v4.0 update. (#2371)

media/docs/pythonDSL/cute_dsl_general/autotuning_gemm.rst

@@ -3,10 +3,6 @@
 Guidance for Auto-Tuning
 ============================= 
 
-.. contents:: Table of Contents
-   :depth: 2
-   :local:
-
 Numerous GEMM kernel code examples are offered within our codebase. 
 When integrating these kernels into frameworks, auto-tuning becomes essential 
 for achieving optimal performance. This involves selecting the appropriate 

media/docs/pythonDSL/cute_dsl_general/debugging.rst

@@ -3,10 +3,6 @@
 Debugging
 =========
 
-.. contents:: Table of Contents
-   :depth: 2
-   :local:
-
 This page provides an overview of debugging techniques and tools for CuTe DSL programs.
 
 

media/docs/pythonDSL/cute_dsl_general/dsl_code_generation.rst

@@ -6,10 +6,6 @@
 End-to-End Code Generation
 ==========================
 
-.. contents::
-   :depth: 2
-   :local:
-
 
 1. Techniques for Turning Python into |IR|
 ------------------------------------------

media/docs/pythonDSL/cute_dsl_general/dsl_control_flow.rst

@@ -4,11 +4,8 @@
 .. |DSL|       replace:: CuTe DSL
 .. |Constexpr| replace:: **Constexpr** (compile-time Python value)
 
-|DSL| Control Flow
+Control Flow
 ==================
-.. contents::
-   :depth: 2
-   :local:
 
 
 Overview

media/docs/pythonDSL/cute_dsl_general/dsl_dynamic_layout.rst

@@ -3,10 +3,6 @@
 .. |SLAY| replace:: static layout
 .. |DLAY| replace:: dynamic layout
 
-.. contents:: Table of Contents
-   :depth: 2
-   :local:
-
 Static vs Dynamic layouts
 =========================
 

media/docs/pythonDSL/cute_dsl_general/dsl_introduction.rst

@@ -4,12 +4,9 @@
 .. |DSL| replace:: CuTe DSL
 
 
-|DSL|
+Introduction
 ======================
 
-.. contents:: Table of Contents
-   :depth: 2
-   :local:
 
 Overview
 --------

media/docs/pythonDSL/cute_dsl_general/dsl_jit_arg_generation.rst

@@ -2,12 +2,9 @@
 .. |DSL| replace:: CuTe DSL
 .. |CUSTOM_TYPES| replace:: customized types
 
-|DSL| JIT Function Argument Generation
+JIT Function Argument Generation
 =======================================
 
-.. contents:: Table of Contents
-   :depth: 2
-   :local:
 
 In a nutshell
 --------------
@@ -39,7 +36,7 @@ By default, |DSL| assumes dynamic arguments and tries to infer the argument type
     import cutlass.cute as cute
 
     @cute.jit
-    def foo(x: cutlass.Int32, y: cute.Constexpr):
+    def foo(x: cutlass.Int32, y: cutlass.Constexpr):
         print("x = ", x)        # Prints x = ?
         print("y = ", y)        # Prints y = 2
         cute.printf("x: {}", x) # Prints x: 2

media/docs/pythonDSL/cute_dsl_general/dsl_jit_caching.rst

@@ -3,11 +3,9 @@
 
 .. _JIT_Caching:
 
-|DSL| JIT Caching
+JIT Caching
 ====================
-.. contents:: Table of Contents
-   :depth: 2
-   :local:
+
 
 Zero Compile and JIT Executor
 -----------------------------

media/docs/pythonDSL/cute_dsl_general/framework_integration.rst

@@ -4,10 +4,6 @@
 Integration with Frameworks
 =============================
 
-.. contents:: Table of Contents
-   :depth: 2
-   :local:
-
 In order to facilitate the integration of CUTLASS Python with popular frameworks, we leverage the
 `DLPack protocol <https://github.com/dmlc/dlpack>`_ and transform tensors originating from these
 frameworks to CuTe tensors. The present page documents the conventions, the API available to the
@@ -257,8 +253,7 @@ layouts. The full signature of ``mark_compact_shape_dynamic`` is as follows:
 
 The ``mode`` parameter determines which shape dimension becomes dynamic. After calling this function,
 the specific shape dimension given by ``mode`` is marked as dynamic immediately. The stride will be
-updated accordingly but this process is delayed until the C ABI of the tensor is constructed.
-For modes that have a shape of size 1, their stride are canonicalized to 0.
+updated accordingly. For modes that have a shape of size 1, their stride are canonicalized to 0.
 
 The ``stride_order`` parameter specifies the ordering of strides in the tensor. It is consistent
 with ``torch.Tensor.dim_order()`` and defaults to ``None``. The parameter indicates the order of
@@ -322,10 +317,6 @@ The following example demonstrates how to use ``mark_compact_shape_dynamic`` to
     import torch
     from cutlass.cute.runtime import from_dlpack
 
-    @cute.jit
-    def kernel(t: cute.Tensor):
-        pass
-
     # (8,4,16,2):(2,16,64,1)
     a = torch.empty(16, 4, 8, 2).permute(2, 1, 0, 3)
     # (1,4,1,32,1):(4,1,4,4,4) => torch tensor when dimension has shape 1, its stride is degenerated to 1,
@@ -337,14 +328,12 @@ The following example demonstrates how to use ``mark_compact_shape_dynamic`` to
     t0 = from_dlpack(a).mark_compact_shape_dynamic(
         mode=0, divisibility=2
     )
-    kernel(t0)
     # (?{div=2},4,16,2):(2,?{div=4},?{div=16},1)
     print(t0)
 
     t1 = from_dlpack(a).mark_compact_shape_dynamic(
         mode=1, divisibility=2
     )
-    kernel(t1)
     # (8,?{div=2},16,2):(2,16,?{div=32},1)
     print(t1)
 
@@ -353,21 +342,18 @@ The following example demonstrates how to use ``mark_compact_shape_dynamic`` to
     ).mark_compact_shape_dynamic(
         mode=3, divisibility=2
     )
-    kernel(t2)
     # (8,?{div=2},16,?{div=2}):(?{div=2},?{div=16},?{div=32},1)
     print(t2)
 
     t3 = from_dlpack(b).mark_compact_shape_dynamic(
         mode=2, divisibility=1, stride_order=(3, 0, 2, 4, 1)
     )
-    kernel(t3)
     # (1,4,?,32,1):(0,1,4,?{div=4},0)
     print(t3)
 
     t4 = from_dlpack(b).mark_compact_shape_dynamic(
         mode=2, divisibility=1, stride_order=(2, 3, 4, 0, 1)
     )
-    kernel(t4)
     # (1,4,?,32,1):(0,1,128,4,0)
     print(t4)