support for fp8

* test batch sampler w varying batch lens

* wip

* multipack batchsampler wip

* wip

* fix for prepare data loader to get correct # of steps based on gpues

* lint and clean up

* calculate len estimate

* fix total num steps calc

* add options for dataloader_num_workers and dataloader_pin_memory

* remove gitbook

* support prefetch_factor for dataloader optimization

* fix the kwarg

README.md

@@ -32,7 +32,6 @@ Features:
   - [How to Use Custom Pretokenized Dataset](#how-to-use-your-custom-pretokenized-dataset)
 - [Config](#config)
   - [Train](#train)
-  - [Training w/ Deepspeed](#training-with-deepspeed)
   - [Inference](#inference)
   - [Merge LORA to Base](#merge-lora-to-base)
 - [Common Errors](#common-errors-)
@@ -75,6 +74,7 @@ Features:
 | gpt-j    | ✅         | ✅    | ✅     | ❌             | ❌                 | ❓          | ❓            |
 | XGen     | ✅         | ❓    | ✅     | ❓             | ❓                 | ❓          | ✅            |
 | phi      | ✅         | ✅    | ✅     | ❓             | ❓                 | ❓          | ❓            |
+| RWKV     | ✅         | ❓    | ❓     | ❓             | ❓                 | ❓          | ❓            |
 
 
 ## Quickstart ⚡
@@ -97,6 +97,10 @@ accelerate launch -m axolotl.cli.train examples/openllama-3b/lora.yml
 # inference
 accelerate launch -m axolotl.cli.inference examples/openllama-3b/lora.yml \
     --lora_model_dir="./lora-out"
+
+# gradio
+accelerate launch -m axolotl.cli.inference examples/openllama-3b/lora.yml \
+    --lora_model_dir="./lora-out" --gradio
 ```
 
 ## Installation
@@ -115,6 +119,25 @@ accelerate launch -m axolotl.cli.inference examples/openllama-3b/lora.yml \
   docker compose up -d
   ```
 
+  <details>
+
+  <summary>Docker advanced</summary>
+
+  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
+  ```
+
+  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.
+
+  [More information on nvidia website](https://docs.nvidia.com/deeplearning/frameworks/user-guide/index.html#setincshmem)
+
+  </details>
+
 #### Conda/Pip venv
   1. Install python >=**3.9**
 
@@ -356,6 +379,13 @@ See [examples](examples) for quick start. It is recommended to duplicate and mod
         - typescript
       type: ... # unimplemented custom format
 
+  # fastchat conversation
+  # See 'conversation' options: https://github.com/lm-sys/FastChat/blob/main/fastchat/conversation.py
+  datasets:
+    - path: ...
+      type: sharegpt
+      conversation: chatml
+
   # local
   datasets:
     - path: data.jsonl # or json
@@ -394,7 +424,7 @@ See [examples](examples) for quick start. It is recommended to duplicate and mod
 
 <details>
 
-<summary>All yaml options</summary>
+<summary>All yaml options (click me)</summary>
 
 ```yaml
 # This is the huggingface model that contains *.pt, *.safetensors, or *.bin files
@@ -461,7 +491,9 @@ 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
-    conversation:  # Optional[str] fastchat conversation type, only used with type: sharegpt
+
+    # 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
 
   # Custom user prompt
   - path: repo
@@ -591,14 +623,14 @@ gradient_accumulation_steps: 1
 # The number of samples to include in each batch. This is the number of samples sent to each GPU.
 micro_batch_size: 2
 eval_batch_size:
-num_epochs: 3
+num_epochs: 4
 warmup_steps: 100
 learning_rate: 0.00003
 lr_quadratic_warmup:
 logging_steps:
 save_strategy: # Set to `no` to skip checkpoint saves
 save_steps: # Leave empty to save at each epoch
-eval_steps: # Leave empty to eval at each epoch
+eval_steps: # Leave empty to eval at each epoch, integers for every N steps. decimal for fraction of total steps
 save_total_limit: # Checkpoints saved at a time
 # Maximum number of iterations to train for. It precedes num_epochs which means that
 # if both are set, num_epochs will not be guaranteed.
@@ -815,14 +847,41 @@ Run
 accelerate launch -m axolotl.cli.train your_config.yml
 ```
 
-#### Multi-GPU
+#### Preprocess dataset
+
+You can optionally pre-tokenize dataset with the following before finetuning.
+This is recommended for large datasets.
+
+- Set `push_dataset_to_hub: hf_user/repo` to push it to Huggingface.
+- Use `--debug` to see preprocessed examples.
 
-You can optionally pre-tokenize dataset with the following before finetuning:
 ```bash
-CUDA_VISIBLE_DEVICES=0 accelerate launch -m axolotl.cli.train your_config.yml --prepare_ds_only
+python -m axolotl.cli.preprocess your_config.yml
 ```
 
-##### Config
+#### Multi-GPU
+
+Below are the options available in axolotl for training with multiple GPUs. Note that DeepSpeed
+is the recommended multi-GPU option currently because FSDP may experience
+[loss instability](https://github.com/huggingface/transformers/issues/26498).
+
+##### DeepSpeed
+
+Deepspeed is an optimization suite for multi-gpu systems allowing you to train much larger models than you
+might typically be able to fit into your GPU's VRAM. More information about the various optimization types
+for deepspeed is available at https://huggingface.co/docs/accelerate/main/en/usage_guides/deepspeed#what-is-integrated
+
+We provide several default deepspeed JSON configurations for ZeRO stage 1, 2, and 3.
+
+```yaml
+deepspeed: deepspeed/zero1.json
+```
+
+```shell
+accelerate launch -m axolotl.cli.train examples/llama-2/config.py --deepspeed deepspeed/zero1.json
+```
+
+##### FSDP
 
 - llama FSDP
 ```yaml
@@ -847,24 +906,6 @@ wandb_run_id:
 wandb_log_model:
 ```
 
-### Training with Deepspeed
-
-Deepspeed is an optimization suite for multi-gpu systems allowing you to train much larger models than you
-might typically be able to fit into your GPU's VRAM. More information about the various optimization types
-for deepspeed is available at https://huggingface.co/docs/accelerate/main/en/usage_guides/deepspeed#what-is-integrated
-
-We provide several default deepspeed JSON configurations for ZeRO stage 1, 2, and 3.
-
-```shell
-accelerate launch -m axolotl.cli.train examples/llama-2/config.py --deepspeed deepspeed/zero1.json
-```
-
-or
-
-```yaml
-deepspeed: deepspeed/zero1.json
-```
-
 ### Inference
 
 Pass the appropriate flag to the train command:
@@ -882,6 +923,10 @@ Pass the appropriate flag to the train command:
   cat /tmp/prompt.txt | python -m axolotl.cli.inference examples/your_config.yml \
     --base_model="./completed-model" --prompter=None --load_in_8bit=True
   ```
+-- With gradio hosting
+  ```bash
+  python -m axolotl.cli.inference examples/your_config.yml --gradio
+  ```
 
 Please use `--sample_packing False` if you have it on and receive the error similar to below:
 

deepspeed/zero3.json

@@ -1,14 +1,6 @@
 {
   "zero_optimization": {
     "stage": 3,
-    "offload_optimizer": {
-      "device": "cpu",
-      "pin_memory": true
-    },
-    "offload_param": {
-      "device": "cpu",
-      "pin_memory": true
-    },
     "overlap_comm": true,
     "contiguous_gradients": true,
     "sub_group_size": 0,
@@ -41,12 +33,13 @@
     }
   },
   "scheduler": {
-    "type": "WarmupLR",
+    "type": "WarmupDecayLR",
     "params": {
       "warmup_min_lr": "auto",
       "warmup_max_lr": "auto",
       "warmup_num_steps": "auto",
-      "warmup_type": "linear"
+      "warmup_type": "linear",
+      "total_num_steps": "auto"
     }
   },
   "gradient_accumulation_steps": "auto",

docker/Dockerfile

@@ -21,9 +21,9 @@ WORKDIR /workspace/axolotl
 # If AXOLOTL_EXTRAS is set, append it in brackets
 RUN sed -i "s/torch==.*/torch==$PYTORCH_VERSION/" requirements.txt
 RUN if [ "$AXOLOTL_EXTRAS" != "" ] ; then \
-        pip install -e .[flash-attn,$AXOLOTL_EXTRAS]; \
+        pip install -e .[deepspeed,flash-attn,$AXOLOTL_EXTRAS]; \
     else \
-        pip install -e .[flash-attn]; \
+        pip install -e .[deepspeed,flash-attn]; \
     fi
 
 # fix so that git fetch/pull from remote works

docker/Dockerfile-base

@@ -10,8 +10,10 @@ ENV PATH="/root/miniconda3/bin:${PATH}"
 ARG PYTHON_VERSION="3.9"
 ARG PYTORCH_VERSION="2.0.1"
 ARG CUDA="118"
+ARG TORCH_CUDA_ARCH_LIST="7.0 7.5 8.0 8.6 9.0+PTX"
 
 ENV PYTHON_VERSION=$PYTHON_VERSION
+ENV TORCH_CUDA_ARCH_LIST=$TORCH_CUDA_ARCH_LIST
 
 RUN apt-get update \
     && apt-get install -y wget git build-essential ninja-build git-lfs libaio-dev && rm -rf /var/lib/apt/lists/* \
@@ -27,47 +29,9 @@ ENV PATH="/root/miniconda3/envs/py${PYTHON_VERSION}/bin:${PATH}"
 WORKDIR /workspace
 
 RUN python3 -m pip install --upgrade pip && pip3 install packaging && \
-    python3 -m pip install --no-cache-dir -U torch==${PYTORCH_VERSION}+cu${CUDA} --extra-index-url https://download.pytorch.org/whl/cu$CUDA
+    python3 -m pip install --no-cache-dir -U torch==${PYTORCH_VERSION}+cu${CUDA} deepspeed-kernels --extra-index-url https://download.pytorch.org/whl/cu$CUDA
 
-FROM base-builder AS deepspeed-builder
+RUN git lfs install --skip-repo && \
-
+    pip3 install awscli && \
-ARG TORCH_CUDA_ARCH_LIST="7.0 7.5 8.0 8.6 9.0+PTX"
-
-WORKDIR /workspace
-
-RUN git clone https://github.com/microsoft/DeepSpeed.git && \
-    cd DeepSpeed && \
-    MAX_CONCURRENCY=8 DS_BUILD_SPARSE_ATTN=0 DS_BUILD_OPS=1 DS_BUILD_EVOFORMER_ATTN=0 python3 setup.py bdist_wheel
-
-FROM base-builder AS bnb-builder
-
-WORKDIR /workspace
-ARG CUDA="118"
-ENV CUDA=$CUDA
-ARG MAX_JOBS="-1"
-ENV MAX_JOBS=$MAX_JOBS
-
-RUN git clone https://github.com/TimDettmers/bitsandbytes.git && \
-    cd bitsandbytes && \
-    CUDA_VERSION=$CUDA make cuda11x && \
-    python setup.py bdist_wheel
-
-FROM base-builder
-
-ARG TORCH_CUDA_ARCH_LIST="7.0 7.5 8.0 8.6 9.0+PTX"
-ENV TORCH_CUDA_ARCH_LIST=$TORCH_CUDA_ARCH_LIST
-
-RUN mkdir -p /workspace/builds
-COPY --from=bnb-builder /workspace/bitsandbytes /workspace/builds/bitsandbytes
-
-RUN mkdir -p /workspace/wheels/bitsandbytes
-COPY --from=deepspeed-builder /workspace/DeepSpeed/dist/deepspeed-*.whl wheels
-COPY --from=bnb-builder /workspace/bitsandbytes/dist/bitsandbytes-*.whl wheels
-COPY --from=bnb-builder /workspace/bitsandbytes/bitsandbytes/libbitsandbytes*.so wheels/bitsandbytes
-
-RUN pip3 install wheels/deepspeed-*.whl
-RUN cd /workspace/builds/bitsandbytes && python3 setup.py install
-RUN git lfs install --skip-repo
-RUN pip3 install awscli && \
     # The base image ships with `pydantic==1.8.2` which is not working
     pip3 install -U --no-cache-dir pydantic==1.10.10

docs/faq.md

@@ -12,3 +12,7 @@ This usually happens when you run out of system RAM.
 > Exitcode -7 while using deepspeed
 
 Try upgrading deepspeed w: `pip install -U deepspeed`
+
+> AttributeError: 'DummyOptim' object has no attribute 'step'
+
+You may be using deepspeed with single gpu. Please don't set `deepspeed:` in yaml or cli.

examples/cerebras/btlm-ft.yml

@@ -14,7 +14,7 @@ datasets:
   - path: mhenrichsen/alpaca_2k_test
     type: alpaca
 dataset_prepared_path: last_prepared_run
-val_set_size: 0.01
+val_set_size: 0.05
 
 adapter:
 lora_model_dir:

examples/cerebras/qlora.yml

@@ -7,7 +7,7 @@ datasets:
   - path: teknium/GPT4-LLM-Cleaned
     type: alpaca
 dataset_prepared_path:
-val_set_size: 0.01
+val_set_size: 0.05
 adapter: qlora
 lora_model_dir:
 sequence_len: 2048
@@ -49,7 +49,7 @@ flash_attention:
 gptq_groupsize:
 gptq_model_v1:
 warmup_steps: 10
-eval_steps: 20
+eval_steps: 0.05
 save_steps:
 debug:
 deepspeed:

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

@@ -11,7 +11,7 @@ datasets:
   - path: mhenrichsen/alpaca_2k_test
     type: alpaca
 dataset_prepared_path:
-val_set_size: 0.01
+val_set_size: 0.05
 output_dir: ./lora-out
 
 sequence_len: 4096
@@ -34,7 +34,7 @@ wandb_log_model:
 
 gradient_accumulation_steps: 4
 micro_batch_size: 2
-num_epochs: 3
+num_epochs: 4
 optimizer: adamw_bnb_8bit
 lr_scheduler: cosine
 learning_rate: 0.0002
@@ -54,7 +54,7 @@ xformers_attention:
 flash_attention: true
 
 warmup_steps: 10
-eval_steps: 20
+eval_steps: 0.05
 save_steps:
 debug:
 deepspeed:

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

@@ -11,7 +11,7 @@ datasets:
   - path: mhenrichsen/alpaca_2k_test
     type: alpaca
 dataset_prepared_path:
-val_set_size: 0.01
+val_set_size: 0.05
 output_dir: ./qlora-out
 
 adapter: qlora
@@ -36,7 +36,7 @@ wandb_log_model:
 
 gradient_accumulation_steps: 4
 micro_batch_size: 2
-num_epochs: 3
+num_epochs: 4
 optimizer: paged_adamw_32bit
 lr_scheduler: cosine
 learning_rate: 0.0002
@@ -56,7 +56,7 @@ xformers_attention:
 flash_attention: true
 
 warmup_steps: 10
-eval_steps: 20
+eval_steps: 0.05
 save_steps:
 debug:
 deepspeed:

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

@@ -11,7 +11,7 @@ datasets:
   - path: mhenrichsen/alpaca_2k_test
     type: alpaca
 dataset_prepared_path:
-val_set_size: 0.01
+val_set_size: 0.05
 output_dir: ./lora-out
 
 sequence_len: 4096
@@ -34,7 +34,7 @@ wandb_log_model:
 
 gradient_accumulation_steps: 4
 micro_batch_size: 2
-num_epochs: 3
+num_epochs: 4
 optimizer: adamw_bnb_8bit
 lr_scheduler: cosine
 learning_rate: 0.0002
@@ -54,7 +54,7 @@ xformers_attention:
 flash_attention: true
 
 warmup_steps: 10
-eval_steps: 20
+eval_steps: 0.05
 save_steps:
 debug:
 deepspeed:

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

@@ -11,7 +11,7 @@ datasets:
   - path: mhenrichsen/alpaca_2k_test
     type: alpaca
 dataset_prepared_path:
-val_set_size: 0.01
+val_set_size: 0.05
 output_dir: ./qlora-out
 
 adapter: qlora
@@ -36,7 +36,7 @@ wandb_log_model:
 
 gradient_accumulation_steps: 4
 micro_batch_size: 2
-num_epochs: 3
+num_epochs: 4
 optimizer: paged_adamw_32bit
 lr_scheduler: cosine
 learning_rate: 0.0002
@@ -56,7 +56,7 @@ xformers_attention:
 flash_attention: true
 
 warmup_steps: 10
-eval_steps: 20
+eval_steps: 0.05
 save_steps:
 debug:
 deepspeed:

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

@@ -11,7 +11,7 @@ datasets:
   - path: mhenrichsen/alpaca_2k_test
     type: alpaca
 dataset_prepared_path:
-val_set_size: 0.01
+val_set_size: 0.05
 output_dir: ./lora-out
 
 sequence_len: 4096
@@ -34,7 +34,7 @@ wandb_log_model:
 
 gradient_accumulation_steps: 4
 micro_batch_size: 2
-num_epochs: 3
+num_epochs: 4
 optimizer: adamw_bnb_8bit
 lr_scheduler: cosine
 learning_rate: 0.0002
@@ -54,7 +54,7 @@ xformers_attention:
 flash_attention: true
 
 warmup_steps: 10
-eval_steps: 20
+eval_steps: 0.05
 save_steps:
 debug:
 deepspeed:

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

@@ -11,7 +11,7 @@ datasets:
   - path: mhenrichsen/alpaca_2k_test
     type: alpaca
 dataset_prepared_path:
-val_set_size: 0.01
+val_set_size: 0.05
 output_dir: ./qlora-out
 
 adapter: qlora
@@ -36,7 +36,7 @@ wandb_log_model:
 
 gradient_accumulation_steps: 4
 micro_batch_size: 2
-num_epochs: 3
+num_epochs: 4
 optimizer: paged_adamw_32bit
 lr_scheduler: cosine
 learning_rate: 0.0002
@@ -56,7 +56,7 @@ xformers_attention:
 flash_attention: true
 
 warmup_steps: 10
-eval_steps: 20
+eval_steps: 0.05
 save_steps:
 debug:
 deepspeed:

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

@@ -12,7 +12,7 @@ datasets:
   - path: teknium/GPT4-LLM-Cleaned
     type: alpaca:chat
 dataset_prepared_path:
-val_set_size: 0.01
+val_set_size: 0.05
 adapter: lora
 lora_model_dir:
 sequence_len: 2048

examples/falcon/config-7b-qlora.yml

@@ -18,7 +18,7 @@ datasets:
       - Chain-of-Thought/formatted_cot_data/gsm8k_train.json
     type: "alpaca:chat"
 dataset_prepared_path:
-val_set_size: 0.01
+val_set_size: 0.05
 # enable QLoRA
 adapter: qlora
 lora_model_dir:
@@ -53,7 +53,7 @@ output_dir: ./qlora-out
 # decrease if OOM, increase for max VRAM utilization
 micro_batch_size: 1
 gradient_accumulation_steps: 2
-num_epochs: 3
+num_epochs: 4
 # Optimizer for QLoRA
 optimizer: paged_adamw_32bit
 torchdistx_path:

examples/falcon/config-7b.yml

@@ -12,7 +12,7 @@ datasets:
   - path: teknium/GPT4-LLM-Cleaned
     type: alpaca:chat
 dataset_prepared_path:
-val_set_size: 0.01
+val_set_size: 0.05
 adapter:
 lora_model_dir:
 sequence_len: 2048

examples/gptj/qlora.yml

@@ -7,7 +7,7 @@ datasets:
   - path: teknium/GPT4-LLM-Cleaned
     type: alpaca
 dataset_prepared_path:
-val_set_size: 0.01
+val_set_size: 0.05
 adapter: qlora
 lora_model_dir:
 sequence_len: 2048
@@ -46,7 +46,7 @@ flash_attention:
 gptq_groupsize:
 gptq_model_v1:
 warmup_steps: 10
-eval_steps: 20
+eval_steps: 0.05
 save_steps:
 debug:
 deepspeed:

examples/jeopardy-bot/config.yml

@@ -24,7 +24,7 @@ wandb_log_model:
 output_dir: ./jeopardy-bot-7b
 gradient_accumulation_steps: 1
 micro_batch_size: 1
-num_epochs: 3
+num_epochs: 4
 optimizer: adamw_bnb_8bit
 torchdistx_path:
 lr_scheduler: cosine

examples/llama-2/fft_optimized.yml

@@ -11,7 +11,7 @@ datasets:
   - path: mhenrichsen/alpaca_2k_test
     type: alpaca
 dataset_prepared_path: last_run_prepared
-val_set_size: 0.01
+val_set_size: 0.05
 output_dir: ./out
 
 sequence_len: 4096

examples/llama-2/gptq-lora.yml

@@ -15,7 +15,7 @@ datasets:
   - path: mhenrichsen/alpaca_2k_test
     type: alpaca
 dataset_prepared_path:
-val_set_size: 0.01
+val_set_size: 0.05
 adapter: lora
 lora_model_dir:
 sequence_len: 4096
@@ -37,7 +37,7 @@ wandb_log_model:
 output_dir: ./model-out
 gradient_accumulation_steps: 1
 micro_batch_size: 1
-num_epochs: 3
+num_epochs: 4
 optimizer: adamw_torch
 adam_beta2: 0.95
 adam_eps: 0.00001

examples/llama-2/lora.yml

@@ -11,7 +11,7 @@ datasets:
   - path: mhenrichsen/alpaca_2k_test
     type: alpaca
 dataset_prepared_path:
-val_set_size: 0.01
+val_set_size: 0.05
 output_dir: ./lora-out
 
 sequence_len: 4096
@@ -34,7 +34,7 @@ wandb_log_model:
 
 gradient_accumulation_steps: 4
 micro_batch_size: 2
-num_epochs: 3
+num_epochs: 4
 optimizer: adamw_bnb_8bit
 lr_scheduler: cosine
 learning_rate: 0.0002
@@ -54,7 +54,7 @@ xformers_attention:
 flash_attention: true
 
 warmup_steps: 10
-eval_steps: 20
+eval_steps: 0.05
 eval_table_size:
 eval_table_max_new_tokens: 128
 save_steps:

examples/llama-2/qlora.yml

@@ -11,7 +11,7 @@ datasets:
   - path: mhenrichsen/alpaca_2k_test
     type: alpaca
 dataset_prepared_path:
-val_set_size: 0.01
+val_set_size: 0.05
 output_dir: ./qlora-out
 
 adapter: qlora
@@ -36,7 +36,7 @@ wandb_log_model:
 
 gradient_accumulation_steps: 4
 micro_batch_size: 2
-num_epochs: 3
+num_epochs: 4
 optimizer: paged_adamw_32bit
 lr_scheduler: cosine
 learning_rate: 0.0002
@@ -56,7 +56,7 @@ xformers_attention:
 flash_attention: true
 
 warmup_steps: 10
-eval_steps: 20
+eval_steps: 0.05
 eval_table_size:
 save_steps:
 debug:

examples/llama-2/relora.yml

@@ -11,7 +11,7 @@ datasets:
   - path: teknium/GPT4-LLM-Cleaned
     type: alpaca
 dataset_prepared_path:
-val_set_size: 0.01
+val_set_size: 0.05
 output_dir: ./relora-out
 
 adapter: qlora
@@ -40,7 +40,7 @@ wandb_log_model:
 
 gradient_accumulation_steps: 4
 micro_batch_size: 4
-num_epochs: 3
+num_epochs: 4
 optimizer: adamw_bnb_8bit
 lr_scheduler: cosine
 learning_rate: 0.0002
@@ -60,7 +60,7 @@ xformers_attention:
 flash_attention: true
 
 warmup_steps: 10
-eval_steps: 20
+eval_steps: 0.05
 save_steps: 50
 debug:
 deepspeed:

examples/llama-2/tiny-llama.yml

@@ -12,7 +12,7 @@ datasets:
   - path: mhenrichsen/alpaca_2k_test
     type: alpaca
 dataset_prepared_path:
-val_set_size: 0.01
+val_set_size: 0.05
 output_dir: ./lora-out
 
 sequence_len: 4096
@@ -34,7 +34,7 @@ wandb_log_model:
 
 gradient_accumulation_steps: 4
 micro_batch_size: 2
-num_epochs: 3
+num_epochs: 4
 optimizer: adamw_bnb_8bit
 lr_scheduler: cosine
 learning_rate: 0.0002
@@ -54,7 +54,7 @@ xformers_attention:
 flash_attention: true
 
 warmup_steps: 10
-eval_steps: 20
+eval_steps: 0.05
 eval_table_size:
 save_steps:
 debug:

examples/mistral/config.yml

@@ -11,7 +11,7 @@ datasets:
   - path: mhenrichsen/alpaca_2k_test
     type: alpaca
 dataset_prepared_path:
-val_set_size: 0.01
+val_set_size: 0.05
 output_dir: ./out
 
 sequence_len: 8192
@@ -26,7 +26,7 @@ wandb_log_model:
 
 gradient_accumulation_steps: 4
 micro_batch_size: 2
-num_epochs: 3
+num_epochs: 4
 optimizer: adamw_bnb_8bit
 lr_scheduler: cosine
 learning_rate: 0.000005
@@ -46,7 +46,7 @@ xformers_attention:
 flash_attention: true
 
 warmup_steps: 10
-eval_steps: 20
+eval_steps: 0.05
 eval_table_size:
 eval_table_max_new_tokens: 128
 save_steps:

examples/mistral/qlora.yml

@@ -11,7 +11,7 @@ datasets:
   - path: mhenrichsen/alpaca_2k_test
     type: alpaca
 dataset_prepared_path: last_run_prepared
-val_set_size: 0.01
+val_set_size: 0.05
 output_dir: ./qlora-out
 
 adapter: qlora
@@ -63,7 +63,7 @@ xformers_attention:
 flash_attention: true
 
 warmup_steps: 10
-eval_steps: 20
+eval_steps: 0.05
 eval_table_size:
 eval_table_max_new_tokens: 128
 save_steps:

examples/mpt-7b/config.yml

@@ -26,7 +26,7 @@ wandb_log_model:
 output_dir: ./mpt-alpaca-7b
 gradient_accumulation_steps: 1
 micro_batch_size: 1
-num_epochs: 3
+num_epochs: 4
 optimizer: adamw_bnb_8bit
 torchdistx_path:
 lr_scheduler: cosine

examples/openllama-3b/qlora.yml

@@ -9,7 +9,7 @@ datasets:
   - path: teknium/GPT4-LLM-Cleaned
     type: alpaca
 dataset_prepared_path:
-val_set_size: 0.01
+val_set_size: 0.05
 adapter: qlora
 lora_model_dir:
 sequence_len: 1024

examples/pythia/lora.yml

@@ -23,7 +23,7 @@ wandb_log_model:
 output_dir: ./lora-alpaca-pythia
 gradient_accumulation_steps: 1
 micro_batch_size: 4
-num_epochs: 3
+num_epochs: 4
 learning_rate: 0.00001
 train_on_inputs: false
 group_by_length: false
@@ -33,5 +33,5 @@ early_stopping_patience:
 resume_from_checkpoint:
 local_rank:
 weight_decay: 0.1
-eval_steps: 20
+eval_steps: 0.05
 logging_steps: 1

examples/redpajama/config-3b.yml

@@ -27,7 +27,7 @@ wandb_log_model:
 output_dir: ./redpajama-alpaca-3b
 batch_size: 4
 micro_batch_size: 1
-num_epochs: 3
+num_epochs: 4
 optimizer: adamw_bnb_8bit
 torchdistx_path:
 lr_scheduler: cosine

examples/replit-3b/config-lora.yml

@@ -26,7 +26,7 @@ wandb_log_model:
 output_dir: ./lora-replit
 batch_size: 8
 micro_batch_size: 1
-num_epochs: 3
+num_epochs: 4
 optimizer:
 torchdistx_path:
 lr_scheduler:

examples/xgen-7b/xgen-7b-8k-qlora.yml

@@ -16,7 +16,7 @@ datasets:
       - openassistant_best_replies_train.jsonl
     type: "completion"
 dataset_prepared_path:
-val_set_size: 0.01
+val_set_size: 0.05
 # enable QLoRA
 adapter: qlora
 lora_model_dir:
@@ -51,7 +51,7 @@ output_dir: ./qlora-out
 # decrease if OOM, increase for max VRAM utilization
 micro_batch_size: 1
 gradient_accumulation_steps: 1
-num_epochs: 3
+num_epochs: 4
 # Optimizer for QLoRA
 optimizer: paged_adamw_32bit
 torchdistx_path:

requirements.txt

@@ -1,9 +1,9 @@
 --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
+auto-gptq==0.4.2
 packaging
-peft @ git+https://github.com/huggingface/peft.git
+peft==0.6.0
 transformers @ git+https://github.com/huggingface/transformers.git@acc394c4f5e1283c19783581790b3dc3105a3697
 bitsandbytes>=0.41.1
 accelerate @ git+https://github.com/huggingface/accelerate@80da9cfb09bb3cc9f1b385cb55d6b90d025a5fd9
@@ -17,7 +17,7 @@ sentencepiece
 wandb
 einops
 xformers>=0.0.22
-optimum
+optimum==1.13.2
 hf_transfer
 colorama
 numba
@@ -31,3 +31,4 @@ scikit-learn==1.2.2
 pynvml
 art
 fschat==0.2.29
+gradio

scripts/finetune.py

@@ -45,8 +45,6 @@ def do_cli(config: Path = Path("examples/"), **kwargs):
         shard(cfg=parsed_cfg, cli_args=parsed_cli_args)
     else:
         dataset_meta = load_datasets(cfg=parsed_cfg, cli_args=parsed_cli_args)
-        if parsed_cli_args.prepare_ds_only:
-            return
         train(cfg=parsed_cfg, cli_args=parsed_cli_args, dataset_meta=dataset_meta)
 
 

src/axolotl/cli/__init__.py

@@ -6,8 +6,10 @@ import os
 import random
 import sys
 from pathlib import Path
+from threading import Thread
 from typing import Any, Dict, List, Optional, Union
 
+import gradio as gr
 import torch
 import yaml
 
@@ -16,7 +18,7 @@ from accelerate.commands.config import config_args
 from art import text2art
 from huggingface_hub import HfApi
 from huggingface_hub.utils import LocalTokenNotFoundError
-from transformers import GenerationConfig, TextStreamer
+from transformers import GenerationConfig, TextIteratorStreamer, TextStreamer
 
 from axolotl.common.cli import TrainerCliArgs, load_model_and_tokenizer
 from axolotl.logging_config import configure_logging
@@ -153,6 +155,91 @@ def do_inference(
         print(tokenizer.decode(generated["sequences"].cpu().tolist()[0]))
 
 
+def do_inference_gradio(
+    *,
+    cfg: DictDefault,
+    cli_args: TrainerCliArgs,
+):
+    model, tokenizer = load_model_and_tokenizer(cfg=cfg, cli_args=cli_args)
+    prompter = cli_args.prompter
+    default_tokens = {"unk_token": "<unk>", "bos_token": "<s>", "eos_token": "</s>"}
+
+    for token, symbol in default_tokens.items():
+        # If the token isn't already specified in the config, add it
+        if not (cfg.special_tokens and token in cfg.special_tokens):
+            tokenizer.add_special_tokens({token: symbol})
+
+    prompter_module = None
+    if prompter:
+        prompter_module = getattr(
+            importlib.import_module("axolotl.prompters"), prompter
+        )
+
+    if cfg.landmark_attention:
+        from axolotl.monkeypatch.llama_landmark_attn import set_model_mem_id
+
+        set_model_mem_id(model, tokenizer)
+        model.set_mem_cache_args(
+            max_seq_len=255, mem_freq=50, top_k=5, max_cache_size=None
+        )
+
+    model = model.to(cfg.device)
+
+    def generate(instruction):
+        if not instruction:
+            return
+        if prompter_module:
+            # pylint: disable=stop-iteration-return
+            prompt: str = next(
+                prompter_module().build_prompt(instruction=instruction.strip("\n"))
+            )
+        else:
+            prompt = instruction.strip()
+        batch = tokenizer(prompt, return_tensors="pt", add_special_tokens=True)
+
+        model.eval()
+        with torch.no_grad():
+            generation_config = GenerationConfig(
+                repetition_penalty=1.1,
+                max_new_tokens=1024,
+                temperature=0.9,
+                top_p=0.95,
+                top_k=40,
+                bos_token_id=tokenizer.bos_token_id,
+                eos_token_id=tokenizer.eos_token_id,
+                pad_token_id=tokenizer.pad_token_id,
+                do_sample=True,
+                use_cache=True,
+                return_dict_in_generate=True,
+                output_attentions=False,
+                output_hidden_states=False,
+                output_scores=False,
+            )
+            streamer = TextIteratorStreamer(tokenizer)
+            generation_kwargs = {
+                "inputs": batch["input_ids"].to(cfg.device),
+                "generation_config": generation_config,
+                "streamer": streamer,
+            }
+
+            thread = Thread(target=model.generate, kwargs=generation_kwargs)
+            thread.start()
+
+            all_text = ""
+
+            for new_text in streamer:
+                all_text += new_text
+                yield all_text
+
+    demo = gr.Interface(
+        fn=generate,
+        inputs="textbox",
+        outputs="text",
+        title=cfg.get("gradio_title", "Axolotl Gradio Interface"),
+    )
+    demo.queue().launch(show_api=False, share=True)
+
+
 def choose_config(path: Path):
     yaml_files = list(path.glob("*.yml"))
 
@@ -222,7 +309,9 @@ def load_datasets(
 ) -> TrainDatasetMeta:
     tokenizer = load_tokenizer(cfg)
 
-    train_dataset, eval_dataset, total_num_steps = prepare_dataset(cfg, tokenizer)
+    train_dataset, eval_dataset, total_num_steps, prompters = prepare_dataset(
+        cfg, tokenizer
+    )
 
     if cli_args.debug or cfg.debug:
         LOG.info("check_dataset_labels...")
@@ -238,6 +327,10 @@ def load_datasets(
             text_only=cli_args.debug_text_only,
         )
 
+        LOG.info("printing prompters...")
+        for prompter in prompters:
+            LOG.info(prompter)
+
     return TrainDatasetMeta(
         train_dataset=train_dataset,
         eval_dataset=eval_dataset,

src/axolotl/cli/inference.py

@@ -6,11 +6,16 @@ from pathlib import Path
 import fire
 import transformers
 
-from axolotl.cli import do_inference, load_cfg, print_axolotl_text_art
+from axolotl.cli import (
+    do_inference,
+    do_inference_gradio,
+    load_cfg,
+    print_axolotl_text_art,
+)
 from axolotl.common.cli import TrainerCliArgs
 
 
-def do_cli(config: Path = Path("examples/"), **kwargs):
+def do_cli(config: Path = Path("examples/"), gradio=False, **kwargs):
     # pylint: disable=duplicate-code
     print_axolotl_text_art()
     parsed_cfg = load_cfg(config, **kwargs)
@@ -21,7 +26,10 @@ def do_cli(config: Path = Path("examples/"), **kwargs):
     )
     parsed_cli_args.inference = True
 
-    do_inference(cfg=parsed_cfg, cli_args=parsed_cli_args)
+    if gradio:
+        do_inference_gradio(cfg=parsed_cfg, cli_args=parsed_cli_args)
+    else:
+        do_inference(cfg=parsed_cfg, cli_args=parsed_cli_args)
 
 
 if __name__ == "__main__":

src/axolotl/cli/preprocess.py

@@ -0,0 +1,53 @@
+"""
+CLI to run training on a model
+"""
+import logging
+from pathlib import Path
+
+import fire
+import transformers
+from colorama import Fore
+
+from axolotl.cli import (
+    check_accelerate_default_config,
+    check_user_token,
+    load_cfg,
+    load_datasets,
+    print_axolotl_text_art,
+)
+from axolotl.common.cli import PreprocessCliArgs
+from axolotl.common.const import DEFAULT_DATASET_PREPARED_PATH
+
+LOG = logging.getLogger("axolotl.cli.preprocess")
+
+
+def do_cli(config: Path = Path("examples/"), **kwargs):
+    # pylint: disable=duplicate-code
+    print_axolotl_text_art()
+    parsed_cfg = load_cfg(config, **kwargs)
+    check_accelerate_default_config()
+    check_user_token()
+    parser = transformers.HfArgumentParser((PreprocessCliArgs))
+    parsed_cli_args, _ = parser.parse_args_into_dataclasses(
+        return_remaining_strings=True
+    )
+    if not parsed_cfg.dataset_prepared_path:
+        msg = (
+            Fore.RED
+            + "preprocess CLI called without dataset_prepared_path set, "
+            + f"using default path: {DEFAULT_DATASET_PREPARED_PATH}"
+            + Fore.RESET
+        )
+        LOG.warning(msg)
+        parsed_cfg.dataset_prepared_path = DEFAULT_DATASET_PREPARED_PATH
+
+    _ = load_datasets(cfg=parsed_cfg, cli_args=parsed_cli_args)
+    LOG.info(
+        Fore.GREEN
+        + f"Success! Preprocessed data path: `dataset_prepared_path: {parsed_cfg.dataset_prepared_path}`"
+        + Fore.RESET
+    )
+
+
+if __name__ == "__main__":
+    fire.Fire(do_cli)

src/axolotl/cli/train.py

@@ -6,7 +6,6 @@ from pathlib import Path
 
 import fire
 import transformers
-from colorama import Fore
 
 from axolotl.cli import (
     check_accelerate_default_config,
@@ -16,7 +15,6 @@ from axolotl.cli import (
     print_axolotl_text_art,
 )
 from axolotl.common.cli import TrainerCliArgs
-from axolotl.common.const import DEFAULT_DATASET_PREPARED_PATH
 from axolotl.train import train
 
 LOG = logging.getLogger("axolotl.cli.train")
@@ -32,18 +30,7 @@ def do_cli(config: Path = Path("examples/"), **kwargs):
     parsed_cli_args, _ = parser.parse_args_into_dataclasses(
         return_remaining_strings=True
     )
-    if parsed_cli_args.prepare_ds_only and not parsed_cfg.dataset_prepared_path:
-        msg = (
-            Fore.RED
-            + "--prepare_ds_only called without dataset_prepared_path set."
-            + Fore.RESET
-        )
-        LOG.warning(msg)
-        parsed_cfg.dataset_prepared_path = DEFAULT_DATASET_PREPARED_PATH
-
     dataset_meta = load_datasets(cfg=parsed_cfg, cli_args=parsed_cli_args)
-    if parsed_cli_args.prepare_ds_only:
-        return
     train(cfg=parsed_cfg, cli_args=parsed_cli_args, dataset_meta=dataset_meta)
 
 

src/axolotl/common/cli.py

@@ -25,11 +25,22 @@ class TrainerCliArgs:
     debug_num_examples: int = field(default=5)
     inference: bool = field(default=False)
     merge_lora: bool = field(default=False)
-    prepare_ds_only: bool = field(default=False)
     prompter: Optional[str] = field(default=None)
     shard: bool = field(default=False)
 
 
+@dataclass
+class PreprocessCliArgs:
+    """
+    dataclass representing arguments for preprocessing only
+    """
+
+    debug: bool = field(default=False)
+    debug_text_only: bool = field(default=False)
+    debug_num_examples: int = field(default=1)
+    prompter: Optional[str] = field(default=None)
+
+
 def load_model_and_tokenizer(
     *,
     cfg: DictDefault,

src/axolotl/core/trainer_builder.py

@@ -6,7 +6,6 @@ import abc
 import importlib
 import logging
 import math
-import os
 import sys
 from abc import abstractmethod
 from dataclasses import dataclass, field
@@ -18,9 +17,9 @@ import torch
 import transformers
 from datasets import Dataset
 from torch.optim.lr_scheduler import OneCycleLR
-from torch.utils.data import DataLoader, DistributedSampler, SequentialSampler
+from torch.utils.data import BatchSampler, DataLoader, RandomSampler, SequentialSampler
 from transformers import EarlyStoppingCallback, Trainer, TrainingArguments
-from transformers.trainer_pt_utils import SequentialDistributedSampler
+from transformers.trainer_utils import seed_worker
 
 from axolotl.monkeypatch.relora import ReLoRACallback, ReLoRAScheduler
 from axolotl.utils.callbacks import (
@@ -31,8 +30,9 @@ from axolotl.utils.callbacks import (
     bench_eval_callback_factory,
     log_prediction_callback_factory,
 )
-from axolotl.utils.collators import DataCollatorForSeq2Seq
+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
 
 try:
@@ -102,6 +102,10 @@ class AxolotlTrainingArguments(TrainingArguments):
     bench_source_max_len: int = field(
         default=2048, metadata={"help": "Maximum source sequence length for bench."}
     )
+    dataloader_prefetch_factor: Optional[int] = field(
+        default=None,
+        metadata={"help": "prefetch_factor argument to the dataloader"},
+    )
 
 
 class AxolotlTrainer(Trainer):
@@ -111,7 +115,8 @@ class AxolotlTrainer(Trainer):
 
     args = None  # type: AxolotlTrainingArguments
 
-    def __init__(self, *args, bench_data_collator=None, **kwargs):
+    def __init__(self, *args, num_epochs=1, bench_data_collator=None, **kwargs):
+        self.num_epochs = num_epochs
         self.bench_data_collator = bench_data_collator
         super().__init__(*args, **kwargs)
 
@@ -144,45 +149,69 @@ class AxolotlTrainer(Trainer):
         return self.lr_scheduler
 
     def _get_train_sampler(self) -> Optional[torch.utils.data.Sampler]:
-        if self.args.world_size > 1 and self.args.sample_packing:
+        if self.args.sample_packing:
-            return DistributedSampler(
+            return MultipackBatchSampler(
-                self.train_dataset,
+                RandomSampler(self.train_dataset),
-                num_replicas=self.args.world_size,
+                self.args.train_batch_size,
-                rank=self.args.process_index,
+                drop_last=True,
-                seed=self.args.seed,
+                batch_max_len=self._train_batch_size * self.args.max_seq_length,
+                lengths=(
+                    self.train_dataset.data.column("position_ids")
+                    .to_pandas()
+                    .apply(lambda x: x[-1] + 1)
+                    .values
+                ),
+                packing_efficiency_estimate=self.args.sample_packing_efficiency,
             )
         return super()._get_train_sampler()
 
     def _get_eval_sampler(
         self, eval_dataset: Dataset
     ) -> Optional[torch.utils.data.Sampler]:
-        if (
+        if self.args.sample_packing and self.args.eval_sample_packing is not False:
-            self.args.world_size > 1
+            return MultipackBatchSampler(
-            and self.args.sample_packing
+                SequentialSampler(eval_dataset),
-            and self.args.eval_sample_packing is not False
+                self.args.per_device_eval_batch_size,
-        ):
+                drop_last=True,
-            return SequentialDistributedSampler(
+                batch_max_len=self.args.eval_batch_size * self.args.max_seq_length,
-                eval_dataset,
+                lengths=(
-                num_replicas=self.args.world_size,
+                    eval_dataset.data.column("position_ids")
-                rank=self.args.process_index,
+                    .to_pandas()
-                batch_size=self.args.per_device_eval_batch_size,
+                    .apply(lambda x: x[-1] + 1)
+                    .values
+                ),
+                packing_efficiency_estimate=self.args.sample_packing_efficiency,
             )
         return super()._get_eval_sampler(eval_dataset)
 
-    def get_train_dataloader(self) -> Union[DataLoader, MultipackDistributedDataloader]:
+    def get_train_dataloader(self) -> DataLoader:
         if self.args.sample_packing:
-            train_sampler = self._get_train_sampler()
+            train_dataset = self.train_dataset
-            return self.accelerator.prepare(
+            train_dataset = train_dataset.remove_columns(["length"])
-                MultipackDistributedDataloader(
+            data_collator = self.data_collator
-                    self.train_dataset,
+            dataloader_params = {
-                    batch_size=self._train_batch_size,
+                "batch_size": self._train_batch_size,
-                    seq_max_length=self.args.max_seq_length,
+                "collate_fn": data_collator,
-                    collate_fn=self.data_collator,
+                "num_workers": self.args.dataloader_num_workers,
-                    sampler=train_sampler,
+                "pin_memory": self.args.dataloader_pin_memory,
-                    packing_efficiency_estimate=self.args.sample_packing_efficiency,
+            }
-                    sample_packing_seq_len_multiplier=self.args.sample_packing_seq_len_multiplier,
+            if self.args.dataloader_prefetch_factor:
-                    device_count=int(os.environ.get("WORLD_SIZE", 1)),
+                dataloader_params[
-                )
+                    "prefetch_factor"
+                ] = self.args.dataloader_prefetch_factor
+
+            sampler = self._get_train_sampler()
+            if isinstance(sampler, BatchSampler):
+                dataloader_params["batch_sampler"] = sampler
+                del dataloader_params["batch_size"]
+            else:
+                dataloader_params["sampler"] = sampler
+                dataloader_params["drop_last"] = self.args.dataloader_drop_last
+            dataloader_params["worker_init_fn"] = seed_worker
+
+            self.accelerator.even_batches = False
+            return self.accelerator.prepare_data_loader(
+                DataLoader(train_dataset, **dataloader_params)
             )
         return super().get_train_dataloader()
 
@@ -195,17 +224,29 @@ class AxolotlTrainer(Trainer):
             )
 
             eval_sampler = self._get_eval_sampler(eval_dataset)
-            return self.accelerator.prepare(
+            eval_dataset = eval_dataset.remove_columns(["length"])
-                MultipackDistributedDataloader(
+            data_collator = self.data_collator
-                    eval_dataset,
+            dataloader_params = {
-                    batch_size=self.args.eval_batch_size,
+                "batch_size": self.args.eval_batch_size,
-                    seq_max_length=self.args.max_seq_length,
+                "collate_fn": data_collator,
-                    collate_fn=self.data_collator,
+                "num_workers": self.args.dataloader_num_workers,
-                    sampler=eval_sampler,
+                "pin_memory": self.args.dataloader_pin_memory,
-                    packing_efficiency_estimate=self.args.sample_packing_efficiency,
+            }
-                    sample_packing_seq_len_multiplier=self.args.eval_batch_size,
+            if self.args.dataloader_prefetch_factor:
-                    device_count=int(os.environ.get("WORLD_SIZE", 1)),
+                dataloader_params[
-                )
+                    "prefetch_factor"
+                ] = self.args.dataloader_prefetch_factor
+
+            if isinstance(eval_sampler, BatchSampler):
+                dataloader_params["batch_sampler"] = eval_sampler
+                del dataloader_params["batch_size"]
+            else:
+                dataloader_params["sampler"] = eval_sampler
+                dataloader_params["drop_last"] = self.args.dataloader_drop_last
+
+            self.accelerator.even_batches = False
+            return self.accelerator.prepare_data_loader(
+                DataLoader(eval_dataset, **dataloader_params)
             )
         return super().get_eval_dataloader(eval_dataset)
 
@@ -226,6 +267,8 @@ class AxolotlTrainer(Trainer):
             "num_workers": self.args.dataloader_num_workers,
             "pin_memory": self.args.dataloader_pin_memory,
         }
+        if self.args.dataloader_prefetch_factor:
+            dataloader_params["prefetch_factor"] = self.args.dataloader_prefetch_factor
 
         if not isinstance(bench_dataset, torch.utils.data.IterableDataset):
             dataloader_params["sampler"] = self._get_bench_sampler(bench_dataset)
@@ -440,6 +483,10 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
         training_arguments_kwargs["fp16"] = (
             self.cfg.fp16 and not self.cfg.bf16
         ) or False
+        if self.cfg.fp8:
+            training_arguments_kwargs["fp16"] = False
+            training_arguments_kwargs["bf16"] = False
+
         training_arguments_kwargs["tf32"] = self.cfg.tf32
         training_arguments_kwargs["warmup_steps"] = warmup_steps
         training_arguments_kwargs["logging_steps"] = logging_steps
@@ -490,6 +537,19 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
                 "sample_packing_efficiency"
             ] = self.cfg.sample_packing_eff_est
 
+        if self.cfg.dataloader_pin_memory is not None:
+            training_arguments_kwargs[
+                "dataloader_pin_memory"
+            ] = self.cfg.dataloader_pin_memory
+        if self.cfg.dataloader_num_workers is not None:
+            training_arguments_kwargs[
+                "dataloader_num_workers"
+            ] = self.cfg.dataloader_num_workers
+        if self.cfg.dataloader_prefetch_factor is not None:
+            training_arguments_kwargs[
+                "dataloader_prefetch_factor"
+            ] = self.cfg.dataloader_prefetch_factor
+
         if self.cfg.eval_steps:
             training_arguments_kwargs["evaluation_strategy"] = "steps"
             training_arguments_kwargs["eval_steps"] = self.cfg.eval_steps
@@ -669,7 +729,7 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
             train_dataset=self.train_dataset,
             eval_dataset=self.eval_dataset,
             args=training_args,
-            data_collator=DataCollatorForSeq2Seq(
+            data_collator=BatchSamplerDataCollatorForSeq2Seq(
                 self.tokenizer,
                 return_tensors="pt",
                 **data_collator_kwargs,
@@ -680,10 +740,16 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
                 **data_collator_kwargs,
             ),
             callbacks=self.get_callbacks(),
+            num_epochs=self.cfg.num_epochs,
             **trainer_kwargs,
         )
         trainer = self.hook_post_create_trainer(trainer)
         for callback in self.get_post_trainer_create_callbacks(trainer):
             trainer.add_callback(callback)
 
+        if self.cfg.deepspeed and self.cfg.sample_packing:
+            trainer.accelerator.state.deepspeed_plugin.deepspeed_config[
+                "train_micro_batch_size_per_gpu"
+            ] = self.cfg.micro_batch_size
+
         return trainer

src/axolotl/datasets.py

@@ -2,7 +2,7 @@
 
 import logging
 import os
-from typing import List
+from typing import List, Optional
 
 import torch
 from datasets import Dataset, IterableDataset
@@ -30,14 +30,20 @@ class TokenizedPromptDataset(Dataset):
         self,
         prompt_tokenizer: PromptTokenizingStrategy,
         dataset: IterableDataset,
+        process_count: Optional[int] = None,
         **kwargs,
     ):
         self.prompt_tokenizer = prompt_tokenizer
+        self.process_count = process_count
         super().__init__(self.process(dataset).data, **kwargs)
 
     def process(self, dataset):
         features = dataset.features.keys()
-        num_proc = min(64, os.cpu_count())
+        num_proc = (
+            min(64, self.process_count)
+            if self.process_count
+            else min(64, os.cpu_count())
+        )
         map_kwargs = {}
         if self.prompt_tokenizer.supports_batched:
             map_kwargs["batched"] = True

src/axolotl/monkeypatch/llama_embeddings_hijack.py

@@ -1,40 +0,0 @@
-"""
-patch to add noisy embeddings per https://arxiv.org/abs/2310.05914
-"""
-
-import torch
-import transformers.models.llama.modeling_llama
-from transformers.utils import logging
-
-logger = logging.get_logger(__name__)
-
-
-def replace_llama_embeddings_with_uniform_distribution(noise_alpha=5):
-    # pylint: disable=duplicate-code
-    def noised_embed(orig_embed, noise_alpha, model):
-        def new_func(input_ids):
-            # during training, we add noise to the embedding
-            # during generation, we don't add noise to the embedding
-            if model.training:
-                embed_init = orig_embed(input_ids)
-                dims = torch.tensor(embed_init.size(1) * embed_init.size(2))
-                mag_norm = noise_alpha / torch.sqrt(dims)
-                return embed_init + torch.zeros_like(embed_init).uniform_(
-                    -mag_norm, mag_norm
-                )
-            return orig_embed(input_ids)
-
-        return new_func
-
-    def post_init(orig_post_init):
-        def new_func(self):
-            orig_post_init(self)
-            self.embed_tokens.forward = noised_embed(
-                self.embed_tokens.forward, noise_alpha, self
-            )
-
-        return new_func
-
-    transformers.models.llama.modeling_llama.LlamaModel.post_init = post_init(
-        transformers.models.llama.modeling_llama.LlamaModel.post_init
-    )

src/axolotl/monkeypatch/mistral_embeddings_hijack.py

@@ -1,40 +0,0 @@
-"""
-patch to add noisy embeddings per https://arxiv.org/abs/2310.05914
-"""
-
-import torch
-import transformers.models.mistral.modeling_mistral
-from transformers.utils import logging
-
-logger = logging.get_logger(__name__)
-
-
-def replace_mistral_embeddings_with_uniform_distribution(noise_alpha=5):
-    # pylint: disable=duplicate-code
-    def noised_embed(orig_embed, noise_alpha, model):
-        def new_func(input_ids):
-            # during training, we add noise to the embedding
-            # during generation, we don't add noise to the embedding
-            if model.training:
-                embed_init = orig_embed(input_ids)
-                dims = torch.tensor(embed_init.size(1) * embed_init.size(2))
-                mag_norm = noise_alpha / torch.sqrt(dims)
-                return embed_init + torch.zeros_like(embed_init).uniform_(
-                    -mag_norm, mag_norm
-                )
-            return orig_embed(input_ids)
-
-        return new_func
-
-    def post_init(orig_post_init):
-        def new_func(self):
-            orig_post_init(self)
-            self.embed_tokens.forward = noised_embed(
-                self.embed_tokens.forward, noise_alpha, self
-            )
-
-        return new_func
-
-    transformers.models.mistral.modeling_mistral.MistralModel.post_init = post_init(
-        transformers.models.mistral.modeling_mistral.MistralModel.post_init
-    )

src/axolotl/monkeypatch/neft_embeddings.py

@@ -0,0 +1,65 @@
+"""
+patches implemented through the trainer hooks to enable NEFT/noisy embeddings per https://arxiv.org/abs/2310.05914
+"""
+import torch
+from peft import PeftModel
+from transformers import PreTrainedModel
+
+
+def patch_neft(alpha, model):
+    embeddings = None
+    if isinstance(model, PreTrainedModel):
+        embeddings = model.get_input_embeddings()
+    if isinstance(model, PeftModel):
+        embeddings = model.base_model.get_input_embeddings()
+    if not embeddings:
+        raise ValueError(f"unhandled model class for neft: {model.__class__.__name__}")
+    embeddings.noisy_embedding_alpha = alpha
+    old_forward = embeddings.forward
+
+    # This hack seems to be needed to properly use a custom forward pass
+    # all credits to: https://discuss.pytorch.org/t/how-can-i-replace-the-forward-method-of-a-predefined-torchvision-model-with-my-customized-forward-function/54224/11
+    bound_method = neft_forward.__get__(  # pylint: disable=no-value-for-parameter
+        embeddings, embeddings.__class__
+    )
+    setattr(embeddings, "forward", bound_method)
+
+    embeddings._old_forward = old_forward  # pylint: disable=protected-access
+    return model
+
+
+def unpatch_neft(model):
+    embeddings = None
+    if isinstance(model, PreTrainedModel):
+        embeddings = model.get_input_embeddings()
+    if isinstance(model, PeftModel):
+        embeddings = model.base_model.get_input_embeddings()
+    if not embeddings:
+        raise ValueError(f"unhandled model class for neft: {model.__class__.__name__}")
+    if hasattr(embeddings, "_old_forward"):
+        embeddings.forward = embeddings._old_forward  # pylint: disable=protected-access
+        del embeddings._old_forward  # pylint: disable=protected-access
+        del embeddings.noisy_embedding_alpha
+
+
+def neft_forward(self, inputs: torch.Tensor):
+    embeddings = self._old_forward(inputs)  # pylint: disable=protected-access
+
+    if self.training:
+        dims = torch.tensor(embeddings.size(1) * embeddings.size(2))
+        mag_norm = self.noisy_embedding_alpha / torch.sqrt(dims)
+        embeddings = embeddings + torch.zeros_like(embeddings).uniform_(
+            -mag_norm, mag_norm
+        )
+
+    return embeddings
+
+
+def pretrain_hook(cfg, trainer):
+    if cfg.noisy_embedding_alpha:
+        trainer.model = patch_neft(cfg.noisy_embedding_alpha, trainer.model)
+
+
+def post_train_hook(cfg, trainer):
+    if cfg.noisy_embedding_alpha:
+        unpatch_neft(trainer.model)

src/axolotl/prompt_strategies/sharegpt.py

@@ -24,7 +24,7 @@ def load(tokenizer, cfg, ds_cfg: Optional[Dict[str, Any]] = None):
     )
     field_human = ds_cfg["field_human"] if ds_cfg and "field_human" in ds_cfg else None
     field_model = ds_cfg["field_model"] if ds_cfg and "field_model" in ds_cfg else None
-    return SimpleShareGPTPromptTokenizingStrategy(
+    strategy = SimpleShareGPTPromptTokenizingStrategy(
         ShareGPTPrompterV2(
             conversation=conversation,
             role_key_model=field_model,
@@ -34,6 +34,9 @@ def load(tokenizer, cfg, ds_cfg: Optional[Dict[str, Any]] = None):
         cfg.train_on_inputs,
         cfg.sequence_len,
     )
+    if ds_cfg and "strict" in ds_cfg:
+        strategy.strict = ds_cfg["strict"]
+    return strategy
 
 
 def load_role(tokenizer, cfg):
@@ -59,8 +62,26 @@ class SimpleShareGPTPromptTokenizingStrategy(ShareGPTPromptTokenizingStrategy):
     basic sharegpt strategy to grab conversations from the sample row
     """
 
+    _strict = True
+
+    @property
+    def strict(self):
+        return self._strict
+
+    @strict.setter
+    def strict(self, strict):
+        self._strict = strict
+
     def get_conversation_thread(self, prompt):
-        return prompt["conversations"]
+        conversations = prompt["conversations"]
+        if self.strict:
+            return conversations
+        # remap roles - allow for assistant turn
+        role_map = {"human": "human", "assistant": "gpt", "gpt": "gpt"}
+        turns = [
+            {"from": role_map[t["from"]], "value": t["value"]} for t in conversations
+        ]
+        return turns
 
 
 class SimpleRoleShareGPTPromptTokenizingStrategy(ShareGPTPromptTokenizingStrategy):

src/axolotl/prompt_tokenizers.py

@@ -245,6 +245,7 @@ class ReflectionPromptTokenizingStrategy(PromptTokenizingStrategy):
         raise NotImplementedError
 
     def tokenize_prompt(self, prompt):
+        # pylint: disable=duplicate-code
         (
             instruction,
             input,  # pylint: disable=redefined-builtin

src/axolotl/prompters.py

@@ -4,10 +4,12 @@ import logging
 from enum import Enum
 from typing import Generator, Optional, Union
 
+from colorama import Fore
 from fastchat.conversation import Conversation, get_conv_template
 
 LOG = logging.getLogger("axolotl")
 IGNORE_TOKEN_ID = -100
+REPR_TEMPLATE = "\n<start>\n" + Fore.CYAN + "{full_prompt}" + Fore.RESET + "\n<end>\n"
 
 
 class PromptStyle(Enum):
@@ -55,20 +57,15 @@ class AlpacaPrompter:
             )
             self.system_format = "<|im_start|>system\n{system}<|im_end|>\n"
 
-    def build_prompt(
+    def _build_result(self, instruction, input_text, output):
-        self,
-        instruction: str,
-        input: Union[None, str] = None,  # pylint: disable=redefined-builtin
-        output: Union[None, str] = None,
-    ) -> Generator[str, None, None]:
         # returns the full prompt from instruction and optional input
         # if a label (=response, =output) is provided, it's also appended.
-        if input:
+        if input_text:
             res = (
                 self.system_format.format(system=self.system_prompt)
                 if self.system_prompt
                 else ""
-            ) + self.turn_format.format(instruction=instruction, input=input)
+            ) + self.turn_format.format(instruction=instruction, input=input_text)
         else:
             res = (
                 self.system_format.format(system=self.system_no_input_prompt)
@@ -77,7 +74,21 @@ class AlpacaPrompter:
             ) + self.turn_no_input_format.format(instruction=instruction)
         if output:
             res = f"{res}{output}"
-        yield res
+
+        return res
+
+    def build_prompt(
+        self,
+        instruction: str,
+        input: Union[None, str] = None,  # pylint: disable=redefined-builtin
+        output: Union[None, str] = None,
+    ) -> Generator[str, None, None]:
+        yield self._build_result(instruction, input, output)
+
+    def __repr__(self) -> str:
+        return REPR_TEMPLATE.format(
+            full_prompt=self._build_result("{instruction}", "{input}", "{output}")
+        )
 
 
 class UnpromptedPrompter(AlpacaPrompter):
@@ -191,14 +202,14 @@ class ReflectAlpacaPrompter:
             )
             self.response_split = "ASSISTANT:"
 
-    def build_prompt(
+    def _build_result(
         self,
         instruction: str,
         input: Union[None, str] = None,  # pylint: disable=redefined-builtin
         output: Union[None, str] = None,
         reflection: Union[None, str] = None,
         corrected: Union[None, str] = None,
-    ) -> Generator[str, None, None]:
+    ):
         # returns the full prompt from instruction and optional input
         # if a label (=response, =output) is provided, it's also appended.
         if input:
@@ -212,7 +223,30 @@ class ReflectAlpacaPrompter:
                 corrected=corrected,
             )
             res = f"{res}{label}"
-        yield res
+
+        return res
+
+    def build_prompt(
+        self,
+        instruction: str,
+        input: Union[None, str] = None,  # pylint: disable=redefined-builtin
+        output: Union[None, str] = None,
+        reflection: Union[None, str] = None,
+        corrected: Union[None, str] = None,
+    ) -> Generator[str, None, None]:
+        # pylint: disable=duplicate-code
+        yield self._build_result(
+            instruction,
+            input,
+            output,
+            reflection,
+            corrected,
+        )
+
+    def __repr__(self) -> str:
+        return REPR_TEMPLATE.format(
+            full_prompt=self._build_result("{instruction}", "{input}", "{output}")
+        )
 
 
 SHAREGPT_ASSERTION_FAILED_ROLE = (
@@ -247,7 +281,7 @@ class ShareGPTPrompter:  # pylint: disable=too-few-public-methods
         if role_key_model:
             self.role_key_model = role_key_model
 
-    def build_prompt(self, source) -> Generator[str, None, None]:
+    def _build_result(self, source):
         if len(source) < 2:
             # If there isn't a back and forth conversation, ignore it
             # also happens on the data splitting leaving empty conversations
@@ -282,11 +316,20 @@ class ShareGPTPrompter:  # pylint: disable=too-few-public-methods
                 LOG.warning(f"{SHAREGPT_ASSERTION_FAILED_ROLE}: {sentence}")
             conv.append_message(role, sentence["value"])
 
-        for part in conv.get_turns():
+        return conv.get_turns()
+
+    def build_prompt(self, source) -> Generator[str, None, None]:
+        turns = self._build_result(source)
+
+        for part in turns:
             if part[0] and not part[1]:
                 LOG.warning(f"role with empty message: {part[0]}")
             yield part
 
+    def __repr__(self) -> str:
+        turns = self._build_result([{"from": "{from}", "value": "{value}"}])
+        return "\n".join([REPR_TEMPLATE.format(full_prompt=part) for part in turns])
+
 
 class ShareGPTPrompterV2(ShareGPTPrompter):
     """
@@ -304,3 +347,15 @@ class ShareGPTPrompterV2(ShareGPTPrompter):
             role_key_human=role_key_human,
             role_key_model=role_key_model,
         )
+
+
+class UnsupportedPrompter:
+    """
+    A dummy class for custom prompters
+    """
+
+    def __init__(self) -> None:
+        pass
+
+    def __repr__(self):
+        return "Pre-tokenized or custom dataset types are unsupported for logging"

src/axolotl/train.py

@@ -1,6 +1,5 @@
 """Prepare and train a model on a dataset. Can also infer from a model or merge lora"""
 
-import logging
 import os
 import signal
 import sys
@@ -10,12 +9,14 @@ from typing import Optional
 
 import torch
 import transformers.modelcard
+from accelerate.logging import get_logger
 from datasets import Dataset
 from optimum.bettertransformer import BetterTransformer
 from transformers.deepspeed import is_deepspeed_zero3_enabled
 
 from axolotl.common.cli import TrainerCliArgs
 from axolotl.logging_config import configure_logging
+from axolotl.monkeypatch import neft_embeddings
 from axolotl.utils.dict import DictDefault
 from axolotl.utils.models import load_model, load_tokenizer
 from axolotl.utils.trainer import setup_trainer
@@ -25,7 +26,7 @@ src_dir = os.path.join(project_root, "src")
 sys.path.insert(0, src_dir)
 
 configure_logging()
-LOG = logging.getLogger("axolotl.train")
+LOG = get_logger("axolotl.train")
 
 
 @dataclass
@@ -43,7 +44,10 @@ def train(
     *, cfg: DictDefault, cli_args: TrainerCliArgs, dataset_meta: TrainDatasetMeta
 ):
     # load the tokenizer first
-    LOG.info(f"loading tokenizer... {cfg.tokenizer_config or cfg.base_model_config}")
+    LOG.debug(
+        f"loading tokenizer... {cfg.tokenizer_config or cfg.base_model_config}",
+        main_process_only=True,
+    )
     tokenizer = load_tokenizer(cfg)
 
     train_dataset = dataset_meta.train_dataset
@@ -51,7 +55,10 @@ def train(
     total_num_steps = dataset_meta.total_num_steps
 
     # Load the model and tokenizer
-    LOG.info("loading model and (optionally) peft_config...")
+    msg = "loading model"
+    if cfg.adapter:
+        msg += " and peft_config..."
+    LOG.debug(msg)
     model, peft_config = load_model(cfg, tokenizer, inference=cli_args.inference)
 
     safe_serialization = cfg.save_safetensors is True
@@ -107,6 +114,7 @@ def train(
     if cfg.group_by_length:
         LOG.info("hang tight... sorting dataset for group_by_length")
 
+    pretrain_hooks(cfg, trainer)
     if cfg.flash_optimum:
         with torch.backends.cuda.sdp_kernel(
             enable_flash=True, enable_math=True, enable_mem_efficient=True
@@ -114,6 +122,7 @@ def train(
             trainer.train(resume_from_checkpoint=resume_from_checkpoint)
     else:
         trainer.train(resume_from_checkpoint=resume_from_checkpoint)
+    post_train_hooks(cfg, trainer)
 
     LOG.info(f"Training Completed!!! Saving pre-trained model to {cfg.output_dir}")
 
@@ -163,3 +172,23 @@ def train(
         trainer.create_model_card(model_name=cfg.output_dir.lstrip("./"))
 
     return model, tokenizer
+
+
+def pretrain_hooks(cfg, trainer):
+    """
+    Run hooks right before kicking off the training
+    :param cfg:
+    :param trainer:
+    :return:
+    """
+    neft_embeddings.pretrain_hook(cfg, trainer)
+
+
+def post_train_hooks(cfg, trainer):
+    """
+    Run hooks right after training completes
+    :param cfg:
+    :param trainer:
+    :return:
+    """
+    neft_embeddings.post_train_hook(cfg, trainer)

src/axolotl/utils/collators.py

@@ -119,3 +119,30 @@ class DataCollatorForSeq2Seq:
             features["decoder_input_ids"] = decoder_input_ids
 
         return features
+
+
+@dataclass
+class BatchSamplerDataCollatorForSeq2Seq(DataCollatorForSeq2Seq):
+    """
+    Collator for multipack specific to the using the BatchSampler
+    """
+
+    def __call__(self, features, return_tensors=None):
+        chunked_data = {}
+        for feature in features[0].keys():
+            if feature == "length":
+                continue
+            if feature == "attention_mask":
+                arrays = [
+                    (1) * np.array(item[feature])
+                    for item in features
+                    if feature in item
+                ]
+                chunked_data[feature] = np.concatenate(arrays)
+            else:
+                arrays = [
+                    np.array(item[feature]) for item in features if feature in item
+                ]
+                chunked_data[feature] = np.concatenate(arrays)
+        features = [chunked_data]
+        return super().__call__(features, return_tensors=return_tensors)

src/axolotl/utils/config.py

@@ -70,7 +70,9 @@ def normalize_config(cfg):
     else:
         torch.backends.cuda.matmul.allow_tf32 = cfg.tf32 or False
 
-    if cfg.bf16 or cfg.bfloat16:
+    if cfg.fp8:
+        cfg.torch_dtype = torch.bfloat16
+    elif cfg.bf16 or cfg.bfloat16:
         cfg.torch_dtype = torch.bfloat16
     elif cfg.load_in_8bit or cfg.fp16 or cfg.float16:
         cfg.torch_dtype = torch.float16

src/axolotl/utils/data.py

@@ -3,7 +3,7 @@ import functools
 import hashlib
 import logging
 from pathlib import Path
-from typing import Dict, List, Tuple, Union
+from typing import Any, Dict, List, Tuple, Union
 
 import torch
 from datasets import (
@@ -36,6 +36,7 @@ from axolotl.prompters import (
     MultipleChoiceExplainPrompter,
     ReflectAlpacaPrompter,
     SummarizeTLDRPrompter,
+    UnsupportedPrompter,
 )
 from axolotl.utils.dict import DictDefault
 from axolotl.utils.distributed import is_main_process, zero_first
@@ -55,9 +56,10 @@ def md5(to_hash: str, encoding: str = "utf-8") -> str:
 
 
 def prepare_dataset(cfg, tokenizer):
+    prompters = []
     if not cfg.pretraining_dataset:
         with zero_first(is_main_process()):
-            train_dataset, eval_dataset = load_prepare_datasets(
+            train_dataset, eval_dataset, prompters = load_prepare_datasets(
                 tokenizer, cfg, DEFAULT_DATASET_PREPARED_PATH
             )
     else:
@@ -70,7 +72,7 @@ def prepare_dataset(cfg, tokenizer):
         # https://discuss.huggingface.co/t/how-to-use-huggingface-trainer-streaming-datasets-without-wrapping-it-with-torchdatas-iterablewrapper/25230
         train_dataset = train_dataset.with_format("torch")
         eval_dataset = None
-        return train_dataset, eval_dataset, cfg.max_steps
+        return train_dataset, eval_dataset, cfg.max_steps, prompters
 
     with zero_first(is_main_process()):
         train_dataset, eval_dataset = process_datasets_for_packing(
@@ -78,12 +80,12 @@ def prepare_dataset(cfg, tokenizer):
         )
     if cfg.max_steps:
         total_num_steps = min(
-            calculate_total_num_steps(cfg, train_dataset, tokenizer), cfg.max_steps
+            calculate_total_num_steps(cfg, train_dataset), cfg.max_steps
         )
         LOG.info(f"Maximum number of steps set at {total_num_steps}")
     else:
-        total_num_steps = calculate_total_num_steps(cfg, train_dataset, tokenizer)
+        total_num_steps = calculate_total_num_steps(cfg, train_dataset)
-    return train_dataset, eval_dataset, total_num_steps
+    return train_dataset, eval_dataset, total_num_steps, prompters
 
 
 def load_tokenized_prepared_datasets(
@@ -109,6 +111,7 @@ def load_tokenized_prepared_datasets(
         else Path(default_dataset_prepared_path) / ds_hash
     )
     dataset = None
+    prompters = []
     use_auth_token = cfg.hf_use_auth_token
     try:
         if cfg.push_dataset_to_hub:
@@ -147,13 +150,13 @@ def load_tokenized_prepared_datasets(
                     yield dataset
 
         # pylint: disable=invalid-name
-        for d in for_d_in_datasets(cfg.datasets):
+        for config_dataset in for_d_in_datasets(cfg.datasets):
             ds: Union[Dataset, DatasetDict] = None
             ds_from_hub = False
             try:
                 load_dataset(
-                    d.path,
+                    config_dataset.path,
-                    name=d.name,
+                    name=config_dataset.name,
                     streaming=True,
                     token=use_auth_token,
                 )
@@ -162,33 +165,33 @@ def load_tokenized_prepared_datasets(
                 pass
 
             # prefer local dataset, even if hub exists
-            local_path = Path(d.path)
+            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(
-                        d.path,
+                        config_dataset.path,
-                        name=d.name,
+                        name=config_dataset.name,
-                        data_files=d.data_files,
+                        data_files=config_dataset.data_files,
                         streaming=False,
                         split=None,
                     )
                 elif local_path.is_file():
                     ds_type = "json"
-                    if d.ds_type:
+                    if config_dataset.ds_type:
-                        ds_type = d.ds_type
+                        ds_type = config_dataset.ds_type
-                    elif ".parquet" in d.path:
+                    elif ".parquet" in config_dataset.path:
                         ds_type = "parquet"
-                    elif ".arrow" in d.path:
+                    elif ".arrow" in config_dataset.path:
                         ds_type = "arrow"
-                    elif ".csv" in d.path:
+                    elif ".csv" in config_dataset.path:
                         ds_type = "csv"
-                    elif ".txt" in d.path:
+                    elif ".txt" in config_dataset.path:
                         ds_type = "text"
                     ds = load_dataset(
                         ds_type,
-                        name=d.name,
+                        name=config_dataset.name,
-                        data_files=d.path,
+                        data_files=config_dataset.path,
                         streaming=False,
                         split=None,
                     )
@@ -198,25 +201,25 @@ def load_tokenized_prepared_datasets(
                     )
             elif ds_from_hub:
                 ds = load_dataset(
-                    d.path,
+                    config_dataset.path,
-                    name=d.name,
+                    name=config_dataset.name,
                     streaming=False,
-                    data_files=d.data_files,
+                    data_files=config_dataset.data_files,
                     token=use_auth_token,
                 )
             else:
-                if isinstance(d.data_files, str):
+                if isinstance(config_dataset.data_files, str):
                     fp = hf_hub_download(
-                        repo_id=d.path,
+                        repo_id=config_dataset.path,
                         repo_type="dataset",
-                        filename=d.data_files,
+                        filename=config_dataset.data_files,
                     )
-                elif isinstance(d.data_files, list):
+                elif isinstance(config_dataset.data_files, list):
                     fp = []
-                    for file in d.data_files:
+                    for file in config_dataset.data_files:
                         fp.append(
                             hf_hub_download(
-                                repo_id=d.path,
+                                repo_id=config_dataset.path,
                                 repo_type="dataset",
                                 filename=file,
                             )
@@ -226,21 +229,27 @@ def load_tokenized_prepared_datasets(
                         "data_files must be either a string or list of strings"
                     )
                 ds = load_dataset(
-                    "json", name=d.name, data_files=fp, streaming=False, split=None
+                    "json",
+                    name=config_dataset.name,
+                    data_files=fp,
+                    streaming=False,
+                    split=None,
                 )
             if not ds:
                 raise ValueError("unhandled dataset load")
             # support for using a subset of the data
-            if d.shards:
+            if config_dataset.shards:
                 if "train" in ds:
                     ds = ds.shuffle(seed=seed)["train"].shard(
-                        num_shards=d.shards, index=0
+                        num_shards=config_dataset.shards, index=0
                     )
                 else:
-                    ds = ds.shuffle(seed=seed).shard(num_shards=d.shards, index=0)
+                    ds = ds.shuffle(seed=seed).shard(
+                        num_shards=config_dataset.shards, index=0
+                    )
 
             d_base_type = d_prompt_style = None
-            d_type = d.type
+            d_type = config_dataset.type
             if isinstance(d_type, str):
                 d_type_split = d_type.split(":")
                 d_base_type = d_type_split[0]
@@ -249,108 +258,26 @@ def load_tokenized_prepared_datasets(
                 ds = ds["train"]
             elif (
                 isinstance(ds, DatasetDict)
-                and d.train_on_split
+                and config_dataset.train_on_split
-                and d.train_on_split in ds
+                and config_dataset.train_on_split in ds
             ):
-                ds = ds[d.train_on_split]
+                ds = ds[config_dataset.train_on_split]
             elif isinstance(ds, DatasetDict):
                 raise ValueError(
-                    f"no train split found for dataset {d.path}, you may specify a split with 'train_on_split: `"
+                    f"no train split found for dataset {config_dataset.path}, you may specify a split with 'train_on_split: `"
-                )
-            if (
-                "input_ids" in ds.features
-                and "attention_mask" in ds.features
-                and "labels" in ds.features
-            ):
-                # dataset is already tokenized, just drop it straight in
-                datasets.append(ds)
-            elif isinstance(d.type, DictDefault):
-                ds_strategy = load("user_defined", tokenizer, cfg, d.type.to_dict())
-                ds_wrapper = TokenizedPromptDataset(ds_strategy, ds)
-                datasets.append(ds_wrapper)
-            elif ds_strategy := load(d.type, tokenizer, cfg, d):
-                ds_wrapper = TokenizedPromptDataset(ds_strategy, ds)
-                datasets.append(ds_wrapper)
-            elif d_base_type == "alpaca":
-                ds_strategy = AlpacaPromptTokenizingStrategy(
-                    AlpacaPrompter(d_prompt_style),
-                    tokenizer,
-                    cfg.train_on_inputs,
-                    cfg.sequence_len,
-                )
-                ds_wrapper = TokenizedPromptDataset(ds_strategy, ds)
-                datasets.append(ds_wrapper)
-            elif d_base_type == "explainchoice":
-                ds_strategy = AlpacaMultipleChoicePromptTokenizingStrategy(
-                    MultipleChoiceExplainPrompter(d_prompt_style),
-                    tokenizer,
-                    cfg.train_on_inputs,
-                    cfg.sequence_len,
-                )
-                ds_wrapper = TokenizedPromptDataset(ds_strategy, ds)
-                datasets.append(ds_wrapper)
-            elif d_base_type == "concisechoice":
-                ds_strategy = AlpacaMultipleChoicePromptTokenizingStrategy(
-                    MultipleChoiceConcisePrompter(d_prompt_style),
-                    tokenizer,
-                    cfg.train_on_inputs,
-                    cfg.sequence_len,
-                )
-                ds_wrapper = TokenizedPromptDataset(ds_strategy, ds)
-                datasets.append(ds_wrapper)
-            elif d_base_type == "summarizetldr":
-                ds_strategy = SummarizeTLDRPromptTokenizingStrategy(
-                    SummarizeTLDRPrompter(d_prompt_style),
-                    tokenizer,
-                    cfg.train_on_inputs,
-                    cfg.sequence_len,
-                )
-                ds_wrapper = TokenizedPromptDataset(ds_strategy, ds)
-                datasets.append(ds_wrapper)
-            elif d_base_type == "jeopardy":
-                ds_strategy = JeopardyPromptTokenizingStrategy(
-                    JeopardyPrompter(d_prompt_style),
-                    tokenizer,
-                    cfg.train_on_inputs,
-                    cfg.sequence_len,
-                )
-                ds_wrapper = TokenizedPromptDataset(ds_strategy, ds)
-                datasets.append(ds_wrapper)
-            elif d_base_type == "oasst":
-                ds_strategy = OpenAssistantPromptTokenizingStrategy(
-                    AlpacaPrompter(d_prompt_style),
-                    tokenizer,
-                    cfg.train_on_inputs,
-                    cfg.sequence_len,
-                )
-                ds_wrapper = TokenizedPromptDataset(ds_strategy, ds)
-                datasets.append(ds_wrapper)
-            elif d_base_type == "gpteacher":
-                ds_strategy = GPTeacherPromptTokenizingStrategy(
-                    GPTeacherPrompter(d_prompt_style),
-                    tokenizer,
-                    cfg.train_on_inputs,
-                    cfg.sequence_len,
-                )
-                ds_wrapper = TokenizedPromptDataset(ds_strategy, ds)
-                datasets.append(ds_wrapper)
-            elif d_base_type == "reflection":
-                ds_strategy = AlpacaReflectionPTStrategy(
-                    ReflectAlpacaPrompter(d_prompt_style),
-                    tokenizer,
-                    cfg.train_on_inputs,
-                    cfg.sequence_len,
-                )
-                ds_wrapper = TokenizedPromptDataset(ds_strategy, ds)
-                datasets.append(ds_wrapper)
-            else:
-                suffix = ""
-                if ":load_" in d.type:
-                    suffix = f" Did you mean {d.type.replace(':load_', '.load_')}?"
-                LOG.error(f"unhandled prompt tokenization strategy: {d.type}. {suffix}")
-                raise ValueError(
-                    f"unhandled prompt tokenization strategy: {d.type} {suffix}"
                 )
+
+            dataset_wrapper, dataset_prompter = get_dataset_wrapper(
+                config_dataset=config_dataset,
+                dataset=ds,
+                tokenizer=tokenizer,
+                cfg=cfg,
+                d_base_type=d_base_type,
+                d_prompt_style=d_prompt_style,
+            )
+            datasets.append(dataset_wrapper)
+            prompters.append(dataset_prompter)
+
         LOG.info("merging datasets")
         dataset = concatenate_datasets(datasets)
 
@@ -368,14 +295,14 @@ def load_tokenized_prepared_datasets(
                     f"{cfg.push_dataset_to_hub}/{ds_hash}", private=True
                 )
 
-    return dataset
+    return dataset, prompters
 
 
 def load_prepare_datasets(
     tokenizer: PreTrainedTokenizerBase,
     cfg,
     default_dataset_prepared_path,
-) -> Tuple[Dataset, Dataset]:
+) -> Tuple[Dataset, Dataset, List[Any]]:
     max_packed_sequence_len = (
         cfg.max_packed_sequence_len if cfg.max_packed_sequence_len else cfg.sequence_len
     )
@@ -384,6 +311,7 @@ def load_prepare_datasets(
     )  # make sure we don't accidentally set it larger than sequence_len
 
     tokenizer_name = tokenizer.__class__.__name__
+    prompters = []
     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 ""
@@ -439,7 +367,7 @@ def load_prepare_datasets(
                     f"{cfg.push_dataset_to_hub}/{ds_hash}", private=True
                 )
         else:
-            dataset = load_tokenized_prepared_datasets(
+            dataset, prompters = load_tokenized_prepared_datasets(
                 tokenizer, cfg, default_dataset_prepared_path
             )
 
@@ -481,7 +409,7 @@ def load_prepare_datasets(
                         private=True,
                     )
     else:
-        dataset = load_tokenized_prepared_datasets(
+        dataset, prompters = load_tokenized_prepared_datasets(
             tokenizer, cfg, default_dataset_prepared_path
         )
 
@@ -532,7 +460,144 @@ def load_prepare_datasets(
         train_dataset = dataset
         eval_dataset = None
 
-    return train_dataset, eval_dataset
+    return train_dataset, eval_dataset, prompters
+
+
+def get_dataset_wrapper(
+    config_dataset, dataset, tokenizer, cfg, d_base_type, d_prompt_style
+):
+    dataset_wrapper = None
+    dataset_prompter = None
+
+    if (
+        "input_ids" in dataset.features
+        and "attention_mask" in dataset.features
+        and "labels" in dataset.features
+    ):
+        # dataset is already tokenized, just drop it straight in
+        dataset_prompter = UnsupportedPrompter()
+        dataset_wrapper = dataset
+    elif isinstance(config_dataset.type, DictDefault):
+        ds_strategy = load(
+            "user_defined", tokenizer, cfg, config_dataset.type.to_dict()
+        )
+        dataset_prompter = UnsupportedPrompter()
+        dataset_wrapper = TokenizedPromptDataset(
+            ds_strategy, dataset, process_count=cfg.dataset_processes
+        )
+    elif ds_strategy := load(config_dataset.type, tokenizer, cfg, config_dataset):
+        dataset_prompter = UnsupportedPrompter()
+        dataset_wrapper = TokenizedPromptDataset(
+            ds_strategy, dataset, process_count=cfg.dataset_processes
+        )
+    elif d_base_type == "alpaca":
+        dataset_prompter = AlpacaPrompter(d_prompt_style)
+        ds_strategy = AlpacaPromptTokenizingStrategy(
+            dataset_prompter,
+            tokenizer,
+            cfg.train_on_inputs,
+            cfg.sequence_len,
+        )
+        ds_wrapper = TokenizedPromptDataset(
+            ds_strategy, dataset, process_count=cfg.dataset_processes
+        )
+        dataset_wrapper = ds_wrapper
+    elif d_base_type == "explainchoice":
+        dataset_prompter = MultipleChoiceExplainPrompter(d_prompt_style)
+        ds_strategy = AlpacaMultipleChoicePromptTokenizingStrategy(
+            dataset_prompter,
+            tokenizer,
+            cfg.train_on_inputs,
+            cfg.sequence_len,
+        )
+        ds_wrapper = TokenizedPromptDataset(
+            ds_strategy, dataset, process_count=cfg.dataset_processes
+        )
+        dataset_wrapper = ds_wrapper
+    elif d_base_type == "concisechoice":
+        dataset_prompter = MultipleChoiceConcisePrompter(d_prompt_style)
+        ds_strategy = AlpacaMultipleChoicePromptTokenizingStrategy(
+            dataset_prompter,
+            tokenizer,
+            cfg.train_on_inputs,
+            cfg.sequence_len,
+        )
+        ds_wrapper = TokenizedPromptDataset(
+            ds_strategy, dataset, process_count=cfg.dataset_processes
+        )
+        dataset_wrapper = ds_wrapper
+    elif d_base_type == "summarizetldr":
+        dataset_prompter = SummarizeTLDRPrompter(d_prompt_style)
+        ds_strategy = SummarizeTLDRPromptTokenizingStrategy(
+            dataset_prompter,
+            tokenizer,
+            cfg.train_on_inputs,
+            cfg.sequence_len,
+        )
+        ds_wrapper = TokenizedPromptDataset(
+            ds_strategy, dataset, process_count=cfg.dataset_processes
+        )
+        dataset_wrapper = ds_wrapper
+    elif d_base_type == "jeopardy":
+        dataset_prompter = JeopardyPrompter(d_prompt_style)
+        ds_strategy = JeopardyPromptTokenizingStrategy(
+            dataset_prompter,
+            tokenizer,
+            cfg.train_on_inputs,
+            cfg.sequence_len,
+        )
+        ds_wrapper = TokenizedPromptDataset(
+            ds_strategy, dataset, process_count=cfg.dataset_processes
+        )
+        dataset_wrapper = ds_wrapper
+    elif d_base_type == "oasst":
+        dataset_prompter = AlpacaPrompter(d_prompt_style)
+        ds_strategy = OpenAssistantPromptTokenizingStrategy(
+            dataset_prompter,
+            tokenizer,
+            cfg.train_on_inputs,
+            cfg.sequence_len,
+        )
+        ds_wrapper = TokenizedPromptDataset(
+            ds_strategy, dataset, process_count=cfg.dataset_processes
+        )
+        dataset_wrapper = ds_wrapper
+    elif d_base_type == "gpteacher":
+        dataset_prompter = GPTeacherPrompter(d_prompt_style)
+        ds_strategy = GPTeacherPromptTokenizingStrategy(
+            dataset_prompter,
+            tokenizer,
+            cfg.train_on_inputs,
+            cfg.sequence_len,
+        )
+        ds_wrapper = TokenizedPromptDataset(
+            ds_strategy, dataset, process_count=cfg.dataset_processes
+        )
+        dataset_wrapper = ds_wrapper
+    elif d_base_type == "reflection":
+        dataset_prompter = ReflectAlpacaPrompter(d_prompt_style)
+        ds_strategy = AlpacaReflectionPTStrategy(
+            dataset_prompter,
+            tokenizer,
+            cfg.train_on_inputs,
+            cfg.sequence_len,
+        )
+        ds_wrapper = TokenizedPromptDataset(
+            ds_strategy, dataset, process_count=cfg.dataset_processes
+        )
+        dataset_wrapper = ds_wrapper
+    else:
+        suffix = ""
+        if ":load_" in config_dataset.type:
+            suffix = f" Did you mean {config_dataset.type.replace(':load_', '.load_')}?"
+        LOG.error(
+            f"unhandled prompt tokenization strategy: {config_dataset.type}. {suffix}"
+        )
+        raise ValueError(
+            f"unhandled prompt tokenization strategy: {config_dataset.type} {suffix}"
+        )
+
+    return dataset_wrapper, dataset_prompter
 
 
 def encode_pretraining(

src/axolotl/utils/dataloader.py

@@ -3,6 +3,9 @@ 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
@@ -149,6 +152,8 @@ class MultipackDistributedDataloader:
         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
@@ -167,6 +172,7 @@ class MultipackDistributedDataloader:
         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
@@ -177,6 +183,44 @@ class MultipackDistributedDataloader:
         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:
@@ -206,11 +250,7 @@ class MultipackDistributedDataloader:
 
         return batches, totseqs
 
-    def __iter__(self):
+    def _internal_batch_generator(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})")
         all_batches, _ = self.generate_batches(set_stats=True)
         features = self.dataset.features.keys()
         len_remaining = self._len_est()

src/axolotl/utils/distributed.py

@@ -50,6 +50,17 @@ def get_world_size():
     return int(os.getenv("WORLD_SIZE", "1"))
 
 
+@contextmanager
+def zero_only():
+    """
+    Context manager that only runs the enclosed block on the main rank.
+    """
+    if is_main_process():
+        yield
+    else:
+        yield None
+
+
 @contextmanager
 def zero_first(is_main):
     """

src/axolotl/utils/models.py

@@ -31,7 +31,7 @@ LOG = logging.getLogger("axolotl")
 
 def load_model_config(cfg):
     model_config_name = cfg.base_model_config or cfg.base_model
-    trust_remote_code: bool = False or cfg.trust_remote_code
+    trust_remote_code = cfg.trust_remote_code is True
     return AutoConfig.from_pretrained(
         model_config_name, trust_remote_code=trust_remote_code
     )
@@ -72,11 +72,6 @@ def load_tokenizer(cfg):
         # set a pad_token, but use eos_token so we don't add a new token
         tokenizer.pad_token = LLAMA_DEFAULT_EOS_TOKEN
 
-    LOG.debug(f"EOS: {tokenizer.eos_token_id} / {tokenizer.eos_token}")
-    LOG.debug(f"BOS: {tokenizer.bos_token_id} / {tokenizer.bos_token}")
-    LOG.debug(f"PAD: {tokenizer.pad_token_id} / {tokenizer.pad_token}")
-    LOG.debug(f"UNK: {tokenizer.unk_token_id} / {tokenizer.unk_token}")
-
     if tokenizer.__class__.__name__ == "GPTNeoXTokenizerFast":
         tokenizer.add_special_tokens({"pad_token": "[PAD]"})
         os.environ["TOKENIZERS_PARALLELISM"] = "false"
@@ -98,6 +93,11 @@ def load_tokenizer(cfg):
             ]
         )
 
+    LOG.debug(f"EOS: {tokenizer.eos_token_id} / {tokenizer.eos_token}")
+    LOG.debug(f"BOS: {tokenizer.bos_token_id} / {tokenizer.bos_token}")
+    LOG.debug(f"PAD: {tokenizer.pad_token_id} / {tokenizer.pad_token}")
+    LOG.debug(f"UNK: {tokenizer.unk_token_id} / {tokenizer.unk_token}")
+
     return tokenizer
 
 
@@ -180,26 +180,6 @@ def load_model(
         LOG.info("patching with flash attention")
         replace_mistral_attn_with_flash_attn(packed=cfg.sample_packing)
 
-    if cfg.is_llama_derived_model and cfg.noisy_embedding_alpha:
-        from axolotl.monkeypatch.llama_embeddings_hijack import (
-            replace_llama_embeddings_with_uniform_distribution,
-        )
-
-        LOG.info("patching with noisy embeddings")
-        replace_llama_embeddings_with_uniform_distribution(
-            noise_alpha=cfg.noisy_embedding_alpha
-        )
-
-    if cfg.is_mistral_derived_model and cfg.noisy_embedding_alpha:
-        from axolotl.monkeypatch.mistral_embeddings_hijack import (
-            replace_mistral_embeddings_with_uniform_distribution,
-        )
-
-        LOG.info("patching with noisy embeddings")
-        replace_mistral_embeddings_with_uniform_distribution(
-            noise_alpha=cfg.noisy_embedding_alpha
-        )
-
     if cfg.is_llama_derived_model and cfg.xpos_rope:
         from axolotl.monkeypatch.xpos_rope_llama_monkey_patch import (
             replace_llama_rope_with_xpos_rope,
@@ -406,6 +386,20 @@ def load_model(
         )
         model.config.max_position_embeddings = cfg.sequence_len
 
+    if (
+        hasattr(model.config, "bos_token_id")
+        and model.config.bos_token_id
+        and model.config.bos_token_id != tokenizer.bos_token_id
+    ):
+        model.config.bos_token_id = tokenizer.bos_token_id
+
+    if (
+        hasattr(model.config, "eos_token_id")
+        and model.config.eos_token_id
+        and model.config.eos_token_id != tokenizer.eos_token_id
+    ):
+        model.config.eos_token_id = tokenizer.eos_token_id
+
     if model.device.type == "cuda":
         log_gpu_memory_usage(LOG, "after model load", model.device)
 
@@ -448,14 +442,7 @@ def load_model(
     if cfg.ddp and not load_in_8bit:
         model.to(f"cuda:{cfg.local_rank}")
 
-    if (
+    if torch.cuda.device_count() > 1 and int(os.getenv("WORLD_SIZE", "1")) == 1:
-        torch.cuda.device_count() > 1
-        and int(os.getenv("WORLD_SIZE", "1")) > 1
-        and (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
-        # https://github.com/johnsmith0031/alpaca_lora_4bit/blob/08b3fca4a4a9e0d3945be1bab4529f100a428636/finetune.py#L130-L133
         setattr(model, "is_parallelizable", True)
         setattr(model, "model_parallel", True)
 

src/axolotl/utils/samplers/__init__.py

@@ -0,0 +1,4 @@
+"""
+axolotl samplers module
+"""
+from .multipack import MultipackBatchSampler  # noqa: F401

src/axolotl/utils/samplers/multipack.py

@@ -0,0 +1,193 @@
+# pylint: skip-file
+"""
+Multipack Batch Sampler
+"""
+import logging
+import math
+import os
+from typing import Any, Iterable, List, Union
+
+import numba
+import numpy as np
+from torch.utils.data import BatchSampler, Sampler
+
+LOG = logging.getLogger("axolotl.utils.samplers.multipack")
+
+
+@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
+
+
+@numba.njit
+def allocate(
+    lengths: np.ndarray, lengths_cumsum: np.ndarray, rank: int, c: int, n: int
+):
+    # 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 = []
+
+    while True:
+        # binary search [l, r)
+        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 l
+        batch = ffd_with_result(
+            lengths[start_index : start_index + left], c, start_index
+        )
+        assert len(batch) <= n
+        if len(batch) < n:
+            break
+
+        start_index += left
+        s = lengths_cumsum[start_index - 1]
+
+        # add local rank
+        result.append(batch[rank])
+
+    return result, s, len(result) * c * n
+
+
+class MultipackBatchSampler(BatchSampler):
+    """
+    Batch Sampler class for multipack
+    """
+
+    def __init__(
+        self,
+        sampler: Union[Sampler[int], Iterable[int]],
+        batch_size: int,
+        drop_last: bool,
+        batch_max_len: int,
+        lengths: np.ndarray,
+        packing_efficiency_estimate: float = 1.0,
+    ):
+        super().__init__(sampler, batch_size, drop_last)
+        self.batch_size = None
+        self.batch_max_len = batch_max_len
+        self.lengths: np.ndarray = lengths
+        self.packing_efficiency_estimate = packing_efficiency_estimate or 1.0
+
+        assert isinstance(self.lengths, np.ndarray)
+
+        self.epoch = 0
+
+        # statistics
+        self.eff_total_used = 0
+        self.eff_total_slots = 0
+
+    def set_epoch(self, epoch: int):
+        self.epoch = epoch
+
+    def generate_batches(self, set_stats=False):
+        indices = [idx for idx in self.sampler]
+
+        lengths = self.lengths[indices]
+        lengths_cumsum = np.cumsum(lengths)
+
+        batches, total_used, total_slots = allocate(
+            lengths=lengths,
+            lengths_cumsum=lengths_cumsum,
+            rank=0,
+            c=self.batch_max_len,
+            n=1,
+        )
+
+        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
+
+    def __iter__(self):
+        batches = self.generate_batches(set_stats=True)
+        return iter(batches)
+
+    def num_batches(self):
+        batches = self.generate_batches(set_stats=True)
+        return len(batches)
+
+    def efficiency(self):
+        return self.eff_total_used / self.eff_total_slots
+
+    def __len__(self):
+        self.num_batches()
+        return self._len_est()
+
+    def _len_est(self):
+        world_size = int(os.getenv("WORLD_SIZE", "1"))
+        lengths_sum = np.sum(self.lengths)
+        lengths_sum_per_device = lengths_sum // world_size
+        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 (
+            world_size
+            * math.floor(
+                0.99
+                * lengths_sum_per_device
+                / self.packing_efficiency_estimate
+                // self.batch_max_len
+            )
+            - 1
+        )

src/axolotl/utils/trainer.py

@@ -1,5 +1,4 @@
 """Module containing the Trainer class and related functions"""
-import logging
 import math
 import os
 from contextlib import contextmanager
@@ -9,21 +8,15 @@ from typing import List
 import numpy as np
 import torch
 import torch.cuda
-import torch.distributed as dist
+from accelerate.logging import get_logger
 from datasets import set_caching_enabled
-from torch.utils.data import DistributedSampler, RandomSampler
+from torch.utils.data import DataLoader, RandomSampler
 
 from axolotl.core.trainer_builder import HFCausalTrainerBuilder
-from axolotl.utils.collators import DataCollatorForSeq2Seq
+from axolotl.utils.distributed import is_main_process, reduce_and_broadcast, zero_first
-from axolotl.utils.dataloader import MultipackDistributedDataloader
+from axolotl.utils.samplers import MultipackBatchSampler
-from axolotl.utils.distributed import (
-    is_distributed,
-    is_main_process,
-    reduce_and_broadcast,
-    zero_first,
-)
 
-LOG = logging.getLogger("axolotl")
+LOG = get_logger("axolotl")
 
 
 @torch.jit.script
@@ -148,19 +141,18 @@ def process_datasets_for_packing(cfg, train_dataset, eval_dataset, tokenizer):
     return train_dataset, eval_dataset
 
 
-def calculate_total_num_steps(cfg, train_dataset, tokenizer):
+def calculate_total_num_steps(cfg, train_dataset):
     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:
-            LOG.info("calculating 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.info(f"total_num_tokens: {total_num_tokens}")
+            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:
@@ -170,7 +162,10 @@ def calculate_total_num_steps(cfg, train_dataset, tokenizer):
                 .apply(lambda x: np.sum(np.array(x) != -100))
                 .sum()
             )
-            LOG.info(f"`total_supervised_tokens: {total_supervised_tokens}`")
+            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:
@@ -189,40 +184,41 @@ def calculate_total_num_steps(cfg, train_dataset, tokenizer):
                 )
                 * cfg.num_epochs
             )
-            LOG.info(
+            LOG.debug(
-                f"total_num_tokens: {cfg.total_num_tokens}, total_num_steps: {total_num_steps}"
+                f"total_num_tokens: {cfg.total_num_tokens}, total_num_steps: {total_num_steps}",
+                main_process_only=True,
             )
         else:
-            if cfg.world_size > 1 and is_distributed():
+            sampler = MultipackBatchSampler(
-                sampler = DistributedSampler(
+                sampler=RandomSampler(train_dataset),
-                    train_dataset,
-                    num_replicas=cfg.world_size,
-                    rank=dist.get_rank(),
-                    seed=cfg.seed or 42,
-                )
-            else:
-                sampler = RandomSampler(train_dataset)
-
-            data_loader = MultipackDistributedDataloader(
-                train_dataset,
                 batch_size=cfg.micro_batch_size,
-                seq_max_length=cfg.max_packed_sequence_len or cfg.sequence_len,
+                drop_last=True,
-                collate_fn=DataCollatorForSeq2Seq(
+                batch_max_len=cfg.micro_batch_size
-                    tokenizer,
+                * (cfg.max_packed_sequence_len or cfg.sequence_len),
-                    return_tensors="pt",
+                lengths=(
-                    padding="longest",
+                    train_dataset.data.column("position_ids")
+                    .to_pandas()
+                    .apply(lambda x: x[-1] + 1)
+                    .values
                 ),
-                sampler=sampler,
-                packing_efficiency_estimate=cfg.sample_packing_eff_est,
-                sample_packing_seq_len_multiplier=cfg.micro_batch_size,
-                device_count=int(os.environ.get("WORLD_SIZE", 1)),
             )
-            data_loader_len = data_loader.len_w_stats()
+
-            actual_eff = data_loader.efficiency()
+            data_loader = DataLoader(
-            LOG.info(f"data_loader_len: {data_loader_len}")
+                train_dataset.remove_columns(["length"]),
+                batch_sampler=sampler,
+            )
+            data_loader_len = len(data_loader)
+            actual_eff = sampler.efficiency()
+            LOG.debug(f"data_loader_len: {data_loader_len}", main_process_only=True)
             # FIXME: is there a bug here somewhere? the total num steps depends
             # on the agreed on value for sample_packing_eff_est
-            total_num_steps = int(math.floor(data_loader_len * cfg.num_epochs))
+            total_num_steps = int(
+                math.floor(
+                    data_loader_len
+                    * cfg.num_epochs
+                    / int(os.environ.get("WORLD_SIZE", 1))
+                )
+            )
 
             def calc_sample_packing_eff_est(estimates: List[float]):
                 LOG.info(f"sample_packing_eff_est across ranks: {repr(estimates)}")
@@ -236,12 +232,20 @@ def calculate_total_num_steps(cfg, train_dataset, tokenizer):
                 math.ceil(sample_packing_actual_eff_all * 100.0) / 100.0
             )
             cfg.sample_packing_eff_est = sample_packing_eff_est
-            LOG.info(f"sample_packing_eff_est: {cfg.sample_packing_eff_est}")
+            LOG.debug(
+                f"sample_packing_eff_est: {cfg.sample_packing_eff_est}",
+                main_process_only=True,
+            )
     else:
         total_num_steps = int(
-            math.ceil(len(train_dataset) * cfg.num_epochs / cfg.batch_size)
+            math.ceil(
+                len(train_dataset)
+                * cfg.num_epochs
+                / int(os.environ.get("WORLD_SIZE", 1))
+                / cfg.batch_size
+            )
         )
-    LOG.info(f"total_num_steps: {total_num_steps}")
+    LOG.debug(f"total_num_steps: {total_num_steps}", main_process_only=True)
     return total_num_steps
 
 
@@ -264,6 +268,8 @@ def setup_trainer(cfg, train_dataset, eval_dataset, model, tokenizer, total_num_
         setup_fsdp_envs(cfg)
     elif cfg.deepspeed:
         os.environ["ACCELERATE_USE_DEEPSPEED"] = "true"
+    if cfg.fp8:
+        os.environ["ACCELERATE_MIXED_PRECISION"] = "fp8"
 
     trainer_builder = HFCausalTrainerBuilder(cfg, model, tokenizer)
     trainer_builder.train_dataset = train_dataset

tests/e2e/test_fused_llama.py

@@ -4,7 +4,6 @@ E2E tests for lora llama
 
 import logging
 import os
-import tempfile
 import unittest
 from pathlib import Path
 
@@ -16,6 +15,8 @@ from axolotl.train import train
 from axolotl.utils.config import normalize_config
 from axolotl.utils.dict import DictDefault
 
+from .utils import with_temp_dir
+
 LOG = logging.getLogger("axolotl.tests.e2e")
 os.environ["WANDB_DISABLED"] = "true"
 
@@ -25,9 +26,9 @@ class TestFusedLlama(unittest.TestCase):
     Test case for Llama models using Fused layers
     """
 
-    def test_fft_packing(self):
+    @with_temp_dir
+    def test_fft_packing(self, temp_dir):
         # pylint: disable=duplicate-code
-        output_dir = tempfile.mkdtemp()
         cfg = DictDefault(
             {
                 "base_model": "JackFram/llama-68m",
@@ -51,7 +52,7 @@ class TestFusedLlama(unittest.TestCase):
                 "num_epochs": 2,
                 "micro_batch_size": 2,
                 "gradient_accumulation_steps": 1,
-                "output_dir": output_dir,
+                "output_dir": temp_dir,
                 "learning_rate": 0.00001,
                 "optimizer": "adamw_torch",
                 "lr_scheduler": "cosine",
@@ -69,4 +70,4 @@ class TestFusedLlama(unittest.TestCase):
         dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
 
         train(cfg=cfg, cli_args=cli_args, dataset_meta=dataset_meta)
-        assert (Path(output_dir) / "pytorch_model.bin").exists()
+        assert (Path(temp_dir) / "pytorch_model.bin").exists()

tests/e2e/test_lora_llama.py

@@ -4,7 +4,6 @@ E2E tests for lora llama
 
 import logging
 import os
-import tempfile
 import unittest
 from pathlib import Path
 
@@ -14,6 +13,8 @@ from axolotl.train import train
 from axolotl.utils.config import normalize_config
 from axolotl.utils.dict import DictDefault
 
+from .utils import with_temp_dir
+
 LOG = logging.getLogger("axolotl.tests.e2e")
 os.environ["WANDB_DISABLED"] = "true"
 
@@ -23,9 +24,9 @@ class TestLoraLlama(unittest.TestCase):
     Test case for Llama models using LoRA
     """
 
-    def test_lora(self):
+    @with_temp_dir
+    def test_lora(self, temp_dir):
         # pylint: disable=duplicate-code
-        output_dir = tempfile.mkdtemp()
         cfg = DictDefault(
             {
                 "base_model": "JackFram/llama-68m",
@@ -52,7 +53,7 @@ class TestLoraLlama(unittest.TestCase):
                 "num_epochs": 2,
                 "micro_batch_size": 8,
                 "gradient_accumulation_steps": 1,
-                "output_dir": output_dir,
+                "output_dir": temp_dir,
                 "learning_rate": 0.00001,
                 "optimizer": "adamw_torch",
                 "lr_scheduler": "cosine",
@@ -63,11 +64,11 @@ class TestLoraLlama(unittest.TestCase):
         dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
 
         train(cfg=cfg, cli_args=cli_args, dataset_meta=dataset_meta)
-        assert (Path(output_dir) / "adapter_model.bin").exists()
+        assert (Path(temp_dir) / "adapter_model.bin").exists()
 
-    def test_lora_packing(self):
+    @with_temp_dir
+    def test_lora_packing(self, temp_dir):
         # pylint: disable=duplicate-code
-        output_dir = tempfile.mkdtemp()
         cfg = DictDefault(
             {
                 "base_model": "JackFram/llama-68m",
@@ -96,7 +97,7 @@ class TestLoraLlama(unittest.TestCase):
                 "num_epochs": 2,
                 "micro_batch_size": 8,
                 "gradient_accumulation_steps": 1,
-                "output_dir": output_dir,
+                "output_dir": temp_dir,
                 "learning_rate": 0.00001,
                 "optimizer": "adamw_torch",
                 "lr_scheduler": "cosine",
@@ -107,11 +108,11 @@ class TestLoraLlama(unittest.TestCase):
         dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
 
         train(cfg=cfg, cli_args=cli_args, dataset_meta=dataset_meta)
-        assert (Path(output_dir) / "adapter_model.bin").exists()
+        assert (Path(temp_dir) / "adapter_model.bin").exists()
 
-    def test_lora_gptq(self):
+    @with_temp_dir
+    def test_lora_gptq(self, temp_dir):
         # pylint: disable=duplicate-code
-        output_dir = tempfile.mkdtemp()
         cfg = DictDefault(
             {
                 "base_model": "TheBlokeAI/jackfram_llama-68m-GPTQ",
@@ -144,7 +145,7 @@ class TestLoraLlama(unittest.TestCase):
                 "save_steps": 0.5,
                 "micro_batch_size": 8,
                 "gradient_accumulation_steps": 1,
-                "output_dir": output_dir,
+                "output_dir": temp_dir,
                 "learning_rate": 0.00001,
                 "optimizer": "adamw_torch",
                 "lr_scheduler": "cosine",
@@ -155,4 +156,4 @@ class TestLoraLlama(unittest.TestCase):
         dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
 
         train(cfg=cfg, cli_args=cli_args, dataset_meta=dataset_meta)
-        assert (Path(output_dir) / "adapter_model.bin").exists()
+        assert (Path(temp_dir) / "adapter_model.bin").exists()

tests/e2e/test_mistral.py

@@ -4,7 +4,6 @@ E2E tests for lora llama
 
 import logging
 import os
-import tempfile
 import unittest
 from pathlib import Path
 
@@ -16,6 +15,8 @@ from axolotl.train import train
 from axolotl.utils.config import normalize_config
 from axolotl.utils.dict import DictDefault
 
+from .utils import with_temp_dir
+
 LOG = logging.getLogger("axolotl.tests.e2e")
 os.environ["WANDB_DISABLED"] = "true"
 
@@ -25,9 +26,9 @@ class TestMistral(unittest.TestCase):
     Test case for Llama models using LoRA
     """
 
-    def test_lora(self):
+    @with_temp_dir
+    def test_lora(self, temp_dir):
         # pylint: disable=duplicate-code
-        output_dir = tempfile.mkdtemp()
         cfg = DictDefault(
             {
                 "base_model": "openaccess-ai-collective/tiny-mistral",
@@ -54,7 +55,7 @@ class TestMistral(unittest.TestCase):
                 "num_epochs": 2,
                 "micro_batch_size": 2,
                 "gradient_accumulation_steps": 1,
-                "output_dir": output_dir,
+                "output_dir": temp_dir,
                 "learning_rate": 0.00001,
                 "optimizer": "adamw_torch",
                 "lr_scheduler": "cosine",
@@ -68,11 +69,11 @@ class TestMistral(unittest.TestCase):
         dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
 
         train(cfg=cfg, cli_args=cli_args, dataset_meta=dataset_meta)
-        assert (Path(output_dir) / "adapter_model.bin").exists()
+        assert (Path(temp_dir) / "adapter_model.bin").exists()
 
-    def test_ft(self):
+    @with_temp_dir
+    def test_ft(self, temp_dir):
         # pylint: disable=duplicate-code
-        output_dir = tempfile.mkdtemp()
         cfg = DictDefault(
             {
                 "base_model": "openaccess-ai-collective/tiny-mistral",
@@ -93,7 +94,7 @@ class TestMistral(unittest.TestCase):
                 "num_epochs": 2,
                 "micro_batch_size": 2,
                 "gradient_accumulation_steps": 1,
-                "output_dir": output_dir,
+                "output_dir": temp_dir,
                 "learning_rate": 0.00001,
                 "optimizer": "adamw_torch",
                 "lr_scheduler": "cosine",
@@ -111,4 +112,4 @@ class TestMistral(unittest.TestCase):
         dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
 
         train(cfg=cfg, cli_args=cli_args, dataset_meta=dataset_meta)
-        assert (Path(output_dir) / "pytorch_model.bin").exists()
+        assert (Path(temp_dir) / "pytorch_model.bin").exists()

tests/e2e/test_mistral_samplepack.py

@@ -4,7 +4,6 @@ E2E tests for lora llama
 
 import logging
 import os
-import tempfile
 import unittest
 from pathlib import Path
 
@@ -16,6 +15,8 @@ from axolotl.train import train
 from axolotl.utils.config import normalize_config
 from axolotl.utils.dict import DictDefault
 
+from .utils import with_temp_dir
+
 LOG = logging.getLogger("axolotl.tests.e2e")
 os.environ["WANDB_DISABLED"] = "true"
 
@@ -25,9 +26,9 @@ class TestMistral(unittest.TestCase):
     Test case for Llama models using LoRA
     """
 
-    def test_lora_packing(self):
+    @with_temp_dir
+    def test_lora_packing(self, temp_dir):
         # pylint: disable=duplicate-code
-        output_dir = tempfile.mkdtemp()
         cfg = DictDefault(
             {
                 "base_model": "openaccess-ai-collective/tiny-mistral",
@@ -55,7 +56,7 @@ class TestMistral(unittest.TestCase):
                 "num_epochs": 2,
                 "micro_batch_size": 2,
                 "gradient_accumulation_steps": 1,
-                "output_dir": output_dir,
+                "output_dir": temp_dir,
                 "learning_rate": 0.00001,
                 "optimizer": "adamw_torch",
                 "lr_scheduler": "cosine",
@@ -69,11 +70,11 @@ class TestMistral(unittest.TestCase):
         dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
 
         train(cfg=cfg, cli_args=cli_args, dataset_meta=dataset_meta)
-        assert (Path(output_dir) / "adapter_model.bin").exists()
+        assert (Path(temp_dir) / "adapter_model.bin").exists()
 
-    def test_ft_packing(self):
+    @with_temp_dir
+    def test_ft_packing(self, temp_dir):
         # pylint: disable=duplicate-code
-        output_dir = tempfile.mkdtemp()
         cfg = DictDefault(
             {
                 "base_model": "openaccess-ai-collective/tiny-mistral",
@@ -95,7 +96,7 @@ class TestMistral(unittest.TestCase):
                 "num_epochs": 2,
                 "micro_batch_size": 2,
                 "gradient_accumulation_steps": 1,
-                "output_dir": output_dir,
+                "output_dir": temp_dir,
                 "learning_rate": 0.00001,
                 "optimizer": "adamw_torch",
                 "lr_scheduler": "cosine",
@@ -113,4 +114,4 @@ class TestMistral(unittest.TestCase):
         dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
 
         train(cfg=cfg, cli_args=cli_args, dataset_meta=dataset_meta)
-        assert (Path(output_dir) / "pytorch_model.bin").exists()
+        assert (Path(temp_dir) / "pytorch_model.bin").exists()

tests/e2e/test_phi.py

@@ -4,8 +4,8 @@ E2E tests for lora llama
 
 import logging
 import os
-import tempfile
 import unittest
+from pathlib import Path
 
 from axolotl.cli import load_datasets
 from axolotl.common.cli import TrainerCliArgs
@@ -13,6 +13,8 @@ from axolotl.train import train
 from axolotl.utils.config import normalize_config
 from axolotl.utils.dict import DictDefault
 
+from .utils import with_temp_dir
+
 LOG = logging.getLogger("axolotl.tests.e2e")
 os.environ["WANDB_DISABLED"] = "true"
 
@@ -22,7 +24,8 @@ class TestPhi(unittest.TestCase):
     Test case for Llama models using LoRA
     """
 
-    def test_ft(self):
+    @with_temp_dir
+    def test_ft(self, temp_dir):
         # pylint: disable=duplicate-code
         cfg = DictDefault(
             {
@@ -52,7 +55,7 @@ class TestPhi(unittest.TestCase):
                 "num_epochs": 1,
                 "micro_batch_size": 1,
                 "gradient_accumulation_steps": 1,
-                "output_dir": tempfile.mkdtemp(),
+                "output_dir": temp_dir,
                 "learning_rate": 0.00001,
                 "optimizer": "adamw_bnb_8bit",
                 "lr_scheduler": "cosine",
@@ -64,8 +67,10 @@ class TestPhi(unittest.TestCase):
         dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
 
         train(cfg=cfg, cli_args=cli_args, dataset_meta=dataset_meta)
+        assert (Path(temp_dir) / "pytorch_model.bin").exists()
 
-    def test_ft_packed(self):
+    @with_temp_dir
+    def test_ft_packed(self, temp_dir):
         # pylint: disable=duplicate-code
         cfg = DictDefault(
             {
@@ -95,7 +100,7 @@ class TestPhi(unittest.TestCase):
                 "num_epochs": 1,
                 "micro_batch_size": 1,
                 "gradient_accumulation_steps": 1,
-                "output_dir": tempfile.mkdtemp(),
+                "output_dir": temp_dir,
                 "learning_rate": 0.00001,
                 "optimizer": "adamw_bnb_8bit",
                 "lr_scheduler": "cosine",
@@ -107,3 +112,4 @@ class TestPhi(unittest.TestCase):
         dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
 
         train(cfg=cfg, cli_args=cli_args, dataset_meta=dataset_meta)
+        assert (Path(temp_dir) / "pytorch_model.bin").exists()

tests/e2e/utils.py

@@ -0,0 +1,22 @@
+"""
+helper utils for tests
+"""
+
+import shutil
+import tempfile
+from functools import wraps
+
+
+def with_temp_dir(test_func):
+    @wraps(test_func)
+    def wrapper(*args, **kwargs):
+        # Create a temporary directory
+        temp_dir = tempfile.mkdtemp()
+        try:
+            # Pass the temporary directory to the test function
+            test_func(*args, temp_dir=temp_dir, **kwargs)
+        finally:
+            # Clean up the directory after the test
+            shutil.rmtree(temp_dir)
+
+    return wrapper