NCF scripts with Distiller integration

This NCF implementation is based on the implementation found in the MLPerf
Training GitHub repository, specifically on the last revision of the code
before the switch to the extended dataset. See:
https://github.com/mlperf/training/tree/fe17e837ed12974d15c86d5173fe8f2c188434d5/recommendation/pytorch

We've made several modifications to the code:
* Removed all MLPerf specific code including logging
* In ncf.py:
  * Added calls to Distiller compression APIs
  * Added progress indication in training and evaluation flows
* In neumf.py:
  * Added option to split final FC layer
  * Replaced all functional calls with modules so they can be detected
    by Distiller
* In dataset.py:
  * Speed up data loading - On first data will is loaded from CSVs and
    then pickled. On subsequent runs the pickle is loaded. This is much
    faster than the original implementation, but still very slow.
  * Added progress indication during data load process
* Removed some irrelevant content from README.md

.gitignore

@@ -8,6 +8,8 @@ logs/
 __pycache__/
 .pytest_cache
 .cache
+examples/ncf/run/
+examples/ncf/ml-20m*
 
 # Virtual env
 env/

distiller/quantization/__init__.py

@@ -16,7 +16,7 @@
 
 from .quantizer import Quantizer
 from .range_linear import RangeLinearQuantWrapper, RangeLinearQuantParamLayerWrapper, PostTrainLinearQuantizer, \
-    LinearQuantMode, QuantAwareTrainRangeLinearQuantizer, add_post_train_quant_args,\
+    LinearQuantMode, QuantAwareTrainRangeLinearQuantizer, add_post_train_quant_args, NCFQuantAwareTrainQuantizer, \
     RangeLinearQuantConcatWrapper, RangeLinearQuantEltwiseAddWrapper, RangeLinearQuantEltwiseMultWrapper, ClipMode
 from .clipped_linear import LinearQuantizeSTE, ClippedLinearQuantization, WRPNQuantizer, DorefaQuantizer, PACTQuantizer
 

distiller/quantization/range_linear.py

@@ -1076,3 +1076,21 @@ class QuantAwareTrainRangeLinearQuantizer(Quantizer):
                                                                   per_channel=perch)
             m.register_buffer(ptq.q_attr_name + '_scale', torch.ones_like(scale))
             m.register_buffer(ptq.q_attr_name + '_zero_point', torch.zeros_like(zero_point))
+
+
+class NCFQuantAwareTrainQuantizer(QuantAwareTrainRangeLinearQuantizer):
+    def __init__(self, model, optimizer=None, bits_activations=32, bits_weights=32, bits_bias=32,
+                 overrides=None, mode=LinearQuantMode.SYMMETRIC, ema_decay=0.999, per_channel_wts=False):
+        super(NCFQuantAwareTrainQuantizer, self).__init__(model, optimizer=optimizer,
+                                                          bits_activations=bits_activations,
+                                                          bits_weights=bits_weights,
+                                                          bits_bias=bits_bias,
+                                                          overrides=overrides,
+                                                          mode=mode, ema_decay=ema_decay,
+                                                          per_channel_wts=per_channel_wts,
+                                                          quantize_inputs=False)
+
+        self.replacement_factory[distiller.modules.EltwiseMult] = self.activation_replace_fn
+        self.replacement_factory[distiller.modules.Concat] = self.activation_replace_fn
+        self.replacement_factory[nn.Linear] = self.activation_replace_fn
+        # self.replacement_factory[nn.Sigmoid] = self.activation_replace_fn

examples/ncf/MLPERF_LICENSE.md

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examples/ncf/README.md

+# NCF - Neural Collaborative Filtering
+
+The NCF implementation provided here is based on the implementation found in the MLPerf Training GitHub repository, specifically on the last revision of the code before the switch to the extended dataset. See [here](https://github.com/mlperf/training/tree/fe17e837ed12974d15c86d5173fe8f2c188434d5/recommendation/pytorch).
+
+We've made several modifications to the code:
+* Removed all MLPerf specific code including logging
+* In `ncf.py`:
+  * Added calls to Distiller compression APIs
+  * Added progress indication in training and evaluation flows
+* In `neumf.py`:
+  * Added option to split final FC layer
+  * Replaced all functional calls with modules so they can be detected by Distiller
+* In `dataset.py`:
+  * Speed up data loading - On first data will is loaded from CSVs and then pickled. On subsequent runs the pickle is loaded. This is much faster than the original implementation, but still very slow.
+  * Added progress indication during data load process
+* Removed some irrelevant content from this README
+
+## Problem
+
+This task benchmarks recommendation with implicit feedback on the [MovieLens 20 Million (ml-20m) dataset](https://grouplens.org/datasets/movielens/20m/) with a [Neural Collaborative Filtering](http://dl.acm.org/citation.cfm?id=3052569) model.
+The model trains on binary information about whether or not a user interacted with a specific item.
+
+## Setup
+
+### Steps to configure machine
+
+1. Install `unzip` and `curl`
+
+```bash
+sudo apt-get install unzip curl
+```
+
+2. Install required python packages
+
+```bash
+pip install -r requirements.txt
+```
+
+3. Download and verify data
+
+```bash
+# Creates ml-20.zip
+source ../download_dataset.sh
+# Confirms the MD5 checksum of ml-20.zip
+source ../verify_dataset.sh
+```
+
+## Running the Sample
+
+### TODO: Add some Distiller specific example command line
+
+## Dataset/Environment
+
+### Publication/Attribution
+
+Harper, F. M. & Konstan, J. A. (2015), 'The MovieLens Datasets: History and Context', ACM Trans. Interact. Intell. Syst. 5(4), 19:1--19:19.
+
+### Data preprocessing
+
+1. Unzip
+2. Remove users with less than 20 reviews
+3. Create training and test data separation described below
+
+### Training and test data separation
+
+Positive training examples are all but the last item each user rated.
+Negative training examples are randomly selected from the unrated items for each user.
+
+The last item each user rated is used as a positive example in the test set.
+A fixed set of 999 unrated items are also selected to calculate hit rate at 10 for predicting the test item.
+
+### Training data order
+
+Data is traversed randomly with 4 negative examples selected on average for every positive example.
+
+## Model
+
+### Publication/Attribution
+
+Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu and Tat-Seng Chua (2017). [Neural Collaborative Filtering](http://dl.acm.org/citation.cfm?id=3052569). In Proceedings of WWW '17, Perth, Australia, April 03-07, 2017.
+
+The author's original code is available at [hexiangnan/neural_collaborative_filtering](https://github.com/hexiangnan/neural_collaborative_filtering).
+
+## Quality
+
+### Quality metric
+
+Hit rate at 10 (HR@10) with 999 negative items.
+
+### Evaluation frequency
+
+After every epoch through the training data.
+
+### Evaluation thoroughness
+
+Every users last item rated, i.e. all held out positive examples.

examples/ncf/convert.py

+import os
+from argparse import ArgumentParser
+from collections import defaultdict
+
+import numpy as np
+import pandas as pd
+from tqdm import tqdm
+
+from load import implicit_load
+
+
+MIN_RATINGS = 20
+
+
+USER_COLUMN = 'user_id'
+ITEM_COLUMN = 'item_id'
+
+
+TRAIN_RATINGS_FILENAME = 'train-ratings.csv'
+TEST_RATINGS_FILENAME = 'test-ratings.csv'
+TEST_NEG_FILENAME = 'test-negative.csv'
+
+
+def parse_args():
+    parser = ArgumentParser()
+    parser.add_argument('path', type=str,
+                        help='Path to reviews CSV file from MovieLens')
+    parser.add_argument('output', type=str,
+                        help='Output directory for train and test CSV files')
+    parser.add_argument('-n', '--negatives', type=int, default=999,
+                        help='Number of negative samples for each positive'
+                             'test example')
+    parser.add_argument('-s', '--seed', type=int, default=0,
+                        help='Random seed to reproduce same negative samples')
+    return parser.parse_args()
+
+
+def main():
+    args = parse_args()
+    np.random.seed(args.seed)
+
+    print("Loading raw data from {}".format(args.path))
+    df = implicit_load(args.path, sort=False)
+    print("Filtering out users with less than {} ratings".format(MIN_RATINGS))
+    grouped = df.groupby(USER_COLUMN)
+    df = grouped.filter(lambda x: len(x) >= MIN_RATINGS)
+
+    print("Mapping original user and item IDs to new sequential IDs")
+    original_users = df[USER_COLUMN].unique()
+    original_items = df[ITEM_COLUMN].unique()
+
+    user_map = {user: index for index, user in enumerate(original_users)}
+    item_map = {item: index for index, item in enumerate(original_items)}
+
+    df[USER_COLUMN] = df[USER_COLUMN].apply(lambda user: user_map[user])
+    df[ITEM_COLUMN] = df[ITEM_COLUMN].apply(lambda item: item_map[item])
+
+    assert df[USER_COLUMN].max() == len(original_users) - 1
+    assert df[ITEM_COLUMN].max() == len(original_items) - 1
+
+    print("Creating list of items for each user")
+    # Need to sort before popping to get last item
+    df.sort_values(by='timestamp', inplace=True)
+    all_ratings = set(zip(df[USER_COLUMN], df[ITEM_COLUMN]))
+    user_to_items = defaultdict(list)
+    for row in tqdm(df.itertuples(), desc='Ratings', total=len(df)):
+        user_to_items[getattr(row, USER_COLUMN)].append(getattr(row, ITEM_COLUMN))  # noqa: E501
+
+    test_ratings = []
+    test_negs = []
+    all_items = set(range(len(original_items)))
+    print("Generating {} negative samples for each user"
+          .format(args.negatives))
+    for user in tqdm(range(len(original_users)), desc='Users', total=len(original_users)):  # noqa: E501
+        test_item = user_to_items[user].pop()
+
+        all_ratings.remove((user, test_item))
+        all_negs = all_items - set(user_to_items[user])
+        all_negs = sorted(list(all_negs))  # determinism
+
+        test_ratings.append((user, test_item))
+        test_negs.append(list(np.random.choice(all_negs, args.negatives)))
+
+    print("Saving train and test CSV files to {}".format(args.output))
+    df_train_ratings = pd.DataFrame(list(all_ratings))
+    df_train_ratings['fake_rating'] = 1
+    df_train_ratings.to_csv(os.path.join(args.output, TRAIN_RATINGS_FILENAME),
+                            index=False, header=False, sep='\t')
+
+    df_test_ratings = pd.DataFrame(test_ratings)
+    df_test_ratings['fake_rating'] = 1
+    df_test_ratings.to_csv(os.path.join(args.output, TEST_RATINGS_FILENAME),
+                           index=False, header=False, sep='\t')
+
+    df_test_negs = pd.DataFrame(test_negs)
+    df_test_negs.to_csv(os.path.join(args.output, TEST_NEG_FILENAME),
+                        index=False, header=False, sep='\t')
+
+
+if __name__ == '__main__':
+    main()

examples/ncf/dataset.py

+import numpy as np
+import scipy
+import scipy.sparse
+import torch
+import torch.utils.data
+import subprocess
+import time
+from tqdm import tqdm
+import os
+import pickle
+import logging
+
+msglogger = logging.getLogger()
+
+
+def wccount(filename):
+    out = subprocess.Popen(['wc', '-l', filename],
+                           stdout=subprocess.PIPE,
+                           stderr=subprocess.STDOUT
+                           ).communicate()[0]
+    return int(out.partition(b' ')[0])
+
+
+class TimingContext(object):
+    def __init__(self, desc):
+        self.desc = desc
+
+    def __enter__(self):
+        msglogger.info(self.desc + ' ... ')
+        self.start = time.time()
+        return self
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        end = time.time()
+        msglogger.info('Done in {0:.4f} seconds'.format(end - self.start))
+        return True
+
+
+class CFTrainDataset(torch.utils.data.dataset.Dataset):
+    def __init__(self, train_fname, nb_neg):
+        self._load_train_matrix(train_fname)
+        self.nb_neg = nb_neg
+
+    def _load_train_matrix(self, train_fname):
+        pkl_name = os.path.splitext(train_fname)[0] + '_data.pkl'
+        npz_name = os.path.splitext(train_fname)[0] + '_mat.npz'
+
+        if os.path.isfile(pkl_name) and os.path.isfile(npz_name):
+            msglogger.info('Found saved dataset data structures')
+            with TimingContext('Loading data list pickle'), open(pkl_name, 'rb') as f:
+                self.data = pickle.load(f)
+            with TimingContext('Loading matrix npz'):
+                self.mat = scipy.sparse.dok_matrix(scipy.sparse.load_npz(npz_name))
+            self.nb_users = self.mat.shape[0]
+            self.nb_items = self.mat.shape[1]
+        else:
+            def process_line(line):
+                tmp = line.split('\t')
+                return [int(tmp[0]), int(tmp[1]), float(tmp[2]) > 0]
+
+            with TimingContext('Loading CSV file'), open(train_fname, 'r') as file:
+                data = list(map(process_line, tqdm(file, total=wccount(train_fname))))
+
+            with TimingContext('Calculating min/max'):
+                self.nb_users = max(data, key=lambda x: x[0])[0] + 1
+                self.nb_items = max(data, key=lambda x: x[1])[1] + 1
+
+            with TimingContext('Constructing data list'):
+                self.data = list(filter(lambda x: x[2], data))
+
+            with TimingContext('Saving data list pickle'), open(pkl_name, 'wb') as f:
+                pickle.dump(self.data, f)
+
+            with TimingContext('Building dok matrix'):
+                self.mat = scipy.sparse.dok_matrix(
+                        (self.nb_users, self.nb_items), dtype=np.float32)
+                for user, item, _ in tqdm(data):
+                    self.mat[user, item] = 1.
+
+            with TimingContext('Converting to COO matrix and saving'):
+                scipy.sparse.save_npz(npz_name, self.mat.tocoo(copy=True))
+
+    def __len__(self):
+        return (self.nb_neg + 1) * len(self.data)
+
+    def __getitem__(self, idx):
+        if idx % (self.nb_neg + 1) == 0:
+            idx = idx // (self.nb_neg + 1)
+            return self.data[idx][0], self.data[idx][1], np.ones(1, dtype=np.float32)  # noqa: E501
+        else:
+            idx = idx // (self.nb_neg + 1)
+            u = self.data[idx][0]
+            j = torch.LongTensor(1).random_(0, self.nb_items).item()
+            while (u, j) in self.mat:
+                j = torch.LongTensor(1).random_(0, self.nb_items).item()
+            return u, j, np.zeros(1, dtype=np.float32)
+
+
+def load_test_ratings(fname):
+    pkl_name = os.path.splitext(fname)[0] + '.pkl'
+    if os.path.isfile(pkl_name):
+        with TimingContext('Found test rating pickle file - loading'), open(pkl_name, 'rb') as f:
+            res = pickle.load(f)
+    else:
+        def process_line(line):
+            tmp = map(int, line.split('\t')[0:2])
+            return list(tmp)
+        with TimingContext('Loading test ratings from csv'), open(fname, 'r') as f:
+            ratings = map(process_line, tqdm(f, total=wccount(fname)))
+            res = list(ratings)
+        with TimingContext('Saving test ratings list pickle'), open(pkl_name, 'wb') as f:
+            pickle.dump(res, f)
+
+    return res
+
+
+def load_test_negs(fname):
+    pkl_name = os.path.splitext(fname)[0] + '.pkl'
+    if os.path.isfile(pkl_name):
+        with TimingContext('Found test negatives pickle file - loading'), open(pkl_name, 'rb') as f:
+            res = pickle.load(f)
+    else:
+        def process_line(line):
+            tmp = map(int, line.split('\t'))
+            return list(tmp)
+        with TimingContext('Loading test negatives from csv'), open(fname, 'r') as f:
+            negs = map(process_line, tqdm(f, total=wccount(fname)))
+            res = list(negs)
+        with TimingContext('Saving test negatives list pickle'), open(pkl_name, 'wb') as f:
+            pickle.dump(res, f)
+
+    return res

examples/ncf/download_dataset.sh

+function download_20m {
+	echo "Download ml-20m"
+	curl -O http://files.grouplens.org/datasets/movielens/ml-20m.zip
+}
+
+function download_1m {
+	echo "Downloading ml-1m"
+	curl -O http://files.grouplens.org/datasets/movielens/ml-1m.zip
+}
+
+if [[ $1 == "ml-1m" ]]
+then
+	download_1m
+else
+	download_20m
+fi

examples/ncf/load.py

+from collections import namedtuple
+
+import pandas as pd
+
+
+RatingData = namedtuple('RatingData',
+                        ['items', 'users', 'ratings', 'min_date', 'max_date'])
+
+
+def describe_ratings(ratings):
+    info = RatingData(items=len(ratings['item_id'].unique()),
+                      users=len(ratings['user_id'].unique()),
+                      ratings=len(ratings),
+                      min_date=ratings['timestamp'].min(),
+                      max_date=ratings['timestamp'].max())
+    print("{ratings} ratings on {items} items from {users} users"
+          " from {min_date} to {max_date}"
+          .format(**(info._asdict())))
+    return info
+
+
+def process_movielens(ratings, sort=True):
+    ratings['timestamp'] = pd.to_datetime(ratings['timestamp'], unit='s')
+    if sort:
+        ratings.sort_values(by='timestamp', inplace=True)
+    describe_ratings(ratings)
+    return ratings
+
+
+def load_ml_100k(filename, sort=True):
+    names = ['user_id', 'item_id', 'rating', 'timestamp']
+    ratings = pd.read_csv(filename, sep='\t', names=names)
+    return process_movielens(ratings, sort=sort)
+
+
+def load_ml_1m(filename, sort=True):
+    names = ['user_id', 'item_id', 'rating', 'timestamp']
+    ratings = pd.read_csv(filename, sep='::', names=names, engine='python')
+    return process_movielens(ratings, sort=sort)
+
+
+def load_ml_10m(filename, sort=True):
+    names = ['user_id', 'item_id', 'rating', 'timestamp']
+    ratings = pd.read_csv(filename, sep='::', names=names, engine='python')
+    return process_movielens(ratings, sort=sort)
+
+
+def load_ml_20m(filename, sort=True):
+    ratings = pd.read_csv(filename)
+    ratings['timestamp'] = pd.to_datetime(ratings['timestamp'], unit='s')
+    names = {'userId': 'user_id', 'movieId': 'item_id'}
+    ratings.rename(columns=names, inplace=True)
+    return process_movielens(ratings, sort=sort)
+
+
+DATASETS = [k.replace('load_', '') for k in locals().keys() if "load_" in k]
+
+
+def get_dataset_name(filename):
+    for dataset in DATASETS:
+        if dataset in filename.replace('-', '_').lower():
+            return dataset
+    raise NotImplementedError
+
+
+def implicit_load(filename, sort=True):
+    func = globals()["load_" + get_dataset_name(filename)]
+    return func(filename, sort=sort)

examples/ncf/logging.conf

+[formatters]
+keys: simple, time_simple
+
+[handlers]
+keys: console, file
+
+[loggers]
+keys: root, app_cfg
+
+[formatter_simple]
+format: %(message)s
+
+[formatter_time_simple]
+format: %(asctime)s - %(message)s
+
+[handler_console]
+class: StreamHandler
+propagate: 0
+args: []
+formatter: simple
+
+[handler_file]
+class: FileHandler
+mode: 'w'
+args=('%(logfilename)s', 'w')
+formatter: time_simple
+
+[logger_root]
+level: INFO
+propagate: 1
+handlers: console, file
+
+[logger_app_cfg]
+# Use this logger to log the application configuration and execution environment
+level: DEBUG
+qualname: app_cfg
+propagate: 0
+handlers: file

examples/ncf/neumf.py

+import numpy as np
+import torch
+import torch.nn as nn
+
+import distiller.modules
+
+import logging
+import os
+msglogger = logging.getLogger()
+
+
+class NeuMF(nn.Module):
+    def __init__(self, nb_users, nb_items,
+                 mf_dim, mf_reg,
+                 mlp_layer_sizes, mlp_layer_regs, split_final=False):
+        if len(mlp_layer_sizes) != len(mlp_layer_regs):
+            raise RuntimeError('u dummy, layer_sizes != layer_regs!')
+        if mlp_layer_sizes[0] % 2 != 0:
+            raise RuntimeError('u dummy, mlp_layer_sizes[0] % 2 != 0')
+        super(NeuMF, self).__init__()
+
+        self.mf_dim = mf_dim
+        self.mlp_layer_sizes = mlp_layer_sizes
+
+        nb_mlp_layers = len(mlp_layer_sizes)
+
+        # TODO: regularization?
+        self.mf_user_embed = nn.Embedding(nb_users, mf_dim)
+        self.mf_item_embed = nn.Embedding(nb_items, mf_dim)
+        self.mlp_user_embed = nn.Embedding(nb_users, mlp_layer_sizes[0] // 2)
+        self.mlp_item_embed = nn.Embedding(nb_items, mlp_layer_sizes[0] // 2)
+
+        self.mf_mult = distiller.modules.EltwiseMult()
+        self.mlp_concat = distiller.modules.Concat(dim=1)
+
+        self.mlp = nn.ModuleList()
+        self.mlp_relu = nn.ModuleList()
+        for i in range(1, nb_mlp_layers):
+            self.mlp.extend([nn.Linear(mlp_layer_sizes[i - 1], mlp_layer_sizes[i])])  # noqa: E501
+            self.mlp_relu.extend([nn.ReLU()])
+
+        self.split_final = split_final
+        if not split_final:
+            self.final_concat = distiller.modules.Concat(dim=1)
+            self.final = nn.Linear(mlp_layer_sizes[-1] + mf_dim, 1)
+        else:
+            self.final_mlp = nn.Linear(mlp_layer_sizes[-1], 1)
+            self.final_mf = nn.Linear(mf_dim, 1)
+            self.final_add = distiller.modules.EltwiseAdd()
+
+        self.sigmoid = nn.Sigmoid()
+
+        self.mf_user_embed.weight.data.normal_(0., 0.01)
+        self.mf_item_embed.weight.data.normal_(0., 0.01)
+        self.mlp_user_embed.weight.data.normal_(0., 0.01)
+        self.mlp_item_embed.weight.data.normal_(0., 0.01)
+
+        def golorot_uniform(layer):
+            fan_in, fan_out = layer.in_features, layer.out_features
+            limit = np.sqrt(6. / (fan_in + fan_out))
+            layer.weight.data.uniform_(-limit, limit)
+
+        def lecunn_uniform(layer):
+            fan_in, fan_out = layer.in_features, layer.out_features  # noqa: F841, E501
+            limit = np.sqrt(3. / fan_in)
+            layer.weight.data.uniform_(-limit, limit)
+
+        for layer in self.mlp:
+            if type(layer) != nn.Linear:
+                continue
+            golorot_uniform(layer)
+        if not split_final:
+            lecunn_uniform(self.final)
+        else:
+            lecunn_uniform(self.final_mlp)
+            lecunn_uniform(self.final_mf)
+
+        # self.post_embed_device = torch.device('cpu')
+
+    def load_state_dict(self, state_dict, strict=True):
+        if 'final.weight' in state_dict and self.split_final:
+            # Loading no-split checkpoint into split model
+
+            # MF weights come first, then MLP
+            final_weight = state_dict.pop('final.weight')
+            state_dict['final_mf.weight'] = final_weight[0][:self.mf_dim].unsqueeze(0)
+            state_dict['final_mlp.weight'] = final_weight[0][self.mf_dim:].unsqueeze(0)
+
+            # Split bias 50-50
+            final_bias = state_dict.pop('final.bias')
+            state_dict['final_mf.bias'] = final_bias * 0.5
+            state_dict['final_mlp.bias'] = final_bias * 0.5
+        elif 'final_mf.weight' in state_dict and not self.split_final:
+            # Loading split checkpoint into no-split model
+            state_dict['final.weight'] = torch.cat((state_dict.pop('final_mf.weight')[0],
+                                                    state_dict.pop('final_mlp.weight')[0])).unsqueeze(0)
+            state_dict['final.bias'] = state_dict.pop('final_mf.bias') + state_dict.pop('final_mlp.bias')
+
+        super(NeuMF, self).load_state_dict(state_dict, strict)
+
+    def forward(self, user, item, sigmoid=False):
+        xmfu = self.mf_user_embed(user)  # .to(self.post_embed_device)
+        xmfi = self.mf_item_embed(item)  # .to(self.post_embed_device)
+        xmf = self.mf_mult(xmfu, xmfi)
+        # @DEBUG
+        # np.save(os.path.join(msglogger.logdir, 'mf_mult'), xmf.cpu().detach().numpy())
+
+        xmlpu = self.mlp_user_embed(user)  # .to(self.post_embed_device)
+        xmlpi = self.mlp_item_embed(item)  # .to(self.post_embed_device)
+        xmlp = self.mlp_concat(xmlpu, xmlpi)
+        # @DEBUG
+        # np.save(os.path.join(msglogger.logdir, 'mlp_concat'), xmlp.cpu().detach().numpy())
+        for i, (layer, act) in enumerate(zip(self.mlp, self.mlp_relu)):
+            xmlp = layer(xmlp)
+            # @DEBUG
+            # np.save(os.path.join(msglogger.logdir, 'mlp.{}'.format(i)), xmlp.detach().cpu().numpy())
+            xmlp = act(xmlp)
+            # @DEBUG
+            # np.save(os.path.join(msglogger.logdir, 'mlp_relu.{}'.format(i)), xmlp.detach().cpu().numpy())
+
+        if not self.split_final:
+            x = self.final_concat(xmf, xmlp)
+            x = self.final(x)
+        else:
+            xmf = self.final_mf(xmf)
+            # @DEBUG
+            # np.save(os.path.join(msglogger.logdir, 'final_mf'), xmf.detach().cpu().numpy())
+            xmlp = self.final_mlp(xmlp)
+            # @DEBUG
+            # np.save(os.path.join(msglogger.logdir, 'final_mlp'), xmlp.detach().cpu().numpy())
+            x = self.final_add(xmf, xmlp)
+            # @DEBUG
+            # np.save(os.path.join(msglogger.logdir, 'final_add'), x.detach().cpu().numpy())
+        if sigmoid:
+            x = self.sigmoid(x)
+        return x
+
+    # def to_cuda(self, device=None, embeds_on_gpu=True):
+    #     self.post_embed_device = device if device is not None else torch.device('cuda')
+    #
+    #     if embeds_on_gpu:
+    #         return self.cuda(device=device)
+    #
+    #     for m in self.modules():
+    #         if isinstance(m, nn.Embedding):
+    #             m.cpu()
+    #         else:
+    #             m.cuda(device=device)
+    #
+    #     return self
+

examples/ncf/requirements.txt

+tqdm==4.20.0
+scipy
+pandas

examples/ncf/run_and_time.sh

+#!/bin/bash
+# runs benchmark and reports time to convergence
+# to use the script:
+#   run_and_time.sh <random seed 1-5>
+
+THRESHOLD=0.635
+BASEDIR=$(dirname -- "$0")
+
+# start timing
+start=$(date +%s)
+start_fmt=$(date +%Y-%m-%d\ %r)
+echo "STARTING TIMING RUN AT $start_fmt"
+
+# Get command line seed
+seed=${1:-1}
+
+echo "unzip ml-20m.zip"
+if unzip ml-20m.zip
+then
+    echo "Start processing ml-20m/ratings.csv"
+    t0=$(date +%s)
+	python $BASEDIR/convert.py ml-20m/ratings.csv ml-20m --negatives 999
+    t1=$(date +%s)
+	delta=$(( $t1 - $t0 ))
+    echo "Finish processing ml-20m/ratings.csv in $delta seconds"
+
+    echo "Start training"
+    t0=$(date +%s)
+	python $BASEDIR/ncf.py ml-20m -l 0.0005 -b 2048 --layers 256 256 128 64 -f 64 \
+		--seed $seed --threshold $THRESHOLD --processes 10
+    t1=$(date +%s)
+	delta=$(( $t1 - $t0 ))
+    echo "Finish training in $delta seconds"
+
+	# end timing
+	end=$(date +%s)
+	end_fmt=$(date +%Y-%m-%d\ %r)
+	echo "ENDING TIMING RUN AT $end_fmt"
+
+
+	# report result
+	result=$(( $end - $start ))
+	result_name="recommendation"
+
+
+	echo "RESULT,$result_name,$seed,$result,$USER,$start_fmt"
+else
+	echo "Problem unzipping ml-20.zip"
+	echo "Please run 'download_data.sh && verify_datset.sh' first"
+fi
+
+
+
+
+

examples/ncf/utils.py

+import os
+import json
+from functools import reduce
+
+
+class AverageMeter(object):
+    """Computes and stores the average and current value"""
+    def __init__(self):
+        self.reset()
+
+    def reset(self):
+        self.val = 0
+        self.avg = 0
+        self.sum = 0
+        self.count = 0
+
+    def update(self, val, n=1):
+        self.val = val
+        self.sum += val * n
+        self.count += n
+        self.avg = self.sum / self.count
+
+
+def count_parameters(model):
+    c = map(lambda p: reduce(lambda x, y: x * y, p.size()), model.parameters())
+    return sum(c)
+
+
+def save_config(config, run_dir):
+    path = os.path.join(run_dir, "config_{}.json".format(config['timestamp']))
+    with open(path, 'w') as config_file:
+        json.dump(config, config_file)
+        config_file.write('\n')
+
+
+def save_result(result, path):
+    write_heading = not os.path.exists(path)
+    with open(path, mode='a') as out:
+        if write_heading:
+            out.write(",".join([str(k) for k, v in result.items()]) + '\n')
+        out.write(",".join([str(v) for k, v in result.items()]) + '\n')

examples/ncf/verify_dataset.sh

+function get_checker {
+    if [[ "$OSTYPE" == "darwin"* ]]; then
+        checkmd5=md5
+    else
+        checkmd5=md5sum
+    fi
+
+    echo $checkmd5
+}
+
+
+function verify_1m {
+    # From: curl -O http://files.grouplens.org/datasets/movielens/ml-1m.zip.md5
+    hash=<(echo "MD5 (ml-1m.zip) = c4d9eecfca2ab87c1945afe126590906")
+    local checkmd5=$(get_checker)
+    if diff <($checkmd5 ml-1m.zip) $hash &> /dev/null
+    then
+        echo "PASSED"
+    else
+        echo "FAILED"
+    fi
+}
+
+function verify_20m {
+    # From: curl -O http://files.grouplens.org/datasets/movielens/ml-20m.zip.md5
+    hash=<(echo "MD5 (ml-20m.zip) = cd245b17a1ae2cc31bb14903e1204af3")
+    local checkmd5=$(get_checker)
+
+    if diff <($checkmd5 ml-20m.zip) $hash &> /dev/null
+    then
+        echo "PASSED"
+    else
+        echo "FAILED"
+    fi
+
+}
+
+
+if [[ $1 == "ml-1m" ]]
+then
+    verify_1m
+else
+    verify_20m
+fi

examples/quantization/quant_aware_train/ncf_quant_aware_train_linear.yaml

+quantizers:
+  linear_quantizer:
+    class: NCFQuantAwareTrainQuantizer
+    bits_activations: 8
+    bits_weights: 8
+    bits_bias: 32
+    mode: 'SYMMETRIC'  # Can try "SYMMETRIC" as well
+    ema_decay: 0.999   # Decay value for exponential moving average tracking of activation ranges
+    per_channel_wts: True
+    overrides:
+      # We want to quantize the last FC layer prior to the sigmoid. So - We set up the quantizer to add fake-quantization
+      # layers after FC layers. But - here we override so that this doesn't actually happen in any of the early FC layers
+      mlp\.*:
+        bits_activations: null
+        bits_weights: 8
+        bits_bias: 32
+      final_concat:
+        bits_activations: null
+        bits_weights: null
+        bits_bias: null
+
+policies:
+    - quantizer:
+        instance_name: linear_quantizer
+      # For now putting a large range here, which should cover both training from scratch or resuming from some
+      # pre-trained checkpoint at some unknown epoch
+      starting_epoch: 0
+      ending_epoch: 300
+      frequency: 1
\ No newline at end of file