test-commit

Author: Ricardo-shuo-liu

_typos.toml

@@ -38,11 +38,6 @@ Similarily = "Similarily"
 Simle = "Simle"
 Sovler = "Sovler"
 Successed = "Successed"
-desgin = "desgin"
-desginated = "desginated"
-desigin = "desigin"
-determinated = "determinated"
-diffcult = "diffcult"
 dimention = "dimention"
 dimentions = "dimentions"
 dirrectories = "dirrectories"
@@ -95,7 +90,6 @@ outpu = "outpu"
 outpus = "outpus"
 overrided = "overrided"
 overwrited = "overwrited"
-porcess = "porcess"
 processer = "processer"
 sacle = "sacle"
 samle = "samle"

docs/design/data_type/float16.md

@@ -130,7 +130,7 @@ fp16_tensor.set(tensor.astype(numpy.float16).view(numpy.uint16), GPUPlace)
 ```
 
 ### Consistent API requirement
-The basic inference in float16 mode requires users to feed input and obtain output both of float16 data type. However, in this way, the inference APIs are not consistent between float16 mode and float mode, and users may find it confusing and diffcult to use float16 inference since they need to do extra steps to provide float16 input data and convert float16 output data back to float. To have consistent API for different inference modes, we need to transpile the program desc in some way so that we can run float16 inference by feeding and fetching variables of float data type.
+The basic inference in float16 mode requires users to feed input and obtain output both of float16 data type. However, in this way, the inference APIs are not consistent between float16 mode and float mode, and users may find it confusing and difficult to use float16 inference since they need to do extra steps to provide float16 input data and convert float16 output data back to float. To have consistent API for different inference modes, we need to transpile the program desc in some way so that we can run float16 inference by feeding and fetching variables of float data type.
 
 This problem can be solved by introducing a type-casting operator which takes an input variable of certain data type, cast it to another specified data type, and put the casted data into the output variable. Insert cast operator where needed can make a program internally run in float16 mode.
 

docs/design/others/graph.md

@@ -56,7 +56,7 @@ For each parameter, like W and b created by `layer.fc`, marked as double circles
 
 ## Block and Graph
 
-The word block and graph are interchangable in the desgin of PaddlePaddle.  A [Block](https://github.com/PaddlePaddle/Paddle/pull/3708) is a metaphore of the code and local variables in a pair of curly braces in programming languages, where operators are like statements or instructions.  A graph of operators and variables is a representation of the block.
+The word block and graph are interchangable in the design of PaddlePaddle.  A [Block](https://github.com/PaddlePaddle/Paddle/pull/3708) is a metaphore of the code and local variables in a pair of curly braces in programming languages, where operators are like statements or instructions.  A graph of operators and variables is a representation of the block.
 
 A Block keeps operators in an array `BlockDesc::ops`
 

docs/design/quantization/fixed_point_quantization.md

@@ -79,7 +79,7 @@ From these formulas, dequantization also can be moved before GEMM, do dequantiza
 Figure 2. Equivalent forward in training with simulated quantization.
 </p>
 
-We use this equivalent workflow in the training. In our desigin, there is a quantization transpiler to insert the quantization operator and the de-quantization operator in the Fluid `ProgramDesc`. Since the outputs of quantization and de-quantization operator are still in floating point, they are called faked quantization and de-quantization operator. And the training framework is called simulated quantization.
+We use this equivalent workflow in the training. In our design, there is a quantization transpiler to insert the quantization operator and the de-quantization operator in the Fluid `ProgramDesc`. Since the outputs of quantization and de-quantization operator are still in floating point, they are called faked quantization and de-quantization operator. And the training framework is called simulated quantization.
 
 #### Backward pass
 

docs/dev_guides/custom_device_docs/custom_runtime_en.rst

@@ -29,7 +29,7 @@ Device APIs
 +---------------------------+----------------------------------------------------------+----------+
 | get_device                | To get the current device                                | Y        |
 +---------------------------+----------------------------------------------------------+----------+
-| synchronize_device        | To synchronize the desginated device                     | Y        |
+| synchronize_device        | To synchronize the designated device                     | Y        |
 +---------------------------+----------------------------------------------------------+----------+
 | get_device_count          | To count available devices                               | Y        |
 +---------------------------+----------------------------------------------------------+----------+

…uides/model_convert/pictures/porcess.png …uides/model_convert/pictures/process.pngdocs/guides/model_convert/pictures/porcess.png renamed to docs/guides/model_convert/pictures/process.png docs/guides/model_convert/pictures/porcess.png renamed to docs/guides/model_convert/pictures/process.png

@@ -130,7 +130,7 @@ import paddle
 import numpy as np
 import random
 
-# set seed for determinated output
+# set seed for determined output
 paddle.seed(2025)
 np.random.seed(2025)
 random.seed(2025)
@@ -172,7 +172,7 @@ import paddle
 import numpy as np
 import random
 
-# set seed for determinated output
+# set seed for determined output
 paddle.seed(2025)
 np.random.seed(2025)
 random.seed(2025)