fix the patch to work properly and work with FSDP

.github/workflows/main.yml

@@ -13,17 +13,17 @@ jobs:
       fail-fast: false
       matrix:
         include:
-          - cuda: 118
+          - cuda: cu118
             cuda_version: 11.8.0
             python_version: "3.9"
             pytorch: 2.0.1
             axolotl_extras:
-          - cuda: 118
+          - cuda: cu118
             cuda_version: 11.8.0
             python_version: "3.10"
             pytorch: 2.0.1
             axolotl_extras:
-          - cuda: 118
+          - cuda: cu118
             cuda_version: 11.8.0
             python_version: "3.9"
             pytorch: 2.0.1
@@ -49,11 +49,10 @@ jobs:
         with:
           context: .
           build-args: |
-            BASE_TAG=${{ github.ref_name }}-base-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}
-            CUDA=${{ matrix.cuda }}
+            BASE_TAG=${{ github.ref_name }}-base-py${{ matrix.python_version }}-${{ matrix.cuda }}-${{ matrix.pytorch }}
           file: ./docker/Dockerfile
           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 }}
   build-axolotl-runpod:
     needs: build-axolotl

.github/workflows/tests.yml

@@ -24,7 +24,7 @@ jobs:
 
       - name: Install dependencies
         run: |
-          pip install -e .[peft]
+          pip install -e .
           pip install -r requirements-tests.txt
 
       - name: Run tests

README.md

@@ -16,7 +16,6 @@ Axolotl is a tool designed to streamline the fine-tuning of various AI models, o
   - [LambdaLabs Installation](#lambdalabs)
 - [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)
   - [Inference](#inference)
@@ -69,9 +68,8 @@ Get started with Axolotl in just a few steps! This quickstart guide will walk yo
 
 ```bash
 git clone https://github.com/OpenAccess-AI-Collective/axolotl
-cd axolotl
 
-pip3 install -e .[flash-attn]
+pip3 install -e .
 pip3 install -U git+https://github.com/huggingface/peft.git
 
 # finetune lora
@@ -100,7 +98,7 @@ accelerate launch scripts/finetune.py examples/openllama-3b/lora.yml \
   ```
 
 - Conda/Pip venv
-  1. Install python >=**3.9**
+  1. Install python **3.9**
 
   2. Install pytorch stable https://pytorch.org/get-started/locally/
 
@@ -153,7 +151,9 @@ accelerate launch scripts/finetune.py examples/openllama-3b/lora.yml \
 
   pip3 install -e . # change depend on needs
   pip3 install protobuf==3.20.3
-  pip3 install -U --ignore-installed requests Pillow psutil scipy
+  pip3 install -U requests
+  pip3 install -U --ignore-installed psutil
+  pip3 install -U scipy
   pip3 install git+https://github.com/huggingface/peft.git # not for gptq
   ```
 
@@ -163,8 +163,6 @@ accelerate launch scripts/finetune.py examples/openllama-3b/lora.yml \
   ```
   </details>
 
-- Windows: Please use WSL or Docker!
-
 ### Dataset
 
 Axolotl supports a variety of dataset formats. Below are some of the formats you can use.
@@ -259,10 +257,6 @@ Have dataset(s) in one of the following format (JSONL recommended):
   ```json
   {"conversations": [{"role": "...", "value": "..."}]}
   ```
-- `metharme`: instruction, adds additional eos tokens
-  ```json
-  {"prompt": "...", "generation": "..."}
-  ```
 - `sharegpt_simple.load_role`: conversations where `role` is used instead of `from`
   ```json
   {"conversations": [{"role": "...", "value": "..."}]}
@@ -280,29 +274,11 @@ Have dataset(s) in one of the following format (JSONL recommended):
 
 #### How to add custom prompts
 
-Using yaml. Example:
-```yaml
-datasets:
-  - path: repo
-    type:
-      system_prompt: ""
-      no_input_format: |-
-        User: {instruction}<|end_of_turn|>
-        Assistant:
-      format: |-
-        User: {instruction}
-        {input}<|end_of_turn|>
-        Assistant:
-```
+  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>`.
 
-Using file:
-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>`.
+Optionally, download some datasets, see [data/README.md](data/README.md)
 
-#### How to use your custom pretokenized dataset
-
-- Do not pass a `type:`
-- Dataset must contain `input_ids`, `attention_mask`, `labels` in columns
 
 
 ### Config
@@ -330,20 +306,11 @@ See [examples](examples) for quick start. It is recommended to duplicate and mod
       name: enron_emails
       type: completion # format from earlier
 
-  # huggingface repo with multiple named configurations/subsets
-  datasets:
-    - path: bigcode/commitpackft
-      name:
-        - ruby
-        - python
-        - typescript
-      type: ... # unimplemented custom format
-
   # local
   datasets:
-    - path: data.jsonl # or json
-      ds_type: json # see other options below
-      type: alpaca
+    - path: json
+      data_files: data.jsonl # or json
+      type: alpaca # format from earlier
   ```
 
 - loading
@@ -418,39 +385,16 @@ fp16: true
 # Use CUDA tf32
 tf32: true # require >=ampere
 
-# No AMP (automatic mixed precision)
-bfloat16: true # require >=ampere
-float16: true
-
 # 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
   - 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>
-    ds_type: # Optional[str] (json|arrow|parquet) defines the datatype when path is a file
     data_files: # path to source data files
     shards: # number of shards to split data into
     name: # name of dataset configuration to load
 
-  # custom user prompt
-  - path: repo
-    type:
-      # the below are defaults. only set what's needed.
-      system_prompt: ""
-      field_system: system
-      field_instruction: instruction
-      field_output: input
-
-      # customizable to be single line or multi-line
-      system_format: "{system}"
-      # 'format' can include {input}
-      format: |-
-        User: {instruction} {input}
-        Assistant:
-      # 'no_input_format' cannot include {input}
-      no_input_format: "{instruction} "
-
 # axolotl attempts to save the dataset as an arrow after packing the data together so
 # subsequent training attempts load faster, relative path
 dataset_prepared_path: data/last_run_prepared
@@ -474,9 +418,6 @@ dataset_shard_idx:
 # the maximum length of an input to train with, this should typically be less than 2048
 # as most models have a token/context limit of 2048
 sequence_len: 2048
-# pad inputs so each step uses constant sized buffers
-# this will reduce memory fragmentation and may prevent OOMs, by re-using memory more efficiently
-pad_to_sequence_len:
 # max sequence length to concatenate training samples together up to
 # inspired by StackLLaMA. see https://huggingface.co/blog/stackllama#supervised-fine-tuning
 # FutureWarning: This will soon be DEPRECATED
@@ -511,12 +452,6 @@ lora_modules_to_save:
 lora_out_dir:
 lora_fan_in_fan_out: false
 
-# ReLoRA configuration
-# must use either 'lora' or 'qlora' adapter, and does not support fsdp or deepspeed
-relora_steps: # number of steps per ReLoRA restart
-relora_warmup_steps: # number of per-restart warmup steps
-relora_cpu_offload: # true to perform lora weight merges on cpu during restarts, for modest gpu memory savings
-
 # wandb configuration if you're using it
 wandb_mode: # "offline" to save run metadata locally and not sync to the server, "disabled" to turn off wandb
 wandb_project: # your wandb project name
@@ -537,9 +472,8 @@ warmup_steps: 100
 learning_rate: 0.00003
 lr_quadratic_warmup:
 logging_steps:
-save_strategy: # set to `no` to skip checkpoint saves
 save_steps: # leave empty to save at each epoch
-eval_steps: # leave empty to eval at each epoch
+eval_steps:
 save_total_limit: # checkpoints saved at a time
 max_steps:
 
@@ -625,14 +559,12 @@ fsdp_config:
 # Deepspeed config path
 deepspeed:
 
-# Advanced DDP Arguments
-ddp_timeout:
-ddp_bucket_cap_mb:
-ddp_broadcast_buffers:
-
 # Path to torch distx for optim 'adamw_anyprecision'
 torchdistx_path:
 
+# Set padding for data collator to 'longest'
+collator_pad_to_longest:
+
 # Set to HF dataset for type: 'completion' for streaming instead of pre-tokenize
 pretraining_dataset:
 
@@ -652,7 +584,7 @@ strict:
 
 Run
 ```bash
-accelerate launch scripts/finetune.py your_config.yml
+accelerate launch scripts/finetune.py configs/your_config.yml
 ```
 
 #### Multi-GPU
@@ -734,9 +666,7 @@ Please reduce any below
   - `gradient_accumulation_steps`
   - `sequence_len`
 
-> `failed (exitcode: -9)`
-
-Usually means your system has run out of system memory.
+> `failed (exitcode: -9)` usually means your system has run out of system memory.
 Similarly, you should consider reducing the same settings as when you run out of VRAM.
 Additionally, look into upgrading your system RAM which should be simpler than GPU upgrades.
 

data/README.md

@@ -0,0 +1,24 @@
+
+## Download some datasets
+```shell
+curl https://raw.githubusercontent.com/tloen/alpaca-lora/main/alpaca_data_gpt4.json -o data/raw/alpaca_data_gpt4.json
+curl https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/ShareGPT_V3_unfiltered_cleaned_split.json -L -o data/raw/vicuna_cleaned.json
+curl https://github.com/teknium1/GPTeacher/blob/main/Instruct/gpt4-instruct-similarity-0.6-dataset.json?raw=true -L -o data/raw/gpt4-instruct-similarity-0.6-dataset.json
+curl https://github.com/teknium1/GPTeacher/blob/main/Roleplay/roleplay-similarity_0.6-instruct-dataset.json?raw=true -L -o data/raw/roleplay-similarity_0.6-instruct-dataset.json
+```
+
+## Convert the JSON data files to JSONL.
+
+```shell
+python3 ./scripts/alpaca_json_to_jsonl.py --file data/alpaca_data_gpt4.json --output data/alpaca_data_gpt4.jsonl
+python3 ./scripts/alpaca_json_to_jsonl.py --file data/raw/vicuna_cleaned.json --output data/vicuna_cleaned.jsonl
+python3 ./scripts/alpaca_json_to_jsonl.py --file data/raw/roleplay-similarity_0.6-instruct-dataset.json --output data/roleplay-similarity_0.6-instruct-dataset.jsonl
+python3 ./scripts/alpaca_json_to_jsonl.py --file data/raw/gpt4-instruct-similarity-0.6-dataset.json --output data/gpt4-instruct-similarity-0.6-dataset.jsonl
+```
+---
+
+Using JSONL makes it easier to subset the data if you want a smaller training set, i.e get 2000 random examples.
+
+```shell
+shuf -n2000 data/vicuna_cleaned.jsonl > data/vicuna_cleaned.subset0.jsonl
+```

data/raw/.gitignore

@@ -0,0 +1 @@
+**

deepspeed/zero2.json

@@ -1,46 +0,0 @@
-{
-    "zero_optimization": {
-      "stage": 2,
-      "offload_optimizer": {
-        "device": "cpu"
-      },
-      "contiguous_gradients": true,
-      "overlap_comm": true
-    },
-    "bf16": {
-      "enabled": "auto"
-    },
-    "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": [
-          0.9,
-          0.999
-        ],
-        "eps": 1e-8,
-        "weight_decay": "auto"
-      }
-    },
-    "scheduler": {
-      "type": "WarmupDecayLR",
-      "params": {
-        "warmup_min_lr": "auto",
-        "warmup_max_lr": "auto",
-        "warmup_num_steps": "auto",
-        "total_num_steps": "auto"
-      }
-    },
-    "train_batch_size": "auto",
-    "train_micro_batch_size_per_gpu": "auto",
-    "wall_clock_breakdown": false
-}

deepspeed/zero3.json

@@ -35,7 +35,10 @@
     "type": "AdamW",
     "params": {
       "lr": "auto",
-      "betas": "auto",
+      "betas": [
+        0.9,
+        0.95
+      ],
       "eps": 1e-8,
       "weight_decay": "auto"
     }

docker/Dockerfile

@@ -11,14 +11,14 @@ RUN apt-get update && \
 
 WORKDIR /workspace
 
-RUN pip3 install "peft @ git+https://github.com/huggingface/peft.git@main"
+RUN pip3 install --force-reinstall "peft @ git+https://github.com/huggingface/peft.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 && \
     if [ "$AXOLOTL_EXTRAS" != "" ] ; then \
-        pip install -e .[flash-attn,$AXOLOTL_EXTRAS]; \
+        pip install -e .[$AXOLOTL_EXTRAS]; \
     else \
-        pip install -e .[flash-attn]; \
+        pip install -e .; \
     fi
 
 # fix so that git fetch/pull from remote works

docker/Dockerfile-base

@@ -31,6 +31,26 @@ 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
 
+
+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"
+
+RUN git clone https://github.com/Dao-AILab/flash-attention.git && \
+    cd flash-attention && \
+    git checkout v2.0.4  && \
+    python3 setup.py bdist_wheel && \
+    cd csrc/fused_dense_lib && \
+    python3 setup.py bdist_wheel && \
+    cd ../xentropy && \
+    python3 setup.py bdist_wheel && \
+    cd ../rotary && \
+    python3 setup.py bdist_wheel && \
+    cd ../layer_norm && \
+    python3 setup.py bdist_wheel
+
 FROM base-builder AS deepspeed-builder
 
 ARG TORCH_CUDA_ARCH_LIST="7.0 7.5 8.0 8.6 9.0+PTX"
@@ -70,8 +90,13 @@ 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
+COPY --from=flash-attn-builder /workspace/flash-attention/dist/flash_attn-*.whl wheels
+COPY --from=flash-attn-builder /workspace/flash-attention/csrc/fused_dense_lib/dist/fused_dense_lib-*.whl wheels
+COPY --from=flash-attn-builder /workspace/flash-attention/csrc/xentropy/dist/xentropy_cuda_lib-*.whl wheels
+COPY --from=flash-attn-builder /workspace/flash-attention/csrc/rotary/dist/rotary_emb-*.whl wheels
+COPY --from=flash-attn-builder /workspace/flash-attention/csrc/layer_norm/dist/dropout_layer_norm-*.whl wheels
 
-RUN pip3 install wheels/deepspeed-*.whl
+RUN pip3 install wheels/deepspeed-*.whl wheels/flash_attn-*.whl wheels/fused_dense_lib-*.whl wheels/xentropy_cuda_lib-*.whl wheels/rotary_emb-*.whl wheels/dropout_layer_norm-*.whl
 RUN cd /workspace/builds/bitsandbytes && python3 setup.py install
 RUN git lfs install --skip-repo
 RUN pip3 install awscli && \

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

@@ -1,67 +0,0 @@
-base_model: codellama/CodeLlama-13b-hf
-base_model_config: codellama/CodeLlama-13b-hf
-model_type: LlamaForCausalLM
-tokenizer_type: CodeLlamaTokenizer
-is_llama_derived_model: true
-
-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: 100000
-sample_packing: true
-
-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_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: 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: 20
-save_steps:
-debug:
-deepspeed:
-weight_decay: 0.0
-fsdp:
-fsdp_config:
-special_tokens:
-  bos_token: "<s>"
-  eos_token: "</s>"
-  unk_token: "<unk>"

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

@@ -1,69 +0,0 @@
-base_model: codellama/CodeLlama-13b-hf
-base_model_config: codellama/CodeLlama-13b-hf
-model_type: LlamaForCausalLM
-tokenizer_type: CodeLlamaTokenizer
-is_llama_derived_model: true
-
-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: 100000
-sample_packing: true
-
-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_entity:
-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: 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: 20
-save_steps:
-debug:
-deepspeed:
-weight_decay: 0.0
-fsdp:
-fsdp_config:
-special_tokens:
-  bos_token: "<s>"
-  eos_token: "</s>"
-  unk_token: "<unk>"

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

@@ -1,67 +0,0 @@
-base_model: codellama/CodeLlama-34b-hf
-base_model_config: codellama/CodeLlama-34b-hf
-model_type: LlamaForCausalLM
-tokenizer_type: CodeLlamaTokenizer
-is_llama_derived_model: true
-
-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: 100000
-sample_packing: true
-
-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_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: 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: 20
-save_steps:
-debug:
-deepspeed:
-weight_decay: 0.0
-fsdp:
-fsdp_config:
-special_tokens:
-  bos_token: "<s>"
-  eos_token: "</s>"
-  unk_token: "<unk>"

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

@@ -1,69 +0,0 @@
-base_model: codellama/CodeLlama-34b-hf
-base_model_config: codellama/CodeLlama-34b-hf
-model_type: LlamaForCausalLM
-tokenizer_type: CodeLlamaTokenizer
-is_llama_derived_model: true
-
-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: 100000
-sample_packing: true
-
-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_entity:
-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: 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: 20
-save_steps:
-debug:
-deepspeed:
-weight_decay: 0.0
-fsdp:
-fsdp_config:
-special_tokens:
-  bos_token: "<s>"
-  eos_token: "</s>"
-  unk_token: "<unk>"

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

@@ -1,67 +0,0 @@
-base_model: codellama/CodeLlama-7b-hf
-base_model_config: codellama/CodeLlama-7b-hf
-model_type: LlamaForCausalLM
-tokenizer_type: CodeLlamaTokenizer
-is_llama_derived_model: true
-
-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: 100000
-sample_packing: true
-
-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_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: 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: 20
-save_steps:
-debug:
-deepspeed:
-weight_decay: 0.0
-fsdp:
-fsdp_config:
-special_tokens:
-  bos_token: "<s>"
-  eos_token: "</s>"
-  unk_token: "<unk>"

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

@@ -1,69 +0,0 @@
-base_model: codellama/CodeLlama-7b-hf
-base_model_config: codellama/CodeLlama-7b-hf
-model_type: LlamaForCausalLM
-tokenizer_type: CodeLlamaTokenizer
-is_llama_derived_model: true
-
-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: 100000
-sample_packing: true
-
-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_entity:
-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: 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: 20
-save_steps:
-debug:
-deepspeed:
-weight_decay: 0.0
-fsdp:
-fsdp_config:
-special_tokens:
-  bos_token: "<s>"
-  eos_token: "</s>"
-  unk_token: "<unk>"

examples/code-llama/README.md

@@ -1,22 +0,0 @@
-# Overview
-
-This is an example of CodeLLaMA configuration for 7b, 13b and 34b.
-
-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
-
-The 34b variant does not fit on 24GB of VRAM - you will need something with +40 gb VRAM that also supports flash attention v2 - A6000 or A100 are good choices.
-
-```shell
-accelerate launch scripts/finetune.py examples/code-llama/[MODEL_SIZE]/qlora.yml
-
-```
-or
-
-```shell
-accelerate launch scripts/finetune.py examples/code-llama/[MODEL_SIZE]/lora.yml
-
-```

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

@@ -57,7 +57,7 @@ weight_decay: 0.0001
 fsdp:
 fsdp_config:
 tokens:
-  pad_token: "<pad>"
+  pad_token: "[PAD]"
   bos_token: "<s>"
   eos_token: "</s>"
   unk_token: "<unk>"

examples/llama-2/relora.yml

@@ -1,73 +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
-is_llama_derived_model: true
-
-load_in_8bit: false
-load_in_4bit: true
-strict: false
-
-datasets:
-  - path: teknium/GPT4-LLM-Cleaned
-    type: alpaca
-dataset_prepared_path: last_run_prepared
-val_set_size: 0.01
-output_dir: ./relora-out
-
-adapter: qlora
-lora_model_dir:
-
-sequence_len: 4096
-sample_packing: true
-
-lora_r: 8
-lora_alpha: 16
-lora_dropout: 0.05
-lora_target_modules:
-lora_target_linear: true
-lora_fan_in_fan_out:
-
-relora_steps: 150
-relora_warmup_steps: 10
-relora_cpu_offload: false
-
-wandb_project:
-wandb_entity:
-wandb_watch:
-wandb_run_id:
-wandb_log_model:
-
-gradient_accumulation_steps: 4
-micro_batch_size: 4
-num_epochs: 3
-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
-
-warmup_steps: 10
-eval_steps: 20
-save_steps: 50
-debug:
-deepspeed:
-weight_decay: 0.0
-fsdp:
-fsdp_config:
-special_tokens:
-  bos_token: "<s>"
-  eos_token: "</s>"
-  unk_token: "<unk>"

examples/pythia-12b/config.yml

@@ -47,3 +47,4 @@ local_rank:
 gradient_checkpointing: true
 fsdp:
 fsdp_config:
+collator_pad_to_longest: true

requirements.txt

@@ -1,23 +1,20 @@
-packaging
 peft @ git+https://github.com/huggingface/peft.git
 transformers @ git+https://github.com/huggingface/transformers.git
 bitsandbytes>=0.41.1
 accelerate @ git+https://github.com/huggingface/accelerate@2a289f6108e77a77a4efffb3f6316bc98538413b
 addict
-evaluate
 fire
-PyYAML>=6.0
+PyYAML==6.0
 datasets
-flash-attn>=2.0.8
+accelerate>=0.19.0
 sentencepiece
 wandb
 einops
 xformers
 optimum
 hf_transfer
-colorama
 numba
-numpy>=1.24.4
+numpy==1.24.4
 # qlora things
 bert-score==0.3.13
 evaluate==0.4.0
@@ -25,4 +22,3 @@ rouge-score==0.1.2
 scipy
 scikit-learn==1.2.2
 pynvml
-art

scripts/alpaca_json_to_jsonl.py

@@ -0,0 +1,52 @@
+"""Module to convert json file to jsonl"""
+
+import os
+import sys
+from pathlib import Path
+from typing import Optional, Union
+
+import fire
+
+from axolotl.convert import (
+    FileReader,
+    FileWriter,
+    JsonlSerializer,
+    JsonParser,
+    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__), ".."))
+src_dir = os.path.join(project_root, "src")
+sys.path.insert(0, src_dir)
+
+
+def main(
+    file: Path,
+    output: Optional[Path] = None,
+    to_stdout: Optional[bool] = False,
+):
+    """
+    Convert a json file to jsonl
+    """
+
+    file_reader = FileReader()
+    writer: Union[StdoutWriter, FileWriter]
+    if to_stdout or output is None:
+        writer = StdoutWriter()
+    else:
+        writer = FileWriter(output)
+    json_parser = JsonParser()
+    jsonl_serializer = JsonlSerializer()
+
+    converter = JsonToJsonlConverter(file_reader, writer, json_parser, jsonl_serializer)
+
+    converter.convert(file, output)
+
+
+if __name__ == "__main__":
+    fire.Fire(main)

scripts/finetune.py

@@ -4,28 +4,27 @@ import importlib
 import logging
 import os
 import random
+import signal
 import sys
 from pathlib import Path
 from typing import Any, Dict, List, Optional, Union
 
 import fire
 import torch
-import transformers
 import yaml
 
 # add src to the pythonpath so we don't need to pip install this
-from art import text2art
+from optimum.bettertransformer import BetterTransformer
 from transformers import GenerationConfig, TextStreamer
 
-from axolotl.common.cli import TrainerCliArgs, load_model_and_tokenizer
 from axolotl.logging_config import configure_logging
-from axolotl.train import TrainDatasetMeta, train
 from axolotl.utils.config import normalize_config, validate_config
 from axolotl.utils.data import prepare_dataset
 from axolotl.utils.dict import DictDefault
 from axolotl.utils.distributed import is_main_process
-from axolotl.utils.models import load_model_config, load_tokenizer
+from axolotl.utils.models import load_model, load_tokenizer
 from axolotl.utils.tokenization import check_dataset_labels
+from axolotl.utils.trainer import setup_trainer
 from axolotl.utils.wandb import setup_wandb_env_vars
 
 project_root = os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))
@@ -38,12 +37,15 @@ LOG = logging.getLogger("axolotl.scripts")
 os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = "1"
 
 
-def print_axolotl_text_art(suffix=None):
-    font = "nancyj"
-    ascii_text = "  axolotl"
-    if suffix:
-        ascii_text += f"  x  {suffix}"
-    ascii_art = text2art(" axolotl", font=font)
+def print_axolotl_text_art():
+    ascii_art = """
+                           dP            dP   dP
+                           88            88   88
+.d8888b. dP.  .dP .d8888b. 88 .d8888b. d8888P 88
+88'  `88  `8bd8'  88'  `88 88 88'  `88   88   88
+88.  .88  .d88b.  88.  .88 88 88.  .88   88   88
+`88888P8 dP'  `dP `88888P' dP `88888P'   dP   dP
+"""
 
     if is_main_process():
         print(ascii_art)
@@ -58,45 +60,7 @@ def get_multi_line_input() -> Optional[str]:
     return instruction
 
 
-def do_merge_lora(
-    *,
-    cfg: DictDefault,
-    cli_args: TrainerCliArgs,
-):
-    model, tokenizer = load_model_and_tokenizer(cfg=cfg, cli_args=cli_args)
-    safe_serialization = cfg.save_safetensors is True
-
-    LOG.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")
-        model.save_pretrained(
-            str(Path(cfg.output_dir) / "merged"),
-            safe_serialization=safe_serialization,
-        )
-        tokenizer.save_pretrained(str(Path(cfg.output_dir) / "merged"))
-
-
-def shard(
-    *,
-    cfg: DictDefault,
-    cli_args: TrainerCliArgs,
-):
-    model, _ = load_model_and_tokenizer(cfg=cfg, cli_args=cli_args)
-    safe_serialization = cfg.save_safetensors is True
-    LOG.debug("Re-saving model w/ sharding")
-    model.save_pretrained(cfg.output_dir, safe_serialization=safe_serialization)
-
-
-def do_inference(
-    *,
-    cfg: DictDefault,
-    cli_args: TrainerCliArgs,
-):
-    model, tokenizer = load_model_and_tokenizer(cfg=cfg, cli_args=cli_args)
-    prompter = cli_args.prompter
+def do_inference(cfg, model, tokenizer, prompter: Optional[str]):
     default_tokens = {"unk_token": "<unk>", "bos_token": "<s>", "eos_token": "</s>"}
 
     for token, symbol in default_tokens.items():
@@ -118,8 +82,6 @@ def do_inference(
             max_seq_len=255, mem_freq=50, top_k=5, max_cache_size=None
         )
 
-    model = model.to(cfg.device)
-
     while True:
         print("=" * 80)
         # support for multiline inputs
@@ -171,10 +133,6 @@ def choose_config(path: Path):
             "No YAML config files found in the specified directory. Are you using a .yml extension?"
         )
 
-    if len(yaml_files) == 1:
-        print(f"Using default YAML file '{yaml_files[0]}'")
-        return yaml_files[0]
-
     print("Choose a YAML file:")
     for idx, file in enumerate(yaml_files):
         print(f"{idx + 1}. {file}")
@@ -197,7 +155,12 @@ def check_not_in(list1: List[str], list2: Union[Dict[str, Any], List[str]]) -> b
     return not any(el in list2 for el in list1)
 
 
-def load_cfg(config: Path = Path("examples/"), **kwargs):
+def train(
+    config: Path = Path("configs/"),
+    prepare_ds_only: bool = False,
+    **kwargs,
+):
+    print_axolotl_text_art()
     if Path(config).is_dir():
         config = choose_config(config)
 
@@ -216,72 +179,137 @@ def load_cfg(config: Path = Path("examples/"), **kwargs):
             else:
                 cfg[k] = kwargs[k]
 
-    model_config = load_model_config(cfg)
-
-    # figure out if the model is llama
-    cfg.is_llama_derived_model = (
-        (hasattr(model_config, "model_type") and model_config.model_type == "llama")
-        or cfg.is_llama_derived_model
-        or "llama" in cfg.base_model
-        or (cfg.model_type and "llama" in cfg.model_type.lower())
-    )
     validate_config(cfg)
 
     normalize_config(cfg)
 
     setup_wandb_env_vars(cfg)
-    return cfg
 
-
-def load_datasets(
-    *,
-    cfg: DictDefault,
-    cli_args: TrainerCliArgs,
-) -> TrainDatasetMeta:
+    # load the tokenizer first
+    LOG.info(f"loading tokenizer... {cfg.tokenizer_config or cfg.base_model_config}")
     tokenizer = load_tokenizer(cfg)
 
-    train_dataset, eval_dataset, total_num_steps = prepare_dataset(cfg, tokenizer)
+    if (
+        check_not_in(["shard", "merge_lora"], kwargs) and not cfg.inference
+    ):  # don't need to load dataset for these
+        train_dataset, eval_dataset, total_num_steps = prepare_dataset(cfg, tokenizer)
 
-    if cli_args.debug or cfg.debug:
+    if cfg.debug or "debug" in kwargs:
         LOG.info("check_dataset_labels...")
         check_dataset_labels(
             train_dataset.select(
-                [
-                    random.randrange(0, len(train_dataset) - 1)  # nosec
-                    for _ in range(cli_args.debug_num_examples)
-                ]
+                [random.randrange(0, len(train_dataset) - 1) for _ in range(5)]  # nosec
             ),
             tokenizer,
-            num_examples=cli_args.debug_num_examples,
-            text_only=cli_args.debug_text_only,
         )
 
-    return TrainDatasetMeta(
-        train_dataset=train_dataset,
-        eval_dataset=eval_dataset,
-        total_num_steps=total_num_steps,
+    if prepare_ds_only:
+        LOG.info("Finished preparing dataset. Exiting...")
+        return
+
+    # Load the model and tokenizer
+    LOG.info("loading model and (optionally) peft_config...")
+    model, peft_config = load_model(cfg, tokenizer)
+
+    safe_serialization = cfg.save_safetensors is True
+
+    if "merge_lora" in kwargs and cfg.adapter is not None:
+        LOG.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")
+            model.save_pretrained(
+                str(Path(cfg.output_dir) / "merged"),
+                safe_serialization=safe_serialization,
+            )
+            tokenizer.save_pretrained(str(Path(cfg.output_dir) / "merged"))
+        return
+
+    if cfg.inference:
+        LOG.info("calling do_inference function")
+        prompter: Optional[str] = "AlpacaPrompter"
+        if "prompter" in kwargs:
+            if kwargs["prompter"] == "None":
+                prompter = None
+            else:
+                prompter = kwargs["prompter"]
+        do_inference(cfg, model, tokenizer, prompter=prompter)
+        return
+
+    if "shard" in kwargs:
+        model.save_pretrained(cfg.output_dir, safe_serialization=safe_serialization)
+        return
+
+    trainer = setup_trainer(
+        cfg, train_dataset, eval_dataset, model, tokenizer, total_num_steps
     )
 
+    model.config.use_cache = False
 
-def do_cli(config: Path = Path("examples/"), **kwargs):
-    print_axolotl_text_art()
-    parsed_cfg = load_cfg(config, **kwargs)
-    parser = transformers.HfArgumentParser((TrainerCliArgs))
-    parsed_cli_args, _ = parser.parse_args_into_dataclasses(
-        return_remaining_strings=True
-    )
-    if parsed_cli_args.inference:
-        do_inference(cfg=parsed_cfg, cli_args=parsed_cli_args)
-    elif parsed_cli_args.merge_lora:
-        do_merge_lora(cfg=parsed_cfg, cli_args=parsed_cli_args)
-    elif parsed_cli_args.shard:
-        shard(cfg=parsed_cfg, cli_args=parsed_cli_args)
+    if torch.__version__ >= "2" and sys.platform != "win32":
+        LOG.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}")
+        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
+    if cfg.local_rank == 0:
+
+        def terminate_handler(_, __, model):
+            if cfg.flash_optimum:
+                model = BetterTransformer.reverse(model)
+            model.save_pretrained(cfg.output_dir, safe_serialization=safe_serialization)
+            sys.exit(0)
+
+        signal.signal(
+            signal.SIGINT, lambda signum, frame: terminate_handler(signum, frame, model)
+        )
+
+    LOG.info("Starting trainer...")
+    if cfg.group_by_length:
+        LOG.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 = [
+            str(cp) for cp in Path(cfg.output_dir).glob("checkpoint-*")
+        ]
+        if len(possible_checkpoints) > 0:
+            sorted_paths = sorted(
+                possible_checkpoints,
+                key=lambda path: int(path.split("-")[-1]),
+            )
+            resume_from_checkpoint = sorted_paths[-1]
+            LOG.info(
+                f"Using Auto-resume functionality to start with checkpoint at {resume_from_checkpoint}"
+            )
+
+    if not Path(cfg.output_dir).is_dir():
+        os.makedirs(cfg.output_dir, exist_ok=True)
+    tokenizer.save_pretrained(cfg.output_dir)
+    if cfg.flash_optimum:
+        with torch.backends.cuda.sdp_kernel(
+            enable_flash=True, enable_math=True, enable_mem_efficient=True
+        ):
+            trainer.train(resume_from_checkpoint=resume_from_checkpoint)
     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)
+        trainer.train(resume_from_checkpoint=resume_from_checkpoint)
+
+    LOG.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:
+        trainer.save_model(cfg.output_dir)
+    elif cfg.local_rank == 0:
+        if cfg.flash_optimum:
+            model = BetterTransformer.reverse(model)
+        model.save_pretrained(cfg.output_dir, safe_serialization=safe_serialization)
 
 
 if __name__ == "__main__":
-    fire.Fire(do_cli)
+    fire.Fire(train)

setup.py

@@ -7,7 +7,6 @@ with open("./requirements.txt", encoding="utf-8") as requirements_file:
     # don't include peft yet until we check the int4
     # need to manually install peft for now...
     reqs = [r.strip() for r in requirements_file.readlines() if "peft" not in r]
-    reqs = [r for r in reqs if "flash-attn" not in r]
     reqs = [r for r in reqs if r and r[0] != "#"]
     for r in reqs:
         install_requires.append(r)
@@ -26,14 +25,9 @@ setup(
         "gptq_triton": [
             "alpaca_lora_4bit[triton] @ git+https://github.com/winglian/alpaca_lora_4bit.git@setup_pip",
         ],
-        "flash-attn": [
-            "flash-attn==2.0.8",
-        ],
         "extras": [
+            "flash-attn",
             "deepspeed",
         ],
-        "peft": [
-            "peft @ git+https://github.com/huggingface/peft.git",
-        ],
     },
 )

src/axolotl/common/cli.py

@@ -1,43 +0,0 @@
-"""
-shared module for cli specific things
-"""
-
-import logging
-from dataclasses import dataclass, field
-from typing import Optional
-
-from axolotl.logging_config import configure_logging
-from axolotl.utils.dict import DictDefault
-from axolotl.utils.models import load_model, load_tokenizer
-
-configure_logging()
-LOG = logging.getLogger("axolotl.common.cli")
-
-
-@dataclass
-class TrainerCliArgs:
-    """
-    dataclass representing the various non-training arguments
-    """
-
-    debug: bool = field(default=False)
-    debug_text_only: bool = field(default=False)
-    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)
-
-
-def load_model_and_tokenizer(
-    *,
-    cfg: DictDefault,
-    cli_args: TrainerCliArgs,
-):
-    LOG.info(f"loading tokenizer... {cfg.tokenizer_config or cfg.base_model_config}")
-    tokenizer = load_tokenizer(cfg)
-    LOG.info("loading model and (optionally) peft_config...")
-    model, _ = load_model(cfg, tokenizer, inference=cli_args.inference)
-
-    return model, tokenizer

src/axolotl/logging_config.py

@@ -1,42 +1,16 @@
-"""
-Common logging module for axolotl
-"""
+"""Logging configuration settings"""
 
 import os
 import sys
-from logging import Formatter
 from logging.config import dictConfig
 from typing import Any, Dict
 
-from colorama import Fore, Style, init
-
-
-class ColorfulFormatter(Formatter):
-    """
-    Formatter to add coloring to log messages by log type
-    """
-
-    COLORS = {
-        "WARNING": Fore.YELLOW,
-        "ERROR": Fore.RED,
-        "CRITICAL": Fore.RED + Style.BRIGHT,
-    }
-
-    def format(self, record):
-        log_message = super().format(record)
-        return self.COLORS.get(record.levelname, "") + log_message + Fore.RESET
-
-
 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",
         },
-        "colorful": {
-            "()": ColorfulFormatter,
-            "format": "[%(asctime)s] [%(levelname)s] [%(name)s.%(funcName)s:%(lineno)d] [PID:%(process)d] %(message)s",
-        },
     },
     "filters": {},
     "handlers": {
@@ -46,25 +20,14 @@ DEFAULT_LOGGING_CONFIG: Dict[str, Any] = {
             "filters": [],
             "stream": sys.stdout,
         },
-        "color_console": {
-            "class": "logging.StreamHandler",
-            "formatter": "colorful",
-            "filters": [],
-            "stream": sys.stdout,
-        },
     },
     "root": {"handlers": ["console"], "level": os.getenv("LOG_LEVEL", "INFO")},
     "loggers": {
-        "axolotl": {
-            "handlers": ["color_console"],
-            "level": "DEBUG",
-            "propagate": False,
-        },
+        "axolotl": {"handlers": ["console"], "level": "DEBUG", "propagate": False},
     },
 }
 
 
 def configure_logging():
     """Configure with default logging"""
-    init()  # Initialize colorama
     dictConfig(DEFAULT_LOGGING_CONFIG)

src/axolotl/monkeypatch/llama_attn_hijack_flash.py

@@ -158,7 +158,7 @@ def flashattn_forward(
     else:
         # turn off FA causal mask after first inference autoregressive iteration
         # only on first autoregressive step q,k,v have same seqlen
-        is_causal = key_states.shape == query_states.shape
+        is_causal = past_key_value is not None
 
     if cu_seqlens is not None and max_seqlen is not None:
         # special handling using sample packing
@@ -169,7 +169,7 @@ def flashattn_forward(
         qkv = rearrange(qkv, "b s ... -> (b s) ...")
 
         output = flash_attn_varlen_qkvpacked_func(
-            qkv, cu_seqlens, max_seqlen, 0.0, softmax_scale=None, causal=True
+            qkv, cu_seqlens, max_seqlen, 0.0, softmax_scale=None, causal=is_causal
         )
         output = rearrange(output, "(b s) ... -> b s ...", b=bsz)
     elif query_states.shape == key_states.shape:

src/axolotl/monkeypatch/relora.py

@@ -1,393 +0,0 @@
-"""Implements the ReLoRA training procedure from https://arxiv.org/abs/2307.05695, minus the initial full fine-tune."""
-import glob
-import json
-import logging
-import os.path
-import shutil
-from pathlib import Path
-from typing import Dict, List, Sequence
-
-import bitsandbytes as bnb
-import peft
-import safetensors.torch as st
-import torch
-from huggingface_hub import snapshot_download
-from torch.optim.lr_scheduler import LRScheduler
-from torch.optim.optimizer import Optimizer
-from transformers import (
-    TrainerCallback,
-    TrainerControl,
-    TrainerState,
-    TrainingArguments,
-)
-from transformers.trainer_utils import PREFIX_CHECKPOINT_DIR
-
-from axolotl.utils.dict import DictDefault
-from axolotl.utils.distributed import is_main_process
-
-LOG = logging.getLogger("axolotl.relora")
-
-
-def reset_optimizer(optimizer: torch.optim.Optimizer):
-    for group in optimizer.param_groups:
-        for param in group["params"]:
-            param_state = optimizer.state[param]
-            for key in param_state:
-                if "qmap" in key:
-                    continue
-
-                if key == "step" and isinstance(param_state[key], int):
-                    param_state[key] = 0
-                else:
-                    param_state[key] = torch.zeros_like(param_state[key])
-
-
-class ReLoRACallback(TrainerCallback):
-    """Callback to merge LoRA weights into the base model and save full-weight checkpoints"""
-
-    def __init__(self, cfg: DictDefault):
-        self.relora_steps = cfg.relora_steps
-        self.cpu_offload = cfg.relora_cpu_offload
-        self.quantized = cfg.load_in_4bit or cfg.load_in_8bit
-        self.last_full_model = cfg.base_model
-        self.resume_from_checkpoint = cfg.resume_from_checkpoint
-
-        if not os.path.exists(self.last_full_model):
-            self.last_full_model = str(Path(snapshot_download(cfg.base_model)))
-
-        assert os.path.exists(
-            self.last_full_model
-        ), "for ReLORA base_model must be a local path"
-
-        self.num_lora_restarts = 0
-        self.need_full_save = False
-
-    def on_train_begin(
-        self,
-        _args: TrainingArguments,
-        _state: TrainerState,
-        control: TrainerControl,
-        model: peft.LoraModel,
-        **_kwargs,
-    ):
-        if self.resume_from_checkpoint:
-            weight_path = os.path.join(self.resume_from_checkpoint, "relora")
-            if not os.path.exists(weight_path):
-                LOG.warning(
-                    "Resuming ReLoRA from checkpoint, but no full-weight save found"
-                )
-            else:
-                LOG.info(f"Loading adjusted base weights from {weight_path}")
-                load_weight_checkpoint(model, weight_path)
-        return control
-
-    def on_step_begin(
-        self,
-        args: TrainingArguments,
-        state: TrainerState,
-        control: TrainerControl,
-        model: peft.LoraModel,
-        optimizer: torch.optim.Optimizer,
-        **_kwargs,
-    ):
-        if state.global_step > 0 and state.global_step % self.relora_steps == 0:
-            checkpoint_folder = os.path.join(
-                args.output_dir,
-                f"{PREFIX_CHECKPOINT_DIR}-{state.global_step}",
-                "relora",
-            )
-
-            with torch.no_grad():
-                merge_and_save(
-                    model,
-                    self.last_full_model,
-                    checkpoint_folder,
-                    reinit=True,
-                    quantized=self.quantized,
-                    actually_save=is_main_process(),
-                    cpu_offload=self.cpu_offload,
-                )
-                reset_optimizer(optimizer)
-
-            if self.quantized:
-                self.last_full_model = checkpoint_folder
-            self.num_lora_restarts += 1
-
-        return control
-
-    def on_save(
-        self,
-        args: TrainingArguments,
-        state: TrainerState,
-        control: TrainerControl,
-        model: peft.LoraModel,
-        **_kwargs,
-    ):
-        checkpoint_folder = os.path.join(
-            args.output_dir, f"{PREFIX_CHECKPOINT_DIR}-{state.global_step}", "relora"
-        )
-        if (
-            state.global_step >= self.relora_steps
-            and state.global_step % self.relora_steps != 0
-        ):
-            if self.quantized:
-                if is_main_process() and self.last_full_model != checkpoint_folder:
-                    # ensure the latest full parameter save is in the latest checkpoint
-                    # folder, so that automatic pruning of checkpoints does not remove it
-                    LOG.info(f"moving last full parameter save to {checkpoint_folder}")
-                    os.makedirs(checkpoint_folder, exist_ok=True)
-                    chunks = glob.glob(
-                        f"{self.last_full_model}/model*.safetensors"
-                    ) + glob.glob(f"{self.last_full_model}/model*.index.json")
-                    for path in chunks:
-                        new_path = os.path.abspath(shutil.move(path, checkpoint_folder))
-                        try:
-                            os.symlink(new_path, path)
-                        except OSError:
-                            # probably on windows without permission to symlink
-                            pass
-
-                    self.last_full_model = checkpoint_folder
-            else:
-                model.model.save_pretrained(checkpoint_folder, safe_serialization=True)
-
-        return control
-
-    def on_log(
-        self,
-        _args: TrainingArguments,
-        _state: TrainerState,
-        control: TrainerControl,
-        logs: Dict[str, float],
-        **_kwargs,
-    ):
-        logs["num_lora_restarts"] = self.num_lora_restarts
-        return control
-
-    def on_train_end(
-        self,
-        args: TrainingArguments,
-        _state: TrainerState,
-        control: TrainerControl,
-        model: peft.LoraModel,
-        **_kwargs,
-    ):
-        if self.quantized:
-            # perform final merge and save
-            with torch.no_grad():
-                merge_and_save(
-                    model,
-                    self.last_full_model,
-                    args.output_dir,
-                    reinit=False,
-                    quantized=self.quantized,
-                    actually_save=is_main_process(),
-                    cpu_offload=self.cpu_offload,
-                )
-        # no need to save if unquantized, as finetune.py will call merge_and_unload()
-        return control
-
-
-class ReLoRAScheduler(LRScheduler):
-    """Wraps another scheduler to apply per-lora-restart learning rate warmups."""
-
-    def __init__(
-        self,
-        optimizer: Optimizer,
-        inner_schedule: LRScheduler,
-        relora_steps: int,
-        warmup_steps: int,
-        min_lr_scale: float = 0.001,
-    ) -> None:
-        self.inner_schedule = inner_schedule
-        self.relora_steps = relora_steps
-        self.warmup_steps = warmup_steps
-        self.min_lr_scale = min_lr_scale
-        super().__init__(optimizer, inner_schedule.last_epoch, inner_schedule.verbose)
-
-    def get_lr(self) -> float:
-        self.inner_schedule.last_epoch = self.last_epoch
-
-        original = self.inner_schedule.get_lr()
-        step = self.last_epoch
-        if step < self.relora_steps:
-            scale = 1
-        else:
-            cycle_t = min(1.0, (step % self.relora_steps) / self.warmup_steps)
-            scale = cycle_t * (1 - self.min_lr_scale) + self.min_lr_scale
-
-        if isinstance(original, Sequence):
-            return [lr * scale for lr in original]
-        return original * scale
-
-
-def sharded_paths(path: str, module_names: List[str]) -> Dict[str, str]:
-    model_name = "model.safetensors"
-    if not os.path.exists(str(Path(path) / model_name)) and not os.path.exists(
-        str(Path(path) / f"{model_name}.index.json")
-    ):
-        model_name = "pytorch_model.bin"
-
-    index_path = str(Path(path) / f"{model_name}.index.json")
-    if os.path.exists(index_path):
-        with open(index_path, "r", encoding="utf-8") as file:
-            data = json.load(file)
-        return data["weight_map"]
-    return {(module_name + ".weight"): model_name for module_name in module_names}
-
-
-def lora_delta_weight(layer: peft.tuners.lora.LoraLayer, device) -> torch.Tensor:
-    if isinstance(layer, (peft.tuners.lora.Linear8bitLt, peft.tuners.lora.Linear4bit)):
-        adapter = layer.active_adapter
-        return (
-            peft.utils.transpose(
-                layer.lora_B[adapter].weight.detach().to(device)
-                @ layer.lora_A[adapter].weight.detach().to(device),
-                getattr(layer, "fan_in_fan_out", False),
-            )
-            * layer.scaling[adapter]
-        )
-
-    return layer.get_delta_weight().to(device)
-
-
-def find_lora_modules(model: peft.LoraModel) -> Dict[str, peft.tuners.lora.LoraLayer]:
-    modules: Dict[str, peft.tuners.lora.LoraLayer] = {}
-
-    key_list = [key for key, _ in model.model.named_modules() if "lora" not in key]
-    for key in key_list:
-        try:
-            # pylint: disable=protected-access
-            _parent, target, _target_name = peft.utils._get_submodules(model.model, key)
-        except AttributeError:
-            continue
-
-        if isinstance(target, peft.tuners.lora.LoraLayer):
-            modules[key] = target
-
-    return modules
-
-
-def update_weights(
-    target: peft.tuners.lora.LoraLayer, new_weight: torch.Tensor, reinit: bool, device
-):
-    if reinit:
-        for adapter_name in target.lora_A:
-            target.reset_lora_parameters(adapter_name)
-        for adapter_name in target.lora_embedding_A:
-            target.reset_lora_parameters(adapter_name)
-
-    if isinstance(target, peft.tuners.lora.Linear4bit):
-        # This could be faster, but the quantization of Linear4bit weights occurs
-        # when the module is moved from cpu to gpu. Without meddling *too* deeply in
-        # PEFT's innards or maintaining a duplicate of that codepath, this is good
-        # enough for now.
-        target.weight.quant_state = None
-        target.weight.data = new_weight.cpu()
-        target.to(device)
-    elif isinstance(target, peft.tuners.lora.Linear8bitLt):
-        target.weight = bnb.nn.Int8Params(new_weight, requires_grad=False).to(device)
-    else:
-        target.weight.data = new_weight.to(device)
-
-
-def merge_and_save(
-    model: peft.LoraModel,
-    model_src: str,
-    model_dst: str,
-    reinit: bool = False,
-    quantized: bool = False,
-    cpu_offload: bool = False,
-    actually_save: bool = True,
-):
-    modules = find_lora_modules(model)
-
-    if not quantized:
-        for module_name, target in modules.items():
-            update = target.get_delta_weight(target.active_adapter).detach()
-            target.weight.data += update
-
-            if reinit:
-                for adapter_name in target.lora_A:
-                    target.reset_lora_parameters(adapter_name)
-                for adapter_name in target.lora_embedding_A:
-                    target.reset_lora_parameters(adapter_name)
-        return
-
-    os.makedirs(model_dst, exist_ok=True)
-    shard_paths = sharded_paths(model_src, modules.keys())
-    out_shard_paths = {}
-
-    unique_shards = list(set(shard_paths.values()))
-    for shard_path in unique_shards:
-        out_tensors = {}
-        if shard_path.endswith(".safetensors"):
-            in_tensors = st.load_file(str(Path(model_src) / shard_path))
-        else:
-            in_tensors = torch.load(Path(model_src) / shard_path)
-            if "state_dict" in in_tensors:
-                in_tensors = in_tensors["state_dict"]
-
-        for module_name, target in modules.items():
-            key = module_name + ".weight"
-            if key not in shard_paths or shard_paths[key] != shard_path:
-                continue
-
-            orig_weight = in_tensors[key]
-            old_dev = target.weight.device
-            math_dev = "cpu" if cpu_offload else old_dev
-
-            delta_weight = lora_delta_weight(target, math_dev)
-            new_weight = orig_weight.to(math_dev) + delta_weight
-            del delta_weight
-
-            if actually_save:
-                out_tensors[key] = new_weight.half().cpu()
-
-            update_weights(target, new_weight, reinit=reinit, device=old_dev)
-
-        if actually_save:
-            out_shard_name = shard_path
-            if out_shard_name.startswith("pytorch_model"):
-                out_shard_name = (
-                    out_shard_name.replace("pytorch_model", "model").rstrip(".bin")
-                    + ".safetensors"
-                )
-
-            for module_name in in_tensors:
-                if module_name not in out_tensors:
-                    out_tensors[module_name] = in_tensors[module_name].half()
-                out_shard_paths[module_name] = out_shard_name
-
-            shard_fn = str(Path(model_dst) / out_shard_name)
-            LOG.info(f"saving tensors to {shard_fn}")
-            st.save_file(out_tensors, shard_fn, metadata={"format": "pt"})
-
-        del in_tensors
-        del out_tensors
-        torch.cuda.empty_cache()
-
-    if actually_save and len(unique_shards) > 1:
-        with open(
-            str(Path(model_dst, "model.safetensors.index.json")), "w", encoding="utf-8"
-        ) as file:
-            json.dump({"metadata": {}, "weight_map": out_shard_paths}, file)
-
-
-def load_weight_checkpoint(model: peft.LoraModel, checkpoint_path: str):
-    modules = find_lora_modules(model)
-    shard_paths = sharded_paths(checkpoint_path, modules.keys())
-    unique_shards = list(set(shard_paths.values()))
-
-    for shard_path in unique_shards:
-        tensors = st.load_file(os.path.join(checkpoint_path, shard_path))
-
-        for module_name, target in modules.items():
-            key = module_name + ".weight"
-            if key not in shard_paths or shard_paths[key] != shard_path:
-                continue
-
-            new_weight = tensors[key]
-            update_weights(
-                target, new_weight, reinit=False, device=target.weight.device
-            )

src/axolotl/prompt_strategies/__init__.py

@@ -2,10 +2,8 @@
 
 import importlib
 
-from axolotl.prompt_strategies.user_defined import UserDefinedDatasetConfig
 
-
-def load(strategy, tokenizer, cfg, ds_cfg):
+def load(strategy, tokenizer, cfg):
     try:
         load_fn = "load"
         if strategy.split(".")[-1].startswith("load_"):
@@ -13,9 +11,6 @@ def load(strategy, tokenizer, cfg, ds_cfg):
             strategy = ".".join(strategy.split(".")[:-1])
         mod = importlib.import_module(f".{strategy}", "axolotl.prompt_strategies")
         func = getattr(mod, load_fn)
-        load_kwargs = {}
-        if strategy == "user_defined":
-            load_kwargs["ds_cfg"] = UserDefinedDatasetConfig(**ds_cfg)
-        return func(tokenizer, cfg, **load_kwargs)
+        return func(tokenizer, cfg)
     except Exception:  # pylint: disable=broad-exception-caught
         return None

src/axolotl/prompt_strategies/alpaca_w_system.py

@@ -57,8 +57,6 @@ class SystemDataPrompter(AlpacaPrompter):
     Alpaca Style Prompter that uses system prompts from the dataset
     """
 
-    system_format: str = "### System:\n{system}\n\n"
-
     def build_prompt_w_system(
         self,
         system: str,

src/axolotl/prompt_strategies/metharme.py

@@ -1,76 +0,0 @@
-"""Module containing the MetharmenPromptTokenizingStrategy and MetharmePrompter class"""
-
-import logging
-from typing import Tuple
-
-from axolotl.prompt_tokenizers import InstructionPromptTokenizingStrategy
-from axolotl.prompters import AlpacaPrompter
-
-LOG = logging.getLogger("axolotl")
-
-IGNORE_TOKEN_ID = -100
-
-# pylint: disable=duplicate-code
-
-
-class MetharmePromptTokenizingStrategy(InstructionPromptTokenizingStrategy):
-    """
-    Tokenizing strategy for the Metharme models
-    """
-
-    def parse_instruction_fields(self, prompt) -> Tuple[str, str, str]:
-        return (prompt["prompt"], "", prompt["generation"])
-
-    def _tokenize(
-        self,
-        prompt: str,
-        add_eos_token: bool = True,
-        strip_bos_token: bool = False,
-        num_eos_tokens: int = 3,
-    ):
-        result = self.tokenizer(
-            prompt,
-            truncation=True,
-            max_length=self.sequence_len,
-            padding=False,
-            return_tensors=None,
-        )
-        if len(result["input_ids"]) == 0:
-            LOG.warning("Tokenizer result is empty. You may want to audit your dataset")
-        # If there's already an EOS token there, subtract from the number added
-        if result["input_ids"][-1] == self.tokenizer.eos_token_id:
-            num_eos_tokens -= 1
-
-        if num_eos_tokens > 0 and add_eos_token and len(result["input_ids"]) > 0:
-            for _ in range(num_eos_tokens):
-                if len(result["input_ids"]) < self.sequence_len:
-                    result["input_ids"].append(self.tokenizer.eos_token_id)
-                    result["attention_mask"].append(1)
-
-        if result["input_ids"][0] == self.tokenizer.bos_token_id and strip_bos_token:
-            result["input_ids"] = result["input_ids"][1:]
-            result["attention_mask"] = result["attention_mask"][1:]
-
-        result["labels"] = result["input_ids"].copy()
-        return result
-
-
-class MetharmePrompter(AlpacaPrompter):
-    """
-    Prompter for the Metharme models.
-    """
-
-    system_prompt = ""
-    system_no_input_prompt = ""
-    system_format = ""
-    turn_format = "{instruction}"
-    turn_no_input_format = "{instruction}"
-
-    def __init__(self, *args, **kwargs):  # pylint: disable=super-init-not-called
-        pass
-
-
-def load(tokenizer, cfg):
-    return MetharmePromptTokenizingStrategy(
-        MetharmePrompter(), tokenizer, cfg.train_on_inputs, cfg.sequence_len
-    )

src/axolotl/prompt_strategies/user_defined.py

@@ -1,98 +0,0 @@
-"""
-User Defined prompts with configuration from the YML config
-"""
-
-from dataclasses import dataclass
-from functools import partial
-from typing import Optional, Tuple
-
-from axolotl.prompt_strategies.alpaca_w_system import (
-    InstructionWSystemPromptTokenizingStrategy,
-    SystemDataPrompter,
-)
-
-
-@dataclass
-class UserDefinedDatasetConfig:
-    """
-    dataclass configuration representing a userdefined dataset type
-    """
-
-    system_prompt: str = ""
-    field_system: str = "system"
-    field_instruction: str = "instruction"
-    field_input: str = "input"
-    field_output: str = "output"
-    format: str = "{instruction} {input} "
-    no_input_format: str = "{instruction} "
-    system_format: str = "{system}"
-
-    def __getitem__(self, item):
-        return getattr(self, item)
-
-
-class UserDefinedPromptTokenizationStrategy(InstructionWSystemPromptTokenizingStrategy):
-    """
-    Prompt Tokenization Strategy for user defined prompts
-    """
-
-
-def load(tokenizer, cfg, ds_cfg: Optional[UserDefinedDatasetConfig] = None):
-    if not ds_cfg:
-        raise ValueError("Missing dataset prompt configuration")
-
-    system_prompt = ""
-    if ds_cfg.system_prompt:
-        system_prompt = ds_cfg.system_prompt
-
-    def parse_instruction_fields(
-        field_instruction,
-        field_input,
-        field_output,
-        field_system,
-        system_prompt,
-        prompt,
-    ) -> Tuple[str, str, str, str]:
-        return (
-            prompt[field_instruction],
-            prompt[field_input] if field_input in prompt else "",
-            prompt[field_output] if field_output in prompt else "",
-            prompt[field_system] if field_system in prompt else system_prompt,
-        )
-
-    turn_format = ds_cfg.format
-    turn_no_input_format = ds_cfg.no_input_format
-    system_format = ds_cfg.system_format
-
-    class UserDefinedPrompter(SystemDataPrompter):
-        """
-        Prompter for user defined prompts
-        """
-
-        def match_prompt_style(self):
-            self.turn_format = turn_format
-            self.turn_no_input_format = turn_no_input_format
-            self.system_format = system_format
-
-    prompter = UserDefinedPrompter()
-
-    strat = UserDefinedPromptTokenizationStrategy(
-        prompter,
-        tokenizer,
-        cfg.train_on_inputs,
-        cfg.sequence_len,
-    )
-
-    setattr(
-        strat,
-        "parse_instruction_fields",
-        partial(
-            parse_instruction_fields,
-            ds_cfg.field_instruction,
-            ds_cfg.field_input,
-            ds_cfg.field_output,
-            ds_cfg.field_system,
-            system_prompt,
-        ),
-    )
-    return strat

src/axolotl/prompt_tokenizers.py

@@ -13,7 +13,7 @@ from axolotl.prompters import IGNORE_TOKEN_ID
 LOG = logging.getLogger("axolotl")
 
 IGNORE_INDEX = -100
-LLAMA_DEFAULT_PAD_TOKEN = "<pad>"  # nosec
+LLAMA_DEFAULT_PAD_TOKEN = "[PAD]"  # nosec
 LLAMA_DEFAULT_EOS_TOKEN = "</s>"  # nosec
 LLAMA_DEFAULT_BOS_TOKEN = "<s>"  # nosec
 LLAMA_DEFAULT_UNK_TOKEN = "<unk>"  # nosec
@@ -85,11 +85,7 @@ class PromptTokenizingStrategy(abc.ABC):
             result["input_ids"].append(self.tokenizer.eos_token_id)
             result["attention_mask"].append(1)
 
-        if (
-            len(result["input_ids"]) > 0
-            and result["input_ids"][0] == self.tokenizer.bos_token_id
-            and strip_bos_token
-        ):
+        if result["input_ids"][0] == self.tokenizer.bos_token_id and strip_bos_token:
             result["input_ids"] = result["input_ids"][1:]
             result["attention_mask"] = result["attention_mask"][1:]
 

src/axolotl/prompters.py

@@ -26,7 +26,7 @@ class AlpacaPrompter:
 
     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}"
+    system_format: str
     turn_format: str
     turn_no_input_format: str
     prompt_style: Optional[PromptStyle] = None
@@ -63,17 +63,13 @@ class AlpacaPrompter:
         # 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)
+            res = self.system_prompt + 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)
+            res = self.system_no_input_prompt + self.turn_no_input_format.format(
+                instruction=instruction
+            )
         if output:
             res = f"{res}{output}"
         yield res

src/axolotl/train.py

@@ -1,139 +0,0 @@
-"""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
-from dataclasses import dataclass
-from pathlib import Path
-from typing import Optional
-
-import torch
-
-# add src to the pythonpath so we don't need to pip install this
-from datasets import Dataset
-from optimum.bettertransformer import BetterTransformer
-
-from axolotl.common.cli import TrainerCliArgs
-from axolotl.logging_config import configure_logging
-from axolotl.utils.dict import DictDefault
-from axolotl.utils.models import load_model, load_tokenizer
-from axolotl.utils.trainer import setup_trainer
-
-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.train")
-
-
-@dataclass
-class TrainDatasetMeta:
-    """
-    dataclass to capture the dataset specific options for training
-    """
-
-    train_dataset: Dataset
-    eval_dataset: Optional[Dataset] = None
-    total_num_steps: Optional[int] = None
-
-
-def train(
-    *,
-    cfg: DictDefault,
-    cli_args: TrainerCliArgs,
-    dataset_meta: TrainDatasetMeta,
-):
-    # load the tokenizer first
-    LOG.info(f"loading tokenizer... {cfg.tokenizer_config or cfg.base_model_config}")
-    tokenizer = load_tokenizer(cfg)
-
-    train_dataset = dataset_meta.train_dataset
-    eval_dataset = dataset_meta.eval_dataset
-    total_num_steps = dataset_meta.total_num_steps
-
-    # Load the model and tokenizer
-    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
-
-    if cfg.resume_from_checkpoint is None and cfg.auto_resume_from_checkpoints:
-        possible_checkpoints = [
-            str(cp) for cp in Path(cfg.output_dir).glob("checkpoint-*")
-        ]
-        if len(possible_checkpoints) > 0:
-            sorted_paths = sorted(
-                possible_checkpoints,
-                key=lambda path: int(path.split("-")[-1]),
-            )
-            cfg.resume_from_checkpoint = sorted_paths[-1]
-            LOG.info(
-                f"Using Auto-resume functionality to start with checkpoint at {cfg.resume_from_checkpoint}"
-            )
-    resume_from_checkpoint = cfg.resume_from_checkpoint
-
-    trainer = setup_trainer(
-        cfg, train_dataset, eval_dataset, model, tokenizer, total_num_steps
-    )
-
-    model.config.use_cache = False
-
-    if torch.__version__ >= "2" and sys.platform != "win32":
-        LOG.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}")
-        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
-    if cfg.local_rank == 0:
-
-        def terminate_handler(_, __, model):
-            if cfg.flash_optimum:
-                model = BetterTransformer.reverse(model)
-            model.save_pretrained(cfg.output_dir, safe_serialization=safe_serialization)
-            sys.exit(0)
-
-        signal.signal(
-            signal.SIGINT, lambda signum, frame: terminate_handler(signum, frame, model)
-        )
-
-    LOG.info("Starting trainer...")
-    if cfg.group_by_length:
-        LOG.info("hang tight... sorting dataset for group_by_length")
-
-    if not Path(cfg.output_dir).is_dir():
-        os.makedirs(cfg.output_dir, exist_ok=True)
-    tokenizer.save_pretrained(cfg.output_dir)
-    if cfg.flash_optimum:
-        with torch.backends.cuda.sdp_kernel(
-            enable_flash=True, enable_math=True, enable_mem_efficient=True
-        ):
-            trainer.train(resume_from_checkpoint=resume_from_checkpoint)
-    else:
-        trainer.train(resume_from_checkpoint=resume_from_checkpoint)
-
-    LOG.info(f"Training Completed!!! Saving pre-trained model to {cfg.output_dir}")
-
-    if cfg.relora_steps:
-        if cfg.adapter == "lora" and not (cfg.load_in_4bit or cfg.load_in_8bit):
-            model = model.merge_and_unload()
-        else:
-            # final model weights have already been saved by `ReLoRACallback.on_train_end`
-            return model, tokenizer
-
-    # 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:
-        trainer.save_model(cfg.output_dir)
-    elif cfg.local_rank == 0:
-        if cfg.flash_optimum:
-            model = BetterTransformer.reverse(model)
-
-        model.save_pretrained(cfg.output_dir, safe_serialization=safe_serialization)
-
-    return model, tokenizer

src/axolotl/utils/callbacks.py

@@ -1,20 +1,9 @@
 """Callbacks for Trainer class"""
 
-from __future__ import annotations
-
 import logging
 import os
-from typing import TYPE_CHECKING, Dict, List
 
-import evaluate
-import numpy as np
-import pandas as pd
-import torch
-import torch.distributed as dist
-from accelerate.state import PartialState
-from datasets import load_dataset
 from optimum.bettertransformer import BetterTransformer
-from tqdm import tqdm
 from transformers import (
     TrainerCallback,
     TrainerControl,
@@ -24,18 +13,8 @@ from transformers import (
 from transformers.trainer_utils import PREFIX_CHECKPOINT_DIR, IntervalStrategy
 
 from axolotl.utils.bench import log_gpu_memory_usage
-from axolotl.utils.distributed import (
-    gather_scalar_from_all_ranks,
-    get_world_size,
-    is_main_process,
-)
-
-if TYPE_CHECKING:
-    from axolotl.utils.trainer import AxolotlTrainingArguments
 
 LOG = logging.getLogger("axolotl.callbacks")
-IGNORE_INDEX = -100
-dist_state = PartialState()
 
 
 class SavePeftModelCallback(TrainerCallback):  # pylint: disable=too-few-public-methods
@@ -54,9 +33,7 @@ class SavePeftModelCallback(TrainerCallback):  # pylint: disable=too-few-public-
         )
 
         peft_model_path = os.path.join(checkpoint_folder, "adapter_model")
-        kwargs["model"].save_pretrained(
-            peft_model_path, save_safetensors=args.save_safetensors
-        )
+        kwargs["model"].save_pretrained(peft_model_path)
 
         return control
 
@@ -117,199 +94,3 @@ class GPUStatsCallback(
             log_gpu_memory_usage(LOG, "while training", self.cfg.device)
             self.logged = True
         return control
-
-
-def bench_eval_callback_factory(trainer, tokenizer):
-    accuracy = evaluate.load("accuracy")
-    abcd_idx = [
-        tokenizer("A", add_special_tokens=False).input_ids[0],
-        tokenizer("B", add_special_tokens=False).input_ids[0],
-        tokenizer("C", add_special_tokens=False).input_ids[0],
-        tokenizer("D", add_special_tokens=False).input_ids[0],
-        tokenizer("E", add_special_tokens=False).input_ids[0],
-        tokenizer("F", add_special_tokens=False).input_ids[0],
-        tokenizer("G", add_special_tokens=False).input_ids[0],
-    ]
-    bench_split = "eval"
-
-    def transform_bench_subject(example):
-        # Split on ':' and trim whitespace
-        parts = example["subject"].split(":")
-        first_part = (
-            parts[0].strip().lower().replace("-", "_")
-        )  # Lowercase the first part
-        second_part = (
-            parts[1].strip().replace("-", "_") if len(parts) > 1 else "all"
-        )  # Replace hyphens with underscores
-
-        # Return the transformed values
-        return {"name": first_part, "subject": second_part}
-
-    if trainer.args.bench_dataset == "mmlu-zs":
-        bench_dataset = load_dataset(
-            "openaccess-ai-collective/mmlu-evals",
-            data_files={
-                "eval": "zero_shot_mmlu_val.json",
-                "test": "zero_shot_mmlu_test.json",
-            },
-        )
-        # bench_dataset = bench_dataset.remove_columns("subject")
-    # MMLU Five-shot (Eval/Test only)
-    elif trainer.args.bench_dataset in ["mmlu", "mmlu-fs"]:
-        bench_dataset = load_dataset(
-            "openaccess-ai-collective/mmlu-evals",
-            data_files={
-                "eval": "five_shot_mmlu_val.json",
-                "test": "five_shot_mmlu_test.json",
-            },
-        )
-        # bench_dataset = bench_dataset.remove_columns('subject')
-    elif "/" in trainer.args.bench_dataset:
-        bench_ds = trainer.args.bench_dataset
-        bench_ds_name = "/".join(bench_ds.split("/", 2)[:2])
-        bench_ds_data_file = "/".join(bench_ds.split("/", 2)[2:])
-        bench_dataset = load_dataset(
-            bench_ds_name,
-            data_files={
-                "eval": bench_ds_data_file,
-            },
-        )
-        bench_dataset["eval"] = bench_dataset["eval"].map(transform_bench_subject)
-    else:
-        raise ValueError(
-            f"unhandled value `{trainer.args.bench_dataset}` for bench_dataset training args"
-        )
-    bench_dataset = bench_dataset[trainer.args.bench_split]
-    if trainer.args.max_bench_samples is not None:
-        bench_dataset = bench_dataset.select(range(trainer.args.max_bench_samples))
-
-    def tokenize_evals(example):
-        source = f"{tokenizer.bos_token}{example['input']}"
-        target = f"{example['output']}{tokenizer.eos_token}"
-
-        tokenized_source = tokenizer(
-            source,
-            max_length=2048,
-            truncation=True,
-            add_special_tokens=False,
-        )
-        tokenized_target = tokenizer(
-            target,
-            max_length=2048,
-            truncation=True,
-            add_special_tokens=False,
-        )
-        input_ids = tokenized_source["input_ids"] + tokenized_target["input_ids"]
-        labels = [IGNORE_INDEX] * len(tokenized_source["input_ids"]) + tokenized_target[
-            "input_ids"
-        ]
-
-        return {
-            "input_ids": input_ids,
-            "labels": labels,
-            "subject": example["subject"],
-        }
-
-    with dist_state.main_process_first():
-        bench_dataset = bench_dataset.map(tokenize_evals)
-        bench_dataset = bench_dataset.filter(lambda x: x["labels"][-2] in abcd_idx)
-
-    class BenchEvalCallback(TrainerCallback):
-        """
-        TrainerCallback that runs the MMLU evals
-        """
-
-        def on_evaluate(
-            self,
-            args: AxolotlTrainingArguments,
-            state: TrainerState,  # pylint: disable=unused-argument
-            control: TrainerControl,  # pylint: disable=unused-argument
-            metrics: Dict[str, float],  # pylint: disable=unused-argument
-            **kwargs,  # pylint: disable=unused-argument
-        ):
-            data_loader = trainer.get_bench_dataloader(
-                bench_dataset.remove_columns(["input", "subject", "output", "name"])
-            )
-            trainer.model.eval()
-            preds, refs = [], []
-            loss_bench = 0
-            for batch in tqdm(data_loader, total=len(data_loader)):
-                (loss, logits, labels) = trainer.prediction_step(
-                    trainer.model,
-                    batch,
-                    prediction_loss_only=False,
-                )
-                # There are two tokens, the output, and eos token.
-                for i, logit in enumerate(logits):
-                    label_non_zero_id = (batch["labels"][i] != IGNORE_INDEX).nonzero()[
-                        0
-                    ][0]
-                    logit_abcd = logit[label_non_zero_id - 1][abcd_idx]
-                    preds.append(torch.argmax(logit_abcd).item())
-                labels = labels[labels != IGNORE_INDEX].view(-1, 2)[:, 0]
-                refs += [
-                    abcd_idx.index(label) if label in abcd_idx else -1
-                    for label in labels.tolist()
-                ]
-                loss_bench += loss.item()
-            # Extract results by subject.
-            bench_name = bench_dataset["name"]
-            bench_names: dict = {s: {"refs": [], "preds": []} for s in set(bench_name)}
-            for s, p, r in zip(bench_name, preds, refs):  # pylint: disable=invalid-name
-                bench_names[s]["preds"].append(p)
-                bench_names[s]["refs"].append(r)
-            dist_state.wait_for_everyone()
-            local_bench_names = bench_names
-            gathered_bench_names: List[Dict] = [{} for _ in range(get_world_size())]
-            # Gather results from all GPUs to GPU 0
-
-            loss_bench_ranks = gather_scalar_from_all_ranks(
-                lambda: loss_bench, get_world_size()
-            )
-            len_data_loader_ranks = gather_scalar_from_all_ranks(
-                lambda: len(data_loader), get_world_size()
-            )
-
-            if not is_main_process():
-                dist.gather_object(local_bench_names, dst=0)
-            else:
-                dist.gather_object(local_bench_names, gathered_bench_names, dst=0)
-                bench_loss = sum(loss_bench_ranks) / sum(len_data_loader_ranks)
-                results = {f"{bench_split}_bench_loss": bench_loss}
-
-                # Combine results from all GPUs
-                combined_bench_names: Dict[str, Dict[str, List]] = {}
-                for bench_name in gathered_bench_names:
-                    for name, data in bench_name.items():
-                        if name not in combined_bench_names:
-                            combined_bench_names[name] = {"refs": [], "preds": []}
-                        combined_bench_names[name]["refs"].extend(data["refs"])
-                        combined_bench_names[name]["preds"].extend(data["preds"])
-
-                bench_scores = []
-                bench_refs = []
-                bench_preds = []
-                for (
-                    bench_name
-                ) in combined_bench_names:  # pylint: disable=consider-using-dict-items
-                    bench_score = accuracy.compute(
-                        references=combined_bench_names[bench_name]["refs"],
-                        predictions=combined_bench_names[bench_name]["preds"],
-                    )["accuracy"]
-                    bench_refs.extend(combined_bench_names[bench_name]["refs"])
-                    bench_preds.extend(combined_bench_names[bench_name]["preds"])
-                    if not pd.isna(bench_score):
-                        results[
-                            f"{bench_split}_bench_accuracy_{bench_name}"
-                        ] = bench_score
-                        bench_scores.append(bench_score)
-                    else:
-                        results[f"{bench_split}_bench_accuracy_{bench_name}"] = 0.0
-                        bench_scores.append(0.0)
-                results[f"{bench_split}_bench_average_accuracy"] = np.mean(bench_scores)
-                results[f"{bench_split}_bench_total_accuracy"] = accuracy.compute(
-                    references=bench_refs, predictions=bench_preds
-                )["accuracy"]
-                trainer.log(results)
-
-    return BenchEvalCallback

src/axolotl/utils/config.py

@@ -62,13 +62,6 @@ def normalize_config(cfg):
     else:
         torch.backends.cuda.matmul.allow_tf32 = cfg.tf32 or False
 
-    if cfg.bf16 or cfg.bfloat16:
-        cfg.torch_dtype = torch.bfloat16
-    elif cfg.load_in_8bit or cfg.fp16 or cfg.float16:
-        cfg.torch_dtype = torch.float16
-    else:
-        cfg.torch_dtype = torch.float32
-
     log_gpu_memory_usage(LOG, "baseline", cfg.device)
 
 
@@ -126,19 +119,6 @@ def validate_config(cfg):
     if not cfg.load_in_8bit and cfg.adapter == "lora":
         LOG.warning("We recommend setting `load_in_8bit: true` for LORA finetuning")
 
-    if cfg.relora_steps:
-        if cfg.adapter not in ("lora", "qlora"):
-            raise ValueError("cfg.adapter must be lora or qlora to use ReLoRA")
-
-        if cfg.fsdp:
-            raise ValueError("fsdp not supported with ReLoRA")
-
-        if cfg.deepspeed:
-            raise ValueError("deepspeed not supported with ReLoRA")
-
-        if cfg.lr_scheduler == "one_cycle":
-            raise ValueError("ReLoRA is not compatible with the one_cycle scheduler")
-
     if cfg.trust_remote_code:
         LOG.warning(
             "`trust_remote_code` is set to true. Please make sure that you reviewed the remote code/model."

src/axolotl/utils/data.py

@@ -7,7 +7,6 @@ from pathlib import Path
 from typing import Tuple, Union
 
 import torch
-from accelerate.state import PartialState
 from datasets import (
     Dataset,
     DatasetDict,
@@ -42,7 +41,7 @@ from axolotl.prompters import (
     ShareGPTPrompter,
     SummarizeTLDRPrompter,
 )
-from axolotl.utils.dict import DictDefault
+from axolotl.utils.distributed import is_main_process, zero_first
 from axolotl.utils.trainer import (
     calculate_total_num_steps,
     process_datasets_for_packing,
@@ -50,15 +49,13 @@ from axolotl.utils.trainer import (
 
 LOG = logging.getLogger("axolotl")
 DEFAULT_DATASET_PREPARED_PATH = "last_run_prepared"
-state = PartialState()
 
 
 def prepare_dataset(cfg, tokenizer):
     if not cfg.pretraining_dataset:
-        with state.main_process_first():
-            train_dataset, eval_dataset = load_prepare_datasets(
-                tokenizer, cfg, DEFAULT_DATASET_PREPARED_PATH
-            )
+        train_dataset, eval_dataset = load_prepare_datasets(
+            tokenizer, cfg, DEFAULT_DATASET_PREPARED_PATH
+        )
     else:
         train_dataset = load_pretraining_dataset(
             cfg.pretraining_dataset,
@@ -70,7 +67,7 @@ def prepare_dataset(cfg, tokenizer):
         train_dataset = train_dataset.with_format("torch")
         eval_dataset = None
 
-    with state.main_process_first():
+    with zero_first(is_main_process()):
         train_dataset, eval_dataset = process_datasets_for_packing(
             cfg, train_dataset, eval_dataset
         )
@@ -135,17 +132,8 @@ def load_tokenized_prepared_datasets(
             seed = 42
 
         datasets = []
-
-        def for_d_in_datasets(dataset_configs):
-            for dataset in dataset_configs:
-                if dataset.name and isinstance(dataset.name, list):
-                    for name in dataset.name:
-                        yield DictDefault({**dataset, "name": name})
-                else:
-                    yield dataset
-
         # pylint: disable=invalid-name
-        for d in for_d_in_datasets(cfg.datasets):
+        for d in cfg.datasets:
             ds: Union[Dataset, DatasetDict] = None
             ds_from_hub = False
             try:
@@ -172,15 +160,8 @@ def load_tokenized_prepared_datasets(
                         split=None,
                     )
                 elif local_path.is_file():
-                    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"
                     ds = load_dataset(
-                        ds_type,
+                        "json",
                         name=d.name,
                         data_files=d.path,
                         streaming=False,
@@ -217,27 +198,13 @@ def load_tokenized_prepared_datasets(
                     )
                 else:
                     ds = ds.shuffle(seed=seed).shard(num_shards=d.shards, index=0)
-
-            d_base_type = d_prompt_style = None
             d_type = d.type
-            if isinstance(d_type, str):
-                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
+            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 (
-                "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):
+            if ds_strategy := load(d.type, tokenizer, cfg):
                 ds_wrapper = TokenizedPromptDataset(ds_strategy, ds)
                 datasets.append(ds_wrapper)
             elif d_base_type == "alpaca":
@@ -508,7 +475,7 @@ def load_prepare_datasets(
             to_hash_test.encode(), usedforsecurity=False
         ).hexdigest()
 
-        with state.main_process_first():
+        with zero_first(is_main_process()):
             dataset = dataset.train_test_split(
                 test_size=cfg.val_set_size,
                 shuffle=False,

src/axolotl/utils/dataloader.py

@@ -243,18 +243,6 @@ class MultipackDistributedDataloader:
             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)

src/axolotl/utils/distributed.py

@@ -1,27 +1,27 @@
 """
 utility helpers for distributed checks
 """
-import torch
+from contextlib import contextmanager
+
 import torch.distributed as dist
-from accelerate import DistributedType
-from accelerate.state import PartialState
-from accelerate.utils import wait_for_everyone
+from accelerate import Accelerator
 
 accelerate = None  # pylint: disable=invalid-name
 
-state = PartialState()
+
+def load_accelerate():
+    global accelerate  # pylint: disable=global-statement
+    accelerate = Accelerator()
 
 
 def is_distributed():
     """
     Check if distributed training is initialized.
     """
-    return state.distributed_type in (
-        DistributedType.MULTI_GPU,
-        DistributedType.MULTI_CPU,
-        DistributedType.DEEPSPEED,
-        DistributedType.FSDP,
-    )
+    global accelerate  # pylint: disable=global-statement
+    if not accelerate:
+        accelerate = Accelerator()
+    return dist.is_available() and dist.is_initialized()
 
 
 def barrier():
@@ -29,48 +29,27 @@ def barrier():
     Acts as a barrier to wait for all processes. This ensures that all processes
     reach the barrier before proceeding further.
     """
-    wait_for_everyone()
+    if is_distributed():
+        dist.barrier()
 
 
-def is_main_process() -> bool:
+def is_main_process():
     """
     Check if the current process is the main process.
     If not in distributed mode, always return True.
     """
-    return state.is_main_process
+    if not is_distributed():
+        return True
+    return dist.get_rank() == 0
 
 
-def get_world_size() -> int:
-    return state.num_processes
-
-
-def gather_scalar_from_all_ranks(fn, world_size=1):  # pylint: disable=invalid-name
+@contextmanager
+def zero_first(is_main):
     """
-    Run a callable 'fn' on all ranks and gather the results on the specified rank.
-
-    Args:
-    - fn (callable): A function that computes the value. This should not have any side effects.
-    - rank (int, optional): The rank that gathers the values. Default is 0.
-    - world_size (int, optional): Total number of processes in the current distributed setup.
-
-    Returns:
-    - A list of computed values from all ranks if on the gathering rank, otherwise None.
+    runs the wrapped context so that rank 0 runs first before other ranks
     """
-    value_scalar = fn()
-    value_tensor = torch.tensor(value_scalar, device=dist.get_rank()).float()
-
-    if not state.is_main_process:
-        dist.gather(value_tensor, dst=0)
-    else:
-        gathered_tensors = [torch.zeros_like(value_tensor) for _ in range(world_size)]
-        dist.gather(value_tensor, gather_list=gathered_tensors, dst=0)
-
-        # Convert tensors back to their original type (int or float)
-        gathered_values = []
-        for tensor in gathered_tensors:
-            if tensor == tensor.int():
-                gathered_values.append(int(tensor.item()))
-            else:
-                gathered_values.append(float(tensor.item()))
-        return gathered_values
-    return None
+    if not is_main:  # other ranks wait first
+        barrier()
+    yield
+    if is_main:  # then rank 0 waits after it has run the context
+        barrier()

src/axolotl/utils/models.py

@@ -5,13 +5,12 @@ import logging
 import math
 import os
 from pathlib import Path
-from typing import Optional, Tuple  # noqa: F401
+from typing import TYPE_CHECKING, Optional, Tuple  # noqa: F401
 
 import bitsandbytes as bnb
 import torch
 import transformers
 from optimum.bettertransformer import BetterTransformer
-from peft import PeftConfig
 from transformers import (  # noqa: F401
     AutoConfig,
     AutoModelForCausalLM,
@@ -22,19 +21,15 @@ from transformers import (  # noqa: F401
     PreTrainedTokenizerBase,
 )
 
-from axolotl.prompt_tokenizers import LLAMA_DEFAULT_EOS_TOKEN
+from axolotl.prompt_tokenizers import LLAMA_DEFAULT_PAD_TOKEN
 from axolotl.utils.bench import log_gpu_memory_usage
-from axolotl.utils.dict import DictDefault
 
 LOG = logging.getLogger("axolotl")
 
+if TYPE_CHECKING:
+    from peft import PeftConfig  # noqa: F401
 
-def load_model_config(cfg):
-    model_config_name = cfg.base_model_config or cfg.base_model
-    trust_remote_code: bool = False or cfg.trust_remote_code
-    return AutoConfig.from_pretrained(
-        model_config_name, trust_remote_code=trust_remote_code
-    )
+    from axolotl.utils.dict import DictDefault  # noqa: F401
 
 
 def load_tokenizer(cfg):
@@ -59,18 +54,11 @@ def load_tokenizer(cfg):
         **tokenizer_kwargs,
     )
 
-    if (
-        tokenizer.__class__.__name__
-        in [
-            "LlamaTokenizer",
-            "LlamaTokenizerFast",
-            "CodeLlamaTokenizer",
-        ]
-        and hasattr(tokenizer, "pad_token")
-        and not tokenizer.pad_token
-    ):
-        # set a pad_token, but use eos_token so we don't add a new token
-        tokenizer.pad_token = LLAMA_DEFAULT_EOS_TOKEN
+    if tokenizer.__class__.__name__ in [
+        "LlamaTokenizer",
+        "LlamaTokenizerFast",
+    ]:
+        tokenizer.pad_token = LLAMA_DEFAULT_PAD_TOKEN
 
     LOG.debug(f"EOS: {tokenizer.eos_token_id} / {tokenizer.eos_token}")
     LOG.debug(f"BOS: {tokenizer.bos_token_id} / {tokenizer.bos_token}")
@@ -91,10 +79,8 @@ def load_tokenizer(cfg):
 
 
 def load_model(
-    cfg: DictDefault,
-    tokenizer: PreTrainedTokenizerBase,
-    inference: bool = False,
-) -> Tuple[PreTrainedModel, Optional[PeftConfig]]:
+    cfg, tokenizer
+):  # type: (DictDefault, PreTrainedTokenizerBase) -> Tuple[PreTrainedModel, Optional[PeftConfig]]
     """
     Load a model for a given configuration and tokenizer.
     """
@@ -104,9 +90,14 @@ def load_model(
 
     # TODO refactor as a kwarg
     load_in_8bit = cfg.load_in_8bit
+    cfg.is_llama_derived_model = (
+        "llama" in base_model
+        or (cfg.model_type and "llama" in cfg.model_type.lower())
+        or cfg.is_llama_derived_model
+    )
 
     if cfg.is_llama_derived_model and cfg.flash_attention:
-        if cfg.device not in ["mps", "cpu"] and not inference:
+        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,
             )
@@ -148,13 +139,19 @@ def load_model(
     if (
         cfg.is_llama_derived_model
         and (cfg.max_packed_sequence_len or cfg.sample_packing)
-        and not inference
+        and not cfg.inference
     ):
         from axolotl.monkeypatch.llama_expand_mask import hijack_expand_mask
 
         LOG.info("patching _expand_mask")
         hijack_expand_mask()
 
+    if cfg.bf16 or cfg.bfloat16:
+        torch_dtype = torch.bfloat16
+    elif cfg.load_in_8bit or cfg.fp16 or cfg.float16:
+        torch_dtype = torch.float16
+    else:
+        torch_dtype = torch.float32
     try:
         if cfg.gptq:
             from alpaca_lora_4bit.monkeypatch.peft_tuners_lora_monkey_patch import (
@@ -186,7 +183,7 @@ def load_model(
             load_in_4bit=True,
             llm_int8_threshold=6.0,
             llm_int8_has_fp16_weight=False,
-            bnb_4bit_compute_dtype=cfg.torch_dtype,
+            bnb_4bit_compute_dtype=torch_dtype,
             bnb_4bit_use_double_quant=True,
             bnb_4bit_quant_type="nf4",
         )
@@ -245,7 +242,7 @@ def load_model(
                 device_map=cfg.device_map,
                 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,
-                torch_dtype=cfg.torch_dtype,
+                torch_dtype=torch_dtype,
                 **model_kwargs,
             )
         # elif model_type == "GPTNeoXForCausalLM" and cfg.flash_attention:
@@ -280,7 +277,7 @@ def load_model(
                 device_map=cfg.device_map,
                 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,
-                torch_dtype=cfg.torch_dtype,
+                torch_dtype=torch_dtype,
                 trust_remote_code=cfg.trust_remote_code or False,
                 **model_kwargs,
             )
@@ -311,7 +308,7 @@ def load_model(
                 device_map=cfg.device_map,
                 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,
-                torch_dtype=cfg.torch_dtype,
+                torch_dtype=torch_dtype,
                 trust_remote_code=cfg.trust_remote_code or False,
                 **model_kwargs,
             )
@@ -325,7 +322,7 @@ def load_model(
             device_map=cfg.device_map,
             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,
-            torch_dtype=cfg.torch_dtype,
+            torch_dtype=torch_dtype,
             trust_remote_code=cfg.trust_remote_code or False,
             **model_kwargs,
         )
@@ -350,15 +347,6 @@ def load_model(
     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)
-    for name, module in model.named_modules():
-        if "norm" in name:
-            module.to(torch.float32)
-        if "lm_head" in name or "embed_tokens" in name:
-            if hasattr(module, "weight"):
-                module.to(torch.float32)
-
-    needs_fa2_dtype = cfg.adapter or cfg.fsdp
     if not cfg.gptq and (
         (cfg.adapter == "lora" and load_in_8bit)
         or (cfg.adapter == "qlora" and cfg.load_in_4bit)
@@ -367,18 +355,16 @@ def load_model(
         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
-    # convert them back to fp16/bf16 for flash-attn compatibility.
-    if needs_fa2_dtype or (cfg.flash_attention and cfg.is_llama_derived_model):
-        LOG.info("converting modules to %s for flash attention", cfg.torch_dtype)
-        for name, module in model.named_modules():
-            if "norm" in name:
-                module.to(cfg.torch_dtype)
-            if "lm_head" in name or "embed_tokens" in name:
-                if hasattr(module, "weight"):
-                    module.to(cfg.torch_dtype)
+        # 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, cfg.adapter)
 
@@ -426,15 +412,15 @@ def load_model(
     return model, lora_config
 
 
-def load_adapter(model, cfg, adapter, inference=False):
-    # type: (PreTrainedModel, DictDefault, Optional[str], bool) -> Tuple[PreTrainedModel, Optional[PeftConfig]]
+def load_adapter(model, cfg, adapter):
+    # type: (PreTrainedModel, DictDefault, Optional[str]) -> Tuple[PreTrainedModel, Optional[PeftConfig]]
 
     if adapter is None:
         return model, None
     if hasattr(model, "enable_input_require_grads"):
         model.enable_input_require_grads()
     if adapter in ["lora", "qlora"]:
-        return load_lora(model, cfg, inference=inference)
+        return load_lora(model, cfg)
     if adapter == "llama-adapter":
         return load_llama_adapter(model, cfg)
 
@@ -452,7 +438,7 @@ def load_llama_adapter(model, cfg):
     )
 
     if cfg.lora_model_dir:
-        LOG.debug("Loading pretained PEFT - llama_adapter")
+        LOG.info("Loading pretained LORA")
         model = PeftModel.from_pretrained(
             model,
             cfg.lora_model_dir,
@@ -466,8 +452,12 @@ def load_llama_adapter(model, cfg):
     return model, peft_config
 
 
-def find_all_linear_names(model):
-    cls = (bnb.nn.Linear4bit, bnb.nn.Linear8bitLt, torch.nn.Linear)
+def find_all_linear_names(bits, model):
+    cls = (
+        bnb.nn.Linear4bit
+        if bits == 4
+        else (bnb.nn.Linear8bitLt if bits == 8 else torch.nn.Linear)
+    )
     lora_module_names = set()
     for name, module in model.named_modules():
         if isinstance(module, cls):
@@ -480,15 +470,21 @@ def find_all_linear_names(model):
     return list(lora_module_names)
 
 
-def load_lora(model, cfg, inference=False):
-    # type: (PreTrainedModel, DictDefault, bool) -> Tuple[PreTrainedModel, Optional[PeftConfig]]
+def load_lora(model, cfg):
+    # type: (PreTrainedModel, DictDefault) -> Tuple[PreTrainedModel, Optional[PeftConfig]]
 
     from peft import LoraConfig, PeftModel, get_peft_model
 
     lora_target_modules = list(cfg.lora_target_modules or [])
 
     if cfg.lora_target_linear:
-        linear_names = find_all_linear_names(model)
+        bits = None
+        if cfg.load_in_4bit:
+            bits = 4
+        elif cfg.load_in_8bit:
+            bits = 8
+
+        linear_names = find_all_linear_names(bits, model)
         LOG.info(f"found linear modules: {repr(linear_names)}")
         lora_target_modules = list(set(lora_target_modules + linear_names))
 
@@ -504,11 +500,10 @@ def load_lora(model, cfg, inference=False):
     )
 
     if cfg.lora_model_dir:
-        LOG.debug("Loading pretained PEFT - LoRA")
         model = PeftModel.from_pretrained(
             model,
             cfg.lora_model_dir,
-            is_trainable=(not inference),
+            is_trainable=not cfg.inference,
         )
     else:
         model = get_peft_model(model, lora_config)

src/axolotl/utils/tokenization.py

@@ -8,13 +8,13 @@ from termcolor import colored
 LOG = logging.getLogger("axolotl")
 
 
-def check_dataset_labels(dataset, tokenizer, num_examples=5, text_only=False):
+def check_dataset_labels(dataset, tokenizer):
     # the dataset is already shuffled, so let's just check the first 5 elements
-    for idx in range(num_examples):
-        check_example_labels(dataset[idx], tokenizer, text_only=text_only)
+    for idx in range(5):
+        check_example_labels(dataset[idx], tokenizer)
 
 
-def check_example_labels(example, tokenizer, text_only=False):
+def check_example_labels(example, tokenizer):
     # Get the input_ids, labels, and attention_mask from the dataset
     input_ids = example["input_ids"]
     labels = example["labels"]
@@ -29,10 +29,8 @@ def check_example_labels(example, tokenizer, text_only=False):
         decoded_input_token = tokenizer.decode(input_id)
         # Choose the color based on whether the label has the ignore value or not
         color = "red" if label_id == -100 else ("yellow" if label_id == 0 else "green")
-        colored_token = colored(decoded_input_token, color) + (
-            not text_only
-            and colored(f"({label_id}, {mask}, {input_id})", "white")
-            or ""
+        colored_token = colored(decoded_input_token, color) + colored(
+            f"({label_id}, {mask}, {input_id})", "white"
         )
         colored_tokens.append(colored_token)
 

src/axolotl/utils/trainer.py

@@ -10,30 +10,28 @@ from functools import partial
 from pathlib import Path
 from typing import Optional, Union
 
+import bitsandbytes as bnb
 import numpy as np
 import torch.cuda
 import transformers
 from datasets import Dataset, set_caching_enabled
+from torch import nn
 from torch.optim.lr_scheduler import OneCycleLR
-from torch.utils.data import (
-    DataLoader,
-    DistributedSampler,
-    RandomSampler,
-    SequentialSampler,
-)
+from torch.utils.data import DataLoader, DistributedSampler, RandomSampler
 from transformers import EarlyStoppingCallback, Trainer, TrainingArguments
-from transformers.trainer_pt_utils import SequentialDistributedSampler
+from transformers.trainer_pt_utils import get_parameter_names
 
-from axolotl.monkeypatch.relora import ReLoRACallback, ReLoRAScheduler
 from axolotl.utils.callbacks import (
     GPUStatsCallback,
     SaveBetterTransformerModelCallback,
     SavePeftModelCallback,
-    bench_eval_callback_factory,
 )
 from axolotl.utils.collators import DataCollatorForSeq2Seq
 from axolotl.utils.dataloader import MultipackDistributedDataloader
-from axolotl.utils.schedulers import get_cosine_schedule_with_quadratic_warmup
+from axolotl.utils.schedulers import (
+    InterpolatingLogScheduler,
+    get_cosine_schedule_with_quadratic_warmup,
+)
 
 LOG = logging.getLogger("axolotl")
 
@@ -126,35 +124,6 @@ class AxolotlTrainingArguments(TrainingArguments):
         default=1,
         metadata={"help": "the multiplier for the max len for packed sequences"},
     )
-    relora_steps: Optional[int] = field(
-        default=None,
-        metadata={"help": "how often to reset for ReLoRA"},
-    )
-    relora_warmup_steps: Optional[int] = field(
-        default=None,
-        metadata={"help": "how many warmup steps to take after reset for ReLoRA"},
-    )
-    bench_split: Optional[str] = field(
-        default="eval", metadata={"help": "The benchmark split to run on"}
-    )
-    bench_dataset: Optional[str] = field(
-        default="pharaouk/dharma-1/dharma_1_mini.json",
-        metadata={
-            "help": "Benchmark dataset to use: options are `mmlu-zs`, `mmlu-fs`, or the full path to the dataset file"
-        },
-    )
-    do_bench_eval: Optional[bool] = field(
-        default=False, metadata={"help": "Whether to run the Benchmark evaluation."}
-    )
-    max_bench_samples: Optional[int] = field(
-        default=None,
-        metadata={
-            "help": "If set, only evaluates on `max_bench_samples` of the benchmark dataset."
-        },
-    )
-    bench_source_max_len: int = field(
-        default=2048, metadata={"help": "Maximum source sequence length for bench."}
-    )
 
 
 class AxolotlTrainer(Trainer):
@@ -164,10 +133,6 @@ class AxolotlTrainer(Trainer):
 
     args = None  # type: AxolotlTrainingArguments
 
-    def __init__(self, *args, bench_data_collator=None, **kwargs):
-        self.bench_data_collator = bench_data_collator
-        super().__init__(*args, **kwargs)
-
     def create_scheduler(
         self, num_training_steps: int, optimizer: torch.optim.Optimizer = None
     ):
@@ -206,18 +171,6 @@ class AxolotlTrainer(Trainer):
             )
         return super()._get_train_sampler()
 
-    def _get_eval_sampler(
-        self, eval_dataset: Dataset
-    ) -> Optional[torch.utils.data.Sampler]:
-        if self.args.world_size > 1 and self.args.sample_packing:
-            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) -> Union[DataLoader, MultipackDistributedDataloader]:
         if self.args.sample_packing:
             train_sampler = self._get_train_sampler()
@@ -242,7 +195,6 @@ class AxolotlTrainer(Trainer):
             eval_dataset = (
                 eval_dataset if eval_dataset is not None else self.eval_dataset
             )
-
             eval_sampler = self._get_eval_sampler(eval_dataset)
             return self.accelerator.prepare(
                 MultipackDistributedDataloader(
@@ -258,31 +210,6 @@ class AxolotlTrainer(Trainer):
             )
         return super().get_eval_dataloader(eval_dataset)
 
-    def _get_bench_sampler(
-        self, bench_dataset: Dataset
-    ) -> Optional[torch.utils.data.Sampler]:
-        if self.args.world_size <= 1:
-            return SequentialSampler(bench_dataset)
-        return None
-
-    def get_bench_dataloader(
-        self,
-        bench_dataset: Dataset,
-    ) -> 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 not isinstance(bench_dataset, torch.utils.data.IterableDataset):
-            dataloader_params["sampler"] = self._get_bench_sampler(bench_dataset)
-            dataloader_params["drop_last"] = self.args.dataloader_drop_last
-
-        return DataLoader(bench_dataset, **dataloader_params)
-        # return self.accelerator.prepare(DataLoader(bench_dataset, **dataloader_params))
-
     def compute_loss(self, model, inputs, return_outputs=False):
         # use one's weighted cross entropy loss calc
         # if self.args.sample_packing:
@@ -322,46 +249,13 @@ class OneCycleLRSchedulerTrainer(AxolotlTrainer):
         return self.lr_scheduler
 
 
-class ReLoRATrainer(AxolotlTrainer):
-    """
-    Trainer subclass that uses the OneCycleLR scheduler
-    """
-
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        self.lr_scheduler = None
-
-    def create_scheduler(
-        self,
-        num_training_steps: int,
-        optimizer: Optional[torch.optim.Optimizer] = None,
-    ):
-        optimizer = self.optimizer if optimizer is None else optimizer
-        lr_scheduler = super().create_scheduler(num_training_steps, optimizer)
-
-        if self.args.relora_steps:
-            warmup_steps = (
-                self.args.relora_warmup_steps if self.args.relora_warmup_steps else 10
-            )
-            self.lr_scheduler = ReLoRAScheduler(
-                optimizer,
-                lr_scheduler,
-                self.args.relora_steps,
-                warmup_steps,
-            )
-        else:
-            self.lr_scheduler = lr_scheduler
-
-        return self.lr_scheduler
-
-
 def add_position_ids(sample):
     sample["position_ids"] = torch.arange(len(sample["input_ids"]))
     return sample
 
 
 def drop_long_seq(sample, sequence_len=2048):
-    return len(sample["input_ids"]) <= sequence_len and len(sample["input_ids"]) > 0
+    return len(sample["input_ids"]) <= sequence_len
 
 
 @contextmanager
@@ -374,15 +268,15 @@ def disable_datasets_caching():
 
 
 def process_datasets_for_packing(cfg, train_dataset, eval_dataset):
-    drop_long = partial(drop_long_seq, sequence_len=cfg.sequence_len)
-    train_dataset = train_dataset.filter(drop_long, num_proc=os.cpu_count())
-    if eval_dataset:
-        eval_dataset = eval_dataset.filter(drop_long, num_proc=os.cpu_count())
-
     if cfg.sample_packing:
-        train_dataset = train_dataset.map(add_position_ids, num_proc=os.cpu_count())
+        drop_long = partial(drop_long_seq, sequence_len=cfg.sequence_len)
+        train_dataset = train_dataset.filter(drop_long, num_proc=os.cpu_count()).map(
+            add_position_ids, num_proc=os.cpu_count()
+        )
         if eval_dataset:
-            eval_dataset = eval_dataset.map(add_position_ids, num_proc=os.cpu_count())
+            eval_dataset = eval_dataset.filter(drop_long, num_proc=os.cpu_count()).map(
+                add_position_ids, num_proc=os.cpu_count()
+            )
     return train_dataset, eval_dataset
 
 
@@ -401,16 +295,6 @@ def calculate_total_num_steps(cfg, train_dataset, tokenizer):
             LOG.info(f"📝 UPDATE CONFIG WITH: `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 = (
                 # match count to len est in dataloader
@@ -471,16 +355,10 @@ def calculate_total_num_steps(cfg, train_dataset, tokenizer):
 
 def setup_fsdp_envs(cfg):
     os.environ["ACCELERATE_USE_FSDP"] = "true"
-    if cfg.fsdp_config.fsdp_offload_params:
-        os.environ["FSDP_OFFLOAD_PARAMS"] = "true"
     if cfg.fsdp_config.fsdp_sync_module_states:
         os.environ["FSDP_SYNC_MODULE_STATES"] = "true"
     if cfg.fsdp_config.fsdp_state_dict_type:
         os.environ["FSDP_STATE_DICT_TYPE"] = cfg.fsdp_config.fsdp_state_dict_type
-    if cfg.fsdp_config.fsdp_transformer_layer_cls_to_wrap:
-        os.environ[
-            "FSDP_TRANSFORMER_CLS_TO_WRAP"
-        ] = cfg.fsdp_config.fsdp_transformer_layer_cls_to_wrap
 
 
 def setup_trainer(cfg, train_dataset, eval_dataset, model, tokenizer, total_num_steps):
@@ -577,27 +455,6 @@ def setup_trainer(cfg, train_dataset, eval_dataset, model, tokenizer, total_num_
         # we have an eval set, but no steps defined, use epoch
         training_arguments_kwargs["evaluation_strategy"] = "epoch"
 
-    if cfg.save_strategy:
-        training_arguments_kwargs["save_strategy"] = cfg.save_strategy
-    else:
-        training_arguments_kwargs["save_strategy"] = (
-            "steps" if cfg.save_steps else "epoch"
-        )
-
-    if cfg.do_bench_eval:
-        training_arguments_kwargs["do_bench_eval"] = cfg.do_bench_eval
-        if cfg.bench_dataset:
-            training_arguments_kwargs["bench_dataset"] = cfg.bench_dataset
-
-    # DDP Config
-    if cfg.ddp_timeout:
-        training_arguments_kwargs["ddp_timeout"] = cfg.ddp_timeout
-    # see https://pytorch.org/docs/stable/generated/torch.nn.parallel.DistributedDataParallel.html
-    if cfg.ddp_bucket_cap_mb:
-        training_arguments_kwargs["ddp_bucket_cap_mb"] = cfg.ddp_bucket_cap_mb
-    if cfg.ddp_broadcast_buffers is not None:
-        training_arguments_kwargs["ddp_broadcast_buffers"] = cfg.ddp_broadcast_buffers
-
     training_args = AxolotlTrainingArguments(  # pylint: disable=unexpected-keyword-arg
         max_steps=total_num_steps if cfg.max_steps else -1,
         max_seq_length=cfg.sequence_len,
@@ -609,6 +466,7 @@ def setup_trainer(cfg, train_dataset, eval_dataset, model, tokenizer, total_num_
         eval_accumulation_steps=cfg.gradient_accumulation_steps,
         num_train_epochs=cfg.num_epochs,
         learning_rate=cfg.learning_rate,
+        save_strategy="steps" if cfg.save_steps else "epoch",
         save_steps=cfg.save_steps,
         output_dir=cfg.output_dir,
         save_total_limit=cfg.save_total_limit if cfg.save_total_limit else 4,
@@ -631,8 +489,6 @@ def setup_trainer(cfg, train_dataset, eval_dataset, model, tokenizer, total_num_
         weight_decay=cfg.weight_decay if cfg.weight_decay is not None else 0.0,
         sample_packing=cfg.sample_packing if cfg.sample_packing else False,
         sample_packing_seq_len_multiplier=cfg.micro_batch_size,
-        relora_steps=cfg.relora_steps,
-        relora_warmup_steps=cfg.relora_warmup_steps,
         **training_arguments_kwargs,
     )
 
@@ -642,13 +498,69 @@ def setup_trainer(cfg, train_dataset, eval_dataset, model, tokenizer, total_num_
         if Path(cfg.torchdistx_path).exists():
             sys.path.append(cfg.torchdistx_path)
             importlib.import_module("torchdistx")
+    if (
+        cfg.optimizer == "adamw_bnb_8bit"
+        and not cfg.gptq
+        and "deepspeed" not in training_arguments_kwargs
+        and not cfg.fsdp
+    ):
+        decay_parameters = get_parameter_names(model, [nn.LayerNorm])
+        decay_parameters = [name for name in decay_parameters if "bias" not in name]
+        optimizer_grouped_parameters = [
+            {
+                "params": [
+                    p
+                    for n, p in model.named_parameters()
+                    if (n in decay_parameters and p.requires_grad)
+                ],
+                "weight_decay": training_args.weight_decay,
+            },
+            {
+                "params": [
+                    p
+                    for n, p in model.named_parameters()
+                    if (n not in decay_parameters and p.requires_grad)
+                ],
+                "weight_decay": 0.0,
+            },
+        ]
+
+        optimizer = bnb.optim.Adam8bit(
+            optimizer_grouped_parameters,
+            betas=(training_args.adam_beta1, training_args.adam_beta2),
+            eps=training_args.adam_epsilon,
+            lr=training_args.learning_rate,
+        )
+
+        if cfg.lr_scheduler == "one_cycle":
+            lr_scheduler_kwargs = (
+                cfg.lr_scheduler_kwargs if cfg.lr_scheduler_kwargs else {}
+            )
+            lr_scheduler = OneCycleLR(
+                optimizer,
+                cfg.learning_rate,
+                total_steps=total_num_steps,
+                epochs=cfg.num_epochs,
+                div_factor=cfg.lr_div_factor if cfg.lr_div_factor else 6,
+                **lr_scheduler_kwargs,
+            )
+        elif cfg.lr_scheduler == "log_sweep":
+            lr_scheduler = InterpolatingLogScheduler(
+                optimizer,
+                cfg.warmup_steps,
+                cfg.log_sweep_min_lr if cfg.log_sweep_min_lr else 1e-10,
+                cfg.log_sweep_max_lr if cfg.log_sweep_max_lr else 10,
+            )
+        else:
+            lr_scheduler = transformers.get_cosine_schedule_with_warmup(
+                optimizer,
+                training_args.warmup_steps,
+                total_num_steps,
+            )
+        trainer_kwargs["optimizers"] = (optimizer, lr_scheduler)
 
     callbacks = []
     callbacks.append(GPUStatsCallback(cfg))
-
-    if cfg.relora_steps:
-        callbacks.append(ReLoRACallback(cfg))
-
     # TODO on_save callback to sync checkpoints to GCP/AWS in background
     if cfg.early_stopping_patience:
         early_stop_cb = EarlyStoppingCallback(
@@ -666,12 +578,10 @@ def setup_trainer(cfg, train_dataset, eval_dataset, model, tokenizer, total_num_
         callbacks.append(SaveBetterTransformerModelCallback)
 
     data_collator_kwargs = {
-        "padding": True,  # True/"longest" is the default
+        "padding": True,
     }
-    if cfg.pad_to_sequence_len:
-        data_collator_kwargs["pad_to_multiple_of"] = 64 * math.ceil(
-            cfg.sequence_len / 64
-        )
+    if cfg.collator_pad_to_longest:
+        data_collator_kwargs["padding"] = "longest"
     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
@@ -695,11 +605,11 @@ def setup_trainer(cfg, train_dataset, eval_dataset, model, tokenizer, total_num_
                 num_proc=32,
             )
 
-    trainer_cls = AxolotlTrainer
-    if cfg.lr_scheduler == "one_cycle" and (cfg.fsdp or cfg.adapter == "qlora"):
-        trainer_cls = OneCycleLRSchedulerTrainer
-    elif cfg.relora_steps:
-        trainer_cls = ReLoRATrainer
+    trainer_cls = (
+        OneCycleLRSchedulerTrainer
+        if cfg.lr_scheduler == "one_cycle" and (cfg.fsdp or cfg.adapter == "qlora")
+        else AxolotlTrainer
+    )
     trainer = trainer_cls(
         model=model,
         train_dataset=train_dataset,
@@ -710,16 +620,8 @@ def setup_trainer(cfg, train_dataset, eval_dataset, model, tokenizer, total_num_
             return_tensors="pt",
             **data_collator_kwargs,
         ),
-        bench_data_collator=transformers.DataCollatorForSeq2Seq(
-            tokenizer,
-            return_tensors="pt",
-            **data_collator_kwargs,
-        ),
         callbacks=callbacks,
         **trainer_kwargs,
     )
 
-    if cfg.do_bench_eval:
-        trainer.add_callback(bench_eval_callback_factory(trainer, tokenizer))
-
     return trainer