Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into…

… replace_opresult_with_value

.gitignore

@@ -109,6 +109,7 @@ paddle/fluid/pir/dialect/operator/ir/op_decomp.cc
 paddle/fluid/pir/dialect/operator/ir/pd_op_vjp.cc
 paddle/fluid/pir/dialect/operator/ir/pd_op.*
 paddle/fluid/pir/dialect/operator/ir/onednn_op.*
+paddle/fluid/pir/dialect/operator/ir/pd_onednn_op.*
 paddle/fluid/pir/dialect/operator/ir/pd_onednn_op_info.*
 paddle/fluid/pir/dialect/operator/ir/pd_op_bwd.*
 paddle/fluid/pir/dialect/operator/ir/pd_op_fused.*

README.md

@@ -15,7 +15,7 @@ English | [简体中文](./README_cn.md) | [日本語](./README_ja.md)
 Welcome to the PaddlePaddle GitHub.
 
 PaddlePaddle, as the first independent R&D deep learning platform in China, has been officially open-sourced to professional communities since 2016. It is an industrial platform with advanced technologies and rich features that cover core deep learning frameworks, basic model libraries, end-to-end development kits, tools & components as well as service platforms.
-PaddlePaddle is originated from industrial practices with dedication and commitments to industrialization. It has been widely adopted by a wide range of sectors including manufacturing, agriculture, enterprise service, and so on while serving more than 8 million developers, 220,000 companies and generating 800,000 models. With such advantages, PaddlePaddle has helped an increasing number of partners commercialize AI.
+PaddlePaddle is originated from industrial practices with dedication and commitments to industrialization. It has been widely adopted by a wide range of sectors including manufacturing, agriculture, enterprise service, and so on while serving more than 10.7 million developers, 235,000 companies and generating 860,000 models. With such advantages, PaddlePaddle has helped an increasing number of partners commercialize AI.
 
 ## Installation
 

README_cn.md

@@ -14,7 +14,7 @@
 
 欢迎来到 PaddlePaddle GitHub
 
-飞桨(PaddlePaddle)以百度多年的深度学习技术研究和业务应用为基础，是中国首个自主研发、功能完备、 开源开放的产业级深度学习平台，集深度学习核心训练和推理框架、基础模型库、端到端开发套件和丰富的工具组件于一体。目前，飞桨累计开发者800万，服务企业22万家，基于飞桨开源深度学习平台产生了80万个模型。飞桨助力开发者快速实现AI想法，快速上线AI业务。帮助越来越多的行业完成AI赋能，实现产业智能化升级。
+飞桨(PaddlePaddle)以百度多年的深度学习技术研究和业务应用为基础，是中国首个自主研发、功能完备、 开源开放的产业级深度学习平台，集深度学习核心训练和推理框架、基础模型库、端到端开发套件和丰富的工具组件于一体。目前，飞桨累计开发者1070万，服务企业23.5万家，基于飞桨开源深度学习平台产生了86万个模型。飞桨助力开发者快速实现AI想法，快速上线AI业务。帮助越来越多的行业完成AI赋能，实现产业智能化升级。
 
 ## 安装
 

README_ja.md

@@ -15,7 +15,7 @@
 PaddlePaddle GitHub へようこそ。
 
 PaddlePaddle は中国初の独立系 R&D ディープラーニングプラットフォームとして、2016年からプロのコミュニティに正式にオープンソース化されました。コアとなる深層学習フレームワーク、基本モデルライブラリ、エンドツーエンドの開発キット、ツール＆コンポーネント、さらにサービスプラットフォームを網羅する、高度な技術と豊富な機能を備えた産業プラットフォームです。
-PaddlePaddle は、工業化に対するコミットメントを持つ工業的実践から生まれたものです。製造業、農業、企業サービスなど幅広い分野で採用され、800万人以上の開発者、22万以上の企業、80万以上のモデルを生み出しています。それにより PaddlePaddle は、ますます多くのパートナーの AI 商用化を支援しています。
+PaddlePaddle は、工業化に対するコミットメントを持つ工業的実践から生まれたものです。製造業、農業、企業サービスなど幅広い分野で採用され、1070万人以上の開発者、23.5万以上の企業、86万以上のモデルを生み出しています。それにより PaddlePaddle は、ますます多くのパートナーの AI 商用化を支援しています。
 
 ## インストール
 

cmake/FindNumPy.cmake

@@ -3,8 +3,6 @@
 # NUMPY_FOUND
 # will be set by this script
 
-cmake_minimum_required(VERSION 2.6)
-
 if(NOT PYTHON_EXECUTABLE)
   if(NumPy_FIND_QUIETLY)
     find_package(PythonInterp QUIET)

cmake/external/eigen.cmake

@@ -53,7 +53,7 @@ if(CMAKE_COMPILER_IS_GNUCC)
   list(GET GCC_VERSION_COMPONENTS 0 GCC_MAJOR)
   list(GET GCC_VERSION_COMPONENTS 1 GCC_MINOR)
   set(GCC_VERSION "${GCC_MAJOR}.${GCC_MINOR}")
-  if(GCC_VERSION GREATER_EQUAL "12.0")
+  if(GCC_VERSION GREATER_EQUAL 12.0)
     file(TO_NATIVE_PATH ${PADDLE_SOURCE_DIR}/patches/eigen/Complex.h.patch
          complex_header)
     set(EIGEN_PATCH_COMMAND

paddle/cinn/common/arithmatic_test.cc

@@ -33,16 +33,16 @@ using namespace ir;  // NOLINT
 
 TEST(GiNaC, simplify) {
   using namespace GiNaC;  // NOLINT
-  symbol x("x");
-  symbol y("y");
+  GiNaC::symbol x("x");
+  GiNaC::symbol y("y");
 
   ex e = x * 0 + 1 + 2 + 3 - 100 + 30 * y - y * 21 + 0 * x;
   LOG(INFO) << "e: " << e;
 }
 
 TEST(GiNaC, diff) {
   using namespace GiNaC;  // NOLINT
-  symbol x("x"), y("y");
+  GiNaC::symbol x("x"), y("y");
   ex e = (x + 1);
   ex e1 = (y + 1);
 
@@ -54,7 +54,7 @@ TEST(GiNaC, diff) {
 
 TEST(GiNaC, solve) {
   using namespace GiNaC;  // NOLINT
-  symbol x("x"), y("y");
+  GiNaC::symbol x("x"), y("y");
 
   lst eqns{2 * x + 3 == 19};
   lst vars{x};

paddle/cinn/common/broadcast_tree.cc

@@ -185,8 +185,8 @@ using Pattern2Placement = std::unordered_map<symbol::DimExpr, symbol::DimExpr>;
 Pattern2Placement ConstructCstrLhsEqRhsReplacement(
     const symbol::Broadcastable<symbol::DimExpr>& broadcastable_condition) {
   auto [lhs, rhs] = *broadcastable_condition;
-  if (lhs.isa<std::string>()) return Pattern2Placement{{lhs, rhs}};
   if (rhs.isa<std::string>()) return Pattern2Placement{{rhs, lhs}};
+  if (lhs.isa<std::string>()) return Pattern2Placement{{lhs, rhs}};
   return Pattern2Placement{{lhs, rhs}};
 }
 
@@ -295,4 +295,54 @@ BroadcastTree ConstructBroadcastTree(const BroadcastLeaf& leaves) {
   return ConstructBroadcastBranch(broadcastable_condition.value(), leaves);
 }
 
+namespace {
+
+std::string ToTxtStringImpl(const BroadcastBranch<BroadcastTree>& branch) {
+  std::stringstream ss;
+  const auto& [cstr, lhs_eq_rhs, lhs_eq_one, rhs_eq_one] = branch.tuple();
+  const auto& [lhs, rhs] = *cstr;
+  const auto& Put = [&](const std::string& key, const auto& value) {
+    ss << "\"" << key << "\": ";
+    ss << ToTxtString(value);
+    ss << ",\n ";
+  };
+  ss << "{";
+  ss << "\"$lhs\": " << lhs << ",\n ";
+  ss << "\"$rhs\": " << rhs << ",\n ";
+  Put("$lhs == $rhs", lhs_eq_rhs);
+  Put("$lhs == 1", lhs_eq_one);
+  Put("$rhs == 1", rhs_eq_one);
+  ss << "}";
+  return ss.str();
+}
+
+std::string ToTxtStringImpl(const BroadcastLeaf& leaf) {
+  std::stringstream ss;
+  ss << "[";
+  for (const auto& dim_exprs : *leaf) {
+    ss << "[";
+    int j = 0;
+    for (const auto& dim_expr : dim_exprs) {
+      if (j++) {
+        ss << ",";
+      }
+      ss << dim_expr;
+    }
+    ss << "]";
+  }
+  ss << "]";
+  return ss.str();
+}
+
+}  // namespace
+
+std::string ToTxtString(const BroadcastTree& tree) {
+  return std::visit([&](const auto& impl) { return ToTxtStringImpl(impl); },
+                    tree.variant());
+}
+
+std::ostream& operator<<(std::ostream& os, const BroadcastTree& tree) {
+  os << ToTxtString(tree);
+}
+
 }  // namespace cinn::common

paddle/cinn/common/broadcast_tree.h

@@ -31,4 +31,8 @@ using BroadcastTree = adt::Tree<BroadcastBranch, BroadcastLeaf>;
 
 BroadcastTree ConstructBroadcastTree(const BroadcastLeaf& leaves);
 
+std::string ToTxtString(const BroadcastTree&);
+
+std::ostream& operator<<(std::ostream& os, const BroadcastTree& tree);
+
 }  // namespace cinn::common

paddle/cinn/hlir/dialect/operator/ir/manual_op.cc

@@ -16,6 +16,8 @@
 
 #include <vector>
 #include "glog/logging.h"
+#include "paddle/cinn/common/dim_expr_simplify.h"
+#include "paddle/cinn/hlir/dialect/operator/ir/generate_shape_util.h"
 #include "paddle/common/ddim.h"
 #include "paddle/common/enforce.h"
 #include "paddle/fluid/pir/dialect/operator/ir/ir_meta_tensor.h"
@@ -190,7 +192,13 @@ void GenerateShapeOp::Build(
     const std::vector<pir::Value>& inputs,
     const std::vector<pir::Attribute>& output_dim_exprs,
     const GenerateShapeOp::SymbolBindings& symbol_bindings) {
-  CHECK(!inputs.empty());
+  CHECK(!inputs.empty()) << ". output_dim_exprs: " << [&] {
+    std::stringstream ss;
+    for (const auto& attr : output_dim_exprs) {
+      ss << attr;
+    }
+    return ss.str();
+  }();
   argument.AddInputs(inputs);
   argument.AddAttribute("output_dim_exprs",
                         builder.array_attr(output_dim_exprs));
@@ -345,6 +353,61 @@ GenerateShapeOp::ConvertAttributeToSymbolBindings(
   return std::move(ret);
 }
 
+bool GenerateShapeOp::InferSymbolicShape(
+    pir::ShapeConstraintIRAnalysis* shape_analysis) {
+  auto GetShapeOrDataDimExprs =
+      [&](pir::Value value) -> const symbol::ShapeOrDataDimExprs& {
+    return shape_analysis->GetShapeOrDataForValue(value);
+  };
+  auto SetShapeOrDataDimExprs =
+      [&](pir::Value value, const symbol::ShapeOrDataDimExprs& dim_exprs) {
+        shape_analysis->SetShapeOrDataForValue(value, dim_exprs);
+      };
+  const auto attr_dim_exprs = [&] {
+    std::vector<symbol::DimExpr> dim_exprs{};
+    pir::Attribute dim_expr_attr = this->attributes().at("output_dim_exprs");
+    CHECK(dim_expr_attr.isa<pir::ArrayAttribute>());
+    auto array = dim_expr_attr.dyn_cast<pir::ArrayAttribute>();
+    for (int i = 0; i < array.size(); ++i) {
+      const auto& dim_expr = ConvertAttributeToDimExpr(array.at(i));
+      CHECK(dim_expr.has_value());
+      dim_exprs.push_back(dim_expr.value());
+    }
+    return dim_exprs;
+  }();
+  const auto symbol_bindings = [&] {
+    pir::Attribute symbol_bindings_attr =
+        this->attributes().at("symbol_bindings");
+    auto symbol_bindings =
+        ConvertAttributeToSymbolBindings(symbol_bindings_attr);
+    CHECK(symbol_bindings.has_value());
+    return symbol_bindings.value();
+  }();
+  auto DimExprs4InputDim =
+      [&](int input_idx) -> const symbol::ShapeOrDataDimExprs& {
+    pir::Value input = this->operand_source(input_idx);
+    return GetShapeOrDataDimExprs(input);
+  };
+  auto DimExprs4SymbolName =
+      MakeGetterDimExpr4SymbolName(symbol_bindings, DimExprs4InputDim);
+  const auto substituted_dim_exprs = [&] {
+    std::vector<symbol::DimExpr> dim_exprs{};
+    dim_exprs.reserve(attr_dim_exprs.size());
+    for (const auto& attr_dim_expr : attr_dim_exprs) {
+      const auto& substituted =
+          SubstituteDimExpr(attr_dim_expr, DimExprs4SymbolName);
+      const auto& simplified = common::SimplifyDimExpr(substituted);
+      dim_exprs.push_back(simplified);
+    }
+    return dim_exprs;
+  }();
+  const auto shape_or_data_dim_exprs =
+      symbol::ShapeOrDataDimExprs::MakeConsistentShapeOrData(
+          substituted_dim_exprs);
+  SetShapeOrDataDimExprs(this->out(), shape_or_data_dim_exprs);
+  return true;
+}
+
 }  // namespace dialect
 }  // namespace cinn
 

paddle/cinn/hlir/dialect/operator/ir/manual_op.h

@@ -14,13 +14,15 @@
 
 #pragma once
 #include <variant>
+#include "paddle/fluid/pir/dialect/operator/interface/infer_symbolic_shape.h"
 #include "paddle/phi/core/infermeta_utils.h"
 #include "paddle/pir/core/builder.h"
 #include "paddle/pir/core/dll_decl.h"
 #include "paddle/pir/core/ir_printer.h"
 #include "paddle/pir/core/op_base.h"
 #include "paddle/pir/core/operation.h"
 #include "paddle/pir/core/operation_utils.h"
+#include "paddle/pir/dialect/shape/utils/shape_utils.h"
 
 namespace cinn {
 namespace dialect {
@@ -83,7 +85,9 @@ class IR_API SplitOp : public pir::Op<SplitOp> {
   void VerifySig() const {}
 };
 
-class IR_API GenerateShapeOp : public pir::Op<GenerateShapeOp> {
+class IR_API GenerateShapeOp
+    : public pir::Op<GenerateShapeOp,
+                     paddle::dialect::InferSymbolicShapeInterface> {
  public:
   using Op::Op;
   static const char *name() { return "cinn_op.generate_shape"; }
@@ -113,6 +117,8 @@ class IR_API GenerateShapeOp : public pir::Op<GenerateShapeOp> {
 
   pir::OpResult out() { return result(0); }
 
+  bool InferSymbolicShape(pir::ShapeConstraintIRAnalysis *shape_analysis);
+
   static pir::Attribute ConvertSymbolBindingsToAttribute(
       pir::Builder &builder, const SymbolBindings &symbol_bindings);  // NOLINT
   static std::optional<SymbolBindings> ConvertAttributeToSymbolBindings(

paddle/cinn/hlir/dialect/operator/transforms/CMakeLists.txt

@@ -11,9 +11,7 @@ if(NOT CINN_ONLY)
       cinn_runtime_dialect
       pir_compiler)
 
-  cc_library(
-    cinn_transforms
-    SRCS ${cinn_transforms_srcs}
-    DEPS ${cinn_transforms_deps})
+  cinn_cc_library(cinn_transforms SRCS ${cinn_transforms_srcs} DEPS
+                  ${cinn_transforms_deps})
 
 endif()

paddle/cinn/hlir/dialect/operator/transforms/fuse_shape_ops_into_generate_shape_op_pass.cc

@@ -139,9 +139,8 @@ bool ProcessOp(paddle::dialect::ExpandOp op, pir::PatternRewriter* rewriter) {
     pir::ShapeConstraintIRAnalysis& shape_analysis =
         pir::ShapeAnalysisManager::Instance().Get(
             op.x().defining_op()->GetParentProgram());
-    CHECK(shape_analysis.value_id_to_shapeordata_.find(GetValueId(&value)) !=
-          shape_analysis.value_id_to_shapeordata_.end());
-    return shape_analysis.value_id_to_shapeordata_.at(GetValueId(&value));
+
+    return shape_analysis.GetShapeOrDataForValue(value);
   };
   std::optional<pir::Value> opt_generated_shape =
       GetOutOfRewritedGenerateShapeOp(