Merge pull request #15 from xixiaoyao/master

fix bugs

config_demo2.yaml

@@ -12,7 +12,7 @@ do_lower_case: True
 max_seq_len: 512
 
 batch_size: 4
-num_epochs: 2
+num_epochs: 0.5
 optimizer: "adam"
 learning_rate: 3e-5
 warmup_proportion: 0.1

paddlepalm/mtl_controller.py

@@ -398,7 +398,7 @@ class Controller(object):
 
         # merge reader input attrs from backbone and task_instances
         joint_input_names, joint_shape_and_dtypes, name_to_position = merge_input_attrs(train_backbone.inputs_attr, task_attrs)
-        pred_joint_input_names, pred_joint_shape_and_dtypes, _ = merge_input_attrs(pred_backbone.inputs_attr, pred_task_attrs, insert_taskid=False)
+        pred_joint_input_names, pred_joint_shape_and_dtypes, _ = merge_input_attrs(pred_backbone.inputs_attr, pred_task_attrs, insert_taskid=False, insert_batchsize=False, insert_seqlen=False, insert_batchsize_x_seqlen=False)
         # shapes: [task_id, shapes_of_backbone, shapes_of_inst1, ..., shapes_of_instN]
 
         if DEBUG:
@@ -448,6 +448,7 @@ class Controller(object):
             pred_net_inputs = create_net_inputs(pred_input_attrs)
             # 别用unique_name.guard了，没用的，无法作用到param_attr里的name上
             # with fluid.unique_name.guard("backbone-"):
+
             pred_bb_output_vars = pred_backbone.build(pred_net_inputs, scope_name='__paddlepalm_')
 
         fluid.framework.switch_main_program(train_prog)

paddlepalm/task_instance.py

@@ -87,7 +87,11 @@ class TaskInstance(object):
         dirpath = self._save_infermodel_path + suffix
         self._pred_input_varname_list = [str(i) for i in self._pred_input_varname_list]
 
-        fluid.io.save_inference_model(dirpath, self._pred_input_varname_list, self._pred_fetch_var_list, self._exe)
+        # del self._pred_input_varname_list[0]
+        # del self._pred_input_varname_list[0]
+        # del self._pred_input_varname_list[0]
+
+        fluid.io.save_inference_model(dirpath, self._pred_input_varname_list, self._pred_fetch_var_list, self._exe, export_for_deployment = True)
         # fluid.io.save_inference_model(dirpath, self._pred_input_varname_list, self._pred_fetch_var_list, self._exe, params_filename='__params__')
         print(self._name + ': inference model saved at ' + dirpath)
 

paddlepalm/task_paradigm/mlm.py

@@ -34,7 +34,6 @@ class TaskParadigm(task_paradigm):
     def inputs_attrs(self):
         reader = {
             "mask_label": [[-1, 1], 'int64'],
-            "batchsize_x_seqlen": [[1], 'int64'],
             "mask_pos": [[-1, 1], 'int64']}
         if not self._is_training:
             del reader['mask_label']
@@ -56,6 +55,7 @@ class TaskParadigm(task_paradigm):
             mask_label = inputs["reader"]["mask_label"] 
             # 多任务学习时才需要引入这个，防止其他run其他任务时导致seqlen过小，gather超范围
             batchsize_x_seqlen = inputs["reader"]["batchsize_x_seqlen"] 
+
         mask_pos = inputs["reader"]["mask_pos"] 
         word_emb = inputs["backbone"]["embedding_table"]
         enc_out = inputs["backbone"]["encoder_outputs"]
@@ -67,7 +67,7 @@ class TaskParadigm(task_paradigm):
 
         if self._is_training:
             # 多任务训练时才需要引入这个，防止其他run其他任务时导致seqlen过小，gather超范围
-            #mask_pos = fluid.layers.cast(x=mask_pos, dtype='int32')
+            # mask_pos = fluid.layers.cast(x=mask_pos, dtype='int32')
             mask_pos = fluid.layers.elementwise_min(mask_pos, batchsize_x_seqlen)
 
         #print(fluid.default_main_program().blocks[0].vars)

paddlepalm/task_paradigm/mrc.py

@@ -50,7 +50,8 @@ class TaskParadigm(task_paradigm):
     def inputs_attrs(self):
         if self._is_training:
             reader = {"start_positions": [[-1, 1], 'int64'],
-                      "end_positions": [[-1, 1], 'int64']}
+                      "end_positions": [[-1, 1], 'int64'],
+                      }
         else:
             reader = {'unique_ids': [[-1, 1], 'int64']}
         bb = {"encoder_outputs": [[-1, -1, self._hidden_size], 'float32']}
@@ -76,6 +77,9 @@ class TaskParadigm(task_paradigm):
         if self._is_training:
             start_positions = inputs['reader']['start_positions']
             end_positions = inputs['reader']['end_positions']
+            seqlen = inputs["reader"]["seqlen"] 
+            start_positions = fluid.layers.elementwise_min(start_positions, seqlen)
+            end_positions = fluid.layers.elementwise_min(end_positions, seqlen)
         else:
             unique_id = inputs['reader']['unique_ids']
 

paddlepalm/utils/reader_helper.py

@@ -166,21 +166,21 @@ def create_joint_iterator_fn(iterators, iterator_prefixes, joint_shape_and_dtype
                 else:
                     outputs = next(iterators[id]) # dict type
 
-                # if 'token_ids' in outputs:
-                #     val1 = len(outputs['token_ids'])
-                #     val = _check_and_adapt_shape_dtype([val1], [[1], 'int64'])
-                #     results[outname_to_pos['batch_size']] = val
-
-                #     val2 = len(outputs['token_ids'][0])
-                #     val = _check_and_adapt_shape_dtype([val2], [[1], 'int64'])
-                #     results[outname_to_pos['seqlen']] = val
-
-                #     val = _check_and_adapt_shape_dtype([val1*val2], [[1], 'int64'])
-                #     results[outname_to_pos['batchsize_x_seqlen']] = val
-                # else:
-                #     if not has_show_warn:
-                #         print('WARNING: token_ids not found in current batch, failed to yield batch_size, seqlen and batchsize_x_seqlen. (This message would be shown only once.)')
-                #         has_show_warn = True
+                if 'token_ids' in outputs:
+                    val1 = len(outputs['token_ids'])
+                    val = _check_and_adapt_shape_dtype([val1], [[1], 'int64'])
+                    results[outname_to_pos['batch_size']] = val
+
+                    val2 = len(outputs['token_ids'][0])
+                    val = _check_and_adapt_shape_dtype([val2], [[1], 'int64'])
+                    results[outname_to_pos['seqlen']] = val
+
+                    val = _check_and_adapt_shape_dtype([val1*val2], [[1], 'int64'])
+                    results[outname_to_pos['batchsize_x_seqlen']] = val
+                else:
+                    if not has_show_warn:
+                        print('WARNING: token_ids not found in current batch, failed to yield batch_size, seqlen and batchsize_x_seqlen. (This message would be shown only once.)')
+                        has_show_warn = True
 
                 prefix = iterator_prefixes[id]
                 for outname, val in outputs.items():
@@ -214,7 +214,7 @@ def create_joint_iterator_fn(iterators, iterator_prefixes, joint_shape_and_dtype
     return iterator
 
 
-def merge_input_attrs(backbone_attr, task_attrs, insert_taskid=True, insert_batchsize=False, insert_seqlen=False, insert_batchsize_x_seqlen=False):
+def merge_input_attrs(backbone_attr, task_attrs, insert_taskid=True, insert_batchsize=True, insert_seqlen=True, insert_batchsize_x_seqlen=True):
     """
     Args:
         task_attrs(list[dict]|dict): task input attributes, key=attr_name, val=[shape, dtype], support single task and nested tasks
@@ -242,7 +242,7 @@ def merge_input_attrs(backbone_attr, task_attrs, insert_taskid=True, insert_batc
 
     if insert_batchsize_x_seqlen:
         ret.append(([1], 'int64'))
-        names.append('batchsize_x_seqlen')
+        names.append(u'batchsize_x_seqlen')
         start += 1
         
     names += sorted(backbone_attr.keys())

run_demo2.sh

-export CUDA_VISIBLE_DEVICES=0
-
+export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
 python -u demo2.py 
 # GLOG_vmodule=lookup_table_op=4 python -u demo2.py > debug2.log 2>&1