multipack sampler support from openchat

.github/FUNDING.yml

@@ -1,13 +0,0 @@
-# These are supported funding model platforms
-
-github: OpenAccess-AI-Collective # Replace with up to 4 GitHub Sponsors-enabled usernames e.g., [user1, user2]
-patreon: # Replace with a single Patreon username
-open_collective: # Replace with a single Open Collective username
-ko_fi: # Replace with a single Ko-fi username
-tidelift: # Replace with a single Tidelift platform-name/package-name e.g., npm/babel
-community_bridge: # Replace with a single Community Bridge project-name e.g., cloud-foundry
-liberapay: # Replace with a single Liberapay username
-issuehunt: # Replace with a single IssueHunt username
-otechie: # Replace with a single Otechie username
-lfx_crowdfunding: # Replace with a single LFX Crowdfunding project-name e.g., cloud-foundry
-custom: # Replace with up to 4 custom sponsorship URLs e.g., ['link1', 'link2']

.github/workflows/base.yml

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

.github/workflows/main.yml

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

LICENSE

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

@@ -24,12 +24,11 @@
 | mpt      | ✅         | ❌    | ❓     | ❌    | ❓            | ❌                 | ❌          | ❓             |
 | falcon   | ✅         | ✅    | ✅     | ❌    | ❓            | ❌                 | ❌          | ✅             |
 | gpt-j    | ✅         | ✅    | ✅     | ❌    | ❓            | ❌                 | ❓          | ✅             |
-| XGen     | ✅         | ❓    | ✅     | ❓    | ❓            | ❓                 | ❓          | ✅
 
 
 ## Quickstart ⚡
 
-**Requirements**: Python >=3.9 and Pytorch >=2.0.
+**Requirements**: Python 3.9 and Pytorch 2.0.
 
 ```bash
 git clone https://github.com/OpenAccess-AI-Collective/axolotl
@@ -37,6 +36,8 @@ git clone https://github.com/OpenAccess-AI-Collective/axolotl
 pip3 install -e .
 pip3 install -U git+https://github.com/huggingface/peft.git
 
+accelerate config
+
 # finetune lora
 accelerate launch scripts/finetune.py examples/openllama-3b/lora.yml
 
@@ -51,10 +52,11 @@ accelerate launch scripts/finetune.py examples/openllama-3b/lora.yml \
 
 - Docker
   ```bash
-  docker run --gpus '"all"' --rm -it winglian/axolotl:main-py3.10-cu118-2.0.1
+  docker run --gpus '"all"' --rm -it winglian/axolotl:main-py3.9-cu118-2.0.0
   ```
-  - `winglian/axolotl-runpod:main-py3.10-cu118-2.0.1`: for runpod
-  - `winglian/axolotl-runpod:main-py3.9-cu118-2.0.1-gptq`: for gptq
+  - `winglian/axolotl-runpod:main-py3.9-cu118-2.0.0`: for runpod
+  - `winglian/axolotl-runpod:main-py3.9-cu118-2.0.0-gptq`: for gptq
+  - `winglian/axolotl:dev`: dev branch (not usually up to date)
 
   Or run on the current files for development:
 
@@ -106,7 +108,7 @@ accelerate launch scripts/finetune.py examples/openllama-3b/lora.yml \
 
   3. Install torch
   ```bash
-  pip3 install -U torch --index-url https://download.pytorch.org/whl/cu118
+  pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118
   ```
 
   4. Axolotl
@@ -136,7 +138,7 @@ Have dataset(s) in one of the following format (JSONL recommended):
   ```json
   {"instruction": "...", "input": "...", "output": "..."}
   ```
-- `sharegpt:chat`: conversations where `from` is `human`/`gpt`
+- `sharegpt:chat`: conversations
   ```json
   {"conversations": [{"from": "...", "value": "..."}]}
   ```
@@ -225,10 +227,6 @@ Have dataset(s) in one of the following format (JSONL recommended):
   ```json
   {"conversations": [{"role": "...", "value": "..."}]}
   ```
-- `sharegpt_simple.load_guanaco`: conversations where `from` is `prompter`/`assistant` instead of default sharegpt
-  ```json
-  {"conversations": [{"from": "...", "value": "..."}]}
-  ```
 - `sharegpt_jokes`: creates a chat where bot is asked to tell a joke, then explain why the joke is funny
   ```json
   {"conversations": [{"title": "...", "text": "...", "explanation": "..."}]}
@@ -247,7 +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
@@ -264,12 +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
@@ -326,9 +318,6 @@ 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
@@ -355,7 +344,6 @@ 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
@@ -363,7 +351,7 @@ 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
@@ -379,14 +367,7 @@ 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
-# FutureWarning: This will soon be DEPRECATED
 max_packed_sequence_len: 1024
-# use efficient multi-packing with block diagonal attention and per sequence position_ids. Recommend set to 'true'
-sample_packing:
-# you can set these packing optimizations AFTER starting a training at least once.
-# The trainer will provide recommended values for these values.
-sample_packing_eff_est:
-total_num_tokens:
 
 # if you want to use 'lora' or 'qlora' or leave blank to train all parameters in original model
 adapter: lora
@@ -412,12 +393,11 @@ lora_out_dir:
 lora_fan_in_fan_out: false
 
 # wandb configuration if you're using it
-wandb_mode: # "offline" to save run metadata locally and not sync to the server, "disabled" to turn off wandb
-wandb_project: # your wandb project name
-wandb_entity: # a wandb Team name if using a Team
+wandb_mode:
+wandb_project:
 wandb_watch:
-wandb_run_id: # set the name of your wandb run
-wandb_log_model: # "checkpoint" to log model to wandb Artifacts every `save_steps` or "end" to log only at the end of training
+wandb_run_id:
+wandb_log_model: # 'checkpoint'
 
 # where to save the finished model to
 output_dir: ./completed-model
@@ -432,16 +412,13 @@ learning_rate: 0.00003
 logging_steps:
 save_steps:
 eval_steps:
-save_total_limit:
 
 # 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
-# group similarly sized data to minimize padding
-# may be slower to start, as it must download and sort the entire dataset
-# note that training loss may have an oscillating pattern with this enabled
+# don't use this, leads to wonky training (according to someone on the internet)
 group_by_length: false
 
 # Whether to use gradient checkpointing https://huggingface.co/docs/transformers/v4.18.0/en/performance#gradient-checkpointing
@@ -487,10 +464,6 @@ landmark_attention:
 # xpos RoPE see https://github.com/kaiokendev/cutoff-len-is-context-len/blob/main/util/xpos_rope_llama_monkey_patch.py
 # llama only
 xpos_rope:
-# RoPE Scaling https://github.com/huggingface/transformers/pull/24653
-rope_scaling:
-  type: # linear | dynamic
-  factor: # float
 
 # resume from a specific checkpoint dir
 resume_from_checkpoint:
@@ -522,9 +495,6 @@ torchdistx_path:
 # Set padding for data collator to 'longest'
 collator_pad_to_longest:
 
-# Set to HF dataset for type: 'completion' for streaming instead of pre-tokenize
-pretraining_dataset:
-
 # Debug mode
 debug:
 
@@ -537,6 +507,17 @@ strict:
 
 </details>
 
+### Accelerate
+
+Configure accelerate
+
+```bash
+accelerate config
+
+# Edit manually
+# nano ~/.cache/huggingface/accelerate/default_config.yaml
+```
+
 ### Train
 
 Run
@@ -544,40 +525,6 @@ Run
 accelerate launch scripts/finetune.py configs/your_config.yml
 ```
 
-#### Multi-GPU
-
-You can optionally pre-tokenize dataset with the following before finetuning:
-```bash
-CUDA_VISIBLE_DEVICES="" accelerate ... --prepare_ds_only
-```
-
-##### 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
-
-##### Weights & Biases Logging
-
-- wandb options
-```yaml
-wandb_mode:
-wandb_project:
-wandb_entity:
-wandb_watch:
-wandb_run_id:
-wandb_log_model:
-```
-
 ### Inference
 
 Pass the appropriate flag to the train command:
@@ -628,10 +575,6 @@ Try set `fp16: true`
 
 Try to turn off xformers.
 
-> accelerate config missing
-
-It's safe to ignore it.
-
 ## Need help? 🙋♂️
 
 Join our [Discord server](https://discord.gg/HhrNrHJPRb) where we can help you

docker/Dockerfile

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

docker/Dockerfile-base

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

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 && \

ds_config.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",

examples/cerebras/qlora.yml

@@ -23,7 +23,6 @@ lora_target_modules:
 lora_target_linear:
 lora_fan_in_fan_out:
 wandb_project:
-wandb_entity:
 wandb_watch:
 wandb_run_id:
 wandb_log_model:
@@ -36,7 +35,7 @@ torchdistx_path:
 lr_scheduler: cosine
 learning_rate: 0.0002
 train_on_inputs: false
-group_by_length: false
+group_by_length: true
 bf16: true
 fp16: false
 tf32: true

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

@@ -24,7 +24,6 @@ lora_target_modules:
 lora_target_linear: true
 lora_fan_in_fan_out:
 wandb_project:
-wandb_entity:
 wandb_watch:
 wandb_run_id:
 wandb_log_model:

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

@@ -38,7 +38,6 @@ lora_target_linear: true
 lora_fan_in_fan_out:
 
 wandb_project:
-wandb_entity:
 wandb_watch:
 wandb_run_id:
 wandb_log_model:

examples/falcon/config-7b.yml

@@ -24,7 +24,6 @@ lora_target_modules:
 lora_target_linear: true
 lora_fan_in_fan_out:
 wandb_project:
-wandb_entity:
 wandb_watch:
 wandb_run_id:
 wandb_log_model:

examples/gptj/qlora.yml

@@ -20,7 +20,6 @@ lora_target_modules:
 lora_target_linear: true
 lora_fan_in_fan_out:
 wandb_project:
-wandb_entity:
 wandb_watch:
 wandb_run_id:
 wandb_log_model:
@@ -33,7 +32,7 @@ torchdistx_path:
 lr_scheduler: cosine
 learning_rate: 0.0001
 train_on_inputs: false
-group_by_length: false
+group_by_length: true
 bf16: true
 fp16: false
 tf32: true

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

@@ -22,7 +22,6 @@ lora_target_modules:
   - v_proj
 lora_fan_in_fan_out: false
 wandb_project: llama-7b-lora-int4
-wandb_entity:
 wandb_watch:
 wandb_run_id:
 wandb_log_model:

examples/jeopardy-bot/config.yml

@@ -18,7 +18,6 @@ lora_dropout:
 lora_target_modules:
 lora_fan_in_fan_out: false
 wandb_project:
-wandb_entity:
 wandb_watch:
 wandb_run_id:
 wandb_log_model:

examples/llama-2/README.md

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

examples/llama-2/lora.yml

@@ -1,67 +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: 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_entity:
-wandb_watch:
-wandb_run_id:
-wandb_log_model:
-
-gradient_accumulation_steps: 4
-micro_batch_size: 2
-num_epochs: 3
-optimizer: adamw_bnb_8bit
-lr_scheduler: cosine
-learning_rate: 0.0002
-
-train_on_inputs: false
-group_by_length: false
-bf16: true
-fp16: false
-tf32: false
-
-gradient_checkpointing: true
-early_stopping_patience:
-resume_from_checkpoint:
-local_rank:
-logging_steps: 1
-xformers_attention: true
-flash_attention:
-
-warmup_steps: 10
-eval_steps: 20
-save_steps:
-debug:
-deepspeed:
-weight_decay: 0.0
-fsdp:
-fsdp_config:
-special_tokens:
-  bos_token: "<s>"
-  eos_token: "</s>"
-  unk_token: "<unk>"
-  pad_token: "<pad>"

examples/llama-2/qlora.yml

@@ -1,68 +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_entity:
-wandb_watch:
-wandb_run_id:
-wandb_log_model:
-
-gradient_accumulation_steps: 4
-micro_batch_size: 2
-num_epochs: 3
-optimizer: paged_adamw_32bit
-lr_scheduler: cosine
-learning_rate: 0.0002
-
-train_on_inputs: false
-group_by_length: false
-bf16: true
-fp16: false
-tf32: false
-
-gradient_checkpointing: true
-early_stopping_patience:
-resume_from_checkpoint:
-local_rank:
-logging_steps: 1
-xformers_attention: true
-flash_attention:
-
-warmup_steps: 10
-eval_steps: 20
-save_steps:
-debug:
-deepspeed:
-weight_decay: 0.0
-fsdp:
-fsdp_config:
-special_tokens:
-  bos_token: "<s>"
-  eos_token: "</s>"
-  unk_token: "<unk>"
-  pad_token: "<pad>"

examples/mpt-7b/config.yml

@@ -20,7 +20,6 @@ lora_target_modules:
   - v_proj
 lora_fan_in_fan_out: false
 wandb_project: mpt-alpaca-7b
-wandb_entity:
 wandb_watch:
 wandb_run_id:
 wandb_log_model:

examples/openllama-3b/config.yml

@@ -22,7 +22,6 @@ lora_target_modules:
 lora_target_linear:
 lora_fan_in_fan_out:
 wandb_project:
-wandb_entity:
 wandb_watch:
 wandb_run_id:
 wandb_log_model:

examples/openllama-3b/lora.yml

@@ -28,7 +28,6 @@ lora_target_modules:
   - o_proj
 lora_fan_in_fan_out:
 wandb_project:
-wandb_entity:
 wandb_watch:
 wandb_run_id:
 wandb_log_model:

examples/openllama-3b/qlora.yml

@@ -22,7 +22,6 @@ lora_target_modules:
 lora_target_linear: true
 lora_fan_in_fan_out:
 wandb_project:
-wandb_entity:
 wandb_watch:
 wandb_run_id:
 wandb_log_model:
@@ -35,7 +34,7 @@ torchdistx_path:
 lr_scheduler: cosine
 learning_rate: 0.0002
 train_on_inputs: false
-group_by_length: false
+group_by_length: true
 bf16: true
 fp16: false
 tf32: true

examples/pythia-12b/config.yml

@@ -23,7 +23,6 @@ lora_target_modules:
 lora_target_linear: true
 lora_fan_in_fan_out: true  # pythia/GPTNeoX lora specific
 wandb_project:
-wandb_entity:
 wandb_watch:
 wandb_run_id:
 wandb_log_model:

examples/pythia/lora.yml

@@ -17,7 +17,6 @@ lora_target_modules:
 lora_target_linear:
 lora_fan_in_fan_out: true  # pythia/GPTNeoX lora specific
 wandb_project:
-wandb_entity:
 wandb_watch:
 wandb_run_id:
 wandb_log_model:

examples/redpajama/config-3b.yml

@@ -21,7 +21,6 @@ lora_target_modules:
   - v_proj
 lora_fan_in_fan_out: false
 wandb_project: redpajama-alpaca-3b
-wandb_entity:
 wandb_watch:
 wandb_run_id:
 wandb_log_model:

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

@@ -20,7 +20,6 @@ lora_target_modules:
   - mlp_down
 lora_fan_in_fan_out:
 wandb_project: lora-replit
-wandb_entity:
 wandb_watch:
 wandb_run_id:
 wandb_log_model:

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

@@ -1,91 +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_entity:
-wandb_watch:
-wandb_run_id:
-wandb_log_model:
-output_dir: ./qlora-out
-
-# QLoRA paper Table 9
-# - 16 for 7b & 13b
-# - 32 for 33b, 64 for 64b
-# Max size tested on A6000
-# - 7b: 40
-# - 40b: 4
-# decrease if OOM, increase for max VRAM utilization
-micro_batch_size: 1
-gradient_accumulation_steps: 1
-num_epochs: 3
-# Optimizer for QLoRA
-optimizer: paged_adamw_32bit
-torchdistx_path:
-lr_scheduler: cosine
-# QLoRA paper Table 9
-# - 2e-4 for 7b & 13b
-# - 1e-4 for 33b & 64b
-learning_rate: 0.00002
-train_on_inputs: false
-group_by_length: false
-bf16: true
-fp16: false
-tf32: false
-gradient_checkpointing: true
-# stop training after this many evaluation losses have increased in a row
-# https://huggingface.co/transformers/v4.2.2/_modules/transformers/trainer_callback.html#EarlyStoppingCallback
-early_stopping_patience: 3
-resume_from_checkpoint:
-auto_resume_from_checkpoints: true
-local_rank:
-logging_steps: 1
-xformers_attention: true
-flash_attention:
-gptq_groupsize:
-gptq_model_v1:
-warmup_steps: 10
-eval_steps: 50
-save_steps: 50
-debug:
-deepspeed:
-weight_decay: 0.0
-special_tokens:
-  eos_token: "<|endoftext|>"
-  bos_token: "<|endoftext|>"
-  unk_token: "<|endoftext|>"
-  pad_token: "<|endoftext|>"

requirements.txt

@@ -1,7 +1,6 @@
 peft @ git+https://github.com/huggingface/peft.git
 transformers @ git+https://github.com/huggingface/transformers.git
-bitsandbytes>=0.41.1
-accelerate @ git+https://github.com/huggingface/accelerate@2a289f6108e77a77a4efffb3f6316bc98538413b
+bitsandbytes>=0.39.0
 addict
 fire
 PyYAML==6.0
@@ -12,13 +11,10 @@ wandb
 einops
 xformers
 optimum
-hf_transfer
-numba
-numpy==1.24.4
 # qlora things
 bert-score==0.3.13
 evaluate==0.4.0
 rouge-score==0.1.2
 scipy
 scikit-learn==1.2.2
-pynvml
+numba

scripts/alpaca_json_to_jsonl.py

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

scripts/finetune.py

@@ -17,30 +17,42 @@ import yaml
 from optimum.bettertransformer import BetterTransformer
 from transformers import GenerationConfig, TextStreamer
 
-from axolotl.logging_config import configure_logging
-from axolotl.utils.config import normalize_config, validate_config
 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
 
 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):
+    def get_device():
+        try:
+            if torch.cuda.is_available():
+                return f"cuda:{cfg.local_rank}"
+
+            if torch.backends.mps.is_available():
+                return "mps"
+
+            raise SystemError("No CUDA/mps device found")
+        except Exception:  # pylint: disable=broad-exception-caught
+            return "cpu"
+
+    cfg.device = get_device()
+    if cfg.device_map != "auto":
+        if cfg.device.startswith("cuda"):
+            cfg.device_map = {"": cfg.local_rank}
+        else:
+            cfg.device_map = {"": cfg.device}
 
 
 def get_multi_line_input() -> Optional[str]:
@@ -172,13 +184,36 @@ def train(
 
     validate_config(cfg)
 
-    normalize_config(cfg)
+    # setup some derived config / hyperparams
+    cfg.gradient_accumulation_steps = cfg.gradient_accumulation_steps or (
+        cfg.batch_size // cfg.micro_batch_size
+    )
+    cfg.batch_size = (
+        cfg.batch_size or cfg.micro_batch_size * cfg.gradient_accumulation_steps
+    )
+    cfg.world_size = int(os.environ.get("WORLD_SIZE", 1))
+    cfg.local_rank = int(os.environ.get("LOCAL_RANK", 0))
+    choose_device(cfg)
+    cfg.ddp = cfg.ddp if cfg.ddp is not None else cfg.world_size != 1
+    if cfg.ddp:
+        cfg.device_map = {"": int(os.environ.get("LOCAL_RANK", 0))}
+        cfg.batch_size = cfg.batch_size * cfg.world_size
 
     setup_wandb_env_vars(cfg)
+    if cfg.device == "mps":
+        cfg.load_in_8bit = False
+        cfg.tf32 = False
+        if cfg.bf16:
+            cfg.fp16 = True
+        cfg.bf16 = False
+
+    if cfg.tf32:
+        torch.backends.cuda.matmul.allow_tf32 = True
 
     # load the tokenizer first
-    LOG.info(f"loading tokenizer... {cfg.tokenizer_config or cfg.base_model_config}")
-    tokenizer = load_tokenizer(cfg)
+    tokenizer_config = cfg.tokenizer_config or cfg.base_model_config
+    logging.info(f"loading tokenizer... {tokenizer_config}")
+    tokenizer = load_tokenizer(tokenizer_config, cfg.tokenizer_type, cfg)
 
     if (
         check_not_in(["shard", "merge_lora"], kwargs) and not cfg.inference
@@ -192,27 +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
@@ -221,31 +243,32 @@ 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 (optionally) peft_config...")
-    model, peft_config = load_model(cfg, tokenizer)
-
-    safe_serialization = cfg.save_safetensors is True
+    logging.info("loading model and peft_config...")
+    model, peft_config = load_model(
+        cfg.base_model,
+        cfg.base_model_config,
+        cfg.model_type,
+        tokenizer,
+        cfg,
+        adapter=cfg.adapter,
+    )
 
     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")
-            model.save_pretrained(
-                str(Path(cfg.output_dir) / "merged"),
-                safe_serialization=safe_serialization,
-            )
-            tokenizer.save_pretrained(str(Path(cfg.output_dir) / "merged"))
+            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":
@@ -256,22 +279,20 @@ def train(
         return
 
     if "shard" in kwargs:
-        model.save_pretrained(cfg.output_dir, safe_serialization=safe_serialization)
+        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
@@ -280,16 +301,16 @@ def train(
         def terminate_handler(_, __, model):
             if cfg.flash_optimum:
                 model = BetterTransformer.reverse(model)
-            model.save_pretrained(cfg.output_dir, safe_serialization=safe_serialization)
+            model.save_pretrained(cfg.output_dir)
             sys.exit(0)
 
         signal.signal(
             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 = [
@@ -301,13 +322,12 @@ 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}"
             )
 
     if not Path(cfg.output_dir).is_dir():
         os.makedirs(cfg.output_dir, exist_ok=True)
-    tokenizer.save_pretrained(cfg.output_dir)
     if cfg.flash_optimum:
         with torch.backends.cuda.sdp_kernel(
             enable_flash=True, enable_math=True, enable_mem_efficient=True
@@ -316,16 +336,16 @@ 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, safe_serialization=safe_serialization)
+        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__":

scripts/runpod-entrypoint.sh

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

src/axolotl/datasets.py

@@ -1,13 +1,12 @@
 """Module containing Dataset functionality"""
 
 import logging
-import os
 from typing import List
 
 import torch
-from datasets import Dataset, IterableDataset
+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
@@ -15,12 +14,10 @@ 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(Dataset):
+class TokenizedPromptDataset(IterableDataset):
     """
-    Dataset that returns tokenized prompts from a stream of text files.
+    Iterable dataset that returns tokenized prompts from a stream of text files.
         Args:
             prompt_tokenizer (PromptTokenizingStrategy): The prompt tokenizing method for proccessing the data.
             dataset (dataset.Dataset): Dataset with text files.
@@ -30,19 +27,22 @@ class TokenizedPromptDataset(Dataset):
         self,
         prompt_tokenizer: PromptTokenizingStrategy,
         dataset: IterableDataset,
-        **kwargs,
     ):
         self.prompt_tokenizer = prompt_tokenizer
-        super().__init__(self.process(dataset).data, **kwargs)
+        self.dataset = dataset
 
-    def process(self, dataset):
-        features = dataset.features.keys()
-        num_proc = min(64, os.cpu_count())
-        return dataset.map(
-            self.prompt_tokenizer.tokenize_prompt,
-            num_proc=num_proc,
-            remove_columns=features,
-        )
+    def __iter__(self):
+        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
@@ -76,21 +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
@@ -112,9 +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()
@@ -123,20 +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
@@ -145,6 +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)
@@ -155,17 +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)

src/axolotl/flash_attn.py

@@ -0,0 +1,140 @@
+"""Flash attention monkey patch for llama model"""
+
+# copied from https://github.com/lm-sys/FastChat/blob/main/fastchat/train/llama_flash_attn_monkey_patch.py
+
+from typing import Optional, Tuple
+
+import torch
+import transformers
+from einops import rearrange
+from flash_attn.bert_padding import pad_input, unpad_input
+from flash_attn.flash_attn_interface import flash_attn_unpadded_qkvpacked_func
+from transformers.models.llama.modeling_llama import apply_rotary_pos_emb
+
+
+def forward(
+    self,
+    hidden_states: torch.Tensor,
+    attention_mask: Optional[torch.Tensor] = None,
+    position_ids: Optional[torch.Tensor] = None,
+    past_key_value: Optional[Tuple[torch.Tensor]] = None,
+    output_attentions: bool = False,
+    use_cache: bool = False,
+) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+    """Input shape: Batch x Time x Channel
+
+    attention_mask: [bsz, q_len]
+    """
+    # pylint: disable=duplicate-code
+    bsz, q_len, _ = hidden_states.size()
+
+    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)
+    )
+    # [bsz, q_len, nh, hd]
+    # [bsz, nh, q_len, hd]
+
+    kv_seq_len = key_states.shape[-2]
+    assert past_key_value is None, "past_key_value is not supported"
+
+    cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
+    query_states, key_states = apply_rotary_pos_emb(
+        query_states, key_states, cos, sin, position_ids
+    )
+    # [bsz, nh, t, hd]
+    assert not output_attentions, "output_attentions is not supported"
+    assert not use_cache, "use_cache is not supported"
+
+    # Flash attention codes from
+    # https://github.com/HazyResearch/flash-attention/blob/main/flash_attn/flash_attention.py
+
+    # transform the data into the format required by flash attention
+    qkv = torch.stack(
+        [query_states, key_states, value_states], dim=2
+    )  # [bsz, nh, 3, q_len, hd]
+    qkv = qkv.transpose(1, 3)  # [bsz, q_len, 3, nh, hd]
+    # We have disabled _prepare_decoder_attention_mask in LlamaModel
+    # the attention_mask should be the same as the key_padding_mask
+    key_padding_mask = attention_mask
+
+    if key_padding_mask is None:
+        qkv = rearrange(qkv, "b s ... -> (b s) ...")
+        max_s = q_len
+        cu_q_lens = torch.arange(
+            0,
+            (bsz + 1) * q_len,
+            step=q_len,
+            dtype=torch.int32,
+            device=qkv.device,
+        )
+        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:
+        nheads = qkv.shape[-2]
+
+        # 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,
+        )
+    return (
+        self.o_proj(rearrange(output, "b s h d -> b s (h d)")),
+        None,
+        None,
+    )
+
+
+# Disable the transformation of the attention mask in LlamaModel as the flash attention
+# requires the attention mask to be the same as the key_padding_mask
+def _prepare_decoder_attention_mask(
+    self,
+    attention_mask,
+    input_shape,
+    inputs_embeds,
+    past_key_values_length,
+):  # pylint: disable=unused-argument
+    # [bsz, seq_len]
+    return attention_mask
+
+
+def replace_llama_attn_with_flash_attn():
+    transformers.models.llama.modeling_llama.LlamaModel._prepare_decoder_attention_mask = (  # pylint: disable=protected-access
+        _prepare_decoder_attention_mask
+    )
+    transformers.models.llama.modeling_llama.LlamaAttention.forward = forward

src/axolotl/logging_config.py

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

src/axolotl/monkeypatch/llama_attn_hijack_flash.py

@@ -1,358 +0,0 @@
-"""Flash attention monkey patch for llama model"""
-
-# copied from https://github.com/lm-sys/FastChat/blob/main/fastchat/train/llama_flash_attn_monkey_patch.py
-
-import warnings
-from typing import Optional, Tuple
-
-import torch
-import torch.nn.functional as F
-import transformers
-from einops import rearrange
-from flash_attn.bert_padding import pad_input, unpad_input
-from transformers.models.llama.modeling_llama import apply_rotary_pos_emb, repeat_kv
-
-from axolotl.monkeypatch.utils import get_cu_seqlens_from_pos_ids
-
-try:
-    from flash_attn.flash_attn_interface import (  # pylint: disable=ungrouped-imports
-        flash_attn_kvpacked_func,
-        flash_attn_varlen_kvpacked_func,
-        flash_attn_varlen_qkvpacked_func,
-    )
-except ImportError:
-    from flash_attn.flash_attn_interface import (
-        flash_attn_unpadded_kvpacked_func as flash_attn_varlen_kvpacked_func,
-    )
-    from flash_attn.flash_attn_interface import (
-        flash_attn_unpadded_qkvpacked_func as flash_attn_varlen_qkvpacked_func,
-    )
-
-
-def replace_llama_attn_with_flash_attn():
-    transformers.models.llama.modeling_llama.LlamaModel._prepare_decoder_attention_mask = (  # pylint: disable=protected-access
-        _prepare_decoder_attention_mask
-    )
-    transformers.models.llama.modeling_llama.LlamaAttention.forward = flashattn_forward
-
-
-# Disable the transformation of the attention mask in LlamaModel as the flash attention
-# requires the attention mask to be the same as the key_padding_mask
-def _prepare_decoder_attention_mask(
-    self,
-    attention_mask,
-    input_shape,
-    inputs_embeds,
-    past_key_values_length,
-):  # pylint: disable=unused-argument
-    # [bsz, seq_len]
-    return attention_mask
-
-
-def flashattn_forward(
-    self,
-    hidden_states: torch.Tensor,
-    attention_mask: Optional[torch.Tensor] = None,
-    position_ids: Optional[torch.Tensor] = None,
-    past_key_value: Optional[Tuple[torch.Tensor]] = None,
-    output_attentions: bool = False,
-    use_cache: bool = False,
-) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
-    """Input shape: Batch x Time x Channel
-
-    attention_mask: [bsz, q_len]
-    """
-    # 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)
-    # [bsz, q_len, nh, hd]
-    # [bsz, nh, q_len, hd]
-
-    kv_seq_len = key_states.shape[-2]
-    if past_key_value is not None:
-        kv_seq_len += past_key_value[0].shape[-2]
-
-    cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
-    query_states, key_states = apply_rotary_pos_emb(
-        query_states, key_states, cos, sin, position_ids
-    )
-    # [bsz, nh, t, hd]
-
-    if past_key_value is not None:
-        # reuse k, v, self_attention
-        key_states = torch.cat([past_key_value[0], key_states], dim=2)
-        value_states = torch.cat([past_key_value[1], value_states], dim=2)
-
-    past_key_value = (key_states, value_states) if use_cache else None
-
-    # repeat k/v heads if n_kv_heads < n_heads
-    key_states = repeat_kv(key_states, self.num_key_value_groups)
-    value_states = repeat_kv(value_states, self.num_key_value_groups)
-
-    if output_attentions:
-        warnings.warn(
-            "Output attentions is not supported for patched `LlamaAttention`, returning `None` instead."
-        )
-
-    #
-    # flash-attn v2 start
-    #
-
-    if self.training:
-        # during training q,k,v always have same seqlen
-        assert key_states.shape == query_states.shape
-        is_causal = True
-    else:
-        # turn off FA causal mask after first inference autoregressive iteration
-        # only on first autoregressive step q,k,v have same seqlen
-        is_causal = past_key_value is not None
-
-    if self.training and attention_mask.shape[0] == 1:
-        # special handling using sample packing
-        qkv = torch.stack(
-            [query_states, key_states, value_states], dim=2
-        )  # [bsz, nh, 3, q_len, hd]
-        qkv = qkv.transpose(1, 3)  # [bsz, q_len, 3, nh, hd]
-        qkv = rearrange(qkv, "b s ... -> (b s) ...")
-        cu_q_lens, max_s = get_cu_seqlens_from_pos_ids(position_ids)
-        cu_q_lens = cu_q_lens.squeeze()
-
-        output = flash_attn_varlen_qkvpacked_func(
-            qkv, cu_q_lens, max_s, 0.0, softmax_scale=None, causal=is_causal
-        )
-        output = rearrange(output, "(b s) ... -> b s ...", b=bsz)
-    elif query_states.shape == key_states.shape:
-        query_states = query_states.transpose(1, 2)
-        key_states = key_states.transpose(1, 2)
-        value_states = value_states.transpose(1, 2)
-        qkv_unpad, cu_seqlens_q, max_seqlen_q, _, output_pad_fn = generate_qkv(
-            query_states,
-            key_states,
-            value_states,
-            qkvpacked=True,
-            # We have disabled _prepare_decoder_attention_mask in LlamaModel
-            # the attention_mask should be the same as the key_padding_mask
-            key_padding_mask=attention_mask,
-            query_padding_mask=attention_mask[:, -query_states.size(1) :]
-            if attention_mask is not None
-            else None,
-        )
-        output_unpad = flash_attn_varlen_qkvpacked_func(
-            qkv_unpad,
-            cu_seqlens_q,
-            max_seqlen_q,
-            0.0,
-            softmax_scale=None,
-            causal=is_causal,
-        )
-        output = output_pad_fn(output_unpad)
-    else:
-        query_states = query_states.transpose(1, 2)
-        key_states = key_states.transpose(1, 2)
-        value_states = value_states.transpose(1, 2)
-        if attention_mask is None or attention_mask.all().item():
-            output = flash_attn_kvpacked_func(
-                query_states,
-                torch.stack([key_states, value_states], 2),
-                causal=is_causal,
-            )
-        else:
-            (  # pylint: disable=unbalanced-tuple-unpacking
-                q_unpad,
-                kv_unpad,
-                cu_seqlens_q,
-                cu_seqlens_k,
-                max_seqlen_q,
-                max_seqlen_k,
-                _,
-                _,
-                output_pad_fn,
-            ) = generate_qkv(
-                query_states,
-                key_states,
-                value_states,
-                kvpacked=True,
-                key_padding_mask=attention_mask,
-                query_padding_mask=attention_mask[:, -query_states.size(1) :]
-                if attention_mask is not None
-                else None,
-            )
-            output_unpad = flash_attn_varlen_kvpacked_func(
-                q_unpad,
-                kv_unpad,
-                cu_seqlens_q,
-                cu_seqlens_k,
-                max_seqlen_q,
-                max_seqlen_k,
-                0.0,
-                softmax_scale=None,
-                causal=is_causal,
-            )
-            output = output_pad_fn(output_unpad)
-
-    attn_output = output
-    if attn_output.size() != (bsz, q_len, self.num_heads, self.head_dim):
-        raise ValueError(
-            f"`attn_output` should be of size {(bsz, q_len, self.num_heads, self.head_dim)}, but is"
-            f" {attn_output.size()}"
-        )
-    attn_output = rearrange(attn_output, "b s h d -> b s (h d)")
-
-    #
-    # flash-attn v2 end
-    #
-
-    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)
-
-    return attn_output, None, past_key_value
-
-
-# based on https://github.com/Dao-AILab/flash-attention/blob/364a5b/tests/test_flash_attn.py#L38
-def generate_qkv(
-    q,
-    k,
-    v,
-    query_padding_mask=None,
-    key_padding_mask=None,
-    kvpacked=False,
-    qkvpacked=False,
-):  # pylint: disable=invalid-name,unnecessary-lambda-assignment
-    """
-    Arguments:
-        q: (batch_size, seqlen_q, nheads, d)
-        k: (batch_size, seqlen_k, nheads_k, d)
-        v: (batch_size, seqlen_k, nheads_k, d)
-        query_padding_mask: (batch_size, seqlen), bool
-        key_padding_mask: (batch_size, seqlen), bool
-    """
-    assert not (kvpacked and qkvpacked)
-    batch_size, seqlen_q, nheads, d = q.shape
-    _, seqlen_k, nheads_k, _ = k.shape
-    assert k.shape == (batch_size, seqlen_k, nheads_k, d)
-    assert v.shape == (batch_size, seqlen_k, nheads_k, d)
-
-    if query_padding_mask is not None:
-        q_unpad, indices_q, cu_seqlens_q, max_seqlen_q = unpad_input(
-            q, query_padding_mask
-        )
-
-        output_pad_fn = lambda output_unpad: pad_input(  # noqa: E731
-            output_unpad, indices_q, batch_size, seqlen_q
-        )
-
-    else:
-        q_unpad = rearrange(q, "b s h d -> (b s) h d")
-        cu_seqlens_q = torch.arange(
-            0,
-            (batch_size + 1) * seqlen_q,
-            step=seqlen_q,
-            dtype=torch.int32,
-            device=q_unpad.device,
-        )
-        max_seqlen_q = seqlen_q
-
-        output_pad_fn = lambda output_unpad: rearrange(  # noqa: E731
-            output_unpad, "(b s) h d -> b s h d", b=batch_size
-        )
-
-    if key_padding_mask is not None:
-        k_unpad, _, cu_seqlens_k, max_seqlen_k = unpad_input(k, key_padding_mask)
-        v_unpad, _, _, _ = unpad_input(v, key_padding_mask)
-    else:
-        k_unpad = rearrange(k, "b s h d -> (b s) h d")
-        v_unpad = rearrange(v, "b s h d -> (b s) h d")
-        cu_seqlens_k = torch.arange(
-            0,
-            (batch_size + 1) * seqlen_k,
-            step=seqlen_k,
-            dtype=torch.int32,
-            device=k_unpad.device,
-        )
-        max_seqlen_k = seqlen_k
-
-    if qkvpacked:
-        assert nheads == nheads_k
-        qkv_unpad = torch.stack([q_unpad, k_unpad, v_unpad], dim=1)
-        qkv = torch.stack([q, k, v], dim=2)
-        return (qkv_unpad, cu_seqlens_q, max_seqlen_q, qkv, output_pad_fn)
-
-    if kvpacked:
-        kv_unpad = torch.stack([k_unpad, v_unpad], dim=1)
-        kv = torch.stack([k, v], dim=2)
-        return (
-            q_unpad,
-            kv_unpad,
-            cu_seqlens_q,
-            cu_seqlens_k,
-            max_seqlen_q,
-            max_seqlen_k,
-            q,
-            kv,
-            output_pad_fn,
-        )
-
-    return (
-        q_unpad,
-        k_unpad,
-        v_unpad,
-        cu_seqlens_q,
-        cu_seqlens_k,
-        max_seqlen_q,
-        max_seqlen_k,
-        q,
-        k,
-        v,
-        output_pad_fn,
-    )

src/axolotl/monkeypatch/llama_attn_hijack_sdp.py

@@ -1,140 +0,0 @@
-"""
-Patched LlamaAttention to use torch.nn.functional.scaled_dot_product_attention
-"""
-
-import warnings
-from typing import Optional, Tuple
-
-import torch
-import torch.nn.functional as F
-import transformers.models.llama.modeling_llama
-from transformers.models.llama.modeling_llama import apply_rotary_pos_emb, repeat_kv
-
-
-def hijack_llama_sdp_attention():
-    transformers.models.llama.modeling_llama.LlamaAttention.forward = (
-        sdp_attention_forward
-    )
-
-
-def sdp_attention_forward(
-    self,
-    hidden_states: torch.Tensor,
-    attention_mask: Optional[torch.Tensor] = None,
-    position_ids: Optional[torch.LongTensor] = None,
-    past_key_value: Optional[Tuple[torch.Tensor]] = None,
-    output_attentions: bool = False,
-    use_cache: bool = False,
-) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
-    # 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)
-    # [bsz, q_len, nh, hd]
-    # [bsz, nh, q_len, hd]
-
-    kv_seq_len = key_states.shape[-2]
-    if past_key_value is not None:
-        kv_seq_len += past_key_value[0].shape[-2]
-
-    cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
-    query_states, key_states = apply_rotary_pos_emb(
-        query_states, key_states, cos, sin, position_ids
-    )
-    # [bsz, nh, t, hd]
-
-    if past_key_value is not None:
-        # reuse k, v, self_attention
-        key_states = torch.cat([past_key_value[0], key_states], dim=2)
-        value_states = torch.cat([past_key_value[1], value_states], dim=2)
-
-    past_key_value = (key_states, value_states) if use_cache else None
-
-    # repeat k/v heads if n_kv_heads < n_heads
-    key_states = repeat_kv(key_states, self.num_key_value_groups)
-    value_states = repeat_kv(value_states, self.num_key_value_groups)
-
-    if output_attentions:
-        warnings.warn(
-            "Output attentions is not supported for patched `LlamaAttention`, returning `None` instead."
-        )
-
-    #
-    # sdp-attn start
-    #
-
-    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,
-        )
-
-    if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
-        raise ValueError(
-            f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
-            f" {attn_output.size()}"
-        )
-    attn_output = attn_output.transpose(1, 2)
-    attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
-
-    #
-    # sdp-attn end
-    #
-
-    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)
-
-    return attn_output, None, past_key_value

src/axolotl/monkeypatch/llama_attn_hijack_xformers.py

@@ -3,13 +3,12 @@ Directly copied the code from https://raw.githubusercontent.com/oobabooga/text-g
 """
 
 import logging
-import warnings
+import math
 from typing import Optional, Tuple
 
 import torch
-import torch.nn.functional as F
 import transformers.models.llama.modeling_llama
-from transformers.models.llama.modeling_llama import apply_rotary_pos_emb, repeat_kv
+from torch import nn
 
 try:
     import xformers.ops
@@ -21,6 +20,12 @@ def hijack_llama_attention():
     transformers.models.llama.modeling_llama.LlamaAttention.forward = xformers_forward
 
 
+def hijack_llama_sdp_attention():
+    transformers.models.llama.modeling_llama.LlamaAttention.forward = (
+        sdp_attention_forward
+    )
+
+
 def xformers_forward(
     self,
     hidden_states: torch.Tensor,
@@ -33,57 +38,30 @@ 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)
-    # [bsz, q_len, nh, hd]
-    # [bsz, nh, q_len, hd]
+    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:
         kv_seq_len += past_key_value[0].shape[-2]
-
     cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
-    query_states, key_states = apply_rotary_pos_emb(
+    (
+        query_states,
+        key_states,
+    ) = transformers.models.llama.modeling_llama.apply_rotary_pos_emb(
         query_states, key_states, cos, sin, position_ids
     )
     # [bsz, nh, t, hd]
@@ -95,61 +73,161 @@ 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 = repeat_kv(key_states, self.num_key_value_groups)
-    value_states = 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)
+        key_states = key_states.transpose(1, 2)
+        value_states = value_states.transpose(1, 2)
 
-    if output_attentions:
-        warnings.warn(
-            "Output attentions is not supported for patched `LlamaAttention`, returning `None` instead."
-        )
-
-    #
-    # xformers-attn start
-    #
-
-    query_states = query_states.transpose(1, 2)
-    key_states = key_states.transpose(1, 2)
-    value_states = value_states.transpose(1, 2)
-
-    # This is a nasty hack. We know attention_mask in transformers is either LowerTriangular or all Zeros.
-    # We therefore check if one element in the upper triangular portion is zero. If it is, then the mask is all zeros.
-    if attention_mask is None or attention_mask[0, 0, 0, 1] == 0:
-        # input and output should be of form (bsz, q_len, num_heads, head_dim)
-        attn_output = xformers.ops.memory_efficient_attention(
-            query_states, key_states, value_states, attn_bias=None
-        )
+        # This is a nasty hack. We know attention_mask in transformers is either LowerTriangular or all Zeros.
+        # We therefore check if one element in the upper triangular portion is zero. If it is, then the mask is all zeros.
+        if attention_mask is None or attention_mask[0, 0, 0, 1] == 0:
+            # input and output should be of form (bsz, q_len, num_heads, head_dim)
+            attn_output = xformers.ops.memory_efficient_attention(
+                query_states, key_states, value_states, attn_bias=None
+            )
+        else:
+            # input and output should be of form (bsz, q_len, num_heads, head_dim)
+            attn_output = xformers.ops.memory_efficient_attention(
+                query_states,
+                key_states,
+                value_states,
+                attn_bias=xformers.ops.LowerTriangularMask(),
+            )
+        attn_weights = None
     else:
-        # input and output should be of form (bsz, q_len, num_heads, head_dim)
-        attn_output = xformers.ops.memory_efficient_attention(
+        attn_weights = torch.matmul(
+            query_states, key_states.transpose(2, 3)
+        ) / math.sqrt(self.head_dim)
+
+        if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
+            raise ValueError(
+                f"Attention weights should be of size {(bsz * self.num_heads, q_len, kv_seq_len)}, but is"
+                f" {attn_weights.size()}"
+            )
+
+        if attention_mask is not None:
+            if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
+                raise ValueError(
+                    f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
+                )
+            attn_weights = attn_weights + attention_mask
+            attn_weights = torch.max(
+                attn_weights, torch.tensor(torch.finfo(attn_weights.dtype).min)
+            )
+
+        # upcast attention to fp32
+        attn_weights = nn.functional.softmax(
+            attn_weights, dim=-1, dtype=torch.float32
+        ).to(query_states.dtype)
+        attn_output = torch.matmul(attn_weights, value_states)
+
+        if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
+            raise ValueError(
+                f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
+                f" {attn_output.size()}"
+            )
+
+        attn_output = attn_output.transpose(1, 2)
+
+    attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
+    attn_output = self.o_proj(attn_output)
+    return attn_output, attn_weights, past_key_value
+
+
+def sdp_attention_forward(
+    self,
+    hidden_states: torch.Tensor,
+    attention_mask: Optional[torch.Tensor] = None,
+    position_ids: Optional[torch.LongTensor] = None,
+    past_key_value: Optional[Tuple[torch.Tensor]] = None,
+    output_attentions: bool = False,
+    use_cache: bool = False,
+) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+    # pylint: disable=duplicate-code
+    bsz, q_len, _ = hidden_states.size()
+
+    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:
+        kv_seq_len += past_key_value[0].shape[-2]
+    cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
+    (
+        query_states,
+        key_states,
+    ) = transformers.models.llama.modeling_llama.apply_rotary_pos_emb(
+        query_states, key_states, cos, sin, position_ids
+    )
+    # [bsz, nh, t, hd]
+
+    if past_key_value is not None:
+        # reuse k, v, self_attention
+        key_states = torch.cat([past_key_value[0], key_states], dim=2)
+        value_states = torch.cat([past_key_value[1], value_states], dim=2)
+
+    past_key_value = (key_states, value_states) if use_cache else None
+
+    # We only apply sdp attention if we don't need to output the whole attention matrix
+    if not output_attentions:
+        attn_output = torch.nn.functional.scaled_dot_product_attention(
             query_states,
             key_states,
             value_states,
-            # attn_bias=attention_mask,
-            attn_bias=xformers.ops.LowerTriangularMask(),
+            attn_mask=attention_mask,
+            is_causal=False,
         )
+        attn_weights = None
+    else:
+        attn_weights = torch.matmul(
+            query_states, key_states.transpose(2, 3)
+        ) / math.sqrt(self.head_dim)
 
-    if attn_output.size() != (bsz, q_len, self.num_heads, self.head_dim):
-        raise ValueError(
-            f"`attn_output` should be of size {(bsz, q_len, self.num_heads, self.head_dim)}, but is"
-            f" {attn_output.size()}"
-        )
+        if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
+            raise ValueError(
+                f"Attention weights should be of size {(bsz * self.num_heads, q_len, kv_seq_len)}, but is"
+                f" {attn_weights.size()}"
+            )
+
+        if attention_mask is not None:
+            if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
+                raise ValueError(
+                    f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
+                )
+            attn_weights = attn_weights + attention_mask
+            attn_weights = torch.max(
+                attn_weights, torch.tensor(torch.finfo(attn_weights.dtype).min)
+            )
+
+        # upcast attention to fp32
+        attn_weights = nn.functional.softmax(
+            attn_weights, dim=-1, dtype=torch.float32
+        ).to(query_states.dtype)
+        attn_output = torch.matmul(attn_weights, value_states)
+
+        if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
+            raise ValueError(
+                f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
+                f" {attn_output.size()}"
+            )
+
+    attn_output = attn_output.transpose(1, 2)
     attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
 
-    #
-    # xformers-attn end
-    #
+    attn_output = self.o_proj(attn_output)
 
-    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)
-
-    return attn_output, None, past_key_value
+    return attn_output, attn_weights, past_key_value

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
-    )

src/axolotl/monkeypatch/llama_landmark_attn.py

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

src/axolotl/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)

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,

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, SHAREGPT_ASSERTION_FAILED_ROLE
-
-
-@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], SHAREGPT_ASSERTION_FAILED_ROLE
-            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,
-    )

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,
-    )

src/axolotl/prompt_strategies/pygmalion.py

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

src/axolotl/prompt_tokenizers.py

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

src/axolotl/prompters.py

@@ -5,7 +5,6 @@ import logging
 from enum import Enum, auto
 from typing import Generator, List, Optional, Tuple, Union
 
-LOG = logging.getLogger("axolotl")
 IGNORE_TOKEN_ID = -100
 
 
@@ -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):
@@ -271,11 +259,6 @@ class Conversation:
         self.messages.append([role, message])
 
 
-SHAREGPT_ASSERTION_FAILED_ROLE = (
-    "Role did not alternate between turns (gpt and human). Please check your data."
-)
-
-
 class ShareGPTPrompter:  # pylint: disable=too-few-public-methods
     """
     A prompter that generates prompts for the ShareGPT
@@ -332,7 +315,7 @@ class ShareGPTPrompter:  # pylint: disable=too-few-public-methods
         conv.messages = []
         for j, sentence in enumerate(source):
             role = roles[sentence["from"]]
-            assert role == conv.roles[j % 2], SHAREGPT_ASSERTION_FAILED_ROLE
+            assert role == conv.roles[j % 2]
             conv.append_message(role, sentence["value"])
 
         for part in conv.get_prompt():

src/axolotl/utils/bench.py

@@ -1,40 +0,0 @@
-"""Benchmarking and measurement utilities"""
-
-import pynvml
-import torch
-
-
-def gpu_memory_usage(device=0):
-    return torch.cuda.memory_allocated(device) / 1024.0**3
-
-
-def gpu_memory_usage_all(device=0):
-    usage = torch.cuda.memory_allocated(device) / 1024.0**3
-    reserved = torch.cuda.memory_reserved(device) / 1024.0**3
-    smi = gpu_memory_usage_smi(device)
-    return usage, reserved - usage, max(0, smi - reserved)
-
-
-def gpu_memory_usage_smi(device=0):
-    if isinstance(device, torch.device):
-        device = device.index
-    if isinstance(device, str) and device.startswith("cuda:"):
-        device = int(device[5:])
-
-    pynvml.nvmlInit()
-    handle = pynvml.nvmlDeviceGetHandleByIndex(device)
-    info = pynvml.nvmlDeviceGetMemoryInfo(handle)
-    return info.used / 1024.0**3
-
-
-def log_gpu_memory_usage(log, msg, device):
-    usage, cache, misc = gpu_memory_usage_all(device)
-    extras = []
-    if cache > 0:
-        extras.append(f"+{cache:.03f}GB cache")
-    if misc > 0:
-        extras.append(f"+{misc:.03f}GB misc")
-    log.info(
-        f"GPU memory usage {msg}: {usage:.03f}GB ({', '.join(extras)})", stacklevel=2
-    )
-    return usage, cache, misc

src/axolotl/utils/callbacks.py

@@ -1,6 +1,5 @@
 """Callbacks for Trainer class"""
 
-import logging
 import os
 
 from optimum.bettertransformer import BetterTransformer
@@ -12,10 +11,6 @@ from transformers import (
 )
 from transformers.trainer_utils import PREFIX_CHECKPOINT_DIR, IntervalStrategy
 
-from axolotl.utils.bench import log_gpu_memory_usage
-
-LOG = logging.getLogger("axolotl.callbacks")
-
 
 class SavePeftModelCallback(TrainerCallback):  # pylint: disable=too-few-public-methods
     """Callback to save the PEFT adapter"""
@@ -72,25 +67,3 @@ class SaveBetterTransformerModelCallback(
             # the trainer will raise an exception since it can't save a BetterTransformer wrapped model
             control.should_save = False
         return control
-
-
-class GPUStatsCallback(
-    TrainerCallback
-):  # pylint: disable=too-few-public-methods disable=unused-argument
-    """Callback to track GPU utilization"""
-
-    def __init__(self, cfg):
-        self.cfg = cfg
-        self.logged = False
-
-    def on_step_end(
-        self,
-        args: TrainingArguments,
-        state: TrainerState,
-        control: TrainerControl,
-        **kwargs,
-    ):
-        if not self.logged and state.global_step > 1:
-            log_gpu_memory_usage(LOG, "while training", self.cfg.device)
-            self.logged = True
-        return control

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

src/axolotl/utils/data.py

@@ -1,19 +1,12 @@
 """Module containing data utilities"""
 import functools
-import hashlib
 import logging
 from hashlib import md5
 from pathlib import Path
-from typing import Tuple, Union
+from typing import List, Tuple, Union
 
 import torch
-from datasets import (
-    Dataset,
-    DatasetDict,
-    concatenate_datasets,
-    load_dataset,
-    load_from_disk,
-)
+from datasets import Dataset, DatasetDict, load_dataset, load_from_disk
 from huggingface_hub import hf_hub_download
 from transformers import PreTrainedTokenizerBase
 
@@ -41,9 +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(
@@ -83,17 +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 = []
@@ -104,7 +94,6 @@ def load_tokenized_prepared_datasets(
             try:
                 load_dataset(
                     d.path,
-                    name=d.name,
                     streaming=True,
                     use_auth_token=use_auth_token,
                 )
@@ -116,10 +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,
@@ -127,7 +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,
@@ -137,22 +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
@@ -266,21 +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("merging datasets")
-        dataset = concatenate_datasets(datasets)
+        logging.info("tokenizing, merging, and shuffling master dataset")
 
-        if len(datasets) > 1:
-            LOG.info("shuffle merged datasets")
-            dataset = dataset.shuffle(seed=seed)
+        samples: List[int] = []
+        for d in datasets:
+            samples = samples + list(d)
+        dataset = Dataset.from_list(samples).shuffle(seed=seed)
         if cfg.local_rank == 0:
-            LOG.info(f"Saving merged prepared dataset to disk... {prepared_ds_path}")
+            logging.info(
+                f"Saving merged prepared dataset to disk... {prepared_ds_path}"
+            )
             dataset.save_to_disk(prepared_ds_path)
             if cfg.push_dataset_to_hub:
-                LOG.info(
+                logging.info(
                     f"Saving merged prepared dataset with push_to_hub... {cfg.push_dataset_to_hub}/{ds_hash}"
                 )
                 dataset.push_to_hub(
@@ -331,7 +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(
@@ -345,13 +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(
@@ -370,16 +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"])
@@ -387,12 +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(
@@ -405,7 +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(
@@ -414,51 +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:
@@ -570,7 +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
 
 

src/axolotl/utils/dataloader.py

@@ -1,288 +0,0 @@
-# pylint: skip-file
-import hashlib
-import itertools
-import logging
-import math
-from typing import Any, Callable, List, Union
-
-import numba
-import numpy as np
-from torch.utils.data import DistributedSampler, Sampler
-
-LOG = logging.getLogger("axolotl.utils.dataloader")
-
-
-@numba.njit
-def ffd_check(a: np.ndarray, c: int, n: int):
-    # First-fit-decreasing bin packing
-    # Check if a[] could fit in n bins with capacity c
-    # https://en.wikipedia.org/wiki/First-fit-decreasing_bin_packing
-
-    a = np.sort(a)[::-1]
-    bins = np.full((n,), c, dtype=a.dtype)
-    for size in a:
-        not_found = True
-        for idx in range(n):
-            if bins[idx] >= size:
-                bins[idx] -= size
-                not_found = False
-                break
-
-        if not_found:
-            return False
-
-    return True
-
-
-@numba.njit
-def ffd_with_result(a: np.ndarray, c: int, start_index: int):
-    # First-fit-decreasing bin packing (with result return)
-
-    indices = np.argsort(a)[::-1]
-    a = a[indices]
-
-    bins: List[Any] = []
-    bins_result: List[Any] = []
-    for a_id, size in enumerate(a):
-        add_new = True
-        for idx in range(len(bins)):
-            if bins[idx] >= size:
-                bins[idx] -= size
-                bins_result[idx].append(indices[a_id] + start_index)
-                add_new = False
-                break
-
-        if add_new:
-            bins.append(c - size)
-            bins_result.append([indices[a_id] + start_index])
-
-    return bins_result, len(a)
-
-
-@numba.njit
-def allocate(
-    lengths: np.ndarray, lengths_cumsum: np.ndarray, rank: int, c: int, n: int
-):
-    """
-    :param lengths: array of lengths of each sample
-    :param lengths_cumsum: cumulative sum of consecutive lengths
-    :param rank: rank for this process
-    :param c: length of tokens per batch
-    :param n: number of ranks
-    :return:
-    """
-    # Dynamic batch allocator, similar to Multifit
-    # https://en.wikipedia.org/wiki/Multifit_algorithm
-    # ~99.5% efficiency on OpenChat training set (12 * 2048 ctx len)
-
-    s = 0
-    start_index = 0
-    result = []
-    result_totseqs = []
-
-    while True:
-        # binary search [left, right)
-        left = 1
-        right = 1 + np.searchsorted(lengths_cumsum[start_index:], s + c * n, "right")
-
-        while right - left > 1:
-            mid = (left + right) // 2
-            if ffd_check(lengths[start_index : start_index + mid], c, n):
-                left = mid
-            else:
-                right = mid
-
-        # use length left
-        batch, tot_seqs = ffd_with_result(
-            lengths[start_index : start_index + left], c, start_index
-        )
-        if len(batch) < n:
-            break
-
-        start_index += left
-        s = lengths_cumsum[start_index - 1]
-
-        # add local rank
-        result.append(batch[rank])
-        # add total seqs for all ranks
-        result_totseqs.append(tot_seqs)
-        # yield batch[rank], tot_seqs, s, len(result) * c * n
-    return result, result_totseqs, s, len(result) * c * n
-
-
-def chunk(iterable, n):
-    """
-    Chunk data into tuples of length n
-    """
-    # batched('ABCDEFG', 3) --> ABC DEF G
-    if n < 1:
-        raise ValueError("n must be at least one")
-    it = iter(iterable)
-    while batch := tuple(itertools.islice(it, n)):
-        yield batch
-
-
-def hash_indices(lst: List[int]) -> str:
-    # Convert the list of integers to a string representation
-    concatenated = ",".join(map(str, lst))
-
-    # Generate the hash
-    sha256 = hashlib.sha256()
-    sha256.update(concatenated.encode())
-
-    return sha256.hexdigest()
-
-
-class MultipackDistributedDataloader:
-    """Unpadded data loading using Multipack.
-    Adapted from https://github.com/imoneoi/openchat/blob/v3_fix_mle_loss/ochat/training_deepspeed/multipack_dataloader.py
-    Approximate (at most ~1.22x) the optimal solution of the identical-machines scheduling problem, which is NP-hard.
-    """
-
-    def __init__(
-        self,
-        dataset: Any,
-        collate_fn: Callable,
-        seq_max_length: int = 2048,
-        batch_size: int = 1,
-        sampler: Union[Sampler, DistributedSampler] = None,
-        packing_efficiency_estimate: float = 1.0,
-        sample_packing_seq_len_multiplier: int = 1,
-        device_count: int = 1,
-    ):
-        # Dataset
-        self.dataset = dataset
-        self.lengths = (
-            dataset.data.column("position_ids")
-            .to_pandas()
-            .apply(lambda x: x[-1] + 1)
-            .values
-        )
-        assert isinstance(self.lengths, np.ndarray)
-        assert batch_size % sample_packing_seq_len_multiplier == 0
-        assert batch_size >= sample_packing_seq_len_multiplier
-        self.sampler = sampler
-        self.batch_size = batch_size
-        self.sample_packing_seq_len_multiplier = sample_packing_seq_len_multiplier
-        self.seq_max_length = seq_max_length
-        self.batch_max_length = batch_size * seq_max_length
-        self.collate_fn = collate_fn
-
-        self.num_replicas = 1
-        self.rank = 0
-
-        # statistics
-        self.eff_total_used = 0
-        self.eff_total_slots = 0
-        self.packing_efficiency_estimate = packing_efficiency_estimate or 1.0
-        self.device_count = device_count
-
-    def generate_batches(self, set_stats=False):
-        LOG.info("generating packed batches")
-        if self.sampler:
-            indices = [idx for idx in self.sampler]
-        else:
-            indices = range(0, len(self.dataset))
-
-        LOG.info(hash_indices(indices))
-        lengths = self.lengths[indices]
-        lengths_cumsum = np.cumsum(lengths)
-
-        batches, totseqs, total_used, total_slots = allocate(
-            lengths=lengths,
-            lengths_cumsum=lengths_cumsum,
-            rank=self.rank,
-            # c=self.batch_max_length,
-            c=self.seq_max_length * self.sample_packing_seq_len_multiplier,
-            n=self.num_replicas,
-        )
-
-        batches = [[indices[b_idx] for b_idx in batch] for batch in batches]
-
-        # statistics
-        if set_stats:
-            self.eff_total_used += total_used
-            self.eff_total_slots += total_slots
-
-        return batches, totseqs
-
-    def __iter__(self):
-        if hasattr(self.sampler, "set_epoch"):
-            new_epoch = self.sampler.epoch + 1
-            self.sampler.set_epoch(new_epoch)
-            LOG.info(f"calling sampler.set_epoch({new_epoch})")
-        all_batches, _ = self.generate_batches(set_stats=True)
-        features = self.dataset.features.keys()
-        len_remaining = self._len_est()
-        for batches in chunk(
-            all_batches, self.batch_size // self.sample_packing_seq_len_multiplier
-        ):
-            chunked_data = []
-            attn_mask_cum_idx = 0
-            for batch in batches:
-                concatenated = {}
-                batched_data = [self.dataset[batch_idx] for batch_idx in batch]
-                for feature in features:
-                    if feature == "attention_mask":
-                        arrays = [
-                            (attn_mask_cum_idx + idx + 1) * np.array(item[feature])
-                            for idx, item in enumerate(batched_data)
-                            if feature in item
-                        ]
-                        attn_mask_cum_idx += len(batched_data)
-                        concatenated[feature] = np.concatenate(arrays)
-                    else:
-                        arrays = [
-                            np.array(item[feature])
-                            for item in batched_data
-                            if feature in item
-                        ]
-                        concatenated[feature] = np.concatenate(arrays)
-                chunked_data.append(concatenated)
-            yield self.collate_fn(chunked_data)
-            len_remaining -= 1
-            if not len_remaining:
-                return
-
-    def _len_est(self):
-        lengths_sum = np.sum(self.lengths)
-        lengths_sum_per_device = lengths_sum // self.device_count
-        LOG.info(
-            f"packing_efficiency_estimate: {self.packing_efficiency_estimate} "
-            f"total_num_tokens per device: {lengths_sum_per_device}"
-        )
-
-        # shave off 1% + 1 for dealing with variance in packing from random sampler to sampler
-        return (
-            math.floor(
-                0.99
-                * lengths_sum_per_device
-                / self.packing_efficiency_estimate
-                // self.seq_max_length
-                // self.batch_size
-            )
-            - 1
-        )
-
-    def __len__(self):
-        # this doesn't return the actual length b/c with distributed samplers, not all dataloaders get
-        # the same share of total tokens
-        # if not self.eff_total_used:
-        #     batches, _ = self.generate_batches(set_stats=True)
-        # LOG.info(
-        #     f"packing_efficiency_estimate: {self.packing_efficiency_estimate} "
-        #     f"actual packing efficiency: {self.efficiency()}"
-        # )
-        return max(1, self._len_est())
-
-    def len_w_stats(self):
-        if not self.eff_total_used:
-            batches, _ = self.generate_batches(set_stats=True)
-        LOG.info(
-            f"packing_efficiency_estimate: {self.packing_efficiency_estimate} "
-            f"actual packing efficiency: {self.efficiency()}"
-        )
-        return max(1, self._len_est())
-
-    def efficiency(self):
-        return self.eff_total_used / self.eff_total_slots

src/axolotl/utils/dict.py

@@ -10,6 +10,3 @@ class DictDefault(Dict):
 
     def __missing__(self, key):
         return None
-
-    def __or__(self, other):
-        return DictDefault(super().__or__(other))

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

src/axolotl/utils/models.py

@@ -22,9 +22,6 @@ from transformers import (  # noqa: F401
 )
 
 from axolotl.prompt_tokenizers import LLAMA_DEFAULT_PAD_TOKEN
-from axolotl.utils.bench import log_gpu_memory_usage
-
-LOG = logging.getLogger("axolotl")
 
 if TYPE_CHECKING:
     from peft import PeftConfig  # noqa: F401
@@ -32,27 +29,31 @@ if TYPE_CHECKING:
     from axolotl.utils.dict import DictDefault  # noqa: F401
 
 
-def load_tokenizer(cfg):
-    tokenizer_kwargs = {}
+def load_tokenizer(
+    tokenizer_config,
+    tokenizer_type,
+    cfg,
+):
     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,
+        )
+    else:
+        tokenizer = AutoTokenizer.from_pretrained(
+            tokenizer_config,
+            trust_remote_code=cfg.trust_remote_code or False,
+            use_fast=use_fast,
+        )
 
-    tokenizer_cls = AutoTokenizer
-    if cfg.tokenizer_type:
-        tokenizer_cls = getattr(transformers, cfg.tokenizer_type)
-
-    tokenizer_config = cfg.tokenizer_config or cfg.base_model_config
-    tokenizer = tokenizer_cls.from_pretrained(
-        tokenizer_config,
-        trust_remote_code=cfg.trust_remote_code or False,
-        use_fast=use_fast,
-        **tokenizer_kwargs,
-    )
+    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",
@@ -60,11 +61,6 @@ def load_tokenizer(cfg):
     ]:
         tokenizer.pad_token = LLAMA_DEFAULT_PAD_TOKEN
 
-    LOG.debug(f"EOS: {tokenizer.eos_token_id} / {tokenizer.eos_token}")
-    LOG.debug(f"BOS: {tokenizer.bos_token_id} / {tokenizer.bos_token}")
-    LOG.debug(f"PAD: {tokenizer.pad_token_id} / {tokenizer.pad_token}")
-    LOG.debug(f"UNK: {tokenizer.unk_token_id} / {tokenizer.unk_token}")
-
     if tokenizer.__class__.__name__ == "GPTNeoXTokenizerFast":
         tokenizer.add_special_tokens({"pad_token": "[PAD]"})
         os.environ["TOKENIZERS_PARALLELISM"] = "false"
@@ -79,42 +75,38 @@ def load_tokenizer(cfg):
 
 
 def load_model(
-    cfg, tokenizer
-):  # type: (DictDefault, PreTrainedTokenizerBase) -> Tuple[PreTrainedModel, Optional[PeftConfig]]
+    base_model, base_model_config, model_type, tokenizer, cfg, adapter="lora"
+):
+    # type: (str, str, str, PreTrainedTokenizerBase, DictDefault, Optional[str]) -> Tuple[PreTrainedModel, Optional[PeftConfig]]
     """
-    Load a model for a given configuration and tokenizer.
+    Load a model from a base model and a model type.
     """
-    base_model = cfg.base_model
-    base_model_config = cfg.base_model_config
-    model_type = cfg.model_type
 
     # 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
+    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_sdp import hijack_llama_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 (
@@ -122,7 +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
@@ -133,17 +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:
@@ -158,20 +142,16 @@ 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:
@@ -207,7 +187,7 @@ 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)
@@ -227,16 +207,14 @@ def load_model(
         elif cfg.is_llama_derived_model and not cfg.trust_remote_code:
             from transformers import LlamaForCausalLM
 
-            config = LlamaConfig.from_pretrained(
-                base_model_config, rope_scaling=cfg.rope_scaling
-            )
+            config = LlamaConfig.from_pretrained(base_model_config)
             model = LlamaForCausalLM.from_pretrained(
                 base_model,
                 config=config,
-                device_map=cfg.device_map,
                 load_in_8bit=cfg.load_in_8bit and cfg.adapter is not None,
                 load_in_4bit=cfg.load_in_4bit and cfg.adapter is not None,
                 torch_dtype=torch_dtype,
+                device_map="auto" if cfg.world_size == 1 else cfg.device_map,
                 **model_kwargs,
             )
         # elif model_type == "GPTNeoXForCausalLM" and cfg.flash_attention:
@@ -268,10 +246,10 @@ def load_model(
         elif model_type and not cfg.trust_remote_code:
             model = getattr(transformers, model_type).from_pretrained(
                 base_model,
-                device_map=cfg.device_map,
                 load_in_8bit=cfg.load_in_8bit and cfg.adapter is not None,
                 load_in_4bit=cfg.load_in_4bit and cfg.adapter is not None,
                 torch_dtype=torch_dtype,
+                device_map=cfg.device_map,
                 trust_remote_code=cfg.trust_remote_code or False,
                 **model_kwargs,
             )
@@ -288,44 +266,40 @@ 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,
-                device_map=cfg.device_map,
                 load_in_8bit=cfg.load_in_8bit and cfg.adapter is not None,
                 load_in_4bit=cfg.load_in_4bit and cfg.adapter is not None,
                 torch_dtype=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,
-            device_map=cfg.device_map,
             load_in_8bit=cfg.load_in_8bit and cfg.adapter is not None,
             load_in_4bit=cfg.load_in_4bit and cfg.adapter is not None,
             torch_dtype=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 (
@@ -333,41 +307,28 @@ 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
 
-    if model.device.type == "cuda":
-        log_gpu_memory_usage(LOG, "after model load", model.device)
-
     if not cfg.gptq and (
         (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, cfg.adapter)
+    model, lora_config = load_adapter(model, cfg, adapter)
 
     if cfg.ddp and not load_in_8bit:
         model.to(f"cuda:{cfg.local_rank}")
 
     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)
@@ -393,15 +354,12 @@ 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:
         model = BetterTransformer.transform(model)
 
-    if cfg.adapter is not None:
-        log_gpu_memory_usage(LOG, "after adapters", model.device)
-
     # TODO resume_from_checkpoint handling
     return model, lora_config
 
@@ -411,8 +369,6 @@ 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":
@@ -432,7 +388,7 @@ 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,
@@ -479,7 +435,7 @@ def load_lora(model, cfg):
             bits = 8
 
         linear_names = find_all_linear_names(bits, model)
-        LOG.info(f"found linear modules: {repr(linear_names)}")
+        logging.info(f"found linear modules: {repr(linear_names)}")
         lora_target_modules = list(set(lora_target_modules + linear_names))
 
     lora_config = LoraConfig(

src/axolotl/utils/sampler.py

@@ -0,0 +1,173 @@
+# pylint: skip-file
+
+from typing import Any, List, Optional
+
+import numba
+import numpy as np
+import torch.distributed as dist
+from torch.utils.data import Sampler
+
+
+@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[int] = []
+    bins_result: List[Any] = []
+    for a_id, size in enumerate(a):
+        add_new = True
+        for idx in range(len(bins)):
+            if bins[idx] >= size:
+                bins[idx] -= size
+                bins_result[idx].append(indices[a_id] + start_index)
+                add_new = False
+                break
+
+        if add_new:
+            bins.append(c - size)
+            bins_result.append([indices[a_id] + start_index])
+
+    return bins_result
+
+
+@numba.njit
+def allocate(
+    lengths: np.ndarray, lengths_cumsum: np.ndarray, rank: int, c: int, n: int
+):
+    # Dynamic batch allocator, similar to Multifit
+    # https://en.wikipedia.org/wiki/Multifit_algorithm
+    # ~99.5% efficiency on OpenChat training set (12 * 2048 ctx len)
+
+    s = 0
+    start_index = 0
+    result = []
+
+    while True:
+        # binary search [l, r)
+        left = 1
+        right = 1 + np.searchsorted(lengths_cumsum[start_index:], s + c * n, "right")
+
+        while right - left > 1:
+            m = (left + right) // 2
+            if ffd_check(lengths[start_index : start_index + m], c, n):
+                left = m
+            else:
+                right = m
+
+        # use length l
+        batch = ffd_with_result(
+            lengths[start_index : start_index + left], c, start_index
+        )
+        assert len(batch) <= n
+        if len(batch) < n:
+            break
+
+        start_index += left
+        s = lengths_cumsum[start_index - 1]
+
+        # add local rank
+        result.append(batch[rank])
+
+    return result, s, len(result) * c * n
+
+
+class MultipackDistributedBatchSampler(Sampler):
+    """Unpadded length sampling using Multipack.
+    Approximate (at most ~1.22x) the optimal solution of the identical-machines scheduling problem, which is NP-hard.
+    """
+
+    def __init__(
+        self,
+        batch_max_length: int,
+        lengths: List[int],
+        num_replicas: Optional[int] = None,
+        rank: Optional[int] = None,
+        seed: int = 0,
+    ):
+        # Get rank
+        if num_replicas is None:
+            if not dist.is_available():
+                raise RuntimeError("Requires distributed package to be available")
+            num_replicas = dist.get_world_size()
+        if rank is None:
+            if not dist.is_available():
+                raise RuntimeError("Requires distributed package to be available")
+            rank = dist.get_rank()
+
+        self.num_replicas = num_replicas
+        self.rank = rank
+        self.seed = seed
+
+        self.batch_max_length = batch_max_length
+        self.lengths = lengths
+        assert isinstance(self.lengths, np.ndarray)
+
+        self.epoch = 0
+
+        # statistics
+        self.eff_total_used = 0
+        self.eff_total_slots = 0
+
+    def set_epoch(self, epoch: int):
+        self.epoch = epoch
+
+    def generate_batches(self, set_stats=False):
+        indices = np.random.default_rng(seed=self.seed + self.epoch).permutation(
+            len(self.lengths)
+        )
+
+        lengths = self.lengths[indices]
+        lengths_cumsum = np.cumsum(lengths)
+
+        batches, total_used, total_slots = allocate(
+            lengths=lengths,
+            lengths_cumsum=lengths_cumsum,
+            rank=self.rank,
+            c=self.batch_max_length,
+            n=self.num_replicas,
+        )
+
+        batches = [indices[batch] for batch in batches]
+
+        # statistics
+        if set_stats:
+            self.eff_total_used += total_used
+            self.eff_total_slots += total_slots
+
+        return batches
+
+    def __iter__(self):
+        batches = self.generate_batches(set_stats=True)
+        return iter(batches)
+
+    def num_batches(self):
+        batches = self.generate_batches()
+        return len(batches)
+
+    def efficiency(self):
+        return self.eff_total_used / self.eff_total_slots

src/axolotl/utils/tokenization.py

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

src/axolotl/utils/trainer.py

@@ -1,101 +1,110 @@
 """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 pathlib import Path
-from typing import Optional, Union
+from typing import Optional
 
 import bitsandbytes as bnb
 import numpy as np
 import torch.cuda
 import transformers
-from datasets import Dataset, set_caching_enabled
 from torch import nn
 from torch.optim.lr_scheduler import OneCycleLR
-from torch.utils.data import DataLoader, DistributedSampler, RandomSampler
+from torch.utils.data import Dataset
 from transformers import EarlyStoppingCallback, Trainer, TrainingArguments
 from transformers.trainer_pt_utils import get_parameter_names
 
 from axolotl.utils.callbacks import (
-    GPUStatsCallback,
     SaveBetterTransformerModelCallback,
     SavePeftModelCallback,
 )
-from axolotl.utils.collators import DataCollatorForSeq2Seq
-from axolotl.utils.dataloader import MultipackDistributedDataloader
+from axolotl.utils.sampler import MultipackDistributedBatchSampler
 from axolotl.utils.schedulers import (
     InterpolatingLogScheduler,
     get_cosine_schedule_with_quadratic_warmup,
 )
 
-LOG = logging.getLogger("axolotl")
+IGNORE_LABEL_ID = -100
 
 
-@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()
+def _find_multiple(val1, val2):
+    return (-(val1 // -val2)) * val2
 
 
-@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)
+def batch_to_tensor(batch, pad_id=0, dtype=torch.long, loss_dtype=torch.bfloat16):
+    # Pad an unused item to reach multiple of 64, for faster GEMM
+    pad_cur_len = sum(list(batch["length"]))
+    pad_len = _find_multiple(pad_cur_len, 64) - pad_cur_len
 
-    weights = torch.zeros_like(labels).float()
-    for i in range(labels.shape[0]):
-        mask = labels[i] != -100
+    if pad_len > 0:
+        assert pad_len < 64
 
-        # Create a tensor to track group ids
-        group_ids = torch.zeros_like(labels[i]).int()
-        curr_group_id = 0
+        batch["input_ids"].append([pad_id] * pad_len)
+        batch["labels"].append([pad_id] * pad_len)
+        batch["attention_mask"].append([0] * pad_len)
+        batch["length"].append(pad_len)
 
-        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
+    # seqlen
+    batch_lengths = torch.tensor(list(batch["length"]), dtype=torch.int32, device="cpu")
 
-        # 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]
+    max_seqlen = torch.max(batch_lengths)
+    cu_seqlens = torch.nn.functional.pad(
+        batch_lengths.cumsum(-1, dtype=torch.int32), (1, 0)
     )
-    if shift_labels:
-        logits = logits[..., :-1, :].contiguous()
-        labels = labels[..., 1:].contiguous()
 
-    weights = create_weighted_mask(labels)
-    return weighted_cross_entropy(logits, labels, weights)
+    # nz elements
+    nz_num = cu_seqlens[-1]
+    nz_input_ids = torch.zeros((nz_num,), dtype=dtype, pin_memory=True, device="cpu")
+    nz_position_ids = torch.zeros((nz_num,), dtype=dtype, pin_memory=True, device="cpu")
+    nz_shifted_label_ids = torch.zeros(
+        (nz_num,), dtype=dtype, pin_memory=True, device="cpu"
+    )
+    nz_shifted_loss_weights = torch.zeros(
+        (nz_num,), dtype=loss_dtype, pin_memory=True, device="cpu"
+    )
+
+    index = 0
+    for token_list, length, labels_list in zip(
+        batch["input_ids"], batch["length"], batch["labels"]
+    ):
+        tokens = torch.tensor(token_list, dtype=dtype, device="cpu")
+        position_ids = torch.arange(length, dtype=dtype, device="cpu")
+
+        # Input IDs & shifted labels
+        # shifted_label_ids = torch.where(masks, tokens, IGNORE_LABEL_ID)
+        shifted_label_ids = labels_list
+        shifted_label_ids = torch.nn.functional.pad(
+            shifted_label_ids[1:], (0, 1), "constant", IGNORE_LABEL_ID
+        )
+
+        nz_input_ids[index : index + length] = tokens
+        nz_position_ids[index : index + length] = position_ids
+        nz_shifted_label_ids[index : index + length] = shifted_label_ids
+
+        # Loss weights
+        mask_count = sum(1 for label in labels_list[1:] if label != IGNORE_LABEL_ID)
+        loss_weight = (
+            1 / mask_count if mask_count > 0 else 0
+        )  # Avoid division by zero for paddings
+
+        nz_shifted_loss_weights[index : index + length] = loss_weight
+
+        index += length
+
+    # inputs
+    return {
+        "max_seqlen": max_seqlen,
+        "cu_seqlens": cu_seqlens,
+        "nz_input_ids": nz_input_ids,
+        "nz_position_ids": nz_position_ids,
+        "nz_shifted_label_ids": nz_shifted_label_ids,
+        "nz_shifted_loss_weights": nz_shifted_loss_weights,
+    }
 
 
 @dataclass
@@ -109,21 +118,13 @@ class AxolotlTrainingArguments(TrainingArguments):
         metadata={"help": "Use quadratic warmup for cosine scheduling."},
     )
     sample_packing: bool = field(
-        default=False,
+        default=True,
         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"},
-    )
 
 
 class AxolotlTrainer(Trainer):
@@ -162,62 +163,24 @@ class AxolotlTrainer(Trainer):
         return self.lr_scheduler
 
     def _get_train_sampler(self) -> Optional[torch.utils.data.Sampler]:
-        if self.args.world_size > 1 and self.args.sample_packing:
-            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()
+        lengths = np.array([len(sample["input_ids"]) for sample in self.train_dataset])
+        return MultipackDistributedBatchSampler(
+            batch_max_length=self.args.per_device_train_batch_size
+            * self.args.max_seq_length,
+            lengths=lengths,
+            seed=self.args.seed,
+        )
 
-    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)),
-                )
-            )
-        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)),
-                )
-            )
-        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)
+    def _get_eval_sampler(
+        self, eval_dataset: Dataset
+    ) -> Optional[torch.utils.data.Sampler]:
+        lengths = np.array([len(sample["input_ids"]) for sample in eval_dataset])
+        return MultipackDistributedBatchSampler(
+            batch_max_length=self.args.per_device_eval_batch_size
+            * self.args.max_seq_length,
+            lengths=lengths,
+            seed=self.args.seed,
+        )
 
 
 class OneCycleLRSchedulerTrainer(AxolotlTrainer):
@@ -249,121 +212,10 @@ 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
-        if not cfg.total_num_tokens:
-            LOG.info("calculating total_num_tokens")
-            total_num_tokens = np.sum(
-                train_dataset.data.column("input_ids")
-                .to_pandas()
-                .apply(lambda x: len(x))  # pylint: disable=unnecessary-lambda
-                .values
-            )
-            LOG.info(f"📝 UPDATE CONFIG WITH: `total_num_tokens: {total_num_tokens}`")
-            cfg.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
-                        * cfg.total_num_tokens
-                        / cfg.sample_packing_eff_est
-                        / cfg.sequence_len
-                        // cfg.batch_size
-                        // int(os.environ.get("WORLD_SIZE", 1))
-                    )
-                    - 1
-                )
-                * cfg.num_epochs
-            )
-            LOG.info(
-                f"total_num_tokens: {cfg.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}`"
-            )
-            cfg.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
@@ -443,14 +295,8 @@ def setup_trainer(cfg, train_dataset, eval_dataset, model, tokenizer, total_num_
     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,
+        max_steps=total_num_steps * cfg.num_epochs,
         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 +310,7 @@ 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
@@ -482,8 +328,6 @@ 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,
         **training_arguments_kwargs,
     )
 
@@ -555,7 +399,6 @@ def setup_trainer(cfg, train_dataset, eval_dataset, model, tokenizer, total_num_
         trainer_kwargs["optimizers"] = (optimizer, lr_scheduler)
 
     callbacks = []
-    callbacks.append(GPUStatsCallback(cfg))
     # TODO on_save callback to sync checkpoints to GCP/AWS in background
     if cfg.early_stopping_patience:
         early_stop_cb = EarlyStoppingCallback(
@@ -578,11 +421,11 @@ 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 +434,7 @@ 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(
@@ -610,7 +453,7 @@ 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,

src/axolotl/utils/validation.py

@@ -1,90 +1,17 @@
-"""Module for working with config dicts"""
+"""Module for validating config files"""
 
 import logging
-import os
 
 import torch
 
-from axolotl.utils.bench import log_gpu_memory_usage
-
-LOG = logging.getLogger("axolotl")
-
-
-def choose_device(cfg):
-    def get_device():
-        try:
-            if torch.cuda.is_available():
-                return f"cuda:{cfg.local_rank}"
-
-            if torch.backends.mps.is_available():
-                return "mps"
-
-            raise SystemError("No CUDA/mps device found")
-        except Exception:  # pylint: disable=broad-exception-caught
-            return "cpu"
-
-    cfg.device = get_device()
-    if cfg.device_map != "auto":
-        if cfg.device.startswith("cuda"):
-            cfg.device_map = {"": cfg.local_rank}
-        else:
-            cfg.device_map = {"": cfg.device}
-
-    # in `accelerate launch`, we need to not pass through any device map and let
-    # accelerate figure out which parts of the model to put on which gpu
-    accelerate_vars = [var for var in os.environ if var.startswith("ACCELERATE_USE_")]
-    if accelerate_vars:
-        cfg.device_map = None
-
-
-def normalize_config(cfg):
-    # setup some derived config / hyperparams
-    cfg.gradient_accumulation_steps = cfg.gradient_accumulation_steps or (
-        cfg.batch_size // cfg.micro_batch_size
-    )
-    cfg.batch_size = (
-        cfg.batch_size or cfg.micro_batch_size * cfg.gradient_accumulation_steps
-    )
-    cfg.world_size = int(os.environ.get("WORLD_SIZE", 1))
-    cfg.local_rank = int(os.environ.get("LOCAL_RANK", 0))
-    choose_device(cfg)
-    cfg.ddp = cfg.ddp if cfg.ddp is not None else cfg.world_size != 1
-    if cfg.ddp:
-        cfg.device_map = {"": int(os.environ.get("LOCAL_RANK", 0))}
-        cfg.batch_size = cfg.batch_size * cfg.world_size
-
-    if cfg.device == "mps":
-        cfg.load_in_8bit = False
-        cfg.tf32 = False
-        if cfg.bf16:
-            cfg.fp16 = True
-        cfg.bf16 = False
-    else:
-        torch.backends.cuda.matmul.allow_tf32 = cfg.tf32 or False
-
-    log_gpu_memory_usage(LOG, "baseline", cfg.device)
-
 
 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.",
@@ -117,10 +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."
         )
 
@@ -139,53 +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.gptq and cfg.model_revision:
-        raise ValueError(
-            "model_revision is not supported for GPTQ models. "
-            + "Please download the model from HuggingFace Hub manually for correct branch, "
-            + "point to its path, and remove model_revision from the config."
-        )
-
-    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

src/axolotl/utils/wandb.py

@@ -9,8 +9,6 @@ def setup_wandb_env_vars(cfg):
     elif cfg.wandb_project and len(cfg.wandb_project) > 0:
         os.environ["WANDB_PROJECT"] = cfg.wandb_project
         cfg.use_wandb = True
-        if cfg.wandb_entity and len(cfg.wandb_entity) > 0:
-            os.environ["WANDB_ENTITY"] = cfg.wandb_entity
         if cfg.wandb_watch and len(cfg.wandb_watch) > 0:
             os.environ["WANDB_WATCH"] = cfg.wandb_watch
         if cfg.wandb_log_model and len(cfg.wandb_log_model) > 0:

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()

tests/test_dict.py

@@ -72,13 +72,6 @@ class DictDefaultTest(unittest.TestCase):
 
         assert cfg.random_key is None, "DictDefault should return None for missing keys"
 
-    def test_dict_or(self):
-        cfg = DictDefault({}) | DictDefault({})
-
-        assert (
-            cfg.random_key is None
-        ), "DictDefault should return None for missing keys after | operation"
-
     def test_dict_nested_missingparentkey(self):
         """
         Due to subclassing Dict, DictDefault will error if we try to access a nested key whose parent key does not exist.

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()

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__":

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__":

tests/test_prompters.py

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

tests/test_tokenizers.py

@@ -13,22 +13,17 @@ class TestTokenizers(unittest.TestCase):
     """
 
     def test_default_use_fast(self):
-        cfg = DictDefault(
-            {
-                "tokenizer_config": "huggyllama/llama-7b",
-            }
-        )
-        tokenizer = load_tokenizer(cfg)
+        cfg = DictDefault({})
+        tokenizer = load_tokenizer("huggyllama/llama-7b", None, cfg)
         assert "Fast" in tokenizer.__class__.__name__
 
     def test_dont_use_fast(self):
         cfg = DictDefault(
             {
-                "tokenizer_config": "huggyllama/llama-7b",
                 "tokenizer_use_fast": False,
             }
         )
-        tokenizer = load_tokenizer(cfg)
+        tokenizer = load_tokenizer("huggyllama/llama-7b", None, cfg)
         assert "Fast" not in tokenizer.__class__.__name__
 
 

tests/test_validation.py

@@ -6,8 +6,8 @@ from typing import Optional
 
 import pytest
 
-from axolotl.utils.config import validate_config
 from axolotl.utils.dict import DictDefault
+from axolotl.utils.validation import validate_config
 
 
 class ValidationTest(unittest.TestCase):
@@ -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)