wehub-resource-sync
97 lines
3.5 KiB
C++
97 lines
3.5 KiB
C++
/* Copyright 2023 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/compiler/tf2xla/tf2xla_opset.h"
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#include <algorithm>
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#include <string>
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#include <vector>
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#include "absl/status/status.h"
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#include "absl/strings/str_cat.h"
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#include "absl/strings/str_join.h"
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#include "absl/strings/string_view.h"
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#include "tensorflow/compiler/tf2xla/tf2xla_util.h"
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#include "tensorflow/compiler/tf2xla/xla_op_registry.h"
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#include "tensorflow/core/framework/kernel_def.pb.h"
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namespace tensorflow {
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const int SUPPORTED_DEVICES_NUM = 2;
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static const char* const SUPPORTED_DEVICES[SUPPORTED_DEVICES_NUM] = {
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DEVICE_GPU_XLA_JIT, DEVICE_CPU_XLA_JIT};
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bool IsSupportedBackend(absl::string_view device_name) {
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for (int i = 0; i < SUPPORTED_DEVICES_NUM; i++) {
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if (SUPPORTED_DEVICES[i] == device_name) return true;
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}
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return false;
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}
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absl::Status RegisterBackends(absl::string_view device_name) {
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if (!IsSupportedBackend(device_name)) {
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return absl::InvalidArgumentError(
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absl::StrCat(device_name, " is not supported. Supported devices are ",
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absl::StrJoin(SUPPORTED_DEVICES, ", ")));
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}
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// All backends need to be registered before DeviceKernels is called
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// because it calls RegisterCompilationKernels which will only run 1x,
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// meaning if a device is registered afterwards the ops for that device
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// will not be included.
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auto op_filter = [](KernelDef* kdef) {
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if (kdef->op() == "Const") {
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AddDtypeToKernelDefConstraint("dtype", DT_STRING, kdef);
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}
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if (kdef->op() == "Assert") {
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AddDtypeToKernelDefConstraint("T", DT_STRING, kdef);
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}
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return true;
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};
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// Backends might already be registered due to preprocesser macros defined
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// in xla_op_registery.h so this first checks to see if they are registered
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// already because re-registering the same device will cause a failure.
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if (!XlaOpRegistry::IsBackendRegistered(DEVICE_GPU_XLA_JIT)) {
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static auto gpu_backend =
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XlaBackendRegistrar(DEVICE_GPU_XLA_JIT, kGpuAllTypes, op_filter);
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}
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if (!XlaOpRegistry::IsBackendRegistered(DEVICE_CPU_XLA_JIT)) {
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static auto cpu_backend =
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XlaBackendRegistrar(DEVICE_CPU_XLA_JIT, kCpuAllTypes, op_filter);
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}
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if (!XlaOpRegistry::IsBackendRegistered(std::string(device_name))) {
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return absl::InternalError(
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absl::StrCat(device_name, " is not registered."));
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}
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return absl::OkStatus();
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}
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absl::StatusOr<std::vector<std::string>> GetRegisteredXlaOpsForDevice(
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absl::string_view device_name) {
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auto status = RegisterBackends(device_name);
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if (!status.ok()) return status;
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std::vector<const KernelDef*> kernel_defs =
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XlaOpRegistry::DeviceKernels(std::string(device_name), true);
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std::vector<std::string> op_names;
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op_names.reserve(kernel_defs.size());
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for (const auto& kernel_def : kernel_defs) {
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op_names.push_back(kernel_def->op());
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}
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std::sort(op_names.begin(), op_names.end());
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return op_names;
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}
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} // namespace tensorflow
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