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/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/
#include "tensorflow/compiler/xla/service/gpu/tests/gpu_codegen_test.h"
// Check that the ftz (flush denormals to zero) flag is reflected in PTX as
// expected.
namespace xla {
namespace gpu {
namespace {
class GpuFtzTest : public GpuCodegenTest {
public:
explicit GpuFtzTest(bool ftz) : ftz_(ftz) {}
// Creates an HLO module that performs the given binary operation on some
// data.
std::unique_ptr<HloModule> CreateBinaryOpModule(HloOpcode op) {
HloComputation::Builder builder(TestName());
Shape param_shape = ShapeUtil::MakeShapeWithLayout(
F32, /*dimensions=*/{100, 100}, /*minor_to_major=*/{1, 0});
HloInstruction* x = builder.AddInstruction(HloInstruction::CreateParameter(
/* parameter_number=*/0, param_shape, "x"));
HloInstruction* y = builder.AddInstruction(HloInstruction::CreateParameter(
/* parameter_number=*/1, param_shape, "y"));
builder.AddInstruction(HloInstruction::CreateBinary(param_shape, op, x, y));
auto hlo_module = CreateNewModuleWithFTZ(ftz_);
hlo_module->AddEntryComputation(builder.Build());
return hlo_module;
}
// Creates an HLO module that performs the given unary operation on some data.
std::unique_ptr<HloModule> CreateUnaryOpModule(HloOpcode op) {
HloComputation::Builder builder(TestName());
Shape param_shape = ShapeUtil::MakeShapeWithLayout(
F32, /*dimensions=*/{100, 100}, /*minor_to_major=*/{1, 0});
HloInstruction* x = builder.AddInstruction(HloInstruction::CreateParameter(
/* parameter_number=*/0, param_shape, "x"));
builder.AddInstruction(HloInstruction::CreateUnary(param_shape, op, x));
auto hlo_module = CreateNewModuleWithFTZ(ftz_);
hlo_module->AddEntryComputation(builder.Build());
return hlo_module;
}
bool ftz_;
};
class GpuFtzEnabledTest : public GpuFtzTest {
public:
GpuFtzEnabledTest() : GpuFtzTest(/*ftz=*/true) {}
};
class GpuFtzDisabledTest : public GpuFtzTest {
public:
GpuFtzDisabledTest() : GpuFtzTest(/*ftz=*/false) {}
};
// Check that we emit mul.ftz.f32 when in ftz mode, and plain mul.f32 otherwise.
TEST_F(GpuFtzEnabledTest, MultiplyFtz) {
CompileAndVerifyPtx(CreateBinaryOpModule(HloOpcode::kMultiply), R"(
CHECK-NOT: mul.f32
CHECK: mul.ftz.f32
CHECK-NOT: mul.f32
)");
}
TEST_F(GpuFtzDisabledTest, MultiplyFtz) {
CompileAndVerifyPtx(CreateBinaryOpModule(HloOpcode::kMultiply), R"(
CHECK-NOT: mul.ftz.f32
CHECK: mul.f32
CHECK-NOT: mul.ftz.f32
)");
}
// In NVPTX, exp(float) is implemented in libdevice, and consults __nvvm_reflect
// to determine whether or not ftz is enabled. The implementation uses two
// calls to ex2.approx. When ftz is on, we get two calls to the ftz version;
// when ftz is off, we get one call to the ftz version and one call to the
// regular version.
TEST_F(GpuFtzEnabledTest, ExpFtz) {
CompileAndVerifyPtx(CreateUnaryOpModule(HloOpcode::kExp), R"(
CHECK-NOT: ex2.approx.f32
CHECK: ex2.approx.ftz.f32
CHECK-NOT: ex2.approx.f32
CHECK: ex2.approx.ftz.f32
CHECK-NOT: ex2.approx.f32
CHECK-NOT: ex2.approx.ftz.f32
)");
}
TEST_F(GpuFtzDisabledTest, ExpFtz) {
CompileAndVerifyPtx(CreateUnaryOpModule(HloOpcode::kExp), R"(
CHECK-NOT: ex2.approx.f32
CHECK-DAG: ex2.approx.ftz.f32
CHECK-DAG: ex2.approx.f32
CHECK-NOT: ex2.approx.f32
CHECK-NOT: ex2.approx.ftz.f32
)");
}
} // namespace
} // namespace gpu
} // namespace xla
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