{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Saving PruneBERT\n",
    "\n",
    "\n",
    "This notebook aims at showcasing how we can leverage standard tools to save (and load) an extremely sparse model fine-pruned with [movement pruning](https://arxiv.org/abs/2005.07683) (or any other unstructured pruning mehtod).\n",
    "\n",
    "In this example, we used BERT (base-uncased, but the procedure described here is not specific to BERT and can be applied to a large variety of models.\n",
    "\n",
    "We first obtain an extremely sparse model by fine-pruning with movement pruning on SQuAD v1.1. We then used the following combination of standard tools:\n",
    "- We reduce the precision of the model with Int8 dynamic quantization using [PyTorch implementation](https://pytorch.org/tutorials/intermediate/dynamic_quantization_bert_tutorial.html). We only quantized the Fully Connected Layers.\n",
    "- Sparse quantized matrices are converted into the [Compressed Sparse Row format](https://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.csr_matrix.html).\n",
    "- We use HDF5 with `gzip` compression to store the weights.\n",
    "\n",
    "We experiment with a question answering model with only 6% of total remaining weights in the encoder (previously obtained with movement pruning). **We are able to reduce the memory size of the encoder from 340MB (original dense BERT) to 11MB**, which fits on a [91' floppy disk](https://en.wikipedia.org/wiki/Floptical)!\n",
    "\n",
    "<img src=\"https://upload.wikimedia.org/wikipedia/commons/thumb/0/00/Floptical_disk_21MB.jpg/440px-Floptical_disk_21MB.jpg\" width=\"200\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Includes\n",
    "\n",
    "import h5py\n",
    "import os\n",
    "import json\n",
    "from collections import OrderedDict\n",
    "\n",
    "from scipy import sparse\n",
    "import numpy as np\n",
    "\n",
    "import torch\n",
    "from torch import nn\n",
    "\n",
    "from transformers import *\n",
    "\n",
    "os.chdir('../../')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Saving"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dynamic quantization induces little or no loss of performance while significantly reducing the memory footprint."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load fine-pruned model and quantize the model\n",
    "\n",
    "model_path = \"serialization_dir/bert-base-uncased/92/squad/l1\"\n",
    "model_name = \"bertarized_l1_with_distil_0._0.1_1_2_l1_1100._3e-5_1e-2_sigmoied_threshold_constant_0._10_epochs\"\n",
    "\n",
    "model = BertForQuestionAnswering.from_pretrained(os.path.join(model_path, model_name))\n",
    "model.to('cpu')\n",
    "\n",
    "quantized_model = torch.quantization.quantize_dynamic(\n",
    "                    model=model,\n",
    "                    qconfig_spec = {\n",
    "                        torch.nn.Linear : torch.quantization.default_dynamic_qconfig,\n",
    "                    },\n",
    "                    dtype=torch.qint8,\n",
    "                )\n",
    "# print(quantized_model)\n",
    "\n",
    "qtz_st = quantized_model.state_dict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Saving the original (encoder + classifier) in the standard torch.save format\n",
    "\n",
    "dense_st = {name: param for name, param in model.state_dict().items() \n",
    "                            if \"embedding\" not in name and \"pooler\" not in name}\n",
    "torch.save(dense_st, 'dbg/dense_squad.pt',)\n",
    "dense_mb_size = os.path.getsize(\"dbg/dense_squad.pt\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decompose quantization for bert.encoder.layer.0.attention.self.query._packed_params.weight\n",
      "Decompose quantization for bert.encoder.layer.0.attention.self.key._packed_params.weight\n",
      "Decompose quantization for bert.encoder.layer.0.attention.self.value._packed_params.weight\n",
      "Decompose quantization for bert.encoder.layer.0.attention.output.dense._packed_params.weight\n",
      "Decompose quantization for bert.encoder.layer.0.intermediate.dense._packed_params.weight\n",
      "Decompose quantization for bert.encoder.layer.0.output.dense._packed_params.weight\n",
      "Decompose quantization for bert.encoder.layer.1.attention.self.query._packed_params.weight\n",
      "Decompose quantization for bert.encoder.layer.1.attention.self.key._packed_params.weight\n",
      "Decompose quantization for bert.encoder.layer.1.attention.self.value._packed_params.weight\n",
      "Decompose quantization for bert.encoder.layer.1.attention.output.dense._packed_params.weight\n",
      "Decompose quantization for bert.encoder.layer.1.intermediate.dense._packed_params.weight\n",
      "Decompose quantization for bert.encoder.layer.1.output.dense._packed_params.weight\n",
      "Decompose quantization for bert.encoder.layer.2.attention.self.query._packed_params.weight\n",
      "Decompose quantization for bert.encoder.layer.2.attention.self.key._packed_params.weight\n",
      "Decompose quantization for bert.encoder.layer.2.attention.self.value._packed_params.weight\n",
      "Decompose quantization for bert.encoder.layer.2.attention.output.dense._packed_params.weight\n",
      "Decompose quantization for bert.encoder.layer.2.intermediate.dense._packed_params.weight\n",
      "Decompose quantization for bert.encoder.layer.2.output.dense._packed_params.weight\n",
      "Decompose quantization for bert.encoder.layer.3.attention.self.query._packed_params.weight\n",
      "Decompose quantization for bert.encoder.layer.3.attention.self.key._packed_params.weight\n",
      "Decompose quantization for bert.encoder.layer.3.attention.self.value._packed_params.weight\n",
      "Decompose quantization for bert.encoder.layer.3.attention.output.dense._packed_params.weight\n",
      "Decompose quantization for bert.encoder.layer.3.intermediate.dense._packed_params.weight\n",
      "Decompose quantization for bert.encoder.layer.3.output.dense._packed_params.weight\n",
      "Decompose quantization for bert.encoder.layer.4.attention.self.query._packed_params.weight\n",
      "Decompose quantization for bert.encoder.layer.4.attention.self.key._packed_params.weight\n",
      "Decompose quantization for bert.encoder.layer.4.attention.self.value._packed_params.weight\n",
      "Decompose quantization for bert.encoder.layer.4.attention.output.dense._packed_params.weight\n",
      "Decompose quantization for bert.encoder.layer.4.intermediate.dense._packed_params.weight\n",
      "Decompose quantization for bert.encoder.layer.4.output.dense._packed_params.weight\n",
      "Decompose quantization for bert.encoder.layer.5.attention.self.query._packed_params.weight\n",
      "Decompose quantization for bert.encoder.layer.5.attention.self.key._packed_params.weight\n",
      "Decompose quantization for bert.encoder.layer.5.attention.self.value._packed_params.weight\n",
      "Decompose quantization for bert.encoder.layer.5.attention.output.dense._packed_params.weight\n",
      "Decompose quantization for bert.encoder.layer.5.intermediate.dense._packed_params.weight\n",
      "Decompose quantization for bert.encoder.layer.5.output.dense._packed_params.weight\n",
      "Decompose quantization for bert.encoder.layer.6.attention.self.query._packed_params.weight\n",
      "Decompose quantization for bert.encoder.layer.6.attention.self.key._packed_params.weight\n",
      "Decompose quantization for bert.encoder.layer.6.attention.self.value._packed_params.weight\n",
      "Decompose quantization for bert.encoder.layer.6.attention.output.dense._packed_params.weight\n",
      "Decompose quantization for bert.encoder.layer.6.intermediate.dense._packed_params.weight\n",
      "Decompose quantization for bert.encoder.layer.6.output.dense._packed_params.weight\n",
      "Decompose quantization for bert.encoder.layer.7.attention.self.query._packed_params.weight\n",
      "Decompose quantization for bert.encoder.layer.7.attention.self.key._packed_params.weight\n",
      "Decompose quantization for bert.encoder.layer.7.attention.self.value._packed_params.weight\n",
      "Decompose quantization for bert.encoder.layer.7.attention.output.dense._packed_params.weight\n",
      "Decompose quantization for bert.encoder.layer.7.intermediate.dense._packed_params.weight\n",
      "Decompose quantization for bert.encoder.layer.7.output.dense._packed_params.weight\n",
      "Decompose quantization for bert.encoder.layer.8.attention.self.query._packed_params.weight\n",
      "Decompose quantization for bert.encoder.layer.8.attention.self.key._packed_params.weight\n",
      "Decompose quantization for bert.encoder.layer.8.attention.self.value._packed_params.weight\n",
      "Decompose quantization for bert.encoder.layer.8.attention.output.dense._packed_params.weight\n",
      "Decompose quantization for bert.encoder.layer.8.intermediate.dense._packed_params.weight\n",
      "Decompose quantization for bert.encoder.layer.8.output.dense._packed_params.weight\n",
      "Decompose quantization for bert.encoder.layer.9.attention.self.query._packed_params.weight\n",
      "Decompose quantization for bert.encoder.layer.9.attention.self.key._packed_params.weight\n",
      "Decompose quantization for bert.encoder.layer.9.attention.self.value._packed_params.weight\n",
      "Decompose quantization for bert.encoder.layer.9.attention.output.dense._packed_params.weight\n",
      "Decompose quantization for bert.encoder.layer.9.intermediate.dense._packed_params.weight\n",
      "Decompose quantization for bert.encoder.layer.9.output.dense._packed_params.weight\n",
      "Decompose quantization for bert.encoder.layer.10.attention.self.query._packed_params.weight\n",
      "Decompose quantization for bert.encoder.layer.10.attention.self.key._packed_params.weight\n",
      "Decompose quantization for bert.encoder.layer.10.attention.self.value._packed_params.weight\n",
      "Decompose quantization for bert.encoder.layer.10.attention.output.dense._packed_params.weight\n",
      "Decompose quantization for bert.encoder.layer.10.intermediate.dense._packed_params.weight\n",
      "Decompose quantization for bert.encoder.layer.10.output.dense._packed_params.weight\n",
      "Decompose quantization for bert.encoder.layer.11.attention.self.query._packed_params.weight\n",
      "Decompose quantization for bert.encoder.layer.11.attention.self.key._packed_params.weight\n",
      "Decompose quantization for bert.encoder.layer.11.attention.self.value._packed_params.weight\n",
      "Decompose quantization for bert.encoder.layer.11.attention.output.dense._packed_params.weight\n",
      "Decompose quantization for bert.encoder.layer.11.intermediate.dense._packed_params.weight\n",
      "Decompose quantization for bert.encoder.layer.11.output.dense._packed_params.weight\n",
      "Decompose quantization for bert.pooler.dense._packed_params.weight\n",
      "Decompose quantization for qa_outputs._packed_params.weight\n"
     ]
    }
   ],
   "source": [
    "# Elementary representation: we decompose the quantized tensors into (scale, zero_point, int_repr).\n",
    "# See https://pytorch.org/docs/stable/quantization.html\n",
    "\n",
    "# We further leverage the fact that int_repr is sparse matrix to optimize the storage: we decompose int_repr into\n",
    "# its CSR representation (data, indptr, indices).\n",
    "\n",
    "elementary_qtz_st = {}\n",
    "for name, param in qtz_st.items():\n",
    "    if param.is_quantized:\n",
    "        print(\"Decompose quantization for\", name)\n",
    "        # We need to extract the scale, the zero_point and the int_repr for the quantized tensor and modules\n",
    "        scale = param.q_scale()                                # torch.tensor(1,) - float32\n",
    "        zero_point = param.q_zero_point()                      # torch.tensor(1,) - int32\n",
    "        elementary_qtz_st[f\"{name}.scale\"] = scale\n",
    "        elementary_qtz_st[f\"{name}.zero_point\"] = zero_point\n",
    "\n",
    "        # We assume the int_repr is sparse and compute its CSR representation\n",
    "        # Only the FCs in the encoder are actually sparse\n",
    "        int_repr = param.int_repr()                         # torch.tensor(nb_rows, nb_columns) - int8\n",
    "        int_repr_cs = sparse.csr_matrix(int_repr)           # scipy.sparse.csr.csr_matrix\n",
    "\n",
    "        elementary_qtz_st[f\"{name}.int_repr.data\"] = int_repr_cs.data                  # np.array int8\n",
    "        elementary_qtz_st[f\"{name}.int_repr.indptr\"] = int_repr_cs.indptr              # np.array int32\n",
    "        assert max(int_repr_cs.indices) < 65535 # If not, we shall fall back to int32\n",
    "        elementary_qtz_st[f\"{name}.int_repr.indices\"] = np.uint16(int_repr_cs.indices) # np.array uint16\n",
    "        elementary_qtz_st[f\"{name}.int_repr.shape\"] = int_repr_cs.shape                # tuple(int, int)\n",
    "    else:\n",
    "        elementary_qtz_st[name] = param\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Encoder Size (MB) - Sparse & Quantized - `torch.save`: 21.29\n"
     ]
    }
   ],
   "source": [
    "# Saving the pruned (encoder + classifier) in the standard torch.save format\n",
    "\n",
    "dense_optimized_st = {name: param for name, param in elementary_qtz_st.items() \n",
    "                                    if \"embedding\" not in name and \"pooler\" not in name}\n",
    "torch.save(dense_optimized_st, 'dbg/dense_squad_optimized.pt',)\n",
    "print(\"Encoder Size (MB) - Sparse & Quantized - `torch.save`:\",\n",
    "      round(os.path.getsize(\"dbg/dense_squad_optimized.pt\")/1e6, 2))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Skip bert.embeddings.word_embeddings.weight\n",
      "Skip bert.embeddings.position_embeddings.weight\n",
      "Skip bert.embeddings.token_type_embeddings.weight\n",
      "Skip bert.embeddings.LayerNorm.weight\n",
      "Skip bert.embeddings.LayerNorm.bias\n",
      "Skip bert.pooler.dense.scale\n",
      "Skip bert.pooler.dense.zero_point\n",
      "Skip bert.pooler.dense._packed_params.weight.scale\n",
      "Skip bert.pooler.dense._packed_params.weight.zero_point\n",
      "Skip bert.pooler.dense._packed_params.weight.int_repr.data\n",
      "Skip bert.pooler.dense._packed_params.weight.int_repr.indptr\n",
      "Skip bert.pooler.dense._packed_params.weight.int_repr.indices\n",
      "Skip bert.pooler.dense._packed_params.weight.int_repr.shape\n",
      "Skip bert.pooler.dense._packed_params.bias\n",
      "\n",
      "Encoder Size (MB) - Dense:              340.25\n",
      "Encoder Size (MB) - Sparse & Quantized: 11.27\n"
     ]
    }
   ],
   "source": [
    "# Save the decomposed state_dict with an HDF5 file\n",
    "# Saving only the encoder + QA Head\n",
    "\n",
    "with h5py.File('dbg/squad_sparse.h5','w') as hf:\n",
    "    for name, param in elementary_qtz_st.items():\n",
    "        if \"embedding\" in name:\n",
    "            print(f\"Skip {name}\")\n",
    "            continue\n",
    "\n",
    "        if \"pooler\" in name:\n",
    "            print(f\"Skip {name}\")\n",
    "            continue\n",
    "\n",
    "        if type(param) == torch.Tensor:\n",
    "            if param.numel() == 1:\n",
    "                # module scale\n",
    "                # module zero_point\n",
    "                hf.attrs[name] = param\n",
    "                continue\n",
    "\n",
    "            if param.requires_grad:\n",
    "                # LayerNorm\n",
    "                param = param.detach().numpy()\n",
    "            hf.create_dataset(name, data=param, compression=\"gzip\", compression_opts=9)\n",
    "\n",
    "        elif type(param) == float or type(param) == int or type(param) == tuple:\n",
    "            # float - tensor _packed_params.weight.scale\n",
    "            # int   - tensor_packed_params.weight.zero_point\n",
    "            # tuple - tensor _packed_params.weight.shape\n",
    "            hf.attrs[name] = param\n",
    "\n",
    "        else:\n",
    "            hf.create_dataset(name, data=param, compression=\"gzip\", compression_opts=9)\n",
    "\n",
    "\n",
    "with open('dbg/metadata.json', 'w') as f:\n",
    "    f.write(json.dumps(qtz_st._metadata))  \n",
    "\n",
    "size = os.path.getsize(\"dbg/squad_sparse.h5\") + os.path.getsize(\"dbg/metadata.json\")\n",
    "print(\"\")\n",
    "print(\"Encoder Size (MB) - Dense:             \", round(dense_mb_size/1e6, 2))\n",
    "print(\"Encoder Size (MB) - Sparse & Quantized:\", round(size/1e6, 2))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Size (MB): 99.39\n"
     ]
    }
   ],
   "source": [
    "# Save the decomposed state_dict to HDF5 storage\n",
    "# Save everything in the architecutre (embedding + encoder + QA Head)\n",
    "\n",
    "with h5py.File('dbg/squad_sparse_with_embs.h5','w') as hf:\n",
    "    for name, param in elementary_qtz_st.items():\n",
    "#         if \"embedding\" in name:\n",
    "#             print(f\"Skip {name}\")\n",
    "#             continue\n",
    "\n",
    "#         if \"pooler\" in name:\n",
    "#             print(f\"Skip {name}\")\n",
    "#             continue\n",
    "\n",
    "        if type(param) == torch.Tensor:\n",
    "            if param.numel() == 1:\n",
    "                # module scale\n",
    "                # module zero_point\n",
    "                hf.attrs[name] = param\n",
    "                continue\n",
    "\n",
    "            if param.requires_grad:\n",
    "                # LayerNorm\n",
    "                param = param.detach().numpy()\n",
    "            hf.create_dataset(name, data=param, compression=\"gzip\", compression_opts=9)\n",
    "\n",
    "        elif type(param) == float or type(param) == int or type(param) == tuple:\n",
    "            # float - tensor _packed_params.weight.scale\n",
    "            # int   - tensor _packed_params.weight.zero_point\n",
    "            # tuple - tensor _packed_params.weight.shape\n",
    "            hf.attrs[name] = param\n",
    "\n",
    "        else:\n",
    "            hf.create_dataset(name, data=param, compression=\"gzip\", compression_opts=9)\n",
    "\n",
    "\n",
    "with open('dbg/metadata.json', 'w') as f:\n",
    "    f.write(json.dumps(qtz_st._metadata))   \n",
    "\n",
    "size = os.path.getsize(\"dbg/squad_sparse_with_embs.h5\") + os.path.getsize(\"dbg/metadata.json\")\n",
    "print('\\nSize (MB):', round(size/1e6, 2))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reconstruct the elementary state dict\n",
    "\n",
    "reconstructed_elementary_qtz_st = {}\n",
    "\n",
    "hf = h5py.File('dbg/squad_sparse_with_embs.h5','r')\n",
    "\n",
    "for attr_name, attr_param in hf.attrs.items():\n",
    "    if 'shape' in attr_name:\n",
    "        attr_param = tuple(attr_param)\n",
    "    elif \".scale\" in attr_name:\n",
    "        if \"_packed_params\" in attr_name:\n",
    "            attr_param = float(attr_param)\n",
    "        else:\n",
    "            attr_param = torch.tensor(attr_param)\n",
    "    elif \".zero_point\" in attr_name:\n",
    "        if \"_packed_params\" in attr_name:\n",
    "            attr_param = int(attr_param)\n",
    "        else:\n",
    "            attr_param = torch.tensor(attr_param)\n",
    "    reconstructed_elementary_qtz_st[attr_name] = attr_param\n",
    "    # print(f\"Unpack {attr_name}\")\n",
    "    \n",
    "# Get the tensors/arrays\n",
    "for data_name, data_param in hf.items():\n",
    "    if \"LayerNorm\" in data_name or \"_packed_params.bias\" in data_name:\n",
    "        reconstructed_elementary_qtz_st[data_name] = torch.from_numpy(np.array(data_param))\n",
    "    elif \"embedding\" in data_name:\n",
    "        reconstructed_elementary_qtz_st[data_name] = torch.from_numpy(np.array(data_param))\n",
    "    else: # _packed_params.weight.int_repr.data, _packed_params.weight.int_repr.indices and _packed_params.weight.int_repr.indptr\n",
    "        data_param = np.array(data_param)\n",
    "        if \"indices\" in data_name:\n",
    "            data_param = np.array(data_param, dtype=np.int32)\n",
    "        reconstructed_elementary_qtz_st[data_name] = data_param\n",
    "    # print(f\"Unpack {data_name}\")\n",
    "    \n",
    "\n",
    "hf.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Sanity checks\n",
    "\n",
    "for name, param in reconstructed_elementary_qtz_st.items():\n",
    "    assert name in elementary_qtz_st\n",
    "for name, param in elementary_qtz_st.items():\n",
    "    assert name in reconstructed_elementary_qtz_st, name\n",
    "\n",
    "for name, param in reconstructed_elementary_qtz_st.items():\n",
    "    assert type(param) == type(elementary_qtz_st[name]), name\n",
    "    if type(param) == torch.Tensor:\n",
    "        assert torch.all(torch.eq(param, elementary_qtz_st[name])), name\n",
    "    elif type(param) == np.ndarray:\n",
    "        assert (param == elementary_qtz_st[name]).all(), name\n",
    "    else:\n",
    "        assert param == elementary_qtz_st[name], name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Re-assemble the sparse int_repr from the CSR format\n",
    "\n",
    "reconstructed_qtz_st = {}\n",
    "\n",
    "for name, param in reconstructed_elementary_qtz_st.items():\n",
    "    if \"weight.int_repr.indptr\" in name:\n",
    "        prefix_ = name[:-16]\n",
    "        data    = reconstructed_elementary_qtz_st[f\"{prefix_}.int_repr.data\"]\n",
    "        indptr  = reconstructed_elementary_qtz_st[f\"{prefix_}.int_repr.indptr\"]\n",
    "        indices = reconstructed_elementary_qtz_st[f\"{prefix_}.int_repr.indices\"]\n",
    "        shape   = reconstructed_elementary_qtz_st[f\"{prefix_}.int_repr.shape\"]\n",
    "\n",
    "        int_repr = sparse.csr_matrix(arg1=(data, indices, indptr),\n",
    "                                     shape=shape)\n",
    "        int_repr = torch.tensor(int_repr.todense())\n",
    "\n",
    "        scale = reconstructed_elementary_qtz_st[f\"{prefix_}.scale\"]\n",
    "        zero_point = reconstructed_elementary_qtz_st[f\"{prefix_}.zero_point\"]\n",
    "        weight = torch._make_per_tensor_quantized_tensor(int_repr,\n",
    "                                                         scale,\n",
    "                                                         zero_point)\n",
    "\n",
    "        reconstructed_qtz_st[f\"{prefix_}\"] = weight\n",
    "    elif \"int_repr.data\" in name or \"int_repr.shape\" in name or \"int_repr.indices\" in name or \\\n",
    "         \"weight.scale\" in name or \"weight.zero_point\" in name:\n",
    "        continue\n",
    "    else:\n",
    "        reconstructed_qtz_st[name] = param\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Sanity checks\n",
    "\n",
    "for name, param in reconstructed_qtz_st.items():\n",
    "    assert name in qtz_st\n",
    "for name, param in qtz_st.items():\n",
    "    assert name in reconstructed_qtz_st, name\n",
    "\n",
    "for name, param in reconstructed_qtz_st.items():\n",
    "    assert type(param) == type(qtz_st[name]), name\n",
    "    if type(param) == torch.Tensor:\n",
    "        assert torch.all(torch.eq(param, qtz_st[name])), name\n",
    "    elif type(param) == np.ndarray:\n",
    "        assert (param == qtz_st[name]).all(), name\n",
    "    else:\n",
    "        assert param == qtz_st[name], name"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sanity checks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<All keys matched successfully>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load the re-constructed state dict into a model\n",
    "\n",
    "dummy_model = BertForQuestionAnswering.from_pretrained('bert-base-uncased')\n",
    "dummy_model.to('cpu')\n",
    "\n",
    "reconstructed_qtz_model = torch.quantization.quantize_dynamic(\n",
    "                            model=dummy_model,\n",
    "                            qconfig_spec = None,\n",
    "                            dtype=torch.qint8,\n",
    "                          )\n",
    "\n",
    "reconstructed_qtz_st = OrderedDict(reconstructed_qtz_st)\n",
    "with open('dbg/metadata.json', 'r') as read_file:\n",
    "    metadata = json.loads(read_file.read())\n",
    "reconstructed_qtz_st._metadata = metadata\n",
    "\n",
    "reconstructed_qtz_model.load_state_dict(reconstructed_qtz_st)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sanity check passed\n"
     ]
    }
   ],
   "source": [
    "# Sanity checks on the infernce\n",
    "\n",
    "N = 32\n",
    "\n",
    "for _ in range(25):\n",
    "    inputs = torch.randint(low=0, high=30000, size=(N, 128))\n",
    "    mask = torch.ones(size=(N, 128))\n",
    "\n",
    "    y_reconstructed = reconstructed_qtz_model(input_ids=inputs, attention_mask=mask)[0]\n",
    "    y               = quantized_model(input_ids=inputs, attention_mask=mask)[0]\n",
    "    \n",
    "    assert torch.all(torch.eq(y, y_reconstructed))\n",
    "print(\"Sanity check passed\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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