8bit) or (
-        cfg.adapter == "qlora" and cfg.load_in_4bit
+    if not cfg.gptq and (
+        (cfg.adapter == "lora" and load_in_8bit)
+        or (cfg.adapter == "qlora" and cfg.load_in_4bit)
     ):
         LOG.info("converting PEFT model w/ prepare_model_for_kbit_training")
-        if cfg.gradient_checkpointing:
-            model.gradient_checkpointing_enable()
         model = prepare_model_for_kbit_training(
             model, use_gradient_checkpointing=cfg.gradient_checkpointing
         )
@@ -342,10 +385,22 @@ def load_model(
     if cfg.ddp and not load_in_8bit:
         model.to(f"cuda:{cfg.local_rank}")
 
+    if cfg.gptq:
+        # Scales to half
+        LOG.info("Fitting 4bit scales and zeros to half")
+        for _, module in model.named_modules():
+            if "Autograd4bitQuantLinear" in str(type(module)) or "Linear4bitLt" in str(
+                type(module)
+            ):
+                if hasattr(module, "is_v1_model") and module.is_v1_model:
+                    module.zeros = module.zeros.half()
+                module.scales = module.scales.half()
+                module.bias = module.bias.half()
+
     if (
         torch.cuda.device_count() > 1
         and int(os.getenv("WORLD_SIZE", "1")) > 1
-        and (cfg.load_in_4bit)
+        and (cfg.gptq or cfg.load_in_4bit)
     ):
         # llama is PROBABLY model parallelizable, but the default isn't that it is
         # so let's only set it for the 4bit, see

src/axolotl/utils/trainer.py

@@ -514,7 +514,23 @@ def setup_trainer(cfg, train_dataset, eval_dataset, model, tokenizer, total_num_
         training_arguments_kwargs["seed"] = cfg.seed
 
     if cfg.gradient_checkpointing:
-        training_arguments_kwargs["gradient_checkpointing"] = cfg.gradient_checkpointing
+        if cfg.gptq:
+            from alpaca_lora_4bit.gradient_checkpointing import (
+                apply_gradient_checkpointing,
+            )
+
+            gradient_checkpointing_ratio = (
+                cfg.gradient_checkpointing_ratio
+                if cfg.gradient_checkpointing_ratio
+                else 1.0
+            )
+            apply_gradient_checkpointing(
+                model, checkpoint_ratio=gradient_checkpointing_ratio
+            )
+        else:
+            training_arguments_kwargs[
+                "gradient_checkpointing"
+            ] = cfg.gradient_checkpointing
     if cfg.fsdp:
         training_arguments_kwargs["fsdp"] = cfg.fsdp
         if cfg.fsdp_config: