[Mlir-commits] [mlir] [Mesh] initialize mesh dialect (PR #68007)

[Chengji Yao]

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@@ -0,0 +1,132 @@
+//===-- MeshOps.td - Mesh dialect operation definitions ----*- tablegen -*-===//
+//
+// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
+// See https://llvm.org/LICENSE.txt for license information.
+// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
+//
+//===----------------------------------------------------------------------===//
+
+#ifndef MLIR_DIALECT_MESH_IR_MESHOPS_TD
+#define MLIR_DIALECT_MESH_IR_MESHOPS_TD
+
+include "mlir/Dialect/Mesh/IR/MeshBase.td"
+include "mlir/Interfaces/InferTypeOpInterface.td"
+include "mlir/Interfaces/SideEffectInterfaces.td"
+include "mlir/IR/BuiltinTypes.td"
+include "mlir/IR/SymbolInterfaces.td"
+
+//===----------------------------------------------------------------------===//
+// Mesh Dialect operations.
+//===----------------------------------------------------------------------===//
+
+class Mesh_Op<string mnemonic, list<Trait> traits = []> :
+    Op<Mesh_Dialect, mnemonic, traits> {
+}
+
+def Mesh_ClusterOp : Mesh_Op<"cluster", [Symbol]> {
+  let summary = "representing a mesh cluster";
+  let description = [{
+    The mesh.cluster operation is a symbol operation that identifies a specific
+    mesh cluster. The operation has three attributes:
+
+    1. `sym_name`: This attribute uniquely identifies the name of the mesh
+    cluster. This name serves as a symbolic reference to the cluster throughout
+    the MLIR module, allowing for consistent referencing and easier debugging.
+
+    2. `rank`: This attribute specifies the number of axes of the cluster. The
+    rank indicates the dimensionality of the mesh cluster and can be used to
+    determine the layout and the addressing space of the computation distributed
+    across the mesh.
+
+    3. `dim_sizes`: This attribute represents the device assignment along the
+    axes of the cluster. Each integer in the array corresponds to the number of
+    devices along a specific axis. If an integer value is <= 0, it implies that
+    the number of devices along that axis is unknown. This flexibility allows
+    for dynamic device assignment or configurations where the exact number of
+    devices might not be determined during compile time.
+
+    Example:
+    ```
+    // A device mesh cluster with 3 axes, the totol device number is 4 * 8 * 12
+    // The dimension sizes are 4, 8, 12 
+    mesh.cluster @mesh0(rank = 3, dim_sizes = [4, 8, 12])
+
+    // A device mesh cluster with 2 axes, the totol device number is unknown
+    // The first dimension size is 4 and the second is unknown
+    mesh.cluster @mesh1(rank = 2, dim_sizes = [4])
+
+    // A device mesh cluster with 2 axes, the totol device number is unknown
+    // The first dimension size is unknown and the second is 4
+    mesh.cluster @mesh2(rank = 2, dim_sizes = [0, 4])
+
+    // A device mesh cluster with 2 axes, the number of devices along both axes
+    // is unknown
+    mesh.cluster @mesh3(rank = 2)
+
+    // A func op's default mesh cluster is @mesh0
+    func.func @foo() -> () attributes { mesh_cluster = @mesh0 } {
+      ...
+    }
+
+    // Used in the mesh sharding attribute to extend the standard tensor to
+    // distributed
+    tensor<4x8xf32, #mesh.shard<[[0]] @mesh0>
+    ```
+  }];
+  let arguments = (ins
+    SymbolNameAttr:$sym_name,
+    I64Attr:$rank,
+    DefaultValuedAttr<DenseI64ArrayAttr, "{}">:$dim_sizes
+  );
+  let assemblyFormat = "$sym_name `(` `rank` `=` $rank (`,` `dim_sizes` `=` $dim_sizes^)? `)` attr-dict";
+  let hasVerifier = 1;
+}
+
+def Mesh_ShardOp : Mesh_Op<"shard", [Pure, SameOperandsAndResultType]> {
+  let summary = "Annotate on how a tensor is sharded across a mesh cluster.";
+  let description = [{
+    The mesh.shard operation is designed to specify and guide the sharding
+    behavior of a tensor value across a mesh topology. This operation has one
+    operand and two attributes:
+
+    1. `input`: This operand represents the tensor value that needs to be
+    annotated for sharding.
+
+    2. `shard`: This attribute is type of `MeshSharding`, which is the core data
+    structure to represent distributed tensor in mesh cluster.
+
+    3. `as_result`: A boolean attribute addressing the scenario when a tensor's
+    sharding annotation differs based on its context of use (either as a result
+    or an operand). If true, the sharding applies to the operation that defines
+    the tensor value. If false, the sharding pertains to specific users of the
+    tensor value, indicating how it should be considered when used as an operand
+    in subsequent operations.
+
+    Example:
+    ```
+    // The first mesh.shard op applies to %arg0, the second mesh.shard op
+    // applies for the operand of op0, the third mesh.shard op applies for the
+    // operand of op2
+    func.func @two_users(%arg0 : tensor<4x8xf32>) -> () {
+      %0 = mesh.shard %arg0 to <@mesh0, [[0]]> : tensor<4x8xf32>
+      %1 = mesh.shard %0 to <@mesh0, [[1]]> {as_result = false} : tensor<4x8xf32>
+      %2 = mesh.shard %0 to <@mesh0, [[2]]> {as_result = false} : tensor<4x8xf32>
+      "op0"(%1) : ...
+      "op1"(%2) : ...
+      ...
+    }
+    ```
+  }];
+  let arguments = (ins
+    Builtin_RankedTensor:$src,
+    MeshSharding:$shard,
+    DefaultValuedAttr<BoolAttr, "true">:$as_result
----------------
yaochengji wrote:

Updated.

https://github.com/llvm/llvm-project/pull/68007