349 lines
11 KiB
Plaintext
349 lines
11 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "e28fb85c",
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"metadata": {},
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"outputs": [],
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"source": [
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"# %pip install gputil\n",
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"# %pip install setuptools\n",
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"# %pip install transformers\n",
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"# %pip install torch\n",
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"\n",
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"# %pip install auto-gptq #==0.4.0"
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]
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},
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{
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"cell_type": "markdown",
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"id": "10e0a35a",
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"metadata": {},
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"source": [
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"What happens if you try to rotate an entire LMM model. Will it still work if you consistently rotate all trained matrices?\n",
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"\n",
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"Doing this is very specific to the internal representations of a particular LMM. Different models have very different internal layers and representations. Layers may have different shapes, or are concatenated (such as the kvq matrices). \n",
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"\n",
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"Should all matrices be rotated, and which should be conjugated? \n",
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"\n",
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"This notebook just offers some base code, it's still far removed from the right approach."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "0667e71a",
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/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",
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" from .autonotebook import tqdm as notebook_tqdm\n"
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]
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}
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],
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"source": [
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"import GPUtil\n",
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"\n",
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"from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline\n",
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"import torch\n",
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"# from auto_gptq import AutoGPTQForCausalLM"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"id": "0273f299",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"No GPU detected on this system.\n"
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]
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}
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],
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"source": [
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"gpus = GPUtil.getGPUs()\n",
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"if not gpus:\n",
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" print(\"No GPU detected on this system.\")\n",
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"else:\n",
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" for gpu in gpus:\n",
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" print(f\"GPU Name: {gpu.name}\")\n",
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" print(f\"Total VRAM: {gpu.memoryTotal} MB\")\n",
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" print(f\"Free VRAM: {gpu.memoryFree} MB\")\n",
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" print(f\"Used VRAM: {gpu.memoryUsed} MB\")\n",
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" print(\"-\" * 40)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "67d7e006",
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"metadata": {},
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"outputs": [],
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"source": [
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"def grab_model(model_name, quantized = False):\n",
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" if quantized:\n",
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" model = AutoGPTQForCausalLM.from_quantized(model_name, device=\"cpu\", use_safetensors=True)\n",
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" else:\n",
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" model = AutoModelForCausalLM.from_pretrained(model_name)\n",
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"\n",
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" tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
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" return model, tokenizer"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 63,
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"id": "153e9ff5",
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"metadata": {},
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"outputs": [],
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"source": [
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"modelA, tokenizerA = grab_model(\"gpt2\")\n",
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"modelB, tokenizerB = grab_model(\"EleutherAI/gpt-neo-125M\")\n",
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"\n",
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"# modelA, tokenizerA = grab_model(\"EleutherAI/gpt-neo-125M-4bit\", quantized=True)\n",
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"# modelB, tokenizerB = grab_model(\"iproskurina/opt-125m-GPTQ-4bit-g128\", quantized=True)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "1da291ed",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"True"
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]
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},
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"execution_count": 5,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"modelA.config.hidden_size == modelB.config.hidden_size "
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]
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},
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{
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"cell_type": "code",
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"execution_count": 113,
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"id": "c62b2f41",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"tensor(False)\n",
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"tensor(22.2842, grad_fn=<MaxBackward1>)\n",
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"tensor(11.5013, grad_fn=<MeanBackward0>)\n"
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]
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}
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],
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"source": [
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"print(torch.isnan(modelB.get_input_embeddings().weight).any())\n",
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"print(torch.norm(modelB.get_input_embeddings().weight, dim=1).max())\n",
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"print(torch.norm(modelB.get_input_embeddings().weight, dim=1).mean())"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "2b9893a3",
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"metadata": {},
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"outputs": [],
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"source": [
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"def check_orthogonal(R):\n",
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" I = torch.eye(R.size(0), device=R.device)\n",
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" delta = torch.norm(R.T @ R - I)\n",
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" print(f\"Delta: {delta:.6e}\")\n",
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" "
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "e1a54c24",
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"metadata": {},
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"outputs": [],
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"source": [
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"# use proscrustes:\n",
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"def procrustes(A: torch.Tensor, B: torch.Tensor) -> torch.Tensor:\n",
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" # A_centered = A - A.mean(dim=0, keepdim=True)\n",
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" # B_centered = B - B.mean(dim=0, keepdim=True)\n",
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"\n",
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" #M = B_centered.T @ A_centered\n",
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" M = B.T @ A\n",
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" # find optimal rotation with svd\n",
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" U, _, Vt = torch.linalg.svd(M)\n",
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"\n",
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" # get rotation matrix that aligns B to A\n",
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" R = U @ Vt\n",
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"\n",
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" check_orthogonal(R)\n",
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" \n",
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" return R # return rotated tensor\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "fedd4d04",
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "ff93495e",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Delta: 6.706436e-05\n",
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"torch.Size([1024, 768])\n"
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]
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}
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],
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"source": [
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"emb1 = modelA.get_input_embeddings().weight\n",
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"emb2 = modelB.get_input_embeddings().weight\n",
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"\n",
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"# get rotation matrix\n",
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"R = procrustes(emb2, emb1)\n",
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"emb1_R = emb1 @ R\n",
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"\n",
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"new_embedding = torch.nn.Embedding.from_pretrained(emb1_R)\n",
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"\n",
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"modelA.set_input_embeddings(new_embedding)\n",
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"modelA.lm_head.weight = new_embedding.weight\n",
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"\n",
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"# def rotate_weight(W, R):\n",
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"# if W.shape[1] == R.shape[0]:\n",
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"# return W @ R\n",
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"# if W.shape[0] == R.shape[0]:\n",
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"# return R.T @ W\n",
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"\n",
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"# now fix the other layers by conjugating:\n",
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"# for block in modelA.transformer.h:\n",
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"# for M in [block.attn.c_attn, block.mlp.c_fc]:\n",
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"# W = M.weight.data\n",
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"# W[:] = R.T @ W\n",
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"# for M in [block.attn.c_proj, block.mlp.c_proj]:\n",
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"# W = M.weight.data\n",
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"# W[:] = R.T @ W @ R\n",
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"\n",
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"def split_rotate_concat(W):\n",
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" parts1 = [x for x in W.split(768, dim=1)]\n",
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" for i, v in enumerate(parts1):\n",
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" parts2 = [x for x in v.split(768, dim=0)]\n",
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" for j, w in enumerate(parts2):\n",
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" parts2[j] = R.T @ w @ R\n",
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" parts1[i] = torch.cat(parts2, dim=0)\n",
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" return torch.cat(parts1, dim=1)\n",
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"\n",
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"\n",
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"def rotate_layernorm(ln):\n",
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" ln.weight.data[:] = ln.weight.data @ R\n",
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" ln.bias.data[:] = ln.bias.data @ R\n",
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"\n",
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"for block in modelA.transformer.h:\n",
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" # print(block.attn.c_attn.weight.data.shape)\n",
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" # print(block.mlp.c_fc.weight.data.shape)\n",
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" # print(block.attn.c_proj.weight.data.shape)\n",
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" # print(block.mlp.c_proj.weight.data.shape)\n",
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" # block.attn.c_attn.weight.data[:] = split_rotate_concat(block.attn.c_attn.weight.data.T).T\n",
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" # block.mlp.c_fc.weight.data[:] = split_rotate_concat(block.mlp.c_fc.weight.data.T).T\n",
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" block.attn.c_attn.weight.data[:] = split_rotate_concat(block.attn.c_attn.weight.data)\n",
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" block.mlp.c_fc.weight.data[:] = split_rotate_concat(block.mlp.c_fc.weight.data)\n",
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" block.attn.c_proj.weight.data[:] = split_rotate_concat(block.attn.c_proj.weight.data)\n",
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" block.mlp.c_proj.weight.data[:] = split_rotate_concat(block.mlp.c_proj.weight.data)\n",
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" rotate_layernorm(block.ln_1)\n",
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" rotate_layernorm(block.ln_2)\n",
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"\n",
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"rotate_layernorm(modelA.transformer.ln_f)\n",
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"\n",
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"\n",
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"print(modelA.transformer.wpe.weight.data.shape)\n",
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"modelA.transformer.wpe.weight.data[:] = modelA.transformer.wpe.weight.data @ R\n",
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"\n",
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" # for name in ['c_attn', 'c_proj']:\n",
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" # W = getattr(block.attn, name).weight.data\n",
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" # W[:] = R.T @ W @ R\n",
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" # w1 = block.mlp.c_fc.weight.data\n",
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" # w2 = block.mlp.c_proj.weight.data\n",
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" # w1[:] = R.T @ W1 @ R\n",
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" # w2[:] = R.T @ W2 @ R\n",
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" \n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 66,
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"id": "d8d9d612",
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"Device set to use cpu\n",
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"Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[{'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"
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]
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}
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],
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"source": [
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"# use model\n",
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"pipe = pipeline(\"text-generation\", model=modelA, tokenizer=tokenizerB)\n",
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"print(pipe(\"Hello, how are you?\"))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "fc72ea8a",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": ".venv",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.12.3"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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