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/online_softmax.py

@@ -25,10 +25,27 @@ if TRITON_AVAILABLE:
         block_size: tl.constexpr,
     ):
         row_idx = tl.program_id(axis=0)
-        # TODO(student): maintain running max and running sum for this row.
-        # TODO(student): process the row in blocks rather than assuming all columns fit at once.
-        # TODO(student): write the final normalized probabilities.
-        pass
+        # First pass: compute running max and sum
+        running_max = float('-inf')
+        running_sum = 0.0
+        for block_start in range(0, num_cols, block_size):
+            col_offsets = block_start + tl.arange(0, block_size)
+            mask = col_offsets < num_cols
+            x_ptrs = x_ptr + row_idx * stride_x_row + col_offsets
+            x_block = tl.load(x_ptrs, mask=mask, other=float('-inf'))
+            block_max = tl.max(x_block, axis=0)
+            new_max = tl.maximum(running_max, block_max)
+            running_sum = running_sum * tl.exp(running_max - new_max) + tl.sum(tl.exp(x_block - new_max), axis=0)
+            running_max = new_max
+        # Second pass: write normalized output
+        for block_start in range(0, num_cols, block_size):
+            col_offsets = block_start + tl.arange(0, block_size)
+            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
+            x_block = tl.load(x_ptrs, mask=mask, other=float('-inf'))
+            result = tl.exp(x_block - running_max) / running_sum
+            tl.store(out_ptrs, result, mask=mask)
 
 
 def triton_online_softmax(x: torch.Tensor, block_size: int = 128) -> torch.Tensor:
@@ -38,5 +55,9 @@ def triton_online_softmax(x: torch.Tensor, block_size: int = 128) -> torch.Tenso
         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 online softmax in Triton.")
+    num_rows, num_cols = x.shape
+    out = torch.empty_like(x)
+    grid = (num_rows,)
+    online_softmax_kernel[grid](x, out, num_cols, x.stride(0), out.stride(0), block_size=block_size)
+    return out