doctor and model test

Playgrounds/doctor.py

@@ -0,0 +1,125 @@
+import random
+import torch
+import pandas as pd
+from pathlib import Path
+import Project_Model.Libs.Embedder as Embedder
+import Project_Model.Libs.BPE as BPE
+import Project_Model.Libs.Transformer as Transformer
+import Project_Model.Libs.TorchShims as torch_shims
+from Project_Model.Libs.Training.learning_rade_shedulers import Custom_lr
+from Project_Model.Libs.Training.logistic_collector import LogitsCollector  # import the external collector
+
+# set a fixed seed
+torch.manual_seed(0)
+random.seed(0)
+DEVICE = torch_shims.get_default_device()
+torch.set_default_device(DEVICE)
+
+# BPE Init
+VOCABULARY_PATH = Path("Assets/Model/toy_10/toy_dictionary.json")
+SPECIAL_VOC = BPE.default_special_tokens()
+
+VOCABULARY = BPE.load_nanos_vocabulary(VOCABULARY_PATH)
+TOKENANO = BPE.TokeNanoCore(VOCABULARY, SPECIAL_VOC)
+
+# Constants
+TOKEN_SPACE_SIZE = TOKENANO.vocabulary_size + 1
+EMBEDDED_SIZE = 256
+FEED_FORWARD_MULTIPLIER = 4
+ATTENTION_HEADS = 4
+SENTENCE_LENGTH = 256
+NUMBER_OF_BLOCKS = 2
+MAX_EPOCHS = int(1e3)
+
+PAD_TOKEN = TOKENANO.encode("<PAD>")[0]
+END_TOKEN = TOKENANO.encode("<END>")[0]
+
+# Load CSV
+TOY_DATASET_PATH = Path("Assets/Dataset/1-hop/toy/rdf_text.csv")
+TOY_DATASET = pd.read_csv(TOY_DATASET_PATH)
+
+TOY_BATCH_INPUT_LIST: list[list[int]] = []
+TOY_BATCH_PADDING_LIST: list[list[bool]] = []
+TOY_BATCH_TARGET_LIST: list[list[int]] = []
+TOY_BATCH_DECODER_DEFAULT: list[list[int]] = []
+
+for index, row in TOY_DATASET.iterrows():
+    RDFs: str = row["RDFs"]
+    Abstract: str = row["Abstract"]
+
+    input_tokens = TOKENANO.encode(RDFs)  # encoder input ids
+    output_tokens = TOKENANO.encode(Abstract)[1:]  # decoder target ids (shifted left)
+    decoder_default_tokens = TOKENANO.encode("<SOS>")  # decoder input starts with <SOS>
+
+    input_tokens, padding = Transformer.normalize_sequence(
+        input_tokens, SENTENCE_LENGTH, PAD_TOKEN, END_TOKEN
+    )  # pad/trim + end token
+    output_tokens, _ = Transformer.normalize_sequence(
+        output_tokens, SENTENCE_LENGTH, PAD_TOKEN, END_TOKEN
+    )  # pad/trim + end token
+    decoder_default_tokens = Transformer.pad_sequence(
+        decoder_default_tokens, SENTENCE_LENGTH, PAD_TOKEN
+    )  # pad with PAD up to SENTENCE_LENGTH
+
+    TOY_BATCH_INPUT_LIST.append(input_tokens)
+    TOY_BATCH_PADDING_LIST.append(padding)
+    TOY_BATCH_TARGET_LIST.append(output_tokens)
+    TOY_BATCH_DECODER_DEFAULT.append(decoder_default_tokens)
+
+# Training loop
+LOSS_HISTORY = []
+NANOSOCRATES = Transformer.TrainingModel(
+    TOKEN_SPACE_SIZE,
+    EMBEDDED_SIZE,
+    FEED_FORWARD_MULTIPLIER,
+    ATTENTION_HEADS,
+    NUMBER_OF_BLOCKS,
+)
+
+collector = LogitsCollector(PAD_TOKEN, END_TOKEN, TOKENANO)  # collects logits and decodes
+
+NANOSOCRATES.train()
+cross_entropy = torch.nn.CrossEntropyLoss(ignore_index=PAD_TOKEN)
+optimizer = torch.optim.AdamW(NANOSOCRATES.parameters())
+scheduler = Custom_lr(EMBEDDED_SIZE, 4000)  # step each optimizer step
+
+current_epoch = 0
+BATCH_SIZE = min(32, len(TOY_BATCH_INPUT_LIST))  # small batch to stabilize
+
+while current_epoch < MAX_EPOCHS:
+    # simple fixed mini-batch from the top; later you can shuffle/slice
+    enc = torch.tensor(TOY_BATCH_INPUT_LIST[:BATCH_SIZE], dtype=torch.long)        # [B,T] encoder token ids
+    pad = torch.tensor(TOY_BATCH_PADDING_LIST[:BATCH_SIZE], dtype=torch.bool)      # [B,T] True where encoder PAD is present
+    tgt = torch.tensor(TOY_BATCH_TARGET_LIST[:BATCH_SIZE], dtype=torch.long)       # [B,T] decoder targets (ground-truth)
+
+    # decoder prefix buffer: <SOS> at pos 0, PAD elsewhere (no shift here)        # we will fill it step by step
+    dec = torch.tensor(TOY_BATCH_DECODER_DEFAULT[:BATCH_SIZE], dtype=torch.long)   # [B,T]
+
+    total_loss = 0.0
+    collector.reset()  # start fresh for this epoch
+
+    T = tgt.size(1)  # sequence length
+    for t in range(T):
+        optimizer.zero_grad(set_to_none=True)                                       # clear grads for this token step
+
+        prefix = dec[:, : t + 1]                                                    # [B, t+1] current decoder prefix
+        dec_pad_mask = prefix.eq(PAD_TOKEN)                                         # [B, t+1] True where PAD inside prefix
+
+        # one-step logits given prefix (trainer model expects 4 args now)
+        logits_t: torch.Tensor = NANOSOCRATES((enc, pad, prefix, dec_pad_mask))     # [B,V] logits for step t
+        collector.add(logits_t)                                                     # store logits for decoding later
+
+        loss_t = cross_entropy(logits_t, tgt[:, t])                                 # CE expects raw logits; PAD ignored
+        loss_t.backward()                                                           # backprop for this step
+        optimizer.step()                                                            # update params
+        scheduler.step()                                                            # Noam/warmup: step per optimizer step
+
+        total_loss = float(loss_t.detach())                                         # keep last step loss for logging
+
+        # teacher forcing: reveal the correct token for next position
+        if t < T - 1:
+            dec[:, t + 1] = tgt[:, t]                                               # write ground-truth into next slot
+
+    current_epoch += 1
+    print(f"EPOCH {current_epoch}\n\tLoss: {total_loss:.6f}")                        # simple log
+    collector.print_decoded()                                                       # print decoded predictions for the batch

Playgrounds/encoder-decoder.ipynb

@@ -0,0 +1,308 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "7a311d4b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[7706, 290, 756, 4270, 7357, 115, 351, 1507, 1213, 410, 3382, 317, 497, 4740, 2784, 7712], [7706, 290, 756, 4270, 7357, 115, 351, 1507, 1213, 410, 3382, 317, 497, 4740, 2784, 7712], [7706, 290, 756, 4270, 7357, 115, 351, 1507, 1213, 410, 3382, 317, 497, 4740, 2784, 7712]]\n",
+      "3\n",
+      "Embedder Tensor: torch.Size([3, 16, 256])\n",
+      "Values:\n",
+      "tensor([[[-0.6981,  0.0804, -2.1672,  ...,  0.3919,  0.3341,  1.0794],\n",
+      "         [ 2.5818, -0.2308,  0.6001,  ..., -0.0500, -0.0408, -0.9852],\n",
+      "         [-0.6967,  0.8109,  1.3108,  ...,  2.1693,  1.4143, -0.1236],\n",
+      "         ...,\n",
+      "         [ 2.1226,  2.5695, -1.6178,  ..., -0.0652, -0.0802,  0.1103],\n",
+      "         [ 0.8770, -2.4782,  0.8536,  ...,  2.0471, -1.5702,  0.7387],\n",
+      "         [ 1.4284, -0.4654,  0.1394,  ...,  1.6520,  0.6728,  1.3851]],\n",
+      "\n",
+      "        [[-0.6981,  0.0804, -2.1672,  ...,  0.3919,  0.3341,  1.0794],\n",
+      "         [ 2.5818, -0.2308,  0.6001,  ..., -0.0500, -0.0408, -0.9852],\n",
+      "         [-0.6967,  0.8109,  1.3108,  ...,  2.1693,  1.4143, -0.1236],\n",
+      "         ...,\n",
+      "         [ 2.1226,  2.5695, -1.6178,  ..., -0.0652, -0.0802,  0.1103],\n",
+      "         [ 0.8770, -2.4782,  0.8536,  ...,  2.0471, -1.5702,  0.7387],\n",
+      "         [ 1.4284, -0.4654,  0.1394,  ...,  1.6520,  0.6728,  1.3851]],\n",
+      "\n",
+      "        [[-0.6981,  0.0804, -2.1672,  ...,  0.3919,  0.3341,  1.0794],\n",
+      "         [ 2.5818, -0.2308,  0.6001,  ..., -0.0500, -0.0408, -0.9852],\n",
+      "         [-0.6967,  0.8109,  1.3108,  ...,  2.1693,  1.4143, -0.1236],\n",
+      "         ...,\n",
+      "         [ 2.1226,  2.5695, -1.6178,  ..., -0.0652, -0.0802,  0.1103],\n",
+      "         [ 0.8770, -2.4782,  0.8536,  ...,  2.0471, -1.5702,  0.7387],\n",
+      "         [ 1.4284, -0.4654,  0.1394,  ...,  1.6520,  0.6728,  1.3851]]],\n",
+      "       grad_fn=<AddBackward0>)\n",
+      "ENCODER Tensor: torch.Size([3, 1, 256])\n",
+      "Values:\n",
+      "tensor([[[ 8.0069e-01,  4.0532e-01, -1.8316e+00, -1.3902e+00, -1.1784e+00,\n",
+      "           1.3667e+00, -9.7890e-01,  6.0696e-01, -1.4899e+00,  5.5765e-01,\n",
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+      "           5.9942e-01]],\n",
+      "\n",
+      "        [[ 1.2683e+00, -4.3200e-01, -1.3333e+00, -3.6705e-01, -5.8895e-01,\n",
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+      "          -1.7712e+00, -1.1628e+00,  9.6813e-01,  8.7314e-01, -9.8027e-01,\n",
+      "           6.9376e-01,  5.3878e-01, -1.6169e+00,  2.2860e-01, -6.2179e-01,\n",
+      "          -1.1043e-01, -3.9658e-01,  2.8712e-01,  8.2201e-02,  2.0888e-01,\n",
+      "          -5.9884e-01,  7.3092e-01,  6.9128e-01,  5.3977e-01, -1.5728e+00,\n",
+      "           1.6878e+00, -8.2669e-01, -9.8076e-01, -3.4203e-01,  4.6939e-02,\n",
+      "          -1.3158e-01,  2.1923e+00, -6.6483e-02, -4.0687e-01, -1.2715e+00,\n",
+      "          -8.1549e-01, -1.2047e+00,  1.3547e+00, -4.2072e-01,  1.1674e+00,\n",
+      "          -5.1421e-01,  1.3055e+00, -1.1277e+00,  1.8372e+00, -1.1215e+00,\n",
+      "           1.4797e+00,  2.8354e-01, -6.3974e-01, -1.2869e+00, -2.7897e-01,\n",
+      "          -1.0397e+00,  1.8622e-01, -5.0397e-02, -4.4865e-02, -7.6067e-01,\n",
+      "           1.7715e+00,  1.5040e+00, -2.6854e-01, -5.2802e-01, -5.3407e-01,\n",
+      "           2.0313e-02, -2.6276e-01, -7.0748e-01, -8.7328e-01, -3.4108e-01,\n",
+      "           1.4313e+00]],\n",
+      "\n",
+      "        [[ 7.7464e-01, -4.2187e-01, -2.0571e+00, -8.6709e-01, -1.5722e+00,\n",
+      "           4.9540e-01, -1.5270e+00,  1.0499e+00, -1.9579e+00, -2.5298e-02,\n",
+      "           4.3419e-01,  5.8822e-01,  1.3392e+00,  6.9604e-01, -9.7883e-01,\n",
+      "          -9.1354e-01, -9.1852e-01, -6.0951e-01, -6.6255e-02,  1.3907e+00,\n",
+      "          -6.2912e-01, -2.7524e-01,  1.9520e-02, -2.7154e-01,  1.5162e-01,\n",
+      "           1.3318e-02, -8.9196e-01,  9.0976e-01, -1.3544e+00,  2.4276e-01,\n",
+      "          -7.4038e-01,  9.7062e-01,  3.2011e-01,  3.4486e-01, -2.3374e+00,\n",
+      "           1.3311e+00, -3.1871e-02, -1.4468e+00, -1.5968e+00,  3.0418e-01,\n",
+      "          -7.7136e-01,  1.3427e+00, -1.2493e+00,  1.4114e+00, -1.2475e+00,\n",
+      "           7.0239e-01, -9.6120e-02, -4.4365e-01,  5.3238e-01, -1.4933e+00,\n",
+      "           5.4476e-01, -1.8490e-02, -5.9936e-01,  1.0878e+00, -1.8892e+00,\n",
+      "           1.2810e+00, -1.0747e+00,  5.3514e-01,  1.7422e-01,  1.1354e+00,\n",
+      "          -7.4837e-01,  4.0327e-01, -1.8950e+00, -7.2336e-01,  2.4441e-01,\n",
+      "          -1.3650e-01, -4.8344e-01,  3.3921e-02,  5.0889e-01, -1.3769e+00,\n",
+      "          -2.5907e-01, -2.7549e-01, -1.9128e-01,  1.9751e+00, -7.1191e-01,\n",
+      "           5.1910e-01,  1.0902e-01,  2.9995e-01, -3.5180e-01, -6.2139e-01,\n",
+      "           7.2905e-01, -5.3177e-01,  4.3340e-01,  1.0071e+00,  1.7586e+00,\n",
+      "          -3.9963e-01, -2.5139e-01, -9.4213e-01,  9.2847e-01,  1.1298e+00,\n",
+      "           7.8545e-01,  1.3188e+00,  3.7466e-01,  9.0773e-01, -4.0454e-02,\n",
+      "           1.3444e+00,  6.0301e-01,  8.9929e-02, -2.0754e+00,  4.8614e-01,\n",
+      "          -9.7160e-01,  8.2446e-01, -1.1813e+00, -9.6185e-01, -9.2922e-02,\n",
+      "           6.0154e-01,  1.6640e+00, -1.0461e+00,  1.5868e-01, -5.7239e-01,\n",
+      "          -6.2726e-01,  3.2848e-01,  5.9609e-01,  1.5563e+00, -4.0883e-01,\n",
+      "           4.4902e-01,  1.4004e+00,  2.2426e-01,  3.8314e-01, -2.0641e-01,\n",
+      "          -1.6465e-01, -6.4645e-01,  1.5772e-01,  6.8907e-01, -1.2703e+00,\n",
+      "           1.8914e-01, -6.2678e-01,  3.0179e-01,  1.2687e+00,  1.6849e+00,\n",
+      "           1.5690e+00,  1.0999e+00,  1.5820e+00, -6.4808e-01,  5.1003e-01,\n",
+      "          -1.6674e+00, -1.2224e+00,  1.9769e-01, -1.3883e-01,  1.2179e+00,\n",
+      "           1.2971e+00,  4.6259e-01, -5.8717e-01,  1.4532e+00, -1.0540e+00,\n",
+      "           2.8689e-01, -1.3895e+00,  1.4014e+00, -4.0430e-01, -2.6099e+00,\n",
+      "          -1.0293e+00, -1.1097e+00,  8.6266e-01, -1.0535e+00,  7.1789e-01,\n",
+      "           6.0642e-01, -1.2493e+00, -3.7762e-01, -4.1281e-02, -7.3049e-01,\n",
+      "          -7.2913e-04, -7.3122e-02, -2.3850e-01,  1.2546e+00,  1.8802e-01,\n",
+      "           1.3135e+00, -5.0367e-01,  1.2456e-01,  2.7475e+00, -1.2486e+00,\n",
+      "           1.4441e+00,  8.7469e-01, -5.6901e-01, -1.2145e-01,  3.1091e-01,\n",
+      "           1.9406e+00, -8.1891e-01,  3.1316e-02, -1.2867e+00,  8.0780e-01,\n",
+      "           7.0041e-01,  2.8903e-01, -1.6387e+00,  6.6553e-01, -1.3696e+00,\n",
+      "          -7.9454e-01,  3.3899e-01, -5.5822e-01, -8.1969e-01, -1.2410e-01,\n",
+      "          -3.7024e-01, -7.2536e-01,  7.5648e-01,  1.6899e+00, -1.7404e-01,\n",
+      "          -1.7191e+00, -7.2603e-01,  1.5046e+00,  8.3216e-01, -1.5304e+00,\n",
+      "          -1.8264e-01,  3.3451e-01, -5.6636e-02,  6.1099e-01, -9.8517e-01,\n",
+      "           4.4856e-01, -8.6275e-01,  6.9264e-02, -1.1572e+00,  2.3373e-01,\n",
+      "           5.9896e-01,  1.2384e-01,  1.0309e+00,  1.4273e+00, -8.4776e-01,\n",
+      "           2.6236e+00, -9.0133e-01, -4.0009e-01, -4.9727e-01,  3.7945e-01,\n",
+      "          -9.0712e-01,  1.5725e+00,  1.6298e-01,  1.1544e-01, -4.3125e-01,\n",
+      "          -8.7131e-01, -2.5880e-01,  2.9032e+00,  2.7154e-01,  1.3677e+00,\n",
+      "          -8.8544e-01,  5.6083e-01, -1.8256e+00,  9.4832e-01, -1.0762e+00,\n",
+      "           7.5421e-01,  6.5008e-01, -8.6361e-01, -1.4911e+00, -7.5930e-02,\n",
+      "          -1.6896e+00,  1.5223e-02, -1.5283e-01, -1.8741e+00,  1.1400e-01,\n",
+      "           1.8822e+00,  2.6615e+00,  2.1607e-01, -5.6243e-01,  3.6730e-01,\n",
+      "           4.0374e-01, -1.1973e+00, -5.3006e-01, -3.4750e-01, -4.4187e-01,\n",
+      "           7.4358e-01]]], grad_fn=<NativeLayerNormBackward0>)\n"
+     ]
+    }
+   ],
+   "source": [
+    "import random\n",
+    "import torch\n",
+    "from pathlib import Path\n",
+    "import Project_Model.Libs.Embedder as Embedder\n",
+    "import Project_Model.Libs.BPE as BPE\n",
+    "import Project_Model.Libs.Transformer as Transformer\n",
+    "\n",
+    "# set a fixed seed\n",
+    "torch.manual_seed(0)\n",
+    "random.seed(0)\n",
+    "\n",
+    "TEXT = (\n",
+    "    \"<ABS>The Dark Knight is a 2008 superhero film directed by Christopher Nolan,<SOTL>\"\n",
+    ")\n",
+    "OUT_TEXT = \"<START>\"\n",
+    "VOCABULARY_PATH = Path(\"Assets/Model/toy_10/toy_dictionary.json\")\n",
+    "SPECIAL_VOC = BPE.default_special_tokens()\n",
+    "\n",
+    "VOCABULARY = BPE.load_nanos_vocabulary(VOCABULARY_PATH)\n",
+    "TOKENANO = BPE.TokeNanoCore(VOCABULARY, SPECIAL_VOC)\n",
+    "\n",
+    "PAD_TOKEN = TOKENANO.encode(\"<PAD>\")[0]\n",
+    "END_TOKEN = TOKENANO.encode(\"<END>\")[0]\n",
+    "\n",
+    "ENCODER_INPUT = TOKENANO.encode(TEXT)\n",
+    "DECODER_INPUT = TOKENANO.encode(OUT_TEXT)\n",
+    "MAX_LEN = len(ENCODER_INPUT) + 1\n",
+    "\n",
+    "EN_IN, PAD_MASK = Transformer.normalize_sequence(ENCODER_INPUT, MAX_LEN, PAD_TOKEN, END_TOKEN)\n",
+    "DEC_IN, _ = Transformer.normalize_sequence(DECODER_INPUT, MAX_LEN, PAD_TOKEN, END_TOKEN)\n",
+    "BATCH_LEN = 3\n",
+    "\n",
+    "INPUT_TOKENIZATION = [\n",
+    "    EN_IN\n",
+    "] * BATCH_LEN\n",
+    "OUTPUT_TOKENIZATION = [\n",
+    "    DEC_IN\n",
+    "] * BATCH_LEN\n",
+    "\n",
+    "\n",
+    "print(INPUT_TOKENIZATION)\n",
+    "\n",
+    "TOKEN_SPACE_SIZE = TOKENANO.vocabulary_size\n",
+    "EMBEDDED_SIZE = 256\n",
+    "FEED_FORWARD_DIM = EMBEDDED_SIZE * 4\n",
+    "\n",
+    "EMBEDDER = Embedder.NanoSocratesEmbedder(TOKEN_SPACE_SIZE, EMBEDDED_SIZE)\n",
+    "encoder_tensor: torch.Tensor = EMBEDDER(INPUT_TOKENIZATION)\n",
+    "ENCODER = torch.nn.Sequential(\n",
+    "    Transformer.Encoder(EMBEDDED_SIZE, FEED_FORWARD_DIM, 4),\n",
+    "    Transformer.Encoder(EMBEDDED_SIZE, FEED_FORWARD_DIM, 4),\n",
+    "    Transformer.Encoder(EMBEDDED_SIZE, FEED_FORWARD_DIM, 4),\n",
+    "    Transformer.Encoder(EMBEDDED_SIZE, FEED_FORWARD_DIM, 4),\n",
+    "    Transformer.Encoder(EMBEDDED_SIZE, FEED_FORWARD_DIM, 4),\n",
+    "    Transformer.Encoder(EMBEDDED_SIZE, FEED_FORWARD_DIM, 4),\n",
+    ")\n",
+    "decoder_tensor: torch.Tensor = EMBEDDER(OUTPUT_TOKENIZATION)\n",
+    "DECODER = torch.nn.Sequential(\n",
+    "    Transformer.Decoder(EMBEDDED_SIZE, FEED_FORWARD_DIM, 4),\n",
+    "    Transformer.Decoder(EMBEDDED_SIZE, FEED_FORWARD_DIM, 4),\n",
+    "    Transformer.Decoder(EMBEDDED_SIZE, FEED_FORWARD_DIM, 4),\n",
+    "    Transformer.Decoder(EMBEDDED_SIZE, FEED_FORWARD_DIM, 4),\n",
+    "    Transformer.Decoder(EMBEDDED_SIZE, FEED_FORWARD_DIM, 4),\n",
+    "    Transformer.Decoder(EMBEDDED_SIZE, FEED_FORWARD_DIM, 4),\n",
+    ")\n",
+    "\n",
+    "print(len(INPUT_TOKENIZATION))\n",
+    "print(f\"Embedder Tensor: {encoder_tensor.shape}\")\n",
+    "print(f\"Values:\\n{encoder_tensor}\")\n",
+    "\n",
+    "BATCH_SIZE, TOKENS, DIMENSIONS = encoder_tensor.shape\n",
+    "PAD_MASK = torch.tensor([PAD_MASK] * BATCH_LEN)\n",
+    "\n",
+    "encoder_out, _ = ENCODER((encoder_tensor, PAD_MASK))\n",
+    "tensor: torch.Tensor\n",
+    "tensor, _, _, _ = DECODER((decoder_tensor, encoder_out, encoder_out, None))\n",
+    "\n",
+    "print(f\"ENCODER Tensor: {tensor.shape}\")\n",
+    "print(f\"Values:\\n{tensor}\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "deep_learning",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

Playgrounds/encoder.ipynb

@@ -0,0 +1,131 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "c64b0e24",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[7706, 290, 756, 4270, 7357, 115, 351, 1507, 1213, 410, 3382, 317, 497, 4740, 2784, 7700], [7706, 290, 756, 4270, 7357, 115, 351, 1507, 1213, 410, 3382, 317, 497, 4740, 2784, 7700]]\n",
+      "2\n",
+      "Embedder Tensor: torch.Size([2, 16, 256])\n",
+      "Values:\n",
+      "tensor([[[-0.6981,  0.0804, -2.1672,  ...,  0.3919,  0.3341,  1.0794],\n",
+      "         [ 2.5818, -0.2308,  0.6001,  ..., -0.0500, -0.0408, -0.9852],\n",
+      "         [-0.6967,  0.8109,  1.3108,  ...,  2.1693,  1.4143, -0.1236],\n",
+      "         ...,\n",
+      "         [ 2.1226,  2.5695, -1.6178,  ..., -0.0652, -0.0802,  0.1103],\n",
+      "         [ 0.8770, -2.4782,  0.8536,  ...,  2.0471, -1.5702,  0.7387],\n",
+      "         [-0.0495, -1.8601,  0.0405,  ...,  2.3944, -0.4297,  1.1141]],\n",
+      "\n",
+      "        [[-0.6981,  0.0804, -2.1672,  ...,  0.3919,  0.3341,  1.0794],\n",
+      "         [ 2.5818, -0.2308,  0.6001,  ..., -0.0500, -0.0408, -0.9852],\n",
+      "         [-0.6967,  0.8109,  1.3108,  ...,  2.1693,  1.4143, -0.1236],\n",
+      "         ...,\n",
+      "         [ 2.1226,  2.5695, -1.6178,  ..., -0.0652, -0.0802,  0.1103],\n",
+      "         [ 0.8770, -2.4782,  0.8536,  ...,  2.0471, -1.5702,  0.7387],\n",
+      "         [-0.0495, -1.8601,  0.0405,  ...,  2.3944, -0.4297,  1.1141]]],\n",
+      "       grad_fn=<AddBackward0>)\n",
+      "ENCODER Tensor: torch.Size([2, 16, 256])\n",
+      "Values:\n",
+      "tensor([[[-1.6325,  0.4094, -2.1403,  ...,  0.4654,  0.5993,  0.9683],\n",
+      "         [ 1.8236,  0.4025, -0.6972,  ...,  0.2430,  0.2536, -1.0889],\n",
+      "         [-0.0587,  0.1618, -0.2335,  ...,  1.7609,  1.2664, -0.4452],\n",
+      "         ...,\n",
+      "         [ 2.0337,  1.3184, -1.3165,  ..., -0.3303,  0.6572,  0.0884],\n",
+      "         [ 0.5752, -2.5594, -0.2393,  ...,  1.3318, -1.4236,  0.4686],\n",
+      "         [ 1.0075, -2.4273, -0.4593,  ...,  1.6660,  0.0359,  0.2927]],\n",
+      "\n",
+      "        [[-1.8300, -0.3079, -1.6585,  ...,  0.4859,  0.5652,  0.8072],\n",
+      "         [ 1.5461, -0.5666, -0.0330,  ...,  0.5651,  0.2974, -1.0879],\n",
+      "         [-0.9060,  0.2700, -0.4585,  ...,  2.0363,  1.2657, -0.7060],\n",
+      "         ...,\n",
+      "         [ 1.6688,  1.7038, -1.9549,  ..., -0.2052,  0.6270,  0.4598],\n",
+      "         [ 0.0482, -2.3951, -0.4351,  ...,  1.6230, -1.3662, -0.0390],\n",
+      "         [ 0.8146, -2.6169, -0.6188,  ...,  1.4525,  0.0507,  0.5177]]],\n",
+      "       grad_fn=<NativeLayerNormBackward0>)\n"
+     ]
+    }
+   ],
+   "source": [
+    "import random\n",
+    "import torch\n",
+    "from pathlib import Path\n",
+    "import Project_Model.Libs.Embedder as Embedder\n",
+    "import Project_Model.Libs.BPE as BPE\n",
+    "import Project_Model.Libs.Transformer as Transformer\n",
+    "\n",
+    "# set a fixed seed\n",
+    "torch.manual_seed(0)\n",
+    "random.seed(0)\n",
+    "\n",
+    "TEXT = \"<ABS>The Dark Knight is a 2008 superhero film directed by Christopher Nolan,<SOTL>\"\n",
+    "\n",
+    "VOCABULARY_PATH = Path(\"Assets/Model/toy_10/toy_dictionary.json\")\n",
+    "SPECIAL_VOC = BPE.default_special_tokens()\n",
+    "\n",
+    "VOCABULARY = BPE.load_nanos_vocabulary(VOCABULARY_PATH)\n",
+    "TOKENANO = BPE.TokeNanoCore(\n",
+    "    VOCABULARY,\n",
+    "    SPECIAL_VOC\n",
+    ")\n",
+    "\n",
+    "TOKENIZATION = [TOKENANO.encode(TEXT), TOKENANO.encode(TEXT)]\n",
+    "print(TOKENIZATION)\n",
+    "\n",
+    "TOKEN_SPACE_SIZE = TOKENANO.vocabulary_size\n",
+    "EMBEDDED_SIZE = 256\n",
+    "FEED_FORWARD_DIM = EMBEDDED_SIZE * 4\n",
+    "\n",
+    "EMBEDDER = Embedder.NanoSocratesEmbedder(TOKEN_SPACE_SIZE, EMBEDDED_SIZE)\n",
+    "tensor: torch.Tensor = EMBEDDER(TOKENIZATION)\n",
+    "ENCODER = torch.nn.Sequential(\n",
+    "    Transformer.Encoder(EMBEDDED_SIZE, FEED_FORWARD_DIM, 4),\n",
+    "    Transformer.Encoder(EMBEDDED_SIZE, FEED_FORWARD_DIM, 4),\n",
+    "    Transformer.Encoder(EMBEDDED_SIZE, FEED_FORWARD_DIM, 4),\n",
+    "    Transformer.Encoder(EMBEDDED_SIZE, FEED_FORWARD_DIM, 4),\n",
+    "    Transformer.Encoder(EMBEDDED_SIZE, FEED_FORWARD_DIM, 4),\n",
+    "    Transformer.Encoder(EMBEDDED_SIZE, FEED_FORWARD_DIM, 4),\n",
+    ")\n",
+    "print(len(TOKENIZATION))\n",
+    "print(f\"Embedder Tensor: {tensor.shape}\")\n",
+    "print(f\"Values:\\n{tensor}\")\n",
+    "\n",
+    "BATCH_SIZE, TOKENS, DIMENSIONS = tensor.shape\n",
+    "PAD_MASK = torch.tensor([[True] * TOKENS] * BATCH_SIZE, dtype=torch.bool)\n",
+    "tensor, _ = ENCODER((tensor, PAD_MASK))\n",
+    "print(f\"ENCODER Tensor: {tensor.shape}\")\n",
+    "print(f\"Values:\\n{tensor}\")\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "deep_learning",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

Playgrounds/locistic_test.ipynb

@@ -0,0 +1,221 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c8741a8f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "EPOCH 1\n",
+      "\tLoss: 7.424792\n",
+      "[0] \n",
+      "[1] \n",
+      "[2] \n",
+      "[3] \n",
+      "[4] \n",
+      "[5] \n",
+      "[6] \n",
+      "[7] \n",
+      "[8] \n",
+      "[9] \n"
+     ]
+    }
+   ],
+   "source": [
+    "import random\n",
+    "import torch\n",
+    "import pandas as pd\n",
+    "from pathlib import Path\n",
+    "import Project_Model.Libs.Embedder as Embedder\n",
+    "import Project_Model.Libs.BPE as BPE\n",
+    "import Project_Model.Libs.Transformer as Transformer\n",
+    "import Project_Model.Libs.TorchShims as torch_shims\n",
+    "from Project_Model.Libs.Training.learning_rade_shedulers import Custom_lr\n",
+    "\n",
+    "import torch\n",
+    "\n",
+    "class LogitsCollector:\n",
+    "    def __init__(self, pad_token: int, end_token: int, tokenizer) -> None:\n",
+    "        self.__pad_token = pad_token                      # used to skip PAD\n",
+    "        self.__end_token = end_token                      # used to stop at END\n",
+    "        self.__tokenizer = tokenizer                      # exposes .decode(list[int]) -> str\n",
+    "        self.__steps: list[torch.Tensor] = []             # list of per-step logits [B,V]\n",
+    "\n",
+    "    def reset(self) -> None:\n",
+    "        self.__steps.clear()                              # clear history\n",
+    "\n",
+    "    def add(self, logits_step: torch.Tensor) -> None:\n",
+    "        if logits_step.dim() == 3:                        # handle [B,1,V]\n",
+    "            logits_step = logits_step[:, -1, :]           # -> [B,V]\n",
+    "        self.__steps.append(logits_step.detach())         # store raw logits (detached)\n",
+    "\n",
+    "    def tokens(self) -> list[list[int]]:\n",
+    "        if not self.__steps:\n",
+    "            return []\n",
+    "        stack = torch.stack(self.__steps, dim=0)          # [T,B,V]\n",
+    "        probs = torch.softmax(stack, dim=-1)              # softmax over vocab -> [T,B,V]\n",
+    "        ids = probs.argmax(dim=-1).transpose(0, 1)        # greedy ids -> [B,T]\n",
+    "        out: list[list[int]] = []\n",
+    "        for row in ids.tolist():\n",
+    "            seq: list[int] = []\n",
+    "            for tok in row:\n",
+    "                if tok == self.__end_token:               # stop on END\n",
+    "                    break\n",
+    "                if tok == self.__pad_token:               # skip PAD\n",
+    "                    continue\n",
+    "                seq.append(tok)\n",
+    "            out.append(seq)\n",
+    "        return out\n",
+    "\n",
+    "    def print_decoded(self) -> None:\n",
+    "        for i, seq in enumerate(self.tokens()):\n",
+    "            try:\n",
+    "                text = self.__tokenizer.decode(seq)       # decode tokens to string\n",
+    "            except Exception:\n",
+    "                text = str(seq)                           # fallback to ids\n",
+    "            print(f\"[{i}] {text}\")                        # simple print\n",
+    "\n",
+    "\n",
+    "# set a fixed seed\n",
+    "torch.manual_seed(0)\n",
+    "random.seed(0)\n",
+    "DEVICE = torch_shims.get_default_device()\n",
+    "torch.set_default_device(DEVICE)\n",
+    "\n",
+    "# BPE Init\n",
+    "VOCABULARY_PATH = Path(\"Assets/Model/toy_10/toy_dictionary.json\")\n",
+    "SPECIAL_VOC = BPE.default_special_tokens()\n",
+    "\n",
+    "VOCABULARY = BPE.load_nanos_vocabulary(VOCABULARY_PATH)\n",
+    "TOKENANO = BPE.TokeNanoCore(VOCABULARY, SPECIAL_VOC)\n",
+    "\n",
+    "# Constants\n",
+    "TOKEN_SPACE_SIZE = TOKENANO.vocabulary_size + 1\n",
+    "EMBEDDED_SIZE = 256\n",
+    "FEED_FORWARD_MULTIPLIER = 4\n",
+    "ATTENTION_HEADS = 4\n",
+    "SENTENCE_LENGTH = 256\n",
+    "NUMBER_OF_BLOCKS = 2\n",
+    "MAX_EPOCHS = int(1e3)\n",
+    "\n",
+    "PAD_TOKEN = TOKENANO.encode(\"<PAD>\")[0]\n",
+    "END_TOKEN = TOKENANO.encode(\"<END>\")[0]\n",
+    "\n",
+    "# Load CSV\n",
+    "TOY_DATASET_PATH = Path(\"Assets/Dataset/1-hop/toy/rdf_text.csv\")\n",
+    "TOY_DATASET = pd.read_csv(TOY_DATASET_PATH)\n",
+    "\n",
+    "TOY_BATCH_INPUT_LIST: list[list[int]] = []\n",
+    "TOY_BATCH_PADDING_LIST: list[list[bool]] = []\n",
+    "TOY_BATCH_TARGET_LIST: list[list[int]] = []\n",
+    "TOY_BATCH_DECODER_DEFAULT: list[list[int]] = []\n",
+    "\n",
+    "for index, row in TOY_DATASET.iterrows():\n",
+    "    RDFs: str = row[\"RDFs\"]\n",
+    "    Abstract: str = row[\"Abstract\"]\n",
+    "\n",
+    "    input_tokens = TOKENANO.encode(RDFs)  # encoder input ids\n",
+    "    output_tokens = TOKENANO.encode(Abstract)[1:]  # decoder target ids (shifted left)\n",
+    "    decoder_default_tokens = TOKENANO.encode(\"<SOS>\")  # decoder input starts with <SOS>\n",
+    "\n",
+    "    input_tokens, padding = Transformer.normalize_sequence(\n",
+    "        input_tokens, SENTENCE_LENGTH, PAD_TOKEN, END_TOKEN\n",
+    "    )  # pad/trim + end token\n",
+    "    output_tokens, _ = Transformer.normalize_sequence(\n",
+    "        output_tokens, SENTENCE_LENGTH, PAD_TOKEN, END_TOKEN\n",
+    "    )  # pad/trim + end token\n",
+    "    decoder_default_tokens = Transformer.pad_sequence(\n",
+    "        decoder_default_tokens, SENTENCE_LENGTH, PAD_TOKEN\n",
+    "    )  # pad with PAD up to SENTENCE_LENGTH\n",
+    "\n",
+    "    TOY_BATCH_INPUT_LIST.append(input_tokens)\n",
+    "    TOY_BATCH_PADDING_LIST.append(padding)\n",
+    "    TOY_BATCH_TARGET_LIST.append(output_tokens)\n",
+    "    TOY_BATCH_DECODER_DEFAULT.append(decoder_default_tokens)\n",
+    "\n",
+    "# Training loop\n",
+    "LOSS_HISTORY = []\n",
+    "NANOSOCRATES = Transformer.TrainingModel(\n",
+    "    TOKEN_SPACE_SIZE,\n",
+    "    EMBEDDED_SIZE,\n",
+    "    FEED_FORWARD_MULTIPLIER,\n",
+    "    ATTENTION_HEADS,\n",
+    "    NUMBER_OF_BLOCKS,\n",
+    ")\n",
+    "\n",
+    "collector = LogitsCollector(PAD_TOKEN, END_TOKEN, TOKENANO)  # collects logits and decodes\n",
+    "\n",
+    "NANOSOCRATES.train()\n",
+    "cross_entropy = torch.nn.CrossEntropyLoss(ignore_index=PAD_TOKEN)\n",
+    "optimizer = torch.optim.AdamW(NANOSOCRATES.parameters())\n",
+    "scheduler = Custom_lr(EMBEDDED_SIZE, 4000)  # step each optimizer step\n",
+    "\n",
+    "current_epoch = 0\n",
+    "BATCH_SIZE = min(32, len(TOY_BATCH_INPUT_LIST))  # small batch to stabilize\n",
+    "\n",
+    "while current_epoch < MAX_EPOCHS:\n",
+    "    # simple fixed mini-batch from the top; later you can shuffle/slice\n",
+    "    enc = torch.tensor(TOY_BATCH_INPUT_LIST[:BATCH_SIZE], dtype=torch.long)        # [B,T] encoder token ids\n",
+    "    pad = torch.tensor(TOY_BATCH_PADDING_LIST[:BATCH_SIZE], dtype=torch.bool)      # [B,T] True where encoder PAD is present\n",
+    "    tgt = torch.tensor(TOY_BATCH_TARGET_LIST[:BATCH_SIZE], dtype=torch.long)       # [B,T] decoder targets (ground-truth)\n",
+    "\n",
+    "    # decoder prefix buffer: <SOS> at pos 0, PAD elsewhere (no shift here)        # we will fill it step by step\n",
+    "    dec = torch.tensor(TOY_BATCH_DECODER_DEFAULT[:BATCH_SIZE], dtype=torch.long)   # [B,T]\n",
+    "\n",
+    "    total_loss = 0.0\n",
+    "    collector.reset()  # start fresh for this epoch\n",
+    "\n",
+    "    T = tgt.size(1)  # sequence length\n",
+    "    for t in range(T):\n",
+    "        optimizer.zero_grad(set_to_none=True)                                       # clear grads for this token step\n",
+    "\n",
+    "        prefix = dec[:, : t + 1]                                                    # [B, t+1] current decoder prefix\n",
+    "        dec_pad_mask = prefix.eq(PAD_TOKEN)                                         # [B, t+1] True where PAD inside prefix\n",
+    "\n",
+    "        # one-step logits given prefix (trainer model expects 4 args now)\n",
+    "        logits_t: torch.Tensor = NANOSOCRATES((enc, pad, prefix, dec_pad_mask))     # [B,V] logits for step t\n",
+    "        collector.add(logits_t)                                                     # store logits for decoding later\n",
+    "\n",
+    "        loss_t = cross_entropy(logits_t, tgt[:, t])                                 # CE expects raw logits; PAD ignored\n",
+    "        loss_t.backward()                                                           # backprop for this step\n",
+    "        optimizer.step()                                                            # update params\n",
+    "        scheduler.step()                                                            # Noam/warmup: step per optimizer step\n",
+    "\n",
+    "        total_loss = float(loss_t.detach())                                         # keep last step loss for logging\n",
+    "\n",
+    "        # teacher forcing: reveal the correct token for next position\n",
+    "        if t < T - 1:\n",
+    "            dec[:, t + 1] = tgt[:, t]                                               # write ground-truth into next slot\n",
+    "\n",
+    "    current_epoch += 1\n",
+    "    print(f\"EPOCH {current_epoch}\\n\\tLoss: {total_loss:.6f}\")                        # simple log\n",
+    "    collector.print_decoded()                                                       # print decoded predictions for the batch\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "deep_learning",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

Playgrounds/model-teacher-forcing.ipynb

@@ -0,0 +1,205 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0afbf498",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "EPOCH 1\n",
+      "\tLoss: 9.174470901489258\n",
+      "EPOCH 2\n",
+      "\tLoss: 9.20919132232666\n",
+      "EPOCH 3\n",
+      "\tLoss: 9.227106094360352\n",
+      "EPOCH 4\n",
+      "\tLoss: 9.172086715698242\n",
+      "EPOCH 5\n",
+      "\tLoss: 9.180150985717773\n"
+     ]
+    },
+    {
+     "ename": "KeyboardInterrupt",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
+      "\u001b[31mKeyboardInterrupt\u001b[39m                         Traceback (most recent call last)",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[1]\u001b[39m\u001b[32m, line 116\u001b[39m\n\u001b[32m    113\u001b[39m step_target = target_logits[:, i]             \u001b[38;5;66;03m# [B]\u001b[39;00m\n\u001b[32m    115\u001b[39m loss = cross_entropy(step_logits,step_target) \u001b[38;5;66;03m# now loss is without softmax\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m116\u001b[39m \u001b[43mloss\u001b[49m\u001b[43m.\u001b[49m\u001b[43mbackward\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m  \u001b[38;5;66;03m# DAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAMN\u001b[39;00m\n\u001b[32m    117\u001b[39m last_loss = loss\n\u001b[32m    118\u001b[39m optimizer.step()\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/deep_learning/lib/python3.13/site-packages/torch/_tensor.py:638\u001b[39m, in \u001b[36mTensor.backward\u001b[39m\u001b[34m(self, gradient, retain_graph, create_graph, inputs)\u001b[39m\n\u001b[32m    595\u001b[39m \u001b[38;5;250m\u001b[39m\u001b[33mr\u001b[39m\u001b[33;03m\"\"\"Computes the gradient of current tensor wrt graph leaves.\u001b[39;00m\n\u001b[32m    596\u001b[39m \n\u001b[32m    597\u001b[39m \u001b[33;03mThe graph is differentiated using the chain rule. If the tensor is\u001b[39;00m\n\u001b[32m   (...)\u001b[39m\u001b[32m    635\u001b[39m \u001b[33;03m        used to compute the :attr:`tensors`. Defaults to ``None``.\u001b[39;00m\n\u001b[32m    636\u001b[39m \u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m    637\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m has_torch_function_unary(\u001b[38;5;28mself\u001b[39m):\n\u001b[32m--> \u001b[39m\u001b[32m638\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mhandle_torch_function\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m    639\u001b[39m \u001b[43m        \u001b[49m\u001b[43mTensor\u001b[49m\u001b[43m.\u001b[49m\u001b[43mbackward\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    640\u001b[39m \u001b[43m        \u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    641\u001b[39m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m    642\u001b[39m \u001b[43m        \u001b[49m\u001b[43mgradient\u001b[49m\u001b[43m=\u001b[49m\u001b[43mgradient\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    643\u001b[39m \u001b[43m        \u001b[49m\u001b[43mretain_graph\u001b[49m\u001b[43m=\u001b[49m\u001b[43mretain_graph\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    644\u001b[39m \u001b[43m        \u001b[49m\u001b[43mcreate_graph\u001b[49m\u001b[43m=\u001b[49m\u001b[43mcreate_graph\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    645\u001b[39m \u001b[43m        \u001b[49m\u001b[43minputs\u001b[49m\u001b[43m=\u001b[49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    646\u001b[39m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    647\u001b[39m torch.autograd.backward(\n\u001b[32m    648\u001b[39m     \u001b[38;5;28mself\u001b[39m, gradient, retain_graph, create_graph, inputs=inputs\n\u001b[32m    649\u001b[39m )\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/deep_learning/lib/python3.13/site-packages/torch/overrides.py:1725\u001b[39m, in \u001b[36mhandle_torch_function\u001b[39m\u001b[34m(public_api, relevant_args, *args, **kwargs)\u001b[39m\n\u001b[32m   1721\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m _is_torch_function_mode_enabled():\n\u001b[32m   1722\u001b[39m     \u001b[38;5;66;03m# if we're here, the mode must be set to a TorchFunctionStackMode\u001b[39;00m\n\u001b[32m   1723\u001b[39m     \u001b[38;5;66;03m# this unsets it and calls directly into TorchFunctionStackMode's torch function\u001b[39;00m\n\u001b[32m   1724\u001b[39m     \u001b[38;5;28;01mwith\u001b[39;00m _pop_mode_temporarily() \u001b[38;5;28;01mas\u001b[39;00m mode:\n\u001b[32m-> \u001b[39m\u001b[32m1725\u001b[39m         result = \u001b[43mmode\u001b[49m\u001b[43m.\u001b[49m\u001b[43m__torch_function__\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpublic_api\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtypes\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m   1726\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m result \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mNotImplemented\u001b[39m:\n\u001b[32m   1727\u001b[39m         \u001b[38;5;28;01mreturn\u001b[39;00m result\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/deep_learning/lib/python3.13/site-packages/torch/utils/_device.py:103\u001b[39m, in \u001b[36mDeviceContext.__torch_function__\u001b[39m\u001b[34m(self, func, types, args, kwargs)\u001b[39m\n\u001b[32m    101\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m func \u001b[38;5;129;01min\u001b[39;00m _device_constructors() \u001b[38;5;129;01mand\u001b[39;00m kwargs.get(\u001b[33m\"\u001b[39m\u001b[33mdevice\u001b[39m\u001b[33m\"\u001b[39m) \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m    102\u001b[39m     kwargs[\u001b[33m\"\u001b[39m\u001b[33mdevice\u001b[39m\u001b[33m\"\u001b[39m] = \u001b[38;5;28mself\u001b[39m.device\n\u001b[32m--> \u001b[39m\u001b[32m103\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/deep_learning/lib/python3.13/site-packages/torch/_tensor.py:647\u001b[39m, in \u001b[36mTensor.backward\u001b[39m\u001b[34m(self, gradient, retain_graph, create_graph, inputs)\u001b[39m\n\u001b[32m    637\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m has_torch_function_unary(\u001b[38;5;28mself\u001b[39m):\n\u001b[32m    638\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m handle_torch_function(\n\u001b[32m    639\u001b[39m         Tensor.backward,\n\u001b[32m    640\u001b[39m         (\u001b[38;5;28mself\u001b[39m,),\n\u001b[32m   (...)\u001b[39m\u001b[32m    645\u001b[39m         inputs=inputs,\n\u001b[32m    646\u001b[39m     )\n\u001b[32m--> \u001b[39m\u001b[32m647\u001b[39m \u001b[43mtorch\u001b[49m\u001b[43m.\u001b[49m\u001b[43mautograd\u001b[49m\u001b[43m.\u001b[49m\u001b[43mbackward\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m    648\u001b[39m \u001b[43m    \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgradient\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mretain_graph\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcreate_graph\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minputs\u001b[49m\u001b[43m=\u001b[49m\u001b[43minputs\u001b[49m\n\u001b[32m    649\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/deep_learning/lib/python3.13/site-packages/torch/autograd/__init__.py:354\u001b[39m, in \u001b[36mbackward\u001b[39m\u001b[34m(tensors, grad_tensors, retain_graph, create_graph, grad_variables, inputs)\u001b[39m\n\u001b[32m    349\u001b[39m     retain_graph = create_graph\n\u001b[32m    351\u001b[39m \u001b[38;5;66;03m# The reason we repeat the same comment below is that\u001b[39;00m\n\u001b[32m    352\u001b[39m \u001b[38;5;66;03m# some Python versions print out the first line of a multi-line function\u001b[39;00m\n\u001b[32m    353\u001b[39m \u001b[38;5;66;03m# calls in the traceback and some print out the last line\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m354\u001b[39m \u001b[43m_engine_run_backward\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m    355\u001b[39m \u001b[43m    \u001b[49m\u001b[43mtensors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    356\u001b[39m \u001b[43m    \u001b[49m\u001b[43mgrad_tensors_\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    357\u001b[39m \u001b[43m    \u001b[49m\u001b[43mretain_graph\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    358\u001b[39m \u001b[43m    \u001b[49m\u001b[43mcreate_graph\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    359\u001b[39m \u001b[43m    \u001b[49m\u001b[43minputs_tuple\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    360\u001b[39m \u001b[43m    \u001b[49m\u001b[43mallow_unreachable\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[32m    361\u001b[39m \u001b[43m    \u001b[49m\u001b[43maccumulate_grad\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[32m    362\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/deep_learning/lib/python3.13/site-packages/torch/autograd/graph.py:829\u001b[39m, in \u001b[36m_engine_run_backward\u001b[39m\u001b[34m(t_outputs, *args, **kwargs)\u001b[39m\n\u001b[32m    827\u001b[39m     unregister_hooks = _register_logging_hooks_on_whole_graph(t_outputs)\n\u001b[32m    828\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m829\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mVariable\u001b[49m\u001b[43m.\u001b[49m\u001b[43m_execution_engine\u001b[49m\u001b[43m.\u001b[49m\u001b[43mrun_backward\u001b[49m\u001b[43m(\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# Calls into the C++ engine to run the backward pass\u001b[39;49;00m\n\u001b[32m    830\u001b[39m \u001b[43m        \u001b[49m\u001b[43mt_outputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\n\u001b[32m    831\u001b[39m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m  \u001b[38;5;66;03m# Calls into the C++ engine to run the backward pass\u001b[39;00m\n\u001b[32m    832\u001b[39m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[32m    833\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m attach_logging_hooks:\n",
+      "\u001b[31mKeyboardInterrupt\u001b[39m: "
+     ]
+    }
+   ],
+   "source": [
+    "import random\n",
+    "import torch\n",
+    "import pandas as pd\n",
+    "from pathlib import Path\n",
+    "import Project_Model.Libs.Embedder as Embedder\n",
+    "import Project_Model.Libs.BPE as BPE\n",
+    "import Project_Model.Libs.Transformer as Transformer\n",
+    "import Project_Model.Libs.TorchShims as torch_shims\n",
+    "\n",
+    "# set a fixed seed\n",
+    "torch.manual_seed(0)\n",
+    "random.seed(0)\n",
+    "DEVICE = torch_shims.get_default_device()\n",
+    "torch.set_default_device(DEVICE)\n",
+    "\n",
+    "# set a default device\n",
+    "\n",
+    "# BPE Init\n",
+    "VOCABULARY_PATH = Path(\"Assets/Model/toy_10/toy_dictionary.json\")\n",
+    "SPECIAL_VOC = BPE.default_special_tokens()\n",
+    "\n",
+    "VOCABULARY = BPE.load_nanos_vocabulary(VOCABULARY_PATH)\n",
+    "TOKENANO = BPE.TokeNanoCore(VOCABULARY, SPECIAL_VOC)\n",
+    "\n",
+    "\n",
+    "# Constants\n",
+    "TOKEN_SPACE_SIZE = TOKENANO.vocabulary_size + 1\n",
+    "EMBEDDED_SIZE = 256\n",
+    "FEED_FORWARD_MULTIPLIER =  4\n",
+    "ATTENTION_HEADS = 4\n",
+    "SENTENCE_LENGTH = 256\n",
+    "NUMBER_OF_BLOCKS = 2\n",
+    "MAX_EPOCHS = int(1e3)\n",
+    "\n",
+    "\n",
+    "PAD_TOKEN = TOKENANO.encode(\"<PAD>\")[0]\n",
+    "END_TOKEN = TOKENANO.encode(\"<END>\")[0]\n",
+    "\n",
+    "\n",
+    "# Load CSV\n",
+    "TOY_DATASET_PATH = Path(\"Assets/Dataset/1-hop/toy/rdf_text.csv\")\n",
+    "\n",
+    "TOY_DATASET = pd.read_csv(TOY_DATASET_PATH)\n",
+    "\n",
+    "TOY_BATCH_INPUT_LIST: list[list[int]] = []\n",
+    "TOY_BATCH_PADDING_LIST: list[list[bool]] = []\n",
+    "TOY_BATCH_TARGET_LIST: list[list[int]] = []\n",
+    "TOY_BATCH_DECODER_DEFAULT: list[list[int]]= []\n",
+    "\n",
+    "\n",
+    "for index, row in TOY_DATASET.iterrows():\n",
+    "\n",
+    "    RDFs: str = row[\"RDFs\"]\n",
+    "    Abstract: str = row[\"Abstract\"]\n",
+    "\n",
+    "    input_tokens = TOKENANO.encode(RDFs)\n",
+    "    output_tokens = TOKENANO.encode(Abstract)[1:]\n",
+    "    decoder_default_tokens = TOKENANO.encode(\"<SOS>\")\n",
+    "\n",
+    "    input_tokens, padding = Transformer.normalize_sequence(\n",
+    "        input_tokens, SENTENCE_LENGTH, PAD_TOKEN, END_TOKEN\n",
+    "    )\n",
+    "    output_tokens, _ = Transformer.normalize_sequence(\n",
+    "        output_tokens, SENTENCE_LENGTH, PAD_TOKEN, END_TOKEN\n",
+    "    )\n",
+    "    decoder_default_tokens, _ = Transformer.normalize_sequence(\n",
+    "        decoder_default_tokens, SENTENCE_LENGTH, PAD_TOKEN, END_TOKEN\n",
+    "    )\n",
+    "\n",
+    "    TOY_BATCH_INPUT_LIST.append(input_tokens)\n",
+    "    TOY_BATCH_PADDING_LIST.append(padding)\n",
+    "    TOY_BATCH_TARGET_LIST.append(output_tokens)\n",
+    "    TOY_BATCH_DECODER_DEFAULT.append(decoder_default_tokens)\n",
+    "\n",
+    "# Training loop\n",
+    "LOSS_HISTORY = []\n",
+    "NANOSOCRATES = Transformer.TrainingModel(\n",
+    "    TOKEN_SPACE_SIZE,\n",
+    "    EMBEDDED_SIZE,\n",
+    "    FEED_FORWARD_MULTIPLIER,\n",
+    "    ATTENTION_HEADS,\n",
+    "    NUMBER_OF_BLOCKS\n",
+    ")\n",
+    "\n",
+    "NANOSOCRATES.train() # nothing important, activates dropout etc \n",
+    "cross_entropy = torch.nn.CrossEntropyLoss(ignore_index=PAD_TOKEN)\n",
+    "optimizer = torch.optim.AdamW(NANOSOCRATES.parameters())\n",
+    "scheduler = torch.optim.lr_scheduler.StepLR(optimizer, 4)\n",
+    "\n",
+    "last_loss = 0\n",
+    "\n",
+    "current_epoch = 0\n",
+    "while current_epoch < MAX_EPOCHS:\n",
+    "\n",
+    "    encoder_list = torch.tensor([TOY_BATCH_INPUT_LIST[0]])\n",
+    "    decoder_list = torch.tensor([TOY_BATCH_DECODER_DEFAULT[0]])\n",
+    "    padding_list = torch.tensor([TOY_BATCH_PADDING_LIST[0]], dtype=torch.bool)\n",
+    "    target_logits = torch.tensor([TOY_BATCH_TARGET_LIST[0]]) # Transform target into logits\n",
+    "\n",
+    "    optimizer.zero_grad() # to clear gradient\n",
+    "\n",
+    "    last_loss = 0.0\n",
+    "\n",
+    "    for i in range(0, SENTENCE_LENGTH):\n",
+    "\n",
+    "        # optimizer.zero_grad()\n",
+    "        # forward \n",
+    "        logits: torch.Tensor = NANOSOCRATES((encoder_list, padding_list, decoder_list))\n",
+    "        # probabilities = torch.softmax(logits,2)\n",
+    "        \n",
+    "\n",
+    "        step_logits = logits[:, i, :]                 # [B, V]\n",
+    "        step_target = target_logits[:, i]             # [B]\n",
+    "\n",
+    "        loss = cross_entropy(step_logits,step_target) # now loss is without softmax\n",
+    "        loss.backward()  # DAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAMN\n",
+    "        last_loss = loss\n",
+    "        optimizer.step()\n",
+    "        optimizer.zero_grad()\n",
+    "        scheduler.step()\n",
+    "        \n",
+    "        probabilities = torch.softmax(logits,2)\n",
+    "        most_probable_tokens = torch.argmax(probabilities, 2) \n",
+    "        if i < SENTENCE_LENGTH - 1:\n",
+    "            decoder_list[:,i+1] = most_probable_tokens[:,i]\n",
+    "\n",
+    "\n",
+    "    current_epoch += 1\n",
+    "\n",
+    "    if current_epoch % 1 == 0:\n",
+    "        print(f\"EPOCH {current_epoch}\\n\\tLoss: {last_loss}\")\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "deep_learning",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

Playgrounds/nanosocrates-sanity-check.ipynb

@@ -0,0 +1,157 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f5762da9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([3, 17, 7714])\n",
+      "torch.Size([3, 17])\n",
+      "tensor([[2034, 6523, 5406, 3985, 5406, 6523, 2034, 2034, 5745,  643, 5406, 7405,\n",
+      "         6523, 6230, 6419, 5745,  657],\n",
+      "        [2458,  830, 5745, 5745, 5406, 3741, 2034, 5745, 6302, 6419, 5406, 2411,\n",
+      "          719,  830, 5745, 3189, 2775],\n",
+      "        [2034, 5745, 5327, 4696, 6523,  643, 6419, 1671, 6302, 4406, 5745,  643,\n",
+      "          643, 1901, 1914, 1914,  719]])\n"
+     ]
+    }
+   ],
+   "source": [
+    "import random\n",
+    "import torch\n",
+    "from pathlib import Path\n",
+    "import Project_Model.Libs.Embedder as Embedder\n",
+    "import Project_Model.Libs.BPE as BPE\n",
+    "import Project_Model.Libs.Transformer as Transformer\n",
+    "\n",
+    "# set a fixed seed\n",
+    "torch.manual_seed(0)\n",
+    "random.seed(0)\n",
+    "\n",
+    "# BPE Init\n",
+    "VOCABULARY_PATH = Path(\"Assets/Model/toy_10/toy_dictionary.json\")\n",
+    "SPECIAL_VOC = BPE.default_special_tokens()\n",
+    "\n",
+    "VOCABULARY = BPE.load_nanos_vocabulary(VOCABULARY_PATH)\n",
+    "TOKENANO = BPE.TokeNanoCore(VOCABULARY, SPECIAL_VOC)\n",
+    "\n",
+    "\n",
+    "# Constants\n",
+    "TOKEN_SPACE_SIZE = TOKENANO.vocabulary_size + 1\n",
+    "EMBEDDED_SIZE = 256\n",
+    "FEED_FORWARD_DIM = EMBEDDED_SIZE * 4\n",
+    "\n",
+    "\n",
+    "# Model Init\n",
+    "ENCODER_EMBEDDER = Embedder.NanoSocratesEmbedder(TOKEN_SPACE_SIZE, EMBEDDED_SIZE)\n",
+    "DECODER_EMBEDDER = Embedder.NanoSocratesEmbedder(TOKEN_SPACE_SIZE, EMBEDDED_SIZE)\n",
+    "\n",
+    "ENCODER = torch.nn.Sequential(\n",
+    "    Transformer.Encoder(EMBEDDED_SIZE, FEED_FORWARD_DIM, 4),\n",
+    "    Transformer.Encoder(EMBEDDED_SIZE, FEED_FORWARD_DIM, 4),\n",
+    "    Transformer.Encoder(EMBEDDED_SIZE, FEED_FORWARD_DIM, 4),\n",
+    "    Transformer.Encoder(EMBEDDED_SIZE, FEED_FORWARD_DIM, 4),\n",
+    "    Transformer.Encoder(EMBEDDED_SIZE, FEED_FORWARD_DIM, 4),\n",
+    "    Transformer.Encoder(EMBEDDED_SIZE, FEED_FORWARD_DIM, 4),\n",
+    ")\n",
+    "\n",
+    "DECODER = torch.nn.Sequential(\n",
+    "    Transformer.Decoder(EMBEDDED_SIZE, FEED_FORWARD_DIM, 4),\n",
+    "    Transformer.Decoder(EMBEDDED_SIZE, FEED_FORWARD_DIM, 4),\n",
+    "    Transformer.Decoder(EMBEDDED_SIZE, FEED_FORWARD_DIM, 4),\n",
+    "    Transformer.Decoder(EMBEDDED_SIZE, FEED_FORWARD_DIM, 4),\n",
+    "    Transformer.Decoder(EMBEDDED_SIZE, FEED_FORWARD_DIM, 4),\n",
+    "    Transformer.Decoder(EMBEDDED_SIZE, FEED_FORWARD_DIM, 4),\n",
+    ")\n",
+    "\n",
+    "DETOKENER = Transformer.DeToken(\n",
+    "    EMBEDDED_SIZE,\n",
+    "    TOKEN_SPACE_SIZE\n",
+    ")\n",
+    "\n",
+    "\n",
+    "# Data\n",
+    "TEXT = (\n",
+    "    \"<ABS>The Dark Knight is a 2008 superhero film directed by Christopher Nolan,<SOTL>\"\n",
+    ")\n",
+    "OUT_TEXT = \"<START>\"\n",
+    "\n",
+    "PAD_TOKEN = TOKENANO.encode(\"<PAD>\")[0]\n",
+    "END_TOKEN = TOKENANO.encode(\"<END>\")[0]\n",
+    "\n",
+    "ENCODER_INPUT = TOKENANO.encode(TEXT)\n",
+    "DECODER_INPUT = TOKENANO.encode(OUT_TEXT)\n",
+    "MAX_LEN = len(ENCODER_INPUT) + 1\n",
+    "\n",
+    "EN_IN, PAD_MASK = Transformer.normalize_sequence(ENCODER_INPUT, MAX_LEN, PAD_TOKEN, END_TOKEN)\n",
+    "DEC_IN, _ = Transformer.normalize_sequence(DECODER_INPUT, MAX_LEN, PAD_TOKEN, END_TOKEN)\n",
+    "\n",
+    "BATCH_LEN = 3\n",
+    "\n",
+    "INPUT_TOKENIZATION = [\n",
+    "    EN_IN\n",
+    "] * BATCH_LEN\n",
+    "OUTPUT_TOKENIZATION = [\n",
+    "    DEC_IN\n",
+    "] * BATCH_LEN\n",
+    "\n",
+    "encoder_tensor_input = ENCODER_EMBEDDER(INPUT_TOKENIZATION)\n",
+    "encoder_padding_mask = torch.tensor([PAD_MASK] * BATCH_LEN)\n",
+    "\n",
+    "encoder_output, _ = ENCODER((encoder_tensor_input, encoder_padding_mask))\n",
+    "\n",
+    "decoder_tensor_input = DECODER_EMBEDDER(OUTPUT_TOKENIZATION)\n",
+    "decoder_padding_mask = torch.tensor([[False] * MAX_LEN] * BATCH_LEN)\n",
+    "\n",
+    "decoder_output, _, _, _ = DECODER((decoder_tensor_input, encoder_output, encoder_output, None))\n",
+    "\n",
+    "logits: torch.Tensor = DETOKENER(decoder_output)\n",
+    "\n",
+    "print(logits.shape)\n",
+    "\n",
+    "# print(logits)\n",
+    "\n",
+    "most_probable_tokens = torch.argmax(logits, 2)\n",
+    "\n",
+    "print(most_probable_tokens.shape)\n",
+    "print(most_probable_tokens)\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "deep_learning",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

Playgrounds/nanosocrates-train-toy.ipynb

@@ -0,0 +1,197 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "adbd9598",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\Chris\\miniconda3\\envs\\deep_learning\\Lib\\site-packages\\torch\\utils\\_device.py:103: UserWarning: Aten Op fallback from XPU to CPU happends. This may have performance implications. If need debug the fallback ops please set environment variable `PYTORCH_DEBUG_XPU_FALLBACK=1`  (Triggered internally at C:\\actions-runner\\_work\\pytorch\\pytorch\\pytorch\\build\\xpu\\ATen\\RegisterXPU_0.cpp:54528.)\n",
+      "  return func(*args, **kwargs)\n",
+      "252.87s - name 'tensor' is not defined\n",
+      "Traceback (most recent call last):\n",
+      "  File \"c:\\Users\\Chris\\miniconda3\\envs\\deep_learning\\Lib\\site-packages\\debugpy\\_vendored\\pydevd\\_pydevd_bundle\\pydevd_vars.py\", line 636, in change_attr_expression\n",
+      "    value = eval(expression, frame.f_globals, frame.f_locals)\n",
+      "  File \"<string>\", line 1, in <module>\n",
+      "NameError: name 'tensor' is not defined\n"
+     ]
+    },
+    {
+     "ename": "",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31mCannot execute code, session has been disposed. Please try restarting the Kernel."
+     ]
+    },
+    {
+     "ename": "",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31mCannot execute code, session has been disposed. Please try restarting the Kernel. \n",
+      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
+     ]
+    }
+   ],
+   "source": [
+    "import random\n",
+    "import torch\n",
+    "import pandas as pd\n",
+    "from pathlib import Path\n",
+    "import Project_Model.Libs.Embedder as Embedder\n",
+    "import Project_Model.Libs.BPE as BPE\n",
+    "import Project_Model.Libs.Transformer as Transformer\n",
+    "import Project_Model.Libs.TorchShims as torch_shims\n",
+    "\n",
+    "# set a fixed seed\n",
+    "torch.manual_seed(0)\n",
+    "random.seed(0)\n",
+    "DEVICE = torch_shims.get_default_device()\n",
+    "torch.set_default_device(DEVICE)\n",
+    "\n",
+    "# set a default device\n",
+    "\n",
+    "# BPE Init\n",
+    "VOCABULARY_PATH = Path(\"Assets/Model/toy_10/toy_dictionary.json\")\n",
+    "SPECIAL_VOC = BPE.default_special_tokens()\n",
+    "\n",
+    "VOCABULARY = BPE.load_nanos_vocabulary(VOCABULARY_PATH)\n",
+    "TOKENANO = BPE.TokeNanoCore(VOCABULARY, SPECIAL_VOC)\n",
+    "\n",
+    "\n",
+    "# Constants\n",
+    "TOKEN_SPACE_SIZE = TOKENANO.vocabulary_size + 1\n",
+    "EMBEDDED_SIZE = 256\n",
+    "FEED_FORWARD_MULTIPLIER =  4\n",
+    "ATTENTION_HEADS = 4\n",
+    "SENTENCE_LENGTH = 256\n",
+    "NUMBER_OF_BLOCKS = 2\n",
+    "MAX_EPOCHS = int(1e3)\n",
+    "\n",
+    "\n",
+    "PAD_TOKEN = TOKENANO.encode(\"<PAD>\")[0]\n",
+    "END_TOKEN = TOKENANO.encode(\"<END>\")[0]\n",
+    "\n",
+    "\n",
+    "# Load CSV\n",
+    "TOY_DATASET_PATH = Path(\"Assets/Dataset/1-hop/toy/rdf_text.csv\")\n",
+    "\n",
+    "TOY_DATASET = pd.read_csv(TOY_DATASET_PATH)\n",
+    "\n",
+    "TOY_BATCH_INPUT_LIST: list[list[int]] = []\n",
+    "TOY_BATCH_PADDING_LIST: list[list[bool]] = []\n",
+    "TOY_BATCH_TARGET_LIST: list[list[int]] = []\n",
+    "TOY_BATCH_DECODER_DEFAULT: list[list[int]]= []\n",
+    "\n",
+    "\n",
+    "for index, row in TOY_DATASET.iterrows():\n",
+    "\n",
+    "    RDFs: str = row[\"RDFs\"]\n",
+    "    Abstract: str = row[\"Abstract\"]\n",
+    "\n",
+    "    input_tokens = TOKENANO.encode(RDFs)\n",
+    "    output_tokens = TOKENANO.encode(Abstract)[1:]\n",
+    "    decoder_default_tokens = TOKENANO.encode(\"<SOS>\")\n",
+    "\n",
+    "    input_tokens, padding = Transformer.normalize_sequence(\n",
+    "        input_tokens, SENTENCE_LENGTH, PAD_TOKEN, END_TOKEN\n",
+    "    )\n",
+    "    output_tokens, _ = Transformer.normalize_sequence(\n",
+    "        output_tokens, SENTENCE_LENGTH, PAD_TOKEN, END_TOKEN\n",
+    "    )\n",
+    "    decoder_default_tokens, _ = Transformer.normalize_sequence(\n",
+    "        decoder_default_tokens, SENTENCE_LENGTH, PAD_TOKEN, END_TOKEN\n",
+    "    )\n",
+    "\n",
+    "    TOY_BATCH_INPUT_LIST.append(input_tokens)\n",
+    "    TOY_BATCH_PADDING_LIST.append(padding)\n",
+    "    TOY_BATCH_TARGET_LIST.append(output_tokens)\n",
+    "    TOY_BATCH_DECODER_DEFAULT.append(decoder_default_tokens)\n",
+    "\n",
+    "# Training loop\n",
+    "LOSS_HISTORY = []\n",
+    "NANOSOCRATES = Transformer.TrainingModel(\n",
+    "    TOKEN_SPACE_SIZE,\n",
+    "    EMBEDDED_SIZE,\n",
+    "    FEED_FORWARD_MULTIPLIER,\n",
+    "    ATTENTION_HEADS,\n",
+    "    NUMBER_OF_BLOCKS\n",
+    ")\n",
+    "cross_entropy = torch.nn.CrossEntropyLoss(ignore_index=PAD_TOKEN)\n",
+    "optimizer = torch.optim.AdamW(NANOSOCRATES.parameters())\n",
+    "scheduler = torch.optim.lr_scheduler.StepLR(optimizer, 4)\n",
+    "last_loss = 0\n",
+    "current_epoch = 0\n",
+    "\n",
+    "while current_epoch < MAX_EPOCHS:\n",
+    "\n",
+    "    optimizer.zero_grad()\n",
+    "\n",
+    "    encoder_list = torch.tensor([TOY_BATCH_INPUT_LIST[0]])\n",
+    "    decoder_list = torch.tensor([TOY_BATCH_DECODER_DEFAULT[0]])\n",
+    "    padding_list = torch.tensor([TOY_BATCH_PADDING_LIST[0]], dtype=torch.bool)\n",
+    "\n",
+    "    # Transform target into logits\n",
+    "    target_logits = torch.tensor([TOY_BATCH_TARGET_LIST[0]])\n",
+    "\n",
+    "    last_loss = 0\n",
+    "\n",
+    "    for i in range(0, SENTENCE_LENGTH):\n",
+    "\n",
+    "        optimizer.zero_grad()\n",
+    "\n",
+    "        logits: torch.Tensor = NANOSOCRATES((encoder_list, padding_list, decoder_list))\n",
+    "\n",
+    "        most_probable_tokens = torch.argmax(logits, 2)\n",
+    "\n",
+    "        logits = logits[:,i,:]\n",
+    "\n",
+    "        loss = cross_entropy(logits, target_logits[:,i])\n",
+    "        last_loss = loss\n",
+    "        optimizer.step()\n",
+    "        scheduler.step()\n",
+    "\n",
+    "        if i < SENTENCE_LENGTH - 1:\n",
+    "            decoder_list[:,i+1] = most_probable_tokens[:,i]\n",
+    "\n",
+    "\n",
+    "    current_epoch += 1\n",
+    "\n",
+    "    if current_epoch % 1 == 0:\n",
+    "        print(f\"EPOCH {current_epoch}\\n\\tLoss: {last_loss}\")\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "deep_learning",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

Playgrounds/prova.ipynb

@@ -0,0 +1,112 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "4ae47336",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "B, T, D = 4, 7, 32\n",
+    "x = torch.randn(B, T, D)\n",
+    "attn_mask = torch.triu(torch.ones(T, T, dtype=torch.bool), diagonal=1)  # [T,T]\n",
+    "pad_mask  = torch.zeros(B, T, dtype=torch.bool)  # no pads\n",
+    "mha = torch.nn.MultiheadAttention(D, num_heads=4, batch_first=True)\n",
+    "y, _ = mha(x, x, x, attn_mask=attn_mask, key_padding_mask=pad_mask)  # should work\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "e38e3fb5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor([[[0, 0, 0, 0, 1, 0, 0, 0, 0, 0],\n",
+       "         [0, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n",
+       "         [0, 0, 0, 0, 0, 0, 0, 0, 0, 1]],\n",
+       "\n",
+       "        [[0, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n",
+       "         [0, 0, 0, 0, 1, 0, 0, 0, 0, 0],\n",
+       "         [0, 0, 0, 0, 0, 1, 0, 0, 0, 0]]])"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "torch.nn.functional.one_hot(torch.tensor([\n",
+    "    [4, 1, 9],\n",
+    "    [2,4,5]\n",
+    "]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "7119ad53",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "device(type='cpu')"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "torch.get_default_device()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "8c95691a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "xpu\n"
+     ]
+    }
+   ],
+   "source": [
+    "from Project_Model.Libs.TorchShims import get_default_device\n",
+    "\n",
+    "print(get_default_device())"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "deep_learning",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

Playgrounds/sanity-check-pytorch.ipynb

@@ -0,0 +1,60 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "dd23cc94",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Current detected architecture is: xpu\n"
+     ]
+    }
+   ],
+   "source": [
+    "import torch\n",
+    "from Project_Model.Libs.TorchShims import get_default_device\n",
+    "\n",
+    "DEVICE = get_default_device()\n",
+    "\n",
+    "print(f\"Current detected architecture is: {DEVICE.type}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6584882e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import Project_Model.Libs.Transformer as Transformer\n",
+    "DECODER = Transformer.Decoder(256, 1024, 4)\n",
+    "print()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "deep_learning",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

Project_Model/Libs/BPE/Classes/NanoSocraTrainerPool.py

@@ -107,7 +107,7 @@ class NanoSocraTrainerPool:
             bpe = NanoSocratesBPE()
         BPE = bpe
 
-        if BPE.vocabulary_size > self.__max_vocabulary:
+        if BPE.vocabulary_size >= self.__max_vocabulary:
             return BPE
 
         exit = False

Project_Model/Libs/BPE/Classes/NanoSocratesSpecial.py

@@ -29,6 +29,10 @@ class NanoSocratesSpecial(Encoder):
         VOC_LENGTH = len(self.__vocabulary)
         return BPE_OFFSET + VOC_LENGTH + 1
 
+    @property
+    def vocabulary_size(self) -> int:
+        return len(self.vocabulary)
+
     @property
     def vocabulary(self) -> dict[str, int]:
         return self.__vocabulary

Project_Model/Libs/BPE/Classes/TokeNanoCore.py

@@ -6,6 +6,7 @@ from ..Classes import NanoSocratesSpecial
 
 from ..Utils import special_regex_maker
 from ..Enums import TokenType
+from ..Enums import SpecialToken
 
 
 class TokeNanoCore:
@@ -24,7 +25,13 @@ class TokeNanoCore:
         self.__splitter = NanoSocratesSplitter(SPECIAL_REGEX, BPE_VOCABULARY_SIZE)
         self.__special_encoder = NanoSocratesSpecial(
             BPE_VOCABULARY_SIZE, special_token_list
-        ) 
+        )
+
+    @property
+    def vocabulary_size(self):
+        BPE_VOC_SIZE = self.__bpe_encoder.vocabulary_size
+        SPECIAL_VOC_SIZE = self.__special_encoder.vocabulary_size
+        return BPE_VOC_SIZE + SPECIAL_VOC_SIZE
 
     def encode(self, corpus: str) -> list[int]:
         output: list[int] = []
@@ -38,6 +45,27 @@ class TokeNanoCore:
                 output.extend(self.__bpe_encoder.encode(piece))
 
         return output
+    
+
+
+    def encode_incomplete_string(self, corpus: str) -> list[int]:
+        """
+        Encode string which don't end with a special token
+        """
+        corpus = corpus + SpecialToken.CORPUS_END.value
+        output: list[int] = []
+        for piece, token_type in self.__splitter.split_text(corpus):
+
+            if token_type == TokenType.SPECIAL:
+                output.extend(self.__special_encoder.encode(piece))
+
+            # slow but clear
+            if token_type == TokenType.BPE:
+                output.extend(self.__bpe_encoder.encode(piece))
+
+        return output[:-1]
+
+
 
     def decode(self, corpus: list[int]) -> str:
         output_str = ""

Project_Model/Libs/BPE/Classes/__init__.py

@@ -5,6 +5,8 @@ from .NanoSocraTrainer import NanoSocraTrainer
 from .NanoSocraTraineRam import NanoSocraTraineRam
 from .NanoSocraTrainerPool import NanoSocraTrainerPool
 from .NanoSocratesSpecial import NanoSocratesSpecial
+from .TokeNanoCore import TokeNanoCore
+from .TokeNano import TokeNano
 
 __all__ = [
     "NanoSocratesChunker",
@@ -12,5 +14,7 @@ __all__ = [
     "NanoSocratesBPE",
     "NanoSocraTrainer",
     "NanoSocraTraineRam",
-    "NanoSocraTrainerPool"
+    "NanoSocraTrainerPool",
+    "TokeNanoCore",
+    "TokeNano"
 ]

Project_Model/Libs/BPE/Enums/SpecialToken.py

@@ -10,7 +10,6 @@ class SpecialToken(Enum):
     RELATIONSHIP = "<PRED>"
     OBJECT = "<OBJ>"
     ABSTRACT = "<ABS>"
-    CORPUS_END = "<END>"
 
     ## Tasks' Token
     RDF_TO_TEXT = "<RDF2TXT>"
@@ -19,3 +18,10 @@ class SpecialToken(Enum):
     MASK = "<MASK>"
 
     # BPE Training:
+    # NanoSocrates
+    START = "<START>"
+    CORPUS_END = "<END>"
+    START_OF_SEQUENCE = "<SOS>"
+    END_OF_SEQUENCE = "<EOS>"
+    PAD = "<PAD>"
+    

Project_Model/Libs/BPE/Enums/__init__.py

@@ -1 +1,6 @@
 from .TokenType import TokenType
+from .SpecialToken import SpecialToken
+
+__all__ = [
+    "SpecialToken"
+]

Project_Model/Libs/BPE/Utils/__init__.py

@@ -3,11 +3,13 @@ from .lag_checker_iterator import iterator_with_checks
 from .vocabulary import save_nanos_vocabulary, load_nanos_vocabulary
 from .json_utils import save_json, load_json
 from .special_regex_maker import special_regex_maker
+from .default_special_tokens import default_special_tokens
 
 __all__ = [
     "special_regex_maker",
     "iterator_with_checks",
     "save_nanos_vocabulary",
     "load_nanos_vocabulary",
-    "save_json", "load_json"
+    "save_json", "load_json",
+    "default_special_tokens"
 ]

Project_Model/Libs/BPE/Utils/default_special_tokens.py

@@ -0,0 +1,4 @@
+from ..Enums import SpecialToken
+
+def default_special_tokens() -> list[str]:
+    return [token.value for token in SpecialToken]

Project_Model/Libs/Batch/Classes/BatchEmbedder.py

@@ -0,0 +1,11 @@
+from ....Libs.Embedder.Classes.NanoSocratesEmbedder import NanoSocratesEmbedder
+import torch
+
+class BatchEmbedder(torch.nn.Module):
+
+    def __init__(self, vocabulary_size: int, embedding_size: int) -> None:
+        super().__init__()
+        self.__embedder = NanoSocratesEmbedder(vocabulary_size,embedding_size)
+        
+
+    def forward(self, )

Project_Model/Libs/Batch/Classes/Batcher.py

@@ -0,0 +1,104 @@
+import random
+from typing import Generator
+import pandas as pd
+
+import Project_Model.Libs.BPE as BPE
+from Scripts.Libs.CleaningPipeline.special_token import SpecialToken
+from Project_Model.Libs.Transformer.Classes.SpannedMasker import SpannedMasker
+from TokenCompletation import TokenCompletationTransformer
+from Project_Model.Libs.BPE.Enums.SpecialToken import SpecialToken
+
+class Batcher:
+
+    def __init__(self, dataset_path: str, batch_size:int, tokenizer: BPE.TokeNanoCore, masker: SpannedMasker) -> None:
+        # ABSTRACT, TRIPLE 
+        # tasks: 
+        #   rdf2text: X: TRIPLE, Y: ABSTRACT 
+        #   text2rdf: X: ABSTRACT, X:TRIPLE 
+        #   masking ( call masker): X: incomplete_triple Y: complete_triple (as exam) 
+        #   completation: X: TRIPLE SUBSET, Y: related TRIPLE SUBSET
+        self._dataset_path = dataset_path
+        self._batch_size = batch_size
+        self._tokenizer = tokenizer
+        self._masker = masker
+
+        sotl = self._tokenizer.encode(SpecialToken.START_TRIPLE_LIST.value)
+        eos = self._tokenizer.encode(SpecialToken.END_OF_SEQUENCE.value)
+        self._token_completation = TokenCompletationTransformer(sotl,eos)
+
+
+    def get_batch(self)-> Generator[pd.DataFrame]:
+        for batch in pd.read_csv(self._dataset_path, chunksize= int(self._batch_size/4)): #now we support 3 task
+
+            tokenized_batch = pd.DataFrame()
+            tokenized_batch[["Abstract","RDFs"]] = (
+                batch[["Abstract","RDFs"]]
+                .map(lambda t: self._tokenizer.encode(t))
+            )
+            
+            rdf2txt_batch = self.__rdf2txt_transformation(tokenized_batch)
+            txt2rdf_batch = self.__txt2rdf_transformation(tokenized_batch)
+            mask_batch = self.__masking_trasformation(tokenized_batch)
+            completation_batch = self.__token_completation_task(tokenized_batch)
+
+            output = pd.concat([rdf2txt_batch,txt2rdf_batch,mask_batch,completation_batch],ignore_index=True)
+            output = output.sample(frac=1).reset_index(drop=True)
+            yield output
+            
+
+    def __random_subset_rdfs(self, batch: pd.DataFrame, seed = 0):
+        # WIP 
+        rng = random.Random(seed)
+
+        def to_list(x):
+            return x.split(SpecialToken.START_TRIPLE.value)[1:]
+
+        batch["RDFs"] = batch["RDFs"].map(
+            to_list
+        )
+
+    def __rdf2txt_transformation(self, batch: pd.DataFrame):
+        batch = batch.rename(columns={"RDFs": "X", "Abstract": "Y"})
+        return batch[["X", "Y"]]
+
+
+    def __txt2rdf_transformation(self, batch: pd.DataFrame):
+        batch = batch.rename(columns={ "Abstract": "X","RDFs": "Y"})
+        return batch[["X", "Y"]]
+    
+    def __masking_trasformation(self, batch: pd.DataFrame):
+        # mask_sequence: List[int] -> Tuple[List[int], List[int]]
+        xy_tuples = batch["RDFs"].apply(self._masker.mask_sequence)  # Series of (X, Y)
+
+        output = batch.copy()
+        # Expand into two columns preserving the original index
+        output[["X", "Y"]] = pd.DataFrame(xy_tuples.tolist(), index=batch.index)
+        return output[["X", "Y"]] 
+        
+    
+    def __token_completation_task(self, batch: pd.DataFrame):
+        xy_tuples = batch["RDFs"].apply(self._token_completation.get_completation_tuple)
+        output = batch.copy()
+        output[["X", "Y"]] = pd.DataFrame(xy_tuples.tolist(), index=batch.index)
+        return output[["X", "Y"]] 
+
+
+
+"""
+DATASET_PATH = "Assets/Dataset/Tmp/rdf_text.csv"
+VOCABULARY_path = "Assets/Dataset/Tmp/trimmed.json"
+
+from pathlib import Path
+VOCABULARY = BPE.load_nanos_vocabulary(Path(VOCABULARY_path))
+SPECIAL_LIST = BPE.default_special_tokens()
+TOKENANO = BPE.TokeNanoCore(VOCABULARY, SPECIAL_LIST)
+SPECIAL_TOKENS: set[int] = set(TOKENANO.encode("".join(SPECIAL_LIST)))
+
+MASKER = SpannedMasker(TOKENANO.vocabulary_size,SPECIAL_TOKENS)
+
+prova = "<ABS>Cactus Flower is a 1969 American screwball comedy film directed by Gene Saks, and starring Walter Matthau, Ingrid Bergman and Goldie Hawn, who won an Academy Award for her performance.The screenplay was adapted by I. A. L. Diamond from the 1965 Broadway play of the same title written by Abe Burrows, which, in turn, is based on the French play Fleur de cactus by Pierre Barillet and Jean-Pierre Gredy. Cactus Flower was the ninth highest-grossing film of 1969."
+print(TOKENANO.encode(prova))
+batcher = Batcher(DATASET_PATH,8,TOKENANO,MASKER)
+for batch in batcher.get_batch():
+    print(batch)
+"""

Project_Model/Libs/Batch/Classes/TokenCompletation.py

@@ -0,0 +1,33 @@
+
+class TokenCompletationTransformer:
+
+    def __init__(self,SOTL_token,EOS_token, input_percent:float = 0.5) -> None:
+        self.__SOTL_token = SOTL_token
+        self.__EOS_token = EOS_token
+        self.__input_percent = input_percent
+        pass
+
+
+    def get_completation_tuple(
+        self,
+        token_sequence: list[int],
+    )-> tuple[list[int], list[int]]:
+        
+        # split the sequence by encoded(<SOTL>), dont take the first, firts pertenge in as X the other as Y
+        sotl_count =int( token_sequence.count(self.__SOTL_token) * self.__input_percent)
+
+        sotl_index = 0
+        percent_index = 0
+        while sotl_index < sotl_count:
+            token = token_sequence[percent_index]
+            if token == self.__SOTL_token:
+                sotl_index += 1
+
+            percent_index+=1
+
+        percent_index = percent_index -1
+        x_list = token_sequence[:percent_index] 
+        x_list.append(self.__EOS_token)
+        y_list = token_sequence[percent_index:]
+        return (x_list,y_list)
+

Project_Model/Libs/Batch/Enums/TaskType.py

@@ -0,0 +1,8 @@
+from enum import Enum, auto
+
+class TaskType(Enum):
+
+    RDF2TXT = auto()
+    TEXT2RDF = auto()
+    MASKING = auto()
+    COMPLETATION = auto()

Project_Model/Libs/Embedder/Classes/NanoSocratesEmbedder.py

@@ -0,0 +1,23 @@
+import torch
+from ..Utils import fixed_positional_encoding
+
+
+# WIP FOR BATCHING
+class NanoSocratesEmbedder(torch.nn.Module):
+
+    def __init__(self, vocabulary_size: int, embedding_size: int) -> None:
+        super().__init__()
+        self.__embedder = torch.nn.Embedding(vocabulary_size, embedding_size)
+
+    def forward(self, tokenized_sentence: torch.Tensor) -> torch.Tensor:
+
+        computed_embeddings: torch.Tensor = self.__embedder(tokenized_sentence)
+
+        _, SENTENCE_LENGHT, EMBEDDING_SIZE = computed_embeddings.shape  # for batching
+
+        POSITIONAL_ENCODINGS = fixed_positional_encoding(
+            SENTENCE_LENGHT, EMBEDDING_SIZE
+        )
+
+        computed_embeddings = computed_embeddings + POSITIONAL_ENCODINGS  # for batching
+        return computed_embeddings

Project_Model/Libs/Embedder/Classes/__init__.py

@@ -0,0 +1,5 @@
+from .NanoSocratesEmbedder import NanoSocratesEmbedder
+
+__all__ = [
+    "NanoSocratesEmbedder"
+]

Project_Model/Libs/Embedder/Utils/__init__.py

@@ -0,0 +1,5 @@
+from .fixed_positional_encoding import fixed_positional_encoding
+
+__all__ = [
+    "fixed_positional_encoding"
+]

Project_Model/Libs/Embedder/Utils/fixed_positional_encoding.py

@@ -0,0 +1,28 @@
+import torch
+
+
+def fixed_positional_encoding(
+    sentence_dimension: int,
+    embedding_dimension: int,
+) -> torch.Tensor:
+
+    BIG_CONST = int(1e4)
+    INITIAL_ENCODING = torch.tensor([i for i in range(0, sentence_dimension)])
+
+    ENCODINGS: list[torch.Tensor] = []
+
+    for i in range(0, embedding_dimension):
+        EMBEDDING_POSITION = i
+
+        # Note: The original paper did not specify
+        #   to compute: pos mod 2!!
+        DIVISOR = BIG_CONST ** ((2 * (EMBEDDING_POSITION // 2)) / embedding_dimension)
+        INTERMEDIATE_ENCODING = INITIAL_ENCODING / DIVISOR
+
+        if EMBEDDING_POSITION % 2 == 0:
+            ENCODINGS.append(torch.sin(INTERMEDIATE_ENCODING))
+            continue
+
+        ENCODINGS.append(torch.cos(INTERMEDIATE_ENCODING))
+
+    return torch.stack(ENCODINGS).transpose(0, 1)

Project_Model/Libs/Embedder/__init__.py

@@ -0,0 +1,7 @@
+from .Utils import *
+from .Classes import *
+
+from . import Utils
+from . import Classes
+
+

Project_Model/Libs/TorchShims/Utils/__init__.py

@@ -0,0 +1,5 @@
+from .get_default_device import get_default_device
+
+__all__ = [
+    "get_default_device"
+]

Project_Model/Libs/TorchShims/Utils/get_default_device.py

@@ -0,0 +1,17 @@
+import torch
+
+def get_default_device() -> torch.device:
+
+    # Cuda or ROCm
+    if torch.cuda.is_available():
+        return torch.device("cuda")
+
+    # Intel GPUs
+    if torch.xpu.is_available():
+        return torch.device("xpu")
+
+    # Apple GPUs
+    if torch.backends.mps.is_available():
+        return torch.device("mps")
+
+    return torch.device("cpu")

Project_Model/Libs/TorchShims/__init__.py

@@ -0,0 +1,7 @@
+from .Utils import *
+
+from .Utils import get_default_device
+
+__all__ = [
+    "get_default_device"
+]

Project_Model/Libs/Training/learning_rade_shedulers.py

@@ -0,0 +1,41 @@
+import numpy as np
+# custom LR from attention is all you need
+class Custom_lr():
+    def __init__(self, d_model: int, warmup_step:int) -> None:
+
+        self.__d_model = d_model
+        self.__warmup_step = warmup_step
+        self.__epoch = 0
+
+
+    def step(self) -> int:
+        self.__epoch += 1
+        return (self.__d_model ** -0.5) * min(self.__epoch ** -0.5,
+                                   self.__epoch * (self.__warmup_step ** -1.5))
+
+# OTHER LR
+
+# Learning rate schedules (matching visualization parameters)
+def step_lr(epoch, lr):
+    # StepLR: step_size=20, gamma=0.5 (from visualization)
+    return lr * 0.5 if epoch % 20 == 0 and epoch > 0 else lr
+ 
+def exp_lr(epoch, lr):
+    # ExponentialLR: gamma=0.95 (from visualization)
+    return lr * 0.95
+ 
+def cosine_lr(epoch, lr):
+    # CosineAnnealingLR: lr_min=0.001, lr_max=0.1, max_epochs=100 (from visualization)
+    lr_min, lr_max = 0.001, 0.1
+    max_epochs = 100
+    return lr_min + 0.5 * (lr_max - lr_min) * (1 + np.cos(epoch * np.pi / max_epochs))
+ 
+def cyclical_lr(epoch, lr):
+    # CyclicalLR: base_lr=0.001, max_lr=0.1, step_size=20 (from visualization)
+    base_lr = 0.001
+    max_lr = 0.1
+    step_size = 20
+    
+    cycle = np.floor(1 + epoch / (2 * step_size))
+    x = np.abs(epoch / step_size - 2 * cycle + 1)
+    return base_lr + (max_lr - base_lr) * max(0, (1 - x))

Project_Model/Libs/Training/logistic_collector.py

@@ -0,0 +1,42 @@
+import torch
+
+class LogitsCollector:
+    def __init__(self, pad_token: int, end_token: int, tokenizer) -> None:
+        self.__pad_token = pad_token                      # used to skip PAD
+        self.__end_token = end_token                      # used to stop at END
+        self.__tokenizer = tokenizer                      # exposes .decode(list[int]) -> str
+        self.__steps: list[torch.Tensor] = []             # list of per-step logits [B,V]
+
+    def reset(self) -> None:
+        self.__steps.clear()                              # clear history
+
+    def add(self, logits_step: torch.Tensor) -> None:
+        if logits_step.dim() == 3:                        # handle [B,1,V]
+            logits_step = logits_step[:, -1, :]           # -> [B,V]
+        self.__steps.append(logits_step.detach())         # store raw logits (detached)
+
+    def tokens(self) -> list[list[int]]:
+        if not self.__steps:
+            return []
+        stack = torch.stack(self.__steps, dim=0)          # [T,B,V]
+        probs = torch.softmax(stack, dim=-1)              # softmax over vocab -> [T,B,V]
+        ids = probs.argmax(dim=-1).transpose(0, 1)        # greedy ids -> [B,T]
+        out: list[list[int]] = []
+        for row in ids.tolist():
+            seq: list[int] = []
+            for tok in row:
+                if tok == self.__end_token:               # stop on END
+                    break
+                if tok == self.__pad_token:               # skip PAD
+                    continue
+                seq.append(tok)
+            out.append(seq)
+        return out
+
+    def print_decoded(self) -> None:
+        for i, seq in enumerate(self.tokens()):
+            try:
+                text = self.__tokenizer.decode(seq)       # decode tokens to string
+            except Exception:
+                text = str(seq)                           # fallback to ids
+            print(f"[{i}] {text}")                        # simple print

Project_Model/Libs/Transformer/Classes/DeToken.py

@@ -0,0 +1,19 @@
+import torch
+
+
+class DeToken(torch.nn.Module):
+
+    def __init__(self, embedding_size: int, vocabulary_size: int) -> None:
+        super().__init__()
+
+        self.__linear = torch.nn.Linear(embedding_size, vocabulary_size)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+
+        # 1) Go from latent space to vocabularu space
+        x = self.__linear(x)
+
+        # 2) Go to logits
+        # x = torch.softmax(x, 2)
+
+        return x

Project_Model/Libs/Transformer/Classes/Decoder.py

@@ -0,0 +1,103 @@
+import torch
+import torch.nn as nn
+from .FeedForwardNetwork import FeedForwardNetwork
+from .TorchMultiHeadAttention import TorchMultiHeadAttention as MultiHeadAttention
+from ..Utils.attention_mask import get_causal_attention_mask
+
+# B, L(T), E_D
+
+
+class Decoder(nn.Module):
+
+    def __init__(
+        self,
+        embedding_dimension: int,
+        feed_forward_hidden_layer_dimension: int,
+        number_of_attention_heads: int,
+    ) -> None:
+        super().__init__()
+
+        self.__masked_attention = MultiHeadAttention(
+            embedding_dimension, number_of_attention_heads, dropout=0.1
+        )
+
+        self.__layer_norm_1 = nn.LayerNorm(embedding_dimension)
+
+        self.__cross_attention = MultiHeadAttention(
+            embedding_dimension, number_of_attention_heads, dropout=0.1
+        )
+        self.__layer_norm_2 = nn.LayerNorm(embedding_dimension)
+
+        self.__dropout = nn.Dropout(0.1)
+
+        self.__feed_forward_network = FeedForwardNetwork(
+            embedding_dimension, feed_forward_hidden_layer_dimension
+        )
+        self.__layer_norm_3 = nn.LayerNorm(embedding_dimension)
+
+    def forward(
+        self,
+        args: tuple[
+            torch.Tensor,
+            torch.Tensor,
+            torch.Tensor,
+            torch.Tensor,
+            torch.Tensor
+        ]
+    ):  # -> list[torch.Tensor]:  # k_x = v_x . While x_q = x
+        # WARNING: args is needed to have sequential
+        x, k_x, v_x, padding_mask,encoder_padding_mask = args
+
+        # build of attention mask
+        attention_mask = get_causal_attention_mask(x.size(1))
+
+        # 1) Masked Attention
+        MASKED_ATTENTION = self.__masked_attention(
+            x, x, x, key_padding_mask=padding_mask, attention_mask=attention_mask
+        )
+
+        # 2) Dropout
+        # DROPPED_MASKED_ATTENTION = self.__dropout(MASKED_ATTENTION)
+        # del MASKED_ATTENTION
+
+        # 3) Residual Connection
+        x = x + MASKED_ATTENTION
+        del MASKED_ATTENTION
+
+        # 4) Layer Normalization
+        x = self.__layer_norm_1(x)
+
+        # 5) Encoder–decoder (cross) attention
+        CROSS_ATTENTION = self.__cross_attention(
+            x, k_x, v_x, key_padding_mask=encoder_padding_mask
+        )
+
+        # 6) Dropout
+        # DROPPED_CROSS_ATTENTION = self.__dropout(CROSS_ATTENTION)
+        # del CROSS_ATTENTION
+
+        # 7) Residual Connection
+        x = x + CROSS_ATTENTION
+        del CROSS_ATTENTION
+
+        # 8) Layer Normalization
+        x = self.__layer_norm_2(x)
+
+        # 9) Position-wise feed-forward
+        FEED_FORWARD = self.__feed_forward_network(x)
+
+        # 10) Dropout
+        # DROPPED_FEED_FORWARD = self.__dropout(FEED_FORWARD)
+        # del FEED_FORWARD
+
+        # 11) Residual Connection
+        x = x + FEED_FORWARD
+        del FEED_FORWARD
+
+        # 12) Layer Normalization
+        x = self.__layer_norm_3(x)
+
+        return (x, k_x, v_x, padding_mask, encoder_padding_mask)
+
+
+# use eval to disable dropout ecc

Project_Model/Libs/Transformer/Classes/Encoder.py

@@ -0,0 +1,73 @@
+import torch
+import torch.nn as nn
+from Project_Model.Libs.Transformer.Classes.FeedForwardNetwork import FeedForwardNetwork
+from Project_Model.Libs.Transformer.Classes.TorchMultiHeadAttention import (
+    TorchMultiHeadAttention as MultiHeadAttention,
+)
+
+
+class Encoder(
+    nn.Module
+):  # in this way we expose the primitive of nn.Module for training purpose
+
+    def __init__(
+        self,
+        embedding_dimension: int,
+        feed_forward_hidden_layer_dimension: int,
+        number_of_attention_heads: int,
+    ) -> None:
+        super().__init__()
+        self.__attention = MultiHeadAttention(
+            embedding_dimension, number_of_attention_heads, dropout=0.1
+        )
+        self.__layer_norm_1 = nn.LayerNorm(
+            embedding_dimension
+        )  # norm of first "Add and Normalize"
+        self.__feed_forward = FeedForwardNetwork(
+            embedding_dimension, feed_forward_hidden_layer_dimension
+        )
+        self.__layer_norm_2 = nn.LayerNorm(
+            embedding_dimension
+        )  # norm of second "Add and Normalize"
+        self.__dropout = nn.Dropout(0.1)  # ...
+
+
+    def forward(self, args: tuple[torch.Tensor, torch.Tensor]):
+        # WARNING: args is needed to have sequential
+        x, padding_mask = args
+
+        # -> ATTENTION -> dropout -> add and normalize -> FF -> dropout -> add and normalize ->
+        # Attention with Residual Connection    [ input + self-attention]
+
+        # 1) Multi Head Attention
+        ATTENTION = self.__attention(x, x, x, key_padding_mask=padding_mask)
+
+        # 2) Dropout
+        # DROPPED_ATTENTION = self.__dropout(ATTENTION)
+        # del ATTENTION
+
+        # 3) Residual Connection
+        x = x + ATTENTION
+        del ATTENTION
+
+        # 4) Layer Normalization
+        x = self.__layer_norm_1(x)
+
+        # 5) Feed Forward
+        FEED_FORWARD = self.__feed_forward(x)
+
+        # 6) Dropout
+        # DROPPED_FEED_FORWARD = self.__dropout(FEED_FORWARD)
+        # del FEED_FORWARD
+
+        # 7) Residual Connection
+        x = x + FEED_FORWARD
+        del FEED_FORWARD
+
+        # 8) Layer Normalization
+        x = self.__layer_norm_2(x)
+
+        return (x, padding_mask)
+
+
+# use eval to disable dropout ecc

Project_Model/Libs/Transformer/Classes/FeedForwardNetwork.py

@@ -0,0 +1,43 @@
+# it is position wise!
+# https://stackoverflow.com/questions/74979359/how-is-position-wise-feed-forward-neural-network-implemented-for-transformers
+
+# Why do we need a fixed size
+# https://ai.stackexchange.com/questions/37624/why-do-transformers-have-a-fixed-input-length
+
+import torch.nn as nn
+
+
+class FeedForwardNetwork(nn.Module):
+
+    def __init__(self, embedding_size: int, feed_forward_hidden_layer_dimension: int):
+
+        super().__init__()
+        self.__fully_connected_1 = nn.Linear(
+            embedding_size, feed_forward_hidden_layer_dimension
+        )  # expand in higher dimension
+
+        self.__relu = nn.ReLU()
+        self.__dropout = nn.Dropout(
+            0.1
+        )  # during training we drop something, with eval it got deactivated
+
+        self.__fully_connected_2 = nn.Linear(
+            feed_forward_hidden_layer_dimension, embedding_size
+        )  # return into the model dimension
+
+    def forward(self, x):
+        # -> NN1 -> RELU -> (Droput during training) -> NN2 ->
+
+        # 1) Linear Layer
+        x = self.__fully_connected_1(x)
+
+        # 2) ReLU
+        x = self.__relu(x)
+
+        # 3) Dropout
+        x = self.__dropout(x)
+
+        # 4) Linear Layer
+        x = self.__fully_connected_2(x)
+
+        return x

Project_Model/Libs/Transformer/Classes/NanoSocrates.py

@@ -0,0 +1,23 @@
+import torch
+from NanoSocratesCore import NanoSocratesCore
+
+class NanoSocrates(torch.nn.Module):
+
+    def __init__(self,
+                embedded_size: int, 
+                feed_forward_dim: int, 
+                encoder_layers: int, 
+                decoder_layers:int,
+                attention_heads: int,
+                vocab_size: int) -> None:
+
+        super().__init__()
+
+        self._model = NanoSocratesCore(            
+                embedded_size, 
+                feed_forward_dim, 
+                encoder_layers, 
+                decoder_layers,
+                attention_heads,
+                vocab_size)
+        

Project_Model/Libs/Transformer/Classes/NanoSocratesCore.py

@@ -0,0 +1,109 @@
+from ..Utils.task_type import TaskType
+from .Decoder import Decoder
+from .Encoder import Encoder
+from ....Libs.Embedder import NanoSocratesEmbedder
+import torch
+
+
+class NanoSocratesCore(torch.nn.Module):
+
+    def __init__(
+        self,
+        sentence_length: int,
+        vocab_size: int,
+        embedding_size: int = 256,
+        feed_forward_multiplier: int = 4,
+        num_encoder_layers: int = 2,
+        num_decoder_layers: int = 2,
+        num_attention_heads: int = 4,
+        pad_token: int = 0, 
+    ) -> None:
+
+        super().__init__() 
+        self.__pad_token = pad_token
+        feed_forward_dim = embedding_size * feed_forward_multiplier
+
+        self.__sentence_length = sentence_length
+
+        self.__encoder_sequence = torch.nn.Sequential(
+            *[
+                Encoder(embedding_size, feed_forward_dim, num_attention_heads)
+                for _ in range(num_encoder_layers)
+            ]
+        )
+
+        # * unpack the list so that each encoder has its own weights
+
+        self.__decoder_sequence = torch.nn.Sequential(
+            *[
+                Decoder(embedding_size, feed_forward_dim, num_attention_heads)
+                for _ in range(num_decoder_layers)
+            ]
+        )
+
+        self.__linear = torch.nn.Linear(embedding_size, vocab_size)
+
+        self.__input_embeder = NanoSocratesEmbedder(vocab_size, embedding_size)
+        self.__output_embedder = NanoSocratesEmbedder(vocab_size, embedding_size)
+
+    @torch.no_grad()  # inference only
+    def forward(
+        self,
+        encoder_input: list[list[int]],
+        decoder_input: list[list[int]],           # must start with <SOS> and PAD elsewhere
+        encoder_padding_mask: list[list[bool]],   # True where encoder is PAD
+    ):
+        # 1) Embed User-Input for encoders
+        ENCODER_INPUT = self.__input_embeder(encoder_input)  # [B,S,E]
+
+        # 2) Encode User-Input
+        ENCODER_OUTPUT, encoder_padding_mask = self.__encoder_sequence(
+            (ENCODER_INPUT, encoder_padding_mask) # as tuple
+        )  # [B,S,E], [B,S]
+        del ENCODER_INPUT
+
+        # 3) Autoregressive Output (greedy)
+        LOGITS_HISTORY: list[torch.Tensor] = []   # keep per-step distributions
+        decoder_token_list = [row[:] for row in decoder_input]  # copy tokens
+        decoder_phase = 0
+        exit_loop = False
+
+        while not exit_loop:
+            decoder_phase += 1  # move to next position
+
+            # 3.1) Build decoder key padding mask from current tokens (True where PAD)
+            DECODER_KEY_PADDING_MASK: list[list[bool]] = [
+                [tok == self.__pad_token for tok in row] for row in decoder_token_list
+            ]  # [B,T]
+
+            # 3.2) Embed Decoder Input (full sequence; decoder builds causal mask inside)
+            DECODER_INPUT = self.__output_embedder(decoder_token_list)  # [B,T,E]
+
+            # 3.3) Decode (self-attn uses causal mask internally; we provide PAD masks)
+            DECODER_OUTPUT, _, _, _ = self.__decoder_sequence(
+                (DECODER_INPUT, ENCODER_OUTPUT, ENCODER_OUTPUT,
+                DECODER_KEY_PADDING_MASK, encoder_padding_mask)
+            )  # [B,T,E]
+            del DECODER_INPUT
+
+            # 3.4) Project to token space
+            LOGITS = self.__linear(DECODER_OUTPUT)  # [B,T,V]
+            del DECODER_OUTPUT
+
+            # 3.5) Probabilities and greedy pick at current step
+            TOKEN_PROBABILITIES = torch.softmax(LOGITS, dim=-1)  # [B,T,V]
+            LOGITS_HISTORY.append(TOKEN_PROBABILITIES)  # store for this step
+
+            step_idx = decoder_phase - 1  # 0-based
+            TOKEN_IDS = TOKEN_PROBABILITIES[:, step_idx, :].argmax(dim=-1).tolist()  # [B] -> list[int]
+
+            # 3.6) Write prediction into next slot (the slot is PAD)
+            if step_idx + 1 < self.__sentence_length:
+                for b, tok in enumerate(TOKEN_IDS):
+                    decoder_token_list[b][step_idx + 1] = tok  # feed next position
+
+            # 3.7) Stop when we filled the sequence
+            if decoder_phase == self.__sentence_length - 1:
+                exit_loop = True
+
+        return LOGITS_HISTORY  # list of [B,T,V] (per step)

Project_Model/Libs/Transformer/Classes/SpannedMasker.py

@@ -0,0 +1,213 @@
+import math
+import random
+import sys
+
+
+class SpannedMasker:
+
+    def __init__(
+        self,
+        max_vocabulary: int,
+        forbidden_tokens: set[int],
+        change_token_probability: float = 0.15,
+        average_span: int = 1,
+        seed: int = random.randint(0, sys.maxsize),
+
+    ) -> None:
+
+        if change_token_probability < 0 or change_token_probability > 1:
+            raise ValueError("received a value that is not between 0 or 1")
+
+        self.__change_token_probability = change_token_probability
+        self.__average_span = average_span
+        self.__rng = random.Random(seed)
+        self.__max_vocabulary = max_vocabulary
+        self.__forbidden_tokens = forbidden_tokens
+
+
+    def mask_sequence(
+        self,
+        token_sequence: list[int],
+    ) -> tuple[list[int], list[int]]:
+
+        MASK = self.__create_mask(token_sequence, self.__forbidden_tokens)
+        MASKED = self.__create_masked_input(token_sequence, MASK, self.__max_vocabulary)
+        TARGET = self.__create_target(token_sequence, MASK, self.__max_vocabulary)
+
+        return (MASKED, TARGET)
+
+
+
+
+    def __number_of_spans(self, legal_token_number: int):
+        EXPECTED_NUM_OF_CORRUPTED_TOKENS = self.__number_of_corrupted_tokens(legal_token_number)
+
+        return math.ceil(EXPECTED_NUM_OF_CORRUPTED_TOKENS / self.__average_span)
+
+    def __number_of_corrupted_tokens(self, legal_token_number: int):
+        EXPECTED_NUM_OF_CORRUPTED_TOKENS = math.ceil(
+            legal_token_number * self.__change_token_probability
+        )
+
+        return EXPECTED_NUM_OF_CORRUPTED_TOKENS
+
+    def __create_mask(self, sequence: list[int], forbidden_tokens: set[int]) -> list[bool]:
+
+        SEQ_LEN = len(sequence)
+        LEGAL_TOKENS = self.__count_legal_tokens(sequence, forbidden_tokens)
+        NUM_OF_CORRUPTIONS = self.__number_of_corrupted_tokens(LEGAL_TOKENS)
+        NUM_OF_SPANS = self.__number_of_spans(LEGAL_TOKENS)
+        MASK = [False] * SEQ_LEN
+
+        mask_index = 0
+        number_of_spans = 0
+        exit_loop = False
+
+        while not exit_loop:
+
+            TOKEN = sequence[mask_index]
+            MASKED = MASK[mask_index]
+            SHOULD_MASK = self.__random_mask()
+            skip = False
+
+
+            if self.__is_illegal_token(TOKEN, forbidden_tokens):
+                skip = True
+
+            if MASKED:
+                skip = True
+
+            if not SHOULD_MASK:
+                skip = True
+
+            if skip:
+                mask_index = (mask_index + 1) % SEQ_LEN
+                continue
+
+
+            CANDIDATE_SPAN = self.__random_span(
+                self.__average_span
+            )
+
+            REMAINING_MASK = SEQ_LEN - (mask_index + 1)
+
+            SPAN_LENGTH = min(CANDIDATE_SPAN, REMAINING_MASK)
+
+            for _ in range(0, SPAN_LENGTH):
+                INNER_TOKEN = sequence[mask_index]
+
+                if self.__is_illegal_token(INNER_TOKEN, forbidden_tokens):
+                    continue
+                
+                MASK[mask_index] = True
+                mask_index += 1
+
+            number_of_spans += 1
+            mask_index += 1
+
+            if number_of_spans == NUM_OF_SPANS:
+                exit_loop = True
+                continue
+
+            if mask_index >= SEQ_LEN - 1:
+                exit_loop = True
+                continue
+
+        return MASK
+
+    def __create_masked_input(self, sequence: list[int], mask: list[bool], max_voc: int) -> list[int]:
+
+        OUT: list[int] = []
+        mask_token_id = max_voc + 1
+        index = 0
+        while index < len(sequence):
+
+            TOKEN = sequence[index]
+            MASKED = mask[index]
+
+            if not MASKED:
+                OUT.append(
+                    TOKEN
+                )
+                index += 1
+                continue
+
+            MASK_TOKEN = mask_token_id
+            OUT.append(
+                MASK_TOKEN
+            )
+
+            while mask[index]:
+                index += 1
+
+            mask_token_id += 1
+
+        return OUT
+
+    def __create_target(self, sequence: list[int], mask: list[bool], max_voc: int) -> list[int]:
+
+        OUT: list[int] = []
+        mask_token_id = max_voc + 1
+        index = 0
+        while index < len(sequence):
+
+            TOKEN = sequence[index]
+            MASKED = mask[index]
+
+            if MASKED:
+                OUT.append(
+                    TOKEN
+                )
+                index += 1
+                continue
+
+            MASK_TOKEN = mask_token_id
+            OUT.append(
+                MASK_TOKEN
+            )
+
+            while index < len(mask) and not mask[index]:
+                index += 1
+
+            mask_token_id += 1
+
+
+        return OUT
+
+    def __is_illegal_token(self, token: int, illegal_voc: set[int]) -> bool:
+        if token in illegal_voc:
+            return True
+
+        return False
+
+    def __count_legal_tokens(self, sequence: list[int], illegal_voc: set[int]) -> int:
+        legal_count = 0
+
+        for token in sequence:
+            if self.__is_illegal_token(token, illegal_voc):
+                continue
+            legal_count += 1
+
+        return legal_count
+
+    def __random_mask(self) -> bool:
+
+        if self.__random_probability() > self.__change_token_probability:
+            return False
+
+        return True
+
+    def __random_probability(self) -> float:
+        return self.__rng.random()
+
+    def __random_token(self, max_vocabulary: int) -> int:
+        return self.__rng.randint(0, max_vocabulary)
+
+    def __random_int_range(self, min: int, max: int) -> int:
+        return self.__rng.randint(min, max)
+
+    def __random_span(self, average: int) -> int:
+        candidate_span = self.__rng.gauss(mu=average)
+        candidate_span = max(1, candidate_span)
+        candidate_span = round(candidate_span)
+        return candidate_span

Project_Model/Libs/Transformer/Classes/TokenMasker.py

@@ -0,0 +1,77 @@
+import random
+import sys
+
+
+class TokenMasker:
+
+    def __init__(
+        self,
+        change_token_probability: float = 0.15,
+        mask_token_probability: float = 0.8,
+        random_token_prob: float = 0.1,
+        seed: int = random.randint(0, sys.maxsize),
+    ) -> None:
+
+        if change_token_probability < 0 or change_token_probability > 1:
+            raise ValueError("received a value that is not between 0 or 1")
+
+        if mask_token_probability < 0 or mask_token_probability > 1:
+            raise ValueError("received a value that is not between 0 or 1")
+
+        if random_token_prob < 0 or random_token_prob > 1:
+            raise ValueError("received a value that is not between 0 or 1")
+
+        if mask_token_probability + random_token_prob > 1:
+            raise ValueError("The sum of probabilities is over 1")
+
+        self.__change_token_probability = change_token_probability
+        self.__mask_token_probability = mask_token_probability
+        self.__random_token_prob = random_token_prob
+        self.__rng = random.Random(seed)
+
+    def mask_sequence(
+        self, token_sequence: list[int], max_vocabulary: int, mask_id: int
+    ) -> list[int]:
+
+        if mask_id <= max_vocabulary:
+            raise ValueError("mask_id is a value of vocabulary")
+
+        MASKED_SEQUENCE: list[int] = []
+
+        for token in token_sequence:
+
+            if token > max_vocabulary:
+                MASKED_SEQUENCE.append(token)
+                continue
+
+            MASKED_TOKEN = self.__mask(token, max_vocabulary, mask_id)
+            MASKED_SEQUENCE.append(MASKED_TOKEN)
+
+        return MASKED_SEQUENCE
+
+    def __mask(self, token: int, max_vocabulary: int, mask_id: int) -> int:
+
+        if self.__random_probability() > self.__change_token_probability:
+            return token
+
+        MASK_TOKEN_TRESH = self.__mask_token_probability
+        RANDOM_TOKEN_TRESH = MASK_TOKEN_TRESH + self.__random_token_prob
+        CHANCE_PROBABILITY = self.__random_probability()
+
+        # It's over both probabilities, return same token
+        if CHANCE_PROBABILITY > RANDOM_TOKEN_TRESH:
+            return token
+
+        # It's over masking treshold, but lower than random
+        #   return random token
+        if CHANCE_PROBABILITY > MASK_TOKEN_TRESH:
+            return self.__random_token(max_vocabulary)
+
+        # It's below masking treshold, mask token
+        return mask_id
+
+    def __random_probability(self) -> float:
+        return self.__rng.random()
+
+    def __random_token(self, max_vocabulary: int) -> int:
+        return self.__rng.randint(0, max_vocabulary)

Project_Model/Libs/Transformer/Classes/TorchMultiHeadAttention.py

@@ -0,0 +1,47 @@
+import torch
+import torch.nn as nn
+from typing import Optional
+
+class TorchMultiHeadAttention(nn.Module):
+
+    def __init__(
+        self,
+        embedding_dimension: int,
+        number_of_attention_heads: int,
+        dropout: float = 0.0
+    ):
+        super().__init__()
+        self.attention = torch.nn.MultiheadAttention(
+            embedding_dimension,
+            num_heads=number_of_attention_heads,
+            dropout=dropout,
+            batch_first=True,
+        )
+
+    def forward(
+        self,
+        x_q: torch.Tensor,
+        x_k: torch.Tensor,
+        x_v: torch.Tensor,
+        key_padding_mask=None,
+        attention_mask: Optional[torch.Tensor] = None
+    ) -> torch.Tensor:
+
+        # x * Wq -> Q
+        # x * Wk -> K
+        # x * Wv -> V
+        # REMEMBER: tochAttention uses Batch internally to build the 3 dimension attention mask given the 2 dimension
+        y, _ = self.attention(
+            x_q, x_k, x_v, attn_mask=attention_mask, key_padding_mask=key_padding_mask,
+            need_weights=False
+        )
+        return y
+
+
+# batch_first=False (default storico)
+# Formato: (L, N, E)
+# L = lunghezza della sequenza (time/posizioni)
+# N = batch size
+# E = dimensione d_model (embed)
+# batch_first=True
+# Formato: (N, L, E) (più naturale per molti modelli)

Project_Model/Libs/Transformer/Classes/__init__.py

@@ -0,0 +1,16 @@
+from .Decoder import Decoder
+from .Encoder import Encoder
+from .FeedForwardNetwork import FeedForwardNetwork
+# from .MultiHeadAttention import MultiheadAttention
+from .TorchMultiHeadAttention import TorchMultiHeadAttention
+from .SpannedMasker import SpannedMasker
+from .DeToken import DeToken
+
+__all__ = [
+    "Decoder",
+    "Encoder",
+    "FeedForwardNetwork",
+    "TorchMultiHeadAttention",
+    "SpannedMasker",
+    "DeToken"
+]

Project_Model/Libs/Transformer/Models/TrainingModel.py

@@ -0,0 +1,72 @@
+import torch
+import Project_Model.Libs.Embedder as Embedder
+from ..Classes import Encoder, Decoder, DeToken
+
+
+class TrainingModel(torch.nn.Module):
+
+    def __init__(
+        self,
+        vocabulary_size: int,
+        latent_space: int = 256,
+        feed_forward_multiplier: int = 4,
+        attention_heads: int = 4,
+        layer_number: int = 2,
+    ) -> None:
+        super().__init__()
+
+        feed_forward_latent_space = latent_space * feed_forward_multiplier
+
+        self.__encoder_embedder = Embedder.NanoSocratesEmbedder(
+            vocabulary_size, latent_space
+        )
+        self.__decoder_embedder = Embedder.NanoSocratesEmbedder(
+            vocabulary_size, latent_space
+        )
+
+        # do NOT share layer weights
+        enc_layers = [
+            Encoder(latent_space, feed_forward_latent_space, attention_heads)
+            for _ in range(layer_number)
+        ]
+        dec_layers = [
+            Decoder(latent_space, feed_forward_latent_space, attention_heads)
+            for _ in range(layer_number)
+        ]
+
+        self.__encoder = torch.nn.Sequential(*enc_layers)
+        self.__decoder = torch.nn.Sequential(*dec_layers)
+
+        self.__detokener = DeToken(latent_space, vocabulary_size)
+
+    def forward(
+        self,
+        args: tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]
+    ):
+        # returns logits for the LAST decoder position only -> [B, V]
+        (
+            encoder_embedder_input,      # [B,S] encoder tokens
+            encoder_padding_mask,        # [B,S] True where encoder is PAD
+            decoder_embedder_prefix,     # [B,Tp] decoder prefix (e.g., <SOS> + tokens so far)
+            decoder_padding_mask,        # [B,Tp] True where decoder prefix has PAD
+        ) = args
+
+        # 1) embeddings
+        encoder_tensor = self.__encoder_embedder(encoder_embedder_input)    # [B,S,E]
+        decoder_tensor = self.__decoder_embedder(decoder_embedder_prefix)   # [B,Tp,E]
+
+        # 2) encode
+        encoder_output, _ = self.__encoder((encoder_tensor, encoder_padding_mask))  # [B,S,E], [B,S]
+
+        # 3) decode (causal mask is built inside the decoder)
+        decoder_output, _, _, _, _ = self.__decoder(
+            (decoder_tensor, encoder_output, encoder_output,
+             decoder_padding_mask, encoder_padding_mask)
+        )  # [B,Tp,E], ...
+
+        # 4) project only the last time step
+        last_hidden = decoder_output[:, -1:, :]       # [B,1,E]
+        step_logits = self.__detokener(last_hidden)   # [B,1,V]
+        step_logits = step_logits[:, -1, :]           # [B,V]
+
+        return step_logits                             # logits for one token

Project_Model/Libs/Transformer/Models/__init__.py

@@ -0,0 +1,5 @@
+from .TrainingModel import TrainingModel
+
+__all__ = [
+    "TrainingModel"
+]

Project_Model/Libs/Transformer/Utils/__init__.py

@@ -0,0 +1,17 @@
+from .attention_mask import get_causal_attention_mask,get_causal_attention_mask_batched
+from .task_type import TaskType
+from .post_tokenization import truncate_sequence, pad_sequence, normalize_sequence, create_padding_mask
+from .inference_masking import inference_masking
+from .truncate_rdf_list import truncate_rdf_list
+
+__all__ = [
+    "TaskType",
+    "get_causal_attention_mask",
+    "get_causal_attention_mask_batched",
+    "truncate_sequence",
+    "pad_sequence",
+    "create_padding_mask",
+    "normalize_sequence",
+    "inference_masking",
+    "truncate_rdf_list"
+]

Project_Model/Libs/Transformer/Utils/attention_mask.py

@@ -0,0 +1,11 @@
+import torch
+
+def get_causal_attention_mask(seq_len: int) -> torch.Tensor:
+    return torch.triu(torch.ones(seq_len, seq_len, dtype=torch.bool), diagonal=1)
+
+
+# there is no need for this since MultiHeadAttention of torch does this internally
+def get_causal_attention_mask_batched(seq_len: int, batch_size: int ) -> torch.Tensor:
+    base_mask = get_causal_attention_mask(seq_len)
+    return base_mask.unsqueeze(0).expand(batch_size, -1, -1)  # add another dimension at the beginning, big as batch_size   
+    # the result is that z,x,y where x,y are repeated along z

Project_Model/Libs/Transformer/Utils/inference_masking.py

@@ -0,0 +1,13 @@
+def inference_masking(sequence: list[int], mask_token: int, max_vocabulary: int) -> list[int]:
+
+    current_mask_token = max_vocabulary + 1
+
+    for i in range(0, len(sequence)):
+
+        if sequence[i] != mask_token:
+            continue
+
+        sequence[i] = current_mask_token
+        current_mask_token += 1
+
+    return sequence

Project_Model/Libs/Transformer/Utils/post_tokenization.py

@@ -0,0 +1,56 @@
+def truncate_sequence(
+    sequence: list[int], truncate_at: int, end_token: int
+) -> list[int]:
+
+    if len(sequence) < truncate_at - 1:
+        sequence.append(end_token)
+        return sequence
+
+    if len(sequence) < truncate_at:
+        sequence[-1] = end_token
+        return sequence
+
+    TRUNCATED_SEQUENCE = sequence[:truncate_at]
+    TRUNCATED_SEQUENCE[-1] = end_token
+
+    return TRUNCATED_SEQUENCE
+
+
+def pad_sequence(sequence: list[int], pad_until: int, pad_token: int) -> list[int]:
+
+    if not (len(sequence) < pad_until):
+        return sequence
+
+    NUM_OF_PADDINGS = pad_until - len(sequence)
+    PADDINGS = [pad_token] * NUM_OF_PADDINGS
+
+    PADDED_SEQUENCE = sequence[:]
+    PADDED_SEQUENCE.extend(PADDINGS)
+
+    return PADDED_SEQUENCE
+
+def create_padding_mask(sequence: list[int], pad_token: int) -> list[bool]:
+
+    PADDING_MASK = [False] * len(sequence)
+
+    for i in range(0, len(sequence)):
+
+        if sequence[i] != pad_token:
+            continue
+
+        PADDING_MASK[i] = True
+
+    return PADDING_MASK
+
+
+def normalize_sequence(
+    sequence: list[int],
+    max_length: int,
+    pad_token: int,
+    end_token: int,
+) -> tuple[list[int], list[bool]]:
+    new_sequence = truncate_sequence(sequence, max_length, end_token)
+    new_sequence = pad_sequence(new_sequence, max_length, pad_token)
+    PADDING_MASK = create_padding_mask(new_sequence, pad_token)
+
+    return (new_sequence, PADDING_MASK)

Project_Model/Libs/Transformer/Utils/task_type.py

@@ -0,0 +1,6 @@
+from enum import Enum, auto
+
+class TaskType(Enum):
+    RDF2TEXT = auto()
+    MASK = auto()
+    COMPLETATION = auto()

Project_Model/Libs/Transformer/Utils/truncate_rdf_list.py

@@ -0,0 +1,65 @@
+from collections import deque
+import random
+import sys
+
+
+def truncate_rdf_list(
+    sequence: list[int],
+    truncation_probability: float,
+    continue_triple_token: int,
+    end_of_triple_token: int,
+    seed: int = random.randint(0, sys.maxsize),
+) -> tuple[list[int], list[int]]:
+
+    if truncation_probability < 0 or truncation_probability > 1:
+        raise ValueError("A probability must be between 0 and 1")
+
+    RNG = random.Random(seed)
+
+    END_OF_TRIPLES: deque[int] = deque()
+
+    for i in range(0, len(sequence)):
+
+        TOKEN = sequence[i]
+        if TOKEN != end_of_triple_token:
+            continue
+
+        END_OF_TRIPLES.append(i + 1)
+
+    TRIPLES_TOKENS: list[int] = []
+    TARGET_TRIPLES: list[int] = []
+
+    start_of_triple = 0
+    exit_loop = False
+
+    while not exit_loop:
+
+        EOT = END_OF_TRIPLES.popleft()
+
+        TRIPLE = sequence[start_of_triple:EOT]
+        TRIPLES_TOKENS.extend(TRIPLE)
+
+        start_of_triple = EOT
+
+        if RNG.random() < truncation_probability:
+            exit_loop = True
+
+        if len(END_OF_TRIPLES) == 1:
+            exit_loop = True
+
+    TRIPLES_TOKENS.append(
+        continue_triple_token
+    )
+
+    while len(END_OF_TRIPLES) > 0:
+
+        EOT = END_OF_TRIPLES.popleft()
+
+        TRIPLE = sequence[start_of_triple:EOT]
+        TARGET_TRIPLES.extend(TRIPLE)
+
+        start_of_triple = EOT
+
+
+    return (TRIPLES_TOKENS, TARGET_TRIPLES)
+

Project_Model/Libs/Transformer/__init__.py

@@ -0,0 +1,7 @@
+from .Classes import *
+from .Utils import *
+from .Models import *
+
+from . import Classes
+from . import Utils
+from . import Models

Project_Model/Libs/__init__.py

@@ -1 +1,4 @@
-from . import BPE
+from . import BPE
+from . import Embedder
+from . import Transformer
+from . import TorchShims

Project_Model/Tests/spanned_masker_test.py

@@ -0,0 +1,92 @@
+from functools import reduce
+from pathlib import Path
+import pytest
+import Project_Model.Libs.BPE as BPE
+import Project_Model.Libs.Transformer as Transformer
+
+VOCABULARY_PATH = Path("Assets/Model/toy_10/toy_dictionary.json")
+VOCABULARY = BPE.load_nanos_vocabulary(VOCABULARY_PATH)
+SPECIAL_LIST = BPE.default_special_tokens()
+
+
+class TestSpannedMasker:
+
+    def test_spanned_masking(self):
+
+        CORPUS_PATH = Path("Project_Model/Tests/spanner_file/mask.txt")
+        TEXT = CORPUS_PATH.read_text("utf-8")
+        CORRUPTION_PERCENTAGE = 0.15
+        TOLERANCE = 0.15
+
+        TOKENIZER = BPE.TokeNanoCore(VOCABULARY, SPECIAL_LIST)
+        VOCABULARY_SIZE = TOKENIZER.vocabulary_size
+
+        
+
+        TOKENS = TOKENIZER.encode(TEXT)
+
+        LEGAL_TOKENS: set[int] = set(TOKENIZER.encode("<SUBJ><OBJ><PRED>"))
+
+        SPECIAL_TOKENS: set[int] = set(TOKENIZER.encode("".join(SPECIAL_LIST)))
+
+        ILLEGAL_TOKENS: set[int] = SPECIAL_TOKENS.difference(LEGAL_TOKENS)
+
+        MASKER = Transformer.SpannedMasker(VOCABULARY_SIZE,ILLEGAL_TOKENS,CORRUPTION_PERCENTAGE, 3)
+
+        SPECIAL_FORMATTER = TOKENIZER.encode("*<SOT>")[0]
+        END_FORMATTER = TOKENIZER.encode("<EOT>")[0]
+
+        OUTPUT, TARGET = MASKER.mask_sequence(TOKENS)
+
+        UNCORRUPTED_TOKENS = list(
+            filter(lambda token: token <= VOCABULARY_SIZE, OUTPUT)
+        )
+        CORRUPTED_TOKENS = list(filter(lambda token: token <= VOCABULARY_SIZE, TARGET))
+
+        TARGET.append(END_FORMATTER)
+
+        OUTPUT = list(
+            map(
+                lambda token: SPECIAL_FORMATTER if token > VOCABULARY_SIZE else token,
+                OUTPUT,
+            )
+        )
+        TARGET = list(
+            map(
+                lambda token: SPECIAL_FORMATTER if token > VOCABULARY_SIZE else token,
+                TARGET,
+            )
+        )
+
+        OUT_TEXT = TOKENIZER.decode(OUTPUT)
+        TAR_TEXT = TOKENIZER.decode(TARGET)
+
+        ACTUAL_CORRUPTION_PERCENTAGE = len(CORRUPTED_TOKENS) / len(TOKENS)
+
+        print(f"Original text:\n\n{TEXT}")
+        print(f"Inputs:\n\n{OUT_TEXT}")
+        print(f"Targets:\n\n{TAR_TEXT}")
+        print(f"Target Tokens:\n\n{OUTPUT}")
+
+        print(
+            "\n".join(
+                [
+                    f"======================",
+                    f"Original length: {len(TOKENS)}",
+                    f"Uncorrupted Chars: {len(UNCORRUPTED_TOKENS)}",
+                    f"Corrupted Chars: {len(CORRUPTED_TOKENS)}",
+                    f"Percentage_corruption: {(len(CORRUPTED_TOKENS)/len(TOKENS))*100}%",
+                    f"======================",
+                ]
+            )
+        )
+
+        for token in TARGET[:len(TARGET) - 1]:
+            assert token not in ILLEGAL_TOKENS
+
+        assert ACTUAL_CORRUPTION_PERCENTAGE > CORRUPTION_PERCENTAGE - TOLERANCE
+        assert ACTUAL_CORRUPTION_PERCENTAGE < CORRUPTION_PERCENTAGE + TOLERANCE
+
+
+if __name__ == "__main__":
+    TestSpannedMasker().test_spanned_masking()

Project_Model/Tests/spanner_file/mask.txt

@@ -0,0 +1 @@
+<SOT><SUBJ>dbp-dbr:How_It_Should_Have_Ended<PRED>dbp-dbp:title<OBJ>dbp-dbr:The_Dark_Knight<EOT><SOT><SUBJ>dbp-dbr:The_Dark_Knight<PRED>dbp-dbp:caption<OBJ>Theatrical release poster<EOT><SOT><SUBJ>dbp-dbr:The_Dark_Knight<PRED>dbp-dbp:director<OBJ>dbp-dbr:Christopher_Nolan<EOT><SOT><SUBJ>dbp-dbr:The_Dark_Knight<PRED>dbp-dbp:distributor<OBJ>Warner Bros. Pictures<EOT><SOT><SUBJ>dbp-dbr:The_Dark_Knight<PRED>dbp-dbp:producer<OBJ>Charles Roven<EOT><SOT><SUBJ>dbp-dbr:The_Dark_Knight<PRED>dbp-dbp:producer<OBJ>Christopher Nolan<EOT><SOT><SUBJ>dbp-dbr:The_Dark_Knight<PRED>dbp-dbp:producer<OBJ>Emma Thomas<EOT><SOT><SUBJ>dbp-dbr:The_Dark_Knight<PRED>dbp-dbp:starring<OBJ>Christian Bale<EOT>

Project_Model/UML/pipeline-utils.excalidraw.json

@@ -0,0 +1,162 @@
+{
+  "type": "excalidraw",
+  "version": 2,
+  "source": "https://marketplace.visualstudio.com/items?itemName=pomdtr.excalidraw-editor",
+  "elements": [
+    {
+      "id": "MR81S4quQLdw7ZYl2sbpi",
+      "type": "text",
+      "x": 309,
+      "y": 131,
+      "width": 649,
+      "height": 200,
+      "angle": 0,
+      "strokeColor": "#1e1e1e",
+      "backgroundColor": "transparent",
+      "fillStyle": "solid",
+      "strokeWidth": 2,
+      "strokeStyle": "solid",
+      "roughness": 1,
+      "opacity": 100,
+      "groupIds": [],
+      "frameId": null,
+      "index": "a0",
+      "roundness": null,
+      "seed": 194891189,
+      "version": 243,
+      "versionNonce": 1614205979,
+      "isDeleted": false,
+      "boundElements": null,
+      "updated": 1759680941870,
+      "link": null,
+      "locked": false,
+      "text": "def truncate(seq: list[int], max_length: int, eos_id: int):\n\n    if len(seq) < max_length:\n        return seq\n\n    seq[max_length - 1] = eos_id\n    return seq[:max_length]\n    ",
+      "fontSize": 20,
+      "fontFamily": 8,
+      "textAlign": "left",
+      "verticalAlign": "top",
+      "containerId": null,
+      "originalText": "def truncate(seq: list[int], max_length: int, eos_id: int):\n\n    if len(seq) < max_length:\n        return seq\n\n    seq[max_length - 1] = eos_id\n    return seq[:max_length]\n    ",
+      "autoResize": true,
+      "lineHeight": 1.25
+    },
+    {
+      "id": "nzMSk0NIeEpMYeUQ1UvVw",
+      "type": "text",
+      "x": 309,
+      "y": 420.5,
+      "width": 594,
+      "height": 350,
+      "angle": 0,
+      "strokeColor": "#1e1e1e",
+      "backgroundColor": "transparent",
+      "fillStyle": "solid",
+      "strokeWidth": 2,
+      "strokeStyle": "solid",
+      "roughness": 1,
+      "opacity": 100,
+      "groupIds": [],
+      "frameId": null,
+      "index": "a1",
+      "roundness": null,
+      "seed": 807602389,
+      "version": 533,
+      "versionNonce": 1455522939,
+      "isDeleted": false,
+      "boundElements": null,
+      "updated": 1759681067736,
+      "link": null,
+      "locked": false,
+      "text": "def pad(seq: list[int], max_length: int, pad_id: int):\n\n    if len(seq) > max_length:\n        raise Exception()\n\n    if len(seq) == max_length:\n        return seq\n    \n    SEQ_LEN = len(seq)\n    PAD_LEN = max_length - SEQ_LEN\n    PADDING = [pad_id] * PAD_LEN\n    seq.extend(PADDING)\n    return seq\n    ",
+      "fontSize": 20,
+      "fontFamily": 8,
+      "textAlign": "left",
+      "verticalAlign": "top",
+      "containerId": null,
+      "originalText": "def pad(seq: list[int], max_length: int, pad_id: int):\n\n    if len(seq) > max_length:\n        raise Exception()\n\n    if len(seq) == max_length:\n        return seq\n    \n    SEQ_LEN = len(seq)\n    PAD_LEN = max_length - SEQ_LEN\n    PADDING = [pad_id] * PAD_LEN\n    seq.extend(PADDING)\n    return seq\n    ",
+      "autoResize": true,
+      "lineHeight": 1.25
+    },
+    {
+      "id": "8ONOUf0ArJ5DMtxeM_fvG",
+      "type": "text",
+      "x": 299,
+      "y": 805.5,
+      "width": 473,
+      "height": 150,
+      "angle": 0,
+      "strokeColor": "#1e1e1e",
+      "backgroundColor": "transparent",
+      "fillStyle": "solid",
+      "strokeWidth": 2,
+      "strokeStyle": "solid",
+      "roughness": 1,
+      "opacity": 100,
+      "groupIds": [],
+      "frameId": null,
+      "index": "a2",
+      "roundness": null,
+      "seed": 1323527029,
+      "version": 294,
+      "versionNonce": 567965627,
+      "isDeleted": false,
+      "boundElements": null,
+      "updated": 1759681345616,
+      "link": null,
+      "locked": false,
+      "text": "def mask(\n    seq: list[int], \n    max_bpe_voc_id: int\n    mask_id: int,\n    masking_probability: MaskingProbability\n)",
+      "fontSize": 20,
+      "fontFamily": 8,
+      "textAlign": "left",
+      "verticalAlign": "top",
+      "containerId": null,
+      "originalText": "def mask(\n    seq: list[int], \n    max_bpe_voc_id: int\n    mask_id: int,\n    masking_probability: MaskingProbability\n)",
+      "autoResize": true,
+      "lineHeight": 1.25
+    },
+    {
+      "id": "cpsIEF7OpTuNkcxiggRpa",
+      "type": "text",
+      "x": 997,
+      "y": 703,
+      "width": 330,
+      "height": 150,
+      "angle": 0,
+      "strokeColor": "#1e1e1e",
+      "backgroundColor": "transparent",
+      "fillStyle": "solid",
+      "strokeWidth": 2,
+      "strokeStyle": "solid",
+      "roughness": 1,
+      "opacity": 100,
+      "groupIds": [],
+      "frameId": null,
+      "index": "a3",
+      "roundness": null,
+      "seed": 516363867,
+      "version": 192,
+      "versionNonce": 430327707,
+      "isDeleted": false,
+      "boundElements": null,
+      "updated": 1759681254424,
+      "link": null,
+      "locked": false,
+      "text": "class MaskingProbability:\n\n    + change_token_prob: float\n    + mask_token_prob: float\n    + same_token_prob: float\n    + random_token_prob: float",
+      "fontSize": 20,
+      "fontFamily": 8,
+      "textAlign": "left",
+      "verticalAlign": "top",
+      "containerId": null,
+      "originalText": "class MaskingProbability:\n\n    + change_token_prob: float\n    + mask_token_prob: float\n    + same_token_prob: float\n    + random_token_prob: float",
+      "autoResize": true,
+      "lineHeight": 1.25
+    }
+  ],
+  "appState": {
+    "gridSize": 20,
+    "gridStep": 5,
+    "gridModeEnabled": false,
+    "viewBackgroundColor": "#ffffff"
+  },
+  "files": {}
+}

Scripts/DataCleaning/data_output_models/debug_csv.py

@@ -0,0 +1,21 @@
+import pandas as pd
+
+class Debug_csv():
+    def __init__(self, output_path:str):
+     
+
+        self.output =  open(output_path, "w")
+        # then the first row as header
+        header = ["MovieURI","SubjectURI","RelationshipURI","ObjectURI","Abstract"]
+        self.output.write(",".join(header) + "\n")
+
+    def close(self):
+        self.output.close()
+
+    def write(self, RDF: pd.DataFrame):
+        """
+        Args:
+            RDF (pd.DataFrame): ["MovieURI","SubjectURI","RelationshipURI","ObjectURI","Abstract"]
+        """        
+
+        RDF.to_csv(self.output, index=False, header=False)

Scripts/DataCleaning/filter.py

@@ -186,3 +186,9 @@ class PipelineApplier():
         # as input two dataframe, one with 2 column  
         return None
 
+    def regex_on_objects(self, RDF: pd.DataFrame) -> pd.DataFrame:
+        RDF["ObjectURI"] = (RDF["ObjectURI"].astype("string")
+                   .str.replace(r"\r?\n+", ", ", regex=True)   # newlines -> ", "
+                   .str.replace(r"\*", "", regex=True))        # delete all asterisks
+
+        return RDF

Scripts/DataCleaning/pipeline.py

@@ -6,17 +6,12 @@ from Scripts.DataCleaning.data_output_models.rdf_mask_task import RDF_mask_task_
 from Scripts.DataCleaning.data_output_models.bpe_corpus import BPE_corpus
 from Scripts.DataCleaning.data_output_models.rdf_text_tasks import RDF_text_task_dataset
 from Scripts.DataCleaning.data_output_models.rdf_completation_task import RDF_completation_task_dataset
+from Scripts.DataCleaning.data_output_models.debug_csv import Debug_csv
 
 import pandas as pd
 
 class Pipeline():
-    def __init__(self, 
+    def __init__(self):
-                 mask_task_dataset_path:str = "./Assets/Dataset/Tmp/rdf_mask.csv",
-                 bpe_corpus_path:str = "./Assets/Dataset/Tmp/corpus.txt",
-                 text_to_rdf_task_dataset_path:str = "./Assets/Dataset/Tmp/rdf_text.csv",
-                 completation_rdf_task_dataset_path:str = "./Assets/Dataset/Tmp/rdf_completation.csv",
-
-                 ):
         self.sql_endpoint = SqlEndpoint()
         # classes to manage taskes' datasets
         self.task_rdf_mask = RDF_mask_task_dataset(mask_task_dataset_path)
@@ -98,6 +93,8 @@ class Pipeline():
             # other filter
             #
             RDF = self.filter_applier.delete_relationship_by_list_filter(RDF)
+            # regex on ObjectURI
+            RDF = self.filter_applier.regex_on_objects(RDF)
             if RDF.empty:
                 continue
             RDF = self.filter_applier.rdf_add_special_token(RDF)  # WARNING, THIS MUST BE DONE AFTER FILTER BY FREQUENCE
@@ -119,22 +116,27 @@ class Pipeline():
         movie_list = [117248, 147074, 113621, 1123, 117586, 90177, 71587, 138952, 144137, 148025]
         self.sql_endpoint.movie_ids = movie_list
 
-    def reduce_movie_list(self, starting_offset:int , ending_offset:int):
+    def generate_csv_debug_file(self, debug_path:str):
-        self.filter_applier.reduce_movie_list(starting_offset,ending_offset)
+        debug_csv = Debug_csv(debug_path)
 
+        for RDF in self._get_cleaned_movie_rows():
+            debug_csv.write(RDF)
+
+        debug_csv.close()
 
 
 # there are a lot of settings to manage
-# you only need to change settings: 
+# you only need to change settings:
 # in the init for file paths, frequency filter limit, banned reletionshipURI
 # in the use_toy_dataset , to change the toy dataset
 # in _get_cleaned_movie_rows: to change how the pipeline behave
 
 #pipeline = Pipeline()
 
-# pipeline.use_toy_dataset()
+pipeline.use_toy_dataset()
 # pipeline.execute_task_bpe_corpus()
 # pipeline.execute_task_rdf_mask()
 # pipeline.execute_tasks_rdf_text()
 # pipeline.execute_task_rdf_completation()
-# pipeline.execute_all_task()
+# pipeline.execute_all_task()
+pipeline.generate_csv_debug_file("Assets/Dataset/Tmp/debug.csv")

Scripts/Libs/CleaningPipeline/special_token.py

@@ -10,6 +10,7 @@ class SpecialToken(Enum):
     RELATIONSHIP = "<PRED>"
     OBJECT = "<OBJ>"
     ABSTRACT = "<ABS>"
+    END_OF_SENTENCE = "<EOS>"
     CORPUS_END = "<END>"
 
     ## Tasks' Token

docs/PAPERS.md

@@ -0,0 +1,57 @@
+# Research Material
+
+## BPE
+
+- [BPE Wikipedia](https://en.wikipedia.org/wiki/Byte-pair_encoding)
+- [BPE Hugging Face](https://huggingface.co/learn/llm-course/chapter6/5)
+- [BPE GeeksForGeeks](https://www.geeksforgeeks.org/nlp/byte-pair-encoding-bpe-in-nlp/)
+- [BPE Medium Chetna Khanna](https://medium.com/data-science/byte-pair-encoding-subword-based-tokenization-algorithm-77828a70bee0)
+- [Stack Overflow "Explain bpe (Byte Pair Encoding) with examples?"](https://stackoverflow.com/questions/50583254/explain-bpe-byte-pair-encoding-with-examples)
+- [Implementing a byte pair encoding(BPE) Tokenizer from scratch](https://sebastianraschka.com/blog/2025/bpe-from-scratch.html)
+- [Thoretical Analysis of Byte-Pair Encoding](https://arxiv.org/pdf/2411.08671)
+- [A Formal Perspective on Byte-Pair Encoding](https://aclanthology.org/2023.findings-acl.38v2.pdf)
+- [Byte Pair Encoding is Suboptimal for Language Model Pretraining](https://arxiv.org/pdf/2004.03720)
+- [Byte pair encoding: a text compression scheme that accelerates pattern matching](https://www.researchgate.net/profile/Takeshi-Shinohara/publication/2310624_Byte_Pair_Encoding_A_Text_Compression_Scheme_That_Accelerates_Pattern_Matching/links/02e7e522f8ea00c318000000/Byte-Pair-Encoding-A-Text-Compression-Scheme-That-Accelerates-Pattern-Matching.pdf)
+- [A Formal Perspective on Byte-Pair Encoding](https://arxiv.org/pdf/2306.16837)
+- [Controlling byte pair encoding for neural machine translation](https://ieeexplore.ieee.org/abstract/document/8300571)
+- [Scaffold-BPE: Enhancing Byte Pair Encoding for Large Language Models with Simple and Effective Scaffold Token Removal](https://ojs.aaai.org/index.php/AAAI/article/view/34633)
+- [Parity-Aware Byte-Pair Encoding: Improving Cross-lingual Fairness in Tokenization](https://arxiv.org/pdf/2508.04796)
+- [Code Completion using Neural A‚ention and Byte Pair Encoding](https://arxiv.org/pdf/2004.06343)
+- [Getting the most out of your tokenizer for pre-training and domain adaptation](https://arxiv.org/html/2402.01035v2)
+
+## Embedder
+
+- [ROFORMER: ENHANCED TRANSFORMER WITH ROTARY POSITION EMBEDDING](https://arxiv.org/pdf/2104.09864)
+- [You could have designed state of the art positional encoding](https://huggingface.co/blog/designing-positional-encoding)
+- [Rotary Embeddings: A Relative Revolution](https://blog.eleuther.ai/rotary-embeddings/)
+- [Round and Round We Go! What makes Rotary Positional Encodings useful?](https://arxiv.org/html/2410.06205v1)
+- [Inside RoPE: Rotary Magic into Position Embeddings](https://learnopencv.com/rope-position-embeddings/)
+- [What Rotary Position Embedding Can Tell Us: Identifying Query and Key Weights Corresponding to Basic Syntactic or High-level Semantic Information](https://openreview.net/pdf?id=e5Mv7iWfVW)
+- [A gentle introduction to Rotary Position Embedding](https://krasserm.github.io/2022/12/13/rotary-position-embedding/)
+- [Context-aware Rotary Position Embedding](https://arxiv.org/pdf/2507.23083)
+- [LIERE: GENERALIZING ROTARY POSITION ENCODINGS TO HIGHER DIMENSIONAL INPUTS](https://openreview.net/pdf?id=xHMMt7r3GW)
+- [Rotary Positional Embeddings (RoPE)](https://nn.labml.ai/transformers/rope/index.html)
+- [Decoding Llama3: An explainer for tinkerers](https://hasgeek.com/simrathanspal/the-llama3-guide/sub/decoding-llama3-part-4-rotary-positional-embedding-3K8ZHpdLi6E56N8ejnaWzm)
+
+## Attention
+
+- [Standard Self-Attention (Attention is all you need)](https://arxiv.org/pdf/1706.03762)
+- [TransMLA: Multi-Head Latent Attention Is All You Need](https://arxiv.org/pdf/2502.07864)
+- [A Gentle Introduction to Multi-Head Latent Attention (MLA)](https://machinelearningmastery.com/a-gentle-introduction-to-multi-head-latent-attention-mla/)
+- [Understanding Multi-Head Latent Attention](https://planetbanatt.net/articles/mla.html)
+- [DeepSeek's Multi-Head Latent Attention](https://liorsinai.github.io/machine-learning/2025/02/22/mla.html)
+- [MatchFormer: Interleaving Attention in Transformers for Feature Matching](https://arxiv.org/pdf/2203.09645)
+- [FIT: Far-reaching Interleaved Transformers](https://arxiv.org/pdf/2305.12689)
+- [Gemma explained: What’s new in Gemma 3](https://developers.googleblog.com/en/gemma-explained-whats-new-in-gemma-3/)
+- [The Llama 4 herd: The beginning of a new era of natively multimodal AI innovation](https://ai.meta.com/blog/llama-4-multimodal-intelligence/)
+- [Attention was never enough: Tracing the rise of hybrid LLMs](https://www.ai21.com/blog/rise-of-hybrid-llms/)
+-
+
+## Spanned Masking
+
+- [Salient Span Masking for Temporal Understanding](https://arxiv.org/pdf/2303.12860)
+- [PMI-MASKING: PRINCIPLED MASKING OF CORRELATED SPANS](https://arxiv.org/pdf/2010.01825)
+
+## Models
+
+- [What Language Model Architecture and Pretraining Objective Work Best for Zero-Shot Generalization?](https://arxiv.org/pdf/2204.05832)