Add PyTorch 2.10 and xformers 0.0.34 support#3985
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- Add cu126/cu128/cu130 xformers 0.0.34 wheel dependencies for torch 2.10 - Add cu126-torch2100, cu128-torch2100, cu130-torch2100 meta-dependencies - Add cu126-ampere-torch2100, cu128-ampere-torch2100, cu130-ampere-torch2100 variants - Update _auto_install.py version detection for torch 2.10.x - Add CUDA check for torch 2.10 (requires CUDA 12.6, 12.8, or 13.0) - Update README.md with torch 2.10 installation instructions
Summary of ChangesHello @danielhanchen, I'm Gemini Code Assist1! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed! This pull request introduces comprehensive support for PyTorch 2.10, coupled with xformers 0.0.34, into the project. It involves updating core dependency configurations and installation scripts to ensure seamless integration and proper environment setup for users utilizing the latest PyTorch version, while also clarifying CUDA compatibility requirements. Highlights
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| elif v < V('2.8.9'): x = 'cu{}{}-torch280' | ||
| elif v < V('2.9.1'): x = 'cu{}{}-torch290' | ||
| elif v < V('2.9.2'): x = 'cu{}{}-torch291' | ||
| elif v < V('2.10.1'): x = 'cu{}{}-torch2100' |
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The new elif v < V('2.10.1') branch also matches torch 2.9.2+, so _auto_install.py now emits extras like cu118-torch2100 for those versions. That breaks installation for valid 2.9.2 users on CUDA 11.8 because pyproject.toml only defines torch2100 extras for cu126/cu128/cu130 (no cu118-torch2100 entry), causing pip to fail on an unknown extra. In practice this makes some previously-installable combinations fail after this commit.
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Code Review
This pull request adds support for PyTorch 2.10 with xformers 0.0.34. The changes across pyproject.toml, README.md, and unsloth/_auto_install.py are generally correct and align with the goal of the PR. I have two suggestions for improvement: one to clarify the supported CUDA versions in the documentation, and another to address potentially redundant dependency groups in pyproject.toml.
| `⚠️Do **NOT** use this if you have Conda.` Pip is a bit more complex since there are dependency issues. The pip command is different for `torch 2.2,2.3,2.4,2.5,2.6,2.7,2.8,2.9,2.10` and CUDA versions. | ||
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| For other torch versions, we support `torch211`, `torch212`, `torch220`, `torch230`, `torch240`, `torch250`, `torch260`, `torch270`, `torch280`, `torch290` and for CUDA versions, we support `cu118` and `cu121` and `cu124`. For Ampere devices (A100, H100, RTX3090) and above, use `cu118-ampere` or `cu121-ampere` or `cu124-ampere`. | ||
| For other torch versions, we support `torch211`, `torch212`, `torch220`, `torch230`, `torch240`, `torch250`, `torch260`, `torch270`, `torch280`, `torch290`, `torch2100` and for CUDA versions, we support `cu118` and `cu121` and `cu124`. For Ampere devices (A100, H100, RTX3090) and above, use `cu118-ampere` or `cu121-ampere` or `cu124-ampere`. Note: torch 2.10 only supports CUDA 12.6, 12.8, and 13.0. |
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The list of supported CUDA versions in this sentence is incomplete. Based on the auto-install script and other parts of the project, cu126, cu128, and cu130 are also supported for various PyTorch versions. To avoid confusion for users, please update the list to include all supported CUDA versions.
| For other torch versions, we support `torch211`, `torch212`, `torch220`, `torch230`, `torch240`, `torch250`, `torch260`, `torch270`, `torch280`, `torch290`, `torch2100` and for CUDA versions, we support `cu118` and `cu121` and `cu124`. For Ampere devices (A100, H100, RTX3090) and above, use `cu118-ampere` or `cu121-ampere` or `cu124-ampere`. Note: torch 2.10 only supports CUDA 12.6, 12.8, and 13.0. | |
| For other torch versions, we support `torch211`, `torch212`, `torch220`, `torch230`, `torch240`, `torch250`, `torch260`, `torch270`, `torch280`, `torch290`, `torch2100` and for CUDA versions, we support `cu118`, `cu121`, `cu124`, `cu126`, `cu128`, and `cu130`. For Ampere devices (A100, H100, RTX3090) and above, use `cu118-ampere` or `cu121-ampere` or `cu124-ampere`. Note: torch 2.10 only supports CUDA 12.6, 12.8, and 13.0. |
| cu126-ampere-torch2100 = [ | ||
| "unsloth[huggingface]", | ||
| "bitsandbytes>=0.45.5,!=0.46.0,!=0.48.0", | ||
| "unsloth[cu126onlytorch2100]", | ||
| ] | ||
| cu128-ampere-torch2100 = [ | ||
| "unsloth[huggingface]", | ||
| "bitsandbytes>=0.45.5,!=0.46.0,!=0.48.0", | ||
| "unsloth[cu128onlytorch2100]", | ||
| ] | ||
| cu130-ampere-torch2100 = [ | ||
| "unsloth[huggingface]", | ||
| "bitsandbytes>=0.45.5,!=0.46.0,!=0.48.0", | ||
| "unsloth[cu130onlytorch2100]", | ||
| ] |
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The ampere variants for torch2100 (e.g., cu126-ampere-torch2100) are identical to their non-ampere counterparts. This pattern is also present for torch290 and torch291 variants. In contrast, older ampere variants included the unsloth[flashattention] dependency. If this omission is intentional due to compatibility issues, these new ampere extras are redundant and could be removed to simplify the configuration. If it's an oversight, unsloth[flashattention] should be added for consistency with other ampere-specific configurations.
- Add cu126/cu128/cu130 xformers 0.0.34 wheel dependencies for torch 2.10 - Add cu126-torch2100, cu128-torch2100, cu130-torch2100 meta-dependencies - Add cu126-ampere-torch2100, cu128-ampere-torch2100, cu130-ampere-torch2100 variants - Update _auto_install.py version detection for torch 2.10.x - Add CUDA check for torch 2.10 (requires CUDA 12.6, 12.8, or 13.0) - Update README.md with torch 2.10 installation instructions Co-authored-by: Daniel Hanchen <danielhanchen@users.noreply.github.com>
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Summary
Adds support for PyTorch 2.10 with xformers 0.0.34.
Changes
pyproject.toml:
cu126onlytorch2100,cu128onlytorch2100,cu130onlytorch2100xformers 0.0.34 wheel dependenciescu126-torch2100,cu128-torch2100,cu130-torch2100meta-dependenciescu126-ampere-torch2100,cu128-ampere-torch2100,cu130-ampere-torch2100ampere variantsunsloth/_auto_install.py:
elif v < V('2.10.1'): x = 'cu{}{}-torch2100'README.md:
torch2100xformers 0.0.34 Wheel Availability
Note: xformers 0.0.34 uses
cp39-abi3(Python stable ABI), which works with Python 3.9+.Test plan