From ba15c23214878339ec0fc493149f27bedb0ea31e Mon Sep 17 00:00:00 2001 From: Max Luebbering <2804731+le1nux@users.noreply.github.com> Date: Fri, 23 Jan 2026 10:37:31 +0100 Subject: [PATCH 1/6] feat: added einsum transformer starter scripts --- tutorials/einsum_transformer/run.sh | 3 ++ tutorials/einsum_transformer/train.py | 52 +++++++++++++++++++++++++++ 2 files changed, 55 insertions(+) create mode 100644 tutorials/einsum_transformer/run.sh create mode 100644 tutorials/einsum_transformer/train.py diff --git a/tutorials/einsum_transformer/run.sh b/tutorials/einsum_transformer/run.sh new file mode 100644 index 00000000..e480d42e --- /dev/null +++ b/tutorials/einsum_transformer/run.sh @@ -0,0 +1,3 @@ +#!/bin/sh + +CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 torchrun --rdzv-endpoint localhost:29804 --nnodes 1 --nproc_per_node 8 train.py \ No newline at end of file diff --git a/tutorials/einsum_transformer/train.py b/tutorials/einsum_transformer/train.py new file mode 100644 index 00000000..ddf8a1ab --- /dev/null +++ b/tutorials/einsum_transformer/train.py @@ -0,0 +1,52 @@ +import os +from pathlib import Path + +from model.einsum_transformer import EinsumTransformer +from pydantic import BaseModel + +from modalities.__main__ import Main +from modalities.config.config import ProcessGroupBackendType +from modalities.config.instantiation_models import TrainingComponentsInstantiationModel +from modalities.running_env.cuda_env import CudaEnv + + +class EinsumTransformerConfig(BaseModel): + prediction_key: str + sample_key: str + vocab_size: int + sequence_length: int + embed_dim: int + num_q_heads: int + num_kv_heads: int + num_layers: int + mlp_expansion_factor: float = 4 + + +def main(): + # load and parse the config file + cwd = Path(__file__).parent + # change to cwd + os.chdir(cwd) + config_file_path = cwd / Path("einsum_transformer_config.yaml") + experiments_root_path = cwd / Path("experiments") + + with CudaEnv(process_group_backend=ProcessGroupBackendType.nccl): + # instantiate the Main entrypoint of modalities by passing in the config path + modalities_main = Main(config_path=config_file_path, experiments_root_path=experiments_root_path) + + # add the custom component to modalities + modalities_main.add_custom_component( + component_key="model", + variant_key="einsum_transformer", + custom_component=EinsumTransformer, + custom_config=EinsumTransformerConfig, + ) + # run the experiment + components: TrainingComponentsInstantiationModel = modalities_main.build_components( + components_model_type=TrainingComponentsInstantiationModel + ) + modalities_main.run(components) + + +if __name__ == "__main__": + main() From 5692d6e6840ce84856b2a11e2f8acac74d7aa4c6 Mon Sep 17 00:00:00 2001 From: Max Luebbering <2804731+le1nux@users.noreply.github.com> Date: Fri, 23 Jan 2026 10:37:54 +0100 Subject: [PATCH 2/6] feat: added tokenizer to einsum example --- .../tokenizer/tokenizer.json | 1 + .../tokenizer/tokenizer_config.json | 19 +++++++++++++++++++ 2 files changed, 20 insertions(+) create mode 100644 tutorials/einsum_transformer/tokenizer/tokenizer.json create mode 100644 tutorials/einsum_transformer/tokenizer/tokenizer_config.json diff --git a/tutorials/einsum_transformer/tokenizer/tokenizer.json b/tutorials/einsum_transformer/tokenizer/tokenizer.json new file mode 100644 index 00000000..4b988bcc --- /dev/null +++ b/tutorials/einsum_transformer/tokenizer/tokenizer.json @@ -0,0 +1 @@ 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5","ĠH ass","Ġsw all","Ġw ax","ĠSurv ival","V S","ĠM urd","v d","stand ard","Ġdrag ons","Ġacceler ation","r ational","f inal","Ġp aired","ĠE thereum","Ġinterf aces","Ġres ent","Ġartif acts","Å «","are l","Ġcompet itor","ĠNich olas","ĠSur face","c pp","ĠT ot","Ġeconom ically","Ġorgan ised","Ġen forced","in ho","Ġvar ieties","Ġab dom","ĠBa iley","id av","ĠSal v","p aid","Ġalt itude","ess ert","ĠG utenberg","are a","op oulos","Ġprofess ors","igg s","ĠF ate","he y","Ġ3 000","D ist","Ġtw ins","c ill","ĠM aps","Ġtra ps","Ġwe ed","ĠK iss","Ġy oga","Ġrecip ients","ĠWest minster","Ġpool s","ĠWal mart","18 8","ĠSchool s","att ack","ĠAR M","par agraph","W arning","j l","Ġself ish","anche z","ĠHe ights","F re","ĠS oph","Ġ --------------------------------","t ml","33 3","Ġraid s","Ġsatell ites","KE Y","Ġlast s","Ñ Ĥ","In s","ĠD ame","Ġunp redict","// /","gh ai","Ġart illery","Ġcru ise","Ġg el","ĠCabin et","Ġbl ows","ĠE sp","Ġprox imity","ot he","ĠSk ills","ĠU pper","ob o","ĠN DP","Ġenjoy s","Ġrepe 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ributed","Ġpop corn","Ġsem inal","f ur","ĠNeuro science","R and","ĠO st","ĠMadd en","ĠIncre asing","ĠDaw kins","ĠSub way","Ġar sen","cons erv","B UR","Ġsp iked","ĠLy ft","ĠImper ium","ĠDrop box","Ġfav oured","Ġencomp asses","gh ost","Ġins pires","Ġbur geoning","ĠY oshi","ĠVert ical","ĠAud itor","Ġint ending","Ġfilib uster","Bl oom","f ac","ĠCav s","ign ing","Ġcowork ers","ĠBarb arian","rem ember","FL AG","Ġaudit ory","ason ry","Col lege","Ġmut ed","gem ony","ob in","ĠPsych o","9 68","Ġlav ish","Ġhierarch ical","ĠDr one","ou k","Ġcripp led","ĠMax im","Sl ot","Ġqu iz","ĠV id","if ling","Ġarchae ologists","Ġabandon ment","d ial","le on","ĠF as","T ed","Ġr aspberry","Ġmaneu vers","Ġbehavi ours","Ġins ure","Ġrem od","Sw itch","h oe","Ġsp aced","Ġafford ability","ĠF ern","not ation","ĠBal anced","Ġoccup ies","en vironment","Ġneck lace","Ġsed an","F U","ĠBrav o","Ġab users","ĠAn ita","met adata","ĠG ithub","ait o","ĠF aster","ĠWass erman","ĠF lesh","Ġth orn","r arily","ĠMer ry","w 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ually","unct ure","18 00","Ġd arn","B razil","Ġleft over","ber us","f red","Mine craft","3 26","ĠForm s","Ġproof s","ĠDes igned","Ġindex es","ĠSupp ose","EM S","ĠL oving","ĠBon nie","im ating","OT US","Ġconduct or","Ġbehav ed","ĠF ren","Ġsy nerg","Ġmillenn ium","Ġcater ing","ĠL auder","W r","ĠY iannopoulos","ĠAT F","Ġensl aved","Ġawaken ed","D VD","ĠED ITION","ĠConc ert","ĠChall enger","ĠH aku","umer ic","Ġdep recated","ĠSH AR","4 12","Ġdy stop","Ġtremb ling","Ġdread ed","ĠSp ac","p adding","Re pl","ĠG arrison","M ini","Ġun paralleled","am ar","URR ENT","w reck","c ertain","t al","ĠC LS","app ings","Ġsens ed","Ġf encing","ĠPas o","ĠDes k","Ġsc off","Ġcontem plate","ĠL iga","l iquid","75 7","Ġapp rentice","ĠUCH IJ","5 70","ĠTh ousand","ĠIll um","Ġchampion ed","ãĤ Į","Ġelect ors","Ġ3 98","ĠH ancock","round ed","ĠJ OHN","Ġuns atisf","Ġqual ifier","ĠGad get","EN E","Ġdead liest","ĠPl ants","Ġ ions","Ġacc ents","Ġtwe aking","Ġsh aved","F REE","ĠCh aser","Again st","9 60","Ġmeth amphetamine","Ġnormal ized","Ġ$ \\","ĠPre cision","ĠGu am","Ġch oked","ĠX II","ĠCast ing","Tor rent","Ġscal p","ĠJagu ar","w it","Ġsem ic","ix ie","ĠG ould","Ġconf ines","N usra","ĠL on","ĠJ ugg","y cle","ĠCod ec","E gypt","Ġrest rain","ĠAl iens","Ġch oking","ĠD unk","ĠBell a","ab c","Ġsl ang","Ġneuro trans","s av","Ġempower ment","â ĨĴ","Ġclim bers","ĠM im","ĠF ra","ros se","Cap ital","ĠCth ulhu","Inter face","Ġprof icient","ĠIN TO","Ġ3 18","ront al","5 80","ĠDes pair","K enn","Ġscrim mage","ĠCo at","as ions","Ġwall paper","ĠJ ol","Ġresurg ence","Ġant iv","ĠB alls","² ¾","Ġbuff ers","Ġsub system","ĠSt ellar","ĠL ung","A IDS","Ġerad icate","Ġblat antly","Ġbehav es","ĠN un","Ġant ics","ex port","DE V","w b","Ġph p","ĠInteg rity","Ġexplore r","Ġrev olving","auth ored","g ans","Ġbas k","Ġas ynchronous","å į","TH ING","69 8","G ene","ĠR acer","ĠN ico","iss ued","Ġser mon","p ossibly","Ġsize of","Ġentrepreneur ial","ox in","ĠMin erva","Ġpl atoon","n os","ri ks","A UT","ĠAval anche","ĠDes 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mer","Ġprot agonists","Ġform atted","ĠBry ce","ĠAch illes","ĠAb edin","sh ock","Ġb um","b os","qu a","ĠW arn","q t","ĠDi abetes","8 64","ĠIn visible","Ġvan ish","Ġtrans mitting","Ġmur ky","ĠFe i","Ġawa ited","ĠJur assic","umm ies","Ġmen acing","g all","C ath","B uilt","ild o","ĠV otes","Ġon t","Ġmun itions","ĠFre em","ÃŃ n","Ġdec ency","lo pp","ie ved","ĠG ord","Ġun thinkable","ĠNews week","Ġ3 21","He at","Ġpresent er","ji ang","Ġpl ank","ĠAval on","Ġben z","ĠR out","Ġslam ming","ĠD ai","ou ter","ĠCook ie","ĠAlic ia","ge y","Ġvan ity","Ġow l","á µ","t ested","ĠAw akens","Ġcan v","Ġblind ly","ĠRid ley","ĠEm ails","Requ ires","ĠSer bian","ograp hed","if rame","eter ia","Ġaltern ating","qu iet","Ġsoc iology","ĠUn lock","ĠCommun ism","Ġo ps","Ġatt ribution","Ġab duction","ĠAb ram","Ġsidel ined","ĠB OOK","Ġref ining","ĠFe eling","ĠOs lo","ĠPru itt","r ack","ang ible","Ġcaut iously","ĠM ARK","eed s","M ouse","ĠStep h","ĠP air","S ab","99 7","ĠBa al","B ec","Ġcomm a","ĠP all","ĠG 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ugs","ĠTrans ition","ĠOcc asionally","Ġ3 26","ĠWh ilst","ĠLe y","Ġspaces hip","cs v","Ġun successfully","ĠA u","le ck","ĠWing ed","ĠGrizz lies",". �","Ġne arer","ĠSorce ress","ĠInd igo","El se","8 40","let es","Co ach","Ġup bringing","ĠK es","Ġseparat ist","Ġrac ists","Ġch ained","Ġabst inence","lear ning","Ġrein stated","Ġsymm etry","Ġremind ers","ĠChe vy","Ġm ont","Ġexempl ary","ĠT OR","Z X","Ġqual itative","ĠSt amp","ĠSav annah","ĠRoss i","Ġp aed","Ġdispens aries","ĠWall s","ĠCh ronic","Ġcompliment ary","ĠBeir ut","Ġ+ ---","igs list","Ġcrypt ographic","mas ters","ĠCap itals","Ġmax imal","Ġent ropy","Point s","Ġcombat ants","l ip","ĠGl ob","ĠB MC","ph ase","th ank","HT TP","Ġcomm uter","Ġ\\( \\",".. /","ĠReg ener","ĠDO I","ĠActiv ision","Ġsl it","os al","RE M","Ġch ants","Y u","Ke ys","Bre xit","ĠFor ced","Ari zona","Ġsquad ron","IS O","ĠMal one","Ġ3 38","Ġcontrast ing","Ġt idal","Ġlib el","Ġimpl anted","Ġupro ar","ĠC ater","Ġpropos itions","M anchester","ĠEuro s","it amin","G 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pr","OV A","Ġkn itting","ĠLim a","Ġf odder","68 4","ĠPerson a","k l","7 01","Ġbreak up","á ¸","Ġapp alled","Ġantidepress ants","ĠSus sex","Har ris","ĠTher mal","ee ee","U pload","Ġg ulf","Ġdoor step","ĠSh ank","L U","ĠM EN","ĠP ond","s orry","Ġmis fortune","n ance","Ġb ona","M ut","Ġde graded","ĠL OG","ĠN ess","an imal","Ġa version","und own","Ġsupplement ed","ĠC ups","Ġ50 4","Ġdep rive","ĠSpark le","Å Ĥ","ĠMed itation","auth ors","ĠSab an","ĠN aked","air d","ĠMand arin","ĠScript ures","ĠPerson nel","ĠMahar ashtra","Ġ19 03","ĠP ai","ĠMir age","omb at","Access ory","Ġfrag mented","T ogether","Ġbelie vable","ĠGl adiator","al igned","ĠSl ug","M AT","Ġconvert ible","ĠBour bon","amer on","ĠRe hab","nt ax","Ġpowd ered","pill ar","Ġsm oker","ĠMans on","ĠB F","5 11","ĠGood ell","ĠD AR","m ud","g art","Ġob edient","ĠTrans mission","ĠDon ation","8 80","Ġbother ing","Material s","ãĤ ±","dest roy","Ġfore going","Ġanarch ism","ĠK ry","ice ps","Ġl ittered","ĠSch iff","Ġanecd otal","un its","Ġf 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NHD + + self.mlp = MLP(embed_dim, mlp_expansion_factor) + self.pre_attn_norm = RMSNorm(embed_dim) + self.pre_mlp_norm = RMSNorm(embed_dim) + + def forward(self, x): + batch_size = x.shape[0] + seq_len = x.shape[1] + + x_new = self.pre_attn_norm(x) # BTD + q = torch.einsum("BTD,DNH->BTNH", x_new, self.W_q) # BTNH + q = q.reshape( + shape=(batch_size, seq_len, self.num_kv_heads, self.num_q_heads_per_kv_head, self.head_dim) + ) # BTKGH + + k = torch.einsum("BTD,DKH->BTKH", x_new, self.W_k) + a = torch.einsum("BTKGH,BSKH->BTSKG", q, k) # BTTKG (here, T=S) + + # apply causal mask + mask = torch.full( + (seq_len, seq_len), float("-inf"), device=x.device, dtype=a.dtype + ) # T by T matrix with -inf entries + mask = torch.triu(mask, diagonal=1)[ + None, :, :, None, None + ] # only keep upper triangular part (future positions), 1TT11 + a = a + mask # BTTKG + + # attention scores -> probabilities + a = a / (self.head_dim**0.5) + a = torch.softmax(a, dim=2) # softmax over S + + v = torch.einsum("BTD,DKH->BTKH", x_new, self.W_v) + x_new = torch.einsum("BTSKG,BSKH->BTKGH", a, v) # BTKGH + x_new = x_new.reshape( + shape=(batch_size, seq_len, self.num_kv_heads * self.num_q_heads_per_kv_head, self.head_dim) + ) # BTNH + x_new = torch.einsum("BTNH,NHD->BTD", x_new, self.W_o) # BTD + + x_new = x_new + x # residual connection + + x_new = self.pre_mlp_norm(x_new) + x_new = x_new + self.mlp(x_new) # residual connection + return x_new + + def reset_parameters(self): + nn.init.normal_(self.W_q, std=0.02) + nn.init.normal_(self.W_k, std=0.02) + nn.init.normal_(self.W_v, std=0.02) + nn.init.normal_(self.W_o, std=0.02) + self.mlp.reset_parameters() + + +class MLP(nn.Module): + def __init__(self, embed_dim: int, expansion_factor: float = 4): + super().__init__() + upscaled_dim = int(embed_dim * expansion_factor) + self.W_in1 = nn.Parameter(torch.zeros(size=(embed_dim, upscaled_dim))) + self.W_in2 = nn.Parameter(torch.zeros(size=(embed_dim, upscaled_dim))) + self.W_out = nn.Parameter(torch.zeros(size=(upscaled_dim, embed_dim))) + self.gelu = nn.GELU() + + def forward(self, x): + x_new1 = torch.einsum("BTD,DF->BTF", x, self.W_in1) # BTF + x_new1 = self.gelu(x_new1) + x_new2 = torch.einsum("BTD,DF->BTF", x, self.W_in2) # BTF + x_new = x_new1 + x_new2 # BTF + x_new = torch.einsum("BTF,FD->BTD", x_new, self.W_out) # BTD + return x_new + + def reset_parameters(self): + nn.init.normal_(self.W_in1, std=0.02) + nn.init.normal_(self.W_in2, std=0.02) + nn.init.normal_(self.W_out, std=0.02) + + +class RMSNorm(nn.Module): + def __init__(self, embed_dim: int, eps=None, device=None, dtype=None): # reusing the pytorch function signature + super().__init__() + self.eps = eps if eps is not None else 1e-8 # for numerical stability + self.gain = nn.Parameter(torch.ones(size=(embed_dim,), dtype=dtype, device=device)) + + def forward(self, x): # input shape: BTD + x = x / torch.sqrt(torch.sum(x**2, dim=-1, keepdim=True) / x.shape[-1] + self.eps) # RMS normalization + x = x * self.gain # elementwise scaling + return x # BTD + + def reset_parameters(self): + nn.init.ones_(self.gain) + + +class EinsumTransformer(nn.Module): + def __init__( + self, + vocab_size: int, + sequence_length: int, + embed_dim: int, + num_q_heads: int, + num_kv_heads: int, + num_layers: int, + mlp_expansion_factor: int = 4, + sample_key: str = "input_ids", + prediction_key: str = "logits", + ): + super().__init__() + self.sample_key = sample_key + self.prediction_key = prediction_key + + self.vocab_size = vocab_size + self.Wte = nn.Parameter(torch.zeros(size=(vocab_size, embed_dim))) + self.Wpe = nn.Parameter(torch.zeros(size=(sequence_length, embed_dim))) + self.pre_lm_head_norm = RMSNorm(embed_dim) + + self.lm_head = nn.Parameter(torch.zeros(size=(embed_dim, vocab_size))) + + self.layers = nn.ModuleList( + [TransformerBlock(embed_dim, num_q_heads, num_kv_heads, mlp_expansion_factor) for _ in range(num_layers)] + ) + + @property + def weight_decay_groups(self): + return { + "linear": [".W_q", ".W_k", ".W_v", ".W_o", ".W_in1", ".W_in2", ".W_out", "lm_head"], + "embedding": ["Wte", "Wpe"], + "layernorm": [".pre_lm_head_norm", ".pre_attn_norm", ".pre_mlp_norm"], + } + + @overload + def forward(self, inputs: dict[str, torch.Tensor]) -> dict[str, torch.Tensor]: + """ + Forward pass of the GPT2LLM module. + + Args: + inputs (dict[str, torch.Tensor]): A dictionary containing input tensors. + - sample_key (str): Key for the input tensor containing token ids. + + Returns: + dict[str, torch.Tensor]: A dictionary containing output tensors. + - prediction_key (str): Key for the output tensor containing logits. + """ + ... + + @overload + def forward(self, inputs: torch.Tensor) -> torch.Tensor: + """ + Forward pass of the module. + + Args: + inputs (torch.Tensor): A tensor containing input token ids. + + Returns: + torch.Tensor: A tensor containing output logits. + """ + ... + + def forward(self, inputs: dict[str, torch.Tensor] | torch.Tensor) -> dict[str, torch.Tensor] | torch.Tensor: + """ + Forward pass of the module. + + Args: + inputs (dict[str, torch.Tensor] | torch.Tensor): Input data. + + Returns: + dict[str, torch.Tensor] | torch.Tensor: Model output. + """ + if isinstance(inputs, dict): + return {self.prediction_key: self.forward_impl(inputs[self.sample_key])} + else: + return self.forward_impl(inputs) + + def forward_impl(self, x: torch.Tensor): + word_embeddings = self.Wte[x] # BTD + pos_indices = torch.arange(start=0, end=x.shape[1], device=x.device)[None, :] # 1T + pos_embeddings = self.Wpe[pos_indices, :] # 1TD + x = word_embeddings + pos_embeddings # BTD + # transformer layers + for layer in self.layers: + x = layer(x) + # lm head + x = self.pre_lm_head_norm(x) # BTD + x = torch.einsum("BTD,DV->BTV", x, self.lm_head) # BTV + return x + + def reset_parameters(self): + nn.init.normal_(self.Wte, std=0.02) + nn.init.normal_(self.Wpe, std=0.02) + nn.init.normal_(self.lm_head, std=0.02) + self.pre_lm_head_norm.reset_parameters() + for layer in self.layers: + layer.reset_parameters() + + +if __name__ == "__main__": + BATCH_SIZE = 2 # B + SEQ_LEN = 64 # T + EMBED_DIM = 512 # D + NUM_Q_HEADS = 8 # N + NUM_KV_HEADS = 2 # K + VOCAB_SIZE = 100 # V + + NUM_LAYERS = 6 + + transformer = EinsumTransformer( + vocab_size=VOCAB_SIZE, + sequence_length=SEQ_LEN, + embed_dim=EMBED_DIM, + num_q_heads=NUM_Q_HEADS, + num_kv_heads=NUM_KV_HEADS, + num_layers=NUM_LAYERS, + ) + transformer.reset_parameters() + x = torch.randint(low=0, high=VOCAB_SIZE, size=(BATCH_SIZE, SEQ_LEN)) + transformer(x) + print(x) From a6ed891669aad02ccabd0a1ecdaffca238662eb4 Mon Sep 17 00:00:00 2001 From: Max Luebbering <2804731+le1nux@users.noreply.github.com> Date: Fri, 23 Jan 2026 10:41:20 +0100 Subject: [PATCH 4/6] feat: added einsum transformer collate fn and fsdp wrapping --- .../einsum_transformer/model/__init__.py | 0 .../einsum_transformer/model/fsdp_wrapping.py | 47 +++++++++++++++++++ 2 files changed, 47 insertions(+) create mode 100644 tutorials/einsum_transformer/model/__init__.py create mode 100644 tutorials/einsum_transformer/model/fsdp_wrapping.py diff --git a/tutorials/einsum_transformer/model/__init__.py b/tutorials/einsum_transformer/model/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/tutorials/einsum_transformer/model/fsdp_wrapping.py b/tutorials/einsum_transformer/model/fsdp_wrapping.py new file mode 100644 index 00000000..5cd3afd7 --- /dev/null +++ b/tutorials/einsum_transformer/model/fsdp_wrapping.py @@ -0,0 +1,47 @@ +from einsum_transformer import EinsumTransformer +from torch.distributed._composable.fsdp import MixedPrecisionPolicy, fully_shard +from torch.distributed.device_mesh import DeviceMesh +from torch.distributed.fsdp import FSDPModule as FSDP2 + +from modalities.running_env.env_utils import FSDP2MixedPrecisionSettings +from modalities.running_env.fsdp.device_mesh import ParallelismDegrees +from modalities.util import get_module_class_from_name + + +def get_fsdp2_wrapped_model( + model: EinsumTransformer, + block_names: list[str], + device_mesh: DeviceMesh, + mixed_precision_settings: FSDP2MixedPrecisionSettings, + reshard_after_forward: bool, +) -> FSDP2: + # map the block names to the actual block class (e.b., GPT2Block) + block_types = tuple([get_module_class_from_name(model, b) for b in block_names]) + + mp_policy = MixedPrecisionPolicy( + param_dtype=mixed_precision_settings.param_dtype.value, + reduce_dtype=mixed_precision_settings.reduce_dtype.value, + ) + # if DP_REPLICATE is not in the mesh, we apply full sharding and hybrid sharding otherwise + fsdp2_degrees = ( + (ParallelismDegrees.DP_REPLICATE.value, ParallelismDegrees.DP_SHARD.value) + if ParallelismDegrees.DP_REPLICATE.value in device_mesh.mesh_dim_names + else (ParallelismDegrees.DP_SHARD.value,) + ) + fsdp_config = {"mesh": device_mesh[fsdp2_degrees], "mp_policy": mp_policy} + + modules = list(model.modules()) + # we first shard all the blocks + for module_id, module in enumerate(modules): + if isinstance(module, block_types): + # As an optimization, we do not reshard after forward for the last + # transformer block since FSDP would prefetch it immediately. + reshard_block_after_forward = reshard_after_forward and int(module_id) < len(modules) - 1 + fully_shard( + module, + **fsdp_config, + reshard_after_forward=reshard_block_after_forward, + ) + # finally, we shard the entire model + fully_shard(model, **fsdp_config, reshard_after_forward=reshard_after_forward) + return model From c1825b84c825eb79852ee29c3b8a8e3007a55417 Mon Sep 17 00:00:00 2001 From: Max Luebbering <2804731+le1nux@users.noreply.github.com> Date: Fri, 23 Jan 2026 10:41:54 +0100 Subject: [PATCH 5/6] feat: added einsum transformer config --- .../einsum_transformer_config.yaml | 324 ++++++++++++++++++ 1 file changed, 324 insertions(+) create mode 100644 tutorials/einsum_transformer/einsum_transformer_config.yaml diff --git a/tutorials/einsum_transformer/einsum_transformer_config.yaml b/tutorials/einsum_transformer/einsum_transformer_config.yaml new file mode 100644 index 00000000..5771cb4a --- /dev/null +++ b/tutorials/einsum_transformer/einsum_transformer_config.yaml @@ -0,0 +1,324 @@ +settings: + experiment_id: ${modalities_env:experiment_id} + config_file_path: ${modalities_env:config_file_path} + referencing_keys: + sample_key: input_ids + target_key: target_ids + prediction_key: logits + cuda_env: + local_rank: ${cuda_env:LOCAL_RANK} + global_rank: ${cuda_env:RANK} + world_size: ${cuda_env:WORLD_SIZE} + paths: + experiments_root_path: ${modalities_env:experiments_root_path} + experiment_folder_path: ${settings.paths.experiments_root_path}/${settings.experiment_id} + train_dataset_path: /raid/s3/opengptx/max_lue/repositories/training_datasets/processed/fineweb_sample-100BT.pbin + intervals: + training_log_interval_in_steps: 1 + checkpointing_interval_in_steps: 1001 + evaluation_interval_in_steps: 1001 + consistency_enforcement: + enforce_tokens_per_step_consistency: true + enforce_last_step_logged: false + enforce_last_step_evaluated: false + enforce_last_step_checkpointed: false + step_profile: + gradient_accumulation_steps: 10 + local_train_micro_batch_size: 1 + sequence_length: 2048 + dp_degree: + instance_key: dp_degree + pass_type: BY_REFERENCE + training_target: + num_target_tokens: + component_key: number_conversion + variant_key: num_tokens_from_num_steps + config: + num_steps: ${settings.training_target.num_target_steps} + dp_degree: + instance_key: dp_degree + pass_type: BY_REFERENCE + local_micro_batch_size: ${settings.step_profile.local_train_micro_batch_size} + sequence_length: ${settings.step_profile.sequence_length} + gradient_accumulation_steps: ${settings.step_profile.gradient_accumulation_steps} + num_target_steps: 1000 + training_progress: + global_num_seen_tokens: 0 + num_seen_steps: 0 + num_seen_samples: 0 + last_step: -1 + +collate_fn: + component_key: collate_fn + variant_key: gpt_2_llm_collator + config: + sample_key: ${settings.referencing_keys.sample_key} + target_key: ${settings.referencing_keys.target_key} + +train_dataset: + component_key: dataset + variant_key: packed_mem_map_dataset_continuous + config: + raw_data_path: ${settings.paths.train_dataset_path} + sequence_length: ${settings.step_profile.sequence_length} + sample_key: ${settings.referencing_keys.sample_key} + +train_dataloader: + component_key: data_loader + variant_key: default + config: + # we set num_workers to 0 so that the the data is loaded in the main process + # this is required to track how often the collator has been called + # in the library tutorials. Otherwise the collator will be copied for each worker + # and the number of call is out of scope. + num_workers: 0 + pin_memory: true + dataloader_tag: train + dataset: + instance_key: train_dataset + pass_type: BY_REFERENCE + batch_sampler: + component_key: batch_sampler + variant_key: default + config: + batch_size: ${settings.step_profile.local_train_micro_batch_size} + drop_last: true + sampler: + component_key: sampler + variant_key: resumable_distributed_sampler + config: + dataset: + instance_key: train_dataset + pass_type: BY_REFERENCE + rank: ${settings.cuda_env.global_rank} + num_replicas: ${settings.cuda_env.world_size} + shuffle: true + seed: 42 + drop_last: true + skip_num_global_samples: ${settings.training_progress.num_seen_samples} + collate_fn: + instance_key: collate_fn + pass_type: BY_REFERENCE + +eval_dataloaders: [] + +checkpoint_saving: + component_key: checkpoint_saving + variant_key: default + config: + checkpoint_saving_strategy: + component_key: checkpoint_saving_strategy + variant_key: save_k_most_recent_checkpoints_strategy + config: + k: -1 # -1 to save all checkpoints + checkpoint_saving_execution: + component_key: checkpoint_saving_execution + variant_key: dcp + config: + checkpoint_path: ${settings.paths.experiment_folder_path} + global_rank: ${settings.cuda_env.global_rank} + experiment_id: ${settings.experiment_id} + +loss_fn: + component_key: loss + variant_key: clm_cross_entropy_loss + config: + target_key: ${settings.referencing_keys.target_key} + prediction_key: ${settings.referencing_keys.prediction_key} + +device_mesh: + component_key: device_mesh + variant_key: default + config: + device_type: cuda + data_parallel_replicate_degree: 1 + data_parallel_shard_degree: ${settings.cuda_env.world_size} # i.e., fully sharded + world_size: ${settings.cuda_env.world_size} + +dp_degree: + component_key: number_conversion + variant_key: parallel_degree + config: # get the parallel degree from the device mesh + device_mesh: + instance_key: device_mesh + pass_type: BY_REFERENCE + parallelism_methods: [dp_shard, dp_replicate] + +app_state: + component_key: app_state + variant_key: raw + config: + model: + instance_key: initialized_model + pass_type: BY_REFERENCE + optimizer: + instance_key: optimizer + pass_type: BY_REFERENCE + lr_scheduler: + instance_key: lr_scheduler + pass_type: BY_REFERENCE + +initialized_model: + component_key: model + variant_key: model_initialized + config: + model: + instance_key: fsdp_model + pass_type: BY_REFERENCE + model_initializer: + component_key: model_initialization + variant_key: composed + config: + model_type: gpt2 + weight_init_type: scaled + mean: 0.0 + std: 0.02 + num_layers: ${model_raw.config.num_layers} + +fsdp_model: + component_key: model + variant_key: fsdp2_wrapped + config: + model: + instance_key: compiled_model + pass_type: BY_REFERENCE + device_mesh: + instance_key: device_mesh + pass_type: BY_REFERENCE + mixed_precision_settings: + param_dtype: BF_16 + reduce_dtype: BF_16 + block_names: [TransformerBlock] + +compiled_model: + component_key: model + variant_key: compiled + config: + model: + instance_key: model_raw + pass_type: BY_REFERENCE + block_names: [TransformerBlock] + +model_raw: + component_key: model + variant_key: einsum_transformer + config: + vocab_size: 128256 + sequence_length: ${settings.step_profile.sequence_length} + embed_dim: 4096 + num_q_heads: 32 + num_kv_heads: 8 + num_layers: 32 + mlp_expansion_factor: 3.5 + prediction_key: ${settings.referencing_keys.prediction_key} + sample_key: ${settings.referencing_keys.sample_key} + +lr_scheduler: + component_key: scheduler + variant_key: onecycle_lr + config: + optimizer: + instance_key: optimizer + pass_type: BY_REFERENCE + max_lr: 6e-4 + div_factor: 10 + final_div_factor: 1 + total_steps: ${settings.training_target.num_target_steps} + pct_start: 0.01 + anneal_strategy: cos + last_epoch: ${settings.training_progress.last_step} + +optimizer: + component_key: optimizer + variant_key: adam_w + config: + lr: 0.0001 + betas: [0.9, 0.95] + eps: 1e-8 + weight_decay: 1e-1 + weight_decay_groups_excluded: [embedding, layernorm] + wrapped_model: + instance_key: initialized_model + pass_type: BY_REFERENCE + +gradient_clipper: + component_key: gradient_clipper + variant_key: fsdp2 + config: + wrapped_model: + instance_key: initialized_model + pass_type: BY_REFERENCE + norm_type: P2_NORM + max_norm: 1.0 + device_mesh: + instance_key: device_mesh + pass_type: BY_REFERENCE + +progress_subscriber: + component_key: progress_subscriber + variant_key: rich + config: + global_rank: ${settings.cuda_env.global_rank} + num_seen_steps: ${settings.training_progress.num_seen_steps} + num_target_steps: ${settings.training_target.num_target_steps} + train_dataloader_tag: ${train_dataloader.config.dataloader_tag} + eval_dataloaders: + instance_key: eval_dataloaders + pass_type: BY_REFERENCE + +evaluation_subscriber: + component_key: results_subscriber + variant_key: to_disc + config: + output_file_path: ${settings.paths.experiment_folder_path}/evaluation_results.jsonl + +mfu_calculator: + component_key: mfu_calculator + variant_key: gpt2 + config: + n_layer: ${model_raw.config.num_layers} + sequence_length: ${settings.step_profile.sequence_length} + n_embd: ${model_raw.config.embed_dim} + world_size: ${settings.cuda_env.world_size} + wrapped_model: + instance_key: initialized_model + pass_type: BY_REFERENCE + device_mesh: + instance_key: device_mesh + pass_type: BY_REFERENCE + +# profiler: +# component_key: steppable_profiler +# variant_key: combined +# config: +# profilers: +# - instance_key: kernel_profiler +# pass_type: BY_REFERENCE +# # - instance_key: memory_profiler +# # pass_type: BY_REFERENCE + +kernel_profiler: + component_key: steppable_profiler + variant_key: kernel_tracing + config: + num_wait_steps: 1 + num_warmup_steps: 1 + num_active_steps: 3 + profiler_activities: [CUDA] + profile_memory: true + record_shapes: true + with_stack: true + with_flops: true + with_modules: true + tracked_ranks: [0] + output_folder_path: ${settings.paths.experiment_folder_path}/profiling + +memory_profiler: + component_key: steppable_profiler + variant_key: memory_tracing + config: + memory_snapshot_folder_path: ${settings.paths.experiment_folder_path}/profiling + num_wait_steps: 1 + num_warmup_steps: 1 + num_active_steps: 3 + tracked_ranks: [0] \ No newline at end of file From 81392e34226488d816c6743eabdf8c68fede20b5 Mon Sep 17 00:00:00 2001 From: Max Luebbering <2804731+le1nux@users.noreply.github.com> Date: Fri, 23 Jan 2026 10:43:46 +0100 Subject: [PATCH 6/6] chore: added example dataset --- .../redpajama_v2_samples_512_train.idx | Bin 0 -> 5121 bytes .../redpajama_v2_samples_512_train.pbin | Bin 0 -> 1577814 bytes 2 files changed, 0 insertions(+), 0 deletions(-) create mode 100644 tutorials/einsum_transformer/data/mem_map/redpajama_v2_samples_512_train.idx create mode 100644 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