222 lines
9.5 KiB
Plaintext
222 lines
9.5 KiB
Plaintext
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "c8741a8f",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"EPOCH 1\n",
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"\tLoss: 7.424792\n",
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"[0] \n",
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"[1] \n",
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"[2] \n",
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"[3] \n",
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"[4] \n",
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"[5] \n",
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"[6] \n",
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"[7] \n",
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"[8] \n",
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"[9] \n"
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]
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}
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],
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"source": [
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"import random\n",
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"import torch\n",
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"import pandas as pd\n",
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"from pathlib import Path\n",
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"import Project_Model.Libs.Embedder as Embedder\n",
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"import Project_Model.Libs.BPE as BPE\n",
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"import Project_Model.Libs.Transformer as Transformer\n",
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"import Project_Model.Libs.TorchShims as torch_shims\n",
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"from Project_Model.Libs.Training.learning_rade_shedulers import Custom_lr\n",
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"\n",
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"import torch\n",
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"\n",
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"class LogitsCollector:\n",
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" def __init__(self, pad_token: int, end_token: int, tokenizer) -> None:\n",
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" self.__pad_token = pad_token # used to skip PAD\n",
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" self.__end_token = end_token # used to stop at END\n",
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" self.__tokenizer = tokenizer # exposes .decode(list[int]) -> str\n",
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" self.__steps: list[torch.Tensor] = [] # list of per-step logits [B,V]\n",
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"\n",
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" def reset(self) -> None:\n",
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" self.__steps.clear() # clear history\n",
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"\n",
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" def add(self, logits_step: torch.Tensor) -> None:\n",
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" if logits_step.dim() == 3: # handle [B,1,V]\n",
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" logits_step = logits_step[:, -1, :] # -> [B,V]\n",
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" self.__steps.append(logits_step.detach()) # store raw logits (detached)\n",
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"\n",
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" def tokens(self) -> list[list[int]]:\n",
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" if not self.__steps:\n",
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" return []\n",
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" stack = torch.stack(self.__steps, dim=0) # [T,B,V]\n",
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" probs = torch.softmax(stack, dim=-1) # softmax over vocab -> [T,B,V]\n",
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" ids = probs.argmax(dim=-1).transpose(0, 1) # greedy ids -> [B,T]\n",
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" out: list[list[int]] = []\n",
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" for row in ids.tolist():\n",
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" seq: list[int] = []\n",
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" for tok in row:\n",
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" if tok == self.__end_token: # stop on END\n",
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" break\n",
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" if tok == self.__pad_token: # skip PAD\n",
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" continue\n",
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" seq.append(tok)\n",
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" out.append(seq)\n",
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" return out\n",
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"\n",
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" def print_decoded(self) -> None:\n",
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" for i, seq in enumerate(self.tokens()):\n",
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" try:\n",
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" text = self.__tokenizer.decode(seq) # decode tokens to string\n",
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" except Exception:\n",
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" text = str(seq) # fallback to ids\n",
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" print(f\"[{i}] {text}\") # simple print\n",
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"\n",
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"\n",
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"# set a fixed seed\n",
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"torch.manual_seed(0)\n",
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"random.seed(0)\n",
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"DEVICE = torch_shims.get_default_device()\n",
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"torch.set_default_device(DEVICE)\n",
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"\n",
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"# BPE Init\n",
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"VOCABULARY_PATH = Path(\"Assets/Model/toy_10/toy_dictionary.json\")\n",
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"SPECIAL_VOC = BPE.default_special_tokens()\n",
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"\n",
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"VOCABULARY = BPE.load_nanos_vocabulary(VOCABULARY_PATH)\n",
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"TOKENANO = BPE.TokeNanoCore(VOCABULARY, SPECIAL_VOC)\n",
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"\n",
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"# Constants\n",
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"TOKEN_SPACE_SIZE = TOKENANO.vocabulary_size + 1\n",
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"EMBEDDED_SIZE = 256\n",
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"FEED_FORWARD_MULTIPLIER = 4\n",
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"ATTENTION_HEADS = 4\n",
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"SENTENCE_LENGTH = 256\n",
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"NUMBER_OF_BLOCKS = 2\n",
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"MAX_EPOCHS = int(1e3)\n",
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"\n",
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"PAD_TOKEN = TOKENANO.encode(\"<PAD>\")[0]\n",
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"END_TOKEN = TOKENANO.encode(\"<END>\")[0]\n",
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"\n",
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"# Load CSV\n",
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"TOY_DATASET_PATH = Path(\"Assets/Dataset/1-hop/toy/rdf_text.csv\")\n",
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"TOY_DATASET = pd.read_csv(TOY_DATASET_PATH)\n",
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"\n",
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"TOY_BATCH_INPUT_LIST: list[list[int]] = []\n",
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"TOY_BATCH_PADDING_LIST: list[list[bool]] = []\n",
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"TOY_BATCH_TARGET_LIST: list[list[int]] = []\n",
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"TOY_BATCH_DECODER_DEFAULT: list[list[int]] = []\n",
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"\n",
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"for index, row in TOY_DATASET.iterrows():\n",
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" RDFs: str = row[\"RDFs\"]\n",
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" Abstract: str = row[\"Abstract\"]\n",
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"\n",
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" input_tokens = TOKENANO.encode(RDFs) # encoder input ids\n",
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" output_tokens = TOKENANO.encode(Abstract)[1:] # decoder target ids (shifted left)\n",
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" decoder_default_tokens = TOKENANO.encode(\"<SOS>\") # decoder input starts with <SOS>\n",
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"\n",
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" input_tokens, padding = Transformer.normalize_sequence(\n",
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" input_tokens, SENTENCE_LENGTH, PAD_TOKEN, END_TOKEN\n",
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" ) # pad/trim + end token\n",
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" output_tokens, _ = Transformer.normalize_sequence(\n",
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" output_tokens, SENTENCE_LENGTH, PAD_TOKEN, END_TOKEN\n",
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" ) # pad/trim + end token\n",
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" decoder_default_tokens = Transformer.pad_sequence(\n",
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" decoder_default_tokens, SENTENCE_LENGTH, PAD_TOKEN\n",
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" ) # pad with PAD up to SENTENCE_LENGTH\n",
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"\n",
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" TOY_BATCH_INPUT_LIST.append(input_tokens)\n",
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" TOY_BATCH_PADDING_LIST.append(padding)\n",
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" TOY_BATCH_TARGET_LIST.append(output_tokens)\n",
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" TOY_BATCH_DECODER_DEFAULT.append(decoder_default_tokens)\n",
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"\n",
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"# Training loop\n",
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"LOSS_HISTORY = []\n",
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"NANOSOCRATES = Transformer.TrainingModel(\n",
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" TOKEN_SPACE_SIZE,\n",
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" EMBEDDED_SIZE,\n",
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" FEED_FORWARD_MULTIPLIER,\n",
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" ATTENTION_HEADS,\n",
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" NUMBER_OF_BLOCKS,\n",
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")\n",
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"\n",
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"collector = LogitsCollector(PAD_TOKEN, END_TOKEN, TOKENANO) # collects logits and decodes\n",
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"\n",
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"NANOSOCRATES.train()\n",
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"cross_entropy = torch.nn.CrossEntropyLoss(ignore_index=PAD_TOKEN)\n",
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"optimizer = torch.optim.AdamW(NANOSOCRATES.parameters())\n",
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"scheduler = Custom_lr(EMBEDDED_SIZE, 4000) # step each optimizer step\n",
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"\n",
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"current_epoch = 0\n",
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"BATCH_SIZE = min(32, len(TOY_BATCH_INPUT_LIST)) # small batch to stabilize\n",
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"\n",
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"while current_epoch < MAX_EPOCHS:\n",
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" # simple fixed mini-batch from the top; later you can shuffle/slice\n",
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" enc = torch.tensor(TOY_BATCH_INPUT_LIST[:BATCH_SIZE], dtype=torch.long) # [B,T] encoder token ids\n",
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" pad = torch.tensor(TOY_BATCH_PADDING_LIST[:BATCH_SIZE], dtype=torch.bool) # [B,T] True where encoder PAD is present\n",
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" tgt = torch.tensor(TOY_BATCH_TARGET_LIST[:BATCH_SIZE], dtype=torch.long) # [B,T] decoder targets (ground-truth)\n",
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"\n",
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" # decoder prefix buffer: <SOS> at pos 0, PAD elsewhere (no shift here) # we will fill it step by step\n",
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" dec = torch.tensor(TOY_BATCH_DECODER_DEFAULT[:BATCH_SIZE], dtype=torch.long) # [B,T]\n",
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"\n",
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" total_loss = 0.0\n",
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" collector.reset() # start fresh for this epoch\n",
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"\n",
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" T = tgt.size(1) # sequence length\n",
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" for t in range(T):\n",
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" optimizer.zero_grad(set_to_none=True) # clear grads for this token step\n",
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"\n",
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" prefix = dec[:, : t + 1] # [B, t+1] current decoder prefix\n",
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" dec_pad_mask = prefix.eq(PAD_TOKEN) # [B, t+1] True where PAD inside prefix\n",
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"\n",
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" # one-step logits given prefix (trainer model expects 4 args now)\n",
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" logits_t: torch.Tensor = NANOSOCRATES((enc, pad, prefix, dec_pad_mask)) # [B,V] logits for step t\n",
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" collector.add(logits_t) # store logits for decoding later\n",
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"\n",
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" loss_t = cross_entropy(logits_t, tgt[:, t]) # CE expects raw logits; PAD ignored\n",
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" loss_t.backward() # backprop for this step\n",
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" optimizer.step() # update params\n",
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" scheduler.step() # Noam/warmup: step per optimizer step\n",
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"\n",
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" total_loss = float(loss_t.detach()) # keep last step loss for logging\n",
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"\n",
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" # teacher forcing: reveal the correct token for next position\n",
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" if t < T - 1:\n",
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" dec[:, t + 1] = tgt[:, t] # write ground-truth into next slot\n",
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"\n",
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" current_epoch += 1\n",
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" print(f\"EPOCH {current_epoch}\\n\\tLoss: {total_loss:.6f}\") # simple log\n",
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" collector.print_decoded() # print decoded predictions for the batch\n"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "deep_learning",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.13.7"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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