submission 780043

+ import torch
  import triton
  import triton.language as tl
- import torch
   
   
  @triton.jit
- def _vecadd_kernel(ptr_a, ptr_b, ptr_c, n_elements, BLOCK_SIZE: tl.constexpr):
-     """Triton kernel for elementwise addition of two float16 tensors.
-     
-     Fused stages: single elementwise add (a + b -> c).
-     No further fusion possible since this is a standalone vector addition.
+ def _add_kernel(
+     x0_ptr,      # Pointer to first input tensor
+     x1_ptr,      # Pointer to second input tensor
+     out_ptr,     # Pointer to output tensor
+     n_elements,  # Total number of elements
+     BLOCK_SIZE: tl.constexpr,  # Number of elements per block
+ ):
      """
-     pid = tl.program_id(0)
+     Triton kernel for elementwise addition of two tensors.
+  
+     Each program instance handles BLOCK_SIZE elements.
+     """
+     # Get the program ID (which block we're processing)
+     pid = tl.program_id(axis=0)
+  
+     # Calculate the starting offset for this block
      block_start = pid * BLOCK_SIZE
+  
+     # Create offsets for elements within this block
      offsets = block_start + tl.arange(0, BLOCK_SIZE)
+  
+     # Create mask to handle boundary conditions (last block may be partial)
      mask = offsets < n_elements
   
-     a = tl.load(ptr_a + offsets, mask=mask)
-     b = tl.load(ptr_b + offsets, mask=mask)
+     # Load elements from both input tensors
+     x0 = tl.load(x0_ptr + offsets, mask=mask, other=0.0)
+     x1 = tl.load(x1_ptr + offsets, mask=mask, other=0.0)
   
-     c = a + b
+     # Perform elementwise addition
+     result = x0 + x1
   
-     tl.store(ptr_c + offsets, c, mask=mask)
+     # Store the result
+     tl.store(out_ptr + offsets, result, mask=mask)
   
   
- def kernel_function(A: torch.Tensor, B: torch.Tensor, C: torch.Tensor) -> torch.Tensor:
-     """Wrapper for float16 vector addition: C = A + B.
+ def kernel_function(x0: torch.Tensor, x1: torch.Tensor, output: torch.Tensor = None) -> torch.Tensor:
+     """
+     Wrapper function for elementwise addition of two tensors using Triton.
   
+     Fused operation: Single kernel performs load + add + store in one pass.
+     No separate stages needed as this is a simple elementwise operation.
+  
      Args:
-         A: Input tensor of shape (N, N), dtype float16, on CUDA.
-         B: Input tensor of shape (N, N), dtype float16, on CUDA.
-         C: Output tensor of shape (N, N), dtype float16, on CUDA (written in-place).
+         x0: First input tensor of shape [N, N], dtype float16
+         x1: Second input tensor of shape [N, N], dtype float16
+         output: Optional output tensor of shape [N, N], dtype float16
   
      Returns:
-         C tensor with result A + B.
+         Output tensor of shape [N, N], dtype float16, containing x0 + x1
      """
-     assert A.is_cuda and B.is_cuda and C.is_cuda
-     assert A.shape == B.shape == C.shape
+     # Validate inputs
+     assert x0.is_cuda and x1.is_cuda, "Both tensors must be on CUDA device"
+     assert x0.shape == x1.shape, "Input tensors must have the same shape"
+     assert x0.dtype == x1.dtype, "Input tensors must have the same dtype"
   
-     # Flatten for 1D indexing - use contiguous views
-     a_flat = A.contiguous().view(-1)
-     b_flat = B.contiguous().view(-1)
-     c_flat = C.contiguous().view(-1)
+     # Ensure tensors are contiguous for proper memory access
+     x0 = x0.contiguous()
+     x1 = x1.contiguous()
   
-     n_elements = a_flat.numel()
+     # Allocate output tensor with same shape and dtype if not provided
+     if output is None:
+         output = torch.empty_like(x0)
+  
+     # Calculate total number of elements
+     n_elements = x0.numel()
+  
+     # Choose block size (power of 2, common choice for good performance)
      BLOCK_SIZE = 1024
+  
+     # Calculate grid size (number of blocks needed)
      grid = (triton.cdiv(n_elements, BLOCK_SIZE),)
   
-     _vecadd_kernel[grid](a_flat, b_flat, c_flat, n_elements, BLOCK_SIZE=BLOCK_SIZE)
+     # Launch the Triton kernel
+     _add_kernel[grid](
+         x0,          # First input pointer
+         x1,          # Second input pointer
+         output,      # Output pointer
+         n_elements,  # Total elements
+         BLOCK_SIZE,  # Block size (compile-time constant)
+     )
   
-     return C
+     return output
   
+  
+ def test_kernel():
+     """
+     Test the Triton kernel against PyTorch reference implementation.
+     """
+     test_cases = [
+         {"seed": 4242, "size": 127},
+         {"seed": 5236, "size": 128},
+         {"seed": 1001, "size": 129},
+         {"seed": 5531, "size": 256},
+         {"seed": 9173, "size": 512},
+     ]
+  
+     all_passed = True
+  
+     for test in test_cases:
+         size = test["size"]
+         seed = test["seed"]
+  
+         # Generate input tensors
+         gen = torch.Generator(device="cuda")
+         gen.manual_seed(seed)
+         A = torch.randn(size, size, device="cuda", dtype=torch.float16, generator=gen).contiguous()
+         B = torch.randn(size, size, device="cuda", dtype=torch.float16, generator=gen).contiguous()
+  
+         # Compute reference result using PyTorch
+         ref_output = A + B
+  
+         # Compute result using Triton kernel
+         triton_output = kernel_function(A, B)
+  
+         # Compare results
+         if torch.allclose(triton_output, ref_output, rtol=2e-2, atol=2e-2):
+             print(f"Test size={size}, seed={seed}: PASS")
+         else:
+             print(f"Test size={size}, seed={seed}: FAIL")
+             max_diff = (triton_output - ref_output).abs().max().item()
+             print(f"  Max difference: {max_diff}")
+             all_passed = False
+  
+     if all_passed:
+         print("PASS")
+     else:
+         print("FAIL")
+         exit(1)
+  
+  
+ if __name__ == "__main__":
+     test_kernel()
+  
+  
  import inspect
   
  def custom_kernel(input):