Compare commits
11 Commits
feat/phi_3
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print_venv
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6
.github/workflows/base.yml
vendored
6
.github/workflows/base.yml
vendored
@@ -5,11 +5,13 @@ on:
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branches:
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- "main"
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paths:
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- 'Dockerfile-base'
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- 'docker/Dockerfile-base'
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- 'docker/Dockerfile-uv-base'
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- '.github/workflows/base.yml'
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pull_request:
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paths:
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- 'Dockerfile-base'
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- 'docker/Dockerfile-base'
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- 'docker/Dockerfile-uv-base'
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- '.github/workflows/base.yml'
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workflow_dispatch:
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115
.github/workflows/tests-nightly.yml
vendored
115
.github/workflows/tests-nightly.yml
vendored
@@ -18,96 +18,9 @@ jobs:
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env:
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SKIP: no-commit-to-branch
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preload-cache:
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name: Preload HF cache
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runs-on: ubuntu-latest
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strategy:
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fail-fast: false
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matrix:
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python_version: ["3.11"]
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pytorch_version: ["2.6.0"]
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timeout-minutes: 20
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env:
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AXOLOTL_IS_CI_CACHE_PRELOAD: "1"
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steps:
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- name: Check out repository code
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uses: actions/checkout@v4
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- name: Restore HF cache
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id: hf-cache-restore
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uses: actions/cache/restore@v4
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with:
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path: |
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/home/runner/.cache/huggingface/hub/datasets--*
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/home/runner/.cache/huggingface/hub/models--*
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key: ${{ runner.os }}-hf-hub-cache-v2
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- name: Setup Python
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uses: actions/setup-python@v5
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with:
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python-version: ${{ matrix.python_version }}
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cache: 'pip' # caching pip dependencies
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- name: upgrade pip
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run: |
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pip3 install --upgrade pip
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pip3 install --upgrade packaging==23.2 setuptools==75.8.0 wheel
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- name: Install PyTorch
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run: |
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pip3 install torch==${{ matrix.pytorch_version }}
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- name: Install dependencies
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run: |
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pip3 show torch
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pip3 install --no-build-isolation -U -e .
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python scripts/unsloth_install.py | sh
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python scripts/cutcrossentropy_install.py | sh
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pip3 install -r requirements-dev.txt -r requirements-tests.txt
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- name: Make sure PyTorch version wasn't clobbered
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run: |
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python -c "import torch; assert '${{ matrix.pytorch_version }}' in torch.__version__"
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- name: Ensure axolotl CLI was installed
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run: |
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axolotl --help
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- name: Pre-Download dataset fixture
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run: |
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huggingface-cli download --repo-type=dataset axolotl-ai-internal/axolotl-oss-dataset-fixtures
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- name: Run tests
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run: |
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pytest -v tests/conftest.py
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- name: Upload coverage to Codecov
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uses: codecov/codecov-action@v5
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with:
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token: ${{ secrets.CODECOV_TOKEN }}
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files: ./coverage.xml
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flags: unittests,pytorch-${{ matrix.pytorch_version }}
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fail_ci_if_error: false
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- name: cleanup pip cache
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run: |
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find "$(pip cache dir)/http-v2" -type f -mtime +14 -exec rm {} \;
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- name: Save HF cache
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id: hf-cache
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uses: actions/cache/save@v4
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with:
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path: |
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/home/runner/.cache/huggingface/hub/datasets--*
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/home/runner/.cache/huggingface/hub/models--*
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key: ${{ steps.hf-cache-restore.outputs.cache-primary-key }}
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pytest:
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name: PyTest
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runs-on: ubuntu-latest
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needs: [preload-cache]
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strategy:
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fail-fast: false
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max-parallel: 2
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@@ -120,14 +33,11 @@ jobs:
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- name: Check out repository code
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uses: actions/checkout@v4
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|
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- name: Restore HF cache
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id: hf-cache-restore
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uses: actions/cache/restore@v4
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with:
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path: |
|
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/home/runner/.cache/huggingface/hub/datasets--*
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/home/runner/.cache/huggingface/hub/models--*
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key: ${{ runner.os }}-hf-hub-cache-v2
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- name: Restore Cache from S3
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id: hf-cache-restore-s3
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run: |
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mkdir -p /home/runner/.cache/huggingface/hub
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curl -L https://d1dttdx32dkk5p.cloudfront.net/hf-cache.tar.zst | tar -xf - -C /home/runner/.cache/huggingface/hub/ --use-compress-program unzstd
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- name: Setup Python
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uses: actions/setup-python@v5
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@@ -168,10 +78,6 @@ jobs:
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run: |
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axolotl --help
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- name: Pre-Download dataset fixture
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run: |
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huggingface-cli download --repo-type=dataset axolotl-ai-internal/axolotl-oss-dataset-fixtures
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- name: Run tests
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run: |
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pytest -v -n8 --dist loadfile --ignore=tests/e2e/ --ignore=tests/patched/ --ignore=tests/cli/ tests/
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@@ -193,15 +99,8 @@ jobs:
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fail-fast: false
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matrix:
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include:
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- cuda: 124
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cuda_version: 12.4.1
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python_version: "3.11"
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pytorch: 2.5.1
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num_gpus: 1
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axolotl_extras:
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nightly_build: "true"
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- cuda: 124
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cuda_version: 12.4.1
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- cuda: 126
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cuda_version: 12.6.3
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python_version: "3.11"
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pytorch: 2.6.0
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num_gpus: 1
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@@ -22,9 +22,11 @@ RUN apt-get update \
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&& mkdir /root/.conda \
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&& bash Miniconda3-latest-Linux-x86_64.sh -b \
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&& rm -f Miniconda3-latest-Linux-x86_64.sh \
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&& conda create -n "py${PYTHON_VERSION}" python="${PYTHON_VERSION}"
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&& conda create -n "axolotl-py${PYTHON_VERSION}" python="${PYTHON_VERSION}" \
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&& conda init bash \
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&& echo "conda activate axolotl-py${PYTHON_VERSION}" >> ~/.bashrc
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ENV PATH="/root/miniconda3/envs/py${PYTHON_VERSION}/bin:${PATH}"
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ENV PATH="/root/miniconda3/envs/axolotl-py${PYTHON_VERSION}/bin:${PATH}"
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WORKDIR /workspace
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@@ -37,3 +39,7 @@ RUN git lfs install --skip-repo && \
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pip3 install awscli && \
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# The base image ships with `pydantic==1.8.2` which is not working
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pip3 install -U --no-cache-dir pydantic==1.10.10
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RUN if [ "$PYTORCH_VERSION" = "2.6.0" ] && [ "$CUDA" = "124" ] ; then \
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FLASH_ATTENTION_FORCE_BUILD="TRUE" pip3 install --no-build-isolation flash-attn==2.8.0.post2; \
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fi
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@@ -22,9 +22,11 @@ RUN apt-get update \
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&& mkdir /root/.conda \
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&& bash Miniconda3-latest-Linux-x86_64.sh -b \
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&& rm -f Miniconda3-latest-Linux-x86_64.sh \
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&& conda create -n "py${PYTHON_VERSION}" python="${PYTHON_VERSION}"
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&& conda create -n "axolotl-py${PYTHON_VERSION}" python="${PYTHON_VERSION}" \
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&& conda init bash \
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&& echo "conda activate axolotl-py${PYTHON_VERSION}" >> ~/.bashrc
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ENV PATH="/root/miniconda3/envs/py${PYTHON_VERSION}/bin:${PATH}"
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ENV PATH="/root/miniconda3/envs/axolotl-py${PYTHON_VERSION}/bin:${PATH}"
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WORKDIR /workspace
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@@ -22,9 +22,11 @@ RUN apt-get update \
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&& mkdir /root/.conda \
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&& bash Miniconda3-latest-Linux-x86_64.sh -b \
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&& rm -f Miniconda3-latest-Linux-x86_64.sh \
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&& conda create -n "py${PYTHON_VERSION}" python="${PYTHON_VERSION}"
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&& conda create -n "axolotl-py${PYTHON_VERSION}" python="${PYTHON_VERSION}" \
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&& conda init bash \
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&& echo "conda activate axolotl-py${PYTHON_VERSION}" >> ~/.bashrc
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ENV PATH="/root/miniconda3/envs/py${PYTHON_VERSION}/bin:${PATH}"
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ENV PATH="/root/miniconda3/envs/axolotl-py${PYTHON_VERSION}/bin:${PATH}"
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WORKDIR /workspace
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@@ -51,6 +51,10 @@ description: Frequently asked questions
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> pad_token: "..."
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> ```
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**Q: `IterableDataset error` or `KeyError: 'input_ids'` when using `preprocess` CLI**
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> A: This is because you may be using `preprocess` CLI with `pretraining_dataset:` or `skip_prepare_dataset: true` respectively. Please use `axolotl train` CLI directly instead as these datasets are prepared on demand.
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### Chat templates
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**Q: `jinja2.exceptions.UndefinedError: 'dict object' has no attribute 'content' / 'role' / ____`**
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@@ -35,6 +35,12 @@ def do_preprocess(cfg: DictDefault, cli_args: PreprocessCliArgs) -> None:
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check_accelerate_default_config()
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check_user_token()
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for key in ["skip_prepare_dataset", "pretraining_dataset"]:
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if cfg.get("key"):
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raise ValueError(
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f"You have set `{key}:`. `preprocess` is not needed. Run the `axolotl train` CLI directly instead."
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)
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if not cfg.dataset_prepared_path:
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msg = (
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Fore.RED
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@@ -219,7 +219,9 @@ class TrainerBuilderBase(abc.ABC):
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if self.cfg.bf16 == "full":
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training_args_kwargs["bf16_full_eval"] = True
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else:
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training_args_kwargs["bf16"] = self.cfg.bf16 or self.cfg.bfloat16
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bf16 = self.cfg.bf16 or self.cfg.bfloat16
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bf16 = bf16 if bf16 is not None else False
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training_args_kwargs["bf16"] = bf16
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def _configure_scheduler(self, training_args_kwargs: dict):
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if self.cfg.lr_scheduler in ["one_cycle", "rex"]:
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@@ -526,8 +526,9 @@ def merge_datasets(datasets: list[Dataset], cfg: DictDefault) -> Dataset:
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if len(datasets) == 1:
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ds = datasets[0]
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# Do not shuffle if curriculum sampling is enabled
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if cfg.curriculum_sampling:
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# Do not shuffle if curriculum sampling is enabled or
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# shuffle_merged_datasets is disabled
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if cfg.curriculum_sampling or not cfg.shuffle_merged_datasets:
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return ds
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return ds.shuffle(seed=cfg.seed)
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@@ -609,6 +609,9 @@ def prepare_opinionated_env(cfg):
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if cfg.qlora_sharded_model_loading:
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# model loading is forked after the tokenizer
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os.environ["TOKENIZERS_PARALLELISM"] = "false"
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if cfg.sample_packing:
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# multipack parallel packing sampler defaults to using fork
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os.environ["TOKENIZERS_PARALLELISM"] = "false"
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|
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def setup_trainer(
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Reference in New Issue
Block a user