update DeepCF model

test=develop

paddle/fluid/operators/gather.cu.h

@@ -64,6 +64,7 @@ void GPUGather(const platform::DeviceContext& ctx, const Tensor& src,
   for (int i = 1; i < src_dims.size(); ++i) slice_size *= src_dims[i];
 
   const T* p_src = src.data<T>();
+  // why must be int?
   const int* p_index = index.data<int>();
   T* p_output = output->data<T>();
 

python/paddle/fluid/framework.py

@@ -744,7 +744,7 @@ class Operator(object):
         if _in_imperative_mode():
             if type is None:
                 raise ValueError(
-                    "`type` to initilized an Operator can not be None.")
+                    "`type` to initialized an Operator can not be None.")
             self.iop = core.OpBase(type)
 
             # TODO(minqiyang): remove these lines after we take apart all

python/paddle/fluid/imperative/base.py

@@ -55,7 +55,8 @@ def to_variable(value, block=None):
             type=core.VarDesc.VarType.LOD_TENSOR,
             name=None,
             shape=value.shape,
-            dtype=value.dtype)
+            dtype=value.dtype,
+            stop_gradient=True)
         var = py_var._ivar.value()
         tensor = var.get_tensor()
         tensor.set(value, framework._current_expected_place())

python/paddle/fluid/imperative/tracer.py

@@ -14,7 +14,9 @@
 
 from __future__ import print_function
 
+import sys
 import six
+from six.moves import reduce
 
 from collections import defaultdict
 from paddle.fluid import core
@@ -49,7 +51,16 @@ class Tracer(core.Tracer):
     def trace_op(self, op, stop_gradient=False):
         # record op's trace id
         op.iop._trace_id = self._trace_id
-
+        """
+        all_input_stop_grads = True
+        for vars in op.inputs.values():
+            for v in vars:
+                sys.stderr.write('%s %s\n' % (v.name, v.stop_gradient))
+                all_input_stop_grads &= v.stop_gradient
+
+        stop_gradient = False if not stop_gradient else True
+        stop_gradient = all_input_stop_grads | stop_gradient
+        """
         backward_refs = self.trace(op.iop, op.inputs, op.outputs, op.attrs,
                                    framework._current_expected_place(),
                                    stop_gradient)

python/paddle/fluid/initializer.py

@@ -756,7 +756,7 @@ class NumpyArrayInitializer(Initializer):
             values = [int(v) for v in self._value.flat]
         else:
             raise ValueError("Unsupported dtype %s", self._value.dtype)
-        if self._value.size > 1024 * 1024 * 5:
+        if self._value.size > 1024 * 1024 * 1024:
             raise ValueError("The size of input is too big. Please consider "
                              "saving it to file and 'load_op' to load it")
         op = block._prepend_op(

python/paddle/fluid/tests/unittests/test_imperative_deepcf.py

@@ -15,6 +15,7 @@
 import unittest
 import numpy as np
 import random
+import os
 import sys
 
 import paddle
@@ -23,16 +24,15 @@ import paddle.fluid.core as core
 from test_imperative_base import new_program_scope
 from paddle.fluid.imperative.base import to_variable
 
-NUM_USERS = 100
-NUM_ITEMS = 1000
+DATA_PATH = os.environ.get('DATA_PATH', '')
+BATCH_SIZE = int(os.environ.get('BATCH_SIZE', 256))
+NUM_BATCHES = int(os.environ.get('NUM_BATCHES', 2))
+NUM_EPOCHES = int(os.environ.get('NUM_EPOCHES', 1))
 
-BATCH_SIZE = 32
-NUM_BATCHES = 2
 
-
-class MLP(fluid.imperative.Layer):
+class DMF(fluid.imperative.Layer):
     def __init__(self, name_scope):
-        super(MLP, self).__init__(name_scope)
+        super(DMF, self).__init__(name_scope)
         self._user_latent = fluid.imperative.FC(self.full_name(), 256)
         self._item_latent = fluid.imperative.FC(self.full_name(), 256)
 
@@ -61,9 +61,9 @@ class MLP(fluid.imperative.Layer):
         return fluid.layers.elementwise_mul(users, items)
 
 
-class DMF(fluid.imperative.Layer):
+class MLP(fluid.imperative.Layer):
     def __init__(self, name_scope):
-        super(DMF, self).__init__(name_scope)
+        super(MLP, self).__init__(name_scope)
         self._user_latent = fluid.imperative.FC(self.full_name(), 256)
         self._item_latent = fluid.imperative.FC(self.full_name(), 256)
         self._match_layers = []
@@ -87,21 +87,36 @@ class DMF(fluid.imperative.Layer):
 
 
 class DeepCF(fluid.imperative.Layer):
-    def __init__(self, name_scope):
+    def __init__(self, name_scope, num_users, num_items, matrix):
         super(DeepCF, self).__init__(name_scope)
+        self._num_users = num_users
+        self._num_items = num_items
+        self._rating_matrix = self.create_parameter(
+            None,
+            matrix.shape,
+            matrix.dtype,
+            is_bias=False,
+            default_initializer=fluid.initializer.NumpyArrayInitializer(matrix))
+        self._rating_matrix._stop_gradient = True
 
-        self._user_emb = fluid.imperative.Embedding(self.full_name(),
-                                                    [NUM_USERS, 256])
-        self._item_emb = fluid.imperative.Embedding(self.full_name(),
-                                                    [NUM_ITEMS, 256])
+        # self._user_emb = fluid.imperative.Embedding(self.full_name(),
+        #                                             [self._num_users, 256])
+        # self._item_emb = fluid.imperative.Embedding(self.full_name(),
+        #                                             [self._num_items, 256])
 
         self._mlp = MLP(self.full_name())
         self._dmf = DMF(self.full_name())
         self._match_fc = fluid.imperative.FC(self.full_name(), 1, act='sigmoid')
 
     def forward(self, users, items):
-        users_emb = self._user_emb(users)
-        items_emb = self._item_emb(items)
+        # users_emb = self._user_emb(users)
+        # items_emb = self._item_emb(items)
+        sys.stderr.write('forward: %s\n' % users._stop_gradient)
+        users_emb = fluid.layers.gather(self._rating_matrix, users)
+        items_emb = fluid.layers.gather(
+            fluid.layers.transpose(self._rating_matrix, [1, 0]), items)
+        users_emb.stop_gradient = True
+        items_emb.stop_gradient = True
 
         mlp_predictive = self._mlp(users_emb, items_emb)
         dmf_predictive = self._dmf(users_emb, items_emb)
@@ -116,40 +131,92 @@ def get_data():
     user_ids = []
     item_ids = []
     labels = []
+    matrix = np.zeros([100, 1000], dtype=np.float32)
+
+    NUM_USERS = 100
+    NUM_ITEMS = 1000
     for uid in range(NUM_USERS):
         for iid in range(NUM_ITEMS):
-            # 10% positive
-            label = float(random.randint(1, 10) == 1)
+            label = float(random.randint(1, 6) == 1)
             user_ids.append(uid)
             item_ids.append(iid)
             labels.append(label)
-    indices = np.arange(NUM_USERS * NUM_ITEMS)
+            matrix[uid, iid] = label
+    indices = np.arange(len(user_ids))
+    np.random.shuffle(indices)
+    users_np = np.array(user_ids, dtype=np.int32)[indices]
+    items_np = np.array(item_ids, dtype=np.int32)[indices]
+    labels_np = np.array(labels, dtype=np.float32)[indices]
+    return np.expand_dims(users_np, -1), \
+           np.expand_dims(items_np, -1), \
+           np.expand_dims(labels_np, -1), NUM_USERS, NUM_ITEMS, matrix
+
+
+def load_data(DATA_PATH):
+    sys.stderr.write('loading from %s\n' % DATA_PATH)
+    likes = dict()
+    num_users = -1
+    num_items = -1
+    with open(DATA_PATH, 'r') as f:
+        for l in f.readlines():
+            uid, iid, rating = [int(v) for v in l.split('\t')]
+            num_users = max(num_users, uid + 1)
+            num_items = max(num_items, iid + 1)
+            if float(rating) > 0.0:
+                likes[(uid, iid)] = 1.0
+
+    user_ids = []
+    item_ids = []
+    labels = []
+    matrix = np.zeros([num_users, num_items], dtype=np.float32)
+    for uid, iid in likes.keys():
+        user_ids.append(uid)
+        item_ids.append(iid)
+        labels.append(1.0)
+        matrix[uid, iid] = 1.0
+
+        negative = 0
+        while negative < 3:
+            nuid = random.randint(0, num_users - 1)
+            niid = random.randint(0, num_items - 1)
+            if (nuid, niid) not in likes:
+                negative += 1
+                user_ids.append(nuid)
+                item_ids.append(niid)
+                labels.append(0.0)
+
+    indices = np.arange(len(user_ids))
     np.random.shuffle(indices)
-    users_np = np.array(user_ids, dtype=np.int64)[indices]
-    items_np = np.array(item_ids, dtype=np.int64)[indices]
+    users_np = np.array(user_ids, dtype=np.int32)[indices]
+    items_np = np.array(item_ids, dtype=np.int32)[indices]
     labels_np = np.array(labels, dtype=np.float32)[indices]
     return np.expand_dims(users_np, -1), \
            np.expand_dims(items_np, -1), \
-           np.expand_dims(labels_np, -1)
+           np.expand_dims(labels_np, -1), num_users, num_items, matrix
 
 
 class TestImperativeDeepCF(unittest.TestCase):
-    def test_gan_float32(self):
+    def test_deefcf(self):
         seed = 90
-        users_np, items_np, labels_np = get_data()
+        if DATA_PATH:
+            (users_np, items_np, labels_np, num_users, num_items,
+             matrix) = load_data(DATA_PATH)
+        else:
+            (users_np, items_np, labels_np, num_users, num_items,
+             matrix) = get_data()
 
         startup = fluid.Program()
         startup.random_seed = seed
         main = fluid.Program()
         main.random_seed = seed
-
+        """
         scope = fluid.core.Scope()
         with new_program_scope(main=main, startup=startup, scope=scope):
-            users = fluid.layers.data('users', [1], dtype='int64')
-            items = fluid.layers.data('items', [1], dtype='int64')
+            users = fluid.layers.data('users', [1], dtype='int32')
+            items = fluid.layers.data('items', [1], dtype='int32')
             labels = fluid.layers.data('labels', [1], dtype='float32')
 
-            deepcf = DeepCF('deepcf')
+            deepcf = DeepCF('deepcf', num_users, num_items, matrix)
             prediction = deepcf(users, items)
             loss = fluid.layers.reduce_sum(
                 fluid.layers.log_loss(prediction, labels))
@@ -159,7 +226,11 @@ class TestImperativeDeepCF(unittest.TestCase):
             exe = fluid.Executor(fluid.CPUPlace(
             ) if not core.is_compiled_with_cuda() else fluid.CUDAPlace(0))
             exe.run(startup)
+            for e in range(NUM_EPOCHES):
+                sys.stderr.write('epoch %d\n' % e)
                 for slice in range(0, BATCH_SIZE * NUM_BATCHES, BATCH_SIZE):
+                    if slice + BATCH_SIZE >= users_np.shape[0]:
+                        break
                     static_loss = exe.run(
                         main,
                         feed={
@@ -169,25 +240,31 @@ class TestImperativeDeepCF(unittest.TestCase):
                         },
                         fetch_list=[loss])[0]
                     sys.stderr.write('static loss %s\n' % static_loss)
+        """
 
         with fluid.imperative.guard():
             fluid.default_startup_program().random_seed = seed
             fluid.default_main_program().random_seed = seed
 
-            deepcf = DeepCF('deepcf')
+            deepcf = DeepCF('deepcf', num_users, num_items, matrix)
+            sys.stderr.write('matrix: %s\n' % deepcf._rating_matrix._numpy())
+            for e in range(NUM_EPOCHES):
+                sys.stderr.write('epoch %d\n' % e)
                 for slice in range(0, BATCH_SIZE * NUM_BATCHES, BATCH_SIZE):
                     prediction = deepcf(
                         to_variable(users_np[slice:slice + BATCH_SIZE]),
                         to_variable(items_np[slice:slice + BATCH_SIZE]))
                     loss = fluid.layers.reduce_sum(
                         fluid.layers.log_loss(prediction,
-                                          to_variable(labels_np[slice:slice +
-                                                                BATCH_SIZE])))
+                                              to_variable(labels_np[
+                                                  slice:slice + BATCH_SIZE])))
                     loss._backward()
                     adam = fluid.optimizer.AdamOptimizer(0.01)
                     adam.minimize(loss)
                     deepcf.clear_gradients()
                     dy_loss = loss._numpy()
+                    sys.stderr.write('dynamic loss: %s\n' % dy_loss)
+            sys.stderr.write('matrix: %s\n' % deepcf._rating_matrix._numpy())
 
         self.assertEqual(static_loss, dy_loss)