Remove trailing whitespaces

Author: Xeratec

CHANGELOG.md

@@ -55,7 +55,7 @@ This file contains the changelog for the Deeploy project. The changelog is divid
 ### Fixed
 - Prevent node duplication for graphs generated via GraphSurgeon
 - Resolved issue with missing `id` in the `Build Cache for Docker` step, used in the `Inject build-cache` step.
-- Fix license CI check. 
+- Fix license CI check.
 
 ### Removed
 - Delete outdated and unused `.gitlab-ci.yml` file

Deeploy/AbstractDataTypes.py

@@ -267,8 +267,8 @@ def partialOrderUpcast(cls, otherCls: Type[Immediate]) -> bool:
     @classmethod
     def checkValue(cls, value: Union[float, Iterable[float], np.ndarray], ctxt: Optional[_NetworkContext] = None):
         """
-        This method tries to manually cast standard python's standard immediate float precision values 
-        (64 bits) to an arbitrary FP representation and check if the new representation is close enough 
+        This method tries to manually cast standard python's standard immediate float precision values
+        (64 bits) to an arbitrary FP representation and check if the new representation is close enough
         to the original value.
         """
         _val_list = []

Deeploy/CommonExtensions/CodeTransformationPasses/PrintInputs.py

@@ -40,9 +40,9 @@
     accessStr += "[" + f"print_iter_{idx}" + "]"
     if idx > 0:
         dimStr += "[" + f"{dim}" + "]"
-formatSpecifier = "%*i" 
+formatSpecifier = "%*i"
 if "float" in bufferType.referencedType.typeName or "double" in bufferType.referencedType.typeName:
-    formatSpecifier = "%*.6f"  
+    formatSpecifier = "%*.6f"
 %>
 printf("${nodeName} ${bufferName}: ${bufferType.referencedType.typeName}, ${bufferShape}, %p\\n", ${bufferName});
 % for idx, dim in enumerate(bufferShape):

Deeploy/Targets/Generic/Templates/FloatGemmTemplate.py

@@ -48,15 +48,15 @@
         % if A_batched:
         ref_${data_out}_${A} += ${M} * ${N};
         % endif
-                                 
+
         % if B_batched:
         ref_${data_out}_${B} += ${N} * ${O};
         % endif
-        
+
         % if C_batched:
         ref_${data_out}_${C} += ${M} * ${O};
         % endif
-                                 
+
         ref_${data_out}_${data_out} += ${M} * ${O};
     }
 END_SINGLE_CORE

Deeploy/Targets/Generic/Templates/FloatLayernormTemplate.py

@@ -27,6 +27,6 @@
 
 referenceTemplate = NodeTemplate("""
 // FloatLayernorm (Name: ${nodeName}, Op: ${nodeOp})
-                                
+
 SINGLE_CORE Layernorm_fp${data_in_type.referencedType.typeWidth}_fp${data_out_type.referencedType.typeWidth}(${data_in}, ${data_out}, ${weight}, ${bias}, ${epsilon}, ${size}, ${lastDimLength});
 """)

Deeploy/Targets/Generic/Templates/FloatMaxPoolTemplate.py

@@ -37,7 +37,7 @@
             ref_${data_out}_${data_in}, ${ch_im_in}, ${dim_im_in_x}, ${dim_im_in_y},${dim_kernel_x}, ${dim_kernel_y}, ${stride_x}, ${stride_y},
             ref_${data_out}_${data_out}
         );
-      
+
     }
 END_SINGLE_CORE
 """)

Deeploy/Targets/Generic/Templates/FloatPadTemplate.py

@@ -29,7 +29,7 @@
 <%
     y_offset_out = dim_im_out_ch*(pad_y*dim_im_out_y)
     x_offset_out = dim_im_out_ch*(pad_x)
-    width = dim_im_in_ch*dim_im_in_y                
+    width = dim_im_in_ch*dim_im_in_y
 
     addoffsetOut = dim_im_out_ch * dim_im_out_y
     addoffsetIn = dim_im_in_ch * dim_im_in_y

Deeploy/Targets/Generic/Templates/FloatReduceSumTemplate.py

@@ -25,7 +25,7 @@
 from Deeploy.DeeployTypes import NodeTemplate
 
 referenceTemplate = NodeTemplate("""
-// Float ReduceSum (Name: ${nodeName}, Op: ${nodeOp}) 
+// Float ReduceSum (Name: ${nodeName}, Op: ${nodeOp})
 BEGIN_SINGLE_CORE
 float32_t ${data_out}_accumulator = 0.0f;
 <% reduceLength = 1

Deeploy/Targets/Generic/Templates/QuantTemplate.py

@@ -35,23 +35,23 @@ def __init__(self, templateStr):
 referenceTemplate = _QuantTemplate("""
 // Quantization (Name: ${nodeName}, Op: ${nodeOp})
 BEGIN_SINGLE_CORE
-    
+
     for (uint32_t i=0; i<${size}; i++) {
         // quantization formula
         float32_t input_val = ${data_in}[i];
         float32_t scaled_val = input_val * ${scale};  // Multiply instead of divide
         float32_t shifted_val = scaled_val + ${zero_point};
-        
+
         // Round to nearest integer
         int32_t quantized = (int32_t)(shifted_val + 0.5f * (shifted_val >= 0 ? 1 : -1));
-        
+
         // Clamp the value
         if (quantized < ${min_val}) quantized = ${min_val};
         if (quantized > ${max_val}) quantized = ${max_val};
-        
+
         // Assign directly with explicit cast
         ${data_out}[i] = (${data_out_type.referencedType.typeName})quantized;
-        
+
     }
 END_SINGLE_CORE
 """)

Deeploy/Targets/Generic/Templates/SliceTemplate.py

@@ -67,7 +67,7 @@ def alignToContext(self, ctxt: NetworkContext,
 for dim in data_in_shape[1:]:
      dimSteps.append(dimSteps[-1]//dim)
 %>
-<%                                   
+<%
 transferSize = dimSteps[int(axes[-1])]
 %>
 <%