polish deepCF model to support real dataset

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>();
 

paddle/fluid/pybind/pybind.cc

@@ -235,6 +235,7 @@ PYBIND11_MODULE(core, m) {
             self.forward_id_ = forward_id;
           },
           py::return_value_policy::reference)
+      .def_property_readonly("type", &imperative::OpBase::Type)
       .def_property(
           "backward_id",
           [](const imperative::OpBase &self) { return self.backward_id_; },

python/paddle/fluid/framework.py

@@ -744,7 +744,7 @@ def __init__(self,
         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
@@ -906,7 +906,10 @@ def __str__(self):
 
     @property
     def type(self):
-        return self.desc.type()
+        if _in_imperative_mode():
+            return self.iop.type
+        else:
+            return self.desc.type()
 
     def input(self, name):
         """

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

@@ -62,7 +62,7 @@ def trace_op(self, op, stop_gradient=False):
             if len(backward_refs) > 0:
                 op.iop.register_backward_hooks(release_op)
 
-                # TODO(minqiyang): remove all inputs and outputs after seperate
+                # TODO(minqiyang): remove all inputs and outputs after separate
                 # var and grad
                 op.backward_refs = defaultdict(list)
                 for k, v in six.iteritems(op.inputs):

python/paddle/fluid/initializer.py

@@ -212,7 +212,7 @@ def __call__(self, var, block):
         if self._seed == 0:
             self._seed = block.program.random_seed
 
-        # to be compatible of fp16 initalizers
+        # to be compatible of fp16 initializers
         if var.dtype == VarDesc.VarType.FP16:
             out_dtype = VarDesc.VarType.FP32
             out_var = block.create_var(
@@ -756,7 +756,7 @@ def __call__(self, var, block):
             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/optimizer.py

@@ -165,6 +165,8 @@ def _add_accumulator(self,
             name = self._name + "_" + name
         if (name in self._accumulators and
                 param.name in self._accumulators[name]):
+            if framework._in_imperative_mode():
+                return self._accumulators[name][param.name]
             raise Exception("Accumulator {} already exists for parameter {}".
                             format(name, param.name))
         if shape == None:

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 +24,17 @@
 from test_imperative_base import new_program_scope
 from paddle.fluid.imperative.base import to_variable
 
-NUM_USERS = 100
-NUM_ITEMS = 1000
+# Can use Amusic dataset as the DeepCF describes.
+DATA_PATH = os.environ.get('DATA_PATH', '')
 
-BATCH_SIZE = 32
-NUM_BATCHES = 2
+BATCH_SIZE = int(os.environ.get('BATCH_SIZE', 128))
+NUM_BATCHES = int(os.environ.get('NUM_BATCHES', 5))
+NUM_EPOCHES = int(os.environ.get('NUM_EPOCHES', 1))
 
 
-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 +63,9 @@ def forward(self, users, items):
         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 +89,30 @@ def forward(self, users, items):
 
 
 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._user_emb = fluid.imperative.Embedding(self.full_name(),
-                                                    [NUM_USERS, 256])
-        self._item_emb = fluid.imperative.Embedding(self.full_name(),
-                                                    [NUM_ITEMS, 256])
+        self._num_users = num_users
+        self._num_items = num_items
+        self._rating_matrix = self.create_parameter(
+            fluid.ParamAttr(trainable=False),
+            matrix.shape,
+            matrix.dtype,
+            is_bias=False,
+            default_initializer=fluid.initializer.NumpyArrayInitializer(matrix))
+        self._rating_matrix._stop_gradient = True
 
         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)
+        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)
@@ -116,27 +127,79 @@ def get_data():
     user_ids = []
     item_ids = []
     labels = []
+    NUM_USERS = 100
+    NUM_ITEMS = 1000
+    matrix = np.zeros([NUM_USERS, NUM_ITEMS], dtype=np.float32)
+
     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
@@ -145,11 +208,11 @@ def test_gan_float32(self):
 
         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))
@@ -159,35 +222,44 @@ def test_gan_float32(self):
             exe = fluid.Executor(fluid.CPUPlace(
             ) if not core.is_compiled_with_cuda() else fluid.CUDAPlace(0))
             exe.run(startup)
-            for slice in range(0, BATCH_SIZE * NUM_BATCHES, BATCH_SIZE):
-                static_loss = exe.run(
-                    main,
-                    feed={
-                        users.name: users_np[slice:slice + BATCH_SIZE],
-                        items.name: items_np[slice:slice + BATCH_SIZE],
-                        labels.name: labels_np[slice:slice + BATCH_SIZE]
-                    },
-                    fetch_list=[loss])[0]
-                sys.stderr.write('static loss %s\n' % static_loss)
+            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={
+                            users.name: users_np[slice:slice + BATCH_SIZE],
+                            items.name: items_np[slice:slice + BATCH_SIZE],
+                            labels.name: labels_np[slice:slice + BATCH_SIZE]
+                        },
+                        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')
-            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])))
-                loss._backward()
-                adam = fluid.optimizer.AdamOptimizer(0.01)
-                adam.minimize(loss)
-                deepcf.clear_gradients()
-                dy_loss = loss._numpy()
+            deepcf = DeepCF('deepcf', num_users, num_items, matrix)
+            adam = fluid.optimizer.AdamOptimizer(0.01)
+            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
+                    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])))
+                    loss._backward()
+                    adam.minimize(loss)
+                    deepcf.clear_gradients()
+                    dy_loss = loss._numpy()
+                    sys.stderr.write('dynamic loss: %s %s\n' % (slice, dy_loss))
 
         self.assertEqual(static_loss, dy_loss)