o TÃiŠã@s‚ddlZddlmZddlZGdd„dejƒZGdd„dejjƒZGdd„dejjƒZGd d „d ejjƒZGd d „d ejjƒZdS) éN)Úautogradc@s8eZdZdZedejdedefdd„ƒZ edd„ƒZ d S) Ú TopKBinarizera Top-k Binarizer. Computes a binary mask M from a real value matrix S such that `M_{i,j} = 1` if and only if `S_{i,j}` is among the k% highest values of S. Implementation is inspired from: https://github.com/yaozhewei/MLPruning ÚinputsÚ thresholdÚsigmoidc Cs||r t |¡ ¡}||_| ¡}| ¡jdd\}}t || ¡¡}| ¡}d|||d…<d||d|…<|  |¡|S)ao Args: inputs (`torch.FloatTensor`) The input matrix from which the binarizer computes the binary mask. threshold (`float`) The percentage of weights to keep (the rest is pruned). `threshold` is a float between 0 and 1. sigmoid (`bool`) Whether to apply a sigmoid on the threshold Returns: mask (`torch.FloatTensor`) Binary matrix of the same size as `inputs` acting as a mask (1 - the associated weight is retained, 0 - the associated weight is pruned). T)Ú descendinggNgð?) ÚtorchrÚitemÚcloneÚflattenÚsortÚmathÚceilÚnumelÚsave_for_backward) ÚctxrrrÚmaskÚ_ÚidxÚjÚflat_out©rúO/home/ubuntu/.local/lib/python3.10/site-packages/deepspeed/compression/utils.pyÚforwards zTopKBinarizer.forwardcCs8|j\}|jr| ¡|| ¡ d¡dfS| ¡ddfS)Néÿÿÿÿ)Ú saved_tensorsrr ÚsumÚview)rÚ gradOutputrrrrÚbackward5szTopKBinarizer.backwardN) Ú__name__Ú __module__Ú __qualname__Ú__doc__Ú staticmethodrÚtensorÚfloatÚboolrrrrrrr s  rc@ó*eZdZdZeddd„ƒZedd„ƒZdS) Ú SymQuantizerz Symmetric quantization Néc CsÆ|dur|dus|dur|dur|dksJ‚d|}|j}|dur6| |d¡}tjt |¡dd |d¡}n t | ¡|¡ d¡}d||} ||  ¡ | d|dd¡| } |  |¡  ¡} | S)áü Args: inputs (`torch.FloatTensor`) The input which needs to be quantized num_bits (int, >=4) Number of bits to use for quantization min_value/max_value (torch.FloatTensor) Used for static activation quantization num_groups (int) How many groups to partition the quantization into Returns: quantized_input (`torch.FloatTensor`) Quantized input Nr*ér©Údim) ÚshapeÚreshaperÚamaxÚabsrÚmaxÚroundÚclampÚ contiguous) rÚinputÚnum_bitsÚ min_valueÚ max_valueÚ num_groupsÚq_rangeÚ input_shapeÚ max_inputÚscaleÚoutputrrrrCs    &zSymQuantizer.forwardcCó| ¡}|ddddfS©N©r ©rÚ grad_outputÚ grad_inputrrrrbózSymQuantizer.backward©NNr*©r r!r"r#r$rrrrrrr)>s  r)c@r() Ú AsymQuantizerz! Asymmetric quantization Nr*c Cs¼|dur|dus|dur|dur|dksJ‚d|}|j}|dur5| |d¡}|jddd}|jddd}|||}|| ¡|} || | ¡ d|d¡|| } |  |¡ ¡} | S)r+Nr*r,rT)r.Úkeepdimr)r/r0Úaminr1r4r5r6) rr7r8r9r:r;r<r=r?Ú zero_pointr@rrrrms    $zAsymQuantizer.forwardcCrArBrCrDrrrrŽrGzAsymQuantizer.backwardrHrIrrrrrJhs   rJc@r() ÚTernaryQuantizerz Ternary quantization Nr*cCsÖ|dur|dus J‚| |d¡}|jd}|jddd |¡}d| dd¡} || k | ¡¡} || k | ¡¡} | ¡| k | ¡¡} | | ¡jdd| jdd dd¡} | | | | }| |j¡ ¡}|S)á Args: inputs (`torch.FloatTensor`) The input which needs to be quantized num_bits (int) Dummy variable min_value/max_value (torch.FloatTensor) Used for static activation quantization; for now they are dummy variable num_groups (int) How many groups to partition the quantization into Returns: quantized_input (`torch.FloatTensor`) Quantized input Nrr*)Úpr.gffffffæ?r-) r0r/ÚnormÚdivrÚtyper2rr6)rr7r8r9r:r;Ú input_flatÚnÚmÚthresÚposÚnegrÚalphar@rrrr™s  (zTernaryQuantizer.forwardcCrArBrCrDrrrr·rGzTernaryQuantizer.backwardrHrIrrrrrN”s  rNc@r() ÚBinaryQuantizerz Binary quantization Nr*c Csb|dur|dus J‚| |d¡}|jd}|jdddd |¡}| ¡ |¡} |  |j¡ ¡} | S)rONrr*T)rPr.rK)r0r/rQrRÚsignÚmulr6) rr7r8r9r:r;rTrUrVr@rrrrÂs  zBinaryQuantizer.forwardcCrArBrCrDrrrrÛrGzBinaryQuantizer.backwardrHrIrrrrr[½s  r[) rrr ÚFunctionrr)rJrNr[rrrrÚs 3*,)