L4_building_quantizer_replace_layers.ipynb

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   "source": [
    "# L4-B - Building your own Quantizer: Replace PyTorch layers with Quantized Layers\n",
    "\n",
    "In this lesson, you will learn about the quantization pipline using your own 8-bit quantizer."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dGQrTCG58GY2"
   },
   "source": [
    "Run the next cell to import all of the functions you have used before in the previous lesson(s) of `Building your own Quantizer` to follow along with the video.\n",
    "\n",
    "- To access the `helper.py` file, you can click `File --> Open...`, on the top left."
   ]
  },
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   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "\n",
    "from helper import W8A16LinearLayer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "TUgZZfZoK9QO"
   },
   "source": [
    "## Step 2: Quantization Pipeline"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {
    "id": "iXO4T8fFLQ04"
   },
   "source": [
    "- Replace all of the `torch.nn.Linear` layers with the `W8A16LinearLayer` layer.\n",
    "- Call `quantize` on the linear layers using the original weights. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ap9-LRqhMvJT"
   },
   "source": [
    "### 2.1 - Model In-place Linear Layer Replacement\n",
    "- Implement `replace_linear_with_target`"
   ]
  },
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   "source": [
    "def replace_linear_with_target(module, \n",
    "                               target_class, module_name_to_exclude):\n",
    "    for name, child in module.named_children():\n",
    "        if isinstance(child, nn.Linear) and not \\\n",
    "          any([x == name for x in module_name_to_exclude]):\n",
    "            old_bias = child.bias\n",
    "\n",
    "            new_module = target_class(child.in_features, \n",
    "                                      child.out_features, \n",
    "                                      old_bias is not None, \n",
    "                                      child.weight.dtype)\n",
    "            setattr(module, name, new_module)\n",
    "            if old_bias is not None:\n",
    "              getattr(module, name).bias = old_bias\n",
    "        else:\n",
    "            # Recursively call the function for nested modules\n",
    "            replace_linear_with_target(\n",
    "                child, target_class, module_name_to_exclude)"
   ]
  },
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   "source": [
    "class DummyModel(torch.nn.Module):\n",
    "  def __init__(self):\n",
    "    super().__init__()\n",
    "    self.emb = torch.nn.Embedding(1, 1)\n",
    "    # Try with bias\n",
    "    self.linear_1 = nn.Linear(1, 1)\n",
    "    # Try without bias\n",
    "    self.linear_2 = nn.Linear(1, 1, bias=False)\n",
    "    # Lm prediction head\n",
    "    self.lm_head = nn.Linear(1, 1, bias=False)"
   ]
  },
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   "source": [
    "model_1 = DummyModel()\n",
    "model_2 = DummyModel()"
   ]
  },
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   "execution_count": 5,
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     "text": [
      "DummyModel(\n",
      "  (emb): Embedding(1, 1)\n",
      "  (linear_1): W8A16LinearLayer()\n",
      "  (linear_2): W8A16LinearLayer()\n",
      "  (lm_head): Linear(in_features=1, out_features=1, bias=False)\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "replace_linear_with_target(model_1, W8A16LinearLayer, [\"lm_head\"])\n",
    "print(model_1)"
   ]
  },
  {
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   "execution_count": 6,
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      "DummyModel(\n",
      "  (emb): Embedding(1, 1)\n",
      "  (linear_1): W8A16LinearLayer()\n",
      "  (linear_2): W8A16LinearLayer()\n",
      "  (lm_head): W8A16LinearLayer()\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "replace_linear_with_target(model_2, W8A16LinearLayer, [])\n",
    "print(model_2)"
   ]
  },
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   "metadata": {
    "id": "KoM2OOk_O_1L"
   },
   "source": [
    "### 2.2 - Linear Layer Replacement + Quantization\n",
    "- Modify the `replace_linear_with_target` function to also perform quantization.\n",
    "- Implement `replace_linear_with_target_and_quantize`."
   ]
  },
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   "source": [
    "def replace_linear_with_target_and_quantize(module, \n",
    "                               target_class, module_name_to_exclude):\n",
    "    for name, child in module.named_children():\n",
    "        if isinstance(child, nn.Linear) and not \\\n",
    "        any([x == name for x in module_name_to_exclude]):\n",
    "            old_bias = child.bias\n",
    "            old_weight = child.weight\n",
    "\n",
    "            new_module = target_class(child.in_features, \n",
    "                                      child.out_features, \n",
    "                                      old_bias is not None, \n",
    "                                      child.weight.dtype)\n",
    "            setattr(module, name, new_module)\n",
    "\n",
    "            getattr(module, name).quantize(old_weight)\n",
    "            \n",
    "            if old_bias is not None:\n",
    "              getattr(module, name).bias = old_bias\n",
    "        else:\n",
    "            # Recursively call the function for nested modules\n",
    "            replace_linear_with_target_and_quantize(child, \n",
    "                     target_class, module_name_to_exclude)"
   ]
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   "source": [
    "model_3 = DummyModel()"
   ]
  },
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   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "height": 66
   },
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     "text": [
      "DummyModel(\n",
      "  (emb): Embedding(1, 1)\n",
      "  (linear_1): W8A16LinearLayer()\n",
      "  (linear_2): W8A16LinearLayer()\n",
      "  (lm_head): Linear(in_features=1, out_features=1, bias=False)\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "replace_linear_with_target_and_quantize(model_3, W8A16LinearLayer, [\"lm_head\"])\n",
    "print(model_3)"
   ]
  },
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