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Fix xla/service tests with new Slice API (includes strides)

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This commit is contained in:
DavidNorman 2017-06-14 14:26:11 +01:00 committed by Martin Wicke
parent 4f513e0555
commit fa3884883d
6 changed files with 24 additions and 19 deletions
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8
tensorflow/compiler/xla/service/algebraic_simplifier_test.cc
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@ -522,7 +522,7 @@ TEST_F(AlgebraicSimplifierTest, RemoveEmptyConcatenateOperands) {
HloInstruction::CreateConstant(LiteralUtil::CreateR1<float>({})));
HloInstruction* empty_slice =
builder.AddInstruction(HloInstruction::CreateSlice(
ShapeUtil::MakeShape(F32, {0}), param1, {42}, {42}));
ShapeUtil::MakeShape(F32, {0}), param1, {42}, {42}, {1}));
Shape result_shape = ShapeUtil::MakeShape(F32, {3 * kParamLength});
builder.AddInstruction(HloInstruction::CreateConcatenate(
result_shape, {empty_literal, param0, param0, empty_slice, param1}, 0));
@ -553,7 +553,7 @@ TEST_F(AlgebraicSimplifierTest, OnlyEmptyConcatenateOperands) {
HloInstruction::CreateConstant(LiteralUtil::CreateR1<float>({})));
HloInstruction* empty_slice =
builder.AddInstruction(HloInstruction::CreateSlice(
ShapeUtil::MakeShape(F32, {0}), param0, {42}, {42}));
ShapeUtil::MakeShape(F32, {0}), param0, {42}, {42}, {1}));
Shape result_shape = ShapeUtil::MakeShape(F32, {0});
builder.AddInstruction(HloInstruction::CreateConcatenate(
result_shape, {empty_literal, empty_slice}, 0));
@ -1134,7 +1134,7 @@ TEST_F(AlgebraicSimplifierTest, RemoveNoopSlice) {
0, ShapeUtil::MakeShape(F32, {dim0, dim1}), "param"));
builder.AddInstruction(HloInstruction::CreateSlice(
ShapeUtil::MakeShape(F32, {dim0, dim1}), param, /*start_indices=*/{0, 0},
/*limit_indices=*/{dim0, dim1}));
/*limit_indices=*/{dim0, dim1}, /*slices=*/{1, 1}));
HloModule module(TestName());
HloComputation* computation = module.AddEntryComputation(builder.Build());
@ -1539,7 +1539,7 @@ TEST_F(AlgebraicSimplifierTest, ScalarBroadcastToSlice) {
Shape slice_shape = ShapeUtil::MakeShape(F32, {2, 2, 3, 3});
HloInstruction* slice = builder.AddInstruction(HloInstruction::CreateSlice(
slice_shape, broadcast, {0, 1, 2, 3}, {2, 3, 5, 6}));
slice_shape, broadcast, {0, 1, 2, 3}, {2, 3, 5, 6}, {1, 1, 1, 1}));
HloModule module(TestName());
auto computation = module.AddEntryComputation(builder.Build());

12
tensorflow/compiler/xla/service/buffer_assignment_test.cc
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@ -730,7 +730,7 @@ TEST_F(BufferAssignmentTest, ReuseNonOperandBuffer) {
auto negate = builder.AddInstruction(
HloInstruction::CreateUnary(f32vec100_, HloOpcode::kNegate, param0));
auto slice = builder.AddInstruction(
HloInstruction::CreateSlice(f32vec10_, negate, {0}, {10}));
HloInstruction::CreateSlice(f32vec10_, negate, {0}, {10}, {1}));
auto broadcast = builder.AddInstruction(
HloInstruction::CreateBroadcast(f32a100x10_, slice, {1}));
@ -762,7 +762,7 @@ TEST_F(BufferAssignmentTest, NoReuseLiveBuffer) {
auto negate = builder.AddInstruction(
HloInstruction::CreateUnary(f32vec100_, HloOpcode::kNegate, param0));
auto slice = builder.AddInstruction(
HloInstruction::CreateSlice(f32vec10_, negate, {0}, {10}));
HloInstruction::CreateSlice(f32vec10_, negate, {0}, {10}, {1}));
auto broadcast = builder.AddInstruction(
HloInstruction::CreateBroadcast(f32a100x10_, slice, {1}));
builder.AddInstruction(HloInstruction::CreateTuple({negate, broadcast}));
@ -799,7 +799,7 @@ TEST_F(BufferAssignmentTest, NoReuseAliasedBuffer) {
auto tuple_element = builder.AddInstruction(
HloInstruction::CreateGetTupleElement(f32vec100_, tuple, 0));
auto slice = builder.AddInstruction(
HloInstruction::CreateSlice(f32vec10_, tuple_element, {0}, {10}));
HloInstruction::CreateSlice(f32vec10_, tuple_element, {0}, {10}, {1}));
auto broadcast = builder.AddInstruction(
HloInstruction::CreateBroadcast(f32a100x10_, slice, {1}));
builder.AddInstruction(HloInstruction::CreateTuple({tuple, broadcast}));
@ -834,7 +834,7 @@ TEST_F(BufferAssignmentTest, DoNotReuseOversizedOutputBuffer) {
HloInstruction::CreateUnary(f32vec100_, HloOpcode::kNegate, param0));
// Slice output is 10 elements.
auto slice = builder.AddInstruction(
HloInstruction::CreateSlice(f32vec10_, negate, {0}, {10}));
HloInstruction::CreateSlice(f32vec10_, negate, {0}, {10}, {1}));
// Broadcast output is 40 elements.
auto broadcast = builder.AddInstruction(HloInstruction::CreateBroadcast(
ShapeUtil::MakeShape(F32, {10, 4}), slice, {0}));
@ -866,7 +866,7 @@ TEST_F(BufferAssignmentTest, ReuseOutputBufferIfExactlySized) {
auto negate = builder.AddInstruction(
HloInstruction::CreateUnary(f32vec100_, HloOpcode::kNegate, param0));
auto slice = builder.AddInstruction(
HloInstruction::CreateSlice(f32vec10_, negate, {0}, {10}));
HloInstruction::CreateSlice(f32vec10_, negate, {0}, {10}, {1}));
// Broadcast output is 40 elements.
auto broadcast = builder.AddInstruction(HloInstruction::CreateBroadcast(
ShapeUtil::MakeShape(F32, {10, 10}), slice, {0}));
@ -903,7 +903,7 @@ TEST_F(BufferAssignmentTest, DoNotReuseOversizedOutputBufferInTuple) {
HloInstruction::CreateUnary(f32vec100_, HloOpcode::kNegate, param0));
// Slice output is 10 elements.
auto slice = builder.AddInstruction(
HloInstruction::CreateSlice(f32vec10_, negate, {0}, {10}));
HloInstruction::CreateSlice(f32vec10_, negate, {0}, {10}, {1}));
// Broadcast output is 40 elements.
auto broadcast = builder.AddInstruction(HloInstruction::CreateBroadcast(
ShapeUtil::MakeShape(F32, {10, 4}), slice, {0}));

2
tensorflow/compiler/xla/service/buffer_liveness_test.cc
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@ -590,7 +590,7 @@ class FusedDynamicUpdateSliceLivenessTest : public BufferLivenessTest {
if (update_uses_tuple_element1) {
// Create a slice instruction as an additional user of 'gte1'.
slice = builder.AddInstruction(
HloInstruction::CreateSlice(update_shape, gte1, {0}, {3}));
HloInstruction::CreateSlice(update_shape, gte1, {0}, {3}, {1}));
update = builder.AddInstruction(HloInstruction::CreateBinary(
update_shape, HloOpcode::kAdd, update, slice));
}

3
tensorflow/compiler/xla/service/hlo_constant_folding_test.cc
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@ -153,6 +153,7 @@ TEST_F(HloConstantFoldingTest, Slice) {
const int64 dimensions[] = {11, 8, 7, 5, 9};
const int64 slice_start[] = {4, 2, 3, 1, 5};
const int64 slice_limits[] = {10, 8, 6, 5, 9};
const int64 slice_strides[] = {1, 1, 1, 1, 1};
TF_ASSIGN_OR_ASSERT_OK(auto literal,
LiteralTestUtil::CreateRandomLiteral<F32>(
ShapeUtil::MakeShape(F32, dimensions), 0.0, 1.0));
@ -160,7 +161,7 @@ TEST_F(HloConstantFoldingTest, Slice) {
HloInstruction::CreateConstant(std::move(literal)));
Shape shape = ShapeUtil::MakeShape(F32, {6, 6, 3, 4, 4});
builder.AddInstruction(HloInstruction::CreateSlice(
shape, literal_instruction, slice_start, slice_limits));
shape, literal_instruction, slice_start, slice_limits, slice_strides));
auto module = CreateNewModule();
auto computation = module->AddEntryComputation(builder.Build());

16
tensorflow/compiler/xla/service/hlo_rematerialization_test.cc
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@ -67,7 +67,8 @@ class HloRematerializationTest : public HloTestBase {
/*dimension=*/0));
auto slice_1 = builder.AddInstruction(HloInstruction::CreateSlice(
vec1_shape_, concat_1, /*start_indices=*/{0},
/*limit_indices=*/{1}));
/*limit_indices=*/{1},
/*strides=*/{1}));
auto concat_2 = builder.AddInstruction(HloInstruction::CreateConcatenate(
ShapeUtil::MakeShape(xla::F32, {1025}), {bcast, slice_1},
/*dimension=*/0));
@ -75,7 +76,8 @@ class HloRematerializationTest : public HloTestBase {
// which is necessary to use this computation in a while.
builder.AddInstruction(HloInstruction::CreateSlice(vec1_shape_, concat_2,
/*start_indices=*/{0},
/*limit_indices=*/{1}));
/*limit_indices=*/{1},
/*strides=*/{1}));
return builder.Build();
}
@ -103,7 +105,8 @@ class HloRematerializationTest : public HloTestBase {
HloInstruction::CreateBroadcast(vec1024_shape_, param, {}));
auto slice_1 = builder.AddInstruction(
HloInstruction::CreateSlice(vec1_shape_, bcast, /*start_indices=*/{0},
/*limit_indices=*/{1}));
/*limit_indices=*/{1},
/*strides=*/{1}));
auto while_inst = builder.AddInstruction(HloInstruction::CreateWhile(
vec1_shape_, while_cond, while_body, slice_1));
auto concat = builder.AddInstruction(HloInstruction::CreateConcatenate(
@ -111,7 +114,8 @@ class HloRematerializationTest : public HloTestBase {
/*dimension=*/0));
builder.AddInstruction(HloInstruction::CreateSlice(vec1_shape_, concat,
/*start_indices=*/{0},
/*limit_indices=*/{1}));
/*limit_indices=*/{1},
/*strides=*/{1}));
return builder.Build();
}
@ -353,7 +357,7 @@ TEST_F(HloRematerializationTest, InstructionRematerializedMultipleTimes) {
/*dimension=*/0));
builder.AddInstruction(HloInstruction::CreateSlice(
vec1024_shape_, concat, /*start_indices=*/{0},
/*limit_indices=*/{1024}));
/*limit_indices=*/{1024}, /*slices=*/{1}));
subcomputation = module->AddEmbeddedComputation(builder.Build());
}
@ -469,7 +473,7 @@ TEST_P(IndirectUseTest, IndirectUseNotRematerialized) {
/*dimension=*/0));
builder.AddInstruction(HloInstruction::CreateSlice(
vec1024_shape_, concat, /*start_indices=*/{0},
/*limit_indices=*/{1024}));
/*limit_indices=*/{1024}, /*slices=*/{1}));
subcomputation = module->AddEmbeddedComputation(builder.Build());
}

2
tensorflow/compiler/xla/service/tuple_points_to_analysis_test.cc
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@ -585,7 +585,7 @@ class FusionPointsToAnalysisTest : public TuplePointsToAnalysisTest {
if (add_additional_gte0_user) {
// Create 'slice' as an additional user of 'input'.
auto slice = builder.AddInstruction(
HloInstruction::CreateSlice(update_shape, input, {0}, {3}));
HloInstruction::CreateSlice(update_shape, input, {0}, {3}, {1}));
// Modify 'update' to take 'slice' output.
update = builder.AddInstruction(HloInstruction::CreateBinary(
update_shape, HloOpcode::kAdd, update, slice));