text: # Copyright (c) Facebook, Inc. and its affiliates.
  #
  # This source code is licensed under the MIT license found in the
  # LICENSE file in the root directory of this source tree.
  #
  # Description: an implementation of a deep learning recommendation model (DLRM)
  # The model input consists of dense and sparse features. The former is a vector
  # of floating point values. The latter is a list of sparse indices into
  # embedding tables, which consist of vectors of floating point values.
  # The selected vectors are passed to mlp networks denoted by triangles,
  # in some cases the vectors are interacted through operators (Ops).
  #
  # output:
  #                         vector of values
  # model:                        |
  #                              /\
  #                             /__\
  #                               |
  #       _____________________> Op  <___________________
  #     /                         |                      \
  #    /\                        /\                      /\
  #   /__\                      /__\           ...      /__\
  #    |                          |                       |
  #    |                         Op                      Op
  #    |                    ____/__\_____           ____/__\____
  #    |                   |_Emb_|____|__|    ...  |_Emb_|__|___|
  # input:
  # [ dense features ]     [sparse indices] , ..., [sparse indices]
  #
  # More precise definition of model layers:
  # 1) fully connected layers of an mlp
  # z = f(y)
  # y = Wx + b
  #
  # 2) embedding lookup (for a list of sparse indices p=[p1,...,pk])
  # z = Op(e1,...,ek)
  # obtain vectors e1=E[:,p1], ..., ek=E[:,pk]
  #
  # 3) Operator Op can be one of the following
  # Sum(e1,...,ek) = e1 + ... + ek
  # Dot(e1,...,ek) = [e1'e1, ..., e1'ek, ..., ek'e1, ..., ek'ek]
  # Cat(e1,...,ek) = [e1', ..., ek']'
  # where ' denotes transpose operation
  #
  # References:
  # [1] Maxim Naumov, Dheevatsa Mudigere, Hao-Jun Michael Shi, Jianyu Huang,
  # Narayanan Sundaram, Jongsoo Park, Xiaodong Wang, Udit Gupta, Carole-Jean Wu,
  # Alisson G. Azzolini, Dmytro Dzhulgakov, Andrey Mallevich, Ilia Cherniavskii,
  # Yinghai Lu, Raghuraman Krishnamoorthi, Ansha Yu, Volodymyr Kondratenko,
  # Stephanie Pereira, Xianjie Chen, Wenlin Chen, Vijay Rao, Bill Jia, Liang Xiong,
  # Misha Smelyanskiy, "Deep Learning Recommendation Model for Personalization and
  # Recommendation Systems", CoRR, arXiv:1906.00091, 2019
  
  from __future__ import absolute_import, division, print_function, unicode_literals
  
  # miscellaneous
  import builtins
  import functools
  # import bisect
  # import shutil
  import time
  import json
  # data generation
  import dlrm_data_pytorch as dp
  
  # numpy
  import numpy as np
  
  # onnx
  # The onnx import causes deprecation warnings every time workers
  # are spawned during testing. So, we filter out those warnings.
  import warnings
  with warnings.catch_warnings():
      warnings.filterwarnings("ignore", category=DeprecationWarning)
  import onnx
  
  # pytorch
  import torch
  import torch.nn as nn
  from torch.nn.parallel.parallel_apply import parallel_apply
  from torch.nn.parallel.replicate import replicate
  from torch.nn.parallel.scatter_gather import gather, scatter
  # quotient-remainder trick
  from tricks.qr_embedding_bag import QREmbeddingBag
  # mixed-dimension trick
  from tricks.md_embedding_bag import PrEmbeddingBag, md_solver
  
  import sklearn.metrics
  
  # from torchviz import make_dot
  # import torch.nn.functional as Functional
  # from torch.nn.parameter import Parameter
  
  from torch.optim.lr_scheduler import _LRScheduler
  
  exc = getattr(builtins, "IOError", "FileNotFoundError")
  
  class LRPolicyScheduler(_LRScheduler):
      def __init__(self, optimizer, num_warmup_steps, decay_start_step, num_decay_steps):
          self.num_warmup_steps = num_warmup_steps
          self.decay_start_step = decay_start_step
          self.decay_end_step = decay_start_step + num_decay_steps
          self.num_decay_steps = num_decay_steps
  
          if self.decay_start_step < self.num_warmup_steps:
              sys.exit("Learning rate warmup must finish before the decay starts")
  
          super(LRPolicyScheduler, self).__init__(optimizer)
  
      def get_lr(self):
          step_count = self._step_count
          if step_count < self.num_warmup_steps:
              # warmup
              scale = 1.0 - (self.num_warmup_steps - step_count) / self.num_warmup_steps
              lr = [base_lr * scale for base_lr in self.base_lrs]
              self.last_lr = lr
          elif self.decay_start_step <= step_count and step_count < self.decay_end_step:
              # decay
              decayed_steps = step_count - self.decay_start_step
              scale = ((self.num_decay_steps - decayed_steps) / self.num_decay_steps) ** 2
              min_lr = 0.0000001
              lr = [max(min_lr, base_lr * scale) for base_lr in self.base_lrs]
              self.last_lr = lr
          else:
              if self.num_decay_steps > 0:
                  # freeze at last, either because we're after decay
                  # or because we're between warmup and decay
                  lr = self.last_lr
              else:
                  # do not adjust
                  lr = self.base_lrs
          return lr
  
  ### define dlrm in PyTorch ###
  class DLRM_Net(nn.Module):
      def create_mlp(self, ln, sigmoid_layer):
          # build MLP layer by layer
          layers = nn.ModuleList()
          for i in range(0, ln.size - 1):
              n = ln[i]
              m = ln[i + 1]
  
              # construct fully connected operator
              LL = nn.Linear(int(n), int(m), bias=True)
  
              # initialize the weights
              # with torch.no_grad():
              # custom Xavier input, output or two-sided fill
              mean = 0.0  # std_dev = np.sqrt(variance)
              std_dev = np.sqrt(2 / (m + n))  # np.sqrt(1 / m) # np.sqrt(1 / n)
              W = np.random.normal(mean, std_dev, size=(m, n)).astype(np.float32)
              std_dev = np.sqrt(1 / m)  # np.sqrt(2 / (m + 1))
              bt = np.random.normal(mean, std_dev, size=m).astype(np.float32)
              # approach 1
              LL.weight.data = torch.tensor(W, requires_grad=True)
              LL.bias.data = torch.tensor(bt, requires_grad=True)
              # approach 2
              # LL.weight.data.copy_(torch.tensor(W))
              # LL.bias.data.copy_(torch.tensor(bt))
              # approach 3
              # LL.weight = Parameter(torch.tensor(W),requires_grad=True)
              # LL.bias = Parameter(torch.tensor(bt),requires_grad=True)
              layers.append(LL)
  
              # construct sigmoid or relu operator
              if i == sigmoid_layer:
                  layers.append(nn.Sigmoid())
              else:
                  layers.append(nn.ReLU())
  
          # approach 1: use ModuleList
          # return layers
          # approach 2: use Sequential container to wrap all layers
          return torch.nn.Sequential(*layers)
  
      def create_emb(self, m, ln):
          emb_l = nn.ModuleList()
          for i in range(0, ln.size):
              n = ln[i]
              # construct embedding operator
              if self.qr_flag and n > self.qr_threshold:
                  EE = QREmbeddingBag(n, m, self.qr_collisions,
                      operation=self.qr_operation, mode="sum", sparse=True)
              elif self.md_flag:
                  base = max(m)
                  _m = m[i] if n > self.md_threshold else base
                  EE = PrEmbeddingBag(n, _m, base)
                  # use np initialization as below for consistency...
                  W = np.random.uniform(
                      low=-np.sqrt(1 / n), high=np.sqrt(1 / n), size=(n, _m)
                  ).astype(np.float32)
                  EE.embs.weight.data = torch.tensor(W, requires_grad=True)
  
              else:
                  EE = nn.EmbeddingBag(n, m, mode="sum", sparse=True)
  
                  # initialize embeddings
                  # nn.init.uniform_(EE.weight, a=-np.sqrt(1 / n), b=np.sqrt(1 / n))
                  W = np.random.uniform(
                      low=-np.sqrt(1 / n), high=np.sqrt(1 / n), size=(n, m)
                  ).astype(np.float32)
                  # approach 1
                  EE.weight.data = torch.tensor(W, requires_grad=True)
                  # approach 2
                  # EE.weight.data.copy_(torch.tensor(W))
                  # approach 3
                  # EE.weight = Parameter(torch.tensor(W),requires_grad=True)
  
              emb_l.append(EE)
  
          return emb_l
  
      def __init__(
          self,
          m_spa=None,
          ln_emb=None,
          ln_bot=None,
          ln_top=None,
          arch_interaction_op=None,
          arch_interaction_itself=False,
          sigmoid_bot=-1,
          sigmoid_top=-1,
          sync_dense_params=True,
          loss_threshold=0.0,
          ndevices=-1,
          qr_flag=False,
          qr_operation="mult",
          qr_collisions=0,
          qr_threshold=200,
          md_flag=False,
          md_threshold=200,
          use_gpu=False
      ):
          super(DLRM_Net, self).__init__()
          self.use_gpu = use_gpu
  
          if (
              (m_spa is not None)
              and (ln_emb is not None)
              and (ln_bot is not None)
              and (ln_top is not None)
              and (arch_interaction_op is not None)
          ):
  
              # save arguments
              self.ndevices = ndevices
              self.output_d = 0
              self.parallel_model_batch_size = -1
              self.parallel_model_is_not_prepared = True
              self.arch_interaction_op = arch_interaction_op
              self.arch_interaction_itself = arch_interaction_itself
              self.sync_dense_params = sync_dense_params
              self.loss_threshold = loss_threshold
              # create variables for QR embedding if applicable
              self.qr_flag = qr_flag
              if self.qr_flag:
                  self.qr_collisions = qr_collisions
                  self.qr_operation = qr_operation
                  self.qr_threshold = qr_threshold
              # create variables for MD embedding if applicable
              self.md_flag = md_flag
              if self.md_flag:
                  self.md_threshold = md_threshold
              # create operators
              # if ndevices <= 1:
              #     self.emb_l = self.create_emb(m_spa, ln_emb)
              self.bot_l = self.create_mlp(ln_bot, sigmoid_bot)
              self.top_l = self.create_mlp(ln_top, sigmoid_top)
  
      def apply_mlp(self, x, layers):
          # approach 1: use ModuleList
          # for layer in layers:
          #     x = layer(x)
          # return x
          # approach 2: use Sequential container to wrap all layers
          return layers(x)
  
      def apply_emb(self, lS_o, lS_i, emb_l):
          # WARNING: notice that we are processing the batch at once. We implicitly
          # assume that the data is laid out such that:
          # 1. each embedding is indexed with a group of sparse indices,
          #   corresponding to a single lookup
          # 2. for each embedding the lookups are further organized into a batch
          # 3. for a list of embedding tables there is a list of batched lookups
  
          ly = []
          for k, sparse_index_group_batch in enumerate(lS_i):
              sparse_offset_group_batch = lS_o[k]
  
              # embedding lookup
              # We are using EmbeddingBag, which implicitly uses sum operator.
              # The embeddings are represented as tall matrices, with sum
              # happening vertically across 0 axis, resulting in a row vector
              E = emb_l[k]
              V = E(sparse_index_group_batch, sparse_offset_group_batch)
              if self.use_gpu:
                  V = V.cuda()
              ly.append(V)
  
          # print(ly)
          return ly
  
      def interact_features(self, x, ly):
          if self.arch_interaction_op == "dot":
              # concatenate dense and sparse features
              (batch_size, d) = x.shape
              # import ipdb; ipdb.set_trace()
              T = torch.cat([x] + ly, dim=1).view((batch_size, -1, d))
              # perform a dot product
              Z = torch.bmm(T, torch.transpose(T, 1, 2))
              # append dense feature with the interactions (into a row vector)
              # approach 1: all
              # Zflat = Z.view((batch_size, -1))
              # approach 2: unique
              _, ni, nj = Z.shape
              # approach 1: tril_indices
              # offset = 0 if self.arch_interaction_itself else -1
              # li, lj = torch.tril_indices(ni, nj, offset=offset)
              # approach 2: custom
              offset = 1 if self.arch_interaction_itself else 0
              li = torch.tensor([i for i in range(ni) for j in range(i + offset)])
              lj = torch.tensor([j for i in range(nj) for j in range(i + offset)])
              Zflat = Z[:, li, lj]
              # concatenate dense features and interactions
              R = torch.cat([x] + [Zflat], dim=1)
          elif self.arch_interaction_op == "cat":
              # concatenation features (into a row vector)
              R = torch.cat([x] + ly, dim=1)
          else:
              sys.exit(
                  "ERROR: --arch-interaction-op="
                  + self.arch_interaction_op
                  + " is not supported"
              )
  
          return R
  
      def forward(self, dense_x, lS_o, lS_i):
          if self.ndevices <= 1:
              return self.sequential_forward(dense_x, lS_o, lS_i)
          else:
              return self.parallel_forward(dense_x, lS_o, lS_i)
  
      def sequential_forward(self, dense_x, lS_o, lS_i):
          # process dense features (using bottom mlp), resulting in a row vector
          x = self.apply_mlp(dense_x, self.bot_l)
          # debug prints
          # print("intermediate")
          # print(x.detach().cpu().numpy())
  
          # process sparse features(using embeddings), resulting in a list of row vectors
          ly = self.apply_emb(lS_o, lS_i, self.emb_l)
          # for y in ly:
          #     print(y.detach().cpu().numpy())
  
          # interact features (dense and sparse)
          z = self.interact_features(x, ly)
          # print(z.detach().cpu().numpy())
  
          # obtain probability of a click (using top mlp)
          p = self.apply_mlp(z, self.top_l)
  
          # clamp output if needed
          if 0.0 < self.loss_threshold and self.loss_threshold < 1.0:
              z = torch.clamp(p, min=self.loss_threshold, max=(1.0 - self.loss_threshold))
          else:
              z = p
  
          return z
  
      def parallel_forward(self, dense_x, lS_o, lS_i):
          ### prepare model (overwrite) ###
          # WARNING: # of devices must be >= batch size in parallel_forward call
          batch_size = dense_x.size()[0]
          ndevices = min(self.ndevices, batch_size, len(self.emb_l))
          device_ids = range(ndevices)
          # WARNING: must redistribute the model if mini-batch size changes(this is common
          # for last mini-batch, when # of elements in the dataset/batch size is not even
          if self.parallel_model_batch_size != batch_size:
              self.parallel_model_is_not_prepared = True
  
          if self.parallel_model_is_not_prepared or self.sync_dense_params:
              # replicate mlp (data parallelism)
              self.bot_l_replicas = replicate(self.bot_l, device_ids)
              self.top_l_replicas = replicate(self.top_l, device_ids)
              self.parallel_model_batch_size = batch_size
  
          if self.parallel_model_is_not_prepared:
              # distribute embeddings (model parallelism)
              t_list = []
              for k, emb in enumerate(self.emb_l):
                  d = torch.device("cuda:" + str(k % ndevices))
                  emb.to(d)
                  t_list.append(emb.to(d))
              self.emb_l = nn.ModuleList(t_list)
              self.parallel_model_is_not_prepared = False
  
          ### prepare input (overwrite) ###
          # scatter dense features (data parallelism)
          # print(dense_x.device)
          dense_x = scatter(dense_x, device_ids, dim=0)
          # distribute sparse features (model parallelism)
          if (len(self.emb_l) != len(lS_o)) or (len(self.emb_l) != len(lS_i)):
              sys.exit("ERROR: corrupted model input detected in parallel_forward call")
  
          t_list = []
          i_list = []
          for k, _ in enumerate(self.emb_l):
              d = torch.device("cuda:" + str(k % ndevices))
              t_list.append(lS_o[k].to(d))
              i_list.append(lS_i[k].to(d))
          lS_o = t_list
          lS_i = i_list
  
          ### compute results in parallel ###
          # bottom mlp
          # WARNING: Note that the self.bot_l is a list of bottom mlp modules
          # that have been replicated across devices, while dense_x is a tuple of dense
          # inputs that has been scattered across devices on the first (batch) dimension.
          # The output is a list of tensors scattered across devices according to the
          # distribution of dense_x.
          x = parallel_apply(self.bot_l_replicas, dense_x, None, device_ids)
          # debug prints
          # print(x)
  
          # embeddings
          ly = self.apply_emb(lS_o, lS_i, self.emb_l)
          # debug prints
          # print(ly)
  
          # butterfly shuffle (implemented inefficiently for now)
          # WARNING: Note that at this point we have the result of the embedding lookup
          # for the entire batch on each device. We would like to obtain partial results
          # corresponding to all embedding lookups, but part of the batch on each device.
          # Therefore, matching the distribution of output of bottom mlp, so that both
          # could be used for subsequent interactions on each device.
          if len(self.emb_l) != len(ly):
              sys.exit("ERROR: corrupted intermediate result in parallel_forward call")
  
          t_list = []
          for k, _ in enumerate(self.emb_l):
              d = torch.device("cuda:" + str(k % ndevices))
              y = scatter(ly[k], device_ids, dim=0)
              t_list.append(y)
          # adjust the list to be ordered per device
          ly = list(map(lambda y: list(y), zip(*t_list)))
          # debug prints
          # print(ly)
  
          # interactions
          z = []
          for k in range(ndevices):
              zk = self.interact_features(x[k], ly[k])
              z.append(zk)
          # debug prints
          # print(z)
  
          # top mlp
          # WARNING: Note that the self.top_l is a list of top mlp modules that
          # have been replicated across devices, while z is a list of interaction results
          # that by construction are scattered across devices on the first (batch) dim.
          # The output is a list of tensors scattered across devices according to the
          # distribution of z.
          p = parallel_apply(self.top_l_replicas, z, None, device_ids)
  
          ### gather the distributed results ###
          p0 = gather(p, self.output_d, dim=0)
  
          # clamp output if needed
          if 0.0 < self.loss_threshold and self.loss_threshold < 1.0:
              z0 = torch.clamp(
                  p0, min=self.loss_threshold, max=(1.0 - self.loss_threshold)
              )
          else:
              z0 = p0
  
          return z0
  
  
  if __name__ == "__main__":
      ### import packages ###
      import sys
      import argparse
  
      ### parse arguments ###
      parser = argparse.ArgumentParser(
          description="Train Deep Learning Recommendation Model (DLRM)"
      )
      # model related parameters
      parser.add_argument("--arch-sparse-feature-size", type=int, default=2)
      parser.add_argument("--arch-embedding-size", type=str, default="4-3-2")
      # j will be replaced with the table number
      parser.add_argument("--arch-mlp-bot", type=str, default="4-3-2")
      parser.add_argument("--arch-mlp-top", type=str, default="4-2-1")
      parser.add_argument("--arch-interaction-op", type=str, default="dot")
      parser.add_argument("--arch-interaction-itself", action="store_true", default=False)
      # embedding table options
      parser.add_argument("--md-flag", action="store_true", default=False)
      parser.add_argument("--md-threshold", type=int, default=200)
      parser.add_argument("--md-temperature", type=float, default=0.3)
      parser.add_argument("--md-round-dims", action="store_true", default=False)
      parser.add_argument("--qr-flag", action="store_true", default=False)
      parser.add_argument("--qr-threshold", type=int, default=200)
      parser.add_argument("--qr-operation", type=str, default="mult")
      parser.add_argument("--qr-collisions", type=int, default=4)
      # activations and loss
      parser.add_argument("--activation-function", type=str, default="relu")
      parser.add_argument("--loss-function", type=str, default="mse")  # or bce or wbce
      parser.add_argument("--loss-weights", type=str, default="1.0-1.0")  # for wbce
      parser.add_argument("--loss-threshold", type=float, default=0.0)  # 1.0e-7
      parser.add_argument("--round-targets", type=bool, default=False)
      # data
      parser.add_argument("--data-size", type=int, default=1)
      parser.add_argument("--num-batches", type=int, default=0)
      parser.add_argument(
          "--data-generation", type=str, default="random"
      )  # synthetic or dataset
      parser.add_argument("--data-trace-file", type=str, default="./input/dist_emb_j.log")
      parser.add_argument("--data-set", type=str, default="kaggle")  # or terabyte
      parser.add_argument("--raw-data-file", type=str, default="")
      parser.add_argument("--processed-data-file", type=str, default="")
      parser.add_argument("--data-randomize", type=str, default="total")  # or day or none
      parser.add_argument("--data-trace-enable-padding", type=bool, default=False)
      parser.add_argument("--max-ind-range", type=int, default=-1)
      parser.add_argument("--data-sub-sample-rate", type=float, default=0.0)  # in [0, 1]
      parser.add_argument("--num-indices-per-lookup", type=int, default=10)
      parser.add_argument("--num-indices-per-lookup-fixed", type=bool, default=False)
      parser.add_argument("--num-workers", type=int, default=0)
      parser.add_argument("--memory-map", action="store_true", default=False)
      # training
      parser.add_argument("--mini-batch-size", type=int, default=1)
      parser.add_argument("--nepochs", type=int, default=1)
      parser.add_argument("--learning-rate", type=float, default=0.01)
      parser.add_argument("--print-precision", type=int, default=5)
      parser.add_argument("--numpy-rand-seed", type=int, default=123)
      parser.add_argument("--sync-dense-params", type=bool, default=True)
      # inference
      parser.add_argument("--inference-only", action="store_true", default=False)
      # onnx
      parser.add_argument("--save-onnx", action="store_true", default=False)
      # gpu
      parser.add_argument("--use-gpu", action="store_true", default=False)
      # debugging and profiling
      parser.add_argument("--print-freq", type=int, default=1)
      parser.add_argument("--test-freq", type=int, default=-1)
      parser.add_argument("--test-mini-batch-size", type=int, default=-1)
      parser.add_argument("--test-num-workers", type=int, default=-1)
      parser.add_argument("--print-time", action="store_true", default=False)
      parser.add_argument("--debug-mode", action="store_true", default=False)
      parser.add_argument("--enable-profiling", action="store_true", default=False)
      parser.add_argument("--plot-compute-graph", action="store_true", default=False)
      # store/load model
      parser.add_argument("--save-model", type=str, default="")
      parser.add_argument("--load-model", type=str, default="")
      # mlperf logging (disables other output and stops early)
      parser.add_argument("--mlperf-logging", action="store_true", default=False)
      # stop at target accuracy Kaggle 0.789, Terabyte (sub-sampled=0.875) 0.8107
      parser.add_argument("--mlperf-acc-threshold", type=float, default=0.0)
      # stop at target AUC Terabyte (no subsampling) 0.8025
      parser.add_argument("--mlperf-auc-threshold", type=float, default=0.0)
      parser.add_argument("--mlperf-bin-loader", action='store_true', default=False)
      parser.add_argument("--mlperf-bin-shuffle", action='store_true', default=False)
      # LR policy
      parser.add_argument("--lr-num-warmup-steps", type=int, default=0)
      parser.add_argument("--lr-decay-start-step", type=int, default=0)
      parser.add_argument("--lr-num-decay-steps", type=int, default=0)
      args = parser.parse_args()
  
      if args.mlperf_logging:
          print('command line args: ', json.dumps(vars(args)))
  
      ### some basic setup ###
      np.random.seed(args.numpy_rand_seed)
      np.set_printoptions(precision=args.print_precision)
      torch.set_printoptions(precision=args.print_precision)
      torch.manual_seed(args.numpy_rand_seed)
  
      if (args.test_mini_batch_size < 0):
          # if the parameter is not set, use the training batch size
          args.test_mini_batch_size = args.mini_batch_size
      if (args.test_num_workers < 0):
          # if the parameter is not set, use the same parameter for training
          args.test_num_workers = args.num_workers
  
      use_gpu = args.use_gpu and torch.cuda.is_available()
      if use_gpu:
          torch.cuda.manual_seed_all(args.numpy_rand_seed)
          torch.backends.cudnn.deterministic = True
          device = torch.device("cuda", 0)
          ngpus = torch.cuda.device_count()  # 1
          print("Using {} GPU(s)...".format(ngpus))
      else:
          device = torch.device("cpu")
          print("Using CPU...")
  
      ### prepare training data ###
      ln_bot = np.fromstring(args.arch_mlp_bot, dtype=int, sep="-")
      # input data
      if (args.data_generation == "dataset"):
  
          train_data, train_ld, test_data, test_ld = \
              dp.make_criteo_data_and_loaders(args)
          nbatches = args.num_batches if args.num_batches > 0 else len(train_ld)
          nbatches_test = len(test_ld)
  
          ln_emb = train_data.counts
          # enforce maximum limit on number of vectors per embedding
          if args.max_ind_range > 0:
              ln_emb = np.array(list(map(
                  lambda x: x if x < args.max_ind_range else args.max_ind_range,
                  ln_emb
              )))
          m_den = train_data.m_den
          ln_bot[0] = m_den
      else:
          # input and target at random
          ln_emb = np.fromstring(args.arch_embedding_size, dtype=int, sep="-")
          m_den = ln_bot[0]
          train_data, train_ld = dp.make_random_data_and_loader(args, ln_emb, m_den)
          nbatches = args.num_batches if args.num_batches > 0 else len(train_ld)
  
      ### parse command line arguments ###
      m_spa = args.arch_sparse_feature_size
      num_fea = ln_emb.size + 1  # num sparse + num dense features
      m_den_out = ln_bot[ln_bot.size - 1]
      if args.arch_interaction_op == "dot":
          # approach 1: all
          # num_int = num_fea * num_fea + m_den_out
          # approach 2: unique
          if args.arch_interaction_itself:
              num_int = (num_fea * (num_fea + 1)) // 2 + m_den_out
          else:
              num_int = (num_fea * (num_fea - 1)) // 2 + m_den_out
      elif args.arch_interaction_op == "cat":
          num_int = num_fea * m_den_out
      else:
          sys.exit(
              "ERROR: --arch-interaction-op="
              + args.arch_interaction_op
              + " is not supported"
          )
      arch_mlp_top_adjusted = str(num_int) + "-" + args.arch_mlp_top
      ln_top = np.fromstring(arch_mlp_top_adjusted, dtype=int, sep="-")
  
      # sanity check: feature sizes and mlp dimensions must match
      if m_den != ln_bot[0]:
          sys.exit(
              "ERROR: arch-dense-feature-size "
              + str(m_den)
              + " does not match first dim of bottom mlp "
              + str(ln_bot[0])
          )
      if args.qr_flag:
          if args.qr_operation == "concat" and 2 * m_spa != m_den_out:
              sys.exit(
                  "ERROR: 2 arch-sparse-feature-size "
                  + str(2 * m_spa)
                  + " does not match last dim of bottom mlp "
                  + str(m_den_out)
                  + " (note that the last dim of bottom mlp must be 2x the embedding dim)"
              )
          if args.qr_operation != "concat" and m_spa != m_den_out:
              sys.exit(
                  "ERROR: arch-sparse-feature-size "
                  + str(m_spa)
                  + " does not match last dim of bottom mlp "
                  + str(m_den_out)
              )
      else:
          if m_spa != m_den_out:
              sys.exit(
                  "ERROR: arch-sparse-feature-size "
                  + str(m_spa)
                  + " does not match last dim of bottom mlp "
                  + str(m_den_out)
              )
      if num_int != ln_top[0]:
          sys.exit(
              "ERROR: # of feature interactions "
              + str(num_int)
              + " does not match first dimension of top mlp "
              + str(ln_top[0])
          )
  
      # assign mixed dimensions if applicable
      if args.md_flag:
          m_spa = md_solver(
              torch.tensor(ln_emb),
              args.md_temperature,  # alpha
              d0=m_spa,
              round_dim=args.md_round_dims
          ).tolist()
  
      # test prints (model arch)
      if args.debug_mode:
          print("model arch:")
          print(
              "mlp top arch "
              + str(ln_top.size - 1)
              + " layers, with input to output dimensions:"
          )
          print(ln_top)
          print("# of interactions")
          print(num_int)
          print(
              "mlp bot arch "
              + str(ln_bot.size - 1)
              + " layers, with input to output dimensions:"
          )
          print(ln_bot)
          print("# of features (sparse and dense)")
          print(num_fea)
          print("dense feature size")
          print(m_den)
          print("sparse feature size")
          print(m_spa)
          print(
              "# of embeddings (= # of sparse features) "
              + str(ln_emb.size)
              + ", with dimensions "
              + str(m_spa)
              + "x:"
          )
          print(ln_emb)
  
          print("data (inputs and targets):")
          for j, (X, lS_o, lS_i, T) in enumerate(train_ld):
              # early exit if nbatches was set by the user and has been exceeded
              if nbatches > 0 and j >= nbatches:
                  break
  
              print("mini-batch: %d" % j)
              print(X.detach().cpu().numpy())
              # transform offsets to lengths when printing
              print(
                  [
                      np.diff(
                          S_o.detach().cpu().tolist() + list(lS_i[i].shape)
                      ).tolist()
                      for i, S_o in enumerate(lS_o)
                  ]
              )
              print([S_i.detach().cpu().tolist() for S_i in lS_i])
              print(T.detach().cpu().numpy())
  
      ndevices = min(ngpus, args.mini_batch_size, num_fea - 1) if use_gpu else -1
  
      ### construct the neural network specified above ###
      # WARNING: to obtain exactly the same initialization for
      # the weights we need to start from the same random seed.
      # np.random.seed(args.numpy_rand_seed)
      # import pdb; pdb.set_trace()
      print('ln_bot', ln_bot, 'ln_top', ln_top)
      dlrm = DLRM_Net(
          m_spa,
          ln_emb,
          ln_bot,
          ln_top,
          arch_interaction_op=args.arch_interaction_op,
          arch_interaction_itself=args.arch_interaction_itself,
          sigmoid_bot=-1,
          sigmoid_top=ln_top.size - 2,
          sync_dense_params=args.sync_dense_params,
          loss_threshold=args.loss_threshold,
          ndevices=ndevices,
          qr_flag=args.qr_flag,
          qr_operation=args.qr_operation,
          qr_collisions=args.qr_collisions,
          qr_threshold=args.qr_threshold,
          md_flag=args.md_flag,
          md_threshold=args.md_threshold,
          use_gpu=args.use_gpu
      )
      # test prints
      if args.debug_mode:
          print("initial parameters (weights and bias):")
          for param in dlrm.parameters():
              print(param.detach().cpu().numpy())
          # print(dlrm)
  
      if use_gpu:
          # Custom Model-Data Parallel
          # the mlps are replicated and use data parallelism, while
          # the embeddings are distributed and use model parallelism
          dlrm = dlrm.to(device)  # .cuda()
          # if dlrm.ndevices > 1:
              # dlrm.emb_l = dlrm.create_emb(m_spa, ln_emb)
          dlrm.emb_l = dlrm.create_emb(m_spa, ln_emb)
  
      # specify the loss function
      if args.loss_function == "mse":
          loss_fn = torch.nn.MSELoss(reduction="mean")
      elif args.loss_function == "bce":
          loss_fn = torch.nn.BCELoss(reduction="mean")
      elif args.loss_function == "wbce":
          loss_ws = torch.tensor(np.fromstring(args.loss_weights, dtype=float, sep="-"))
          loss_fn = torch.nn.BCELoss(reduction="none")
      else:
          sys.exit("ERROR: --loss-function=" + args.loss_function + " is not supported")
  
      if not args.inference_only:
          # specify the optimizer algorithm
          optimizer = torch.optim.SGD(dlrm.parameters(), lr=args.learning_rate)
          lr_scheduler = LRPolicyScheduler(optimizer, args.lr_num_warmup_steps, args.lr_decay_start_step,
                                           args.lr_num_decay_steps)
  
      ### main loop ###
      def time_wrap(use_gpu):
          if use_gpu:
              torch.cuda.synchronize()
          return time.time()
  
      def dlrm_wrap(X, lS_o, lS_i, use_gpu, device):
          if use_gpu:  # .cuda()
              # lS_i can be either a list of tensors or a stacked tensor.
              # Handle each case below:
              # lS_i = [S_i.to(device) for S_i in lS_i] if isinstance(lS_i, list) \
              #     else lS_i.to(device)
              # lS_o = [S_o.to(device) for S_o in lS_o] if isinstance(lS_o, list) \
              #     else lS_o.to(device)
              return dlrm(
                  X.to(device),
                  lS_o,
                  lS_i
              )
          else:
              return dlrm(X, lS_o, lS_i)
  
      def loss_fn_wrap(Z, T, use_gpu, device):
          if args.loss_function == "mse" or args.loss_function == "bce":
              if use_gpu:
                  return loss_fn(Z, T.to(device))
              else:
                  return loss_fn(Z, T)
          elif args.loss_function == "wbce":
              if use_gpu:
                  loss_ws_ = loss_ws[T.data.view(-1).long()].view_as(T).to(device)
                  loss_fn_ = loss_fn(Z, T.to(device))
              else:
                  loss_ws_ = loss_ws[T.data.view(-1).long()].view_as(T)
                  loss_fn_ = loss_fn(Z, T.to(device))
              loss_sc_ = loss_ws_ * loss_fn_
              # debug prints
              # print(loss_ws_)
              # print(loss_fn_)
              return loss_sc_.mean()
  
      # training or inference
      best_gA_test = 0
      best_auc_test = 0
      skip_upto_epoch = 0
      skip_upto_batch = 0
      total_time = 0
      total_loss = 0
      total_accu = 0
      total_iter = 0
      total_samp = 0
      k = 0
  
      # Load model is specified
      if not (args.load_model == ""):
          print("Loading saved model {}".format(args.load_model))
          if use_gpu:
              if dlrm.ndevices > 1:
                  # NOTE: when targeting inference on multiple GPUs,
                  # load the model as is on CPU or GPU, with the move
                  # to multiple GPUs to be done in parallel_forward
                  ld_model = torch.load(args.load_model)
              else:
                  # NOTE: when targeting inference on single GPU,
                  # note that the call to .to(device) has already happened
                  ld_model = torch.load(
                      args.load_model,
                      map_location=torch.device('cuda')
                      # map_location=lambda storage, loc: storage.cuda(0)
                  )
          else:
              # when targeting inference on CPU
              ld_model = torch.load(args.load_model, map_location=torch.device('cpu'))
          dlrm.load_state_dict(ld_model["state_dict"])
          ld_j = ld_model["iter"]
          ld_k = ld_model["epoch"]
          ld_nepochs = ld_model["nepochs"]
          ld_nbatches = ld_model["nbatches"]
          ld_nbatches_test = ld_model["nbatches_test"]
          ld_gA = ld_model["train_acc"]
          ld_gL = ld_model["train_loss"]
          ld_total_loss = ld_model["total_loss"]
          ld_total_accu = ld_model["total_accu"]
          ld_gA_test = ld_model["test_acc"]
          ld_gL_test = ld_model["test_loss"]
          if not args.inference_only:
              optimizer.load_state_dict(ld_model["opt_state_dict"])
              best_gA_test = ld_gA_test
              total_loss = ld_total_loss
              total_accu = ld_total_accu
              skip_upto_epoch = ld_k  # epochs
              skip_upto_batch = ld_j  # batches
          else:
              args.print_freq = ld_nbatches
              args.test_freq = 0
  
          print(
              "Saved at: epoch = {:d}/{:d}, batch = {:d}/{:d}, ntbatch = {:d}".format(
                  ld_k, ld_nepochs, ld_j, ld_nbatches, ld_nbatches_test
              )
          )
          print(
              "Training state: loss = {:.6f}, accuracy = {:3.3f} %".format(
                  ld_gL, ld_gA * 100
              )
          )
          print(
              "Testing state: loss = {:.6f}, accuracy = {:3.3f} %".format(
                  ld_gL_test, ld_gA_test * 100
              )
          )
  
      print("time/loss/accuracy (if enabled):")
      with torch.autograd.profiler.profile(args.enable_profiling, use_gpu) as prof:
          while k < args.nepochs:
              if k < skip_upto_epoch:
                  continue
  
              accum_time_begin = time_wrap(use_gpu)
  
              if args.mlperf_logging:
                  previous_iteration_time = None
  
              for j, (X, lS_o, lS_i, T) in enumerate(train_ld):
                  if j == 0 and args.save_onnx:
                      (X_onnx, lS_o_onnx, lS_i_onnx) = (X, lS_o, lS_i)
  
                  if j < skip_upto_batch:
                      continue
  
                  if args.mlperf_logging:
                      current_time = time_wrap(use_gpu)
                      if previous_iteration_time:
                          iteration_time = current_time - previous_iteration_time
                      else:
                          iteration_time = 0
                      previous_iteration_time = current_time
                  else:
                      t1 = time_wrap(use_gpu)
  
                  # early exit if nbatches was set by the user and has been exceeded
                  if nbatches > 0 and j >= nbatches:
                      break
                  '''
                  # debug prints
                  print("input and targets")
                  print(X.detach().cpu().numpy())
                  print([np.diff(S_o.detach().cpu().tolist()
                         + list(lS_i[i].shape)).tolist() for i, S_o in enumerate(lS_o)])
                  print([S_i.detach().cpu().numpy().tolist() for S_i in lS_i])
                  print(T.detach().cpu().numpy())
                  '''
  
                  # forward pass
                  Z = dlrm_wrap(X, lS_o, lS_i, use_gpu, device)
  
                  # loss
                  E = loss_fn_wrap(Z, T, use_gpu, device)
                  '''
                  # debug prints
                  print("output and loss")
                  print(Z.detach().cpu().numpy())
                  print(E.detach().cpu().numpy())
                  '''
                  # compute loss and accuracy
                  L = E.detach().cpu().numpy()  # numpy array
                  S = Z.detach().cpu().numpy()  # numpy array
                  T = T.detach().cpu().numpy()  # numpy array
                  mbs = T.shape[0]  # = args.mini_batch_size except maybe for last
                  A = np.sum((np.round(S, 0) == T).astype(np.uint8))
  
                  if not args.inference_only:
                      # scaled error gradient propagation
                      # (where we do not accumulate gradients across mini-batches)
                      optimizer.zero_grad()
                      # backward pass
                      E.backward()
                      # debug prints (check gradient norm)
                      # for l in mlp.layers:
                      #     if hasattr(l, 'weight'):
                      #          print(l.weight.grad.norm().item())
  
                      # optimizer
                      optimizer.step()
                      lr_scheduler.step()
  
                  if args.mlperf_logging:
                      total_time += iteration_time
                  else:
                      t2 = time_wrap(use_gpu)
                      total_time += t2 - t1
                  total_accu += A
                  total_loss += L * mbs
                  total_iter += 1
                  total_samp += mbs
  
                  should_print = ((j + 1) % args.print_freq == 0) or (j + 1 == nbatches)
                  should_test = (
                      (args.test_freq > 0)
                      and (args.data_generation == "dataset")
                      and (((j + 1) % args.test_freq == 0) or (j + 1 == nbatches))
                  )
  
                  # print time, loss and accuracy
                  if should_print or should_test:
                      gT = 1000.0 * total_time / total_iter if args.print_time else -1
                      total_time = 0
  
                      gA = total_accu / total_samp
                      total_accu = 0
  
                      gL = total_loss / total_samp
                      total_loss = 0
  
                      str_run_type = "inference" if args.inference_only else "training"
                      print(
                          "Finished {} it {}/{} of epoch {}, {:.2f} ms/it, ".format(
                              str_run_type, j + 1, nbatches, k, gT
                          )
                          + "loss {:.6f}, accuracy {:3.3f} %".format(gL, gA * 100)
                      )
                      # Uncomment the line below to print out the total time with overhead
                      # print("Accumulated time so far: {}" \
                      # .format(time_wrap(use_gpu) - accum_time_begin))
                      total_iter = 0
                      total_samp = 0
  
                  # testing
                  if should_test and not args.inference_only:
                      # don't measure training iter time in a test iteration
                      if args.mlperf_logging:
                          previous_iteration_time = None
  
                      test_accu = 0
                      test_loss = 0
                      test_samp = 0
  
                      accum_test_time_begin = time_wrap(use_gpu)
                      if args.mlperf_logging:
                          scores = []
                          targets = []
  
                      for i, (X_test, lS_o_test, lS_i_test, T_test) in enumerate(test_ld):
                          # early exit if nbatches was set by the user and was exceeded
                          if nbatches > 0 and i >= nbatches:
                              break
  
                          t1_test = time_wrap(use_gpu)
  
                          # forward pass
                          Z_test = dlrm_wrap(
                              X_test, lS_o_test, lS_i_test, use_gpu, device
                          )
                          if args.mlperf_logging:
                              S_test = Z_test.detach().cpu().numpy()  # numpy array
                              T_test = T_test.detach().cpu().numpy()  # numpy array
                              scores.append(S_test)
                              targets.append(T_test)
                          else:
                              # loss
                              E_test = loss_fn_wrap(Z_test, T_test, use_gpu, device)
  
                              # compute loss and accuracy
                              L_test = E_test.detach().cpu().numpy()  # numpy array
                              S_test = Z_test.detach().cpu().numpy()  # numpy array
                              T_test = T_test.detach().cpu().numpy()  # numpy array
                              mbs_test = T_test.shape[0]  # = mini_batch_size except last
                              A_test = np.sum((np.round(S_test, 0) == T_test).astype(np.uint8))
                              test_accu += A_test
                              test_loss += L_test * mbs_test
                              test_samp += mbs_test
  
                          t2_test = time_wrap(use_gpu)
  
                      if args.mlperf_logging:
                          scores = np.concatenate(scores, axis=0)
                          targets = np.concatenate(targets, axis=0)
  
                          metrics = {
                              'loss' : sklearn.metrics.log_loss,
                              'recall' : lambda y_true, y_score:
                              sklearn.metrics.recall_score(
                                  y_true=y_true,
                                  y_pred=np.round(y_score)
                              ),
                              'precision' : lambda y_true, y_score:
                              sklearn.metrics.precision_score(
                                  y_true=y_true,
                                  y_pred=np.round(y_score)
                              ),
                              'f1' : lambda y_true, y_score:
                              sklearn.metrics.f1_score(
                                  y_true=y_true,
                                  y_pred=np.round(y_score)
                              ),
                              'ap' : sklearn.metrics.average_precision_score,
                              'roc_auc' : sklearn.metrics.roc_auc_score,
                              'accuracy' : lambda y_true, y_score:
                              sklearn.metrics.accuracy_score(
                                  y_true=y_true,
                                  y_pred=np.round(y_score)
                              ),
                              # 'pre_curve' : sklearn.metrics.precision_recall_curve,
                              # 'roc_curve' :  sklearn.metrics.roc_curve,
                          }
  
                          # print("Compute time for validation metric : ", end="")
                          # first_it = True
                          validation_results = {}
                          for metric_name, metric_function in metrics.items():
                              # if first_it:
                              #     first_it = False
                              # else:
                              #     print(", ", end="")
                              # metric_compute_start = time_wrap(False)
                              validation_results[metric_name] = metric_function(
                                  targets,
                                  scores
                              )
                              # metric_compute_end = time_wrap(False)
                              # met_time = metric_compute_end - metric_compute_start
                              # print("{} {:.4f}".format(metric_name, 1000 * (met_time)),
                              #      end="")
                          # print(" ms")
                          gA_test = validation_results['accuracy']
                          gL_test = validation_results['loss']
                      else:
                          gA_test = test_accu / test_samp
                          gL_test = test_loss / test_samp
  
                      is_best = gA_test > best_gA_test
                      if is_best:
                          best_gA_test = gA_test
                          if not (args.save_model == ""):
                              print("Saving model to {}".format(args.save_model))
                              torch.save(
                                  {
                                      "epoch": k,
                                      "nepochs": args.nepochs,
                                      "nbatches": nbatches,
                                      "nbatches_test": nbatches_test,
                                      "iter": j + 1,
                                      "state_dict": dlrm.state_dict(),
                                      "train_acc": gA,
                                      "train_loss": gL,
                                      "test_acc": gA_test,
                                      "test_loss": gL_test,
                                      "total_loss": total_loss,
                                      "total_accu": total_accu,
                                      "opt_state_dict": optimizer.state_dict(),
                                  },
                                  args.save_model,
                              )
  
                      if args.mlperf_logging:
                          is_best = validation_results['roc_auc'] > best_auc_test
                          if is_best:
                              best_auc_test = validation_results['roc_auc']
  
                          print(
                              "Testing at - {}/{} of epoch {},".format(j + 1, nbatches, k)
                              + " loss {:.6f}, recall {:.4f}, precision {:.4f},".format(
                                  validation_results['loss'],
                                  validation_results['recall'],
                                  validation_results['precision']
                              )
                              + " f1 {:.4f}, ap {:.4f},".format(
                                  validation_results['f1'],
                                  validation_results['ap'],
                              )
                              + " auc {:.4f}, best auc {:.4f},".format(
                                  validation_results['roc_auc'],
                                  best_auc_test
                              )
                              + " accuracy {:3.3f} %, best accuracy {:3.3f} %".format(
                                  validation_results['accuracy'] * 100,
                                  best_gA_test * 100
                              )
                          )
                      else:
                          print(
                              "Testing at - {}/{} of epoch {},".format(j + 1, nbatches, 0)
                              + " loss {:.6f}, accuracy {:3.3f} %, best {:3.3f} %".format(
                                  gL_test, gA_test * 100, best_gA_test * 100
                              )
                          )
                      # Uncomment the line below to print out the total time with overhead
                      # print("Total test time for this group: {}" \
                      # .format(time_wrap(use_gpu) - accum_test_time_begin))
  
                      if (args.mlperf_logging
                          and (args.mlperf_acc_threshold > 0)
                          and (best_gA_test > args.mlperf_acc_threshold)):
                          print("MLPerf testing accuracy threshold "
                                + str(args.mlperf_acc_threshold)
                                + " reached, stop training")
                          break
  
                      if (args.mlperf_logging
                          and (args.mlperf_auc_threshold > 0)
                          and (best_auc_test > args.mlperf_auc_threshold)):
                          print("MLPerf testing auc threshold "
                                + str(args.mlperf_auc_threshold)
                                + " reached, stop training")
                          break
  
              k += 1  # nepochs
  
      # profiling
      if args.enable_profiling:
          with open("dlrm_s_pytorch.prof", "w") as prof_f:
              prof_f.write(prof.key_averages().table(sort_by="cuda_time_total"))
              prof.export_chrome_trace("./dlrm_s_pytorch.json")
          # print(prof.key_averages().table(sort_by="cpu_time_total"))
  
      # plot compute graph
      if args.plot_compute_graph:
          sys.exit(
              "ERROR: Please install pytorchviz package in order to use the"
              + " visualization. Then, uncomment its import above as well as"
              + " three lines below and run the code again."
          )
          # V = Z.mean() if args.inference_only else E
          # dot = make_dot(V, params=dict(dlrm.named_parameters()))
          # dot.render('dlrm_s_pytorch_graph') # write .pdf file
  
      # test prints
      if not args.inference_only and args.debug_mode:
          print("updated parameters (weights and bias):")
          for param in dlrm.parameters():
              print(param.detach().cpu().numpy())
  
      # export the model in onnx
      if args.save_onnx:
          dlrm_pytorch_onnx_file = "dlrm_s_pytorch.onnx"
          torch.onnx.export(
              dlrm, (X_onnx, lS_o_onnx, lS_i_onnx), dlrm_pytorch_onnx_file, verbose=True, use_external_data_format=True
          )
          # recover the model back
          dlrm_pytorch_onnx = onnx.load("dlrm_s_pytorch.onnx")
          # check the onnx model
          onnx.checker.check_model(dlrm_pytorch_onnx)
  