PaddlePaddle
diff --git a/‎paddle/cinn/hlir/dialect/operator/transforms/pd_to_cinn_pass.cc
Copy file name to clipboard
+5Lines changed: 5 additions & 0 deletions b/‎paddle/cinn/hlir/dialect/operator/transforms/pd_to_cinn_pass.cc
Copy file name to clipboard
+5Lines changed: 5 additions & 0 deletions
diff --git a/‎paddle/cinn/hlir/dialect/operator/transforms/pd_to_cinn_pass.h
Copy file name to clipboard
+4Lines changed: 4 additions & 0 deletions b/‎paddle/cinn/hlir/dialect/operator/transforms/pd_to_cinn_pass.h
Copy file name to clipboard
+4Lines changed: 4 additions & 0 deletions
diff --git a/‎paddle/fluid/pir/transforms/shape_optimization_pass.cc
Copy file name to clipboard
-297Lines changed: 0 additions & 297 deletions b/‎paddle/fluid/pir/transforms/shape_optimization_pass.cc
Copy file name to clipboard
-297Lines changed: 0 additions & 297 deletions
diff --git a/‎paddle/fluid/pybind/pir.cc
Copy file name to clipboard
+2-4Lines changed: 2 additions & 4 deletions b/‎paddle/fluid/pybind/pir.cc
Copy file name to clipboard
+2-4Lines changed: 2 additions & 4 deletions
diff --git a/‎paddle/pir/dialect/shape/utils/shape_optimization_utils.cc
Copy file name to clipboard
+2-14Lines changed: 2 additions & 14 deletions b/‎paddle/pir/dialect/shape/utils/shape_optimization_utils.cc
Copy file name to clipboard
+2-14Lines changed: 2 additions & 14 deletions
@@ -673,6 +673,11 @@ void PdOp2CinnOpConverter(::pir::Program *program) {
 
   pm.Run(program);
 }
+
+std::unique_ptr<pir::Pass> CreatePdOpToCinnOpPass() {
+  return std::make_unique<PdOpToCinnOpPass>();
+}
+
 }  // namespace ir
 }  // namespace dialect
 }  // namespace cinn
@@ -31,8 +31,12 @@ class PdOpToCinnOpPass : public pir::PatternRewritePass {
   bool CanApplyOn(pir::Operation *op) const override;
 };
 
+// TODO(lanxianghit): delete this and use CreatePdOpToCinnOpPass() in
+// corresponding unit tests.
 void PdOp2CinnOpConverter(::pir::Program *program);
 
+IR_API std::unique_ptr<pir::Pass> CreatePdOpToCinnOpPass();
+
 }  // namespace ir
 }  // namespace dialect
 }  // namespace cinn
@@ -29,306 +29,9 @@
 namespace pir {
 namespace {
 
-bool InsertTieShapeOnValue(pir::Value value,
-                           pir::Builder& builder) {  // NOLINT
-  // Insert TieShapeOp only for non-zero ranked tensor type.
-  auto type = value.type().dyn_cast<DenseTensorType>();
-  if (!type || type.dims().size() == 0) return true;
-
-  std::vector<pir::Value> dim_sizes;
-  for (int64_t dim = 0, rank = type.dims().size(); dim < rank; ++dim) {
-    auto dim_op = builder.Build<shape::TensorDimOp>(value, dim);
-    dim_sizes.push_back(dim_op.out());
-  }
-  builder.Build<shape::TieShapeOp>(value, dim_sizes);
-  return true;
-}
-
-// Forward declaration
-bool InsertTieShapeOnRegion(pir::Region* region);
-
-bool InsertTieShapeOnOperation(pir::Operation* op,
-                               pir::Builder& builder) {  // NOLINT
-  // TODO(zhangbopd): skip more specialized Ops.
-  if (op->isa<shape::TieShapeOp>() || op->isa<shape::FuncOp>()) return true;
-
-  for (size_t i = 0; i < op->num_regions(); ++i) {
-    if (!InsertTieShapeOnRegion(&(op->region(i)))) return false;
-  }
-  builder.SetInsertionPointAfter(op);
-  for (pir::OpResult v : op->results()) {
-    if (!InsertTieShapeOnValue(v, builder)) return false;
-  }
-
-  return true;
-}
-
-bool InsertTieShapeOnBlock(pir::Block* block) {
-  pir::Builder builder =
-      pir::Builder(pir::IrContext::Instance(), block, block->begin());
-  // TODO(zhangbopd): mapping block arguments
-
-  std::vector<pir::Operation*> op_list;
-  for (auto& op : *block) op_list.push_back(&op);
-  for (pir::Operation* op : op_list) {
-    if (!InsertTieShapeOnOperation(op, builder)) return false;
-  }
-  return true;
-}
-
-bool InsertTieShapeOnRegion(pir::Region* region) {
-  for (auto& block : *region) {
-    if (!InsertTieShapeOnBlock(&block)) return false;
-  }
-  return true;
-}
-
-// Convert:
-//   %shape = shape.shape_of %0 : tensor<?x?xf32> -> tensor<2xindex>
-// To:
-//   %d0 = tensor.dim %0, %c0 : tensor<?x?xf32>
-//   %d1 = tensor.dim %0, %c1 : tensor<?x?xf32>
-//   %shape = tensor.from_elements %d0, %d1 : tensor<2xindex>
-struct ExpandShapeOfOpPattern : public OpRewritePattern<shape::ShapeOfOp> {
-  using OpRewritePattern<shape::ShapeOfOp>::OpRewritePattern;
-
-  bool MatchAndRewrite(shape::ShapeOfOp op,
-                       PatternRewriter& rewriter) const override {
-    VLOG(3) << "Apply ExpandShapeOfOpPattern...";
-
-    auto type = op.out().type().dyn_cast<pir::DenseTensorType>();
-
-    if (!type || !type.dyn_cast<ShapedTypeInterface>().HasStaticShape() ||
-        !type.dyn_cast<ShapedTypeInterface>().GetElementType().IsIndex())
-      return false;
-
-    std::vector<Value> dim_sizes;
-    for (int dim = 0,
-             rank = type.dyn_cast<ShapedTypeInterface>().GetDyShape()[0];
-         dim < rank;
-         ++dim) {
-      dim_sizes.push_back(
-          rewriter.Build<shape::TensorDimOp>(op.input(), dim).out());
-    }
-    rewriter.ReplaceOpWithNewOp<shape::FromElementsOp>(op, dim_sizes);
-    return true;
-  }
-};
-
-// Fold dim of an operation that implements the InferSymbolicShapeInterface
-template <typename OpTy>
-struct DimOfShapedTypeOpInterfacePattern : public OpRewritePattern<OpTy> {
-  using OpRewritePattern<OpTy>::OpRewritePattern;
-
-  bool MatchAndRewrite(OpTy dim_op, PatternRewriter& rewriter) const override {
-    return true;
-  }
-};
-
 using PassPipelineRunner =
     std::function<bool(pir::PassManager&, pir::ModuleOp)>;
 
-// Returns true if the type is possible to be a shape tensor type.
-// Shape tensor type :
-//    - rank-1 static-shaped tensor type
-//    - element type of the tensor is int or index
-//    - number of elements of the tensor < 32, supposing that the
-//      higiest possible rank is smaller than 32.
-bool IsCandidateShapeTensorType(Type type) {
-  auto tensor_type = type.dyn_cast<DenseTensorType>();
-  auto shaped_type = tensor_type.dyn_cast<ShapedTypeInterface>();
-
-  return (tensor_type && tensor_type && shaped_type.GetRank() == 1 &&
-          shaped_type.HasStaticShape() &&
-          shaped_type.GetElementType().IsIntOrIndex() &&
-          shaped_type.GetDyShape()[0] < 32);
-}
-
-class ShapeComputationIRAnalysis {
- public:
-  using func = std::function<bool(Operation* op)>;
-  explicit ShapeComputationIRAnalysis(ModuleOp m,
-                                      SymbolicDimMgr& mgr);  // NOLINT
-  bool Run();
-
- private:
-  bool RunOnRegion(Region* region, func fn);
-  bool RunOnBlock(Block* block, func fn);
-  bool RunOnOperation(Operation* op, func fn);
-
-  bool BuildShapeOnOperation(Operation* op);
-  bool BuildShapeOnValue(Value value);
-
-  bool ApplyOpConstraint(Operation* op);
-  bool ApplyIndexOpConstraint(Operation* op);
-  bool ApplyTieShapeOpConstraint(Operation* op);
-
-  bool initialized_ = false;
-  ModuleOp m_;
-  SymbolicDimMgr& mgr_;
-
-  std::unordered_map<Value, SymbolicDimOp> value_to_sym_dim_;
-
-  // shape tensor is the 1D ranked tensor with int/index dtype.
-  std::unordered_map<Value, std::vector<SymbolicDimOp>>
-      shape_tensor_to_sym_dims_;
-
-  std::unordered_map<Value, std::vector<SymbolicDimOp>>
-      dense_tensor_to_sym_dims_;
-};
-
-ShapeComputationIRAnalysis::ShapeComputationIRAnalysis(ModuleOp m,
-                                                       SymbolicDimMgr& mgr)
-    : m_(m), mgr_(mgr) {}
-
-bool ShapeComputationIRAnalysis::Run() {
-  // Make sure only run once.
-  if (initialized_) return false;
-  initialized_ = true;
-  return true;
-}
-
-bool ShapeComputationIRAnalysis::RunOnRegion(Region* region, func fn) {
-  for (auto& block : *region) {
-    if (!RunOnBlock(&block, fn)) return false;
-  }
-  return true;
-}
-
-bool ShapeComputationIRAnalysis::RunOnBlock(Block* block, func fn) {
-  // TODO(zhangbopd): mapping block arguments
-
-  std::vector<Operation*> op_list;
-  for (auto& op : *block) op_list.push_back(&op);
-  for (Operation* op : op_list) {
-    if (!RunOnOperation(op, fn)) return false;
-  }
-  return true;
-}
-
-bool ShapeComputationIRAnalysis::RunOnOperation(Operation* op, func fn) {
-  for (size_t i = 0; i < op->num_regions(); ++i) {
-    if (!RunOnRegion(&(op->region(i)), fn)) return false;
-  }
-  return fn(op);
-}
-
-bool ShapeComputationIRAnalysis::BuildShapeOnOperation(Operation* op) {
-  if (op->isa<shape::FuncOp>()) return true;
-  if (op->isa<shape::TieShapeOp>()) {
-    Value value = op->operand_source(0);
-    std::vector<SymbolicDimOp> symbols;
-    if (op->HasAttribute(SymbolicDimOp::GetSymbolicDimAttrName())) {
-      auto attrs =
-          op->attribute<ArrayAttribute>(SymbolicDimOp::GetSymbolicDimAttrName())
-              .AsVector();
-      for (Attribute attr : attrs) {
-        auto sym = mgr_.symbolTable().Lookup<SymbolicDimOp>(
-            attr.dyn_cast<StrAttribute>().AsString());
-        IR_ENFORCE(sym);
-        SymbolicDimOp root = mgr_.GetRootSymbolicDim(sym);
-        symbols.push_back(root);
-      }
-    } else {
-      symbols = mgr_.CreateSymbolicDimsForRankedValue(value);
-      std::vector<Attribute> attrs;
-      for (SymbolicDimOp sym : symbols) {
-        Attribute rootSymbol =
-            StrAttribute::get(m_->ir_context(), sym.GetSymName());
-        attrs.push_back(rootSymbol);
-      }
-      op->set_attribute(SymbolicDimOp::GetSymbolicDimAttrName(),
-                        ArrayAttribute::get(m_->ir_context(), attrs));
-    }
-    dense_tensor_to_sym_dims_[value] = std::move(symbols);
-    return true;
-  }
-  for (auto& result : op->results()) {
-    if (!BuildShapeOnValue(result)) return false;
-  }
-  return true;
-}
-
-bool ShapeComputationIRAnalysis::BuildShapeOnValue(Value value) {
-  Type type = value.type();
-  if (type.IsIntOrIndex()) {
-    SymbolicDimOp sym = mgr_.NewSymbolicDim();
-    value_to_sym_dim_[value] = sym;
-  } else if (IsCandidateShapeTensorType(type)) {
-    auto shaped_type = type.dyn_cast<ShapedTypeInterface>();
-    std::vector<SymbolicDimOp> symbols;
-    for (size_t i = 0, d = shaped_type.GetDyShape()[0]; i < d; ++i)
-      symbols.push_back(mgr_.NewSymbolicDim());
-    shape_tensor_to_sym_dims_[value] = std::move(symbols);
-  }
-  return true;
-}
-
-bool ShapeComputationIRAnalysis::ApplyOpConstraint(Operation* op) {
-  IR_ENFORCE(ApplyIndexOpConstraint(op),
-             "Fail to apply constraint for index op");
-  IR_ENFORCE(ApplyTieShapeOpConstraint(op),
-             "Fail to apply constraint for tie_shape op");
-
-  // TODO(zhangbopd): add more constraints
-  return true;
-}
-
-bool ShapeComputationIRAnalysis::ApplyIndexOpConstraint(Operation* op) {
-  if (op->num_results() == 0) return true;
-
-  Type type = op->result(0).type();
-  if (!type.IsIntOrIndex()) return true;
-
-  if (auto dim_op = op->dyn_cast<shape::TensorDimOp>()) {
-    int64_t dim_index = dim_op.index()
-                            .dyn_cast<OpResult>()
-                            .owner()
-                            ->attribute<Int64Attribute>("value")
-                            .data();
-    value_to_sym_dim_[dim_op.out()].UpdateKnownNonNegative(true);
-    if (!mgr_.MapSymbolicDimEqual(
-            value_to_sym_dim_[dim_op.out()],
-            dense_tensor_to_sym_dims_[dim_op.source()][dim_index])) {
-      return false;
-    }
-
-  } else if (auto const_op = op->dyn_cast<ConstantOp>()) {
-    int64_t val = const_op.value().dyn_cast<Int64Attribute>().data();
-    if (!mgr_.MapSymbolicDimEqual(value_to_sym_dim_[op->result(0)],
-                                  mgr_.NewConstantSymbolicDim(val))) {
-      return false;
-    }
-  }
-  // TODO(zhangbopd): add support for reifyInferShape. (e.g. mul/add)
-  return true;
-}
-
-bool ShapeComputationIRAnalysis::ApplyTieShapeOpConstraint(Operation* op) {
-  if (auto tie_shape = op->dyn_cast<shape::TieShapeOp>()) {
-    auto& value = dense_tensor_to_sym_dims_[op->operand_source(0)];
-    for (size_t idx = 0; idx < tie_shape.dims().size(); ++idx) {
-      if (!mgr_.MapSymbolicDimEqual(value_to_sym_dim_[tie_shape.dims()[idx]],
-                                    value[idx]))
-        return false;
-      mgr_.GetRootSymbolicDim(value[idx]).UpdateKnownNonNegative(true);
-    }
-  }
-  return true;
-}
-
-bool OptimizeShapeComputation(pir::ModuleOp m, PassPipelineRunner runner) {
-  // TODO(zhangbopd): Do some Canonicalizer.
-  pir::SymbolicDimMgr mgr(m);
-
-  ShapeComputationIRAnalysis analysis(m, mgr);
-  if (!analysis.Run()) {
-    return false;
-  }
-
-  return true;
-}
-
 void PrintProgram(pir::ModuleOp m, std::string mgs) {
   std::ostringstream print_stream;
   print_stream << "\n\n";
 
@@ -1564,8 +1564,7 @@ void AddCinnPass(std::shared_ptr<PassManager> &pass_manager,  // NOLINT
       has_dynamic_shape ? std::make_shared<pir::ShapeConstraintIRAnalysis>(ctx)
                         : nullptr;
 
-  cinn::dialect::ir::PdOp2CinnOpConverter(&program);
-
+  pass_manager->AddPass(cinn::dialect::ir::CreatePdOpToCinnOpPass());
   pass_manager->AddPass(
       std::make_unique<cinn::dialect::ir::AddBroadcastToElementwisePass>());
   pass_manager->AddPass(pir::CreateDeadCodeEliminationPass());
@@ -1583,8 +1582,7 @@ void AddCinnPass(std::shared_ptr<PassManager> &pass_manager,  // NOLINT
 }
 
 void InferSymbolicShapePass(
-    std::shared_ptr<PassManager> &pass_manager,  // NOLINT
-    Program &program) {                          // NOLINT
+    std::shared_ptr<PassManager> &pass_manager) {  // NOLINT
   if (FLAGS_pir_apply_shape_optimization_pass) {
     pir::IrContext *ctx = pir::IrContext::Instance();
     ctx->GetOrRegisterDialect<pir::shape::ShapeDialect>();
 
@@ -48,17 +48,7 @@ bool CompareSymbolicDimProduct(SymbolicDimProduct& lhs,    // NOLINT
   return false;
 }
 
-SymbolicDimMgr::SymbolicDimMgr(ModuleOp m) : m_(m) {
-  for (auto& op : m.block()) {
-    if (op.isa<shape::FuncOp>()) {
-      symbol_table_ = SymbolTable(&op);
-      return;
-    }
-  }
-  Builder builder = Builder(m_.ir_context(), &m_.block(), m_.block().begin());
-  shape::FuncOp func = builder.Build<shape::FuncOp>();
-  symbol_table_ = SymbolTable(func);
-}
+SymbolicDimMgr::SymbolicDimMgr(ModuleOp m) : m_(m) {}
 
 bool SymbolicDimMgr::MapSymbolicDimProductEqual(const SymbolicDimProduct& lhs,
                                                 const SymbolicDimProduct& rhs) {
@@ -176,9 +166,7 @@ const std::string SymbolicDimMgr::GetNextName() {
 }
 
 SymbolicDimOp SymbolicDimMgr::NewSymbolicDim(const std::string& name) {
-  auto func_op = symbol_table_.getOp()->dyn_cast<shape::FuncOp>();
-  IR_ENFORCE(func_op);
-  Builder builder = Builder(m_.ir_context(), func_op.block());
+  Builder builder = Builder(m_.ir_context(), nullptr, Block::Iterator{}, false);
   // default settting dim != 0
   SymbolicDimOp symbol =
       builder.Build<SymbolicDimOp>(name.empty() ? GetNextName() : name,