experiments/STT-tensorflow
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[XLA:CPU] Remove the global/module-level fast math flags

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These are deprecated in favor of instruction-level fast math, and most of
LLVM's backend code was updated to use those instead. Not having them gives us
more fine-grained control of fast math flags without loss of performance.

Disabling UnsafeFPMath has the side effect of requiring __truncdfhf2 for
double->half conversions, so provide that. Also always allow FMA formation,
while it's not IEEE754 compliant it never decreases accuracy.

PiperOrigin-RevId: 281801638
Change-Id: I2d96220fefebad4d11b1dab8f75b06ccb88a05bf
This commit is contained in:
Benjamin Kramer 2019-11-21 11:58:53 -08:00 committed by TensorFlower Gardener
parent 1703690e1e
commit d04bfee679
5 changed files with 13 additions and 28 deletions
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16
tensorflow/compiler/xla/service/cpu/cpu_compiler.cc
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@ -409,20 +409,8 @@ auto memory_alignment = [](LogicalBuffer::Color) { return kMemoryAlignment; };
llvm::TargetOptions CompilerTargetOptions(
const HloModuleConfig& module_config) {
llvm::TargetOptions target_options;
// In LLVM backend flags, UnsafeFPMath does not explicitly imply NoInfs, etc.
if (module_config.debug_options().xla_cpu_enable_fast_math()) {
target_options.UnsafeFPMath = true;
target_options.NoInfsFPMath =
!module_config.debug_options().xla_cpu_fast_math_honor_infs();
target_options.NoNaNsFPMath =
!module_config.debug_options().xla_cpu_fast_math_honor_nans();
target_options.NoSignedZerosFPMath = true;
} else {
target_options.UnsafeFPMath = false;
target_options.NoInfsFPMath = false;
target_options.NoNaNsFPMath = false;
target_options.NoSignedZerosFPMath = false;
}
// Always allow FMA fusion. This increases precision instead of decreasing it.
target_options.AllowFPOpFusion = llvm::FPOpFusion::Fast;
return target_options;
}

6
tensorflow/compiler/xla/service/cpu/runtime_fp16.cc
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@ -131,3 +131,9 @@ float TF_ATTRIBUTE_WEAK __gnu_h2f_ieee(uint16 h) {
o.set_uint(o.as_uint() | (h & 0x8000) << 16); // sign bit
return o.as_float();
}
uint16 TF_ATTRIBUTE_WEAK __truncdfhf2(double d) {
// This does a double rounding step, but it's precise enough for our use
// cases.
return __gnu_f2h_ieee(static_cast<float>(d));
}

3
tensorflow/compiler/xla/service/cpu/runtime_fp16.h
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@ -24,4 +24,7 @@ extern "C" tensorflow::uint16 __gnu_f2h_ieee(float);
// Converts an F16 value to a F32.
extern "C" float __gnu_h2f_ieee(tensorflow::uint16);
// Converts an F64 value to a F16.
extern "C" tensorflow::uint16 __truncdfhf2(double);
#endif // TENSORFLOW_COMPILER_XLA_SERVICE_CPU_RUNTIME_FP16_H_

2
tensorflow/compiler/xla/service/cpu/simple_orc_jit.cc
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@ -250,6 +250,8 @@ bool RegisterKnownJITSymbols() {
"Host");
registry->Register("__gnu_h2f_ieee", reinterpret_cast<void*>(__gnu_h2f_ieee),
"Host");
registry->Register("__truncdfhf2", reinterpret_cast<void*>(__truncdfhf2),
"Host");
#undef REGISTER_CPU_RUNTIME_SYMBOL

14
tensorflow/compiler/xla/service/llvm_ir/llvm_util.cc
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@ -607,20 +607,6 @@ llvm::Function* CreateCpuFunction(llvm::FunctionType* function_type,
// created by the JIT compiled code.
function->setHasUWTable();
if (module_config.debug_options().xla_cpu_enable_fast_math()) {
function->addFnAttr("unsafe-fp-math", "true");
function->addFnAttr("no-signed-zeros-fp-math", "true");
if (!module_config.debug_options().xla_cpu_fast_math_honor_nans()) {
function->addFnAttr("no-nans-fp-math", "true");
}
if (!module_config.debug_options().xla_cpu_fast_math_honor_infs()) {
function->addFnAttr("no-infs-fp-math", "true");
}
if (module_config.debug_options().xla_cpu_fast_math_honor_division()) {
function->addFnAttr("reciprocal-estimates", "none");
}
}
// Add the optize attribute to the function if optimizing for size. This
// controls internal behavior of some optimization passes (e.g. loop
// unrolling).