# 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 from . 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, ): super(DLRM_Net, self).__init__() 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): for k in range(len(lS_i)): sparse_index_group_batch = lS_i[k] 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) 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 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 def dash_separated_ints(value): vals = value.split('-') for val in vals: try: int(val) except ValueError: raise argparse.ArgumentTypeError( "%s is not a valid dash separated list of ints" % value) return value def dash_separated_floats(value): vals = value.split('-') for val in vals: try: float(val) except ValueError: raise argparse.ArgumentTypeError( "%s is not a valid dash separated list of floats" % value) return value 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=dash_separated_ints, default="4-3-2") # j will be replaced with the table number parser.add_argument( "--arch-mlp-bot", type=dash_separated_ints, default="4-3-2") parser.add_argument( "--arch-mlp-top", type=dash_separated_ints, default="4-2-1") parser.add_argument( "--arch-interaction-op", type=str, choices=['dot', 'cat'], 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=dash_separated_floats, 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) 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, ) # 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) # 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="cpu_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" batch_size = X_onnx.shape[0] # debug prints # print("batch_size", batch_size) # print("inputs", X_onnx, lS_o_onnx, lS_i_onnx) # print("output", dlrm_wrap(X_onnx, lS_o_onnx, lS_i_onnx, use_gpu, device)) # force list conversion # if torch.is_tensor(lS_o_onnx): # lS_o_onnx = [lS_o_onnx[j] for j in range(len(lS_o_onnx))] # if torch.is_tensor(lS_i_onnx): # lS_i_onnx = [lS_i_onnx[j] for j in range(len(lS_i_onnx))] # force tensor conversion # if isinstance(lS_o_onnx, list): # lS_o_onnx = torch.stack(lS_o_onnx) # if isinstance(lS_i_onnx, list): # lS_i_onnx = torch.stack(lS_i_onnx) # debug prints print("X_onnx.shape", X_onnx.shape) if torch.is_tensor(lS_o_onnx): print("lS_o_onnx.shape", lS_o_onnx.shape) else: for oo in lS_o_onnx: print("oo.shape", oo.shape) if torch.is_tensor(lS_i_onnx): print("lS_i_onnx.shape", lS_i_onnx.shape) else: for ii in lS_i_onnx: print("ii.shape", ii.shape) # name inputs and outputs o_inputs = ["offsets"] if torch.is_tensor(lS_o_onnx) else ["offsets_"+str(i) for i in range(len(lS_o_onnx))] i_inputs = ["indices"] if torch.is_tensor(lS_i_onnx) else ["indices_"+str(i) for i in range(len(lS_i_onnx))] all_inputs = ["dense_x"] + o_inputs + i_inputs #debug prints print("inputs", all_inputs) # create dynamic_axis dictionaries do_inputs = [{'offsets': {1 : 'batch_size' }}] if torch.is_tensor(lS_o_onnx) else [{"offsets_"+str(i) :{0 : 'batch _size'}} for i in range(len(lS_o_onnx))] di_inputs = [{'indices': {1 : 'batch_size' }}] if torch.is_tensor(lS_i_onnx) else [{"indices_"+str(i) :{0 : 'batch _size'}} for i in range(len(lS_i_onnx))] dynamic_axes = {'dense_x' : {0 : 'batch _size'}, 'pred' : {0 : 'batch_size'}} for do in do_inputs: dynamic_axes.update(do) for di in di_inputs: dynamic_axes.update(di) # debug prints print(dynamic_axes) # export model torch.onnx.export( dlrm, (X_onnx, lS_o_onnx, lS_i_onnx), dlrm_pytorch_onnx_file, verbose=True, use_external_data_format=True, opset_version=11, input_names=all_inputs, output_names=["pred"], dynamic_axes=dynamic_axes ) # recover the model back dlrm_pytorch_onnx = onnx.load(dlrm_pytorch_onnx_file) # check the onnx model onnx.checker.check_model(dlrm_pytorch_onnx) ''' # run model using onnxruntime import onnxruntime as rt dict_inputs = {} dict_inputs["dense_x"] = X_onnx.numpy().astype(np.float32) if torch.is_tensor(lS_o_onnx): dict_inputs["offsets"] = lS_o_onnx.numpy().astype(np.int64) else: for i in range(len(lS_o_onnx)): dict_inputs["offsets_"+str(i)] = lS_o_onnx[i].numpy().astype(np.int64) if torch.is_tensor(lS_i_onnx): dict_inputs["indices"] = lS_i_onnx.numpy().astype(np.int64) else: for i in range(len(lS_i_onnx)): dict_inputs["indices_"+str(i)] = lS_i_onnx[i].numpy().astype(np.int64) print("dict_inputs", dict_inputs) sess = rt.InferenceSession(dlrm_pytorch_onnx_file, rt.SessionOptions()) prediction = sess.run(output_names=["pred"], input_feed=dict_inputs) print("prediction", prediction) '''