fix the sed command to replace the version w the tag

* replace tags, build dist for pypi publish

* missing trailing comma

.github/workflows/pypi.yml

@@ -0,0 +1,45 @@
+name: publish pypi
+
+on:
+  push:
+    tags:
+      - '*'
+
+jobs:
+  pypi-publish:
+    name: Upload release to PyPI
+    runs-on: ubuntu-latest
+    environment:
+      name: pypi
+      url: https://pypi.org/p/axolotl
+    permissions:
+      id-token: write  # IMPORTANT: this permission is mandatory for trusted publishing
+    steps:
+      - name: Check out repository code
+        uses: actions/checkout@v3
+
+      - name: Setup Python
+        uses: actions/setup-python@v4
+        with:
+          python-version: "3.10"
+
+      - name: Install dependencies
+        run: |
+          pip3 install wheel
+          pip3 install -e .
+          pip3 install -r requirements-tests.txt
+
+      - name: Extract tag name
+        id: tag
+        run: echo ::set-output name=TAG_NAME::$(echo $GITHUB_REF | cut -d / -f 3)
+
+      - name: Update version in setup.py
+        run: >-
+          sed -i -E 's/version="([0-9.]+)",/version="${{ steps.tag.outputs.TAG_NAME }}",/g' setup.py
+
+      - name: Build a binary wheel
+        run: >-
+          python setup.py sdist bdist_wheel
+
+      - name: Publish package distributions to PyPI
+        uses: pypa/gh-action-pypi-publish@release/v1

.github/workflows/tests.yml

@@ -24,8 +24,8 @@ jobs:
 
       - name: Install dependencies
         run: |
-          pip install -e .
-          pip install -r requirements-tests.txt
+          pip3 install -e .
+          pip3 install -r requirements-tests.txt
 
       - name: Run tests
         run: |

README.md

@@ -90,8 +90,7 @@ accelerate launch scripts/finetune.py examples/openllama-3b/lora.yml \
   ```bash
   docker run --gpus '"all"' --rm -it winglian/axolotl:main-py3.10-cu118-2.0.1
   ```
-  - `winglian/axolotl-runpod:main-py3.10-cu118-2.0.1`: for runpod
-  - `winglian/axolotl-runpod:main-py3.9-cu118-2.0.1-gptq`: for gptq
+  - `winglian/axolotl-runpod:main-latest`: for runpod or use this [direct link](https://runpod.io/gsc?template=v2ickqhz9s&ref=6i7fkpdz)
 
   Or run on the current files for development:
 
@@ -104,19 +103,9 @@ accelerate launch scripts/finetune.py examples/openllama-3b/lora.yml \
 
   2. Install pytorch stable https://pytorch.org/get-started/locally/
 
-  3. Install python dependencies with ONE of the following:
-      - Recommended, supports QLoRA, NO gptq/int4 support
+  3. Install axolotl along with python dependencies
         ```bash
-        pip3 install -e .
-        pip3 install -U git+https://github.com/huggingface/peft.git
-        ```
-      - gptq/int4 support, NO QLoRA
-        ```bash
-        pip3 install -e .[gptq]
-        ```
-      - same as above but not recommended
-        ```bash
-        pip3 install -e .[gptq_triton]
+        pip3 install -e .[flash-attn]
         ```
 
 - LambdaLabs
@@ -151,10 +140,9 @@ accelerate launch scripts/finetune.py examples/openllama-3b/lora.yml \
   git clone https://github.com/OpenAccess-AI-Collective/axolotl
   cd axolotl
 
-  pip3 install -e . # change depend on needs
+  pip3 install -e .
   pip3 install protobuf==3.20.3
   pip3 install -U --ignore-installed requests Pillow psutil scipy
-  pip3 install git+https://github.com/huggingface/peft.git # not for gptq
   ```
 
   5. Set path
@@ -572,6 +560,30 @@ log_sweep_min_lr:
 log_sweep_max_lr:
 
 # specify optimizer
+# Valid values are driven by the Transformers OptimizerNames class, see:
+# https://github.com/huggingface/transformers/blob/95b374952dc27d8511541d6f5a4e22c9ec11fb24/src/transformers/training_args.py#L134
+#
+# Note that not all optimizers may be available in your environment, ex: 'adamw_anyprecision' is part of
+# torchdistx, 'adamw_bnb_8bit' is part of bnb.optim.Adam8bit, etc. When in doubt, it is recommended to start with the optimizer used
+# in the examples/ for your model and fine-tuning use case.
+#
+# Valid values for 'optimizer' include:
+# - adamw_hf
+# - adamw_torch
+# - adamw_torch_fused
+# - adamw_torch_xla
+# - adamw_apex_fused
+# - adafactor
+# - adamw_anyprecision
+# - sgd
+# - adagrad
+# - adamw_bnb_8bit
+# - lion_8bit
+# - lion_32bit
+# - paged_adamw_32bit
+# - paged_adamw_8bit
+# - paged_lion_32bit
+# - paged_lion_8bit
 optimizer:
 # specify weight decay
 weight_decay:
@@ -752,6 +764,10 @@ Try to turn off xformers.
 
 It's safe to ignore it.
 
+> NCCL Timeouts during training
+
+See the [NCCL](docs/nccl.md) guide.
+
 ## Need help? 🙋♂️
 
 Join our [Discord server](https://discord.gg/HhrNrHJPRb) where we can help you

docker-compose.yaml

@@ -9,6 +9,11 @@ services:
       - ~/.cache/huggingface/:/root/.cache/huggingface/
     # set environment variables
     environment:
+      # Set environment variables
+      - GIT_AUTHOR_NAME=${GIT_AUTHOR_NAME}
+      - GIT_AUTHOR_EMAIL=${GIT_AUTHOR_EMAIL}
+      - GIT_COMMITTER_NAME=${GIT_COMMITTER_NAME}
+      - GIT_COMMITTER_EMAIL=${GIT_COMMITTER_EMAIL}
       - WANDB_API_KEY=${WANDB_API_KEY}
     deploy:
       resources:

docker/Dockerfile

@@ -15,9 +15,9 @@ 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

docs/nccl.md

@@ -0,0 +1,46 @@
+# NCCL
+
+NVIDIA NCCL is a library to facilitate and optimize multi-GPU communication operations, such as broadcast, all-gather, reduce, all-reduce, etc. Broadly, NCCL configuration is highly environment-specific and is configured via several [environment variables](https://docs.nvidia.com/deeplearning/nccl/user-guide/docs/env.html). A common NCCL-related problem occurs when a long-running operation times out causing the training process to abort:
+
+```text
+Watchdog caught collective operation timeout: WorkNCCL(SeqNum=42, OpType=ALLGATHER, Timeout(ms)=1800000) ran for 1806948 milliseconds before timing out.
+```
+
+Often, this timeout will happen after 30 minutes (the default setting) and is accompanied by below-average power consumption with near 100% GPU utilization before the error is raised. Nvidia recommends [disabling PCI access control services (ACS)](https://docs.nvidia.com/deeplearning/nccl/user-guide/docs/troubleshooting.html#pci-access-control-services-acs) as a possible solution if this is available to you.
+
+Forcing cross-GPU communication via [NVLink](https://en.wikipedia.org/wiki/NVLink) may help without increasing timeouts. To verify that your configuration is leveraging NVLink run the following command:
+
+```shell
+nvidia-smi nvlink --status
+```
+
+To force NCCL to use NVLink, simply set this in the environment:
+
+```shell
+export NCCL_P2P_LEVEL=NVL
+```
+
+If NVLink is not available in your environment there are other options for ``NCCL_P2P_LEVEL`` in the table below:
+
+| NCCL_P2P_LEVEL | Description |
+| -------------- | ----------- |
+| PIX | P2P data transfers through no more than a single PCIe bridge. Faster data transfer rates vs to paths involving multiple bridges, but slower compared to direct GPU-to-GPU communication. |
+| PXB | P2P data transfers through multiple PCIe bridges but not going through the PCIe Host Bridge; this path involves a complex routing process, potentially incurring a moderate level of latency. |
+| PHB | P2P data transfers occur over the PCIe and through a PCIe Host Bridge, typically involving the CPU, which can facilitate direct memory access but might introduce additional latency compared to more direct paths (ex PIX, NVL) |
+
+To validate that acceptable data transfer speeds exist for your training job, running [NCCL Tests](https://github.com/NVIDIA/nccl-tests/blob/master/README.md) can help pinpoint bottlenecks, for example:
+
+```shell
+./build/all_reduce_perf -b 8 -e 128M -f 2 -g 3
+```
+
+It can be useful when debugging NCCL communication timeouts to activate additional logging in both PyTorch and NCCL:
+
+```shell
+export NCCL_DEBUG=INFO
+export NCCL_DEBUG_SUBSYS=ALL
+export TORCH_DISTRIBUTED_DEBUG=INFO
+export TORCHELASTIC_ERROR_FILE=/PATH/TO/torcherror.log
+```
+
+Finally, if you believe your training job needs more time you can increase the timeout past 30 minutes by setting the ``ddp_timeout`` value in the Axolotl configuration. See [PyTorch init_process_group](https://pytorch.org/docs/stable/distributed.html#torch.distributed.init_process_group) for documentation on this value.

examples/code-llama/13b/lora.yml

@@ -17,6 +17,7 @@ output_dir: ./lora-out
 
 sequence_len: 100000
 sample_packing: true
+pad_to_sequence_len: true
 
 adapter: lora
 lora_model_dir:

examples/code-llama/13b/qlora.yml

@@ -20,6 +20,7 @@ lora_model_dir:
 
 sequence_len: 100000
 sample_packing: true
+pad_to_sequence_len: true
 
 lora_r: 32
 lora_alpha: 16

examples/code-llama/34b/lora.yml

@@ -17,6 +17,7 @@ output_dir: ./lora-out
 
 sequence_len: 100000
 sample_packing: true
+pad_to_sequence_len: true
 
 adapter: lora
 lora_model_dir:

examples/code-llama/34b/qlora.yml

@@ -20,6 +20,7 @@ lora_model_dir:
 
 sequence_len: 100000
 sample_packing: true
+pad_to_sequence_len: true
 
 lora_r: 32
 lora_alpha: 16

examples/code-llama/7b/lora.yml

@@ -17,6 +17,7 @@ output_dir: ./lora-out
 
 sequence_len: 100000
 sample_packing: true
+pad_to_sequence_len: true
 
 adapter: lora
 lora_model_dir:

examples/code-llama/7b/qlora.yml

@@ -20,6 +20,7 @@ lora_model_dir:
 
 sequence_len: 100000
 sample_packing: true
+pad_to_sequence_len: true
 
 lora_r: 32
 lora_alpha: 16

examples/llama-2/lora.yml

@@ -17,6 +17,7 @@ output_dir: ./lora-out
 
 sequence_len: 4096
 sample_packing: true
+pad_to_sequence_len: true
 
 adapter: lora
 lora_model_dir:

examples/llama-2/qlora.yml

@@ -20,6 +20,7 @@ lora_model_dir:
 
 sequence_len: 4096
 sample_packing: true
+pad_to_sequence_len: true
 
 lora_r: 32
 lora_alpha: 16

examples/llama-2/relora.yml

@@ -20,6 +20,7 @@ lora_model_dir:
 
 sequence_len: 4096
 sample_packing: true
+pad_to_sequence_len: true
 
 lora_r: 8
 lora_alpha: 16

requirements.txt

@@ -6,13 +6,13 @@ packaging
 peft @ git+https://github.com/huggingface/peft.git
 transformers @ git+https://github.com/huggingface/transformers.git
 bitsandbytes>=0.41.1
-accelerate @ git+https://github.com/huggingface/accelerate@2a289f6108e77a77a4efffb3f6316bc98538413b
+accelerate @ git+https://github.com/huggingface/accelerate
 addict
 evaluate
 fire
 PyYAML>=6.0
 datasets
-flash-attn>=2.0.8
+flash-attn>=2.2.1
 sentencepiece
 wandb
 einops

setup.py

@@ -7,9 +7,7 @@ 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
-        ]
+        lines = [r.strip() for r in requirements_file.readlines()]
         for line in lines:
             if line.startswith("--extra-index-url"):
                 # Handle custom index URLs
@@ -26,18 +24,16 @@ install_requires, dependency_links = parse_requirements()
 
 setup(
     name="axolotl",
-    version="0.1",
-    description="You know you're going to axolotl questions",
+    version="0.3.0",
+    description="LLM Trainer",
+    long_description="Axolotl is a tool designed to streamline the fine-tuning of various AI models, offering support for multiple configurations and architectures.",
     package_dir={"": "src"},
     packages=find_packages(),
     install_requires=install_requires,
     dependency_links=dependency_links,
     extras_require={
-        "gptq": [
-            "auto-gptq",
-        ],
         "flash-attn": [
-            "flash-attn==2.0.8",
+            "flash-attn>=2.2.1",
         ],
         "extras": [
             "deepspeed",

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/logging_config.py

@@ -23,6 +23,7 @@ class ColorfulFormatter(Formatter):
     }
 
     def format(self, record):
+        record.rank = int(os.getenv("LOCAL_RANK", "0"))
         log_message = super().format(record)
         return self.COLORS.get(record.levelname, "") + log_message + Fore.RESET
 
@@ -35,7 +36,7 @@ DEFAULT_LOGGING_CONFIG: Dict[str, Any] = {
         },
         "colorful": {
             "()": ColorfulFormatter,
-            "format": "[%(asctime)s] [%(levelname)s] [%(name)s.%(funcName)s:%(lineno)d] [PID:%(process)d] %(message)s",
+            "format": "[%(asctime)s] [%(levelname)s] [%(name)s.%(funcName)s:%(lineno)d] [PID:%(process)d] [RANK:%(rank)d] %(message)s",
         },
     },
     "filters": {},

src/axolotl/train.py

@@ -88,6 +88,11 @@ def train(
     if peft_config:
         LOG.info(f"Pre-saving adapter config to {cfg.output_dir}")
         peft_config.save_pretrained(cfg.output_dir)
+    # additionally presave the tokenizer and model configs
+    if not Path(cfg.output_dir).is_dir():
+        os.makedirs(cfg.output_dir, exist_ok=True)
+    tokenizer.save_pretrained(str(Path(cfg.output_dir)))
+    model.config.save_pretrained(str(Path(cfg.output_dir)))
 
     # In case we want to stop early with ctrl+c, this is a nice to have to save the pretrained model
     if cfg.local_rank == 0:
@@ -106,9 +111,6 @@ def train(
     if cfg.group_by_length:
         LOG.info("hang tight... sorting dataset for group_by_length")
 
-    if not Path(cfg.output_dir).is_dir():
-        os.makedirs(cfg.output_dir, exist_ok=True)
-    tokenizer.save_pretrained(cfg.output_dir)
     if cfg.flash_optimum:
         with torch.backends.cuda.sdp_kernel(
             enable_flash=True, enable_math=True, enable_mem_efficient=True

src/axolotl/utils/callbacks.py

@@ -25,6 +25,7 @@ from transformers.trainer_utils import PREFIX_CHECKPOINT_DIR, IntervalStrategy
 from axolotl.utils.bench import log_gpu_memory_usage
 from axolotl.utils.distributed import (
     barrier,
+    broadcast_dict,
     gather_scalar_from_all_ranks,
     get_world_size,
     is_distributed,
@@ -271,6 +272,7 @@ def bench_eval_callback_factory(trainer, tokenizer):
                 lambda: len(data_loader), get_world_size()
             )
 
+            results = {}
             if is_distributed() and not is_main_process():
                 dist.gather_object(local_bench_names, dst=0)
             else:
@@ -316,4 +318,8 @@ def bench_eval_callback_factory(trainer, tokenizer):
                 )["accuracy"]
                 trainer.log(results)
 
+            results = broadcast_dict(results)
+            for key, val in results.items():
+                metrics[key] = val
+
     return BenchEvalCallback

src/axolotl/utils/config.py

@@ -97,6 +97,11 @@ def validate_config(cfg):
             )
         )
 
+    if cfg.sample_packing and not cfg.pad_to_sequence_len:
+        LOG.warning(
+            "`pad_to_sequence_len: true` is recommended when using sample_packing"
+        )
+
     if cfg.gradient_accumulation_steps and cfg.batch_size:
         raise ValueError(
             "please set only one of gradient_accumulation_steps or batch_size"
@@ -215,6 +220,15 @@ def validate_config(cfg):
             "sample_packing not compatible with xformers_attention. Use flash_attention"
         )
 
+    if cfg.early_stopping_patience:
+        if not cfg.save_steps or not cfg.eval_steps:
+            raise ValueError(
+                "`early_stopping_patience` requires save_steps and eval_steps to be set. eval_steps should evenly divide save_steps."
+            )
+        if cfg.save_steps % cfg.eval_steps != 0:
+            raise ValueError(
+                "`early_stopping_patience` requires that eval_steps should evenly divide save_steps."
+            )
     # TODO
     # MPT 7b
     # https://github.com/facebookresearch/bitsandbytes/issues/25

src/axolotl/utils/data.py

@@ -2,7 +2,6 @@
 import functools
 import hashlib
 import logging
-from hashlib import md5
 from pathlib import Path
 from typing import Tuple, Union
 
@@ -52,6 +51,13 @@ LOG = logging.getLogger("axolotl")
 DEFAULT_DATASET_PREPARED_PATH = "last_run_prepared"
 
 
+def md5(to_hash: str, encoding: str = "utf-8") -> str:
+    try:
+        return hashlib.md5(to_hash.encode(encoding), usedforsecurity=False).hexdigest()
+    except TypeError:
+        return hashlib.md5(to_hash.encode(encoding)).hexdigest()  # nosec
+
+
 def prepare_dataset(cfg, tokenizer):
     if not cfg.pretraining_dataset:
         with zero_first(is_main_process()):
@@ -88,7 +94,7 @@ def load_tokenized_prepared_datasets(
 ) -> DatasetDict:
     tokenizer_name = tokenizer.__class__.__name__
     ds_hash = str(
-        md5(  # nosec
+        md5(
             (
                 str(cfg.sequence_len)
                 + "@"
@@ -97,8 +103,8 @@ def load_tokenized_prepared_datasets(
                 )
                 + "|"
                 + tokenizer_name
-            ).encode("utf-8")
-        ).hexdigest()
+            )
+        )
     )
     prepared_ds_path = (
         Path(cfg.dataset_prepared_path) / ds_hash
@@ -374,7 +380,7 @@ def load_prepare_datasets(
         # see if we can go ahead and load the stacked dataset
         seed = f"@{str(cfg.seed)}" if cfg.seed else ""
         ds_hash = str(
-            md5(  # nosec
+            md5(
                 (
                     str(cfg.sequence_len)
                     + "@"
@@ -385,8 +391,8 @@ def load_prepare_datasets(
                     )
                     + "|"
                     + tokenizer_name
-                ).encode("utf-8")
-            ).hexdigest()
+                )
+            )
         )
         prepared_ds_path = (
             Path(cfg.dataset_prepared_path) / ds_hash
@@ -500,12 +506,8 @@ def load_prepare_datasets(
             + "|"
             + str(cfg.seed or 42)
         )
-        train_fingerprint = hashlib.md5(
-            to_hash_train.encode(), usedforsecurity=False
-        ).hexdigest()
-        test_fingerprint = hashlib.md5(
-            to_hash_test.encode(), usedforsecurity=False
-        ).hexdigest()
+        train_fingerprint = md5(to_hash_train)
+        test_fingerprint = md5(to_hash_test)
 
         with zero_first(is_main_process()):
             dataset = dataset.train_test_split(

src/axolotl/utils/distributed.py

@@ -2,6 +2,7 @@
 utility helpers for distributed checks
 """
 import os
+import pickle  # nosec
 from contextlib import contextmanager
 
 import torch
@@ -93,3 +94,30 @@ def gather_scalar_from_all_ranks(fn, world_size=1):  # pylint: disable=invalid-n
                 gathered_values.append(float(tensor.item()))
         return gathered_values
     return None
+
+
+def broadcast_dict(vals: dict):
+    if not is_distributed():
+        return vals
+
+    if is_main_process():
+        data_byte = pickle.dumps(vals)
+        data_tensor = torch.ByteTensor(list(data_byte)).to("cuda")
+        data_size = torch.IntTensor([len(data_byte)]).to("cuda")
+    else:
+        data_tensor = torch.empty([1024], dtype=torch.uint8, device="cuda")
+        data_size = torch.IntTensor([0]).to("cuda")
+
+    dist.broadcast(data_size, 0)
+    if not is_main_process():
+        # resize
+        data_tensor = data_tensor.new_empty([data_size.item()])
+
+    dist.broadcast(data_tensor, 0)
+
+    if not is_main_process():
+        data_list = data_tensor.cpu().tolist()
+        data_byte = bytes(data_list[: data_size.item()])
+        vals = pickle.loads(data_byte)  # nosec
+
+    return vals

src/axolotl/utils/models.py

@@ -160,7 +160,7 @@ def load_model(
         model_kwargs["revision"] = cfg.model_revision
     if cfg.gptq:
         model_config = load_model_config(cfg)
-        if hasattr(model_config, "quantization_config"):
+        if not hasattr(model_config, "quantization_config"):
             LOG.warning("model config does not contain quantization_config information")
         else:
             model_kwargs["quantization_config"] = GPTQConfig(

src/axolotl/utils/trainer.py

@@ -33,6 +33,7 @@ from axolotl.utils.callbacks import (
 )
 from axolotl.utils.collators import DataCollatorForSeq2Seq
 from axolotl.utils.dataloader import MultipackDistributedDataloader
+from axolotl.utils.distributed import is_main_process, zero_first
 from axolotl.utils.schedulers import get_cosine_schedule_with_quadratic_warmup
 
 LOG = logging.getLogger("axolotl")
@@ -375,14 +376,17 @@ def disable_datasets_caching():
 
 def process_datasets_for_packing(cfg, train_dataset, eval_dataset):
     drop_long = partial(drop_long_seq, sequence_len=cfg.sequence_len)
-    train_dataset = train_dataset.filter(drop_long, num_proc=os.cpu_count())
-    if eval_dataset:
-        eval_dataset = eval_dataset.filter(drop_long, num_proc=os.cpu_count())
-
-    if cfg.sample_packing:
-        train_dataset = train_dataset.map(add_position_ids, num_proc=os.cpu_count())
+    with zero_first(is_main_process()):
+        train_dataset = train_dataset.filter(drop_long, num_proc=os.cpu_count())
         if eval_dataset:
-            eval_dataset = eval_dataset.map(add_position_ids, num_proc=os.cpu_count())
+            eval_dataset = eval_dataset.filter(drop_long, num_proc=os.cpu_count())
+
+        if cfg.sample_packing:
+            train_dataset = train_dataset.map(add_position_ids, num_proc=os.cpu_count())
+            if eval_dataset:
+                eval_dataset = eval_dataset.map(
+                    add_position_ids, num_proc=os.cpu_count()
+                )
     return train_dataset, eval_dataset
 
 
@@ -572,6 +576,10 @@ def setup_trainer(cfg, train_dataset, eval_dataset, model, tokenizer, total_num_
         training_arguments_kwargs["do_bench_eval"] = cfg.do_bench_eval
         if cfg.bench_dataset:
             training_arguments_kwargs["bench_dataset"] = cfg.bench_dataset
+    if cfg.metric_for_best_model:
+        training_arguments_kwargs["metric_for_best_model"] = cfg.metric_for_best_model
+    if cfg.greater_is_better:
+        training_arguments_kwargs["greater_is_better"] = cfg.greater_is_better
 
     # DDP Config
     if cfg.ddp_timeout:
@@ -597,11 +605,10 @@ def setup_trainer(cfg, train_dataset, eval_dataset, model, tokenizer, total_num_
         output_dir=cfg.output_dir,
         save_total_limit=cfg.save_total_limit if cfg.save_total_limit else 4,
         load_best_model_at_end=(
-            cfg.load_best_model_at_end is not False
+            (cfg.load_best_model_at_end is not False or cfg.early_stopping_patience)
             and cfg.val_set_size > 0
             and cfg.save_steps
             and cfg.save_steps % cfg.eval_steps == 0
-            and cfg.load_in_8bit is not True
         )
         or False,
         ddp_find_unused_parameters=False if cfg.ddp else None,
@@ -633,13 +640,6 @@ def setup_trainer(cfg, train_dataset, eval_dataset, model, tokenizer, total_num_
     if cfg.relora_steps:
         callbacks.append(ReLoRACallback(cfg))
 
-    # TODO on_save callback to sync checkpoints to GCP/AWS in background
-    if cfg.early_stopping_patience:
-        early_stop_cb = EarlyStoppingCallback(
-            cfg.early_stopping_patience,
-        )
-        callbacks.append(early_stop_cb)
-
     if cfg.local_rank == 0 and cfg.adapter in [
         "lora",
         "qlora",
@@ -706,4 +706,11 @@ def setup_trainer(cfg, train_dataset, eval_dataset, model, tokenizer, total_num_
     if cfg.do_bench_eval:
         trainer.add_callback(bench_eval_callback_factory(trainer, tokenizer))
 
+    # TODO on_save callback to sync checkpoints to GCP/AWS in background
+    if cfg.early_stopping_patience:
+        early_stop_cb = EarlyStoppingCallback(
+            cfg.early_stopping_patience,
+        )
+        trainer.add_callback(early_stop_cb)
+
     return trainer

tests/test_data.py

@@ -0,0 +1,64 @@
+"""
+test module for the axolotl.utis.data module
+"""
+import unittest
+
+from transformers import LlamaTokenizer
+
+from axolotl.utils.data import encode_pretraining, md5
+
+
+class TestEncodePretraining(unittest.TestCase):
+    """
+    test class for encode pretraining and md5 helper
+    """
+
+    def setUp(self):
+        self.tokenizer = LlamaTokenizer.from_pretrained("huggyllama/llama-7b")
+        self.tokenizer.add_special_tokens(
+            {
+                "eos_token": "</s>",
+                "bos_token": "<s>",
+                "unk_token": "<unk>",
+                "pad_token": "<pad>",
+            }
+        )
+        self.max_tokens = 15  # set a small number for easy inspection
+
+    def test_encode_pretraining(self):
+        examples = {
+            "text": [
+                "Hello, world!",
+                "Nice to meet you.",
+                "lorem ipsum dolor sit amet.",
+                "Nice to meet you again!.",
+                "hello, hello",
+            ]
+        }
+        result = encode_pretraining(self.tokenizer, self.max_tokens, examples)
+
+        self.assertEqual(len(result["input_ids"]), 3)
+
+        # Assert the length of input_ids and attention_mask is correct
+        self.assertEqual(len(result["input_ids"][0]), self.max_tokens)
+        self.assertEqual(len(result["attention_mask"][0]), self.max_tokens)
+
+        # Assert EOS and PAD tokens are correctly added
+        # hello world! is 4 tokens
+        self.assertEqual(result["input_ids"][0][0], self.tokenizer.bos_token_id)
+        self.assertEqual(result["input_ids"][0][5], self.tokenizer.eos_token_id)
+        self.assertEqual(result["input_ids"][0][6], self.tokenizer.pad_token_id)
+        # second part, 5 tokens
+        self.assertEqual(result["input_ids"][0][7], self.tokenizer.bos_token_id)
+        self.assertEqual(result["input_ids"][0][13], self.tokenizer.eos_token_id)
+        self.assertEqual(result["input_ids"][0][14], self.tokenizer.pad_token_id)
+
+    def test_md5(self):
+        self.assertEqual(md5("hello world"), "5eb63bbbe01eeed093cb22bb8f5acdc3")
+        self.assertEqual(
+            md5("hello world", "utf-8"), "5eb63bbbe01eeed093cb22bb8f5acdc3"
+        )
+
+
+if __name__ == "__main__":
+    unittest.main()

tests/test_validation.py

@@ -328,6 +328,20 @@ class ValidationTest(unittest.TestCase):
                 for record in self._caplog.records
             )
 
+        cfg = DictDefault(
+            {
+                "sample_packing": True,
+                "pad_to_sequence_len": None,
+            }
+        )
+        with self._caplog.at_level(logging.WARNING):
+            validate_config(cfg)
+            assert any(
+                "`pad_to_sequence_len: true` is recommended when using sample_packing"
+                in record.message
+                for record in self._caplog.records
+            )
+
         cfg = DictDefault(
             {
                 "max_packed_sequence_len": 2048,