{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
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     "shell.execute_reply.started": "2024-06-26T20:02:10.809229Z"
    }
   },
   "outputs": [],
   "source": [
    "import sys\n",
    "sys.path.insert(0, \"/home/tony/Work/neon/sunoGPT/\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-06-26T20:02:10.814297Z",
     "iopub.status.busy": "2024-06-26T20:02:10.814163Z",
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     "shell.execute_reply.started": "2024-06-26T20:02:10.814282Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WILL USE FLASH ATTN: True\n",
      "WARNING: feature_extractor_cqt requires the libray 'nnAudio'\n",
      "Failed to import Bio.\n"
     ]
    }
   ],
   "source": [
    "from data_utils import get_batch, read_jsonl, write_jsonl\n",
    "from suno_utils.gpt.chirp_v2_5 import GPT_CKPT_PATH\n",
    "\n",
    "from suno_utils.gpt.engine import Engine, GPTConfig\n",
    "import os\n",
    "import numpy as np\n",
    "import json\n",
    "import random\n",
    "from collections import defaultdict\n",
    "\n",
    "# gpt_ckpt_path = \"/app/suno/checkpoints/2024-01-29_18-30-58/last_ckpt_infer.pt\"\n",
    "# # gpt_ckpt_path = _get_model_if_needed(GPT_CKPT_PATH)\n",
    "# engine = Engine(\n",
    "#     gpt_ckpt_path,\n",
    "#     \"/home/victor/data/models/chirp_v2/tokenizer_60k.json\",\n",
    "# )\n",
    "# model = engine.model\n",
    "# cfg = model.config\n",
    "# cfg\n",
    "cfg = GPTConfig(\n",
    "    n_layer=32,\n",
    "    n_head=32,\n",
    "    n_kv_head=4,\n",
    "    d_head=128,\n",
    "    block_size=8832,\n",
    "    bias=False,\n",
    "    text_vocab_size=60032,\n",
    "    text_codebook_size=60001,\n",
    "    text_pad_token=60001,\n",
    "    semantic_vocab_size=4032,\n",
    "    semantic_codebook_size=4000,\n",
    "    semantic_n_codebooks=1,\n",
    "    semantic_pad_token=4000,\n",
    "    semantic_infer_token=4001,\n",
    "    semantic_mask_token=4002,\n",
    "    semantic_cover_token=4003,\n",
    "    semantic_artist_token=4004,\n",
    "    semantic_rate_hz=25,\n",
    "    semantic_shift_factor=50,\n",
    "    coarse_vocab_size=2112,\n",
    "    coarse_codebook_size=2048,\n",
    "    coarse_n_codebooks=12,\n",
    "    coarse_pad_token=2048,\n",
    "    coarse_infer_token=2049,\n",
    "    coarse_rate_hz=25,\n",
    "    coarse_shift_factor=5,\n",
    "    t_text=2560,\n",
    "    t_audio=6272,\n",
    "    t_memmap=6016,\n",
    "    use_sin_pos_emb=False,\n",
    "    use_rotary_pos_emb=False,\n",
    "    rope_theta=10000,\n",
    "    attention_type=\"torch\",\n",
    "    use_qk_norm=False,\n",
    "    activation_f=\"gelu\",\n",
    "    embed_scale_factor=1.0,\n",
    "    attention_sliding_window_size=-1,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-06-26T20:02:15.705667Z",
     "iopub.status.busy": "2024-06-26T20:02:15.705435Z",
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     "shell.execute_reply.started": "2024-06-26T20:02:15.705649Z"
    }
   },
   "outputs": [],
   "source": [
    "train_filename = \"data_tr.bin\"\n",
    "train_metas_filename = \"metas_tr.jsonl\"\n",
    "train_info_filename = \"info_tr.json\"\n",
    "val_filename = \"data_val.bin\"\n",
    "val_metas_filename = \"metas_val.jsonl\"\n",
    "val_info_filename = \"info_val.json\"\n",
    "tokenizer_filename = \"tokenizer_60k.json\"\n",
    "data_dir = \"/app/suno/data/chirp_v4/base\"\n",
    "# data_dir = \"/app/suno/data/chirp_v4/discogs_covers/\"\n",
    "# data_dir = \"/app/suno/data/chirp_v4_test_artists\"  # artists\n",
    "\n",
    "if \"covers\" in data_dir:\n",
    "    cfg.t_text = 2560\n",
    "    cfg.t_memmap = 6016\n",
    "    cfg.t_audio = 12288\n",
    "    cfg.block_size = 14848\n",
    "\n",
    "if \"artists\" in data_dir:\n",
    "    cfg.t_text = 1152\n",
    "    cfg.t_memmap = 3008\n",
    "    cfg.t_audio = 6672\n",
    "    cfg.block_size = 7824\n",
    "\n",
    "t_memmap = cfg.t_memmap\n",
    "semantic_n_codebooks = cfg.semantic_n_codebooks\n",
    "coarse_n_codebooks = cfg.coarse_n_codebooks\n",
    "semantic_vocab_size = cfg.semantic_vocab_size\n",
    "coarse_vocab_size = cfg.coarse_vocab_size\n",
    "\n",
    "\n",
    "def load_dataset(\n",
    "    data_dir: str,\n",
    "    filename: str,\n",
    "    info_filename: str,\n",
    "    metas_filename: str,\n",
    "    weights_multiplier_map: dict,\n",
    "    is_finetune: bool,\n",
    ") -> list:\n",
    "    dataset_names = []\n",
    "    data_idx_lists = []  # used to randomly sample from the dataset\n",
    "    data_weights = []\n",
    "    data = np.memmap(os.path.join(data_dir, filename), dtype=np.uint16, mode=\"r\")\n",
    "    data = data.reshape(-1, t_memmap, semantic_n_codebooks + coarse_n_codebooks)\n",
    "    assert data[:100, :, :semantic_n_codebooks].max() <= semantic_vocab_size\n",
    "    assert data[:100, :, semantic_n_codebooks:].max() <= coarse_vocab_size\n",
    "    with open(os.path.join(data_dir, info_filename)) as f:\n",
    "        infos = json.load(f)\n",
    "    metas = read_jsonl(os.path.join(data_dir, metas_filename))\n",
    "    assert len(data) == len(metas), (len(data), len(metas))\n",
    "\n",
    "    artist_to_songs = defaultdict(list)\n",
    "    for i, m in enumerate(metas):\n",
    "        if \"artist\" in m:\n",
    "            artist_to_songs[m[\"artist\"]].append(i)\n",
    "\n",
    "    idx_set = set()  # for checking that we don't have any duplicates\n",
    "    for dset_name, info in infos.items():\n",
    "        dataset_names.append(dset_name)\n",
    "\n",
    "        if \"idx_list\" in info:\n",
    "            idx_list = [idx for idx in info[\"idx_list\"]]\n",
    "        elif \"idx_map_covers\" in info:\n",
    "            # dict from original idx to list of covers idx\n",
    "            # use original idxs as the idx_list\n",
    "            idx_list = [int(idx) for idx in info[\"idx_map_covers\"].keys()]\n",
    "        else:\n",
    "            raise ValueError(f\"info for {dset_name} has neither idx_list nor idx_map_covers\")\n",
    "\n",
    "        idx_set |= set(idx_list)\n",
    "        random.shuffle(idx_list)\n",
    "\n",
    "        data_idx_lists.append(idx_list)\n",
    "        data_weights.append(len(idx_list) * weights_multiplier_map.get(dset_name, 1.0))\n",
    "\n",
    "    weights_norm = np.sum(data_weights)\n",
    "    data_weights = [v / weights_norm for v in data_weights]\n",
    "\n",
    "    if not is_finetune and \"idx_list\" in info:\n",
    "        assert len(idx_set) == len(data), (len(idx_set), len(data))\n",
    "    for k in weights_multiplier_map.keys():\n",
    "        assert k in dataset_names\n",
    "\n",
    "    del idx_set\n",
    "    return dataset_names, data_idx_lists, data_weights, data, metas, infos, artist_to_songs\n",
    "\n",
    "\n",
    "(\n",
    "    val_dataset_names,\n",
    "    val_data_idx_lists,\n",
    "    val_data_weights,\n",
    "    val_data,\n",
    "    val_metas,\n",
    "    val_info,\n",
    "    val_artist_to_songs,\n",
    ") = load_dataset(\n",
    "    data_dir,\n",
    "    val_filename,\n",
    "    val_info_filename,\n",
    "    val_metas_filename,\n",
    "    {},\n",
    "    False,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-06-26T20:02:15.765206Z",
     "iopub.status.busy": "2024-06-26T20:02:15.764926Z",
     "iopub.status.idle": "2024-06-26T20:02:15.789816Z",
     "shell.execute_reply": "2024-06-26T20:02:15.789411Z",
     "shell.execute_reply.started": "2024-06-26T20:02:15.765189Z"
    }
   },
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "# data_dir = \"/app/suno/data/chirp_v4_test\"\n",
    "data_sampling_info = {\n",
    "    \"cfg\": cfg,\n",
    "    \"batch_size\": 1,\n",
    "    \"batch_size_tokens\": 2 * cfg.block_size,\n",
    "    \"tokenizer_fp\": os.path.join(data_dir, \"tokenizer_60k.json\"),\n",
    "    \"device\": \"cpu\",\n",
    "    \"device_type\": \"cpu\",\n",
    "    \"val\": {\n",
    "        \"data\": val_data,\n",
    "        \"metas\": val_metas,\n",
    "        \"infos\": val_info,\n",
    "        \"artist_to_songs\": val_artist_to_songs,\n",
    "        \"names\": val_dataset_names,\n",
    "        \"weights\": val_data_weights,\n",
    "        \"idx_lists\": val_data_idx_lists,\n",
    "    },\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-06-26T20:02:15.791416Z",
     "iopub.status.busy": "2024-06-26T20:02:15.791135Z",
     "iopub.status.idle": "2024-06-26T20:02:15.866816Z",
     "shell.execute_reply": "2024-06-26T20:02:15.866474Z",
     "shell.execute_reply.started": "2024-06-26T20:02:15.791399Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X.shape=torch.Size([1, 14, 8832])\n",
      "Y.shape=torch.Size([1, 13, 8831])\n",
      "tensor([[[   92, 19933,    64,  ..., 60001, 60001, 60001],\n",
      "         [ 4000,  4000,  4000,  ...,  4000,  4000,  4000],\n",
      "         [ 2048,  2048,  2048,  ...,  2048,  2048,  2048],\n",
      "         ...,\n",
      "         [ 2048,  2048,  2048,  ...,  2048,  2048,  2048],\n",
      "         [ 2048,  2048,  2048,  ...,  2048,  2048,  2048],\n",
      "         [ 2048,  2048,  2048,  ...,  2048,  2048,  2048]]])\n",
      "tensor(759353627)\n"
     ]
    }
   ],
   "source": [
    "out = get_batch(\n",
    "    data_sampling_info,\n",
    "    \"val\",\n",
    "    inference=True,\n",
    "    dataset_idx=0,\n",
    "    # row_idx=0,\n",
    ")\n",
    "if isinstance(out[0], int):\n",
    "    te, X, Y = out\n",
    "else:\n",
    "    X, Y, seq_lens = out\n",
    "print(f\"{X.shape=}\")\n",
    "print(f\"{Y.shape=}\")\n",
    "print(X)\n",
    "print(X.sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-06-26T20:02:15.867382Z",
     "iopub.status.busy": "2024-06-26T20:02:15.867242Z",
     "iopub.status.idle": "2024-06-26T20:02:15.883062Z",
     "shell.execute_reply": "2024-06-26T20:02:15.882641Z",
     "shell.execute_reply.started": "2024-06-26T20:02:15.867367Z"
    }
   },
   "outputs": [],
   "source": [
    "# %%timeit\n",
    "# X, Y, seq_lens = get_batch(data_sampling_info, \"val\", inference=True, mask_padding=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-06-26T20:02:15.883777Z",
     "iopub.status.busy": "2024-06-26T20:02:15.883636Z",
     "iopub.status.idle": "2024-06-26T20:02:15.917645Z",
     "shell.execute_reply": "2024-06-26T20:02:15.917249Z",
     "shell.execute_reply.started": "2024-06-26T20:02:15.883763Z"
    }
   },
   "outputs": [],
   "source": [
    "# %%timeit\n",
    "# X, Y_pad, seq_lens = get_batch(data_sampling_info, \"val\", inference=False, mask_padding=True, pack=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-06-26T20:02:15.918325Z",
     "iopub.status.busy": "2024-06-26T20:02:15.918184Z",
     "iopub.status.idle": "2024-06-26T20:02:18.524846Z",
     "shell.execute_reply": "2024-06-26T20:02:18.524319Z",
     "shell.execute_reply.started": "2024-06-26T20:02:15.918310Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 3135 [3135]\n",
      "0 4950 [3135, 1815]\n",
      "0 7954 [3135, 1815, 3004]\n",
      "0 8258 [3135, 1815, 3004, 304]\n",
      "0 10810 [3135, 1815, 3004, 304, 2552]\n",
      "0 11839 [3135, 1815, 3004, 304, 2552, 1029]\n",
      "0 13437 [3135, 1815, 3004, 304, 2552, 1029, 1598]\n",
      "0 13711 [3135, 1815, 3004, 304, 2552, 1029, 1598, 274]\n",
      "0 17440 [3135, 1815, 3004, 304, 2552, 1029, 1598, 274, 3729]\n",
      "0 22015 [3135, 1815, 3004, 304, 2552, 1029, 1598, 274, 3729, 4575]\n",
      "torch.Size([1, 14, 17664])\n"
     ]
    },
    {
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      "text/plain": [
       "<Figure size 10000x1600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 10000x1600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from suno_utils.gpt.prompt import Prompt\n",
    "\n",
    "# cfg.t_audio = 3336\n",
    "# cfg.block_size = 4488\n",
    "\n",
    "X, Y, seq = get_batch(\n",
    "    data_sampling_info,\n",
    "    \"val\",\n",
    "    inference=False,\n",
    "    mask_padding=True,\n",
    "    suffix_first=True,\n",
    "    dropout_semantic=False,\n",
    "    pack=True,\n",
    ")\n",
    "print(X.shape)\n",
    "prompt = Prompt(None, cfg)\n",
    "prompt.visualize(X[0])\n",
    "prompt.visualize(X[0], compress=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-06-26T20:02:18.525834Z",
     "iopub.status.busy": "2024-06-26T20:02:18.525562Z",
     "iopub.status.idle": "2024-06-26T20:02:18.530019Z",
     "shell.execute_reply": "2024-06-26T20:02:18.529550Z",
     "shell.execute_reply.started": "2024-06-26T20:02:18.525816Z"
    }
   },
   "outputs": [],
   "source": [
    "X2 = X.clone()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-06-26T20:02:18.530630Z",
     "iopub.status.busy": "2024-06-26T20:02:18.530482Z",
     "iopub.status.idle": "2024-06-26T20:02:18.566980Z",
     "shell.execute_reply": "2024-06-26T20:02:18.566617Z",
     "shell.execute_reply.started": "2024-06-26T20:02:18.530614Z"
    }
   },
   "outputs": [],
   "source": [
    "X2[:, 1:, :-1] = Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-06-26T20:02:18.568618Z",
     "iopub.status.busy": "2024-06-26T20:02:18.567590Z",
     "iopub.status.idle": "2024-06-26T20:02:20.656443Z",
     "shell.execute_reply": "2024-06-26T20:02:20.655901Z",
     "shell.execute_reply.started": "2024-06-26T20:02:18.568597Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 10000x1600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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AAAAAAAAAAAAAAAAAAAAAAAAAAAAA0AgYqwUAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAaASM1QIAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAANALGagEAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAGgFjtQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAjYCxWgAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACARuD/AdKmigHcilpkAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 10000x1600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "prompt.visualize(X2[0])\n",
    "prompt.visualize(X2[0], compress=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-06-26T20:02:20.657269Z",
     "iopub.status.busy": "2024-06-26T20:02:20.657107Z",
     "iopub.status.idle": "2024-06-26T20:02:20.660462Z",
     "shell.execute_reply": "2024-06-26T20:02:20.660008Z",
     "shell.execute_reply.started": "2024-06-26T20:02:20.657252Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17664"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(seq)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-06-26T20:02:20.661227Z",
     "iopub.status.busy": "2024-06-26T20:02:20.661073Z",
     "iopub.status.idle": "2024-06-26T20:02:20.698989Z",
     "shell.execute_reply": "2024-06-26T20:02:20.698572Z",
     "shell.execute_reply.started": "2024-06-26T20:02:20.661210Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[3135, 1815, 3004, 304, 2552, 1029, 1598, 274, 3729, 224]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-06-26T20:02:20.699703Z",
     "iopub.status.busy": "2024-06-26T20:02:20.699556Z",
     "iopub.status.idle": "2024-06-26T20:02:20.734568Z",
     "shell.execute_reply": "2024-06-26T20:02:20.734170Z",
     "shell.execute_reply.started": "2024-06-26T20:02:20.699688Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17664"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "2 * cfg.block_size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[[-0.2925, -0.5174,  2.9404,  0.2570],\n",
      "         [ 1.2981,  0.6507, -1.4695,  1.0934],\n",
      "         [ 0.9320, -0.3677, -0.1593,  0.0427]],\n",
      "\n",
      "        [[ 0.6315,  0.0369,  0.3513, -0.3650],\n",
      "         [ 0.3621,  0.2844, -0.7134,  0.1427],\n",
      "         [ 1.9496, -0.3718,  0.0455,  0.2745]]])\n"
     ]
    }
   ],
   "source": [
    "test_t = torch.randn((2, 3, 4))\n",
    "print(test_t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "max_p = torch.max(test_t, dim=-1).values\n",
    "min_p = torch.tensor((0.1, 0))\n",
    "min_p = min_p.unsqueeze(-1)\n",
    "test_t[test_t < (min_p * max_p).unsqueeze(-1)] = 0\n",
    "test_t = test_t / test_t.sum(dim=-1, keepdim=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[[0.0000, 0.0000, 1.0000, 0.0000],\n",
       "         [0.4267, 0.2139, 0.0000, 0.3594],\n",
       "         [1.0000, 0.0000, 0.0000, 0.0000]],\n",
       "\n",
       "        [[0.6193, 0.0362, 0.3445, 0.0000],\n",
       "         [0.4588, 0.3604, 0.0000, 0.1808],\n",
       "         [0.8590, 0.0000, 0.0201, 0.1210]]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[[0.8289, 0.0234, 2.3174, 0.1800],\n",
      "         [1.1475, 1.0098, 1.6582, 1.0866],\n",
      "         [2.8178, 0.0037, 1.0044, 1.2684]],\n",
      "\n",
      "        [[0.5528, 0.1589, 0.0062, 0.3192],\n",
      "         [0.6989, 0.4086, 1.7007, 0.7787],\n",
      "         [0.1134, 1.6015, 0.9773, 0.5315]]])\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "tensor([[[2],\n",
       "         [0],\n",
       "         [0]],\n",
       "\n",
       "        [[2],\n",
       "         [1],\n",
       "         [0]]])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q = torch.empty_like(test_t).exponential_(1)\n",
    "print(q)\n",
    "torch.argmax(test_t / q, dim=-1, keepdim=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "top_p = torch.tensor([0.98, 0], device=\"cpu\", dtype=torch.float32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor(0.9800)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_p[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9800000190734863"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "float(top_p[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[[0.0000, 0.0000, 1.0000, 0.0000],\n",
       "         [0.4267, 0.2139, 0.0000, 0.3594],\n",
       "         [1.0000, 0.0000, 0.0000, 0.0000]],\n",
       "\n",
       "        [[0.6193, 0.0362, 0.3445, 0.0000],\n",
       "         [0.4588, 0.3604, 0.0000, 0.1808],\n",
       "         [0.8590, 0.0000, 0.0201, 0.1210]]])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[[0.1732, 0.1732, 0.4805, 0.1732],\n",
       "         [0.2954, 0.2377, 0.1911, 0.2758],\n",
       "         [0.4805, 0.1732, 0.1732, 0.1732]],\n",
       "\n",
       "        [[   nan,    nan,    nan,    nan],\n",
       "         [   nan,    nan,    nan,    nan],\n",
       "         [   nan,    nan,    nan,    nan]]])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.softmax(test_t / torch.tensor([0.98, 0])[:, None, None], dim=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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