* fix: Remove moot has_blackwell_gpu() function
Fixes unslothai/unsloth#6961. This function skipped flash-attn on Blackwell GPUs because no prebuilt wheel existed;
Dao-AILab now ships one and url_exists() already gates resolution.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
* fix: use torchao 0.17.0 for Blackwell
Fixes#6961. Torchao 0.16.0's cpp extensions are built against CUDA 12, so on a CUDA-13
torch (cu130 / Blackwell) they fail to load with "libcudart.so.12: cannot
open shared object file". Select 0.17.0 there instead: its cpp targets torch
2.11, so it is skipped cleanly rather than crashing. CUDA-12 / ROCm / CPU
torch 2.10 keeps 0.16.0 and its working kernels.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
* Condense torchao version-selection comments (no behavior change)
* Support torch 2.11 in the Studio installer via the torch2.10 prebuilt wheels
Map torch 2.11 to the torch2.10 prebuilt wheels for flash-attn, causal-conv1d,
and mamba through wheel_utils.prebuilt_wheel_torch_mm, applied in direct_wheel_url
(filename) and flash_attn_wheel_url (version). Those torch2.10 CUDA wheels load and
pass each project's own test suite on torch 2.11 (verified on B200), so a torch 2.11
environment gets the prebuilt accelerators instead of skipping or building from source.
Raise _CUDA_TORCH_PKG_SPEC to <2.12.0 (torchvision <0.27.0, torchaudio <2.12.0) so
the CUDA torch repair path can install torch 2.11, where torchao 0.17's cpp kernels
load cleanly. Add tests for the mapping.
* Keep has_blackwell_gpu as a False stub for future arch gating
* Restore has_blackwell_gpu as a return-False probe kept for future arch gating
Keep the nvidia-smi compute_cap detection and its two call sites, but short-circuit
with return False at the top so flash-attn is no longer skipped on Blackwell (sm_100+
now has prebuilt wheels and url_exists gates resolution). Drop the early return to
re-enable arch-based detection later.
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Co-authored-by: Claude Opus 4.8 <noreply@anthropic.com>
Co-authored-by: Daniel Han <danielhanchen@gmail.com>
* Fix: skip fp16/bf16 validation for full finetuning in RL trainers
When doing full finetuning (FFT) of a bfloat16 model, the fp16/bf16
mismatch validation fires before the corrective logic runs, causing a
misleading error even though the code would properly handle it downstream.
Skip the validation when full_finetuning is active.
Fixes#6731
* Fix: auto-correct fp16/bf16 mismatches for full finetuning before validation
Instead of entirely skipping validation (which could let mismatches
through when mixed_precision_dtype is float32), auto-correct explicit
fp16/bf16 settings that conflict with the model's dtype for FFT. This
way the existing validation still catches real mismatches for non-FFT
cases, and the corrective logic below handles the normalized settings.
Fixes the issue raised in Codex review of PR #6813.
* Guard Windows ROCm torchao override skip
Detect installed ROCm torch directly before applying the torchao override so Windows ROCm environments never install the crashing torchao package even if the earlier ROCm-installed flag is missing.
* Update unsloth/models/rl.py
Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>
* Update studio/install_python_stack.py
Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>
* Harden ROCm probe and sync RL precision flags
Tolerate stray stdout noise when probing Windows ROCm torch installs by checking the last non-empty output line, matching the existing torch version probe behavior. Also keep args.fp16 and args.bf16 synchronized with the full-finetuning precision auto-corrections in the RL trainer patch so downstream eval settings see a consistent TrainingArguments state.
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* Add MLX trainer compatibility shims
Patch imported MLXTrainer and MLXTrainingConfig objects to preserve the expected dataclass field ordering and to provide a _train_dataset_for_batches fallback when older trainers or test doubles only expose train_dataset. Also add focused worker tests covering both compatibility paths.
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* Scope PR to Windows ROCm torchao guard
* Restore PR scope to Windows ROCm guard
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* test: cover Windows ROCm torchao skip behavior
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---------
Co-authored-by: Ayushman Paul <ayushman@HP>
Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Lee Jackson <130007945+Imagineer99@users.noreply.github.com>
Co-authored-by: imagineer99 <samleejackson0@gmail.com>
torchao has no working Windows ROCm build. transformers.quantizers imports it,
and it loads torch's c10d distributed backend at module level, which the AMD
Windows wheels omit (no RCCL). The import aborts, transformers can no longer
expose PreTrainedModel, and the sentence-transformers embedder silently falls
back to the llama-server GGUF embedder. Linux ROCm and NVIDIA are unaffected
(the c10d ops are present / torchao is real there).
The training and export workers already install the shared torchao stub before
importing transformers, but the RAG embedder runs in the main backend process,
which never did. Two fixes, both no-ops off Windows ROCm:
- embeddings.py: install_torchao_windows_rocm_stub() before the first
sentence-transformers import, so an already-installed torchao is neutralized
(fixes existing venvs).
- install_python_stack.py: stop installing torchao on Windows ROCm; it can only
crash on import there, so new venvs never ship it.
Add tests covering the embedder stub call and the install skip.
* studio: select torchao version from the installed torch
The Studio installer pins CUDA torch to torch>=2.4,<2.11 and its driver
ladder selects the cu130 wheel index on recent NVIDIA drivers, so pip
resolves torch 2.10.0. overrides.txt hard-pinned torchao==0.14.0, whose
C++ extensions are built against torch 2.9.0, so torchao skipped its cpp
kernels ("Skipping import of cpp extensions due to incompatible torch
version 2.10.0+cu130 for torchao version 0.14.0") and fell back to the
slow Python path. Every CUDA index now tops out at torch 2.10.0, so this
hit most modern installs, not just cu130.
Pick the torchao version matching the torch actually installed in the
venv (table: pytorch/ao#2919): torch 2.10.x -> torchao 0.16.0, 2.11.x ->
torchao 0.17.0, otherwise the previous 0.14.0 (so torch <=2.9 is
unchanged). The installer reads torch.__version__ from the venv via a
cross-platform sys.executable probe (probe_torch_wheel_env is Linux-only)
and passes the computed spec positionally to the existing force-reinstall
override step; overrides.txt becomes a pointer to that logic. torchao's
Python API (Float8Tensor, used by unsloth/kernels/utils.py) imports
cleanly on 0.16.0/0.17.0, verified against torch 2.9.1.
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* studio: address review on torchao selection
- Clean the torch minor of pre-release/dev suffixes before parsing
(e.g. '2.10rc1' -> minor 10), matching wheel_utils.probe_torch_wheel_env.
- Pass _windows_hidden_subprocess_kwargs() to the torch-version probe so
it does not flash a console window on Windows (no-op elsewhere).
- Use _safe_print for the selection log line, consistent with the file's
other status output (safe on non-UTF-8 consoles).
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