use different perplexity calc

fix perplexity calculation and make it configurable
compute perplexity from cross entropy
2023-07-10 13:43:50 -04:00 · 2023-07-10 12:49:51 -04:00 · 2023-07-10 12:49:47 -04:00
44 changed files with 322 additions and 2406 deletions
--- a/.github/workflows/base.yml
+++ b/.github/workflows/base.yml
@@ -18,13 +18,23 @@ jobs:
          - cuda: "118"
            cuda_version: 11.8.0
            python_version: "3.9"
-            pytorch: 2.0.1
-            torch_cuda_arch_list: "7.0 7.5 8.0 8.6 9.0+PTX"
+            pytorch: 2.0.0
+            axolotl_extras:
          - cuda: "118"
            cuda_version: 11.8.0
            python_version: "3.10"
-            pytorch: 2.0.1
-            torch_cuda_arch_list: "7.0 7.5 8.0 8.6 9.0+PTX"
+            pytorch: 2.0.0
+            axolotl_extras:
+          - cuda: "117"
+            cuda_version: 11.7.1
+            python_version: "3.9"
+            pytorch: 1.13.1
+            axolotl_extras:
+          - cuda: "118"
+            cuda_version: 11.8.0
+            python_version: "3.9"
+            pytorch: 2.0.0
+            axolotl_extras: gptq
    steps:
      - name: Checkout
        uses: actions/checkout@v3
@@ -48,9 +58,11 @@ jobs:
          push: ${{ github.event_name != 'pull_request' }}
          tags: ${{ steps.metadata.outputs.tags }}-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}${{ matrix.axolotl_extras != '' && '-' || '' }}${{ matrix.axolotl_extras }}
          labels: ${{ steps.metadata.outputs.labels }}
+          cache-from: type=gha
+          cache-to: type=gha,mode=max
          build-args: |
            CUDA_VERSION=${{ matrix.cuda_version }}
            CUDA=${{ matrix.cuda }}
            PYTHON_VERSION=${{ matrix.python_version }}
            PYTORCH_VERSION=${{ matrix.pytorch }}
-            TORCH_CUDA_ARCH_LIST=${{ matrix.torch_cuda_arch_list }}
+            AXOLOTL_EXTRAS=${{ matrix.axolotl_extras }}
--- a/.github/workflows/main.yml
+++ b/.github/workflows/main.yml
@@ -17,18 +17,23 @@ jobs:
          - cuda: cu118
            cuda_version: 11.8.0
            python_version: "3.9"
-            pytorch: 2.0.1
+            pytorch: 2.0.0
            axolotl_extras:
          - cuda: cu118
            cuda_version: 11.8.0
            python_version: "3.10"
-            pytorch: 2.0.1
+            pytorch: 2.0.0
            axolotl_extras:
          - cuda: cu118
            cuda_version: 11.8.0
            python_version: "3.9"
-            pytorch: 2.0.1
+            pytorch: 2.0.0
            axolotl_extras: gptq
+          - cuda: cu117
+            cuda_version: 11.7.1
+            python_version: "3.9"
+            pytorch: 1.13.1
+            axolotl_extras:
    runs-on: self-hosted
    steps:
      - name: Checkout
@@ -50,11 +55,13 @@ jobs:
        with:
          context: .
          build-args: |
-            BASE_TAG=${{ github.ref_name }}-base-py${{ matrix.python_version }}-${{ matrix.cuda }}-${{ matrix.pytorch }}
+            BASE_TAG=${{ github.ref_name }}-base-py${{ matrix.python_version }}-${{ matrix.cuda }}-${{ matrix.pytorch }}${{ matrix.axolotl_extras != '' && '-' || '' }}${{ matrix.axolotl_extras }}
          file: ./docker/Dockerfile
          push: ${{ github.event_name != 'pull_request' }}
          tags: ${{ steps.metadata.outputs.tags }}-py${{ matrix.python_version }}-${{ matrix.cuda }}-${{ matrix.pytorch }}${{ matrix.axolotl_extras != '' && '-' || '' }}${{ matrix.axolotl_extras }}
          labels: ${{ steps.metadata.outputs.labels }}
+          cache-from: type=gha
+          cache-to: type=gha,mode=max
  build-axolotl-runpod:
    needs: build-axolotl
    if: github.repository_owner == 'OpenAccess-AI-Collective'
@@ -62,21 +69,26 @@ jobs:
    strategy:
      matrix:
        include:
-          - cuda: 118
+          - cuda: cu118
            cuda_version: 11.8.0
            python_version: "3.9"
-            pytorch: 2.0.1
+            pytorch: 2.0.0
            axolotl_extras:
-          - cuda: 118
+          - cuda: cu118
            cuda_version: 11.8.0
            python_version: "3.10"
-            pytorch: 2.0.1
+            pytorch: 2.0.0
            axolotl_extras:
-          - cuda: 118
+          - cuda: cu118
            cuda_version: 11.8.0
            python_version: "3.9"
-            pytorch: 2.0.1
+            pytorch: 2.0.0
            axolotl_extras: gptq
+          - cuda: cu117
+            cuda_version: 11.7.1
+            python_version: "3.9"
+            pytorch: 1.13.1
+            axolotl_extras:
    runs-on: self-hosted
    steps:
      - name: Checkout
@@ -98,9 +110,10 @@ jobs:
        with:
          context: .
          build-args: |
-            BASE_TAG=${{ github.ref_name }}-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}${{ matrix.axolotl_extras != '' && '-' || '' }}${{ matrix.axolotl_extras }}
-            CUDA=${{ matrix.cuda }}
+            BASE_TAG=${{ github.ref_name }}-py${{ matrix.python_version }}-${{ matrix.cuda }}-${{ matrix.pytorch }}${{ matrix.axolotl_extras != '' && '-' || '' }}${{ matrix.axolotl_extras }}
          file: ./docker/Dockerfile-runpod
          push: ${{ github.event_name != 'pull_request' }}
-          tags: ${{ steps.metadata.outputs.tags }}-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}${{ matrix.axolotl_extras != '' && '-' || '' }}${{ matrix.axolotl_extras }}
+          tags: ${{ steps.metadata.outputs.tags }}-py${{ matrix.python_version }}-${{ matrix.cuda }}-${{ matrix.pytorch }}${{ matrix.axolotl_extras != '' && '-' || '' }}${{ matrix.axolotl_extras }}
          labels: ${{ steps.metadata.outputs.labels }}
+          cache-from: type=gha
+          cache-to: type=gha,mode=max
--- a/202
+++ b/202
@@ -1,202 +0,0 @@
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--- a/README.md
+++ b/README.md
@@ -24,12 +24,11 @@
 | mpt      | ✅         | ❌    | ❓     | ❌    | ❓            | ❌                 | ❌          | ❓             |
 | falcon   | ✅         | ✅    | ✅     | ❌    | ❓            | ❌                 | ❌          | ✅             |
 | gpt-j    | ✅         | ✅    | ✅     | ❌    | ❓            | ❌                 | ❓          | ✅             |
-| XGen     | ✅         | ❓    | ✅     | ❓    | ❓            | ❓                 | ❓          | ✅


 ## Quickstart ⚡

-**Requirements**: Python >=3.9 and Pytorch >=2.0.
+**Requirements**: Python 3.9 and Pytorch 2.0.

 ```bash
 git clone https://github.com/OpenAccess-AI-Collective/axolotl
@@ -37,6 +36,8 @@ git clone https://github.com/OpenAccess-AI-Collective/axolotl
 pip3 install -e .
 pip3 install -U git+https://github.com/huggingface/peft.git

+accelerate config
+
 # finetune lora
 accelerate launch scripts/finetune.py examples/openllama-3b/lora.yml

@@ -51,10 +52,11 @@ accelerate launch scripts/finetune.py examples/openllama-3b/lora.yml \

 - Docker
  ```bash
-  docker run --gpus '"all"' --rm -it winglian/axolotl:main-py3.10-cu118-2.0.1
+  docker run --gpus '"all"' --rm -it winglian/axolotl:main-py3.9-cu118-2.0.0
  ```
-  - `winglian/axolotl-runpod:main-py3.10-cu118-2.0.1`: for runpod
-  - `winglian/axolotl-runpod:main-py3.9-cu118-2.0.1-gptq`: for gptq
+  - `winglian/axolotl-runpod:main-py3.9-cu118-2.0.0`: for runpod
+  - `winglian/axolotl-runpod:main-py3.9-cu118-2.0.0-gptq`: for gptq
+  - `winglian/axolotl:dev`: dev branch (not usually up to date)

  Or run on the current files for development:

@@ -106,7 +108,7 @@ accelerate launch scripts/finetune.py examples/openllama-3b/lora.yml \

  3. Install torch
  ```bash
-  pip3 install -U torch --index-url https://download.pytorch.org/whl/cu118
+  pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118
  ```

  4. Axolotl
@@ -243,7 +245,7 @@ Optionally, download some datasets, see [data/README.md](data/README.md)

 ### Config

-See [examples](examples) for quick start. It is recommended to duplicate and modify to your needs. The most important options are:
+See sample configs in [configs](configs) folder or [examples](examples) for quick start. It is recommended to duplicate and modify to your needs. The most important options are:

 - model
  ```yaml
@@ -260,12 +262,6 @@ See [examples](examples) for quick start. It is recommended to duplicate and mod
    - path: vicgalle/alpaca-gpt4
      type: alpaca # format from earlier

-  # huggingface repo with specific configuration/subset
-  datasets:
-    - path: EleutherAI/pile
-      name: enron_emails
-      type: completion # format from earlier
-
  # local
  datasets:
    - path: json
@@ -309,8 +305,6 @@ base_model_ignore_patterns:
 # if the base_model repo on hf hub doesn't include configuration .json files,
 # you can set that here, or leave this empty to default to base_model
 base_model_config: ./llama-7b-hf
-# you can specify to choose a specific model revision from huggingface hub
-model_revision:
 # Optional tokenizer configuration override in case you want to use a different tokenizer
 # than the one defined in the base model
 tokenizer_config:
@@ -322,9 +316,6 @@ tokenizer_type: AutoTokenizer
 trust_remote_code:
 # use_fast option for tokenizer loading from_pretrained, default to True
 tokenizer_use_fast:
-# resize the model embeddings when new tokens are added to multiples of 32
-# this is reported to improve training speed on some models
-resize_token_embeddings_to_32x:

 # whether you are training a 4-bit GPTQ quantized model
 gptq: true
@@ -351,7 +342,6 @@ datasets:
    type: alpaca # format | format:<prompt_style> (chat/instruct) | <prompt_strategies>.load_<load_fn>
    data_files: # path to source data files
    shards: # number of shards to split data into
-    name: # name of dataset configuration to load

 # axolotl attempts to save the dataset as an arrow after packing the data together so
 # subsequent training attempts load faster, relative path
@@ -359,7 +349,7 @@ dataset_prepared_path: data/last_run_prepared
 # push prepared dataset to hub
 push_dataset_to_hub: # repo path
 # push checkpoints to hub
-hub_model_id: # repo path to push finetuned model
+hub_model_id: # repo path
 # whether to use hf `use_auth_token` for loading datasets. Useful for fetching private datasets
 # required to be true when used in combination with `push_dataset_to_hub`
 hf_use_auth_token: # boolean
@@ -375,10 +365,7 @@ dataset_shard_idx:
 sequence_len: 2048
 # max sequence length to concatenate training samples together up to
 # inspired by StackLLaMA. see https://huggingface.co/blog/stackllama#supervised-fine-tuning
-# soon to be DEPRECATED
 max_packed_sequence_len: 1024
-# use efficient multi-packing with block diagonal attention and per sequence position_ids
-sample_packing:

 # if you want to use 'lora' or 'qlora' or leave blank to train all parameters in original model
 adapter: lora
@@ -424,9 +411,6 @@ logging_steps:
 save_steps:
 eval_steps:

-# save model as safetensors (require safetensors package)
-save_safetensors:
-
 # whether to mask out or include the human's prompt from the training labels
 train_on_inputs: false
 # don't use this, leads to wonky training (according to someone on the internet)
@@ -518,6 +502,17 @@ strict:

 </details>

+### Accelerate
+
+Configure accelerate
+
+```bash
+accelerate config
+
+# Edit manually
+# nano ~/.cache/huggingface/accelerate/default_config.yaml
+```
+
 ### Train

 Run
@@ -525,21 +520,6 @@ Run
 accelerate launch scripts/finetune.py configs/your_config.yml
 ```

-#### Multi-GPU Config
-
- llama FSDP
-```yaml
-fsdp:
-  - full_shard
-  - auto_wrap
-fsdp_config:
-  fsdp_offload_params: true
-  fsdp_state_dict_type: FULL_STATE_DICT
-  fsdp_transformer_layer_cls_to_wrap: LlamaDecoderLayer
-```
-
- llama Deepspeed: append `ACCELERATE_USE_DEEPSPEED=true` in front of finetune command
-
 ### Inference

 Pass the appropriate flag to the train command:
@@ -590,10 +570,6 @@ Try set `fp16: true`

 Try to turn off xformers.

-> accelerate config missing
-
-It's safe to ignore it.
-
 ## Need help? 🙋♂️

 Join our [Discord server](https://discord.gg/HhrNrHJPRb) where we can help you
--- a/docker/Dockerfile
+++ b/docker/Dockerfile
@@ -3,15 +3,16 @@ FROM winglian/axolotl-base:$BASE_TAG

 ARG TORCH_CUDA_ARCH_LIST="7.0 7.5 8.0 8.6+PTX"
 ARG AXOLOTL_EXTRAS=""
-ARG CUDA="118"
-ENV BNB_CUDA_VERSION=$CUDA

 RUN apt-get update && \
    apt-get install -y vim curl

 WORKDIR /workspace

-RUN pip3 install --force-reinstall "peft @ git+https://github.com/huggingface/peft.git@main"
+RUN pip3 install --force-reinstall "peft @ git+https://github.com/huggingface/peft.git@main" \
+            "accelerate @ git+https://github.com/huggingface/accelerate.git@main" \
+            "transformers @ git+https://github.com/huggingface/transformers.git@main"
+
 RUN git clone --depth=1 https://github.com/OpenAccess-AI-Collective/axolotl.git
 # If AXOLOTL_EXTRAS is set, append it in brackets
 RUN cd axolotl && \
@@ -21,10 +22,5 @@ RUN cd axolotl && \
        pip install -e .; \
    fi

-# fix so that git fetch/pull from remote works
-RUN cd axolotl && \
-    git config remote.origin.fetch "+refs/heads/*:refs/remotes/origin/*" && \
-    git config --get remote.origin.fetch
-
 # helper for huggingface-login cli
 RUN git config --global credential.helper store
--- a/docker/Dockerfile-base
+++ b/docker/Dockerfile-base
@@ -8,7 +8,7 @@ FROM nvidia/cuda:$CUDA_VERSION-cudnn$CUDNN_VERSION-devel-ubuntu$UBUNTU_VERSION a
 ENV PATH="/root/miniconda3/bin:${PATH}"

 ARG PYTHON_VERSION="3.9"
-ARG PYTORCH_VERSION="2.0.1"
+ARG PYTORCH="2.0.0"
 ARG CUDA="118"

 ENV PYTHON_VERSION=$PYTHON_VERSION
@@ -29,18 +29,17 @@ ENV PATH="/root/miniconda3/envs/py${PYTHON_VERSION}/bin:${PATH}"
 WORKDIR /workspace

 RUN python3 -m pip install --upgrade pip && pip3 install packaging && \
-    python3 -m pip install --no-cache-dir -U torch==${PYTORCH_VERSION}+cu${CUDA} --extra-index-url https://download.pytorch.org/whl/cu$CUDA
+    python3 -m pip install --no-cache-dir -U torch==${PYTORCH} torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu$CUDA


 FROM base-builder AS flash-attn-builder

 WORKDIR /workspace

-ARG TORCH_CUDA_ARCH_LIST="7.0 7.5 8.0 8.6 9.0+PTX"
+ARG TORCH_CUDA_ARCH_LIST="7.0 7.5 8.0 8.6+PTX"

-RUN git clone https://github.com/Dao-AILab/flash-attention.git && \
+RUN git clone https://github.com/HazyResearch/flash-attention.git && \
    cd flash-attention && \
-    git checkout v2.0.1  && \
    python3 setup.py bdist_wheel && \
    cd csrc/fused_dense_lib && \
    python3 setup.py bdist_wheel && \
@@ -53,7 +52,7 @@ RUN git clone https://github.com/Dao-AILab/flash-attention.git && \

 FROM base-builder AS deepspeed-builder

-ARG TORCH_CUDA_ARCH_LIST="7.0 7.5 8.0 8.6 9.0+PTX"
+ARG TORCH_CUDA_ARCH_LIST="7.0 7.5 8.0 8.6+PTX"

 WORKDIR /workspace

@@ -74,9 +73,6 @@ RUN git clone https://github.com/TimDettmers/bitsandbytes.git && \

 FROM base-builder

-ARG TORCH_CUDA_ARCH_LIST="7.0 7.5 8.0 8.6 9.0+PTX"
-ENV TORCH_CUDA_ARCH_LIST=$TORCH_CUDA_ARCH_LIST
-
 # recompile apex
 RUN python3 -m pip uninstall -y apex
 RUN git clone https://github.com/NVIDIA/apex
--- a/docker/Dockerfile-runpod
+++ b/docker/Dockerfile-runpod
@@ -1,10 +1,6 @@
 ARG BASE_TAG=main
 FROM winglian/axolotl:$BASE_TAG

-ENV HF_DATASETS_CACHE="/workspace/data/huggingface-cache/datasets"
-ENV HUGGINGFACE_HUB_CACHE="/workspace/data/huggingface-cache/hub"
-ENV TRANSFORMERS_CACHE="/workspace/data/huggingface-cache/hub"
-
 COPY scripts/runpod-entrypoint.sh /root/runpod-entrypoint.sh

 RUN apt install --yes --no-install-recommends openssh-server tmux && \
--- a/deepspeed/zero3.json
+++ b/deepspeed/zero3.json
@@ -37,18 +37,18 @@
      "lr": "auto",
      "betas": [
        0.9,
-        0.95
+        0.999
      ],
      "eps": 1e-8,
      "weight_decay": "auto"
    }
  },
  "scheduler": {
-    "type": "WarmupLR",
+    "type": "OneCycle",
    "params": {
-      "warmup_min_lr": "auto",
-      "warmup_max_lr": "auto",
-      "warmup_num_steps": "auto"
+      "cycle_min_lr": 0.00001,
+      "cycle_max_lr": 0.00003,
+      "cycle_first_step_size": 120
    }
  },
  "train_batch_size": "auto",
--- a/examples/llama-2/README.md
+++ b/examples/llama-2/README.md
@@ -1,20 +0,0 @@
-# Overview
-
-This is an example of a llama-2 configuration for 7b and 13b. The yaml file contains configuration for the 7b variant, but you can just aswell use the same settings for 13b.
-
-The 7b variant fits on any 24GB VRAM GPU and will take up about 17 GB of VRAM during training if using qlora and 20 GB if using lora. On a RTX 4090 it trains 3 epochs of the default dataset in about 15 minutes.
-
-The 13b variant will fit if you change these settings to these values:
-gradient_accumulation_steps: 2
-micro_batch_size: 1
-
-```shell
-accelerate launch scripts/finetune.py examples/llama-2/qlora.yml
-
-```
-or
-
-```shell
-accelerate launch scripts/finetune.py examples/llama-2/lora.yml
-
-```
--- a/examples/llama-2/lora.yml
+++ b/examples/llama-2/lora.yml
@@ -1,66 +0,0 @@
-base_model: meta-llama/Llama-2-7b-hf
-base_model_config: meta-llama/Llama-2-7b-hf
-model_type: LlamaForCausalLM
-tokenizer_type: LlamaTokenizer
-
-load_in_8bit: true
-load_in_4bit: false
-strict: false
-
-datasets:
-  - path: mhenrichsen/alpaca_2k_test
-    type: alpaca
-dataset_prepared_path: last_run_prepared
-val_set_size: 0.01
-output_dir: ./lora-out
-
-sequence_len: 4096
-max_packed_sequence_len: 4096
-
-adapter: lora
-lora_model_dir:
-lora_r: 32
-lora_alpha: 16
-lora_dropout: 0.05
-lora_target_linear: true
-lora_fan_in_fan_out:
-
-wandb_project:
-wandb_watch:
-wandb_run_id:
-wandb_log_model:
-
-gradient_accumulation_steps: 4
-micro_batch_size: 2
-num_epochs: 3
-optimizer: adamw_bnb_8bit
-lr_scheduler: cosine
-learning_rate: 0.0002
-
-train_on_inputs: false
-group_by_length: true
-bf16: true
-fp16: false
-tf32: false
-
-gradient_checkpointing: true
-early_stopping_patience:
-resume_from_checkpoint:
-local_rank:
-logging_steps: 1
-xformers_attention: true
-flash_attention:
-
-warmup_steps: 10
-eval_steps: 20
-save_steps:
-debug:
-deepspeed:
-weight_decay: 0.0
-fsdp:
-fsdp_config:
-special_tokens:
-  bos_token: "<s>"
-  eos_token: "</s>"
-  unk_token: "<unk>"
-  pad_token: "<pad>"
--- a/examples/llama-2/qlora.yml
+++ b/examples/llama-2/qlora.yml
@@ -1,67 +0,0 @@
-base_model: meta-llama/Llama-2-7b-hf
-base_model_config: meta-llama/Llama-2-7b-hf
-model_type: LlamaForCausalLM
-tokenizer_type: LlamaTokenizer
-
-load_in_8bit: false
-load_in_4bit: true
-strict: false
-
-datasets:
-  - path: mhenrichsen/alpaca_2k_test
-    type: alpaca
-dataset_prepared_path: last_run_prepared
-val_set_size: 0.01
-output_dir: ./qlora-out
-
-adapter: qlora
-lora_model_dir:
-
-sequence_len: 4096
-max_packed_sequence_len: 4096
-lora_r: 32
-lora_alpha: 16
-lora_dropout: 0.05
-lora_target_modules:
-lora_target_linear: true
-lora_fan_in_fan_out:
-
-wandb_project:
-wandb_watch:
-wandb_run_id:
-wandb_log_model:
-
-gradient_accumulation_steps: 4
-micro_batch_size: 2
-num_epochs: 3
-optimizer: paged_adamw_32bit
-lr_scheduler: cosine
-learning_rate: 0.0002
-
-train_on_inputs: false
-group_by_length: true
-bf16: true
-fp16: false
-tf32: false
-
-gradient_checkpointing: true
-early_stopping_patience:
-resume_from_checkpoint:
-local_rank:
-logging_steps: 1
-xformers_attention: true
-flash_attention:
-
-warmup_steps: 10
-eval_steps: 20
-save_steps:
-debug:
-deepspeed:
-weight_decay: 0.0
-fsdp:
-fsdp_config:
-special_tokens:
-  bos_token: "<s>"
-  eos_token: "</s>"
-  unk_token: "<unk>"
-  pad_token: "<pad>"
--- a/examples/xgen-7b/xgen-7b-8k-qlora.yml
+++ b/examples/xgen-7b/xgen-7b-8k-qlora.yml
@@ -1,90 +0,0 @@
-# An example finetuning Saleforce's XGen-7b model with 8k context using qlora
-# on Tim Dettmer's Guanaco dataset.
-base_model: Salesforce/xgen-7b-8k-base
-base_model_config: Salesforce/xgen-7b-8k-base
-trust_remote_code: true
-model_type: AutoModelForCausalLM
-tokenizer_type: AutoTokenizer
-load_in_8bit: false
-# enable 4bit for QLoRA
-load_in_4bit: true
-gptq: false
-strict: false
-push_dataset_to_hub:
-datasets:
-  - path: timdettmers/openassistant-guanaco
-    data_files:
-      - openassistant_best_replies_train.jsonl
-    type: "completion"
-dataset_prepared_path: last_run_prepared
-val_set_size: 0.01
-# enable QLoRA
-adapter: qlora
-lora_model_dir:
-sequence_len: 8192
-max_packed_sequence_len:
-
-# hyperparameters from QLoRA paper Appendix B.2
-# "We find hyperparameters to be largely robust across datasets"
-lora_r: 64
-lora_alpha: 16
-# 0.1 for models up to 13B
-# 0.05 for 33B and 65B models
-lora_dropout: 0.05
-# add LoRA modules on all linear layers of the base model
-lora_target_modules:
-lora_target_linear: true
-lora_fan_in_fan_out:
-
-wandb_project:
-wandb_watch:
-wandb_run_id:
-wandb_log_model:
-output_dir: ./qlora-out
-
-# QLoRA paper Table 9
-# - 16 for 7b & 13b
-# - 32 for 33b, 64 for 64b
-# Max size tested on A6000
-# - 7b: 40
-# - 40b: 4
-# decrease if OOM, increase for max VRAM utilization
-micro_batch_size: 1
-gradient_accumulation_steps: 1
-num_epochs: 3
-# Optimizer for QLoRA
-optimizer: paged_adamw_32bit
-torchdistx_path:
-lr_scheduler: cosine
-# QLoRA paper Table 9
-# - 2e-4 for 7b & 13b
-# - 1e-4 for 33b & 64b
-learning_rate: 0.00002
-train_on_inputs: false
-group_by_length: false
-bf16: true
-fp16: false
-tf32: false
-gradient_checkpointing: true
-# stop training after this many evaluation losses have increased in a row
-# https://huggingface.co/transformers/v4.2.2/_modules/transformers/trainer_callback.html#EarlyStoppingCallback
-early_stopping_patience: 3
-resume_from_checkpoint:
-auto_resume_from_checkpoints: true
-local_rank:
-logging_steps: 1
-xformers_attention: true
-flash_attention:
-gptq_groupsize:
-gptq_model_v1:
-warmup_steps: 10
-eval_steps: 50
-save_steps: 50
-debug:
-deepspeed:
-weight_decay: 0.0
-special_tokens:
-  eos_token: "<|endoftext|>"
-  bos_token: "<|endoftext|>"
-  unk_token: "<|endoftext|>"
-  pad_token: "<|endoftext|>"
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,7 +1,7 @@
 peft @ git+https://github.com/huggingface/peft.git
 transformers @ git+https://github.com/huggingface/transformers.git
 bitsandbytes>=0.39.0
-accelerate @ git+https://github.com/huggingface/accelerate@2a289f6108e77a77a4efffb3f6316bc98538413b
+accelerate
 addict
 fire
 PyYAML==6.0
@@ -12,9 +12,6 @@ wandb
 einops
 xformers
 optimum
-hf_transfer
-numba
-numpy==1.24.4
 # qlora things
 bert-score==0.3.13
 evaluate==0.4.0
--- a/scripts/alpaca_json_to_jsonl.py
+++ b/scripts/alpaca_json_to_jsonl.py
@@ -15,9 +15,6 @@ from axolotl.convert import (
    JsonToJsonlConverter,
    StdoutWriter,
 )
-from axolotl.logging_config import configure_logging
-
-configure_logging()

 # add src to the pythonpath so we don't need to pip install this
 project_root = os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))
--- a/scripts/finetune.py
+++ b/scripts/finetune.py
@@ -17,17 +17,11 @@ import yaml
 from optimum.bettertransformer import BetterTransformer
 from transformers import GenerationConfig, TextStreamer

-from axolotl.logging_config import configure_logging
 from axolotl.utils.data import load_prepare_datasets, load_pretraining_dataset
 from axolotl.utils.dict import DictDefault
-from axolotl.utils.distributed import barrier, is_main_process
 from axolotl.utils.models import load_model, load_tokenizer
 from axolotl.utils.tokenization import check_dataset_labels
-from axolotl.utils.trainer import (
-    calculate_total_num_steps,
-    process_datasets_for_packing,
-    setup_trainer,
-)
+from axolotl.utils.trainer import setup_trainer
 from axolotl.utils.validation import validate_config
 from axolotl.utils.wandb import setup_wandb_env_vars

@@ -35,12 +29,9 @@ project_root = os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))
 src_dir = os.path.join(project_root, "src")
 sys.path.insert(0, src_dir)

-configure_logging()
-LOG = logging.getLogger("axolotl.scripts")
-

+logging.basicConfig(level=os.getenv("LOG_LEVEL", "INFO"))
 DEFAULT_DATASET_PREPARED_PATH = "last_run_prepared"
-os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = "1"


 def choose_device(cfg):
@@ -221,7 +212,7 @@ def train(

    # load the tokenizer first
    tokenizer_config = cfg.tokenizer_config or cfg.base_model_config
-    LOG.info(f"loading tokenizer... {tokenizer_config}")
+    logging.info(f"loading tokenizer... {tokenizer_config}")
    tokenizer = load_tokenizer(tokenizer_config, cfg.tokenizer_type, cfg)

    if (
@@ -236,27 +227,14 @@ def train(
                cfg.pretraining_dataset,
                tokenizer,
                max_tokens=cfg.sequence_len,
-                seed=cfg.seed or 42,
+                seed=cfg.seed,
            )
            # https://discuss.huggingface.co/t/how-to-use-huggingface-trainer-streaming-datasets-without-wrapping-it-with-torchdatas-iterablewrapper/25230
            train_dataset = train_dataset.with_format("torch")
            eval_dataset = None

-        if is_main_process():
-            # process on rank 0 first so it gets cached so other ranks load from cache
-            train_dataset, eval_dataset = process_datasets_for_packing(
-                cfg, train_dataset, eval_dataset
-            )
-        barrier()
-        if not is_main_process():
-            train_dataset, eval_dataset = process_datasets_for_packing(
-                cfg, train_dataset, eval_dataset
-            )
-        barrier()
-        total_num_steps = calculate_total_num_steps(cfg, train_dataset, tokenizer)
-
    if cfg.debug or "debug" in kwargs:
-        LOG.info("check_dataset_labels...")
+        logging.info("check_dataset_labels...")
        check_dataset_labels(
            train_dataset.select(
                [random.randrange(0, len(train_dataset) - 1) for _ in range(5)]  # nosec
@@ -265,11 +243,11 @@ def train(
        )

    if prepare_ds_only:
-        LOG.info("Finished preparing dataset. Exiting...")
+        logging.info("Finished preparing dataset. Exiting...")
        return

    # Load the model and tokenizer
-    LOG.info("loading model and peft_config...")
+    logging.info("loading model and peft_config...")
    model, peft_config = load_model(
        cfg.base_model,
        cfg.base_model_config,
@@ -280,17 +258,17 @@ def train(
    )

    if "merge_lora" in kwargs and cfg.adapter is not None:
-        LOG.info("running merge of LoRA with base model")
+        logging.info("running merge of LoRA with base model")
        model = model.merge_and_unload()
        model.to(dtype=torch.float16)

        if cfg.local_rank == 0:
-            LOG.info("saving merged model")
+            logging.info("saving merged model")
            model.save_pretrained(str(Path(cfg.output_dir) / "merged"))
        return

    if cfg.inference:
-        LOG.info("calling do_inference function")
+        logging.info("calling do_inference function")
        prompter: Optional[str] = "AlpacaPrompter"
        if "prompter" in kwargs:
            if kwargs["prompter"] == "None":
@@ -304,19 +282,17 @@ def train(
        model.save_pretrained(cfg.output_dir)
        return

-    trainer = setup_trainer(
-        cfg, train_dataset, eval_dataset, model, tokenizer, total_num_steps
-    )
+    trainer = setup_trainer(cfg, train_dataset, eval_dataset, model, tokenizer)

    model.config.use_cache = False

    if torch.__version__ >= "2" and sys.platform != "win32":
-        LOG.info("Compiling torch model")
+        logging.info("Compiling torch model")
        model = torch.compile(model)

    # go ahead and presave, so we have the adapter config available to inspect
    if peft_config:
-        LOG.info(f"Pre-saving adapter config to {cfg.output_dir}")
+        logging.info(f"Pre-saving adapter config to {cfg.output_dir}")
        peft_config.save_pretrained(cfg.output_dir)

    # In case we want to stop early with ctrl+c, this is a nice to have to save the pretrained model
@@ -332,9 +308,9 @@ def train(
            signal.SIGINT, lambda signum, frame: terminate_handler(signum, frame, model)
        )

-    LOG.info("Starting trainer...")
+    logging.info("Starting trainer...")
    if cfg.group_by_length:
-        LOG.info("hang tight... sorting dataset for group_by_length")
+        logging.info("hang tight... sorting dataset for group_by_length")
    resume_from_checkpoint = cfg.resume_from_checkpoint
    if cfg.resume_from_checkpoint is None and cfg.auto_resume_from_checkpoints:
        possible_checkpoints = [
@@ -346,7 +322,7 @@ def train(
                key=lambda path: int(path.split("-")[-1]),
            )
            resume_from_checkpoint = sorted_paths[-1]
-            LOG.info(
+            logging.info(
                f"Using Auto-resume functionality to start with checkpoint at {resume_from_checkpoint}"
            )

@@ -360,17 +336,17 @@ def train(
    else:
        trainer.train(resume_from_checkpoint=resume_from_checkpoint)

-    LOG.info(f"Training Completed!!! Saving pre-trained model to {cfg.output_dir}")
+    logging.info(f"Training Completed!!! Saving pre-trained model to {cfg.output_dir}")

    # TODO do we need this fix? https://huggingface.co/docs/accelerate/usage_guides/fsdp#saving-and-loading
    # only save on rank 0, otherwise it corrupts output on multi-GPU when multiple processes attempt to write the same file
-    if cfg.fsdp:
-        trainer.save_model(cfg.output_dir)
-    elif cfg.local_rank == 0:
+    if cfg.local_rank == 0:
        if cfg.flash_optimum:
            model = BetterTransformer.reverse(model)
        model.save_pretrained(cfg.output_dir)

+    # trainer.save_model(cfg.output_dir)  # TODO this may be needed for deepspeed to work? need to review another time
+

 if __name__ == "__main__":
    fire.Fire(train)
--- a/scripts/runpod-entrypoint.sh
+++ b/scripts/runpod-entrypoint.sh
@@ -1,21 +1,10 @@
 #!/bin/bash

-# Export specific ENV variables to /etc/rp_environment
-echo "Exporting environment variables..."
-printenv | grep -E '^RUNPOD_|^PATH=|^_=' | sed 's/^\(.*\)=\(.*\)$/export \1="\2"/' >> /etc/rp_environment
-echo 'source /etc/rp_environment' >> ~/.bashrc
+echo $PUBLIC_KEY >> ~/.ssh/authorized_keys
+chmod 700 -R ~/.ssh

-if [[ $PUBLIC_KEY ]]
-then
-    mkdir -p ~/.ssh
-    chmod 700 ~/.ssh
-    echo $PUBLIC_KEY >> ~/.ssh/authorized_keys
-    chmod 700 -R ~/.ssh
-    # Start the SSH service in the background
-    service ssh start
-else
-    echo "No PUBLIC_KEY ENV variable provided, not starting openSSH daemon"
-fi
+# Start the SSH service in the background
+service ssh start

 # Execute the passed arguments (CMD)
 exec "$@"
--- a/src/axolotl/datasets.py
+++ b/src/axolotl/datasets.py
@@ -1,13 +1,12 @@
 """Module containing Dataset functionality"""

 import logging
-import os
 from typing import List

 import torch
 from datasets import IterableDataset

-from .prompt_tokenizers import PromptTokenizingStrategy
+from .prompt_tokenizers import InvalidDataException, PromptTokenizingStrategy

 # We want this to be a wrapper for an existing dataset that we have loaded
 # lets use the concept of middlewares to wrap each dataset, for example
@@ -15,8 +14,6 @@ from .prompt_tokenizers import PromptTokenizingStrategy
 # let's check to ensure we don't truncate an item in the middle, we'll use
 # the collators later on to pad the datasets

-LOG = logging.getLogger("axolotl")
-

 class TokenizedPromptDataset(IterableDataset):
    """
@@ -35,15 +32,17 @@ class TokenizedPromptDataset(IterableDataset):
        self.dataset = dataset

    def __iter__(self):
-        features = self.dataset.features.keys()
-        num_proc = os.cpu_count()
-        return iter(
-            self.dataset.map(
-                self.prompt_tokenizer.tokenize_prompt,
-                num_proc=num_proc,
-                remove_columns=features,
-            )
-        )
+        iterator = iter(self.dataset)
+        count = 0
+        # Loop through the entire dataset
+        for example in iterator:
+            try:
+                yield self.prompt_tokenizer.tokenize_prompt(example)
+                count += 1
+            except InvalidDataException:
+                pass
+        if count == 0:
+            raise RuntimeError("Expected at least one datapoint in dataset.")


 # TODO this isn't the best since it can't interleave datasets
@@ -77,21 +76,14 @@ class ConstantLengthDataset(IterableDataset):
            self.tokens_dtype = torch.int64

    def __iter__(self):
-        buffer = {
-            "input_ids": [],
-            "attention_mask": [],
-            "labels": [],
-            "position_ids": [],
-        }
+        buffer = {"input_ids": [], "attention_mask": [], "labels": []}
        buffer_len = 0
        for dataset in self.datasets:
-            idx = 0
            iterator = iter(dataset)
            more_examples = True
            while more_examples:
                try:
                    example = next(iterator)
-                    idx += 1
                except StopIteration:
                    more_examples = False
                    example = None
@@ -113,9 +105,6 @@ class ConstantLengthDataset(IterableDataset):
                        attention_mask = torch.cat(buffer["attention_mask"], dim=-1)[
                            : self.seq_length
                        ]
-                        position_ids = torch.cat(buffer["position_ids"], dim=-1)[
-                            : self.seq_length
-                        ]
                        labels = torch.cat(buffer["labels"], dim=-1)[: self.seq_length]
                        if labels.size() == input_ids.size() and (
                            attention_mask.size() == input_ids.size()
@@ -124,20 +113,17 @@ class ConstantLengthDataset(IterableDataset):
                                "input_ids": input_ids,
                                "labels": labels,
                                "attention_mask": attention_mask,
-                                "position_ids": position_ids,
                            }
                        else:
-                            LOG.warning(
+                            logging.warning(
                                f"dropping batch due to tensor size mismatch input_ids: {input_ids.size()}, labels: {labels.size()}, attention_mask: {attention_mask.size()}"
                            )
                    buffer = {
                        "input_ids": [],
                        "attention_mask": [],
                        "labels": [],
-                        "position_ids": [],
                    }
                    buffer_len = 0
-                    idx = 1

                if example:
                    # FIXME
@@ -146,6 +132,11 @@ class ConstantLengthDataset(IterableDataset):
                        input_ids = example["input_ids"]
                        attention_mask = example["attention_mask"]
                        labels = example["labels"]
+                        if (
+                            buffer["input_ids"]
+                            and input_ids[0] == self.tokenizer.bos_token_id
+                        ):
+                            attention_mask[0] = 0

                        if add_concat_token:
                            input_ids.append(self.concat_token_id)
@@ -156,17 +147,13 @@ class ConstantLengthDataset(IterableDataset):
                            input_ids, dtype=self.tokens_dtype
                        )
                        attention_mask_with_concat = torch.tensor(
-                            [idx * m for m in attention_mask], dtype=torch.int16
+                            attention_mask, dtype=self.tokens_dtype
                        )
                        labels_with_concat = torch.tensor(
                            labels, dtype=self.tokens_dtype
                        )
-                        position_ids = torch.arange(
-                            len(input_ids), dtype=self.tokens_dtype
-                        )

                        buffer["input_ids"].append(input_ids_with_concat)
                        buffer["attention_mask"].append(attention_mask_with_concat)
                        buffer["labels"].append(labels_with_concat)
-                        buffer["position_ids"].append(position_ids)
                        buffer_len += len(input_ids)
--- a/src/axolotl/monkeypatch/llama_attn_hijack_flash.py
+++ b/src/axolotl/monkeypatch/llama_attn_hijack_flash.py
@@ -7,18 +7,10 @@ from typing import Optional, Tuple
 import torch
 import transformers
 from einops import rearrange
-
-try:
-    from flash_attn.flash_attn_interface import flash_attn_varlen_qkvpacked_func
-except ImportError:
-    from flash_attn.flash_attn_interface import (
-        flash_attn_unpadded_qkvpacked_func as flash_attn_varlen_qkvpacked_func,
-    )
-
+from flash_attn.bert_padding import pad_input, unpad_input
+from flash_attn.flash_attn_interface import flash_attn_unpadded_qkvpacked_func
 from transformers.models.llama.modeling_llama import apply_rotary_pos_emb

-from axolotl.monkeypatch.utils import get_cu_seqlens_from_pos_ids
-

 def forward(
    self,
@@ -87,20 +79,40 @@ def forward(
            dtype=torch.int32,
            device=qkv.device,
        )
-        output = flash_attn_varlen_qkvpacked_func(
+        output = flash_attn_unpadded_qkvpacked_func(
            qkv, cu_q_lens, max_s, 0.0, softmax_scale=None, causal=True
        )
        output = rearrange(output, "(b s) ... -> b s ...", b=bsz)
    else:
-        qkv = rearrange(qkv, "b s ... -> (b s) ...")
-        cu_q_lens, max_s = get_cu_seqlens_from_pos_ids(position_ids)
-        cu_q_lens = cu_q_lens.squeeze()
+        nheads = qkv.shape[-2]

-        output = flash_attn_varlen_qkvpacked_func(
-            qkv, cu_q_lens, max_s, 0.0, softmax_scale=None, causal=True
+        # pylint: disable=invalid-name
+        x = rearrange(qkv, "b s three h d -> b s (three h d)")
+        x_unpad, indices, cu_q_lens, max_s = unpad_input(x, key_padding_mask)
+        x_unpad = rearrange(
+            x_unpad,
+            "nnz (three h d) -> nnz three h d",
+            three=3,
+            h=nheads,
+        )
+        output_unpad = flash_attn_unpadded_qkvpacked_func(
+            x_unpad,
+            cu_q_lens,
+            max_s,
+            0.0,
+            softmax_scale=None,
+            causal=True,
+        )
+        output = rearrange(
+            pad_input(
+                rearrange(output_unpad, "nnz h d -> nnz (h d)"),
+                indices,
+                bsz,
+                q_len,
+            ),
+            "b s (h d) -> b s h d",
+            h=nheads,
        )
-        output = rearrange(output, "(b s) ... -> b s ...", b=bsz)
-
    return (
        self.o_proj(rearrange(output, "b s h d -> b s (h d)")),
        None,
--- a/src/axolotl/logging_config.py
+++ b/src/axolotl/logging_config.py
@@ -1,33 +0,0 @@
-"""Logging configuration settings"""
-
-import os
-import sys
-from logging.config import dictConfig
-from typing import Any, Dict
-
-DEFAULT_LOGGING_CONFIG: Dict[str, Any] = {
-    "version": 1,
-    "formatters": {
-        "simple": {
-            "format": "[%(asctime)s] [%(levelname)s] [%(name)s.%(funcName)s:%(lineno)d] [PID:%(process)d] %(message)s",
-        },
-    },
-    "filters": {},
-    "handlers": {
-        "console": {
-            "class": "logging.StreamHandler",
-            "formatter": "simple",
-            "filters": [],
-            "stream": sys.stdout,
-        },
-    },
-    "root": {"handlers": ["console"], "level": os.getenv("LOG_LEVEL", "INFO")},
-    "loggers": {
-        "axolotl": {"handlers": ["console"], "level": "DEBUG", "propagate": False},
-    },
-}
-
-
-def configure_logging():
-    """Configure with default logging"""
-    dictConfig(DEFAULT_LOGGING_CONFIG)
--- a/src/axolotl/monkeypatch/llama_attn_hijack_xformers.py
+++ b/src/axolotl/monkeypatch/llama_attn_hijack_xformers.py
@@ -7,7 +7,6 @@ import math
 from typing import Optional, Tuple

 import torch
-import torch.nn.functional as F
 import transformers.models.llama.modeling_llama
 from torch import nn

@@ -39,48 +38,21 @@ def xformers_forward(
    # pylint: disable=duplicate-code
    bsz, q_len, _ = hidden_states.size()

-    if not hasattr(self, "pretraining_tp"):
-        self.pretraining_tp = 1
-
-    if self.pretraining_tp > 1:
-        key_value_slicing = (
-            self.num_key_value_heads * self.head_dim
-        ) // self.pretraining_tp
-        query_slices = self.q_proj.weight.split(
-            (self.num_heads * self.head_dim) // self.pretraining_tp, dim=0
-        )
-        key_slices = self.k_proj.weight.split(key_value_slicing, dim=0)
-        value_slices = self.v_proj.weight.split(key_value_slicing, dim=0)
-
-        query_states = [
-            F.linear(hidden_states, query_slices[i]) for i in range(self.pretraining_tp)
-        ]
-        query_states = torch.cat(query_states, dim=-1)
-
-        key_states = [
-            F.linear(hidden_states, key_slices[i]) for i in range(self.pretraining_tp)
-        ]
-        key_states = torch.cat(key_states, dim=-1)
-
-        value_states = [
-            F.linear(hidden_states, value_slices[i]) for i in range(self.pretraining_tp)
-        ]
-        value_states = torch.cat(value_states, dim=-1)
-
-    else:
-        query_states = self.q_proj(hidden_states)
-        key_states = self.k_proj(hidden_states)
-        value_states = self.v_proj(hidden_states)
-
-    query_states = query_states.view(
-        bsz, q_len, self.num_heads, self.head_dim
-    ).transpose(1, 2)
-    key_states = key_states.view(
-        bsz, q_len, self.num_key_value_heads, self.head_dim
-    ).transpose(1, 2)
-    value_states = value_states.view(
-        bsz, q_len, self.num_key_value_heads, self.head_dim
-    ).transpose(1, 2)
+    query_states = (
+        self.q_proj(hidden_states)
+        .view(bsz, q_len, self.num_heads, self.head_dim)
+        .transpose(1, 2)
+    )
+    key_states = (
+        self.k_proj(hidden_states)
+        .view(bsz, q_len, self.num_heads, self.head_dim)
+        .transpose(1, 2)
+    )
+    value_states = (
+        self.v_proj(hidden_states)
+        .view(bsz, q_len, self.num_heads, self.head_dim)
+        .transpose(1, 2)
+    )

    kv_seq_len = key_states.shape[-2]
    if past_key_value is not None:
@@ -101,14 +73,6 @@ def xformers_forward(

    past_key_value = (key_states, value_states) if use_cache else None

-    # repeat k/v heads if n_kv_heads < n_heads
-    key_states = transformers.models.llama.modeling_llama.repeat_kv(
-        key_states, self.num_key_value_groups
-    )
-    value_states = transformers.models.llama.modeling_llama.repeat_kv(
-        value_states, self.num_key_value_groups
-    )
-
    # We only apply xformers optimizations if we don't need to output the whole attention matrix
    if not output_attentions:
        query_states = query_states.transpose(1, 2)
@@ -128,7 +92,6 @@ def xformers_forward(
                query_states,
                key_states,
                value_states,
-                # attn_bias=attention_mask,
                attn_bias=xformers.ops.LowerTriangularMask(),
            )
        attn_weights = None
@@ -165,23 +128,10 @@ def xformers_forward(
                f" {attn_output.size()}"
            )

-        attn_output = attn_output.transpose(1, 2).contiguous()
-        # end x-formers vs. not x-formers if-else block
+        attn_output = attn_output.transpose(1, 2)

    attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
-
-    if self.pretraining_tp > 1:
-        attn_output = attn_output.split(self.hidden_size // self.pretraining_tp, dim=2)
-        o_proj_slices = self.o_proj.weight.split(
-            self.hidden_size // self.pretraining_tp, dim=1
-        )
-        attn_output = sum(
-            F.linear(attn_output[i], o_proj_slices[i])
-            for i in range(self.pretraining_tp)
-        )
-    else:
-        attn_output = self.o_proj(attn_output)
-
+    attn_output = self.o_proj(attn_output)
    return attn_output, attn_weights, past_key_value


@@ -234,15 +184,14 @@ def sdp_attention_forward(

    # We only apply sdp attention if we don't need to output the whole attention matrix
    if not output_attentions:
-        with torch.backends.cuda.sdp_kernel():
-            attn_output = torch.nn.functional.scaled_dot_product_attention(
-                query_states,
-                key_states,
-                value_states,
-                attn_mask=attention_mask,
-                is_causal=False,
-            )
-            attn_weights = None
+        attn_output = torch.nn.functional.scaled_dot_product_attention(
+            query_states,
+            key_states,
+            value_states,
+            attn_mask=attention_mask,
+            is_causal=False,
+        )
+        attn_weights = None
    else:
        attn_weights = torch.matmul(
            query_states, key_states.transpose(2, 3)
--- a/src/axolotl/monkeypatch/llama_expand_mask.py
+++ b/src/axolotl/monkeypatch/llama_expand_mask.py
@@ -1,52 +0,0 @@
-"""
-expands the binary attention mask per 3.2.2 of https://arxiv.org/pdf/2107.02027.pdf
-"""
-from typing import Optional
-
-import torch
-
-
-def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
-    """
-    Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
-    This expansion handles packed sequences so that sequences share the same attention mask integer value
-    when they attend to each other within that sequence.
-    This expansion transforms the mask to lower triangular form to prevent future peeking.
-    """
-    bsz, src_len = mask.size()
-    tgt_len = tgt_len if tgt_len is not None else src_len
-
-    mask = mask.unsqueeze(1).unsqueeze(2)
-    mask = mask.expand(bsz, 1, tgt_len, src_len)
-
-    # Create a binary mask from the original mask where zeros remain zeros and all other values are set to one
-    binary_mask = torch.where(
-        mask != 0,
-        torch.tensor(1).to(dtype),
-        torch.tensor(0).to(dtype),
-    )
-
-    # Create a block-diagonal mask.
-    # we multiply by the binary mask so that 0's in the original mask are correctly excluded
-    zero_one_mask = torch.eq(mask, mask.transpose(-1, -2)).int() * binary_mask
-
-    # Now let's create a lower triangular mask of ones that will zero out the upper triangular part
-    lower_triangular_ones = torch.tril(torch.ones((tgt_len, src_len), dtype=dtype)).to(
-        mask.device
-    )
-
-    # Use the lower triangular mask to zero out the upper triangular part of the zero_one_mask
-    masked_zero_one_mask = zero_one_mask * lower_triangular_ones
-    inverted_mask = 1.0 - masked_zero_one_mask
-
-    return inverted_mask.masked_fill(
-        inverted_mask.to(torch.bool), torch.finfo(dtype).min
-    )
-
-
-def hijack_expand_mask():
-    import transformers
-
-    transformers.models.llama.modeling_llama._expand_mask = (  # pylint: disable=protected-access
-        _expand_mask
-    )
--- a/src/axolotl/monkeypatch/llama_landmark_attn.py
+++ b/src/axolotl/monkeypatch/llama_landmark_attn.py
@@ -53,7 +53,7 @@ from transformers.utils import (
    replace_return_docstrings,
 )

-LOG = logging.getLogger("axolotl")
+logger = logging.get_logger(__name__)

 _CONFIG_FOR_DOC = "LlamaConfig"

@@ -862,7 +862,7 @@ class LlamaModel(LlamaPreTrainedModel):

        if self.gradient_checkpointing and self.training:
            if use_cache:
-                LOG.warning_once(
+                logger.warning_once(
                    "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
                )
                use_cache = False
--- a/src/axolotl/monkeypatch/utils.py
+++ b/src/axolotl/monkeypatch/utils.py
@@ -1,103 +0,0 @@
-"""
-Shared utils for the monkeypatches
-"""
-import torch
-
-
-def get_cu_seqlens(attn_mask):
-    """generate a cumulative sequence length mask for flash attention using attn mask"""
-    if len(attn_mask.shape) == 1:
-        attn_mask = attn_mask.unsqueeze(0)
-
-    device = attn_mask.device
-    results = []
-    max_seq_lens = []
-
-    for row in attn_mask:
-        # Exclude zeros to avoid adding their positions to the mask
-        t_non_zeros = row[row != 0]
-        # Find where the sequence number changes (including the first position)
-        seq_change = torch.cat(
-            [
-                torch.tensor([1], dtype=torch.int32, device=device),
-                t_non_zeros[1:] != t_non_zeros[:-1],
-            ]
-        )
-        # Get the indices where the sequence changes
-        change_indices = torch.cat(
-            [
-                (seq_change == 1).nonzero(as_tuple=True)[0],
-                torch.tensor([len(t_non_zeros)], dtype=torch.int32, device=device),
-            ]
-        )
-        # Calculate the sequence lengths
-        seq_lengths = change_indices[1:] - change_indices[:-1]
-        # Calculate the length of the final sequence or padding
-        final_seq_length = len(row) - change_indices[-1]
-        # Append the length of the final sequence or padding to seq_lengths
-        if final_seq_length.item():
-            seq_lengths = torch.cat(
-                [
-                    seq_lengths,
-                    torch.tensor(
-                        [final_seq_length.item()], dtype=torch.int32, device=device
-                    ),
-                ]
-            )
-        # Calculate the cumulative sequence lengths
-        cu_seqlens = torch.cat(
-            [torch.tensor([0], dtype=torch.int32, device=device), seq_lengths.cumsum(0)]
-        )
-        max_seq_len = (cu_seqlens[1:] - cu_seqlens[:-1]).max()
-        results.append(cu_seqlens)
-        max_seq_lens.append(max_seq_len)
-
-    return torch.stack(results).to(dtype=torch.int32), torch.stack(max_seq_lens)
-
-
-def get_cu_seqlens_from_pos_ids(position_ids):
-    """generate a cumulative sequence length mask for flash attention using pos ids"""
-    if len(position_ids.shape) == 1:
-        position_ids = position_ids.unsqueeze(0)
-
-    device = position_ids.device
-    results = []
-    max_seq_lens = []
-
-    for row in position_ids:
-        # Count the number of consecutive zeros from the right side
-        padding_length = (row == 0).int().flip(dims=[0]).cumprod(dim=0).sum().item()
-
-        # Adjust the row to exclude padding
-        adjusted_row = row[:-padding_length] if padding_length else row.clone()
-
-        # Find where the position resets to 0 (indicating a new sequence)
-        seq_starts = torch.cat(
-            [
-                torch.tensor([True], dtype=torch.bool, device=device),
-                adjusted_row[1:] == 0,
-            ]
-        )
-        # Get the indices where the sequence starts
-        start_indices = torch.cat(
-            [
-                (seq_starts).nonzero(as_tuple=True)[0],
-                torch.tensor([len(adjusted_row)], dtype=torch.int32, device=device),
-            ]
-        )
-        # Calculate the sequence lengths
-        seq_lengths = start_indices[1:] - start_indices[:-1]
-        # Calculate the cumulative sequence lengths
-        cu_seqlens = torch.cat(
-            [torch.tensor([0], dtype=torch.int32, device=device), seq_lengths.cumsum(0)]
-        )
-        # Append the padding length to the cumulative sequence lengths
-        if padding_length:
-            cu_seqlens = torch.cat(
-                [cu_seqlens, torch.tensor([len(row)], dtype=torch.int32, device=device)]
-            )
-        max_seq_len = (cu_seqlens[1:] - cu_seqlens[:-1]).max()
-        results.append(cu_seqlens)
-        max_seq_lens.append(max_seq_len)
-
-    return torch.stack(results).to(dtype=torch.int32), torch.stack(max_seq_lens)
--- a/src/axolotl/prompt_strategies/alpaca_w_system.py
+++ b/src/axolotl/prompt_strategies/alpaca_w_system.py
@@ -66,46 +66,15 @@ class SystemDataPrompter(AlpacaPrompter):
    ) -> Generator[str, None, None]:
        # returns the full prompt from instruction and optional input
        # if a label (=response, =output) is provided, it's also appended.
-        formatted_sys_prompt = (
-            self.system_format.format(system=system)
-            if system and self.system_format
-            else ""
-        )
        if input:
-            res = formatted_sys_prompt + self.turn_format.format(
-                instruction=instruction, input=input
-            )
+            res = system + self.turn_format.format(instruction=instruction, input=input)
        else:
-            res = formatted_sys_prompt + self.turn_no_input_format.format(
-                instruction=instruction
-            )
+            res = system + self.turn_no_input_format.format(instruction=instruction)
        if output:
            res = f"{res}{output}"
        yield res


-class OpenOrcaSystemDataPrompter(SystemDataPrompter):
-    """
-    Alpaca Style Prompter that uses system prompts from the dataset, with OpenOrca prompts
-    """
-
-    def match_prompt_style(self):
-        # pylint: disable=duplicate-code
-        if self.prompt_style == PromptStyle.INSTRUCT.value:
-            self.turn_format = "### User:\n{instruction}\n\n### Additional Context:\n{input}\n\n### Assistant:\n"
-            self.turn_no_input_format = "### User:\n{instruction}\n\n### Assistant:\n"
-        if self.prompt_style == PromptStyle.CHAT.value:
-            self.turn_format = "User: {instruction}\n{input}\nAssistant:"
-            self.turn_no_input_format = "User: {instruction}\nAssistant:"
-            self.system_format = "System: {system}\n"
-        if self.prompt_style == PromptStyle.CHATML.value:
-            self.turn_format = "<|im_start|>user\n{instruction}\n{input}<|im_end|>\n<|im_start|>assistant\n"
-            self.turn_no_input_format = (
-                "<|im_start|>user\n{instruction}<|im_end|>\n<|im_start|>assistant\n"
-            )
-            self.system_format = "<|im_start|>system\n{system}<|im_end|>\n"
-
-
 class OpenOrcaPromptTokenizingStrategy(InstructionWSystemPromptTokenizingStrategy):
    """
    Tokenizing strategy for OpenOrca datasets
@@ -144,16 +113,7 @@ def load_chat(tokenizer, cfg):

 def load_open_orca(tokenizer, cfg):
    return OpenOrcaPromptTokenizingStrategy(
-        OpenOrcaSystemDataPrompter(PromptStyle.INSTRUCT.value),
-        tokenizer,
-        cfg.train_on_inputs,
-        cfg.sequence_len,
-    )
-
-
-def load_open_orca_chatml(tokenizer, cfg):
-    return OpenOrcaPromptTokenizingStrategy(
-        OpenOrcaSystemDataPrompter(PromptStyle.CHATML.value),
+        SystemDataPrompter(PromptStyle.INSTRUCT.value),
        tokenizer,
        cfg.train_on_inputs,
        cfg.sequence_len,
--- a/src/axolotl/prompt_strategies/llama2_chat.py
+++ b/src/axolotl/prompt_strategies/llama2_chat.py
@@ -1,205 +0,0 @@
-"""
-Prompt Strategy for finetuning Llama2 chat models
-see also https://github.com/facebookresearch/llama/blob/6c7fe276574e78057f917549435a2554000a876d/llama/generation.py#L213 for ma reference implementation.
-
-This implementation is based on the Vicuna PR and the fastchat repo, see also:
-https://github.com/lm-sys/FastChat/blob/cdd7730686cb1bf9ae2b768ee171bdf7d1ff04f3/fastchat/conversation.py#L847
-
-Use dataset type: "llama2_chat" in conig.yml to use this prompt style.
-
-E.g. in the config.yml:
-```
-datasets:
-  - path: llama_finetune_train.jsonl
-    type: llama2_chat
-```
-
-The dataset itself should look like this:
-```
-{'conversations':[{"from": "human", "value": "Who are you?"}, {"from": "gpt", "value": "I am Vicuna"},...]}
-```
-in a jsonl file. The first message should be from the human, the second from gpt.
-For a custom system message, the first "from" can be "system" (followed by alternating "human" and "gpt" turns).
-
-Important: Don't use "special_tokens:" in your config.yml if you are not sure what you are doing!
-"""
-
-import logging
-from dataclasses import dataclass, field
-from typing import Generator, List, Sequence
-
-from axolotl.prompt_tokenizers import PromptTokenizingStrategy
-from axolotl.prompters import IGNORE_TOKEN_ID
-
-
-@dataclass
-class Llama2ChatConversation:
-    """A class that manages prompt templates and keeps all conversation history.
-    copied from https://github.com/lm-sys/FastChat/blob/main/fastchat/conversation.py"""
-
-    name: str = "llama2"
-    # The system prompt
-    system: str = (
-        "[INST] <<SYS>>\nYou are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. "
-        "Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. "
-        "Please ensure that your responses are socially unbiased and positive in nature.\n\n"
-        "If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. "
-        "If you don't know the answer to a question, please don't share false information.\n<</SYS>>\n\n"
-    )
-    roles: Sequence[str] = ("[INST]", "[/INST]")
-    messages: List[List[str]] = field(default_factory=list)
-    offset: int = 0
-    sep = " "
-    sep2 = " </s><s>"
-    stop_token_ids = [2]
-
-    def get_prompt(self) -> str:
-        """Get the prompt for generation."""
-        seps = [self.sep, self.sep2]
-        ret = ""
-        for i, (role, message) in enumerate(self.messages):
-            if (i == len(self.messages) - 1) and (role == self.roles[0]):
-                # last message is from user (due to length),
-                #  return prompt without it for training
-                return ret
-            if i == 0:
-                ret += self.system + message.strip()
-            else:
-                ret += role + " " + message.strip() + seps[i % 2]
-        return ret
-
-    def append_message(self, role: str, message: str):
-        """Append a new message."""
-        self.messages.append([role, message])
-
-
-class LLama2ChatTokenizingStrategy(PromptTokenizingStrategy):
-    """
-    Tokenizing strategy for ShareGPT prompts.
-    adapted from https://github.com/lm-sys/FastChat/blob/main/fastchat/train/train.py
-    """
-
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        self.sequence_len = 4096
-        self.tokenizer.add_special_tokens({"pad_token": "<pad>"})
-        # https://huggingface.co/meta-llama/Llama-2-7b-chat-hf/blob/main/added_tokens.json
-
-    def tokenize_prompt(self, prompt):
-        conv = next(self.prompter.build_prompt(prompt))
-        conversation_str = conv.get_prompt()
-
-        # Tokenize conversations
-        input_ids = self.tokenizer(
-            conversation_str,
-            return_tensors="pt",
-            padding="max_length",
-            max_length=self.sequence_len,
-            truncation=True,
-        ).input_ids[0]
-        target = input_ids.clone()
-
-        # Mask targets. Only compute loss on the assistant outputs.
-        sep = conv.roles[1]
-
-        total_len = int(target.ne(self.tokenizer.pad_token_id).sum())
-
-        turns = conversation_str.split(conv.sep2)
-        cur_len = 1
-        target[:cur_len] = IGNORE_TOKEN_ID
-        for turn in turns:
-            if turn == "":
-                break
-            turn_len = len(self.tokenizer(turn).input_ids)
-
-            parts = turn.split(sep)
-            if len(parts) != 2:
-                break
-            parts[0] += sep
-            # "-1" is hardcoded for the LLaMA tokenizer to make the offset correct.
-            instruction_len = len(self.tokenizer(parts[0]).input_ids) - 1
-
-            # Ignore the user instructions
-            target[cur_len - 1 : cur_len + instruction_len] = IGNORE_TOKEN_ID
-            cur_len += turn_len + 2  # due to length of role token
-
-        target[cur_len:] = IGNORE_TOKEN_ID
-
-        if cur_len < self.sequence_len:
-            if cur_len != total_len:
-                target[:] = IGNORE_TOKEN_ID
-                logging.warning(
-                    f"WARNING: tokenization mismatch: {cur_len} vs. {total_len}."
-                    f" (ignored)"
-                )
-
-        attention_mask = input_ids.ne(self.tokenizer.pad_token_id).tolist()
-        input_ids = input_ids.tolist()
-        target = target.tolist()
-        # this is a fix for the tokenizer which tokenizes [ differently with eos tokens and
-        # follows the original llama implementation
-        for i in range(2, total_len - 2):
-            if input_ids[i] == 29961:
-                input_ids[i] = 518
-            if target[i] == 29961:
-                target[i] = 518
-        return {
-            "input_ids": input_ids,
-            "labels": target,
-            "attention_mask": attention_mask,
-        }
-
-
-class Llama2ChatPrompter:  # pylint: disable=too-few-public-methods
-    """
-    A prompter that generates prompts for Llama2 models.
-    """
-
-    system_prompt = (
-        "[INST] <<SYS>>\nYou are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. "
-        "Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. "
-        "Please ensure that your responses are socially unbiased and positive in nature.\n\n"
-        "If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. "
-        "If you don't know the answer to a question, please don't share false information.\n<</SYS>>\n\n"
-    )
-
-    def build_prompt(self, source) -> Generator[Llama2ChatConversation, None, None]:
-        # see https://github.com/lm-sys/FastChat/blob/da0641e567cf93756b0978ab5a6b092e96f06240/fastchat/train/train.py#L78
-        source = source["conversations"]  # fix data structure for datasets
-
-        # if system prompt provided, use it
-        if source[0]["from"] == "system":
-            system = f"[INST] <<SYS>>\n{source[0]['value']}\n<</SYS>>\n\n"
-            source = source[1:]
-        else:
-            system = self.system_prompt
-
-        conv = Llama2ChatConversation(system=system)
-
-        if len(source) < 2:
-            # If there isn't a back and forth conversation, ignore it
-            # also happens on the data splitting leaving empty conversations
-            raise IndexError
-
-        roles = {"human": conv.roles[0], "gpt": conv.roles[1]}
-
-        if roles[source[0]["from"]] != conv.roles[0]:
-            # Skip the first one if it is not from human
-            source = source[1:]
-
-        conv.messages = []  # pylint: disable=R0801
-        for j, sentence in enumerate(source):
-            role = roles[sentence["from"]]
-            assert role == conv.roles[j % 2]
-            if sentence["value"]:
-                conv.append_message(role, sentence["value"])
-        yield conv
-
-
-def load(tokenizer, cfg) -> LLama2ChatTokenizingStrategy:
-    return LLama2ChatTokenizingStrategy(
-        Llama2ChatPrompter(),
-        tokenizer,
-        cfg.train_on_inputs,
-        cfg.sequence_len,
-    )
--- a/src/axolotl/prompt_strategies/orcamini.py
+++ b/src/axolotl/prompt_strategies/orcamini.py
@@ -1,46 +0,0 @@
-"""
-Prompt Strategy for finetuning Orca Mini (v2) models
-see also https://huggingface.co/psmathur/orca_mini_v2_7b for more information
-
-Use dataset type: orcamini in conig.yml to use this prompt style.
-
-Compared to the alpaca_w_system.open_orca dataset type,
-this one specifies the system prompt with "### System:".
-
-Not suited/tested for multiple-turn conversations without further adjustments.
-"""
-from typing import Generator, Union
-
-from axolotl.prompt_strategies.alpaca_w_system import OpenOrcaPromptTokenizingStrategy
-from axolotl.prompters import AlpacaPrompter
-
-
-class OrcaMiniPrompter(AlpacaPrompter):
-    """Adjusted Prompter for Orca Mini (v2) datasets"""
-
-    def match_prompt_style(self):
-        self.turn_no_input_format = (
-            "### System:\n{system}\n\n### User:\n{instruction}\n\n### Response:\n"
-        )
-
-    def build_prompt_w_system(
-        self,
-        system: str,
-        instruction: str,
-        output: Union[None, str] = None,
-    ) -> Generator[str, None, None]:
-        # returns the full prompt from instruction and optional input
-        # if a label (=response, =output) is provided, it's also appended.
-        res = self.turn_no_input_format.format(system=system, instruction=instruction)
-        if output:
-            res = f"{res}{output}"
-        yield res
-
-
-def load(tokenizer, cfg):
-    return OpenOrcaPromptTokenizingStrategy(
-        OrcaMiniPrompter(),
-        tokenizer,
-        cfg.train_on_inputs,
-        cfg.sequence_len,
-    )
--- a/src/axolotl/prompt_strategies/pygmalion.py
+++ b/src/axolotl/prompt_strategies/pygmalion.py
@@ -11,8 +11,6 @@ from axolotl.prompt_tokenizers import (
    tokenize_prompt_default,
 )

-LOG = logging.getLogger("axolotl")
-
 IGNORE_TOKEN_ID = -100


@@ -66,7 +64,7 @@ class PygmalionPromptTokenizingStrategy(PromptTokenizingStrategy):
                    *copy.deepcopy(res["input_ids"])
                ][len(self.bot_prefix_token_ids) :]
            else:
-                LOG.warning(f"unknown role in conversation: {role}")
+                logging.warning(f"unknown role in conversation: {role}")
                res = defaultdict(lambda: [])

            # pylint: disable=duplicate-code
--- a/src/axolotl/prompt_tokenizers.py
+++ b/src/axolotl/prompt_tokenizers.py
@@ -10,8 +10,6 @@ from transformers import PreTrainedTokenizer

 from axolotl.prompters import IGNORE_TOKEN_ID

-LOG = logging.getLogger("axolotl")
-
 IGNORE_INDEX = -100
 LLAMA_DEFAULT_PAD_TOKEN = "[PAD]"  # nosec
 LLAMA_DEFAULT_EOS_TOKEN = "</s>"  # nosec
@@ -48,22 +46,16 @@ class PromptTokenizingStrategy(abc.ABC):

    @functools.lru_cache(maxsize=128)
    def _get_user_token(self):
-        try:
-            id_or_ids = self.tokenizer.convert_tokens_to_ids("<|USER|>")
-            if isinstance(id_or_ids, (int,)):
-                return id_or_ids
-        except KeyError:
-            pass
+        id_or_ids = self.tokenizer.convert_tokens_to_ids("<|USER|>")
+        if isinstance(id_or_ids, (int,)):
+            return id_or_ids
        return False

    @functools.lru_cache(maxsize=128)
    def _get_assistant_token(self):
-        try:
-            id_or_ids = self.tokenizer.convert_tokens_to_ids("<|ASSISTANT|>")
-            if isinstance(id_or_ids, (int,)):
-                return id_or_ids
-        except KeyError:
-            pass
+        id_or_ids = self.tokenizer.convert_tokens_to_ids("<|ASSISTANT|>")
+        if isinstance(id_or_ids, (int,)):
+            return id_or_ids
        return False

    def _tokenize(self, prompt: str, add_eos_token=True, strip_bos_token=False):
@@ -392,7 +384,7 @@ class ShareGPTPromptTokenizingStrategy(PromptTokenizingStrategy):
                        # everything from this is masked out from the labels
                        labels = [IGNORE_TOKEN_ID] * len(res["input_ids"])
                    else:
-                        LOG.warning(f"unhandled role: {part[0]}")
+                        logging.warning(f"unhandled role: {part[0]}")

                # pylint: disable=duplicate-code
                result, current_len = parse_tokenized_to_result(
--- a/src/axolotl/prompters.py
+++ b/src/axolotl/prompters.py
@@ -5,7 +5,6 @@ import logging
 from enum import Enum, auto
 from typing import Generator, List, Optional, Tuple, Union

-LOG = logging.getLogger("axolotl")
 IGNORE_TOKEN_ID = -100


@@ -16,7 +15,6 @@ class PromptStyle(Enum):

    INSTRUCT = "instruct"
    CHAT = "chat"
-    CHATML = "chatml"


 class AlpacaPrompter:
@@ -26,7 +24,6 @@ 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
    turn_format: str
    turn_no_input_format: str
    prompt_style: Optional[PromptStyle] = None
@@ -36,23 +33,14 @@ class AlpacaPrompter:
        self.match_prompt_style()

    def match_prompt_style(self):
-        # pylint: disable=duplicate-code
        if self.prompt_style == PromptStyle.INSTRUCT.value:
            self.turn_format = "### Instruction:\n{instruction}\n\n### Input:\n{input}\n\n### Response:\n"
            self.turn_no_input_format = (
                "### Instruction:\n{instruction}\n\n### Response:\n"
            )
-            self.system_format = "### System:\n{system}\n\n"
        if self.prompt_style == PromptStyle.CHAT.value:
            self.turn_format = "USER: {instruction}\n{input}\nASSISTANT:"
            self.turn_no_input_format = "USER: {instruction}\nASSISTANT:"
-            self.system_format = "SYSTEM: {system}\n"
-        if self.prompt_style == PromptStyle.CHATML.value:
-            self.turn_format = "<|im_start|>user\n{instruction}\n{input}<|im_end|>\n<|im_start|>assistant\n"
-            self.turn_no_input_format = (
-                "<|im_start|>user\n{instruction}<|im_end|>\n<|im_start|>assistant\n"
-            )
-            self.system_format = "<|im_start|>system\n{system}<|im_end|>\n"

    def build_prompt(
        self,
@@ -253,7 +241,7 @@ class Conversation:
            if message:
                yield (role + ":", " " + message)
            else:
-                LOG.warning(f"role with empty message: {role}")
+                logging.warning(f"role with empty message: {role}")
                yield (role + ":", "")

    def copy(self):
--- a/src/axolotl/utils/collators.py
+++ b/src/axolotl/utils/collators.py
@@ -1,121 +0,0 @@
-"""
-DataCollator for axolotl to pad labels and position_ids for packed sequences
-"""
-from dataclasses import dataclass
-from typing import Any, Optional, Union
-
-import numpy as np
-from transformers import PreTrainedTokenizerBase
-from transformers.utils import PaddingStrategy
-
-
-@dataclass
-class DataCollatorForSeq2Seq:
-    """
-    Data collator that will dynamically pad the inputs received, as well as the labels and position_ids
-
-    Args:
-        tokenizer ([`PreTrainedTokenizer`] or [`PreTrainedTokenizerFast`]):
-            The tokenizer used for encoding the data.
-        model ([`PreTrainedModel`]):
-            The model that is being trained. If set and has the *prepare_decoder_input_ids_from_labels*, use it to
-            prepare the *decoder_input_ids*
-
-            This is useful when using *label_smoothing* to avoid calculating loss twice.
-        padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `True`):
-            Select a strategy to pad the returned sequences (according to the model's padding side and padding index)
-            among:
-
-            - `True` or `'longest'` (default): Pad to the longest sequence in the batch (or no padding if only a single
-              sequence is provided).
-            - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum
-              acceptable input length for the model if that argument is not provided.
-            - `False` or `'do_not_pad'`: No padding (i.e., can output a batch with sequences of different lengths).
-        max_length (`int`, *optional*):
-            Maximum length of the returned list and optionally padding length (see above).
-        pad_to_multiple_of (`int`, *optional*):
-            If set will pad the sequence to a multiple of the provided value.
-
-            This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability >=
-            7.5 (Volta).
-        label_pad_token_id (`int`, *optional*, defaults to -100):
-            The id to use when padding the labels (-100 will be automatically ignored by PyTorch loss functions).
-        return_tensors (`str`):
-            The type of Tensor to return. Allowable values are "np", "pt" and "tf".
-    """
-
-    tokenizer: PreTrainedTokenizerBase
-    model: Optional[Any] = None
-    padding: Union[bool, str, PaddingStrategy] = True
-    max_length: Optional[int] = None
-    pad_to_multiple_of: Optional[int] = None
-    label_pad_token_id: int = -100
-    position_pad_token_id: int = 0
-    return_tensors: str = "pt"
-
-    def __call__(self, features, return_tensors=None):
-        labels = None
-        if return_tensors is None:
-            return_tensors = self.return_tensors
-
-        for feature_name, pad_token_id in [
-            ("labels", self.label_pad_token_id),
-            ("position_ids", self.position_pad_token_id),
-        ]:
-            feat = (
-                [feature[feature_name] for feature in features]
-                if feature_name in features[0].keys()
-                else None
-            )
-            labels = feat if feat and feature_name == "labels" else labels
-            # We have to pad the labels before calling `tokenizer.pad` as this method won't pad them and needs them of the
-            # same length to return tensors.
-            if feat is not None:
-                max_feature_length = max(len(l) for l in feat)  # noqa: E741
-                if self.pad_to_multiple_of is not None:
-                    max_feature_length = (
-                        (max_feature_length + self.pad_to_multiple_of - 1)
-                        // self.pad_to_multiple_of
-                        * self.pad_to_multiple_of
-                    )
-
-                padding_side = self.tokenizer.padding_side
-                for feature in features:
-                    remainder = [pad_token_id] * (
-                        max_feature_length - len(feature[feature_name])
-                    )
-                    if isinstance(feature[feature_name], list):
-                        feature[feature_name] = (
-                            feature[feature_name] + remainder
-                            if padding_side == "right"
-                            else remainder + feature[feature_name]
-                        )
-                    elif padding_side == "right":
-                        feature[feature_name] = np.concatenate(
-                            [feature[feature_name], remainder]
-                        ).astype(np.int64)
-                    else:
-                        feature[feature_name] = np.concatenate(
-                            [remainder, feature[feature_name]]
-                        ).astype(np.int64)
-
-        features = self.tokenizer.pad(
-            features,
-            padding=self.padding,
-            max_length=self.max_length,
-            pad_to_multiple_of=self.pad_to_multiple_of,
-            return_tensors=return_tensors,
-        )
-
-        # prepare decoder_input_ids
-        if (
-            labels is not None
-            and self.model is not None
-            and hasattr(self.model, "prepare_decoder_input_ids_from_labels")
-        ):
-            decoder_input_ids = self.model.prepare_decoder_input_ids_from_labels(
-                labels=features["labels"]
-            )
-            features["decoder_input_ids"] = decoder_input_ids
-
-        return features
--- a/src/axolotl/utils/data.py
+++ b/src/axolotl/utils/data.py
@@ -1,7 +1,5 @@
 """Module containing data utilities"""
 import functools
-import hashlib
-import itertools
 import logging
 from hashlib import md5
 from pathlib import Path
@@ -36,9 +34,6 @@ from axolotl.prompters import (
    ShareGPTPrompter,
    SummarizeTLDRPrompter,
 )
-from axolotl.utils.distributed import barrier, is_main_process
-
-LOG = logging.getLogger("axolotl")


 def load_tokenized_prepared_datasets(
@@ -78,17 +73,17 @@ def load_tokenized_prepared_datasets(
    if dataset:
        ...
    elif any(prepared_ds_path.glob("*")):
-        LOG.info(f"Loading prepared dataset from disk at {prepared_ds_path}...")
+        logging.info(f"Loading prepared dataset from disk at {prepared_ds_path}...")
        dataset = load_from_disk(str(prepared_ds_path))
-        LOG.info("Prepared dataset loaded from disk...")
+        logging.info("Prepared dataset loaded from disk...")
    else:
-        LOG.info(f"Unable to find prepared dataset in {prepared_ds_path}")
-        LOG.info("Loading raw datasets...")
+        logging.info(f"Unable to find prepared dataset in {prepared_ds_path}")
+        logging.info("Loading raw datasets...")

        if cfg.seed:
            seed = cfg.seed
        else:
-            LOG.info("No seed provided, using default seed of 42")
+            logging.info("No seed provided, using default seed of 42")
            seed = 42

        datasets = []
@@ -99,7 +94,6 @@ def load_tokenized_prepared_datasets(
            try:
                load_dataset(
                    d.path,
-                    name=d.name,
                    streaming=True,
                    use_auth_token=use_auth_token,
                )
@@ -111,10 +105,8 @@ def load_tokenized_prepared_datasets(
            local_path = Path(d.path)
            if local_path.exists():
                if local_path.is_dir():
-                    # TODO dirs with arrow or parquet files could be loaded with `load_from_disk`
                    ds = load_dataset(
                        d.path,
-                        name=d.name,
                        data_files=d.data_files,
                        streaming=False,
                        split=None,
@@ -122,7 +114,6 @@ def load_tokenized_prepared_datasets(
                elif local_path.is_file():
                    ds = load_dataset(
                        "json",
-                        name=d.name,
                        data_files=d.path,
                        streaming=False,
                        split=None,
@@ -132,22 +123,26 @@ def load_tokenized_prepared_datasets(
                        "unhandled dataset load: local path exists, but is neither a directory or a file"
                    )
            elif ds_from_hub:
-                ds = load_dataset(
-                    d.path,
-                    name=d.name,
-                    streaming=False,
-                    data_files=d.data_files,
-                    use_auth_token=use_auth_token,
-                )
+                if d.data_files:
+                    ds = load_dataset(
+                        d.path,
+                        streaming=False,
+                        data_files=d.data_files,
+                        use_auth_token=use_auth_token,
+                    )
+                else:
+                    ds = load_dataset(
+                        d.path,
+                        streaming=False,
+                        use_auth_token=use_auth_token,
+                    )
            else:
                fp = hf_hub_download(
                    repo_id=d.path,
                    repo_type="dataset",
                    filename=d.data_files,
                )
-                ds = load_dataset(
-                    "json", name=d.name, data_files=fp, streaming=False, split=None
-                )
+                ds = load_dataset("json", data_files=fp, streaming=False, split=None)
            if not ds:
                raise ValueError("unhandled dataset load")
            # support for using a subset of the data
@@ -261,29 +256,25 @@ def load_tokenized_prepared_datasets(
                suffix = ""
                if ":load_" in d.type:
                    suffix = f" Did you mean {d.type.replace(':load_', '.load_')}?"
-                LOG.error(f"unhandled prompt tokenization strategy: {d.type}. {suffix}")
+                logging.error(
+                    f"unhandled prompt tokenization strategy: {d.type}. {suffix}"
+                )
                raise ValueError(
                    f"unhandled prompt tokenization strategy: {d.type} {suffix}"
                )
-        LOG.info("tokenizing, merging, and shuffling master dataset")
+        logging.info("tokenizing, merging, and shuffling master dataset")

        samples: List[int] = []
-        chunk_size = 1000
        for d in datasets:
-            d_iter = iter(d)
-            while True:
-                chunk = list(itertools.islice(d_iter, chunk_size))
-                if not chunk:
-                    break
-                samples.extend(chunk)
-
-        LOG.info("shuffle")
+            samples = samples + list(d)
        dataset = Dataset.from_list(samples).shuffle(seed=seed)
        if cfg.local_rank == 0:
-            LOG.info(f"Saving merged prepared dataset to disk... {prepared_ds_path}")
+            logging.info(
+                f"Saving merged prepared dataset to disk... {prepared_ds_path}"
+            )
            dataset.save_to_disk(prepared_ds_path)
            if cfg.push_dataset_to_hub:
-                LOG.info(
+                logging.info(
                    f"Saving merged prepared dataset with push_to_hub... {cfg.push_dataset_to_hub}/{ds_hash}"
                )
                dataset.push_to_hub(
@@ -334,7 +325,7 @@ def load_prepare_datasets(
        use_auth_token = cfg.hf_use_auth_token
        try:
            if cfg.push_dataset_to_hub:
-                LOG.info(
+                logging.info(
                    f"Checking for packed prepared dataset from hub... {cfg.push_dataset_to_hub}/{ds_hash}"
                )
                dataset = load_dataset(
@@ -348,13 +339,13 @@ def load_prepare_datasets(
        if dataset:
            ...
        elif any(prepared_ds_path.glob("*")):
-            LOG.info(
+            logging.info(
                f"Loading prepared packed dataset from disk at {prepared_ds_path}..."
            )
            dataset = load_from_disk(str(prepared_ds_path))
-            LOG.info("Prepared packed dataset loaded from disk...")
+            logging.info("Prepared packed dataset loaded from disk...")
            if cfg.push_dataset_to_hub:
-                LOG.info(
+                logging.info(
                    f"Saving packed prepared dataset with push_to_hub... {cfg.push_dataset_to_hub}/{ds_hash}"
                )
                dataset.push_to_hub(
@@ -373,16 +364,17 @@ def load_prepare_datasets(
                [dataset],
                seq_length=max_packed_sequence_len,
            )
-            LOG.info(f"packing master dataset to len: {cfg.max_packed_sequence_len}")
+            logging.info(
+                f"packing master dataset to len: {cfg.max_packed_sequence_len}"
+            )
            dataset = Dataset.from_list(list(constant_len_dataset))

            # filter out bad data
-            # TODO convert to dataset.filter(...)
            dataset = Dataset.from_list(
                [
                    d
                    for d in dataset
-                    if len(d["input_ids"]) <= cfg.sequence_len
+                    if len(d["input_ids"]) < cfg.sequence_len
                    and len(d["input_ids"]) > 0
                    and len(d["input_ids"]) == len(d["attention_mask"])
                    and len(d["input_ids"]) == len(d["labels"])
@@ -390,12 +382,12 @@ def load_prepare_datasets(
            )

            if cfg.local_rank == 0:
-                LOG.info(
+                logging.info(
                    f"Saving packed prepared dataset to disk... {prepared_ds_path}"
                )
                dataset.save_to_disk(prepared_ds_path)
                if cfg.push_dataset_to_hub:
-                    LOG.info(
+                    logging.info(
                        f"Saving packed prepared dataset with push_to_hub... {cfg.push_dataset_to_hub}/{ds_hash}"
                    )
                    dataset.push_to_hub(
@@ -408,7 +400,7 @@ def load_prepare_datasets(
        )

    if cfg.dataset_shard_num and cfg.dataset_shard_idx is not None:
-        LOG.info(
+        logging.info(
            f"Using index #{cfg.dataset_shard_idx} of {cfg.dataset_shard_num} shards"
        )
        dataset = dataset.shard(
@@ -417,51 +409,7 @@ def load_prepare_datasets(
        )

    if cfg.val_set_size:
-        # ensure we end up with the same fingerprint by doing rank0 first and being able to cache
-        to_hash_train = (
-            dataset._fingerprint  # pylint: disable=protected-access
-            + "|"
-            + str(cfg.val_set_size)
-            + "|"
-            + "train"
-            + "|"
-            + str(cfg.seed or 42)
-        )
-        to_hash_test = (
-            dataset._fingerprint  # pylint: disable=protected-access
-            + "|"
-            + str(cfg.val_set_size)
-            + "|"
-            + "test"
-            + "|"
-            + str(cfg.seed or 42)
-        )
-        train_fingerprint = hashlib.md5(
-            to_hash_train.encode(), usedforsecurity=False
-        ).hexdigest()
-        test_fingerprint = hashlib.md5(
-            to_hash_test.encode(), usedforsecurity=False
-        ).hexdigest()
-
-        if is_main_process():
-            dataset = dataset.train_test_split(
-                test_size=cfg.val_set_size,
-                shuffle=False,
-                seed=cfg.seed or 42,
-                train_new_fingerprint=train_fingerprint,
-                test_new_fingerprint=test_fingerprint,
-            )
-        barrier()
-        if not is_main_process():
-            dataset = dataset.train_test_split(
-                test_size=cfg.val_set_size,
-                shuffle=False,
-                seed=cfg.seed or 42,
-                train_new_fingerprint=train_fingerprint,
-                test_new_fingerprint=test_fingerprint,
-            )
-        barrier()
-
+        dataset = dataset.train_test_split(test_size=cfg.val_set_size, shuffle=False)
        train_dataset = dataset["train"]
        eval_dataset = dataset["test"]
    else:
@@ -573,7 +521,7 @@ def encode_pretraining(tokenizer, max_tokens, examples):
        "attention_mask": [seq.tolist() for seq in new_attention_mask],
    }

-    LOG.debug(len(ret["input_ids"]))
+    logging.debug(len(ret["input_ids"]))
    return ret


--- a/src/axolotl/utils/dataloader.py
+++ b/src/axolotl/utils/dataloader.py
@@ -1,310 +0,0 @@
-# pylint: skip-file
-import hashlib
-import itertools
-import logging
-import math
-import queue
-import threading
-from typing import Any, Callable, List, Optional, Union
-
-import numba
-import numpy as np
-from torch.utils.data import DistributedSampler, Sampler
-
-LOG = logging.getLogger("axolotl.utils.dataloader")
-
-
-@numba.njit
-def ffd_check(a: np.ndarray, c: int, n: int):
-    # First-fit-decreasing bin packing
-    # Check if a[] could fit in n bins with capacity c
-    # https://en.wikipedia.org/wiki/First-fit-decreasing_bin_packing
-
-    a = np.sort(a)[::-1]
-    bins = np.full((n,), c, dtype=a.dtype)
-    for size in a:
-        not_found = True
-        for idx in range(n):
-            if bins[idx] >= size:
-                bins[idx] -= size
-                not_found = False
-                break
-
-        if not_found:
-            return False
-
-    return True
-
-
-@numba.njit
-def ffd_with_result(a: np.ndarray, c: int, start_index: int):
-    # First-fit-decreasing bin packing (with result return)
-
-    indices = np.argsort(a)[::-1]
-    a = a[indices]
-
-    bins: List[Any] = []
-    bins_result: List[Any] = []
-    for a_id, size in enumerate(a):
-        add_new = True
-        for idx in range(len(bins)):
-            if bins[idx] >= size:
-                bins[idx] -= size
-                bins_result[idx].append(indices[a_id] + start_index)
-                add_new = False
-                break
-
-        if add_new:
-            bins.append(c - size)
-            bins_result.append([indices[a_id] + start_index])
-
-    return bins_result, len(a)
-
-
-@numba.njit
-def allocate(
-    lengths: np.ndarray, lengths_cumsum: np.ndarray, rank: int, c: int, n: int
-):
-    """
-    :param lengths: array of lengths of each sample
-    :param lengths_cumsum: cumulative sum of consecutive lengths
-    :param rank: rank for this process
-    :param c: length of tokens per batch
-    :param n: number of ranks
-    :return:
-    """
-    # Dynamic batch allocator, similar to Multifit
-    # https://en.wikipedia.org/wiki/Multifit_algorithm
-    # ~99.5% efficiency on OpenChat training set (12 * 2048 ctx len)
-
-    s = 0
-    start_index = 0
-    result = []
-
-    while True:
-        # binary search [left, right)
-        left = 1
-        right = 1 + np.searchsorted(lengths_cumsum[start_index:], s + c * n, "right")
-
-        while right - left > 1:
-            mid = (left + right) // 2
-            if ffd_check(lengths[start_index : start_index + mid], c, n):
-                left = mid
-            else:
-                right = mid
-
-        # use length left
-        batch, tot_seqs = ffd_with_result(
-            lengths[start_index : start_index + left], c, start_index
-        )
-        if len(batch) < n:
-            break
-
-        start_index += left
-        s = lengths_cumsum[start_index - 1]
-
-        # add local rank
-        result.append(batch[rank])
-
-        yield batch[rank], tot_seqs, s, len(result) * c * n
-
-
-def chunk(iterable, n):
-    """
-    Chunk data into tuples of length n
-    """
-    # batched('ABCDEFG', 3) --> ABC DEF G
-    if n < 1:
-        raise ValueError("n must be at least one")
-    it = iter(iterable)
-    while batch := tuple(itertools.islice(it, n)):
-        yield batch
-
-
-def hash_indices(lst: List[int]) -> str:
-    # Convert the list of integers to a string representation
-    concatenated = ",".join(map(str, lst))
-
-    # Generate the hash
-    sha256 = hashlib.sha256()
-    sha256.update(concatenated.encode())
-
-    return sha256.hexdigest()
-
-
-class MultipackDistributedDataloader:
-    """Unpadded data loading using Multipack.
-    Adapted from https://github.com/imoneoi/openchat/blob/v3_fix_mle_loss/ochat/training_deepspeed/multipack_dataloader.py
-    Approximate (at most ~1.22x) the optimal solution of the identical-machines scheduling problem, which is NP-hard.
-    """
-
-    def __init__(
-        self,
-        dataset: Any,
-        collate_fn: Callable,
-        seq_max_length: int = 2048,
-        batch_size: int = 1,
-        sampler: Union[Sampler, DistributedSampler] = None,
-        packing_efficiency_estimate: float = 1.0,
-        sample_packing_seq_len_multiplier: int = 1,
-        device_count: int = 1,
-        total_num_tokens: Optional[int] = None,
-    ):
-        # Dataset
-        self.dataset = dataset
-        lengths_series = (
-            dataset.data.column("position_ids").to_pandas().apply(lambda x: x[-1] + 1)
-        )
-        self.lengths: np.ndarray = lengths_series.values
-        assert isinstance(self.lengths, np.ndarray)
-        assert batch_size % sample_packing_seq_len_multiplier == 0
-        assert batch_size >= sample_packing_seq_len_multiplier
-        self.sampler = sampler
-        self.batch_size = batch_size
-        self.sample_packing_seq_len_multiplier = sample_packing_seq_len_multiplier
-        self.seq_max_length = seq_max_length
-        self.batch_max_length = batch_size * seq_max_length
-        self.collate_fn = collate_fn
-
-        self.num_replicas = 1
-        self.rank = 0
-
-        # statistics
-        self.total_num_tokens = total_num_tokens
-        self.eff_total_used = 0
-        self.eff_total_slots = 0
-        self.packing_efficiency_estimate = packing_efficiency_estimate or 1.0
-        self.device_count = device_count
-
-        # for non-blocking batch creation
-        self.batch_queue: queue.Queue = queue.Queue(
-            maxsize=10
-        )  # Adjust maxsize as needed
-
-    def generate_batches(self, set_stats=False):
-        LOG.info("generating packed batches")
-        if self.sampler:
-            indices = [idx for idx in self.sampler]
-        else:
-            indices = range(0, len(self.dataset))
-
-        LOG.info(hash_indices(indices))
-        lengths = self.lengths[indices]
-        lengths_cumsum = np.cumsum(lengths)
-
-        alloc_iter = iter(
-            allocate(
-                lengths=lengths,
-                lengths_cumsum=lengths_cumsum,
-                rank=self.rank,
-                # c=self.batch_max_length,
-                c=self.seq_max_length * self.sample_packing_seq_len_multiplier,
-                n=self.num_replicas,
-            )
-        )
-
-        for batch, tot_seqs, total_used, total_slots in alloc_iter:
-            self.batch_queue.put([indices[b_idx] for b_idx in batch])
-            # statistics
-            if set_stats:
-                self.eff_total_used = total_used
-                self.eff_total_slots = total_slots
-        self.batch_queue.put(None)  # Signal the end of batch generation
-
-    def _generate_batches_thread(self):
-        try:
-            self.generate_batches(set_stats=True)
-        except Exception as e:
-            LOG.error(f"Error in batch generation thread: {e}")
-            self.batch_queue.put(
-                None
-            )  # Signal the end of batch generation in case of error
-
-    def __iter__(self):
-        if hasattr(self.sampler, "set_epoch"):
-            new_epoch = self.sampler.epoch + 1
-            self.sampler.set_epoch(new_epoch)
-            LOG.info(f"calling sampler.set_epoch({new_epoch})")
-        # Start the batch generation in a separate thread
-        batch_gen_thread = threading.Thread(target=self._generate_batches_thread)
-        batch_gen_thread.start()
-
-        features = self.dataset.features.keys()
-        len_remaining = self._len_est()
-        while True:
-            batch = self.batch_queue.get()
-            if batch is None:  # Sentinel value received, stop iteration
-                break
-            chunked_data = []
-            attn_mask_cum_idx = 0
-            concatenated = {}
-            batched_data = [self.dataset[batch_idx] for batch_idx in batch]
-            for feature in features:
-                if feature == "attention_mask":
-                    arrays = [
-                        (attn_mask_cum_idx + idx + 1) * np.array(item[feature])
-                        for idx, item in enumerate(batched_data)
-                        if feature in item
-                    ]
-                    attn_mask_cum_idx += len(batched_data)
-                    concatenated[feature] = np.concatenate(arrays)
-                else:
-                    arrays = [
-                        np.array(item[feature])
-                        for item in batched_data
-                        if feature in item
-                    ]
-                    concatenated[feature] = np.concatenate(arrays)
-            chunked_data.append(concatenated)
-
-            yield self.collate_fn(chunked_data)
-            len_remaining -= 1
-            if not len_remaining:
-                break
-        # Wait for the batch generation thread to finish
-        batch_gen_thread.join(timeout=5)
-        LOG.info(f"actual packing efficiency: {self.efficiency()}")
-
-    def _len_est(self):
-        if not self.total_num_tokens:
-            self.total_num_tokens = np.sum(self.lengths)
-        lengths_sum_per_device = self.total_num_tokens // self.device_count
-        LOG.info(
-            f"packing_efficiency_estimate: {self.packing_efficiency_estimate} "
-            f"total_num_tokens per device: {lengths_sum_per_device}"
-        )
-
-        # shave off 1% + 1 for dealing with variance in packing from random sampler to sampler
-        return (
-            math.floor(
-                0.99
-                * lengths_sum_per_device
-                / self.packing_efficiency_estimate
-                // self.seq_max_length
-                // self.batch_size
-            )
-            - 1
-        )
-
-    def __len__(self):
-        # this doesn't return the actual length b/c with distributed samplers, not all dataloaders get
-        # the same share of total tokens
-        # if not self.eff_total_used:
-        #     batches, _ = self.generate_batches(set_stats=True)
-        # LOG.info(
-        #     f"packing_efficiency_estimate: {self.packing_efficiency_estimate} "
-        #     f"actual packing efficiency: {self.efficiency()}"
-        # )
-        return max(1, self._len_est())
-
-    def len_w_stats(self):
-        if not self.eff_total_used:
-            batches, _ = self.generate_batches(set_stats=True)
-        LOG.info(
-            f"packing_efficiency_estimate: {self.packing_efficiency_estimate} "
-            f"actual packing efficiency: {self.efficiency()}"
-        )
-        return max(1, self._len_est())
-
-    def efficiency(self):
-        return self.eff_total_used / self.eff_total_slots
--- a/src/axolotl/utils/distributed.py
+++ b/src/axolotl/utils/distributed.py
@@ -1,41 +0,0 @@
-"""
-utility helpers for distributed checks
-"""
-import torch.distributed as dist
-from accelerate import Accelerator
-
-accelerate = None  # pylint: disable=invalid-name
-
-
-def load_accelerate():
-    global accelerate  # pylint: disable=global-statement
-    accelerate = Accelerator()
-
-
-def is_distributed():
-    """
-    Check if distributed training is initialized.
-    """
-    global accelerate  # pylint: disable=global-statement
-    if not accelerate:
-        accelerate = Accelerator()
-    return dist.is_available() and dist.is_initialized()
-
-
-def barrier():
-    """
-    Acts as a barrier to wait for all processes. This ensures that all processes
-    reach the barrier before proceeding further.
-    """
-    if is_distributed():
-        dist.barrier()
-
-
-def is_main_process():
-    """
-    Check if the current process is the main process.
-    If not in distributed mode, always return True.
-    """
-    if not is_distributed():
-        return True
-    return dist.get_rank() == 0
--- a/src/axolotl/utils/models.py
+++ b/src/axolotl/utils/models.py
@@ -23,8 +23,6 @@ from transformers import (  # noqa: F401

 from axolotl.prompt_tokenizers import LLAMA_DEFAULT_PAD_TOKEN

-LOG = logging.getLogger("axolotl")
-
 if TYPE_CHECKING:
    from peft import PeftConfig  # noqa: F401

@@ -36,32 +34,26 @@ def load_tokenizer(
    tokenizer_type,
    cfg,
 ):
-    tokenizer_kwargs = {}
    use_fast = True  # this is the default
    if cfg.tokenizer_use_fast is not None:
        use_fast = cfg.tokenizer_use_fast
-    if cfg.tokenizer_legacy is not None:
-        # True is the default w/ https://github.com/huggingface/transformers/pull/25224
-        tokenizer_kwargs["legacy"] = cfg.tokenizer_legacy
    if tokenizer_type:
        tokenizer = getattr(transformers, tokenizer_type).from_pretrained(
            tokenizer_config,
            trust_remote_code=cfg.trust_remote_code or False,
            use_fast=use_fast,
-            **tokenizer_kwargs,
        )
    else:
        tokenizer = AutoTokenizer.from_pretrained(
            tokenizer_config,
            trust_remote_code=cfg.trust_remote_code or False,
            use_fast=use_fast,
-            **tokenizer_kwargs,
        )

-    LOG.debug(f"EOS: {tokenizer.eos_token_id} / {tokenizer.eos_token}")
-    LOG.debug(f"BOS: {tokenizer.bos_token_id} / {tokenizer.bos_token}")
-    LOG.debug(f"PAD: {tokenizer.pad_token_id} / {tokenizer.pad_token}")
-    LOG.debug(f"UNK: {tokenizer.unk_token_id} / {tokenizer.unk_token}")
+    logging.debug(f"EOS: {tokenizer.eos_token_id} / {tokenizer.eos_token}")
+    logging.debug(f"BOS: {tokenizer.bos_token_id} / {tokenizer.bos_token}")
+    logging.debug(f"PAD: {tokenizer.pad_token_id} / {tokenizer.pad_token}")
+    logging.debug(f"UNK: {tokenizer.unk_token_id} / {tokenizer.unk_token}")

    if tokenizer.__class__.__name__ in [
        "LlamaTokenizer",
@@ -92,33 +84,29 @@ 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 is True
+    cfg.is_llama_derived_model = "llama" in base_model or (
+        cfg.model_type and "llama" in cfg.model_type.lower()
    )

    if cfg.is_llama_derived_model and cfg.flash_attention:
        if cfg.device not in ["mps", "cpu"] and not cfg.inference:
-            from axolotl.monkeypatch.llama_attn_hijack_flash import (
-                replace_llama_attn_with_flash_attn,
-            )
+            from axolotl.flash_attn import replace_llama_attn_with_flash_attn

-            LOG.info("patching with flash attention")
+            logging.info("patching with flash attention")
            replace_llama_attn_with_flash_attn()
    elif cfg.is_llama_derived_model and cfg.xformers_attention:
        from axolotl.monkeypatch.llama_attn_hijack_xformers import (
            hijack_llama_attention,
        )

-        LOG.info("patching with xformers attention")
+        logging.info("patching with xformers attention")
        hijack_llama_attention()
    elif cfg.is_llama_derived_model and cfg.sdp_attention:
        from axolotl.monkeypatch.llama_attn_hijack_xformers import (
            hijack_llama_sdp_attention,
        )

-        LOG.info("patching with sdp attention")
+        logging.info("patching with sdp attention")
        hijack_llama_sdp_attention()
    elif cfg.is_llama_derived_model and cfg.landmark_attention:
        from axolotl.monkeypatch.llama_landmark_attn import (
@@ -126,7 +114,7 @@ def load_model(
            patch_llama_with_landmark_attn,
        )

-        LOG.info("patching with landmark attention")
+        logging.info("patching with landmark attention")
        patch_llama_with_landmark_attn()

        # Note: This might overwrite previous additional_special_tokens
@@ -137,17 +125,9 @@ def load_model(
            replace_llama_rope_with_xpos_rope,
        )

-        LOG.info("patching with xpos rope")
+        logging.info("patching with xpos rope")
        replace_llama_rope_with_xpos_rope()

-    if cfg.is_llama_derived_model and (
-        cfg.max_packed_sequence_len or cfg.sample_packing
-    ):
-        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:
@@ -162,24 +142,18 @@ def load_model(

            replace_peft_model_with_int4_lora_model()
    except Exception as err:
-        LOG.exception(err)
+        logging.exception(err)
        raise err

-    if not cfg.gptq and (
-        (cfg.adapter == "lora" and load_in_8bit)
-        or (cfg.adapter == "qlora" and cfg.load_in_4bit)
-    ):
-        try:
-            from peft import prepare_model_for_kbit_training
-        except ImportError:
-            # For backward compatibility
-            from peft import (
-                prepare_model_for_int8_training as prepare_model_for_kbit_training,
-            )
+    try:
+        from peft import prepare_model_for_kbit_training
+    except ImportError:
+        # For backward compatibility
+        from peft import (
+            prepare_model_for_int8_training as prepare_model_for_kbit_training,
+        )

    model_kwargs = {}
-    if cfg.model_revision:
-        model_kwargs["revision"] = cfg.model_revision
    if cfg.adapter == "qlora" and cfg.load_in_4bit:
        model_kwargs["quantization_config"] = BitsAndBytesConfig(
            load_in_4bit=True,
@@ -211,7 +185,7 @@ def load_model(
                if len(files) > 0:
                    model_path = str(files[0])
                else:
-                    LOG.warning(
+                    logging.warning(
                        "unable to find a cached model file, this will likely fail..."
                    )
                    model_path = str(cache_model_path)
@@ -238,6 +212,7 @@ def load_model(
                load_in_8bit=cfg.load_in_8bit and cfg.adapter is not None,
                load_in_4bit=cfg.load_in_4bit and cfg.adapter is not None,
                torch_dtype=torch_dtype,
+                device_map="auto" if cfg.world_size == 1 else cfg.device_map,
                **model_kwargs,
            )
        # elif model_type == "GPTNeoXForCausalLM" and cfg.flash_attention:
@@ -272,6 +247,7 @@ def load_model(
                load_in_8bit=cfg.load_in_8bit and cfg.adapter is not None,
                load_in_4bit=cfg.load_in_4bit and cfg.adapter is not None,
                torch_dtype=torch_dtype,
+                device_map=cfg.device_map,
                trust_remote_code=cfg.trust_remote_code or False,
                **model_kwargs,
            )
@@ -288,42 +264,40 @@ def load_model(
                and cfg.sequence_len > config.max_seq_len
            ):
                config.max_seq_len = cfg.sequence_len
-                LOG.warning(f"increasing context length to {cfg.sequence_len}")
+                logging.warning(f"increasing context length to {cfg.sequence_len}")
            elif (
                hasattr(config, "max_sequence_length")
                and config.max_sequence_length
                and cfg.sequence_len > config.max_sequence_length
            ):
                config.max_sequence_length = cfg.sequence_len
-                LOG.warning(f"increasing context length to {cfg.sequence_len}")
+                logging.warning(f"increasing context length to {cfg.sequence_len}")
            model = AutoModelForCausalLM.from_pretrained(
                base_model,
                config=config,
                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=torch_dtype,
+                device_map=cfg.device_map,
                trust_remote_code=cfg.trust_remote_code or False,
                **model_kwargs,
            )
    except Exception as err:  # pylint: disable=broad-exception-caught
-        LOG.error(
+        logging.error(
            "Exception raised attempting to load model, retrying with AutoModelForCausalLM"
        )
-        LOG.exception(err)
+        logging.exception(err)
        model = AutoModelForCausalLM.from_pretrained(
            base_model,
            load_in_8bit=cfg.load_in_8bit and cfg.adapter is not None,
            load_in_4bit=cfg.load_in_4bit and cfg.adapter is not None,
            torch_dtype=torch_dtype,
+            device_map=cfg.device_map,
            trust_remote_code=cfg.trust_remote_code or False,
            **model_kwargs,
        )

-    embeddings_len = (
-        math.ceil(len(tokenizer) / 32) * 32
-        if cfg.resize_token_embeddings_to_32x
-        else len(tokenizer)
-    )
+    embeddings_len = math.ceil(len(tokenizer) / 32) * 32
    model.resize_token_embeddings(embeddings_len)

    if (
@@ -331,7 +305,7 @@ def load_model(
        and model.config.max_position_embeddings
        and cfg.sequence_len >= model.config.max_position_embeddings
    ):
-        LOG.warning(
+        logging.warning(
            f"increasing model.config.max_position_embeddings to {cfg.sequence_len}"
        )
        model.config.max_position_embeddings = cfg.sequence_len
@@ -340,21 +314,11 @@ def load_model(
        (cfg.adapter == "lora" and load_in_8bit)
        or (cfg.adapter == "qlora" and cfg.load_in_4bit)
    ):
-        LOG.info("converting PEFT model w/ prepare_model_for_kbit_training")
+        logging.info("converting PEFT model w/ prepare_model_for_kbit_training")
        model = prepare_model_for_kbit_training(
            model, use_gradient_checkpointing=cfg.gradient_checkpointing
        )

-        # LlamaRMSNorm layers are in fp32 after kbit_training, so we need to
-        # convert them back to fp16/bf16 for flash-attn compatibility.
-        if cfg.flash_attention and cfg.is_llama_derived_model:
-            for name, module in model.named_modules():
-                if "norm" in name:
-                    module.to(torch_dtype)
-                if "lm_head" in name or "embed_tokens" in name:
-                    if hasattr(module, "weight"):
-                        module.to(torch_dtype)
-
    model, lora_config = load_adapter(model, cfg, adapter)

    if cfg.ddp and not load_in_8bit:
@@ -362,7 +326,7 @@ def load_model(

    if cfg.gptq:
        # Scales to half
-        LOG.info("Fitting 4bit scales and zeros to half")
+        logging.info("Fitting 4bit scales and zeros to half")
        for _, module in model.named_modules():
            if "Autograd4bitQuantLinear" in str(type(module)) or "Linear4bitLt" in str(
                type(module)
@@ -388,7 +352,7 @@ def load_model(
        if param.requires_grad:
            requires_grad.append(f"{name}: {param.requires_grad}")
    if len(requires_grad) == 0:
-        LOG.warning("there are no parameters that require gradient updates")
+        logging.warning("there are no parameters that require gradient updates")
    model.config.use_cache = False

    if cfg.flash_optimum:
@@ -403,8 +367,6 @@ def load_adapter(model, cfg, adapter):

    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)
    if adapter == "llama-adapter":
@@ -424,7 +386,7 @@ def load_llama_adapter(model, cfg):
    )

    if cfg.lora_model_dir:
-        LOG.info("Loading pretained LORA")
+        logging.info("Loading pretained LORA")
        model = PeftModel.from_pretrained(
            model,
            cfg.lora_model_dir,
@@ -471,7 +433,7 @@ def load_lora(model, cfg):
            bits = 8

        linear_names = find_all_linear_names(bits, model)
-        LOG.info(f"found linear modules: {repr(linear_names)}")
+        logging.info(f"found linear modules: {repr(linear_names)}")
        lora_target_modules = list(set(lora_target_modules + linear_names))

    lora_config = LoraConfig(
--- a/src/axolotl/utils/tokenization.py
+++ b/src/axolotl/utils/tokenization.py
@@ -5,8 +5,6 @@ import logging

 from termcolor import colored

-LOG = logging.getLogger("axolotl")
-

 def check_dataset_labels(dataset, tokenizer):
    # the dataset is already shuffled, so let's just check the first 5 elements
@@ -34,7 +32,7 @@ def check_example_labels(example, tokenizer):
        )
        colored_tokens.append(colored_token)

-    LOG.info(" ".join(colored_tokens))
-    LOG.info("\n\n\n")
+    logging.info(" ".join(colored_tokens))
+    logging.info("\n\n\n")

    return " ".join(colored_tokens)
--- a/src/axolotl/utils/trainer.py
+++ b/src/axolotl/utils/trainer.py
@@ -1,102 +1,38 @@
 """Module containing the Trainer class and related functions"""
+
 import importlib
 import logging
 import math
 import os
 import sys
-from contextlib import contextmanager
-from dataclasses import dataclass, field
-from functools import partial
+from dataclasses import field
 from pathlib import Path
-from typing import Optional, Union
+from typing import Any, Dict, Optional

 import bitsandbytes as bnb
 import torch.cuda
+import torch.nn.functional as F
 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
-from transformers import EarlyStoppingCallback, Trainer, TrainingArguments
+from transformers import (
+    EarlyStoppingCallback,
+    EvalPrediction,
+    Trainer,
+    TrainingArguments,
+)
 from transformers.trainer_pt_utils import get_parameter_names

 from axolotl.utils.callbacks import (
    SaveBetterTransformerModelCallback,
    SavePeftModelCallback,
 )
-from axolotl.utils.collators import DataCollatorForSeq2Seq
-from axolotl.utils.dataloader import MultipackDistributedDataloader
 from axolotl.utils.schedulers import (
    InterpolatingLogScheduler,
    get_cosine_schedule_with_quadratic_warmup,
 )

-LOG = logging.getLogger("axolotl")

-
-@torch.jit.script
-def weighted_cross_entropy(
-    logits: torch.Tensor, labels: torch.Tensor, weights: torch.Tensor
-):
-    # Flatten the logits, labels, and weights tensors
-    logits = logits.view(
-        -1, logits.size(-1)
-    )  # logits becomes of shape [batch_size*sequence_length, vocab_size]
-    labels = labels.view(-1)  # labels becomes of shape [batch_size*sequence_length]
-    weights = weights.view(-1)  # weights becomes of shape [batch_size*sequence_length]
-
-    # Compute the unweighted cross entropy loss
-    losses = torch.nn.functional.cross_entropy(logits, labels, reduction="none")
-
-    # Apply the weights to the losses and compute their sum
-    return (weights * losses).sum()
-
-
-@torch.jit.script
-def create_weighted_mask(labels: torch.Tensor):
-    # Check if the tensor is 2D. If not, unsqueeze it to make it 2D
-    if len(labels.shape) == 1:
-        labels = labels.unsqueeze(0)
-
-    weights = torch.zeros_like(labels).float()
-    for i in range(labels.shape[0]):
-        mask = labels[i] != -100
-
-        # Create a tensor to track group ids
-        group_ids = torch.zeros_like(labels[i]).int()
-        curr_group_id = 0
-
-        for j in range(1, len(labels[i])):
-            if mask[j] and not mask[j - 1]:  # switch from masked to unmasked label
-                curr_group_id += 1  # start new group
-            group_ids[j] = (
-                curr_group_id if mask[j] else 0
-            )  # assign group id if unmasked label
-
-        # Count only unmasked labels in each group
-        group_counts = torch.bincount(group_ids[mask])
-
-        mask_weights = torch.zeros_like(labels[i]).float()
-        mask_weights[mask] = 1.0 / group_counts[group_ids[mask]]
-
-        weights[i] = mask_weights
-
-    return weights.squeeze()  # squeeze the output to match the input dimension
-
-
-def trainer_weighted_loss(model_output, labels, shift_labels=True):
-    logits = (
-        model_output["logits"] if isinstance(model_output, dict) else model_output[0]
-    )
-    if shift_labels:
-        logits = logits[..., :-1, :].contiguous()
-        labels = labels[..., 1:].contiguous()
-
-    weights = create_weighted_mask(labels)
-    return weighted_cross_entropy(logits, labels, weights)
-
-
-@dataclass
 class AxolotlTrainingArguments(TrainingArguments):
    """
    Extend the base TrainingArguments for axolotl helpers
@@ -106,26 +42,6 @@ class AxolotlTrainingArguments(TrainingArguments):
        default=False,
        metadata={"help": "Use quadratic warmup for cosine scheduling."},
    )
-    sample_packing: bool = field(
-        default=False,
-        metadata={"help": "Use sample packing for efficient training."},
-    )
-    sample_packing_efficiency: float = field(
-        default=1.0,
-        metadata={"help": "Sample packing efficiency for calculating batch length."},
-    )
-    max_seq_length: int = field(
-        default=2048,
-        metadata={"help": "The maximum sequence length the model can handle"},
-    )
-    sample_packing_seq_len_multiplier: int = field(
-        default=1,
-        metadata={"help": "the multiplier for the max len for packed sequences"},
-    )
-    train_data_total_num_tokens: Optional[int] = field(
-        default=None,
-        metadata={"help": "the total number of tokens in the train dataset"},
-    )


 class AxolotlTrainer(Trainer):
@@ -163,66 +79,6 @@ class AxolotlTrainer(Trainer):
                return super().create_scheduler(num_training_steps, optimizer)
        return self.lr_scheduler

-    def _get_train_sampler(self) -> Optional[torch.utils.data.Sampler]:
-        if self.args.world_size > 1 and self.args.sample_packing:
-            return DistributedSampler(
-                self.train_dataset,
-                num_replicas=self.args.world_size,
-                rank=self.args.process_index,
-                seed=self.args.seed,
-            )
-        return super()._get_train_sampler()
-
-    def get_train_dataloader(self) -> Union[DataLoader, MultipackDistributedDataloader]:
-        if self.args.sample_packing:
-            train_sampler = self._get_train_sampler()
-            return self.accelerator.prepare(
-                MultipackDistributedDataloader(
-                    self.train_dataset,
-                    batch_size=self._train_batch_size,
-                    seq_max_length=self.args.max_seq_length,
-                    collate_fn=self.data_collator,
-                    sampler=train_sampler,
-                    packing_efficiency_estimate=self.args.sample_packing_efficiency,
-                    sample_packing_seq_len_multiplier=self.args.sample_packing_seq_len_multiplier,
-                    device_count=int(os.environ.get("WORLD_SIZE", 1)),
-                    total_num_tokens=self.args.train_data_total_num_tokens,
-                )
-            )
-        return super().get_train_dataloader()
-
-    def get_eval_dataloader(
-        self, eval_dataset: Optional[Dataset] = None
-    ) -> Union[DataLoader, MultipackDistributedDataloader]:
-        if self.args.sample_packing:
-            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(
-                    eval_dataset,
-                    batch_size=self.args.eval_batch_size,
-                    seq_max_length=self.args.max_seq_length,
-                    collate_fn=self.data_collator,
-                    sampler=eval_sampler,
-                    packing_efficiency_estimate=self.args.sample_packing_efficiency,
-                    sample_packing_seq_len_multiplier=self.args.eval_batch_size,
-                    device_count=int(os.environ.get("WORLD_SIZE", 1)),
-                    total_num_tokens=None,
-                )
-            )
-        return super().get_eval_dataloader(eval_dataset)
-
-    def compute_loss(self, model, inputs, return_outputs=False):
-        # use one's weighted cross entropy loss calc
-        # if self.args.sample_packing:
-        #     labels = inputs.pop("labels")
-        #     outputs = model(**inputs)
-        #     loss = trainer_weighted_loss(outputs, labels, shift_labels=True)
-        #     return (loss, outputs) if return_outputs else loss
-        return super().compute_loss(model, inputs, return_outputs=return_outputs)
-

 class OneCycleLRSchedulerTrainer(AxolotlTrainer):
    """
@@ -253,117 +109,10 @@ class OneCycleLRSchedulerTrainer(AxolotlTrainer):
        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
-
-
-@contextmanager
-def disable_datasets_caching():
-    try:
-        set_caching_enabled(False)
-        yield
-    finally:
-        set_caching_enabled(True)
-
-
-def process_datasets_for_packing(cfg, train_dataset, eval_dataset):
-    if cfg.sample_packing:
-        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.filter(drop_long, num_proc=os.cpu_count()).map(
-                add_position_ids, num_proc=os.cpu_count()
-            )
-    return train_dataset, eval_dataset
-
-
-def calculate_total_num_steps(cfg, train_dataset, tokenizer):
-    if cfg.sample_packing:
-        # we have to drop anything longer then sequence len otherwise
-        # flash attention with position ids fails
-        total_num_tokens = (
-            cfg.total_num_tokens
-            if cfg.total_num_tokens
-            else sum(len(s["input_ids"]) for s in train_dataset)
-        )
-        if not cfg.total_num_tokens:
-            LOG.info(f"📝 UPDATE CONFIG WITH: `total_num_tokens: {total_num_tokens}`")
-
-        if cfg.sample_packing_eff_est:
-            total_num_steps = (
-                # match count to len est in dataloader
-                (
-                    math.floor(
-                        0.99
-                        * total_num_tokens
-                        / cfg.sample_packing_eff_est
-                        / 2048
-                        // cfg.batch_size
-                        // int(os.environ.get("WORLD_SIZE", 1))
-                    )
-                    - 1
-                )
-                * cfg.num_epochs
-            )
-            LOG.info(
-                f"total_num_tokens: {total_num_tokens}, total_num_steps: {total_num_steps}"
-            )
-        else:
-            sampler = RandomSampler(train_dataset)
-            data_loader = MultipackDistributedDataloader(
-                train_dataset,
-                batch_size=cfg.micro_batch_size,
-                seq_max_length=cfg.max_packed_sequence_len or cfg.sequence_len,
-                collate_fn=DataCollatorForSeq2Seq(
-                    tokenizer,
-                    return_tensors="pt",
-                    padding="longest",
-                ),
-                sampler=sampler,
-                packing_efficiency_estimate=cfg.sample_packing_eff_est,
-                sample_packing_seq_len_multiplier=cfg.micro_batch_size,
-                device_count=int(os.environ.get("WORLD_SIZE", 1)),
-            )
-            data_loader_len = data_loader.len_w_stats()
-            actual_eff = data_loader.efficiency()
-            LOG.info(f"data_loader_len: {data_loader_len}")
-            total_num_steps = int(
-                math.floor(
-                    data_loader_len
-                    * cfg.micro_batch_size
-                    * cfg.num_epochs
-                    // cfg.batch_size
-                )
-            )
-            LOG.info(
-                f"📝 UPDATE CONFIG WITH: `sample_packing_eff_est: {math.ceil(actual_eff * 100.0) / 100.0}`"
-            )
-    else:
-        total_num_steps = int(
-            math.ceil(len(train_dataset) * cfg.num_epochs / cfg.batch_size)
-        )
-    LOG.info(f"total_num_steps: {total_num_steps}")
-    return total_num_steps
-
-
-def setup_fsdp_envs(cfg):
-    os.environ["ACCELERATE_USE_FSDP"] = "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
-
-
-def setup_trainer(cfg, train_dataset, eval_dataset, model, tokenizer, total_num_steps):
-    if cfg.fsdp:
-        setup_fsdp_envs(cfg)
+def setup_trainer(cfg, train_dataset, eval_dataset, model, tokenizer):
+    total_num_steps = int(
+        math.ceil(len(train_dataset) * cfg.num_epochs / cfg.batch_size)
+    )
    warmup_steps = (
        cfg.warmup_steps
        if cfg.warmup_steps is not None
@@ -438,19 +187,8 @@ def setup_trainer(cfg, train_dataset, eval_dataset, model, tokenizer, total_num_
    if cfg.hub_model_id:
        training_arguments_kwargs["hub_model_id"] = cfg.hub_model_id
        training_arguments_kwargs["push_to_hub"] = True
-        training_arguments_kwargs["hub_private_repo"] = True

-    if cfg.save_safetensors:
-        training_arguments_kwargs["save_safetensors"] = cfg.save_safetensors
-
-    if cfg.sample_packing_eff_est:
-        training_arguments_kwargs[
-            "sample_packing_efficiency"
-        ] = cfg.sample_packing_eff_est
-
-    training_args = AxolotlTrainingArguments(  # pylint: disable=unexpected-keyword-arg
-        # max_steps=total_num_steps,  # this is helpful in case we don't actually know total # of steps
-        max_seq_length=cfg.sequence_len,
+    training_args = AxolotlTrainingArguments(
        per_device_train_batch_size=cfg.micro_batch_size,
        per_device_eval_batch_size=cfg.eval_batch_size
        if cfg.eval_batch_size is not None
@@ -464,7 +202,7 @@ def setup_trainer(cfg, train_dataset, eval_dataset, model, tokenizer, total_num_
        eval_steps=cfg.eval_steps if cfg.val_set_size > 0 else None,
        save_steps=cfg.save_steps,
        output_dir=cfg.output_dir,
-        save_total_limit=cfg.save_total_limit if cfg.save_total_limit else 4,
+        save_total_limit=3,
        load_best_model_at_end=(
            cfg.load_best_model_at_end is not False
            and cfg.val_set_size > 0
@@ -482,9 +220,6 @@ def setup_trainer(cfg, train_dataset, eval_dataset, model, tokenizer, total_num_
        if cfg.lr_scheduler and cfg.lr_scheduler not in ("one_cycle", "log_sweep")
        else "cosine",
        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 or 1,
-        train_data_total_num_tokens=cfg.total_num_tokens,
        **training_arguments_kwargs,
    )

@@ -578,11 +313,11 @@ def setup_trainer(cfg, train_dataset, eval_dataset, model, tokenizer, total_num_
    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
-        data_collator_kwargs["pad_to_multiple_of"] = 64
+        data_collator_kwargs["pad_to_multiple_of"] = 8

    if cfg.is_llama_derived_model and cfg.landmark_attention:
+        from functools import partial
+
        from axolotl.monkeypatch.llama_landmark_attn import (
            add_mem_tokens,
            get_mem_id,
@@ -591,7 +326,7 @@ def setup_trainer(cfg, train_dataset, eval_dataset, model, tokenizer, total_num_

        set_model_mem_id(model, tokenizer)

-        LOG.info("Adding landmark attention tokens to dataset")
+        logging.info("Adding landmark attention tokens to dataset")

        for dataset in [train_dataset, eval_dataset]:
            dataset = dataset.map(
@@ -600,6 +335,19 @@ def setup_trainer(cfg, train_dataset, eval_dataset, model, tokenizer, total_num_
                num_proc=32,
            )

+    if cfg.compute_perplexity_metrics:
+
+        def compute_metrics(eval_preds: EvalPrediction) -> Dict[str, Any]:
+            logits = eval_preds.predictions
+            labels = eval_preds.label_ids
+            cross_entropy_loss = F.cross_entropy(
+                logits.view(-1, model.config.vocab_size), labels.view(-1)
+            )
+            perplexity = torch.exp(cross_entropy_loss)
+            return {"cross_entropy_loss": cross_entropy_loss, "perplexity": perplexity}
+
+        trainer_kwargs["compute_metrics"] = compute_metrics
+
    trainer_cls = (
        OneCycleLRSchedulerTrainer
        if cfg.lr_scheduler == "one_cycle" and (cfg.fsdp or cfg.adapter == "qlora")
@@ -610,7 +358,7 @@ def setup_trainer(cfg, train_dataset, eval_dataset, model, tokenizer, total_num_
        train_dataset=train_dataset,
        eval_dataset=eval_dataset,
        args=training_args,
-        data_collator=DataCollatorForSeq2Seq(
+        data_collator=transformers.DataCollatorForSeq2Seq(
            tokenizer,
            return_tensors="pt",
            **data_collator_kwargs,
--- a/src/axolotl/utils/validation.py
+++ b/src/axolotl/utils/validation.py
@@ -4,29 +4,14 @@ import logging

 import torch

-LOG = logging.getLogger("axolotl")
-

 def validate_config(cfg):
-    if cfg.max_packed_sequence_len and cfg.sample_packing:
-        raise ValueError(
-            "please set only one of max_packed_sequence_len (deprecated soon) or sample_packing"
-        )
-    if cfg.max_packed_sequence_len:
-        LOG.warning(
-            str(
-                PendingDeprecationWarning(
-                    "max_packed_sequence_len will be deprecated in favor of sample_packing"
-                )
-            )
-        )
-
    if cfg.gradient_accumulation_steps and cfg.batch_size:
        raise ValueError(
            "please set only one of gradient_accumulation_steps or batch_size"
        )
    if cfg.batch_size:
-        LOG.warning(
+        logging.warning(
            "%s\n%s",
            "batch_size is not recommended. Please use gradient_accumulation_steps instead.",
            "To calculate the equivalent gradient_accumulation_steps, divide batch_size / micro_batch_size / number of gpus.",
@@ -59,10 +44,10 @@ def validate_config(cfg):
                raise ValueError("Require cfg.load_in_4bit to be True for qlora")

    if not cfg.load_in_8bit and cfg.adapter == "lora":
-        LOG.warning("We recommend setting `load_in_8bit: true` for LORA finetuning")
+        logging.warning("We recommend setting `load_in_8bit: true` for LORA finetuning")

    if cfg.trust_remote_code:
-        LOG.warning(
+        logging.warning(
            "`trust_remote_code` is set to true. Please make sure that you reviewed the remote code/model."
        )

@@ -81,46 +66,37 @@ def validate_config(cfg):

    if cfg.flash_optimum is True:
        if cfg.adapter:
-            LOG.warning("BetterTransformers probably doesn't work with PEFT adapters")
+            logging.warning(
+                "BetterTransformers probably doesn't work with PEFT adapters"
+            )
        if cfg.fp16 or cfg.bf16:
            raise ValueError("AMP is not supported with BetterTransformer")
        if cfg.float16 is not True and cfg.bloat16 is not True:
-            LOG.warning(
+            logging.warning(
                "You should probably set bfloat16 or float16 to true to "
                "load the model in float16 for BetterTransformers"
            )
        if int(torch.__version__.split(".")[0]) < 2:
-            LOG.warning("torch>=2.0.0 required")
+            logging.warning("torch>=2.0.0 required")
            raise ValueError(
                f"flash_optimum for BetterTransformers may not be used with {torch.__version__}"
            )

    if cfg.pretraining_dataset and cfg.group_by_length:
-        LOG.warning(
+        logging.warning(
            "You probably want to disable group_by_length as it will force a streamed dataset to download completely."
        )

    if any([cfg.adam_beta1, cfg.adam_beta2, cfg.adam_epsilon]) and (
        not cfg.optimizer or "adamw" not in cfg.optimizer
    ):
-        LOG.warning("adamw hyperparameters found, but no adamw optimizer set")
+        logging.warning("adamw hyperparameters found, but no adamw optimizer set")

    if cfg.push_to_hub_model_id:
        raise ValueError(
            "push_to_hub_model_id is deprecated. Please use hub_model_id instead."
        )

-    if cfg.sample_packing and cfg.sdp_attention:
-        # incompatible due to bug w/ accelerate causing 0.0 loss when using llama2
-        raise ValueError(
-            "sample_packing not compatible with sdp_attention. Use flash_attention"
-        )
-
-    if cfg.sample_packing and cfg.xformers_attention:
-        raise ValueError(
-            "sample_packing not compatible with xformers_attention. Use flash_attention"
-        )
-
    # TODO
    # MPT 7b
    # https://github.com/facebookresearch/bitsandbytes/issues/25
--- a/tests/fixtures/conversation.tokenized_llama2chat.json
+++ b/tests/fixtures/conversation.tokenized_llama2chat.json
--- a/tests/monkeypatch/test_llama_attn_hijack_flash.py
+++ b/tests/monkeypatch/test_llama_attn_hijack_flash.py
@@ -1,30 +0,0 @@
-"""
-Unit tests for the monkeypatch utils
-"""
-import unittest
-
-import torch
-
-from axolotl.monkeypatch.utils import get_cu_seqlens, get_cu_seqlens_from_pos_ids
-
-
-class TestMonkeyPatchUtils(unittest.TestCase):
-    """
-    Unit test class for monkeypatch utils
-    """
-
-    def test_get_cu_seqlens_1d(self):
-        attn_mask = torch.tensor([[1, 1, 1, 1, 2, 2, 2, 3, 3, 3, 3, 3, 4, 4, 0, 0]])
-        target_res = torch.tensor([0, 4, 7, 12, 14, 16], dtype=torch.int32)
-        self.assertTrue(torch.allclose(get_cu_seqlens(attn_mask)[0], target_res))
-
-    def test_get_cu_seqlens_from_pos_ids_1d(self):
-        position_ids = torch.tensor([[0, 1, 2, 3, 0, 1, 2, 0, 1, 2, 3, 4, 0, 1, 0, 0]])
-        target_res = torch.tensor([0, 4, 7, 12, 14, 16], dtype=torch.int32)
-        self.assertTrue(
-            torch.allclose(get_cu_seqlens_from_pos_ids(position_ids)[0], target_res)
-        )
-
-
-if __name__ == "__main__":
-    unittest.main()
--- a/tests/test_expand_mask.py
+++ b/tests/test_expand_mask.py
@@ -1,44 +0,0 @@
-"""
-Unit tests for the monkey patch for expand mask to handle packed sequences
-"""
-import unittest
-
-import torch
-
-from axolotl.monkeypatch.llama_expand_mask import _expand_mask
-
-
-class TestExpandMask(unittest.TestCase):
-    """
-    Test class for attention mask expansion for packed sequences
-    """
-
-    def test_output(self):
-        mask = torch.tensor([[1, 1, 1, 2], [2, 3, 3, 0]])
-        dtype = torch.float32
-        expected_output = torch.tensor(
-            [
-                [
-                    [
-                        [0.0000e00, -3.4028e38, -3.4028e38, -3.4028e38],
-                        [0.0000e00, 0.0000e00, -3.4028e38, -3.4028e38],
-                        [0.0000e00, 0.0000e00, 0.0000e00, -3.4028e38],
-                        [-3.4028e38, -3.4028e38, -3.4028e38, 0.0000e00],
-                    ]
-                ],
-                [
-                    [
-                        [0.0000e00, -3.4028e38, -3.4028e38, -3.4028e38],
-                        [-3.4028e38, 0.0000e00, -3.4028e38, -3.4028e38],
-                        [-3.4028e38, 0.0000e00, 0.0000e00, -3.4028e38],
-                        [-3.4028e38, -3.4028e38, -3.4028e38, -3.4028e38],
-                    ]
-                ],
-            ]
-        )
-        # Check that the output matches the expected output
-        self.assertTrue(torch.allclose(_expand_mask(mask, dtype), expected_output))
-
-
-if __name__ == "__main__":
-    unittest.main()
--- a/tests/test_packed_dataset.py
+++ b/tests/test_packed_dataset.py
@@ -27,7 +27,7 @@ class TestPacking(unittest.TestCase):
            }
        )

-    def test_increments_attention(self):
+    def test_resets_attention(self):
        prompter = AlpacaPrompter("chat")
        strat = AlpacaPromptTokenizingStrategy(
            prompter,
@@ -55,14 +55,10 @@ class TestPacking(unittest.TestCase):
        # first example doesn't have mask reset
        assert example["input_ids"][0] == self.tokenizer.bos_token_id
        assert example["attention_mask"][0] == 1
-        assert example["position_ids"][0] == 0
-        assert example["position_ids"][1] == 1

        # but subsequent one does
        assert example["input_ids"][next_bos_index] == self.tokenizer.bos_token_id
-        assert example["attention_mask"][next_bos_index] == 2
-        assert example["position_ids"][next_bos_index] == 0
-        assert example["position_ids"][next_bos_index + 1] == 1
+        assert example["attention_mask"][next_bos_index] == 0


 if __name__ == "__main__":
--- a/tests/test_prompt_tokenizers.py
+++ b/tests/test_prompt_tokenizers.py
@@ -4,24 +4,20 @@ import logging
 import unittest
 from pathlib import Path

-from transformers import AutoTokenizer, LlamaTokenizer
+from transformers import AutoTokenizer

 from axolotl.prompt_strategies.alpaca_chat import NoSystemPrompter
 from axolotl.prompt_strategies.alpaca_w_system import (
    InstructionWSystemPromptTokenizingStrategy,
    SystemDataPrompter,
 )
-from axolotl.prompt_strategies.llama2_chat import (
-    Llama2ChatPrompter,
-    LLama2ChatTokenizingStrategy,
-)
 from axolotl.prompt_tokenizers import (
    AlpacaPromptTokenizingStrategy,
    ShareGPTPromptTokenizingStrategy,
 )
 from axolotl.prompters import AlpacaPrompter, PromptStyle, ShareGPTPrompter

-LOG = logging.getLogger("axolotl")
+logging.basicConfig(level="INFO")


 class TestPromptTokenizationStrategies(unittest.TestCase):
@@ -134,95 +130,8 @@ class InstructionWSystemPromptTokenizingStrategyTest(unittest.TestCase):
            "output": "Hi! How can I help?",
        }
        example = strat.tokenize_prompt(sample)
-        assert example["input_ids"][0:5] == [
-            1,
-            28962,
-            1254,
-            12665,
-            29901,
-        ]  # "<s>SYSTEM:"
-        assert example["input_ids"][5:7] == [671, 20118]  # " use cot"
-        assert example["input_ids"][8] == 11889  # USER
-
-
-class Llama2ChatTokenizationTest(unittest.TestCase):
-    """
-    Test class for prompt tokenization strategies with sys prompt from the dataset
-    """
-
-    def setUp(self) -> None:
-        # pylint: disable=duplicate-code
-        self.tokenizer = LlamaTokenizer.from_pretrained("NousResearch/Llama-2-7b-hf")
-        # woraround because official Meta repos are not open
-
-    def test_llama2_chat_integration(self):
-        with open(
-            Path(__file__).parent / "fixtures/conversation.json", encoding="utf-8"
-        ) as fin:
-            data = fin.read()
-            conversation = json.loads(data)
-        with open(
-            Path(__file__).parent / "fixtures/conversation.tokenized_llama2chat.json",
-            encoding="utf-8",
-        ) as fin:
-            data = fin.read()
-            tokenized_conversation = json.loads(data)
-        prompter = Llama2ChatPrompter()
-        strat = LLama2ChatTokenizingStrategy(
-            prompter,
-            self.tokenizer,
-            False,
-            4096,
-        )
-        example = strat.tokenize_prompt(conversation)
-        for fields in ["input_ids", "attention_mask", "labels"]:
-            self.assertEqual(len(example[fields]), len(tokenized_conversation[fields]))
-            self.assertEqual(example[fields], tokenized_conversation[fields])
-
-    def compare_with_transformers_integration(self):
-        # this needs transformers >= v4.31.0
-        from transformers.models.llama.tokenization_llama import B_SYS, E_SYS
-        from transformers.pipelines.conversational import Conversation
-
-        # from transformers.models.llama.tokenization_llama import DEFAULT_SYSTEM_PROMPT
-        # broken as of 23/7/20
-        # see https://github.com/huggingface/transformers/pull/24935
-        # pylint: disable=C0103
-        DEFAULT_SYSTEM_PROMPT = """\
-You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature.
-
-If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information."""
-        with open(
-            Path(__file__).parent / "fixtures/conversation.json", encoding="utf-8"
-        ) as fin:
-            data = fin.read()
-            conversation = json.loads(data)
-        with open(
-            Path(__file__).parent / "fixtures/conversation.tokenized_llama2chat.json",
-            encoding="utf-8",
-        ) as fin:
-            data = fin.read()
-            tokenized_conversation = json.loads(data)
-
-        user_input = []
-        answers = []
-        for msg in conversation["conversations"]:
-            if msg["from"] == "human":
-                user_input.append(msg["value"])
-            else:
-                answers.append(msg["value"])
-        hf_conf = Conversation(
-            text=user_input[-1],
-            past_user_inputs=[B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + user_input[0]]
-            + user_input[1:-1],
-            generated_responses=answers,
-        )
-        # pylint: disable=W0212
-        hf_tokens = self.tokenizer._build_conversation_input_ids(hf_conf)
-
-        self.assertEqual(
-            hf_tokens, tokenized_conversation["input_ids"][: len(hf_tokens)]
-        )
+        assert example["input_ids"][0:3] == [1, 671, 20118]  # <s>use cot
+        assert example["input_ids"][3] == 11889  # USER


 if __name__ == "__main__":
--- a/tests/test_prompters.py
+++ b/tests/test_prompters.py
@@ -70,7 +70,7 @@ class AlpacaPrompterTest(unittest.TestCase):
            )
        )
        assert "use cot" in res
-        assert res.startswith("SYSTEM:")
+        assert res.startswith("use cot")
        assert "### Instruction:" not in res
        assert "### Input:" not in res
        assert "alpacas" in res
--- a/tests/test_validation.py
+++ b/tests/test_validation.py
@@ -313,27 +313,3 @@ class ValidationTest(unittest.TestCase):
        )

        validate_config(cfg)
-
-    def test_packing(self):
-        cfg = DictDefault(
-            {
-                "max_packed_sequence_len": 2048,
-            }
-        )
-        with self._caplog.at_level(logging.WARNING):
-            validate_config(cfg)
-            assert any(
-                "max_packed_sequence_len will be deprecated in favor of sample_packing"
-                in record.message
-                for record in self._caplog.records
-            )
-
-        cfg = DictDefault(
-            {
-                "max_packed_sequence_len": 2048,
-                "sample_packing": True,
-            }
-        )
-        regex_exp = r".*set only one of max_packed_sequence_len \(deprecated soon\) or sample_packing.*"
-        with pytest.raises(ValueError, match=regex_exp):
-            validate_config(cfg)
Author	SHA1	Message	Date
Wing Lian	0f2a16aa33	use different perplexity calc Some checks failed pre-commit / pre-commit (push) Has been cancelled Details PyTest / test (3.10) (push) Has been cancelled Details PyTest / test (3.9) (push) Has been cancelled Details	2023-07-10 13:43:50 -04:00
Wing Lian	e7c84254ba	fix perplexity calculation and make it configurable	2023-07-10 12:49:51 -04:00
Wing Lian	1d02606934	compute perplexity from cross entropy	2023-07-10 12:49:47 -04:00