!1398 Update the bert scripts according to rules of modelzoo

Merge pull request !1398 from chenhaozhe/update_bert_script

mindspore/ops/_grad/grad_nn_ops.py

@@ -308,7 +308,7 @@ def get_bprop_softmax(self):
     axis = self.axis
 
     def bprop(x, out, dout):
-        dx = mul(sub(dout, sum_func(mul(dout, out), axis)), out)
+        dx = mul(out, sub(dout, sum_func(mul(out, dout), axis)))
         return (dx,)
 
     return bprop

model_zoo/bert/README.md

@@ -16,12 +16,12 @@ This example implements pre-training, fine-tuning and evaluation of [BERT-base](
 - Run `run_standalone_pretrain.sh` for non-distributed pre-training of BERT-base and BERT-NEZHA model.
 
     ``` bash   
-    sh run_standalone_pretrain.sh DEVICE_ID EPOCH_SIZE DATA_DIR SCHEMA_DIR
+    sh scripts/run_standalone_pretrain.sh DEVICE_ID EPOCH_SIZE DATA_DIR SCHEMA_DIR
     ```
 - Run `run_distribute_pretrain.sh` for distributed pre-training of BERT-base and BERT-NEZHA model.
 
     ``` bash   
-    sh run_distribute_pretrain.sh DEVICE_NUM EPOCH_SIZE DATA_DIR SCHEMA_DIR MINDSPORE_HCCL_CONFIG_PATH
+    sh scripts/run_distribute_pretrain.sh DEVICE_NUM EPOCH_SIZE DATA_DIR SCHEMA_DIR MINDSPORE_HCCL_CONFIG_PATH
     ```  
 
 ### Fine-Tuning

model_zoo/bert/evaluation.py

@@ -19,8 +19,6 @@ Bert evaluation script.
 
 import os
 import numpy as np
-from evaluation_config import cfg, bert_net_cfg
-from utils import BertNER, BertCLS
 import mindspore.common.dtype as mstype
 from mindspore import context
 from mindspore.common.tensor import Tensor
@@ -28,9 +26,11 @@ import mindspore.dataset as de
 import mindspore.dataset.transforms.c_transforms as C
 from mindspore.train.model import Model
 from mindspore.train.serialization import load_checkpoint, load_param_into_net
-from CRF import postprocess
-from cluener_evaluation import submit
-from finetune_config import tag_to_index
+from src.evaluation_config import cfg, bert_net_cfg
+from src.utils import BertNER, BertCLS
+from src.CRF import postprocess
+from src.cluener_evaluation import submit
+from src.finetune_config import tag_to_index
 
 class Accuracy():
     '''

model_zoo/bert/finetune.py

@@ -18,8 +18,8 @@ Bert finetune script.
 '''
 
 import os
-from utils import BertFinetuneCell, BertCLS, BertNER
-from finetune_config import cfg, bert_net_cfg, tag_to_index
+from src.utils import BertFinetuneCell, BertCLS, BertNER
+from src.finetune_config import cfg, bert_net_cfg, tag_to_index
 import mindspore.common.dtype as mstype
 import mindspore.communication.management as D
 from mindspore import context

model_zoo/bert/run_pretrain.py

@@ -26,10 +26,10 @@ from mindspore.train.parallel_utils import ParallelMode
 from mindspore.nn.wrap.loss_scale import DynamicLossScaleUpdateCell
 from mindspore.train.callback import Callback, ModelCheckpoint, CheckpointConfig, TimeMonitor
 from mindspore.train.serialization import load_checkpoint, load_param_into_net
-from mindspore.model_zoo.Bert_NEZHA import BertNetworkWithLoss, BertTrainOneStepCell, BertTrainOneStepWithLossScaleCell
 from mindspore.nn.optim import Lamb, Momentum, AdamWeightDecayDynamicLR
-from dataset import create_bert_dataset
-from config import cfg, bert_net_cfg
+from src import BertNetworkWithLoss, BertTrainOneStepCell, BertTrainOneStepWithLossScaleCell
+from src.dataset import create_bert_dataset
+from src.config import cfg, bert_net_cfg
 _current_dir = os.path.dirname(os.path.realpath(__file__))
 
 class LossCallBack(Callback):
@@ -48,10 +48,8 @@ class LossCallBack(Callback):
         self._per_print_times = per_print_times
     def step_end(self, run_context):
         cb_params = run_context.original_args()
-        with open("./loss.log", "a+") as f:
-            f.write("epoch: {}, step: {}, outputs are {}".format(cb_params.cur_epoch_num, cb_params.cur_step_num,
+        print("epoch: {}, step: {}, outputs are {}".format(cb_params.cur_epoch_num, cb_params.cur_step_num,
                                                            str(cb_params.net_outputs)))
-            f.write('\n')
 
 def run_pretrain():
     """pre-train bert_clue"""
@@ -81,6 +79,11 @@ def run_pretrain():
         context.reset_auto_parallel_context()
         context.set_auto_parallel_context(parallel_mode=ParallelMode.DATA_PARALLEL, mirror_mean=True,
                                           device_num=device_num)
+        from mindspore.parallel._auto_parallel_context import auto_parallel_context
+        if bert_net_cfg.num_hidden_layers == 12:
+            auto_parallel_context().set_all_reduce_fusion_split_indices([28, 55, 82, 109, 136, 163, 190, 205])
+        elif bert_net_cfg.num_hidden_layers == 24:
+            auto_parallel_context().set_all_reduce_fusion_split_indices([38, 93, 148, 203, 258, 313, 368, 397])
         D.init()
         rank = args_opt.device_id % device_num
     else:

model_zoo/bert/scripts/run_distribute_pretrain.sh

@@ -16,8 +16,8 @@
 
 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"
-echo "for example: sh run_distribute_pretrain.sh 8 40 /path/zh-wiki/ /path/Schema.json /path/hccl.json"
+echo "bash run_distribute_pretrain.sh DEVICE_NUM EPOCH_SIZE DATA_DIR SCHEMA_DIR MINDSPORE_HCCL_CONFIG_PATH"
+echo "for example: bash run_distribute_pretrain.sh 8 40 /path/zh-wiki/ /path/Schema.json /path/hccl.json"
 echo "It is better to use absolute path."
 echo "=============================================================================================================="
 
@@ -49,6 +49,10 @@ do
     cp  *.py ./LOG$i
     cd ./LOG$i || exit
     echo "start training for rank $i, device $DEVICE_ID"
+    mkdir -p ms_log
+    CUR_DIR=`pwd`
+    export GLOG_log_dir=${CUR_DIR}/ms_log
+    export GLOG_logtostderr=0
     env > env.log
     taskset -c $cmdopt python ../run_pretrain.py  \
     --distribute="true" \
@@ -59,7 +63,7 @@ do
     --enable_lossscale="true" \
     --do_shuffle="true" \
     --enable_data_sink="true" \
-    --data_sink_steps=1 \
+    --data_sink_steps=100 \
     --checkpoint_path="" \
     --save_checkpoint_steps=10000 \
     --save_checkpoint_num=1 \

model_zoo/bert/scripts/run_standalone_pretrain.sh

@@ -16,8 +16,8 @@
 
 echo "=============================================================================================================="
 echo "Please run the scipt as: "
-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 "bash run_standalone_pretrain.sh DEVICE_ID EPOCH_SIZE DATA_DIR SCHEMA_DIR"
+echo "for example: bash run_standalone_pretrain.sh 0 40 /path/zh-wiki/ /path/Schema.json"
 echo "=============================================================================================================="
 
 DEVICE_ID=$1
@@ -25,6 +25,10 @@ EPOCH_SIZE=$2
 DATA_DIR=$3
 SCHEMA_DIR=$4
 
+mkdir -p ms_log 
+CUR_DIR=`pwd`
+export GLOG_log_dir=${CUR_DIR}/ms_log
+export GLOG_logtostderr=0
 python run_pretrain.py  \
     --distribute="false" \
     --epoch_size=$EPOCH_SIZE \
@@ -33,7 +37,7 @@ python run_pretrain.py  \
     --enable_lossscale="true" \
     --do_shuffle="true" \
     --enable_data_sink="true" \
-    --data_sink_steps=1 \
+    --data_sink_steps=100 \
     --checkpoint_path="" \
     --save_checkpoint_steps=10000 \
     --save_checkpoint_num=1 \

model_zoo/bert/src/bert_for_pre_training.py

@@ -357,10 +357,10 @@ class BertTrainOneStepWithLossScaleCell(nn.Cell):
         if self.parallel_mode in [ParallelMode.DATA_PARALLEL, ParallelMode.HYBRID_PARALLEL]:
             self.reducer_flag = True
         self.grad_reducer = F.identity
+        self.degree = 1
         if self.reducer_flag:
-            mean = context.get_auto_parallel_context("mirror_mean")
-            degree = get_group_size()
-            self.grad_reducer = DistributedGradReducer(optimizer.parameters, mean, degree)
+            self.degree = get_group_size()
+            self.grad_reducer = DistributedGradReducer(optimizer.parameters, False, self.degree)
         self.is_distributed = (self.parallel_mode != ParallelMode.STAND_ALONE)
         self.cast = P.Cast()
         self.alloc_status = P.NPUAllocFloatStatus()
@@ -411,10 +411,10 @@ class BertTrainOneStepWithLossScaleCell(nn.Cell):
                                                  masked_lm_weights,
                                                  self.cast(scaling_sens,
                                                            mstype.float32))
-        grads = self.hyper_map(F.partial(grad_scale, scaling_sens), grads)
-        grads = self.hyper_map(F.partial(clip_grad, GRADIENT_CLIP_TYPE, GRADIENT_CLIP_VALUE), grads)
         # apply grad reducer on grads
         grads = self.grad_reducer(grads)
+        grads = self.hyper_map(F.partial(grad_scale, scaling_sens * self.degree), grads)
+        grads = self.hyper_map(F.partial(clip_grad, GRADIENT_CLIP_TYPE, GRADIENT_CLIP_VALUE), grads)
         self.get_status(init)
         flag_sum = self.reduce_sum(init, (0,))
         if self.is_distributed:

model_zoo/bert/src/bert_model.py

@@ -25,6 +25,7 @@ from mindspore.ops import operations as P
 from mindspore.ops import composite as C
 from mindspore.common.tensor import Tensor
 from mindspore.common.parameter import Parameter
+from .fused_layer_norm import FusedLayerNorm
 
 
 class BertConfig:
@@ -77,7 +78,8 @@ class BertConfig:
                  input_mask_from_dataset=True,
                  token_type_ids_from_dataset=True,
                  dtype=mstype.float32,
-                 compute_type=mstype.float32):
+                 compute_type=mstype.float32,
+                 enable_fused_layernorm=False):
         self.batch_size = batch_size
         self.seq_length = seq_length
         self.vocab_size = vocab_size
@@ -96,6 +98,7 @@ class BertConfig:
         self.use_relative_positions = use_relative_positions
         self.dtype = dtype
         self.compute_type = compute_type
+        self.enable_fused_layernorm = enable_fused_layernorm
 
 
 class EmbeddingLookup(nn.Cell):
@@ -240,12 +243,18 @@ class BertOutput(nn.Cell):
                  out_channels,
                  initializer_range=0.02,
                  dropout_prob=0.1,
-                 compute_type=mstype.float32):
+                 compute_type=mstype.float32,
+                 enable_fused_layernorm=False):
         super(BertOutput, self).__init__()
         self.dense = nn.Dense(in_channels, out_channels,
                               weight_init=TruncatedNormal(initializer_range)).to_float(compute_type)
         self.dropout = nn.Dropout(1 - dropout_prob)
+        self.dropout_prob = dropout_prob
         self.add = P.TensorAdd()
+        if compute_type == mstype.float16:
+            self.layernorm = FusedLayerNorm((out_channels,),
+                                            use_batch_norm=enable_fused_layernorm).to_float(compute_type)
+        else:
             self.layernorm = nn.LayerNorm((out_channels,)).to_float(compute_type)
         self.cast = P.Cast()
 
@@ -481,6 +490,7 @@ class BertAttention(nn.Cell):
             self.shape_return = (batch_size, from_seq_length, num_attention_heads * size_per_head)
 
         self.cast_compute_type = SaturateCast(dst_type=compute_type)
+        if self.use_relative_positions:
             self._generate_relative_positions_embeddings = \
                 RelaPosEmbeddingsGenerator(length=to_seq_length,
                                            depth=size_per_head,
@@ -529,7 +539,7 @@ class BertAttention(nn.Cell):
                                                      self.trans_shape_position)
             attention_scores = attention_scores + key_position_scores_r_t
 
-        attention_scores = self.multiply(attention_scores, self.scores_mul)
+        attention_scores = self.multiply(self.scores_mul, attention_scores)
 
         if self.has_attention_mask:
             attention_mask = self.expand_dims(attention_mask, 1)
@@ -606,7 +616,8 @@ class BertSelfAttention(nn.Cell):
                  initializer_range=0.02,
                  hidden_dropout_prob=0.1,
                  use_relative_positions=False,
-                 compute_type=mstype.float32):
+                 compute_type=mstype.float32,
+                 enable_fused_layernorm=False):
         super(BertSelfAttention, self).__init__()
         if hidden_size % num_attention_heads != 0:
             raise ValueError("The hidden size (%d) is not a multiple of the number "
@@ -634,7 +645,8 @@ class BertSelfAttention(nn.Cell):
                                  out_channels=hidden_size,
                                  initializer_range=initializer_range,
                                  dropout_prob=hidden_dropout_prob,
-                                 compute_type=compute_type)
+                                 compute_type=compute_type,
+                                 enable_fused_layernorm=enable_fused_layernorm)
         self.reshape = P.Reshape()
         self.shape = (-1, hidden_size)
 
@@ -676,7 +688,8 @@ class BertEncoderCell(nn.Cell):
                  hidden_dropout_prob=0.1,
                  use_relative_positions=False,
                  hidden_act="gelu",
-                 compute_type=mstype.float32):
+                 compute_type=mstype.float32,
+                 enable_fused_layernorm=False):
         super(BertEncoderCell, self).__init__()
         self.attention = BertSelfAttention(
             batch_size=batch_size,
@@ -688,7 +701,8 @@ class BertEncoderCell(nn.Cell):
             initializer_range=initializer_range,
             hidden_dropout_prob=hidden_dropout_prob,
             use_relative_positions=use_relative_positions,
-            compute_type=compute_type)
+            compute_type=compute_type,
+            enable_fused_layernorm=enable_fused_layernorm)
         self.intermediate = nn.Dense(in_channels=hidden_size,
                                      out_channels=intermediate_size,
                                      activation=hidden_act,
@@ -697,7 +711,8 @@ class BertEncoderCell(nn.Cell):
                                  out_channels=hidden_size,
                                  initializer_range=initializer_range,
                                  dropout_prob=hidden_dropout_prob,
-                                 compute_type=compute_type)
+                                 compute_type=compute_type,
+                                 enable_fused_layernorm=enable_fused_layernorm)
 
     def construct(self, hidden_states, attention_mask):
         # self-attention
@@ -744,7 +759,8 @@ class BertTransformer(nn.Cell):
                  use_relative_positions=False,
                  hidden_act="gelu",
                  compute_type=mstype.float32,
-                 return_all_encoders=False):
+                 return_all_encoders=False,
+                 enable_fused_layernorm=False):
         super(BertTransformer, self).__init__()
         self.return_all_encoders = return_all_encoders
 
@@ -761,7 +777,8 @@ class BertTransformer(nn.Cell):
                                     hidden_dropout_prob=hidden_dropout_prob,
                                     use_relative_positions=use_relative_positions,
                                     hidden_act=hidden_act,
-                                    compute_type=compute_type)
+                                    compute_type=compute_type,
+                                    enable_fused_layernorm=enable_fused_layernorm)
             layers.append(layer)
 
         self.layers = nn.CellList(layers)
@@ -888,7 +905,8 @@ class BertModel(nn.Cell):
             use_relative_positions=config.use_relative_positions,
             hidden_act=config.hidden_act,
             compute_type=config.compute_type,
-            return_all_encoders=True)
+            return_all_encoders=True,
+            enable_fused_layernorm=config.enable_fused_layernorm)
 
         self.cast = P.Cast()
         self.dtype = config.dtype

model_zoo/bert/src/cluener_evaluation.py

@@ -17,12 +17,12 @@
 
 import json
 import numpy as np
-from evaluation_config import cfg
 import mindspore.common.dtype as mstype
 from mindspore.common.tensor import Tensor
-from CRF import postprocess
 import tokenization
 from sample_process import label_generation, process_one_example_p
+from .evaluation_config import cfg
+from .CRF import postprocess
 
 vocab_file = "./vocab.txt"
 tokenizer_ = tokenization.FullTokenizer(vocab_file=vocab_file)

model_zoo/bert/src/config.py

@@ -17,16 +17,16 @@ network config setting, will be used in dataset.py, run_pretrain.py
 """
 from easydict import EasyDict as edict
 import mindspore.common.dtype as mstype
-from mindspore.model_zoo.Bert_NEZHA import BertConfig
+from .bert_model import BertConfig
 cfg = edict({
     'bert_network': 'base',
-    'loss_scale_value': 2**32,
+    'loss_scale_value': 65536,
     'scale_factor': 2,
     'scale_window': 1000,
     'optimizer': 'Lamb',
     'AdamWeightDecayDynamicLR': edict({
         'learning_rate': 3e-5,
-        'end_learning_rate': 1e-7,
+        'end_learning_rate': 1e-10,
         'power': 5.0,
         'weight_decay': 1e-5,
         'eps': 1e-6,
@@ -34,7 +34,7 @@ cfg = edict({
     }),
     'Lamb': edict({
         'start_learning_rate': 3e-5,
-        'end_learning_rate': 1e-7,
+        'end_learning_rate': 1e-10,
         'power': 10.0,
         'warmup_steps': 10000,
         'weight_decay': 0.01,
@@ -56,7 +56,7 @@ if cfg.bert_network == 'base':
     bert_net_cfg = BertConfig(
         batch_size=32,
         seq_length=128,
-        vocab_size=21128,
+        vocab_size=21136,
         hidden_size=768,
         num_hidden_layers=12,
         num_attention_heads=12,
@@ -71,13 +71,13 @@ if cfg.bert_network == 'base':
         input_mask_from_dataset=True,
         token_type_ids_from_dataset=True,
         dtype=mstype.float32,
-        compute_type=mstype.float16,
+        compute_type=mstype.float16
     )
 if cfg.bert_network == 'nezha':
     bert_net_cfg = BertConfig(
         batch_size=32,
         seq_length=128,
-        vocab_size=21128,
+        vocab_size=21136,
         hidden_size=1024,
         num_hidden_layers=24,
         num_attention_heads=16,
@@ -92,5 +92,27 @@ if cfg.bert_network == 'nezha':
         input_mask_from_dataset=True,
         token_type_ids_from_dataset=True,
         dtype=mstype.float32,
-        compute_type=mstype.float16,
+        compute_type=mstype.float16
+    )
+if cfg.bert_network == 'large':
+    bert_net_cfg = BertConfig(
+        batch_size=16,
+        seq_length=512,
+        vocab_size=30528,
+        hidden_size=1024,
+        num_hidden_layers=24,
+        num_attention_heads=16,
+        intermediate_size=4096,
+        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=False,
+        input_mask_from_dataset=True,
+        token_type_ids_from_dataset=True,
+        dtype=mstype.float32,
+        compute_type=mstype.float16,
+        enable_fused_layernorm=True
     )

model_zoo/bert/src/dataset.py

@@ -20,7 +20,7 @@ import mindspore.common.dtype as mstype
 import mindspore.dataset.engine.datasets as de
 import mindspore.dataset.transforms.c_transforms as C
 from mindspore import log as logger
-from config import bert_net_cfg
+from .config import bert_net_cfg
 
 
 def create_bert_dataset(epoch_size=1, device_num=1, rank=0, do_shuffle="true", enable_data_sink="true",
@@ -31,8 +31,9 @@ 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:
+        if "tfrecord" in file_name:
             data_files.append(os.path.join(data_dir, file_name))
-    ds = de.TFRecordDataset(data_files, schema_dir,
+    ds = de.TFRecordDataset(data_files, schema_dir if schema_dir != "" else None,
                             columns_list=["input_ids", "input_mask", "segment_ids", "next_sentence_labels",
                                           "masked_lm_positions", "masked_lm_ids", "masked_lm_weights"],
                             shuffle=(do_shuffle == "true"), num_shards=device_num, shard_id=rank,

model_zoo/bert/src/evaluation_config.py

@@ -19,7 +19,7 @@ config settings, will be used in finetune.py
 
 from easydict import EasyDict as edict
 import mindspore.common.dtype as mstype
-from mindspore.model_zoo.Bert_NEZHA import BertConfig
+from .bert_model import BertConfig
 
 cfg = edict({
     'task': 'NER',

model_zoo/bert/src/finetune_config.py

@@ -19,7 +19,7 @@ config settings, will be used in finetune.py
 
 from easydict import EasyDict as edict
 import mindspore.common.dtype as mstype
-from mindspore.model_zoo.Bert_NEZHA import BertConfig
+from .bert_model import BertConfig
 
 cfg = edict({
     'task': 'NER',

model_zoo/bert/src/fused_layer_norm.py

@@ -0,0 +1,121 @@
+# 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.
+# ============================================================================
+"""fused layernorm"""
+from mindspore.ops import operations as P
+from mindspore.ops import functional as F
+from mindspore.common.parameter import Parameter
+from mindspore.common.initializer import initializer
+from mindspore.ops.primitive import constexpr
+import mindspore.common.dtype as mstype
+from mindspore.nn.cell import Cell
+
+import numpy as np
+
+
+__all__ = ['FusedLayerNorm']
+
+@constexpr
+def get_shape_for_norm(x_shape, begin_norm_axis):
+    print("input_shape: ", x_shape)
+    norm_shape = x_shape[begin_norm_axis:]
+    output_shape = (1, -1, 1, int(np.prod(norm_shape)))
+    print("output_shape: ", output_shape)
+    return output_shape
+
+class FusedLayerNorm(Cell):
+    r"""
+    Applies Layer Normalization over a mini-batch of inputs.
+
+    Layer normalization is widely used in recurrent neural networks. It applies
+    normalization over a mini-batch of inputs for each single training case as described
+    in the paper `Layer Normalization <https://arxiv.org/pdf/1607.06450.pdf>`_. Unlike batch
+    normalization, layer normalization performs exactly the same computation at training and
+    testing times. It can be described using the following formula. It is applied across all channels
+    and pixel but only one batch size.
+
+    .. math::
+        y = \frac{x - \mathrm{E}[x]}{\sqrt{\mathrm{Var}[x] + \epsilon}} * \gamma + \beta
+
+    Args:
+        normalized_shape (Union(tuple[int], list[int]): The normalization is performed over axis
+            `begin_norm_axis ... R - 1`.
+        begin_norm_axis (int): It first normalization dimension: normalization will be performed along dimensions
+            `begin_norm_axis: rank(inputs)`, the value should be in [-1, rank(input)). Default: -1.
+        begin_params_axis (int): The first parameter(beta, gamma)dimension: scale and centering parameters
+            will have dimensions `begin_params_axis: rank(inputs)` and will be broadcast with
+            the normalized inputs accordingly, the value should be in [-1, rank(input)). Default: -1.
+        gamma_init (Union[Tensor, str, Initializer, numbers.Number]): Initializer for the gamma weight.
+            The values of str refer to the function `initializer` including 'zeros', 'ones', 'xavier_uniform',
+            'he_uniform', etc. Default: 'ones'.
+        beta_init (Union[Tensor, str, Initializer, numbers.Number]): Initializer for the beta weight.
+            The values of str refer to the function `initializer` including 'zeros', 'ones', 'xavier_uniform',
+            'he_uniform', etc. Default: 'zeros'.
+        use_batch_nrom (bool): Whether use batchnorm to preocess.
+
+    Inputs:
+        - **input_x** (Tensor) - The shape of 'input_x' is :math:`(x_1, x_2, ..., x_R)`,
+          and `input_shape[begin_norm_axis:]` is equal to `normalized_shape`.
+
+    Outputs:
+        Tensor, the normalized and scaled offset tensor, has the same shape and data type as the `input_x`.
+
+    Examples:
+        >>> x = Tensor(np.ones([20, 5, 10, 10]), mindspore.float32)
+        >>> shape1 = x.shape()[1:]
+        >>> m = nn.LayerNorm(shape1,  begin_norm_axis=1, begin_params_axis=1)
+        >>> m(x)
+    """
+    def __init__(self,
+                 normalized_shape,
+                 begin_norm_axis=-1,
+                 begin_params_axis=-1,
+                 gamma_init='ones',
+                 beta_init='zeros',
+                 use_batch_norm=False):
+        super(FusedLayerNorm, self).__init__()
+        if not isinstance(normalized_shape, (tuple, list)):
+            raise TypeError("The type of 'normalized_shape' should be tuple[int] or list[int], but '{}' type is {}."
+                            .format(normalized_shape, type(normalized_shape)))
+        self.normalized_shape = normalized_shape
+        self.begin_norm_axis = begin_norm_axis
+        self.begin_params_axis = begin_params_axis
+        self.gamma = Parameter(initializer(
+            gamma_init, normalized_shape), name="gamma")
+        self.beta = Parameter(initializer(
+            beta_init, normalized_shape), name="beta")
+        self.layer_norm = P.LayerNorm(begin_norm_axis=self.begin_norm_axis, begin_params_axis=self.begin_params_axis)
+
+        self.batch_norm = P.BatchNorm(is_training=True, epsilon=1e-5)
+        self.use_batch_norm = use_batch_norm
+
+    def construct(self, input_x):
+        if self.use_batch_norm and self.training:
+            ones = P.Fill()(mstype.float32, F.shape(input_x)[:self.begin_norm_axis], 1.0)
+            zeros = P.Fill()(mstype.float32, F.shape(input_x)[:self.begin_norm_axis], 0.0)
+            shape_x = F.shape(input_x)
+            norm_shape = get_shape_for_norm(shape_x, self.begin_norm_axis)
+            input_x = F.reshape(input_x, norm_shape)
+            output, _, _, _, _, _ = self.batch_norm(input_x, ones, zeros, None, None)
+            output = F.reshape(output, shape_x)
+            y = output * self.gamma + self.beta
+        else:
+            y, _, _ = self.layer_norm(input_x, self.gamma, self.beta)
+        return y
+
+    def extend_repr(self):
+        """Display instance object as string."""
+        s = 'normalized_shape={}, begin_norm_axis={}, begin_params_axis={}, gamma{}, beta={}'.format(
+            self.normalized_shape, self.begin_norm_axis, self.begin_params_axis, self.gamma, self.beta)
+        return s

model_zoo/bert/src/utils.py

@@ -30,8 +30,8 @@ from mindspore.train.parallel_utils import ParallelMode
 from mindspore.communication.management import get_group_size
 from mindspore import context
 from mindspore.model_zoo.Bert_NEZHA.bert_model import BertModel
-from mindspore.model_zoo.Bert_NEZHA.bert_for_pre_training import clip_grad
-from CRF import CRF
+from .bert_for_pre_training import clip_grad
+from .CRF import CRF
 
 GRADIENT_CLIP_TYPE = 1
 GRADIENT_CLIP_VALUE = 1.0

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

@@ -25,7 +25,8 @@ import mindspore.dataset.transforms.c_transforms as C
 from mindspore import context
 from mindspore import log as logger
 from mindspore.common.tensor import Tensor
-from mindspore.model_zoo.Bert_NEZHA import BertConfig, BertNetworkWithLoss, BertTrainOneStepWithLossScaleCell
+from src.bert_model import BertConfig
+from src.bert_for_pre_training import BertNetworkWithLoss, BertTrainOneStepWithLossScaleCell
 from mindspore.nn.optim import Lamb
 from mindspore.train.callback import Callback
 from mindspore.train.loss_scale_manager import DynamicLossScaleManager
@@ -77,7 +78,8 @@ def get_config(version='base', batch_size=1):
             input_mask_from_dataset=True,
             token_type_ids_from_dataset=True,
             dtype=mstype.float32,
-            compute_type=mstype.float16)
+            compute_type=mstype.float16,
+            enable_fused_layernorm=False)
     else:
         bert_config = BertConfig(batch_size=batch_size)
     return bert_config

tests/st/networks/models/bert/src/CRF.py

@@ -0,0 +1,177 @@
+# 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.
+# ============================================================================
+
+'''
+CRF script.
+'''
+
+import numpy as np
+import mindspore.nn as nn
+from mindspore.ops import operations as P
+from mindspore.common.tensor import Tensor
+from mindspore.common.parameter import Parameter
+import mindspore.common.dtype as mstype
+
+class CRF(nn.Cell):
+    '''
+    Conditional Random Field
+    Args:
+        tag_to_index: The dict for tag to index mapping with extra "<START>" and "<STOP>"sign.
+        batch_size: Batch size, i.e., the length of the first dimension.
+        seq_length: Sequence length, i.e., the length of the second dimention.
+        is_training: Specifies whether to use training mode.
+    Returns:
+        Training mode: Tensor, total loss.
+        Evaluation mode: Tuple, the index for each step with the highest score; Tuple, the index for the last
+        step with the highest score.
+    '''
+    def __init__(self, tag_to_index, batch_size=1, seq_length=128, is_training=True):
+
+        super(CRF, self).__init__()
+        self.target_size = len(tag_to_index)
+        self.is_training = is_training
+        self.tag_to_index = tag_to_index
+        self.batch_size = batch_size
+        self.seq_length = seq_length
+        self.START_TAG = "<START>"
+        self.STOP_TAG = "<STOP>"
+        self.START_VALUE = Tensor(self.target_size-2, dtype=mstype.int32)
+        self.STOP_VALUE = Tensor(self.target_size-1, dtype=mstype.int32)
+        transitions = np.random.normal(size=(self.target_size, self.target_size)).astype(np.float32)
+        transitions[tag_to_index[self.START_TAG], :] = -10000
+        transitions[:, tag_to_index[self.STOP_TAG]] = -10000
+        self.transitions = Parameter(Tensor(transitions), name="transition_matrix")
+        self.cat = P.Concat(axis=-1)
+        self.argmax = P.ArgMaxWithValue(axis=-1)
+        self.log = P.Log()
+        self.exp = P.Exp()
+        self.sum = P.ReduceSum()
+        self.tile = P.Tile()
+        self.reduce_sum = P.ReduceSum(keep_dims=True)
+        self.reshape = P.Reshape()
+        self.expand = P.ExpandDims()
+        self.mean = P.ReduceMean()
+        init_alphas = np.ones(shape=(self.batch_size, self.target_size)) * -10000.0
+        init_alphas[:, self.tag_to_index[self.START_TAG]] = 0.
+        self.init_alphas = Tensor(init_alphas, dtype=mstype.float32)
+        self.cast = P.Cast()
+        self.reduce_max = P.ReduceMax(keep_dims=True)
+        self.on_value = Tensor(1.0, dtype=mstype.float32)
+        self.off_value = Tensor(0.0, dtype=mstype.float32)
+        self.onehot = P.OneHot()
+
+    def log_sum_exp(self, logits):
+        '''
+        Compute the log_sum_exp score for normalization factor.
+        '''
+        max_score = self.reduce_max(logits, -1)  #16 5 5
+        score = self.log(self.reduce_sum(self.exp(logits - max_score), -1))
+        score = max_score + score
+        return score
+
+    def _realpath_score(self, features, label):
+        '''
+        Compute the emission and transition score for the real path.
+        '''
+        label = label * 1
+        concat_A = self.tile(self.reshape(self.START_VALUE, (1,)), (self.batch_size,))
+        concat_A = self.reshape(concat_A, (self.batch_size, 1))
+        labels = self.cat((concat_A, label))
+        onehot_label = self.onehot(label, self.target_size, self.on_value, self.off_value)
+        emits = features * onehot_label
+        labels = self.onehot(labels, self.target_size, self.on_value, self.off_value)
+        label1 = labels[:, 1:, :]
+        label2 = labels[:, :self.seq_length, :]
+        label1 = self.expand(label1, 3)
+        label2 = self.expand(label2, 2)
+        label_trans = label1 * label2
+        transitions = self.expand(self.expand(self.transitions, 0), 0)
+        trans = transitions * label_trans
+        score = self.sum(emits, (1, 2)) + self.sum(trans, (1, 2, 3))
+        stop_value_index = labels[:, (self.seq_length-1):self.seq_length, :]
+        stop_value = self.transitions[(self.target_size-1):self.target_size, :]
+        stop_score = stop_value * self.reshape(stop_value_index, (self.batch_size, self.target_size))
+        score = score + self.sum(stop_score, 1)
+        score = self.reshape(score, (self.batch_size, -1))
+        return score
+
+    def _normalization_factor(self, features):
+        '''
+        Compute the total score for all the paths.
+        '''
+        forward_var = self.init_alphas
+        forward_var = self.expand(forward_var, 1)
+        for idx in range(self.seq_length):
+            feat = features[:, idx:(idx+1), :]
+            emit_score = self.reshape(feat, (self.batch_size, self.target_size, 1))
+            next_tag_var = emit_score + self.transitions + forward_var
+            forward_var = self.log_sum_exp(next_tag_var)
+            forward_var = self.reshape(forward_var, (self.batch_size, 1, self.target_size))
+        terminal_var = forward_var + self.reshape(self.transitions[(self.target_size-1):self.target_size, :], (1, -1))
+        alpha = self.log_sum_exp(terminal_var)
+        alpha = self.reshape(alpha, (self.batch_size, -1))
+        return alpha
+
+    def _decoder(self, features):
+        '''
+        Viterbi decode for evaluation.
+        '''
+        backpointers = ()
+        forward_var = self.init_alphas
+        for idx in range(self.seq_length):
+            feat = features[:, idx:(idx+1), :]
+            feat = self.reshape(feat, (self.batch_size, self.target_size))
+            bptrs_t = ()
+
+            next_tag_var = self.expand(forward_var, 1) + self.transitions
+            best_tag_id, best_tag_value = self.argmax(next_tag_var)
+            bptrs_t += (best_tag_id,)
+            forward_var = best_tag_value + feat
+
+            backpointers += (bptrs_t,)
+        terminal_var = forward_var + self.reshape(self.transitions[(self.target_size-1):self.target_size, :], (1, -1))
+        best_tag_id, _ = self.argmax(terminal_var)
+        return backpointers, best_tag_id
+
+    def construct(self, features, label):
+        if self.is_training:
+            forward_score = self._normalization_factor(features)
+            gold_score = self._realpath_score(features, label)
+            return_value = self.mean(forward_score - gold_score)
+        else:
+            path_list, tag = self._decoder(features)
+            return_value = path_list, tag
+        return return_value
+
+def postprocess(backpointers, best_tag_id):
+    '''
+    Do postprocess
+    '''
+    best_tag_id = best_tag_id.asnumpy()
+    batch_size = len(best_tag_id)
+    best_path = []
+    for i in range(batch_size):
+        best_path.append([])
+        best_local_id = best_tag_id[i]
+        best_path[-1].append(best_local_id)
+        for bptrs_t in reversed(backpointers):
+            bptrs_t = bptrs_t[0].asnumpy()
+            local_idx = bptrs_t[i]
+            best_local_id = local_idx[best_local_id]
+            best_path[-1].append(best_local_id)
+        # Pop off the start tag (we dont want to return that to the caller)
+        best_path[-1].pop()
+        best_path[-1].reverse()
+    return best_path

tests/st/networks/models/bert/src/__init__.py

@@ -0,0 +1,31 @@
+# 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.
+# ============================================================================
+"""Bert Init."""
+from .bert_for_pre_training import BertNetworkWithLoss, BertPreTraining, \
+    BertPretrainingLoss, GetMaskedLMOutput, GetNextSentenceOutput, \
+    BertTrainOneStepCell, BertTrainOneStepWithLossScaleCell
+from .bert_model import BertAttention, BertConfig, BertEncoderCell, BertModel, \
+    BertOutput, BertSelfAttention, BertTransformer, EmbeddingLookup, \
+    EmbeddingPostprocessor, RelaPosEmbeddingsGenerator, RelaPosMatrixGenerator, \
+    SaturateCast, CreateAttentionMaskFromInputMask
+
+__all__ = [
+    "BertNetworkWithLoss", "BertPreTraining", "BertPretrainingLoss",
+    "GetMaskedLMOutput", "GetNextSentenceOutput", "BertTrainOneStepCell", "BertTrainOneStepWithLossScaleCell",
+    "BertAttention", "BertConfig", "BertEncoderCell", "BertModel", "BertOutput",
+    "BertSelfAttention", "BertTransformer", "EmbeddingLookup",
+    "EmbeddingPostprocessor", "RelaPosEmbeddingsGenerator",
+    "RelaPosMatrixGenerator", "SaturateCast", "CreateAttentionMaskFromInputMask"
+]

tests/st/networks/models/bert/src/bert_for_pre_training.py

@@ -0,0 +1,434 @@
+# 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.
+# ============================================================================
+"""Bert for pretraining."""
+import numpy as np
+
+import mindspore.nn as nn
+from mindspore.common.initializer import initializer, TruncatedNormal
+from mindspore.ops import operations as P
+from mindspore.ops import functional as F
+from mindspore.ops import composite as C
+from mindspore.common.tensor import Tensor
+from mindspore.common.parameter import Parameter, ParameterTuple
+from mindspore.common import dtype as mstype
+from mindspore.nn.wrap.grad_reducer import DistributedGradReducer
+from mindspore.train.parallel_utils import ParallelMode
+from mindspore.communication.management import get_group_size
+from mindspore import context
+from .bert_model import BertModel
+
+GRADIENT_CLIP_TYPE = 1
+GRADIENT_CLIP_VALUE = 1.0
+
+_nn_clip_by_norm = nn.ClipByNorm()
+clip_grad = C.MultitypeFuncGraph("clip_grad")
+@clip_grad.register("Number", "Number", "Tensor")
+def _clip_grad(clip_type, clip_value, grad):
+    """
+    Clip gradients.
+
+    Inputs:
+        clip_type (int): The way to clip, 0 for 'value', 1 for 'norm'.
+        clip_value (float): Specifies how much to clip.
+        grad (tuple[Tensor]): Gradients.
+
+    Outputs:
+        tuple[Tensor], clipped gradients.
+    """
+    if clip_type != 0 and clip_type != 1:
+        return grad
+    dt = F.dtype(grad)
+    if clip_type == 0:
+        new_grad = C.clip_by_value(grad, F.cast(F.tuple_to_array((-clip_value,)), dt),
+                                   F.cast(F.tuple_to_array((clip_value,)), dt))
+    else:
+        new_grad = _nn_clip_by_norm(grad, F.cast(F.tuple_to_array((clip_value,)), dt))
+    return new_grad
+
+class GetMaskedLMOutput(nn.Cell):
+    """
+    Get masked lm output.
+
+    Args:
+        config (BertConfig): The config of BertModel.
+
+    Returns:
+        Tensor, masked lm output.
+    """
+    def __init__(self, config):
+        super(GetMaskedLMOutput, self).__init__()
+        self.width = config.hidden_size
+        self.reshape = P.Reshape()
+        self.gather = P.GatherV2()
+
+        weight_init = TruncatedNormal(config.initializer_range)
+        self.dense = nn.Dense(self.width,
+                              config.hidden_size,
+                              weight_init=weight_init,
+                              activation=config.hidden_act).to_float(config.compute_type)
+        self.layernorm = nn.LayerNorm((config.hidden_size,)).to_float(config.compute_type)
+        self.output_bias = Parameter(
+            initializer(
+                'zero',
+                config.vocab_size),
+            name='output_bias')
+        self.matmul = P.MatMul(transpose_b=True)
+        self.log_softmax = nn.LogSoftmax(axis=-1)
+        self.shape_flat_offsets = (-1, 1)
+        self.rng = Tensor(np.array(range(0, config.batch_size)).astype(np.int32))
+        self.last_idx = (-1,)
+        self.shape_flat_sequence_tensor = (config.batch_size * config.seq_length, self.width)
+        self.seq_length_tensor = Tensor(np.array((config.seq_length,)).astype(np.int32))
+        self.cast = P.Cast()
+        self.compute_type = config.compute_type
+        self.dtype = config.dtype
+
+    def construct(self,
+                  input_tensor,
+                  output_weights,
+                  positions):
+        flat_offsets = self.reshape(
+            self.rng * self.seq_length_tensor, self.shape_flat_offsets)
+        flat_position = self.reshape(positions + flat_offsets, self.last_idx)
+        flat_sequence_tensor = self.reshape(input_tensor, self.shape_flat_sequence_tensor)
+        input_tensor = self.gather(flat_sequence_tensor, flat_position, 0)
+        input_tensor = self.cast(input_tensor, self.compute_type)
+        output_weights = self.cast(output_weights, self.compute_type)
+        input_tensor = self.dense(input_tensor)
+        input_tensor = self.layernorm(input_tensor)
+        logits = self.matmul(input_tensor, output_weights)
+        logits = self.cast(logits, self.dtype)
+        logits = logits + self.output_bias
+        log_probs = self.log_softmax(logits)
+        return log_probs
+
+
+class GetNextSentenceOutput(nn.Cell):
+    """
+    Get next sentence output.
+
+    Args:
+        config (BertConfig): The config of Bert.
+
+    Returns:
+        Tensor, next sentence output.
+    """
+    def __init__(self, config):
+        super(GetNextSentenceOutput, self).__init__()
+        self.log_softmax = P.LogSoftmax()
+        self.weight_init = TruncatedNormal(config.initializer_range)
+        self.dense = nn.Dense(config.hidden_size, 2,
+                              weight_init=self.weight_init, has_bias=True).to_float(config.compute_type)
+        self.dtype = config.dtype
+        self.cast = P.Cast()
+
+    def construct(self, input_tensor):
+        logits = self.dense(input_tensor)
+        logits = self.cast(logits, self.dtype)
+        log_prob = self.log_softmax(logits)
+        return log_prob
+
+
+class BertPreTraining(nn.Cell):
+    """
+    Bert pretraining network.
+
+    Args:
+        config (BertConfig): The config of BertModel.
+        is_training (bool): Specifies whether to use the training mode.
+        use_one_hot_embeddings (bool): Specifies whether to use one-hot for embeddings.
+
+    Returns:
+        Tensor, prediction_scores, seq_relationship_score.
+    """
+    def __init__(self, config, is_training, use_one_hot_embeddings):
+        super(BertPreTraining, self).__init__()
+        self.bert = BertModel(config, is_training, use_one_hot_embeddings)
+        self.cls1 = GetMaskedLMOutput(config)
+        self.cls2 = GetNextSentenceOutput(config)
+
+    def construct(self, input_ids, input_mask, token_type_id,
+                  masked_lm_positions):
+        sequence_output, pooled_output, embedding_table = \
+            self.bert(input_ids, token_type_id, input_mask)
+        prediction_scores = self.cls1(sequence_output,
+                                      embedding_table,
+                                      masked_lm_positions)
+        seq_relationship_score = self.cls2(pooled_output)
+        return prediction_scores, seq_relationship_score
+
+
+class BertPretrainingLoss(nn.Cell):
+    """
+    Provide bert pre-training loss.
+
+    Args:
+        config (BertConfig): The config of BertModel.
+
+    Returns:
+        Tensor, total loss.
+    """
+    def __init__(self, config):
+        super(BertPretrainingLoss, self).__init__()
+        self.vocab_size = config.vocab_size
+        self.onehot = P.OneHot()
+        self.on_value = Tensor(1.0, mstype.float32)
+        self.off_value = Tensor(0.0, mstype.float32)
+        self.reduce_sum = P.ReduceSum()
+        self.reduce_mean = P.ReduceMean()
+        self.reshape = P.Reshape()
+        self.last_idx = (-1,)
+        self.neg = P.Neg()
+        self.cast = P.Cast()
+
+    def construct(self, prediction_scores, seq_relationship_score, masked_lm_ids,
+                  masked_lm_weights, next_sentence_labels):
+        """Defines the computation performed."""
+        label_ids = self.reshape(masked_lm_ids, self.last_idx)
+        label_weights = self.cast(self.reshape(masked_lm_weights, self.last_idx), mstype.float32)
+        one_hot_labels = self.onehot(label_ids, self.vocab_size, self.on_value, self.off_value)
+
+        per_example_loss = self.neg(self.reduce_sum(prediction_scores * one_hot_labels, self.last_idx))
+        numerator = self.reduce_sum(label_weights * per_example_loss, ())
+        denominator = self.reduce_sum(label_weights, ()) + self.cast(F.tuple_to_array((1e-5,)), mstype.float32)
+        masked_lm_loss = numerator / denominator
+
+        # next_sentence_loss
+        labels = self.reshape(next_sentence_labels, self.last_idx)
+        one_hot_labels = self.onehot(labels, 2, self.on_value, self.off_value)
+        per_example_loss = self.neg(self.reduce_sum(
+            one_hot_labels * seq_relationship_score, self.last_idx))
+        next_sentence_loss = self.reduce_mean(per_example_loss, self.last_idx)
+
+        # total_loss
+        total_loss = masked_lm_loss + next_sentence_loss
+
+        return total_loss
+
+
+class BertNetworkWithLoss(nn.Cell):
+    """
+    Provide bert pre-training loss through network.
+
+    Args:
+        config (BertConfig): The config of BertModel.
+        is_training (bool): Specifies whether to use the training mode.
+        use_one_hot_embeddings (bool): Specifies whether to use one-hot for embeddings. Default: False.
+
+    Returns:
+        Tensor, the loss of the network.
+    """
+    def __init__(self, config, is_training, use_one_hot_embeddings=False):
+        super(BertNetworkWithLoss, self).__init__()
+        self.bert = BertPreTraining(config, is_training, use_one_hot_embeddings)
+        self.loss = BertPretrainingLoss(config)
+        self.cast = P.Cast()
+
+    def construct(self,
+                  input_ids,
+                  input_mask,
+                  token_type_id,
+                  next_sentence_labels,
+                  masked_lm_positions,
+                  masked_lm_ids,
+                  masked_lm_weights):
+        prediction_scores, seq_relationship_score = \
+            self.bert(input_ids, input_mask, token_type_id, masked_lm_positions)
+        total_loss = self.loss(prediction_scores, seq_relationship_score,
+                               masked_lm_ids, masked_lm_weights, next_sentence_labels)
+        return self.cast(total_loss, mstype.float32)
+
+
+class BertTrainOneStepCell(nn.Cell):
+    """
+    Encapsulation class of bert network training.
+
+    Append an optimizer to the training network after that the construct
+    function can be called to create the backward graph.
+
+    Args:
+        network (Cell): The training network. Note that loss function should have been added.
+        optimizer (Optimizer): Optimizer for updating the weights.
+        sens (Number): The adjust parameter. Default: 1.0.
+    """
+    def __init__(self, network, optimizer, sens=1.0):
+        super(BertTrainOneStepCell, self).__init__(auto_prefix=False)
+        self.network = network
+        self.weights = ParameterTuple(network.trainable_params())
+        self.optimizer = optimizer
+        self.grad = C.GradOperation('grad', get_by_list=True, sens_param=True)
+        self.sens = sens
+        self.reducer_flag = False
+        self.parallel_mode = context.get_auto_parallel_context("parallel_mode")
+        if self.parallel_mode in [ParallelMode.DATA_PARALLEL, ParallelMode.HYBRID_PARALLEL]:
+            self.reducer_flag = True
+        self.grad_reducer = None
+        if self.reducer_flag:
+            mean = context.get_auto_parallel_context("mirror_mean")
+            degree = get_group_size()
+            self.grad_reducer = DistributedGradReducer(optimizer.parameters, mean, degree)
+
+        self.cast = P.Cast()
+        self.hyper_map = C.HyperMap()
+
+    def set_sens(self, value):
+        self.sens = value
+
+    def construct(self,
+                  input_ids,
+                  input_mask,
+                  token_type_id,
+                  next_sentence_labels,
+                  masked_lm_positions,
+                  masked_lm_ids,
+                  masked_lm_weights):
+        """Defines the computation performed."""
+        weights = self.weights
+
+        loss = self.network(input_ids,
+                            input_mask,
+                            token_type_id,
+                            next_sentence_labels,
+                            masked_lm_positions,
+                            masked_lm_ids,
+                            masked_lm_weights)
+        grads = self.grad(self.network, weights)(input_ids,
+                                                 input_mask,
+                                                 token_type_id,
+                                                 next_sentence_labels,
+                                                 masked_lm_positions,
+                                                 masked_lm_ids,
+                                                 masked_lm_weights,
+                                                 self.cast(F.tuple_to_array((self.sens,)),
+                                                           mstype.float32))
+        grads = self.hyper_map(F.partial(clip_grad, GRADIENT_CLIP_TYPE, GRADIENT_CLIP_VALUE), grads)
+        if self.reducer_flag:
+            # apply grad reducer on grads
+            grads = self.grad_reducer(grads)
+
+        succ = self.optimizer(grads)
+        return F.depend(loss, succ)
+
+
+grad_scale = C.MultitypeFuncGraph("grad_scale")
+reciprocal = P.Reciprocal()
+
+
+@grad_scale.register("Tensor", "Tensor")
+def tensor_grad_scale(scale, grad):
+    return grad * reciprocal(scale)
+
+
+class BertTrainOneStepWithLossScaleCell(nn.Cell):
+    """
+    Encapsulation class of bert network training.
+
+    Append an optimizer to the training network after that the construct
+    function can be called to create the backward graph.
+
+    Args:
+        network (Cell): The training network. Note that loss function should have been added.
+        optimizer (Optimizer): Optimizer for updating the weights.
+        scale_update_cell (Cell): Cell to do the loss scale. Default: None.
+    """
+    def __init__(self, network, optimizer, scale_update_cell=None):
+        super(BertTrainOneStepWithLossScaleCell, self).__init__(auto_prefix=False)
+        self.network = network
+        self.weights = ParameterTuple(network.trainable_params())
+        self.optimizer = optimizer
+        self.grad = C.GradOperation('grad',
+                                    get_by_list=True,
+                                    sens_param=True)
+        self.reducer_flag = False
+        self.allreduce = P.AllReduce()
+        self.parallel_mode = context.get_auto_parallel_context("parallel_mode")
+        if self.parallel_mode in [ParallelMode.DATA_PARALLEL, ParallelMode.HYBRID_PARALLEL]:
+            self.reducer_flag = True
+        self.grad_reducer = F.identity
+        self.degree = 1
+        if self.reducer_flag:
+            self.degree = get_group_size()
+            self.grad_reducer = DistributedGradReducer(optimizer.parameters, False, self.degree)
+        self.is_distributed = (self.parallel_mode != ParallelMode.STAND_ALONE)
+        self.cast = P.Cast()
+        self.alloc_status = P.NPUAllocFloatStatus()
+        self.get_status = P.NPUGetFloatStatus()
+        self.clear_before_grad = P.NPUClearFloatStatus()
+        self.reduce_sum = P.ReduceSum(keep_dims=False)
+        self.depend_parameter_use = P.ControlDepend(depend_mode=1)
+        self.base = Tensor(1, mstype.float32)
+        self.less_equal = P.LessEqual()
+        self.hyper_map = C.HyperMap()
+        self.loss_scale = None
+        self.loss_scaling_manager = scale_update_cell
+        if scale_update_cell:
+            self.loss_scale = Parameter(Tensor(scale_update_cell.get_loss_scale(), dtype=mstype.float32),
+                                        name="loss_scale")
+        self.add_flags(has_effect=True)
+    def construct(self,
+                  input_ids,
+                  input_mask,
+                  token_type_id,
+                  next_sentence_labels,
+                  masked_lm_positions,
+                  masked_lm_ids,
+                  masked_lm_weights,
+                  sens=None):
+        """Defines the computation performed."""
+        weights = self.weights
+        loss = self.network(input_ids,
+                            input_mask,
+                            token_type_id,
+                            next_sentence_labels,
+                            masked_lm_positions,
+                            masked_lm_ids,
+                            masked_lm_weights)
+        if sens is None:
+            scaling_sens = self.loss_scale
+        else:
+            scaling_sens = sens
+        # alloc status and clear should be right before gradoperation
+        init = self.alloc_status()
+        self.clear_before_grad(init)
+        grads = self.grad(self.network, weights)(input_ids,
+                                                 input_mask,
+                                                 token_type_id,
+                                                 next_sentence_labels,
+                                                 masked_lm_positions,
+                                                 masked_lm_ids,
+                                                 masked_lm_weights,
+                                                 self.cast(scaling_sens,
+                                                           mstype.float32))
+        # apply grad reducer on grads
+        grads = self.grad_reducer(grads)
+        grads = self.hyper_map(F.partial(grad_scale, scaling_sens * self.degree), grads)
+        grads = self.hyper_map(F.partial(clip_grad, GRADIENT_CLIP_TYPE, GRADIENT_CLIP_VALUE), grads)
+        self.get_status(init)
+        flag_sum = self.reduce_sum(init, (0,))
+        if self.is_distributed:
+            # sum overflow flag over devices
+            flag_reduce = self.allreduce(flag_sum)
+            cond = self.less_equal(self.base, flag_reduce)
+        else:
+            cond = self.less_equal(self.base, flag_sum)
+        overflow = cond
+        if sens is None:
+            overflow = self.loss_scaling_manager(self.loss_scale, cond)
+        if overflow:
+            succ = False
+        else:
+            succ = self.optimizer(grads)
+        ret = (loss, cond, scaling_sens)
+        return F.depend(ret, succ)

tests/st/networks/models/bert/src/bert_model.py

@@ -0,0 +1,949 @@
+# 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.
+# ============================================================================
+"""Bert model."""
+
+import math
+import copy
+import numpy as np
+import mindspore.common.dtype as mstype
+import mindspore.nn as nn
+import mindspore.ops.functional as F
+from mindspore.common.initializer import TruncatedNormal, initializer
+from mindspore.ops import operations as P
+from mindspore.ops import composite as C
+from mindspore.common.tensor import Tensor
+from mindspore.common.parameter import Parameter
+from .fused_layer_norm import FusedLayerNorm
+
+
+class BertConfig:
+    """
+    Configuration for `BertModel`.
+
+    Args:
+        batch_size (int): Batch size of input dataset.
+        seq_length (int): Length of input sequence. Default: 128.
+        vocab_size (int): The shape of each embedding vector. Default: 32000.
+        hidden_size (int): Size of the bert encoder layers. Default: 768.
+        num_hidden_layers (int): Number of hidden layers in the BertTransformer encoder
+                           cell. Default: 12.
+        num_attention_heads (int): Number of attention heads in the BertTransformer
+                             encoder cell. Default: 12.
+        intermediate_size (int): Size of intermediate layer in the BertTransformer
+                           encoder cell. Default: 3072.
+        hidden_act (str): Activation function used in the BertTransformer encoder
+                    cell. Default: "gelu".
+        hidden_dropout_prob (float): The dropout probability for BertOutput. Default: 0.1.
+        attention_probs_dropout_prob (float): The dropout probability for
+                                      BertAttention. Default: 0.1.
+        max_position_embeddings (int): Maximum length of sequences used in this
+                                 model. Default: 512.
+        type_vocab_size (int): Size of token type vocab. Default: 16.
+        initializer_range (float): Initialization value of TruncatedNormal. Default: 0.02.
+        use_relative_positions (bool): Specifies whether to use relative positions. Default: False.
+        input_mask_from_dataset (bool): Specifies whether to use the input mask that loaded from
+                                 dataset. Default: True.
+        token_type_ids_from_dataset (bool): Specifies whether to use the token type ids that loaded
+                                     from dataset. Default: True.
+        dtype (:class:`mindspore.dtype`): Data type of the input. Default: mstype.float32.
+        compute_type (:class:`mindspore.dtype`): Compute type in BertTransformer. Default: mstype.float32.
+    """
+    def __init__(self,
+                 batch_size,
+                 seq_length=128,
+                 vocab_size=32000,
+                 hidden_size=768,
+                 num_hidden_layers=12,
+                 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=16,
+                 initializer_range=0.02,
+                 use_relative_positions=False,
+                 input_mask_from_dataset=True,
+                 token_type_ids_from_dataset=True,
+                 dtype=mstype.float32,
+                 compute_type=mstype.float32,
+                 enable_fused_layernorm=False):
+        self.batch_size = batch_size
+        self.seq_length = seq_length
+        self.vocab_size = vocab_size
+        self.hidden_size = hidden_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.hidden_act = hidden_act
+        self.intermediate_size = intermediate_size
+        self.hidden_dropout_prob = hidden_dropout_prob
+        self.attention_probs_dropout_prob = attention_probs_dropout_prob
+        self.max_position_embeddings = max_position_embeddings
+        self.type_vocab_size = type_vocab_size
+        self.initializer_range = initializer_range
+        self.input_mask_from_dataset = input_mask_from_dataset
+        self.token_type_ids_from_dataset = token_type_ids_from_dataset
+        self.use_relative_positions = use_relative_positions
+        self.dtype = dtype
+        self.compute_type = compute_type
+        self.enable_fused_layernorm = enable_fused_layernorm
+
+
+class EmbeddingLookup(nn.Cell):
+    """
+    A embeddings lookup table with a fixed dictionary and size.
+
+    Args:
+        vocab_size (int): Size of the dictionary of embeddings.
+        embedding_size (int): The size of each embedding vector.
+        embedding_shape (list): [batch_size, seq_length, embedding_size], the shape of
+                         each embedding vector.
+        use_one_hot_embeddings (bool): Specifies whether to use one hot encoding form. Default: False.
+        initializer_range (float): Initialization value of TruncatedNormal. Default: 0.02.
+    """
+    def __init__(self,
+                 vocab_size,
+                 embedding_size,
+                 embedding_shape,
+                 use_one_hot_embeddings=False,
+                 initializer_range=0.02):
+        super(EmbeddingLookup, self).__init__()
+        self.vocab_size = vocab_size
+        self.use_one_hot_embeddings = use_one_hot_embeddings
+        self.embedding_table = Parameter(initializer
+                                         (TruncatedNormal(initializer_range),
+                                          [vocab_size, embedding_size]),
+                                         name='embedding_table')
+        self.expand = P.ExpandDims()
+        self.shape_flat = (-1,)
+        self.gather = P.GatherV2()
+        self.one_hot = P.OneHot()
+        self.on_value = Tensor(1.0, mstype.float32)
+        self.off_value = Tensor(0.0, mstype.float32)
+        self.array_mul = P.MatMul()
+        self.reshape = P.Reshape()
+        self.shape = tuple(embedding_shape)
+
+    def construct(self, input_ids):
+        extended_ids = self.expand(input_ids, -1)
+        flat_ids = self.reshape(extended_ids, self.shape_flat)
+        if self.use_one_hot_embeddings:
+            one_hot_ids = self.one_hot(flat_ids, self.vocab_size, self.on_value, self.off_value)
+            output_for_reshape = self.array_mul(
+                one_hot_ids, self.embedding_table)
+        else:
+            output_for_reshape = self.gather(self.embedding_table, flat_ids, 0)
+        output = self.reshape(output_for_reshape, self.shape)
+        return output, self.embedding_table
+
+
+class EmbeddingPostprocessor(nn.Cell):
+    """
+    Postprocessors apply positional and token type embeddings to word embeddings.
+
+    Args:
+        embedding_size (int): The size of each embedding vector.
+        embedding_shape (list): [batch_size, seq_length, embedding_size], the shape of
+                         each embedding vector.
+        use_token_type (bool): Specifies whether to use token type embeddings. Default: False.
+        token_type_vocab_size (int): Size of token type vocab. Default: 16.
+        use_one_hot_embeddings (bool): Specifies whether to use one hot encoding form. Default: False.
+        initializer_range (float): Initialization value of TruncatedNormal. Default: 0.02.
+        max_position_embeddings (int): Maximum length of sequences used in this
+                                 model. Default: 512.
+        dropout_prob (float): The dropout probability. Default: 0.1.
+    """
+    def __init__(self,
+                 embedding_size,
+                 embedding_shape,
+                 use_relative_positions=False,
+                 use_token_type=False,
+                 token_type_vocab_size=16,
+                 use_one_hot_embeddings=False,
+                 initializer_range=0.02,
+                 max_position_embeddings=512,
+                 dropout_prob=0.1):
+        super(EmbeddingPostprocessor, self).__init__()
+        self.use_token_type = use_token_type
+        self.token_type_vocab_size = token_type_vocab_size
+        self.use_one_hot_embeddings = use_one_hot_embeddings
+        self.max_position_embeddings = max_position_embeddings
+        self.embedding_table = Parameter(initializer
+                                         (TruncatedNormal(initializer_range),
+                                          [token_type_vocab_size,
+                                           embedding_size]),
+                                         name='embedding_table')
+
+        self.shape_flat = (-1,)
+        self.one_hot = P.OneHot()
+        self.on_value = Tensor(1.0, mstype.float32)
+        self.off_value = Tensor(0.1, mstype.float32)
+        self.array_mul = P.MatMul()
+        self.reshape = P.Reshape()
+        self.shape = tuple(embedding_shape)
+        self.layernorm = nn.LayerNorm((embedding_size,))
+        self.dropout = nn.Dropout(1 - dropout_prob)
+        self.gather = P.GatherV2()
+        self.use_relative_positions = use_relative_positions
+        self.slice = P.StridedSlice()
+        self.full_position_embeddings = Parameter(initializer
+                                                  (TruncatedNormal(initializer_range),
+                                                   [max_position_embeddings,
+                                                    embedding_size]),
+                                                  name='full_position_embeddings')
+
+    def construct(self, token_type_ids, word_embeddings):
+        output = word_embeddings
+        if self.use_token_type:
+            flat_ids = self.reshape(token_type_ids, self.shape_flat)
+            if self.use_one_hot_embeddings:
+                one_hot_ids = self.one_hot(flat_ids,
+                                           self.token_type_vocab_size, self.on_value, self.off_value)
+                token_type_embeddings = self.array_mul(one_hot_ids,
+                                                       self.embedding_table)
+            else:
+                token_type_embeddings = self.gather(self.embedding_table, flat_ids, 0)
+            token_type_embeddings = self.reshape(token_type_embeddings, self.shape)
+            output += token_type_embeddings
+        if not self.use_relative_positions:
+            _, seq, width = self.shape
+            position_embeddings = self.slice(self.full_position_embeddings, (0, 0), (seq, width), (1, 1))
+            position_embeddings = self.reshape(position_embeddings, (1, seq, width))
+            output += position_embeddings
+        output = self.layernorm(output)
+        output = self.dropout(output)
+        return output
+
+
+class BertOutput(nn.Cell):
+    """
+    Apply a linear computation to hidden status and a residual computation to input.
+
+    Args:
+        in_channels (int): Input channels.
+        out_channels (int): Output channels.
+        initializer_range (float): Initialization value of TruncatedNormal. Default: 0.02.
+        dropout_prob (float): The dropout probability. Default: 0.1.
+        compute_type (:class:`mindspore.dtype`): Compute type in BertTransformer. Default: mstype.float32.
+    """
+    def __init__(self,
+                 in_channels,
+                 out_channels,
+                 initializer_range=0.02,
+                 dropout_prob=0.1,
+                 compute_type=mstype.float32,
+                 enable_fused_layernorm=False):
+        super(BertOutput, self).__init__()
+        self.dense = nn.Dense(in_channels, out_channels,
+                              weight_init=TruncatedNormal(initializer_range)).to_float(compute_type)
+        self.dropout = nn.Dropout(1 - dropout_prob)
+        self.dropout_prob = dropout_prob
+        self.add = P.TensorAdd()
+        if compute_type == mstype.float16:
+            self.layernorm = FusedLayerNorm((out_channels,),
+                                            use_batch_norm=enable_fused_layernorm).to_float(compute_type)
+        else:
+            self.layernorm = nn.LayerNorm((out_channels,)).to_float(compute_type)
+        self.cast = P.Cast()
+
+    def construct(self, hidden_status, input_tensor):
+        output = self.dense(hidden_status)
+        output = self.dropout(output)
+        output = self.add(output, input_tensor)
+        output = self.layernorm(output)
+        return output
+
+
+class RelaPosMatrixGenerator(nn.Cell):
+    """
+    Generates matrix of relative positions between inputs.
+
+    Args:
+        length (int): Length of one dim for the matrix to be generated.
+        max_relative_position (int): Max value of relative position.
+    """
+    def __init__(self, length, max_relative_position):
+        super(RelaPosMatrixGenerator, self).__init__()
+        self._length = length
+        self._max_relative_position = Tensor(max_relative_position, dtype=mstype.int32)
+        self._min_relative_position = Tensor(-max_relative_position, dtype=mstype.int32)
+        self.range_length = -length + 1
+
+        self.tile = P.Tile()
+        self.range_mat = P.Reshape()
+        self.sub = P.Sub()
+        self.expanddims = P.ExpandDims()
+        self.cast = P.Cast()
+
+    def construct(self):
+        range_vec_row_out = self.cast(F.tuple_to_array(F.make_range(self._length)), mstype.int32)
+        range_vec_col_out = self.range_mat(range_vec_row_out, (self._length, -1))
+        tile_row_out = self.tile(range_vec_row_out, (self._length,))
+        tile_col_out = self.tile(range_vec_col_out, (1, self._length))
+        range_mat_out = self.range_mat(tile_row_out, (self._length, self._length))
+        transpose_out = self.range_mat(tile_col_out, (self._length, self._length))
+        distance_mat = self.sub(range_mat_out, transpose_out)
+
+        distance_mat_clipped = C.clip_by_value(distance_mat,
+                                               self._min_relative_position,
+                                               self._max_relative_position)
+
+        # Shift values to be >=0. Each integer still uniquely identifies a
+        # relative position difference.
+        final_mat = distance_mat_clipped + self._max_relative_position
+        return final_mat
+
+
+class RelaPosEmbeddingsGenerator(nn.Cell):
+    """
+    Generates tensor of size [length, length, depth].
+
+    Args:
+        length (int): Length of one dim for the matrix to be generated.
+        depth (int): Size of each attention head.
+        max_relative_position (int): Maxmum value of relative position.
+        initializer_range (float): Initialization value of TruncatedNormal.
+        use_one_hot_embeddings (bool): Specifies whether to use one hot encoding form. Default: False.
+    """
+    def __init__(self,
+                 length,
+                 depth,
+                 max_relative_position,
+                 initializer_range,
+                 use_one_hot_embeddings=False):
+        super(RelaPosEmbeddingsGenerator, self).__init__()
+        self.depth = depth
+        self.vocab_size = max_relative_position * 2 + 1
+        self.use_one_hot_embeddings = use_one_hot_embeddings
+
+        self.embeddings_table = Parameter(
+            initializer(TruncatedNormal(initializer_range),
+                        [self.vocab_size, self.depth]),
+            name='embeddings_for_position')
+
+        self.relative_positions_matrix = RelaPosMatrixGenerator(length=length,
+                                                                max_relative_position=max_relative_position)
+        self.reshape = P.Reshape()
+        self.one_hot = P.OneHot()
+        self.on_value = Tensor(1.0, mstype.float32)
+        self.off_value = Tensor(0.0, mstype.float32)
+        self.shape = P.Shape()
+        self.gather = P.GatherV2()  # index_select
+        self.matmul = P.BatchMatMul()
+
+    def construct(self):
+        relative_positions_matrix_out = self.relative_positions_matrix()
+
+        # Generate embedding for each relative position of dimension depth.
+        if self.use_one_hot_embeddings:
+            flat_relative_positions_matrix = self.reshape(relative_positions_matrix_out, (-1,))
+            one_hot_relative_positions_matrix = self.one_hot(
+                flat_relative_positions_matrix, self.vocab_size, self.on_value, self.off_value)
+            embeddings = self.matmul(one_hot_relative_positions_matrix, self.embeddings_table)
+            my_shape = self.shape(relative_positions_matrix_out) + (self.depth,)
+            embeddings = self.reshape(embeddings, my_shape)
+        else:
+            embeddings = self.gather(self.embeddings_table,
+                                     relative_positions_matrix_out, 0)
+        return embeddings
+
+
+class SaturateCast(nn.Cell):
+    """
+    Performs a safe saturating cast. This operation applies proper clamping before casting to prevent
+    the danger that the value will overflow or underflow.
+
+    Args:
+        src_type (:class:`mindspore.dtype`): The type of the elements of the input tensor. Default: mstype.float32.
+        dst_type (:class:`mindspore.dtype`): The type of the elements of the output tensor. Default: mstype.float32.
+    """
+    def __init__(self, src_type=mstype.float32, dst_type=mstype.float32):
+        super(SaturateCast, self).__init__()
+        np_type = mstype.dtype_to_nptype(dst_type)
+        min_type = np.finfo(np_type).min
+        max_type = np.finfo(np_type).max
+
+        self.tensor_min_type = Tensor([min_type], dtype=src_type)
+        self.tensor_max_type = Tensor([max_type], dtype=src_type)
+
+        self.min_op = P.Minimum()
+        self.max_op = P.Maximum()
+        self.cast = P.Cast()
+        self.dst_type = dst_type
+
+    def construct(self, x):
+        out = self.max_op(x, self.tensor_min_type)
+        out = self.min_op(out, self.tensor_max_type)
+        return self.cast(out, self.dst_type)
+
+
+class BertAttention(nn.Cell):
+    """
+    Apply multi-headed attention from "from_tensor" to "to_tensor".
+
+    Args:
+        batch_size (int): Batch size of input datasets.
+        from_tensor_width (int): Size of last dim of from_tensor.
+        to_tensor_width (int): Size of last dim of to_tensor.
+        from_seq_length (int): Length of from_tensor sequence.
+        to_seq_length (int): Length of to_tensor sequence.
+        num_attention_heads (int): Number of attention heads. Default: 1.
+        size_per_head (int): Size of each attention head. Default: 512.
+        query_act (str): Activation function for the query transform. Default: None.
+        key_act (str): Activation function for the key transform. Default: None.
+        value_act (str): Activation function for the value transform. Default: None.
+        has_attention_mask (bool): Specifies whether to use attention mask. Default: False.
+        attention_probs_dropout_prob (float): The dropout probability for
+                                      BertAttention. Default: 0.0.
+        use_one_hot_embeddings (bool): Specifies whether to use one hot encoding form. Default: False.
+        initializer_range (float): Initialization value of TruncatedNormal. Default: 0.02.
+        do_return_2d_tensor (bool): True for return 2d tensor. False for return 3d
+                             tensor. Default: False.
+        use_relative_positions (bool): Specifies whether to use relative positions. Default: False.
+        compute_type (:class:`mindspore.dtype`): Compute type in BertAttention. Default: mstype.float32.
+    """
+    def __init__(self,
+                 batch_size,
+                 from_tensor_width,
+                 to_tensor_width,
+                 from_seq_length,
+                 to_seq_length,
+                 num_attention_heads=1,
+                 size_per_head=512,
+                 query_act=None,
+                 key_act=None,
+                 value_act=None,
+                 has_attention_mask=False,
+                 attention_probs_dropout_prob=0.0,
+                 use_one_hot_embeddings=False,
+                 initializer_range=0.02,
+                 do_return_2d_tensor=False,
+                 use_relative_positions=False,
+                 compute_type=mstype.float32):
+
+        super(BertAttention, self).__init__()
+        self.batch_size = batch_size
+        self.from_seq_length = from_seq_length
+        self.to_seq_length = to_seq_length
+        self.num_attention_heads = num_attention_heads
+        self.size_per_head = size_per_head
+        self.has_attention_mask = has_attention_mask
+        self.use_relative_positions = use_relative_positions
+
+        self.scores_mul = Tensor([1.0 / math.sqrt(float(self.size_per_head))], dtype=compute_type)
+        self.reshape = P.Reshape()
+        self.shape_from_2d = (-1, from_tensor_width)
+        self.shape_to_2d = (-1, to_tensor_width)
+        weight = TruncatedNormal(initializer_range)
+        units = num_attention_heads * size_per_head
+        self.query_layer = nn.Dense(from_tensor_width,
+                                    units,
+                                    activation=query_act,
+                                    weight_init=weight).to_float(compute_type)
+        self.key_layer = nn.Dense(to_tensor_width,
+                                  units,
+                                  activation=key_act,
+                                  weight_init=weight).to_float(compute_type)
+        self.value_layer = nn.Dense(to_tensor_width,
+                                    units,
+                                    activation=value_act,
+                                    weight_init=weight).to_float(compute_type)
+
+        self.shape_from = (batch_size, from_seq_length, num_attention_heads, size_per_head)
+        self.shape_to = (
+            batch_size, to_seq_length, num_attention_heads, size_per_head)
+
+        self.matmul_trans_b = P.BatchMatMul(transpose_b=True)
+        self.multiply = P.Mul()
+        self.transpose = P.Transpose()
+        self.trans_shape = (0, 2, 1, 3)
+        self.trans_shape_relative = (2, 0, 1, 3)
+        self.trans_shape_position = (1, 2, 0, 3)
+        self.multiply_data = Tensor([-10000.0,], dtype=compute_type)
+        self.batch_num = batch_size * num_attention_heads
+        self.matmul = P.BatchMatMul()
+
+        self.softmax = nn.Softmax()
+        self.dropout = nn.Dropout(1 - attention_probs_dropout_prob)
+
+        if self.has_attention_mask:
+            self.expand_dims = P.ExpandDims()
+            self.sub = P.Sub()
+            self.add = P.TensorAdd()
+            self.cast = P.Cast()
+            self.get_dtype = P.DType()
+        if do_return_2d_tensor:
+            self.shape_return = (batch_size * from_seq_length, num_attention_heads * size_per_head)
+        else:
+            self.shape_return = (batch_size, from_seq_length, num_attention_heads * size_per_head)
+
+        self.cast_compute_type = SaturateCast(dst_type=compute_type)
+        if self.use_relative_positions:
+            self._generate_relative_positions_embeddings = \
+                RelaPosEmbeddingsGenerator(length=to_seq_length,
+                                           depth=size_per_head,
+                                           max_relative_position=16,
+                                           initializer_range=initializer_range,
+                                           use_one_hot_embeddings=use_one_hot_embeddings)
+
+    def construct(self, from_tensor, to_tensor, attention_mask):
+        # reshape 2d/3d input tensors to 2d
+        from_tensor_2d = self.reshape(from_tensor, self.shape_from_2d)
+        to_tensor_2d = self.reshape(to_tensor, self.shape_to_2d)
+        query_out = self.query_layer(from_tensor_2d)
+        key_out = self.key_layer(to_tensor_2d)
+        value_out = self.value_layer(to_tensor_2d)
+
+        query_layer = self.reshape(query_out, self.shape_from)
+        query_layer = self.transpose(query_layer, self.trans_shape)
+        key_layer = self.reshape(key_out, self.shape_to)
+        key_layer = self.transpose(key_layer, self.trans_shape)
+
+        attention_scores = self.matmul_trans_b(query_layer, key_layer)
+
+        # use_relative_position, supplementary logic
+        if self.use_relative_positions:
+            # 'relations_keys' = [F|T, F|T, H]
+            relations_keys = self._generate_relative_positions_embeddings()
+            relations_keys = self.cast_compute_type(relations_keys)
+            # query_layer_t is [F, B, N, H]
+            query_layer_t = self.transpose(query_layer, self.trans_shape_relative)
+            # query_layer_r is [F, B * N, H]
+            query_layer_r = self.reshape(query_layer_t,
+                                         (self.from_seq_length,
+                                          self.batch_num,
+                                          self.size_per_head))
+            # key_position_scores is [F, B * N, F|T]
+            key_position_scores = self.matmul_trans_b(query_layer_r,
+                                                      relations_keys)
+            # key_position_scores_r is [F, B, N, F|T]
+            key_position_scores_r = self.reshape(key_position_scores,
+                                                 (self.from_seq_length,
+                                                  self.batch_size,
+                                                  self.num_attention_heads,
+                                                  self.from_seq_length))
+            # key_position_scores_r_t is [B, N, F, F|T]
+            key_position_scores_r_t = self.transpose(key_position_scores_r,
+                                                     self.trans_shape_position)
+            attention_scores = attention_scores + key_position_scores_r_t
+
+        attention_scores = self.multiply(self.scores_mul, attention_scores)
+
+        if self.has_attention_mask:
+            attention_mask = self.expand_dims(attention_mask, 1)
+            multiply_out = self.sub(self.cast(F.tuple_to_array((1.0,)), self.get_dtype(attention_scores)),
+                                    self.cast(attention_mask, self.get_dtype(attention_scores)))
+
+            adder = self.multiply(multiply_out, self.multiply_data)
+            attention_scores = self.add(adder, attention_scores)
+
+        attention_probs = self.softmax(attention_scores)
+        attention_probs = self.dropout(attention_probs)
+
+        value_layer = self.reshape(value_out, self.shape_to)
+        value_layer = self.transpose(value_layer, self.trans_shape)
+        context_layer = self.matmul(attention_probs, value_layer)
+
+        # use_relative_position, supplementary logic
+        if self.use_relative_positions:
+            # 'relations_values' = [F|T, F|T, H]
+            relations_values = self._generate_relative_positions_embeddings()
+            relations_values = self.cast_compute_type(relations_values)
+            # attention_probs_t is [F, B, N, T]
+            attention_probs_t = self.transpose(attention_probs, self.trans_shape_relative)
+            # attention_probs_r is [F, B * N, T]
+            attention_probs_r = self.reshape(
+                attention_probs_t,
+                (self.from_seq_length,
+                 self.batch_num,
+                 self.to_seq_length))
+            # value_position_scores is [F, B * N, H]
+            value_position_scores = self.matmul(attention_probs_r,
+                                                relations_values)
+            # value_position_scores_r is [F, B, N, H]
+            value_position_scores_r = self.reshape(value_position_scores,
+                                                   (self.from_seq_length,
+                                                    self.batch_size,
+                                                    self.num_attention_heads,
+                                                    self.size_per_head))
+            # value_position_scores_r_t is [B, N, F, H]
+            value_position_scores_r_t = self.transpose(value_position_scores_r,
+                                                       self.trans_shape_position)
+            context_layer = context_layer + value_position_scores_r_t
+
+        context_layer = self.transpose(context_layer, self.trans_shape)
+        context_layer = self.reshape(context_layer, self.shape_return)
+
+        return context_layer
+
+
+class BertSelfAttention(nn.Cell):
+    """
+    Apply self-attention.
+
+    Args:
+        batch_size (int): Batch size of input dataset.
+        seq_length (int): Length of input sequence.
+        hidden_size (int): Size of the bert encoder layers.
+        num_attention_heads (int): Number of attention heads. Default: 12.
+        attention_probs_dropout_prob (float): The dropout probability for
+                                      BertAttention. Default: 0.1.
+        use_one_hot_embeddings (bool): Specifies whether to use one_hot encoding form. Default: False.
+        initializer_range (float): Initialization value of TruncatedNormal. Default: 0.02.
+        hidden_dropout_prob (float): The dropout probability for BertOutput. Default: 0.1.
+        use_relative_positions (bool): Specifies whether to use relative positions. Default: False.
+        compute_type (:class:`mindspore.dtype`): Compute type in BertSelfAttention. Default: mstype.float32.
+    """
+    def __init__(self,
+                 batch_size,
+                 seq_length,
+                 hidden_size,
+                 num_attention_heads=12,
+                 attention_probs_dropout_prob=0.1,
+                 use_one_hot_embeddings=False,
+                 initializer_range=0.02,
+                 hidden_dropout_prob=0.1,
+                 use_relative_positions=False,
+                 compute_type=mstype.float32,
+                 enable_fused_layernorm=False):
+        super(BertSelfAttention, self).__init__()
+        if hidden_size % num_attention_heads != 0:
+            raise ValueError("The hidden size (%d) is not a multiple of the number "
+                             "of attention heads (%d)" % (hidden_size, num_attention_heads))
+
+        self.size_per_head = int(hidden_size / num_attention_heads)
+
+        self.attention = BertAttention(
+            batch_size=batch_size,
+            from_tensor_width=hidden_size,
+            to_tensor_width=hidden_size,
+            from_seq_length=seq_length,
+            to_seq_length=seq_length,
+            num_attention_heads=num_attention_heads,
+            size_per_head=self.size_per_head,
+            attention_probs_dropout_prob=attention_probs_dropout_prob,
+            use_one_hot_embeddings=use_one_hot_embeddings,
+            initializer_range=initializer_range,
+            use_relative_positions=use_relative_positions,
+            has_attention_mask=True,
+            do_return_2d_tensor=True,
+            compute_type=compute_type)
+
+        self.output = BertOutput(in_channels=hidden_size,
+                                 out_channels=hidden_size,
+                                 initializer_range=initializer_range,
+                                 dropout_prob=hidden_dropout_prob,
+                                 compute_type=compute_type,
+                                 enable_fused_layernorm=enable_fused_layernorm)
+        self.reshape = P.Reshape()
+        self.shape = (-1, hidden_size)
+
+    def construct(self, input_tensor, attention_mask):
+        input_tensor = self.reshape(input_tensor, self.shape)
+        attention_output = self.attention(input_tensor, input_tensor, attention_mask)
+        output = self.output(attention_output, input_tensor)
+        return output
+
+
+class BertEncoderCell(nn.Cell):
+    """
+    Encoder cells used in BertTransformer.
+
+    Args:
+        batch_size (int): Batch size of input dataset.
+        hidden_size (int): Size of the bert encoder layers. Default: 768.
+        seq_length (int): Length of input sequence. Default: 512.
+        num_attention_heads (int): Number of attention heads. Default: 12.
+        intermediate_size (int): Size of intermediate layer. Default: 3072.
+        attention_probs_dropout_prob (float): The dropout probability for
+                                      BertAttention. Default: 0.02.
+        use_one_hot_embeddings (bool): Specifies whether to use one hot encoding form. Default: False.
+        initializer_range (float): Initialization value of TruncatedNormal. Default: 0.02.
+        hidden_dropout_prob (float): The dropout probability for BertOutput. Default: 0.1.
+        use_relative_positions (bool): Specifies whether to use relative positions. Default: False.
+        hidden_act (str): Activation function. Default: "gelu".
+        compute_type (:class:`mindspore.dtype`): Compute type in attention. Default: mstype.float32.
+    """
+    def __init__(self,
+                 batch_size,
+                 hidden_size=768,
+                 seq_length=512,
+                 num_attention_heads=12,
+                 intermediate_size=3072,
+                 attention_probs_dropout_prob=0.02,
+                 use_one_hot_embeddings=False,
+                 initializer_range=0.02,
+                 hidden_dropout_prob=0.1,
+                 use_relative_positions=False,
+                 hidden_act="gelu",
+                 compute_type=mstype.float32,
+                 enable_fused_layernorm=False):
+        super(BertEncoderCell, self).__init__()
+        self.attention = BertSelfAttention(
+            batch_size=batch_size,
+            hidden_size=hidden_size,
+            seq_length=seq_length,
+            num_attention_heads=num_attention_heads,
+            attention_probs_dropout_prob=attention_probs_dropout_prob,
+            use_one_hot_embeddings=use_one_hot_embeddings,
+            initializer_range=initializer_range,
+            hidden_dropout_prob=hidden_dropout_prob,
+            use_relative_positions=use_relative_positions,
+            compute_type=compute_type,
+            enable_fused_layernorm=enable_fused_layernorm)
+        self.intermediate = nn.Dense(in_channels=hidden_size,
+                                     out_channels=intermediate_size,
+                                     activation=hidden_act,
+                                     weight_init=TruncatedNormal(initializer_range)).to_float(compute_type)
+        self.output = BertOutput(in_channels=intermediate_size,
+                                 out_channels=hidden_size,
+                                 initializer_range=initializer_range,
+                                 dropout_prob=hidden_dropout_prob,
+                                 compute_type=compute_type,
+                                 enable_fused_layernorm=enable_fused_layernorm)
+
+    def construct(self, hidden_states, attention_mask):
+        # self-attention
+        attention_output = self.attention(hidden_states, attention_mask)
+        # feed construct
+        intermediate_output = self.intermediate(attention_output)
+        # add and normalize
+        output = self.output(intermediate_output, attention_output)
+        return output
+
+
+class BertTransformer(nn.Cell):
+    """
+    Multi-layer bert transformer.
+
+    Args:
+        batch_size (int): Batch size of input dataset.
+        hidden_size (int): Size of the encoder layers.
+        seq_length (int): Length of input sequence.
+        num_hidden_layers (int): Number of hidden layers in encoder cells.
+        num_attention_heads (int): Number of attention heads in encoder cells. Default: 12.
+        intermediate_size (int): Size of intermediate layer in encoder cells. Default: 3072.
+        attention_probs_dropout_prob (float): The dropout probability for
+                                      BertAttention. Default: 0.1.
+        use_one_hot_embeddings (bool): Specifies whether to use one hot encoding form. Default: False.
+        initializer_range (float): Initialization value of TruncatedNormal. Default: 0.02.
+        hidden_dropout_prob (float): The dropout probability for BertOutput. Default: 0.1.
+        use_relative_positions (bool): Specifies whether to use relative positions. Default: False.
+        hidden_act (str): Activation function used in the encoder cells. Default: "gelu".
+        compute_type (:class:`mindspore.dtype`): Compute type in BertTransformer. Default: mstype.float32.
+        return_all_encoders (bool): Specifies whether to return all encoders. Default: False.
+    """
+    def __init__(self,
+                 batch_size,
+                 hidden_size,
+                 seq_length,
+                 num_hidden_layers,
+                 num_attention_heads=12,
+                 intermediate_size=3072,
+                 attention_probs_dropout_prob=0.1,
+                 use_one_hot_embeddings=False,
+                 initializer_range=0.02,
+                 hidden_dropout_prob=0.1,
+                 use_relative_positions=False,
+                 hidden_act="gelu",
+                 compute_type=mstype.float32,
+                 return_all_encoders=False,
+                 enable_fused_layernorm=False):
+        super(BertTransformer, self).__init__()
+        self.return_all_encoders = return_all_encoders
+
+        layers = []
+        for _ in range(num_hidden_layers):
+            layer = BertEncoderCell(batch_size=batch_size,
+                                    hidden_size=hidden_size,
+                                    seq_length=seq_length,
+                                    num_attention_heads=num_attention_heads,
+                                    intermediate_size=intermediate_size,
+                                    attention_probs_dropout_prob=attention_probs_dropout_prob,
+                                    use_one_hot_embeddings=use_one_hot_embeddings,
+                                    initializer_range=initializer_range,
+                                    hidden_dropout_prob=hidden_dropout_prob,
+                                    use_relative_positions=use_relative_positions,
+                                    hidden_act=hidden_act,
+                                    compute_type=compute_type,
+                                    enable_fused_layernorm=enable_fused_layernorm)
+            layers.append(layer)
+
+        self.layers = nn.CellList(layers)
+
+        self.reshape = P.Reshape()
+        self.shape = (-1, hidden_size)
+        self.out_shape = (batch_size, seq_length, hidden_size)
+
+    def construct(self, input_tensor, attention_mask):
+        prev_output = self.reshape(input_tensor, self.shape)
+
+        all_encoder_layers = ()
+        for layer_module in self.layers:
+            layer_output = layer_module(prev_output, attention_mask)
+            prev_output = layer_output
+
+            if self.return_all_encoders:
+                layer_output = self.reshape(layer_output, self.out_shape)
+                all_encoder_layers = all_encoder_layers + (layer_output,)
+
+        if not self.return_all_encoders:
+            prev_output = self.reshape(prev_output, self.out_shape)
+            all_encoder_layers = all_encoder_layers + (prev_output,)
+        return all_encoder_layers
+
+
+class CreateAttentionMaskFromInputMask(nn.Cell):
+    """
+    Create attention mask according to input mask.
+
+    Args:
+        config (Class): Configuration for BertModel.
+    """
+    def __init__(self, config):
+        super(CreateAttentionMaskFromInputMask, self).__init__()
+        self.input_mask_from_dataset = config.input_mask_from_dataset
+        self.input_mask = None
+
+        if not self.input_mask_from_dataset:
+            self.input_mask = initializer(
+                "ones", [config.batch_size, config.seq_length], mstype.int32).to_tensor()
+
+        self.cast = P.Cast()
+        self.reshape = P.Reshape()
+        self.shape = (config.batch_size, 1, config.seq_length)
+        self.broadcast_ones = initializer(
+            "ones", [config.batch_size, config.seq_length, 1], mstype.float32).to_tensor()
+        self.batch_matmul = P.BatchMatMul()
+
+    def construct(self, input_mask):
+        if not self.input_mask_from_dataset:
+            input_mask = self.input_mask
+
+        input_mask = self.cast(self.reshape(input_mask, self.shape), mstype.float32)
+        attention_mask = self.batch_matmul(self.broadcast_ones, input_mask)
+        return attention_mask
+
+
+class BertModel(nn.Cell):
+    """
+    Bidirectional Encoder Representations from Transformers.
+
+    Args:
+        config (Class): Configuration for BertModel.
+        is_training (bool): True for training mode. False for eval mode.
+        use_one_hot_embeddings (bool): Specifies whether to use one hot encoding form. Default: False.
+    """
+    def __init__(self,
+                 config,
+                 is_training,
+                 use_one_hot_embeddings=False):
+        super(BertModel, self).__init__()
+        config = copy.deepcopy(config)
+        if not is_training:
+            config.hidden_dropout_prob = 0.0
+            config.attention_probs_dropout_prob = 0.0
+
+        self.input_mask_from_dataset = config.input_mask_from_dataset
+        self.token_type_ids_from_dataset = config.token_type_ids_from_dataset
+        self.batch_size = config.batch_size
+        self.seq_length = config.seq_length
+        self.hidden_size = config.hidden_size
+        self.num_hidden_layers = config.num_hidden_layers
+        self.embedding_size = config.hidden_size
+        self.token_type_ids = None
+
+        self.last_idx = self.num_hidden_layers - 1
+        output_embedding_shape = [self.batch_size, self.seq_length,
+                                  self.embedding_size]
+
+        if not self.token_type_ids_from_dataset:
+            self.token_type_ids = initializer(
+                "zeros", [self.batch_size, self.seq_length], mstype.int32).to_tensor()
+
+        self.bert_embedding_lookup = EmbeddingLookup(
+            vocab_size=config.vocab_size,
+            embedding_size=self.embedding_size,
+            embedding_shape=output_embedding_shape,
+            use_one_hot_embeddings=use_one_hot_embeddings,
+            initializer_range=config.initializer_range)
+
+        self.bert_embedding_postprocessor = EmbeddingPostprocessor(
+            embedding_size=self.embedding_size,
+            embedding_shape=output_embedding_shape,
+            use_relative_positions=config.use_relative_positions,
+            use_token_type=True,
+            token_type_vocab_size=config.type_vocab_size,
+            use_one_hot_embeddings=use_one_hot_embeddings,
+            initializer_range=0.02,
+            max_position_embeddings=config.max_position_embeddings,
+            dropout_prob=config.hidden_dropout_prob)
+
+        self.bert_encoder = BertTransformer(
+            batch_size=self.batch_size,
+            hidden_size=self.hidden_size,
+            seq_length=self.seq_length,
+            num_attention_heads=config.num_attention_heads,
+            num_hidden_layers=self.num_hidden_layers,
+            intermediate_size=config.intermediate_size,
+            attention_probs_dropout_prob=config.attention_probs_dropout_prob,
+            use_one_hot_embeddings=use_one_hot_embeddings,
+            initializer_range=config.initializer_range,
+            hidden_dropout_prob=config.hidden_dropout_prob,
+            use_relative_positions=config.use_relative_positions,
+            hidden_act=config.hidden_act,
+            compute_type=config.compute_type,
+            return_all_encoders=True,
+            enable_fused_layernorm=config.enable_fused_layernorm)
+
+        self.cast = P.Cast()
+        self.dtype = config.dtype
+        self.cast_compute_type = SaturateCast(dst_type=config.compute_type)
+        self.slice = P.StridedSlice()
+
+        self.squeeze_1 = P.Squeeze(axis=1)
+        self.dense = nn.Dense(self.hidden_size, self.hidden_size,
+                              activation="tanh",
+                              weight_init=TruncatedNormal(config.initializer_range)).to_float(config.compute_type)
+        self._create_attention_mask_from_input_mask = CreateAttentionMaskFromInputMask(config)
+
+    def construct(self, input_ids, token_type_ids, input_mask):
+
+        # embedding
+        if not self.token_type_ids_from_dataset:
+            token_type_ids = self.token_type_ids
+        word_embeddings, embedding_tables = self.bert_embedding_lookup(input_ids)
+        embedding_output = self.bert_embedding_postprocessor(token_type_ids,
+                                                             word_embeddings)
+
+        # attention mask [batch_size, seq_length, seq_length]
+        attention_mask = self._create_attention_mask_from_input_mask(input_mask)
+
+        # bert encoder
+        encoder_output = self.bert_encoder(self.cast_compute_type(embedding_output),
+                                           attention_mask)
+
+        sequence_output = self.cast(encoder_output[self.last_idx], self.dtype)
+
+        # pooler
+        sequence_slice = self.slice(sequence_output,
+                                    (0, 0, 0),
+                                    (self.batch_size, 1, self.hidden_size),
+                                    (1, 1, 1))
+        first_token = self.squeeze_1(sequence_slice)
+        pooled_output = self.dense(first_token)
+        pooled_output = self.cast(pooled_output, self.dtype)
+
+        return sequence_output, pooled_output, embedding_tables

tests/st/networks/models/bert/src/cluener_evaluation.py

@@ -0,0 +1,73 @@
+# 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.
+# ============================================================================
+
+'''bert clue evaluation'''
+
+import json
+import numpy as np
+import mindspore.common.dtype as mstype
+from mindspore.common.tensor import Tensor
+import tokenization
+from sample_process import label_generation, process_one_example_p
+from .evaluation_config import cfg
+from .CRF import postprocess
+
+vocab_file = "./vocab.txt"
+tokenizer_ = tokenization.FullTokenizer(vocab_file=vocab_file)
+
+def process(model, text, sequence_length):
+    """
+    process text.
+    """
+    data = [text]
+    features = []
+    res = []
+    ids = []
+    for i in data:
+        feature = process_one_example_p(tokenizer_, i, max_seq_len=sequence_length)
+        features.append(feature)
+        input_ids, input_mask, token_type_id = feature
+        input_ids = Tensor(np.array(input_ids), mstype.int32)
+        input_mask = Tensor(np.array(input_mask), mstype.int32)
+        token_type_id = Tensor(np.array(token_type_id), mstype.int32)
+        if cfg.use_crf:
+            backpointers, best_tag_id = model.predict(input_ids, input_mask, token_type_id, Tensor(1))
+            best_path = postprocess(backpointers, best_tag_id)
+            logits = []
+            for ele in best_path:
+                logits.extend(ele)
+            ids = logits
+        else:
+            logits = model.predict(input_ids, input_mask, token_type_id, Tensor(1))
+            ids = logits.asnumpy()
+            ids = np.argmax(ids, axis=-1)
+            ids = list(ids)
+    res = label_generation(text, ids)
+    return res
+
+def submit(model, path, sequence_length):
+    """
+    submit task
+    """
+    data = []
+    for line in open(path):
+        if not line.strip():
+            continue
+        oneline = json.loads(line.strip())
+        res = process(model, oneline["text"], sequence_length)
+        print("text", oneline["text"])
+        print("res:", res)
+        data.append(json.dumps({"label": res}, ensure_ascii=False))
+    open("ner_predict.json", "w").write("\n".join(data))

tests/st/networks/models/bert/src/config.py

@@ -0,0 +1,118 @@
+# 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.
+# ============================================================================
+"""
+network config setting, will be used in dataset.py, run_pretrain.py
+"""
+from easydict import EasyDict as edict
+import mindspore.common.dtype as mstype
+from .bert_model import BertConfig
+cfg = edict({
+    'bert_network': 'base',
+    'loss_scale_value': 65536,
+    'scale_factor': 2,
+    'scale_window': 1000,
+    'optimizer': 'Lamb',
+    'AdamWeightDecayDynamicLR': edict({
+        'learning_rate': 3e-5,
+        'end_learning_rate': 1e-10,
+        'power': 5.0,
+        'weight_decay': 1e-5,
+        'eps': 1e-6,
+        'warmup_steps': 10000,
+    }),
+    'Lamb': edict({
+        'start_learning_rate': 3e-5,
+        'end_learning_rate': 1e-10,
+        'power': 10.0,
+        'warmup_steps': 10000,
+        'weight_decay': 0.01,
+        'eps': 1e-6,
+    }),
+    '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=32,
+        seq_length=128,
+        vocab_size=21136,
+        hidden_size=768,
+        num_hidden_layers=12,
+        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=False,
+        input_mask_from_dataset=True,
+        token_type_ids_from_dataset=True,
+        dtype=mstype.float32,
+        compute_type=mstype.float16
+    )
+if cfg.bert_network == 'nezha':
+    bert_net_cfg = BertConfig(
+        batch_size=32,
+        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=0.1,
+        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.float16
+    )
+if cfg.bert_network == 'large':
+    bert_net_cfg = BertConfig(
+        batch_size=16,
+        seq_length=512,
+        vocab_size=30528,
+        hidden_size=1024,
+        num_hidden_layers=24,
+        num_attention_heads=16,
+        intermediate_size=4096,
+        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=False,
+        input_mask_from_dataset=True,
+        token_type_ids_from_dataset=True,
+        dtype=mstype.float32,
+        compute_type=mstype.float16,
+        enable_fused_layernorm=True
+    )

tests/st/networks/models/bert/src/dataset.py

@@ -0,0 +1,59 @@
+# 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.
+# ============================================================================
+"""
+Data operations, will be used in run_pretrain.py
+"""
+import os
+import mindspore.common.dtype as mstype
+import mindspore.dataset.engine.datasets as de
+import mindspore.dataset.transforms.c_transforms as C
+from mindspore import log as logger
+from .config import bert_net_cfg
+
+
+def create_bert_dataset(epoch_size=1, device_num=1, rank=0, do_shuffle="true", enable_data_sink="true",
+                        data_sink_steps=1, data_dir=None, schema_dir=None):
+    """create train dataset"""
+    # apply repeat operations
+    repeat_count = epoch_size
+    files = os.listdir(data_dir)
+    data_files = []
+    for file_name in files:
+        if "tfrecord" in file_name:
+            data_files.append(os.path.join(data_dir, file_name))
+    ds = de.TFRecordDataset(data_files, schema_dir if schema_dir != "" else None,
+                            columns_list=["input_ids", "input_mask", "segment_ids", "next_sentence_labels",
+                                          "masked_lm_positions", "masked_lm_ids", "masked_lm_weights"],
+                            shuffle=(do_shuffle == "true"), num_shards=device_num, shard_id=rank,
+                            shard_equal_rows=True)
+    ori_dataset_size = ds.get_dataset_size()
+    new_size = ori_dataset_size
+    if enable_data_sink == "true":
+        new_size = data_sink_steps * bert_net_cfg.batch_size
+    ds.set_dataset_size(new_size)
+    new_repeat_count = int(repeat_count * ori_dataset_size // ds.get_dataset_size())
+    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
+    ds = ds.batch(bert_net_cfg.batch_size, drop_remainder=True)
+    ds = ds.repeat(new_repeat_count)
+    logger.info("data size: {}".format(ds.get_dataset_size()))
+    logger.info("repeatcount: {}".format(ds.get_repeat_count()))
+    return ds, new_repeat_count

tests/st/networks/models/bert/src/evaluation_config.py

@@ -0,0 +1,53 @@
+# 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.
+# ============================================================================
+
+"""
+config settings, will be used in finetune.py
+"""
+
+from easydict import EasyDict as edict
+import mindspore.common.dtype as mstype
+from .bert_model import BertConfig
+
+cfg = edict({
+    'task': 'NER',
+    'num_labels': 41,
+    'data_file': '/your/path/evaluation.tfrecord',
+    'schema_file': '/your/path/schema.json',
+    'finetune_ckpt': '/your/path/your.ckpt',
+    'use_crf': False,
+    'clue_benchmark': False,
+})
+
+bert_net_cfg = BertConfig(
+    batch_size=16 if not cfg.clue_benchmark else 1,
+    seq_length=128,
+    vocab_size=21128,
+    hidden_size=768,
+    num_hidden_layers=12,
+    num_attention_heads=12,
+    intermediate_size=3072,
+    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=False,
+    input_mask_from_dataset=True,
+    token_type_ids_from_dataset=True,
+    dtype=mstype.float32,
+    compute_type=mstype.float16,
+)

tests/st/networks/models/bert/src/finetune_config.py

@@ -0,0 +1,119 @@
+# 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.
+# ============================================================================
+
+"""
+config settings, will be used in finetune.py
+"""
+
+from easydict import EasyDict as edict
+import mindspore.common.dtype as mstype
+from .bert_model import BertConfig
+
+cfg = edict({
+    'task': 'NER',
+    'num_labels': 41,
+    'data_file': '/your/path/train.tfrecord',
+    'schema_file': '/your/path/schema.json',
+    'epoch_num': 5,
+    'ckpt_prefix': 'bert',
+    'ckpt_dir': None,
+    'pre_training_ckpt': '/your/path/pre_training.ckpt',
+    'use_crf': False,
+    'optimizer': 'Lamb',
+    'AdamWeightDecayDynamicLR': edict({
+        'learning_rate': 2e-5,
+        'end_learning_rate': 1e-7,
+        'power': 1.0,
+        'weight_decay': 1e-5,
+        'eps': 1e-6,
+    }),
+    'Lamb': edict({
+        'start_learning_rate': 2e-5,
+        'end_learning_rate': 1e-7,
+        'power': 1.0,
+        'decay_filter': lambda x: False,
+    }),
+    'Momentum': edict({
+        'learning_rate': 2e-5,
+        'momentum': 0.9,
+    }),
+})
+
+bert_net_cfg = BertConfig(
+    batch_size=16,
+    seq_length=128,
+    vocab_size=21128,
+    hidden_size=768,
+    num_hidden_layers=12,
+    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=False,
+    input_mask_from_dataset=True,
+    token_type_ids_from_dataset=True,
+    dtype=mstype.float32,
+    compute_type=mstype.float16,
+)
+
+tag_to_index = {
+    "O": 0,
+    "S_address": 1,
+    "B_address": 2,
+    "M_address": 3,
+    "E_address": 4,
+    "S_book": 5,
+    "B_book": 6,
+    "M_book": 7,
+    "E_book": 8,
+    "S_company": 9,
+    "B_company": 10,
+    "M_company": 11,
+    "E_company": 12,
+    "S_game": 13,
+    "B_game": 14,
+    "M_game": 15,
+    "E_game": 16,
+    "S_government": 17,
+    "B_government": 18,
+    "M_government": 19,
+    "E_government": 20,
+    "S_movie": 21,
+    "B_movie": 22,
+    "M_movie": 23,
+    "E_movie": 24,
+    "S_name": 25,
+    "B_name": 26,
+    "M_name": 27,
+    "E_name": 28,
+    "S_organization": 29,
+    "B_organization": 30,
+    "M_organization": 31,
+    "E_organization": 32,
+    "S_position": 33,
+    "B_position": 34,
+    "M_position": 35,
+    "E_position": 36,
+    "S_scene": 37,
+    "B_scene": 38,
+    "M_scene": 39,
+    "E_scene": 40,
+    "<START>": 41,
+    "<STOP>": 42
+}

tests/st/networks/models/bert/src/fused_layer_norm.py

@@ -0,0 +1,121 @@
+# 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.
+# ============================================================================
+"""fused layernorm"""
+from mindspore.ops import operations as P
+from mindspore.ops import functional as F
+from mindspore.common.parameter import Parameter
+from mindspore.common.initializer import initializer
+from mindspore.ops.primitive import constexpr
+import mindspore.common.dtype as mstype
+from mindspore.nn.cell import Cell
+
+import numpy as np
+
+
+__all__ = ['FusedLayerNorm']
+
+@constexpr
+def get_shape_for_norm(x_shape, begin_norm_axis):
+    print("input_shape: ", x_shape)
+    norm_shape = x_shape[begin_norm_axis:]
+    output_shape = (1, -1, 1, int(np.prod(norm_shape)))
+    print("output_shape: ", output_shape)
+    return output_shape
+
+class FusedLayerNorm(Cell):
+    r"""
+    Applies Layer Normalization over a mini-batch of inputs.
+
+    Layer normalization is widely used in recurrent neural networks. It applies
+    normalization over a mini-batch of inputs for each single training case as described
+    in the paper `Layer Normalization <https://arxiv.org/pdf/1607.06450.pdf>`_. Unlike batch
+    normalization, layer normalization performs exactly the same computation at training and
+    testing times. It can be described using the following formula. It is applied across all channels
+    and pixel but only one batch size.
+
+    .. math::
+        y = \frac{x - \mathrm{E}[x]}{\sqrt{\mathrm{Var}[x] + \epsilon}} * \gamma + \beta
+
+    Args:
+        normalized_shape (Union(tuple[int], list[int]): The normalization is performed over axis
+            `begin_norm_axis ... R - 1`.
+        begin_norm_axis (int): It first normalization dimension: normalization will be performed along dimensions
+            `begin_norm_axis: rank(inputs)`, the value should be in [-1, rank(input)). Default: -1.
+        begin_params_axis (int): The first parameter(beta, gamma)dimension: scale and centering parameters
+            will have dimensions `begin_params_axis: rank(inputs)` and will be broadcast with
+            the normalized inputs accordingly, the value should be in [-1, rank(input)). Default: -1.
+        gamma_init (Union[Tensor, str, Initializer, numbers.Number]): Initializer for the gamma weight.
+            The values of str refer to the function `initializer` including 'zeros', 'ones', 'xavier_uniform',
+            'he_uniform', etc. Default: 'ones'.
+        beta_init (Union[Tensor, str, Initializer, numbers.Number]): Initializer for the beta weight.
+            The values of str refer to the function `initializer` including 'zeros', 'ones', 'xavier_uniform',
+            'he_uniform', etc. Default: 'zeros'.
+        use_batch_nrom (bool): Whether use batchnorm to preocess.
+
+    Inputs:
+        - **input_x** (Tensor) - The shape of 'input_x' is :math:`(x_1, x_2, ..., x_R)`,
+          and `input_shape[begin_norm_axis:]` is equal to `normalized_shape`.
+
+    Outputs:
+        Tensor, the normalized and scaled offset tensor, has the same shape and data type as the `input_x`.
+
+    Examples:
+        >>> x = Tensor(np.ones([20, 5, 10, 10]), mindspore.float32)
+        >>> shape1 = x.shape()[1:]
+        >>> m = nn.LayerNorm(shape1,  begin_norm_axis=1, begin_params_axis=1)
+        >>> m(x)
+    """
+    def __init__(self,
+                 normalized_shape,
+                 begin_norm_axis=-1,
+                 begin_params_axis=-1,
+                 gamma_init='ones',
+                 beta_init='zeros',
+                 use_batch_norm=False):
+        super(FusedLayerNorm, self).__init__()
+        if not isinstance(normalized_shape, (tuple, list)):
+            raise TypeError("The type of 'normalized_shape' should be tuple[int] or list[int], but '{}' type is {}."
+                            .format(normalized_shape, type(normalized_shape)))
+        self.normalized_shape = normalized_shape
+        self.begin_norm_axis = begin_norm_axis
+        self.begin_params_axis = begin_params_axis
+        self.gamma = Parameter(initializer(
+            gamma_init, normalized_shape), name="gamma")
+        self.beta = Parameter(initializer(
+            beta_init, normalized_shape), name="beta")
+        self.layer_norm = P.LayerNorm(begin_norm_axis=self.begin_norm_axis, begin_params_axis=self.begin_params_axis)
+
+        self.batch_norm = P.BatchNorm(is_training=True, epsilon=1e-5)
+        self.use_batch_norm = use_batch_norm
+
+    def construct(self, input_x):
+        if self.use_batch_norm and self.training:
+            ones = P.Fill()(mstype.float32, F.shape(input_x)[:self.begin_norm_axis], 1.0)
+            zeros = P.Fill()(mstype.float32, F.shape(input_x)[:self.begin_norm_axis], 0.0)
+            shape_x = F.shape(input_x)
+            norm_shape = get_shape_for_norm(shape_x, self.begin_norm_axis)
+            input_x = F.reshape(input_x, norm_shape)
+            output, _, _, _, _, _ = self.batch_norm(input_x, ones, zeros, None, None)
+            output = F.reshape(output, shape_x)
+            y = output * self.gamma + self.beta
+        else:
+            y, _, _ = self.layer_norm(input_x, self.gamma, self.beta)
+        return y
+
+    def extend_repr(self):
+        """Display instance object as string."""
+        s = 'normalized_shape={}, begin_norm_axis={}, begin_params_axis={}, gamma{}, beta={}'.format(
+            self.normalized_shape, self.begin_norm_axis, self.begin_params_axis, self.gamma, self.beta)
+        return s

tests/st/networks/models/bert/src/sample_process.py

@@ -0,0 +1,100 @@
+# 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.
+# ============================================================================
+
+"""process txt"""
+
+import re
+import json
+
+def process_one_example_p(tokenizer, text, max_seq_len=128):
+    """process one testline"""
+    textlist = list(text)
+    tokens = []
+    for _, word in enumerate(textlist):
+        token = tokenizer.tokenize(word)
+        tokens.extend(token)
+    if len(tokens) >= max_seq_len - 1:
+        tokens = tokens[0:(max_seq_len - 2)]
+    ntokens = []
+    segment_ids = []
+    label_ids = []
+    ntokens.append("[CLS]")
+    segment_ids.append(0)
+    for _, token in enumerate(tokens):
+        ntokens.append(token)
+        segment_ids.append(0)
+    ntokens.append("[SEP]")
+    segment_ids.append(0)
+    input_ids = tokenizer.convert_tokens_to_ids(ntokens)
+    input_mask = [1] * len(input_ids)
+    while len(input_ids) < max_seq_len:
+        input_ids.append(0)
+        input_mask.append(0)
+        segment_ids.append(0)
+        label_ids.append(0)
+        ntokens.append("**NULL**")
+    assert len(input_ids) == max_seq_len
+    assert len(input_mask) == max_seq_len
+    assert len(segment_ids) == max_seq_len
+
+    feature = (input_ids, input_mask, segment_ids)
+    return feature
+
+def label_generation(text, probs):
+    """generate label"""
+    data = [text]
+    probs = [probs]
+    result = []
+    label2id = json.loads(open("./label2id.json").read())
+    id2label = [k for k, v in label2id.items()]
+
+    for index, prob in enumerate(probs):
+        for v in prob[1:len(data[index]) + 1]:
+            result.append(id2label[int(v)])
+
+    labels = {}
+    start = None
+    index = 0
+    for _, t in zip("".join(data), result):
+        if re.search("^[BS]", t):
+            if start is not None:
+                label = result[index - 1][2:]
+                if labels.get(label):
+                    te_ = text[start:index]
+                    labels[label][te_] = [[start, index - 1]]
+                else:
+                    te_ = text[start:index]
+                    labels[label] = {te_: [[start, index - 1]]}
+            start = index
+        if re.search("^O", t):
+            if start is not None:
+                label = result[index - 1][2:]
+                if labels.get(label):
+                    te_ = text[start:index]
+                    labels[label][te_] = [[start, index - 1]]
+                else:
+                    te_ = text[start:index]
+                    labels[label] = {te_: [[start, index - 1]]}
+            start = None
+        index += 1
+    if start is not None:
+        label = result[start][2:]
+        if labels.get(label):
+            te_ = text[start:index]
+            labels[label][te_] = [[start, index - 1]]
+        else:
+            te_ = text[start:index]
+            labels[label] = {te_: [[start, index - 1]]}
+    return labels

tests/st/networks/models/bert/src/utils.py

@@ -0,0 +1,263 @@
+# 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.
+# ============================================================================
+
+'''
+Functional Cells used in Bert finetune and evaluation.
+'''
+
+import mindspore.nn as nn
+from mindspore.common.initializer import TruncatedNormal
+from mindspore.ops import operations as P
+from mindspore.ops import functional as F
+from mindspore.ops import composite as C
+from mindspore.common.tensor import Tensor
+from mindspore.common.parameter import Parameter, ParameterTuple
+from mindspore.common import dtype as mstype
+from mindspore.nn.wrap.grad_reducer import DistributedGradReducer
+from mindspore.train.parallel_utils import ParallelMode
+from mindspore.communication.management import get_group_size
+from mindspore import context
+from mindspore.model_zoo.Bert_NEZHA.bert_model import BertModel
+from .bert_for_pre_training import clip_grad
+from .CRF import CRF
+
+GRADIENT_CLIP_TYPE = 1
+GRADIENT_CLIP_VALUE = 1.0
+grad_scale = C.MultitypeFuncGraph("grad_scale")
+reciprocal = P.Reciprocal()
+
+@grad_scale.register("Tensor", "Tensor")
+def tensor_grad_scale(scale, grad):
+    return grad * reciprocal(scale)
+
+class BertFinetuneCell(nn.Cell):
+    """
+    Especifically defined for finetuning where only four inputs tensor are needed.
+    """
+    def __init__(self, network, optimizer, scale_update_cell=None):
+
+        super(BertFinetuneCell, self).__init__(auto_prefix=False)
+        self.network = network
+        self.weights = ParameterTuple(network.trainable_params())
+        self.optimizer = optimizer
+        self.grad = C.GradOperation('grad',
+                                    get_by_list=True,
+                                    sens_param=True)
+        self.reducer_flag = False
+        self.allreduce = P.AllReduce()
+        self.parallel_mode = context.get_auto_parallel_context("parallel_mode")
+        if self.parallel_mode in [ParallelMode.DATA_PARALLEL, ParallelMode.HYBRID_PARALLEL]:
+            self.reducer_flag = True
+        self.grad_reducer = None
+        if self.reducer_flag:
+            mean = context.get_auto_parallel_context("mirror_mean")
+            degree = get_group_size()
+            self.grad_reducer = DistributedGradReducer(optimizer.parameters, mean, degree)
+        self.is_distributed = (self.parallel_mode != ParallelMode.STAND_ALONE)
+        self.cast = P.Cast()
+        self.alloc_status = P.NPUAllocFloatStatus()
+        self.get_status = P.NPUGetFloatStatus()
+        self.clear_before_grad = P.NPUClearFloatStatus()
+        self.reduce_sum = P.ReduceSum(keep_dims=False)
+        self.depend_parameter_use = P.ControlDepend(depend_mode=1)
+        self.base = Tensor(1, mstype.float32)
+        self.less_equal = P.LessEqual()
+        self.hyper_map = C.HyperMap()
+        self.loss_scale = None
+        self.loss_scaling_manager = scale_update_cell
+        if scale_update_cell:
+            self.loss_scale = Parameter(Tensor(scale_update_cell.get_loss_scale(), dtype=mstype.float32),
+                                        name="loss_scale")
+
+    def construct(self,
+                  input_ids,
+                  input_mask,
+                  token_type_id,
+                  label_ids,
+                  sens=None):
+
+
+        weights = self.weights
+        init = self.alloc_status()
+        loss = self.network(input_ids,
+                            input_mask,
+                            token_type_id,
+                            label_ids)
+        if sens is None:
+            scaling_sens = self.loss_scale
+        else:
+            scaling_sens = sens
+        grads = self.grad(self.network, weights)(input_ids,
+                                                 input_mask,
+                                                 token_type_id,
+                                                 label_ids,
+                                                 self.cast(scaling_sens,
+                                                           mstype.float32))
+        clear_before_grad = self.clear_before_grad(init)
+        F.control_depend(loss, init)
+        self.depend_parameter_use(clear_before_grad, scaling_sens)
+        grads = self.hyper_map(F.partial(grad_scale, scaling_sens), grads)
+        grads = self.hyper_map(F.partial(clip_grad, GRADIENT_CLIP_TYPE, GRADIENT_CLIP_VALUE), grads)
+        if self.reducer_flag:
+            grads = self.grad_reducer(grads)
+        flag = self.get_status(init)
+        flag_sum = self.reduce_sum(init, (0,))
+        if self.is_distributed:
+            flag_reduce = self.allreduce(flag_sum)
+            cond = self.less_equal(self.base, flag_reduce)
+        else:
+            cond = self.less_equal(self.base, flag_sum)
+        F.control_depend(grads, flag)
+        F.control_depend(flag, flag_sum)
+        overflow = cond
+        if sens is None:
+            overflow = self.loss_scaling_manager(self.loss_scale, cond)
+        if overflow:
+            succ = False
+        else:
+            succ = self.optimizer(grads)
+        ret = (loss, cond)
+        return F.depend(ret, succ)
+
+class BertCLSModel(nn.Cell):
+    """
+    This class is responsible for classification task evaluation, i.e. XNLI(num_labels=3),
+    LCQMC(num_labels=2), Chnsenti(num_labels=2). The returned output represents the final
+    logits as the results of log_softmax is propotional to that of softmax.
+    """
+    def __init__(self, config, is_training, num_labels=2, dropout_prob=0.0, use_one_hot_embeddings=False):
+        super(BertCLSModel, self).__init__()
+        self.bert = BertModel(config, is_training, use_one_hot_embeddings)
+        self.cast = P.Cast()
+        self.weight_init = TruncatedNormal(config.initializer_range)
+        self.log_softmax = P.LogSoftmax(axis=-1)
+        self.dtype = config.dtype
+        self.num_labels = num_labels
+        self.dense_1 = nn.Dense(config.hidden_size, self.num_labels, weight_init=self.weight_init,
+                                has_bias=True).to_float(config.compute_type)
+        self.dropout = nn.Dropout(1 - dropout_prob)
+
+    def construct(self, input_ids, input_mask, token_type_id):
+        _, pooled_output, _ = \
+            self.bert(input_ids, token_type_id, input_mask)
+        cls = self.cast(pooled_output, self.dtype)
+        cls = self.dropout(cls)
+        logits = self.dense_1(cls)
+        logits = self.cast(logits, self.dtype)
+        log_probs = self.log_softmax(logits)
+        return log_probs
+
+
+class BertNERModel(nn.Cell):
+    """
+    This class is responsible for sequence labeling task evaluation, i.e. NER(num_labels=11).
+    The returned output represents the final logits as the results of log_softmax is propotional to that of softmax.
+    """
+    def __init__(self, config, is_training, num_labels=11, use_crf=False, dropout_prob=0.0,
+                 use_one_hot_embeddings=False):
+        super(BertNERModel, self).__init__()
+        self.bert = BertModel(config, is_training, use_one_hot_embeddings)
+        self.cast = P.Cast()
+        self.weight_init = TruncatedNormal(config.initializer_range)
+        self.log_softmax = P.LogSoftmax(axis=-1)
+        self.dtype = config.dtype
+        self.num_labels = num_labels
+        self.dense_1 = nn.Dense(config.hidden_size, self.num_labels, weight_init=self.weight_init,
+                                has_bias=True).to_float(config.compute_type)
+        self.dropout = nn.Dropout(1 - dropout_prob)
+        self.reshape = P.Reshape()
+        self.shape = (-1, config.hidden_size)
+        self.use_crf = use_crf
+        self.origin_shape = (config.batch_size, config.seq_length, self.num_labels)
+
+    def construct(self, input_ids, input_mask, token_type_id):
+        sequence_output, _, _ = \
+            self.bert(input_ids, token_type_id, input_mask)
+        seq = self.dropout(sequence_output)
+        seq = self.reshape(seq, self.shape)
+        logits = self.dense_1(seq)
+        logits = self.cast(logits, self.dtype)
+        if self.use_crf:
+            return_value = self.reshape(logits, self.origin_shape)
+        else:
+            return_value = self.log_softmax(logits)
+        return return_value
+
+class CrossEntropyCalculation(nn.Cell):
+    """
+    Cross Entropy loss
+    """
+    def __init__(self, is_training=True):
+        super(CrossEntropyCalculation, self).__init__()
+        self.onehot = P.OneHot()
+        self.on_value = Tensor(1.0, mstype.float32)
+        self.off_value = Tensor(0.0, mstype.float32)
+        self.reduce_sum = P.ReduceSum()
+        self.reduce_mean = P.ReduceMean()
+        self.reshape = P.Reshape()
+        self.last_idx = (-1,)
+        self.neg = P.Neg()
+        self.cast = P.Cast()
+        self.is_training = is_training
+
+    def construct(self, logits, label_ids, num_labels):
+        if self.is_training:
+            label_ids = self.reshape(label_ids, self.last_idx)
+            one_hot_labels = self.onehot(label_ids, num_labels, self.on_value, self.off_value)
+            per_example_loss = self.neg(self.reduce_sum(one_hot_labels * logits, self.last_idx))
+            loss = self.reduce_mean(per_example_loss, self.last_idx)
+            return_value = self.cast(loss, mstype.float32)
+        else:
+            return_value = logits * 1.0
+        return return_value
+
+class BertCLS(nn.Cell):
+    """
+    Train interface for classification finetuning task.
+    """
+    def __init__(self, config, is_training, num_labels=2, dropout_prob=0.0, use_one_hot_embeddings=False):
+        super(BertCLS, self).__init__()
+        self.bert = BertCLSModel(config, is_training, num_labels, dropout_prob, use_one_hot_embeddings)
+        self.loss = CrossEntropyCalculation(is_training)
+        self.num_labels = num_labels
+    def construct(self, input_ids, input_mask, token_type_id, label_ids):
+        log_probs = self.bert(input_ids, input_mask, token_type_id)
+        loss = self.loss(log_probs, label_ids, self.num_labels)
+        return loss
+
+
+class BertNER(nn.Cell):
+    """
+    Train interface for sequence labeling finetuning task.
+    """
+    def __init__(self, config, is_training, num_labels=11, use_crf=False, tag_to_index=None, dropout_prob=0.0,
+                 use_one_hot_embeddings=False):
+        super(BertNER, self).__init__()
+        self.bert = BertNERModel(config, is_training, num_labels, use_crf, dropout_prob, use_one_hot_embeddings)
+        if use_crf:
+            if not tag_to_index:
+                raise Exception("The dict for tag-index mapping should be provided for CRF.")
+            self.loss = CRF(tag_to_index, config.batch_size, config.seq_length, is_training)
+        else:
+            self.loss = CrossEntropyCalculation(is_training)
+        self.num_labels = num_labels
+        self.use_crf = use_crf
+    def construct(self, input_ids, input_mask, token_type_id, label_ids):
+        logits = self.bert(input_ids, input_mask, token_type_id)
+        if self.use_crf:
+            loss = self.loss(logits, label_ids)
+        else:
+            loss = self.loss(logits, label_ids, self.num_labels)
+        return loss

tests/ut/python/model/test_bert.py

@@ -1,52 +0,0 @@
-# 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.
-# ============================================================================
-""" test bert cell """
-import numpy as np
-import pytest
-
-from mindspore.model_zoo.Bert_NEZHA import BertConfig, BertModel
-from ....dataset_mock import MindData
-
-
-def map_bert(record):
-    target_data = {'input_ids': None, 'input_mask': None,
-                   'segment_ids': None, 'next_sentence_labels': None,
-                   'masked_lm_positions': None, 'masked_lm_ids': None,
-                   'masked_lm_weights': None}
-
-    sample = dt.parse_single_example(record, target_data)
-
-    return sample['input_ids'], sample['input_mask'], sample['segment_ids'], \
-           sample['next_sentence_labels'], sample['masked_lm_positions'], \
-           sample['masked_lm_ids'], sample['masked_lm_weights']
-
-
-def test_bert_model():
-    # test for config.hidden_size % config.num_attention_heads != 0
-    config_error = BertConfig(32, hidden_size=512, num_attention_heads=10)
-    with pytest.raises(ValueError):
-        BertModel(config_error, True)
-
-
-def get_dataset(batch_size=1):
-    dataset_types = (np.int32, np.int32, np.int32, np.int32, np.int32, np.int32, np.int32)
-    dataset_shapes = ((batch_size, 128), (batch_size, 128), (batch_size, 128), (batch_size, 1),
-                      (batch_size, 20), (batch_size, 20), (batch_size, 20))
-
-    dataset = MindData(size=2, batch_size=batch_size,
-                       np_types=dataset_types,
-                       output_shapes=dataset_shapes,
-                       input_indexs=(0, 1))
-    return dataset

tests/ut/python/model/test_bert_cell.py

@@ -1,437 +0,0 @@
-# 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.
-# ============================================================================
-""" test bert of graph compile """
-import functools
-import numpy as np
-
-import mindspore.common.dtype as mstype
-import mindspore.nn as nn
-import mindspore.ops.composite as C
-from mindspore.ops import functional as F
-from mindspore.common.initializer import TruncatedNormal
-from mindspore.common.parameter import ParameterTuple
-from mindspore.common.tensor import Tensor
-from mindspore.model_zoo.Bert_NEZHA import BertPretrainingLoss, GetNextSentenceOutput
-from mindspore.model_zoo.Bert_NEZHA.bert_for_pre_training import clip_grad
-from mindspore.model_zoo.Bert_NEZHA.bert_model import BertConfig, \
-    EmbeddingLookup, EmbeddingPostprocessor, BertOutput, RelaPosMatrixGenerator, \
-    RelaPosEmbeddingsGenerator, SaturateCast, BertAttention, BertSelfAttention, \
-    BertEncoderCell, BertTransformer, CreateAttentionMaskFromInputMask, BertModel
-from mindspore.nn.layer.basic import Norm
-from mindspore.nn.optim import AdamWeightDecay, AdamWeightDecayDynamicLR
-from ....mindspore_test_framework.mindspore_test import mindspore_test
-from ....mindspore_test_framework.pipeline.forward.compile_forward import \
-    pipeline_for_compile_forward_ge_graph_for_case_by_case_config
-from ....mindspore_test_framework.pipeline.gradient.compile_gradient import \
-    pipeline_for_compile_grad_ge_graph_for_case_by_case_config
-from ....ops_common import convert
-
-
-def bert_trans():
-    """bert_trans"""
-    net = BertTransformer(batch_size=1,
-                          hidden_size=768,
-                          seq_length=128,
-                          num_hidden_layers=1,
-                          num_attention_heads=12,
-                          intermediate_size=768,
-                          attention_probs_dropout_prob=0.1,
-                          use_one_hot_embeddings=False,
-                          initializer_range=0.02,
-                          use_relative_positions=False,
-                          hidden_act="gelu",
-                          compute_type=mstype.float32,
-                          return_all_encoders=True)
-    net.set_train()
-    return net
-
-
-def set_train(net):
-    net.set_train()
-    return net
-
-
-class NetForAdam(nn.Cell):
-    def __init__(self):
-        super(NetForAdam, self).__init__()
-        self.dense = nn.Dense(64, 10)
-
-    def construct(self, x):
-        x = self.dense(x)
-        return x
-
-
-class TrainStepWrapForAdam(nn.Cell):
-    """TrainStepWrapForAdam definition"""
-
-    def __init__(self, network):
-        super(TrainStepWrapForAdam, self).__init__()
-        self.network = network
-        self.weights = ParameterTuple(network.get_parameters())
-        self.optimizer = AdamWeightDecay(self.weights)
-        self.hyper_map = C.HyperMap()
-
-    def construct(self, x, sens):
-        weights = self.weights
-        grads = C.grad_by_list_with_sens(self.network, weights)(x, sens)
-        grads = self.hyper_map(F.partial(clip_grad, 1, 1.0), grads)
-        return self.optimizer(grads)
-
-
-class TrainStepWrapForAdamDynamicLr(nn.Cell):
-    """TrainStepWrapForAdamDynamicLr definition"""
-
-    def __init__(self, network):
-        super(TrainStepWrapForAdamDynamicLr, self).__init__()
-        self.network = network
-        self.weights = ParameterTuple(network.get_parameters())
-        self.optimizer = AdamWeightDecayDynamicLR(self.weights, 10)
-        self.sens = Tensor(np.ones(shape=(1, 10)).astype(np.float32))
-
-    def construct(self, x):
-        weights = self.weights
-        grads = C.grad_by_list_with_sens(self.network, weights)(x, self.sens)
-        return self.optimizer(grads)
-
-
-class TempC2Wrap(nn.Cell):
-    def __init__(self, op, c1=None, c2=None,):
-        super(TempC2Wrap, self).__init__()
-        self.op = op
-        self.c1 = c1
-        self.c2 = c2
-        self.hyper_map = C.HyperMap()
-
-    def construct(self, x1):
-        x = self.hyper_map(F.partial(self.op, self.c1, self.c2), x1)
-        return x
-
-
-test_case_cell_ops = [
-    ('Norm_keepdims', {
-        'block': Norm(keep_dims=True),
-        'desc_inputs': [[1, 3, 4, 4]],
-        'desc_bprop': [[1]]}),
-    ('SaturateCast', {
-        'block': SaturateCast(),
-        'desc_inputs': [[1, 3, 4, 4]],
-        'desc_bprop': [[1, 3, 4, 4]]}),
-    ('RelaPosMatrixGenerator_0', {
-        'block': RelaPosMatrixGenerator(length=128, max_relative_position=16),
-        'desc_inputs': [],
-        'desc_bprop': [[128, 128]],
-        'skip': ['backward']}),
-    ('RelaPosEmbeddingsGenerator_0', {
-        'block': RelaPosEmbeddingsGenerator(length=128, depth=512,
-                                            max_relative_position=16,
-                                            initializer_range=0.2),
-        'desc_inputs': [],
-        'desc_bprop': [[16384, 512]],
-        'skip': ['backward']}),
-    ('RelaPosEmbeddingsGenerator_1', {
-        'block': RelaPosEmbeddingsGenerator(length=128, depth=512,
-                                            max_relative_position=16,
-                                            initializer_range=0.2,
-                                            use_one_hot_embeddings=False),
-        'desc_inputs': [],
-        'desc_bprop': [[128, 128, 512]],
-        'skip': ['backward']}),
-    ('RelaPosEmbeddingsGenerator_2', {
-        'block': RelaPosEmbeddingsGenerator(length=128, depth=64,
-                                            max_relative_position=16,
-                                            initializer_range=0.2,
-                                            use_one_hot_embeddings=False),
-        'desc_inputs': [],
-        'desc_bprop': [[128, 128, 64]],
-        'skip': ['backward']}),
-    ('BertAttention_0', {
-        'block': BertAttention(batch_size=64,
-                               from_tensor_width=768,
-                               to_tensor_width=768,
-                               from_seq_length=128,
-                               to_seq_length=128,
-                               num_attention_heads=12,
-                               size_per_head=64,
-                               query_act=None,
-                               key_act=None,
-                               value_act=None,
-                               has_attention_mask=True,
-                               attention_probs_dropout_prob=0.1,
-                               use_one_hot_embeddings=False,
-                               initializer_range=0.02,
-                               do_return_2d_tensor=True,
-                               use_relative_positions=False,
-                               compute_type=mstype.float32),
-        'desc_inputs': [[64, 128, 768], [64, 128, 768], [64, 128, 128]],
-        'desc_bprop': [[8192, 768]]}),
-    ('BertAttention_1', {
-        'block': BertAttention(batch_size=64,
-                               from_tensor_width=768,
-                               to_tensor_width=768,
-                               from_seq_length=128,
-                               to_seq_length=128,
-                               num_attention_heads=12,
-                               size_per_head=64,
-                               query_act=None,
-                               key_act=None,
-                               value_act=None,
-                               has_attention_mask=True,
-                               attention_probs_dropout_prob=0.1,
-                               use_one_hot_embeddings=False,
-                               initializer_range=0.02,
-                               do_return_2d_tensor=True,
-                               use_relative_positions=True,
-                               compute_type=mstype.float32),
-        'desc_inputs': [[64, 128, 768], [64, 128, 768], [64, 128, 128]],
-        'desc_bprop': [[8192, 768]]}),
-    ('BertAttention_2', {
-        'block': BertAttention(batch_size=64,
-                               from_tensor_width=768,
-                               to_tensor_width=768,
-                               from_seq_length=128,
-                               to_seq_length=128,
-                               num_attention_heads=12,
-                               size_per_head=64,
-                               query_act=None,
-                               key_act=None,
-                               value_act=None,
-                               has_attention_mask=False,
-                               attention_probs_dropout_prob=0.1,
-                               use_one_hot_embeddings=False,
-                               initializer_range=0.02,
-                               do_return_2d_tensor=True,
-                               use_relative_positions=True,
-                               compute_type=mstype.float32),
-        'desc_inputs': [[64, 128, 768], [64, 128, 768], [64, 128, 128]],
-        'desc_bprop': [[8192, 768]]}),
-    ('BertAttention_3', {
-        'block': BertAttention(batch_size=64,
-                               from_tensor_width=768,
-                               to_tensor_width=768,
-                               from_seq_length=128,
-                               to_seq_length=128,
-                               num_attention_heads=12,
-                               size_per_head=64,
-                               query_act=None,
-                               key_act=None,
-                               value_act=None,
-                               has_attention_mask=True,
-                               attention_probs_dropout_prob=0.1,
-                               use_one_hot_embeddings=False,
-                               initializer_range=0.02,
-                               do_return_2d_tensor=False,
-                               use_relative_positions=True,
-                               compute_type=mstype.float32),
-        'desc_inputs': [[64, 128, 768], [64, 128, 768], [64, 128, 128]],
-        'desc_bprop': [[8192, 768]]}),
-    ('BertOutput', {
-        'block': BertOutput(in_channels=768,
-                            out_channels=768,
-                            initializer_range=0.02,
-                            dropout_prob=0.1),
-        'desc_inputs': [[8192, 768], [8192, 768]],
-        'desc_bprop': [[8192, 768]]}),
-    ('BertSelfAttention_0', {
-        'block': BertSelfAttention(batch_size=64,
-                                   seq_length=128,
-                                   hidden_size=768,
-                                   num_attention_heads=12,
-                                   attention_probs_dropout_prob=0.1,
-                                   use_one_hot_embeddings=False,
-                                   initializer_range=0.02,
-                                   hidden_dropout_prob=0.1,
-                                   use_relative_positions=False,
-                                   compute_type=mstype.float32),
-        'desc_inputs': [[64, 128, 768], [64, 128, 128]],
-        'desc_bprop': [[8192, 768]]}),
-    ('BertEncoderCell', {
-        'block': BertEncoderCell(batch_size=64,
-                                 hidden_size=768,
-                                 seq_length=128,
-                                 num_attention_heads=12,
-                                 intermediate_size=768,
-                                 attention_probs_dropout_prob=0.02,
-                                 use_one_hot_embeddings=False,
-                                 initializer_range=0.02,
-                                 hidden_dropout_prob=0.1,
-                                 use_relative_positions=False,
-                                 hidden_act="gelu",
-                                 compute_type=mstype.float32),
-        'desc_inputs': [[64, 128, 768], [64, 128, 128]],
-        'desc_bprop': [[8192, 768]]}),
-    ('BertTransformer_0', {
-        'block': BertTransformer(batch_size=1,
-                                 hidden_size=768,
-                                 seq_length=128,
-                                 num_hidden_layers=1,
-                                 num_attention_heads=12,
-                                 intermediate_size=768,
-                                 attention_probs_dropout_prob=0.1,
-                                 use_one_hot_embeddings=False,
-                                 initializer_range=0.02,
-                                 use_relative_positions=False,
-                                 hidden_act="gelu",
-                                 compute_type=mstype.float32,
-                                 return_all_encoders=True),
-        'desc_inputs': [[1, 128, 768], [1, 128, 128]]}),
-    ('BertTransformer_1', {
-        'block': BertTransformer(batch_size=64,
-                                 hidden_size=768,
-                                 seq_length=128,
-                                 num_hidden_layers=2,
-                                 num_attention_heads=12,
-                                 intermediate_size=768,
-                                 attention_probs_dropout_prob=0.1,
-                                 use_one_hot_embeddings=False,
-                                 initializer_range=0.02,
-                                 use_relative_positions=True,
-                                 hidden_act="gelu",
-                                 compute_type=mstype.float32,
-                                 return_all_encoders=False),
-        'desc_inputs': [[64, 128, 768], [64, 128, 128]]}),
-    ('EmbeddingLookup', {
-        'block': EmbeddingLookup(vocab_size=32000,
-                                 embedding_size=768,
-                                 embedding_shape=[1, 128, 768],
-                                 use_one_hot_embeddings=False,
-                                 initializer_range=0.02),
-        'desc_inputs': [Tensor(np.random.rand(128).astype(np.int32))],
-        'desc_bprop': [[1, 128, 768], [1, 128, 768]],
-        'num_output': 2}),
-    ('EmbeddingPostprocessor', {
-        'block': EmbeddingPostprocessor(embedding_size=768,
-                                        embedding_shape=[1, 128, 768],
-                                        use_token_type=True,
-                                        token_type_vocab_size=16,
-                                        use_one_hot_embeddings=False,
-                                        initializer_range=0.02,
-                                        max_position_embeddings=512,
-                                        dropout_prob=0.1),
-        'desc_inputs': [Tensor(np.random.rand(128).astype(np.int32)), [1, 128, 768]],
-        'desc_bprop': [[1, 128, 768]]}),
-    ('CreateAttentionMaskFromInputMask', {
-        'block': CreateAttentionMaskFromInputMask(config=BertConfig(batch_size=1)),
-        'desc_inputs': [[128]],
-        'desc_bprop': [[1, 128, 128]]}),
-    ('BertOutput_0', {
-        'block': BertOutput(in_channels=768,
-                            out_channels=768,
-                            initializer_range=0.02,
-                            dropout_prob=0.1),
-        'desc_inputs': [[1, 768], [1, 768]],
-        'desc_bprop': [[1, 768]]}),
-    ('BertTransformer_2', {
-        'block': bert_trans(),
-        'desc_inputs': [[1, 128, 768], [1, 128, 128]]}),
-
-    ('BertModel', {
-        'block': BertModel(config=BertConfig(batch_size=1,
-                                             num_hidden_layers=1,
-                                             intermediate_size=768,
-                                             token_type_ids_from_dataset=False),
-                           is_training=True),
-        'desc_inputs': [Tensor(np.random.rand(128).astype(np.int32)),
-                        Tensor(np.random.rand(128).astype(np.int32)), [128]],
-        'desc_bprop': [[1, 128, 768], [1, 128, 768], [1, 128, 768]],
-        'num_output': 3}),
-
-    ('BertModel_1', {
-        'block': BertModel(config=BertConfig(batch_size=1,
-                                             num_hidden_layers=1,
-                                             intermediate_size=768,
-                                             token_type_ids_from_dataset=False),
-                           is_training=False),
-        'desc_inputs': [Tensor(np.random.rand(128).astype(np.int32)),
-                        Tensor(np.random.rand(128).astype(np.int32)), [128]],
-        'desc_bprop': [[1, 128, 768], [1, 128, 768], [1, 128, 768]],
-        'num_output': 3}),
-
-    ('BertModel_2', {
-        'block': BertModel(config=BertConfig(batch_size=1,
-                                             num_hidden_layers=1,
-                                             intermediate_size=768,
-                                             token_type_ids_from_dataset=False,
-                                             input_mask_from_dataset=False),
-                           is_training=True),
-        'desc_inputs': [Tensor(np.random.rand(128).astype(np.int32)),
-                        Tensor(np.random.rand(128).astype(np.int32)), [128]],
-        'desc_bprop': [[1, 128, 768], [1, 128, 768], [1, 128, 768]],
-        'num_output': 3}),
-
-    ('BertPretrainingLoss', {
-        'block': BertPretrainingLoss(config=BertConfig(batch_size=1)),
-        'desc_inputs': [[32000], [20, 2], Tensor(np.array([1]).astype(np.int32)),
-                        [20], Tensor(np.array([20]).astype(np.int32))],
-        'desc_bprop': [[1]],
-        'num_output': 1}),
-    ('Dense_1', {
-        'block': nn.Dense(in_channels=768,
-                          out_channels=3072,
-                          activation='gelu',
-                          weight_init=TruncatedNormal(0.02)),
-        'desc_inputs': [[3, 768]],
-        'desc_bprop': [[3, 3072]]}),
-    ('Dense_2', {
-        'block': set_train(nn.Dense(in_channels=768,
-                                    out_channels=3072,
-                                    activation='gelu',
-                                    weight_init=TruncatedNormal(0.02),)),
-        'desc_inputs': [[3, 768]],
-        'desc_bprop': [[3, 3072]]}),
-    ('GetNextSentenceOutput', {
-        'block': GetNextSentenceOutput(BertConfig(batch_size=1)),
-        'desc_inputs': [[128, 768]],
-        'desc_bprop': [[128, 2]]}),
-    ('Adam_1', {
-        'block': set_train(TrainStepWrapForAdam(NetForAdam())),
-        'desc_inputs': [[1, 64], [1, 10]],
-        'skip': ['backward']}),
-    ('Adam_2', {
-        'block': set_train(TrainStepWrapForAdam(GetNextSentenceOutput(BertConfig(batch_size=1)))),
-        'desc_inputs': [[128, 768], [128, 2]],
-        'skip': ['backward']}),
-    ('AdamWeightDecayDynamicLR', {
-        'block': set_train(TrainStepWrapForAdamDynamicLr(NetForAdam())),
-        'desc_inputs': [[1, 64]],
-        'skip': ['backward']}),
-    ('ClipGradients', {
-        'block': TempC2Wrap(clip_grad, 1, 1.0),
-        'desc_inputs': [tuple(convert(shp) for shp in [[1], [1], [1]])],
-        'skip': ['backward', 'exec']}),
-]
-
-test_case = functools.reduce(lambda x, y: x + y, [test_case_cell_ops])
-# use -k to select certain testcast
-# pytest  tests/python/ops/test_ops.py::test_backward -k LayerNorm
-
-
-test_exec_case = filter(lambda x: 'skip' not in x[1] or
-                                  'exec' not in x[1]['skip'], test_case)
-test_backward_exec_case = filter(lambda x: 'skip' not in x[1] or
-                                           'backward' not in x[1]['skip'] and 'backward_exec'
-                                           not in x[1]['skip'], test_case)
-test_check_gradient_case = filter(lambda x: 'skip' not in x[1] or
-                                            'backward' not in x[1]['skip'] and 'backward_exec'
-                                            not in x[1]['skip'], test_case)
-
-
-@mindspore_test(pipeline_for_compile_forward_ge_graph_for_case_by_case_config)
-def test_exec():
-    return test_exec_case
-
-
-@mindspore_test(pipeline_for_compile_grad_ge_graph_for_case_by_case_config)
-def test_backward_exec():
-    return test_backward_exec_case

tests/ut/python/nn/test_embedding.py

@@ -1,66 +0,0 @@
-# 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.
-# ============================================================================
-""" test_embedding """
-import numpy as np
-
-from mindspore import Tensor
-from mindspore import dtype as mstype
-from mindspore.model_zoo.Bert_NEZHA import EmbeddingLookup, EmbeddingPostprocessor
-from ..ut_filter import non_graph_engine
-
-
-@non_graph_engine
-def test_check_embedding_lookup_1():
-    m = EmbeddingLookup(vocab_size=32000,
-                        embedding_size=768,
-                        embedding_shape=[1, 128, 768],
-                        use_one_hot_embeddings=False)
-    m(Tensor(np.ones([128]), mstype.int32))
-
-
-@non_graph_engine
-def test_check_embedding_lookup_2():
-    m = EmbeddingLookup(vocab_size=32000,
-                        embedding_size=768,
-                        embedding_shape=[1, 128, 768],
-                        use_one_hot_embeddings=True)
-    m(Tensor(np.ones([128]), mstype.int32))
-
-
-@non_graph_engine
-def test_check_embedding_lookup_3():
-    m = EmbeddingLookup(vocab_size=32000,
-                        embedding_size=768,
-                        embedding_shape=[1, 128, 768],
-                        use_one_hot_embeddings=True,
-                        initializer_range=0.01)
-    m(Tensor(np.ones([128]), mstype.int32))
-
-
-@non_graph_engine
-def test_embedding_post_1():
-    m = EmbeddingPostprocessor(embedding_size=768,
-                               embedding_shape=[1, 128, 768],
-                               use_token_type=True)
-    m(Tensor(np.ones([128]), mstype.int32), Tensor(np.ones([1, 128, 768]), mstype.float32))
-
-
-@non_graph_engine
-def test_embedding_post_2():
-    m = EmbeddingPostprocessor(embedding_size=768,
-                               embedding_shape=[1, 128, 768],
-                               use_token_type=True,
-                               initializer_range=0.3)
-    m(Tensor(np.ones([128]), mstype.int32), Tensor(np.ones([1, 128, 768]), mstype.float32))