experiments/STT-tensorflow
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Add logging in case we fallback to the secondary backend.

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This makes it possible to see from the logs whether the failover compiler used
the secondary (classic xla gpu) backend instead of the mlir_gpu backend.

PiperOrigin-RevId: 297583118
Change-Id: Ic76736684abec9764a16408fda1d52a7b2323bfc
This commit is contained in:
Adrian Kuegel 2020-02-27 06:36:46 -08:00 committed by TensorFlower Gardener
parent 58ac75a553
commit 8187dd591f
1 changed files with 6 additions and 0 deletions
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6
tensorflow/compiler/xla/service/mlir_gpu/failover_compiler.cc
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@ -32,6 +32,8 @@ StatusOr<std::unique_ptr<HloModule>> FailoverCompiler::RunHloPasses(
auto result =
primary_->RunHloPasses(module->Clone(), stream_exec, device_allocator);
if (IsUnimplemented(result)) {
VLOG(2) << "RunHloPasses resulted in " << result.status()
<< ", falling back to secondary backend";
return secondary_->RunHloPasses(std::move(module), stream_exec,
device_allocator);
}
@ -44,6 +46,8 @@ StatusOr<std::unique_ptr<Executable>> FailoverCompiler::RunBackend(
auto result =
primary_->RunBackend(module->Clone(), stream_exec, device_allocator);
if (IsUnimplemented(result)) {
VLOG(2) << "RunBackend resulted in " << result.status()
<< ", falling back to secondary backend";
return secondary_->RunBackend(std::move(module), stream_exec,
device_allocator);
}
@ -78,6 +82,8 @@ StatusOr<std::vector<std::unique_ptr<Executable>>> FailoverCompiler::Compile(
}(modules[i]->Clone());
if (IsUnimplemented(executable)) {
VLOG(2) << "Compile resulted in " << executable.status()
<< ", falling back to secondary backend";
TF_ASSIGN_OR_RETURN(
modules[i],
secondary_->RunHloPasses(std::move(modules[i]), stream_execs[i][0],