09d0ef73ff
Adding an HLO pass that expands the kLogistic HLO into a desired sequence of different HLOs. Currently two different strategies are added. 1) A lowering through an expansion using TAHN (0.5 + 0.5 * tanh(0.5 * x)) 2) A lowering through an expansion using EXP (1.0 / (1.0 + exp(-x))) PiperOrigin-RevId: 318208462 Change-Id: Ibcfba8e95f76c85cdbffc42566f5cec5e663c72b
104 lines
4.2 KiB
C++
104 lines
4.2 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/service/logistic_expander.h"
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#include "absl/memory/memory.h"
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#include "absl/strings/str_cat.h"
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#include "absl/strings/str_join.h"
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#include "tensorflow/compiler/xla/layout_util.h"
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#include "tensorflow/compiler/xla/literal.h"
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#include "tensorflow/compiler/xla/service/hlo_casting_utils.h"
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#include "tensorflow/compiler/xla/service/hlo_computation.h"
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#include "tensorflow/compiler/xla/service/hlo_creation_utils.h"
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#include "tensorflow/compiler/xla/service/hlo_instruction.h"
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#include "tensorflow/compiler/xla/service/hlo_instructions.h"
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#include "tensorflow/compiler/xla/service/hlo_opcode.h"
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#include "tensorflow/compiler/xla/service/hlo_parser.h"
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#include "tensorflow/compiler/xla/service/hlo_pass_fix.h"
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#include "tensorflow/compiler/xla/service/hlo_pass_pipeline.h"
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#include "tensorflow/compiler/xla/service/pattern_matcher.h"
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#include "tensorflow/compiler/xla/service/pattern_matcher_gmock.h"
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#include "tensorflow/compiler/xla/service/shape_inference.h"
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#include "tensorflow/compiler/xla/shape_util.h"
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#include "tensorflow/compiler/xla/test.h"
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#include "tensorflow/compiler/xla/tests/hlo_test_base.h"
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#include "tensorflow/compiler/xla/types.h"
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#include "tensorflow/compiler/xla/window_util.h"
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#include "tensorflow/compiler/xla/xla_data.pb.h"
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#include "tensorflow/core/lib/core/status_test_util.h"
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namespace xla {
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namespace {
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namespace m = match;
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class LogisticExpanderTest : public HloTestBase {};
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// Test that we expand kLogistic with 0.5 + 0.5 * tanh(0.5*x) when the proper
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// option is enabled.
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TEST_F(LogisticExpanderTest, ExpandWithTanh) {
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const char* kModuleStr = R"(
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HloModule m
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test {
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p = f32[2,3] parameter(0)
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ROOT r = f32[2,3] logistic(p)
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}
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)";
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TF_ASSERT_OK_AND_ASSIGN(auto m, ParseAndReturnVerifiedModule(kModuleStr));
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auto computation = m->entry_computation();
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HloInstruction* root = computation->root_instruction();
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EXPECT_EQ(root->opcode(), HloOpcode::kLogistic);
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LogisticExpander logistic_expander(LogisticExpansionType::kTanh);
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ASSERT_TRUE(logistic_expander.Run(m.get()).ValueOrDie());
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root = computation->root_instruction();
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EXPECT_THAT(m->entry_computation()->root_instruction(),
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GmockMatch(m::AddAnyOrder(
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m::MultiplyAnyOrder(m::Broadcast(m::ConstantScalar(0.5)),
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m::Tanh(m::MultiplyAnyOrder(
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m::Broadcast(m::ConstantScalar(0.5)),
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m::Parameter(0)))),
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m::Broadcast(m::ConstantScalar(0.5)))));
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}
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// Test that we expand kLogistic with 1.0 / (1.0 + exp(-x)) when the proper
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// option is enabled.
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TEST_F(LogisticExpanderTest, ExpandWithEXP) {
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const char* kModuleStr = R"(
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HloModule m
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test {
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p = f32[2,3] parameter(0)
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ROOT r = f32[2,3] logistic(p)
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}
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)";
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TF_ASSERT_OK_AND_ASSIGN(auto m, ParseAndReturnVerifiedModule(kModuleStr));
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auto computation = m->entry_computation();
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HloInstruction* root = computation->root_instruction();
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EXPECT_EQ(root->opcode(), HloOpcode::kLogistic);
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LogisticExpander logistic_expander(LogisticExpansionType::kExp);
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ASSERT_TRUE(logistic_expander.Run(m.get()).ValueOrDie());
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root = computation->root_instruction();
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EXPECT_THAT(m->entry_computation()->root_instruction(),
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GmockMatch(m::Divide(
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m::Broadcast(m::ConstantScalar(1.0)),
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m::AddAnyOrder(m::Broadcast(m::ConstantScalar(1.0)),
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m::Exp(m::Negate(m::Parameter(0)))))));
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}
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} // namespace
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} // namespace xla
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