shampoo checkpoint save workaround

* bump accelerate

* add fixture to predownload the test model

* change fixture

.github/workflows/lint.yml

@@ -6,7 +6,7 @@ on:
        - '**.py'
        - 'requirements.txt'
        - '.github/workflows/*.yml'
-       - "*.md"
+       - "*.[q]md"
        - "examples/**/*.y[a]?ml"
   workflow_dispatch:
 

.github/workflows/multi-gpu-e2e.yml

@@ -1,6 +1,9 @@
 name: docker-multigpu-tests-biweekly
 
 on:
+  pull_request:
+    paths:
+      - 'tests/e2e/multigpu/*.py'
   workflow_dispatch:
   schedule:
     - cron: '0 0 * * 1,4'  # Runs at 00:00 UTC every monday & thursday

.github/workflows/tests-nightly.yml

@@ -25,6 +25,7 @@ jobs:
       fail-fast: false
       matrix:
         python_version: ["3.10", "3.11"]
+        pytorch_version: ["2.3.1", "2.4.0"]
     timeout-minutes: 20
 
     steps:
@@ -37,12 +38,15 @@ jobs:
           python-version: ${{ matrix.python_version }}
           cache: 'pip' # caching pip dependencies
 
+      - name: Install PyTorch
+        run: |
+          pip3 install torch==${{ matrix.pytorch_version }} --index-url https://download.pytorch.org/whl/cpu
+
       - name: Update requirements.txt
         run: |
           sed -i 's#^transformers.*#transformers @ git+https://github.com/huggingface/transformers.git@main#' requirements.txt
           sed -i 's#^peft.*#peft @ git+https://github.com/huggingface/peft.git@main#' requirements.txt
           sed -i 's#^accelerate.*#accelerate @ git+https://github.com/huggingface/accelerate.git@main#' requirements.txt
-          sed -i 's#^bitsandbytes.*#bitsandbytes @ git+https://github.com/bitsandbytes-foundation/bitsandbytes.git@main#' requirements.txt
 
       - name: Install dependencies
         run: |

.github/workflows/tests.yml

@@ -36,6 +36,7 @@ jobs:
       fail-fast: false
       matrix:
         python_version: ["3.10", "3.11"]
+        pytorch_version: ["2.3.1", "2.4.0"]
     timeout-minutes: 20
 
     steps:
@@ -48,6 +49,10 @@ jobs:
           python-version: ${{ matrix.python_version }}
           cache: 'pip' # caching pip dependencies
 
+      - name: Install PyTorch
+        run: |
+          pip3 install torch==${{ matrix.pytorch_version }} --index-url https://download.pytorch.org/whl/cpu
+
       - name: Install dependencies
         run: |
           pip3 install --upgrade pip

.mypy.ini

@@ -11,6 +11,9 @@ ignore_errors = True
 [mypy-axolotl.models.mixtral.*]
 ignore_errors = True
 
+[mypy-axolotl.integrations.liger.models.*]
+ignore_errors = True
+
 [mypy-axolotl.models.phi.*]
 ignore_errors = True
 

README.md

@@ -11,7 +11,7 @@ Features:
 - Supports fullfinetune, lora, qlora, relora, and gptq
 - Customize configurations using a simple yaml file or CLI overwrite
 - Load different dataset formats, use custom formats, or bring your own tokenized datasets
-- Integrated with xformer, flash attention, rope scaling, and multipacking
+- Integrated with xformer, flash attention, [liger kernel](https://github.com/linkedin/Liger-Kernel), rope scaling, and multipacking
 - Works with single GPU or multiple GPUs via FSDP or Deepspeed
 - Easily run with Docker locally or on the cloud
 - Log results and optionally checkpoints to wandb or mlflow
@@ -55,6 +55,7 @@ Features:
         - [FSDP + QLoRA](#fsdp--qlora)
         - [Weights \& Biases Logging](#weights--biases-logging)
         - [Special Tokens](#special-tokens)
+      - [Liger Kernel](#liger-kernel)
     - [Inference Playground](#inference-playground)
     - [Merge LORA to base](#merge-lora-to-base)
   - [Common Errors 🧰](#common-errors-)
@@ -530,6 +531,25 @@ tokens: # these are delimiters
 
 When you include these tokens in your axolotl config, axolotl adds these tokens to the tokenizer's vocabulary.
 
+##### Liger Kernel
+
+Liger Kernel: Efficient Triton Kernels for LLM Training
+
+https://github.com/linkedin/Liger-Kernel
+
+Liger (LinkedIn GPU Efficient Runtime) Kernel is a collection of Triton kernels designed specifically for LLM training.
+It can effectively increase multi-GPU training throughput by 20% and reduces memory usage by 60%. The Liger Kernel
+composes well and is compatible with both FSDP and Deepspeed.
+
+```yaml
+plugins:
+  - axolotl.integrations.liger.LigerPlugin
+liger_rope: true
+liger_rms_norm: true
+liger_swiglu: true
+liger_fused_linear_cross_entropy: true
+```
+
 ### Inference Playground
 
 Axolotl allows you to load your model in an interactive terminal playground for quick experimentation.

_quarto.yml

@@ -37,6 +37,7 @@ website:
             - docs/mac.qmd
             - docs/multi-node.qmd
             - docs/unsloth.qmd
+            - docs/amd_hpc.qmd
         - section: "Dataset Formats"
           contents: docs/dataset-formats/*
         - section: "Reference"

cicd/Dockerfile.jinja

@@ -28,7 +28,6 @@ RUN if [ "$NIGHTLY_BUILD" = "true" ] ; then \
         sed -i 's#^transformers.*#transformers @ git+https://github.com/huggingface/transformers.git@main#' requirements.txt; \
         sed -i 's#^peft.*#peft @ git+https://github.com/huggingface/peft.git@main#' requirements.txt; \
         sed -i 's#^accelerate.*#accelerate @ git+https://github.com/huggingface/accelerate.git@main#' requirements.txt; \
-        sed -i 's#^bitsandbytes.*#bitsandbytes @ git+https://github.com/bitsandbytes-foundation/bitsandbytes.git@main#' requirements.txt; \
     fi
 
 RUN if [ "$AXOLOTL_EXTRAS" != "" ] ; then \

cicd/cicd.sh

@@ -2,5 +2,5 @@
 set -e
 
 pytest --ignore=tests/e2e/ /workspace/axolotl/tests/
-pytest -n1 --dist loadfile -v /workspace/axolotl/tests/e2e/patched/
-pytest --ignore=tests/e2e/patched/ --ignore=tests/e2e/multigpu/ /workspace/axolotl/tests/e2e/
+pytest -n1 --dist loadfile -v /workspace/axolotl/tests/e2e/patched/ /workspace/axolotl/tests/e2e/integrations/
+pytest --ignore=tests/e2e/patched/ --ignore=tests/e2e/multigpu/ --ignore=tests/e2e/integrations/ /workspace/axolotl/tests/e2e/

docs/amd_hpc.qmd

@@ -0,0 +1,108 @@
+---
+title: Training with AMD GPUs on HPC Systems
+description: A comprehensive guide for using Axolotl on distributed systems with AMD GPUs
+---
+
+This guide provides step-by-step instructions for installing and configuring Axolotl on a High-Performance Computing (HPC) environment equipped with AMD GPUs.
+
+## Setup
+
+### 1. Install Python
+
+We recommend using Miniforge, a minimal conda-based Python distribution:
+
+```bash
+curl -L -O "https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-$(uname)-$(uname -m).sh"
+bash Miniforge3-$(uname)-$(uname -m).sh
+```
+
+### 2. Configure Python Environment
+Add Python to your PATH and ensure it's available at login:
+
+```bash
+echo 'export PATH=~/miniforge3/bin:$PATH' >> ~/.bashrc
+echo 'if [ -f ~/.bashrc ]; then . ~/.bashrc; fi' >> ~/.bash_profile
+```
+
+### 3. Load AMD GPU Software
+
+Load the ROCm module:
+
+```bash
+module load rocm/5.7.1
+```
+
+Note: The specific module name and version may vary depending on your HPC system. Consult your system documentation for the correct module name.
+
+### 4. Install PyTorch
+
+Install PyTorch with ROCm support:
+
+```bash
+pip install -U torch torchvision torchaudio --index-url https://download.pytorch.org/whl/rocm5.7 --force-reinstall
+```
+
+### 5. Install Flash Attention
+
+Clone and install the Flash Attention repository:
+
+```bash
+git clone --recursive https://github.com/ROCmSoftwarePlatform/flash-attention.git
+export GPU_ARCHS="gfx90a"
+cd flash-attention
+export PYTHON_SITE_PACKAGES=$(python -c 'import site; print(site.getsitepackages()[0])')
+patch "${PYTHON_SITE_PACKAGES}/torch/utils/hipify/hipify_python.py" hipify_patch.patch
+pip install .
+```
+
+### 6. Install Axolotl
+
+Clone and install Axolotl:
+
+```bash
+git clone https://github.com/axolotl-ai-cloud/axolotl
+cd axolotl
+pip install packaging ninja
+pip install -e .
+```
+
+### 7. Apply xformers Workaround
+
+xformers appears to be incompatible with ROCm. Apply the following workarounds:
+ - Edit $HOME/packages/axolotl/src/axolotl/monkeypatch/llama_attn_hijack_flash.py modifying the code to always return `False` for SwiGLU availability from xformers.
+ - Edit $HOME/miniforge3/lib/python3.10/site-packages/xformers/ops/swiglu_op.py replacing the "SwiGLU" function with a pass statement.
+
+### 8. Prepare Job Submission Script
+
+Create a script for job submission using your HPC's particular software (e.g. Slurm, PBS). Include necessary environment setup and the command to run Axolotl training. If the compute node(s) do(es) not have internet access, it is recommended to include
+
+```bash
+export TRANSFORMERS_OFFLINE=1
+export HF_DATASETS_OFFLINE=1
+```
+
+### 9. Download Base Model
+
+Download a base model using the Hugging Face CLI:
+
+```bash
+huggingface-cli download meta-llama/Meta-Llama-3.1-8B --local-dir ~/hfdata/llama3.1-8B
+```
+
+### 10. Create Axolotl Configuration
+
+Create an Axolotl configuration file (YAML format) tailored to your specific training requirements and dataset. Use FSDP for multi-node training.
+
+Note: Deepspeed did not work at the time of testing. However, if anyone managed to get it working, please let us know.
+
+### 11. Preprocess Data
+
+Run preprocessing on the login node:
+
+```bash
+CUDA_VISIBLE_DEVICES="" python -m axolotl.cli.preprocess /path/to/your/config.yaml
+```
+
+### 12. Train
+
+You are now ready to submit your previously prepared job script. 🚂

docs/dataset-formats/tokenized.qmd

@@ -7,7 +7,7 @@ order: 5
 - Pass an empty `type:` in your axolotl config.
 - Columns in Dataset must be exactly `input_ids`, `attention_mask`, `labels`
 - To indicate that a token should be ignored during training, set its corresponding label to `-100`.
-- Do not add BOS/EOS. Axolotl will add them for you based on the default tokenizer for the model you're using.
+- You must add BOS and EOS, and make sure that you are training on EOS by not setting its label to -100.
 - For pretraining, do not truncate/pad documents to the context window length.
 - For instruction training, documents must be truncated/padded as desired.
 

docs/optimizers.qmd

@@ -0,0 +1,17 @@
+# Optimizers
+
+## Shampoo
+
+```yaml
+optimizer: shampoo
+optim_shampoo_betas: [0.9, 0.999]
+optim_args:
+    epsilon: 1e-12
+    max_preconditioner_dim: 8192
+    precondition_frequency: 100
+    use_decoupled_weight_decay: true
+optim_shampoo_grafting_config_type: adam
+optim_shampoo_grafting_config_kwargs:
+    beta2: 0.999
+    epsilon: 1e-12
+```

examples/deepseek-v2/fft-fsdp-16b.yaml

@@ -0,0 +1,67 @@
+base_model: deepseek-ai/DeepSeek-V2-Lite
+trust_remote_code: true
+
+load_in_8bit: false
+load_in_4bit: false
+strict: false
+
+datasets:
+  - path: tatsu-lab/alpaca
+    type: alpaca
+dataset_prepared_path: last_run_prepared
+val_set_size: 0.0
+output_dir: ./outputs/out
+
+sequence_len: 2048
+sample_packing: true
+pad_to_sequence_len: true
+
+wandb_project:
+wandb_entity:
+wandb_watch:
+wandb_name:
+wandb_log_model:
+
+gradient_accumulation_steps: 8
+micro_batch_size: 1
+num_epochs: 1
+optimizer: adamw_torch
+lr_scheduler: cosine
+learning_rate: 2e-5
+
+train_on_inputs: false
+group_by_length: false
+bf16: auto
+fp16:
+tf32: false
+
+gradient_checkpointing: true
+gradient_checkpointing_kwargs:
+  use_reentrant: false
+early_stopping_patience:
+resume_from_checkpoint:
+logging_steps: 1
+xformers_attention:
+flash_attention: true
+
+warmup_steps: 100
+evals_per_epoch: 2
+eval_table_size:
+saves_per_epoch: 1
+debug:
+deepspeed:
+weight_decay: 0.0
+special_tokens:
+fsdp:
+  - full_shard
+  - auto_wrap
+fsdp_config:
+  fsdp_limit_all_gathers: true
+  fsdp_sync_module_states: true
+  fsdp_offload_params: true
+  fsdp_use_orig_params: false
+  fsdp_cpu_ram_efficient_loading: true
+  fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
+  fsdp_transformer_layer_cls_to_wrap: DeepseekV2DecoderLayer
+  fsdp_state_dict_type: FULL_STATE_DICT
+  fsdp_sharding_strategy: FULL_SHARD

examples/deepseek-v2/qlora-fsdp-2_5.yaml

@@ -0,0 +1,83 @@
+base_model: axolotl-quants/DeepSeek-V2.5-bnb-nf4-bf16
+trust_remote_code: true
+
+load_in_8bit: false
+load_in_4bit: true
+strict: false
+
+
+plugins:
+  - axolotl.integrations.liger.LigerPlugin
+liger_rms_norm: true
+liger_swiglu: true
+liger_fused_linear_cross_entropy: true
+
+chat_template: deepseek_v2
+datasets:
+  - path: mlabonne/FineTome-100k
+    type: chat_template
+    split: train
+
+dataset_prepared_path: last_run_prepared
+val_set_size: 0.0
+output_dir: ./outputs/out
+
+sequence_len: 4096
+sample_packing: true
+pad_to_sequence_len: true
+
+wandb_project:
+wandb_entity:
+wandb_watch:
+wandb_name:
+wandb_log_model:
+
+adapter: qlora
+lora_r: 256
+lora_alpha: 256
+lora_target_linear: true
+peft_use_rslora: true
+
+gradient_accumulation_steps: 1
+micro_batch_size: 8
+num_epochs: 1
+optimizer: adamw_torch
+lr_scheduler: cosine
+learning_rate: 2e-5
+
+train_on_inputs: false
+group_by_length: false
+bf16: auto
+fp16:
+tf32: false
+
+gradient_checkpointing: true
+gradient_checkpointing_kwargs:
+  use_reentrant: false
+early_stopping_patience:
+resume_from_checkpoint:
+logging_steps: 1
+xformers_attention:
+flash_attention: true
+
+warmup_steps: 100
+evals_per_epoch: 2
+eval_table_size:
+saves_per_epoch: 1
+debug:
+deepspeed:
+weight_decay: 0.0
+special_tokens:
+fsdp:
+  - full_shard
+  - auto_wrap
+fsdp_config:
+  fsdp_limit_all_gathers: true
+  fsdp_sync_module_states: true
+  fsdp_offload_params: true
+  fsdp_use_orig_params: false
+  fsdp_cpu_ram_efficient_loading: true
+  fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
+  fsdp_transformer_layer_cls_to_wrap: DeepseekV2DecoderLayer
+  fsdp_state_dict_type: FULL_STATE_DICT
+  fsdp_sharding_strategy: FULL_SHARD

examples/llama-3/fft-8b-liger-fsdp.yaml

@@ -0,0 +1,76 @@
+base_model: NousResearch/Meta-Llama-3.1-8B
+
+plugins:
+  - axolotl.integrations.liger.LigerPlugin
+liger_rope: true
+liger_rms_norm: true
+liger_swiglu: true
+liger_fused_linear_cross_entropy: true
+
+strict: false
+
+chat_template: llama3
+datasets:
+  - path: mlabonne/FineTome-100k
+    type: chat_template
+    split: train[:20%]
+dataset_prepared_path: last_run_prepared
+val_set_size: 0.02
+output_dir: ./outputs/out
+
+sequence_len: 4096
+sample_packing: true
+pad_to_sequence_len: true
+
+wandb_project:
+wandb_entity:
+wandb_watch:
+wandb_name:
+wandb_log_model:
+
+gradient_accumulation_steps: 4
+micro_batch_size: 2
+num_epochs: 1
+optimizer: adamw_torch
+lr_scheduler: cosine
+learning_rate: 2e-5
+
+train_on_inputs: false
+group_by_length: false
+bf16: auto
+fp16:
+tf32: false
+
+gradient_checkpointing: true
+gradient_checkpointing_kwargs:
+  use_reentrant: false
+early_stopping_patience:
+resume_from_checkpoint:
+logging_steps: 1
+xformers_attention:
+flash_attention: true
+
+warmup_steps: 100
+evals_per_epoch: 2
+eval_table_size:
+saves_per_epoch: 1
+debug:
+deepspeed:
+weight_decay: 0.0
+fsdp:
+  - full_shard
+  - auto_wrap
+fsdp_config:
+  fsdp_limit_all_gathers: true
+  fsdp_sync_module_states: true
+  fsdp_offload_params: true
+  fsdp_use_orig_params: false
+  fsdp_cpu_ram_efficient_loading: true
+  fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
+  fsdp_transformer_layer_cls_to_wrap: LlamaDecoderLayer
+  fsdp_state_dict_type: FULL_STATE_DICT
+  fsdp_sharding_strategy: FULL_SHARD
+  fsdp_backward_prefetch: BACKWARD_PRE
+special_tokens:
+  pad_token: <|finetune_right_pad_id|>
+  eos_token: <|eot_id|>

examples/llama-3/fft-8b.yaml

@@ -1,6 +1,4 @@
-base_model: NousResearch/Meta-Llama-3-8B
-model_type: LlamaForCausalLM
-tokenizer_type: AutoTokenizer
+base_model: NousResearch/Meta-Llama-3.1-8B
 
 load_in_8bit: false
 load_in_4bit: false

examples/phi/lora-3.5.yaml

@@ -0,0 +1,76 @@
+base_model: microsoft/Phi-3.5-mini-instruct
+model_type: AutoModelForCausalLM
+tokenizer_type: AutoTokenizer
+
+load_in_8bit: true
+load_in_4bit: false
+strict: false
+
+chat_template: phi_3
+datasets:
+  - path: fozziethebeat/alpaca_messages_2k_test
+    type: chat_template
+    chat_template: phi_3
+    field_messages: messages
+    message_field_role: role
+    message_field_content: content
+    roles:
+      user:
+        - user
+      assistant:
+        - assistant
+
+dataset_prepared_path:
+val_set_size: 0.05
+output_dir: ./outputs/lora-out
+
+sequence_len: 4096
+sample_packing: false
+pad_to_sequence_len: true
+
+adapter: lora
+lora_model_dir:
+lora_r: 32
+lora_alpha: 16
+lora_dropout: 0.05
+lora_target_linear: true
+lora_fan_in_fan_out:
+
+wandb_project:
+wandb_entity:
+wandb_watch:
+wandb_name:
+wandb_log_model:
+
+gradient_accumulation_steps: 4
+micro_batch_size: 4
+num_epochs: 2
+optimizer: adamw_bnb_8bit
+lr_scheduler: cosine
+learning_rate: 0.0002
+
+train_on_inputs: false
+group_by_length: false
+bfloat16: true
+bf16: true
+fp16:
+tf32: false
+
+gradient_checkpointing: true
+early_stopping_patience:
+resume_from_checkpoint:
+local_rank:
+logging_steps: 1
+xformers_attention:
+s2_attention:
+
+warmup_steps: 10
+evals_per_epoch: 4
+eval_table_size:
+eval_max_new_tokens: 128
+saves_per_epoch: 4
+debug:
+deepspeed:
+weight_decay: 0.0
+fsdp:
+fsdp_config:

requirements.txt

@@ -1,11 +1,11 @@
 --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/
 packaging==23.2
 peft==0.12.0
-transformers==4.44.0
+transformers @ git+https://github.com/huggingface/transformers.git@0963229e287501bed52ae1dabc17922524de6992
 tokenizers>=0.19.1
 bitsandbytes==0.43.3
-accelerate==0.33.0
-datasets==2.20.0
+accelerate==0.34.2
+datasets==2.21.0
 deepspeed==0.14.4
 pydantic==2.6.3
 addict
@@ -33,6 +33,9 @@ gradio==3.50.2
 tensorboard
 python-dotenv==1.0.1
 autoawq>=0.2.5
+triton>=2.3.0
+liger-kernel==0.2.1
+distributed_shampoo @ git+https://github.com/facebookresearch/optimizers.git@main
 
 mamba-ssm==1.2.0.post1
 

src/axolotl/cli/__init__.py

@@ -27,6 +27,7 @@ from transformers.utils import is_torch_bf16_gpu_available
 from transformers.utils.import_utils import _is_package_available
 
 from axolotl.common.cli import TrainerCliArgs, load_model_and_tokenizer
+from axolotl.integrations.base import PluginManager
 from axolotl.logging_config import configure_logging
 from axolotl.train import TrainDatasetMeta
 from axolotl.utils.config import (
@@ -365,6 +366,11 @@ def load_cfg(config: Union[str, Path] = Path("examples/"), **kwargs):
 
     cfg.axolotl_config_path = config
 
+    if cfg.get("plugins"):
+        plugin_manager = PluginManager.get_instance()
+        for plugin_name in cfg["plugins"]:
+            plugin_manager.register(plugin_name)
+
     try:
         device_props = torch.cuda.get_device_properties("cuda")
         gpu_version = "sm_" + str(device_props.major) + str(device_props.minor)

src/axolotl/core/trainer_builder.py

@@ -4,6 +4,7 @@ Builder for the training args and trainer
 """
 
 import abc
+import gc
 import importlib
 import importlib.util
 import logging
@@ -15,11 +16,12 @@ from collections import defaultdict
 from dataclasses import dataclass, field
 from functools import wraps
 from pathlib import Path
-from typing import Dict, List, Literal, Optional, Type, Union
+from typing import Any, Dict, List, Literal, Optional, Tuple, Type, Union
 
 import torch
 import transformers
 from datasets import Dataset
+from torch import nn
 from torch.optim.lr_scheduler import OneCycleLR
 from torch.utils.data import BatchSampler, DataLoader, RandomSampler, SequentialSampler
 from transformers import (
@@ -248,6 +250,11 @@ class AxolotlTrainingMixins:
             "help": "workaround to pass an alternate lr scheduler to the HF trainer"
         },
     )
+    optim_shampoo_grafting_config_type: Optional[
+        Literal["adam", "sgd", "adagrad"]
+    ] = None
+    optim_shampoo_grafting_config_kwargs: Optional[Dict[str, Any]] = None
+    optim_shampoo_betas: Optional[Tuple[float, float]] = None
 
 
 @dataclass
@@ -420,7 +427,13 @@ class AxolotlTrainer(SchedulerMixin, Trainer):
         if (
             self.args.loraplus_lr_ratio is None
             and self.args.alternate_optimizer
-            not in ["optimi_adamw", "ao_adamw_8bit", "ao_adamw_4bit", "ao_adamw_fp8"]
+            not in [
+                "optimi_adamw",
+                "ao_adamw_8bit",
+                "ao_adamw_4bit",
+                "ao_adamw_fp8",
+                "shampoo",
+            ]
         ):
             return super().create_optimizer()
 
@@ -463,6 +476,102 @@ class AxolotlTrainer(SchedulerMixin, Trainer):
                     loraplus_lr_ratio,
                     loraplus_lr_embedding,
                 )
+            elif self.args.alternate_optimizer == "shampoo":
+                from distributed_shampoo.distributed_shampoo import DistributedShampoo
+                from distributed_shampoo.shampoo_types import (
+                    AdaGradGraftingConfig,
+                    AdamGraftingConfig,
+                    CommunicationDType,
+                    DDPShampooConfig,
+                    FSDPShampooConfig,
+                    PrecisionConfig,
+                    SGDGraftingConfig,
+                )
+                from distributed_shampoo.utils.shampoo_fsdp_utils import (
+                    compile_fsdp_parameter_metadata,
+                )
+
+                # parse args.optim_args
+                optim_args = {}
+                if self.args.optim_args:
+                    for mapping in self.args.optim_args.replace(" ", "").split(","):
+                        key, value = mapping.split("=")
+                        optim_args[key] = value
+
+                optim_args["betas"] = self.args.optim_shampoo_betas
+                if "max_preconditioner_dim" in optim_args:
+                    optim_args["max_preconditioner_dim"] = int(
+                        optim_args["max_preconditioner_dim"]
+                    )
+                if "precondition_frequency" in optim_args:
+                    optim_args["precondition_frequency"] = int(
+                        optim_args["precondition_frequency"]
+                    )
+                if "use_decoupled_weight_decay" in optim_args:
+                    optim_args["use_decoupled_weight_decay"] = bool(
+                        optim_args["use_decoupled_weight_decay"]
+                    )
+                if isinstance(optim_args["epsilon"], str):
+                    optim_args["epsilon"] = float(optim_args["epsilon"])
+                optim_args["lr"] = self.args.learning_rate
+                optim_args["weight_decay"] = self.args.weight_decay
+
+                if "epsilon" in self.args.optim_shampoo_grafting_config_kwargs:
+                    if isinstance(
+                        self.args.optim_shampoo_grafting_config_kwargs["epsilon"], str
+                    ):
+                        self.args.optim_shampoo_grafting_config_kwargs[
+                            "epsilon"
+                        ] = float(
+                            self.args.optim_shampoo_grafting_config_kwargs["epsilon"]
+                        )
+                if self.args.optim_shampoo_grafting_config_type == "adam":
+                    grafting_config = AdamGraftingConfig(
+                        **self.args.optim_shampoo_grafting_config_kwargs
+                    )
+                elif self.args.optim_shampoo_grafting_config_type == "sgd":
+                    grafting_config = SGDGraftingConfig(
+                        **self.args.optim_shampoo_grafting_config_kwargs
+                    )
+                elif self.args.optim_shampoo_grafting_config_type == "adagrad":
+                    grafting_config = AdaGradGraftingConfig(
+                        **self.args.optim_shampoo_grafting_config_kwargs
+                    )
+
+                distributed_config = None
+                if self.args.world_size > 1:
+                    if self.args.fsdp and self.args.fsdp_config:
+                        distributed_config = FSDPShampooConfig(
+                            param_to_metadata=compile_fsdp_parameter_metadata(
+                                self.model_wrapped
+                            )
+                        )
+                    else:
+                        distributed_config = DDPShampooConfig(
+                            communication_dtype=CommunicationDType.BF16,
+                            num_trainers_per_group=self.args.world_size,
+                            communicate_params=False,
+                        )
+
+                precision_config = None
+                if self.args.bf16:
+                    precision_config = PrecisionConfig(
+                        computation_dtype=torch.bfloat16,
+                        factor_matrix_dtype=torch.bfloat16,
+                        inv_factor_matrix_dtype=torch.bfloat16,
+                        filtered_grad_dtype=torch.bfloat16,
+                        momentum_dtype=torch.bfloat16,
+                        grafting_state_dtype=torch.bfloat16,
+                    )
+                self.optimizer = (  # pylint: disable=attribute-defined-outside-init
+                    DistributedShampoo(
+                        optimizer_grouped_parameters,
+                        grafting_config=grafting_config,
+                        distributed_config=distributed_config,
+                        precision_config=precision_config,
+                        **optim_args,
+                    )
+                )
             elif self.args.alternate_optimizer == "optimi_adamw":
                 from optimi import AdamW
 
@@ -504,9 +613,10 @@ class AxolotlTrainer(SchedulerMixin, Trainer):
                 batch_max_len = self.args.max_seq_length
             else:
                 batch_size = 1
-                batch_max_len = (
-                    self.args.per_device_train_batch_size * self.args.max_seq_length
+                train_batch_size = (
+                    self.state.train_batch_size or self.args.per_device_train_batch_size
                 )
+                batch_max_len = train_batch_size * self.args.max_seq_length
             return MultipackBatchSampler(
                 RandomSampler(self.train_dataset),
                 lengths=get_dataset_lengths(self.train_dataset),
@@ -867,7 +977,11 @@ class AxolotlTrainer(SchedulerMixin, Trainer):
         run_dir = self._get_output_dir(trial=trial)
         output_dir = os.path.join(run_dir, checkpoint_folder)
         os.makedirs(output_dir, exist_ok=True)
-        return super()._save_checkpoint(model, trial, metrics=metrics)
+        try:
+            return super()._save_checkpoint(model, trial, metrics=metrics)
+        except NotImplementedError as exc:
+            LOG.warning(f"Failed to save checkpoint: {exc}")
+            return None
 
 
 class AxolotlMambaTrainer(AxolotlTrainer):
@@ -997,6 +1111,14 @@ class AxolotlDPOTrainer(SchedulerMixin, DPOTrainer):
                 res[key] = res[key][1:]
         return res
 
+    def training_step(
+        self, model: nn.Module, inputs: Dict[str, Union[torch.Tensor, Any]]
+    ) -> torch.Tensor:
+        loss: torch.Tensor = super().training_step(model, inputs)
+        gc.collect()
+        torch.cuda.empty_cache()
+        return loss
+
 
 class AxolotlORPOTrainer(SchedulerMixin, ORPOTrainer):
     """
@@ -1369,6 +1491,10 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
             training_arguments_kwargs[
                 "per_device_eval_batch_size"
             ] = self.cfg.eval_batch_size
+        if self.cfg.auto_find_batch_size is not None:
+            training_arguments_kwargs[
+                "auto_find_batch_size"
+            ] = self.cfg.auto_find_batch_size
         training_arguments_kwargs[
             "gradient_accumulation_steps"
         ] = self.cfg.gradient_accumulation_steps
@@ -1426,6 +1552,21 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
             training_arguments_kwargs[
                 "optim_target_modules"
             ] = self.cfg.optim_target_modules
+
+        # shampoo optimizer config
+        if self.cfg.optim_shampoo_betas:
+            training_arguments_kwargs[
+                "optim_shampoo_betas"
+            ] = self.cfg.optim_shampoo_betas
+        if self.cfg.optim_shampoo_grafting_config_type:
+            training_arguments_kwargs[
+                "optim_shampoo_grafting_config_type"
+            ] = self.cfg.optim_shampoo_grafting_config_type
+            if self.cfg.optim_shampoo_grafting_config_kwargs:
+                training_arguments_kwargs[
+                    "optim_shampoo_grafting_config_kwargs"
+                ] = self.cfg.optim_shampoo_grafting_config_kwargs
+
         training_arguments_kwargs["loraplus_lr_ratio"] = self.cfg.loraplus_lr_ratio
         training_arguments_kwargs[
             "loraplus_lr_embedding"
@@ -1451,9 +1592,9 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
         )
 
         training_arguments_kwargs["sample_packing"] = bool(self.cfg.sample_packing)
-        training_arguments_kwargs[
-            "multipack_real_batches"
-        ] = not self.cfg.flash_attention
+        training_arguments_kwargs["multipack_real_batches"] = (
+            not self.cfg.flash_attention or self.cfg.multipack_real_batches
+        )
         training_arguments_kwargs["eval_sample_packing"] = bool(
             self.cfg.eval_sample_packing
         )
@@ -1510,10 +1651,12 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
         trainer_kwargs = {}
 
         if self.cfg.optimizer in [
+            # pylint: disable=duplicate-code
             "optimi_adamw",
             "ao_adamw_4bit",
             "ao_adamw_8bit",
             "ao_adamw_fp8",
+            "shampoo",
         ]:
             # Set default so transformers doesn't throw
             training_arguments_kwargs["optim"] = "adamw_hf"

src/axolotl/integrations/LICENSE.md

@@ -0,0 +1,58 @@
+### AXOLOTL COMMUNITY LICENSE AGREEMENT
+
+This Axolotl Community License Agreement (“Agreement”) is entered into by and between Axolotl AI Corp. (“Axolotl”) and
+any individual or entity (“Licensee”) who wishes to use the Software (as defined below) in accordance with the terms
+and conditions set forth in this Agreement.
+
+1.  Definitions
+    1.1 “Licensee” refers to any individual or entity who has obtained a copy of the Software under this Agreement.
+    1.2 “Plugin Integration” means independent integration software modules which may or may not be offered by Axolotl,
+        which may be licensed separately by their respective  authors and/or licensors.
+    1.3 “Software” refers to the specific sub-directory of the Axolotl, Inc. software located at
+        https://github.com/axolotl-ai-cloud/axolotl/tree/main/src/axolotl/integrations and its subdirectories which
+        permits Plugin Integrations to integrate with the Axolotl service.
+2.  Grant of License
+    2.1	Axolotl hereby grants Licensee a worldwide, non-exclusive, royalty-free, license to use, copy, modify, merge,
+        publish, distribute, sublicense, and/or otherwise exploit the Software, subject to the following conditions:
+        - Licensee must comply with all the terms and conditions of this Agreement.
+        - Licensee must include the original copyright notice and disclaimer of warranty in all copies or substantial
+          portions of the Software.
+    2.2 Licensee may use the Software for any lawful purpose, except as restricted in Section 3.
+3.  Restrictions
+    3.1 Licensee shall not use the Software for any activity that constitutes a commercial activity of offering for
+        free or for sale any services, platform, or equivalent  to third parties for the purposes of allowing such
+        third parties to fine-tune artificial intelligence models.
+    3.2 Licensee shall not:
+        - Use the Software for any illegal or unauthorized purpose.
+        - Reverse engineer, decompile, or disassemble the Software.
+        - Remove or modify any copyright, trademark, or other proprietary notices contained in the Software.
+        - Use the Software in a way that could damage, disable, overburden, or impair the functionality of the
+          Software or interfere with any third-party use of the Software.
+    3.3 Axolotl reserves the right to restrict certain Plugin Integrations for use with the Software. To the extent Licensee integrates a permitted, applicable Plugin Integration with the Software, Licensee shall comply with any additional terms and conditions imposed by the licensors of such Plugin Integration for use of such Plugin Integrations. Licensee shall contact Axolotl if it has questions about whether its use of the Software falls beyond the scope of this Agreement.
+4.  Intellectual Property Rights
+    4.1 Axolotl and its contributors retain all intellectual property rights in and to the Software. Licensee
+        acknowledges that this Agreement does not transfer any ownership rights or intellectual property rights to
+        Licensee.
+5.  Disclaimer of Warranty
+    5.1 THE SOFTWARE IS PROVIDED “AS IS,” WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED
+        TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, AND NON-INFRINGEMENT. IN NO EVENT SHALL
+        THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES, OR OTHER LIABILITY, WHETHER IN AN ACTION OF
+        CONTRACT, TORT, OR OTHERWISE, ARISING FROM, OUT OF, OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
+        DEALINGS IN THE SOFTWARE.
+6.  Termination
+    6.1 Axolotl may terminate this Agreement at any time if Licensee fails to comply with any of the terms and
+        conditions set forth herein. Upon termination, Licensee shall cease all use of the Software and destroy any
+        copies in its possession.
+7.  Governing Law
+    7.1 This Agreement shall be governed by and construed in accordance with the laws of the State of California,
+        without regards to conflicts of laws provisions thereof.
+8.  Entire Agreement
+    8.1 This Agreement constitutes the entire agreement between Axolotl and Licensee with respect to the subject matter
+        hereof and supersedes all prior or contemporaneous understandings or agreements between the parties concerning
+        the Software, whether written or oral. Axolotl may update the terms of this Agreement from time to time, and
+        Licensee’s continued use of the Software after any such updates shall constitute acceptance of updated terms
+        on a go-forward basis.  Axolotl will use commercially reasonable efforts to provide Licensee notice of any
+        material updates. By using the Software, Licensee acknowledges that it has read, understood, and agrees to be
+        bound by the terms and conditions of this Agreement.
+
+This Agreement was last updated on August 23, 2024.

src/axolotl/integrations/base.py

@@ -0,0 +1,383 @@
+# Copyright 2024 Axolotl AI. All rights reserved.
+#
+# This software may be used and distributed according to
+# the terms of the Axolotl Community License Agreement (the "License");
+# you may not use this file except in compliance with the License.
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+# License for the specific language governing permissions and limitations under
+# the License.
+
+"""
+Base class for all plugins.
+
+A plugin is a reusable, modular, and self-contained piece of code that extends the functionality of Axolotl.
+Plugins can be used to integrate third-party models, modify the training process, or add new features.
+
+To create a new plugin, you need to inherit from the BasePlugin class and implement the required methods.
+"""
+import importlib
+import logging
+from typing import List
+
+
+class BasePlugin:
+    """
+    Base class for all plugins. Defines the interface for plugin methods.
+
+    Attributes:
+    None
+
+    Methods:
+    register(cfg): Registers the plugin with the given configuration.
+    pre_model_load(cfg): Performs actions before the model is loaded.
+    post_model_load(cfg, model): Performs actions after the model is loaded.
+    pre_lora_load(cfg, model): Performs actions before LoRA weights are loaded.
+    post_lora_load(cfg, model): Performs actions after LoRA weights are loaded.
+    create_optimizer(cfg, trainer): Creates and returns an optimizer for training.
+    create_lr_scheduler(cfg, trainer, optimizer): Creates and returns a learning rate scheduler.
+    add_callbacks_pre_trainer(cfg, model): Adds callbacks to the trainer before training.
+    add_callbacks_post_trainer(cfg, trainer): Adds callbacks to the trainer after training.
+    """
+
+    def __init__(self):
+        """
+        Initializes the BasePlugin.
+        """
+
+    def register(self, cfg):
+        """
+        Registers the plugin with the given configuration.
+
+        Parameters:
+        cfg (dict): The configuration for the plugin.
+
+        Returns:
+        None
+        """
+
+    def get_input_args(self):
+        """
+        Returns a pydantic model for the plugin's input arguments.
+        """
+
+    def pre_model_load(self, cfg):
+        """
+        Performs actions before the model is loaded.
+
+        Parameters:
+        cfg (dict): The configuration for the plugin.
+
+        Returns:
+        None
+        """
+
+    def post_model_load(self, cfg, model):
+        """
+        Performs actions after the model is loaded.
+
+        Parameters:
+        cfg (dict): The configuration for the plugin.
+        model (object): The loaded model.
+
+        Returns:
+        None
+        """
+
+    def pre_lora_load(self, cfg, model):
+        """
+        Performs actions before LoRA weights are loaded.
+
+        Parameters:
+        cfg (dict): The configuration for the plugin.
+        model (object): The loaded model.
+
+        Returns:
+        None
+        """
+
+    def post_lora_load(self, cfg, model):
+        """
+        Performs actions after LoRA weights are loaded.
+
+        Parameters:
+        cfg (dict): The configuration for the plugin.
+        model (object): The loaded model.
+
+        Returns:
+        None
+        """
+
+    def create_optimizer(self, cfg, trainer):
+        """
+        Creates and returns an optimizer for training.
+
+        Parameters:
+        cfg (dict): The configuration for the plugin.
+        trainer (object): The trainer object for training.
+
+        Returns:
+        object: The created optimizer.
+        """
+
+    def create_lr_scheduler(self, cfg, trainer, optimizer):
+        """
+        Creates and returns a learning rate scheduler.
+
+        Parameters:
+        cfg (dict): The configuration for the plugin.
+        trainer (object): The trainer object for training.
+        optimizer (object): The optimizer for training.
+
+        Returns:
+        object: The created learning rate scheduler.
+        """
+
+    def add_callbacks_pre_trainer(self, cfg, model):
+        """
+        Adds callbacks to the trainer before training.
+
+        Parameters:
+        cfg (dict): The configuration for the plugin.
+        model (object): The loaded model.
+
+        Returns:
+        List[callable]: A list of callback functions to be added to the TrainingArgs
+        """
+
+    def add_callbacks_post_trainer(self, cfg, trainer):
+        """
+        Adds callbacks to the trainer after training.
+
+        Parameters:
+        cfg (dict): The configuration for the plugin.
+        trainer (object): The trainer object for training.
+
+        Returns:
+        List[callable]: A list of callback functions to be added to the TrainingArgs
+        """
+
+
+def load_plugin(plugin_name: str) -> BasePlugin:
+    """
+    Loads a plugin based on the given plugin name.
+
+    The plugin name should be in the format "module_name.class_name".
+    This function splits the plugin name into module and class, imports the module,
+    retrieves the class from the module, and creates an instance of the class.
+
+    Parameters:
+    plugin_name (str): The name of the plugin to be loaded. The name should be in the format "module_name.class_name".
+
+    Returns:
+    BasePlugin: An instance of the loaded plugin.
+
+    Raises:
+    ImportError: If the plugin module cannot be imported.
+    """
+    # split the plugin name into module and class
+    module_name, class_name = plugin_name.rsplit(".", 1)
+
+    # import the module
+    module = importlib.import_module(module_name)
+    # instantiate the class
+    plugin_class = getattr(module, class_name)
+    # create an instance of the class
+    plugin = plugin_class()
+
+    return plugin
+
+
+class PluginManager:
+    """
+    The PluginManager class is responsible for loading and managing plugins.
+    It should be a singleton so it can be accessed from anywhere in the codebase.
+
+    Attributes:
+    plugins (List[BasePlugin]): A list of loaded plugins.
+
+    Methods:
+    get_instance(): Static method to get the singleton instance of PluginManager.
+    register(plugin_name: str): Registers a new plugin by its name.
+    pre_model_load(cfg): Calls the pre_model_load method of all registered plugins.
+    """
+
+    plugins: List[BasePlugin] = []
+
+    _instance = None
+
+    def __new__(cls):
+        """
+        Creates a new instance of PluginManager if it doesn't exist yet.
+        """
+        if cls._instance is None:
+            cls._instance = super(PluginManager, cls).__new__(cls)
+            cls._instance.plugins: List[BasePlugin] = []
+        return cls._instance
+
+    @staticmethod
+    def get_instance() -> "PluginManager":
+        """
+        Returns the singleton instance of PluginManager.
+        If the instance doesn't exist, it creates a new one.
+        """
+        if PluginManager._instance is None:
+            PluginManager()
+        return PluginManager._instance  # type: ignore
+
+    def register(self, plugin_name: str):
+        """
+        Registers a new plugin by its name.
+
+        Parameters:
+        plugin_name (str): The name of the plugin to be registered.
+
+        Returns:
+        None
+
+        Raises:
+        ImportError: If the plugin module cannot be imported.
+        """
+        try:
+            plugin = load_plugin(plugin_name)
+            self.plugins.append(plugin)
+        except ImportError:
+            logging.error(f"Failed to load plugin: {plugin_name}")
+
+    def get_input_args(self):
+        """
+        Returns a list of Pydantic classes for all registered plugins' input arguments.'
+
+        Returns:
+        list[str]: A list of Pydantic classes for all registered plugins' input arguments.'
+        """
+        input_args = []
+        for plugin in self.plugins:
+            input_args_from_plugin = plugin.get_input_args()
+            if input_args_from_plugin is not None:
+                input_args.append(input_args_from_plugin)
+        return input_args
+
+    def pre_model_load(self, cfg):
+        """
+        Calls the pre_model_load method of all registered plugins.
+
+        Parameters:
+        cfg (dict): The configuration for the plugins.
+
+        Returns:
+        None
+        """
+        for plugin in self.plugins:
+            plugin.pre_model_load(cfg)
+
+    def post_model_load(self, cfg, model):
+        """
+        Calls the post_model_load method of all registered plugins.
+
+        Parameters:
+        cfg (dict): The configuration for the plugins.
+        model (object): The loaded model.
+
+        Returns:
+        None
+        """
+        for plugin in self.plugins:
+            plugin.post_model_load(cfg, model)
+
+    def pre_lora_load(self, cfg, model):
+        """
+        Calls the pre_lora_load method of all registered plugins.
+
+        Parameters:
+        cfg (dict): The configuration for the plugins.
+        model (object): The loaded model.
+
+        Returns:
+        None
+        """
+        for plugin in self.plugins:
+            plugin.pre_lora_load(cfg, model)
+
+    def post_lora_load(self, cfg, model):
+        """
+        Calls the post_lora_load method of all registered plugins.
+
+        Parameters:
+        cfg (dict): The configuration for the plugins.
+        model (object): The loaded model.
+
+        Returns:
+        None
+        """
+        for plugin in self.plugins:
+            plugin.post_lora_load(cfg, model)
+
+    def create_optimizer(self, cfg, trainer):
+        """
+        Calls the create_optimizer method of all registered plugins and returns the first non-None optimizer.
+
+        Parameters:
+        cfg (dict): The configuration for the plugins.
+        trainer (object): The trainer object for training.
+
+        Returns:
+        object: The created optimizer, or None if none was found.
+        """
+        for plugin in self.plugins:
+            optimizer = plugin.create_optimizer(cfg, trainer)
+            if optimizer is not None:
+                return optimizer
+        return None
+
+    def create_lr_scheduler(self, cfg, trainer, optimizer):
+        """
+        Calls the create_lr_scheduler method of all registered plugins and returns the first non-None scheduler.
+
+        Parameters:
+        cfg (dict): The configuration for the plugins.
+        trainer (object): The trainer object for training.
+        optimizer (object): The optimizer for training.
+
+        Returns:
+        object: The created learning rate scheduler, or None if none was found.
+        """
+        for plugin in self.plugins:
+            scheduler = plugin.create_lr_scheduler(cfg, trainer, optimizer)
+            if scheduler is not None:
+                return scheduler
+        return None
+
+    def add_callbacks_pre_trainer(self, cfg, model):
+        """
+        Calls the add_callbacks_pre_trainer method of all registered plugins.
+
+        Parameters:
+        cfg (dict): The configuration for the plugins.
+        model (object): The loaded model.
+
+        Returns:
+        List[callable]: A list of callback functions to be added to the TrainingArgs.
+        """
+        callbacks = []
+        for plugin in self.plugins:
+            callbacks.extend(plugin.add_callbacks_pre_trainer(cfg, model))
+        return callbacks
+
+    def add_callbacks_post_trainer(self, cfg, trainer):
+        """
+        Calls the add_callbacks_post_trainer method of all registered plugins.
+
+        Parameters:
+        cfg (dict): The configuration for the plugins.
+        trainer (object): The trainer object for training.
+
+        Returns:
+        List[callable]: A list of callback functions to be added to the TrainingArgs.
+        """
+        callbacks = []
+        for plugin in self.plugins:
+            callbacks.extend(plugin.add_callbacks_post_trainer(cfg, trainer))
+        return callbacks

src/axolotl/integrations/config.py

@@ -0,0 +1,65 @@
+# Copyright 2024 Axolotl AI. All rights reserved.
+#
+# This software may be used and distributed according to
+# the terms of the Axolotl Community License Agreement (the "License");
+# you may not use this file except in compliance with the License.
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+# License for the specific language governing permissions and limitations under
+# the License.
+
+"""
+module to handle merging the plugins' input arguments with the base configurations.
+
+this was moved here to prevent circular imports
+"""
+
+from typing import Any, Dict, List
+
+from axolotl.utils.config.models.input.v0_4_1 import (
+    AxolotlConfigWCapabilities as AxolotlConfigWCapabilitiesBase,
+)
+from axolotl.utils.config.models.input.v0_4_1 import (
+    AxolotlInputConfig as AxolotlInputConfigBase,
+)
+
+
+def merge_input_args():
+    """
+    Merges input arguments from registered plugins with the base configurations.
+
+    This function retrieves the input arguments from registered plugins using the PluginManager.
+    It then dynamically creates new classes, AxolotlConfigWCapabilities and AxolotlInputConfig,
+    that inherit from the base configurations and include the input arguments from the plugins.
+
+    Returns:
+    tuple: A tuple containing the newly created classes, AxolotlConfigWCapabilities and AxolotlInputConfig.
+    """
+    from axolotl.integrations.base import PluginManager
+
+    plugin_manager = PluginManager.get_instance()
+    input_args: List[str] = plugin_manager.get_input_args()
+    plugin_classes = []
+    dynamic_input = ""
+    for plugin_args in input_args:
+        plugin_module, plugin_cls = plugin_args.rsplit(".", 1)
+        dynamic_input += f"from {plugin_module} import {plugin_cls}\n"
+        plugin_classes.append(plugin_cls)
+    if dynamic_input:
+        dynamic_input += f"class AxolotlConfigWCapabilities(AxolotlConfigWCapabilitiesBase, {', '.join(plugin_classes)}):\n    pass\n"
+        dynamic_input += f"class AxolotlInputConfig(AxolotlInputConfigBase, {', '.join(plugin_classes)}):\n    pass\n"
+
+        namespace: Dict[Any, Any] = {}
+        exec(  # pylint: disable=exec-used  # nosec B102
+            dynamic_input, globals(), namespace
+        )
+        AxolotlInputConfig = namespace[  # pylint: disable=invalid-name
+            "AxolotlInputConfig"
+        ]
+        AxolotlConfigWCapabilities = namespace[  # pylint: disable=invalid-name
+            "AxolotlConfigWCapabilities"
+        ]
+        return AxolotlConfigWCapabilities, AxolotlInputConfig
+    return AxolotlConfigWCapabilitiesBase, AxolotlInputConfigBase

src/axolotl/integrations/liger/LICENSE

@@ -0,0 +1,202 @@
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src/axolotl/integrations/liger/__init__.py

@@ -0,0 +1,189 @@
+# Copyright 2024 Axolotl AI. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+Module for the Plugin for LIGER integraton with Axolotl.
+
+Liger Kernel is the collection of Triton-native kernels for LLM Training.
+It is designed to be performant, correct, and light-weight.
+"""
+import logging
+import sys
+from functools import partial
+
+from liger_kernel.transformers.cross_entropy import LigerCrossEntropyLoss
+from liger_kernel.transformers.geglu import LigerGEGLUMLP
+from liger_kernel.transformers.rms_norm import LigerRMSNorm
+from liger_kernel.transformers.rope import liger_rotary_pos_emb
+from liger_kernel.transformers.swiglu import LigerSwiGLUMLP
+
+from axolotl.integrations.base import BasePlugin
+
+from .args import LigerArgs  # pylint: disable=unused-import. # noqa: F401
+
+
+class LigerPlugin(BasePlugin):
+    """
+    Plugin for LIGER integraton with Axolotl.
+    """
+
+    def get_input_args(self):
+        return "axolotl.integrations.liger.LigerArgs"
+
+    def pre_model_load(self, cfg):
+        if cfg.model_config_type == "llama":
+            from liger_kernel.transformers.model.llama import (
+                lce_forward as llama_lce_forward,
+            )
+            from transformers.models.llama import modeling_llama
+
+            if cfg.liger_rope:
+                modeling_llama.apply_rotary_pos_emb = liger_rotary_pos_emb
+            if cfg.liger_rms_norm:
+                modeling_llama.LlamaRMSNorm = LigerRMSNorm
+            if cfg.liger_swiglu:
+                modeling_llama.LlamaMLP = LigerSwiGLUMLP
+            if cfg.liger_cross_entropy:
+                modeling_llama.CrossEntropyLoss = LigerCrossEntropyLoss
+            elif cfg.liger_fused_linear_cross_entropy:
+                modeling_llama.LlamaForCausalLM.forward = llama_lce_forward
+
+        elif cfg.model_config_type == "mistral":
+            from liger_kernel.transformers.model.mistral import (
+                lce_forward as mistral_lce_forward,
+            )
+            from transformers.models.mistral import modeling_mistral
+
+            if cfg.liger_rope:
+                modeling_mistral.apply_rotary_pos_emb = liger_rotary_pos_emb
+            if cfg.liger_rms_norm:
+                modeling_mistral.MistralRMSNorm = LigerRMSNorm
+            if cfg.liger_swiglu:
+                modeling_mistral.MistralMLP = LigerSwiGLUMLP
+            if cfg.liger_cross_entropy:
+                modeling_mistral.CrossEntropyLoss = LigerCrossEntropyLoss
+            if cfg.liger_fused_linear_cross_entropy:
+                modeling_mistral.MistralForCausalLM.forward = mistral_lce_forward
+
+        elif cfg.model_config_type == "gemma":
+            from liger_kernel.transformers.model.gemma import (
+                lce_forward as gemma_lce_forward,
+            )
+            from transformers.models.gemma import modeling_gemma
+
+            if cfg.liger_rope:
+                modeling_gemma.apply_rotary_pos_emb = liger_rotary_pos_emb
+            if cfg.liger_rms_norm:
+                modeling_gemma.GemmaRMSNorm = partial(
+                    LigerRMSNorm, offset=1.0, init_fn="zeros", casting_mode="gemma"
+                )
+            if cfg.liger_swiglu:
+                modeling_gemma.GemmaMLP = LigerGEGLUMLP
+            if cfg.liger_cross_entropy:
+                modeling_gemma.CrossEntropyLoss = LigerCrossEntropyLoss
+            if cfg.liger_fused_linear_cross_entropy:
+                modeling_gemma.GemmaForCausalLM.forward = gemma_lce_forward
+
+        elif cfg.model_config_type == "jamba":
+            from transformers.models.jamba import modeling_jamba
+
+            from .models.jamba import lce_forward as jamba_lce_forward
+
+            if cfg.liger_rope:
+                modeling_jamba.apply_rotary_pos_emb = liger_rotary_pos_emb
+            if cfg.liger_rms_norm:
+                modeling_jamba.JambaRMSNorm = LigerRMSNorm
+            if cfg.liger_swiglu:
+                modeling_jamba.JambaMLP = LigerSwiGLUMLP
+            if cfg.liger_cross_entropy:
+                modeling_jamba.CrossEntropyLoss = LigerCrossEntropyLoss
+            if cfg.liger_fused_linear_cross_entropy:
+                modeling_jamba.JambaForCausalLM.forward = jamba_lce_forward
+
+        elif cfg.model_config_type == "qwen2":
+            from liger_kernel.transformers.model.qwen2 import (
+                lce_forward as qwen2_lce_forward,
+            )
+            from transformers.models.qwen2 import modeling_qwen2
+
+            if cfg.liger_rope:
+                modeling_qwen2.apply_rotary_pos_emb = liger_rotary_pos_emb
+            if cfg.liger_rms_norm:
+                modeling_qwen2.Qwen2RMSNorm = LigerRMSNorm
+            if cfg.liger_swiglu:
+                modeling_qwen2.Qwen2MLP = LigerSwiGLUMLP
+            if cfg.liger_cross_entropy:
+                modeling_qwen2.CrossEntropyLoss = LigerCrossEntropyLoss
+            if cfg.liger_fused_linear_cross_entropy:
+                modeling_qwen2.Qwen2ForCausalLM.forward = qwen2_lce_forward
+
+        elif cfg.model_config_type == "deepseek_v2":
+            from accelerate import init_empty_weights
+            from transformers import AutoModelForCausalLM
+
+            with init_empty_weights():
+                model = AutoModelForCausalLM.from_pretrained(
+                    cfg.base_model, trust_remote_code=cfg.trust_remote_code or False
+                )
+                modeling_mod = sys.modules[model.__class__.__module__]
+
+            from .models.deepseekv2 import lce_forward as deepseekv2_lce_forward
+
+            if cfg.liger_rope:
+                # The DeepseekV2 version of RoPE is different than upstream LLaMA.
+                # See https://github.com/linkedin/Liger-Kernel/issues/129#issuecomment-2313763528
+                logging.warning("Fused liger_rope is not supported for DeepseekV2.")
+            if cfg.liger_rms_norm:
+                modeling_mod.DeepseekV2RMSNorm = LigerRMSNorm
+            if cfg.liger_swiglu:
+                modeling_mod.DeepseekV2MLP.forward = LigerSwiGLUMLP.forward
+            if cfg.liger_cross_entropy:
+                modeling_mod.CrossEntropyLoss = LigerCrossEntropyLoss
+            if cfg.liger_fused_linear_cross_entropy:
+                modeling_mod.DeepseekV2ForCausalLM.forward = deepseekv2_lce_forward
+
+        elif cfg.model_config_type == "gemma2":
+            from transformers.models.gemma2 import modeling_gemma2
+
+            if cfg.liger_rope:
+                modeling_gemma2.apply_rotary_pos_emb = liger_rotary_pos_emb
+            if cfg.liger_rms_norm:
+                modeling_gemma2.Gemma2RMSNorm = partial(
+                    LigerRMSNorm, offset=1.0, init_fn="zeros", casting_mode="gemma"
+                )
+            if cfg.liger_swiglu:
+                modeling_gemma2.Gemma2MLP = LigerGEGLUMLP
+            if cfg.liger_cross_entropy:
+                modeling_gemma2.CrossEntropyLoss = LigerCrossEntropyLoss
+            if cfg.liger_fused_linear_cross_entropy:
+                logging.warning(
+                    "Fused linear cross entropy is not supported for Gemma 2."
+                )
+
+        elif cfg.model_config_type == "phi3":
+            from liger_kernel.transformers.model.phi3 import (
+                lce_forward as phi3_lce_forward,
+            )
+            from transformers.models.phi3 import modeling_phi3
+
+            if cfg.liger_rope:
+                modeling_phi3.apply_rotary_pos_emb = liger_rotary_pos_emb
+            if cfg.liger_rms_norm:
+                modeling_phi3.Phi3RMSNorm = LigerRMSNorm
+            if cfg.liger_swiglu:
+                modeling_phi3.Phi3MLP = LigerSwiGLUMLP
+            if cfg.liger_cross_entropy:
+                modeling_phi3.CrossEntropyLoss = LigerCrossEntropyLoss
+            if cfg.liger_fused_linear_cross_entropy:
+                modeling_phi3.Phi3ForCausalLM.forward = phi3_lce_forward

src/axolotl/integrations/liger/args.py

@@ -0,0 +1,32 @@
+# Copyright 2024 Axolotl AI. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+Module for handling LIGER input arguments.
+"""
+from typing import Optional
+
+from pydantic import BaseModel
+
+
+class LigerArgs(BaseModel):
+    """
+    Input args for LIGER.
+    """
+
+    liger_rope: Optional[bool] = None
+    liger_rms_norm: Optional[bool] = None
+    liger_swiglu: Optional[bool] = None
+    liger_cross_entropy: Optional[bool] = None
+    liger_fused_linear_cross_entropy: Optional[bool] = None

src/axolotl/integrations/liger/models/deepseekv2.py

@@ -0,0 +1,127 @@
+"""
+DeepseekV2 model with LigerFusedLinearCrossEntropyLoss
+"""
+# pylint: disable=duplicate-code
+
+from typing import List, Optional, Tuple, Union
+
+import torch
+from liger_kernel.transformers.fused_linear_cross_entropy import (
+    LigerFusedLinearCrossEntropyLoss,
+)
+from torch.nn import CrossEntropyLoss
+from transformers.modeling_outputs import CausalLMOutputWithPast
+
+
+# @add_start_docstrings_to_model_forward(DeepseekV2_INPUTS_DOCSTRING)
+# @replace_return_docstrings(
+#    output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC
+# )
+def lce_forward(
+    self,
+    input_ids: torch.LongTensor = None,
+    attention_mask: Optional[torch.Tensor] = None,
+    position_ids: Optional[torch.LongTensor] = None,
+    past_key_values: Optional[List[torch.FloatTensor]] = None,
+    inputs_embeds: Optional[torch.FloatTensor] = None,
+    labels: Optional[torch.LongTensor] = None,
+    use_cache: Optional[bool] = None,
+    output_attentions: Optional[bool] = None,
+    output_hidden_states: Optional[bool] = None,
+    return_dict: Optional[bool] = None,
+) -> Union[Tuple, CausalLMOutputWithPast]:
+    r"""
+    Args:
+        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Labels for computing the masked language modeling loss. Indices should either be in `[0, transformers.,
+            config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+            (masked), the loss is only computed for the tokens with labels in `[0, transformers., config.vocab_size]`.
+
+    Returns:
+
+    Example:
+
+    ```python
+    >>> from transformers import AutoTokenizer, DeepseekV2ForCausalLM
+
+    >>> model = DeepseekV2ForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS)
+    >>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER)
+
+    >>> prompt = "Hey, are you conscious? Can you talk to me?"
+    >>> inputs = tokenizer(prompt, return_tensors="pt")
+
+    >>> # Generate
+    >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
+    >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+    "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
+    ```"""
+    output_attentions = (
+        output_attentions
+        if output_attentions is not None
+        else self.config.output_attentions
+    )
+    output_hidden_states = (
+        output_hidden_states
+        if output_hidden_states is not None
+        else self.config.output_hidden_states
+    )
+    return_dict = (
+        return_dict if return_dict is not None else self.config.use_return_dict
+    )
+
+    # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
+    outputs = self.model(
+        input_ids=input_ids,
+        attention_mask=attention_mask,
+        position_ids=position_ids,
+        past_key_values=past_key_values,
+        inputs_embeds=inputs_embeds,
+        use_cache=use_cache,
+        output_attentions=output_attentions,
+        output_hidden_states=output_hidden_states,
+        return_dict=return_dict,
+    )
+
+    hidden_states = outputs[0]
+
+    loss = None
+    logits = None
+
+    if self.training:
+        shift_hidden_states = hidden_states[..., :-1, :].contiguous()
+        shift_labels = labels[..., 1:].contiguous()
+
+        # flatten tokens
+        shift_hidden_states = shift_hidden_states.view(-1, self.config.hidden_size)
+        shift_labels = shift_labels.view(-1)
+
+        lce = LigerFusedLinearCrossEntropyLoss()
+        loss = lce(self.lm_head.weight, shift_hidden_states, shift_labels)
+    else:
+        logits = self.lm_head(hidden_states)
+        logits = logits.float()
+
+        loss = None
+        if labels is not None:
+            # Shift so that tokens < n predict n
+            shift_logits = logits[..., :-1, :].contiguous()
+            shift_labels = labels[..., 1:].contiguous()
+            # Flatten the tokens
+            loss_fct = CrossEntropyLoss()
+            shift_logits = shift_logits.view(-1, self.config.vocab_size)
+            shift_labels = shift_labels.view(-1)
+            # Enable model parallelism
+            shift_labels = shift_labels.to(shift_logits.device)
+            loss = loss_fct(shift_logits, shift_labels)
+
+    if not return_dict:
+        output = (logits,) + outputs[1:]
+        return (loss,) + output if loss is not None else output
+
+    return CausalLMOutputWithPast(
+        loss=loss,
+        logits=logits,
+        past_key_values=outputs.past_key_values,
+        hidden_states=outputs.hidden_states,
+        attentions=outputs.attentions,
+    )

src/axolotl/integrations/liger/models/jamba.py

@@ -0,0 +1,173 @@
+"""
+Jamba model with LigerFusedLinearCrossEntropyLoss
+"""
+# pylint: disable=duplicate-code
+
+from typing import Optional, Tuple, Union
+
+import torch
+from liger_kernel.transformers.fused_linear_cross_entropy import (
+    LigerFusedLinearCrossEntropyLoss,
+)
+from torch.nn import CrossEntropyLoss
+from transformers.modeling_outputs import MoeCausalLMOutputWithPast
+from transformers.models.jamba.modeling_jamba import (
+    _CONFIG_FOR_DOC,
+    JAMBA_INPUTS_DOCSTRING,
+    HybridMambaAttentionDynamicCache,
+    load_balancing_loss_func,
+)
+from transformers.utils import (
+    add_start_docstrings_to_model_forward,
+    replace_return_docstrings,
+)
+
+
+@add_start_docstrings_to_model_forward(JAMBA_INPUTS_DOCSTRING)
+@replace_return_docstrings(
+    output_type=MoeCausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC
+)
+def lce_forward(
+    self,
+    input_ids: torch.LongTensor = None,
+    attention_mask: Optional[torch.Tensor] = None,
+    position_ids: Optional[torch.LongTensor] = None,
+    past_key_values: Optional[HybridMambaAttentionDynamicCache] = None,
+    inputs_embeds: Optional[torch.FloatTensor] = None,
+    labels: Optional[torch.LongTensor] = None,
+    use_cache: Optional[bool] = None,
+    output_attentions: Optional[bool] = None,
+    output_hidden_states: Optional[bool] = None,
+    output_router_logits: Optional[bool] = None,
+    return_dict: Optional[bool] = None,
+    cache_position: Optional[torch.LongTensor] = None,
+    num_logits_to_keep: Optional[Union[int, None]] = None,
+) -> Union[Tuple, MoeCausalLMOutputWithPast]:
+    r"""
+    Args:
+        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+            config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+            (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+
+        num_logits_to_keep (`int` or `None`, *optional*):
+            Calculate logits for the last `num_logits_to_keep` tokens. If `None`, calculate logits for all
+            `input_ids`. Only last token logits are needed for generation, and calculating them only for that token
+            can save memory, which becomes pretty significant for long sequences.
+
+    Returns:
+
+    Example:
+
+    ```python
+    >>> from transformers import AutoTokenizer, JambaForCausalLM
+
+    >>> model = JambaForCausalLM.from_pretrained("ai21labs/Jamba-v0.1")
+    >>> tokenizer = AutoTokenizer.from_pretrained("ai21labs/Jamba-v0.1")
+
+    >>> prompt = "Hey, are you conscious? Can you talk to me?"
+    >>> inputs = tokenizer(prompt, return_tensors="pt")
+
+    >>> # Generate
+    >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
+    >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+    "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
+    ```"""
+
+    output_attentions = (
+        output_attentions
+        if output_attentions is not None
+        else self.config.output_attentions
+    )
+    output_router_logits = (
+        output_router_logits
+        if output_router_logits is not None
+        else self.config.output_router_logits
+    )
+
+    output_hidden_states = (
+        output_hidden_states
+        if output_hidden_states is not None
+        else self.config.output_hidden_states
+    )
+    return_dict = (
+        return_dict if return_dict is not None else self.config.use_return_dict
+    )
+
+    # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
+    outputs = self.model(
+        input_ids=input_ids,
+        attention_mask=attention_mask,
+        position_ids=position_ids,
+        past_key_values=past_key_values,
+        inputs_embeds=inputs_embeds,
+        use_cache=use_cache,
+        output_attentions=output_attentions,
+        output_hidden_states=output_hidden_states,
+        output_router_logits=output_router_logits,
+        cache_position=cache_position,
+        return_dict=return_dict,
+    )
+
+    hidden_states = outputs[0]
+
+    loss = None
+    logits = None
+
+    if self.training:
+        shift_hidden_states = hidden_states[..., :-1, :].contiguous()
+        shift_labels = labels[..., 1:].contiguous()
+
+        # flatten tokens
+        shift_hidden_states = shift_hidden_states.view(-1, self.config.hidden_size)
+        shift_labels = shift_labels.view(-1)
+
+        lce = LigerFusedLinearCrossEntropyLoss()
+        loss = lce(self.lm_head.weight, shift_hidden_states, shift_labels)
+    else:
+        if num_logits_to_keep is None:
+            logits = self.lm_head(hidden_states)
+        else:
+            logits = self.lm_head(hidden_states[..., -num_logits_to_keep:, :])
+        logits = logits.float()
+
+        if labels is not None:
+            # Shift so that tokens < n predict n
+            shift_logits = logits[..., :-1, :].contiguous()
+            shift_labels = labels[..., 1:].contiguous()
+            # Flatten the tokens
+            loss_fct = CrossEntropyLoss()
+            shift_logits = shift_logits.view(-1, self.config.vocab_size)
+            shift_labels = shift_labels.view(-1)
+            # Enable model parallelism
+            shift_labels = shift_labels.to(shift_logits.device)
+            loss = loss_fct(shift_logits, shift_labels)
+
+    aux_loss = None
+    if output_router_logits:
+        aux_loss = load_balancing_loss_func(
+            outputs.router_logits if return_dict else outputs[-1],
+            self.num_experts,
+            self.num_experts_per_tok,
+            attention_mask,
+        )
+        if labels is not None:
+            loss += self.router_aux_loss_coef * aux_loss.to(
+                loss.device
+            )  # make sure to reside in the same device
+
+    if not return_dict:
+        output = (logits,) + outputs[1:]
+        if output_router_logits:
+            output = (aux_loss,) + output
+        return (loss,) + output if loss is not None else output
+
+    return MoeCausalLMOutputWithPast(
+        loss=loss,
+        aux_loss=aux_loss,
+        logits=logits,
+        past_key_values=outputs.past_key_values,
+        hidden_states=outputs.hidden_states,
+        attentions=outputs.attentions,
+        router_logits=outputs.router_logits,
+    )

src/axolotl/integrations/spectrum/LICENSE

@@ -0,0 +1,202 @@
+
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src/axolotl/integrations/spectrum/README.md

@@ -0,0 +1,21 @@
+## Spectrum: Targeted Training on Signal to Noise Ratio
+
+by Eric Hartford, Lucas Atkins, Fernando Fernandes, David Golchinfar
+
+This plugin contains code to freeze the bottom fraction of modules in a model, based on the Signal-to-Noise Ratio (SNR).
+
+### Overview
+
+Spectrum is a tool for scanning and evaluating the Signal-to-Noise Ratio (SNR) of layers in large language models.
+By identifying the top n% of layers with the highest SNR, you can optimize training efficiency.
+
+### Usage
+
+```yaml
+plugins:
+  - axolotl.integrations.spectrum.SpectrumPlugin
+
+spectrum_top_fraction: 0.5
+# Optional if using a pre-scanned model as your base_model. Useful if using a model mirror
+spectrum_model_name: meta-llama/Meta-Llama-3.1-8B
+```

src/axolotl/integrations/spectrum/__init__.py

@@ -0,0 +1,102 @@
+# Copyright 2024 Axolotl AI. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+Spectrum Plugin to automatically generate unfrozen parameters based on SNR data.
+"""
+
+import json
+import logging
+
+import requests
+
+from axolotl.integrations.base import BasePlugin
+
+from .args import SpectrumArgs  # pylint: disable=unused-import. # noqa: F401
+
+
+def _generate_unfrozen_params_yaml(snr_data, top_fraction=0.5):
+    unfrozen_parameters = {}
+    for layer_name, info in snr_data.items():
+        layer_type = info["type"]
+        if layer_type not in unfrozen_parameters:
+            unfrozen_parameters[layer_type] = []
+        unfrozen_parameters[layer_type].append((layer_name, info["snr"]))
+    top_layers_by_type = {}
+    for layer_type, layers in unfrozen_parameters.items():
+        layers_sorted = sorted(layers, key=lambda x: x[1], reverse=True)
+        num_top_layers = int(len(layers) * top_fraction)
+        top_layers_by_type[layer_type] = [
+            layer[0] for layer in layers_sorted[:num_top_layers]
+        ]
+    unfrozen_parameters = [
+        "^lm_head.weight$",
+        "^model.embed_tokens.weight$",
+    ]
+    for layer_type, layer_names in top_layers_by_type.items():
+        for layer_name in layer_names:
+            unfrozen_parameters.append(layer_name)
+    return unfrozen_parameters
+
+
+class SpectrumPlugin(BasePlugin):
+    """
+    Spectrum Plugin to automatically generate unfrozen parameters based on SNR data.
+    """
+
+    base_url = "https://raw.githubusercontent.com/cognitivecomputations/spectrum/main/model_snr_results/"
+    base_path = "./model_snr_results/"
+    snr_file_template = "snr_results_{model_name_slug}.json"
+
+    def get_input_args(self):
+        return "axolotl.integrations.spectrum.SpectrumArgs"
+
+    def pre_model_load(self, cfg):
+        if cfg.get("spectrum_model_name"):
+            model_name = cfg["spectrum_model_name"]
+        else:
+            model_name = cfg["base_model"]
+        top_fraction = cfg.get("spectrum_top_fraction", 50)
+        model_slug = model_name.replace("/", "-").replace("_", "-")
+        snr_url = self.base_url + self.snr_file_template.format(
+            model_name_slug=model_slug
+        )
+        snr_path = self.base_path + self.snr_file_template.format(
+            model_name_slug=model_slug
+        )
+        # first check if the files exist locally and read the json
+        snr_data = None
+        try:
+            with open(snr_path, "r", encoding="utf-8") as fin:
+                snr_data = json.load(fin)
+        except FileNotFoundError:
+            pass
+        except Exception as exc:  # pylint: disable=broad-exception-caught
+            logging.warning(f"Failed to read SNR data from {snr_path}: {exc}")
+
+        if not snr_data:
+            try:
+                snr_data = requests.get(snr_url, timeout=60).json()
+            except requests.exceptions.RequestException as exc:
+                logging.warning(f"Failed to fetch SNR data from {snr_url}: {exc}")
+                return
+            # also catch json parsing errors
+            except json.JSONDecodeError as exc:
+                logging.warning(f"Failed to parse SNR data from {snr_url}: {exc}")
+                return
+
+        unfrozen_parameters = _generate_unfrozen_params_yaml(
+            snr_data, top_fraction=top_fraction
+        )
+        cfg["unfrozen_parameters"] = unfrozen_parameters

src/axolotl/integrations/spectrum/args.py

@@ -0,0 +1,29 @@
+# Copyright 2024 Axolotl AI. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+Module for handling Spectrum input arguments.
+"""
+from typing import Optional
+
+from pydantic import BaseModel
+
+
+class SpectrumArgs(BaseModel):
+    """
+    Input args for Spectrum.
+    """
+
+    spectrum_top_fraction: Optional[float] = 0.5
+    spectrum_model_name: Optional[str] = None

src/axolotl/monkeypatch/llama_patch_multipack.py

@@ -9,18 +9,18 @@ from axolotl.monkeypatch.utils import (
 
 
 def hijack_llama_prepare_4d_mask():
-    import transformers.modeling_attn_mask_utils
-    import transformers.models.llama.modeling_llama
+    from transformers import modeling_attn_mask_utils
+    from transformers.models.llama import modeling_llama
 
-    transformers.models.llama.modeling_llama._prepare_4d_causal_attention_mask_for_sdpa = (  # pylint: disable=protected-access
+    modeling_llama._prepare_4d_causal_attention_mask_for_sdpa = (  # pylint: disable=protected-access
         patched_prepare_4d_causal_attention_mask_for_sdpa
     )
-    transformers.modeling_attn_mask_utils._prepare_4d_causal_attention_mask_for_sdpa = (  # pylint: disable=protected-access
+    modeling_attn_mask_utils._prepare_4d_causal_attention_mask_for_sdpa = (  # pylint: disable=protected-access
         patched_prepare_4d_causal_attention_mask_for_sdpa
     )
-    transformers.models.llama.modeling_llama._prepare_4d_causal_attention_mask = (  # pylint: disable=protected-access
+    modeling_llama._prepare_4d_causal_attention_mask = (  # pylint: disable=protected-access
         patched_prepare_4d_causal_attention_mask
     )
-    transformers.modeling_attn_mask_utils._prepare_4d_causal_attention_mask = (  # pylint: disable=protected-access
+    modeling_attn_mask_utils._prepare_4d_causal_attention_mask = (  # pylint: disable=protected-access
         patched_prepare_4d_causal_attention_mask
     )

src/axolotl/monkeypatch/unsloth_.py

@@ -16,8 +16,7 @@ from transformers.models.llama.modeling_llama import (
 
 LOG = get_logger("axolotl.monkeypatch.unsloth")
 
-ORIGINAL_CEL_CODE = """    if labels is not None:
-        # Shift so that tokens < n predict n
+ORIGINAL_CEL_CODE = """# Shift so that tokens < n predict n
         shift_logits = logits[..., :-1, :].contiguous()
         shift_labels = labels[..., 1:].contiguous()
         # Flatten the tokens
@@ -29,8 +28,7 @@ ORIGINAL_CEL_CODE = """    if labels is not None:
         loss = loss_fct(shift_logits, shift_labels)
 """
 
-PATCHED_CEL_CODE = """    if labels is not None:
-        shift_logits = logits[..., :-1, :].contiguous()
+PATCHED_CEL_CODE = """shift_logits = logits[..., :-1, :].contiguous()
         shift_labels = labels[..., 1:].contiguous()
         loss = fast_cross_entropy_loss(
             logits = shift_logits,

src/axolotl/monkeypatch/utils.py

@@ -17,11 +17,9 @@ def get_max_seqlen_in_batch(attention_mask: torch.Tensor) -> torch.Tensor:
     max_num = int(torch.max(attention_mask).item())
     batch_size, _ = attention_mask.shape
     counts = torch.zeros((batch_size, max_num), dtype=torch.int32)
-
     for i in range(1, max_num + 1):
         mask = attention_mask == i
         counts[:, i - 1] = torch.sum(mask, dim=-1).to(dtype=torch.int32)
-
     result = counts.flatten()
     nonzero_indices = torch.nonzero(result).squeeze(-1)
     return result[nonzero_indices]

src/axolotl/prompt_strategies/chat_template.py

@@ -24,8 +24,8 @@ class ChatTemplatePrompter(Prompter):
         max_length=2048,
         message_field_role: str = "from",
         message_field_content: str = "value",
-        message_field_training: str = "train",
-        message_field_training_detail: str = "train_detail",
+        message_field_training: Optional[str] = None,
+        message_field_training_detail: Optional[str] = None,
         roles: Optional[Dict[str, List[str]]] = None,
         drop_system_message: bool = False,
     ):
@@ -186,7 +186,7 @@ class ChatTemplateStrategy(PromptTokenizingStrategy):
         train_on_inputs,
         sequence_len,
         roles_to_train=None,
-        train_on_eos="last",
+        train_on_eos=None,
     ):
         super().__init__(prompter, tokenizer, train_on_inputs, sequence_len)
         self.roles_to_train = roles_to_train if roles_to_train is not None else []
@@ -201,6 +201,37 @@ class ChatTemplateStrategy(PromptTokenizingStrategy):
         self._messages = messages
 
     def tokenize_prompt(self, prompt):
+        # Old simple legacy behavior that works reliably.
+        if (
+            not self.roles_to_train
+            and not self.train_on_eos
+            and not self.prompter.message_field_training
+            and not self.prompter.message_field_training_detail
+        ):
+            turns = self.get_conversation_thread(prompt)
+            prompt_ids = self.prompter.build_prompt(
+                turns[:-1], add_generation_prompt=True
+            )
+            input_ids = self.prompter.build_prompt(turns)
+
+            if not self.train_on_inputs:
+                user_prompt_len = len(prompt_ids)
+                labels = [-100] * user_prompt_len + input_ids[user_prompt_len:]
+            else:
+                labels = input_ids
+
+            tokenized_prompt = {
+                "input_ids": input_ids,
+                "labels": labels,
+                "attention_mask": [1] * len(input_ids),
+            }
+
+            return tokenized_prompt
+        LOG.info(self.roles_to_train)
+        LOG.info(self.train_on_eos)
+        LOG.info(self.prompter.message_field_training)
+        LOG.info(self.prompter.message_field_training_detail)
+
         turns = prompt[self.messages]
         input_ids = self.prompter.build_prompt(turns)
         labels = [IGNORE_TOKEN_ID] * len(input_ids)
@@ -219,9 +250,11 @@ class ChatTemplateStrategy(PromptTokenizingStrategy):
             should_train = (
                 train_turn
                 if train_turn is not None
-                else bool(train_detail is not None)
-                if train_detail is not None
-                else self.train_on_inputs or role in self.roles_to_train
+                else (
+                    bool(train_detail is not None)
+                    if train_detail is not None
+                    else self.train_on_inputs or role in self.roles_to_train
+                )
             )
 
             LOG.debug(f"Should train: {should_train}")
@@ -344,20 +377,22 @@ def load(tokenizer, cfg, ds_cfg: Optional[Dict[str, Any]] = None):
         "chat_template": chat_templates(ds_cfg.get("chat_template", "chatml")),
         "message_field_role": ds_cfg.get("message_field_role", "from"),
         "message_field_content": ds_cfg.get("message_field_content", "value"),
-        "message_field_training": ds_cfg.get("message_field_training", "training"),
+        "message_field_training": ds_cfg.get("message_field_training", None),
         "message_field_training_detail": ds_cfg.get(
-            "message_field_training_detail", "train_detail"
+            "message_field_training_detail",
+            None,
         ),
         "roles": ds_cfg.get("roles"),
         "drop_system_message": ds_cfg.get("drop_system_message", False),
-        "max_length": cfg.sequence_len,
+        # we need to add one for detecting sequences with exceeding the `sequence_len` limit.
+        "max_length": cfg.sequence_len + 1,
     }
 
     strategy_params = {
         "train_on_inputs": cfg.train_on_inputs,
         "sequence_len": cfg.sequence_len,
-        "roles_to_train": ds_cfg.get("roles_to_train", ["gpt", "assistant"]),
-        "train_on_eos": ds_cfg.get("train_on_eos", "turn"),
+        "roles_to_train": ds_cfg.get("roles_to_train", []),
+        "train_on_eos": ds_cfg.get("train_on_eos", None),
     }
 
     strategy = ChatTemplateStrategy(

src/axolotl/prompters.py

@@ -352,9 +352,12 @@ class ShareGPTPrompter(Prompter):  # pylint: disable=too-few-public-methods
                         "Please help us by creating an Issue to add support for this conversation type."
                     )
 
-                role = CONVERSATION_ROLE_FORMAT[self._conversation.name].format(
-                    ROLE=from_role
-                )
+                if self._conversation.name in ["llama3"]:
+                    role = from_role
+                else:
+                    role = CONVERSATION_ROLE_FORMAT[self._conversation.name].format(
+                        ROLE=from_role
+                    )
 
             if len(conv.messages) > 0 and ((role == conv.messages[-1][0])):
                 if (

src/axolotl/utils/config/__init__.py

@@ -8,11 +8,14 @@ from typing import Optional
 import torch
 from transformers.utils import is_torch_bf16_gpu_available
 
+from axolotl.integrations.config import merge_input_args
 from axolotl.utils.bench import log_gpu_memory_usage
+from axolotl.utils.config.models.input.v0_4_1 import SUPPORTED_METRICS
 from axolotl.utils.config.models.input.v0_4_1 import (
-    SUPPORTED_METRICS,
-    AxolotlConfigWCapabilities,
-    AxolotlInputConfig,
+    AxolotlConfigWCapabilities as AxolotlConfigWCapabilitiesBase,
+)
+from axolotl.utils.config.models.input.v0_4_1 import (
+    AxolotlInputConfig as AxolotlInputConfigBase,
 )
 from axolotl.utils.dict import DictDefault
 from axolotl.utils.models import load_model_config
@@ -207,6 +210,15 @@ def normalize_cfg_datasets(cfg):
 
 
 def validate_config(cfg: DictDefault, capabilities: Optional[dict] = None):
+    AxolotlConfigWCapabilities = AxolotlConfigWCapabilitiesBase
+    AxolotlInputConfig = AxolotlInputConfigBase
+
+    if cfg.plugins:
+        (
+            AxolotlConfigWCapabilities,  # pylint: disable=invalid-name
+            AxolotlInputConfig,  # pylint: disable=invalid-name
+        ) = merge_input_args()
+
     if capabilities:
         return DictDefault(
             dict(

src/axolotl/utils/config/models/input/v0_4_1/__init__.py

@@ -189,6 +189,7 @@ class ChatTemplate(str, Enum):
     cohere = "cohere"  # pylint: disable=invalid-name
     llama3 = "llama3"  # pylint: disable=invalid-name
     phi_3 = "phi_3"  # pylint: disable=invalid-name
+    phi_35 = "phi_35"  # pylint: disable=invalid-name
     deepseek_v2 = "deepseek_v2"  # pylint: disable=invalid-name
     jamba = "jamba"  # pylint: disable=invalid-name
 
@@ -355,6 +356,8 @@ class HyperparametersConfig(BaseModel):
         },
     )
 
+    auto_find_batch_size: Optional[bool] = None
+
     train_on_inputs: Optional[bool] = False
     group_by_length: Optional[bool] = None
 
@@ -369,6 +372,7 @@ class HyperparametersConfig(BaseModel):
                 "ao_adamw_4bit",
                 "ao_adamw_8bit",
                 "ao_adamw_fp8",
+                "shampoo",
             ],
         ]
     ] = OptimizerNames.ADAMW_HF.value
@@ -381,6 +385,12 @@ class HyperparametersConfig(BaseModel):
             "help": "The target modules to optimize, i.e. the module names that you would like to train."
         },
     )
+    optim_shampoo_grafting_config_type: Optional[
+        Literal["adam", "sgd", "adagrad"]
+    ] = None
+    optim_shampoo_grafting_config_kwargs: Optional[Dict[str, Any]] = None
+    optim_shampoo_betas: Optional[Tuple[float, float]] = None
+
     torchdistx_path: Optional[str] = None
     lr_scheduler: Optional[Union[SchedulerType, Literal["one_cycle"]]] = "cosine"
     lr_scheduler_kwargs: Optional[Dict[str, Any]] = None
@@ -592,6 +602,7 @@ class AxolotlInputConfig(
     eval_sample_packing: Optional[bool] = None
     pad_to_sequence_len: Optional[bool] = None
     curriculum_sampling: Optional[bool] = None
+    multipack_real_batches: Optional[bool] = None
 
     # for PoSE context length extension
     use_pose: Optional[bool] = None

src/axolotl/utils/models.py

@@ -96,6 +96,12 @@ def check_model_config(cfg: DictDefault, model_config: Union[AutoConfig, DictDef
             "Please make sure to point to a GPTQ model."
         )
 
+    if not cfg.gptq and quant_config_exists and not cfg.load_in_4bit:
+        raise ValueError(
+            "model_config.quantization_config is set but `gptq` flag is not. "
+            "Please use the `gptq` flag to train quantized model or point to a non-quantized model."
+        )
+
     lora_modules_to_save = get_linear_embedding_layers(model_config.model_type)
     if (
         cfg.adapter
@@ -302,10 +308,17 @@ def load_model(
     """
     Load a model for a given configuration and tokenizer.
     """
+
     base_model = cfg.base_model
     model_type = cfg.type_of_model
     model_config = load_model_config(cfg)
 
+    # load any patches from plugins
+    from axolotl.integrations.base import PluginManager
+
+    plugin_manager = PluginManager.get_instance()
+    plugin_manager.pre_model_load(cfg)
+
     # TODO refactor as a kwarg
     load_in_8bit = cfg.load_in_8bit
 
@@ -576,19 +589,12 @@ def load_model(
 
     # sample packing uses custom FA2 patch
     if cfg.flash_attention:
-        if not cfg.sample_packing:
-            if cfg.s2_attention:
-                pass
-            # most other models support flash attention, we can define exceptions as they come up
-            model_kwargs["attn_implementation"] = "flash_attention_2"
-            model_config._attn_implementation = (  # pylint: disable=protected-access
-                "flash_attention_2"
-            )
-        else:
-            model_kwargs["attn_implementation"] = "flash_attention_2"
-            model_config._attn_implementation = (  # pylint: disable=protected-access
-                "flash_attention_2"
-            )
+        if not cfg.sample_packing and cfg.s2_attention:
+            pass
+        model_kwargs["attn_implementation"] = "flash_attention_2"
+        model_config._attn_implementation = (  # pylint: disable=protected-access
+            "flash_attention_2"
+        )
     elif cfg.sdp_attention:
         model_kwargs["attn_implementation"] = "sdpa"
         model_config._attn_implementation = "sdpa"  # pylint: disable=protected-access

src/axolotl/utils/samplers/multipack.py

@@ -11,6 +11,8 @@ import numba
 import numpy as np
 from torch.utils.data import BatchSampler, Sampler
 
+from axolotl.utils.distributed import reduce_and_broadcast
+
 LOG = logging.getLogger("axolotl.utils.samplers.multipack")
 
 
@@ -174,16 +176,46 @@ class MultipackBatchSampler(BatchSampler):
     def efficiency(self):
         return self.eff_total_used / self.eff_total_slots
 
+    def gather_efficiency(self):
+        def calc_sample_packing_eff_est(estimates: List[float]):
+            LOG.debug(f"sample_packing_eff_est across ranks: {repr(estimates)}")
+            return math.floor(0.997 * max(estimates))
+
+        sample_packing_actual_eff_all = reduce_and_broadcast(
+            lambda: self.efficiency(),  # pylint: disable=unnecessary-lambda
+            calc_sample_packing_eff_est,
+        )
+        sample_packing_eff_est = (
+            math.ceil(sample_packing_actual_eff_all * 200.0) / 200.0
+        )
+        return sample_packing_eff_est
+
+    def gather_len_batches(self, num):
+        def calc_min_len(estimates: list[(int, float)]):
+            LOG.info(f"gather_len_batches: {repr(estimates)}")
+            return math.floor(0.998 * min(estimates))
+
+        min_len_batches = reduce_and_broadcast(
+            lambda: num,
+            calc_min_len,
+        )
+        return min_len_batches
+
     def __len__(self):
-        self.num_batches()
-        return self._len_est()
+        len_batches = self.num_batches()
+        return self.gather_len_batches(len_batches)
 
     def _len_est(self):
+        efficiency = (
+            self.packing_efficiency_estimate
+            if self.packing_efficiency_estimate
+            else self.gather_efficiency()
+        )
         world_size = int(os.getenv("WORLD_SIZE", "1"))
         lengths_sum = np.sum(self.lengths)
         lengths_sum_per_device = lengths_sum // world_size
         LOG.info(
-            f"packing_efficiency_estimate: {self.packing_efficiency_estimate} "
+            f"packing_efficiency_estimate: {efficiency} "
             f"total_num_tokens per device: {lengths_sum_per_device}"
         )
 
@@ -195,7 +227,7 @@ class MultipackBatchSampler(BatchSampler):
                 * math.floor(
                     0.99
                     * lengths_sum_per_device
-                    / self.packing_efficiency_estimate
+                    / efficiency
                     // (self.batch_max_len * self.batch_size)
                 )
                 - 1

src/axolotl/utils/trainer.py

@@ -217,6 +217,24 @@ def process_datasets_for_packing(cfg, train_dataset, eval_dataset):
             desc="Dropping Long Sequences",
         )
 
+    # drop samples with where the number of elements with labels not equal to -100 is zero
+    def drop_no_trainable_tokens(sample):
+        return np.sum(np.array(sample["labels"]) != -100) > 0
+
+    train_dataset = train_dataset.filter(
+        drop_no_trainable_tokens,
+        num_proc=cfg.dataset_processes,
+        load_from_cache_file=not cfg.is_preprocess,
+        desc="Drop Samples with Zero Trainable Tokens",
+    )
+    if eval_dataset:
+        eval_dataset = eval_dataset.filter(
+            drop_no_trainable_tokens,
+            num_proc=cfg.dataset_processes,
+            load_from_cache_file=not cfg.is_preprocess,
+            desc="Drop Samples with Zero Trainable Tokens",
+        )
+
     if cfg.group_by_length:
         train_dataset = train_dataset.map(
             add_length,
@@ -339,7 +357,7 @@ def calculate_total_num_steps(cfg, train_dataset, update=True):
                 main_process_only=True,
             )
         else:
-            if cfg.flash_attention:
+            if cfg.flash_attention and not cfg.multipack_real_batches:
                 sampler_batch_size = 1
                 batch_max_len = cfg.micro_batch_size * cfg.sequence_len
             else:
@@ -407,7 +425,8 @@ def setup_deepspeed_env(cfg, stage=None):
         os.environ["ACCELERATE_DEEPSPEED_ZERO_STAGE"] = str(stage)
         if stage == 3:
             os.environ["ACCELERATE_DEEPSPEED_ZERO3_INIT"] = "true"
-    HfTrainerDeepSpeedConfig(cfg.deepspeed)
+    # If we don't assign this, it doesn't actually get set in the accelerate weakref
+    _ = HfTrainerDeepSpeedConfig(cfg.deepspeed)
 
 
 def setup_fsdp_envs(cfg):

tests/e2e/integrations/liger.py

@@ -0,0 +1,110 @@
+"""
+Simple end-to-end test for Liger integration
+"""
+
+import unittest
+from pathlib import Path
+
+from axolotl.cli import load_datasets
+from axolotl.common.cli import TrainerCliArgs
+from axolotl.train import train
+from axolotl.utils.config import normalize_config
+from axolotl.utils.dict import DictDefault
+
+from ..utils import with_temp_dir
+
+
+class LigerIntegrationTestCase(unittest.TestCase):
+    """
+    e2e tests for liger integration with Axolotl
+    """
+
+    @with_temp_dir
+    def test_llama_wo_flce(self, temp_dir):
+        cfg = DictDefault(
+            {
+                "base_model": "JackFram/llama-68m",
+                "tokenizer_type": "LlamaTokenizer",
+                "plugins": [
+                    "axolotl.integrations.liger.LigerPlugin",
+                ],
+                "liger_rope": True,
+                "liger_rms_norm": True,
+                "liger_swiglu": True,
+                "liger_cross_entropy": True,
+                "liger_fused_linear_cross_entropy": False,
+                "sequence_len": 1024,
+                "val_set_size": 0.1,
+                "special_tokens": {
+                    "unk_token": "<unk>",
+                    "bos_token": "<s>",
+                    "eos_token": "</s>",
+                },
+                "datasets": [
+                    {
+                        "path": "mhenrichsen/alpaca_2k_test",
+                        "type": "alpaca",
+                    },
+                ],
+                "num_epochs": 1,
+                "micro_batch_size": 8,
+                "gradient_accumulation_steps": 1,
+                "output_dir": temp_dir,
+                "learning_rate": 0.00001,
+                "optimizer": "adamw_torch",
+                "lr_scheduler": "cosine",
+                "save_safetensors": True,
+                "bf16": "auto",
+            }
+        )
+        normalize_config(cfg)
+        cli_args = TrainerCliArgs()
+        dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
+
+        train(cfg=cfg, cli_args=cli_args, dataset_meta=dataset_meta)
+        assert (Path(temp_dir) / "model.safetensors").exists()
+
+    @with_temp_dir
+    def test_llama_w_flce(self, temp_dir):
+        cfg = DictDefault(
+            {
+                "base_model": "JackFram/llama-68m",
+                "tokenizer_type": "LlamaTokenizer",
+                "plugins": [
+                    "axolotl.integrations.liger.LigerPlugin",
+                ],
+                "liger_rope": True,
+                "liger_rms_norm": True,
+                "liger_swiglu": True,
+                "liger_cross_entropy": False,
+                "liger_fused_linear_cross_entropy": True,
+                "sequence_len": 1024,
+                "val_set_size": 0.1,
+                "special_tokens": {
+                    "unk_token": "<unk>",
+                    "bos_token": "<s>",
+                    "eos_token": "</s>",
+                },
+                "datasets": [
+                    {
+                        "path": "mhenrichsen/alpaca_2k_test",
+                        "type": "alpaca",
+                    },
+                ],
+                "num_epochs": 1,
+                "micro_batch_size": 8,
+                "gradient_accumulation_steps": 1,
+                "output_dir": temp_dir,
+                "learning_rate": 0.00001,
+                "optimizer": "adamw_torch",
+                "lr_scheduler": "cosine",
+                "save_safetensors": True,
+                "bf16": "auto",
+            }
+        )
+        normalize_config(cfg)
+        cli_args = TrainerCliArgs()
+        dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
+
+        train(cfg=cfg, cli_args=cli_args, dataset_meta=dataset_meta)
+        assert (Path(temp_dir) / "model.safetensors").exists()

tests/e2e/multigpu/test_llama.py

@@ -10,6 +10,7 @@ from pathlib import Path
 import pytest
 import yaml
 from accelerate.test_utils import execute_subprocess_async
+from huggingface_hub import snapshot_download
 
 from axolotl.utils.dict import DictDefault
 
@@ -19,6 +20,12 @@ LOG = logging.getLogger("axolotl.tests.e2e.multigpu")
 os.environ["WANDB_DISABLED"] = "true"
 
 
+@pytest.fixture(scope="session", autouse=True)
+def download_model():
+    # download the model
+    snapshot_download("TinyLlama/TinyLlama_v1.1")
+
+
 class TestMultiGPULlama(unittest.TestCase):
     """
     Test case for Llama models using LoRA

tests/prompt_strategies/conftest.py

@@ -0,0 +1,71 @@
+"""
+shared fixtures for prompt strategies tests
+"""
+
+import pytest
+from datasets import Dataset
+from transformers import AutoTokenizer
+
+
+@pytest.fixture(name="assistant_dataset")
+def fixture_assistant_dataset():
+    return Dataset.from_list(
+        [
+            {
+                "messages": [
+                    {"role": "user", "content": "hello"},
+                    {"role": "assistant", "content": "hello"},
+                    {"role": "user", "content": "goodbye"},
+                    {"role": "assistant", "content": "goodbye"},
+                ]
+            }
+        ]
+    )
+
+
+@pytest.fixture(name="sharegpt_dataset")
+def fixture_sharegpt_dataset():
+    # pylint: disable=duplicate-code
+    return Dataset.from_list(
+        [
+            {
+                "conversations": [
+                    {"from": "human", "value": "hello"},
+                    {"from": "gpt", "value": "hello"},
+                    {"from": "human", "value": "goodbye"},
+                    {"from": "gpt", "value": "goodbye"},
+                ]
+            }
+        ]
+    )
+
+
+@pytest.fixture(name="basic_dataset")
+def fixture_basic_dataset():
+    # pylint: disable=duplicate-code
+    return Dataset.from_list(
+        [
+            {
+                "conversations": [
+                    {"from": "system", "value": "You are an AI assistant."},
+                    {"from": "human", "value": "Hello"},
+                    {"from": "assistant", "value": "Hi there!"},
+                    {"from": "human", "value": "How are you?"},
+                    {"from": "assistant", "value": "I'm doing well, thank you!"},
+                ]
+            }
+        ]
+    )
+
+
+@pytest.fixture(name="llama3_tokenizer")
+def fixture_llama3_tokenizer():
+    tokenizer = AutoTokenizer.from_pretrained("NousResearch/Meta-Llama-3-8B-Instruct")
+
+    return tokenizer
+
+
+@pytest.fixture(name="phi35_tokenizer")
+def fixture_phi35_tokenizer():
+    tokenizer = AutoTokenizer.from_pretrained("microsoft/Phi-3.5-mini-instruct")
+    return tokenizer

tests/prompt_strategies/test_chat_templates.py

@@ -5,10 +5,6 @@ tests for chat_template prompt strategy
 import logging
 import unittest
 
-import pytest
-from datasets import Dataset
-from transformers import AutoTokenizer
-
 from axolotl.prompt_strategies.chat_template import (
     ChatTemplatePrompter,
     ChatTemplateStrategy,
@@ -22,657 +18,6 @@ logging.basicConfig(level=logging.DEBUG)
 LOG = logging.getLogger("axolotl")
 
 
-@pytest.fixture(name="assistant_dataset")
-def fixture_assistant_dataset():
-    return Dataset.from_list(
-        [
-            {
-                "messages": [
-                    {"role": "user", "content": "hello"},
-                    {"role": "assistant", "content": "hello"},
-                    {"role": "user", "content": "goodbye"},
-                    {"role": "assistant", "content": "goodbye"},
-                ]
-            }
-        ]
-    )
-
-
-@pytest.fixture(name="sharegpt_dataset")
-def fixture_sharegpt_dataset():
-    # pylint: disable=duplicate-code
-    return Dataset.from_list(
-        [
-            {
-                "conversations": [
-                    {"from": "human", "value": "hello"},
-                    {"from": "gpt", "value": "hello"},
-                    {"from": "human", "value": "goodbye"},
-                    {"from": "gpt", "value": "goodbye"},
-                ]
-            }
-        ]
-    )
-
-
-@pytest.fixture(name="basic_dataset")
-def fixture_basic_dataset():
-    # pylint: disable=duplicate-code
-    return Dataset.from_list(
-        [
-            {
-                "conversations": [
-                    {"from": "system", "value": "You are an AI assistant."},
-                    {"from": "human", "value": "Hello"},
-                    {"from": "assistant", "value": "Hi there!"},
-                    {"from": "human", "value": "How are you?"},
-                    {"from": "assistant", "value": "I'm doing well, thank you!"},
-                ]
-            }
-        ]
-    )
-
-
-@pytest.fixture(name="llama3_tokenizer")
-def fixture_llama3_tokenizer():
-    tokenizer = AutoTokenizer.from_pretrained("NousResearch/Meta-Llama-3-8B-Instruct")
-
-    return tokenizer
-
-
-class TestChatTemplateConfigurations:
-    """
-    Test class for various configurations of ChatTemplateStrategy.
-    """
-
-    @staticmethod
-    def find_sublist(full_list, sub_list):
-        token_count = len(sub_list)
-        for index in range(len(full_list) - token_count + 1):
-            if full_list[index : index + token_count] == sub_list:
-                return index
-        return -1
-
-    def test_train_on_inputs_true(self, llama3_tokenizer, basic_dataset):
-        LOG.info("Testing with train_on_inputs=True")
-        strategy = ChatTemplateStrategy(
-            ChatTemplatePrompter(llama3_tokenizer, chat_templates("llama3")),
-            tokenizer=llama3_tokenizer,
-            train_on_inputs=True,
-            sequence_len=512,
-            roles_to_train=["assistant"],
-        )
-        res = strategy.tokenize_prompt(basic_dataset[0])
-        labels = res["labels"]
-        input_ids = res["input_ids"]
-
-        # Verify that assistant responses are labeled
-        assistant_responses = ["Hi there!", "I'm doing well, thank you!"]
-        for response in assistant_responses:
-            response_ids = llama3_tokenizer.encode(response, add_special_tokens=False)
-            start_idx = self.find_sublist(input_ids, response_ids)
-            LOG.debug(
-                f"Assistant response '{response}' expected IDs: {response_ids}, found at: {start_idx}"
-            )
-            assert start_idx != -1, f"Could not find '{response}' in input_ids"
-            assert all(
-                label != IGNORE_TOKEN_ID
-                for label in labels[start_idx : start_idx + len(response_ids)]
-            ), f"Expected labels for assistant response '{response}' to be set, but got {labels[start_idx:start_idx+len(response_ids)]}"
-
-        # Check the behavior of human inputs
-        human_inputs = ["Hello", "How are you?"]
-        for input_text in human_inputs:
-            input_ids = llama3_tokenizer.encode(input_text, add_special_tokens=False)
-            start_idx = self.find_sublist(input_ids, input_ids)
-            labeled = all(
-                label != IGNORE_TOKEN_ID
-                for label in labels[start_idx : start_idx + len(input_ids)]
-            )
-            LOG.debug(
-                f"Human input '{input_text}' is {'labeled' if labeled else 'not labeled'}, expected IDs: {input_ids}, found at: {start_idx}"
-            )
-
-        LOG.debug("Full labels: %s", labels)
-        LOG.debug("Full input_ids: %s", input_ids)
-
-    def test_train_on_inputs_false(self, llama3_tokenizer, basic_dataset):
-        LOG.info("Testing with train_on_inputs=False")
-        strategy = ChatTemplateStrategy(
-            ChatTemplatePrompter(llama3_tokenizer, chat_templates("llama3")),
-            tokenizer=llama3_tokenizer,
-            train_on_inputs=False,
-            sequence_len=512,
-            roles_to_train=["assistant"],
-        )
-        res = strategy.tokenize_prompt(basic_dataset[0])
-        labels = res["labels"]
-        input_ids = res["input_ids"]
-
-        # Verify that only assistant responses are labeled
-        assistant_responses = ["Hi there!", "I'm doing well, thank you!"]
-        for response in assistant_responses:
-            response_ids = llama3_tokenizer.encode(response, add_special_tokens=False)
-            start_idx = self.find_sublist(input_ids, response_ids)
-            LOG.debug(
-                f"Assistant response '{response}' expected IDs: {response_ids}, found at: {start_idx}"
-            )
-            assert start_idx != -1, f"Could not find '{response}' in input_ids"
-            assert all(
-                label != IGNORE_TOKEN_ID
-                for label in labels[start_idx : start_idx + len(response_ids)]
-            ), f"Expected labels for assistant response '{response}' to be set, but got {labels[start_idx:start_idx+len(response_ids)]}"
-
-        # Verify that human inputs are not labeled
-        human_inputs = ["Hello", "How are you?"]
-        for input_text in human_inputs:
-            input_ids = llama3_tokenizer.encode(input_text, add_special_tokens=False)
-            start_idx = self.find_sublist(input_ids, input_ids)
-            LOG.debug(
-                f"Human input '{input_text}' expected IDs: {input_ids}, found at: {start_idx}"
-            )
-            assert start_idx != -1, f"Could not find '{input_text}' in input_ids"
-            assert all(
-                label == IGNORE_TOKEN_ID
-                for label in labels[start_idx : start_idx + len(input_ids)]
-            ), f"Expected labels for human input '{input_text}' to be IGNORE_TOKEN_ID, but got {labels[start_idx:start_idx+len(input_ids)]}"
-
-    def test_roles_to_train_assistant_only(self, llama3_tokenizer, basic_dataset):
-        LOG.info("Testing roles_to_train with assistant only")
-        strategy = ChatTemplateStrategy(
-            ChatTemplatePrompter(llama3_tokenizer, chat_templates("llama3")),
-            tokenizer=llama3_tokenizer,
-            train_on_inputs=False,
-            sequence_len=512,
-            roles_to_train=["assistant"],
-        )
-        res = strategy.tokenize_prompt(basic_dataset[0])
-        labels = res["labels"]
-        input_ids = res["input_ids"]
-
-        # Verify that only assistant responses are labeled
-        assistant_responses = ["Hi there!", "I'm doing well, thank you!"]
-        for response in assistant_responses:
-            response_ids = llama3_tokenizer.encode(response, add_special_tokens=False)
-            start_idx = self.find_sublist(input_ids, response_ids)
-            LOG.debug(
-                f"Assistant response '{response}' expected IDs: {response_ids}, found at: {start_idx}"
-            )
-            assert all(
-                label != IGNORE_TOKEN_ID
-                for label in labels[start_idx : start_idx + len(response_ids)]
-            ), f"Expected labels for assistant response '{response}' to be set, but got {labels[start_idx:start_idx+len(response_ids)]}"
-
-    def test_roles_to_train_all(self, llama3_tokenizer, basic_dataset):
-        LOG.info("Testing roles_to_train with all roles")
-        strategy = ChatTemplateStrategy(
-            ChatTemplatePrompter(llama3_tokenizer, chat_templates("llama3")),
-            tokenizer=llama3_tokenizer,
-            train_on_inputs=True,
-            sequence_len=512,
-            roles_to_train=["human", "assistant"],
-        )
-        res = strategy.tokenize_prompt(basic_dataset[0])
-        labels = res["labels"]
-        input_ids = res["input_ids"]
-
-        # Verify that all responses are labeled (except for special tokens)
-        all_responses = [
-            "Hello",
-            "Hi there!",
-            "How are you?",
-            "I'm doing well, thank you!",
-        ]
-        for response in all_responses:
-            response_ids = llama3_tokenizer.encode(response, add_special_tokens=False)
-            start_idx = self.find_sublist(input_ids, response_ids)
-            LOG.debug(
-                f"Response '{response}' expected IDs: {response_ids}, found at: {start_idx}"
-            )
-            assert all(
-                label != IGNORE_TOKEN_ID
-                for label in labels[start_idx : start_idx + len(response_ids)]
-            ), f"Expected labels for response '{response}' to be set, but got {labels[start_idx:start_idx+len(response_ids)]}"
-
-    def test_empty_roles_to_train(self, llama3_tokenizer, basic_dataset):
-        LOG.info("Testing with empty roles_to_train")
-        strategy = ChatTemplateStrategy(
-            ChatTemplatePrompter(llama3_tokenizer, chat_templates("llama3")),
-            tokenizer=llama3_tokenizer,
-            train_on_inputs=False,
-            sequence_len=512,
-            roles_to_train=[],
-            train_on_eos="none",  # Add this line
-        )
-        res = strategy.tokenize_prompt(basic_dataset[0])
-        labels = res["labels"]
-
-        # Verify that no labels are set when roles_to_train is empty
-        LOG.debug("Full labels: %s", labels)
-        assert all(
-            label == IGNORE_TOKEN_ID for label in labels
-        ), "Expected all labels to be IGNORE_TOKEN_ID when roles_to_train is empty"
-
-    def test_train_on_eos_all(self, llama3_tokenizer, basic_dataset):
-        LOG.info("Testing with train_on_eos='all'")
-        strategy = ChatTemplateStrategy(
-            ChatTemplatePrompter(llama3_tokenizer, chat_templates("llama3")),
-            tokenizer=llama3_tokenizer,
-            train_on_inputs=False,
-            sequence_len=512,
-            roles_to_train=["assistant"],
-            train_on_eos="all",
-        )
-        res = strategy.tokenize_prompt(basic_dataset[0])
-        labels = res["labels"]
-        input_ids = res["input_ids"]
-
-        eos_token_id = llama3_tokenizer.eos_token_id
-        eos_indices = [
-            i for i, token_id in enumerate(input_ids) if token_id == eos_token_id
-        ]
-
-        assert len(eos_indices) > 0, "Expected at least one EOS token in the input"
-        for eos_idx in eos_indices:
-            assert (
-                labels[eos_idx] != IGNORE_TOKEN_ID
-            ), f"Expected EOS token at index {eos_idx} to be labeled"
-
-    def test_train_on_eos_turn(self, llama3_tokenizer, basic_dataset):
-        LOG.info("Testing with train_on_eos='turn'")
-        strategy = ChatTemplateStrategy(
-            ChatTemplatePrompter(llama3_tokenizer, chat_templates("llama3")),
-            tokenizer=llama3_tokenizer,
-            train_on_inputs=False,
-            sequence_len=512,
-            roles_to_train=["assistant"],
-            train_on_eos="turn",
-        )
-        res = strategy.tokenize_prompt(basic_dataset[0])
-        labels = res["labels"]
-        input_ids = res["input_ids"]
-
-        eos_token_id = llama3_tokenizer.eos_token_id
-        assistant_responses = ["Hi there!", "I'm doing well, thank you!"]
-
-        for response in assistant_responses:
-            response_ids = llama3_tokenizer.encode(response, add_special_tokens=False)
-            start_idx = self.find_sublist(input_ids, response_ids)
-            assert start_idx != -1, f"Could not find '{response}' in input_ids"
-
-            eos_idx = start_idx + len(response_ids)
-            while eos_idx < len(input_ids) and input_ids[eos_idx] != eos_token_id:
-                eos_idx += 1
-
-            assert eos_idx < len(
-                input_ids
-            ), f"Could not find EOS token after '{response}'"
-            assert (
-                labels[eos_idx] != IGNORE_TOKEN_ID
-            ), f"Expected EOS token after assistant response '{response}' to be labeled"
-
-        # Check that EOS tokens after human inputs are not labeled
-        human_inputs = ["Hello", "How are you?"]
-        for input_text in human_inputs:
-            input_ids = llama3_tokenizer.encode(input_text, add_special_tokens=False)
-            start_idx = self.find_sublist(input_ids, input_ids)
-            assert start_idx != -1, f"Could not find '{input_text}' in input_ids"
-
-            eos_idx = start_idx + len(input_ids)
-            while eos_idx < len(input_ids) and input_ids[eos_idx] != eos_token_id:
-                eos_idx += 1
-
-            assert (
-                labels[eos_idx] == IGNORE_TOKEN_ID
-            ), f"Expected EOS token after human input '{input_text}' to not be labeled"
-
-    def test_train_on_eos_last(self, llama3_tokenizer, basic_dataset):
-        LOG.info("Testing with train_on_eos='last'")
-        strategy = ChatTemplateStrategy(
-            ChatTemplatePrompter(llama3_tokenizer, chat_templates("llama3")),
-            tokenizer=llama3_tokenizer,
-            train_on_inputs=False,
-            sequence_len=512,
-            roles_to_train=["assistant"],
-            train_on_eos="last",
-        )
-        res = strategy.tokenize_prompt(basic_dataset[0])
-        labels = res["labels"]
-        input_ids = res["input_ids"]
-
-        eos_token_id = llama3_tokenizer.eos_token_id
-        eos_indices = [
-            i for i, token_id in enumerate(input_ids) if token_id == eos_token_id
-        ]
-
-        assert len(eos_indices) > 0, "Expected at least one EOS token in the input"
-        last_eos_idx = eos_indices[-1]
-
-        # Check that only the last EOS token is labeled
-        for idx in eos_indices[:-1]:
-            assert (
-                labels[idx] == IGNORE_TOKEN_ID
-            ), f"Expected EOS token at index {idx} to not be labeled"
-        assert (
-            labels[last_eos_idx] != IGNORE_TOKEN_ID
-        ), f"Expected last EOS token at index {last_eos_idx} to be labeled"
-
-    def test_train_on_eos_none(self, llama3_tokenizer, basic_dataset):
-        LOG.info("Testing with train_on_eos='none'")
-        strategy = ChatTemplateStrategy(
-            ChatTemplatePrompter(llama3_tokenizer, chat_templates("llama3")),
-            tokenizer=llama3_tokenizer,
-            train_on_inputs=False,
-            sequence_len=512,
-            roles_to_train=["assistant"],
-            train_on_eos="none",
-        )
-        res = strategy.tokenize_prompt(basic_dataset[0])
-        labels = res["labels"]
-        input_ids = res["input_ids"]
-
-        eos_token_id = llama3_tokenizer.eos_token_id
-        eos_indices = [
-            i for i, token_id in enumerate(input_ids) if token_id == eos_token_id
-        ]
-
-        assert len(eos_indices) > 0, "Expected at least one EOS token in the input"
-        for eos_idx in eos_indices:
-            assert (
-                labels[eos_idx] == IGNORE_TOKEN_ID
-            ), f"Expected EOS token at index {eos_idx} to not be labeled"
-
-    def test_drop_system_message(self, llama3_tokenizer, basic_dataset):
-        LOG.info("Testing with drop_system_message=True")
-        strategy = ChatTemplateStrategy(
-            ChatTemplatePrompter(
-                llama3_tokenizer, chat_templates("llama3"), drop_system_message=True
-            ),
-            tokenizer=llama3_tokenizer,
-            train_on_inputs=False,
-            sequence_len=512,
-            roles_to_train=["assistant"],
-        )
-        res = strategy.tokenize_prompt(basic_dataset[0])
-        input_ids = res["input_ids"]
-
-        # Check if system message is not present in input_ids
-        system_message = "You are an AI assistant."
-        system_ids = llama3_tokenizer.encode(system_message, add_special_tokens=False)
-        assert (
-            self.find_sublist(input_ids, system_ids) == -1
-        ), "Expected system message to be dropped"
-
-    def test_custom_roles(self, llama3_tokenizer):
-        LOG.info("Testing with custom roles mapping")
-        custom_roles = {
-            "user": ["human", "user"],
-            "assistant": ["ai", "assistant"],
-            "system": ["context"],
-        }
-        strategy = ChatTemplateStrategy(
-            ChatTemplatePrompter(
-                llama3_tokenizer, chat_templates("llama3"), roles=custom_roles
-            ),
-            tokenizer=llama3_tokenizer,
-            train_on_inputs=False,
-            sequence_len=512,
-            roles_to_train=["ai"],
-        )
-
-        # Create a new dataset with modified role names
-        modified_conversations = [
-            {"from": "context", "value": "You are an AI assistant."},
-            {"from": "human", "value": "Hello"},
-            {"from": "ai", "value": "Hi there!"},
-            {"from": "human", "value": "How are you?"},
-            {"from": "ai", "value": "I'm doing well, thank you!"},
-        ]
-
-        modified_dataset = Dataset.from_dict(
-            {"conversations": [modified_conversations]}
-        )
-
-        res = strategy.tokenize_prompt(modified_dataset[0])
-        labels = res["labels"]
-        input_ids = res["input_ids"]
-
-        # Check if AI responses are labeled correctly
-        ai_responses = ["Hi there!", "I'm doing well, thank you!"]
-        for response in ai_responses:
-            response_ids = llama3_tokenizer.encode(response, add_special_tokens=False)
-            start_idx = self.find_sublist(input_ids, response_ids)
-            assert start_idx != -1, f"Could not find response '{response}' in input_ids"
-            assert all(
-                label != IGNORE_TOKEN_ID
-                for label in labels[start_idx : start_idx + len(response_ids)]
-            ), f"Expected labels for AI response '{response}' to be set"
-
-        # Check if human messages are not labeled
-        human_messages = ["Hello", "How are you?"]
-        for message in human_messages:
-            message_ids = llama3_tokenizer.encode(message, add_special_tokens=False)
-            start_idx = self.find_sublist(input_ids, message_ids)
-            assert start_idx != -1, f"Could not find message '{message}' in input_ids"
-            assert all(
-                label == IGNORE_TOKEN_ID
-                for label in labels[start_idx : start_idx + len(message_ids)]
-            ), f"Expected labels for human message '{message}' to be IGNORE_TOKEN_ID"
-
-    def test_message_field_training(self, llama3_tokenizer):
-        LOG.info("Testing with message_field_training")
-        strategy = ChatTemplateStrategy(
-            ChatTemplatePrompter(
-                llama3_tokenizer,
-                chat_templates("llama3"),
-                message_field_training="train",
-                message_field_training_detail="train_detail",
-            ),
-            tokenizer=llama3_tokenizer,
-            train_on_inputs=False,
-            sequence_len=512,
-            roles_to_train=[],
-        )
-
-        # Create a new dataset with the train and train_detail fields
-        modified_conversation = [
-            {"from": "system", "value": "You are an AI assistant.", "train": False},
-            {"from": "human", "value": "Hello", "train": False},
-            {"from": "assistant", "value": "Hello", "train": True},
-            {"from": "human", "value": "How are you?", "train": True},
-            {
-                "from": "assistant",
-                "value": "I'm doing very well, thank you!",
-                "train_detail": [
-                    {"begin_offset": 0, "end_offset": 8, "train": False},
-                    {"begin_offset": 9, "end_offset": 18, "train": True},
-                    {"begin_offset": 19, "end_offset": 30, "train": False},
-                ],
-            },
-            {
-                "from": "human",
-                "value": "I'm doing very well, thank you!",
-                "train": False,
-            },
-            {"from": "assistant", "value": "Hi there!", "train": True},
-        ]
-
-        modified_dataset = Dataset.from_dict({"conversations": [modified_conversation]})
-
-        res = strategy.tokenize_prompt(modified_dataset[0])
-        labels = res["labels"]
-        input_ids = res["input_ids"]
-
-        # Function to find all occurrences of a sublist
-        def find_all_sublists(full_list, sub_list):
-            indices = []
-            for index in range(len(full_list) - len(sub_list) + 1):
-                if full_list[index : index + len(sub_list)] == sub_list:
-                    indices.append(index)
-            return indices
-
-        # Keep track of which occurrences we've processed
-        processed_occurrences = {}
-        # Check if messages are labeled correctly based on train or train_detail
-        for i, turn in enumerate(modified_conversation):
-            turn_tokens = llama3_tokenizer.encode(
-                turn["value"], add_special_tokens=False
-            )
-            occurrences = find_all_sublists(input_ids, turn_tokens)
-            turn_key = turn["value"]
-            if turn_key not in processed_occurrences:
-                processed_occurrences[turn_key] = 0
-            current_occurrence = processed_occurrences[turn_key]
-
-            if current_occurrence >= len(occurrences):
-                assert (
-                    False
-                ), f"Not enough occurrences found for message: {turn['value']}"
-
-            start_idx = occurrences[current_occurrence]
-            processed_occurrences[turn_key] += 1
-            end_idx = start_idx + len(turn_tokens)
-
-            LOG.debug(
-                f"Processing turn {i}: role={turn['from']}, content='{turn['value']}', start_idx={start_idx}, end_idx={end_idx}"
-            )
-
-            if "train_detail" in turn:
-                # Get token offsets
-                tokenized_output = llama3_tokenizer(
-                    turn["value"], return_offsets_mapping=True, add_special_tokens=False
-                )
-                token_offsets = tokenized_output["offset_mapping"]
-
-                # Adjust token offsets as done in the implementation
-                for i in range(len(token_offsets) - 1):
-                    token_offsets[i] = (
-                        token_offsets[i][0],
-                        token_offsets[i + 1][0] - 1,
-                    )
-                token_offsets[-1] = (token_offsets[-1][0], len(turn["value"]) - 1)
-
-                # Adjust train_details
-                adjusted_train_details = strategy.prompter.adjust_train_details(
-                    turn["train_detail"], token_offsets
-                )
-
-                LOG.debug(f"Original train_details: {turn['train_detail']}")
-                LOG.debug(f"Adjusted train_details: {adjusted_train_details}")
-
-                # Handle train_detail
-                token_offsets = strategy.prompter.get_offsets_for_train_detail(
-                    text=turn["value"],
-                    train_details=adjusted_train_details,
-                    mask_untrainable=False,
-                )
-                token_offsets_masked = strategy.prompter.get_offsets_for_train_detail(
-                    text=turn["value"],
-                    train_details=adjusted_train_details,
-                    mask_untrainable=True,
-                )
-                LOG.debug(f"Token offsets: {token_offsets_masked}")
-
-                expected_labels = [IGNORE_TOKEN_ID] * len(turn_tokens)
-                for i, offset in enumerate(token_offsets_masked):
-                    if offset != IGNORE_TOKEN_ID:
-                        expected_labels[i] = turn_tokens[i]
-                actual_labels = labels[
-                    start_idx : start_idx + len(token_offsets_masked)
-                ]
-                assert (
-                    actual_labels == expected_labels
-                ), f"Labels mismatch for turn: {turn['value']}\nExpected: {expected_labels}\nActual: {actual_labels}"
-
-                for detail in adjusted_train_details:
-                    # Find the token indices that correspond to the character offsets
-                    detail_start = start_idx + next(
-                        i
-                        for i, offset in enumerate(token_offsets)
-                        if offset >= detail["begin_offset"]
-                    )
-                    detail_end = start_idx + next(
-                        (
-                            i
-                            for i, offset in enumerate(token_offsets)
-                            if offset > detail["end_offset"]
-                        ),
-                        len(token_offsets),
-                    )
-
-                    detail_text = turn["value"][
-                        detail["begin_offset"] : detail["end_offset"] + 1
-                    ]
-                    detail_labels = labels[detail_start:detail_end]
-                    detail_input_ids = input_ids[detail_start:detail_end]
-
-                    LOG.debug(
-                        f"Detail: '{detail_text}', Start: {detail_start}, End: {detail_end}"
-                    )
-                    LOG.debug(f"Detail input_ids: {detail_input_ids}")
-                    LOG.debug(f"Detail labels: {detail_labels}")
-                    LOG.debug(
-                        f"Decoded detail: {llama3_tokenizer.decode(detail_input_ids)}"
-                    )
-                    LOG.debug(
-                        f"Token offsets for this detail: {token_offsets[detail_start-start_idx:detail_end-start_idx]}"
-                    )
-
-                    if detail["train"]:
-                        assert all(
-                            label != IGNORE_TOKEN_ID for label in detail_labels
-                        ), (
-                            f"Expected labels for trainable detail '{detail_text}' to be set, but some were IGNORE_TOKEN_ID. "
-                            f"Labels({detail_start}:{detail_end}): {detail_labels}, "
-                            f"InputIDs: {detail_input_ids}, "
-                            f"Decoded: '{llama3_tokenizer.decode(detail_input_ids)}'"
-                        )
-                    else:
-                        assert all(
-                            label == IGNORE_TOKEN_ID for label in detail_labels
-                        ), (
-                            f"Expected all labels for non-trainable detail '{detail_text}' to be IGNORE_TOKEN_ID, but some were not. "
-                            f"Labels({detail_start}:{detail_end}): {detail_labels}, "
-                            f"InputIDs: {detail_input_ids}, "
-                            f"Decoded: '{llama3_tokenizer.decode(detail_input_ids)}'"
-                        )
-            else:
-                should_train = turn.get("train", False)
-                turn_labels = labels[start_idx:end_idx]
-
-                LOG.debug(f"Should train: {should_train}")
-                LOG.debug(f"Turn indices: start={start_idx}, end={end_idx}")
-                LOG.debug(f"Turn labels: {turn_labels}")
-                LOG.debug(f"Turn input IDs: {input_ids[start_idx:end_idx]}")
-                LOG.debug(
-                    f"Decoded turn: {llama3_tokenizer.decode(input_ids[start_idx:end_idx])}"
-                )
-
-                if should_train:
-                    assert all(label != IGNORE_TOKEN_ID for label in turn_labels), (
-                        f"Expected all labels for '{turn['value']}' to be set\n"
-                        f"Labels({start_idx}:{end_idx}): {turn_labels}, "
-                        f"InputIDs: {input_ids[start_idx:end_idx]}, "
-                        f"Decoded: '{llama3_tokenizer.decode(input_ids[start_idx:end_idx])}'"
-                    )
-                else:
-                    assert all(label == IGNORE_TOKEN_ID for label in turn_labels), (
-                        f"Expected all labels for '{turn['value']}' to be IGNORE_TOKEN_ID\n"
-                        f"Labels({start_idx}:{end_idx}): {turn_labels}, "
-                        f"InputIDs: {input_ids[start_idx:end_idx]}, "
-                        f"Decoded: '{llama3_tokenizer.decode(input_ids[start_idx:end_idx])}'"
-                    )
-
-                LOG.debug(
-                    f"Processed turn: {turn['from']}, content: '{turn['value']}', "
-                    f"start_idx: {start_idx}, end_idx: {end_idx}, "
-                    f"labels: {labels[start_idx:end_idx]}"
-                )
-
-        LOG.debug(f"Final labels: {labels}")
-        LOG.debug(f"Final input_ids: {input_ids}")
-
-
 class TestAssistantChatTemplateLlama3:
     """
     Test class for assistant style datasets with llama-3 prompts using the chat_template strategy.
@@ -740,7 +85,6 @@ class TestAssistantChatTemplateLlama3:
             tokenizer=llama3_tokenizer,
             train_on_inputs=False,
             sequence_len=512,
-            roles_to_train=["assistant"],
         )
         strategy.messages = "messages"
         res = strategy.tokenize_prompt(assistant_dataset[0])
@@ -764,6 +108,64 @@ class TestAssistantChatTemplateLlama3:
             input_ids == expected_input_ids
         ), f"Input IDs mismatch: {input_ids} != {expected_input_ids}"
 
+    def test_phi35(self, phi35_tokenizer, assistant_dataset):
+        LOG.info("Testing phi-3.5 with assistant dataset")
+        strategy = ChatTemplateStrategy(
+            ChatTemplatePrompter(
+                phi35_tokenizer,
+                chat_templates("phi_35"),
+                message_field_role="role",
+                message_field_content="content",
+                roles={
+                    "user": ["user"],
+                    "assistant": ["assistant"],
+                    "system": ["system"],
+                },
+            ),
+            tokenizer=phi35_tokenizer,
+            train_on_inputs=False,
+            sequence_len=512,
+        )
+        strategy.messages = "messages"
+        res = strategy.tokenize_prompt(assistant_dataset[0])
+        input_ids = res["input_ids"]
+        labels = res["labels"]
+        # fmt: off
+        expected_input_ids = [
+            32010,  # user
+            22172, 32007,  # user eot
+            32001,  # assistant
+            22172, 32007,  # assistant eot
+            32010,  # user
+            1781, 26966, 32007,  # user eot
+            32001,  # assistant
+            1781, 26966, 32007,  # assistant eot
+            32000,  # eos
+        ]
+        expected_labels = [
+            -100,  # user
+            -100, -100,  # user eot
+            -100,  # assistant
+            -100, -100,  # assistant eot,
+            -100,  # user
+            -100, -100, -100,  # user eot
+            -100,  # assistant
+            1781, 26966, 32007,  # assistant eot
+            32000,  # eos
+        ]
+        # fmt: on
+        LOG.debug(f"Expected input_ids: {expected_input_ids}")
+        LOG.debug(f"Actual input_ids: {input_ids}")
+        assert (
+            input_ids == expected_input_ids
+        ), f"Input IDs mismatch: {input_ids} != {expected_input_ids}"
+
+        LOG.debug(f"Expected labels : {expected_labels}")
+        LOG.debug(f"Actual labels : {labels}")
+        assert (
+            labels == expected_labels
+        ), f"Input IDs mismatch: {labels} != {expected_labels}"
+
     def test_llama3_with_training_data(self, llama3_tokenizer, assistant_dataset):
         LOG.info("Testing llama-3 with assistant dataset including training data")
         strategy = ChatTemplateStrategy(

tests/prompt_strategies/test_chat_templates_advanced.py

@@ -0,0 +1,615 @@
+"""
+tests for chat_template prompt strategy
+"""
+
+import logging
+import unittest
+
+from datasets import Dataset
+
+from axolotl.prompt_strategies.chat_template import (
+    ChatTemplatePrompter,
+    ChatTemplateStrategy,
+)
+from axolotl.prompters import IGNORE_TOKEN_ID
+from axolotl.utils.chat_templates import chat_templates
+
+logging.basicConfig(level=logging.DEBUG)
+LOG = logging.getLogger("axolotl")
+
+
+class TestChatTemplateConfigurations:
+    """
+    Test class for various configurations of ChatTemplateStrategy.
+    """
+
+    @staticmethod
+    def find_sublist(full_list, sub_list):
+        token_count = len(sub_list)
+        for index in range(len(full_list) - token_count + 1):
+            if full_list[index : index + token_count] == sub_list:
+                return index
+        return -1
+
+    def test_train_on_inputs_true(self, llama3_tokenizer, basic_dataset):
+        LOG.info("Testing with train_on_inputs=True")
+        strategy = ChatTemplateStrategy(
+            ChatTemplatePrompter(llama3_tokenizer, chat_templates("llama3")),
+            tokenizer=llama3_tokenizer,
+            train_on_inputs=True,
+            sequence_len=512,
+            roles_to_train=["assistant"],
+        )
+        res = strategy.tokenize_prompt(basic_dataset[0])
+        labels = res["labels"]
+        input_ids = res["input_ids"]
+
+        # Verify that assistant responses are labeled
+        assistant_responses = ["Hi there!", "I'm doing well, thank you!"]
+        for response in assistant_responses:
+            response_ids = llama3_tokenizer.encode(response, add_special_tokens=False)
+            start_idx = self.find_sublist(input_ids, response_ids)
+            LOG.debug(
+                f"Assistant response '{response}' expected IDs: {response_ids}, found at: {start_idx}"
+            )
+            assert start_idx != -1, f"Could not find '{response}' in input_ids"
+            assert all(
+                label != IGNORE_TOKEN_ID
+                for label in labels[start_idx : start_idx + len(response_ids)]
+            ), f"Expected labels for assistant response '{response}' to be set, but got {labels[start_idx:start_idx+len(response_ids)]}"
+
+        # Check the behavior of human inputs
+        human_inputs = ["Hello", "How are you?"]
+        for input_text in human_inputs:
+            input_ids = llama3_tokenizer.encode(input_text, add_special_tokens=False)
+            start_idx = self.find_sublist(input_ids, input_ids)
+            labeled = all(
+                label != IGNORE_TOKEN_ID
+                for label in labels[start_idx : start_idx + len(input_ids)]
+            )
+            LOG.debug(
+                f"Human input '{input_text}' is {'labeled' if labeled else 'not labeled'}, expected IDs: {input_ids}, found at: {start_idx}"
+            )
+
+        LOG.debug("Full labels: %s", labels)
+        LOG.debug("Full input_ids: %s", input_ids)
+
+    def test_train_on_inputs_false(self, llama3_tokenizer, basic_dataset):
+        LOG.info("Testing with train_on_inputs=False")
+        strategy = ChatTemplateStrategy(
+            ChatTemplatePrompter(llama3_tokenizer, chat_templates("llama3")),
+            tokenizer=llama3_tokenizer,
+            train_on_inputs=False,
+            sequence_len=512,
+            roles_to_train=["assistant"],
+        )
+        res = strategy.tokenize_prompt(basic_dataset[0])
+        labels = res["labels"]
+        input_ids = res["input_ids"]
+
+        # Verify that only assistant responses are labeled
+        assistant_responses = ["Hi there!", "I'm doing well, thank you!"]
+        for response in assistant_responses:
+            response_ids = llama3_tokenizer.encode(response, add_special_tokens=False)
+            start_idx = self.find_sublist(input_ids, response_ids)
+            LOG.debug(
+                f"Assistant response '{response}' expected IDs: {response_ids}, found at: {start_idx}"
+            )
+            assert start_idx != -1, f"Could not find '{response}' in input_ids"
+            assert all(
+                label != IGNORE_TOKEN_ID
+                for label in labels[start_idx : start_idx + len(response_ids)]
+            ), f"Expected labels for assistant response '{response}' to be set, but got {labels[start_idx:start_idx+len(response_ids)]}"
+
+        # Verify that human inputs are not labeled
+        human_inputs = ["Hello", "How are you?"]
+        for input_text in human_inputs:
+            input_ids = llama3_tokenizer.encode(input_text, add_special_tokens=False)
+            start_idx = self.find_sublist(input_ids, input_ids)
+            LOG.debug(
+                f"Human input '{input_text}' expected IDs: {input_ids}, found at: {start_idx}"
+            )
+            assert start_idx != -1, f"Could not find '{input_text}' in input_ids"
+            assert all(
+                label == IGNORE_TOKEN_ID
+                for label in labels[start_idx : start_idx + len(input_ids)]
+            ), f"Expected labels for human input '{input_text}' to be IGNORE_TOKEN_ID, but got {labels[start_idx:start_idx+len(input_ids)]}"
+
+    def test_roles_to_train_assistant_only(self, llama3_tokenizer, basic_dataset):
+        LOG.info("Testing roles_to_train with assistant only")
+        strategy = ChatTemplateStrategy(
+            ChatTemplatePrompter(llama3_tokenizer, chat_templates("llama3")),
+            tokenizer=llama3_tokenizer,
+            train_on_inputs=False,
+            sequence_len=512,
+            roles_to_train=["assistant"],
+        )
+        res = strategy.tokenize_prompt(basic_dataset[0])
+        labels = res["labels"]
+        input_ids = res["input_ids"]
+
+        # Verify that only assistant responses are labeled
+        assistant_responses = ["Hi there!", "I'm doing well, thank you!"]
+        for response in assistant_responses:
+            response_ids = llama3_tokenizer.encode(response, add_special_tokens=False)
+            start_idx = self.find_sublist(input_ids, response_ids)
+            LOG.debug(
+                f"Assistant response '{response}' expected IDs: {response_ids}, found at: {start_idx}"
+            )
+            assert all(
+                label != IGNORE_TOKEN_ID
+                for label in labels[start_idx : start_idx + len(response_ids)]
+            ), f"Expected labels for assistant response '{response}' to be set, but got {labels[start_idx:start_idx+len(response_ids)]}"
+
+    def test_roles_to_train_all(self, llama3_tokenizer, basic_dataset):
+        LOG.info("Testing roles_to_train with all roles")
+        strategy = ChatTemplateStrategy(
+            ChatTemplatePrompter(llama3_tokenizer, chat_templates("llama3")),
+            tokenizer=llama3_tokenizer,
+            train_on_inputs=True,
+            sequence_len=512,
+            roles_to_train=["human", "assistant"],
+        )
+        res = strategy.tokenize_prompt(basic_dataset[0])
+        labels = res["labels"]
+        input_ids = res["input_ids"]
+
+        # Verify that all responses are labeled (except for special tokens)
+        all_responses = [
+            "Hello",
+            "Hi there!",
+            "How are you?",
+            "I'm doing well, thank you!",
+        ]
+        for response in all_responses:
+            response_ids = llama3_tokenizer.encode(response, add_special_tokens=False)
+            start_idx = self.find_sublist(input_ids, response_ids)
+            LOG.debug(
+                f"Response '{response}' expected IDs: {response_ids}, found at: {start_idx}"
+            )
+            assert all(
+                label != IGNORE_TOKEN_ID
+                for label in labels[start_idx : start_idx + len(response_ids)]
+            ), f"Expected labels for response '{response}' to be set, but got {labels[start_idx:start_idx+len(response_ids)]}"
+
+    def test_empty_roles_to_train(self, llama3_tokenizer, basic_dataset):
+        LOG.info("Testing with empty roles_to_train")
+        strategy = ChatTemplateStrategy(
+            ChatTemplatePrompter(llama3_tokenizer, chat_templates("llama3")),
+            tokenizer=llama3_tokenizer,
+            train_on_inputs=False,
+            sequence_len=512,
+            roles_to_train=[],
+            train_on_eos="none",  # Add this line
+        )
+        res = strategy.tokenize_prompt(basic_dataset[0])
+        labels = res["labels"]
+
+        # Verify that no labels are set when roles_to_train is empty
+        LOG.debug("Full labels: %s", labels)
+        assert all(
+            label == IGNORE_TOKEN_ID for label in labels
+        ), "Expected all labels to be IGNORE_TOKEN_ID when roles_to_train is empty"
+
+    def test_train_on_eos_all(self, llama3_tokenizer, basic_dataset):
+        LOG.info("Testing with train_on_eos='all'")
+        strategy = ChatTemplateStrategy(
+            ChatTemplatePrompter(llama3_tokenizer, chat_templates("llama3")),
+            tokenizer=llama3_tokenizer,
+            train_on_inputs=False,
+            sequence_len=512,
+            roles_to_train=["assistant"],
+            train_on_eos="all",
+        )
+        res = strategy.tokenize_prompt(basic_dataset[0])
+        labels = res["labels"]
+        input_ids = res["input_ids"]
+
+        eos_token_id = llama3_tokenizer.eos_token_id
+        eos_indices = [
+            i for i, token_id in enumerate(input_ids) if token_id == eos_token_id
+        ]
+
+        assert len(eos_indices) > 0, "Expected at least one EOS token in the input"
+        for eos_idx in eos_indices:
+            assert (
+                labels[eos_idx] != IGNORE_TOKEN_ID
+            ), f"Expected EOS token at index {eos_idx} to be labeled"
+
+    def test_train_on_eos_turn(self, llama3_tokenizer, basic_dataset):
+        LOG.info("Testing with train_on_eos='turn'")
+        strategy = ChatTemplateStrategy(
+            ChatTemplatePrompter(llama3_tokenizer, chat_templates("llama3")),
+            tokenizer=llama3_tokenizer,
+            train_on_inputs=False,
+            sequence_len=512,
+            roles_to_train=["assistant"],
+            train_on_eos="turn",
+        )
+        res = strategy.tokenize_prompt(basic_dataset[0])
+        labels = res["labels"]
+        input_ids = res["input_ids"]
+
+        eos_token_id = llama3_tokenizer.eos_token_id
+        assistant_responses = ["Hi there!", "I'm doing well, thank you!"]
+
+        for response in assistant_responses:
+            response_ids = llama3_tokenizer.encode(response, add_special_tokens=False)
+            start_idx = self.find_sublist(input_ids, response_ids)
+            assert start_idx != -1, f"Could not find '{response}' in input_ids"
+
+            eos_idx = start_idx + len(response_ids)
+            while eos_idx < len(input_ids) and input_ids[eos_idx] != eos_token_id:
+                eos_idx += 1
+
+            assert eos_idx < len(
+                input_ids
+            ), f"Could not find EOS token after '{response}'"
+            assert (
+                labels[eos_idx] != IGNORE_TOKEN_ID
+            ), f"Expected EOS token after assistant response '{response}' to be labeled"
+
+        # Check that EOS tokens after human inputs are not labeled
+        human_inputs = ["Hello", "How are you?"]
+        for input_text in human_inputs:
+            input_ids = llama3_tokenizer.encode(input_text, add_special_tokens=False)
+            start_idx = self.find_sublist(input_ids, input_ids)
+            assert start_idx != -1, f"Could not find '{input_text}' in input_ids"
+
+            eos_idx = start_idx + len(input_ids)
+            while eos_idx < len(input_ids) and input_ids[eos_idx] != eos_token_id:
+                eos_idx += 1
+
+            assert (
+                labels[eos_idx] == IGNORE_TOKEN_ID
+            ), f"Expected EOS token after human input '{input_text}' to not be labeled"
+
+    def test_train_on_eos_last(self, llama3_tokenizer, basic_dataset):
+        LOG.info("Testing with train_on_eos='last'")
+        strategy = ChatTemplateStrategy(
+            ChatTemplatePrompter(llama3_tokenizer, chat_templates("llama3")),
+            tokenizer=llama3_tokenizer,
+            train_on_inputs=False,
+            sequence_len=512,
+            roles_to_train=["assistant"],
+            train_on_eos="last",
+        )
+        res = strategy.tokenize_prompt(basic_dataset[0])
+        labels = res["labels"]
+        input_ids = res["input_ids"]
+
+        eos_token_id = llama3_tokenizer.eos_token_id
+        eos_indices = [
+            i for i, token_id in enumerate(input_ids) if token_id == eos_token_id
+        ]
+
+        assert len(eos_indices) > 0, "Expected at least one EOS token in the input"
+        last_eos_idx = eos_indices[-1]
+
+        # Check that only the last EOS token is labeled
+        for idx in eos_indices[:-1]:
+            assert (
+                labels[idx] == IGNORE_TOKEN_ID
+            ), f"Expected EOS token at index {idx} to not be labeled"
+        assert (
+            labels[last_eos_idx] != IGNORE_TOKEN_ID
+        ), f"Expected last EOS token at index {last_eos_idx} to be labeled"
+
+    def test_train_on_eos_none(self, llama3_tokenizer, basic_dataset):
+        LOG.info("Testing with train_on_eos='none'")
+        strategy = ChatTemplateStrategy(
+            ChatTemplatePrompter(llama3_tokenizer, chat_templates("llama3")),
+            tokenizer=llama3_tokenizer,
+            train_on_inputs=False,
+            sequence_len=512,
+            roles_to_train=["assistant"],
+            train_on_eos="none",
+        )
+        res = strategy.tokenize_prompt(basic_dataset[0])
+        labels = res["labels"]
+        input_ids = res["input_ids"]
+
+        eos_token_id = llama3_tokenizer.eos_token_id
+        eos_indices = [
+            i for i, token_id in enumerate(input_ids) if token_id == eos_token_id
+        ]
+
+        assert len(eos_indices) > 0, "Expected at least one EOS token in the input"
+        for eos_idx in eos_indices:
+            assert (
+                labels[eos_idx] == IGNORE_TOKEN_ID
+            ), f"Expected EOS token at index {eos_idx} to not be labeled"
+
+    def test_drop_system_message(self, llama3_tokenizer, basic_dataset):
+        LOG.info("Testing with drop_system_message=True")
+        strategy = ChatTemplateStrategy(
+            ChatTemplatePrompter(
+                llama3_tokenizer, chat_templates("llama3"), drop_system_message=True
+            ),
+            tokenizer=llama3_tokenizer,
+            train_on_inputs=False,
+            sequence_len=512,
+            roles_to_train=["assistant"],
+        )
+        res = strategy.tokenize_prompt(basic_dataset[0])
+        input_ids = res["input_ids"]
+
+        # Check if system message is not present in input_ids
+        system_message = "You are an AI assistant."
+        system_ids = llama3_tokenizer.encode(system_message, add_special_tokens=False)
+        assert (
+            self.find_sublist(input_ids, system_ids) == -1
+        ), "Expected system message to be dropped"
+
+    def test_custom_roles(self, llama3_tokenizer):
+        LOG.info("Testing with custom roles mapping")
+        custom_roles = {
+            "user": ["human", "user"],
+            "assistant": ["ai", "assistant"],
+            "system": ["context"],
+        }
+        strategy = ChatTemplateStrategy(
+            ChatTemplatePrompter(
+                llama3_tokenizer, chat_templates("llama3"), roles=custom_roles
+            ),
+            tokenizer=llama3_tokenizer,
+            train_on_inputs=False,
+            sequence_len=512,
+            roles_to_train=["ai"],
+        )
+
+        # Create a new dataset with modified role names
+        modified_conversations = [
+            {"from": "context", "value": "You are an AI assistant."},
+            {"from": "human", "value": "Hello"},
+            {"from": "ai", "value": "Hi there!"},
+            {"from": "human", "value": "How are you?"},
+            {"from": "ai", "value": "I'm doing well, thank you!"},
+        ]
+
+        modified_dataset = Dataset.from_dict(
+            {"conversations": [modified_conversations]}
+        )
+
+        res = strategy.tokenize_prompt(modified_dataset[0])
+        labels = res["labels"]
+        input_ids = res["input_ids"]
+
+        # Check if AI responses are labeled correctly
+        ai_responses = ["Hi there!", "I'm doing well, thank you!"]
+        for response in ai_responses:
+            response_ids = llama3_tokenizer.encode(response, add_special_tokens=False)
+            start_idx = self.find_sublist(input_ids, response_ids)
+            assert start_idx != -1, f"Could not find response '{response}' in input_ids"
+            assert all(
+                label != IGNORE_TOKEN_ID
+                for label in labels[start_idx : start_idx + len(response_ids)]
+            ), f"Expected labels for AI response '{response}' to be set"
+
+        # Check if human messages are not labeled
+        human_messages = ["Hello", "How are you?"]
+        for message in human_messages:
+            message_ids = llama3_tokenizer.encode(message, add_special_tokens=False)
+            start_idx = self.find_sublist(input_ids, message_ids)
+            assert start_idx != -1, f"Could not find message '{message}' in input_ids"
+            assert all(
+                label == IGNORE_TOKEN_ID
+                for label in labels[start_idx : start_idx + len(message_ids)]
+            ), f"Expected labels for human message '{message}' to be IGNORE_TOKEN_ID"
+
+    def test_message_field_training(self, llama3_tokenizer):
+        LOG.info("Testing with message_field_training")
+        strategy = ChatTemplateStrategy(
+            ChatTemplatePrompter(
+                llama3_tokenizer,
+                chat_templates("llama3"),
+                message_field_training="train",
+                message_field_training_detail="train_detail",
+            ),
+            tokenizer=llama3_tokenizer,
+            train_on_inputs=False,
+            sequence_len=512,
+            roles_to_train=[],
+        )
+
+        # Create a new dataset with the train and train_detail fields
+        modified_conversation = [
+            {"from": "system", "value": "You are an AI assistant.", "train": False},
+            {"from": "human", "value": "Hello", "train": False},
+            {"from": "assistant", "value": "Hello", "train": True},
+            {"from": "human", "value": "How are you?", "train": True},
+            {
+                "from": "assistant",
+                "value": "I'm doing very well, thank you!",
+                "train_detail": [
+                    {"begin_offset": 0, "end_offset": 8, "train": False},
+                    {"begin_offset": 9, "end_offset": 18, "train": True},
+                    {"begin_offset": 19, "end_offset": 30, "train": False},
+                ],
+            },
+            {
+                "from": "human",
+                "value": "I'm doing very well, thank you!",
+                "train": False,
+            },
+            {"from": "assistant", "value": "Hi there!", "train": True},
+        ]
+
+        modified_dataset = Dataset.from_dict({"conversations": [modified_conversation]})
+
+        res = strategy.tokenize_prompt(modified_dataset[0])
+        labels = res["labels"]
+        input_ids = res["input_ids"]
+
+        # Function to find all occurrences of a sublist
+        def find_all_sublists(full_list, sub_list):
+            indices = []
+            for index in range(len(full_list) - len(sub_list) + 1):
+                if full_list[index : index + len(sub_list)] == sub_list:
+                    indices.append(index)
+            return indices
+
+        # Keep track of which occurrences we've processed
+        processed_occurrences = {}
+        # Check if messages are labeled correctly based on train or train_detail
+        for i, turn in enumerate(modified_conversation):
+            turn_tokens = llama3_tokenizer.encode(
+                turn["value"], add_special_tokens=False
+            )
+            occurrences = find_all_sublists(input_ids, turn_tokens)
+            turn_key = turn["value"]
+            if turn_key not in processed_occurrences:
+                processed_occurrences[turn_key] = 0
+            current_occurrence = processed_occurrences[turn_key]
+
+            if current_occurrence >= len(occurrences):
+                assert (
+                    False
+                ), f"Not enough occurrences found for message: {turn['value']}"
+
+            start_idx = occurrences[current_occurrence]
+            processed_occurrences[turn_key] += 1
+            end_idx = start_idx + len(turn_tokens)
+
+            LOG.debug(
+                f"Processing turn {i}: role={turn['from']}, content='{turn['value']}', start_idx={start_idx}, end_idx={end_idx}"
+            )
+
+            if "train_detail" in turn:
+                # Get token offsets
+                tokenized_output = llama3_tokenizer(
+                    turn["value"], return_offsets_mapping=True, add_special_tokens=False
+                )
+                token_offsets = tokenized_output["offset_mapping"]
+
+                # Adjust token offsets as done in the implementation
+                for i in range(len(token_offsets) - 1):
+                    token_offsets[i] = (
+                        token_offsets[i][0],
+                        token_offsets[i + 1][0] - 1,
+                    )
+                token_offsets[-1] = (token_offsets[-1][0], len(turn["value"]) - 1)
+
+                # Adjust train_details
+                adjusted_train_details = strategy.prompter.adjust_train_details(
+                    turn["train_detail"], token_offsets
+                )
+
+                LOG.debug(f"Original train_details: {turn['train_detail']}")
+                LOG.debug(f"Adjusted train_details: {adjusted_train_details}")
+
+                # Handle train_detail
+                token_offsets = strategy.prompter.get_offsets_for_train_detail(
+                    text=turn["value"],
+                    train_details=adjusted_train_details,
+                    mask_untrainable=False,
+                )
+                token_offsets_masked = strategy.prompter.get_offsets_for_train_detail(
+                    text=turn["value"],
+                    train_details=adjusted_train_details,
+                    mask_untrainable=True,
+                )
+                LOG.debug(f"Token offsets: {token_offsets_masked}")
+
+                expected_labels = [IGNORE_TOKEN_ID] * len(turn_tokens)
+                for i, offset in enumerate(token_offsets_masked):
+                    if offset != IGNORE_TOKEN_ID:
+                        expected_labels[i] = turn_tokens[i]
+                actual_labels = labels[
+                    start_idx : start_idx + len(token_offsets_masked)
+                ]
+                assert (
+                    actual_labels == expected_labels
+                ), f"Labels mismatch for turn: {turn['value']}\nExpected: {expected_labels}\nActual: {actual_labels}"
+
+                for detail in adjusted_train_details:
+                    # Find the token indices that correspond to the character offsets
+                    detail_start = start_idx + next(
+                        i
+                        for i, offset in enumerate(token_offsets)
+                        if offset >= detail["begin_offset"]
+                    )
+                    detail_end = start_idx + next(
+                        (
+                            i
+                            for i, offset in enumerate(token_offsets)
+                            if offset > detail["end_offset"]
+                        ),
+                        len(token_offsets),
+                    )
+
+                    detail_text = turn["value"][
+                        detail["begin_offset"] : detail["end_offset"] + 1
+                    ]
+                    detail_labels = labels[detail_start:detail_end]
+                    detail_input_ids = input_ids[detail_start:detail_end]
+
+                    LOG.debug(
+                        f"Detail: '{detail_text}', Start: {detail_start}, End: {detail_end}"
+                    )
+                    LOG.debug(f"Detail input_ids: {detail_input_ids}")
+                    LOG.debug(f"Detail labels: {detail_labels}")
+                    LOG.debug(
+                        f"Decoded detail: {llama3_tokenizer.decode(detail_input_ids)}"
+                    )
+                    LOG.debug(
+                        f"Token offsets for this detail: {token_offsets[detail_start-start_idx:detail_end-start_idx]}"
+                    )
+
+                    if detail["train"]:
+                        assert all(
+                            label != IGNORE_TOKEN_ID for label in detail_labels
+                        ), (
+                            f"Expected labels for trainable detail '{detail_text}' to be set, but some were IGNORE_TOKEN_ID. "
+                            f"Labels({detail_start}:{detail_end}): {detail_labels}, "
+                            f"InputIDs: {detail_input_ids}, "
+                            f"Decoded: '{llama3_tokenizer.decode(detail_input_ids)}'"
+                        )
+                    else:
+                        assert all(
+                            label == IGNORE_TOKEN_ID for label in detail_labels
+                        ), (
+                            f"Expected all labels for non-trainable detail '{detail_text}' to be IGNORE_TOKEN_ID, but some were not. "
+                            f"Labels({detail_start}:{detail_end}): {detail_labels}, "
+                            f"InputIDs: {detail_input_ids}, "
+                            f"Decoded: '{llama3_tokenizer.decode(detail_input_ids)}'"
+                        )
+            else:
+                should_train = turn.get("train", False)
+                turn_labels = labels[start_idx:end_idx]
+
+                LOG.debug(f"Should train: {should_train}")
+                LOG.debug(f"Turn indices: start={start_idx}, end={end_idx}")
+                LOG.debug(f"Turn labels: {turn_labels}")
+                LOG.debug(f"Turn input IDs: {input_ids[start_idx:end_idx]}")
+                LOG.debug(
+                    f"Decoded turn: {llama3_tokenizer.decode(input_ids[start_idx:end_idx])}"
+                )
+
+                if should_train:
+                    assert all(label != IGNORE_TOKEN_ID for label in turn_labels), (
+                        f"Expected all labels for '{turn['value']}' to be set\n"
+                        f"Labels({start_idx}:{end_idx}): {turn_labels}, "
+                        f"InputIDs: {input_ids[start_idx:end_idx]}, "
+                        f"Decoded: '{llama3_tokenizer.decode(input_ids[start_idx:end_idx])}'"
+                    )
+                else:
+                    assert all(label == IGNORE_TOKEN_ID for label in turn_labels), (
+                        f"Expected all labels for '{turn['value']}' to be IGNORE_TOKEN_ID\n"
+                        f"Labels({start_idx}:{end_idx}): {turn_labels}, "
+                        f"InputIDs: {input_ids[start_idx:end_idx]}, "
+                        f"Decoded: '{llama3_tokenizer.decode(input_ids[start_idx:end_idx])}'"
+                    )
+
+                LOG.debug(
+                    f"Processed turn: {turn['from']}, content: '{turn['value']}', "
+                    f"start_idx: {start_idx}, end_idx: {end_idx}, "
+                    f"labels: {labels[start_idx:end_idx]}"
+                )
+
+        LOG.debug(f"Final labels: {labels}")
+        LOG.debug(f"Final input_ids: {input_ids}")
+
+
+if __name__ == "__main__":
+    unittest.main()