finish1

docs/arch/index.rst

@@ -306,13 +306,33 @@ in the IRModule. Please refer to the :ref:`Relax Deep Dive <relax-deep-dive>` fo
 tvm/tirx
 --------
 
-tirx contains the definition of the low-level program representations. We use ``tirx::PrimFunc`` to represent functions that can be transformed by tirx passes.
-Besides the IR data structures, the tirx module also includes:
+``tirx`` contains the core IR definitions and lowering infrastructure for
+TensorIR. ``tirx::PrimFunc`` represents low-level tensor functions that can be
+transformed by tirx passes.
 
-- A set of analysis passes to analyze the tirx functions in ``tirx/analysis``.
-- A set of transformation passes to lower or optimize the tirx functions in ``tirx/transform``.
+The tirx module includes:
 
-The schedule primitives and tensor intrinsics are in ``s_tir/schedule`` and ``s_tir/tensor_intrin`` respectively.
+- IR data structures (PrimFunc, Buffer, SBlock, expressions, statements).
+- Analysis passes in ``tirx/analysis``.
+- Transformation and lowering passes in ``tirx/transform``.
+- Hardware-aware layout abstractions (TileLayout, SwizzleLayout, ComposeLayout).
+- Operator dispatch framework for mapping high-level operators to hardware-specific
+  implementations.
+- Async pipeline primitives (MBarrier, TMABar, TCGen05Bar) for Hopper/Blackwell.
+
+tvm/s_tir
+---------
+
+``s_tir`` (Schedulable TIR) contains schedule primitives and auto-tuning tools
+that operate on ``tirx::PrimFunc``:
+
+- Schedule primitives to control code generation (tiling, vectorization, thread
+  binding) in ``s_tir/schedule``.
+- Builtin tensor intrinsics in ``s_tir/tensor_intrin``.
+- MetaSchedule for automated performance tuning.
+- DLight for pre-defined, high-performance schedules.
+
+``s_tir`` depends on ``tirx``; ``tirx`` does not depend on ``s_tir``.
 
 Please refer to the :ref:`TensorIR Deep Dive <tensor-ir-deep-dive>` for more details.
 

docs/arch/pass_infra.rst

@@ -31,7 +31,7 @@ transformation using the analysis result collected during and/or before traversa
 However, as TVM evolves quickly, the need for a more systematic and efficient
 way to manage these passes is becoming apparent. In addition, a generic
 framework that manages the passes across different layers of the TVM stack (e.g.
-Relax and tirx) paves the way for developers to quickly prototype and plug the
+Relax and TensorIR) paves the way for developers to quickly prototype and plug the
 implemented passes into the system.
 
 This doc describes the design of such an infra that takes the advantage of the
@@ -166,7 +166,7 @@ Pass Constructs
 ^^^^^^^^^^^^^^^
 
 The pass infra is designed in a hierarchical manner, and it could work at
-different granularities of Relax/tirx programs. A pure virtual class ``PassNode`` is
+different granularities of Relax/TensorIR programs. A pure virtual class ``PassNode`` is
 introduced to serve as the base of the different optimization passes. This class
 contains several virtual methods that must be implemented by the
 subclasses at the level of modules, functions, or sequences of passes.
@@ -222,13 +222,13 @@ Function-Level Passes
 ^^^^^^^^^^^^^^^^^^^^^
 
 Function-level passes are used to implement various intra-function level
-optimizations for a given Relax/tirx module. It fetches one function at a time from
+optimizations for a given Relax/TensorIR module. It fetches one function at a time from
 the function list of a module for optimization and yields a rewritten Relax
-``Function`` or tirx ``PrimFunc``. Most of passes can be classified into this category, such as
+``Function`` or TensorIR ``PrimFunc``. Most of passes can be classified into this category, such as
 common subexpression elimination and inference simplification in Relax as well as vectorization
-and flattening storage in tirx, etc.
+and flattening storage in TensorIR, etc.
 
-Note that the scope of passes at this level is either a Relax function or a tirx primitive function.
+Note that the scope of passes at this level is either a Relax function or a TensorIR primitive function.
 Therefore, we cannot add or delete a function through these passes as they are not aware of
 the global information.
 

docs/arch/runtimes/vulkan.rst

@@ -254,6 +254,6 @@ string are all false boolean flags.
   validated with `spvValidate`_.
 
 * ``TVM_VULKAN_DEBUG_SHADER_SAVEPATH`` - A path to a directory.  If
-  set to a non-empty string, the Vulkan codegen will save tirx, binary
+  set to a non-empty string, the Vulkan codegen will save TIR, binary
   SPIR-V, and disassembled SPIR-V shaders to this directory, to be
   used for debugging purposes.

docs/deep_dive/tensor_ir/abstraction.rst

@@ -15,7 +15,7 @@
     specific language governing permissions and limitations
     under the License.
 
-.. _tir-abstraction:
+.. _tirx-abstraction-basics:
 
 Tensor Program Abstraction
 --------------------------

docs/deep_dive/tensor_ir/index.rst

@@ -19,8 +19,20 @@
 
 TensorIR
 ========
-TensorIR is one of the core abstraction in Apache TVM stack, which is used to
-represent and optimize the primitive tensor functions.
+TensorIR is one of the core abstractions in the Apache TVM stack, used to
+represent and optimize primitive tensor functions.
+
+The TensorIR codebase is organized into two modules:
+
+- **tirx** — Core IR definitions and lowering (PrimFunc, Buffer, SBlock,
+  expressions, statements, lowering passes).
+- **s_tir** (Schedulable TIR) — Schedule primitives, MetaSchedule, DLight, and
+  tensor intrinsics. These tools operate on tirx IR to apply performance
+  optimizations.
+
+In TVMScript, the recommended alias for new tirx code is
+``from tvm.script import tirx as Tx``. Existing code uses
+``from tvm.script import tirx as T``.
 
 .. toctree::
     :maxdepth: 2

docs/deep_dive/tensor_ir/tutorials/tir_creation.py

@@ -17,7 +17,7 @@
 # ruff: noqa: E402
 
 """
-.. _tir-creation:
+.. _tirx-creation:
 
 TensorIR Creation
 -----------------

docs/deep_dive/tensor_ir/tutorials/tir_transformation.py

@@ -19,10 +19,10 @@
 """
 .. _tirx-transform:
 
-Transformation
---------------
-In this section, we will get to the main ingredients of the compilation flows -
-transformations of primitive tensor functions.
+Transformation (s_tir)
+----------------------
+This section covers **s_tir** (Schedulable TIR) schedule primitives — the main
+tools for transforming primitive tensor functions for performance optimization.
 """
 
 ######################################################################

docs/reference/api/python/tirx/tirx.rst

@@ -20,4 +20,4 @@ tvm.tirx
 .. automodule:: tvm.tirx
    :members:
    :imported-members:
-   :exclude-members: PrimExpr, const, StmtSRef, SBlockScope, ScheduleState, Schedule, ScheduleError
+   :exclude-members: PrimExpr, const