Return freed activations to the OS on Linux; recommend jemalloc (A+B)
MIMALLOC_PURGE_DELAY=0 only bites on Windows (bundled mimalloc). On a Linux server PyTorch uses glibc ptmalloc, which keeps a BiRefNet@2048 forward's freed activations in the arena, so RSS ratchets up across requests and the env var is a no-op there. A. bgfilter/memtune.py: release_freed_memory() calls glibc malloc_trim(0) at runtime (no-op on Windows/musl/other allocators). Wired into service.process (after each request) and the CLI batch loop (after each image), the two long-lived paths where RSS accumulates. Single-image CLI exits, so it is left alone. B. DEPLOY(_ZH): document preloading jemalloc via LD_PRELOAD + MALLOC_CONF (also improves CPU throughput) as the production alternative, with MALLOC_ARENA_MAX/ MALLOC_TRIM_THRESHOLD_ as an allocator-free fallback. Under jemalloc/tcmalloc malloc_trim simply no-ops. Also fixes the stale off-by-default cross-check heading in DEPLOY.md. Verified: modules import; release_freed_memory() returns False (no-op) on Windows. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
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@@ -7,6 +7,8 @@ import os
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# BiRefNet@2048 pass, stacking under ViTMatte's attention peak (measured
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# 2048x2048 bf16: peak 25.2 -> 21.4 GB with purge 0, and slightly *faster*).
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# Must be set before the torch DLL loads; harmless where mimalloc is not used.
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# The Linux/glibc equivalent (ptmalloc arena retention) is handled at runtime by
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# bgfilter.memtune.release_freed_memory(); see DEPLOY for jemalloc as an alt.
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os.environ.setdefault("MIMALLOC_PURGE_DELAY", "0")
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__version__ = "0.1.0"
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@@ -6,6 +6,7 @@ import typer
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from rich import print
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from .config import load_settings, override_settings
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from .memtune import release_freed_memory
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from .pipeline import MattingPipeline, run_image
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app = typer.Typer(help="Offline flat-background character matting.")
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@@ -69,6 +70,9 @@ def main(
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dbg = None if debug_dir is None else debug_dir / image_path.stem
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result = pipeline.run_image(image_path, out_path, dbg)
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print(f"[green]wrote[/green] {result['output']}")
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# Free this image's activations back to the OS before the next one
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# so batch RSS tracks a single image, not the whole run.
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release_freed_memory()
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return
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if input is None or output is None:
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@@ -0,0 +1,44 @@
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from __future__ import annotations
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"""Cross-platform release of freed allocator memory after a heavy inference.
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Both of the process allocators this pipeline runs under retain large freed
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blocks instead of returning them to the OS, so a BiRefNet@2048 forward's
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activations keep inflating RSS long after the tensors are gone:
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- Windows PyTorch bundles mimalloc; its lazy purge is defused at import time by
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``MIMALLOC_PURGE_DELAY=0`` (set in ``bgfilter/__init__.py`` before torch loads).
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- Linux/glibc PyTorch uses ptmalloc, which keeps freed arenas committed;
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``malloc_trim(0)`` hands those pages back and is called at runtime via
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:func:`release_freed_memory` after each image.
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Neither hook exists on the other's platform, so this stays a no-op there (and on
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musl, which has no ``malloc_trim``, or under a preloaded jemalloc/tcmalloc that
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manages its own release). See DEPLOY for the recommended jemalloc LD_PRELOAD.
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"""
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import ctypes
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import ctypes.util
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import sys
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_malloc_trim = None
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if sys.platform.startswith("linux"):
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try:
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_libc = ctypes.CDLL(ctypes.util.find_library("c") or "libc.so.6", use_errno=True)
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_malloc_trim = _libc.malloc_trim # glibc only; AttributeError on musl
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_malloc_trim.argtypes = [ctypes.c_size_t]
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_malloc_trim.restype = ctypes.c_int
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except (OSError, AttributeError):
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_malloc_trim = None
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def release_freed_memory() -> bool:
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"""Return freed heap pages to the OS; True if a release was actually issued.
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Cheap (sub-millisecond) and safe to call after every processed image. No-op
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where no supported hook is available (Windows, musl, non-glibc allocators).
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"""
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if _malloc_trim is None:
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return False
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_malloc_trim(0)
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return True
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+7
-1
@@ -7,6 +7,7 @@ from dataclasses import replace
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import numpy as np
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from .config import _SEG_MODELS, override_settings
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from .memtune import release_freed_memory
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from .pipeline import MattingPipeline, MattingResult
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from .segmentation import BiRefNetSegmenter, make_segmenter
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from .settings import PipelineSettings, SegmentationSettings
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@@ -151,4 +152,9 @@ class PipelineManager:
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pipeline = MattingPipeline(
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settings, runner=runner, segmenter=segmenter, cross_checker=cross_checker
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)
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return pipeline.run_rgb(rgb)
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try:
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return pipeline.run_rgb(rgb)
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finally:
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# Return this request's large freed activations to the OS so RSS does
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# not ratchet up across requests in the long-lived service process.
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release_freed_memory()
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