Add virtual memory support: PE_MMU, VA allocator, fabric MmuMapMsg

Implement VA/MMU layer (ADR-0011 Phase 1) enabling Triton kernels to use
contiguous virtual addresses on sharded tensors.

Key changes:
- PE_MMU component: hybrid inbox (MmuMapMsg) + sync translate() for PE_DMA
- VirtualAllocator + PEMemAllocator: free-list with coalescing
- MmuMapMsg/MmuUnmapMsg fabric path with SIP-level routing
- DPPolicy-based mapping: replicate=local, sharded=broadcast
- Tensor lifecycle: del + weakref cleanup, context manager
- Rename: TensorHandle.pa→addr, DmaReadCmd.src_pa→src_addr, ctx→torch

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

benches/qkv_gemm_multi_pe.py

@@ -26,14 +26,14 @@ def _gemm_kernel(a_ptr, b_ptr, out_ptr, M, K, N, tl, DTYPE="f16"):
     tl.wait(handle)
 
 
-def run(ctx):
+def run(torch):
     """Run the multi-PE QKV GEMM benchmark."""
     # DP placement: a=replicate (cube-level), b/out=column_wise (N-axis split)
-    a = ctx.zeros((M, K), dtype=DTYPE, dp=DPPolicy(cube="replicate", pe="replicate"), name="a")
-    b = ctx.zeros((K, N), dtype=DTYPE, dp=DPPolicy(cube="replicate", pe="column_wise"), name="b")
-    out = ctx.empty(
+    a = torch.zeros((M, K), dtype=DTYPE, dp=DPPolicy(cube="replicate", pe="replicate"), name="a")
+    b = torch.zeros((K, N), dtype=DTYPE, dp=DPPolicy(cube="replicate", pe="column_wise"), name="b")
+    out = torch.empty(
         (M, N), dtype=DTYPE, dp=DPPolicy(cube="replicate", pe="column_wise"), name="out",
     )
 
     # Launch GEMM kernel on all PEs
-    ctx.launch("qkv_gemm_multi", _gemm_kernel, a, b, out, M, K, N)
+    torch.launch("qkv_gemm_multi", _gemm_kernel, a, b, out, M, K, N)