Fix FT doc (PaddlePaddle#1028)

* fix doc

* tran -> trans

Author: FrostML

examples/machine_translation/transformer/faster_transformer/README.md

@@ -109,11 +109,11 @@ datasets = load_dataset('wmt14ende', splits=('test'))
 
 使用模型推断前提是需要指定一个合适的 checkpoint，需要在对应的 `../configs/transformer.base.yaml` 中修改对应的模型载入的路径参数 `init_from_params`。
 
-我们提供一个已经训练好的动态图的 base model 的 checkpoint 以供使用，可以通过[tranformer-base-wmt_ende_bpe](https://paddlenlp.bj.bcebos.com/models/transformers/transformer/tranformer-base-wmt_ende_bpe.tar.gz)下载。
+我们提供一个已经训练好的动态图的 base model 的 checkpoint 以供使用，可以通过[transformer-base-wmt_ende_bpe](https://paddlenlp.bj.bcebos.com/models/transformers/transformer/transformer-base-wmt_ende_bpe.tar.gz)下载。
 
 ``` sh
-wget https://paddlenlp.bj.bcebos.com/models/transformers/transformer/tranformer-base-wmt_ende_bpe.tar.gz
-tar -zxf tranformer-base-wmt_ende_bpe.tar.gz
+wget https://paddlenlp.bj.bcebos.com/models/transformers/transformer/transformer-base-wmt_ende_bpe.tar.gz
+tar -zxf transformer-base-wmt_ende_bpe.tar.gz
 ```
 
 然后，需要修改对应的 `../configs/transformer.base.yaml` 配置文件中的 `init_from_params` 的值为 `./base_trained_models/step_final/`。
@@ -126,7 +126,7 @@ tar -zxf tranformer-base-wmt_ende_bpe.tar.gz
 # setting visible devices for prediction
 export CUDA_VISIBLE_DEVICES=0
 export FLAGS_fraction_of_gpu_memory_to_use=0.1
-cp -rf ../../../../paddlenlp/ops/build/third-party/build/bin/decoding_gemm ./
+cp -rf ../../../../paddlenlp/ops/build/third-party/build/fastertransformer/bin/decoding_gemm ./
 ./decoding_gemm 8 4 8 64 38512 32 512 0
 python encoder_decoding_predict.py --config ../configs/transformer.base.yaml --decoding_lib ../../../../paddlenlp/ops/build/lib/libdecoding_op.so --decoding_strategy beam_search --beam_size 5
 ```
@@ -153,7 +153,7 @@ float16 与 float32 预测的基本流程相同，不过在使用 float16 的 de
 # setting visible devices for prediction
 export CUDA_VISIBLE_DEVICES=0
 export FLAGS_fraction_of_gpu_memory_to_use=0.1
-cp -rf ../../../../paddlenlp/ops/build/third-party/build/bin/decoding_gemm ./
+cp -rf ../../../../paddlenlp/ops/build/third-party/build/fastertransformer/bin/decoding_gemm ./
 ./decoding_gemm 8 4 8 64 38512 32 512 1
 python encoder_decoding_predict.py --config ../configs/transformer.base.yaml --decoding_lib ../../../../paddlenlp/ops/build/lib/libdecoding_op.so --use_fp16_decoding --decoding_strategy beam_search --beam_size 5
 ```
@@ -240,7 +240,7 @@ cd ../
 
 ### 导出基于 Faster Transformer 自定义 op 的预测库可使用模型文件
 
-我们提供一个已经基于动态图训练好的 base model 的 checkpoint 以供使用，当前 checkpoint 是基于 WMT 英德翻译的任务训练。可以通过[tranformer-base-wmt_ende_bpe](https://paddlenlp.bj.bcebos.com/models/transformers/transformer/tranformer-base-wmt_ende_bpe.tar.gz)下载。
+我们提供一个已经基于动态图训练好的 base model 的 checkpoint 以供使用，当前 checkpoint 是基于 WMT 英德翻译的任务训练。可以通过[transformer-base-wmt_ende_bpe](https://paddlenlp.bj.bcebos.com/models/transformers/transformer/transformer-base-wmt_ende_bpe.tar.gz)下载。
 
 使用 C++ 预测库，首先，我们需要做的是将动态图的 checkpoint 导出成预测库能使用的模型文件和参数文件。可以执行 `export_model.py` 实现这个过程。
 
@@ -276,7 +276,7 @@ cd bin/
 
 ``` sh
 cd bin/
-../third-party/build/bin/decoding_gemm 8 5 8 64 38512 256 512 0
+../third-party/build/fastertransformer/bin/decoding_gemm 8 5 8 64 38512 256 512 0
 ./transformer_e2e -batch_size 8 -gpu_id 0 -model_dir ./infer_model/ -vocab_dir DATA_HOME/WMT14ende/WMT14.en-de/wmt14_ende_data_bpe/vocab_all.bpe.33708 -data_dir DATA_HOME/WMT14ende/WMT14.en-de/wmt14_ende_data_bpe/newstest2014.tok.bpe.33708.en
 ```
 

paddlenlp/ops/README.md

@@ -106,7 +106,7 @@ transformer = FasterTransformer(
 ``` sh
 export CUDA_VISIBLE_DEVICES=0
 export FLAGS_fraction_of_gpu_memory_to_use=0.1
-./build/third-party/build/bin/decoding_gemm 32 4 8 64 30000 32 512 0
+./build/third-party/build/fastertransformer/bin/decoding_gemm 32 4 8 64 30000 32 512 0
 python ./faster_transformer/sample/decoding_sample.py --config ./faster_transformer/sample/config/decoding.sample.yaml --decoding_lib ./build/lib/libdecoding_op.so
 ```
 
@@ -116,7 +116,7 @@ python ./faster_transformer/sample/decoding_sample.py --config ./faster_transfor
 ``` sh
 export CUDA_VISIBLE_DEVICES=0
 export FLAGS_fraction_of_gpu_memory_to_use=0.1
-./build/third-party/build/bin/decoding_gemm 32 4 8 64 30000 32 512 1
+./build/third-party/build/fastertransformer/bin/decoding_gemm 32 4 8 64 30000 32 512 1
 python ./faster_transformer/sample/decoding_sample.py --config ./faster_transformer/sample/config/decoding.sample.yaml --decoding_lib ./build/lib/libdecoding_op.so --use_fp16_decoding
 ```
 
@@ -243,7 +243,7 @@ cd bin/
 
 ``` sh
 cd bin/
-../third-party/build/bin/decoding_gemm 8 5 8 64 38512 256 512 0
+../third-party/build/fastertransformer/bin/decoding_gemm 8 5 8 64 38512 256 512 0
 ./transformer_e2e -batch_size 8 -gpu_id 0 -model_dir ./infer_model/ -vocab_dir DATA_HOME/WMT14ende/WMT14.en-de/wmt14_ende_data_bpe/vocab_all.bpe.33708 -data_dir DATA_HOME/WMT14ende/WMT14.en-de/wmt14_ende_data_bpe/newstest2014.tok.bpe.33708.en
 ```
 

tests/prepare.sh

@@ -72,9 +72,9 @@ elif [ ${MODE} = "whole_infer" ]; then
 
     # Trained transformer base model checkpoint. 
     # For infer. 
-    if [ ! -f tranformer-base-wmt_ende_bpe.tar.gz ]; then
-        wget https://paddlenlp.bj.bcebos.com/models/transformers/transformer/tranformer-base-wmt_ende_bpe.tar.gz
-        tar -zxf tranformer-base-wmt_ende_bpe.tar.gz
+    if [ ! -f transformer-base-wmt_ende_bpe.tar.gz ]; then
+        wget https://paddlenlp.bj.bcebos.com/models/transformers/transformer/transformer-base-wmt_ende_bpe.tar.gz
+        tar -zxf transformer-base-wmt_ende_bpe.tar.gz
         mv base_trained_models/ trained_models/
     fi
     # For train. 
@@ -191,9 +191,9 @@ else # infer
     sed -i "s/^shuffle:.*/shuffle: True/g" configs/transformer.big.yaml
 
     # Trained transformer base model checkpoint. 
-    if [ ! -f tranformer-base-wmt_ende_bpe.tar.gz ]; then
-        wget https://paddlenlp.bj.bcebos.com/models/transformers/transformer/tranformer-base-wmt_ende_bpe.tar.gz
-        tar -zxf tranformer-base-wmt_ende_bpe.tar.gz
+    if [ ! -f transformer-base-wmt_ende_bpe.tar.gz ]; then
+        wget https://paddlenlp.bj.bcebos.com/models/transformers/transformer/transformer-base-wmt_ende_bpe.tar.gz
+        tar -zxf transformer-base-wmt_ende_bpe.tar.gz
         mv base_trained_models/ trained_models/
     fi
     # Whole data set prepared.