tweak to make it work when we have no explicit test split

.github/workflows/base.yml

@@ -18,13 +18,23 @@ jobs:
           - cuda: "118"
             cuda_version: 11.8.0
             python_version: "3.9"
-            pytorch: 2.0.1
-            torch_cuda_arch_list: "7.0 7.5 8.0 8.6 9.0+PTX"
+            pytorch: 2.0.0
+            axolotl_extras:
           - cuda: "118"
             cuda_version: 11.8.0
             python_version: "3.10"
-            pytorch: 2.0.1
-            torch_cuda_arch_list: "7.0 7.5 8.0 8.6 9.0+PTX"
+            pytorch: 2.0.0
+            axolotl_extras:
+          - cuda: "117"
+            cuda_version: 11.7.1
+            python_version: "3.9"
+            pytorch: 1.13.1
+            axolotl_extras:
+          - cuda: "118"
+            cuda_version: 11.8.0
+            python_version: "3.9"
+            pytorch: 2.0.0
+            axolotl_extras: gptq
     steps:
       - name: Checkout
         uses: actions/checkout@v3
@@ -48,9 +58,11 @@ jobs:
           push: ${{ github.event_name != 'pull_request' }}
           tags: ${{ steps.metadata.outputs.tags }}-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}${{ matrix.axolotl_extras != '' && '-' || '' }}${{ matrix.axolotl_extras }}
           labels: ${{ steps.metadata.outputs.labels }}
+          cache-from: type=gha
+          cache-to: type=gha,mode=max
           build-args: |
             CUDA_VERSION=${{ matrix.cuda_version }}
             CUDA=${{ matrix.cuda }}
             PYTHON_VERSION=${{ matrix.python_version }}
             PYTORCH_VERSION=${{ matrix.pytorch }}
-            TORCH_CUDA_ARCH_LIST=${{ matrix.torch_cuda_arch_list }}
+            AXOLOTL_EXTRAS=${{ matrix.axolotl_extras }}

.github/workflows/main.yml

@@ -17,18 +17,23 @@ jobs:
           - cuda: cu118
             cuda_version: 11.8.0
             python_version: "3.9"
-            pytorch: 2.0.1
+            pytorch: 2.0.0
             axolotl_extras:
           - cuda: cu118
             cuda_version: 11.8.0
             python_version: "3.10"
-            pytorch: 2.0.1
+            pytorch: 2.0.0
             axolotl_extras:
           - cuda: cu118
             cuda_version: 11.8.0
             python_version: "3.9"
-            pytorch: 2.0.1
+            pytorch: 2.0.0
             axolotl_extras: gptq
+          - cuda: cu117
+            cuda_version: 11.7.1
+            python_version: "3.9"
+            pytorch: 1.13.1
+            axolotl_extras:
     runs-on: self-hosted
     steps:
       - name: Checkout
@@ -50,11 +55,13 @@ jobs:
         with:
           context: .
           build-args: |
-            BASE_TAG=${{ github.ref_name }}-base-py${{ matrix.python_version }}-${{ matrix.cuda }}-${{ matrix.pytorch }}
+            BASE_TAG=${{ github.ref_name }}-base-py${{ matrix.python_version }}-${{ matrix.cuda }}-${{ matrix.pytorch }}${{ matrix.axolotl_extras != '' && '-' || '' }}${{ matrix.axolotl_extras }}
           file: ./docker/Dockerfile
           push: ${{ github.event_name != 'pull_request' }}
           tags: ${{ steps.metadata.outputs.tags }}-py${{ matrix.python_version }}-${{ matrix.cuda }}-${{ matrix.pytorch }}${{ matrix.axolotl_extras != '' && '-' || '' }}${{ matrix.axolotl_extras }}
           labels: ${{ steps.metadata.outputs.labels }}
+          cache-from: type=gha
+          cache-to: type=gha,mode=max
   build-axolotl-runpod:
     needs: build-axolotl
     if: github.repository_owner == 'OpenAccess-AI-Collective'
@@ -62,21 +69,26 @@ jobs:
     strategy:
       matrix:
         include:
-          - cuda: 118
+          - cuda: cu118
             cuda_version: 11.8.0
             python_version: "3.9"
-            pytorch: 2.0.1
+            pytorch: 2.0.0
             axolotl_extras:
-          - cuda: 118
+          - cuda: cu118
             cuda_version: 11.8.0
             python_version: "3.10"
-            pytorch: 2.0.1
+            pytorch: 2.0.0
             axolotl_extras:
-          - cuda: 118
+          - cuda: cu118
             cuda_version: 11.8.0
             python_version: "3.9"
-            pytorch: 2.0.1
+            pytorch: 2.0.0
             axolotl_extras: gptq
+          - cuda: cu117
+            cuda_version: 11.7.1
+            python_version: "3.9"
+            pytorch: 1.13.1
+            axolotl_extras:
     runs-on: self-hosted
     steps:
       - name: Checkout
@@ -98,9 +110,10 @@ jobs:
         with:
           context: .
           build-args: |
-            BASE_TAG=${{ github.ref_name }}-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}${{ matrix.axolotl_extras != '' && '-' || '' }}${{ matrix.axolotl_extras }}
-            CUDA=${{ matrix.cuda }}
+            BASE_TAG=${{ github.ref_name }}-py${{ matrix.python_version }}-${{ matrix.cuda }}-${{ matrix.pytorch }}${{ matrix.axolotl_extras != '' && '-' || '' }}${{ matrix.axolotl_extras }}
           file: ./docker/Dockerfile-runpod
           push: ${{ github.event_name != 'pull_request' }}
-          tags: ${{ steps.metadata.outputs.tags }}-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}${{ matrix.axolotl_extras != '' && '-' || '' }}${{ matrix.axolotl_extras }}
+          tags: ${{ steps.metadata.outputs.tags }}-py${{ matrix.python_version }}-${{ matrix.cuda }}-${{ matrix.pytorch }}${{ matrix.axolotl_extras != '' && '-' || '' }}${{ matrix.axolotl_extras }}
           labels: ${{ steps.metadata.outputs.labels }}
+          cache-from: type=gha
+          cache-to: type=gha,mode=max

LICENSE

@@ -1,202 +0,0 @@
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README.md

@@ -24,12 +24,11 @@
 | mpt      | ✅         | ❌    | ❓     | ❌    | ❓            | ❌                 | ❌          | ❓             |
 | falcon   | ✅         | ✅    | ✅     | ❌    | ❓            | ❌                 | ❌          | ✅             |
 | gpt-j    | ✅         | ✅    | ✅     | ❌    | ❓            | ❌                 | ❓          | ✅             |
-| XGen     | ✅         | ❓    | ✅     | ❓    | ❓            | ❓                 | ❓          | ✅
 
 
 ## Quickstart ⚡
 
-**Requirements**: Python >=3.9 and Pytorch >=2.0.
+**Requirements**: Python 3.9 and Pytorch 2.0.
 
 ```bash
 git clone https://github.com/OpenAccess-AI-Collective/axolotl
@@ -37,6 +36,8 @@ git clone https://github.com/OpenAccess-AI-Collective/axolotl
 pip3 install -e .
 pip3 install -U git+https://github.com/huggingface/peft.git
 
+accelerate config
+
 # finetune lora
 accelerate launch scripts/finetune.py examples/openllama-3b/lora.yml
 
@@ -51,10 +52,11 @@ accelerate launch scripts/finetune.py examples/openllama-3b/lora.yml \
 
 - Docker
   ```bash
-  docker run --gpus '"all"' --rm -it winglian/axolotl:main-py3.10-cu118-2.0.1
+  docker run --gpus '"all"' --rm -it winglian/axolotl:main-py3.9-cu118-2.0.0
   ```
-  - `winglian/axolotl-runpod:main-py3.10-cu118-2.0.1`: for runpod
-  - `winglian/axolotl-runpod:main-py3.9-cu118-2.0.1-gptq`: for gptq
+  - `winglian/axolotl-runpod:main-py3.9-cu118-2.0.0`: for runpod
+  - `winglian/axolotl-runpod:main-py3.9-cu118-2.0.0-gptq`: for gptq
+  - `winglian/axolotl:dev`: dev branch (not usually up to date)
 
   Or run on the current files for development:
 
@@ -106,7 +108,7 @@ accelerate launch scripts/finetune.py examples/openllama-3b/lora.yml \
 
   3. Install torch
   ```bash
-  pip3 install -U torch --index-url https://download.pytorch.org/whl/cu118
+  pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118
   ```
 
   4. Axolotl
@@ -235,7 +237,7 @@ Have dataset(s) in one of the following format (JSONL recommended):
 #### How to add custom prompts
 
   1. Add your method to a file in [prompt_strategies](src/axolotl/prompt_strategies). Please see other files as example.
-  2. Use your custom file name as the dataset type `<prompt_strategies_file>.load_<load_fn>`.
+  2. Use your custom file name as the dataset type.
 
 Optionally, download some datasets, see [data/README.md](data/README.md)
 
@@ -243,7 +245,7 @@ Optionally, download some datasets, see [data/README.md](data/README.md)
 
 ### Config
 
-See [examples](examples) for quick start. It is recommended to duplicate and modify to your needs. The most important options are:
+See sample configs in [configs](configs) folder or [examples](examples) for quick start. It is recommended to duplicate and modify to your needs. The most important options are:
 
 - model
   ```yaml
@@ -253,24 +255,10 @@ See [examples](examples) for quick start. It is recommended to duplicate and mod
 
 - dataset
   ```yaml
-  sequence_len: 2048 # max token length for prompt
-
-  # huggingface repo
   datasets:
-    - path: vicgalle/alpaca-gpt4
-      type: alpaca # format from earlier
-
-  # huggingface repo with specific configuration/subset
-  datasets:
-    - path: EleutherAI/pile
-      name: enron_emails
-      type: completion # format from earlier
-
-  # local
-  datasets:
-    - path: json
-      data_files: data.jsonl # or json
+    - path: vicgalle/alpaca-gpt4 # local or huggingface repo
       type: alpaca # format from earlier
+  sequence_len: 2048 # max token length / prompt
   ```
 
 - loading
@@ -309,8 +297,6 @@ base_model_ignore_patterns:
 # if the base_model repo on hf hub doesn't include configuration .json files,
 # you can set that here, or leave this empty to default to base_model
 base_model_config: ./llama-7b-hf
-# you can specify to choose a specific model revision from huggingface hub
-model_revision:
 # Optional tokenizer configuration override in case you want to use a different tokenizer
 # than the one defined in the base model
 tokenizer_config:
@@ -322,9 +308,6 @@ tokenizer_type: AutoTokenizer
 trust_remote_code:
 # use_fast option for tokenizer loading from_pretrained, default to True
 tokenizer_use_fast:
-# resize the model embeddings when new tokens are added to multiples of 32
-# this is reported to improve training speed on some models
-resize_token_embeddings_to_32x:
 
 # whether you are training a 4-bit GPTQ quantized model
 gptq: true
@@ -345,13 +328,12 @@ tf32: true # require >=ampere
 
 # a list of one or more datasets to finetune the model with
 datasets:
-  # hf dataset repo | "json" for local dataset, make sure to fill data_files
+  # this can be either a hf dataset, or relative path
   - 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>
+    type: alpaca # format OR format:prompt_style (chat/instruct)
     data_files: # path to source data files
     shards: # number of shards to split data into
-    name: # name of dataset configuration to load
 
 # axolotl attempts to save the dataset as an arrow after packing the data together so
 # subsequent training attempts load faster, relative path
@@ -359,7 +341,7 @@ dataset_prepared_path: data/last_run_prepared
 # push prepared dataset to hub
 push_dataset_to_hub: # repo path
 # push checkpoints to hub
-hub_model_id: # repo path to push finetuned model
+push_to_hub_model_id: # repo path
 # whether to use hf `use_auth_token` for loading datasets. Useful for fetching private datasets
 # required to be true when used in combination with `push_dataset_to_hub`
 hf_use_auth_token: # boolean
@@ -421,9 +403,6 @@ logging_steps:
 save_steps:
 eval_steps:
 
-# save model as safetensors (require safetensors package)
-save_safetensors:
-
 # whether to mask out or include the human's prompt from the training labels
 train_on_inputs: false
 # don't use this, leads to wonky training (according to someone on the internet)
@@ -515,6 +494,17 @@ strict:
 
 </details>
 
+### Accelerate
+
+Configure accelerate
+
+```bash
+accelerate config
+
+# Edit manually
+# nano ~/.cache/huggingface/accelerate/default_config.yaml
+```
+
 ### Train
 
 Run
@@ -522,21 +512,6 @@ Run
 accelerate launch scripts/finetune.py configs/your_config.yml
 ```
 
-#### Multi-GPU Config
-
-- llama FSDP
-```yaml
-fsdp:
-  - full_shard
-  - auto_wrap
-fsdp_config:
-  fsdp_offload_params: true
-  fsdp_state_dict_type: FULL_STATE_DICT
-  fsdp_transformer_layer_cls_to_wrap: LlamaDecoderLayer
-```
-
-- llama Deepspeed: append `ACCELERATE_USE_DEEPSPEED=true` in front of finetune command
-
 ### Inference
 
 Pass the appropriate flag to the train command:
@@ -587,10 +562,6 @@ Try set `fp16: true`
 
 Try to turn off xformers.
 
-> accelerate config missing
-
-It's safe to ignore it.
-
 ## Need help? 🙋♂️
 
 Join our [Discord server](https://discord.gg/HhrNrHJPRb) where we can help you

docker/Dockerfile

@@ -3,15 +3,16 @@ FROM winglian/axolotl-base:$BASE_TAG
 
 ARG TORCH_CUDA_ARCH_LIST="7.0 7.5 8.0 8.6+PTX"
 ARG AXOLOTL_EXTRAS=""
-ARG CUDA="118"
-ENV BNB_CUDA_VERSION=$CUDA
 
 RUN apt-get update && \
     apt-get install -y vim curl
 
 WORKDIR /workspace
 
-RUN pip3 install --force-reinstall "peft @ git+https://github.com/huggingface/peft.git@main"
+RUN pip3 install --force-reinstall "peft @ git+https://github.com/huggingface/peft.git@main" \
+            "accelerate @ git+https://github.com/huggingface/accelerate.git@main" \
+            "transformers @ git+https://github.com/huggingface/transformers.git@main"
+
 RUN git clone --depth=1 https://github.com/OpenAccess-AI-Collective/axolotl.git
 # If AXOLOTL_EXTRAS is set, append it in brackets
 RUN cd axolotl && \
@@ -21,10 +22,5 @@ RUN cd axolotl && \
         pip install -e .; \
     fi
 
-# fix so that git fetch/pull from remote works
-RUN cd axolotl && \
-    git config remote.origin.fetch "+refs/heads/*:refs/remotes/origin/*" && \
-    git config --get remote.origin.fetch
-
 # helper for huggingface-login cli
 RUN git config --global credential.helper store

docker/Dockerfile-base

@@ -8,7 +8,7 @@ FROM nvidia/cuda:$CUDA_VERSION-cudnn$CUDNN_VERSION-devel-ubuntu$UBUNTU_VERSION a
 ENV PATH="/root/miniconda3/bin:${PATH}"
 
 ARG PYTHON_VERSION="3.9"
-ARG PYTORCH_VERSION="2.0.1"
+ARG PYTORCH="2.0.0"
 ARG CUDA="118"
 
 ENV PYTHON_VERSION=$PYTHON_VERSION
@@ -29,18 +29,17 @@ ENV PATH="/root/miniconda3/envs/py${PYTHON_VERSION}/bin:${PATH}"
 WORKDIR /workspace
 
 RUN python3 -m pip install --upgrade pip && pip3 install packaging && \
-    python3 -m pip install --no-cache-dir -U torch==${PYTORCH_VERSION}+cu${CUDA} --extra-index-url https://download.pytorch.org/whl/cu$CUDA
+    python3 -m pip install --no-cache-dir -U torch==${PYTORCH} torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu$CUDA
 
 
 FROM base-builder AS flash-attn-builder
 
 WORKDIR /workspace
 
-ARG TORCH_CUDA_ARCH_LIST="7.0 7.5 8.0 8.6 9.0+PTX"
+ARG TORCH_CUDA_ARCH_LIST="7.0 7.5 8.0 8.6+PTX"
 
-RUN git clone https://github.com/Dao-AILab/flash-attention.git && \
+RUN git clone https://github.com/HazyResearch/flash-attention.git && \
     cd flash-attention && \
-    git checkout v2.0.1  && \
     python3 setup.py bdist_wheel && \
     cd csrc/fused_dense_lib && \
     python3 setup.py bdist_wheel && \
@@ -53,7 +52,7 @@ RUN git clone https://github.com/Dao-AILab/flash-attention.git && \
 
 FROM base-builder AS deepspeed-builder
 
-ARG TORCH_CUDA_ARCH_LIST="7.0 7.5 8.0 8.6 9.0+PTX"
+ARG TORCH_CUDA_ARCH_LIST="7.0 7.5 8.0 8.6+PTX"
 
 WORKDIR /workspace
 
@@ -74,9 +73,6 @@ RUN git clone https://github.com/TimDettmers/bitsandbytes.git && \
 
 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
-
 # recompile apex
 RUN python3 -m pip uninstall -y apex
 RUN git clone https://github.com/NVIDIA/apex
@@ -101,4 +97,4 @@ 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
+    pip3 install -U --no-cache-dir pydantic

docker/Dockerfile-runpod

@@ -1,10 +1,6 @@
 ARG BASE_TAG=main
 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"
-
 COPY scripts/runpod-entrypoint.sh /root/runpod-entrypoint.sh
 
 RUN apt install --yes --no-install-recommends openssh-server tmux && \

examples/llama-2/README.md

@@ -1,20 +0,0 @@
-# Overview
-
-This is an example of a llama-2 configuration for 7b and 13b. The yaml file contains configuration for the 7b variant, but you can just aswell use the same settings for 13b.
-
-The 7b variant fits on any 24GB VRAM GPU and will take up about 17 GB of VRAM during training if using qlora and 20 GB if using lora. On a RTX 4090 it trains 3 epochs of the default dataset in about 15 minutes.
-
-The 13b variant will fit if you change these settings to these values:
-gradient_accumulation_steps: 2
-micro_batch_size: 1
-
-```shell
-accelerate launch scripts/finetune.py examples/llama-2/qlora.yml
-
-```
-or
-
-```shell
-accelerate launch scripts/finetune.py examples/llama-2/lora.yml
-
-```

examples/llama-2/lora.yml

@@ -1,66 +0,0 @@
-base_model: meta-llama/Llama-2-7b-hf
-base_model_config: meta-llama/Llama-2-7b-hf
-model_type: LlamaForCausalLM
-tokenizer_type: LlamaTokenizer
-
-load_in_8bit: true
-load_in_4bit: false
-strict: false
-
-datasets:
-  - path: mhenrichsen/alpaca_2k_test
-    type: alpaca
-dataset_prepared_path: last_run_prepared
-val_set_size: 0.01
-output_dir: ./lora-out
-
-sequence_len: 4096
-max_packed_sequence_len: 4096
-
-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_watch:
-wandb_run_id:
-wandb_log_model:
-
-gradient_accumulation_steps: 4
-micro_batch_size: 2
-num_epochs: 3
-optimizer: adamw_bnb_8bit
-lr_scheduler: cosine
-learning_rate: 0.0002
-
-train_on_inputs: false
-group_by_length: true
-bf16: true
-fp16: false
-tf32: false
-
-gradient_checkpointing: true
-early_stopping_patience:
-resume_from_checkpoint:
-local_rank:
-logging_steps: 1
-xformers_attention: true
-flash_attention:
-
-warmup_steps: 10
-eval_steps: 20
-save_steps:
-debug:
-deepspeed:
-weight_decay: 0.0
-fsdp:
-fsdp_config:
-special_tokens:
-  bos_token: "<s>"
-  eos_token: "</s>"
-  unk_token: "<unk>"
-  pad_token: "<pad>"

examples/llama-2/qlora.yml

@@ -1,67 +0,0 @@
-base_model: meta-llama/Llama-2-7b-hf
-base_model_config: meta-llama/Llama-2-7b-hf
-model_type: LlamaForCausalLM
-tokenizer_type: LlamaTokenizer
-
-load_in_8bit: false
-load_in_4bit: true
-strict: false
-
-datasets:
-  - path: mhenrichsen/alpaca_2k_test
-    type: alpaca
-dataset_prepared_path: last_run_prepared
-val_set_size: 0.01
-output_dir: ./qlora-out
-
-adapter: qlora
-lora_model_dir:
-
-sequence_len: 4096
-max_packed_sequence_len: 4096
-lora_r: 32
-lora_alpha: 16
-lora_dropout: 0.05
-lora_target_modules:
-lora_target_linear: true
-lora_fan_in_fan_out:
-
-wandb_project:
-wandb_watch:
-wandb_run_id:
-wandb_log_model:
-
-gradient_accumulation_steps: 4
-micro_batch_size: 2
-num_epochs: 3
-optimizer: paged_adamw_32bit
-lr_scheduler: cosine
-learning_rate: 0.0002
-
-train_on_inputs: false
-group_by_length: true
-bf16: true
-fp16: false
-tf32: false
-
-gradient_checkpointing: true
-early_stopping_patience:
-resume_from_checkpoint:
-local_rank:
-logging_steps: 1
-xformers_attention: true
-flash_attention:
-
-warmup_steps: 10
-eval_steps: 20
-save_steps:
-debug:
-deepspeed:
-weight_decay: 0.0
-fsdp:
-fsdp_config:
-special_tokens:
-  bos_token: "<s>"
-  eos_token: "</s>"
-  unk_token: "<unk>"
-  pad_token: "<pad>"

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

@@ -1,90 +0,0 @@
-# An example finetuning Saleforce's XGen-7b model with 8k context using qlora
-# on Tim Dettmer's Guanaco dataset.
-base_model: Salesforce/xgen-7b-8k-base
-base_model_config: Salesforce/xgen-7b-8k-base
-trust_remote_code: true
-model_type: AutoModelForCausalLM
-tokenizer_type: AutoTokenizer
-load_in_8bit: false
-# enable 4bit for QLoRA
-load_in_4bit: true
-gptq: false
-strict: false
-push_dataset_to_hub:
-datasets:
-  - path: timdettmers/openassistant-guanaco
-    data_files:
-      - openassistant_best_replies_train.jsonl
-    type: "completion"
-dataset_prepared_path: last_run_prepared
-val_set_size: 0.01
-# enable QLoRA
-adapter: qlora
-lora_model_dir:
-sequence_len: 8192
-max_packed_sequence_len:
-
-# hyperparameters from QLoRA paper Appendix B.2
-# "We find hyperparameters to be largely robust across datasets"
-lora_r: 64
-lora_alpha: 16
-# 0.1 for models up to 13B
-# 0.05 for 33B and 65B models
-lora_dropout: 0.05
-# add LoRA modules on all linear layers of the base model
-lora_target_modules:
-lora_target_linear: true
-lora_fan_in_fan_out:
-
-wandb_project:
-wandb_watch:
-wandb_run_id:
-wandb_log_model:
-output_dir: ./qlora-out
-
-# QLoRA paper Table 9
-# - 16 for 7b & 13b
-# - 32 for 33b, 64 for 64b
-# Max size tested on A6000
-# - 7b: 40
-# - 40b: 4
-# decrease if OOM, increase for max VRAM utilization
-micro_batch_size: 1
-gradient_accumulation_steps: 1
-num_epochs: 3
-# Optimizer for QLoRA
-optimizer: paged_adamw_32bit
-torchdistx_path:
-lr_scheduler: cosine
-# QLoRA paper Table 9
-# - 2e-4 for 7b & 13b
-# - 1e-4 for 33b & 64b
-learning_rate: 0.00002
-train_on_inputs: false
-group_by_length: false
-bf16: true
-fp16: false
-tf32: false
-gradient_checkpointing: true
-# stop training after this many evaluation losses have increased in a row
-# https://huggingface.co/transformers/v4.2.2/_modules/transformers/trainer_callback.html#EarlyStoppingCallback
-early_stopping_patience: 3
-resume_from_checkpoint:
-auto_resume_from_checkpoints: true
-local_rank:
-logging_steps: 1
-xformers_attention: true
-flash_attention:
-gptq_groupsize:
-gptq_model_v1:
-warmup_steps: 10
-eval_steps: 50
-save_steps: 50
-debug:
-deepspeed:
-weight_decay: 0.0
-special_tokens:
-  eos_token: "<|endoftext|>"
-  bos_token: "<|endoftext|>"
-  unk_token: "<|endoftext|>"
-  pad_token: "<|endoftext|>"

requirements.txt

@@ -1,7 +1,7 @@
 peft @ git+https://github.com/huggingface/peft.git
 transformers @ git+https://github.com/huggingface/transformers.git
 bitsandbytes>=0.39.0
-accelerate @ git+https://github.com/huggingface/accelerate@2a289f6108e77a77a4efffb3f6316bc98538413b
+accelerate
 addict
 fire
 PyYAML==6.0
@@ -12,7 +12,6 @@ wandb
 einops
 xformers
 optimum
-hf_transfer
 # qlora things
 bert-score==0.3.13
 evaluate==0.4.0

scripts/alpaca_json_to_jsonl.py

@@ -15,9 +15,6 @@ from axolotl.convert import (
     JsonToJsonlConverter,
     StdoutWriter,
 )
-from axolotl.logging_config import configure_logging
-
-configure_logging()
 
 # add src to the pythonpath so we don't need to pip install this
 project_root = os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))

scripts/finetune.py

@@ -17,7 +17,6 @@ import yaml
 from optimum.bettertransformer import BetterTransformer
 from transformers import GenerationConfig, TextStreamer
 
-from axolotl.logging_config import configure_logging
 from axolotl.utils.data import load_prepare_datasets, load_pretraining_dataset
 from axolotl.utils.dict import DictDefault
 from axolotl.utils.models import load_model, load_tokenizer
@@ -30,12 +29,9 @@ 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 = logging.getLogger("axolotl.scripts")
-
 
+logging.basicConfig(level=os.getenv("LOG_LEVEL", "INFO"))
 DEFAULT_DATASET_PREPARED_PATH = "last_run_prepared"
-os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = "1"
 
 
 def choose_device(cfg):
@@ -216,7 +212,7 @@ def train(
 
     # load the tokenizer first
     tokenizer_config = cfg.tokenizer_config or cfg.base_model_config
-    LOG.info(f"loading tokenizer... {tokenizer_config}")
+    logging.info(f"loading tokenizer... {tokenizer_config}")
     tokenizer = load_tokenizer(tokenizer_config, cfg.tokenizer_type, cfg)
 
     if (
@@ -238,7 +234,7 @@ def train(
             eval_dataset = None
 
     if cfg.debug or "debug" in kwargs:
-        LOG.info("check_dataset_labels...")
+        logging.info("check_dataset_labels...")
         check_dataset_labels(
             train_dataset.select(
                 [random.randrange(0, len(train_dataset) - 1) for _ in range(5)]  # nosec
@@ -247,11 +243,11 @@ def train(
         )
 
     if prepare_ds_only:
-        LOG.info("Finished preparing dataset. Exiting...")
+        logging.info("Finished preparing dataset. Exiting...")
         return
 
     # Load the model and tokenizer
-    LOG.info("loading model and peft_config...")
+    logging.info("loading model and peft_config...")
     model, peft_config = load_model(
         cfg.base_model,
         cfg.base_model_config,
@@ -262,17 +258,17 @@ def train(
     )
 
     if "merge_lora" in kwargs and cfg.adapter is not None:
-        LOG.info("running merge of LoRA with base model")
+        logging.info("running merge of LoRA with base model")
         model = model.merge_and_unload()
         model.to(dtype=torch.float16)
 
         if cfg.local_rank == 0:
-            LOG.info("saving merged model")
+            logging.info("saving merged model")
             model.save_pretrained(str(Path(cfg.output_dir) / "merged"))
         return
 
     if cfg.inference:
-        LOG.info("calling do_inference function")
+        logging.info("calling do_inference function")
         prompter: Optional[str] = "AlpacaPrompter"
         if "prompter" in kwargs:
             if kwargs["prompter"] == "None":
@@ -291,12 +287,12 @@ def train(
     model.config.use_cache = False
 
     if torch.__version__ >= "2" and sys.platform != "win32":
-        LOG.info("Compiling torch model")
+        logging.info("Compiling torch model")
         model = torch.compile(model)
 
     # go ahead and presave, so we have the adapter config available to inspect
     if peft_config:
-        LOG.info(f"Pre-saving adapter config to {cfg.output_dir}")
+        logging.info(f"Pre-saving adapter config to {cfg.output_dir}")
         peft_config.save_pretrained(cfg.output_dir)
 
     # In case we want to stop early with ctrl+c, this is a nice to have to save the pretrained model
@@ -312,9 +308,9 @@ def train(
             signal.SIGINT, lambda signum, frame: terminate_handler(signum, frame, model)
         )
 
-    LOG.info("Starting trainer...")
+    logging.info("Starting trainer...")
     if cfg.group_by_length:
-        LOG.info("hang tight... sorting dataset for group_by_length")
+        logging.info("hang tight... sorting dataset for group_by_length")
     resume_from_checkpoint = cfg.resume_from_checkpoint
     if cfg.resume_from_checkpoint is None and cfg.auto_resume_from_checkpoints:
         possible_checkpoints = [
@@ -326,7 +322,7 @@ def train(
                 key=lambda path: int(path.split("-")[-1]),
             )
             resume_from_checkpoint = sorted_paths[-1]
-            LOG.info(
+            logging.info(
                 f"Using Auto-resume functionality to start with checkpoint at {resume_from_checkpoint}"
             )
 
@@ -340,13 +336,11 @@ def train(
     else:
         trainer.train(resume_from_checkpoint=resume_from_checkpoint)
 
-    LOG.info(f"Training Completed!!! Saving pre-trained model to {cfg.output_dir}")
+    logging.info(f"Training Completed!!! Saving pre-trained model to {cfg.output_dir}")
 
     # TODO do we need this fix? https://huggingface.co/docs/accelerate/usage_guides/fsdp#saving-and-loading
     # only save on rank 0, otherwise it corrupts output on multi-GPU when multiple processes attempt to write the same file
-    if cfg.fsdp:
-        model.save_pretrained(cfg.output_dir)
-    elif cfg.local_rank == 0:
+    if cfg.local_rank == 0:
         if cfg.flash_optimum:
             model = BetterTransformer.reverse(model)
         model.save_pretrained(cfg.output_dir)

scripts/runpod-entrypoint.sh

@@ -1,21 +1,10 @@
 #!/bin/bash
 
-# Export specific ENV variables to /etc/rp_environment
-echo "Exporting environment variables..."
-printenv | grep -E '^RUNPOD_|^PATH=|^_=' | sed 's/^\(.*\)=\(.*\)$/export \1="\2"/' >> /etc/rp_environment
-echo 'source /etc/rp_environment' >> ~/.bashrc
+echo $PUBLIC_KEY >> ~/.ssh/authorized_keys
+chmod 700 -R ~/.ssh
 
-if [[ $PUBLIC_KEY ]]
-then
-    mkdir -p ~/.ssh
-    chmod 700 ~/.ssh
-    echo $PUBLIC_KEY >> ~/.ssh/authorized_keys
-    chmod 700 -R ~/.ssh
-    # Start the SSH service in the background
-    service ssh start
-else
-    echo "No PUBLIC_KEY ENV variable provided, not starting openSSH daemon"
-fi
+# Start the SSH service in the background
+service ssh start
 
 # Execute the passed arguments (CMD)
 exec "$@"

src/axolotl/datasets.py

@@ -1,13 +1,12 @@
 """Module containing Dataset functionality"""
 
 import logging
-import os
 from typing import List
 
 import torch
 from datasets import IterableDataset
 
-from .prompt_tokenizers import PromptTokenizingStrategy
+from .prompt_tokenizers import InvalidDataException, PromptTokenizingStrategy
 
 # We want this to be a wrapper for an existing dataset that we have loaded
 # lets use the concept of middlewares to wrap each dataset, for example
@@ -15,8 +14,6 @@ from .prompt_tokenizers import PromptTokenizingStrategy
 # let's check to ensure we don't truncate an item in the middle, we'll use
 # the collators later on to pad the datasets
 
-LOG = logging.getLogger("axolotl")
-
 
 class TokenizedPromptDataset(IterableDataset):
     """
@@ -35,15 +32,17 @@ class TokenizedPromptDataset(IterableDataset):
         self.dataset = dataset
 
     def __iter__(self):
-        features = self.dataset.features.keys()
-        num_proc = os.cpu_count()
-        return iter(
-            self.dataset.map(
-                self.prompt_tokenizer.tokenize_prompt,
-                num_proc=num_proc,
-                remove_columns=features,
-            )
-        )
+        iterator = iter(self.dataset)
+        count = 0
+        # Loop through the entire dataset
+        for example in iterator:
+            try:
+                yield self.prompt_tokenizer.tokenize_prompt(example)
+                count += 1
+            except InvalidDataException:
+                pass
+        if count == 0:
+            raise RuntimeError("Expected at least one datapoint in dataset.")
 
 
 # TODO this isn't the best since it can't interleave datasets
@@ -116,7 +115,7 @@ class ConstantLengthDataset(IterableDataset):
                                 "attention_mask": attention_mask,
                             }
                         else:
-                            LOG.warning(
+                            logging.warning(
                                 f"dropping batch due to tensor size mismatch input_ids: {input_ids.size()}, labels: {labels.size()}, attention_mask: {attention_mask.size()}"
                             )
                     buffer = {

src/axolotl/flash_attn.py

@@ -8,7 +8,7 @@ import torch
 import transformers
 from einops import rearrange
 from flash_attn.bert_padding import pad_input, unpad_input
-from flash_attn.flash_attn_interface import flash_attn_varlen_qkvpacked_func
+from flash_attn.flash_attn_interface import flash_attn_unpadded_qkvpacked_func
 from transformers.models.llama.modeling_llama import apply_rotary_pos_emb
 
 
@@ -79,7 +79,7 @@ def forward(
             dtype=torch.int32,
             device=qkv.device,
         )
-        output = flash_attn_varlen_qkvpacked_func(
+        output = flash_attn_unpadded_qkvpacked_func(
             qkv, cu_q_lens, max_s, 0.0, softmax_scale=None, causal=True
         )
         output = rearrange(output, "(b s) ... -> b s ...", b=bsz)
@@ -95,7 +95,7 @@ def forward(
             three=3,
             h=nheads,
         )
-        output_unpad = flash_attn_varlen_qkvpacked_func(
+        output_unpad = flash_attn_unpadded_qkvpacked_func(
             x_unpad,
             cu_q_lens,
             max_s,

src/axolotl/logging_config.py

@@ -1,33 +0,0 @@
-"""Logging configuration settings"""
-
-import os
-import sys
-from logging.config import dictConfig
-from typing import Any, Dict
-
-DEFAULT_LOGGING_CONFIG: Dict[str, Any] = {
-    "version": 1,
-    "formatters": {
-        "simple": {
-            "format": "[%(asctime)s] [%(levelname)s] [%(name)s.%(funcName)s:%(lineno)d] [PID:%(process)d] %(message)s",
-        },
-    },
-    "filters": {},
-    "handlers": {
-        "console": {
-            "class": "logging.StreamHandler",
-            "formatter": "simple",
-            "filters": [],
-            "stream": sys.stdout,
-        },
-    },
-    "root": {"handlers": ["console"], "level": os.getenv("LOG_LEVEL", "INFO")},
-    "loggers": {
-        "axolotl": {"handlers": ["console"], "level": "DEBUG", "propagate": False},
-    },
-}
-
-
-def configure_logging():
-    """Configure with default logging"""
-    dictConfig(DEFAULT_LOGGING_CONFIG)

src/axolotl/monkeypatch/llama_attn_hijack_xformers.py

@@ -7,7 +7,6 @@ import math
 from typing import Optional, Tuple
 
 import torch
-import torch.nn.functional as F
 import transformers.models.llama.modeling_llama
 from torch import nn
 
@@ -39,48 +38,21 @@ def xformers_forward(
     # pylint: disable=duplicate-code
     bsz, q_len, _ = hidden_states.size()
 
-    if not hasattr(self, "pretraining_tp"):
-        self.pretraining_tp = 1
-
-    if self.pretraining_tp > 1:
-        key_value_slicing = (
-            self.num_key_value_heads * self.head_dim
-        ) // self.pretraining_tp
-        query_slices = self.q_proj.weight.split(
-            (self.num_heads * self.head_dim) // self.pretraining_tp, dim=0
-        )
-        key_slices = self.k_proj.weight.split(key_value_slicing, dim=0)
-        value_slices = self.v_proj.weight.split(key_value_slicing, dim=0)
-
-        query_states = [
-            F.linear(hidden_states, query_slices[i]) for i in range(self.pretraining_tp)
-        ]
-        query_states = torch.cat(query_states, dim=-1)
-
-        key_states = [
-            F.linear(hidden_states, key_slices[i]) for i in range(self.pretraining_tp)
-        ]
-        key_states = torch.cat(key_states, dim=-1)
-
-        value_states = [
-            F.linear(hidden_states, value_slices[i]) for i in range(self.pretraining_tp)
-        ]
-        value_states = torch.cat(value_states, dim=-1)
-
-    else:
-        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)
+    query_states = (
+        self.q_proj(hidden_states)
+        .view(bsz, q_len, self.num_heads, self.head_dim)
+        .transpose(1, 2)
+    )
+    key_states = (
+        self.k_proj(hidden_states)
+        .view(bsz, q_len, self.num_heads, self.head_dim)
+        .transpose(1, 2)
+    )
+    value_states = (
+        self.v_proj(hidden_states)
+        .view(bsz, q_len, self.num_heads, self.head_dim)
+        .transpose(1, 2)
+    )
 
     kv_seq_len = key_states.shape[-2]
     if past_key_value is not None:
@@ -101,14 +73,6 @@ def xformers_forward(
 
     past_key_value = (key_states, value_states) if use_cache else None
 
-    # repeat k/v heads if n_kv_heads < n_heads
-    key_states = transformers.models.llama.modeling_llama.repeat_kv(
-        key_states, self.num_key_value_groups
-    )
-    value_states = transformers.models.llama.modeling_llama.repeat_kv(
-        value_states, self.num_key_value_groups
-    )
-
     # We only apply xformers optimizations if we don't need to output the whole attention matrix
     if not output_attentions:
         query_states = query_states.transpose(1, 2)
@@ -164,23 +128,10 @@ def xformers_forward(
                 f" {attn_output.size()}"
             )
 
-        attn_output = attn_output.transpose(1, 2).contiguous()
-        # end x-formers vs. not x-formers if-else block
+        attn_output = attn_output.transpose(1, 2)
 
     attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
-
-    if self.pretraining_tp > 1:
-        attn_output = attn_output.split(self.hidden_size // self.pretraining_tp, dim=2)
-        o_proj_slices = self.o_proj.weight.split(
-            self.hidden_size // self.pretraining_tp, dim=1
-        )
-        attn_output = sum(
-            F.linear(attn_output[i], o_proj_slices[i])
-            for i in range(self.pretraining_tp)
-        )
-    else:
-        attn_output = self.o_proj(attn_output)
-
+    attn_output = self.o_proj(attn_output)
     return attn_output, attn_weights, past_key_value
 
 
@@ -233,15 +184,14 @@ def sdp_attention_forward(
 
     # We only apply sdp attention if we don't need to output the whole attention matrix
     if not output_attentions:
-        with torch.backends.cuda.sdp_kernel():
-            attn_output = torch.nn.functional.scaled_dot_product_attention(
-                query_states,
-                key_states,
-                value_states,
-                attn_mask=attention_mask,
-                is_causal=False,
-            )
-            attn_weights = None
+        attn_output = torch.nn.functional.scaled_dot_product_attention(
+            query_states,
+            key_states,
+            value_states,
+            attn_mask=attention_mask,
+            is_causal=False,
+        )
+        attn_weights = None
     else:
         attn_weights = torch.matmul(
             query_states, key_states.transpose(2, 3)

src/axolotl/monkeypatch/llama_landmark_attn.py

@@ -53,7 +53,7 @@ from transformers.utils import (
     replace_return_docstrings,
 )
 
-LOG = logging.getLogger("axolotl")
+logger = logging.get_logger(__name__)
 
 _CONFIG_FOR_DOC = "LlamaConfig"
 
@@ -862,7 +862,7 @@ class LlamaModel(LlamaPreTrainedModel):
 
         if self.gradient_checkpointing and self.training:
             if use_cache:
-                LOG.warning_once(
+                logger.warning_once(
                     "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
                 )
                 use_cache = False

src/axolotl/prompt_strategies/alpaca_w_system.py

@@ -66,34 +66,15 @@ class SystemDataPrompter(AlpacaPrompter):
     ) -> Generator[str, None, None]:
         # returns the full prompt from instruction and optional input
         # if a label (=response, =output) is provided, it's also appended.
-        formatted_sys_prompt = f"### System:\n{system}\n\n" if system else ""
         if input:
-            res = formatted_sys_prompt + self.turn_format.format(
-                instruction=instruction, input=input
-            )
+            res = system + self.turn_format.format(instruction=instruction, input=input)
         else:
-            res = formatted_sys_prompt + self.turn_no_input_format.format(
-                instruction=instruction
-            )
+            res = system + self.turn_no_input_format.format(instruction=instruction)
         if output:
             res = f"{res}{output}"
         yield res
 
 
-class OpenOrcaSystemDataPrompter(SystemDataPrompter):
-    """
-    Alpaca Style Prompter that uses system prompts from the dataset, with OpenOrca prompts
-    """
-
-    def match_prompt_style(self):
-        if self.prompt_style == PromptStyle.INSTRUCT.value:
-            self.turn_format = "### User:\n{instruction}\n\n### Additional Context:\n{input}\n\n### Assistant:\n"
-            self.turn_no_input_format = "### User:\n{instruction}\n\n### Assistant:\n"
-        if self.prompt_style == PromptStyle.CHAT.value:
-            self.turn_format = "USER: {instruction}\n{input}\nASSISTANT:"
-            self.turn_no_input_format = "USER: {instruction}\nASSISTANT:"
-
-
 class OpenOrcaPromptTokenizingStrategy(InstructionWSystemPromptTokenizingStrategy):
     """
     Tokenizing strategy for OpenOrca datasets
@@ -132,7 +113,7 @@ def load_chat(tokenizer, cfg):
 
 def load_open_orca(tokenizer, cfg):
     return OpenOrcaPromptTokenizingStrategy(
-        OpenOrcaSystemDataPrompter(PromptStyle.INSTRUCT.value),
+        SystemDataPrompter(PromptStyle.INSTRUCT.value),
         tokenizer,
         cfg.train_on_inputs,
         cfg.sequence_len,

src/axolotl/prompt_strategies/pygmalion.py

@@ -11,8 +11,6 @@ from axolotl.prompt_tokenizers import (
     tokenize_prompt_default,
 )
 
-LOG = logging.getLogger("axolotl")
-
 IGNORE_TOKEN_ID = -100
 
 
@@ -66,7 +64,7 @@ class PygmalionPromptTokenizingStrategy(PromptTokenizingStrategy):
                     *copy.deepcopy(res["input_ids"])
                 ][len(self.bot_prefix_token_ids) :]
             else:
-                LOG.warning(f"unknown role in conversation: {role}")
+                logging.warning(f"unknown role in conversation: {role}")
                 res = defaultdict(lambda: [])
 
             # pylint: disable=duplicate-code

src/axolotl/prompt_tokenizers.py

@@ -10,8 +10,6 @@ from transformers import PreTrainedTokenizer
 
 from axolotl.prompters import IGNORE_TOKEN_ID
 
-LOG = logging.getLogger("axolotl")
-
 IGNORE_INDEX = -100
 LLAMA_DEFAULT_PAD_TOKEN = "[PAD]"  # nosec
 LLAMA_DEFAULT_EOS_TOKEN = "</s>"  # nosec
@@ -48,22 +46,16 @@ class PromptTokenizingStrategy(abc.ABC):
 
     @functools.lru_cache(maxsize=128)
     def _get_user_token(self):
-        try:
-            id_or_ids = self.tokenizer.convert_tokens_to_ids("<|USER|>")
-            if isinstance(id_or_ids, (int,)):
-                return id_or_ids
-        except KeyError:
-            pass
+        id_or_ids = self.tokenizer.convert_tokens_to_ids("<|USER|>")
+        if isinstance(id_or_ids, (int,)):
+            return id_or_ids
         return False
 
     @functools.lru_cache(maxsize=128)
     def _get_assistant_token(self):
-        try:
-            id_or_ids = self.tokenizer.convert_tokens_to_ids("<|ASSISTANT|>")
-            if isinstance(id_or_ids, (int,)):
-                return id_or_ids
-        except KeyError:
-            pass
+        id_or_ids = self.tokenizer.convert_tokens_to_ids("<|ASSISTANT|>")
+        if isinstance(id_or_ids, (int,)):
+            return id_or_ids
         return False
 
     def _tokenize(self, prompt: str, add_eos_token=True, strip_bos_token=False):
@@ -392,7 +384,7 @@ class ShareGPTPromptTokenizingStrategy(PromptTokenizingStrategy):
                         # everything from this is masked out from the labels
                         labels = [IGNORE_TOKEN_ID] * len(res["input_ids"])
                     else:
-                        LOG.warning(f"unhandled role: {part[0]}")
+                        logging.warning(f"unhandled role: {part[0]}")
 
                 # pylint: disable=duplicate-code
                 result, current_len = parse_tokenized_to_result(

src/axolotl/prompters.py

@@ -5,7 +5,6 @@ import logging
 from enum import Enum, auto
 from typing import Generator, List, Optional, Tuple, Union
 
-LOG = logging.getLogger("axolotl")
 IGNORE_TOKEN_ID = -100
 
 
@@ -242,7 +241,7 @@ class Conversation:
             if message:
                 yield (role + ":", " " + message)
             else:
-                LOG.warning(f"role with empty message: {role}")
+                logging.warning(f"role with empty message: {role}")
                 yield (role + ":", "")
 
     def copy(self):

src/axolotl/utils/data.py

@@ -1,6 +1,5 @@
 """Module containing data utilities"""
 import functools
-import itertools
 import logging
 from hashlib import md5
 from pathlib import Path
@@ -36,11 +35,9 @@ from axolotl.prompters import (
     SummarizeTLDRPrompter,
 )
 
-LOG = logging.getLogger("axolotl")
-
 
 def load_tokenized_prepared_datasets(
-    tokenizer, cfg, default_dataset_prepared_path
+    split, tokenizer, cfg, default_dataset_prepared_path
 ) -> DatasetDict:
     tokenizer_name = tokenizer.__class__.__name__
     ds_hash = str(
@@ -52,6 +49,8 @@ def load_tokenized_prepared_datasets(
                     sorted([f"{d.path}:{d.type}:{d.shards}" for d in cfg.datasets])
                 )
                 + "|"
+                + split
+                + "|"
                 + tokenizer_name
             ).encode("utf-8")
         ).hexdigest()
@@ -69,24 +68,24 @@ def load_tokenized_prepared_datasets(
                 f"{cfg.push_dataset_to_hub}/{ds_hash}",
                 use_auth_token=use_auth_token,
             )
-            dataset = dataset["train"]
+            dataset = dataset[split]
     except Exception:  # pylint: disable=broad-except # nosec
         pass
 
     if dataset:
         ...
     elif any(prepared_ds_path.glob("*")):
-        LOG.info(f"Loading prepared dataset from disk at {prepared_ds_path}...")
+        logging.info(f"Loading prepared dataset from disk at {prepared_ds_path}...")
         dataset = load_from_disk(str(prepared_ds_path))
-        LOG.info("Prepared dataset loaded from disk...")
+        logging.info("Prepared dataset loaded from disk...")
     else:
-        LOG.info(f"Unable to find prepared dataset in {prepared_ds_path}")
-        LOG.info("Loading raw datasets...")
+        logging.info(f"Unable to find prepared dataset in {prepared_ds_path}")
+        logging.info("Loading raw datasets...")
 
         if cfg.seed:
             seed = cfg.seed
         else:
-            LOG.info("No seed provided, using default seed of 42")
+            logging.info("No seed provided, using default seed of 42")
             seed = 42
 
         datasets = []
@@ -97,7 +96,6 @@ def load_tokenized_prepared_datasets(
             try:
                 load_dataset(
                     d.path,
-                    name=d.name,
                     streaming=True,
                     use_auth_token=use_auth_token,
                 )
@@ -106,51 +104,40 @@ def load_tokenized_prepared_datasets(
                 pass
 
             # prefer local dataset, even if hub exists
-            local_path = Path(d.path)
-            if local_path.exists():
-                if local_path.is_dir():
+            if Path(d.path).exists():
+                ds = load_dataset(
+                    "json",
+                    data_files=d.path,
+                    streaming=False,
+                    split=None,
+                )
+            elif ds_from_hub:
+                if d.data_files:
                     ds = load_dataset(
                         d.path,
-                        name=d.name,
+                        streaming=False,
                         data_files=d.data_files,
-                        streaming=False,
-                        split=None,
-                    )
-                elif local_path.is_file():
-                    ds = load_dataset(
-                        "json",
-                        name=d.name,
-                        data_files=d.path,
-                        streaming=False,
-                        split=None,
+                        use_auth_token=use_auth_token,
                     )
                 else:
-                    raise ValueError(
-                        "unhandled dataset load: local path exists, but is neither a directory or a file"
+                    ds = load_dataset(
+                        d.path,
+                        streaming=False,
+                        use_auth_token=use_auth_token,
                     )
-            elif ds_from_hub:
-                ds = load_dataset(
-                    d.path,
-                    name=d.name,
-                    streaming=False,
-                    data_files=d.data_files,
-                    use_auth_token=use_auth_token,
-                )
             else:
                 fp = hf_hub_download(
                     repo_id=d.path,
                     repo_type="dataset",
                     filename=d.data_files,
                 )
-                ds = load_dataset(
-                    "json", name=d.name, data_files=fp, streaming=False, split=None
-                )
+                ds = load_dataset("json", data_files=fp, streaming=False, split=None)
             if not ds:
                 raise ValueError("unhandled dataset load")
             # support for using a subset of the data
             if d.shards:
-                if "train" in ds:
-                    ds = ds.shuffle(seed=seed)["train"].shard(
+                if split in ds:
+                    ds = ds.shuffle(seed=seed)[split].shard(
                         num_shards=d.shards, index=0
                     )
                 else:
@@ -159,8 +146,8 @@ def load_tokenized_prepared_datasets(
             d_type_split = d_type.split(":")
             d_base_type = d_type_split[0]
             d_prompt_style = d_type_split[1] if len(d_type_split) > 1 else None
-            if "train" in ds:
-                ds = ds["train"]
+            if split in ds:
+                ds = ds[split]
             if ds_strategy := load(d.type, tokenizer, cfg):
                 ds_wrapper = TokenizedPromptDataset(ds_strategy, ds)
                 datasets.append(ds_wrapper)
@@ -258,29 +245,25 @@ def load_tokenized_prepared_datasets(
                 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}")
+                logging.error(
+                    f"unhandled prompt tokenization strategy: {d.type}. {suffix}"
+                )
                 raise ValueError(
                     f"unhandled prompt tokenization strategy: {d.type} {suffix}"
                 )
-        LOG.info("tokenizing, merging, and shuffling master dataset")
+        logging.info("tokenizing, merging, and shuffling master dataset")
 
         samples: List[int] = []
-        chunk_size = 1000
         for d in datasets:
-            d_iter = iter(d)
-            while True:
-                chunk = list(itertools.islice(d_iter, chunk_size))
-                if not chunk:
-                    break
-                samples.extend(chunk)
-
-        LOG.info("shuffle")
+            samples = samples + list(d)
         dataset = Dataset.from_list(samples).shuffle(seed=seed)
         if cfg.local_rank == 0:
-            LOG.info(f"Saving merged prepared dataset to disk... {prepared_ds_path}")
+            logging.info(
+                f"Saving merged prepared dataset to disk... {prepared_ds_path}"
+            )
             dataset.save_to_disk(prepared_ds_path)
             if cfg.push_dataset_to_hub:
-                LOG.info(
+                logging.info(
                     f"Saving merged prepared dataset with push_to_hub... {cfg.push_dataset_to_hub}/{ds_hash}"
                 )
                 dataset.push_to_hub(
@@ -331,53 +314,63 @@ def load_prepare_datasets(
         use_auth_token = cfg.hf_use_auth_token
         try:
             if cfg.push_dataset_to_hub:
-                LOG.info(
+                logging.info(
                     f"Checking for packed prepared dataset from hub... {cfg.push_dataset_to_hub}/{ds_hash}"
                 )
                 dataset = load_dataset(
                     f"{cfg.push_dataset_to_hub}/{ds_hash}",
                     use_auth_token=use_auth_token,
                 )
-                dataset = dataset["train"]
         except Exception:  # pylint: disable=broad-except # nosec
             pass
 
         if dataset:
             ...
         elif any(prepared_ds_path.glob("*")):
-            LOG.info(
+            logging.info(
                 f"Loading prepared packed dataset from disk at {prepared_ds_path}..."
             )
             dataset = load_from_disk(str(prepared_ds_path))
-            LOG.info("Prepared packed dataset loaded from disk...")
+            logging.info("Prepared packed dataset loaded from disk...")
             if cfg.push_dataset_to_hub:
-                LOG.info(
+                logging.info(
                     f"Saving packed prepared dataset with push_to_hub... {cfg.push_dataset_to_hub}/{ds_hash}"
                 )
                 dataset.push_to_hub(
                     f"{cfg.push_dataset_to_hub}/{ds_hash}", private=True
                 )
         else:
-            dataset = load_tokenized_prepared_datasets(
-                tokenizer, cfg, default_dataset_prepared_path
+            dataset_train = load_tokenized_prepared_datasets(
+                "train", tokenizer, cfg, default_dataset_prepared_path
             )
-
+            dataset_test = load_tokenized_prepared_datasets(
+                "test", tokenizer, cfg, default_dataset_prepared_path
+            )
+            dataset = DatasetDict({"train": dataset_train, "test": dataset_test})
             if cfg.seed:
                 dataset = dataset.shuffle(seed=cfg.seed)
 
-            constant_len_dataset = ConstantLengthDataset(
+            constant_len_dataset_train = ConstantLengthDataset(
                 tokenizer,
-                [dataset],
+                [dataset["train"]],
                 seq_length=max_packed_sequence_len,
             )
-            LOG.info(f"packing master dataset to len: {cfg.max_packed_sequence_len}")
-            dataset = Dataset.from_list(list(constant_len_dataset))
+            constant_len_dataset_test = ConstantLengthDataset(
+                tokenizer,
+                [dataset["test"]],
+                seq_length=max_packed_sequence_len,
+            )
+            logging.info(
+                f"packing master dataset to len: {cfg.max_packed_sequence_len}"
+            )
+            dataset_train = Dataset.from_list(list(constant_len_dataset_train))
+            dataset_test = Dataset.from_list(list(constant_len_dataset_test))
 
             # filter out bad data
-            dataset = Dataset.from_list(
+            dataset_train = Dataset.from_list(
                 [
                     d
-                    for d in dataset
+                    for d in dataset_train
                     if len(d["input_ids"]) < cfg.sequence_len
                     and len(d["input_ids"]) > 0
                     and len(d["input_ids"]) == len(d["attention_mask"])
@@ -385,13 +378,26 @@ def load_prepare_datasets(
                 ]
             )
 
+            # filter out bad data
+            dataset_test = Dataset.from_list(
+                [
+                    d
+                    for d in dataset_test
+                    if len(d["input_ids"]) < cfg.sequence_len
+                    and len(d["input_ids"]) > 0
+                    and len(d["input_ids"]) == len(d["attention_mask"])
+                    and len(d["input_ids"]) == len(d["labels"])
+                ]
+            )
+            dataset = DatasetDict({"train": dataset_train, "test": dataset_test})
+
             if cfg.local_rank == 0:
-                LOG.info(
+                logging.info(
                     f"Saving packed prepared dataset to disk... {prepared_ds_path}"
                 )
                 dataset.save_to_disk(prepared_ds_path)
                 if cfg.push_dataset_to_hub:
-                    LOG.info(
+                    logging.info(
                         f"Saving packed prepared dataset with push_to_hub... {cfg.push_dataset_to_hub}/{ds_hash}"
                     )
                     dataset.push_to_hub(
@@ -399,12 +405,17 @@ def load_prepare_datasets(
                         private=True,
                     )
     else:
+        # dataset_train = load_tokenized_prepared_datasets(
         dataset = load_tokenized_prepared_datasets(
-            tokenizer, cfg, default_dataset_prepared_path
+            "train", tokenizer, cfg, default_dataset_prepared_path
         )
+        # dataset_test = load_tokenized_prepared_datasets(
+        #     "test", tokenizer, cfg, default_dataset_prepared_path
+        # )
+        # dataset = DatasetDict({"train": dataset_train, "test": dataset_test})
 
     if cfg.dataset_shard_num and cfg.dataset_shard_idx is not None:
-        LOG.info(
+        logging.info(
             f"Using index #{cfg.dataset_shard_idx} of {cfg.dataset_shard_num} shards"
         )
         dataset = dataset.shard(
@@ -416,6 +427,9 @@ def load_prepare_datasets(
         dataset = dataset.train_test_split(test_size=cfg.val_set_size, shuffle=False)
         train_dataset = dataset["train"]
         eval_dataset = dataset["test"]
+    elif "train" in dataset:
+        train_dataset = dataset["train"]
+        eval_dataset = dataset["test"]
     else:
         train_dataset = dataset
         eval_dataset = None
@@ -525,7 +539,7 @@ def encode_pretraining(tokenizer, max_tokens, examples):
         "attention_mask": [seq.tolist() for seq in new_attention_mask],
     }
 
-    LOG.debug(len(ret["input_ids"]))
+    logging.debug(len(ret["input_ids"]))
     return ret
 
 

src/axolotl/utils/models.py

@@ -23,8 +23,6 @@ from transformers import (  # noqa: F401
 
 from axolotl.prompt_tokenizers import LLAMA_DEFAULT_PAD_TOKEN
 
-LOG = logging.getLogger("axolotl")
-
 if TYPE_CHECKING:
     from peft import PeftConfig  # noqa: F401
 
@@ -52,10 +50,10 @@ def load_tokenizer(
             use_fast=use_fast,
         )
 
-    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}")
+    logging.debug(f"EOS: {tokenizer.eos_token_id} / {tokenizer.eos_token}")
+    logging.debug(f"BOS: {tokenizer.bos_token_id} / {tokenizer.bos_token}")
+    logging.debug(f"PAD: {tokenizer.pad_token_id} / {tokenizer.pad_token}")
+    logging.debug(f"UNK: {tokenizer.unk_token_id} / {tokenizer.unk_token}")
 
     if tokenizer.__class__.__name__ in [
         "LlamaTokenizer",
@@ -92,25 +90,23 @@ def load_model(
 
     if cfg.is_llama_derived_model and cfg.flash_attention:
         if cfg.device not in ["mps", "cpu"] and not cfg.inference:
-            from axolotl.monkeypatch.llama_attn_hijack_flash import (
-                replace_llama_attn_with_flash_attn,
-            )
+            from axolotl.flash_attn import replace_llama_attn_with_flash_attn
 
-            LOG.info("patching with flash attention")
+            logging.info("patching with flash attention")
             replace_llama_attn_with_flash_attn()
     elif cfg.is_llama_derived_model and cfg.xformers_attention:
         from axolotl.monkeypatch.llama_attn_hijack_xformers import (
             hijack_llama_attention,
         )
 
-        LOG.info("patching with xformers attention")
+        logging.info("patching with xformers attention")
         hijack_llama_attention()
     elif cfg.is_llama_derived_model and cfg.sdp_attention:
         from axolotl.monkeypatch.llama_attn_hijack_xformers import (
             hijack_llama_sdp_attention,
         )
 
-        LOG.info("patching with sdp attention")
+        logging.info("patching with sdp attention")
         hijack_llama_sdp_attention()
     elif cfg.is_llama_derived_model and cfg.landmark_attention:
         from axolotl.monkeypatch.llama_landmark_attn import (
@@ -118,7 +114,7 @@ def load_model(
             patch_llama_with_landmark_attn,
         )
 
-        LOG.info("patching with landmark attention")
+        logging.info("patching with landmark attention")
         patch_llama_with_landmark_attn()
 
         # Note: This might overwrite previous additional_special_tokens
@@ -129,7 +125,7 @@ def load_model(
             replace_llama_rope_with_xpos_rope,
         )
 
-        LOG.info("patching with xpos rope")
+        logging.info("patching with xpos rope")
         replace_llama_rope_with_xpos_rope()
 
     if cfg.bf16 or cfg.bfloat16:
@@ -146,24 +142,18 @@ def load_model(
 
             replace_peft_model_with_int4_lora_model()
     except Exception as err:
-        LOG.exception(err)
+        logging.exception(err)
         raise err
 
-    if not cfg.gptq and (
-        (cfg.adapter == "lora" and load_in_8bit)
-        or (cfg.adapter == "qlora" and cfg.load_in_4bit)
-    ):
-        try:
-            from peft import prepare_model_for_kbit_training
-        except ImportError:
-            # For backward compatibility
-            from peft import (
-                prepare_model_for_int8_training as prepare_model_for_kbit_training,
-            )
+    try:
+        from peft import prepare_model_for_kbit_training
+    except ImportError:
+        # For backward compatibility
+        from peft import (
+            prepare_model_for_int8_training as prepare_model_for_kbit_training,
+        )
 
     model_kwargs = {}
-    if cfg.model_revision:
-        model_kwargs["revision"] = cfg.model_revision
     if cfg.adapter == "qlora" and cfg.load_in_4bit:
         model_kwargs["quantization_config"] = BitsAndBytesConfig(
             load_in_4bit=True,
@@ -195,7 +185,7 @@ def load_model(
                 if len(files) > 0:
                     model_path = str(files[0])
                 else:
-                    LOG.warning(
+                    logging.warning(
                         "unable to find a cached model file, this will likely fail..."
                     )
                     model_path = str(cache_model_path)
@@ -212,7 +202,7 @@ def load_model(
                 else True,
             )
             load_in_8bit = False
-        elif cfg.is_llama_derived_model and not cfg.trust_remote_code:
+        elif cfg.is_llama_derived_model:
             from transformers import LlamaForCausalLM
 
             config = LlamaConfig.from_pretrained(base_model_config)
@@ -251,7 +241,7 @@ def load_model(
         #         device=cfg.device,
         #     )
         #     model.train() # sets to train instead of eval mode
-        elif model_type and not cfg.trust_remote_code:
+        elif model_type:
             model = getattr(transformers, model_type).from_pretrained(
                 base_model,
                 load_in_8bit=cfg.load_in_8bit and cfg.adapter is not None,
@@ -274,14 +264,14 @@ def load_model(
                 and cfg.sequence_len > config.max_seq_len
             ):
                 config.max_seq_len = cfg.sequence_len
-                LOG.warning(f"increasing context length to {cfg.sequence_len}")
+                logging.warning(f"increasing context length to {cfg.sequence_len}")
             elif (
                 hasattr(config, "max_sequence_length")
                 and config.max_sequence_length
                 and cfg.sequence_len > config.max_sequence_length
             ):
                 config.max_sequence_length = cfg.sequence_len
-                LOG.warning(f"increasing context length to {cfg.sequence_len}")
+                logging.warning(f"increasing context length to {cfg.sequence_len}")
             model = AutoModelForCausalLM.from_pretrained(
                 base_model,
                 config=config,
@@ -293,10 +283,10 @@ def load_model(
                 **model_kwargs,
             )
     except Exception as err:  # pylint: disable=broad-exception-caught
-        LOG.error(
+        logging.error(
             "Exception raised attempting to load model, retrying with AutoModelForCausalLM"
         )
-        LOG.exception(err)
+        logging.exception(err)
         model = AutoModelForCausalLM.from_pretrained(
             base_model,
             load_in_8bit=cfg.load_in_8bit and cfg.adapter is not None,
@@ -307,11 +297,7 @@ def load_model(
             **model_kwargs,
         )
 
-    embeddings_len = (
-        math.ceil(len(tokenizer) / 32) * 32
-        if cfg.resize_token_embeddings_to_32x
-        else len(tokenizer)
-    )
+    embeddings_len = math.ceil(len(tokenizer) / 32) * 32
     model.resize_token_embeddings(embeddings_len)
 
     if (
@@ -319,7 +305,7 @@ def load_model(
         and model.config.max_position_embeddings
         and cfg.sequence_len >= model.config.max_position_embeddings
     ):
-        LOG.warning(
+        logging.warning(
             f"increasing model.config.max_position_embeddings to {cfg.sequence_len}"
         )
         model.config.max_position_embeddings = cfg.sequence_len
@@ -328,21 +314,11 @@ def load_model(
         (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")
+        logging.info("converting PEFT model w/ prepare_model_for_kbit_training")
         model = prepare_model_for_kbit_training(
             model, use_gradient_checkpointing=cfg.gradient_checkpointing
         )
 
-        # LlamaRMSNorm layers are in fp32 after kbit_training, so we need to
-        # convert them back to fp16/bf16 for flash-attn compatibility.
-        if cfg.flash_attention and cfg.is_llama_derived_model:
-            for name, module in model.named_modules():
-                if "norm" in name:
-                    module.to(torch_dtype)
-                if "lm_head" in name or "embed_tokens" in name:
-                    if hasattr(module, "weight"):
-                        module.to(torch_dtype)
-
     model, lora_config = load_adapter(model, cfg, adapter)
 
     if cfg.ddp and not load_in_8bit:
@@ -350,7 +326,7 @@ def load_model(
 
     if cfg.gptq:
         # Scales to half
-        LOG.info("Fitting 4bit scales and zeros to half")
+        logging.info("Fitting 4bit scales and zeros to half")
         for _, module in model.named_modules():
             if "Autograd4bitQuantLinear" in str(type(module)) or "Linear4bitLt" in str(
                 type(module)
@@ -376,7 +352,7 @@ def load_model(
         if param.requires_grad:
             requires_grad.append(f"{name}: {param.requires_grad}")
     if len(requires_grad) == 0:
-        LOG.warning("there are no parameters that require gradient updates")
+        logging.warning("there are no parameters that require gradient updates")
     model.config.use_cache = False
 
     if cfg.flash_optimum:
@@ -410,7 +386,7 @@ def load_llama_adapter(model, cfg):
     )
 
     if cfg.lora_model_dir:
-        LOG.info("Loading pretained LORA")
+        logging.info("Loading pretained LORA")
         model = PeftModel.from_pretrained(
             model,
             cfg.lora_model_dir,
@@ -457,7 +433,7 @@ def load_lora(model, cfg):
             bits = 8
 
         linear_names = find_all_linear_names(bits, model)
-        LOG.info(f"found linear modules: {repr(linear_names)}")
+        logging.info(f"found linear modules: {repr(linear_names)}")
         lora_target_modules = list(set(lora_target_modules + linear_names))
 
     lora_config = LoraConfig(

src/axolotl/utils/schedulers.py

@@ -1,9 +1,6 @@
 """Module for custom LRScheduler class"""
-import math
-from functools import partial
 
-from torch.optim import Optimizer
-from torch.optim.lr_scheduler import LambdaLR, LRScheduler
+from torch.optim.lr_scheduler import LRScheduler
 
 
 class InterpolatingLogScheduler(LRScheduler):
@@ -45,58 +42,3 @@ class InterpolatingLogScheduler(LRScheduler):
             lrs = [self.max_lr for base_lr in self.base_lrs]
 
         return lrs
-
-
-def _get_cosine_schedule_with_quadratic_warmup_lr_lambda(
-    current_step: int,
-    *,
-    num_warmup_steps: int,
-    num_training_steps: int,
-    num_cycles: float
-):
-    if current_step < num_warmup_steps:
-        return (float(current_step) / float(max(1, num_warmup_steps))) ** 2
-    progress = float(current_step - num_warmup_steps) / float(
-        max(1, num_training_steps - num_warmup_steps)
-    )
-    return max(
-        0.0, 0.5 * (1.0 + math.cos(math.pi * float(num_cycles) * 2.0 * progress))
-    )
-
-
-def get_cosine_schedule_with_quadratic_warmup(
-    optimizer: Optimizer,
-    num_warmup_steps: int,
-    num_training_steps: int,
-    num_cycles: float = 0.5,
-    last_epoch: int = -1,
-):
-    """
-    Create a schedule with a learning rate that decreases following the values of the cosine function between the
-    initial lr set in the optimizer to 0, after a warmup period during which it increases linearly between 0 and the
-    initial lr set in the optimizer.
-
-    Args:
-        optimizer ([`~torch.optim.Optimizer`]):
-            The optimizer for which to schedule the learning rate.
-        num_warmup_steps (`int`):
-            The number of steps for the warmup phase.
-        num_training_steps (`int`):
-            The total number of training steps.
-        num_cycles (`float`, *optional*, defaults to 0.5):
-            The number of waves in the cosine schedule (the defaults is to just decrease from the max value to 0
-            following a half-cosine).
-        last_epoch (`int`, *optional*, defaults to -1):
-            The index of the last epoch when resuming training.
-
-    Return:
-        `torch.optim.lr_scheduler.LambdaLR` with the appropriate schedule.
-    """
-
-    lr_lambda = partial(
-        _get_cosine_schedule_with_quadratic_warmup_lr_lambda,
-        num_warmup_steps=num_warmup_steps,
-        num_training_steps=num_training_steps,
-        num_cycles=num_cycles,
-    )
-    return LambdaLR(optimizer, lr_lambda, last_epoch)

src/axolotl/utils/tokenization.py

@@ -5,8 +5,6 @@ import logging
 
 from termcolor import colored
 
-LOG = logging.getLogger("axolotl")
-
 
 def check_dataset_labels(dataset, tokenizer):
     # the dataset is already shuffled, so let's just check the first 5 elements
@@ -34,7 +32,7 @@ def check_example_labels(example, tokenizer):
         )
         colored_tokens.append(colored_token)
 
-    LOG.info(" ".join(colored_tokens))
-    LOG.info("\n\n\n")
+    logging.info(" ".join(colored_tokens))
+    logging.info("\n\n\n")
 
     return " ".join(colored_tokens)

src/axolotl/utils/trainer.py

@@ -5,7 +5,6 @@ import logging
 import math
 import os
 import sys
-from dataclasses import dataclass, field
 from pathlib import Path
 from typing import Optional
 
@@ -14,70 +13,17 @@ import torch.cuda
 import transformers
 from torch import nn
 from torch.optim.lr_scheduler import OneCycleLR
-from transformers import EarlyStoppingCallback, Trainer, TrainingArguments
+from transformers import EarlyStoppingCallback, Trainer
 from transformers.trainer_pt_utils import get_parameter_names
 
 from axolotl.utils.callbacks import (
     SaveBetterTransformerModelCallback,
     SavePeftModelCallback,
 )
-from axolotl.utils.schedulers import (
-    InterpolatingLogScheduler,
-    get_cosine_schedule_with_quadratic_warmup,
-)
-
-LOG = logging.getLogger("axolotl")
+from axolotl.utils.schedulers import InterpolatingLogScheduler
 
 
-@dataclass
-class AxolotlTrainingArguments(TrainingArguments):
-    """
-    Extend the base TrainingArguments for axolotl helpers
-    """
-
-    lr_quadratic_warmup: bool = field(
-        default=False,
-        metadata={"help": "Use quadratic warmup for cosine scheduling."},
-    )
-
-
-class AxolotlTrainer(Trainer):
-    """
-    Extend the base Trainer for axolotl helpers
-    """
-
-    args = None  # type: AxolotlTrainingArguments
-
-    def create_scheduler(
-        self, num_training_steps: int, optimizer: torch.optim.Optimizer = None
-    ):
-        """
-        Setup the scheduler. The optimizer of the trainer must have been set up either before this method is called or
-        passed as an argument.
-
-        Args:
-            num_training_steps (int): The number of training steps to do.
-            optimizer (torch.optim.Optimizer): The training optimizer
-        """
-
-        # fmt: off
-        if self.lr_scheduler is None:  # type: ignore  # pylint: disable=access-member-before-definition
-            # fmt: on
-            if (
-                self.args.lr_scheduler_type == "cosine"
-                and self.args.lr_quadratic_warmup is True
-            ):
-                self.lr_scheduler = get_cosine_schedule_with_quadratic_warmup(  # pylint: disable=attribute-defined-outside-init
-                    optimizer,
-                    num_warmup_steps=self.args.get_warmup_steps(num_training_steps),
-                    num_training_steps=num_training_steps,
-                )
-            else:
-                return super().create_scheduler(num_training_steps, optimizer)
-        return self.lr_scheduler
-
-
-class OneCycleLRSchedulerTrainer(AxolotlTrainer):
+class OneCycleLRSchedulerTrainer(Trainer):
     """
     Trainer subclass that uses the OneCycleLR scheduler
     """
@@ -157,9 +103,6 @@ def setup_trainer(cfg, train_dataset, eval_dataset, model, tokenizer):
         if cfg.fsdp_config:
             training_arguments_kwargs["fsdp_config"] = dict(cfg.fsdp_config)
 
-    if cfg.lr_quadratic_warmup is not None:
-        training_arguments_kwargs["lr_quadratic_warmup"] = cfg.lr_quadratic_warmup
-
     # deepspeed
     if (
         os.environ.get("ACCELERATE_USE_DEEPSPEED") == "true"
@@ -181,15 +124,11 @@ def setup_trainer(cfg, train_dataset, eval_dataset, model, tokenizer):
     if cfg.max_grad_norm:
         training_arguments_kwargs["max_grad_norm"] = cfg.max_grad_norm
 
-    if cfg.hub_model_id:
-        training_arguments_kwargs["hub_model_id"] = cfg.hub_model_id
+    if cfg.push_to_hub_model_id:
+        training_arguments_kwargs["push_to_hub_model_id"] = cfg.push_to_hub_model_id
         training_arguments_kwargs["push_to_hub"] = True
-        training_arguments_kwargs["hub_private_repo"] = True
 
-    if cfg.save_safetensors:
-        training_arguments_kwargs["save_safetensors"] = cfg.save_safetensors
-
-    training_args = AxolotlTrainingArguments(  # pylint: disable=unexpected-keyword-arg
+    training_args = transformers.TrainingArguments(
         per_device_train_batch_size=cfg.micro_batch_size,
         per_device_eval_batch_size=cfg.eval_batch_size
         if cfg.eval_batch_size is not None
@@ -198,9 +137,9 @@ def setup_trainer(cfg, train_dataset, eval_dataset, model, tokenizer):
         eval_accumulation_steps=cfg.gradient_accumulation_steps,
         num_train_epochs=cfg.num_epochs,
         learning_rate=cfg.learning_rate,
-        evaluation_strategy="steps" if cfg.val_set_size > 0 else "no",
+        evaluation_strategy="steps",
         save_strategy="steps" if cfg.save_steps else "epoch",
-        eval_steps=cfg.eval_steps if cfg.val_set_size > 0 else None,
+        eval_steps=cfg.eval_steps,
         save_steps=cfg.save_steps,
         output_dir=cfg.output_dir,
         save_total_limit=3,
@@ -327,7 +266,7 @@ def setup_trainer(cfg, train_dataset, eval_dataset, model, tokenizer):
 
         set_model_mem_id(model, tokenizer)
 
-        LOG.info("Adding landmark attention tokens to dataset")
+        logging.info("Adding landmark attention tokens to dataset")
 
         for dataset in [train_dataset, eval_dataset]:
             dataset = dataset.map(
@@ -339,7 +278,7 @@ def setup_trainer(cfg, train_dataset, eval_dataset, model, tokenizer):
     trainer_cls = (
         OneCycleLRSchedulerTrainer
         if cfg.lr_scheduler == "one_cycle" and (cfg.fsdp or cfg.adapter == "qlora")
-        else AxolotlTrainer
+        else transformers.Trainer
     )
     trainer = trainer_cls(
         model=model,

src/axolotl/utils/validation.py

@@ -4,8 +4,6 @@ import logging
 
 import torch
 
-LOG = logging.getLogger("axolotl")
-
 
 def validate_config(cfg):
     if cfg.gradient_accumulation_steps and cfg.batch_size:
@@ -13,7 +11,7 @@ def validate_config(cfg):
             "please set only one of gradient_accumulation_steps or batch_size"
         )
     if cfg.batch_size:
-        LOG.warning(
+        logging.warning(
             "%s\n%s",
             "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.",
@@ -46,10 +44,10 @@ def validate_config(cfg):
                 raise ValueError("Require cfg.load_in_4bit to be True for qlora")
 
     if not cfg.load_in_8bit and cfg.adapter == "lora":
-        LOG.warning("We recommend setting `load_in_8bit: true` for LORA finetuning")
+        logging.warning("We recommend setting `load_in_8bit: true` for LORA finetuning")
 
     if cfg.trust_remote_code:
-        LOG.warning(
+        logging.warning(
             "`trust_remote_code` is set to true. Please make sure that you reviewed the remote code/model."
         )
 
@@ -68,34 +66,31 @@ def validate_config(cfg):
 
     if cfg.flash_optimum is True:
         if cfg.adapter:
-            LOG.warning("BetterTransformers probably doesn't work with PEFT adapters")
+            logging.warning(
+                "BetterTransformers probably doesn't work with PEFT adapters"
+            )
         if cfg.fp16 or cfg.bf16:
             raise ValueError("AMP is not supported with BetterTransformer")
         if cfg.float16 is not True and cfg.bloat16 is not True:
-            LOG.warning(
+            logging.warning(
                 "You should probably set bfloat16 or float16 to true to "
                 "load the model in float16 for BetterTransformers"
             )
         if int(torch.__version__.split(".")[0]) < 2:
-            LOG.warning("torch>=2.0.0 required")
+            logging.warning("torch>=2.0.0 required")
             raise ValueError(
                 f"flash_optimum for BetterTransformers may not be used with {torch.__version__}"
             )
 
     if cfg.pretraining_dataset and cfg.group_by_length:
-        LOG.warning(
+        logging.warning(
             "You probably want to disable group_by_length as it will force a streamed dataset to download completely."
         )
 
-    if any([cfg.adam_beta1, cfg.adam_beta2, cfg.adam_epsilon]) and (
+    if any([cfg.adamw_beta1, cfg.adamw_beta2, cfg.adamw_epsilon]) and (
         not cfg.optimizer or "adamw" not in cfg.optimizer
     ):
-        LOG.warning("adamw hyperparameters found, but no adamw optimizer set")
-
-    if cfg.push_to_hub_model_id:
-        raise ValueError(
-            "push_to_hub_model_id is deprecated. Please use hub_model_id instead."
-        )
+        logging.warning("adamw hyperparameters found, but no adamw optimizer set")
 
     # TODO
     # MPT 7b

tests/test_prompt_tokenizers.py

@@ -17,7 +17,7 @@ from axolotl.prompt_tokenizers import (
 )
 from axolotl.prompters import AlpacaPrompter, PromptStyle, ShareGPTPrompter
 
-LOG = logging.getLogger("axolotl")
+logging.basicConfig(level="INFO")
 
 
 class TestPromptTokenizationStrategies(unittest.TestCase):
@@ -130,9 +130,8 @@ class InstructionWSystemPromptTokenizingStrategyTest(unittest.TestCase):
             "output": "Hi! How can I help?",
         }
         example = strat.tokenize_prompt(sample)
-        assert example["input_ids"][0:4] == [1, 835, 2184, 29901]  # "<s>### System:"
-        assert example["input_ids"][5:7] == [1509, 20118]  # "use cot"
-        assert example["input_ids"][9] == 11889  # USER
+        assert example["input_ids"][0:3] == [1, 671, 20118]  # <s>use cot
+        assert example["input_ids"][3] == 11889  # USER
 
 
 if __name__ == "__main__":

tests/test_prompters.py

@@ -70,7 +70,7 @@ class AlpacaPrompterTest(unittest.TestCase):
             )
         )
         assert "use cot" in res
-        assert res.startswith("### System:")
+        assert res.startswith("use cot")
         assert "### Instruction:" not in res
         assert "### Input:" not in res
         assert "alpacas" in res

tests/test_validation.py

@@ -268,7 +268,7 @@ class ValidationTest(unittest.TestCase):
         cfg = DictDefault(
             {
                 "optimizer": None,
-                "adam_epsilon": 0.0001,
+                "adamw_epsilon": 0.0001,
             }
         )
 
@@ -283,7 +283,7 @@ class ValidationTest(unittest.TestCase):
         cfg = DictDefault(
             {
                 "optimizer": "adafactor",
-                "adam_beta1": 0.0001,
+                "adamw_beta1": 0.0001,
             }
         )
 
@@ -298,9 +298,9 @@ class ValidationTest(unittest.TestCase):
         cfg = DictDefault(
             {
                 "optimizer": "adamw_bnb_8bit",
-                "adam_beta1": 0.9,
-                "adam_beta2": 0.99,
-                "adam_epsilon": 0.0001,
+                "adamw_beta1": 0.0001,
+                "adamw_beta2": 0.0001,
+                "adamw_epsilon": 0.0001,
             }
         )