[Docs] Align documentation with tirx/s_tir namespace split

docs/arch/index.rst

@@ -83,14 +83,14 @@ relax transformations
 relax transformations contain a collection of passes that apply to relax functions. The optimizations include common graph-level
 optimizations such as constant folding and dead-code elimination for operators, and backend-specific optimizations such as library dispatch.
 
-tirx transformations
-^^^^^^^^^^^^^^^^^^^^
+TensorIR transformations
+^^^^^^^^^^^^^^^^^^^^^^^^
 
 - **TensorIR schedule**: TensorIR schedules are designed to optimize the TensorIR functions for a specific target, with user-guided instructions and control how the target code is generated.
-  For CPU targets, tirx PrimFunc can generate valid code and execute on the target device without schedule but with very-low performance. However, for GPU targets, the schedule is essential
+  For CPU targets, a TensorIR PrimFunc can generate valid code and execute on the target device without schedule but with very-low performance. However, for GPU targets, the schedule is essential
   for generating valid code with thread bindings. For more details, please refer to the :ref:`TensorIR Transformation <tirx-transform>` section. Additionally, we provides ``MetaSchedule`` to
   automate the search of TensorIR schedule.
-- **Lowering Passes**: These passes usually perform after the schedule is applied, transforming a tirx PrimFunc into another functionally equivalent PrimFunc, but closer to the
+- **Lowering Passes**: These passes usually perform after the schedule is applied, transforming a TensorIR PrimFunc into another functionally equivalent PrimFunc, but closer to the
   target-specific representation. For example, there are passes to flatten multi-dimensional access to one-dimensional pointer access, to expand the intrinsics into target-specific ones,
   and to decorate the function entry to meet the runtime calling convention.
 
@@ -101,12 +101,12 @@ focus on optimizations that are not covered by them.
 
 cross-level transformations
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^
-Apache TVM enables cross-level optimization of end-to-end models. As the IRModule includes both relax and tirx functions, the cross-level transformations are designed to mutate
+Apache TVM enables cross-level optimization of end-to-end models. As the IRModule includes both Relax and TensorIR functions, the cross-level transformations are designed to mutate
 the IRModule by applying different transformations to these two types of functions.
 
-For example, ``relax.LegalizeOps`` pass mutates the IRModule by lowering relax operators, adding corresponding tirx PrimFunc into the IRModule, and replacing the relax operators
-with calls to the lowered tirx PrimFunc. Another example is operator fusion pipeline in relax (including ``relax.FuseOps`` and ``relax.FuseTIR``), which fuses multiple consecutive tensor operations
-into one. Different from the previous implementations, relax fusion pipeline analyzes the pattern of tirx functions and detects the best fusion rules automatically rather
+For example, ``relax.LegalizeOps`` pass mutates the IRModule by lowering relax operators, adding corresponding TensorIR PrimFunc into the IRModule, and replacing the relax operators
+with calls to the lowered TensorIR PrimFunc. Another example is operator fusion pipeline in relax (including ``relax.FuseOps`` and ``relax.FuseTIR``), which fuses multiple consecutive tensor operations
+into one. Different from the previous implementations, relax fusion pipeline analyzes the pattern of TensorIR functions and detects the best fusion rules automatically rather
 than human-defined operator fusion patterns.
 
 Target Translation
@@ -306,22 +306,36 @@ 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 (split from the former ``tir`` module). ``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``.
+
+tvm/s_tir
+---------
+
+``s_tir`` (Schedulable TIR, split from the former ``tir`` module) 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.
 
 Please refer to the :ref:`TensorIR Deep Dive <tensor-ir-deep-dive>` for more details.
 
 tvm/arith
 ---------
 
-This module is closely tied to tirx. One of the key problems in the low-level code generation is the analysis of the indices'
+This module is closely tied to TensorIR. One of the key problems in the low-level code generation is the analysis of the indices'
 arithmetic properties — the positiveness, variable bound, and the integer set that describes the iterator space. arith module provides
-a collection of tools that do (primarily integer) analysis. A tirx pass can use these analyses to simplify and optimize the code.
+a collection of tools that do (primarily integer) analysis. A TensorIR pass can use these analyses to simplify and optimize the code.
 
 tvm/te and tvm/topi
 -------------------
@@ -330,7 +344,7 @@ TE stands for Tensor Expression. TE is a domain-specific language (DSL) for desc
 itself is not a self-contained function that can be stored into IRModule. We can use ``te.create_prim_func`` to convert a tensor expression to a ``tirx::PrimFunc``
 and then integrate it into the IRModule.
 
-While possible to construct operators directly via tirx or tensor expressions (TE) for each use case, it is tedious to do so.
+While possible to construct operators directly via TensorIR or tensor expressions (TE) for each use case, it is tedious to do so.
 `topi` (Tensor operator inventory) provides a set of pre-defined operators defined by numpy and found in common deep learning workloads.
 
 tvm/s_tir/meta_schedule
@@ -339,10 +353,10 @@ tvm/s_tir/meta_schedule
 MetaSchedule is a system for automated search-based program optimization,
 and can be used to optimize TensorIR schedules. Note that MetaSchedule only works with static-shape workloads.
 
-tvm/dlight
-----------
+tvm/s_tir/dlight
+----------------
 
-DLight is a set of pre-defined, easy-to-use, and performant tirx schedules. DLight aims:
+DLight is a set of pre-defined, easy-to-use, and performant s_tir schedules. DLight aims:
 
 - Fully support **dynamic shape workloads**.
 - **Light weight**. DLight schedules provides tuning-free schedule with reasonable performance.

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/index.rst

@@ -19,8 +19,18 @@
 
 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 consists of two modules (split from the former ``tir``):
+
+- **tirx** — Core IR definitions and lowering (PrimFunc, Buffer, SBlock,
+  expressions, statements, lowering passes).
+- **s_tir** (Schedulable TIR) — Schedule primitives, MetaSchedule, DLight,
+  and tensor intrinsics.
+
+In TVMScript, both modules are accessed via
+``from tvm.script import tirx as T``.
 
 .. toctree::
     :maxdepth: 2