48 commits
| Author | SHA1 | Message | Date | |
|---|---|---|---|---|
Daniel Han
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09ca2a1777 |
Studio: retry agentic loop on validation failure
Adds a pre-dispatch validation pass inside both agentic tool loops (generate_chat_completion_with_tools in core/inference/llama_cpp.py and run_safetensors_tool_loop in core/inference/safetensors_agentic.py). The pass catches two failure modes between parser and dispatch: * Unknown tool name (not in the request's tools[] array). * Arguments that cannot decode to a JSON object when auto_heal is off. On a caught call the loop appends a corrective tool-result message tied to the hallucinated tool_call_id (not a fabricated id, so the OpenAI chat template stays valid) and re-enters the model. When the call has no usable id we fall back to a user-role correction since tool-role messages require a matching prior call id. The retry pass is bounded by max_validation_retries (default 2, new ChatCompletionRequest field, threaded through the route layer). On budget exhaustion the call falls through to the existing per-tool error path so today's behavior is preserved. When auto_heal_tool_calls is on the heal path still runs in the dispatch loop unchanged; F3 only catches the strict-shape failures the coercer cannot fix. |
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Daniel Han
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bb4eb88fdc |
Studio: tools, thinking blocks, code execution and web search for safetensors (#5520)
Adds tools, thinking blocks, code execution, and web search support to the safetensors / transformers and MLX inference backends in Studio, bringing them to parity with the GGUF path. What ships - safetensors / transformers agentic tool loop with cumulative-text state machine, tool-call XML parser, and template kwarg forwarding (tools / enable_thinking / reasoning_effort / preserve_thinking). - MLX backend: same kwargs accepted on Apple Silicon; chat_template_info shipped through worker IPC; pills enable for Qwen / Qwen3 / Qwen3.5 / Gemma reasoning. - Capability classifier (_detect_safetensors_features) gates supports_tools on actual parser-compatible emission markers (<tool_call> / <function=) so Llama-3 / Mistral / Gemma 4 do not advertise toggles the parser cannot honour. - gpt-oss override stays: reasoning on, tools off (Harmony channel, not <tool_call> XML). - CWE-209 hygiene: safetensors SSE error path emits a constant message and logs the trace server-side. Validation - 256 unit tests green (43 tool-loop, 11 capability advertise, 7 MLX backend, 5 main-added, 190 adjacent inference / anthropic / openai regression). - Cross-OS staging CI green on ubuntu-latest / macos-14 / windows-latest plus a dedicated MLX cartesian probe against real unsloth/Qwen3.5-0.8B on macos-14 (CI 26098107440). - Capability parity verified across Qwen3 / Qwen3.5 / Llama-3 / Mistral / Gemma / DeepSeek-R1 / gpt-oss (incl. BF16). - Manual confirmation from Imagineer99 on Qwen3.5-2B: think + search + code exec working. Closes the safetensors / MLX gap with the GGUF backend. |
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Daniel Han
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65b4028560 |
Pin bitsandbytes to continuous-release_main on ROCm (4-bit decode fix) (#4954)
* Pin bitsandbytes to continuous-release_main on ROCm for 4-bit decode fix
bitsandbytes 0.49.2 on PyPI ships with a broken 4-bit GEMV kernel on
every ROCm target:
- CDNA (gfx90a / gfx942 / gfx950 = MI210 / MI300X / MI350) via a
broken blocksize=32/64 warp64 GEMV kernel whose tests were
explicitly skipped with ROCM_WARP_SIZE_64 guards because the
code was known broken.
- RDNA3 / RDNA3.5 (gfx1100-1103 / gfx1150-1152) via a compile-time
BNB_WARP_SIZE macro in the host-side dispatch that resolves to
64 when the multi-arch wheel is compiled with CDNA as the
primary target, so num_blocks is wrong on RDNA and half the GEMV
output is never written.
At decode shape (1, 1, hidden) both bugs produce NaN. Training is
unaffected because training shapes are (batch, seq_len > 1, hidden)
and never touch the GEMV path. The crash during autoregressive
inference surfaces as _assert_async_cuda_kernel in torch.multinomial
which on HIP becomes a hard HSA_STATUS_ERROR_EXCEPTION instead of
a clean Python error.
Both bugs are fixed by bitsandbytes commit 713a3b8 ("[ROCm] Enable
blocksize 32 4-bit quantization and GEMV kernels on AMD CDNA",
PR #1887, merged 2026-03-09) which replaces BNB_WARP_SIZE with a
runtime hipDeviceGetAttribute query and ships a working CDNA warp64
kernel. That commit has not shipped to PyPI yet, but
continuous-release_main wheels are published on every push to bnb
main via GitHub Releases.
Point the ROCm install path at the continuous-release_main x86_64 and
aarch64 wheels and fall back to PyPI >=0.49.1 when the pre-release is
unreachable (offline installs, firewalled hosts, or architectures not
covered by the pre-release wheels). Drop the pin once bnb cuts a
0.50+ tag on PyPI.
Verified on MI300X (gfx942, ROCm 7.2, torch 2.10.0+rocm7.1): direct
bnb GEMV shape test now returns 0.0078 max abs error at seq_len=1
(no NaN) vs NaN on 0.49.2, and full Unsloth + for_inference + 4-bit
sampling generation works end-to-end.
NVIDIA / CPU / Mac / Windows paths are unaffected -- the helper is
gated on the ROCm torch index and platform.machine() respectively.
* Drop Studio ROCm 16-bit fallback now that bnb 0.50+ fixes 4-bit decode
The 16-bit fallback in studio/backend/core/inference/inference.py was
added as a workaround for a bug that this PR already fixes at the
install layer: bitsandbytes <= 0.49.2 has a broken 4-bit GEMV kernel
on every ROCm target, which NaNs at decode shape (seq_len=1) and
crashes autoregressive inference. bnb PR #1887 (commit 713a3b8, in
0.50.0.dev0+, pinned by install.sh / install_python_stack.py in this
PR) restores correct 4-bit decode on MI300X and verified working
end-to-end with full Unsloth + for_inference + sampling.
Revert the dual code path so ROCm and NVIDIA both go through the
normal FastLanguageModel.from_pretrained + for_inference flow:
- Remove the conditional `from unsloth import` that skipped the
import on ROCm. The monkey-patches it was trying to avoid were
never the cause of the crash; bnb 4-bit GEMV was.
- Remove the `if _hw_module.IS_ROCM:` branch in load_model that
loaded with plain transformers + PEFT + bfloat16, and the
`_resolve_fp16_base` helper it relied on.
- Remove the `get_chat_template is not None` fallback in
_load_chat_template_info -- get_chat_template is now always
imported.
- Refactor the audio/vision ROCm guard to check _hw_module.IS_ROCM
directly instead of the removed _IS_ROCM_ENV global. Audio and
vision on ROCm still need separate validation (FastVisionModel
and the CSM audio codecs were never tested on HIP) so the guard
stays for now.
Add _bnb_rocm_4bit_ok() as a runtime safety net for users who
install from this PR before the install.sh bnb pin kicks in, or
whose installer fell back to the PyPI pin because the continuous-
release wheel was unreachable. When the installed bnb is < 0.50 on
ROCm, force load_in_4bit=False and strip any -unsloth-bnb-4bit /
-bnb-4bit suffix from the model path so a pre-quantized repo
resolves to its FP16 sibling instead of pulling bnb back in via
the repo's quantization_config. LoRA adapters whose base is a
pre-quantized repo on old bnb will still fail inside Unsloth's
loader -- the only real fix there is `unsloth studio update`.
Verified on MI300X (gfx942, ROCm 7.2, torch 2.10.0+rocm7.1):
- HAPPY path (bnb 0.50.0.dev0, load_in_4bit=True, pre-quantized
repo): loads in 4-bit via the fixed GEMV, generation returns
"Paris." for greedy and sampling.
- SAFETY-NET path (simulated old bnb, suffix-stripped to the
FP16 sibling, load_in_4bit=False): loads in bf16, generation
returns "Paris." for greedy and sampling.
Net diff is ~45 lines smaller than the pre-revert state because
the entire plain-transformers 16-bit branch is gone.
* Cache _bnb_rocm_4bit_ok() with functools.cache
load_model() can be called many times in a single session but the bnb
version and hardware state cannot change at runtime, so memoise the
check. First call is ~1.9 ms (dominated by the lazy `import bitsandbytes`
inside the try block), subsequent calls drop to sub-microsecond dict
lookups. Zero behavioral change.
* Shorten verbose bnb/ROCm comments
Comment-only cleanup across install.sh, studio/install_python_stack.py,
and studio/backend/core/inference/inference.py. No behavioral change.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Remove _bnb_rocm_4bit_ok safety net from inference.py
Studio's ROCm support is brand new (PR #4720, merged today) and every
fresh install pulls the bnb continuous-release_main wheel via
install.sh / install_python_stack.py in this same PR. There are no
existing ROCm Studio installs carrying bnb < 0.50, so the defensive
version-check fallback is guarding against a scenario that cannot
actually occur. Delete the helper, the functools import, and the
safety-net block -- inference.py now calls FastLanguageModel.from_pretrained
directly with no ROCm branching.
* Drop audio/vision ROCm guard in inference.py — verified unblocked by bnb fix
Vision inference was blocked by the same bnb 4-bit GEMV bug that affected
text inference (vision models use bnb 4-bit for the LM backbone). With
bnb 0.50+ pinned in install.sh / install_python_stack.py, vision works
end-to-end on MI300X: Llama-3.2-11B-Vision-Instruct-unsloth-bnb-4bit
loaded in 4-bit via FastVisionModel + for_inference returns a correct
answer to a multimodal prompt.
Audio (CSM) was never actually blocked by HIP — on this hardware CSM
loads and runs its backbone forward pass fine with bnb 0.50, then fails
during generate() with a transformers-level kwarg validation mismatch
in generation_csm.py (`backbone_last_hidden_state` rejected). That's a
pre-existing transformers/CSM integration bug that reproduces identically
on NVIDIA, so the ROCm-gated guard was never actually protecting users
from anything HIP-specific.
Remove the combined audio/vision guard and the now-unused _hw_module
import. Also restore the one-word "Can be" in an inline comment that
drifted during the earlier comment-shortening pass, so the inference.py
delta vs pre-#4720 is exactly the max_seq_length<=0 crash fix and
nothing else.
* Shorten max_seq_length=0 guard comment to one line
---------
Co-authored-by: Daniel Han <danielhanchen@users.noreply.github.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
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Daniel Han
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cad8c6ad05 |
Add AMD ROCm/HIP support across installer and hardware detection (#4720)
* Add ROCm detection to install.sh and expand shell tests Add AMD ROCm GPU detection to get_torch_index_url() in install.sh. When nvidia-smi is not found, probe for ROCm via amd-smi, /opt/rocm version file, hipconfig, dpkg-query, and rpm. Includes validation guard for malformed _rocm_tag, Debian epoch prefix stripping, ROCm 7.2+ cap to rocm7.1 index, bitsandbytes AMD install, and status messaging. Shell tests expanded to 23 cases. Co-authored-by: Daniel Han <danielhanchen@gmail.com> * Add ROCm torch reinstall support to install_python_stack.py Add _detect_rocm_version() and _ensure_rocm_torch() to detect when a Linux host has ROCm but the venv received CPU-only torch, and reinstall with the correct ROCm wheels. Covers ROCm 6.0 through 7.1 with a 30-second timeout on the torch GPU probe subprocess. Co-authored-by: Daniel Han <danielhanchen@gmail.com> * Add ROCm support to llama.cpp prebuilt installer Add has_rocm field to HostInfo, extend detect_host() to probe for ROCm via hipcc/amd-smi/rocm-smi/ROCM_PATH, and route ROCm hosts to upstream prebuilts (Linux ROCm 7.2 prebuilt with source fallback, Windows HIP prebuilt with CPU fallback). Add linux-rocm and windows-hip install kinds to runtime_patterns_for_choice(). Co-authored-by: Daniel Han <danielhanchen@gmail.com> * Add IS_ROCM hardware flag and fix AMD error message Add IS_ROCM flag to hardware.py detect_hardware() (set when torch.version.hip is present, DeviceType stays CUDA). Export IS_ROCM from __init__.py. Add "rocm" key to get_package_versions(). Replace "We do not support AMD" error in tokenizer_utils.py with a helpful message pointing to ROCm installation docs. Co-authored-by: Daniel Han <danielhanchen@gmail.com> * Add comprehensive ROCm support test suite (68 tests) Add tests/studio/install/test_rocm_support.py covering all ROCm code paths across install_llama_prebuilt.py, install_python_stack.py, hardware.py, tokenizer_utils.py, and install.sh. All tests use mocks and run without AMD hardware. Covers: asset selection (11), runtime patterns (5), HostInfo (4), ROCm version detection (9), torch reinstall (9), index mapping (8), hardware flag (8), tokenizer message (2), install.sh structure (10), and live regression (1). * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Harden ROCm support: probe error handling, version cap, validation Address review findings from 8 independent reviewers: - Wrap _ensure_rocm_torch() torch probe in try/except for TimeoutExpired and OSError so a hung or broken torch import does not crash the installer (8/8 reviewers flagged this) - Add torch>=2.4,<2.11.0 version cap to the ROCm reinstall path to prevent installing unsupported torch 2.11.0 from the rocm7.1 index - Use with-statement for file reads in _detect_rocm_version() to avoid resource leaks - Handle ROCM_PATH="" correctly (use `or "/opt/rocm"` instead of default parameter to avoid relative path resolution) - Strengthen shell validation guard from rocm[0-9] to rocm[1-9] to reject rocm0.x tags that would produce nonexistent PyTorch index URLs - Switch shell version cap from blocklist to allowlist (rocm6.*|rocm7.0* |rocm7.1* pass through, everything else caps to rocm7.1) so future ROCm 10+ does not fall through to a nonexistent index - Add sorted() to _ROCM_TORCH_INDEX lookup for defensive ordering - Fix test_probe_timeout_handled: replace zero-assertion test with proper assertions verifying reinstall proceeds after timeout * Clean up rocm_paths list construction in detect_host() Filter None from the ROCM_PATH env var lookup at list construction time instead of relying on the inline `if p` guard in the any() call. * Require actual AMD GPU presence before selecting ROCm paths All 8 reviewers across 2 cycles independently flagged that ROCm detection used toolkit/filesystem hints (hipcc, /opt/rocm, rocm-core) as a proxy for GPU presence, which would misroute CPU-only or NVIDIA hosts that happen to have ROCm tools installed. Now all 3 detection points (install.sh, install_python_stack.py, install_llama_prebuilt.py) probe for an actual AMD GPU before entering the ROCm path: - install.sh: check rocminfo for gfx* GPU names, or amd-smi list for device rows, before version detection - install_python_stack.py: new _has_rocm_gpu() function probes rocminfo and amd-smi list before _ensure_rocm_torch() proceeds - install_llama_prebuilt.py: detect_host() probes rocminfo/amd-smi list instead of just checking tool existence or directory paths Also: - Shell test mock amd-smi now handles "list" subcommand - Python tests updated to mock _has_rocm_gpu where needed - Added test_no_gpu_with_rocm_tools_skips to verify the new guard - Test index lookups now use sorted() to match production code * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Harden hipconfig version parsing and torch probe compatibility - Add parts[1].isdigit() check in hipconfig version parsing to handle versions like "6.3-HIP" where the minor component has non-numeric suffix (strip "-" prefix before int() conversion) - Use getattr() in torch probe subprocess to safely handle old or custom torch builds that may lack torch.version.hip/cuda attributes * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Strengthen AMD GPU detection and add NVIDIA precedence guard - Change amd-smi list detection from any-non-empty-output to requiring "gpu" marker in output, matching the shell-side NR>1 check. Prevents false positives from header-only amd-smi list output. - Add nvidia-smi check at the top of _ensure_rocm_torch() so mixed AMD+NVIDIA hosts preserve NVIDIA precedence (matching install.sh and install_llama_prebuilt.py behavior). - Apply the same amd-smi marker fix to install_llama_prebuilt.py detect_host() for consistency. * Add Windows-specific ROCm/HIP detection in detect_host() The previous detect_host() ROCm check used rocminfo and amd-smi list which are Linux-only tools. On Windows, has_rocm would always be False, making the Windows HIP prebuilt path at line 1794 unreachable. Now detect_host() uses platform-specific detection: - Linux: rocminfo (check for gfx GPU names) or amd-smi list - Windows: hipinfo.exe, amd-smi, or amdhip64.dll on PATH This allows Windows AMD users to get the HIP prebuilt binary instead of silently falling through to the CPU prebuilt. * Add AMD ROCm gaps: Mamba/SSM source builds, GPU monitoring, Windows messaging, RDNA expansion - worker.py: Add HIP detection to causal-conv1d/mamba-ssm probe, check for hipcc before ROCm source builds, improve status messages and error reporting, add timeout and uv support for the source build fallback - amd.py: New AMD GPU monitoring module via amd-smi metric --json, mirroring nvidia.py structure (utilization, temperature, power, VRAM) - hardware.py: Branch to amd.py when IS_ROCM is True for GPU utilization, visible GPU queries, and physical GPU count - install_python_stack.py: Detect AMD GPUs on Windows and warn that ROCm-enabled PyTorch must be installed manually - kernels/utils.py: Expand is_rdna() to cover RDNA2 (gfx1030-1032), RDNA3 (gfx1102-1103), RDNA3.5 (gfx1150-1152) alongside existing entries - tests: Add 32 new tests covering all changes (95/95 pass) * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Harden ROCm detection, fix VRAM heuristic, and expand RDNA2 coverage - Windows ROCm detection: validate actual GPU presence via hipinfo/amd-smi output markers instead of just checking tool existence on PATH - _ensure_rocm_torch: validate nvidia-smi actually reports a GPU before giving NVIDIA precedence (fixes AMD-only hosts with stale NVIDIA tools) - amd.py _parse_numeric: handle dict-shaped metric objects from newer amd-smi versions ({"value": 10, "unit": "W"}) and strip MiB/GiB units - amd.py VRAM heuristic: raise threshold from 100k to 10M to correctly handle MI300X (192 GB = 196608 MB) and other high-VRAM GPUs - amd.py visible GPU: use AMD-reported GPU IDs instead of enumerate index so non-dense sets like CUDA_VISIBLE_DEVICES=1,3 report correctly - install.sh: add ROCm <6.0 minimum version guard (no PyTorch wheels exist for older versions); fix rocm7.1* glob to not match rocm7.10+ - is_rdna: add gfx1033-1036 for RDNA2 mobile GPUs (RX 6600M etc.) - worker.py: increase ROCm source build timeout from 600s to 1800s; fix success log message for ROCm source builds - Tests: update mocks for _has_usable_nvidia_gpu, add RDNA2 target asserts * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Add HIP_VISIBLE_DEVICES support, unit-aware VRAM parsing, Windows GPU validation - hardware.py: check HIP_VISIBLE_DEVICES and ROCR_VISIBLE_DEVICES on ROCm before falling back to CUDA_VISIBLE_DEVICES, so multi-GPU AMD setups with HIP-specific env vars report the correct visible device set - amd.py: add _parse_memory_mb() that reads "unit" from dict-shaped amd-smi JSON (e.g. {"value": 192, "unit": "GiB"}) and converts to MB correctly; fixes MI300X VRAM misreported as 0.19 GB instead of 192 GB - install_python_stack.py: Windows AMD warning now validates actual GPU presence via hipinfo/amd-smi output markers before printing - install_llama_prebuilt.py: restore amdhip64.dll fallback for Windows HIP detection after tool-based checks, so Windows HIP installs without CLI tools on PATH are still detected - hardware.py: fix IS_ROCM comment to accurately describe its role * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Fix HIP_VISIBLE_DEVICES empty-string handling in GPU visibility spec Use explicit None checks instead of Python `or` operator when reading HIP_VISIBLE_DEVICES / ROCR_VISIBLE_DEVICES, so that an empty string ("") is correctly honored as "no visible GPUs" rather than silently falling through to CUDA_VISIBLE_DEVICES on mixed ROCm+CUDA systems. * Fix IS_ROCM test assertion for multi-line formatting * Cap torchvision/torchaudio versions, remove amdhip64.dll fallback, fix visible GPU count - Cap torchvision<0.26.0 and torchaudio<2.11.0 alongside torch<2.11.0 in both install.sh and install_python_stack.py to prevent resolver from selecting incompatible companion packages from ROCm wheel index - Remove amdhip64.dll fallback in Windows ROCm detection (DLL presence without hipinfo/amd-smi is not proof of GPU existence) - Fix get_visible_gpu_count() to use _get_parent_visible_gpu_spec() which respects HIP_VISIBLE_DEVICES/ROCR_VISIBLE_DEVICES on ROCm hosts * Attribute is_rdna() RDNA2/3/3.5/4 expansion to PR #4428 The is_rdna() expansion to cover RDNA2 (gfx1030-1036), RDNA3 (gfx1100-1103), RDNA3.5 (gfx1150-1152), and RDNA4 (gfx1200-1201) architectures is based on the original work from PR #4428. Co-authored-by: GoldenGrapeGentleman <yueyuan@amd.com> Co-authored-by: billishyahao <bill.he@amd.com> * Support AMD Radeon for studio (#4770) Co-authored-by: Iswarya Alex <iswarya.alex@amd.com> * Remove ROCm test files from main PR Move test_rocm_support.py and shell test additions to a separate PR to keep the main ROCm support PR focused on implementation changes. * Fix installer and hardware detection issues for PR #4720 - Fix empty _tri_arg passed to uv pip install in Radeon path (causes "Empty field is not allowed for PEP508" error) - Fix Radeon fallback: use ROCm index instead of CPU-only when repo.radeon.com is unreachable (TORCH_INDEX_URL already has ROCm) - Use $TORCH_CONSTRAINT in fallback paths instead of hardcoded strings - Fix _pick_radeon_wheel: relax suffix to match manylinux_2_28_x86_64 wheels (AMD Radeon repo does not use bare linux_x86_64 platform tag) - Fix IS_ROCM export: use __getattr__ so callers always see the live value after detect_hardware() runs - Fix apply_gpu_ids: set HIP_VISIBLE_DEVICES and ROCR_VISIBLE_DEVICES on ROCm so _get_parent_visible_gpu_spec picks up narrowed GPU set - Fix _parse_memory_mb: distinguish GB (1000 MB) from GiB (1024 MiB) - Add amd-smi version as a fallback in _detect_rocm_version - Fix trailing whitespace and missing newline at EOF in install.sh * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Fix GPU detection false positives and add missing health groups - Fix _has_rocm_gpu() false positive: require "GPU: <number>" data rows from amd-smi list, not just header containing "gpu" - Apply same fix in detect_host() in install_llama_prebuilt.py - Add runtime_payload_health_groups for linux-rocm and windows-hip so partial/corrupt ROCm/HIP prebuilt installs are properly detected - Add bitsandbytes install to Radeon fallback paths (was only in the success path, skipped when repo.radeon.com was unreachable) - Keep DEVICE/CHAT_ONLY as direct imports in __init__.py (matching main) and only use __getattr__ for IS_ROCM * Fix _ensure_rocm_torch and Windows AMD warning false positives - _ensure_rocm_torch: only skip when HIP is already present, not for CUDA builds (which are unusable on AMD-only hosts). Fixes the case where a venv has a stale CUDA wheel and the repair step is skipped. - Windows AMD warning: use GPU data row check (same as Linux fix) to avoid false positives from amd-smi list header-only output. * Fix amd-smi GPU detection for GPU[N] output format Older amd-smi versions output "GPU[0] : Card series: ..." instead of "GPU: 0". The regex now matches both "GPU: <digit>" and "GPU[<digit>" formats to detect actual GPU data rows. * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Harden AMD GPU detection against false positives - install.sh: replace weak amd-smi list check (awk 'NR>1 && NF') with strict pattern matching GPU data rows (/^GPU[[:space:]]*[:\[]/) - All files: reject rocminfo gfx000 (CPU HSA agent) by requiring gfx[1-9] instead of gfx[0-9] in the rocminfo GPU probe - Fixes false positives on hosts with ROCm tools but no AMD GPU * Remove duplicate comment from pre-commit merge * Refactor: deduplicate AMD detection, consolidate bitsandbytes, clean up imports - Extract _has_amd_rocm_gpu() shell function to avoid duplicating the rocminfo/amd-smi GPU detection logic in get_torch_index_url and the Radeon auto-detect block - Consolidate bitsandbytes install into a single case block after torch install (was duplicated 4 times across Radeon success/fallback paths) - Move math and re imports to top of amd.py (were inline in functions) - Add _smi_query() helper in hardware.py to centralize IS_ROCM backend selection for get_gpu_utilization and get_visible_gpu_utilization Addresses Gemini code review suggestions. * Fix VRAM parsing for string values and GB/GiB consistency - Extract unit from string-valued VRAM fields (e.g. "192 GiB") so _parse_memory_mb correctly applies the unit multiplier instead of treating the value as bare MB - Treat GB and GiB identically (both as binary x1024) since GPU tools including amd-smi use binary units even when labeling them "GB" - Fixes incorrect VRAM reporting on MI300-class cards (was showing ~0.19 GB instead of 192 GB for string-valued outputs) * Add --no-cache to uv for ROCm HIP source builds Avoid stale cache artifacts from partial HIP source builds when uv is used for causal-conv1d/mamba-ssm compilation on ROCm. The pip path already uses --no-cache-dir; this adds the uv equivalent (--no-cache) only when is_hip is True. * Fix critical: initialize _amd_gpu_radeon before case block _amd_gpu_radeon was only set inside the */rocm*) case arm, so on NVIDIA/CPU/macOS paths where TORCH_INDEX_URL does not contain "rocm", the variable was unbound. With set -u (nounset) enabled, this crashes the installer for every non-AMD user. Move initialization to before the case block so it is always defined. * Fix Windows AMD: route has_rocm hosts to HIP prebuilt path resolve_release_asset_choice was selecting windows-cpu for all Windows x86_64 hosts including those with has_rocm=True. Windows AMD users should fall through to resolve_upstream_asset_choice which tries the HIP prebuilt first. Add "not host.has_rocm" guard to the published windows-cpu selection. * Harden ROCm detection, Radeon wheel fallback, and HIP visibility Addresses review findings from parallel reviewers on PR #4720: - install.sh: add _has_usable_nvidia_gpu() helper requiring nvidia-smi -L to actually list a GPU before treating the host as NVIDIA. Fixes the stale-nvidia-smi-on-PATH regression where AMD-only hosts fell into the CUDA branch. - install.sh: fix hipconfig awk blocks to propagate a non-zero exit code when the output is not a recognisable version string, so the ||-chain continues to dpkg-query / rpm instead of terminating early. - install.sh: fail-closed on Radeon wheel fallback. When torch, torchvision or torchaudio is missing from the Radeon repo for the active Python tag, fall back to the standard ROCm index instead of silently mixing Radeon wheels with PyPI defaults. Quote all wheel arguments individually so wheel filenames cannot be word-split or glob-expanded. - install_llama_prebuilt.py: detect_host() now requires nvidia-smi -L to list a GPU before setting has_physical_nvidia. Routes AMD ROCm hosts with a broken leftover nvidia-smi to the ROCm path instead of misclassifying them as NVIDIA. - install_llama_prebuilt.py: scan upstream assets for any rocm-<version> prebuilt instead of hard-coding rocm-7.2, so ROCm 6.x / 7.0 / 7.1 / 7.3+ users pick up a matching upstream prebuilt when one exists. - install_llama_prebuilt.py: validate_server() adds --n-gpu-layers 1 for linux-rocm and windows-hip hosts, so new HIP prebuilts are preflighted on the GPU path instead of passing validation on CPU only. - install_llama_prebuilt.py: restore the published windows-cpu fallback for AMD Windows hosts without a HIP prebuilt so hash-approved bundles are still preferred over the raw upstream CPU asset. - install_python_stack.py: drop the /opt/rocm / hipcc gate in _ensure_rocm_torch() and rely on _has_rocm_gpu(). Runtime-only ROCm installs (package-managed minimal installs, Radeon software) that ship amd-smi / rocminfo without hipcc can now repair a CPU-only venv via "unsloth studio update". Adds an explicit IS_WINDOWS / IS_MACOS guard. - studio/backend/utils/hardware/amd.py: honour HIP_VISIBLE_DEVICES / ROCR_VISIBLE_DEVICES / CUDA_VISIBLE_DEVICES in get_primary_gpu_utilization(). A process restricted to GPU 2 now reports metrics for GPU 2 instead of physical GPU 0. Tighten the plain bytes unit detection to an explicit allowlist. - studio/backend/utils/hardware/hardware.py: route get_backend_visible_gpu_info()'s backend_cuda_visible_devices field through a helper that reads HIP_VISIBLE_DEVICES on ROCm. Drop the unconditional "(rocm=False)" suffix in apply_gpu_ids() logs. * Fix round 2 regressions: ROCm validate_server and Windows HIP routing Follow-up to |
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Datta Nimmaturi
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3b5a49776b |
[studio] multi gpu: revert to balanced for inference. (#4698)
* Revert to balanced for inference * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Remove unused for_inference parameter from get_device_map Since inference and training both use "balanced" now, the for_inference flag is dead code. Remove it from the function signature, the call site in inference.py, and simplify the tests accordingly. * Remove redundant TestDeviceMapForInference test class TestGpuAutoSelection already covers the same multi-gpu and single-gpu device_map assertions. The TestDeviceMapForInference class was left over from when for_inference had distinct behavior. * Remove redundant test_get_device_map_multi_gpu_uses_balanced Its assertions ([0,1] -> balanced, [0] -> sequential) are already covered by test_get_device_map_uses_explicit_gpu_selection. --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Daniel Han <danielhanchen@gmail.com> |
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Datta Nimmaturi
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9311df2b29 |
[Studio] multi gpu finetuning/inference via "balanced_low0/sequential" device_map (#4602)
* [WIP] balanced device map for studio * gpus as a request parameter * API for multi GPU stuff * return multi gpu util in new API * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Use balanced_low0 instead of balanced * Use balanced_low0 instead of balanced * Fix device_map typo, UUID parsing crash, set() filter bug, and broken tests - balanced_low0 -> balanced_low_0 (transformers/accelerate rejects the old string) - get_parent_visible_gpu_ids() now handles UUID/MIG CUDA_VISIBLE_DEVICES gracefully instead of crashing on int() parse - _get_backend_visible_gpu_info() set() or None bug: empty set is falsy so CUDA_VISIBLE_DEVICES=-1 would disable filtering and report all GPUs - test_gpu_selection.py: add missing get_visible_gpu_utilization import and add required job_id arg to start_training() calls * Smart GPU determinism using estimates * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * disallow gpu selection for gguf for now * cleanup * Slightly larger baseline * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Treat empty list as auto * Verbose logging/debug * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Cleanup and revert unnecessary deletions * Cleanup excessive logs and guard against disk/cpu offload * auth for visibility API. cleanup redundant imports. Adjust QLoRA estimate * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * support for non cuda gpus * Fix multi-GPU auto-selection memory accounting The multi_gpu_factor was applied uniformly to all GPUs including the first one, which unfairly penalizes single-GPU capacity when transitioning to multi-GPU. This created a discontinuity where a model that barely fits 1 GPU would suddenly require 2 GPUs because the first GPU's free memory was discounted by 20%. Now the first GPU keeps its full free memory, and only additional GPUs have an overhead factor (0.85) applied to account for inter-GPU communication and sharding overhead. This gives more accurate auto-selection and avoids unnecessary multi-GPU for models that comfortably fit on one device. * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Add sandbox tests for multi-GPU selection logic 24 tests covering model size estimation, memory requirements, automatic GPU selection, device map generation, GPU ID validation, and multi-GPU overhead accounting. All tests use mocks so they run without GPUs on Linux, macOS, and Windows. * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Fix reviewer findings: 4bit inference estimate, fallback, GGUF gpu_ids, retry 1. 4-bit inference now uses reduced memory estimate (model_size/3 + buffer) instead of the FP16 1.3x multiplier. This prevents over-sharding quantized models across unnecessary GPUs. 2. When model size estimation fails, auto_select_gpu_ids now falls back to all visible GPUs instead of returning None (which could default to single-GPU loading for an unknown-size model). 3. GGUF inference route now treats gpu_ids=[] as auto-selection (same as None) instead of rejecting it as an unsupported explicit request. 4. Training retry path for "could not get source code" now preserves the gpu_ids parameter so the retry lands on the same GPUs. 5. Updated sandbox tests to cover the new 4-bit inference estimate branch. * Remove accidentally added unsloth-zoo submodule * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Fix UUID/MIG visibility and update test expectations 1. nvidia.py: When CUDA_VISIBLE_DEVICES uses UUID/MIG tokens, the visibility APIs now return "unresolved" with empty device lists instead of exposing all physical GPUs. This prevents the UI from showing GPUs that the backend process cannot actually use. 2. test_gpu_selection.py: Updated test expectations to match the new multi-GPU overhead accounting (first GPU at full capacity, 0.85x for additional GPUs) and 4-bit inference memory estimation formula. All 60 tests now pass. * Add CPU/disk offload guard to audio inference path The audio model loading branch returned before the common get_offloaded_device_map_entries() check, so audio models loaded with a multi-GPU device_map that spilled layers to CPU/disk would be accepted instead of rejected. Now audio loads also verify no modules are offloaded. * Improve VRAM requirement estimates * Replace balanced_low_0 with balanced * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * refine calculations for slightly easier nums * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * adjust estimates * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Use nums instead of obj to avoid seralisation error * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Harden nvidia-smi parsing and fix fallback GPU list 1. nvidia.py: Wrap int() casts for GPU index and memory in try/except so MIG slices, N/A values, or unexpected nvidia-smi output skip the unparseable row instead of aborting the entire GPU list. 2. nvidia.py: Handle GPU names containing commas by using the last field as memory instead of a fixed positional index. 3. hardware.py: fallback_all now uses gpu_candidates (GPUs with verified VRAM data) instead of raw devices list, which could include GPUs with null VRAM that were excluded from the ranking. * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * cleanup * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * consolidate raise_if_offload * Improve MoE support. Guard against nvidia-smi failures * Improve MoE support. Guard against nvidia-smi failures * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Fix shared-expert LoRA undercount, torch VRAM fallback, and apply_gpu_ids edge case 1. vram_estimation.py: compute_lora_params now includes shared experts (n_shared_experts) alongside routed experts when computing MoE LoRA adapter parameters. Previously only n_experts were counted, causing the estimator to undercount adapter, optimizer, and gradient memory for DeepSeek/GLM-style models with shared experts. 2. hardware.py: _torch_get_per_device_info now uses mem_get_info (which reports system-wide VRAM usage) instead of memory_allocated (which only reports this process's PyTorch allocations). This prevents auto-selection from treating a GPU as mostly free when another process is consuming VRAM. Falls back to memory_allocated when mem_get_info is unavailable. 3. hardware.py: apply_gpu_ids([]) now returns early instead of setting CUDA_VISIBLE_DEVICES="" which would disable CUDA entirely. Empty list inherits the parent visibility, same as None. 4. hardware.py: Upgraded fallback_all GPU selection log from debug to warning so operators are notified when the model likely will not fit in available VRAM. * Guard nvidia-smi subprocess calls against OSError and TimeoutExpired get_visible_gpu_utilization and get_backend_visible_gpu_info now catch OSError (nvidia-smi not found) and TimeoutExpired internally instead of relying on callers to wrap every invocation. Returns the standard available=False sentinel on failure so the torch-based fallback in hardware.py can take over. * Guard get_primary_gpu_utilization and reset GPU caches between tests 1. nvidia.py: get_primary_gpu_utilization now catches OSError and TimeoutExpired internally, matching the pattern already used in get_visible_gpu_utilization and get_backend_visible_gpu_info. All three nvidia-smi callers are now self-contained. 2. test_gpu_selection.py: Added _GpuCacheResetMixin that resets the module-level _physical_gpu_count and _visible_gpu_count caches in tearDown. Applied to all test classes that exercise GPU selection, device map, or visibility functions. This prevents stale cache values from leaking between tests and causing flaky results on machines with real GPUs. * Fix nvidia-smi fallback regression and physical GPU count validation 1. hardware.py: get_gpu_utilization, get_visible_gpu_utilization, and get_backend_visible_gpu_info now check result.get("available") before returning the nvidia-smi result. When nvidia-smi is unavailable or returns no data (e.g., containers without nvidia-smi, UUID/MIG masks), the functions fall through to the torch-based fallback instead of returning an empty result. This fixes a regression where the internal exception handling in nvidia.py prevented the caller's except block from triggering the fallback. 2. hardware.py: resolve_requested_gpu_ids now separates negative-ID validation from physical upper-bound validation. The physical count check is only enforced when it is plausibly a true physical count (i.e., higher than the largest parent-visible ID), since torch.cuda.device_count() under CUDA_VISIBLE_DEVICES returns the visible count, not the physical total. The parent-visible-set check remains authoritative in all cases. This prevents valid physical IDs like [2, 3] from being rejected as "out of range" when nvidia-smi is unavailable and CUDA_VISIBLE_DEVICES="2,3" makes torch report only 2 devices. * Fix UUID/MIG torch fallback to enumerate devices by ordinal When CUDA_VISIBLE_DEVICES uses UUID or MIG identifiers, get_parent_visible_gpu_ids() returns [] because the tokens are non-numeric. The torch fallback in get_visible_gpu_utilization() and get_backend_visible_gpu_info() previously passed that empty list to _torch_get_per_device_info(), getting nothing back. Now both functions detect the empty-list case and fall back to enumerating torch-visible ordinals (0..device_count-1) with index_kind="relative". This means the UI and auto-selection still see real device data in Kubernetes, MIG, and Slurm-style UUID environments where nvidia-smi output cannot be mapped to physical indices. Updated test_uuid_parent_visibility to verify the new torch fallback path returns available=True with relative ordinals. * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Add type hint for gpu_ids parameter in InferenceOrchestrator.load_model --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Daniel Han <danielhanchen@gmail.com> |
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Wasim Yousef Said
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c8057d911b |
fix: system prompt ignored in unsloth inference (#4528)
* fix: system prompt was dropped in unsloth text and vision inference * refactor: simplify system prompt message construction * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * fix: use multimodal typed content parts for vision system message and add fallback The system message content must use typed content parts ([{"type": "text", "text": ...}]) instead of a plain string to match the multimodal processor contract (consistent with the audio path). Plain strings cause some processors (e.g. LLaVA) to silently drop the system prompt. Also wraps processor.apply_chat_template in try/except so models that reject the system role gracefully fall back to no system message with a warning log. * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * fix: capture and log original exception in vision system prompt fallback --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Daniel Han <danielhanchen@gmail.com> |
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NuoFang
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4cedeba8c2 |
fix(studio): prevent ModuleNotFoundError in dataset.map() on Windows (#4473)
* fix(studio): prevent ModuleNotFoundError in dataset.map() on Windows On Windows, dataset.map() uses "spawn", which requires workers to import compiled modules from disk. Previously, clear_unsloth_compiled_cache() deleted the entire directory, causing workers to crash when looking for UnslothSFTTrainer.py. Changes: 1. Added `preserve_patterns` to cache cleanup to keep `Unsloth*Trainer.py` on Windows while clearing model-specific files. 2. Added the cache directory to PYTHONPATH for spawn workers. Linux/macOS behavior is unchanged. * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Fix spawn-platform coverage, CWD path mismatch, and race condition for PR #4473 - Extend platform guard from win32-only to include macOS (also uses spawn since Python 3.8, same ModuleNotFoundError would occur) - Replace fragile CWD-based PYTHONPATH registration with centralized register_compiled_cache_on_path() that uses the same __file__-relative _CACHE_DIRS already used by cache_cleanup -- fixes path mismatch when studio is launched from a directory other than the repo root - Move PYTHONPATH registration to the top of _train_worker(), before any dataset.map() call (previously it ran late in config assembly, after dataset formatting which also calls dataset.map()) - Update inference.py model-unload to preserve trainer files on spawn platforms, preventing a race where unloading a model via inference tab would delete UnslothSFTTrainer.py while training workers are importing it * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Fix cache-dir precedence reversal in register_compiled_cache_on_path() Iterating _CACHE_DIRS in forward order while calling insert(0) each time reverses the declared priority: later entries shadow earlier ones. When multiple compiled-cache directories exist, spawned workers could import a stale trainer from the wrong cache. Fix: iterate in reverse so that the highest-priority entry (first in _CACHE_DIRS) is inserted last and ends up at position 0 in sys.path and PYTHONPATH. * fix: harden worker-count helpers against cpu_count=None and desired<=0 - safe_num_proc: guard os.cpu_count() with `or 1`, clamp multi-GPU path with max(1, min(4, desired)), clamp return with max(1, desired) - safe_thread_num_proc: same os.cpu_count() guard and return clamp - Add regression tests (31 L1 unit + 10 sandbox edge-case tests) * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * remove regression tests from PR --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Daniel Han <danielhanchen@users.noreply.github.com> Co-authored-by: Daniel Han <danielhanchen@gmail.com> |
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Daniel Han
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0acd1c7eec |
studio: improve onboarding UX, tooltips, and training defaults (#4355)
* studio: improve onboarding UX, tooltips, and training defaults - Change splash text to "Train and run LLMs locally" - Add "Chat Only" card with BubbleChatIcon to skip directly to chat - Add Skip/Skip to Chat buttons in sidebar and footer - Back button on step 1 returns to splash screen instead of being disabled - Change "Watch video guide" to "Get started with our guide" with new URL - Update intro text to mention all model types + chat - Make all tooltips clickable (in addition to hover) via React context - Strip surrounding quotes from pasted HF tokens - Rename "Eval Split" to "Evaluation Split" - Add SparklesIcon to "Auto Detect" format option - Change step 4 heading to "Choose your training parameters" - Default max_steps to 60 - Learning rate displayed in scientific notation with +/- stepper - Context length options capped by model's max_position_embeddings (via AutoConfig) - Fix "QLORA"/"LORA" to "QLoRA"/"LoRA" in summary step - Backend: add max_position_embeddings to model config endpoint * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * compare for 2 diff models * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * resolving gemini comments * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * studio: disable thinking for Qwen3.5 <9B and always for AI Assist - Change Qwen3.5 thinking threshold from <=2B to <9B (0.8B, 2B, 4B all disable thinking by default; 9B+ enables it) - Always pass enable_thinking=False in AI Assist helper calls (_run_with_helper and _generate_with_backend) regardless of chat thinking settings * studio: address PR review comments - Extract _get_max_position_embeddings helper to DRY config extraction - Fix "Skip to Chat" to navigate to /chat on step 1 (was /studio) * fix: comment out debug print statements * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * studio: skip Shiki highlighting for incomplete SVG code fences While streaming SVG content, the syntax highlighter (Shiki) re-parses the entire growing SVG on every token, blocking the main thread and freezing the code area until the fence closes. Show a plain-text preview for incomplete SVG fences instead, similar to how Mermaid diagrams show a placeholder while streaming. * studio: fix default top_k from 50/40 to 20 for chat inference Per Qwen3.5 docs (unsloth.ai/docs/models/qwen3.5), top_k should be 20 for both thinking and non-thinking modes. The model-specific config in inference_defaults.json already had top_k=20 for Qwen3.5, but the generic fallback defaults were wrong: - Frontend DEFAULT_INFERENCE_PARAMS.topK: 50 -> 20 - Backend generate_chat_completion top_k: 40 -> 20 - Backend generate_chat_completion_with_tools top_k: 40 -> 20 - Frontend title generation top_k: 40 -> 20 * studio: set universal inference defaults for unknown models Default params for any model without specific config: temperature=0.6, top_p=0.95, top_k=20, min_p=0.01, presence_penalty=0.0, repetition_penalty=1.0 Models with entries in inference_defaults.json (Qwen3.5, Gemma-3, Llama, etc.) override these with their recommended values. Updated in: frontend DEFAULT_INFERENCE_PARAMS, backend Pydantic request models, and backend generate_chat_completion defaults. * studio: only trust_remote_code for unsloth/ models in AutoConfig Only set trust_remote_code=True when the model name starts with "unsloth/". All other models default to False for safety. * studio: move Generating spinner above the composer The "Generating" spinner was below the send message bar, causing the bar to jump up and down. Move it above the composer in both the regular thread view and the welcome/empty view. * studio: adjust toast close button position away from edge Move the X close button on toasts (like "Starting model...") from top-1.5 to top-3 and add right-3, giving more breathing room from the top-right corner. * studio: make Think button smaller with tighter icon-text gap Reduce gap from 1.5 to 0.5, padding from px-2.5/py-1 to px-2/py-0.5, and icon from size-3.5 to size-3. * studio: multiple onboarding and chat UX improvements - Move Generating spinner above composer (fixes jumping send bar) - Make Think button smaller with tighter icon-text gap - Chat card now inside grid (same size as Audio/Embeddings cards) - Rename "Chat Only" to "Chat" - Chat card requires Continue to proceed (no auto-advance) - Continue on Chat selection skips onboarding and goes to /chat - Tooltip (i) click on Chat card doesn't trigger navigation - Step 1 footer Back button goes back to splash (label is "Back") - Splash "Skip Onboarding" renamed to "Skip to Chat", navigates to /chat - Toast close button moved away from edge * studio: align Skip to Chat button, add Skip to footer - Sidebar "Skip to Chat" now uses primary (green) Button style with arrow icon, full width, aligned like step items. Shows on all steps. - Footer: added "Skip" outline button next to Continue that goes directly to /studio with progress saved (markOnboardingDone) * studio: change default max steps from 30 to 60 in toggle hook The DEFAULT_MAX_STEPS in use-max-steps-epochs-toggle.ts was still 30, used as fallback when toggling from epochs back to max steps. * studio: extend context length options to 262K CONTEXT_LENGTHS now includes 65536, 131072, 262144 in addition to the existing 512-32768 range. The onboarding step filters these by the model's max_position_embeddings (e.g. Nemotron-3-Nano-4B has 262144), showing powers of 2 up to the model's maximum. * studio: auto-select LoRA vs QLoRA based on model size and GPU memory After selecting a model in onboarding, detect the total model weight file size from HF Hub (safetensors/bin files). Then estimate memory needed: model_size_gb * 1.5 * context_scale, where context_scale is: - <=8192 tokens: 1.0x - >8192 tokens: 1.7x - >=16384 tokens: 2.0x - >=32768 tokens: 4.0x If the estimate fits in free GPU VRAM, default to LoRA (16-bit). Otherwise default to QLoRA (4-bit). Backend changes: - Add model_size_bytes to ModelDetails (models.py) - Add _get_model_size_bytes() using HfApi.repo_info (routes/models.py) - Add vram_free_gb to get_gpu_summary (hardware.py) Frontend changes: - Add autoSelectTrainingMethod() in training-config-store.ts - Called after model defaults are loaded - Add model_size_bytes to ModelConfigResponse type - Add vramFreeGb to HardwareInfo hook * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * studio: rename "Importing ML libraries..." to "Importing Unsloth..." * studio: show model/dataset in training status, fix LoRA/QLoRA casing - Training status now shows 'Training "model_name"' and 'Dataset = ...' instead of generic "Starting training..." - Fix Studio progress section to show QLoRA/LoRA instead of QLORA/LORA * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * studio: rename 'Skip to Chat' to 'Skip Onboarding' on splash screen * studio: add presence_penalty support for chat inference Add presence_penalty as a parameter across the full stack: - Backend: llama_cpp.py generate_chat_completion/with_tools, Pydantic models (inference.py), routes/inference.py pass-through - Frontend: InferenceParams type, DEFAULT_INFERENCE_PARAMS (0.0), chat-adapter.ts payload, chat-settings-sheet.tsx slider (0-2), model defaults loading from inference_defaults.json - Set Qwen3.5 default presence_penalty to 1.5 per official docs - Default for unknown models is 0.0 (off) * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * studio: fix Chat card deselecting Text and aligning with other cards * studio: fix presence_penalty not loading from inference defaults The inference_config.py load_inference_config() was not including presence_penalty in the returned config dict, so the Qwen3.5 default of 1.5 from inference_defaults.json never reached the frontend. Added it to the config builder. * studio: add delete button for cached models in model selector Add trash icon on each downloaded model row (GGUF and safetensors) with confirmation dialog. Backend DELETE /api/models/delete-cached endpoint uses huggingface_hub scan_cache_dir + delete_revisions to cleanly remove cached repos, refusing if the model is currently loaded. * studio: restore inference defaults, reasoning, and tools on page refresh On page refresh with a model already loaded, the frontend was not re-applying model-specific inference defaults (presence_penalty, temperature, etc.) or restoring reasoning/tools support flags. Backend: Add inference config, supports_reasoning, supports_tools, and context_length to InferenceStatusResponse. Frontend: In the refresh callback, when an active model is detected, apply mergeRecommendedInference and restore reasoning/tools flags with proper Qwen3.5 size-based defaults. * studio: fix delete dialog closing before async completes Prevent AlertDialogAction's default close behavior with e.preventDefault() so the dialog stays open during deletion. Also block onOpenChange dismiss while deleting is in progress. * fix: add Dict and Any imports to inference models * studio: fix Qwen3.5 reasoning threshold in frontend load path The frontend loadModel handler had the old threshold (<=2) for disabling reasoning on small Qwen3.5 models. Changed to <9 to match the backend. This was causing 4B to not properly disable thinking by default when auto-loaded. * studio: move GGUF delete to per-variant level For GGUF repos, the trash icon now appears on each downloaded variant row inside the quantization expander instead of on the repo-level row. Backend accepts optional variant param to delete specific GGUF files (blob + symlink) rather than the entire repo cache. * studio: restore ggufContextLength on page refresh The Max Tokens slider was capped at 32768 on page refresh because ggufContextLength was not restored from the status response. Now set it from statusRes.context_length on reconnect. * fix: remove <think> from Qwen3.5 response template marker The train-on-responses-only feature uses template markers to find where the assistant response starts. The Qwen3.5 response marker included '<think>\n' which is only present when thinking mode is enabled. With thinking disabled (default for <9B), the marker never matched, causing 100% of samples to be dropped. Changed response marker from '<|im_start|>assistant\n<think>\n' to '<|im_start|>assistant\n' which works regardless of thinking mode. * studio: fix sloth ASCII art alignment in training overlay * fix: correct sloth ASCII art alignment to match Unsloth banner * studio: add Python and terminal tool calling to chat Register python and terminal tools alongside web search. Python executor validates imports (stdlib only) via unsloth_zoo rl_environments, runs code in a subprocess sandbox with 5-min timeout and cancel support. Terminal executor blocks dangerous commands (rm, sudo, etc.) and runs in a temp directory. Update llama_cpp tool loop to show tool-specific status messages and pass cancel_event through to executors. Rename composer toggle from "Search" to "Tools" and show TerminalIcon for execution status pills. * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * studio: fix Nemotron/transformers 5.x support, onboarding navigation, port binding Backend: - Dynamic transformers 5.x detection via tokenizer_config.json fetch (checks for TokenizersBackend class, cached per-model) - Bump transformers 5.x version from 5.2.0 to 5.3.0 across all workers, setup scripts (setup.sh, setup.ps1) - Auto-enable trust_remote_code for unsloth/* models needing transformers 5.x (workaround for NemotronH config parsing bug in transformers) - Auto-install mamba-ssm/causal-conv1d for SSM models (NemotronH, Falcon-H1) with --no-build-isolation --no-deps to avoid torch version conflicts - Add SO_REUSEADDR to port check in run.py (fixes Colab proxy stale connection falsely reporting port as in-use) Frontend: - Fix "Skip to Chat" navigation: use window.location.href instead of React Router navigate() to bypass useEffect redirect race - Fix "Skip Onboarding" on splash: navigates to /studio (not /chat) - Fix onboarding guard: only check isOnboardingDone() on initial mount - Fix Chat card on step 1: add sr-only spacer for consistent alignment - Fix Chat+Text both selected: clear RadioGroup value when Chat is selected * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * studio: split tools toggle into Search and Code buttons Replace the single "Tools" toggle with two independent toggles: - "Search" (globe icon) enables web search only - "Code" (terminal icon) enables Python and terminal execution Add enabled_tools list field to the inference payload so the backend only registers the tools the user has toggled on. Both toggles appear in the main composer and the compare composer. * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * studio: fix tool calling import validation and error logging Replace unsloth_zoo-dependent import checker with a standalone ast-based validator using sys.stdlib_module_names. This properly blocks non-stdlib imports (numpy, requests, etc.) and returns a clear error message to the model so it can rewrite using only stdlib. Add full traceback to tool streaming error logs for debugging. * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * fix: parse gpt-oss harmony channels for clean safetensors chat output gpt-oss models emit multi-channel output via harmony protocol tokens (<|channel|>analysis<|message|>... and <|channel|>final<|message|>...). TextIteratorStreamer with skip_special_tokens=True strips the special tokens but leaves channel names concatenated with content, producing garbled output like "analysisWe need to...assistantfinalHello!". Add HarmonyTextStreamer that decodes with skip_special_tokens=False, parses harmony markup via regex, and emits <think>analysis</think> for the analysis channel and plain text for the final channel -- reusing the existing frontend reasoning UI. Also expose supports_reasoning=True for non-GGUF gpt-oss models in the /status endpoint so the frontend enables the Think toggle. * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * studio: use unsloth_zoo for Python sandbox validation Set UNSLOTH_IS_PRESENT=1 and import check_python_modules and check_signal_escape_patterns directly from unsloth_zoo instead of a standalone fallback. This gives us the full Unsloth validation including stdlib-only import checks and signal/timeout escape pattern detection. * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * studio: allow all imports in Python tool sandbox Remove stdlib-only import restriction. Keep signal escape pattern detection via unsloth_zoo for safety. * studio: fix ReadTimeout on tool streaming final pass The 0.5s read timeout used for cancel-checking during streaming also fires when waiting for the first response from llama-server (e.g. reasoning model thinking for 15+ seconds). Add _stream_with_retry() context manager that retries on ReadTimeout while checking cancel_event, so the model has unlimited time to think before producing the first token. Applied to both the regular streaming path and the tool-calling final pass. * fix: rewrite HarmonyTextStreamer with stateful incremental parsing The delta-on-transformed approach had two critical bugs: 1. Before the full <|channel|>X<|message|> pattern was complete, the strip-tokens fallback emitted "analysis" as plain text. Then when the regex matched, _transform returned a completely different format (<think>...</think>) and the delta was computed against the wrong base string, producing fragments like "think>", "nk>", ">". 2. Even with full matches, the closing </think> tag shifted position as content grew, so text[prev_len:] produced garbled deltas. Replace with stateful incremental parsing that: - Buffers until a complete channel+message pair is seen - Emits <think> once when analysis channel first appears - Streams analysis content deltas (computed on channel content directly) - Emits </think> once when final channel first appears - Streams final content deltas - Closes open think tags in end() Also skip the generic all_special_tokens stripping in _clean_generated_text for gpt-oss since HarmonyTextStreamer already produces clean output and the generic stripping was mangling <think> tags. * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * fix: strip all <|...|> tokens in gpt-oss cleanup, not just harmony subset The gpt-oss tokenizer has added tokens like <|return|> (id=200002) that are not part of the harmony channel protocol but can leak into output. The previous regex only stripped channel|message|start|end tokens. Broaden the _clean_generated_text regex for gpt-oss to <\|[a-z_]+\|> which catches all pipe-delimited tokens (return, constrain, reserved, etc.) without matching <think>/<\/think> tags. Verified: gpt-oss all_special_tokens are only <|return|>, <|reserved_200017|>, <|startoftext|> -- none overlap with <think>. The harmony tokens (channel, message, start, end) are added_tokens but not in all_special_tokens. * fix: hide config-only model repos from cached models list Repos that only have metadata/config files cached (no .safetensors or .bin weight files) were showing up in the Downloaded list with tiny sizes like "1.8 KB" or "24 KB". These are just leftover config snapshots from architecture checks, not usable models. Filter the cached-models endpoint to only include repos that contain actual model weight files (.safetensors or .bin). * studio: fix toast description text contrast in dark mode Add explicit !text-muted-foreground to toast description classNames so secondary text (e.g. "Releases VRAM and resets inference state.") is readable in dark mode. * studio: fix Chat card icon alignment with size-4 spacer Replace sr-only span (takes no space) with a size-4 shrink-0 div matching the RadioGroupItem dimensions in other cards, so the Chat icon aligns vertically with Text/Audio/Vision/Embeddings icons. --------- Co-authored-by: workspace <user@workspace.local> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Manan17 <shahmanan170602@gmail.com> Co-authored-by: Roland Tannous <rolandtannous@gravityq.ai> |
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Daniel Han
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44dcf30b9b |
studio: per-model inference defaults, GGUF slider fix, reasoning toggle (#4325)
* studio: extract param count from model name as fallback When HuggingFace API doesn't return totalParams for a model, extract the param count from the model name (e.g. "Qwen3-0.6B" -> "0.6B", "Llama-3.2-1B-Instruct" -> "1B"). Applied to both the recommended list and HF search results. * studio: read GGUF context_length via fast header parser, set max tokens - Fast GGUF metadata reader (~30-55ms) parses only KV header, skips tensor data and large arrays (tokenizer vocab etc) - Extracts context_length and chat_template from GGUF metadata - Returns context_length in LoadResponse for frontend to use - Frontend sets maxTokens to actual context_length for GGUFs (e.g. 262144 for Qwen3.5-9B, 131072 for Qwen2.5-7B) - Max Tokens slider shows "Max" and is locked for GGUFs - Auto-load path also uses actual context_length from load response - Toast auto-dismiss (5s) and close button for auto-load toast * studio: GGUF TTS audio support (from PR #4318) Add GGUF TTS audio generation via llama-server. When a GGUF model loads, the backend probes its vocabulary to detect audio codecs (SNAC/BiCodec/DAC/CSM/Whisper). If detected, the codec is pre-loaded and the model is reported as audio to the frontend. During chat, TTS models route to the audio generation path which sends a per-codec prompt to llama-server's /completion endpoint, extracts generated tokens/text, and decodes to WAV using AudioCodecManager. Also strips base64 audio data from prior assistant messages to prevent context overflow. Co-authored-by: Manan Shah <mananshah511@gmail.com> * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Remove package-lock.json from tracking * studio: per-model inference defaults, GGUF max tokens fix, reasoning toggle - Add inference_defaults.json with per-model-family sampling parameters for ~50 families (Qwen3.5, Qwen3, Gemma-3, Llama-3, DeepSeek, etc.). Values sourced from unslothai/docs and Ollama params blobs. - Family-based lookup in inference_config.py: extracts model family from identifier, matches against patterns (longest match first), merges with priority: model-specific YAML > family JSON > default.yaml. - Fix GGUF Max Tokens slider locked at "Max": store ggufContextLength separately from maxTokens so the slider is adjustable (step=64). - Fix Ministral YAML: top_p was literal string "default", now 0.95. - Add reasoning toggle for thinking models (Qwen3.5, Qwen3, DeepSeek-R1, DeepSeek-V3.1, etc.): detect enable_thinking support from GGUF chat template metadata, pass --jinja to llama-server, send chat_template_kwargs per-request. Frontend shows "Reasoning is ON/OFF" pill button next to attachment button in composer. * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * studio: remove default system prompt injection Backend was injecting "You are a helpful AI assistant." when no system prompt was provided. Neither unslothai/docs nor Ollama specify a default system prompt for most models. Now defaults to empty string, letting the model's own chat template handle system behavior. * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * studio: use lightbulb icons and "Think" label for reasoning toggle Lightbulb on when thinking enabled, lightbulb-off when disabled. Label is just "Think" in both states; grayed out styling when off. * studio: fix HTML file upload breaking chat Replace SimpleTextAttachmentAdapter with custom TextAttachmentAdapter (excludes text/html) and HtmlAttachmentAdapter that strips tags via DOMParser, removing scripts/styles and extracting readable text content instead of dumping raw HTML markup into the conversation. * studio: show chat template in Configuration panel Display the model's Jinja2 chat template in a new "Chat Template" section under Settings (now open by default). For GGUFs, reads from GGUF metadata; for safetensors, reads from tokenizer.chat_template. Template is editable with a "Restore default chat template" button that appears when modified. Section only shows when a model with a chat template is loaded. * studio: editable chat template with Apply & Reload Chat template section now functional: - Editing the template shows "Apply & Reload" (reloads model with custom template) and "Revert changes" buttons - For GGUFs: writes template to temp .jinja file, passes --chat-template-file to llama-server on reload - For non-GGUF: passes chat_template_override in load request - Settings section now open by default - selectModel supports forceReload to reload same model * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * studio: fix DeepSeek reasoning detection and auto-load metadata - Set _model_identifier before _read_gguf_metadata so DeepSeek "thinking" template detection works (was always None before) - Populate ggufContextLength, supportsReasoning, reasoningEnabled, defaultChatTemplate in autoLoadSmallestModel GGUF path * studio: add spacing before BETA badge in navbar Add gap-1.5 on the logo Link container to space the BETA label from the wordmark. Co-authored-by: Imagineer99 <Imagineer99@users.noreply.github.com> * studio: vertically center BETA badge with logo --------- Co-authored-by: Manan Shah <mananshah511@gmail.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Imagineer99 <Imagineer99@users.noreply.github.com> |
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Daniel Han
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20c6d9a26a |
Set repetition_penalty default to 1.0 (disabled) everywhere
Change all repetition_penalty defaults from 1.1 (or 1.05/1.2 in presets) to 1.0 across the entire backend and frontend. Most models handle repetition well on their own and a non-1.0 penalty can degrade output quality, especially for code, structured output, and creative tasks. Files changed: - Backend: inference.py, llama_cpp.py, orchestrator.py, worker.py, models/inference.py (Field defaults) - Frontend: chat-settings-sheet.tsx (Creative/Precise presets), runtime-provider.tsx (auto-title generation) |
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Manan Shah
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b2dce8e3a8 |
chat only with gguf for mac devices (#4300)
* chat only with gguf for mac devices * resolving gpt comments * add change-password for chat only * hide lora adaptors dropdown * solving gpt comments * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * addressing the comment * fixing auth flow --------- Co-authored-by: Datta Nimmaturi <venkatadattasainimmaturi@gmail.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> |
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Roland Tannous
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477e68675b |
Fix: Compare Mode Deadlock, Cancel Event Poisoning & IPC Optimization (#4303)
* fix: resolve compare mode deadlock, cancel_event poisoning, and add dispatcher-based IPC optimization * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * revert to 2048 tokens * refactor: extract dispatcher timeout values into named constants * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * fix: guard dispatcher shutdown against active compare mailboxes --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> |
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Daniel Han
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88c7b08faa |
fix: prevent ai-assist model config RCE via untrusted Hugging Face repos (#4274)
* fix: disable remote code loading for ai-assist model hint lookup * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Co-authored-by: Roland Tannous <115670425+rolandtannous@users.noreply.github.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> |
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Roland Tannous
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e539965740 | fix error for chat template | ||
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Roland Tannous
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47654cb91c | Final cleanup | ||
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Roland Tannous
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a2baf80511 | Update license headers | ||
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Roland Tannous
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817f2e8dcc | feat: integrate structlog, configure workers for prod logging, and migrate print statements | ||
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Roland Tannous
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d882678fe4 | Add AGPL-3.0 SPDX headers to all source files | ||
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samit
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86e94b5844 | exposed trust_remote_code through the UI | ||
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Manan17
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80b704d7b7 | Audio_VLM bug fix | ||
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Manan17
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9909111982 | resolved merge conflicts | ||
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Manan17
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c636fd5a42 | code cleanup | ||
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Manan17
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c48437848d | revamping up the code and adding inference | ||
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samit
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862b4100d2 | deleted duplicate definitions | ||
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Manan17
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aeb198f52d | Fixing base model export issue for vlms | ||
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Roland Tannous
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dbbcdb4f09 | feat: clear unsloth_compiled_cache on startup, shutdown, and between model loads | ||
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Roland Tannous
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c051e3d532 | fix: load proper vision processor from base model when FastVisionModel returns raw tokenizer, add tokenize=False to vision chat template | ||
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Manan17
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f6ebeb1d42 | Mapping proper tokenizer for VLMs | ||
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Manan17
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3fa9e773c2 | fixed the vlm's text only errors | ||
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Manan17
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fdeccec259 | Fixing compare feature | ||
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Shine1i
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0db7da96cc |
feat: support disabling top-k sampling with -1 and standardize normalization logic
- Updated top-k parameter range to accept -1 in models and frontend. - Added utility to normalize top-k for backend compatibility. |
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Roland Tannous
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909955767b | feat: add min_p sampling parameter to /chat/completions generation pipeline | ||
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Shine1i
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571959e383 | feat: add cancelation support for chat generation and streaming tasks | ||
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Roland Tannous
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be3934860f | strip extra debug statements | ||
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Roland Tannous
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3ff3def555 | replace model unloading and peft loading mechanism for compare feature | ||
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Roland Tannous
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e7ae901737 | del model.peft_config instead of using model.delete_adapter | ||
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Roland Tannous
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7d8e991c1f | added print statements for activate_lora_adapter | ||
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Roland Tannous
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6fefbe9f0b | swipped logger for print statements as logger isn't propagating | ||
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Roland Tannous
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d0b94eae75 | added logging | ||
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Roland Tannous
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b5c8136957 | exclude default from model.delete_adapter | ||
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Roland Tannous
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35a6e40268 | _apply_adapter_state now calls revert_to_base_model and activate_lora_adapter properly | ||
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Roland Tannous
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f67ee58347 | feat(inference): add use_adapter field for per-request adapter toggling in compare mode | ||
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Roland Tannous
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418a374125 | migrate _generate_vision_response to use TextIteratorStreamer + background thread | ||
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Roland Tannous
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da1cde971c | use get_device() for device selection and clear_gpu_cache() for GPU memory cleanup in inference, trainer, and export | ||
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Roland Tannous
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b4ec0389f0 | refactor/inference-api-routes-part-1 | ||
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Roland Tannous
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62ddcfa019 | Refactor [dataset_utils.py](cci:7://file:///home/support/new-ui-prototype/studio/backend/utils/datasets/dataset_utils.py:0:0-0:0) into focused modules | ||
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Roland Tannous
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544d6944d1 | root studio folder |
Renamed from backend/core/inference/inference.py (Browse further)