wehub-resource-sync
419 lines
15 KiB
Python
419 lines
15 KiB
Python
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import os
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import random
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import sys
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import unittest
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import numpy as np
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from op_test import get_device_place, is_custom_device
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from test_imperative_base import new_program_scope
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import paddle
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from paddle import base
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from paddle.base import core
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from paddle.nn import Linear
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class DMF(paddle.nn.Layer):
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def __init__(self):
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super().__init__()
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self._user_latent = Linear(1000, 256)
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self._item_latent = Linear(100, 256)
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self._user_layers = []
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self._item_layers = []
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self._hid_sizes = [128, 64]
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for i in range(len(self._hid_sizes)):
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self._user_layers.append(
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self.add_sublayer(
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f'user_layer_{i}',
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Linear(
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256 if i == 0 else self._hid_sizes[i - 1],
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self._hid_sizes[i],
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),
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)
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)
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self._user_layers.append(
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self.add_sublayer(
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f'user_layer_act_{i}',
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paddle.nn.ReLU(),
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)
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)
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self._item_layers.append(
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self.add_sublayer(
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f'item_layer_{i}',
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Linear(
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256 if i == 0 else self._hid_sizes[i - 1],
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self._hid_sizes[i],
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),
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)
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)
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self._item_layers.append(
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self.add_sublayer(
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f'item_layer_act_{i}',
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paddle.nn.ReLU(),
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)
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)
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def forward(self, users, items):
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users = self._user_latent(users)
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items = self._item_latent(items)
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for ul, il in zip(self._user_layers, self._item_layers):
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users = ul(users)
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items = il(items)
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return paddle.multiply(users, items)
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class MLP(paddle.nn.Layer):
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def __init__(self):
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super().__init__()
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self._user_latent = Linear(1000, 256)
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self._item_latent = Linear(100, 256)
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self._match_layers = []
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self._hid_sizes = [128, 64]
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for i in range(len(self._hid_sizes)):
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self._match_layers.append(
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self.add_sublayer(
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f'match_layer_{i}',
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Linear(
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256 * 2 if i == 0 else self._hid_sizes[i - 1],
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self._hid_sizes[i],
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),
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)
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)
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self._match_layers.append(
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self.add_sublayer(
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f'match_layer_act_{i}',
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paddle.nn.ReLU(),
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)
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)
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def forward(self, users, items):
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users = self._user_latent(users)
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items = self._item_latent(items)
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match_vec = paddle.concat([users, items], axis=len(users.shape) - 1)
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for l in self._match_layers:
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match_vec = l(match_vec)
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return match_vec
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class DeepCF(paddle.nn.Layer):
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def __init__(self, num_users, num_items, matrix):
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super().__init__()
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self._num_users = num_users
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self._num_items = num_items
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self._rating_matrix = self.create_parameter(
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attr=base.ParamAttr(trainable=False),
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shape=matrix.shape,
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dtype=matrix.dtype,
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is_bias=False,
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default_initializer=paddle.nn.initializer.Assign(matrix),
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)
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self._rating_matrix.stop_gradient = True
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self._mlp = MLP()
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self._dmf = DMF()
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self._match_fc = Linear(128, 1)
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def forward(self, users, items):
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# users_emb = self._user_emb(users)
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# items_emb = self._item_emb(items)
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users_emb = paddle.gather(self._rating_matrix, users)
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items_emb = paddle.gather(
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paddle.transpose(self._rating_matrix, [1, 0]), items
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)
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users_emb.stop_gradient = True
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items_emb.stop_gradient = True
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mlp_predictive = self._mlp(users_emb, items_emb)
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dmf_predictive = self._dmf(users_emb, items_emb)
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predictive = paddle.concat(
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[mlp_predictive, dmf_predictive], axis=len(mlp_predictive.shape) - 1
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)
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prediction = self._match_fc(predictive)
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prediction = paddle.nn.functional.sigmoid(prediction)
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return prediction
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class TestDygraphDeepCF(unittest.TestCase):
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def setUp(self):
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# Can use Amusic dataset as the DeepCF describes.
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self.data_path = os.environ.get('DATA_PATH', '')
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self.batch_size = int(os.environ.get('BATCH_SIZE', 128))
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self.num_batches = int(os.environ.get('NUM_BATCHES', 5))
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self.num_epochs = int(os.environ.get('NUM_EPOCHS', 1))
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def get_data(self):
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user_ids = []
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item_ids = []
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labels = []
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NUM_USERS = 100
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NUM_ITEMS = 1000
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matrix = np.zeros([NUM_USERS, NUM_ITEMS], dtype=np.float32)
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for uid in range(NUM_USERS):
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for iid in range(NUM_ITEMS):
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label = float(random.randint(1, 6) == 1)
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user_ids.append(uid)
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item_ids.append(iid)
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labels.append(label)
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matrix[uid, iid] = label
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indices = np.arange(len(user_ids))
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np.random.shuffle(indices)
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users_np = np.array(user_ids, dtype=np.int32)[indices]
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items_np = np.array(item_ids, dtype=np.int32)[indices]
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labels_np = np.array(labels, dtype=np.float32)[indices]
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return (
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np.expand_dims(users_np, -1),
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np.expand_dims(items_np, -1),
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np.expand_dims(labels_np, -1),
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NUM_USERS,
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NUM_ITEMS,
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matrix,
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)
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def load_data(self):
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sys.stderr.write(f'loading from {self.data_path}\n')
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likes = {}
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num_users = -1
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num_items = -1
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with open(self.data_path, 'r') as f:
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for l in f:
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uid, iid, rating = (int(v) for v in l.split('\t'))
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num_users = max(num_users, uid + 1)
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num_items = max(num_items, iid + 1)
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if float(rating) > 0.0:
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likes[(uid, iid)] = 1.0
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user_ids = []
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item_ids = []
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labels = []
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matrix = np.zeros([num_users, num_items], dtype=np.float32)
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for uid, iid in likes.keys():
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user_ids.append(uid)
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item_ids.append(iid)
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labels.append(1.0)
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matrix[uid, iid] = 1.0
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negative = 0
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while negative < 3:
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nuid = random.randint(0, num_users - 1)
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niid = random.randint(0, num_items - 1)
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if (nuid, niid) not in likes:
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negative += 1
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user_ids.append(nuid)
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item_ids.append(niid)
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labels.append(0.0)
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indices = np.arange(len(user_ids))
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np.random.shuffle(indices)
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users_np = np.array(user_ids, dtype=np.int32)[indices]
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items_np = np.array(item_ids, dtype=np.int32)[indices]
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labels_np = np.array(labels, dtype=np.float32)[indices]
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return (
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np.expand_dims(users_np, -1),
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np.expand_dims(items_np, -1),
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np.expand_dims(labels_np, -1),
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num_users,
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num_items,
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matrix,
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)
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def test_deefcf(self):
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seed = 90
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if self.data_path:
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(
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users_np,
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items_np,
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labels_np,
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num_users,
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num_items,
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matrix,
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) = self.load_data()
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else:
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(
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users_np,
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items_np,
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labels_np,
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num_users,
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num_items,
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matrix,
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) = self.get_data()
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paddle.seed(seed)
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paddle.framework.random._manual_program_seed(seed)
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startup = base.Program()
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main = base.Program()
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scope = base.core.Scope()
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with new_program_scope(main=main, startup=startup, scope=scope):
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users = paddle.static.data('users', [-1, 1], dtype='int32')
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items = paddle.static.data('items', [-1, 1], dtype='int32')
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labels = paddle.static.data('labels', [-1, 1], dtype='float32')
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deepcf = DeepCF(num_users, num_items, matrix)
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prediction = deepcf(users, items)
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loss = paddle.sum(paddle.nn.functional.log_loss(prediction, labels))
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adam = paddle.optimizer.Adam(0.01)
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adam.minimize(loss)
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exe = base.Executor(
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base.CPUPlace()
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if not (core.is_compiled_with_cuda() or is_custom_device())
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else get_device_place()
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)
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exe.run(startup)
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for e in range(self.num_epochs):
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sys.stderr.write(f'epoch {e}\n')
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for slice in range(
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0, self.batch_size * self.num_batches, self.batch_size
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):
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if slice + self.batch_size >= users_np.shape[0]:
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break
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static_loss = exe.run(
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main,
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feed={
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users.name: users_np[
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slice : slice + self.batch_size
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],
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items.name: items_np[
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slice : slice + self.batch_size
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],
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labels.name: labels_np[
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slice : slice + self.batch_size
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],
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},
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fetch_list=[loss],
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)[0]
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sys.stderr.write(f'static loss {static_loss}\n')
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with base.dygraph.guard():
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paddle.seed(seed)
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paddle.framework.random._manual_program_seed(seed)
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deepcf = DeepCF(num_users, num_items, matrix)
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adam = paddle.optimizer.Adam(0.01, parameters=deepcf.parameters())
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for e in range(self.num_epochs):
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sys.stderr.write(f'epoch {e}\n')
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for slice in range(
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0, self.batch_size * self.num_batches, self.batch_size
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):
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if slice + self.batch_size >= users_np.shape[0]:
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break
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prediction = deepcf(
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paddle.to_tensor(
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users_np[slice : slice + self.batch_size]
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),
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paddle.to_tensor(
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items_np[slice : slice + self.batch_size]
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),
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)
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loss = paddle.sum(
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paddle.nn.functional.log_loss(
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prediction,
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paddle.to_tensor(
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labels_np[slice : slice + self.batch_size]
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),
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)
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)
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loss.backward()
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adam.minimize(loss)
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deepcf.clear_gradients()
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dy_loss = loss.numpy()
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sys.stderr.write(f'dynamic loss: {slice} {dy_loss}\n')
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with base.dygraph.guard():
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paddle.seed(seed)
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paddle.framework.random._manual_program_seed(seed)
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deepcf2 = DeepCF(num_users, num_items, matrix)
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adam2 = paddle.optimizer.Adam(0.01, parameters=deepcf2.parameters())
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base.set_flags({'FLAGS_sort_sum_gradient': True})
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for e in range(self.num_epochs):
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sys.stderr.write(f'epoch {e}\n')
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for slice in range(
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0, self.batch_size * self.num_batches, self.batch_size
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):
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if slice + self.batch_size >= users_np.shape[0]:
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break
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prediction2 = deepcf2(
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paddle.to_tensor(
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users_np[slice : slice + self.batch_size]
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),
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paddle.to_tensor(
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items_np[slice : slice + self.batch_size]
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),
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)
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loss2 = paddle.sum(
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paddle.nn.functional.log_loss(
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prediction2,
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paddle.to_tensor(
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labels_np[slice : slice + self.batch_size]
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),
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)
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)
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loss2.backward()
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adam2.minimize(loss2)
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deepcf2.clear_gradients()
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dy_loss2 = loss2.numpy()
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sys.stderr.write(f'dynamic loss: {slice} {dy_loss2}\n')
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with base.dygraph.guard():
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paddle.seed(seed)
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paddle.framework.random._manual_program_seed(seed)
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deepcf = DeepCF(num_users, num_items, matrix)
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adam = paddle.optimizer.Adam(0.01, parameters=deepcf.parameters())
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for e in range(self.num_epochs):
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sys.stderr.write(f'epoch {e}\n')
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for slice in range(
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0, self.batch_size * self.num_batches, self.batch_size
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):
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if slice + self.batch_size >= users_np.shape[0]:
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break
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prediction = deepcf(
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paddle.to_tensor(
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users_np[slice : slice + self.batch_size]
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),
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paddle.to_tensor(
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items_np[slice : slice + self.batch_size]
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),
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)
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loss = paddle.sum(
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paddle.nn.functional.log_loss(
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prediction,
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paddle.to_tensor(
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labels_np[slice : slice + self.batch_size]
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),
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)
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)
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loss.backward()
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adam.minimize(loss)
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deepcf.clear_gradients()
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eager_loss = loss.numpy()
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sys.stderr.write(f'eager loss: {slice} {eager_loss}\n')
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self.assertEqual(static_loss, dy_loss)
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self.assertEqual(static_loss, dy_loss2)
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self.assertEqual(static_loss, eager_loss)
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if __name__ == '__main__':
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paddle.enable_static()
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unittest.main()
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