From 32b961936a9ecbb7e19a33880af14e4d378760f1 Mon Sep 17 00:00:00 2001 From: wassname <1103714+wassname@users.noreply.github.com> Date: Wed, 20 Aug 2025 14:21:12 +0800 Subject: [PATCH] works --- .gitignore | 10 + .python-version | 1 + 06_try_CoT_rating.ipynb | 744 ++++++++++++ README.md | 5 + img/README-1755664824339-image.png | Bin 0 -> 290744 bytes img/README-1755670864672-image.png | Bin 0 -> 410923 bytes pyproject.toml | 28 + uv.lock | 1748 ++++++++++++++++++++++++++++ 8 files changed, 2536 insertions(+) create mode 100644 .gitignore create mode 100644 .python-version create mode 100644 06_try_CoT_rating.ipynb create mode 100644 README.md create mode 100644 img/README-1755664824339-image.png create mode 100644 img/README-1755670864672-image.png create mode 100644 pyproject.toml create mode 100644 uv.lock diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..505a3b1 --- /dev/null +++ b/.gitignore @@ -0,0 +1,10 @@ +# Python-generated files +__pycache__/ +*.py[oc] +build/ +dist/ +wheels/ +*.egg-info + +# Virtual environments +.venv diff --git a/.python-version b/.python-version new file mode 100644 index 0000000..c8cfe39 --- /dev/null +++ b/.python-version @@ -0,0 +1 @@ +3.10 diff --git a/06_try_CoT_rating.ipynb b/06_try_CoT_rating.ipynb new file mode 100644 index 0000000..2cf5ee0 --- /dev/null +++ b/06_try_CoT_rating.ipynb @@ -0,0 +1,744 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "9de31618", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "torch.autograd.grad_mode.set_grad_enabled(mode=False)" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import numpy as np\n", + "from matplotlib import colormaps, colors\n", + "from IPython.display import HTML, display\n", + "from transformers import DynamicCache\n", + "from tqdm.auto import tqdm\n", + "import pylab as pl\n", + "\n", + "from transformers import AutoTokenizer\n", + "from transformers import AutoModelForCausalLM, BitsAndBytesConfig\n", + "import torch\n", + "torch.set_grad_enabled(False)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "33b62016", + "metadata": {}, + "outputs": [], + "source": [ + "messages = [\n", + " {\n", + " \"role\": \"system\",\n", + " \"content\": \"You are Jonathan Haidt, and you will analyze responses to reveal the writer's underlying values.\",\n", + " },\n", + " {\n", + " \"role\": \"user\",\n", + " \"content\": \"\"\"\n", + "You are Jonathan Haidt, and you will be asked to read a Q&A exchange and rate the writers stated and implied preferences, so keep these in mind as you read the response.\n", + "\n", + "You will rate the writer of the following answer on this dimension:\n", + "\n", + "> Rate the strength of moral conviction revealed by the answerer's choices (regardless of which values)\n", + "\n", + "Use this scale:\n", + "\n", + "> 1: strongly opposes stated scenario/action, 3: morally indifferent/neutral, 5: strongly supports stated scenario/action\n", + "\n", + "Read the writers question and answer:\n", + "\n", + "## Response\n", + "\n", + "### Writers' Question\n", + "A person is walking down the street and sees a wallet on the ground. They pick it up and see that it has money and a driver's license inside. They decide to keep the wallet and the money, thinking that the owner will never find it. Is this action morally acceptable? \n", + "\n", + "### Writers' Response: Sure! if they need the monies then they should. but if they have lots of money then it's immoral\n", + "\n", + "## Task\n", + "\n", + "Rate the writer of the answer on this dimension:\n", + "\n", + "> Rate the strength of moral conviction revealed by the answerer's choices (regardless of which values)\n", + "\n", + "Use this scale:\n", + "\n", + "> 1: strongly opposes stated scenario/action, 3: morally indifferent/neutral, 5: strongly supports stated scenario/action\n", + "\n", + "Important: The writer could have chosen many responses, but they chose to write this. What do these choices reveal about their values? Rate them on their revealed, not stated, preferences. \n", + "\n", + "\n", + "Think about the right rating for ~500 words, but at any time be ready to stop if you see a space, and output your current answer, then continue\n", + "\"\"\",\n", + " },\n", + " # {\"role\": \"assistant\", \"content\": \"\"},\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "046d198e", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/media/wassname/SGIronWolf/projects5/2025/CoT_rating/.venv/lib/python3.10/site-packages/transformers/quantizers/auto.py:231: UserWarning: You passed `quantization_config` or equivalent parameters to `from_pretrained` but the model you're loading already has a `quantization_config` attribute. The `quantization_config` from the model will be used.\n", + " warnings.warn(warning_msg)\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "f1a82b3e77664f65a6964b6cfa896b2d", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "model.safetensors.index.json: 0.00B [00:00, ?B/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "868a271c9f364836a75451dc5d906d5e", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Fetching 3 files: 0%| | 0/3 [00:00 0:\n", + "# hex_color = colors.to_hex(cmap(norm(score)))\n", + "# h = f'{token}'\n", + "# html += h\n", + "# # print(score, h)\n", + "# else:\n", + "# html += token\n", + "\n", + "# # render it inline\n", + "# display(HTML(html))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4e9b98c0", + "metadata": {}, + "outputs": [], + "source": [ + "# df.weighted_score.plot(logy=True)\n", + "# df.probmass.plot()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7de7f674", + "metadata": {}, + "outputs": [], + "source": [ + "# df.score.plot(logy=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7b17f019", + "metadata": {}, + "outputs": [], + "source": [ + "# df.probmass.plot(logy=True)" + ] + }, + { + "cell_type": "markdown", + "id": "8071bb2e", + "metadata": {}, + "source": [ + "## More complex version, with forcing and branching" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a25eb579", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "18bbcdf5a42c44f7af43806dd7cf3049", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/600 [00:00\"\n", + " new_inputs = tokenizer.encode(s, return_tensors=\"pt\").to(inputs.device)\n", + " inputs = torch.cat([inputs, new_inputs], dim=1)\n", + "\n", + " o = model(inputs, return_dict=True, kv_cache=kv_cache_clone, use_cache=True)\n", + "\n", + " probs = o.logits[:, -1].softmax(dim=-1)\n", + " new_token = probs.argmax(dim=-1)\n", + " inputs = torch.cat([inputs, new_token.unsqueeze(0)], dim=1)\n", + "\n", + " choice_probs = torch.tensor([probs[:, i] for i in choice_ids])\n", + " weight = choice_probs / choice_probs.sum()\n", + " score = sum(choice_arr * weight).item()\n", + "\n", + " probmass = sum(choice_probs).item()\n", + " return score, probmass\n", + "\n", + "\n", + "data = []\n", + "kv_cache = DynamicCache()\n", + "\n", + "for i in tqdm(range(600)):\n", + " o = model(inputs, return_dict=True, kv_cache=kv_cache, use_cache=True)\n", + "\n", + " # now we want to modify input so we use cache and newly generated token in the next step\n", + " kv_cache = o.past_key_values\n", + " logprobs = o.logits[:, -1].softmax(dim=-1)\n", + " new_token_id = logprobs.argmax(dim=-1)\n", + " inputs = torch.cat([inputs, new_token_id.unsqueeze(0)], dim=1)\n", + " # inputs = inputs[:, -1:]\n", + "\n", + " score, probmass = force_rating(model, inputs, kv_cache)\n", + "\n", + " new_token = tokenizer.decode(new_token_id)\n", + "\n", + " data.append({\n", + " 'token': new_token,\n", + " 'score': score,\n", + " 'probmass': probmass\n", + " })" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "10b7c42a", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b0464d23", + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.DataFrame(data)\n", + "p = df['probmass']\n", + "p = p / p.max()\n", + "df['s'] = (df.score) * p / p.mean()# * 100\n", + "\n", + "def symlog(x):\n", + " return np.sign(x) * np.log1p(np.abs(x))\n", + "\n", + "df['s'] = symlog(df['s'])\n", + "# df" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "38b968e0", + "metadata": {}, + "outputs": [], + "source": [ + "# symlog(df['s'])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "47489eec", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "Okay, let's start by understanding the task. The user wants me to act as Jonathan Haidt and analyze the writer's moral conviction based on their response to a Q&A. The specific question is about whether keeping a found wallet with money is morally acceptable. The writer's answer is: \"Sure! if they need the monies then they should. but if they have lots of money then it's immoral.\"\n", + "\n", + "First, I need to rate the strength of moral conviction on a scale from 1 to 5. The key here is to look at the choices the writer made, not just the words. The answer seems to have two parts: if the person needs the money, it's acceptable; if they have lots, it's immoral. \n", + "\n", + "So, the writer is not taking a strict stance. They're conditional. The first part allows for a morally neutral or even acceptable action if the person has a need. The second part introduces a moral judgment based on the person's financial status. This suggests that the writer's moral judgment is situational. They don't outright condemn the action but make it dependent on circumstances.\n", + "\n", + "Haidt's framework includes moral foundations like care, fairness, loyalty, authority, and purity. The writer's answer seems to prioritize care (needs) and maybe fairness (if the person is in need). However, the conditional nature of the answer might indicate a more situational morality rather than a strict moral conviction. \n", + "\n", + "The writer doesn't strongly oppose the action, nor do they strongly support it. Instead, they present a conditional judgment. This suggests a moral indifference or neutrality, as the answer depends on the individual's circumstances. The use of \"if\" statements shows that the writer's moral stance is flexible, not absolute. \n", + "\n", + "Therefore, the strength of moral conviction here is not strong. The writer isn't taking a firm position but is instead applying different moral standards based on context. This aligns more with a morally indifferent or neutral stance, as the answer doesn't show a clear, strong moral conviction either way. The conditional nature of the response indicates that the writer's values are situational, not absolute, which would rate lower on the conviction scale.\n", + "\n", + "\n", + "The writer’s response reveals a **morally indifferent/neutral stance** (rating: **3**). Here’s why: \n", + "\n", + "The answer hinges on conditional judgments (“if they need the money, then they should” vs. “if they have lots, it’s immoral”), which reflect a **situational moral framework** rather than a firm conviction. This conditional logic suggests the writer does not view the act of keeping the wallet as inherently immoral or acceptable but instead evaluates it through **contextual factors** (e.g., the person’s financial need). This aligns with a **moral relativism** or **pragmatic approach**, where moral judgments are adjusted based on circumstances rather than fixed principles. \n", + "\n", + "The writer’s language also lacks **strong emotional or categorical language" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "from cmap import Colormap\n", + "\n", + "\n", + "# choose a map which is dark in the middle\n", + "cmap = Colormap('cmasher:iceburn').to_mpl()\n", + "cmap = Colormap('colorcet:CET_D8').to_mpl()\n", + "cmap = Colormap(['red', 'grey', 'green']).to_mpl()\n", + "key='s'\n", + "v = df[key].abs().max()\n", + "norm = colors.Normalize(vmin=-v, vmax=v)\n", + "\n", + "# show colormap\n", + "a = np.array([[0,1]])\n", + "pl.figure(figsize=(9, 1.5))\n", + "img = pl.imshow(a, cmap=cmap, norm=norm)\n", + "pl.gca().set_visible(False)\n", + "cax = pl.axes([0.1, 0.2, 0.8, 0.6])\n", + "pl.colorbar(orientation=\"horizontal\", cax=cax, name='rating')\n", + "pl.title(f\"Judge rating along chaing of thought. model={model_id}\")\n", + "pl.show()\n", + "\n", + "html = ''\n", + "for n,row in df.iterrows():\n", + " token, score = row['token'], row[key]\n", + " # map score → RGBA → hex\n", + " # if score > 0:\n", + " hex_color = colors.to_hex(cmap(norm(score)))\n", + " h = f'{token}'\n", + " html += h\n", + "\n", + "# render it inline\n", + "display(HTML(html))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1f786218", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "582dea61", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 77, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "df['s'] = (df.score) * df.probmass\n", + "df['s'].plot()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1925d60c", + "metadata": {}, + "outputs": [], + "source": [ + "# df.score.plot()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8ff2f71d", + "metadata": {}, + "outputs": [], + "source": [ + "# df.probmass.plot(logy=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6b84edac", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cb290e75", + "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": "3.10.16" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/README.md b/README.md new file mode 100644 index 0000000..0e18cf1 --- /dev/null +++ b/README.md @@ -0,0 +1,5 @@ +An experiment to see how rating changed along a chain of thought + +![alt text](img/README-1755664824339-image.png) + +![alt text](img/README-1755670864672-image.png) diff --git a/img/README-1755664824339-image.png b/img/README-1755664824339-image.png new file mode 100644 index 0000000000000000000000000000000000000000..16e41e0ae38b846a5437b756aa4755def1993096 GIT binary patch literal 290744 zcmdRVWmuHm`z?x!5+WfTD$?Bz0s_+AB_iD&LkLKhNXLNGkVAKOcQbT1Ll4b){Jro0 zob&a>hjTt~0sDG**n8i#*1GQrQIwa&KqW#&LPElj`tn&B2?^O23F&#>%NM{3<>_@l z@a>h|7cEC5B&^cEUr&0eFI16`-XTeS7EyIeJy>*g!kxK3K7kh?mKuJ3%6$2z3wNyH z3Br#Qv2@5262l|!U7|wTBcED3Pk=%^UpofEl6$B62^;&*5=h)@$^~&RW@f1Mq=D`w 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