6412efd7d9* Auto-detect completion masking markers with template table fallback Studio's train_on_completions previously relied only on the hardcoded MODEL_TO_TEMPLATE_MAPPER / TEMPLATE_TO_RESPONSES_MAPPER tables and silently disabled masking when a model was not in the table, so unmapped models (LFM2-8B-A1B, DeepSeek, and others) trained on full sequences without telling the user. Several mapped templates (glm, mistral, llama, starling, zephyr, qwen3-thinking) also carried markers that mask every assistant token, which made every row drop in the post-masking filter. Both training callsites (CUDA trainer.py and MLX worker.py) now share utils.datasets.completion_masking.apply_completion_masking: - Try unsloth_zoo chat template auto-detection first; it raises loudly when the template cannot be parsed and never masks the EOS token. - gpt-oss models keep their manual markers so non-final assistant <|end|> tokens stay trained, matching current behavior. - If auto-detection raises, fall back to the template table exactly as before. - If the table also misses, emit an explicit user-visible warning that completion masking could not be applied and full-sequence training will occur, instead of a quiet log line. The >30 percent dropped-rows safety net in trainer.py now guards the auto path as well. Table consumers for inference and chat templates are unchanged. Validated against one representative tokenizer for every template in TEMPLATE_TO_RESPONSES_MAPPER plus the unmapped models: no template regresses; unit tests cover the four decision paths. * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Restrict masking fallback to marker detection failures The auto branch wrapped the whole train_on_responses_only call, so a real failure while applying the masking (dataset map, tokenization) was treated as a detection miss and training silently proceeded on full sequences. Detect markers separately via get_chat_template_parts (test seam via detect_fn), then apply them with errors propagating, matching the manual path. Tokenizers with preset unsloth marker attrs skip detection and call bare so zoo reuses the stored parts. * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Fail the run when applying completion masking raises The helper already falls back internally on detection failures and returns applied=False on a double miss, so an exception reaching the callsites is a real failure applying the masking. Remove the callsite catches that downgraded it to full-sequence training; the run now fails visibly instead. Also use the explicit re-export alias form in utils/datasets/__init__.py for the two new names, satisfying the import-hoist source lint. * Import completion masking from its submodule The import-hoist source lint counts only real name loads, so package-level re-exports of the two new names cannot satisfy it. Import apply_completion_masking from utils.datasets.completion_masking directly at both callsites and leave utils/datasets/__init__.py untouched. * Completion masking: gpt-oss renames and MLX raw/alpaca parity Renamed or private gpt-oss checkpoints are name-detected as gpt-oss but miss the exact-name table; default them to the gpt-oss template markers instead of falling through to full-sequence training. Gate the MLX masking call on not raw_text_mode and format_type != alpaca, mirroring the CUDA path: raw/CPT text has no chat turns to mask and Alpaca-rendered text lacks the tokenizer's chat markers. * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Define raw_text_mode outside the MLX feature-detect block With an older zoo lacking the append_eos config field, the masking gate referenced raw_text_mode before assignment. Hoist the assignment above the feature detection so both consumers see it. * Gate MLX masking on the formatter's resolved format format_type auto can resolve to alpaca or raw text; the masking skip checked only the requested value, so auto-detected Alpaca data got chat-template markers applied to rendered prompt text. Track the final_format returned by format_and_template_dataset and gate on it, matching the CUDA path. * Unwrap the mlx-lm TokenizerWrapper before marker checks The wrapper delegates plain reads to the wrapped HF tokenizer but hides underscore attrs, so preset unsloth markers were invisible and detection relied on the loader's call patch. Unwrap to the real tokenizer first, as the zoo MLX resolver does. * Tighten masking comments * gpt-oss: auto-detect markers first like every other template The quantized and BF16 gpt-oss checkpoints ship a chat template without the channel final header, so the pinned manual markers match nothing there and masking trained zero tokens. Auto-detection derives markers from whichever template the checkpoint ships and keeps the final terminator trained; the manual gpt-oss markers remain the detection failure fallback, including for renamed checkpoints. * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Tighten comments --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
3802 lines
149 KiB
Python
3802 lines
149 KiB
Python
# SPDX-License-Identifier: AGPL-3.0-only
|
||
# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
|
||
|
||
"""
|
||
Training subprocess entry point.
|
||
|
||
Each job runs in a fresh subprocess (mp.get_context("spawn")): a clean
|
||
interpreter with no stale module state, which solves transformers
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||
version-switching. Pattern follows core/data_recipe/jobs/worker.py.
|
||
"""
|
||
|
||
from __future__ import annotations
|
||
|
||
import structlog
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||
from loggers import get_logger
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||
import math
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||
import os
|
||
import shutil
|
||
import sys
|
||
import time
|
||
import traceback
|
||
import gc
|
||
import re
|
||
import types
|
||
import subprocess as _sp
|
||
from pathlib import Path
|
||
from typing import Any, Callable
|
||
|
||
# ── WSL AMD Strix Halo (gfx1151): enable ROCDXG before any torch import ──────
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# Mirrors main.py. In WSL the AMD GPU is reached via the ROCDXG bridge
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# (librocdxg.so over /dev/dxg), which HSA loads only when HSA_ENABLE_DXG_
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# DETECTION=1 is set before torch touches the GPU. A worker spawned outside a
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# login shell misses the installer's persisted env and falls back to CPU.
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||
# Gated to no-op unless BOTH /dev/dxg and librocdxg.so exist, so native Linux
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# ROCm, NVIDIA, macOS and Windows are unaffected.
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if sys.platform.startswith("linux") and "HSA_ENABLE_DXG_DETECTION" not in os.environ:
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try:
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if os.path.exists("/dev/dxg") and any(
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os.path.exists(_p + "/librocdxg.so") for _p in ("/opt/rocm/lib", "/opt/rocm/lib64")
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):
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os.environ["HSA_ENABLE_DXG_DETECTION"] = "1"
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except Exception:
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pass
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||
logger = get_logger(__name__)
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from utils.hardware import apply_gpu_ids
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from utils.training_runs import build_default_output_dir_name
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from utils.wheel_utils import (
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direct_wheel_url,
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flash_attn_wheel_url,
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has_blackwell_gpu,
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install_wheel,
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||
probe_torch_wheel_env,
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url_exists,
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||
)
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||
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||
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||
def _output_dir_from_resume_checkpoint(resume_from_checkpoint: str | None) -> str | None:
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if not resume_from_checkpoint:
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return None
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path = Path(resume_from_checkpoint)
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return str(path.parent if path.name.startswith("checkpoint-") else path)
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||
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||
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_CAUSAL_CONV1D_RELEASE_TAG = "v1.6.1.post4"
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||
_CAUSAL_CONV1D_PACKAGE_VERSION = "1.6.1"
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||
_MAMBA_SSM_RELEASE_TAG = "v2.3.1"
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||
_MAMBA_SSM_PACKAGE_VERSION = "2.3.1"
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_FLASH_ATTN_RUNTIME_MIN_SEQ_LEN = 32768
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_FLASH_ATTN_SKIP_ENV = "UNSLOTH_STUDIO_SKIP_FLASHATTN_INSTALL"
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# apache-tvm-ffi 0.1.10/0.1.11 crash Triton with "CUDA: misaligned address" on sm_100.
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_TILELANG_PACKAGE_VERSION = "0.1.8"
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_APACHE_TVM_FFI_PACKAGE_VERSION = "0.1.9"
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_TILELANG_SKIP_ENV = "UNSLOTH_STUDIO_SKIP_TILELANG_INSTALL"
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# Pin both so plain pip can't silently upgrade torch under the worker (fla-core needs torch>=2.7).
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_FLA_PACKAGE_VERSION = "0.5.0"
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_FLA_CORE_PACKAGE_VERSION = "0.5.0"
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_FLA_SKIP_ENV = "UNSLOTH_STUDIO_SKIP_FLA_INSTALL"
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# `--no-deps` saves torch but loses fla-core's transitive deps; `packaging` is also undeclared upstream.
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_FLA_RUNTIME_DEPS = ("einops", "packaging", "triton")
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_FLA_MIN_TORCH = (2, 7)
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_FLA_MIN_PYTHON = (3, 10)
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||
# tilelang 0.1.8 ships wheels only for these Linux arches and macOS arm64; never fall back to its 93MB sdist.
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_TILELANG_SUPPORTED_LINUX_MACHINES = frozenset(("x86_64", "amd64", "aarch64", "arm64"))
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||
_TILELANG_INSTALL_TIMEOUT_S = 600
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||
_TVM_FFI_BROKEN_VERSIONS = ("0.1.10", "0.1.11")
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_FAST_PATH_HOOKS_SKIP_ENV = "UNSLOTH_STUDIO_SKIP_FAST_PATH_HOOKS"
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||
|
||
# Module-level handle so the torch.library.Library registration survives past
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||
# run_training_process() and isn't GC'd mid-run.
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_WINDOWS_ROCM_GROUPED_MM_LIB = None
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||
|
||
# Subprocesses don't inherit os.add_dll_directory registrations. Replicate
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# main.py's Windows ROCm DLL setup so the first `import torch` finds
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# amdhip64.dll. Handles retained at module scope so they aren't GC'd.
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_ROCM_DLL_HANDLES: list = []
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if sys.platform == "win32":
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||
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def _add_rocm_dll_dirs_worker() -> None:
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_candidates: list[str] = []
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for _var in ("HIP_PATH", "ROCM_PATH"):
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_val = os.environ.get(_var)
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||
if _val:
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_candidates.append(os.path.join(_val, "bin"))
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||
_default_root = os.path.join(
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os.environ.get("ProgramFiles", r"C:\Program Files"), "AMD", "ROCm"
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)
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||
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||
def _ver_key(name: str) -> tuple:
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||
# Numeric tuple key so "10.0" sorts after "7.0".
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||
parts = []
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||
for chunk in name.split("."):
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try:
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parts.append((0, int(chunk)))
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||
except ValueError:
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parts.append((1, chunk))
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||
return tuple(parts)
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||
|
||
try:
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||
if os.path.isdir(_default_root):
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||
for _ver in sorted(os.listdir(_default_root), key = _ver_key, reverse = True):
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||
_bin = os.path.join(_default_root, _ver, "bin")
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if os.path.isdir(_bin):
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_candidates.append(_bin)
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except OSError:
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pass
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for _d in _candidates:
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if os.path.isdir(_d):
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try:
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_ROCM_DLL_HANDLES.append(os.add_dll_directory(_d))
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except (OSError, AttributeError):
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||
pass
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_add_rocm_dll_dirs_worker()
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del _add_rocm_dll_dirs_worker
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||
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def _model_wants_causal_conv1d(model_name: str) -> bool:
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name = model_name.lower()
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return any(
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key in name
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for key in (
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"qwen3.5",
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"qwen3_5",
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"qwen3.6",
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"qwen3_6",
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"qwen3-next",
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"qwen3_next",
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"nemotron_h",
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"nemotron-h",
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"nemotron-3-nano",
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"falcon_h1",
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||
"falcon-h1",
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||
"granite-4.0-h",
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"granitemoehybrid",
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"lfm2",
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||
)
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)
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def _hipcc_gcc_install_dir() -> str | None:
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"""Highest-numbered ``/usr/lib/gcc/x86_64-linux-gnu/<N>`` that has BOTH the
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gcc runtime dir AND ``/usr/include/c++/<N>`` headers, or None.
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Ubuntu 24.04 ships gcc-14 runtime but not ``/usr/include/c++/14``; ROCm
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clang-20 picks the highest runtime dir, finds no ``<cstdlib>``, and the HIP
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build fails. The returned path is passed to clang via
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``--gcc-install-dir``. Mirrors bbf004c in studio/setup.sh (PR #5301).
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"""
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if not sys.platform.startswith("linux"):
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return None
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import platform as _platform
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if _platform.machine().lower() != "x86_64":
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return None
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for _ver in (14, 13, 12, 11):
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_runtime = f"/usr/lib/gcc/x86_64-linux-gnu/{_ver}/include"
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_headers = f"/usr/include/c++/{_ver}"
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if os.path.isdir(_runtime) and os.path.isdir(_headers):
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return f"/usr/lib/gcc/x86_64-linux-gnu/{_ver}"
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return None
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||
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||
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def _install_package_wheel_first(
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*,
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event_queue: Any,
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import_name: str,
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display_name: str,
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||
pypi_name: str,
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||
pypi_version: str | None = None,
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||
filename_prefix: str | None = None,
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||
release_tag: str | None = None,
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||
release_base_url: str | None = None,
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||
wheel_url_builder: Callable[[dict[str, str] | None], str | None] | None = None,
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pypi_spec: str | None = None,
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pypi_status_message: str | None = None,
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) -> bool:
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try:
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__import__(import_name)
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logger.info("%s already installed", display_name)
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return True
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except ImportError:
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pass
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||
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env = probe_torch_wheel_env(timeout = 30)
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if wheel_url_builder is not None:
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wheel_url = wheel_url_builder(env)
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else:
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wheel_url = direct_wheel_url(
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filename_prefix = filename_prefix,
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package_version = pypi_version,
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release_tag = release_tag,
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release_base_url = release_base_url,
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env = env,
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||
)
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||
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if wheel_url is None:
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logger.info("No compatible %s wheel candidate", display_name)
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elif url_exists(wheel_url):
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_send_status(event_queue, f"Installing {display_name} for faster training...")
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for installer, result in install_wheel(
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wheel_url,
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python_executable = sys.executable,
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use_uv = bool(shutil.which("uv")),
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||
run = _sp.run,
|
||
):
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||
if result.returncode == 0:
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logger.info("Installed prebuilt %s wheel successfully", display_name)
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||
return True
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||
logger.warning(
|
||
"%s failed to install %s wheel:\n%s",
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||
installer,
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||
display_name,
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||
result.stdout,
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||
)
|
||
else:
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||
logger.info("No published %s wheel found: %s", display_name, wheel_url)
|
||
|
||
is_hip = env and env.get("hip_version")
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||
if is_hip and not shutil.which("hipcc"):
|
||
logger.error(
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"%s requires hipcc for source compilation on ROCm. "
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||
"Install the ROCm HIP SDK: https://rocm.docs.amd.com",
|
||
display_name,
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||
)
|
||
_send_status(
|
||
event_queue,
|
||
f"{display_name}: hipcc not found (ROCm HIP SDK required)",
|
||
)
|
||
return False
|
||
|
||
if pypi_spec is None:
|
||
pypi_spec = f"{pypi_name}=={pypi_version}"
|
||
|
||
if pypi_status_message is None:
|
||
if is_hip:
|
||
pypi_status_message = (
|
||
f"Compiling {display_name} from source for ROCm "
|
||
"(this may take several minutes)..."
|
||
)
|
||
else:
|
||
pypi_status_message = f"Installing {display_name} from PyPI for faster training..."
|
||
|
||
_send_status(event_queue, pypi_status_message)
|
||
|
||
# Prefer uv for faster dependency resolution when available
|
||
plain_pypi_install = pypi_version is None
|
||
if plain_pypi_install:
|
||
if shutil.which("uv"):
|
||
pypi_cmd = [
|
||
"uv",
|
||
"pip",
|
||
"install",
|
||
"--python",
|
||
sys.executable,
|
||
pypi_spec,
|
||
]
|
||
else:
|
||
pypi_cmd = [sys.executable, "-m", "pip", "install", pypi_spec]
|
||
else:
|
||
if shutil.which("uv"):
|
||
pypi_cmd = [
|
||
"uv",
|
||
"pip",
|
||
"install",
|
||
"--python",
|
||
sys.executable,
|
||
"--no-build-isolation",
|
||
"--no-deps",
|
||
]
|
||
# Avoid stale cache artifacts from partial HIP source builds
|
||
if is_hip:
|
||
pypi_cmd.append("--no-cache")
|
||
pypi_cmd.append(pypi_spec)
|
||
else:
|
||
pypi_cmd = [
|
||
sys.executable,
|
||
"-m",
|
||
"pip",
|
||
"install",
|
||
"--no-build-isolation",
|
||
"--no-deps",
|
||
"--no-cache-dir",
|
||
pypi_spec,
|
||
]
|
||
|
||
# ROCm source compilation can take 10-30 min; use a generous timeout.
|
||
# Non-HIP installs keep the pre-existing "no timeout" behaviour so unrelated
|
||
# slow installs (e.g. causal-conv1d source build on Linux aarch64, or
|
||
# unsupported torch/CUDA combos) aren't aborted at 5 minutes.
|
||
_run_kwargs: dict[str, Any] = {
|
||
"stdout": _sp.PIPE,
|
||
"stderr": _sp.STDOUT,
|
||
"text": True,
|
||
}
|
||
if is_hip:
|
||
_run_kwargs["timeout"] = 1800
|
||
# On Ubuntu 24.04 + ROCm clang-20 the HIP source build dies on a missing
|
||
# <cstdlib> (gcc-14 runtime dir lacks C++ headers). Inject
|
||
# --gcc-install-dir for a gcc whose headers exist, respecting any
|
||
# pre-existing one. Mirrors bbf004c in studio/setup.sh (PR #5301).
|
||
_existing_flags = os.environ.get("HIPCC_COMPILE_FLAGS_APPEND", "")
|
||
if "--gcc-install-dir" not in _existing_flags:
|
||
_gcc_dir = _hipcc_gcc_install_dir()
|
||
if _gcc_dir is not None:
|
||
_appended = (f"{_existing_flags} --gcc-install-dir={_gcc_dir}").strip()
|
||
_env = _run_kwargs.get("env", os.environ).copy()
|
||
_env["HIPCC_COMPILE_FLAGS_APPEND"] = _appended
|
||
_run_kwargs["env"] = _env
|
||
logger.info(
|
||
"HIP source build for %s: appended "
|
||
"--gcc-install-dir=%s to HIPCC_COMPILE_FLAGS_APPEND",
|
||
display_name,
|
||
_gcc_dir,
|
||
)
|
||
|
||
try:
|
||
result = _sp.run(pypi_cmd, **_run_kwargs)
|
||
except _sp.TimeoutExpired:
|
||
logger.error(
|
||
"%s installation timed out after %ds",
|
||
display_name,
|
||
_run_kwargs.get("timeout"),
|
||
)
|
||
_send_status(
|
||
event_queue,
|
||
f"{display_name} installation timed out after " f"{_run_kwargs.get('timeout')}s",
|
||
)
|
||
return False
|
||
|
||
if result.returncode != 0:
|
||
if is_hip:
|
||
# Surface a clear error for ROCm source build failures
|
||
error_lines = (result.stdout or "").strip().splitlines()
|
||
snippet = "\n".join(error_lines[-5:]) if error_lines else "(no output)"
|
||
logger.error(
|
||
"Failed to compile %s for ROCm:\n%s",
|
||
display_name,
|
||
result.stdout,
|
||
)
|
||
_send_status(
|
||
event_queue,
|
||
f"Failed to compile {display_name} for ROCm. "
|
||
"Check that hipcc and ROCm development headers are installed.\n"
|
||
f"{snippet}",
|
||
)
|
||
else:
|
||
if sys.platform == "win32":
|
||
# No prebuilt wheel and no source toolchain on Windows --
|
||
# expected for packages like causal-conv1d. Log at info so
|
||
# users aren't alarmed by what looks like an error.
|
||
logger.info(
|
||
"%s is not available on Windows (no prebuilt wheel); skipping",
|
||
display_name,
|
||
)
|
||
logger.debug("Install output:\n%s", result.stdout)
|
||
else:
|
||
logger.error(
|
||
"Failed to install %s from PyPI:\n%s",
|
||
display_name,
|
||
result.stdout,
|
||
)
|
||
return False
|
||
|
||
if is_hip:
|
||
logger.info("Compiled and installed %s from source for ROCm", display_name)
|
||
else:
|
||
logger.info("Installed %s from PyPI", display_name)
|
||
return True
|
||
|
||
|
||
def _ensure_causal_conv1d_fast_path(event_queue: Any, model_name: str) -> None:
|
||
if not _model_wants_causal_conv1d(model_name):
|
||
return
|
||
if sys.platform == "win32":
|
||
logger.info("causal-conv1d: no prebuilt wheel for Windows; skipping")
|
||
return
|
||
|
||
_install_package_wheel_first(
|
||
event_queue = event_queue,
|
||
import_name = "causal_conv1d",
|
||
display_name = "causal-conv1d",
|
||
pypi_name = "causal-conv1d",
|
||
pypi_version = _CAUSAL_CONV1D_PACKAGE_VERSION,
|
||
filename_prefix = "causal_conv1d",
|
||
release_tag = _CAUSAL_CONV1D_RELEASE_TAG,
|
||
release_base_url = "https://github.com/Dao-AILab/causal-conv1d/releases/download",
|
||
)
|
||
|
||
|
||
def _installed_torch_version_tuple() -> tuple[int, int] | None:
|
||
"""Return ``(major, minor)`` of the installed torch, else None."""
|
||
try:
|
||
from importlib.metadata import version as _pkg_version
|
||
|
||
raw = _pkg_version("torch").split("+", 1)[0]
|
||
parts = raw.split(".")
|
||
return (int(parts[0]), int(parts[1]))
|
||
except Exception:
|
||
return None
|
||
|
||
|
||
def _flash_linear_attention_importable() -> bool:
|
||
"""Catch any exception (not just ImportError) so a broken native lib doesn't abort the worker."""
|
||
try:
|
||
import fla.modules # noqa: F401
|
||
import fla.ops.gated_delta_rule # noqa: F401
|
||
return True
|
||
except Exception as exc:
|
||
logger.warning(
|
||
"flash-linear-attention is not importable; continuing with install/fallback: %s",
|
||
exc,
|
||
)
|
||
return False
|
||
|
||
|
||
def _flash_linear_attention_current(already_importable: bool | None = None) -> bool:
|
||
"""True iff FLA imports AND is at the pinned version (older FLA lacks gated_delta_rule kernels)."""
|
||
if already_importable is None:
|
||
already_importable = _flash_linear_attention_importable()
|
||
if not already_importable:
|
||
return False
|
||
try:
|
||
from importlib.metadata import version as _pkg_version
|
||
from packaging.version import Version
|
||
|
||
fla_v = Version(_pkg_version("flash-linear-attention"))
|
||
core_v = Version(_pkg_version("fla-core"))
|
||
return fla_v >= Version(_FLA_PACKAGE_VERSION) and core_v >= Version(
|
||
_FLA_CORE_PACKAGE_VERSION
|
||
)
|
||
except Exception as exc:
|
||
logger.warning(
|
||
"flash-linear-attention importable but version check failed; treating as stale: %s",
|
||
exc,
|
||
)
|
||
return False
|
||
|
||
|
||
def _ensure_flash_linear_attention_unconditional(event_queue: Any) -> bool:
|
||
"""Install pinned FLA + fla-core with --no-deps. Returns True iff importable post-call."""
|
||
if os.getenv(_FLA_SKIP_ENV) == "1":
|
||
return False
|
||
if sys.platform == "win32":
|
||
logger.info("Skipping flash-linear-attention install: no prebuilt wheel for Windows")
|
||
return False
|
||
if sys.version_info < _FLA_MIN_PYTHON:
|
||
logger.info(
|
||
"Skipping flash-linear-attention install: requires Python >= %d.%d, have %s",
|
||
_FLA_MIN_PYTHON[0],
|
||
_FLA_MIN_PYTHON[1],
|
||
sys.version.split()[0],
|
||
)
|
||
return False
|
||
torch_ver = _installed_torch_version_tuple()
|
||
if torch_ver is not None and torch_ver < _FLA_MIN_TORCH:
|
||
_send_status(
|
||
event_queue,
|
||
(
|
||
f"Skipping flash-linear-attention install: fla-core requires "
|
||
f"torch>={_FLA_MIN_TORCH[0]}.{_FLA_MIN_TORCH[1]}, have "
|
||
f"{torch_ver[0]}.{torch_ver[1]}"
|
||
),
|
||
)
|
||
return False
|
||
|
||
# Probe once; reuse so the --force-reinstall decision and the short-circuit
|
||
# share the same call count (stable for tests).
|
||
already_importable = _flash_linear_attention_importable()
|
||
if already_importable and _flash_linear_attention_current(already_importable = True):
|
||
logger.info("flash-linear-attention already importable at the pinned version")
|
||
return True
|
||
|
||
_send_status(
|
||
event_queue,
|
||
f"Installing flash-linear-attention=={_FLA_PACKAGE_VERSION} for faster training...",
|
||
)
|
||
|
||
# `--no-deps` blocks the silent torch upgrade; bring non-torch runtime deps in by hand.
|
||
specs = [
|
||
*_FLA_RUNTIME_DEPS,
|
||
f"fla-core=={_FLA_CORE_PACKAGE_VERSION}",
|
||
f"flash-linear-attention=={_FLA_PACKAGE_VERSION}",
|
||
]
|
||
extra_args = ["--no-deps"]
|
||
if already_importable:
|
||
# Older FLA already imported; pip skips reinstall without this flag.
|
||
extra_args.append("--force-reinstall")
|
||
|
||
if shutil.which("uv"):
|
||
pypi_cmd = [
|
||
"uv",
|
||
"pip",
|
||
"install",
|
||
"--python",
|
||
sys.executable,
|
||
*extra_args,
|
||
*specs,
|
||
]
|
||
else:
|
||
pypi_cmd = [
|
||
sys.executable,
|
||
"-m",
|
||
"pip",
|
||
"install",
|
||
*extra_args,
|
||
*specs,
|
||
]
|
||
|
||
try:
|
||
result = _sp.run(
|
||
pypi_cmd,
|
||
stdout = _sp.PIPE,
|
||
stderr = _sp.STDOUT,
|
||
text = True,
|
||
timeout = _TILELANG_INSTALL_TIMEOUT_S,
|
||
)
|
||
except _sp.TimeoutExpired:
|
||
logger.warning("flash-linear-attention install timed out; continuing")
|
||
_send_status(event_queue, "flash-linear-attention install timed out; continuing")
|
||
return False
|
||
|
||
if result.returncode != 0:
|
||
if sys.platform == "win32":
|
||
logger.info(
|
||
"flash-linear-attention not available on Windows (no prebuilt wheel); "
|
||
"continuing on torch fallback"
|
||
)
|
||
logger.debug("Install output:\n%s", result.stdout)
|
||
else:
|
||
logger.warning(
|
||
"flash-linear-attention install failed (continuing on torch fallback):\n%s",
|
||
result.stdout,
|
||
)
|
||
_send_status(
|
||
event_queue,
|
||
"flash-linear-attention install failed; continuing without it",
|
||
)
|
||
return False
|
||
|
||
# pip can exit 0 with a missing transitive runtime dep; verify the import.
|
||
if not _flash_linear_attention_importable():
|
||
_send_status(
|
||
event_queue,
|
||
"flash-linear-attention installed but is not importable; continuing without it",
|
||
)
|
||
return False
|
||
|
||
logger.info("Installed flash-linear-attention for the FLA fast path")
|
||
return True
|
||
|
||
|
||
def _ensure_flash_linear_attention(event_queue: Any, model_name: str) -> None:
|
||
"""Legacy model-name-gated FLA install, used when UNSLOTH_STUDIO_SKIP_FAST_PATH_HOOKS=1."""
|
||
if not _model_wants_tilelang(model_name):
|
||
return
|
||
_ensure_flash_linear_attention_unconditional(event_queue)
|
||
|
||
|
||
_SSM_MODEL_SUBSTRINGS = (
|
||
"nemotron_h",
|
||
"nemotron-h",
|
||
"nemotron-3-nano",
|
||
"falcon_h1",
|
||
"falcon-h1",
|
||
"granite-4.0-h",
|
||
"granitemoehybrid",
|
||
)
|
||
|
||
|
||
def _ensure_mamba_ssm(event_queue: Any, model_name: str) -> None:
|
||
if not any(sub in model_name.lower() for sub in _SSM_MODEL_SUBSTRINGS):
|
||
return
|
||
|
||
logger.info("SSM model detected; setting up mamba-ssm after causal-conv1d")
|
||
_install_package_wheel_first(
|
||
event_queue = event_queue,
|
||
import_name = "mamba_ssm",
|
||
display_name = "mamba-ssm",
|
||
pypi_name = "mamba-ssm",
|
||
pypi_version = _MAMBA_SSM_PACKAGE_VERSION,
|
||
filename_prefix = "mamba_ssm",
|
||
release_tag = _MAMBA_SSM_RELEASE_TAG,
|
||
release_base_url = "https://github.com/state-spaces/mamba/releases/download",
|
||
)
|
||
|
||
|
||
# Auto-derived from installed transformers: model_types whose modeling_*.py imports `from fla.*`.
|
||
# Cached per process. Empty when transformers can't be inspected -> we skip tilelang pre-install
|
||
# (the FLA Triton path still runs via the runtime hook).
|
||
_TRANSFORMERS_FLA_MODEL_TYPES_CACHE: frozenset[str] | None = None
|
||
_MODEL_NAME_SEP_CHARS = ("-", ".", "/", " ")
|
||
|
||
|
||
def _discover_fla_model_types() -> frozenset[str]:
|
||
"""Installed-transformers model_types whose modeling file imports `from fla.*`."""
|
||
global _TRANSFORMERS_FLA_MODEL_TYPES_CACHE
|
||
if _TRANSFORMERS_FLA_MODEL_TYPES_CACHE is not None:
|
||
return _TRANSFORMERS_FLA_MODEL_TYPES_CACHE
|
||
found: set[str] = set()
|
||
try:
|
||
import transformers
|
||
models_root = Path(transformers.__file__).parent / "models"
|
||
for modeling in models_root.glob("*/modeling_*.py"):
|
||
try:
|
||
src = modeling.read_text(encoding = "utf-8", errors = "ignore")
|
||
except OSError:
|
||
continue
|
||
if "from fla." in src:
|
||
found.add(modeling.parent.name)
|
||
except Exception as exc:
|
||
logger.debug("FLA model-type discovery skipped: %s", exc)
|
||
_TRANSFORMERS_FLA_MODEL_TYPES_CACHE = frozenset(found)
|
||
return _TRANSFORMERS_FLA_MODEL_TYPES_CACHE
|
||
|
||
|
||
def _model_wants_tilelang(model_name: str) -> bool:
|
||
"""True iff model_name normalizes to contain a discovered FLA model_type."""
|
||
types = _discover_fla_model_types()
|
||
if not types:
|
||
return False
|
||
name = model_name.lower()
|
||
for sep in _MODEL_NAME_SEP_CHARS:
|
||
name = name.replace(sep, "_")
|
||
return any(t in name for t in types)
|
||
|
||
|
||
def _installed_tvm_ffi_version() -> str | None:
|
||
"""Installed apache-tvm-ffi version, or None if missing/unimportable."""
|
||
try:
|
||
from importlib.metadata import version as _pkg_version
|
||
return _pkg_version("apache-tvm-ffi")
|
||
except Exception:
|
||
return None
|
||
|
||
|
||
def _tilelang_importable() -> bool:
|
||
"""Catch any exception (not just ImportError) so a broken native lib doesn't abort the worker."""
|
||
try:
|
||
import tilelang # noqa: F401
|
||
import tvm_ffi # noqa: F401
|
||
return True
|
||
except Exception as exc:
|
||
logger.warning(
|
||
"tilelang/tvm_ffi is not importable; continuing with install/fallback: %s",
|
||
exc,
|
||
)
|
||
return False
|
||
|
||
|
||
def _torch_has_hip() -> bool:
|
||
"""True iff torch is a ROCm build.
|
||
|
||
`torch.version.hip` covers official PyTorch ROCm wheels; AMD SDK / Radeon
|
||
wheels can leave it unset but still encode "rocm" in `torch.__version__`.
|
||
"""
|
||
try:
|
||
import torch as _torch
|
||
return bool(
|
||
getattr(_torch.version, "hip", None)
|
||
or "rocm" in getattr(_torch, "__version__", "").lower()
|
||
)
|
||
except Exception:
|
||
return False
|
||
|
||
|
||
def _rocm_classify_unified_memory(props: Any) -> tuple[str, bool]:
|
||
"""Classify a ROCm device as unified-memory (APU) or discrete.
|
||
|
||
Returns ``(gcn_arch, is_unified)``:
|
||
- ``gcn_arch``: canonical arch string (e.g. ``"gfx1151"``) when a known
|
||
attribute is present, else ``""``.
|
||
- ``is_unified``: ``True`` for AMD APUs with a shared GPU/system-RAM pool
|
||
(gfx1150 Strix Point, gfx1151 Strix Halo) — these need a lower
|
||
``set_per_process_memory_fraction`` cap to leave OS headroom.
|
||
|
||
Classification priority:
|
||
1. ``props.is_integrated`` truthy (hipDeviceProp_t.integrated -- the
|
||
driver's own unified-memory answer; covers APUs beyond the hardcoded
|
||
arch set, e.g. gfx1103 Phoenix iGPUs). Only ever upgrades to unified.
|
||
2. ``gcnArchName`` / variant spellings (stable, naming-independent).
|
||
3. Device-name substring match (last resort when all arch attrs absent;
|
||
AMD SDK / Radeon wheels may not populate them):
|
||
- gfx1150 Strix Point: ``Radeon 890M``, ``Radeon 880M``
|
||
- gfx1151 Strix Halo: ``Radeon 8060S`` (Ryzen AI MAX+ 395),
|
||
``Radeon 8050S`` (cut-down SKU)
|
||
"""
|
||
gcn_arch = ""
|
||
for _attr in ("gcnArchName", "gcn_arch_name", "arch_name", "gfx_arch_name"):
|
||
_v = (getattr(props, _attr, "") or "").split(":")[0].strip()
|
||
if _v:
|
||
gcn_arch = _v
|
||
break
|
||
|
||
# Driver's own answer first: hipDeviceProp_t.integrated (exposed as
|
||
# props.is_integrated; same gate PR #5988's UMA safetensors fast-load
|
||
# uses). Strictly additive -- only a truthy value upgrades to unified;
|
||
# 0/absent falls through to the arch/name logic below, so a wheel that
|
||
# omits or zeroes the field can never downgrade the known APU set. This
|
||
# covers unified APUs outside the hardcoded arches (gfx1103 Phoenix
|
||
# iGPUs, future parts) with one universal signal.
|
||
if getattr(props, "is_integrated", 0):
|
||
return gcn_arch, True
|
||
|
||
if gcn_arch:
|
||
return gcn_arch, gcn_arch in {"gfx1150", "gfx1151"}
|
||
|
||
# Arch attrs absent — fall back to device-name matching.
|
||
dev_lower = (getattr(props, "name", "") or "").lower()
|
||
is_unified = (
|
||
"890m" in dev_lower or "880m" in dev_lower or "8060s" in dev_lower or "8050s" in dev_lower
|
||
)
|
||
return gcn_arch, is_unified
|
||
|
||
|
||
def _tilelang_platform_supported() -> bool:
|
||
"""True iff a tilelang 0.1.8 wheel will load: Linux x86_64/aarch64, non-HIP torch.
|
||
|
||
HIP excluded: tilelang 0.1.8 has no HIP GEMM and crashes mid-backward.
|
||
"""
|
||
import platform as _platform
|
||
|
||
if not sys.platform.startswith("linux"):
|
||
return False
|
||
if _platform.machine().lower() not in _TILELANG_SUPPORTED_LINUX_MACHINES:
|
||
return False
|
||
if _torch_has_hip():
|
||
return False
|
||
return True
|
||
|
||
|
||
def _pip_install_cmd(*args: str) -> list[str]:
|
||
"""`uv pip install` if uv is on PATH, else `python -m pip install`."""
|
||
if shutil.which("uv"):
|
||
return ["uv", "pip", "install", "--python", sys.executable, *args]
|
||
return [sys.executable, "-m", "pip", "install", *args]
|
||
|
||
|
||
def _run_pip(cmd: list[str], event_queue: Any, label: str) -> bool:
|
||
"""Run a pip install and surface success/failure via status events."""
|
||
try:
|
||
result = _sp.run(
|
||
cmd,
|
||
stdout = _sp.PIPE,
|
||
stderr = _sp.STDOUT,
|
||
text = True,
|
||
timeout = _TILELANG_INSTALL_TIMEOUT_S,
|
||
)
|
||
except _sp.TimeoutExpired:
|
||
logger.warning("%s install timed out; continuing", label)
|
||
_send_status(event_queue, f"{label} install timed out; continuing")
|
||
return False
|
||
if result.returncode != 0:
|
||
logger.warning("%s install failed (continuing without it):\n%s", label, result.stdout)
|
||
_send_status(event_queue, f"{label} install failed; continuing")
|
||
return False
|
||
return True
|
||
|
||
|
||
def _ensure_tilelang_backend_unconditional(event_queue: Any) -> bool:
|
||
"""Install pinned tilelang + apache-tvm-ffi; two-step repair if a broken tvm-ffi is present.
|
||
|
||
Returns True iff both import post-call. Step 1 downgrades a broken tvm-ffi
|
||
with --force-reinstall --no-deps so torch / CUDA stay untouched; step 2 is a
|
||
regular install for missing transitive deps. Bypass via
|
||
UNSLOTH_STUDIO_SKIP_TILELANG_INSTALL=1.
|
||
"""
|
||
if os.getenv(_TILELANG_SKIP_ENV) == "1":
|
||
return False
|
||
if sys.version_info < _FLA_MIN_PYTHON:
|
||
logger.info(
|
||
"Skipping tilelang install: requires Python >= %d.%d, have %s",
|
||
_FLA_MIN_PYTHON[0],
|
||
_FLA_MIN_PYTHON[1],
|
||
sys.version.split()[0],
|
||
)
|
||
return False
|
||
if not _tilelang_platform_supported():
|
||
import platform as _platform
|
||
logger.info(
|
||
"Skipping tilelang install: no prebuilt wheel for %s/%s",
|
||
sys.platform,
|
||
_platform.machine(),
|
||
)
|
||
return False
|
||
|
||
existing_tvm_ffi = _installed_tvm_ffi_version()
|
||
needs_repair = existing_tvm_ffi in _TVM_FFI_BROKEN_VERSIONS
|
||
|
||
if not needs_repair and _tilelang_importable():
|
||
logger.info("tilelang + apache-tvm-ffi already installed")
|
||
return True
|
||
|
||
# Step 1: --no-deps keeps --force-reinstall off torch/CUDA via the dep graph.
|
||
if needs_repair:
|
||
logger.info(
|
||
"Forcing apache-tvm-ffi downgrade: %s is on the broken list",
|
||
existing_tvm_ffi,
|
||
)
|
||
_send_status(
|
||
event_queue,
|
||
(
|
||
f"Downgrading apache-tvm-ffi {existing_tvm_ffi} -> "
|
||
f"{_APACHE_TVM_FFI_PACKAGE_VERSION} (broken-versions list)"
|
||
),
|
||
)
|
||
repair_cmd = _pip_install_cmd(
|
||
"--only-binary=:all:",
|
||
"--force-reinstall",
|
||
"--no-deps",
|
||
f"apache-tvm-ffi=={_APACHE_TVM_FFI_PACKAGE_VERSION}",
|
||
)
|
||
if not _run_pip(repair_cmd, event_queue, "TileLang backend repair"):
|
||
return False
|
||
|
||
# Step 2: regular install pulls transitive deps (z3-solver, ml-dtypes) without touching torch.
|
||
_send_status(
|
||
event_queue,
|
||
f"Installing TileLang=={_TILELANG_PACKAGE_VERSION} for faster training...",
|
||
)
|
||
install_cmd = _pip_install_cmd(
|
||
"--only-binary=:all:",
|
||
f"apache-tvm-ffi=={_APACHE_TVM_FFI_PACKAGE_VERSION}",
|
||
f"tilelang=={_TILELANG_PACKAGE_VERSION}",
|
||
)
|
||
if not _run_pip(install_cmd, event_queue, "TileLang backend"):
|
||
return False
|
||
|
||
# pip can exit 0 while a native lib (libz3.so) is missing; verify the import.
|
||
if not _tilelang_importable():
|
||
_send_status(
|
||
event_queue,
|
||
"TileLang backend installed but is not importable; continuing on the FLA Triton path",
|
||
)
|
||
return False
|
||
|
||
logger.info("Installed TileLang backend for FLA fast path")
|
||
return True
|
||
|
||
|
||
def _ensure_tilelang_backend(event_queue: Any, model_name: str) -> None:
|
||
"""Legacy substring-gated tilelang installer (opt-out path)."""
|
||
if not _model_wants_tilelang(model_name):
|
||
return
|
||
_ensure_tilelang_backend_unconditional(event_queue)
|
||
|
||
|
||
# ── Fast-path hooks ──
|
||
# Wrap transformers' is_{flash_linear_attention,causal_conv1d}_available so the
|
||
# first call (at modeling import) drives the install. Models that never query
|
||
# the gate (Llama, Gemma, dense Qwen) pay nothing.
|
||
# UNSLOTH_STUDIO_SKIP_FAST_PATH_HOOKS=1 falls back to the substring path.
|
||
|
||
|
||
def _rebind_in_already_imported_modules(*, attr_name: str, old_obj: Any, new_obj: Any) -> int:
|
||
"""Rebind `attr_name -> new_obj` in every module that imported `old_obj`.
|
||
|
||
`from X import Y` creates a local binding that reassigning X.Y won't reach.
|
||
Uses `__dict__.get` to skip lazy `__getattr__` aliases.
|
||
"""
|
||
count = 0
|
||
missing = object()
|
||
for mod_name, mod in list(sys.modules.items()):
|
||
if mod is None:
|
||
continue
|
||
module_dict = getattr(mod, "__dict__", None)
|
||
if not isinstance(module_dict, dict):
|
||
continue
|
||
existing = module_dict.get(attr_name, missing)
|
||
if existing is old_obj:
|
||
try:
|
||
setattr(mod, attr_name, new_obj)
|
||
count += 1
|
||
except Exception as exc:
|
||
logger.debug("Could not rebind %s in %s: %s", attr_name, mod_name, exc)
|
||
return count
|
||
|
||
|
||
def _install_fast_path_hooks(event_queue: Any, model_name: str) -> None:
|
||
"""Hook transformers' is_*_available gates so the first call drives the install.
|
||
|
||
Idempotent. UNSLOTH_STUDIO_SKIP_FAST_PATH_HOOKS=1 falls back to the substring gate.
|
||
"""
|
||
if os.getenv(_FAST_PATH_HOOKS_SKIP_ENV) == "1":
|
||
logger.info("Fast-path hooks disabled via env; using substring fallback")
|
||
return
|
||
|
||
# On HIP torch, even installed tilelang crashes FLA's TileLang dispatch.
|
||
# Override with FLA_TILELANG=1.
|
||
if _torch_has_hip() and os.environ.get("FLA_TILELANG") is None:
|
||
os.environ["FLA_TILELANG"] = "0"
|
||
logger.info(
|
||
"HIP/ROCm torch detected; setting FLA_TILELANG=0 (no HIP GEMM in tilelang 0.1.8)"
|
||
)
|
||
|
||
try:
|
||
from transformers.utils import import_utils as _iu
|
||
except Exception as exc:
|
||
logger.warning(
|
||
"transformers.utils.import_utils not importable; skipping fast-path hooks: %s",
|
||
exc,
|
||
)
|
||
return
|
||
|
||
def _make_wrapper(
|
||
original: Callable[[], bool],
|
||
install_fn: Callable[[Any], bool],
|
||
gate_name: str,
|
||
post_available_fn: Callable[[Any], None] | None = None,
|
||
) -> Callable[[], bool]:
|
||
state = {"installed": False}
|
||
|
||
def wrapper() -> bool:
|
||
if state["installed"]:
|
||
return original()
|
||
try:
|
||
original.cache_clear() # defensive; worker subprocess is fresh
|
||
except AttributeError:
|
||
pass
|
||
ok = original()
|
||
ran_install = False
|
||
if not ok:
|
||
ran_install = True
|
||
logger.info("Hook fired for %s; triggering install", gate_name)
|
||
try:
|
||
ok = bool(install_fn(event_queue))
|
||
except Exception as exc:
|
||
logger.warning("%s install raised: %s; falling back to torch", gate_name, exc)
|
||
ok = False
|
||
logger.info("%s hook done; available=%s", gate_name, ok)
|
||
# post_available_fn handles "gate already True but ancillary kernel broken"
|
||
# (e.g. tilelang missing while FLA imports); skip when install_fn already chained it.
|
||
if ok and not ran_install and post_available_fn is not None:
|
||
try:
|
||
post_available_fn(event_queue)
|
||
except Exception as exc:
|
||
logger.warning("%s post-available step raised: %s; continuing", gate_name, exc)
|
||
state["installed"] = True
|
||
return ok
|
||
|
||
wrapper.__wrapped__ = original # type: ignore[attr-defined]
|
||
wrapper.cache_clear = getattr(original, "cache_clear", lambda: None) # type: ignore[attr-defined]
|
||
return wrapper
|
||
|
||
def _fla_install(eq: Any) -> bool:
|
||
# FLA alone ~2.35x; +tilelang adds ~26%. tilelang is GDN-only (Qwen3.5 family).
|
||
if not _ensure_flash_linear_attention_unconditional(eq):
|
||
logger.info("FLA install did not produce an importable runtime; skipping TileLang")
|
||
return False
|
||
if _model_wants_tilelang(model_name):
|
||
_ensure_tilelang_backend_unconditional(eq)
|
||
else:
|
||
logger.info(
|
||
"Model %r outside TileLang allowlist; FLA Triton path is sufficient",
|
||
model_name,
|
||
)
|
||
return True
|
||
|
||
def _fla_post_available(eq: Any) -> None:
|
||
# FLA imports; repair tilelang if missing or on the broken tvm-ffi list.
|
||
if not _model_wants_tilelang(model_name):
|
||
return
|
||
if _installed_tvm_ffi_version() not in _TVM_FFI_BROKEN_VERSIONS and _tilelang_importable():
|
||
return
|
||
_ensure_tilelang_backend_unconditional(eq)
|
||
|
||
def _causal_conv1d_install(eq: Any) -> bool:
|
||
if sys.platform == "win32":
|
||
logger.info("causal-conv1d: no prebuilt wheel for Windows; skipping")
|
||
return False
|
||
ok = _install_package_wheel_first(
|
||
event_queue = eq,
|
||
import_name = "causal_conv1d",
|
||
display_name = "causal-conv1d",
|
||
pypi_name = "causal-conv1d",
|
||
pypi_version = _CAUSAL_CONV1D_PACKAGE_VERSION,
|
||
filename_prefix = "causal_conv1d",
|
||
release_tag = _CAUSAL_CONV1D_RELEASE_TAG,
|
||
release_base_url = ("https://github.com/Dao-AILab/causal-conv1d/releases/download"),
|
||
)
|
||
return bool(ok)
|
||
|
||
for gate_name, install_fn, post_fn in (
|
||
("is_flash_linear_attention_available", _fla_install, _fla_post_available),
|
||
("is_causal_conv1d_available", _causal_conv1d_install, None),
|
||
):
|
||
original = getattr(_iu, gate_name, None)
|
||
if original is None:
|
||
logger.info(
|
||
"%s missing on transformers.utils.import_utils; skipping hook",
|
||
gate_name,
|
||
)
|
||
continue
|
||
wrapped = _make_wrapper(original, install_fn, gate_name, post_fn)
|
||
setattr(_iu, gate_name, wrapped)
|
||
rebound = _rebind_in_already_imported_modules(
|
||
attr_name = gate_name, old_obj = original, new_obj = wrapped
|
||
)
|
||
logger.info("Installed fast-path hook on %s (rebound %d modules)", gate_name, rebound)
|
||
|
||
|
||
def _should_try_runtime_flash_attn_install(max_seq_length: int) -> bool:
|
||
if os.getenv(_FLASH_ATTN_SKIP_ENV) == "1":
|
||
return False
|
||
if max_seq_length < _FLASH_ATTN_RUNTIME_MIN_SEQ_LEN:
|
||
return False
|
||
return sys.platform.startswith("linux")
|
||
|
||
|
||
def _ensure_flash_attn_for_long_context(event_queue: Any, max_seq_length: int) -> None:
|
||
if not _should_try_runtime_flash_attn_install(max_seq_length):
|
||
return
|
||
if has_blackwell_gpu():
|
||
_send_status(
|
||
event_queue,
|
||
"Skipping flash-attn install: Blackwell GPU detected (sm_100+); no compatible prebuilt wheel",
|
||
)
|
||
return
|
||
|
||
installed = _install_package_wheel_first(
|
||
event_queue = event_queue,
|
||
import_name = "flash_attn",
|
||
display_name = "flash-attn",
|
||
pypi_name = "flash-attn",
|
||
wheel_url_builder = flash_attn_wheel_url,
|
||
pypi_spec = "flash-attn",
|
||
pypi_status_message = "Installing flash-attn from PyPI for long-context training...",
|
||
)
|
||
if not installed:
|
||
_send_status(event_queue, "Continuing without flash-attn")
|
||
|
||
|
||
def _activate_transformers_version(model_name: str, hf_token: str | None = None) -> None:
|
||
"""Activate the correct transformers version BEFORE any ML imports."""
|
||
# Ensure backend is on path for utils imports
|
||
backend_path = str(Path(__file__).resolve().parent.parent.parent)
|
||
if backend_path not in sys.path:
|
||
sys.path.insert(0, backend_path)
|
||
|
||
from utils.transformers_version import activate_transformers_for_subprocess
|
||
|
||
activate_transformers_for_subprocess(model_name, hf_token)
|
||
|
||
|
||
def _activate_transformers_version_or_warn(model_name: str, hf_token: str | None = None) -> None:
|
||
"""Activate the required transformers version for the MLX fast-path.
|
||
|
||
Unlike the non-MLX path (which treats activation failure as fatal and
|
||
reports it via the event queue), the MLX path is intentionally non-fatal:
|
||
it falls through with whatever transformers version is installed. The
|
||
failure used to be swallowed by a bare ``except: pass``, leaving no trace
|
||
and only a confusing downstream crash. Log a warning instead so the cause
|
||
is visible, while keeping the fall-through behaviour.
|
||
"""
|
||
try:
|
||
_activate_transformers_version(model_name, hf_token)
|
||
except Exception as exc:
|
||
logger.warning(
|
||
"Failed to activate transformers version for '%s' (MLX); "
|
||
"training may fail if this model requires a specific version. Error: %s",
|
||
model_name,
|
||
exc,
|
||
)
|
||
|
||
|
||
def _mlx_vlm_max_resized_size(width: int, height: int, target: int) -> tuple[int, int]:
|
||
if width <= 0 or height <= 0 or target <= 0:
|
||
return width, height
|
||
largest_side = max(width, height)
|
||
if largest_side <= target:
|
||
return width, height
|
||
# Integer formula matches unsloth_zoo's collator (Python round() differs by
|
||
# 1px on half-pixel cases). max(1, _) avoids a zero-side degenerate output.
|
||
new_w = max(1, (width * target + largest_side // 2) // largest_side)
|
||
new_h = max(1, (height * target + largest_side // 2) // largest_side)
|
||
return new_w, new_h
|
||
|
||
|
||
_MLX_VLM_RESIZED_IMAGE_LAYOUT_CACHE = {}
|
||
|
||
|
||
def _mlx_vlm_resized_image_layout(processor = None) -> str | None:
|
||
"""Return the numpy image layout expected after Studio-side VLM resizing."""
|
||
image_processor = getattr(processor, "image_processor", None)
|
||
if image_processor is None:
|
||
return None
|
||
cls = image_processor.__class__
|
||
key = (getattr(cls, "__module__", ""), getattr(cls, "__qualname__", cls.__name__))
|
||
if key in _MLX_VLM_RESIZED_IMAGE_LAYOUT_CACHE:
|
||
return _MLX_VLM_RESIZED_IMAGE_LAYOUT_CACHE[key]
|
||
copied_image_processor = _copy_mlx_vlm_image_processor(image_processor)
|
||
layout = (
|
||
_probe_mlx_vlm_numpy_image_layout(copied_image_processor)
|
||
if copied_image_processor is not None
|
||
else None
|
||
)
|
||
_MLX_VLM_RESIZED_IMAGE_LAYOUT_CACHE[key] = layout
|
||
return layout
|
||
|
||
|
||
def _copy_mlx_vlm_image_processor(image_processor):
|
||
import copy
|
||
try:
|
||
return copy.deepcopy(image_processor)
|
||
except Exception:
|
||
try:
|
||
return copy.copy(image_processor)
|
||
except Exception:
|
||
return None
|
||
|
||
|
||
def _probe_mlx_vlm_numpy_image_layout(image_processor) -> str | None:
|
||
try:
|
||
import numpy as np
|
||
except ImportError:
|
||
return None
|
||
|
||
def _accepts(candidate) -> bool:
|
||
try:
|
||
image_processor(images = [candidate])
|
||
return True
|
||
except TypeError:
|
||
try:
|
||
image_processor([candidate])
|
||
return True
|
||
except Exception:
|
||
return False
|
||
except Exception:
|
||
return False
|
||
|
||
# Use an asymmetric image so CHW-vs-HWC mistakes are visible to processors
|
||
# that skip conversion for 3D numpy arrays.
|
||
hwc = np.zeros((64, 96, 3), dtype = np.uint8)
|
||
chw = np.ascontiguousarray(hwc.transpose(2, 0, 1))
|
||
if _accepts(hwc):
|
||
return None
|
||
if _accepts(chw):
|
||
return "chw"
|
||
return None
|
||
|
||
|
||
def _resize_mlx_vlm_image(
|
||
image,
|
||
resize,
|
||
image_layout = None,
|
||
):
|
||
if resize is None:
|
||
return image
|
||
try:
|
||
from PIL import Image
|
||
import numpy as np
|
||
except ImportError:
|
||
return image
|
||
if not isinstance(image, Image.Image):
|
||
return image
|
||
image = image.convert("RGB")
|
||
new_size = _mlx_vlm_max_resized_size(*image.size, int(resize))
|
||
if new_size != image.size:
|
||
resampling = getattr(Image, "Resampling", Image).LANCZOS
|
||
image = image.resize(new_size, resampling)
|
||
# On resize, hand mlx-vlm a writable RGB ndarray so its PIL-path
|
||
# square-resize is skipped and HF processors don't warn on non-writable
|
||
# views. resize=None above keeps the original PIL.
|
||
array = np.array(image, copy = True)
|
||
if image_layout == "chw":
|
||
return np.ascontiguousarray(array.transpose(2, 0, 1))
|
||
return array
|
||
|
||
|
||
def _resize_mlx_vlm_images(
|
||
value,
|
||
resize,
|
||
image_layout = None,
|
||
):
|
||
if isinstance(value, list):
|
||
return [_resize_mlx_vlm_image(image, resize, image_layout = image_layout) for image in value]
|
||
return _resize_mlx_vlm_image(value, resize, image_layout = image_layout)
|
||
|
||
|
||
def _adapt_for_mlx_vlm(
|
||
items,
|
||
resize = None,
|
||
image_layout = None,
|
||
):
|
||
"""Adapt GPU-path VLM dataset output for mlx-vlm.
|
||
|
||
The GPU path embeds PIL images in message content as
|
||
{"type": "image", "image": PIL_Image}, but mlx-vlm's prepare_inputs needs
|
||
images at top-level to produce pixel_values (any model type). Extract them
|
||
and leave bare {"type": "image"} placeholders.
|
||
"""
|
||
adapted = []
|
||
for item in items:
|
||
images = []
|
||
messages = []
|
||
for msg in item.get("messages", []):
|
||
content = msg.get("content", "")
|
||
if isinstance(content, list):
|
||
new_content = []
|
||
for part in content:
|
||
if isinstance(part, dict) and part.get("type") == "image":
|
||
img = part.get("image")
|
||
if img is not None:
|
||
images.append(
|
||
_resize_mlx_vlm_image(
|
||
img,
|
||
resize,
|
||
image_layout = image_layout,
|
||
)
|
||
)
|
||
new_content.append({"type": "image"})
|
||
else:
|
||
new_content.append(part)
|
||
messages.append({"role": msg["role"], "content": new_content})
|
||
else:
|
||
messages.append(msg)
|
||
out = {"messages": messages}
|
||
if images:
|
||
out["image"] = images[0] if len(images) == 1 else images
|
||
elif "image" in item:
|
||
out["image"] = _resize_mlx_vlm_images(
|
||
item["image"],
|
||
resize,
|
||
image_layout = image_layout,
|
||
)
|
||
elif "images" in item:
|
||
out["images"] = _resize_mlx_vlm_images(
|
||
item["images"],
|
||
resize,
|
||
image_layout = image_layout,
|
||
)
|
||
adapted.append(out)
|
||
return adapted
|
||
|
||
|
||
_MLX_STUDIO_LR_SCHEDULERS = {"linear", "cosine", "constant"}
|
||
|
||
|
||
# Fallback alias map mirroring unsloth_zoo._normalize_mlx_optimizer_name, used
|
||
# only when mlx (Apple Silicon) is not importable so Studio config validation
|
||
# still works on non-MLX hosts. The zoo function stays the source of truth.
|
||
_MLX_STUDIO_ADAMW_ALIASES = frozenset(
|
||
(
|
||
"adamw_8bit",
|
||
"paged_adamw_8bit",
|
||
"adamw_bnb_8bit",
|
||
"paged_adamw_32bit",
|
||
"adamw_torch",
|
||
"adamw_torch_fused",
|
||
"paged_adamw",
|
||
"adamw_32bit",
|
||
"adamw_hf",
|
||
"adamw_anyprecision",
|
||
"adamw_apex_fused",
|
||
)
|
||
)
|
||
_MLX_STUDIO_NATIVE_OPTIMIZERS = ("adafactor", "adamw", "adam", "sgd", "muon", "lion")
|
||
|
||
|
||
def _normalize_mlx_studio_optimizer(value):
|
||
try:
|
||
from unsloth_zoo.mlx.trainer import _normalize_mlx_optimizer_name
|
||
return _normalize_mlx_optimizer_name(value or "adamw_8bit")
|
||
except (ImportError, ValueError):
|
||
# Missing mlx, or an older unsloth-zoo whose normalizer lacks CUDA/TRL
|
||
# aliases: map common adamw_* names locally so notebook defaults work.
|
||
opt = str(getattr(value, "value", value) or "adamw_8bit").strip().lower()
|
||
opt = opt.rsplit(".", 1)[-1].replace("-", "_")
|
||
if opt in _MLX_STUDIO_ADAMW_ALIASES:
|
||
opt = "adamw"
|
||
if opt not in _MLX_STUDIO_NATIVE_OPTIMIZERS:
|
||
supported = ", ".join(_MLX_STUDIO_NATIVE_OPTIMIZERS)
|
||
raise ValueError(
|
||
f"Unsupported optimizer for MLX training: {value!r}. "
|
||
f"Supported optimizers: {supported}."
|
||
)
|
||
return opt
|
||
|
||
|
||
def _normalize_mlx_studio_scheduler(value):
|
||
raw = str(value or "linear").strip().lower()
|
||
if raw not in _MLX_STUDIO_LR_SCHEDULERS:
|
||
supported = ", ".join(sorted(_MLX_STUDIO_LR_SCHEDULERS))
|
||
raise ValueError(
|
||
f"Unsupported LR scheduler for MLX training: {value!r}. "
|
||
f"Supported values: {supported}."
|
||
)
|
||
return raw
|
||
|
||
|
||
def _resolve_mlx_local_dataset_files(file_paths: list) -> list[str]:
|
||
"""Resolve CLI paths and Studio local dataset uploads without importing the GPU trainer."""
|
||
from utils.paths import resolve_dataset_path
|
||
|
||
all_files: list[str] = []
|
||
for dataset_file in file_paths or []:
|
||
dataset_path = Path(os.path.expanduser(str(dataset_file)))
|
||
if dataset_path.is_absolute():
|
||
file_path = str(dataset_path)
|
||
elif dataset_path.exists():
|
||
file_path = str(dataset_path.resolve())
|
||
else:
|
||
file_path = str(resolve_dataset_path(str(dataset_file)))
|
||
file_path_obj = Path(file_path)
|
||
|
||
if file_path_obj.is_dir():
|
||
parquet_dir = (
|
||
file_path_obj / "parquet-files"
|
||
if (file_path_obj / "parquet-files").exists()
|
||
else file_path_obj
|
||
)
|
||
parquet_files = sorted(parquet_dir.glob("*.parquet"))
|
||
if parquet_files:
|
||
all_files.extend(str(p) for p in parquet_files)
|
||
continue
|
||
|
||
candidates: list[Path] = []
|
||
for ext in (".json", ".jsonl", ".csv", ".parquet"):
|
||
candidates.extend(sorted(file_path_obj.glob(f"*{ext}")))
|
||
if candidates:
|
||
all_files.extend(str(c) for c in candidates)
|
||
continue
|
||
|
||
raise ValueError(f"No supported data files in directory: {file_path_obj}")
|
||
|
||
all_files.append(str(file_path_obj))
|
||
|
||
return all_files
|
||
|
||
|
||
def _mlx_local_dataset_loader_for_files(files: list[str]) -> str:
|
||
first_ext = Path(files[0]).suffix.lower()
|
||
if first_ext in (".json", ".jsonl"):
|
||
return "json"
|
||
if first_ext == ".csv":
|
||
return "csv"
|
||
if first_ext == ".parquet":
|
||
return "parquet"
|
||
raise ValueError(f"Unsupported dataset format: {files[0]}")
|
||
|
||
|
||
_MLX_WORKER_COMPLETE = "_mlx_worker_complete"
|
||
|
||
|
||
def _start_mlx_stop_poller(stop_queue):
|
||
import queue as _queue
|
||
import threading
|
||
|
||
stop_save = [True]
|
||
stop_requested = [False]
|
||
trainer_ref = [None]
|
||
|
||
def is_stop_requested():
|
||
return stop_requested[0]
|
||
|
||
def poll_stop():
|
||
while True:
|
||
try:
|
||
msg = stop_queue.get(timeout = 0.25)
|
||
if msg and msg.get("type") == _MLX_WORKER_COMPLETE:
|
||
return
|
||
if msg and msg.get("type") == "stop":
|
||
stop_save[0] = msg.get("save", True)
|
||
stop_requested[0] = True
|
||
trainer = trainer_ref[0]
|
||
if trainer is not None:
|
||
trainer.stop_requested = True
|
||
return
|
||
except _queue.Empty:
|
||
continue
|
||
except (EOFError, OSError):
|
||
return
|
||
|
||
stop_thread = threading.Thread(target = poll_stop, daemon = True)
|
||
stop_thread.start()
|
||
return stop_save, stop_requested, trainer_ref, is_stop_requested, stop_thread
|
||
|
||
|
||
def _resolve_mlx_output_dir(config, model_name):
|
||
from utils.paths import resolve_output_dir, default_run_dir_name
|
||
|
||
output_dir = config.get("output_dir", "")
|
||
if not output_dir:
|
||
output_dir = f"{default_run_dir_name(model_name)}_{int(time.time())}"
|
||
return str(resolve_output_dir(output_dir))
|
||
if config.get("allow_external_output_dir"):
|
||
output_path = Path(output_dir).expanduser()
|
||
if not output_path.is_absolute():
|
||
output_path = Path.cwd() / output_path
|
||
return str(output_path.resolve())
|
||
return str(resolve_output_dir(output_dir))
|
||
|
||
|
||
def _run_mlx_training(event_queue, stop_queue, config):
|
||
"""Self-contained MLX training path for Apple Silicon.
|
||
|
||
Uses unsloth_zoo's MLXTrainer directly (no torch/SFTTrainer). Mirrors the
|
||
event_queue protocol so the parent process pump works unchanged.
|
||
"""
|
||
import time
|
||
import math
|
||
from pathlib import Path
|
||
|
||
def _send(event_type, **kwargs):
|
||
if event_type == "status" and "message" not in kwargs:
|
||
sm = kwargs.get("status_message")
|
||
if sm is not None:
|
||
kwargs["message"] = sm
|
||
event_queue.put({"type": event_type, "ts": time.time(), **kwargs})
|
||
|
||
_stop_save, _stop_requested, _trainer_ref, _is_stop_requested, _stop_thread = (
|
||
_start_mlx_stop_poller(stop_queue)
|
||
)
|
||
|
||
_send("status", status_message = "Loading MLX libraries...")
|
||
|
||
import mlx.core as mx
|
||
|
||
try:
|
||
from unsloth_zoo.mlx.loader import FastMLXModel
|
||
from unsloth_zoo.mlx.trainer import (
|
||
MLXTrainer,
|
||
MLXTrainingConfig,
|
||
train_on_responses_only,
|
||
)
|
||
except ImportError as e:
|
||
raise ImportError(
|
||
"Unsloth: MLX training requires unsloth-zoo with the MLX modules "
|
||
"(unsloth_zoo.mlx.loader / unsloth_zoo.mlx.trainer). Reinstall via "
|
||
"install.sh on Apple Silicon."
|
||
) from e
|
||
from utils.datasets.cache_safe import load_dataset_cache_safe as load_dataset
|
||
|
||
if mx.metal.is_available():
|
||
info = mx.device_info()
|
||
rec_bytes = info.get("max_recommended_working_set_size", 0) or 0
|
||
if rec_bytes > 0:
|
||
memory_cap = int(rec_bytes * 0.85)
|
||
wired_cap = min(int(rec_bytes), memory_cap)
|
||
mx.set_memory_limit(memory_cap)
|
||
mx.set_wired_limit(wired_cap)
|
||
|
||
model_name = config["model_name"]
|
||
hf_token = config.get("hf_token") or None
|
||
if hf_token:
|
||
os.environ["HF_TOKEN"] = hf_token
|
||
|
||
if config.get("use_loftq"):
|
||
message = "LoftQ is not supported for MLX training yet."
|
||
_send("error", error = message)
|
||
raise NotImplementedError(message)
|
||
if config.get("is_embedding"):
|
||
message = "Embedding model training is not supported for MLX training yet."
|
||
_send("error", error = message)
|
||
raise NotImplementedError(message)
|
||
if config.get("training_type") == "Continued Pretraining":
|
||
message = "Continued Pretraining is not supported for MLX training yet."
|
||
_send("error", error = message)
|
||
raise NotImplementedError(message)
|
||
|
||
optim_name = _normalize_mlx_studio_optimizer(config.get("optim", "adamw_8bit"))
|
||
lr_scheduler_type = _normalize_mlx_studio_scheduler(config.get("lr_scheduler_type", "linear"))
|
||
|
||
# ── 1. Load model ──
|
||
# Force text-only for non-image datasets even on vision-capable models
|
||
# (e.g. Qwen3.5-VL trained on plain alpaca text).
|
||
_send("status", status_message = f"Loading {model_name}...")
|
||
# Pull through resume_from_checkpoint so MLXTrainer.train() can restore
|
||
# optimizer + step state and continue cleanly. Was previously dropped on
|
||
# the floor for the MLX path, so the Resume UI button silently restarted
|
||
# from step 0 (the CUDA path at lines 2729 / 3108 has been forwarding
|
||
# this all along).
|
||
resume_from_checkpoint = config.get("resume_from_checkpoint") or None
|
||
is_dataset_image = bool(config.get("is_dataset_image", False))
|
||
training_type = config.get("training_type", "LoRA/QLoRA")
|
||
use_lora = training_type == "LoRA/QLoRA"
|
||
# Normalize seed; explicit None must not reach the seed chain.
|
||
_raw_seed = config.get("random_seed", 3407)
|
||
random_seed = 3407 if _raw_seed is None else int(_raw_seed)
|
||
# `config.get(k, d)` only fills d when key is missing; handle explicit None too.
|
||
_model_seed = config.get("model_random_state")
|
||
model_random_state = random_seed if _model_seed is None else int(_model_seed)
|
||
_lora_seed = config.get("lora_random_state")
|
||
lora_random_state = random_seed if _lora_seed is None else int(_lora_seed)
|
||
|
||
# Malware gate (MLX): a poisoned pickle deserializes on load even with
|
||
# trust_remote_code False, so check HF's security scan (metadata-only) first.
|
||
# For a LoRA, gate the base whose weights deserialize.
|
||
from utils.security import evaluate_file_security
|
||
|
||
malware_targets = [model_name]
|
||
try:
|
||
from utils.models.model_config import get_base_model_from_lora_identifier
|
||
|
||
# Resolve a LOCAL or REMOTE adapter's base so a remote LoRA base is gated too.
|
||
_base = get_base_model_from_lora_identifier(model_name, config.get("hf_token") or None)
|
||
if _base:
|
||
malware_targets.append(_base)
|
||
except Exception as exc:
|
||
logger.debug("Could not resolve LoRA base for malware scan: %s", exc)
|
||
from utils.security import security_load_subdirs
|
||
|
||
for target in dict.fromkeys(malware_targets):
|
||
_fs = evaluate_file_security(
|
||
target, hf_token = hf_token, load_subdirs = security_load_subdirs(target, hf_token)
|
||
)
|
||
if _fs.blocked:
|
||
_send(
|
||
"error",
|
||
error = _fs.reason,
|
||
error_kind = "malware_blocked",
|
||
security = _fs.response_payload(),
|
||
)
|
||
return
|
||
|
||
# Consent gate (MLX): the CUDA path gates in run_training_process, but MLX returns
|
||
# before that, so scan auto_map code here before FastMLXModel runs it. Block
|
||
# CRITICAL/HIGH unless pinned-approved; for a LoRA, gate the base whose code runs.
|
||
if config.get("trust_remote_code", False):
|
||
from utils.security import evaluate_remote_code_consent_for_targets
|
||
|
||
consent_targets = [model_name]
|
||
try:
|
||
from utils.models.model_config import get_base_model_from_lora_identifier
|
||
|
||
# Resolve a LOCAL or REMOTE adapter's base so a remote LoRA base is gated too.
|
||
base_model = get_base_model_from_lora_identifier(
|
||
model_name, config.get("hf_token") or None
|
||
)
|
||
if base_model:
|
||
consent_targets.append(base_model)
|
||
except Exception as exc:
|
||
logger.debug("Could not resolve LoRA base for consent scan: %s", exc)
|
||
# Scan adapter + base as one combined unit, pinned by a single fingerprint.
|
||
_rc = evaluate_remote_code_consent_for_targets(
|
||
consent_targets,
|
||
hf_token = hf_token,
|
||
trust_remote_code = True,
|
||
approved_fingerprint = config.get("approved_remote_code_fingerprint"),
|
||
subject = config.get("subject"),
|
||
)
|
||
if _rc.blocked:
|
||
_send(
|
||
"error",
|
||
error = (
|
||
f"Model '{_rc.model_name}' ships custom code flagged as "
|
||
f"{_rc.max_severity} by the security scan. Review it and "
|
||
f"re-run with approval to proceed.\n\n{_rc.findings_summary}"
|
||
),
|
||
error_kind = "remote_code_blocked",
|
||
remote_code = _rc.response_payload(),
|
||
)
|
||
return
|
||
|
||
model, tokenizer = FastMLXModel.from_pretrained(
|
||
model_name,
|
||
load_in_4bit = config.get("load_in_4bit", True),
|
||
full_finetuning = not use_lora,
|
||
text_only = None if is_dataset_image else True,
|
||
token = hf_token,
|
||
trust_remote_code = bool(config.get("trust_remote_code", False)),
|
||
random_state = model_random_state,
|
||
)
|
||
|
||
is_vlm = bool(is_dataset_image and getattr(model, "_is_vlm_model", False))
|
||
model._is_vlm_model = is_vlm
|
||
vision_image_size = config.get("vision_image_size")
|
||
# DeepSeek OCR uses a coupled preset tuple; skip resize like the Torch path.
|
||
_model_name_lower = str(config.get("model_name", "")).lower()
|
||
_is_deepseek_ocr = "deepseek" in _model_name_lower and "ocr" in _model_name_lower
|
||
if is_vlm and vision_image_size is not None and _is_deepseek_ocr:
|
||
_send(
|
||
"status",
|
||
status_message = (
|
||
"MLX vision image resize ignored for DeepSeek OCR (uses fixed Gundam preset)."
|
||
),
|
||
)
|
||
vision_image_size = None
|
||
elif is_vlm and vision_image_size is not None:
|
||
vision_image_size = int(vision_image_size)
|
||
_send(
|
||
"status",
|
||
status_message = f"MLX vision image resize: {vision_image_size} (max dimension)",
|
||
)
|
||
# ── 2. Apply LoRA / full FT ──
|
||
# gradient_checkpointing stays a string ("mlx"/"unsloth"/"none"/etc.);
|
||
# get_peft_model and MLXTrainer both accept and handle strings.
|
||
gc_setting = config.get("gradient_checkpointing", "mlx")
|
||
if isinstance(gc_setting, str):
|
||
use_grad_checkpoint = (
|
||
gc_setting if gc_setting.lower() not in ("false", "none", "") else False
|
||
)
|
||
else:
|
||
use_grad_checkpoint = gc_setting
|
||
|
||
if use_lora:
|
||
_send("status", status_message = "Configuring LoRA adapters...")
|
||
peft_kwargs = dict(
|
||
r = config.get("lora_r", 16),
|
||
lora_alpha = config.get("lora_alpha", 16),
|
||
lora_dropout = config.get("lora_dropout", 0.0),
|
||
use_rslora = config.get("use_rslora", False),
|
||
init_lora_weights = config.get("init_lora_weights", True),
|
||
random_state = lora_random_state,
|
||
target_modules = config.get("target_modules")
|
||
or [
|
||
"q_proj",
|
||
"k_proj",
|
||
"v_proj",
|
||
"o_proj",
|
||
"gate_proj",
|
||
"up_proj",
|
||
"down_proj",
|
||
],
|
||
use_gradient_checkpointing = use_grad_checkpoint,
|
||
)
|
||
finetune_language = config.get("finetune_language_layers", True)
|
||
finetune_attention = config.get("finetune_attention_modules", True)
|
||
finetune_mlp = config.get("finetune_mlp_modules", True)
|
||
finetune_vision = config.get("finetune_vision_layers", False) if is_vlm else False
|
||
|
||
if (finetune_attention or finetune_mlp) and not finetune_language and not finetune_vision:
|
||
finetune_language = True
|
||
|
||
peft_kwargs["finetune_language_layers"] = finetune_language
|
||
peft_kwargs["finetune_attention_modules"] = finetune_attention
|
||
peft_kwargs["finetune_mlp_modules"] = finetune_mlp
|
||
if is_vlm:
|
||
peft_kwargs["finetune_vision_layers"] = finetune_vision
|
||
model = FastMLXModel.get_peft_model(model, **peft_kwargs)
|
||
|
||
# ── 3. Load dataset ──
|
||
_send("status", status_message = "Loading dataset...")
|
||
hf_dataset = config.get("hf_dataset", "")
|
||
subset = config.get("subset")
|
||
train_split = config.get("train_split", "train") or "train"
|
||
eval_split = config.get("eval_split")
|
||
slice_start = config.get("dataset_slice_start")
|
||
slice_end = config.get("dataset_slice_end")
|
||
|
||
def _slice(ds):
|
||
if slice_start is not None or slice_end is not None:
|
||
start = slice_start if slice_start is not None else 0
|
||
end = slice_end if slice_end is not None else len(ds) - 1
|
||
if end < start:
|
||
return ds.select([])
|
||
ds = ds.select(range(start, min(end + 1, len(ds))))
|
||
return ds
|
||
|
||
def _load_local(file_paths):
|
||
from datasets import load_from_disk
|
||
|
||
if len(file_paths) == 1:
|
||
p = Path(file_paths[0])
|
||
if p.is_dir() and ((p / "dataset_info.json").exists() or (p / "state.json").exists()):
|
||
return load_from_disk(str(p))
|
||
all_files = _resolve_mlx_local_dataset_files(file_paths)
|
||
if not all_files:
|
||
raise ValueError("No local dataset files found")
|
||
loader = _mlx_local_dataset_loader_for_files(all_files)
|
||
return load_dataset(loader, data_files = all_files, split = "train")
|
||
|
||
if hf_dataset:
|
||
load_kwargs = {"split": train_split, "token": hf_token}
|
||
if subset:
|
||
load_kwargs["name"] = subset
|
||
dataset = load_dataset(hf_dataset, **load_kwargs)
|
||
dataset = _slice(dataset)
|
||
elif config.get("local_datasets"):
|
||
dataset = _load_local(config["local_datasets"])
|
||
dataset = _slice(dataset)
|
||
elif config.get("s3_config"):
|
||
from core.training.s3_dataset import (
|
||
S3DownloadCancelled,
|
||
prepare_s3_dataset_download,
|
||
)
|
||
|
||
_send("status", status_message = "Downloading dataset from S3...")
|
||
try:
|
||
s3_download = prepare_s3_dataset_download(
|
||
config["s3_config"],
|
||
cancel_callback = _is_stop_requested,
|
||
)
|
||
try:
|
||
dataset = _load_local(s3_download.files)
|
||
finally:
|
||
s3_download.cleanup()
|
||
except S3DownloadCancelled:
|
||
_send("complete", output_dir = None, status_message = "Training cancelled")
|
||
return
|
||
dataset = _slice(dataset)
|
||
else:
|
||
raise ValueError("No dataset specified")
|
||
|
||
# Eval dataset (separate split or local file)
|
||
eval_dataset = None
|
||
if eval_split and hf_dataset:
|
||
eval_kwargs = {"split": eval_split, "token": hf_token}
|
||
if subset:
|
||
eval_kwargs["name"] = subset
|
||
try:
|
||
eval_dataset = load_dataset(hf_dataset, **eval_kwargs)
|
||
except Exception as e:
|
||
_send("status", status_message = f"Eval split load failed: {e}")
|
||
eval_dataset = None
|
||
elif config.get("local_eval_datasets"):
|
||
eval_dataset = _load_local(config["local_eval_datasets"])
|
||
|
||
# ── 3b. Format dataset (VLM or text) ──
|
||
# Reuse the GPU format pipeline for VLM (auto-detects OCR/caption/llava/
|
||
# sharegpt+images) and text (alpaca/sharegpt/chatml → "text" column).
|
||
format_type = config.get("format_type", "")
|
||
custom_format_mapping = config.get("custom_format_mapping")
|
||
dataset_final_format = ""
|
||
try:
|
||
from utils.datasets import format_and_template_dataset
|
||
def _fmt_progress(status_message = "", **_kw):
|
||
_send("status", status_message = status_message)
|
||
|
||
if is_vlm:
|
||
_send("status", status_message = "Formatting VLM dataset...")
|
||
vlm_info = format_and_template_dataset(
|
||
dataset,
|
||
model_name = model_name,
|
||
tokenizer = tokenizer,
|
||
is_vlm = True,
|
||
dataset_name = hf_dataset or "local",
|
||
custom_format_mapping = custom_format_mapping,
|
||
progress_callback = _fmt_progress,
|
||
)
|
||
if vlm_info.get("success"):
|
||
vision_image_layout = (
|
||
_mlx_vlm_resized_image_layout(tokenizer)
|
||
if vision_image_size is not None
|
||
else None
|
||
)
|
||
dataset = _adapt_for_mlx_vlm(
|
||
vlm_info["dataset"],
|
||
resize = vision_image_size,
|
||
image_layout = vision_image_layout,
|
||
)
|
||
else:
|
||
errors = vlm_info.get("errors", [])
|
||
raise ValueError(f"VLM dataset format conversion failed: {'; '.join(errors)}")
|
||
if eval_dataset is not None:
|
||
ev_info = format_and_template_dataset(
|
||
eval_dataset,
|
||
model_name = model_name,
|
||
tokenizer = tokenizer,
|
||
is_vlm = True,
|
||
dataset_name = hf_dataset or "local",
|
||
custom_format_mapping = custom_format_mapping,
|
||
)
|
||
if ev_info.get("success"):
|
||
vision_image_layout = (
|
||
_mlx_vlm_resized_image_layout(tokenizer)
|
||
if vision_image_size is not None
|
||
else None
|
||
)
|
||
eval_dataset = _adapt_for_mlx_vlm(
|
||
ev_info["dataset"],
|
||
resize = vision_image_size,
|
||
image_layout = vision_image_layout,
|
||
)
|
||
|
||
elif format_type:
|
||
_send("status", status_message = f"Formatting dataset ({format_type})...")
|
||
info = format_and_template_dataset(
|
||
dataset,
|
||
model_name = model_name,
|
||
tokenizer = tokenizer,
|
||
is_vlm = False,
|
||
format_type = format_type,
|
||
dataset_name = hf_dataset or "local",
|
||
custom_format_mapping = custom_format_mapping,
|
||
progress_callback = _fmt_progress,
|
||
)
|
||
if info.get("success", True):
|
||
dataset = info.get("dataset", dataset)
|
||
dataset_final_format = str(info.get("final_format", "") or "").lower()
|
||
if eval_dataset is not None:
|
||
ev = format_and_template_dataset(
|
||
eval_dataset,
|
||
model_name = model_name,
|
||
tokenizer = tokenizer,
|
||
is_vlm = False,
|
||
format_type = format_type,
|
||
dataset_name = hf_dataset or "local",
|
||
custom_format_mapping = custom_format_mapping,
|
||
)
|
||
if ev.get("success", True):
|
||
eval_dataset = ev.get("dataset", eval_dataset)
|
||
except ImportError:
|
||
_send("status", status_message = "Format helper unavailable, using raw dataset")
|
||
|
||
# ── 4. Resolve training steps ──
|
||
max_steps = config.get("max_steps", 0) or 0
|
||
num_epochs = config.get("num_epochs", 3)
|
||
max_seq_length = config.get("max_seq_length", 2048)
|
||
batch_size = config.get("batch_size", 4)
|
||
grad_accum = config.get("gradient_accumulation_steps", 4)
|
||
|
||
if max_steps <= 0:
|
||
max_steps = max(
|
||
1,
|
||
math.ceil(len(dataset) / batch_size / grad_accum) * num_epochs,
|
||
)
|
||
|
||
lr_value = float(config.get("learning_rate", "2e-4"))
|
||
|
||
# Warmup: prefer warmup_steps; fall back to warmup_ratio
|
||
warmup_steps = config.get("warmup_steps")
|
||
warmup_ratio = config.get("warmup_ratio")
|
||
if warmup_steps is None and warmup_ratio is not None:
|
||
warmup_steps = int(round(warmup_ratio * max_steps))
|
||
if warmup_steps is None:
|
||
warmup_steps = 5
|
||
|
||
# ── 5. Build output dir ──
|
||
# Resolve to ~/.unsloth/studio/outputs/ so the export page finds it
|
||
from utils.paths import ensure_dir
|
||
|
||
output_dir = _resolve_mlx_output_dir(config, model_name)
|
||
ensure_dir(Path(output_dir))
|
||
|
||
# ── 6. Create trainer ──
|
||
eval_steps_val = config.get("eval_steps", 0) or 0
|
||
if isinstance(eval_steps_val, float) and 0 < eval_steps_val < 1:
|
||
eval_steps_val = max(1, int(eval_steps_val * max_steps))
|
||
else:
|
||
eval_steps_val = int(eval_steps_val)
|
||
|
||
# Per-element clipping only; trainer owns the None default. Re-validate
|
||
# for direct worker callers (training.py normalizes the main path).
|
||
max_grad_norm = 0.0
|
||
max_grad_value = config.get("max_grad_value")
|
||
if max_grad_value is not None:
|
||
max_grad_value = float(max_grad_value)
|
||
if max_grad_value < 0:
|
||
raise ValueError(
|
||
f"Unsloth MLX: max_grad_value={max_grad_value} must be >= 0 "
|
||
"(0 or None disables elementwise clipping)."
|
||
)
|
||
max_grad_leaf_norm = config.get("max_grad_leaf_norm")
|
||
if max_grad_leaf_norm is not None:
|
||
max_grad_leaf_norm = float(max_grad_leaf_norm)
|
||
if max_grad_leaf_norm < 0:
|
||
raise ValueError(
|
||
f"Unsloth MLX: max_grad_leaf_norm={max_grad_leaf_norm} must be >= 0 "
|
||
"(0 or None disables proportional leaf-norm clipping)."
|
||
)
|
||
weight_decay = config.get("weight_decay", 0.001)
|
||
weight_decay = 0.001 if weight_decay is None else float(weight_decay)
|
||
|
||
mlx_config_kwargs = dict(
|
||
per_device_train_batch_size = batch_size,
|
||
gradient_accumulation_steps = grad_accum,
|
||
max_steps = max_steps,
|
||
learning_rate = lr_value,
|
||
warmup_steps = warmup_steps,
|
||
lr_scheduler_type = lr_scheduler_type,
|
||
optim = optim_name,
|
||
weight_decay = weight_decay,
|
||
max_grad_norm = max_grad_norm,
|
||
max_grad_value = max_grad_value,
|
||
logging_steps = 1,
|
||
max_seq_length = max_seq_length,
|
||
seed = random_seed,
|
||
use_cce = True,
|
||
compile = True,
|
||
gradient_checkpointing = use_grad_checkpoint,
|
||
streaming = is_vlm,
|
||
packing = bool(config.get("packing", False)),
|
||
output_dir = output_dir,
|
||
save_steps = int(config.get("save_steps", 0) or 0),
|
||
eval_steps = eval_steps_val,
|
||
)
|
||
|
||
# Also gates the masking skip below, so defined outside the feature-detect block.
|
||
raw_text_mode = training_type == "Continued Pretraining" or format_type == "raw"
|
||
|
||
# Feature-detect optional fields so this PR works without the paired zoo bump.
|
||
_supported_fields = getattr(MLXTrainingConfig, "__dataclass_fields__", {})
|
||
if "cast_norm_output_to_input_dtype" in _supported_fields:
|
||
# Explicit None falls back to True (default).
|
||
_raw_cast = config.get("cast_norm_output_to_input_dtype", True)
|
||
mlx_config_kwargs["cast_norm_output_to_input_dtype"] = (
|
||
True if _raw_cast is None else bool(_raw_cast)
|
||
)
|
||
if "dataset_order" in _supported_fields:
|
||
mlx_config_kwargs["dataset_order"] = "torch_randperm"
|
||
if "max_grad_leaf_norm" in _supported_fields:
|
||
mlx_config_kwargs["max_grad_leaf_norm"] = max_grad_leaf_norm
|
||
if "append_eos" in _supported_fields:
|
||
# Studio SFT formatting owns rendered examples; raw/CPT text still
|
||
# needs MLX to append EOS like the CUDA raw-text path.
|
||
mlx_config_kwargs["append_eos"] = bool(raw_text_mode)
|
||
|
||
trainer = MLXTrainer(
|
||
model = model,
|
||
tokenizer = tokenizer,
|
||
train_dataset = dataset,
|
||
eval_dataset = eval_dataset,
|
||
args = MLXTrainingConfig(**mlx_config_kwargs),
|
||
)
|
||
_trainer_ref[0] = trainer
|
||
if _stop_requested[0]:
|
||
trainer.stop_requested = True
|
||
|
||
# Tell the parent eval is configured so the frontend shows the eval chart
|
||
if eval_dataset is not None and eval_steps_val > 0:
|
||
_send("eval_configured")
|
||
|
||
# ── 7. Apply train_on_responses_only if requested ──
|
||
# Auto-detect markers from the chat template first, manual table as
|
||
# fallback. Mirror the CUDA skips: raw/CPT text has no chat turns and
|
||
# Alpaca-rendered text lacks the chat markers. Also check the resolved
|
||
# format, since format_type="auto" can land on alpaca or raw text.
|
||
if (
|
||
config.get("train_on_completions", False)
|
||
and not raw_text_mode
|
||
and format_type != "alpaca"
|
||
and dataset_final_format not in ("alpaca", "raw_text")
|
||
):
|
||
_send("status", status_message = "Configuring response-only training...")
|
||
# No catch: the helper handles detection failures and double misses, so
|
||
# an exception here is a real masking failure that must fail the run,
|
||
# not silently train on full sequences.
|
||
from utils.datasets.completion_masking import apply_completion_masking
|
||
trainer, _masking_applied = apply_completion_masking(
|
||
trainer,
|
||
model_name,
|
||
train_on_responses_only,
|
||
notify = lambda level, message: _send("status", status_message = message),
|
||
)
|
||
|
||
# ── 8. Setup wandb / tensorboard ──
|
||
wandb_run = None
|
||
tb_writer = None
|
||
if config.get("enable_wandb", False):
|
||
try:
|
||
import wandb as _wandb
|
||
|
||
wandb_token = config.get("wandb_token")
|
||
if wandb_token:
|
||
os.environ["WANDB_API_KEY"] = wandb_token
|
||
# Keep the authenticated subject out of W&B run config (mirrors _sanitize_db_config).
|
||
_wandb_sensitive = {"hf_token", "wandb_token", "s3_config", "subject"}
|
||
wandb_run = _wandb.init(
|
||
project = config.get("wandb_project") or "unsloth-mlx",
|
||
config = {k: v for k, v in config.items() if k not in _wandb_sensitive},
|
||
reinit = True,
|
||
)
|
||
except Exception as e:
|
||
_send("status", status_message = f"wandb init failed: {e}")
|
||
if config.get("enable_tensorboard", False):
|
||
try:
|
||
from tensorboardX import SummaryWriter
|
||
except ImportError:
|
||
try:
|
||
from torch.utils.tensorboard import SummaryWriter
|
||
except ImportError:
|
||
SummaryWriter = None
|
||
if SummaryWriter is not None:
|
||
try:
|
||
tb_dir = config.get("tensorboard_dir") or f"{output_dir}/runs"
|
||
tb_writer = SummaryWriter(log_dir = tb_dir)
|
||
except Exception as e:
|
||
_send("status", status_message = f"tensorboard init failed: {e}")
|
||
else:
|
||
_send(
|
||
"status",
|
||
status_message = "tensorboard unavailable (install tensorboardX)",
|
||
)
|
||
|
||
# ── 9. Real-time progress callback ──
|
||
_send("status", status_message = f"Training {model_name}...")
|
||
|
||
def _on_step(
|
||
step,
|
||
total,
|
||
loss,
|
||
lr,
|
||
tok_s,
|
||
peak_gb,
|
||
elapsed,
|
||
num_tokens,
|
||
grad_norm = None,
|
||
):
|
||
eta = (elapsed / step * (total - step)) if step > 0 else 0
|
||
_send(
|
||
"progress",
|
||
step = step,
|
||
epoch = round(step / total * num_epochs, 2) if total > 0 else 0,
|
||
loss = loss,
|
||
learning_rate = lr,
|
||
total_steps = total,
|
||
elapsed_seconds = elapsed,
|
||
eta_seconds = max(0, eta),
|
||
grad_norm = grad_norm,
|
||
num_tokens = num_tokens,
|
||
eval_loss = None,
|
||
status_message = None,
|
||
peak_memory_gb = peak_gb,
|
||
)
|
||
if wandb_run is not None:
|
||
try:
|
||
wandb_run.log(
|
||
{
|
||
"train/loss": loss,
|
||
"train/learning_rate": lr,
|
||
"train/tokens_per_sec": tok_s,
|
||
"train/peak_gb": peak_gb,
|
||
"train/num_tokens": num_tokens,
|
||
**({"train/grad_norm": grad_norm} if grad_norm is not None else {}),
|
||
},
|
||
step = step,
|
||
)
|
||
except Exception:
|
||
pass
|
||
if tb_writer is not None:
|
||
try:
|
||
tb_writer.add_scalar("train/loss", loss, step)
|
||
tb_writer.add_scalar("train/learning_rate", lr, step)
|
||
tb_writer.add_scalar("train/tokens_per_sec", tok_s, step)
|
||
tb_writer.add_scalar("train/peak_gb", peak_gb, step)
|
||
if grad_norm is not None:
|
||
tb_writer.add_scalar("train/grad_norm", grad_norm, step)
|
||
except Exception:
|
||
pass
|
||
|
||
trainer.add_step_callback(_on_step)
|
||
|
||
def _on_eval(step, eval_loss, perplexity):
|
||
_send("progress", step = step, eval_loss = eval_loss)
|
||
if wandb_run is not None:
|
||
try:
|
||
wandb_run.log({"eval/loss": eval_loss, "eval/perplexity": perplexity}, step = step)
|
||
except Exception:
|
||
pass
|
||
if tb_writer is not None:
|
||
try:
|
||
tb_writer.add_scalar("eval/loss", eval_loss, step)
|
||
tb_writer.add_scalar("eval/perplexity", perplexity, step)
|
||
except Exception:
|
||
pass
|
||
|
||
trainer.add_eval_callback(_on_eval)
|
||
|
||
# ── 11. Run training ──
|
||
gc.collect()
|
||
mx.synchronize()
|
||
_save_model = trainer.save_model
|
||
|
||
def _skip_internal_final_save(*args, **kwargs):
|
||
raise ValueError("worker owns final save")
|
||
|
||
trainer.save_model = _skip_internal_final_save
|
||
try:
|
||
trainer.train(resume_from_checkpoint = resume_from_checkpoint)
|
||
finally:
|
||
trainer.save_model = _save_model
|
||
|
||
# ── 12. Save and finalize ──
|
||
if trainer.stop_requested:
|
||
if not _stop_save[0]:
|
||
# Cancel (save=False): skip saving.
|
||
_send("complete", output_dir = None, status_message = "Training cancelled")
|
||
else:
|
||
_send("status", status_message = "Saving stopped model...")
|
||
mx.synchronize()
|
||
trainer.save_model(output_dir)
|
||
_send("complete", output_dir = output_dir, status_message = "Training stopped")
|
||
else:
|
||
_send("status", status_message = "Saving model...")
|
||
mx.synchronize()
|
||
trainer.save_model(output_dir)
|
||
_send("complete", output_dir = output_dir, status_message = "Training completed")
|
||
|
||
if tb_writer is not None:
|
||
try:
|
||
tb_writer.close()
|
||
except Exception:
|
||
pass
|
||
if wandb_run is not None:
|
||
try:
|
||
wandb_run.finish()
|
||
except Exception:
|
||
pass
|
||
|
||
|
||
def _is_current_process_apple_silicon() -> bool:
|
||
import platform
|
||
return platform.system() == "Darwin" and platform.machine() == "arm64"
|
||
|
||
|
||
def run_mlx_training_process(
|
||
*,
|
||
event_queue: Any,
|
||
stop_queue: Any,
|
||
config: dict,
|
||
transformers_activated: bool = False,
|
||
) -> None:
|
||
"""MLX worker entrypoint shared by Studio subprocesses and the CLI adapter."""
|
||
model_name = config["model_name"]
|
||
|
||
backend_path = str(Path(__file__).resolve().parent.parent.parent)
|
||
if backend_path not in sys.path:
|
||
sys.path.insert(0, backend_path)
|
||
|
||
from utils.hf_xet_fallback import child_should_disable_xet
|
||
|
||
if child_should_disable_xet(config):
|
||
os.environ["HF_HUB_DISABLE_XET"] = "1"
|
||
os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = "0"
|
||
|
||
if not transformers_activated:
|
||
# Must precede detect_hardware(): its MLX stack check imports mlx_lm, hence transformers.
|
||
_activate_transformers_version_or_warn(model_name, config.get("hf_token") or None)
|
||
|
||
from utils.hardware import hardware as _hw
|
||
|
||
_hw.detect_hardware()
|
||
if _hw.DEVICE != _hw.DeviceType.MLX:
|
||
event_queue.put(
|
||
{
|
||
"type": "error",
|
||
"error": "MLX training requires Apple Silicon with the MLX backend available.",
|
||
"stack": "",
|
||
"ts": time.time(),
|
||
}
|
||
)
|
||
return
|
||
|
||
if config.get("is_dataset_audio"):
|
||
event_queue.put(
|
||
{
|
||
"type": "error",
|
||
"error": "Audio dataset training is not yet supported on Apple Silicon.",
|
||
"stack": "",
|
||
"ts": time.time(),
|
||
}
|
||
)
|
||
return
|
||
|
||
try:
|
||
try:
|
||
_run_mlx_training(event_queue, stop_queue, config)
|
||
finally:
|
||
try:
|
||
stop_queue.put({"type": _MLX_WORKER_COMPLETE})
|
||
except (EOFError, OSError, ValueError):
|
||
pass
|
||
except Exception as exc:
|
||
event_queue.put(
|
||
{
|
||
"type": "error",
|
||
"error": str(exc),
|
||
"stack": traceback.format_exc(limit = 20),
|
||
"ts": time.time(),
|
||
}
|
||
)
|
||
|
||
|
||
def run_training_process(*, event_queue: Any, stop_queue: Any, config: dict) -> None:
|
||
"""Subprocess entrypoint. Fresh Python — no stale module state.
|
||
|
||
Args:
|
||
event_queue: mp.Queue for progress/status/error events to the parent.
|
||
stop_queue: mp.Queue for stop commands from the parent.
|
||
config: Training config dict with all parameters.
|
||
"""
|
||
os.environ["TOKENIZERS_PARALLELISM"] = "false"
|
||
os.environ["PYTHONWARNINGS"] = "ignore" # before imports
|
||
|
||
# HTTP-fallback respawn: disable Xet before any huggingface_hub import (the
|
||
# var is read at import time). Mirrors core/inference/worker.py.
|
||
from utils.hf_xet_fallback import child_should_disable_xet
|
||
|
||
if child_should_disable_xet(config):
|
||
os.environ["HF_HUB_DISABLE_XET"] = "1"
|
||
os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = "0"
|
||
print(
|
||
"Xet transport disabled for this training worker (HF_HUB_DISABLE_XET=1).",
|
||
file = sys.stderr,
|
||
flush = True,
|
||
)
|
||
|
||
# Offline auto-detect: skip ~25s of HF retries per call when DNS is dead.
|
||
if "HF_HUB_OFFLINE" not in os.environ:
|
||
import socket as _socket
|
||
import threading as _threading
|
||
|
||
# Daemon thread so we don't mutate process-wide setdefaulttimeout.
|
||
_result: list = [None]
|
||
|
||
def _probe() -> None:
|
||
try:
|
||
_socket.gethostbyname("huggingface.co")
|
||
_result[0] = False
|
||
except Exception:
|
||
_result[0] = True
|
||
|
||
_t = _threading.Thread(target = _probe, daemon = True)
|
||
_t.start()
|
||
_t.join(2.0)
|
||
if _result[0] is None or _result[0] is True:
|
||
os.environ["HF_HUB_OFFLINE"] = "1"
|
||
os.environ.setdefault("TRANSFORMERS_OFFLINE", "1")
|
||
os.environ.setdefault("HF_DATASETS_OFFLINE", "1")
|
||
# logger isn't configured yet; print to stderr instead.
|
||
print(
|
||
"huggingface.co unreachable; HF_HUB_OFFLINE=1 set for this worker.",
|
||
file = sys.stderr,
|
||
flush = True,
|
||
)
|
||
|
||
import warnings
|
||
from loggers.config import LogConfig
|
||
|
||
if os.getenv("ENVIRONMENT_TYPE", "production") == "production":
|
||
warnings.filterwarnings("ignore")
|
||
|
||
LogConfig.setup_logging(
|
||
service_name = "unsloth-studio-training-worker",
|
||
env = os.getenv("ENVIRONMENT_TYPE", "production"),
|
||
)
|
||
|
||
apply_gpu_ids(config.get("resolved_gpu_ids"))
|
||
|
||
model_name = config["model_name"]
|
||
|
||
# ── 0. MLX FAST-PATH (must run before any torch/transformers imports) ──
|
||
# Apple Silicon uses MLXTrainer directly -- skip torch imports / installs.
|
||
backend_path = str(Path(__file__).resolve().parent.parent.parent)
|
||
if backend_path not in sys.path:
|
||
sys.path.insert(0, backend_path)
|
||
|
||
from .training import is_apple_silicon_training_platform, should_use_mlx_training_backend
|
||
|
||
mlx_backend_requested = is_apple_silicon_training_platform()
|
||
|
||
mlx_transformers_activated = False
|
||
if mlx_backend_requested and _is_current_process_apple_silicon():
|
||
# Must precede detect_hardware(): its MLX stack check imports mlx_lm, hence transformers.
|
||
_activate_transformers_version_or_warn(model_name, config.get("hf_token") or None)
|
||
mlx_transformers_activated = True
|
||
|
||
from utils.hardware import hardware as _hw
|
||
|
||
_hw.detect_hardware()
|
||
if mlx_backend_requested or should_use_mlx_training_backend(device = _hw.DEVICE):
|
||
run_mlx_training_process(
|
||
event_queue = event_queue,
|
||
stop_queue = stop_queue,
|
||
config = config,
|
||
transformers_activated = mlx_transformers_activated,
|
||
)
|
||
return
|
||
|
||
# ── 1. Activate correct transformers version BEFORE any ML imports ──
|
||
try:
|
||
_activate_transformers_version(model_name, config.get("hf_token") or None)
|
||
except Exception as exc:
|
||
event_queue.put(
|
||
{
|
||
"type": "error",
|
||
"error": f"Failed to activate transformers version: {exc}",
|
||
"stack": traceback.format_exc(limit = 20),
|
||
"ts": time.time(),
|
||
}
|
||
)
|
||
return
|
||
|
||
# ── 1a. Auto-enable trust_remote_code for NemotronH/Nano models ──
|
||
# NemotronH needs trust_remote_code=True to work around config-parsing bugs.
|
||
# Other 5.x models are native and don't need it (it bypasses the compiler,
|
||
# disabling fused CE). Must NOT match Llama-Nemotron (standard Llama arch).
|
||
from utils.security.trusted_org import is_trusted_org_repo
|
||
|
||
_NEMOTRON_TRUST_SUBSTRINGS = ("nemotron_h", "nemotron-h", "nemotron-3-nano")
|
||
_lowered = model_name.lower()
|
||
if (
|
||
any(sub in _lowered for sub in _NEMOTRON_TRUST_SUBSTRINGS)
|
||
and (_lowered.startswith("unsloth/") or _lowered.startswith("nvidia/"))
|
||
# Confirm a genuine first-party Hub repo (not a local/spoofed name starting
|
||
# with "unsloth/"); authenticated so private first-party repos resolve.
|
||
and is_trusted_org_repo(model_name, hf_token = config.get("hf_token") or None)
|
||
and not config.get("trust_remote_code", False)
|
||
):
|
||
config["trust_remote_code"] = True
|
||
logger.info(
|
||
"Auto-enabled trust_remote_code for Nemotron model: %s",
|
||
model_name,
|
||
)
|
||
|
||
# 1a. Malware gate: a poisoned pickle deserializes on load even with
|
||
# trust_remote_code False, so check HF's security scan (metadata-only) first.
|
||
# For a LoRA, gate the base whose weights deserialize.
|
||
from utils.security import evaluate_file_security
|
||
|
||
malware_targets = [model_name]
|
||
try:
|
||
from utils.models.model_config import get_base_model_from_lora_identifier
|
||
|
||
# Resolve a LOCAL or REMOTE adapter's base so a remote LoRA base is gated too.
|
||
_base = get_base_model_from_lora_identifier(model_name, config.get("hf_token") or None)
|
||
if _base:
|
||
malware_targets.append(_base)
|
||
except Exception as exc:
|
||
logger.debug("Could not resolve LoRA base for malware scan: %s", exc)
|
||
from utils.security import security_load_subdirs
|
||
|
||
_ls_hf = config.get("hf_token") or None
|
||
for target in dict.fromkeys(malware_targets):
|
||
_fs = evaluate_file_security(
|
||
target, hf_token = _ls_hf, load_subdirs = security_load_subdirs(target, _ls_hf)
|
||
)
|
||
if _fs.blocked:
|
||
event_queue.put(
|
||
{
|
||
"type": "error",
|
||
"error": _fs.reason,
|
||
"error_kind": "malware_blocked",
|
||
"security": _fs.response_payload(),
|
||
"ts": time.time(),
|
||
}
|
||
)
|
||
return
|
||
|
||
# 1a'. Consent gate: scan auto_map Python before it runs; refuse CRITICAL/HIGH
|
||
# unless pinned-approved.
|
||
if config.get("trust_remote_code", False):
|
||
from utils.security import evaluate_remote_code_consent_for_targets
|
||
|
||
# A LoRA adapter's base is where custom code runs, so gate it too.
|
||
consent_targets = [model_name]
|
||
try:
|
||
from utils.models.model_config import get_base_model_from_lora_identifier
|
||
|
||
# Resolve a LOCAL or REMOTE adapter's base so a remote LoRA base is gated too.
|
||
base_model = get_base_model_from_lora_identifier(
|
||
model_name, config.get("hf_token") or None
|
||
)
|
||
if base_model:
|
||
consent_targets.append(base_model)
|
||
except Exception as exc:
|
||
logger.debug("Could not resolve LoRA base for consent scan: %s", exc)
|
||
# Scan adapter + base as one combined unit, pinned by a single fingerprint.
|
||
_rc = evaluate_remote_code_consent_for_targets(
|
||
consent_targets,
|
||
hf_token = config.get("hf_token") or None,
|
||
trust_remote_code = True,
|
||
approved_fingerprint = config.get("approved_remote_code_fingerprint"),
|
||
subject = config.get("subject"),
|
||
)
|
||
if _rc.blocked:
|
||
event_queue.put(
|
||
{
|
||
"type": "error",
|
||
"error": (
|
||
f"Model '{_rc.model_name}' ships custom code flagged as "
|
||
f"{_rc.max_severity} by the security scan. Review it and "
|
||
f"re-run with approval to proceed.\n\n{_rc.findings_summary}"
|
||
),
|
||
"error_kind": "remote_code_blocked",
|
||
"remote_code": _rc.response_payload(),
|
||
"ts": time.time(),
|
||
}
|
||
)
|
||
return
|
||
|
||
# ── 1b. Install fast-path kernel libraries for the chosen model.
|
||
# 1) causal-conv1d ALWAYS runs eagerly via the substring path: some SSM
|
||
# modeling files lazy_load it without calling is_causal_conv1d_available.
|
||
# 2) FLA + tilelang: gated by the runtime hook on
|
||
# is_flash_linear_attention_available (hooks also wrap causal-conv1d).
|
||
# 3) mamba-ssm + flash-attn keep their substring / size gates.
|
||
# 4) UNSLOTH_STUDIO_SKIP_FAST_PATH_HOOKS=1 falls back to the substring path.
|
||
try:
|
||
_ensure_causal_conv1d_fast_path(event_queue, model_name)
|
||
if os.getenv(_FAST_PATH_HOOKS_SKIP_ENV) == "1":
|
||
_ensure_flash_linear_attention(event_queue, model_name)
|
||
_ensure_tilelang_backend(event_queue, model_name)
|
||
else:
|
||
_install_fast_path_hooks(event_queue, model_name)
|
||
_ensure_mamba_ssm(event_queue, model_name)
|
||
_ensure_flash_attn_for_long_context(
|
||
event_queue,
|
||
int(config.get("max_seq_length", 2048)),
|
||
)
|
||
except Exception as exc:
|
||
event_queue.put(
|
||
{
|
||
"type": "error",
|
||
"error": (
|
||
f"Please choose another model to train, since "
|
||
f"a fast-path kernel library "
|
||
f"(causal-conv1d / flash-linear-attention / "
|
||
f"mamba-ssm / tilelang) failed to install "
|
||
f"with error: {exc}"
|
||
),
|
||
"stack": traceback.format_exc(limit = 20),
|
||
"ts": time.time(),
|
||
}
|
||
)
|
||
return
|
||
|
||
# ── 1c. Set fork start method so dataset.map() can multiprocess ──
|
||
# The compiled SFTTrainer disables num_proc if start method isn't "fork".
|
||
# Linux only and safe here (no CUDA context yet); macOS/Windows excluded.
|
||
if sys.platform == "linux":
|
||
import multiprocessing as _mp
|
||
try:
|
||
_mp.set_start_method("fork", force = True)
|
||
except RuntimeError:
|
||
pass # Already set
|
||
|
||
# ── 1c. On Windows, check Triton availability (must be before import torch) ──
|
||
if sys.platform == "win32":
|
||
try:
|
||
import triton # noqa: F401
|
||
logger.info("Triton available — torch.compile enabled")
|
||
except ImportError:
|
||
os.environ["TORCHDYNAMO_DISABLE"] = "1"
|
||
logger.warning(
|
||
"Triton not found on Windows — torch.compile disabled. "
|
||
'Install for better performance: pip install "triton-windows<3.7"'
|
||
)
|
||
|
||
# ── 1d. Stub torchao on Windows ROCm ──
|
||
# See core/_torchao_stub.py for the rationale (no RCCL backend on Windows
|
||
# ROCm). No-op elsewhere. Must run before importing transformers/unsloth_zoo.
|
||
from core._torchao_stub import install_torchao_windows_rocm_stub
|
||
|
||
install_torchao_windows_rocm_stub()
|
||
|
||
# ── 1e. Ensure torch.distributed helper attrs are present ──
|
||
# Single-GPU never inits the process group, but transformers/trl import
|
||
# these unconditionally.
|
||
_td_stubs = {
|
||
"is_initialized": lambda: False,
|
||
"is_available": lambda: False,
|
||
"is_torchelastic_launched": lambda: False,
|
||
"get_rank": lambda: 0,
|
||
"get_world_size": lambda: 1,
|
||
"barrier": lambda: None,
|
||
}
|
||
|
||
try:
|
||
import torch.distributed as _td
|
||
for _name, _stub in _td_stubs.items():
|
||
if not hasattr(_td, _name):
|
||
setattr(_td, _name, _stub)
|
||
except Exception:
|
||
_td_mock = types.ModuleType("torch.distributed")
|
||
for _name, _stub in _td_stubs.items():
|
||
setattr(_td_mock, _name, _stub)
|
||
sys.modules["torch.distributed"] = _td_mock
|
||
try:
|
||
import torch as _torch
|
||
_torch.distributed = _td_mock
|
||
except Exception:
|
||
pass
|
||
|
||
# ── 1f. Windows ROCm runtime patches ──
|
||
# torch._grouped_mm has a null HIP kernel on gfx1200 (ROCm ≤ 7.12 Windows),
|
||
# causing 0xC0000005 during training. Root cause: JitDecomp (not
|
||
# torch.compile) dispatches _grouped_mm → null crash; TORCHDYNAMO_DISABLE
|
||
# doesn't cover JitDecomp, so we also override the CUDA dispatch key with a
|
||
# Python fallback. Fixed in torch==2.11.0+rocm7.13.0, so gate on HIP < 7.13.
|
||
# Schema: _grouped_mm(self, mat2, offs=None, bias=None, out_dtype=None);
|
||
# offs: optional group-split offsets (MoE-style variable-size batches).
|
||
# _WINDOWS_ROCM_GROUPED_MM_LIB keeps the registration alive past return/GC.
|
||
global _WINDOWS_ROCM_GROUPED_MM_LIB
|
||
if sys.platform == "win32":
|
||
_torch_for_rocm = sys.modules.get("torch")
|
||
# Broad check (torch.version.hip OR "rocm" in __version__): AMD SDK /
|
||
# Radeon wheels don't always set torch.version.hip, and without it the
|
||
# BNB pin, dynamo-disable, and _grouped_mm fallback would silently skip.
|
||
_build_version_for_rocm = (
|
||
getattr(_torch_for_rocm, "__version__", "").lower()
|
||
if _torch_for_rocm is not None
|
||
else ""
|
||
)
|
||
_is_win_rocm_torch = bool(
|
||
_torch_for_rocm is not None
|
||
and (
|
||
getattr(getattr(_torch_for_rocm, "version", None), "hip", None)
|
||
or "rocm" in _build_version_for_rocm
|
||
)
|
||
)
|
||
if _is_win_rocm_torch:
|
||
# Disable dynamo (belt-and-suspenders; the JitDecomp patch is the
|
||
# real fix, but this avoids other compile paths).
|
||
if "TORCHDYNAMO_DISABLE" not in os.environ:
|
||
os.environ["TORCHDYNAMO_DISABLE"] = "1"
|
||
logger.info("Windows ROCm: torch.compile (dynamo) disabled")
|
||
|
||
# bitsandbytes' import-time get_rocm_gpu_arch() probe runs
|
||
# `hipinfo.exe` from PATH; the AMD torch wheel ships it in the venv
|
||
# Scripts dir, which is on PATH only for activated venvs. Prepend
|
||
# it so the probe succeeds instead of logging a scary (harmless)
|
||
# "Could not detect ROCm GPU architecture" ERROR on every import.
|
||
# Normally inherited from main.py's env, but workers can also be
|
||
# spawned standalone (tests, CLI) -- keep the guard here too.
|
||
_scripts_dir = os.path.dirname(sys.executable)
|
||
if os.path.isfile(os.path.join(_scripts_dir, "hipInfo.exe")):
|
||
import shutil as _shutil
|
||
if not _shutil.which("hipinfo.exe"):
|
||
os.environ["PATH"] = _scripts_dir + os.pathsep + os.environ.get("PATH", "")
|
||
|
||
# BNB picks a rocm DLL from torch.version.hip, but AMD's Windows BNB
|
||
# wheel may ship a DLL whose suffix doesn't match. Detect the actual
|
||
# DLL name and override. Values seeded by the installer are
|
||
# redetectable defaults, while caller overrides remain authoritative.
|
||
if (
|
||
"BNB_ROCM_VERSION" not in os.environ
|
||
or os.environ.get("UNSLOTH_BNB_ROCM_VERSION_SOURCE") == "sitecustomize"
|
||
):
|
||
_bnb_rocm_ver = None
|
||
_found_rocm_bnb = False
|
||
try:
|
||
import glob as _glob
|
||
import importlib.util as _ilu
|
||
import re as _re
|
||
|
||
_bnb_spec = _ilu.find_spec("bitsandbytes")
|
||
if _bnb_spec and _bnb_spec.submodule_search_locations:
|
||
_all_vers: list[str] = []
|
||
for _pkg_dir in _bnb_spec.submodule_search_locations:
|
||
for _dll in _glob.glob(
|
||
os.path.join(_pkg_dir, "libbitsandbytes_rocm*.dll")
|
||
):
|
||
_found_rocm_bnb = True
|
||
_m = _re.search(
|
||
r"libbitsandbytes_rocm(\d+)\.dll",
|
||
os.path.basename(_dll),
|
||
)
|
||
if _m:
|
||
_all_vers.append(_m.group(1))
|
||
# Highest numeric suffix wins (glob order isn't sorted).
|
||
if _all_vers:
|
||
_bnb_rocm_ver = max(_all_vers, key = lambda v: int(v))
|
||
except Exception:
|
||
pass
|
||
# Only when a ROCm bnb DLL actually exists (mirrors main.py):
|
||
# without one the seeded value and its marker stay untouched,
|
||
# so later import fixes can still redetect or opt out. DLL
|
||
# with unparsable name -> seeded value or "72".
|
||
if _found_rocm_bnb:
|
||
_bnb_rocm_ver = _bnb_rocm_ver or os.environ.get("BNB_ROCM_VERSION") or "72"
|
||
os.environ["BNB_ROCM_VERSION"] = _bnb_rocm_ver
|
||
os.environ["UNSLOTH_BNB_ROCM_VERSION_SOURCE"] = "detected"
|
||
logger.info(
|
||
"Windows ROCm: set BNB_ROCM_VERSION=%s "
|
||
"(detected from installed BNB wheel; "
|
||
"overrides torch.version.hip auto-detection)",
|
||
_bnb_rocm_ver,
|
||
)
|
||
|
||
# Parse HIP version for the kernel-fix gate below, falling back to
|
||
# the rocm version embedded in torch.__version__ when version.hip is
|
||
# unset (AMD SDK / Radeon wheels).
|
||
def _hip_ver_at_least(major: int, minor: int) -> bool:
|
||
_hip_str = getattr(getattr(_torch_for_rocm, "version", None), "hip", None)
|
||
if not _hip_str:
|
||
# Try the standard "+rocmX.Y.Z" embedded version first.
|
||
_ver_match = re.search(r"rocm(\d+)\.(\d+)", _build_version_for_rocm)
|
||
if _ver_match:
|
||
return (
|
||
int(_ver_match.group(1)),
|
||
int(_ver_match.group(2)),
|
||
) >= (major, minor)
|
||
# "+rocmsdk<date>" wheels postdate the gfx120X null-kernel
|
||
# fix (ROCm 7.13), so treat them as >= 7.13 (no workaround).
|
||
if "rocmsdk" in _build_version_for_rocm:
|
||
logger.debug(
|
||
"Windows ROCm: AMD SDK wheel detected (%r); "
|
||
"assuming HIP >= %d.%d (rocmsdk wheels post-date "
|
||
"the gfx120X null-kernel fix)",
|
||
_build_version_for_rocm,
|
||
major,
|
||
minor,
|
||
)
|
||
return True
|
||
return False
|
||
try:
|
||
_parts = [int(x) for x in str(_hip_str).split(".")[:2]]
|
||
if len(_parts) < 2:
|
||
logger.warning(
|
||
"Windows ROCm: torch.version.hip %r has fewer than "
|
||
"two components; cannot compare against %d.%d",
|
||
_hip_str,
|
||
major,
|
||
minor,
|
||
)
|
||
return False
|
||
return (_parts[0], _parts[1]) >= (major, minor)
|
||
except ValueError:
|
||
logger.warning(
|
||
"Windows ROCm: could not parse torch.version.hip %r as "
|
||
"a version number; assuming HIP < %d.%d",
|
||
_hip_str,
|
||
major,
|
||
minor,
|
||
)
|
||
return False
|
||
|
||
# Install the Python fallback only on affected versions (ROCm ≤ 7.12)
|
||
# so 7.13+ uses the real GPU kernel.
|
||
if not _hip_ver_at_least(7, 13):
|
||
try:
|
||
import warnings as _warnings
|
||
|
||
_gm_lib = _torch_for_rocm.library.Library("aten", "IMPL")
|
||
|
||
def _grouped_mm_safe_impl(
|
||
self,
|
||
mat2,
|
||
offs = None,
|
||
bias = None,
|
||
out_dtype = None,
|
||
):
|
||
"""Python mm/bmm fallback for _grouped_mm on gfx1200 (null HIP kernel, ROCm ≤ 7.12)."""
|
||
_t = _torch_for_rocm
|
||
if offs is None:
|
||
# No offsets: 2-D -> mm, 3-D batched -> bmm
|
||
# (unconditional mm broke 3-D MoE).
|
||
if self.dim() == 3 and mat2.dim() == 3:
|
||
result = _t.bmm(self.contiguous(), mat2.contiguous())
|
||
elif self.dim() == 3 and mat2.dim() == 2:
|
||
# Broadcast 2-D mat2 across the batch dim.
|
||
result = _t.matmul(self.contiguous(), mat2.contiguous())
|
||
elif self.dim() == 2 and mat2.dim() == 3:
|
||
# Broadcast 2-D self across batch via matmul.
|
||
result = _t.matmul(self.contiguous(), mat2.contiguous())
|
||
else:
|
||
result = _t.mm(self.contiguous(), mat2.contiguous())
|
||
else:
|
||
# Grouped: offs[i] is the exclusive end-row of group i.
|
||
offs_list = offs.tolist()
|
||
pieces = []
|
||
prev = 0
|
||
for idx, end in enumerate(offs_list):
|
||
end = int(end)
|
||
a_part = self[prev:end].contiguous()
|
||
if mat2.dim() == 3:
|
||
b_part = mat2[idx].contiguous()
|
||
else:
|
||
b_part = mat2.contiguous()
|
||
pieces.append(_t.mm(a_part, b_part))
|
||
prev = end
|
||
# Include trailing rows not covered by offs.
|
||
if prev < self.shape[0]:
|
||
a_tail = self[prev:].contiguous()
|
||
b_tail = (
|
||
mat2[-1].contiguous() if mat2.dim() == 3 else mat2.contiguous()
|
||
)
|
||
pieces.append(_t.mm(a_tail, b_tail))
|
||
result = (
|
||
_t.cat(pieces, dim = 0)
|
||
if pieces
|
||
else _t.zeros(
|
||
0,
|
||
mat2.shape[-1],
|
||
device = self.device,
|
||
dtype = self.dtype,
|
||
)
|
||
)
|
||
if bias is not None:
|
||
result = result + bias
|
||
if out_dtype is not None:
|
||
result = result.to(out_dtype)
|
||
elif result.dtype != self.dtype:
|
||
result = result.to(self.dtype)
|
||
return result
|
||
|
||
with _warnings.catch_warnings():
|
||
_warnings.simplefilter("ignore")
|
||
_gm_lib.impl("_grouped_mm", _grouped_mm_safe_impl, "CUDA")
|
||
|
||
_WINDOWS_ROCM_GROUPED_MM_LIB = _gm_lib # prevent GC
|
||
logger.info(
|
||
"Windows ROCm: patched _grouped_mm CUDA dispatch "
|
||
"(null HIP kernel on gfx1200, ROCm ≤ 7.12 — "
|
||
"bypassed with Python mm fallback)"
|
||
)
|
||
except Exception as _patch_exc:
|
||
logger.warning(
|
||
"Windows ROCm: could not patch _grouped_mm — "
|
||
"training may crash with 0xC0000005: %s",
|
||
_patch_exc,
|
||
)
|
||
else:
|
||
logger.info(
|
||
"Windows ROCm: HIP >= 7.13 — _grouped_mm kernel is functional, "
|
||
"skipping Python fallback (AMD fixed gfx1200 null kernel in ROCm 7.13)"
|
||
)
|
||
|
||
# ── 1g. ROCm OOM guard ──
|
||
# On ROCm, exhausting VRAM can hang the HIP driver instead of raising.
|
||
# set_per_process_memory_fraction caps the allocator so PyTorch raises
|
||
# OutOfMemoryError first (NVIDIA already has a graceful OOM path).
|
||
# Unified-memory APUs (gfx1150/gfx1151) share GPU+system RAM, so use 0.80
|
||
# vs 0.90 for discrete. Classify via gcnArchName, else device-name markers.
|
||
# Non-fatal: skipped if torch is not importable.
|
||
if _hw.IS_ROCM:
|
||
try:
|
||
import torch as _torch_mem
|
||
if _torch_mem.cuda.is_available():
|
||
# Classify unified vs discrete via _rocm_classify_unified_memory
|
||
# (see its docstring for classification priority).
|
||
_props = _torch_mem.cuda.get_device_properties(0)
|
||
_dev_name = _props.name
|
||
_gcn_arch, _is_unified = _rocm_classify_unified_memory(_props)
|
||
if _is_unified and not _gcn_arch:
|
||
logger.debug(
|
||
"ROCm OOM guard: gcnArchName absent -- inferred "
|
||
"unified memory from device name %r; applying unified cap",
|
||
_dev_name,
|
||
)
|
||
# Unified hosts on native Windows: mem_get_info's total is the
|
||
# WDDM budget the driver grants HIP (BIOS carve + ~half of the
|
||
# remaining RAM) -- the OS share is already outside it, so the
|
||
# Linux 0.80 starve-protection double-taxes (48.49 GiB budget →
|
||
# 38.79 allowed) and blocks loads that fit in free memory.
|
||
# 1.0 removes the double-tax. Current AMD Windows wheels only
|
||
# enforce sub-1.0 fractions (measured on gfx1151: 0.5 caps,
|
||
# 1.0 still allocates past the budget via WDDM overcommit), so
|
||
# 1.0 behaves like torch's uncapped default, with WDDM
|
||
# arbitrating residency; on wheels that do enforce it, it caps
|
||
# at exactly the driver-granted budget. On Linux the total
|
||
# spans nearly all RAM, so keep the 0.80 OS headroom there.
|
||
if _is_unified:
|
||
_mem_fraction = 1.0 if sys.platform == "win32" else 0.80
|
||
else:
|
||
_mem_fraction = 0.90
|
||
_torch_mem.cuda.set_per_process_memory_fraction(_mem_fraction)
|
||
logger.info(
|
||
"ROCm OOM guard: set_per_process_memory_fraction(%.2f) — "
|
||
"%s memory host (%s, %s)",
|
||
_mem_fraction,
|
||
"unified" if _is_unified else "discrete",
|
||
_dev_name,
|
||
_gcn_arch or "unknown arch",
|
||
)
|
||
# Unified Windows APUs: the WDDM budget is user-raisable, but
|
||
# nothing on the box says so -- users see "48 GB VRAM" on a
|
||
# 96 GB machine and assume a Studio bug. Say where the limit
|
||
# comes from and how to raise it.
|
||
if _is_unified and sys.platform == "win32":
|
||
try:
|
||
import psutil as _psutil
|
||
|
||
_phys = _psutil.virtual_memory().total
|
||
_granted = _torch_mem.cuda.mem_get_info(0)[1]
|
||
if _granted < 0.75 * _phys:
|
||
logger.info(
|
||
"Windows grants the GPU %.1f GiB of %.1f GiB "
|
||
"system RAM (driver/WDDM budget). To raise it: "
|
||
"increase the BIOS UMA frame buffer size, or "
|
||
"AMD Software > Performance > Tuning > "
|
||
"Variable Graphics Memory.",
|
||
_granted / 1024**3,
|
||
_phys / 1024**3,
|
||
)
|
||
except Exception:
|
||
pass
|
||
except Exception as _oom_guard_err:
|
||
logger.debug("Could not set GPU memory fraction: %s", _oom_guard_err)
|
||
|
||
# ── 2. Now import ML libraries (fresh in this clean process) ──
|
||
try:
|
||
_send_status(event_queue, "Importing Unsloth...")
|
||
|
||
backend_path = str(Path(__file__).resolve().parent.parent.parent)
|
||
if backend_path not in sys.path:
|
||
sys.path.insert(0, backend_path)
|
||
|
||
from core.training.training import TrainingProgress
|
||
from core.training.trainer import UnslothTrainer
|
||
from utils.paths import (
|
||
ensure_dir,
|
||
resolve_output_dir,
|
||
resolve_tensorboard_dir,
|
||
datasets_root,
|
||
default_run_dir_name,
|
||
)
|
||
|
||
import transformers
|
||
|
||
logger.info("Subprocess loaded transformers %s", transformers.__version__)
|
||
except Exception as exc:
|
||
event_queue.put(
|
||
{
|
||
"type": "error",
|
||
"error": f"Failed to import ML libraries: {exc}",
|
||
"stack": traceback.format_exc(limit = 20),
|
||
"ts": time.time(),
|
||
}
|
||
)
|
||
return
|
||
|
||
# ── 2b. EMBEDDING MODEL FAST-PATH ──
|
||
# Embedding models use a different pipeline (FastSentenceTransformer +
|
||
# SentenceTransformerTrainer + MultipleNegativesRankingLoss), so branch early
|
||
# and handle the whole flow in a self-contained function.
|
||
if config.get("is_embedding", False):
|
||
try:
|
||
_run_embedding_training(event_queue, stop_queue, config)
|
||
except Exception as exc:
|
||
event_queue.put(
|
||
{
|
||
"type": "error",
|
||
"error": str(exc),
|
||
"stack": traceback.format_exc(limit = 20),
|
||
"ts": time.time(),
|
||
}
|
||
)
|
||
return
|
||
|
||
# ── 3. Create a fresh trainer instance ──
|
||
trainer = UnslothTrainer()
|
||
|
||
# Wire up progress callback → event_queue
|
||
def _on_progress(progress: TrainingProgress):
|
||
has_train_loss = progress.step > 0 and progress.loss is not None
|
||
has_eval_loss = progress.eval_loss is not None
|
||
if (progress.step == 0 and progress.total_steps > 0) or has_train_loss or has_eval_loss:
|
||
event_queue.put(
|
||
{
|
||
"type": "progress",
|
||
"step": progress.step,
|
||
"epoch": progress.epoch,
|
||
"loss": progress.loss,
|
||
"learning_rate": progress.learning_rate,
|
||
"total_steps": progress.total_steps,
|
||
"elapsed_seconds": progress.elapsed_seconds,
|
||
"eta_seconds": progress.eta_seconds,
|
||
"grad_norm": progress.grad_norm,
|
||
"num_tokens": progress.num_tokens,
|
||
"eval_loss": progress.eval_loss,
|
||
"status_message": progress.status_message,
|
||
"ts": time.time(),
|
||
}
|
||
)
|
||
if progress.status_message:
|
||
_send_status(event_queue, progress.status_message)
|
||
|
||
trainer.add_progress_callback(_on_progress)
|
||
|
||
# Wire up stop_queue polling to trainer.should_stop
|
||
import threading
|
||
import queue as _queue
|
||
|
||
def _poll_stop():
|
||
while True:
|
||
try:
|
||
msg = stop_queue.get(timeout = 1.0)
|
||
if msg and msg.get("type") == "stop":
|
||
save = msg.get("save", True)
|
||
trainer.should_stop = True
|
||
trainer.save_on_stop = save
|
||
logger.info("Stop signal received (save=%s)", save)
|
||
return
|
||
except _queue.Empty:
|
||
continue
|
||
except (EOFError, OSError):
|
||
return
|
||
|
||
stop_thread = threading.Thread(target = _poll_stop, daemon = True)
|
||
stop_thread.start()
|
||
|
||
# ── 4. Execute the training pipeline ──
|
||
# Order: detect → dataset → model → prepare → train. Dataset processing runs
|
||
# BEFORE model loading so both never occupy VRAM at once.
|
||
try:
|
||
hf_token = config.get("hf_token", "")
|
||
hf_token = hf_token if hf_token and hf_token.strip() else None
|
||
|
||
# ── 4a. Lightweight detection + tokenizer (no VRAM) ──
|
||
_send_status(event_queue, "Detecting model type...")
|
||
trainer.pre_detect_and_load_tokenizer(
|
||
model_name = model_name,
|
||
max_seq_length = config["max_seq_length"],
|
||
hf_token = hf_token,
|
||
is_dataset_image = config.get("is_dataset_image", False),
|
||
is_dataset_audio = config.get("is_dataset_audio", False),
|
||
trust_remote_code = config.get("trust_remote_code", False),
|
||
)
|
||
if trainer.should_stop:
|
||
event_queue.put({"type": "complete", "output_dir": None, "ts": time.time()})
|
||
return
|
||
|
||
# ── 4b. Load and format dataset (LLM helper may use VRAM briefly) ──
|
||
_send_status(event_queue, "Loading and formatting dataset...")
|
||
hf_dataset = config.get("hf_dataset", "")
|
||
training_type = config.get("training_type", "LoRA/QLoRA")
|
||
_is_cpt_for_dataset = training_type == "Continued Pretraining"
|
||
dataset_result = trainer.load_and_format_dataset(
|
||
dataset_source = hf_dataset if hf_dataset and hf_dataset.strip() else None,
|
||
format_type = config.get("format_type", ""),
|
||
local_datasets = config.get("local_datasets") or None,
|
||
local_eval_datasets = config.get("local_eval_datasets") or None,
|
||
custom_format_mapping = config.get("custom_format_mapping"),
|
||
subset = config.get("subset"),
|
||
train_split = config.get("train_split", "train"),
|
||
eval_split = config.get("eval_split"),
|
||
dataset_streaming = config.get("dataset_streaming", False),
|
||
eval_steps = config.get("eval_steps", 0.00),
|
||
dataset_slice_start = config.get("dataset_slice_start"),
|
||
dataset_slice_end = config.get("dataset_slice_end"),
|
||
is_cpt = _is_cpt_for_dataset,
|
||
s3_config = config.get("s3_config"),
|
||
)
|
||
|
||
if isinstance(dataset_result, tuple):
|
||
dataset, eval_dataset = dataset_result
|
||
else:
|
||
dataset = dataset_result
|
||
eval_dataset = None
|
||
|
||
# Disable eval if eval_steps <= 0
|
||
eval_steps = config.get("eval_steps", 0.00)
|
||
if eval_steps is not None and float(eval_steps) <= 0:
|
||
eval_dataset = None
|
||
|
||
# Tell the parent eval is configured so the frontend shows
|
||
# "Waiting for first evaluation step..." instead of "not configured".
|
||
if eval_dataset is not None:
|
||
event_queue.put(
|
||
{
|
||
"type": "eval_configured",
|
||
"ts": time.time(),
|
||
}
|
||
)
|
||
|
||
if dataset is None or trainer.should_stop:
|
||
if trainer.should_stop:
|
||
event_queue.put({"type": "complete", "output_dir": None, "ts": time.time()})
|
||
else:
|
||
event_queue.put(
|
||
{
|
||
"type": "error",
|
||
"error": trainer.training_progress.error or "Failed to load dataset",
|
||
"stack": "",
|
||
"ts": time.time(),
|
||
}
|
||
)
|
||
return
|
||
|
||
# ── Start tqdm monitor early to capture download + tokenization bars ──
|
||
import threading as _th
|
||
|
||
_tqdm_stop = _th.Event()
|
||
|
||
def _monitor_tqdm():
|
||
from tqdm.auto import tqdm as _tqdm_cls
|
||
while not _tqdm_stop.is_set():
|
||
for bar in list(getattr(_tqdm_cls, "_instances", set())):
|
||
try:
|
||
n, total = bar.n or 0, bar.total or 0
|
||
desc = getattr(bar, "desc", "") or ""
|
||
if total > 0 and n > 0 and desc:
|
||
pct = min(int(n * 100 / total), 100)
|
||
_send_status(event_queue, f"{desc.strip()} {pct}% ({n:,}/{total:,})")
|
||
except (AttributeError, ReferenceError):
|
||
pass
|
||
_tqdm_stop.wait(3)
|
||
|
||
_tqdm_thread = _th.Thread(target = _monitor_tqdm, daemon = True)
|
||
_tqdm_thread.start()
|
||
|
||
training_type = config.get("training_type", "LoRA/QLoRA")
|
||
is_cpt = training_type == "Continued Pretraining"
|
||
use_lora = training_type in ("LoRA/QLoRA", "Continued Pretraining")
|
||
cpt_trains_embeddings = False
|
||
|
||
# ── 4c. Load training model (uses VRAM — dataset already formatted) ──
|
||
# Watchdog lets the parent recover a stalled Xet download via respawn.
|
||
_send_status(event_queue, "Loading model...")
|
||
from utils.hf_xet_fallback import start_watchdog
|
||
|
||
event_queue.put({"type": "model_load_started", "ts": time.time()})
|
||
_load_watchdog_stop = start_watchdog(
|
||
repo_ids = [model_name],
|
||
on_stall = lambda msg: event_queue.put(
|
||
{"type": "stall", "message": msg, "ts": time.time()}
|
||
),
|
||
xet_disabled = os.environ.get("HF_HUB_DISABLE_XET") == "1",
|
||
)
|
||
try:
|
||
success = trainer.load_model(
|
||
model_name = model_name,
|
||
max_seq_length = config["max_seq_length"],
|
||
load_in_4bit = config["load_in_4bit"],
|
||
full_finetuning = not use_lora,
|
||
hf_token = hf_token,
|
||
is_dataset_image = config.get("is_dataset_image", False),
|
||
is_dataset_audio = config.get("is_dataset_audio", False),
|
||
trust_remote_code = config.get("trust_remote_code", False),
|
||
gpu_ids = config.get("resolved_gpu_ids"),
|
||
)
|
||
finally:
|
||
_load_watchdog_stop.set()
|
||
event_queue.put({"type": "model_load_completed", "ts": time.time()})
|
||
if not success or trainer.should_stop:
|
||
if trainer.should_stop:
|
||
event_queue.put({"type": "complete", "output_dir": None, "ts": time.time()})
|
||
else:
|
||
error_msg = trainer.training_progress.error or "Failed to load model"
|
||
event_queue.put(
|
||
{
|
||
"type": "error",
|
||
"error": error_msg,
|
||
"stack": "",
|
||
"ts": time.time(),
|
||
}
|
||
)
|
||
return
|
||
|
||
# ── 4d. Prepare model (LoRA, full finetuning, or CPT) ──
|
||
if is_cpt:
|
||
_send_status(event_queue, "Configuring LoRA for continued pretraining...")
|
||
# embed_tokens (if included) goes to modules_to_save — trained
|
||
# full-precision at embedding_learning_rate. lm_head stays a LoRA
|
||
# target for merge compatibility (see unsloth PR #4106).
|
||
_user_modules = config.get("target_modules") or []
|
||
wants_embed = "embed_tokens" in _user_modules
|
||
cpt_trains_embeddings = wants_embed
|
||
cpt_target_modules = [m for m in _user_modules if m != "embed_tokens"]
|
||
if not cpt_target_modules:
|
||
cpt_target_modules = [
|
||
"q_proj",
|
||
"k_proj",
|
||
"v_proj",
|
||
"o_proj",
|
||
"gate_proj",
|
||
"up_proj",
|
||
"down_proj",
|
||
"lm_head",
|
||
]
|
||
success = trainer.prepare_model_for_training(
|
||
use_lora = True,
|
||
target_modules = cpt_target_modules,
|
||
modules_to_save = ["embed_tokens"] if wants_embed else None,
|
||
lora_r = config.get("lora_r", 128),
|
||
lora_alpha = config.get("lora_alpha", 32),
|
||
lora_dropout = config.get("lora_dropout", 0.0),
|
||
use_gradient_checkpointing = config.get("gradient_checkpointing", "unsloth"),
|
||
use_rslora = config.get("use_rslora", False),
|
||
use_loftq = config.get("use_loftq", False),
|
||
)
|
||
elif use_lora:
|
||
_send_status(event_queue, "Configuring LoRA adapters...")
|
||
success = trainer.prepare_model_for_training(
|
||
use_lora = True,
|
||
finetune_vision_layers = config.get("finetune_vision_layers", True),
|
||
finetune_language_layers = config.get("finetune_language_layers", True),
|
||
finetune_attention_modules = config.get("finetune_attention_modules", True),
|
||
finetune_mlp_modules = config.get("finetune_mlp_modules", True),
|
||
target_modules = config.get("target_modules"),
|
||
lora_r = config.get("lora_r", 16),
|
||
lora_alpha = config.get("lora_alpha", 16),
|
||
lora_dropout = config.get("lora_dropout", 0.0),
|
||
use_gradient_checkpointing = config.get("gradient_checkpointing", "unsloth"),
|
||
use_rslora = config.get("use_rslora", False),
|
||
use_loftq = config.get("use_loftq", False),
|
||
)
|
||
else:
|
||
_send_status(event_queue, "Preparing model for full finetuning...")
|
||
success = trainer.prepare_model_for_training(use_lora = False)
|
||
|
||
if not success or trainer.should_stop:
|
||
if trainer.should_stop:
|
||
event_queue.put({"type": "complete", "output_dir": None, "ts": time.time()})
|
||
else:
|
||
event_queue.put(
|
||
{
|
||
"type": "error",
|
||
"error": trainer.training_progress.error or "Failed to prepare model",
|
||
"stack": "",
|
||
"ts": time.time(),
|
||
}
|
||
)
|
||
return
|
||
|
||
lr_default = "5e-5" if is_cpt else "2e-4"
|
||
try:
|
||
lr_value = float(config.get("learning_rate", lr_default))
|
||
except ValueError:
|
||
event_queue.put(
|
||
{
|
||
"type": "error",
|
||
"error": f"Invalid learning rate: {config.get('learning_rate')}",
|
||
"stack": "",
|
||
"ts": time.time(),
|
||
}
|
||
)
|
||
return
|
||
|
||
# embedding_learning_rate is validated by Pydantic (Optional[float],
|
||
# gt=0, lt=1.0); if present it's already a finite float in range.
|
||
embedding_lr_value = config.get("embedding_learning_rate")
|
||
if is_cpt:
|
||
if cpt_trains_embeddings:
|
||
if embedding_lr_value is None:
|
||
# Default embedding_learning_rate = lr/10 (Unsloth CPT notebook).
|
||
embedding_lr_value = lr_value / 10.0
|
||
logger.info(
|
||
f"CPT: using default embedding_learning_rate={embedding_lr_value:.1e} "
|
||
f"(lr/10). Set explicitly to override.\n"
|
||
)
|
||
elif embedding_lr_value is not None:
|
||
logger.warning(
|
||
"CPT: embedding_learning_rate was provided but embed_tokens is "
|
||
"not being trained; ignoring the override.\n"
|
||
)
|
||
embedding_lr_value = None
|
||
|
||
# Generate output dir
|
||
resume_from_checkpoint = config.get("resume_from_checkpoint")
|
||
output_dir = config.get("output_dir") or _output_dir_from_resume_checkpoint(
|
||
resume_from_checkpoint
|
||
)
|
||
if not output_dir:
|
||
output_dir = build_default_output_dir_name(
|
||
model_name,
|
||
config.get("project_name"),
|
||
)
|
||
output_dir = str(resolve_output_dir(output_dir))
|
||
ensure_dir(Path(output_dir))
|
||
|
||
tensorboard_dir = config.get("tensorboard_dir")
|
||
if config.get("enable_tensorboard", False):
|
||
tensorboard_dir = str(resolve_tensorboard_dir(tensorboard_dir))
|
||
ensure_dir(Path(tensorboard_dir))
|
||
|
||
# Start training directly — no inner thread, we ARE the subprocess.
|
||
dataset_display = config.get("hf_dataset", "") or config.get("uploaded_file", "") or ""
|
||
_send_status(
|
||
event_queue,
|
||
f'Training "{model_name}"'
|
||
+ (f"\nDataset = {dataset_display}" if dataset_display else ""),
|
||
)
|
||
max_steps = config.get("max_steps", 0)
|
||
save_steps = config.get("save_steps", 0)
|
||
|
||
trainer._train_worker(
|
||
dataset,
|
||
output_dir = output_dir,
|
||
num_epochs = config.get("num_epochs", 3),
|
||
learning_rate = lr_value,
|
||
embedding_learning_rate = embedding_lr_value,
|
||
batch_size = config.get("batch_size", 2),
|
||
gradient_accumulation_steps = config.get("gradient_accumulation_steps", 4),
|
||
warmup_steps = config.get("warmup_steps"),
|
||
warmup_ratio = config.get("warmup_ratio"),
|
||
max_steps = max_steps if max_steps and max_steps > 0 else 0,
|
||
save_steps = save_steps if save_steps and save_steps > 0 else 0,
|
||
weight_decay = config.get("weight_decay", 0.001),
|
||
random_seed = config.get("random_seed", 3407),
|
||
packing = config.get("packing", False),
|
||
train_on_completions = False if is_cpt else config.get("train_on_completions", False),
|
||
enable_wandb = config.get("enable_wandb", False),
|
||
wandb_project = config.get("wandb_project", "unsloth-training"),
|
||
wandb_token = config.get("wandb_token"),
|
||
enable_tensorboard = config.get("enable_tensorboard", False),
|
||
tensorboard_dir = tensorboard_dir,
|
||
eval_dataset = eval_dataset,
|
||
eval_steps = eval_steps,
|
||
max_seq_length = config.get("max_seq_length", 2048),
|
||
vision_image_size = config.get("vision_image_size"),
|
||
optim = config.get("optim", "adamw_8bit"),
|
||
lr_scheduler_type = config.get("lr_scheduler_type", "linear"),
|
||
is_cpt = is_cpt,
|
||
resume_from_checkpoint = resume_from_checkpoint,
|
||
)
|
||
|
||
_tqdm_stop.set()
|
||
|
||
# Check final state
|
||
progress = trainer.get_training_progress()
|
||
if progress.error:
|
||
event_queue.put(
|
||
{
|
||
"type": "error",
|
||
"error": progress.error,
|
||
"stack": "",
|
||
"ts": time.time(),
|
||
}
|
||
)
|
||
else:
|
||
saved_output_dir = (
|
||
None if trainer.should_stop and not trainer.save_on_stop else output_dir
|
||
)
|
||
event_queue.put(
|
||
{
|
||
"type": "complete",
|
||
"output_dir": saved_output_dir,
|
||
"status_message": progress.status_message or "Training completed",
|
||
"ts": time.time(),
|
||
}
|
||
)
|
||
|
||
except Exception as exc:
|
||
_exc_str = str(exc).lower()
|
||
_is_oom = (
|
||
"out of memory" in _exc_str
|
||
or "hip out of memory" in _exc_str
|
||
or "cuda out of memory" in _exc_str
|
||
or type(exc).__name__ == "OutOfMemoryError"
|
||
)
|
||
if _is_oom:
|
||
_oom_msg = (
|
||
"GPU ran out of VRAM during training.\n"
|
||
"To fix: reduce max_seq_length (e.g. 2048–4096), enable "
|
||
"gradient_checkpointing=True, lower per_device_train_batch_size, "
|
||
"or use a smaller model / higher quantization."
|
||
)
|
||
logger.error("Training stopped: GPU OOM — %s", exc)
|
||
event_queue.put(
|
||
{
|
||
"type": "error",
|
||
"error": _oom_msg,
|
||
"stack": traceback.format_exc(limit = 20),
|
||
"ts": time.time(),
|
||
}
|
||
)
|
||
else:
|
||
event_queue.put(
|
||
{
|
||
"type": "error",
|
||
"error": str(exc),
|
||
"stack": traceback.format_exc(limit = 20),
|
||
"ts": time.time(),
|
||
}
|
||
)
|
||
|
||
|
||
def _send_status(event_queue: Any, message: str) -> None:
|
||
"""Send a status update to the parent process."""
|
||
event_queue.put(
|
||
{
|
||
"type": "status",
|
||
"message": message,
|
||
"ts": time.time(),
|
||
}
|
||
)
|
||
|
||
|
||
def _run_embedding_training(event_queue: Any, stop_queue: Any, config: dict) -> None:
|
||
"""Self-contained embedding model training pipeline.
|
||
|
||
Uses FastSentenceTransformer + SentenceTransformerTrainer +
|
||
MultipleNegativesRankingLoss — separate from UnslothTrainer's LLM/VLM/audio
|
||
paths. Mirrors the reference embedding notebooks:
|
||
All_MiniLM_L6_v2.py, BGE_M3.py, EmbeddingGemma_300M.py,
|
||
ModernBert.py, Qwen3_Embedding_0_6B.py
|
||
"""
|
||
import math
|
||
import queue as _queue
|
||
import threading
|
||
|
||
model_name = config["model_name"]
|
||
training_start_time = time.time()
|
||
|
||
# ── 1. Import embedding-specific libraries ──
|
||
_send_status(event_queue, "Importing embedding libraries...")
|
||
try:
|
||
# Recover from a namespace-package shadow (embedding imports unsloth directly).
|
||
from core.import_guards import ensure_real_packages
|
||
|
||
ensure_real_packages("unsloth_zoo", "unsloth")
|
||
from unsloth import FastSentenceTransformer, is_bfloat16_supported
|
||
from sentence_transformers import (
|
||
SentenceTransformerTrainer,
|
||
SentenceTransformerTrainingArguments,
|
||
)
|
||
from sentence_transformers.losses import MultipleNegativesRankingLoss
|
||
from sentence_transformers.training_args import BatchSamplers
|
||
from datasets import Dataset
|
||
from utils.datasets.cache_safe import load_dataset_cache_safe as load_dataset
|
||
from transformers import TrainerCallback
|
||
from utils.paths import datasets_root, resolve_output_dir, default_run_dir_name
|
||
except ImportError as e:
|
||
event_queue.put(
|
||
{
|
||
"type": "error",
|
||
"error": f"Failed to import embedding libraries: {e}. "
|
||
"Ensure 'sentence_transformers' and 'unsloth' are installed.",
|
||
"stack": traceback.format_exc(limit = 20),
|
||
"ts": time.time(),
|
||
}
|
||
)
|
||
return
|
||
|
||
# ── Stop signal handling ──
|
||
_should_stop = False
|
||
_save_on_stop = True
|
||
|
||
def _poll_stop():
|
||
nonlocal _should_stop, _save_on_stop
|
||
while True:
|
||
try:
|
||
msg = stop_queue.get(timeout = 1.0)
|
||
if msg and msg.get("type") == "stop":
|
||
_save_on_stop = msg.get("save", True)
|
||
_should_stop = True
|
||
logger.info(
|
||
"Embedding training: stop signal received (save=%s)",
|
||
_save_on_stop,
|
||
)
|
||
return
|
||
except _queue.Empty:
|
||
continue
|
||
except (EOFError, OSError):
|
||
return
|
||
|
||
stop_thread = threading.Thread(target = _poll_stop, daemon = True)
|
||
stop_thread.start()
|
||
|
||
# ── 2. Load model ──
|
||
_send_status(event_queue, "Loading embedding model...")
|
||
try:
|
||
hf_token = config.get("hf_token", "")
|
||
hf_token = hf_token if hf_token and hf_token.strip() else None
|
||
max_seq_length = config.get("max_seq_length", 512)
|
||
training_type = config.get("training_type", "LoRA/QLoRA")
|
||
use_lora = training_type == "LoRA/QLoRA"
|
||
|
||
# Malware gate (embedding): a poisoned pickle deserializes on load even with
|
||
# trust_remote_code False, so check HF's security scan (metadata-only) first.
|
||
# For a LoRA, gate the base whose weights deserialize.
|
||
from utils.security import evaluate_file_security
|
||
|
||
malware_targets = [model_name]
|
||
try:
|
||
from utils.models.model_config import get_base_model_from_lora_identifier
|
||
_base = get_base_model_from_lora_identifier(model_name, hf_token)
|
||
if _base:
|
||
malware_targets.append(_base)
|
||
except Exception as exc:
|
||
logger.debug("Could not resolve LoRA base for malware scan: %s", exc)
|
||
from utils.security import security_load_subdirs
|
||
|
||
for target in dict.fromkeys(malware_targets):
|
||
_fs = evaluate_file_security(
|
||
target, hf_token = hf_token, load_subdirs = security_load_subdirs(target, hf_token)
|
||
)
|
||
if _fs.blocked:
|
||
event_queue.put(
|
||
{
|
||
"type": "error",
|
||
"error": _fs.reason,
|
||
"error_kind": "malware_blocked",
|
||
"security": _fs.response_payload(),
|
||
"ts": time.time(),
|
||
}
|
||
)
|
||
return
|
||
|
||
# Consent gate (embedding): scan any auto_map code before it runs; block
|
||
# CRITICAL/HIGH unless pinned-approved. A no-op without auto_map.
|
||
if config.get("trust_remote_code", False):
|
||
from utils.security import evaluate_remote_code_consent_for_targets
|
||
|
||
consent_targets = [model_name]
|
||
try:
|
||
from utils.models.model_config import get_base_model_from_lora_identifier
|
||
_cbase = get_base_model_from_lora_identifier(model_name, hf_token)
|
||
if _cbase:
|
||
consent_targets.append(_cbase)
|
||
except Exception as exc:
|
||
logger.debug("Could not resolve LoRA base for consent scan: %s", exc)
|
||
# Scan adapter + base as one combined unit, pinned by a single fingerprint.
|
||
_rc = evaluate_remote_code_consent_for_targets(
|
||
consent_targets,
|
||
hf_token = hf_token,
|
||
trust_remote_code = True,
|
||
approved_fingerprint = config.get("approved_remote_code_fingerprint"),
|
||
subject = config.get("subject"),
|
||
)
|
||
if _rc.blocked:
|
||
event_queue.put(
|
||
{
|
||
"type": "error",
|
||
"error": (
|
||
f"Model '{_rc.model_name}' ships custom code flagged as "
|
||
f"{_rc.max_severity} by the security scan. Review it and "
|
||
f"re-run with approval to proceed.\n\n{_rc.findings_summary}"
|
||
),
|
||
"error_kind": "remote_code_blocked",
|
||
"remote_code": _rc.response_payload(),
|
||
"ts": time.time(),
|
||
}
|
||
)
|
||
return
|
||
|
||
model = FastSentenceTransformer.from_pretrained(
|
||
model_name = model_name,
|
||
max_seq_length = max_seq_length,
|
||
full_finetuning = not use_lora,
|
||
token = hf_token,
|
||
)
|
||
except Exception as e:
|
||
event_queue.put(
|
||
{
|
||
"type": "error",
|
||
"error": f"Failed to load embedding model '{model_name}': {e}",
|
||
"stack": traceback.format_exc(limit = 20),
|
||
"ts": time.time(),
|
||
}
|
||
)
|
||
return
|
||
|
||
if _should_stop:
|
||
event_queue.put({"type": "complete", "output_dir": None, "ts": time.time()})
|
||
return
|
||
|
||
# ── 3. Apply LoRA ──
|
||
if use_lora:
|
||
_send_status(event_queue, "Configuring LoRA adapters (FEATURE_EXTRACTION)...")
|
||
try:
|
||
gradient_checkpointing = config.get("gradient_checkpointing", False)
|
||
# Normalize "none"/empty → False.
|
||
if gradient_checkpointing in ("none", "", None):
|
||
gradient_checkpointing = False
|
||
|
||
model = FastSentenceTransformer.get_peft_model(
|
||
model,
|
||
r = config.get("lora_r", 32),
|
||
target_modules = config.get("target_modules")
|
||
or ["q_proj", "k_proj", "v_proj", "o_proj"],
|
||
lora_alpha = config.get("lora_alpha", 64),
|
||
lora_dropout = config.get("lora_dropout", 0.0),
|
||
bias = "none",
|
||
use_gradient_checkpointing = gradient_checkpointing,
|
||
random_state = config.get("random_seed", 3407),
|
||
use_rslora = config.get("use_rslora", False),
|
||
loftq_config = {"loftq_bits": 4, "loftq_iter": 1}
|
||
if config.get("use_loftq")
|
||
else None,
|
||
task_type = "FEATURE_EXTRACTION",
|
||
)
|
||
except Exception as e:
|
||
event_queue.put(
|
||
{
|
||
"type": "error",
|
||
"error": f"Failed to configure LoRA for embedding model: {e}",
|
||
"stack": traceback.format_exc(limit = 20),
|
||
"ts": time.time(),
|
||
}
|
||
)
|
||
return
|
||
|
||
if _should_stop:
|
||
event_queue.put({"type": "complete", "output_dir": None, "ts": time.time()})
|
||
return
|
||
|
||
# ── 4. Load dataset ──
|
||
_send_status(event_queue, "Loading dataset...")
|
||
try:
|
||
hf_dataset = config.get("hf_dataset", "")
|
||
local_datasets = config.get("local_datasets") or []
|
||
subset = config.get("subset") or None
|
||
train_split = config.get("train_split", "train") or "train"
|
||
|
||
def _load_local_embedding_dataset(dataset_paths: list[str]):
|
||
all_files: list[str] = []
|
||
for dataset_file in dataset_paths:
|
||
file_path = (
|
||
dataset_file
|
||
if os.path.isabs(dataset_file)
|
||
else os.path.join(
|
||
str(datasets_root()),
|
||
dataset_file,
|
||
)
|
||
)
|
||
if os.path.isdir(file_path):
|
||
file_path_obj = Path(file_path)
|
||
parquet_dir = (
|
||
file_path_obj / "parquet-files"
|
||
if (file_path_obj / "parquet-files").exists()
|
||
else file_path_obj
|
||
)
|
||
parquet_files = sorted(parquet_dir.glob("*.parquet"))
|
||
if parquet_files:
|
||
all_files.extend(str(p) for p in parquet_files)
|
||
continue
|
||
candidates: list[Path] = []
|
||
for ext in (".json", ".jsonl", ".csv", ".parquet"):
|
||
candidates.extend(sorted(file_path_obj.glob(f"*{ext}")))
|
||
if candidates:
|
||
all_files.extend(str(c) for c in candidates)
|
||
continue
|
||
raise ValueError(f"No supported data files in directory: {file_path_obj}")
|
||
else:
|
||
all_files.append(file_path)
|
||
|
||
if not all_files:
|
||
raise ValueError("No local dataset files found")
|
||
|
||
first_ext = Path(all_files[0]).suffix.lower()
|
||
if first_ext in (".json", ".jsonl"):
|
||
loader = "json"
|
||
elif first_ext == ".csv":
|
||
loader = "csv"
|
||
elif first_ext == ".parquet":
|
||
loader = "parquet"
|
||
else:
|
||
raise ValueError(f"Unsupported local dataset format: {all_files[0]}")
|
||
return load_dataset(loader, data_files = all_files, split = "train")
|
||
|
||
if hf_dataset and hf_dataset.strip():
|
||
hf_token = config.get("hf_token", "")
|
||
hf_token = hf_token if hf_token and hf_token.strip() else None
|
||
dataset = load_dataset(
|
||
hf_dataset.strip(),
|
||
subset,
|
||
split = train_split,
|
||
token = hf_token,
|
||
)
|
||
elif local_datasets:
|
||
dataset = _load_local_embedding_dataset(local_datasets)
|
||
elif config.get("s3_config"):
|
||
from core.training.s3_dataset import (
|
||
S3DownloadCancelled,
|
||
prepare_s3_dataset_download,
|
||
)
|
||
|
||
_send_status(event_queue, "Downloading dataset from S3...")
|
||
s3_download = None
|
||
try:
|
||
s3_download = prepare_s3_dataset_download(
|
||
config["s3_config"],
|
||
cancel_callback = lambda: _should_stop,
|
||
)
|
||
dataset = _load_local_embedding_dataset(s3_download.files)
|
||
except S3DownloadCancelled:
|
||
event_queue.put(
|
||
{
|
||
"type": "complete",
|
||
"output_dir": None,
|
||
"status_message": "Training cancelled",
|
||
"ts": time.time(),
|
||
}
|
||
)
|
||
return
|
||
finally:
|
||
if s3_download is not None:
|
||
s3_download.cleanup()
|
||
else:
|
||
event_queue.put(
|
||
{
|
||
"type": "error",
|
||
"error": "No dataset specified for embedding training.",
|
||
"stack": "",
|
||
"ts": time.time(),
|
||
}
|
||
)
|
||
return
|
||
|
||
# Apply dataset slicing if specified
|
||
slice_start = config.get("dataset_slice_start")
|
||
slice_end = config.get("dataset_slice_end")
|
||
if slice_start is not None or slice_end is not None:
|
||
start = slice_start if slice_start is not None else 0
|
||
end = slice_end if slice_end is not None else len(dataset)
|
||
dataset = dataset.select(range(start, min(end + 1, len(dataset))))
|
||
|
||
logger.info(f"Embedding dataset loaded: {len(dataset)} samples")
|
||
except Exception as e:
|
||
event_queue.put(
|
||
{
|
||
"type": "error",
|
||
"error": f"Failed to load dataset: {e}",
|
||
"stack": traceback.format_exc(limit = 20),
|
||
"ts": time.time(),
|
||
}
|
||
)
|
||
return
|
||
|
||
if _should_stop:
|
||
event_queue.put({"type": "complete", "output_dir": None, "ts": time.time()})
|
||
return
|
||
|
||
# ── 5. Create loss function ──
|
||
loss = MultipleNegativesRankingLoss(model)
|
||
|
||
# ── 6. Build training arguments ──
|
||
_send_status(event_queue, "Configuring training...")
|
||
try:
|
||
lr_value = float(config.get("learning_rate", "2e-4"))
|
||
except ValueError:
|
||
event_queue.put(
|
||
{
|
||
"type": "error",
|
||
"error": f"Invalid learning rate: {config.get('learning_rate')}",
|
||
"stack": "",
|
||
"ts": time.time(),
|
||
}
|
||
)
|
||
return
|
||
|
||
resume_from_checkpoint = config.get("resume_from_checkpoint")
|
||
output_dir = config.get("output_dir") or _output_dir_from_resume_checkpoint(
|
||
resume_from_checkpoint
|
||
)
|
||
if not output_dir:
|
||
output_dir = build_default_output_dir_name(
|
||
model_name,
|
||
config.get("project_name"),
|
||
)
|
||
output_dir = str(resolve_output_dir(output_dir))
|
||
|
||
num_epochs = config.get("num_epochs", 2)
|
||
batch_size = config.get("batch_size", 256)
|
||
gradient_accumulation_steps = config.get("gradient_accumulation_steps", 1)
|
||
max_steps_val = config.get("max_steps", 0)
|
||
save_steps_val = config.get("save_steps", 0)
|
||
warmup_ratio = config.get("warmup_ratio", 0.03)
|
||
warmup_steps_val = config.get("warmup_steps")
|
||
log_frequency = config.get("log_frequency", 50)
|
||
|
||
# Build args dict
|
||
training_args_kwargs = {
|
||
"output_dir": output_dir,
|
||
"per_device_train_batch_size": batch_size,
|
||
"gradient_accumulation_steps": gradient_accumulation_steps,
|
||
"learning_rate": lr_value,
|
||
"fp16": not is_bfloat16_supported(),
|
||
"bf16": is_bfloat16_supported(),
|
||
"logging_steps": 1,
|
||
"report_to": ["wandb"] if config.get("enable_wandb") else "none",
|
||
"lr_scheduler_type": config.get("lr_scheduler_type", "linear"),
|
||
"batch_sampler": BatchSamplers.NO_DUPLICATES,
|
||
"optim": config.get("optim", "adamw_8bit"),
|
||
"weight_decay": config.get("weight_decay", 0.001),
|
||
"seed": config.get("random_seed", 3407),
|
||
}
|
||
|
||
# max_steps vs epochs
|
||
if max_steps_val and max_steps_val > 0:
|
||
training_args_kwargs["max_steps"] = max_steps_val
|
||
else:
|
||
training_args_kwargs["num_train_epochs"] = num_epochs if num_epochs > 0 else 2
|
||
|
||
# warmup: prefer warmup_ratio (standard for embedding scripts), else steps
|
||
if warmup_ratio is not None and warmup_ratio > 0:
|
||
training_args_kwargs["warmup_ratio"] = warmup_ratio
|
||
elif warmup_steps_val is not None and warmup_steps_val > 0:
|
||
training_args_kwargs["warmup_steps"] = warmup_steps_val
|
||
|
||
# save_steps
|
||
if save_steps_val and save_steps_val > 0:
|
||
training_args_kwargs["save_steps"] = save_steps_val
|
||
training_args_kwargs["save_strategy"] = "steps"
|
||
|
||
args = SentenceTransformerTrainingArguments(**training_args_kwargs)
|
||
|
||
# ── 7. Calculate total steps for progress tracking ──
|
||
if max_steps_val and max_steps_val > 0:
|
||
total_steps = max_steps_val
|
||
else:
|
||
effective_epochs = num_epochs if num_epochs > 0 else 2
|
||
len_dataloader = math.ceil(len(dataset) / batch_size)
|
||
steps_per_epoch = max(len_dataloader // gradient_accumulation_steps, 1)
|
||
total_steps = steps_per_epoch * effective_epochs
|
||
|
||
# ── 8. Create progress callback ──
|
||
class _EmbeddingProgressCallback(TrainerCallback):
|
||
"""Send training progress events to the parent via event_queue."""
|
||
|
||
def on_log(
|
||
self,
|
||
args,
|
||
state,
|
||
control,
|
||
logs = None,
|
||
**kwargs,
|
||
):
|
||
if not logs:
|
||
return
|
||
loss_value = logs.get("loss", logs.get("train_loss", None))
|
||
current_step = state.global_step
|
||
|
||
elapsed = time.time() - training_start_time
|
||
eta = None
|
||
if current_step > 0 and total_steps > 0:
|
||
remaining = total_steps - current_step
|
||
if remaining > 0:
|
||
eta = (elapsed / current_step) * remaining
|
||
|
||
event_queue.put(
|
||
{
|
||
"type": "progress",
|
||
"step": current_step,
|
||
"epoch": round(state.epoch, 2) if state.epoch else 0,
|
||
"loss": loss_value,
|
||
"learning_rate": logs.get("learning_rate", None),
|
||
"total_steps": total_steps,
|
||
"elapsed_seconds": elapsed,
|
||
"eta_seconds": eta,
|
||
"grad_norm": logs.get("grad_norm"),
|
||
"num_tokens": getattr(state, "num_input_tokens_seen", None),
|
||
"eval_loss": logs.get("eval_loss"),
|
||
"status_message": "",
|
||
"ts": time.time(),
|
||
}
|
||
)
|
||
|
||
def on_step_end(self, args, state, control, **kwargs):
|
||
if _should_stop:
|
||
logger.info("Embedding training: stop at step %d", state.global_step)
|
||
control.should_training_stop = True
|
||
return control
|
||
|
||
# ── 9. Create trainer and train ──
|
||
_send_status(event_queue, "Starting embedding training...")
|
||
try:
|
||
trainer = SentenceTransformerTrainer(
|
||
model = model,
|
||
train_dataset = dataset,
|
||
loss = loss,
|
||
args = args,
|
||
callbacks = [_EmbeddingProgressCallback()],
|
||
)
|
||
|
||
trainer.train(resume_from_checkpoint = resume_from_checkpoint)
|
||
except Exception as e:
|
||
event_queue.put(
|
||
{
|
||
"type": "error",
|
||
"error": f"Embedding training failed: {e}",
|
||
"stack": traceback.format_exc(limit = 20),
|
||
"ts": time.time(),
|
||
}
|
||
)
|
||
return
|
||
|
||
# ── 10. Save model ──
|
||
if _should_stop and not _save_on_stop:
|
||
event_queue.put(
|
||
{
|
||
"type": "complete",
|
||
"output_dir": None,
|
||
"status_message": "Training cancelled",
|
||
"ts": time.time(),
|
||
}
|
||
)
|
||
return
|
||
|
||
_send_status(event_queue, "Saving model...")
|
||
try:
|
||
if _should_stop and _save_on_stop:
|
||
trainer._save_checkpoint(trainer.model, trial = None)
|
||
model.save_pretrained(output_dir)
|
||
model.tokenizer.save_pretrained(output_dir)
|
||
logger.info("Embedding model saved to %s", output_dir)
|
||
except Exception as e:
|
||
logger.error("Failed to save embedding model: %s", e)
|
||
event_queue.put(
|
||
{
|
||
"type": "error",
|
||
"error": f"Training completed but failed to save: {e}",
|
||
"stack": traceback.format_exc(limit = 20),
|
||
"ts": time.time(),
|
||
}
|
||
)
|
||
return
|
||
|
||
# ── 11. Done ──
|
||
event_queue.put(
|
||
{
|
||
"type": "complete",
|
||
"output_dir": output_dir,
|
||
"status_message": "Embedding training completed",
|
||
"ts": time.time(),
|
||
}
|
||
)
|