coderabbit comments

.github/workflows/base.yml

@@ -16,7 +16,6 @@ on:
 jobs:
   build-base:
     if: github.repository_owner == 'axolotl-ai-cloud'
-    timeout-minutes: 480
     # this job needs to be run on self-hosted GPU runners...
     runs-on: ubuntu-latest-m
     strategy:
@@ -48,14 +47,14 @@ jobs:
             cuda_version: 12.6.3
             cudnn_version: ""
             python_version: "3.11"
-            pytorch: 2.7.1
+            pytorch: 2.7.0
             torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
             dockerfile: "Dockerfile-base"
           - cuda: "128"
             cuda_version: 12.6.3
             cudnn_version: ""
             python_version: "3.11"
-            pytorch: 2.7.1
+            pytorch: 2.7.0
             torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
             dockerfile: "Dockerfile-base"
           - cuda: "128"
@@ -107,7 +106,6 @@ jobs:
             TORCH_CUDA_ARCH_LIST=${{ matrix.torch_cuda_arch_list }}
   build-base-uv:
     if: github.repository_owner == 'axolotl-ai-cloud'
-    timeout-minutes: 480
     runs-on: ubuntu-latest-m
     strategy:
       fail-fast: false
@@ -124,7 +122,7 @@ jobs:
             cuda_version: 12.8.1
             cudnn_version: ""
             python_version: "3.11"
-            pytorch: 2.7.1
+            pytorch: 2.7.0
             torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
             dockerfile: "Dockerfile-uv-base"
     steps:

.github/workflows/main.yml

@@ -29,12 +29,12 @@ jobs:
           - cuda: 126
             cuda_version: 12.6.3
             python_version: "3.11"
-            pytorch: 2.7.1
+            pytorch: 2.7.0
             axolotl_extras:
           - cuda: 128
             cuda_version: 12.8.1
             python_version: "3.11"
-            pytorch: 2.7.1
+            pytorch: 2.7.0
             axolotl_extras:
     runs-on: axolotl-gpu-runner
     steps:
@@ -97,12 +97,12 @@ jobs:
           - cuda: 126
             cuda_version: 12.6.3
             python_version: "3.11"
-            pytorch: 2.7.1
+            pytorch: 2.7.0
             axolotl_extras:
           - cuda: 128
             cuda_version: 12.8.1
             python_version: "3.11"
-            pytorch: 2.7.1
+            pytorch: 2.7.0
             axolotl_extras:
     runs-on: axolotl-gpu-runner
     steps:

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

@@ -43,7 +43,7 @@ jobs:
           - cuda: 126
             cuda_version: 12.6.3
             python_version: "3.11"
-            pytorch: 2.7.1
+            pytorch: 2.7.0
             axolotl_extras:
             num_gpus: 2
             nightly_build: "true"

.github/workflows/tests.yml

@@ -52,7 +52,7 @@ jobs:
       fail-fast: false
       matrix:
         python_version: ["3.11"]
-        pytorch_version: ["2.5.1", "2.6.0", "2.7.1"]
+        pytorch_version: ["2.5.1", "2.6.0", "2.7.0"]
     timeout-minutes: 20
 
     steps:
@@ -125,7 +125,7 @@ jobs:
       fail-fast: false
       matrix:
         python_version: ["3.11"]
-        pytorch_version: ["2.5.1", "2.6.0", "2.7.1"]
+        pytorch_version: ["2.5.1", "2.6.0", "2.7.0"]
     timeout-minutes: 20
 
     steps:
@@ -188,7 +188,7 @@ jobs:
     if: ${{ ! contains(github.event.commits[0].message, '[skip e2e]') && github.repository_owner == 'axolotl-ai-cloud' }}
     # this job needs to be run on self-hosted GPU runners...
     runs-on: [self-hosted, modal]
-    timeout-minutes: 120
+    timeout-minutes: 90
     needs: [pre-commit, pytest, pytest-sdist]
 
     strategy:
@@ -238,7 +238,7 @@ jobs:
     if: github.repository_owner == 'axolotl-ai-cloud'
     # this job needs to be run on self-hosted GPU runners...
     runs-on: [self-hosted, modal]
-    timeout-minutes: 120
+    timeout-minutes: 90
     # Only run the remainder of the matrix if the first e2e check passed;
     # this is to save on wasted compute costs for known failures that get caught in the first run
     needs: [pre-commit, pytest, docker-e2e-tests-1st]
@@ -262,13 +262,13 @@ jobs:
           - cuda: 126
             cuda_version: 12.6.3
             python_version: "3.11"
-            pytorch: 2.7.1
+            pytorch: 2.7.0
             num_gpus: 1
             axolotl_extras:
           - cuda: 128
             cuda_version: 12.8.1
             python_version: "3.11"
-            pytorch: 2.7.1
+            pytorch: 2.7.0
             num_gpus: 1
             axolotl_extras:
     steps:

README.md

@@ -22,32 +22,28 @@
     <img src="https://github.com/axolotl-ai-cloud/axolotl/actions/workflows/multi-gpu-e2e.yml/badge.svg" alt="multigpu-semi-weekly tests">
 </p>
 
-
-## 🎉 Latest Updates
-
-- 2025/06: Magistral with mistral-common tokenizer support has been added to Axolotl. See [examples](https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/magistral) to start training your own Magistral models with Axolotl!
-- 2025/05: Quantization Aware Training (QAT) support has been added to Axolotl. Explore the [docs](https://docs.axolotl.ai/docs/qat.html) to learn more!
-- 2025/04: Llama 4 support has been added in Axolotl. See [examples](https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/llama-4) to start training your own Llama 4 models with Axolotl's linearized version!
-- 2025/03: Axolotl has implemented Sequence Parallelism (SP) support. Read the [blog](https://huggingface.co/blog/axolotl-ai-co/long-context-with-sequence-parallelism-in-axolotl) and [docs](https://docs.axolotl.ai/docs/sequence_parallelism.html) to learn how to scale your context length when fine-tuning.
-- 2025/03: (Beta) Fine-tuning Multimodal models is now supported in Axolotl. Check out the [docs](https://docs.axolotl.ai/docs/multimodal.html) to fine-tune your own!
-- 2025/02: Axolotl has added LoRA optimizations to reduce memory usage and improve training speed for LoRA and QLoRA in single GPU and multi-GPU training (DDP and DeepSpeed). Jump into the [docs](https://docs.axolotl.ai/docs/lora_optims.html) to give it a try.
-- 2025/02: Axolotl has added GRPO support. Dive into our [blog](https://huggingface.co/blog/axolotl-ai-co/training-llms-w-interpreter-feedback-wasm) and [GRPO example](https://github.com/axolotl-ai-cloud/grpo_code) and have some fun!
-- 2025/01: Axolotl has added Reward Modelling / Process Reward Modelling fine-tuning support. See [docs](https://docs.axolotl.ai/docs/reward_modelling.html).
-
-## ✨ Overview
-
 Axolotl is a tool designed to streamline post-training for various AI models.
+Post-training refers to any modifications or additional training performed on
+pre-trained models - including full model fine-tuning, parameter-efficient tuning (like
+LoRA and QLoRA), supervised fine-tuning (SFT), instruction tuning, and alignment
+techniques. With support for multiple model architectures and training configurations,
+Axolotl makes it easy to get started with these techniques.
+
+Axolotl is designed to work with YAML config files that contain everything you need to
+preprocess a dataset, train or fine-tune a model, run model inference or evaluation,
+and much more.
 
 Features:
 
-- **Multiple Model Support**: Train various models like LLaMA, Mistral, Mixtral, Pythia, and more. We are compatible with HuggingFace transformers causal language models.
+- Train various Huggingface models such as llama, pythia, falcon, mpt
-- **Training Methods**: Full fine-tuning, LoRA, QLoRA, GPTQ, QAT, Preference Tuning (DPO, IPO, KTO, ORPO), RL (GRPO), Multimodal, and Reward Modelling (RM) / Process Reward Modelling (PRM).
+- Supports fullfinetune, lora, qlora, relora, and gptq
-- **Easy Configuration**: Re-use a single YAML file between dataset preprocess, training, evaluation, quantization, and inference.
+- Customize configurations using a simple yaml file or CLI overwrite
-- **Performance Optimizations**: [Multipacking](https://docs.axolotl.ai/docs/multipack.html), [Flash Attention](https://github.com/Dao-AILab/flash-attention), [Xformers](https://github.com/facebookresearch/xformers), [Flex Attention](https://pytorch.org/blog/flexattention/), [Liger Kernel](https://github.com/linkedin/Liger-Kernel), [Cut Cross Entropy](https://github.com/apple/ml-cross-entropy/tree/main), Sequence Parallelism (SP), LoRA optimizations, Multi-GPU training (FSDP1, FSDP2, DeepSpeed), Multi-node training (Torchrun, Ray), and many more!
+- Load different dataset formats, use custom formats, or bring your own tokenized datasets
-- **Flexible Dataset Handling**: Load from local, HuggingFace, and cloud (S3, Azure, GCP, OCI) datasets.
+- Integrated with [xformers](https://github.com/facebookresearch/xformers), flash attention, [liger kernel](https://github.com/linkedin/Liger-Kernel), rope scaling, and multipacking
-- **Cloud Ready**: We ship [Docker images](https://hub.docker.com/u/axolotlai) and also [PyPI packages](https://pypi.org/project/axolotl/) for use on cloud platforms and local hardware.
+- 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, mlflow or Comet
+- And more!
 
 ## 🚀 Quick Start
 
@@ -85,12 +81,19 @@ axolotl train examples/llama-3/lora-1b.yml
 
 That's it! Check out our [Getting Started Guide](https://docs.axolotl.ai/docs/getting-started.html) for a more detailed walkthrough.
 
+## ✨ Key Features
+
+- **Multiple Model Support**: Train various models like LLaMA, Mistral, Mixtral, Pythia, and more
+- **Training Methods**: Full fine-tuning, LoRA, QLoRA, and more
+- **Easy Configuration**: Simple YAML files to control your training setup
+- **Performance Optimizations**: Flash Attention, xformers, multi-GPU training
+- **Flexible Dataset Handling**: Use various formats and custom datasets
+- **Cloud Ready**: Run on cloud platforms or local hardware
 
 ## 📚 Documentation
 
 - [Installation Options](https://docs.axolotl.ai/docs/installation.html) - Detailed setup instructions for different environments
 - [Configuration Guide](https://docs.axolotl.ai/docs/config.html) - Full configuration options and examples
-- [Dataset Loading](https://docs.axolotl.ai/docs/dataset_loading.html) - Loading datasets from various sources
 - [Dataset Guide](https://docs.axolotl.ai/docs/dataset-formats/) - Supported formats and how to use them
 - [Multi-GPU Training](https://docs.axolotl.ai/docs/multi-gpu.html)
 - [Multi-Node Training](https://docs.axolotl.ai/docs/multi-node.html)
@@ -109,6 +112,38 @@ That's it! Check out our [Getting Started Guide](https://docs.axolotl.ai/docs/ge
 
 Contributions are welcome! Please see our [Contributing Guide](https://github.com/axolotl-ai-cloud/axolotl/blob/main/.github/CONTRIBUTING.md) for details.
 
+## 📈 Telemetry
+
+Axolotl has opt-in telemetry that helps us understand how the project is being used
+and prioritize improvements. We collect basic system information, model types, and
+error rates—never personal data or file paths. Telemetry is disabled by default. To
+enable it, set AXOLOTL_DO_NOT_TRACK=0. For more details, see our [telemetry documentation](https://axolotl-ai-cloud.github.io/axolotl/docs/telemetry.html).
+
+## Supported Models
+
+|             | fp16/fp32 | lora | qlora | gptq | gptq w/flash attn | flash attn | xformers attn |
+|-------------|:----------|:-----|-------|------|-------------------|------------|--------------|
+| llama       | ✅         | ✅    | ✅     | ✅             | ✅                 | ✅          | ✅            |
+| Mistral     | ✅         | ✅    | ✅     | ✅             | ✅                 | ✅          | ✅            |
+| Mixtral-MoE | ✅         | ✅    | ✅     | ❓             | ❓                 | ❓          | ❓            |
+| Mixtral8X22 | ✅         | ✅    | ✅     | ❓             | ❓                 | ❓          | ❓            |
+| Pythia      | ✅         | ✅    | ✅     | ❌             | ❌                 | ❌          | ❓            |
+| cerebras    | ✅         | ✅    | ✅     | ❌             | ❌                 | ❌          | ❓            |
+| btlm        | ✅         | ✅    | ✅     | ❌             | ❌                 | ❌          | ❓            |
+| mpt         | ✅         | ❌    | ❓     | ❌             | ❌                 | ❌          | ❓            |
+| falcon      | ✅         | ✅    | ✅     | ❌             | ❌                 | ❌          | ❓            |
+| gpt-j       | ✅         | ✅    | ✅     | ❌             | ❌                 | ❓          | ❓            |
+| XGen        | ✅         | ❓    | ✅     | ❓             | ❓                 | ❓          | ✅            |
+| phi         | ✅         | ✅    | ✅     | ❓             | ❓                 | ❓          | ❓            |
+| RWKV        | ✅         | ❓    | ❓     | ❓             | ❓                 | ❓          | ❓            |
+| Qwen        | ✅         | ✅    | ✅     | ❓             | ❓                 | ❓          | ❓            |
+| Gemma       | ✅         | ✅    | ✅     | ❓             | ❓                 | ✅          | ❓            |
+| Jamba       | ✅         | ✅    | ✅     | ❓             | ❓                 | ✅          | ❓            |
+
+✅: supported
+❌: not supported
+❓: untested
+
 ## ❤️ Sponsors
 
 Thank you to our sponsors who help make Axolotl possible:

_quarto.yml

@@ -236,6 +236,7 @@ website:
             - docs/inference.qmd
             - docs/cli.qmd
             - docs/config.qmd
+            - docs/telemetry.qmd
             - text: "API Reference"
               href: docs/api
 

deepspeed_configs/zero2_torch_compile.json

@@ -1,31 +0,0 @@
-{
-  "compile": {
-    "disable": false,
-    "backend": "inductor"
-  },
-  "zero_optimization": {
-    "stage": 2,
-    "offload_optimizer": {
-      "device": "cpu"
-    },
-    "contiguous_gradients": true,
-    "overlap_comm": true
-  },
-  "bf16": {
-    "enabled": "auto"
-  },
-  "fp16": {
-    "enabled": "auto",
-    "auto_cast": false,
-    "loss_scale": 0,
-    "initial_scale_power": 32,
-    "loss_scale_window": 1000,
-    "hysteresis": 2,
-    "min_loss_scale": 1
-  },
-  "gradient_accumulation_steps": "auto",
-  "gradient_clipping": "auto",
-  "train_batch_size": "auto",
-  "train_micro_batch_size_per_gpu": "auto",
-  "wall_clock_breakdown": false
-}

docker/Dockerfile-base

@@ -38,6 +38,6 @@ RUN git lfs install --skip-repo && \
     # The base image ships with `pydantic==1.8.2` which is not working
     pip3 install -U --no-cache-dir pydantic==1.10.10
 
-RUN if [ "$PYTORCH_VERSION" = "2.7.1" ] ; then \
+RUN if [ "$PYTORCH_VERSION" = "2.7.0" ] ; then \
         pip3 install flash-attn==2.7.4.post1; \
     fi

docker/Dockerfile-base-next

@@ -29,7 +29,7 @@ ENV PATH="/root/miniconda3/envs/py${PYTHON_VERSION}/bin:${PATH}"
 WORKDIR /workspace
 
 RUN python3 -m pip install --upgrade pip && pip3 install packaging && \
-    python3 -m pip install --no-cache-dir -U torch==2.7.1 --extra-index-url https://download.pytorch.org/whl/test/cu$CUDA && \
+    python3 -m pip install --no-cache-dir -U torch==2.7.0 --extra-index-url https://download.pytorch.org/whl/test/cu$CUDA && \
     python3 -m pip install --no-cache-dir "causal_conv1d @ git+https://github.com/Dao-AILab/causal-conv1d.git@main" && \
     python3 -m pip install --no-cache-dir "mamba_ssm @ git+https://github.com/state-spaces/mamba.git@main"
 

docker/Dockerfile-uv-base

@@ -29,12 +29,8 @@ RUN uv venv --no-project --relocatable axolotl-venv
 
 ENV PATH="/workspace/axolotl-venv/bin:${PATH}"
 
-RUN uv pip install packaging setuptools wheel psutil \
+RUN uv pip install packaging setuptools wheel \
     && uv pip install torch==${PYTORCH_VERSION} \
     && uv pip install --no-build-isolation "causal_conv1d @ git+https://github.com/Dao-AILab/causal-conv1d.git@main" \
     && uv pip install "mamba_ssm @ git+https://github.com/state-spaces/mamba.git@main" \
     && uv pip install awscli pydantic
-
-RUN if [ "$PYTORCH_VERSION" = "2.7.1" ] ; then \
-        uv pip install --no-build-isolation flash-attn==2.7.4.post1; \
-    fi

docs/config.qmd

@@ -27,8 +27,6 @@ trust_remote_code:
 tokenizer_use_fast:
 # Whether to use the legacy tokenizer setting, defaults to True
 tokenizer_legacy:
-# Whether to use mistral-common tokenizer. If set to True, it will use the mistral-common tokenizer.
-tokenizer_use_mistral_common:
 # Resize the model embeddings when new tokens are added to multiples of 32
 # This is reported to improve training speed on some models
 resize_token_embeddings_to_32x:
@@ -175,10 +173,6 @@ datasets:
     # Key containing the messages (default: "messages")
     field_messages: messages
 
-    # Key containing the tools (default: "tools")
-    # Must be a list[dict] and follow [JSON schema](https://json-schema.org/learn/getting-started-step-by-step).
-    field_tools: tools
-
     # Key containing the system message (default: "system")
     # If the system message is not present in the dataset sample, it will be loaded from the field_system property.
     field_system: system

docs/dataset-formats/conversation.qmd

@@ -52,9 +52,7 @@ We recommend checking the below examples for other usecases.
 
 ### Examples
 
-#### Training on last message
+1. (Legacy) Using the default chat template in the tokenizer_config.json on OpenAI messages format, training on only last message.
-
-(Legacy) Using the default chat template in the tokenizer_config.json on OpenAI messages format, training on only last message.
 
 ```yaml
 datasets:
@@ -68,9 +66,7 @@ datasets:
 If you receive an error like "`chat_template` choice is `tokenizer_default` but tokenizer's `chat_template` is null.", it means the tokenizer does not have a default `chat_template`. Follow the examples below instead to set a custom `chat_template`.
 :::
 
-#### Overriding default chat template
+2. Using the `gemma` chat template to override the tokenizer_config.json's chat template on OpenAI messages format, training on all assistant messages.
-
-Using the `gemma` chat template to override the tokenizer_config.json's chat template on OpenAI messages format, training on all assistant messages.
 
 ```yaml
 chat_template: gemma # this overwrites the tokenizer's chat_template
@@ -80,13 +76,7 @@ datasets:
     roles_to_train: ["assistant"]  # default value
 ```
 
-::: {.callout-note}
+3. Using the tokenizer_config.json's chat template or `chatml` as fallback if the former's chat template does not exist, on OpenAI messages format, training on all assistant messages.
-If you want to use built-in chat_template, use `chat_template: tokenizer_default` (this is set by default).
-:::
-
-#### Using default chat template with fallback
-
-Using the tokenizer_config.json's chat template or `chatml` as fallback if the former's chat template does not exist, on OpenAI messages format, training on all assistant messages.
 
 ```yaml
 chat_template: tokenizer_default_fallback_chatml # this overwrites the tokenizer's chat_template
@@ -95,9 +85,7 @@ datasets:
     type: chat_template
 ```
 
-#### Custom Jinja template
+4. Using a custom jinja template on OpenAI messages format, training on all assistant messages.
-
-Using a custom jinja template on OpenAI messages format, training on all assistant messages.
 
 ```yaml
 # chat_template: jinja # `jinja` will be implied if the `chat_template_jinja` is set and this field is empty
@@ -112,9 +100,7 @@ datasets:
 Please make sure that your `tokenizer.eos_token` is same as EOS (End-of-Sequence) token in template. Otherwise, set `eos_token` under `special_tokens: `.
 :::
 
-#### Using template with different token for EOT and EOS
+5. If you are using a template that has a different EOT (End-of-Turn) token from EOS token or multiple EOT tokens (like Mistral V7 Tekken), set the `eot_tokens: ` config. The handling of EOT tokens follows `train_on_eos: ` which defaults to turn.
-
-- If you are using a template that has a different EOT (End-of-Turn) token from EOS token or multiple EOT tokens (like Mistral V7 Tekken), set the `eot_tokens: ` config. The handling of EOT tokens follows `train_on_eos: ` which defaults to turn.
 
 ```yaml
 eot_tokens:
@@ -139,7 +125,7 @@ Using `eot_tokens` requires each token that exists in `chat_template` to be a si
 You can add those tokens as new tokens under `tokens: ` or (recommended) override unused added_tokens via `added_tokens_overrides: `. See [config](../config.qmd) for more details.
 :::
 
-- Continuing from the previous example, if you want to train on all EOT token trainable turns but only last EOS token, set `train_on_eos: last`.
+6. Continuing from the previous example, if you want to train on all EOT token trainable turns but only last EOS token, set `train_on_eos: last`.
 
 ```yaml
 eot_tokens:
@@ -159,73 +145,7 @@ If EOS token only appears at the end of a prompt, `train_on_eos: last` is equiva
 :::
 
 
-#### Using tool use
+7. (Advanced) Using fine-grained control over tokens and turns to train in a conversation
-
-Instead of passing `tools` via the system prompt, an alternative method would be to have the `tools` in a separate column and loaded via `chat_template` to let the template dynamically build it.
-
-```json
-{
-    "tools": [
-        {
-            "type": "...",
-            "function": {
-                "name": "...",
-                "description": "...",
-                "parameters": {
-                    "type": "...",
-                    "properties": {
-                        // ...
-                    },
-                    "required": ["..."],
-                },
-            },
-        },
-    ],
-    "messages": [
-        // ...
-        {
-            "role": "assistant", // call the function via assistant
-            "tool_calls": [
-                {
-                    "type": "function",
-                    "function": {
-                        "name": "...",
-                        "arguments": {
-                            "...": "...",
-                        }
-                    }
-                }
-            ]
-        },
-        {
-            "role": "tool",
-            "name": "...",
-            "content": "..."
-        },
-    ],
-}
-```
-
-::: {.callout-note}
-Tools need to follow [JSON schema](https://json-schema.org/learn/getting-started-step-by-step).
-:::
-
-```yaml
-chat_template: llama4
-datasets:
-  - path: ...
-    type: chat_template
-    # field_tools: tools # default is `tools`
-```
-
-::: {.callout-tip}
-Look into the `chat_template` you are using to see if it supports `tools` and what the expected role is for the tool answer. In the example above, the tool answer is expected to be in the `tool` or `ipython` role for `llama4` template.
-:::
-
-
-#### Using fine-grained control over token masking
-
-(Advanced) Using fine-grained control over tokens and turns to train in a conversation
 
 For a data sample that looks like:
 
@@ -276,9 +196,7 @@ datasets:
 It is not necessary to set both `message_field_training` and `message_field_training_detail` at once.
 :::
 
-#### Reasoning split
+8. (For Qwen3 template only) Enable reasoning split, where the reasoning is split from the content and passed as a separate field into the template.
-
-(For Qwen3 template only) Enable reasoning split, where the reasoning is split from the content and passed as a separate field into the template.
 
 ```yaml
 datasets:

docs/docker.qmd

@@ -9,7 +9,7 @@ format:
 This section describes the different Docker images that are released by AxolotlAI at [Docker Hub](https://hub.docker.com/u/axolotlai).
 
 ::: {.callout-important}
-For Blackwell GPUs, please use the tags with Pytorch 2.7.1 and CUDA 12.8.
+For Blackwell GPUs, please use the tags with Pytorch 2.7.0 and CUDA 12.8.
 :::
 
 ## Base
@@ -32,8 +32,8 @@ main-base-py{python_version}-cu{cuda_version}-{pytorch_version}
 
 Tags examples:
 
-- `main-base-py3.11-cu128-2.7.1`
+- `main-base-py3.11-cu128-2.7.0`
-- `main-base-py3.11-cu126-2.7.1`
+- `main-base-py3.11-cu126-2.7.0`
 - `main-base-py3.11-cu124-2.6.0`
 - `main-base-py3.11-cu124-2.5.1`
 

docs/qat.qmd

@@ -29,4 +29,4 @@ qat:
   fake_quant_after_n_steps: # Optional[int] = None. The number of steps to apply fake quantization after
 ```
 
-Once you have finished training, you must quantize your model by using the same quantization configuration which you used to train the model with. You can use the [`quantize`](./quantize.qmd) command to do this.
+Once you have finished training, you must quantize your model by using the same quantization configuration which you used to train the model with. You can use the [`quantize` command](./quantize.md) to do this.

docs/rlhf.qmd

@@ -500,7 +500,7 @@ The input format is a simple JSON input with customizable fields based on the ab
 ### GRPO
 
 ::: {.callout-tip}
-Check out our [GRPO cookbook](https://github.com/axolotl-ai-cloud/grpo_code).
+Check out our [GRPO cookbook](https://github.com/axolotl-ai-cloud/axolotl-cookbook/tree/main/grpo#training-an-r1-style-large-language-model-using-grpo).
 :::
 
 In the latest GRPO implementation, `vLLM` is used to significantly speedup trajectory generation during training. In this example, we're using 4 GPUs - 2 for training, and 2 for vLLM:

docs/telemetry.qmd

@@ -0,0 +1,59 @@
+---
+title: Telemetry
+description: A description of the opt-in telemetry implementation in Axolotl.
+---
+
+# Telemetry in Axolotl
+
+Axolotl implements anonymous telemetry to help maintainers understand how the library
+is used and where users encounter issues. This data helps prioritize features, optimize
+performance, and fix bugs.
+
+## Data Collection
+
+We collect:
+
+- System info: OS, Python version, Axolotl version, PyTorch version, Transformers
+version, etc.
+- Hardware info: CPU count, memory, GPU count and models
+- Runtime metrics: Training progress, memory usage, timing information
+- Usage patterns: Models (from a whitelist) and configurations used
+- Error tracking: Stack traces and error messages (sanitized to remove personal
+information)
+
+Personally identifiable information (PII) is not collected.
+
+## Implementation
+
+Telemetry is implemented using PostHog and consists of:
+
+- `axolotl.telemetry.TelemetryManager`: A singleton class that initializes the
+telemetry system and provides methods for tracking events.
+- `axolotl.telemetry.errors.send_errors`: A decorator that captures exceptions and
+sends sanitized stack traces.
+- `axolotl.telemetry.runtime_metrics.RuntimeMetricsTracker`: A class that tracks
+runtime metrics during training.
+- `axolotl.telemetry.callbacks.TelemetryCallback`: A Trainer callback that sends
+runtime metrics telemetry.
+
+The telemetry system will block training startup for 15 seconds to ensure users are
+aware of data collection, unless telemetry is explicitly enabled or disabled.
+
+## Opt-In Mechanism
+
+Telemetry is **disabled by default** on an opt-in basis. To enable it, set `AXOLOTL_DO_NOT_TRACK=0`.
+
+To remove the warning message about telemetry that is displayed on train, etc. startup,
+explicitly set: `AXOLOTL_DO_NOT_TRACK=0` (enable telemetry) or `AXOLOTL_DO_NOT_TRACK=1`
+(explicitly disable telemetry).
+
+**Note**: Telemetry will move to an opt-out model in a later release.
+
+## Privacy
+
+- All path-like config information is automatically redacted from telemetry data
+- Model information is only collected for whitelisted organizations
+    - See `axolotl/telemetry/whitelist.yaml` for the set of whitelisted organizations
+- Each run generates a unique anonymous ID
+    - This allows us to link different telemetry events in a single same training run
+- Telemetry is only sent from the main process to avoid duplicate events

examples/llama-3/instruct-dpo-lora-8b.yml

@@ -5,10 +5,6 @@ tokenizer_type: AutoTokenizer
 # Automatically upload checkpoint and final model to HF
 # hub_model_id: username/custom_model_name
 
-special_tokens:
-  pad_token: <|finetune_right_pad_id|>
-  eos_token: <|eot_id|>
-
 load_in_8bit: true
 load_in_4bit: false
 

examples/magistral/README.md

@@ -1,71 +0,0 @@
-# Finetune Magistral Small with Axolotl
-
-Magistral Small is a 24B parameter opensource model from MistralAI found on [HuggingFace](https://huggingface.co/mistralai/Magistral-Small-2506). This guide shows how to fine-tune it with Axolotl with multi-turn conversations with proper masking.
-
-MistralAI has also released a proprietary medium-sized version called Magistral Medium.
-
-Thanks to the team at MistralAI for giving us early access to prepare for this release.
-
-## Getting started
-
-1. Install Axolotl following the [installation guide](https://docs.axolotl.ai/docs/installation.html). You need to install from main as Magistral is only on nightly or use our latest [Docker images](https://docs.axolotl.ai/docs/docker.html).
-
-    Here is an example of how to install from main for pip:
-
-```bash
-# Ensure you have Pytorch installed (Pytorch 2.6.0 recommended)
-git clone https://github.com/axolotl-ai-cloud/axolotl.git
-cd axolotl
-
-pip3 install packaging==23.2 setuptools==75.8.0 wheel ninja
-pip3 install --no-build-isolation -e '.[flash-attn,mistral]'
-```
-
-2. Download the example config:
-
-```bash
-axolotl fetch examples
-```
-
-3. Run the finetuning example:
-
-```bash
-axolotl train examples/magistral/magistral-small-qlora.yaml
-```
-
-This config uses about 24GB VRAM.
-
-Let us know how it goes. Happy finetuning! 🚀
-
-### TIPS
-
-- For inference, the official MistralAI team recommends `top_p: 0.95` and `temperature: 0.7` with `max_tokens: 40960`.
-- You can run a full finetuning by removing the `adapter: qlora` and `load_in_4bit: true` from the config.
-- Read more on how to load your own dataset at [docs](https://docs.axolotl.ai/docs/dataset_loading.html).
-- The dataset format is the OpenAI Messages format as seen [here](https://docs.axolotl.ai/docs/dataset-formats/conversation.html#chat_template).
-
-## Optimization Guides
-
-- [Multi-GPU Training](https://docs.axolotl.ai/docs/multi-gpu.html)
-- [Multi-Node Training](https://docs.axolotl.ai/docs/multi-node.html)
-- [LoRA Optimizations](https://docs.axolotl.ai/docs/lora_optims.html)
-
-## Limitations
-
-We only support the `mistral-common` tokenizer for Supervised Fine-tuning at the moment and for `type: chat_template` only.
-
-The tokenizer does not work with `dataset.map` with multiprocessing, so we had to disable it. In addition, we do not support overriding tokens yet.
-
-## Related Resources
-
-- [MistralAI Magistral Blog](https://mistral.ai/news/magistral/)
-- [Axolotl Docs](https://docs.axolotl.ai)
-- [Axolotl Website](https://axolotl.ai)
-- [Axolotl GitHub](https://github.com/axolotl-ai-cloud/axolotl)
-- [Axolotl Discord](https://discord.gg/7m9sfhzaf3)
-
-
-## Future Work
-
-- Add parity to Preference Tuning, RL, Multi-modal, etc.
-- Add parity to other tokenizer configs like overriding tokens.

examples/magistral/magistral-small-fsdp-qlora.yaml

@@ -1,72 +0,0 @@
-base_model: mistralai/Magistral-Small-2506
-
-# Enable to use mistral-common tokenizer
-tokenizer_use_mistral_common: true
-
-# Automatically upload checkpoint and final model to HF
-# hub_model_id: username/custom_model_name
-
-load_in_8bit: false
-load_in_4bit: true
-
-datasets:
-  - path: fozziethebeat/alpaca_messages_2k_test
-    type: chat_template
-
-dataset_prepared_path: last_run_prepared
-val_set_size: 0.1
-output_dir: ./outputs/lora-out
-
-adapter: qlora
-lora_model_dir:
-
-sequence_len: 2048
-sample_packing: true
-eval_sample_packing: false
-pad_to_sequence_len: true
-
-lora_r: 32
-lora_alpha: 16
-lora_dropout: 0.05
-lora_target_linear: true
-lora_target_modules:
-  - gate_proj
-  - down_proj
-  - up_proj
-  - q_proj
-  - v_proj
-  - k_proj
-  - o_proj
-
-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_fused
-lr_scheduler: cosine
-learning_rate: 0.0002
-
-bf16: auto
-tf32: false
-
-gradient_checkpointing:
-resume_from_checkpoint:
-logging_steps: 1
-flash_attention: true
-
-warmup_ratio: 0.1
-evals_per_epoch: 1
-saves_per_epoch: 1
-
-fsdp:
-  - full_shard
-  - auto_wrap
-fsdp_config:
-  fsdp_state_dict_type: FULL_STATE_DICT
-  fsdp_transformer_layer_cls_to_wrap: MistralDecoderLayer
-  fsdp_activation_checkpointing: true

examples/magistral/magistral-small-qlora.yaml

@@ -1,63 +0,0 @@
-base_model: mistralai/Magistral-Small-2506
-
-# Enable to use mistral-common tokenizer
-tokenizer_use_mistral_common: true
-
-# Automatically upload checkpoint and final model to HF
-# hub_model_id: username/custom_model_name
-
-load_in_8bit: false
-load_in_4bit: true
-
-datasets:
-  - path: fozziethebeat/alpaca_messages_2k_test
-    type: chat_template
-
-dataset_prepared_path: last_run_prepared
-val_set_size: 0.1
-output_dir: ./outputs/lora-out
-
-adapter: qlora
-lora_model_dir:
-
-sequence_len: 2048
-sample_packing: true
-pad_to_sequence_len: true
-
-lora_r: 32
-lora_alpha: 16
-lora_dropout: 0.05
-lora_target_linear: true
-lora_target_modules:
-  - gate_proj
-  - down_proj
-  - up_proj
-  - q_proj
-  - v_proj
-  - k_proj
-  - o_proj
-
-wandb_project:
-wandb_entity:
-wandb_watch:
-wandb_name:
-wandb_log_model:
-
-gradient_accumulation_steps: 4
-micro_batch_size: 2
-num_epochs: 1
-optimizer: adamw_bnb_8bit
-lr_scheduler: cosine
-learning_rate: 0.0002
-
-bf16: auto
-tf32: false
-
-gradient_checkpointing: true
-resume_from_checkpoint:
-logging_steps: 1
-flash_attention: true
-
-warmup_ratio: 0.1
-evals_per_epoch: 1
-saves_per_epoch: 1

examples/qwen2-vl/lora-7b.yaml

@@ -25,7 +25,7 @@ pad_to_sequence_len: false
 lora_r: 32
 lora_alpha: 16
 lora_dropout: 0.05
-lora_target_modules: 'model.language_model.layers.[\d]+.(mlp|cross_attn|self_attn).(up|down|gate|q|k|v|o)_proj'
+lora_target_modules: 'model.layers.[\d]+.(mlp|cross_attn|self_attn).(up|down|gate|q|k|v|o)_proj'
 
 wandb_project:
 wandb_entity:

requirements.txt

@@ -68,4 +68,5 @@ schedulefree==1.4.1
 axolotl-contribs-lgpl==0.0.6
 axolotl-contribs-mit==0.0.3
 
-mistral-common==1.6.0
+# telemetry
+posthog>=4.2.0

src/axolotl/__init__.py

@@ -4,4 +4,4 @@ import pkgutil
 
 __path__ = pkgutil.extend_path(__path__, __name__)  # Make this a namespace package
 
-__version__ = "0.10.0"
+__version__ = "0.10.0.dev0"

src/axolotl/cli/config.py

@@ -14,6 +14,8 @@ import yaml
 from transformers.utils import is_torch_bf16_gpu_available
 
 from axolotl.integrations.base import PluginManager
+from axolotl.telemetry.errors import send_errors
+from axolotl.telemetry.manager import TelemetryManager
 from axolotl.utils.comet_ import setup_comet_env_vars
 from axolotl.utils.config import (
     normalize_cfg_datasets,
@@ -28,6 +30,8 @@ from axolotl.utils.wandb_ import setup_wandb_env_vars
 
 LOG = get_logger(__name__, use_environ=True)
 
+TELEMETRY_MANAGER = TelemetryManager.get_instance()
+
 
 def check_remote_config(config: Union[str, Path]) -> Union[str, Path]:
     """
@@ -159,6 +163,7 @@ def plugin_set_cfg(cfg: DictDefault):
         plugin_manager.cfg = cfg
 
 
+@send_errors
 def load_cfg(
     config: str | Path | DictDefault = Path("examples/"), **kwargs
 ) -> DictDefault:
@@ -192,6 +197,8 @@ def load_cfg(
             temp_file.close()
         cfg.axolotl_config_path = temp_file.name
 
+    TELEMETRY_MANAGER.send_event(event_type="config-loaded", properties=cfg)
+
     # If there are any options passed in the cli, if it is something that seems valid
     # from the yaml, then overwrite the value
     cfg_keys = cfg.keys()
@@ -233,4 +240,6 @@ def load_cfg(
     setup_comet_env_vars(cfg)
     plugin_set_cfg(cfg)
 
+    TELEMETRY_MANAGER.send_event(event_type="config-processed", properties=cfg)
+
     return cfg

src/axolotl/cli/inference.py

@@ -16,6 +16,7 @@ from axolotl.cli.args import InferenceCliArgs
 from axolotl.cli.art import print_axolotl_text_art
 from axolotl.cli.config import load_cfg
 from axolotl.cli.utils import load_model_and_tokenizer
+from axolotl.telemetry.errors import send_errors
 from axolotl.utils.chat_templates import (
     get_chat_template,
     get_chat_template_from_config,
@@ -42,6 +43,7 @@ def get_multi_line_input() -> str:
     return instruction
 
 
+@send_errors
 def do_inference(
     *,
     cfg: DictDefault,
@@ -135,6 +137,7 @@ def do_inference(
         print(tokenizer.decode(generated["sequences"].cpu().tolist()[0]))
 
 
+@send_errors
 def do_inference_gradio(
     *,
     cfg: DictDefault,

src/axolotl/cli/merge_lora.py

@@ -9,12 +9,14 @@ from dotenv import load_dotenv
 from axolotl.cli.art import print_axolotl_text_art
 from axolotl.cli.config import load_cfg
 from axolotl.cli.utils import load_model_and_tokenizer
+from axolotl.telemetry.errors import send_errors
 from axolotl.utils.dict import DictDefault
 from axolotl.utils.logging import get_logger
 
 LOG = get_logger(__name__)
 
 
+@send_errors
 def do_merge_lora(*, cfg: DictDefault) -> None:
     """
     Calls `transformers`' `merge_and_unload` on the model given in the `axolotl` config

src/axolotl/cli/merge_sharded_fsdp_weights.py

@@ -24,6 +24,7 @@ from torch.distributed.checkpoint.format_utils import _EmptyStateDictLoadPlanner
 
 from axolotl.cli.art import print_axolotl_text_art
 from axolotl.cli.config import load_cfg
+from axolotl.telemetry.errors import send_errors
 from axolotl.utils.logging import get_logger
 
 LOG = get_logger(__name__)
@@ -118,6 +119,7 @@ def _distributed_checkpoint_to_merged_weights(
     return save_path_
 
 
+@send_errors
 def merge_fsdp_weights(
     checkpoint_dir: str,
     output_path: str,

src/axolotl/cli/preprocess.py

@@ -18,6 +18,7 @@ from axolotl.cli.config import load_cfg
 from axolotl.common.const import DEFAULT_DATASET_PREPARED_PATH
 from axolotl.common.datasets import load_datasets, load_preference_datasets
 from axolotl.integrations.base import PluginManager
+from axolotl.telemetry.errors import send_errors
 from axolotl.utils.dict import DictDefault
 from axolotl.utils.logging import get_logger
 from axolotl.utils.trainer import disable_datasets_caching
@@ -25,6 +26,7 @@ from axolotl.utils.trainer import disable_datasets_caching
 LOG = get_logger(__name__)
 
 
+@send_errors
 def do_preprocess(cfg: DictDefault, cli_args: PreprocessCliArgs) -> None:
     """
     Preprocesses dataset specified in axolotl config.

src/axolotl/common/const.py

@@ -1,3 +1,5 @@
-"""Various shared constants"""
+"""
+Various shared constants
+"""
 
 DEFAULT_DATASET_PREPARED_PATH = "last_run_prepared"

src/axolotl/common/datasets.py

@@ -3,13 +3,16 @@
 import math
 import random
 from dataclasses import dataclass
+from typing import Optional, Union
 
 from datasets import Dataset
 
 import axolotl.monkeypatch.data.batch_dataset_fetcher  # pylint: disable=unused-import  # noqa: F401
 from axolotl.cli.args import PreprocessCliArgs, TrainerCliArgs
 from axolotl.loaders import load_processor, load_tokenizer
-from axolotl.utils.data import prepare_datasets, prepare_preference_datasets
+from axolotl.telemetry.errors import send_errors
+from axolotl.utils.data import prepare_dataset
+from axolotl.utils.data.rl import load_prepare_preference_datasets
 from axolotl.utils.dict import DictDefault
 from axolotl.utils.logging import get_logger
 from axolotl.utils.schemas.enums import RLType
@@ -28,49 +31,66 @@ class TrainDatasetMeta:
 
 
 def sample_dataset(dataset: Dataset, num_samples: int) -> Dataset:
-    """Randomly sample `num_samples` samples with replacement from `dataset`."""
+    """
+    Randomly sample `num_samples` samples from `dataset`.
+
+    Args:
+        dataset: Dataset.
+        num_samples: Number of samples to return.
+
+    Returns:
+        Random sample (with replacement) of examples in `dataset`.
+    """
     return dataset.select(
         [random.randrange(0, len(dataset) - 1) for _ in range(num_samples)]  # nosec
     )
 
 
+@send_errors
 def load_datasets(
     *,
     cfg: DictDefault,
     cli_args: PreprocessCliArgs | TrainerCliArgs | None = None,
     debug: bool = False,
 ) -> TrainDatasetMeta:
-    """Loads one or more training or evaluation datasets, calling
+    """
-    `axolotl.utils.data.prepare_datasets`. Optionally, logs out debug information.
+    Loads one or more training or evaluation datasets, calling
+    `axolotl.utils.data.prepare_dataset`. Optionally, logs out debug information.
 
     Args:
         cfg: Dictionary mapping `axolotl` config keys to values.
         cli_args: Command-specific CLI arguments.
-        debug: Whether to print out tokenization of sample. This is duplicated in
+        debug: Whether to print out tokenization of sample
-            `cfg` and `cli_args`, but is kept due to use in our Colab notebooks.
 
     Returns:
         Dataclass with fields for training and evaluation datasets and the computed
-            `total_num_steps`.
+        `total_num_steps`.
     """
     tokenizer = load_tokenizer(cfg)
     processor = load_processor(cfg, tokenizer=tokenizer) if cfg.processor_type else None
-    preprocess_iterable = getattr(cli_args, "iterable", False)
+    preprocess_iterable = (
+        cli_args
+        and hasattr(cli_args, "iterable")
+        and cli_args.iterable is not None
+        and cli_args.iterable
+    )
 
-    train_dataset, eval_dataset, total_num_steps, prompters = prepare_datasets(
+    train_dataset, eval_dataset, total_num_steps, prompters = prepare_dataset(
         cfg,
         tokenizer,
         processor=processor,
         preprocess_iterable=preprocess_iterable,
     )
 
-    if (
+    if (  # pylint: disable=too-many-boolean-expressions
-        cfg.debug
+        cli_args
-        or getattr(cli_args, "debug", False)
+        and (
-        or getattr(cli_args, "debug_text_only", False)
+            cli_args.debug
-        or getattr(cli_args, "debug_num_examples", 0) > 0
+            or cfg.debug
-        or debug
+            or cli_args.debug_text_only
-    ):
+            or int(cli_args.debug_num_examples) > 0
+        )
+    ) or debug:
         LOG.info("check_dataset_labels...")
 
         num_examples = cli_args.debug_num_examples if cli_args else 1
@@ -94,11 +114,15 @@ def load_datasets(
     )
 
 
+@send_errors
 def load_preference_datasets(
-    *, cfg: DictDefault, cli_args: PreprocessCliArgs | TrainerCliArgs | None = None
+    *,
+    cfg: DictDefault,
+    cli_args: Union[PreprocessCliArgs, TrainerCliArgs],
 ) -> TrainDatasetMeta:
-    """Loads one or more training or evaluation datasets for RL training using paired
+    """
-    preference data, calling `axolotl.utils.data.rl.prepare_preference_datasets`.
+    Loads one or more training or evaluation datasets for RL training using paired
+    preference data, calling `axolotl.utils.data.rl.load_prepare_preference_datasets`.
     Optionally, logs out debug information.
 
     Args:
@@ -109,28 +133,23 @@ def load_preference_datasets(
         Dataclass with fields for training and evaluation datasets and the computed
         `total_num_steps`.
     """
-    tokenizer = load_tokenizer(cfg)
+    train_dataset, eval_dataset = load_prepare_preference_datasets(cfg)
-    train_dataset, eval_dataset = prepare_preference_datasets(cfg, tokenizer)
+    total_num_steps: Optional[int] = int(
+        math.ceil(len(train_dataset) * cfg.num_epochs / cfg.batch_size)
+    )
+    if cfg.rl is RLType.GRPO:
+        total_num_steps = None
 
-    total_num_steps: int | None = None
+    if cli_args.debug or cfg.debug:
-    if cfg.rl is not RLType.GRPO:
-        total_num_steps = int(
-            math.ceil(len(train_dataset) * cfg.num_epochs / cfg.batch_size)
-        )
-
-    if (cli_args and cli_args.debug) or cfg.debug:
         LOG.info("check_dataset_labels...")
 
-        num_examples = cli_args.debug_num_examples if cli_args else 1
-        text_only = cli_args.debug_text_only if cli_args else False
-
         tokenizer = load_tokenizer(cfg)
-        train_samples = sample_dataset(train_dataset, num_examples)
+        train_samples = sample_dataset(train_dataset, cli_args.debug_num_examples)
         check_dataset_labels(
-            dataset=train_samples,
+            train_samples,
-            tokenizer=tokenizer,
+            tokenizer,
-            num_examples=num_examples,
+            num_examples=cli_args.debug_num_examples,
-            text_only=text_only,
+            text_only=cli_args.debug_text_only,
             rl_mode=True,
         )
 

src/axolotl/core/builders/base.py

@@ -31,6 +31,8 @@ from transformers.training_args import OptimizerNames
 
 from axolotl.integrations.base import PluginManager
 from axolotl.monkeypatch.trainer.lr import patch_trainer_get_lr
+from axolotl.telemetry.callbacks import TelemetryCallback
+from axolotl.telemetry.manager import TelemetryManager
 from axolotl.utils import is_comet_available, is_mlflow_available
 from axolotl.utils.callbacks import (
     GCCallback,
@@ -145,6 +147,10 @@ class TrainerBuilderBase(abc.ABC):
 
         callbacks.append(GPUStatsCallback(cfg=self.cfg))
 
+        telemetry_manager = TelemetryManager.get_instance()
+        if telemetry_manager.enabled:
+            callbacks.append(TelemetryCallback())
+
         return callbacks
 
     def get_post_trainer_create_callbacks(self, trainer):
@@ -380,16 +386,14 @@ class TrainerBuilderBase(abc.ABC):
         )
 
         # eval_strategy and eval_steps
-        if not self.eval_dataset and self.cfg.val_set_size == 0:
+        if not self.eval_dataset or self.cfg.val_set_size == 0:
-            # do not eval if no eval_dataset and val_set_size=0
+            # do not eval if no eval_dataset or val_set_size=0
             training_args_kwargs["eval_strategy"] = "no"
         elif self.cfg.eval_steps:
             training_args_kwargs["eval_strategy"] = "steps"
             training_args_kwargs["eval_steps"] = self.cfg.eval_steps
-            training_args_kwargs["eval_on_start"] = True
         elif self.cfg.eval_strategy:
             training_args_kwargs["eval_strategy"] = self.cfg.eval_strategy
-            training_args_kwargs["eval_on_start"] = True
 
     def _configure_reporting(self, training_args_kwargs: dict):
         report_to = []
@@ -492,9 +496,6 @@ class TrainerBuilderBase(abc.ABC):
         training_args_kwargs["max_steps"] = self.cfg.max_steps or total_num_steps or -1
         training_args_kwargs["num_train_epochs"] = self.cfg.num_epochs
 
-        if self.cfg.dataset_processes:
-            training_args_kwargs["dataset_num_proc"] = self.cfg.dataset_processes
-
         # max_length is not used in CausalTrainer
         if self.cfg.reward_model or self.cfg.rl:
             training_args_kwargs["max_length"] = self.cfg.sequence_len

src/axolotl/core/builders/causal.py

@@ -21,12 +21,18 @@ from axolotl.core.trainers import (
     AxolotlTrainer,
     ReLoRATrainer,
 )
+from axolotl.core.training_args import (
+    AxolotlPRMConfig,
+    AxolotlRewardConfig,
+    AxolotlTrainingArguments,
+)
 from axolotl.integrations.base import PluginManager
 from axolotl.monkeypatch.multipack import SUPPORTED_MULTIPACK_MODEL_TYPES
 from axolotl.monkeypatch.relora import ReLoRACallback
 from axolotl.processing_strategies import get_processing_strategy
 from axolotl.utils import is_comet_available, is_mlflow_available
 from axolotl.utils.callbacks import (
+    EvalFirstStepCallback,
     LossWatchDogCallback,
     SaveBetterTransformerModelCallback,
     bench_eval_callback_factory,
@@ -57,6 +63,7 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
 
     def get_callbacks(self):
         callbacks = super().get_callbacks()
+        callbacks.append(EvalFirstStepCallback())
 
         if self.cfg.relora_steps:
             callbacks.append(ReLoRACallback(self.cfg))
@@ -123,9 +130,6 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
         return callbacks
 
     def _get_trainer_cls(self):
-        """
-        Gets the trainer class for the given configuration.
-        """
         if self.cfg.plugins:
             plugin_manager = PluginManager.get_instance()
             trainer_cls = plugin_manager.get_trainer_cls(self.cfg)
@@ -142,12 +146,6 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
         return AxolotlTrainer
 
     def build(self, total_num_steps):
-        from axolotl.core.training_args import (
-            AxolotlPRMConfig,
-            AxolotlRewardConfig,
-            AxolotlTrainingArguments,
-        )
-
         training_arguments_kwargs, trainer_kwargs = self._set_base_training_args(
             total_num_steps
         )
@@ -316,12 +314,20 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
             training_arguments_kwargs["image_resize_algorithm"] = (
                 self.cfg.image_resize_algorithm
             )
-
+        if self.cfg.kd_ce_alpha is not None:
-        if self.cfg.plugins:
+            training_arguments_kwargs["kd_ce_alpha"] = self.cfg.kd_ce_alpha
-            plugin_manager = PluginManager.get_instance()
+        if self.cfg.kd_alpha is not None:
-            plugin_training_args = plugin_manager.get_training_args(self.cfg)
+            training_arguments_kwargs["kd_alpha"] = self.cfg.kd_alpha
-            if plugin_training_args:
+        if self.cfg.kd_temperature is not None:
-                training_arguments_kwargs.update(plugin_training_args)
+            training_arguments_kwargs["kd_temperature"] = self.cfg.kd_temperature
+        if self.cfg.kd_zscore_base_temp is not None:
+            training_arguments_kwargs["kd_zscore_base_temp"] = (
+                self.cfg.kd_zscore_base_temp
+            )
+        if self.cfg.kd_top_k_before_softmax is not None:
+            training_arguments_kwargs["kd_top_k_before_softmax"] = (
+                self.cfg.kd_top_k_before_softmax
+            )
 
         if self.cfg.reward_model:
             training_args_cls = AxolotlRewardConfig
@@ -375,7 +381,7 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
         elif "tokenizer" in sig.parameters:
             trainer_kwargs["tokenizer"] = self.tokenizer
         if (
-            trainer_cls not in [AxolotlRewardTrainer, AxolotlPRMTrainer]
+            not (trainer_cls in [AxolotlRewardTrainer, AxolotlPRMTrainer])
             and self.cfg.datasets is not None
         ):
             trainer_kwargs["dataset_tags"] = [
@@ -402,10 +408,7 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
         return trainer
 
     def build_collator(
-        self,
+        self, training_args: AxolotlTrainingArguments, is_eval=False, **kwargs
-        training_args,  # type: "AxolotlTrainingArguments"  # type: ignore
-        is_eval=False,
-        **kwargs,
     ):
         if training_args.pretraining:
             if (
@@ -434,19 +437,7 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
             ]
         ]
         collator_args = [self.tokenizer]
-
+        if self.cfg.reward_model:
-        collator_cls_and_kwargs = None
-        if self.cfg.plugins:
-            plugin_manager = PluginManager.get_instance()
-            collator_cls_and_kwargs = plugin_manager.get_collator_cls_and_kwargs(
-                self.cfg, is_eval=is_eval
-            )
-
-        if collator_cls_and_kwargs:
-            collator = collator_cls_and_kwargs[0]
-            if kwargs and isinstance(kwargs, dict):
-                kwargs.update(collator_cls_and_kwargs[1])
-        elif self.cfg.reward_model:
             collator = RewardDataCollatorWithPadding
         elif use_batch_sampler_collator:
             # Use V2BatchSamplerDataCollatorForSeq2Seq for flex attention,
@@ -477,6 +468,16 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
                 collator_args.pop(0)
                 kwargs.pop("pad_to_multiple_of", None)
                 kwargs.pop("padding", None)
+            elif self.cfg.kd_trainer:
+                from axolotl.integrations.kd.collator import (
+                    DataCollatorForKD,
+                    KDBatchSamplerDataCollatorForSeq2Seq,
+                )
+
+                if self.cfg.sample_packing:
+                    collator = KDBatchSamplerDataCollatorForSeq2Seq
+                else:
+                    collator = DataCollatorForKD
             else:
                 collator = DataCollatorForSeq2Seq
 

src/axolotl/core/builders/rl.py

@@ -12,9 +12,13 @@ from axolotl.core.trainers import (
 from axolotl.core.trainers.dpo import DPOStrategy
 from axolotl.core.trainers.dpo.args import AxolotlDPOConfig
 from axolotl.core.trainers.grpo import GRPOStrategy
+from axolotl.core.training_args import (
+    AxolotlCPOConfig,
+    AxolotlKTOConfig,
+    AxolotlORPOConfig,
+)
 from axolotl.integrations.base import PluginManager
 from axolotl.loaders.utils import ensure_dtype
-from axolotl.utils.callbacks.qat import QATCallback
 from axolotl.utils.logging import get_logger
 from axolotl.utils.schemas.enums import RLType
 
@@ -27,9 +31,6 @@ class HFRLTrainerBuilder(TrainerBuilderBase):
     def get_callbacks(self):
         callbacks = super().get_callbacks()
 
-        if self.cfg.qat:
-            callbacks.append(QATCallback(self.cfg.qat))
-
         return callbacks
 
     def get_post_trainer_create_callbacks(self, trainer):
@@ -78,12 +79,6 @@ class HFRLTrainerBuilder(TrainerBuilderBase):
         """
         Returns training_args and trainer_kwargs
         """
-        from axolotl.core.training_args import (
-            AxolotlCPOConfig,
-            AxolotlKTOConfig,
-            AxolotlORPOConfig,
-        )
-
         training_args_kwargs, trainer_kwargs = self._set_base_training_args(
             total_num_steps=total_num_steps
         )
@@ -95,6 +90,10 @@ class HFRLTrainerBuilder(TrainerBuilderBase):
         else:
             training_args_kwargs["remove_unused_columns"] = False
 
+        # only rlhf
+        if self.cfg.dataset_processes:
+            training_args_kwargs["dataset_num_proc"] = self.cfg.dataset_processes
+
         if self.cfg.trl and self.cfg.trl.beta is not None:
             training_args_kwargs["beta"] = self.cfg.trl.beta
         elif self.cfg.rl_beta is not None:
@@ -143,7 +142,22 @@ class HFRLTrainerBuilder(TrainerBuilderBase):
 
         elif self.cfg.rl in [RLType.DPO, RLType.IPO]:
             training_args_cls = AxolotlDPOConfig
-            training_args_kwargs.update(DPOStrategy.set_training_args_kwargs(self.cfg))
+            if self.cfg.rl is RLType.IPO:
+                training_args_kwargs["loss_type"] = "ipo"
+
+            # Not compatible with IPO
+            if self.cfg.rl is RLType.DPO and self.cfg.dpo_label_smoothing:
+                training_args_kwargs["label_smoothing"] = self.cfg.dpo_label_smoothing
+
+            training_args_kwargs["max_completion_length"] = None
+            training_args_kwargs["max_prompt_length"] = self.cfg.sequence_len
+            training_args_kwargs["generate_during_eval"] = self.cfg.use_wandb
+            if self.cfg.dpo_use_weighting is not None:
+                training_args_kwargs["use_weighting"] = self.cfg.dpo_use_weighting
+            if self.cfg.dpo_use_logits_to_keep is not None:
+                training_args_kwargs["use_logits_to_keep"] = (
+                    self.cfg.dpo_use_logits_to_keep
+                )
         else:
             raise ValueError(f"Unsupported RL: {self.cfg.rl}")
 
@@ -151,12 +165,6 @@ class HFRLTrainerBuilder(TrainerBuilderBase):
             if blocklist_key in training_args_kwargs:
                 del training_args_kwargs[blocklist_key]
 
-        if self.cfg.plugins:
-            plugin_manager = PluginManager.get_instance()
-            plugin_training_args = plugin_manager.get_training_args(self.cfg)
-            if plugin_training_args:
-                training_args_kwargs.update(plugin_training_args)
-
         training_args = training_args_cls(  # pylint: disable=unexpected-keyword-arg
             logging_first_step=True,
             **training_args_kwargs,

src/axolotl/core/trainers/base.py

@@ -25,7 +25,6 @@ from trl.trainer.utils import pad_to_length
 from typing_extensions import override
 
 from axolotl.core.trainers.mixins import (
-    CheckpointSaveMixin,
     OptimizerMixin,
     RngLoaderMixin,
     SchedulerMixin,
@@ -34,16 +33,13 @@ from axolotl.core.trainers.utils import (
     sanitize_kwargs_for_ds_tagging,
     sanitize_kwargs_for_tagging,
 )
-from axolotl.utils import get_not_null
 from axolotl.utils.logging import get_logger
 from axolotl.utils.samplers import MultipackBatchSampler, get_dataset_lengths
 
 LOG = get_logger(__name__)
 
 
-class AxolotlTrainer(
+class AxolotlTrainer(SchedulerMixin, OptimizerMixin, RngLoaderMixin, Trainer):
-    SchedulerMixin, OptimizerMixin, RngLoaderMixin, CheckpointSaveMixin, Trainer
-):
     """Extend the base Trainer for axolotl helpers"""
 
     args = None  # type: "AxolotlTrainingArguments"  # type: ignore[name-defined]
@@ -105,7 +101,7 @@ class AxolotlTrainer(
             )
             batch_max_len = train_batch_size * self.args.max_seq_length
 
-        sampler = MultipackBatchSampler(
+        return MultipackBatchSampler(
             base_sampler,
             lengths=get_dataset_lengths(dataset),
             packing_efficiency_estimate=self.args.sample_packing_efficiency,
@@ -115,12 +111,8 @@ class AxolotlTrainer(
             bin_size=self.args.sample_packing_bin_size,
             sequential=self.args.sample_packing_sequentially,
             drop_last=True,
-            num_processes=self.args.dataset_num_proc,
         )
 
-        len(sampler)
-        return sampler
-
     def _get_train_sampler(
         self, train_dataset: Optional[Dataset] = None
     ) -> Optional[Sampler]:
@@ -228,9 +220,7 @@ class AxolotlTrainer(
         }
 
         if not isinstance(dataset, torch.utils.data.IterableDataset):
-            dataloader_params["drop_last"] = get_not_null(
+            dataloader_params["drop_last"] = self.args.dataloader_drop_last
-                self.args.dataloader_drop_last, True
-            )
             if sampler_fn is not None:
                 sampler = sampler_fn(dataset)
                 if isinstance(sampler, BatchSampler):

src/axolotl/core/trainers/dpo/__init__.py

@@ -22,19 +22,10 @@ class DPOStrategy:
         training_args_kwargs = {}
         if cfg.rl is RLType.IPO:
             training_args_kwargs["loss_type"] = "ipo"
-        # Label smoothing is not compatible with IPO
-        if cfg.rl is RLType.DPO and cfg.dpo_label_smoothing:
-            training_args_kwargs["label_smoothing"] = cfg.dpo_label_smoothing
-        training_args_kwargs["max_completion_length"] = None
         training_args_kwargs["max_length"] = cfg.sequence_len
+        training_args_kwargs["max_completion_length"] = None
         training_args_kwargs["max_prompt_length"] = cfg.sequence_len
         training_args_kwargs["generate_during_eval"] = cfg.use_wandb
         if cfg.dpo_use_weighting is not None:
             training_args_kwargs["use_weighting"] = cfg.dpo_use_weighting
-        if cfg.dpo_padding_free is not None:
-            training_args_kwargs["padding_free"] = cfg.dpo_padding_free
-        if cfg.dpo_norm_loss is not None:
-            training_args_kwargs["dpo_norm_loss"] = cfg.dpo_norm_loss
-        if cfg.dpo_use_logits_to_keep is not None:
-            training_args_kwargs["use_logits_to_keep"] = cfg.dpo_use_logits_to_keep
         return training_args_kwargs

src/axolotl/core/trainers/dpo/args.py

@@ -14,5 +14,3 @@ class AxolotlDPOConfig(AxolotlTrainingMixins, DPOConfig):
     """
     DPO config for DPO training
     """
-
-    dpo_norm_loss: bool | None = False

src/axolotl/core/trainers/dpo/trainer.py

@@ -83,20 +83,3 @@ class AxolotlDPOTrainer(
         gc.collect()
         torch.cuda.empty_cache()
         return loss
-
-    def concatenated_forward(
-        self,
-        model: nn.Module,
-        batch: dict[str, Union[list, torch.LongTensor]],
-        is_ref_model: bool = False,
-    ) -> dict[str, torch.Tensor]:
-        if self.args.dpo_norm_loss:
-            # fmt: off
-            loss_type: str = self.loss_type  # type: ignore[has-type]  # pylint: disable=access-member-before-definition
-            # fmt: on
-            # concatenated_forward handles avg token logprob for ipo case already
-            self.loss_type = "ipo"  # pylint: disable=attribute-defined-outside-init
-            res = super().concatenated_forward(model, batch, is_ref_model=is_ref_model)
-            self.loss_type = loss_type  # pylint: disable=attribute-defined-outside-init
-            return res
-        return super().concatenated_forward(model, batch, is_ref_model=is_ref_model)

src/axolotl/core/trainers/grpo/trainer.py

@@ -3,7 +3,6 @@
 # pylint: disable=too-many-lines,duplicate-code,protected-access,no-member
 
 import warnings
-from functools import partial
 from typing import Any
 
 import datasets
@@ -59,42 +58,6 @@ class AxolotlGRPOTrainer(
 
     _tag_names = ["trl", "grpo", "axolotl"]
 
-    def get_train_dataloader(self):
-        if self.train_dataset is None:
-            raise ValueError("Trainer: training requires a train_dataset.")
-
-        train_dataset = self.train_dataset
-        data_collator = self.data_collator
-        if isinstance(train_dataset, datasets.Dataset):
-            train_dataset = self._remove_unused_columns(
-                train_dataset, description="training"
-            )
-        else:
-            data_collator = self._get_collator_with_removed_columns(
-                data_collator, description="training"
-            )
-
-        dataloader_params = {
-            "batch_size": self._train_batch_size
-            * self.args.steps_per_generation,  # < this is the change
-            "collate_fn": data_collator,
-            "num_workers": self.args.dataloader_num_workers,
-            "pin_memory": self.args.dataloader_pin_memory,
-            "persistent_workers": self.args.dataloader_persistent_workers,
-        }
-
-        if not isinstance(train_dataset, torch.utils.data.IterableDataset):
-            dataloader_params["sampler"] = self._get_train_sampler()
-            dataloader_params["drop_last"] = self.args.dataloader_drop_last
-            dataloader_params["worker_init_fn"] = partial(
-                seed_worker,
-                num_workers=self.args.dataloader_num_workers,
-                rank=self.args.process_index,
-            )
-            dataloader_params["prefetch_factor"] = self.args.dataloader_prefetch_factor
-
-        return self.accelerator.prepare(DataLoader(train_dataset, **dataloader_params))
-
 
 class AxolotlGRPOSequenceParallelTrainer(AxolotlGRPOTrainer):
     """Extend the base GRPOTrainer for sequence parallelism handling"""

src/axolotl/core/trainers/mixins/__init__.py

@@ -3,7 +3,6 @@
 # pylint: disable=unused-import
 # flake8: noqa
 
-from .checkpoints import CheckpointSaveMixin
 from .optimizer import OptimizerMixin
 from .rng_state_loader import RngLoaderMixin
 from .scheduler import SchedulerMixin

src/axolotl/core/trainers/mixins/checkpoints.py

@@ -1,21 +0,0 @@
-"""Custom handling to not fail training if fsdp optimizer is not savable"""
-
-from transformers import Trainer
-
-from axolotl.utils.logging import get_logger
-
-LOG = get_logger(__name__)
-
-
-class CheckpointSaveMixin(Trainer):
-    """Mixin to handle saving the optimizer and scheduler if they are not savable."""
-
-    def _save_optimizer_and_scheduler(self, output_dir):
-        try:
-            super()._save_optimizer_and_scheduler(output_dir)
-        except NotImplementedError as exc:
-            LOG.warning(
-                f"Trainer does not support saving optimizer and scheduler:  {exc}\n"
-                "Optimizer and scheduler states were not saved - resuming from checkpoints "
-                "for this training run will not be possible."
-            )

src/axolotl/core/training_args.py

@@ -2,17 +2,238 @@
 extra axolotl specific training args
 """
 
-from __future__ import annotations
-
 from dataclasses import dataclass, field
-from typing import Optional, Type
+from typing import Optional
 
+from PIL.Image import Resampling
 from transformers import TrainingArguments
 from trl import CPOConfig, KTOConfig, ORPOConfig, PRMConfig, RewardConfig
 
-from axolotl.integrations.config import merge_training_args
 
-AxolotlTrainingMixins: Type = merge_training_args()
+@dataclass
+class AxolotlTrainingMixins:
+    """
+    Mixin class for the Axolotl training args.
+    """
+
+    # pylint: disable=duplicate-code
+    model_type: Optional[str] = field(
+        default=None, metadata={"help": "HF model configuration model_type."}
+    )
+    lr_quadratic_warmup: bool = field(
+        default=False,
+        metadata={"help": "Use quadratic warmup for cosine scheduling."},
+    )
+    pretraining: bool = field(
+        default=False,
+        metadata={
+            "help": "Indicates to trainer whether we are doing continued pretraining."
+        },
+    )
+    sample_packing: bool = field(
+        default=False,
+        metadata={"help": "Use sample packing for efficient training."},
+    )
+    sample_packing_sequentially: bool = field(
+        default=False,
+        metadata={
+            "help": "Use next-fit sample packing that preserves the order of samples coming from the sampler. Use in combination with curriculum_sampling for fully sequential packing."
+        },
+    )
+    multipack_real_batches: bool = field(
+        default=False,
+        metadata={"help": "Use real batches for efficient training."},
+    )
+    eval_sample_packing: Optional[bool] = field(
+        default=None,
+        metadata={"help": "Use sample packing for efficient evals."},
+    )
+    sample_packing_efficiency: float = field(
+        default=1.0,
+        metadata={"help": "Sample packing efficiency for calculating batch length."},
+    )
+    sample_packing_bin_size: int = field(
+        default=200,
+        metadata={
+            "help": "The max number of samples that packed sample can contain after packing. Increase for better packing."
+        },
+    )
+    sample_packing_group_size: int = field(
+        default=100000,
+        metadata={
+            "help": "The number of samples to group together for packing. Increase for better packing."
+        },
+    )
+    max_seq_length: int = field(
+        default=2048,
+        metadata={"help": "The maximum sequence length the model can handle"},
+    )
+    relora_steps: Optional[int] = field(
+        default=None,
+        metadata={"help": "how often to reset for ReLoRA"},
+    )
+    relora_warmup_steps: Optional[int] = field(
+        default=None,
+        metadata={"help": "how many warmup steps to take after reset for ReLoRA"},
+    )
+    relora_anneal_steps: Optional[int] = field(
+        default=None,
+        metadata={"help": "how many warmup steps to take after reset for ReLoRA"},
+    )
+    relora_prune_ratio: Optional[float] = field(
+        default=0.9,
+        metadata={"help": "prune ratio for magnitude pruning of the optimizer"},
+    )
+    bench_split: Optional[str] = field(
+        default="eval", metadata={"help": "The benchmark split to run on"}
+    )
+    bench_dataset: Optional[str] = field(
+        default="pharaouk/dharma-1/dharma_1_mini.json",
+        metadata={
+            "help": "Benchmark dataset to use: options are `mmlu-zs`, `mmlu-fs`, or the full path to the dataset file"
+        },
+    )
+    do_bench_eval: Optional[bool] = field(
+        default=False, metadata={"help": "Whether to run the Benchmark evaluation."}
+    )
+    do_causal_lm_eval: Optional[bool] = field(
+        default=False, metadata={"help": "Whether to run the Causal LM evaluation."}
+    )
+    max_bench_samples: Optional[int] = field(
+        default=None,
+        metadata={
+            "help": "If set, only evaluates on `max_bench_samples` of the benchmark dataset."
+        },
+    )
+    bench_source_max_len: int = field(
+        default=2048, metadata={"help": "Maximum source sequence length for bench."}
+    )
+    dataloader_prefetch_factor: Optional[int] = field(
+        default=None,
+        metadata={"help": "prefetch_factor argument to the dataloader"},
+    )
+    cosine_min_lr_ratio: Optional[float] = field(
+        default=None,
+        metadata={"help": "Minimum learning rate is min_lr_ratio * learning_rate"},
+    )
+    cosine_constant_lr_ratio: Optional[float] = field(
+        default=None,
+        metadata={
+            "help": "Starting constant learning rate step is cosine_constant_lr_ratio * max_steps"
+        },
+    )
+    loraplus_lr_ratio: Optional[float] = field(
+        default=None, metadata={"help": "loraplus learning rate ratio lr_B / lr_A."}
+    )
+    loraplus_lr_embedding: Optional[float] = field(
+        default=1e-6,
+        metadata={"help": "loraplus learning rate for lora embedding layers."},
+    )
+    embedding_lr_scale: Optional[float] = field(
+        default=None,
+        metadata={"help": "Scale the learning rate for the embedding layers."},
+    )
+    lr_groups: Optional[list[dict]] = field(
+        default=None,
+        metadata={"help": "Specify learning rate groups for with different LRs."},
+    )
+    embedding_lr: Optional[float] = field(
+        default=None,
+        metadata={"help": "absolute learning rate for the embedding layers."},
+    )
+    qlora: bool = field(
+        default=False,
+        metadata={"help": "whether this is a qlora training"},
+    )
+    orpo_alpha: Optional[float] = field(
+        default=None,
+    )
+    lisa_n_layers: Optional[int] = field(
+        default=None,
+        metadata={"help": "the number of activate layers in LISA"},
+    )
+    lisa_step_interval: Optional[int] = field(
+        default=None,
+        metadata={"help": "how often to switch layers in LISA"},
+    )
+    lisa_layers_attribute: Optional[str] = field(
+        default=None,
+        metadata={"help": "path under the model to access the layers"},
+    )
+    curriculum_sampling: Optional[bool] = field(
+        default=None,
+        metadata={"help": "whether to use sequential sampling for curriculum learning"},
+    )
+    alternate_lr_scheduler_type: Optional[str] = field(
+        default=None,
+        metadata={
+            "help": "workaround to pass an alternate lr scheduler to the HF trainer"
+        },
+    )
+    chat_template: Optional[str] = field(
+        default=None,
+        metadata={"help": "Chat template converting chat messages to text"},
+    )
+
+    kd_ce_alpha: Optional[float] = field(
+        default=None,
+        metadata={
+            "help": "The alpha scaling parameter for SFT cross entropy loss when using KD"
+        },
+    )
+
+    kd_alpha: Optional[float] = field(
+        default=1.0,
+        metadata={"help": "The alpha scaling parameter for KD loss"},
+    )
+
+    kd_temperature: Optional[float] = field(
+        default=1.0,
+        metadata={
+            "help": "the temperature parameter for KL divergence loss when using KD"
+        },
+    )
+
+    kd_zscore_base_temp: Optional[float] = field(
+        default=None,
+        metadata={
+            "help": "the base temperature parameter for KL divergence with z-score when using KD"
+        },
+    )
+
+    kd_top_k_before_softmax: Optional[bool] = field(
+        default=None,
+        metadata={
+            "help": "Whether to apply top_k_before_softmax to the logits when using KD"
+        },
+    )
+
+    adam_beta3: Optional[float] = field(
+        default=None,
+        metadata={
+            "help": "The beta3 hyperparameter used in some optimizers such as CAME"
+        },
+    )
+    adam_epsilon2: Optional[float] = field(
+        default=None,
+        metadata={
+            "help": "The epsilon2 hyperparameter used in some optimizers such as CAME"
+        },
+    )
+
+    # multi-modal section
+
+    image_size: int | tuple[int, int] | None = field(
+        default=None,
+        metadata={"help": "The size of the image to resize to"},
+    )
+
+    image_resize_algorithm: Resampling | None = field(
+        default=None,
+        metadata={"help": "The algorithm to use for image resizing"},
+    )
+
+    # end of multi-modal section
 
 
 @dataclass

src/axolotl/core/training_args_base.py

@@ -1,224 +0,0 @@
-"""
-Base Axolotl Training Mixins shared across various trainer configs
-"""
-
-from dataclasses import dataclass, field
-from typing import Optional
-
-from PIL.Image import Resampling
-
-
-@dataclass
-class AxolotlTrainingMixins:
-    """
-    Mixin class for the Axolotl training args.
-    """
-
-    # pylint: disable=duplicate-code
-    model_type: Optional[str] = field(
-        default=None, metadata={"help": "HF model configuration model_type."}
-    )
-    lr_quadratic_warmup: bool = field(
-        default=False,
-        metadata={"help": "Use quadratic warmup for cosine scheduling."},
-    )
-    pretraining: bool = field(
-        default=False,
-        metadata={
-            "help": "Indicates to trainer whether we are doing continued pretraining."
-        },
-    )
-    sample_packing: bool = field(
-        default=False,
-        metadata={"help": "Use sample packing for efficient training."},
-    )
-    sample_packing_sequentially: bool = field(
-        default=False,
-        metadata={
-            "help": "Use next-fit sample packing that preserves the order of samples coming from the sampler. Use in combination with curriculum_sampling for fully sequential packing."
-        },
-    )
-    multipack_real_batches: bool = field(
-        default=False,
-        metadata={"help": "Use real batches for efficient training."},
-    )
-    eval_sample_packing: Optional[bool] = field(
-        default=None,
-        metadata={"help": "Use sample packing for efficient evals."},
-    )
-    sample_packing_efficiency: float = field(
-        default=1.0,
-        metadata={"help": "Sample packing efficiency for calculating batch length."},
-    )
-    sample_packing_bin_size: int = field(
-        default=200,
-        metadata={
-            "help": "The max number of samples that packed sample can contain after packing. Increase for better packing."
-        },
-    )
-    sample_packing_group_size: int = field(
-        default=100000,
-        metadata={
-            "help": "The number of samples to group together for packing. Increase for better packing."
-        },
-    )
-    max_seq_length: int = field(
-        default=2048,
-        metadata={"help": "The maximum sequence length the model can handle"},
-    )
-    dataset_num_proc: int | None = field(
-        default=None,
-        metadata={"help": "The number of processes to use for data processing"},
-    )
-    relora_steps: Optional[int] = field(
-        default=None,
-        metadata={"help": "how often to reset for ReLoRA"},
-    )
-    relora_warmup_steps: Optional[int] = field(
-        default=None,
-        metadata={"help": "how many warmup steps to take after reset for ReLoRA"},
-    )
-    relora_anneal_steps: Optional[int] = field(
-        default=None,
-        metadata={"help": "how many warmup steps to take after reset for ReLoRA"},
-    )
-    relora_prune_ratio: Optional[float] = field(
-        default=0.9,
-        metadata={"help": "prune ratio for magnitude pruning of the optimizer"},
-    )
-    bench_split: Optional[str] = field(
-        default="eval", metadata={"help": "The benchmark split to run on"}
-    )
-    bench_dataset: Optional[str] = field(
-        default="pharaouk/dharma-1/dharma_1_mini.json",
-        metadata={
-            "help": "Benchmark dataset to use: options are `mmlu-zs`, `mmlu-fs`, or the full path to the dataset file"
-        },
-    )
-    do_bench_eval: Optional[bool] = field(
-        default=False, metadata={"help": "Whether to run the Benchmark evaluation."}
-    )
-    do_causal_lm_eval: Optional[bool] = field(
-        default=False, metadata={"help": "Whether to run the Causal LM evaluation."}
-    )
-    max_bench_samples: Optional[int] = field(
-        default=None,
-        metadata={
-            "help": "If set, only evaluates on `max_bench_samples` of the benchmark dataset."
-        },
-    )
-    bench_source_max_len: int = field(
-        default=2048, metadata={"help": "Maximum source sequence length for bench."}
-    )
-    dataloader_prefetch_factor: Optional[int] = field(
-        default=None,
-        metadata={"help": "prefetch_factor argument to the dataloader"},
-    )
-    cosine_min_lr_ratio: Optional[float] = field(
-        default=None,
-        metadata={"help": "Minimum learning rate is min_lr_ratio * learning_rate"},
-    )
-    cosine_constant_lr_ratio: Optional[float] = field(
-        default=None,
-        metadata={
-            "help": "Starting constant learning rate step is cosine_constant_lr_ratio * max_steps"
-        },
-    )
-    loraplus_lr_ratio: Optional[float] = field(
-        default=None, metadata={"help": "loraplus learning rate ratio lr_B / lr_A."}
-    )
-    loraplus_lr_embedding: Optional[float] = field(
-        default=1e-6,
-        metadata={"help": "loraplus learning rate for lora embedding layers."},
-    )
-    embedding_lr_scale: Optional[float] = field(
-        default=None,
-        metadata={"help": "Scale the learning rate for the embedding layers."},
-    )
-    lr_groups: Optional[list[dict]] = field(
-        default=None,
-        metadata={"help": "Specify learning rate groups for with different LRs."},
-    )
-    embedding_lr: Optional[float] = field(
-        default=None,
-        metadata={"help": "absolute learning rate for the embedding layers."},
-    )
-    qlora: bool = field(
-        default=False,
-        metadata={"help": "whether this is a qlora training"},
-    )
-    orpo_alpha: Optional[float] = field(
-        default=None,
-    )
-    lisa_n_layers: Optional[int] = field(
-        default=None,
-        metadata={"help": "the number of activate layers in LISA"},
-    )
-    lisa_step_interval: Optional[int] = field(
-        default=None,
-        metadata={"help": "how often to switch layers in LISA"},
-    )
-    lisa_layers_attribute: Optional[str] = field(
-        default=None,
-        metadata={"help": "path under the model to access the layers"},
-    )
-    curriculum_sampling: Optional[bool] = field(
-        default=None,
-        metadata={"help": "whether to use sequential sampling for curriculum learning"},
-    )
-    alternate_lr_scheduler_type: Optional[str] = field(
-        default=None,
-        metadata={
-            "help": "workaround to pass an alternate lr scheduler to the HF trainer"
-        },
-    )
-    chat_template: Optional[str] = field(
-        default=None,
-        metadata={"help": "Chat template converting chat messages to text"},
-    )
-
-    # kd_ce_alpha: Optional[float] = field(
-    #     default=None,
-    #     metadata={
-    #         "help": "The alpha scaling parameter for SFT cross entropy loss when using KD"
-    #     },
-    # )
-    #
-    # kd_alpha: Optional[float] = field(
-    #     default=1.0,
-    #     metadata={"help": "The alpha scaling parameter for KD loss"},
-    # )
-    #
-    # kd_temperature: Optional[float] = field(
-    #     default=1.0,
-    #     metadata={
-    #         "help": "the temperature parameter for KL divergence loss when using KD"
-    #     },
-    # )
-
-    adam_beta3: Optional[float] = field(
-        default=None,
-        metadata={
-            "help": "The beta3 hyperparameter used in some optimizers such as CAME"
-        },
-    )
-    adam_epsilon2: Optional[float] = field(
-        default=None,
-        metadata={
-            "help": "The epsilon2 hyperparameter used in some optimizers such as CAME"
-        },
-    )
-
-    # multi-modal section
-
-    image_size: int | tuple[int, int] | None = field(
-        default=None,
-        metadata={"help": "The size of the image to resize to"},
-    )
-
-    image_resize_algorithm: Resampling | None = field(
-        default=None,
-        metadata={"help": "The algorithm to use for image resizing"},
-    )
-
-    # end of multi-modal section

src/axolotl/datasets.py

@@ -1,6 +1,7 @@
 """Module containing Dataset functionality"""
 
 import os
+from typing import List, Optional, Union
 
 import torch
 from datasets import Dataset, IterableDataset
@@ -19,21 +20,21 @@ LOG = get_logger(__name__)
 
 
 class TokenizedPromptDataset(Dataset):
-    """Dataset that returns tokenized prompts from a stream of text files.
+    """
-
+    Dataset that returns tokenized prompts from a stream of text files.
-    Args:
+        Args:
-        prompt_tokenizer: The prompt tokenizing method for processing the data.
+            prompt_tokenizer (PromptTokenizingStrategy): The prompt tokenizing method for processing the data.
-        dataset: Dataset with text files.
+            dataset (dataset.Dataset): Dataset with text files.
-        process_count: Number of processes to use for tokenizing.
+            process_count (int): Number of processes to use for tokenizing.
-        keep_in_memory: Whether to keep the tokenized dataset in memory.
+            keep_in_memory (bool): Whether to keep the tokenized dataset in memory.
     """
 
     def __init__(  # pylint: disable=super-init-not-called
         self,
         prompt_tokenizer: PromptTokenizingStrategy,
         dataset: Dataset,
-        process_count: int | None = None,
+        process_count: Optional[int] = None,
-        keep_in_memory: bool | None = False,
+        keep_in_memory: Optional[bool] = False,
         **kwargs,
     ):
         self.prompt_tokenizer = prompt_tokenizer
@@ -48,13 +49,6 @@ class TokenizedPromptDataset(Dataset):
         features = dataset.features.keys()
         num_proc = min(64, self.process_count if self.process_count else os.cpu_count())
 
-        # Disable multiprocessing if the tokenizer doesn't support it (e.g., mistral_common)
-        if not getattr(self.prompt_tokenizer, "supports_multiprocessing", True):
-            LOG.info(
-                "Disabling multiprocessing for tokenizer as it doesn't support it (e.g., mistral_common)"
-            )
-            num_proc = 1
-
         map_kwargs = {}
         if self.prompt_tokenizer.supports_batched:
             map_kwargs["batched"] = True
@@ -82,14 +76,14 @@ class TokenizedPromptDataset(Dataset):
 
 def wrap_dataset_for_tokenized_prompt(
     prompt_tokenizer: PromptTokenizingStrategy,
-    dataset: Dataset | IterableDataset,
+    dataset: Union[Dataset, IterableDataset],
     **kwargs,
 ):
     if isinstance(dataset, IterableDataset):
         map_kwargs = {}
         if prompt_tokenizer.supports_batched:
             map_kwargs["batched"] = True
-        features = list(dataset.features.keys())
+        features = dataset.features.keys()
         return dataset.map(
             prompt_tokenizer.tokenize_prompt,
             remove_columns=features,
@@ -100,13 +94,12 @@ def wrap_dataset_for_tokenized_prompt(
 
 # TODO this isn't the best since it can't interleave datasets
 class ConstantLengthDataset(IterableDataset):
-    """Iterable dataset that returns constant length chunks of tokens from stream of
+    """
-    text files.
+    Iterable dataset that returns constant length chunks of tokens from stream of text files.
-
+        Args:
-    Args:
+            tokenizer (Tokenizer): The processor used for processing the data.
-        tokenizer: The processor used for processing the data.
+            dataset (dataset.Dataset): Dataset with text files.
-        dataset: Dataset with text files.
+            seq_length (int): Length of token sequences to return.
-        seq_length: Length of token sequences to return.
     """
 
     def __init__(  # pylint: disable=super-init-not-called
@@ -117,7 +110,7 @@ class ConstantLengthDataset(IterableDataset):
     ):
         self.tokenizer = tokenizer
         self.concat_token_id = tokenizer.eos_token_id
-        self.datasets: list[IterableDataset] = datasets
+        self.datasets: List[IterableDataset] = datasets
         self.seq_length = seq_length
 
         vocab_size = len(tokenizer.get_vocab())
@@ -181,10 +174,7 @@ class ConstantLengthDataset(IterableDataset):
                             }
                         else:
                             LOG.warning(
-                                "Dropping batch due to tensor size mismatch "
+                                f"dropping batch due to tensor size mismatch input_ids: {input_ids.size()}, labels: {labels.size()}, attention_mask: {attention_mask.size()}"
-                                f"input_ids: {input_ids.size()}, "
-                                f"labels: {labels.size()}, "
-                                f"attention_mask: {attention_mask.size()}"
                             )
                     buffer = {
                         "input_ids": [],

src/axolotl/evaluate.py

@@ -7,16 +7,17 @@ from pathlib import Path
 from typing import Dict, Optional
 
 import torch
+from accelerate.logging import get_logger
 from datasets import Dataset
 from transformers.trainer import Trainer
 
+from axolotl.telemetry.errors import send_errors
 from axolotl.train import (
     TrainDatasetMeta,
     setup_model_and_tokenizer,
 )
 from axolotl.utils.dict import DictDefault
 from axolotl.utils.distributed import cleanup_distributed
-from axolotl.utils.logging import get_logger
 from axolotl.utils.trainer import setup_trainer
 
 project_root = os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))
@@ -63,6 +64,7 @@ def evaluate_dataset(
     return metrics
 
 
+@send_errors
 def evaluate(*, cfg: DictDefault, dataset_meta: TrainDatasetMeta) -> Dict[str, float]:
     """
     Evaluate a model on training and validation datasets.

src/axolotl/integrations/base.py

@@ -22,7 +22,6 @@ from __future__ import annotations
 
 import collections
 import importlib
-import traceback
 from typing import TYPE_CHECKING, Callable, OrderedDict, Union
 
 from peft import PeftModel
@@ -84,11 +83,6 @@ class BasePlugin:
     def get_input_args(self) -> str | None:
         """Returns a pydantic model for the plugin's input arguments."""
 
-    def get_training_args_mixin(self) -> str | None:
-        """
-        Returns a dataclass model for the plugin's training arguments.
-        """
-
     def load_datasets(
         self, cfg: DictDefault, preprocess: bool = False
     ) -> Union["TrainDatasetMeta", None]:
@@ -164,31 +158,6 @@ class BasePlugin:
             trainer: The trainer object for training.
         """
 
-    def get_training_args(self, cfg: DictDefault):  # pylint: disable=unused-argument):
-        """
-        Returns custom training arguments to set on TrainingArgs.
-
-        Args:
-            cfg: The global axolotl configuration.
-
-        Returns:
-            object: dict containing the training arguments.
-        """
-
-    def get_collator_cls_and_kwargs(
-        self, cfg: DictDefault, is_eval: bool = False
-    ):  # pylint: disable=unused-argument):
-        """
-        Returns a custom class for the collator.
-
-        Args:
-            cfg: The global axolotl configuration.
-            is_eval: Whether this is an eval split.
-
-        Returns:
-            class: The class for the collator.
-        """
-
     # pylint: disable=unused-argument
     def create_optimizer(self, cfg: DictDefault, trainer: Trainer) -> Optimizer | None:
         """Creates and returns an optimizer for training.
@@ -309,7 +278,7 @@ def load_plugin(plugin_name: str) -> BasePlugin:
     return plugin
 
 
-class PluginManager:  # pylint: disable=too-many-public-methods
+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.
 
@@ -368,11 +337,8 @@ class PluginManager:  # pylint: disable=too-many-public-methods
             plugin = load_plugin(plugin_name)
             self.plugins[plugin_name] = plugin
             LOG.info(f"Plugin loaded successfully: {plugin_name}")
-        except ImportError as exc:
+        except ImportError:
             LOG.error(f"Failed to load plugin: {plugin_name}")
-            # print stacktrace
-            traceback.print_exc()
-            print(f"Error: {exc}")
 
     def get_input_args(self) -> list[str]:
         """Returns a list of Pydantic classes for all registered plugins' input arguments.'
@@ -387,20 +353,6 @@ class PluginManager:  # pylint: disable=too-many-public-methods
                 input_args.append(input_args_from_plugin)
         return input_args
 
-    def get_training_args_mixin(self):
-        """
-        Returns a list of dataclasses for all registered plugins' training args mixins'
-
-        Returns:
-        list[str]: A list of dataclsses
-        """
-        training_args = []
-        for plugin in self.plugins.values():
-            training_args_from_plugin = plugin.get_training_args_mixin()
-            if training_args_from_plugin is not None:
-                training_args.append(training_args_from_plugin)
-        return training_args
-
     def load_datasets(
         self, cfg: DictDefault, preprocess: bool = False
     ) -> Union["TrainDatasetMeta", None]:
@@ -490,42 +442,6 @@ class PluginManager:  # pylint: disable=too-many-public-methods
                 return trainer_cls
         return None
 
-    def get_training_args(self, cfg):
-        """
-        Calls the get_training_args method of all registered plugins and returns the combined training arguments.
-
-        Parameters:
-        cfg (dict): The configuration for the plugins.
-
-        Returns:
-        object: The training arguments
-        """
-        training_args_kwargs = {}
-        for plugin in self.plugins.values():
-            training_args = plugin.get_training_args(cfg)
-            if training_args is not None:
-                training_args_kwargs.update(training_args)
-
-        return training_args_kwargs
-
-    def get_collator_cls_and_kwargs(self, cfg, is_eval=False):
-        """
-        Calls the get_collator_cls_and_kwargs method of all registered plugins and returns the first non-None collator class.
-
-        Parameters:
-        cfg (dict): The configuration for the plugins.
-        is_eval (bool): Whether this is an eval split.
-
-        Returns:
-        object: The collator class, or None if none was found.
-        """
-        for plugin in self.plugins.values():
-            collator = plugin.get_collator_cls_and_kwargs(cfg, is_eval=is_eval)
-            if collator is not None:
-                collator_cls, collator_kwargs = collator
-                return collator_cls, collator_kwargs
-        return None
-
     def post_trainer_create(self, cfg: DictDefault, trainer: Trainer):
         """Calls the `post_trainer_create` method of all registered plugins.
 

src/axolotl/integrations/config.py

@@ -16,7 +16,7 @@ Module to handle merging the plugins' input arguments with the base configuratio
 This was moved here to prevent circular imports.
 """
 
-from typing import Any, Dict, List, Type
+from typing import Any, Dict, List
 
 from axolotl.utils.schemas.config import (
     AxolotlConfigWCapabilities as AxolotlConfigWCapabilitiesBase,
@@ -61,43 +61,3 @@ def merge_input_args():
         ]
         return AxolotlConfigWCapabilities, AxolotlInputConfig
     return AxolotlConfigWCapabilitiesBase, AxolotlInputConfigBase
-
-
-def merge_training_args() -> Type:
-    """
-    Merges training arguments from registered plugins with the base TrainingArguments.
-
-    This function retrieves the training arguments from registered plugins using the PluginManager.
-    It then dynamically creates new classes, AxolotlTrainingMixins,
-    that inherit from the base configurations and include the training arguments from the plugins.
-
-    Returns:
-    tuple: A tuple containing the newly created classes, AxolotlTrainingMixins.
-    """
-    # pylint: disable=duplicate-code
-    from axolotl.core.training_args_base import (
-        AxolotlTrainingMixins as AxolotlTrainingMixinsBase,
-    )
-    from axolotl.integrations.base import PluginManager
-
-    plugin_manager = PluginManager.get_instance()
-    training_args_mixins: List[str] = plugin_manager.get_training_args_mixin()
-    mixin_classes = []
-    dynamic_input = ""
-    for plugin_args in training_args_mixins:
-        plugin_module, plugin_cls = plugin_args.rsplit(".", 1)
-        dynamic_input += f"from {plugin_module} import {plugin_cls}\n"
-        mixin_classes.append(plugin_cls)
-    if dynamic_input:
-        dynamic_input += f"class AxolotlTrainingMixins(AxolotlTrainingMixinsBase, {', '.join(mixin_classes)}):\n    pass\n"
-
-        namespace: Dict[Any, Any] = {}
-        local_vars = {"AxolotlTrainingMixinsBase": AxolotlTrainingMixinsBase}
-        exec(  # pylint: disable=exec-used  # nosec B102
-            dynamic_input, {**globals(), **local_vars}, namespace
-        )
-        AxolotlTrainingMixins = namespace[  # pylint: disable=invalid-name
-            "AxolotlTrainingMixins"
-        ]
-        return AxolotlTrainingMixins
-    return AxolotlTrainingMixinsBase

src/axolotl/integrations/cut_cross_entropy/README.md

@@ -24,14 +24,6 @@ pip3 uninstall -y cut-cross-entropy && pip3 install "cut-cross-entropy[transform
 
 ## Usage
 
-**NOTE**: If you are training a VLM model, please use older version of Axolotl as upstream has applied a major VLM refactor, and our patches have not been updated yet.
-
-```bash
-git checkout 787880215b3ab32ccaf81c1b2e9588c6f3e6e764
-
-pip3 install --no-build-isolation -e .
-```
-
 ```yaml
 plugins:
   - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin

src/axolotl/integrations/kd/__init__.py

@@ -15,12 +15,7 @@
 """
 Plugin init to add KD support to Axolotl.
 """
-from typing import Any
-
-from transformers import Trainer
-
 from axolotl.integrations.base import BasePlugin
-from axolotl.integrations.kd.callbacks import KDTemperatureSchedulerCallback
 
 from .args import KDArgs  # pylint: disable=unused-import. # noqa: F401
 
@@ -33,75 +28,9 @@ class KDPlugin(BasePlugin):
     def get_input_args(self):
         return "axolotl.integrations.kd.KDArgs"
 
-    def get_training_args_mixin(self):
-        return "axolotl.integrations.kd.args.KDTrainingArgsMixin"
-
     def get_trainer_cls(self, cfg):
         if cfg.kd_trainer:
             from .trainer import AxolotlKDTrainer
 
             return AxolotlKDTrainer
         return None
-
-    def get_training_args(self, cfg):
-        return {
-            "kd_ce_alpha": cfg.kd_ce_alpha,
-            "kd_alpha": cfg.kd_alpha,
-            "kd_temperature": cfg.kd_temperature,
-            "kd_beta": cfg.kd_beta,
-            "kd_normalize_topk": cfg.kd_normalize_topk,
-        }
-
-    def get_collator_cls_and_kwargs(self, cfg, is_eval=False):
-        if not cfg.kd_trainer:
-            return None, None
-
-        from .collator import DataCollatorForKD, KDBatchSamplerDataCollatorForSeq2Seq
-
-        use_batch_sampler_collator = False
-        if is_eval is False and cfg.sample_packing:
-            use_batch_sampler_collator = True
-        if cfg.eval_sample_packing and is_eval:
-            use_batch_sampler_collator = True
-
-        if cfg.kd_online_server_base_url:
-            from .collator_online_teacher import OnlineTeacherCollator
-
-            return OnlineTeacherCollator, {
-                "kd_online_server_base_url": cfg.kd_online_server_base_url,
-                "kd_online_topk": cfg.kd_online_topk,
-                "kd_temperature": cfg.kd_temperature,
-                "kd_online_server": cfg.kd_online_server,
-                "kd_online_timeout": cfg.kd_online_timeout,
-                "kd_normalize_topk": cfg.kd_normalize_topk,
-            }
-
-        if use_batch_sampler_collator:
-            return KDBatchSamplerDataCollatorForSeq2Seq, {}
-        return DataCollatorForKD, {}
-
-    def pre_model_load(self, cfg):
-        from .kernels.models import apply_kernel
-
-        apply_kernel(cfg.model_config_type)
-
-    def add_callbacks_post_trainer(self, cfg: Any, trainer: Trainer) -> list:
-        """
-        Adds temp scheduler callback to the Trainer instance.
-
-        Args:
-            cfg (Any): Configuration object containing the sparse recipe.
-            trainer (Trainer): Huggingface Trainer instance.
-
-        Returns:
-            list: List containing the configured callback instances.
-        """
-        if cfg.kd_temperature_min is not None and cfg.kd_online_server_base_url:
-            callback = KDTemperatureSchedulerCallback(
-                cfg.kd_temperature,
-                cfg.kd_temperature_min,
-                trainer,
-            )
-            return [callback]
-
-        return []

src/axolotl/integrations/kd/args.py

@@ -15,19 +15,9 @@
 """
 Plugin args for KD support.
 """
-from dataclasses import dataclass
+from typing import Optional
-from enum import Enum
 
-from pydantic import BaseModel, Field
+from pydantic import BaseModel
-
-
-class InferenceServerType(str, Enum):
-    """
-    Online inferences server types to handle different request args
-    """
-
-    vllm = "vllm"  # pylint: disable=invalid-name
-    sglang = "sglang"  # pylint: disable=invalid-name
 
 
 class KDArgs(BaseModel):
@@ -35,41 +25,13 @@ class KDArgs(BaseModel):
     Input args for knowledge distillation.
     """
 
-    kd_trainer: float | None = None  # whether to use KD trainer
+    kd_trainer: Optional[bool] = None  # whether to use KD trainer
-    kd_ce_alpha: float | None = (
+    kd_ce_alpha: Optional[float] = (
         None  # loss coefficient for cross-entropy loss during KD
     )
-    kd_alpha: float | None = None  # loss coefficient for KD loss
+    kd_alpha: Optional[float] = None  # loss coefficient for KD loss
-    kd_temperature: float | None = None  # temperature for sampling during KD
+    kd_temperature: Optional[float] = None  # temperature for sampling during KD
-    kd_beta: float | None = 0.0  # beta coefficient for ratio of fwd and reverse KL
+    kd_zscore_base_temp: Optional[float] = None  # base temperature for zscore scaling
-    kd_normalize_topk: bool | None = (
+    kd_top_k_before_softmax: Optional[bool] = (
-        None  # whether to normalize student logits during KD
+        None  # whether to sample top k before softmax during KD
-    )
-
-    # TODO online kd
-    kd_online_server_base_url: str | None = None
-    kd_online_topk: int | None = None
-    kd_online_server: InferenceServerType | None = Field(
-        default_factory=lambda: InferenceServerType.vllm
-    )
-    kd_online_timeout: int | None = 120
-    kd_temperature_min: float | None = (
-        None  # kd temperature scheduling during online kd
-    )
-
-
-@dataclass
-class KDTrainingArgsMixin:
-    """
-    Additional args for KD training.
-    """
-
-    kd_ce_alpha: float | None = (
-        None  # loss coefficient for cross-entropy loss during KD
-    )
-    kd_alpha: float | None = None  # loss coefficient for KD loss
-    kd_temperature: float | None = None  # temperature for sampling during KD
-    kd_beta: float | None = None  # beta coefficient for ratio of fwd and reverse KL
-    kd_normalize_topk: float | None = (
-        None  # whether to normalize student logits during KD
     )

src/axolotl/integrations/kd/callbacks.py

@@ -1,36 +0,0 @@
-"""
-Transformers trainer callbacks to schedule the KD temperature during training
-"""
-
-import math
-
-from transformers.trainer_callback import TrainerCallback
-
-
-class KDTemperatureSchedulerCallback(TrainerCallback):
-    """
-    KD temperature scheduler callback for the trainer.
-    """
-
-    def __init__(self, temperature_start, temperature_min, trainer):
-        self.temperature_start = temperature_start
-        self.temperature_min = temperature_min
-        self.temperature = temperature_start
-
-        self.trainer = trainer
-
-    def on_step_end(
-        self, args, state, control, **kwargs
-    ):  # pylint: disable=unused-argument
-        # cosine decay temperature over the max steps
-
-        progress = state.global_step / state.max_steps
-        # Cosine decay factor: 0.5 * (1 + cos(pi * progress))
-        # This factor goes from 1 (at progress=0) to 0 (at progress=1)
-        decay_factor = 0.5 * (1.0 + math.cos(math.pi * progress))
-        self.temperature = self.temperature_start - (
-            (self.temperature_start - self.temperature_min) * (1.0 - decay_factor)
-        )
-
-        if hasattr(self.trainer.data_collator, "kd_temperature"):
-            self.trainer.data_collator.kd_temperature = self.temperature

src/axolotl/integrations/kd/chat_template.py

@@ -15,15 +15,12 @@
 """
 Chat template prompt strategy loader with KD support
 """
-import logging
 from typing import Any, Dict
 
 import torch
 
 from axolotl.prompt_strategies.chat_template import ChatTemplateStrategy, StrategyLoader
 
-LOG = logging.getLogger(__name__)
-
 
 class ChatTemplateStrategyWithKD(ChatTemplateStrategy):
     """
@@ -104,8 +101,10 @@ class ChatTemplateStrategyWithKD(ChatTemplateStrategy):
         # fill with -inf for padding_len tokens for top_k tokens
         # extend target_logprobs with a padding_len x top_k 2D list filled with -inf
 
-        # we shift for causal models in the trainer, so start the range from 0
+        # for causal models, if we start the range at 1, then we don't need to shift in the trainer
-        for _ in range(0, input_padding_len):
+        # otherwise, we need to shift in the trainer
+        shift = 0
+        for _ in range(shift, input_padding_len):
             target_logprobs.append([-float("inf")] * top_k)
             target_token_ids.append(list(range(top_k)))
             target_mask.append([0] * top_k)
@@ -144,10 +143,6 @@ class ChatTemplateStrategyWithKD(ChatTemplateStrategy):
             #
             # Convert from log to probability
             teacher_probs_t1 = position_logprobs_tensor.exp()
-            # normalize probabilities to sum to 1 in case they aren't already
-            teacher_probs_t1_sum = teacher_probs_t1.sum(dim=0, keepdim=True)
-            if teacher_probs_t1_sum > 1e-9:
-                teacher_probs_t1 = teacher_probs_t1 / teacher_probs_t1_sum
             if self.kd_temperature != self.gen_temperature:
                 # Exponentiate by factor (T1 / T2)
                 exponent = self.gen_temperature / self.kd_temperature
@@ -167,115 +162,12 @@ class ChatTemplateStrategyWithKD(ChatTemplateStrategy):
             target_logprobs.append(position_logprobs_scaled)
             target_token_ids.append(position_token_ids)
 
-        # Update sample with transformed logprobs
+        if shift == 1:
-        sample["target_logprobs"] = target_logprobs
+            # since we started at index 1 for causal, we need one more padding token
-        sample["target_token_ids"] = target_token_ids
-        sample["target_mask"] = target_mask
-
-        return sample
-
-    def _tokenize_single_prompt(self, prompt):
-        logprobs = prompt.pop(self.logprobs_field)
-        tokenized_prompt = super()._tokenize_single_prompt(prompt)
-        tokenized_prompt[self.logprobs_field] = logprobs
-        tokenized_prompt = self.transform_logprobs(tokenized_prompt)
-
-        return tokenized_prompt
-
-
-class ChatTemplateStrategyWithKDv2(ChatTemplateStrategyWithKD):
-    """
-    Strat for datasets with complete structured KD logprob data
-    """
-
-    def transform_logprobs(self, sample):
-        """
-        Transform logprobs to target format for KD training
-        """
-        # pylint: disable=duplicate-code
-
-        logprobs = sample.pop(self.logprobs_field)
-        target_seq_len = len(logprobs)
-        input_seq_len = len(sample["input_ids"])
-        input_padding_len = input_seq_len - target_seq_len
-        # get non-zero top-k (prune None logprobs from vllm data step)
-        top_k_vals = [
-            len(logprobs[i])
-            for i in range(len(logprobs))
-            if logprobs[i] is not None and len(logprobs[i])
-        ]
-        max_top_k = max(set(top_k_vals), key=top_k_vals.count)
-        min_top_k = min(set(top_k_vals), key=top_k_vals.count)
-        top_k = min(max_top_k, min_top_k)
-        if top_k == 0:
-            raise ValueError("No non-zero top-k logprobs found.")
-
-        target_logprobs = []
-        target_token_ids = []
-        target_mask = []
-
-        if input_padding_len < 0:
-            # logprobs is longer than target_seq_len,
-            # so we need to slice from the left/beginning of logprobs
-            logprobs = logprobs[:-input_seq_len]
-            input_padding_len = 0
-            # target_seq_len = input_seq_len
-
-        # truncate the second dimension of the logprobs to top_k
-        logprobs = [row[:top_k] for row in logprobs]
-
-        # fill with -inf for padding_len tokens for top_k tokens
-        # extend target_logprobs with a padding_len x top_k 2D list filled with -inf
-
-        # we shift for causal models in the trainer, so start the range from 0
-        for _ in range(0, input_padding_len):
             target_logprobs.append([-float("inf")] * top_k)
             target_token_ids.append(list(range(top_k)))
             target_mask.append([0] * top_k)
 
-        for position in range(input_padding_len, input_seq_len):
-            if sample["labels"][position] == -100:
-                target_mask.append([0] * top_k)
-            else:
-                target_mask.append([1] * top_k)
-
-        for token_pos_logprobs, pos_target_token_ids in zip(
-            logprobs, sample["target_token_ids"]
-        ):
-            # Convert to a tensor for easier manipulation
-            position_logprobs_tensor = torch.tensor(
-                token_pos_logprobs, dtype=torch.float
-            )
-
-            # Now we have distribution at T1 in log form, i.e. log p_{T1}(k).
-            # Next, re-scale to T2 = self.kd_temperature via exponent-based trick
-            # p_{T2}(k) = [p_{T1}(k)]^(T1 / T2) / Z
-            #
-            # Convert from log to probability
-            teacher_probs_t1 = position_logprobs_tensor.exp()
-            # normalize probabilities to sum to 1 in case they aren't already
-            teacher_probs_t1_sum = teacher_probs_t1.sum(dim=0, keepdim=True)
-            if teacher_probs_t1_sum > 1e-9:
-                teacher_probs_t1 = teacher_probs_t1 / teacher_probs_t1_sum
-            if self.kd_temperature != self.gen_temperature:
-                # Exponentiate by factor (T1 / T2)
-                exponent = self.gen_temperature / self.kd_temperature
-                teacher_probs_t2 = teacher_probs_t1**exponent
-            else:
-                teacher_probs_t2 = teacher_probs_t1
-            # Re-normalize
-            teacher_probs_t2 = teacher_probs_t2 / teacher_probs_t2.sum(
-                dim=0, keepdim=True
-            )
-            # Convert back to log
-            position_logprobs_tensor = torch.log(teacher_probs_t2)
-
-            # Now we have log p_{teacher, T2}(k) stored in position_logprobs_tensor
-            position_logprobs_scaled = position_logprobs_tensor.tolist()
-
-            target_logprobs.append(position_logprobs_scaled)
-            target_token_ids.append(pos_target_token_ids)
-
         # Update sample with transformed logprobs
         sample["target_logprobs"] = target_logprobs
         sample["target_token_ids"] = target_token_ids
@@ -285,10 +177,8 @@ class ChatTemplateStrategyWithKDv2(ChatTemplateStrategyWithKD):
 
     def _tokenize_single_prompt(self, prompt):
         logprobs = prompt.pop(self.logprobs_field)
-        target_token_ids = prompt.pop("target_token_ids")
         tokenized_prompt = super()._tokenize_single_prompt(prompt)
         tokenized_prompt[self.logprobs_field] = logprobs
-        tokenized_prompt["target_token_ids"] = target_token_ids
         tokenized_prompt = self.transform_logprobs(tokenized_prompt)
 
         return tokenized_prompt
@@ -299,7 +189,7 @@ class KDStrategyLoader(StrategyLoader):
     Load ChatTemplateStrategy with KD support using StrategyLoader.
     """
 
-    def _get_strategy_cls(self, cfg):  # pylint: disable=unused-argument
+    def _get_strategy_cls(self):
         return ChatTemplateStrategyWithKD
 
     def _get_strategy_params(self, cfg, ds_cfg: Dict[str, Any]):
@@ -314,14 +204,4 @@ class KDStrategyLoader(StrategyLoader):
         return strategy_params
 
 
-class KDStrategyLoaderV2(KDStrategyLoader):
+load = KDStrategyLoader()
-    """
-    Load KD chat template datasets with pre-tokenized logprob data
-    """
-
-    def _get_strategy_cls(self, cfg):  # pylint: disable=unused-argument
-        return ChatTemplateStrategyWithKDv2
-
-
-load_legacy = KDStrategyLoader()
-load = KDStrategyLoaderV2()

src/axolotl/integrations/kd/collator.py

@@ -47,16 +47,11 @@ class DataCollatorForKD(DataCollatorForSeq2Seq):
     position_pad_token_id: int = 0
     return_tensors: str = "pt"
 
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        self.tokenizer.deprecation_warnings["Asking-to-pad-a-fast-tokenizer"] = True
-
     def __call__(self, features, return_tensors=None):
         if return_tensors is None:
             return_tensors = self.return_tensors
 
         padding_side = self.tokenizer.padding_side
-        max_len = 0
 
         # Pad labels and position_ids first
         for feature_name, pad_token_id in [
@@ -107,9 +102,7 @@ class DataCollatorForKD(DataCollatorForSeq2Seq):
                 target_mask_list.append(f.pop("target_mask"))
 
             # Determine max lengths
-            max_teacher_seq_len = max_len or max(
+            max_teacher_seq_len = max(len(seq) for seq in target_logprobs_list)
-                len(seq) for seq in target_logprobs_list
-            )
             max_k = max(len(seq_k) for seq in target_logprobs_list for seq_k in seq)
 
             padded_target_logprobs = []
@@ -216,9 +209,7 @@ class KDBatchSamplerDataCollatorForSeq2Seq(DataCollatorForKD):
         #    We want to produce a single "merged" feature dict for each sub-batch.
         out_features = [{} for _ in features]
 
-        for i, sub_features in enumerate(  # pylint: disable=too-many-nested-blocks
+        for i, sub_features in enumerate(features):
-            features
-        ):
             # sub_features is a list of dicts, each dict = one sequence’s features
             # We'll merge them into out_features[i].
             #
@@ -252,17 +243,10 @@ class KDBatchSamplerDataCollatorForSeq2Seq(DataCollatorForKD):
                     # For example, input_ids or labels are often arrays.
                     arrays = []
                     for feat in sub_features:
-                        if field_name in feat and isinstance(
+                        if field_name in feat:
-                            feat[field_name], (list, torch.Tensor)
-                        ):
-                            if isinstance(
-                                feat[field_name][0], (dict, str)
-                            ):  # pylint: disable=too-many-nested-blocks
-                                continue
                             arr = np.array(feat[field_name])
                             arrays.append(arr)
-                    if arrays:
+                    out_features[i][field_name] = np.concatenate(arrays)
-                        out_features[i][field_name] = np.concatenate(arrays)
 
         # 3) Now call the parent collator, which will do:
         #    - padding of labels/position_ids

src/axolotl/integrations/kd/collator_online_teacher.py

@@ -1,561 +0,0 @@
-"""
-Packed data loader for online teacher training supporting vllm and sglang.
-"""
-
-import hashlib
-import hmac
-import logging
-from typing import Any, Dict, List, Optional
-
-import requests
-import torch
-from orjson import orjson
-
-from axolotl.integrations.kd.collator import KDBatchSamplerDataCollatorForSeq2Seq
-from axolotl.integrations.kd.utils import normalize_logprobs
-from axolotl.utils.data.utils import retry_on_request_exceptions
-
-LOG = logging.getLogger(__name__)
-
-
-def hmac_sha_from_int_list(int_list, key, hash_func=hashlib.sha256):
-    """
-    Create HMAC-SHA hash from a list of integers
-
-    Args:
-        int_list: List of integers
-        key: Secret key (string or bytes)
-        hash_func: Hash function (default: sha256)
-
-    Returns:
-        HMAC digest as hex string
-    """
-    # Convert key to bytes if it's a string
-    if isinstance(key, str):
-        key = key.encode("utf-8")
-
-    # Convert list of ints to bytes
-    # Method 1: Convert each int to bytes and concatenate
-    data = b"".join(i.to_bytes(4, byteorder="big") for i in int_list)
-
-    # Create HMAC
-    h = hmac.new(key, data, hash_func)
-    return h.hexdigest()
-
-
-class OnlineTeacherCollator(KDBatchSamplerDataCollatorForSeq2Seq):
-    """
-    Collator for online teacher training.
-    """
-
-    DEFAULT_LABEL_PAD_TOKEN_ID: int = -100
-
-    def __init__(
-        self,
-        *args: Any,
-        kd_online_server_base_url: Optional[str] = None,
-        kd_online_topk: Optional[int] = None,
-        kd_temperature: Optional[float] = 1.0,
-        kd_online_server: Optional[str] = "vllm",
-        kd_online_timeout: Optional[int] = 120,
-        kd_cache_dir: Optional[str] = None,
-        kd_normalize_topk: Optional[bool] = True,
-        **kwargs: Any,
-    ):
-        super().__init__(*args, **kwargs)
-
-        if kd_online_server_base_url is None:
-            raise ValueError(
-                "kd_online_server_base_url must be provided for OnlineTeacherDataloader"
-            )
-        if kd_online_topk is None or kd_online_topk <= 0:
-            raise ValueError(
-                "kd_online_topk must be a positive integer for OnlineTeacherDataloader"
-            )
-
-        self.kd_online_server_base_url = kd_online_server_base_url.rstrip("/")
-        self.kd_online_topk = kd_online_topk
-        self.kd_temperature = kd_temperature
-        self.kd_online_server = kd_online_server
-        self.http_session = requests.Session()
-        self.kd_online_timeout = kd_online_timeout
-        self.kd_cache_dir = kd_cache_dir
-        self.kd_normalize_topk = kd_normalize_topk
-
-    def _normalize_logprobs(self, raw_logprobs: List[float]) -> List[float]:
-        """
-        Re-normalizes top-k raw logprobs as probabilities, and converts back to logprobs.
-        """
-        if not raw_logprobs or self.kd_online_topk == 0:
-            return (
-                [-float("inf")] * self.kd_online_topk if self.kd_online_topk > 0 else []
-            )
-
-        raw_logprobs_tensor = torch.tensor(raw_logprobs, dtype=torch.float32)
-        return normalize_logprobs(raw_logprobs_tensor, self.kd_online_topk).tolist()
-
-    @retry_on_request_exceptions(max_retries=10, delay=5)
-    def fetch_online_logprobs_sglang(
-        self, batch_input_ids: List[List[int]], labels: List[List[int]]
-    ):
-        """
-        Fetches logprobs from an online teacher served by sglang for a batch of input_ids.
-        Assumes API returns token IDs as strings in logprob dictionary keys.
-        """
-        api_endpoint = f"{self.kd_online_server_base_url}/generate"
-
-        payload = {
-            "input_ids": batch_input_ids,
-            "return_logprob": True,
-            "top_logprobs_num": self.kd_online_topk,
-            "logprob_start_len": 0,
-            "return_text_in_logprobs": True,
-            "echo": True,
-            "sampling_params": {
-                "max_new_tokens": 0,
-                "temperature": self.kd_temperature,
-                "skip_special_tokens": False,
-            },
-        }
-
-        # Initialize with empty lists, so if API call fails, these are returned.
-        ret_data_target_token_ids: List[List[List[int]]] = []
-        ret_data_target_logprobs: List[List[List[float]]] = []
-        ret_data_target_mask: List[List[List[int]]] = []
-
-        try:
-            response = self.http_session.post(
-                api_endpoint, json=payload, timeout=self.kd_online_timeout
-            )
-            response.raise_for_status()
-            api_data: list[dict] = response.json()
-
-            # Ensure api_data is a list, and its length matches batch_input_ids
-            if not isinstance(api_data, list) or len(api_data) != len(batch_input_ids):
-                LOG.error(
-                    f"API response format error. Expected a list of {len(batch_input_ids)} "
-                    f"items, got {type(api_data)} with length {len(api_data) if isinstance(api_data, list) else 'N/A'}."
-                )
-                # Return empty data; items processed later will get default empty KD fields
-                return {
-                    "target_token_ids": ret_data_target_token_ids,
-                    "target_logprobs": ret_data_target_logprobs,
-                    "target_mask": ret_data_target_mask,
-                }
-
-            for sequence_data, seq_input_ids, seq_labels in zip(
-                api_data, batch_input_ids, labels
-            ):
-                current_target_logprobs = []
-                current_target_token_ids = []
-                current_target_mask = []
-
-                meta_info = sequence_data.pop("meta_info", {})
-                # Ensure input_top_logprobs is a list
-                input_top_logprobs: Optional[list[None | list[tuple]]] = meta_info.pop(
-                    "input_top_logprobs", []
-                )
-                if not isinstance(input_top_logprobs, list):
-                    LOG.warning(
-                        f"Received non-list input_top_logprobs: {input_top_logprobs}. Skipping sequence."
-                    )
-                    input_top_logprobs = []  # Treat as empty
-
-                # basic check that the logprob data len matches the input len, so no need to handle padding
-                assert len(seq_input_ids) == len(input_top_logprobs)
-
-                for i, _, label in zip(
-                    range(len(seq_input_ids)), seq_input_ids, seq_labels
-                ):
-                    if i < len(input_top_logprobs) and input_top_logprobs[i] is None:
-                        # this is always the case for the first token.
-                        # there is never logprob data for the first token since that's a true input
-                        # so we replace the None value with padding data
-                        current_target_logprobs.append(
-                            [-float("inf")] * self.kd_online_topk
-                        )
-                        current_target_token_ids.append([0] * self.kd_online_topk)
-                        current_target_mask.append([0] * self.kd_online_topk)
-                    elif (
-                        i < len(input_top_logprobs)
-                        and input_top_logprobs[i] is not None
-                    ):
-                        pos_top_logprobs_data = input_top_logprobs[i]
-                        # Ensure pos_top_logprobs_data is a list of lists as expected
-                        if not (
-                            isinstance(pos_top_logprobs_data, list)
-                            and all(
-                                isinstance(item, list) for item in pos_top_logprobs_data
-                            )
-                            and len(pos_top_logprobs_data) > 0
-                            and len(pos_top_logprobs_data[0]) == 3
-                        ):  # [logprob, token_id, token_str]
-                            LOG.warning(
-                                f"Malformed pos_top_logprobs_data: {pos_top_logprobs_data}. Padding this position."
-                            )
-                            current_target_logprobs.append(
-                                [-float("inf")] * self.kd_online_topk
-                            )
-                            current_target_token_ids.append([0] * self.kd_online_topk)
-                            current_target_mask.append([0] * self.kd_online_topk)
-                            continue
-
-                        # pos_top_logprobs: list of logprobs, pos_token_ids: list of token_ids
-                        pos_logprobs_raw, pos_token_ids, _ = [
-                            list(row) for row in zip(*pos_top_logprobs_data)
-                        ]
-
-                        # Ensure correct length (top_k)
-                        if len(pos_logprobs_raw) < self.kd_online_topk:
-                            pad_len = self.kd_online_topk - len(pos_logprobs_raw)
-                            pos_logprobs_raw.extend([-float("inf")] * pad_len)
-                            pos_token_ids.extend([0] * pad_len)  # Pad with 0 token_id
-
-                        # truncate to top_k in case the response was longer
-                        current_target_token_ids.append(
-                            pos_token_ids[: self.kd_online_topk]
-                        )
-
-                        if self.kd_normalize_topk:
-                            normalized_logprobs_for_position = self._normalize_logprobs(
-                                pos_logprobs_raw[: self.kd_online_topk]
-                            )
-                            current_target_logprobs.append(
-                                normalized_logprobs_for_position
-                            )
-                        else:
-                            current_target_logprobs.append(
-                                pos_logprobs_raw[: self.kd_online_topk]
-                            )
-
-                        # Mask depends on the corresponding label for the student
-                        if label == self.DEFAULT_LABEL_PAD_TOKEN_ID:
-                            current_target_mask.append([0] * self.kd_online_topk)
-                        else:
-                            current_target_mask.append([1] * self.kd_online_topk)
-                    else:
-                        # Pad if no logprobs for this position (either due to length mismatch or None entry)
-                        current_target_logprobs.append(
-                            [-float("inf")] * self.kd_online_topk
-                        )
-                        current_target_token_ids.append([0] * self.kd_online_topk)
-                        current_target_mask.append([0] * self.kd_online_topk)
-
-                ret_data_target_token_ids.append(current_target_token_ids)
-                ret_data_target_logprobs.append(current_target_logprobs)
-                ret_data_target_mask.append(current_target_mask)
-
-        except requests.exceptions.RequestException as e:
-            LOG.error(f"Error fetching logprobs from online teacher: {e}")
-            raise e
-            # ret_logprobs_data will be returned with empty lists, handled by the caller.
-        except Exception as e:  # Catch other potential errors during processing
-            LOG.error(
-                f"Unexpected error processing API response in fetch_online_logprobs: {e}",
-                exc_info=True,
-            )
-            raise e
-
-        return {
-            "target_token_ids": ret_data_target_token_ids,
-            "target_logprobs": ret_data_target_logprobs,
-            "target_mask": ret_data_target_mask,
-        }
-
-    @retry_on_request_exceptions(max_retries=10, delay=5)
-    def fetch_online_logprobs_vllm(
-        self, batch_input_ids: List[List[int]], labels: List[List[int]]
-    ):
-        """
-        Fetches logprobs from an online teacher served by vllm for a batch of input_ids.
-        Assumes API returns token IDs as strings in logprob dictionary keys.
-        """
-        api_endpoint = f"{self.kd_online_server_base_url}/v1/completions"
-
-        payload = {
-            "prompt": batch_input_ids,
-            "echo": True,
-            "logprobs": True,
-            "prompt_logprobs": self.kd_online_topk,
-            "top_logprobs": self.kd_online_topk,
-            "max_new_tokens": 0,
-            "skip_special_tokens": False,
-            "temperature": self.kd_temperature,
-            "sampling_params": {
-                "max_tokens": 0,
-            },
-        }
-
-        # Initialize with empty lists, so if API call fails, these are returned.
-        ret_data_target_token_ids: List[List[List[int]]] = []
-        ret_data_target_logprobs: List[List[List[float]]] = []
-        ret_data_target_mask: List[List[List[int]]] = []
-
-        try:
-            headers = {"Accept-Encoding": "deflate, gzip, br, zstd"}
-            response = self.http_session.post(
-                api_endpoint,
-                json=payload,
-                headers=headers,
-                timeout=self.kd_online_timeout,
-            )
-            response.raise_for_status()
-            api_data: dict = orjson.loads(response.content)
-            choices: list[dict] = api_data["choices"]
-
-            # Ensure api_data is a list, and its length matches batch_input_ids
-            if not isinstance(choices, list) or len(choices) != len(batch_input_ids):
-                LOG.error(
-                    f"API response format error. Expected a list of {len(batch_input_ids)} "
-                    f"items, got {type(api_data)} with length {len(api_data) if isinstance(api_data, list) else 'N/A'}."
-                )
-                # Return empty data; items processed later will get default empty KD fields
-                return {
-                    "target_token_ids": ret_data_target_token_ids,
-                    "target_logprobs": ret_data_target_logprobs,
-                    "target_mask": ret_data_target_mask,
-                }
-
-            for sequence_data, seq_input_ids, seq_labels in zip(
-                choices, batch_input_ids, labels
-            ):
-                # seq_input_ids: List[int]
-                # seq_labels: List[int]
-
-                current_target_logprobs = []
-                current_target_token_ids = []
-                current_target_mask = []
-
-                # Ensure input_top_logprobs is a list
-                input_top_logprobs: Optional[list[None | dict[str, dict]]] = (
-                    sequence_data.pop("prompt_logprobs", [])
-                )
-
-                if not isinstance(input_top_logprobs, list):
-                    LOG.warning(
-                        f"Received non-list input_top_logprobs: {input_top_logprobs}. Skipping sequence."
-                    )
-                    input_top_logprobs = []  # Treat as empty
-
-                # basic check that the logprob data len matches the input len, so no need to handle padding
-                assert len(seq_input_ids) == len(input_top_logprobs)
-
-                seq_len = len(seq_input_ids)
-
-                for i, _, label in zip(range(seq_len), seq_input_ids, seq_labels):
-                    if i < len(input_top_logprobs) and input_top_logprobs[i] is None:
-                        # this is always the case for the first token.
-                        # there is never logprob data for the first token since that's a true input
-                        continue
-                    if (
-                        i < len(input_top_logprobs)
-                        and input_top_logprobs[i] is not None
-                    ):
-                        pos_top_logprobs_data: dict[str, dict] = input_top_logprobs[i]  # type: ignore[assignment]
-                        # Ensure pos_top_logprobs_data is a list of lists as expected
-                        if not (
-                            isinstance(pos_top_logprobs_data, dict)
-                            and all(
-                                isinstance(item, dict)
-                                for item in pos_top_logprobs_data.values()
-                            )
-                            and len(pos_top_logprobs_data.keys()) > 0
-                        ):  # [logprob, token_id, token_str]
-                            LOG.warning(
-                                f"Malformed pos_top_logprobs_data: {pos_top_logprobs_data}. Padding this position."
-                            )
-                            current_target_logprobs.append(
-                                [-float("inf")] * self.kd_online_topk
-                            )
-                            current_target_token_ids.append(
-                                list(range(self.kd_online_topk))
-                            )
-                            current_target_mask.append([0] * self.kd_online_topk)
-                            continue
-
-                        # pos_top_logprobs: list of logprobs, pos_token_ids: list of token_ids
-                        pos_token_ids_str = list(pos_top_logprobs_data.keys())
-                        pos_logprobs_dict = pos_top_logprobs_data.values()
-                        pos_token_ids = [
-                            int(token_id) for token_id in pos_token_ids_str
-                        ]
-                        pos_logprobs_raw = [
-                            float(logprob.get("logprob", -float("inf")))
-                            for logprob in pos_logprobs_dict
-                        ]
-
-                        # Ensure correct length (top_k)
-                        if len(pos_logprobs_raw) < self.kd_online_topk:
-                            pad_len = self.kd_online_topk - len(pos_logprobs_raw)
-                            LOG.warning(
-                                f"Padding position {i} with {pad_len} top-k tokens and logprobs."
-                            )
-                            pos_logprobs_raw.extend([-float("inf")] * pad_len)
-                            pos_token_ids.extend([0] * pad_len)  # Pad with 0 token_id
-
-                        # truncate to top_k in case the response was longer
-                        current_target_token_ids.append(
-                            pos_token_ids[: self.kd_online_topk]
-                        )
-
-                        if self.kd_normalize_topk:
-                            normalized_logprobs_for_position = self._normalize_logprobs(
-                                pos_logprobs_raw[: self.kd_online_topk]
-                            )
-                            current_target_logprobs.append(
-                                normalized_logprobs_for_position
-                            )
-                        else:
-                            current_target_logprobs.append(
-                                pos_logprobs_raw[: self.kd_online_topk]
-                            )
-
-                        # Mask depends on the corresponding label for the student
-                        if label == self.DEFAULT_LABEL_PAD_TOKEN_ID:
-                            current_target_mask.append([0] * self.kd_online_topk)
-                        else:
-                            current_target_mask.append([1] * self.kd_online_topk)
-                    else:
-                        # Pad if no logprobs for this position (either due to length mismatch or None entry)
-                        current_target_logprobs.append(
-                            [-float("inf")] * self.kd_online_topk
-                        )
-                        current_target_token_ids.append(
-                            list(range(self.kd_online_topk))
-                        )
-                        current_target_mask.append([0] * self.kd_online_topk)
-                for i in range(max(0, seq_len - len(current_target_logprobs))):
-                    current_target_logprobs.append(
-                        [-float("inf")] * self.kd_online_topk
-                    )
-                    current_target_token_ids.append(list(range(self.kd_online_topk)))
-                    current_target_mask.append([0] * self.kd_online_topk)
-
-                ret_data_target_token_ids.append(current_target_token_ids)
-                ret_data_target_logprobs.append(current_target_logprobs)
-                ret_data_target_mask.append(current_target_mask)
-
-                # TODO save and load targets to disk for caching for next epoch
-                # generate a hmac SHA256 hash over the list seq_input_ids and convert it to an int
-                # if self.kd_cache_dir:
-                #     hash_input_ids = hmac_sha_from_int_list(
-                #         seq_input_ids, f"{self.kd_online_server_base_url}:{self.kd_online_topk}"
-                #     )
-                #     with open(f"{self.kd_cache_dir}/{hash_input_ids}.parquet", "wb") as f:
-                #         pd.DataFrame(ret_logprobs_data).to_parquet(f, index=False)
-
-        except requests.exceptions.RequestException as e:
-            LOG.error(f"Error fetching logprobs from online teacher: {e}")
-            raise e
-            # ret_logprobs_data will be returned with empty lists, handled by the caller.
-        except Exception as e:  # Catch other potential errors during processing
-            LOG.error(
-                f"Unexpected error processing API response in fetch_online_logprobs: {e}",
-                exc_info=True,
-            )
-            raise e
-
-        return {
-            "target_token_ids": ret_data_target_token_ids,
-            "target_logprobs": ret_data_target_logprobs,
-            "target_mask": ret_data_target_mask,
-        }
-
-    def __call__(
-        self, features: List[List[Dict[str, Any]]], return_tensors: Optional[str] = None
-    ) -> Dict[str, Any]:
-        if not features:
-            return super().__call__(features, return_tensors=return_tensors)
-
-        for (
-            sub_batch_features
-        ) in features:  # sub_batch_features is List[Dict[str, Any]]
-            if not sub_batch_features:
-                continue
-
-            input_ids_for_api_call: List[List[int]] = []
-            labels_for_api_call: List[List[int]] = []
-            # Store references to the original item dictionaries to update them in-place
-            items_for_api_call: List[Dict[str, Any]] = []
-
-            for item_dict in sub_batch_features:
-                if not isinstance(item_dict, dict):
-                    LOG.warning(
-                        f"Skipping non-dict item in sub_batch_features: {item_dict}"
-                    )
-                    continue
-
-                current_input_ids = item_dict.get("input_ids")
-                current_labels = item_dict.get("labels")
-
-                if current_input_ids is not None and current_labels is not None:
-                    # Ensure input_ids and labels are lists of ints for JSON serialization
-                    input_ids_list = (
-                        current_input_ids.tolist()
-                        if hasattr(current_input_ids, "tolist")
-                        else list(current_input_ids)
-                    )
-                    labels_list = (
-                        current_labels.tolist()
-                        if hasattr(current_labels, "tolist")
-                        else list(current_labels)
-                    )
-
-                    input_ids_for_api_call.append(input_ids_list)
-                    labels_for_api_call.append(labels_list)
-                    items_for_api_call.append(item_dict)
-                else:
-                    # This item will not get teacher logprobs from the API.
-                    # Initialize KD fields to empty lists so downstream collators handle them uniformly.
-                    item_dict.setdefault("target_token_ids", [])
-                    item_dict.setdefault("target_logprobs", [])
-                    item_dict.setdefault("target_mask", [])
-
-            # print(items_for_api_call)
-            if items_for_api_call:  # Only call API if there's something to process
-                if self.kd_online_server == "sglang":
-                    api_responses_for_sub_batch = self.fetch_online_logprobs_sglang(
-                        input_ids_for_api_call, labels_for_api_call
-                    )
-                else:
-                    api_responses_for_sub_batch = self.fetch_online_logprobs_vllm(
-                        input_ids_for_api_call, labels_for_api_call
-                    )
-
-                # api_responses_for_sub_batch has keys: "target_token_ids", "target_logprobs", "target_mask"
-                # Each value is a list, corresponding to items_for_api_call
-                for i, item_to_update in enumerate(items_for_api_call):
-                    # TODO make sure to figure out which input in sub_batch_features to update the batch in the original `features` object so the super class can handle it properly.
-                    if api_responses_for_sub_batch and i < len(
-                        api_responses_for_sub_batch["target_token_ids"]
-                    ):  # Check bounds
-                        assert len(
-                            api_responses_for_sub_batch["target_token_ids"][i]
-                        ) == len(item_to_update["input_ids"])
-                        assert len(
-                            api_responses_for_sub_batch["target_logprobs"][i]
-                        ) == len(item_to_update["input_ids"])
-                        assert len(
-                            api_responses_for_sub_batch["target_mask"][i]
-                        ) == len(item_to_update["labels"])
-                        item_to_update["target_token_ids"] = (
-                            api_responses_for_sub_batch["target_token_ids"][i]
-                        )
-                        item_to_update["target_logprobs"] = api_responses_for_sub_batch[
-                            "target_logprobs"
-                        ][i]
-                        item_to_update["target_mask"] = api_responses_for_sub_batch[
-                            "target_mask"
-                        ][i]
-                    else:
-                        # API call failed for this item, or response was shorter than expected.
-                        # Ensure KD fields are initialized as empty lists.
-                        LOG.warning(
-                            f" (index {i}), or API response was too short. "
-                            f"API response keys: {list(api_responses_for_sub_batch.keys()) if api_responses_for_sub_batch else 'None'}"
-                        )
-                        item_to_update.setdefault("target_token_ids", [])
-                        item_to_update.setdefault("target_logprobs", [])
-                        item_to_update.setdefault("target_mask", [])
-
-        return super().__call__(features, return_tensors=return_tensors)

src/axolotl/integrations/kd/kernels/__init__.py

@@ -1,8 +0,0 @@
-"""
-Liger Chunked loss optimizations module
-"""
-
-from .liger import LigerFusedLinearKLTopKLogprobLoss
-from .models import apply_kernel
-
-__all__ = ["LigerFusedLinearKLTopKLogprobLoss", "apply_kernel"]

src/axolotl/integrations/kd/kernels/liger.py

@@ -1,485 +0,0 @@
-"""
-Liger Kernels for Chunked Top-K Log-Prob Distillation
-"""
-
-import torch
-import torch.nn.functional as F
-from liger_kernel.chunked_loss.fused_linear_distillation import (
-    LigerFusedLinearDistillationBase,
-)
-
-from axolotl.integrations.kd.utils import normalize_logprobs
-
-
-class LigerFusedLinearKLTopKLogprobFunction(LigerFusedLinearDistillationBase):
-    """
-    Chunked kl-div loss for top-k logprobs
-    """
-
-    @staticmethod
-    def distillation_loss_fn(
-        student_logits_temp_scaled: torch.Tensor,  # [chunk_size, vocab_size], already temp-scaled
-        target_token_ids_chunk: torch.Tensor,  # [chunk_size, top_k]
-        target_logprobs_chunk: torch.Tensor,  # [chunk_size, top_k], already temp-scaled and normalized logprobs
-        target_mask_chunk: torch.Tensor,  # [chunk_size, top_k]
-        beta: float = 0.0,
-        normalize_topk: bool = True,
-    ) -> torch.Tensor:
-        """
-        Compute Top-K KL divergence loss for a chunk.
-        Args:
-            student_logits_temp_scaled: Student logits, scaled by temperature. Shape: (N, V).
-            target_token_ids_chunk: Top-k teacher token IDs. Shape: (N, K).
-            target_logprobs_chunk: Top-k teacher log probabilities (temp-scaled, normalized). Shape: (N, K).
-            target_mask_chunk: Mask for valid top-k tokens. Shape: (N, K).
-            beta: Controls the type of KL divergence.
-                  0.0 for Forward KL (P_teacher || P_student).
-                  1.0 for Reverse KL (P_student || P_teacher).
-                  0.5 for Symmetric KL (average of Forward and Reverse).
-            normalize_topk: Whether to normalize the log probabilities
-        Returns:
-            Sum of KL divergence losses for the chunk.
-        """
-        topk = target_token_ids_chunk.shape[-1]
-        student_logits_temp_scaled = (  # [chunk_size, vocab_size]
-            student_logits_temp_scaled.float()
-        )
-        target_logprobs_chunk = target_logprobs_chunk.float()
-
-        # Gather student logits for the top-k teacher token IDs
-        # target_token_ids_chunk: [chunk_size, top_k]
-        # student_logits_topk_temp_scaled: [chunk_size, top_k]
-        student_logits_topk_temp_scaled = torch.gather(
-            student_logits_temp_scaled, dim=-1, index=target_token_ids_chunk
-        )
-
-        # Student log-probabilities for the gathered top-k tokens
-        student_lse = torch.logsumexp(
-            student_logits_temp_scaled, dim=-1, keepdim=True
-        )  # [chunk_size, 1]
-        student_logprobs_topk_temp_scaled = (
-            student_logits_topk_temp_scaled - student_lse
-        )
-
-        # we have the top-k student logprobs, normalize them
-        if normalize_topk:
-            student_logprobs_topk_temp_scaled = normalize_logprobs(
-                student_logprobs_topk_temp_scaled, topk
-            )
-
-        valid_mask = target_mask_chunk.to(torch.bool)  # [chunk_size, top_k]
-
-        student_logprobs_topk_valid = student_logprobs_topk_temp_scaled[valid_mask]
-        teacher_logprobs_valid = target_logprobs_chunk[valid_mask]
-
-        # Teacher probabilities P(y|x_teacher) from logprobs
-        # target_logprobs_valid are already normalized (log(softmax(teacher_logits/T)))
-        teacher_probs_valid = teacher_logprobs_valid.exp()
-        # Student probabilities P_student from log P_student
-        student_probs_topk_valid = student_logprobs_topk_valid.exp()
-
-        # kd_loss_per_token = torch.zeros_like(target_logprobs_valid)
-
-        # KL divergence: sum(P_teacher * (log P_teacher - log P_student))
-        # = sum(P_teacher * log P_teacher) - sum(P_teacher * log P_student)
-        # The distillation loss is often formulated as -sum(P_teacher * log P_student)
-        # or as sum(P_teacher * (log_softmax_teacher - log_softmax_student))
-        # Here, target_logprobs_valid are log_softmax_teacher.
-        # student_logprobs_topk_valid are log_softmax_student (for the selected K indices).
-        if beta == 0.0:  # Contribution from Forward KL
-            fwd_kl_per_token = teacher_probs_valid * (
-                teacher_logprobs_valid - student_logprobs_topk_valid
-            )
-            kd_loss = fwd_kl_per_token.sum()
-        elif beta == 1.0:  # Contribution from Reverse KL
-            rev_kl_per_token = student_probs_topk_valid * (
-                student_logprobs_topk_valid - teacher_logprobs_valid
-            )
-            kd_loss = rev_kl_per_token.sum()
-        else:
-            # JSD - Jensen-Shannon Divergence / Symmetric
-            mean_probs = (
-                1 - beta
-            ) * student_probs_topk_valid + beta * teacher_probs_valid
-            log_mean_probs = mean_probs.log()
-            student_kl = F.kl_div(
-                log_mean_probs,
-                student_logprobs_topk_valid,
-                reduction="sum",
-                log_target=True,
-            )
-            teacher_kl = F.kl_div(
-                log_mean_probs, teacher_logprobs_valid, reduction="sum", log_target=True
-            )
-            jsd_loss = beta * teacher_kl + (1 - beta) * student_kl
-            kd_loss = jsd_loss
-
-        return kd_loss
-
-    @staticmethod
-    def _compute_loss_kl_topk(
-        student_input_chunk: torch.Tensor,
-        student_weight: torch.Tensor,
-        # Args for student_bias, target_token_ids_chunk etc. are passed to the lambda wrapped by grad_and_value
-        # or through `partial`. Let's make them explicit here for clarity.
-        target_token_ids_chunk: torch.Tensor,
-        target_logprobs_chunk: torch.Tensor,
-        target_mask_chunk: torch.Tensor,
-        target_chunk: torch.Tensor,  # For hard loss (true labels)
-        student_bias: torch.Tensor = None,  # This will be one of the grad targets
-        # Other params passed via `partial` from `forward`
-        distillation_loss_fn=None,
-        ignore_index: int = -100,
-        weight_hard_loss: float = 0.5,
-        weight_soft_loss: float = 0.5,
-        compute_ce_loss: bool = True,
-        temperature: float = 1.0,
-        beta: float = 0.0,
-        normalize_topk: bool = True,
-    ):
-        # Compute student logits for the chunk from hidden states and LM head
-        # student_input_chunk: [chunk_size, hidden_dim]
-        # student_lm_head_weight: [vocab_size, hidden_dim]
-        # student_logits_chunk: [chunk_size, vocab_size]
-        student_logits_chunk = F.linear(
-            student_input_chunk, student_weight, student_bias
-        )
-
-        ce_loss = torch.tensor(
-            0.0, device=student_logits_chunk.device, dtype=student_logits_chunk.dtype
-        )
-        if compute_ce_loss and weight_hard_loss > 0.0:
-            ce_loss = F.cross_entropy(
-                student_logits_chunk.view(-1, student_logits_chunk.shape[-1]),
-                target_chunk.view(-1),
-                reduction="sum",
-                ignore_index=ignore_index,
-            )
-
-        soft_loss = torch.tensor(
-            0.0, device=student_logits_chunk.device, dtype=student_logits_chunk.dtype
-        )
-        if weight_soft_loss > 0.0:
-            student_logits_chunk_temp_scaled = student_logits_chunk / temperature
-
-            # Assuming student_weight.shape[0] (vocab_size) is adequate for target_token_ids_chunk.max()
-            # No explicit padding here; user must ensure vocab alignment or pre-pad student_weight.
-
-            soft_loss = distillation_loss_fn(
-                student_logits_chunk_temp_scaled,
-                target_token_ids_chunk,
-                target_logprobs_chunk,
-                target_mask_chunk,
-                beta=beta,
-                normalize_topk=normalize_topk,
-            )
-
-        return soft_loss, ce_loss
-
-    @classmethod
-    def forward(
-        cls,
-        ctx,
-        student_input: torch.Tensor,  # [batch_size, seq_len, dim]
-        student_lm_head_weight: torch.Tensor,  # [dim, vocab_size]
-        target_token_ids: torch.Tensor,  # [batch_size, seq_len, top_k]
-        target_logprobs: torch.Tensor,  # [batch_size, seq_len, top_k]
-        target_mask: torch.Tensor,  # [batch_size, seq_len, top_k]
-        true_labels: torch.Tensor,  # [batch_size, seq_len]
-        student_lm_head_bias: torch.Tensor = None,
-        weight_hard_loss: float = 0.5,
-        weight_soft_loss: float = 0.5,
-        ignore_index: int = -100,
-        temperature: float = 1.0,
-        beta: float = 0.0,
-        compiled: bool = False,
-        chunk_size: int = 1024,
-        compute_ce_loss: bool = True,
-        normalize_topk: bool = True,
-    ):
-        CHUNK_SIZE = chunk_size  # pylint: disable=invalid-name
-        grad_weight_acc = torch.zeros_like(student_lm_head_weight)
-        grad_inputs_list = []
-        grad_bias_acc = (
-            torch.zeros_like(student_lm_head_bias)
-            if student_lm_head_bias is not None
-            else None
-        )
-        kd_loss_acc = torch.zeros(
-            (), device=student_input.device, dtype=student_input.dtype
-        )
-        ce_loss_acc = torch.zeros(
-            (), device=student_input.device, dtype=student_input.dtype
-        )
-
-        # This function will be what torch.func.grad_and_value differentiates.
-        # It takes student_input_chunk, student_weight (full), student_bias (full) as primals.
-        # Other necessary data (target_*, etc.) are passed as non-differentiable arguments.
-        def loss_fn_for_grad(
-            _student_input_chunk,
-            _student_lm_head_weight,  # full weight
-            _student_lm_head_bias,  # full bias
-            # Fixed arguments for a given chunk, not differentiated:
-            _target_token_ids_chunk,
-            _target_logprobs_chunk,
-            _target_mask_chunk,
-            _true_labels_chunk,
-        ):
-            return cls._compute_loss_kl_topk(
-                student_input_chunk=_student_input_chunk,
-                student_weight=_student_lm_head_weight,
-                target_token_ids_chunk=_target_token_ids_chunk,
-                target_logprobs_chunk=_target_logprobs_chunk,
-                target_mask_chunk=_target_mask_chunk,
-                target_chunk=_true_labels_chunk,
-                student_bias=_student_lm_head_bias,
-                distillation_loss_fn=cls.distillation_loss_fn,
-                ignore_index=ignore_index,
-                weight_hard_loss=weight_hard_loss,
-                weight_soft_loss=weight_soft_loss,
-                compute_ce_loss=compute_ce_loss,
-                temperature=temperature,
-                beta=beta,
-                normalize_topk=normalize_topk,
-            )
-
-        def accumulate_chunk_grads(
-            student_input_chunk_ac,
-            target_token_ids_chunk_ac,
-            target_logprobs_chunk_ac,
-            target_mask_chunk_ac,
-            true_labels_chunk_ac,
-        ):
-            # student_weight and student_bias are closed over from the outer scope (full tensors)
-            if student_lm_head_bias is not None:
-                (
-                    (chunk_grad_input, chunk_grad_weight, chunk_grad_bias),
-                    (chunk_kd_loss, chunk_ce_loss),
-                ) = torch.func.grad_and_value(
-                    loss_fn_for_grad, argnums=(0, 1, 2), has_aux=True
-                )(
-                    student_input_chunk_ac,
-                    student_lm_head_weight,
-                    student_lm_head_bias,  # primals
-                    target_token_ids_chunk_ac,
-                    target_logprobs_chunk_ac,
-                    target_mask_chunk_ac,
-                    true_labels_chunk_ac,
-                )  # non-primals
-                grad_bias_acc.add_(chunk_grad_bias)
-            else:
-                argnums_for_grad = (0, 1)  # Differentiate wrt input_chunk, weight
-                (
-                    (chunk_grad_input, chunk_grad_weight),  # No grad for bias
-                    (chunk_kd_loss, chunk_ce_loss),
-                ) = torch.func.grad_and_value(
-                    loss_fn_for_grad, argnums=argnums_for_grad, has_aux=True
-                )(
-                    student_input_chunk_ac,
-                    student_lm_head_weight,
-                    None,  # Pass None for student_bias primal
-                    target_token_ids_chunk_ac,
-                    target_logprobs_chunk_ac,
-                    target_mask_chunk_ac,
-                    true_labels_chunk_ac,
-                )
-
-            grad_weight_acc.add_(chunk_grad_weight)
-            kd_loss_acc.add_(chunk_kd_loss)
-            ce_loss_acc.add_(chunk_ce_loss)
-
-            return chunk_grad_input
-
-        if compiled:
-            accumulate_chunk_grads_compiled = torch.compile(
-                accumulate_chunk_grads, dynamic=True, backend="inductor"
-            )  # dynamic=True often helpful
-        else:
-            accumulate_chunk_grads_compiled = accumulate_chunk_grads
-
-        # Use the same chunking logic as LigerFusedLinearDistillationBase.forward
-        B, N, D = student_input.shape  # pylint: disable=invalid-name
-        K = target_token_ids.shape[-1]  # pylint: disable=invalid-name
-
-        student_input_flat = student_input.reshape(-1, student_input.shape[-1])
-        target_token_ids_flat = target_token_ids.reshape(-1, target_token_ids.shape[-1])
-        target_logprobs_flat = target_logprobs.reshape(-1, target_logprobs.shape[-1])
-        target_mask_flat = target_mask.reshape(-1, target_mask.shape[-1])
-        # pad and shift for cross entropy loss
-        true_labels = torch.nn.functional.pad(true_labels, (0, 1), value=ignore_index)
-        true_labels_flat = true_labels[:, 1:].contiguous().view(-1)
-
-        num_chunks = max(1, student_input_flat.shape[0] // CHUNK_SIZE)
-
-        _student_input_chunks = torch.chunk(
-            student_input_flat, chunks=num_chunks, dim=0
-        )
-        _target_token_ids_chunks = torch.chunk(
-            target_token_ids_flat, chunks=num_chunks, dim=0
-        )
-        _target_logprobs_chunks = torch.chunk(
-            target_logprobs_flat, chunks=num_chunks, dim=0
-        )
-        _target_mask_chunks = torch.chunk(target_mask_flat, chunks=num_chunks, dim=0)
-        _true_labels_chunks = torch.chunk(true_labels_flat, chunks=num_chunks, dim=0)
-
-        for i in range(num_chunks):
-            grad_input_chunk = accumulate_chunk_grads_compiled(
-                _student_input_chunks[i],
-                _target_token_ids_chunks[i],
-                _target_logprobs_chunks[i],
-                _target_mask_chunks[i],
-                _true_labels_chunks[i],
-            )
-            grad_inputs_list.append(grad_input_chunk)
-
-        grad_inputs_combined = torch.cat(grad_inputs_list, dim=0)
-        ctx.save_for_backward(grad_inputs_combined, grad_weight_acc, grad_bias_acc)
-
-        # For matching None returns in backward for non-tensor/non-grad_requiring inputs
-        ctx.hyperparams_count = 9  # Corresponds to number of hyperparams after main tensors in fwd signature
-        ctx.bias_was_none = student_lm_head_bias is None
-        ctx.orig_dims = (B, N, D, K)
-
-        # since this is packed, there is simply a single batch, so batchmean reduction of kl-div is simply the accumulated sum
-        # we still need to scale the kd_loss by the temp^2
-        kd_loss_acc = kd_loss_acc * (temperature**2)
-        final_loss = weight_soft_loss * kd_loss_acc + weight_hard_loss * ce_loss_acc
-
-        return final_loss
-
-    @staticmethod
-    def backward(ctx, grad_output):
-        grad_input_flat, grad_weight, grad_bias_maybe = (
-            ctx.saved_tensors
-        )  # grad_input_flat is (B*N, D)
-
-        # Scale gradients by grad_output if it's not 1.0
-        if not torch.equal(
-            grad_output,
-            torch.tensor(1.0, device=grad_output.device, dtype=grad_output.dtype),
-        ):
-            grad_input_flat = grad_input_flat * grad_output
-            grad_weight = grad_weight * grad_output
-            if grad_bias_maybe is not None:
-                grad_bias_maybe = grad_bias_maybe * grad_output
-
-        # Reshape grad_input_flat to match original student_input shape (B, N, D)
-        # ctx.orig_dims stores (B, N, D, K)
-        # We need the first three dimensions for student_input's shape.
-        # Ensure that orig_dims are not (0,0,0,K) for empty inputs leading to view errors
-        if (
-            ctx.orig_dims[0] * ctx.orig_dims[1] * ctx.orig_dims[2] == 0
-            and grad_input_flat.numel() == 0
-        ):
-            # If original input was empty, gradient should also be empty with correct shape
-            grad_input_reshaped = torch.zeros(
-                ctx.orig_dims[0],
-                ctx.orig_dims[1],
-                ctx.orig_dims[2],
-                dtype=grad_input_flat.dtype,
-                device=grad_input_flat.device,
-            )
-        elif grad_input_flat.numel() == 0 and not (
-            ctx.orig_dims[0] * ctx.orig_dims[1] * ctx.orig_dims[2] == 0
-        ):
-            # This case should ideally not happen if forward path is correct (non-empty input -> non-empty flat grad)
-            # but as a safeguard:
-            grad_input_reshaped = torch.zeros(
-                ctx.orig_dims[0],
-                ctx.orig_dims[1],
-                ctx.orig_dims[2],
-                dtype=grad_input_flat.dtype,
-                device=grad_input_flat.device,
-            )
-        else:
-            grad_input_reshaped = grad_input_flat.view(
-                ctx.orig_dims[0], ctx.orig_dims[1], ctx.orig_dims[2]
-            )
-
-        nones_for_hyperparams = [None] * ctx.hyperparams_count
-        grad_bias_return = grad_bias_maybe if not ctx.bias_was_none else None
-
-        return (
-            grad_input_reshaped,  # Gradient for student_input (reshaped)
-            grad_weight,  # Gradient for student_lm_head_weight
-            None,  # Gradient for target_token_ids
-            None,  # Gradient for target_logprobs
-            None,  # Gradient for target_mask
-            None,  # Gradient for true_labels
-            grad_bias_return,  # Gradient for student_lm_head_bias
-            *nones_for_hyperparams,  # Grads for weight_hard_loss, ..., compute_ce_loss
-        )
-
-
-class LigerFusedLinearKLTopKLogprobLoss(torch.nn.Module):
-    """
-    wrapper for chunked top-k logprob kl-d
-    """
-
-    def __init__(
-        self,
-        weight_hard_loss: float = 0.5,
-        weight_soft_loss: float = 0.5,
-        temperature: float = 1.0,  # This is the kd_temperature
-        beta: float = 1.0,
-        ignore_index: int = -100,
-        compiled: bool = True,
-        chunk_size: int = 1024,
-        compute_ce_loss: bool = True,
-        normalize_topk: bool = True,
-    ):
-        super().__init__()
-        if not (0.0 <= weight_hard_loss <= 1.0 and 0.0 <= weight_soft_loss <= 1.0):
-            raise ValueError("Loss weights must be between 0.0 and 1.0.")
-        if temperature <= 0:
-            raise ValueError("Temperature must be positive.")
-
-        self.weight_hard_loss = weight_hard_loss
-        self.weight_soft_loss = weight_soft_loss
-        self.temperature = temperature
-        self.beta = beta
-        self.ignore_index = ignore_index
-        self.compiled = compiled
-        self.chunk_size = chunk_size
-        self.compute_ce_loss = compute_ce_loss
-        self.normalize_topk = normalize_topk
-
-        if not self.compute_ce_loss and self.weight_hard_loss > 0.0:
-            print(
-                f"Warning: compute_ce_loss is False, but weight_hard_loss ({self.weight_hard_loss}) > 0. Hard loss will effectively be zero."
-            )
-            # self.weight_hard_loss = 0.0 # Or let user manage this
-        if self.weight_soft_loss == 0.0:
-            print(
-                "Warning: weight_soft_loss is 0.0. Soft (KD) loss will not be computed."
-            )
-
-    def forward(
-        self,
-        lm_head_weight: torch.Tensor,  # Weights of the linear layer in the LM head
-        student_hidden_states: torch.Tensor,  # student_hidden_states before the lm_head
-        target_token_ids: torch.Tensor,
-        target_logprobs: torch.Tensor,
-        target_mask: torch.Tensor,
-        true_labels: torch.Tensor,
-        student_bias: torch.Tensor = None,
-    ) -> torch.Tensor:
-        return LigerFusedLinearKLTopKLogprobFunction.apply(
-            student_hidden_states,
-            lm_head_weight,
-            target_token_ids,
-            target_logprobs,
-            target_mask,
-            true_labels,
-            student_bias,
-            self.weight_hard_loss,
-            self.weight_soft_loss,
-            self.ignore_index,
-            self.temperature,
-            self.beta,
-            self.compiled,
-            self.chunk_size,
-            self.compute_ce_loss,
-            self.normalize_topk,
-        )

src/axolotl/integrations/kd/kernels/models.py

@@ -1,98 +0,0 @@
-"""
-model patcher for chunked top-k kl-div
-"""
-
-from types import MethodType
-from typing import Optional, Union, Unpack
-
-import torch
-from transformers import Cache
-from transformers.modeling_flash_attention_utils import FlashAttentionKwargs
-from transformers.modeling_outputs import CausalLMOutputWithPast
-from transformers.utils import LossKwargs
-
-
-class KwargsForCausalLM(FlashAttentionKwargs, LossKwargs):
-    """
-    placeholder kwargs for hf model classes
-    """
-
-
-def kldiv_forward_llama_like(
-    self,
-    input_ids: Optional[torch.LongTensor] = None,
-    target_logprobs: Optional[torch.Tensor] = None,
-    target_token_ids: Optional[torch.LongTensor] = None,
-    target_mask: Optional[torch.Tensor] = None,
-    attention_mask: Optional[torch.Tensor] = None,
-    position_ids: Optional[torch.LongTensor] = None,
-    past_key_values: Optional[Cache] = 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,
-    cache_position: Optional[torch.LongTensor] = None,
-    logits_to_keep: Union[int, torch.Tensor] = 0,  # pylint: disable=unused-argument
-    **kwargs: Unpack[KwargsForCausalLM],  # type: ignore[misc]
-) -> CausalLMOutputWithPast:
-    # pylint: disable=duplicate-code
-    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
-    )
-
-    # 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,
-        cache_position=cache_position,
-        **kwargs,
-    )
-
-    hidden_states = outputs.last_hidden_state
-
-    # Only compute necessary logits, and do not upcast them to float if we are not computing the loss
-    # TODO, we can optimize this further by filtering hidden_states on sequence dimension using labels != -100
-    # self.loss_function should be LigerFusedLinearKLTopKLogprobLoss
-
-    loss = self.loss_function(
-        self.lm_head.weight,
-        hidden_states,
-        target_token_ids,
-        target_logprobs,
-        target_mask,
-        true_labels=labels,
-    )
-    num_items_in_batch = kwargs.pop("num_items_in_batch", -1)
-    if num_items_in_batch is not None and num_items_in_batch > 0:
-        loss = loss / num_items_in_batch
-
-    return CausalLMOutputWithPast(
-        loss=loss,
-        logits=None,
-        past_key_values=outputs.past_key_values,
-        hidden_states=outputs.hidden_states,
-        attentions=outputs.attentions,
-    )
-
-
-def apply_kernel(model_type):
-    # Dynamically import the module and attention class
-    module_path = f"transformers.models.{model_type}.modeling_{model_type}"
-    model_cls_prefix = "".join([part.capitalize() for part in model_type.split("_")])
-    module = __import__(module_path, fromlist=[f"{model_cls_prefix}ForCausalLM"])
-    model_cls = getattr(module, f"{model_cls_prefix}ForCausalLM")
-    model_cls.forward = MethodType(kldiv_forward_llama_like, model_cls)

src/axolotl/integrations/kd/topk_logprob/forward_kl.py

@@ -16,7 +16,40 @@
 loss for top_k KL divergence
 """
 import torch
-from torch import nn
+
+
+def zscore_standardize(
+    logits: torch.Tensor,
+    mask: torch.Tensor = None,
+    base_temperature: float = 1.0,
+    eps: float = 1e-9,
+):
+    """
+    Z-score standardize along the last dimension of `logits`.
+    i.e., for each [B, seq_len] row, across K entries:
+        z = (logits - mean) / std,
+    then scale by 1 / base_temperature if desired.
+
+    mask can be broadcastable or None. If None, we standardize all elements.
+    """
+    if mask is None:
+        # shape: [B, seq_len, K]
+        # Mean and std over dim=-1
+        mean = logits.mean(dim=-1, keepdim=True)
+        var = logits.var(dim=-1, unbiased=False, keepdim=True)
+    else:
+        # If you have to exclude some tokens, multiply by mask, etc.
+        float_mask = mask.to(logits.dtype)
+        count = float_mask.sum(dim=-1, keepdim=True).clamp_min(1.0)
+        mean = (logits * float_mask).sum(dim=-1, keepdim=True) / count
+        var = (float_mask * (logits - mean) ** 2).sum(dim=-1, keepdim=True) / count
+
+    std = torch.sqrt(var.clamp_min(eps))
+    z = (logits - mean) / std
+
+    # Scale by 1 / base_temperature
+    z = z / base_temperature
+    return z
 
 
 @torch.jit.script
@@ -27,6 +60,7 @@ def loss(
     target_mask: torch.Tensor,
     num_items_in_batch: int = -1,  # Use -1 to indicate "None"
     kd_temperature: float = 1.0,
+    top_k_before_softmax: int = 0,
 ) -> torch.Tensor:
     """
     A KD loss function that is TorchScript-friendly.
@@ -43,6 +77,8 @@ def loss(
         num_items_in_batch (int, optional): The number of items in the batch.
         kd_temperature (float, optional): The temperature for KD.
             Default: 1.0
+        top_k_before_softmax (int, optional): Flag of whether to apply softmax before gathering student top-k logits
+            Default: 0
     """
 
     target_logprobs = target_logprobs.float()
@@ -52,24 +88,46 @@ def loss(
     # student_logits shape:   [B, student_seq_len, vocab_size]
     teacher_seq_len = target_token_ids.shape[1]
 
-    # Slice student logits to match teacher-provided sequence length
+    if top_k_before_softmax:
-    student_logits_for_kd = (
+        # Slice student logits to match teacher-provided sequence length
-        student_logits[:, :teacher_seq_len, :] / kd_temperature
+        student_logits_for_kd = student_logits[
-    )  # [B, teacher_seq_len, vocab_size]
+            :, :teacher_seq_len, :
+        ]  # [B, teacher_seq_len, vocab_size]
 
-    # keep in full precision for numerical stability of loss
+        # Gather student logits for teacher's top-K tokens
-    student_logits_for_kd = student_logits_for_kd.float()
+        student_logits_topk = torch.gather(
+            student_logits_for_kd, dim=-1, index=target_token_ids
+        )  # [B, teacher_seq_len, K]
 
-    # Gather student logits for teacher's top-K tokens
+        student_logits_topk = student_logits_topk.float()
-    student_logits_topk = torch.gather(
-        student_logits_for_kd, dim=-1, index=target_token_ids
-    )  # [B, teacher_seq_len, K]
 
-    # Compute logsumexp across full vocabulary
+        # Apply KD temperature to student’s logits
-    student_lse = torch.logsumexp(student_logits_for_kd, dim=-1, keepdim=True)
+        if kd_temperature != 1.0:
+            student_logits_topk = student_logits_topk / kd_temperature
 
-    #  Convert just the top-k logits to logprobs
+        # Convert student top-k logits to logprobs
-    student_logprobs_topk = student_logits_topk - student_lse
+        student_logprobs_topk = student_logits_topk - torch.logsumexp(
+            student_logits_topk, dim=-1, keepdim=True
+        )  # [B, teacher_seq_len, K]
+    else:
+        # Slice student logits to match teacher-provided sequence length
+        student_logits_for_kd = (
+            student_logits[:, :teacher_seq_len, :] / kd_temperature
+        )  # [B, teacher_seq_len, vocab_size]
+
+        # keep in full precision for numerical stability of loss
+        student_logits_for_kd = student_logits_for_kd.float()
+
+        # Gather student logits for teacher's top-K tokens
+        student_logits_topk = torch.gather(
+            student_logits_for_kd, dim=-1, index=target_token_ids
+        )  # [B, teacher_seq_len, K]
+
+        # Compute logsumexp across full vocabulary
+        student_lse = torch.logsumexp(student_logits_for_kd, dim=-1, keepdim=True)
+
+        #  Convert just the top-k logits to logprobs
+        student_logprobs_topk = student_logits_topk - student_lse
 
     # Convert teacher_mask to boolean for indexing
     # In TorchScript, .bool() is sometimes unsupported, so we do:
@@ -86,6 +144,10 @@ def loss(
     kd_loss_per_token = teacher_probs * (target_logprobs - student_logprobs_topk)
     kd_loss = kd_loss_per_token.sum()
 
+    # Multiply by T^2 (classical KD scaling)
+    if kd_temperature != 1.0:
+        kd_loss = kd_loss * (kd_temperature**2)
+
     # Normalize by number of items (if provided) or by valid tokens
     if num_items_in_batch > 0:
         kd_loss = kd_loss / float(num_items_in_batch)
@@ -96,74 +158,80 @@ def loss(
     return kd_loss
 
 
-class ChunkedTopKKDLoss(nn.Module):
+def topk_kd_loss_with_zscore(
+    student_logits: torch.Tensor,  # [B, seq_len, vocab_size]
+    target_token_ids: torch.Tensor,  # [B, seq_len, K]
+    target_logprobs: torch.Tensor,  # [B, seq_len, K], sums to 1.0 in prob space
+    target_mask: torch.Tensor,  # [B, seq_len, K] or [B, seq_len]
+    kd_temperature: float = 1.0,  # classic KD temperature
+    zscore_base_temp: float = 1.0,  # from the paper
+    num_items_in_batch: int = -1,
+):
     """
-    A wrapper that chunks (splits) the student and teacher outputs along the time dimension
+    A variant of top_k KL divergence with Z-score scaling
-    to reduce peak memory usage when upcasting from bf16 to fp32, especially for large vocabularies.
+    from "Logit Standardization in Knowledge Distillation".
-
-    Usage is analogous to ForwardKLWithChunkedOutputLoss but adapted to top-K teacher logprobs.
     """
 
-    def __init__(self, num_output_chunks: int = 8, kd_temperature: float = 1.0):
+    target_logprobs = target_logprobs.float()
-        super().__init__()
-        self.num_output_chunks = num_output_chunks
-        self.kd_temperature = kd_temperature
 
-    def forward(
+    B, teacher_seq_len, K = target_logprobs.shape  # pylint: disable=invalid-name
-        self,
+    # 1) Gather the student's top-k logits to match teacher
-        student_logits: torch.Tensor,  # [B, seq_len, vocab_size]
+    student_logits_for_kd = student_logits[
-        target_token_ids: torch.Tensor,  # [B, seq_len, K]
+        :, :teacher_seq_len, :
-        target_logprobs: torch.Tensor,  # [B, seq_len, K]
+    ]  # [B, seq_len, vocab]
-        target_mask: torch.Tensor,  # [B, seq_len, K]
+    student_topk_logits = torch.gather(
-        num_items_in_batch: int = -1,  # optional batch size for normalization
+        student_logits_for_kd, dim=-1, index=target_token_ids
-    ) -> torch.Tensor:
+    )  # [B, seq_len, K]
 
-        # 1. Split along the "token" dimension (dim=1).
+    student_topk_logits = student_topk_logits.float()
-        student_logits_chunks = student_logits.chunk(self.num_output_chunks, dim=1)
-        token_ids_chunks = target_token_ids.chunk(self.num_output_chunks, dim=1)
-        logprobs_chunks = target_logprobs.chunk(self.num_output_chunks, dim=1)
-        mask_chunks = target_mask.chunk(self.num_output_chunks, dim=1)
 
-        # We'll accumulate a global "sum of losses" and "sum of valid tokens"
+    # 2) If you want to keep the "classical" T scaling, apply it first
-        # so that our final average is consistent with the entire sequence/batch.
+    if kd_temperature != 1.0:
-        total_loss = 0.0
+        student_topk_logits = student_topk_logits / kd_temperature
-        total_valid_tokens = 0
 
-        # 2. Loop over each chunk and compute a chunk-specific loss.
+    # 3) Convert teacher logprobs -> treat them as “logits” for z-score
-        for st_chunk, tid_chunk, lp_chunk, msk_chunk in zip(
+    #    (They differ by +some_constant from real logits, but in z-score
-            student_logits_chunks, token_ids_chunks, logprobs_chunks, mask_chunks
+    #     that constant is subtracted out anyway.)
-        ):
+    teacher_logits_for_zscore = target_logprobs  # rename variable for clarity
-            # We pass num_items_in_batch=-1 so that the kd_loss
-            # will average over *this chunk's* valid tokens only.
-            chunk_loss = loss(
-                student_logits=st_chunk,
-                target_token_ids=tid_chunk,
-                target_logprobs=lp_chunk,
-                target_mask=msk_chunk,
-                num_items_in_batch=-1,  # ensure per-chunk averaging by valid tokens
-                kd_temperature=self.kd_temperature,
-            )
 
-            # kd_loss returns an average over the chunk's valid tokens.
+    # 4) Z-score teacher and student
-            # We want a global average in the end, so we need to re‐weight
+    #    If target_mask is 2D, expand to 3D for the K dimension
-            # by the number of valid tokens in this chunk and keep track of the total.
+    if target_mask.dim() == 2 and target_mask.shape[:2] == (B, teacher_seq_len):
-            chunk_valid_mask = msk_chunk.to(torch.bool)
+        target_mask = target_mask.unsqueeze(-1).expand(-1, -1, K)
-            chunk_valid_count = chunk_valid_mask.sum()  # scalar tensor
 
-            # Re-scale "chunk average" back to "chunk sum"
+    teacher_z = zscore_standardize(
-            chunk_loss_sum = chunk_loss * chunk_valid_count
+        teacher_logits_for_zscore, mask=target_mask, base_temperature=zscore_base_temp
+    )
+    student_z = zscore_standardize(
+        student_topk_logits, mask=target_mask, base_temperature=zscore_base_temp
+    )
 
-            total_loss += chunk_loss_sum
+    # 5) Convert to log-probs for KL
-            total_valid_tokens += chunk_valid_count
+    teacher_logprobs_z = teacher_z - torch.logsumexp(teacher_z, dim=-1, keepdim=True)
+    student_logprobs_z = student_z - torch.logsumexp(student_z, dim=-1, keepdim=True)
 
-        # 3. Normalize *once* at the end.
+    # 6) Restrict to valid tokens if needed
-        if num_items_in_batch > 0:
+    valid_mask = target_mask.bool()  # shape [B, seq_len, K]
-            # If the user gave us a manual denominator (e.g. total items in batch),
+    teacher_probs_z = teacher_logprobs_z.exp()
-            # we divide by it. Typically used if each item is of different length.
+    teacher_probs_z = teacher_probs_z[valid_mask]
-            final_loss = total_loss / float(num_items_in_batch)
+    teacher_logprobs_z = teacher_logprobs_z[valid_mask]
-        else:
+    student_logprobs_z = student_logprobs_z[valid_mask]
-            # Otherwise, divide by total valid tokens across all chunks.
-            # to get the same result as a non-chunked approach.
-            final_loss = total_loss / float(total_valid_tokens)
 
-        return final_loss
+    # 7) forward KL:  sum( p_teacher * [log(p_teacher) - log(p_student)] )
+    kd_loss_per_token = teacher_probs_z * (teacher_logprobs_z - student_logprobs_z)
+    kd_loss = kd_loss_per_token.sum()
+
+    # 8) If using classical KD scaling by T^2
+    if kd_temperature != 1.0:
+        kd_loss = kd_loss * (kd_temperature**2)
+
+    # Optionally scale by zscore_base_temp**2 if you want (paper might differ).
+    # kd_loss = kd_loss * (zscore_base_temp**2)
+
+    # 9) Normalize
+    if num_items_in_batch is not None and num_items_in_batch > 0:
+        kd_loss = kd_loss / float(num_items_in_batch)
+    else:
+        kd_loss = kd_loss / float(kd_loss_per_token.size(0))
+
+    return kd_loss

src/axolotl/integrations/kd/trainer.py

@@ -18,7 +18,8 @@ KD trainer
 
 from axolotl.core.trainers.base import AxolotlTrainer
 
-from .kernels.liger import LigerFusedLinearKLTopKLogprobLoss
+from .topk_logprob.forward_kl import loss as topk_kd_loss
+from .topk_logprob.forward_kl import topk_kd_loss_with_zscore
 
 
 class AxolotlKDTrainer(AxolotlTrainer):
@@ -26,18 +27,6 @@ class AxolotlKDTrainer(AxolotlTrainer):
     Custom trainer subclass for Knowledge Distillation (KD)
     """
 
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        self.model_accepts_loss_kwargs = True
-        self.model._loss_function = LigerFusedLinearKLTopKLogprobLoss(
-            self.args.kd_ce_alpha,  # hard label loss
-            self.args.kd_alpha,  # kd loss
-            self.args.kd_temperature,
-            self.args.kd_beta or 0.0,
-            compute_ce_loss=bool(self.args.kd_ce_alpha),
-            normalize_topk=self.args.kd_normalize_topk,
-        )
-
     def _set_signature_columns_if_needed(self):
         super()._set_signature_columns_if_needed()
         columns_to_add = []
@@ -63,12 +52,12 @@ class AxolotlKDTrainer(AxolotlTrainer):
 
         Subclass and override for custom behavior.
         """
-        if (
+
-            self.args.sample_packing
+        target_logprobs = inputs.pop("target_logprobs")
-            and hasattr(inputs, "attention_mask")
+        target_token_ids = inputs.pop("target_token_ids")
-            and hasattr(inputs, "position_ids")
+        target_mask = inputs.pop("target_mask")
-        ):
+
-            del inputs["attention_mask"]
+        seq_len = target_token_ids.shape[1]
 
         if self.model_accepts_loss_kwargs:
             loss_kwargs = {}
@@ -76,4 +65,49 @@ class AxolotlKDTrainer(AxolotlTrainer):
                 loss_kwargs["num_items_in_batch"] = num_items_in_batch
             inputs = {**inputs, **loss_kwargs}
         outputs = model(**inputs)
-        return outputs[0]
+
+        # FIXME: account for tokenizer.padding_side
+        student_logits = outputs["logits"][:, : seq_len - 1, :].contiguous()
+
+        shift_logits = student_logits.contiguous()
+        target_logprobs_for_loss = target_logprobs[..., 1:, :].contiguous()
+        target_token_ids_for_loss = target_token_ids[..., 1:, :].contiguous()
+        target_mask_for_loss = target_mask[..., 1:, :].contiguous()
+
+        if self.args.kd_zscore_base_temp:
+            loss_kd = topk_kd_loss_with_zscore(
+                shift_logits,
+                target_token_ids_for_loss,
+                target_logprobs_for_loss,
+                target_mask_for_loss,
+                kd_temperature=self.args.kd_temperature,
+                zscore_base_temp=self.args.kd_zscore_base_temp,
+                num_items_in_batch=num_items_in_batch,
+            )
+        else:
+            loss_kd = topk_kd_loss(
+                shift_logits,
+                target_token_ids_for_loss,
+                target_logprobs_for_loss,
+                target_mask_for_loss,
+                num_items_in_batch=num_items_in_batch,
+                kd_temperature=self.args.kd_temperature,
+                top_k_before_softmax=1 if self.args.kd_top_k_before_softmax else 0,
+            )
+
+        if self.args.kd_ce_alpha > 0:
+            kd_alpha = self.args.kd_alpha
+            loss = self.args.kd_ce_alpha * outputs["loss"] + kd_alpha * loss_kd
+        else:
+            loss = loss_kd
+        # Save past state if it exists
+        # TODO: this needs to be fixed and made cleaner later.
+        if self.args.past_index >= 0:
+            self._past = outputs[  # pylint: disable=attribute-defined-outside-init
+                self.args.past_index
+            ]
+
+        if self.args.average_tokens_across_devices and self.model_accepts_loss_kwargs:
+            loss *= self.accelerator.num_processes
+
+        return (loss, outputs) if return_outputs else loss

src/axolotl/integrations/kd/utils.py

@@ -1,100 +0,0 @@
-"""Helper KD utils"""
-
-import math
-from typing import List, Union
-
-import numpy as np
-import torch
-from torch import FloatTensor, Tensor
-
-
-def normalize_logprobs(logprobs: FloatTensor, topk: int) -> FloatTensor:
-    """
-    Re-normalizes top-k raw logprobs as probabilities, and converts back to logprobs.
-    """
-    # Ensure raw_logprobs matches kd_online_topk length for tensor operations
-    # This should ideally be handled by the caller ensuring correct padding/truncation first
-    if logprobs.shape[-1] != topk:
-        # pad last dimension of logprobs to match topk length with -inf
-        padding_len = topk - logprobs.shape[-1]
-        padding_tensor = torch.full(
-            (
-                *logprobs.shape[:-1],
-                padding_len,
-            ),  # Takes all dimensions of logprobs except the last, then appends padding_needed
-            float("-inf"),
-            dtype=logprobs.dtype,
-            device=logprobs.device,
-        )
-        logprobs = torch.cat((logprobs, padding_tensor), dim=-1)
-
-    # Convert logprobs at T_online to probabilities
-    # use log sum exp trick to avoid underflow
-    position_logprobs_lse = torch.logsumexp(logprobs, dim=-1, keepdim=True)
-    teacher_probs_t_online = torch.exp(logprobs - position_logprobs_lse)
-
-    # Normalize probabilities (sum to 1)
-    # This is important if the top-k from server aren't a full distribution
-    teacher_probs_t_online_sum = teacher_probs_t_online.sum(dim=-1, keepdim=True)
-    teacher_probs_t_online = teacher_probs_t_online / teacher_probs_t_online_sum
-
-    final_logprobs_tensor = torch.log(teacher_probs_t_online)
-
-    return final_logprobs_tensor
-
-
-def strided_chunk_views(
-    tensor: Union[np.ndarray, torch.Tensor],
-    chunks: int,
-    dim: int = 0,
-    stride: int = 1,
-    chunk_size: int | None = None,
-) -> List[Union[np.ndarray, torch.Tensor]]:
-    """
-    Split a tensor into chunks along a dimension with striding, prioritizing views over copies.
-
-    Args:
-        tensor: Input tensor (numpy array or torch tensor)
-        chunks: Number of chunks to create
-        dim: Dimension along which to chunk (default: 0)
-        stride: Stride between chunk starting positions (default: 1)
-        chunk_size: Size of each chunk. If None, calculated automatically (default: None)
-
-    Returns:
-        List of tensor chunks (views when possible, copies when necessary)
-    """
-
-    # Get the size of the specified dimension
-    dim_size = tensor.shape[dim]
-
-    # Calculate chunk size if not provided
-    if chunk_size is None:
-        chunk_size = (dim_size + chunks - 1) // chunks  # Ceiling division
-
-    chunks_list = []
-
-    for i in range(chunks):
-        start_idx = i * stride
-        end_idx = min(start_idx + chunk_size, dim_size)
-
-        # Break if we've gone beyond the tensor
-        if start_idx >= dim_size:
-            break
-
-        # Create slice objects for all dimensions
-        slices = [slice(None)] * tensor.ndim
-        slices[dim] = slice(start_idx, end_idx)
-
-        chunk = tensor[tuple(slices)]
-        chunks_list.append(chunk)
-
-    return chunks_list
-
-
-def chunk_overlap(input_tensor: Tensor, chunks: int, dim: int = 0, overlap: int = 1):
-    dim_size = input_tensor.shape[dim]
-    stride = math.ceil(dim_size / chunks)
-
-    return strided_chunk_views(
-        input_tensor, chunks, dim, stride=stride, chunk_size=stride + overlap
-    )

src/axolotl/loaders/adapter.py

@@ -19,6 +19,7 @@ from peft import (
 from transformers import PreTrainedModel
 
 from axolotl.loaders.utils import get_linear_embedding_layers
+from axolotl.telemetry.errors import send_errors
 from axolotl.utils.dict import DictDefault
 from axolotl.utils.logging import get_logger
 
@@ -162,6 +163,7 @@ def load_lora(
     return model, lora_config
 
 
+@send_errors
 def load_adapter(
     model: PreTrainedModel,
     cfg: DictDefault,

src/axolotl/loaders/model.py

@@ -46,6 +46,7 @@ from axolotl.loaders.utils import (
     load_model_config,
 )
 from axolotl.models.mamba import fix_mamba_attn_for_loss
+from axolotl.telemetry.errors import send_errors
 from axolotl.utils.bench import log_gpu_memory_usage
 from axolotl.utils.dict import DictDefault
 from axolotl.utils.distributed import (
@@ -145,6 +146,7 @@ class ModelLoader:
         """Property that determines if FSDP with QLoRA is enabled."""
         return self.cfg.fsdp and self.cfg.adapter == "qlora"
 
+    @send_errors
     def load(self) -> tuple[PreTrainedModel | PeftModelForCausalLM, PeftConfig | None]:
         """Load and prepare the model with all configurations and patches.
 

src/axolotl/loaders/patch_manager.py

@@ -166,17 +166,6 @@ class PatchManager:
     def _apply_self_attention_lora_patch(self):
         """Apply self-attention LoRA patches if configured."""
         if self.cfg.lora_qkv_kernel or self.cfg.lora_o_kernel:
-            # Only patch if conditions are met
-            can_patch = (
-                self.cfg.lora_dropout == 0
-                if hasattr(self.cfg, "lora_dropout")
-                else True
-            )  # default to True if lora_dropout is not set
-
-            if not can_patch:
-                LOG.warning("Cannot patch self-attention - requires no dropout")
-                return
-
             from axolotl.monkeypatch.lora_kernels import patch_self_attn_lora
 
             patch_self_attn_lora(self.cfg)

src/axolotl/loaders/processor.py

@@ -8,12 +8,14 @@ from transformers import (
     PreTrainedTokenizerBase,
 )
 
+from axolotl.telemetry.errors import send_errors
 from axolotl.utils.dict import DictDefault
 from axolotl.utils.logging import get_logger
 
 LOG = get_logger(__name__)
 
 
+@send_errors
 def load_processor(cfg: DictDefault, tokenizer: PreTrainedTokenizerBase):
     processor_kwargs: dict[str, Any] = {}  # Do we actually need this?
 

src/axolotl/loaders/tokenizer.py

@@ -7,14 +7,13 @@ import transformers
 from transformers import (
     AddedToken,
     AutoTokenizer,
-    PreTrainedTokenizer,
 )
 
 from axolotl.integrations.base import PluginManager
 from axolotl.loaders.utils import get_linear_embedding_layers, load_model_config
 from axolotl.prompt_tokenizers import LLAMA_DEFAULT_EOS_TOKEN
+from axolotl.telemetry.errors import send_errors
 from axolotl.utils.chat_templates import get_chat_template_from_config
-from axolotl.utils.dict import DictDefault
 from axolotl.utils.distributed import (
     barrier,
     is_local_main_process,
@@ -119,21 +118,9 @@ def modify_tokenizer_files(
     return tokenizer_dir
 
 
-def load_tokenizer(cfg: DictDefault) -> PreTrainedTokenizer:
+@send_errors
+def load_tokenizer(cfg):
     """Load and configure the tokenizer based on the provided config."""
-
-    def _load_mistral_common_tokenizer(cfg: DictDefault):
-        """Load mistral-common tokenizer"""
-        from axolotl.utils.mistral_tokenizer import HFMistralTokenizer
-
-        # Load the HF-compatible wrapper around MistralTokenizer
-        tokenizer = HFMistralTokenizer.from_pretrained(cfg.tokenizer_config)
-
-        return tokenizer
-
-    if cfg.tokenizer_use_mistral_common:
-        return _load_mistral_common_tokenizer(cfg)
-
     model_config = load_model_config(cfg)
     tokenizer_kwargs = {}
     use_fast = True  # this is the default
@@ -222,12 +209,11 @@ def load_tokenizer(cfg: DictDefault) -> PreTrainedTokenizer:
                 )
                 and k != "pad_token"
             ):
-                lora_modules_to_save_str = ", ".join(
+                lora_modules_to_save = ", ".join(
                     [f"`{x}`" for x in lora_modules_to_save]
                 )
                 raise ValueError(
-                    f"Please set lora_modules_to_save to [{lora_modules_to_save_str}] "
+                    f"Please set lora_modules_to_save to [{lora_modules_to_save}] when using an adapter and changing the special tokens."
-                    "when using an adapter and changing the special tokens."
                 )
 
             tokenizer.add_special_tokens(

src/axolotl/monkeypatch/lora_kernels.py

@@ -145,11 +145,6 @@ def get_attention_cls_from_config(cfg: DictDefault) -> Type[nn.Module]:
 
         return Qwen2Attention
 
-    if model_type == "mllama":
-        from transformers.models.mllama.modeling_mllama import MllamaTextSelfAttention
-
-        return MllamaTextSelfAttention
-
     try:
         # Dynamically import the module and attention class
         module_path = f"transformers.models.{model_type}.modeling_{model_type}"
@@ -274,29 +269,6 @@ def find_mlp_in_layer(
                 )
 
 
-def get_layers(model: PeftModelForCausalLM) -> list[nn.Module]:
-    """
-    Get the layers of the model. Handles text-only and multimodal models.
-
-    Args:
-        model: A PEFT model.
-
-    Returns:
-        A list of layers.
-    """
-    pretrained_model = model.model
-
-    # check for multimodal models first
-    if hasattr(pretrained_model, "language_model"):
-        return pretrained_model.language_model.layers
-    if hasattr(pretrained_model, "model"):
-        return pretrained_model.model.layers
-
-    raise NotImplementedError(
-        f"Model type {model.config.model_type} is not supported yet. Please create an Issue."
-    )
-
-
 def apply_lora_kernel_patches(
     model: PeftModelForCausalLM, cfg: DictDefault
 ) -> PeftModelForCausalLM:
@@ -368,7 +340,17 @@ def apply_lora_kernel_patches(
     if activation not in SUPPORTED_ACTIVATIONS:
         raise NotImplementedError(f"Activation {activation} is not supported")
 
-    layers = get_layers(model)
+    layers = []
+    # check for multimodal models first
+    pretrained_model = model.model
+    if hasattr(pretrained_model, "language_model"):
+        layers = pretrained_model.language_model.layers
+    elif hasattr(pretrained_model, "model"):
+        layers = pretrained_model.model.layers
+    else:
+        raise NotImplementedError(
+            f"Model type {model.config.model_type} is not supported yet. Please create an Issue."
+        )
 
     # Patch each layer
     for layer in layers:

src/axolotl/prompt_strategies/__init__.py

@@ -17,10 +17,7 @@ def load(strategy, tokenizer, cfg, ds_cfg, processor=None):
             return messages_load(tokenizer, cfg, ds_cfg, processor=processor)
         load_fn = "load"
         package = "axolotl.prompt_strategies"
-        if (
+        if strategy.split(".")[-1].startswith("load_"):
-            strategy.split(".")[-1].startswith("load_")
-            or strategy.split(".")[-1] == "load"
-        ):
             load_fn = strategy.split(".")[-1]
             strategy = ".".join(strategy.split(".")[:-1])
         elif len(strategy.split(".")) > 1:

src/axolotl/prompt_strategies/chat_template.py

@@ -2,10 +2,8 @@
 HF Chat Templates prompt strategy
 """
 
-# pylint: disable=too-many-lines
-
 from collections import defaultdict
-from typing import TYPE_CHECKING, Any, Dict, List, Set, Union
+from typing import Any, Dict, List, Set, Union
 
 from pydantic import BaseModel
 from transformers import ProcessorMixin
@@ -17,9 +15,6 @@ from axolotl.utils.chat_templates import get_chat_template_from_config
 from axolotl.utils.logging import get_logger
 from axolotl.utils.schemas.datasets import DatasetConfig
 
-if TYPE_CHECKING:
-    from axolotl.utils.mistral_tokenizer import HFMistralTokenizer
-
 # Configure the logger
 LOG = get_logger(__name__)
 LOG.setLevel("INFO")
@@ -39,7 +34,6 @@ class ChatTemplatePrompter(Prompter):
         message_field_training_detail: str | None = None,
         field_messages: str = "messages",
         field_system: str = "system",
-        field_tools: str = "tools",
         roles: dict[str, list[str]] | None = None,
         chat_template_kwargs: dict[str, Any] | None = None,
         drop_system_message: bool = False,
@@ -72,7 +66,6 @@ class ChatTemplatePrompter(Prompter):
         self.message_field_training_detail = message_field_training_detail
         self.field_messages = field_messages
         self.field_system = field_system
-        self.field_tools = field_tools
         self.tokenizer = tokenizer
         self.processor: ProcessorMixin | None = processor
         self.chat_template = chat_template
@@ -84,38 +77,17 @@ class ChatTemplatePrompter(Prompter):
     def chat_template_msg_variables(self) -> Set[str]:
         return self._chat_template_msg_variables
 
-    def build_prompt(
+    def build_prompt(self, conversation, add_generation_prompt=False, images=None):
-        self,
-        conversation: list[dict],
-        add_generation_prompt=False,
-        images=None,
-        tools=None,
-    ):
-        """
-        Build a prompt from a conversation.
-
-        Args:
-            conversation: A list of messages.
-            add_generation_prompt: Whether to add a generation prompt.
-            images: A list of images. (optional)
-            tools: A list of tools. (optional)
-        """
-        chat_template_kwargs = {
-            "chat_template": self.chat_template,
-            "add_generation_prompt": add_generation_prompt,
-        }
-
-        if tools:
-            chat_template_kwargs["tools"] = tools
-
         if self.processor:
             if not callable(self.processor):
                 raise TypeError("Processor must be callable")
 
             text = self.processor.apply_chat_template(
                 conversation,
+                chat_template=self.chat_template,
                 tokenize=False,
-                **chat_template_kwargs,
+                add_generation_prompt=add_generation_prompt,
+                **self.chat_template_kwargs,
             )
             batch = self.processor(
                 text=text,
@@ -132,7 +104,9 @@ class ChatTemplatePrompter(Prompter):
 
         return self.tokenizer.apply_chat_template(
             conversation,
-            **chat_template_kwargs,
+            add_generation_prompt=add_generation_prompt,
+            chat_template=self.chat_template,
+            **self.chat_template_kwargs,
         )
 
     def get_offsets_for_train_detail(
@@ -276,15 +250,9 @@ class ChatTemplateStrategy(PromptTokenizingStrategy):
         self.train_on_eot = train_on_eot if train_on_eot is not None else train_on_eos
 
         # Default to eos_token if eot_tokens not provided
-        self.eot_tokens = []
+        self.eot_tokens = (
-        if eot_tokens is not None:
+            eot_tokens if eot_tokens is not None else [self.tokenizer.eos_token]
-            self.eot_tokens = eot_tokens
+        )
-        elif (
-            hasattr(self.tokenizer, "eos_token")
-            and self.tokenizer.eos_token is not None
-        ):
-            self.eot_tokens = [self.tokenizer.eos_token]
-
         self.split_thinking = split_thinking
 
         self.images = "images"
@@ -408,7 +376,7 @@ class ChatTemplateStrategy(PromptTokenizingStrategy):
             and not self.prompter.message_field_training_detail  # type: ignore
         ):
             turns = self.get_conversation_thread(prompt)
-            images = self._get_images(prompt)
+            images = self.get_images(prompt)
             prompt_ids = self.prompter.build_prompt(  # type: ignore
                 turns[:-1],
                 add_generation_prompt=True,
@@ -437,8 +405,7 @@ class ChatTemplateStrategy(PromptTokenizingStrategy):
             return tokenized_prompt
 
         turns = self.get_conversation_thread(prompt)
-        tools = self._get_tools(prompt)
+        input_ids = self.prompter.build_prompt(turns)  # type: ignore
-        input_ids = self.prompter.build_prompt(turns, tools=tools)  # type: ignore
         labels = [IGNORE_TOKEN_ID] * len(input_ids)
 
         last_eos_idx = -1
@@ -477,9 +444,7 @@ class ChatTemplateStrategy(PromptTokenizingStrategy):
 
                 continue
 
-            turn_start_idx, turn_end_idx = self.find_turn(
+            turn_start_idx, turn_end_idx = self.find_turn(turns=turns, turn_idx=index)
-                turns=turns, turn_idx=index, tools=tools
-            )
 
             LOG.debug(f"Turn indices: start={turn_start_idx}, end={turn_end_idx}")
 
@@ -581,9 +546,7 @@ class ChatTemplateStrategy(PromptTokenizingStrategy):
                 return i
         return -1
 
-    def find_turn(
+    def find_turn(self, turns: list[dict], turn_idx: int):
-        self, turns: list[dict], turn_idx: int, tools: list[dict] | None = None
-    ):
         """
         Locate the starting and ending indices of the specified turn in a conversation.
         """
@@ -614,10 +577,10 @@ class ChatTemplateStrategy(PromptTokenizingStrategy):
         turns_with_content = turns[: turn_idx + 1]
 
         # Generate the conversation up to the turn, with final turn replaced with dummy content
-        dummy_ids = self.prompter.build_prompt(turns_with_empty, tools=tools)  # type: ignore
+        dummy_ids = self.prompter.build_prompt(turns_with_empty)  # type: ignore
 
         # Generate the conversation up to the turn, with final turn included
-        full_ids = self.prompter.build_prompt(turns_with_content, tools=tools)  # type: ignore
+        full_ids = self.prompter.build_prompt(turns_with_content)  # type: ignore
 
         if not full_ids or not dummy_ids:
             LOG.warning(f"Empty template generated for turn {turn_idx}")
@@ -670,10 +633,9 @@ class ChatTemplateStrategy(PromptTokenizingStrategy):
     def get_conversation_thread(self, prompt):
         turns = []
 
-        messages = self._get_messages(prompt)
+        possible_sys_turn = self.transform_message(
-
+            prompt[self.prompter.field_messages][0]
-        possible_sys_turn = self.transform_message(messages[0])
+        )
-
         if (
             possible_sys_turn["role"] != "system"
             and self.prompter.field_system in prompt
@@ -681,7 +643,7 @@ class ChatTemplateStrategy(PromptTokenizingStrategy):
             turn = {"role": "system", "content": prompt[self.prompter.field_system]}
             turns.append(turn)
 
-        for message in messages:
+        for message in prompt[self.prompter.field_messages]:
             transformed_message = self.transform_message(message)
 
             turn = {
@@ -699,7 +661,7 @@ class ChatTemplateStrategy(PromptTokenizingStrategy):
 
         return turns
 
-    def transform_message(self, message: dict) -> dict:
+    def transform_message(self, message):
         # Build the initial transformed message from the mappings
         transformed_message = {}
         for key, value in self.prompter.message_property_mappings.items():
@@ -776,135 +738,18 @@ class ChatTemplateStrategy(PromptTokenizingStrategy):
 
         return transformed_message
 
-    def _get_images(self, prompt):
+    def get_images(self, prompt):
         return prompt.get(self.images, None)
 
-    def _get_tools(self, prompt) -> list[dict] | None:
-        """Get tools from prompt if available."""
-        tools = prompt.get(self.prompter.field_tools, None)
-        if tools is None:
-            return None
-
-        if isinstance(tools, list):
-            return tools
-
-        raise ValueError(
-            "Unknown tools format. Please convert it into a list[dict].\n"
-            f"Current format: {type(tools)}"
-        )
-
-    def _get_messages(self, prompt):
-        messages = prompt.get(self.prompter.field_messages, None)
-        if messages is None:
-            raise ValueError("Messages is null. Please check `field_messages`.")
-
-        if isinstance(messages, list):
-            return messages
-
-        raise ValueError(
-            "Unknown messages format. Please convert it into a list[dict].\n"
-            f"Current format: {type(messages)}"
-        )
-
-
-class MistralStrategy(ChatTemplateStrategy):
-    """
-    Mistral strategy for chat template.
-    """
-
-    def __init__(
-        self,
-        prompter: "ChatTemplatePrompter",
-        tokenizer: "HFMistralTokenizer",
-        train_on_inputs: bool,
-        sequence_len: int,
-        roles_to_train: list[str] | None = None,
-        train_on_eos: str | None = None,
-        train_on_eot: str | None = None,
-        eot_tokens: list[str] | None = None,
-        split_thinking: bool | None = False,
-    ):
-        # Call the parent's parent __init__ (PromptTokenizingStrategy) to skip ChatTemplateStrategy's validation
-        # pylint: disable=non-parent-init-called,super-init-not-called
-        PromptTokenizingStrategy.__init__(
-            self, prompter, tokenizer, train_on_inputs, sequence_len
-        )
-        self.prompter: ChatTemplatePrompter = prompter
-
-        self.roles_to_train = []
-        if roles_to_train:
-            # map roles if exist in prompter.roles else use the role as is
-            self.roles_to_train = [
-                prompter.roles.get(role, role) for role in roles_to_train
-            ]
-
-        self.train_on_eos = train_on_eos
-        # Backward compatibility, load from train_on_eos
-        self.train_on_eot = train_on_eot if train_on_eot is not None else train_on_eos
-
-        # Default to eos_token if eot_tokens not provided
-        self.eot_tokens = []
-        if eot_tokens is not None:
-            self.eot_tokens = eot_tokens
-        else:
-            # set eot_tokens to the eos_token
-            self.eot_tokens = [self.tokenizer.eos_token]
-
-        self.split_thinking = split_thinking
-
-        self.images = "images"
-
-        LOG.debug(
-            f"The chat template uses the following properites on the message: {self.prompter.chat_template_msg_variables}"
-        )
-
-        # Skip the validation that ChatTemplateStrategy calls
-        # TODO: address this in the future with mistral-specific checks
-        # self._validate_eot_and_eos_tokens()
-
-    @property
-    def supports_multiprocessing(self) -> bool:
-        """
-        Whether this tokenizing strategy supports multiprocessing.
-        mistral_common tokenizers cannot be pickled for multiprocessing.
-        """
-
-        return False
-
-    def find_first_eot_token(self, input_ids, start_idx):
-        """Find the first EOT token in the input_ids starting from start_idx."""
-        # mistral-common tokenizer does not support eot_tokens
-        return self.find_first_eos_token(input_ids, start_idx)
-
-
-class MistralPrompter(ChatTemplatePrompter):
-    """
-    Mistral prompter for chat template.
-    """
-
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-
-        self._chat_template_msg_variables = set(["tool_call_id", "name", "tool_calls"])
-
 
 class StrategyLoader:
     """
     Load chat template strategy based on configuration.
     """
 
-    def _get_strategy_cls(self, cfg):
+    def _get_strategy_cls(self):
-        if cfg.tokenizer_use_mistral_common:
-            return MistralStrategy
-
         return ChatTemplateStrategy
 
-    def _get_prompter_cls(self, cfg):
-        if cfg.tokenizer_use_mistral_common:
-            return MistralPrompter
-
-        return ChatTemplatePrompter
-
     def _get_strategy_params(self, cfg, ds_cfg: Dict[str, Any]):
         return {
             "train_on_inputs": cfg.train_on_inputs,
@@ -930,14 +775,9 @@ class StrategyLoader:
         else:
             dataset_config = ds_cfg
 
-        if cfg.tokenizer_use_mistral_common:
+        chat_template_string = get_chat_template_from_config(
-            # mistral-common does not use this, so we pass an empty string
+            cfg=cfg, ds_cfg=dataset_config, tokenizer=tokenizer
-            chat_template_string = ""
+        )
-        else:
-            chat_template_string = get_chat_template_from_config(
-                cfg=cfg, ds_cfg=dataset_config, tokenizer=tokenizer
-            )
-
         LOG.info(f"Using chat template:\n---\n{chat_template_string!s}\n---")
 
         prompter_params = {
@@ -963,11 +803,10 @@ class StrategyLoader:
         }
 
         strategy_params = self._get_strategy_params(cfg, dataset_config)
-        strategy_cls = self._get_strategy_cls(cfg)
+        strategy_cls = self._get_strategy_cls()
-        prompter_cls = self._get_prompter_cls(cfg)
 
         strategy = strategy_cls(
-            prompter_cls(**prompter_params),
+            ChatTemplatePrompter(**prompter_params),
             tokenizer=tokenizer,
             **strategy_params,
         )

src/axolotl/prompt_strategies/dpo/chat_template.py

@@ -46,14 +46,6 @@ def default(
         )
 
         messages = sample[field_messages]
-        if isinstance(messages, str):
-            messages = [
-                {
-                    message_property_mappings["role"]: "user",
-                    message_property_mappings["content"]: messages,
-                }
-            ]
-
         messages = [
             {
                 "role": role_map[m[message_property_mappings["role"]]],
@@ -61,35 +53,13 @@ def default(
             }
             for m in messages
         ]
-
-        chosen_raw = sample[field_chosen]
-        if isinstance(chosen_raw, str):
-            chosen_msg = {
-                message_property_mappings["role"]: "assistant",
-                message_property_mappings["content"]: chosen_raw,
-            }
-        elif isinstance(chosen_raw, dict):
-            chosen_msg = chosen_raw
-        else:
-            chosen_msg = chosen_raw[-1]
         chosen = {
-            "role": role_map[chosen_msg[message_property_mappings["role"]]],
+            "role": role_map[sample[field_chosen][message_property_mappings["role"]]],
-            "content": chosen_msg[message_property_mappings["content"]],
+            "content": sample[field_chosen][message_property_mappings["content"]],
         }
-
-        rejected_raw = sample[field_rejected]
-        if isinstance(rejected_raw, str):
-            rejected_msg = {
-                message_property_mappings["role"]: "assistant",
-                message_property_mappings["content"]: rejected_raw,
-            }
-        elif isinstance(rejected_raw, dict):
-            rejected_msg = rejected_raw
-        else:
-            rejected_msg = rejected_raw[-1]
         rejected = {
-            "role": role_map[rejected_msg[message_property_mappings["role"]]],
+            "role": role_map[sample[field_rejected][message_property_mappings["role"]]],
-            "content": rejected_msg[message_property_mappings["content"]],
+            "content": sample[field_rejected][message_property_mappings["content"]],
         }
         dummy_user_message = {"role": "user", "content": "[[dummy_message]]"}
 

src/axolotl/prompt_strategies/messages/__init__.py

@@ -32,3 +32,4 @@ def load(tokenizer, cfg, ds_cfg, processor=None):
     except Exception as exc:  # pylint: disable=broad-exception-caught
         LOG.error(f"Failed to load prompt strategy `{strategy}`: {str(exc)}")
         raise exc
+    return None

src/axolotl/prompt_tokenizers.py

@@ -3,7 +3,6 @@
 import abc
 from typing import Callable, Dict, List, Optional, Tuple, Union
 
-from datasets import Dataset
 from transformers import BatchEncoding, PreTrainedTokenizer
 
 from axolotl.prompters import Prompter
@@ -29,16 +28,6 @@ class DatasetWrappingStrategy(abc.ABC):
     Abstract class for wrapping datasets for Chat Messages
     """
 
-    @abc.abstractmethod
-    def wrap_dataset(
-        self,
-        dataset,
-        process_count: int | None = None,
-        keep_in_memory: bool | None = False,
-        **kwargs,
-    ) -> Dataset:
-        pass
-
 
 class PromptTokenizingStrategy(abc.ABC):
     """
@@ -70,14 +59,6 @@ class PromptTokenizingStrategy(abc.ABC):
     def supports_batched(self):
         return False
 
-    @property
-    def supports_multiprocessing(self):
-        """
-        Whether this tokenizing strategy supports multiprocessing.
-        Should return False if the tokenizer has unpicklable objects.
-        """
-        return True
-
     def _tokenize(
         self, prompt: str, add_eos_token: bool = True, strip_bos_token: bool = False
     ) -> BatchEncoding:

src/axolotl/telemetry/callbacks.py

@@ -0,0 +1,164 @@
+"""Trainer callbacks for reporting runtime metrics at regular intervals."""
+
+import logging
+import time
+
+from transformers import (
+    TrainerCallback,
+    TrainerControl,
+    TrainerState,
+    TrainingArguments,
+)
+
+from axolotl.telemetry.manager import TelemetryManager
+from axolotl.telemetry.runtime_metrics import RuntimeMetricsTracker
+
+LOG = logging.getLogger(__name__)
+
+TIME_SINCE_LAST = 30
+
+
+class TelemetryCallback(TrainerCallback):
+    """
+    Trainer callback for tracking and reporting runtime metrics.
+
+    This callback tracks training progress, runtime, and memory usage,
+    sending telemetry at configurable intervals.
+    """
+
+    report_interval_steps: int = 100
+
+    def __init__(self):
+        """Initialize the metrics callback."""
+        self.tracker = RuntimeMetricsTracker()
+        self.telemetry_manager = TelemetryManager.get_instance()
+        self.current_epoch = -1
+        self.start_time = time.time()
+        self.last_report_time = None
+        self.last_report_step = 0
+
+    # pylint: disable=unused-argument
+    def on_train_begin(
+        self,
+        args: TrainingArguments,
+        state: TrainerState,
+        control: TrainerControl,
+        **kwargs,
+    ):
+        """Handle training start."""
+        self.telemetry_manager.send_event(event_type="train-start")
+
+    # pylint: disable=unused-argument
+    def on_train_end(
+        self,
+        args: TrainingArguments,
+        state: TrainerState,
+        control: TrainerControl,
+        **kwargs,
+    ):
+        """Handle training end."""
+        # Send training completion event
+        self.telemetry_manager.send_event(
+            event_type="train-end",
+            properties=self._extract_last_metrics(state)
+            | self.tracker.metrics.to_dict(),
+        )
+
+    # pylint: disable=unused-argument
+    def on_epoch_begin(
+        self,
+        args: TrainingArguments,
+        state: TrainerState,
+        control: TrainerControl,
+        **kwargs,
+    ):
+        """Handle epoch start."""
+        self.current_epoch += 1
+        self.tracker.start_epoch(self.current_epoch)
+
+    # pylint: disable=unused-argument
+    def on_epoch_end(
+        self,
+        args: TrainingArguments,
+        state: TrainerState,
+        control: TrainerControl,
+        **kwargs,
+    ):
+        """Handle epoch end."""
+        self.tracker.end_epoch(self.current_epoch)
+
+    # pylint: disable=unused-argument
+    def on_step_end(
+        self,
+        args: TrainingArguments,
+        state: TrainerState,
+        control: TrainerControl,
+        **kwargs,
+    ):
+        """Handle step end."""
+        step = state.global_step
+        self.tracker.update_step(step)
+
+        # Check if we should report metrics
+        should_report = (
+            step % self.report_interval_steps == 0
+            or step == 1  # Always report first step
+            or step - self.last_report_step >= self.report_interval_steps
+        )
+
+        if should_report:
+            current_time = time.time()
+            if self.last_report_time is not None:
+                time_since_last_report = current_time - self.last_report_time
+            else:
+                time_since_last_report = current_time - self.start_time
+            steps_since_last_report = step - self.last_report_step
+
+            # Only report if enough time has passed
+            if (
+                step == 1
+                or time_since_last_report >= TIME_SINCE_LAST
+                or steps_since_last_report >= self.report_interval_steps
+            ):
+                # Calculate steps per second for this interval
+                if time_since_last_report > 0 and steps_since_last_report > 0:
+                    steps_per_second = steps_since_last_report / time_since_last_report
+                else:
+                    steps_per_second = 0
+
+                # Update memory metrics
+                self.tracker.update_memory_metrics()
+
+                # Prepare metrics to report
+                metrics = self._extract_last_metrics(state) | {
+                    "step": step,
+                    "epoch": self.current_epoch,
+                    "progress": state.epoch,  # Fractional epoch progress
+                    "steps_per_second": steps_per_second,
+                    "elapsed_time": current_time - self.start_time,
+                    "time_since_last_report": time_since_last_report,
+                }
+
+                # Add memory metrics
+                memory_metrics = self.tracker.get_memory_metrics()
+                metrics.update({"memory": memory_metrics})
+
+                # Send telemetry
+                self.telemetry_manager.send_event(
+                    event_type="train-progress", properties=metrics
+                )
+
+                # Update last report time and step
+                self.last_report_time = current_time
+                self.last_report_step = step
+
+    def _extract_last_metrics(self, state: TrainerState) -> dict:
+        """Extract last loss and learning_rate from log history."""
+        if not state.log_history:
+            return {"loss": 0, "learning_rate": 0}
+
+        last_log = state.log_history[-1]
+        return {
+            "loss": last_log.get("loss", 0),
+            "learning_rate": last_log.get("learning_rate", 0),
+        }

src/axolotl/telemetry/errors.py

@@ -0,0 +1,160 @@
+"""Telemetry utilities for exception and traceback information."""
+
+import logging
+import os
+import re
+import traceback
+from functools import wraps
+from inspect import getmodule
+from typing import Any, Callable
+
+from axolotl.telemetry.manager import TelemetryManager
+
+LOG = logging.getLogger(__name__)
+
+ERROR_HANDLED = False
+
+
+def sanitize_stack_trace(stack_trace: str) -> str:
+    """
+    Remove personal information from stack trace messages while keeping Python package codepaths.
+
+    This function identifies Python packages by looking for common patterns in virtual environment
+    and site-packages directories, preserving the package path while removing user-specific paths.
+
+    Args:
+        stack_trace: The original stack trace string.
+
+    Returns:
+        A sanitized version of the stack trace with Python package paths preserved.
+    """
+    # Split the stack trace into lines to process each file path separately
+    lines = stack_trace.split("\n")
+    sanitized_lines = []
+
+    # Regular expression to find file paths in the stack trace
+    path_pattern = re.compile(r'(?:File ")(.*?)(?:")')
+
+    # Regular expression to identify paths in site-packages or dist-packages
+    # This matches path segments like "site-packages/package_name" or "dist-packages/package_name"
+    site_packages_pattern = re.compile(
+        r"(?:site-packages|dist-packages)[/\\]([\w\-\.]+)"
+    )
+
+    # Additional common virtual environment patterns
+    venv_lib_pattern = re.compile(
+        r"(?:lib|Lib)[/\\](?:python\d+(?:\.\d+)?[/\\])?(?:site-packages|dist-packages)[/\\]([\w\-\.]+)"
+    )
+
+    for line in lines:
+        # Check if this line contains a file path
+        path_match = path_pattern.search(line)
+
+        if path_match:
+            full_path = path_match.group(1)
+            sanitized_path = ""
+
+            # Try to match site-packages pattern
+            site_packages_match = site_packages_pattern.search(full_path)
+            venv_lib_match = venv_lib_pattern.search(full_path)
+
+            if site_packages_match:
+                # Find the index where the matched pattern starts
+                idx = full_path.find("site-packages")
+                if idx == -1:
+                    idx = full_path.find("dist-packages")
+
+                # Keep from 'site-packages' onward
+                if idx >= 0:
+                    sanitized_path = full_path[idx:]
+            elif venv_lib_match:
+                # For other virtual environment patterns, find the package directory
+                match_idx = venv_lib_match.start(1)
+                if match_idx > 0:
+                    # Keep from the package name onward
+                    package_name = venv_lib_match.group(1)
+                    idx = full_path.rfind(
+                        package_name, 0, match_idx + len(package_name)
+                    )
+                    if idx >= 0:
+                        sanitized_path = full_path[idx:]
+
+            # If we couldn't identify a package pattern but path contains 'axolotl'
+            elif "axolotl" in full_path:
+                idx = full_path.rfind("axolotl")
+                if idx >= 0:
+                    sanitized_path = full_path[idx:]
+
+            # Apply the sanitization to the line
+            if sanitized_path:
+                line = line.replace(full_path, sanitized_path)
+            else:
+                # If we couldn't identify a package pattern, just keep the filename
+                filename = os.path.basename(full_path)
+                if filename:
+                    line = line.replace(full_path, filename)
+                else:
+                    line = line.replace(full_path, "")
+
+        sanitized_lines.append(line)
+
+    return "\n".join(sanitized_lines)
+
+
+def send_errors(func: Callable) -> Callable:
+    """
+    Decorator to send exception info in a function. If an exception is raised, we send
+    telemetry containing the stack trace and error message.
+
+    If an error occurs in a decorated function that is called by another decorated
+    function, we'll only send telemetry corresponding to the lower-level function.
+
+    Args:
+        func: Function to decorate.
+
+    Returns:
+        Decorated function.
+    """
+
+    @wraps(func)
+    def wrapper(*args, **kwargs) -> Any:
+        telemetry_manager = TelemetryManager.get_instance()
+
+        if not telemetry_manager.enabled:
+            return func(*args, **kwargs)
+
+        try:
+            return func(*args, **kwargs)
+        except Exception as exception:
+            # Only track if we're not already handling an error. This prevents us from
+            # capturing an error more than once in nested decorated function calls.
+            global ERROR_HANDLED  # pylint: disable=global-statement
+            if not ERROR_HANDLED:
+                ERROR_HANDLED = True
+
+                # Get function module path
+                module = getmodule(func)
+                module_path = (
+                    f"{module.__name__}.{func.__name__}" if module else func.__name__
+                )
+
+                # Get stack trace
+                stack_trace = "".join(
+                    traceback.format_exception(
+                        type(exception), exception, exception.__traceback__
+                    )
+                )
+                stack_trace = sanitize_stack_trace(stack_trace)
+
+                # Send error telemetry
+                telemetry_manager.send_event(
+                    event_type=f"{module_path}-error",
+                    properties={
+                        "exception": str(exception),
+                        "stack_trace": stack_trace,
+                    },
+                )
+
+            raise
+
+    return wrapper

src/axolotl/telemetry/manager.py

@@ -0,0 +1,417 @@
+"""Telemetry manager and associated utilities."""
+
+import atexit
+import importlib
+import logging
+import os
+import platform
+import time
+import uuid
+from pathlib import Path
+from typing import Any
+
+import posthog
+import psutil
+import torch
+import yaml
+
+LOG = logging.getLogger(__name__)
+
+POSTHOG_HOST = "https://app.posthog.com"
+POSTHOG_WRITE_KEY = "phc_1kUR0o04oJKKTTeSsIz2Mfm5mpiVsQEf2WOlzljMD7y"
+
+OPT_IN_WARNING_SLEEP_SECONDS = 10
+OPT_IN_WARNING = (
+    "\nTelemetry is currently disabled by default. If you'd like to help improve "
+    "Axolotl, consider enabling it by setting AXOLOTL_DO_NOT_TRACK=0 in your environment.\n\n"
+    "Telemetry data helps us understand:\n"
+    "- Which features are most used\n"
+    "- What hardware configurations to prioritize\n"
+    "- Where users encounter errors\n\n"
+    "Personally identifiable information (PII) is not collected.\n\n"
+    "To remove this warning, explicitly set AXOLOTL_DO_NOT_TRACK=0 (enable telemetry) "
+    "or AXOLOTL_DO_NOT_TRACK=1 (explicitly disable telemetry).\n\n"
+    "Note: Telemetry will move to an opt-out in a later release.\n\n"
+    "For details, see: https://axolotl-ai-cloud.github.io/axolotl/docs/telemetry.html\n\n"
+    f"Sleeping for {OPT_IN_WARNING_SLEEP_SECONDS}s..."
+)
+
+WHITELIST_PATH = str(Path(__file__).parent / "whitelist.yaml")
+
+# NOTE: Need to keep these up to date with any config schema changes
+FIELDS_TO_REDACT = {
+    "base_model",
+    "tokenizer_config",
+    "base_model_config",
+    "pretraining_dataset",  # NOTE: this field may be a string or a dictionary
+    "resume_from_checkpoint",
+    "hub_model_id",
+}
+PREFIXES_TO_REDACT = {"wandb_", "comet_", "mlflow_", "gradio_"}
+PATH_INDICATORS = {"path", "dir"}
+
+# pylint: disable=duplicate-code
+RELEVANT_PACKAGES = {
+    "torch",
+    "transformers",
+    "trl",
+    "datasets",
+    "peft",
+    "bitsandbytes",
+    "accelerate",
+    "optimum",
+    "deepspeed",
+    "ray",
+    "axolotl",
+    "triton",
+    "mamba-ssm",
+    "flash-attn",
+    "xformers",
+    "autoawq",
+    "tokenizers",
+    "sentencepiece",
+    "torchao",
+    "lm_eval",
+}
+
+
+def is_main_process() -> bool:
+    """
+    Check whether we're running in the main process.
+
+    Note:
+        We're using this function instead of `torch.utils.distributed.is_main_process`
+        causes issues with DeepSpeed world_size since. This function avoids that issue
+        by checking env vars that are set by various launchers.
+
+    Returns:
+        Whether we're running in the main process.
+    """
+    # If PyTorch distributed is already initialized, use it
+    if torch.distributed.is_initialized():
+        return torch.distributed.get_rank() == 0
+
+    # Otherwise check environment variables for global rank
+    # NOTE: need to verify this in SLURM / OpenMPI environments
+    global_rank = int(
+        os.environ.get(
+            "RANK",
+            os.environ.get(
+                "GLOBAL_RANK",
+                os.environ.get(
+                    "SLURM_PROCID",
+                    os.environ.get(
+                        "OMPI_COMM_WORLD_RANK",
+                        "0",
+                    ),
+                ),
+            ),
+        )
+    )
+
+    return global_rank == 0
+
+
+class TelemetryManager:
+    """Manages telemetry collection and transmission"""
+
+    _instance = None
+    _initialized = False
+
+    def __new__(cls):
+        """
+        Telemetry manager constructor. Creates the singleton instance of this class if
+        it doesn't already exist.
+        """
+        if cls._instance is None:
+            cls._instance = super(TelemetryManager, cls).__new__(cls)
+            cls._instance._initialized = False
+
+        return cls._instance
+
+    def __init__(self):
+        """Telemetry manager initializer"""
+        if self._initialized:
+            return
+
+        self.enabled = self._check_telemetry_enabled()
+
+        if self.enabled:
+            self.run_id = str(uuid.uuid4())
+            self.whitelist = self._load_whitelist()
+
+            try:
+                self.system_info = self._get_system_info()
+            except Exception as e:  # pylint: disable=broad-exception-caught
+                LOG.warning(f"Error during system info collection: {e}")
+                self.system_info = None
+
+            self._init_posthog()
+
+            # Register shutdown method to flush posthog telemetry
+            atexit.register(self.shutdown)
+
+        self._initialized = True
+
+    @classmethod
+    def get_instance(cls) -> "TelemetryManager":
+        if cls._instance is None:
+            cls._instance = TelemetryManager()
+
+        return cls._instance
+
+    def _check_telemetry_enabled(self) -> bool:
+        """
+        Check if telemetry is enabled based on environment variables. We also check
+        whether this is the main process (for the distributed setting and to avoid
+        sending duplicate PostHog events per GPU).
+
+        Note: This is disabled by default on an opt-in basis. Set
+        `AXOLOTL_DO_NOT_TRACK=0` to enable telemetry. We plan to move to an opt-out
+        model in a later release. For more details, see
+        https://axolotl-ai-cloud.github.io/axolotl/docs/telemetry.html.
+
+        Returns:
+            Boolean denoting whether telemetry is enabled or not.
+        """
+        # Parse relevant env vars
+        axolotl_do_not_track = os.getenv("AXOLOTL_DO_NOT_TRACK")
+        do_not_track = os.getenv("DO_NOT_TRACK")
+
+        # Default to disabled (opt-in model for initial release)
+        if axolotl_do_not_track is None or axolotl_do_not_track.lower() not in (
+            "0",
+            "1",
+            "false",
+            "true",
+        ):
+            # Print opt-in info message for main process only
+            if is_main_process():
+                LOG.warning(OPT_IN_WARNING)
+            time.sleep(OPT_IN_WARNING_SLEEP_SECONDS)
+
+            return False
+
+        # Only rank 0 will send telemetry
+        if not is_main_process():
+            return False
+
+        if do_not_track is None:
+            do_not_track = "0"
+
+        # Respect AXOLOTL_DO_NOT_TRACK, DO_NOT_TRACK if enabled
+        enabled = axolotl_do_not_track.lower() not in (
+            "1",
+            "true",
+        ) and do_not_track.lower() not in ("1", "true")
+
+        return enabled
+
+    def _load_whitelist(self) -> dict:
+        """Load HuggingFace Hub organization whitelist"""
+        with open(WHITELIST_PATH, encoding="utf-8") as f:
+            whitelist = yaml.safe_load(f)
+
+            # Send org strings to lowercase since model names are case insensitive
+            whitelist["organizations"] = {
+                org.lower() for org in whitelist["organizations"]
+            }
+
+            return whitelist
+
+    def _is_whitelisted(self, value: str) -> bool:
+        """
+        Check if model / dataset / etc. org is in whitelist.
+
+        Args:
+            value: Value for one of `axolotl.telemetry.manager.FIELDS_WITH_ORGS`
+                ("base_model", etc.).
+
+        Returns:
+            Boolean indicating whitelist membership.
+        """
+        # NOTE: This membership-checking logic can be improved.
+        # What happens when a local model path matches a whitelisted org?
+        parts = value.split("/")
+        if len(parts) < 2:
+            return False
+        org = parts[0]
+        whitelisted = org.lower() in self.whitelist["organizations"]
+
+        return whitelisted
+
+    def _init_posthog(self):
+        """Initialize PostHog client"""
+        posthog.host = POSTHOG_HOST
+        posthog.project_api_key = POSTHOG_WRITE_KEY
+
+    def _redact_paths(self, properties: dict[str, Any]) -> dict[str, Any]:
+        """
+        Redact properties to remove any paths, so as to avoid inadvertently collecting
+        private or personally identifiable information (PII). We also remove
+        information related to Wandb, MLflow, etc. configuration.
+
+        Args:
+            properties: Dictionary of properties to redact.
+
+        Returns:
+            Properties dictionary with redaction applied.
+        """
+        if not properties:
+            return {}
+
+        def redact_value(value: Any, key: str = "") -> Any:
+            """Recursively sanitize values, redacting those with path-like keys"""
+            if isinstance(key, str) and isinstance(value, str):
+                # Other redaction special cases
+                if (
+                    key in FIELDS_TO_REDACT
+                    or any(prefix in key for prefix in PREFIXES_TO_REDACT)
+                    or any(indicator in key.lower() for indicator in PATH_INDICATORS)
+                ):
+                    # Fields with whitelisted orgs don't need to be redacted
+                    if not self._is_whitelisted(value):
+                        return "[REDACTED]"
+
+            # Handle nested values
+            if isinstance(value, dict):
+                return {k: redact_value(v, k) for k, v in value.items()}
+            if isinstance(value, list):
+                return [redact_value(item) for item in value]
+
+            return value
+
+        # Create new dict with redacted values
+        redacted = {k: redact_value(v, k) for k, v in properties.items()}
+
+        return redacted
+
+    def _get_system_info(self) -> dict[str, Any]:
+        """Collect system information for various hardware accelerators"""
+        gpu_info = []
+        accelerator_type = "none"
+
+        # NVIDIA GPUs
+        if torch.cuda.is_available():
+            accelerator_type = "cuda"
+            for i in range(torch.cuda.device_count()):
+                gpu_info.append(
+                    {
+                        "name": torch.cuda.get_device_name(i),
+                        "memory": torch.cuda.get_device_properties(i).total_memory,
+                    }
+                )
+
+        # AMD GPUs
+        elif hasattr(torch, "hip") and torch.hip.is_available():
+            accelerator_type = "hip"
+            for i in range(torch.hip.device_count()):
+                gpu_info.append(
+                    {
+                        "name": torch.hip.get_device_name(i),
+                        "memory": (
+                            torch.hip.get_device_properties(i).total_memory
+                            if hasattr(torch.hip, "get_device_properties")
+                            else None
+                        ),
+                    }
+                )
+
+        # Apple Silicon
+        elif hasattr(torch.backends, "mps") and torch.backends.mps.is_available():
+            accelerator_type = "mps"
+            gpu_info.append(
+                {
+                    "name": "Apple Silicon",
+                    # NOTE: this is memory allocated to this process, not total memory
+                    "memory": torch.mps.driver_allocated_memory(),
+                }
+            )
+
+        # Intel GPUs
+        elif hasattr(torch, "xpu") and torch.xpu.is_available():
+            accelerator_type = "xpu"
+            for i in range(torch.xpu.device_count()):
+                memory = None
+                if hasattr(torch.xpu, "get_device_properties"):
+                    memory = torch.xpu.get_device_properties(i).total_memory
+
+                gpu_info.append(
+                    {
+                        "name": torch.xpu.get_device_name(i),
+                        "memory": memory,
+                    }
+                )
+
+        # NPUs
+        elif hasattr(torch, "npu") and torch.npu.is_available():
+            accelerator_type = "npu"
+            for i in range(torch.npu.device_count()):
+                memory = None
+                if hasattr(torch.npu, "get_device_properties"):
+                    memory = torch.npu.get_device_properties(i).total_memory
+
+                gpu_info.append(
+                    {
+                        "name": torch.npu.get_device_name(i),
+                        "memory": memory,
+                    }
+                )
+
+        # Get relevant package versions
+        installed_packages = {}
+        for package in RELEVANT_PACKAGES:
+            try:
+                version = importlib.metadata.version(package)
+                installed_packages[f"{package}_version"] = version
+            except importlib.metadata.PackageNotFoundError:
+                pass
+
+        return {
+            "os": platform.system(),
+            "python_version": platform.python_version(),
+            "cpu_count": psutil.cpu_count(),
+            "memory_total": psutil.virtual_memory().total,
+            "accelerator_type": accelerator_type,
+            "accelerator_count": len(gpu_info),
+            "accelerator_info": gpu_info,
+            **installed_packages,
+        }
+
+    def send_event(self, event_type: str, properties: dict[str, Any] | None = None):
+        """Send a telemetry event"""
+        if not self.enabled:
+            return
+
+        if properties is None:
+            properties = {}
+
+        # Sanitize properties to remove PII
+        properties = self._redact_paths(properties)
+
+        # Wrap PostHog errors in try / except to not raise errors during Axolotl usage
+        try:
+            # Send event via PostHog
+            posthog.capture(
+                distinct_id=self.run_id,
+                event=event_type,
+                properties=properties,
+                disable_geoip=True,
+            )
+        except Exception as e:  # pylint: disable=broad-exception-caught
+            LOG.warning(f"Failed to send telemetry event: {e}")
+
+        # Additionally, send system info telemetry when loading config.
+        # NOTE: Is this the best place for this?
+        if event_type == "config-loaded":
+            self.send_system_info()
+
+    def send_system_info(self):
+        """Helper method for sending system info"""
+        if self.system_info is not None:
+            self.send_event(event_type="system-info", properties=self.system_info)
+
+    def shutdown(self):
+        """Ensure all queued events are processed before shutdown"""
+        if self.enabled:
+            posthog.flush()

src/axolotl/telemetry/runtime_metrics.py

@@ -0,0 +1,209 @@
+"""Telemetry utilities for runtime and memory metrics."""
+
+import logging
+import time
+from dataclasses import dataclass, field
+from typing import Any
+
+import psutil
+import torch
+
+from axolotl.telemetry.manager import TelemetryManager
+
+LOG = logging.getLogger(__name__)
+
+
+@dataclass
+class RuntimeMetrics:
+    """Container for runtime metrics to be tracked throughout training."""
+
+    # Timing metrics
+    start_time: float
+    epoch_start_times: dict[int, float] = field(init=False)
+    epoch_end_times: dict[int, float] = field(init=False)
+
+    # Memory metrics
+    peak_cpu_memory: int = 0
+    peak_gpu_memory: dict[int, int] = field(init=False)
+
+    # Progress metrics
+    total_steps: int = 0
+    current_epoch: int = 0
+    current_step: int = 0
+
+    def __post_init__(self):
+        """Initialize empty metric mappings."""
+        self.epoch_start_times = {}
+        self.epoch_end_times = {}
+        self.peak_gpu_memory = {}
+
+    @property
+    def elapsed_time(self) -> float:
+        """Calculate total elapsed time in seconds."""
+        return time.time() - self.start_time
+
+    def epoch_time(self, epoch: int) -> float | None:
+        """Calculate time taken for a specific epoch in seconds."""
+        if epoch in self.epoch_start_times and epoch in self.epoch_end_times:
+            return self.epoch_end_times[epoch] - self.epoch_start_times[epoch]
+
+        return None
+
+    def average_epoch_time(self) -> float | None:
+        """Calculate average time per epoch in seconds."""
+        completed_epochs = [
+            epoch for epoch in self.epoch_start_times if epoch in self.epoch_end_times
+        ]
+        if not completed_epochs:
+            return None
+
+        total_time = 0.0
+        for epoch in completed_epochs:
+            epoch_time = self.epoch_time(epoch)
+            if epoch_time is not None:  # Check to avoid mypy warning
+                total_time += epoch_time
+
+        return total_time / len(completed_epochs)
+
+    def steps_per_second(self) -> float | None:
+        """Calculate average steps per second across all training."""
+        if self.total_steps == 0 or self.elapsed_time == 0:
+            return None
+
+        return self.total_steps / self.elapsed_time
+
+    def to_dict(self) -> dict[str, Any]:
+        """Convert metrics to a dictionary for telemetry reporting."""
+        metrics = {
+            "total_time_seconds": self.elapsed_time,
+            "total_steps": self.total_steps,
+            "steps_per_second": self.steps_per_second(),
+            "epochs_completed": len(
+                [
+                    epoch
+                    for epoch in self.epoch_start_times
+                    if epoch in self.epoch_end_times
+                ]
+            ),
+            "peak_cpu_memory_bytes": self.peak_cpu_memory,
+        }
+
+        # Add per-epoch timing if available
+        epoch_times: dict[str, float] = {}
+        for epoch in sorted(self.epoch_end_times.keys()):
+            time_taken = self.epoch_time(epoch)
+            if time_taken is not None:
+                epoch_times[f"epoch_{epoch}_seconds"] = time_taken
+
+        if epoch_times:
+            metrics["epoch_times"] = epoch_times  # type: ignore
+            metrics["average_epoch_time_seconds"] = self.average_epoch_time()
+
+        # Add GPU memory metrics if available
+        if self.peak_gpu_memory:
+            gpu_metrics: dict[str, int] = {}
+            for gpu_id, memory in self.peak_gpu_memory.items():
+                gpu_metrics[f"gpu_{gpu_id}_peak_memory_bytes"] = memory
+            metrics["gpu_memory"] = gpu_metrics  # type: ignore
+
+        return metrics
+
+
+class RuntimeMetricsTracker:
+    """Tracker for runtime metrics during training."""
+
+    update_interval = 100
+
+    def __init__(self):
+        """Initialize the runtime metrics tracker."""
+        self.metrics = RuntimeMetrics(start_time=time.time())
+        self.telemetry_manager = TelemetryManager.get_instance()
+
+    def start_epoch(self, epoch: int):
+        """Record the start of a new epoch."""
+        self.metrics.current_epoch = epoch
+        self.metrics.epoch_start_times[epoch] = time.time()
+        self.update_memory_metrics()
+
+    def end_epoch(self, epoch: int):
+        """Record the end of an epoch."""
+        self.metrics.epoch_end_times[epoch] = time.time()
+
+    def update_step(self, step: int):
+        """Update the current step count."""
+        self.metrics.current_step = step
+        self.metrics.total_steps += 1
+
+        # Periodically update memory metrics
+        if step % self.update_interval == 0:
+            self.update_memory_metrics()
+
+    def _get_allocated_memory(self) -> dict[int, int]:
+        """
+        Helper function for getting accelerator-agnostic allocated memory.
+
+        Returns:
+            A dictionary mapping device IDs to allocated memory in bytes
+        """
+        memory_used: dict[int, int] = {}
+
+        # NVIDIA GPUs
+        if torch.cuda.is_available():
+            for i in range(torch.cuda.device_count()):
+                memory_used[i] = torch.cuda.memory_allocated(i)
+
+        # AMD GPUs
+        elif hasattr(torch, "hip") and torch.hip.is_available():
+            for i in range(torch.hip.device_count()):
+                if hasattr(torch.hip, "memory_allocated"):
+                    memory_used[i] = torch.hip.memory_allocated(i)
+
+        # Apple Silicon
+        elif hasattr(torch.backends, "mps") and torch.backends.mps.is_available():
+            # MPS doesn't have per-device memory stats since there's only one device
+            if hasattr(torch.mps, "current_allocated_memory"):
+                memory_used[0] = torch.mps.current_allocated_memory()
+
+        # Intel GPUs
+        elif hasattr(torch, "xpu") and torch.xpu.is_available():
+            for i in range(torch.xpu.device_count()):
+                if hasattr(torch.xpu, "memory_allocated"):
+                    memory_used[i] = torch.xpu.memory_allocated(i)
+
+        # NPUs
+        elif hasattr(torch, "npu") and torch.npu.is_available():
+            for i in range(torch.npu.device_count()):
+                if hasattr(torch.npu, "memory_allocated"):
+                    memory_used[i] = torch.npu.memory_allocated(i)
+
+        return memory_used
+
+    def update_memory_metrics(self):
+        """Update peak memory usage metrics."""
+        # CPU memory
+        cpu_memory = psutil.Process().memory_info().rss
+        self.metrics.peak_cpu_memory = max(self.metrics.peak_cpu_memory, cpu_memory)
+
+        # GPU memory (if available)
+        memory_used = self._get_allocated_memory()
+        for i, memory in memory_used.items():
+            self.metrics.peak_gpu_memory[i] = max(
+                self.metrics.peak_gpu_memory.get(i, 0), memory
+            )
+
+    def get_memory_metrics(self) -> dict[str, Any]:
+        """Get the current memory metrics as a dictionary."""
+        memory_metrics = {
+            "cpu_memory_bytes": psutil.Process().memory_info().rss,
+            "peak_cpu_memory_bytes": self.metrics.peak_cpu_memory,
+        }
+
+        # GPU memory (if available)
+        memory_used = self._get_allocated_memory()
+        for i, memory in memory_used.items():
+            memory_metrics[f"gpu_{i}_memory_bytes"] = memory
+            memory_metrics[f"gpu_{i}_peak_memory_bytes"] = (
+                self.metrics.peak_gpu_memory.get(i, 0)
+            )
+
+        return memory_metrics

src/axolotl/telemetry/whitelist.yaml

@@ -0,0 +1,17 @@
+organizations:
+  - "axolotl-ai-co"
+  - "meta-llama"
+  - "huggingface"
+  - "nvidia"
+  - "facebook"
+  - "google"
+  - "microsoft"
+  - "deepseek-ai"
+  - "HuggingFaceTB"
+  - "mistralai"
+  - "Qwen"
+  - "unsloth"
+  - "NousResearch"
+  - "allenai"
+  - "amd"
+  - "tiiuae"

src/axolotl/train.py

@@ -1,13 +1,10 @@
 """Prepare and train a model on a dataset. Can also infer from a model or merge lora"""
 
-from __future__ import annotations
-
 import importlib
 import inspect
 import os
 import signal
 import sys
-import typing
 import weakref
 from contextlib import ExitStack
 from pathlib import Path
@@ -28,12 +25,15 @@ from axolotl.common.datasets import TrainDatasetMeta
 from axolotl.contribs.lgpl import (  # pylint: disable = no-name-in-module
     fix_untrained_tokens,
 )
+from axolotl.core.builders import HFCausalTrainerBuilder, HFRLTrainerBuilder
 from axolotl.integrations.base import PluginManager
 from axolotl.loaders import (
     ModelLoader,
     load_processor,
     load_tokenizer,
 )
+from axolotl.telemetry.errors import send_errors
+from axolotl.telemetry.manager import TelemetryManager
 from axolotl.utils.ctx_managers.sequence_parallel import SequenceParallelContextManager
 from axolotl.utils.dict import DictDefault
 from axolotl.utils.distributed import cleanup_distributed
@@ -47,19 +47,19 @@ try:
 except ImportError:
     BetterTransformer = None
 
-if typing.TYPE_CHECKING:
-    from axolotl.core.trainer_builder import HFCausalTrainerBuilder, HFRLTrainerBuilder
-
 LOG = get_logger(__name__)
 
+TELEMETRY_MANAGER = TelemetryManager.get_instance()
+PLUGIN_MANAGER = PluginManager.get_instance()
+
 
 def setup_model_and_tokenizer(
     cfg: DictDefault,
 ) -> tuple[
     PreTrainedModel, PreTrainedTokenizer, PeftConfig | None, ProcessorMixin | None
 ]:
-    """Load the tokenizer, processor (for multimodal models), and model based on
+    """
-    configuration.
+    Load the tokenizer, processor (for multimodal models), and model based on configuration.
 
     Args:
         cfg: Dictionary mapping `axolotl` config keys to values.
@@ -69,7 +69,10 @@ def setup_model_and_tokenizer(
             `None`), and processor (if multimodal, else `None`).
     """
     # Load tokenizer
-    LOG.debug(f"loading tokenizer... {cfg.tokenizer_config or cfg.base_model_config}")
+    LOG.debug(
+        f"loading tokenizer... {cfg.tokenizer_config or cfg.base_model_config}",
+        main_process_only=True,
+    )
     tokenizer = load_tokenizer(cfg)
 
     # Load processor for multimodal models if needed
@@ -88,6 +91,14 @@ def setup_model_and_tokenizer(
     if model.generation_config is not None:
         model.generation_config.do_sample = True
 
+    TELEMETRY_MANAGER.send_event(
+        event_type="model-load", properties=model.config.to_dict()
+    )
+    if peft_config:
+        TELEMETRY_MANAGER.send_event(
+            event_type="peft-config-load", properties=peft_config.to_dict()
+        )
+
     # Apply freezing if specified
     if cfg.unfrozen_parameters:
         freeze_layers_except(model, cfg.unfrozen_parameters)
@@ -477,7 +488,7 @@ def handle_untrained_tokens_fix(
 
 
 def setup_model_and_trainer(cfg: DictDefault, dataset_meta: TrainDatasetMeta) -> tuple[
-    "HFRLTrainerBuilder" | "HFCausalTrainerBuilder",
+    HFRLTrainerBuilder | HFCausalTrainerBuilder,
     PeftModel | PreTrainedModel,
     PreTrainedTokenizer,
     PeftConfig | None,
@@ -522,6 +533,7 @@ def setup_model_and_trainer(cfg: DictDefault, dataset_meta: TrainDatasetMeta) ->
         model_ref=model_ref,
         peft_config=peft_config,
     )
+    PLUGIN_MANAGER.post_trainer_create(cfg, trainer)
 
     return (
         trainer,
@@ -532,6 +544,7 @@ def setup_model_and_trainer(cfg: DictDefault, dataset_meta: TrainDatasetMeta) ->
     )
 
 
+@send_errors
 def train(
     cfg: DictDefault, dataset_meta: TrainDatasetMeta
 ) -> tuple[PeftModel | PreTrainedModel, PreTrainedTokenizer, Trainer]:
@@ -556,8 +569,11 @@ def train(
         processor,
     ) = setup_model_and_trainer(cfg, dataset_meta)
 
-    plugin_manager = PluginManager.get_instance()
+    # Determine if we need to resume from a checkpoint
-    plugin_manager.post_trainer_create(cfg, trainer)
+    resume_from_checkpoint = determine_resume_checkpoint(cfg)
+
+    # Configuration for saving
+    safe_serialization = cfg.save_safetensors is True
 
     # Handle untrained tokens if configured
     safe_serialization = cfg.save_safetensors is True
@@ -572,7 +588,6 @@ def train(
     setup_model_card(cfg)
 
     # Execute the training
-    resume_from_checkpoint = determine_resume_checkpoint(cfg)
     execute_training(cfg, trainer, resume_from_checkpoint)
 
     # Save the trained model and cleanup
@@ -580,7 +595,6 @@ def train(
     create_model_card(cfg, trainer)
     if not cfg.use_ray:
         cleanup_distributed()
-
+    PLUGIN_MANAGER.post_train(cfg, model)
-    plugin_manager.post_train(cfg, model)
 
     return model, tokenizer, trainer

src/axolotl/utils/__init__.py

@@ -52,10 +52,3 @@ def patch_optimized_env():
     if os.getenv("HF_HUB_ENABLE_HF_TRANSFER") is None:
         os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = "1"
     set_pytorch_cuda_alloc_conf()
-
-
-def get_not_null(value, default=None):
-    """
-    return the value if it's not None, otherwise return the default value
-    """
-    return value if value is not None else default

src/axolotl/utils/callbacks/__init__.py

@@ -53,6 +53,25 @@ IGNORE_INDEX = -100
 LOG = get_logger(__name__)
 
 
+class EvalFirstStepCallback(
+    TrainerCallback
+):  # pylint: disable=too-few-public-methods disable=unused-argument
+    """
+    Callback to trigger evals on the first step
+    """
+
+    def on_step_end(
+        self,
+        args: TrainingArguments,
+        state: TrainerState,
+        control: TrainerControl,
+        **kwargs,
+    ):
+        if args.eval_strategy == IntervalStrategy.STEPS and state.global_step == 1:
+            control.should_evaluate = True
+        return control
+
+
 class SaveBetterTransformerModelCallback(
     TrainerCallback
 ):  # pylint: disable=too-few-public-methods

src/axolotl/utils/collators/batching.py

@@ -1,7 +1,7 @@
 """Data collators for axolotl to pad labels and position_ids for packed sequences"""
 
 from dataclasses import dataclass
-from typing import Any, List
+from typing import Any
 
 import numpy as np
 from transformers import PreTrainedTokenizerBase
@@ -81,11 +81,9 @@ class DataCollatorForSeq2Seq:
 
                 padding_side = self.tokenizer.padding_side
                 for feature in features:
-                    remainder_len = max_feature_length - len(feature[feature_name])
+                    remainder = [pad_token_id] * (
-                    if feature_name == "position_ids":
+                        max_feature_length - len(feature[feature_name])
-                        remainder = list(range(remainder_len))
+                    )
-                    else:
-                        remainder = [pad_token_id] * remainder_len
                     if isinstance(feature[feature_name], list):
                         feature[feature_name] = (
                             feature[feature_name] + remainder
@@ -163,7 +161,7 @@ class V2BatchSamplerDataCollatorForSeq2Seq(DataCollatorForSeq2Seq):
 
     def __call__(self, features, return_tensors=None):
         if not isinstance(features[0], list):
-            features: List[List[dict]] = [features]
+            features = [features]
         out_features = [{} for _ in features]
         for i, features_ in enumerate(features):
             for feature in features_[0].keys():

src/axolotl/utils/data/__init__.py

@@ -1,21 +1,16 @@
-"""Init for `axolotl.utils.data` module."""
+"""
+Data processing modules
+"""
 
-from axolotl.utils.data.pretraining import (
+from axolotl.utils.data.pretraining import (  # noqa: F401
     encode_pretraining,
     wrap_pretraining_dataset,
 )
-from axolotl.utils.data.rl import prepare_preference_datasets
+from axolotl.utils.data.rl import load_prepare_preference_datasets  # noqa: F401
-from axolotl.utils.data.sft import (
+from axolotl.utils.data.sft import (  # noqa: F401
     get_dataset_wrapper,
-    prepare_datasets,
+    load_prepare_datasets,
+    load_tokenized_prepared_datasets,
+    prepare_dataset,
 )
-from axolotl.utils.data.utils import md5
+from axolotl.utils.data.utils import md5  # noqa: F401
-
-__all__ = [
-    "encode_pretraining",
-    "wrap_pretraining_dataset",
-    "prepare_preference_datasets",
-    "get_dataset_wrapper",
-    "prepare_datasets",
-    "md5",
-]

src/axolotl/utils/data/lock.py

@@ -1,66 +0,0 @@
-"""Logic for loading / preparing a dataset once over all processes."""
-
-import time
-from pathlib import Path
-from typing import Any, Callable
-
-from filelock import FileLock
-
-from axolotl.common.const import DEFAULT_DATASET_PREPARED_PATH
-from axolotl.utils.dict import DictDefault
-
-LOCK_FILE_NAME = "datasets_prep.lock"
-READY_FILE_NAME = "datasets_ready.flag"
-PROCESS_COUNTER_FILE_NAME = "process_counter.txt"
-
-
-class FileLockLoader:
-    """
-    Simple class for abstracting single process data loading / processing. The first
-    process that creates a lock file does the work; the remaining procesees simply load
-    the preprocessed dataset once the first process is done.
-    """
-
-    def __init__(self, cfg: DictDefault):
-        self.cfg = cfg
-        self.dataset_prepared_path = (
-            cfg.dataset_prepared_path or DEFAULT_DATASET_PREPARED_PATH
-        )
-        self.lock_file_path = Path(self.dataset_prepared_path) / LOCK_FILE_NAME
-        self.ready_flag_path = Path(self.dataset_prepared_path) / READY_FILE_NAME
-        self.counter_path = Path(self.dataset_prepared_path) / PROCESS_COUNTER_FILE_NAME
-
-    def load(self, load_fn: Callable[[], Any]) -> Any:
-        with FileLock(str(self.lock_file_path)):
-            self._increment_counter()
-
-            if not self.ready_flag_path.exists():
-                result = load_fn()
-                self.ready_flag_path.touch()
-                return result
-
-            while not self.ready_flag_path.exists():
-                time.sleep(1)
-            return load_fn()
-
-    def _increment_counter(self):
-        """Safely increment the process counter."""
-        if self.counter_path.exists():
-            count = int(self.counter_path.read_text().strip())
-        else:
-            count = 0
-        self.counter_path.write_text(str(count + 1))
-
-    def cleanup(self):
-        """Clean up ready flag when last process is done."""
-        with FileLock(str(self.lock_file_path)):
-            count = int(self.counter_path.read_text().strip())
-            count -= 1
-
-            if count == 0:
-                # Last process cleans everything up
-                self.ready_flag_path.unlink(missing_ok=True)
-                self.counter_path.unlink(missing_ok=True)
-            else:
-                # Still have active processes
-                self.counter_path.write_text(str(count))

src/axolotl/utils/data/pretraining.py

@@ -250,7 +250,7 @@ def encode_packed_pretraining(
     # pylint: disable=duplicate-code
     # tokenize all the examples
     # rows get split with stride (overlap)
-    train_dataset = ds_wrapper(dataset=Dataset.from_dict(examples))[0]
+    train_dataset = ds_wrapper(Dataset.from_dict(examples))[0]
 
     train_dataset = process_pretraining_datasets_for_packing(
         train_dataset,

src/axolotl/utils/data/rl.py

@@ -1,117 +1,75 @@
-"""Data handling specific to RL trainers."""
+"""data handling specific to DPO"""
 
 import inspect
 from functools import partial
-from typing import Any, Callable, Literal
+from pathlib import Path
+from typing import Any, List, Union
 
-from datasets import Dataset, DatasetDict
+import yaml
-from transformers import PreTrainedTokenizer
+from datasets import Dataset, DatasetDict, concatenate_datasets, load_from_disk
 
+from axolotl.common.const import DEFAULT_DATASET_PREPARED_PATH
 from axolotl.loaders import load_tokenizer
 from axolotl.prompt_strategies.dpo import load as load_dpo
 from axolotl.prompt_strategies.kto import load as load_kto
 from axolotl.prompt_strategies.orpo import load as load_orpo
-from axolotl.utils.data.lock import FileLockLoader
+from axolotl.utils.data.shared import datasets_w_name_generator, load_dataset_w_config
-from axolotl.utils.data.shared import (
+from axolotl.utils.data.utils import deduplicate_and_log_datasets, md5
-    create_train_validation_split,
-    datasets_with_name_generator,
-    generate_dataset_hash_from_config,
-    load_dataset_with_config,
-    load_preprocessed_dataset,
-    merge_datasets,
-    save_preprocessed_dataset,
-    try_load_from_hub,
-)
-from axolotl.utils.data.utils import (
-    deduplicate_and_log_datasets,
-    retry_on_request_exceptions,
-)
 from axolotl.utils.dict import DictDefault
+from axolotl.utils.distributed import is_main_process, zero_first
 from axolotl.utils.logging import get_logger
 from axolotl.utils.schemas.enums import RLType
 
 LOG = get_logger(__name__)
 
 
-@retry_on_request_exceptions(max_retries=3, delay=5)
+def _get_path(ds_hash, cfg):
-def prepare_preference_datasets(
+    prepared_ds_path = (
-    cfg: DictDefault, tokenizer: PreTrainedTokenizer
+        Path(cfg.dataset_prepared_path) / ds_hash
-) -> tuple[Dataset, Dataset | None]:
+        if cfg.dataset_prepared_path
-    """Load and prepare preference datasets for RL training.
+        else Path(DEFAULT_DATASET_PREPARED_PATH) / ds_hash
+    )
 
-    Loads training and evaluation datasets, handling preprocessing, caching, and
+    return prepared_ds_path
-    deduplication as configured. Uses FileLock for distributed coordination.
-
-    Args:
-        cfg: Configuration object containing dataset and training settings.
-        tokenizer: Tokenizer to use for processing text.
-
-    Returns:
-        Tuple of (train_dataset, eval_dataset). eval_dataset may be None
-            if no evaluation dataset is configured.
-    """
-
-    def _load_datasets():
-        # Load training dataset
-        train_dataset = _load_or_create_dataset_split(cfg, tokenizer, split="train")
-
-        # Load or create evaluation dataset
-        eval_dataset: Dataset | None = None
-        if cfg.test_datasets:
-            eval_dataset = _load_or_create_dataset_split(cfg, tokenizer, split="test")
-        elif cfg.val_set_size:
-            # Create validation split from training data
-            train_dataset, eval_dataset = create_train_validation_split(
-                train_dataset, cfg, cfg.val_set_size
-            )
-
-        return train_dataset, eval_dataset
-
-    # Prepare datasets (with file locking logic for multiple ranks)
-    loader = FileLockLoader(cfg)
-    try:
-        train_dataset, eval_dataset = loader.load(_load_datasets)
-    finally:
-        loader.cleanup()
-
-    # Apply deduplication if configured
-    if cfg.dataset_exact_deduplication:
-        train_dataset, eval_dataset = deduplicate_and_log_datasets(
-            dataset=train_dataset, other_dataset=eval_dataset
-        )
-
-    return train_dataset, eval_dataset
 
 
-def _map_dataset(
+def _load_preprocessed_ds(cfg, sub_cfg):
-    cfg: DictDefault,
+    ds_hash = md5(yaml.dump(sub_cfg, Dumper=yaml.Dumper))
-    dataset: Dataset | DatasetDict,
+    prepared_ds_path = _get_path(ds_hash, cfg)
-    ds_transform_fn: Callable[..., Any],
+    dataset = None
-    tokenizer: Any | None = None,
-    **map_kwargs: Any,
-) -> Dataset:
-    """Apply transformation function to dataset.
 
-    Args:
+    # pylint: disable=duplicate-code
-        cfg: Configuration object.
+    if (
-        dataset: Dataset to transform.
+        cfg.dataset_prepared_path
-        ds_transform_fn: Transformation function to apply.
+        and any(prepared_ds_path.glob("*"))
-        tokenizer: Optional tokenizer for transformation.
+        and not cfg.is_preprocess
-        **map_kwargs: Additional arguments for dataset mapping.
+    ):
+        LOG.info(f"Loading prepared dataset from disk at {prepared_ds_path}...")
+        dataset = load_from_disk(str(prepared_ds_path))
 
-    Returns:
+    return dataset
-        Transformed dataset.
+
-    """
+
+def _save_preprocessed_ds(cfg, sub_cfg, dataset):
+    ds_hash = md5(yaml.dump(sub_cfg, Dumper=yaml.Dumper))
+    prepared_ds_path = _get_path(ds_hash, cfg)
+
+    if cfg.is_preprocess and is_main_process():
+        LOG.info(f"Loading prepared dataset from disk at {prepared_ds_path}...")
+        dataset.save_to_disk(str(prepared_ds_path))
+
+
+def map_dataset(cfg, data_set, ds_transform_fn, tokenizer, **map_kwargs):
     sig = inspect.signature(ds_transform_fn)
     if "tokenizer" in sig.parameters:
         if not tokenizer:
             tokenizer = load_tokenizer(cfg)
         ds_transform_fn = partial(ds_transform_fn, tokenizer=tokenizer)
 
-    if isinstance(dataset, DatasetDict):
+    if isinstance(data_set, DatasetDict):
-        dataset = dataset["train"]
+        data_set = data_set["train"]
 
-    dataset = dataset.map(
+    data_set = data_set.map(
         ds_transform_fn,
         num_proc=cfg.dataset_processes,
         load_from_cache_file=not cfg.is_preprocess,
@@ -119,27 +77,13 @@ def _map_dataset(
         **map_kwargs,
     )
 
-    return dataset
+    return data_set
 
 
-def _drop_long_sequences(
+def drop_long_rl_seq(
-    sample: dict[str, Any], rl: RLType, tokenizer: Any, sequence_len: int
+    sample, rl, tokenizer, sequence_len  # pylint: disable=invalid-name
-) -> bool:
+):
-    """Filter out samples that exceed maximum sequence length.
+    if rl in (RLType.DPO, RLType.IPO, RLType.ORPO, RLType.SIMPO):
-
-    Args:
-        sample: Dataset sample to check.
-        rl: Reinforcement learning type.
-        tokenizer: Tokenizer for length calculation.
-        sequence_len: Maximum allowed sequence length.
-
-    Returns:
-        True if sample should be kept, False if it should be dropped.
-
-    Raises:
-        ValueError: If required keys are missing or RL type is unknown.
-    """
-    if rl in {RLType.DPO, RLType.IPO, RLType.ORPO, RLType.SIMPO}:
         if not (
             sample.get("prompt") and sample.get("chosen") and sample.get("rejected")
         ):
@@ -179,115 +123,132 @@ def _drop_long_sequences(
     raise ValueError("Unknown RL type")
 
 
-def _load_split(cfg: DictDefault, split: Literal["train", "test"]) -> Dataset:
+def load_prepare_preference_datasets(cfg):
-    """Load and process dataset split for RL training.
+    def load_split(dataset_cfgs, _cfg):
+        split_datasets: List[Any] = []
+        use_auth_token = _cfg.hf_use_auth_token
+        for config_dataset in datasets_w_name_generator(dataset_cfgs):
+            ds: Union[Dataset, DatasetDict] = load_dataset_w_config(
+                config_dataset, use_auth_token, streaming=False
+            )
+            split_datasets.append(ds)
 
-    Args:
+        tokenizer = load_tokenizer(cfg)
-        cfg: Configuration object containing dataset settings.
-        split: Dataset split to load ("train" or "test").
 
-    Returns:
+        for i, data_set in enumerate(split_datasets):
-        Combined and processed dataset for the specified split.
+            _type = dataset_cfgs[i]["type"]
-    """
+            if _type:
-    datasets_configs = cfg.datasets if split == "train" else cfg.test_datasets
+                if isinstance(_type, DictDefault):
-    split_datasets: list[Dataset | DatasetDict] = []
+                    _type = "user_defined.default"
+                if _cfg.rl is RLType.ORPO:
+                    ds_transform_fn = load_orpo(_type, _cfg, dataset_idx=i)
+                elif _cfg.rl is RLType.KTO:
+                    ds_transform_fn = load_kto(_type, _cfg, dataset_idx=i)
+                else:
+                    ds_transform_fn = load_dpo(_type, _cfg, dataset_idx=i)
 
-    for dataset_config in datasets_with_name_generator(datasets_configs):
+                map_kwargs = {}
-        dataset: Dataset | DatasetDict = load_dataset_with_config(
+                if isinstance(ds_transform_fn, tuple):
-            dataset_config, cfg.hf_use_auth_token, streaming=False
+                    ds_transform_fn, map_kwargs = ds_transform_fn
-        )
+                split_datasets[i] = map_dataset(
-        split_datasets.append(dataset)
+                    cfg, data_set, ds_transform_fn, tokenizer, **map_kwargs
-
+                )
-    tokenizer = load_tokenizer(cfg)
+            elif _cfg.rl is RLType.KTO:
-
+                ds_transform_fn = load_kto(_type, _cfg, dataset_idx=i)
-    for i, dataset in enumerate(split_datasets):
+                map_kwargs = {}
-        _type = datasets_configs[i]["type"]
+                if isinstance(ds_transform_fn, tuple):
-        if _type:
+                    ds_transform_fn, map_kwargs = ds_transform_fn
-            if isinstance(_type, DictDefault):
+                split_datasets[i] = map_dataset(
-                _type = "user_defined.default"
+                    cfg, data_set, ds_transform_fn, tokenizer, **map_kwargs
-            if cfg.rl is RLType.ORPO:
+                )
-                ds_transform_fn = load_orpo(_type, cfg, dataset_idx=i)
-            elif cfg.rl is RLType.KTO:
-                ds_transform_fn = load_kto(_type, cfg, dataset_idx=i)
             else:
-                ds_transform_fn = load_dpo(_type, cfg, dataset_idx=i)
+                # If no `type` is provided, assume the dataset is already in the expected format with
+                # "prompt", "chosen" and "rejected" already preprocessed
+                split_datasets[i] = data_set
 
-            map_kwargs: dict[str, Any] = {}
+            if not cfg.skip_prepare_dataset:
-            if isinstance(ds_transform_fn, tuple):
+                drop_long = partial(
-                ds_transform_fn, map_kwargs = ds_transform_fn
+                    drop_long_rl_seq,
-            split_datasets[i] = _map_dataset(
+                    rl=_cfg.rl,
-                cfg, dataset, ds_transform_fn, tokenizer, **map_kwargs
+                    tokenizer=tokenizer,
-            )
+                    sequence_len=cfg.sequence_len,
+                )
+
+                prior_len = len(split_datasets[i])
+                split_datasets[i] = split_datasets[i].filter(
+                    drop_long,
+                    num_proc=cfg.dataset_processes,
+                    load_from_cache_file=not cfg.is_preprocess,
+                    desc="Dropping Long Sequences",
+                )
+                dropped = prior_len - len(split_datasets[i])
+                if dropped:
+                    LOG.warning(
+                        f"Dropped {dropped} long samples from dataset index {i}"
+                    )
+
+        combined_datasets = concatenate_datasets(split_datasets)
+        combined_datasets = combined_datasets.shuffle(seed=cfg.seed or 42)
+
+        return combined_datasets
+
+    with zero_first(is_main_process()):
+        train_is_preprocessed = False
+        eval_is_preprocessed = False
+        if train_dataset := _load_preprocessed_ds(cfg, cfg.datasets):
+            train_is_preprocessed = True
         else:
-            # If no `type` is provided, assume the dataset is already in the expected format with
+            train_dataset = load_split(cfg.datasets, cfg)
-            # "prompt", "chosen", and "rejected" already preprocessed
-            split_datasets[i] = dataset
 
-        if not cfg.skip_prepare_dataset:
+        eval_dataset = None
-            drop_long = partial(
+        if cfg.test_datasets:
-                _drop_long_sequences,
+            if eval_dataset := _load_preprocessed_ds(cfg, cfg.test_datasets):
-                rl=cfg.rl,
+                eval_is_preprocessed = True
-                tokenizer=tokenizer,
+            else:
-                sequence_len=cfg.sequence_len,
+                eval_dataset = load_split(cfg.test_datasets, cfg)
-            )
+        if not eval_dataset:
+            if cfg.val_set_size:
+                seed = cfg.seed if cfg.seed is not None else 42
 
-            prior_len = len(split_datasets[i])
+                # ensure we end up with the same fingerprint by doing rank0 first and being able to cache
-            split_datasets[i] = split_datasets[i].filter(
+                to_hash_train = (
-                drop_long,
+                    train_dataset._fingerprint  # pylint: disable=protected-access
-                num_proc=cfg.dataset_processes,
+                    + "|"
-                load_from_cache_file=not cfg.is_preprocess,
+                    + str(cfg.val_set_size)
-                desc="Dropping Long Sequences",
+                    + "|"
-            )
+                    + "train"
-            dropped = prior_len - len(split_datasets[i])
+                    + "|"
-            if dropped:
+                    + str(cfg.seed or 42)
-                LOG.warning(f"Dropped {dropped} long samples from dataset index {i}")
+                )
+                to_hash_test = (
+                    train_dataset._fingerprint  # pylint: disable=protected-access
+                    + "|"
+                    + str(cfg.val_set_size)
+                    + "|"
+                    + "test"
+                    + "|"
+                    + str(cfg.seed or 42)
+                )
+                train_fingerprint = md5(to_hash_train)
+                test_fingerprint = md5(to_hash_test)
+                ds_w_test_split = train_dataset.train_test_split(
+                    test_size=cfg.val_set_size,
+                    seed=seed,
+                    shuffle=False,
+                    train_new_fingerprint=train_fingerprint,
+                    test_new_fingerprint=test_fingerprint,
+                )
+                eval_dataset = ds_w_test_split["test"]
+                train_dataset = ds_w_test_split["train"]
 
-    # Merge datasets
+        if not train_is_preprocessed:
-    dataset = merge_datasets(split_datasets, cfg)
+            _save_preprocessed_ds(cfg, cfg.datasets, train_dataset)
+        if eval_dataset and not eval_is_preprocessed:
+            _save_preprocessed_ds(cfg, cfg.test_datasets, eval_dataset)
 
-    if not cfg.skip_prepare_dataset:
+    if cfg.dataset_exact_deduplication:
-        # Save preprocessed dataset
+        train_dataset, eval_dataset, _ = deduplicate_and_log_datasets(
-        dataset_hash = generate_dataset_hash_from_config(
+            train_dataset=train_dataset, eval_dataset=eval_dataset
-            cfg, datasets_configs, tokenizer.name_or_path
         )
-        save_preprocessed_dataset(cfg, dataset, dataset_hash, split)
 
-    return dataset
+    return train_dataset, eval_dataset
-
-
-# pylint: disable=duplicate-code
-def _load_or_create_dataset_split(
-    cfg: DictDefault, tokenizer: PreTrainedTokenizer, split: Literal["train", "test"]
-) -> Dataset:
-    """Load preprocessed dataset or create new one for given split.
-
-    Args:
-        cfg: Configuration object.
-        tokenizer: Tokenizer to use for processing text.
-        split: Dataset split to load.
-
-    Returns:
-        Tuple of (dataset, is_preprocessed).
-    """
-    # Select correct dataset configuration based on split
-    datasets_config = cfg.datasets if split == "train" else cfg.test_datasets
-
-    # Generate dataset hash for caching
-    dataset_hash = generate_dataset_hash_from_config(
-        cfg, datasets_config, tokenizer.name_or_path
-    )
-
-    # Try loading from hub if push_dataset_to_hub is configured
-    dataset = None
-    if cfg.push_dataset_to_hub:
-        dataset = try_load_from_hub(cfg, dataset_hash, split)
-
-    # Attempt to load preprocessed dataset
-    if dataset is None:
-        dataset = load_preprocessed_dataset(cfg, dataset_hash)
-
-    # Otherwise, load it
-    if dataset is None:
-        dataset = _load_split(cfg, split=split)
-
-    return dataset

src/axolotl/utils/data/shared.py

@@ -1,21 +1,11 @@
-"""Dataset loading shared utils."""
+"""
+dataset loading shared utils
+"""
 
-from __future__ import annotations
-
-import functools
-import os
 from pathlib import Path
-from typing import TYPE_CHECKING, Any, Generator
+from typing import Optional, Union
 
-from datasets import (
+from datasets import Dataset, DatasetDict, load_dataset, load_from_disk
-    Dataset,
-    DatasetDict,
-    IterableDataset,
-    IterableDatasetDict,
-    concatenate_datasets,
-    load_dataset,
-    load_from_disk,
-)
 from huggingface_hub import hf_hub_download, snapshot_download
 from huggingface_hub.errors import (
     HFValidationError,
@@ -23,141 +13,78 @@ from huggingface_hub.errors import (
     RevisionNotFoundError,
 )
 
-from axolotl.common.const import DEFAULT_DATASET_PREPARED_PATH
-from axolotl.utils.data.utils import deduplicate_and_log_datasets, md5
 from axolotl.utils.dict import DictDefault
-from axolotl.utils.logging import get_logger
-
-if TYPE_CHECKING:
-    from adlfs import AzureBlobFileSystem
-    from gcsfs import GCSFileSystem
-    from ocifs import OCIFileSystem
-    from s3fs import S3FileSystem
-
-LOG = get_logger(__name__)
-
-EXTENSIONS_TO_DATASET_TYPES = {
-    ".parquet": "parquet",
-    ".arrow": "arrow",
-    ".csv": "csv",
-    ".txt": "text",
-}
 
 
-def get_dataset_type(dataset_config: DictDefault) -> str:
+def get_ds_type(config_dataset: DictDefault):
-    """Get the dataset type from the path if it's not specified."""
+    """
-    if dataset_config.ds_type:
+    Get the dataset type from the path if it's not specified
-        return dataset_config.ds_type
+    """
-
+    ds_type = "json"
-    for extension, dataset_type in EXTENSIONS_TO_DATASET_TYPES.items():
+    if config_dataset.ds_type:
-        if extension in dataset_config.path:
+        ds_type = config_dataset.ds_type
-            return dataset_type
+    elif ".parquet" in config_dataset.path:
-
+        ds_type = "parquet"
-    return "json"
+    elif ".arrow" in config_dataset.path:
+        ds_type = "arrow"
+    elif ".csv" in config_dataset.path:
+        ds_type = "csv"
+    elif ".txt" in config_dataset.path:
+        ds_type = "text"
+    return ds_type
 
 
-def datasets_with_name_generator(
+def datasets_w_name_generator(dataset_configs: list[DictDefault]):
-    dataset_configs: list[DictDefault],
+    """
-) -> Generator[DictDefault, None, None]:
+    Yields dataset configs handling multiple names or preprocess_shards
-    """Yields expanded dataset configurations based on multiple names or preprocessing
-    shards.
-
-    When a dataset config has a list of names, it yields separate configs for each
-    name. When a dataset config specifies preprocessing shards, it yields configs for
-    each shard.
 
     Args:
-        dataset_configs: List of dataset configuration objects.
+        dataset_configs: list of dataset configs (equivalent to cfg.datasets)
-
-    Yields:
-        Individual dataset configurations, expanded as needed for names or shards.
     """
-    for config in dataset_configs:
+    for dataset in dataset_configs:
-        if config.name and isinstance(config.name, list):
+        if dataset.name and isinstance(dataset.name, list):
-            for name in config.name:
+            # load_dataset doesn't properly handle multiple named configurations
-                yield DictDefault({**config, "name": name})
+            # at the same time for a given dataset
-        elif config.preprocess_shards and not config.shards:
+            for name in dataset.name:
-            for shard_idx in range(config.preprocess_shards):
+                yield DictDefault({**dataset, "name": name})
+        elif dataset.preprocess_shards and not dataset.shards:
+            for shard in range(dataset.preprocess_shards):
                 yield DictDefault(
                     {
-                        **config,
+                        **dataset,
-                        "shards": config.preprocess_shards,
+                        "shards": dataset.preprocess_shards,
-                        "shards_idx": shard_idx,
+                        "shards_idx": shard,
                     }
                 )
         else:
-            yield config
+            yield dataset
 
 
-def load_dataset_with_config(
+def load_dataset_w_config(
-    dataset_config: DictDefault, use_auth_token: bool, streaming=False
+    config_dataset: DictDefault, use_auth_token: bool, streaming=False
-) -> Dataset | IterableDataset:
+) -> Union[Dataset, DatasetDict]:
-    """Load a dataset from a config. Handles datasets that are stored locally, in the
+    """
-    HuggingFace Hub, in a remote filesystem (S3, GCS, Azure, OCI), a URL, or
+    Load a dataset from a config
-    `data_files`.
 
     Args:
-        dataset_config: Single dataset config.
+        config_dataset: single dataset config
-        use_auth_token: Whether to use HF auth token.
+        use_auth_token: whether to use HF auth token
-        streaming: Whether to stream the dataset.
+        streaming: whether to stream the dataset
-
-    Returns:
-        Loaded dataset.
     """
-    # Set up common kwargs for dataset loading
+    # pylint: disable=invalid-name
-    load_dataset_kwargs = {
+    ds: Optional[Union[Dataset, DatasetDict]] = None  # pylint: disable=invalid-name
-        "split": dataset_config.split if dataset_config.split else None,
+    ds_from_hub = False
-        "name": dataset_config.name,
-        "streaming": streaming,
-        "trust_remote_code": dataset_config.trust_remote_code,
-    }
-
-    # First check if it's a local path
-    if Path(dataset_config.path).exists():
-        return _load_from_local_path(dataset_config, load_dataset_kwargs)
-
-    # Check if it's a HuggingFace dataset
-    is_hub_dataset = _check_if_hub_dataset(dataset_config, use_auth_token)
-
-    # Check if it's a cloud storage path and get appropriate filesystem
-    remote_fs, storage_options = _get_remote_filesystem(dataset_config.path)
-    is_cloud_dataset = False
-    if remote_fs:
-        try:
-            is_cloud_dataset = remote_fs.exists(dataset_config.path)
-        except (FileNotFoundError, ConnectionError):
-            pass
-
-    # Load from appropriate source
-    if is_hub_dataset:
-        return _load_from_hub(dataset_config, use_auth_token, load_dataset_kwargs)
-    if is_cloud_dataset:
-        return _load_from_cloud(
-            dataset_config, remote_fs, storage_options, load_dataset_kwargs
-        )
-    if dataset_config.path.startswith("https://"):
-        return _load_from_url(dataset_config, load_dataset_kwargs)
-    if dataset_config.data_files:
-        return _load_from_data_files(dataset_config, load_dataset_kwargs)
-
-    raise ValueError(
-        f"The dataset could not be loaded. This could be due to a misconfigured dataset path "
-        f"({dataset_config.path}). Try double-check your path / name / data_files. "
-        f"This is not caused by the dataset type."
-    )
-
-
-def _check_if_hub_dataset(dataset_config: DictDefault, use_auth_token: bool) -> bool:
-    """Check if a dataset exists on the HuggingFace Hub."""
     try:
+        # this is just a basic check to see if the path is a
+        # valid HF dataset that's loadable
         snapshot_download(
-            repo_id=dataset_config.path,
+            repo_id=config_dataset.path,
             repo_type="dataset",
             token=use_auth_token,
-            revision=dataset_config.revision,
+            revision=config_dataset.revision,
             ignore_patterns=["*"],
         )
-        return True
+        ds_from_hub = True
     except (
         RepositoryNotFoundError,
         RevisionNotFoundError,
@@ -166,373 +93,198 @@ def _check_if_hub_dataset(dataset_config: DictDefault, use_auth_token: bool) ->
         HFValidationError,
         ValueError,
     ):
-        return False
+        pass
 
-
+    ds_from_cloud = False
-def _get_remote_filesystem(
+    storage_options: dict = {}
-    path: str,
+    remote_file_system = None
-) -> tuple[
+    if config_dataset.path.startswith("s3://"):
-    S3FileSystem | GCSFileSystem | AzureBlobFileSystem | OCIFileSystem | None, dict
-]:
-    """Get the appropriate filesystem for a remote path."""
-    if path.startswith("s3://"):
         try:
-            import s3fs
+            import s3fs  # type: ignore
-
-            storage_options = {"anon": False}
-            return s3fs.S3FileSystem(**storage_options), storage_options
         except ImportError as exc:
             raise ImportError("s3:// paths require s3fs to be installed") from exc
 
-    elif path.startswith(("gs://", "gcs://")):
+        # Reads env, credentials from ~/.aws/credentials, or IAM metadata provider
+        # https://s3fs.readthedocs.io/en/latest/index.html?highlight=storage_options#credentials
+        storage_options = {"anon": False}
+        remote_file_system = s3fs.S3FileSystem(**storage_options)
+    elif config_dataset.path.startswith("gs://") or config_dataset.path.startswith(
+        "gcs://"
+    ):
         try:
-            import gcsfs
+            import gcsfs  # type: ignore
-
-            storage_options = {"token": None}  # type: ignore
-            return gcsfs.GCSFileSystem(**storage_options), storage_options
         except ImportError as exc:
             raise ImportError(
                 "gs:// or gcs:// paths require gcsfs to be installed"
             ) from exc
 
-    elif path.startswith(("adl://", "abfs://", "az://")):
+        # gcsfs will use default credentials from the environment else anon
+        # https://gcsfs.readthedocs.io/en/latest/#credentials
+        storage_options = {"token": None}
+        remote_file_system = gcsfs.GCSFileSystem(**storage_options)
+    elif (
+        config_dataset.path.startswith("adl://")
+        or config_dataset.path.startswith("abfs://")
+        or config_dataset.path.startswith("az://")
+    ):
         try:
             import adlfs
-
-            storage_options = {"anon": False}
-            return adlfs.AzureBlobFileSystem(**storage_options), storage_options
         except ImportError as exc:
             raise ImportError(
                 "adl:// or abfs:// paths require adlfs to be installed"
             ) from exc
 
-    elif path.startswith("oci://"):
+        # # Ensure you have the following environment variables set:
+        # # Gen 1
+        # storage_options = {
+        #     "tenant_id": AZURE_STORAGE_TENANT_ID,
+        #     "client_id": AZURE_STORAGE_CLIENT_ID,
+        #     "client_secret": AZURE_STORAGE_CLIENT_SECRET,
+        # }
+        # # Gen 2
+        # storage_options = {
+        #     "account_name": AZURE_STORAGE_ACCOUNT_NAME,
+        #     "account_key": AZURE_STORAGE_ACCOUNT_KEY,
+        # }
+
+        # Reads env
+        # https://github.com/fsspec/adlfs?tab=readme-ov-file#setting-credentials
+        storage_options = {"anon": False}
+        remote_file_system = adlfs.AzureBlobFileSystem(**storage_options)
+    elif config_dataset.path.startswith("oci://"):
         try:
             import ocifs
-
-            storage_options = {}
-            return ocifs.OCIFileSystem(**storage_options), storage_options
         except ImportError as exc:
             raise ImportError("oci:// paths require ocifs to be installed") from exc
 
-    return None, {}
+        # https://ocifs.readthedocs.io/en/latest/getting-connected.html#Using-Environment-Variables
+        remote_file_system = ocifs.OCIFileSystem(**storage_options)
 
-
-def _load_from_local_path(
-    dataset_config: DictDefault, load_dataset_kwargs: dict
-) -> Dataset | IterableDataset | DatasetDict | IterableDatasetDict:
-    """Load a dataset from a local path."""
-    local_path = Path(dataset_config.path)
-
-    if local_path.is_dir():
-        if dataset_config.data_files:
-            dataset_type = get_dataset_type(dataset_config)
-            return load_dataset(
-                dataset_type,
-                data_files=dataset_config.data_files,
-                **load_dataset_kwargs,
-            )
-        try:
-            return load_from_disk(dataset_config.path)
-        except FileNotFoundError:
-            load_dataset_kwargs["streaming"] = False
-            return load_dataset(dataset_config.path, **load_dataset_kwargs)
-    elif local_path.is_file():
-        dataset_type = get_dataset_type(dataset_config)
-        load_dataset_kwargs["streaming"] = False
-        return load_dataset(
-            dataset_type,
-            data_files=dataset_config.path,
-            **load_dataset_kwargs,
-        )
-    else:
-        raise ValueError(
-            "Unhandled dataset load: local path exists, but is neither a directory or a file"
-        )
-
-
-def _load_from_hub(
-    dataset_config: DictDefault, use_auth_token: bool, load_dataset_kwargs: dict
-) -> Dataset | IterableDataset | DatasetDict | IterableDatasetDict:
-    """Load a dataset from the HuggingFace Hub."""
-    return load_dataset(
-        dataset_config.path,
-        data_files=dataset_config.data_files,
-        token=use_auth_token,
-        revision=dataset_config.revision,
-        **load_dataset_kwargs,
-    )
-
-
-def _load_from_cloud(
-    dataset_config: DictDefault,
-    remote_fs: S3FileSystem | GCSFileSystem | AzureBlobFileSystem | OCIFileSystem,
-    storage_options: dict,
-    load_dataset_kwargs: dict,
-) -> Dataset | IterableDataset | DatasetDict | IterableDatasetDict:
-    """Load a dataset from cloud storage."""
-    if remote_fs.isdir(dataset_config.path):
-        return load_from_disk(
-            dataset_config.path,
-            storage_options=storage_options,
-        )
-
-    if remote_fs.isfile(dataset_config.path):
-        dataset_type = get_dataset_type(dataset_config)
-        return load_dataset(
-            dataset_type,
-            data_files=dataset_config.path,
-            storage_options=storage_options,
-            **load_dataset_kwargs,
-        )
-
-    raise ValueError(
-        f"Cloud path {dataset_config.path} is neither a directory nor a file"
-    )
-
-
-def _load_from_url(
-    dataset_config: DictDefault, load_dataset_kwargs: dict
-) -> Dataset | IterableDataset | DatasetDict | IterableDatasetDict:
-    """Load a dataset from a URL."""
-    dataset_type = get_dataset_type(dataset_config)
-    return load_dataset(
-        dataset_type,
-        data_files=dataset_config.path,
-        **load_dataset_kwargs,
-    )
-
-
-def _load_from_data_files(
-    dataset_config: DictDefault, load_dataset_kwargs: dict
-) -> Dataset | IterableDataset | DatasetDict | IterableDatasetDict:
-    """Load a dataset from data files."""
-    file_path = None
-
-    if isinstance(dataset_config.data_files, str):
-        file_path = hf_hub_download(
-            repo_id=dataset_config.path,
-            repo_type="dataset",
-            filename=dataset_config.data_files,
-            revision=dataset_config.revision,
-        )
-    elif isinstance(dataset_config.data_files, list):
-        file_path = [
-            hf_hub_download(
-                repo_id=dataset_config.path,
-                repo_type="dataset",
-                filename=file,
-                revision=dataset_config.revision,
-            )
-            for file in dataset_config.data_files
-        ]
-    else:
-        raise ValueError("data_files must be either a string or list of strings")
-
-    return load_dataset("json", data_files=file_path, **load_dataset_kwargs)
-
-
-def generate_split_fingerprints(
-    dataset: Dataset, val_set_size: int | float, seed: int
-) -> tuple[str, str]:
-    """Generate consistent fingerprints for train/test splits."""
-    fingerprint = dataset._fingerprint  # pylint: disable=protected-access
-
-    train_hash_input = f"{fingerprint}|{val_set_size}|train|{seed}"
-    test_hash_input = f"{fingerprint}|{val_set_size}|test|{seed}"
-
-    train_fingerprint = md5(train_hash_input)
-    test_fingerprint = md5(test_hash_input)
-
-    return train_fingerprint, test_fingerprint
-
-
-def get_prepared_dataset_path(cfg: DictDefault, dataset_hash: str) -> Path:
-    """Get standardized path for prepared datasets.
-
-    Args:
-        cfg: Configuration object.
-        dataset_hash: Hash identifying the specific dataset configuration.
-
-    Returns:
-        Path where the prepared dataset should be stored.
-    """
-    base_path = cfg.dataset_prepared_path or DEFAULT_DATASET_PREPARED_PATH
-    return Path(base_path) / dataset_hash
-
-
-def create_train_validation_split(
-    dataset: Dataset, cfg: DictDefault, val_set_size: int | float
-) -> tuple[Dataset, Dataset]:
-    """Create train/validation split with consistent fingerprinting.
-
-    Args:
-        dataset: Dataset to split.
-        cfg: Configuration object containing seed and other settings.
-        val_set_size: Size of validation set (absolute number or fraction).
-
-    Returns:
-        Tuple of (train_dataset, eval_dataset).
-    """
-    train_fingerprint, test_fingerprint = generate_split_fingerprints(
-        dataset, val_set_size, cfg.seed
-    )
-
-    # Apply deduplication before splitting if configured
-    if cfg.dataset_exact_deduplication:
-        dataset, _ = deduplicate_and_log_datasets(dataset=dataset)
-
-    split_dataset = dataset.train_test_split(
-        test_size=val_set_size,
-        shuffle=False,
-        seed=cfg.seed,
-        train_new_fingerprint=train_fingerprint,
-        test_new_fingerprint=test_fingerprint,
-    )
-
-    return split_dataset["train"], split_dataset["test"]
-
-
-def _generate_from_iterable_dataset(
-    dataset: IterableDataset, worker_id: list[int], num_workers: list[int]
-) -> Generator[Any, None, None]:
-    """Generator function to correctly split the dataset for each worker"""
-    for i, item in enumerate(dataset):
-        if i % num_workers[0] == worker_id[0]:
-            yield item
-
-
-def save_preprocessed_dataset(
-    cfg: DictDefault,
-    dataset: Dataset,
-    dataset_hash: str,
-    split: str,
-) -> None:
-    """Save preprocessed dataset to disk and optionally push to the HF Hub."""
-    prepared_ds_path = get_prepared_dataset_path(cfg, dataset_hash)
-    if isinstance(dataset, IterableDataset):
-        num_workers = cfg.dataset_processes
-
-        ds_from_iter = Dataset.from_generator(
-            functools.partial(_generate_from_iterable_dataset, dataset),
-            features=dataset.features,
-            num_proc=num_workers,
-            split=split,
-            gen_kwargs={
-                "worker_id": list(range(num_workers)),
-                "num_workers": [num_workers] * num_workers,
-            },
-        )
-        ds_from_iter.save_to_disk(str(prepared_ds_path))
-    else:
-        os.makedirs(prepared_ds_path, exist_ok=True)
-        dataset.save_to_disk(str(prepared_ds_path))
-    if cfg.push_dataset_to_hub:
-        LOG.info(
-            "Pushing merged prepared dataset to Huggingface hub at "
-            f"{cfg.push_dataset_to_hub} (version {dataset_hash})...",
-            main_process_only=False,
-        )
-        dataset.push_to_hub(
-            cfg.push_dataset_to_hub,
-            dataset_hash,
-            private=True,
-        )
-
-
-def load_preprocessed_dataset(cfg: DictDefault, dataset_hash: str) -> Dataset | None:
-    """Load preprocessed dataset from disk if available.
-
-    Args:
-        cfg: Configuration object.
-        dataset_hash: Hash identifying the dataset configuration.
-
-    Returns:
-        Loaded dataset if found and conditions are met, None otherwise.
-    """
-    prepared_ds_path = get_prepared_dataset_path(cfg, dataset_hash)
-
-    if (
-        cfg.dataset_prepared_path
-        and any(prepared_ds_path.glob("*"))
-        and not cfg.skip_prepare_dataset
-        and not cfg.is_preprocess
-    ):
-        LOG.info(
-            f"Loading prepared dataset from disk at {prepared_ds_path}...",
-            main_process_only=False,
-        )
-        return load_from_disk(str(prepared_ds_path))
-
-    LOG.info(
-        f"Unable to find prepared dataset in {prepared_ds_path}",
-        main_process_only=False,
-    )
-    return None
-
-
-def try_load_from_hub(
-    cfg: DictDefault, dataset_hash: str, split: str
-) -> Dataset | None:
-    """Try to load the prepared dataset from HuggingFace Hub."""
     try:
-        LOG.info(
+        if remote_file_system and remote_file_system.exists(config_dataset.path):
-            "Attempting to load prepared dataset from HuggingFace Hub at "
+            ds_from_cloud = True
-            f"{cfg.push_dataset_to_hub} (version {dataset_hash})..."
+    except (FileNotFoundError, ConnectionError):
-        )
+        pass
-        dataset = load_dataset(
-            cfg.push_dataset_to_hub,
-            dataset_hash,
-            token=cfg.hf_use_auth_token,
-        )
-        return dataset[split]
-    except Exception:  # pylint: disable=broad-except # nosec
-        LOG.info("Unable to find prepared dataset in HuggingFace Hub")
-        return None
 
-
+    # gather extra args from the config
-def generate_dataset_hash_from_config(
+    load_ds_kwargs = {}
-    cfg: DictDefault, cfg_datasets: list, tokenizer_name: str
+    if config_dataset.split:
-) -> str:
+        load_ds_kwargs["split"] = config_dataset.split
-    """Generate a hash to uniquely identify a dataset configuration for SFT.
-
-    Args:
-        cfg: Main configuration object.
-        cfg_datasets: List of dataset configurations.
-        tokenizer_name: Name of the tokenizer being used.
-
-    Returns:
-        MD5 hash string representing the configuration.
-    """
-    config_str = (
-        f"{cfg.sequence_len}@{cfg.sample_packing}@{cfg.eval_sample_packing}@"
-        f"{cfg.group_by_length}@{cfg.kd_temperature or 1.0}|"
-        f"{'|'.join(sorted([f'{d.path}:{d.type}:{d.shards}:{d.conversation}:{d.split}:{d.temperature or 1.0}' for d in cfg_datasets]))}"
-        f"|{tokenizer_name}"
-    )
-    return str(md5(config_str))
-
-
-def merge_datasets(datasets: list[Dataset], cfg: DictDefault) -> Dataset:
-    """Merge multiple datasets into one with optional shuffling.
-
-    Args:
-        datasets: List of datasets to merge.
-        cfg: Configuration object containing shuffle settings.
-
-    Returns:
-        Merged dataset.
-    """
-    if len(datasets) == 1:
-        return datasets[0]
-
-    LOG.info("Merging datasets...")
-    merged_dataset = concatenate_datasets(datasets)
-
-    if cfg.shuffle_merged_datasets:
-        LOG.debug("Shuffling merged datasets...")
-        merged_dataset = merged_dataset.shuffle(seed=cfg.seed)
     else:
-        LOG.debug("Not shuffling merged datasets.")
+        load_ds_kwargs["split"] = None
 
-    return merged_dataset
+    # prefer local dataset, even if hub exists
+    local_path = Path(config_dataset.path)
+    if local_path.exists():
+        if local_path.is_dir():
+            if config_dataset.data_files:
+                ds_type = get_ds_type(config_dataset)
+                ds = load_dataset(  # pylint: disable=invalid-name
+                    ds_type,
+                    name=config_dataset.name,
+                    data_files=config_dataset.data_files,
+                    streaming=streaming,
+                    **load_ds_kwargs,
+                )
+            else:
+                try:
+                    ds = load_from_disk(
+                        config_dataset.path
+                    )  # pylint: disable=invalid-name
+                except FileNotFoundError:
+                    ds = load_dataset(
+                        config_dataset.path,
+                        name=config_dataset.name,
+                        streaming=False,
+                        **load_ds_kwargs,
+                    )
+        elif local_path.is_file():
+            ds_type = get_ds_type(config_dataset)
+
+            ds = load_dataset(  # pylint: disable=invalid-name
+                ds_type,
+                name=config_dataset.name,
+                data_files=config_dataset.path,
+                streaming=False,
+                **load_ds_kwargs,
+            )
+        else:
+            raise ValueError(
+                "unhandled dataset load: local path exists, but is neither a directory or a file"
+            )
+    elif ds_from_hub:
+        ds = load_dataset(
+            config_dataset.path,
+            name=config_dataset.name,
+            streaming=streaming,
+            data_files=config_dataset.data_files,
+            token=use_auth_token,
+            revision=config_dataset.revision,
+            trust_remote_code=config_dataset.trust_remote_code,
+            **load_ds_kwargs,
+        )
+    elif ds_from_cloud and remote_file_system:
+        if remote_file_system.isdir(config_dataset.path):
+            ds = load_from_disk(
+                config_dataset.path,
+                storage_options=storage_options,
+            )
+        elif remote_file_system.isfile(config_dataset.path):
+            ds_type = get_ds_type(config_dataset)
+            ds = load_dataset(
+                ds_type,
+                name=config_dataset.name,
+                data_files=config_dataset.path,
+                streaming=streaming,
+                storage_options=storage_options,
+                trust_remote_code=config_dataset.trust_remote_code,
+                **load_ds_kwargs,
+            )
+    elif config_dataset.path.startswith("https://"):
+        ds_type = get_ds_type(config_dataset)
+        ds = load_dataset(
+            ds_type,
+            name=config_dataset.name,
+            data_files=config_dataset.path,
+            streaming=streaming,
+            storage_options=storage_options,
+            trust_remote_code=config_dataset.trust_remote_code,
+            **load_ds_kwargs,
+        )
+    elif config_dataset.data_files:
+        fp: str | list[str] | None = None
+        if isinstance(config_dataset.data_files, str):
+            fp = hf_hub_download(
+                repo_id=config_dataset.path,
+                repo_type="dataset",
+                filename=config_dataset.data_files,
+                revision=config_dataset.revision,
+            )
+        elif isinstance(config_dataset.data_files, list):
+            fp = []
+            for file in config_dataset.data_files:
+                fp.append(
+                    hf_hub_download(
+                        repo_id=config_dataset.path,
+                        repo_type="dataset",
+                        filename=file,
+                        revision=config_dataset.revision,
+                    )
+                )
+        else:
+            raise ValueError("data_files must be either a string or list of strings")
+        ds = load_dataset(
+            "json",
+            name=config_dataset.name,
+            data_files=fp,
+            streaming=streaming,
+            **load_ds_kwargs,
+        )
+    if not ds:
+        raise ValueError(
+            "The dataset could not be loaded. This could be due to a misconfigured dataset path "
+            f"({config_dataset.path}). Try double-check your path / name / data_files. "
+            "This is not caused by the dataset type."
+        )
+
+    return ds

src/axolotl/utils/data/utils.py

@@ -1,11 +1,9 @@
-"""Data handling helpers"""
+"""data handling helpers"""
 
-import contextlib
 import functools
 import hashlib
 import time
 from enum import Enum
-from typing import Callable
 
 import huggingface_hub
 import numpy as np
@@ -21,7 +19,9 @@ LOG = get_logger(__name__)
 
 
 class RetryStrategy(Enum):
-    """Enum for retry strategies."""
+    """
+    Enum for retry strategies.
+    """
 
     CONSTANT = 1
     LINEAR = 2
@@ -30,18 +30,7 @@ class RetryStrategy(Enum):
 
 def retry_on_request_exceptions(
     max_retries=3, delay=1, retry_strategy: RetryStrategy = RetryStrategy.LINEAR
-) -> Callable:
+):
-    """Decorator that retries function calls on specific request exceptions.
-
-    Args:
-        max_retries: Maximum number of retry attempts.
-        delay: Base delay between retries in seconds.
-        retry_strategy: Strategy for calculating retry delays.
-
-    Returns:
-        Decorated function with retry logic.
-    """
-
     def decorator(func):
         @functools.wraps(func)
         def wrapper(*args, **kwargs):  # pylint: disable=inconsistent-return-statements
@@ -51,7 +40,6 @@ def retry_on_request_exceptions(
                 except (
                     requests.exceptions.ReadTimeout,
                     requests.exceptions.ConnectionError,
-                    requests.exceptions.HTTPError,
                     huggingface_hub.errors.HfHubHTTPError,
                 ) as exc:
                     if attempt < max_retries - 1:
@@ -71,7 +59,6 @@ def retry_on_request_exceptions(
 
 
 def md5(to_hash: str, encoding: str = "utf-8") -> str:
-    """Generate MD5 hash of a string."""
     try:
         return hashlib.md5(to_hash.encode(encoding), usedforsecurity=False).hexdigest()
     except TypeError:
@@ -79,89 +66,102 @@ def md5(to_hash: str, encoding: str = "utf-8") -> str:
 
 
 def sha256(to_hash: str, encoding: str = "utf-8") -> str:
-    """Generate SHA256 hash of a string."""
     return hashlib.sha256(to_hash.encode(encoding)).hexdigest()
 
 
-def _deduplicate_dataset(
+def deduplicate_dataset(
-    dataset: Dataset,
+    dataset: Dataset, seen_hashes: dict[str, list[int]], other_dataset: Dataset = None
-    seen_hashes: set[str] | None = None,
+) -> Dataset:
-) -> tuple[Dataset, set[str]]:
-    """Remove duplicate rows from a dataset using SHA256 hashes.
-
-    Args:
-        dataset: Dataset to deduplicate.
-        seen_hashes: Set of previously seen row hashes (for cross-deduplication).
-
-    Returns:
-        Tuple of deduplicated dataset and the set of seen hashes.
-    """
-    if seen_hashes is None:
-        seen_hashes = set()
-
     unique_indices = []
-    for idx, row in enumerate(dataset):
-        row_hash = sha256(str(row))  # Using SHA256 for collision resistance
-        if row_hash not in seen_hashes:
-            seen_hashes.add(row_hash)
-            unique_indices.append(idx)
 
-    return dataset.select(unique_indices), seen_hashes
+    for idx, row in enumerate(dataset):
+        row_hash = sha256(str(row))  # Using SHA256 for collision resistance.
+        if row_hash not in seen_hashes:
+            seen_hashes[row_hash] = [idx]
+            unique_indices.append(idx)
+        else:
+            # Check for collision by looking up the original dataset indices
+            original_indices = seen_hashes[row_hash]
+            is_duplicate = False
+            for original_idx in original_indices:
+                if (
+                    not idx == original_idx
+                    and original_idx < len(dataset)
+                    and str(dataset[original_idx]) == str(row)
+                ):
+                    is_duplicate = True
+                    break
+                # Check in the other dataset if provided
+                if other_dataset is not None:
+                    if original_idx < len(other_dataset) and str(
+                        other_dataset[original_idx]
+                    ) == str(row):
+                        is_duplicate = True
+                        break
+            if not is_duplicate:
+                seen_hashes[row_hash].append(idx)
+                unique_indices.append(idx)
+                continue
+    return dataset.select(unique_indices)
 
 
 def deduplicate_and_log_datasets(
-    dataset: Dataset,
+    *,
-    other_dataset: Dataset | None = None,
+    train_dataset: Dataset = None,
-    dataset_name: str | None = "train",
+    eval_dataset: Dataset = None,
-    other_name: str | None = "eval",
+    dataset: Dataset = None,
-) -> tuple[Dataset, Dataset | None]:
+) -> tuple[Dataset, Dataset, Dataset]:
-    """Deduplicate datasets, with optional cross-dataset deduplication.
+    """
-
+    Deduplicates train, eval, and an optional dataset if provided, logging original and new sizes.
-    Args:
-        dataset: Primary dataset to deduplicate.
-        other_dataset: Optional second dataset to deduplicate against the first.
-        dataset_name: Name for the primary dataset (for logging).
-        other_name: Name for the second dataset (for logging).
 
     Returns:
-        Tuple of (deduplicated_dataset, deduplicated_other_dataset).
+        tuple: Deduplicated train, eval, and additional datasets.
     """
-    # Deduplicate primary dataset
+    seen_hashes: dict[str, list[int]] = {}
-    LOG.info(
-        f"Starting deduplication for {dataset_name} dataset. Original size: {len(dataset)}"
-    )
-    dataset, seen_rows = _deduplicate_dataset(dataset)
-    LOG.info(
-        f"Deduplication complete for {dataset_name} dataset. New size: {len(dataset)}"
-    )
 
-    # Deduplicate second dataset if provided
+    # Handle cases where datasets are None
-    if other_dataset is not None:
+    if train_dataset is not None:
         LOG.info(
-            f"Starting deduplication for {other_name} dataset. Original size: {len(other_dataset)}"
+            f"Starting deduplication for train dataset. Original size: {len(train_dataset)}"
+        )
+        train_dataset = deduplicate_dataset(
+            dataset=train_dataset, seen_hashes=seen_hashes
         )
-        other_dataset, _ = _deduplicate_dataset(other_dataset, seen_rows)
         LOG.info(
-            f"Deduplication complete for {other_name} dataset. New size: {len(other_dataset)}"
+            f"Deduplication complete for train dataset. New size: {len(train_dataset)}"
+        )
+    else:
+        LOG.info("Train dataset is None. Skipping deduplication.")
+
+    if eval_dataset is not None:
+        LOG.info(
+            f"Starting deduplication for eval dataset. Original size: {len(eval_dataset)}"
+        )
+        eval_dataset = deduplicate_dataset(
+            dataset=eval_dataset, seen_hashes=seen_hashes, other_dataset=train_dataset
+        )
+        LOG.info(
+            f"Deduplication complete for eval dataset. New size: {len(eval_dataset)}"
+        )
+    else:
+        LOG.info("Eval dataset is None. Skipping deduplication.")
+
+    if dataset is not None and (eval_dataset is None and train_dataset is None):
+        LOG.info(
+            f"Starting deduplication for combined dataset. Original size: {len(dataset)}"
+        )
+        dataset = deduplicate_dataset(dataset=dataset, seen_hashes=seen_hashes)
+        LOG.info(
+            f"Deduplication complete for combined dataset. New size: {len(dataset)}"
         )
 
-    return dataset, other_dataset
+    return train_dataset, eval_dataset, dataset
 
 
-def drop_long_seq_in_dataset(dataset: Dataset, cfg: DictDefault) -> Dataset:
+def drop_long_seq_in_dataset(dataset: Dataset, cfg: DictDefault):
-    """Remove sequences longer than configured maximum from dataset.
-
-    Args:
-        dataset: Dataset to filter.
-        cfg: Dictionary mapping `axolotl` config keys to values.
-
-    Returns:
-        Filtered dataset with long sequences removed.
-    """
     if "input_ids" not in dataset.column_names:
         LOG.warning(
-            "Dataset does not contain 'input_ids' column. Skip drop long seq. This is "
+            "Dataset does not contain 'input_ids' column. Skip drop long seq. This is expected for RewardModeling."
-            "expected for reward modeling."
         )
         return dataset
 
@@ -171,14 +171,20 @@ def drop_long_seq_in_dataset(dataset: Dataset, cfg: DictDefault) -> Dataset:
         min_sequence_len=cfg.min_sample_len,
     )
 
-    with contextlib.suppress(AttributeError):
+    try:
         ds_lengths = get_dataset_lengths(dataset, from_arrow=True)
         min_input_len = np.min(ds_lengths)
         LOG.info(f"min_input_len: {min_input_len}")
         max_input_len = np.max(ds_lengths)
         LOG.info(f"max_input_len: {max_input_len}")
+    except AttributeError:
+        pass
 
-    prior_len = len(dataset) if hasattr(dataset, "__len__") else None
+    try:
+        prior_len = len(dataset)
+    except TypeError:
+        # handle iterable datasets case
+        prior_len = None
 
     filter_map_kwargs = {}
     if not isinstance(dataset, IterableDataset):

src/axolotl/utils/data/wrappers.py

@@ -1,425 +0,0 @@
-"""Data handling specific to SFT."""
-
-import logging
-from typing import Any, NoReturn, cast
-
-from datasets import (
-    Dataset,
-    IterableDataset,
-    Sequence,
-    Value,
-)
-from transformers import PreTrainedTokenizer
-from transformers.processing_utils import ProcessorMixin
-
-from axolotl.datasets import TokenizedPromptDataset, wrap_dataset_for_tokenized_prompt
-from axolotl.prompt_strategies import load
-from axolotl.prompt_strategies.bradley_terry import load as bradley_terry_load
-from axolotl.prompt_tokenizers import (
-    AlpacaMultipleChoicePromptTokenizingStrategy,
-    AlpacaPromptTokenizingStrategy,
-    AlpacaReflectionPTStrategy,
-    DatasetWrappingStrategy,
-    GPTeacherPromptTokenizingStrategy,
-    JeopardyPromptTokenizingStrategy,
-    OpenAssistantPromptTokenizingStrategy,
-    PromptTokenizingStrategy,
-    SummarizeTLDRPromptTokenizingStrategy,
-)
-from axolotl.prompters import (
-    AlpacaPrompter,
-    GPTeacherPrompter,
-    JeopardyPrompter,
-    MultipleChoiceConcisePrompter,
-    MultipleChoiceExplainPrompter,
-    Prompter,
-    ReflectAlpacaPrompter,
-    SummarizeTLDRPrompter,
-    UnsupportedPrompter,
-)
-from axolotl.utils.dict import DictDefault
-
-LOG = logging.getLogger(__name__)
-
-
-def handle_unknown_dataset_strategy(dataset_config: DictDefault) -> NoReturn:
-    """Raise error for unknown dataset strategy."""
-    ds_type = dataset_config.type
-    suffix = ""
-    if ":load_" in ds_type:
-        suffix = f"Did you mean {ds_type.replace(':load_', '.load_')}?"
-
-    error_message = f"unhandled prompt tokenization strategy: {ds_type}. {suffix}"
-    LOG.error(error_message)
-    raise ValueError(error_message)
-
-
-# pylint: disable=too-many-return-statements
-def get_dataset_wrapper(
-    dataset_config: DictDefault,
-    tokenizer: PreTrainedTokenizer,
-    cfg: DictDefault,
-    dataset_base_type: str | None,
-    dataset: Dataset | IterableDataset,
-    dataset_prompt_style: str | None = None,
-    processor: ProcessorMixin | None = None,  # pylint: disable=unused-argument
-) -> tuple[Dataset | IterableDataset, Prompter | None]:
-    """Create an appropriate dataset wrapper and prompter based on dataset
-    configuration.
-
-    Args:
-        dataset_config: Configuration for the dataset.
-        tokenizer: Tokenizer to use for processing text.
-        cfg: Global configuration object.
-        dataset_base_type: The base type of the dataset.
-        dataset: The actual dataset object.
-        dataset_prompt_style: Optional prompt style specification.
-        processor: Optional processor for multimodal datasets.
-
-    Returns:
-        tuple of (dataset_wrapper, dataset_prompter).
-    """
-    # Common parameters for dataset wrapping
-    dataset_kwargs: dict[str, Any] = {
-        "process_count": cfg.dataset_processes,
-        "keep_in_memory": cfg.dataset_keep_in_memory is True,
-    }
-
-    LOG.info(
-        f"Loading dataset: {dataset_config['path']} with base_type: "
-        f"{dataset_base_type} and prompt_style: {dataset_prompt_style}"
-    )
-
-    # Dataset is already tokenized
-    if _is_dataset_already_tokenized(dataset):
-        return dataset, UnsupportedPrompter()
-
-    # Custom dataset type definition
-    if isinstance(dataset_config.type, DictDefault):
-        return _handle_custom_dataset_type(
-            dataset_config, tokenizer, cfg, dataset, dataset_kwargs
-        )
-
-    # Skip preparation if configured
-    if cfg.skip_prepare_dataset:
-        return dataset, None
-
-    # Bradley-Terry dataset
-    if dataset_config.type.startswith("bradley_terry"):
-        return _handle_bradley_terry_dataset(
-            dataset_config, tokenizer, cfg, dataset, dataset_kwargs
-        )
-
-    # Stepwise supervised dataset
-    if dataset_config.type.startswith("stepwise_supervised"):
-        return _handle_stepwise_supervised_dataset(
-            dataset_config, tokenizer, cfg, dataset, dataset_kwargs
-        )
-
-    # Try to load prompt tokenizer / dataset wrapper strategy from registry
-    dataset_strategy = load(
-        dataset_config.type, tokenizer, cfg, dataset_config, processor=processor
-    )
-    if dataset_strategy:
-        return _handle_loaded_strategy(dataset_strategy, dataset, dataset_kwargs)
-
-    # Known dataset types with specific handling
-    if dataset_base_type in DATASET_HANDLERS:
-        handler = DATASET_HANDLERS[dataset_base_type]
-        return handler(dataset_prompt_style, tokenizer, cfg, dataset, dataset_kwargs)
-
-    # Unhandled dataset type
-    handle_unknown_dataset_strategy(dataset_config)
-
-
-def _is_dataset_already_tokenized(dataset: Dataset | IterableDataset) -> bool:
-    """Check if the dataset is already tokenized."""
-    return (
-        isinstance(dataset, Dataset)
-        and "input_ids" in dataset.features
-        and "attention_mask" in dataset.features
-        and "labels" in dataset.features
-    )
-
-
-def _handle_custom_dataset_type(
-    dataset_config: DictDefault,
-    tokenizer: PreTrainedTokenizer,
-    cfg: DictDefault,
-    dataset: Dataset | IterableDataset,
-    dataset_kwargs: dict[str, Any],
-) -> tuple[Dataset | IterableDataset, Prompter]:
-    """Handle a custom dataset type defined in the configuration."""
-    dataset_strategy = cast(
-        PromptTokenizingStrategy,
-        load("user_defined", tokenizer, cfg, dataset_config.type.to_dict()),
-    )
-    dataset_prompter = UnsupportedPrompter()
-    dataset_wrapper = wrap_dataset_for_tokenized_prompt(
-        dataset_strategy,
-        dataset,
-        **dataset_kwargs,
-    )
-    return dataset_wrapper, dataset_prompter
-
-
-def _handle_bradley_terry_dataset(
-    dataset_config: DictDefault,
-    tokenizer: PreTrainedTokenizer,
-    cfg: DictDefault,
-    dataset: Dataset | IterableDataset,
-    dataset_kwargs: dict[str, Any],
-) -> tuple[Dataset | IterableDataset, Prompter | None]:
-    """Handle a Bradley-Terry dataset."""
-    bt_type = dataset_config.type.split(".", 1)[1]
-    dataset_strategy = bradley_terry_load(bt_type, tokenizer, cfg, dataset_config)
-
-    if not dataset_strategy:
-        handle_unknown_dataset_strategy(dataset_config)
-
-    dataset_prompter = UnsupportedPrompter()
-    dataset_wrapper = wrap_dataset_for_tokenized_prompt(
-        dataset_strategy,
-        dataset,
-        **dataset_kwargs,
-    )
-
-    return dataset_wrapper, dataset_prompter
-
-
-def _handle_stepwise_supervised_dataset(
-    dataset_config: DictDefault,
-    tokenizer: PreTrainedTokenizer,
-    cfg: DictDefault,
-    dataset: Dataset | IterableDataset,
-    dataset_kwargs: dict[str, Any],
-) -> tuple[Dataset | IterableDataset, Prompter]:
-    """Handle a stepwise supervised dataset."""
-    dataset_prompter = UnsupportedPrompter()
-    dataset_strategy = load(dataset_config.type, tokenizer, cfg, dataset_config)
-
-    # We need to explicitly cast boolean labels to int
-    # for compatibility with how trl's PRMTrainer works
-    if isinstance(dataset, Dataset):
-        dataset = dataset.cast_column("labels", Sequence(Value("int64")))
-
-    dataset_wrapper = TokenizedPromptDataset(
-        dataset_strategy,
-        dataset,
-        **dataset_kwargs,
-    )
-    return dataset_wrapper, dataset_prompter
-
-
-def _handle_loaded_strategy(
-    dataset_strategy: PromptTokenizingStrategy | DatasetWrappingStrategy,
-    dataset: Dataset | IterableDataset,
-    dataset_kwargs: dict[str, Any],
-) -> tuple[Dataset | IterableDataset, Prompter | None]:
-    """Handle a dataset with a strategy loaded from the registry."""
-    if isinstance(dataset_strategy, DatasetWrappingStrategy):
-        return dataset_strategy.wrap_dataset(dataset, **dataset_kwargs), None
-
-    dataset_prompter = UnsupportedPrompter()
-    dataset_wrapper = wrap_dataset_for_tokenized_prompt(
-        dataset_strategy,
-        dataset,
-        **dataset_kwargs,
-    )
-    return dataset_wrapper, dataset_prompter
-
-
-def _handle_alpaca_dataset(
-    dataset_prompt_style: str | None,
-    tokenizer: PreTrainedTokenizer,
-    cfg: DictDefault,
-    dataset: Dataset | IterableDataset,
-    dataset_kwargs: dict[str, Any],
-) -> tuple[Dataset | IterableDataset, Prompter]:
-    """Handle an Alpaca dataset."""
-    dataset_prompter = AlpacaPrompter(dataset_prompt_style)
-    dataset_strategy = AlpacaPromptTokenizingStrategy(
-        dataset_prompter,
-        tokenizer,
-        cfg.train_on_inputs,
-        cfg.sequence_len,
-    )
-    dataset_wrapper = wrap_dataset_for_tokenized_prompt(
-        dataset_strategy,
-        dataset,
-        **dataset_kwargs,
-    )
-    return dataset_wrapper, dataset_prompter
-
-
-def _handle_explainchoice_dataset(
-    dataset_prompt_style: str | None,
-    tokenizer: PreTrainedTokenizer,
-    cfg: DictDefault,
-    dataset: Dataset | IterableDataset,
-    dataset_kwargs: dict[str, Any],
-) -> tuple[Dataset | IterableDataset, Prompter]:
-    """Handle an ExplainChoice dataset."""
-    dataset_prompter = MultipleChoiceExplainPrompter(dataset_prompt_style)
-    dataset_strategy = AlpacaMultipleChoicePromptTokenizingStrategy(
-        dataset_prompter,
-        tokenizer,
-        cfg.train_on_inputs,
-        cfg.sequence_len,
-    )
-    dataset_wrapper = wrap_dataset_for_tokenized_prompt(
-        dataset_strategy,
-        dataset,
-        **dataset_kwargs,
-    )
-    return dataset_wrapper, dataset_prompter
-
-
-def _handle_concisechoice_dataset(
-    dataset_prompt_style: str | None,
-    tokenizer: PreTrainedTokenizer,
-    cfg: DictDefault,
-    dataset: Dataset | IterableDataset,
-    dataset_kwargs: dict[str, Any],
-) -> tuple[Dataset | IterableDataset, Prompter]:
-    """Handle a ConciseChoice dataset."""
-    dataset_prompter = MultipleChoiceConcisePrompter(dataset_prompt_style)
-    dataset_strategy = AlpacaMultipleChoicePromptTokenizingStrategy(
-        dataset_prompter,
-        tokenizer,
-        cfg.train_on_inputs,
-        cfg.sequence_len,
-    )
-    dataset_wrapper = wrap_dataset_for_tokenized_prompt(
-        dataset_strategy,
-        dataset,
-        **dataset_kwargs,
-    )
-    return dataset_wrapper, dataset_prompter
-
-
-def _handle_summarizetldr_dataset(
-    dataset_prompt_style: str | None,
-    tokenizer: PreTrainedTokenizer,
-    cfg: DictDefault,
-    dataset: Dataset | IterableDataset,
-    dataset_kwargs: dict[str, Any],
-) -> tuple[Dataset | IterableDataset, Prompter]:
-    """Handle a SummarizeTLDR dataset."""
-    dataset_prompter = SummarizeTLDRPrompter(dataset_prompt_style)
-    dataset_strategy = SummarizeTLDRPromptTokenizingStrategy(
-        dataset_prompter,
-        tokenizer,
-        cfg.train_on_inputs,
-        cfg.sequence_len,
-    )
-    dataset_wrapper = wrap_dataset_for_tokenized_prompt(
-        dataset_strategy,
-        dataset,
-        **dataset_kwargs,
-    )
-    return dataset_wrapper, dataset_prompter
-
-
-def _handle_jeopardy_dataset(
-    dataset_prompt_style: str | None,
-    tokenizer: PreTrainedTokenizer,
-    cfg: DictDefault,
-    dataset: Dataset | IterableDataset,
-    dataset_kwargs: dict[str, Any],
-) -> tuple[Dataset | IterableDataset, Prompter]:
-    """Handle a Jeopardy dataset."""
-    dataset_prompter = JeopardyPrompter(dataset_prompt_style)
-    dataset_strategy = JeopardyPromptTokenizingStrategy(
-        dataset_prompter,
-        tokenizer,
-        cfg.train_on_inputs,
-        cfg.sequence_len,
-    )
-    dataset_wrapper = wrap_dataset_for_tokenized_prompt(
-        dataset_strategy,
-        dataset,
-        **dataset_kwargs,
-    )
-    return dataset_wrapper, dataset_prompter
-
-
-def _handle_oasst_dataset(
-    dataset_prompt_style: str | None,
-    tokenizer: PreTrainedTokenizer,
-    cfg: DictDefault,
-    dataset: Dataset | IterableDataset,
-    dataset_kwargs: dict[str, Any],
-) -> tuple[Dataset | IterableDataset, Prompter]:
-    """Handle an OpenAssistant dataset."""
-    dataset_prompter = AlpacaPrompter(dataset_prompt_style)
-    dataset_strategy = OpenAssistantPromptTokenizingStrategy(
-        dataset_prompter,
-        tokenizer,
-        cfg.train_on_inputs,
-        cfg.sequence_len,
-    )
-    dataset_wrapper = wrap_dataset_for_tokenized_prompt(
-        dataset_strategy,
-        dataset,
-        **dataset_kwargs,
-    )
-    return dataset_wrapper, dataset_prompter
-
-
-def _handle_gpteacher_dataset(
-    dataset_prompt_style: str | None,
-    tokenizer: PreTrainedTokenizer,
-    cfg: DictDefault,
-    dataset: Dataset | IterableDataset,
-    dataset_kwargs: dict[str, Any],
-) -> tuple[Dataset | IterableDataset, Prompter]:
-    """Handle a GPTeacher dataset."""
-    dataset_prompter = GPTeacherPrompter(dataset_prompt_style)
-    dataset_strategy = GPTeacherPromptTokenizingStrategy(
-        dataset_prompter,
-        tokenizer,
-        cfg.train_on_inputs,
-        cfg.sequence_len,
-    )
-    dataset_wrapper = wrap_dataset_for_tokenized_prompt(
-        dataset_strategy,
-        dataset,
-        **dataset_kwargs,
-    )
-    return dataset_wrapper, dataset_prompter
-
-
-def _handle_reflection_dataset(
-    dataset_prompt_style: str | None,
-    tokenizer: PreTrainedTokenizer,
-    cfg: DictDefault,
-    dataset: Dataset | IterableDataset,
-    dataset_kwargs: dict[str, Any],
-) -> tuple[Dataset | IterableDataset, Prompter]:
-    """Handle a Reflection dataset."""
-    dataset_prompter = ReflectAlpacaPrompter(dataset_prompt_style)
-    dataset_strategy = AlpacaReflectionPTStrategy(
-        dataset_prompter,
-        tokenizer,
-        cfg.train_on_inputs,
-        cfg.sequence_len,
-    )
-    dataset_wrapper = wrap_dataset_for_tokenized_prompt(
-        dataset_strategy,
-        dataset,
-        **dataset_kwargs,
-    )
-    return dataset_wrapper, dataset_prompter
-
-
-DATASET_HANDLERS = {
-    "alpaca": _handle_alpaca_dataset,
-    "explainchoice": _handle_explainchoice_dataset,
-    "concisechoice": _handle_concisechoice_dataset,
-    "summarizetldr": _handle_summarizetldr_dataset,
-    "jeopardy": _handle_jeopardy_dataset,
-    "oasst": _handle_oasst_dataset,
-    "gpteacher": _handle_gpteacher_dataset,
-    "reflection": _handle_reflection_dataset,
-}

src/axolotl/utils/mistral_tokenizer.py

@@ -1,567 +0,0 @@
-"""Wrapper for MistralTokenizer from mistral-common"""
-
-import math
-import os
-from shutil import copyfile
-from typing import TYPE_CHECKING, Optional
-
-import numpy as np
-from huggingface_hub import hf_hub_download
-from mistral_common.tokens.tokenizers.mistral import MistralTokenizer
-from mistral_common.tokens.tokenizers.tekken import Tekkenizer
-from torch import Tensor
-from transformers.utils import PaddingStrategy
-
-from axolotl.utils.collators.core import IGNORE_INDEX
-
-if TYPE_CHECKING:
-    from mistral_common.protocol.instruct.request import ChatCompletionRequest
-
-
-def _get_file_path(path_or_repo_id: str, filename: str) -> str:
-    """Get the file path from local or HF Hub"""
-    if os.path.exists(path_or_repo_id):
-        maybe_file_path = os.path.join(path_or_repo_id, filename)
-        if os.path.exists(maybe_file_path):
-            return maybe_file_path
-
-        raise FileNotFoundError(f"File not found at {path_or_repo_id}")
-
-    return hf_hub_download(repo_id=path_or_repo_id, filename=filename)
-
-
-class HFMistralTokenizer:
-    """
-    Wraps mistral_common.tokens.tokenizers.mistral.MistralTokenizer
-    and exposes HuggingFace API for special tokens.
-    """
-
-    def __init__(
-        self, mistral: MistralTokenizer, name_or_path: str, tokenizer_path: str
-    ):
-        """
-        Args:
-            mistral: The mistral-common tokenizer to wrap.
-            name_or_path: The name or path to the tokenizer files or the repo id.
-        """
-        self._mistral = mistral
-        self._padding_side = "right"
-        self._name_or_path = name_or_path
-        self._tokenizer_path = tokenizer_path
-
-        # Manual set to training mode
-        from mistral_common.protocol.instruct.validator import (
-            MistralRequestValidator,
-            ValidationMode,
-        )
-
-        # Check if MistralRequestValidator has a _mode attribute.
-        # This is a private API and may change in the future.
-        # pylint: disable=protected-access
-        if not (
-            hasattr(self._mistral, "_chat_completion_request_validator")
-            and isinstance(
-                self._mistral._chat_completion_request_validator,
-                MistralRequestValidator,
-            )
-            and hasattr(self._mistral._chat_completion_request_validator, "_mode")
-        ):
-            raise RuntimeError(
-                "Unable to switch mistral tokenizer to finetuning mode – "
-                "private API `_chat_completion_request_validator._mode` missing."
-            )
-
-        self._mistral._chat_completion_request_validator._mode = (
-            ValidationMode.finetuning
-        )
-
-    def _load_system_prompt(self, path_or_repo_id: str) -> str:
-        """Load system prompt from local or HF Hub.
-
-        Note: Unused for now as we don't want to explicitly set the system prompt if a user does
-        not provide one.
-
-        Args:
-            path_or_repo_id: The path to the tokenizer files or the repo id.
-
-        Returns:
-            The system prompt.
-        """
-        file_path = _get_file_path(path_or_repo_id, "SYSTEM_PROMPT.txt")
-
-        if not os.path.exists(file_path):
-            raise FileNotFoundError(f"System prompt file not found at {file_path}")
-
-        with open(file_path, "r", encoding="utf-8") as file:
-            return file.read()
-
-    @property
-    def bos_token_id(self) -> int:
-        return self._mistral.instruct_tokenizer.tokenizer.bos_id
-
-    @property
-    def eos_token_id(self) -> int:
-        return self._mistral.instruct_tokenizer.tokenizer.eos_id
-
-    @property
-    def pad_token_id(self) -> int:
-        return self._mistral.instruct_tokenizer.tokenizer.pad_id
-
-    @property
-    def unk_token_id(self) -> int:
-        return self._mistral.instruct_tokenizer.tokenizer.unk_id
-
-    @property
-    def bos_token(self) -> str:
-        return self._mistral.instruct_tokenizer.tokenizer.id_to_piece(self.bos_token_id)
-
-    @property
-    def eos_token(self) -> str:
-        return self._mistral.instruct_tokenizer.tokenizer.id_to_piece(self.eos_token_id)
-
-    @property
-    def pad_token(self) -> str:
-        return self._mistral.instruct_tokenizer.tokenizer.id_to_piece(self.pad_token_id)
-
-    @property
-    def unk_token(self) -> str:
-        return self._mistral.instruct_tokenizer.tokenizer.id_to_piece(self.unk_token_id)
-
-    @property
-    def padding_side(self) -> str:
-        return self._padding_side
-
-    @property
-    def name_or_path(self) -> str:
-        return self._name_or_path
-
-    @property
-    def chat_template(self) -> str | None:
-        """Chat template is not supported. Dummy method to satisfy HuggingFace API."""
-        return None
-
-    def __len__(self) -> int:
-        return self._mistral.instruct_tokenizer.tokenizer.n_words
-
-    @classmethod
-    def from_pretrained(
-        cls,
-        name_or_path: str,
-        *,
-        revision: Optional[str] = None,
-        **kwargs,  # pylint: disable=unused-argument
-    ) -> "HFMistralTokenizer":
-        """
-        Load a mistral tekken tokenizer from a local file or HF Hub and wrap it.
-
-        Args:
-            path_or_repo_id: The path to the tokenizer files or the repo id.
-            revision: The revision of the tokenizer to download.
-            kwargs: Additional keyword arguments.
-
-        Returns:
-            A HFMistralTokenizer instance.
-        """
-        if revision:
-            raise NotImplementedError(
-                "Revision not supported yet for mistral-common tokenizer"
-            )
-
-        # only support Tekken tokenizer for now
-        # downloads from HF Hub if not local
-        tokenizer_path = _get_file_path(name_or_path, "tekken.json")
-
-        base = MistralTokenizer.from_file(tokenizer_path)
-
-        return cls(
-            base,
-            name_or_path=name_or_path,
-            tokenizer_path=tokenizer_path,
-        )
-
-    def save_pretrained(self, save_directory: str) -> None:
-        """
-        Save the Tekken/SentencePiece model file so that from_pretrained can pick it up again.
-
-        Only Tekken models are supported.
-
-        Args:
-            save_directory: The directory to save the tokenizer files.
-        """
-        inner = self._mistral.instruct_tokenizer.tokenizer
-        if isinstance(inner, Tekkenizer):
-            # Create the directory and save the model
-            try:
-                os.makedirs(save_directory, exist_ok=True)
-
-                # Verify directory was created
-                if not os.path.exists(save_directory):
-                    raise RuntimeError(f"Failed to create directory: {save_directory}")
-
-                # Verify source file exists
-                if not os.path.exists(self._tokenizer_path):
-                    raise FileNotFoundError(
-                        f"Source tokenizer file not found: {self._tokenizer_path}"
-                    )
-
-                destination_path = os.path.join(save_directory, "tekken.json")
-                copyfile(self._tokenizer_path, destination_path)
-
-            except Exception as e:
-                raise RuntimeError(
-                    f"Failed to save tokenizer to {save_directory}: {e}. "
-                    f"Source path: {self._tokenizer_path}, "
-                    f"Directory exists: {os.path.exists(save_directory)}"
-                ) from e
-
-        else:
-            raise RuntimeError(f"Unknown tokenizer type: {type(inner)}")
-
-    def encode(self, text: str, add_special_tokens: bool = True) -> list[int]:
-        """
-        Encode a text string into a list of token IDs.
-
-        Args:
-            text: The text string to encode.
-            add_special_tokens: Whether to add special tokens to the encoded tokens.
-
-        Returns:
-            A list of token IDs.
-        """
-        return self._mistral.instruct_tokenizer.tokenizer.encode(
-            text,
-            bos=add_special_tokens,
-            eos=add_special_tokens,
-        )
-
-    def decode(
-        self, token_ids: int | list[int], skip_special_tokens: bool = False
-    ) -> str:
-        """
-        Decode a list of token IDs into a text string.
-
-        Args:
-            token_ids: The int or list of token IDs to decode.
-            skip_special_tokens: Whether to skip special tokens in the decoded text.
-
-        Returns:
-            The decoded text string.
-        """
-        if isinstance(token_ids, int):
-            token_ids = [token_ids]
-
-        if skip_special_tokens:
-            return self._mistral.instruct_tokenizer.tokenizer.decode(token_ids)
-
-        # to_string returns a string with special tokens
-        return self._mistral.instruct_tokenizer.tokenizer.to_string(token_ids)
-
-    def _create_mistral_chat_completion_request(
-        self, conversation: list[dict], tools: list[dict] | None = None
-    ) -> "ChatCompletionRequest":
-        from mistral_common.protocol.instruct.messages import (
-            AssistantMessage,
-            SystemMessage,
-            ToolMessage,
-            UserMessage,
-        )
-        from mistral_common.protocol.instruct.request import ChatCompletionRequest
-        from mistral_common.protocol.instruct.tool_calls import Function, Tool
-
-        messages: list[UserMessage | AssistantMessage | ToolMessage | SystemMessage] = (
-            []
-        )
-        for turn in conversation:
-            role = turn.get("role")
-
-            if role == "user":
-                messages.append(UserMessage(content=turn["content"]))
-            elif role == "assistant":
-                messages.append(
-                    AssistantMessage(
-                        content=turn.get("content"),
-                        tool_calls=turn.get("tool_calls"),
-                    )
-                )
-            elif role == "tool":
-                messages.append(
-                    ToolMessage(
-                        content=turn.get("content"),
-                        tool_call_id=turn.get("tool_call_id"),
-                        name=turn.get("name"),
-                    )
-                )
-            elif role == "system":
-                messages.append(SystemMessage(content=turn["content"]))
-            else:
-                raise ValueError(
-                    f"Unknown role for use with mistral-common tokenizer: {turn['role']}"
-                )
-
-        tool_calls: list[Tool] = []
-        if tools:
-            # convert to Tool
-            for tool in tools:
-                if tool["type"] != "function":
-                    continue
-
-                function = tool["function"]
-
-                tool_calls.append(
-                    Tool(
-                        function=Function(
-                            name=function["name"],
-                            description=function["description"],
-                            # set parameters to empty dict if not provided
-                            parameters=function.get("parameters", {}),
-                        )
-                    )
-                )
-
-        chat_completion: ChatCompletionRequest = ChatCompletionRequest(
-            messages=messages,
-            tools=tool_calls,
-        )
-
-        return chat_completion
-
-    def apply_chat_template(
-        self,
-        messages: list[dict],
-        tokenize: bool = True,
-        tools: list[dict] | None = None,
-        chat_template: str | None = None,  # pylint: disable=unused-argument
-        add_generation_prompt: bool = False,  # pylint: disable=unused-argument
-    ) -> list[int] | str:
-        if chat_template:
-            raise NotImplementedError("chat_template not supported yet")
-
-        if add_generation_prompt:
-            raise NotImplementedError("add_generation_prompt not supported yet")
-
-        chat_completion: ChatCompletionRequest = (
-            self._create_mistral_chat_completion_request(messages, tools)
-        )
-
-        tokens: list[int] = self._mistral.encode_chat_completion(chat_completion).tokens
-
-        if tokenize:
-            return tokens
-
-        return self.decode(tokens)
-
-    def pad(
-        self,
-        features: list[dict[str, list[int] | np.ndarray]],
-        *,
-        padding: bool | str | PaddingStrategy = True,
-        max_length: int | None = None,
-        pad_to_multiple_of: int | None = None,
-        return_tensors: str | None = None,  # "np", "pt", or "tf"
-    ) -> dict[str, np.ndarray | Tensor]:
-        """
-        HF-style pad method that properly handles all sequence-related features:
-        - pad 'input_ids' & 'labels' to the longest (or to max_length)
-        """
-        import torch
-        from torch.nn import functional as F
-
-        # Check for unsupported fields
-        if any("token_type_ids" in f for f in features):
-            raise ValueError("token_type_ids is not supported by this tokenizer")
-
-        # Determine desired sequence length
-        lengths = [len(f["input_ids"]) for f in features]
-        if padding in (True, "longest", PaddingStrategy.LONGEST):
-            target_length = max(lengths)
-        elif padding in ("max_length", PaddingStrategy.MAX_LENGTH):
-            if max_length is None:
-                raise ValueError("max_length must be set for 'max_length' padding")
-            target_length = max_length
-        elif padding in (False, "do_not_pad", PaddingStrategy.DO_NOT_PAD):
-            target_length = None
-        else:
-            raise ValueError(f"Unknown padding strategy: {padding}")
-
-        # Apply pad_to_multiple_of
-        if target_length is not None and pad_to_multiple_of is not None:
-            target_length = (
-                math.ceil(target_length / pad_to_multiple_of) * pad_to_multiple_of
-            )
-
-        # If no padding requested, just stack tensors
-        do_pad = target_length is not None
-
-        # Pad sequences using torch.nn.utils.rnn.pad_sequence
-        input_ids = torch.nn.utils.rnn.pad_sequence(
-            [torch.tensor(x["input_ids"], dtype=torch.long) for x in features],
-            batch_first=True,
-            padding_value=self.pad_token_id if self.pad_token_id is not None else 0,
-        )
-
-        labels = torch.nn.utils.rnn.pad_sequence(
-            [torch.tensor(x["labels"], dtype=torch.long) for x in features],
-            batch_first=True,
-            padding_value=IGNORE_INDEX,
-        )
-
-        attention_mask = torch.nn.utils.rnn.pad_sequence(
-            [torch.tensor(x["attention_mask"], dtype=torch.long) for x in features],
-            batch_first=True,
-            padding_value=0,
-        )
-
-        # Handle position_ids - pad with sequential values for right padding, 0s for left padding
-        if "position_ids" in features[0]:
-            if self.padding_side == "left":
-                # Likely not needed, but keeping for now
-                # For left padding, we'll pad with 0s using pad_sequence, then handle manually
-                position_ids = torch.nn.utils.rnn.pad_sequence(
-                    [
-                        torch.tensor(x["position_ids"], dtype=torch.long)
-                        for x in features
-                    ],
-                    batch_first=True,
-                    padding_value=0,
-                )
-            else:
-                # For right padding, continue the sequence
-                max_pos_len = max(len(f["position_ids"]) for f in features)
-                position_ids_list = []
-                for f in features:
-                    pos_seq = torch.tensor(f["position_ids"], dtype=torch.long)
-                    if len(pos_seq) < max_pos_len:
-                        # Continue the sequence
-                        last_pos = pos_seq[-1].item() if len(pos_seq) > 0 else -1
-                        pad_len = max_pos_len - len(pos_seq)
-                        pad_positions = torch.arange(
-                            last_pos + 1, last_pos + 1 + pad_len, dtype=torch.long
-                        )
-                        pos_seq = torch.cat([pos_seq, pad_positions])
-                    position_ids_list.append(pos_seq)
-                position_ids = torch.stack(position_ids_list)
-        else:
-            # Create position_ids if not present
-            seq_len = input_ids.size(1)
-            position_ids = (
-                torch.arange(seq_len, dtype=torch.long)
-                .unsqueeze(0)
-                .expand(input_ids.size(0), -1)
-            )
-
-        # Ensure all tensors have the same sequence length
-        max_seq_len = max(
-            input_ids.size(1),
-            labels.size(1),
-            attention_mask.size(1),
-            position_ids.size(1),
-        )
-
-        # TODO: check if trimming is needed? and correct.
-
-        if do_pad and target_length is not None:
-            max_seq_len = target_length
-
-        # Pad all tensors to the same length
-        if input_ids.size(1) < max_seq_len:
-            pad_len = max_seq_len - input_ids.size(1)
-            if self.padding_side == "right":
-                input_ids = F.pad(
-                    input_ids,
-                    (0, pad_len),
-                    value=self.pad_token_id if self.pad_token_id is not None else 0,
-                )
-            else:
-                input_ids = F.pad(
-                    input_ids,
-                    (pad_len, 0),
-                    value=self.pad_token_id if self.pad_token_id is not None else 0,
-                )
-        elif input_ids.size(1) > max_seq_len:
-            input_ids = input_ids[:, :max_seq_len]
-
-        if labels.size(1) < max_seq_len:
-            pad_len = max_seq_len - labels.size(1)
-            if self.padding_side == "right":
-                labels = F.pad(labels, (0, pad_len), value=IGNORE_INDEX)
-            else:
-                labels = F.pad(labels, (pad_len, 0), value=IGNORE_INDEX)
-        elif labels.size(1) > max_seq_len:
-            labels = labels[:, :max_seq_len]
-
-        if attention_mask.size(1) < max_seq_len:
-            pad_len = max_seq_len - attention_mask.size(1)
-            if self.padding_side == "right":
-                attention_mask = F.pad(attention_mask, (0, pad_len), value=0)
-            else:
-                attention_mask = F.pad(attention_mask, (pad_len, 0), value=0)
-        elif attention_mask.size(1) > max_seq_len:
-            attention_mask = attention_mask[:, :max_seq_len]
-
-        if position_ids.size(1) < max_seq_len:
-            pad_len = max_seq_len - position_ids.size(1)
-            if self.padding_side == "right":
-                batch_size = position_ids.size(0)
-                new_position_ids = []
-                for i in range(batch_size):
-                    seq = position_ids[i]
-                    if len(seq) > 0:
-                        # get last position and pad with sequential values
-                        last_pos = seq[-1].item()
-                        pad_positions = torch.arange(
-                            last_pos + 1, last_pos + 1 + pad_len, dtype=torch.long
-                        )
-                        new_seq = torch.cat([seq, pad_positions])
-                    else:
-                        new_seq = torch.arange(pad_len, dtype=torch.long)
-                    new_position_ids.append(new_seq)
-                position_ids = torch.stack(new_position_ids)
-            else:
-                position_ids = F.pad(position_ids, (pad_len, 0), value=0)
-        elif position_ids.size(1) > max_seq_len:
-            position_ids = position_ids[:, :max_seq_len]
-
-        final_batch = {
-            "input_ids": input_ids,
-            "labels": labels,
-            "attention_mask": attention_mask,
-            "position_ids": position_ids,
-        }
-
-        # Handle non-sequence fields (raise error)
-        sequence_fields = {"input_ids", "labels", "attention_mask", "position_ids"}
-        for f in features:
-            for key in f.keys():
-                if key not in sequence_fields:
-                    raise NotImplementedError(
-                        f"Non-sequence field {key} not handled yet"
-                    )
-
-        # Convert to requested tensor type
-        if return_tensors is None or return_tensors == "np":
-            result = {}
-            for k, v in final_batch.items():
-                if isinstance(v, torch.Tensor):
-                    result[k] = v.numpy().astype(np.long)
-                else:
-                    result[k] = v
-            return result
-
-        if return_tensors == "pt":
-            return final_batch
-
-        raise ValueError(f"Unsupported return_tensors='{return_tensors}'")
-
-    def convert_ids_to_tokens(self, ids: list[int]) -> list[str]:
-        """
-        Convert a list of token IDs to a list of tokens.
-
-        Args:
-            ids: The list of token IDs to convert.
-
-        Returns:
-            The list of tokens.
-        """
-        return [
-            self._mistral.instruct_tokenizer.tokenizer.id_to_piece(id) for id in ids
-        ]

src/axolotl/utils/samplers/multipack.py

@@ -3,7 +3,6 @@ Multipack Batch Sampler - An efficient batch sampler for packing variable-length
 into fixed-capacity batches to optimize memory usage and training throughput.
 """
 
-import gc
 import math
 from concurrent.futures import ProcessPoolExecutor
 from multiprocessing import cpu_count, get_context
@@ -146,7 +145,7 @@ def pack_parallel(
     """
     num_items = len(sequence_lengths)
     if num_processes is None:
-        num_processes = max(1, min(num_items // group_size, cpu_count(), 16))
+        num_processes = max(1, min(num_items // group_size, cpu_count()))
 
     # Create tasks for parallel processing
     tasks = []
@@ -259,8 +258,8 @@ class MultipackBatchSampler(BatchSampler):
         batch_max_len: int,  # Maximum sequence length (bin capacity)
         lengths: np.ndarray,  # Sequence lengths
         packing_efficiency_estimate: float = 1.0,  # Initial efficiency estimate
-        drop_last: bool = True,  # Whether to drop final batches (might be incomplete)
+        drop_last: bool = False,  # Whether to drop final batches (might be incomplete)
-        num_count_samples: int = 8,  # Number of times to estimate batch count
+        num_count_samples: int = 16,  # Number of times to estimate batch count
         sequential: bool = False,  # Whether to use sequential packing
         group_size: int = 100_000,  # Size of groups for parallel packing
         bin_size: int = 200,  # The max number of samples that can be packed in a single bin
@@ -350,7 +349,6 @@ class MultipackBatchSampler(BatchSampler):
             # Calculate efficiency statistics
             total_used = lengths.sum()
             total_slots = len(all_bins) * self.batch_max_len
-            del all_bins
 
         # Group bins into batches (each batch contains batch_size bins)
         batches = [
@@ -370,7 +368,6 @@ class MultipackBatchSampler(BatchSampler):
             self.total_token_slots += total_slots
 
         self._batches = batches
-        gc.collect()
         return batches
 
     def __iter__(self) -> Iterator[list[list[int]]]:
@@ -446,18 +443,10 @@ class MultipackBatchSampler(BatchSampler):
 
         if self._len_across_ranks is None:
             # Sample multiple times to get stable estimate
-            _sampled_lens = []
+            len_batches = min(  # pylint: disable=consider-using-generator
-            for _ in range(self.num_count_samples):
+                [len(self._batches) for _ in range(self.num_count_samples)]
-                self._batches = None  # Reset cached batches
+            )
-                _sampled_lens.append(len(self.generate_batches(set_stats=False)))
-            len_batches = min(_sampled_lens)
-
             # Gather minimum across all ranks
-            if self._len_across_ranks is None:
+            self._len_across_ranks = self.gather_len_batches(len_batches)
-                self._len_across_ranks = self.gather_len_batches(len_batches)
-            else:
-                self._len_across_ranks = min(
-                    self._len_across_ranks, self.gather_len_batches(len_batches)
-                )
 
         return self._len_across_ranks

src/axolotl/utils/schemas/config.py

@@ -102,8 +102,6 @@ class AxolotlInputConfig(
     dpo_use_weighting: bool | None = None
     dpo_use_logits_to_keep: bool | None = None
     dpo_label_smoothing: float | None = None
-    dpo_norm_loss: bool | None = None
-    dpo_padding_free: bool | None = None
 
     datasets: (
         Annotated[
@@ -338,14 +336,6 @@ class AxolotlInputConfig(
 
     plugins: list[str] | None = Field(default=None)
 
-    @field_validator("seed", mode="after")
-    @classmethod
-    def set_default_seed(cls, seed):
-        if seed is None:
-            LOG.info("`seed` not set in config; setting to 42")
-            seed = 42
-        return seed
-
     @field_validator("datasets", mode="before")
     @classmethod
     def deprecate_sharegpt_datasets(cls, datasets):
@@ -1209,7 +1199,7 @@ class AxolotlInputConfig(
                     "flash_attention: true must be set with sequence_parallel_degree > 1"
                 )
 
-            if self.sample_packing and getattr(self, "micro_batch_size", 1) > 1:
+            if self.sample_packing and self.micro_batch_size > 1:
                 raise ValueError(
                     "micro_batch_size must be set to 1 when sample_packing is enabled "
                     "due to a `ring-flash-attn` requirement"
@@ -1267,71 +1257,9 @@ class AxolotlInputConfig(
             )
         return data
 
-    @model_validator(mode="before")
-    @classmethod
-    def check_tokenizer_use_mistral_common(cls, data):
-        if data.get("tokenizer_use_mistral_common") is None:
-            if any(
-                "magistral" in name.lower()
-                for name in [
-                    data.get("base_model", ""),
-                    data.get("base_model_config", ""),
-                    data.get("tokenizer_config", ""),
-                ]
-            ):
-                LOG.warning(
-                    "tokenizer_use_mistral_common auto inferred to True for Magistral models. Please set it to True explicitly if you want to use mistral-common tokenizer."
-                )
-                data["tokenizer_use_mistral_common"] = True
-
-        return data
-
-    @field_validator("tokenizer_use_mistral_common", mode="after")
-    @classmethod
-    def check_mistral_common_import(cls, tokenizer_use_mistral_common):
-        if tokenizer_use_mistral_common:
-            try:
-                import mistral_common  # noqa: F401 # pylint:disable=unused-import
-            except ImportError as exception:
-                raise ImportError(
-                    "mistral-common is required for mistral models. Please install it with `pip install axolotl` or `pip install -e .`."
-                ) from exception
-
-        return tokenizer_use_mistral_common
-
-    @model_validator(mode="before")
-    @classmethod
-    def check_mistral_common_incompatible_options(cls, data):
-        if not data.get("tokenizer_use_mistral_common"):
-            return data
-
-        # NOTE: mistral-common tokenizer is not compatible with editing tokenizer at the moment
-
-        if data.get("added_tokens_overrides"):
-            raise ValueError(
-                "added_tokens_overrides is not supported with mistral-common tokenizer"
-            )
-
-        if data.get("special_tokens"):
-            raise ValueError(
-                "special_tokens override is not supported with mistral-common tokenizer"
-            )
-
-        if data.get("tokens"):
-            raise ValueError(
-                "tokens override is not supported with mistral-common tokenizer"
-            )
-
-        if data.get("chat_template"):
-            raise ValueError(
-                "Setting chat_template is not supported with mistral-common tokenizer"
-            )
-
-        return data
-
 
 class AxolotlConfigWCapabilities(AxolotlInputConfig):
-    """wrapper to valdiate gpu capabilities with the configured options"""
+    """Wrapper to validate GPU capabilities with the config options"""
 
     capabilities: GPUCapabilities
     env_capabilities: EnvCapabilities

src/axolotl/utils/schemas/datasets.py

@@ -43,7 +43,6 @@ class SFTDataset(BaseModel):
     field_human: str | None = None
     field_model: str | None = None
     field_messages: str | None = None
-    field_tools: str | None = None
     # deprecated, use message_property_mappings
     message_field_role: str | None = None
     # deprecated, use message_property_mappings

src/axolotl/utils/schemas/model.py

@@ -18,7 +18,6 @@ class ModelInputConfig(BaseModel):
     tokenizer_config: str | None = None
     tokenizer_use_fast: bool | None = None
     tokenizer_legacy: bool | None = None
-    tokenizer_use_mistral_common: bool | None = None
     tokenizer_type: str | None = Field(
         default=None, json_schema_extra={"description": "transformers tokenizer class"}
     )

src/axolotl/utils/trainer.py

@@ -16,6 +16,7 @@ from datasets import IterableDataset, disable_caching, enable_caching
 from torch.utils.data import DataLoader, RandomSampler, SequentialSampler
 from transformers.utils import is_torch_bf16_gpu_available
 
+from axolotl.core.builders import HFCausalTrainerBuilder, HFRLTrainerBuilder
 from axolotl.monkeypatch.trainer_eval_guard import patch_evaluation_loop_for_fsdp2
 from axolotl.utils.distributed import reduce_and_broadcast
 from axolotl.utils.environment import check_cuda_p2p_ib_support
@@ -466,7 +467,6 @@ def calculate_total_num_steps(cfg, train_dataset, update=True):
                 bin_size=cfg.sample_packing_bin_size,
                 sequential=cfg.sample_packing_sequentially,
                 drop_last=True,
-                num_processes=cfg.dataset_processes,
             )
 
             data_loader = DataLoader(
@@ -482,9 +482,6 @@ def calculate_total_num_steps(cfg, train_dataset, update=True):
                     data_loader_len * cfg.num_epochs * cfg.sequence_parallel_degree
                 )
             )
-            if cfg.dataloader_drop_last:
-                # drop the last batch for each epoch
-                total_num_steps -= int(math.ceil(cfg.num_epochs))
 
             def calc_sample_packing_eff_est(estimates: List[float]):
                 LOG.info(f"sample_packing_eff_est across ranks: {repr(estimates)}")
@@ -632,8 +629,6 @@ def setup_trainer(
         A trainer instance (either `HFRLTrainer` or `HFCausalTrainer`) configured based
             on the provided parameters.
     """
-    from axolotl.core.builders import HFCausalTrainerBuilder, HFRLTrainerBuilder
-
     if (
         cfg.torch_compile
         and cfg.fsdp_config

tests/conftest.py

@@ -1,6 +1,4 @@
-"""
+"""Shared pytest fixtures"""
-shared pytest fixtures
-"""
 
 import functools
 import importlib
@@ -559,3 +557,9 @@ def test_load_fixtures(
     download_llama2_model_fixture,
 ):
     pass
+
+
+@pytest.fixture(autouse=True)
+def disable_telemetry(monkeypatch):
+    monkeypatch.setenv("AXOLOTL_DO_NOT_TRACK", "1")
+    yield

tests/core/test_builders.py

@@ -12,7 +12,7 @@ from axolotl.common.datasets import load_datasets
 from axolotl.core.builders import HFCausalTrainerBuilder, HFRLTrainerBuilder
 from axolotl.loaders import ModelLoader, load_tokenizer
 from axolotl.utils.config import normalize_config
-from axolotl.utils.data import prepare_preference_datasets
+from axolotl.utils.data.rl import load_prepare_preference_datasets
 from axolotl.utils.dict import DictDefault
 from axolotl.utils.schemas.enums import RLType
 
@@ -451,19 +451,15 @@ def rand_reward_func(prompts, completions) -> list[float]:
             # Only use mock for the commented out configs
             if dataset_name is not None:
                 with patch(
-                    "axolotl.utils.data.rl.load_dataset_with_config"
+                    "axolotl.utils.data.rl.load_dataset_w_config"
                 ) as mock_load_dataset:
                     mock_load_dataset.return_value = request.getfixturevalue(
                         dataset_name
                     )
-                    train_dataset, eval_dataset = prepare_preference_datasets(
+                    train_dataset, eval_dataset = load_prepare_preference_datasets(cfg)
-                        cfg, tokenizer
-                    )
             else:
                 # Load actual datasets for orpo_cfg and kto_cfg
-                train_dataset, eval_dataset = prepare_preference_datasets(
+                train_dataset, eval_dataset = load_prepare_preference_datasets(cfg)
-                    cfg, tokenizer
-                )
 
             builder.train_dataset = train_dataset
             builder.eval_dataset = eval_dataset

tests/e2e/integrations/test_cut_cross_entropy.py

@@ -4,6 +4,7 @@ Simple end-to-end test for Cut Cross Entropy integration
 
 import pytest
 
+from axolotl.cli.args import TrainerCliArgs
 from axolotl.common.datasets import load_datasets
 from axolotl.train import train
 from axolotl.utils import get_pytorch_version
@@ -58,7 +59,8 @@ class TestCutCrossEntropyIntegration:
         cfg = validate_config(cfg)
         prepare_plugins(cfg)
         normalize_config(cfg)
-        dataset_meta = load_datasets(cfg=cfg)
+        cli_args = TrainerCliArgs()
+        dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
 
         major, minor, _ = get_pytorch_version()
         if (major, minor) < (2, 4):
@@ -103,7 +105,8 @@ class TestCutCrossEntropyIntegration:
         cfg = validate_config(cfg)
         prepare_plugins(cfg)
         normalize_config(cfg)
-        dataset_meta = load_datasets(cfg=cfg)
+        cli_args = TrainerCliArgs()
+        dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
 
         major, minor, _ = get_pytorch_version()
         if (major, minor) < (2, 4):
@@ -131,7 +134,8 @@ class TestCutCrossEntropyIntegration:
         cfg = validate_config(cfg)
         prepare_plugins(cfg)
         normalize_config(cfg)
-        dataset_meta = load_datasets(cfg=cfg)
+        cli_args = TrainerCliArgs()
+        dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
 
         major, minor, _ = get_pytorch_version()
         if (major, minor) < (2, 4):

tests/e2e/integrations/test_hooks.py

@@ -5,6 +5,7 @@ e2e tests to make sure all the hooks are fired on the plugin
 import os
 from pathlib import Path
 
+from axolotl.cli.args import TrainerCliArgs
 from axolotl.common.datasets import load_datasets
 from axolotl.integrations.base import BasePlugin
 from axolotl.train import train
@@ -159,7 +160,8 @@ class TestPluginHooks:
         cfg = validate_config(cfg)
         prepare_plugins(cfg)
         normalize_config(cfg)
-        dataset_meta = load_datasets(cfg=cfg)
+        cli_args = TrainerCliArgs()
+        dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
 
         train(cfg=cfg, dataset_meta=dataset_meta)
         check_model_output_exists(temp_dir, cfg)

tests/e2e/integrations/test_kd.py

@@ -5,9 +5,11 @@ e2e tests for kd trainer support in Axolotl
 from pathlib import Path
 
 import pytest
-import yaml
-from accelerate.test_utils import execute_subprocess_async, get_torch_dist_unique_port
 
+from axolotl.cli.args import TrainerCliArgs
+from axolotl.common.datasets import load_datasets
+from axolotl.train import train
+from axolotl.utils.config import normalize_config, prepare_plugins, validate_config
 from axolotl.utils.dict import DictDefault
 
 from tests.e2e.utils import check_tensorboard, require_torch_2_5_1
@@ -16,8 +18,8 @@ from tests.e2e.utils import check_tensorboard, require_torch_2_5_1
 @pytest.fixture(name="kd_min_cfg")
 def min_cfg(temp_dir):
     return {
-        "base_model": "Qwen/Qwen3-0.6B",
+        "base_model": "osllmai-community/Llama-3.2-1B",
-        "tokenizer_config": "winglian/qwen3-14b-math",
+        "tokenizer_config": "axolotl-ai-co/Llama-3.3-70B-Instruct-tokenizer",
         "plugins": [
             "axolotl.integrations.kd.KDPlugin",
             "axolotl.integrations.liger.LigerPlugin",
@@ -30,22 +32,20 @@ def min_cfg(temp_dir):
         "kd_ce_alpha": 0.1,
         "kd_alpha": 0.9,
         "kd_temperature": 1.0,
-        "kd_beta": 0.0,
-        "kd_normalize_topk": True,
         "dataloader_prefetch_factor": 8,
         "dataloader_num_workers": 4,
         "dataloader_pin_memory": True,
         "datasets": [
             {
-                "path": "winglian/OpenThoughts-114k-math-correct-qwen3-14b-math-prepared-topk128-normalized",
+                "path": "axolotl-ai-co/evolkit-logprobs-pipeline-75k-v2-sample",
-                "type": "chat_template",
+                "type": "axolotl.integrations.kd.chat_template",
+                "field_messages": "messages_combined",
                 "split": "train",
-                "split_thinking": True,
+                "logprobs_field": "llm_text_generation_vllm_logprobs",
-                "eot_tokens": ["<|im_end|>"],
+                "temperature": 1.0,
-                "data_files": ["train/batch-000000.parquet"],
+                "preprocess_shards": 2,
             },
         ],
-        "skip_prepare_dataset": True,
         "val_set_size": 0.0,
         "sequence_len": 2048,
         "sample_packing": True,
@@ -81,29 +81,18 @@ class TestKnowledgeDistillation:
     def test_llama_kd(self, temp_dir, kd_min_cfg):
         cfg = DictDefault(kd_min_cfg)
         # pylint: disable=duplicate-code
-        # write cfg to yaml file
+        cfg = validate_config(cfg)
-        Path(temp_dir).mkdir(parents=True, exist_ok=True)
+        prepare_plugins(cfg)
-        with open(Path(temp_dir) / "config.yaml", "w", encoding="utf-8") as fout:
+        normalize_config(cfg)
-            fout.write(yaml.dump(cfg.to_dict(), Dumper=yaml.Dumper))
+        cli_args = TrainerCliArgs()
-
+        dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
-        execute_subprocess_async(
-            [
-                "axolotl",
-                "train",
-                str(Path(temp_dir) / "config.yaml"),
-                "--num-processes",
-                "1",
-                "--main-process-port",
-                f"{get_torch_dist_unique_port()}",
-            ]
-        )
 
+        train(cfg=cfg, dataset_meta=dataset_meta)
         assert (Path(temp_dir) / "model.safetensors").exists()
         check_tensorboard(
             temp_dir + "/runs", "train/loss", 1.4, "Train Loss (%s) is too high"
         )
 
-    @pytest.mark.skip(reason="Chunked KD loss doesn't support PEFT/LoRA")
     @pytest.mark.parametrize(
         "load_in_8bit",
         [True, False],
@@ -123,22 +112,13 @@ class TestKnowledgeDistillation:
             | kd_min_cfg
         )
         # pylint: disable=duplicate-code
-        # write cfg to yaml file
+        cfg = validate_config(cfg)
-        Path(temp_dir).mkdir(parents=True, exist_ok=True)
+        prepare_plugins(cfg)
-        with open(Path(temp_dir) / "config.yaml", "w", encoding="utf-8") as fout:
+        normalize_config(cfg)
-            fout.write(yaml.dump(cfg.to_dict(), Dumper=yaml.Dumper))
+        cli_args = TrainerCliArgs()
+        dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
 
-        execute_subprocess_async(
+        train(cfg=cfg, dataset_meta=dataset_meta)
-            [
-                "axolotl",
-                "train",
-                str(Path(temp_dir) / "config.yaml"),
-                "--num-processes",
-                "1",
-                "--main-process-port",
-                f"{get_torch_dist_unique_port()}",
-            ]
-        )
         assert (Path(temp_dir) / "adapter_model.safetensors").exists()
         check_tensorboard(
             temp_dir + "/runs", "train/loss", 1.2, "Train Loss (%s) is too high"