2021-10-06 02:16:20 +00:00
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# Copyright (c) Facebook, Inc. and its affiliates.
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#
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# This source code is licensed under the MIT license found in the
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# LICENSE file in the root directory of this source tree.
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2022-06-30 15:14:20 +00:00
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import ctypes as ct
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2021-10-06 02:16:20 +00:00
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import random
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2022-06-30 15:14:20 +00:00
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from typing import Tuple
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2021-10-06 02:16:20 +00:00
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import torch
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from torch import Tensor
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2022-07-01 14:25:44 +00:00
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from .cextension import lib, COMPILED_WITH_CUDA
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2022-07-01 14:16:10 +00:00
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2021-10-06 02:16:20 +00:00
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name2qmap = {}
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2022-07-01 14:16:10 +00:00
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if COMPILED_WITH_CUDA:
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''' C FUNCTIONS FOR OPTIMIZERS '''
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str2optimizer32bit = {}
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str2optimizer32bit['adam'] = (lib.cadam32bit_g32, lib.cadam32bit_g16)
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str2optimizer32bit['momentum'] = (lib.cmomentum32bit_g32, lib.cmomentum32bit_g16)
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str2optimizer32bit['rmsprop'] = (lib.crmsprop32bit_g32, lib.crmsprop32bit_g16)
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str2optimizer32bit['adagrad'] = (lib.cadagrad32bit_g32, lib.cadagrad32bit_g16)
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str2optimizer32bit['lars'] = (lib.cmomentum32bit_g32, lib.cmomentum32bit_g16)
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str2optimizer32bit['lamb'] = (lib.cadam32bit_g32, lib.cadam32bit_g16)
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str2optimizer8bit = {}
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str2optimizer8bit['adam'] = (lib.cadam_static_8bit_g32, lib.cadam_static_8bit_g16)
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str2optimizer8bit['momentum'] = (lib.cmomentum_static_8bit_g32, lib.cmomentum_static_8bit_g16)
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str2optimizer8bit['rmsprop'] = (lib.crmsprop_static_8bit_g32, lib.crmsprop_static_8bit_g16)
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str2optimizer8bit['lamb'] = (lib.cadam_static_8bit_g32, lib.cadam_static_8bit_g16)
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str2optimizer8bit['lars'] = (lib.cmomentum_static_8bit_g32, lib.cmomentum_static_8bit_g16)
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str2optimizer8bit_blockwise = {}
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str2optimizer8bit_blockwise['adam'] = (lib.cadam_8bit_blockwise_fp32, lib.cadam_8bit_blockwise_fp16)
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str2optimizer8bit_blockwise['momentum'] = (lib.cmomentum_8bit_blockwise_fp32, lib.cmomentum_8bit_blockwise_fp16)
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str2optimizer8bit_blockwise['rmsprop'] = (lib.crmsprop_8bit_blockwise_fp32, lib.crmsprop_8bit_blockwise_fp16)
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str2optimizer8bit_blockwise['adagrad'] = (lib.cadagrad_8bit_blockwise_fp32, lib.cadagrad_8bit_blockwise_fp16)
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2021-10-06 02:16:20 +00:00
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optimal_normal = [-0.9939730167388916, -0.8727636337280273, -0.8097418546676636, -0.7660024166107178, -0.7318882346153259, -0.6793879270553589, -0.657649040222168, -0.6385974884033203, -0.6211113333702087, -0.5901028513908386, -0.5762918591499329, -0.5630806684494019, -0.5509274005889893, -0.5394591689109802, -0.5283197164535522, -0.517780065536499, -0.5074946284294128, -0.4980469048023224, -0.48867011070251465, -0.48003149032592773, -0.47125306725502014, -0.4629971981048584, -0.4547359049320221, -0.446626216173172, -0.43902668356895447, -0.43158355355262756, -0.4244747757911682, -0.4173796474933624, -0.41038978099823, -0.4055633544921875, -0.4035947024822235, -0.39701032638549805, -0.39057496190071106, -0.38439232110977173, -0.3782760500907898, -0.3721940815448761, -0.3661896586418152, -0.3604033589363098, -0.354605108499527, -0.34892538189888, -0.34320303797721863, -0.3376772701740265, -0.3323028087615967, -0.3269782066345215, -0.32166096568107605, -0.316457599401474, -0.3112771809101105, -0.3061025142669678, -0.30106794834136963, -0.2961243987083435, -0.2912728488445282, -0.28644347190856934, -0.28165507316589355, -0.2769731283187866, -0.2722635865211487, -0.26779335737228394, -0.26314786076545715, -0.2586647868156433, -0.2541804611682892, -0.2496625930070877, -0.24527113139629364, -0.24097171425819397, -0.23659978806972504, -0.23218469321727753, -0.22799566388130188, -0.22380566596984863, -0.21965542435646057, -0.2154538631439209, -0.2113603949546814, -0.20735277235507965, -0.20334717631340027, -0.19932441413402557, -0.19530178606510162, -0.19136647880077362, -0.18736697733402252, -0.18337111175060272, -0.17951400578022003, -0.1757056713104248, -0.17182783782482147, -0.1680615097284317, -0.16431649029254913, -0.16053077578544617, -0.15685945749282837, -0.15298527479171753, -0.1493264138698578, -0.14566898345947266, -0.14188314974308014, -0.13819937407970428, -0.1344561129808426, -0.1306886374950409, -0.1271020770072937, -0.12346585839986801, -0.11981867253780365, -0.11614970862865448, -0.11256207525730133, -0.10889036953449249, -0.10525048524141312, -0.1016591489315033, -0.09824034571647644, -0.09469068050384521, -0.0911419615149498, -0.08773849159479141, -0.08416644483804703, -0.08071305602788925, -0.07720902562141418, -0.07371306419372559, -0.07019119709730148, -0.06673648208379745, -0.06329209357500076, -0.059800852090120316, -0.0564190037548542, -0.05296570807695389, -0.049522045999765396, -0.04609023034572601, -0.04262964054942131, -0.039246633648872375, -0.03577171266078949, -0.03236335143446922, -0.028855687007308006, -0.02542758360505104, -0.022069433704018593, -0.018754752352833748, -0.015386369079351425, -0.01194947212934494, -0.008439815603196621, -0.004995611496269703, -0.0016682245768606663, 0.0, 0.0015510577941313386, 0.005062474869191647, 0.008417150937020779, 0.011741090565919876, 0.015184164978563786, 0.018582714721560478, 0.02204744517803192, 0.025471193715929985, 0.02889077737927437, 0.0323684960603714, 0.03579240292310715, 0.039281025528907776, 0.0427563451230526, 0.04619763046503067, 0.04968220740556717, 0.05326594039797783, 0.05679265409708023, 0.060245808213949203, 0.06372645497322083, 0.06721872836351395, 0.0706876739859581, 0.0742349922657013, 0.07774098962545395, 0.08123527467250824, 0.08468879014253616, 0.08810535818338394, 0.09155989438295364, 0.09498448669910431, 0.0985206812620163, 0.10206405073404312, 0.10563778132200241, 0.10921968519687653, 0.11284469068050385, 0.11653254181146622, 0.12008969485759735, 0.12368203699588776, 0.1272617131471634, 0.13089501857757568, 0.134552001953125, 0.1382799744606018, 0.14194637537002563, 0.14563234150409698, 0.14930322766304016, 0.15303383767604828, 0.1567956507205963, 0.16050070524215698, 0.16431072354316711, 0.16813558340072632, 0.17204202711582184, 0.1758781224489212, 0.17973239719867706, 0.1836014688014984, 0.18753431737422943, 0.19138391315937042, 0.19535475969314575, 0.19931404292583466, 0.20333819091320038, 0.20738255977630615, 0.21152682602405548, 0.21568812429904938, 0.21978361904621124, 0.22393859922885895, 0.22814159095287323, 0.232410684
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optimal_half_normal = [0.0025565922260284424, 0.005811259150505066, 0.00961565226316452, 0.010822802782058716, 0.013123787939548492, 0.014242202043533325, 0.0143156498670578, 0.016469404101371765, 0.017666727304458618, 0.01773911714553833, 0.0199756920337677, 0.0210941880941391, 0.021161124110221863, 0.02451971173286438, 0.024580076336860657, 0.02685210108757019, 0.028012827038764954, 0.030198264867067337, 0.0302925705909729, 0.03136435151100159, 0.03374280035495758, 0.03487399220466614, 0.035243816673755646, 0.037192340940237045, 0.03822284936904907, 0.04164902865886688, 0.04173608124256134, 0.04401407018303871, 0.04508155584335327, 0.047482021152973175, 0.04756556823849678, 0.050963032990694046, 0.05196474492549896, 0.055417388677597046, 0.05793146416544914, 0.05799369141459465, 0.05887940526008606, 0.05895659327507019, 0.062420234084129333, 0.06493274495005608, 0.06499008461833, 0.06935599446296692, 0.07197384163737297, 0.07201516255736351, 0.07276943325996399, 0.07283210754394531, 0.07550075277686119, 0.07975354790687561, 0.07980883121490479, 0.08257630094885826, 0.0867777168750763, 0.08682405948638916, 0.08967285975813866, 0.09323835000395775, 0.09386616945266724, 0.09735457599163055, 0.09739077091217041, 0.10092401504516602, 0.10444298386573792, 0.10447832942008972, 0.10770941898226738, 0.10803905129432678, 0.11161200702190399, 0.1151546835899353, 0.11520349979400635, 0.11875157058238983, 0.11879390478134155, 0.1222602017223835, 0.122351735830307, 0.12240418791770935, 0.12594850733876228, 0.12597402930259705, 0.12602100148797035, 0.12960633635520935, 0.1296597123146057, 0.12966342642903328, 0.13227657973766327, 0.13325360417366028, 0.1333133578300476, 0.13691483438014984, 0.1371927298605442, 0.14066261053085327, 0.14088113978505135, 0.1447291411459446, 0.14805573225021362, 0.148526418954134, 0.15170684456825256, 0.15178103744983673, 0.15225710347294807, 0.1554398238658905, 0.15609459951519966, 0.15618794038891792, 0.1592724472284317, 0.1629735231399536, 0.16382690146565437, 0.16676269471645355, 0.16873238794505596, 0.17066434025764465, 0.17068277299404144, 0.1717144437134266, 0.17558929696679115, 0.17827065289020538, 0.17835864424705505, 0.18222273886203766, 0.18353315070271492, 0.18604370951652527, 0.18611834943294525, 0.1876586265861988, 0.18996606767177582, 0.19170701876282692, 0.19398853182792664, 0.19786442816257477, 0.19795633852481842, 0.20195159316062927, 0.2058800607919693, 0.2099103182554245, 0.2122517265379429, 0.21410366892814636, 0.21819619834423065, 0.22221362590789795, 0.22233009338378906, 0.22500130906701088, 0.2251257635653019, 0.22638091444969177, 0.23067741096019745, 0.23368822410702705, 0.2348879873752594, 0.2382080741226673, 0.2390350103378296, 0.2391497790813446, 0.24253453686833382, 0.24265171959996223, 0.2470107562839985, 0.24764248728752136, 0.24777774512767792, 0.2516774423420429, 0.256104726344347, 0.2564055472612381, 0.2607169933617115, 0.265461727976799, 0.26985861361026764, 0.2701106257736683, 0.2702729292213917, 0.274574413895607, 0.2750340588390827, 0.27919672429561615, 0.283704474568367, 0.28386808931827545, 0.28953738883137703, 0.2896753139793873, 0.29320384562015533, 0.29451676085591316, 0.295327290892601, 0.29802779853343964, 0.29818175733089447, 0.29972871020436287, 0.30290623009204865, 0.30305664241313934, 0.30486901476979256, 0.31299956142902374, 0.31518544629216194, 0.31790371239185333, 0.3205283172428608, 0.3230419009923935, 0.32595496252179146, 0.32612212374806404, 0.3282426446676254, 0.3283906430006027, 0.33146094158291817, 0.3316439874470234, 0.33365286886692047, 0.33723779395222664, 0.3390095978975296, 0.3427443392574787, 0.34853987768292427, 0.34869300201535225, 0.35457711294293404, 0.35537679493427277, 0.3604113645851612, 0.36124424636363983, 0.3665340431034565, 0.36667295172810555, 0.3727492541074753, 0.3729033060371876, 0.37888188660144806, 0.37907837703824043, 0.3792510814964771, 0.38557394221425056, 0.38573457673192024, 0.39108292758464813, 0.39911722019314766, 0.40589402988553047, 0.40604450181126595, 0.410498782992363, 0.4106704741716385, 0.4129834659397602, 0.4
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def create_linear_map(signed=True):
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if signed:
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return torch.linspace(-1.0, 1.0, 256)
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else:
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return torch.linspace(0.0, 1.0, 256)
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def create_dynamic_map(signed=True, n=7):
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'''
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Creates the dynamic quantiztion map.
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The dynamic data type is made up of a dynamic exponent and
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fraction. As the exponent increase from 0 to -7 the number
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of bits available for the fraction shrinks.
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This is a generalization of the dynamic type where a certain
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number of the bits and be reserved for the linear quantization
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region (the fraction). n determines the maximum number of
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exponent bits.
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For more details see
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(8-Bit Approximations for Parallelism in Deep Learning)[https://arxiv.org/abs/1511.04561]
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'''
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data = []
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# these are additional items that come from the case
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# where all the exponent bits are zero and no
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# indicator bit is present
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additional_items = 2**(7-n)-1
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if not signed: additional_items = 2*additional_items
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for i in range(n):
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fraction_items = 2**(i+7-n)+1 if signed else 2**(i+7-n+1)+1
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boundaries = torch.linspace(0.1, 1, fraction_items)
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means = (boundaries[:-1]+boundaries[1:])/2.0
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data += ((10**(-(n-1)+i))*means).tolist()
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if signed:
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data += (-(10**(-(n-1)+i))*means).tolist()
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if additional_items > 0:
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boundaries = torch.linspace(0.1, 1, additional_items+1)
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means = (boundaries[:-1]+boundaries[1:])/2.0
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data += ((10**(-(n-1)+i))*means).tolist()
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if signed:
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data += (-(10**(-(n-1)+i))*means).tolist()
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data.append(0)
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data.append(1.0)
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data.sort()
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return Tensor(data)
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def get_ptr(A: Tensor) -> ct.c_void_p:
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'''
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Get the ctypes pointer from a PyTorch Tensor.
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Parameters
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----------
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A : torch.tensor
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The PyTorch tensor.
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Returns
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-------
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ctypes.c_void_p
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'''
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if A is None: return None
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else: return ct.c_void_p(A.data.storage().data_ptr())
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def estimate_quantiles(A: Tensor, out: Tensor=None, offset: float=1/512) -> Tensor:
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'''
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Estimates 256 equidistant quantiles on the input tensor eCDF.
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Uses SRAM-Quantiles algorithm to quickly estimate 256 equidistant quantiles
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via the eCDF of the input tensor `A`. This is a fast but approximate algorithm
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and the extreme quantiles close to 0 and 1 have high variance / large estimation
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errors. These large errors can be avoided by using the offset variable which trims
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the distribution. The default offset value of 1/512 ensures minimum entropy encoding -- it
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trims 1/512 = 0.2% from each side of the distrivution. An offset value of 0.01 to 0.02
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usually has a much lower error but is not a minimum entropy encoding. Given an offset
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of 0.02 equidistance points in the range [0.02, 0.98] are used for the quantiles.
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Parameters
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----------
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A : torch.Tensor
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The input tensor. Any shape.
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out : torch.Tensor
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Tensor with the 256 estimated quantiles.
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offset : float
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The offset for the first and last quantile from 0 and 1. Default: 1/512
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Returns
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-------
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torch.Tensor:
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The 256 quantiles in float32 datatype.
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'''
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if out is None: out = torch.zeros((256,), dtype=torch.float32, device=A.device)
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if A.dtype == torch.float32:
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lib.cestimate_quantiles_fp32(get_ptr(A), get_ptr(out), ct.c_float(offset), ct.c_int(A.numel()))
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elif A.dtype == torch.float16:
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lib.cestimate_quantiles_fp16(get_ptr(A), get_ptr(out), ct.c_float(offset), ct.c_int(A.numel()))
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else:
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2022-07-01 14:16:10 +00:00
|
|
|
raise NotImplementedError(f'Not supported data type {A.dtype}')
|
2021-10-06 02:16:20 +00:00
|
|
|
return out
|
|
|
|
|
|
|
|
|
|
def quantize_blockwise(A: Tensor, code: Tensor=None, absmax: Tensor=None, rand=None, out: Tensor=None) -> Tensor:
|
|
|
|
|
'''
|
|
|
|
|
Quantize tensor A in blocks of size 4096 values.
|
|
|
|
|
|
|
|
|
|
Quantizes tensor A by dividing it into blocks of 4096 values.
|
|
|
|
|
Then the absolute maximum value within these blocks is calculated
|
|
|
|
|
for the non-linear quantization.
|
|
|
|
|
|
|
|
|
|
Parameters
|
|
|
|
|
----------
|
|
|
|
|
A : torch.Tensor
|
|
|
|
|
The input tensor.
|
|
|
|
|
code : torch.Tensor
|
|
|
|
|
The quantization map.
|
|
|
|
|
absmax : torch.Tensor
|
|
|
|
|
The absmax values.
|
|
|
|
|
rand : torch.Tensor
|
|
|
|
|
The tensor for stochastic rounding.
|
|
|
|
|
out : torch.Tensor
|
|
|
|
|
The output tensor (8-bit).
|
|
|
|
|
|
|
|
|
|
Returns
|
|
|
|
|
-------
|
|
|
|
|
torch.Tensor:
|
|
|
|
|
The 8-bit tensor.
|
|
|
|
|
tuple(torch.Tensor, torch.Tensor):
|
|
|
|
|
The quantization state to undo the quantization.
|
|
|
|
|
'''
|
|
|
|
|
|
|
|
|
|
if code is None:
|
|
|
|
|
if 'dynamic' not in name2qmap: name2qmap['dynamic'] = create_dynamic_map().to(A.device)
|
|
|
|
|
code = name2qmap['dynamic']
|
|
|
|
|
code = code.to(A.device)
|
|
|
|
|
|
|
|
|
|
if absmax is None:
|
|
|
|
|
n = A.numel()
|
|
|
|
|
num_blocks = 4096
|
|
|
|
|
blocks = n//num_blocks
|
|
|
|
|
blocks += 1 if n % num_blocks > 0 else 0
|
|
|
|
|
absmax = torch.zeros((blocks,), device=A.device)
|
|
|
|
|
|
|
|
|
|
if out is None: out = torch.zeros_like(A, dtype=torch.uint8)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
if A.device.type != 'cpu':
|
|
|
|
|
if rand is not None:
|
|
|
|
|
assert rand.numel() >= 1024
|
|
|
|
|
rand_offset = random.randint(0, 1023)
|
|
|
|
|
if A.dtype == torch.float32:
|
|
|
|
|
lib.cquantize_blockwise_stochastic_fp32(get_ptr(code), get_ptr(A), get_ptr(absmax), get_ptr(out), get_ptr(rand), ct.c_int32(rand_offset), ct.c_int(A.numel()))
|
|
|
|
|
elif A.dtype == torch.float16:
|
|
|
|
|
lib.cquantize_blockwise_stochastic_fp16(get_ptr(code), get_ptr(A), get_ptr(absmax), get_ptr(out), get_ptr(rand), ct.c_int32(rand_offset), ct.c_int(A.numel()))
|
|
|
|
|
else:
|
|
|
|
|
raise ValueError(f'Blockwise quantization only supports 16/32-bit floats, but got {A.dtype}')
|
|
|
|
|
else:
|
|
|
|
|
if A.dtype == torch.float32:
|
|
|
|
|
lib.cquantize_blockwise_fp32(get_ptr(code), get_ptr(A), get_ptr(absmax), get_ptr(out), ct.c_int(A.numel()))
|
|
|
|
|
elif A.dtype == torch.float16:
|
|
|
|
|
lib.cquantize_blockwise_fp16(get_ptr(code), get_ptr(A), get_ptr(absmax), get_ptr(out), ct.c_int(A.numel()))
|
|
|
|
|
else:
|
|
|
|
|
raise ValueError(f'Blockwise quantization only supports 16/32-bit floats, but got {A.dtype}')
|
|
|
|
|
else:
|
|
|
|
|
# cpu
|
|
|
|
|
assert rand is None
|
|
|
|
|
lib.cquantize_blockwise_cpu_fp32(get_ptr(code), get_ptr(A), get_ptr(absmax), get_ptr(out), ct.c_int(A.numel()))
|
|
|
|
|
|
|
|
|
|
return out, (absmax, code)
|
|
|
|
|
|
|
|
|
|
def dequantize_blockwise(A: Tensor, quant_state: Tuple[Tensor, Tensor]=None,
|
|
|
|
|
absmax: Tensor=None, code: Tensor=None, out: Tensor=None,
|
|
|
|
|
blocksize: int=4096) -> Tensor:
|
|
|
|
|
'''
|
|
|
|
|
Dequantizes blockwise quantized values.
|
|
|
|
|
|
|
|
|
|
Dequantizes the tensor A with maximum absolute values absmax in
|
|
|
|
|
blocks of size 4096.
|
|
|
|
|
|
|
|
|
|
Parameters
|
|
|
|
|
----------
|
|
|
|
|
A : torch.Tensor
|
|
|
|
|
The input 8-bit tensor.
|
|
|
|
|
quant_state : tuple(torch.Tensor, torch.Tensor)
|
|
|
|
|
Tuple of code and absmax values.
|
|
|
|
|
absmax : torch.Tensor
|
|
|
|
|
The absmax values.
|
|
|
|
|
code : torch.Tensor
|
|
|
|
|
The quantization map.
|
|
|
|
|
out : torch.Tensor
|
|
|
|
|
Dequantized output tensor (default: float32)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Returns
|
|
|
|
|
-------
|
|
|
|
|
torch.Tensor:
|
|
|
|
|
Dequantized tensor (default: float32)
|
|
|
|
|
'''
|
|
|
|
|
assert quant_state is not None or absmax is not None
|
|
|
|
|
if code is None and quant_state is None:
|
|
|
|
|
if 'dynamic' not in name2qmap: name2qmap['dynamic'] = create_dynamic_map().to(A.device)
|
|
|
|
|
code = name2qmap['dynamic']
|
|
|
|
|
code = code.to(A.device)
|
|
|
|
|
|
|
|
|
|
if out is None: out = torch.zeros_like(A, dtype=torch.float32)
|
|
|
|
|
if quant_state is None: quant_state = (absmax, code)
|
|
|
|
|
|
|
|
|
|
if blocksize not in [2048, 4096]:
|
|
|
|
|
raise ValueError(f'The blockwise of {blocksize} is not supported. Supported values: [2048 4096]')
|
|
|
|
|
|
|
|
|
|
if A.device.type != 'cpu':
|
|
|
|
|
if out.dtype == torch.float32:
|
|
|
|
|
lib.cdequantize_blockwise_fp32(get_ptr(quant_state[1]), get_ptr(A), get_ptr(quant_state[0]), get_ptr(out), ct.c_int(blocksize), ct.c_int(A.numel()))
|
|
|
|
|
elif out.dtype == torch.float16:
|
|
|
|
|
lib.cdequantize_blockwise_fp16(get_ptr(quant_state[1]), get_ptr(A), get_ptr(quant_state[0]), get_ptr(out), ct.c_int(blocksize), ct.c_int(A.numel()))
|
|
|
|
|
else:
|
|
|
|
|
raise ValueError(f'Blockwise quantization only supports 16/32-bit floats, but got {A.dtype}')
|
|
|
|
|
else:
|
|
|
|
|
lib.cdequantize_blockwise_cpu_fp32(get_ptr(quant_state[1]), get_ptr(A), get_ptr(quant_state[0]), get_ptr(out), ct.c_int(A.numel()))
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
return out
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
def quantize(A: Tensor, code: Tensor=None, out: Tensor=None) -> Tensor:
|
|
|
|
|
if code is None:
|
|
|
|
|
if 'dynamic' not in name2qmap: name2qmap['dynamic'] = create_dynamic_map().to(A.device)
|
|
|
|
|
code = name2qmap['dynamic']
|
|
|
|
|
code = code.to(A.device)
|
|
|
|
|
|
|
|
|
|
absmax = torch.abs(A).max()
|
|
|
|
|
inp = A/absmax
|
|
|
|
|
out = quantize_no_absmax(inp, code, out)
|
|
|
|
|
return out, (absmax, code)
|
|
|
|
|
|
|
|
|
|
def dequantize(A: Tensor, quant_state: Tuple[Tensor, Tensor]=None, absmax: Tensor=None, code: Tensor=None, out: Tensor=None) -> Tensor:
|
|
|
|
|
assert quant_state is not None or absmax is not None
|
|
|
|
|
if code is None and quant_state is None:
|
|
|
|
|
if 'dynamic' not in name2qmap: name2qmap['dynamic'] = create_dynamic_map().to(A.device)
|
|
|
|
|
code = name2qmap['dynamic']
|
|
|
|
|
code = code.to(A.device)
|
|
|
|
|
|
|
|
|
|
if quant_state is None: quant_state = (absmax, code)
|
|
|
|
|
out = dequantize_no_absmax(A, quant_state[1], out)
|
|
|
|
|
return out*quant_state[0]
|
|
|
|
|
|
|
|
|
|
def quantize_no_absmax(A: Tensor, code: Tensor, out: Tensor=None) -> Tensor:
|
|
|
|
|
'''
|
|
|
|
|
Quantizes input tensor to 8-bit.
|
|
|
|
|
|
|
|
|
|
Quantizes the 32-bit input tensor `A` to the 8-bit output tensor
|
|
|
|
|
`out` using the quantization map `code`.
|
|
|
|
|
|
|
|
|
|
Parameters
|
|
|
|
|
----------
|
|
|
|
|
A : torch.Tensor
|
|
|
|
|
The input tensor.
|
|
|
|
|
code : torch.Tensor
|
|
|
|
|
The quantization map.
|
|
|
|
|
out : torch.Tensor, optional
|
|
|
|
|
The output tensor. Needs to be of type byte.
|
|
|
|
|
|
|
|
|
|
Returns
|
|
|
|
|
-------
|
|
|
|
|
torch.Tensor:
|
|
|
|
|
Quantized 8-bit tensor.
|
|
|
|
|
'''
|
|
|
|
|
if out is None: out = torch.zeros_like(A, dtype=torch.uint8)
|
|
|
|
|
lib.cquantize(get_ptr(code), get_ptr(A), get_ptr(out), ct.c_int(A.numel()))
|
|
|
|
|
return out
|
|
|
|
|
|
|
|
|
|
def dequantize_no_absmax(A: Tensor, code: Tensor, out: Tensor=None) -> Tensor:
|
|
|
|
|
'''
|
|
|
|
|
Dequantizes the 8-bit tensor to 32-bit.
|
|
|
|
|
|
|
|
|
|
Dequantizes the 8-bit tensor `A` to the 32-bit tensor `out` via
|
|
|
|
|
the quantization map `code`.
|
|
|
|
|
|
|
|
|
|
Parameters
|
|
|
|
|
----------
|
|
|
|
|
A : torch.Tensor
|
|
|
|
|
The 8-bit input tensor.
|
|
|
|
|
code : torch.Tensor
|
|
|
|
|
The quantization map.
|
|
|
|
|
out : torch.Tensor
|
|
|
|
|
The 32-bit output tensor.
|
|
|
|
|
|
|
|
|
|
Returns
|
|
|
|
|
-------
|
|
|
|
|
torch.Tensor:
|
|
|
|
|
32-bit output tensor.
|
|
|
|
|
'''
|
|
|
|
|
if out is None: out = torch.zeros_like(A, dtype=torch.float32)
|
|
|
|
|
lib.cdequantize(get_ptr(code), get_ptr(A), get_ptr(out), ct.c_int(A.numel()))
|
|
|
|
|
return out
|
|
|
|
|
|
|
|
|
|
def optimizer_update_32bit(optimizer_name:str, g: Tensor, p: Tensor, state1: Tensor,
|
|
|
|
|
beta1: float, eps: float, step: int, lr: float,
|
|
|
|
|
state2: Tensor=None, beta2: float=0.0,
|
|
|
|
|
weight_decay: float=0.0, gnorm_scale: float=1.0,
|
2021-10-21 01:37:44 +00:00
|
|
|
unorm_vec: Tensor=None, max_unorm: float=0.0, skip_zeros=False) -> None:
|
2021-10-06 02:16:20 +00:00
|
|
|
'''
|
|
|
|
|
Performs an inplace optimizer update with one or two optimizer states.
|
|
|
|
|
|
|
|
|
|
Universal optimizer update for 32-bit state and 32/16-bit gradients/weights.
|
|
|
|
|
|
|
|
|
|
Parameters
|
|
|
|
|
----------
|
|
|
|
|
optimizer_name : str
|
|
|
|
|
The name of the optimizer: {adam}.
|
|
|
|
|
g : torch.Tensor
|
|
|
|
|
Gradient tensor.
|
|
|
|
|
p : torch.Tensor
|
|
|
|
|
Parameter tensor.
|
|
|
|
|
state1 : torch.Tensor
|
|
|
|
|
Optimizer state 1.
|
|
|
|
|
beta1 : float
|
|
|
|
|
Optimizer beta1.
|
|
|
|
|
eps : float
|
|
|
|
|
Optimizer epsilon.
|
|
|
|
|
weight_decay : float
|
|
|
|
|
Weight decay.
|
|
|
|
|
step : int
|
|
|
|
|
Current optimizer step.
|
|
|
|
|
lr : float
|
|
|
|
|
The learning rate.
|
|
|
|
|
state2 : torch.Tensor
|
|
|
|
|
Optimizer state 2.
|
|
|
|
|
beta2 : float
|
|
|
|
|
Optimizer beta2.
|
|
|
|
|
gnorm_scale : float
|
|
|
|
|
The factor to rescale the gradient to the max clip value.
|
2021-10-21 01:37:44 +00:00
|
|
|
unorm_vec : torch.Tensor
|
|
|
|
|
The tensor for the update norm.
|
|
|
|
|
max_unorm : float
|
|
|
|
|
The maximum update norm relative to the weight norm.
|
|
|
|
|
skip_zeros : bool
|
|
|
|
|
Whether to skip zero-valued gradients or not (default: False).
|
2021-10-06 02:16:20 +00:00
|
|
|
'''
|
|
|
|
|
|
|
|
|
|
param_norm = 0.0
|
|
|
|
|
if max_unorm > 0.0:
|
|
|
|
|
param_norm = torch.norm(p.data.float())
|
|
|
|
|
|
|
|
|
|
if optimizer_name not in str2optimizer32bit:
|
2022-07-01 14:16:10 +00:00
|
|
|
raise NotImplementedError(f'Optimizer not implemented: {optimizer_name}. Choices: {",".join(str2optimizer32bit.keys())}')
|
2021-10-06 02:16:20 +00:00
|
|
|
|
|
|
|
|
if g.dtype == torch.float32 and state1.dtype == torch.float32:
|
|
|
|
|
str2optimizer32bit[optimizer_name][0](get_ptr(g), get_ptr(p), get_ptr(state1), get_ptr(state2), get_ptr(unorm_vec), ct.c_float(max_unorm),
|
|
|
|
|
ct.c_float(param_norm), ct.c_float(beta1), ct.c_float(beta2), ct.c_float(eps), ct.c_float(weight_decay),
|
2021-10-21 01:37:44 +00:00
|
|
|
ct.c_int32(step), ct.c_float(lr), ct.c_float(gnorm_scale), ct.c_bool(skip_zeros), ct.c_int32(g.numel()))
|
2021-10-06 02:16:20 +00:00
|
|
|
elif g.dtype == torch.float16 and state1.dtype == torch.float32:
|
|
|
|
|
str2optimizer32bit[optimizer_name][1](get_ptr(g), get_ptr(p), get_ptr(state1), get_ptr(state2), get_ptr(unorm_vec), ct.c_float(max_unorm),
|
|
|
|
|
ct.c_float(param_norm), ct.c_float(beta1), ct.c_float(beta2), ct.c_float(eps), ct.c_float(weight_decay),
|
2021-10-21 01:37:44 +00:00
|
|
|
ct.c_int32(step), ct.c_float(lr), ct.c_float(gnorm_scale), ct.c_bool(skip_zeros), ct.c_int32(g.numel()))
|
2021-10-06 02:16:20 +00:00
|
|
|
else:
|
|
|
|
|
raise ValueError(f'Gradient+optimizer bit data type combination not supported: grad {g.dtype}, optimizer {state1.dtype}')
|
|
|
|
|
|
|
|
|
|
def optimizer_update_8bit(optimizer_name: str, g: Tensor, p: Tensor, state1: Tensor, state2: Tensor,
|
|
|
|
|
beta1: float, beta2: float, eps: float,
|
|
|
|
|
step: int, lr: float, qmap1: Tensor, qmap2: Tensor,
|
|
|
|
|
max1: Tensor, max2: Tensor, new_max1: Tensor, new_max2: Tensor,
|
|
|
|
|
weight_decay: float=0.0, gnorm_scale: float=1.0,
|
|
|
|
|
unorm_vec: Tensor=None, max_unorm: float=0.0) -> None:
|
|
|
|
|
'''
|
|
|
|
|
Performs an inplace Adam update.
|
|
|
|
|
|
|
|
|
|
Universal Adam update for 32/8-bit state and 32/16-bit gradients/weights.
|
|
|
|
|
Uses AdamW formulation if weight decay > 0.0.
|
|
|
|
|
|
|
|
|
|
Parameters
|
|
|
|
|
----------
|
|
|
|
|
optimizer_name : str
|
|
|
|
|
The name of the optimizer. Choices {adam, momentum}
|
|
|
|
|
g : torch.Tensor
|
|
|
|
|
Gradient tensor.
|
|
|
|
|
p : torch.Tensor
|
|
|
|
|
Parameter tensor.
|
|
|
|
|
state1 : torch.Tensor
|
|
|
|
|
Adam state 1.
|
|
|
|
|
state2 : torch.Tensor
|
|
|
|
|
Adam state 2.
|
|
|
|
|
beta1 : float
|
|
|
|
|
Adam beta1.
|
|
|
|
|
beta2 : float
|
|
|
|
|
Adam beta2.
|
|
|
|
|
eps : float
|
|
|
|
|
Adam epsilon.
|
|
|
|
|
weight_decay : float
|
|
|
|
|
Weight decay.
|
|
|
|
|
step : int
|
|
|
|
|
Current optimizer step.
|
|
|
|
|
lr : float
|
|
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The learning rate.
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qmap1 : torch.Tensor
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Quantization map for first Adam state.
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qmap2 : torch.Tensor
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Quantization map for second Adam state.
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max1 : torch.Tensor
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Max value for first Adam state update.
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max2 : torch.Tensor
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Max value for second Adam state update.
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new_max1 : torch.Tensor
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Max value for the next Adam update of the first state.
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new_max2 : torch.Tensor
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Max value for the next Adam update of the second state.
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gnorm_scale : float
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The factor to rescale the gradient to the max clip value.
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2021-10-21 01:37:44 +00:00
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unorm_vec : torch.Tensor
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The tensor for the update norm.
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max_unorm : float
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The maximum update norm relative to the weight norm.
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2021-10-06 02:16:20 +00:00
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'''
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param_norm = 0.0
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if max_unorm > 0.0:
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param_norm = torch.norm(p.data.float())
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if g.dtype == torch.float32 and state1.dtype == torch.uint8:
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str2optimizer8bit[optimizer_name][0](get_ptr(p), get_ptr(g), get_ptr(state1), get_ptr(state2),
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get_ptr(unorm_vec), ct.c_float(max_unorm), ct.c_float(param_norm),
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ct.c_float(beta1), ct.c_float(beta2), ct.c_float(eps),
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ct.c_int32(step), ct.c_float(lr),
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get_ptr(qmap1), get_ptr(qmap2),
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get_ptr(max1), get_ptr(max2), get_ptr(new_max1), get_ptr(new_max2),
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ct.c_float(weight_decay),ct.c_float(gnorm_scale), ct.c_int32(g.numel()))
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elif g.dtype == torch.float16 and state1.dtype == torch.uint8:
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str2optimizer8bit[optimizer_name][1](get_ptr(p), get_ptr(g), get_ptr(state1), get_ptr(state2),
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get_ptr(unorm_vec), ct.c_float(max_unorm), ct.c_float(param_norm),
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ct.c_float(beta1), ct.c_float(beta2), ct.c_float(eps),
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ct.c_int32(step), ct.c_float(lr),
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get_ptr(qmap1), get_ptr(qmap2),
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get_ptr(max1), get_ptr(max2), get_ptr(new_max1), get_ptr(new_max2),
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ct.c_float(weight_decay),ct.c_float(gnorm_scale), ct.c_int32(g.numel()))
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else:
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raise ValueError(f'Gradient+optimizer bit data type combination not supported: grad {g.dtype}, optimizer {state1.dtype}')
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def optimizer_update_8bit_blockwise(optimizer_name: str, g: Tensor, p: Tensor, state1: Tensor, state2: Tensor,
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beta1: float, beta2: float, eps: float,
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step: int, lr: float, qmap1: Tensor, qmap2: Tensor,
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2021-10-21 01:37:44 +00:00
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absmax1: Tensor, absmax2: Tensor, weight_decay: float=0.0, gnorm_scale: float=1.0,
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skip_zeros=False) -> None:
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2021-10-06 02:16:20 +00:00
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if g.dtype == torch.float32 and state1.dtype == torch.uint8:
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str2optimizer8bit_blockwise[optimizer_name][0](get_ptr(p), get_ptr(g), get_ptr(state1), get_ptr(state2),
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ct.c_float(beta1), ct.c_float(beta2), ct.c_float(eps),
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ct.c_int32(step), ct.c_float(lr), get_ptr(qmap1), get_ptr(qmap2),
|
2021-10-21 02:15:47 +00:00
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get_ptr(absmax1), get_ptr(absmax2), ct.c_float(weight_decay), ct.c_float(gnorm_scale),
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2021-10-21 01:37:44 +00:00
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ct.c_bool(skip_zeros), ct.c_int32(g.numel()))
|
2021-10-06 02:16:20 +00:00
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elif g.dtype == torch.float16 and state1.dtype == torch.uint8:
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str2optimizer8bit_blockwise[optimizer_name][1](get_ptr(p), get_ptr(g), get_ptr(state1), get_ptr(state2),
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ct.c_float(beta1), ct.c_float(beta2), ct.c_float(eps),
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ct.c_int32(step), ct.c_float(lr), get_ptr(qmap1), get_ptr(qmap2),
|
2021-10-21 02:15:47 +00:00
|
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get_ptr(absmax1), get_ptr(absmax2), ct.c_float(weight_decay), ct.c_float(gnorm_scale),
|
2021-10-21 01:37:44 +00:00
|
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|
ct.c_bool(skip_zeros), ct.c_int32(g.numel()))
|
2021-10-06 02:16:20 +00:00
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|
else:
|
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|
raise ValueError(f'Gradient+optimizer bit data type combination not supported: grad {g.dtype}, optimizer {state1.dtype}')
|
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|
def percentile_clipping(grad: Tensor, gnorm_vec: Tensor, step: int, percentile: int=5):
|
|
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|
|
"""Applies percentile clipping
|
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|
grad: torch.Tensor
|
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|
The gradient tensor.
|
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|
gnorm_vec: torch.Tensor
|
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|
|
Vector of gradient norms. 100 elements expected.
|
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|
|
step: int
|
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|
|
The current optimiation steps (number of past gradient norms).
|
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|
|
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|
|
"""
|
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|
|
if grad.dtype == torch.float32:
|
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|
|
lib.cpercentile_clipping_g32(get_ptr(grad), get_ptr(gnorm_vec), ct.c_int32(step), ct.c_int32(grad.numel()))
|
|
|
|
|
elif grad.dtype == torch.float16:
|
|
|
|
|
lib.cpercentile_clipping_g16(get_ptr(grad), get_ptr(gnorm_vec), ct.c_int32(step), ct.c_int32(grad.numel()))
|
|
|
|
|
else:
|
|
|
|
|
raise ValueError(f'Gradient type {grad.dtype} not supported!')
|
|
|
|
|
|
|
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|
|
current_gnorm = torch.sqrt(gnorm_vec[step % 100])
|
|
|
|
|
vals, idx = torch.sort(gnorm_vec)
|
|
|
|
|
clip_value = torch.sqrt(vals[percentile])
|
|
|
|
|
gnorm_scale = 1.0
|
|
|
|
|
|
|
|
|
|
if current_gnorm > clip_value:
|
|
|
|
|
gnorm_scale = clip_value/current_gnorm
|
|
|
|
|
|
|
|
|
|
return current_gnorm, clip_value, gnorm_scale
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
def histogram_scatter_add_2d(histogram: Tensor, index1: Tensor, index2: Tensor, source: Tensor):
|
|
|
|
|
assert len(histogram.shape) == 2
|
|
|
|
|
assert histogram.dtype == torch.float32
|
|
|
|
|
assert source.dtype == torch.float32
|
|
|
|
|
assert index1.dtype == torch.int32
|
|
|
|
|
assert index2.dtype == torch.int32
|
|
|
|
|
|
|
|
|
|
assert histogram.device.type == 'cuda'
|
|
|
|
|
assert index1.device.type == 'cuda'
|
|
|
|
|
assert index2.device.type == 'cuda'
|
|
|
|
|
assert source.device.type == 'cuda'
|
|
|
|
|
|
|
|
|
|
maxdim1 = ct.c_int32(histogram.shape[0])
|
|
|
|
|
n = ct.c_int32(index1.numel())
|
|
|
|
|
lib.chistogram_scatter_add_2d(get_ptr(histogram), get_ptr(index1), get_ptr(index2), get_ptr(source), maxdim1, n)
|
