wehub-resource-sync
99 lines
3.9 KiB
C++
99 lines
3.9 KiB
C++
/* Copyright 2022 The TensorFlow Authors. All Rights Reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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==============================================================================*/
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#include "tensorflow/dtensor/mlir/collectives_common.h"
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#include <cstddef>
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#include <cstdint>
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#include <map>
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#include <string>
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#include <vector>
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#include "absl/container/flat_hash_set.h"
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#include "tensorflow/core/platform/errors.h"
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#include "tensorflow/core/platform/types.h"
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#include "tensorflow/dtensor/cc/dstatus.h"
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#include "tensorflow/dtensor/cc/tensor_layout.h"
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namespace tensorflow {
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namespace dtensor {
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// A map from a unique set of kept mesh dimension values (a partition) to
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// IDs of devices in that partition.
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//
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// Users will typically ignore the key, but use the map values as the group
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// assignment for collective operations. This is intentionally a
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// std::map instead of absl::flat_hash_map to guarantee all hosts in
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// a multi-host cluster will generate the same grouping, and therefore the same
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// XLA program fingerprint, independently. std::map guarantees the same
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// iteration order.
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using AllReducePartitions = std::map<DeviceLocation, std::vector<int32_t>>;
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// Computes AllReduce partitions using reduced mesh dimension names.
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//
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// Reduction groups are formed across all _non_-reduced dimensions. For example,
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// in the following scenario:
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//
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// output_layout.dims() = [a, b]
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// output_layout.mesh() = [(x, 8), (y, 4)]
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// reduced_dims = `x`
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//
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// We first reduce over `a` locally on each device, producing 32 local
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// reductions. We then AllReduce within each of the 4 partitions. Each partition
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// corresponds to one unique value of `y` and has 8 devices. The end result is
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// sharded over the y mesh dimension and replicated 8 times.
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//
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// The returned map should have four entries with key values from [0] to [3]
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// (unique values of `y`). Each key maps to IDs of devices with that `y` value.
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StatusOr<AllReducePartitions> GetAllReducePartitionsFromReducedDims(
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const dtensor::Layout& output_layout,
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const absl::flat_hash_set<std::string>& reduced_dims) {
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AllReducePartitions partitions;
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for (int64_t device = 0; device < output_layout.num_devices(); ++device) {
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TF_ASSIGN_OR_RETURN(const DeviceLocation device_loc,
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output_layout.mesh().device_location(device));
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DeviceLocation kept_dims;
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for (int64_t dim_idx = 0; dim_idx < device_loc.size(); ++dim_idx) {
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if (!reduced_dims.contains(output_layout.mesh().dim_name(dim_idx))) {
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kept_dims.push_back(device_loc[dim_idx]);
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}
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}
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partitions[kept_dims].push_back(device);
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}
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return partitions;
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}
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// Use the first device in the mesh to extract the device name. For example:
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//
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// device_path = "/job:localhost/replica:0/task:0/device:TPU:0"
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// device_type = "/job:localhost/replica:0/task:0/device:TPU"
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// device_id = 0
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//
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// The device ID can be obtained through DeviceId as a runtime input. We may
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// need it in the future to enable device ID-based branch divergence.
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StatusOr<std::string> DeviceTypeFromMesh(const Mesh& mesh) {
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std::string device_path =
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mesh.is_remote() ? mesh.global_devices()[0] : mesh.local_devices()[0];
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size_t device_path_pos = device_path.find_last_of(':');
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if (device_path_pos == std::string::npos) {
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return absl::InvalidArgumentError(
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absl::StrCat("Unexpected device path: ", device_path));
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}
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return device_path.substr(0, device_path_pos);
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}
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} // namespace dtensor
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} // namespace tensorflow
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