[Dy2Stat] Add test for ptb model. (#24076)

* [Dy2Stat] Add test for ptb model. test=develop

* Simplify code for gast.If in is_control_flow_to_transform. test=develop

* Move IsControlFlowVisitor to file utils. test=develop

* Don't use convert_call for build-in func in CallTransformer. test=develop

* Optimize api is_control_flow_to_transform. test=develop

* Polish the document of IsControlFlowVisitor. test=develop

* Use declarative instead of dygraph_to_static_func. test=develop

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

+#   Copyright (c) 2018 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 __future__ import absolute_import, division, print_function
+
+import logging
+import time
+import unittest
+
+import numpy as np
+
+import paddle.fluid as fluid
+from paddle.fluid.dygraph import ProgramTranslator
+from paddle.fluid.dygraph.base import to_variable
+from paddle.fluid.dygraph.jit import declarative
+from paddle.fluid.dygraph.nn import Embedding
+from paddle.fluid.optimizer import SGDOptimizer
+
+PRINT_STEP = 20
+SEED = 2020
+
+
+class SimpleLSTMRNN(fluid.Layer):
+    def __init__(self,
+                 hidden_size,
+                 num_steps,
+                 num_layers=2,
+                 init_scale=0.1,
+                 dropout=None):
+        super(SimpleLSTMRNN, self).__init__()
+        self._hidden_size = hidden_size
+        self._num_layers = num_layers
+        self._init_scale = init_scale
+        self._dropout = dropout
+        self._num_steps = num_steps
+        self.cell_array = []
+        self.hidden_array = []
+
+        self.weight_1_arr = []
+        self.weight_2_arr = []
+        self.bias_arr = []
+        self.mask_array = []
+
+        for i in range(self._num_layers):
+            weight_1 = self.create_parameter(
+                attr=fluid.ParamAttr(
+                    initializer=fluid.initializer.UniformInitializer(
+                        low=-self._init_scale, high=self._init_scale)),
+                shape=[self._hidden_size * 2, self._hidden_size * 4],
+                dtype="float32",
+                default_initializer=fluid.initializer.UniformInitializer(
+                    low=-self._init_scale, high=self._init_scale))
+            self.weight_1_arr.append(self.add_parameter('w_%d' % i, weight_1))
+            bias_1 = self.create_parameter(
+                attr=fluid.ParamAttr(
+                    initializer=fluid.initializer.UniformInitializer(
+                        low=-self._init_scale, high=self._init_scale)),
+                shape=[self._hidden_size * 4],
+                dtype="float32",
+                default_initializer=fluid.initializer.Constant(0.0))
+            self.bias_arr.append(self.add_parameter('b_%d' % i, bias_1))
+
+    def forward(self, input_embedding, init_hidden=None, init_cell=None):
+        cell_array = []
+        hidden_array = []
+
+        for i in range(self._num_layers):
+            hidden_array.append(init_hidden[i])
+            cell_array.append(init_cell[i])
+
+        res = []
+        for index in range(self._num_steps):
+            step_input = input_embedding[:, index, :]
+            for k in range(self._num_layers):
+                pre_hidden = hidden_array[k]
+                pre_cell = cell_array[k]
+                weight_1 = self.weight_1_arr[k]
+                bias = self.bias_arr[k]
+
+                nn = fluid.layers.concat([step_input, pre_hidden], 1)
+                gate_input = fluid.layers.matmul(x=nn, y=weight_1)
+
+                gate_input = fluid.layers.elementwise_add(gate_input, bias)
+                i, j, f, o = fluid.layers.split(
+                    gate_input, num_or_sections=4, dim=-1)
+                c = pre_cell * fluid.layers.sigmoid(f) + fluid.layers.sigmoid(
+                    i) * fluid.layers.tanh(j)
+                m = fluid.layers.tanh(c) * fluid.layers.sigmoid(o)
+                hidden_array[k] = m
+                cell_array[k] = c
+                step_input = m
+
+                if self._dropout is not None and self._dropout > 0.0:
+                    step_input = fluid.layers.dropout(
+                        step_input,
+                        dropout_prob=self._dropout,
+                        dropout_implementation='upscale_in_train')
+            res.append(step_input)
+        real_res = fluid.layers.concat(res, 1)
+        real_res = fluid.layers.reshape(
+            real_res, [-1, self._num_steps, self._hidden_size])
+        last_hidden = fluid.layers.concat(hidden_array, 1)
+        last_hidden = fluid.layers.reshape(
+            last_hidden, shape=[-1, self._num_layers, self._hidden_size])
+        last_hidden = fluid.layers.transpose(x=last_hidden, perm=[1, 0, 2])
+        last_cell = fluid.layers.concat(cell_array, 1)
+        last_cell = fluid.layers.reshape(
+            last_cell, shape=[-1, self._num_layers, self._hidden_size])
+        last_cell = fluid.layers.transpose(x=last_cell, perm=[1, 0, 2])
+        return real_res, last_hidden, last_cell
+
+
+class PtbModel(fluid.Layer):
+    def __init__(self,
+                 hidden_size,
+                 vocab_size,
+                 num_layers=2,
+                 num_steps=20,
+                 init_scale=0.1,
+                 dropout=None):
+        super(PtbModel, self).__init__()
+        self.hidden_size = hidden_size
+        self.vocab_size = vocab_size
+        self.init_scale = init_scale
+        self.num_layers = num_layers
+        self.num_steps = num_steps
+        self.dropout = dropout
+        self.simple_lstm_rnn = SimpleLSTMRNN(
+            hidden_size,
+            num_steps,
+            num_layers=num_layers,
+            init_scale=init_scale,
+            dropout=dropout)
+        self.embedding = Embedding(
+            size=[vocab_size, hidden_size],
+            dtype='float32',
+            is_sparse=False,
+            param_attr=fluid.ParamAttr(
+                name='embedding_para',
+                initializer=fluid.initializer.UniformInitializer(
+                    low=-init_scale, high=init_scale)))
+        self.softmax_weight = self.create_parameter(
+            attr=fluid.ParamAttr(),
+            shape=[self.hidden_size, self.vocab_size],
+            dtype="float32",
+            default_initializer=fluid.initializer.UniformInitializer(
+                low=-self.init_scale, high=self.init_scale))
+        self.softmax_bias = self.create_parameter(
+            attr=fluid.ParamAttr(),
+            shape=[self.vocab_size],
+            dtype="float32",
+            default_initializer=fluid.initializer.UniformInitializer(
+                low=-self.init_scale, high=self.init_scale))
+
+    def build_once(self, input, label, init_hidden, init_cell):
+        pass
+
+    @declarative
+    def forward(self, input, label, init_hidden, init_cell):
+
+        # TODO(liym27): Call `to_variable` to feed data successfully.
+        #  Remove to_variable statements later
+        input = to_variable(input)
+        label = to_variable(label)
+        init_hidden = to_variable(init_hidden)
+        init_cell = to_variable(init_cell)
+
+        init_h = fluid.layers.reshape(
+            init_hidden, shape=[self.num_layers, -1, self.hidden_size])
+
+        init_c = fluid.layers.reshape(
+            init_cell, shape=[self.num_layers, -1, self.hidden_size])
+
+        x_emb = self.embedding(input)
+
+        x_emb = fluid.layers.reshape(
+            x_emb, shape=[-1, self.num_steps, self.hidden_size])
+        if self.dropout is not None and self.dropout > 0.0:
+            x_emb = fluid.layers.dropout(
+                x_emb,
+                dropout_prob=self.dropout,
+                dropout_implementation='upscale_in_train')
+        rnn_out, last_hidden, last_cell = self.simple_lstm_rnn(x_emb, init_h,
+                                                               init_c)
+
+        projection = fluid.layers.matmul(rnn_out, self.softmax_weight)
+        projection = fluid.layers.elementwise_add(projection, self.softmax_bias)
+
+        loss = fluid.layers.softmax_with_cross_entropy(
+            logits=projection, label=label, soft_label=False)
+        loss = fluid.layers.reshape(loss, shape=[-1, self.num_steps])
+        loss = fluid.layers.reduce_mean(loss, dim=[0])
+        loss = fluid.layers.reduce_sum(loss)
+
+        return loss, last_hidden, last_cell
+
+    def debug_emb(self):
+
+        np.save("emb_grad", self.x_emb.gradient())
+
+
+def train_dygraph(place):
+    num_layers = 1
+    batch_size = 4
+    hidden_size = 10
+    num_steps = 3
+    init_scale = 0.1
+    max_epoch = 1
+    dropout = 0.0
+    vocab_size = 1000
+    batch_num = 200
+
+    with fluid.dygraph.guard(place):
+        fluid.default_startup_program().random_seed = SEED
+        fluid.default_main_program().random_seed = SEED
+        ptb_model = PtbModel(
+            hidden_size=hidden_size,
+            vocab_size=vocab_size,
+            num_layers=num_layers,
+            num_steps=num_steps,
+            init_scale=init_scale,
+            dropout=dropout)
+
+        sgd = SGDOptimizer(
+            learning_rate=1e-3, parameter_list=ptb_model.parameters())
+
+        for epoch_id in range(max_epoch):
+
+            total_loss = 0.0
+            iters = 0.0
+            total_sample = 0
+
+            init_hidden_data = np.zeros(
+                (num_layers, batch_size, hidden_size), dtype='float32')
+            init_cell_data = np.zeros(
+                (num_layers, batch_size, hidden_size), dtype='float32')
+
+            init_hidden = to_variable(init_hidden_data)
+            init_cell = to_variable(init_cell_data)
+            for step_id in range(batch_num):
+                x_data = np.arange(12).reshape(4, 3).astype('int64')
+                y_data = np.arange(1, 13).reshape(4, 3).astype('int64')
+                y_data = y_data.reshape((-1, 1))
+
+                x_data = x_data.reshape((-1, num_steps, 1))
+                y_data = y_data.reshape((-1, num_steps, 1))
+
+                x = to_variable(x_data)
+                y = to_variable(y_data)
+
+                dy_loss, last_hidden, last_cell = ptb_model(x, y, init_hidden,
+                                                            init_cell)
+                out_loss = dy_loss.numpy()
+
+                dy_loss.backward()
+                sgd.minimize(dy_loss)
+                ptb_model.clear_gradients()
+
+                total_loss += out_loss
+                iters += num_steps
+                total_sample += 1
+                if step_id % PRINT_STEP == 0:
+                    if step_id == 0:
+                        logging.info("epoch %d | step %d, loss %0.3f" % (
+                            epoch_id, step_id, total_loss / total_sample))
+                        avg_batch_time = time.time()
+                    else:
+                        speed = PRINT_STEP / (time.time() - avg_batch_time)
+                        logging.info(
+                            "epoch %d | step %d, loss %0.3f, speed %.3f steps/s"
+                            % (epoch_id, step_id, total_loss / total_sample,
+                               speed))
+                        avg_batch_time = time.time()
+
+            return out_loss, last_hidden.numpy(), last_cell.numpy()
+
+
+def train_static(place):
+    num_layers = 1
+    batch_size = 4
+    hidden_size = 10
+    num_steps = 3
+    init_scale = 0.1
+    max_epoch = 1
+    dropout = 0.0
+    vocab_size = 1000
+    batch_num = 200
+
+    main_prog = fluid.Program()
+    startup_prog = fluid.Program()
+    main_prog.random_seed = SEED
+    startup_prog.random_seed = SEED
+    with fluid.program_guard(main_prog, startup_prog):
+
+        ptb_model = PtbModel(
+            hidden_size=hidden_size,
+            vocab_size=vocab_size,
+            num_layers=num_layers,
+            num_steps=num_steps,
+            init_scale=init_scale,
+            dropout=dropout)
+
+        sgd = SGDOptimizer(
+            learning_rate=1e-3, parameter_list=ptb_model.parameters())
+        program_translator = ProgramTranslator()
+        program_translator.set_optimizer(sgd, index_of_loss=0)
+
+        for epoch_id in range(max_epoch):
+
+            total_loss = 0.0
+            iters = 0.0
+            total_sample = 0
+
+            init_hidden_data = np.zeros(
+                (num_layers, batch_size, hidden_size), dtype='float32')
+            init_cell_data = np.zeros(
+                (num_layers, batch_size, hidden_size), dtype='float32')
+
+            for step_id in range(batch_num):
+                x_data = np.arange(12).reshape(4, 3).astype('int64')
+                y_data = np.arange(1, 13).reshape(4, 3).astype('int64')
+                y_data = y_data.reshape((-1, 1))
+
+                x_data = x_data.reshape((-1, num_steps, 1))
+                y_data = y_data.reshape((-1, num_steps, 1))
+
+                dy_loss, last_hidden, last_cell = ptb_model(
+                    x_data, y_data, init_hidden_data, init_cell_data)
+
+                out_loss = dy_loss.numpy()
+                total_loss += out_loss
+                iters += num_steps
+                total_sample += 1
+
+                if step_id % PRINT_STEP == 0:
+                    if step_id == 0:
+                        logging.info(
+                            "epoch %d | step %d, loss %0.3f" %
+                            (epoch_id, step_id, total_loss / total_sample))
+                        avg_batch_time = time.time()
+                    else:
+                        speed = PRINT_STEP / (time.time() - avg_batch_time)
+                        logging.info(
+                            "epoch %d | step %d, loss %0.3f, speed %.3f steps/s"
+                            % (epoch_id, step_id, total_loss / total_sample,
+                               speed))
+                        avg_batch_time = time.time()
+
+            return out_loss, last_hidden.numpy(), last_cell.numpy()
+
+
+class TestPtb(unittest.TestCase):
+    def setUp(self):
+        self.place = fluid.CUDAPlace(0) if fluid.is_compiled_with_cuda() \
+            else fluid.CPUPlace()
+
+    def test_check_result(self):
+        loss_1, hidden_1, cell_1 = train_static(self.place)
+        loss_2, hidden_2, cell_2 = train_dygraph(self.place)
+
+        self.assertTrue(
+            np.allclose(loss_1, loss_2),
+            msg="static loss: {} \ndygraph loss: {}".format(loss_1, loss_2))
+        self.assertTrue(
+            np.allclose(hidden_1, hidden_2),
+            msg="static hidden: {} \ndygraph acc1: {}".format(hidden_1,
+                                                              hidden_2))
+        self.assertTrue(
+            np.allclose(cell_1, cell_2),
+            msg="static cell: {} \ndygraph cell: {}".format(cell_1, cell_2))
+
+
+if __name__ == '__main__':
+    unittest.main()