!792 [Model][bert] Modify ST test script of bert model.

Merge pull request !792 from wsc/lossscale_script

example/bert_clue/README.md

@@ -78,7 +78,7 @@ It contains of parameters of BERT model and options for training, which is set i
 ### Options:
 ```
 Pre-Training:
-    bert_network                    version of BERT model: base | large, default is base
+    bert_network                    version of BERT model: base | nezha, default is base
     loss_scale_value                initial value of loss scale: N, default is 2^32
     scale_factor                    factor used to update loss scale: N, default is 2
     scale_window                    steps for once updatation of loss scale: N, default is 1000   

example/bert_clue/config.py

@@ -26,30 +26,36 @@ cfg = edict({
     'optimizer': 'Lamb',
     'AdamWeightDecayDynamicLR': edict({
         'learning_rate': 3e-5,
-        'end_learning_rate': 0.0,
+        'end_learning_rate': 1e-7,
         'power': 5.0,
         'weight_decay': 1e-5,
         'eps': 1e-6,
     }),
     'Lamb': edict({
         'start_learning_rate': 3e-5,
-        'end_learning_rate': 0.0,
+        'end_learning_rate': 1e-7,
         'power': 10.0,
         'warmup_steps': 10000,
         'weight_decay': 0.01,
         'eps': 1e-6,
-        'decay_filter': lambda x: False,
     }),
     'Momentum': edict({
         'learning_rate': 2e-5,
         'momentum': 0.9,
     }),
 })
+
+'''
+Including two kinds of network: \
+base: Goole BERT-base(the base version of BERT model).
+large: BERT-NEZHA(a Chinese pretrained language model developed by Huawei, which introduced a improvement of \
+       Functional Relative Posetional Encoding as an effective positional encoding scheme).
+'''
 if cfg.bert_network == 'base':
     bert_net_cfg = BertConfig(
-        batch_size=16,
+        batch_size=32,
         seq_length=128,
-        vocab_size=21136,
+        vocab_size=21128,
         hidden_size=768,
         num_hidden_layers=12,
         num_attention_heads=12,
@@ -66,13 +72,13 @@ if cfg.bert_network == 'base':
         dtype=mstype.float32,
         compute_type=mstype.float16,
     )
-else:
+if cfg.bert_network == 'nezha':
     bert_net_cfg = BertConfig(
-        batch_size=16,
+        batch_size=32,
         seq_length=128,
-        vocab_size=21136,
+        vocab_size=21128,
         hidden_size=1024,
-        num_hidden_layers=12,
+        num_hidden_layers=24,
         num_attention_heads=16,
         intermediate_size=4096,
         hidden_act="gelu",

example/bert_clue/dataset.py

@@ -31,7 +31,7 @@ def create_bert_dataset(epoch_size=1, device_num=1, rank=0, do_shuffle="true", e
     files = os.listdir(data_dir)
     data_files = []
     for file_name in files:
-        data_files.append(data_dir+file_name)
+        data_files.append(os.path.join(data_dir, file_name))
     ds = de.TFRecordDataset(data_files, schema_dir,
                             columns_list=["input_ids", "input_mask", "segment_ids", "next_sentence_labels",
                                           "masked_lm_positions", "masked_lm_ids", "masked_lm_weights"],

example/bert_clue/run_distribute_pretrain.sh

@@ -16,17 +16,15 @@
 
 echo "=============================================================================================================="
 echo "Please run the scipt as: "
-echo "sh run_distribute_pretrain.sh DEVICE_NUM EPOCH_SIZE DATA_DIR SCHEMA_DIR MINDSPORE_HCCL_CONFIG_PATH MINDSPORE_PATH"
-echo "for example: sh run_distribute_pretrain.sh 8 40 /path/zh-wiki/ /path/Schema.json /path/hccl.json /path/mindspore"
+echo "sh run_distribute_pretrain.sh DEVICE_NUM EPOCH_SIZE DATA_DIR SCHEMA_DIR MINDSPORE_HCCL_CONFIG_PATH"
+echo "for example: sh run_distribute_pretrain.sh 8 40 /path/zh-wiki/ /path/Schema.json /path/hccl.json"
 echo "It is better to use absolute path."
 echo "=============================================================================================================="
 
 EPOCH_SIZE=$2
 DATA_DIR=$3
 SCHEMA_DIR=$4
-MINDSPORE_PATH=$6
 
-export PYTHONPATH=$MINDSPORE_PATH/build/package:$PYTHONPATH
 export MINDSPORE_HCCL_CONFIG_PATH=$5
 export RANK_SIZE=$1
 

example/bert_clue/run_standalone_pretrain.sh

@@ -16,16 +16,14 @@
 
 echo "=============================================================================================================="
 echo "Please run the scipt as: "
-echo "sh run_standalone_pretrain.sh DEVICE_ID EPOCH_SIZE DATA_DIR SCHEMA_DIR MINDSPORE_PATH"
-echo "for example: sh run_standalone_pretrain.sh 0 40 /path/zh-wiki/ /path/Schema.json /path/mindspore"
+echo "sh run_standalone_pretrain.sh DEVICE_ID EPOCH_SIZE DATA_DIR SCHEMA_DIR"
+echo "for example: sh run_standalone_pretrain.sh 0 40 /path/zh-wiki/ /path/Schema.json"
 echo "=============================================================================================================="
 
 DEVICE_ID=$1
 EPOCH_SIZE=$2
 DATA_DIR=$3
 SCHEMA_DIR=$4
-MINDSPORE_PATH=$5
-export PYTHONPATH=$MINDSPORE_PATH/build/package:$PYTHONPATH
 
 python run_pretrain.py  \
     --distribute="false" \

tests/st/networks/models/bert/bert_tdt_lossscale.py

@@ -135,9 +135,10 @@ class ModelCallback(Callback):
 
     def step_end(self, run_context):
         cb_params = run_context.original_args()
-        self.loss_list.append(cb_params.net_outputs[0])
+        self.loss_list.append(cb_params.net_outputs[0].asnumpy()[0])
         self.overflow_list.append(cb_params.net_outputs[1])
         self.lossscale_list.append(cb_params.net_outputs[2])
+        print("epoch: {}, outputs are: {}".format(cb_params.cur_epoch_num, str(cb_params.net_outputs)))
 
 @pytest.mark.level0
 @pytest.mark.platform_arm_ascend_training
@@ -192,7 +193,11 @@ def test_bert_tdt():
             if count == scale_window:
                 count = 0
                 assert callback.lossscale_list[i] == callback.lossscale_list[i - 1] * Tensor(2.0, mstype.float32)
-
+    # assertion occurs while the loss value is wrong
+    loss_value = np.array(callback.loss_list)
+    expect_value = [12.1918125, 11.966035, 11.972114, 11.982671, 11.976399, 12.616986, 12.180658, 12.850562, 12.415608, 12.640145]
+    print("loss value: {}".format(loss_value))
+    assert np.allclose(loss_value, expect_value, 0.00001, 0.00001)
 
 if __name__ == '__main__':
     test_bert_tdt()

tests/st/networks/models/bert/bert_tdt_no_lossscale.py

-# Copyright 2020 Huawei Technologies Co., Ltd
-#
-# 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.
-# ============================================================================
-
-"""train bert network without lossscale"""
-
-import os
-import pytest
-import numpy as np
-import mindspore.context as context
-import mindspore.common.dtype as mstype
-import mindspore.dataset.engine.datasets as de
-import mindspore.dataset.transforms.c_transforms as C
-from mindspore import Tensor
-from mindspore.train.model import Model
-from mindspore.train.callback import Callback
-from mindspore.model_zoo.Bert_NEZHA import BertConfig, BertNetworkWithLoss, BertTrainOneStepCell
-from mindspore.nn.optim import Momentum
-from mindspore import log as logger
-
-_current_dir = os.path.dirname(os.path.realpath(__file__))
-DATA_DIR = ["/home/workspace/mindspore_dataset/bert/example/examples.tfrecord"]
-SCHEMA_DIR = "/home/workspace/mindspore_dataset/bert/example/datasetSchema.json"
-
-
-def get_config(version='base', batch_size=1):
-    """get config"""
-    if version == 'base':
-        bert_config = BertConfig(
-            batch_size=batch_size,
-            seq_length=128,
-            vocab_size=21136,
-            hidden_size=768,
-            num_hidden_layers=2,
-            num_attention_heads=12,
-            intermediate_size=3072,
-            hidden_act="gelu",
-            hidden_dropout_prob=0.1,
-            attention_probs_dropout_prob=0.1,
-            max_position_embeddings=512,
-            type_vocab_size=2,
-            initializer_range=0.02,
-            use_relative_positions=True,
-            input_mask_from_dataset=True,
-            token_type_ids_from_dataset=True,
-            dtype=mstype.float32,
-            compute_type=mstype.float32)
-    elif version == 'large':
-        bert_config = BertConfig(
-            batch_size=batch_size,
-            seq_length=128,
-            vocab_size=21136,
-            hidden_size=1024,
-            num_hidden_layers=2,
-            num_attention_heads=16,
-            intermediate_size=4096,
-            hidden_act="gelu",
-            hidden_dropout_prob=0.0,
-            attention_probs_dropout_prob=0.0,
-            max_position_embeddings=512,
-            type_vocab_size=2,
-            initializer_range=0.02,
-            use_relative_positions=True,
-            input_mask_from_dataset=True,
-            token_type_ids_from_dataset=True,
-            dtype=mstype.float32,
-            compute_type=mstype.float16)
-    elif version == 'large_mixed':
-        bert_config = BertConfig(
-            batch_size=batch_size,
-            seq_length=128,
-            vocab_size=21136,
-            hidden_size=1024,
-            num_hidden_layers=24,
-            num_attention_heads=16,
-            intermediate_size=4096,
-            hidden_act="gelu",
-            hidden_dropout_prob=0.0,
-            attention_probs_dropout_prob=0.0,
-            max_position_embeddings=512,
-            type_vocab_size=2,
-            initializer_range=0.02,
-            use_relative_positions=True,
-            input_mask_from_dataset=True,
-            token_type_ids_from_dataset=True,
-            dtype=mstype.float32,
-            compute_type=mstype.float32)
-    else:
-        bert_config = BertConfig(batch_size=batch_size)
-    return bert_config
-
-
-def me_de_train_dataset():
-    """test me de train dataset"""
-    # apply repeat operations
-    repeat_count = 1
-    ds = de.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["input_ids", "input_mask", "segment_ids",
-                                                                "next_sentence_labels", "masked_lm_positions",
-                                                                "masked_lm_ids", "masked_lm_weights"], shuffle=False)
-    type_cast_op = C.TypeCast(mstype.int32)
-    ds = ds.map(input_columns="masked_lm_ids", operations=type_cast_op)
-    ds = ds.map(input_columns="masked_lm_positions", operations=type_cast_op)
-    ds = ds.map(input_columns="next_sentence_labels", operations=type_cast_op)
-    ds = ds.map(input_columns="segment_ids", operations=type_cast_op)
-    ds = ds.map(input_columns="input_mask", operations=type_cast_op)
-    ds = ds.map(input_columns="input_ids", operations=type_cast_op)
-    # apply batch operations
-    batch_size = 16
-    ds = ds.batch(batch_size, drop_remainder=True)
-    ds = ds.repeat(repeat_count)
-    return ds
-
-
-def weight_variable(shape):
-    """weight variable"""
-    np.random.seed(1)
-    ones = np.random.uniform(-0.1, 0.1, size=shape).astype(np.float32)
-    return Tensor(ones)
-
-
-class ModelCallback(Callback):
-    def __init__(self):
-        super(ModelCallback, self).__init__()
-        self.loss_list = []
-
-    def step_end(self, run_context):
-        cb_params = run_context.original_args()
-        self.loss_list.append(cb_params.net_outputs.asnumpy()[0])
-        logger.info("epoch: {}, outputs are {}".format(cb_params.cur_epoch_num, str(cb_params.net_outputs)))
-
-
-@pytest.mark.level0
-@pytest.mark.platform_arm_ascend_training
-@pytest.mark.platform_x86_ascend_training
-@pytest.mark.env_onecard
-def test_bert_tdt():
-    """test bert tdt"""
-    context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", reserve_class_name_in_scope=False)
-    context.set_context(enable_task_sink=True)
-    context.set_context(enable_loop_sink=True)
-    context.set_context(enable_mem_reuse=True)
-    parallel_callback = ModelCallback()
-    ds = me_de_train_dataset()
-    version = os.getenv('VERSION', 'large')
-    batch_size = int(os.getenv('BATCH_SIZE', '16'))
-    config = get_config(version=version, batch_size=batch_size)
-    netwithloss = BertNetworkWithLoss(config, True)
-    optimizer = Momentum(netwithloss.trainable_params(), learning_rate=2e-5, momentum=0.9)
-    netwithgrads = BertTrainOneStepCell(netwithloss, optimizer=optimizer)
-    netwithgrads.set_train(True)
-    model = Model(netwithgrads)
-    params = netwithloss.trainable_params()
-    for param in params:
-        value = param.default_input
-        name = param.name
-        if isinstance(value, Tensor):
-            if name.split('.')[-1] in ['weight']:
-                if name.split('.')[-3] in ['cls2']:
-                    logger.info("***************** BERT param name is 1 {}".format(name))
-                    param.default_input = weight_variable(value.asnumpy().shape)
-                else:
-                    logger.info("***************** BERT param name is 2 {}".format(name))
-                    tempshape = value.asnumpy().shape
-                    shape = (tempshape[1], tempshape[0])
-                    weight_value = weight_variable(shape).asnumpy()
-                    param.default_input = Tensor(np.transpose(weight_value, [1, 0]))
-            else:
-                logger.info("***************** BERT param name is 3 {}".format(name))
-                param.default_input = weight_variable(value.asnumpy().shape)
-    model.train(ds.get_repeat_count(), ds, callbacks=parallel_callback, dataset_sink_mode=False)
-    loss_value = np.array(parallel_callback.loss_list)
-    expect_out = [12.19179, 11.965041, 11.969687, 11.97815, 11.969171, 12.603289, 12.165594,
-                  12.824818, 12.38842, 12.604046]
-    logger.info("expected loss value output: {}".format(expect_out))
-    assert np.allclose(loss_value, expect_out, 0.00001, 0.00001)
-
-
-if __name__ == '__main__':
-    test_bert_tdt()