{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e28fb85c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# %pip install gputil\n",
    "# %pip install setuptools\n",
    "# %pip install transformers\n",
    "# %pip install torch\n",
    "\n",
    "# %pip install auto-gptq #==0.4.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0667e71a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/mick/pycharmprojects/Frankenstein/.venv/lib/python3.12/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 GPUtil\n",
    "\n",
    "from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline\n",
    "import torch\n",
    "# from auto_gptq import AutoGPTQForCausalLM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0273f299",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No GPU detected on this system.\n"
     ]
    }
   ],
   "source": [
    "gpus = GPUtil.getGPUs()\n",
    "if not gpus:\n",
    "    print(\"No GPU detected on this system.\")\n",
    "else:\n",
    "    for gpu in gpus:\n",
    "        print(f\"GPU Name: {gpu.name}\")\n",
    "        print(f\"Total VRAM: {gpu.memoryTotal} MB\")\n",
    "        print(f\"Free VRAM: {gpu.memoryFree} MB\")\n",
    "        print(f\"Used VRAM: {gpu.memoryUsed} MB\")\n",
    "        print(\"-\" * 40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "67d7e006",
   "metadata": {},
   "outputs": [],
   "source": [
    "def grab_model(model_name, quantized = False):\n",
    "    if quantized:\n",
    "        model = AutoGPTQForCausalLM.from_quantized(model_name, device=\"cpu\", use_safetensors=True)\n",
    "    else:\n",
    "        model = AutoModelForCausalLM.from_pretrained(model_name)\n",
    "\n",
    "    tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
    "    return model, tokenizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "153e9ff5",
   "metadata": {},
   "outputs": [],
   "source": [
    "modelA, tokenizerA = grab_model(\"gpt2\")\n",
    "modelB, tokenizerB = grab_model(\"EleutherAI/gpt-neo-125M\")\n",
    "\n",
    "# modelA, tokenizerA = grab_model(\"EleutherAI/gpt-neo-125M-4bit\", quantized=True)\n",
    "# modelB, tokenizerB = grab_model(\"iproskurina/opt-125m-GPTQ-4bit-g128\", quantized=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1da291ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "modelA.config.hidden_size == modelB.config.hidden_size "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "dcfc2d85",
   "metadata": {},
   "outputs": [],
   "source": [
    "# replace tokenizer:\n",
    "# modelA.Tokenizer = tokenizerB   # optional when not accessing directly \n",
    "\n",
    "# replace token embeddings for input and output:\n",
    "# modelA.set_input_embeddings(modelB.get_input_embeddings())\n",
    "# modelA.lm_head.weight = modelB.get_input_embeddings().weight\n",
    "# modelA.resize_token_embeddings(tokenizerB.vocab_size)\n",
    "\n",
    "# modelA.transformer.wpe.weight = modelB.transformer.wpe.weight\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1011d3ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "# emb1 = modelA.get_input_embeddings().weight\n",
    "# emb2 = modelB.get_input_embeddings().weight\n",
    "\n",
    "# print(\"ModelA mean norms:\", torch.norm(emb1, dim=1).mean().item())\n",
    "# print(\"ModelB mean norms:\", torch.norm(emb2, dim=1).mean().item())\n",
    "\n",
    "# scaling_factor = torch.norm(emb1, dim=1).mean().item() / torch.norm(emb2, dim=1).mean().item()\n",
    "\n",
    "# print(scaling_factor)\n",
    "\n",
    "# new_embedding = torch.nn.Embedding.from_pretrained(emb2*scaling_factor)\n",
    "\n",
    "# print(\"new_embedding mean norms:\", torch.norm(new_embedding.weight, dim=1).mean().item())\n",
    "\n",
    "# modelA.set_input_embeddings(new_embedding)\n",
    "# modelA.lm_head.weight = new_embedding.weight\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "c62b2f41",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(False)\n",
      "tensor(22.2842, grad_fn=<MaxBackward1>)\n",
      "tensor(11.5013, grad_fn=<MeanBackward0>)\n"
     ]
    }
   ],
   "source": [
    "print(torch.isnan(modelB.get_input_embeddings().weight).any())\n",
    "print(torch.norm(modelB.get_input_embeddings().weight, dim=1).max())\n",
    "print(torch.norm(modelB.get_input_embeddings().weight, dim=1).mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2b9893a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "def check_orthogonal(R):\n",
    "    I = torch.eye(R.size(0), device=R.device)\n",
    "    delta = torch.norm(R.T @ R - I)\n",
    "    print(f\"Delta: {delta:.6e}\")\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e1a54c24",
   "metadata": {},
   "outputs": [],
   "source": [
    "# use proscrustes:\n",
    "def procrustes(A: torch.Tensor, B: torch.Tensor) -> torch.Tensor:\n",
    "    # A_centered = A - A.mean(dim=0, keepdim=True)\n",
    "    # B_centered = B - B.mean(dim=0, keepdim=True)\n",
    "\n",
    "    #M = B_centered.T @ A_centered\n",
    "    M = B.T @ A\n",
    "    # find optimal rotation with svd\n",
    "    U, _, Vt = torch.linalg.svd(M)\n",
    "\n",
    "    # get rotation matrix that aligns B to A\n",
    "    R = U @ Vt\n",
    "\n",
    "    check_orthogonal(R)\n",
    "    \n",
    "    return R # return rotated tensor\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fedd4d04",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ff93495e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Delta: 6.706436e-05\n",
      "torch.Size([1024, 768])\n"
     ]
    }
   ],
   "source": [
    "emb1 = modelA.get_input_embeddings().weight\n",
    "emb2 = modelB.get_input_embeddings().weight\n",
    "\n",
    "# get rotation matrix\n",
    "R = procrustes(emb2, emb1)\n",
    "emb1_R = emb1 @ R\n",
    "\n",
    "new_embedding = torch.nn.Embedding.from_pretrained(emb1_R)\n",
    "\n",
    "modelA.set_input_embeddings(new_embedding)\n",
    "modelA.lm_head.weight = new_embedding.weight\n",
    "\n",
    "# def rotate_weight(W, R):\n",
    "#     if W.shape[1] == R.shape[0]:\n",
    "#         return W @ R\n",
    "#     if W.shape[0] == R.shape[0]:\n",
    "#         return R.T @ W\n",
    "\n",
    "# now fix the other layers by conjugating:\n",
    "# for block in modelA.transformer.h:\n",
    "#     for M in [block.attn.c_attn, block.mlp.c_fc]:\n",
    "#         W = M.weight.data\n",
    "#         W[:] = R.T @ W\n",
    "#     for M in [block.attn.c_proj, block.mlp.c_proj]:\n",
    "#         W = M.weight.data\n",
    "#         W[:] = R.T @ W @ R\n",
    "\n",
    "def split_rotate_concat(W):\n",
    "    parts1 = [x for x in W.split(768, dim=1)]\n",
    "    for i, v in enumerate(parts1):\n",
    "        parts2 = [x for x in v.split(768, dim=0)]\n",
    "        for j, w in enumerate(parts2):\n",
    "            parts2[j] = R.T @ w @ R\n",
    "        parts1[i] = torch.cat(parts2, dim=0)\n",
    "    return torch.cat(parts1, dim=1)\n",
    "\n",
    "\n",
    "def rotate_layernorm(ln):\n",
    "    ln.weight.data[:] = ln.weight.data @ R\n",
    "    ln.bias.data[:] = ln.bias.data @ R\n",
    "\n",
    "for block in modelA.transformer.h:\n",
    "    # print(block.attn.c_attn.weight.data.shape)\n",
    "    # print(block.mlp.c_fc.weight.data.shape)\n",
    "    # print(block.attn.c_proj.weight.data.shape)\n",
    "    # print(block.mlp.c_proj.weight.data.shape)\n",
    "    # block.attn.c_attn.weight.data[:] = split_rotate_concat(block.attn.c_attn.weight.data.T).T\n",
    "    # block.mlp.c_fc.weight.data[:] = split_rotate_concat(block.mlp.c_fc.weight.data.T).T\n",
    "    block.attn.c_attn.weight.data[:] = split_rotate_concat(block.attn.c_attn.weight.data)\n",
    "    block.mlp.c_fc.weight.data[:] = split_rotate_concat(block.mlp.c_fc.weight.data)\n",
    "    block.attn.c_proj.weight.data[:] = split_rotate_concat(block.attn.c_proj.weight.data)\n",
    "    block.mlp.c_proj.weight.data[:] = split_rotate_concat(block.mlp.c_proj.weight.data)\n",
    "    rotate_layernorm(block.ln_1)\n",
    "    rotate_layernorm(block.ln_2)\n",
    "\n",
    "rotate_layernorm(modelA.transformer.ln_f)\n",
    "\n",
    "\n",
    "print(modelA.transformer.wpe.weight.data.shape)\n",
    "modelA.transformer.wpe.weight.data[:] = modelA.transformer.wpe.weight.data @ R\n",
    "\n",
    "    # for name in ['c_attn', 'c_proj']:\n",
    "    #     W = getattr(block.attn, name).weight.data\n",
    "    #     W[:] = R.T @ W @ R\n",
    "    # w1 = block.mlp.c_fc.weight.data\n",
    "    # w2 = block.mlp.c_proj.weight.data\n",
    "    # w1[:] = R.T @ W1 @ R\n",
    "    # w2[:] = R.T @ W2 @ R\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9b671b41",
   "metadata": {},
   "outputs": [],
   "source": [
    "# modelA.transformer.wpe.weight = modelB.transformer.wpe.weight"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "85957357",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# modelA.lm_head.out_features == tokenizerA.vocab_size"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6b39638",
   "metadata": {},
   "source": [
    "Text:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fbfa8d62",
   "metadata": {},
   "source": [
    "With it:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "998a0ed6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# modelA.lm_head.weight = modelA.get_input_embeddings().weight # should change nothing: they are the same object.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa8b7ca4",
   "metadata": {},
   "source": [
    "Text:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "d8d9d612",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Device set to use cpu\n",
      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[{'generated_text': 'Hello, how are you?orm Coulormormorm Coulorm Coul Coulorm Coul Coulinion Coulorm Coulonomousonomous Coulonomousonomousonomousonomousonomousormonomous Coulorm Coulonomousonomousonomous Coulonomousonomous Coulonomousonomousonomous Coulonomousonomousonomousonomousonomousonomousonomousonomousonomousonomousonomousonomousonomousonomousonomousonomousonomous Coulonomousonomousonomousonomousonomousonomousonomousonomousonomousonomousonomousonomousonomousonomousonomousonomousonomousonomousonomousonomous Coulonomousonomousonomousonomousonomousonomousonomous Coulonomousonomousonomousonomousonomousonomousonomousonomousonomousonomousonomousonomous Amenonomousonomousonomousonomousonomousonomousonomousonomousonomousonomousonomousonomousonomousonomousonomousonomousonomous Coulonomous Coulonomousonomousonomousonomousonomousonomousonomoushered…] Coulonomousonomousonomous Amenomniaifulonomousonomouskeleyifulonomous Amenomniaifulhered Amenkeleyomniastad Coulonomousifulifulomniaifulomniaifulomniaifulifulifulomniaifulomnia…]hered…]ifulomniaifulifulomniastadkeleyomniaifulifulomniaifulomniaifulomniakeleyomniaomniaomnia Coulomniaifulomnia Coulifulomnia Coul Coulkeleyomniastad Coulomnia Coulkeleyomnia Coulkeleyomnia Coulkeleyomniaomnia Coulkeleyomniaomniaomniaomniastadomniaomniaomniaomnia Coulkeleyonomousomnia Coulomniaomniaomnia Coulkeleyomnia Coulomniaomniaomniaomnia Coulomniaomniakeleyomniakeleyomniakeleyomniaomniaomniaomniakeleystadkeleyomniakeleyomniaomnia'}]\n"
     ]
    }
   ],
   "source": [
    "# use model\n",
    "pipe = pipeline(\"text-generation\", model=modelA, tokenizer=tokenizerB)\n",
    "print(pipe(\"Hello, how are you?\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fc72ea8a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d7673a5e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "79616f5c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "modelA.lm_head.weight.data_ptr() == modelA.get_input_embeddings().weight.data_ptr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "7fc76499",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "modelB.lm_head.weight.data_ptr() == modelB.get_input_embeddings().weight.data_ptr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "7d51d201",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([ 1212,   318,   257,  1332,   290,  1312,  4240,  1521,   262, 11241,\n",
      "        11341,   389,   262,   976])\n"
     ]
    }
   ],
   "source": [
    "tok = tokenizerA(\"This is a test and i wonder why the tokenizers are the same\", return_tensors = \"pt\")\n",
    "print(tok.input_ids[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2e76534a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([ 1212,   318,   257,  1332,   290,  1312,  4240,  1521,   262, 11241,\n",
      "        11341,   389,   262,   976])\n"
     ]
    }
   ],
   "source": [
    "tok = tokenizerB(\"This is a test and i wonder why the tokenizers are the same\", return_tensors = \"pt\")\n",
    "print(tok.input_ids[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a44c465a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "381c712f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "153995fe",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv",
   "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": "3.12.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}