tinyllama

* Feat: Add dataset loading from S3, GCS

* chore: update docs

* chore: add more info on cloud loading

README.md

@@ -25,8 +25,10 @@ Features:
 - [Installation](#installation)
   - [Docker](#docker)
   - [Conda/Pip venv](#condapip-venv)
+  - [Runpod](#runpod)
   - [LambdaLabs](#lambdalabs)
   - [Windows](#windows)
+  - [Launching on public clouds via SkyPilot](#launching-on-public-clouds-via-skypilot)
 - [Dataset](#dataset)
   - [How to Add Custom Prompts](#how-to-add-custom-prompts)
   - [How to Use Custom Pretokenized Dataset](#how-to-use-your-custom-pretokenized-dataset)
@@ -111,7 +113,6 @@ accelerate launch -m axolotl.cli.inference 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-latest`: for runpod or use this [direct link](https://runpod.io/gsc?template=v2ickqhz9s&ref=6i7fkpdz)
 
   Or run on the current files for development:
 
@@ -126,13 +127,15 @@ accelerate launch -m axolotl.cli.inference examples/openllama-3b/lora.yml \
   A more powerful Docker command to run would be this:
 
   ```bash
-  docker run --gpus '"all"' --rm -it --name axolotl --ipc=host --ulimit memlock=-1 --ulimit stack=67108864 --mount type=volume,src=axolotl,target=/workspace/axolotl -v ${HOME}/.cache/huggingface:/root/.cache/huggingface winglian/axolotl:main-py3.10-cu118-2.0.1
+  docker run --privileged --gpus '"all"' --shm-size 10g --rm -it --name axolotl --ipc=host --ulimit memlock=-1 --ulimit stack=67108864 --mount type=volume,src=axolotl,target=/workspace/axolotl -v ${HOME}/.cache/huggingface:/root/.cache/huggingface winglian/axolotl:main-py3.10-cu118-2.0.1
   ```
 
   It additionally:
   * Prevents memory issues when running e.g. deepspeed (e.g. you could hit SIGBUS/signal 7 error) through `--ipc` and `--ulimit` args.
   * Persists the downloaded HF data (models etc.) and your modifications to axolotl code through `--mount`/`-v` args.
   * The `--name` argument simply makes it easier to refer to the container in vscode (`Dev Containers: Attach to Running Container...`) or in your terminal.
+  * The `--privileged` flag gives all capabilities to the container.
+  * The `--shm-size 10g` argument increases the shared memory size. Use this if you see `exitcode: -7` errors using deepspeed.
 
   [More information on nvidia website](https://docs.nvidia.com/deeplearning/frameworks/user-guide/index.html#setincshmem)
 
@@ -154,6 +157,10 @@ accelerate launch -m axolotl.cli.inference examples/openllama-3b/lora.yml \
         ```
         Get the token at huggingface.co/settings/tokens
 
+#### Runpod
+
+Use `winglian/axolotl-runpod:main-latest` or use this [direct link](https://runpod.io/gsc?template=v2ickqhz9s&ref=6i7fkpdz)
+
 #### LambdaLabs
   <details>
 
@@ -201,6 +208,28 @@ accelerate launch -m axolotl.cli.inference examples/openllama-3b/lora.yml \
 #### Windows
 Please use WSL or Docker!
 
+
+#### Launching on public clouds via SkyPilot
+To launch on GPU instances (both on-demand and spot instances) on 7+ clouds (GCP, AWS, Azure, OCI, and more), you can use [SkyPilot](https://skypilot.readthedocs.io/en/latest/index.html):
+```bash
+pip install "skypilot-nightly[gcp,aws,azure,oci,lambda,kubernetes,ibm,scp]"  # choose your clouds
+sky check
+```
+Get the [example YAMLs](https://github.com/skypilot-org/skypilot/tree/master/llm/axolotl) of using Axolotl to finetune `mistralai/Mistral-7B-v0.1`:
+```
+git clone https://github.com/skypilot-org/skypilot.git
+cd skypilot/llm/axolotl
+```
+Use one command to launch:
+```bash
+# On-demand
+HF_TOKEN=xx sky launch axolotl.yaml --env HF_TOKEN
+
+# Managed spot (auto-recovery on preemption)
+HF_TOKEN=xx BUCKET=<unique-name> sky spot launch axolotl-spot.yaml --env HF_TOKEN --env BUCKET
+```
+
+
 ### Dataset
 
 Axolotl supports a variety of dataset formats. Below are some of the formats you can use.
@@ -397,6 +426,12 @@ See [examples](examples) for quick start. It is recommended to duplicate and mod
     - path: knowrohit07/know_sql
       type: context_qa.load_v2
       train_on_split: validation
+
+  # loading from s3 or gcs
+  # s3 creds will be loaded from the system default and gcs only supports public access
+  dataset:
+    - path: s3://path_to_ds # Accepts folder with arrow/parquet or file path like above. Supports s3, gcs.
+      ...
   ```
 
 - loading
@@ -460,6 +495,14 @@ is_llama_derived_model:
 # Please note that if you set this to true, `padding_side` will be set to "left" by default
 is_mistral_derived_model:
 
+# optional overrides to the base model configuration
+model_config:
+  # RoPE Scaling https://github.com/huggingface/transformers/pull/24653
+  rope_scaling:
+    type: # linear | dynamic
+    factor: # float
+
+
 # Whether you are training a 4-bit GPTQ quantized model
 gptq: true
 gptq_groupsize: 128 # group size
@@ -483,7 +526,7 @@ float16: true
 
 # A list of one or more datasets to finetune the model with
 datasets:
-  # HuggingFace dataset repo | "json" for local dataset, make sure to fill data_files
+  # HuggingFace dataset repo | s3://,gs:// path | "json" for local dataset, make sure to fill data_files
   - path: vicgalle/alpaca-gpt4
   # The type of prompt to use for training. [alpaca, sharegpt, gpteacher, oasst, reflection]
     type: alpaca # format | format:<prompt_style> (chat/instruct) | <prompt_strategies>.load_<load_fn>
@@ -491,6 +534,7 @@ datasets:
     data_files: # Optional[str] path to source data files
     shards: # Optional[int] number of shards to split data into
     name: # Optional[str] name of dataset configuration to load
+    train_on_split: train # Optional[str] name of dataset split to load from
 
     # Optional[str] fastchat conversation type, only used with type: sharegpt
     conversation:  # Options (see Conversation 'name'): https://github.com/lm-sys/FastChat/blob/main/fastchat/conversation.py
@@ -726,10 +770,6 @@ landmark_attention:
 # xpos RoPE see https://github.com/kaiokendev/cutoff-len-is-context-len/blob/main/util/xpos_rope_llama_monkey_patch.py
 # LLaMA only
 xpos_rope:
-# RoPE Scaling https://github.com/huggingface/transformers/pull/24653
-rope_scaling:
-  type: # linear | dynamic
-  factor: # float
 
 # Resume from a specific checkpoint dir
 resume_from_checkpoint:

examples/llama-2/tiny-llama.yml

@@ -1,22 +1,23 @@
-base_model: PY007/TinyLlama-1.1B-step-50K-105b
+base_model: PY007/TinyLlama-1.1B-intermediate-step-715k-1.5T
 
 model_type: LlamaForCausalLM
 tokenizer_type: LlamaTokenizer
 is_llama_derived_model: true
 
-load_in_8bit: true
+load_in_8bit: false
 load_in_4bit: false
 strict: false
 
 datasets:
-  - path: mhenrichsen/alpaca_2k_test
+  - path: mhenrichsen/context-aware-splits-english
     type: alpaca
 dataset_prepared_path:
-val_set_size: 0.05
-output_dir: ./lora-out
+val_set_size: 200
+output_dir: ./tiny-llama
 
-sequence_len: 4096
+sequence_len: 8192
 sample_packing: true
+pad_to_sequence_len: true
 
 adapter: lora
 lora_model_dir:
@@ -32,9 +33,9 @@ wandb_watch:
 wandb_run_id:
 wandb_log_model:
 
-gradient_accumulation_steps: 4
-micro_batch_size: 2
-num_epochs: 4
+gradient_accumulation_steps: 1
+micro_batch_size: 8
+num_epochs: 3
 optimizer: adamw_bnb_8bit
 lr_scheduler: cosine
 learning_rate: 0.0002
@@ -53,13 +54,13 @@ logging_steps: 1
 xformers_attention:
 flash_attention: true
 
-warmup_steps: 10
+warmup_steps: 50
 eval_steps: 0.05
 eval_table_size:
-save_steps:
+save_steps: 0.50
 debug:
 deepspeed:
-weight_decay: 0.0
+weight_decay: 0.1
 fsdp:
 fsdp_config:
 special_tokens:

requirements.txt

@@ -11,7 +11,7 @@ deepspeed
 addict
 fire
 PyYAML>=6.0
-datasets
+datasets>=2.14.0
 flash-attn>=2.3.0
 sentencepiece
 wandb
@@ -32,3 +32,9 @@ pynvml
 art
 fschat==0.2.29
 gradio
+tensorboard
+
+# remote filesystems
+s3fs
+gcsfs
+# adlfs

src/axolotl/cli/__init__.py

@@ -46,7 +46,7 @@ def print_axolotl_text_art(suffix=None):
     ascii_text = "  axolotl"
     if suffix:
         ascii_text += f"  x  {suffix}"
-    ascii_art = text2art(" axolotl", font=font)
+    ascii_art = text2art(ascii_text, font=font)
 
     if is_main_process():
         print(ascii_art)

src/axolotl/core/trainer_builder.py

@@ -11,7 +11,7 @@ from abc import abstractmethod
 from dataclasses import dataclass, field
 from functools import partial
 from pathlib import Path
-from typing import Optional, Union
+from typing import Optional
 
 import torch
 import transformers
@@ -31,7 +31,6 @@ from axolotl.utils.callbacks import (
     log_prediction_callback_factory,
 )
 from axolotl.utils.collators import BatchSamplerDataCollatorForSeq2Seq
-from axolotl.utils.dataloader import MultipackDistributedDataloader
 from axolotl.utils.samplers import MultipackBatchSampler
 from axolotl.utils.schedulers import get_cosine_schedule_with_quadratic_warmup
 
@@ -215,9 +214,7 @@ class AxolotlTrainer(Trainer):
             )
         return super().get_train_dataloader()
 
-    def get_eval_dataloader(
-        self, eval_dataset: Optional[Dataset] = None
-    ) -> Union[DataLoader, MultipackDistributedDataloader]:
+    def get_eval_dataloader(self, eval_dataset: Optional[Dataset] = None) -> DataLoader:
         if self.args.sample_packing and self.args.eval_sample_packing is not False:
             eval_dataset = (
                 eval_dataset if eval_dataset is not None else self.eval_dataset
@@ -260,7 +257,7 @@ class AxolotlTrainer(Trainer):
     def get_bench_dataloader(
         self,
         bench_dataset: Dataset,
-    ) -> Union[DataLoader, MultipackDistributedDataloader]:
+    ) -> DataLoader:
         dataloader_params = {
             "batch_size": self.args.eval_batch_size,
             "collate_fn": self.bench_data_collator,
@@ -546,16 +543,16 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
                 "dataloader_prefetch_factor"
             ] = self.cfg.dataloader_prefetch_factor
 
-        if self.cfg.eval_steps:
+        if self.cfg.val_set_size == 0:
+            # no eval set, so don't eval
+            training_arguments_kwargs["evaluation_strategy"] = "no"
+        elif self.cfg.eval_steps:
             training_arguments_kwargs["evaluation_strategy"] = "steps"
             training_arguments_kwargs["eval_steps"] = self.cfg.eval_steps
         elif self.cfg.evaluation_strategy:
             training_arguments_kwargs[
                 "evaluation_strategy"
             ] = self.cfg.evaluation_strategy
-        elif self.cfg.val_set_size == 0:
-            # no eval set, so don't eval
-            training_arguments_kwargs["evaluation_strategy"] = "no"
         else:
             # we have an eval set, but no steps defined, default to use epoch
             training_arguments_kwargs["evaluation_strategy"] = "epoch"

src/axolotl/monkeypatch/llama_attn_hijack_flash.py

@@ -321,6 +321,8 @@ def flashattn_forward(
         # only on first autoregressive step q,k,v have same seqlen
         is_causal = key_states.shape == query_states.shape
 
+    dropout_rate = 0.0 if not self.training else getattr(self, "attention_dropout", 0.0)
+
     if cu_seqlens is not None and max_seqlen is not None and cu_seqlens.dim() == 1:
         # special handling using sample packing
         qkv = torch.stack(
@@ -330,7 +332,12 @@ def flashattn_forward(
         qkv = rearrange(qkv, "b s ... -> (b s) ...")
 
         output = flash_attn_varlen_qkvpacked_func(
-            qkv, cu_seqlens, max_seqlen, 0.0, softmax_scale=None, causal=True
+            qkv,
+            cu_seqlens,
+            max_seqlen,
+            dropout_p=dropout_rate,
+            softmax_scale=None,
+            causal=True,
         )
         output = rearrange(output, "(b s) ... -> b s ...", b=bsz)
     elif query_states.shape == key_states.shape:
@@ -353,7 +360,7 @@ def flashattn_forward(
             qkv_unpad,
             cu_seqlens_q,
             max_seqlen_q,
-            0.0,
+            dropout_p=dropout_rate,
             softmax_scale=None,
             causal=is_causal,
         )
@@ -366,6 +373,7 @@ def flashattn_forward(
             output = flash_attn_kvpacked_func(
                 query_states,
                 torch.stack([key_states, value_states], 2),
+                dropout_p=dropout_rate,
                 causal=is_causal,
             )
         else:
@@ -398,7 +406,7 @@ def flashattn_forward(
                 cu_seqlens_k,
                 max_seqlen_q,
                 max_seqlen_k,
-                0.0,
+                dropout_p=dropout_rate,
                 softmax_scale=None,
                 causal=is_causal,
             )

src/axolotl/monkeypatch/llama_attn_hijack_sdp.py

@@ -25,6 +25,8 @@ def sdp_attention_forward(
     past_key_value: Optional[Tuple[torch.Tensor]] = None,
     output_attentions: bool = False,
     use_cache: bool = False,
+    padding_mask: Optional[torch.LongTensor] = None,  # pylint: disable=unused-argument
+    **kwargs,  # pylint: disable=unused-argument
 ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
     # pylint: disable=duplicate-code
     bsz, q_len, _ = hidden_states.size()

src/axolotl/monkeypatch/llama_attn_hijack_xformers.py

@@ -29,6 +29,8 @@ def xformers_forward(
     past_key_value: Optional[Tuple[torch.Tensor]] = None,
     output_attentions: bool = False,
     use_cache: bool = False,
+    padding_mask: Optional[torch.LongTensor] = None,  # pylint: disable=unused-argument
+    **kwargs,  # pylint: disable=unused-argument
 ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
     # pylint: disable=duplicate-code
     bsz, q_len, _ = hidden_states.size()

src/axolotl/monkeypatch/mistral_attn_hijack_flash.py

@@ -201,6 +201,8 @@ def flashattn_forward(
         # only on first autoregressive step q,k,v have same seqlen
         is_causal = key_states.shape == query_states.shape
 
+    dropout_rate = 0.0 if not self.training else getattr(self, "attention_dropout", 0.0)
+
     if cu_seqlens is not None and max_seqlen is not None and cu_seqlens.dim() == 1:
         # special handling using sample packing
         qkv = torch.stack(
@@ -213,7 +215,7 @@ def flashattn_forward(
             qkv,
             cu_seqlens,
             max_seqlen,
-            0.0,
+            dropout_p=dropout_rate,
             softmax_scale=None,
             causal=True,
             window_size=window_size,
@@ -239,7 +241,7 @@ def flashattn_forward(
             qkv_unpad,
             cu_seqlens_q,
             max_seqlen_q,
-            0.0,
+            dropout_p=dropout_rate,
             softmax_scale=None,
             causal=is_causal,
             window_size=window_size,
@@ -253,6 +255,7 @@ def flashattn_forward(
             output = flash_attn_kvpacked_func(
                 query_states,
                 torch.stack([key_states, value_states], 2),
+                dropout_p=dropout_rate,
                 causal=is_causal,
                 window_size=window_size,
             )
@@ -286,7 +289,7 @@ def flashattn_forward(
                 cu_seqlens_k,
                 max_seqlen_q,
                 max_seqlen_k,
-                0.0,
+                dropout_p=dropout_rate,
                 softmax_scale=None,
                 causal=is_causal,
                 window_size=window_size,

src/axolotl/prompters.py

@@ -22,7 +22,13 @@ class PromptStyle(Enum):
     CHATML = "chatml"
 
 
-class AlpacaPrompter:
+class Prompter:
+    """
+    Base prompter class for all prompters
+    """
+
+
+class AlpacaPrompter(Prompter):
     """
     Base class for alpaca prompters
     """
@@ -69,7 +75,7 @@ class AlpacaPrompter:
         else:
             res = (
                 self.system_format.format(system=self.system_no_input_prompt)
-                if self.system_prompt
+                if self.system_no_input_prompt
                 else ""
             ) + self.turn_no_input_format.format(instruction=instruction)
         if output:
@@ -159,7 +165,7 @@ class NomicGPT4AllPrompter(AlpacaPrompter):
     """
 
 
-class ReflectAlpacaPrompter:
+class ReflectAlpacaPrompter(Prompter):
     """
     Prompter for ReflectAlpaca
     """
@@ -254,7 +260,7 @@ SHAREGPT_ASSERTION_FAILED_ROLE = (
 )
 
 
-class ShareGPTPrompter:  # pylint: disable=too-few-public-methods
+class ShareGPTPrompter(Prompter):  # pylint: disable=too-few-public-methods
     """
     A prompter that generates prompts for the ShareGPT
     """
@@ -349,7 +355,7 @@ class ShareGPTPrompterV2(ShareGPTPrompter):
         )
 
 
-class UnsupportedPrompter:
+class UnsupportedPrompter(Prompter):
     """
     A dummy class for custom prompters
     """

src/axolotl/utils/config.py

@@ -369,6 +369,9 @@ def validate_config(cfg):
             "If you want to full finetune, please turn off load_in_8bit and load_in_4bit."
         )
 
+    if cfg.rope_scaling:
+        LOG.warning("`rope_scaling` should now be be a key under `model_config`")
+
     # TODO
     # MPT 7b
     # https://github.com/facebookresearch/bitsandbytes/issues/25

src/axolotl/utils/data.py

@@ -3,7 +3,7 @@ import functools
 import hashlib
 import logging
 from pathlib import Path
-from typing import Any, Dict, List, Tuple, Union
+from typing import Dict, List, Tuple, Union
 
 import torch
 from datasets import (
@@ -34,6 +34,7 @@ from axolotl.prompters import (
     JeopardyPrompter,
     MultipleChoiceConcisePrompter,
     MultipleChoiceExplainPrompter,
+    Prompter,
     ReflectAlpacaPrompter,
     SummarizeTLDRPrompter,
     UnsupportedPrompter,
@@ -90,7 +91,7 @@ def prepare_dataset(cfg, tokenizer):
 
 def load_tokenized_prepared_datasets(
     tokenizer, cfg, default_dataset_prepared_path
-) -> DatasetDict:
+) -> Tuple[DatasetDict, List[Prompter]]:
     tokenizer_name = tokenizer.__class__.__name__
     ds_hash = str(
         md5(
@@ -98,7 +99,12 @@ def load_tokenized_prepared_datasets(
                 str(cfg.sequence_len)
                 + "@"
                 + "|".join(
-                    sorted([f"{d.path}:{d.type}:{d.shards}" for d in cfg.datasets])
+                    sorted(
+                        [
+                            f"{d.path}:{d.type}:{d.shards}:{d.conversation}"
+                            for d in cfg.datasets
+                        ]
+                    )
                 )
                 + "|"
                 + tokenizer_name
@@ -164,30 +170,74 @@ def load_tokenized_prepared_datasets(
             except (FileNotFoundError, ConnectionError):
                 pass
 
+            ds_from_cloud = False
+            storage_options = {}
+            remote_file_system = None
+            if config_dataset.path.startswith("s3://"):
+                try:
+                    import aiobotocore.session  # type: ignore
+                    import s3fs  # type: ignore
+                except ImportError as exc:
+                    raise ImportError(
+                        "s3:// paths require aiobotocore and s3fs to be installed"
+                    ) from exc
+
+                # Takes credentials from ~/.aws/credentials for default profile
+                s3_session = aiobotocore.session.AioSession(profile="default")
+                storage_options = {"session": s3_session}
+                remote_file_system = s3fs.S3FileSystem(**storage_options)
+            elif config_dataset.path.startswith(
+                "gs://"
+            ) or config_dataset.path.startswith("gcs://"):
+                try:
+                    import gcsfs  # type: ignore
+                except ImportError as exc:
+                    raise ImportError(
+                        "gs:// or gcs:// paths require gcsfs to be installed"
+                    ) from exc
+
+                # gcsfs will use default credentials from the environment else anon
+                # https://gcsfs.readthedocs.io/en/latest/#credentials
+                storage_options = {"token": None}
+                remote_file_system = gcsfs.GCSFileSystem(**storage_options)
+            # TODO: Figure out how to get auth creds passed
+            # elif config_dataset.path.startswith("adl://") or config_dataset.path.startswith("abfs://"):
+            #     try:
+            #         import adlfs
+            #     except ImportError as exc:
+            #        raise ImportError(
+            #            "adl:// or abfs:// paths require adlfs to be installed"
+            #        ) from exc
+
+            #     # Gen 1
+            #     storage_options = {
+            #         "tenant_id": TENANT_ID,
+            #         "client_id": CLIENT_ID,
+            #         "client_secret": CLIENT_SECRET,
+            #     }
+            #     # Gen 2
+            #     storage_options = {
+            #         "account_name": ACCOUNT_NAME,
+            #         "account_key": ACCOUNT_KEY,
+            #     }
+
+            #     remote_file_system = adlfs.AzureBlobFileSystem(**storage_options)
+            try:
+                if remote_file_system and remote_file_system.exists(
+                    config_dataset.path
+                ):
+                    ds_from_cloud = True
+            except (FileNotFoundError, ConnectionError):
+                pass
+
             # prefer local dataset, even if hub exists
             local_path = Path(config_dataset.path)
             if local_path.exists():
                 if local_path.is_dir():
-                    # TODO dirs with arrow or parquet files could be loaded with `load_from_disk`
-                    ds = load_dataset(
-                        config_dataset.path,
-                        name=config_dataset.name,
-                        data_files=config_dataset.data_files,
-                        streaming=False,
-                        split=None,
-                    )
+                    ds = load_from_disk(config_dataset.path)
                 elif local_path.is_file():
-                    ds_type = "json"
-                    if config_dataset.ds_type:
-                        ds_type = config_dataset.ds_type
-                    elif ".parquet" in config_dataset.path:
-                        ds_type = "parquet"
-                    elif ".arrow" in config_dataset.path:
-                        ds_type = "arrow"
-                    elif ".csv" in config_dataset.path:
-                        ds_type = "csv"
-                    elif ".txt" in config_dataset.path:
-                        ds_type = "text"
+                    ds_type = get_ds_type(config_dataset)
+
                     ds = load_dataset(
                         ds_type,
                         name=config_dataset.name,
@@ -207,6 +257,22 @@ def load_tokenized_prepared_datasets(
                     data_files=config_dataset.data_files,
                     token=use_auth_token,
                 )
+            elif ds_from_cloud and remote_file_system:
+                if remote_file_system.isdir(config_dataset.path):
+                    ds = load_from_disk(
+                        config_dataset.path,
+                        storage_options=storage_options,
+                    )
+                elif remote_file_system.isfile(config_dataset.path):
+                    ds_type = get_ds_type(config_dataset)
+                    ds = load_dataset(
+                        ds_type,
+                        name=config_dataset.name,
+                        data_files=config_dataset.path,
+                        streaming=False,
+                        split=None,
+                        storage_options=storage_options,
+                    )
             else:
                 if isinstance(config_dataset.data_files, str):
                     fp = hf_hub_download(
@@ -298,11 +364,29 @@ def load_tokenized_prepared_datasets(
     return dataset, prompters
 
 
+def get_ds_type(config_dataset: DictDefault):
+    """
+    Get the dataset type from the path if it's not specified
+    """
+    ds_type = "json"
+    if config_dataset.ds_type:
+        ds_type = config_dataset.ds_type
+    elif ".parquet" in config_dataset.path:
+        ds_type = "parquet"
+    elif ".arrow" in config_dataset.path:
+        ds_type = "arrow"
+    elif ".csv" in config_dataset.path:
+        ds_type = "csv"
+    elif ".txt" in config_dataset.path:
+        ds_type = "text"
+    return ds_type
+
+
 def load_prepare_datasets(
     tokenizer: PreTrainedTokenizerBase,
     cfg,
     default_dataset_prepared_path,
-) -> Tuple[Dataset, Dataset, List[Any]]:
+) -> Tuple[Dataset, Dataset, List[Prompter]]:
     max_packed_sequence_len = (
         cfg.max_packed_sequence_len if cfg.max_packed_sequence_len else cfg.sequence_len
     )
@@ -311,7 +395,7 @@ def load_prepare_datasets(
     )  # make sure we don't accidentally set it larger than sequence_len
 
     tokenizer_name = tokenizer.__class__.__name__
-    prompters = []
+    prompters: List[Prompter] = []
     if cfg.max_packed_sequence_len is not None:
         # see if we can go ahead and load the stacked dataset
         seed = f"@{str(cfg.seed)}" if cfg.seed else ""
@@ -445,14 +529,13 @@ def load_prepare_datasets(
         train_fingerprint = md5(to_hash_train)
         test_fingerprint = md5(to_hash_test)
 
-        with zero_first(is_main_process()):
-            dataset = dataset.train_test_split(
-                test_size=cfg.val_set_size,
-                shuffle=False,
-                seed=cfg.seed or 42,
-                train_new_fingerprint=train_fingerprint,
-                test_new_fingerprint=test_fingerprint,
-            )
+        dataset = dataset.train_test_split(
+            test_size=cfg.val_set_size,
+            shuffle=False,
+            seed=cfg.seed or 42,
+            train_new_fingerprint=train_fingerprint,
+            test_new_fingerprint=test_fingerprint,
+        )
 
         train_dataset = dataset["train"]
         eval_dataset = dataset["test"]

src/axolotl/utils/dataloader.py

@@ -1,342 +0,0 @@
-# pylint: skip-file
-import hashlib
-import itertools
-import logging
-import math
-import time
-from queue import Queue
-from threading import Thread
-from typing import Any, Callable, List, Union
-
-import numba
-import numpy as np
-from torch.utils.data import DistributedSampler, Sampler
-
-LOG = logging.getLogger("axolotl.utils.dataloader")
-
-
-@numba.njit
-def ffd_check(a: np.ndarray, c: int, n: int):
-    # First-fit-decreasing bin packing
-    # Check if a[] could fit in n bins with capacity c
-    # https://en.wikipedia.org/wiki/First-fit-decreasing_bin_packing
-
-    a = np.sort(a)[::-1]
-    bins = np.full((n,), c, dtype=a.dtype)
-    for size in a:
-        not_found = True
-        for idx in range(n):
-            if bins[idx] >= size:
-                bins[idx] -= size
-                not_found = False
-                break
-
-        if not_found:
-            return False
-
-    return True
-
-
-@numba.njit
-def ffd_with_result(a: np.ndarray, c: int, start_index: int):
-    # First-fit-decreasing bin packing (with result return)
-
-    indices = np.argsort(a)[::-1]
-    a = a[indices]
-
-    bins: List[Any] = []
-    bins_result: List[Any] = []
-    for a_id, size in enumerate(a):
-        add_new = True
-        for idx in range(len(bins)):
-            if bins[idx] >= size:
-                bins[idx] -= size
-                bins_result[idx].append(indices[a_id] + start_index)
-                add_new = False
-                break
-
-        if add_new:
-            bins.append(c - size)
-            bins_result.append([indices[a_id] + start_index])
-
-    return bins_result, len(a)
-
-
-@numba.njit
-def allocate(
-    lengths: np.ndarray, lengths_cumsum: np.ndarray, rank: int, c: int, n: int
-):
-    """
-    :param lengths: array of lengths of each sample
-    :param lengths_cumsum: cumulative sum of consecutive lengths
-    :param rank: rank for this process
-    :param c: length of tokens per batch
-    :param n: number of ranks
-    :return:
-    """
-    # Dynamic batch allocator, similar to Multifit
-    # https://en.wikipedia.org/wiki/Multifit_algorithm
-    # ~99.5% efficiency on OpenChat training set (12 * 2048 ctx len)
-
-    s = 0
-    start_index = 0
-    result = []
-    result_totseqs = []
-
-    while True:
-        # binary search [left, right)
-        left = 1
-        right = 1 + np.searchsorted(lengths_cumsum[start_index:], s + c * n, "right")
-
-        while right - left > 1:
-            mid = (left + right) // 2
-            if ffd_check(lengths[start_index : start_index + mid], c, n):
-                left = mid
-            else:
-                right = mid
-
-        # use length left
-        batch, tot_seqs = ffd_with_result(
-            lengths[start_index : start_index + left], c, start_index
-        )
-        if len(batch) < n:
-            break
-
-        start_index += left
-        s = lengths_cumsum[start_index - 1]
-
-        # add local rank
-        result.append(batch[rank])
-        # add total seqs for all ranks
-        result_totseqs.append(tot_seqs)
-        # yield batch[rank], tot_seqs, s, len(result) * c * n
-    return result, result_totseqs, s, len(result) * c * n
-
-
-def chunk(iterable, n):
-    """
-    Chunk data into tuples of length n
-    """
-    # batched('ABCDEFG', 3) --> ABC DEF G
-    if n < 1:
-        raise ValueError("n must be at least one")
-    it = iter(iterable)
-    while batch := tuple(itertools.islice(it, n)):
-        yield batch
-
-
-def hash_indices(lst: List[int]) -> str:
-    # Convert the list of integers to a string representation
-    concatenated = ",".join(map(str, lst))
-
-    # Generate the hash
-    sha256 = hashlib.sha256()
-    sha256.update(concatenated.encode())
-
-    return sha256.hexdigest()
-
-
-class MultipackDistributedDataloader:
-    """Unpadded data loading using Multipack.
-    Adapted from https://github.com/imoneoi/openchat/blob/v3_fix_mle_loss/ochat/training_deepspeed/multipack_dataloader.py
-    Approximate (at most ~1.22x) the optimal solution of the identical-machines scheduling problem, which is NP-hard.
-    """
-
-    def __init__(
-        self,
-        dataset: Any,
-        collate_fn: Callable,
-        seq_max_length: int = 2048,
-        batch_size: int = 1,
-        sampler: Union[Sampler, DistributedSampler] = None,
-        packing_efficiency_estimate: float = 1.0,
-        sample_packing_seq_len_multiplier: int = 1,
-        device_count: int = 1,
-        prefetch_max: int = 1000,
-        num_epochs: int = 1,
-    ):
-        # Dataset
-        self.dataset = dataset
-        self.lengths = (
-            dataset.data.column("position_ids")
-            .to_pandas()
-            .apply(lambda x: x[-1] + 1)
-            .values
-        )
-        assert isinstance(self.lengths, np.ndarray)
-        assert batch_size % sample_packing_seq_len_multiplier == 0
-        assert batch_size >= sample_packing_seq_len_multiplier
-        self.sampler = sampler
-        self.batch_size = batch_size
-        self.sample_packing_seq_len_multiplier = sample_packing_seq_len_multiplier
-        self.seq_max_length = seq_max_length
-        self.batch_max_length = batch_size * seq_max_length
-        self.collate_fn = collate_fn
-        self.num_epochs = num_epochs
-
-        self.num_replicas = 1
-        self.rank = 0
-
-        # statistics
-        self.eff_total_used = 0
-        self.eff_total_slots = 0
-        self.packing_efficiency_estimate = packing_efficiency_estimate or 1.0
-        self.device_count = device_count
-
-        # maxsize is maximum number of samples in queue
-        self.prefetch_max = prefetch_max
-        self.queue: Queue = Queue(maxsize=prefetch_max)
-        self.thread = None
-
-    def _worker(self):
-        LOG.info(
-            f"[WORKER] Epochs: {self.num_epochs}, Samples: {self.len_w_stats()*self.batch_size}"
-        )
-        for epoch in range(self.num_epochs):
-            for sample in self._internal_batch_generator():
-                while True:
-                    if self.queue.full():
-                        time.sleep(1)
-                    else:
-                        break
-                self.queue.put(sample)
-
-            # stop the queue when epoch is done
-            self.queue.put(None)
-
-    def __iter__(self):
-        if hasattr(self.sampler, "set_epoch"):
-            new_epoch = self.sampler.epoch + 1
-            self.sampler.set_epoch(new_epoch)
-            LOG.info(f"calling sampler.set_epoch({new_epoch})")
-
-        if self.thread is None:
-            self.thread = Thread(target=self._worker, daemon=True)
-            self.thread.start()
-
-        while True:
-            item = self.queue.get()
-
-            if item is None:
-                break
-            yield item
-
-    def generate_batches(self, set_stats=False):
-        LOG.info("generating packed batches")
-        if self.sampler:
-            indices = [idx for idx in self.sampler]
-        else:
-            indices = range(0, len(self.dataset))
-
-        LOG.info(hash_indices(indices))
-        lengths = self.lengths[indices]
-        lengths_cumsum = np.cumsum(lengths)
-
-        batches, totseqs, total_used, total_slots = allocate(
-            lengths=lengths,
-            lengths_cumsum=lengths_cumsum,
-            rank=self.rank,
-            # c=self.batch_max_length,
-            c=self.seq_max_length * self.sample_packing_seq_len_multiplier,
-            n=self.num_replicas,
-        )
-
-        batches = [[indices[b_idx] for b_idx in batch] for batch in batches]
-
-        # statistics
-        if set_stats:
-            self.eff_total_used += total_used
-            self.eff_total_slots += total_slots
-
-        return batches, totseqs
-
-    def _internal_batch_generator(self):
-        all_batches, _ = self.generate_batches(set_stats=True)
-        features = self.dataset.features.keys()
-        len_remaining = self._len_est()
-        for batches in chunk(
-            all_batches, self.batch_size // self.sample_packing_seq_len_multiplier
-        ):
-            chunked_data = []
-            attn_mask_cum_idx = 0
-            for batch in batches:
-                concatenated = {}
-                batched_data = [self.dataset[batch_idx] for batch_idx in batch]
-                for feature in features:
-                    if feature == "length":
-                        continue
-                    if feature == "attention_mask":
-                        arrays = [
-                            (attn_mask_cum_idx + idx + 1) * np.array(item[feature])
-                            for idx, item in enumerate(batched_data)
-                            if feature in item
-                        ]
-                        attn_mask_cum_idx += len(batched_data)
-                        concatenated[feature] = np.concatenate(arrays)
-                    else:
-                        arrays = [
-                            np.array(item[feature])
-                            for item in batched_data
-                            if feature in item
-                        ]
-                        concatenated[feature] = np.concatenate(arrays)
-                chunked_data.append(concatenated)
-            yield self.collate_fn(chunked_data)
-            len_remaining -= 1
-            if not len_remaining:
-                return
-        # yield a no-op for cases where we don't have any data left to pack
-        for i in range(0, len_remaining):
-            yield self.collate_fn(
-                [
-                    {
-                        "input_ids": [0],
-                        "labels": [-100],
-                        "attention_mask": [True],
-                        "position_ids": [0],
-                    }
-                ]
-            )
-
-    def _len_est(self):
-        lengths_sum = np.sum(self.lengths)
-        lengths_sum_per_device = lengths_sum // self.device_count
-        LOG.info(
-            f"packing_efficiency_estimate: {self.packing_efficiency_estimate} "
-            f"total_num_tokens per device: {lengths_sum_per_device}"
-        )
-
-        # shave off 1% + 1 for dealing with variance in packing from random sampler to sampler
-        return (
-            math.floor(
-                0.99
-                * lengths_sum_per_device
-                / self.packing_efficiency_estimate
-                // self.seq_max_length
-                // self.batch_size
-            )
-            - 1
-        )
-
-    def __len__(self):
-        # this doesn't return the actual length b/c with distributed samplers, not all dataloaders get
-        # the same share of total tokens
-        # if not self.eff_total_used:
-        #     batches, _ = self.generate_batches(set_stats=True)
-        # LOG.info(
-        #     f"packing_efficiency_estimate: {self.packing_efficiency_estimate} "
-        #     f"actual packing efficiency: {self.efficiency()}"
-        # )
-        return max(1, self._len_est())
-
-    def len_w_stats(self):
-        if not self.eff_total_used:
-            batches, _ = self.generate_batches(set_stats=True)
-        LOG.info(
-            f"packing_efficiency_estimate: {self.packing_efficiency_estimate} "
-            f"actual packing efficiency: {self.efficiency()}"
-        )
-        return max(1, self._len_est())
-
-    def efficiency(self):
-        return self.eff_total_used / self.eff_total_slots

src/axolotl/utils/models.py

@@ -17,7 +17,6 @@ from transformers import (  # noqa: F401
     AutoTokenizer,
     BitsAndBytesConfig,
     GPTQConfig,
-    LlamaConfig,
     PreTrainedModel,
     PreTrainedTokenizerBase,
 )
@@ -32,9 +31,14 @@ LOG = logging.getLogger("axolotl")
 def load_model_config(cfg):
     model_config_name = cfg.base_model_config or cfg.base_model
     trust_remote_code = cfg.trust_remote_code is True
-    return AutoConfig.from_pretrained(
+    model_config = AutoConfig.from_pretrained(
         model_config_name, trust_remote_code=trust_remote_code
     )
+    if cfg.model_config:
+        for key, val in cfg.model_config.items():
+            setattr(model_config, key, val)
+
+    return model_config
 
 
 def load_tokenizer(cfg):
@@ -51,7 +55,7 @@ def load_tokenizer(cfg):
     if cfg.tokenizer_type:
         tokenizer_cls = getattr(transformers, cfg.tokenizer_type)
 
-    tokenizer_config = cfg.tokenizer_config or cfg.base_model_config
+    tokenizer_config = cfg.tokenizer_config or cfg.base_model_config or cfg.base_model
     tokenizer = tokenizer_cls.from_pretrained(
         tokenizer_config,
         trust_remote_code=cfg.trust_remote_code or False,
@@ -110,7 +114,6 @@ def load_model(
     Load a model for a given configuration and tokenizer.
     """
     base_model = cfg.base_model
-    base_model_config = cfg.base_model_config
     model_type = cfg.model_type
     model_config = load_model_config(cfg)
 
@@ -238,16 +241,9 @@ def load_model(
         if cfg.is_llama_derived_model and not cfg.trust_remote_code and not cfg.gptq:
             from transformers import LlamaForCausalLM
 
-            config_kwargs = {}
-            if cfg.rope_scaling:
-                config_kwargs["rope_scaling"] = cfg.rope_scaling
-            config = LlamaConfig.from_pretrained(
-                base_model_config,
-                **config_kwargs,
-            )
             model = LlamaForCausalLM.from_pretrained(
                 base_model,
-                config=config,
+                config=model_config,
                 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,
                 **model_kwargs,
@@ -305,66 +301,55 @@ def load_model(
             if cfg.gptq:
                 model = AutoModelForCausalLM.from_pretrained(
                     base_model,
+                    config=model_config,
                     trust_remote_code=cfg.trust_remote_code or False,
                     **model_kwargs,
                 )
             else:
                 model = getattr(transformers, model_type).from_pretrained(
                     base_model,
+                    config=model_config,
                     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,
                     trust_remote_code=cfg.trust_remote_code or False,
                     **model_kwargs,
                 )
         else:
-            config = AutoConfig.from_pretrained(
-                base_model,
-                trust_remote_code=cfg.trust_remote_code or False,
-            )
             # Shouldn't be a problem most of the time. will obviously error if the model doesn't support this
             # when training starts
             if (
-                hasattr(config, "max_seq_len")
-                and config.max_seq_len
-                and cfg.sequence_len > config.max_seq_len
+                hasattr(model_config, "max_seq_len")
+                and model_config.max_seq_len
+                and cfg.sequence_len > model_config.max_seq_len
             ):
-                config.max_seq_len = cfg.sequence_len
+                model_config.max_seq_len = cfg.sequence_len
                 LOG.warning(f"increasing context length to {cfg.sequence_len}")
             elif (
-                hasattr(config, "max_sequence_length")
-                and config.max_sequence_length
-                and cfg.sequence_len > config.max_sequence_length
+                hasattr(model_config, "max_sequence_length")
+                and model_config.max_sequence_length
+                and cfg.sequence_len > model_config.max_sequence_length
             ):
-                config.max_sequence_length = cfg.sequence_len
+                model_config.max_sequence_length = cfg.sequence_len
                 LOG.warning(f"increasing context length to {cfg.sequence_len}")
             if cfg.gptq:
                 model = AutoModelForCausalLM.from_pretrained(
                     base_model,
-                    config=config,
+                    config=model_config,
                     trust_remote_code=cfg.trust_remote_code or False,
                     **model_kwargs,
                 )
             else:
                 model = AutoModelForCausalLM.from_pretrained(
                     base_model,
-                    config=config,
+                    config=model_config,
                     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,
                     trust_remote_code=cfg.trust_remote_code or False,
                     **model_kwargs,
                 )
     except Exception as err:  # pylint: disable=broad-exception-caught
-        LOG.error(
-            "Exception raised attempting to load model, retrying with AutoModelForCausalLM"
-        )
         LOG.exception(err)
-        model = AutoModelForCausalLM.from_pretrained(
-            base_model,
-            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,
-            trust_remote_code=cfg.trust_remote_code or False,
-            **model_kwargs,
-        )
+        raise err
 
     embeddings_len = (
         math.ceil(len(tokenizer) / 32) * 32

src/axolotl/utils/samplers/multipack.py

@@ -181,13 +181,16 @@ class MultipackBatchSampler(BatchSampler):
         )
 
         # shave off 1% + 1 for dealing with variance in packing from random sampler to sampler
-        return (
-            world_size
-            * math.floor(
-                0.99
-                * lengths_sum_per_device
-                / self.packing_efficiency_estimate
-                // self.batch_max_len
-            )
-            - 1
+        return max(
+            1,
+            (
+                world_size
+                * math.floor(
+                    0.99
+                    * lengths_sum_per_device
+                    / self.packing_efficiency_estimate
+                    // self.batch_max_len
+                )
+                - 1
+            ),
         )

src/axolotl/utils/trainer.py

@@ -142,31 +142,32 @@ def process_datasets_for_packing(cfg, train_dataset, eval_dataset, tokenizer):
 
 
 def calculate_total_num_steps(cfg, train_dataset):
+    if not cfg.total_num_tokens:
+        total_num_tokens = np.sum(
+            train_dataset.data.column("input_ids")
+            .to_pandas()
+            .apply(lambda x: len(x))  # pylint: disable=unnecessary-lambda
+            .values
+        )
+        LOG.debug(f"total_num_tokens: {total_num_tokens}", main_process_only=True)
+        cfg.total_num_tokens = total_num_tokens
+
+    if not cfg.total_supervised_tokens:
+        total_supervised_tokens = (
+            train_dataset.data.column("labels")
+            .to_pandas()
+            .apply(lambda x: np.sum(np.array(x) != -100))
+            .sum()
+        )
+        LOG.debug(
+            f"`total_supervised_tokens: {total_supervised_tokens}`",
+            main_process_only=True,
+        )
+        cfg.total_supervised_tokens = total_supervised_tokens
+
     if cfg.sample_packing:
         # we have to drop anything longer then sequence len otherwise
         # flash attention with position ids fails
-        if not cfg.total_num_tokens:
-            total_num_tokens = np.sum(
-                train_dataset.data.column("input_ids")
-                .to_pandas()
-                .apply(lambda x: len(x))  # pylint: disable=unnecessary-lambda
-                .values
-            )
-            LOG.debug(f"total_num_tokens: {total_num_tokens}", main_process_only=True)
-            cfg.total_num_tokens = total_num_tokens
-
-        if not cfg.total_supervised_tokens:
-            total_supervised_tokens = (
-                train_dataset.data.column("labels")
-                .to_pandas()
-                .apply(lambda x: np.sum(np.array(x) != -100))
-                .sum()
-            )
-            LOG.debug(
-                f"`total_supervised_tokens: {total_supervised_tokens}`",
-                main_process_only=True,
-            )
-            cfg.total_supervised_tokens = total_supervised_tokens
 
         if cfg.sample_packing_eff_est:
             total_num_steps = (

tests/e2e/test_lora_llama.py

@@ -101,6 +101,7 @@ class TestLoraLlama(unittest.TestCase):
                 "learning_rate": 0.00001,
                 "optimizer": "adamw_torch",
                 "lr_scheduler": "cosine",
+                "bf16": True,
             }
         )
         normalize_config(cfg)

tests/e2e/test_resume.py

@@ -0,0 +1,95 @@
+"""
+E2E tests for resuming training
+"""
+
+import logging
+import os
+import re
+import subprocess
+import unittest
+from pathlib import Path
+
+from transformers.utils import is_torch_bf16_gpu_available
+
+from axolotl.cli import load_datasets
+from axolotl.common.cli import TrainerCliArgs
+from axolotl.train import train
+from axolotl.utils.config import normalize_config
+from axolotl.utils.dict import DictDefault
+
+from .utils import most_recent_subdir, with_temp_dir
+
+LOG = logging.getLogger("axolotl.tests.e2e")
+os.environ["WANDB_DISABLED"] = "true"
+
+
+class TestResumeLlama(unittest.TestCase):
+    """
+    Test case for resuming training of llama models
+    """
+
+    @with_temp_dir
+    def test_resume_qlora(self, temp_dir):
+        # pylint: disable=duplicate-code
+        cfg = DictDefault(
+            {
+                "base_model": "JackFram/llama-68m",
+                "tokenizer_type": "LlamaTokenizer",
+                "sequence_len": 1024,
+                "sample_packing": True,
+                "flash_attention": True,
+                "load_in_4bit": True,
+                "adapter": "qlora",
+                "lora_r": 32,
+                "lora_alpha": 64,
+                "lora_dropout": 0.05,
+                "lora_target_linear": True,
+                "val_set_size": 0.1,
+                "special_tokens": {},
+                "datasets": [
+                    {
+                        "path": "vicgalle/alpaca-gpt4",
+                        "type": "alpaca",
+                    },
+                ],
+                "num_epochs": 2,
+                "micro_batch_size": 1,
+                "gradient_accumulation_steps": 1,
+                "output_dir": temp_dir,
+                "learning_rate": 0.00001,
+                "optimizer": "adamw_torch",
+                "lr_scheduler": "cosine",
+                "save_steps": 10,
+                "save_total_limit": 5,
+                "max_steps": 40,
+            }
+        )
+        if is_torch_bf16_gpu_available():
+            cfg.bf16 = True
+        else:
+            cfg.fp16 = True
+        normalize_config(cfg)
+        cli_args = TrainerCliArgs()
+        dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
+
+        train(cfg=cfg, cli_args=cli_args, dataset_meta=dataset_meta)
+
+        resume_cfg = cfg | DictDefault(
+            {
+                "resume_from_checkpoint": f"{temp_dir}/checkpoint-30/",
+            }
+        )
+        normalize_config(resume_cfg)
+        cli_args = TrainerCliArgs()
+
+        train(cfg=resume_cfg, cli_args=cli_args, dataset_meta=dataset_meta)
+        assert (Path(temp_dir) / "adapter_model.bin").exists()
+
+        tb_log_path_1 = most_recent_subdir(temp_dir + "/runs")
+        cmd = f"tensorboard --inspect  --logdir {tb_log_path_1}"
+        res = subprocess.run(
+            cmd, shell=True, text=True, capture_output=True, check=True
+        )
+        pattern = r"first_step\s+(\d+)"
+        first_steps = int(re.findall(pattern, res.stdout)[0])
+        assert first_steps == 31

tests/e2e/utils.py

@@ -1,10 +1,11 @@
 """
 helper utils for tests
 """
-
+import os
 import shutil
 import tempfile
 from functools import wraps
+from pathlib import Path
 
 
 def with_temp_dir(test_func):
@@ -20,3 +21,13 @@ def with_temp_dir(test_func):
             shutil.rmtree(temp_dir)
 
     return wrapper
+
+
+def most_recent_subdir(path):
+    base_path = Path(path)
+    subdirectories = [d for d in base_path.iterdir() if d.is_dir()]
+    if not subdirectories:
+        return None
+    subdir = max(subdirectories, key=os.path.getctime)
+
+    return subdir