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/ipcq_allreduce.py

@@ -1,2 +1,2 @@
-def run(ctx):
+def run(torch):
     print("IPCQ all reduce kernel bench")

benches/loader.py

@@ -15,7 +15,7 @@ def resolve_bench(bench_id: str) -> BenchFn:
 
     Expected layout (repo root):
         benches/<bench_id>.py
-            def run(ctx: RuntimeContext) -> Any
+            def run(torch: RuntimeContext) -> Any
     """
     bench_id = bench_id.strip()
     if not bench_id:
@@ -30,7 +30,7 @@ def resolve_bench(bench_id: str) -> BenchFn:
 
     run_fn = getattr(mod, "run", None)
     if run_fn is None:
-        raise ValueError(f"Bench module {module_path} must define a 'run(ctx)' function.")
+        raise ValueError(f"Bench module {module_path} must define a 'run(torch)' function.")
     if not callable(run_fn):
         raise ValueError(f"'run' in {module_path} is not callable.")
 

benches/qkv_gemm.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 QKV GEMM benchmark."""
     # DP placement: a=replicate (cube-level), b/out=column_wise (N-axis, single PE)
-    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
-    ctx.launch("qkv_gemm", _gemm_kernel, a, b, out, M, K, N)
+    torch.launch("qkv_gemm", _gemm_kernel, a, b, out, M, K, N)

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)