# Copyright 2024 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. "HIGGS through FLUTE (Flexible Lookup Table Engine for LUT-quantized LLMs) integration file" from math import sqrt from ..utils import ( is_flute_available, is_hadamard_available, is_torch_available, ) if is_torch_available(): import torch from torch import nn if is_flute_available(): from flute.integrations.higgs import prepare_data_transposed from flute.tune import TuneMetaData, qgemm_v2 if is_hadamard_available(): from fast_hadamard_transform import hadamard_transform def pad_to_block(tensor, dims, had_block_size, value=0): pad_dims = [0 for _ in range(2 * len(tensor.shape))] for dim in dims: size = tensor.shape[dim] next_multiple_of_1024 = ((size - 1) // had_block_size + 1) * had_block_size delta = next_multiple_of_1024 - size pad_dims[-2 * dim - 1] = delta return nn.functional.pad(tensor, pad_dims, "constant", value) def get_higgs_grid(p: int, n: int): if (p, n) == (2, 256): return torch.tensor( [ [-2.501467704772949, 0.17954708635807037], [-0.6761789321899414, 1.2728623151779175], [-1.8025816679000854, 0.7613157629966736], [-0.538287878036499, -2.6028504371643066], [0.8415029644966125, -0.8600977659225464], [0.7023013234138489, 3.3138747215270996], [0.5699077844619751, 2.5782253742218018], [3.292393207550049, -0.6016128063201904], [0.5561617016792297, -1.7723814249038696], [-2.1012380123138428, 0.020958125591278076], [0.46085724234580994, 0.8428705334663391], [1.4548040628433228, -0.6156039237976074], [3.210029363632202, 0.3546904921531677], [0.8893890976905823, -0.5967988967895508], [0.8618854284286499, -3.2061192989349365], [1.1360996961593628, -0.23852407932281494], [1.6646337509155273, -0.9265465140342712], [1.4767773151397705, 1.2476022243499756], [-1.0511897802352905, 1.94503915309906], [-1.56318998336792, -0.3264186680316925], [-0.1829211413860321, 0.2922491431236267], [-0.8950616717338562, -1.3887052536010742], [-0.08206957578659058, -1.329533576965332], [-0.487422913312912, 1.4817842245101929], [-1.6769757270812988, -2.8269758224487305], [-1.5057679414749146, 1.8905963897705078], [1.8335362672805786, 1.0515104532241821], [0.3273945450782776, 1.0491033792495728], [-3.295924186706543, -0.7021600008010864], [-1.8428784608840942, -1.2315762042999268], [-0.8575026392936707, -1.7005949020385742], [-1.120667815208435, 0.6467998027801514], [-0.1588846743106842, -1.804071068763733], [-0.8539647459983826, 0.5645008683204651], [-1.4192019701004028, -0.6175029873847961], [1.0799058675765991, 1.7871345281600952], [1.171311855316162, 0.7511613965034485], [2.162078380584717, 0.8044339418411255], [1.3969420194625854, -1.243762493133545], [-0.23818807303905487, 0.053944624960422516], [2.304199457168579, -1.2667627334594727], [1.4225027561187744, 0.568610668182373], [0.376836895942688, -0.7134661674499512], [2.0404467582702637, 0.4087389409542084], [0.7639489769935608, -1.1367933750152588], [0.3622530400753021, -1.4827953577041626], [0.4100743532180786, 0.36108437180519104], [-1.5867475271224976, -1.618212342262268], [-2.2769672870635986, -1.2132309675216675], [0.9184022545814514, -0.34428009390830994], [-0.3902314603328705, 0.21785245835781097], [3.120687484741211, 1.3077973127365112], [1.587440848350525, -1.6506884098052979], [-1.718808889389038, -0.038405973464250565], [-0.6888407468795776, -0.8402308821678162], [-0.7981445789337158, -1.1117373704910278], [-2.4124443531036377, 1.3419722318649292], [-0.6611530184745789, 0.9939885139465332], [-0.33103418350219727, -0.16702833771705627], [-2.4091389179229736, -2.326857566833496], [1.6610108613967896, -2.159703254699707], [0.014884627424180508, 0.3887578248977661], [0.029668325558304787, 1.8786455392837524], [1.180362582206726, 2.699317216873169], [1.821286678314209, -0.5960053205490112], [-0.44835323095321655, 3.327436685562134], [-0.3714401423931122, -2.1466753482818604], [-1.1103475093841553, -2.4536871910095215], [-0.39110705256462097, 0.6670510172843933], [0.474752813577652, -1.1959707736968994], [-0.013110585510730743, -2.52519154548645], [-2.0836575031280518, -1.703289270401001], [-1.1077687740325928, -0.1252644956111908], [-0.4138077199459076, 1.1837692260742188], [-1.977599024772644, 1.688241720199585], [-1.659559965133667, -2.1387736797332764], [0.03242531046271324, 0.6526556015014648], [0.9127950072288513, 0.6099498867988586], [-0.38478314876556396, 0.433487206697464], [0.27454206347465515, -0.27719801664352417], [0.10388526320457458, 2.2812814712524414], [-0.014394169673323631, -3.177137613296509], [-1.2871228456497192, -0.8961855173110962], [0.5720916986465454, -0.921597957611084], [1.1159656047821045, -0.7609877586364746], [2.4383342266082764, -2.2983546257019043], [-0.294057160615921, -0.9770799875259399], [-0.9342701435089111, 1.107579231262207], [-1.549338698387146, 3.090520143508911], [2.6076579093933105, 2.051239013671875], [-0.9259037375450134, 1.407211184501648], [-0.1747353971004486, 0.540488600730896], [-0.8963701725006104, 0.8271111249923706], [0.6480194926261902, 1.0128909349441528], [0.980783998966217, -0.06156221032142639], [-0.16883476078510284, 1.0601658821105957], [0.5839992761611938, 0.004697148688137531], [-0.34228450059890747, -1.2423977851867676], [2.500824451446533, 0.3665279746055603], [-0.17641609907150269, 1.3529551029205322], [0.05378641560673714, 2.817232847213745], [-1.2391047477722168, 2.354328155517578], [0.630434513092041, -0.668536365032196], [1.7576488256454468, 0.6738647818565369], [0.4435231387615204, 0.6000469326972961], [-0.08794835954904556, -0.11511358618736267], [1.6540337800979614, 0.33995017409324646], [-0.04202975332736969, -0.5375117063522339], [-0.4247745871543884, -0.7897617220878601], [0.06695003807544708, 1.2000739574432373], [-3.2508881092071533, 0.28734830021858215], [-1.613816261291504, 0.4944162368774414], [1.3598989248275757, 0.26117825508117676], [2.308382511138916, 1.3462618589401245], [-1.2137469053268433, -1.9254342317581177], [-0.4889402985572815, 1.8136259317398071], [-0.1870335340499878, -0.3480615019798279], [1.0766386985778809, -1.0627082586288452], [0.4651014506816864, 2.131748914718628], [-0.1306295394897461, -0.7811847925186157], [0.06433182954788208, -1.5397958755493164], [-0.2894323468208313, -0.5789554715156555], [-0.6081662178039551, 0.4845278263092041], [2.697964668273926, -0.18515698611736298], [0.1277363896369934, -0.7221432328224182], [0.8700758218765259, 0.35042452812194824], [0.22088994085788727, 0.495242178440094], [-2.5843818187713623, -0.8000828623771667], [0.6732649803161621, -1.4362232685089111], [-1.5286413431167603, 1.0417330265045166], [-1.1222513914108276, -0.6269875764846802], [-0.9752035140991211, -0.8750635385513306], [-2.6369473934173584, 0.6918523907661438], [0.14478731155395508, -0.041986867785453796], [-1.5629483461380005, 1.4369450807571411], [0.38952457904815674, -2.16428804397583], [-0.16885095834732056, 0.7976621985435486], [-3.12416934967041, 1.256506085395813], [0.6843105554580688, -0.4203019142150879], [1.9345275163650513, 1.934950351715088], [0.012184220366179943, -2.1080918312072754], [-0.6350273489952087, 0.7358828186988831], [-0.837304949760437, -0.6214472651481628], [0.08211923390626907, -0.9472538232803345], [2.9332995414733887, -1.4956780672073364], [1.3806978464126587, -0.2916182279586792], [0.06773144006729126, 0.9285762310028076], [-1.1943119764328003, 1.5963770151138306], [1.6395620107650757, -0.32285431027412415], [-1.390851378440857, -0.08273141086101532], [1.816330909729004, -1.2812227010726929], [0.7921574711799622, -2.1135804653167725], [0.5817914605140686, 1.2644577026367188], [1.929347038269043, -0.2386285960674286], [0.8877345323562622, 1.190008521080017], [1.4732073545455933, 0.8935023546218872], [-2.8518524169921875, -1.5478795766830444], [0.2439267635345459, 0.7576767802238464], [0.5246709585189819, -2.606659412384033], [1.150876760482788, 1.4073830842971802], [-0.2643202245235443, 2.0634236335754395], [1.555483341217041, -0.0023102816194295883], [2.0830578804016113, -1.7225427627563477], [-0.5424830317497253, -1.070199728012085], [0.9168899655342102, 0.8955540060997009], [-0.8120972514152527, 2.696739912033081], [-0.29908373951911926, -1.5310651063919067], [1.2320337295532227, -1.556247353553772], [1.8612544536590576, 0.08704725652933121], [0.22133447229862213, -1.8091708421707153], [-0.4403655230998993, -0.38571012020111084], [-1.88539457321167, 1.192205786705017], [2.239687919616699, 0.004709010478109121], [1.139495611190796, 0.45733731985092163], [-1.507995367050171, 0.19716016948223114], [0.46986445784568787, 1.5422041416168213], [-1.2573751211166382, -0.35984551906585693], [-1.7415345907211304, -0.6020717024803162], [1.0751984119415283, 0.19006384909152985], [2.24186635017395, -0.46343153715133667], [0.3610347509384155, -0.07658443599939346], [-1.3111497163772583, 0.432013601064682], [0.6164408326148987, 0.24538464844226837], [-1.9266542196273804, -0.3256155550479889], [-0.5870336890220642, -0.1879584938287735], [-1.0476511716842651, 0.3677721917629242], [-1.229940414428711, 1.2433830499649048], [0.18550436198711395, 0.22753673791885376], [-0.017921989783644676, 0.12625974416732788], [1.1659504175186157, -0.5020995736122131], [-0.5983408093452454, -1.40438973903656], [0.7519024014472961, -0.16282692551612854], [0.9920787811279297, -1.344896912574768], [-0.8103678226470947, 0.3064485788345337], [0.6956969499588013, 1.8208192586898804], [-2.7830491065979004, -0.2299390584230423], [-0.34681546688079834, 2.4890666007995605], [-1.4452646970748901, -1.2216600179672241], [-2.1872897148132324, 0.8926076292991638], [1.706072211265564, -2.8440372943878174], [1.1119003295898438, -2.4923460483551025], [-2.582794666290283, 2.0973289012908936], [0.04987720400094986, -0.2964983284473419], [-2.063807487487793, -0.7847916483879089], [-0.4068813621997833, 0.9135897755622864], [-0.9814359545707703, -0.3874954879283905], [-1.4227229356765747, 0.7337291240692139], [0.3065044581890106, 1.3125417232513428], [1.2160996198654175, -1.9643305540084839], [-1.2163853645324707, 0.14608727395534515], [-2.3030710220336914, -0.37558120489120483], [0.9232977628707886, 2.1843791007995605], [-0.1989777386188507, 1.651851773262024], [-0.714374840259552, -0.39365994930267334], [-0.7805715799331665, -2.099881887435913], [0.9015759229660034, -1.7053706645965576], [0.1033422127366066, 1.5256654024124146], [-1.8773194551467896, 2.324174165725708], [1.9227174520492554, 2.7441604137420654], [-0.5994020104408264, 0.23984014987945557], [1.3496100902557373, -0.9126054644584656], [-0.8765304088592529, -3.1877026557922363], [-1.2040035724639893, -1.5169521570205688], [1.4261796474456787, 2.150200128555298], [1.463774561882019, 1.6656692028045654], [0.20364105701446533, -0.4988172650337219], [0.5195154547691345, -0.24067887663841248], [-1.1116786003112793, -1.1599653959274292], [-0.8490808606147766, -0.1681060940027237], [0.3189965784549713, -0.9641751646995544], [-0.5664751529693604, -0.5951744318008423], [-1.6347930431365967, -0.9137664437294006], [0.44048091769218445, -0.47259435057640076], [-2.147747039794922, 0.47442489862442017], [1.834734320640564, 1.4462147951126099], [1.1777573823928833, 1.0659226179122925], [-0.9568989872932434, 0.09495053440332413], [-1.838529348373413, 0.2950586676597595], [-0.4800611734390259, 0.014894310384988785], [-0.5235516428947449, -1.7687653303146362], [2.0735011100769043, -0.8825281262397766], [2.637502431869507, 0.8455678224563599], [2.606602907180786, -0.7848446369171143], [-1.1886937618255615, 0.9330510497093201], [0.38082656264305115, 0.13328030705451965], [0.6847941875457764, 0.7384101152420044], [1.2638574838638306, -0.007309418171644211], [0.18292222917079926, -1.22371244430542], [0.8143821954727173, 1.4976691007614136], [0.6571850776672363, 0.48368802666664124], [-0.6991601586341858, 2.150190830230713], [0.8101756572723389, 0.10206498205661774], [-0.08768226951360703, -1.084917664527893], [-0.7208092212677002, 0.03657956421375275], [0.3211449086666107, 1.803687334060669], [-0.7835946083068848, 1.6869111061096191], ] ) if (p, n) == (2, 64): return torch.tensor( [ [-2.7216711044311523, 0.14431366324424744], [-0.766914427280426, 1.7193410396575928], [-2.2575762271881104, 1.2476624250411987], [1.233758807182312, -2.3560616970062256], [0.8701965808868408, -0.2649352252483368], [1.4506438970565796, 2.1776366233825684], [-0.06305818259716034, 1.9049758911132812], [2.536226511001587, 0.563927412033081], [0.4599496126174927, -1.8745561838150024], [-1.900517225265503, -0.30703988671302795], [0.09386251866817474, 0.8755807280540466], [1.946500539779663, -0.6743080615997314], [2.1338934898376465, 1.4581491947174072], [0.9429940581321716, -0.8038390278816223], [2.0697755813598633, -1.614896535873413], [0.772676408290863, 0.22017823159694672], [1.0689979791641235, -1.525044322013855], [0.6813604831695557, 1.1345642805099487], [0.4706456661224365, 2.606626272201538], [-1.294018030166626, -0.4372096061706543], [-0.09134224057197571, 0.4610418677330017], [-0.7907772064208984, -0.48412787914276123], [0.060459110885858536, -0.9172890186309814], [-0.5855047702789307, 2.56172513961792], [0.11484206467866898, -2.659848213195801], [-1.5893300771713257, 2.188580274581909], [1.6750942468643188, 0.7089915871620178], [-0.445697546005249, 0.7452405095100403], [-1.8539940118789673, -1.8377939462661743], [-1.5791912078857422, -1.017285943031311], [-1.030419945716858, -1.5746369361877441], [-1.9511750936508179, 0.43696075677871704], [-0.3446580767631531, -1.8953213691711426], [-1.4219647645950317, 0.7676230669021606], [-0.9191089272499084, 0.5021472573280334], [0.20464491844177246, 1.3684605360031128], [0.5402919054031372, 0.6699410676956177], [1.8903915882110596, 0.03638288006186485], [0.4723062515258789, -0.6216739416122437], [-0.41345009207725525, -0.22752176225185394], [2.7119064331054688, -0.5111885070800781], [1.065286636352539, 0.6950305700302124], [0.40629103779792786, -0.14339995384216309], [1.2815024852752686, 0.17108257114887238], [0.01785222627222538, -0.43778058886528015], [0.054590027779340744, -1.4225547313690186], [0.3076786696910858, 0.30697619915008545], [-0.9498570561408997, -0.9576997756958008], [-2.4640724658966064, -0.9660449028015137], [1.3714425563812256, -0.39760473370552063], [-0.4857747256755829, 0.2386789172887802], [1.2797833681106567, 1.3097363710403442], [0.5508887767791748, -1.1777795553207397], [-1.384316325187683, 0.1465839296579361], [-0.46556955575942993, -1.2442727088928223], [-0.3915477693080902, -0.7319604158401489], [-1.4005504846572876, 1.3890998363494873], [-0.8647305965423584, 1.0617644786834717], [-0.8901953101158142, -0.01650036871433258], [-0.9893633723258972, -2.4662880897521973], [1.445534110069275, -1.049334168434143], [-0.041650623083114624, 0.012734669260680676], [-0.3302375078201294, 1.26217782497406], [0.6934980154037476, 1.7714335918426514], ] ) elif (p, n) == (2, 16): return torch.tensor( [ [-0.8996632695198059, -1.6360418796539307], [-0.961183488368988, 1.5999565124511719], [-1.882026195526123, 0.678778350353241], [0.36300793290138245, -1.9667866230010986], [-0.6814072728157043, -0.576818585395813], [0.7270012497901917, 0.6186859607696533], [0.3359416127204895, 1.8371193408966064], [1.859930396080017, 0.036668598651885986], [0.17208248376846313, -0.9401724338531494], [-1.7599700689315796, -0.6244229674339294], [-0.8993809223175049, 0.32267823815345764], [0.839488685131073, -0.3017036020755768], [1.5314953327178955, 1.2942044734954834], [-0.0011779458727687597, 0.00022069070837460458], [1.4274526834487915, -1.207889199256897], [-0.16123905777931213, 0.8787511587142944], ] ) elif (p, n) == (1, 16): return torch.tensor( [ [-2.7325894832611084], [-2.069017171859741], [-1.6180464029312134], [-1.2562311887741089], [-0.9423404335975647], [-0.6567591428756714], [-0.38804829120635986], [-0.12839503586292267], [0.12839503586292267], [0.38804829120635986], [0.6567591428756714], [0.9423404335975647], [1.2562311887741089], [1.6180464029312134], [2.069017171859741], [2.7325894832611084], ] ) elif (p, n) == (1, 8): return torch.tensor( [ [-2.1519455909729004], [-1.3439092636108398], [-0.7560052871704102], [-0.2450941801071167], [0.2450941801071167], [0.7560052871704102], [1.3439092636108398], [2.1519455909729004], ] ) elif (p, n) == (1, 4): return torch.tensor([[-1.5104175806045532], [-0.4527800381183624], [0.4527800381183624], [1.5104175806045532]]) else: raise NotImplementedError(f"Unsupported p={p}, n={n}") def quantize_with_higgs(weight, bits: int = 4, p: int = 2, group_size: int = 256, hadamard_size: int = 1024): assert len(weight.shape) == 2, "Only 2D weights are supported for now" grid = get_higgs_grid(p, 2 ** (p * bits)).to(weight.device) grid_norm_2 = torch.linalg.norm(grid, axis=-1) ** 2 device = weight.device dtype = weight.dtype weight = weight.to(copy=True, dtype=torch.float32) # Pad to Hadamard transform size weight = pad_to_block(weight, [1], hadamard_size) # Scale and Hadamard transform mult = weight.shape[1] // hadamard_size weight = weight.reshape(-1, mult, hadamard_size) scales = torch.linalg.norm(weight, axis=-1) weight = hadamard_transform(weight, 1) / scales[:, :, None] # Pad to edenn_d and project weight = pad_to_block(weight, [2], p).reshape(weight.shape[0], mult, -1, p) # Quantize codes = torch.empty(weight.shape[:-1], device=device, dtype=torch.uint8) for i in range(0, weight.shape[0], 16): codes[i : i + 16] = torch.argmax(2 * weight[i : i + 16] @ grid.T - grid_norm_2, dim=-1).to(torch.uint8) del weight codes = codes.reshape(codes.shape[0], -1) scales = scales / sqrt(hadamard_size) weight, scales, tables, tables2, tune_metadata = prepare_data_transposed( codes, torch.repeat_interleave(scales.to(dtype), hadamard_size // group_size, dim=1), grid.to(dtype), num_bits=bits, group_size=group_size, vector_size=p, dtype=dtype, device=device, check_correctness=False, ) return { "weight": weight, "scales": scales, "tables": tables, "tables2": tables2.view(dtype=torch.float16), "tune_metadata": tune_metadata, } class HiggsLinear(torch.nn.Module): def __init__( self, in_features: int, out_features: int, num_bits: int, bias=True, dtype: torch.dtype = None, device: torch.device = None, group_size: int = 256, hadamard_size: int = 1024, ): super().__init__() self.in_features = in_features self.out_features = out_features self.num_bits = num_bits self.group_size = group_size self.hadamard_size = hadamard_size assert in_features % group_size == 0 assert num_bits in [2, 3, 4] self.weight = nn.Parameter( torch.empty((out_features * num_bits // 16, in_features), dtype=torch.int16, device=device), requires_grad=False, ) self.scales = nn.Parameter( torch.empty((out_features, in_features // group_size), dtype=dtype, device=device), requires_grad=False ) self.tables = nn.Parameter(torch.empty((2**num_bits,), dtype=dtype, device=device), requires_grad=False) self.tables2 = nn.Parameter( torch.empty((2**num_bits, 2**num_bits, 2), dtype=dtype, device=device), requires_grad=False ) if bias: self.bias = nn.Parameter(torch.empty(out_features, device=device, dtype=dtype), requires_grad=False) else: self.register_parameter("bias", None) self.workspace = None # must be set externally to be reused among layers self.tune_metadata: TuneMetaData = None # must be set externally because architecture dependent def forward(self, x): x = pad_to_block(x, [-1], self.hadamard_size) if self.workspace is None: raise Exception("Workspace must be set before calling forward") return qgemm_v2( x, self.weight, self.scales, self.tables, self.tables2.view(dtype=torch.float32), self.workspace, self.tune_metadata, hadamard_size=self.hadamard_size, ) def replace_with_higgs_linear( model, quantization_config=None, current_key_name=None, has_been_replaced=False, ): """ Public method that recursively replaces the Linear layers of the given model with HIGGS quantized layers. `accelerate` is needed to use this method. Returns the converted model and a boolean that indicates if the conversion has been successfull or not. Args: model (`torch.nn.Module`): The model to convert, can be any `torch.nn.Module` instance. quantization_config (`HiggsConfig`): The quantization config object that contains the quantization parameters. current_key_name (`list`, *optional*): A list that contains the current key name. This is used for recursion and should not be passed by the user. has_been_replaced (`bool`, *optional*): A boolean that indicates if the conversion has been successful or not. This is used for recursion and should not be passed by the user. """ from accelerate import init_empty_weights for name, module in model.named_children(): if current_key_name is None: current_key_name = [] current_key_name.append(name) if isinstance(module, nn.Linear): # Check if the current key is not in the `quantization_config.modules_to_not_convert` current_key_name_str = ".".join(current_key_name) if not any(current_key_name_str.endswith(key) for key in quantization_config.modules_to_not_convert): with init_empty_weights(): in_features = module.in_features out_features = module.out_features model._modules[name] = HiggsLinear( in_features, out_features, bias=module.bias is not None, num_bits=quantization_config.bits, hadamard_size=quantization_config.hadamard_size, group_size=quantization_config.group_size, ) has_been_replaced = True # Store the module class in case we need to transpose the weight later model._modules[name].source_cls = type(module) # Force requires grad to False to avoid unexpected errors model._modules[name].requires_grad_(False) if len(list(module.children())) > 0: _, has_been_replaced = replace_with_higgs_linear( module, quantization_config=quantization_config, current_key_name=current_key_name, has_been_replaced=has_been_replaced, ) # Remove the last key for recursion current_key_name.pop(-1) return model, has_been_replaced def dequantize_higgs(model, current_key_name=None): """ Dequantizes the HiggsLinear layers in the given model by replacing them with standard torch.nn.Linear layers. Args: model (torch.nn.Module): The model containing HiggsLinear layers to be dequantized. current_key_name (list, optional): A list to keep track of the current module names during recursion. Defaults to None. Returns: torch.nn.Module: The model with HiggsLinear layers replaced by torch.nn.Linear layers. """ with torch.no_grad(): for name, module in model.named_children(): if current_key_name is None: current_key_name = [] current_key_name.append(name) if isinstance(module, HiggsLinear): in_features = module.in_features out_features = module.out_features model._modules[name] = torch.nn.Linear( in_features, out_features, bias=module.bias is not None, device=module.scales.device, dtype=module.scales.dtype, ) model._modules[name].weight.data = module( torch.eye(in_features, device=module.scales.device, dtype=module.scales.dtype) ).T.contiguous() if len(list(module.children())) > 0: _ = dequantize_higgs( module, current_key_name=current_key_name, ) # Remove the last key for recursion current_key_name.pop(-1) return model