handle load_model splat

.github/workflows/tests.yml

@@ -71,9 +71,8 @@ jobs:
 
       - name: Install dependencies
         run: |
-          pip3 install --extra-index-url https://download.pytorch.org/whl/cu118 -U torch==2.0.1
           pip3 uninstall -y transformers accelerate
-          pip3 install -U -e .[flash-attn,mamba-ssm]
+          pip3 install -U -e .[flash-attn]
           pip3 install -r requirements-tests.txt
 
       - name: Run e2e tests

.mypy.ini

@@ -8,9 +8,6 @@ ignore_missing_imports = True
 [mypy-axolotl.monkeypatch.*]
 ignore_errors = True
 
-[mypy-axolotl.models.mixtral.*]
-ignore_errors = True
-
 [mypy-axolotl.models.phi.*]
 ignore_errors = True
 

README.md

@@ -25,20 +25,17 @@ Features:
 - [Installation](#installation)
   - [Docker](#docker)
   - [Conda/Pip venv](#condapip-venv)
-  - [Runpod](#runpod)
   - [LambdaLabs](#lambdalabs)
   - [Windows](#windows)
-  - [Launching on public clouds via SkyPilot](#launching-on-public-clouds-via-skypilot)
 - [Dataset](#dataset)
   - [How to Add Custom Prompts](#how-to-add-custom-prompts)
   - [How to Use Custom Pretokenized Dataset](#how-to-use-your-custom-pretokenized-dataset)
 - [Config](#config)
   - [Train](#train)
+  - [Training w/ Deepspeed](#training-with-deepspeed)
   - [Inference](#inference)
   - [Merge LORA to Base](#merge-lora-to-base)
-  - [Special Tokens](#special-tokens)
 - [Common Errors](#common-errors-)
-  - [Tokenization Mismatch b/w Training & Inference](#tokenization-mismatch-bw-inference--training)
 - [Need Help?](#need-help-)
 - [Badge](#badge-)
 - [Community Showcase](#community-showcase)
@@ -67,21 +64,17 @@ Features:
 
 ## Axolotl supports
 
-|             | fp16/fp32 | lora | qlora | gptq | gptq w/flash attn | flash attn | xformers attn |
-|-------------|:----------|:-----|-------|------|-------------------|------------|--------------|
-| llama       | ✅         | ✅    | ✅     | ✅             | ✅                 | ✅          | ✅            |
-| Mistral     | ✅         | ✅    | ✅     | ✅             | ✅                 | ✅          | ✅            |
-| Mixtral-MoE | ✅         | ✅    | ✅     | ❓             | ❓                 | ❓          | ❓            |
-| Pythia      | ✅         | ✅    | ✅     | ❌             | ❌                 | ❌          | ❓            |
-| cerebras    | ✅         | ✅    | ✅     | ❌             | ❌                 | ❌          | ❓            |
-| btlm        | ✅         | ✅    | ✅     | ❌             | ❌                 | ❌          | ❓            |
-| mpt         | ✅         | ❌    | ❓     | ❌             | ❌                 | ❌          | ❓            |
-| falcon      | ✅         | ✅    | ✅     | ❌             | ❌                 | ❌          | ❓            |
-| gpt-j       | ✅         | ✅    | ✅     | ❌             | ❌                 | ❓          | ❓            |
-| XGen        | ✅         | ❓    | ✅     | ❓             | ❓                 | ❓          | ✅            |
-| phi         | ✅         | ✅    | ✅     | ❓             | ❓                 | ❓          | ❓            |
-| RWKV        | ✅         | ❓    | ❓     | ❓             | ❓                 | ❓          | ❓            |
-| Qwen        | ✅         | ✅    | ✅     | ❓             | ❓                 | ❓          | ❓            |
+|          | fp16/fp32 | lora | qlora | gptq | gptq w/flash attn | flash attn | xformers attn |
+|----------|:----------|:-----|-------|------|-------------------|------------|--------------|
+| llama    | ✅         | ✅    | ✅     | ✅             | ✅                 | ✅          | ✅            |
+| Pythia   | ✅         | ✅    | ✅     | ❌             | ❌                 | ❌          | ❓            |
+| cerebras | ✅         | ✅    | ✅     | ❌             | ❌                 | ❌          | ❓            |
+| btlm     | ✅         | ✅    | ✅     | ❌             | ❌                 | ❌          | ❓            |
+| mpt      | ✅         | ❌    | ❓     | ❌             | ❌                 | ❌          | ❓            |
+| falcon   | ✅         | ✅    | ✅     | ❌             | ❌                 | ❌          | ❓            |
+| gpt-j    | ✅         | ✅    | ✅     | ❌             | ❌                 | ❓          | ❓            |
+| XGen     | ✅         | ❓    | ✅     | ❓             | ❓                 | ❓          | ✅            |
+| phi      | ✅         | ✅    | ✅     | ❓             | ❓                 | ❓          | ❓            |
 
 
 ## Quickstart ⚡
@@ -90,29 +83,20 @@ Get started with Axolotl in just a few steps! This quickstart guide will walk yo
 
 **Requirements**: Python >=3.9 and Pytorch >=2.0.
 
-`pip3 install "axolotl[flash-attn,deepspeed] @ git+https://github.com/OpenAccess-AI-Collective/axolotl"`
-
-### For developers
 ```bash
 git clone https://github.com/OpenAccess-AI-Collective/axolotl
 cd axolotl
 
 pip3 install packaging
 pip3 install -e '.[flash-attn,deepspeed]'
-```
+pip3 install -U git+https://github.com/huggingface/peft.git
 
-### Usage
-```bashtet
 # finetune lora
 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
@@ -123,6 +107,7 @@ accelerate launch -m axolotl.cli.inference examples/openllama-3b/lora.yml \
   ```bash
   docker run --gpus '"all"' --rm -it winglian/axolotl:main-py3.10-cu118-2.0.1
   ```
+  - `winglian/axolotl-runpod:main-latest`: for runpod or use this [direct link](https://runpod.io/gsc?template=v2ickqhz9s&ref=6i7fkpdz)
 
   Or run on the current files for development:
 
@@ -130,27 +115,6 @@ 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 --privileged --gpus '"all"' --shm-size 10g --rm -it --name axolotl --ipc=host --ulimit memlock=-1 --ulimit stack=67108864 --mount type=volume,src=axolotl,target=/workspace/axolotl -v ${HOME}/.cache/huggingface:/root/.cache/huggingface winglian/axolotl:main-py3.10-cu118-2.0.1
-  ```
-
-  It additionally:
-  * Prevents memory issues when running e.g. deepspeed (e.g. you could hit SIGBUS/signal 7 error) through `--ipc` and `--ulimit` args.
-  * Persists the downloaded HF data (models etc.) and your modifications to axolotl code through `--mount`/`-v` args.
-  * The `--name` argument simply makes it easier to refer to the container in vscode (`Dev Containers: Attach to Running Container...`) or in your terminal.
-  * The `--privileged` flag gives all capabilities to the container.
-  * The `--shm-size 10g` argument increases the shared memory size. Use this if you see `exitcode: -7` errors using deepspeed.
-
-  [More information on nvidia website](https://docs.nvidia.com/deeplearning/frameworks/user-guide/index.html#setincshmem)
-
-  </details>
-
 #### Conda/Pip venv
   1. Install python >=**3.9**
 
@@ -167,10 +131,6 @@ accelerate launch -m axolotl.cli.inference examples/openllama-3b/lora.yml \
         ```
         Get the token at huggingface.co/settings/tokens
 
-#### Runpod
-
-Use `winglian/axolotl-runpod:main-latest` or use this [direct link](https://runpod.io/gsc?template=v2ickqhz9s&ref=6i7fkpdz)
-
 #### LambdaLabs
   <details>
 
@@ -218,28 +178,6 @@ Use `winglian/axolotl-runpod:main-latest` or use this [direct link](https://runp
 #### Windows
 Please use WSL or Docker!
 
-
-#### Launching on public clouds via SkyPilot
-To launch on GPU instances (both on-demand and spot instances) on 7+ clouds (GCP, AWS, Azure, OCI, and more), you can use [SkyPilot](https://skypilot.readthedocs.io/en/latest/index.html):
-```bash
-pip install "skypilot-nightly[gcp,aws,azure,oci,lambda,kubernetes,ibm,scp]"  # choose your clouds
-sky check
-```
-Get the [example YAMLs](https://github.com/skypilot-org/skypilot/tree/master/llm/axolotl) of using Axolotl to finetune `mistralai/Mistral-7B-v0.1`:
-```
-git clone https://github.com/skypilot-org/skypilot.git
-cd skypilot/llm/axolotl
-```
-Use one command to launch:
-```bash
-# On-demand
-HF_TOKEN=xx sky launch axolotl.yaml --env HF_TOKEN
-
-# Managed spot (auto-recovery on preemption)
-HF_TOKEN=xx BUCKET=<unique-name> sky spot launch axolotl-spot.yaml --env HF_TOKEN --env BUCKET
-```
-
-
 ### Dataset
 
 Axolotl supports a variety of dataset formats. Below are some of the formats you can use.
@@ -249,17 +187,10 @@ Have dataset(s) in one of the following format (JSONL recommended):
   ```json
   {"instruction": "...", "input": "...", "output": "..."}
   ```
-- `sharegpt`: conversations where `from` is `human`/`gpt`. (optional: `system` to override default system prompt)
+- `sharegpt`: conversations where `from` is `human`/`gpt`
   ```json
   {"conversations": [{"from": "...", "value": "..."}]}
   ```
-- `llama-2`: the json is the same format as `sharegpt` above, with the following config (see the [config section](#config) for more details)
-    ```yml
-    datasets:
-      - path: <your-path>
-        type: sharegpt
-        conversation: llama-2
-    ```
 - `completion`: raw corpus
   ```json
   {"text": "..."}
@@ -425,13 +356,6 @@ 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
@@ -443,12 +367,6 @@ See [examples](examples) for quick start. It is recommended to duplicate and mod
     - path: knowrohit07/know_sql
       type: context_qa.load_v2
       train_on_split: validation
-
-  # loading from s3 or gcs
-  # s3 creds will be loaded from the system default and gcs only supports public access
-  dataset:
-    - path: s3://path_to_ds # Accepts folder with arrow/parquet or file path like above. Supports s3, gcs.
-      ...
   ```
 
 - loading
@@ -476,7 +394,7 @@ See [examples](examples) for quick start. It is recommended to duplicate and mod
 
 <details>
 
-<summary>All yaml options (click me)</summary>
+<summary>All yaml options</summary>
 
 ```yaml
 # This is the huggingface model that contains *.pt, *.safetensors, or *.bin files
@@ -511,15 +429,6 @@ is_falcon_derived_model:
 is_llama_derived_model:
 # Please note that if you set this to true, `padding_side` will be set to "left" by default
 is_mistral_derived_model:
-is_qwen_derived_model:
-
-# optional overrides to the base model configuration
-model_config:
-  # RoPE Scaling https://github.com/huggingface/transformers/pull/24653
-  rope_scaling:
-    type: # linear | dynamic
-    factor: # float
-
 
 # Whether you are training a 4-bit GPTQ quantized model
 gptq: true
@@ -544,7 +453,7 @@ float16: true
 
 # A list of one or more datasets to finetune the model with
 datasets:
-  # HuggingFace dataset repo | s3://,gs:// path | "json" for local dataset, make sure to fill data_files
+  # HuggingFace dataset repo | "json" for local dataset, make sure to fill data_files
   - path: vicgalle/alpaca-gpt4
   # The type of prompt to use for training. [alpaca, sharegpt, gpteacher, oasst, reflection]
     type: alpaca # format | format:<prompt_style> (chat/instruct) | <prompt_strategies>.load_<load_fn>
@@ -552,12 +461,7 @@ datasets:
     data_files: # Optional[str] path to source data files
     shards: # Optional[int] number of shards to split data into
     name: # Optional[str] name of dataset configuration to load
-    train_on_split: train # Optional[str] name of dataset split to load from
-
-    # Optional[str] fastchat conversation type, only used with type: sharegpt
-    conversation:  # Options (see Conversation 'name'): https://github.com/lm-sys/FastChat/blob/main/fastchat/conversation.py
-    field_human: # Optional[str]. Human key to use for conversation.
-    field_model: # Optional[str]. Assistant key to use for conversation.
+    conversation:  # Optional[str] fastchat conversation type, only used with type: sharegpt
 
   # Custom user prompt
   - path: repo
@@ -623,12 +527,6 @@ eval_sample_packing:
 sample_packing_eff_est:
 total_num_tokens:
 
-# Passed through to transformers when loading the model when launched without accelerate
-# Use `sequential` when training w/ model parallelism to limit memory
-device_map:
-# Defines the max memory usage per gpu on the system. Passed through to transformers when loading the model.
-max_memory:
-
 # If you want to use 'lora' or 'qlora' or leave blank to train all parameters in original model
 adapter: lora
 # If you already have a lora model trained that you want to load, put that here.
@@ -676,8 +574,7 @@ wandb_mode: # "offline" to save run metadata locally and not sync to the server,
 wandb_project: # Your wandb project name
 wandb_entity: # A wandb Team name if using a Team
 wandb_watch:
-wandb_name: # Set the name of your wandb run
-wandb_run_id: # Set the ID of your wandb run
+wandb_run_id: # Set the name of your wandb run
 wandb_log_model: # "checkpoint" to log model to wandb Artifacts every `save_steps` or "end" to log only at the end of training
 
 # Where to save the full-finetuned model to
@@ -694,17 +591,14 @@ 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: 4
-warmup_steps: 100  # cannot use with warmup_ratio
-warmup_ratio: 0.05  # cannot use with warmup_steps
+num_epochs: 3
+warmup_steps: 100
 learning_rate: 0.00003
 lr_quadratic_warmup:
 logging_steps:
-eval_steps: # Leave empty to eval at each epoch, integers for every N steps. decimal for fraction of total steps
-evals_per_epoch: # number of times per epoch to run evals, mutually exclusive with eval_steps
 save_strategy: # Set to `no` to skip checkpoint saves
 save_steps: # Leave empty to save at each epoch
-saves_per_epoch: # number of times per epoch to save a checkpoint, mutually exclusive with save_steps
+eval_steps: # Leave empty to eval at each epoch
 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.
@@ -714,9 +608,6 @@ max_steps:
 eval_table_size: # Approximate number of predictions sent to wandb depending on batch size. Enabled above 0. Default is 0
 eval_table_max_new_tokens: # Total number of tokens generated for predictions sent to wandb. Default is 128
 
-loss_watchdog_threshold: # High loss value, indicating the learning has broken down (a good estimate is ~2 times the loss at the start of training)
-loss_watchdog_patience: # Number of high-loss steps in a row before the trainer aborts (default: 3)
-
 # Save model as safetensors (require safetensors package)
 save_safetensors:
 
@@ -783,7 +674,7 @@ max_grad_norm:
 # Augmentation techniques
 # NEFT https://arxiv.org/abs/2310.05914, set this to a number (paper default is 5) to add noise to embeddings
 # currently only supported on Llama and Mistral
-neftune_noise_alpha:
+noisy_embedding_alpha:
 
 # Whether to bettertransformers
 flash_optimum:
@@ -803,6 +694,10 @@ landmark_attention:
 # xpos RoPE see https://github.com/kaiokendev/cutoff-len-is-context-len/blob/main/util/xpos_rope_llama_monkey_patch.py
 # LLaMA only
 xpos_rope:
+# RoPE Scaling https://github.com/huggingface/transformers/pull/24653
+rope_scaling:
+  type: # linear | dynamic
+  factor: # float
 
 # Resume from a specific checkpoint dir
 resume_from_checkpoint:
@@ -920,41 +815,14 @@ Run
 accelerate launch -m axolotl.cli.train your_config.yml
 ```
 
-#### 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.
-
-```bash
-python -m axolotl.cli.preprocess your_config.yml
-```
-
 #### 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
+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
 ```
 
-```shell
-accelerate launch -m axolotl.cli.train examples/llama-2/config.py --deepspeed deepspeed/zero1.json
-```
-
-##### FSDP
+##### Config
 
 - llama FSDP
 ```yaml
@@ -975,25 +843,27 @@ wandb_mode:
 wandb_project:
 wandb_entity:
 wandb_watch:
-wandb_name:
+wandb_run_id:
 wandb_log_model:
 ```
 
-##### Special Tokens
+### Training with Deepspeed
 
-It is important to have special tokens like delimiters, end-of-sequence, beginning-of-sequence in your tokenizer's vocabulary.  This will help you avoid tokenization issues and help your model train better.  You can do this in axolotl like this:
+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
 
-```yml
-special_tokens:
-  bos_token: "<s>"
-  eos_token: "</s>"
-  unk_token: "<unk>"
-tokens: # these are delimiters
-  - "<|im_start|>"
-  - "<|im_end|>"
+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
 ```
 
-When you include these tokens in your axolotl config, axolotl adds these tokens to the tokenizer's vocabulary.
+or
+
+```yaml
+deepspeed: deepspeed/zero1.json
+```
 
 ### Inference
 
@@ -1012,10 +882,6 @@ 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:
 
@@ -1047,10 +913,6 @@ Please reduce any below
   - `gradient_accumulation_steps`
   - `sequence_len`
 
-If it does not help, try running without deepspeed and without accelerate (replace "accelerate launch" with "python") in the command.
-
-Using adamw_bnb_8bit might also save you some memory.
-
 > `failed (exitcode: -9)`
 
 Usually means your system has run out of system memory.
@@ -1073,20 +935,6 @@ It's safe to ignore it.
 
 See the [NCCL](docs/nccl.md) guide.
 
-
-### Tokenization Mismatch b/w Inference & Training
-
-For many formats, Axolotl constructs prompts by concatenating token ids _after_ tokenizing strings.  The reason for concatenating token ids rather than operating on strings is to maintain precise accounting for attention masks.
-
-If you decode a prompt constructed by axolotl, you might see spaces between tokens (or lack thereof) that you do not expect, especially around delimiters and special tokens.  When you are starting out with a new format, you should always do the following:
-
-1. Materialize some data using `python -m axolotl.cli.preprocess your_config.yml --debug`, and then decode the first few rows with your model's tokenizer.
-2. During inference, right before you pass a tensor of token ids to your model, decode these tokens back into a string.
-3. Make sure the inference string from #2 looks **exactly** like the data you fine tuned on from #1, including spaces and new lines.  If they aren't the same adjust your inference server accordingly.
-4. As an additional troubleshooting step, you can look look at the token ids between 1 and 2 to make sure they are identical.
-
-Having misalignment between your prompts during training and inference can cause models to perform very poorly, so it is worth checking this.  See [this blog post](https://hamel.dev/notes/llm/05_tokenizer_gotchas.html) for a concrete example.
-
 ## Need help? 🙋♂️
 
 Join our [Discord server](https://discord.gg/HhrNrHJPRb) where we can help you

deepspeed/zero1.json

@@ -24,6 +24,16 @@
       "weight_decay": "auto"
     }
   },
+  "scheduler": {
+    "type": "WarmupDecayLR",
+    "params": {
+      "warmup_min_lr": "auto",
+      "warmup_max_lr": "auto",
+      "warmup_num_steps": "auto",
+      "warmup_type": "linear",
+      "total_num_steps": "auto"
+    }
+  },
   "gradient_accumulation_steps": "auto",
   "train_batch_size": "auto",
   "train_micro_batch_size_per_gpu": "auto",

deepspeed/zero2.json

@@ -28,6 +28,16 @@
       "weight_decay": "auto"
     }
   },
+  "scheduler": {
+    "type": "WarmupDecayLR",
+    "params": {
+      "warmup_min_lr": "auto",
+      "warmup_max_lr": "auto",
+      "warmup_num_steps": "auto",
+      "warmup_type": "linear",
+      "total_num_steps": "auto"
+    }
+  },
   "gradient_accumulation_steps": "auto",
   "train_batch_size": "auto",
   "train_micro_batch_size_per_gpu": "auto",

deepspeed/zero3.json

@@ -1,6 +1,14 @@
 {
   "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,
@@ -32,6 +40,15 @@
       "weight_decay": "auto"
     }
   },
+  "scheduler": {
+    "type": "WarmupLR",
+    "params": {
+      "warmup_min_lr": "auto",
+      "warmup_max_lr": "auto",
+      "warmup_num_steps": "auto",
+      "warmup_type": "linear"
+    }
+  },
   "gradient_accumulation_steps": "auto",
   "train_batch_size": "auto",
   "train_micro_batch_size_per_gpu": "auto",

deepspeed/zero3_bf16.json

@@ -1,39 +0,0 @@
-{
-  "zero_optimization": {
-    "stage": 3,
-    "overlap_comm": true,
-    "contiguous_gradients": true,
-    "sub_group_size": 0,
-    "reduce_bucket_size": "auto",
-    "stage3_prefetch_bucket_size": "auto",
-    "stage3_param_persistence_threshold": "auto",
-    "stage3_max_live_parameters": 0,
-    "stage3_max_reuse_distance": 0,
-    "stage3_gather_16bit_weights_on_model_save": true
-  },
-  "bf16": {
-    "enabled": true
-  },
-  "fp16": {
-    "enabled": "auto",
-    "auto_cast": false,
-    "loss_scale": 0,
-    "initial_scale_power": 32,
-    "loss_scale_window": 1000,
-    "hysteresis": 2,
-    "min_loss_scale": 1
-  },
-  "optimizer": {
-    "type": "AdamW",
-    "params": {
-      "lr": "auto",
-      "betas": "auto",
-      "eps": "auto",
-      "weight_decay": "auto"
-    }
-  },
-  "gradient_accumulation_steps": "auto",
-  "train_batch_size": "auto",
-  "train_micro_batch_size_per_gpu": "auto",
-  "wall_clock_breakdown": false
-}

docker/Dockerfile

@@ -10,7 +10,7 @@ ARG PYTORCH_VERSION="2.0.1"
 ENV PYTORCH_VERSION=$PYTORCH_VERSION
 
 RUN apt-get update && \
-    apt-get install -y --allow-change-held-packages vim curl nano libnccl2 libnccl-dev
+    apt-get install -y vim curl
 
 WORKDIR /workspace
 
@@ -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 .[deepspeed,flash-attn,$AXOLOTL_EXTRAS]; \
+        pip install -e .[flash-attn,$AXOLOTL_EXTRAS]; \
     else \
-        pip install -e .[deepspeed,flash-attn]; \
+        pip install -e .[flash-attn]; \
     fi
 
 # fix so that git fetch/pull from remote works

docker/Dockerfile-base

@@ -10,10 +10,8 @@ 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/* \
@@ -29,9 +27,47 @@ 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} deepspeed-kernels --extra-index-url https://download.pytorch.org/whl/cu$CUDA
+    python3 -m pip install --no-cache-dir -U torch==${PYTORCH_VERSION}+cu${CUDA} --extra-index-url https://download.pytorch.org/whl/cu$CUDA
 
-RUN git lfs install --skip-repo && \
-    pip3 install awscli && \
+FROM base-builder AS deepspeed-builder
+
+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

docker/Dockerfile-runpod

@@ -4,7 +4,6 @@ FROM winglian/axolotl:$BASE_TAG
 ENV HF_DATASETS_CACHE="/workspace/data/huggingface-cache/datasets"
 ENV HUGGINGFACE_HUB_CACHE="/workspace/data/huggingface-cache/hub"
 ENV TRANSFORMERS_CACHE="/workspace/data/huggingface-cache/hub"
-ENV HF_HOME="/workspace/data/huggingface-cache/hub"
 
 COPY scripts/runpod-entrypoint.sh /root/runpod-entrypoint.sh
 

docs/faq.md

@@ -12,7 +12,3 @@ 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: 0.05
+val_set_size: 0.01
 
 adapter:
 lora_model_dir:
@@ -35,7 +35,7 @@ lora_fan_in_fan_out:
 wandb_project:
 wandb_entity:
 wandb_watch:
-wandb_name:
+wandb_run_id:
 wandb_log_model:
 
 output_dir: btlm-out
@@ -72,8 +72,8 @@ gptq_groupsize:
 gptq_model_v1:
 
 warmup_steps: 32
-evals_per_epoch: 4
-saves_per_epoch: 1
+eval_steps:
+save_steps:
 save_total_limit:
 
 debug:

examples/cerebras/qlora.yml

@@ -7,7 +7,7 @@ datasets:
   - path: teknium/GPT4-LLM-Cleaned
     type: alpaca
 dataset_prepared_path:
-val_set_size: 0.05
+val_set_size: 0.01
 adapter: qlora
 lora_model_dir:
 sequence_len: 2048
@@ -24,7 +24,7 @@ lora_fan_in_fan_out:
 wandb_project:
 wandb_entity:
 wandb_watch:
-wandb_name:
+wandb_run_id:
 wandb_log_model:
 output_dir: ./qlora-out
 batch_size: 4
@@ -49,8 +49,8 @@ flash_attention:
 gptq_groupsize:
 gptq_model_v1:
 warmup_steps: 10
-evals_per_epoch: 4
-saves_per_epoch: 1
+eval_steps: 20
+save_steps:
 debug:
 deepspeed:
 weight_decay: 0.1

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

@@ -11,7 +11,7 @@ datasets:
   - path: mhenrichsen/alpaca_2k_test
     type: alpaca
 dataset_prepared_path:
-val_set_size: 0.05
+val_set_size: 0.01
 output_dir: ./lora-out
 
 sequence_len: 4096
@@ -29,12 +29,12 @@ lora_fan_in_fan_out:
 wandb_project:
 wandb_entity:
 wandb_watch:
-wandb_name:
+wandb_run_id:
 wandb_log_model:
 
 gradient_accumulation_steps: 4
 micro_batch_size: 2
-num_epochs: 4
+num_epochs: 3
 optimizer: adamw_bnb_8bit
 lr_scheduler: cosine
 learning_rate: 0.0002
@@ -54,8 +54,8 @@ xformers_attention:
 flash_attention: true
 
 warmup_steps: 10
-evals_per_epoch: 4
-saves_per_epoch: 1
+eval_steps: 20
+save_steps:
 debug:
 deepspeed:
 weight_decay: 0.0

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

@@ -11,7 +11,7 @@ datasets:
   - path: mhenrichsen/alpaca_2k_test
     type: alpaca
 dataset_prepared_path:
-val_set_size: 0.05
+val_set_size: 0.01
 output_dir: ./qlora-out
 
 adapter: qlora
@@ -31,12 +31,12 @@ lora_fan_in_fan_out:
 wandb_project:
 wandb_entity:
 wandb_watch:
-wandb_name:
+wandb_run_id:
 wandb_log_model:
 
 gradient_accumulation_steps: 4
 micro_batch_size: 2
-num_epochs: 4
+num_epochs: 3
 optimizer: paged_adamw_32bit
 lr_scheduler: cosine
 learning_rate: 0.0002
@@ -56,8 +56,8 @@ xformers_attention:
 flash_attention: true
 
 warmup_steps: 10
-evals_per_epoch: 4
-saves_per_epoch: 1
+eval_steps: 20
+save_steps:
 debug:
 deepspeed:
 weight_decay: 0.0

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

@@ -11,7 +11,7 @@ datasets:
   - path: mhenrichsen/alpaca_2k_test
     type: alpaca
 dataset_prepared_path:
-val_set_size: 0.05
+val_set_size: 0.01
 output_dir: ./lora-out
 
 sequence_len: 4096
@@ -29,12 +29,12 @@ lora_fan_in_fan_out:
 wandb_project:
 wandb_entity:
 wandb_watch:
-wandb_name:
+wandb_run_id:
 wandb_log_model:
 
 gradient_accumulation_steps: 4
 micro_batch_size: 2
-num_epochs: 4
+num_epochs: 3
 optimizer: adamw_bnb_8bit
 lr_scheduler: cosine
 learning_rate: 0.0002
@@ -54,8 +54,8 @@ xformers_attention:
 flash_attention: true
 
 warmup_steps: 10
-evals_per_epoch: 4
-saves_per_epoch: 1
+eval_steps: 20
+save_steps:
 debug:
 deepspeed:
 weight_decay: 0.0

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

@@ -11,7 +11,7 @@ datasets:
   - path: mhenrichsen/alpaca_2k_test
     type: alpaca
 dataset_prepared_path:
-val_set_size: 0.05
+val_set_size: 0.01
 output_dir: ./qlora-out
 
 adapter: qlora
@@ -31,12 +31,12 @@ lora_fan_in_fan_out:
 wandb_project:
 wandb_entity:
 wandb_watch:
-wandb_name:
+wandb_run_id:
 wandb_log_model:
 
 gradient_accumulation_steps: 4
 micro_batch_size: 2
-num_epochs: 4
+num_epochs: 3
 optimizer: paged_adamw_32bit
 lr_scheduler: cosine
 learning_rate: 0.0002
@@ -56,8 +56,8 @@ xformers_attention:
 flash_attention: true
 
 warmup_steps: 10
-evals_per_epoch: 4
-saves_per_epoch: 1
+eval_steps: 20
+save_steps:
 debug:
 deepspeed:
 weight_decay: 0.0

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

@@ -11,7 +11,7 @@ datasets:
   - path: mhenrichsen/alpaca_2k_test
     type: alpaca
 dataset_prepared_path:
-val_set_size: 0.05
+val_set_size: 0.01
 output_dir: ./lora-out
 
 sequence_len: 4096
@@ -29,12 +29,12 @@ lora_fan_in_fan_out:
 wandb_project:
 wandb_entity:
 wandb_watch:
-wandb_name:
+wandb_run_id:
 wandb_log_model:
 
 gradient_accumulation_steps: 4
 micro_batch_size: 2
-num_epochs: 4
+num_epochs: 3
 optimizer: adamw_bnb_8bit
 lr_scheduler: cosine
 learning_rate: 0.0002
@@ -54,8 +54,8 @@ xformers_attention:
 flash_attention: true
 
 warmup_steps: 10
-evals_per_epoch: 4
-saves_per_epoch: 1
+eval_steps: 20
+save_steps:
 debug:
 deepspeed:
 weight_decay: 0.0

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

@@ -11,7 +11,7 @@ datasets:
   - path: mhenrichsen/alpaca_2k_test
     type: alpaca
 dataset_prepared_path:
-val_set_size: 0.05
+val_set_size: 0.01
 output_dir: ./qlora-out
 
 adapter: qlora
@@ -31,12 +31,12 @@ lora_fan_in_fan_out:
 wandb_project:
 wandb_entity:
 wandb_watch:
-wandb_name:
+wandb_run_id:
 wandb_log_model:
 
 gradient_accumulation_steps: 4
 micro_batch_size: 2
-num_epochs: 4
+num_epochs: 3
 optimizer: paged_adamw_32bit
 lr_scheduler: cosine
 learning_rate: 0.0002
@@ -56,8 +56,8 @@ xformers_attention:
 flash_attention: true
 
 warmup_steps: 10
-evals_per_epoch: 4
-saves_per_epoch: 1
+eval_steps: 20
+save_steps:
 debug:
 deepspeed:
 weight_decay: 0.0

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: 0.05
+val_set_size: 0.01
 adapter: lora
 lora_model_dir:
 sequence_len: 2048
@@ -26,7 +26,7 @@ lora_fan_in_fan_out:
 wandb_project:
 wandb_entity:
 wandb_watch:
-wandb_name:
+wandb_run_id:
 wandb_log_model:
 output_dir: ./falcon-7b
 batch_size: 2
@@ -51,8 +51,8 @@ flash_attention:
 gptq_groupsize:
 gptq_model_v1:
 warmup_steps: 40
-evals_per_epoch: 4
-saves_per_epoch: 1
+eval_steps: 5
+save_steps: 43
 debug:
 deepspeed:
 weight_decay: 0.0

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: 0.05
+val_set_size: 0.01
 # enable QLoRA
 adapter: qlora
 lora_model_dir:
@@ -40,7 +40,7 @@ lora_fan_in_fan_out:
 wandb_project:
 wandb_entity:
 wandb_watch:
-wandb_name:
+wandb_run_id:
 wandb_log_model:
 output_dir: ./qlora-out
 
@@ -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: 4
+num_epochs: 3
 # Optimizer for QLoRA
 optimizer: paged_adamw_32bit
 torchdistx_path:
@@ -80,8 +80,8 @@ flash_attention:
 gptq_groupsize:
 gptq_model_v1:
 warmup_steps: 10
-evals_per_epoch: 4
-saves_per_epoch: 1
+eval_steps: 5
+save_steps: 10
 debug:
 deepspeed:
 weight_decay: 0.000001

examples/falcon/config-7b.yml

@@ -12,7 +12,7 @@ datasets:
   - path: teknium/GPT4-LLM-Cleaned
     type: alpaca:chat
 dataset_prepared_path:
-val_set_size: 0.05
+val_set_size: 0.01
 adapter:
 lora_model_dir:
 sequence_len: 2048
@@ -26,7 +26,7 @@ lora_fan_in_fan_out:
 wandb_project:
 wandb_entity:
 wandb_watch:
-wandb_name:
+wandb_run_id:
 wandb_log_model:
 output_dir: ./falcon-7b
 batch_size: 2
@@ -51,8 +51,8 @@ flash_attention:
 gptq_groupsize:
 gptq_model_v1:
 warmup_steps: 40
-evals_per_epoch: 4
-saves_per_epoch: 1
+eval_steps: 5
+save_steps: 43
 debug:
 deepspeed:
 weight_decay: 0.0

examples/gptj/qlora.yml

@@ -7,7 +7,7 @@ datasets:
   - path: teknium/GPT4-LLM-Cleaned
     type: alpaca
 dataset_prepared_path:
-val_set_size: 0.05
+val_set_size: 0.01
 adapter: qlora
 lora_model_dir:
 sequence_len: 2048
@@ -21,7 +21,7 @@ lora_fan_in_fan_out:
 wandb_project:
 wandb_entity:
 wandb_watch:
-wandb_name:
+wandb_run_id:
 wandb_log_model:
 output_dir: ./qlora-out
 gradient_accumulation_steps: 2
@@ -46,8 +46,8 @@ flash_attention:
 gptq_groupsize:
 gptq_model_v1:
 warmup_steps: 10
-evals_per_epoch: 4
-saves_per_epoch: 1
+eval_steps: 20
+save_steps:
 debug:
 deepspeed:
 weight_decay: 0.1

examples/jeopardy-bot/config.yml

@@ -19,12 +19,12 @@ lora_fan_in_fan_out: false
 wandb_project:
 wandb_entity:
 wandb_watch:
-wandb_name:
+wandb_run_id:
 wandb_log_model:
 output_dir: ./jeopardy-bot-7b
 gradient_accumulation_steps: 1
 micro_batch_size: 1
-num_epochs: 4
+num_epochs: 3
 optimizer: adamw_bnb_8bit
 torchdistx_path:
 lr_scheduler: cosine
@@ -42,8 +42,8 @@ flash_attention:
 gptq_groupsize:
 gptq_model_v1:
 warmup_steps: 20
-evals_per_epoch: 4
-saves_per_epoch: 1
+eval_steps: 110
+save_steps: 660
 debug:
 deepspeed:
 weight_decay: 0.1

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: 0.05
+val_set_size: 0.01
 output_dir: ./out
 
 sequence_len: 4096
@@ -29,7 +29,7 @@ lora_fan_in_fan_out:
 wandb_project:
 wandb_entity:
 wandb_watch:
-wandb_name:
+wandb_run_id:
 wandb_log_model:
 
 gradient_accumulation_steps: 1
@@ -58,9 +58,9 @@ flash_attn_fuse_qkv: false
 flash_attn_fuse_mlp: true
 
 warmup_steps: 100
-evals_per_epoch: 4
+eval_steps: 0.05
 eval_table_size:
-saves_per_epoch: 1
+save_steps:
 debug:
 deepspeed: #deepspeed/zero2.json # multi-gpu only
 weight_decay: 0.1

examples/llama-2/gptq-lora.yml

@@ -15,7 +15,7 @@ datasets:
   - path: mhenrichsen/alpaca_2k_test
     type: alpaca
 dataset_prepared_path:
-val_set_size: 0.05
+val_set_size: 0.01
 adapter: lora
 lora_model_dir:
 sequence_len: 4096
@@ -32,12 +32,12 @@ lora_target_linear:
 lora_fan_in_fan_out:
 wandb_project:
 wandb_watch:
-wandb_name:
+wandb_run_id:
 wandb_log_model:
 output_dir: ./model-out
 gradient_accumulation_steps: 1
 micro_batch_size: 1
-num_epochs: 4
+num_epochs: 3
 optimizer: adamw_torch
 adam_beta2: 0.95
 adam_eps: 0.00001
@@ -62,8 +62,8 @@ flash_attention:
 sdp_attention:
 flash_optimum:
 warmup_steps: 100
-evals_per_epoch: 4
-saves_per_epoch: 1
+eval_steps:
+save_steps:
 debug:
 deepspeed:
 weight_decay: 0.1

examples/llama-2/lora.yml

@@ -11,7 +11,7 @@ datasets:
   - path: mhenrichsen/alpaca_2k_test
     type: alpaca
 dataset_prepared_path:
-val_set_size: 0.05
+val_set_size: 0.01
 output_dir: ./lora-out
 
 sequence_len: 4096
@@ -29,12 +29,12 @@ lora_fan_in_fan_out:
 wandb_project:
 wandb_entity:
 wandb_watch:
-wandb_name:
+wandb_run_id:
 wandb_log_model:
 
 gradient_accumulation_steps: 4
 micro_batch_size: 2
-num_epochs: 4
+num_epochs: 3
 optimizer: adamw_bnb_8bit
 lr_scheduler: cosine
 learning_rate: 0.0002
@@ -54,10 +54,10 @@ xformers_attention:
 flash_attention: true
 
 warmup_steps: 10
-evals_per_epoch: 4
+eval_steps: 20
 eval_table_size:
 eval_table_max_new_tokens: 128
-saves_per_epoch: 1
+save_steps:
 debug:
 deepspeed:
 weight_decay: 0.0

examples/llama-2/qlora.yml

@@ -11,7 +11,7 @@ datasets:
   - path: mhenrichsen/alpaca_2k_test
     type: alpaca
 dataset_prepared_path:
-val_set_size: 0.05
+val_set_size: 0.01
 output_dir: ./qlora-out
 
 adapter: qlora
@@ -31,12 +31,12 @@ lora_fan_in_fan_out:
 wandb_project:
 wandb_entity:
 wandb_watch:
-wandb_name:
+wandb_run_id:
 wandb_log_model:
 
 gradient_accumulation_steps: 4
 micro_batch_size: 2
-num_epochs: 4
+num_epochs: 3
 optimizer: paged_adamw_32bit
 lr_scheduler: cosine
 learning_rate: 0.0002
@@ -56,9 +56,9 @@ xformers_attention:
 flash_attention: true
 
 warmup_steps: 10
-evals_per_epoch: 4
+eval_steps: 20
 eval_table_size:
-saves_per_epoch: 1
+save_steps:
 debug:
 deepspeed:
 weight_decay: 0.0

examples/llama-2/relora.yml

@@ -11,7 +11,7 @@ datasets:
   - path: teknium/GPT4-LLM-Cleaned
     type: alpaca
 dataset_prepared_path:
-val_set_size: 0.05
+val_set_size: 0.01
 output_dir: ./relora-out
 
 adapter: qlora
@@ -35,12 +35,12 @@ relora_cpu_offload: false
 wandb_project:
 wandb_entity:
 wandb_watch:
-wandb_name:
+wandb_run_id:
 wandb_log_model:
 
 gradient_accumulation_steps: 4
 micro_batch_size: 4
-num_epochs: 4
+num_epochs: 3
 optimizer: adamw_bnb_8bit
 lr_scheduler: cosine
 learning_rate: 0.0002
@@ -60,8 +60,8 @@ xformers_attention:
 flash_attention: true
 
 warmup_steps: 10
-evals_per_epoch: 4
-saves_per_epoch: 1
+eval_steps: 20
+save_steps: 50
 debug:
 deepspeed:
 weight_decay: 0.0

examples/llama-2/tiny-llama.yml

@@ -1,4 +1,4 @@
-base_model: PY007/TinyLlama-1.1B-intermediate-step-715k-1.5T
+base_model: PY007/TinyLlama-1.1B-step-50K-105b
 
 model_type: LlamaForCausalLM
 tokenizer_type: LlamaTokenizer
@@ -12,7 +12,7 @@ datasets:
   - path: mhenrichsen/alpaca_2k_test
     type: alpaca
 dataset_prepared_path:
-val_set_size: 0.05
+val_set_size: 0.01
 output_dir: ./lora-out
 
 sequence_len: 4096
@@ -29,12 +29,12 @@ lora_fan_in_fan_out:
 wandb_project:
 wandb_entity:
 wandb_watch:
-wandb_name:
+wandb_run_id:
 wandb_log_model:
 
 gradient_accumulation_steps: 4
 micro_batch_size: 2
-num_epochs: 4
+num_epochs: 3
 optimizer: adamw_bnb_8bit
 lr_scheduler: cosine
 learning_rate: 0.0002
@@ -54,9 +54,9 @@ xformers_attention:
 flash_attention: true
 
 warmup_steps: 10
-evals_per_epoch: 4
+eval_steps: 20
 eval_table_size:
-saves_per_epoch: 1
+save_steps:
 debug:
 deepspeed:
 weight_decay: 0.0

examples/mamba/config.yml

@@ -1,61 +0,0 @@
-base_model: state-spaces/mamba-2.8b
-model_type: MambaLMHeadModel
-tokenizer_type: AutoTokenizer
-tokenizer_config: EleutherAI/gpt-neox-20b
-
-load_in_8bit: false
-load_in_4bit: false
-strict: false
-
-datasets:
-  - path: mhenrichsen/alpaca_2k_test
-    type: alpaca
-dataset_prepared_path:
-val_set_size: 0.0
-output_dir: ./out
-
-sequence_len: 2048
-sample_packing: false
-pad_to_sequence_len: false
-
-wandb_project:
-wandb_entity:
-wandb_watch:
-wandb_name:
-wandb_log_model:
-
-gradient_accumulation_steps: 4
-micro_batch_size: 1
-num_epochs: 2
-optimizer: paged_adamw_8bit
-lr_scheduler: cosine
-learning_rate: 5e-5
-
-train_on_inputs: false
-group_by_length: true
-
-bf16: true
-fp16: false
-tf32: true
-
-gradient_checkpointing: false
-early_stopping_patience:
-resume_from_checkpoint:
-local_rank:
-logging_steps: 1
-xformers_attention:
-flash_attention:
-
-warmup_steps: 10
-evals_per_epoch: 4
-eval_table_size:
-eval_table_max_new_tokens: 128
-saves_per_epoch: 1
-debug:
-deepspeed:
-weight_decay: 0.0
-fsdp:
-fsdp_config:
-special_tokens:
-tokens:
-save_safetensors: False

examples/mistral/config.yml

@@ -11,7 +11,7 @@ datasets:
   - path: mhenrichsen/alpaca_2k_test
     type: alpaca
 dataset_prepared_path:
-val_set_size: 0.05
+val_set_size: 0.01
 output_dir: ./out
 
 sequence_len: 8192
@@ -21,12 +21,12 @@ pad_to_sequence_len: true
 wandb_project:
 wandb_entity:
 wandb_watch:
-wandb_name:
+wandb_run_id:
 wandb_log_model:
 
 gradient_accumulation_steps: 4
 micro_batch_size: 2
-num_epochs: 4
+num_epochs: 3
 optimizer: adamw_bnb_8bit
 lr_scheduler: cosine
 learning_rate: 0.000005
@@ -46,10 +46,10 @@ xformers_attention:
 flash_attention: true
 
 warmup_steps: 10
-evals_per_epoch: 4
+eval_steps: 20
 eval_table_size:
 eval_table_max_new_tokens: 128
-saves_per_epoch: 1
+save_steps:
 debug:
 deepspeed:
 weight_decay: 0.0

examples/mistral/mixtral.yml

@@ -1,88 +0,0 @@
-base_model: mistralai/Mixtral-8x7B-v0.1
-model_type: AutoModelForCausalLM
-tokenizer_type: LlamaTokenizer
-trust_remote_code: true
-
-load_in_8bit: false
-load_in_4bit: true
-strict: false
-
-datasets:
-  - path: tatsu-lab/alpaca
-    type: alpaca
-dataset_prepared_path: last_run_prepared
-val_set_size: 0.0
-output_dir: ./qlora-out
-
-## You can optionally freeze the entire model and unfreeze a subset of parameters
-unfrozen_parameters:
-#  - lm_head.*
-#  - model.embed_tokens.*
-#  - model.layers.2[0-9]+.block_sparse_moe.gate.*
-#  - model.layers.2[0-9]+.block_sparse_moe.experts.*
-#  - model.layers.3[0-9]+.block_sparse_moe.gate.*
-#  - model.layers.3[0-9]+.block_sparse_moe.experts.*
-
-adapter: qlora
-lora_model_dir:
-
-sequence_len: 4096
-sample_packing: true
-pad_to_sequence_len: true
-
-lora_r: 32
-lora_alpha: 16
-lora_dropout: 0.05
-lora_target_linear: true
-lora_fan_in_fan_out:
-#lora_target_modules:
-#  - gate
-#  - q_proj
-#  - k_proj
-#  - v_proj
-#  - o_proj
-#  - w1
-#  - w2
-#  - w3
-
-wandb_project:
-wandb_entity:
-wandb_watch:
-wandb_name:
-wandb_log_model:
-
-gradient_accumulation_steps: 2
-micro_batch_size: 1
-num_epochs: 1
-optimizer: adamw_bnb_8bit
-lr_scheduler: cosine
-learning_rate: 0.0002
-
-train_on_inputs: false
-group_by_length: false
-bf16: true
-fp16: false
-tf32: false
-
-gradient_checkpointing: true
-early_stopping_patience:
-resume_from_checkpoint:
-local_rank:
-logging_steps: 1
-xformers_attention:
-flash_attention: true
-
-loss_watchdog_threshold: 5.0
-loss_watchdog_patience: 3
-
-warmup_steps: 10
-evals_per_epoch: 4
-eval_table_size:
-eval_table_max_new_tokens: 128
-saves_per_epoch: 1
-debug:
-deepspeed: deepspeed/zero2.json
-weight_decay: 0.0
-fsdp:
-fsdp_config:
-special_tokens:

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: 0.05
+val_set_size: 0.01
 output_dir: ./qlora-out
 
 adapter: qlora
@@ -38,7 +38,7 @@ lora_target_modules:
 wandb_project:
 wandb_entity:
 wandb_watch:
-wandb_name:
+wandb_run_id:
 wandb_log_model:
 
 gradient_accumulation_steps: 4
@@ -62,14 +62,11 @@ logging_steps: 1
 xformers_attention:
 flash_attention: true
 
-loss_watchdog_threshold: 5.0
-loss_watchdog_patience: 3
-
 warmup_steps: 10
-evals_per_epoch: 4
+eval_steps: 20
 eval_table_size:
 eval_table_max_new_tokens: 128
-saves_per_epoch: 1
+save_steps:
 debug:
 deepspeed:
 weight_decay: 0.0

examples/mpt-7b/config.yml

@@ -21,12 +21,12 @@ lora_fan_in_fan_out: false
 wandb_project: mpt-alpaca-7b
 wandb_entity:
 wandb_watch:
-wandb_name:
+wandb_run_id:
 wandb_log_model:
 output_dir: ./mpt-alpaca-7b
 gradient_accumulation_steps: 1
 micro_batch_size: 1
-num_epochs: 4
+num_epochs: 3
 optimizer: adamw_bnb_8bit
 torchdistx_path:
 lr_scheduler: cosine
@@ -44,8 +44,8 @@ flash_attention:
 gptq_groupsize:
 gptq_model_v1:
 warmup_steps: 20
-evals_per_epoch: 4
-saves_per_epoch: 1
+eval_steps: 110
+save_steps: 660
 debug:
 deepspeed:
 weight_decay: 0.0001

examples/multimodal/llava-mistral.yml

@@ -0,0 +1,63 @@
+base_model: mistralai/Mistral-7B-v0.1
+model_type: MistralForCausalLM
+tokenizer_type: LlamaTokenizer
+is_mistral_derived_model: true
+multimodal: true
+
+vision_tower: openai/clip-vit-large-patch14
+tune_mm_mlp_adapter: true
+mm_vision_select_layer: -2
+mm_projector_type: mlp2x_gelu
+mm_image_folder: ./llava/
+
+load_in_8bit: false
+load_in_4bit: false
+strict: false
+
+datasets:
+  - path: liuhaotian/LLaVA-CC3M-Pretrain-595K
+dataset_prepared_path:
+val_set_size: 0.01
+output_dir: ./out
+
+sequence_len: 2048
+sample_packing: true
+pad_to_sequence_len: true
+
+wandb_project:
+wandb_entity:
+wandb_watch:
+wandb_run_id:
+wandb_log_model:
+
+gradient_accumulation_steps: 4
+micro_batch_size: 2
+num_epochs: 4
+optimizer: adamw_bnb_8bit
+lr_scheduler: cosine
+learning_rate: 0.002
+
+train_on_inputs: false
+group_by_length: false
+bf16: true
+fp16: false
+tf32: false
+
+gradient_checkpointing: true
+early_stopping_patience:
+resume_from_checkpoint:
+local_rank:
+logging_steps: 1
+xformers_attention:
+flash_attention: true
+
+warmup_steps: 10
+eval_steps: 0.05
+save_steps:
+debug:
+deepspeed:
+weight_decay: 0.0
+fsdp:
+fsdp_config:
+special_tokens:
+  pad_token: "<unk>"

examples/openllama-3b/config.yml

@@ -23,7 +23,7 @@ lora_fan_in_fan_out:
 wandb_project:
 wandb_entity:
 wandb_watch:
-wandb_name:
+wandb_run_id:
 wandb_log_model:
 output_dir: ./openllama-out
 gradient_accumulation_steps: 1
@@ -49,8 +49,8 @@ flash_attention: true
 gptq_groupsize:
 gptq_model_v1:
 warmup_steps: 20
-evals_per_epoch: 4
-saves_per_epoch: 1
+eval_steps: 0.05
+save_steps:
 debug:
 deepspeed:
 weight_decay: 0.1

examples/openllama-3b/lora.yml

@@ -29,7 +29,7 @@ lora_fan_in_fan_out:
 wandb_project:
 wandb_entity:
 wandb_watch:
-wandb_name:
+wandb_run_id:
 wandb_log_model:
 output_dir: ./lora-out
 gradient_accumulation_steps: 1
@@ -54,8 +54,8 @@ flash_attention: true
 gptq_groupsize:
 gptq_model_v1:
 warmup_steps: 20
-evals_per_epoch: 4
-saves_per_epoch: 1
+eval_steps: 0.05
+save_steps:
 debug:
 deepspeed:
 weight_decay: 0.1

examples/openllama-3b/qlora.yml

@@ -9,7 +9,7 @@ datasets:
   - path: teknium/GPT4-LLM-Cleaned
     type: alpaca
 dataset_prepared_path:
-val_set_size: 0.05
+val_set_size: 0.01
 adapter: qlora
 lora_model_dir:
 sequence_len: 1024
@@ -23,7 +23,7 @@ lora_fan_in_fan_out:
 wandb_project:
 wandb_entity:
 wandb_watch:
-wandb_name:
+wandb_run_id:
 wandb_log_model:
 output_dir: ./qlora-out
 gradient_accumulation_steps: 1
@@ -48,8 +48,8 @@ flash_attention: true
 gptq_groupsize:
 gptq_model_v1:
 warmup_steps: 20
-evals_per_epoch: 4
-saves_per_epoch: 1
+eval_steps: 0.05
+save_steps:
 debug:
 deepspeed:
 weight_decay: 0.1

examples/phi/phi-ft.yml

@@ -1,5 +1,5 @@
 base_model: microsoft/phi-1_5
-model_type: PhiForCausalLM
+model_type: MixFormerSequentialForCausalLM
 tokenizer_type: AutoTokenizer
 is_llama_derived_model: false
 trust_remote_code: true
@@ -31,7 +31,7 @@ lora_fan_in_fan_out:
 wandb_project:
 wandb_entity:
 wandb_watch:
-wandb_name:
+wandb_run_id:
 wandb_log_model:
 
 gradient_accumulation_steps: 1
@@ -59,8 +59,8 @@ xformers_attention:
 flash_attention:
 
 warmup_steps: 100
-evals_per_epoch: 4
-saves_per_epoch: 1
+eval_steps: 0.05
+save_steps:
 debug:
 deepspeed:
 weight_decay: 0.1

examples/phi/phi-qlora.yml

@@ -31,7 +31,7 @@ lora_fan_in_fan_out:
 wandb_project:
 wandb_entity:
 wandb_watch:
-wandb_name:
+wandb_run_id:
 wandb_log_model:
 
 gradient_accumulation_steps: 1
@@ -59,8 +59,8 @@ xformers_attention:
 flash_attention:
 
 warmup_steps: 100
-evals_per_epoch: 4
-saves_per_epoch: 1
+eval_steps: 0.05
+save_steps:
 debug:
 deepspeed:
 weight_decay: 0.1

examples/pythia-12b/config.yml

@@ -24,7 +24,7 @@ lora_fan_in_fan_out: true  # pythia/GPTNeoX lora specific
 wandb_project:
 wandb_entity:
 wandb_watch:
-wandb_name:
+wandb_run_id:
 wandb_log_model:
 output_dir: ./pythia-12b
 gradient_accumulation_steps: 1

examples/pythia/lora.yml

@@ -18,12 +18,12 @@ lora_fan_in_fan_out: true  # pythia/GPTNeoX lora specific
 wandb_project:
 wandb_entity:
 wandb_watch:
-wandb_name:
+wandb_run_id:
 wandb_log_model:
 output_dir: ./lora-alpaca-pythia
 gradient_accumulation_steps: 1
 micro_batch_size: 4
-num_epochs: 4
+num_epochs: 3
 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
-evals_per_epoch: 4
+eval_steps: 20
 logging_steps: 1

examples/qwen/lora.yml

@@ -1,68 +0,0 @@
-base_model: Qwen/Qwen-7B
-model_type: AutoModelForCausalLM
-tokenizer_type: AutoTokenizer
-
-is_qwen_derived_model: true
-trust_remote_code: true
-
-load_in_8bit: true
-load_in_4bit: false
-strict: false
-
-datasets:
-  - path: mhenrichsen/alpaca_2k_test
-    type: alpaca
-dataset_prepared_path:
-val_set_size: 0.05
-output_dir: ./lora-out
-
-sequence_len: 2048  # supports up to 8192
-sample_packing: false
-pad_to_sequence_len:
-
-adapter: lora
-lora_model_dir:
-lora_r: 32
-lora_alpha: 16
-lora_dropout: 0.05
-lora_target_linear: true
-lora_fan_in_fan_out:
-
-wandb_project:
-wandb_entity:
-wandb_watch:
-wandb_name:
-wandb_log_model:
-
-gradient_accumulation_steps: 4
-micro_batch_size: 2
-num_epochs: 4
-optimizer: adamw_bnb_8bit
-lr_scheduler: cosine
-learning_rate: 0.0002
-
-train_on_inputs: false
-group_by_length: false
-bf16: true
-fp16: false
-tf32: false
-
-gradient_checkpointing: false
-early_stopping_patience:
-resume_from_checkpoint:
-local_rank:
-logging_steps: 1
-xformers_attention:
-flash_attention:
-
-warmup_steps: 10
-evals_per_epoch: 4
-eval_table_size:
-eval_table_max_new_tokens: 128
-saves_per_epoch: 1
-debug:
-deepspeed:
-weight_decay: 0.0
-fsdp:
-fsdp_config:
-special_tokens:

examples/qwen/qlora.yml

@@ -1,68 +0,0 @@
-base_model: Qwen/Qwen-7B
-model_type: AutoModelForCausalLM
-tokenizer_type: AutoTokenizer
-
-is_qwen_derived_model: true
-trust_remote_code: true
-
-load_in_8bit: false
-load_in_4bit: true
-strict: false
-
-datasets:
-  - path: mhenrichsen/alpaca_2k_test
-    type: alpaca
-dataset_prepared_path:
-val_set_size: 0.05
-output_dir: ./lora-out
-
-sequence_len: 2048  # supports up to 8192
-sample_packing: false
-pad_to_sequence_len:
-
-adapter: qlora
-lora_model_dir:
-lora_r: 32
-lora_alpha: 16
-lora_dropout: 0.05
-lora_target_linear: true
-lora_fan_in_fan_out:
-
-wandb_project:
-wandb_entity:
-wandb_watch:
-wandb_name:
-wandb_log_model:
-
-gradient_accumulation_steps: 4
-micro_batch_size: 2
-num_epochs: 4
-optimizer: adamw_bnb_8bit
-lr_scheduler: cosine
-learning_rate: 0.0002
-
-train_on_inputs: false
-group_by_length: false
-bf16: true
-fp16: false
-tf32: false
-
-gradient_checkpointing: false
-early_stopping_patience:
-resume_from_checkpoint:
-local_rank:
-logging_steps: 1
-xformers_attention:
-flash_attention:
-
-warmup_steps: 10
-evals_per_epoch: 4
-eval_table_size:
-eval_table_max_new_tokens: 128
-saves_per_epoch: 1
-debug:
-deepspeed:
-weight_decay: 0.0
-fsdp:
-fsdp_config:
-special_tokens:

examples/redpajama/config-3b.yml

@@ -22,12 +22,12 @@ lora_fan_in_fan_out: false
 wandb_project: redpajama-alpaca-3b
 wandb_entity:
 wandb_watch:
-wandb_name:
+wandb_run_id:
 wandb_log_model:
 output_dir: ./redpajama-alpaca-3b
 batch_size: 4
 micro_batch_size: 1
-num_epochs: 4
+num_epochs: 3
 optimizer: adamw_bnb_8bit
 torchdistx_path:
 lr_scheduler: cosine
@@ -45,8 +45,8 @@ flash_attention:
 gptq_groupsize:
 gptq_model_v1:
 warmup_steps: 20
-evals_per_epoch: 4
-saves_per_epoch: 1
+eval_steps: 110
+save_steps: 660
 debug:
 deepspeed:
 weight_decay: 0.0001

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

@@ -21,12 +21,12 @@ lora_fan_in_fan_out:
 wandb_project: lora-replit
 wandb_entity:
 wandb_watch:
-wandb_name:
+wandb_run_id:
 wandb_log_model:
 output_dir: ./lora-replit
 batch_size: 8
 micro_batch_size: 1
-num_epochs: 4
+num_epochs: 3
 optimizer:
 torchdistx_path:
 lr_scheduler:
@@ -45,8 +45,8 @@ flash_attention:
 gptq_groupsize:
 gptq_model_v1:
 warmup_steps: 20
-evals_per_epoch: 4
-saves_per_epoch: 1
+eval_steps: 50
+save_steps:
 debug:
 deepspeed:
 weight_decay: 0

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: 0.05
+val_set_size: 0.01
 # enable QLoRA
 adapter: qlora
 lora_model_dir:
@@ -38,7 +38,7 @@ lora_fan_in_fan_out:
 wandb_project:
 wandb_entity:
 wandb_watch:
-wandb_name:
+wandb_run_id:
 wandb_log_model:
 output_dir: ./qlora-out
 
@@ -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: 4
+num_epochs: 3
 # Optimizer for QLoRA
 optimizer: paged_adamw_32bit
 torchdistx_path:
@@ -78,8 +78,8 @@ flash_attention:
 gptq_groupsize:
 gptq_model_v1:
 warmup_steps: 10
-evals_per_epoch: 4
-saves_per_epoch: 1
+eval_steps: 50
+save_steps: 50
 debug:
 deepspeed:
 weight_decay: 0.0

requirements.txt

@@ -1,22 +1,23 @@
+--extra-index-url https://download.pytorch.org/whl/cu118
 --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/
-auto-gptq==0.5.1
+torch==2.0.1
+auto-gptq
 packaging
-peft==0.6.0
-transformers @ git+https://github.com/huggingface/transformers.git@ebfdb9ca62205279d5019ef1403877461b3b2da4
-tokenizers==0.15.0
+peft @ git+https://github.com/huggingface/peft.git
+transformers @ git+https://github.com/huggingface/transformers.git@acc394c4f5e1283c19783581790b3dc3105a3697
 bitsandbytes>=0.41.1
-accelerate==0.24.1
+accelerate @ git+https://github.com/huggingface/accelerate@80da9cfb09bb3cc9f1b385cb55d6b90d025a5fd9
 deepspeed
 addict
 fire
 PyYAML>=6.0
-datasets>=2.15.0
-flash-attn==2.3.3
+datasets
+flash-attn>=2.3.0
 sentencepiece
 wandb
 einops
-xformers==0.0.22
-optimum==1.13.2
+xformers>=0.0.22
+optimum
 hf_transfer
 colorama
 numba
@@ -29,11 +30,4 @@ scipy
 scikit-learn==1.2.2
 pynvml
 art
-fschat==0.2.34
-gradio==3.50.2
-tensorboard
-
-# remote filesystems
-s3fs
-gcsfs
-# adlfs
+fschat==0.2.29

scripts/finetune.py

@@ -45,6 +45,8 @@ 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)
 
 

setup.py

@@ -46,13 +46,10 @@ setup(
     dependency_links=dependency_links,
     extras_require={
         "flash-attn": [
-            "flash-attn==2.3.3",
+            "flash-attn>=2.3.0",
         ],
         "deepspeed": [
             "deepspeed",
         ],
-        "mamba-ssm": [
-            "mamba-ssm==1.0.1",
-        ],
     },
 )

src/axolotl/cli/__init__.py

@@ -2,14 +2,13 @@
 
 import importlib
 import logging
+import math
 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
 
@@ -18,7 +17,7 @@ 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, TextIteratorStreamer, TextStreamer
+from transformers import GenerationConfig, TextStreamer
 
 from axolotl.common.cli import TrainerCliArgs, load_model_and_tokenizer
 from axolotl.logging_config import configure_logging
@@ -29,7 +28,6 @@ from axolotl.utils.dict import DictDefault
 from axolotl.utils.distributed import is_main_process
 from axolotl.utils.models import load_tokenizer
 from axolotl.utils.tokenization import check_dataset_labels
-from axolotl.utils.trainer import prepare_optim_env
 from axolotl.utils.wandb_ import setup_wandb_env_vars
 
 project_root = os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))
@@ -47,7 +45,7 @@ def print_axolotl_text_art(suffix=None):
     ascii_text = "  axolotl"
     if suffix:
         ascii_text += f"  x  {suffix}"
-    ascii_art = text2art(ascii_text, font=font)
+    ascii_art = text2art(" axolotl", font=font)
 
     if is_main_process():
         print(ascii_art)
@@ -72,7 +70,7 @@ def do_merge_lora(
 
     LOG.info("running merge of LoRA with base model")
     model = model.merge_and_unload()
-    model.to(dtype=cfg.torch_dtype)
+    model.to(dtype=torch.float16)
 
     if cfg.local_rank == 0:
         LOG.info(f"saving merged model to: {str(Path(cfg.output_dir) / 'merged')}")
@@ -156,91 +154,6 @@ 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"))
 
@@ -297,14 +210,51 @@ def load_cfg(config: Path = Path("examples/"), **kwargs):
 
     validate_config(cfg)
 
-    prepare_optim_env(cfg)
-
     normalize_config(cfg)
 
     setup_wandb_env_vars(cfg)
     return cfg
 
 
+def load_mm_dataset(
+    *,
+    cfg: DictDefault,
+    cli_args: TrainerCliArgs,  # pylint: disable=unused-argument
+    model,
+):
+    # pylint: disable=duplicate-code
+    from llava.train.train import DataArguments, LazySupervisedDataset
+
+    vision_tower = model.get_vision_tower()
+    data_args = DataArguments(
+        data_path=cfg.datasets[0]["path"],
+        lazy_preprocess=cfg.mm_lazy_preprocess
+        if cfg.mm_lazy_preprocess is not None
+        else True,
+        is_multimodal=True,
+        image_folder=cfg.mm_image_folder or None,
+        image_aspect_ratio=cfg.mm_image_aspect_ratio or "square",
+        image_grid_pinpoints=cfg.mm_image_grid_pinpoints or None,
+    )
+    data_args.image_processor = vision_tower.image_processor
+    tokenizer = load_tokenizer(cfg)
+    train_dataset = LazySupervisedDataset(
+        tokenizer=tokenizer,
+        data_path=data_args["data_path"],
+        data_args=data_args,
+    )
+
+    total_num_steps = int(
+        math.ceil(len(train_dataset) * cfg.num_epochs / cfg.batch_size)
+    )
+
+    return TrainDatasetMeta(
+        train_dataset=train_dataset,
+        eval_dataset=None,
+        total_num_steps=total_num_steps,
+    )
+
+
 def load_datasets(
     *,
     cfg: DictDefault,
@@ -312,9 +262,7 @@ def load_datasets(
 ) -> TrainDatasetMeta:
     tokenizer = load_tokenizer(cfg)
 
-    train_dataset, eval_dataset, total_num_steps, prompters = prepare_dataset(
-        cfg, tokenizer
-    )
+    train_dataset, eval_dataset, total_num_steps = prepare_dataset(cfg, tokenizer)
 
     if cli_args.debug or cfg.debug:
         LOG.info("check_dataset_labels...")
@@ -330,10 +278,6 @@ 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

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

src/axolotl/cli/train.py

@@ -6,6 +6,7 @@ from pathlib import Path
 
 import fire
 import transformers
+from colorama import Fore
 
 from axolotl.cli import (
     check_accelerate_default_config,
@@ -15,6 +16,7 @@ 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")
@@ -22,15 +24,26 @@ LOG = logging.getLogger("axolotl.cli.train")
 
 def do_cli(config: Path = Path("examples/"), **kwargs):
     # pylint: disable=duplicate-code
-    parsed_cfg = load_cfg(config, **kwargs)
     print_axolotl_text_art()
+    parsed_cfg = load_cfg(config, **kwargs)
     check_accelerate_default_config()
     check_user_token()
     parser = transformers.HfArgumentParser((TrainerCliArgs))
     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/cli/train_mm.py

@@ -12,13 +12,15 @@ from axolotl.cli import (
     check_accelerate_default_config,
     check_user_token,
     load_cfg,
-    load_datasets,
+    load_mm_dataset,
     print_axolotl_text_art,
 )
-from axolotl.common.cli import PreprocessCliArgs
+from axolotl.common.cli import TrainerCliArgs
 from axolotl.common.const import DEFAULT_DATASET_PREPARED_PATH
+from axolotl.train import train
+from axolotl.utils.models import load_model, load_tokenizer
 
-LOG = logging.getLogger("axolotl.cli.preprocess")
+LOG = logging.getLogger("axolotl.cli.train")
 
 
 def do_cli(config: Path = Path("examples/"), **kwargs):
@@ -27,26 +29,27 @@ def do_cli(config: Path = Path("examples/"), **kwargs):
     parsed_cfg = load_cfg(config, **kwargs)
     check_accelerate_default_config()
     check_user_token()
-    parser = transformers.HfArgumentParser((PreprocessCliArgs))
+    parser = transformers.HfArgumentParser((TrainerCliArgs))
     parsed_cli_args, _ = parser.parse_args_into_dataclasses(
         return_remaining_strings=True
     )
-    if not parsed_cfg.dataset_prepared_path:
+    if parsed_cli_args.prepare_ds_only and 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}"
+            + "--prepare_ds_only called without dataset_prepared_path set."
             + 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
+    tokenizer = load_tokenizer(parsed_cfg)
+    model, _ = load_model(parsed_cfg, tokenizer)
+    dataset_meta = load_mm_dataset(
+        cfg=parsed_cfg, cli_args=parsed_cli_args, model=model
     )
+    if parsed_cli_args.prepare_ds_only:
+        return
+    train(cfg=parsed_cfg, cli_args=parsed_cli_args, dataset_meta=dataset_meta)
 
 
 if __name__ == "__main__":

src/axolotl/common/cli.py

@@ -25,22 +25,11 @@ 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

@@ -6,36 +6,33 @@ import abc
 import importlib
 import logging
 import math
+import os
 import sys
 from abc import abstractmethod
 from dataclasses import dataclass, field
 from functools import partial
 from pathlib import Path
-from typing import Optional
+from typing import Optional, Union
 
 import torch
 import transformers
 from datasets import Dataset
 from torch.optim.lr_scheduler import OneCycleLR
-from torch.utils.data import BatchSampler, DataLoader, RandomSampler, SequentialSampler
+from torch.utils.data import DataLoader, DistributedSampler, SequentialSampler
 from transformers import EarlyStoppingCallback, Trainer, TrainingArguments
-from transformers.trainer_utils import seed_worker
+from transformers.trainer_pt_utils import SequentialDistributedSampler
 
 from axolotl.monkeypatch.relora import ReLoRACallback, ReLoRAScheduler
 from axolotl.utils.callbacks import (
     EvalFirstStepCallback,
     GPUStatsCallback,
-    LossWatchDogCallback,
     SaveAxolotlConfigtoWandBCallback,
     SaveBetterTransformerModelCallback,
     bench_eval_callback_factory,
     log_prediction_callback_factory,
 )
-from axolotl.utils.collators import (
-    BatchSamplerDataCollatorForSeq2Seq,
-    MambaDataCollator,
-)
-from axolotl.utils.samplers import MultipackBatchSampler
+from axolotl.utils.collators import DataCollatorForSeq2Seq
+from axolotl.utils.dataloader import MultipackDistributedDataloader
 from axolotl.utils.schedulers import get_cosine_schedule_with_quadratic_warmup
 
 try:
@@ -43,6 +40,14 @@ try:
 except ImportError:
     pass
 
+try:
+    from llava.train.llava_trainer import get_mm_adapter_state_maybe_zero_3
+except ImportError:
+
+    def get_mm_adapter_state_maybe_zero_3(named_params, keys_to_match):
+        raise ImportError("missing LLaVA package")
+
+
 LOG = logging.getLogger("axolotl.core.trainer_builder")
 
 
@@ -52,9 +57,6 @@ class AxolotlTrainingArguments(TrainingArguments):
     Extend the base TrainingArguments for axolotl helpers
     """
 
-    model_type: Optional[str] = field(
-        default=None, metadata={"help": "HF model configuration model_type."}
-    )
     lr_quadratic_warmup: bool = field(
         default=False,
         metadata={"help": "Use quadratic warmup for cosine scheduling."},
@@ -108,10 +110,6 @@ 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):
@@ -121,8 +119,7 @@ class AxolotlTrainer(Trainer):
 
     args = None  # type: AxolotlTrainingArguments
 
-    def __init__(self, *args, num_epochs=1, bench_data_collator=None, **kwargs):
-        self.num_epochs = num_epochs
+    def __init__(self, *args, bench_data_collator=None, **kwargs):
         self.bench_data_collator = bench_data_collator
         super().__init__(*args, **kwargs)
 
@@ -155,102 +152,68 @@ class AxolotlTrainer(Trainer):
         return self.lr_scheduler
 
     def _get_train_sampler(self) -> Optional[torch.utils.data.Sampler]:
-        if self.args.sample_packing:
-            return MultipackBatchSampler(
-                RandomSampler(self.train_dataset),
-                self.args.train_batch_size,
-                drop_last=True,
-                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,
+        if self.args.world_size > 1 and self.args.sample_packing:
+            return DistributedSampler(
+                self.train_dataset,
+                num_replicas=self.args.world_size,
+                rank=self.args.process_index,
+                seed=self.args.seed,
             )
         return super()._get_train_sampler()
 
     def _get_eval_sampler(
         self, eval_dataset: Dataset
     ) -> Optional[torch.utils.data.Sampler]:
-        if self.args.sample_packing and self.args.eval_sample_packing is not False:
-            return MultipackBatchSampler(
-                SequentialSampler(eval_dataset),
-                self.args.per_device_eval_batch_size,
-                drop_last=True,
-                batch_max_len=self.args.eval_batch_size * self.args.max_seq_length,
-                lengths=(
-                    eval_dataset.data.column("position_ids")
-                    .to_pandas()
-                    .apply(lambda x: x[-1] + 1)
-                    .values
-                ),
-                packing_efficiency_estimate=self.args.sample_packing_efficiency,
+        if (
+            self.args.world_size > 1
+            and self.args.sample_packing
+            and self.args.eval_sample_packing is not False
+        ):
+            return SequentialDistributedSampler(
+                eval_dataset,
+                num_replicas=self.args.world_size,
+                rank=self.args.process_index,
+                batch_size=self.args.per_device_eval_batch_size,
             )
         return super()._get_eval_sampler(eval_dataset)
 
-    def get_train_dataloader(self) -> DataLoader:
+    def get_train_dataloader(self) -> Union[DataLoader, MultipackDistributedDataloader]:
         if self.args.sample_packing:
-            train_dataset = self.train_dataset
-            train_dataset = train_dataset.remove_columns(["length"])
-            data_collator = self.data_collator
-            dataloader_params = {
-                "batch_size": self._train_batch_size,
-                "collate_fn": data_collator,
-                "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
-
-            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)
+            train_sampler = self._get_train_sampler()
+            return self.accelerator.prepare(
+                MultipackDistributedDataloader(
+                    self.train_dataset,
+                    batch_size=self._train_batch_size,
+                    seq_max_length=self.args.max_seq_length,
+                    collate_fn=self.data_collator,
+                    sampler=train_sampler,
+                    packing_efficiency_estimate=self.args.sample_packing_efficiency,
+                    sample_packing_seq_len_multiplier=self.args.sample_packing_seq_len_multiplier,
+                    device_count=int(os.environ.get("WORLD_SIZE", 1)),
+                )
             )
         return super().get_train_dataloader()
 
-    def get_eval_dataloader(self, eval_dataset: Optional[Dataset] = None) -> DataLoader:
+    def get_eval_dataloader(
+        self, eval_dataset: Optional[Dataset] = None
+    ) -> Union[DataLoader, MultipackDistributedDataloader]:
         if self.args.sample_packing and self.args.eval_sample_packing is not False:
             eval_dataset = (
                 eval_dataset if eval_dataset is not None else self.eval_dataset
             )
 
             eval_sampler = self._get_eval_sampler(eval_dataset)
-            eval_dataset = eval_dataset.remove_columns(["length"])
-            data_collator = self.data_collator
-            dataloader_params = {
-                "batch_size": self.args.eval_batch_size,
-                "collate_fn": data_collator,
-                "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 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 self.accelerator.prepare(
+                MultipackDistributedDataloader(
+                    eval_dataset,
+                    batch_size=self.args.eval_batch_size,
+                    seq_max_length=self.args.max_seq_length,
+                    collate_fn=self.data_collator,
+                    sampler=eval_sampler,
+                    packing_efficiency_estimate=self.args.sample_packing_efficiency,
+                    sample_packing_seq_len_multiplier=self.args.eval_batch_size,
+                    device_count=int(os.environ.get("WORLD_SIZE", 1)),
+                )
             )
         return super().get_eval_dataloader(eval_dataset)
 
@@ -264,15 +227,13 @@ class AxolotlTrainer(Trainer):
     def get_bench_dataloader(
         self,
         bench_dataset: Dataset,
-    ) -> DataLoader:
+    ) -> Union[DataLoader, MultipackDistributedDataloader]:
         dataloader_params = {
             "batch_size": self.args.eval_batch_size,
             "collate_fn": self.bench_data_collator,
             "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)
@@ -290,31 +251,35 @@ class AxolotlTrainer(Trainer):
         #     return (loss, outputs) if return_outputs else loss
         return super().compute_loss(model, inputs, return_outputs=return_outputs)
 
+    def _save_checkpoint(self, model, trial, metrics=None):
+        if getattr(self.args, "tune_mm_mlp_adapter", False):
+            from transformers.trainer_utils import PREFIX_CHECKPOINT_DIR
 
-class AxolotlMambaTrainer(AxolotlTrainer):
-    """
-    Mamba specific trainer to handle loss calculation
-    """
+            checkpoint_folder = f"{PREFIX_CHECKPOINT_DIR}-{self.state.global_step}"
 
-    def compute_loss(
-        self,
-        model,
-        inputs,
-        return_outputs=False,  # pylint: disable=unused-argument
-    ):
-        input_ids = inputs.pop("input_ids")
-        lm_logits = model(input_ids).logits
+            run_dir = self._get_output_dir(trial=trial)
+            output_dir = os.path.join(run_dir, checkpoint_folder)
 
-        labels = input_ids.to(lm_logits.device)
-        shift_logits = lm_logits[:, :-1, :].contiguous()
-        labels = labels[:, 1:].contiguous()
+            # Only save Adapter
+            keys_to_match = ["mm_projector", "vision_resampler"]
+            if getattr(self.args, "use_im_start_end", False):
+                keys_to_match.extend(["embed_tokens", "embed_in"])
 
-        loss_fct = torch.nn.CrossEntropyLoss()
-        lm_loss = loss_fct(
-            shift_logits.view(-1, shift_logits.size(-1)), labels.view(-1)
-        )
+            weight_to_save = get_mm_adapter_state_maybe_zero_3(
+                self.model.named_parameters(), keys_to_match
+            )
 
-        return lm_loss
+            if self.args.local_rank in (0, -1):
+                self.model.config.save_pretrained(output_dir)
+                torch.save(weight_to_save, os.path.join(output_dir, "mm_projector.bin"))
+        else:
+            super()._save_checkpoint(model, trial, metrics)
+
+    def _save(self, output_dir: Optional[str] = None, state_dict=None):
+        if getattr(self.args, "tune_mm_mlp_adapter", False):
+            pass
+        else:
+            super()._save(output_dir, state_dict)
 
 
 class OneCycleLRSchedulerTrainer(AxolotlTrainer):
@@ -463,9 +428,6 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
                 SaveAxolotlConfigtoWandBCallback(self.cfg.axolotl_config_path)
             )
 
-        if self.cfg.loss_watchdog_threshold is not None:
-            callbacks.append(LossWatchDogCallback(self.cfg))
-
         return callbacks
 
     def get_post_trainer_create_callbacks(self, trainer):
@@ -494,19 +456,14 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
             return OneCycleLRSchedulerTrainer
         if self.cfg.relora_steps:
             return ReLoRATrainer
-        if self.cfg.model_config_type == "mamba":
-            return AxolotlMambaTrainer
         return AxolotlTrainer
 
     def build(self, total_num_steps):
-        warmup_steps = None
-        if self.cfg.warmup_steps is not None:
-            warmup_steps = self.cfg.warmup_steps
-        elif self.cfg.warmup_ratio is not None:
-            warmup_steps = max(int(self.cfg.warmup_ratio * total_num_steps), 0)
-        else:
-            warmup_steps = min(int(0.03 * total_num_steps), 100)
-
+        warmup_steps = (
+            self.cfg.warmup_steps
+            if self.cfg.warmup_steps is not None
+            else min(int(0.03 * total_num_steps), 100)
+        )
         logging_steps = (
             self.cfg.logging_steps
             if self.cfg.logging_steps is not None
@@ -563,7 +520,7 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
             if self.cfg.hub_strategy:
                 training_arguments_kwargs["hub_strategy"] = self.cfg.hub_strategy
 
-        if self.cfg.save_safetensors is not None:
+        if self.cfg.save_safetensors:
             training_arguments_kwargs["save_safetensors"] = self.cfg.save_safetensors
 
         if self.cfg.sample_packing_eff_est:
@@ -571,29 +528,16 @@ 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.val_set_size == 0:
-            # no eval set, so don't eval
-            training_arguments_kwargs["evaluation_strategy"] = "no"
-        elif self.cfg.eval_steps:
+        if self.cfg.eval_steps:
             training_arguments_kwargs["evaluation_strategy"] = "steps"
             training_arguments_kwargs["eval_steps"] = self.cfg.eval_steps
         elif self.cfg.evaluation_strategy:
             training_arguments_kwargs[
                 "evaluation_strategy"
             ] = self.cfg.evaluation_strategy
+        elif self.cfg.val_set_size == 0:
+            # no eval set, so don't eval
+            training_arguments_kwargs["evaluation_strategy"] = "no"
         else:
             # we have an eval set, but no steps defined, default to use epoch
             training_arguments_kwargs["evaluation_strategy"] = "epoch"
@@ -681,7 +625,7 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
         training_arguments_kwargs["group_by_length"] = self.cfg.group_by_length
         training_arguments_kwargs["report_to"] = "wandb" if self.cfg.use_wandb else None
         training_arguments_kwargs["run_name"] = (
-            self.cfg.wandb_name if self.cfg.use_wandb else None
+            self.cfg.wandb_run_id if self.cfg.use_wandb else None
         )
         training_arguments_kwargs["optim"] = (
             self.cfg.optimizer if self.cfg.optimizer else "adamw_hf"
@@ -692,9 +636,6 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
             and self.cfg.lr_scheduler not in ("one_cycle", "log_sweep")
             else "cosine"
         )
-        training_arguments_kwargs["lr_scheduler_kwargs"] = (
-            self.cfg.lr_scheduler_kwargs if self.cfg.lr_scheduler_kwargs else {}
-        )
         training_arguments_kwargs["weight_decay"] = (
             self.cfg.weight_decay if self.cfg.weight_decay is not None else 0.0
         )
@@ -702,9 +643,7 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
             self.cfg.sample_packing if self.cfg.sample_packing else False
         )
         training_arguments_kwargs["eval_sample_packing"] = (
-            self.cfg.sample_packing
-            if self.cfg.eval_sample_packing is not False
-            else False
+            self.cfg.sample_packing if self.cfg.sample_packing else False
         )
         training_arguments_kwargs[
             "sample_packing_seq_len_multiplier"
@@ -714,13 +653,6 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
         training_arguments_kwargs = self.hook_pre_create_training_args(
             training_arguments_kwargs
         )
-        training_arguments_kwargs["model_type"] = self.cfg.model_config_type
-
-        if self.cfg.neftune_noise_alpha is not None:
-            training_arguments_kwargs[
-                "neftune_noise_alpha"
-            ] = self.cfg.neftune_noise_alpha
-
         training_args = (
             AxolotlTrainingArguments(  # pylint: disable=unexpected-keyword-arg
                 **training_arguments_kwargs,
@@ -734,18 +666,6 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
                 sys.path.append(self.cfg.torchdistx_path)
                 importlib.import_module("torchdistx")
 
-        data_collator_kwargs = {
-            "padding": True,  # True/"longest" is the default
-        }
-        if self.cfg.pad_to_sequence_len:
-            data_collator_kwargs["pad_to_multiple_of"] = 64 * math.ceil(
-                self.cfg.sequence_len / 64
-            )
-        else:
-            # A100 is best at 64, while others at 8. Let's use the larger so we don't have to check
-            # https://docs.nvidia.com/deeplearning/performance/dl-performance-matrix-multiplication/index.html
-            data_collator_kwargs["pad_to_multiple_of"] = 64
-
         if self.cfg.is_llama_derived_model and self.cfg.landmark_attention:
             from axolotl.monkeypatch.llama_landmark_attn import (
                 add_mem_tokens,
@@ -770,38 +690,57 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
         trainer_kwargs, trainer_cls = self.hook_pre_create_trainer(
             trainer_kwargs, trainer_cls
         )
+        trainer_collator_kwargs = self.build_data_collator()
+
         trainer = trainer_cls(
             model=self.model,
             train_dataset=self.train_dataset,
             eval_dataset=self.eval_dataset,
             args=training_args,
-            data_collator=self.build_collator(**data_collator_kwargs),
-            bench_data_collator=transformers.DataCollatorForSeq2Seq(
-                self.tokenizer,
-                return_tensors="pt",
-                **data_collator_kwargs,
-            ),
             callbacks=self.get_callbacks(),
-            num_epochs=self.cfg.num_epochs,
+            **trainer_collator_kwargs,
             **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
 
-    def build_collator(self, **kwargs):
-        if self.cfg.model_config_type == "mamba":
-            return MambaDataCollator(tokenizer=self.tokenizer)
+    def build_data_collator(self):
+        data_collator_kwargs = {
+            "padding": True,  # True/"longest" is the default
+        }
+        if self.cfg.pad_to_sequence_len:
+            data_collator_kwargs["pad_to_multiple_of"] = 64 * math.ceil(
+                self.cfg.sequence_len / 64
+            )
+        else:
+            # A100 is best at 64, while others at 8. Let's use the larger so we don't have to check
+            # https://docs.nvidia.com/deeplearning/performance/dl-performance-matrix-multiplication/index.html
+            data_collator_kwargs["pad_to_multiple_of"] = 64
 
-        return BatchSamplerDataCollatorForSeq2Seq(
-            self.tokenizer,
-            return_tensors="pt",
-            **kwargs,
-        )
+        collator_kwargs = {}
+        if self.cfg.multimodal:
+            from llava.train.train import DataCollatorForSupervisedDataset
+
+            collator_kwargs["data_collator"] = DataCollatorForSupervisedDataset(
+                tokenizer=self.tokenizer,
+            )
+        else:
+            collator_kwargs["data_collator"] = DataCollatorForSeq2Seq(
+                self.tokenizer,
+                return_tensors="pt",
+                **data_collator_kwargs,
+            )
+
+        if self.cfg.do_bench_eval:
+            collator_kwargs[
+                "bench_data_collator"
+            ] = transformers.DataCollatorForSeq2Seq(
+                self.tokenizer,
+                return_tensors="pt",
+                **data_collator_kwargs,
+            )
+
+        return collator_kwargs

src/axolotl/datasets.py

@@ -2,7 +2,7 @@
 
 import logging
 import os
-from typing import List, Optional
+from typing import List
 
 import torch
 from datasets import Dataset, IterableDataset
@@ -30,20 +30,14 @@ 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, self.process_count)
-            if self.process_count
-            else min(64, os.cpu_count())
-        )
+        num_proc = min(64, os.cpu_count())
         map_kwargs = {}
         if self.prompt_tokenizer.supports_batched:
             map_kwargs["batched"] = True

src/axolotl/models/llava/llava_mistral.py

@@ -0,0 +1,167 @@
+"""
+LLaVA Mistral classes
+"""
+
+from typing import List, Optional, Tuple, Union
+
+import torch
+from llava.model.llava_arch import LlavaMetaForCausalLM, LlavaMetaModel
+from torch import nn
+from torch.nn import CrossEntropyLoss
+from transformers import (
+    AutoConfig,
+    AutoModelForCausalLM,
+    MistralConfig,
+    MistralForCausalLM,
+    MistralModel,
+)
+from transformers.modeling_outputs import CausalLMOutputWithPast
+
+
+class LlavaMistralConfig(MistralConfig):
+    """
+    HF Transformers Config for Mistral w LLaVA
+    """
+
+    model_type = "llava_mistral"
+
+
+class LlavaMistralModel(LlavaMetaModel, MistralModel):
+    """
+    HF Transformers Model for Mistral w LLaVA
+    """
+
+    config_class = LlavaMistralConfig
+
+    def __init__(
+        self, config: LlavaMistralConfig
+    ):  # pylint: disable=useless-parent-delegation
+        super().__init__(config)
+
+
+class LlavaMistralForCausalLM(MistralForCausalLM, LlavaMetaForCausalLM):
+    """
+    HF Transformers Causal Model for Mistral w LLaVA
+    """
+
+    config_class = LlavaMistralConfig
+
+    def __init__(self, config: LlavaMistralConfig):
+        super().__init__(config)
+        self.model = LlavaMistralModel(config)
+
+        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_model(self):
+        return self.model
+
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        images: Optional[torch.FloatTensor] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, CausalLMOutputWithPast]:
+        output_attentions = (
+            output_attentions
+            if output_attentions is not None
+            else self.config.output_attentions
+        )
+        output_hidden_states = (
+            output_hidden_states
+            if output_hidden_states is not None
+            else self.config.output_hidden_states
+        )
+        return_dict = (
+            return_dict if return_dict is not None else self.config.use_return_dict
+        )
+
+        (
+            input_ids,
+            attention_mask,
+            past_key_values,
+            inputs_embeds,
+            labels,
+        ) = self.prepare_inputs_labels_for_multimodal(
+            input_ids, attention_mask, past_key_values, labels, images
+        )
+
+        # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
+        outputs = self.model(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        hidden_states = outputs[0]
+        logits = self.lm_head(hidden_states)
+
+        loss = None
+        if labels is not None:
+            # Shift so that tokens < n predict n
+            shift_logits = logits[..., :-1, :].contiguous()
+            shift_labels = labels[..., 1:].contiguous()
+            # Flatten the tokens
+            loss_fct = CrossEntropyLoss()
+            shift_logits = shift_logits.view(-1, self.config.vocab_size)
+            shift_labels = shift_labels.view(-1)
+            # Enable model/pipeline parallelism
+            shift_labels = shift_labels.to(shift_logits.device)
+            loss = loss_fct(shift_logits, shift_labels)
+
+        if not return_dict:
+            output = (logits,) + outputs[1:]
+            return (loss,) + output if loss is not None else output
+
+        return CausalLMOutputWithPast(
+            loss=loss,
+            logits=logits,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+    def prepare_inputs_for_generation(
+        self,
+        input_ids,
+        past_key_values=None,
+        attention_mask=None,
+        inputs_embeds=None,
+        **kwargs
+    ):
+        if past_key_values:
+            input_ids = input_ids[:, -1:]
+
+        # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
+        if inputs_embeds is not None and past_key_values is None:
+            model_inputs = {"inputs_embeds": inputs_embeds}
+        else:
+            model_inputs = {"input_ids": input_ids}
+
+        model_inputs.update(
+            {
+                "past_key_values": past_key_values,
+                "use_cache": kwargs.get("use_cache"),
+                "attention_mask": attention_mask,
+                "images": kwargs.get("images", None),
+            }
+        )
+        return model_inputs
+
+
+AutoConfig.register("llava_mistral", LlavaMistralConfig)
+AutoModelForCausalLM.register(LlavaMistralConfig, LlavaMistralForCausalLM)

src/axolotl/models/mamba/__init__.py

@@ -1,12 +0,0 @@
-"""
-Modeling module for Mamba models
-"""
-
-
-def fix_mamba_attn_for_loss():
-    from mamba_ssm.models import mixer_seq_simple
-
-    from .modeling_mamba import MambaLMHeadModel as MambaLMHeadModelFixed
-
-    mixer_seq_simple.MambaLMHeadModel = MambaLMHeadModelFixed
-    return mixer_seq_simple.MambaLMHeadModel  # pylint: disable=invalid-name

src/axolotl/models/mamba/configuration_mamba.py

@@ -1,42 +0,0 @@
-"""
-HF Transformers MambaConfig
-"""
-from transformers import PretrainedConfig
-
-
-class MambaConfig(PretrainedConfig):
-    """
-    modeling configuration for state space model/mamba
-    """
-
-    model_type = "mamba"
-
-    def __init__(
-        self,
-        vocab_size=50280,
-        d_model=2560,
-        n_layer=64,
-        rms_norm=True,
-        residual_in_fp32=True,
-        fused_add_norm=True,
-        pad_vocab_size_multiple=8,
-        pad_token_id=50277,
-        bos_token_id=0,
-        eos_token_id=0,
-        tie_word_embeddings=False,
-        **kwargs,
-    ):
-        self.vocab_size = vocab_size
-        self.d_model = d_model
-        self.n_layer = n_layer
-        self.rms_norm = rms_norm
-        self.residual_in_fp32 = residual_in_fp32
-        self.fused_add_norm = fused_add_norm
-        self.pad_vocab_size_multiple = pad_vocab_size_multiple
-        super().__init__(
-            pad_token_id=pad_token_id,
-            bos_token_id=bos_token_id,
-            eos_token_id=eos_token_id,
-            tie_word_embeddings=tie_word_embeddings,
-            **kwargs,
-        )

src/axolotl/models/mamba/modeling_mamba.py

@@ -1,128 +0,0 @@
-# pylint: skip-file
-import os
-from collections import namedtuple
-from functools import partial
-from typing import Optional, Union
-
-import torch
-from mamba_ssm.models.mixer_seq_simple import MixerModel, _init_weights
-from mamba_ssm.utils.generation import GenerationMixin
-from mamba_ssm.utils.hf import load_config_hf, load_state_dict_hf
-from torch import nn
-from torch.nn import CrossEntropyLoss
-
-from axolotl.models.mamba.configuration_mamba import MambaConfig
-
-
-class MambaLMHeadModel(nn.Module, GenerationMixin):
-    def __init__(
-        self,
-        d_model: int,
-        n_layer: int,
-        vocab_size: int,
-        initializer_cfg=None,
-        pad_vocab_size_multiple: int = 1,
-        device=None,
-        dtype=None,
-        **backbone_kwargs,
-    ) -> None:
-        factory_kwargs = {"device": device, "dtype": dtype}
-        super().__init__()
-        if vocab_size % pad_vocab_size_multiple != 0:
-            vocab_size += pad_vocab_size_multiple - (
-                vocab_size % pad_vocab_size_multiple
-            )
-        self.config = MambaConfig(
-            vocab_size=vocab_size,
-            d_model=d_model,
-            n_layer=n_layer,
-            pad_vocab_size_multiple=pad_vocab_size_multiple,
-        )
-        self.backbone = MixerModel(
-            d_model=d_model,
-            n_layer=n_layer,
-            vocab_size=vocab_size,
-            initializer_cfg=initializer_cfg,
-            **backbone_kwargs,
-            **factory_kwargs,
-        )
-        self.lm_head = nn.Linear(d_model, vocab_size, bias=False, **factory_kwargs)
-
-        # Initialize weights and apply final processing
-        self.apply(
-            partial(
-                _init_weights,
-                n_layer=n_layer,
-                **(initializer_cfg if initializer_cfg is not None else {}),
-            )
-        )
-        self.tie_weights()
-
-    def tie_weights(self):
-        self.lm_head.weight = self.backbone.embedding.weight
-
-    def allocate_inference_cache(self, batch_size, max_seqlen, dtype=None, **kwargs):
-        return self.backbone.allocate_inference_cache(
-            batch_size, max_seqlen, dtype=dtype, **kwargs
-        )
-
-    def forward(
-        self,
-        input_ids,
-        position_ids=None,
-        inference_params=None,
-        num_last_tokens=0,
-        labels=None,
-        **kwargs,
-    ):
-        """
-        "position_ids" is just to be compatible with Transformer generation. We don't use it.
-        num_last_tokens: if > 0, only return the logits for the last n tokens
-        """
-        hidden_states = self.backbone(input_ids, inference_params=inference_params)
-        if num_last_tokens > 0:
-            hidden_states = hidden_states[:, -num_last_tokens:]
-        lm_logits = self.lm_head(hidden_states)
-
-        CausalLMOutput = namedtuple("CausalLMOutput", ["logits"])
-        return CausalLMOutput(logits=lm_logits)
-
-        loss = None
-        if labels is not None:
-            logits = lm_logits
-            # Shift so that tokens < n predict n
-            shift_logits = logits[..., :-1, :].contiguous()
-            shift_labels = labels[..., 1:].contiguous()
-            # Flatten the tokens
-            loss_fct = CrossEntropyLoss()
-            shift_logits = shift_logits.view(-1, self.config.vocab_size)
-            shift_labels = shift_labels.view(-1)
-            # Enable model parallelism
-            shift_labels = shift_labels.to(shift_logits.device)
-            loss = loss_fct(shift_logits, shift_labels)
-            CausalLMOutput = namedtuple("CausalLMOutput", ["logits", "loss"])
-            print(loss)
-            return CausalLMOutput(logits=lm_logits, loss=loss)
-
-        else:
-            CausalLMOutput = namedtuple("CausalLMOutput", ["logits"])
-            return CausalLMOutput(logits=lm_logits)
-
-    def save_pretrained(
-        self,
-        save_directory: Union[str, os.PathLike],
-        state_dict: Optional[dict] = None,
-        safe_serialization: Optional[bool] = None,  # pylint: disable=unused-argument
-    ):
-        if state_dict is None:
-            state_dict = self.state_dict()
-        torch.save(state_dict, os.path.join(save_directory, "pytorch_model.bin"))
-
-    @classmethod
-    def from_pretrained(cls, pretrained_model_name, device=None, dtype=None, **kwargs):
-        config = load_config_hf(pretrained_model_name)
-        model = cls(**config, device=device, dtype=dtype, **kwargs)
-        model.load_state_dict(
-            load_state_dict_hf(pretrained_model_name, device={"": device}, dtype=dtype)
-        )
-        return model

src/axolotl/models/phi/__init__.py

@@ -3,6 +3,4 @@ MixFormers model architecture used for phi models
 """
 
 from .configuration_mixformer_sequential import MixFormerSequentialConfig  # noqa
-from .configuration_phi import PhiConfig  # noqa
 from .modeling_mixformer_sequential import MixFormerSequentialForCausalLM  # noqa
-from .modeling_phi import PhiForCausalLM  # noqa

src/axolotl/models/phi/configuration_phi.py

@@ -1,65 +0,0 @@
-# pylint: skip-file
-# Copyright (c) Microsoft Corporation.
-# Licensed under the MIT license.
-
-import math
-from typing import Optional
-
-from transformers import PretrainedConfig
-
-
-class PhiConfig(PretrainedConfig):
-    """Phi configuration."""
-
-    model_type = "phi"
-    attribute_map = {
-        "max_position_embeddings": "n_positions",
-        "hidden_size": "n_embd",
-        "num_attention_heads": "n_head",
-        "num_hidden_layers": "n_layer",
-    }
-
-    def __init__(
-        self,
-        vocab_size: int = 50304,
-        n_positions: int = 2048,
-        n_embd: int = 1024,
-        n_layer: int = 20,
-        n_inner: Optional[int] = None,
-        n_head: int = 16,
-        n_head_kv: Optional[int] = None,
-        rotary_dim: Optional[int] = 32,
-        activation_function: Optional[str] = "gelu_new",
-        flash_attn: bool = False,
-        flash_rotary: bool = False,
-        fused_dense: bool = False,
-        attn_pdrop: float = 0.0,
-        embd_pdrop: float = 0.0,
-        resid_pdrop: float = 0.0,
-        layer_norm_epsilon: float = 1e-5,
-        initializer_range: float = 0.02,
-        tie_word_embeddings: bool = False,
-        pad_vocab_size_multiple: int = 64,
-        **kwargs
-    ) -> None:
-        self.vocab_size = int(
-            math.ceil(vocab_size / pad_vocab_size_multiple) * pad_vocab_size_multiple
-        )
-        self.n_positions = n_positions
-        self.n_embd = n_embd
-        self.n_layer = n_layer
-        self.n_inner = n_inner
-        self.n_head = n_head
-        self.n_head_kv = n_head_kv
-        self.rotary_dim = min(rotary_dim, n_embd // n_head)
-        self.activation_function = activation_function
-        self.flash_attn = flash_attn
-        self.flash_rotary = flash_rotary
-        self.fused_dense = fused_dense
-        self.attn_pdrop = attn_pdrop
-        self.embd_pdrop = embd_pdrop
-        self.resid_pdrop = resid_pdrop
-        self.layer_norm_epsilon = layer_norm_epsilon
-        self.initializer_range = initializer_range
-
-        super().__init__(tie_word_embeddings=tie_word_embeddings, **kwargs)

src/axolotl/monkeypatch/fastchat_conversation_turns.py

@@ -83,21 +83,14 @@ def get_turns(  # pylint: disable=too-many-return-statements
                 yield role + ":", ""
         return
     if self.sep_style == SeparatorStyle.LLAMA2:
+        seps = [self.sep, self.sep2]
         if self.system_message:
-            if self.messages:
-                # For llama, the system message is incorporated into the first human instruction
-                first_role, first_msg = self.messages[0]
-                if first_role == self.roles[0]:
-                    system_prompt += first_msg
-                    self.messages.pop(0)
             yield "", system_prompt
-        for i, (role, message) in enumerate(self.messages):
+        else:
+            yield "", "[INST] "
+        for i, (role, message) in enumerate(self.messages[1:]):
             if message:
-                if (i % 2 == 0 and not self.system_message) or (
-                    i % 2 != 0 and self.system_message
-                ):
-                    role = "<s> " + role
-                yield role + " ", message
+                yield role + " ", message + seps[i % 2]
             else:
                 yield role, ""
         return

src/axolotl/monkeypatch/llama_attn_hijack_flash.py

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

src/axolotl/monkeypatch/llama_attn_hijack_sdp.py

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

src/axolotl/monkeypatch/llama_attn_hijack_xformers.py

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

src/axolotl/monkeypatch/llama_embeddings_hijack.py

@@ -0,0 +1,40 @@
+"""
+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_attn_hijack_flash.py

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

src/axolotl/monkeypatch/mistral_embeddings_hijack.py

@@ -0,0 +1,40 @@
+"""
+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/mixtral/__init__.py

@@ -1,22 +0,0 @@
-"""
-Patches to support multipack for mixtral
-"""
-import transformers
-
-
-def replace_mixtral_attn_with_multipack_flash_attn():
-    from .modeling_mixtral import (
-        MixtralMultipackFlashAttention2,
-        mixtral_decoder_layer_forward,
-        mixtral_model_forward,
-    )
-
-    transformers.models.mixtral.modeling_mixtral.MixtralDecoderLayer.forward = (
-        mixtral_decoder_layer_forward
-    )
-    transformers.models.mixtral.modeling_mixtral.MixtralModel.forward = (
-        mixtral_model_forward
-    )
-    transformers.models.mixtral.modeling_mixtral.MISTRAL_ATTENTION_CLASSES[
-        "flash_attention_2"
-    ] = MixtralMultipackFlashAttention2

src/axolotl/monkeypatch/mixtral/modeling_mixtral.py

@@ -1,379 +0,0 @@
-"""
-Mixtral modeling for multipack
-"""
-# pylint: disable=missing-module-docstring,unused-argument,protected-access,pointless-string-statement,duplicate-code
-import logging
-import warnings
-from typing import List, Optional, Tuple, Union
-
-import torch
-from einops import rearrange
-from flash_attn import flash_attn_varlen_qkvpacked_func
-from transformers import Cache, DynamicCache
-from transformers.modeling_attn_mask_utils import _prepare_4d_causal_attention_mask
-from transformers.modeling_outputs import MoeModelOutputWithPast
-from transformers.models.mixtral.modeling_mixtral import (
-    MixtralFlashAttention2,
-    apply_rotary_pos_emb,
-    repeat_kv,
-)
-
-from axolotl.monkeypatch.utils import get_cu_seqlens_from_pos_ids
-
-LOG = logging.getLogger("axolotl.monkeypatch.mixtral")
-
-
-class MixtralMultipackFlashAttention2(MixtralFlashAttention2):
-    """
-    Custom multipack implementation w flash attention 2
-    """
-
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        self._flash_attn_uses_top_left_mask = True
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        attention_mask: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        past_key_value: Optional[Cache] = None,
-        output_attentions: bool = False,
-        use_cache: bool = False,
-        cu_seqlens: Optional[torch.Tensor] = None,
-        max_seqlen: Optional[torch.Tensor] = None,
-        **kwargs,
-    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
-        if "padding_mask" in kwargs:
-            warnings.warn(
-                "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
-            )
-        bsz, q_len, _ = hidden_states.size()
-
-        query_states = self.q_proj(hidden_states)
-        key_states = self.k_proj(hidden_states)
-        value_states = self.v_proj(hidden_states)
-
-        query_states = query_states.view(
-            bsz, q_len, self.num_heads, self.head_dim
-        ).transpose(1, 2)
-        key_states = key_states.view(
-            bsz, q_len, self.num_key_value_heads, self.head_dim
-        ).transpose(1, 2)
-        value_states = value_states.view(
-            bsz, q_len, self.num_key_value_heads, self.head_dim
-        ).transpose(1, 2)
-
-        kv_seq_len = key_states.shape[-2]
-        if past_key_value is not None:
-            if self.layer_idx is None:
-                raise ValueError(
-                    f"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} "
-                    "for auto-regressive decoding with k/v caching, please make sure to initialize the attention class "
-                    "with a layer index."
-                )
-            kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
-        cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
-        query_states, key_states = apply_rotary_pos_emb(
-            query_states, key_states, cos, sin, position_ids
-        )
-
-        if past_key_value is not None:
-            cache_kwargs = {"sin": sin, "cos": cos}  # Specific to RoPE models
-            key_states, value_states = past_key_value.update(
-                key_states, value_states, self.layer_idx, cache_kwargs
-            )
-
-        # repeat k/v heads if n_kv_heads < n_heads
-        key_states = repeat_kv(key_states, self.num_key_value_groups)
-        value_states = repeat_kv(value_states, self.num_key_value_groups)
-
-        if cu_seqlens is not None and max_seqlen is not None and cu_seqlens.dim() == 1:
-            # special handling using sample packing
-            qkv = torch.stack(
-                [query_states, key_states, value_states], dim=2
-            )  # [bsz, nh, 3, q_len, hd]
-            qkv = qkv.transpose(1, 3)  # [bsz, q_len, 3, nh, hd]
-            qkv = rearrange(qkv, "b s ... -> (b s) ...")
-
-            attn_output = flash_attn_varlen_qkvpacked_func(
-                qkv,
-                cu_seqlens,
-                max_seqlen,
-                dropout_p=self.attention_dropout,
-                softmax_scale=None,
-                causal=True,
-            )
-            attn_output = rearrange(attn_output, "(b s) ... -> b s ...", b=bsz)
-
-        attn_output = attn_output.reshape(bsz, q_len, self.hidden_size).contiguous()
-        attn_output = self.o_proj(attn_output)
-
-        if not output_attentions:
-            attn_weights = None
-
-        return attn_output, attn_weights, past_key_value
-
-
-def mixtral_decoder_layer_forward(
-    self,
-    hidden_states: torch.Tensor,
-    attention_mask: Optional[torch.Tensor] = None,
-    position_ids: Optional[torch.LongTensor] = None,
-    past_key_value: Optional[Tuple[torch.Tensor]] = None,
-    output_attentions: Optional[bool] = False,
-    output_router_logits: Optional[bool] = False,
-    use_cache: Optional[bool] = False,
-    cu_seqlens: Optional[torch.Tensor] = None,
-    max_seqlen: Optional[torch.Tensor] = None,
-    **kwargs,
-) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
-    if "padding_mask" in kwargs:
-        warnings.warn(
-            "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
-        )
-    """
-    Args:
-        hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
-        attention_mask (`torch.FloatTensor`, *optional*): attention mask of size
-            `(batch, sequence_length)` where padding elements are indicated by 0.
-        past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
-        output_attentions (`bool`, *optional*):
-            Whether or not to return the attentions tensors of all attention layers. See `attentions` under
-            returned tensors for more detail.
-        output_router_logits (`bool`, *optional*):
-            Whether or not to return the logits of all the routers. They are useful for computing the router loss, and
-            should not be returned during inference.
-        use_cache (`bool`, *optional*):
-            If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
-            (see `past_key_values`).
-    """
-
-    residual = hidden_states
-
-    hidden_states = self.input_layernorm(hidden_states)
-
-    # Self Attention
-    hidden_states, self_attn_weights, present_key_value = self.self_attn(
-        hidden_states=hidden_states,
-        attention_mask=attention_mask,
-        position_ids=position_ids,
-        past_key_value=past_key_value,
-        output_attentions=output_attentions,
-        use_cache=use_cache,
-        cu_seqlens=cu_seqlens,
-        max_seqlen=max_seqlen,
-    )
-    hidden_states = residual + hidden_states
-
-    # Fully Connected
-    residual = hidden_states
-    hidden_states = self.post_attention_layernorm(hidden_states)
-    hidden_states, router_logits = self.block_sparse_moe(hidden_states)
-    hidden_states = residual + hidden_states
-
-    outputs = (hidden_states,)
-
-    if output_attentions:
-        outputs += (self_attn_weights,)
-
-    if use_cache:
-        outputs += (present_key_value,)
-
-    if output_router_logits:
-        outputs += (router_logits,)
-
-    return outputs
-
-
-def mixtral_model_forward(
-    self,
-    input_ids: torch.LongTensor = None,
-    attention_mask: Optional[torch.Tensor] = None,
-    position_ids: Optional[torch.LongTensor] = None,
-    past_key_values: Optional[List[torch.FloatTensor]] = None,
-    inputs_embeds: Optional[torch.FloatTensor] = None,
-    use_cache: Optional[bool] = None,
-    output_attentions: Optional[bool] = None,
-    output_hidden_states: Optional[bool] = None,
-    output_router_logits: Optional[bool] = None,
-    return_dict: Optional[bool] = None,
-) -> Union[Tuple, MoeModelOutputWithPast]:
-    output_attentions = (
-        output_attentions
-        if output_attentions is not None
-        else self.config.output_attentions
-    )
-    output_router_logits = (
-        output_router_logits
-        if output_router_logits is not None
-        else self.config.output_router_logits
-    )
-    output_hidden_states = (
-        output_hidden_states
-        if output_hidden_states is not None
-        else self.config.output_hidden_states
-    )
-    use_cache = use_cache if use_cache is not None else self.config.use_cache
-
-    return_dict = (
-        return_dict if return_dict is not None else self.config.use_return_dict
-    )
-
-    # retrieve input_ids and inputs_embeds
-    if input_ids is not None and inputs_embeds is not None:
-        raise ValueError(
-            "You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time"
-        )
-    if input_ids is not None:
-        batch_size, seq_length = input_ids.shape
-    elif inputs_embeds is not None:
-        batch_size, seq_length, _ = inputs_embeds.shape
-    else:
-        raise ValueError(
-            "You have to specify either decoder_input_ids or decoder_inputs_embeds"
-        )
-
-    past_key_values_length = 0
-
-    if use_cache:
-        use_legacy_cache = not isinstance(past_key_values, Cache)
-        if use_legacy_cache:
-            past_key_values = DynamicCache.from_legacy_cache(past_key_values)
-        past_key_values_length = past_key_values.get_usable_length(seq_length)
-
-    cu_seqlens = None
-    max_seqlen = None
-    if position_ids is None:
-        device = input_ids.device if input_ids is not None else inputs_embeds.device
-        position_ids = torch.arange(
-            past_key_values_length,
-            seq_length + past_key_values_length,
-            dtype=torch.long,
-            device=device,
-        )
-        position_ids = position_ids.unsqueeze(0).view(-1, seq_length)
-    else:
-        position_ids = position_ids.view(-1, seq_length).long()
-        cu_seqlens, max_seqlen = get_cu_seqlens_from_pos_ids(position_ids)
-        cu_seqlens = cu_seqlens.squeeze()
-
-    if inputs_embeds is None:
-        inputs_embeds = self.embed_tokens(input_ids)
-
-    if attention_mask is not None and self._use_flash_attention_2 and use_cache:
-        is_padding_right = attention_mask[:, -1].sum().item() != batch_size
-        if is_padding_right:
-            raise ValueError(
-                "You are attempting to perform batched generation with padding_side='right'"
-                " this may lead to unexpected behaviour for Flash Attention version of Mixtral. Make sure to "
-                " call `tokenizer.padding_side  = 'left'` before tokenizing the input. "
-            )
-
-    if self._use_flash_attention_2:
-        # 2d mask is passed through the layers
-        attention_mask = (
-            attention_mask
-            if (attention_mask is not None and 0 in attention_mask)
-            else None
-        )
-    else:
-        # 4d mask is passed through the layers
-        attention_mask = _prepare_4d_causal_attention_mask(
-            attention_mask,
-            (batch_size, seq_length),
-            inputs_embeds,
-            past_key_values_length,
-            sliding_window=self.config.sliding_window,
-        )
-
-    hidden_states = inputs_embeds
-
-    if self.gradient_checkpointing and self.training:
-        if use_cache:
-            LOG.warning_once(
-                "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
-            )
-            use_cache = False
-
-    # decoder layers
-    all_hidden_states = () if output_hidden_states else None
-    all_self_attns = () if output_attentions else None
-    all_router_logits = () if output_router_logits else None
-    next_decoder_cache = None
-
-    for decoder_layer in self.layers:
-        if output_hidden_states:
-            all_hidden_states += (hidden_states,)
-
-        if self.gradient_checkpointing and self.training:
-            layer_outputs = self._gradient_checkpointing_func(
-                decoder_layer.__call__,
-                hidden_states,
-                attention_mask,
-                position_ids,
-                past_key_values,
-                output_attentions,
-                output_router_logits,
-                use_cache,
-                cu_seqlens,
-                max_seqlen,
-            )
-        else:
-            layer_outputs = decoder_layer(
-                hidden_states,
-                attention_mask=attention_mask,
-                position_ids=position_ids,
-                past_key_value=past_key_values,
-                output_attentions=output_attentions,
-                output_router_logits=output_router_logits,
-                use_cache=use_cache,
-                cu_seqlens=cu_seqlens,
-                max_seqlen=max_seqlen,
-            )
-
-        hidden_states = layer_outputs[0]
-
-        if use_cache:
-            next_decoder_cache = layer_outputs[2 if output_attentions else 1]
-
-        if output_attentions:
-            all_self_attns += (layer_outputs[1],)
-
-        if output_router_logits:
-            all_router_logits += (layer_outputs[-1],)
-
-    hidden_states = self.norm(hidden_states)
-
-    # add hidden states from the last decoder layer
-    if output_hidden_states:
-        all_hidden_states += (hidden_states,)
-
-    next_cache = None
-    if use_cache:
-        next_cache = (
-            next_decoder_cache.to_legacy_cache()
-            if use_legacy_cache
-            else next_decoder_cache
-        )
-
-    if not return_dict:
-        return tuple(
-            v
-            for v in [
-                hidden_states,
-                next_cache,
-                all_hidden_states,
-                all_self_attns,
-                all_router_logits,
-            ]
-            if v is not None
-        )
-
-    return MoeModelOutputWithPast(
-        last_hidden_state=hidden_states,
-        past_key_values=next_cache,
-        hidden_states=all_hidden_states,
-        attentions=all_self_attns,
-        router_logits=all_router_logits,
-    )

src/axolotl/prompt_strategies/llama2_chat.py

@@ -81,9 +81,8 @@ class LLama2ChatTokenizingStrategy(PromptTokenizingStrategy):
 
     def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
-        self.tokenizer.add_special_tokens(
-            {"pad_token": getattr(self.tokenizer, "pad_token", "<pad>")}
-        )
+        self.sequence_len = 4096
+        self.tokenizer.add_special_tokens({"pad_token": "<pad>"})
         # https://huggingface.co/meta-llama/Llama-2-7b-chat-hf/blob/main/added_tokens.json
 
     def tokenize_prompt(self, prompt):

src/axolotl/prompt_strategies/sharegpt.py

@@ -13,7 +13,7 @@ register_conv_template(
         system_message="You are a helpful assistant.",
         roles=["<|im_start|>user", "<|im_start|>assistant"],
         sep_style=SeparatorStyle.CHATML,
-        sep="<|im_end|>",
+        sep="<|im_end|>\n",
     )
 )
 
@@ -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
-    strategy = SimpleShareGPTPromptTokenizingStrategy(
+    return SimpleShareGPTPromptTokenizingStrategy(
         ShareGPTPrompterV2(
             conversation=conversation,
             role_key_model=field_model,
@@ -34,9 +34,6 @@ 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):
@@ -62,26 +59,8 @@ 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):
-        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
+        return prompt["conversations"]
 
 
 class SimpleRoleShareGPTPromptTokenizingStrategy(ShareGPTPromptTokenizingStrategy):

src/axolotl/prompt_tokenizers.py

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

src/axolotl/prompters.py

@@ -4,12 +4,10 @@ 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):
@@ -22,19 +20,13 @@ class PromptStyle(Enum):
     CHATML = "chatml"
 
 
-class Prompter:
-    """
-    Base prompter class for all prompters
-    """
-
-
-class AlpacaPrompter(Prompter):
+class AlpacaPrompter:
     """
     Base class for alpaca prompters
     """
 
-    system_prompt = "Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request."
-    system_no_input_prompt = "Below is an instruction that describes a task. Write a response that appropriately completes the request."
+    system_prompt = "Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n\n"
+    system_no_input_prompt = "Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n"
     system_format: str = "{system}"
     turn_format: str
     turn_no_input_format: str
@@ -63,38 +55,29 @@ class AlpacaPrompter(Prompter):
             )
             self.system_format = "<|im_start|>system\n{system}<|im_end|>\n"
 
-    def _build_result(self, instruction, input_text, output):
-        # returns the full prompt from instruction and optional input
-        # if a label (=response, =output) is provided, it's also appended.
-        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_text)
-        else:
-            res = (
-                self.system_format.format(system=self.system_no_input_prompt)
-                if self.system_no_input_prompt
-                else ""
-            ) + self.turn_no_input_format.format(instruction=instruction)
-        if output:
-            res = f"{res}{output}"
-
-        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}")
-        )
+        # returns the full prompt from instruction and optional input
+        # if a label (=response, =output) is provided, it's also appended.
+        if input:
+            res = (
+                self.system_format.format(system=self.system_prompt)
+                if self.system_prompt
+                else ""
+            ) + self.turn_format.format(instruction=instruction, input=input)
+        else:
+            res = (
+                self.system_format.format(system=self.system_no_input_prompt)
+                if self.system_prompt
+                else ""
+            ) + self.turn_no_input_format.format(instruction=instruction)
+        if output:
+            res = f"{res}{output}"
+        yield res
 
 
 class UnpromptedPrompter(AlpacaPrompter):
@@ -165,7 +148,7 @@ class NomicGPT4AllPrompter(AlpacaPrompter):
     """
 
 
-class ReflectAlpacaPrompter(Prompter):
+class ReflectAlpacaPrompter:
     """
     Prompter for ReflectAlpaca
     """
@@ -208,14 +191,14 @@ class ReflectAlpacaPrompter(Prompter):
             )
             self.response_split = "ASSISTANT:"
 
-    def _build_result(
+    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]:
         # returns the full prompt from instruction and optional input
         # if a label (=response, =output) is provided, it's also appended.
         if input:
@@ -229,30 +212,7 @@ class ReflectAlpacaPrompter(Prompter):
                 corrected=corrected,
             )
             res = f"{res}{label}"
-
-        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}")
-        )
+        yield res
 
 
 SHAREGPT_ASSERTION_FAILED_ROLE = (
@@ -260,7 +220,7 @@ SHAREGPT_ASSERTION_FAILED_ROLE = (
 )
 
 
-class ShareGPTPrompter(Prompter):  # pylint: disable=too-few-public-methods
+class ShareGPTPrompter:  # pylint: disable=too-few-public-methods
     """
     A prompter that generates prompts for the ShareGPT
     """
@@ -287,7 +247,7 @@ class ShareGPTPrompter(Prompter):  # pylint: disable=too-few-public-methods
         if role_key_model:
             self.role_key_model = role_key_model
 
-    def _build_result(self, source):
+    def build_prompt(self, source) -> Generator[str, None, None]:
         if len(source) < 2:
             # If there isn't a back and forth conversation, ignore it
             # also happens on the data splitting leaving empty conversations
@@ -322,20 +282,11 @@ class ShareGPTPrompter(Prompter):  # pylint: disable=too-few-public-methods
                 LOG.warning(f"{SHAREGPT_ASSERTION_FAILED_ROLE}: {sentence}")
             conv.append_message(role, sentence["value"])
 
-        return conv.get_turns()
-
-    def build_prompt(self, source) -> Generator[str, None, None]:
-        turns = self._build_result(source)
-
-        for part in turns:
+        for part in conv.get_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):
     """
@@ -353,15 +304,3 @@ class ShareGPTPrompterV2(ShareGPTPrompter):
             role_key_human=role_key_human,
             role_key_model=role_key_model,
         )
-
-
-class UnsupportedPrompter(Prompter):
-    """
-    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

@@ -1,5 +1,6 @@
 """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
@@ -9,7 +10,6 @@ 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
@@ -17,16 +17,23 @@ from transformers.deepspeed import is_deepspeed_zero3_enabled
 from axolotl.common.cli import TrainerCliArgs
 from axolotl.logging_config import configure_logging
 from axolotl.utils.dict import DictDefault
-from axolotl.utils.freeze import freeze_parameters_except
 from axolotl.utils.models import load_model, load_tokenizer
 from axolotl.utils.trainer import setup_trainer
 
+try:
+    from llava.train.train import safe_save_model_for_hf_trainer
+except ImportError:
+
+    def safe_save_model_for_hf_trainer(trainer: transformers.Trainer, output_dir: str):
+        raise ImportError("missing LLaVA package")
+
+
 project_root = os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))
 src_dir = os.path.join(project_root, "src")
 sys.path.insert(0, src_dir)
 
 configure_logging()
-LOG = get_logger("axolotl.train")
+LOG = logging.getLogger("axolotl.train")
 
 
 @dataclass
@@ -44,10 +51,7 @@ def train(
     *, cfg: DictDefault, cli_args: TrainerCliArgs, dataset_meta: TrainDatasetMeta
 ):
     # load the tokenizer first
-    LOG.debug(
-        f"loading tokenizer... {cfg.tokenizer_config or cfg.base_model_config}",
-        main_process_only=True,
-    )
+    LOG.info(f"loading tokenizer... {cfg.tokenizer_config or cfg.base_model_config}")
     tokenizer = load_tokenizer(cfg)
 
     train_dataset = dataset_meta.train_dataset
@@ -55,10 +59,7 @@ def train(
     total_num_steps = dataset_meta.total_num_steps
 
     # Load the model and tokenizer
-    msg = "loading model"
-    if cfg.adapter:
-        msg += " and peft_config..."
-    LOG.debug(msg)
+    LOG.info("loading model and (optionally) peft_config...")
     model, peft_config = load_model(cfg, tokenizer, inference=cli_args.inference)
 
     safe_serialization = cfg.save_safetensors is True
@@ -78,15 +79,11 @@ def train(
             )
     resume_from_checkpoint = cfg.resume_from_checkpoint
 
-    if cfg.unfrozen_parameters:
-        freeze_parameters_except(model, cfg.unfrozen_parameters)
-
     trainer = setup_trainer(
         cfg, train_dataset, eval_dataset, model, tokenizer, total_num_steps
     )
 
-    if hasattr(model, "config"):
-        model.config.use_cache = False
+    model.config.use_cache = False
 
     # go ahead and presave, so we have the adapter config available to inspect
     if peft_config:
@@ -96,8 +93,7 @@ def train(
     if not Path(cfg.output_dir).is_dir():
         os.makedirs(cfg.output_dir, exist_ok=True)
     tokenizer.save_pretrained(str(Path(cfg.output_dir)))
-    if hasattr(model, "config"):
-        model.config.save_pretrained(str(Path(cfg.output_dir)))
+    model.config.save_pretrained(str(Path(cfg.output_dir)))
 
     # In case we want to stop early with ctrl+c, this is a nice to have to save the pretrained model
     if cfg.local_rank == 0:
@@ -119,7 +115,6 @@ 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
@@ -127,7 +122,6 @@ 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}")
 
@@ -151,6 +145,8 @@ def train(
     # only save on rank 0, otherwise it corrupts output on multi-GPU when multiple processes attempt to write the same file
     if cfg.fsdp:
         trainer.save_model(cfg.output_dir)
+    elif cfg.multimodal:
+        safe_save_model_for_hf_trainer(trainer=trainer, output_dir=cfg.output_dir)
     elif cfg.deepspeed and is_deepspeed_zero3_enabled():
         # Copied over from: https://github.com/huggingface/accelerate/blob/5ae611118057232f441055f7ef9ba0b0f2b8d533/docs/source/usage_guides/deepspeed.md#saving-and-loading
         trainer.accelerator.wait_for_everyone()
@@ -177,21 +173,3 @@ 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:
-    """
-
-
-def post_train_hooks(_cfg, _trainer):
-    """
-    Run hooks right after training completes
-    :param cfg:
-    :param trainer:
-    :return:
-    """

src/axolotl/utils/callbacks.py

@@ -124,36 +124,6 @@ class GPUStatsCallback(
         return control
 
 
-class LossWatchDogCallback(TrainerCallback):
-    """Callback to track loss and stop training if loss is too high"""
-
-    def __init__(self, cfg):
-        self.cfg = cfg
-        self.logged = False
-        self.violations = 0
-        self.threshold = cfg.loss_watchdog_threshold
-        self.patience = cfg.loss_watchdog_patience or 3
-
-    def on_step_end(
-        self,
-        _args: TrainingArguments,
-        state: TrainerState,
-        control: TrainerControl,
-        **_kwargs,
-    ):
-        if len(state.log_history) > 0 and "loss" in state.log_history[-1]:
-            if state.log_history[-1]["loss"] > self.threshold:
-                self.violations += 1
-                if self.violations >= self.patience:
-                    LOG.warning(
-                        "Loss is too high, stopping training (loss_watchdog_threshold)"
-                    )
-                    control.should_training_stop = True
-            else:
-                self.violations = 0
-        return control
-
-
 def bench_eval_callback_factory(trainer, tokenizer):
     accuracy = evaluate.load("accuracy")
     abcd_idx = [

src/axolotl/utils/collators.py

@@ -2,16 +2,12 @@
 DataCollator for axolotl to pad labels and position_ids for packed sequences
 """
 from dataclasses import dataclass
-from typing import Any, Dict, Optional, Sequence, Union
+from typing import Any, Optional, Union
 
 import numpy as np
-import torch
-import transformers
 from transformers import PreTrainedTokenizerBase
 from transformers.utils import PaddingStrategy
 
-IGNORE_INDEX = -100
-
 
 @dataclass
 class DataCollatorForSeq2Seq:
@@ -123,58 +119,3 @@ 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)
-
-
-@dataclass
-class MambaDataCollator:
-    """
-    Collator for State Space Models (Mamba)
-    """
-
-    tokenizer: transformers.PreTrainedTokenizer
-
-    def __call__(self, instances: Sequence[Dict]) -> Dict[str, torch.Tensor]:
-        input_ids, labels = tuple(
-            [torch.LongTensor(instance[key]) for instance in instances]
-            for key in ("input_ids", "labels")
-        )
-        input_ids = torch.nn.utils.rnn.pad_sequence(
-            input_ids,
-            batch_first=True,
-            padding_value=self.tokenizer.pad_token_id,
-        )
-        labels = torch.nn.utils.rnn.pad_sequence(
-            labels, batch_first=True, padding_value=IGNORE_INDEX
-        )
-
-        return {
-            "input_ids": input_ids,
-            "labels": labels,
-        }

src/axolotl/utils/config.py

@@ -27,7 +27,7 @@ def choose_device(cfg):
 
     cfg.device = get_device()
     if cfg.world_size == 1:
-        cfg.device_map = cfg.device_map or "auto"
+        cfg.device_map = "auto"
     else:
         if cfg.device.startswith("cuda"):
             cfg.device_map = {"": torch.cuda.current_device()}
@@ -77,15 +77,6 @@ def normalize_config(cfg):
     else:
         cfg.torch_dtype = torch.float32
 
-    if cfg.saves_per_epoch:
-        save_steps = 1.0 / (cfg.saves_per_epoch * cfg.num_epochs)
-        if save_steps < 1.0:  # prevent saves on every step
-            cfg.save_steps = save_steps
-    if cfg.evals_per_epoch:
-        eval_steps = 1.0 / (cfg.evals_per_epoch * cfg.num_epochs)
-        if eval_steps < 1.0:  # prevent evals on every step
-            cfg.eval_steps = eval_steps
-
     cfg.dataset_processes = cfg.dataset_processes or os.cpu_count()
 
     if not cfg.base_model_config:
@@ -131,19 +122,6 @@ def normalize_config(cfg):
         or (cfg.model_type and "mistral" in cfg.model_type.lower())
     )
 
-    cfg.is_qwen_derived_model = (
-        (
-            hasattr(model_config, "model_type")
-            and model_config.model_type
-            in [
-                "qwen",
-            ]
-        )
-        or cfg.is_qwen_derived_model
-        or "qwen" in cfg.base_model.lower()
-        or (cfg.model_type and "qwen" in cfg.model_type.lower())
-    )
-
     if isinstance(cfg.learning_rate, str):
         cfg.learning_rate = float(cfg.learning_rate)
 
@@ -187,11 +165,7 @@ def validate_config(cfg):
             "batch_size is not recommended. Please use gradient_accumulation_steps instead.",
             "To calculate the equivalent gradient_accumulation_steps, divide batch_size / micro_batch_size / number of gpus.",
         )
-    if (
-        cfg.eval_batch_size
-        and cfg.micro_batch_size
-        and cfg.eval_batch_size != cfg.micro_batch_size
-    ):
+    if cfg.eval_batch_size != cfg.micro_batch_size:
         LOG.warning(
             "eval_batch_size != micro_batch_size. This can lead to VRAM instability."
         )
@@ -361,27 +335,6 @@ def validate_config(cfg):
                 cfg.datasets[idx].type = cfg.datasets[idx].type.replace(
                     "sharegpt_simple", "sharegpt"
                 )
-
-    if cfg.saves_per_epoch and cfg.save_steps:
-        raise ValueError(
-            "save_steps and saves_per_epoch are mutually exclusive and cannot be used together."
-        )
-    if cfg.saves_per_epoch and cfg.save_strategy and cfg.save_strategy != "steps":
-        raise ValueError(
-            "save_strategy must be empty or set to `steps` when used with saves_per_epoch."
-        )
-    if cfg.evals_per_epoch and cfg.eval_steps:
-        raise ValueError(
-            "eval_steps and evals_per_epoch are mutually exclusive and cannot be used together."
-        )
-    if (
-        cfg.evals_per_epoch
-        and cfg.evaluation_strategy
-        and cfg.evaluation_strategy != "steps"
-    ):
-        raise ValueError(
-            "evaluation_strategy must be empty or set to `steps` when used with evals_per_epoch."
-        )
     if cfg.save_strategy and cfg.save_steps and cfg.save_strategy != "steps":
         raise ValueError(
             "save_strategy and save_steps mismatch. Please set save_strategy to 'steps' or remove save_steps."
@@ -416,37 +369,14 @@ def validate_config(cfg):
             "If you want to full finetune, please turn off load_in_8bit and load_in_4bit."
         )
 
-    if cfg.rope_scaling:
-        LOG.warning("`rope_scaling` should now be be a key under `model_config`")
-
-    if cfg.warmup_steps and cfg.warmup_ratio:
-        raise ValueError("warmup_steps and warmup_ratio are mutually exclusive")
-
-    if cfg.is_qwen_derived_model and cfg.gradient_checkpointing:
-        LOG.warning(
-            "Gradient checkpointing is broken for Qwen models for transformers>=4.35.0, except main branch."
-        )
-
-    if cfg.wandb_run_id and not cfg.wandb_name:
-        cfg.wandb_name = cfg.wandb_run_id
-
-        LOG.warning(
-            "wandb_run_id sets the ID of the run. If you would like to set the name, please use wandb_name instead."
-        )
-
-    if cfg.noisy_embedding_alpha is not None:
-        # Deprecated, use neftune_noise_alpha
-        LOG.warning("noisy_embedding_alpha is deprecated, use neftune_noise_alpha")
-        if cfg.neftune_noise_alpha is None:
-            cfg.neftune_noise_alpha = cfg.noisy_embedding_alpha
-        else:
-            # User is providing both; bail and have them sort out their settings
-            raise ValueError(
-                "noisy_embedding_alpha is deprecated, use neftune_noise_alpha; both are set, please remove the deprecated noisy_embedding_alpha setting"
+    if cfg.multimodal:
+        try:
+            import llava  # noqa: F401 # pylint:disable=unused-import
+        except ImportError as exc:
+            LOG.warning(
+                "LLaVA package required for multimodal training. See docs/llava.md for more information."
             )
-
-    if cfg.neftune_noise_alpha is not None and cfg.neftune_noise_alpha <= 0.0:
-        raise ValueError("neftune_noise_alpha must be > 0.0")
+            raise exc
 
     # TODO
     # MPT 7b

src/axolotl/utils/data.py

@@ -34,10 +34,8 @@ from axolotl.prompters import (
     JeopardyPrompter,
     MultipleChoiceConcisePrompter,
     MultipleChoiceExplainPrompter,
-    Prompter,
     ReflectAlpacaPrompter,
     SummarizeTLDRPrompter,
-    UnsupportedPrompter,
 )
 from axolotl.utils.dict import DictDefault
 from axolotl.utils.distributed import is_main_process, zero_first
@@ -56,11 +54,21 @@ def md5(to_hash: str, encoding: str = "utf-8") -> str:
         return hashlib.md5(to_hash.encode(encoding)).hexdigest()  # nosec
 
 
-def prepare_dataset(cfg, tokenizer):
-    prompters = []
-    if not cfg.pretraining_dataset:
+def prepare_dataset(cfg, tokenizer, model=None):
+    if cfg.multimodal:
+        if not model:
+            raise ValueError("missing model argument")
+        from llava.train.train import LazySupervisedDataset
+
         with zero_first(is_main_process()):
-            train_dataset, eval_dataset, prompters = load_prepare_datasets(
+            eval_dataset = None
+            train_dataset = LazySupervisedDataset(
+                tokenizer=tokenizer,
+            )
+
+    elif not cfg.pretraining_dataset:
+        with zero_first(is_main_process()):
+            train_dataset, eval_dataset = load_prepare_datasets(
                 tokenizer, cfg, DEFAULT_DATASET_PREPARED_PATH
             )
     else:
@@ -73,33 +81,25 @@ 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, prompters
+        return train_dataset, eval_dataset, cfg.max_steps
 
     with zero_first(is_main_process()):
         train_dataset, eval_dataset = process_datasets_for_packing(
             cfg, train_dataset, eval_dataset, tokenizer
         )
-
-    if eval_dataset and cfg.sample_packing and cfg.eval_sample_packing is not False:
-        total_eval_steps = calculate_total_num_steps(cfg, eval_dataset, update=False)
-        if total_eval_steps == 0:
-            raise ValueError(
-                "eval dataset split is too small for sample_packing. You should set `eval_sample_packing: False`. "
-            )
-
     if cfg.max_steps:
         total_num_steps = min(
-            calculate_total_num_steps(cfg, train_dataset), cfg.max_steps
+            calculate_total_num_steps(cfg, train_dataset, tokenizer), 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)
-    return train_dataset, eval_dataset, total_num_steps, prompters
+        total_num_steps = calculate_total_num_steps(cfg, train_dataset, tokenizer)
+    return train_dataset, eval_dataset, total_num_steps
 
 
 def load_tokenized_prepared_datasets(
     tokenizer, cfg, default_dataset_prepared_path
-) -> Tuple[DatasetDict, List[Prompter]]:
+) -> DatasetDict:
     tokenizer_name = tokenizer.__class__.__name__
     ds_hash = str(
         md5(
@@ -107,12 +107,7 @@ def load_tokenized_prepared_datasets(
                 str(cfg.sequence_len)
                 + "@"
                 + "|".join(
-                    sorted(
-                        [
-                            f"{d.path}:{d.type}:{d.shards}:{d.conversation}"
-                            for d in cfg.datasets
-                        ]
-                    )
+                    sorted([f"{d.path}:{d.type}:{d.shards}" for d in cfg.datasets])
                 )
                 + "|"
                 + tokenizer_name
@@ -125,7 +120,6 @@ 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:
@@ -164,13 +158,13 @@ def load_tokenized_prepared_datasets(
                     yield dataset
 
         # pylint: disable=invalid-name
-        for config_dataset in for_d_in_datasets(cfg.datasets):
+        for d in for_d_in_datasets(cfg.datasets):
             ds: Union[Dataset, DatasetDict] = None
             ds_from_hub = False
             try:
                 load_dataset(
-                    config_dataset.path,
-                    name=config_dataset.name,
+                    d.path,
+                    name=d.name,
                     streaming=True,
                     token=use_auth_token,
                 )
@@ -178,85 +172,34 @@ def load_tokenized_prepared_datasets(
             except (FileNotFoundError, ConnectionError):
                 pass
 
-            ds_from_cloud = False
-            storage_options = {}
-            remote_file_system = None
-            if config_dataset.path.startswith("s3://"):
-                try:
-                    import aiobotocore.session  # type: ignore
-                    import s3fs  # type: ignore
-                except ImportError as exc:
-                    raise ImportError(
-                        "s3:// paths require aiobotocore and s3fs to be installed"
-                    ) from exc
-
-                # Takes credentials from ~/.aws/credentials for default profile
-                s3_session = aiobotocore.session.AioSession(profile="default")
-                storage_options = {"session": s3_session}
-                remote_file_system = s3fs.S3FileSystem(**storage_options)
-            elif config_dataset.path.startswith(
-                "gs://"
-            ) or config_dataset.path.startswith("gcs://"):
-                try:
-                    import gcsfs  # type: ignore
-                except ImportError as exc:
-                    raise ImportError(
-                        "gs:// or gcs:// paths require gcsfs to be installed"
-                    ) from exc
-
-                # gcsfs will use default credentials from the environment else anon
-                # https://gcsfs.readthedocs.io/en/latest/#credentials
-                storage_options = {"token": None}
-                remote_file_system = gcsfs.GCSFileSystem(**storage_options)
-            # TODO: Figure out how to get auth creds passed
-            # elif config_dataset.path.startswith("adl://") or config_dataset.path.startswith("abfs://"):
-            #     try:
-            #         import adlfs
-            #     except ImportError as exc:
-            #        raise ImportError(
-            #            "adl:// or abfs:// paths require adlfs to be installed"
-            #        ) from exc
-
-            #     # Gen 1
-            #     storage_options = {
-            #         "tenant_id": TENANT_ID,
-            #         "client_id": CLIENT_ID,
-            #         "client_secret": CLIENT_SECRET,
-            #     }
-            #     # Gen 2
-            #     storage_options = {
-            #         "account_name": ACCOUNT_NAME,
-            #         "account_key": ACCOUNT_KEY,
-            #     }
-
-            #     remote_file_system = adlfs.AzureBlobFileSystem(**storage_options)
-            try:
-                if remote_file_system and remote_file_system.exists(
-                    config_dataset.path
-                ):
-                    ds_from_cloud = True
-            except (FileNotFoundError, ConnectionError):
-                pass
-
             # prefer local dataset, even if hub exists
-            local_path = Path(config_dataset.path)
+            local_path = Path(d.path)
             if local_path.exists():
                 if local_path.is_dir():
                     # TODO dirs with arrow or parquet files could be loaded with `load_from_disk`
                     ds = load_dataset(
-                        config_dataset.path,
-                        name=config_dataset.name,
-                        data_files=config_dataset.data_files,
+                        d.path,
+                        name=d.name,
+                        data_files=d.data_files,
                         streaming=False,
                         split=None,
                     )
                 elif local_path.is_file():
-                    ds_type = get_ds_type(config_dataset)
-
+                    ds_type = "json"
+                    if d.ds_type:
+                        ds_type = d.ds_type
+                    elif ".parquet" in d.path:
+                        ds_type = "parquet"
+                    elif ".arrow" in d.path:
+                        ds_type = "arrow"
+                    elif ".csv" in d.path:
+                        ds_type = "csv"
+                    elif ".txt" in d.path:
+                        ds_type = "text"
                     ds = load_dataset(
                         ds_type,
-                        name=config_dataset.name,
-                        data_files=config_dataset.path,
+                        name=d.name,
+                        data_files=d.path,
                         streaming=False,
                         split=None,
                     )
@@ -266,41 +209,25 @@ def load_tokenized_prepared_datasets(
                     )
             elif ds_from_hub:
                 ds = load_dataset(
-                    config_dataset.path,
-                    name=config_dataset.name,
+                    d.path,
+                    name=d.name,
                     streaming=False,
-                    data_files=config_dataset.data_files,
+                    data_files=d.data_files,
                     token=use_auth_token,
                 )
-            elif ds_from_cloud and remote_file_system:
-                if remote_file_system.isdir(config_dataset.path):
-                    ds = load_from_disk(
-                        config_dataset.path,
-                        storage_options=storage_options,
-                    )
-                elif remote_file_system.isfile(config_dataset.path):
-                    ds_type = get_ds_type(config_dataset)
-                    ds = load_dataset(
-                        ds_type,
-                        name=config_dataset.name,
-                        data_files=config_dataset.path,
-                        streaming=False,
-                        split=None,
-                        storage_options=storage_options,
-                    )
             else:
-                if isinstance(config_dataset.data_files, str):
+                if isinstance(d.data_files, str):
                     fp = hf_hub_download(
-                        repo_id=config_dataset.path,
+                        repo_id=d.path,
                         repo_type="dataset",
-                        filename=config_dataset.data_files,
+                        filename=d.data_files,
                     )
-                elif isinstance(config_dataset.data_files, list):
+                elif isinstance(d.data_files, list):
                     fp = []
-                    for file in config_dataset.data_files:
+                    for file in d.data_files:
                         fp.append(
                             hf_hub_download(
-                                repo_id=config_dataset.path,
+                                repo_id=d.path,
                                 repo_type="dataset",
                                 filename=file,
                             )
@@ -310,27 +237,21 @@ def load_tokenized_prepared_datasets(
                         "data_files must be either a string or list of strings"
                     )
                 ds = load_dataset(
-                    "json",
-                    name=config_dataset.name,
-                    data_files=fp,
-                    streaming=False,
-                    split=None,
+                    "json", name=d.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 config_dataset.shards:
+            if d.shards:
                 if "train" in ds:
                     ds = ds.shuffle(seed=seed)["train"].shard(
-                        num_shards=config_dataset.shards, index=0
+                        num_shards=d.shards, index=0
                     )
                 else:
-                    ds = ds.shuffle(seed=seed).shard(
-                        num_shards=config_dataset.shards, index=0
-                    )
+                    ds = ds.shuffle(seed=seed).shard(num_shards=d.shards, index=0)
 
             d_base_type = d_prompt_style = None
-            d_type = config_dataset.type
+            d_type = d.type
             if isinstance(d_type, str):
                 d_type_split = d_type.split(":")
                 d_base_type = d_type_split[0]
@@ -339,26 +260,108 @@ def load_tokenized_prepared_datasets(
                 ds = ds["train"]
             elif (
                 isinstance(ds, DatasetDict)
-                and config_dataset.train_on_split
-                and config_dataset.train_on_split in ds
+                and d.train_on_split
+                and d.train_on_split in ds
             ):
-                ds = ds[config_dataset.train_on_split]
+                ds = ds[d.train_on_split]
             elif isinstance(ds, DatasetDict):
                 raise ValueError(
-                    f"no train split found for dataset {config_dataset.path}, you may specify a split with 'train_on_split: `"
+                    f"no train split found for dataset {d.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)
 
@@ -376,32 +379,14 @@ def load_tokenized_prepared_datasets(
                     f"{cfg.push_dataset_to_hub}/{ds_hash}", private=True
                 )
 
-    return dataset, prompters
-
-
-def get_ds_type(config_dataset: DictDefault):
-    """
-    Get the dataset type from the path if it's not specified
-    """
-    ds_type = "json"
-    if config_dataset.ds_type:
-        ds_type = config_dataset.ds_type
-    elif ".parquet" in config_dataset.path:
-        ds_type = "parquet"
-    elif ".arrow" in config_dataset.path:
-        ds_type = "arrow"
-    elif ".csv" in config_dataset.path:
-        ds_type = "csv"
-    elif ".txt" in config_dataset.path:
-        ds_type = "text"
-    return ds_type
+    return dataset
 
 
 def load_prepare_datasets(
     tokenizer: PreTrainedTokenizerBase,
     cfg,
     default_dataset_prepared_path,
-) -> Tuple[Dataset, Dataset, List[Prompter]]:
+) -> Tuple[Dataset, Dataset]:
     max_packed_sequence_len = (
         cfg.max_packed_sequence_len if cfg.max_packed_sequence_len else cfg.sequence_len
     )
@@ -410,7 +395,6 @@ def load_prepare_datasets(
     )  # make sure we don't accidentally set it larger than sequence_len
 
     tokenizer_name = tokenizer.__class__.__name__
-    prompters: List[Prompter] = []
     if cfg.max_packed_sequence_len is not None:
         # see if we can go ahead and load the stacked dataset
         seed = f"@{str(cfg.seed)}" if cfg.seed else ""
@@ -466,7 +450,7 @@ def load_prepare_datasets(
                     f"{cfg.push_dataset_to_hub}/{ds_hash}", private=True
                 )
         else:
-            dataset, prompters = load_tokenized_prepared_datasets(
+            dataset = load_tokenized_prepared_datasets(
                 tokenizer, cfg, default_dataset_prepared_path
             )
 
@@ -508,7 +492,7 @@ def load_prepare_datasets(
                         private=True,
                     )
     else:
-        dataset, prompters = load_tokenized_prepared_datasets(
+        dataset = load_tokenized_prepared_datasets(
             tokenizer, cfg, default_dataset_prepared_path
         )
 
@@ -544,13 +528,14 @@ def load_prepare_datasets(
         train_fingerprint = md5(to_hash_train)
         test_fingerprint = md5(to_hash_test)
 
-        dataset = dataset.train_test_split(
-            test_size=cfg.val_set_size,
-            shuffle=False,
-            seed=cfg.seed or 42,
-            train_new_fingerprint=train_fingerprint,
-            test_new_fingerprint=test_fingerprint,
-        )
+        with zero_first(is_main_process()):
+            dataset = dataset.train_test_split(
+                test_size=cfg.val_set_size,
+                shuffle=False,
+                seed=cfg.seed or 42,
+                train_new_fingerprint=train_fingerprint,
+                test_new_fingerprint=test_fingerprint,
+            )
 
         train_dataset = dataset["train"]
         eval_dataset = dataset["test"]
@@ -558,144 +543,7 @@ def load_prepare_datasets(
         train_dataset = dataset
         eval_dataset = None
 
-    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
+    return train_dataset, eval_dataset
 
 
 def encode_pretraining(

src/axolotl/utils/dataloader.py

@@ -0,0 +1,302 @@
+# pylint: skip-file
+import hashlib
+import itertools
+import logging
+import math
+from typing import Any, Callable, List, Union
+
+import numba
+import numpy as np
+from torch.utils.data import DistributedSampler, Sampler
+
+LOG = logging.getLogger("axolotl.utils.dataloader")
+
+
+@numba.njit
+def ffd_check(a: np.ndarray, c: int, n: int):
+    # First-fit-decreasing bin packing
+    # Check if a[] could fit in n bins with capacity c
+    # https://en.wikipedia.org/wiki/First-fit-decreasing_bin_packing
+
+    a = np.sort(a)[::-1]
+    bins = np.full((n,), c, dtype=a.dtype)
+    for size in a:
+        not_found = True
+        for idx in range(n):
+            if bins[idx] >= size:
+                bins[idx] -= size
+                not_found = False
+                break
+
+        if not_found:
+            return False
+
+    return True
+
+
+@numba.njit
+def ffd_with_result(a: np.ndarray, c: int, start_index: int):
+    # First-fit-decreasing bin packing (with result return)
+
+    indices = np.argsort(a)[::-1]
+    a = a[indices]
+
+    bins: List[Any] = []
+    bins_result: List[Any] = []
+    for a_id, size in enumerate(a):
+        add_new = True
+        for idx in range(len(bins)):
+            if bins[idx] >= size:
+                bins[idx] -= size
+                bins_result[idx].append(indices[a_id] + start_index)
+                add_new = False
+                break
+
+        if add_new:
+            bins.append(c - size)
+            bins_result.append([indices[a_id] + start_index])
+
+    return bins_result, len(a)
+
+
+@numba.njit
+def allocate(
+    lengths: np.ndarray, lengths_cumsum: np.ndarray, rank: int, c: int, n: int
+):
+    """
+    :param lengths: array of lengths of each sample
+    :param lengths_cumsum: cumulative sum of consecutive lengths
+    :param rank: rank for this process
+    :param c: length of tokens per batch
+    :param n: number of ranks
+    :return:
+    """
+    # Dynamic batch allocator, similar to Multifit
+    # https://en.wikipedia.org/wiki/Multifit_algorithm
+    # ~99.5% efficiency on OpenChat training set (12 * 2048 ctx len)
+
+    s = 0
+    start_index = 0
+    result = []
+    result_totseqs = []
+
+    while True:
+        # binary search [left, right)
+        left = 1
+        right = 1 + np.searchsorted(lengths_cumsum[start_index:], s + c * n, "right")
+
+        while right - left > 1:
+            mid = (left + right) // 2
+            if ffd_check(lengths[start_index : start_index + mid], c, n):
+                left = mid
+            else:
+                right = mid
+
+        # use length left
+        batch, tot_seqs = ffd_with_result(
+            lengths[start_index : start_index + left], c, start_index
+        )
+        if len(batch) < n:
+            break
+
+        start_index += left
+        s = lengths_cumsum[start_index - 1]
+
+        # add local rank
+        result.append(batch[rank])
+        # add total seqs for all ranks
+        result_totseqs.append(tot_seqs)
+        # yield batch[rank], tot_seqs, s, len(result) * c * n
+    return result, result_totseqs, s, len(result) * c * n
+
+
+def chunk(iterable, n):
+    """
+    Chunk data into tuples of length n
+    """
+    # batched('ABCDEFG', 3) --> ABC DEF G
+    if n < 1:
+        raise ValueError("n must be at least one")
+    it = iter(iterable)
+    while batch := tuple(itertools.islice(it, n)):
+        yield batch
+
+
+def hash_indices(lst: List[int]) -> str:
+    # Convert the list of integers to a string representation
+    concatenated = ",".join(map(str, lst))
+
+    # Generate the hash
+    sha256 = hashlib.sha256()
+    sha256.update(concatenated.encode())
+
+    return sha256.hexdigest()
+
+
+class MultipackDistributedDataloader:
+    """Unpadded data loading using Multipack.
+    Adapted from https://github.com/imoneoi/openchat/blob/v3_fix_mle_loss/ochat/training_deepspeed/multipack_dataloader.py
+    Approximate (at most ~1.22x) the optimal solution of the identical-machines scheduling problem, which is NP-hard.
+    """
+
+    def __init__(
+        self,
+        dataset: Any,
+        collate_fn: Callable,
+        seq_max_length: int = 2048,
+        batch_size: int = 1,
+        sampler: Union[Sampler, DistributedSampler] = None,
+        packing_efficiency_estimate: float = 1.0,
+        sample_packing_seq_len_multiplier: int = 1,
+        device_count: int = 1,
+    ):
+        # Dataset
+        self.dataset = dataset
+        self.lengths = (
+            dataset.data.column("position_ids")
+            .to_pandas()
+            .apply(lambda x: x[-1] + 1)
+            .values
+        )
+        assert isinstance(self.lengths, np.ndarray)
+        assert batch_size % sample_packing_seq_len_multiplier == 0
+        assert batch_size >= sample_packing_seq_len_multiplier
+        self.sampler = sampler
+        self.batch_size = batch_size
+        self.sample_packing_seq_len_multiplier = sample_packing_seq_len_multiplier
+        self.seq_max_length = seq_max_length
+        self.batch_max_length = batch_size * seq_max_length
+        self.collate_fn = collate_fn
+
+        self.num_replicas = 1
+        self.rank = 0
+
+        # statistics
+        self.eff_total_used = 0
+        self.eff_total_slots = 0
+        self.packing_efficiency_estimate = packing_efficiency_estimate or 1.0
+        self.device_count = device_count
+
+    def generate_batches(self, set_stats=False):
+        LOG.info("generating packed batches")
+        if self.sampler:
+            indices = [idx for idx in self.sampler]
+        else:
+            indices = range(0, len(self.dataset))
+
+        LOG.info(hash_indices(indices))
+        lengths = self.lengths[indices]
+        lengths_cumsum = np.cumsum(lengths)
+
+        batches, totseqs, total_used, total_slots = allocate(
+            lengths=lengths,
+            lengths_cumsum=lengths_cumsum,
+            rank=self.rank,
+            # c=self.batch_max_length,
+            c=self.seq_max_length * self.sample_packing_seq_len_multiplier,
+            n=self.num_replicas,
+        )
+
+        batches = [[indices[b_idx] for b_idx in batch] for batch in batches]
+
+        # statistics
+        if set_stats:
+            self.eff_total_used += total_used
+            self.eff_total_slots += total_slots
+
+        return batches, totseqs
+
+    def __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})")
+        all_batches, _ = self.generate_batches(set_stats=True)
+        features = self.dataset.features.keys()
+        len_remaining = self._len_est()
+        for batches in chunk(
+            all_batches, self.batch_size // self.sample_packing_seq_len_multiplier
+        ):
+            chunked_data = []
+            attn_mask_cum_idx = 0
+            for batch in batches:
+                concatenated = {}
+                batched_data = [self.dataset[batch_idx] for batch_idx in batch]
+                for feature in features:
+                    if feature == "length":
+                        continue
+                    if feature == "attention_mask":
+                        arrays = [
+                            (attn_mask_cum_idx + idx + 1) * np.array(item[feature])
+                            for idx, item in enumerate(batched_data)
+                            if feature in item
+                        ]
+                        attn_mask_cum_idx += len(batched_data)
+                        concatenated[feature] = np.concatenate(arrays)
+                    else:
+                        arrays = [
+                            np.array(item[feature])
+                            for item in batched_data
+                            if feature in item
+                        ]
+                        concatenated[feature] = np.concatenate(arrays)
+                chunked_data.append(concatenated)
+            yield self.collate_fn(chunked_data)
+            len_remaining -= 1
+            if not len_remaining:
+                return
+        # yield a no-op for cases where we don't have any data left to pack
+        for i in range(0, len_remaining):
+            yield self.collate_fn(
+                [
+                    {
+                        "input_ids": [0],
+                        "labels": [-100],
+                        "attention_mask": [True],
+                        "position_ids": [0],
+                    }
+                ]
+            )
+
+    def _len_est(self):
+        lengths_sum = np.sum(self.lengths)
+        lengths_sum_per_device = lengths_sum // self.device_count
+        LOG.info(
+            f"packing_efficiency_estimate: {self.packing_efficiency_estimate} "
+            f"total_num_tokens per device: {lengths_sum_per_device}"
+        )
+
+        # shave off 1% + 1 for dealing with variance in packing from random sampler to sampler
+        return (
+            math.floor(
+                0.99
+                * lengths_sum_per_device
+                / self.packing_efficiency_estimate
+                // self.seq_max_length
+                // self.batch_size
+            )
+            - 1
+        )
+
+    def __len__(self):
+        # this doesn't return the actual length b/c with distributed samplers, not all dataloaders get
+        # the same share of total tokens
+        # if not self.eff_total_used:
+        #     batches, _ = self.generate_batches(set_stats=True)
+        # LOG.info(
+        #     f"packing_efficiency_estimate: {self.packing_efficiency_estimate} "
+        #     f"actual packing efficiency: {self.efficiency()}"
+        # )
+        return max(1, self._len_est())
+
+    def len_w_stats(self):
+        if not self.eff_total_used:
+            batches, _ = self.generate_batches(set_stats=True)
+        LOG.info(
+            f"packing_efficiency_estimate: {self.packing_efficiency_estimate} "
+            f"actual packing efficiency: {self.efficiency()}"
+        )
+        return max(1, self._len_est())
+
+    def efficiency(self):
+        return self.eff_total_used / self.eff_total_slots

src/axolotl/utils/distributed.py

@@ -50,17 +50,6 @@ 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/freeze.py

@@ -1,38 +0,0 @@
-"""
-module to freeze/unfreeze parameters by name
-"""
-import logging
-import re
-
-from axolotl.utils.distributed import is_main_process
-
-LOG = logging.getLogger("axolotl.utils.freeze")
-
-
-def freeze_parameters_except(model, regex_patterns):
-    """
-    Freezes all layers of the given model except for the layers that match given regex patterns.
-    Periods in the patterns are treated as literal periods, not as wildcard characters.
-
-    Parameters:
-    - model (nn.Module): The PyTorch model to be modified.
-    - regex_patterns (list of str): List of regex patterns to match layer names to keep unfrozen.
-
-    Returns:
-    None; the model is modified in place.
-    """
-    # Escape periods and compile the regex patterns
-    compiled_patterns = [
-        re.compile(pattern.replace(".", "\\.")) for pattern in regex_patterns
-    ]
-
-    # First, freeze all parameters in the model
-    for param in model.parameters():
-        param.requires_grad = False
-
-    # Unfreeze layers that match the regex patterns
-    for name, param in model.named_parameters():
-        if any(pattern.match(name) for pattern in compiled_patterns):
-            if is_main_process():
-                LOG.debug(f"unfreezing {name}")
-            param.requires_grad = True

src/axolotl/utils/models.py

@@ -4,7 +4,6 @@ import math
 import os
 from typing import Optional, Tuple  # noqa: F401
 
-import addict
 import bitsandbytes as bnb
 import torch
 import transformers
@@ -18,12 +17,11 @@ from transformers import (  # noqa: F401
     AutoTokenizer,
     BitsAndBytesConfig,
     GPTQConfig,
+    LlamaConfig,
     PreTrainedModel,
     PreTrainedTokenizerBase,
 )
-from transformers.deepspeed import is_deepspeed_zero3_enabled
 
-from axolotl.models.mamba import fix_mamba_attn_for_loss
 from axolotl.prompt_tokenizers import LLAMA_DEFAULT_EOS_TOKEN
 from axolotl.utils.bench import log_gpu_memory_usage
 from axolotl.utils.dict import DictDefault
@@ -31,51 +29,12 @@ from axolotl.utils.dict import DictDefault
 LOG = logging.getLogger("axolotl")
 
 
-def check_model_config(cfg: DictDefault, model_config: AutoConfig):
-    quant_config_exists = hasattr(model_config, "quantization_config")
-    quant_config_method_is_gptq = (
-        quant_config_exists
-        and "quant_method" in model_config.quantization_config
-        and model_config.quantization_config["quant_method"] == "gptq"
-    )
-
-    if cfg.gptq and not quant_config_method_is_gptq:
-        raise ValueError(
-            "model_config.quantization_config is not set or quant_method is not set to gptq. "
-            "Please make sure to point to a GPTQ model."
-        )
-
-    if not cfg.gptq and quant_config_exists:
-        raise ValueError(
-            "model_config.quantization_config is set but `gptq` flag is not. "
-            "Please use the `gptq` flag to train quantized model or point to a non-quantized model."
-        )
-
-
 def load_model_config(cfg):
     model_config_name = cfg.base_model_config or cfg.base_model
-    trust_remote_code = cfg.trust_remote_code is True
-
-    try:
-        model_config = AutoConfig.from_pretrained(
-            model_config_name, trust_remote_code=trust_remote_code
-        )
-    except ValueError as err:
-        if "mamba" in model_config_name:
-            return addict.Dict(
-                {
-                    "model_type": "mamba",
-                }
-            )
-        raise err
-
-    if cfg.model_config:
-        for key, val in cfg.model_config.items():
-            setattr(model_config, key, val)
-
-    check_model_config(cfg, model_config)
-
-    return model_config
+    trust_remote_code: bool = False or cfg.trust_remote_code
+    return AutoConfig.from_pretrained(
+        model_config_name, trust_remote_code=trust_remote_code
+    )
 
 
 def load_tokenizer(cfg):
@@ -92,7 +51,7 @@ def load_tokenizer(cfg):
     if cfg.tokenizer_type:
         tokenizer_cls = getattr(transformers, cfg.tokenizer_type)
 
-    tokenizer_config = cfg.tokenizer_config or cfg.base_model_config or cfg.base_model
+    tokenizer_config = cfg.tokenizer_config or cfg.base_model_config
     tokenizer = tokenizer_cls.from_pretrained(
         tokenizer_config,
         trust_remote_code=cfg.trust_remote_code or False,
@@ -106,7 +65,6 @@ def load_tokenizer(cfg):
             "LlamaTokenizer",
             "LlamaTokenizerFast",
             "CodeLlamaTokenizer",
-            "CodeLlamaTokenizerFast",
         ]
         and hasattr(tokenizer, "pad_token")
         and not tokenizer.pad_token
@@ -114,6 +72,11 @@ 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"
@@ -122,40 +85,11 @@ def load_tokenizer(cfg):
     if cfg.is_mistral_derived_model and cfg.flash_attention and not cfg.sample_packing:
         tokenizer.padding_side = "left"
 
-    # Qwen base only has single token, so we need to set the special tokens
-    if cfg.is_qwen_derived_model:
-        token_ids = ["bos_token_id", "eos_token_id", "pad_token_id", "unk_token_id"]
-        for attr_name in token_ids:
-            if getattr(tokenizer, attr_name) is None:
-                setattr(tokenizer, attr_name, tokenizer.eod_id)
-
-        token_names = ["bos_token", "eos_token", "pad_token", "unk_token"]
-        for attr_name in token_names:
-            if getattr(tokenizer, attr_name) is None:
-                setattr(tokenizer, attr_name, "<|endoftext|>")
-
     if cfg.special_tokens:
         for k, val in cfg.special_tokens.items():
             tokenizer.add_special_tokens(
                 {k: AddedToken(val, rstrip=False, lstrip=False, normalized=False)}
             )
-
-        # If we add bos_token and eos_token, we need to update the post processor to
-        # handle them correctly.
-        # https://github.com/huggingface/transformers/pull/24132
-        bos_or_eos_in_special_tokens = (
-            "bos_token" in cfg.special_tokens and "eos_token" in cfg.special_tokens
-        )
-        if (
-            tokenizer.__class__.__name__
-            in (
-                "LlamaTokenizerFast",
-                "CodeLlamaTokenizerFast",
-            )
-            and bos_or_eos_in_special_tokens
-        ):
-            tokenizer.update_post_processor()
-
     if cfg.tokens:
         tokenizer.add_tokens(
             [
@@ -164,11 +98,6 @@ 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
 
 
@@ -181,6 +110,7 @@ def load_model(
     Load a model for a given configuration and tokenizer.
     """
     base_model = cfg.base_model
+    base_model_config = cfg.base_model_config
     model_type = cfg.model_type
     model_config = load_model_config(cfg)
 
@@ -250,17 +180,25 @@ def load_model(
         LOG.info("patching with flash attention")
         replace_mistral_attn_with_flash_attn(packed=cfg.sample_packing)
 
-    if (
-        cfg.model_config_type == "mixtral"
-        and cfg.flash_attention
-        and cfg.sample_packing
-    ):
-        from axolotl.monkeypatch.mixtral import (
-            replace_mixtral_attn_with_multipack_flash_attn,
+    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 flash attention")
-        replace_mixtral_attn_with_multipack_flash_attn()
+        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 (
@@ -283,12 +221,8 @@ def load_model(
     model_kwargs = {}
 
     model_kwargs["device_map"] = cfg.device_map
-    model_kwargs["max_memory"] = cfg.max_memory
     model_kwargs["torch_dtype"] = cfg.torch_dtype
 
-    if is_deepspeed_zero3_enabled():
-        del model_kwargs["device_map"]
-
     if cfg.model_revision:
         model_kwargs["revision"] = cfg.model_revision
     if cfg.gptq:
@@ -312,34 +246,114 @@ def load_model(
             bnb_4bit_quant_type="nf4",
         )
     # sample packing uses custom FA2 patch
-    if cfg.flash_attention:
-        if not cfg.sample_packing:
-            if (
-                cfg.is_llama_derived_model
-                or cfg.is_falcon_derived_model
-                or cfg.is_mistral_derived_model
-                or model_config.model_type == "mixtral"
-            ):
-                model_config._attn_implementation = (  # pylint: disable=protected-access
-                    "flash_attention_2"
-                )
-        else:
-            if model_config.model_type == "mixtral":
-                model_config._attn_implementation = (  # pylint: disable=protected-access
-                    "flash_attention_2"
-                )
-            else:
-                model_config._attn_implementation = (  # pylint: disable=protected-access
-                    "eager"
-                )
+    if cfg.flash_attention and not cfg.sample_packing:
+        if (
+            cfg.is_llama_derived_model
+            or cfg.is_falcon_derived_model
+            or cfg.is_mistral_derived_model
+        ):
+            model_kwargs["use_flash_attention_2"] = True
 
     try:
-        if cfg.is_llama_derived_model and not cfg.trust_remote_code and not cfg.gptq:
+        if cfg.multimodal:
+            from llava.train.train import DataArguments, ModelArguments
+
+            if cfg.is_llama_derived_model:
+                from llava.model.language_model.llava_llama import LlavaLlamaForCausalLM
+
+                model = LlavaLlamaForCausalLM.from_pretrained(
+                    cfg.base_model,
+                )
+            elif cfg.is_mistral_derived_model:
+                from axolotl.models.llava.llava_mistral import LlavaMistralForCausalLM
+
+                model = LlavaMistralForCausalLM.from_pretrained(
+                    cfg.base_model,
+                )
+            else:
+                raise NotImplementedError(
+                    "unhandled model architecture for multimodal training"
+                )
+
+            if cfg.mm_freeze_backbone:
+                model.model.requires_grad_(False)
+
+            def make_inputs_require_grad(
+                module, input, output
+            ):  # pylint: disable=redefined-builtin,unused-argument
+                output.requires_grad_(True)
+
+            model.get_input_embeddings().register_forward_hook(make_inputs_require_grad)
+
+            model_args = ModelArguments(
+                model_name_or_path=cfg.base_model,
+                version="v0",
+                freeze_backbone=cfg.mm_freeze_backbone or False,
+                tune_mm_mlp_adapter=cfg.tune_mm_mlp_adapter or False,
+                vision_tower=cfg.mm_vision_tower,
+                mm_vision_select_layer=cfg.mm_vision_select_layer or -1,
+                pretrain_mm_mlp_adapter=cfg.pretrain_mm_mlp_adapter,
+                mm_projector_type=cfg.mm_projector_type or "linear",
+                mm_use_im_start_end=cfg.mm_use_im_start_end or False,
+                mm_use_im_patch_token=cfg.mm_use_im_patch_token or True,
+                mm_vision_select_feature=cfg.mm_vision_select_feature or "patch",
+            )
+
+            if cfg.mm_vision_tower:
+                model.get_model().initialize_vision_modules(
+                    model_args=model_args, fsdp=cfg.fsdp
+                )
+
+            vision_tower = model.get_vision_tower()
+            vision_tower.to(dtype=cfg.torch_dtype)
+
+            # pylint: disable=duplicate-code
+            data_args = DataArguments(
+                data_path=cfg.datasets[0]["path"],
+                lazy_preprocess=cfg.mm_lazy_preprocess
+                if cfg.mm_lazy_preprocess is not None
+                else True,
+                is_multimodal=True,
+                image_folder=cfg.mm_image_folder or None,
+                image_aspect_ratio=cfg.mm_image_aspect_ratio or "square",
+                image_grid_pinpoints=cfg.mm_image_grid_pinpoints or None,
+            )
+            data_args.image_processor = vision_tower.image_processor
+            model.config.image_aspect_ratio = data_args.image_aspect_ratio
+            model.config.image_grid_pinpoints = data_args.image_grid_pinpoints
+            model.config.tune_mm_mlp_adapter = model_args.tune_mm_mlp_adapter
+            if model_args.tune_mm_mlp_adapter:
+                model.requires_grad_(False)
+                for (
+                    p  # pylint: disable=invalid-name
+                ) in model.get_model().mm_projector.parameters():
+                    p.requires_grad = True
+
+            model.config.freeze_mm_mlp_adapter = cfg.freeze_mm_mlp_adapter
+            if cfg.freeze_mm_mlp_adapter:
+                for (
+                    p  # pylint: disable=invalid-name
+                ) in model.get_model().mm_projector.parameters():
+                    p.requires_grad = False
+
+            model.config.mm_use_im_start_end = (
+                data_args.mm_use_im_start_end
+            ) = model_args.mm_use_im_start_end
+            model.config.mm_use_im_patch_token = model_args.mm_use_im_patch_token
+            model.initialize_vision_tokenizer(model_args, tokenizer=tokenizer)
+        elif cfg.is_llama_derived_model and not cfg.trust_remote_code and not cfg.gptq:
             from transformers import LlamaForCausalLM
 
+            config_kwargs = {}
+            if cfg.rope_scaling:
+                config_kwargs["rope_scaling"] = cfg.rope_scaling
+            config = LlamaConfig.from_pretrained(
+                base_model_config,
+                **config_kwargs,
+            )
             model = LlamaForCausalLM.from_pretrained(
                 base_model,
-                config=model_config,
+                config=config,
                 load_in_8bit=cfg.load_in_8bit and cfg.adapter is not None,
                 load_in_4bit=cfg.load_in_4bit and cfg.adapter is not None,
                 **model_kwargs,
@@ -384,99 +398,92 @@ def load_model(
         #         device=cfg.device,
         #     )
         #     model.train() # sets to train instead of eval mode
-        elif model_type == "PhiForCausalLM":
-            from axolotl.models.phi import PhiForCausalLM
+        elif model_type == "MixFormerSequentialForCausalLM":
+            from axolotl.models.phi import MixFormerSequentialForCausalLM
 
-            model = PhiForCausalLM.from_pretrained(
+            model = MixFormerSequentialForCausalLM.from_pretrained(
                 base_model,
                 load_in_8bit=cfg.load_in_8bit and cfg.adapter is not None,
                 load_in_4bit=cfg.load_in_4bit and cfg.adapter is not None,
                 **model_kwargs,
             )
-        elif model_type == "MambaLMHeadModel":
-            # FIXME this is janky at best and hacked together to make it work
-            MambaLMHeadModel = fix_mamba_attn_for_loss()  # pylint: disable=invalid-name
-
-            model_kwargs["dtype"] = model_kwargs["torch_dtype"]
-            model_kwargs["device"] = torch.cuda.current_device()
-            del model_kwargs["torch_dtype"]
-            del model_kwargs["device_map"]
-            del model_kwargs["max_memory"]
-
-            model = MambaLMHeadModel.from_pretrained(
-                base_model,
-                **model_kwargs,
-            )
         elif model_type and not cfg.trust_remote_code:
             if cfg.gptq:
                 model = AutoModelForCausalLM.from_pretrained(
                     base_model,
-                    config=model_config,
                     trust_remote_code=cfg.trust_remote_code or False,
                     **model_kwargs,
                 )
             else:
                 model = getattr(transformers, model_type).from_pretrained(
                     base_model,
-                    config=model_config,
                     load_in_8bit=cfg.load_in_8bit and cfg.adapter is not None,
                     load_in_4bit=cfg.load_in_4bit and cfg.adapter is not None,
                     trust_remote_code=cfg.trust_remote_code or False,
                     **model_kwargs,
                 )
         else:
+            config = AutoConfig.from_pretrained(
+                base_model,
+                trust_remote_code=cfg.trust_remote_code or False,
+            )
             # Shouldn't be a problem most of the time. will obviously error if the model doesn't support this
             # when training starts
             if (
-                hasattr(model_config, "max_seq_len")
-                and model_config.max_seq_len
-                and cfg.sequence_len > model_config.max_seq_len
+                hasattr(config, "max_seq_len")
+                and config.max_seq_len
+                and cfg.sequence_len > config.max_seq_len
             ):
-                model_config.max_seq_len = cfg.sequence_len
+                config.max_seq_len = cfg.sequence_len
                 LOG.warning(f"increasing context length to {cfg.sequence_len}")
             elif (
-                hasattr(model_config, "max_sequence_length")
-                and model_config.max_sequence_length
-                and cfg.sequence_len > model_config.max_sequence_length
+                hasattr(config, "max_sequence_length")
+                and config.max_sequence_length
+                and cfg.sequence_len > config.max_sequence_length
             ):
-                model_config.max_sequence_length = cfg.sequence_len
+                config.max_sequence_length = cfg.sequence_len
                 LOG.warning(f"increasing context length to {cfg.sequence_len}")
             if cfg.gptq:
                 model = AutoModelForCausalLM.from_pretrained(
                     base_model,
-                    config=model_config,
+                    config=config,
                     trust_remote_code=cfg.trust_remote_code or False,
                     **model_kwargs,
                 )
             else:
                 model = AutoModelForCausalLM.from_pretrained(
                     base_model,
-                    config=model_config,
+                    config=config,
                     load_in_8bit=cfg.load_in_8bit and cfg.adapter is not None,
                     load_in_4bit=cfg.load_in_4bit and cfg.adapter is not None,
                     trust_remote_code=cfg.trust_remote_code or False,
                     **model_kwargs,
                 )
     except Exception as err:  # pylint: disable=broad-exception-caught
+        LOG.error(
+            "Exception raised attempting to load model, retrying with AutoModelForCausalLM"
+        )
         LOG.exception(err)
-        raise err
+        model = AutoModelForCausalLM.from_pretrained(
+            base_model,
+            load_in_8bit=cfg.load_in_8bit and cfg.adapter is not None,
+            load_in_4bit=cfg.load_in_4bit and cfg.adapter is not None,
+            trust_remote_code=cfg.trust_remote_code or False,
+            **model_kwargs,
+        )
 
     embeddings_len = (
         math.ceil(len(tokenizer) / 32) * 32
         if cfg.resize_token_embeddings_to_32x
         else len(tokenizer)
     )
-    if (
-        hasattr(model, "get_input_embeddings")
-        and model.get_input_embeddings().num_embeddings < embeddings_len
-    ):
+    if model.get_input_embeddings().num_embeddings < embeddings_len:
         model.resize_token_embeddings(embeddings_len)
     else:
         model.tie_weights()
 
     if (
-        hasattr(model, "config")
-        and hasattr(model.config, "max_position_embeddings")
+        hasattr(model.config, "max_position_embeddings")
         and model.config.max_position_embeddings
         and cfg.sequence_len > model.config.max_position_embeddings
     ):
@@ -485,23 +492,7 @@ def load_model(
         )
         model.config.max_position_embeddings = cfg.sequence_len
 
-    if (
-        hasattr(model, "config")
-        and 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")
-        and 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 hasattr(model, "device") and model.device.type == "cuda":
+    if model.device.type == "cuda":
         log_gpu_memory_usage(LOG, "after model load", model.device)
 
     # make sure these are fp32 per Ramesh et al. (2021)
@@ -516,22 +507,15 @@ def load_model(
                 module.to(torch.float32)
 
     needs_fa2_dtype = cfg.adapter or cfg.fsdp
-    skip_prepare_model_for_kbit_training = False
-
-    if cfg.model_config_type == "qwen" and cfg.adapter == "lora":
-        # Qwen doesn't play nicely with LoRA if this is enabled
-        skip_prepare_model_for_kbit_training = True
-
     if (cfg.adapter == "lora" and load_in_8bit) or (
         cfg.adapter == "qlora" and cfg.load_in_4bit
     ):
         LOG.info("converting PEFT model w/ prepare_model_for_kbit_training")
         if cfg.gradient_checkpointing:
             model.gradient_checkpointing_enable()
-        if not skip_prepare_model_for_kbit_training:
-            model = prepare_model_for_kbit_training(
-                model, use_gradient_checkpointing=cfg.gradient_checkpointing
-            )
+        model = prepare_model_for_kbit_training(
+            model, use_gradient_checkpointing=cfg.gradient_checkpointing
+        )
         needs_fa2_dtype = True
 
     # LlamaRMSNorm layers are in fp32 after kbit_training or full finetune, so we need to
@@ -550,7 +534,14 @@ def load_model(
     if cfg.ddp and not load_in_8bit:
         model.to(f"cuda:{cfg.local_rank}")
 
-    if torch.cuda.device_count() > 1 and int(os.getenv("WORLD_SIZE", "1")) == 1:
+    if (
+        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)
 
@@ -560,8 +551,7 @@ def load_model(
             requires_grad.append(f"{name}: {param.requires_grad}")
     if len(requires_grad) == 0:
         LOG.warning("there are no parameters that require gradient updates")
-    if hasattr(model, "config"):
-        model.config.use_cache = False
+    model.config.use_cache = False
 
     if cfg.flash_optimum:
         model = BetterTransformer.transform(model)
@@ -616,7 +606,14 @@ def load_llama_adapter(model, cfg):
 def find_all_linear_names(model):
     cls = (bnb.nn.Linear4bit, bnb.nn.Linear8bitLt, torch.nn.Linear, QuantLinear)
     lora_module_names = set()
+    multimodal_keywords = [
+        "mm_projector",
+        "vision_tower",
+        "vision_resampler",
+    ]  # for LLaVA
     for name, module in model.named_modules():
+        if any(mm_keyword in name for mm_keyword in multimodal_keywords):
+            continue
         if (
             isinstance(module, cls)
             or "Linear" in module.__class__.__name__

src/axolotl/utils/samplers/__init__.py

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

src/axolotl/utils/samplers/multipack.py

@@ -1,196 +0,0 @@
-# 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 max(
-            0,
-            (
-                world_size
-                * math.floor(
-                    0.99
-                    * lengths_sum_per_device
-                    / self.packing_efficiency_estimate
-                    // self.batch_max_len
-                )
-                - 1
-            ),
-        )

src/axolotl/utils/trainer.py

@@ -1,4 +1,5 @@
 """Module containing the Trainer class and related functions"""
+import logging
 import math
 import os
 from contextlib import contextmanager
@@ -8,15 +9,21 @@ from typing import List
 import numpy as np
 import torch
 import torch.cuda
-from accelerate.logging import get_logger
+import torch.distributed as dist
 from datasets import set_caching_enabled
-from torch.utils.data import DataLoader, RandomSampler
+from torch.utils.data import DistributedSampler, RandomSampler
 
 from axolotl.core.trainer_builder import HFCausalTrainerBuilder
-from axolotl.utils.distributed import is_main_process, reduce_and_broadcast, zero_first
-from axolotl.utils.samplers import MultipackBatchSampler
+from axolotl.utils.collators import DataCollatorForSeq2Seq
+from axolotl.utils.dataloader import MultipackDistributedDataloader
+from axolotl.utils.distributed import (
+    is_distributed,
+    is_main_process,
+    reduce_and_broadcast,
+    zero_first,
+)
 
-LOG = get_logger("axolotl")
+LOG = logging.getLogger("axolotl")
 
 
 @torch.jit.script
@@ -131,10 +138,8 @@ def process_datasets_for_packing(cfg, train_dataset, eval_dataset, tokenizer):
                     )
 
         # Phi doesn't want the attention_mask feature when training
-        if (
-            "CodeGenTokenizer" in tokenizer.__class__.__name__
-            or (cfg.is_mistral_derived_model and cfg.flash_attention)
-            or cfg.model_config_type == "mamba"
+        if "CodeGenTokenizer" in tokenizer.__class__.__name__ or (
+            cfg.is_mistral_derived_model and cfg.flash_attention
         ):
             train_dataset = train_dataset.remove_columns("attention_mask")
             if eval_dataset:
@@ -143,37 +148,30 @@ def process_datasets_for_packing(cfg, train_dataset, eval_dataset, tokenizer):
     return train_dataset, eval_dataset
 
 
-def calculate_total_num_steps(cfg, train_dataset, update=True):
-    if not cfg.total_num_tokens:
-        total_num_tokens = np.sum(
-            train_dataset.data.column("input_ids")
-            .to_pandas()
-            .apply(lambda x: len(x))  # pylint: disable=unnecessary-lambda
-            .values
-        )
-        LOG.debug(f"total_num_tokens: {total_num_tokens}", main_process_only=True)
-        if update:
-            cfg.total_num_tokens = total_num_tokens
-
-    skip_estimates = cfg.model_config_type == "mamba"
-
-    if not skip_estimates and not cfg.total_supervised_tokens:
-        total_supervised_tokens = (
-            train_dataset.data.column("labels")
-            .to_pandas()
-            .apply(lambda x: np.sum(np.array(x) != -100))
-            .sum()
-        )
-        LOG.debug(
-            f"`total_supervised_tokens: {total_supervised_tokens}`",
-            main_process_only=True,
-        )
-        if update:
-            cfg.total_supervised_tokens = total_supervised_tokens
-
-    if not skip_estimates and cfg.sample_packing:
+def calculate_total_num_steps(cfg, train_dataset, tokenizer):
+    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}")
+            cfg.total_num_tokens = total_num_tokens
+
+        if not cfg.total_supervised_tokens:
+            total_supervised_tokens = (
+                train_dataset.data.column("labels")
+                .to_pandas()
+                .apply(lambda x: np.sum(np.array(x) != -100))
+                .sum()
+            )
+            LOG.info(f"`total_supervised_tokens: {total_supervised_tokens}`")
+            cfg.total_supervised_tokens = total_supervised_tokens
 
         if cfg.sample_packing_eff_est:
             total_num_steps = (
@@ -191,41 +189,40 @@ def calculate_total_num_steps(cfg, train_dataset, update=True):
                 )
                 * cfg.num_epochs
             )
-            LOG.debug(
-                f"total_num_tokens: {cfg.total_num_tokens}, total_num_steps: {total_num_steps}",
-                main_process_only=True,
+            LOG.info(
+                f"total_num_tokens: {cfg.total_num_tokens}, total_num_steps: {total_num_steps}"
             )
         else:
-            sampler = MultipackBatchSampler(
-                sampler=RandomSampler(train_dataset),
-                batch_size=cfg.micro_batch_size,
-                drop_last=True,
-                batch_max_len=cfg.micro_batch_size
-                * (cfg.max_packed_sequence_len or cfg.sequence_len),
-                lengths=(
-                    train_dataset.data.column("position_ids")
-                    .to_pandas()
-                    .apply(lambda x: x[-1] + 1)
-                    .values
-                ),
-            )
+            if cfg.world_size > 1 and is_distributed():
+                sampler = DistributedSampler(
+                    train_dataset,
+                    num_replicas=cfg.world_size,
+                    rank=dist.get_rank(),
+                    seed=cfg.seed or 42,
+                )
+            else:
+                sampler = RandomSampler(train_dataset)
 
-            data_loader = DataLoader(
-                train_dataset.remove_columns(["length"]),
-                batch_sampler=sampler,
+            data_loader = MultipackDistributedDataloader(
+                train_dataset,
+                batch_size=cfg.micro_batch_size,
+                seq_max_length=cfg.max_packed_sequence_len or cfg.sequence_len,
+                collate_fn=DataCollatorForSeq2Seq(
+                    tokenizer,
+                    return_tensors="pt",
+                    padding="longest",
+                ),
+                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 = len(data_loader)
-            actual_eff = sampler.efficiency()
-            LOG.debug(f"data_loader_len: {data_loader_len}", main_process_only=True)
+            data_loader_len = data_loader.len_w_stats()
+            actual_eff = data_loader.efficiency()
+            LOG.info(f"data_loader_len: {data_loader_len}")
             # 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
-                    / int(os.environ.get("WORLD_SIZE", 1))
-                )
-            )
+            total_num_steps = int(math.floor(data_loader_len * cfg.num_epochs))
 
             def calc_sample_packing_eff_est(estimates: List[float]):
                 LOG.info(f"sample_packing_eff_est across ranks: {repr(estimates)}")
@@ -238,22 +235,13 @@ def calculate_total_num_steps(cfg, train_dataset, update=True):
             sample_packing_eff_est = (
                 math.ceil(sample_packing_actual_eff_all * 100.0) / 100.0
             )
-            if update:
-                cfg.sample_packing_eff_est = sample_packing_eff_est
-            LOG.debug(
-                f"sample_packing_eff_est: {cfg.sample_packing_eff_est}",
-                main_process_only=True,
-            )
+            cfg.sample_packing_eff_est = sample_packing_eff_est
+            LOG.info(f"sample_packing_eff_est: {cfg.sample_packing_eff_est}")
     else:
         total_num_steps = int(
-            math.ceil(
-                len(train_dataset)
-                * cfg.num_epochs
-                / int(os.environ.get("WORLD_SIZE", 1))
-                / cfg.batch_size
-            )
+            math.ceil(len(train_dataset) * cfg.num_epochs / cfg.batch_size)
         )
-    LOG.debug(f"total_num_steps: {total_num_steps}", main_process_only=True)
+    LOG.info(f"total_num_steps: {total_num_steps}")
     return total_num_steps
 
 
@@ -271,15 +259,12 @@ def setup_fsdp_envs(cfg):
         ] = cfg.fsdp_config.fsdp_transformer_layer_cls_to_wrap
 
 
-def prepare_optim_env(cfg):
+def setup_trainer(cfg, train_dataset, eval_dataset, model, tokenizer, total_num_steps):
     if cfg.fsdp:
         setup_fsdp_envs(cfg)
     elif cfg.deepspeed:
         os.environ["ACCELERATE_USE_DEEPSPEED"] = "true"
-        os.environ["ACCELERATE_DEEPSPEED_CONFIG_FILE"] = cfg.deepspeed
 
-
-def setup_trainer(cfg, train_dataset, eval_dataset, model, tokenizer, total_num_steps):
     trainer_builder = HFCausalTrainerBuilder(cfg, model, tokenizer)
     trainer_builder.train_dataset = train_dataset
     trainer_builder.eval_dataset = eval_dataset

src/axolotl/utils/wandb_.py

@@ -2,20 +2,20 @@
 
 import os
 
-from axolotl.utils.dict import DictDefault
 
-
-def setup_wandb_env_vars(cfg: DictDefault):
-    for key in cfg.keys():
-        if key.startswith("wandb_"):
-            value = cfg.get(key, "")
-
-            if value and isinstance(value, str) and len(value) > 0:
-                os.environ[key.upper()] = value
-
-    # Enable wandb if project name is present
-    if cfg.wandb_project and len(cfg.wandb_project) > 0:
+def setup_wandb_env_vars(cfg):
+    if cfg.wandb_mode and cfg.wandb_mode == "offline":
+        os.environ["WANDB_MODE"] = cfg.wandb_mode
+    elif cfg.wandb_project and len(cfg.wandb_project) > 0:
+        os.environ["WANDB_PROJECT"] = cfg.wandb_project
         cfg.use_wandb = True
-        os.environ.pop("WANDB_DISABLED", None)  # Remove if present
+        if cfg.wandb_entity and len(cfg.wandb_entity) > 0:
+            os.environ["WANDB_ENTITY"] = cfg.wandb_entity
+        if cfg.wandb_watch and len(cfg.wandb_watch) > 0:
+            os.environ["WANDB_WATCH"] = cfg.wandb_watch
+        if cfg.wandb_log_model and len(cfg.wandb_log_model) > 0:
+            os.environ["WANDB_LOG_MODEL"] = cfg.wandb_log_model
+        if cfg.wandb_run_id and len(cfg.wandb_run_id) > 0:
+            os.environ["WANDB_RUN_ID"] = cfg.wandb_run_id
     else:
         os.environ["WANDB_DISABLED"] = "true"

tests/e2e/test_fused_llama.py

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

tests/e2e/test_lora_llama.py

@@ -4,6 +4,7 @@ E2E tests for lora llama
 
 import logging
 import os
+import tempfile
 import unittest
 from pathlib import Path
 
@@ -13,8 +14,6 @@ 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"
 
@@ -24,9 +23,9 @@ class TestLoraLlama(unittest.TestCase):
     Test case for Llama models using LoRA
     """
 
-    @with_temp_dir
-    def test_lora(self, temp_dir):
+    def test_lora(self):
         # pylint: disable=duplicate-code
+        output_dir = tempfile.mkdtemp()
         cfg = DictDefault(
             {
                 "base_model": "JackFram/llama-68m",
@@ -53,7 +52,7 @@ class TestLoraLlama(unittest.TestCase):
                 "num_epochs": 2,
                 "micro_batch_size": 8,
                 "gradient_accumulation_steps": 1,
-                "output_dir": temp_dir,
+                "output_dir": output_dir,
                 "learning_rate": 0.00001,
                 "optimizer": "adamw_torch",
                 "lr_scheduler": "cosine",
@@ -64,11 +63,11 @@ 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(temp_dir) / "adapter_model.bin").exists()
+        assert (Path(output_dir) / "adapter_model.bin").exists()
 
-    @with_temp_dir
-    def test_lora_packing(self, temp_dir):
+    def test_lora_packing(self):
         # pylint: disable=duplicate-code
+        output_dir = tempfile.mkdtemp()
         cfg = DictDefault(
             {
                 "base_model": "JackFram/llama-68m",
@@ -97,11 +96,10 @@ class TestLoraLlama(unittest.TestCase):
                 "num_epochs": 2,
                 "micro_batch_size": 8,
                 "gradient_accumulation_steps": 1,
-                "output_dir": temp_dir,
+                "output_dir": output_dir,
                 "learning_rate": 0.00001,
                 "optimizer": "adamw_torch",
                 "lr_scheduler": "cosine",
-                "bf16": True,
             }
         )
         normalize_config(cfg)
@@ -109,11 +107,11 @@ 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(temp_dir) / "adapter_model.bin").exists()
+        assert (Path(output_dir) / "adapter_model.bin").exists()
 
-    @with_temp_dir
-    def test_lora_gptq(self, temp_dir):
+    def test_lora_gptq(self):
         # pylint: disable=duplicate-code
+        output_dir = tempfile.mkdtemp()
         cfg = DictDefault(
             {
                 "base_model": "TheBlokeAI/jackfram_llama-68m-GPTQ",
@@ -146,7 +144,7 @@ class TestLoraLlama(unittest.TestCase):
                 "save_steps": 0.5,
                 "micro_batch_size": 8,
                 "gradient_accumulation_steps": 1,
-                "output_dir": temp_dir,
+                "output_dir": output_dir,
                 "learning_rate": 0.00001,
                 "optimizer": "adamw_torch",
                 "lr_scheduler": "cosine",
@@ -157,4 +155,4 @@ 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(temp_dir) / "adapter_model.bin").exists()
+        assert (Path(output_dir) / "adapter_model.bin").exists()

tests/e2e/test_mamba.py

@@ -1,65 +0,0 @@
-"""
-E2E tests for lora llama
-"""
-
-import logging
-import os
-import unittest
-from pathlib import Path
-
-from axolotl.cli import load_datasets
-from axolotl.common.cli import TrainerCliArgs
-from axolotl.train import train
-from axolotl.utils.config import normalize_config
-from axolotl.utils.dict import DictDefault
-
-from .utils import with_temp_dir
-
-LOG = logging.getLogger("axolotl.tests.e2e")
-os.environ["WANDB_DISABLED"] = "true"
-
-
-class TestMistral(unittest.TestCase):
-    """
-    Test case for Llama models using LoRA
-    """
-
-    @with_temp_dir
-    def test_fft(self, temp_dir):
-        # pylint: disable=duplicate-code
-        cfg = DictDefault(
-            {
-                "base_model": "state-spaces/mamba-130m",
-                "model_type": "MambaLMHeadModel",
-                "tokenizer_type": "AutoTokenizer",
-                "tokenizer_config": "EleutherAI/gpt-neox-20b",
-                "flash_attention": False,
-                "sequence_len": 1024,
-                "load_in_8bit": False,
-                "val_set_size": 0.0,
-                "datasets": [
-                    {
-                        "path": "mhenrichsen/alpaca_2k_test",
-                        "type": "alpaca",
-                    },
-                ],
-                "gradient_checkpointing": False,
-                "num_epochs": 2,
-                "micro_batch_size": 2,
-                "gradient_accumulation_steps": 1,
-                "output_dir": temp_dir,
-                "learning_rate": 0.00001,
-                "optimizer": "adamw_torch",
-                "lr_scheduler": "cosine",
-                "max_steps": 20,
-                "save_steps": 10,
-                "eval_steps": None,
-                "save_safetensors": False,
-            }
-        )
-        normalize_config(cfg)
-        cli_args = TrainerCliArgs()
-        dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
-
-        train(cfg=cfg, cli_args=cli_args, dataset_meta=dataset_meta)
-        assert (Path(temp_dir) / "pytorch_model.bin").exists()

tests/e2e/test_mistral.py

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

tests/e2e/test_mistral_samplepack.py

@@ -4,6 +4,7 @@ E2E tests for lora llama
 
 import logging
 import os
+import tempfile
 import unittest
 from pathlib import Path
 
@@ -15,8 +16,6 @@ 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"
 
@@ -26,9 +25,9 @@ class TestMistral(unittest.TestCase):
     Test case for Llama models using LoRA
     """
 
-    @with_temp_dir
-    def test_lora_packing(self, temp_dir):
+    def test_lora_packing(self):
         # pylint: disable=duplicate-code
+        output_dir = tempfile.mkdtemp()
         cfg = DictDefault(
             {
                 "base_model": "openaccess-ai-collective/tiny-mistral",
@@ -56,7 +55,7 @@ class TestMistral(unittest.TestCase):
                 "num_epochs": 2,
                 "micro_batch_size": 2,
                 "gradient_accumulation_steps": 1,
-                "output_dir": temp_dir,
+                "output_dir": output_dir,
                 "learning_rate": 0.00001,
                 "optimizer": "adamw_torch",
                 "lr_scheduler": "cosine",
@@ -70,11 +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(temp_dir) / "adapter_model.bin").exists()
+        assert (Path(output_dir) / "adapter_model.bin").exists()
 
-    @with_temp_dir
-    def test_ft_packing(self, temp_dir):
+    def test_ft_packing(self):
         # pylint: disable=duplicate-code
+        output_dir = tempfile.mkdtemp()
         cfg = DictDefault(
             {
                 "base_model": "openaccess-ai-collective/tiny-mistral",
@@ -96,7 +95,7 @@ class TestMistral(unittest.TestCase):
                 "num_epochs": 2,
                 "micro_batch_size": 2,
                 "gradient_accumulation_steps": 1,
-                "output_dir": temp_dir,
+                "output_dir": output_dir,
                 "learning_rate": 0.00001,
                 "optimizer": "adamw_torch",
                 "lr_scheduler": "cosine",
@@ -114,4 +113,4 @@ 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(temp_dir) / "pytorch_model.bin").exists()
+        assert (Path(output_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
@@ -13,8 +13,6 @@ 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"
 
@@ -24,14 +22,13 @@ class TestPhi(unittest.TestCase):
     Test case for Llama models using LoRA
     """
 
-    @with_temp_dir
-    def test_ft(self, temp_dir):
+    def test_ft(self):
         # pylint: disable=duplicate-code
         cfg = DictDefault(
             {
                 "base_model": "microsoft/phi-1_5",
                 "trust_remote_code": True,
-                "model_type": "PhiForCausalLM",
+                "model_type": "MixFormerSequentialForCausalLM",
                 "tokenizer_type": "AutoTokenizer",
                 "sequence_len": 512,
                 "sample_packing": False,
@@ -55,7 +52,7 @@ class TestPhi(unittest.TestCase):
                 "num_epochs": 1,
                 "micro_batch_size": 1,
                 "gradient_accumulation_steps": 1,
-                "output_dir": temp_dir,
+                "output_dir": tempfile.mkdtemp(),
                 "learning_rate": 0.00001,
                 "optimizer": "adamw_bnb_8bit",
                 "lr_scheduler": "cosine",
@@ -67,16 +64,14 @@ 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()
 
-    @with_temp_dir
-    def test_ft_packed(self, temp_dir):
+    def test_ft_packed(self):
         # pylint: disable=duplicate-code
         cfg = DictDefault(
             {
                 "base_model": "microsoft/phi-1_5",
                 "trust_remote_code": True,
-                "model_type": "PhiForCausalLM",
+                "model_type": "MixFormerSequentialForCausalLM",
                 "tokenizer_type": "AutoTokenizer",
                 "sequence_len": 512,
                 "sample_packing": True,
@@ -100,7 +95,7 @@ class TestPhi(unittest.TestCase):
                 "num_epochs": 1,
                 "micro_batch_size": 1,
                 "gradient_accumulation_steps": 1,
-                "output_dir": temp_dir,
+                "output_dir": tempfile.mkdtemp(),
                 "learning_rate": 0.00001,
                 "optimizer": "adamw_bnb_8bit",
                 "lr_scheduler": "cosine",
@@ -112,4 +107,3 @@ 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/test_resume.py

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

tests/e2e/utils.py

@@ -1,33 +0,0 @@
-"""
-helper utils for tests
-"""
-import os
-import shutil
-import tempfile
-from functools import wraps
-from pathlib import Path
-
-
-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
-
-
-def most_recent_subdir(path):
-    base_path = Path(path)
-    subdirectories = [d for d in base_path.iterdir() if d.is_dir()]
-    if not subdirectories:
-        return None
-    subdir = max(subdirectories, key=os.path.getctime)
-
-    return subdir