Update dygraph code

demo/sequence_labeling/sequence_label_dygraph.py

+#coding:utf-8
+import argparse
+import os
+
+import numpy as np
+import paddlehub as hub
+import paddle.fluid as fluid
+from paddle.fluid.dygraph import Linear
+from paddle.fluid.dygraph.base import to_variable
+from paddle.fluid.optimizer import AdamOptimizer
+from paddlehub.finetune.evaluate import chunk_eval, calculate_f1
+
+# yapf: disable
+parser = argparse.ArgumentParser(__doc__)
+parser.add_argument("--num_epoch",          type=int,               default=1,                                  help="Number of epoches for fine-tuning.")
+parser.add_argument("--batch_size",         type=int,               default=16,                                 help="Total examples' number in batch for training.")
+parser.add_argument("--log_interval",       type=int,               default=10,                                 help="log interval.")
+parser.add_argument("--save_interval",      type=int,               default=10,                                 help="save interval.")
+parser.add_argument("--checkpoint_dir",     type=str,               default="paddlehub_finetune_ckpt_dygraph",  help="Path to save log data.")
+parser.add_argument("--max_seq_len",        type=int,               default=512,                                help="Number of words of the longest seqence.")
+# yapf: enable.
+
+
+class TransformerSequenceLabelLayer(fluid.dygraph.Layer):
+    def __init__(self, num_classes, transformer):
+        super(TransformerSequenceLabelLayer, self).__init__()
+        self.num_classes = num_classes
+        self.transformer = transformer
+        self.fc = Linear(input_dim=768, output_dim=num_classes)
+
+    def forward(self, input_ids, position_ids, segment_ids, input_mask):
+        result = self.transformer(input_ids, position_ids, segment_ids,
+                                  input_mask)
+        pred = self.fc(result['sequence_output'])
+        ret_infers = fluid.layers.reshape(
+            x=fluid.layers.argmax(pred, axis=2), shape=[-1, 1])
+        pred = fluid.layers.reshape(pred, shape=[-1, self.num_classes])
+        return fluid.layers.softmax(pred), ret_infers
+
+
+def finetune(args):
+    ernie = hub.Module(name="ernie", max_seq_len=args.max_seq_len)
+    with fluid.dygraph.guard():
+        dataset = hub.dataset.MSRA_NER()
+        ts = TransformerSequenceLabelLayer(
+            num_classes=dataset.num_labels, transformer=ernie)
+        adam = AdamOptimizer(learning_rate=1e-5, parameter_list=ts.parameters())
+        state_dict_path = os.path.join(args.checkpoint_dir,
+                                       'dygraph_state_dict')
+        if os.path.exists(state_dict_path + '.pdparams'):
+            state_dict, _ = fluid.load_dygraph(state_dict_path)
+            ts.load_dict(state_dict)
+
+        reader = hub.reader.SequenceLabelReader(
+            dataset=dataset,
+            vocab_path=ernie.get_vocab_path(),
+            max_seq_len=args.max_seq_len,
+            sp_model_path=ernie.get_spm_path(),
+            word_dict_path=ernie.get_word_dict_path())
+        train_reader = reader.data_generator(
+            batch_size=args.batch_size, phase='train')
+
+        loss_sum = total_infer = total_label = total_correct = cnt = 0
+        # 执行epoch_num次训练
+        for epoch in range(args.num_epoch):
+            # 读取训练数据进行训练
+            for batch_id, data in enumerate(train_reader()):
+                input_ids = np.array(data[0][0]).astype(np.int64)
+                position_ids = np.array(data[0][1]).astype(np.int64)
+                segment_ids = np.array(data[0][2]).astype(np.int64)
+                input_mask = np.array(data[0][3]).astype(np.float32)
+                labels = np.array(data[0][4]).astype(np.int64).reshape(-1, 1)
+                seq_len = np.squeeze(
+                    np.array(data[0][5]).astype(np.int64), axis=1)
+                pred, ret_infers = ts(input_ids, position_ids, segment_ids,
+                                      input_mask)
+
+                loss = fluid.layers.cross_entropy(pred, to_variable(labels))
+                avg_loss = fluid.layers.mean(loss)
+                avg_loss.backward()
+                # 参数更新
+                adam.minimize(avg_loss)
+
+                loss_sum += avg_loss.numpy() * labels.shape[0]
+                label_num, infer_num, correct_num = chunk_eval(
+                    labels, ret_infers.numpy(), seq_len, dataset.num_labels, 1)
+                cnt += labels.shape[0]
+
+                total_infer += infer_num
+                total_label += label_num
+                total_correct += correct_num
+
+                if batch_id % args.log_interval == 0:
+                    precision, recall, f1 = calculate_f1(
+                        total_label, total_infer, total_correct)
+                    print('epoch {}: loss {}, f1 {} recall {} precision {}'.
+                          format(epoch, loss_sum / cnt, f1, recall, precision))
+                    loss_sum = total_infer = total_label = total_correct = cnt = 0
+
+                if batch_id % args.save_interval == 0:
+                    state_dict = ts.state_dict()
+                    fluid.save_dygraph(state_dict, state_dict_path)
+
+
+if __name__ == "__main__":
+    args = parser.parse_args()
+    finetune(args)

demo/text_classification/text_classifier_dygraph.py

@@ -28,10 +28,10 @@ class TransformerClassifier(fluid.dygraph.Layer):
         self.fc = Linear(input_dim=768, output_dim=num_classes)
 
     def forward(self, input_ids, position_ids, segment_ids, input_mask):
-        pooled_output, sequence_output = self.transformer(
-            input_ids, position_ids, segment_ids, input_mask)
+        result = self.transformer(input_ids, position_ids, segment_ids,
+                                  input_mask)
         cls_feats = fluid.layers.dropout(
-            pooled_output,
+            result['pooled_output'],
             dropout_prob=0.1,
             dropout_implementation="upscale_in_train")
         cls_feats = fluid.layers.reshape(cls_feats, shape=[-1, 768])
@@ -40,14 +40,12 @@ class TransformerClassifier(fluid.dygraph.Layer):
 
 
 def finetune(args):
+    ernie = hub.Module(name="ernie", max_seq_len=args.max_seq_len)
     with fluid.dygraph.guard():
-        ernie = hub.Module(name="ernie")
         dataset = hub.dataset.ChnSentiCorp()
         tc = TransformerClassifier(
             num_classes=dataset.num_labels, transformer=ernie)
-        adam = AdamOptimizer(
-            learning_rate=0.001, parameter_list=tc.parameters())
-        print(len(tc.parameters()))
+        adam = AdamOptimizer(learning_rate=1e-5, parameter_list=tc.parameters())
         state_dict_path = os.path.join(args.checkpoint_dir,
                                        'dygraph_state_dict')
         if os.path.exists(state_dict_path + '.pdparams'):

paddlehub/common/paddle_helper.py

@@ -19,10 +19,11 @@ from __future__ import print_function
 
 import copy
 
+import paddle
 import paddle.fluid as fluid
 
 from paddlehub.module import module_desc_pb2
-from paddlehub.common.utils import from_pyobj_to_module_attr, from_module_attr_to_pyobj
+from paddlehub.common.utils import from_pyobj_to_module_attr, from_module_attr_to_pyobj, version_compare
 from paddlehub.common.logger import logger
 
 dtype_map = {
@@ -62,7 +63,8 @@ def get_variable_info(var):
         var_info['trainable'] = var.trainable
         var_info['optimize_attr'] = var.optimize_attr
         var_info['regularizer'] = var.regularizer
-        var_info['gradient_clip_attr'] = var.gradient_clip_attr
+        if not version_compare(paddle.__version__, '1.8'):
+            var_info['gradient_clip_attr'] = var.gradient_clip_attr
         var_info['do_model_average'] = var.do_model_average
     else:
         var_info['persistable'] = var.persistable

paddlehub/module/module.py

@@ -146,12 +146,6 @@ class Module(fluid.dygraph.Layer):
         self._initialize(**kwargs)
         self._is_initialize = True
         self._code_version = "v2"
-        self.model_runner = fluid.dygraph.StaticModelRunner(
-            self.pretrained_model_path)
-
-    @property
-    def pretrained_model_path(self):
-        return self.default_pretrained_model_path
 
     def _get_func_name(self, current_cls, module_func_dict):
         mod = current_cls.__module__ + "." + current_cls.__name__
@@ -256,7 +250,8 @@ class Module(fluid.dygraph.Layer):
         pass
 
     def forward(self, *args, **kwargs):
-        return self.model_runner(*args, **kwargs)
+        raise RuntimeError('{} does not support dynamic graph mode yet.'.format(
+            self.name))
 
 
 class ModuleHelper(object):

paddlehub/module/nlp_module.py

@@ -24,13 +24,15 @@ import os
 import re
 import six
 
+import paddle
 import numpy as np
 import paddle.fluid as fluid
-from paddlehub.common import paddle_helper
-from paddle.fluid.core import PaddleTensor, AnalysisConfig, create_paddle_predictor
+
 import paddlehub as hub
+from paddle.fluid.core import PaddleTensor, AnalysisConfig, create_paddle_predictor
+from paddlehub.common import paddle_helper, tmp_dir
 from paddlehub.common.logger import logger
-from paddlehub.common.utils import sys_stdin_encoding
+from paddlehub.common.utils import sys_stdin_encoding, version_compare
 from paddlehub.io.parser import txt_parser
 from paddlehub.module.module import runnable
 
@@ -246,9 +248,44 @@ class TransformerModule(NLPBaseModule):
     Tranformer Module base class can be used by BERT, ERNIE, RoBERTa and so on.
     """
 
-    @property
-    def pretrained_model_path(self):
-        return self.params_path
+    def __init__(self,
+                 name=None,
+                 directory=None,
+                 module_dir=None,
+                 version=None,
+                 max_seq_len=128,
+                 **kwargs):
+        if not directory:
+            return
+        super(TransformerModule, self).__init__(
+            name=name,
+            directory=directory,
+            module_dir=module_dir,
+            version=version,
+            **kwargs)
+
+        self.max_seq_len = max_seq_len
+        if version_compare(paddle.__version__, '1.8.0'):
+            with tmp_dir() as _dir:
+                input_dict, output_dict, program = self.context(
+                    max_seq_len=max_seq_len)
+                fluid.io.save_inference_model(
+                    dirname=_dir,
+                    main_program=program,
+                    feeded_var_names=[
+                        input_dict['input_ids'].name,
+                        input_dict['position_ids'].name,
+                        input_dict['segment_ids'].name,
+                        input_dict['input_mask'].name
+                    ],
+                    target_vars=[
+                        output_dict["pooled_output"],
+                        output_dict["sequence_output"]
+                    ],
+                    executor=fluid.Executor(fluid.CPUPlace()))
+
+                with fluid.dygraph.guard():
+                    self.model_runner = fluid.dygraph.StaticModelRunner(_dir)
 
     def init_pretraining_params(self, exe, pretraining_params_path,
                                 main_program):
@@ -275,7 +312,7 @@ class TransformerModule(NLPBaseModule):
 
     def context(
             self,
-            max_seq_len=128,
+            max_seq_len=None,
             trainable=True,
     ):
         """
@@ -291,6 +328,9 @@ class TransformerModule(NLPBaseModule):
 
         """
 
+        if not max_seq_len:
+            max_seq_len = self.max_seq_len
+
         assert max_seq_len <= self.MAX_SEQ_LEN and max_seq_len >= 1, "max_seq_len({}) should be in the range of [1, {}]".format(
             max_seq_len, self.MAX_SEQ_LEN)
 
@@ -357,14 +397,6 @@ class TransformerModule(NLPBaseModule):
 
         return inputs, outputs, module_program
 
-
-#     @property
-#     def model_runner(self):
-#         if not self._model_runner:
-#             self._model_runner = fluid.dygraph.StaticModelRunner(
-#                 self.params_path)
-#         return self._model_runner
-
     def get_embedding(self, texts, use_gpu=False, batch_size=1):
         """
         get pooled_output and sequence_output for input texts.
@@ -443,3 +475,16 @@ class TransformerModule(NLPBaseModule):
                 "The module context has not been initialized. "
                 "Please call context() before using get_params_layer")
         return self.params_layer
+
+    def forward(self, input_ids, position_ids, segment_ids, input_mask):
+        if version_compare(paddle.__version__, '1.8.0'):
+            pooled_output, sequence_output = self.model_runner(
+                input_ids, position_ids, segment_ids, input_mask)
+            return {
+                'pooled_output': pooled_output,
+                'sequence_output': sequence_output
+            }
+        else:
+            raise RuntimeError(
+                '{} only support dynamic graph mode in paddle >= 1.8.0'.format(
+                    self.name))