1  """CuTile GEMM Conceptual Implementation.
  2  
  3  This module demonstrates the conceptual API and programming model
  4  of cuTile for GEMM operations.
  5  
  6  Note: cuTile is an experimental feature in CUDA 13.1+.
  7  This implementation shows the expected API patterns.
  8  """
  9  
 10  import functools
 11  from collections.abc import Callable
 12  from dataclasses import dataclass
 13  from typing import Any, TypeVar
 14  
 15  import numpy as np
 16  
 17  F = TypeVar("F", bound=Callable[..., Any])
 18  
 19  # ============================================================================
 20  # Conceptual cuTile Types
 21  # ============================================================================
 22  
 23  
 24  @dataclass
 25  class TileShape:
 26      """Shape of a tile in the cuTile programming model."""
 27  
 28      m: int  # Rows
 29      n: int  # Columns
 30      k: int = 1  # Depth (for 3D tiles)
 31  
 32      def __repr__(self) -> str:
 33          if self.k == 1:
 34              return f"TileShape({self.m}, {self.n})"
 35          return f"TileShape({self.m}, {self.n}, {self.k})"
 36  
 37  
 38  class TileArray:
 39      """
 40      Conceptual TileArray class representing cuTile's tile abstraction.
 41  
 42      In cuTile, a TileArray is a multi-dimensional array that can be
 43      automatically partitioned and distributed across GPU threads.
 44      The compiler handles thread mapping and memory access patterns.
 45      """
 46  
 47      def __init__(self, data: np.ndarray, tile_shape: TileShape | None = None):
 48          self.data = data
 49          self.shape = data.shape
 50          self.dtype = data.dtype
 51          self.tile_shape = tile_shape or TileShape(32, 32)
 52  
 53      def __repr__(self) -> str:
 54          return f"TileArray(shape={self.shape}, dtype={self.dtype}, tile={self.tile_shape})"
 55  
 56      def __getitem__(self, key) -> "TileArray":
 57          """Tile-aware indexing."""
 58          return TileArray(self.data[key], self.tile_shape)
 59  
 60      def __setitem__(self, key, value) -> None:
 61          """Tile-aware assignment."""
 62          if isinstance(value, TileArray):
 63              self.data[key] = value.data
 64          else:
 65              self.data[key] = value
 66  
 67      def __add__(self, other) -> "TileArray":
 68          if isinstance(other, TileArray):
 69              return TileArray(self.data + other.data, self.tile_shape)
 70          return TileArray(self.data + other, self.tile_shape)
 71  
 72      def __mul__(self, other) -> "TileArray":
 73          if isinstance(other, TileArray):
 74              return TileArray(self.data * other.data, self.tile_shape)
 75          return TileArray(self.data * other, self.tile_shape)
 76  
 77      def __matmul__(self, other) -> "TileArray":
 78          """Matrix multiplication using @ operator."""
 79          if isinstance(other, TileArray):
 80              return TileArray(self.data @ other.data, self.tile_shape)
 81          return TileArray(self.data @ other, self.tile_shape)
 82  
 83  
 84  # ============================================================================
 85  # cuTile Kernel Decorator (Conceptual)
 86  # ============================================================================
 87  
 88  
 89  def tile_kernel(func: F) -> F:
 90      """
 91      Conceptual decorator for cuTile kernels.
 92  
 93      In the actual cuTile implementation, this decorator would:
 94      1. Parse the function to extract tile operations
 95      2. Generate optimized CUDA code
 96      3. Handle thread mapping automatically
 97      4. Apply Tensor Core optimizations when applicable
 98      """
 99  
100      @functools.wraps(func)
101      def wrapper(*args, **kwargs):
102          # In production, this would compile to CUDA
103          # For now, we execute on CPU for demonstration
104          return func(*args, **kwargs)
105  
106      wrapper._is_tile_kernel = True
107      return wrapper
108  
109  
110  # ============================================================================
111  # cuTile GEMM Implementation
112  # ============================================================================
113  
114  
115  @tile_kernel
116  def tiled_gemm(
117      A: TileArray, B: TileArray, block_shape: tuple[int, int, int] = (128, 128, 32)
118  ) -> TileArray:
119      """
120      GEMM using cuTile's tile abstraction.
121  
122      In cuTile, the programmer specifies the computation at the tile level,
123      and the compiler automatically:
124      - Maps tiles to thread blocks
125      - Handles shared memory allocation
126      - Optimizes memory access patterns
127      - Utilizes Tensor Cores when available
128  
129      Args:
130          A: Input matrix [M, K]
131          B: Input matrix [K, N]
132          block_shape: Tile dimensions (M, N, K)
133  
134      Returns:
135          Output matrix C [M, N] = A @ B
136      """
137      M, K = A.shape
138      K2, N = B.shape
139      assert K == K2, f"Dimension mismatch: A.shape[1]={K} != B.shape[0]={K2}"
140  
141      block_m, block_n, block_k = block_shape
142  
143      # In cuTile, block_shape would guide the compiler's tiling strategy
144      # The compiler would:
145      # 1. Map tiles to thread blocks
146      # 2. Optimize shared memory usage for the given tile dimensions
147      # 3. Generate efficient memory access patterns
148  
149      # Conceptual implementation (CPU fallback)
150      C_data = A.data @ B.data
151  
152      return TileArray(C_data, TileShape(block_m, block_n))
153  
154  
155  @tile_kernel
156  def tiled_gemm_with_epilogue(
157      A: TileArray,
158      B: TileArray,
159      C: TileArray,
160      alpha: float = 1.0,
161      beta: float = 0.0,
162      block_shape: tuple[int, int, int] = (128, 128, 32),
163  ) -> TileArray:
164      """
165      GEMM with epilogue: D = alpha * A @ B + beta * C
166  
167      cuTile supports fused epilogue operations, allowing the compiler
168      to generate a single kernel that computes the matrix multiply
169      and applies the scaling/addition without extra memory traffic.
170      """
171      M, K = A.shape
172      K2, N = B.shape
173  
174      # Fused computation
175      D_data = alpha * (A.data @ B.data) + beta * C.data
176  
177      return TileArray(D_data, TileShape(block_shape[0], block_shape[1]))
178  
179  
180  # ============================================================================
181  # Example Usage
182  # ============================================================================
183  
184  
185  def demonstrate_tiled_gemm() -> None:
186      """Demonstrate cuTile GEMM usage."""
187      print("=" * 60)
188      print("cuTile GEMM Demonstration")
189      print("=" * 60)
190  
191      # Create input matrices
192      M, N, K = 1024, 1024, 512
193  
194      print(f"\nMatrix dimensions: M={M}, N={N}, K={K}")
195  
196      # Initialize with random data
197      np.random.seed(42)
198      A_data = np.random.randn(M, K).astype(np.float32)
199      B_data = np.random.randn(K, N).astype(np.float32)
200  
201      # Create TileArrays
202      A = TileArray(A_data, TileShape(128, 32))
203      B = TileArray(B_data, TileShape(32, 128))
204  
205      print(f"\nInput A: {A}")
206      print(f"Input B: {B}")
207  
208      # Run tiled GEMM
209      print("\nRunning tiled GEMM...")
210      C = tiled_gemm(A, B, block_shape=(128, 128, 32))
211  
212      print(f"Output C: {C}")
213  
214      # Verify against numpy
215      C_ref = A_data @ B_data
216      max_diff = np.max(np.abs(C.data - C_ref))
217      print(f"\nMax difference from numpy: {max_diff:.2e}")
218  
219      # Demonstrate GEMM with epilogue
220      print("\n" + "-" * 40)
221      print("GEMM with epilogue: D = 2.0 * A @ B + 0.5 * C")
222  
223      C_init = TileArray(np.random.randn(M, N).astype(np.float32))
224      D = tiled_gemm_with_epilogue(A, B, C_init, alpha=2.0, beta=0.5)
225  
226      D_ref = 2.0 * (A_data @ B_data) + 0.5 * C_init.data
227      max_diff = np.max(np.abs(D.data - D_ref))
228      print(f"Max difference from reference: {max_diff:.2e}")
229  
230      print("\n" + "=" * 60)
231      print("Demonstration complete")
232      print("=" * 60)
233  
234  
235  if __name__ == "__main__":
236      demonstrate_tiled_gemm()