BitLinear_model_test_CPU.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from bitnet_llama.model import LlamaForCausalLM\n",
    "from transformers import AutoTokenizer\n",
    "from transformers import LlamaConfig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "config = LlamaConfig()\n",
    "tokenizer = AutoTokenizer.from_pretrained(\"EleutherAI/polyglot-ko-5.8b\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# model = LlamaForCausalLM(config=config)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "from torch import nn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(torch.Size([1000, 20]), torch.Size([1000, 30]))"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating a synthetic dataset\n",
    "\n",
    "# Parameters\n",
    "input_size = 20\n",
    "output_size = 30\n",
    "num_samples = 1000\n",
    "\n",
    "# Random input data\n",
    "inputs = torch.randn(num_samples, input_size)\n",
    "\n",
    "# True weights for generating synthetic targets (randomly initialized)\n",
    "true_weights = torch.randn(input_size, output_size)\n",
    "\n",
    "# Generating synthetic targets: y = inputs x true_weights + noise\n",
    "noise = 0.05 * torch.randn(num_samples, output_size)\n",
    "targets = inputs @ true_weights + noise\n",
    "\n",
    "inputs.shape, targets.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from bitnet_llama.module import BitLinear"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define a simple neural network with the BitLinear layer\n",
    "class SimpleNet(nn.Module):\n",
    "    def __init__(self, input_size, output_size, num_groups):\n",
    "        super(SimpleNet, self).__init__()\n",
    "        self.bitlinear = BitLinear(input_size, output_size, num_groups=num_groups)\n",
    "    \n",
    "    def forward(self, x):\n",
    "        return self.bitlinear(x)\n",
    "\n",
    "# Initialize the model, loss function, and optimizer\n",
    "model = SimpleNet(input_size, output_size, num_groups=5)\n",
    "criterion = nn.MSELoss()\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=0.01)\n",
    "\n",
    "# Train the model\n",
    "num_epochs = 10000\n",
    "losses = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[501.3132019042969,\n",
       " 503.0736389160156,\n",
       " 502.09466552734375,\n",
       " 514.0336303710938,\n",
       " 525.6828002929688]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "for epoch in range(num_epochs):\n",
    "    # Forward pass\n",
    "    outputs = model(inputs)\n",
    "    loss = criterion(outputs, targets)\n",
    "    \n",
    "    # Backward pass and optimization\n",
    "    optimizer.zero_grad()\n",
    "    loss.backward()\n",
    "    optimizer.step()\n",
    "    \n",
    "    losses.append(loss.item())\n",
    "#     print(loss.item())\n",
    "\n",
    "losses[-5:]  # Display the last 5 loss values\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Plotting the training losses\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(losses, label='Training Loss', color='blue')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Loss')\n",
    "plt.title('Training Loss over Epochs')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Parameter containing:\n",
       "tensor([[-1.7925e+01, -1.7919e+01, -1.7918e+01, -6.2251e+01, -1.7916e+01,\n",
       "         -1.7916e+01, -1.7917e+01, -1.7921e+01, -1.7921e+01, -1.7919e+01,\n",
       "         -1.7918e+01, -4.2988e+01, -1.7916e+01, -1.7919e+01, -6.9264e+01,\n",
       "         -1.7916e+01, -1.7919e+01, -1.7918e+01, -1.7922e+01, -1.7918e+01],\n",
       "        [ 9.9200e+01, -9.9046e+01, -9.9248e+01,  9.9226e+01, -9.5423e+01,\n",
       "          9.8967e+01, -9.8388e+01, -9.8877e+01,  9.9062e+01,  9.7910e+01,\n",
       "         -9.9103e+01, -9.8796e+01, -9.8792e+01,  9.8951e+01, -9.7427e+01,\n",
       "          9.8804e+01, -9.8593e+01,  9.9049e+01, -1.7916e+01,  9.9113e+01],\n",
       "        [-1.7916e+01, -1.7917e+01, -1.7922e+01, -1.7921e+01, -1.7916e+01,\n",
       "         -1.7920e+01, -1.7924e+01, -1.7919e+01, -3.7513e+01, -1.7919e+01,\n",
       "         -1.7920e+01, -1.7919e+01, -1.7920e+01, -1.7921e+01, -1.7918e+01,\n",
       "         -1.7919e+01, -1.7920e+01, -1.7917e+01, -1.7918e+01, -1.7925e+01],\n",
       "        [-1.7921e+01, -1.7918e+01, -1.7922e+01, -1.7918e+01, -1.7917e+01,\n",
       "         -1.7917e+01, -1.7925e+01, -5.8180e+01, -1.7917e+01, -1.7931e+01,\n",
       "         -1.7917e+01, -1.7918e+01, -1.7922e+01, -1.7918e+01, -1.7918e+01,\n",
       "         -1.7917e+01, -1.7919e+01, -1.7922e+01, -1.7920e+01, -8.4137e+01],\n",
       "        [-1.7922e+01, -1.7921e+01, -1.7919e+01, -1.7918e+01, -1.7919e+01,\n",
       "         -1.7920e+01, -1.7923e+01, -1.7921e+01, -1.7916e+01, -1.7919e+01,\n",
       "         -1.7919e+01, -1.7923e+01, -1.7920e+01, -1.7920e+01, -1.7919e+01,\n",
       "         -1.7917e+01, -1.7921e+01, -1.7941e+01, -1.7918e+01, -1.7919e+01],\n",
       "        [-1.7918e+01, -1.7923e+01, -1.7918e+01, -1.7917e+01, -1.7920e+01,\n",
       "         -1.7917e+01, -1.7917e+01, -1.7918e+01, -1.7917e+01, -1.7919e+01,\n",
       "         -1.7921e+01, -1.7928e+01, -1.7916e+01, -1.7920e+01, -1.7917e+01,\n",
       "         -1.7922e+01, -1.7918e+01, -1.7920e+01, -1.7916e+01, -1.7924e+01],\n",
       "        [ 1.3436e-02,  1.5098e-02,  1.3088e-02,  1.3801e-02,  1.3205e-02,\n",
       "          1.3340e-02,  1.4059e-02,  8.8665e-03,  1.2230e-02,  1.2490e-02,\n",
       "          1.2858e-02,  1.2180e-02,  1.1214e-02,  1.1854e-02,  1.1403e-02,\n",
       "          1.0829e-02,  9.8548e-03,  1.4234e-02,  1.0678e-02,  1.0626e-02],\n",
       "        [ 1.2733e-02,  1.0965e-02,  1.1340e-02,  1.2856e-02,  1.1742e-02,\n",
       "          1.2702e-02,  1.2328e-02,  1.2356e-02,  1.6069e-02,  1.2472e-02,\n",
       "          9.9897e-03,  1.2471e-02,  1.0023e-02,  1.4750e-02,  1.1388e-02,\n",
       "          1.3790e-02,  9.7276e-03,  1.3238e-02,  1.1918e-02,  1.3234e-02],\n",
       "        [ 1.2981e-02,  1.0967e-02,  1.5249e-02,  9.4122e-03,  1.1672e-02,\n",
       "          1.3388e-02,  1.1385e-02,  1.0326e-02,  9.1163e-03,  1.4024e-02,\n",
       "          1.1541e-02,  1.0089e-02,  1.1406e-02,  1.4509e-02,  1.2937e-02,\n",
       "          1.2577e-02,  1.1633e-02,  1.1507e-02,  1.3582e-02,  1.4656e-02],\n",
       "        [ 1.2264e-02,  1.1793e-02,  1.2808e-02,  9.6683e-03,  1.2846e-02,\n",
       "          1.1072e-02,  1.3572e-02,  1.2310e-02,  1.4005e-02,  1.3776e-02,\n",
       "          1.3509e-02,  1.2879e-02,  1.2759e-02,  8.2176e-03,  1.4410e-02,\n",
       "          9.5539e-03,  1.3563e-02,  1.2559e-02,  1.0821e-02,  1.3824e-02],\n",
       "        [ 7.3133e-03,  1.2436e-02,  1.2086e-02,  1.0732e-02,  1.1551e-02,\n",
       "          1.3902e-02,  1.4852e-02,  1.2805e-02,  1.4506e-02,  1.4494e-02,\n",
       "          1.4011e-02,  1.3109e-02,  1.1132e-02,  1.3408e-02,  1.4753e-02,\n",
       "          1.2471e-02,  9.2312e-03,  1.3265e-02,  9.6795e-03,  1.3404e-02],\n",
       "        [ 1.1284e-02,  1.4052e-02,  1.2821e-02,  1.2635e-02,  1.2077e-02,\n",
       "          1.1100e-02,  1.3444e-02,  8.1394e-03,  1.2471e-02,  1.1994e-02,\n",
       "          6.3918e-03,  1.1683e-02,  1.2988e-02,  1.1780e-02,  1.1994e-02,\n",
       "          1.0887e-02,  1.1030e-02,  1.1230e-02,  1.1082e-02,  9.8597e-03],\n",
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       "          9.5156e+01, -9.5117e+01,  9.7691e+01,  9.7580e+01, -5.4698e+01,\n",
       "          6.1105e+01, -9.5816e+01, -9.7990e+01, -9.5720e+01, -9.1514e+01],\n",
       "        [-7.9496e+00, -7.9496e+00, -7.9505e+00, -7.9491e+00, -7.9501e+00,\n",
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       "         -7.9477e+00, -7.9478e+00, -7.9511e+00, -7.9494e+00, -7.9498e+00],\n",
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       "         -7.9508e+00, -7.9494e+00, -7.9416e+00, -7.9486e+00, -7.9538e+00],\n",
       "        [-7.9500e+00, -7.9512e+00, -7.9521e+00, -7.9506e+00, -7.9487e+00,\n",
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       "         -7.9491e+00, -7.9484e+00, -7.9506e+00, -7.9517e+00, -7.9505e+00],\n",
       "        [ 9.7275e+01, -7.9535e+00, -9.4461e+01, -9.7654e+01,  9.7213e+01,\n",
       "         -9.6531e+01, -9.7937e+01, -9.7698e+01,  9.7582e+01, -7.9498e+00,\n",
       "         -9.7478e+01, -7.9484e+00, -7.9487e+00, -9.7185e+01,  9.7328e+01,\n",
       "          9.7505e+01, -9.7645e+01, -9.7569e+01,  9.7493e+01,  9.7376e+01],\n",
       "        [ 4.0858e+01, -9.7917e+01,  9.7290e+01, -9.7847e+01,  9.7335e+01,\n",
       "         -7.9496e+00, -9.5972e+01, -9.7842e+01,  8.9385e+01,  9.8104e+01,\n",
       "         -9.7905e+01,  9.7927e+01, -9.7398e+01,  9.7818e+01, -9.6485e+01,\n",
       "          9.7860e+01, -9.7505e+01, -9.7805e+01,  9.8008e+01,  9.7997e+01],\n",
       "        [ 4.5500e+00,  4.5396e+00,  4.5417e+00,  4.5441e+00,  4.5413e+00,\n",
       "          4.5416e+00,  4.5417e+00,  4.5418e+00,  4.5395e+00,  4.5435e+00,\n",
       "          4.5418e+00,  4.5419e+00,  4.5419e+00,  4.5427e+00,  4.5414e+00,\n",
       "          4.5407e+00,  4.5398e+00,  4.5422e+00,  4.5401e+00,  4.5420e+00],\n",
       "        [ 4.5412e+00,  4.5399e+00,  4.5413e+00,  4.5405e+00,  4.5425e+00,\n",
       "          4.5442e+00,  4.5449e+00,  4.5376e+00,  4.5470e+00,  4.5395e+00,\n",
       "          4.5393e+00,  4.5412e+00,  4.5432e+00,  4.5424e+00,  4.5425e+00,\n",
       "          4.5412e+00,  4.5389e+00,  4.5427e+00,  4.5419e+00,  4.5440e+00],\n",
       "        [ 4.5399e+00,  4.5421e+00,  4.5393e+00,  4.5442e+00,  4.5423e+00,\n",
       "          4.5400e+00,  4.5428e+00,  4.5437e+00,  4.5429e+00,  4.5410e+00,\n",
       "          4.5393e+00,  4.5391e+00,  4.5403e+00,  4.5381e+00,  4.5439e+00,\n",
       "          4.5396e+00,  4.5401e+00,  4.5396e+00,  4.5421e+00,  4.5419e+00],\n",
       "        [ 4.5418e+00,  4.5383e+00,  4.5397e+00,  4.5421e+00,  4.5418e+00,\n",
       "          4.5431e+00,  4.5403e+00,  4.5390e+00,  4.5424e+00,  4.5414e+00,\n",
       "          4.5391e+00,  4.5395e+00,  4.5413e+00,  4.5419e+00,  4.5437e+00,\n",
       "          4.5387e+00,  4.5438e+00,  4.5382e+00,  4.5362e+00,  4.5410e+00],\n",
       "        [-9.7027e+01, -9.8170e+01, -8.8779e+01,  9.6834e+01,  9.6541e+01,\n",
       "          9.0469e+01,  4.5331e+00, -9.6856e+01,  7.8105e+01,  4.5371e+00,\n",
       "         -9.7240e+01,  9.7096e+01, -9.6805e+01, -9.7511e+01,  9.7023e+01,\n",
       "          9.3606e+01,  9.6799e+01,  9.5375e+01,  5.8668e+00, -9.3671e+01],\n",
       "        [ 4.5419e+00,  4.5404e+00,  4.5390e+00,  4.5405e+00,  4.5416e+00,\n",
       "          4.5393e+00,  4.5413e+00,  4.5387e+00,  4.5407e+00,  4.5402e+00,\n",
       "          4.5404e+00,  4.5407e+00,  4.5417e+00,  4.5378e+00,  4.5398e+00,\n",
       "          4.5403e+00,  4.5438e+00,  4.5418e+00,  4.5402e+00,  4.5410e+00],\n",
       "        [ 1.8678e-02,  1.8798e-02,  1.8756e-02,  1.9242e-02,  1.8219e-02,\n",
       "          1.7877e-02,  2.0317e-02,  1.8320e-02,  1.7264e-02,  1.8715e-02,\n",
       "          2.0095e-02,  1.8884e-02,  1.7180e-02,  1.8121e-02,  1.8814e-02,\n",
       "          2.1301e-02,  1.9500e-02,  1.6028e-02,  1.9342e-02,  2.1884e-02],\n",
       "        [ 1.8828e-02,  1.5169e-02,  1.8959e-02,  2.0069e-02,  1.9119e-02,\n",
       "          1.8552e-02,  1.7821e-02,  1.4615e-02,  2.0386e-02,  2.0320e-02,\n",
       "          1.8790e-02,  1.8466e-02,  1.8894e-02,  1.8485e-02,  2.0437e-02,\n",
       "          2.4381e-02,  1.8508e-02,  1.9447e-02,  2.0580e-02,  1.8814e-02],\n",
       "        [ 2.1292e-02,  1.8672e-02,  1.7962e-02,  1.9158e-02,  1.9886e-02,\n",
       "          1.8955e-02,  1.5937e-02,  1.7312e-02,  1.7946e-02,  1.7304e-02,\n",
       "          1.9449e-02,  2.0923e-02,  1.8422e-02,  2.2087e-02,  1.5395e-02,\n",
       "          1.6287e-02,  1.9771e-02,  1.5987e-02,  1.7978e-02,  1.7045e-02],\n",
       "        [ 1.8205e-02,  1.9395e-02,  1.9326e-02,  1.9986e-02,  1.8018e-02,\n",
       "          1.6771e-02,  1.7593e-02,  2.0156e-02,  1.9439e-02,  1.7182e-02,\n",
       "          1.9751e-02,  1.6615e-02,  2.2857e-02,  2.0719e-02,  2.0067e-02,\n",
       "          2.4799e-02,  2.0019e-02,  1.8848e-02,  1.9456e-02,  1.9809e-02],\n",
       "        [ 1.9629e-02,  2.3279e-02,  1.8689e-02,  1.7063e-02,  1.6595e-02,\n",
       "          1.8578e-02,  1.8171e-02,  2.1818e-02,  1.7253e-02,  1.8830e-02,\n",
       "          1.8043e-02,  1.8210e-02,  1.9566e-02,  2.1204e-02,  2.3083e-02,\n",
       "          1.9922e-02,  1.9320e-02,  1.8799e-02,  1.9115e-02,  1.5289e-02],\n",
       "        [ 1.8745e-02,  1.8358e-02,  1.7873e-02,  1.7334e-02,  1.9805e-02,\n",
       "          1.6712e-02,  1.8485e-02,  2.0933e-02,  1.8630e-02,  1.7529e-02,\n",
       "          2.0464e-02,  1.7698e-02,  1.8815e-02,  1.9965e-02,  2.1467e-02,\n",
       "          1.8848e-02,  1.8479e-02,  1.8147e-02,  1.8596e-02,  1.9575e-02]],\n",
       "       requires_grad=True)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.bitlinear.weight"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.float32"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.bitlinear.weight.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[-1., -1., -1., -1.,  1.,  1.,  1., -1., -1., -1., -1., -1.,  1., -1.,\n",
       "         -1.,  1., -1., -1., -1., -1.],\n",
       "        [ 1., -1., -1.,  1., -1.,  1., -1., -1.,  1.,  1., -1., -1., -1.,  1.,\n",
       "         -1.,  1., -1.,  1.,  1.,  1.],\n",
       "        [ 1.,  1., -1., -1.,  1., -1., -1., -1., -1., -1., -1., -1., -1., -1.,\n",
       "         -1., -1., -1.,  1., -1., -1.],\n",
       "        [-1., -1., -1., -1.,  1.,  1., -1., -1.,  1., -1.,  1., -1., -1., -1.,\n",
       "         -1.,  1., -1., -1., -1., -1.],\n",
       "        [-1., -1., -1., -1., -1., -1., -1., -1.,  1., -1., -1., -1., -1., -1.,\n",
       "         -1.,  1., -1., -1., -1., -1.],\n",
       "        [-1., -1., -1.,  1., -1.,  1.,  1., -1.,  1., -1., -1., -1.,  1., -1.,\n",
       "          1., -1., -1., -1.,  1., -1.],\n",
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       "         -1., -1., -1.,  1., -1., -1.],\n",
       "        [ 1., -1., -1.,  1., -1.,  1.,  1.,  1.,  1.,  1., -1.,  1., -1.,  1.,\n",
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       "        [ 1., -1.,  1., -1.,  1., -1.,  1.,  1.,  1.,  1.,  1.,  1.,  1., -1.,\n",
       "          1., -1.,  1.,  1., -1.,  1.],\n",
       "        [-1.,  1., -1., -1., -1.,  1.,  1.,  1.,  1.,  1.,  1.,  1., -1.,  1.,\n",
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       "        [-1.,  1.,  1.,  1., -1., -1.,  1., -1.,  1., -1., -1., -1.,  1., -1.,\n",
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       "        [-1., -1., -1.,  1.,  1.,  1., -1., -1.,  1., -1., -1.,  1., -1., -1.,\n",
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       "        [ 1.,  1., -1.,  1.,  1., -1.,  1., -1.,  1., -1.,  1.,  1.,  1., -1.,\n",
       "         -1., -1.,  1.,  1., -1.,  1.],\n",
       "        [-1., -1., -1.,  1., -1., -1.,  1., -1., -1., -1.,  1., -1., -1., -1.,\n",
       "         -1.,  1.,  1., -1.,  1.,  1.],\n",
       "        [-1., -1.,  1.,  1.,  1., -1., -1., -1.,  1.,  1., -1., -1., -1., -1.,\n",
       "          1.,  1., -1.,  1.,  1., -1.],\n",
       "        [ 1., -1., -1.,  1.,  1.,  1., -1., -1., -1., -1.,  1.,  1., -1.,  1.,\n",
       "         -1., -1.,  1., -1., -1., -1.],\n",
       "        [-1.,  1.,  1.,  1., -1., -1., -1.,  1.,  1., -1.,  1., -1.,  1.,  1.,\n",
       "          1.,  1.,  1., -1.,  1.,  1.],\n",
       "        [ 1.,  1., -1., -1., -1., -1., -1.,  1., -1., -1., -1., -1.,  1.,  1.,\n",
       "          1.,  1.,  1., -1.,  1., -1.],\n",
       "        [-1., -1., -1., -1.,  1., -1., -1.,  1., -1., -1.,  1., -1., -1.,  1.,\n",
       "          1., -1., -1., -1., -1.,  1.]], grad_fn=<CopySlices>)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.bitlinear.binarize_weights_groupwise()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'./binarized_weights.pth'"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2. Saving the 1-bit quantized weights\n",
    "\n",
    "# Extract the binarized weights from the model\n",
    "binarized_weights = model.bitlinear.binarize_weights_groupwise()\n",
    "\n",
    "# Define the path to save the binarized weights\n",
    "binarized_weights_path = \"./binarized_weights.pth\"\n",
    "\n",
    "# Save the binarized weights\n",
    "torch.save(binarized_weights, binarized_weights_path)\n",
    "\n",
    "binarized_weights_path\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([1000, 30])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1. Load the binarized weights\n",
    "loaded_binarized_weights = torch.load(binarized_weights_path)\n",
    "\n",
    "# 2. Set the model's weights to the loaded binarized weights\n",
    "# (Note: Here we're directly setting the weights without binarizing again. This is specific to our current implementation.)\n",
    "model.bitlinear.weight.data = loaded_binarized_weights\n",
    "\n",
    "# 3. Perform inference\n",
    "# For demonstration, let's use the same input data we used for training\n",
    "with torch.no_grad():  # Ensure no gradients are computed for inference\n",
    "    inference_output = model(inputs)\n",
    "\n",
    "inference_output.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.float32"
      ]
     },
     "execution_count": 18,
     "metadata": {},
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       "Parameter containing:\n",
       "tensor([[-1., -1., -1., -1.,  1.,  1.,  1., -1., -1., -1., -1., -1.,  1., -1.,\n",
       "         -1.,  1., -1., -1., -1., -1.],\n",
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       "        [-1., -1.,  1.,  1.,  1., -1., -1., -1.,  1.,  1., -1., -1., -1., -1.,\n",
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       "        [-1.,  1.,  1.,  1., -1., -1., -1.,  1.,  1., -1.,  1., -1.,  1.,  1.,\n",
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       "          1., -1., -1., -1., -1.,  1.]], requires_grad=True)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
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    "model.bitlinear.weight"
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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
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   "source": []
  }
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