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-rw-r--r--bitsandbytes/__init__.py10
-rw-r--r--bitsandbytes/functional.py531
-rw-r--r--bitsandbytes/nn/__init__.py5
-rw-r--r--bitsandbytes/nn/modules.py44
-rw-r--r--bitsandbytes/optim/__init__.py10
-rw-r--r--bitsandbytes/optim/adam.py28
-rw-r--r--bitsandbytes/optim/lamb.py29
-rw-r--r--bitsandbytes/optim/lars.py115
-rw-r--r--bitsandbytes/optim/optimizer.py460
-rw-r--r--bitsandbytes/optim/rmsprop.py37
-rw-r--r--bitsandbytes/optim/sgd.py32
11 files changed, 1301 insertions, 0 deletions
diff --git a/bitsandbytes/__init__.py b/bitsandbytes/__init__.py
new file mode 100644
index 0000000..6e29322
--- /dev/null
+++ b/bitsandbytes/__init__.py
@@ -0,0 +1,10 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+from .optim import adam
+from .nn import modules
+__pdoc__ = {'libBitsNBytes' : False,
+ 'optim.optimizer.Optimizer8bit': False,
+ 'optim.optimizer.MockArgs': False
+ }
diff --git a/bitsandbytes/functional.py b/bitsandbytes/functional.py
new file mode 100644
index 0000000..65c697d
--- /dev/null
+++ b/bitsandbytes/functional.py
@@ -0,0 +1,531 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+import os
+import random
+import math
+import ctypes as ct
+import torch
+from torch import Tensor
+from typing import Tuple
+
+lib = ct.cdll.LoadLibrary(os.path.dirname(__file__) + '/libbitsandbytes.so')
+name2qmap = {}
+
+''' C FUNCTIONS FOR OPTIMIZERS '''
+
+str2optimizer32bit = {}
+str2optimizer32bit['adam'] = (lib.cadam32bit_g32, lib.cadam32bit_g16)
+str2optimizer32bit['momentum'] = (lib.cmomentum32bit_g32, lib.cmomentum32bit_g16)
+str2optimizer32bit['rmsprop'] = (lib.crmsprop32bit_g32, lib.crmsprop32bit_g16)
+str2optimizer32bit['lars'] = (lib.cmomentum32bit_g32, lib.cmomentum32bit_g16)
+str2optimizer32bit['lamb'] = (lib.cadam32bit_g32, lib.cadam32bit_g16)
+
+str2optimizer8bit = {}
+str2optimizer8bit['adam'] = (lib.cadam_static_8bit_g32, lib.cadam_static_8bit_g16)
+str2optimizer8bit['momentum'] = (lib.cmomentum_static_8bit_g32, lib.cmomentum_static_8bit_g16)
+str2optimizer8bit['rmsprop'] = (lib.crmsprop_static_8bit_g32, lib.crmsprop_static_8bit_g16)
+str2optimizer8bit['lamb'] = (lib.cadam_static_8bit_g32, lib.cadam_static_8bit_g16)
+str2optimizer8bit['lars'] = (lib.cmomentum_static_8bit_g32, lib.cmomentum_static_8bit_g16)
+
+str2optimizer8bit_blockwise = {}
+str2optimizer8bit_blockwise['adam'] = (lib.cadam_8bit_blockwise_fp32, lib.cadam_8bit_blockwise_fp16)
+str2optimizer8bit_blockwise['momentum'] = (lib.cmomentum_8bit_blockwise_fp32, lib.cmomentum_8bit_blockwise_fp16)
+str2optimizer8bit_blockwise['rmsprop'] = (lib.crmsprop_8bit_blockwise_fp32, lib.crmsprop_8bit_blockwise_fp16)
+
+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, 0.23241068422794342, 0.23675410449504852, 0.24123944342136383, 0.24569889903068542, 0.2500703036785126, 0.25904011726379395, 0.26349544525146484, 0.2682226300239563, 0.272907555103302, 0.2774306833744049, 0.28220856189727783, 0.2869136929512024, 0.2916390895843506, 0.29649388790130615, 0.30142995715141296, 0.3065022826194763, 0.3114383816719055, 0.31648796796798706, 0.3216581642627716, 0.32700115442276, 0.3322487473487854, 0.33778008818626404, 0.3431521952152252, 0.3487405776977539, 0.3543166518211365, 0.3601346015930176, 0.36605337262153625, 0.37217751145362854, 0.378179669380188, 0.3843980133533478, 0.3906566798686981, 0.39714935421943665, 0.40357843041419983, 0.4104187488555908, 0.4171563684940338, 0.42418959736824036, 0.43136918544769287, 0.4389212429523468, 0.44673123955726624, 0.45457619428634644, 0.4627031683921814, 0.47130417823791504, 0.4798591434955597, 0.48897242546081543, 0.4979848861694336, 0.5, 0.5076631307601929, 0.5177803635597229, 0.5282770991325378, 0.5392990112304688, 0.5506287813186646, 0.5632893443107605, 0.5764452815055847, 0.5903191566467285, 0.6051878333091736, 0.6209936141967773, 0.6382884979248047, 0.6573970913887024, 0.6795773506164551, 0.7037051916122437, 0.7327037453651428, 0.7677436470985413, 0.8111193776130676, 0.875165581703186, 1.0]
+
+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, 0.4131447561085224, 0.4172855168581009, 0.4202354736626148, 0.4204071946442127, 0.43538858368992805, 0.4355536885559559, 0.4432900734245777, 0.44603554904460907, 0.4461968094110489, 0.451409537345171, 0.4598204083740711, 0.46002377942204475, 0.46178819239139557, 0.46868549659848213, 0.46995367109775543, 0.4868385046720505, 0.48702501133084297, 0.4958047419786453, 0.4960057884454727, 0.5051481872797012, 0.506847757846117, 0.5148334950208664, 0.5150565356016159, 0.5174009390175343, 0.5249751061201096, 0.5283288545906544, 0.5355450958013535, 0.539984006434679, 0.5467876642942429, 0.5522958822548389, 0.5584012717008591, 0.5706631988286972, 0.5836620181798935, 0.5836880058050156, 0.5942088551819324, 0.5975865572690964, 0.6102624125778675, 0.6124880760908127, 0.6286389082670212, 0.646102175116539, 0.6471664495766163, 0.665437325835228, 0.6687244363129139, 0.687017485499382, 0.6932839937508106, 0.7115348428487778, 0.7218200154602528, 0.7219699807465076, 0.7747527211904526, 0.7749756425619125, 0.8192005604505539, 0.8194110840559006, 0.8830635994672775, 0.9217727445065975, 0.9245667457580566, 0.947742685675621, 0.9674464613199234, 0.9890814647078514, 0.9891453236341476, 0.9925699159502983]
+
+def create_linear_map(signed=True):
+ if signed:
+ return torch.linspace(-1.0, 1.0, 256)
+ else:
+ return torch.linspace(0.0, 1.0, 256)
+
+def create_dynamic_map(signed=True, n=7):
+ '''
+ Creates the dynamic quantiztion map.
+
+ The dynamic data type is made up of a dynamic exponent and
+ fraction. As the exponent increase from 0 to -7 the number
+ of bits available for the fraction shrinks.
+
+ This is a generalization of the dynamic type where a certain
+ number of the bits and be reserved for the linear quantization
+ region (the fraction). n determines the maximum number of
+ exponent bits.
+
+ For more details see
+ (8-Bit Approximations for Parallelism in Deep Learning)[https://arxiv.org/abs/1511.04561]
+ '''
+
+ data = []
+ # these are additional items that come from the case
+ # where all the exponent bits are zero and no
+ # indicator bit is present
+ additional_items = 2**(7-n)-1
+ if not signed: additional_items = 2*additional_items
+ for i in range(n):
+ fraction_items = 2**(i+7-n)+1 if signed else 2**(i+7-n+1)+1
+ boundaries = torch.linspace(0.1, 1, fraction_items)
+ means = (boundaries[:-1]+boundaries[1:])/2.0
+ data += ((10**(-(n-1)+i))*means).tolist()
+ if signed:
+ data += (-(10**(-(n-1)+i))*means).tolist()
+
+ if additional_items > 0:
+ boundaries = torch.linspace(0.1, 1, additional_items+1)
+ means = (boundaries[:-1]+boundaries[1:])/2.0
+ data += ((10**(-(n-1)+i))*means).tolist()
+ if signed:
+ data += (-(10**(-(n-1)+i))*means).tolist()
+
+ data.append(0)
+ data.append(1.0)
+ data.sort()
+ return Tensor(data)
+
+def get_ptr(A: Tensor) -> ct.c_void_p:
+ '''
+ Get the ctypes pointer from a PyTorch Tensor.
+
+ Parameters
+ ----------
+ A : torch.tensor
+ The PyTorch tensor.
+
+ Returns
+ -------
+ ctypes.c_void_p
+ '''
+ if A is None: return None
+ else: return ct.c_void_p(A.data.storage().data_ptr())
+
+def estimate_quantiles(A: Tensor, out: Tensor=None, offset: float=1/512) -> Tensor:
+ '''
+ Estimates 256 equidistant quantiles on the input tensor eCDF.
+
+ Uses SRAM-Quantiles algorithm to quickly estimate 256 equidistant quantiles
+ via the eCDF of the input tensor `A`. This is a fast but approximate algorithm
+ and the extreme quantiles close to 0 and 1 have high variance / large estimation
+ errors. These large errors can be avoided by using the offset variable which trims
+ the distribution. The default offset value of 1/512 ensures minimum entropy encoding -- it
+ trims 1/512 = 0.2% from each side of the distrivution. An offset value of 0.01 to 0.02
+ usually has a much lower error but is not a minimum entropy encoding. Given an offset
+ of 0.02 equidistance points in the range [0.02, 0.98] are used for the quantiles.
+
+ Parameters
+ ----------
+ A : torch.Tensor
+ The input tensor. Any shape.
+ out : torch.Tensor
+ Tensor with the 256 estimated quantiles.
+ offset : float
+ The offset for the first and last quantile from 0 and 1. Default: 1/512
+
+ Returns
+ -------
+ torch.Tensor:
+ The 256 quantiles in float32 datatype.
+ '''
+ if out is None: out = torch.zeros((256,), dtype=torch.float32, device=A.device)
+ if A.dtype == torch.float32:
+ lib.cestimate_quantiles_fp32(get_ptr(A), get_ptr(out), ct.c_float(offset), ct.c_int(A.numel()))
+ elif A.dtype == torch.float16:
+ lib.cestimate_quantiles_fp16(get_ptr(A), get_ptr(out), ct.c_float(offset), ct.c_int(A.numel()))
+ else:
+ raise NotImplementError(f'Not supported data type {A.dtype}')
+ 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,
+ unorm_vec: Tensor=None, max_unorm: float=0.0) -> None:
+ '''
+ 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.
+ '''
+
+ param_norm = 0.0
+ if max_unorm > 0.0:
+ param_norm = torch.norm(p.data.float())
+
+ if optimizer_name not in str2optimizer32bit:
+ raise NotImplementError(f'Optimizer not implemented: {optimizer_name}. Choices: {",".join(str2optimizer32bit.keys())}')
+
+ 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),
+ ct.c_int32(step), ct.c_float(lr), ct.c_float(gnorm_scale), ct.c_int32(g.numel()))
+ 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),
+ ct.c_int32(step), ct.c_float(lr), ct.c_float(gnorm_scale), ct.c_int32(g.numel()))
+ 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
+ The learning rate.
+ qmap1 : torch.Tensor
+ Quantization map for first Adam state.
+ qmap2 : torch.Tensor
+ Quantization map for second Adam state.
+ max1 : torch.Tensor
+ Max value for first Adam state update.
+ max2 : torch.Tensor
+ Max value for second Adam state update.
+ new_max1 : torch.Tensor
+ Max value for the next Adam update of the first state.
+ new_max2 : torch.Tensor
+ Max value for the next Adam update of the second state.
+ gnorm_scale : float
+ The factor to rescale the gradient to the max clip value.
+ '''
+
+ param_norm = 0.0
+ if max_unorm > 0.0:
+ param_norm = torch.norm(p.data.float())
+
+ if g.dtype == torch.float32 and state1.dtype == torch.uint8:
+ str2optimizer8bit[optimizer_name][0](get_ptr(p), get_ptr(g), 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_int32(step), ct.c_float(lr),
+ get_ptr(qmap1), get_ptr(qmap2),
+ get_ptr(max1), get_ptr(max2), get_ptr(new_max1), get_ptr(new_max2),
+ ct.c_float(weight_decay),ct.c_float(gnorm_scale), ct.c_int32(g.numel()))
+ elif g.dtype == torch.float16 and state1.dtype == torch.uint8:
+ str2optimizer8bit[optimizer_name][1](get_ptr(p), get_ptr(g), 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_int32(step), ct.c_float(lr),
+ get_ptr(qmap1), get_ptr(qmap2),
+ get_ptr(max1), get_ptr(max2), get_ptr(new_max1), get_ptr(new_max2),
+ ct.c_float(weight_decay),ct.c_float(gnorm_scale), ct.c_int32(g.numel()))
+ else:
+ raise ValueError(f'Gradient+optimizer bit data type combination not supported: grad {g.dtype}, optimizer {state1.dtype}')
+
+
+def optimizer_update_8bit_blockwise(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,
+ absmax1: Tensor, absmax2: Tensor, weight_decay: float=0.0, gnorm_scale: float=1.0) -> None:
+
+
+ if g.dtype == torch.float32 and state1.dtype == torch.uint8:
+ str2optimizer8bit_blockwise[optimizer_name][0](get_ptr(p), get_ptr(g), get_ptr(state1), get_ptr(state2),
+ ct.c_float(beta1), ct.c_float(beta2), ct.c_float(eps),
+ ct.c_int32(step), ct.c_float(lr), get_ptr(qmap1), get_ptr(qmap2),
+ get_ptr(absmax1), get_ptr(absmax2), ct.c_float(weight_decay), ct.c_float(gnorm_scale), ct.c_int32(g.numel()))
+ elif g.dtype == torch.float16 and state1.dtype == torch.uint8:
+ str2optimizer8bit_blockwise[optimizer_name][1](get_ptr(p), get_ptr(g), get_ptr(state1), get_ptr(state2),
+ ct.c_float(beta1), ct.c_float(beta2), ct.c_float(eps),
+ ct.c_int32(step), ct.c_float(lr), get_ptr(qmap1), get_ptr(qmap2),
+ get_ptr(absmax1), get_ptr(absmax2), ct.c_float(weight_decay), ct.c_float(gnorm_scale), ct.c_int32(g.numel()))
+ else:
+ raise ValueError(f'Gradient+optimizer bit data type combination not supported: grad {g.dtype}, optimizer {state1.dtype}')
+
+
+def percentile_clipping(grad: Tensor, gnorm_vec: Tensor, step: int, percentile: int=5):
+ """Applies percentile clipping
+
+ grad: torch.Tensor
+ The gradient tensor.
+ gnorm_vec: torch.Tensor
+ Vector of gradient norms. 100 elements expected.
+ step: int
+ The current optimiation steps (number of past gradient norms).
+
+ """
+ if grad.dtype == torch.float32:
+ 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!')
+
+ 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)
diff --git a/bitsandbytes/nn/__init__.py b/bitsandbytes/nn/__init__.py
new file mode 100644
index 0000000..177540f
--- /dev/null
+++ b/bitsandbytes/nn/__init__.py
@@ -0,0 +1,5 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+from .modules import StableEmbedding
diff --git a/bitsandbytes/nn/modules.py b/bitsandbytes/nn/modules.py
new file mode 100644
index 0000000..bf0945c
--- /dev/null
+++ b/bitsandbytes/nn/modules.py
@@ -0,0 +1,44 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+import torch
+
+from typing import Optional
+
+from torch import Tensor
+from torch.nn.parameter import Parameter
+import torch.nn.functional as F
+
+from bitsandbytes.optim import GlobalOptimManager
+
+class StableEmbedding(torch.nn.Embedding):
+ def __init__(self, num_embeddings: int, embedding_dim: int, padding_idx: Optional[int] = None,
+ max_norm: Optional[float] = None, norm_type: float = 2., scale_grad_by_freq: bool = False,
+ sparse: bool = True, _weight: Optional[Tensor] = None) -> None:
+ super(StableEmbedding, self).__init__(num_embeddings, embedding_dim, padding_idx, max_norm, norm_type, scale_grad_by_freq, False, _weight)
+ self.norm = torch.nn.LayerNorm(embedding_dim)
+ GlobalOptimManager.get_instance().register_parameters(self.weight)
+ GlobalOptimManager.get_instance().override_config(self.weight, 'optim_bits', 32)
+
+ def reset_parameters(self) -> None:
+ torch.nn.init.xavier_uniform_(self.weight)
+ self._fill_padding_idx_with_zero()
+
+ ''' !!! This is a redefinition of _fill_padding_idx_with_zero in torch.nn.Embedding
+ to make the Layer compatible with Pytorch < 1.9.
+ This means that if this changes in future PyTorch releases this need to change too
+ which is cumbersome. However, with this we can ensure compatibility with previous
+ PyTorch releases.
+ '''
+ def _fill_padding_idx_with_zero(self) -> None:
+ if self.padding_idx is not None:
+ with torch.no_grad():
+ self.weight[self.padding_idx].fill_(0)
+
+ def forward(self, input: Tensor) -> Tensor:
+ emb = F.embedding(
+ input, self.weight, self.padding_idx, self.max_norm,
+ self.norm_type, self.scale_grad_by_freq, self.sparse)
+
+ return self.norm(emb)
diff --git a/bitsandbytes/optim/__init__.py b/bitsandbytes/optim/__init__.py
new file mode 100644
index 0000000..92c83b1
--- /dev/null
+++ b/bitsandbytes/optim/__init__.py
@@ -0,0 +1,10 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+from .adam import Adam, Adam8bit, Adam32bit
+from .sgd import SGD, SGD8bit, SGD32bit
+from .lars import LARS, LARS8bit, LARS32bit, PytorchLARS
+from .lamb import LAMB, LAMB8bit, LAMB32bit
+from .rmsprop import RMSprop, RMSprop8bit, RMSprop32bit
+from .optimizer import GlobalOptimManager
diff --git a/bitsandbytes/optim/adam.py b/bitsandbytes/optim/adam.py
new file mode 100644
index 0000000..99a6d10
--- /dev/null
+++ b/bitsandbytes/optim/adam.py
@@ -0,0 +1,28 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+from bitsandbytes.optim.optimizer import Optimizer2State
+
+class Adam(Optimizer2State):
+ def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8,
+ weight_decay=0, amsgrad=False, optim_bits=32, args=None,
+ min_8bit_size=4096, percentile_clipping=100, block_wise=True):
+ super(Adam, self).__init__('adam', params, lr, betas, eps,
+ weight_decay, optim_bits, args, min_8bit_size, percentile_clipping, block_wise)
+
+class Adam8bit(Optimizer2State):
+ def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8,
+ weight_decay=0, amsgrad=False, args=None,
+ min_8bit_size=4096, percentile_clipping=100, block_wise=True):
+ super(Adam8bit, self).__init__('adam', params, lr, betas, eps,
+ weight_decay, 8, args, min_8bit_size, percentile_clipping, block_wise)
+
+class Adam32bit(Optimizer2State):
+ def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8,
+ weight_decay=0, amsgrad=False, args=None,
+ min_8bit_size=4096, percentile_clipping=100, block_wise=True):
+ super(Adam32bit, self).__init__('adam', params, lr, betas, eps,
+ weight_decay, 32, args, min_8bit_size, percentile_clipping, block_wise)
+
+
diff --git a/bitsandbytes/optim/lamb.py b/bitsandbytes/optim/lamb.py
new file mode 100644
index 0000000..b8d4b1e
--- /dev/null
+++ b/bitsandbytes/optim/lamb.py
@@ -0,0 +1,29 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+import apex
+from bitsandbytes.optim.optimizer import Optimizer2State
+
+class LAMB(Optimizer2State):
+ def __init__(self, params, lr=1e-3, bias_correction=True, betas=(0.9, 0.999), eps=1e-8,
+ weight_decay=0, amsgrad=False, adam_w_mode=True, optim_bits=32, args=None,
+ min_8bit_size=4096, percentile_clipping=100, block_wise=False, max_unorm=1.0):
+ super(LAMB, self).__init__('lamb', params, lr, betas, eps,
+ weight_decay, optim_bits, args, min_8bit_size, percentile_clipping, block_wise, max_unorm=1.0)
+
+class LAMB8bit(Optimizer2State):
+ def __init__(self, params, lr=1e-3, bias_correction=True, betas=(0.9, 0.999), eps=1e-8,
+ weight_decay=0, amsgrad=False, adam_w_mode=True, args=None,
+ min_8bit_size=4096, percentile_clipping=100, block_wise=False, max_unorm=1.0):
+ super(LAMB8bit, self).__init__('lamb', params, lr, betas, eps,
+ weight_decay, 8, args, min_8bit_size, percentile_clipping, block_wise, max_unorm=1.0)
+
+class LAMB32bit(Optimizer2State):
+ def __init__(self, params, lr=1e-3, bias_correction=True, betas=(0.9, 0.999), eps=1e-8,
+ weight_decay=0, amsgrad=False, adam_w_mode=True, args=None,
+ min_8bit_size=4096, percentile_clipping=100, block_wise=False, max_unorm=1.0):
+ super(LAMB32bit, self).__init__('lamb', params, lr, betas, eps,
+ weight_decay, 32, args, min_8bit_size, percentile_clipping, block_wise, max_unorm=1.0)
+
+
diff --git a/bitsandbytes/optim/lars.py b/bitsandbytes/optim/lars.py
new file mode 100644
index 0000000..40dede7
--- /dev/null
+++ b/bitsandbytes/optim/lars.py
@@ -0,0 +1,115 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+import torch
+
+from torch.optim import Optimizer
+from bitsandbytes.optim.optimizer import Optimizer1State
+
+class LARS(Optimizer1State):
+ def __init__(self, params, lr, momentum=0, dampening=0,
+ weight_decay=0, nesterov=False, optim_bits=32, args=None,
+ min_8bit_size=4096, percentile_clipping=100, max_unorm=0.02):
+ if momentum == 0:
+ raise NotImplementError(f'LARS without momentum is not supported!')
+ super(LARS, self).__init__('lars', params, lr, (momentum, dampening), 0.0,
+ weight_decay, optim_bits, args, min_8bit_size, percentile_clipping, max_unorm=max_unorm, block_wise=False)
+
+class LARS8bit(Optimizer1State):
+ def __init__(self, params, lr, momentum=0, dampening=0,
+ weight_decay=0, nesterov=False, args=None,
+ min_8bit_size=4096, percentile_clipping=100, max_unorm=0.02):
+ if momentum == 0:
+ raise NotImplementError(f'LARS without momentum is not supported!')
+ super(LARS8bit, self).__init__('lars', params, lr, (momentum, dampening), 0.0,
+ weight_decay, 8, args, min_8bit_size, percentile_clipping, max_unorm=max_unorm, block_wise=False)
+
+class LARS32bit(Optimizer1State):
+ def __init__(self, params, lr, momentum=0, dampening=0,
+ weight_decay=0, nesterov=False, args=None,
+ min_8bit_size=4096, percentile_clipping=100, max_unorm=0.02):
+ if momentum == 0:
+ raise NotImplementError(f'LARS without momentum is not supported!')
+ super(LARS32bit, self).__init__('lars', params, lr, (momentum, dampening), 0.0,
+ weight_decay, 32, args, min_8bit_size, percentile_clipping, max_unorm=max_unorm, block_wise=False)
+
+
+class PytorchLARS(Optimizer):
+ def __init__(self, params, lr=0.01, momentum=0, dampening=0,
+ weight_decay=0, nesterov=False, max_unorm=0.02):
+ if lr < 0.0:
+ raise ValueError("Invalid learning rate: {}".format(lr))
+ if momentum < 0.0:
+ raise ValueError("Invalid momentum value: {}".format(momentum))
+ if weight_decay < 0.0:
+ raise ValueError("Invalid weight_decay value: {}".format(weight_decay))
+
+ defaults = dict(lr=lr, momentum=momentum, dampening=dampening,
+ weight_decay=weight_decay, nesterov=nesterov, max_unorm=max_unorm)
+ if nesterov and (momentum <= 0 or dampening != 0):
+ raise ValueError("Nesterov momentum requires a momentum and zero dampening")
+ super(PytorchLARS, self).__init__(params, defaults)
+
+ def __setstate__(self, state):
+ super(PytorchLARS, self).__setstate__(state)
+ for group in self.param_groups:
+ group.setdefault('nesterov', False)
+
+ @torch.no_grad()
+ def step(self, closure=None):
+ """Performs a single optimization step.
+
+ Args:
+ closure (callable, optional): A closure that reevaluates the model
+ and returns the loss.
+ """
+ loss = None
+ if closure is not None:
+ with torch.enable_grad():
+ loss = closure()
+
+ for group in self.param_groups:
+ params_with_grad = []
+ d_p_list = []
+ momentum_buffer_list = []
+ weight_decay = group['weight_decay']
+ momentum = group['momentum']
+ dampening = group['dampening']
+ nesterov = group['nesterov']
+ max_unorm = group['max_unorm']
+ lr = group['lr']
+
+ for p in group['params']:
+ if p.grad is None: continue
+
+ state = self.state[p]
+ d_p = p.grad
+ if weight_decay != 0:
+ d_p = d_p.add(param, alpha=weight_decay)
+
+ if momentum != 0:
+ buf = state.get('momentum_buffer', None)
+
+ if buf is None:
+ buf = torch.clone(d_p).detach()
+ state['momentum_buffer']= buf
+ else:
+ buf.mul_(momentum).add_(d_p, alpha=1 - dampening)
+
+ if nesterov:
+ update = d_p + buf*momentum
+ else:
+ update = buf
+
+ update_scale = 1.0
+ if max_unorm > 0.0:
+ assert p.dtype == torch.float32
+ pnorm = torch.norm(p.detach())
+ unorm = torch.norm(update)
+ if unorm > max_unorm*pnorm:
+ update_scale = max_unorm*pnorm/unorm
+
+ p.add_(update, alpha=-lr*update_scale)
+
+ return loss
diff --git a/bitsandbytes/optim/optimizer.py b/bitsandbytes/optim/optimizer.py
new file mode 100644
index 0000000..6743c15
--- /dev/null
+++ b/bitsandbytes/optim/optimizer.py
@@ -0,0 +1,460 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+import torch
+import bitsandbytes.functional as F
+
+from copy import deepcopy
+from itertools import chain
+from collections import defaultdict, abc as container_abcs
+
+class MockArgs(object):
+ def __init__(self, initial_data):
+ for key in initial_data:
+ setattr(self, key, initial_data[key])
+
+
+class GlobalOptimManager(object):
+ _instance = None
+
+ def __init__(self):
+ raise RuntimeError('Call get_instance() instead')
+
+ def initialize(self):
+ self.pid2config = {}
+ self.index2config = {}
+ self.optimizer = None
+ self.uses_config_override = False
+
+ @classmethod
+ def get_instance(cls):
+ if cls._instance is None:
+ cls._instance = cls.__new__(cls)
+ cls._instance.initialize()
+ return cls._instance
+
+ def register_parameters(self, params):
+ param_groups = list(params)
+ if not isinstance(param_groups[0], dict):
+ param_groups = [{'params': param_groups}]
+
+ for group_index, group in enumerate(param_groups):
+ for p_index, p in enumerate(group['params']):
+ if id(p) in self.pid2config:
+ self.index2config[(group_index, p_index)] = self.pid2config[id(p)]
+
+ def override_config(self, parameters, key=None, value=None, key_value_dict=None):
+ '''
+ Overrides initial optimizer config for specific parameters.
+
+ The key-values of the optimizer config for the input parameters are overidden
+ This can be both, optimizer parameters like "betas", or "lr" or it can be
+ 8-bit specific paramters like "optim_bits", "percentile_clipping".
+
+ Parameters
+ ----------
+ parameters : torch.Tensor or list(torch.Tensors)
+ The input parameters.
+ key : str
+ The hyperparamter to override.
+ value : object
+ The value for the hyperparamters.
+ key_value_dict : dict
+ A dictionary with multiple key-values to override.
+ '''
+ self.uses_config_override = True
+ if isinstance(parameters, torch.nn.Parameter):
+ parameters = [parameters]
+ if isinstance(parameters, torch.Tensor):
+ parameters = [parameters]
+ if key is not None and value is not None:
+ assert key_value_dict is None
+ key_value_dict = {key: value}
+
+ if key_value_dict is not None:
+ for p in parameters:
+ if id(p) in self.pid2config:self.pid2config[id(p)].update(key_value_dict)
+ else: self.pid2config[id(p)] = key_value_dict
+
+
+class Optimizer8bit(torch.optim.Optimizer):
+
+ def __init__(self, params, defaults, optim_bits=32):
+ super(Optimizer8bit, self).__init__(params, defaults)
+ self.checked_if_on_gpu = False
+ self.name2qmap = {}
+
+ self.mng = GlobalOptimManager.get_instance()
+ self.non_castable_tensor_keys = set(
+ ['qmap1', 'qmap2',
+ 'max1', 'max2',
+ 'new_max1', 'new_max2',
+ 'state1', 'state2',
+ 'gnorm_vec', 'absmax1', 'absmax2',
+ 'unorm_vec'])
+
+ if optim_bits == 8: self.fill_qmap()
+
+ def fill_qmap(self):
+ self.name2qmap['dynamic'] = F.create_dynamic_map(signed=True)
+ self.name2qmap['udynamic'] = F.create_dynamic_map(signed=False)
+
+ def __setstate__(self, state):
+ super(Optimizer8bit, self).__setstate__(state)
+
+
+ def load_state_dict(self, state_dict):
+ r"""Loads the optimizer state.
+
+ Args:
+ state_dict (dict): optimizer state. Should be an object returned
+ from a call to :meth:`state_dict`.
+ """
+ # deepcopy, to be consistent with module API
+ state_dict = deepcopy(state_dict)
+ # Validate the state_dict
+ groups = self.param_groups
+ saved_groups = state_dict['param_groups']
+
+ if len(groups) != len(saved_groups):
+ raise ValueError("loaded state dict has a different number of "
+ "parameter groups")
+ param_lens = (len(g['params']) for g in groups)
+ saved_lens = (len(g['params']) for g in saved_groups)
+ if any(p_len != s_len for p_len, s_len in zip(param_lens, saved_lens)):
+ raise ValueError("loaded state dict contains a parameter group "
+ "that doesn't match the size of optimizer's group")
+
+ # Update the state
+ id_map = {old_id: p for old_id, p in
+ zip(chain.from_iterable((g['params'] for g in saved_groups)),
+ chain.from_iterable((g['params'] for g in groups)))}
+
+ def cast(param, value):
+ r"""Make a deep copy of value, casting all tensors to device of param."""
+ if isinstance(value, torch.Tensor):
+ # Floating-point types are a bit special here. They are the only ones
+ # that are assumed to always match the type of params.
+ if param.is_floating_point() and value.dtype != torch.uint8:
+ value = value.to(param.dtype)
+ return value
+ elif isinstance(value, dict):
+ for k, v in value.items():
+ if k in self.non_castable_tensor_keys:
+ value[k] = v.to(param.device)
+ else:
+ value[k] = cast(param, v)
+
+ return value
+ elif isinstance(value, container_abcs.Iterable):
+ return type(value)(cast(param, v) for v in value)
+ else:
+ return value
+
+ # Copy state assigned to params (and cast tensors to appropriate types).
+ # State that is not assigned to params is copied as is (needed for
+ # backward compatibility).
+ state = defaultdict(dict)
+ for k, v in state_dict['state'].items():
+ if k in id_map:
+ param = id_map[k]
+ state[param] = cast(param, v)
+ else:
+ state[k] = v
+
+ # Update parameter groups, setting their 'params' value
+ def update_group(group, new_group):
+ new_group['params'] = group['params']
+ return new_group
+ param_groups = [
+ update_group(g, ng) for g, ng in zip(groups, saved_groups)]
+ self.__setstate__({'state': state, 'param_groups': param_groups})
+
+ def to_gpu(self):
+ self.checked_if_on_gpu = True
+ for gindex, group in enumerate(self.param_groups):
+ for pindex, p in enumerate(group['params']):
+ if p in self.state:
+ values = self.state[p]
+ for k, v in values.items():
+ if isinstance(v, torch.Tensor):
+ self.state[p][k] = v.to(p.device)
+
+ @torch.no_grad()
+ def step(self, closure=None):
+ """Performs a single optimization step.
+
+ Arguments:
+ closure (callable, optional): A closure that reevaluates the model
+ and returns the loss.
+ """
+ loss = None
+ if closure is not None:
+ with torch.enable_grad():
+ loss = closure()
+
+ overflows = []
+
+ if not self.checked_if_on_gpu: self.to_gpu() # needed for fairseq pure fp16 training
+ for gindex, group in enumerate(self.param_groups):
+ for pindex, p in enumerate(group['params']):
+ if p.grad is None:
+ continue
+ state = self.state[p]
+ if len(state) == 0:
+ self.init_state(group, p, gindex, pindex)
+
+ self.update_step(group, p, gindex, pindex)
+
+ return loss
+
+ def get_config(self, gindex, pindex, group):
+ config = {}
+ config['betas'] = group['betas']
+ config['eps'] = group['eps']
+ config['weight_decay'] = group['weight_decay']
+ config['lr'] = group['lr']
+ config['optim_bits'] = self.args.optim_bits
+ config['min_8bit_size'] = self.args.min_8bit_size
+ config['percentile_clipping'] = self.args.percentile_clipping
+ config['block_wise'] = self.args.block_wise
+ config['max_unorm'] = self.args.max_unorm
+
+ if (gindex, pindex) in self.mng.index2config:
+ config.update(self.mng.index2config[(gindex, pindex)])
+ return config
+
+ def init_state(self, group, p, gindex, pindex):
+ raise NotImplementedError(f'init_state method needs to be overidden')
+
+ def update_step(self, group, p, gindex, pindex):
+ raise NotImplementedError(f'The update_step method needs to be overidden')
+
+class Optimizer2State(Optimizer8bit):
+ def __init__(self, optimizer_name, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8,
+ weight_decay=0.0, optim_bits=32, args=None,
+ min_8bit_size=4096, percentile_clipping=100, block_wise=True, max_unorm=0.0):
+ if not 0.0 <= lr:
+ raise ValueError("Invalid learning rate: {}".format(lr))
+ if not 0.0 <= eps:
+ raise ValueError("Invalid epsilon value: {}".format(eps))
+ if isinstance(betas, str):
+ betas = eval(betas)
+ print(betas, 'parsed')
+ for i in range(len(betas)):
+ if not 0.0 <= betas[i] < 1.0:
+ raise ValueError(f"Invalid beta parameter at index {i}: {betas[i]}")
+ if not 0.0 <= weight_decay:
+ raise ValueError("Invalid weight_decay value: {}".format(weight_decay))
+ defaults = dict(lr=lr, betas=betas, eps=eps,
+ weight_decay=weight_decay)
+ super(Optimizer2State, self).__init__(params, defaults, optim_bits)
+
+ if args is None:
+ args = {}
+ args['optim_bits'] = optim_bits
+ args['percentile_clipping'] = 100
+ args['min_8bit_size'] = min_8bit_size
+ args['percentile_clipping'] = percentile_clipping
+ args['block_wise'] = block_wise
+ args['max_unorm'] = max_unorm
+
+ self.args = MockArgs(args)
+ else:
+ self.args = args
+
+ self.optimizer_name = optimizer_name
+
+ @torch.no_grad()
+ def init_state(self, group, p, gindex, pindex):
+ config = self.get_config(gindex, pindex, group)
+
+ if config['optim_bits'] == 32:
+ dtype = torch.float32
+ elif config['optim_bits'] == 8:
+ dtype = torch.uint8
+ else: raise NotImplementedError(f'Amount of optimizer bits not supported: {config["optim_bits"]}')
+
+ if p.numel() < config['min_8bit_size']: dtype = torch.float32
+
+ state = self.state[p]
+ state['step'] = 0
+
+ if dtype == torch.float32 or (dtype == torch.uint8 and p.numel() < 4096):
+ state['state1'] = torch.zeros_like(p, memory_format=torch.preserve_format, dtype=torch.float32, device=p.device)
+ state['state2'] = torch.zeros_like(p, memory_format=torch.preserve_format, dtype=torch.float32, device=p.device)
+ elif dtype == torch.uint8:
+ if state['step'] == 0:
+ if 'dynamic' not in self.name2qmap: self.fill_qmap()
+ self.name2qmap['dynamic'] = self.name2qmap['dynamic'].to(p.device)
+ self.name2qmap['udynamic'] = self.name2qmap['udynamic'].to(p.device)
+
+ state['state1'] = torch.zeros_like(p, memory_format=torch.preserve_format, dtype=torch.uint8, device=p.device)
+ state['qmap1'] = self.name2qmap['dynamic']
+
+ state['state2'] = torch.zeros_like(p, memory_format=torch.preserve_format, dtype=torch.uint8, device=p.device)
+ state['qmap2'] = self.name2qmap['udynamic']
+
+ if config['block_wise']:
+ n = p.numel()
+ blocks = n//2048
+ blocks += 1 if n % 2048 > 0 else 0
+
+ state['absmax1'] = torch.zeros((blocks,), dtype=torch.float32, device=p.device)
+ state['absmax2'] = torch.zeros((blocks,), dtype=torch.float32, device=p.device)
+ else:
+ state['max1'] = torch.zeros((1,), dtype=torch.float32, device=p.device)
+ state['new_max1'] = torch.zeros((1,), dtype=torch.float32, device=p.device)
+ state['max2'] = torch.zeros((1,), dtype=torch.float32, device=p.device)
+ state['new_max2'] = torch.zeros((1,), dtype=torch.float32, device=p.device)
+
+ if config['percentile_clipping'] < 100:
+ state['gnorm_vec'] = torch.zeros((100,), device=p.device)
+
+ if config['max_unorm'] > 0.0:
+ state['unorm_vec'] = torch.zeros((1,), device=p.device)
+
+ @torch.no_grad()
+ def update_step(self, group, p, gindex, pindex):
+ state = self.state[p]
+ grad = p.grad
+
+ config = self.get_config(gindex, pindex, group)
+
+ state['step'] += 1
+ step = state['step']
+
+ if config['percentile_clipping'] < 100:
+ current_gnorm, clip_value, gnorm_scale = F.percentile_clipping(grad, state['gnorm_vec'], step, config['percentile_clipping'])
+ else:
+ gnorm_scale = 1.0
+
+ if state['state1'].dtype == torch.float:
+ F.optimizer_update_32bit(self.optimizer_name, grad, p, state['state1'], config['betas'][0], config['eps'], step, config['lr'],
+ state['state2'], config['betas'][1], config['weight_decay'], gnorm_scale,
+ state['unorm_vec'] if config['max_unorm'] > 0.0 else None, max_unorm=config['max_unorm'])
+
+ elif state['state1'].dtype == torch.uint8 and not config['block_wise']:
+ F.optimizer_update_8bit(self.optimizer_name, grad, p, state['state1'], state['state2'], config['betas'][0], config['betas'][1],
+ config['eps'], step, config['lr'],
+ state['qmap1'], state['qmap2'], state['max1'], state['max2'], state['new_max1'], state['new_max2'],
+ config['weight_decay'], gnorm_scale=gnorm_scale,
+ unorm_vec=state['unorm_vec'] if config['max_unorm'] > 0.0 else None, max_unorm=config['max_unorm'])
+
+ # swap maxes
+ state['max1'], state['new_max1'] = state['new_max1'], state['max1']
+ state['max2'], state['new_max2'] = state['new_max2'], state['max2']
+ elif state['state1'].dtype == torch.uint8 and config['block_wise']:
+ F.optimizer_update_8bit_blockwise(self.optimizer_name, grad, p, state['state1'], state['state2'], config['betas'][0], config['betas'][1],
+ config['eps'], step, config['lr'],
+ state['qmap1'], state['qmap2'], state['absmax1'], state['absmax2'],
+ config['weight_decay'], gnorm_scale=gnorm_scale)
+
+
+class Optimizer1State(Optimizer8bit):
+ def __init__(self, optimizer_name, params, lr=1e-3, betas=(0.9, 0.0), eps=1e-8,
+ weight_decay=0.0, optim_bits=32, args=None,
+ min_8bit_size=4096, percentile_clipping=100, block_wise=True, max_unorm=0.0):
+ if not 0.0 <= lr:
+ raise ValueError("Invalid learning rate: {}".format(lr))
+ if not 0.0 <= eps:
+ raise ValueError("Invalid epsilon value: {}".format(eps))
+ for i in range(len(betas)):
+ if not 0.0 <= betas[i] < 1.0:
+ raise ValueError(f"Invalid beta parameter at index {i}: {betas[i]}")
+ if not 0.0 <= weight_decay:
+ raise ValueError("Invalid weight_decay value: {}".format(weight_decay))
+ defaults = dict(lr=lr, betas=betas, eps=eps,
+ weight_decay=weight_decay)
+ super(Optimizer1State, self).__init__(params, defaults, optim_bits)
+
+ if args is None:
+ args = {}
+ args['optim_bits'] = optim_bits
+ args['percentile_clipping'] = 100
+ args['min_8bit_size'] = min_8bit_size
+ args['percentile_clipping'] = percentile_clipping
+ args['block_wise'] = block_wise
+ args['max_unorm'] = max_unorm
+
+ self.args = MockArgs(args)
+ else:
+ self.args = args
+
+ self.optimizer_name = optimizer_name
+
+ @torch.no_grad()
+ def init_state(self, group, p, gindex, pindex):
+ config = self.get_config(gindex, pindex, group)
+
+ if config['optim_bits'] == 32:
+ dtype = torch.float32
+ elif config['optim_bits'] == 8:
+ dtype = torch.uint8
+ else: raise NotImplementedError(f'Amount of optimizer bits not supported: {config["optim_bits"]}')
+
+ if p.numel() < config['min_8bit_size']: dtype = torch.float32
+
+ state = self.state[p]
+ state['step'] = 0
+
+ if dtype == torch.float32 or (dtype == torch.uint8 and p.numel() < 4096):
+ state['state1'] = torch.zeros_like(p, memory_format=torch.preserve_format, dtype=torch.float32, device=p.device)
+ elif dtype == torch.uint8:
+ if state['step'] == 0:
+ if 'dynamic' not in self.name2qmap: self.fill_qmap()
+ self.name2qmap['dynamic'] = self.name2qmap['dynamic'].to(p.device)
+
+ state['state1'] = torch.zeros_like(p, memory_format=torch.preserve_format, dtype=torch.uint8, device=p.device)
+ state['qmap1'] = self.name2qmap['dynamic']
+
+ if config['block_wise']:
+ n = p.numel()
+ blocks = n//2048
+ blocks += 1 if n % 2048 > 0 else 0
+
+ state['absmax1'] = torch.zeros((blocks,), dtype=torch.float32, device=p.device)
+ else:
+ state['max1'] = torch.zeros((1,), dtype=torch.float32, device=p.device)
+ state['new_max1'] = torch.zeros((1,), dtype=torch.float32, device=p.device)
+
+ if config['percentile_clipping'] < 100:
+ state['gnorm_vec'] = torch.zeros((100,), device=p.device)
+
+ if config['max_unorm'] > 0.0:
+ state['unorm_vec'] = torch.zeros((1,), device=p.device)
+
+
+ @torch.no_grad()
+ def update_step(self, group, p, gindex, pindex):
+ state = self.state[p]
+ grad = p.grad
+
+ config = self.get_config(gindex, pindex, group)
+
+ state['step'] += 1
+ step = state['step']
+
+ if config['percentile_clipping'] < 100:
+ current_gnorm, clip_value, gnorm_scale = F.percentile_clipping(grad, state['gnorm_vec'], step, config['percentile_clipping'])
+ else:
+ gnorm_scale = 1.0
+
+ if state['state1'].dtype == torch.float:
+ F.optimizer_update_32bit(self.optimizer_name, grad, p, state['state1'], config['betas'][0], config['eps'], step, config['lr'],
+ None, 0.0, config['weight_decay'], gnorm_scale,
+ state['unorm_vec'] if config['max_unorm'] > 0.0 else None, max_unorm=config['max_unorm'])
+
+ elif state['state1'].dtype == torch.uint8 and not config['block_wise']:
+ F.optimizer_update_8bit(self.optimizer_name, grad, p, state['state1'], None, config['betas'][0], config['betas'][1],
+ config['eps'], step, config['lr'], state['qmap1'], None, state['max1'], None, state['new_max1'], None,
+ config['weight_decay'], gnorm_scale,
+ state['unorm_vec'] if config['max_unorm'] > 0.0 else None, max_unorm=config['max_unorm'])
+
+ state['max1'], state['new_max1'] = state['new_max1'], state['max1']
+ elif state['state1'].dtype == torch.uint8 and config['block_wise']:
+ F.optimizer_update_8bit_blockwise(self.optimizer_name, grad, p, state['state1'], None, config['betas'][0], config['betas'][1],
+ config['eps'], step, config['lr'],
+ state['qmap1'], None, state['absmax1'], None,
+ config['weight_decay'], gnorm_scale=gnorm_scale)
diff --git a/bitsandbytes/optim/rmsprop.py b/bitsandbytes/optim/rmsprop.py
new file mode 100644
index 0000000..99b718e
--- /dev/null
+++ b/bitsandbytes/optim/rmsprop.py
@@ -0,0 +1,37 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+import torch
+from bitsandbytes.optim.optimizer import Optimizer1State
+
+class RMSprop(Optimizer1State):
+ def __init__(self, params, lr=1e-2, alpha=0.99, eps=1e-8, weight_decay=0, momentum=0, centered=False, optim_bits=32, args=None,
+ min_8bit_size=4096, percentile_clipping=100, block_wise=True):
+ if alpha == 0:
+ raise NotImplementError(f'RMSprop with alpha==0.0 is not supported!')
+ if centered:
+ raise NotImplementError(f'Centered RMSprop is not supported!')
+ super(RMSprop, self).__init__('rmsprop', params, lr, (alpha, momentum), eps,
+ weight_decay, optim_bits, args, min_8bit_size, percentile_clipping, block_wise)
+
+class RMSprop8bit(Optimizer1State):
+ def __init__(self, params, lr=1e-2, alpha=0.99, eps=1e-8, weight_decay=0, momentum=0, centered=False, args=None,
+ min_8bit_size=4096, percentile_clipping=100, block_wise=True):
+ if alpha == 0:
+ raise NotImplementError(f'RMSprop with alpha==0.0 is not supported!')
+ if centered:
+ raise NotImplementError(f'Centered RMSprop is not supported!')
+ super(RMSprop8bit, self).__init__('rmsprop', params, lr, (alpha, momentum), eps,
+ weight_decay, 8, args, min_8bit_size, percentile_clipping, block_wise)
+
+class RMSprop32bit(Optimizer1State):
+ def __init__(self, params, lr=1e-2, alpha=0.99, eps=1e-8, weight_decay=0, momentum=0, centered=False, args=None,
+ min_8bit_size=4096, percentile_clipping=100, block_wise=True):
+
+ if alpha == 0:
+ raise NotImplementError(f'RMSprop with alpha==0.0 is not supported!')
+ if centered:
+ raise NotImplementError(f'Centered RMSprop is not supported!')
+ super(RMSprop32bit, self).__init__('rmsprop', params, lr, (alpha, momentum), eps,
+ weight_decay, 32, args, min_8bit_size, percentile_clipping, block_wise)
diff --git a/bitsandbytes/optim/sgd.py b/bitsandbytes/optim/sgd.py
new file mode 100644
index 0000000..926d804
--- /dev/null
+++ b/bitsandbytes/optim/sgd.py
@@ -0,0 +1,32 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+from bitsandbytes.optim.optimizer import Optimizer1State
+
+class SGD(Optimizer1State):
+ def __init__(self, params, lr, momentum=0, dampening=0,
+ weight_decay=0, nesterov=False, optim_bits=32, args=None,
+ min_8bit_size=4096, percentile_clipping=100, block_wise=True):
+ if momentum == 0:
+ raise NotImplementError(f'SGD without momentum is not supported!')
+ super(SGD, self).__init__('momentum', params, lr, (momentum, dampening), 0.0,
+ weight_decay, optim_bits, args, min_8bit_size, percentile_clipping, block_wise)
+
+class SGD8bit(Optimizer1State):
+ def __init__(self, params, lr, momentum=0, dampening=0,
+ weight_decay=0, nesterov=False, args=None,
+ min_8bit_size=4096, percentile_clipping=100, block_wise=True):
+ if momentum == 0:
+ raise NotImplementError(f'SGD without momentum is not supported!')
+ super(SGD8bit, self).__init__('momentum', params, lr, (momentum, dampening), 0.0,
+ weight_decay, 8, args, min_8bit_size, percentile_clipping, block_wise)
+
+class SGD32bit(Optimizer1State):
+ def __init__(self, params, lr, momentum=0, dampening=0,
+ weight_decay=0, nesterov=False, args=None,
+ min_8bit_size=4096, percentile_clipping=100, block_wise=True):
+ if momentum == 0:
+ raise NotImplementError(f'SGD without momentum is not supported!')
+ super(SGD32bit, self).__init__('momentum', params, lr, (momentum, dampening), 0.0,
+ weight_decay, 32, args, min_8bit_size, percentile_clipping, block_wise)
generated by cgit v1.2.3 (git 2.25.1) at 2024-12-27 10:26:49 +0000