[XLA] Several improvements to dynamic padder.

- Support partial slice on dynamic dimensions -- this is achieved by letting the client to set the dynamic dimension after building a xla slice.
- Support dynamic pad on padded dimension.
- Fix a terrible bug exposed by rxsang's experiment where transpose creates wrong dynamic dimension.

PiperOrigin-RevId: 303033314
Change-Id: Id76f4619d12e88b8c0b3e9ec75baa1d78d1a7270

tensorflow/compiler/tf2xla/kernels/slice_op.cc

@@ -21,6 +21,7 @@ limitations under the License.
 #include "tensorflow/compiler/tf2xla/xla_op_kernel.h"
 #include "tensorflow/compiler/tf2xla/xla_op_registry.h"
 #include "tensorflow/compiler/xla/client/xla_builder.h"
+#include "tensorflow/compiler/xla/util.h"
 #include "tensorflow/core/framework/op_kernel.h"
 #include "tensorflow/core/framework/ops_util.h"
 #include "tensorflow/core/framework/register_types.h"
@@ -58,18 +59,21 @@ class SliceOp : public XlaOpKernel {
     std::vector<int64> begin;
     std::vector<int64> size;
     OP_REQUIRES_OK(ctx, ctx->ConstantInputAsIntVector(2, &size));
+    std::vector<int64> wrapped_size(size.size());
     if (ctx->ConstantInputAsIntVector(1, &begin).ok()) {
       // `begin` is a compile-time constant.
       for (int i = 0; i < input_dims; ++i) {
         if (size[i] == -1) {
           // A size[i] of -1 means "all elements from begin[i] to dim_size(i)".
-          size[i] = input_shape.dim_size(i) - begin[i];
+          wrapped_size[i] = input_shape.dim_size(i) - begin[i];
+        } else {
+          wrapped_size[i] = size[i];
         }
       }
 
       for (int i = 0; i < input_dims; ++i) {
         int64 b = begin[i];
-        int64 s = size[i];
+        int64 s = wrapped_size[i];
         if (input_shape.dim_size(i) == 0) {
           OP_REQUIRES(ctx, b == 0 && s == 0,
                       errors::InvalidArgument(
@@ -91,10 +95,28 @@ class SliceOp : public XlaOpKernel {
       std::vector<int64> limits;
       limits.reserve(begin.size());
       for (int i = 0; i < begin.size(); ++i) {
-        limits.push_back(begin[i] + size[i]);
+        limits.push_back(begin[i] + wrapped_size[i]);
       }
       std::vector<int64> strides(begin.size(), 1);
-      ctx->SetOutput(0, xla::Slice(ctx->Input(0), begin, limits, strides));
+      auto slice = xla::Slice(ctx->Input(0), begin, limits, strides);
+      // Check for slice on dynamic dimensions.
+      ctx->set_dynamic_dimension_is_minus_one(true);
+      std::vector<int64> dynamic_size;
+      OP_REQUIRES_OK(ctx, ctx->ConstantInputAsIntVector(2, &dynamic_size));
+
+      for (int64 i = 0; i < size.size(); ++i) {
+        if (dynamic_size[i] == -1) {
+          if (size[i] != -1) {
+            // If there is a dynamic dimension, properly set dimension size of
+            // the slice.
+            auto dynamic_size =
+                xla::Reshape(xla::Slice(ctx->Input(2), {i}, {i + 1}, {1}), {});
+
+            slice = xla::SetDimensionSize(slice, dynamic_size, i);
+          }
+        }
+      }
+      ctx->SetOutput(0, slice);
     } else {
       // `begin` is not a compile-time constant.
       for (int i = 0; i < input_dims; ++i) {

tensorflow/compiler/tf2xla/kernels/strided_slice_op.cc

@@ -28,6 +28,7 @@ limitations under the License.
 #include "tensorflow/core/framework/register_types.h"
 #include "tensorflow/core/framework/tensor.h"
 #include "tensorflow/core/lib/core/status.h"
+#include "tensorflow/core/platform/errors.h"
 #include "tensorflow/core/platform/mem.h"
 
 namespace tensorflow {
@@ -115,6 +116,72 @@ class StridedSliceOp : public XlaOpKernel {
         slice = xla::Rev(slice, dimensions_to_reverse);
       }
       slice = xla::Slice(slice, slice_begin, slice_end, slice_strides);
+      auto operand_shape_or = ctx->builder()->GetShape(ctx->Input(0));
+      OP_REQUIRES_OK(ctx, operand_shape_or.status());
+      xla::Shape xla_shape = operand_shape_or.ValueOrDie();
+      if (xla_shape.is_static()) {
+        // Static output shape, return a static slice.
+        slice = xla::Reshape(slice, final_shape.dim_sizes());
+        ctx->SetOutput(0, slice);
+        return;
+      }
+      auto input_dim_sizes = input_shape.dim_sizes();
+
+      for (int64 i = 0; i < xla_shape.rank(); ++i) {
+        if (xla_shape.is_dynamic_dimension(i)) {
+          input_dim_sizes[i] = -1;
+        }
+      }
+      PartialTensorShape input_partial_shape(input_dim_sizes);
+      partial_final_shape.Clear();
+      end.clear();
+      strides.clear();
+      begin.clear();
+      // Run shape inferenference again with partial shape.
+      OP_REQUIRES_OK(ctx, ValidateStridedSliceOp(
+                              &begin_tensor, &end_tensor, strides_tensor,
+                              input_partial_shape, begin_mask_, end_mask_,
+                              ellipsis_mask_, new_axis_mask_, shrink_axis_mask_,
+                              &dummy_processing_shape, &partial_final_shape,
+                              &dummy, &dummy, &dummy, &begin, &end, &strides));
+      if (partial_final_shape.AsTensorShape(&final_shape)) {
+        // Static output shape, return a static slice.
+        slice = xla::Reshape(slice, final_shape.dim_sizes());
+        ctx->SetOutput(0, slice);
+        return;
+      }
+
+      // We consider slicing a dynamic tensor t with negative indices as a
+      // dynamic sized slice. E.g., t[: -n], the result length is shape(t) - n
+      for (int64 i = 0; i < partial_final_shape.dims(); ++i) {
+        bool dynamic_dim = partial_final_shape.dim_size(i) - 1;
+        bool backward_slice = end[i] < 0;
+        if (dynamic_dim && backward_slice) {
+          OP_REQUIRES(
+              ctx, strides[i] == 1,
+              errors::InvalidArgument("XLA has not implemented dynamic "
+                                      "sized slice with non-trival stride yet. "
+                                      "Please file a bug against XLA"));
+
+          OP_REQUIRES(ctx, begin[i] >= 0,
+                      errors::InvalidArgument(
+                          "XLA has not implemented dynamic "
+                          "sized slice with negative begin index %lld. "
+                          "Please file a bug against XLA",
+                          begin[i]));
+          // If there is a dynamic dimension, properly set dimension size of
+          // the result.
+          auto operand_size = xla::GetDimensionSize(ctx->Input(0), i);
+
+          operand_size = xla::Add(
+              operand_size, xla::ConstantR0<int32>(ctx->builder(), end[i]));
+          slice = xla::SetDimensionSize(
+              slice,
+              xla::Sub(operand_size,
+                       xla::ConstantR0<int32>(ctx->builder(), begin[i])),
+              i);
+        }
+      }
     } else {
       // When output shape is fully defined, it must be a size one slice:
       //

tensorflow/compiler/xla/service/BUILD

@@ -2404,6 +2404,7 @@ cc_library(
         "//tensorflow/compiler/xla:status",
         "//tensorflow/compiler/xla:statusor",
         "//tensorflow/compiler/xla:types",
+        "//tensorflow/compiler/xla:util",
         "//tensorflow/compiler/xla:window_util",
         "//tensorflow/core/platform:macros",
         "@com_google_absl//absl/container:flat_hash_map",

tensorflow/compiler/xla/service/dynamic_dimension_inference.cc

@@ -24,6 +24,7 @@ limitations under the License.
 #include "tensorflow/compiler/xla/service/hlo_instructions.h"
 #include "tensorflow/compiler/xla/service/hlo_module.h"
 #include "tensorflow/compiler/xla/service/while_util.h"
+#include "tensorflow/compiler/xla/util.h"
 #include "tensorflow/compiler/xla/window_util.h"
 
 namespace xla {
@@ -250,15 +251,25 @@ Status DynamicDimensionInferenceVisitor::HandlePad(HloInstruction* hlo) {
         }
         const PaddingConfig_PaddingConfigDimension& padding_config =
             hlo->padding_config().dimensions(dimension);
-        if (padding_config.interior_padding() == 0 &&
-            padding_config.edge_padding_low() == 0 &&
-            padding_config.edge_padding_high() == 0) {
-          parent_->SetDynamicSize(hlo, {}, dimension, dynamic_size, constraint);
+        if (padding_config.interior_padding() == 0) {
+          HloInstruction* dynamic_size_adjusted = dynamic_size;
+          HloInstruction* adjustment = hlo->parent()->AddInstruction(
+              HloInstruction::CreateConstant(LiteralUtil::CreateR0<int32>(
+                  padding_config.edge_padding_low() +
+                  padding_config.edge_padding_high())));
+          dynamic_size_adjusted =
+              hlo->parent()->AddInstruction(HloInstruction::CreateBinary(
+                  dynamic_size_adjusted->shape(), HloOpcode::kAdd,
+                  dynamic_size_adjusted, adjustment));
+          parent_->SetDynamicSize(hlo, {}, dimension, dynamic_size_adjusted,
+                                  constraint);
           return Status::OK();
         } else {
           return Unimplemented(
-              "Dynamic dimension propagation on padding dimension is not "
-              "supported.");
+              "Dynamic dimension propagation on interio padding dimension is "
+              "not "
+              "supported: %s",
+              hlo->ToString());
         }
       });
 }
@@ -400,11 +411,19 @@ Status DynamicDimensionInferenceVisitor::HandleDot(HloInstruction* hlo) {
 
 Status DynamicDimensionInferenceVisitor::HandleTranspose(HloInstruction* hlo) {
   return ForEachOperandDynamicDimension(
-      hlo, [&](HloInstruction* operand, ShapeIndex index, int64 dimension,
-               int64 operand_index, HloInstruction* dynamic_size,
-               DimensionConstraint constraint) {
-        parent_->SetDynamicSize(hlo, {}, hlo->dimensions()[dimension],
-                                dynamic_size, constraint);
+      hlo,
+      [&](HloInstruction* operand, ShapeIndex index, int64 dimension,
+          int64 operand_index, HloInstruction* dynamic_size,
+          DimensionConstraint constraint) -> Status {
+        int64 permuted_dim = -1;
+        for (int64 i = 0; i < hlo->dimensions().size(); ++i) {
+          if (hlo->dimensions()[i] == dimension) {
+            TF_RET_CHECK(permuted_dim == -1);
+            permuted_dim = i;
+          }
+        }
+        parent_->SetDynamicSize(hlo, {}, permuted_dim, dynamic_size,
+                                constraint);
         return Status::OK();
       });
 }
@@ -979,14 +998,8 @@ Status DynamicDimensionInferenceVisitor::HandleSlice(HloInstruction* hlo) {
             hlo->slice_strides(dimension) != 1 ||
             hlo->slice_limits(dimension) !=
                 operand->shape().dimensions(dimension)) {
-          // Slicing a single element out eliminates the dynamic dimension.
-          if (hlo->shape().dimensions(dimension) == 1) {
-            return Status::OK();
-          }
-          return Unimplemented(
-              "Dynamic dimension propagation on Slice where it doesn't slice "
-              "out an entire dimension is not supported %s",
-              hlo->ToString());
+          // Slicing a partial element out eliminates the dynamic dimension.
+          return Status::OK();
         }
 
         parent_->SetDynamicSize(hlo, {}, dimension, dynamic_size, constraint);

tensorflow/compiler/xla/service/dynamic_dimension_inference_test.cc

@@ -386,6 +386,53 @@ TEST_F(DynamicDimensionInferenceTest, DotTestBatch) {
   EXPECT_EQ(inference_->GetDynamicSize(dot, {}, 3), nullptr);
 }
 
+TEST_F(DynamicDimensionInferenceTest, DotTestMultiContracting) {
+  auto builder = HloComputation::Builder(TestName());
+  auto lhs_shape = ShapeUtil::MakeShape(F32, {2, 2, 8, 64});
+  auto rhs_shape = ShapeUtil::MakeShape(F32, {2, 2, 512});
+  auto output_shape = ShapeUtil::MakeShape(F32, {8, 64, 512});
+
+  auto* a_param = builder.AddInstruction(HloInstruction::CreateParameter(
+      /*parameter_number=*/0, lhs_shape, "A"));
+  auto* b_param = builder.AddInstruction(HloInstruction::CreateParameter(
+      /*parameter_number=*/1, rhs_shape, "B"));
+  builder.AddInstruction(HloInstruction::CreateParameter(
+      /*parameter_number=*/2, scalar_shape_, "size_param"));
+
+  DotDimensionNumbers dot_dnums;
+  dot_dnums.add_lhs_contracting_dimensions(0);
+  dot_dnums.add_lhs_contracting_dimensions(1);
+  dot_dnums.add_rhs_contracting_dimensions(0);
+  dot_dnums.add_rhs_contracting_dimensions(1);
+  auto dot = builder.AddInstruction(
+      HloInstruction::CreateDot(output_shape, a_param, b_param, dot_dnums,
+                                HloTestBase::DefaultPrecisionConfig(2)));
+
+  module_->AddEntryComputation(builder.Build());
+
+  TF_CHECK_OK(module_->dynamic_parameter_binding().Bind(
+      DynamicParameterBinding::DynamicParameter{2, {}},
+      DynamicParameterBinding::DynamicDimension{0, {}, 0}));
+
+  TF_CHECK_OK(module_->dynamic_parameter_binding().Bind(
+      DynamicParameterBinding::DynamicParameter{2, {}},
+      DynamicParameterBinding::DynamicDimension{0, {}, 1}));
+  TF_CHECK_OK(module_->dynamic_parameter_binding().Bind(
+      DynamicParameterBinding::DynamicParameter{2, {}},
+      DynamicParameterBinding::DynamicDimension{1, {}, 0}));
+
+  TF_CHECK_OK(module_->dynamic_parameter_binding().Bind(
+      DynamicParameterBinding::DynamicParameter{2, {}},
+      DynamicParameterBinding::DynamicDimension{1, {}, 1}));
+
+  SCOPED_TRACE(module_->ToString());
+  TF_ASSERT_OK(RunInference());
+  // Nothing is dynamic in the output.
+  EXPECT_EQ(inference_->GetDynamicSize(dot, {}, 0), nullptr);
+  EXPECT_EQ(inference_->GetDynamicSize(dot, {}, 1), nullptr);
+  EXPECT_EQ(inference_->GetDynamicSize(dot, {}, 2), nullptr);
+}
+
 TEST_F(DynamicDimensionInferenceTest, ConvolutionTest) {
   auto builder = HloComputation::Builder(TestName());
   constexpr int xdim = 3;
@@ -474,6 +521,45 @@ TEST_F(DynamicDimensionInferenceTest, TransposeTest) {
   EXPECT_EQ(inference_->GetDynamicSize(transpose, {}, 2), size_param_1);
 }
 
+TEST_F(DynamicDimensionInferenceTest, NonDescendingTransposeTest) {
+  // Test the ability to trace unmodified dimensions
+  auto builder = HloComputation::Builder(TestName());
+  auto input_shape = ShapeUtil::MakeShape(F32, {1, 2, 3});
+  auto output_shape = ShapeUtil::MakeShape(F32, {3, 1, 2});
+
+  auto* a_param = builder.AddInstruction(HloInstruction::CreateParameter(
+      /*parameter_number=*/0, input_shape, "A"));
+  auto* size_param_1 = builder.AddInstruction(HloInstruction::CreateParameter(
+      /*parameter_number=*/1, scalar_shape_, "size_param"));
+  auto* size_param_2 = builder.AddInstruction(HloInstruction::CreateParameter(
+      /*parameter_number=*/2, scalar_shape_, "size_param"));
+  auto* size_param_3 = builder.AddInstruction(HloInstruction::CreateParameter(
+      /*parameter_number=*/3, scalar_shape_, "size_param"));
+
+  auto* transpose = builder.AddInstruction(
+      HloInstruction::CreateTranspose(output_shape, a_param, {2, 0, 1}));
+
+  module_->AddEntryComputation(builder.Build());
+
+  TF_CHECK_OK(module_->dynamic_parameter_binding().Bind(
+      DynamicParameterBinding::DynamicParameter{1, {}},
+      DynamicParameterBinding::DynamicDimension{0, {}, 0}));
+
+  TF_CHECK_OK(module_->dynamic_parameter_binding().Bind(
+      DynamicParameterBinding::DynamicParameter{2, {}},
+      DynamicParameterBinding::DynamicDimension{0, {}, 1}));
+
+  TF_CHECK_OK(module_->dynamic_parameter_binding().Bind(
+      DynamicParameterBinding::DynamicParameter{3, {}},
+      DynamicParameterBinding::DynamicDimension{0, {}, 2}));
+
+  SCOPED_TRACE(module_->ToString());
+  TF_ASSERT_OK(RunInference());
+  EXPECT_EQ(inference_->GetDynamicSize(transpose, {}, 0), size_param_3);
+  EXPECT_EQ(inference_->GetDynamicSize(transpose, {}, 1), size_param_1);
+  EXPECT_EQ(inference_->GetDynamicSize(transpose, {}, 2), size_param_2);
+}
+
 TEST_F(DynamicDimensionInferenceTest, ReshapeTest) {
   // Test the ability to trace unmodified reshape dimensions.
   auto builder = HloComputation::Builder(TestName());

tensorflow/compiler/xla/service/dynamic_padder_test.cc

@@ -865,6 +865,45 @@ ENTRY main {
   EXPECT_EQ(result, expected);
 }
 
+XLA_TEST_F(ExecutionTest, DynamicPad) {
+  const string hlo_text = R"(
+HloModule TEST
+
+update_s32 (lhs: s32[], rhs: s32[]) -> s32[] {
+  lhs = s32[] parameter(0)
+  rhs = s32[] parameter(1)
+  ROOT add = s32[] add(lhs, rhs)
+}
+
+ENTRY main {
+  param = s32[4] parameter(0)
+  size = s32[] constant(3)
+  padding = s32[] constant(2)
+  param_dynamic = s32[4] set-dimension-size(param, size),
+    dimensions={0}
+  // pad head and tail to 2
+  pad = s32[6] pad(param_dynamic, padding), padding=1_1
+
+  init = s32[] constant(0)
+  ROOT reduce = s32[] reduce(pad, init),
+    dimensions={0},
+    to_apply=update_s32
+}
+)";
+
+  Literal operand = LiteralUtil::CreateR1<int32>({1, 4, 3, 5});
+  auto module = GetHloModule(hlo_text);
+
+  // After padding head and tail with "2", the effective data will be [2, 1, 4,
+  // 3, 2]
+
+  Literal result = PadAndExecute(std::move(module), {&operand},
+                                 /*slice_dynamic_output=*/false);
+  Literal expected = LiteralUtil::CreateR0<int32>(12);
+
+  EXPECT_EQ(result, expected);
+}
+
 XLA_TEST_F(ExecutionTest, DynamicTupleSort) {
   const string hlo_text = R"(
 HloModule TEST