build_for_pynq.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "275f2cdd",
   "metadata": {},
   "source": [
    "# Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "52bf15d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import finn.builder.build_dataflow as build\n",
    "import finn.builder.build_dataflow_config as build_cfg\n",
    "\n",
    "import json\n",
    "import netron\n",
    "from IPython.display import IFrame\n",
    "\n",
    "import numpy as np\n",
    "from finn.core.modelwrapper import ModelWrapper\n",
    "import onnx\n",
    "from finn.util.test import get_test_model_trained\n",
    "import brevitas.onnx as bo\n",
    "import random\n",
    "\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",
    "from finn.transformation.streamline import Streamline\n",
    "from finn.transformation.lower_convs_to_matmul import LowerConvsToMatMul\n",
    "from finn.transformation.bipolar_to_xnor import ConvertBipolarMatMulToXnorPopcount\n",
    "import finn.transformation.streamline.absorb as absorb\n",
    "from finn.transformation.streamline.reorder import MakeMaxPoolNHWC, MoveScalarLinearPastInvariants\n",
    "from finn.transformation.infer_data_layouts import InferDataLayouts\n",
    "from finn.transformation.general import RemoveUnusedTensors\n",
    "import finn.transformation.fpgadataflow.convert_to_hls_layers as to_hls\n",
    "from finn.transformation.fpgadataflow.create_dataflow_partition import (\n",
    "    CreateDataflowPartition,\n",
    ")\n",
    "\n",
    "from finn.transformation.fpgadataflow.insert_dwc import InsertDWC\n",
    "from finn.transformation.move_reshape import RemoveCNVtoFCFlatten\n",
    "from finn.custom_op.registry import getCustomOp\n",
    "from finn.transformation.infer_data_layouts import InferDataLayouts\n",
    "from finn.util.pytorch import ToTensor\n",
    "from finn.transformation.merge_onnx_models import MergeONNXModels\n",
    "from finn.core.datatype import DataType\n",
    "from finn.transformation.insert_topk import InsertTopK\n",
    "from finn.transformation.infer_datatypes import InferDataTypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97ec2256",
   "metadata": {},
   "source": [
    "# Build architecture for unpruned model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "75b8d49e",
   "metadata": {},
   "outputs": [],
   "source": [
    "steps = [\n",
    "    \"step_tidy_up\",\n",
    "    \"step_streamline\",\n",
    "    \"step_convert_to_hls\",\n",
    "    \"step_create_dataflow_partition\",\n",
    "    \"step_target_fps_parallelization\",\n",
    "    \"step_apply_folding_config\",\n",
    "    \"step_generate_estimate_reports\",\n",
    "    \"step_hls_codegen\",\n",
    "    \"step_hls_ipgen\",\n",
    "    \"step_set_fifo_depths\",\n",
    "    \"step_create_stitched_ip\",\n",
    "    \"step_measure_rtlsim_performance\",\n",
    "    #\"step_make_pynq_driver\",\n",
    "    \"step_out_of_context_synthesis\",\n",
    "    #\"step_synthesize_bitfile\",\n",
    "    #\"step_deployment_package\",\n",
    "]\n",
    "\n",
    "model_file_path = \"best_2bit_nonpruned.onnx\" # replace this with a file containing model you would like to synthesize\n",
    "\n",
    "estimates_output_dir = f\"resource_monitoring/nonpruned_\" +  model_file_path[5:-5]\n",
    "cfg = build.DataflowBuildConfig(\n",
    "    output_dir          = estimates_output_dir,\n",
    "    folding_config_file = \"w4a4_folding_config.json\", # replace this with a file containing the configuration you got from pruning algorithm\n",
    "    auto_fifo_depths    = False,\n",
    "    synth_clk_period_ns = 10.0,\n",
    "    board               = \"Pynq-Z1\",\n",
    "    shell_flow_type     = build_cfg.ShellFlowType.VIVADO_ZYNQ,\n",
    "    steps               = steps,\n",
    "    generate_outputs = [\n",
    "        build_cfg.DataflowOutputType.ESTIMATE_REPORTS,\n",
    "        build_cfg.DataflowOutputType.STITCHED_IP,\n",
    "        build_cfg.DataflowOutputType.OOC_SYNTH,\n",
    "    ]\n",
    ")\n",
    "build.build_dataflow_cfg(\"pruned_models/model_2bit/\" + model_file_path, cfg)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "982f336c",
   "metadata": {},
   "source": [
    "# Build architecture for pruned model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "362eb863",
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_mask(amount, simd_list):\n",
    "    # flattened dimensions of each layer\n",
    "    dims = [27,576,576,1152,1152,2304]\n",
    "    masks = []\n",
    "    for i, (dim, simd) in enumerate(zip(dims, simd_list)):\n",
    "        # new shape of the array, which makes it easier to create the blocks afterward \n",
    "        # by repeating the elements simd times\n",
    "        new_shape = dim // simd\n",
    "        # number of pruned parameters have to be divisible by simd\n",
    "        params_to_keep = new_shape - int(np.round(new_shape * amount))\n",
    "        if params_to_keep > simd:\n",
    "            params_to_keep += simd\n",
    "            params_to_keep -= params_to_keep % simd\n",
    "\n",
    "        # create mask full of 1\n",
    "        layer_mask = [1] * new_shape\n",
    "        # set params_pruned elements to 0\n",
    "        layer_mask[:params_to_keep] = [0] * params_to_keep\n",
    "        # shuffle the elements\n",
    "        random.shuffle(layer_mask)\n",
    "        masks.append(np.repeat(layer_mask, simd).tolist())\n",
    "    return masks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9b6d26ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "steps = [\n",
    "    \"step_tidy_up\",\n",
    "    \"step_streamline\",\n",
    "    \"step_convert_to_hls\",\n",
    "    \"step_create_dataflow_partition\",\n",
    "    \"step_target_fps_parallelization\",\n",
    "    \"step_apply_folding_config\",\n",
    "    \"step_generate_estimate_reports\",\n",
    "    \"step_hls_codegen\",\n",
    "    \"step_hls_ipgen\",\n",
    "    \"step_set_fifo_depths\",\n",
    "    \"step_create_stitched_ip\",\n",
    "    \"step_measure_rtlsim_performance\",\n",
    "    #\"step_make_pynq_driver\",\n",
    "    \"step_out_of_context_synthesis\",\n",
    "    #\"step_synthesize_bitfile\",\n",
    "    #\"step_deployment_package\",\n",
    "]\n",
    "\n",
    "model_file_path = \"best_2bit_nonpruned.onnx\" # replace this with a file containing model you would like to synthesize\n",
    "\n",
    "fc_simds = [3, 8, 6, 8, 3, 4]\n",
    "cnv_simds = [1, 8, 2, 2, 1, 1]\n",
    "\n",
    "# replace this with the results of extract_masks.ipynb\n",
    "pruning_masks = [[[[0.0, 1.0, 0.0], [0.0, 1.0, 0.0], [0.0, 1.0, 0.0]], [[0.0, 1.0, 0.0], [0.0, 1.0, 0.0], [0.0, 1.0, 0.0]], [[0.0, 1.0, 0.0], [0.0, 1.0, 0.0], [0.0, 1.0, 0.0]]], [[[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 1.0]], [[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 1.0]], [[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 1.0]], [[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 1.0]], [[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 1.0]], [[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 1.0]], [[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 1.0]], [[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 1.0]], [[1.0, 1.0, 1.0], [1.0, 1.0, 0.0], [1.0, 0.0, 1.0]], [[1.0, 1.0, 1.0], [1.0, 1.0, 0.0], [1.0, 0.0, 1.0]], [[1.0, 1.0, 1.0], [1.0, 1.0, 0.0], [1.0, 0.0, 1.0]], [[1.0, 1.0, 1.0], [1.0, 1.0, 0.0], [1.0, 0.0, 1.0]], [[1.0, 1.0, 1.0], [1.0, 1.0, 0.0], [1.0, 0.0, 1.0]], [[1.0, 1.0, 1.0], [1.0, 1.0, 0.0], [1.0, 0.0, 1.0]], [[1.0, 1.0, 1.0], [1.0, 1.0, 0.0], [1.0, 0.0, 1.0]], [[1.0, 1.0, 1.0], [1.0, 1.0, 0.0], [1.0, 0.0, 1.0]], [[0.0, 1.0, 1.0], [1.0, 1.0, 0.0], [0.0, 0.0, 0.0]], [[0.0, 1.0, 1.0], [1.0, 1.0, 0.0], [0.0, 0.0, 0.0]], [[0.0, 1.0, 1.0], [1.0, 1.0, 0.0], [0.0, 0.0, 0.0]], [[0.0, 1.0, 1.0], [1.0, 1.0, 0.0], [0.0, 0.0, 0.0]], [[0.0, 1.0, 1.0], [1.0, 1.0, 0.0], [0.0, 0.0, 0.0]], [[0.0, 1.0, 1.0], [1.0, 1.0, 0.0], [0.0, 0.0, 0.0]], [[0.0, 1.0, 1.0], [1.0, 1.0, 0.0], [0.0, 0.0, 0.0]], [[0.0, 1.0, 1.0], [1.0, 1.0, 0.0], [0.0, 0.0, 0.0]], [[0.0, 0.0, 0.0], [0.0, 0.0, 1.0], [0.0, 0.0, 1.0]], [[0.0, 0.0, 0.0], [0.0, 0.0, 1.0], [0.0, 0.0, 1.0]], [[0.0, 0.0, 0.0], [0.0, 0.0, 1.0], [0.0, 0.0, 1.0]], [[0.0, 0.0, 0.0], [0.0, 0.0, 1.0], [0.0, 0.0, 1.0]], [[0.0, 0.0, 0.0], [0.0, 0.0, 1.0], [0.0, 0.0, 1.0]], [[0.0, 0.0, 0.0], [0.0, 0.0, 1.0], [0.0, 0.0, 1.0]], [[0.0, 0.0, 0.0], [0.0, 0.0, 1.0], [0.0, 0.0, 1.0]], [[0.0, 0.0, 0.0], [0.0, 0.0, 1.0], [0.0, 0.0, 1.0]], [[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]], [[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]], [[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]], [[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]], [[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]], [[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]], [[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]], [[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]], [[0.0, 0.0, 0.0], [1.0, 1.0, 1.0], [0.0, 1.0, 1.0]], [[0.0, 0.0, 0.0], [1.0, 1.0, 1.0], [0.0, 1.0, 1.0]], [[0.0, 0.0, 0.0], [1.0, 1.0, 1.0], [0.0, 1.0, 1.0]], [[0.0, 0.0, 0.0], [1.0, 1.0, 1.0], [0.0, 1.0, 1.0]], [[0.0, 0.0, 0.0], [1.0, 1.0, 1.0], [0.0, 1.0, 1.0]], [[0.0, 0.0, 0.0], [1.0, 1.0, 1.0], [0.0, 1.0, 1.0]], [[0.0, 0.0, 0.0], [1.0, 1.0, 1.0], [0.0, 1.0, 1.0]], [[0.0, 0.0, 0.0], [1.0, 1.0, 1.0], [0.0, 1.0, 1.0]], [[0.0, 1.0, 0.0], [0.0, 0.0, 0.0], [0.0, 1.0, 0.0]], [[0.0, 1.0, 0.0], [0.0, 0.0, 0.0], [0.0, 1.0, 0.0]], [[0.0, 1.0, 0.0], [0.0, 0.0, 0.0], [0.0, 1.0, 0.0]], [[0.0, 1.0, 0.0], [0.0, 0.0, 0.0], [0.0, 1.0, 0.0]], [[0.0, 1.0, 0.0], [0.0, 0.0, 0.0], [0.0, 1.0, 0.0]], [[0.0, 1.0, 0.0], [0.0, 0.0, 0.0], [0.0, 1.0, 0.0]], [[0.0, 1.0, 0.0], [0.0, 0.0, 0.0], [0.0, 1.0, 0.0]], [[0.0, 1.0, 0.0], [0.0, 0.0, 0.0], [0.0, 1.0, 0.0]], [[0.0, 0.0, 0.0], [1.0, 0.0, 1.0], [0.0, 1.0, 0.0]], [[0.0, 0.0, 0.0], [1.0, 0.0, 1.0], [0.0, 1.0, 0.0]], [[0.0, 0.0, 0.0], [1.0, 0.0, 1.0], [0.0, 1.0, 0.0]], [[0.0, 0.0, 0.0], [1.0, 0.0, 1.0], [0.0, 1.0, 0.0]], [[0.0, 0.0, 0.0], [1.0, 0.0, 1.0], [0.0, 1.0, 0.0]], [[0.0, 0.0, 0.0], [1.0, 0.0, 1.0], [0.0, 1.0, 0.0]], [[0.0, 0.0, 0.0], [1.0, 0.0, 1.0], [0.0, 1.0, 0.0]], [[0.0, 0.0, 0.0], [1.0, 0.0, 1.0], [0.0, 1.0, 0.0]]], [[[0.0, 0.0, 1.0], [0.0, 0.0, 0.0], [0.0, 0.0, 1.0]], [[0.0, 0.0, 1.0], [0.0, 0.0, 0.0], [0.0, 0.0, 1.0]], [[0.0, 1.0, 1.0], [0.0, 0.0, 0.0], [0.0, 1.0, 1.0]], [[0.0, 1.0, 1.0], [0.0, 0.0, 0.0], [0.0, 1.0, 1.0]], [[0.0, 1.0, 0.0], [0.0, 0.0, 0.0], [0.0, 1.0, 0.0]], [[0.0, 1.0, 0.0], [0.0, 0.0, 0.0], [0.0, 1.0, 0.0]], [[0.0, 1.0, 0.0], [0.0, 0.0, 0.0], [1.0, 1.0, 0.0]], [[0.0, 1.0, 0.0], [0.0, 0.0, 0.0], [1.0, 1.0, 0.0]], [[0.0, 1.0, 0.0], [0.0, 0.0, 0.0], [1.0, 0.0, 0.0]], [[0.0, 1.0, 0.0], [0.0, 0.0, 0.0], [1.0, 0.0, 0.0]], [[0.0, 1.0, 1.0], [0.0, 0.0, 0.0], [1.0, 0.0, 0.0]], [[0.0, 1.0, 1.0], [0.0, 0.0, 0.0], [1.0, 0.0, 0.0]], [[1.0, 1.0, 1.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]], [[1.0, 1.0, 1.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]], [[1.0, 1.0, 1.0], [0.0, 0.0, 0.0], [0.0, 1.0, 0.0]], [[1.0, 1.0, 1.0], [0.0, 0.0, 0.0], [0.0, 1.0, 0.0]], [[1.0, 1.0, 1.0], [0.0, 0.0, 0.0], [0.0, 1.0, 0.0]], [[1.0, 1.0, 1.0], [0.0, 0.0, 0.0], [0.0, 1.0, 0.0]], [[0.0, 1.0, 1.0], [1.0, 0.0, 0.0], [0.0, 1.0, 0.0]], [[0.0, 1.0, 1.0], [1.0, 0.0, 0.0], [0.0, 1.0, 0.0]], [[0.0, 1.0, 1.0], [1.0, 0.0, 0.0], [0.0, 1.0, 0.0]], [[0.0, 1.0, 1.0], [1.0, 0.0, 0.0], [0.0, 1.0, 0.0]], [[0.0, 1.0, 0.0], [1.0, 0.0, 0.0], [0.0, 1.0, 1.0]], [[0.0, 1.0, 0.0], [1.0, 0.0, 0.0], [0.0, 1.0, 1.0]], [[0.0, 1.0, 0.0], [0.0, 0.0, 0.0], [0.0, 1.0, 1.0]], [[0.0, 1.0, 0.0], [0.0, 0.0, 0.0], [0.0, 1.0, 1.0]], [[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], 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0.0]], [[1.0, 0.0, 0.0], [1.0, 0.0, 1.0], [0.0, 0.0, 0.0]], [[1.0, 0.0, 0.0], [1.0, 0.0, 1.0], [0.0, 0.0, 0.0]], [[1.0, 0.0, 0.0], [1.0, 0.0, 1.0], [0.0, 0.0, 0.0]]]]\n",
    "\n",
    "for i, layer in enumerate(pruning_masks):\n",
    "    pruning_masks[i] = np.array(np.transpose(layer, (1,2,0))).flatten().tolist()\n",
    "    pruning_masks[i] = np.logical_not(pruning_masks[i]).astype(int)\n",
    "\n",
    "estimates_output_dir = f\"resource_monitoring/nonpruned_\" +  model_file_path[5:-5]\n",
    "cfg = build.DataflowBuildConfig(\n",
    "    output_dir          = estimates_output_dir,\n",
    "    folding_config_file = \"w4a4_folding_config.json\", # replace this with a file containing the configuration you got from pruning algorithm\n",
    "    auto_fifo_depths    = False,\n",
    "    synth_clk_period_ns = 10.0,\n",
    "    board               = \"Pynq-Z1\",\n",
    "    shell_flow_type     = build_cfg.ShellFlowType.VIVADO_ZYNQ,\n",
    "    pruning_masks       = pruning_masks, # use a custom mask, for quick test you can use a random mask using make_mask(pruning_ratio, fc_simds)\n",
    "    simd_list           = fc_simds,\n",
    "    simd_list_gen       = cnv_simds,\n",
    "    steps               = steps,\n",
    "    generate_outputs = [\n",
    "        build_cfg.DataflowOutputType.ESTIMATE_REPORTS,\n",
    "        build_cfg.DataflowOutputType.STITCHED_IP,\n",
    "        build_cfg.DataflowOutputType.OOC_SYNTH,\n",
    "    ]\n",
    ")\n",
    "build.build_dataflow_cfg(\"pruned_models/model_2bit/\" + model_file_path, cfg)"
   ]
  }
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