Add SIP-level tensor parallelism, component registry YAML, VA offset verification

- DPPolicy: 3-level (sip/cube/pe), unified naming (column_wise/row_wise)
- PE_CPU: auto num_programs from cube shard count
- context.launch(): per-SIP KernelLaunchMsg with local va_base + auto local shape
- deploy_tensor: removed mmus param, MMU mapping is context-only responsibility
- ComponentRegistry: YAML-based lazy loading (components.yaml), impls→builtin rename
- VA offset bench + tests: 2D/1D, standard Triton kernel pattern

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

benches/va_offset_verify.py

@@ -0,0 +1,42 @@
+"""VA offset verification benchmark.
+
+Verifies that Triton-style base_ptr + pid * stride addressing works correctly
+with full TP sharding (sip/cube/pe all column_wise). Each PE loads its own
+block from a sharded tensor and stores it back.
+
+The kernel uses standard Triton patterns:
+  - tl.program_id(0) for PE index within cube
+  - tl.num_programs(0) for PE count within cube
+  - Shape args are automatically localized by launch()
+"""
+from kernbench.policy.placement.dp import DPPolicy
+
+M, K = 128, 256
+DTYPE = "f16"
+
+
+def _copy_kernel(src_ptr, dst_ptr, M, K, tl, DTYPE="f16"):
+    """Standard Triton copy kernel. M and K are cube-local (set by launch)."""
+    pid = tl.program_id(0)
+    num_pe = tl.num_programs(0)
+    cols_per_pe = K // num_pe
+    elem_bytes = 2  # f16
+    offset = pid * M * cols_per_pe * elem_bytes
+    data = tl.load(src_ptr + offset, shape=(M, cols_per_pe), dtype=DTYPE)
+    tl.store(dst_ptr + offset, data)
+
+
+def run(torch):
+    """Run the VA offset verification benchmark with full TP sharding."""
+    dp = DPPolicy(sip="column_wise", cube="column_wise", pe="column_wise")
+    src = torch.zeros((M, K), dtype=DTYPE, dp=dp, name="src")
+    dst = torch.empty((M, K), dtype=DTYPE, dp=dp, name="dst")
+
+    # launch() automatically converts M, K to cube-local values
+    torch.launch("va_offset_copy", _copy_kernel, src, dst, M, K)
+
+    # Sanity check: kernel completed with non-zero latency
+    kernel_traces = [t for t in torch._traces if t["phase"] == "kernel"]
+    assert len(kernel_traces) > 0, "No kernel traces recorded"
+    for kt in kernel_traces:
+        assert kt["total_ns"] > 0, f"Kernel latency is zero for {kt}"