572442eb16
Add a new `PjRtBuffer::HostBufferSemantics` enum that describes the possible contracts between caller and runtime. * Change `FromHostBuffer(..., force_copy, ...)` to `FromHostBuffer(..., host_buffer_semantics, ...)`. We were seeing some data races between modifications to a NumPy array and JAX on CPU, due to unintended buffer aliasing. This change allows clients to control whether they want zero-copy behavior or not. PiperOrigin-RevId: 316672280 Change-Id: Ibee296305005e0aa306a2c0aacf4b35a3d6c3ac1
67 lines
2.6 KiB
C++
67 lines
2.6 KiB
C++
/* Copyright 2020 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/xla/pjrt/cpu_device.h"
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#include "absl/strings/str_cat.h"
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#include "tensorflow/compiler/xla/client/client_library.h"
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#include "tensorflow/compiler/xla/service/platform_util.h"
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namespace xla {
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static const char kCpuPlatformName[] = "cpu";
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CpuDevice::CpuDevice(int id,
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std::unique_ptr<LocalDeviceState> local_device_state)
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: Device(id, std::move(local_device_state), kCpuPlatformName,
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/*device_kind=*/kCpuPlatformName) {}
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StatusOr<std::shared_ptr<PjRtClient>> GetCpuClient(bool asynchronous) {
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TF_ASSIGN_OR_RETURN(se::Platform * platform,
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PlatformUtil::GetPlatform("Host"));
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if (platform->VisibleDeviceCount() <= 0) {
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return FailedPrecondition("CPU platform has no visible devices.");
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}
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LocalClientOptions options;
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options.set_platform(platform);
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TF_ASSIGN_OR_RETURN(LocalClient * client,
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ClientLibrary::GetOrCreateLocalClient(options));
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std::vector<std::unique_ptr<Device>> devices;
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for (int i = 0; i < client->device_count(); ++i) {
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se::StreamExecutorConfig config;
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config.ordinal = i;
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// 8MiB stacks seem to be necessary for running LAPACK/OpenBLAS
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// computations.
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config.device_options.non_portable_tags["host_thread_stack_size_in_bytes"] =
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absl::StrCat(8192 * 1024);
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TF_ASSIGN_OR_RETURN(se::StreamExecutor * executor,
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platform->GetExecutor(config));
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auto device_state = absl::make_unique<LocalDeviceState>(
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executor, client, LocalDeviceState::kSynchronous, asynchronous,
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/*allow_event_reuse=*/false);
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auto device = absl::make_unique<CpuDevice>(i, std::move(device_state));
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devices.push_back(std::move(device));
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}
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return std::make_shared<PjRtClient>(
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kCpuPlatformName, client, std::move(devices), /*host_id=*/0,
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/*allocator=*/nullptr, /*host_memory_allocator=*/nullptr,
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/*should_stage_host_to_device_transfers=*/false,
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/*gpu_run_options=*/nullptr);
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}
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} // namespace xla
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