Reland fix to multi-row reduction triggering.

Apparently there was no actual breakage, just a numerically
unstable model.

Reverts 87e62ee

PiperOrigin-RevId: 679548584

xla/service/gpu/fusions/reduction_mlir.cc

@@ -770,32 +770,11 @@ llvm::SmallVector<mlir::Value> MlirSmallColumnReductionFusion::EmitReduction(
                                      shared_rows_ / 2);
 }
 
-std::unique_ptr<MlirReductionFusion> CreateMlirReductionFusion(
-    const HloFusionAnalysis& analysis) {
-  auto* hero_reduction = analysis.FindHeroReduction();
-  CHECK_NE(hero_reduction, nullptr);
-  ReductionDimensions reduction_dimensions =
-      GetReductionKindAndContiguousComponents(*hero_reduction);
-  if (reduction_dimensions.is_row_reduction) {
-    if (RowReductionGetRowsPerWarp(
-            reduction_dimensions.dimensions[kRowMinorReduced]) > 1) {
-      return std::make_unique<MlirMultiRowReductionFusion>(analysis);
-    }
-    return std::make_unique<MlirRowReductionFusion>(analysis);
-  }
-
-  if (WarpSize() % reduction_dimensions.dimensions[kColMinorKept] == 0) {
-    return std::make_unique<MlirSmallColumnReductionFusion>(analysis);
-  }
-  return std::make_unique<MlirColumnReductionFusion>(analysis);
-}
-
 MlirRowReductionFusion::MlirRowReductionFusion(
     const HloFusionAnalysis& analysis)
     : MlirReductionFusion(analysis) {
   CHECK(reduction_dimensions_.is_row_reduction);
   Vector3 shape = reduction_dimensions_.dimensions;
-  CHECK_EQ(RowReductionGetRowsPerWarp(shape[kRowMinorReduced]), 1);
   constexpr int64_t kMinorReducedElementsPerThread = 16;
 
   int64_t num_threads_kept = 1;
@@ -931,58 +910,104 @@ llvm::SmallVector<mlir::Value> MlirRowReductionFusion::EmitReduction(
 }
 
 MlirMultiRowReductionFusion::MlirMultiRowReductionFusion(
-    const HloFusionAnalysis& analysis)
+    const HloFusionAnalysis& analysis, int vector_size)
     : MlirReductionFusion(analysis) {
   CHECK(reduction_dimensions_.is_row_reduction);
   Vector3 shape = reduction_dimensions_.dimensions;
-  int64_t rows_per_warp = RowReductionGetRowsPerWarp(shape[kRowMinorReduced]);
   input_shape_ = {shape[0], shape[1], shape[2]};
-  CHECK_GT(rows_per_warp, 1);
-
-  auto compute_block_size = [&](int vector_size) {
-    int64_t num_threads_reduced = shape[kRowMinorReduced] / vector_size;
-
-    constexpr int64_t kThreadsPerBlockTarget = 256;
-    int64_t kept_size = reduction_dimensions_.dimensions[kRowKept];
-    int64_t num_threads_kept = 1;
-    if (kept_size * num_threads_reduced <= kThreadsPerBlockTarget) {
-      num_threads_kept = kept_size;
-    } else {
-      num_threads_kept = kThreadsPerBlockTarget / num_threads_reduced;
-    }
-    num_threads_ = {num_threads_kept, num_threads_reduced};
-    tile_sizes_per_thread_ = {shape[0], vector_size};
-    num_blocks_ = {CeilOfRatio(input_shape_[kRowKept], num_threads_kept)};
-  };
+  num_threads_ = GetNumThreads(reduction_dimensions_, vector_size);
+  num_blocks_ = {GetNumBlocks(reduction_dimensions_, num_threads_)};
+  tile_sizes_per_thread_ = {shape[0], vector_size};
+}
 
-  // Compute the launch grid without vectorization. We use the results to
-  // compute the vectorized launch grid.
-  compute_block_size(1);
+std::unique_ptr<MlirReductionFusion> MlirMultiRowReductionFusion::TryCreate(
+    const HloFusionAnalysis& analysis) {
+  auto* hero_reduction = analysis.FindHeroReduction();
+  CHECK_NE(hero_reduction, nullptr);
+  auto reduction_dimensions =
+      GetReductionKindAndContiguousComponents(*hero_reduction);
+  auto shape = reduction_dimensions.dimensions;
+  // This emitter only supports reductions where the reduced dimension is a
+  // power of 2.
+  if (shape[kRowMinorReduced] & (shape[kRowMinorReduced] - 1)) {
+    return nullptr;
+  }
 
   // Normally, we only consider input types for vectorization. However, in
   // multi-row reductions, the input:output ratio is much higher, so we consider
   // both inputs and outputs.
   int smallest_input_or_output_bits =
       std::min(analysis.input_output_info().smallest_input_dtype_bits,
                analysis.input_output_info().smallest_output_dtype_bits);
+  int largest_input_or_output_bits =
+      std::max(analysis.input_output_info().smallest_input_dtype_bits,
+               analysis.input_output_info().smallest_output_dtype_bits);
 
-  // This vector size is always valid: we know that the reduced dimension is a
-  // power of 2, since otherwise RowReductionGetRowsPerWarp would have
-  // returned 1.
   // Our codegen can't currently deal with vectorization across rows, so we
   // limit the vector size to the size of the row. Note that this emitter
   // essentially reverts to the loop emitter in this case, except for side
   // outputs.
-  int vector_size = std::min(static_cast<int>(input_shape_[kRowMinorReduced]),
-                             32 / smallest_input_or_output_bits);
-
-  // We target 8 warps per block, which means there could be up to 8 blocks per
-  // SM, but we have no good way of knowing. In practice, enabling vectorization
-  // for decently sized reductions at least does not hurt.
-  if (num_blocks_.front() > analysis.device_info().core_count() &&
-      vector_size > 1) {
-    compute_block_size(vector_size);
+  int vector_size = std::min(static_cast<int>(shape[kRowMinorReduced]),
+                             64 / smallest_input_or_output_bits);
+
+  // Very large vector sizes for f32 can be detrimental, so we limit the vector
+  // size to 16 bytes if we have some >= 32 bit inputs or outputs. This is still
+  // a bit on the high side, but remember that we also have very small inputs
+  // or outputs.
+  if (largest_input_or_output_bits >= 32) {
+    vector_size = std::min(128 / largest_input_or_output_bits, vector_size);
+  }
+
+  // The reduced dimension must fit into a single warp.
+  if (shape[kRowMinorReduced] > WarpSize() * vector_size) {
+    return nullptr;
+  }
+
+  // At the very least, we want to have work for every SM.
+  // TODO(jreiffers): This limit is probably too low: if we have as many blocks
+  // as SMs, we'll only run about 8 warps per SM, so occupancy will be very low.
+  // Further measurements are needed to refine this heuristic.
+  int64_t min_desired_blocks = analysis.device_info().core_count();
+  while (vector_size > 1 &&
+         GetNumBlocks(reduction_dimensions,
+                      GetNumThreads(reduction_dimensions, vector_size)) <
+             min_desired_blocks) {
+    vector_size /= 2;
   }
+
+  // Check again that the reduced dimension fits after potentially reducing the
+  // vector size.
+  if (shape[kRowMinorReduced] > WarpSize() * vector_size) {
+    return nullptr;
+  }
+
+  return std::make_unique<MlirMultiRowReductionFusion>(analysis, vector_size);
+}
+
+absl::InlinedVector<int64_t, 4> MlirMultiRowReductionFusion::GetNumThreads(
+    const ReductionDimensions& reduction_dimensions, int vector_size) {
+  int64_t num_threads_reduced =
+      reduction_dimensions.dimensions[kRowMinorReduced] / vector_size;
+
+  constexpr int64_t kThreadsPerBlockTarget = 256;
+  int64_t kept_size = reduction_dimensions.dimensions[kRowKept];
+  int64_t num_threads_kept = 1;
+  if (kept_size * num_threads_reduced <= kThreadsPerBlockTarget) {
+    num_threads_kept = kept_size;
+  } else {
+    num_threads_kept = kThreadsPerBlockTarget / num_threads_reduced;
+  }
+  return {num_threads_kept, num_threads_reduced};
+}
+
+int64_t MlirMultiRowReductionFusion::GetNumBlocks(
+    const ReductionDimensions& reduction_dimensions,
+    const absl::InlinedVector<int64_t, 4>& num_threads) {
+  CHECK_EQ(num_threads.size(), 2)
+      << "Expected num_threads to contain the number of threads in the {kept, "
+         "reduced} dimensions.";
+  return CeilOfRatio(reduction_dimensions.dimensions[kRowKept],
+                     num_threads.front());
 }
 
 IndexingMap MlirMultiRowReductionFusion::ComputeReductionInputIndexing(
@@ -1013,8 +1038,7 @@ IndexingMap MlirMultiRowReductionFusion::ComputeReductionOutputIndexing(
                                            : mlir::getAffineDimExpr(3, ctx);
   IndexingMap projected_index =
       GetIndexingMap(block_id * num_threads_[0] + thread_id[0]);
-  projected_index.AddConstraint(thread_id[1] % (WarpSize() / GetRowsPerWarp()),
-                                {0, 0});
+  projected_index.AddConstraint(thread_id[1] % num_threads_[1], {0, 0});
   // We don't need a constraint on the loop dimensions, because they are removed
   // by GetIndexingMap (since they don't show up in the output index
   // computation).
@@ -1034,10 +1058,30 @@ llvm::SmallVector<mlir::Value> MlirMultiRowReductionFusion::EmitReduction(
   auto per_thread =
       state.EmitPerThreadElements(group_id, inits, state.FusionOutputs());
   auto reduced = state.ShuffleReduce(reductions, per_thread.reduction_scalars,
-                                     WarpSize() / 2 / GetRowsPerWarp());
+                                     num_threads_[1] / 2);
   return EvaluateEpilogue(reduced, std::move(per_thread.outputs), state,
                           group_id, /*symbol_values=*/{});
 }
 
+std::unique_ptr<MlirReductionFusion> CreateMlirReductionFusion(
+    const HloFusionAnalysis& analysis) {
+  auto* hero_reduction = analysis.FindHeroReduction();
+  CHECK_NE(hero_reduction, nullptr);
+  ReductionDimensions reduction_dimensions =
+      GetReductionKindAndContiguousComponents(*hero_reduction);
+  if (reduction_dimensions.is_row_reduction) {
+    auto multi_row_emitter = MlirMultiRowReductionFusion::TryCreate(analysis);
+    if (multi_row_emitter != nullptr) {
+      return multi_row_emitter;
+    }
+    return std::make_unique<MlirRowReductionFusion>(analysis);
+  }
+
+  if (WarpSize() % reduction_dimensions.dimensions[kColMinorKept] == 0) {
+    return std::make_unique<MlirSmallColumnReductionFusion>(analysis);
+  }
+  return std::make_unique<MlirColumnReductionFusion>(analysis);
+}
+
 }  // namespace gpu
 }  // namespace xla

xla/service/gpu/fusions/reduction_mlir.h

@@ -16,6 +16,7 @@ limitations under the License.
 #define XLA_SERVICE_GPU_FUSIONS_REDUCTION_MLIR_H_
 
 #include <cstdint>
+#include <memory>
 #include <optional>
 #include <utility>
 #include <vector>
@@ -168,9 +169,23 @@ class MlirRowReductionFusion : public MlirReductionFusion {
 
 class MlirMultiRowReductionFusion : public MlirReductionFusion {
  public:
-  explicit MlirMultiRowReductionFusion(const HloFusionAnalysis& analysis);
+  MlirMultiRowReductionFusion(const HloFusionAnalysis& analysis,
+                              int vector_size);
+
+  // Attempts to create a multi-row reduction emitter for the given analysis.
+  // Returns nullptr if the fusion is not supported.
+  static std::unique_ptr<MlirReductionFusion> TryCreate(
+      const HloFusionAnalysis& analysis);
 
  protected:
+  // Returns the number of {kept, reduced} threads for the given reduction and
+  // vector size.
+  static absl::InlinedVector<int64_t, 4> GetNumThreads(
+      const ReductionDimensions& reduction_dimensions, int vector_size);
+  static int64_t GetNumBlocks(
+      const ReductionDimensions& reduction_dimensions,
+      const absl::InlinedVector<int64_t, 4>& num_threads);
+
   int GetRowsPerWarp() const;
   llvm::SmallVector<mlir::Value> EmitReduction(
       int group_id, EmitterState& state) const override;

xla/service/gpu/fusions/tests/reduce_multirow/f16_v4.hlo

@@ -0,0 +1,22 @@
+// RUN: fusion_to_mlir %s | mlir_fusions_opt -xla-gpu-test-optimize \
+// RUN:   -xla-gpu-test-transform-loops | FileCheck %s
+
+// The reference implementation reduces in f64, so we need a larger tolerance.
+// RUN: test_correctness %s --bijection_inputs=reduce:0 \
+// RUN:  --bijection_outputs=reduce --abs_error_bound=0.005 --rel_error_bound=0.005
+
+add {
+  lhs = f16[] parameter(0)
+  rhs = f16[] parameter(1)
+  ROOT add = f16[] add(lhs, rhs)
+}
+
+fusion {
+  param_0 = f16[2048,64] parameter(0)
+  c = f16[] constant(0)
+  ROOT reduce = f16[2048] reduce(param_0, c), dimensions={1}, to_apply=add
+}
+
+// If unvectorized, this would be a regular row reduction. However, since we can
+// vectorize to size four, we can emit this as a multi-row reduction.
+// CHECK: vector.transfer_read {{.*}} vector<4xf16>