!8704 BugFix for GPT

From: @alouhahahahaha
Reviewed-by: @stsuteng,@yingjy
Signed-off-by: @stsuteng

model_zoo/official/nlp/gpt/scripts/run_distribute_train.sh

@@ -21,13 +21,13 @@ echo "for example: bash run_distributed_pretrain_ascend.sh /path/dataset /path/h
 echo "It is better to use absolute path."
 echo "=============================================================================================================="
 
-ROOT_PATH='pwd'
+ROOT_PATH=`pwd`
 DATA_DIR=$1
 export RANK_TABLE_FILE=$2
 RANK_SIZE=$3
 
 
-for((i=0;i<=${RANK_SIZE};i++));
+for((i=0;i<${RANK_SIZE};i++));
 do
     rm ${ROOT_PATH}/device$i/ -rf
     mkdir ${ROOT_PATH}/device$i

model_zoo/official/nlp/gpt/src/gpt.py

@@ -21,7 +21,7 @@ import mindspore.nn as nn
 from mindspore.common.tensor import Tensor
 from mindspore.common.parameter import Parameter
 import mindspore.common.dtype as mstype
-from mindspore.common.initializer import TruncatedNormal, initializer
+from mindspore.common.initializer import TruncatedNormal, initializer, Normal
 from mindspore.ops import operations as P
 from mindspore.ops import functional as F
 
@@ -48,7 +48,7 @@ class LayerNorm(nn.Cell):
 
     def construct(self, x):
         mean = self.mean(x, -1)
-        variance = self.mean(F.square(x - mean))
+        variance = self.mean(F.square(x - mean), -1)
         output = (x - mean) / F.sqrt(variance + self.eps)
         rescaled_output = output * self.gamma + self.beta
         return rescaled_output
@@ -100,10 +100,8 @@ class Mapping(nn.Cell):
         super(Mapping, self).__init__()
         self.output_size = output_size
         self.input_size = input_size
-        weight = np.random.normal(loc=0.0, scale=0.02*scale, size=(input_size, output_size))
-        bias = np.zeros(shape=(output_size,))
-        self.weight = Parameter(Tensor(weight, mstype.float32), name="mapping_weight")
-        self.bias = Parameter(Tensor(bias, mstype.float32), name="mapping_bias")
+        self.weight = Parameter(initializer(Normal(sigma=0.02*scale), [input_size, output_size]), name="mapping_weight")
+        self.bias = Parameter(initializer("zeros", [output_size,]), name="mapping_bias")
         self.dtype = dtype
         self.cast = P.Cast()
 
@@ -363,7 +361,6 @@ class Block(nn.Cell):
     """
     def __init__(self, config, layer_idx):
         super(Block, self).__init__()
-        scale = 1 / math.sqrt(2.0*layer_idx)
         self.layernorm1 = LayerNorm((config.embedding_size,)).to_float(config.compute_dtype)
         self.attention = Attention(config, scale, layer_idx)
         self.layernorm2 = LayerNorm((config.embedding_size,)).to_float(config.compute_dtype)