[Dy2stat] Add Simnet Test for V2 APIs (#27460)

Add Simnet Test for V2 APIs.

We used tool from https://www.paddlepaddle.org.cn/documentation/docs/zh/2.0-beta/guides/migration_cn.html#paddle1-xpaddle2-0beta to do v1.x to v2 transformation.

This PR pulled changes from #27430, please DO NOT merge before #27430 is merged

python/paddle/fluid/tests/unittests/dygraph_to_static/simnet_dygraph_model_v2.py

+#   Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from functools import reduce
+import paddle
+
+
+class EmbeddingLayer(object):
+    """
+    Embedding Layer class
+    """
+
+    def __init__(self, dict_size, emb_dim, name="emb", padding_idx=None):
+        """
+        initialize
+        """
+        self.dict_size = dict_size
+        self.emb_dim = emb_dim
+        self.name = name
+        self.padding_idx = padding_idx
+
+    def ops(self):
+        """
+        operation
+        """
+        # TODO(huihuangzheng): The original code set the is_sparse=True, but it
+        # causes crush in dy2stat. Set it to True after fixing it.
+        emb = paddle.fluid.dygraph.Embedding(
+            size=[self.dict_size, self.emb_dim],
+            is_sparse=True,
+            padding_idx=self.padding_idx,
+            param_attr=paddle.ParamAttr(
+                name=self.name, initializer=paddle.nn.initializer.Xavier()))
+
+        return emb
+
+
+class FCLayer(object):
+    """
+    Fully Connect Layer class
+    """
+
+    def __init__(self, fc_dim, act, name="fc"):
+        """
+        initialize
+        """
+        self.fc_dim = fc_dim
+        self.act = act
+        self.name = name
+
+    def ops(self):
+        """
+        operation
+        """
+        fc = FC(size=self.fc_dim,
+                param_attr=paddle.ParamAttr(name="%s.w" % self.name),
+                bias_attr=paddle.ParamAttr(name="%s.b" % self.name),
+                act=self.act)
+        return fc
+
+
+class ConcatLayer(object):
+    """
+    Connection Layer class
+    """
+
+    def __init__(self, axis):
+        """
+        initialize
+        """
+        self.axis = axis
+
+    def ops(self, inputs):
+        """
+        operation
+        """
+        concat = paddle.concat(x=inputs, axis=self.axis)
+        return concat
+
+
+class ReduceMeanLayer(object):
+    """
+    Reduce Mean Layer class
+    """
+
+    def __init__(self):
+        """
+        initialize
+        """
+        pass
+
+    def ops(self, input):
+        """
+        operation
+        """
+        mean = paddle.reduce_mean(input)
+        return mean
+
+
+class CosSimLayer(object):
+    """
+    Cos Similarly Calculate Layer
+    """
+
+    def __init__(self):
+        """
+        initialize
+        """
+        pass
+
+    def ops(self, x, y):
+        """
+        operation
+        """
+        sim = paddle.nn.functional.cosine_similarity(x, y)
+        return sim
+
+
+class ElementwiseMaxLayer(object):
+    """
+    Elementwise Max Layer class
+    """
+
+    def __init__(self):
+        """
+        initialize
+        """
+        pass
+
+    def ops(self, x, y):
+        """
+        operation
+        """
+        max = paddle.maximum(x=x, y=y)
+        return max
+
+
+class ElementwiseAddLayer(object):
+    """
+    Elementwise Add Layer class
+    """
+
+    def __init__(self):
+        """
+        initialize
+        """
+        pass
+
+    def ops(self, x, y):
+        """
+        operation
+        """
+        add = paddle.add(x=x, y=y)
+        return add
+
+
+class ElementwiseSubLayer(object):
+    """
+    Elementwise Add Layer class
+    """
+
+    def __init__(self):
+        """
+        initialize
+        """
+        pass
+
+    def ops(self, x, y):
+        """
+        operation
+        """
+        sub = paddle.elementwise_sub(x, y)
+        return sub
+
+
+class ConstantLayer(object):
+    """
+    Generate A Constant Layer class
+    """
+
+    def __init__(self):
+        """
+        initialize
+        """
+        pass
+
+    def ops(self, input, shape, dtype, value):
+        """
+        operation
+        """
+        shape = list(shape)
+        input_shape = paddle.shape(input)
+        shape[0] = input_shape[0]
+        constant = paddle.fill_constant(shape, dtype, value)
+        return constant
+
+
+class SoftsignLayer(object):
+    """
+    Softsign Layer class
+    """
+
+    def __init__(self):
+        """
+        initialize
+        """
+        pass
+
+    def ops(self, input):
+        """
+        operation
+        """
+        softsign = paddle.nn.functional.softsign(input)
+        return softsign
+
+
+class FC(paddle.nn.Layer):
+    """
+    This interface is used to construct a callable object of the ``FC`` class.
+    For more details, refer to code examples.
+    It creates a fully connected layer in the network. It can take
+    one or multiple ``Tensor`` as its inputs. It creates a Variable called weights for each input tensor,
+    which represents a fully connected weight matrix from each input unit to
+    each output unit. The fully connected layer multiplies each input tensor
+    with its corresponding weight to produce an output Tensor with shape [N, `size`],
+    where N is batch size. If multiple input tensors are given, the results of
+    multiple output tensors with shape [N, `size`] will be summed up. If ``bias_attr``
+    is not None, a bias variable will be created and added to the output.
+    Finally, if ``act`` is not None, it will be applied to the output as well.
+    When the input is single ``Tensor`` :
+    .. math::
+        Out = Act({XW + b})
+    When the input are multiple ``Tensor`` :
+    .. math::
+        Out = Act({\sum_{i=0}^{N-1}X_iW_i + b})
+    In the above equation:
+    * :math:`N`: Number of the input. N equals to len(input) if input is list of ``Tensor`` .
+    * :math:`X_i`: The i-th input ``Tensor`` .
+    * :math:`W_i`: The i-th weights matrix corresponding i-th input tensor.
+    * :math:`b`: The bias parameter created by this layer (if needed).
+    * :math:`Act`: The activation function.
+    * :math:`Out`: The output ``Tensor`` .
+    See below for an example.
+    .. code-block:: text
+        Given:
+            data_1.data = [[[0.1, 0.2]]]
+            data_1.shape = (1, 1, 2) # 1 is batch_size
+            data_2.data = [[[0.1, 0.2, 0.3]]]
+            data_2.shape = (1, 1, 3) # 1 is batch_size
+            fc = FC("fc", 2, num_flatten_dims=2)
+            out = fc(input=[data_1, data_2])
+        Then:
+            out.data = [[[0.182996 -0.474117]]]
+            out.shape = (1, 1, 2)
+    Parameters:
+        
+        size(int): The number of output units in this layer.
+        num_flatten_dims (int, optional): The fc layer can accept an input tensor with more than
+            two dimensions. If this happens, the multi-dimension tensor will first be flattened
+            into a 2-dimensional matrix. The parameter `num_flatten_dims` determines how the input
+            tensor is flattened: the first `num_flatten_dims` (inclusive, index starts from 1)
+            dimensions will be flatten to form the first dimension of the final matrix (height of
+            the matrix), and the rest `rank(X) - num_flatten_dims` dimensions are flattened to
+            form the second dimension of the final matrix (width of the matrix). For example, suppose
+            `X` is a 5-dimensional tensor with a shape [2, 3, 4, 5, 6], and `num_flatten_dims` = 3.
+            Then, the flattened matrix will have a shape [2 x 3 x 4, 5 x 6] = [24, 30]. Default: 1
+        param_attr (ParamAttr or list of ParamAttr, optional): The parameter attribute for learnable
+            weights(Parameter) of this layer. Default: None.
+        bias_attr (ParamAttr or list of ParamAttr, optional): The attribute for the bias
+            of this layer. If it is set to False, no bias will be added to the output units.
+            If it is set to None, the bias is initialized zero. Default: None.
+        act (str, optional): Activation to be applied to the output of this layer. Default: None.
+        is_test(bool, optional): A flag indicating whether execution is in test phase. Default: False.
+        dtype(str, optional): Dtype used for weight, it can be "float32" or "float64". Default: "float32".
+    Attribute:
+        **weight** (list of Parameter): the learnable weights of this layer.
+        **bias** (Parameter or None): the learnable bias of this layer.
+    Returns:
+        None
+    
+    """
+
+    def __init__(self,
+                 size,
+                 num_flatten_dims=1,
+                 param_attr=None,
+                 bias_attr=None,
+                 act=None,
+                 is_test=False,
+                 dtype="float32"):
+        super(FC, self).__init__(dtype)
+
+        self._size = size
+        self._num_flatten_dims = num_flatten_dims
+        self._dtype = dtype
+        self._param_attr = param_attr
+        self._bias_attr = bias_attr
+        self._act = act
+        self.__w = list()
+
+    def _build_once(self, input):
+        i = 0
+        for inp, param in self._helper.iter_inputs_and_params(input,
+                                                              self._param_attr):
+            input_shape = inp.shape
+
+            param_shape = [
+                reduce(lambda a, b: a * b, input_shape[self._num_flatten_dims:],
+                       1)
+            ] + [self._size]
+            self.__w.append(
+                self.add_parameter(
+                    '_w%d' % i,
+                    self.create_parameter(
+                        attr=param,
+                        shape=param_shape,
+                        dtype=self._dtype,
+                        is_bias=False)))
+            i += 1
+
+        size = list([self._size])
+        self._b = self.create_parameter(
+            attr=self._bias_attr, shape=size, dtype=self._dtype, is_bias=True)
+
+    # TODO(songyouwei): We should remove _w property
+    @property
+    def _w(self, i=0):
+        return self.__w[i]
+
+    @_w.setter
+    def _w(self, value, i=0):
+        assert isinstance(self.__w[i], Variable)
+        self.__w[i].set_value(value)
+
+    @property
+    def weight(self):
+        if len(self.__w) > 1:
+            return self.__w
+        else:
+            return self.__w[0]
+
+    @weight.setter
+    def weight(self, value):
+        if len(self.__w) == 1:
+            self.__w[0] = value
+
+    @property
+    def bias(self):
+        return self._b
+
+    @bias.setter
+    def bias(self, value):
+        self._b = value
+
+    def forward(self, input):
+        mul_results = list()
+        i = 0
+        for inp, param in self._helper.iter_inputs_and_params(input,
+                                                              self._param_attr):
+            tmp = self._helper.create_variable_for_type_inference(self._dtype)
+            self._helper.append_op(
+                type="mul",
+                inputs={"X": inp,
+                        "Y": self.__w[i]},
+                outputs={"Out": tmp},
+                attrs={
+                    "x_num_col_dims": self._num_flatten_dims,
+                    "y_num_col_dims": 1
+                })
+            i += 1
+            mul_results.append(tmp)
+
+        if len(mul_results) == 1:
+            pre_bias = mul_results[0]
+        else:
+            pre_bias = self._helper.create_variable_for_type_inference(
+                self._dtype)
+            self._helper.append_op(
+                type="sum",
+                inputs={"X": mul_results},
+                outputs={"Out": pre_bias},
+                attrs={"use_mkldnn": False})
+
+        if self._b is not None:
+            pre_activation = self._helper.create_variable_for_type_inference(
+                dtype=self._dtype)
+            self._helper.append_op(
+                type='elementwise_add',
+                inputs={'X': [pre_bias],
+                        'Y': [self._b]},
+                outputs={'Out': [pre_activation]},
+                attrs={'axis': self._num_flatten_dims})
+        else:
+            pre_activation = pre_bias
+        # Currently, we don't support inplace in dygraph mode
+        return self._helper.append_activation(pre_activation, act=self._act)
+
+
+class HingeLoss(object):
+    """
+    Hing Loss Calculate class
+    """
+
+    def __init__(self, conf_dict):
+        """
+        initialize
+        """
+        self.margin = conf_dict["loss"]["margin"]
+
+    def compute(self, pos, neg):
+        """
+        compute loss
+        """
+        elementwise_max = ElementwiseMaxLayer()
+        elementwise_add = ElementwiseAddLayer()
+        elementwise_sub = ElementwiseSubLayer()
+        constant = ConstantLayer()
+        reduce_mean = ReduceMeanLayer()
+        loss = reduce_mean.ops(
+            elementwise_max.ops(
+                constant.ops(neg, neg.shape, "float32", 0.0),
+                elementwise_add.ops(
+                    elementwise_sub.ops(neg, pos),
+                    constant.ops(neg, neg.shape, "float32", self.margin))))
+        return loss
+
+
+class BOW(paddle.nn.Layer):
+    """
+    BOW
+    """
+
+    def __init__(self, conf_dict):
+        """
+        initialize
+        """
+        super(BOW, self).__init__()
+        self.dict_size = conf_dict["dict_size"]
+        self.task_mode = conf_dict["task_mode"]
+        self.emb_dim = conf_dict["net"]["emb_dim"]
+        self.bow_dim = conf_dict["net"]["bow_dim"]
+        self.seq_len = conf_dict["seq_len"]
+        self.emb_layer = EmbeddingLayer(self.dict_size, self.emb_dim,
+                                        "emb").ops()
+        self.bow_layer = paddle.nn.Linear(
+            in_features=self.bow_dim, out_features=self.bow_dim)
+        self.bow_layer_po = FCLayer(self.bow_dim, None, "fc").ops()
+        self.softmax_layer = FCLayer(2, "softmax", "cos_sim").ops()
+
+    @paddle.jit.to_static
+    def forward(self, left, right):
+        """
+        Forward network
+        """
+
+        # embedding layer
+        left_emb = self.emb_layer(left)
+        right_emb = self.emb_layer(right)
+        left_emb = paddle.reshape(
+            left_emb, shape=[-1, self.seq_len, self.bow_dim])
+        right_emb = paddle.reshape(
+            right_emb, shape=[-1, self.seq_len, self.bow_dim])
+
+        bow_left = paddle.reduce_sum(left_emb, dim=1)
+        bow_right = paddle.reduce_sum(right_emb, dim=1)
+        softsign_layer = SoftsignLayer()
+        left_soft = softsign_layer.ops(bow_left)
+        right_soft = softsign_layer.ops(bow_right)
+
+        # matching layer
+        if self.task_mode == "pairwise":
+            left_bow = self.bow_layer(left_soft)
+            right_bow = self.bow_layer(right_soft)
+            cos_sim_layer = CosSimLayer()
+            pred = cos_sim_layer.ops(left_bow, right_bow)
+            return left_bow, pred
+        else:
+            concat_layer = ConcatLayer(1)
+            concat = concat_layer.ops([left_soft, right_soft])
+            concat_fc = self.bow_layer_po(concat)
+            pred = self.softmax_layer(concat_fc)
+            return left_soft, pred

python/paddle/fluid/tests/unittests/dygraph_to_static/test_simnet_v2.py

+#   Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import argparse
+import numpy as np
+import paddle
+import random
+import unittest
+
+from simnet_dygraph_model_v2 import BOW, HingeLoss
+
+SEED = 102
+random.seed(SEED)
+
+
+def create_conf_dict():
+    conf_dict = {}
+    conf_dict["task_mode"] = "pairwise"
+    conf_dict["net"] = {"emb_dim": 128, "bow_dim": 128, "hidden_dim": 128}
+    conf_dict["loss"] = {"margin": 0.1}
+    return conf_dict
+
+
+def parse_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--batch_size",
+        type=int,
+        default=32,
+        help="Total examples' number in batch for training.")
+    parser.add_argument(
+        "--seq_len", type=int, default=32, help="The length of each sentence.")
+    parser.add_argument(
+        "--epoch", type=int, default=1, help="The number of training epoch.")
+    parser.add_argument(
+        "--fake_sample_size",
+        type=int,
+        default=128,
+        help="The number of samples of fake data.")
+    args = parser.parse_args([])
+    return args
+
+
+args = parse_args()
+
+
+def fake_vocabulary():
+    vocab = {}
+    vocab["<unk>"] = 0
+    for i in range(26):
+        c = chr(ord('a') + i)
+        vocab[c] = i + 1
+    return vocab
+
+
+vocab = fake_vocabulary()
+
+
+class FakeReaderProcessor(object):
+    def __init__(self, args, vocab):
+        self.vocab = vocab
+        self.seq_len = args.seq_len
+        self.sample_size = args.fake_sample_size
+        self.data_samples = []
+        for i in range(self.sample_size):
+            query = [random.randint(0, 26) for i in range(self.seq_len)]
+            pos_title = query[:]
+            neg_title = [26 - q for q in query]
+            self.data_samples.append(
+                np.array([query, pos_title, neg_title]).astype(np.int64))
+
+    def get_reader(self, mode, epoch=0):
+        def reader_with_pairwise():
+            if mode == "train":
+                for i in range(self.sample_size):
+                    yield self.data_samples[i]
+
+        return reader_with_pairwise
+
+
+simnet_process = FakeReaderProcessor(args, vocab)
+
+
+def train(conf_dict, to_static):
+    """
+    train process
+    """
+    program_translator = paddle.jit.ProgramTranslator()
+    program_translator.enable(to_static)
+
+    # Get device
+    if paddle.is_compiled_with_cuda():
+        place = paddle.CUDAPlace(0)
+    else:
+        place = paddle.CPUPlace()
+
+    paddle.disable_static(place)
+    paddle.manual_seed(SEED)
+    paddle.framework.random._manual_program_seed(SEED)
+
+    conf_dict['dict_size'] = len(vocab)
+    conf_dict['seq_len'] = args.seq_len
+
+    net = BOW(conf_dict)
+    loss = HingeLoss(conf_dict)
+    optimizer = paddle.optimizer.Adam(
+        learning_rate=0.001,
+        beta1=0.9,
+        beta2=0.999,
+        epsilon=1e-08,
+        parameters=net.parameters())
+
+    metric = paddle.metric.Auc(name="auc")
+
+    global_step = 0
+    losses = []
+
+    train_loader = paddle.io.DataLoader.from_generator(
+        capacity=16, return_list=True, iterable=True, use_double_buffer=True)
+    get_train_examples = simnet_process.get_reader("train", epoch=args.epoch)
+    train_loader.set_sample_list_generator(
+        paddle.batch(
+            get_train_examples, batch_size=args.batch_size), place)
+
+    for left, pos_right, neg_right in train_loader():
+        left = paddle.reshape(left, shape=[-1, 1])
+        pos_right = paddle.reshape(pos_right, shape=[-1, 1])
+        neg_right = paddle.reshape(neg_right, shape=[-1, 1])
+        net.train()
+        global_step += 1
+        left_feat, pos_score = net(left, pos_right)
+        pred = pos_score
+        _, neg_score = net(left, neg_right)
+        avg_cost = loss.compute(pos_score, neg_score)
+        losses.append(np.mean(avg_cost.numpy()))
+        avg_cost.backward()
+        optimizer.minimize(avg_cost)
+        net.clear_gradients()
+    paddle.enable_static()
+    return losses
+
+
+class TestSimnet(unittest.TestCase):
+    def test_dygraph_static_same_loss(self):
+        if paddle.is_compiled_with_cuda():
+            paddle.fluid.set_flags({"FLAGS_cudnn_deterministic": True})
+        conf_dict = create_conf_dict()
+        dygraph_loss = train(conf_dict, to_static=False)
+        static_loss = train(conf_dict, to_static=True)
+
+        self.assertEqual(len(dygraph_loss), len(static_loss))
+        for i in range(len(dygraph_loss)):
+            self.assertAlmostEqual(dygraph_loss[i], static_loss[i])
+
+
+if __name__ == '__main__':
+    unittest.main()