{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "daee88ca",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/annt68re/anaconda3/envs/quantize/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "import numpy as np\n",
    "from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig\n",
    "from datasets import load_dataset\n",
    "import gc\n",
    "import os\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3acda586",
   "metadata": {},
   "source": [
    "## Example: Load Mixtral model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b6829f5",
   "metadata": {},
   "source": [
    "Rename the selected_model and LOAD_PATH to load other models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "81e82b58",
   "metadata": {},
   "outputs": [],
   "source": [
    "selected_model = \"qwen-moe-a2.7b\"\n",
    "LOAD_PATH = \"outputs/qwen-moe-a2.7b_20251009_084529\" \n",
    "\n",
    "input_file = os.path.join(LOAD_PATH, 'tokenized_input.npz')\n",
    "metadata_file = os.path.join(LOAD_PATH, 'metadata.txt')\n",
    "attention_file = os.path.join(LOAD_PATH, 'attention_matrices_multihead.npz')\n",
    "routing_file = os.path.join(LOAD_PATH, 'routing_matrices.npz')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b4782df",
   "metadata": {},
   "source": [
    "### Overview of the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "37e42ac1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--- Metadata ---\n",
      "model_name: mixtral-8x7b\n",
      "model_id: mistralai/Mixtral-8x7B-Instruct-v0.1\n",
      "sequence_length: 5000\n",
      "num_attention_layers: 32\n",
      "num_routing_layers: 32\n",
      "timestamp: 20251008_035141\n",
      "attention_shape_per_layer: (5000, 5000)\n",
      "routing_shape_per_layer: torch.Size([5000, 8])\n",
      "has_routing_matrices: Yes\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"--- Metadata ---\")\n",
    "with open(metadata_file, 'r') as f:\n",
    "    print(f.read())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1aabb0d",
   "metadata": {},
   "source": [
    "### Load input data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "550042ea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "File size: 0.04 MB\n",
      "\n",
      "Tokenized Input:\n",
      "Shape: (5000,)\n",
      "First 20 token IDs: [    1   327   550  1093 28724  3931 23967  4992  6950   327 28705    13\n",
      " 28705  5355 28768 28934   708   550  1093 28724]\n",
      "\n",
      "Original text length: 50000 characters\n",
      "First 200 characters:  = Valkyria Chronicles III = \n",
      "  Senjō no Valkyria 3 : Unrecorded Chronicles ( Japanese : 戦場のヴァルキュリア3 , lit . Valkyria of the Battlefield 3 ) , commonly referred to as Valkyria Chronicles III outside J...\n"
     ]
    }
   ],
   "source": [
    "input_data = np.load(input_file)\n",
    "print(f\"File size: {os.path.getsize(input_file) / (1024**2):.2f} MB\")\n",
    "\n",
    "input_ids = input_data['input_ids']\n",
    "print(f\"\\nTokenized Input:\")\n",
    "print(f\"Shape: {input_ids.shape}\")\n",
    "print(f\"First 20 token IDs: {input_ids[:20]}\")\n",
    "\n",
    "\n",
    "input_text = str(input_data['input_text'])\n",
    "print(f\"\\nOriginal text length: {len(input_text)} characters\")\n",
    "print(f\"First 200 characters: {input_text[:200]}...\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be1042d0",
   "metadata": {},
   "source": [
    "### Load attention matrices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c73d0e93",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "File size: 581.18 MB\n",
      "Available layers: 32\n",
      "Layer names: ['layer_0', 'layer_1', 'layer_2', 'layer_3', 'layer_4', 'layer_5', 'layer_6', 'layer_7', 'layer_8', 'layer_9', 'layer_10', 'layer_11', 'layer_12', 'layer_13', 'layer_14', 'layer_15', 'layer_16', 'layer_17', 'layer_18', 'layer_19', 'layer_20', 'layer_21', 'layer_22', 'layer_23', 'layer_24', 'layer_25', 'layer_26', 'layer_27', 'layer_28', 'layer_29', 'layer_30', 'layer_31']\n"
     ]
    }
   ],
   "source": [
    "attn_data = np.load(attention_file, allow_pickle=True)\n",
    "print(f\"File size: {os.path.getsize(attention_file) / (1024**2):.2f} MB\")\n",
    "print(f\"Available layers: {len(attn_data.files)}\")\n",
    "print(f\"Layer names: {attn_data.files}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1dbcb935",
   "metadata": {},
   "source": [
    "Example: Extract attention matrix of layer 0. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2c2aecf1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Layer 0 attention matrix shape: (5000, 5000)\n",
      "Layer 0 sample attention (first 5 tokens):\n",
      "[[1.      0.      0.      0.      0.     ]\n",
      " [0.91    0.0879  0.      0.      0.     ]\n",
      " [0.8164  0.1406  0.0437  0.      0.     ]\n",
      " [0.785   0.0884  0.0962  0.02917 0.     ]\n",
      " [0.7773  0.05298 0.0718  0.07666 0.02332]]\n"
     ]
    }
   ],
   "source": [
    "layer_0 = attn_data['layer_0']\n",
    "print(f\"\\nLayer 0 attention matrix:\")\n",
    "print(f\"Shape: {layer_0.shape} [num_heads, seq_len, seq_len]\")\n",
    "print(f\"Number of heads: {layer_0.shape[0]}\")\n",
    "print(f\"Min value: {layer_0.min():.6f}\")\n",
    "print(f\"Max value: {layer_0.max():.6f}\")\n",
    "print(f\"Mean value: {layer_0.mean():.6f}\")\n",
    "\n",
    "print(f\"\\n  Head 0 attention sample (first 5x5 tokens):\")\n",
    "print(layer_0[0, :5, :5])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2fbf90d1",
   "metadata": {},
   "source": [
    "### Load routing matrices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "439c1bc1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "File size: 1.88 MB\n",
      "Available layers: 32\n",
      "Layer names: ['layer_0', 'layer_1', 'layer_2', 'layer_3', 'layer_4', 'layer_5', 'layer_6', 'layer_7', 'layer_8', 'layer_9', 'layer_10', 'layer_11', 'layer_12', 'layer_13', 'layer_14', 'layer_15', 'layer_16', 'layer_17', 'layer_18', 'layer_19', 'layer_20', 'layer_21', 'layer_22', 'layer_23', 'layer_24', 'layer_25', 'layer_26', 'layer_27', 'layer_28', 'layer_29', 'layer_30', 'layer_31']\n"
     ]
    }
   ],
   "source": [
    "routing_data = np.load(routing_file)\n",
    "print(f\"File size: {os.path.getsize(routing_file) / (1024**2):.2f} MB\")\n",
    "print(f\"Available layers: {len(routing_data.files)}\")\n",
    "print(f\"Layer names: {routing_data.files}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b79defd",
   "metadata": {},
   "source": [
    "Example: Extract routing matrix of layer 0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ea00690c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Routing logits sample (first 5 tokens, all experts):\n",
      "[[-0.3574   0.1875   0.01233 -0.1157  -0.496    1.195   -0.1914  -0.00653]\n",
      " [-0.617   -2.016    1.523    0.6367  -1.086    1.25     0.84     0.01318]\n",
      " [-0.832   -1.602    2.36     0.3926  -0.926    1.578   -0.1494  -0.463  ]\n",
      " [-1.32     0.1177   2.25     3.156   -1.6875   0.5      0.1631  -3.266  ]\n",
      " [ 1.414   -1.578    0.377    0.91    -0.1367   0.9844  -0.2617  -1.57   ]]\n"
     ]
    }
   ],
   "source": [
    "layer_0 = routing_data['layer_0']\n",
    "print(f\"Routing logits sample (first 5 tokens, all experts):\")\n",
    "print(layer_0[:5, :])\n"
   ]
  }
 ],
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