#2362: Added workaround to bug in full to shard with shard type repli…

…cate

runtime/lib/ttnn/operations/ccl/mesh_shard.cpp

@@ -18,8 +18,16 @@ void FullToShardShape(const ::ttnn::Tensor &input, ::ttnn::Tensor &out,
                       const std::vector<int64_t> &shardShape,
                       const std::vector<int64_t> &shardDims) {
   if (shardType == ::tt::target::ttnn::MeshShardType::Replicate) {
+
+    // todo: (tapspatel) - metal issue #18842. Currently ReplicateTensorToMesh
+    // map API cannot take as input a borrowed storage tensor, so we need to
+    // create a deep copy of this tensor and convert to owned storage.
+    // https://github.com/tenstorrent/tt-metal/issues/18842
+    auto copiedTensorChunk = ::ttnn::experimental::xtensor::chunk(input, 1, 0);
+    auto copiedTensor =
+        ::ttnn::experimental::xtensor::concat(copiedTensorChunk, 0);
     out = ::ttnn::distributed::distribute_tensor(
-        input,
+        copiedTensor,
         *::ttnn::distributed::replicate_tensor_to_mesh_mapper(meshDevice));
   } else {
     DEBUG_ASSERT(input.get_logical_shape().rank() > 1,

test/ttmlir/Silicon/TTNN/n300/device_parallel/device_parallel_1x2.mlir

@@ -28,3 +28,27 @@ func.func public @jit_tensor_parallel_n300(%arg0: tensor<64x1x1024x2048xf32>, %a
   // CHECK: "ttnn.mesh_shard"
   return %14 : tensor<64x1x1024x512xf32>
 }
+
+func.func public @jit_data_parallel_n300(%arg0: tensor<64x1x1024x2048xf32>, %arg1: tensor<1x1x2048x512xf32>) -> (tensor<64x1x1024x512xf32> {jax.result_info = ""}) {
+  %0 = tensor.empty() : tensor<32x1x1024x2048xf32>
+  %1 = "ttir.mesh_shard"(%arg0, %0) <{shard_dims = array<i64: -1, 0>, shard_direction = #tt.shard_direction<full_to_shard>, shard_shape = array<i64: 2, 1, 1, 1>, shard_type = #tt.shard_type<devices>}> : (tensor<64x1x1024x2048xf32>, tensor<32x1x1024x2048xf32>) -> tensor<32x1x1024x2048xf32>
+  // CHECK: "ttnn.mesh_shard"
+  %2 = tensor.empty() : tensor<1x1x2048x512xf32>
+  %3 = "ttir.mesh_shard"(%arg1, %2) <{shard_dims = array<i64: -1>, shard_direction = #tt.shard_direction<full_to_shard>, shard_shape = array<i64: 1>, shard_type = #tt.shard_type<replicate>}> : (tensor<1x1x2048x512xf32>, tensor<1x1x2048x512xf32>) -> tensor<1x1x2048x512xf32>
+  // CHECK: "ttnn.mesh_shard"
+  %4 = tensor.empty() : tensor<32x1024x2048xf32>
+  %5 = "ttir.reshape"(%1, %4) <{shape = [32 : i32, 1024 : i32, 2048 : i32]}> : (tensor<32x1x1024x2048xf32>, tensor<32x1024x2048xf32>) -> tensor<32x1024x2048xf32>
+  // CHECK: = "ttnn.reshape"
+  %6 = tensor.empty() : tensor<1x2048x512xf32>
+  %7 = "ttir.reshape"(%3, %6) <{shape = [1 : i32, 2048 : i32, 512 : i32]}> : (tensor<1x1x2048x512xf32>, tensor<1x2048x512xf32>) -> tensor<1x2048x512xf32>
+  // CHECK: = "ttnn.reshape"
+  %8 = "ttir.dot_general"(%5, %7) <{batch_dims_lhs = array<i64>, batch_dims_rhs = array<i64>, contract_dims_lhs = array<i64: 2>, contract_dims_rhs = array<i64: 1>}> : (tensor<32x1024x2048xf32>, tensor<1x2048x512xf32>) -> tensor<32x1024x1x512xf32>
+  // CHECK: "ttnn.matmul"
+  %9 = tensor.empty() : tensor<32x1x1024x512xf32>
+  %10 = "ttir.permute"(%8, %9) <{permutation = array<i64: 0, 2, 1, 3>}> : (tensor<32x1024x1x512xf32>, tensor<32x1x1024x512xf32>) -> tensor<32x1x1024x512xf32>
+  // CHECK: "ttnn.permute"
+  %11 = tensor.empty() : tensor<64x1x1024x512xf32>
+  %12 = "ttir.mesh_shard"(%10, %11) <{shard_dims = array<i64: -1, 0>, shard_direction = #tt.shard_direction<shard_to_full>, shard_shape = array<i64: 2, 1, 1, 1>, shard_type = #tt.shard_type<devices>}> : (tensor<32x1x1024x512xf32>, tensor<64x1x1024x512xf32>) -> tensor<64x1x1024x512xf32>
+  // CHECK: "ttnn.mesh_shard"
+  return %12 : tensor<64x1x1024x512xf32>
+}