{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "375fa8b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "! pip install https://github.com/state-spaces/mamba/releases/download/v2.2.5/mamba_ssm-2.2.5+cu12torch2.8cxx11abiFALSE-cp312-cp312-linux_x86_64.whl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e088c6c2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wed Jan 28 10:49:49 2026       \n",
      "+-----------------------------------------------------------------------------------------+\n",
      "| NVIDIA-SMI 580.95.05              Driver Version: 580.95.05      CUDA Version: 13.0     |\n",
      "+-----------------------------------------+------------------------+----------------------+\n",
      "| GPU  Name                 Persistence-M | Bus-Id          Disp.A | Volatile Uncorr. ECC |\n",
      "| Fan  Temp   Perf          Pwr:Usage/Cap |           Memory-Usage | GPU-Util  Compute M. |\n",
      "|                                         |                        |               MIG M. |\n",
      "|=========================================+========================+======================|\n",
      "|   0  NVIDIA GeForce RTX 4090        Off |   00000000:41:00.0 Off |                  Off |\n",
      "|  0%   37C    P8             25W /  450W |    3915MiB /  24564MiB |      0%      Default |\n",
      "|                                         |                        |                  N/A |\n",
      "+-----------------------------------------+------------------------+----------------------+\n",
      "\n",
      "+-----------------------------------------------------------------------------------------+\n",
      "| Processes:                                                                              |\n",
      "|  GPU   GI   CI              PID   Type   Process name                        GPU Memory |\n",
      "|        ID   ID                                                               Usage      |\n",
      "|=========================================================================================|\n",
      "|  No running processes found                                                             |\n",
      "+-----------------------------------------------------------------------------------------+\n"
     ]
    }
   ],
   "source": [
    "! nvidia-smi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d6c970d4",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using device: cuda\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "You are using a model of type pmnet to instantiate a model of type PMNet. This is not supported for all configurations of models and can yield errors.\n",
      "You are using a model of type pmnet to instantiate a model of type PMNet. This is not supported for all configurations of models and can yield errors.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of MambaByte test samples: 19\n",
      "MambaByte PG19 Parameters: 373,950,464\n",
      "MambaByte PG19 Approximate Size: 0.374B\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Evaluating MambaByte PG19 Samples: 100%|██████████| 19/19 [00:01<00:00, 11.33it/s]\n",
      "You are using a model of type pmnet to instantiate a model of type PMNet. This is not supported for all configurations of models and can yield errors.\n",
      "You are using a model of type pmnet to instantiate a model of type PMNet. This is not supported for all configurations of models and can yield errors.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of MambaByte test samples: 19\n",
      "MambaByte Arxiv Parameters: 373,950,464\n",
      "MambaByte Arxiv Approximate Size: 0.374B\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Evaluating MambaByte Arxiv Samples: 100%|██████████| 19/19 [00:01<00:00, 11.21it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SmolLM Base Context Length: 2048\n",
      "Fetching a long sample for SmolLM...\n",
      "Pre-calculating byte lengths for SmolLM mapping...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Evaluating SmolLM: 100%|██████████| 32/32 [00:00<00:00, 33.93it/s]\n",
      "You are using a model of type pmnet to instantiate a model of type PMNet. This is not supported for all configurations of models and can yield errors.\n",
      "You are using a model of type pmnet to instantiate a model of type PMNet. This is not supported for all configurations of models and can yield errors.\n",
      "Tokenizing (num_proc=16): 100%|██████████| 100/100 [00:18<00:00,  5.33 examples/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of PMNet test samples: 19\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "You are using a model of type pmnet to instantiate a model of type PMNet. This is not supported for all configurations of models and can yield errors.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PMNet Parameters : 118,970,368\n",
      "PMNet Approximate Size: 0.119B\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Evaluating PMNet Samples: 100%|██████████| 19/19 [00:01<00:00, 10.43it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Graph saved to data/long_context_bpb_comparison.pdf\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "from datasets import load_dataset\n",
    "from tqdm import tqdm\n",
    "from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer\n",
    "\n",
    "from mamba_ssm.models.mixer_seq_simple import MambaLMHeadModel\n",
    "\n",
    "try:\n",
    "\tfrom mamba_ssm.utils.generation import InferenceParams\n",
    "except Exception:\n",
    "\tInferenceParams = None\n",
    "\n",
    "DATASET_ID = \"emozilla/pg19\"\n",
    "DATA_SPLIT = \"test\"\n",
    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
    "print(f\"Using device: {device}\")\n",
    "\n",
    "MambaByteCKPT_PATH = \"JunxiongWang/MambaByte_PG19_353M\"\n",
    "MambaByteArxivCKPT_PATH = \"JunxiongWang/MambaByte_Arxiv\"\n",
    "PMNet_MODEL_ID = \"phasorkinetics/pmnet\"\n",
    "DATA_PROCESS_BATCH_SIZE = 1000\n",
    "NUM_PROC = 16\n",
    "SEQ_LENGTH_MAMBA = 512 * 1024\n",
    "NUM_EVAL_SAMPLES = 19\n",
    "MAMBA_LOSS_DIR = \"data/mambabyte_losses_512k\"\n",
    "os.makedirs(MAMBA_LOSS_DIR, exist_ok=True)\n",
    "\n",
    "SMOL_CKPT_PATH = \"HuggingFaceTB/SmolLM-135M\"\n",
    "SEQ_LENGTH_SMOL = 16384\n",
    "CHUNK_SIZE_SMOL = 512\n",
    "\n",
    "PMNET_CKPT_PATH = \"phasorkinetics/pmnet\"\n",
    "SEQ_LENGTH_PMNET = 512 * 1024\n",
    "PMNET_NUM_EVAL_SAMPLES = 19\n",
    "PMNET_LOSS_DIR = \"data/byte_losses_pg19_512k\"\n",
    "os.makedirs(PMNET_LOSS_DIR, exist_ok=True)\n",
    "\n",
    "\n",
    "def _extract_logits(outputs):\n",
    "\tif hasattr(outputs, \"logits\"):\n",
    "\t\treturn outputs.logits\n",
    "\tif isinstance(outputs, (tuple, list)) and len(outputs) > 0:\n",
    "\t\treturn outputs[0]\n",
    "\treturn outputs\n",
    "\n",
    "\n",
    "def _byte_lengths_for_tokens(tokenizer, input_ids):\n",
    "\tdecoded_tokens = [len(tokenizer.decode([t]).encode(\"utf-8\")) for t in input_ids]\n",
    "\treturn decoded_tokens\n",
    "\n",
    "\n",
    "def _byte_indices_from_lengths(byte_lengths):\n",
    "\tbyte_indices = []\n",
    "\tcurrent = 0\n",
    "\tfor b in byte_lengths:\n",
    "\t\tcurrent += b\n",
    "\t\tbyte_indices.append(current)\n",
    "\treturn byte_indices\n",
    "\n",
    "\n",
    "def get_loss_over_positions(model, input_ids, ablation=False, chunk_size=1024 * 30):\n",
    "\tif ablation:\n",
    "\t\traise NotImplementedError(\n",
    "\t\t\t\"MambaByte ablation is not implemented in this script.\"\n",
    "\t\t)\n",
    "\n",
    "\tmodel.eval()\n",
    "\tseq_len = len(input_ids)\n",
    "\tall_losses = []\n",
    "\tcriterion = nn.CrossEntropyLoss(reduction=\"none\")\n",
    "\n",
    "\twith torch.no_grad():\n",
    "\t\tfor i in range(0, seq_len, chunk_size):\n",
    "\t\t\tend_idx = min(i + chunk_size, seq_len)\n",
    "\t\t\tchunk_input = input_ids[i:end_idx]\n",
    "\n",
    "\t\t\tlabel_end_idx = min(i + chunk_size + 1, seq_len)\n",
    "\t\t\tchunk_labels_ids = input_ids[i + 1 : label_end_idx]\n",
    "\n",
    "\t\t\tinput_tensor = torch.tensor(\n",
    "\t\t\t\tchunk_input,\n",
    "\t\t\t\tdtype=torch.long,\n",
    "\t\t\t\tdevice=\"cuda\" if torch.cuda.is_available() else \"cpu\",\n",
    "\t\t\t).unsqueeze(0)\n",
    "\n",
    "\t\t\toutputs = model(input_ids=input_tensor)\n",
    "\t\t\tlogits = _extract_logits(outputs)\n",
    "\n",
    "\t\t\tif len(chunk_labels_ids) == len(chunk_input):\n",
    "\t\t\t\tshift_logits = logits.contiguous()\n",
    "\t\t\t\tshift_labels = torch.tensor(\n",
    "\t\t\t\t\tchunk_labels_ids,\n",
    "\t\t\t\t\tdtype=torch.long,\n",
    "\t\t\t\t\tdevice=\"cuda\" if torch.cuda.is_available() else \"cpu\",\n",
    "\t\t\t\t)\n",
    "\t\t\telse:\n",
    "\t\t\t\tshift_logits = logits[..., :-1, :].contiguous()\n",
    "\t\t\t\tshift_labels = input_tensor[..., 1:].contiguous()\n",
    "\n",
    "\t\t\tloss = criterion(\n",
    "\t\t\t\tshift_logits.view(-1, shift_logits.size(-1)),\n",
    "\t\t\t\tshift_labels.view(-1),\n",
    "\t\t\t)\n",
    "\t\t\tall_losses.extend(loss.detach().float().cpu().numpy().tolist())\n",
    "\n",
    "\t\t\tdel outputs, logits\n",
    "\t\t\tif torch.cuda.is_available():\n",
    "\t\t\t\ttorch.cuda.empty_cache()\n",
    "\n",
    "\treturn all_losses\n",
    "\n",
    "\n",
    "def get_loss_over_positions_pmnet(model, input_ids, ablation=False, chunk_size=1024 * 30):\n",
    "\tmodel.eval()\n",
    "\tdevice_local = model.device\n",
    "\tseq_len = len(input_ids)\n",
    "\tall_losses = []\n",
    "\n",
    "\tpast_key_values = None\n",
    "\n",
    "\toriginal_cumsum_config = model.config.memory_cumsum\n",
    "\tif ablation:\n",
    "\t\tmodel.config.memory_cumsum = False\n",
    "\n",
    "\tcriterion = nn.CrossEntropyLoss(reduction=\"none\")\n",
    "\n",
    "\twith torch.no_grad():\n",
    "\t\tfor i in range(0, seq_len, chunk_size):\n",
    "\t\t\tend_idx = min(i + chunk_size, seq_len)\n",
    "\t\t\tchunk_input = input_ids[i:end_idx]\n",
    "\n",
    "\t\t\tlabel_end_idx = min(i + chunk_size + 1, seq_len)\n",
    "\t\t\tchunk_labels_ids = input_ids[i + 1 : label_end_idx]\n",
    "\n",
    "\t\t\tinput_tensor = (\n",
    "\t\t\t\ttorch.tensor(chunk_input, dtype=torch.long).unsqueeze(0).to(device_local)\n",
    "\t\t\t)\n",
    "\n",
    "\t\t\toutputs = model(\n",
    "\t\t\t\tinput_ids=input_tensor,\n",
    "\t\t\t\tpast_key_values=past_key_values,\n",
    "\t\t\t\tuse_cache=True,\n",
    "\t\t\t)\n",
    "\n",
    "\t\t\tpast_key_values = outputs.past_key_values\n",
    "\n",
    "\t\t\tif ablation and past_key_values is not None:\n",
    "\t\t\t\tif hasattr(past_key_values, \"_memory_states_storage\"):\n",
    "\t\t\t\t\tfor block_idx in past_key_values._memory_states_storage:\n",
    "\t\t\t\t\t\tpast_key_values._memory_states_storage[block_idx].zero_()\n",
    "\n",
    "\t\t\tlogits = outputs.logits\n",
    "\n",
    "\t\t\tif len(chunk_labels_ids) == len(chunk_input):\n",
    "\t\t\t\tshift_logits = logits.contiguous()\n",
    "\t\t\t\tshift_labels = torch.tensor(chunk_labels_ids, dtype=torch.long).to(\n",
    "\t\t\t\t\tdevice_local\n",
    "\t\t\t\t)\n",
    "\t\t\telse:\n",
    "\t\t\t\tshift_logits = logits[..., :-1, :].contiguous()\n",
    "\t\t\t\tshift_labels = input_tensor[..., 1:].contiguous()\n",
    "\n",
    "\t\t\tloss = criterion(\n",
    "\t\t\t\tshift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1)\n",
    "\t\t\t)\n",
    "\t\t\tall_losses.extend(loss.cpu().numpy().tolist())\n",
    "\n",
    "\t\t\tdel outputs\n",
    "\t\t\tdel logits\n",
    "\t\t\tif torch.cuda.is_available():\n",
    "\t\t\t\ttorch.cuda.empty_cache()\n",
    "\n",
    "\tmodel.config.memory_cumsum = original_cumsum_config\n",
    "\n",
    "\treturn all_losses\n",
    "\n",
    "\n",
    "def prepare_mamba_dataset(tokenizer):\n",
    "\ttest_dataset = load_dataset(DATASET_ID, split=DATA_SPLIT)\n",
    "\n",
    "\tdef process_batch(examples):\n",
    "\t\ttokenized = tokenizer(examples[\"text\"])\n",
    "\t\tinput_ids = tokenized[\"input_ids\"]\n",
    "\n",
    "\t\tinput_ids = [ids[:SEQ_LENGTH_MAMBA] for ids in input_ids if len(ids) >= SEQ_LENGTH_MAMBA]\n",
    "\t\tresult = {\"input_ids\": input_ids}\n",
    "\t\treturn result\n",
    "\n",
    "\ttest_dataset = test_dataset.map(\n",
    "\t\tprocess_batch,\n",
    "\t\tbatched=True,\n",
    "\t\tbatch_size=DATA_PROCESS_BATCH_SIZE,\n",
    "\t\tremove_columns=test_dataset.column_names,\n",
    "\t\tnum_proc=NUM_PROC,\n",
    "\t\tdesc=\"Tokenizing\",\n",
    "\t)\n",
    "\n",
    "\tprint(f\"Number of MambaByte test samples: {len(test_dataset)}\")\n",
    "\treturn test_dataset\n",
    "\n",
    "\n",
    "def prepare_pmnet_dataset(tokenizer):\n",
    "\ttest_dataset = load_dataset(DATASET_ID, split=DATA_SPLIT)\n",
    "\n",
    "\tdef process_batch(examples):\n",
    "\t\ttokenized = tokenizer(examples[\"text\"])\n",
    "\t\tinput_ids = tokenized[\"input_ids\"]\n",
    "\n",
    "\t\tinput_ids = [ids[:SEQ_LENGTH_PMNET] for ids in input_ids if len(ids) >= SEQ_LENGTH_PMNET]\n",
    "\t\tresult = {\"input_ids\": input_ids}\n",
    "\t\treturn result\n",
    "\n",
    "\ttest_dataset = test_dataset.map(\n",
    "\t\tprocess_batch,\n",
    "\t\tbatched=True,\n",
    "\t\tbatch_size=DATA_PROCESS_BATCH_SIZE,\n",
    "\t\tremove_columns=test_dataset.column_names,\n",
    "\t\tnum_proc=NUM_PROC,\n",
    "\t\tdesc=\"Tokenizing\",\n",
    "\t)\n",
    "\n",
    "\tprint(f\"Number of PMNet test samples: {len(test_dataset)}\")\n",
    "\treturn test_dataset\n",
    "\n",
    "\n",
    "def evaluate_mamba_byte(ckpt_path, label):\n",
    "\ttokenizer = AutoTokenizer.from_pretrained(PMNet_MODEL_ID, trust_remote_code=True)\n",
    "\ttest_dataset = prepare_mamba_dataset(tokenizer)\n",
    "\n",
    "\tmodel = MambaLMHeadModel.from_pretrained(\n",
    "\t\tckpt_path,\n",
    "\t\tdevice=device,\n",
    "\t\tdtype=torch.bfloat16 if device == \"cuda\" else torch.float32,\n",
    "\t)\n",
    "\n",
    "\tparam_size = sum(p.numel() for p in model.parameters())\n",
    "\tprint(f\"{label} Parameters: {param_size:,}\")\n",
    "\tprint(f\"{label} Approximate Size: {param_size / 1_000_000_000:.3f}B\")\n",
    "\n",
    "\tdataset_for_eval = test_dataset.select(range(NUM_EVAL_SAMPLES))\n",
    "\tlosses_list = []\n",
    "\n",
    "\tfor i, sample in enumerate(\n",
    "\t\ttqdm(dataset_for_eval, desc=f\"Evaluating {label} Samples\", total=len(dataset_for_eval))\n",
    "\t):\n",
    "\t\tsave_path = f\"{MAMBA_LOSS_DIR}/sample_{i}_losses_{label.replace(' ', '_')}.npy\"\n",
    "\t\tif os.path.exists(save_path):\n",
    "\t\t\tlosses = np.load(save_path)\n",
    "\t\t\tlosses_list.append(losses)\n",
    "\t\t\tcontinue\n",
    "\n",
    "\t\tlosses = get_loss_over_positions(model, sample[\"input_ids\"], ablation=False)\n",
    "\t\tnp.save(save_path, np.array(losses))\n",
    "\t\tlosses_list.append(losses)\n",
    "\n",
    "\tlosses_mean = np.array(losses_list).mean(axis=0)\n",
    "\n",
    "\tref_tokens = dataset_for_eval[0][\"input_ids\"]\n",
    "\tref_byte_lengths = _byte_lengths_for_tokens(tokenizer, ref_tokens)\n",
    "\ttarget_byte_lengths = np.array(ref_byte_lengths[1: 1 + len(losses_mean)])\n",
    "\ttarget_byte_lengths[target_byte_lengths == 0] = 1\n",
    "\tbyte_indices = _byte_indices_from_lengths(target_byte_lengths.tolist())\n",
    "\n",
    "\tmamba_bpb = losses_mean / (np.log(2) * target_byte_lengths)\n",
    "\treturn np.array(byte_indices), np.array(mamba_bpb)\n",
    "\n",
    "\n",
    "def get_long_sample(tokenizer, min_len=SEQ_LENGTH_SMOL):\n",
    "\tdataset = load_dataset(DATASET_ID, split=DATA_SPLIT, streaming=True)\n",
    "\tfor sample in dataset:\n",
    "\t\ttokens = tokenizer(sample[\"text\"], add_special_tokens=False)[\"input_ids\"]\n",
    "\t\tif len(tokens) >= min_len:\n",
    "\t\t\treturn tokens[:min_len]\n",
    "\treturn None\n",
    "\n",
    "\n",
    "def evaluate_smol_lm():\n",
    "\ttokenizer = AutoTokenizer.from_pretrained(SMOL_CKPT_PATH)\n",
    "\tconfig = AutoConfig.from_pretrained(SMOL_CKPT_PATH)\n",
    "\n",
    "\tmodel = AutoModelForCausalLM.from_pretrained(\n",
    "\t\tSMOL_CKPT_PATH,\n",
    "\t\tconfig=config,\n",
    "\t\tdtype=torch.float16 if device == \"cuda\" else torch.float32,\n",
    "\t\tdevice_map=\"auto\",\n",
    "\t)\n",
    "\tmodel.eval()\n",
    "\n",
    "\tbase_ctx_len = getattr(config, \"max_position_embeddings\", 2048)\n",
    "\tprint(f\"SmolLM Base Context Length: {base_ctx_len}\")\n",
    "\n",
    "\tprint(\"Fetching a long sample for SmolLM...\")\n",
    "\tlong_tokens = get_long_sample(tokenizer, SEQ_LENGTH_SMOL)\n",
    "\tif long_tokens is None:\n",
    "\t\traise ValueError(\"Failed to find a sufficiently long SmolLM sample in the dataset.\")\n",
    "\n",
    "\tinput_tensor = torch.tensor(\n",
    "\t\tlong_tokens, dtype=torch.long, device=model.device\n",
    "\t).unsqueeze(0)\n",
    "\tseq_len = input_tensor.size(1)\n",
    "\n",
    "\tnlls = []\n",
    "\tbyte_indices = []\n",
    "\tbyte_lengths = []\n",
    "\tcurrent_byte_pos = 0\n",
    "\tpast_key_values = None\n",
    "\n",
    "\tcriterion = nn.CrossEntropyLoss(reduction=\"none\")\n",
    "\n",
    "\tprint(\"Pre-calculating byte lengths for SmolLM mapping...\")\n",
    "\tdecoded_tokens = _byte_lengths_for_tokens(tokenizer, long_tokens)\n",
    "\ttarget_byte_lengths = decoded_tokens[1:]\n",
    "\n",
    "\tpbar = tqdm(range(0, seq_len, CHUNK_SIZE_SMOL), desc=\"Evaluating SmolLM\")\n",
    "\n",
    "\twith torch.no_grad():\n",
    "\t\tloss_buffer_idx = 0\n",
    "\n",
    "\t\tfor i in pbar:\n",
    "\t\t\tend_loc = min(i + CHUNK_SIZE_SMOL, seq_len)\n",
    "\t\t\tinput_chunk = input_tensor[:, i:end_loc]\n",
    "\n",
    "\t\t\toutputs = model(\n",
    "\t\t\t\tinput_chunk, past_key_values=past_key_values, use_cache=True\n",
    "\t\t\t)\n",
    "\t\t\tlogits = outputs.logits\n",
    "\t\t\tpast_key_values = outputs.past_key_values\n",
    "\n",
    "\t\t\tshift_logits = logits[..., :-1, :].contiguous()\n",
    "\t\t\tshift_labels = input_chunk[..., 1:].contiguous()\n",
    "\n",
    "\t\t\tif shift_labels.size(1) > 0:\n",
    "\t\t\t\tloss = criterion(\n",
    "\t\t\t\t\tshift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1)\n",
    "\t\t\t\t)\n",
    "\t\t\t\tchunk_nlls = loss.float().cpu().numpy().tolist()\n",
    "\t\t\t\tnlls.extend(chunk_nlls)\n",
    "\n",
    "\t\t\t\tnum_losses = len(chunk_nlls)\n",
    "\t\t\t\tchunk_byte_lens = target_byte_lengths[\n",
    "\t\t\t\t\tloss_buffer_idx : loss_buffer_idx + num_losses\n",
    "\t\t\t\t]\n",
    "\t\t\t\tbyte_lengths.extend(chunk_byte_lens)\n",
    "\n",
    "\t\t\t\tfor b_len in chunk_byte_lens:\n",
    "\t\t\t\t\tcurrent_byte_pos += b_len\n",
    "\t\t\t\t\tbyte_indices.append(current_byte_pos)\n",
    "\n",
    "\t\t\t\tloss_buffer_idx += num_losses\n",
    "\n",
    "\t\t\tdel outputs, logits, shift_logits, shift_labels\n",
    "\t\t\tif torch.cuda.is_available():\n",
    "\t\t\t\ttorch.cuda.empty_cache()\n",
    "\n",
    "\tsafe_byte_lens = np.array(byte_lengths)\n",
    "\tsafe_byte_lens[safe_byte_lens == 0] = 1\n",
    "\tbpb_values = np.array(nlls) / (np.log(2) * safe_byte_lens)\n",
    "\n",
    "\treturn np.array(byte_indices), bpb_values, base_ctx_len\n",
    "\n",
    "\n",
    "def evaluate_pmnet_ablation():\n",
    "\ttokenizer = AutoTokenizer.from_pretrained(PMNET_CKPT_PATH, trust_remote_code=True)\n",
    "\tdataset = prepare_pmnet_dataset(tokenizer)\n",
    "\n",
    "\tmodel = AutoModelForCausalLM.from_pretrained(\n",
    "\t\tPMNET_CKPT_PATH,\n",
    "\t\tdtype=torch.float32,\n",
    "\t\tdevice_map=\"cuda\" if device == \"cuda\" else \"cpu\",\n",
    "\t\ttrust_remote_code=True,\n",
    "\t)\n",
    "\tparam_size = sum(p.numel() for p in model.parameters())\n",
    "\tprint(f\"PMNet Parameters : {param_size:,}\")\n",
    "\tprint(f\"PMNet Approximate Size: {param_size / 1_000_000_000:.3f}B\")\n",
    "\n",
    "\tlosses_off_list = []\n",
    "\tlosses_on_list = []\n",
    "\tdataset_for_eval = dataset.select(range(PMNET_NUM_EVAL_SAMPLES))\n",
    "\n",
    "\tfor i, sample in enumerate(\n",
    "\t\ttqdm(dataset_for_eval, desc=\"Evaluating PMNet Samples\", total=len(dataset_for_eval))\n",
    "\t):\n",
    "\t\tlosses_off_path = f\"{PMNET_LOSS_DIR}/sample_{i}_losses_off.npy\"\n",
    "\t\tlosses_on_path = f\"{PMNET_LOSS_DIR}/sample_{i}_losses_on.npy\"\n",
    "\t\tif os.path.exists(losses_off_path) and os.path.exists(losses_on_path):\n",
    "\t\t\tlosses_off = np.load(losses_off_path)\n",
    "\t\t\tlosses_on = np.load(losses_on_path)\n",
    "\t\t\tlosses_off_list.append(losses_off)\n",
    "\t\t\tlosses_on_list.append(losses_on)\n",
    "\t\t\tcontinue\n",
    "\n",
    "\t\tlosses_on = get_loss_over_positions_pmnet(\n",
    "\t\t\tmodel, sample[\"input_ids\"], ablation=False\n",
    "\t\t)\n",
    "\t\tnp.save(losses_on_path, np.array(losses_on))\n",
    "\t\tlosses_on_list.append(losses_on)\n",
    "\n",
    "\t\tlosses_off = get_loss_over_positions_pmnet(\n",
    "\t\t\tmodel, sample[\"input_ids\"], ablation=True\n",
    "\t\t)\n",
    "\t\tnp.save(losses_off_path, np.array(losses_off))\n",
    "\t\tlosses_off_list.append(losses_off)\n",
    "\n",
    "\tlosses_off_mean = np.array(losses_off_list).mean(axis=0)\n",
    "\tlosses_on_mean = np.array(losses_on_list).mean(axis=0)\n",
    "\n",
    "\treturn losses_on_mean, losses_off_mean, model.config.sliding_window\n",
    "\n",
    "\n",
    "def moving_average_with_indices(vals, idxs, n=300):\n",
    "\tif len(vals) < n:\n",
    "\t\treturn vals, idxs\n",
    "\tret_vals = np.cumsum(vals, dtype=float)\n",
    "\tret_vals[n:] = ret_vals[n:] - ret_vals[:-n]\n",
    "\tsmoothed_vals = ret_vals[n - 1 :] / n\n",
    "\treturn smoothed_vals, idxs[n - 1 :]\n",
    "\n",
    "\n",
    "def main():\n",
    "\tmamba_bytes, mamba_bpb = evaluate_mamba_byte(MambaByteCKPT_PATH, \"MambaByte PG19\")\n",
    "\tmamba_arxiv_bytes, mamba_arxiv_bpb = evaluate_mamba_byte(MambaByteArxivCKPT_PATH, \"MambaByte Arxiv\")\n",
    "\tsmol_bytes, smol_bpb, smol_ctx = evaluate_smol_lm()\n",
    "\tpmnet_on, pmnet_off, pmnet_ws = evaluate_pmnet_ablation()\n",
    "\n",
    "\tmamba_smooth, mamba_x = moving_average_with_indices(mamba_bpb, mamba_bytes, n=1000)\n",
    "\tmamba_arxiv_smooth, mamba_arxiv_x = moving_average_with_indices(mamba_arxiv_bpb, mamba_arxiv_bytes, n=1000)\n",
    "\tsmol_smooth, smol_x = moving_average_with_indices(smol_bpb, smol_bytes, n=300)\n",
    "\n",
    "\tplt.figure(figsize=(12, 6))\n",
    "\tplt.plot(mamba_x, mamba_smooth, label=\"MambaByte_PG19 (353M)\", color=\"blue\", alpha=0.6)\n",
    "\tplt.plot(mamba_arxiv_x, mamba_arxiv_smooth, label=\"MambaByte_Arxiv (353M)\", color=\"cyan\", alpha=0.6)\n",
    "\tplt.plot(smol_x, smol_smooth, label=\"SmolLM (135M)\", color=\"grey\", alpha=0.6)\n",
    "\n",
    "\tdef moving_average(a, n=1000):\n",
    "\t\tret = np.cumsum(a, dtype=float)\n",
    "\t\tret[n:] = ret[n:] - ret[:-n]\n",
    "\t\treturn ret[n - 1 :] / n\n",
    "\n",
    "\tsmooth_on = moving_average(pmnet_on)\n",
    "\tsmooth_off = moving_average(pmnet_off)\n",
    "\n",
    "\tsmooth_on = smooth_on / 0.693\n",
    "\tsmooth_off = smooth_off / 0.693\n",
    "\n",
    "\tx_pmnet = range(len(smooth_on))\n",
    "\n",
    "\tplt.plot(\n",
    "\t\tx_pmnet,\n",
    "\t\tsmooth_off,\n",
    "\t\tlabel=f\"PMNet (119M, No Recurrence)\",\n",
    "\t\tcolor=\"red\",\n",
    "\t\talpha=1,\n",
    "\t\tlinestyle=\"--\",\n",
    "\t)\n",
    "\tplt.plot(\n",
    "\t\tx_pmnet,\n",
    "\t\tsmooth_on,\n",
    "\t\tlabel=f\"PMNet (119M)\",\n",
    "\t\tcolor=\"green\",\n",
    "\t\talpha=1,\n",
    "\t)\n",
    "\n",
    "\tapprox_byte_limit = smol_ctx * 4\n",
    "\tplt.axvline(\n",
    "\t\tx=approx_byte_limit,\n",
    "\t\tcolor=\"gray\",\n",
    "\t\tlinestyle=\"--\",\n",
    "\t\tlabel=f\"SmolLM Context Limit (~{smol_ctx} tokens)\",\n",
    "\t)\n",
    "\n",
    "\tplt.title(\"Long Context BPB Comparison (Byte-Aligned)\\nData: PG19 Test\", fontsize=20, fontweight='bold')\n",
    "\tplt.xlabel(\"Position in Bytes\", fontsize=20)\n",
    "\tplt.ylabel(\"Bits Per Byte (BPB)\", fontsize=20)\n",
    "\tplt.yscale(\"log\")\n",
    "\tplt.xticks(fontsize=20)\n",
    "\tplt.yticks(fontsize=20)\n",
    "\n",
    "\thandles, labels = plt.gca().get_legend_handles_labels()\n",
    "\t\n",
    "\torder =[ 'PMNet (119M)', 'PMNet (119M, No Recurrence)', 'MambaByte_Arxiv (353M)', 'MambaByte_PG19 (353M)', 'SmolLM (135M)', f'SmolLM Context Limit (~{smol_ctx} tokens)']\n",
    "\tindex = [labels.index(label) for label in order]\n",
    "\tplt.legend([handles[i] for i in index], [labels[i] for i in index], fontsize=16)\n",
    "\tplt.grid(True, alpha=0.3)\n",
    "\tplt.ylim(top=4.0)\n",
    "\tplt.tight_layout()\n",
    "\tsave_path = \"data/long_context_bpb_comparison.pdf\"\n",
    "\tplt.savefig(save_path)\n",
    "\tprint(f\"Graph saved to {save_path}\")\n",
    "\n",
    "\n",
    "\n",
    "main()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1c0c657f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "from transformers import AutoTokenizer, AutoModelForCausalLM\n",
    "from datasets import load_dataset\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from tqdm import tqdm\n",
    "\n",
    "CKPT_PATH = \"phasorkinetics/pmnet\"\n",
    "DATA_PROCESS_BATCH_SIZE = 1000\n",
    "NUM_PROC = 16\n",
    "SEQ_LENGTH = 128 * 1024\n",
    "DATASET_ID = \"emozilla/pg19\"\n",
    "DATA_SPLIT = \"test\"\n",
    "NUM_EVAL_SAMPLES = 30\n",
    "loss_save_dir = \"data/byte_losses_pg19_128k_hierarchy_test\"\n",
    "os.makedirs(loss_save_dir, exist_ok=True)\n",
    "\n",
    "tokenizer = AutoTokenizer.from_pretrained(CKPT_PATH, trust_remote_code=True)\n",
    "test_dataset = load_dataset(DATASET_ID, split=DATA_SPLIT)\n",
    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
    "\n",
    "\n",
    "def process_batch(examples):\n",
    "    tokenized = tokenizer(examples[\"text\"])\n",
    "    input_ids = tokenized[\"input_ids\"]\n",
    "    input_ids = [ids[:SEQ_LENGTH] for ids in input_ids if len(ids) >= SEQ_LENGTH]\n",
    "    result = {\"input_ids\": input_ids}\n",
    "    return result\n",
    "\n",
    "\n",
    "test_dataset = test_dataset.map(\n",
    "    process_batch,\n",
    "    batched=True,\n",
    "    batch_size=DATA_PROCESS_BATCH_SIZE,\n",
    "    remove_columns=test_dataset.column_names,\n",
    "    num_proc=NUM_PROC,\n",
    "    desc=\"Tokenizing\",\n",
    ")\n",
    "\n",
    "print(f\"Number of test samples: {len(test_dataset)}\")\n",
    "\n",
    "\n",
    "def get_loss_over_positions(model, input_ids, chunk_size=1024 * 30):\n",
    "    model.eval()\n",
    "    device = model.device\n",
    "    seq_len = len(input_ids)\n",
    "    all_losses = []\n",
    "    past_key_values = None\n",
    "    criterion = nn.CrossEntropyLoss(reduction=\"none\")\n",
    "\n",
    "    with torch.no_grad():\n",
    "        for i in range(0, seq_len, chunk_size):\n",
    "            end_idx = min(i + chunk_size, seq_len)\n",
    "            chunk_input = input_ids[i:end_idx]\n",
    "\n",
    "            label_end_idx = min(i + chunk_size + 1, seq_len)\n",
    "            chunk_labels_ids = input_ids[i + 1 : label_end_idx]\n",
    "\n",
    "            input_tensor = (\n",
    "                torch.tensor(chunk_input, dtype=torch.long).unsqueeze(0).to(device)\n",
    "            )\n",
    "\n",
    "            outputs = model(\n",
    "                input_ids=input_tensor,\n",
    "                past_key_values=past_key_values,\n",
    "                use_cache=True,\n",
    "            )\n",
    "\n",
    "            past_key_values = outputs.past_key_values\n",
    "            logits = outputs.logits\n",
    "\n",
    "            if len(chunk_labels_ids) == len(chunk_input):\n",
    "                shift_logits = logits.contiguous()\n",
    "                shift_labels = torch.tensor(chunk_labels_ids, dtype=torch.long).to(\n",
    "                    device\n",
    "                )\n",
    "            else:\n",
    "                shift_logits = logits[..., :-1, :].contiguous()\n",
    "                shift_labels = input_tensor[..., 1:].contiguous()\n",
    "\n",
    "            loss = criterion(\n",
    "                shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1)\n",
    "            )\n",
    "            all_losses.extend(loss.cpu().numpy().tolist())\n",
    "\n",
    "            del outputs, logits\n",
    "            torch.cuda.empty_cache()\n",
    "\n",
    "    return all_losses\n",
    "\n",
    "\n",
    "def evaluate_hierarchy_ablation():\n",
    "    print(f\"Loading Model from {CKPT_PATH}...\")\n",
    "    model = AutoModelForCausalLM.from_pretrained(\n",
    "        CKPT_PATH,\n",
    "        dtype=torch.float32,\n",
    "        device_map=\"cuda\",\n",
    "        trust_remote_code=True,\n",
    "    )\n",
    "    param_size = sum(p.numel() for p in model.parameters())\n",
    "    print(f\"Model Size: {param_size / 1_000_000_000:.3f}B\")\n",
    "\n",
    "    original_embeddings = [\n",
    "        emb.clone().detach() for emb in model.model.memory_embeddings\n",
    "    ]\n",
    "\n",
    "    modes = [\n",
    "        (\"Normal\", \"normal\", \"blue\", \"Normal (Baseline)\"),\n",
    "        (\"Leaf Zero\", \"leaf_zero\", \"green\", \"No Leaf Memory Embeddings\"),\n",
    "        (\"Root Zero\", \"root_zero\", \"red\", \"No Root Memory Embeddings\"),\n",
    "        (\"All Zero\", \"all_zero\", \"orange\", \"No Memory Embeddings\"),\n",
    "    ]\n",
    "\n",
    "    dataset = test_dataset.select(range(NUM_EVAL_SAMPLES))\n",
    " \n",
    "    results = {mode_name: [] for mode_name, _, _, _ in modes}\n",
    "\n",
    "    for i, sample in enumerate(tqdm(dataset, desc=\"Samples\", total=len(dataset))):\n",
    "\n",
    "        for mode_name, suffix, _, _ in modes:\n",
    "            save_file = f\"{loss_save_dir}/sample_{i}_losses_{suffix}.npy\"\n",
    "\n",
    "            if os.path.exists(save_file):\n",
    "                losses = np.load(save_file)\n",
    "                results[mode_name].append(losses)\n",
    "                continue\n",
    "\n",
    "            with torch.no_grad():\n",
    "                for idx, emb in enumerate(model.model.memory_embeddings):\n",
    "                    emb.data.copy_(original_embeddings[idx])\n",
    "\n",
    "            with torch.no_grad():\n",
    "                if mode_name == \"Normal\":\n",
    "                    pass  \n",
    "\n",
    "                elif mode_name == \"No Memory Embeddings\":\n",
    "                    for emb in model.model.memory_embeddings:\n",
    "                        emb.data.fill_(0.0)\n",
    "\n",
    "                elif mode_name == \"No Root Memory Embeddings\":\n",
    "                    model.model.memory_embeddings[0].data.fill_(0.0)\n",
    "\n",
    "                elif mode_name == \"No Leaf Memory Embeddings\":\n",
    "                    model.model.memory_embeddings[-1].data.fill_(0.0)\n",
    "\n",
    "            losses = get_loss_over_positions(model, sample[\"input_ids\"])\n",
    "            np.save(save_file, np.array(losses))\n",
    "            results[mode_name].append(losses)\n",
    "\n",
    "    plt.figure(figsize=(8, 6))\n",
    "\n",
    "    def moving_average(a, n=10000):\n",
    "        ret = np.cumsum(a, dtype=float)\n",
    "        ret[n:] = ret[n:] - ret[:-n]\n",
    "        return ret[n - 1 :] / n\n",
    "\n",
    "    normal_losses = np.array(results[\"Normal\"]).mean(axis=0)\n",
    "    normal_bpb = moving_average(normal_losses) / 0.693\n",
    "\n",
    "    for mode_name, suffix, color, label in modes:\n",
    "        loss_list = results[mode_name]\n",
    "        if not loss_list:\n",
    "            continue\n",
    "\n",
    "        avg_losses = np.array(loss_list).mean(axis=0)\n",
    "        current_bpb = moving_average(avg_losses) / 0.693\n",
    "\n",
    "        delta_bpb = current_bpb - normal_bpb\n",
    "\n",
    "        x_axis = range(len(delta_bpb))\n",
    "        cumulative_delta = np.cumsum(delta_bpb)\n",
    "        if mode_name == \"Normal\":\n",
    "\n",
    "            plt.plot(\n",
    "                x_axis,\n",
    "                cumulative_delta,\n",
    "                label=label,\n",
    "                color=\"black\",\n",
    "                linestyle=\"--\",\n",
    "                linewidth=1.5,\n",
    "                alpha=0.8,\n",
    "            )\n",
    "        else:\n",
    "            plt.plot(\n",
    "                x_axis,\n",
    "                cumulative_delta,\n",
    "                label=f\"Δ {label}\",\n",
    "                color=color,\n",
    "                linewidth=2,\n",
    "                alpha=0.9,\n",
    "            )\n",
    "\n",
    "    plt.title(\n",
    "        f\"Cumulative Delta BPB relative to Baseline\\n Seq Len: {SEQ_LENGTH//1024}k Tokens\",\n",
    "        fontsize=20,\n",
    "        fontweight='bold'\n",
    "    )\n",
    "    plt.xlabel(\"Token Position\", fontsize=20)\n",
    "    plt.ylabel(\"Cumulative BPB Degradation (Bits)\", fontsize=20)\n",
    "    plt.axhline(y=0, color=\"black\", linestyle=\"-\", alpha=0.2)\n",
    "    plt.legend(loc=\"upper left\", fontsize=20)\n",
    "    plt.grid(True, which=\"both\", ls=\"-\", alpha=0.2)\n",
    "    plt.xticks(fontsize=18)\n",
    "    plt.yticks(fontsize=18)\n",
    "\n",
    "\n",
    "    save_path = \"pmnet_delta_bpb_analysis.pdf\"\n",
    "    plt.tight_layout()\n",
    "    plt.savefig(save_path, dpi=300)\n",
    "    print(f\"Delta graph saved to {save_path}\")\n",
    "\n",
    "    print(\"\\n=== Final Results (Avg BPB) ===\")\n",
    "    for mode_name, _, _, _ in modes:\n",
    "        if results[mode_name]:\n",
    "            avg_all_loss = np.mean([np.mean(l) for l in results[mode_name]])\n",
    "            print(f\"{mode_name:10s}: {avg_all_loss / 0.693:.4f} BPB\")\n",
    "\n",
    "\n",
    "\n",
    "evaluate_hierarchy_ablation()"
   ]
  }
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