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Open XLA pin update - updated to 20231010
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,184 @@ | ||
| diff --git a/xla/service/topk_rewriter.cc b/xla/service/topk_rewriter.cc | ||
| index da872d962..1b7141055 100644 | ||
| --- a/xla/service/topk_rewriter.cc | ||
| +++ b/xla/service/topk_rewriter.cc | ||
| @@ -196,6 +196,8 @@ std::optional<int64_t> TopkRewriter::SortIsInTopK(HloInstruction* inst) { | ||
| return std::nullopt; | ||
| } | ||
| const int64_t sort_dim = sort->sort_dimension(); | ||
| + const int64_t batch_dim = sort_dim == 1 ? 0 : 1; | ||
| + const bool has_batch = data->shape().rank() == 2; | ||
|
|
||
| bool supported = true; | ||
| std::optional<int64_t> k; | ||
| @@ -220,15 +222,10 @@ std::optional<int64_t> TopkRewriter::SortIsInTopK(HloInstruction* inst) { | ||
| supported = false; | ||
| break; | ||
| } | ||
| - for (int64_t i = 0; i < slice->slice_limits().size(); ++i) { | ||
| - if (i != sort_dim && | ||
| - slice->slice_limits(i) != slice->operand(0)->shape().dimensions(i)) { | ||
| - // Slicing along a non-sort dimension isn't supported. | ||
| - supported = false; | ||
| - break; | ||
| - } | ||
| - } | ||
| - if (!supported) { | ||
| + if (has_batch && slice->slice_limits(batch_dim) != | ||
| + slice->operand(0)->shape().dimensions(batch_dim)) { | ||
| + // Slicing along the batch dimension isn't supported. | ||
| + supported = false; | ||
| break; | ||
| } | ||
| if (k == std::nullopt) { | ||
| @@ -260,57 +257,29 @@ StatusOr<bool> TopkRewriter::TransformToCustomCall( | ||
| HloSortInstruction* sort = DynCast<HloSortInstruction>(inst); | ||
| HloInstruction* data = sort->mutable_operand(0); | ||
| const PrimitiveType element_type = data->shape().element_type(); | ||
| - const Shape data_shape = data->shape(); | ||
|
|
||
| - if (element_type != F32 && element_type != BF16) { | ||
| + if ((data->shape().rank() != 1 && data->shape().rank() != 2) || | ||
| + (element_type != F32 && element_type != BF16)) { | ||
| continue; | ||
| } | ||
|
|
||
| - // Sort dimension must be the first or last dimension. | ||
| const int64_t sort_dim = sort->sort_dimension(); | ||
| - if (sort_dim != 0 && sort_dim != data_shape.rank() - 1) { | ||
| - continue; | ||
| - } | ||
| + const int64_t batch_dim = sort_dim == 1 ? 0 : 1; | ||
| + const bool has_batch = data->shape().rank() == 2; | ||
|
|
||
| // Profitability check. | ||
| if (!is_profitable_to_convert_(sort, *k)) { | ||
| continue; | ||
| } | ||
|
|
||
| - HloInstruction* input = data; | ||
| - const bool has_batch = data_shape.rank() >= 2; | ||
| - const int64_t input_size = data_shape.dimensions(sort_dim); | ||
| - int64_t batch_size = 1; | ||
| - Shape topk_input_shape; | ||
| - | ||
| - if (has_batch) { | ||
| - // The TopK custom call expects either a 1d tensor or a 2d tensor with | ||
| - // the last dimension being the sort dimension. An input with rank > 2 | ||
| - // is reshaped into a 2d tensor by combining non-sort dimensions into a | ||
| - // single batch dimension. The original non-sort dimensions are | ||
| - // restored for the outputs with another reshape after the custom call. | ||
| - batch_size = | ||
| - ShapeUtil::ElementsIn(data_shape) / data_shape.dimensions(sort_dim); | ||
| - topk_input_shape = | ||
| - ShapeUtil::MakeShape(element_type, {batch_size, input_size}); | ||
| - | ||
| - if (data_shape.rank() > 2) { | ||
| - // Reshape to 2d. | ||
| - input = comp->AddInstruction(HloInstruction::CreateReshape( | ||
| - sort_dim == 0 | ||
| - ? ShapeUtil::MakeShape(element_type, {input_size, batch_size}) | ||
| - : ShapeUtil::MakeShape(element_type, | ||
| - {batch_size, input_size}), | ||
| - input)); | ||
| - } | ||
| - | ||
| - if (sort_dim == 0) { | ||
| - // Transpose for the custom call when sorting the first dimension. | ||
| - input = comp->AddInstruction( | ||
| - HloInstruction::CreateTranspose(topk_input_shape, input, {1, 0})); | ||
| - } | ||
| - } else { | ||
| - topk_input_shape = data_shape; | ||
| + const int64_t batch_size = | ||
| + has_batch ? sort->operand(0)->shape().dimensions(batch_dim) : 1; | ||
| + const int64_t input_size = sort->operand(0)->shape().dimensions(sort_dim); | ||
| + HloInstruction* input = sort->mutable_operand(0); | ||
| + if (has_batch && sort_dim == 0) { | ||
| + input = comp->AddInstruction(HloInstruction::CreateTranspose( | ||
| + ShapeUtil::MakeShape(element_type, {batch_size, input_size}), input, | ||
| + {1, 0})); | ||
| } | ||
|
|
||
| Shape topk_shape = | ||
| @@ -331,26 +300,13 @@ StatusOr<bool> TopkRewriter::TransformToCustomCall( | ||
| comp->AddInstruction(HloInstruction::CreateGetTupleElement( | ||
| topk->shape().tuple_shapes(1), topk, 1)); | ||
|
|
||
| - if (has_batch) { | ||
| - if (sort_dim == 0) { | ||
| - // Transpose back. | ||
| - value_gte = comp->AddInstruction(HloInstruction::CreateTranspose( | ||
| - ShapeUtil::MakeShape(element_type, {k.value(), batch_size}), | ||
| - value_gte, {1, 0})); | ||
| - index_gte = comp->AddInstruction(HloInstruction::CreateTranspose( | ||
| - ShapeUtil::MakeShape(S32, {k.value(), batch_size}), index_gte, | ||
| - {1, 0})); | ||
| - } | ||
| - if (data_shape.rank() > 2) { | ||
| - // Reshape back. | ||
| - std::vector<int64_t> shape_dim(data_shape.dimensions().begin(), | ||
| - data_shape.dimensions().end()); | ||
| - shape_dim[sort_dim] = k.value(); | ||
| - value_gte = comp->AddInstruction(HloInstruction::CreateReshape( | ||
| - ShapeUtil::MakeShape(element_type, shape_dim), value_gte)); | ||
| - index_gte = comp->AddInstruction(HloInstruction::CreateReshape( | ||
| - ShapeUtil::MakeShape(S32, shape_dim), index_gte)); | ||
| - } | ||
| + if (has_batch && sort_dim == 0) { | ||
| + value_gte = comp->AddInstruction(HloInstruction::CreateTranspose( | ||
| + ShapeUtil::MakeShape(element_type, {k.value(), batch_size}), | ||
| + value_gte, {1, 0})); | ||
| + index_gte = comp->AddInstruction(HloInstruction::CreateTranspose( | ||
| + ShapeUtil::MakeShape(S32, {k.value(), batch_size}), index_gte, | ||
| + {1, 0})); | ||
| } | ||
|
|
||
| for (HloInstruction* user : sort->users()) { | ||
| diff --git a/xla/service/topk_rewriter_test.cc b/xla/service/topk_rewriter_test.cc | ||
| index 36e723737..25ce150e0 100644 | ||
| --- a/xla/service/topk_rewriter_test.cc | ||
| +++ b/xla/service/topk_rewriter_test.cc | ||
| @@ -326,42 +326,6 @@ ENTRY cluster { | ||
| EXPECT_THAT(cc->custom_call_target(), "TopK"); | ||
| } | ||
|
|
||
| -TEST_F(TopkRewriterTest, RewriteReshape) { | ||
| - const std::string hlo_string = R"( | ||
| -HloModule module | ||
| -)" + getComparator() + R"( | ||
| -ENTRY cluster { | ||
| - %arg_tuple.1 = f32[3,8,1234567] parameter(0) | ||
| - %iota.4 = s32[3,8,1234567] iota(), iota_dimension=2 | ||
| - %sort.27 = (f32[3,8,1234567], s32[3,8,1234567]) sort(%arg_tuple.1, %iota.4), | ||
| - dimensions={2}, is_stable=true, to_apply=%compare | ||
| - %get-tuple-element.28 = f32[3, 8,1234567] get-tuple-element(%sort.27), index=0 | ||
| - %slice.29 = f32[3,8,5] slice(%get-tuple-element.28), slice={[0:3], [0:8], [0:5]} | ||
| - %get-tuple-element.30 = s32[3,8,1234567] get-tuple-element(%sort.27), index=1 | ||
| - %slice.31 = s32[3,8,5] slice(%get-tuple-element.30), slice={[0:3], [0:8], [0:5]} | ||
| - ROOT %tuple.32 = (f32[3,8,5], s32[3,8,5]) tuple(%slice.29, %slice.31) | ||
| -})"; | ||
| - TF_ASSERT_OK_AND_ASSIGN(auto module, | ||
| - ParseAndReturnVerifiedModule(hlo_string)); | ||
| - TopkRewriter rewriter( | ||
| - [](const HloSortInstruction*, int64_t) { return true; }); | ||
| - TF_ASSERT_OK_AND_ASSIGN(bool changed, rewriter.Run(module.get())); | ||
| - TF_ASSERT_OK(HloDCE().Run(module.get()).status()); | ||
| - EXPECT_TRUE(changed); | ||
| - EXPECT_THAT(module->entry_computation()->root_instruction(), | ||
| - GmockMatch(m::Tuple( | ||
| - m::Reshape(m::GetTupleElement( | ||
| - m::CustomCall(m::Reshape(m::Parameter(0))), 0)), | ||
| - m::Reshape(m::GetTupleElement( | ||
| - m::CustomCall(m::Reshape(m::Parameter(0))), 1))))); | ||
| - const HloInstruction* cc = module->entry_computation() | ||
| - ->root_instruction() | ||
| - ->operand(0) | ||
| - ->operand(0) | ||
| - ->operand(0); | ||
| - EXPECT_THAT(cc->custom_call_target(), "TopK"); | ||
| -} | ||
| - | ||
| TEST_F(TopkRewriterTest, RewriteNoIota) { | ||
| const std::string hlo_string = R"( | ||
| HloModule module |
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