L4_building_quantizer_custom_quantizer.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vKSxr-q27Xk5"
   },
   "source": [
    "# L4-A - Building your own Quantizer: Custom Build an 8-Bit Quantizer\n",
    "\n",
    "In this lesson, you will learn how to compress any model in 8-bit precision."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dGQrTCG58GY2"
   },
   "source": [
    "## Step 1: class `W8A16LinearLayer`\n",
    "\n",
    "- Build the target class, `W8A16LinearLayer()`, that will be responsible for quantizing your model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.1 - `w8_a16_forward` Function\n",
    "\n",
    "-\n",
    "```Python\n",
    "W8A16LinearLayer\n",
    "                    # 8-bit  # 16-bit         # optional\n",
    "* w8_a16_forward -> weights, input,   scales, bias=None\n",
    "                    \n",
    "```\n",
    "- Cast the 8-bit `weights` to the same data type as the `input`, \"casted weights\",\n",
    "- keeping the \"casted weights\" in the same range as before, [-128, 127]\n",
    "- Next, $$(({inputs} \\cdot \\text{``casted weights''}) * {scale}) + {bias}$$ "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 25310,
     "status": "ok",
     "timestamp": 1700733851446,
     "user": {
      "displayName": "Younes Belkada",
      "userId": "15414910276690549281"
     },
     "user_tz": -60
    },
    "height": 64,
    "id": "6DdI19lk8IWY",
    "outputId": "5cf6438b-36c4-4c91-e9e3-97a2a912dcd2"
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "height": 81
   },
   "outputs": [],
   "source": [
    "random_int8 = torch.randint(-128, 127, (32, 16)).to(torch.int8)\n",
    "random_hs = torch.randn((1, 16), dtype=torch.bfloat16)\n",
    "scales = torch.randn((1, 32), dtype=torch.bfloat16)\n",
    "bias = torch.randn((1, 32), dtype=torch.bfloat16)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note:** Since the values are random, what you see in the video might be different than what you will get."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "height": 30
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[-190.0000,  260.0000,  135.0000,   18.2500,  163.0000, -548.0000,\n",
       "           99.0000, -113.0000,  241.0000,  147.0000,  -67.0000, -255.0000,\n",
       "         -628.0000, -302.0000, -494.0000,  203.0000,   -4.8750,   58.7500,\n",
       "           71.5000, -164.0000,   36.0000,  390.0000,  398.0000,  111.0000,\n",
       "         -444.0000,  -24.0000,   39.5000,   -6.7188,   27.0000, -110.0000,\n",
       "          362.0000,  552.0000]], dtype=torch.bfloat16)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "F.linear(random_hs, random_int8.to(random_hs.dtype))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "height": 30
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[ 2.0300e+02,  6.6250e+00,  6.0938e-01, -2.5250e+01, -8.0000e+01,\n",
       "          7.7000e+01, -7.7000e+01, -5.1000e+01,  1.8100e+02, -5.4250e+01,\n",
       "          4.7750e+01,  7.0800e+02,  2.7800e+02, -1.3900e+02, -7.1200e+02,\n",
       "          3.3400e+02,  2.5000e+00, -3.3250e+01,  2.6625e+01,  2.1800e+02,\n",
       "          3.0938e+00, -2.0600e+02,  1.0080e+03,  4.7250e+01,  1.7400e+02,\n",
       "          2.9500e+01,  1.4125e+01,  2.7188e+00, -3.3500e+01,  8.2000e+01,\n",
       "          4.3600e+02, -2.0600e+02]], dtype=torch.bfloat16)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "F.linear(random_hs, random_int8.to(random_hs.dtype)) * scales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "height": 30
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[ 2.0300e+02,  4.1875e+00,  7.5391e-01, -2.5375e+01, -7.9000e+01,\n",
       "          7.7500e+01, -7.3500e+01, -5.1000e+01,  1.8000e+02, -5.4750e+01,\n",
       "          4.7750e+01,  7.0800e+02,  2.7600e+02, -1.4000e+02, -7.1200e+02,\n",
       "          3.3400e+02,  3.6719e+00, -3.4000e+01,  2.6000e+01,  2.1900e+02,\n",
       "          1.1875e+00, -2.0600e+02,  1.0080e+03,  4.8250e+01,  1.7200e+02,\n",
       "          2.9125e+01,  1.4125e+01,  3.1875e+00, -3.1875e+01,  8.2000e+01,\n",
       "          4.3600e+02, -2.0600e+02]], dtype=torch.bfloat16)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(F.linear(random_hs, random_int8.to(random_hs.dtype)) * scales) + bias"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1e_ftI5x89Tb"
   },
   "source": [
    "- Implement all this as a function, `w8_a16_forward`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "height": 166,
    "id": "gPu1yzvG-ej3"
   },
   "outputs": [],
   "source": [
    "def w8_a16_forward(weight, input, scales, bias=None):\n",
    "    \n",
    "    casted_weights = weight.to(input.dtype)\n",
    "    output = F.linear(input, casted_weights) * scales\n",
    "    \n",
    "    if bias is not None:\n",
    "        output = output + bias\n",
    "      \n",
    "    return output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "height": 98
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "With bias:\n",
      "\n",
      " tensor([[ 2.0300e+02,  4.1875e+00,  7.5391e-01, -2.5375e+01, -7.9000e+01,\n",
      "          7.7500e+01, -7.3500e+01, -5.1000e+01,  1.8000e+02, -5.4750e+01,\n",
      "          4.7750e+01,  7.0800e+02,  2.7600e+02, -1.4000e+02, -7.1200e+02,\n",
      "          3.3400e+02,  3.6719e+00, -3.4000e+01,  2.6000e+01,  2.1900e+02,\n",
      "          1.1875e+00, -2.0600e+02,  1.0080e+03,  4.8250e+01,  1.7200e+02,\n",
      "          2.9125e+01,  1.4125e+01,  3.1875e+00, -3.1875e+01,  8.2000e+01,\n",
      "          4.3600e+02, -2.0600e+02]], dtype=torch.bfloat16)\n",
      "\n",
      "Without bias:\n",
      "\n",
      " tensor([[ 2.0300e+02,  6.6250e+00,  6.0938e-01, -2.5250e+01, -8.0000e+01,\n",
      "          7.7000e+01, -7.7000e+01, -5.1000e+01,  1.8100e+02, -5.4250e+01,\n",
      "          4.7750e+01,  7.0800e+02,  2.7800e+02, -1.3900e+02, -7.1200e+02,\n",
      "          3.3400e+02,  2.5000e+00, -3.3250e+01,  2.6625e+01,  2.1800e+02,\n",
      "          3.0938e+00, -2.0600e+02,  1.0080e+03,  4.7250e+01,  1.7400e+02,\n",
      "          2.9500e+01,  1.4125e+01,  2.7188e+00, -3.3500e+01,  8.2000e+01,\n",
      "          4.3600e+02, -2.0600e+02]], dtype=torch.bfloat16)\n"
     ]
    }
   ],
   "source": [
    "print(\"With bias:\\n\\n\", \n",
    "      w8_a16_forward(random_int8, random_hs, scales, bias))\n",
    "\n",
    "print(\"\\nWithout bias:\\n\\n\", \n",
    "      w8_a16_forward(random_int8, random_hs, scales))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xex3DAyQ-sTd"
   },
   "source": [
    "### 1.2 - `init` Function of class `W8A16LinearLayer`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- This is how the `init` is of [PyTorch Linear layer](https://pytorch.org/docs/stable/_modules/torch/nn/modules/linear.html#Linear):\n",
    "```Python\n",
    "def __init__(self, in_features, out_features, bias=True,\n",
    "             device=None, dtype=None)\n",
    "\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "height": 268
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[91m RuntimeError :  Only Tensors of floating point and complex dtype can require gradients \u001b[0m\n"
     ]
    }
   ],
   "source": [
    "### running this will result in an error\n",
    "class W8A16LinearLayer(nn.Module):\n",
    "    def __init__(self, in_features, out_features, \n",
    "                 bias=True, dtype=torch.float32):\n",
    "        super().__init__()\n",
    "        \n",
    "        self.int8_weights = nn.Parameter(torch.Tensor([0, 1]\n",
    "                                     ).to(dtype=torch.int8))\n",
    "\n",
    "try:\n",
    "    \n",
    "    W8A16LinearLayer(1, 1)\n",
    "    \n",
    "except Exception as error:\n",
    "    print(\"\\033[91m\", type(error).__name__, \": \", error, \"\\033[0m\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "height": 404,
    "id": "o0sOv9N6_Y9y"
   },
   "outputs": [],
   "source": [
    "class W8A16LinearLayer(nn.Module):\n",
    "    def __init__(self, in_features, out_features, \n",
    "                 bias=True, dtype=torch.float32):\n",
    "        super().__init__()\n",
    "        \n",
    "        \n",
    "        self.register_buffer(\n",
    "            \"int8_weights\",\n",
    "            torch.randint(\n",
    "                -128, 127, (out_features, in_features), dtype=torch.int8\n",
    "            )\n",
    "        )\n",
    "        \n",
    "        self.register_buffer(\"scales\", \n",
    "                             torch.randn((out_features), dtype=dtype))\n",
    "        \n",
    "        if bias:\n",
    "            self.register_buffer(\"bias\", \n",
    "                                 torch.randn((1, out_features), \n",
    "                                             dtype=dtype))\n",
    "        \n",
    "        else:\n",
    "            self.bias = None"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Test your implementation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "height": 30
   },
   "outputs": [],
   "source": [
    "dummy_instance = W8A16LinearLayer(16, 32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "height": 47
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([32, 16])\n",
      "torch.Size([32])\n"
     ]
    }
   ],
   "source": [
    "print(dummy_instance.int8_weights.shape)\n",
    "print(dummy_instance.scales.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "u4Mg4qnAA45H"
   },
   "source": [
    "### 1.3 - `forward` Function of class `W8A16LinearLayer`\n",
    "\n",
    "- Use the `w8_a16_forward` defined earlier (Step 1.1) to define the `forward` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "executionInfo": {
     "elapsed": 1,
     "status": "ok",
     "timestamp": 1700735280123,
     "user": {
      "displayName": "Younes Belkada",
      "userId": "15414910276690549281"
     },
     "user_tz": -60
    },
    "height": 472,
    "id": "3RnW6NKGBIpZ"
   },
   "outputs": [],
   "source": [
    "class W8A16LinearLayer(nn.Module):\n",
    "    def __init__(self, in_features, out_features, \n",
    "                 bias=True, dtype=torch.float32):\n",
    "        super().__init__()\n",
    "        \n",
    "        \n",
    "        self.register_buffer(\n",
    "            \"int8_weights\",\n",
    "            torch.randint(\n",
    "                -128, 127, (out_features, in_features), dtype=torch.int8\n",
    "            )\n",
    "        )\n",
    "        \n",
    "        self.register_buffer(\"scales\", \n",
    "                             torch.randn((out_features), dtype=dtype))\n",
    "        \n",
    "        if bias:\n",
    "            self.register_buffer(\"bias\", \n",
    "                                 torch.randn((1, out_features), \n",
    "                                             dtype=dtype))\n",
    "        \n",
    "        else:\n",
    "            self.bias = None\n",
    "\n",
    "    def forward(self, input):\n",
    "        return w8_a16_forward(self.int8_weights, \n",
    "                              input, self.scales, self.bias)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "height": 47
   },
   "outputs": [],
   "source": [
    "module = W8A16LinearLayer(16, 32)\n",
    "dummy_hidden_states = torch.randn(1, 6, 16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "height": 30
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([1, 6, 32])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "module(dummy_hidden_states).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "height": 30
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.float32"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "module(dummy_hidden_states).dtype"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "e8BuIGbWCvTW"
   },
   "source": [
    "### 1.4 - `quantize` Function of class `W8A16LinearLayer`\n",
    "\n",
    "- `quantize` function will dynamically quantize half-precision weights into `torch.int8`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "executionInfo": {
     "elapsed": 2,
     "status": "ok",
     "timestamp": 1700735285050,
     "user": {
      "displayName": "Younes Belkada",
      "userId": "15414910276690549281"
     },
     "user_tz": -60
    },
    "height": 676,
    "id": "I85htZILD4Hm"
   },
   "outputs": [],
   "source": [
    "class W8A16LinearLayer(nn.Module):\n",
    "    def __init__(self, in_features, out_features, \n",
    "                 bias=True, dtype=torch.float32):\n",
    "        super().__init__()\n",
    "        \n",
    "        \n",
    "        self.register_buffer(\n",
    "            \"int8_weights\",\n",
    "            torch.randint(\n",
    "                -128, 127, (out_features, in_features), dtype=torch.int8\n",
    "            )\n",
    "        )\n",
    "        \n",
    "        self.register_buffer(\"scales\", \n",
    "                             torch.randn((out_features), dtype=dtype))\n",
    "        \n",
    "        if bias:\n",
    "            self.register_buffer(\"bias\", \n",
    "                                 torch.randn((1, out_features), \n",
    "                                             dtype=dtype))\n",
    "        \n",
    "        else:\n",
    "            self.bias = None\n",
    "\n",
    "    def quantize(self, weights):\n",
    "        w_fp32 = weights.clone().to(torch.float32)\n",
    "\n",
    "        scales = w_fp32.abs().max(dim=-1).values / 127\n",
    "        scales = scales.to(weights.dtype)\n",
    "\n",
    "        int8_weights = torch.round(weights\n",
    "                        /scales.unsqueeze(1)).to(torch.int8)\n",
    "\n",
    "        self.int8_weights = int8_weights\n",
    "        self.scales = scales\n",
    "    \n",
    "    def forward(self, input):\n",
    "        return w8_a16_forward(self.int8_weights, \n",
    "                              input, self.scales, self.bias)      "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 4,
     "status": "ok",
     "timestamp": 1700735287821,
     "user": {
      "displayName": "Younes Belkada",
      "userId": "15414910276690549281"
     },
     "user_tz": -60
    },
    "height": 30,
    "id": "ZkZnUcRHEhS-",
    "outputId": "ecf21a98-a2c6-4e32-f6ea-30ac56e60a50"
   },
   "outputs": [],
   "source": [
    "module = W8A16LinearLayer(4, 8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "height": 30
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Weights before:\n",
      " tensor([[  78,   71,   61,   36],\n",
      "        [  -3,   34,  -47,  -75],\n",
      "        [ -73,   94,  -60,  119],\n",
      "        [ -28,   31,  -75,   33],\n",
      "        [  81,  -22,   75,   21],\n",
      "        [  27,  -87,   79,   44],\n",
      "        [  78, -111, -103,   68],\n",
      "        [  24,  -50,   22, -102]], dtype=torch.int8)\n"
     ]
    }
   ],
   "source": [
    "print(\"Weights before:\\n\" , module.int8_weights)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "height": 30
   },
   "outputs": [],
   "source": [
    "random_matrix = torch.randn((4, 8), dtype=torch.bfloat16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "height": 30
   },
   "outputs": [],
   "source": [
    "module.quantize(random_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "height": 30
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Weights After:\n",
      " tensor([[  96,  -98, -127,   -9,   35,  -96,  -81,   -9],\n",
      "        [  26, -127,  -76,    7,   42,   61,   60,  -30],\n",
      "        [  74,   -8,   90,  -28,   58,  -14, -127,   -9],\n",
      "        [  53,   54,  127, -114,    7,    4,  -60,   51]], dtype=torch.int8)\n"
     ]
    }
   ],
   "source": [
    "print(\"Weights After:\\n\" , module.int8_weights)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "height": 30
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([0.0081, 0.0081, 0.0139, 0.0080], dtype=torch.bfloat16)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "module.scales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "height": 30
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([4])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "module.scales.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "height": 30
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([4, 8])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "module.int8_weights.shape"
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  },
  {
   "cell_type": "code",
   "execution_count": 25,
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    "height": 47
   },
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    {
     "data": {
      "text/plain": [
       "tensor([[ 0.7734, -0.7891, -1.0234, -0.0723,  0.2812, -0.7734, -0.6523, -0.0723],\n",
       "        [ 0.2109, -1.0312, -0.6172,  0.0569,  0.3418,  0.4961,  0.4863, -0.2432],\n",
       "        [ 1.0234, -0.1108,  1.2500, -0.3887,  0.8047, -0.1943, -1.7578, -0.1245],\n",
       "        [ 0.4238,  0.4316,  1.0156, -0.9102,  0.0559,  0.0320, -0.4805,  0.4082]],\n",
       "       dtype=torch.bfloat16)"
      ]
     },
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   ],
   "source": [
    "### dequantized weights\n",
    "module.int8_weights * module.scales.unsqueeze(1)"
   ]
  },
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   "cell_type": "code",
   "execution_count": 26,
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      "text/plain": [
       "tensor([[ 0.7773, -0.7852, -1.0234, -0.0703,  0.2832, -0.7734, -0.6523, -0.0757],\n",
       "        [ 0.2139, -1.0312, -0.6211,  0.0540,  0.3438,  0.4922,  0.4863, -0.2402],\n",
       "        [ 1.0234, -0.1060,  1.2422, -0.3848,  0.8086, -0.1973, -1.7578, -0.1240],\n",
       "        [ 0.4219,  0.4316,  1.0156, -0.9102,  0.0598,  0.0282, -0.4766,  0.4062]],\n",
       "       dtype=torch.bfloat16)"
      ]
     },
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    "### original weights\n",
    "random_matrix"
   ]
  },
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   "execution_count": 27,
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       "tensor(0.0023, dtype=torch.bfloat16)"
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     "execution_count": 27,
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    "(random_matrix - module.int8_weights \n",
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