use narrow as a view on the student logits instead of slicing

.github/workflows/base.yml

@@ -22,6 +22,12 @@ jobs:
       fail-fast: false
       matrix:
         include:
+          - cuda: "124"
+            cuda_version: 12.4.1
+            cudnn_version: ""
+            python_version: "3.10"
+            pytorch: 2.4.1
+            torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
           - cuda: "124"
             cuda_version: 12.4.1
             cudnn_version: ""
@@ -34,12 +40,6 @@ jobs:
             python_version: "3.11"
             pytorch: 2.5.1
             torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
-          - cuda: "124"
-            cuda_version: 12.4.1
-            cudnn_version: ""
-            python_version: "3.11"
-            pytorch: 2.6.0
-            torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
     steps:
       - name: Checkout
         uses: actions/checkout@v4

.github/workflows/docs.yml

@@ -19,7 +19,7 @@ jobs:
         - name: Setup Python
           uses: actions/setup-python@v5
           with:
-            python-version: '3.11'
+            python-version: '3.10'
         - name: install dependencies
           run: |
             python3 -m pip install jupyter

.github/workflows/lint.yml

@@ -19,6 +19,6 @@ jobs:
       - uses: actions/checkout@v4
       - uses: actions/setup-python@v5
         with:
-          python-version: "3.11"
+          python-version: "3.10"
           cache: 'pip' # caching pip dependencies
       - uses: pre-commit/action@v3.0.1

.github/workflows/main.yml

@@ -26,11 +26,6 @@ jobs:
             pytorch: 2.5.1
             axolotl_extras:
             is_latest: true
-          - cuda: 124
-            cuda_version: 12.4.1
-            python_version: "3.11"
-            pytorch: 2.6.0
-            axolotl_extras:
     runs-on: axolotl-gpu-runner
     steps:
       - name: Checkout

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

@@ -34,13 +34,6 @@ jobs:
             axolotl_extras:
             num_gpus: 2
             nightly_build: "true"
-          - cuda: 124
-            cuda_version: 12.4.1
-            python_version: "3.11"
-            pytorch: 2.6.0
-            axolotl_extras:
-            num_gpus: 2
-            nightly_build: "true"
     runs-on: [self-hosted, modal]
     timeout-minutes: 120
     steps:
@@ -49,7 +42,7 @@ jobs:
       - name: Install Python
         uses: actions/setup-python@v5
         with:
-          python-version: "3.11"
+          python-version: "3.10"
       - name: Install Modal
         run: |
           python -m pip install --upgrade pip

.github/workflows/nightlies.yml

@@ -22,11 +22,6 @@ jobs:
             python_version: "3.11"
             pytorch: 2.5.1
             axolotl_extras:
-          - cuda: 124
-            cuda_version: 12.4.1
-            python_version: "3.11"
-            pytorch: 2.6.0
-            axolotl_extras:
     runs-on: axolotl-gpu-runner
     steps:
       - name: Checkout

.github/workflows/pypi.yml

@@ -36,7 +36,7 @@ jobs:
       - name: Setup Python
         uses: actions/setup-python@v5
         with:
-          python-version: "3.11"
+          python-version: "3.10"
 
       - name: Install dependencies
         run: |

.github/workflows/tests-nightly.yml

@@ -12,7 +12,7 @@ jobs:
       - uses: actions/checkout@v4
       - uses: actions/setup-python@v5
         with:
-          python-version: "3.11"
+          python-version: "3.10"
           cache: 'pip' # caching pip dependencies
       - uses: pre-commit/action@v3.0.1
         env:
@@ -25,8 +25,13 @@ jobs:
       fail-fast: false
       max-parallel: 2
       matrix:
-        python_version: ["3.11"]
-        pytorch_version: ["2.4.1", "2.5.1", "2.6.0"]
+        python_version: ["3.10", "3.11"]
+        pytorch_version: ["2.4.1", "2.5.1"]
+        exclude:
+          - python_version: "3.10"
+            pytorch_version: "2.4.1"
+          - python_version: "3.10"
+            pytorch_version: "2.5.1"
     timeout-minutes: 20
 
     steps:
@@ -107,20 +112,13 @@ jobs:
             num_gpus: 1
             axolotl_extras:
             nightly_build: "true"
-          - cuda: 124
-            cuda_version: 12.4.1
-            python_version: "3.11"
-            pytorch: 2.6.0
-            num_gpus: 1
-            axolotl_extras:
-            nightly_build: "true"
     steps:
       - name: Checkout
         uses: actions/checkout@v4
       - name: Install Python
         uses: actions/setup-python@v5
         with:
-          python-version: "3.11"
+          python-version: "3.10"
       - name: Install Modal
         run: |
           python -m pip install --upgrade pip

.github/workflows/tests.yml

@@ -35,7 +35,7 @@ jobs:
       - uses: actions/checkout@v4
       - uses: actions/setup-python@v5
         with:
-          python-version: "3.11"
+          python-version: "3.10"
           cache: 'pip' # caching pip dependencies
       - uses: pre-commit/action@v3.0.1
         env:
@@ -48,8 +48,13 @@ jobs:
       fail-fast: false
       max-parallel: 2
       matrix:
-        python_version: ["3.11"]
-        pytorch_version: ["2.4.1", "2.5.1", "2.6.0"]
+        python_version: ["3.10", "3.11"]
+        pytorch_version: ["2.4.1", "2.5.1"]
+        exclude:
+          - python_version: "3.10"
+            pytorch_version: "2.4.1"
+          - python_version: "3.10"
+            pytorch_version: "2.5.1"
     timeout-minutes: 20
 
     steps:
@@ -122,7 +127,7 @@ jobs:
       max-parallel: 1
       matrix:
         python_version: ["3.11"]
-        pytorch_version: ["2.4.1", "2.5.1", "2.6.0"]
+        pytorch_version: ["2.4.1", "2.5.1"]
     timeout-minutes: 20
 
     steps:
@@ -211,7 +216,7 @@ jobs:
       - name: Install Python
         uses: actions/setup-python@v5
         with:
-          python-version: "3.11"
+          python-version: "3.10"
       - name: Install Modal
         run: |
           python -m pip install --upgrade pip
@@ -246,19 +251,13 @@ jobs:
             pytorch: 2.4.1
             num_gpus: 1
             axolotl_extras:
-          - cuda: 124
-            cuda_version: 12.4.1
-            python_version: "3.11"
-            pytorch: 2.6.0
-            num_gpus: 1
-            axolotl_extras:
     steps:
       - name: Checkout
         uses: actions/checkout@v4
       - name: Install Python
         uses: actions/setup-python@v5
         with:
-          python-version: "3.11"
+          python-version: "3.10"
       - name: Install Modal
         run: |
           python -m pip install --upgrade pip

README.md

@@ -51,7 +51,7 @@ Features:
 
 **Requirements**:
 - NVIDIA GPU (Ampere or newer for `bf16` and Flash Attention) or AMD GPU
-- Python 3.11
+- Python ≥3.10
 - PyTorch ≥2.4.1
 
 ### Installation

docs/config.qmd

@@ -46,10 +46,6 @@ overrides_of_model_config:
     type: # linear | dynamic
     factor: # float
 
-# optional overrides the base model loading from_pretrained
-overrides_of_model_kwargs:
-  # use_cache: False
-
 # optional overrides to the bnb 4bit quantization configuration
 # https://huggingface.co/docs/transformers/main/main_classes/quantization#transformers.BitsAndBytesConfig
 bnb_config_kwargs:

docs/faq.qmd

@@ -19,7 +19,3 @@ description: Frequently asked questions
 **Q: AttributeError: 'DummyOptim' object has no attribute 'step'**
 
 > A: You may be using deepspeed with single gpu. Please don't set `deepspeed:` in yaml or cli.
-
-**Q: The codes is stuck on saving preprocessed datasets.**
-
-> A: This is usually an issue with the GPU. This can be resolved through setting the os environment variable `CUDA_VISIBLE_DEVICES=0`. If you are on runpod, this is usually a pod issue. Starting a new pod should take care of it. 

docs/multi-node.qmd

@@ -3,18 +3,6 @@ title: Multi Node
 description: How to use Axolotl on multiple machines
 ---
 
-The below are three ways to train multi-node in Axolotl.
-
-::: {.callout-important}
-Each machine needs a copy of Axolotl, we suggest using the same commit to ensure compatibility.
-
-You will also need to have the same configuration file for your model on each machine.
-
-Make sure the main machine is reachable by other machines.
-:::
-
-# Accelerate
-
 You will need to create a configuration for accelerate, either by using `accelerate config` and follow the instructions or you can use one of the preset below:
 
 ~/.cache/huggingface/accelerate/default_config.yaml
@@ -38,7 +26,7 @@ tpu_use_sudo: false
 use_cpu: false
 ```
 
-Configure your model to use FSDP in the Axolotl yaml. For example:
+Configure your model to use FSDP with for example:
 ```yaml
 fsdp:
   - full_shard
@@ -49,40 +37,12 @@ fsdp_config:
   fsdp_transformer_layer_cls_to_wrap: LlamaDecoderLayer
 ```
 
+## Machine configuration
+
+On each machine you need a copy of Axolotl, we suggest using the same commit to ensure compatibility.
+
+You will also need to have the same configuration file for your model on each machine.
+
+On the main machine only, make sure the port you set as `main_process_port` is open in TCP and reachable by other machines.
+
 All you have to do now is launch using accelerate as you would usually do on each machine and voila, the processes will start once you have launched accelerate on every machine.
-
-# Raytrain
-
-Please see ray train doc [here](ray-integration.qmd).
-
-# Torchrun
-
-If you are using Infiniband, we recommend torchrun to utilize the full bandwidth.
-
-Set the following env (change buffersize/socketname depending on your system):
-
-```yaml
-export NCCL_IB_DISABLE=0
-export NCCL_SOCKET_IFNAME="eth0,en,eth,em,bond"
-export NCCL_BUFFSIZE=2097152
-```
-
-Run the following on each node:
-
-```bash
-torchrun --nnodes $num_nodes --nproc_per_node $gpu_per_node --rdzv_id $rdzv_id --rdzv_backend c10d --rdzv_endpoint "$head_node_ip:$head_node_port" -m axolotl.cli.train config.yaml
-```
-
-Please make sure to substitute the placeholder variables.
-
-- `num_nodes`: Number of nodes (containing GPUs)
-- `gpu_per_node`: Number of gpus per node
-- `head_node_ip`: IP of the head node (make sure other machines can connect to this)
-- `head_node_port`: Port of the head node (make sure other machines can connect to this. Default 29400)
-- `rdzv_id`: A unique job ID that is used by the job across nodes.
-
-::: {.callout-note}
-You need to call `axolotl.cli.train` instead of `axolotl train` as the latter calls accelerate under the hood
-:::
-
-More info on the available configs can be found on the Pytorch docs [here](https://pytorch.org/docs/stable/elastic/run.html)

requirements.txt

@@ -1,10 +1,10 @@
 --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/
 
 # START section of dependencies that don't install on Darwin/MacOS
-bitsandbytes==0.45.2
+bitsandbytes==0.45.1
 triton>=3.0.0
 mamba-ssm==1.2.0.post1
-flash-attn==2.7.4.post1
+flash-attn==2.7.0.post2
 xformers>=0.0.23.post1
 autoawq==0.2.7.post3
 liger-kernel==0.5.2
@@ -13,7 +13,7 @@ liger-kernel==0.5.2
 packaging==23.2
 
 peft==0.14.0
-transformers==4.48.3
+transformers==4.48.1
 tokenizers>=0.21.0
 accelerate==1.3.0
 datasets==3.2.0

setup.py

@@ -71,15 +71,12 @@ def parse_requirements():
             else:
                 raise ValueError("Invalid version format")
 
-            if (major, minor) >= (2, 6):
-                _install_requires.pop(_install_requires.index(xformers_version))
-                _install_requires.append("xformers==0.0.29.post2")
-            elif (major, minor) >= (2, 5):
+            if (major, minor) >= (2, 5):
                 _install_requires.pop(_install_requires.index(xformers_version))
                 if patch == 0:
                     _install_requires.append("xformers==0.0.28.post2")
                 else:
-                    _install_requires.append("xformers==0.0.29")
+                    _install_requires.append("xformers==0.0.28.post3")
                 _install_requires.pop(_install_requires.index(autoawq_version))
             elif (major, minor) >= (2, 4):
                 if patch == 0:

src/axolotl/integrations/kd/trainer.py

@@ -67,9 +67,8 @@ class AxolotlKDTrainer(AxolotlTrainer):
         outputs = model(**inputs)
 
         # FIXME: account for tokenizer.padding_side
-        student_logits = outputs["logits"][:, : seq_len - 1, :].contiguous()
+        shift_logits = outputs["logits"].narrow(1, 0, 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()

src/axolotl/integrations/spectrum/model_snr_results/snr_results_meta-llama-Llama-3.2-1B-Instruct.json

@@ -1,590 +0,0 @@
-{
-    "model.layers.0.input_layernorm": {
-        "snr": Infinity,
-        "type": "input_layernorm"
-    },
-    "model.layers.1.input_layernorm": {
-        "snr": Infinity,
-        "type": "input_layernorm"
-    },
-    "model.layers.2.input_layernorm": {
-        "snr": Infinity,
-        "type": "input_layernorm"
-    },
-    "model.layers.3.input_layernorm": {
-        "snr": Infinity,
-        "type": "input_layernorm"
-    },
-    "model.layers.4.input_layernorm": {
-        "snr": Infinity,
-        "type": "input_layernorm"
-    },
-    "model.layers.5.input_layernorm": {
-        "snr": Infinity,
-        "type": "input_layernorm"
-    },
-    "model.layers.6.input_layernorm": {
-        "snr": Infinity,
-        "type": "input_layernorm"
-    },
-    "model.layers.7.input_layernorm": {
-        "snr": Infinity,
-        "type": "input_layernorm"
-    },
-    "model.layers.8.input_layernorm": {
-        "snr": Infinity,
-        "type": "input_layernorm"
-    },
-    "model.layers.9.input_layernorm": {
-        "snr": Infinity,
-        "type": "input_layernorm"
-    },
-    "model.layers.10.input_layernorm": {
-        "snr": Infinity,
-        "type": "input_layernorm"
-    },
-    "model.layers.11.input_layernorm": {
-        "snr": Infinity,
-        "type": "input_layernorm"
-    },
-    "model.layers.12.input_layernorm": {
-        "snr": Infinity,
-        "type": "input_layernorm"
-    },
-    "model.layers.13.input_layernorm": {
-        "snr": Infinity,
-        "type": "input_layernorm"
-    },
-    "model.layers.14.input_layernorm": {
-        "snr": Infinity,
-        "type": "input_layernorm"
-    },
-    "model.layers.15.input_layernorm": {
-        "snr": Infinity,
-        "type": "input_layernorm"
-    },
-    "lm_head": {
-        "snr": Infinity,
-        "type": "lm_head"
-    },
-    "model.layers.0.mlp.down_proj": {
-        "snr": 70.0594253540039,
-        "type": "mlp.down_proj"
-    },
-    "model.layers.1.mlp.down_proj": {
-        "snr": 11.135851860046387,
-        "type": "mlp.down_proj"
-    },
-    "model.layers.2.mlp.down_proj": {
-        "snr": 7.035482883453369,
-        "type": "mlp.down_proj"
-    },
-    "model.layers.3.mlp.down_proj": {
-        "snr": 6.422532081604004,
-        "type": "mlp.down_proj"
-    },
-    "model.layers.4.mlp.down_proj": {
-        "snr": 5.748020172119141,
-        "type": "mlp.down_proj"
-    },
-    "model.layers.5.mlp.down_proj": {
-        "snr": 3.885556697845459,
-        "type": "mlp.down_proj"
-    },
-    "model.layers.6.mlp.down_proj": {
-        "snr": 3.4336745738983154,
-        "type": "mlp.down_proj"
-    },
-    "model.layers.7.mlp.down_proj": {
-        "snr": 2.791595935821533,
-        "type": "mlp.down_proj"
-    },
-    "model.layers.8.mlp.down_proj": {
-        "snr": 5.36277961730957,
-        "type": "mlp.down_proj"
-    },
-    "model.layers.9.mlp.down_proj": {
-        "snr": 4.459208011627197,
-        "type": "mlp.down_proj"
-    },
-    "model.layers.10.mlp.down_proj": {
-        "snr": 6.272170066833496,
-        "type": "mlp.down_proj"
-    },
-    "model.layers.11.mlp.down_proj": {
-        "snr": 5.264761447906494,
-        "type": "mlp.down_proj"
-    },
-    "model.layers.12.mlp.down_proj": {
-        "snr": 4.324735641479492,
-        "type": "mlp.down_proj"
-    },
-    "model.layers.13.mlp.down_proj": {
-        "snr": 3.878648042678833,
-        "type": "mlp.down_proj"
-    },
-    "model.layers.14.mlp.down_proj": {
-        "snr": 2.9773054122924805,
-        "type": "mlp.down_proj"
-    },
-    "model.layers.15.mlp.down_proj": {
-        "snr": 4.471445560455322,
-        "type": "mlp.down_proj"
-    },
-    "model.layers.0.mlp.gate_proj": {
-        "snr": 25.227100372314453,
-        "type": "mlp.gate_proj"
-    },
-    "model.layers.1.mlp.gate_proj": {
-        "snr": 6.58299446105957,
-        "type": "mlp.gate_proj"
-    },
-    "model.layers.2.mlp.gate_proj": {
-        "snr": 3.4688243865966797,
-        "type": "mlp.gate_proj"
-    },
-    "model.layers.3.mlp.gate_proj": {
-        "snr": 1.555246114730835,
-        "type": "mlp.gate_proj"
-    },
-    "model.layers.4.mlp.gate_proj": {
-        "snr": 0.7770601511001587,
-        "type": "mlp.gate_proj"
-    },
-    "model.layers.5.mlp.gate_proj": {
-        "snr": 0.6239906549453735,
-        "type": "mlp.gate_proj"
-    },
-    "model.layers.6.mlp.gate_proj": {
-        "snr": 0.6440379023551941,
-        "type": "mlp.gate_proj"
-    },
-    "model.layers.7.mlp.gate_proj": {
-        "snr": 0.5120116472244263,
-        "type": "mlp.gate_proj"
-    },
-    "model.layers.8.mlp.gate_proj": {
-        "snr": 0.6544050574302673,
-        "type": "mlp.gate_proj"
-    },
-    "model.layers.9.mlp.gate_proj": {
-        "snr": 0.5381016731262207,
-        "type": "mlp.gate_proj"
-    },
-    "model.layers.10.mlp.gate_proj": {
-        "snr": 0.622873842716217,
-        "type": "mlp.gate_proj"
-    },
-    "model.layers.11.mlp.gate_proj": {
-        "snr": 0.9361700415611267,
-        "type": "mlp.gate_proj"
-    },
-    "model.layers.12.mlp.gate_proj": {
-        "snr": 1.475605845451355,
-        "type": "mlp.gate_proj"
-    },
-    "model.layers.13.mlp.gate_proj": {
-        "snr": 1.608325719833374,
-        "type": "mlp.gate_proj"
-    },
-    "model.layers.14.mlp.gate_proj": {
-        "snr": 1.0720024108886719,
-        "type": "mlp.gate_proj"
-    },
-    "model.layers.15.mlp.gate_proj": {
-        "snr": 0.7111338973045349,
-        "type": "mlp.gate_proj"
-    },
-    "model.layers.0.mlp.up_proj": {
-        "snr": 28.431896209716797,
-        "type": "mlp.up_proj"
-    },
-    "model.layers.1.mlp.up_proj": {
-        "snr": 15.546019554138184,
-        "type": "mlp.up_proj"
-    },
-    "model.layers.2.mlp.up_proj": {
-        "snr": 23.048023223876953,
-        "type": "mlp.up_proj"
-    },
-    "model.layers.3.mlp.up_proj": {
-        "snr": 25.790977478027344,
-        "type": "mlp.up_proj"
-    },
-    "model.layers.4.mlp.up_proj": {
-        "snr": 18.552549362182617,
-        "type": "mlp.up_proj"
-    },
-    "model.layers.5.mlp.up_proj": {
-        "snr": 8.85106372833252,
-        "type": "mlp.up_proj"
-    },
-    "model.layers.6.mlp.up_proj": {
-        "snr": 10.653799057006836,
-        "type": "mlp.up_proj"
-    },
-    "model.layers.7.mlp.up_proj": {
-        "snr": 7.365357875823975,
-        "type": "mlp.up_proj"
-    },
-    "model.layers.8.mlp.up_proj": {
-        "snr": 11.98373794555664,
-        "type": "mlp.up_proj"
-    },
-    "model.layers.9.mlp.up_proj": {
-        "snr": 8.04493236541748,
-        "type": "mlp.up_proj"
-    },
-    "model.layers.10.mlp.up_proj": {
-        "snr": 8.523039817810059,
-        "type": "mlp.up_proj"
-    },
-    "model.layers.11.mlp.up_proj": {
-        "snr": 5.381742477416992,
-        "type": "mlp.up_proj"
-    },
-    "model.layers.12.mlp.up_proj": {
-        "snr": 3.9845118522644043,
-        "type": "mlp.up_proj"
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src/axolotl/integrations/spectrum/model_snr_results/snr_results_meta-llama-Llama-3.2-1B.json

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-{
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-        "type": "mlp.up_proj"
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-        "type": "mlp.up_proj"
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-        "snr": Infinity,
-        "type": "post_attention_layernorm"
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-        "snr": Infinity,
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-        "type": "post_attention_layernorm"
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-        "type": "self_attn.v_proj"
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-        "type": "self_attn.v_proj"
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-    "model.layers.13.self_attn.v_proj": {
-        "snr": 17.722007751464844,
-        "type": "self_attn.v_proj"
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-    "model.layers.14.self_attn.v_proj": {
-        "snr": 1.48500657081604,
-        "type": "self_attn.v_proj"
-    },
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-        "snr": 2.5776851177215576,
-        "type": "self_attn.v_proj"
-    }
-}

src/axolotl/prompt_strategies/bradley_terry/chat_template.py

@@ -47,7 +47,7 @@ class BTChatTemplateStrategy(ChatTemplateStrategy):
 
         if len(chosen_tokenized["input_ids"]) > max_length:
             LOG.warning(
-                f"To-be-trimmed chosen sequence exceeds max sequence length: {len(chosen_tokenized['input_ids'])}",
+                f"Chosen sequence exceeds max sequence length: {len(chosen_tokenized['input_ids'])}",
             )
 
             chosen_tokenized["input_ids"] = chosen_tokenized["input_ids"][:max_length]
@@ -70,7 +70,7 @@ class BTChatTemplateStrategy(ChatTemplateStrategy):
 
         if len(rejected_tokenized["input_ids"]) > max_length:
             LOG.warning(
-                f"To-be-trimmed rejected sequence exceeds max sequence length: {len(rejected_tokenized['input_ids'])}",
+                f"Rejected sequence exceeds max sequence length: {len(rejected_tokenized['input_ids'])}",
             )
 
             rejected_tokenized["input_ids"] = rejected_tokenized["input_ids"][

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

@@ -115,9 +115,6 @@ class RemappedParameters(BaseModel):
     overrides_of_model_config: Optional[Dict[str, Any]] = Field(
         default=None, alias="model_config"
     )
-    overrides_of_model_kwargs: Optional[Dict[str, Any]] = Field(
-        default=None, alias="model_kwargs"
-    )
     type_of_model: Optional[str] = Field(default=None, alias="model_type")
     revision_of_model: Optional[str] = Field(default=None, alias="model_revision")
 
@@ -429,6 +426,8 @@ class ModelInputConfig(BaseModel):
     )
     trust_remote_code: Optional[bool] = None
 
+    model_kwargs: Optional[Dict[str, Any]] = None
+
     @field_validator("trust_remote_code")
     @classmethod
     def hint_trust_remote_code(cls, trust_remote_code):

src/axolotl/utils/data/sft.py

@@ -46,7 +46,6 @@ from axolotl.utils.data.pretraining import wrap_pretraining_dataset
 from axolotl.utils.data.shared import load_dataset_w_config
 from axolotl.utils.data.utils import (
     deduplicate_and_log_datasets,
-    drop_long_seq_in_dataset,
     md5,
     retry_on_request_exceptions,
 )
@@ -57,7 +56,7 @@ from axolotl.utils.trainer import (
     process_datasets_for_packing,
 )
 
-LOG = logging.getLogger(__name__)
+LOG = logging.getLogger("axolotl")
 
 
 @retry_on_request_exceptions(max_retries=3, delay=5)
@@ -340,11 +339,8 @@ def load_tokenized_prepared_datasets(
             else:
                 LOG.debug("NOT shuffling merged datasets")
 
-        if not cfg.skip_prepare_dataset:
-            dataset = drop_long_seq_in_dataset(dataset, cfg)
-
-            if cfg.sample_packing:
-                dataset, _ = process_datasets_for_packing(cfg, dataset, None)
+        if cfg.sample_packing and not cfg.skip_prepare_dataset:
+            dataset, _ = process_datasets_for_packing(cfg, dataset, None)
 
         if cfg.local_rank == 0 and not cfg.skip_prepare_dataset:
             LOG.info(f"Saving merged prepared dataset to disk... {prepared_ds_path}")

src/axolotl/utils/data/utils.py

@@ -1,5 +1,4 @@
 """data handling helpers"""
-
 import functools
 import hashlib
 import logging
@@ -7,15 +6,10 @@ import time
 from enum import Enum
 
 import huggingface_hub
-import numpy as np
 import requests
-from datasets import Dataset, IterableDataset
+from datasets import Dataset
 
-from axolotl.utils.dict import DictDefault
-from axolotl.utils.samplers.utils import get_dataset_lengths
-from axolotl.utils.trainer import drop_long_seq
-
-LOG = logging.getLogger(__name__)
+LOG = logging.getLogger("axolotl")
 
 
 class RetryStrategy(Enum):
@@ -156,53 +150,3 @@ def deduplicate_and_log_datasets(
         )
 
     return train_dataset, eval_dataset, dataset
-
-
-def drop_long_seq_in_dataset(dataset: Dataset, cfg: DictDefault):
-    if "input_ids" not in dataset.column_names:
-        LOG.warning(
-            "Dataset does not contain 'input_ids' column. Skip drop long seq. This is expected for RewardModeling."
-        )
-        return dataset
-
-    drop_long = functools.partial(
-        drop_long_seq,
-        sequence_len=cfg.sequence_len,
-        min_sequence_len=cfg.min_sample_len,
-    )
-
-    try:
-        min_input_len = np.min(get_dataset_lengths(dataset))
-        LOG.debug(f"min_input_len: {min_input_len}")
-        max_input_len = np.max(get_dataset_lengths(dataset))
-        LOG.debug(f"max_input_len: {max_input_len}")
-    except AttributeError:
-        pass
-
-    try:
-        prior_len = len(dataset)
-    except TypeError:
-        # handle iterable datasets case
-        prior_len = None
-
-    filter_map_kwargs = {}
-    if not isinstance(dataset, IterableDataset):
-        filter_map_kwargs["num_proc"] = cfg.dataset_processes
-        filter_map_kwargs["load_from_cache_file"] = not cfg.is_preprocess
-
-    drop_long_kwargs = {}
-    if filter_map_kwargs:
-        drop_long_kwargs["desc"] = "Dropping Long Sequences"
-
-    dataset = dataset.filter(
-        drop_long,
-        batched=True,
-        **filter_map_kwargs,
-        **drop_long_kwargs,
-    )
-    if prior_len:
-        dropped = prior_len - len(dataset)
-        if dropped:
-            LOG.warning(f"Dropped {dropped} long samples from dataset")
-
-    return dataset

src/axolotl/utils/models.py

@@ -357,8 +357,8 @@ class ModelLoader:
 
         # init model kwargs
         self.model_kwargs: Dict[str, Any] = {}
-        if cfg.overrides_of_model_kwargs:
-            for key, val in cfg.overrides_of_model_kwargs.items():
+        if cfg.model_kwargs:
+            for key, val in cfg.model_kwargs.items():
                 self.model_kwargs[key] = val
 
         # init model

src/axolotl/utils/samplers/utils.py

@@ -13,4 +13,5 @@ def get_dataset_lengths(dataset):
     else:
         input_ids = dataset.data.column("input_ids")
         lengths = np.vectorize(len)(np.array(input_ids, dtype=object))
+        return lengths
     return lengths

src/axolotl/utils/trainer.py

@@ -1,5 +1,4 @@
 """Module containing the Trainer class and related functions"""
-
 import json
 import math
 import os
@@ -211,8 +210,6 @@ def drop_long_seq(sample, sequence_len=2048, min_sequence_len=2):
 
     Works for both single-example (list[int]) or batched (list[list[int]]).
     """
-    min_sequence_len = min_sequence_len or 2
-
     input_ids = sample["input_ids"]
 
     # Edge case: if input_ids is empty
@@ -235,6 +232,20 @@ def drop_long_seq(sample, sequence_len=2048, min_sequence_len=2):
 
 
 def process_datasets_for_packing(cfg, train_dataset, eval_dataset):
+    drop_long = partial(
+        drop_long_seq,
+        sequence_len=cfg.sequence_len,
+        min_sequence_len=cfg.min_sample_len or 2,
+    )
+
+    try:
+        min_input_len = np.min(get_dataset_lengths(train_dataset))
+        LOG.debug(f"min_input_len: {min_input_len}", main_process_only=True)
+        max_input_len = np.max(get_dataset_lengths(train_dataset))
+        LOG.debug(f"max_input_len: {max_input_len}", main_process_only=True)
+    except AttributeError:
+        pass
+
     if cfg.model_config_type == "mamba":
         LOG.info("dropping attention_mask column")
         train_dataset = train_dataset.remove_columns("attention_mask")
@@ -248,6 +259,46 @@ def process_datasets_for_packing(cfg, train_dataset, eval_dataset):
         if eval_dataset and "token_type_ids" in eval_dataset.column_names:
             eval_dataset = eval_dataset.remove_columns("token_type_ids")
 
+    filter_map_kwargs = {}
+    if not isinstance(train_dataset, IterableDataset):
+        filter_map_kwargs["num_proc"] = cfg.dataset_processes
+        filter_map_kwargs["load_from_cache_file"] = not cfg.is_preprocess
+
+    try:
+        prior_len = len(train_dataset)
+    except TypeError:
+        # handle iterable datasets case
+        prior_len = None
+    drop_long_kwargs = {}
+    if filter_map_kwargs:
+        drop_long_kwargs["desc"] = "Dropping Long Sequences"
+    train_dataset = train_dataset.filter(
+        drop_long,
+        batched=True,
+        **filter_map_kwargs,
+        **drop_long_kwargs,
+    )
+    if prior_len:
+        dropped = prior_len - len(train_dataset)
+        if dropped:
+            LOG.warning(f"Dropped {dropped} long samples from train dataset")
+
+    if eval_dataset:
+        try:
+            prior_len = len(eval_dataset)
+        except TypeError:
+            # handle iterable datasets case
+            prior_len = None
+        eval_dataset = eval_dataset.filter(
+            drop_long,
+            **filter_map_kwargs,
+            **drop_long_kwargs,
+        )
+        if prior_len:
+            dropped = prior_len - len(eval_dataset)
+            if dropped:
+                LOG.warning(f"Dropped {dropped} long samples from eval dataset")
+
     def drop_no_trainable_tokens(sample):
         """
         Drop samples if all labels are -100 (i.e., zero trainable tokens).
@@ -274,11 +325,6 @@ def process_datasets_for_packing(cfg, train_dataset, eval_dataset):
     except TypeError:
         # handle iterable datasets case
         prior_len = None
-    filter_map_kwargs = {}
-    if not isinstance(train_dataset, IterableDataset):
-        filter_map_kwargs["num_proc"] = cfg.dataset_processes
-        filter_map_kwargs["load_from_cache_file"] = not cfg.is_preprocess
-
     drop_long_kwargs = {}
     if filter_map_kwargs:
         drop_long_kwargs["desc"] = "Drop Samples with Zero Trainable Tokens"

tests/e2e/test_reward_model_smollm2.py

@@ -33,7 +33,7 @@ class TestRewardModelLoraSmolLM2(unittest.TestCase):
                 "num_labels": 1,
                 "chat_template": "alpaca",
                 "reward_model": True,
-                "sequence_len": 2048,
+                "sequence_len": 1024,
                 "pad_to_sequence_len": True,
                 "adapter": "lora",
                 "lora_r": 8,