{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "ExecuteTime": { "end_time": "2024-04-12T04:01:08.135821Z", "start_time": "2024-04-12T04:01:06.908474Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2744755/4201459797.py:1: DeprecationWarning: \n", "Pyarrow will become a required dependency of pandas in the next major release of pandas (pandas 3.0),\n", "(to allow more performant data types, such as the Arrow string type, and better interoperability with other libraries)\n", "but was not found to be installed on your system.\n", "If this would cause problems for you,\n", "please provide us feedback at https://github.com/pandas-dev/pandas/issues/54466\n", " \n", " import pandas as pd\n" ] } ], "source": [ "import pandas as pd\n", "import numpy as np\n", "import os\n", "from tqdm import tqdm\n", "from sklearn.model_selection import train_test_split\n", "from suno_utils.utils.s3 import download_s3_files\n", "import sys\n", "from collections import defaultdict\n", "from suno_utils.utils.text import (\n", " write_jsonl,\n", " read_jsonl,\n", " write_json,\n", " read_json,\n", ")\n", "import shutil\n", "import ast\n", "from preference_helper import *\n", "\n", "sys.path.insert(0, \"/home/tony/Work/glockenspiel/sunoGPT/scripts/\")\n", "\n", "from data_preparation_7b import *\n", "import numpy as np\n", "\n", "pd.set_option('display.max_rows', 500)\n", "pd.set_option('display.max_columns', 500)\n", "pd.set_option('display.width', 1000)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "ExecuteTime": { "end_time": "2024-04-12T04:01:08.204676Z", "start_time": "2024-04-12T04:01:08.137401Z" } }, "outputs": [], "source": [ "OUT_DATA_DIR = \"/app/suno/data/dpo/7v_r2_v0_ft/\"\n", "# OUT_DATA_DIR = \"/home/tony/Data/test/7v_v6_full/\"\n", "os.makedirs(OUT_DATA_DIR, exist_ok=True)\n", "shutil.copyfile(\"/app/suno/data/dpo/7v_v1_full/tokenizer_60k.json\", os.path.join(OUT_DATA_DIR, \"tokenizer_60k.json\"))\n", "NPZ_DIR = \"/app/suno/data/dpo/v3_npz\"" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "ExecuteTime": { "end_time": "2024-04-12T04:01:08.207994Z", "start_time": "2024-04-12T04:01:08.206371Z" } }, "outputs": [], "source": [ "# df_origin = pd.read_csv(\"/home/tony/Data/Preference/7b_v1/interesting_clips.csv\")\n", "# df_origin[df_origin[\"id\"] == \"75f69d46-1d81-4327-9253-a8a4886777d1\"]\n", "# df_origin[df_origin[\"request_id\"] == \"9414f356-88d6-40ba-b95e-9f4c5004a4aa\"]\n", "# df_origin[df_origin[\"request_id\"] == \"b2a150c1-f058-4bff-a013-1961e574631d\"]\n", "# df_origin[df_origin[\"id\"] == \"574ba431-18a1-4e28-ac7c-d3e401dba6fe\"]" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "ExecuteTime": { "end_time": "2024-04-12T04:03:04.120523Z", "start_time": "2024-04-12T04:01:08.209971Z" } }, "outputs": [ { "data": { "text/plain": [ "(2322354, 71)" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.read_csv(\"/home/tony/Data/Preference/7b_v2/r0_pre_filter.csv\", engine='python')\n", "df.shape\n", "# v0: (68746, 31)\n", "# v5: (938008, 37)\n", "# v6: (1097024, 38)\n", "# v21: (1822704, 41)\n", "# r1 \n", "# v2: 2620268 (pre fixes...lots of imperfections...)\n", "# v3: 1552512" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "ExecuteTime": { "end_time": "2024-04-12T04:03:04.551988Z", "start_time": "2024-04-12T04:03:04.122041Z" } }, "outputs": [ { "data": { "text/plain": [ "is_7b\n", "True 2322352\n", "False 2\n", "Name: count, dtype: int64" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[\"is_7b\"] = df[\"model_name\"].str.contains(\"v3\")\n", "df[\"is_7b\"].value_counts()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# LET's do the data prep" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "ExecuteTime": { "end_time": "2024-04-12T04:03:04.968040Z", "start_time": "2024-04-12T04:03:04.553488Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "preference model_name \n", "False chirp-v3-engine-i 882207\n", " chirp-v3-engine-v0 147486\n", " chirp-v3-engine-d 111256\n", " chirp-v3-engine-s 18239\n", " chirp-v3-engine-i-d 1988\n", "True chirp-v3-engine-i 886806\n", " chirp-v3-engine-d 184570\n", " chirp-v3-engine-v0 74197\n", " chirp-v3-engine-s 14008\n", " chirp-v3-engine-i-d 1595\n", "Name: count, dtype: int64\n", "(2322354, 71)\n", "(221683, 71)\n" ] } ], "source": [ "# # let's also kick out the ... ipo and ipo-dpoed model for now?\n", "print(df.groupby([\"preference\"])[\"model_name\"].value_counts())\n", "print(df.shape)\n", "# df = df[df[\"model_name\"].isin([\"chirp-v3-engine-d\", \"chirp-v3-engine-v0\", \"chirp-v3-engine-i\"])]\n", "df = df[df[\"model_name\"].isin([\"chirp-v3-engine-v0\"])]\n", "# only IPO\n", "# df = df[(df[\"model_name\"].isin([\"chirp-v3-engine-i\"]) ) & (df[\"created_at\"] >= date_cut)]\n", "# for some reason...we can't train dpo on the ipo data...it just doesn't follow lyrics...X.x\n", "# df = df[\n", "# (\n", "# (df[\"model_name\"].isin([\"chirp-v3-engine-d\", \"chirp-v3-engine-v0\"]))\n", "# & (df[\"preference\"] == True)\n", "# )\n", "# | (df[\"preference\"] == False)\n", "# ]\n", "print(df.shape)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "ExecuteTime": { "end_time": "2024-04-12T04:03:09.174142Z", "start_time": "2024-04-12T04:03:04.969647Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(221683, 71)\n", "(83112, 71)\n", "preference model_name \n", "False chirp-v3-engine-v0 41556\n", "True chirp-v3-engine-v0 41556\n", "Name: count, dtype: int64\n" ] } ], "source": [ "print(df.shape)\n", "df = df[\n", " df[\"request_id\"].isin(\n", " df[\"request_id\"].value_counts().index[df[\"request_id\"].value_counts() == 2]\n", " )\n", "]\n", "print(df.shape)\n", "print(df.groupby([\"preference\"])[\"model_name\"].value_counts())\n", "assert df.shape[0] == df[\"request_id\"].nunique() * 2" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "ExecuteTime": { "end_time": "2024-04-12T04:03:09.305386Z", "start_time": "2024-04-12T04:03:09.176925Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "requests 41556 clips 83112 total khrs 2.286; N gpus for 1000 iters 2.597; n unique users 21130\n" ] } ], "source": [ "df_slice = df.copy()\n", "print(\n", " \"requests\",\n", " df_slice[\"request_id\"].nunique(),\n", " \"clips\",\n", " df_slice.shape[0],\n", " f\"total khrs {sum(df_slice['duration'] / 3600 / 1000):.3f};\",\n", " f\"N gpus for 1000 iters {df_slice.shape[0] / 8 / 4 / 1000:.3f};\",\n", " f\"n unique users {df_slice['user_id'].nunique()}\",\n", ")\n", "# 76171 152342 total khrs 2.880 n gpus for 1250 iters 3.809" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "ExecuteTime": { "end_time": "2024-04-12T04:03:09.326696Z", "start_time": "2024-04-12T04:03:09.307706Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "positive in playlist (5204, 71)\n" ] } ], "source": [ "test_mask = (df_slice[\"preference\"] == True) & (\n", " (df_slice[\"is_in_playlist\"] == True) | (df_slice[\"concat_in_playlist\"] == True)\n", ")\n", "print(\"positive in playlist\", df_slice[test_mask].shape)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "ExecuteTime": { "end_time": "2024-04-12T04:03:09.398722Z", "start_time": "2024-04-12T04:03:09.328501Z" } }, "outputs": [], "source": [ "interesting_clips_must_be_positive_mask = (\n", " (df_slice[\"upvoted\"] == True)\n", " | (df_slice[\"has_action\"] == True)\n", " | (df_slice[\"part_of_concat\"] == True)\n", ")\n", "interesting_clips_must_be_not_negative_mask = df_slice[\"downvoted\"] == False\n", "interesting_clips_mask = interesting_clips_must_be_positive_mask & interesting_clips_must_be_not_negative_mask\n", "assert interesting_clips_mask.eq(df_slice[\"preference\"]).all()" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "ExecuteTime": { "end_time": "2024-04-12T04:03:09.466860Z", "start_time": "2024-04-12T04:03:09.400349Z" } }, "outputs": [], "source": [ "# BREAK" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Need to kick out the ones has gpt prompt -- these are pairs with different text inputs" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "ExecuteTime": { "end_time": "2024-04-12T04:03:09.613178Z", "start_time": "2024-04-12T04:03:09.468374Z" } }, "outputs": [ { "data": { "text/plain": [ "41447" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# don't have continue at\n", "df_slice[df_slice[\"continue_at\"].isna()][\"request_id\"].nunique()" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "ExecuteTime": { "end_time": "2024-04-12T04:03:09.872174Z", "start_time": "2024-04-12T04:03:09.615824Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "41556\n" ] } ], "source": [ "final_filtered_requests = df_slice[\"request_id\"].unique()\n", "print(len(final_filtered_requests))" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "ExecuteTime": { "end_time": "2024-04-12T04:03:10.202054Z", "start_time": "2024-04-12T04:03:09.873612Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "41140 416\n", "(82280, 72) (832, 72)\n" ] } ], "source": [ "train_requests, val_requests = train_test_split(\n", " sorted(list(final_filtered_requests)), test_size=0.01, random_state=42\n", ")\n", "print(len(train_requests), len(val_requests))\n", "\n", "train_df = df_slice[df_slice[\"request_id\"].isin(set(train_requests))].copy()\n", "val_df = df_slice[df_slice[\"request_id\"].isin(set(val_requests))].copy()\n", "train_df = train_df.sort_values(by=[\"request_id\", \"preference\"])\n", "train_df = train_df.reset_index()\n", "val_df = val_df.sort_values(by=[\"request_id\", \"preference\"])\n", "val_df = val_df.reset_index()\n", "\n", "print(train_df.shape, val_df.shape)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "ExecuteTime": { "end_time": "2024-04-12T04:03:10.218256Z", "start_time": "2024-04-12T04:03:10.203721Z" } }, "outputs": [], "source": [ "def reshift(arr):\n", " sem_start_idx = 0\n", " sem_end_idx = len(arr) - 1\n", " semantic_arr = arr[:, :SEMANTIC_N_CODEBOOKS]\n", " coarse_arr = arr[:, SEMANTIC_N_CODEBOOKS:]\n", "\n", " coarse_start_idx = int(round(sem_start_idx * COARSE_RATE_HZ / SEMANTIC_RATE_HZ))\n", " coarse_end_idx = int(round(sem_end_idx * COARSE_RATE_HZ / SEMANTIC_RATE_HZ))\n", " assert sem_end_idx >= 0 and coarse_start_idx >= 0\n", " assert not (sem_end_idx > len(semantic_arr) or coarse_end_idx > len(coarse_arr))\n", "\n", " # get array segments\n", " arr_s = semantic_arr[sem_start_idx:sem_end_idx, :].copy()\n", " arr_c = coarse_arr[coarse_start_idx:coarse_end_idx, :].copy()\n", " assert arr_s.max() <= SEMANTIC_PAD_TOKEN\n", " assert arr_c.max() <= COARSE_PAD_TOKEN\n", " assert len(arr_s) == len(arr_c)\n", " # concat and stack\n", " if len(arr_c) < N_TOKENS_AUDIO:\n", " arr_c = np.pad(\n", " arr_c,\n", " ((0, N_TOKENS_AUDIO - len(arr_c)), (0, 0)),\n", " constant_values=COARSE_PAD_TOKEN,\n", " mode=\"constant\",\n", " )\n", " arr_s = np.pad(\n", " arr_s,\n", " ((0, N_TOKENS_AUDIO - len(arr_s)), (0, 0)),\n", " constant_values=SEMANTIC_PAD_TOKEN,\n", " mode=\"constant\",\n", " )\n", " arr = np.concatenate([arr_s, arr_c], axis=-1)\n", " arr = arr.astype(np.uint16)\n", " assert arr.shape == (N_TOKENS_AUDIO, SEMANTIC_N_CODEBOOKS + COARSE_N_CODEBOOKS)\n", " return arr\n", "\n", "\n", "def make_dataset(input_df, is_val=False):\n", " dset_type = \"val\" if is_val else \"tr\"\n", " out_mmap_path = os.path.join(OUT_DATA_DIR, f\"data_{dset_type}.bin\")\n", " out_metas_path = os.path.join(OUT_DATA_DIR, f\"meta_{dset_type}.jsonl\")\n", " out_info_filepath = os.path.join(OUT_DATA_DIR, f\"info_{dset_type}.json\")\n", "\n", " # gather the data\n", " _ = np.memmap(out_mmap_path, dtype=np.uint16, mode=\"w+\", shape=(1,))\n", " n_offs = 0\n", " tot_duration_dict = defaultdict(int)\n", " datasets_info = defaultdict(dict)\n", " n = 0\n", " for i, row in tqdm.tqdm(input_df.iterrows(), total=len(input_df)):\n", " # we need to alternate between preference: neg, pos\n", " # print(i, row)\n", " assert row[\"preference\"] == (i % 2 == 1)\n", " # make mmap -- two different paths\n", " local_path = (\n", " f\"{NPZ_DIR}/{row['s3_id']}.npz\"\n", " if row[\"is_7b\"]\n", " else f\"/app/suno/data/dpo/7b_npz/{row['s3_id']}.npz\"\n", " )\n", " if not os.path.exists(local_path):\n", " # print(row, local_path)\n", " raise ValueError()\n", " # print(local_path)\n", " # print( np.load(local_path))\n", " try:\n", " arr = (\n", " np.load(local_path)[\"v3.0_raw\"]\n", " if row[\"is_7b\"]\n", " else np.load(local_path)[\"v2_raw\"]\n", " )\n", " except Exception as e:\n", " print(local_path)\n", " raise e\n", " assert arr.shape[0] <= 3000\n", " assert arr.shape[1] == 13\n", " arr_duration = arr.shape[0] / 25\n", " # print(arr.shape)\n", " arr = reshift(arr)\n", " # print(\"after shift and pad\", arr.shape)\n", " arr = arr.reshape(\n", " -1,\n", " )\n", " # print(arr.shape)\n", " out_mm = np.memmap(\n", " out_mmap_path,\n", " dtype=np.uint16,\n", " mode=\"r+\",\n", " shape=(n_offs + arr.size,),\n", " )\n", " out_mm[n_offs : n_offs + arr.size] = arr\n", " # print(f\"offset is: {n_offs}\")\n", " # break\n", " # write it once\n", " out_mm.flush()\n", " del out_mm\n", "\n", " add_metas = []\n", " add_meta = {\n", " \"dataset\": f\"perference_{int(row['preference'])}\",\n", " \"id\": row[\"s3_id\"], # this is the row s3_id\n", " \"start_s\": row[\"total_start_s\"] if row[\"total_start_s\"] >= 0 else None,\n", " \"end_s\": (\n", " row[\"total_clip_s\"] if row[\"total_clip_s\"] >= 0 else None\n", " ), # for full clips we do know it has an edding, other wise, we don't know\n", " \"original_duration_s\": (\n", " row[\"original_duration_s\"]\n", " if row[\"original_duration_s\"] >= 0\n", " else arr_duration\n", " ), # this nees to be... a bit more complicated, only works with concat!\n", " \"vocal_start_s\": None, # these are unfortunately missing for now\n", " \"vocal_end_s\": None, # these are unfortunately missing for now\n", " \"tags\": [\n", " row[\"tags\"] if not pd.isna(row[\"tags\"]) else \"\"\n", " ], # tags is a list, do you know :)\n", " \"text\": row[\"prompt\"] if not pd.isna(row[\"prompt\"]) else \"\",\n", " }\n", " add_metas.append(add_meta)\n", " tot_duration_dict[row[\"preference\"]] += arr_duration\n", " write_jsonl(\n", " add_metas,\n", " os.path.join(out_metas_path),\n", " do_append=bool(n_offs != 0),\n", " )\n", " if \"idx_list\" not in datasets_info[add_meta[\"dataset\"]]:\n", " datasets_info[add_meta[\"dataset\"]][\"idx_list\"] = [n]\n", " else:\n", " datasets_info[add_meta[\"dataset\"]][\"idx_list\"].append(n)\n", " n += 1\n", " n_offs += arr.size\n", "\n", " write_json(datasets_info, out_info_filepath)\n", " print(f\"Total {n} clips\")\n", " for k, v in tot_duration_dict.items():\n", " print(f\"{round(v / 60 / 60):,} hours of {k}\")\n", " print(f\"Done\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Actually make" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "ExecuteTime": { "end_time": "2024-04-12T04:03:10.288968Z", "start_time": "2024-04-12T04:03:10.219497Z" } }, "outputs": [], "source": [ "# val_df[[\"request_id\", \"metadata\", \"updated_at\", \"user_id\", \"preference\"]].head()" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "ExecuteTime": { "end_time": "2024-04-12T04:03:13.316512Z", "start_time": "2024-04-12T04:03:10.290183Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████| 82280/82280 [00:02<00:00, 27803.83it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "2,263 hours of 82280 clips, 2.57125 nodes\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "total_duration = 0\n", "for i, row in tqdm.tqdm(train_df.iterrows(), total=len(train_df)):\n", " # we need to alternate between preference: neg, pos\n", " # print(i, row)\n", " try:\n", " assert row[\"preference\"] == (i % 2 == 1)\n", " except:\n", " print(i, row)\n", " total_duration += row[\"duration\"]\n", "print(f\"{round(total_duration / 60 / 60):,} hours of {train_df.shape[0]} clips, {train_df.shape[0] / 8 / 4 / 1000} nodes\")" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "ExecuteTime": { "end_time": "2024-04-12T04:03:26.148340Z", "start_time": "2024-04-12T04:03:13.319498Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████| 832/832 [00:12<00:00, 65.52it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Total 832 clips\n", "11 hours of False\n", "11 hours of True\n", "Done\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "make_dataset(val_df, is_val=True)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "ExecuteTime": { "end_time": "2024-04-12T04:23:57.318137Z", "start_time": "2024-04-12T04:03:26.149927Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████| 82280/82280 [20:24<00:00, 67.18it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Total 82280 clips\n", "1,135 hours of False\n", "1,131 hours of True\n", "Done\n" ] } ], "source": [ "make_dataset(train_df, is_val=False)" ] }, { "cell_type": "markdown", "metadata": { "ExecuteTime": { "end_time": "2024-01-29T19:46:47.549860Z", "start_time": "2024-01-29T19:46:47.548015Z" } }, "source": [ "# Validation" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "ExecuteTime": { "end_time": "2024-04-12T04:23:57.339134Z", "start_time": "2024-04-12T04:23:57.320512Z" } }, "outputs": [], "source": [ "# verify\n", "mm = np.memmap(os.path.join(OUT_DATA_DIR, f\"data_val.bin\"), dtype=np.uint16, mode=\"r\")\n", "test_metas = read_jsonl(os.path.join(OUT_DATA_DIR, f\"meta_val.jsonl\"))\n", "test_info = read_json(os.path.join(OUT_DATA_DIR, f\"info_val.json\"))\n", "mm = mm.reshape(-1, 3008, 13)\n", "assert len(mm) == len(test_metas)\n", "assert mm[:100, :, 0].min() >= 0\n", "assert mm[:100, :, 0].max() <= 4000\n", "assert mm[:100, :, 1:].min() >= 0\n", "assert mm[:100, :, 1:].max() <= 2048" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "ExecuteTime": { "end_time": "2024-04-12T04:23:57.398494Z", "start_time": "2024-04-12T04:23:57.340409Z" } }, "outputs": [], "source": [ "# # randomly listen to some stuff\n", "# from suno_utils.tasks.dac_2c_12cb import preload_models as preload_codec_models\n", "# from suno_utils.tasks.dac_2c_12cb import (\n", "# encode as codec_encode,\n", "# decode_stream_to_full_audio as codec_decode,\n", "# EMBEDDING_RATE as CODEC_EMBEDDING_RATE,\n", "# decode as decode\n", "# )\n", "# os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"3\"\n", "# _ = preload_codec_models(\"/app/suno/tony/v3/dac_2c_25x12.pt\", device=\"cuda\")\n", "# assert len(test_metas) == len(mm)\n", "# idx_list = list(range(len(test_metas)))\n", "# # random.shuffle(idx_list)\n", "# # idx_list = [idx for idx in idx_list if \"text\" in test_metas[idx]]\n", "# print(len(mm))" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "ExecuteTime": { "end_time": "2024-04-12T04:23:57.465191Z", "start_time": "2024-04-12T04:23:57.401020Z" } }, "outputs": [], "source": [ "# idx = random.choice(test_info[\"perference_0\"][\"idx_list\"])\n", "# assert \"original_duration_s\" in test_metas[idx]\n", "# # positive index should be shifted by 1\n", "# pos_idx = idx + 1\n", "# print(\n", "# \"tags:\",\n", "# test_metas[idx].get(\"tags\") == test_metas[pos_idx].get(\"tags\"),\n", "# test_metas[idx].get(\"tags\"),\n", "# )\n", "# arr = mm[idx, 1:].copy().astype(np.int16)[:, 1:]\n", "# pos_arr = mm[pos_idx, 1:].copy().astype(np.int16)[:, 1:]\n", "# pad_idx_arr = np.where(arr == COARSE_PAD_TOKEN)[0]\n", "# if len(pad_idx_arr) > 0:\n", "# arr = arr[: pad_idx_arr[0], :]\n", "# pos_pad_idx_arr = np.where(pos_arr == COARSE_PAD_TOKEN)[0]\n", "# if len(pos_pad_idx_arr) > 0:\n", "# pos_arr = pos_arr[: pos_pad_idx_arr[0], :]\n", "# a = decode(arr)\n", "# print(\"negative example\", test_metas[idx])\n", "# a.play(compress=False)\n", "# pos_a = decode(pos_arr)\n", "# print(\"positive example\", test_metas[pos_idx])\n", "# pos_a.play(compress=False)\n", "# print(\n", "# \"text:\",\n", "# test_metas[idx].get(\"text\") == test_metas[pos_idx].get(\"text\"),\n", "# test_metas[idx].get(\"text\"),\n", "# )" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "ExecuteTime": { "end_time": "2024-04-12T04:23:57.532015Z", "start_time": "2024-04-12T04:23:57.466170Z" } }, "outputs": [], "source": [ "# val_df[val_df[\"tags\"] == 'a vibrant blend of experimental jazz fusion, drum-and-bass and swagger fuzzed-out guitars']" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "ExecuteTime": { "end_time": "2024-04-12T04:23:57.606611Z", "start_time": "2024-04-12T04:23:57.533186Z" } }, "outputs": [], "source": [ "# from collections import Counter\n", "# c = Counter()\n", "# for _, row in df_slice.iterrows():\n", "# # print(row[\"metadata\"])\n", "# for k in ast.literal_eval(row[\"metadata\"]).keys():\n", "# c[k] += 1" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "ExecuteTime": { "end_time": "2024-04-12T04:23:57.688405Z", "start_time": "2024-04-12T04:23:57.607722Z" } }, "outputs": [], "source": [ "# original_npz_path = f\"/app/suno/data/dpo/7b_npz/{test_metas[idx]['id']}.npz\"\n", "# original_npz_path = \"/app/suno/data/dpo/7b_npz/729c3011-f672-4ccd-8d82-1cbf2b52ff69.npz\"\n", "# original_arr = np.load(original_npz_path)[\"v2_raw\"]" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "ExecuteTime": { "end_time": "2024-04-12T04:23:57.763197Z", "start_time": "2024-04-12T04:23:57.689931Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "416 0\n" ] } ], "source": [ "def validation_on_metas(input_metas):\n", "\n", " total_bad = 0\n", " total_good = 0\n", " for idx in range(len(input_metas)):\n", " if idx % 2 == 0:\n", " pos_idx = idx + 1\n", " if input_metas[idx].get(\"tags\") != input_metas[pos_idx].get(\"tags\"):\n", " # print(test_metas[idx].get(\"text\") == test_metas[pos_idx].get(\"text\"), test_metas[idx].get(\"tags\"), test_metas[pos_idx].get(\"tags\"))\n", " total_bad += 1\n", " else:\n", " total_good += 1\n", " print(total_good, total_bad)\n", " return\n", "\n", "\n", "validation_on_metas(test_metas)" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "ExecuteTime": { "end_time": "2024-04-12T04:23:57.966730Z", "start_time": "2024-04-12T04:23:57.764237Z" } }, "outputs": [], "source": [ "train_info = read_json(os.path.join(OUT_DATA_DIR, f\"info_tr.json\"))" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "ExecuteTime": { "end_time": "2024-04-12T04:23:58.029693Z", "start_time": "2024-04-12T04:23:57.967950Z" } }, "outputs": [], "source": [ "n_neg_tr = train_info[\"perference_0\"][\"idx_list\"]\n", "n_pos_tr = train_info[\"perference_1\"][\"idx_list\"]\n", "assert len(n_pos_tr) == len(n_neg_tr)" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "ExecuteTime": { "end_time": "2024-04-12T04:23:58.093133Z", "start_time": "2024-04-12T04:23:58.030714Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "total samples 82280 (82280, 72)\n" ] } ], "source": [ "total_iters = (len(n_neg_tr) + len(n_pos_tr))\n", "print(\"total samples\", total_iters, train_df.shape)" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "ExecuteTime": { "end_time": "2024-04-12T04:23:58.175010Z", "start_time": "2024-04-12T04:23:58.094391Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1 epoch per batch 4, total 642.8125\n" ] } ], "source": [ "print(\"1 epoch per batch 4, total\", total_iters / 8 / 4 / 4)" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "ExecuteTime": { "end_time": "2024-04-12T04:23:59.000576Z", "start_time": "2024-04-12T04:23:58.175899Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Submitted batch job 1358\r\n" ] } ], "source": [ "!cd /home/tony/Work/tony/slurm && sbatch sbatch_dpo_ft" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "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": "3.10.13" }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": true, "sideBar": true, "skip_h1_title": false, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": {}, "toc_section_display": true, "toc_window_display": false } }, "nbformat": 4, "nbformat_minor": 2 }