Add CudnnPadForConvolutions and CudnnVecotrizeConvolutions HLO pass

xla/service/gpu/BUILD

@@ -3907,6 +3907,9 @@ cc_library(
         ":conv_algorithm_picker",
         ":cublas_pad_for_gemms",
         ":cublas_padding_requirements",
+        ":cudnn_pad_for_convolutions",
+        ":cudnn_simplify_padding",
+        ":cudnn_vectorize_convolutions",
         ":cusolver_rewriter",
         ":gemm_algorithm_picker",
         ":gemm_rewriter",

xla/service/gpu/amdgpu_compiler.cc

@@ -28,6 +28,9 @@ limitations under the License.
 #include "xla/service/gpu/conv_algorithm_picker.h"
 #include "xla/service/gpu/cublas_pad_for_gemms.h"
 #include "xla/service/gpu/cublas_padding_requirements.h"
+#include "xla/service/gpu/cudnn_pad_for_convolutions.h"
+#include "xla/service/gpu/cudnn_simplify_padding.h"
+#include "xla/service/gpu/cudnn_vectorize_convolutions.h"
 #include "xla/service/gpu/cusolver_rewriter.h"
 #include "xla/service/gpu/gemm_algorithm_picker.h"
 #include "xla/service/gpu/gemm_rewriter.h"
@@ -88,6 +91,8 @@ absl::Status AMDGPUCompiler::OptimizeHloConvolutionCanonicalization(
     HloModule* hlo_module, se::GpuComputeCapability gpu_version,
     se::dnn::VersionInfo dnn_version,
     se::DeviceMemoryAllocator* device_allocator) {
+  auto rocm_compute_capability =
+    std::get<se::RocmComputeCapability>(gpu_version);
   // Convert convolutions into CustomCalls to MIOpen, then canonicalize them
   // (PadInsertion).
   HloPassPipeline pipeline("conv_canonicalization");
@@ -96,13 +101,14 @@ absl::Status AMDGPUCompiler::OptimizeHloConvolutionCanonicalization(
       /*allow_mixed_precision=*/false);
 
   // Convert upsupported bf16 convolutions to f32.
-  ConvBfloat16Support conv_bf16_support(
-    std::get<se::RocmComputeCapability>(gpu_version));
+  ConvBfloat16Support conv_bf16_support(rocm_compute_capability);
   pipeline.AddPass<FloatNormalization>(&conv_bf16_support);
 
   pipeline.AddPass<GpusolverRewriter>();
   pipeline.AddPass<GpuConvRewriter>();
   pipeline.AddPass<GpuConvPaddingLegalization>();
+  pipeline.AddPass<CudnnPadForConvolutions>(rocm_compute_capability);
+  pipeline.AddPass<CudnnVectorizeConvolutions>(rocm_compute_capability);
 
   // The conv padding/vectorization passes which we need to get rid of.  They
   // also leave behind unnecessary tuple/get-tuple-element pairs that
@@ -119,6 +125,13 @@ absl::Status AMDGPUCompiler::OptimizeHloConvolutionCanonicalization(
   options.set_enable_unconditional_reduce_of_concat_replacement(false);
   pipeline.AddPass<HloPassFix<AlgebraicSimplifier>>(options);
 
+  // CudnnSimplifyPadding gets rid of some padding introduced by
+  // CudnnPadForConvolutions and used by CudnnVectorizeConvolutions.  The
+  // pattern-matches in this pass need to be run after inlining and simplifying
+  // tuples from CudnnVectorizeConvolutions.  We also need to run algsimp to
+  // e.g. clean up unnecessary nop `convert`s.
+  pipeline.AddPass<CudnnSimplifyPadding>();
+
   pipeline.AddPass<HloConstantFolding>();
   TF_RETURN_IF_ERROR(pipeline.Run(hlo_module).status());
 

xla/service/gpu/cudnn_pad_for_convolutions.cc

@@ -315,7 +315,7 @@ static absl::StatusOr<bool> TryResolvePaddedShapesForTensorCore(
 // Adds padding to cudnn integer convolutions to make input and output feature
 // maps multiples of pad_to (usually 4 or 32).
 absl::StatusOr<bool> TryResolvePaddedShapesForIntegerConvolution(
-    int pad_to, const se::CudaComputeCapability& compute_capability,
+    int pad_to, const se::GpuComputeCapability& compute_capability,
     HloCustomCallInstruction* conv, std::vector<Shape>* new_input_shapes_ptr,
     Shape* new_result_shape_ptr) {
   TF_ASSIGN_OR_RETURN(auto kind, GetCudnnConvKind(conv));
@@ -490,13 +490,16 @@ absl::StatusOr<bool> CudnnPadForConvolutions::Run(
     HloModule* module,
     const absl::flat_hash_set<absl::string_view>& execution_threads) {
   bool changed = false;
+  auto *ccc = std::get_if<se::CudaComputeCapability>(&compute_capability_);
   for (HloComputation* comp :
        module->MakeNonfusionComputations(execution_threads)) {
     for (HloCustomCallInstruction* conv : GetRelevantConvs(comp)) {
       // On Turing and later (sm75+), pad to multiples of 32 bytes if possible,
       // because that lets us use the fast int8x32 data type.
       bool local_changed = false;
-      if (compute_capability_.IsAtLeast(7, 5)) {
+      bool isSM75_and_later = false;
+      if (ccc) isSM75_and_later = ccc->IsAtLeast(7, 5);
+      if (isSM75_and_later || se::isROCm(compute_capability_)) {
         TF_ASSIGN_OR_RETURN(
             local_changed,
             ResolveAndPad(conv, absl::bind_front(
@@ -512,7 +515,9 @@ absl::StatusOr<bool> CudnnPadForConvolutions::Run(
       }
       changed |= local_changed;
     }
-    if (compute_capability_.IsAtLeast(se::CudaComputeCapability::VOLTA)) {
+    bool isVOLTA = false;
+    if (ccc) isVOLTA = ccc->IsAtLeast(se::CudaComputeCapability::VOLTA);
+    if (isVOLTA || se::isROCm(compute_capability_)) {
       for (HloCustomCallInstruction* conv : GetRelevantConvs(comp)) {
         TF_ASSIGN_OR_RETURN(
             bool local_changed,

xla/service/gpu/cudnn_pad_for_convolutions.h

@@ -34,6 +34,9 @@ class CudnnPadForConvolutions : public HloModulePass {
   explicit CudnnPadForConvolutions(se::CudaComputeCapability compute_capability)
       : compute_capability_(compute_capability) {}
 
+  explicit CudnnPadForConvolutions(se::RocmComputeCapability compute_capability)
+      : compute_capability_(compute_capability) {}
+
   absl::string_view name() const override {
     return "cudnn_pad_for_convolutions";
   }
@@ -44,7 +47,7 @@ class CudnnPadForConvolutions : public HloModulePass {
       const absl::flat_hash_set<absl::string_view>& execution_threads) override;
 
  private:
-  const se::CudaComputeCapability compute_capability_;
+  const se::GpuComputeCapability compute_capability_;
 };
 
 }  // namespace gpu

xla/service/gpu/cudnn_support_utils.cc

@@ -33,7 +33,7 @@ namespace xla {
 namespace gpu {
 
 absl::StatusOr<bool> CudnnSupportsOptimizedIntegerConvolution(
-    const se::CudaComputeCapability& compute_capability,
+    const se::GpuComputeCapability& compute_capability,
     HloCustomCallInstruction& conv, int vector_size) {
   TF_ASSIGN_OR_RETURN(auto kind, GetCudnnConvKind(&conv));
   const Shape& input_shape = conv.operand(0)->shape();
@@ -50,9 +50,11 @@ absl::StatusOr<bool> CudnnSupportsOptimizedIntegerConvolution(
 
   // Require cc6.1+ for any vectorized integer convolutions
   // Require cc7.5+ for any IMMA convolutions
-  if ((vector_size == 32 && !compute_capability.IsAtLeast(7, 5)) ||
-      !compute_capability.IsAtLeast(6, 1)) {
-    VLOG(3) << "Compute capability " << compute_capability.ToString()
+  bool isCUDA = std::holds_alternative<se::CudaComputeCapability>(compute_capability);
+  auto cuda_compute_capability = std::get<se::CudaComputeCapability>(compute_capability);
+  if ((vector_size == 32 && !cuda_compute_capability.IsAtLeast(7, 5)) ||
+      !cuda_compute_capability.IsAtLeast(6, 1)) {
+    VLOG(3) << "Compute capability " << cuda_compute_capability.ToString()
             << " is not sufficent for int8x" << vector_size
             << " vectorization.";
     return false;

xla/service/gpu/cudnn_support_utils.h

@@ -32,7 +32,7 @@ namespace gpu {
 // This function does not guarantee that a convolution will be padded and/or
 // vectorized. It only checks that it is a valid candiate for such optimization.
 absl::StatusOr<bool> CudnnSupportsOptimizedIntegerConvolution(
-    const se::CudaComputeCapability& compute_capability,
+    const se::GpuComputeCapability& compute_capability,
     HloCustomCallInstruction& conv, int vector_size);
 
 // Represents configuration for the reshape-transpose-reshape operations that

xla/service/gpu/cudnn_vectorize_convolutions.cc

@@ -335,7 +335,7 @@ absl::Status ReorderInt8NchwVect(HloCustomCallInstruction* conv,
 // (The dimensions can appear in any order; which is N/C/etc is determined by
 // the convolutions' dnums.)
 static absl::StatusOr<bool> TryRevectorizeConv(
-    const se::CudaComputeCapability& compute_capability,
+    const se::GpuComputeCapability& compute_capability,
     const se::dnn::VersionInfo& cudnn_version, HloCustomCallInstruction* conv,
     int vect_size) {
   const Shape& input_shape = conv->operand(0)->shape();
@@ -496,7 +496,7 @@ static absl::StatusOr<bool> TryRevectorizeConv(
 // This requires that C be a multiple of vect_size.  CudnnPadForConvolutions can
 // add padding to make this true.
 static absl::StatusOr<bool> TryVectorizeConv(
-    const se::CudaComputeCapability& compute_capability,
+    const se::GpuComputeCapability& compute_capability,
     const se::dnn::VersionInfo& cudnn_version, HloCustomCallInstruction* conv,
     int64_t vect_size) {
   const Shape& input_shape = conv->operand(0)->shape();
@@ -625,7 +625,10 @@ absl::StatusOr<bool> CudnnVectorizeConvolutions::Run(
       // Try to (re)vectorize to int8x32 if this is an sm75+ GPU.  If we can't,
       // fall back to int8x4.
       bool local_changed = false;
-      if (compute_capability_.IsAtLeast(7, 5)) {
+      auto *ccc = std::get_if<se::CudaComputeCapability>(&compute_capability_);
+      bool isSM75_and_later = false;
+      if (ccc) isSM75_and_later = ccc->IsAtLeast(7, 5);
+      if (isSM75_and_later || se::isROCm(compute_capability_)) {
         TF_ASSIGN_OR_RETURN(
             local_changed,
             TryRevectorizeConv(compute_capability_, cudnn_version_, conv, 32));

xla/service/gpu/cudnn_vectorize_convolutions.h

@@ -52,6 +52,11 @@ class CudnnVectorizeConvolutions : public HloModulePass {
       : compute_capability_(compute_capability),
         cudnn_version_(cudnn_version) {}
 
+  explicit CudnnVectorizeConvolutions(
+      se::RocmComputeCapability compute_capability)
+      : compute_capability_(compute_capability) {}
+
+
   absl::string_view name() const override {
     return "cudnn_vectorize_convolutions";
   }
@@ -61,7 +66,7 @@ class CudnnVectorizeConvolutions : public HloModulePass {
       const absl::flat_hash_set<absl::string_view>& execution_threads) override;
 
  private:
-  const se::CudaComputeCapability compute_capability_;
+  const se::GpuComputeCapability compute_capability_;
   const se::dnn::VersionInfo cudnn_version_;
 };
 

xla/stream_executor/device_description.h

@@ -223,6 +223,14 @@ class RocmComputeCapability {
 using GpuComputeCapability =
     std::variant<CudaComputeCapability, RocmComputeCapability>;
 
+static inline bool isCUDA(const GpuComputeCapability& gcc) {
+  return std::holds_alternative<CudaComputeCapability>(gcc);
+}
+
+static inline bool isROCm(const GpuComputeCapability& gcc) {
+  return std::holds_alternative<RocmComputeCapability>(gcc);
+}
+
 // Data that describes the execution target of the StreamExecutor, in terms of
 // important logical parameters. These include dimensionality limits and
 // physical parameters of interest, such as number of cores present on the