diff --git a/include/mlir-hlo/Dialect/mhlo/IR/hlo_ops.td b/include/mlir-hlo/Dialect/mhlo/IR/hlo_ops.td
index 05fc6bf..0f8d721 100644
--- a/include/mlir-hlo/Dialect/mhlo/IR/hlo_ops.td
+++ b/include/mlir-hlo/Dialect/mhlo/IR/hlo_ops.td
@@ -909,6 +909,26 @@ def HLO_WhileOp: HLO_Op<"while", [
   let hasCustomHLOConverter = 1;
 }
 
+def HLO_AllGatherOp : HLO_Op<"all_gather", [SameOperandsAndResultElementType]> {
+
+  string summary = "AllGather operator";
+
+  string description = [{
+    Performs concatenation across replicas.
+
+    See https://www.tensorflow.org/xla/operation_semantics#allgather
+  }];
+
+  let arguments = (ins
+    HLO_Tensor:$operand,
+    I64Attr:$all_gather_dim,
+    I64ElementsAttr:$replica_groups,
+    OptionalAttr<ChannelHandle>:$channel_handle
+  );
+  let results = (outs HLO_Tensor);
+  let hasCustomHLOConverter = 1;
+}
+
 def HLO_AllReduceOp : HLO_Op<"all_reduce",
     [SameOperandsAndResultType]> {
   let summary = "AllReduce operator";
@@ -921,7 +941,7 @@ def HLO_AllReduceOp : HLO_Op<"all_reduce",
   let arguments = (ins
     HLO_Tensor:$operand,
     I64ElementsAttr:$replica_groups,
-    OptionalAttr<ChannelHandle>:$channel_id
+    OptionalAttr<ChannelHandle>:$channel_handle
   );
   let regions = (region SizedRegion<1>:$computation);
   let results = (outs HLO_Tensor);
diff --git a/lib/Dialect/mhlo/IR/hlo_ops.cc b/lib/Dialect/mhlo/IR/hlo_ops.cc
index f91bf4b..d363844 100644
--- a/lib/Dialect/mhlo/IR/hlo_ops.cc
+++ b/lib/Dialect/mhlo/IR/hlo_ops.cc
@@ -725,6 +725,34 @@ static LogicalResult Verify(AllToAllOp op) {
   return success();
 }
 
+//===----------------------------------------------------------------------===//
+// AllGatherOp
+//===----------------------------------------------------------------------===//
+
+static LogicalResult Verify(AllGatherOp op) {
+  // If operand and result are both ranked, then the size of the gather
+  // dimension in the result should be a multiple of the size of the gather
+  // dimension in the operand.
+  auto operandType = op.operand().getType().dyn_cast<RankedTensorType>();
+  auto resultType = op.getType().dyn_cast<RankedTensorType>();
+  uint64_t allGatherDimIndex = op.all_gather_dim();
+  if (!operandType || !resultType ||
+      operandType.isDynamicDim(allGatherDimIndex) ||
+      resultType.isDynamicDim(allGatherDimIndex))
+    return success();
+  if (operandType.getDimSize(allGatherDimIndex) == 0)
+    return op.emitOpError() << "operand gather dimension cannot be zero.";
+  if ((resultType.getDimSize(allGatherDimIndex) %
+       operandType.getDimSize(allGatherDimIndex)) != 0)
+    return op.emitOpError()
+           << "result gather dimension has size "
+           << resultType.getDimSize(allGatherDimIndex)
+           << ", expected to be a multiple of operand gather dimension size "
+           << operandType.getDimSize(allGatherDimIndex);
+
+  return success();
+}
+
 //===----------------------------------------------------------------------===//
 // BroadcastOp
 //===----------------------------------------------------------------------===//
diff --git a/tests/ops.mlir b/tests/ops.mlir
index 7c33d2f..48192fe 100644
--- a/tests/ops.mlir
+++ b/tests/ops.mlir
@@ -52,6 +52,42 @@ func @alltoall_invalid_split_dim_size(%data: tensor<4x16xf32>) -> tensor<16x4xf3
 
 // -----
 
+func @allgather_incompatible_types(%arg0: tensor<128x32xf32>) -> tensor<128x100xf32> {
+  // expected-error@+1 {{result gather dimension has size 100, expected to be a multiple of operand gather dimension size 32}}
+  %0 = "mhlo.all_gather"(%arg0) {
+    all_gather_dim = 1 : i64,
+    channel_handle = {handle = 1 : i64, type = 0 : i64},
+    replica_groups = dense<[[0, 2, 4, 6], [1, 3, 5, 7]]> : tensor<2x4xi64>
+  } : (tensor<128x32xf32>) -> tensor<128x100xf32>
+  return %0 : tensor<128x100xf32>
+}
+
+// -----
+
+func @allgather_gather_along_zero_dimension(%arg0: tensor<128x0x32xf32>) -> tensor<128x100xf32> {
+  // expected-error@+1 {{operand gather dimension cannot be zero}}
+  %0 = "mhlo.all_gather"(%arg0) {
+    all_gather_dim = 1 : i64,
+    channel_handle = {handle = 1 : i64, type = 0 : i64},
+    replica_groups = dense<[[0, 2, 4, 6], [1, 3, 5, 7]]> : tensor<2x4xi64>
+  } : (tensor<128x0x32xf32>) -> tensor<128x100xf32>
+  return %0 : tensor<128x100xf32>
+}
+
+// -----
+
+// CHECK-LABEL: func @allgather_dynamic_gather_dim
+func @allgather_dynamic_gather_dim(%arg0: tensor<128x32xf32>) -> tensor<128x?xf32> {
+  %0 = "mhlo.all_gather"(%arg0) {
+    all_gather_dim = 1 : i64,
+    channel_handle = {handle = 1 : i64, type = 0 : i64},
+    replica_groups = dense<[[0, 2, 4, 6], [1, 3, 5, 7]]> : tensor<2x4xi64>
+  } : (tensor<128x32xf32>) -> tensor<128x?xf32>
+  return %0 : tensor<128x?xf32>
+}
+
+// -----
+
 // CHECK-LABEL: func @broadcast
 func @broadcast(%arg0: tensor<3xi32>) -> tensor<1x2x3xi32> {
   %0 = "mhlo.broadcast"(%arg0) {broadcast_sizes = dense<[1, 2]> : tensor<2xi64>} : (tensor<3xi32>) -> tensor<1x2x3xi32>