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[PJRT] Enable layering_check in the Bazel BUILD.
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| Original file line number | Diff line number | Diff line change |
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| /* Copyright 2020 The TensorFlow Authors. All Rights Reserved. | ||
| Licensed under the Apache License, Version 2.0 (the "License"); | ||
| you may not use this file except in compliance with the License. | ||
| You may obtain a copy of the License at | ||
| http://www.apache.org/licenses/LICENSE-2.0 | ||
| Unless required by applicable law or agreed to in writing, software | ||
| distributed under the License is distributed on an "AS IS" BASIS, | ||
| WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| See the License for the specific language governing permissions and | ||
| limitations under the License. | ||
| ==============================================================================*/ | ||
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| #include "tensorflow/compiler/xla/pjrt/nccl_id_store.h" | ||
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| #include <string> | ||
| #include <utility> | ||
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| #ifdef NCCL_ENABLED | ||
| #include "third_party/nccl/nccl.h" | ||
| #endif // NCCL_ENABLED | ||
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| #include "tensorflow/compiler/xla/util.h" | ||
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| namespace xla { | ||
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| StatusOr<std::string> NcclIdStore::GetNcclUniqueId( | ||
| const gpu::NcclCliqueKey& key) { | ||
| // The caller must ensure that threads calling this method concurrently have | ||
| // unique keys, otherwise the global key-value store may hold the wrong value. | ||
| { | ||
| absl::MutexLock lock(&mu_); | ||
| auto it = cache_.find(key); | ||
| if (it != cache_.end()) { | ||
| return it->second; | ||
| } | ||
| } | ||
| std::string id_string; | ||
| int primary_node_id = device_to_node_.at(key.devices()[0]); | ||
| if (node_id_ == primary_node_id) { | ||
| #ifdef NCCL_ENABLED | ||
| ncclUniqueId id; | ||
| ncclResult_t r = ncclGetUniqueId(&id); | ||
| TF_RET_CHECK(r == ncclSuccess); | ||
| id_string = std::string(id.internal, NCCL_UNIQUE_ID_BYTES); | ||
| TF_RETURN_IF_ERROR(client_->KeyValueSet(key.ToString(), id_string)); | ||
| #else | ||
| return FailedPrecondition("NCCL support was not built into XLA binary."); | ||
| #endif | ||
| } else { | ||
| TF_ASSIGN_OR_RETURN(id_string, client_->BlockingKeyValueGet( | ||
| key.ToString(), absl::Minutes(5))); | ||
| } | ||
| absl::MutexLock lock(&mu_); | ||
| auto result = cache_.emplace(key, std::move(id_string)); | ||
| TF_RET_CHECK(result.second) << "Unique ID already in cache."; | ||
| return result.first->second; | ||
| } | ||
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| } // namespace xla |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,59 @@ | ||
| /* Copyright 2020 The TensorFlow Authors. All Rights Reserved. | ||
| Licensed under the Apache License, Version 2.0 (the "License"); | ||
| you may not use this file except in compliance with the License. | ||
| You may obtain a copy of the License at | ||
| http://www.apache.org/licenses/LICENSE-2.0 | ||
| Unless required by applicable law or agreed to in writing, software | ||
| distributed under the License is distributed on an "AS IS" BASIS, | ||
| WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| See the License for the specific language governing permissions and | ||
| limitations under the License. | ||
| ==============================================================================*/ | ||
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| #ifndef TENSORFLOW_COMPILER_XLA_PJRT_NCCL_ID_STORE_H_ | ||
| #define TENSORFLOW_COMPILER_XLA_PJRT_NCCL_ID_STORE_H_ | ||
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| #include <memory> | ||
| #include <utility> | ||
| #include <string> | ||
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| #include "absl/base/attributes.h" | ||
| #include "absl/container/flat_hash_map.h" | ||
| #include "absl/synchronization/mutex.h" | ||
| #include "tensorflow/compiler/xla/pjrt/distributed/client.h" | ||
| #include "tensorflow/compiler/xla/service/global_device_id.h" | ||
| #include "tensorflow/compiler/xla/service/gpu/gpu_executable_run_options.h" | ||
| #include "tensorflow/compiler/xla/statusor.h" | ||
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| namespace xla { | ||
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| // A table mapping NcclCliqueKeys to ncclUniqueId values encoded as strings. | ||
| // In a distributed setup the table of NCCL IDs is kept on the master node | ||
| // (node 0). The node of the first participating device will create the unique | ||
| // id. | ||
| class NcclIdStore { | ||
| public: | ||
| NcclIdStore(int node_id, std::shared_ptr<DistributedRuntimeClient> client, | ||
| absl::flat_hash_map<GlobalDeviceId, int> device_to_node) | ||
| : node_id_(node_id), | ||
| client_(std::move(client)), | ||
| device_to_node_(std::move(device_to_node)) {} | ||
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| StatusOr<std::string> GetNcclUniqueId(const gpu::NcclCliqueKey& key); | ||
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| private: | ||
| const int node_id_; | ||
| const std::shared_ptr<DistributedRuntimeClient> client_; | ||
| const absl::flat_hash_map<GlobalDeviceId, int> device_to_node_; | ||
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| absl::Mutex mu_; | ||
| absl::flat_hash_map<gpu::NcclCliqueKey, std::string> cache_ | ||
| ABSL_GUARDED_BY(mu_); | ||
| }; | ||
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| } // namespace xla | ||
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| #endif // TENSORFLOW_COMPILER_XLA_PJRT_NCCL_ID_STORE_H_ |