9jdZddlZddlZddlZddlZddlZd\ZZdddZdZ dZ d \a a d a iZd Zdd ZdZdZdZdZdZdZdS)a,MMGP RAM allocator for PyTorch's CPU tensors (Windows and Linux, PyTorch 2.6 to 2.15). from mmgp.allocator import ram ram.install() # after importing torch: the CPU tensors allocated from then on use it ram.release() # the freed blocks it keeps go back to the system now (the end of a generation, a model release) The CPU tensors of min_mb and more get their own pages, committed at their size, instead of a block of PyTorch's CPU allocator (mimalloc on Windows, glibc's arenas on Linux), which keeps the memory of freed tensors for the rest of the process and on Windows commits up to twice what a tensor of 1 to 64 MiB needs. The freed blocks are kept for reuse (a loop that allocates the same sizes again reuses pages already in memory, without page faults), at most cache_gb of them. They go back to the system on demand, with release() (call it at the end of a workload) or when the MMGP VRAM allocator would otherwise refuse to spill into RAM, and when the workload needs it: beyond cache_gb, or when new pages are wanted while the system's available RAM is under pressure_gb, in the thread that frees or allocates: no background thread, no timer, nothing runs while the GPU works. Smaller tensors keep PyTorch's allocator. The library also carries the hooks of the RAM debug mode (ram_debug.py).N)i@izram_alloc_win_amd64.dllzram_alloc_linux_x86_64.so))win32amd64)linuxx86_64)))r) enabledlivecachedpeak allocationshitsfreshtrims fallbacksreleasesreleased_bytesreserved)NNFcttttfStdtjdddddD}t tj tj f}|dn^tj tj tj t"|}|%dtj dtj ant$d|cxkrt$d ksnd tjd an`tj d rtjsd an*tj |s|dan t-j|}t,jt,jt,jf|j_t,j|j_t,jf|j_t-jt,jf|j_t-jt,j t,jf|j!_t,j|j!_|an#tD$r}|d|daYd}~nd}~wwxYwttfS)zJ(the library, None), or (None, the reason it cannot be used): loaded once.Nc34K|]}t|VdSN)int).0parts :C:\Users\johnn\OneDrive\Documents\AI\mmgp\allocator\ram.py zlibrary..$s(YY$CIIYYYYYY+r.rzno library for  zPyTorch z7 is not among the versions checked for it (2.6 to 2.15)rz'this PyTorch build uses the old C++ ABIz, is missing (python -m mmgp.allocator.build)z could not be loaded ())#_lib_reasontupletorch __version__split _LIBRARIESgetsysplatformmachinelowerospathjoindirnameabspath__file__TORCH_VERSIONS startswithcompiled_with_cxx11_abiisfilectypesCDLLc_int64 ra_installargtypesc_intrestypera_set_enabledPOINTERra_statsc_uint64 ra_rangesOSError)versionnamer1liberrors rlibraryrKsf 7.W}YY%*;*A*A#*F*Fq*I*O*OPS*T*TUWVWUW*XYYYYYG >>3<)9););)A)A)C)CD E ED<44RW\\"'//"'//RZB[B[2\2\^b%c%cD |GCLGG83C3E3EGG A ' > > > >^A-> > > > >gU.ggg   ) )%2O2Q2Q; W^^D ! !GGG +d##C(.~v~v~&VCN #%+\CN "+1 > >==U===GGGGGG > =sJ?? K KKr"ct\}}|td|dddl}|j}|t |t zntdt z|dz}|t |t zntdt z|dz|z}| t |tz||dkrtd d a tt|t|t z dt|t z d dS) aMakes the allocator PyTorch's CPU allocator for the tensors of min_mb and more. cache_gb: the most freed blocks kept for reuse (default: 5% of the RAM, at most 2 GiB); pressure_gb: under this much available RAM, the freed blocks go back at once (default: the margin the VRAM allocator leaves before spilling into RAM, 10% of the RAM and at least 4 GiB, plus cache_gb). Raises RuntimeError when the allocator cannot be used here.Nz'The MMGP RAM allocator cannot be used: r rr zUThe MMGP RAM allocator cannot be used: PyTorch's CPU allocator could not be replaced.T)min_mbcache_gb pressure_gb)rK RuntimeErrorpsutilvirtual_memorytotalrGBminmaxr=MBactive_link_vram_allocatorsettingsupdateround) rPrQrRrIreasonrTphysicalcachepressures rinstallrd?s7 ))KC {NVNNNOOOMMM$$&&,H"*"6C2   CBTVzranges..ys3 K K KUSU^SUQ/ 0 K K Kr)r$r:rDrXrErange)countr~s @rrangesrss\ | ?T ! $ $C dnnS$// 0 0E K K K KeEll K K KKr)r"NN)__doc__r:r0r-r,r'rWrZr*r6rvr$r%r[r]rKrdr\rmrprsrzrrorrrs  b b  B";Rm n n " N g  @gggg(XXX---   $$$LLLLLr