NFC: Optimize includes in header files to limit header size

- Adds a utility script to analyze header file sizes after preprocessing.
- Optimize includes in some of the largest headers in order to reduce
  size using pruning of included files or forward declarations where
  relevant.

GitOrigin-RevId: 8f30fbbba54a4cfae6e4904352f2c04bc2677727

Author: christopherbate

mlir-tensorrt/common/include/mlir-tensorrt-common/Dialect/LinalgExt/Transforms/ToLoopsOpInterfaceImpl.h

@@ -26,15 +26,16 @@
 #define MLIR_TENSORRT_COMMON_DIALECT_LINALGEXT_TRANSFORMS_TOLOOPSOPINTERFACEIMPL
 
 #include "mlir-tensorrt-common/Interfaces/ToLoopsOpInterface.h"
-#include "mlir/IR/DialectRegistry.h"
 
-namespace mlir::linalg {
+namespace mlir {
+class DialectRegistry;
+namespace linalg {
 class LinalgOp;
 }
-
-namespace mlir::scf {
+namespace scf {
 class ForOp;
 }
+} // namespace mlir
 
 namespace mlir::linalg_ext {
 

mlir-tensorrt/common/include/mlir-tensorrt-common/Interfaces/BufferizationScopeInterface.h

@@ -1,6 +1,6 @@
 //===- BufferizationScopeInterface.h ---------------------------*- C++ -*-===//
 //
-// SPDX-FileCopyrightText: Copyright 2025 NVIDIA CORPORATION & AFFILIATES.
+// SPDX-FileCopyrightText: Copyright 2025-2026 NVIDIA CORPORATION & AFFILIATES.
 // All rights reserved.
 // SPDX-License-Identifier: Apache-2.0
 //
@@ -24,7 +24,7 @@
 #ifndef MLIR_TENSORRT_COMMON_INTERFACES_BUFFERIZATIONSCOPEINTERFACE_H
 #define MLIR_TENSORRT_COMMON_INTERFACES_BUFFERIZATIONSCOPEINTERFACE_H
 
-#include "mlir/Dialect/Bufferization/Transforms/OneShotAnalysis.h"
+#include "mlir/Dialect/Bufferization/Transforms/OneShotAnalysis.h" // IWYU pragma: keep
 
 #include "mlir-tensorrt-common/Interfaces/BufferizationScopeInterface.h.inc"
 

mlir-tensorrt/common/include/mlir-tensorrt-common/Interfaces/TensorKindAttrInterface.h

@@ -0,0 +1,79 @@
+#ifndef MLIR_TENSORRT_COMMON_INTERFACES_TENSORKINDATTRINTERFACE
+#define MLIR_TENSORRT_COMMON_INTERFACES_TENSORKINDATTRINTERFACE
+
+#include "mlir/Analysis/DataFlowFramework.h"
+#include "mlir/Support/LLVM.h"
+namespace mlir {
+
+enum class TensorKind { Unknown = 0, Device, Host, Both };
+
+/// Represents a lattice point. The lattice picture looks like this:
+///
+/// ```
+///     "both" (top)
+///     /    \               (host and device states are at same level)
+/// "host"  "device"
+///    \     /
+///     unknown
+///       |
+///      uninitialized (bottom)
+///
+/// ```
+struct TensorKindInfo {
+  /// Create the type info in an uninitialized state.
+  TensorKindInfo();
+  TensorKindInfo(TensorKind initialKind);
+
+  /// Print a description of this lattice point to the stream.
+  void print(raw_ostream &os) const;
+
+  /// Set the value and return whether there was a change.
+  ChangeResult setKind(TensorKind kind);
+
+  /// Return whether this lattice point has an uninitialied value.
+  bool isUninitialized() const;
+
+  /// Return whether this lattice point is unknown.
+  bool isUnknown() const;
+
+  static TensorKindInfo join(const TensorKindInfo &lhs,
+                             const TensorKindInfo &rhs);
+  static TensorKindInfo meet(const TensorKindInfo &lhs,
+                             const TensorKindInfo &rhs);
+
+  TensorKind getKind() const;
+
+  /// Return true if the kind has `Host` value.
+  bool isHostOnly() const;
+
+  /// Returns true if the kind is `host` or `both`.
+  bool isHostVisible() const;
+
+  /// Return true if this is an device tensor and not 'both'.
+  bool isDeviceOnly() const;
+
+  /// Returns true if  this is a tensor visible on the device (device or both).
+  bool isDeviceVisible() const;
+
+  /// Return true if the kind is both a host tensor and an device tensor.
+  bool isBothHostAndDevice() const;
+
+  bool operator<(const TensorKindInfo &other) const;
+
+  bool operator==(const TensorKindInfo &other) const;
+  bool operator!=(const TensorKindInfo &other) const;
+
+  std::optional<TensorKind> kind;
+};
+
+llvm::StringRef stringifyTensorKind(TensorKind kind);
+
+/// Return the name of the function arg attr UnitAttr
+/// that should be used to mark an argument as a shape tensor.
+StringRef getHostTensorArgAttrName();
+
+} // namespace mlir
+
+#include "mlir-tensorrt-common/Interfaces/TensorKindAttrInterface.h.inc"
+
+#endif // MLIR_TENSORRT_COMMON_INTERFACES_TENSORKINDATTRINTERFACE

mlir-tensorrt/common/include/mlir-tensorrt-common/Interfaces/TensorKindOpInterface.h

@@ -1,6 +1,6 @@
 //===- TensorKindOpInterface.h ----------------------------------*- C++ -*-===//
 //
-// SPDX-FileCopyrightText: Copyright 2024 NVIDIA CORPORATION & AFFILIATES.
+// SPDX-FileCopyrightText: Copyright 2024-2026 NVIDIA CORPORATION & AFFILIATES.
 // All rights reserved.
 // SPDX-License-Identifier: Apache-2.0
 //
@@ -26,125 +26,17 @@
 #ifndef MLIR_TENSORRT_INTERFACE_TENSORKINDOPINTERFACE_H
 #define MLIR_TENSORRT_INTERFACE_TENSORKINDOPINTERFACE_H
 
+#include "mlir-tensorrt-common/Interfaces/TensorKindAttrInterface.h"
 #include "mlir/Analysis/DataFlow/SparseAnalysis.h"
-#include "mlir/Analysis/DataFlowFramework.h"
-#include "mlir/IR/BuiltinTypes.h"
-#include "mlir/IR/OpDefinition.h"
-#include "mlir/Support/LLVM.h"
 
 namespace mlir {
 
-enum class TensorKind { Unknown = 0, Device, Host, Both };
-
 llvm::StringRef stringifyTensorKind(TensorKind kind);
 
 /// Return the name of the function arg attr UnitAttr
 /// that should be used to mark an argument as a shape tensor.
 StringRef getHostTensorArgAttrName();
 
-/// Represents a lattice point. The lattice picture looks like this:
-///
-/// ```
-///     "both" (top)
-///     /    \               (host and device states are at same level)
-/// "host"  "device"
-///    \     /
-///     unknown
-///       |
-///      uninitialized (bottom)
-///
-/// ```
-struct TensorKindInfo {
-  /// Create the type info in an uninitialized state.
-  TensorKindInfo() : kind(std::nullopt) {}
-  TensorKindInfo(TensorKind initialKind) : kind(initialKind) {}
-
-  /// Print a description of this lattice point to the stream.
-  void print(raw_ostream &os) const;
-
-  /// Set the value and return whether there was a change.
-  ChangeResult setKind(TensorKind kind) {
-    auto newKind = kind;
-    ChangeResult result = isUninitialized() || newKind != *this->kind
-                              ? ChangeResult::Change
-                              : ChangeResult::NoChange;
-    this->kind = newKind;
-    return result;
-  }
-
-  /// Return whether this lattice point has an uninitialied value.
-  bool isUninitialized() const { return !kind.has_value(); }
-
-  /// Return whether this lattice point is unknown.
-  bool isUnknown() const { return *kind == TensorKind::Unknown; }
-
-  static TensorKindInfo join(const TensorKindInfo &lhs,
-                             const TensorKindInfo &rhs);
-  static TensorKindInfo meet(const TensorKindInfo &lhs,
-                             const TensorKindInfo &rhs);
-
-  TensorKind getKind() const {
-    assert(!isUninitialized());
-    return *kind;
-  }
-
-  /// Return true if the kind has `Host` value.
-  bool isHostOnly() const {
-    assert(!isUninitialized() && "expected initialized value");
-    return getKind() == TensorKind::Host;
-  }
-
-  /// Returns true if the kind is `host` or `both`.
-  bool isHostVisible() const {
-    assert(!isUninitialized() && "expected initialized value");
-    return getKind() == TensorKind::Host || getKind() == TensorKind::Both;
-  }
-
-  /// Return true if this is an device tensor and not 'both'.
-  bool isDeviceOnly() const {
-    assert(!isUninitialized() && "expected initialized value");
-    return getKind() == TensorKind::Device;
-  }
-
-  /// Returns true if  this is a tensor visible on the device (device or both).
-  bool isDeviceVisible() const {
-    assert(!isUninitialized() && "expected initialized value");
-    return getKind() == TensorKind::Device || getKind() == TensorKind::Both;
-  }
-
-  /// Return true if the kind is both a host tensor and an device tensor.
-  bool isBothHostAndDevice() const {
-    assert(!isUninitialized() && "expected initialized value");
-    return getKind() == TensorKind::Both;
-  }
-
-  bool operator<(const TensorKindInfo &other) const {
-    // Uninitialized value is always less than other.
-    if (isUninitialized())
-      return true;
-    // Uninitialized other can't be greater than anything.
-    if (other.isUninitialized())
-      return false;
-    // If we are unknown, we are less than any host/device/both.
-    if (isUnknown())
-      return static_cast<int32_t>(getKind()) <
-             static_cast<int32_t>(other.getKind());
-    // If we are known, we are less than 'both'.
-    if ((isHostOnly() || isDeviceOnly()) && other.isBothHostAndDevice())
-      return true;
-    return false;
-  }
-
-  bool operator==(const TensorKindInfo &other) const {
-    return other.kind == this->kind;
-  }
-  bool operator!=(const TensorKindInfo &other) const {
-    return !(*this == other);
-  }
-
-  std::optional<TensorKind> kind;
-};
-
 /// Wraps the `TensorKindInfo` into a lattice class. This is required because
 /// upstream `dataflow::Lattice` has some issues that make it incompatible with
 /// backward analysis. TODO: fix the static switch for the `meet` operation
@@ -198,7 +90,6 @@ bool isHostTensorCandidate(Type type);
 } // namespace mlir
 
 // Include the generated interface declarations.
-#include "mlir-tensorrt-common/Interfaces/TensorKindAttrInterface.h.inc"
 #include "mlir-tensorrt-common/Interfaces/TensorKindOpInterface.h.inc"
 
 #endif // MLIR_TENSORRT_INTERFACE_TENSORKINDOPINTERFACE_H

mlir-tensorrt/common/include/mlir-tensorrt-common/Interfaces/ToLoopsOpInterface.h

@@ -24,7 +24,7 @@
 #ifndef MLIR_TENSORRT_COMMON_INTERFACES_TOLOOPSOPINTERFACE
 #define MLIR_TENSORRT_COMMON_INTERFACES_TOLOOPSOPINTERFACE
 
-#include "mlir/IR/OpDefinition.h"
+#include "mlir/IR/OpDefinition.h" // IWYU pragma: keep
 
 namespace mlir {
 class RewriterBase;

mlir-tensorrt/common/include/mlir-tensorrt-common/Support/ADTExtras.h

@@ -27,8 +27,7 @@
 #include "llvm/ADT/SmallVector.h"
 #include "llvm/ADT/StringRef.h"
 #include "llvm/ADT/iterator_range.h"
-#include "llvm/Support/Debug.h"
-#include "llvm/Support/FormatProviders.h"
+#include "llvm/Support/FormatProviders.h" // IWYU pragma: keep
 #include "llvm/Support/raw_ostream.h"
 #include <array>
 #include <type_traits>

mlir-tensorrt/common/include/mlir-tensorrt-common/Utils/DataFlowUtils.h

@@ -1,6 +1,6 @@
-//===- DataFlowUtils.h ------------------------------------------*- C++ -*-===//
+//===- DataFlowUtils.h ---------------------------------------------------===//
 //
-// SPDX-FileCopyrightText: Copyright 2025 NVIDIA CORPORATION & AFFILIATES.
+// SPDX-FileCopyrightText: Copyright 2025-2026 NVIDIA CORPORATION & AFFILIATES.
 // All rights reserved.
 // SPDX-License-Identifier: Apache-2.0
 //
@@ -25,9 +25,6 @@
 #define MLIR_TENSORRT_COMMON_UTILS_DATAFLOWUTILS
 
 #include "mlir/Analysis/DataFlow/DeadCodeAnalysis.h"
-#include "mlir/Analysis/DataFlowFramework.h"
-#include "mlir/IR/Operation.h"
-#include "mlir/IR/PatternMatch.h"
 
 namespace mlir {
 

…ude/mlir-executor/Utils/ModuleLikePass.h …r-tensorrt-common/Utils/ModuleLikePass.hmlir-tensorrt/executor/include/mlir-executor/Utils/ModuleLikePass.h renamed to mlir-tensorrt/common/include/mlir-tensorrt-common/Utils/ModuleLikePass.h mlir-tensorrt/executor/include/mlir-executor/Utils/ModuleLikePass.h renamed to mlir-tensorrt/common/include/mlir-tensorrt-common/Utils/ModuleLikePass.h

@@ -1,18 +1,18 @@
-//===- ModulePass.h ---------------------------------------------*- C++ -*-===//
+//===- ModuleLikePass.h --------------------------------------------------===//
 //
-// Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved.
+// SPDX-FileCopyrightText: Copyright 2026 NVIDIA CORPORATION & AFFILIATES.
+// All rights reserved.
+// SPDX-License-Identifier: Apache-2.0
 //
 //===----------------------------------------------------------------------===//
 ///
 /// Defines a pass with static scheduling filter that only allows it to be run
 /// on "module-like" operations.
 ///
 //===----------------------------------------------------------------------===//
-#ifndef MLIR_EXECUTOR_UTILS_MODULEPASS
-#define MLIR_EXECUTOR_UTILS_MODULEPASS
+#ifndef MLIR_TENSORRT_COMMON_UTILS_MODULELIKEPASS
+#define MLIR_TENSORRT_COMMON_UTILS_MODULELIKEPASS
 
-#include "mlir/IR/OpDefinition.h"
-#include "mlir/IR/SymbolTable.h"
 #include "mlir/Pass/Pass.h"
 
 namespace mlir {
@@ -41,21 +41,12 @@ class ModuleLikePass : public OperationPass<> {
   /// Indicate if the current pass can be scheduled on the given operation type.
   /// For an InterfacePass, this checks if the operation implements the given
   /// interface.
-  bool canScheduleOn(RegisteredOperationName opName) const final {
-    return opName.hasTrait<OpTrait::IsIsolatedFromAbove>() &&
-           opName.hasTrait<OpTrait::SymbolTable>();
-  }
+  bool canScheduleOn(RegisteredOperationName opName) const final;
 
   /// Used in pass `runOnOperation` implementation to ensure that the op meets
   /// requirements that can't be checked in 'canScheduleOn'.
-  static LogicalResult checkIsModuleLike(Operation *op) {
-    if (op->getNumRegions() != 1 || !op->getRegion(0).hasOneBlock())
-      return emitError(op->getLoc())
-             << "expected a module-like operation with a single region "
-                "containing a single block";
-    return success();
-  }
+  static LogicalResult checkIsModuleLike(Operation *op);
 };
 } // namespace mlir
 
-#endif // MLIR_EXECUTOR_UTILS_MODULEPASS
+#endif // MLIR_TENSORRT_COMMON_UTILS_MODULELIKEPASS

mlir-tensorrt/common/include/mlir-tensorrt-common/Utils/ModuleUtils.h

@@ -1,6 +1,6 @@
-//===- ModuleUtils.h --------------------------------------------*- C++ -*-===//
+//===- ModuleUtils.h -----------------------------------------------------===//
 //
-// SPDX-FileCopyrightText: Copyright 2025 NVIDIA CORPORATION & AFFILIATES.
+// SPDX-FileCopyrightText: Copyright 2025-2026 NVIDIA CORPORATION & AFFILIATES.
 // All rights reserved.
 // SPDX-License-Identifier: Apache-2.0
 //

mlir-tensorrt/common/include/mlir-tensorrt-common/Utils/PDLUtils.h

@@ -1,6 +1,6 @@
-//===- PDLUtils.h -----------------------------------------------*- C++ -*-===//
+//===- PDLUtils.h --------------------------------------------------------===//
 //
-// SPDX-FileCopyrightText: Copyright 2024 NVIDIA CORPORATION & AFFILIATES.
+// SPDX-FileCopyrightText: Copyright 2024-2026 NVIDIA CORPORATION & AFFILIATES.
 // All rights reserved.
 // SPDX-License-Identifier: Apache-2.0
 //
@@ -24,7 +24,6 @@
 #ifndef MLIR_TENSORRT_COMMON_UTILS_PDLUTILS
 #define MLIR_TENSORRT_COMMON_UTILS_PDLUTILS
 
-#include "mlir/IR/PatternMatch.h"
 #include "llvm/ADT/TypeSwitch.h"
 
 namespace mlir {