[Doc] Update doc regarding dynamic index

docs/lang/articles/advanced/meta.md

@@ -166,26 +166,27 @@ def func():
   print(3)
 ```
 
-## When to use `ti.static` with for loops
-
-There are two reasons to use `ti.static` with for loops:
-
-- Loop unrolling for improving runtime performance (see [Compile-time evaluations](#compile-time-evaluations)).
-- Accessing elements of Taichi matrices/vectors. Indices for accessing Taichi fields can be runtime variables, while indices for Taichi matrices/vectors **must be a compile-time constant**.
+:::note
+Before v1.4.0, indices for accessing Taichi matrices/vectors must be compile-time constants.
+Therefore, if the indices come from a loop, the loop must be unrolled:
 
-For example, when accessing a vector field `x` with `x[field_index][vector_component_index]`, the `field_index` can be a runtime variable, while the `vector_component_index` must be a compile-time constant:
+```python {7}
+# Here we declare a field containing 3 vectors. Each vector contains 8 elements.
+x = ti.Vector.field(8, ti.f32, shape=3)
 
-```python {6}
-# Here we declare a field contains 3 vector. Each vector contains 8 elements.
-x = ti.Vector.field(8, ti.f32, shape=(3))
 @ti.kernel
 def reset():
-  for i in x:
-    for j in ti.static(range(x.n)):
-      # The inner loop must be unrolled since j is an index for accessing a vector
-      x[i][j] = 0
+    for i in x:
+        for j in ti.static(range(x.n)):
+            # The inner loop must be unrolled since j is an index for accessing a vector.
+            x[i][j] = 0
 ```
 
+Starting from v1.4.0, indices for accessing Taichi matrices/vectors can be runtime variables.
+Therefore, the loop above is no longer required to be unrolled.
+That said, unrolling it will still help you reduce runtime overhead.
+:::
+
 ## Compile-time recursion of `ti.func`
 
 A compile-time recursive function is a function with recursion that can be recursively inlined at compile time. The condition which determines whether to recurse is evaluated at compile time.

docs/lang/articles/contribution/write_test.md

@@ -283,4 +283,3 @@ Now, Taichi supports the following extensions:
 | bls           | Block-local storage                                           |
 | assertion     | Run-time asserts in Taichi kernels                            |
 | extfunc       | Support inserting external function calls or backend source   |
-| dynamic_index | Dynamic index support for tensors                             |

docs/lang/articles/reference/global_settings.md

@@ -58,9 +58,6 @@ Following are some frequently-used configurations in `ti.init()`:
     default_ip: [ti.i32, ti.i64]
         Set the default precision of integers in the Taichi scope.
 
-    dynamic_index: bool
-        Enable/disable the use of variables as indices when accessing vector/matrix elements in the Taichi scope.
-
     kernel_profiler: bool
         Turn on/off kernel performance profiling.
 

docs/lang/articles/reference/language_reference.md

@@ -252,12 +252,6 @@ Otherwise, `primary` has a Taichi type. All Taichi types excluding primitive
 types support subscriptions. You can refer to documentation of these types
 for subscription usage.
 
-:::note
-When `primary` has a Taichi matrix type, all expressions in `expression_list`
-are required to be evaluated to Python values. This restriction can be got rid
-of by setting `ti.init(dynamic_index=True)`.
-:::
-
 #### Slicings
 
 ```