[Lint] Add ruff config to check for useless spaces (#807)

* update lint config

* Remove spaces for blank line

* update

testing/python/autotune/test_tilelang_autotune_with_inputs.py

@@ -133,7 +133,7 @@ def run_autotune(M: int, N: int, K: int):
 def test_autotune_matmul():
     """
     Run the autotuning validation for the matmul kernel on a 1024x1024x1024 problem.
-    
+
     This test constructs random CUDA tensors, autotunes the JIT-compiled block-level matrix-multiplication kernel,
     executes it, and asserts the result matches a reference CPU implementation within tolerances.
     """

testing/python/transform/test_tilelang_transform_lower_hopper_intrin.py

@@ -55,4 +55,4 @@ def test_lower_hopper_intrin_barrier():
 
 
 if __name__ == "__main__":
-    tilelang.testing.main()
\ No newline at end of file
+    tilelang.testing.main()

testing/python/transform/test_tilelang_transform_warp_specialized.py

@@ -118,4 +118,4 @@ def test_warp_specialized():
 
 
 if __name__ == "__main__":
-    tilelang.testing.main()
\ No newline at end of file
+    tilelang.testing.main()

tilelang/autotuner/tuner.py

@@ -713,7 +713,7 @@ def autotune(  # This is the new public interface
 
     This decorator can be used without arguments (e.g., `@tilelang.jit`):
        Applies JIT compilation with default settings.
-    
+
     Tips:
         - If you want to skip the auto-tuning process, you can set override the tunable parameters in the function signature.
             ```python

tilelang/carver/roller/rasterization.py

@@ -78,7 +78,7 @@ __device__ __inline__ dim3 rasterization2DColumn(const int panel_width) {
     const auto bx = (panelIdx & 1) ? gridDim.x -(baseBlockIdx - panelIdx * panel_width * gridDim.x) /strideLd - 1 : (baseBlockIdx - panelIdx * panel_width *gridDim.x) / strideLd;
     const auto by = (baseBlockIdx - panelIdx * panel_width *gridDim.x) % strideLd + panelIdx * panel_width;
     const auto bz = blockIdx.z;
-    
+
     dim3 blockIdx(bx, by, bz);
     return blockIdx;
 }

tilelang/carver/template/__init__.py

@@ -6,4 +6,4 @@ from .gemv import GEMVTemplate  # noqa: F401
 from .elementwise import ElementwiseTemplate  # noqa: F401
 from .general_reduce import GeneralReductionTemplate  # noqa: F401
 from .flashattention import FlashAttentionTemplate  # noqa: F401
-from .conv import ConvTemplate  # noqa: F401
\ No newline at end of file
+from .conv import ConvTemplate  # noqa: F401

tilelang/carver/template/base.py

@@ -12,8 +12,8 @@ from tvm.tir import PrimFunc  # Import PrimFunc for handling tensor IR functions
 @dataclass
 class BaseTemplate(ABC):
     """
-    Base class template for hardware-aware configurations. 
-    This serves as an abstract base class (ABC) that defines the structure 
+    Base class template for hardware-aware configurations.
+    This serves as an abstract base class (ABC) that defines the structure
     for subclasses implementing hardware-specific optimizations.
     """
 
@@ -30,9 +30,9 @@ class BaseTemplate(ABC):
     def get_hardware_aware_configs(self, arch: TileDevice = None, topk: int = 10) -> List[Hint]:
         """
         Abstract method that must be implemented by subclasses.
-        It should return a list of hardware-aware configurations (hints) 
+        It should return a list of hardware-aware configurations (hints)
         based on the specified architecture.
-        
+
         Args:
             arch (TileDevice, optional): The target architecture. Defaults to None.
             topk (int, optional): Number of top configurations to return. Defaults to 10.
@@ -104,7 +104,7 @@ class BaseTemplate(ABC):
         """
         Placeholder method that should be implemented by subclasses.
         This method is responsible for initializing the function.
-        
+
         Raises:
             NotImplementedError: If not implemented in the subclass.
         """

tilelang/carver/template/conv.py

@@ -62,8 +62,8 @@ class ConvTemplate(BaseTemplate):
         """
         Defines and initializes the convolution computation.
 
-        This method sets up placeholders for input matrices, computes 
-        the convolution using TVM's compute API, 
+        This method sets up placeholders for input matrices, computes
+        the convolution using TVM's compute API,
         and optionally applies bias and type casting.
 
         Raises:

tilelang/carver/template/flashattention.py

@@ -44,8 +44,8 @@ class FlashAttentionTemplate(BaseTemplate):
         """
         Defines and initializes the matrix multiplication computation.
 
-        This method sets up placeholders for input matrices, computes 
-        the matrix multiplication using TVM's compute API, 
+        This method sets up placeholders for input matrices, computes
+        the matrix multiplication using TVM's compute API,
         and optionally applies bias and type casting.
 
         Raises:

tilelang/carver/template/gemv.py

@@ -12,7 +12,7 @@ class GEMVTemplate(BaseTemplate):
     """
     A template for Generalized Matrix-Vector Multiplication (GEMV).
 
-    This template defines the computation for a matrix-vector multiplication 
+    This template defines the computation for a matrix-vector multiplication
     with configurable parameters such as transposition, data types, and bias addition.
     """
 
@@ -43,8 +43,8 @@ class GEMVTemplate(BaseTemplate):
         """
         Defines and initializes the GEMV computation function.
 
-        This method sets up placeholders for input matrices, computes 
-        the matrix-vector multiplication using TVM's compute API, 
+        This method sets up placeholders for input matrices, computes
+        the matrix-vector multiplication using TVM's compute API,
         and optionally applies bias and type casting.
         """
         M: int = 1  # Fixed M value, representing a single batch dimension

tilelang/carver/template/matmul.py

@@ -56,8 +56,8 @@ class MatmulTemplate(BaseTemplate):
         """
         Defines and initializes the matrix multiplication computation.
 
-        This method sets up placeholders for input matrices, computes 
-        the matrix multiplication using TVM's compute API, 
+        This method sets up placeholders for input matrices, computes
+        the matrix multiplication using TVM's compute API,
         and optionally applies bias and type casting.
 
         Raises:

tilelang/contrib/nvcc.py

@@ -126,7 +126,7 @@ def compile_cuda(code,
 
 def find_cuda_path():
     """Utility function to find cuda path
-    
+
     Returns
     -------
     path : str

tilelang/engine/callback.py

@@ -5,7 +5,7 @@ from tvm.target import Target
 
 def register_cuda_postproc(func: Callable[[str, Target], str], override: bool = True):
     """Register a post-processing function for CUDA code generation.
-    
+
     Args:
         func: A callable that takes generated code (str) and target (Target) as input,
              and returns the processed code (str).
@@ -16,7 +16,7 @@ def register_cuda_postproc(func: Callable[[str, Target], str], override: bool =
 
 def register_hip_postproc(func: Callable[[str, Target], str], override: bool = True):
     """Register a post-processing function for HIP code generation.
-    
+
     Args:
         func: A callable that takes generated code (str) and target (Target) as input,
              and returns the processed code (str).
@@ -27,17 +27,17 @@ def register_hip_postproc(func: Callable[[str, Target], str], override: bool = T
 
 def register_cuda_postproc_callback(func: Union[Callable, bool] = None, override: bool = True):
     """Decorator for registering CUDA post-processing callback function.
-    
+
     Can be used with or without parentheses:
         @register_cuda_postproc_callback
         def func(code, target): ...
-        
+
         @register_cuda_postproc_callback()
         def func(code, target): ...
-        
+
         @register_cuda_postproc_callback(override=False)
         def func(code, target): ...
-    
+
     Args:
         func: The function to be decorated or a boolean override flag
         override: Whether to override existing registered function. Defaults to True.
@@ -60,17 +60,17 @@ def register_cuda_postproc_callback(func: Union[Callable, bool] = None, override
 
 def register_hip_postproc_callback(func: Union[Callable, bool] = None, override: bool = True):
     """Decorator for registering HIP post-processing callback function.
-    
+
     Can be used with or without parentheses:
         @register_hip_postproc_callback
         def func(code, target): ...
-        
+
         @register_hip_postproc_callback()
         def func(code, target): ...
-        
+
         @register_hip_postproc_callback(override=False)
         def func(code, target): ...
-    
+
     Args:
         func: The function to be decorated or a boolean override flag
         override: Whether to override existing registered function. Defaults to True.

tilelang/engine/param.py

@@ -21,13 +21,13 @@ class KernelParam:
     def from_buffer(cls, buffer: Buffer):
         """
         Creates a KernelParam instance from a TVM Buffer object.
-        
+
         Args:
             buffer: TVM Buffer object containing dtype and shape information
-            
+
         Returns:
             KernelParam instance with converted dtype and shape
-            
+
         Raises:
             ValueError: If dimension type is not supported (not IntImm or Var)
         """
@@ -47,10 +47,10 @@ class KernelParam:
         """
         Creates a KernelParam instance from a TVM Variable object.
         Used for scalar parameters.
-        
+
         Args:
             var: TVM Variable object containing dtype information
-            
+
         Returns:
             KernelParam instance representing a scalar (empty shape)
         """
@@ -60,7 +60,7 @@ class KernelParam:
     def is_scalar(self) -> bool:
         """
         Checks if the parameter represents a scalar value.
-        
+
         Returns:
             bool: True if parameter has no dimensions (empty shape), False otherwise
         """
@@ -69,7 +69,7 @@ class KernelParam:
     def is_unsigned(self) -> bool:
         """
         Checks if the parameter represents an unsigned integer type.
-        
+
         Returns:
             bool: True if parameter is an unsigned integer type, False otherwise
         """
@@ -81,7 +81,7 @@ class KernelParam:
     def is_float8(self) -> bool:
         """
         Checks if the parameter represents a float8 type.
-        
+
         Returns:
             bool: True if parameter is a float8 type, False otherwise
         """
@@ -93,7 +93,7 @@ class KernelParam:
     def is_boolean(self) -> bool:
         """
         Checks if the parameter represents a boolean type.
-        
+
         Returns:
             bool: True if parameter is a boolean type, False otherwise
         """

tilelang/engine/phase.py

@@ -65,7 +65,7 @@ def LowerAndLegalize(mod: IRModule, target: Target) -> IRModule:
     # Bind the target device information to the module
     """
     Bind target information and progressively legalize and lower frontend Tile IR into a form suitable for downstream optimization and codegen.
-    
+
     This pass pipeline:
     - Binds the provided target to the module.
     - Legalizes frontend Tile IR into TVM-compatible constructs.
@@ -75,11 +75,11 @@ def LowerAndLegalize(mod: IRModule, target: Target) -> IRModule:
     - Legalizes vectorized loops and inserts safety checks for memory accesses.
     - Re-simplifies to remove redundancies introduced by safety checks.
     - Attempts loop vectorization for dynamic-shaped loops.
-    
+
     Parameters:
         mod (IRModule): The input IR module containing frontend Tile IR.
         target (Target): Target device information to bind into the module.
-    
+
     Returns:
         IRModule: The transformed module, ready for target-specific optimization passes.
     """

tilelang/intrinsics/utils.py

@@ -91,14 +91,14 @@ def get_mma_micro_size(dtype: Literal["float16", "int8"]):
     # Basic Tensor Core Matrix Multiply operation Unit
     """
     Return the MMA (Tensor Core) micro-tile dimensions for a given data type.
-    
+
     This function returns the micro tile sizes (x, y, k) used by MMA/Tensor Core operations.
     - x: tile width in the output/result dimension
     - y: tile height in the output/result dimension
     - k: tile depth in the reduction/K dimension
-    
+
     Accepted dtype strings include "float16", "int8" and some FP8 identifiers ("float8_e4m3", "float8_e5m2"). For FP8 and int8 types the reduction depth (`k`) is 32; for float16 it is 16.
-    
+
     Returns:
         tuple[int, int, int]: (micro_size_x, micro_size_y, micro_size_k)
     """

tilelang/jit/__init__.py

 """
-This module provides an auto-tuning infrastructure for TileLang (tl) programs. 
-It includes functionality to JIT-compile TileLang programs into a runnable 
+This module provides an auto-tuning infrastructure for TileLang (tl) programs.
+It includes functionality to JIT-compile TileLang programs into a runnable
 kernel adapter using TVM.
 """
 

tilelang/jit/adapter/__init__.py

@@ -2,4 +2,4 @@ from .base import BaseKernelAdapter  # noqa: F401
 from .dlpack import TorchDLPackKernelAdapter  # noqa: F401
 from .ctypes import CtypesKernelAdapter  # noqa: F401
 from .cython import CythonKernelAdapter  # noqa: F401
-from .nvrtc import NVRTCKernelAdapter  # noqa: F401
\ No newline at end of file
+from .nvrtc import NVRTCKernelAdapter  # noqa: F401

tilelang/jit/adapter/ctypes/adapter.py

@@ -16,7 +16,7 @@ from tilelang.utils.language import retrieve_func_from_module
 
 class CtypesKernelAdapter(BaseKernelAdapter):
     """Adapter class that converts TVM/TIR functions to callable CUDA kernels using ctypes.
-    
+
     This adapter handles:
     1. Converting TIR functions to compiled CUDA libraries
     2. Managing dynamic shapes in tensor operations
@@ -52,7 +52,7 @@ class CtypesKernelAdapter(BaseKernelAdapter):
                  pass_configs: Optional[Dict[str, Any]] = None,
                  compile_flags: Optional[List[str]] = None):
         """Initialize the adapter with the given TIR function or module.
-        
+
         Args:
             params: List of tensor types for inputs/outputs
             result_idx: Indices of output tensors
@@ -157,7 +157,7 @@ class CtypesKernelAdapter(BaseKernelAdapter):
 
     def _process_dynamic_symbolic(self) -> Dict[tir.Var, Tuple[int, int, int]]:
         """Extract information about dynamic shapes from the TIR function.
-        
+
         Maps symbolic variables to their corresponding (id, buffer_index, dimension)
         for runtime shape resolution.
         id represents shape or stride, 0 represents shape, 1 represents stride
@@ -184,7 +184,7 @@ class CtypesKernelAdapter(BaseKernelAdapter):
 
     def _forward_from_prebuild_lib(self, *args, stream: Optional[int] = None):
         """Low-level function to call the compiled CUDA kernel.
-        
+
         Converts PyTorch tensor pointers to C void pointers for ctypes interface.
         """
         ctypes_args = [
@@ -197,17 +197,17 @@ class CtypesKernelAdapter(BaseKernelAdapter):
                                         *ins: List[torch.Tensor],
                                         stream: Optional[int] = None):
         """High-level wrapper for kernel execution.
-        
+
         Handles:
         1. Input validation
         2. Output tensor allocation
         3. Dynamic shape resolution
         4. CUDA stream management
-        
+
         Args:
             ins: Input PyTorch tensors
             stream: Optional CUDA stream for asynchronous execution
-        
+
         Returns:
             Single tensor or list of tensors containing the kernel results
         """

tilelang/jit/adapter/cython/adapter.py

@@ -176,7 +176,7 @@ from cython_wrapper import CythonKernelWrapper
 
 class CythonKernelAdapter(BaseKernelAdapter):
     """Adapter class that converts TVM/TIR functions to callable CUDA kernels using ctypes.
-    
+
     This adapter handles:
     1. Converting TIR functions to compiled CUDA libraries
     2. Managing dynamic shapes in tensor operations
@@ -222,7 +222,7 @@ class CythonKernelAdapter(BaseKernelAdapter):
                  pass_configs: Optional[Dict[str, Any]] = None,
                  compile_flags: Optional[List[str]] = None):
         """Initialize the adapter with the given TIR function or module.
-        
+
         Args:
             params: List of tensor types for inputs/outputs
             result_idx: Indices of output tensors
@@ -347,7 +347,7 @@ class CythonKernelAdapter(BaseKernelAdapter):
 
     def _process_dynamic_symbolic(self) -> Dict[tir.Var, Tuple[int, int, int]]:
         """Extract information about dynamic shapes from the TIR function.
-        
+
         Maps symbolic variables to their corresponding (id, buffer_index, dimension)
         for runtime shape resolution.
         id represents shape or stride, 0 represents shape, 1 represents stride
@@ -374,7 +374,7 @@ class CythonKernelAdapter(BaseKernelAdapter):
 
     def _process_buffer_dtype(self) -> Dict[tir.Var, Tuple[int, torch.dtype]]:
         """Extract information about buffer dtypes from the TIR function.
-        
+
         Maps buffer variables to their corresponding dtypes.
         """
         func = self.prim_func
@@ -390,7 +390,7 @@ class CythonKernelAdapter(BaseKernelAdapter):
 
     def _process_ptr_map(self) -> Dict[int, str]:
         """Extract information about pointer arguments from the TIR function.
-        
+
         Maps pointer arguments to their corresponding (buffer_index, shape_dimension)
         for runtime shape resolution.
         """
@@ -407,7 +407,7 @@ class CythonKernelAdapter(BaseKernelAdapter):
                   Dict[tir.Var, Tuple[int, List[Tuple[int, int]]]],
                   List[Tuple[tir.Var]]]:
         """Extract information about static shapes from the TIR function.
-        
+
         Maps buffer variables to their corresponding static shapes.
         """
         func = self.prim_func
@@ -438,7 +438,7 @@ class CythonKernelAdapter(BaseKernelAdapter):
 
     def _process_buffer_device(self) -> Dict[tir.Var, Tuple[int, torch.device]]:
         """Extract information about buffer devices from the TIR function.
-        
+
         Maps buffer variables to their corresponding devices.
         """
         func = self.prim_func
@@ -462,7 +462,7 @@ class CythonKernelAdapter(BaseKernelAdapter):
 
     def _forward_from_prebuild_lib(self, *args, stream: Optional[int] = None):
         """Low-level function to call the compiled CUDA kernel.
-        
+
         Converts PyTorch tensor pointers to C void pointers for ctypes interface.
         """
         ctypes_args = [