feat(distributed): pld.alloc_window_buffer / pld.window ops; remodel WindowBuffer as a Var

CMakeLists.txt

@@ -109,6 +109,7 @@ set(PYPTO_SOURCES
     src/ir/op/sync_ops/sync.cpp
     src/ir/op/sync_ops/cross_core.cpp
     src/ir/op/sync_ops/task.cpp
+    src/ir/op/distributed/memory.cpp
     src/ir/op/tensor_ops/broadcast.cpp
     src/ir/op/tensor_ops/elementwise.cpp
     src/ir/op/tensor_ops/matmul.cpp

docs/en/dev/ir/02-types.md

@@ -69,9 +69,13 @@ assert isinstance(t, ir.TensorType)            # C++ inheritance preserved
 # As<TensorType>(t) → null; As<DistributedTensorType>(t) → cast.
 ```
 
-Allocation-side metadata (the buffer name, host-staging flags) lives on the
-allocation op (`pld.alloc_window_buffer`, added in a later milestone) and on
-the `ir.WindowBuffer` slot in `program.comm_groups`, not on the type itself.
+Allocation-side metadata (per-rank size, host-staging flags) lives on the
+`ir.WindowBuffer` `Var` subclass that the `pld.alloc_window_buffer` op binds.
+Slices materialised through `pld.window(buf, [shape], dtype=...)` carry an
+optional back-reference (`DistributedTensorType.window_buffer`) to the source
+`WindowBuffer`, so two same-shape / same-dtype slices of different
+allocations stay structurally distinct. User-declared parameter annotations
+like `pld.DistributedTensor[[shape], dtype]` leave this field as `None`.
 Tile types do not have a distributed variant; cross-rank ops always operate
 on `DistributedTensor`.
 

docs/zh-cn/dev/ir/02-types.md

@@ -68,10 +68,14 @@ assert isinstance(t, ir.TensorType)            # C++ 继承关系保留
 # As<TensorType>(t) → null；As<DistributedTensorType>(t) → 转型成功
 ```
 
-分配侧的元数据（buffer 名字、host staging 标志）挂在 alloc op
-（`pld.alloc_window_buffer`，后续 milestone 引入）和 `program.comm_groups`
-中的 `ir.WindowBuffer` slot 上，**不**在类型本身。Tile 类型没有 distributed
-变体；跨 rank op 始终作用在 `DistributedTensor` 上。
+分配侧的元数据（每 rank 大小、host staging 标志）挂在 `pld.alloc_window_buffer`
+op 所绑定的 `ir.WindowBuffer`（`Var` 子类）上。通过
+`pld.window(buf, [shape], dtype=...)` 物化的切片在
+`DistributedTensorType.window_buffer` 上保留指向源 `WindowBuffer` 的可选反向
+引用，从而让两个 shape/dtype 相同但分配来源不同的切片在结构上保持不同。
+用户在签名中写的 `pld.DistributedTensor[[shape], dtype]` 不填该字段（为
+`None`）。Tile 类型没有 distributed 变体；跨 rank op 始终作用在
+`DistributedTensor` 上。
 
 ### 带 TensorView 的 TensorType
 

include/pypto/ir/core.h

@@ -113,6 +113,7 @@ enum class ObjectKind {
   TileType,
   ArrayType,
   TupleType,
+  WindowBufferType,
 
   // Other IR node kinds
   Function,

include/pypto/ir/kind_traits.h

@@ -114,6 +114,7 @@ DEFINE_KIND_TRAIT(ArrayType, ObjectKind::ArrayType)
 DEFINE_KIND_TRAIT(TupleType, ObjectKind::TupleType)
 DEFINE_KIND_TRAIT(MemRefType, ObjectKind::MemRefType)
 DEFINE_KIND_TRAIT(PtrType, ObjectKind::PtrType)
+DEFINE_KIND_TRAIT(WindowBufferType, ObjectKind::WindowBufferType)
 
 // Other IR node types
 DEFINE_KIND_TRAIT(Function, ObjectKind::Function)
@@ -203,7 +204,8 @@ struct KindTrait<Type> {
                                          ObjectKind::DistributedTensorType,
                                          ObjectKind::TileType,
                                          ObjectKind::ArrayType,
-                                         ObjectKind::TupleType};
+                                         ObjectKind::TupleType,
+                                         ObjectKind::WindowBufferType};
   static constexpr size_t count = sizeof(kinds) / sizeof(ObjectKind);
 };
 
@@ -278,7 +280,10 @@ std::shared_ptr<const T> As(const std::shared_ptr<const Base>& base) {
  *
  * As<Var>() uses exact ObjectKind matching and won't match IterArg.
  * This utility matches both Var and IterArg (which inherits from Var).
- * MemRef is intentionally excluded — use As<MemRef>() for that.
+ * MemRef and WindowBuffer are intentionally excluded — they are Var
+ * subclasses that carry allocation-source / window-slot semantics rather
+ * than the plain bound-name model AsVarLike's callers assume. Use
+ * As<MemRef>() / As<WindowBuffer>() when you specifically want them.
  */
 inline VarPtr AsVarLike(const ExprPtr& expr) {
   if (!expr) return nullptr;

include/pypto/ir/program.h

@@ -19,7 +19,6 @@
 #include <utility>
 #include <vector>
 
-#include "pypto/core/dtype.h"
 #include "pypto/ir/core.h"
 #include "pypto/ir/expr.h"
 #include "pypto/ir/function.h"
@@ -30,39 +29,34 @@ namespace pypto {
 namespace ir {
 
 /**
- * @brief Per-rank allocation spec for one named CommGroup HCCL window buffer.
+ * @brief Per-rank CommGroup HCCL window-buffer allocation, modelled as a Var.
  *
- * Maps 1:1 to ``simpler.task_interface.ChipBufferSpec`` at submit-time. Pure
- * allocation metadata: does NOT describe how the buffer is used in code.
- * Code-level use is expressed in the function signature via
- * ``pld.DistributedTensor[[shape], dtype]``; the alloc op
- * (``pld.alloc_window_buffer``, added in N2) materialises one of these slots
- * into the program's :class:`CommGroup`.
+ * A specialised :class:`Var` subclass whose SSA-edge type is the singleton
+ * :class:`WindowBufferType`. The buffer's runtime-unique identifier flows
+ * through the inherited ``Var::name_hint_``; there is no separate ``name_``
+ * field so structural equality does not depend on the chosen variable name.
  *
- * ``size_`` is the **element count** of one rank's slice (a single scalar; this
- * struct is allocation-only and intentionally does not carry a multi-dim
- * shape). ``size_`` may be a ``ConstInt`` (compile-time known) or a symbolic
- * expression referring to the world size.
- *
- * ``load_from_host_`` / ``store_to_host_`` are simple boolean flags marking
- * whether the slot participates in pre-fork H2D / post-task D2H staging. The
- * specific host tensor that supplies / receives the staged data is recorded
- * on the alloc op, not on this allocation spec.
+ * Fields:
+ *   * ``base_`` — :class:`Var` holding the underlying ``Ptr`` allocation
+ *     identity. Multiple ``WindowBuffer`` instances built from the same alloc
+ *     Var share allocation identity through this field.
+ *   * ``size_`` — per-rank allocation size in **bytes**; ``ConstInt`` or
+ *     symbolic :class:`ExprPtr`.
+ *   * ``load_from_host_`` / ``store_to_host_`` — pre-fork H2D / post-task
+ *     D2H staging flags.
  */
-class WindowBuffer : public IRNode {
+class WindowBuffer : public Var {
  public:
-  std::string name_;             ///< Buffer name (parser-extracted from alloc-op LHS)
-  ExprPtr size_;                 ///< Per-rank element count (ConstInt or symbolic Expr)
-  DataType dtype_;               ///< Element data type
-  bool load_from_host_ = false;  ///< Pre-fork H2D copy from a host staging tensor
-  bool store_to_host_ = false;   ///< Post-task D2H copy back into a host staging tensor
-
-  WindowBuffer(std::string name, ExprPtr size, DataType dtype, bool load_from_host = false,
-               bool store_to_host = false, Span span = Span::unknown())
-      : IRNode(std::move(span)),
-        name_(std::move(name)),
+  VarPtr base_;                  ///< Ptr Var from the alloc op (allocation identity)
+  ExprPtr size_;                 ///< Per-rank allocation size in bytes
+  bool load_from_host_ = false;  ///< Pre-fork H2D staging flag
+  bool store_to_host_ = false;   ///< Post-task D2H staging flag
+
+  WindowBuffer(VarPtr base, ExprPtr size, bool load_from_host = false, bool store_to_host = false,
+               Span span = Span::unknown())
+      : Var(base->name_hint_, GetWindowBufferType(), std::move(span)),
+        base_(std::move(base)),
         size_(std::move(size)),
-        dtype_(dtype),
         load_from_host_(load_from_host),
         store_to_host_(store_to_host) {}
 
@@ -71,15 +65,17 @@ class WindowBuffer : public IRNode {
 
   static constexpr auto GetFieldDescriptors() {
     return std::tuple_cat(
-        IRNode::GetFieldDescriptors(),
-        std::make_tuple(reflection::UsualField(&WindowBuffer::name_, "name"),
+        Var::GetFieldDescriptors(),
+        std::make_tuple(reflection::UsualField(&WindowBuffer::base_, "base"),
                         reflection::UsualField(&WindowBuffer::size_, "size"),
-                        reflection::UsualField(&WindowBuffer::dtype_, "dtype"),
                         reflection::UsualField(&WindowBuffer::load_from_host_, "load_from_host"),
                         reflection::UsualField(&WindowBuffer::store_to_host_, "store_to_host")));
   }
 };
 
+// WindowBufferPtr is forward-declared in include/pypto/ir/type.h so that
+// DistributedTensorType::window_buffer_ can hold it without a circular
+// include.
 using WindowBufferPtr = std::shared_ptr<const WindowBuffer>;
 
 /**

include/pypto/ir/type.h

@@ -36,6 +36,9 @@ using ExprPtr = std::shared_ptr<const Expr>;
 class MemRef;
 using MemRefPtr = std::shared_ptr<const MemRef>;
 
+class WindowBuffer;
+using WindowBufferPtr = std::shared_ptr<const WindowBuffer>;
+
 /**
  * @brief Base class for type representations in the IR
  *
@@ -500,32 +503,53 @@ using TensorTypePtr = std::shared_ptr<const TensorType>;
  */
 class DistributedTensorType : public TensorType {
  public:
-  DistributedTensorType(std::vector<ExprPtr> shape, DataType dtype) : TensorType(std::move(shape), dtype) {}
+  /// Optional back-reference to the :class:`WindowBuffer` whose allocation this
+  /// tensor is a view of. Populated by ``pld.window``'s type deducer;
+  /// ``std::nullopt`` for user-declared parameter annotations like
+  /// ``pld.DistributedTensor[[shape], dtype]``. Two DistributedTensorTypes with
+  /// the same shape / dtype but different ``window_buffer_`` values are
+  /// structurally distinct, so passes can tell apart slices of different
+  /// CommGroup window buffers.
+  std::optional<WindowBufferPtr> window_buffer_;
+
+  DistributedTensorType(std::vector<ExprPtr> shape, DataType dtype)
+      : TensorType(std::move(shape), dtype), window_buffer_(std::nullopt) {}
 
   DistributedTensorType(std::vector<ExprPtr> shape, DataType dtype, MemRefPtr memref)
-      : TensorType(std::move(shape), dtype, std::move(memref)) {}
+      : TensorType(std::move(shape), dtype, std::move(memref)), window_buffer_(std::nullopt) {}
 
   DistributedTensorType(std::vector<ExprPtr> shape, DataType dtype, std::optional<MemRefPtr> memref)
-      : TensorType(std::move(shape), dtype, std::move(memref)) {}
+      : TensorType(std::move(shape), dtype, std::move(memref)), window_buffer_(std::nullopt) {}
 
   DistributedTensorType(std::vector<ExprPtr> shape, DataType dtype, std::optional<MemRefPtr> memref,
                         std::optional<TensorView> tensor_view)
-      : TensorType(std::move(shape), dtype, std::move(memref), std::move(tensor_view)) {}
+      : TensorType(std::move(shape), dtype, std::move(memref), std::move(tensor_view)),
+        window_buffer_(std::nullopt) {}
 
   DistributedTensorType(const std::vector<int64_t>& shape, DataType dtype)
-      : TensorType(shape, dtype, std::nullopt) {}
+      : TensorType(shape, dtype, std::nullopt), window_buffer_(std::nullopt) {}
 
   DistributedTensorType(const std::vector<int64_t>& shape, DataType dtype, std::optional<MemRefPtr> memref)
-      : TensorType(shape, dtype, std::move(memref)) {}
+      : TensorType(shape, dtype, std::move(memref)), window_buffer_(std::nullopt) {}
 
   DistributedTensorType(const std::vector<int64_t>& shape, DataType dtype, std::optional<MemRefPtr> memref,
                         std::optional<TensorView> tensor_view)
-      : TensorType(shape, dtype, std::move(memref), std::move(tensor_view)) {}
+      : TensorType(shape, dtype, std::move(memref), std::move(tensor_view)), window_buffer_(std::nullopt) {}
+
+  /// Construct a DistributedTensorType produced by ``pld.window``: the result
+  /// is paired with the originating :class:`WindowBuffer` so passes can recover
+  /// the comm-group / slot identity later.
+  DistributedTensorType(std::vector<ExprPtr> shape, DataType dtype, WindowBufferPtr window_buffer)
+      : TensorType(std::move(shape), dtype), window_buffer_(std::move(window_buffer)) {}
 
   [[nodiscard]] ObjectKind GetKind() const override { return ObjectKind::DistributedTensorType; }
   [[nodiscard]] std::string TypeName() const override { return "DistributedTensorType"; }
 
-  static constexpr auto GetFieldDescriptors() { return TensorType::GetFieldDescriptors(); }
+  static constexpr auto GetFieldDescriptors() {
+    return std::tuple_cat(
+        TensorType::GetFieldDescriptors(),
+        std::make_tuple(reflection::UsualField(&DistributedTensorType::window_buffer_, "window_buffer")));
+  }
 };
 
 using DistributedTensorTypePtr = std::shared_ptr<const DistributedTensorType>;
@@ -711,6 +735,32 @@ inline PtrTypePtr GetPtrType() {
   return ptr_type;
 }
 
+/**
+ * @brief Singleton marker type for ``pld.alloc_window_buffer`` results.
+ *
+ * Carries no per-instance fields; all allocation metadata (size, host-staging
+ * flags, etc.) lives on the :class:`WindowBuffer` Var subclass that the alloc
+ * op binds. Cross-rank op verifiers dispatch on this marker
+ * (``As<WindowBufferType>``) to reject non-window arguments.
+ */
+class WindowBufferType : public Type {
+ public:
+  WindowBufferType() = default;
+
+  [[nodiscard]] ObjectKind GetKind() const override { return ObjectKind::WindowBufferType; }
+  [[nodiscard]] std::string TypeName() const override { return "WindowBufferType"; }
+
+  static constexpr auto GetFieldDescriptors() { return Type::GetFieldDescriptors(); }
+};
+
+using WindowBufferTypePtr = std::shared_ptr<const WindowBufferType>;
+
+/// Get the shared singleton WindowBufferType instance.
+inline WindowBufferTypePtr GetWindowBufferType() {
+  static const auto window_buffer_type = std::make_shared<WindowBufferType>();
+  return window_buffer_type;
+}
+
 }  // namespace ir
 }  // namespace pypto
 

python/bindings/modules/ir.cpp

@@ -454,6 +454,11 @@ void BindIR(nb::module_& m) {
       nb::init<const std::vector<int64_t>&, DataType, std::optional<MemRefPtr>, std::optional<TensorView>>(),
       nb::arg("shape"), nb::arg("dtype"), nb::arg("memref") = nb::none(), nb::arg("tensor_view") = nb::none(),
       "Create a distributed tensor type with constant shape, optional memref and tensor_view");
+  dist_tensor_type_class.def(
+      nb::init<std::vector<ExprPtr>, DataType, WindowBufferPtr>(), nb::arg("shape"), nb::arg("dtype"),
+      nb::arg("window_buffer"),
+      "Create a distributed tensor type produced by pld.window; window_buffer is the back-"
+      "reference to the source WindowBuffer allocation.");
   BindFields<DistributedTensorType>(dist_tensor_type_class);
 
   // TileType - const shared_ptr
@@ -507,6 +512,18 @@ void BindIR(nb::module_& m) {
   ptr_type_class.def_static("get", &GetPtrType, "Get the singleton PtrType instance");
   BindFields<PtrType>(ptr_type_class);
 
+  // WindowBufferType - singleton marker type for pld.alloc_window_buffer outputs.
+  // Mirrors MemRefType: no per-instance fields. The WindowBuffer Var subclass
+  // (bindings below) carries the allocation metadata.
+  auto window_buffer_type_class = nb::class_<WindowBufferType, Type>(
+      ir, "WindowBufferType",
+      "Singleton marker type for pld.alloc_window_buffer outputs. The companion WindowBuffer "
+      "Var subclass carries the allocation metadata (name, size, dtype, host flags).");
+  window_buffer_type_class.def(nb::init<>(), "Create the singleton WindowBufferType instance.");
+  window_buffer_type_class.def_static("get", &GetWindowBufferType,
+                                      "Get the shared singleton WindowBufferType instance.");
+  BindFields<WindowBufferType>(window_buffer_type_class);
+
   // MemorySpace enum
   nb::enum_<MemorySpace>(ir, "MemorySpace", "Memory space enumeration")
       .value("DDR", MemorySpace::DDR, "DDR memory (off-chip)")
@@ -1487,18 +1504,23 @@ void BindIR(nb::module_& m) {
                        "List of CommGroups declared on the program. CommGroups participate "
                        "in structural equality / hashing through reflection.");
 
-  // CommGroup / WindowBuffer — IRNode-typed host-side metadata attached to a Program.
-  auto window_buffer_class = nb::class_<WindowBuffer, IRNode>(
+  // WindowBuffer — a specialised Var subclass that carries CommGroup window-buffer
+  // allocation metadata. Mirrors MemRef's Var-subclass shape; the inherited
+  // ``name_hint`` is mirrored from ``name_`` (UsualField, unique-id role).
+  auto window_buffer_class = nb::class_<WindowBuffer, Var>(
       ir, "WindowBuffer",
-      "Per-rank allocation spec for a named CommGroup HCCL window buffer. "
-      "Maps 1:1 to simpler.task_interface.ChipBufferSpec at submit-time. "
-      "size_ is the per-rank element count; load_from_host_ / store_to_host_ "
-      "are bool flags marking host-staging participation (the actual host "
-      "tensor binding is recorded on the alloc op, not here).");
-  window_buffer_class.def(nb::init<std::string, ExprPtr, DataType, bool, bool, Span>(), nb::arg("name"),
-                          nb::arg("size"), nb::arg("dtype"), nb::arg("load_from_host") = false,
-                          nb::arg("store_to_host") = false, nb::arg("span") = Span::unknown(),
-                          "Create a WindowBuffer.");
+      "Per-rank CommGroup window-buffer allocation, modelled as a specialised Var. "
+      "Its SSA-edge type is the singleton WindowBufferType; the allocation metadata "
+      "(name, size, dtype, host-staging flags) lives on the Var subclass directly — "
+      "the exact mirror of how MemRef carries (base, byte_offset, size) under MemRefType. "
+      "Constructed by the comm-collection pass; the alloc op's LHS at parse time is a "
+      "plain Var(PtrType).");
+  window_buffer_class.def(nb::init<VarPtr, ExprPtr, bool, bool, Span>(), nb::arg("base"), nb::arg("size"),
+                          nb::arg("load_from_host") = false, nb::arg("store_to_host") = false,
+                          nb::arg("span") = Span::unknown(),
+                          "Create a WindowBuffer wrapping the given Ptr Var. The buffer's "
+                          "runtime-unique identifier flows through the inherited "
+                          "Var.name_hint (taken from base.name_hint).");
   BindFields<WindowBuffer>(window_buffer_class);
 
   auto comm_group_class =