experiments/STT-tensorflow
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[TF:XLA] Remove flag --xla_enable_buffer_reuse.

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The corresponding issues have been fixed, so this should not be needed anymore.

PiperOrigin-RevId: 161120874
This commit is contained in:
A. Unique TensorFlower 2017-07-06 13:17:39 -07:00 committed by TensorFlower Gardener
parent a22dad9836
commit b87774c9d8
3 changed files with 1 additions and 11 deletions
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6
tensorflow/compiler/xla/legacy_flags/debug_options_flags.cc
View File

@ -42,7 +42,6 @@ struct DebugOptionsFlags {
string xla_dump_ir_to;
string xla_dump_debug_json_to;
bool xla_eliminate_hlo_implicit_broadcast;
bool xla_enable_buffer_reuse;
bool xla_cpu_multi_thread_eigen;
@ -79,7 +78,6 @@ void AllocateFlags() {
flag_values->xla_dump_ir_to = "";
flag_values->xla_dump_debug_json_to = "";
flag_values->xla_eliminate_hlo_implicit_broadcast = false;
flag_values->xla_enable_buffer_reuse = true;
flag_values->xla_cpu_multi_thread_eigen = true;
flag_values->xla_gpu_cuda_data_dir = "./cuda_sdk_lib";
flag_values->xla_gpu_ftz = false;
@ -146,9 +144,6 @@ void AllocateFlags() {
"Eliminate implicit broadcasts when lowering user "
"computations to HLO instructions; use explicit "
"broadcast instead."),
tensorflow::Flag("xla_enable_buffer_reuse",
&flag_values->xla_enable_buffer_reuse,
"Enable reuse of buffers between HLO operations."),
tensorflow::Flag("xla_cpu_multi_thread_eigen",
&flag_values->xla_cpu_multi_thread_eigen,
"When generating calls to Eigen in the CPU backend, "
@ -207,7 +202,6 @@ xla::DebugOptions GetDebugOptionsFromFlags() {
options.set_xla_dump_ir_to(flag_values->xla_dump_ir_to);
options.set_xla_eliminate_hlo_implicit_broadcast(
flag_values->xla_eliminate_hlo_implicit_broadcast);
options.set_xla_enable_buffer_reuse(flag_values->xla_enable_buffer_reuse);
options.set_xla_dump_debug_json_to(flag_values->xla_dump_debug_json_to);
options.set_xla_cpu_multi_thread_eigen(

3
tensorflow/compiler/xla/service/buffer_assignment.cc
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@ -796,8 +796,7 @@ Status BufferAssigner::AssignBuffersForComputation(
continue;
}
if (!debug_options.xla_enable_buffer_reuse() || is_thread_local ||
instruction->opcode() == HloOpcode::kCustomCall) {
if (is_thread_local || instruction->opcode() == HloOpcode::kCustomCall) {
// Custom call operations never have reusable buffers. Also we do not
// reuse thread-local buffers for now, because they are dynamically
// allocated and their lifetimes are hard to compute.

3
tensorflow/compiler/xla/xla.proto
View File

@ -99,9 +99,6 @@ message DebugOptions {
// the generated IR.
bool xla_llvm_enable_invariant_load_metadata = 65;
// Enable reuse of buffers between HLO operations.
bool xla_enable_buffer_reuse = 66;
// Extra options to pass to the compilation backend; specific interpretation
// of these values is left to the backend.
map<string, string> xla_backend_extra_options = 500;