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Add replicate_on_last_tile_dims handling when validating tiled output sharding. #64861

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Apr 1, 2024
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Add replicate_on_last_tile_dims handling when validating tiled output sharding. #64861

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32 changes: 17 additions & 15 deletions tensorflow/compiler/mlir/tensorflow/utils/xla_sharding_util.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -609,33 +609,35 @@ mlir::LogicalResult ValidateAndGetTiledExecuteOutputShape(
const mlir::TensorType cluster_func_output_type,
const xla::OpSharding& output_sharding,
mlir::Type* tiled_logical_computation_type) {
auto new_output_shape =
llvm::to_vector<4>(cluster_func_output_type.getShape());
for (const auto& dimension_and_output_splits :
llvm::enumerate(output_sharding.tile_assignment_dimensions())) {
const auto dimension_index = dimension_and_output_splits.index();
const auto output_splits = dimension_and_output_splits.value();
const auto output_shape = cluster_func_output_type.getShape();

if (output_shape[dimension_index] == mlir::ShapedType::kDynamic) {
const auto output_shape = cluster_func_output_type.getShape();
auto new_output_shape = llvm::to_vector<4>(output_shape);
auto dimension_to_splits_map =
GetDimensionIndicesAndNumSplitsFromSharding(output_sharding);
if (!dimension_to_splits_map.ok()) {
LOG(ERROR) << dimension_to_splits_map.status();
return mlir::failure();
}

for (const auto& dimension_and_output_splits : *dimension_to_splits_map) {
const auto dimension = dimension_and_output_splits.first;
const auto output_splits = dimension_and_output_splits.second;

if (output_shape[dimension] == mlir::ShapedType::kDynamic) {
*tiled_logical_computation_type = cluster_func_output_type;
break;
}

auto output_shape_at_dim =
cluster_func_output_type.getShape()[dimension_index];
if (output_shape_at_dim % output_splits != 0) {
if (output_shape[dimension] % output_splits != 0) {
mlir::emitError(
location,
llvm::formatv("incorrect output sharding received. "
"{0}-th dimension of the output must be "
"evenly divisible by {1}, got dimension "
"shape {2}",
dimension_index, output_splits, output_shape_at_dim));
dimension, output_splits, output_shape[dimension]));
}

new_output_shape[dimension_index] =
output_shape[dimension_index] / output_splits;
new_output_shape[dimension] = output_shape[dimension] / output_splits;
}

*tiled_logical_computation_type = mlir::RankedTensorType::get(
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