194 lines
10 KiB
Plaintext
194 lines
10 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": 1,
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"id": "ddfb4457",
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"metadata": {},
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"outputs": [
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{
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"ename": "AssertionError",
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"evalue": "target id 3872 >= V (256). Fix TOKEN_SPACE_SIZE.",
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"output_type": "error",
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"traceback": [
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"\u001b[31m---------------------------------------------------------------------------\u001b[39m",
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"\u001b[31mAssertionError\u001b[39m Traceback (most recent call last)",
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"\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[1]\u001b[39m\u001b[32m, line 126\u001b[39m\n\u001b[32m 124\u001b[39m \u001b[38;5;66;03m# sanity guard (helps debug vocab mismatches fast)\u001b[39;00m\n\u001b[32m 125\u001b[39m max_seen = tgt[:, :Tp].max().item()\n\u001b[32m--> \u001b[39m\u001b[32m126\u001b[39m \u001b[38;5;28;01massert\u001b[39;00m max_seen < V \u001b[38;5;129;01mor\u001b[39;00m (tgt[:, :Tp] == PAD_TOKEN).all(), \\\n\u001b[32m 127\u001b[39m \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mtarget id \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mmax_seen\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m >= V (\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mV\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m). Fix TOKEN_SPACE_SIZE.\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 129\u001b[39m \u001b[38;5;66;03m# CE over all tokens produced so far (0..t). PAD is ignored by ignore_index\u001b[39;00m\n\u001b[32m 130\u001b[39m loss_t = cross_entropy(\n\u001b[32m 131\u001b[39m logits_btV.reshape(-\u001b[32m1\u001b[39m, V), \u001b[38;5;66;03m# [B*(t+1), V]\u001b[39;00m\n\u001b[32m 132\u001b[39m tgt[:, :Tp].reshape(-\u001b[32m1\u001b[39m) \u001b[38;5;66;03m# [B*(t+1)]\u001b[39;00m\n\u001b[32m 133\u001b[39m )\n",
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"\u001b[31mAssertionError\u001b[39m: target id 3872 >= V (256). Fix TOKEN_SPACE_SIZE."
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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 CustomLR\n",
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"from Project_Model.Libs.Training.logistic_collector import LogitsCollector # external collector\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 (TEMP size; will be corrected after dataset scan below)\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(1e4)\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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"# fix V to cover ALL ids (including specials) # <- important\n",
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"max_enc_id = max(max(row) for row in TOY_BATCH_INPUT_LIST) if TOY_BATCH_INPUT_LIST else 0\n",
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"max_tgt_id = max(max(row) for row in TOY_BATCH_TARGET_LIST) if TOY_BATCH_TARGET_LIST else 0\n",
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"TOKEN_SPACE_SIZE = max(TOKEN_SPACE_SIZE, max(PAD_TOKEN, END_TOKEN, max_enc_id, max_tgt_id) + 1)\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(), lr=1.0) # base lr works as factor\n",
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"scheduler = CustomLR(optimizer, EMBEDDED_SIZE, warmup_steps=4000, factor=1.0) # 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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" # skip all-PAD steps to avoid CE divide-by-zero late in the sequence\n",
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" if (tgt[:, t] == PAD_TOKEN).all(): # all PAD at this timestep\n",
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" break\n",
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"\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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" # now decoder returns all steps up to t -> [B, t+1, V]\n",
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" logits_btV: torch.Tensor = NANOSOCRATES((enc, pad, prefix, dec_pad_mask)) # full logits for learning\n",
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" collector.add(logits_btV) # collector will take the last step\n",
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"\n",
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" Tp = logits_btV.size(1) # t+1\n",
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" V = logits_btV.size(-1) # vocab size\n",
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"\n",
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" # sanity guard (helps debug vocab mismatches fast)\n",
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" max_seen = tgt[:, :Tp].max().item()\n",
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" assert max_seen < V or (tgt[:, :Tp] == PAD_TOKEN).all(), \\\n",
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" f\"target id {max_seen} >= V ({V}). Fix TOKEN_SPACE_SIZE.\"\n",
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"\n",
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" # CE over all tokens produced so far (0..t). PAD is ignored by ignore_index\n",
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" loss_t = cross_entropy(\n",
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" logits_btV.reshape(-1, V), # [B*(t+1), V]\n",
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" tgt[:, :Tp].reshape(-1) # [B*(t+1)]\n",
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" )\n",
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"\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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