Add --verify-data CLI flag, Tensor.data property, parallel DataExecutor

- CLI: --verify-data flag enables Phase 2 data verification (ADR-0020)
- Tensor.data: returns actual numpy values (verify-data) or zeros placeholder
- Tensor.__repr__: shows value summary or data=N/A (placeholder)
- DataExecutor: ThreadPoolExecutor for same-timestamp parallel op execution
- BenchResult.engine: exposes op_log/memory_store for Phase 2 access

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

src/kernbench/runtime_api/tensor.py

@@ -5,6 +5,8 @@ import weakref
 from dataclasses import dataclass
 from typing import Literal
 
+import numpy as np
+
 from kernbench.policy.address.allocator import PEMemAllocator
 from kernbench.policy.placement.dp import DPPolicy, ShardSpec
 from kernbench.runtime_api.kernel import TensorArg, TensorArgShard
@@ -50,6 +52,20 @@ def dtype_itemsize(dtype: str) -> int:
     return _DTYPE_ITEMSIZE[dtype]
 
 
+_NUMPY_DTYPE = {
+    "f16": np.float16, "fp16": np.float16, "float16": np.float16,
+    "f32": np.float32, "fp32": np.float32, "float32": np.float32,
+    "bf16": np.float16,
+    "i8": np.int8, "int8": np.int8,
+    "i16": np.int16, "int16": np.int16,
+    "i32": np.int32, "int32": np.int32,
+}
+
+
+def _numpy_dtype(dtype: str) -> np.dtype:
+    return np.dtype(_NUMPY_DTYPE.get(dtype, np.float16))
+
+
 def deploy_tensor(
     *,
     name: str,
@@ -129,6 +145,7 @@ class Tensor:
         self._dp_metadata: DPMetadata | None = None
         self._handle: TensorHandle | None = None
         self._ctx_ref: weakref.ref | None = None  # set by RuntimeContext
+        self._memory_store = None  # set by RuntimeContext when enable_data=True
 
     def __del__(self) -> None:
         if self._ctx_ref is None or self._handle is None:
@@ -137,6 +154,28 @@ class Tensor:
         if ctx is not None:
             ctx._free_tensor(self)
 
+    def __repr__(self) -> str:
+        parts = [f"tensor(name={self.name}, shape={self.shape}, dtype={self.dtype}"]
+        if self._memory_store is not None and self._handle is not None:
+            arr = self.data
+            parts.append(f", mean={float(arr.mean()):.4g}, norm={float(np.linalg.norm(arr)):.4g}")
+        else:
+            parts.append(", data=N/A (placeholder)")
+        parts.append(")")
+        return "".join(parts)
+
+    @property
+    def data(self) -> np.ndarray:
+        """Tensor data as numpy array. Returns actual values when enable_data=True,
+        zeros placeholder otherwise (like an uninitialized tensor)."""
+        if self._memory_store is not None and self._handle is not None:
+            shard = self._handle.shards[0]
+            try:
+                return self._memory_store.read("hbm", shard.pa, shape=self.shape, dtype=self.dtype)
+            except KeyError:
+                pass
+        return np.zeros(self.shape, dtype=_numpy_dtype(self.dtype))
+
     @property
     def itemsize(self) -> int:
         return dtype_itemsize(self.dtype)