extract_masks.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "16770c6e",
   "metadata": {},
   "source": [
    "# Define the network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "abf31d4a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files already downloaded and verified\n",
      "Files already downloaded and verified\n"
     ]
    }
   ],
   "source": [
    "from finn.util.basic import make_build_dir\n",
    "from finn.util.visualization import showInNetron\n",
    "import numpy as np\n",
    "from collections import OrderedDict\n",
    "\n",
    "import onnx\n",
    "from finn.util.test import get_test_model_trained\n",
    "import brevitas.onnx as bo\n",
    "from finn.core.modelwrapper import ModelWrapper\n",
    "from finn.transformation.infer_shapes import InferShapes\n",
    "from finn.transformation.fold_constants import FoldConstants\n",
    "from finn.transformation.general import GiveReadableTensorNames, GiveUniqueNodeNames, RemoveStaticGraphInputs\n",
    "import time\n",
    "import torch\n",
    "import torch.nn.utils.prune as prune\n",
    "import torch.optim as optim\n",
    "from brevitas.nn import QuantConv2d, QuantLinear\n",
    "\n",
    "\n",
    "from dependencies import value\n",
    "\n",
    "from brevitas.inject import ExtendedInjector\n",
    "from brevitas.quant.solver import WeightQuantSolver, ActQuantSolver\n",
    "from brevitas.core.bit_width import BitWidthImplType\n",
    "from brevitas.core.quant import QuantType\n",
    "from brevitas.core.restrict_val import RestrictValueType, FloatToIntImplType\n",
    "from brevitas.core.scaling import ScalingImplType\n",
    "from brevitas.core.zero_point import ZeroZeroPoint\n",
    "\n",
    "\n",
    "class CommonQuant(ExtendedInjector):\n",
    "    bit_width_impl_type = BitWidthImplType.CONST\n",
    "    scaling_impl_type = ScalingImplType.CONST\n",
    "    restrict_scaling_type = RestrictValueType.FP\n",
    "    zero_point_impl = ZeroZeroPoint\n",
    "    float_to_int_impl_type = FloatToIntImplType.ROUND\n",
    "    scaling_per_output_channel = False\n",
    "    narrow_range = True\n",
    "    signed = True\n",
    "\n",
    "    @value\n",
    "    def quant_type(bit_width):\n",
    "        if bit_width is None:\n",
    "            return QuantType.FP\n",
    "        elif bit_width == 1:\n",
    "            return QuantType.BINARY\n",
    "        else:\n",
    "            return QuantType.INT\n",
    "\n",
    "\n",
    "class CommonWeightQuant(CommonQuant, WeightQuantSolver):\n",
    "    scaling_const = 1.0\n",
    "\n",
    "\n",
    "class CommonActQuant(CommonQuant, ActQuantSolver):\n",
    "    min_val = -1.0\n",
    "    max_val = 1.0\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.init as init\n",
    "\n",
    "\n",
    "class TensorNorm(nn.Module):\n",
    "    def __init__(self, eps=1e-4, momentum=0.1):\n",
    "        super().__init__()\n",
    "\n",
    "        self.eps = eps\n",
    "        self.momentum = momentum\n",
    "        self.weight = nn.Parameter(torch.rand(1))\n",
    "        self.bias = nn.Parameter(torch.rand(1))\n",
    "        self.register_buffer('running_mean', torch.zeros(1))\n",
    "        self.register_buffer('running_var', torch.ones(1))\n",
    "        self.reset_running_stats()\n",
    "\n",
    "    def reset_running_stats(self):\n",
    "        self.running_mean.zero_()\n",
    "        self.running_var.fill_(1)\n",
    "        init.ones_(self.weight)\n",
    "        init.zeros_(self.bias)\n",
    "\n",
    "    def forward(self, x):\n",
    "        if self.training:\n",
    "            mean = x.mean()\n",
    "            unbias_var = x.var(unbiased=True)\n",
    "            biased_var = x.var(unbiased=False)\n",
    "            self.running_mean = (1 - self.momentum) * self.running_mean + self.momentum * mean.detach()\n",
    "            self.running_var = (1 - self.momentum) * self.running_var + self.momentum * unbias_var.detach()\n",
    "            inv_std = 1 / (biased_var + self.eps).pow(0.5)\n",
    "            return (x - mean) * inv_std * self.weight + self.bias\n",
    "        else:\n",
    "            return ((x - self.running_mean) / (self.running_var + self.eps).pow(0.5)) * self.weight + self.bias\n",
    "\n",
    "import torch\n",
    "from torch.nn import Module, ModuleList, BatchNorm2d, MaxPool2d, BatchNorm1d\n",
    "\n",
    "from brevitas.nn import QuantConv2d, QuantIdentity, QuantLinear\n",
    "from brevitas.core.restrict_val import RestrictValueType\n",
    "\n",
    "\n",
    "CNV_OUT_CH_POOL = [(64, False), (64, True), (128, False), (128, True), (256, False), (256, False)]\n",
    "INTERMEDIATE_FC_FEATURES = [(256, 512), (512, 512)]\n",
    "LAST_FC_IN_FEATURES = 512\n",
    "LAST_FC_PER_OUT_CH_SCALING = False\n",
    "POOL_SIZE = 2\n",
    "KERNEL_SIZE = 3\n",
    "\n",
    "\n",
    "class CNV(Module):\n",
    "\n",
    "    def __init__(self, num_classes, weight_bit_width, act_bit_width, in_bit_width, in_ch):\n",
    "        super(CNV, self).__init__()\n",
    "\n",
    "        self.conv_features = ModuleList()\n",
    "        self.linear_features = ModuleList()\n",
    "\n",
    "        self.conv_features.append(QuantIdentity( # for Q1.7 input format\n",
    "            act_quant=CommonActQuant,\n",
    "            bit_width=in_bit_width,\n",
    "            min_val=- 1.0,\n",
    "            max_val=1.0 - 2.0 ** (-7),\n",
    "            narrow_range=False,\n",
    "            restrict_scaling_type=RestrictValueType.POWER_OF_TWO))\n",
    "\n",
    "        for out_ch, is_pool_enabled in CNV_OUT_CH_POOL:\n",
    "            self.conv_features.append(QuantConv2d(\n",
    "                kernel_size=KERNEL_SIZE,\n",
    "                in_channels=in_ch,\n",
    "                out_channels=out_ch,\n",
    "                bias=False,\n",
    "            \n",
    "                weight_quant=CommonWeightQuant,\n",
    "                weight_bit_width=weight_bit_width))\n",
    "            in_ch = out_ch\n",
    "            self.conv_features.append(BatchNorm2d(in_ch, eps=1e-4))\n",
    "            self.conv_features.append(QuantIdentity(\n",
    "                act_quant=CommonActQuant,\n",
    "                bit_width=act_bit_width))\n",
    "            if is_pool_enabled:\n",
    "                self.conv_features.append(MaxPool2d(kernel_size=2))\n",
    "\n",
    "        for in_features, out_features in INTERMEDIATE_FC_FEATURES:\n",
    "            self.linear_features.append(QuantLinear(\n",
    "                in_features=in_features,\n",
    "                out_features=out_features,\n",
    "                bias=False,\n",
    "                weight_quant=CommonWeightQuant,\n",
    "                weight_bit_width=weight_bit_width))\n",
    "            self.linear_features.append(BatchNorm1d(out_features, eps=1e-4))\n",
    "            self.linear_features.append(QuantIdentity(\n",
    "                act_quant=CommonActQuant,\n",
    "                bit_width=act_bit_width))\n",
    "\n",
    "        self.linear_features.append(QuantLinear(\n",
    "            in_features=LAST_FC_IN_FEATURES,\n",
    "            out_features=num_classes,\n",
    "            bias=False,\n",
    "            weight_quant=CommonWeightQuant,\n",
    "            weight_bit_width=weight_bit_width))\n",
    "        self.linear_features.append(TensorNorm())\n",
    "        \n",
    "        for m in self.modules():\n",
    "            if isinstance(m, QuantConv2d) or isinstance(m, QuantLinear):\n",
    "                torch.nn.init.uniform_(m.weight.data, -1, 1)\n",
    "\n",
    "\n",
    "    def clip_weights(self, min_val, max_val):\n",
    "        for mod in self.conv_features:\n",
    "            if isinstance(mod, QuantConv2d):\n",
    "                mod.weight.data.clamp_(min_val, max_val)\n",
    "        for mod in self.linear_features:\n",
    "            if isinstance(mod, QuantLinear):\n",
    "                mod.weight.data.clamp_(min_val, max_val)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = 2.0 * x - torch.tensor([1.0], device=x.device)\n",
    "        for mod in self.conv_features:\n",
    "            x = mod(x)\n",
    "        x = x.view(x.shape[0], -1)\n",
    "        for mod in self.linear_features:\n",
    "            x = mod(x)\n",
    "        return x\n",
    "\n",
    "\n",
    "def cnv(weight_bit_width, act_bit_width, in_bit_width):\n",
    "    num_classes = 10\n",
    "    in_channels = 3\n",
    "    net = CNV(weight_bit_width=weight_bit_width,\n",
    "              act_bit_width=act_bit_width,\n",
    "              in_bit_width=in_bit_width,\n",
    "              num_classes=num_classes,\n",
    "              in_ch=in_channels)\n",
    "    return net\n",
    "\n",
    "import torchvision\n",
    "import torchvision.transforms as transforms\n",
    "\n",
    "transform = transforms.Compose(\n",
    "    [transforms.ToTensor()])\n",
    "\n",
    "train_transforms_list = [transforms.RandomCrop(32, padding=4),\n",
    "                                     transforms.RandomHorizontalFlip(),\n",
    "                                     transforms.ToTensor()]\n",
    "transform_train = transforms.Compose(train_transforms_list)\n",
    "\n",
    "trainset = torchvision.datasets.CIFAR10(root='./data', train=True,\n",
    "                                        download=True, transform=transform_train)\n",
    "trainloader = torch.utils.data.DataLoader(trainset, batch_size=128,\n",
    "                                          shuffle=True, num_workers=1)\n",
    "\n",
    "testset = torchvision.datasets.CIFAR10(root='./data', train=False,\n",
    "                                       download=True, transform=transform)\n",
    "testloader = torch.utils.data.DataLoader(testset, batch_size=128,\n",
    "                                         shuffle=False, num_workers=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "efb55c99",
   "metadata": {},
   "source": [
    "# Extract masks from the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a3272bcf",
   "metadata": {},
   "outputs": [],
   "source": [
    "import brevitas.onnx as bo\n",
    "\n",
    "# this function works on a model checkpoint exported before making the pruning permanent\n",
    "# it is neccessary to change the state dictionary to make it permanent before loading\n",
    "def make_weights(filename):\n",
    "    # change this to get masks for model with different bitwidth (weight bitwidth, activation bitwidth, input bitwidth)\n",
    "    model = cnv(4,4,8)\n",
    "    optimizer = optim.Adam(model.parameters(), lr=0.001)\n",
    "    device = 'cuda:0'\n",
    "    criterion = nn.CrossEntropyLoss().to(device)\n",
    "    package = torch.load(filename, map_location='cpu')\n",
    "    model_state_dict = package['state_dict']\n",
    "    export_mask_list = []\n",
    "    for orig_weight_key, mask_key in [('conv_features.1.weight_orig', 'conv_features.1.weight_mask'), ('conv_features.4.weight_orig', 'conv_features.4.weight_mask'), ('conv_features.8.weight_orig', 'conv_features.8.weight_mask') , ('conv_features.11.weight_orig', 'conv_features.11.weight_mask'), ('conv_features.15.weight_orig', 'conv_features.15.weight_mask'), ('conv_features.18.weight_orig', 'conv_features.18.weight_mask')]:\n",
    "        orig_weight = model_state_dict[orig_weight_key]\n",
    "        export_mask = model_state_dict[mask_key][0]\n",
    "        export_mask_list.append(export_mask.tolist())\n",
    "        # save the pruning mask in a file\n",
    "        torch.save(model_state_dict[mask_key], mask_key + \".tar\")\n",
    "        mask = model_state_dict[mask_key].bool()\n",
    "        orig_weight[~mask] = 0\n",
    "        weight = orig_weight\n",
    "        weight_key = '.'.join(orig_weight_key.split(\".\")[:2])+ \".weight\"\n",
    "        model_state_dict = OrderedDict([(weight_key, weight) if k == orig_weight_key else (k, v) for k, v in model_state_dict.items()])\n",
    "        model_state_dict.pop(mask_key)\n",
    "    model.load_state_dict(model_state_dict, strict=True)\n",
    "    optimizer = optim.Adam(model.parameters(), lr=0.001)\n",
    "    # print pruning masks for quick use\n",
    "    print(export_mask_list)\n",
    "    bo.export_finn_onnx(model, (1, 3, 32, 32), filename[:-3] + \"onnx\")\n",
    "    return export_mask_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "524e3422",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:176: TracerWarning: torch.tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.\n",
      "/home/roba/.local/lib/python3.6/site-packages/brevitas/quant_tensor/__init__.py:74: TracerWarning: torch.tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.\n",
      "  training = torch.tensor(training, dtype=torch.bool)\n"
     ]
    }
   ],
   "source": [
    "masks = make_weights(\"pruned_models_tuned/best_4bit_68_pruned_0.75.tar\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7667f54c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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