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| Original file line number | Diff line number | Diff line change |
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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. | ||
| ==============================================================================*/ | ||
|
|
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| #include "tensorflow/compiler/xla/service/conditional_to_select.h" | ||
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| #include "tensorflow/compiler/xla/service/call_graph.h" | ||
| #include "tensorflow/compiler/xla/service/call_inliner.h" | ||
| #include "tensorflow/compiler/xla/service/hlo_computation.h" | ||
| #include "tensorflow/compiler/xla/service/hlo_creation_utils.h" | ||
| #include "tensorflow/compiler/xla/service/hlo_instruction.h" | ||
| #include "tensorflow/compiler/xla/service/hlo_opcode.h" | ||
| #include "tensorflow/compiler/xla/status_macros.h" | ||
| #include "tensorflow/compiler/xla/types.h" | ||
| #include "tensorflow/core/lib/core/errors.h" | ||
| #include "tensorflow/core/lib/core/status.h" | ||
| #include "tensorflow/core/platform/logging.h" | ||
|
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| namespace xla { | ||
|
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| static StatusOr<bool> DoConditionalToSelect(HloInstruction* conditional) { | ||
| // Only allow conditional to select if the called computations | ||
| // do not have side effects. | ||
| if (conditional->true_computation()->HasSideEffect() || | ||
| conditional->false_computation()->HasSideEffect()) { | ||
| VLOG(1) << "Not transforming conditional; branches have side effects:" | ||
| << conditional->ToString(); | ||
| return false; | ||
| } | ||
|
|
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| auto computation = conditional->parent(); | ||
|
|
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| // Create new instructions | ||
| HloInstruction* if_call_op = | ||
| computation->AddInstruction(HloInstruction::CreateCall( | ||
| conditional->shape(), {conditional->mutable_operand(1)}, | ||
| conditional->true_computation())); | ||
| conditional->SetupDerivedInstruction(if_call_op); | ||
| HloInstruction* else_call_op = | ||
| computation->AddInstruction(HloInstruction::CreateCall( | ||
| conditional->shape(), {conditional->mutable_operand(2)}, | ||
| conditional->false_computation())); | ||
| conditional->SetupDerivedInstruction(else_call_op); | ||
| HloInstruction* condition = conditional->mutable_operand(0); | ||
| TF_ASSIGN_OR_RETURN( | ||
| HloInstruction * select_op, | ||
| MakeSelectHlo(condition, if_call_op, else_call_op, conditional)); | ||
| TF_RETURN_IF_ERROR(computation->ReplaceInstruction(conditional, select_op)); | ||
| TF_RETURN_IF_ERROR(CallInliner::Inline(if_call_op).status()); | ||
| TF_RETURN_IF_ERROR(CallInliner::Inline(else_call_op).status()); | ||
| return true; | ||
| } | ||
|
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| StatusOr<bool> ConditionalToSelect::Run(HloModule* module) { | ||
| std::unique_ptr<CallGraph> call_graph = CallGraph::Build(module); | ||
| bool did_mutate = false; | ||
| VLOG(1) << "Running conditional-to-select pass"; | ||
| TF_RETURN_IF_ERROR( | ||
| call_graph->VisitNodes([&](const CallGraphNode& node) -> Status { | ||
| std::vector<HloInstruction*> ToInline; | ||
| if (node.context() != CallContext::kParallel) { | ||
| return Status::OK(); | ||
| } | ||
| for (const CallSite& callsite : node.callsites()) { | ||
| if (callsite.instruction()->opcode() == HloOpcode::kConditional) { | ||
| VLOG(1) << "Visiting conditional: " << callsite.ToString(); | ||
| HloInstruction* conditional = callsite.instruction(); | ||
| TF_ASSIGN_OR_RETURN(bool result, | ||
| DoConditionalToSelect(conditional)); | ||
| did_mutate |= result; | ||
| } | ||
| } | ||
| return Status::OK(); | ||
| })); | ||
| return did_mutate; | ||
| } | ||
|
|
||
| } // namespace xla |
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| @@ -0,0 +1,38 @@ | ||
| /* 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. | ||
| ==============================================================================*/ | ||
|
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| #ifndef TENSORFLOW_COMPILER_XLA_SERVICE_CONDITIONAL_TO_SELECT_H_ | ||
| #define TENSORFLOW_COMPILER_XLA_SERVICE_CONDITIONAL_TO_SELECT_H_ | ||
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| #include "tensorflow/compiler/xla/service/hlo_module.h" | ||
| #include "tensorflow/compiler/xla/service/hlo_pass_interface.h" | ||
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| namespace xla { | ||
|
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| // A pass which transforms conditionals to selects in places where conditionals | ||
| // are legal, but not currently supported by the backends (e.g. inside kMap) | ||
| class ConditionalToSelect : public HloModulePass { | ||
| public: | ||
| ~ConditionalToSelect() override = default; | ||
| absl::string_view name() const override { return "conditional-to-select"; } | ||
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| // Run conditional to select on the given computation. Returns whether the | ||
| // computation was changed. | ||
| StatusOr<bool> Run(HloModule* module) override; | ||
| }; | ||
|
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| } // namespace xla | ||
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| #endif // TENSORFLOW_COMPILER_XLA_SERVICE_CONDITIONAL_TO_SELECT_H_ |
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tensorflow/compiler/xla/service/conditional_to_select_test.cc
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,188 @@ | ||
| /* 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. | ||
| ==============================================================================*/ | ||
|
|
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| #include "tensorflow/compiler/xla/service/conditional_to_select.h" | ||
|
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| #include <memory> | ||
| #include <utility> | ||
|
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| #include "absl/memory/memory.h" | ||
| #include "tensorflow/compiler/xla/literal.h" | ||
| #include "tensorflow/compiler/xla/service/hlo_computation.h" | ||
| #include "tensorflow/compiler/xla/service/hlo_instruction.h" | ||
| #include "tensorflow/compiler/xla/service/hlo_matchers.h" | ||
| #include "tensorflow/compiler/xla/service/hlo_opcode.h" | ||
| #include "tensorflow/compiler/xla/test.h" | ||
| #include "tensorflow/compiler/xla/tests/hlo_test_base.h" | ||
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| namespace op = xla::testing::opcode_matchers; | ||
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| namespace xla { | ||
| namespace { | ||
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| using ConditionalToSelectTest = HloTestBase; | ||
| using ::testing::_; | ||
|
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| // Test that a conditional of simple constants is transformed to a select | ||
| TEST_F(ConditionalToSelectTest, MapConditionalConstants) { | ||
| const string hlo_text = R"( | ||
| HloModule MapConditionalConstants | ||
| if { | ||
| %pif = () parameter(0) | ||
| ROOT %cif = f32[] constant(0) | ||
| } | ||
| else { | ||
| %pelse = () parameter(0) | ||
| ROOT %celse = f32[] constant(1) | ||
| } | ||
| mapped { | ||
| %a = f32[] parameter(0) | ||
| %b = f32[] parameter(1) | ||
| %lt = pred[] compare(%a, %b), direction=LT | ||
| %t = () tuple() | ||
| ROOT %conditional = f32[] conditional(%lt, %t, %t), true_computation=if, false_computation=else | ||
| } | ||
| ENTRY comp { | ||
| %p1 = f32[1000]{0} parameter(0) | ||
| %p2 = f32[1000]{0} parameter(1) | ||
| ROOT %mapped = f32[1000]{0} map(%p1, %p2), dimensions={0}, to_apply=mapped | ||
| } | ||
| )"; | ||
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| auto module = ParseAndReturnVerifiedModule(hlo_text).ValueOrDie(); | ||
| ConditionalToSelect pass; | ||
| ASSERT_TRUE(pass.Run(&*module).ValueOrDie()); | ||
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| HloInstruction* root = module->entry_computation()->root_instruction(); | ||
| ASSERT_EQ(root->opcode(), HloOpcode::kMap); | ||
| HloComputation* mapped = root->called_computations()[0]; | ||
| EXPECT_THAT(mapped->root_instruction(), | ||
| op::Select(op::Lt(op::Parameter(0), op::Parameter(1)), | ||
| op::Constant(), op::Constant())); | ||
| } | ||
|
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| // Test that the condition gets broadcasted for feeding into | ||
| // select when the output is non-scalar. | ||
| TEST_F(ConditionalToSelectTest, MapConditionalNonScalar) { | ||
| const string hlo_text = R"( | ||
| HloModule MapConditionalNonScalar | ||
| if { | ||
| %pif = () parameter(0) | ||
| %zero = f32[] constant(0) | ||
| ROOT %zero_broadcasted = f32[2,2]{1,0} broadcast(%zero), dimensions={} | ||
| } | ||
| else { | ||
| %pelse = () parameter(0) | ||
| %one = f32[] constant(0) | ||
| ROOT %one_broadcasted = f32[2,2]{1,0} broadcast(%one), dimensions={} | ||
| } | ||
| add { | ||
| %add_lhs = f32[] parameter(0) | ||
| %add_rhs = f32[] parameter(1) | ||
| ROOT %add = f32[] add(%add_lhs, %add_rhs) | ||
| } | ||
| mapped { | ||
| %a = f32[] parameter(0) | ||
| %b = f32[] parameter(1) | ||
| %lt = pred[] compare(%a, %b), direction=LT | ||
| %t = () tuple() | ||
| %conditional = f32[2,2]{1,0} conditional(%lt, %t, %t), true_computation=if, false_computation=else | ||
| %zero = f32[] constant(0) | ||
| ROOT %reduced = f32[] reduce(%conditional, %zero), dimensions={0,1}, to_apply=add | ||
| } | ||
| ENTRY comp { | ||
| %p1 = f32[1000]{0} parameter(0) | ||
| %p2 = f32[1000]{0} parameter(1) | ||
| ROOT %mapped = f32[1000]{0} map(%p1, %p2), dimensions={0}, to_apply=mapped | ||
| } | ||
| )"; | ||
|
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| auto module = ParseAndReturnVerifiedModule(hlo_text).ValueOrDie(); | ||
| ConditionalToSelect pass; | ||
| ASSERT_TRUE(pass.Run(&*module).ValueOrDie()); | ||
|
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| HloInstruction* root = module->entry_computation()->root_instruction(); | ||
| ASSERT_EQ(root->opcode(), HloOpcode::kMap); | ||
| HloComputation* mapped = root->called_computations()[0]; | ||
| EXPECT_THAT( | ||
| mapped->root_instruction(), | ||
| op::Reduce( | ||
| op::Select(op::Broadcast(op::Lt(op::Parameter(0), op::Parameter(1))), | ||
| _, _), | ||
| _)); | ||
| } | ||
|
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| // Test that conditionals of tuple type get turned into kTupleSelect | ||
| TEST_F(ConditionalToSelectTest, MapConditionalTuples) { | ||
| const string hlo_text = R"( | ||
| HloModule MapConditionalTuples | ||
| if { | ||
| %pif = () parameter(0) | ||
| %zero = f32[] constant(0) | ||
| ROOT %tup = (f32[],f32[]) tuple(%zero, %zero) | ||
| } | ||
| else { | ||
| %pelse = () parameter(0) | ||
| %one = f32[] constant(0) | ||
| ROOT %tup = (f32[],f32[]) tuple(%one, %one) | ||
| } | ||
| add { | ||
| %add_lhs = f32[] parameter(0) | ||
| %add_rhs = f32[] parameter(1) | ||
| ROOT %add = f32[] add(%add_lhs, %add_rhs) | ||
| } | ||
| mapped { | ||
| %a = f32[] parameter(0) | ||
| %b = f32[] parameter(1) | ||
| %lt = pred[] compare(%a, %b), direction=LT | ||
| %t = () tuple() | ||
| %conditional = (f32[], f32[]) conditional(%lt, %t, %t), true_computation=if, false_computation=else | ||
| %el1 = f32[] get-tuple-element(%conditional), index=0 | ||
| %el2 = f32[] get-tuple-element(%conditional), index=1 | ||
| %reduced = f32[] add(%el1, %el2) | ||
| } | ||
| ENTRY comp { | ||
| %p1 = f32[1000]{0} parameter(0) | ||
| %p2 = f32[1000]{0} parameter(1) | ||
| ROOT %mapped = f32[1000]{0} map(%p1, %p2), dimensions={0}, to_apply=mapped | ||
| } | ||
| )"; | ||
|
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| auto module = ParseAndReturnVerifiedModule(hlo_text).ValueOrDie(); | ||
| ConditionalToSelect pass; | ||
| ASSERT_TRUE(pass.Run(&*module).ValueOrDie()); | ||
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| HloInstruction* root = module->entry_computation()->root_instruction(); | ||
| ASSERT_EQ(root->opcode(), HloOpcode::kMap); | ||
| HloComputation* mapped = root->called_computations()[0]; | ||
| EXPECT_THAT(mapped->root_instruction(), | ||
| op::Add(op::GetTupleElement(op::TupleSelect(_, _, _)), _)); | ||
| } | ||
| } // namespace | ||
| } // namespace xla |
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