[compiler] Trim trailing whitespace

This commit removes trailing whitespace from all files in the compiler directory.

ONE-DCO-1.0-Signed-off-by: Hyeongseok Oh <hseok82.oh@samsung.com>

Author: hseok-oh

compiler/circle-execution-plan/README.md

@@ -15,12 +15,12 @@ In order to record and read this data, we use `luci::CircleNodeExecutionPlan`.
 ### Execution plan building
 
 In order to build "execution plan" we use `ExecutionPlanner` class.
-The main method is `make_execution_plan()` which for each node finds and writes to its annotations 
+The main method is `make_execution_plan()` which for each node finds and writes to its annotations
 "execution plan". For this purpose there are two steps:
 - determining the order of execution of nodes, which is stored in `_ordered_nodes` vector.
 Now for this purpose there is only one default method `get_default_execution_order_plan()` that uses `loco::postorder_traversal(const std::vector<loco::Node *> &roots)`.
   In the future we can add new method and find the most suitable way to graph traversal.
-  
+
 - determining memory offsets for nodes from the beginning of shared memory buffer, which is stored in `_offsets`.
 Now for this purpose there is one method `get_offsets_with_greedy_by_size()` that is the implementation of the "Greedy by Size" algorithm, which is described in https://arxiv.org/pdf/2001.03288.pdf article.
   The main objective is to minimize the size of the allocated memory block.

compiler/circle-mpqsolver/README.md

@@ -1,20 +1,20 @@
 # circle-mpqsolver
-_circle-mpqsolver_ provides light-weight methods for finding a high-quality mixed-precision model 
+_circle-mpqsolver_ provides light-weight methods for finding a high-quality mixed-precision model
 within a reasonable time.
 
 ## Methods
 
 ### Bisection
-A model is split into two parts: front and back. One of them is quantized in uint8 and another in 
-int16. The precision of front and back is determined by our proxy metric, upperbound of total layer 
+A model is split into two parts: front and back. One of them is quantized in uint8 and another in
+int16. The precision of front and back is determined by our proxy metric, upperbound of total layer
 errors. (See https://github.com/Samsung/ONE/pull/10170#discussion_r1042246598 for more details)
 
-The boundary between the front and the back is decided by the depth of operators (depth: distance 
-from input to the operator), i.e., given a depth d, layers with a depth less than d are included 
-in front, and the rest are included in back. Bisection performs binary search to find a proper 
+The boundary between the front and the back is decided by the depth of operators (depth: distance
+from input to the operator), i.e., given a depth d, layers with a depth less than d are included
+in front, and the rest are included in back. Bisection performs binary search to find a proper
 depth which achieves a qerror less than target_qerror.
 
-In case front is quantized into Q16 the pseudocode is the following: 
+In case front is quantized into Q16 the pseudocode is the following:
 ```
     until |_depth_max_ - _depth_min_| <=1 do
         _current_depth_ = 0.5 * (_depth_max_ + _depth_min_)
@@ -23,12 +23,12 @@ In case front is quantized into Q16 the pseudocode is the following:
         else
             _depth_min_ = _current_depth_
 ```
-, where Loss(current_depth) is the qerror of the mixied-precision model split at current_depth. 
-As every iteration halves the remaining range (|depth_max - depth_min|), it converges in 
+, where Loss(current_depth) is the qerror of the mixied-precision model split at current_depth.
+As every iteration halves the remaining range (|depth_max - depth_min|), it converges in
 _~log2(max_depth)_ iterations.
 
-## Usage 
-Run _circle-mpqsolver_ with the following arguments.  
+## Usage
+Run _circle-mpqsolver_ with the following arguments.
 
 --data: .h5 file with test data
 
@@ -63,8 +63,8 @@ $./circle-mpqsolver
     --bisection true
 ```
 
-It will produce _model.q_opt.circle_, which is _model.recorded.circle_ quantized to mixed precision 
-using _dataset.h5_, with input nodes set to _Q16_ precision and quantization error (_qerror_) of 
+It will produce _model.q_opt.circle_, which is _model.recorded.circle_ quantized to mixed precision
+using _dataset.h5_, with input nodes set to _Q16_ precision and quantization error (_qerror_) of
 _model.q_opt.circle_ will be less than
 ```
  _qerror(full_q16) + qerror_ratio * (qerror(full_q8) - qerror(full_q16))_

compiler/circle-part-value-test/part_eval_all.sh

@@ -25,7 +25,7 @@ for TESTCASE in "$@"; do
 
   # for simplicity, folder uses same ${TESTCASE}
   TESTCASE_FOLDER="${WORKDIR}/${TESTCASE}"
-  
+
   PASSED_TAG="${TESTCASE_FOLDER}.passed"
   rm -f "${PASSED_TAG}"
 

compiler/circle-quantizer-dredd-recipe-test/CMakeLists.txt

@@ -52,7 +52,7 @@ macro(Add RECIPE)
       --quantize_with_minmax float32 ${ARG_DTYPE} ${ARG_GRANULARITY}
       ${QCONFIG_OPT} ${RECORDED_CIRCLE_PATH} ${QUANT_CIRCLE_PATH}
       ${INPUT_DTYPE_OPT} ${OUTPUT_DTYPE_OPT}
-    DEPENDS 
+    DEPENDS
       circle-quantizer
       record-minmax
       ${CIRCLE_PATH}

compiler/circle-resizer-dredd-recipe-test/CMakeLists.txt

@@ -33,7 +33,7 @@ macro(Add RECIPE)
   # Generate resized .circle
   add_custom_command(OUTPUT ${RESIZED_CIRCLE_PATH}
     COMMAND $<TARGET_FILE:circle_resizer> --input_path ${CIRCLE_PATH} --output_path ${RESIZED_CIRCLE_PATH} ${NEW_INPUTS_SIZES}
-    DEPENDS 
+    DEPENDS
       circle_resizer
       ${CIRCLE_PATH}
     COMMENT "Generate ${RECIPE}.resized.circle"

compiler/circle-resizer/app/CMakeLists.txt

@@ -4,8 +4,8 @@ target_link_libraries(circle_resizer PRIVATE arser)
 target_link_libraries(circle_resizer PRIVATE safemain)
 target_link_libraries(circle_resizer PRIVATE vconone)
 
-set_target_properties(circle_resizer PROPERTIES 
-  OUTPUT_NAME "circle-resizer" 
+set_target_properties(circle_resizer PROPERTIES
+  OUTPUT_NAME "circle-resizer"
 )
 
 install(TARGETS circle_resizer DESTINATION bin)

compiler/circle-tensordump/README.md

@@ -10,7 +10,7 @@ dump tensors in circle file
 
 ```
 $ ./circle-tensordump --tensors ../luci/tests/Conv2D_000.circle
-                      
+
 ----------------------------------------------------------------------
 [ifm]
  └── shape : (1, 3, 3, 2)
@@ -21,15 +21,15 @@ $ ./circle-tensordump --tensors ../luci/tests/Conv2D_000.circle
  └── buffer
      ├── index : 3
      ├── size  : 8
-     └── data  : 0.727939, 0.320132, 
+     └── data  : 0.727939, 0.320132,
 
 ----------------------------------------------------------------------
 [bias]
  ├── shape : (1)
  └── buffer
      ├── index : 4
      ├── size  : 4
-     └── data  : -0.794465, 
+     └── data  : -0.794465,
 
 ----------------------------------------------------------------------
 [ofm]

compiler/circlechef/proto/circlechef.proto

@@ -129,7 +129,7 @@ message Operation {
   optional GRUOptions gru_options = 104;
   optional FullyConnectedOptions fullyconnected_options = 105;
   optional RmsNormOptions rms_norm_options = 106;
-  optional RoPEOptions rope_options = 107;  
+  optional RoPEOptions rope_options = 107;
 }
 
 // For additional subgraphs

compiler/dredd-rule-lib/rule-lib.sh

@@ -265,7 +265,7 @@ tensor_shape()
           awk -v tensor_name="$1" '{ if ($1 == tensor_name) print $2}'`
 
   check_success_exit_code $? 0
-  
+
   echo ${ACTUAL}
 }
 

compiler/encodump/README.md

@@ -30,13 +30,13 @@ Output:
     <Inst>:
       Eval (0x10cfa90)
         out: 0x10cf960
-        <op>: 
+        <op>:
           Load(0x10cf600, obj: 0x10cd670)
           Conv2D(0x10cf8a0, ker obj: 0x10cf2d0, padding [T/B/L/R=0,0,0,0], stride [V/H = 1,1])
     <Inst>:
       Eval (0x10cff80)
         out: 0x10cfb20
-        <op>: 
+        <op>:
           Load(0x10cfe70, obj: 0x10cfcc0)
           Load(0x10cfdd0, obj: 0x10cf960)
           Add
@@ -51,13 +51,13 @@ Output:
   <Input>: bag 0x10ce650, name=ifm
   <Output>: bag 0x10ce9c0, name=ofm
   <Bag>:
-    0x10ce650, obj: [0x10cd670], size: 18, input, const, reader: [x], updater: [x], 
-    0x10ce770, obj: [0x10cf2d0], size: 2, const, reader: [x], updater: [x], 
-    0x10ce890, obj: [0x10cfcc0], size: 1, const, reader: [x], updater: [x], 
-    0x10ce9c0, obj: [0x10cf210], size: 9, output, const, reader: [x], updater: [x], 
-    0x10cf9d0, obj: [0x10cf960], size: 9, const, reader: [x], updater: [x], 
-    0x10cfbe0, obj: [0x10cfb20], size: 9, const, reader: [x], updater: [x], 
-    0x10d0060, obj: [0x10cfff0], size: 9, const, reader: [x], updater: [x], 
+    0x10ce650, obj: [0x10cd670], size: 18, input, const, reader: [x], updater: [x],
+    0x10ce770, obj: [0x10cf2d0], size: 2, const, reader: [x], updater: [x],
+    0x10ce890, obj: [0x10cfcc0], size: 1, const, reader: [x], updater: [x],
+    0x10ce9c0, obj: [0x10cf210], size: 9, output, const, reader: [x], updater: [x],
+    0x10cf9d0, obj: [0x10cf960], size: 9, const, reader: [x], updater: [x],
+    0x10cfbe0, obj: [0x10cfb20], size: 9, const, reader: [x], updater: [x],
+    0x10d0060, obj: [0x10cfff0], size: 9, const, reader: [x], updater: [x],
   <Object>:
     0x10cd670, bag: 0x10ce650, kind: Feature, Shape [H/W/D=3,3,2], producer: x, comsumer: [op: 0x10cf600]
     0x10cf210, bag: 0x10ce9c0, kind: Feature, Shape [H/W/D=3,3,1], producer: instr: 0x10d01f0, comsumer: [x]