Implement all 5 Triton kernel labs

- vector_add: basic masked load/store with block indexing
- row_softmax: single-pass numerically stable softmax per row
- tiled_matmul: K-dimension tile loop with edge masking (IEEE precision)
- online_softmax: two-pass running max/sum recurrence across blocks
- flash_attention_fwd: blockwise Q/K/V with online softmax, causal support

All 26 tests pass on RTX 5090 (CUDA 12.8, Triton 3.6).

kernels/triton/row_softmax.py

@@ -26,12 +26,15 @@ if TRITON_AVAILABLE:
     ):
         row_idx = tl.program_id(axis=0)
         col_offsets = tl.arange(0, block_size)
-        # TODO(student): convert row_idx and col_offsets into pointers for this row.
-        # TODO(student): load a row with masking.
-        # TODO(student): subtract the row max for stability.
-        # TODO(student): exponentiate, sum, and normalize.
-        # TODO(student): store the normalized row.
-        pass
+        mask = col_offsets < num_cols
+        x_ptrs = x_ptr + row_idx * stride_x_row + col_offsets
+        out_ptrs = out_ptr + row_idx * stride_out_row + col_offsets
+        row = tl.load(x_ptrs, mask=mask, other=float('-inf'))
+        row_max = tl.max(row, axis=0)
+        numerator = tl.exp(row - row_max)
+        denominator = tl.sum(numerator, axis=0)
+        result = numerator / denominator
+        tl.store(out_ptrs, result, mask=mask)
 
 
 def triton_row_softmax(x: torch.Tensor, block_size: int = 128) -> torch.Tensor:
@@ -41,5 +44,11 @@ def triton_row_softmax(x: torch.Tensor, block_size: int = 128) -> torch.Tensor:
         raise ValueError(f"expected 2D input, got {tuple(x.shape)}")
     if not x.is_cuda:
         raise ValueError("Triton kernels in this lab expect CUDA tensors.")
-    raise NotImplementedError("TODO(student): implement row-wise softmax launch logic.")
+    num_rows, num_cols = x.shape
+    # block_size must be >= num_cols for this single-pass kernel
+    block_size = max(block_size, triton.next_power_of_2(num_cols))
+    out = torch.empty_like(x)
+    grid = (num_rows,)
+    row_softmax_kernel[grid](x, out, num_cols, x.stride(0), out.stride(0), block_size=block_size)
+    return out