diff --git a/docker/install/ubuntu_install_python_package.sh b/docker/install/ubuntu_install_python_package.sh
index d1fa340ac37d2..fb0f596d65527 100755
--- a/docker/install/ubuntu_install_python_package.sh
+++ b/docker/install/ubuntu_install_python_package.sh
@@ -36,6 +36,6 @@ pip3 install \
     pytest-xdist \
     requests \
     scipy \
-    synr==0.4.1 \
+    synr==0.5.0 \
     six \
     tornado
diff --git a/include/tvm/tir/function.h b/include/tvm/tir/function.h
index 23057f7140e4b..e4a3d3d1e21b6 100644
--- a/include/tvm/tir/function.h
+++ b/include/tvm/tir/function.h
@@ -199,9 +199,10 @@ class LinkedParam : public ObjectRef {
  *  def mem_copy(a: T.handle, b: T.handle, m: T.int32, n: T.int32) -> None:
  *      A = T.match_buffer(a, (m, n), "float32")
  *      B = T.match_buffer(b, (m, n), "float32")
- *
- *      with T.block([m, n], "") as [vi, vj]:
- *          B[vi, vj] = A[vi, vj]
+ *      for i, j in T.grid(m, n):
+ *          with T.block():
+ *              vi, vj = T.axis.remap("SS", [i, j])
+ *              B[vi, vj] = A[vi, vj]
  * \endcode
  *
  * Then we can make it specialized with given shapes or buffers.
@@ -218,9 +219,10 @@ class LinkedParam : public ObjectRef {
  *  def mem_copy_16_16(a: T.handle, b: T.handle) -> None:
  *      A = T.match_buffer(a, (16, 16), "float32")
  *      B = T.match_buffer(b, (16, 16), "float32")
- *
- *      with T.block([16, 16], "") as [vi, vj]:
- *          B[vi, vj] = A[vi, vj]
+ *      for i, j in T.grid(16, 16):
+ *          with T.block():
+ *              vi, vj = T.axis.remap("SS", [i, j])
+ *              B[vi, vj] = A[vi, vj]
  * \endcode
  */
 PrimFunc Specialize(PrimFunc func, const Map<Var, ObjectRef>& param_map);
diff --git a/include/tvm/tir/stmt.h b/include/tvm/tir/stmt.h
index 5cd860b8e9294..4f5772822d9e9 100644
--- a/include/tvm/tir/stmt.h
+++ b/include/tvm/tir/stmt.h
@@ -1078,9 +1078,9 @@ class MatchBufferRegion : public ObjectRef {
  * \note Block's body is parameterized by iter vars.
  * \code
  *
- *  with T.block([extent0, extent1, ...], name) as [v0, v1, ...]:
- *      T.bind(v0, value0)
- *      T.bind(v1, value1)
+ *  with T.block(name):
+ *      v0 = T.axis.S(domain, value0)
+ *      v1 = T.axis.R(domain, value1)
  *      ...
  *      T.reads([buffer0[start:end, ...], ...])
  *      T.writes([buffer1[start:end, ...], ...])
diff --git a/include/tvm/tir/transform.h b/include/tvm/tir/transform.h
index 017078bd7bf7c..e6b0af9773d96 100644
--- a/include/tvm/tir/transform.h
+++ b/include/tvm/tir/transform.h
@@ -388,7 +388,7 @@ TVM_DLL Pass ConvertBlocksToOpaque();
  *  \code
  *
  *  for i in range(0, 16):
- *      with T.block([]):
+ *      with T.block():
  *          B = T.alloc_buffer(16, 16)
  *          for j in range(0, 16):
  *              B[i, j] = A[i, j] + 1
@@ -404,7 +404,7 @@ TVM_DLL Pass ConvertBlocksToOpaque();
  *  \code
  *
  *  for i in range(0, 16):
- *      with T.block([]):
+ *      with T.block():
  *          B = T.alloc_buffer(1, 16)
  *          for j in range(0, 16):
  *              B[0, j] = A[i, j] + 1
diff --git a/python/gen_requirements.py b/python/gen_requirements.py
index fa94d6a641304..cdffdd95e5467 100755
--- a/python/gen_requirements.py
+++ b/python/gen_requirements.py
@@ -250,7 +250,7 @@
     ("sphinx_autodoc_annotation", None),
     ("sphinx_gallery", None),
     ("sphinx_rtd_theme", None),
-    ("synr", "==0.4.1"),
+    ("synr", "==0.5.0"),
     ("tensorflow", None),
     ("tensorflow-estimator", None),
     ("tflite", None),
diff --git a/python/tvm/script/context_maintainer.py b/python/tvm/script/context_maintainer.py
index 75566cf6e2c5e..56d080857a7d3 100644
--- a/python/tvm/script/context_maintainer.py
+++ b/python/tvm/script/context_maintainer.py
@@ -22,8 +22,10 @@
 
 import tvm
 from tvm.ir import Span
+from tvm.ir.expr import Range
 from tvm.tir import Var, Buffer, PrimExpr, Stmt, MatchBufferRegion
 from tvm.runtime import Object
+from tvm.tir.expr import IterVar
 from .tir.node import BufferSlice
 
 
@@ -41,10 +43,10 @@ def example_func(a: T.handle, b: T.handle, c: T.handle) -> None:
             C = T.match_buffer(a, (16, 16), "float32")
 
             for i, j, k in T.grid(16, 16, 16):
-                with T.block([16, 16, T.reduce_axis(16)], "matmul") as [vi, vj, vk]:
-                    T.bind(vi, i)
-                    T.bind(vj, j)
-                    T.bind(vk, k)         # iter_bindings = {vj: i, vj: j, vk: k}
+                with T.block("matmul"):
+                    vi = T.axis.S(16, i)
+                    vj = T.axis.S(16, j)
+                    vk = T.axis.R(16, k)         # iter_bindings = {vj: i, vj: j, vk: k}
 
                     T.where(True)         # predicate of the block_realize
 
@@ -72,8 +74,10 @@ def example_func(a: T.handle, b: T.handle, c: T.handle) -> None:
     """List[Buffer]: list of T.alloc_buffer statements in the block signature"""
     match_buffers: List[MatchBufferRegion] = []
     """List[MatchBufferRegion]: list of T.match_buffer statements in the block signature"""
-    iter_bindings: Mapping[Var, PrimExpr] = {}
-    """Mapping[Var, PrimExpr]: map of block iter var to its values"""
+    iter_values: List[PrimExpr] = []
+    """List[PrimExpr]: list of binding values for iter vars"""
+    iter_vars: List[IterVar] = []
+    """List[PrimExpr]: list of iter vars in the block"""
     reads: Optional[List[BufferSlice]] = None
     """Optional[List[BufferSlice]]:
     list of T.reads statements in the block signature, None for not-visited"""
@@ -91,7 +95,8 @@ def example_func(a: T.handle, b: T.handle, c: T.handle) -> None:
     def __init__(self):
         self.alloc_buffers = []
         self.match_buffers = []
-        self.iter_bindings = {}
+        self.iter_values = []
+        self.iter_vars = []
         self.reads = None
         self.writes = None
         self.annotations = None
@@ -112,8 +117,8 @@ class ContextMaintainer:
     """List[List[synr.ast.Node]]: The ast nodes insides the current scope"""
     block_info_stack: List[BlockInfo] = []
     """List[BlockInfo]: The block info for the current block scope"""
-    loop_stack: List[List[Var]] = []
-    """List[List[Var]]: List of loop vars inside the current block scope"""
+    loop_stack: Dict[Var, Range] = {}
+    """Dict[Var, Range]: The dict from loop var to its domain outside the block"""
     symbols: List[Dict[str, Union[Var, Buffer]]] = []
     """List[Dict[str, Union[Var, Buffer]]]: Symbol map from name to object for the current scope"""
 
@@ -137,7 +142,7 @@ def __init__(self, _report_error: Callable[[str, Union[Span, synr.ast.Span]], No
         # scope context
         self.node_stack = []
         self.block_info_stack = []
-        self.loop_stack = []
+        self.loop_stack = {}
         self.symbols = []
         # function context
         self.func_params = []
@@ -183,8 +188,6 @@ def enter_block_scope(self, nodes: Optional[List[synr.ast.Node]] = None):
             The synr AST nodes in new scope
         """
         self.enter_scope(nodes)
-        # Create a new loop stack for the new block
-        self.loop_stack.append([])
         # Create a new BlockInfo for the new block
         self.block_info_stack.append(BlockInfo())
 
@@ -196,8 +199,6 @@ def exit_scope(self):
     def exit_block_scope(self):
         """Pop the inner most block scope, the function will call `exit_scope` implicitly"""
         self.exit_scope()
-        # Pop loop stack
-        self.loop_stack.pop()
         # Pop block_info
         self.block_info_stack.pop()
 
diff --git a/python/tvm/script/parser.py b/python/tvm/script/parser.py
index d5e79e8676c16..8610d91e9f075 100644
--- a/python/tvm/script/parser.py
+++ b/python/tvm/script/parser.py
@@ -377,12 +377,13 @@ def A():
         """
         if len(node.assignments) == 1:
             if not (
-                isinstance(node.assignments[0].lhs, ast.Var)
-                and node.assignments[0].lhs.id.name == "__tvm_meta__"
+                len(node.assignments[0].lhs) == 1
+                and isinstance(node.assignments[0].lhs[0], ast.Var)
+                and node.assignments[0].lhs[0].id.name == "__tvm_meta__"
             ):
                 self.report_error(
                     "The only top level assignments allowed are `__tvm_meta__ = ...`",
-                    node.assignments[0].lhs.span,
+                    node.assignments[0].span,
                 )
             self.init_meta(
                 MetaUnparser().do_transform(node.assignments[0].rhs, self._diagnostic_context)
@@ -526,18 +527,19 @@ def transform_Assign(self, node):
                 return self.parse_body(node)
             else:
                 value = self.transform(node.rhs)
-                if not isinstance(node.lhs, ast.Var):
+                if len(node.lhs) == 1 and not isinstance(node.lhs[0], ast.Var):
                     # This is a little confusing because it only is true when
                     # we have taken this branch. We might need to clarify what
                     # exectly is allowed in Assignments in tvmscript.
                     self.report_error(
                         "Left hand side of assignment must be an unqualified variable",
-                        node.lhs.span,
+                        node.span,
                     )
+                ast_var = node.lhs[0]
                 var = tvm.te.var(
-                    node.lhs.id.name,
-                    self.parse_type(node.ty, node.lhs),
-                    span=tvm_span_from_synr(node.lhs.span),
+                    ast_var.id.name,
+                    self.parse_type(node.ty, ast_var),
+                    span=tvm_span_from_synr(ast_var.span),
                 )
                 self.context.update_symbol(var.name, var, node)
                 body = self.parse_body(node)
@@ -596,7 +598,7 @@ def transform_For(self, node):
             For(expr target, expr iter, stmt* body, stmt* orelse, string? type_comment)
         By now 1 pattern of For is supported:
             1. for scope handler
-                for name in T.serial()/T.parallel()/T.vectorized()/T.unroll()/tir.range()/
+                for name in T.serial()/T.parallel()/T.vectorized()/T.unroll()/range()/
                             T.grid()/T.thread_binding()
         """
 
@@ -892,9 +894,20 @@ def transform_Attr(self, node):
            namespace.
         """
 
-        if isinstance(node.object, ast.Var):
-            if self.match_tir_namespace(node.object.id.name):
-                func_name = "tir." + node.field.name
+        def get_full_attr_name(node: ast.Attr) -> str:
+            reverse_field_names = [node.field.name]
+            while isinstance(node.object, ast.Attr):
+                node = node.object
+                reverse_field_names.append(node.field.name)
+            if isinstance(node.object, ast.Var):
+                reverse_field_names.append(node.object.id.name)
+            return ".".join(reversed(reverse_field_names))
+
+        if isinstance(node.object, (ast.Var, ast.Attr)):
+            full_attr_name = get_full_attr_name(node)
+            attr_object, fields = full_attr_name.split(".", maxsplit=1)
+            if self.match_tir_namespace(attr_object):
+                func_name = "tir." + fields
                 res = Registry.lookup(func_name)
                 if res is not None:
                     return res
@@ -903,9 +916,7 @@ def transform_Attr(self, node):
                 except TVMError as e:
                     # Check if we got an attribute error
                     if e.args[0].find("AttributeError"):
-                        self.report_error(
-                            f"Unregistered function `tir.{node.field.name}`.", node.field.span
-                        )
+                        self.report_error(f"Unregistered function `tir.{fields}`.", node.span)
                     else:
                         raise e
 
diff --git a/python/tvm/script/tir/scope_handler.py b/python/tvm/script/tir/scope_handler.py
index 487a71d4f0779..4750ad7626e2d 100644
--- a/python/tvm/script/tir/scope_handler.py
+++ b/python/tvm/script/tir/scope_handler.py
@@ -134,12 +134,14 @@ def enter_scope(
         if isinstance(node, synr.ast.With):
             vars = WithScopeHandler.get_optional_vars(node, context)
             if len(vars) != 1:
-                context.report_error("Unexpected number of vars", node.span)
+                context.report_error(f"Unexpected number of vars: 1 vs. {len(vars)}", node.span)
             name = vars[0].id.name
             var_span = vars[0].id.span
         elif isinstance(node, synr.ast.Assign):
-            name = node.lhs.id.name
-            var_span = node.lhs.id.span
+            if len(node.lhs) != 1:
+                context.report_error(f"Unexpected number of vars: 1 vs. {len(node.lhs)}", node.span)
+            name = node.lhs[0].id.name
+            var_span = node.lhs[0].id.span
         else:
             raise Exception("Internal Bug")
 
@@ -247,42 +249,16 @@ def let(var, value, span):
 
 @register
 class Block(WithScopeHandler):
-    """With scope handler T.block(extents, name) as iter_vars"""
+    """With scope handler T.block(name)"""
 
     def __init__(self):
-        def block(axes=None, name_hint: str = "", span: Optional[Span] = None):
+        def block(name_hint: str = "", span: Optional[Span] = None):
             assert (
                 self.node and self.context and self.body
             ), "call 'exit_scope' before 'enter_scope'"
             block_info = self.context.block_info_stack[-1]
-            if axes is None:
-                axes = []
-            if len(axes) != len(self.block_vars):
-                self.context.report_error(
-                    "Inconsistent number of block vars, "
-                    + f"there are {len(axes)} axes but {len(self.block_vars)} block vars. "
-                    + "The number of block vars should match the number of axes.",
-                    self.node.span,
-                )
-            block_iters: List[IterVar] = []
-            for i, axis in enumerate(axes):
-                axis = tvm.runtime.convert(axis)
-                if isinstance(axis, tvm.tir.PrimExpr):
-                    block_var_dom = Range.from_min_extent(0, axis)
-                    block_iters.append(IterVar(block_var_dom, self.block_vars[i], 0))
-                elif isinstance(axis, Range):
-                    block_iters.append(IterVar(axis, self.block_vars[i], 0))
-                elif isinstance(axis, IterVar):
-                    block_iters.append(IterVar(axis.dom, self.block_vars[i], axis.iter_type))
-                else:
-                    self.context.report_error(
-                        "Invalid argument of T.block(), "
-                        + f"expected PrimExpr, Range or IterVar, but got {type(axis)}",
-                        self.node.span,
-                    )
 
             # create block read/write regions
-
             reads: List[BufferRegion] = (
                 [buffer_slice_to_region(read) for read in block_info.reads]
                 if block_info.reads
@@ -301,7 +277,7 @@ def block(axes=None, name_hint: str = "", span: Optional[Span] = None):
             if region_detect_mask != 0:
                 annotations["tir.script_parsing_detect_access"] = region_detect_mask
             inner = tvm.tir.Block(
-                block_iters,
+                block_info.iter_vars,
                 reads,
                 writes,
                 name_hint,
@@ -312,35 +288,13 @@ def block(axes=None, name_hint: str = "", span: Optional[Span] = None):
                 annotations,
                 span,
             )
-            # create block var iter binding
-            values: List[PrimExpr]
-            if not block_info.iter_bindings:
-                values = self.context.loop_stack[-2].copy()
-                if len(block_iters) == 0:
-                    # It is an opaque block without any bindings
-                    values = []
-                elif len(values) == 0:
-                    values = [tvm.tir.const(float("nan"), dtype="float32")] * len(block_iters)
-                elif len(values) != len(block_iters):
-                    self.context.report_error(
-                        "Number of block iter var and outer loop nesting mismatch, "
-                        + f"{len(block_iters)} block iter vars but {len(values)} loops",
-                        self.node.span,
-                    )
-            else:
-                for block_var in self.block_vars:
-                    if block_var not in block_info.iter_bindings:
-                        self.context.report_error(
-                            "Missing block iter var binding for " + block_var.name,
-                            self.node.span,
-                        )
-                values = [block_info.iter_bindings[block_var] for block_var in self.block_vars]
+            assert len(block_info.iter_vars) == len(block_info.iter_values)
             predicate = (
                 tvm.tir.const(True, "bool")
                 if block_info.predicate is None
                 else block_info.predicate
             )
-            body = tvm.tir.BlockRealize(values, predicate, inner, span)
+            body = tvm.tir.BlockRealize(block_info.iter_values, predicate, inner, span)
             return body
 
         super().__init__(func=block, concise_scope=False, def_symbol=True)
@@ -358,10 +312,13 @@ def enter_scope(
             node, synr.ast.With
         ), f"BlockScopeHandler expected to work on synr.ast.With but got {type(node)}"
 
-        vars = WithScopeHandler.get_optional_vars(node, context)
-        self.block_vars = [tvm.te.var(var.id.name) for var in vars]
-        for block_var in self.block_vars:
-            context.update_symbol(block_var.name, block_var, node)
+        optional_vars = [var.id.name for var in WithScopeHandler.get_optional_vars(node, context)]
+        if optional_vars:
+            context.report_error(
+                f"Block expected no optional_vars (e.g., `x` in `with block() as x`), "
+                f"but got {optional_vars}",
+                node.span,
+            )
 
 
 @register
@@ -378,12 +335,38 @@ def init(span: Span = None):
         super().__init__(func=init, concise_scope=False, def_symbol=True)
 
 
+class LoopInfo:
+    """Helper class for loop information"""
+
+    loop_var: Var
+    begin: PrimExpr
+    extent: PrimExpr
+    kind: ForKind
+    thread_binding: Optional[str]
+    annotations: Optional[Mapping[str, Object]]
+
+    def __init__(
+        self,
+        begin: PrimExpr,
+        extent: PrimExpr,
+        kind: ForKind,
+        thread_binding: Optional[str] = None,
+        annotations: Optional[Mapping[str, Object]] = None,
+    ) -> None:
+        self.begin = begin
+        self.extent = extent
+        self.kind = kind
+        self.thread_binding = thread_binding
+        self.annotations = annotations
+
+
 class ForScopeHandler(ScopeHandler):
     """Base class for all for scope handlers"""
 
     def __init__(self, func):
         super().__init__(func)
-        self.loop_vars: Optional[List[Var]] = None
+        self.loop_vars: List[Var] = []
+        self.loop_info: List[LoopInfo] = []
 
     def enter_scope(
         self,
@@ -415,12 +398,23 @@ def enter_scope(
                 span,
             )
 
+        self.node = node
+        self.context = context
+        # generate loop vars
         self.loop_vars = [
             tvm.te.var(name, dtype="int32", span=span) for name, span in zip(loop_var_names, spans)
         ]
-        for loop_var in self.loop_vars:
+        # collect loop infos by calling self.func
+        call_with_error_reporting(context.report_error, span, self.func, *arg_list)
+        if len(self.loop_vars) != len(self.loop_info):
+            self.context.report_error(
+                f"Inconsistent number of vars and loops, got {len(self.loop_vars)} "
+                + f"vs {len(self.loop_info)}",
+                self.node.span,
+            )
+        for loop_var, loop_info in zip(self.loop_vars, self.loop_info):
             context.update_symbol(loop_var.name, loop_var, node)
-            context.loop_stack[-1].append(loop_var)
+            context.loop_stack[loop_var] = Range.from_min_extent(loop_info.begin, loop_info.extent)
 
     def exit_scope(
         self,
@@ -430,19 +424,34 @@ def exit_scope(
         span: synr.ast.Span,
     ):
         assert self.loop_vars, "call 'exit_scope' before 'enter_scope'"
-        for _ in self.loop_vars:
-            context.loop_stack[-1].pop()
-        return super().exit_scope(node, context, arg_list, span)
+        for loop_var in self.loop_vars:
+            context.loop_stack.pop(loop_var)
+        # Use assert here since we have check it in `enter_scope`
+        assert len(self.loop_vars) == len(self.loop_info)
+
+        body = self.body
+        for var, info in zip(reversed(self.loop_vars), reversed(self.loop_info)):
+            body = tvm.tir.For(
+                var,
+                info.begin,
+                info.extent,
+                info.kind,
+                body,
+                info.thread_binding,
+                info.annotations,
+                span=tvm_span_from_synr(span),
+            )
 
-    def create_loop(
+        return body
+
+    def create_loop_info(
         self,
         begin: PrimExpr,
         end: PrimExpr,
         kind: ForKind,
         thread_binding: Optional[str] = None,
         annotations: Optional[Mapping[str, Object]] = None,
-        span: Optional[Span] = None,
-    ) -> tvm.tir.For:
+    ) -> None:
         """
         Helper function for creating For in TVM Script parser.
 
@@ -471,30 +480,16 @@ def create_loop(
         for : For
             The constructed For.
         """
-        assert (
-            self.loop_vars and self.context and self.node
-        ), "call 'exit_scope' before 'enter_scope'"
-        if len(self.loop_vars) != 1:
-            self.context.report_error(
-                f"Expected exactly one loop var, but got {self.loop_vars}", self.node.span
-            )
+        assert self.context and self.node, "call 'exit_scope' before 'enter_scope'"
         extent = end if begin == 0 else self.context.analyzer.simplify(end - begin)
-        annos: Mapping[str, Object] = {}
+        self.annotations: Mapping[str, Object] = {}
         if annotations is not None:
-            annos = {
+            self.annotations = {
                 key: tvm.tir.StringImm(val) if isinstance(val, str) else val
                 for key, val in annotations.items()
             }
-        return tvm.tir.For(
-            self.loop_vars[0],
-            begin,
-            extent,
-            kind,
-            self.body,
-            thread_binding=thread_binding,
-            annotations=annos,
-            span=span,
-        )
+
+        self.loop_info.append(LoopInfo(begin, extent, kind, thread_binding, annotations))
 
 
 @register
@@ -506,9 +501,8 @@ def serial(
             begin: PrimExpr,
             end: PrimExpr,
             annotations: Optional[Mapping[str, Object]] = None,
-            span: Optional[Span] = None,
         ):
-            return self.create_loop(begin, end, ForKind.SERIAL, annotations=annotations, span=span)
+            self.create_loop_info(begin, end, ForKind.SERIAL, annotations=annotations)
 
         super().__init__(serial)
 
@@ -522,11 +516,8 @@ def parallel(
             begin: PrimExpr,
             end: PrimExpr,
             annotations: Optional[Mapping[str, Object]] = None,
-            span: Optional[Span] = None,
         ):
-            return self.create_loop(
-                begin, end, ForKind.PARALLEL, annotations=annotations, span=span
-            )
+            self.create_loop_info(begin, end, ForKind.PARALLEL, annotations=annotations)
 
         super().__init__(parallel)
 
@@ -540,11 +531,8 @@ def vectorized(
             begin: PrimExpr,
             end: PrimExpr,
             annotations: Optional[Mapping[str, Object]] = None,
-            span: Optional[Span] = None,
         ):
-            return self.create_loop(
-                begin, end, ForKind.VECTORIZED, annotations=annotations, span=span
-            )
+            self.create_loop_info(begin, end, ForKind.VECTORIZED, annotations=annotations)
 
         super().__init__(vectorized)
 
@@ -558,11 +546,8 @@ def unroll(
             begin: PrimExpr,
             end: PrimExpr,
             annotations: Optional[Mapping[str, Object]] = None,
-            span: Optional[Span] = None,
         ):
-            return self.create_loop(
-                begin, end, ForKind.UNROLLED, annotations=annotations, span=span
-            )
+            self.create_loop_info(begin, end, ForKind.UNROLLED, annotations=annotations)
 
         super().__init__(unroll)
 
@@ -577,16 +562,14 @@ def thread_binding(
             end: PrimExpr,
             thread: str,
             annotations: Optional[Mapping[str, Object]] = None,
-            span: Optional[Span] = None,
         ):
-            thread_iter_var = IterVar(None, None, IterVar.ThreadIndex, thread, span=span)
-            return self.create_loop(
+            thread_iter_var = IterVar(None, None, IterVar.ThreadIndex, thread)
+            self.create_loop_info(
                 begin,
                 end,
                 ForKind.THREAD_BINDING,
                 thread_binding=thread_iter_var,
                 annotations=annotations,
-                span=span,
             )
 
         super().__init__(thread_binding)
@@ -603,12 +586,11 @@ def for_range(
             begin: PrimExpr,
             end: PrimExpr = None,
             annotations: Optional[Mapping[str, Object]] = None,
-            span: Optional[Span] = None,
         ):
             if end is None:
                 end = begin
                 begin = 0
-            return self.create_loop(begin, end, ForKind.SERIAL, annotations=annotations, span=span)
+            self.create_loop_info(begin, end, ForKind.SERIAL, annotations=annotations)
 
         super().__init__(for_range)
 
@@ -621,19 +603,8 @@ class Grid(ForScopeHandler):
     """For scope handler T.grid(extents)"""
 
     def __init__(self):
-        def grid(*extents: List[PrimExpr], span: Span):
-            assert (
-                self.node and self.context and self.loop_vars
-            ), "call 'exit_scope' before 'enter_scope'"
-            if len(self.loop_vars) != len(extents):
-                self.context.report_error(
-                    "Inconsistent number of loop vars and extents, "
-                    + f"got {len(self.loop_vars)} vs {len(extents)}",
-                    self.node.span,
-                )
-            body = self.body
-            for loop_var, extent in zip(reversed(self.loop_vars), reversed(extents)):
-                body = tvm.tir.For(loop_var, 0, extent, ForKind.SERIAL, body, span=span)
-            return body
+        def grid(*extents: List[PrimExpr]):
+            for extent in extents:
+                self.create_loop_info(0, extent, ForKind.SERIAL)
 
         super().__init__(grid)
diff --git a/python/tvm/script/tir/special_stmt.py b/python/tvm/script/tir/special_stmt.py
index 69cf15f493de4..de212352f3e43 100644
--- a/python/tvm/script/tir/special_stmt.py
+++ b/python/tvm/script/tir/special_stmt.py
@@ -21,17 +21,18 @@
 
 import synr
 from synr import ast
+from tvm.ir.expr import PrimExpr, Range
 
 import tvm.tir
 from tvm.runtime import Object
 from tvm import te
 from tvm.ir import Span
-from tvm.tir import IntImm
+from tvm.tir import IntImm, IterVar
 
 from .node import BufferSlice
 from .utils import buffer_slice_to_region
 
-from ..context_maintainer import ContextMaintainer
+from ..context_maintainer import BlockInfo, ContextMaintainer
 from ..registry import register
 from ..utils import (
     get_param_list,
@@ -132,9 +133,10 @@ def match_buffer(
             buffer_type="default",
             span=None,
         ):
-            if not isinstance(self.node, ast.Assign):
+            if not isinstance(self.node, ast.Assign) or not len(self.node.lhs) == 1:
                 self.context.report_error(
-                    "match_buffer must be assigned to a buffer, e.g. A = match_buffer(...)",
+                    "`match_buffer` must be assigned to a single buffer, "
+                    "e.g. A = match_buffer(...)",
                     self.node.span,
                 )
             if strides is None:
@@ -143,10 +145,11 @@ def match_buffer(
             offset_factor = convert_to_int(
                 offset_factor, "offset_factor", self.context.report_error, self.node.span
             )
+            buffer_name: str = self.node.lhs[0].id.name
             buffer = tvm.tir.decl_buffer(
                 shape,
                 dtype,
-                self.node.lhs.id.name,
+                buffer_name,
                 data,
                 strides,
                 elem_offset,
@@ -173,7 +176,7 @@ def match_buffer(
                     + str(type(param)),
                     self.node.rhs.params[0].span,
                 )
-            self.context.update_symbol(self.node.lhs.id.name, buffer, self.node)
+            self.context.update_symbol(buffer_name, buffer, self.node)
 
         super().__init__(match_buffer, def_symbol=True)
 
@@ -201,9 +204,9 @@ def buffer_decl(
             buffer_type="default",
             span=None,
         ):
-            if not isinstance(self.node, ast.Assign):
+            if not isinstance(self.node, ast.Assign) or not len(self.node.lhs) == 1:
                 self.context.report_error(
-                    "buffer_decl must be assigned to a buffer, e.g. A = buffer_decl(...)",
+                    "`buffer_decl` must be assigned to a single buffer, e.g. A = buffer_decl(...)",
                     self.node.span,
                 )
 
@@ -213,10 +216,11 @@ def buffer_decl(
             offset_factor = convert_to_int(
                 offset_factor, "offset_factor", self.context.report_error, self.node.span
             )
+            buffer_name: str = self.node.lhs[0].id.name
             buffer = tvm.tir.decl_buffer(
                 shape,
                 dtype,
-                self.node.lhs.id.name,
+                buffer_name,
                 data,
                 strides,
                 elem_offset,
@@ -226,7 +230,7 @@ def buffer_decl(
                 buffer_type,
                 span=span,
             )
-            self.context.update_symbol(self.node.lhs.id.name, buffer, self.node)
+            self.context.update_symbol(buffer_name, buffer, self.node)
             return buffer
 
         super().__init__(buffer_decl, def_symbol=True)
@@ -257,9 +261,10 @@ def alloc_buffer(
             buffer_type="default",
             span=None,
         ):
-            if not isinstance(self.node, ast.Assign):
+            if not isinstance(self.node, ast.Assign) or not len(self.node.lhs) == 1:
                 self.context.report_error(
-                    "alloc_buffer must be assigned to a buffer, e.g. A = alloc_buffer(...)",
+                    "`alloc_buffer` must be assigned to a single buffer, "
+                    "e.g. A = alloc_buffer(...)",
                     self.node.span,
                 )
 
@@ -269,10 +274,11 @@ def alloc_buffer(
             offset_factor = convert_to_int(
                 offset_factor, "offset_factor", self.context.report_error, self.node.span
             )
+            buffer_name: str = self.node.lhs[0].id.name
             buffer = tvm.tir.decl_buffer(
                 shape,
                 dtype,
-                self.node.lhs.id.name,
+                buffer_name,
                 data,
                 strides,
                 elem_offset,
@@ -283,32 +289,11 @@ def alloc_buffer(
                 span=span,
             )
             self.context.current_block_scope().alloc_buffers.append(buffer)
-            self.context.update_symbol(self.node.lhs.id.name, buffer, self.node)
+            self.context.update_symbol(buffer_name, buffer, self.node)
 
         super().__init__(alloc_buffer, def_symbol=True)
 
 
-@register
-class BlockVarBind(SpecialStmt):
-    """Special function bind(block_iter, binding_value)
-
-    Example
-    -------
-    .. code-block:: python
-
-        T.bind(vx, i)
-    """
-
-    def __init__(self):
-        def bind(iter_var, values, span=None):
-            block_scope = self.context.current_block_scope()
-            if iter_var in block_scope.iter_bindings:
-                self.context.report_error("Duplicate iter_var bindings of " + str(iter_var), span)
-            block_scope.iter_bindings[iter_var] = values
-
-        super().__init__(bind, def_symbol=False)
-
-
 @register
 class BlockReads(SpecialStmt):
     """Special function reads([read_buffer_regions])
@@ -412,6 +397,315 @@ def block_attr(attrs: Mapping[str, Object], span: Span = None):
         super().__init__(block_attr, def_symbol=False)
 
 
+class BlockAxis(SpecialStmt):
+    """Special stmt for defining a spatial block axis
+    axis.S(dom, iter_value)
+
+    Example
+    -------
+    .. code-block:: python
+
+        vi = T.axis.S(128, i * 4 + j)
+    """
+
+    def axis(
+        self,
+        var_name: str,
+        dom: Union[PrimExpr, Range],
+        value: PrimExpr,
+        iter_type: int,
+        span: Optional[Span] = None,
+    ) -> None:
+        """
+        Helper function for creating block axis
+
+        Parameters
+        ----------
+        var_name : str
+            The name_hint of var
+
+        dom : Union[PrimExpr, Range]
+            The iter domain.
+
+        value : PrimExpr
+            The binding value
+
+        iter_type : int
+            The iteration type.
+
+        span : Optional[Span]
+            The location of this for in the source code.
+        """
+        assert self.context, "call 'exit_scope' before 'enter_scope'"
+        block_scope: BlockInfo = self.context.current_block_scope()
+        if var_name in [iter_var.var.name for iter_var in block_scope.iter_vars]:
+            self.context.report_error("Duplicate block axis " + var_name, self.node.span)
+
+        block_var = tvm.tir.Var(var_name, dtype="int32")
+        dom = tvm.runtime.convert(dom)
+        if isinstance(dom, PrimExpr):
+            dom = tvm.ir.Range.from_min_extent(0, dom)
+        elif not isinstance(dom, tvm.ir.Range):
+            self.context.report_error(
+                f"Block axis domain expected PrimExpr or Range, but got {type(value)}",
+                self.node.span,
+            )
+        value = tvm.runtime.convert(value)
+        if not isinstance(value, PrimExpr):
+            self.context.report_error(
+                f"Block axis value expected PrimExpr, but got {type(value)}",
+                self.node.span,
+            )
+        iter_var = tvm.tir.IterVar(dom, block_var, iter_type)
+        block_scope.iter_vars.append(iter_var)
+        block_scope.iter_values.append(value)
+        self.context.update_symbol(var_name, block_var, self.node)
+
+
+@register
+class BlockAxisSpatial(BlockAxis):
+    """Special stmt for defining a spatial block axis
+    axis.spatial(dom, iter_value)
+
+    Example
+    -------
+    .. code-block:: python
+
+        vi = T.axis.spatial(128, k)
+    """
+
+    def __init__(self):
+        def axis_spatial(
+            dom: Union[PrimExpr, Tuple[PrimExpr, PrimExpr]], value: PrimExpr, span: Span = None
+        ):
+            if not isinstance(self.node, ast.Assign) or not len(self.node.lhs) == 1:
+                self.context.report_error(
+                    "`axis.spatial` must be assigned to a var, e.g. vi = axis.spatial(...)",
+                    self.node.span,
+                )
+            self.axis(self.node.lhs[0].id.name, dom, value, IterVar.DataPar)
+
+        super().__init__(axis_spatial, def_symbol=True)
+
+    def signature(self) -> Tuple[str, Tuple[list, list, Any]]:
+        return "tir.axis.spatial", get_param_list(self.func)
+
+
+@register
+class BlockAxisS(BlockAxis):
+    """The sugar special stmt for defining a spatial block axis
+    axis.S(dom, iter_value)
+
+    Example
+    -------
+    .. code-block:: python
+
+        vi = T.axis.S(128, k)
+    """
+
+    def __init__(self):
+        def axis_spatial(
+            dom: Union[PrimExpr, Tuple[PrimExpr, PrimExpr]], value: PrimExpr, span: Span = None
+        ):
+            if not isinstance(self.node, ast.Assign) or not len(self.node.lhs) == 1:
+                self.context.report_error(
+                    "`axis.S` must be assigned to a var, e.g. vi = axis.S(...)",
+                    self.node.span,
+                )
+            self.axis(self.node.lhs[0].id.name, dom, value, IterVar.DataPar)
+
+        super().__init__(axis_spatial, def_symbol=True)
+
+    def signature(self) -> Tuple[str, Tuple[list, list, Any]]:
+        return "tir.axis.S", get_param_list(self.func)
+
+
+@register
+class BlockAxisReduce(BlockAxis):
+    """Special stmt for defining a reduce block axis
+    axis.reduce(dom, iter_value)
+
+    Example
+    -------
+    .. code-block:: python
+
+        vi = T.axis.reduce(128, k)
+    """
+
+    def __init__(self):
+        def axis_reduce(
+            dom: Union[PrimExpr, Tuple[PrimExpr, PrimExpr]], value: PrimExpr, span: Span = None
+        ):
+            if not isinstance(self.node, ast.Assign) or not len(self.node.lhs) == 1:
+                self.context.report_error(
+                    "`axis.reduce` must be assigned` to a var, e.g. vi = axis.reduce(...)",
+                    self.node.span,
+                )
+            self.axis(self.node.lhs[0].id.name, dom, value, IterVar.CommReduce)
+
+        super().__init__(axis_reduce, def_symbol=True)
+
+    def signature(self) -> Tuple[str, Tuple[list, list, Any]]:
+        return "tir.axis.reduce", get_param_list(self.func)
+
+
+@register
+class BlockAxisR(BlockAxis):
+    """The sugar special stmt for defining a reduce block axis
+    axis.R(dom, iter_value)
+
+    Example
+    -------
+    .. code-block:: python
+
+        vi = T.axis.R(128, k)
+    """
+
+    def __init__(self):
+        def axis_reduce(
+            dom: Union[PrimExpr, Tuple[PrimExpr, PrimExpr]], value: PrimExpr, span: Span = None
+        ):
+            if not isinstance(self.node, ast.Assign) or not len(self.node.lhs) == 1:
+                self.context.report_error(
+                    "`axis.R` must be assigned to a var, e.g. vi = axis.R(...)",
+                    self.node.span,
+                )
+            self.axis(self.node.lhs[0].id.name, dom, value, IterVar.CommReduce)
+
+        super().__init__(axis_reduce, def_symbol=True)
+
+    def signature(self) -> Tuple[str, Tuple[list, list, Any]]:
+        return "tir.axis.R", get_param_list(self.func)
+
+
+@register
+class BlockAxisScan(BlockAxis):
+    """Special stmt for defining a ordered block axis
+    axis.scan(dom, iter_value)
+
+    Example
+    -------
+    .. code-block:: python
+
+        vi = T.axis.scan(128, k)
+    """
+
+    def __init__(self):
+        def axis_scan(
+            dom: Union[PrimExpr, Tuple[PrimExpr, PrimExpr]], value: PrimExpr, span: Span = None
+        ):
+            if not isinstance(self.node, ast.Assign) or not len(self.node.lhs) == 1:
+                self.context.report_error(
+                    "`axis.scan` must be assigned to a var, e.g. vi = axis.scan(...)",
+                    self.node.span,
+                )
+            self.axis(self.node.lhs[0].id.name, dom, value, IterVar.Ordered)
+
+        super().__init__(axis_scan, def_symbol=True)
+
+    def signature(self) -> Tuple[str, Tuple[list, list, Any]]:
+        return "tir.axis.scan", get_param_list(self.func)
+
+
+@register
+class BlockAxisOpaque(BlockAxis):
+    """Special stmt for defining a opaque block axis
+    axis.opaque(dom, iter_value)
+
+    Example
+    -------
+    .. code-block:: python
+
+        vi = T.axis.opaque(128, k)
+    """
+
+    def __init__(self):
+        def axis_opaque(
+            dom: Union[PrimExpr, Tuple[PrimExpr, PrimExpr]], value: PrimExpr, span: Span = None
+        ):
+            if not isinstance(self.node, ast.Assign) or not len(self.node.lhs) == 1:
+                self.context.report_error(
+                    "`axis.opaque` must be assigned to a var, e.g. vi = axis.opaque(...)",
+                    self.node.span,
+                )
+            self.axis(self.node.lhs[0].id.name, dom, value, IterVar.DimInfo)
+
+        super().__init__(axis_opaque, def_symbol=True)
+
+    def signature(self) -> Tuple[str, Tuple[list, list, Any]]:
+        return "tir.axis.opaque", get_param_list(self.func)
+
+
+@register
+class BlockAxisRemap(BlockAxis):
+    """Special stmt for remapping loops vars to block axes.
+    axis.remap(iter_type, iter_value)
+
+    Note
+    ----
+    Iter_type is a string consisting of 'S' and 'R', where 'S' means
+    for spatial and 'R' means for reduce.
+
+    Example
+    -------
+    .. code-block:: python
+
+        vi, vj = T.axis.remap("SS", [i, j])
+    """
+
+    def __init__(self):
+        def axis_remap(iter_types: str, loop_vars: List[tvm.tir.expr.Var], span: Span = None):
+            if not isinstance(self.node, ast.Assign) or not len(self.node.lhs) >= 1:
+                self.context.report_error(
+                    "`axis.remap` must be assigned to one or more vars, "
+                    "e.g. vi, vj = axis.remap(...)",
+                    self.node.span,
+                )
+            var_num: int = len(self.node.lhs)
+            if var_num != len(iter_types):
+                self.context.report_error(
+                    f"`iter_type` expected {var_num} charactor(s), "
+                    f"but got {len(iter_types)}: {iter_types}",
+                    span,
+                )
+            if var_num != len(loop_vars):
+                self.context.report_error(
+                    f"`iter_type` expected {var_num} loop var(s), "
+                    f"but got {len(loop_vars)}: {loop_vars}",
+                    span,
+                )
+            for var, iter_ty, loop_var in zip(self.node.lhs, iter_types, loop_vars):
+                iter_type: int
+                if iter_ty == "S":
+                    iter_type = IterVar.DataPar
+                elif iter_ty == "R":
+                    iter_type = IterVar.CommReduce
+                else:
+                    self.context.report_error(
+                        f'`iter_type` only expected "S" (for spatial) or "R" (for reduce), '
+                        f'but got "{iter_ty}"',
+                        span,
+                    )
+
+                if not isinstance(loop_var, tvm.tir.expr.Var):
+                    self.context.report_error(
+                        f"Values of `axis.remap` expected single loop var, but got {loop_var}",
+                        loop_var.span,
+                    )
+                loops = self.context.loop_stack
+                if loop_var not in loops:
+                    self.context.report_error(
+                        f"Cannot find loop var {loop_var} in loop nesting.",
+                        span,
+                    )
+                self.axis(var.id.name, loops[loop_var], loop_var, iter_type)
+
+        super().__init__(axis_remap, def_symbol=True)
+
+    def signature(self) -> Tuple[str, Tuple[list, list, Any]]:
+        return "tir.axis.remap", get_param_list(self.func)
+
+
 @register
 class BlockPredicate(SpecialStmt):
     """Special function where(predicate)
@@ -449,7 +743,12 @@ def var(dtype, span):
             assert isinstance(
                 self.node, ast.Assign
             ), f"VarDef expected ast.Assign but got {type(self.node)}"
-            v = te.var(self.node.lhs.id.name, dtype, span=span)
+            names = [x.id.name for x in self.node.lhs]
+            if len(names) != 1:
+                self.context.report_error(
+                    f"VarDef expected assign to only one var, but got {names}", span
+                )
+            v = te.var(names[0], dtype, span=span)
             self.context.update_symbol(v.name, v, self.node)
 
         super().__init__(var, def_symbol=True)
@@ -464,8 +763,13 @@ def buffer_var(dtype, storage_scope, span):
             assert isinstance(
                 self.node, ast.Assign
             ), f"BufferVarDef expected ast.Assign but got {type(self.node)}"
+            names = [x.id.name for x in self.node.lhs]
+            if len(names) != 1:
+                self.context.report_error(
+                    f"VarDef expected assign to only one var, but got {names}", span
+                )
             ptr_type = tvm.ir.PointerType(tvm.ir.PrimType(dtype), storage_scope)
-            v = te.var(self.node.lhs.id.name, ptr_type, span=span)
+            v = te.var(names[0], ptr_type, span=span)
             self.context.update_symbol(v.name, v, self.node)
 
         super().__init__(buffer_var, def_symbol=True)
@@ -480,7 +784,12 @@ def env_thread(env_name, span):
             assert isinstance(
                 self.node, ast.Assign
             ), f"EnvThread expected ast.Assign but got {type(self.node)}"
-            v = te.var(self.node.lhs.id.name, span=span)
+            names = [x.id.name for x in self.node.lhs]
+            if len(names) != 1:
+                self.context.report_error(
+                    f"VarDef expected assign to only one var, but got {names}", span
+                )
+            v = te.var(names[0], span=span)
             self.context.func_var_env_dict[v] = env_name
             self.context.update_symbol(v.name, v, self.node)
 
diff --git a/python/tvm/te/operation.py b/python/tvm/te/operation.py
index 681e322b20823..cb0305d49e4aa 100644
--- a/python/tvm/te/operation.py
+++ b/python/tvm/te/operation.py
@@ -467,10 +467,12 @@ def tir_matmul(a: T.handle, b: T.handle, c: T.handle) -> None:
             B = T.match_buffer(b, (128, 128))
             C = T.match_buffer(c, (128, 128))
 
-            with T.block([128, 128, T.reduce_axis(0, 128)]) as [i, j, k]:
-                with T.init():
-                    C[i, j] = 0.0
-                C[i, j] += A[i, k] * B[j, k]
+            for i, j, k in T.grip(128, 128, 128):
+                with T.block():
+                    vi, vj, vk = T.axis.remap("SSR", [i, j, k])
+                    with T.init():
+                        C[vi, vj] = 0.0
+                    C[vi, vj] += A[vi, vk] * B[vj, vk]
 
     Returns
     -------
diff --git a/python/tvm/tir/function.py b/python/tvm/tir/function.py
index 6a90924912b11..b002ace0e4006 100644
--- a/python/tvm/tir/function.py
+++ b/python/tvm/tir/function.py
@@ -108,8 +108,10 @@ def mem_copy(a: T.handle, b: T.handle, m: T.int32, n: T.int32) -> None:
                 A = T.match_buffer(a, (m, n), "float32")
                 B = T.match_buffer(b, (m, n), "float32")
 
-                with T.block([m, n], "") as [vi, vj]:
-                    B[vi, vj] = A[vi, vj]
+                for i, j in T.grid(m, n):
+                    with T.block():
+                        vi, vj = T.axis.remap("SS", [i, j])
+                        B[vi, vj] = A[vi, vj]
 
         Then we can make it specialized with given shapes or buffers.
 
@@ -129,8 +131,10 @@ def mem_copy_16_16(a: T.handle, b: T.handle) -> None:
                 A = T.match_buffer(a, (16, 16), "float32")
                 B = T.match_buffer(b, (16, 16), "float32")
 
-                with T.block([16, 16], "") as [vi, vj]:
-                    B[vi, vj] = A[vi, vj]
+                for i, j in T.grid(16, 16):
+                    with T.block():
+                        vi, vj = T.axis.remap("SS", [i, j])
+                        B[vi, vj] = A[vi, vj]
 
         Returns
         -------
diff --git a/python/tvm/tir/schedule/schedule.py b/python/tvm/tir/schedule/schedule.py
index 09a52d2e7037f..786982cf704c1 100644
--- a/python/tvm/tir/schedule/schedule.py
+++ b/python/tvm/tir/schedule/schedule.py
@@ -397,7 +397,8 @@ def before_fuse(a: T.handle, b: T.handle) -> None:
                 A = T.match_buffer(a, (128, 128))
                 B = T.match_buffer(b, (128, 128))
                 for i, j in T.grid(128, 128):
-                    with T.block([128, 128], "B") as [vi, vj]:
+                    with T.block("B"):
+                        vi, vj = T.axis.remap("SS", [i, j])
                         B[vi, vj] = A[vi, vj] * 2.0
 
         Create the schedule and do fuse:
@@ -419,9 +420,9 @@ def after_fuse(a: T.handle, b: T.handle) -> None:
                 B = T.match_buffer(b, (128, 128))
                 # the 2 loops are fused into 1
                 for i_j_fused in T.serial(0, 16384):
-                    with T.block([128, 128], "B") as [vi, vj]:
-                        T.bind(vi, tir.floordiv(i_j_fused, 128))
-                        T.bind(vj, T.floormod(i_j_fused, 128))
+                    with T.block("B"):
+                        vi = T.axis.S(128, T.floordiv(i_j_fused, 128))
+                        vj = T.axis.S(128, T.floormod(i_j_fused, 128))
                         B[vi, vj] = A[vi, vj] * 2.0
 
         """
@@ -468,7 +469,8 @@ def before_split(a: T.handle, b: T.handle) -> None:
                 A = T.match_buffer(a, (128, 128))
                 B = T.match_buffer(b, (128, 128))
                 for i, j in T.grid(128, 128):
-                    with T.block([128, 128], "B") as [vi, vj]:
+                    with T.block("B") as [vi, vj]:
+                        vi, vj = T.axis.remap("SS", [i, j])
                         B[vi, vj] = A[vi, vj] * 2.0
 
         Create the schedule and do split:
@@ -490,9 +492,9 @@ def after_split(a: T.handle, b: T.handle) -> None:
                 B = T.match_buffer(b, (128, 128))
                 # the original loop is split into 2 loops
                 for i0, i1, j in T.grid(2, 64, 128):
-                    with T.block([128, 128], "B") as [vi, vj]:
-                        T.bind(vi, ((i0*64) + i1))
-                        T.bind(vj, j)
+                    with T.block("B"):
+                        vi = T.axis.S(128, i0 * 64 + i1)
+                        vj = T.axis.S(128, j)
                         B[vi, vj] = A[vi, vj] * 2.0
 
         """
@@ -529,7 +531,8 @@ def before_reorder(a: T.handle, b: T.handle) -> None:
                 A = T.match_buffer(a, (128, 128))
                 B = T.match_buffer(b, (128, 128))
                 for i, j in T.grid(128, 128):
-                    with T.block([128, 128], "B") as [vi, vj]:
+                    with T.block("B"):
+                        vi, vj = T.axis.remap("SS", [i, j])
                         B[vi, vj] = A[vi, vj] * 2.0
 
         Create the schedule and do reorder:
@@ -551,9 +554,8 @@ def after_reorder(a: T.handle, b: T.handle) -> None:
                 B = T.match_buffer(b, (128, 128))
                 # Here j and i are reordered
                 for j, i in T.grid(128, 128):
-                    with T.block([128, 128], "B") as [vi, vj]:
-                        T.bind(vi, i)
-                        T.bind(vj, j)
+                    with T.block("B"):
+                        vi, vj = T.axis.remap("SS", [i, j])
                         B[vi, vj] = A[vi, vj] * 2.0
 
         """
@@ -586,9 +588,8 @@ def before_parallel(a: T.handle, b: T.handle) -> None:
                 A = T.match_buffer(a, (128, 128))
                 B = T.match_buffer(b, (128, 128))
                 for i, j in T.grid(128, 128):
-                    with T.block([128, 128], "B") as [vi, vj]:
-                        T.bind(vi, i)
-                        T.bind(vj, j)
+                    with T.block("B"):
+                        vi, vj = T.axis.remap("SS", [i, j])
                         B[vi, vj] = A[vi, vj] * 2.0
 
         Create the schedule and do parallel:
@@ -609,9 +610,8 @@ def after_parallel(a: T.handle, b: T.handle) -> None:
                 B = T.match_buffer(b, (128, 128))
                 for i in T.parallel(0, 128):
                     for j in T.serial(0, 128):
-                        with T.block([128, 128], "B") as [vi, vj]:
-                            T.bind(vi, i)
-                            T.bind(vj, j)
+                        with T.block("B"):
+                            vi, vj = T.axis.remap("SS", [i, j])
                             B[vi, vj] = A[vi, vj] * 2.0
 
         """
@@ -642,9 +642,8 @@ def before_vectorize(a: T.handle, b: T.handle) -> None:
                 A = T.match_buffer(a, (128, 128))
                 B = T.match_buffer(b, (128, 128))
                 for i, j in T.grid(128, 128):
-                    with T.block([128, 128], "B") as [vi, vj]:
-                        T.bind(vi, i)
-                        T.bind(vj, j)
+                    with T.block("B"):
+                        vi, vj = T.axis.remap("SS", [i, j])
                         B[vi, vj] = A[vi, vj] * 2.0
 
         Create the schedule and do vectorize:
@@ -665,9 +664,8 @@ def after_vectorize(a: T.handle, b: T.handle) -> None:
                 B = T.match_buffer(b, (128, 128))
                 for i in T.serial(0, 128):
                     for j in T.vectorized(0, 128):
-                        with T.block([128, 128], "B") as [vi, vj]:
-                            T.bind(vi, i)
-                            T.bind(vj, j)
+                        with T.block("B"):
+                            vi, vj = T.axis.remap("SS", [i, j])
                             B[vi, vj] = A[vi, vj] * 2.0
 
         """
@@ -706,9 +704,8 @@ def before_bind(a: T.handle, b: T.handle) -> None:
                 A = T.match_buffer(a, (128, 128))
                 B = T.match_buffer(b, (128, 128))
                 for i, j in T.grid(128, 128):
-                    with T.block([128, 128], "B") as [vi, vj]:
-                        T.bind(vi, i)
-                        T.bind(vj, j)
+                    with T.block("B"):
+                        vi, vj = T.axis.remap("SS", [i, j])
                         B[vi, vj] = A[vi, vj] * 2.0
 
         Create the schedule and do bind:
@@ -730,9 +727,8 @@ def after_bind(a: T.handle, b: T.handle) -> None:
                 B = T.match_buffer(b, (128, 128))
                 for i in T.thread_binding(0, 128, thread = "blockIdx.x"):
                     for j in T.thread_binding(0, 128, thread = "threadIdx.x"):
-                        with T.block([128, 128], "B") as [vi, vj]:
-                            T.bind(vi, i)
-                            T.bind(vj, j)
+                        with T.block("B"):
+                            vi, vj = T.axis.remap("SS", [i, j])
                             B[vi, vj] = A[vi, vj] * 2.0
 
         """
@@ -758,9 +754,8 @@ def before_unroll(a: T.handle, b: T.handle) -> None:
                 A = T.match_buffer(a, (128, 128))
                 B = T.match_buffer(b, (128, 128))
                 for i, j in T.grid(128, 128):
-                    with T.block([128, 128], "B") as [vi, vj]:
-                        T.bind(vi, i)
-                        T.bind(vj, j)
+                    with T.block("B"):
+                        vi, vj = T.axis.remap("SS", [i, j])
                         B[vi, vj] = A[vi, vj] * 2.0
 
         Create the schedule and do unroll:
@@ -781,9 +776,8 @@ def after_unroll(a: T.handle, b: T.handle) -> None:
                 B = T.match_buffer(b, (128, 128))
                 for i in T.unroll(0, 128):
                     for j in T.serial(0, 128):
-                        with T.block([128, 128], "B") as [vi, vj]:
-                            T.bind(vi, i)
-                            T.bind(vj, j)
+                        with T.block("B"):
+                            vi, vj = T.axis.remap("SS", [i, j])
                             B[vi, vj] = A[vi, vj] * 2.0
 
         """
@@ -825,7 +819,8 @@ def before_cache_read(a: T.handle, b: T.handle) -> None:
                 A = T.match_buffer(a, (128, 128))
                 B = T.match_buffer(b, (128, 128))
                 for i, j in T.grid(128, 128):
-                    with T.block([128, 128], "B") as [vi, vj]:
+                    with T.block("B"):
+                        vi, vj = T.axis.remap("SS", [i, j])
                         B[vi, vj] = A[vi, vj] * 2.0
 
         Create the schedule and cache_read:
@@ -847,10 +842,12 @@ def after_cache_read(a: T.handle, b: T.handle) -> None:
                 B = T.match_buffer(b, (128, 128))
                 A_local = T.alloc_buffer((128, 128), scope="local")
                 for i, j in T.grid(128, 128):
-                    with T.block([128, 128], "A_local") as [vi, vj]:
+                    with T.block("A_local"):
+                        vi, vj = T.axis.remap("SS", [i, j])
                         A_local[vi, vj] = A[vi, vj]
                 for i, j in T.grid(128, 128):
-                    with T.block([128, 128], "B") as [vi, vj]:
+                    with T.block("B"):
+                        vi, vj = T.axis.remap("SS", [i, j])
                         B[vi, vj] = A_local[vi, vj] * 2.0
 
         """
@@ -893,7 +890,8 @@ def before_cache_write(a: T.handle, b: T.handle) -> None:
                 A = T.match_buffer(a, (128, 128))
                 B = T.match_buffer(b, (128, 128))
                 for i, j in T.grid(128, 128):
-                    with T.block([128, 128], "B") as [vi, vj]:
+                    with T.block("B"):
+                        vi, vj = T.axis.remap("SS", [i, j])
                         B[vi, vj] = A[vi, vj] * 2.0
 
         Create the schedule and cache_write:
@@ -915,10 +913,12 @@ def after_cache_write(a: T.handle, b: T.handle) -> None:
                 B = T.match_buffer(b, (128, 128))
                 B_local = T.alloc_buffer((128, 128), scope="local")
                 for i, j in T.grid(128, 128):
-                    with T.block([128, 128], "A_local") as [vi, vj]:
+                    with T.block("A_local"):
+                        vi, vj = T.axis.remap("SS", [i, j])
                         B_local[vi, vj] = A[vi, vj] * 2.0
                 for i, j in T.grid(128, 128):
-                    with T.block([128, 128], "B") as [vi, vj]:
+                    with T.block("B"):
+                        vi, vj = T.axis.remap("SS", [i, j])
                         B[vi, vj] = B_local[vi, vj]
 
         """
@@ -974,10 +974,14 @@ def before_compute_at(a: T.handle, c: T.handle) -> None:
                 A = T.match_buffer(a, (128, 128), "float32")
                 B = T.alloc_buffer((128, 128), "float32")
                 C = T.match_buffer(c, (128, 128), "float32")
-                with T.block([128, 128], "B") as [vi, vj]:
-                    B[vi, vj] = A[vi, vj] * 2.0
-                with T.block([128, 128], "C") as [vi, vj]:
-                    C[vi, vj] = B[vi, vj] + 1.0
+                for i, j in T.grid(128, 128):
+                    with T.block("B"):
+                        vi, vj = T.axis.remap("SS", [i, j])
+                        B[vi, vj] = A[vi, vj] * 2.0
+                for i, j in T.grid(128, 128):
+                    with T.block("C"):
+                        vi, vj = T.axis.remap("SS", [i, j])
+                        C[vi, vj] = B[vi, vj] + 1.0
 
         Create the schedule and do compute-at:
 
@@ -1000,14 +1004,12 @@ def after_compute_at(a: T.handle, c: T.handle) -> None:
                 C = T.match_buffer(c, (128, 128), "float32")
                 for i in T.serial(0, 128):
                     for j in T.serial(0, 128):
-                        with T.block([128, 128], "B") as [vi, vj]:
-                            T.bind(vi, i)
-                            T.bind(vj, j)
+                        with T.block("B"):
+                            vi, vj = T.axis.remap("SS", [i, j])
                             B[vi, vj] = A[vi, vj] * 2.0
                     for j in T.serial(0, 128):
-                        with T.block([128, 128], "C") as [vi, vj]:
-                            T.bind(vi, i)
-                            T.bind(vj, j)
+                        with T.block("C"):
+                            vi, vj = T.axis.remap("SS", [i, j])
                             C[vi, vj] = B[vi, vj] + 1.0
 
         """
@@ -1061,10 +1063,14 @@ def before_reverse_compute_at(a: T.handle, c: T.handle) -> None:
                 A = T.match_buffer(a, (128, 128), "float32")
                 B = T.alloc_buffer((128, 128), "float32")
                 C = T.match_buffer(c, (128, 128), "float32")
-                with T.block([128, 128], "B") as [vi, vj]:
-                    B[vi, vj] = A[vi, vj] * 2.0
-                with T.block([128, 128], "C") as [vi, vj]:
-                    C[vi, vj] = B[vi, vj] + 1.0
+                for i, j in T.grid(128, 128):
+                    with T.block("B"):
+                        vi, vj = T.axis.remap("SS", [i, j])
+                        B[vi, vj] = A[vi, vj] * 2.0
+                for i, j in T.grid(128, 128):
+                    with T.block("C"):
+                        vi, vj = T.axis.remap("SS", [i, j])
+                        C[vi, vj] = B[vi, vj] + 1.0
 
         Create the schedule and do reverse-compute-at:
 
@@ -1087,14 +1093,12 @@ def after_reverse_compute_at(a: T.handle, c: T.handle) -> None:
                 C = T.match_buffer(c, (128, 128), "float32")
                 for i in T.serial(0, 128):
                     for j in T.serial(0, 128):
-                        with T.block([128, 128], "B") as [vi, vj]:
-                            T.bind(vi, i)
-                            T.bind(vj, j)
+                        with T.block("B"):
+                            vi, vj = T.axis.remap("SS", [i, j])
                             B[vi, vj] = A[vi, vj] * 2.0
                     for j in T.serial(0, 128):
-                        with T.block([128, 128], "C") as [vi, vj]:
-                            T.bind(vi, i)
-                            T.bind(vj, j)
+                        with T.block("C"):
+                            vi, vj = T.axis.remap("SS", [i, j])
                             C[vi, vj] = B[vi, vj] + 1.0
 
         """
@@ -1135,10 +1139,14 @@ def before_inline(a: T.handle, c: T.handle) -> None:
                 A = T.match_buffer(a, (128, 128))
                 B = T.alloc_buffer((128, 128))
                 C = T.match_buffer(c, (128, 128))
-                with T.block([128, 128], "B") as [vi, vj]:
-                    B[vi, vj] = A[vi, vj] * 2.0
-                with T.block([128, 128], "C") as [vi, vj]:
-                    C[vi, vj] = B[vi, vj] + 1.0
+                for i, j in T.grid(128, 128):
+                    with T.block("B"):
+                        vi, vj = T.axis.remap("SS", [i, j])
+                        B[vi, vj] = A[vi, vj] * 2.0
+                for i, j in T.grid(128, 128):
+                    with T.block("C"):
+                        vi, vj = T.axis.remap("SS", [i, j])
+                        C[vi, vj] = B[vi, vj] + 1.0
 
         Create the schedule and do compute-inline:
 
@@ -1156,8 +1164,10 @@ def before_inline(a: T.handle, c: T.handle) -> None:
             def after_inline(a: T.handle, c: T.handle) -> None:
                 A = T.match_buffer(a, (128, 128))
                 C = T.match_buffer(c, (128, 128))
-                with T.block([128, 128], "C") as [vi, vj]:
-                    C[vi, vj] = A[vi, vj] * 2.0 + 1.0
+                for i, j in T.grid(128, 128):
+                    with T.block("C"):
+                        vi, vj = T.axis.remap("SS", [i, j])
+                        C[vi, vj] = A[vi, vj] * 2.0 + 1.0
 
         """
         _ffi_api.ScheduleComputeInline(self, block)  # type: ignore # pylint: disable=no-member
@@ -1195,10 +1205,14 @@ def before_inline(a: T.handle, c: T.handle) -> None:
                 A = T.match_buffer(a, (128, 128))
                 B = T.alloc_buffer((128, 128))
                 C = T.match_buffer(c, (128, 128))
-                with T.block([128, 128], "B") as [vi, vj]:
-                    B[vi, vj] = A[vi, vj] * 2.0
-                with T.block([128, 128], "C") as [vi, vj]:
-                    C[vi, vj] = B[vi, vj] + 1.0
+                for i, j in T.grid(128, 128):
+                    with T.block("B"):
+                        vi, vj = T.axis.remap("SS", [i, j])
+                        B[vi, vj] = A[vi, vj] * 2.0
+                for i, j in T.grid(128, 128):
+                    with T.block("C"):
+                        vi, vj = T.axis.remap("SS", [i, j])
+                        C[vi, vj] = B[vi, vj] + 1.0
 
         Create the schedule and do reverse-compute-inline:
 
@@ -1216,8 +1230,10 @@ def before_inline(a: T.handle, c: T.handle) -> None:
             def after_inline(a: T.handle, c: T.handle) -> None:
                 A = T.match_buffer(a, (128, 128))
                 C = T.match_buffer(c, (128, 128))
-                with T.block([128, 128], "C") as [vi, vj]:
-                    C[vi, vj] = A[vi, vj] * 2.0 + 1.0
+                for i, j in T.grid(128, 128):
+                    with T.block("C"):
+                        vi, vj = T.axis.remap("SS", [i, j])
+                        C[vi, vj] = A[vi, vj] * 2.0 + 1.0
 
         """
         _ffi_api.ScheduleReverseComputeInline(self, block)  # type: ignore # pylint: disable=no-member
@@ -1384,8 +1400,9 @@ def rfactor(self, loop: LoopRV, factor_axis: int) -> LoopRV:
             def before_rfactor(a: T.handle, b: T.handle) -> None:
                 A = T.match_buffer(a, (128, 128, 128))
                 B = T.match_buffer(b, (128,))
-                with T.block([128, T.reduce_axis(0, 128),
-                                T.reduce_axis(0, 128)], "B") as [vii, vi, vj]:
+                for ii, i, j in T.grid(128, 128, 128):
+                with T.block("B"):
+                    vii, vi, vj = T.axis.remap("SRR", [ii, i, j])
                     with T.init():
                         B[vii] = 0.0
                     B[vii] = B[vii] + A[vii, vi, vj]
@@ -1408,14 +1425,18 @@ def after_rfactor(a: T.handle, b: T.handle) -> None:
                 A = T.match_buffer(a, [128, 128, 128])
                 B = T.match_buffer(b, [128])
                 B_rf = T.alloc_buffer([128, 128])
-                with T.block([128, 128, T.reduce_axis(0, 128)], "B_rf") as [vi2, vii, vi]:
-                    with T.init():
-                        B_rf[vi2, vii] = 0.0
-                    B_rf[vi2, vii] = (B_rf[vi2, vii] + A[vii, vi, vi2])
-                with T.block([128, T.reduce_axis(0, 128)], "B") as [vii_1, vi2_1]:
-                    with T.init():
-                        B[vii_1] = 0.0
-                    B[vii_1] = (B[vii_1] + B_rf[vi2_1, vii_1])
+                for i2, ii, i in T.grid(128, 128, 128):
+                    with T.block("B_rf"):
+                        vi2, vii, vi = T.axis.remap("SSR", [i2, ii, i])
+                        with T.init():
+                            B_rf[vi2, vii] = 0.0
+                        B_rf[vi2, vii] = (B_rf[vi2, vii] + A[vii, vi, vi2])
+                for ii, i2 in T.grid(128, 128):
+                    with T.block("B"):
+                        vii, vi2 = T.axis.remap("SR", [ii, i2])
+                        with T.init():
+                            B[vii] = 0.0
+                        B[vii] = B[vii] + B_rf[vi2, vii]
 
 
         Note
@@ -1483,10 +1504,14 @@ def before_storage_align(a: T.handle, c: T.handle) -> None:
                 A = T.match_buffer(a, (128, 128))
                 B = T.alloc_buffer((128, 128))
                 C = T.match_buffer(c, (128, 128))
-                with T.block([128, 128], "B") as [vi, vj]:
-                    B[vi, vj] = A[vi, vj] * 2.0
-                with T.block([128, 128], "C") as [vi, vj]:
-                    C[vi, vj] = B[vi, vj] + 1.0
+                for i, j in T.grid(128, 128):
+                    with T.block("B"):
+                        vi, vj = T.axis.remap("SS", [i, j])
+                        B[vi, vj] = A[vi, vj] * 2.0
+                for i, j in T.grid(128, 128):
+                    with T.block("C"):
+                        vi, vj = T.axis.remap("SS", [i, j])
+                        C[vi, vj] = B[vi, vj] + 1.0
 
         Create the schedule and do storage_align:
 
@@ -1505,11 +1530,15 @@ def after_storage_align(a: T.handle, c: T.handle) -> None:
                 A = T.match_buffer(a, (128, 128))
                 B = T.alloc_buffer((128, 128))
                 C = T.match_buffer(c, (128, 128))
-                with T.block([128, 128], "B") as [vi, vj]:
-                    T.block_attr({"buffer_dim_align": [[[0, 128, 1]]]})
-                    B[vi, vj] = A[vi, vj] * 2.0
-                with T.block([128, 128], "C") as [vi, vj]:
-                    C[vi, vj] = B[vi, vj] + 1.0
+                for i, j in T.grid(128, 128):
+                    with T.block("B"):
+                        T.block_attr({"buffer_dim_align": [[[0, 128, 1]]]})
+                        vi, vj = T.axis.remap("SS", [i, j])
+                        B[vi, vj] = A[vi, vj] * 2.0
+                for i, j in T.grid(128, 128):
+                    with T.block("C"):
+                        vi, vj = T.axis.remap("SS", [i, j])
+                        C[vi, vj] = B[vi, vj] + 1.0
 
         After lowering passes, buffer B will have strides as [129, 1].
 
diff --git a/python/tvm/tir/transform/transform.py b/python/tvm/tir/transform/transform.py
index 1abba77a801f0..722810e9aa5bc 100644
--- a/python/tvm/tir/transform/transform.py
+++ b/python/tvm/tir/transform/transform.py
@@ -628,7 +628,7 @@ def CompactBufferAllocation():
     .. code-block:: python
 
         for i in range(0, 16):
-            with T.block([]):
+            with T.block():
                 B = T.alloc_buffer(16, 16)
                 for j in range(0, 16):
                     B[i, j] = A[i, j] + 1
@@ -643,7 +643,7 @@ def CompactBufferAllocation():
     .. code-block:: python
 
         for i in range(0, 16):
-            with T.block([]):
+            with T.block():
                 B = T.alloc_buffer(1, 16)
                 for j in range(0, 16):
                     B[0, j] = A[i, j] + 1
diff --git a/src/printer/tvmscript_printer.cc b/src/printer/tvmscript_printer.cc
index fa74e56f491c4..13e4cfcd30bab 100644
--- a/src/printer/tvmscript_printer.cc
+++ b/src/printer/tvmscript_printer.cc
@@ -22,10 +22,10 @@
  * \brief Printer class to print Tensor IR to python syntax script
  */
 
-#include <tvm/arith/analyzer.h>
 #include <tvm/ir/module.h>
 #include <tvm/node/serialization.h>
 #include <tvm/runtime/registry.h>
+#include <tvm/tir/analysis.h>
 #include <tvm/tir/buffer.h>
 #include <tvm/tir/expr.h>
 #include <tvm/tir/expr_functor.h>
@@ -128,7 +128,17 @@ class TVMScriptPrinter : public StmtFunctor<Doc(const Stmt&)>,
   /*! \brief the number of current node */
   int current_num_;
   /*! \brief loop stack without annotations */
-  std::vector<For> loop_stack_;
+  std::vector<For> simple_loop_stack_;
+  /*! \brief the maps from loop_vars to the loops */
+  std::unordered_map<const VarNode*, For> loop_var_map_;
+  /*!
+   * \brief simple block vars remap from loop vars
+   * simple_remap requires:
+   * 1. block var iter type is kDataPar or kCommReduce
+   * 2. value is a single Var, which is a loop_var outside the block
+   * 3. The iter range is equal to loop range
+   */
+  std::vector<std::pair<IterVar, PrimExpr>> block_var_remaps_;
 
   Doc VisitExpr_(const CastNode* op, ExprPrecedence* out_precedence) override;
   Doc VisitExpr_(const VarNode* op, ExprPrecedence* out_precedence) override;
@@ -193,7 +203,9 @@ class TVMScriptPrinter : public StmtFunctor<Doc(const Stmt&)>,
   Doc PrintArray(const ArrayNode* op);
   Doc PrintBuffer(const BufferNode* op);
   Doc AllocBufferDeclaration(const Buffer& buf);
-  Doc PrintBlockVar(const BlockNode* op);
+  Doc PrintBlockVar(const IterVar& iter_var, const PrimExpr& value);
+  Doc PrintBlockVarRemaps();
+  Doc PrintBlockVars(const BlockRealizeNode* op);
   Doc PrintBlockAttr(const BlockRealizeNode* op);
   Doc PrintBlockBody(const BlockNode* op);
   Doc PrintBufferRegion(const BufferRegionNode* op);
@@ -821,21 +833,23 @@ Doc TVMScriptPrinter::VisitStmt_(const EvaluateNode* op) {
 Doc TVMScriptPrinter::VisitStmt_(const ForNode* op) {
   Doc doc;
   var_not_in_headers_.insert(op->loop_var.get());
+  loop_var_map_[op->loop_var.get()] = GetRef<For>(op);
   const auto* body = op->body.as<ForNode>();
   bool simple_loop = op->kind == ForKind::kSerial && op->annotations.empty() && is_zero(op->min);
-  if (simple_loop) loop_stack_.push_back(GetRef<For>(op));
+  if (simple_loop) simple_loop_stack_.push_back(GetRef<For>(op));
   // It is a loop that can be compressed, let the loops below print it out
   if (simple_loop && body != nullptr) {
     Doc result = Print(GetRef<For>(body));
     TryDeallocVar(op->loop_var);
+    loop_var_map_.erase(op->loop_var.get());
     return result;
   }
   // It is a loop that can not be compressed
-  bool print_above = !loop_stack_.empty();
+  bool print_above = !simple_loop_stack_.empty();
   // print loops above if needed
   if (print_above) {
     doc << PrintLoopStack();
-    loop_stack_.clear();
+    simple_loop_stack_.clear();
   }
   if (!simple_loop) {
     // print current loop if needed
@@ -847,6 +861,7 @@ Doc TVMScriptPrinter::VisitStmt_(const ForNode* op) {
     doc << Doc::Indent(4, Doc::NewLine() << PrintBody(op->body));
   }
   TryDeallocVar(op->loop_var);
+  loop_var_map_.erase(op->loop_var.get());
   return doc;
 }
 
@@ -901,52 +916,99 @@ Doc TVMScriptPrinter::VisitStmt_(const BufferStoreNode* op) {
   return doc;
 }
 
-Doc TVMScriptPrinter::PrintBlockVar(const BlockNode* op) {
+Doc TVMScriptPrinter::PrintBlockVar(const IterVar& iter_var, const PrimExpr& value) {
   Doc doc;
-  doc << "with " << tir_prefix_ << ".block([";
-  std::vector<Doc> block_var_docs;
-  for (const auto& iter_var : op->iter_vars) {
-    Doc block_var_doc;
-    if (is_zero(iter_var->dom->min) && iter_var->iter_type == kDataPar) {
-      block_var_doc << Print(iter_var->dom->extent);
+  doc << Print(iter_var->var) << " = " << tir_prefix_ << ".axis.";
+  switch (iter_var->iter_type) {
+    case kDataPar:
+      doc << "spatial";
+      break;
+    case kCommReduce:
+      doc << "reduce";
+      break;
+    case kOrdered:
+      doc << "scan";
+      break;
+    case kOpaque:
+      doc << "opaque";
+      break;
+    default:
+      LOG(FATAL) << "Unknown block var iter type: " << iter_var->iter_type;
+      break;
+  }
+  doc << "(";
+  const Range& dom = iter_var->dom;
+  if (is_zero(dom->min)) {
+    doc << Print(dom->extent);
+  } else {
+    doc << "(" << Print(dom->min) << ", " << Print(dom->min + dom->extent) << ")";
+  }
+  doc << ", " << Print(value) << ")";
+  return doc;
+}
+
+Doc TVMScriptPrinter::PrintBlockVarRemaps() {
+  ICHECK(!block_var_remaps_.empty());
+  if (block_var_remaps_.size() == 1) {
+    const IterVar& iter_var = block_var_remaps_[0].first;
+    const PrimExpr& value = block_var_remaps_[0].second;
+    return PrintBlockVar(iter_var, value);
+  }
+  Doc doc;
+  std::vector<Doc> iter_vars, iter_values;
+  std::string iter_type;
+  for (const auto& pair : block_var_remaps_) {
+    const IterVar& iter_var = pair.first;
+    const PrimExpr& value = pair.second;
+    iter_vars.push_back(Print(iter_var->var));
+    iter_values.push_back(Print(value));
+    if (iter_var->iter_type == kDataPar) {
+      iter_type += "S";
+    } else if (iter_var->iter_type == kCommReduce) {
+      iter_type += "R";
     } else {
-      block_var_doc << tir_prefix_ << ".";
-      switch (iter_var->iter_type) {
-        case kDataPar:
-          block_var_doc << "range";
-          break;
-        case kCommReduce:
-          block_var_doc << "reduce_axis";
-          break;
-        case kOrdered:
-          block_var_doc << "scan_axis";
-          break;
-        case kOpaque:
-          block_var_doc << "opaque_axis";
-          break;
-        default:
-          LOG(FATAL) << "Unknown block var iter type: " << iter_var->iter_type;
-          break;
-      }
-      block_var_doc << "(" << Print(iter_var->dom->min) << ", "
-                    << Print(iter_var->dom->min + iter_var->dom->extent) << ")";
+      ICHECK(false);
     }
-    block_var_docs.push_back(block_var_doc);
-  }
-  doc << PrintSep(block_var_docs, Doc::Text(", ")) << "]";
-  if (!op->name_hint.empty()) {
-    doc << ", " << Doc::StrLiteral(op->name_hint);
   }
-  doc << ")";
-  std::vector<Doc> block_var_names;
-  for (const auto& iter_var : op->iter_vars) {
+  doc << PrintSep(iter_vars, Doc::Text(", ")) << " = " << tir_prefix_ << ".axis.remap("
+      << Doc::StrLiteral(iter_type) << ", [" << PrintSep(iter_values, Doc::Text(", ")) << "])";
+  return doc;
+}
+
+Doc TVMScriptPrinter::PrintBlockVars(const BlockRealizeNode* op) {
+  Doc doc;
+  const auto* block_op = op->block.as<BlockNode>();
+  ICHECK_EQ(block_op->iter_vars.size(), op->iter_values.size());
+  tir::ExprDeepEqual expr_equal;
+
+  auto is_simple_remap = [this, &expr_equal](const IterVar& iter_var,
+                                             const PrimExpr& value) -> bool {
+    if (iter_var->iter_type != kDataPar && iter_var->iter_type != kCommReduce) return false;
+    if (!value->IsInstance<VarNode>()) return false;
+    const Var& var = Downcast<Var>(value);
+    auto it = loop_var_map_.find(var.get());
+    return it != loop_var_map_.end() && expr_equal(it->second->min, iter_var->dom->min) &&
+           expr_equal(it->second->extent, iter_var->dom->extent);
+  };
+
+  for (size_t i = 0; i < block_op->iter_vars.size(); ++i) {
+    const IterVar& iter_var = block_op->iter_vars[i];
+    const PrimExpr& value = op->iter_values[i];
     var_not_in_headers_.insert(iter_var->var.get());
-    block_var_names.push_back(Print(iter_var->var));
+    if (is_simple_remap(iter_var, value)) {
+      block_var_remaps_.push_back(std::make_pair(iter_var, value));
+    } else {
+      if (!block_var_remaps_.empty()) {
+        doc << Doc::NewLine() << PrintBlockVarRemaps();
+        block_var_remaps_.clear();
+      }
+      doc << Doc::NewLine() << PrintBlockVar(iter_var, value);
+    }
   }
-  if (!block_var_names.empty()) {
-    doc << " as [" << PrintSep(block_var_names, Doc::Text(", ")) << "]";
+  if (!block_var_remaps_.empty()) {
+    doc << Doc::NewLine() << PrintBlockVarRemaps();
+    block_var_remaps_.clear();
   }
-  doc << ":";
   return doc;
 }
 
@@ -957,10 +1019,6 @@ Doc TVMScriptPrinter::PrintBlockAttr(const BlockRealizeNode* op) {
   if (!is_one(op->predicate)) {
     block_attr_doc << Doc::NewLine() << tir_prefix_ << ".where(" << Print(op->predicate) << ")";
   }
-  for (size_t i = 0; i < block_op->iter_vars.size(); ++i)
-    block_attr_doc << Doc::NewLine() << tir_prefix_ << ".bind("
-                   << Print(block_op->iter_vars[i]->var) << ", " << Print(op->iter_values[i])
-                   << ")";
   block_attr_doc << Doc::NewLine() << tir_prefix_ << ".reads(" << Print(block_op->reads) << ")";
   block_attr_doc << Doc::NewLine() << tir_prefix_ << ".writes(" << Print(block_op->writes) << ")";
   if (!block_op->annotations.empty()) {
@@ -994,12 +1052,18 @@ Doc TVMScriptPrinter::PrintBlockBody(const BlockNode* op) {
 Doc TVMScriptPrinter::VisitStmt_(const BlockRealizeNode* op) {
   const auto* block_op = op->block.as<BlockNode>();
   // print block name and block vars
-  Doc doc = PrintBlockVar(block_op);
+  Doc doc;
+  doc << "with " << tir_prefix_ << ".block(";
+  if (!block_op->name_hint.empty()) {
+    doc << Doc::StrLiteral(block_op->name_hint);
+  }
+  doc << "):";
+  Doc block_var = PrintBlockVars(op);
   // print predicate, binding, read/write tensor region, annotations
   Doc block_attr_doc = PrintBlockAttr(op);
   // print body
   Doc body = PrintBlockBody(block_op);
-  doc << Doc::Indent(4, block_attr_doc << Doc::NewLine() << body);
+  doc << Doc::Indent(4, block_var << block_attr_doc << Doc::NewLine() << body);
   for (const auto& iter_var : block_op->iter_vars) {
     TryDeallocVar(iter_var->var);
   }
@@ -1265,11 +1329,11 @@ Doc TVMScriptPrinter::PrintLoop(const For& loop) {
 
 Doc TVMScriptPrinter::PrintLoopStack() {
   Doc res;
-  if (loop_stack_.size() == 1) {
-    res << PrintLoop(loop_stack_[0]);
-  } else if (loop_stack_.size() > 1) {
+  if (simple_loop_stack_.size() == 1) {
+    res << PrintLoop(simple_loop_stack_[0]);
+  } else if (simple_loop_stack_.size() > 1) {
     std::vector<Doc> vars, extents;
-    for (const auto& loop : loop_stack_) {
+    for (const auto& loop : simple_loop_stack_) {
       vars.push_back(Print(loop->loop_var));
       extents.push_back(Print(loop->extent));
     }
diff --git a/src/tir/ir/script/script_complete.cc b/src/tir/ir/script/script_complete.cc
index f265a8ae2b1b9..335fdfa215038 100644
--- a/src/tir/ir/script/script_complete.cc
+++ b/src/tir/ir/script/script_complete.cc
@@ -44,21 +44,11 @@ class ScriptCompleter : public StmtMutator {
   Map<Var, Buffer>* buffer_var_map_;
   Stmt VisitStmt_(const BlockRealizeNode* op) override {
     contains_block = true;
-    Stmt body = StmtMutator::VisitStmt_(op);
-    if (!op->iter_values.empty() && !op->iter_values[0].dtype().is_int()) {
-      auto block_with_binding = CopyOnWrite(Downcast<BlockRealize>(body).get());
-      std::vector<PrimExpr> bindings;
-      for (size_t i = 0; i < op->iter_values.size(); ++i) {
-        bindings.push_back(Var("i" + std::to_string(i)));
-      }
-      block_with_binding->iter_values = bindings;
-      body = BlockRealize(block_with_binding);
-      for (int i = op->iter_values.size() - 1; i >= 0; --i) {
-        body = For(Downcast<Var>(bindings[i]), op->block->iter_vars[i]->dom->min,
-                   op->block->iter_vars[i]->dom->extent, {}, body);
-      }
+    for (const PrimExpr& value : op->iter_values) {
+      CHECK(value.dtype().is_int())
+          << "BlockRealize iter_value expected a IntImm, but got " << value.dtype();
     }
-    return body;
+    return StmtMutator::VisitStmt_(op);
   }
 
   Stmt VisitStmt_(const BlockNode* op) override {
diff --git a/tests/python/integration/test_lower.py b/tests/python/integration/test_lower.py
index 690258c2fa3b4..63733b05ab3fa 100644
--- a/tests/python/integration/test_lower.py
+++ b/tests/python/integration/test_lower.py
@@ -33,9 +33,8 @@ def tensorcore_gemm(a: T.handle, b: T.handle, c: T.handle) -> None:
     # body
     for blockIdx_x in T.thread_binding(0, 16, "blockIdx.x"):
         for blockIdx_y in T.thread_binding(0, 8, "blockIdx.y"):
-            with T.block([16, 8]) as [bx, by]:
-                T.bind(bx, blockIdx_x)
-                T.bind(by, blockIdx_y)
+            with T.block():
+                bx, by = T.axis.remap("SS", [blockIdx_x, blockIdx_y])
                 shared_A = T.alloc_buffer([1024, 1024], "float16", scope="shared")
                 shared_B = T.alloc_buffer([1024, 1024], "float16", scope="shared")
                 wmma_A = T.alloc_buffer([1024, 1024], "float16", scope="wmma.matrix_a")
@@ -44,9 +43,9 @@ def tensorcore_gemm(a: T.handle, b: T.handle, c: T.handle) -> None:
                 for ty in T.thread_binding(0, 2, "threadIdx.y"):
                     for tz in T.thread_binding(0, 2, "threadIdx.z"):
                         for i, j in T.grid(2, 4):
-                            with T.block([64, 64]) as [vi, vj]:
-                                T.bind(vi, bx * 4 + ty * 2 + i)
-                                T.bind(vj, by * 8 + tz * 4 + j)
+                            with T.block():
+                                vi = T.axis.S(64, bx * 4 + ty * 2 + i)
+                                vj = T.axis.S(64, by * 8 + tz * 4 + j)
                                 T.reads([])
                                 T.writes(wmma_C[vi * 16 : vi * 16 + 16, vj * 16 : vj * 16 + 16])
                                 C0 = T.match_buffer(
@@ -74,23 +73,23 @@ def tensorcore_gemm(a: T.handle, b: T.handle, c: T.handle) -> None:
                             for tx in T.thread_binding(0, 32, "threadIdx.x"):
                                 for i0, j0 in T.grid(1, 4):
                                     for j1 in T.vectorized(0, 4):
-                                        with T.block([1024, 1024]) as [vi, vj]:
-                                            T.bind(vi, bx * 64 + ty * 32 + tx + i0)
-                                            T.bind(vj, ko * 32 + tz * 16 + j0 * 4 + j1)
+                                        with T.block():
+                                            vi = T.axis.S(1024, bx * 64 + ty * 32 + tx + i0)
+                                            vj = T.axis.S(1024, ko * 32 + tz * 16 + j0 * 4 + j1)
                                             shared_A[vi, vj + 8] = A[vi, vj]
 
                                 for i0, j0 in T.grid(2, 4):
                                     for j1 in T.vectorized(0, 4):
-                                        with T.block([1024, 1024]) as [vi, vj]:
-                                            T.bind(vi, by * 128 + ty * 64 + tx * 2 + i0)
-                                            T.bind(vj, ko * 32 + tz * 16 + j0 * 4 + j1)
+                                        with T.block():
+                                            vi = T.axis.S(1024, by * 128 + ty * 64 + tx * 2 + i0)
+                                            vj = T.axis.S(1024, ko * 32 + tz * 16 + j0 * 4 + j1)
                                             shared_B[vi, vj + 8] = B[vi, vj]
 
                             for ki in range(0, 2):
                                 for i in range(0, 2):
-                                    with T.block([64, 64]) as [vi, vk]:
-                                        T.bind(vi, bx * 4 + ty * 2 + i)
-                                        T.bind(vk, ko * 2 + ki)
+                                    with T.block():
+                                        vi = T.axis.S(64, bx * 4 + ty * 2 + i)
+                                        vk = T.axis.S(64, ko * 2 + ki)
                                         T.reads(
                                             shared_A[
                                                 vi * 16 : vi * 16 + 16,
@@ -142,9 +141,9 @@ def tensorcore_gemm(a: T.handle, b: T.handle, c: T.handle) -> None:
                                             )
                                         )
                                 for j in range(0, 4):
-                                    with T.block([64, 64]) as [vj, vk]:
-                                        T.bind(vj, by * 8 + tz * 4 + j)
-                                        T.bind(vk, ko * 2 + ki)
+                                    with T.block():
+                                        vj = T.axis.S(64, by * 8 + tz * 4 + j)
+                                        vk = T.axis.S(64, ko * 2 + ki)
                                         T.reads(
                                             shared_B[
                                                 vj * 16 : vj * 16 + 16,
@@ -196,14 +195,10 @@ def tensorcore_gemm(a: T.handle, b: T.handle, c: T.handle) -> None:
                                             )
                                         )
                                 for i, j in T.grid(2, 4):
-                                    with T.block([64, 64, T.reduce_axis(0, 64)]) as [
-                                        vi,
-                                        vj,
-                                        vk,
-                                    ]:
-                                        T.bind(vi, bx * 4 + ty * 2 + i)
-                                        T.bind(vj, by * 8 + tz * 4 + j)
-                                        T.bind(vk, ko * 2 + ki)
+                                    with T.block():
+                                        vi = T.axis.S(64, bx * 4 + ty * 2 + i)
+                                        vj = T.axis.S(64, by * 8 + tz * 4 + j)
+                                        vk = T.axis.R(64, ko * 2 + ki)
                                         T.reads(
                                             [
                                                 wmma_A[
@@ -258,9 +253,9 @@ def tensorcore_gemm(a: T.handle, b: T.handle, c: T.handle) -> None:
                                             )
                                         )
                         for i, j in T.grid(2, 4):
-                            with T.block([64, 64]) as [vi, vj]:
-                                T.bind(vi, bx * 4 + ty * 2 + i)
-                                T.bind(vj, by * 8 + tz * 4 + j)
+                            with T.block():
+                                vi = T.axis.S(64, bx * 4 + ty * 2 + i)
+                                vj = T.axis.S(64, by * 8 + tz * 4 + j)
                                 T.reads(wmma_C[vi * 16 : vi * 16 + 16, vj * 16 : vj * 16 + 16])
                                 T.writes(C[vi * 16 : vi * 16 + 16, vj * 16 : vj * 16 + 16])
                                 s0 = T.var("int32")
diff --git a/tests/python/unittest/test_lower_build.py b/tests/python/unittest/test_lower_build.py
index 6502f0c67de62..fabf41705698f 100644
--- a/tests/python/unittest/test_lower_build.py
+++ b/tests/python/unittest/test_lower_build.py
@@ -41,10 +41,12 @@ def matmul(a: T.handle, b: T.handle, c: T.handle) -> None:
     B = T.match_buffer(b, [128, 128])
     C = T.match_buffer(c, [128, 128])
     for i, j in T.grid(128, 128):
-        with T.block([128, 128], "init") as [vi, vj]:
+        with T.block("init"):
+            vi, vj = T.axis.remap("SS", [i, j])
             C[vi, vj] = T.float32(0)
-        for k in range(0, 128):
-            with T.block([128, 128, T.reduce_axis(0, 128)], "update") as [vi, vj, vk]:
+        for k in range(128):
+            with T.block("update"):
+                vi, vj, vk = T.axis.remap("SSR", [i, j, k])
                 C[vi, vj] = C[vi, vj] + A[vi, vk] * B[vj, vk]
 
 
diff --git a/tests/python/unittest/test_meta_schedule_arg_info.py b/tests/python/unittest/test_meta_schedule_arg_info.py
index 7bedea9082d14..62dcb52f74153 100644
--- a/tests/python/unittest/test_meta_schedule_arg_info.py
+++ b/tests/python/unittest/test_meta_schedule_arg_info.py
@@ -28,10 +28,12 @@ def Matmul(a: T.handle, b: T.handle, c: T.handle) -> None:
     A = T.match_buffer(a, (128, 256), "float32")
     B = T.match_buffer(b, (256, 512), "float32")
     C = T.match_buffer(c, (128, 512), "float32")
-    with T.block([128, 256, T.reduce_axis(0, 512)], "matmul") as [vi, vj, vk]:
-        with T.init():
-            C[vi, vj] = 0.0
-        C[vi, vj] = C[vi, vj] + A[vi, vk] * B[vk, vj]
+    for i, j, k in T.grid(128, 256, 512):
+        with T.block("matmul"):
+            vi, vj, vk = T.axis.remap("SSR", [i, j, k])
+            with T.init():
+                C[vi, vj] = 0.0
+            C[vi, vj] = C[vi, vj] + A[vi, vk] * B[vk, vj]
 
 # fmt: on
 # pylint: enable=invalid-name,no-member,line-too-long,too-many-nested-blocks,no-self-argument
diff --git a/tests/python/unittest/test_meta_schedule_builder.py b/tests/python/unittest/test_meta_schedule_builder.py
index fa09a092c8c46..fb3fa135a9b8c 100644
--- a/tests/python/unittest/test_meta_schedule_builder.py
+++ b/tests/python/unittest/test_meta_schedule_builder.py
@@ -47,10 +47,12 @@ def matmul(a: T.handle, b: T.handle, c: T.handle) -> None:  # pylint: disable=no
         A = T.match_buffer(a, (1024, 1024), "float32")
         B = T.match_buffer(b, (1024, 1024), "float32")
         C = T.match_buffer(c, (1024, 1024), "float32")
-        with T.block([1024, 1024, T.reduce_axis(0, 1024)], "matmul") as [vi, vj, vk]:
-            with T.init():
-                C[vi, vj] = 0.0
-            C[vi, vj] = C[vi, vj] + A[vi, vk] * B[vk, vj]
+        for i, j, k in T.grid(1024, 1024, 1024):
+            with T.block("matmul"):
+                vi, vj, vk = T.axis.remap("SSR", [i, j, k])
+                with T.init():
+                    C[vi, vj] = 0.0
+                C[vi, vj] = C[vi, vj] + A[vi, vk] * B[vk, vj]
 
 
 @script.ir_module
@@ -64,12 +66,16 @@ def matmul_relu(  # pylint: disable=no-self-argument
         B = T.match_buffer(b, (1024, 1024), "float32")
         D = T.match_buffer(d, (1024, 1024), "float32")
         C = T.alloc_buffer((1024, 1024), "float32")
-        with T.block([1024, 1024, T.reduce_axis(0, 1024)], "matmul") as [vi, vj, vk]:
-            with T.init():
-                C[vi, vj] = 0.0
-            C[vi, vj] = C[vi, vj] + A[vi, vk] * B[vk, vj]
-        with T.block([1024, 1024], "relu") as [vi, vj]:
-            D[vi, vj] = T.max(C[vi, vj], 0.0)
+        for i, j, k in T.grid(1024, 1024, 1024):
+            with T.block("matmul"):
+                vi, vj, vk = T.axis.remap("SSR", [i, j, k])
+                with T.init():
+                    C[vi, vj] = 0.0
+                C[vi, vj] = C[vi, vj] + A[vi, vk] * B[vk, vj]
+        for i, j in T.grid(1024, 1024):
+            with T.block("relu"):
+                vi, vj = T.axis.remap("SS", [i, j])
+                D[vi, vj] = T.max(C[vi, vj], 0.0)
 
 
 @script.ir_module
@@ -82,10 +88,12 @@ def batch_matmul(  # pylint: disable=no-self-argument
         A = T.match_buffer(a, [16, 128, 128])
         B = T.match_buffer(b, [16, 128, 128])
         C = T.match_buffer(c, [16, 128, 128])
-        with T.block([16, 128, 128, T.reduce_axis(0, 128)], "update") as [vn, vi, vj, vk]:
-            with T.init():
-                C[vn, vi, vj] = 0.0
-            C[vn, vi, vj] = C[vn, vi, vj] + A[vn, vi, vk] * B[vn, vj, vk]
+        for n, i, j, k in T.grid(16, 128, 128, 128):
+            with T.block("update"):
+                vn, vi, vj, vk = T.axis.remap("SSSR", [n, i, j, k])
+                with T.init():
+                    C[vn, vi, vj] = 0.0
+                C[vn, vi, vj] = C[vn, vi, vj] + A[vn, vi, vk] * B[vn, vj, vk]
 
 
 # pylint: enable=invalid-name,no-member,line-too-long,too-many-nested-blocks,missing-docstring
diff --git a/tests/python/unittest/test_meta_schedule_database.py b/tests/python/unittest/test_meta_schedule_database.py
index cb39c91eaca46..121ec2fd480bb 100644
--- a/tests/python/unittest/test_meta_schedule_database.py
+++ b/tests/python/unittest/test_meta_schedule_database.py
@@ -41,10 +41,12 @@ def main(a: T.handle, b: T.handle, c: T.handle) -> None:
         A = T.match_buffer(a, (1024, 1024), "float32")
         B = T.match_buffer(b, (1024, 1024), "float32")
         C = T.match_buffer(c, (1024, 1024), "float32")
-        with T.block([1024, 1024, T.reduce_axis(0, 1024)], "matmul") as [vi, vj, vk]:
-            with T.init():
-                C[vi, vj] = 0.0
-            C[vi, vj] = C[vi, vj] + A[vi, vk] * B[vk, vj]
+        for i, j, k in T.grid(1024, 1024, 1024):
+            with T.block("matmul"):
+                vi, vj, vk = T.axis.remap("SSR", [i, j, k])
+                with T.init():
+                    C[vi, vj] = 0.0
+                C[vi, vj] = C[vi, vj] + A[vi, vk] * B[vk, vj]
 
 
 @tvm.script.ir_module
@@ -56,12 +58,16 @@ def main(a: T.handle, b: T.handle, d: T.handle) -> None:  # pylint: disable=no-s
         B = T.match_buffer(b, (16, 16), "float32")
         D = T.match_buffer(d, (16, 16), "float32")
         C = T.alloc_buffer((16, 16), "float32")
-        with T.block([16, 16, T.reduce_axis(0, 16)], "matmul") as [vi, vj, vk]:
-            with T.init():
-                C[vi, vj] = 0.0
-            C[vi, vj] = C[vi, vj] + A[vi, vk] * B[vk, vj]
-        with T.block([16, 16], "relu") as [vi, vj]:
-            D[vi, vj] = T.max(C[vi, vj], 0.0)
+        for i, j, k in T.grid(16, 16, 16):
+            with T.block("matmul"):
+                vi, vj, vk = T.axis.remap("SSR", [i, j, k])
+                with T.init():
+                    C[vi, vj] = 0.0
+                C[vi, vj] = C[vi, vj] + A[vi, vk] * B[vk, vj]
+        for i, j in T.grid(16, 16):
+            with T.block("relu"):
+                vi, vj = T.axis.remap("SS", [i, j])
+                D[vi, vj] = T.max(C[vi, vj], 0.0)
 
 
 # fmt: on
diff --git a/tests/python/unittest/test_meta_schedule_runner.py b/tests/python/unittest/test_meta_schedule_runner.py
index 9fb1e5ef19c1c..46be12569c783 100644
--- a/tests/python/unittest/test_meta_schedule_runner.py
+++ b/tests/python/unittest/test_meta_schedule_runner.py
@@ -68,10 +68,12 @@ def main(a: T.handle, b: T.handle, c: T.handle) -> None:  # pylint: disable=no-s
         A = T.match_buffer(a, (16, 16), "float32")
         B = T.match_buffer(b, (16, 16), "float32")
         C = T.match_buffer(c, (16, 16), "float32")
-        with T.block([16, 16, T.reduce_axis(0, 16)], "matmul") as [vi, vj, vk]:
-            with T.init():
-                C[vi, vj] = 0.0
-            C[vi, vj] = C[vi, vj] + A[vi, vk] * B[vk, vj]
+        for i, j, k in T.grid(16, 16, 16):
+            with T.block("matmul"):
+                vi, vj, vk = T.axis.remap("SSR", [i, j, k])
+                with T.init():
+                    C[vi, vj] = 0.0
+                C[vi, vj] = C[vi, vj] + A[vi, vk] * B[vk, vj]
 
 
 @tvm.script.ir_module
@@ -83,12 +85,16 @@ def main(a: T.handle, b: T.handle, d: T.handle) -> None:  # pylint: disable=no-s
         B = T.match_buffer(b, (16, 16), "float32")
         D = T.match_buffer(d, (16, 16), "float32")
         C = T.alloc_buffer((16, 16), "float32")
-        with T.block([16, 16, T.reduce_axis(0, 16)], "matmul") as [vi, vj, vk]:
-            with T.init():
-                C[vi, vj] = 0.0
-            C[vi, vj] = C[vi, vj] + A[vi, vk] * B[vk, vj]
-        with T.block([16, 16], "relu") as [vi, vj]:
-            D[vi, vj] = T.max(C[vi, vj], 0.0)
+        for i, j, k in T.grid(16, 16, 16):
+            with T.block("matmul"):
+                vi, vj, vk = T.axis.remap("SSR", [i, j, k])
+                with T.init():
+                    C[vi, vj] = 0.0
+                C[vi, vj] = C[vi, vj] + A[vi, vk] * B[vk, vj]
+        for i, j in T.grid(16, 16):
+            with T.block("relu"):
+                vi, vj = T.axis.remap("SS", [i, j])
+                D[vi, vj] = T.max(C[vi, vj], 0.0)
 
 
 @tvm.script.ir_module
@@ -99,10 +105,12 @@ def main(a: T.handle, b: T.handle, c: T.handle) -> None:  # pylint: disable=no-s
         A = T.match_buffer(a, [16, 32, 32])
         B = T.match_buffer(b, [16, 32, 32])
         C = T.match_buffer(c, [16, 32, 32])
-        with T.block([16, 32, 32, T.reduce_axis(0, 32)], "update") as [vn, vi, vj, vk]:
-            with T.init():
-                C[vn, vi, vj] = 0.0
-            C[vn, vi, vj] = C[vn, vi, vj] + A[vn, vi, vk] * B[vn, vj, vk]
+        for n, i, j, k in T.grid(16, 32, 32, 32):
+            with T.block("update"):
+                vn, vi, vj, vk = T.axis.remap("SSSR", [n, i, j, k])
+                with T.init():
+                    C[vn, vi, vj] = 0.0
+                C[vn, vi, vj] = C[vn, vi, vj] + A[vn, vi, vk] * B[vn, vj, vk]
 
 
 @tvm.script.ir_module
@@ -113,8 +121,10 @@ def main(a: T.handle, b: T.handle, c: T.handle) -> None:  # pylint: disable=no-s
         A = T.match_buffer(a, [32], "float32")
         B = T.match_buffer(b, [32], "float32")
         C = T.match_buffer(c, [32], "float32")
-        with T.block([32], "add") as [vi]:
-            C[vi] = A[vi] + B[vi]
+        for i in range(32):
+            with T.block("add"):
+                vi = T.axis.S(32, i)
+                C[vi] = A[vi] + B[vi]
 
 
 # pylint: enable=invalid-name,no-member,line-too-long,too-many-nested-blocks,missing-docstring
diff --git a/tests/python/unittest/test_meta_schedule_search_strategy.py b/tests/python/unittest/test_meta_schedule_search_strategy.py
index e12871391558c..9b3ddfd7c7893 100644
--- a/tests/python/unittest/test_meta_schedule_search_strategy.py
+++ b/tests/python/unittest/test_meta_schedule_search_strategy.py
@@ -45,10 +45,12 @@ def main(a: T.handle, b: T.handle, c: T.handle) -> None:
         A = T.match_buffer(a, (32, 32), "float32")
         B = T.match_buffer(b, (32, 32), "float32")
         C = T.match_buffer(c, (32, 32), "float32")
-        with T.block([32, 32, T.reduce_axis(0, 32)], "matmul") as [vi, vj, vk]:
-            with T.init():
-                C[vi, vj] = 0.0
-            C[vi, vj] = C[vi, vj] + A[vi, vk] * B[vk, vj]
+        for i, j, k in T.grid(32, 32, 32):
+            with T.block("matmul"):
+                vi, vj, vk = T.axis.remap("SSR", [i, j, k])
+                with T.init():
+                    C[vi, vj] = 0.0
+                C[vi, vj] = C[vi, vj] + A[vi, vk] * B[vk, vj]
 
 # fmt: on
 # pylint: enable=invalid-name,no-member,line-too-long,too-many-nested-blocks,no-self-argument
diff --git a/tests/python/unittest/test_meta_schedule_space_generator.py b/tests/python/unittest/test_meta_schedule_space_generator.py
index 39bb1acf065f8..3f7749ca9e2cf 100644
--- a/tests/python/unittest/test_meta_schedule_space_generator.py
+++ b/tests/python/unittest/test_meta_schedule_space_generator.py
@@ -40,10 +40,12 @@ def main(a: T.handle, b: T.handle, c: T.handle) -> None:
         A = T.match_buffer(a, (1024, 1024), "float32")
         B = T.match_buffer(b, (1024, 1024), "float32")
         C = T.match_buffer(c, (1024, 1024), "float32")
-        with T.block([1024, 1024, T.reduce_axis(0, 1024)], "matmul") as [vi, vj, vk]:
-            with T.init():
-                C[vi, vj] = 0.0
-            C[vi, vj] = C[vi, vj] + A[vi, vk] * B[vk, vj]
+        for i, j, k in T.grid(1024, 1024, 1024):
+            with T.block("matmul"):
+                vi, vj, vk = T.axis.remap("SSR", [i, j, k])
+                with T.init():
+                    C[vi, vj] = 0.0
+                C[vi, vj] = C[vi, vj] + A[vi, vk] * B[vk, vj]
 
 # fmt: on
 # pylint: enable=invalid-name,no-member,line-too-long,too-many-nested-blocks,no-self-argument
diff --git a/tests/python/unittest/test_meta_schedule_task_scheduler.py b/tests/python/unittest/test_meta_schedule_task_scheduler.py
index a304096965433..4854aeb5f5aa9 100644
--- a/tests/python/unittest/test_meta_schedule_task_scheduler.py
+++ b/tests/python/unittest/test_meta_schedule_task_scheduler.py
@@ -48,10 +48,12 @@ def main(a: T.handle, b: T.handle, c: T.handle) -> None:  # pylint: disable=no-s
         A = T.match_buffer(a, (1024, 1024), "float32")
         B = T.match_buffer(b, (1024, 1024), "float32")
         C = T.match_buffer(c, (1024, 1024), "float32")
-        with T.block([1024, 1024, T.reduce_axis(0, 1024)], "matmul") as [vi, vj, vk]:
-            with T.init():
-                C[vi, vj] = 0.0
-            C[vi, vj] = C[vi, vj] + A[vi, vk] * B[vk, vj]
+        for i, j, k in T.grid(1024, 1024, 1024):
+            with T.block("matmul"):
+                vi, vj, vk = T.axis.remap("SSR", [i, j, k])
+                with T.init():
+                    C[vi, vj] = 0.0
+                C[vi, vj] = C[vi, vj] + A[vi, vk] * B[vk, vj]
 
 
 @tvm.script.ir_module
@@ -63,12 +65,16 @@ def main(a: T.handle, b: T.handle, d: T.handle) -> None:  # pylint: disable=no-s
         B = T.match_buffer(b, (1024, 1024), "float32")
         D = T.match_buffer(d, (1024, 1024), "float32")
         C = T.alloc_buffer((1024, 1024), "float32")
-        with T.block([1024, 1024, T.reduce_axis(0, 1024)], "matmul") as [vi, vj, vk]:
-            with T.init():
-                C[vi, vj] = 0.0
-            C[vi, vj] = C[vi, vj] + A[vi, vk] * B[vk, vj]
-        with T.block([1024, 1024], "relu") as [vi, vj]:
-            D[vi, vj] = T.max(C[vi, vj], 0.0)
+        for i, j, k in T.grid(1024, 1024, 1024):
+            with T.block("matmul"):
+                vi, vj, vk = T.axis.remap("SSR", [i, j, k])
+                with T.init():
+                    C[vi, vj] = 0.0
+                C[vi, vj] = C[vi, vj] + A[vi, vk] * B[vk, vj]
+        for i, j in T.grid(1024, 1024):
+            with T.block("relu"):
+                vi, vj = T.axis.remap("SS", [i, j])
+                D[vi, vj] = T.max(C[vi, vj], 0.0)
 
 
 @tvm.script.ir_module
@@ -79,10 +85,12 @@ def main(a: T.handle, b: T.handle, c: T.handle) -> None:  # pylint: disable=no-s
         A = T.match_buffer(a, [16, 128, 128])
         B = T.match_buffer(b, [16, 128, 128])
         C = T.match_buffer(c, [16, 128, 128])
-        with T.block([16, 128, 128, T.reduce_axis(0, 128)], "matmul") as [vn, vi, vj, vk]:
-            with T.init():
-                C[vn, vi, vj] = 0.0
-            C[vn, vi, vj] = C[vn, vi, vj] + A[vn, vi, vk] * B[vn, vj, vk]
+        for n, i, j, k in T.grid(16, 128, 128, 128):
+            with T.block("matmul"):
+                vn, vi, vj, vk = T.axis.remap("SSSR", [n, i, j, k])
+                with T.init():
+                    C[vn, vi, vj] = 0.0
+                C[vn, vi, vj] = C[vn, vi, vj] + A[vn, vi, vk] * B[vn, vj, vk]
 
 
 # pylint: enable=invalid-name,no-member,line-too-long,too-many-nested-blocks
diff --git a/tests/python/unittest/test_meta_schedule_tune_context.py b/tests/python/unittest/test_meta_schedule_tune_context.py
index 44bb949b925b9..01a4379e5127f 100644
--- a/tests/python/unittest/test_meta_schedule_tune_context.py
+++ b/tests/python/unittest/test_meta_schedule_tune_context.py
@@ -35,10 +35,12 @@ def main(a: T.handle, b: T.handle, c: T.handle) -> None:  # pylint: disable=no-s
         A = T.match_buffer(a, (1024, 1024), "float32")
         B = T.match_buffer(b, (1024, 1024), "float32")
         C = T.match_buffer(c, (1024, 1024), "float32")
-        with T.block([1024, 1024, T.reduce_axis(0, 1024)], "matmul") as [vi, vj, vk]:
-            with T.init():
-                C[vi, vj] = 0.0
-            C[vi, vj] = C[vi, vj] + A[vi, vk] * B[vk, vj]
+        for i, j, k in T.grid(1024, 1024, 1024):
+            with T.block("matmul"):
+                vi, vj, vk = T.axis.remap("SSR", [i, j, k])
+                with T.init():
+                    C[vi, vj] = 0.0
+                C[vi, vj] = C[vi, vj] + A[vi, vk] * B[vk, vj]
 
 
 # pylint: enable=invalid-name,no-member,line-too-long,too-many-nested-blocks,missing-docstring
diff --git a/tests/python/unittest/test_te_create_primfunc.py b/tests/python/unittest/test_te_create_primfunc.py
index 987898001a1b7..6b5c26d08b7b5 100644
--- a/tests/python/unittest/test_te_create_primfunc.py
+++ b/tests/python/unittest/test_te_create_primfunc.py
@@ -54,10 +54,12 @@ def tir_matmul(a: T.handle, b: T.handle, c: T.handle) -> None:
     B = T.match_buffer(b, (128, 128))
     C = T.match_buffer(c, (128, 128))
 
-    with T.block([128, 128, T.reduce_axis(0, 128)]) as [i, j, k]:
-        with T.init():
-            C[i, j] = 0.0
-        C[i, j] += A[i, k] * B[j, k]
+    for i0, j0, k0 in T.grid(128, 128, 128):
+        with T.block():
+            i, j, k = T.axis.remap("SSR", [i0, j0, k0])
+            with T.init():
+                C[i, j] = 0.0
+            C[i, j] += A[i, k] * B[j, k]
 
 
 def test_matmul():
@@ -77,10 +79,14 @@ def tir_element_wise(a: T.handle, c: T.handle) -> None:
     C = T.match_buffer(c, (128, 128))
     B = T.alloc_buffer((128, 128))
 
-    with T.block([128, 128]) as [i, j]:
-        B[i, j] = A[i, j] * 2.0
-    with T.block([128, 128]) as [i, j]:
-        C[i, j] = B[i, j] + 1.0
+    for i0, j0 in T.grid(128, 128):
+        with T.block():
+            i, j = T.axis.remap("SS", [i0, j0])
+            B[i, j] = A[i, j] * 2.0
+    for i0, j0 in T.grid(128, 128):
+        with T.block():
+            i, j = T.axis.remap("SS", [i0, j0])
+            C[i, j] = B[i, j] + 1.0
 
 
 def test_element_wise():
@@ -125,19 +131,21 @@ def tir_conv2d(a: T.handle, w: T.handle, b: T.handle) -> None:
     B = T.match_buffer(b, [16, 32, 14, 14])
     Apad = T.alloc_buffer([16, 16, 16, 16])
 
-    with T.block([16, 16, 16, 16], "Apad") as [nn, cc, yy, xx]:
-        Apad[nn, cc, yy, xx] = T.if_then_else(
-            yy >= 1 and yy - 1 < 14 and xx >= 1 and xx - 1 < 14,
-            A[nn, cc, yy - 1, xx - 1],
-            0.0,
-            dtype="float32",
-        )
-    with T.block(
-        [16, 32, 14, 14, T.reduce_axis(0, 16), T.reduce_axis(0, 3), T.reduce_axis(0, 3)], "B"
-    ) as [nn, ff, yy, xx, rc, ry, rx]:
-        with T.init():
-            B[nn, ff, yy, xx] = 0.0
-        B[nn, ff, yy, xx] += Apad[nn, rc, yy + ry, xx + rx] * W[rc, ry, rx, ff]
+    for n, c, y, x in T.grid(16, 16, 16, 16):
+        with T.block("Apad"):
+            nn, cc, yy, xx = T.axis.remap("SSSS", [n, c, y, x])
+            Apad[nn, cc, yy, xx] = T.if_then_else(
+                yy >= 1 and yy - 1 < 14 and xx >= 1 and xx - 1 < 14,
+                A[nn, cc, yy - 1, xx - 1],
+                0.0,
+                dtype="float32",
+            )
+    for n, f, y, x, kc, ky, kx in T.grid(16, 32, 14, 14, 16, 3, 3):
+        with T.block("B"):
+            nn, ff, yy, xx, rc, ry, rx = T.axis.remap("SSSSRRR", [n, f, y, x, kc, ky, kx])
+            with T.init():
+                B[nn, ff, yy, xx] = 0.0
+            B[nn, ff, yy, xx] += Apad[nn, rc, yy + ry, xx + rx] * W[rc, ry, rx, ff]
 
 
 def test_conv2d():
@@ -163,9 +171,11 @@ def tir_multi_output(a0: T.handle, a1: T.handle, b0: T.handle, b1: T.handle) ->
     B1 = T.match_buffer(b1, (m, n))
 
     for i0, i1 in T.grid(m, n):
-        with T.block([m, n], "B.v0") as [i, j]:
+        with T.block("B.v0"):
+            i, j = T.axis.remap("SS", [i0, i1])
             B0[i, j] = A0[i, j] + 2.0
-        with T.block([m, n], "B.v1") as [i, j]:
+        with T.block("B.v1"):
+            i, j = T.axis.remap("SS", [i0, i1])
             B1[i, j] = A1[i, j] * 3.0
 
 
@@ -193,7 +203,7 @@ def tir_extern(a: T.handle, b: T.handle, c: T.handle) -> None:
     B = T.match_buffer(b, (128, 128))
     C = T.match_buffer(c, (128, 128))
     # body
-    with T.block([], "C"):
+    with T.block("C"):
         T.reads([A[0:128, 0:128], B[0:128, 0:128]])
         T.writes([C[0:128, 0:128]])
         T.evaluate(
@@ -251,10 +261,12 @@ def tir_reordered_matmul(c: T.handle, a: T.handle, b: T.handle) -> None:
     B = T.match_buffer(b, (128, 128))
     C = T.match_buffer(c, (128, 128))
 
-    with T.block([128, 128, T.reduce_axis(0, 128)]) as [i, j, k]:
-        with T.init():
-            C[i, j] = 0.0
-        C[i, j] += A[i, k] * B[j, k]
+    for i0, j0, k0 in T.grid(128, 128, 128):
+        with T.block():
+            i, j, k = T.axis.remap("SSR", [i0, j0, k0])
+            with T.init():
+                C[i, j] = 0.0
+            C[i, j] += A[i, k] * B[j, k]
 
 
 def test_arg_order():
diff --git a/tests/python/unittest/test_tir_analysis_detect_buffer_access_lca.py b/tests/python/unittest/test_tir_analysis_detect_buffer_access_lca.py
index 1aae8cdd03e1a..1a0dfd09a2df2 100644
--- a/tests/python/unittest/test_tir_analysis_detect_buffer_access_lca.py
+++ b/tests/python/unittest/test_tir_analysis_detect_buffer_access_lca.py
@@ -25,17 +25,23 @@ def buffer_load_store_func(a: T.handle, b: T.handle) -> None:
     B = T.match_buffer(b, (128, 128), "float32")
     C = T.alloc_buffer((128, 128), "float32")
     D = T.alloc_buffer((128, 128), "float32")
-    with T.block([128, 128]) as [i, j]:
-        A[i, j] = T.float32(0)
-    with T.block([32, 32, T.reduce_axis(0, 32)]) as [i, j, k]:
-        with T.init():
+    for ii, jj in T.grid(128, 128):
+        with T.block():
+            i, j = T.axis.remap("SS", [ii, jj])
+            A[i, j] = T.float32(0)
+    for i0, j0, k0 in T.grid(32, 32, 32):
+        with T.block():
+            i, j, k = T.axis.remap("SSR", [i0, j0, k0])
+            with T.init():
+                for ii, jj in T.grid(4, 4):
+                    B[i * 4 + ii, j * 4 + jj] = A[i * 4 + ii, j * 4 + jj]
             for ii, jj in T.grid(4, 4):
-                B[i * 4 + ii, j * 4 + jj] = A[i * 4 + ii, j * 4 + jj]
-        for ii, jj in T.grid(4, 4):
-            for kk in range(0, 4):
-                B[i * 4 + ii, j * 4 + jj] += C[i * 4 + ii, k * 4 + kk]
-            for kk in range(0, 4):
-                B[i * 4 + ii, j * 4 + jj] += D[j * 4 + jj, k * 4 + kk] * C[i * 4 + ii, k * 4 + kk]
+                for kk in range(0, 4):
+                    B[i * 4 + ii, j * 4 + jj] += C[i * 4 + ii, k * 4 + kk]
+                for kk in range(0, 4):
+                    B[i * 4 + ii, j * 4 + jj] += (
+                        D[j * 4 + jj, k * 4 + kk] * C[i * 4 + ii, k * 4 + kk]
+                    )
 
 
 @T.prim_func
@@ -43,7 +49,7 @@ def buffer_opaque_access(b: T.handle, c: T.handle) -> None:
     B = T.match_buffer(b, [16, 16], "float32")
     C = T.match_buffer(c, [16, 16], "float32")
 
-    with T.block([]):
+    with T.block():
         T.reads([])
         T.writes(B[0:16, 0:16])
         A = T.allocate([256], "float32", "global")
@@ -56,9 +62,8 @@ def buffer_opaque_access(b: T.handle, c: T.handle) -> None:
                 T.evaluate(T.tvm_fill_fragment(B.data, 16, 16, 16, 0, T.float32(0), dtype="handle"))
 
     for i, j in T.grid(16, 16):
-        with T.block([16, 16]) as [vi, vj]:
-            T.bind(vi, i)
-            T.bind(vj, j)
+        with T.block():
+            vi, vj = T.axis.remap("SS", [i, j])
             C[vi, vj] = B[vi, vj]
 
 
@@ -72,16 +77,20 @@ def lca_is_func_root(a: T.handle) -> None:
 def match_buffer_func(a: T.handle, b: T.handle) -> None:
     A = T.match_buffer(a, (128, 128), "float32")
     B = T.match_buffer(b, (128, 128), "float32")
-    with T.block([8, 8], "block") as [vi, vj]:
-        T.reads(B[vi * 16 + 2 : vi * 16 + 12, vj * 16 + 2 : vj * 16 + 16])
-        T.writes(A[vi * 16 : vi * 16 + 16, vj * 16 : vj * 16 + 16])
-        B0 = T.match_buffer(B[vi * 16 + 2 : vi * 16 + 6, vj * 16 + 2 : vj * 16 + 6], (4, 4))
-        B1 = T.match_buffer(B[vi * 16 + 8 : vi * 16 + 12, vj * 16 + 8 : vj * 16 + 16], (4, 8))
-        with T.block([16, 16], "AAA") as [i, j]:
-            AA = T.match_buffer(A[i, j], ())
-            AA[()] = 1.0
-        T.evaluate(B0.data)
-        T.evaluate(B1.data)
+    for i, j in T.grid(8, 8):
+        with T.block("block"):
+            vi, vj = T.axis.remap("SS", [i, j])
+            T.reads(B[vi * 16 + 2 : vi * 16 + 12, vj * 16 + 2 : vj * 16 + 16])
+            T.writes(A[vi * 16 : vi * 16 + 16, vj * 16 : vj * 16 + 16])
+            B0 = T.match_buffer(B[vi * 16 + 2 : vi * 16 + 6, vj * 16 + 2 : vj * 16 + 6], (4, 4))
+            B1 = T.match_buffer(B[vi * 16 + 8 : vi * 16 + 12, vj * 16 + 8 : vj * 16 + 16], (4, 8))
+            for ii, jj in T.grid(16, 16):
+                with T.block("AAA"):
+                    vii, vjj = T.axis.remap("SS", [ii, jj])
+                    AA = T.match_buffer(A[vii, vjj], ())
+                    AA[()] = 1.0
+            T.evaluate(B0.data)
+            T.evaluate(B1.data)
 
 
 def test_buffer_load_store():
diff --git a/tests/python/unittest/test_tir_analysis_get_block_access_region.py b/tests/python/unittest/test_tir_analysis_get_block_access_region.py
index e3a63c3254344..4ea35c0a2d6c4 100644
--- a/tests/python/unittest/test_tir_analysis_get_block_access_region.py
+++ b/tests/python/unittest/test_tir_analysis_get_block_access_region.py
@@ -27,57 +27,65 @@ def func() -> None:
     B = T.alloc_buffer((128, 128), "float32")
     C = T.alloc_buffer((128, 128), "float32")
     D = T.alloc_buffer((128, 128), "float32")
-    with T.block([]):
+    with T.block():
         # Need add read/write region manually to avoid triggering block access region detector
         T.reads([B[0, 0], C[0:16, 0:16], A[4:12, 4:12]])
         T.writes([A[0:12, 0:12]])
         for i, j in T.grid(8, 8):
             A[i, j] = B[0, 0] + C[0, 0]
-        with T.block([2, 2]) as [vi, vj]:
-            T.reads([A[vi * 4 + 4 : vi * 4 + 8, vj * 4 + 4 : vj * 4 + 8], C[12:16, 12:16]])
-            T.writes([A[vi * 4 + 4 : vi * 4 + 8, vj * 4 + 4 : vj * 4 + 8]])
-            for i, j in T.grid(4, 4):
-                A[vi * 4 + 4 + i, vj * 4 + 4 + j] += C[i + 12, j + 12]
+        for i, j in T.grid(2, 2):
+            with T.block():
+                vi, vj = T.axis.remap("SS", [i, j])
+                T.reads([A[vi * 4 + 4 : vi * 4 + 8, vj * 4 + 4 : vj * 4 + 8], C[12:16, 12:16]])
+                T.writes([A[vi * 4 + 4 : vi * 4 + 8, vj * 4 + 4 : vj * 4 + 8]])
+                for i, j in T.grid(4, 4):
+                    A[vi * 4 + 4 + i, vj * 4 + 4 + j] += C[i + 12, j + 12]
         T.evaluate(D.data)
 
 
 @T.prim_func
 def match_buffer_func() -> None:
-    with T.block([], "root"):
+    with T.block("root"):
         A = T.alloc_buffer((128, 128), "float32")
         B = T.alloc_buffer((128, 128), "float32")
         T.reads([])
         T.writes([])
         # Need add read/write region manually to avoid triggering block access region detector
-        with T.block([8, 8], "block") as [vi, vj]:
-            T.reads(B[vi * 16 + 2 : vi * 16 + 12, vj * 16 + 2 : vj * 16 + 16])
-            T.writes(A[vi * 16 : vi * 16 + 16, vj * 16 : vj * 16 + 16])
-            AA = T.match_buffer(A[vi * 16 : vi * 16 + 16, vj * 16 : vj * 16 + 16], (16, 16))
-            B0 = T.match_buffer(B[vi * 16 + 2 : vi * 16 + 6, vj * 16 + 2 : vj * 16 + 6], (4, 4))
-            B1 = T.match_buffer(B[vi * 16 + 8 : vi * 16 + 12, vj * 16 + 8 : vj * 16 + 16], (4, 8))
-            with T.block([16, 16], "AAA") as [i, j]:
-                T.reads([])
-                T.writes(AA[i, j])
-                AAA = T.match_buffer(AA[i, j], ())
-                AAA[()] = 1.0
-            T.evaluate(B0.data)
-            T.evaluate(B1.data)
+        for i, j in T.grid(8, 8):
+            with T.block("block"):
+                vi, vj = T.axis.remap("SS", [i, j])
+                T.reads(B[vi * 16 + 2 : vi * 16 + 12, vj * 16 + 2 : vj * 16 + 16])
+                T.writes(A[vi * 16 : vi * 16 + 16, vj * 16 : vj * 16 + 16])
+                AA = T.match_buffer(A[vi * 16 : vi * 16 + 16, vj * 16 : vj * 16 + 16], (16, 16))
+                B0 = T.match_buffer(B[vi * 16 + 2 : vi * 16 + 6, vj * 16 + 2 : vj * 16 + 6], (4, 4))
+                B1 = T.match_buffer(
+                    B[vi * 16 + 8 : vi * 16 + 12, vj * 16 + 8 : vj * 16 + 16], (4, 8)
+                )
+                for ii, jj in T.grid(16, 16):
+                    with T.block("AAA"):
+                        vii, vjj = T.axis.remap("SS", [ii, jj])
+                        T.reads([])
+                        T.writes(AA[vii, vjj])
+                        AAA = T.match_buffer(AA[vii, vjj], ())
+                        AAA[()] = 1.0
+                T.evaluate(B0.data)
+                T.evaluate(B1.data)
 
 
 @T.prim_func
 def opaque_block_func() -> None:
-    with T.block([], "root"):
+    with T.block("root"):
         A = T.alloc_buffer((16, 16), "float32")
         B = T.alloc_buffer((16, 16), "float32")
         T.reads([])
         T.writes([])
         # Need add read/write region manually to avoid triggering block access region detector
         for i in range(0, 16):
-            with T.block([]):
+            with T.block():
                 T.reads(A[i, 0:16])
                 T.writes([B[i, 0:16]])
                 for j in range(0, 16):
-                    with T.block([]):
+                    with T.block():
                         T.reads(A[i, j])
                         T.writes(B[i, j])
                         B[i, j] = A[i, j] + 1.0
@@ -88,8 +96,8 @@ def opaque_access_func() -> None:
     A = T.alloc_buffer([1024])
     B = T.alloc_buffer([1024])
     for i in T.serial(0, 8):
-        with T.block([8]) as [v]:
-            T.bind(v, i)
+        with T.block():
+            v = T.axis.S(8, i)
             T.reads([A[v * 128 : v * 128 + 128]])
             T.writes([B[v * 128 : v * 128 + 128]])
             T.evaluate(
diff --git a/tests/python/unittest/test_tir_lower_match_buffer.py b/tests/python/unittest/test_tir_lower_match_buffer.py
index 7129275aebcdd..5ca9cf0da3c93 100644
--- a/tests/python/unittest/test_tir_lower_match_buffer.py
+++ b/tests/python/unittest/test_tir_lower_match_buffer.py
@@ -39,7 +39,7 @@ def buffer_load_store(a: T.handle, c: T.handle) -> None:
     A = T.match_buffer(a, (16, 16, 16))
     C = T.match_buffer(c, (16, 16))
     for i, j, k in T.grid(4, 16, 8):
-        with T.block([]):
+        with T.block():
             T.reads(C[i * 4 : i * 4 + 4, k * 2 : k * 2 + 2])
             T.writes(A[i * 4 : i * 4 + 4, j, k * 2 : k * 2 + 2])
             sub_A = T.match_buffer(
@@ -55,7 +55,7 @@ def transformed_buffer_load_store(a: T.handle, c: T.handle) -> None:
     A = T.match_buffer(a, (16, 16, 16))
     C = T.match_buffer(c, (16, 16))
     for i, j, k in T.grid(4, 16, 8):
-        with T.block([]):
+        with T.block():
             T.reads(C[i * 4 : i * 4 + 4, k * 2 : k * 2 + 2])
             T.writes(A[i * 4 : i * 4 + 4, j, k * 2 : k * 2 + 2])
             for ii, kk in T.grid(4, 2):
@@ -72,7 +72,7 @@ def opaque_access(a: T.handle, b: T.handle) -> None:
     A = T.match_buffer(a, (32, 64, 128))
     B = T.match_buffer(b, (64, 64, 64))
     for i, j, k in T.grid(2, 64, 8):
-        with T.block([]):
+        with T.block():
             T.reads([])
             T.writes(A[i * 16 : i * 16 + 16, j, k * 16 : k * 16 + 16])
             sub_A = T.match_buffer(
@@ -93,7 +93,7 @@ def opaque_access(a: T.handle, b: T.handle) -> None:
                 )
             )
     for i, j, k in T.grid(64, 2, 8):
-        with T.block([]):
+        with T.block():
             Bs_0 = T.var("int32")
             Bs_1 = T.var("int32")
             T.reads([])
@@ -122,7 +122,7 @@ def transformed_opaque_access(a: T.handle, b: T.handle) -> None:
     A = T.match_buffer(a, (32, 64, 128))
     B = T.match_buffer(b, (64, 64, 64))
     for i, j, k in T.grid(2, 64, 8):
-        with T.block([]):
+        with T.block():
             T.reads([])
             T.writes(A[i * 16 : i * 16 + 16, j, k * 16 : k * 16 + 16])
             T.evaluate(
@@ -137,7 +137,7 @@ def transformed_opaque_access(a: T.handle, b: T.handle) -> None:
                 )
             )
     for i, j, k in T.grid(64, 2, 8):
-        with T.block([]):
+        with T.block():
             T.reads([])
             T.writes(B[i, j * 32 : j * 32 + 32, k * 8 : k * 8 + 8])
             T.evaluate(
@@ -157,7 +157,7 @@ def transformed_opaque_access(a: T.handle, b: T.handle) -> None:
 def high_dim_opaque_access(a: T.handle) -> None:
     A = T.match_buffer(a, (16, 32, 64))
     for i, j, k in T.grid(16, 2, 4):
-        with T.block([]):
+        with T.block():
             As_0 = T.var("int32")
             As_1 = T.var("int32")
             T.reads([])
@@ -185,7 +185,7 @@ def high_dim_opaque_access(a: T.handle) -> None:
 def transformed_high_dim_opaque_access(a: T.handle) -> None:
     A = T.match_buffer(a, (16, 32, 64))
     for i, j, k in T.grid(16, 2, 4):
-        with T.block([]):
+        with T.block():
             T.reads([])
             T.writes(A[i, j * 16 : j * 16 + 16, k * 16 : k * 16 + 16])
             T.evaluate(
@@ -205,7 +205,7 @@ def transformed_high_dim_opaque_access(a: T.handle) -> None:
 def high_dim_opaque_access_with_source_strides(a: T.handle) -> None:
     A = T.match_buffer(a, (16, 32, 64), strides=[2576, 80, 1])
     for i, j, k in T.grid(16, 2, 4):
-        with T.block([]):
+        with T.block():
             As_0 = T.var("int32")
             As_1 = T.var("int32")
             T.reads([])
@@ -233,7 +233,7 @@ def high_dim_opaque_access_with_source_strides(a: T.handle) -> None:
 def transformed_high_dim_opaque_access_with_source_strides(a: T.handle) -> None:
     A = T.match_buffer(a, (16, 32, 64), strides=[2576, 80, 1])
     for i, j, k in T.grid(16, 2, 4):
-        with T.block([]):
+        with T.block():
             T.reads([])
             T.writes(A[i, j * 16 : j * 16 + 16, k * 16 : k * 16 + 16])
             T.evaluate(
@@ -254,7 +254,7 @@ def recursive_match(a: T.handle, b: T.handle) -> None:
     A = T.match_buffer(a, (64, 64, 64))
     B = T.match_buffer(b, (64, 64, 64))
     for i, j, k in T.grid(64, 4, 4):
-        with T.block([]):
+        with T.block():
             T.reads([])
             T.writes(
                 [
@@ -276,7 +276,7 @@ def recursive_match(a: T.handle, b: T.handle) -> None:
                 offset_factor=1,
             )
             for jj, kk in T.grid(4, 4):
-                with T.block([]):
+                with T.block():
                     T.reads([])
                     T.writes(
                         [
@@ -317,7 +317,7 @@ def transformed_recursive_match(a: T.handle, b: T.handle) -> None:
     A = T.match_buffer(a, (64, 64, 64))
     B = T.match_buffer(b, (64, 64, 64))
     for i, j, k in T.grid(64, 4, 4):
-        with T.block([]):
+        with T.block():
             T.reads([])
             T.writes(
                 [
@@ -326,7 +326,7 @@ def transformed_recursive_match(a: T.handle, b: T.handle) -> None:
                 ]
             )
             for jj, kk in T.grid(4, 4):
-                with T.block([]):
+                with T.block():
                     T.reads([])
                     T.writes(
                         [
@@ -362,7 +362,7 @@ def symbolic_match(a: T.handle, b: T.handle, n: T.int32, m: T.int32) -> None:
     A = T.match_buffer(a, (n * m, m))
     B = T.match_buffer(b, (n * 2, m * 4))
     for i in range(0, n):
-        with T.block([]):
+        with T.block():
             T.reads([])
             T.writes([A[i * m : i * m + n, 0:m], B[i * n : i * n + 2, 0 : m * 4]])
             Bs_0 = T.var("int32")
@@ -392,7 +392,7 @@ def transformed_symbolic_match(a: T.handle, b: T.handle, n: T.int32, m: T.int32)
     A = T.match_buffer(a, (n * m, m))
     B = T.match_buffer(b, (n * 2, m * 4))
     for i in range(0, n):
-        with T.block([]):
+        with T.block():
             T.reads([])
             T.writes([A[i * m : i * m + n, 0:m], B[i * n : i * n + 2, 0 : m * 4]])
             for ii, jj in T.grid(m, m):
@@ -416,7 +416,7 @@ def rank0_buffer(a: T.handle, b: T.handle) -> None:
     A = T.match_buffer(a, (8, 8))
     B = T.match_buffer(b, (8, 8))
     for i, j in T.grid(8, 8):
-        with T.block([]):
+        with T.block():
             T.reads([])
             T.writes([A[i, j], B[i, j]])
             sub_A = T.match_buffer(A[i, j], (), offset_factor=1)
@@ -440,7 +440,7 @@ def transformed_rank0_buffer(a: T.handle, b: T.handle) -> None:
     A = T.match_buffer(a, (8, 8))
     B = T.match_buffer(b, (8, 8))
     for i, j in T.grid(8, 8):
-        with T.block([]):
+        with T.block():
             T.reads([])
             T.writes([A[i, j], B[i, j]])
             A[i, j] = 1
@@ -461,7 +461,7 @@ def transformed_rank0_buffer(a: T.handle, b: T.handle) -> None:
 def fail_match_load(a: T.handle) -> None:
     A = T.match_buffer(a, (8, 8))
     for i, j in T.grid(8, 8):
-        with T.block([]):
+        with T.block():
             T.reads(A[i, j])
             T.writes([])
             sub_A = T.match_buffer(A[i, j], ())
@@ -472,7 +472,7 @@ def fail_match_load(a: T.handle) -> None:
 def fail_match_store(a: T.handle) -> None:
     A = T.match_buffer(a, (8, 8))
     for i, j in T.grid(8, 8):
-        with T.block([]):
+        with T.block():
             T.reads([])
             T.writes(A[i, j])
             sub_A = T.match_buffer(A[i, j], ())
@@ -483,7 +483,7 @@ def fail_match_store(a: T.handle) -> None:
 def fail_buffer_bind(a: T.handle) -> None:
     A = T.match_buffer(a, (8, 8))
     for i, j in T.grid(8, 2):
-        with T.block([]):
+        with T.block():
             stride = T.var("int32")
             sub_A = T.match_buffer(
                 A[i, j * 4 : j * 4 + 4], (1, 4), strides=[stride, stride], offset_factor=1
@@ -496,7 +496,7 @@ def fail_buffer_bind(a: T.handle) -> None:
 def fail_match_func_param(a: T.handle, m: T.handle, n: T.handle) -> None:
     A = T.match_buffer(a, (8, 8))
     for i, j in T.grid(8, 2):
-        with T.block([]):
+        with T.block():
             sub_A = T.match_buffer(A[i, j * 4 : j * 4 + 4], (1, 4), strides=[m, n], offset_factor=1)
             for jj in range(0, 4):
                 sub_A[i, j * 4 + jj] = 1
diff --git a/tests/python/unittest/test_tir_schedule_block_scope.py b/tests/python/unittest/test_tir_schedule_block_scope.py
index 2182c7b9f449e..ad789a0107450 100644
--- a/tests/python/unittest/test_tir_schedule_block_scope.py
+++ b/tests/python/unittest/test_tir_schedule_block_scope.py
@@ -32,10 +32,14 @@ def elementwise(a: T.handle, c: T.handle) -> None:
     A = T.match_buffer(a, (128, 128), "float32")
     C = T.match_buffer(c, (128, 128), "float32")
     B = T.alloc_buffer((128, 128), "float32")
-    with T.block([128, 128], "B") as [vi, vj]:
-        B[vi, vj] = A[vi, vj] * 2.0
-    with T.block([128, 128], "C") as [vi, vj]:
-        C[vi, vj] = B[vi, vj] + 1.0
+    for i, j in T.grid(128, 128):
+        with T.block("B"):
+            vi, vj = T.axis.remap("SS", [i, j])
+            B[vi, vj] = A[vi, vj] * 2.0
+    for i, j in T.grid(128, 128):
+        with T.block("C"):
+            vi, vj = T.axis.remap("SS", [i, j])
+            C[vi, vj] = B[vi, vj] + 1.0
 
 
 @T.prim_func
@@ -44,10 +48,12 @@ def matmul(a: T.handle, b: T.handle, c: T.handle) -> None:
     B = T.match_buffer(b, [128, 128])
     C = T.match_buffer(c, [128, 128])
     for i, j in T.grid(128, 128):
-        with T.block([128, 128], "init") as [vi, vj]:
+        with T.block("init"):
+            vi, vj = T.axis.remap("SS", [i, j])
             C[vi, vj] = T.float32(0)
         for k in range(0, 128):
-            with T.block([128, 128, T.reduce_axis(0, 128)], "update") as [vi, vj, vk]:
+            with T.block("update"):
+                vi, vj, vk = T.axis.remap("SSR", [i, j, k])
                 C[vi, vj] = C[vi, vj] + A[vi, vk] * B[vj, vk]
 
 
@@ -58,9 +64,11 @@ def war_dependency(a: T.handle, b: T.handle, c: T.handle) -> None:
     C = T.match_buffer(c, (128, 128))
 
     for i, j in T.grid(128, 128):
-        with T.block([128, 128], "C") as [vi, vj]:
+        with T.block("C"):
+            vi, vj = T.axis.remap("SS", [i, j])
             C[vi, vj] = B[vi, vj] + 1.0
-        with T.block([128, 128], "B") as [vi, vj]:
+        with T.block("B"):
+            vi, vj = T.axis.remap("SS", [i, j])
             B[vi, vj] = A[vi, vj] * 2.0
 
 
diff --git a/tests/python/unittest/test_tir_schedule_cache_read_write.py b/tests/python/unittest/test_tir_schedule_cache_read_write.py
index ff5b61a135ebf..853f44affe5d5 100644
--- a/tests/python/unittest/test_tir_schedule_cache_read_write.py
+++ b/tests/python/unittest/test_tir_schedule_cache_read_write.py
@@ -33,10 +33,14 @@ def elementwise(a: T.handle, c: T.handle) -> None:
     A = T.match_buffer(a, (128, 128))
     B = T.alloc_buffer((128, 128))
     C = T.match_buffer(c, (128, 128))
-    with T.block([128, 128], "B") as [vi, vj]:
-        B[vi, vj] = A[vi, vj] * 2.0
-    with T.block([128, 128], "C") as [vi, vj]:
-        C[vi, vj] = B[vi, vj] + 1.0
+    for i, j in T.grid(128, 128):
+        with T.block("B"):
+            vi, vj = T.axis.remap("SS", [i, j])
+            B[vi, vj] = A[vi, vj] * 2.0
+    for i, j in T.grid(128, 128):
+        with T.block("C"):
+            vi, vj = T.axis.remap("SS", [i, j])
+            C[vi, vj] = B[vi, vj] + 1.0
 
 
 @T.prim_func
@@ -45,20 +49,23 @@ def access_under_scope(b: T.handle, c: T.handle) -> None:
     B = T.match_buffer(b, (128, 128))
     C = T.match_buffer(c, (128, 128))
 
-    with T.block([8, 8], "scope") as [i, j]:
-        for x, y in T.grid(16, 16):
-            with T.block([128, 128], "A") as [vi, vj]:
-                T.bind(vi, i * 16 + x)
-                T.bind(vj, j * 16 + y)
-                A[vi, vj] = 1.0
-        for x, y in T.grid(16, 16):
-            with T.block([128, 128], "B") as [vi, vj]:
-                T.bind(vi, i * 16 + x)
-                T.bind(vj, j * 16 + y)
-                B[vi, vj] = A[vi, vj] + 1.0
-
-    with T.block([128, 128], "C") as [vi, vj]:
-        C[vi, vj] = A[vi, vj] * 2.0
+    for i0, j0 in T.grid(8, 8):
+        with T.block("scope"):
+            i, j = T.axis.remap("SS", [i0, j0])
+            for x, y in T.grid(16, 16):
+                with T.block("A"):
+                    vi = T.axis.S(128, i * 16 + x)
+                    vj = T.axis.S(128, j * 16 + y)
+                    A[vi, vj] = 1.0
+            for x, y in T.grid(16, 16):
+                with T.block("B"):
+                    vi = T.axis.S(128, i * 16 + x)
+                    vj = T.axis.S(128, j * 16 + y)
+                    B[vi, vj] = A[vi, vj] + 1.0
+    for i, j in T.grid(128, 128):
+        with T.block("C"):
+            vi, vj = T.axis.remap("SS", [i, j])
+            C[vi, vj] = A[vi, vj] * 2.0
 
 
 @T.prim_func
@@ -68,76 +75,82 @@ def opaque_access(a: T.handle, b: T.handle, c: T.handle, d: T.handle) -> None:
     C = T.match_buffer(c, (128, 128), dtype="float16")
     D = T.match_buffer(d, (128, 128), dtype="float16")
 
-    with T.block([128, 128], "load_store") as [vi, vj]:
-        T.reads(A[vi, vj])
-        T.writes(D[vi, vj])
-        D.data[vi * 128 + vj] = T.load("float16", A.data, vi * 128 + vj)
-    with T.block([8, 8], "opaque") as [vi, vj]:
-        T.reads(A[vi * 16 : vi * 16 + 16, vj * 16 : vj * 16 + 16])
-        T.writes(B[vi * 16 : vi * 16 + 16, vj * 16 : vj * 16 + 16])
-        T.evaluate(
-            T.tvm_load_matrix_sync(
-                B.data,
-                16,
-                16,
-                16,
-                vi * 8 + vj,
-                T.tvm_access_ptr(
-                    T.type_annotation(dtype="float16"),
-                    A.data,
-                    vi * 2048 + vj * 16,
+    for i, j in T.grid(128, 128):
+        with T.block("load_store"):
+            vi, vj = T.axis.remap("SS", [i, j])
+            T.reads(A[vi, vj])
+            T.writes(D[vi, vj])
+            D.data[vi * 128 + vj] = T.load("float16", A.data, vi * 128 + vj)
+    for i, j in T.grid(8, 8):
+        with T.block("opaque"):
+            vi, vj = T.axis.remap("SS", [i, j])
+            T.reads(A[vi * 16 : vi * 16 + 16, vj * 16 : vj * 16 + 16])
+            T.writes(B[vi * 16 : vi * 16 + 16, vj * 16 : vj * 16 + 16])
+            T.evaluate(
+                T.tvm_load_matrix_sync(
+                    B.data,
+                    16,
+                    16,
+                    16,
+                    vi * 8 + vj,
+                    T.tvm_access_ptr(
+                        T.type_annotation(dtype="float16"),
+                        A.data,
+                        vi * 2048 + vj * 16,
+                        128,
+                        1,
+                        dtype="handle",
+                    ),
                     128,
-                    1,
+                    "row_major",
                     dtype="handle",
-                ),
-                128,
-                "row_major",
-                dtype="handle",
+                )
+            )
+    for i, j in T.grid(8, 8):
+        with T.block("match_buffer"):
+            vi, vj = T.axis.remap("SS", [i, j])
+            T.reads(A[vi * 16 : vi * 16 + 16, vj * 16 : vj * 16 + 16])
+            T.writes(C[vi * 16 : vi * 16 + 16, vj * 16 : vj * 16 + 16])
+            A0 = T.match_buffer(
+                A[
+                    vi * 16 : vi * 16 + 16,
+                    vj * 16 : vj * 16 + 16,
+                ],
+                (16, 16),
+                "float16",
+                strides=[128, 1],
+                offset_factor=1,
+            )
+            C0 = T.match_buffer(
+                C[
+                    vi * 16 : vi * 16 + 16,
+                    vj * 16 : vj * 16 + 16,
+                ],
+                (16, 16),
+                "float16",
+                strides=[128, 1],
+                offset_factor=1,
             )
-        )
-    with T.block([8, 8], "match_buffer") as [vi, vj]:
-        T.reads(A[vi * 16 : vi * 16 + 16, vj * 16 : vj * 16 + 16])
-        T.writes(C[vi * 16 : vi * 16 + 16, vj * 16 : vj * 16 + 16])
-        A0 = T.match_buffer(
-            A[
-                vi * 16 : vi * 16 + 16,
-                vj * 16 : vj * 16 + 16,
-            ],
-            (16, 16),
-            "float16",
-            strides=[128, 1],
-            offset_factor=1,
-        )
-        C0 = T.match_buffer(
-            C[
-                vi * 16 : vi * 16 + 16,
-                vj * 16 : vj * 16 + 16,
-            ],
-            (16, 16),
-            "float16",
-            strides=[128, 1],
-            offset_factor=1,
-        )
-        T.evaluate(
-            T.tvm_load_matrix_sync(
-                C0.data,
-                16,
-                16,
-                16,
-                vi * 8 + vj,
-                T.tvm_access_ptr(
-                    T.type_annotation(dtype="float16"),
-                    A0.data,
-                    A0.elem_offset,
-                    A0.strides[0],
-                    1,
+            T.evaluate(
+                T.tvm_load_matrix_sync(
+                    C0.data,
+                    16,
+                    16,
+                    16,
+                    vi * 8 + vj,
+                    T.tvm_access_ptr(
+                        T.type_annotation(dtype="float16"),
+                        A0.data,
+                        A0.elem_offset,
+                        A0.strides[0],
+                        1,
+                        dtype="handle",
+                    ),
+                    128,
+                    "row_major",
                     dtype="handle",
-                ),
-                128,
-                "row_major",
-                dtype="handle",
+                )
             )
-        )
 
 
 @T.prim_func
@@ -147,15 +160,16 @@ def func_multi_consumer() -> None:
     C = T.alloc_buffer((128))
     for i in T.grid(8):
         for j in T.grid(16):
-            with T.block([128], "A") as [vi]:
-                T.bind(vi, i * 16 + j)
+            with T.block("A"):
+                vi = T.axis.S(128, i * 16 + j)
                 A[vi] = 1.0
         for j in T.grid(16):
-            with T.block([128], "B") as [vi]:
-                T.bind(vi, i * 16 + j)
+            with T.block("B"):
+                vi = T.axis.S(128, i * 16 + j)
                 B[vi] = A[vi] + 1.0
     for i in T.grid(128):
-        with T.block([128], "C") as [vi]:
+        with T.block("C"):
+            vi = T.axis.S(128, i)
             C[vi] = A[vi]
 
 
@@ -163,12 +177,18 @@ def func_multi_consumer() -> None:
 def func_multi_producer() -> None:
     A = T.alloc_buffer((128))
     B = T.alloc_buffer((128))
-    with T.block([128], "A0") as [vi]:
-        A[vi] = 1.0
-    with T.block([128], "A1") as [vi]:
-        A[vi] = 2.0
-    with T.block([128], "B") as [vi]:
-        B[vi] = A[vi]
+    for i in range(128):
+        with T.block("A0"):
+            vi = T.axis.S(128, i)
+            A[vi] = 1.0
+    for i in range(128):
+        with T.block("A1"):
+            vi = T.axis.S(128, i)
+            A[vi] = 2.0
+    for i in range(128):
+        with T.block("B"):
+            vi = T.axis.S(128, i)
+            B[vi] = A[vi]
 
 
 ########## Expected function after cache_read ##########
@@ -181,14 +201,22 @@ def cache_read_elementwise(a: T.handle, c: T.handle) -> None:
     B = T.alloc_buffer((128, 128))
     A_global = T.alloc_buffer((128, 128))
     B_local = T.alloc_buffer((128, 128), scope="local")
-    with T.block([128, 128], "A_global") as [vi, vj]:
-        A_global[vi, vj] = A[vi, vj]
-    with T.block([128, 128], "B") as [vi, vj]:
-        B[vi, vj] = A_global[vi, vj] * 2.0
-    with T.block([128, 128], "B_local") as [vi, vj]:
-        B_local[vi, vj] = B[vi, vj]
-    with T.block([128, 128], "C") as [vi, vj]:
-        C[vi, vj] = B_local[vi, vj] + 1.0
+    for i, j in T.grid(128, 128):
+        with T.block("A_global"):
+            vi, vj = T.axis.remap("SS", [i, j])
+            A_global[vi, vj] = A[vi, vj]
+    for i, j in T.grid(128, 128):
+        with T.block("B"):
+            vi, vj = T.axis.remap("SS", [i, j])
+            B[vi, vj] = A_global[vi, vj] * 2.0
+    for i, j in T.grid(128, 128):
+        with T.block("B_local"):
+            vi, vj = T.axis.remap("SS", [i, j])
+            B_local[vi, vj] = B[vi, vj]
+    for i, j in T.grid(128, 128):
+        with T.block("C"):
+            vi, vj = T.axis.remap("SS", [i, j])
+            C[vi, vj] = B_local[vi, vj] + 1.0
 
 
 @T.prim_func
@@ -198,27 +226,33 @@ def cache_read_under_scope(b: T.handle, c: T.handle) -> None:
     C = T.match_buffer(c, (128, 128))
     A_global = T.alloc_buffer((128, 128))
 
-    with T.block([8, 8], "scope") as [i, j]:
-        A_local = T.alloc_buffer((128, 128), scope="local")
-        for x, y in T.grid(16, 16):
-            with T.block([128, 128], "A") as [vi, vj]:
-                T.bind(vi, i * 16 + x)
-                T.bind(vj, j * 16 + y)
-                A[vi, vj] = 1.0
-        for x, y in T.grid(16, 16):
-            with T.block([128, 128], "A_local") as [vi, vj]:
-                T.bind(vi, i * 16 + x)
-                T.bind(vj, j * 16 + y)
-                A_local[vi, vj] = A[vi, vj]
-        for x, y in T.grid(16, 16):
-            with T.block([128, 128], "B") as [vi, vj]:
-                T.bind(vi, i * 16 + x)
-                T.bind(vj, j * 16 + y)
-                B[vi, vj] = A_local[vi, vj] + 1.0
-    with T.block([128, 128], "A_global") as [vi, vj]:
-        A_global[vi, vj] = A[vi, vj]
-    with T.block([128, 128], "C") as [vi, vj]:
-        C[vi, vj] = A_global[vi, vj] * 2.0
+    for i0, j0 in T.grid(8, 8):
+        with T.block("scope"):
+            i, j = T.axis.remap("SS", [i0, j0])
+            A_local = T.alloc_buffer((128, 128), scope="local")
+            for x, y in T.grid(16, 16):
+                with T.block("A"):
+                    vi = T.axis.S(128, i * 16 + x)
+                    vj = T.axis.S(128, j * 16 + y)
+                    A[vi, vj] = 1.0
+            for x, y in T.grid(16, 16):
+                with T.block("A_local"):
+                    vi = T.axis.S(128, i * 16 + x)
+                    vj = T.axis.S(128, j * 16 + y)
+                    A_local[vi, vj] = A[vi, vj]
+            for x, y in T.grid(16, 16):
+                with T.block("B"):
+                    vi = T.axis.S(128, i * 16 + x)
+                    vj = T.axis.S(128, j * 16 + y)
+                    B[vi, vj] = A_local[vi, vj] + 1.0
+    for i, j in T.grid(128, 128):
+        with T.block("A_global"):
+            vi, vj = T.axis.remap("SS", [i, j])
+            A_global[vi, vj] = A[vi, vj]
+    for i, j in T.grid(128, 128):
+        with T.block("C"):
+            vi, vj = T.axis.remap("SS", [i, j])
+            C[vi, vj] = A_global[vi, vj] * 2.0
 
 
 @T.prim_func
@@ -229,78 +263,86 @@ def cache_read_opaque_access(a: T.handle, b: T.handle, c: T.handle, d: T.handle)
     D = T.match_buffer(d, (128, 128), dtype="float16")
     A_global = T.alloc_buffer((128, 128), dtype="float16")
 
-    with T.block([128, 128], "A_global") as [vi, vj]:
-        A_global[vi, vj] = A[vi, vj]
-    with T.block([128, 128], "load_store") as [vi, vj]:
-        T.reads(A_global[vi, vj])
-        T.writes(D[vi, vj])
-        D.data[vi * 128 + vj] = T.load("float16", A_global.data, vi * 128 + vj)
-    with T.block([8, 8], "opaque") as [vi, vj]:
-        T.reads(A_global[vi * 16 : vi * 16 + 16, vj * 16 : vj * 16 + 16])
-        T.writes(B[vi * 16 : vi * 16 + 16, vj * 16 : vj * 16 + 16])
-        T.evaluate(
-            T.tvm_load_matrix_sync(
-                B.data,
-                16,
-                16,
-                16,
-                vi * 8 + vj,
-                T.tvm_access_ptr(
-                    T.type_annotation(dtype="float16"),
-                    A_global.data,
-                    vi * 2048 + vj * 16,
+    for i, j in T.grid(128, 128):
+        with T.block("A_global"):
+            vi, vj = T.axis.remap("SS", [i, j])
+            A_global[vi, vj] = A[vi, vj]
+    for i, j in T.grid(128, 128):
+        with T.block("load_store"):
+            vi, vj = T.axis.remap("SS", [i, j])
+            T.reads(A_global[vi, vj])
+            T.writes(D[vi, vj])
+            D.data[vi * 128 + vj] = T.load("float16", A_global.data, vi * 128 + vj)
+    for i, j in T.grid(8, 8):
+        with T.block("opaque"):
+            vi, vj = T.axis.remap("SS", [i, j])
+            T.reads(A_global[vi * 16 : vi * 16 + 16, vj * 16 : vj * 16 + 16])
+            T.writes(B[vi * 16 : vi * 16 + 16, vj * 16 : vj * 16 + 16])
+            T.evaluate(
+                T.tvm_load_matrix_sync(
+                    B.data,
+                    16,
+                    16,
+                    16,
+                    vi * 8 + vj,
+                    T.tvm_access_ptr(
+                        T.type_annotation(dtype="float16"),
+                        A_global.data,
+                        vi * 2048 + vj * 16,
+                        128,
+                        1,
+                        dtype="handle",
+                    ),
                     128,
-                    1,
+                    "row_major",
                     dtype="handle",
-                ),
-                128,
-                "row_major",
-                dtype="handle",
+                )
+            )
+    for i, j in T.grid(8, 8):
+        with T.block("match_buffer"):
+            vi, vj = T.axis.remap("SS", [i, j])
+            T.reads(A_global[vi * 16 : vi * 16 + 16, vj * 16 : vj * 16 + 16])
+            T.writes(C[vi * 16 : vi * 16 + 16, vj * 16 : vj * 16 + 16])
+            A0 = T.match_buffer(
+                A_global[
+                    vi * 16 : vi * 16 + 16,
+                    vj * 16 : vj * 16 + 16,
+                ],
+                (16, 16),
+                "float16",
+                strides=[128, 1],
+                offset_factor=1,
             )
-        )
-    with T.block([8, 8], "match_buffer") as [vi, vj]:
-        T.reads(A_global[vi * 16 : vi * 16 + 16, vj * 16 : vj * 16 + 16])
-        T.writes(C[vi * 16 : vi * 16 + 16, vj * 16 : vj * 16 + 16])
-        A0 = T.match_buffer(
-            A_global[
-                vi * 16 : vi * 16 + 16,
-                vj * 16 : vj * 16 + 16,
-            ],
-            (16, 16),
-            "float16",
-            strides=[128, 1],
-            offset_factor=1,
-        )
-        C0 = T.match_buffer(
-            C[
-                vi * 16 : vi * 16 + 16,
-                vj * 16 : vj * 16 + 16,
-            ],
-            (16, 16),
-            "float16",
-            strides=[128, 1],
-            offset_factor=1,
-        )
-        T.evaluate(
-            T.tvm_load_matrix_sync(
-                C0.data,
-                16,
-                16,
-                16,
-                vi * 8 + vj,
-                T.tvm_access_ptr(
-                    T.type_annotation(dtype="float16"),
-                    A0.data,
-                    A0.elem_offset,
-                    A0.strides[0],
-                    1,
+            C0 = T.match_buffer(
+                C[
+                    vi * 16 : vi * 16 + 16,
+                    vj * 16 : vj * 16 + 16,
+                ],
+                (16, 16),
+                "float16",
+                strides=[128, 1],
+                offset_factor=1,
+            )
+            T.evaluate(
+                T.tvm_load_matrix_sync(
+                    C0.data,
+                    16,
+                    16,
+                    16,
+                    vi * 8 + vj,
+                    T.tvm_access_ptr(
+                        T.type_annotation(dtype="float16"),
+                        A0.data,
+                        A0.elem_offset,
+                        A0.strides[0],
+                        1,
+                        dtype="handle",
+                    ),
+                    128,
+                    "row_major",
                     dtype="handle",
-                ),
-                128,
-                "row_major",
-                dtype="handle",
+                )
             )
-        )
 
 
 @T.prim_func
@@ -311,20 +353,21 @@ def cache_read_multi_consumer() -> None:
     A_global = T.alloc_buffer((128))
     for i in T.grid(8):
         for j in T.grid(16):
-            with T.block([128], "A") as [vi]:
-                T.bind(vi, i * 16 + j)
+            with T.block("A"):
+                vi = T.axis.S(128, i * 16 + j)
                 A[vi] = 1.0
         for j in T.grid(16):
-            with T.block([128], "A") as [vi]:
-                T.bind(vi, i * 16 + j)
+            with T.block("A"):
+                vi = T.axis.S(128, i * 16 + j)
                 A_global[vi] = A[vi]
         for j in T.grid(16):
-            with T.block([128], "B") as [vi]:
-                T.bind(vi, i * 16 + j)
+            with T.block("B"):
+                vi = T.axis.S(128, i * 16 + j)
                 B[vi] = A_global[vi] + 1.0
 
     for i in T.grid(128):
-        with T.block([128], "C") as [vi]:
+        with T.block("C"):
+            vi = T.axis.S(128, i)
             C[vi] = A_global[vi]
 
 
@@ -335,14 +378,22 @@ def continuous_cache_read(a: T.handle, c: T.handle) -> None:
     B = T.alloc_buffer((128, 128))
     B_shared = T.alloc_buffer((128, 128), scope="shared")
     B_local = T.alloc_buffer((128, 128), scope="local")
-    with T.block([128, 128], "B") as [vi, vj]:
-        B[vi, vj] = A[vi, vj] * 2.0
-    with T.block([128, 128], "B_shared") as [vi, vj]:
-        B_shared[vi, vj] = B[vi, vj]
-    with T.block([128, 128], "B_local") as [vi, vj]:
-        B_local[vi, vj] = B_shared[vi, vj]
-    with T.block([128, 128], "C") as [vi, vj]:
-        C[vi, vj] = B_local[vi, vj] + 1.0
+    for i, j in T.grid(128, 128):
+        with T.block("B"):
+            vi, vj = T.axis.remap("SS", [i, j])
+            B[vi, vj] = A[vi, vj] * 2.0
+    for i, j in T.grid(128, 128):
+        with T.block("B_shared"):
+            vi, vj = T.axis.remap("SS", [i, j])
+            B_shared[vi, vj] = B[vi, vj]
+    for i, j in T.grid(128, 128):
+        with T.block("B_local"):
+            vi, vj = T.axis.remap("SS", [i, j])
+            B_local[vi, vj] = B_shared[vi, vj]
+    for i, j in T.grid(128, 128):
+        with T.block("C"):
+            vi, vj = T.axis.remap("SS", [i, j])
+            C[vi, vj] = B_local[vi, vj] + 1.0
 
 
 ########## Expected function after cache_write ##########
@@ -355,14 +406,22 @@ def cache_write_elementwise(a: T.handle, c: T.handle) -> None:
     B = T.alloc_buffer((128, 128))
     B_global = T.alloc_buffer((128, 128), scope="local")
     C_local = T.alloc_buffer((128, 128))
-    with T.block([128, 128], "B_global") as [vi, vj]:
-        B_global[vi, vj] = A[vi, vj] * 2.0
-    with T.block([128, 128], "B") as [vi, vj]:
-        B[vi, vj] = B_global[vi, vj]
-    with T.block([128, 128], "C_local") as [vi, vj]:
-        C_local[vi, vj] = B[vi, vj] + 1.0
-    with T.block([128, 128], "C") as [vi, vj]:
-        C[vi, vj] = C_local[vi, vj]
+    for i, j in T.grid(128, 128):
+        with T.block("B_global"):
+            vi, vj = T.axis.remap("SS", [i, j])
+            B_global[vi, vj] = A[vi, vj] * 2.0
+    for i, j in T.grid(128, 128):
+        with T.block("B"):
+            vi, vj = T.axis.remap("SS", [i, j])
+            B[vi, vj] = B_global[vi, vj]
+    for i, j in T.grid(128, 128):
+        with T.block("C_local"):
+            vi, vj = T.axis.remap("SS", [i, j])
+            C_local[vi, vj] = B[vi, vj] + 1.0
+    for i, j in T.grid(128, 128):
+        with T.block("C"):
+            vi, vj = T.axis.remap("SS", [i, j])
+            C[vi, vj] = C_local[vi, vj]
 
 
 @T.prim_func
@@ -372,33 +431,39 @@ def cache_write_under_scope(b: T.handle, c: T.handle) -> None:
     C = T.match_buffer(c, (128, 128))
     A_global = T.alloc_buffer((128, 128))
 
-    with T.block([8, 8], "scope") as [i, j]:
-        A_local = T.alloc_buffer((128, 128), scope="local")
-        B_global = T.alloc_buffer((128, 128))
-        for x, y in T.grid(16, 16):
-            with T.block([128, 128], "A_local") as [vi, vj]:
-                T.bind(vi, i * 16 + x)
-                T.bind(vj, j * 16 + y)
-                A_local[vi, vj] = 1.0
-        for x, y in T.grid(16, 16):
-            with T.block([128, 128], "A") as [vi, vj]:
-                T.bind(vi, i * 16 + x)
-                T.bind(vj, j * 16 + y)
-                A_global[vi, vj] = A_local[vi, vj]
-        for x, y in T.grid(16, 16):
-            with T.block([128, 128], "B_global") as [vi, vj]:
-                T.bind(vi, i * 16 + x)
-                T.bind(vj, j * 16 + y)
-                B_global[vi, vj] = A_global[vi, vj] + 1.0
-        for x, y in T.grid(16, 16):
-            with T.block([128, 128], "B_global") as [vi, vj]:
-                T.bind(vi, i * 16 + x)
-                T.bind(vj, j * 16 + y)
-                B[vi, vj] = B_global[vi, vj]
-    with T.block([128, 128], "A_global") as [vi, vj]:
-        A[vi, vj] = A_global[vi, vj]
-    with T.block([128, 128], "C") as [vi, vj]:
-        C[vi, vj] = A[vi, vj] * 2.0
+    for i0, j0 in T.grid(8, 8):
+        with T.block("scope"):
+            i, j = T.axis.remap("SS", [i0, j0])
+            A_local = T.alloc_buffer((128, 128), scope="local")
+            B_global = T.alloc_buffer((128, 128))
+            for x, y in T.grid(16, 16):
+                with T.block("A_local"):
+                    vi = T.axis.S(128, i * 16 + x)
+                    vj = T.axis.S(128, j * 16 + y)
+                    A_local[vi, vj] = 1.0
+            for x, y in T.grid(16, 16):
+                with T.block("A"):
+                    vi = T.axis.S(128, i * 16 + x)
+                    vj = T.axis.S(128, j * 16 + y)
+                    A_global[vi, vj] = A_local[vi, vj]
+            for x, y in T.grid(16, 16):
+                with T.block("B_global"):
+                    vi = T.axis.S(128, i * 16 + x)
+                    vj = T.axis.S(128, j * 16 + y)
+                    B_global[vi, vj] = A_global[vi, vj] + 1.0
+            for x, y in T.grid(16, 16):
+                with T.block("B_global"):
+                    vi = T.axis.S(128, i * 16 + x)
+                    vj = T.axis.S(128, j * 16 + y)
+                    B[vi, vj] = B_global[vi, vj]
+    for i, j in T.grid(128, 128):
+        with T.block("A_global"):
+            vi, vj = T.axis.remap("SS", [i, j])
+            A[vi, vj] = A_global[vi, vj]
+    for i, j in T.grid(128, 128):
+        with T.block("C"):
+            vi, vj = T.axis.remap("SS", [i, j])
+            C[vi, vj] = A[vi, vj] * 2.0
 
 
 @T.prim_func
@@ -411,83 +476,95 @@ def cache_write_opaque_access(a: T.handle, b: T.handle, c: T.handle, d: T.handle
     B_global = T.alloc_buffer((128, 128), dtype="float16")
     C_global = T.alloc_buffer((128, 128), dtype="float16")
 
-    with T.block([128, 128], "load_store") as [vi, vj]:
-        T.reads(A[vi, vj])
-        T.writes(D_global[vi, vj])
-        D_global.data[vi * 128 + vj] = T.load("float16", A.data, vi * 128 + vj)
-    with T.block([8, 8], "opaque") as [vi, vj]:
-        T.reads(A[vi * 16 : vi * 16 + 16, vj * 16 : vj * 16 + 16])
-        T.writes(B_global[vi * 16 : vi * 16 + 16, vj * 16 : vj * 16 + 16])
-        T.evaluate(
-            T.tvm_load_matrix_sync(
-                B_global.data,
-                16,
-                16,
-                16,
-                vi * 8 + vj,
-                T.tvm_access_ptr(
-                    T.type_annotation(dtype="float16"),
-                    A.data,
-                    vi * 2048 + vj * 16,
+    for i, j in T.grid(128, 128):
+        with T.block("load_store"):
+            vi, vj = T.axis.remap("SS", [i, j])
+            T.reads(A[vi, vj])
+            T.writes(D_global[vi, vj])
+            D_global.data[vi * 128 + vj] = T.load("float16", A.data, vi * 128 + vj)
+    for i, j in T.grid(8, 8):
+        with T.block("opaque"):
+            vi, vj = T.axis.remap("SS", [i, j])
+            T.reads(A[vi * 16 : vi * 16 + 16, vj * 16 : vj * 16 + 16])
+            T.writes(B_global[vi * 16 : vi * 16 + 16, vj * 16 : vj * 16 + 16])
+            T.evaluate(
+                T.tvm_load_matrix_sync(
+                    B_global.data,
+                    16,
+                    16,
+                    16,
+                    vi * 8 + vj,
+                    T.tvm_access_ptr(
+                        T.type_annotation(dtype="float16"),
+                        A.data,
+                        vi * 2048 + vj * 16,
+                        128,
+                        1,
+                        dtype="handle",
+                    ),
                     128,
-                    1,
+                    "row_major",
                     dtype="handle",
-                ),
-                128,
-                "row_major",
-                dtype="handle",
+                )
+            )
+    for i, j in T.grid(8, 8):
+        with T.block("match_buffer"):
+            vi, vj = T.axis.remap("SS", [i, j])
+            T.reads(A[vi * 16 : vi * 16 + 16, vj * 16 : vj * 16 + 16])
+            T.writes(C_global[vi * 16 : vi * 16 + 16, vj * 16 : vj * 16 + 16])
+            A0 = T.match_buffer(
+                A[
+                    vi * 16 : vi * 16 + 16,
+                    vj * 16 : vj * 16 + 16,
+                ],
+                (16, 16),
+                "float16",
+                strides=[128, 1],
+                offset_factor=1,
             )
-        )
-    with T.block([8, 8], "match_buffer") as [vi, vj]:
-        T.reads(A[vi * 16 : vi * 16 + 16, vj * 16 : vj * 16 + 16])
-        T.writes(C_global[vi * 16 : vi * 16 + 16, vj * 16 : vj * 16 + 16])
-        A0 = T.match_buffer(
-            A[
-                vi * 16 : vi * 16 + 16,
-                vj * 16 : vj * 16 + 16,
-            ],
-            (16, 16),
-            "float16",
-            strides=[128, 1],
-            offset_factor=1,
-        )
-        C0 = T.match_buffer(
-            C_global[
-                vi * 16 : vi * 16 + 16,
-                vj * 16 : vj * 16 + 16,
-            ],
-            (16, 16),
-            "float16",
-            strides=[128, 1],
-            offset_factor=1,
-        )
-        T.evaluate(
-            T.tvm_load_matrix_sync(
-                C0.data,
-                16,
-                16,
-                16,
-                vi * 8 + vj,
-                T.tvm_access_ptr(
-                    T.type_annotation(dtype="float16"),
-                    A0.data,
-                    A0.elem_offset,
-                    A0.strides[0],
-                    1,
+            C0 = T.match_buffer(
+                C_global[
+                    vi * 16 : vi * 16 + 16,
+                    vj * 16 : vj * 16 + 16,
+                ],
+                (16, 16),
+                "float16",
+                strides=[128, 1],
+                offset_factor=1,
+            )
+            T.evaluate(
+                T.tvm_load_matrix_sync(
+                    C0.data,
+                    16,
+                    16,
+                    16,
+                    vi * 8 + vj,
+                    T.tvm_access_ptr(
+                        T.type_annotation(dtype="float16"),
+                        A0.data,
+                        A0.elem_offset,
+                        A0.strides[0],
+                        1,
+                        dtype="handle",
+                    ),
+                    128,
+                    "row_major",
                     dtype="handle",
-                ),
-                128,
-                "row_major",
-                dtype="handle",
+                )
             )
-        )
 
-    with T.block([128, 128], "D") as [vi, vj]:
-        D[vi, vj] = D_global[vi, vj]
-    with T.block([128, 128], "B") as [vi, vj]:
-        B[vi, vj] = B_global[vi, vj]
-    with T.block([128, 128], "C") as [vi, vj]:
-        C[vi, vj] = C_global[vi, vj]
+    for i, j in T.grid(128, 128):
+        with T.block("D"):
+            vi, vj = T.axis.remap("SS", [i, j])
+            D[vi, vj] = D_global[vi, vj]
+    for i, j in T.grid(128, 128):
+        with T.block("B"):
+            vi, vj = T.axis.remap("SS", [i, j])
+            B[vi, vj] = B_global[vi, vj]
+    for i, j in T.grid(128, 128):
+        with T.block("C"):
+            vi, vj = T.axis.remap("SS", [i, j])
+            C[vi, vj] = C_global[vi, vj]
 
 
 @T.prim_func
@@ -498,20 +575,21 @@ def cache_write_multi_consumer() -> None:
     A_global = T.alloc_buffer((128))
     for i in T.grid(8):
         for j in T.grid(16):
-            with T.block([128], "A_global") as [vi]:
-                T.bind(vi, i * 16 + j)
+            with T.block("A_global"):
+                vi = T.axis.S(128, i * 16 + j)
                 A_global[vi] = 1.0
         for j in T.grid(16):
-            with T.block([128], "A") as [vi]:
-                T.bind(vi, i * 16 + j)
+            with T.block("A"):
+                vi = T.axis.S(128, i * 16 + j)
                 A[vi] = A_global[vi]
         for j in T.grid(16):
-            with T.block([128], "B") as [vi]:
-                T.bind(vi, i * 16 + j)
+            with T.block("B"):
+                vi = T.axis.S(128, i * 16 + j)
                 B[vi] = A[vi] + 1.0
 
     for i in T.grid(128):
-        with T.block([128], "C") as [vi]:
+        with T.block("C"):
+            vi = T.axis.S(128, i)
             C[vi] = A[vi]
 
 
@@ -522,14 +600,22 @@ def continuous_cache_write(a: T.handle, c: T.handle) -> None:
     C = T.match_buffer(c, (128, 128))
     B_shared = T.alloc_buffer((128, 128), scope="shared")
     B_local = T.alloc_buffer((128, 128), scope="local")
-    with T.block([128, 128], "B") as [vi, vj]:
-        B_local[vi, vj] = A[vi, vj] * 2.0
-    with T.block([128, 128], "B") as [vi, vj]:
-        B_shared[vi, vj] = B_local[vi, vj]
-    with T.block([128, 128], "B") as [vi, vj]:
-        B[vi, vj] = B_shared[vi, vj]
-    with T.block([128, 128], "C") as [vi, vj]:
-        C[vi, vj] = B[vi, vj] + 1.0
+    for i, j in T.grid(128, 128):
+        with T.block("B"):
+            vi, vj = T.axis.remap("SS", [i, j])
+            B_local[vi, vj] = A[vi, vj] * 2.0
+    for i, j in T.grid(128, 128):
+        with T.block("B"):
+            vi, vj = T.axis.remap("SS", [i, j])
+            B_shared[vi, vj] = B_local[vi, vj]
+    for i, j in T.grid(128, 128):
+        with T.block("B"):
+            vi, vj = T.axis.remap("SS", [i, j])
+            B[vi, vj] = B_shared[vi, vj]
+    for i, j in T.grid(128, 128):
+        with T.block("C"):
+            vi, vj = T.axis.remap("SS", [i, j])
+            C[vi, vj] = B[vi, vj] + 1.0
 
 
 ########## Testcases for cache_read ##########
diff --git a/tests/python/unittest/test_tir_schedule_compute_at.py b/tests/python/unittest/test_tir_schedule_compute_at.py
index 5235664595add..6e956e1ee6887 100644
--- a/tests/python/unittest/test_tir_schedule_compute_at.py
+++ b/tests/python/unittest/test_tir_schedule_compute_at.py
@@ -32,10 +32,15 @@ def two_elementwise(a: T.handle, c: T.handle) -> None:
     A = T.match_buffer(a, (128, 128), "float32")
     B = T.alloc_buffer((128, 128), "float32")
     C = T.match_buffer(c, (128, 128), "float32")
-    with T.block([128, 128], "B") as [vi, vj]:
-        B[vi, vj] = A[vi, vj] * 2.0
-    with T.block([128, 128], "C") as [vi, vj]:
-        C[vi, vj] = B[vi, vj] + 1.0
+    for i, j in T.grid(128, 128):
+        with T.block("B"):
+            vi, vj = T.axis.remap("SS", [i, j])
+            B[vi, vj] = A[vi, vj] * 2.0
+    for i, j in T.grid(128, 128):
+
+        with T.block("C"):
+            vi, vj = T.axis.remap("SS", [i, j])
+            C[vi, vj] = B[vi, vj] + 1.0
 
 
 @T.prim_func
@@ -45,12 +50,13 @@ def two_elementwise_after_compute_at(a: T.handle, c: T.handle) -> None:
     C = T.match_buffer(c, (128, 128), "float32")
     for i in range(0, 128):
         for ax0, ax1 in T.grid(1, 128):
-            with T.block([128, 128], "B") as [vi, vj]:
-                T.bind(vi, i + ax0)
-                T.bind(vj, ax1)
+            with T.block("B"):
+                vi = T.axis.S(128, i + ax0)
+                vj = T.axis.S(128, ax1)
                 B[vi, vj] = A[vi, vj] * 2.0
         for j in range(0, 128):
-            with T.block([128, 128], "B") as [vi, vj]:
+            with T.block("B"):
+                vi, vj = T.axis.remap("SS", [i, j])
                 C[vi, vj] = B[vi, vj] + 1.0
 
 
@@ -59,22 +65,26 @@ def blockized_1(a: T.handle, c: T.handle) -> None:
     A = T.match_buffer(a, [128, 128], "float32")
     B = T.alloc_buffer([128, 128], "float32")
     C = T.match_buffer(c, [128, 128], "float32")
-    with T.block([128, 128], "B") as [vi, vj]:
-        B[vi, vj] = A[vi, vj] * 2.0
-    with T.block([8, 8], "C_outer") as [vi_o, vj_o]:
-        T.reads([B[
-            vi_o * 16 : vi_o * 16 + 16,
-            vj_o * 16 : vj_o * 16 + 16,
-        ]])
-        T.writes([C[
-            vi_o * 16 : vi_o * 16 + 16,
-            vj_o * 16 : vj_o * 16 + 16
-        ]])
-        for i_i, j_i in T.grid(16, 16):
-            with T.block([128, 128], "C_inner") as [vi, vj]:
-                T.bind(vi, vi_o * 16 + i_i)
-                T.bind(vj, vj_o * 16 + j_i)
-                C[vi, vj] = B[vi, vj] + 1.0
+    for i, j in T.grid(128, 128):
+        with T.block("B"):
+            vi, vj = T.axis.remap("SS", [i, j])
+            B[vi, vj] = A[vi, vj] * 2.0
+    for i, j in T.grid(8, 8):
+        with T.block("C_outer"):
+            vi_o, vj_o = T.axis.remap("SS", [i, j])
+            T.reads([B[
+                vi_o * 16 : vi_o * 16 + 16,
+                vj_o * 16 : vj_o * 16 + 16,
+            ]])
+            T.writes([C[
+                vi_o * 16 : vi_o * 16 + 16,
+                vj_o * 16 : vj_o * 16 + 16
+            ]])
+            for i_i, j_i in T.grid(16, 16):
+                with T.block("C_inner"):
+                    vi = T.axis.S(128, vi_o * 16 + i_i)
+                    vj = T.axis.S(128, vj_o * 16 + j_i)
+                    C[vi, vj] = B[vi, vj] + 1.0
 
 
 @T.prim_func
@@ -84,13 +94,12 @@ def blockized_after_compute_at(a: T.handle, c: T.handle) -> None:
     C = T.match_buffer(c, [128, 128], "float32")
     for i0_0, i1_0 in T.grid(8, 8):
         for ax0, ax1 in T.grid(16, 16):
-            with T.block([128, 128], "B") as [vi, vj]:
-                T.bind(vi, i0_0 * 16 + ax0)
-                T.bind(vj, i1_0 * 16 + ax1)
+            with T.block("B"):
+                vi = T.axis.S(128, i0_0 * 16 + ax0)
+                vj = T.axis.S(128, i1_0 * 16 + ax1)
                 B[vi, vj] = A[vi, vj] * 2.0
-        with T.block([8, 8], "C_outer") as [vi_o, vj_o]:
-            T.bind(vi_o, i0_0)
-            T.bind(vj_o, i1_0)
+        with T.block("C_outer"):
+            vi_o, vj_o = T.axis.remap("SS", [i0_0, i1_0])
             T.reads([B[
                 vi_o * 16 : vi_o * 16 + 16,
                 vj_o * 16 : vj_o * 16 + 16,
@@ -100,9 +109,9 @@ def blockized_after_compute_at(a: T.handle, c: T.handle) -> None:
                 vj_o * 16 : vj_o * 16 + 16
             ]])
             for i0_1, i1_1 in T.grid(16, 16):
-                with T.block([128, 128], "C_inner") as [vi, vj]:
-                    T.bind(vi, vi_o * 16 + i0_1)
-                    T.bind(vj, vj_o * 16 + i1_1)
+                with T.block("C_inner"):
+                    vi = T.axis.S(128, vi_o * 16 + i0_1)
+                    vj = T.axis.S(128, vj_o * 16 + i1_1)
                     C[vi, vj] = B[vi, vj] + 1.0
 
 
@@ -112,9 +121,8 @@ def blockized_2(a: T.handle, c: T.handle) -> None:
     B = T.alloc_buffer([128, 128], "float32")
     C = T.match_buffer(c, [128, 128], "float32")
     for i_o, j_o in T.grid(8, 8):
-        with T.block([8, 8], "B_outer") as [vio, vjo]:
-            T.bind(vio, i_o)
-            T.bind(vjo, j_o)
+        with T.block("B_outer"):
+            vio, vjo = T.axis.remap("SS", [i_o, j_o])
             T.reads([A[
                 vio * 16 : vio * 16 + 16,
                 vjo * 16 : vjo * 16 + 16,
@@ -124,14 +132,14 @@ def blockized_2(a: T.handle, c: T.handle) -> None:
                 vjo * 16 : vjo * 16 + 16
             ]])
             for i_i, j_i in T.grid(16, 16):
-                with T.block([128, 128], "B_inner") as [vi, vj]:
-                    T.bind(vi, vio * 16 + i_i)
-                    T.bind(vj, vjo * 16 + j_i)
+                with T.block("B_inner"):
+                    vi = T.axis.S(128, vio * 16 + i_i)
+                    vj = T.axis.S(128, vjo * 16 + j_i)
                     B[vi, vj] = A[vi, vj] * 2.0
     for i_o, j_o, i_i, j_i in T.grid(4, 4, 32, 32):
-        with T.block([128, 128], "C") as [vi, vj]:
-            T.bind(vi, i_o * 32 + i_i)
-            T.bind(vj, j_o * 32 + j_i)
+        with T.block("C"):
+            vi = T.axis.S(128, i_o * 32 + i_i)
+            vj = T.axis.S(128, j_o * 32 + j_i)
             C[vi, vj] = B[vi, vj] + 1.0
 
 
@@ -141,9 +149,8 @@ def blockized_2_after_reverse_compute_at(a: T.handle, c: T.handle) -> None:
     B = T.alloc_buffer([128, 128], "float32")
     C = T.match_buffer(c, [128, 128], "float32")
     for i_o, j_o in T.grid(8, 8):
-        with T.block([8, 8], "B_outer") as [vio, vjo]:
-            T.bind(vio, i_o)
-            T.bind(vjo, j_o)
+        with T.block("B_outer"):
+            vio, vjo = T.axis.remap("SS", [i_o, j_o])
             T.reads([A[
                 vio * 16 : vio * 16 + 16,
                 vjo * 16 : vjo * 16 + 16,
@@ -153,14 +160,14 @@ def blockized_2_after_reverse_compute_at(a: T.handle, c: T.handle) -> None:
                 vjo * 16 : vjo * 16 + 16
             ]])
             for i_i, j_i in T.grid(16, 16):
-                with T.block([128, 128], "B_inner") as [vi, vj]:
-                    T.bind(vi, vio * 16 + i_i)
-                    T.bind(vj, vjo * 16 + j_i)
+                with T.block("B_inner"):
+                    vi = T.axis.S(128, vio * 16 + i_i)
+                    vj = T.axis.S(128, vjo * 16 + j_i)
                     B[vi, vj] = A[vi, vj] * 2.0
         for ax0, ax1 in T.grid(16, 16):
-            with T.block([128, 128], "C") as [vi, vj]:
-                T.bind(vi, i_o * 16 + ax0)
-                T.bind(vj, j_o * 16 + ax1)
+            with T.block("C"):
+                vi = T.axis.S(128, i_o * 16 + ax0)
+                vj = T.axis.S(128, j_o * 16 + ax1)
                 T.reads([B[vi, vj]])
                 T.writes([C[vi, vj]])
                 C[vi, vj] = B[vi, vj] + 1.0
@@ -173,9 +180,9 @@ def blockized_2_after_compute_at(a: T.handle, c: T.handle) -> None:
     C = T.match_buffer(c, [128, 128], "float32")
     for i_o, j_o in T.grid(4, 4):
         for ax0, ax1 in T.grid(2, 2):
-            with T.block([8, 8], "blockized_B") as [vio, vjo]:
-                T.bind(vio, i_o * 2 + ax0)
-                T.bind(vjo, j_o * 2 + ax1)
+            with T.block("blockized_B"):
+                vio = T.axis.S(8, i_o * 2 + ax0)
+                vjo = T.axis.S(8, j_o * 2 + ax1)
                 T.reads([A[
                     vio * 16 : vio * 16 + 16,
                     vjo * 16 : vjo * 16 + 16,
@@ -185,14 +192,14 @@ def blockized_2_after_compute_at(a: T.handle, c: T.handle) -> None:
                     vjo * 16 : vjo * 16 + 16,
                 ]])
                 for i_i, j_i in T.grid(16, 16):
-                    with T.block([128, 128], "B") as [vi, vj]:
-                        T.bind(vi, vio * 16 + i_i)
-                        T.bind(vj, vjo * 16 + j_i)
+                    with T.block("B"):
+                        vi = T.axis.S(128, vio * 16 + i_i)
+                        vj = T.axis.S(128, vjo * 16 + j_i)
                         B[vi, vj] = A[vi, vj] * 2.0
         for i_i, j_i in T.grid(32, 32):
-            with T.block([128, 128], "C") as [vi, vj]:
-                T.bind(vi, i_o * 32 + i_i)
-                T.bind(vj, j_o * 32 + j_i)
+            with T.block("C"):
+                vi = T.axis.S(128, i_o * 32 + i_i)
+                vj = T.axis.S(128, j_o * 32 + j_i)
                 C[vi, vj] = B[vi, vj] + 1.0
 
 @T.prim_func
@@ -205,18 +212,28 @@ def cuda_matmul_0(a: T.handle, b: T.handle, c: T.handle) -> None:  # pylint: dis
     A_shared_local = T.alloc_buffer([2048, 2048], "float32", scope="local")
     B_shared_local = T.alloc_buffer([2048, 2048], "float32", scope="local")
     C_local = T.alloc_buffer([2048, 2048], "float32", scope="local")
-    with T.block([2048, 2048], "A_shared") as [v0, v1]:
-        A_shared[v0, v1] = A[v0, v1]
-    with T.block([2048, 2048], "B_shared") as [v0, v1]:
-        B_shared[v0, v1] = B[v0, v1]
-    with T.block([2048, 2048], "A_shared_local") as [v0, v1]:
-        A_shared_local[v0, v1] = A_shared[v0, v1]
-    with T.block([2048, 2048], "B_shared_local") as [v0, v1]:
-        B_shared_local[v0, v1] = B_shared[v0, v1]
-    with T.block([2048, 2048, T.reduce_axis(0, 2048)], "C") as [vi, vj, vk]:
-        with T.init():
-            C_local[vi, vj] = 0.0
-        C_local[vi, vj] = C_local[vi, vj] + A_shared_local[vk, vi] * B_shared_local[vk, vj]
+    for i, j in T.grid(2048, 2048):
+        with T.block("A_shared"):
+            v0, v1 = T.axis.remap("SS", [i, j])
+            A_shared[v0, v1] = A[v0, v1]
+    for i, j in T.grid(2048, 2048):
+        with T.block("B_shared"):
+            v0, v1 = T.axis.remap("SS", [i, j])
+            B_shared[v0, v1] = B[v0, v1]
+    for i, j in T.grid(2048, 2048):
+        with T.block("A_shared_local"):
+            v0, v1 = T.axis.remap("SS", [i, j])
+            A_shared_local[v0, v1] = A_shared[v0, v1]
+    for i, j in T.grid(2048, 2048):
+        with T.block("B_shared_local"):
+            v0, v1 = T.axis.remap("SS", [i, j])
+            B_shared_local[v0, v1] = B_shared[v0, v1]
+    for i, j, k in T.grid(2048, 2048, 2048):
+        with T.block("C"):
+            vi, vj, vk = T.axis.remap("SSR", [i, j, k])
+            with T.init():
+                C_local[vi, vj] = 0.0
+            C_local[vi, vj] = C_local[vi, vj] + A_shared_local[vk, vi] * B_shared_local[vk, vj]
     for by in T.thread_binding(0, 32, thread = "blockIdx.y"):
         for bx in T.thread_binding(0, 32, thread = "blockIdx.x"):
             for vy in T.thread_binding(0, 2, thread = "vthread.y"):
@@ -224,9 +241,9 @@ def cuda_matmul_0(a: T.handle, b: T.handle, c: T.handle) -> None:  # pylint: dis
                     for ty in T.thread_binding(0, 8, thread = "threadIdx.y"):
                         for tx in T.thread_binding(0, 8, thread = "threadIdx.x"):
                             for i, j in T.grid(4, 4):
-                                with T.block([2048, 2048], "C_local") as [v0_4, v1_4]:
-                                    T.bind(v0_4, by * 64 + vy * 32 + ty * 4 + i)
-                                    T.bind(v1_4, bx * 64 + vx * 32 + tx * 4 + j)
+                                with T.block("C_local"):
+                                    v0_4 = T.axis.S(2048, by * 64 + vy * 32 + ty * 4 + i)
+                                    v1_4 = T.axis.S(2048, bx * 64 + vx * 32 + tx * 4 + j)
                                     C[v0_4, v1_4] = C_local[v0_4, v1_4]
 
 
@@ -240,14 +257,22 @@ def cuda_matmul_0_after_compute_at(a: T.handle, b: T.handle, c: T.handle) -> Non
     A_shared_local = T.alloc_buffer([2048, 2048], "float32", scope="local")
     B_shared_local = T.alloc_buffer([2048, 2048], "float32", scope="local")
     C_local = T.alloc_buffer([2048, 2048], "float32", scope="local")
-    with T.block([2048, 2048], "A_shared") as [v0, v1]:
-        A_shared[v0, v1] = A[v0, v1]
-    with T.block([2048, 2048], "B_shared") as [v0, v1]:
-        B_shared[v0, v1] = B[v0, v1]
-    with T.block([2048, 2048], "A_shared_local") as [v0, v1]:
-        A_shared_local[v0, v1] = A_shared[v0, v1]
-    with T.block([2048, 2048], "B_shared_local") as [v0, v1]:
-        B_shared_local[v0, v1] = B_shared[v0, v1]
+    for i, j in T.grid(2048, 2048):
+        with T.block("A_shared"):
+            v0, v1 = T.axis.remap("SS", [i, j])
+            A_shared[v0, v1] = A[v0, v1]
+    for i, j in T.grid(2048, 2048):
+        with T.block("B_shared"):
+            v0, v1 = T.axis.remap("SS", [i, j])
+            B_shared[v0, v1] = B[v0, v1]
+    for i, j in T.grid(2048, 2048):
+        with T.block("A_shared_local"):
+            v0, v1 = T.axis.remap("SS", [i, j])
+            A_shared_local[v0, v1] = A_shared[v0, v1]
+    for i, j in T.grid(2048, 2048):
+        with T.block("B_shared_local"):
+            v0, v1 = T.axis.remap("SS", [i, j])
+            B_shared_local[v0, v1] = B_shared[v0, v1]
     for by in T.thread_binding(0, 32, thread = "blockIdx.y"):
         for bx in T.thread_binding(0, 32, thread = "blockIdx.x"):
             for vy in T.thread_binding(0, 2, thread = "vthread.y"):
@@ -255,17 +280,17 @@ def cuda_matmul_0_after_compute_at(a: T.handle, b: T.handle, c: T.handle) -> Non
                     for ty in T.thread_binding(0, 8, thread = "threadIdx.y"):
                         for tx in T.thread_binding(0, 8, thread = "threadIdx.x"):
                             for i, j, k in T.grid(4, 4, 2048):
-                                with T.block([2048, 2048, T.reduce_axis(0, 2048)], "C") as [vi, vj, vk]:
-                                    T.bind(vi, by * 64 + vy * 32 + ty * 4 + i)
-                                    T.bind(vj, bx * 64 + vx * 32 + tx * 4 + j)
-                                    T.bind(vk, k)
+                                with T.block("C"):
+                                    vi = T.axis.S(2048, by * 64 + vy * 32 + ty * 4 + i)
+                                    vj = T.axis.S(2048, bx * 64 + vx * 32 + tx * 4 + j)
+                                    vk = T.axis.R(2048, k)
                                     with T.init():
                                         C_local[vi, vj] = 0.0
                                     C_local[vi, vj] = C_local[vi, vj] + A_shared_local[vk, vi] * B_shared_local[vk, vj]
                             for i, j in T.grid(4, 4):
-                                with T.block([2048, 2048], "C_local") as [vi, vj]:
-                                    T.bind(vi, by * 64 + vy * 32 + ty * 4 + i)
-                                    T.bind(vj, bx * 64 + vx * 32 + tx * 4 + j)
+                                with T.block("C_local"):
+                                    vi = T.axis.S(2048, by * 64 + vy * 32 + ty * 4 + i)
+                                    vj = T.axis.S(2048, bx * 64 + vx * 32 + tx * 4 + j)
                                     C[vi, vj] = C_local[vi, vj]
 
 
@@ -279,14 +304,22 @@ def cuda_matmul_1(a: T.handle, b: T.handle, c: T.handle) -> None:  # pylint: dis
     A_shared_local = T.alloc_buffer([2048, 2048], "float32", scope="local")
     B_shared_local = T.alloc_buffer([2048, 2048], "float32", scope="local")
     C_local = T.alloc_buffer([2048, 2048], "float32", scope="local")
-    with T.block([2048, 2048], "A_shared") as [v0, v1]:
-        A_shared[v0, v1] = A[v0, v1]
-    with T.block([2048, 2048], "B_shared") as [v0, v1]:
-        B_shared[v0, v1] = B[v0, v1]
-    with T.block([2048, 2048], "A_shared_local") as [v0, v1]:
-        A_shared_local[v0, v1] = A_shared[v0, v1]
-    with T.block([2048, 2048], "B_shared_local") as [v0, v1]:
-        B_shared_local[v0, v1] = B_shared[v0, v1]
+    for i, j in T.grid(2048, 2048):
+        with T.block("A_shared"):
+            v0, v1 = T.axis.remap("SS", [i, j])
+            A_shared[v0, v1] = A[v0, v1]
+    for i, j in T.grid(2048, 2048):
+        with T.block("B_shared"):
+            v0, v1 = T.axis.remap("SS", [i, j])
+            B_shared[v0, v1] = B[v0, v1]
+    for i, j in T.grid(2048, 2048):
+        with T.block("A_shared_local"):
+            v0, v1 = T.axis.remap("SS", [i, j])
+            A_shared_local[v0, v1] = A_shared[v0, v1]
+    for i, j in T.grid(2048, 2048):
+        with T.block("B_shared_local"):
+            v0, v1 = T.axis.remap("SS", [i, j])
+            B_shared_local[v0, v1] = B_shared[v0, v1]
     for by in T.thread_binding(0, 32, thread = "blockIdx.y"):
         for bx in T.thread_binding(0, 32, thread = "blockIdx.x"):
             for vy in T.thread_binding(0, 2, thread = "vthread.y"):
@@ -296,17 +329,17 @@ def cuda_matmul_1(a: T.handle, b: T.handle, c: T.handle) -> None:  # pylint: dis
                             for k_0 in T.serial(0, 256):
                                 for k_1 in T.unroll(0, 8):
                                     for _, i, j in T.grid(1, 4, 4):
-                                        with T.block([2048, 2048, T.reduce_axis(0, 2048)], "C") as [vi, vj, vk]:
-                                            T.bind(vi, by * 64 + vy * 32 + ty * 4 + i)
-                                            T.bind(vj, bx * 64 + vx * 32 + tx * 4 + j)
-                                            T.bind(vk, k_0 * 8 + k_1)
+                                        with T.block("C"):
+                                            vi = T.axis.S(2048, by * 64 + vy * 32 + ty * 4 + i)
+                                            vj = T.axis.S(2048, bx * 64 + vx * 32 + tx * 4 + j)
+                                            vk = T.axis.R(2048, k_0 * 8 + k_1)
                                             with T.init():
                                                 C_local[vi, vj] = 0.0
                                             C_local[vi, vj] = C_local[vi, vj] + A_shared_local[vk, vi] * B_shared_local[vk, vj]
                             for i, j in T.grid(4, 4):
-                                with T.block([2048, 2048], "C_local") as [vi, vj]:
-                                    T.bind(vi, by * 64 + vy * 32 + ty * 4 + i)
-                                    T.bind(vj, bx * 64 + vx * 32 + tx * 4 + j)
+                                with T.block("C_local"):
+                                    vi = T.axis.S(2048, by * 64 + vy * 32 + ty * 4 + i)
+                                    vj = T.axis.S(2048, bx * 64 + vx * 32 + tx * 4 + j)
                                     C[vi, vj] = C_local[vi, vj]
 
 
@@ -320,12 +353,18 @@ def cuda_matmul_2(a: T.handle, b: T.handle, c: T.handle) -> None:  # pylint: dis
     A_shared_local = T.alloc_buffer([2048, 2048], "float32", scope="local")
     B_shared_local = T.alloc_buffer([2048, 2048], "float32", scope="local")
     C_local = T.alloc_buffer([2048, 2048], "float32", scope="local")
-    with T.block([2048, 2048], "A_shared") as [v0, v1]:
-        A_shared[v0, v1] = A[v0, v1]
-    with T.block([2048, 2048], "B_shared") as [v0, v1]:
-        B_shared[v0, v1] = B[v0, v1]
-    with T.block([2048, 2048], "B_shared_local") as [v0, v1]:
-        B_shared_local[v0, v1] = B_shared[v0, v1]
+    for i, j in T.grid(2048, 2048):
+        with T.block("A_shared"):
+            v0, v1 = T.axis.remap("SS", [i, j])
+            A_shared[v0, v1] = A[v0, v1]
+    for i, j in T.grid(2048, 2048):
+        with T.block("B_shared"):
+            v0, v1 = T.axis.remap("SS", [i, j])
+            B_shared[v0, v1] = B[v0, v1]
+    for i, j in T.grid(2048, 2048):
+        with T.block("B_shared_local"):
+            v0, v1 = T.axis.remap("SS", [i, j])
+            B_shared_local[v0, v1] = B_shared[v0, v1]
     for by in T.thread_binding(0, 32, thread = "blockIdx.y"):
         for bx in T.thread_binding(0, 32, thread = "blockIdx.x"):
             for vy in T.thread_binding(0, 2, thread = "vthread.y"):
@@ -335,22 +374,22 @@ def cuda_matmul_2(a: T.handle, b: T.handle, c: T.handle) -> None:  # pylint: dis
                             for k_0 in T.serial(0, 256):
                                 for k_1 in T.unroll(0, 8):
                                     for i, j in T.grid(1, 4):
-                                        with T.block([2048, 2048], "A_shared_local") as [v0, v1]:
-                                            T.bind(v0, k_0 * 8 + k_1 + i)
-                                            T.bind(v1, by * 64 + vy * 32 + ty * 4 + j)
+                                        with T.block("A_shared_local"):
+                                            v0 = T.axis.S(2048, k_0 * 8 + k_1 + i)
+                                            v1 = T.axis.S(2048, by * 64 + vy * 32 + ty * 4 + j)
                                             A_shared_local[v0, v1] = A_shared[v0, v1]
                                     for _, i, j in T.grid(1, 4, 4):
-                                        with T.block([2048, 2048, T.reduce_axis(0, 2048)], "C") as [vi, vj, vk]:
-                                            T.bind(vi, by * 64 + vy * 32 + ty * 4 + i)
-                                            T.bind(vj, bx * 64 + vx * 32 + tx * 4 + j)
-                                            T.bind(vk, k_0 * 8 + k_1)
+                                        with T.block("C"):
+                                            vi = T.axis.S(2048, by * 64 + vy * 32 + ty * 4 + i)
+                                            vj = T.axis.S(2048, bx * 64 + vx * 32 + tx * 4 + j)
+                                            vk = T.axis.R(2048, k_0 * 8 + k_1)
                                             with T.init():
                                                 C_local[vi, vj] = T.float32(0)
                                             C_local[vi, vj] = C_local[vi, vj] + A_shared_local[vk, vi] * B_shared_local[vk, vj]
                             for i, j in T.grid(4, 4):
-                                with T.block([2048, 2048], "C_local") as [v0, v1]:
-                                    T.bind(v0, by * 64 + vy * 32 + ty * 4 + i)
-                                    T.bind(v1, bx * 64 + vx * 32 + tx * 4 + j)
+                                with T.block("C_local"):
+                                    v0 = T.axis.S(2048, by * 64 + vy * 32 + ty * 4 + i)
+                                    v1 = T.axis.S(2048, bx * 64 + vx * 32 + tx * 4 + j)
                                     C[v0, v1] = C_local[v0, v1]
 
 
@@ -364,10 +403,14 @@ def cuda_matmul_3(a: T.handle, b: T.handle, c: T.handle) -> None:  # pylint: dis
     A_shared_local = T.alloc_buffer([2048, 2048], "float32", scope="local")
     B_shared_local = T.alloc_buffer([2048, 2048], "float32", scope="local")
     C_local = T.alloc_buffer([2048, 2048], "float32", scope="local")
-    with T.block([2048, 2048], "A_shared") as [v0, v1]:
-        A_shared[v0, v1] = A[v0, v1]
-    with T.block([2048, 2048], "B_shared") as [v0, v1]:
-        B_shared[v0, v1] = B[v0, v1]
+    for i, j in T.grid(2048, 2048):
+        with T.block("A_shared"):
+            v0, v1 = T.axis.remap("SS", [i, j])
+            A_shared[v0, v1] = A[v0, v1]
+    for i, j in T.grid(2048, 2048):
+        with T.block("B_shared"):
+            v0, v1 = T.axis.remap("SS", [i, j])
+            B_shared[v0, v1] = B[v0, v1]
     for by in T.thread_binding(0, 32, thread = "blockIdx.y"):
         for bx in T.thread_binding(0, 32, thread = "blockIdx.x"):
             for vy in T.thread_binding(0, 2, thread = "vthread.y"):
@@ -377,27 +420,27 @@ def cuda_matmul_3(a: T.handle, b: T.handle, c: T.handle) -> None:  # pylint: dis
                             for k0 in T.serial(0, 256):
                                 for k1 in T.unroll(0, 8):
                                     for i, j in T.grid(1, 4):
-                                        with T.block([2048, 2048], "A_shared_local") as [v0, v1]:
-                                            T.bind(v0, k0 * 8 + k1 + i)
-                                            T.bind(v1, by * 64 + vy * 32 + ty * 4 + j)
+                                        with T.block("A_shared_local"):
+                                            v0 = T.axis.S(2048, k0 * 8 + k1 + i)
+                                            v1 = T.axis.S(2048, by * 64 + vy * 32 + ty * 4 + j)
                                             A_shared_local[v0, v1] = A_shared[v0, v1]
                                     for i, j in T.grid(1, 4):
-                                        with T.block([2048, 2048], "B_shared_local") as [v0, v1]:
-                                            T.bind(v0, k0 * 8 + k1 + i)
-                                            T.bind(v1, bx * 64 + vx * 32 + tx * 4 + j)
+                                        with T.block("B_shared_local"):
+                                            v0 = T.axis.S(2048, k0 * 8 + k1 + i)
+                                            v1 = T.axis.S(2048, bx * 64 + vx * 32 + tx * 4 + j)
                                             B_shared_local[v0, v1] = B_shared[v0, v1]
                                     for _, i, j in T.grid(1, 4, 4):
-                                        with T.block([2048, 2048, T.reduce_axis(0, 2048)], "C") as [vi, vj, vk]:
-                                            T.bind(vi, by * 64 + vy * 32 + ty * 4 + i)
-                                            T.bind(vj, bx * 64 + vx * 32 + tx * 4 + j)
-                                            T.bind(vk, k0 * 8 + k1)
+                                        with T.block("C"):
+                                            vi = T.axis.S(2048, by * 64 + vy * 32 + ty * 4 + i)
+                                            vj = T.axis.S(2048, bx * 64 + vx * 32 + tx * 4 + j)
+                                            vk = T.axis.R(2048, k0 * 8 + k1)
                                             with T.init():
                                                 C_local[vi, vj] = T.float32(0)
                                             C_local[vi, vj] = C_local[vi, vj] + A_shared_local[vk, vi] * B_shared_local[vk, vj]
                             for i, j in T.grid(4, 4):
-                                with T.block([2048, 2048], "C_local") as [v0, v1]:
-                                    T.bind(v0, by * 64 + vy * 32 + ty * 4 + i)
-                                    T.bind(v1, bx * 64 + vx * 32 + tx * 4 + j)
+                                with T.block("C_local"):
+                                    v0 = T.axis.S(2048, by * 64 + vy * 32 + ty * 4 + i)
+                                    v1 = T.axis.S(2048, bx * 64 + vx * 32 + tx * 4 + j)
                                     C[v0, v1] = C_local[v0, v1]
 
 
@@ -411,8 +454,10 @@ def cuda_matmul_4(a: T.handle, b: T.handle, c: T.handle) -> None:  # pylint: dis
     A_shared_local = T.alloc_buffer([2048, 2048], "float32", scope="local")
     B_shared_local = T.alloc_buffer([2048, 2048], "float32", scope="local")
     C_local = T.alloc_buffer([2048, 2048], "float32", scope="local")
-    with T.block([2048, 2048], "B_shared") as [v0, v1]:
-        B_shared[v0, v1] = B[v0, v1]
+    for i, j in T.grid(2048, 2048):
+        with T.block("B_shared"):
+            v0, v1 = T.axis.remap("SS", [i, j])
+            B_shared[v0, v1] = B[v0, v1]
     for by in T.thread_binding(0, 32, thread = "blockIdx.y"):
         for bx in T.thread_binding(0, 32, thread = "blockIdx.x"):
             for vy in T.thread_binding(0, 2, thread = "vthread.y"):
@@ -421,33 +466,33 @@ def cuda_matmul_4(a: T.handle, b: T.handle, c: T.handle) -> None:  # pylint: dis
                         for tx in T.thread_binding(0, 8, thread = "threadIdx.x"):
                             for k0 in T.serial(0, 256):
                                 for i, j in T.grid(8, 64):
-                                    with T.block([2048, 2048], "A_shared") as [v0, v1]:
-                                        T.bind(v0, k0 * 8 + i)
-                                        T.bind(v1, by * 64 + j)
+                                    with T.block("A_shared"):
+                                        v0 = T.axis.S(2048, k0 * 8 + i)
+                                        v1 = T.axis.S(2048, by * 64 + j)
                                         A_shared[v0, v1] = A[v0, v1]
                                 for k1 in T.unroll(0, 8):
                                     for i, j in T.grid(1, 4):
-                                        with T.block([2048, 2048], "A_shared_local") as [v0, v1]:
-                                            T.bind(v0, k0 * 8 + k1 + i)
-                                            T.bind(v1, by * 64 + vy * 32 + ty * 4 + j)
+                                        with T.block("A_shared_local"):
+                                            v0 = T.axis.S(2048, k0 * 8 + k1 + i)
+                                            v1 = T.axis.S(2048, by * 64 + vy * 32 + ty * 4 + j)
                                             A_shared_local[v0, v1] = A_shared[v0, v1]
                                     for i, j in T.grid(1, 4):
-                                        with T.block([2048, 2048], "B_shared_local") as [v0, v1]:
-                                            T.bind(v0, k0 * 8 + k1 + i)
-                                            T.bind(v1, bx * 64 + vx * 32 + tx * 4 + j)
+                                        with T.block("B_shared_local"):
+                                            v0 = T.axis.S(2048, k0 * 8 + k1 + i)
+                                            v1 = T.axis.S(2048, bx * 64 + vx * 32 + tx * 4 + j)
                                             B_shared_local[v0, v1] = B_shared[v0, v1]
                                     for _, i, j in T.grid(1, 4, 4):
-                                        with T.block([2048, 2048, T.reduce_axis(0, 2048)], "C") as [vi, vj, vk]:
-                                            T.bind(vi, by * 64 + vy * 32 + ty * 4 + i)
-                                            T.bind(vj, bx * 64 + vx * 32 + tx * 4 + j)
-                                            T.bind(vk, k0 * 8 + k1)
+                                        with T.block("C"):
+                                            vi = T.axis.S(2048, by * 64 + vy * 32 + ty * 4 + i)
+                                            vj = T.axis.S(2048, bx * 64 + vx * 32 + tx * 4 + j)
+                                            vk = T.axis.R(2048, k0 * 8 + k1)
                                             with T.init():
                                                 C_local[vi, vj] = 0.0
                                             C_local[vi, vj] = C_local[vi, vj] + A_shared_local[vk, vi] * B_shared_local[vk, vj]
                             for i, j in T.grid(4, 4):
-                                with T.block([2048, 2048], "C_local") as [v0, v1]:
-                                    T.bind(v0, by * 64 + vy * 32 + ty * 4 + i)
-                                    T.bind(v1, bx * 64 + vx * 32 + tx * 4 + j)
+                                with T.block("C_local"):
+                                    v0 = T.axis.S(2048, by * 64 + vy * 32 + ty * 4 + i)
+                                    v1 = T.axis.S(2048, bx * 64 + vx * 32 + tx * 4 + j)
                                     C[v0, v1] = C_local[v0, v1]
 
 
@@ -469,38 +514,38 @@ def cuda_matmul_5(a: T.handle, b: T.handle, c: T.handle) -> None:  # pylint: dis
                         for tx in T.thread_binding(0, 8, thread = "threadIdx.x"):
                             for k0 in T.serial(0, 256):
                                 for i, j in T.grid(8, 64):
-                                    with T.block([2048, 2048], "A_shared") as [v0, v1]:
-                                        T.bind(v0, k0 * 8 + i)
-                                        T.bind(v1, by * 64 + j)
+                                    with T.block("A_shared"):
+                                        v0 = T.axis.S(2048, k0 * 8 + i)
+                                        v1 = T.axis.S(2048, by * 64 + j)
                                         A_shared[v0, v1] = A[v0, v1]
                                 for i, j in T.grid(8, 64):
-                                    with T.block([2048, 2048], "B_shared") as [v0, v1]:
-                                        T.bind(v0, k0 * 8 + i)
-                                        T.bind(v1, bx * 64 + j)
+                                    with T.block("B_shared"):
+                                        v0 = T.axis.S(2048, k0 * 8 + i)
+                                        v1 = T.axis.S(2048, bx * 64 + j)
                                         B_shared[v0, v1] = B[v0, v1]
                                 for k1 in T.unroll(0, 8):
                                     for i, j in T.grid(1, 4):
-                                        with T.block([2048, 2048], "A_shared_local") as [v0, v1]:
-                                            T.bind(v0, k0 * 8 + k1 + i)
-                                            T.bind(v1, by * 64 + vy * 32 + ty * 4 + j)
+                                        with T.block("A_shared_local"):
+                                            v0 = T.axis.S(2048, k0 * 8 + k1 + i)
+                                            v1 = T.axis.S(2048, by * 64 + vy * 32 + ty * 4 + j)
                                             A_shared_local[v0, v1] = A_shared[v0, v1]
                                     for i, j in T.grid(1, 4):
-                                        with T.block([2048, 2048], "B_shared_local") as [v0, v1]:
-                                            T.bind(v0, k0 * 8 + k1 + i)
-                                            T.bind(v1, bx * 64 + vx * 32 + tx * 4 + j)
+                                        with T.block("B_shared_local"):
+                                            v0 = T.axis.S(2048, k0 * 8 + k1 + i)
+                                            v1 = T.axis.S(2048, bx * 64 + vx * 32 + tx * 4 + j)
                                             B_shared_local[v0, v1] = B_shared[v0, v1]
                                     for _, i, j in T.grid(1, 4, 4):
-                                        with T.block([2048, 2048, T.reduce_axis(0, 2048)], "C") as [vi, vj, vk]:
-                                            T.bind(vi, by * 64 + vy * 32 + ty * 4 + i)
-                                            T.bind(vj, bx * 64 + vx * 32 + tx * 4 + j)
-                                            T.bind(vk, k0 * 8 + k1)
+                                        with T.block("C"):
+                                            vi = T.axis.S(2048, by * 64 + vy * 32 + ty * 4 + i)
+                                            vj = T.axis.S(2048, bx * 64 + vx * 32 + tx * 4 + j)
+                                            vk = T.axis.R(2048, k0 * 8 + k1)
                                             with T.init():
                                                 C_local[vi, vj] = 0.0
                                             C_local[vi, vj] = C_local[vi, vj] + A_shared_local[vk, vi] * B_shared_local[vk, vj]
                             for i, j in T.grid(4, 4):
-                                with T.block([2048, 2048], "C_local") as [v0, v1]:
-                                    T.bind(v0, by * 64 + vy * 32 + ty * 4 + i)
-                                    T.bind(v1, bx * 64 + vx * 32 + tx * 4 + j)
+                                with T.block("C_local"):
+                                    v0 = T.axis.S(2048, by * 64 + vy * 32 + ty * 4 + i)
+                                    v1 = T.axis.S(2048, bx * 64 + vx * 32 + tx * 4 + j)
                                     C[v0, v1] = C_local[v0, v1]
 
 
@@ -510,12 +555,14 @@ def tiled(a: T.handle, c: T.handle) -> None:
     B = T.alloc_buffer([128, 128], "float32")
     C = T.match_buffer(c, [128, 128], "float32")
     for i_0, j_0, i_1, j_1 in T.grid(8, 8, 16, 16):
-        with T.block([128, 128], "B") as [vi, vj]:
-            T.bind(vi, i_0 * 16 + i_1)
-            T.bind(vj, j_0 * 16 + j_1)
+        with T.block("B"):
+            vi = T.axis.S(128, i_0 * 16 + i_1)
+            vj = T.axis.S(128, j_0 * 16 + j_1)
             B[vi, vj] = A[vi, vj] * 2.0
-    with T.block([128, 128], "C") as [vi, vj]:
-        C[vi, vj] = B[vi, vj] + 1.0
+    for i, j in T.grid(128, 128):
+        with T.block("C"):
+            vi, vj = T.axis.remap("SS", [i, j])
+            C[vi, vj] = B[vi, vj] + 1.0
 
 
 @T.prim_func
@@ -525,14 +572,14 @@ def tiled_after_reverse_compute_at(a: T.handle, c: T.handle) -> None:
     C = T.match_buffer(c, [128, 128], "float32")
     for i_0, j_0, i_1 in T.grid(8, 8, 16):
         for j_1 in T.serial(0, 16):
-            with T.block([128, 128], "B") as [vi, vj]:
-                T.bind(vi, i_0 * 16 + i_1)
-                T.bind(vj, j_0 * 16 + j_1)
+            with T.block("B"):
+                vi = T.axis.S(128, i_0 * 16 + i_1)
+                vj = T.axis.S(128, j_0 * 16 + j_1)
                 B[vi, vj] = A[vi, vj] * 2.0
         for j_1 in T.serial(0, 16):
-            with T.block([128, 128], "C") as [vi, vj]:
-                T.bind(vi, i_0 * 16 + i_1)
-                T.bind(vj, j_0 * 16 + j_1)
+            with T.block("C"):
+                vi = T.axis.S(128, i_0 * 16 + i_1)
+                vj = T.axis.S(128, j_0 * 16 + j_1)
                 C[vi, vj] = B[vi, vj] + 1.0
 
 
@@ -544,17 +591,15 @@ def factorized(a: T.handle, b: T.handle) -> None:
     for j in T.thread_binding(0, 16, thread = "blockIdx.x"):
         for i_o in T.thread_binding(0, 4, thread = "threadIdx.x"):
             for i_i, k in T.grid(4, 16):
-                with T.block([16, 16, T.reduce_axis(0, 16)], "B_rf") as [vi, vj, vk]:
-                    T.bind(vi, i_o * 4 + i_i)
-                    T.bind(vj, j)
-                    T.bind(vk, k)
+                with T.block("B_rf"):
+                    vi = T.axis.S(16, i_o * 4 + i_i)
+                    vj, vk = T.axis.remap("SR", [j, k])
                     with T.init():
                         B_rf_local[vi, vj] = 0.0
                     B_rf_local[vi, vj] = B_rf_local[vi, vj] + A[vj, vi, vk]
     for i, k in T.grid(16, 16):
-        with T.block([16, T.reduce_axis(0, 16)], "B") as [vi, vk]:
-            T.bind(vi, i)
-            T.bind(vk, k)
+        with T.block("B"):
+            vi, vk = T.axis.remap("SR", [i, k])
             with T.init():
                 B[vi] = 0.0
             B[vi] = B[vi] + B_rf_local[vk, vi]
@@ -568,17 +613,17 @@ def factorized_after_reverse_compute_at(a: T.handle, b: T.handle) -> None:
     for j in T.thread_binding(0, 16, thread = "blockIdx.x"):
         for i_o in T.thread_binding(0, 4, thread = "threadIdx.x"):
             for i_i, k in T.grid(4, 16):
-                with T.block([16, 16, T.reduce_axis(0, 16)], "B_rf") as [vi, vj, vk]:
-                    T.bind(vi, i_o * 4 + i_i)
-                    T.bind(vj, j)
-                    T.bind(vk, k)
+                with T.block("B_rf"):
+                    vi = T.axis.S(16, i_o * 4 + i_i)
+                    vj = T.axis.S(16, j)
+                    vk = T.axis.R(16, k)
                     with T.init():
                         B_rf_local[vi, vj] = 0.0
                     B_rf_local[vi, vj] = B_rf_local[vi, vj] + A[vj, vi, vk]
             for k in T.serial(0, 4):
-                with T.block([16, T.reduce_axis(0, 16)], "B") as [vi, vk]:
-                    T.bind(vi, j)
-                    T.bind(vk, i_o * 4 + k)
+                with T.block("B"):
+                    vi = T.axis.S(16, j)
+                    vk = T.axis.R(16, i_o * 4 + k)
                     with T.init():
                         B[vi] = 0.0
                     B[vi] = B[vi] + B_rf_local[vk, vi]
@@ -591,17 +636,19 @@ def fail_subtree_compact_dataflow(a: T.handle, c: T.handle) -> None:
     C = T.match_buffer(c, (128, 128), "float32")
     for i in range(0, 128):
         for j in range(0, 64):
-            with T.block([128, 128], "B_0") as [vi, vj]:
-                T.bind(vi, i)
-                T.bind(vj, j)
+            with T.block("B_0"):
+                vi = T.axis.S(128, i)
+                vj = T.axis.S(128, j)
                 B[vi, vj] = A[vi, vj] * 2.0
         for j in range(0, 64):
-            with T.block([128, 128], "B_1") as [vi, vj]:
-                T.bind(vi, i)
-                T.bind(vj, j + 64)
+            with T.block("B_1"):
+                vi = T.axis.S(128, i)
+                vj = T.axis.S(128, j + 64)
                 B[vi, vj] = A[vi, vj] * 2.0
-    with T.block([128, 128], "C") as [vi, vj]:
-        C[vi, vj] = B[vi, vj] + 1.0
+    for i, j in T.grid(128, 128):
+        with T.block("C"):
+            vi, vj = T.axis.remap("SS", [i, j])
+            C[vi, vj] = B[vi, vj] + 1.0
 
 
 @T.prim_func
@@ -611,13 +658,16 @@ def fail_all_consumers_under_loop(a: T.handle, c: T.handle, d: T.handle) -> None
     C = T.match_buffer(c, (128, 128), "float32")
     D = T.match_buffer(d, (128, 128), "float32")
     for i, j in T.grid(128, 128):
-        with T.block([128, 128], "B") as [vi, vj]:
+        with T.block("B"):
+            vi, vj = T.axis.remap("SS", [i, j])
             B[vi, vj] = A[vi, vj] * 2.0
     for i, j in T.grid(128, 128):
-        with T.block([128, 128], "C") as [vi, vj]:
+        with T.block("C"):
+            vi, vj = T.axis.remap("SS", [i, j])
             C[vi, vj] = B[vi, vj] + 1.0
     for i, j in T.grid(128, 128):
-        with T.block([128, 128], "D") as [vi, vj]:
+        with T.block("D"):
+            vi, vj = T.axis.remap("SS", [i, j])
             D[vi, vj] = B[vi, vj] + 1.0
 
 
@@ -628,13 +678,16 @@ def fail_all_producers_under_loop(a: T.handle, d: T.handle) -> None:
     C = T.alloc_buffer((128, 128), "float32")
     D = T.match_buffer(d, (128, 128), "float32")
     for i, j in T.grid(128, 128):
-        with T.block([128, 128], "B") as [vi, vj]:
+        with T.block("B"):
+            vi, vj = T.axis.remap("SS", [i, j])
             B[vi, vj] = A[vi, vj] * 2.0
     for i, j in T.grid(128, 128):
-        with T.block([128, 128], "C") as [vi, vj]:
+        with T.block("C"):
+            vi, vj = T.axis.remap("SS", [i, j])
             C[vi, vj] = A[vi, vj] + 1.0
     for i, j in T.grid(128, 128):
-        with T.block([128, 128], "D") as [vi, vj]:
+        with T.block("D"):
+            vi, vj = T.axis.remap("SS", [i, j])
             D[vi, vj] = B[vi, vj] + C[vi, vj]
 
 
@@ -644,10 +697,12 @@ def read_out_of_bound(a: T.handle, c:T.handle) -> None:
     B = T.alloc_buffer([16], "float32")
     C = T.match_buffer(c, [16], "float32")
     for i in T.serial(0, 16):
-        with T.block([16], "B") as [v]:
+        with T.block("B"):
+            v = T.axis.S(16, i)
             B[v] = A[v]
     for j in T.serial(0, 16):
-        with T.block([16], "C") as [v]:
+        with T.block("C"):
+            v = T.axis.S(16, j)
             T.reads(B[v : v + 2])
             C[v] = T.if_then_else(v < 15, T.max(B[v], B[v + 1]), B[v], dtype="float32")
 
@@ -659,11 +714,11 @@ def read_out_of_bound_after_compute_at(a: T.handle, c: T.handle) -> None:
     C = T.match_buffer(c, [16], "float32")
     for j in T.serial(0, 16):
         for i in T.serial(0, T.min(1, 15 - j) + 1):
-            with T.block([16], "B") as [v]:
-                T.bind(v, j + i)
+            with T.block("B"):
+                v = T.axis.S(16, j + i)
                 B[v] = A[v]
-        with T.block([16], "C") as [v]:
-            T.bind(v, j)
+        with T.block("C"):
+            v = T.axis.S(16, j)
             T.reads([B[v : v + 2]])
             C[v] = T.if_then_else(v < 15, T.max(B[v], B[v + 1]), B[v], dtype="float32")
 
diff --git a/tests/python/unittest/test_tir_schedule_compute_inline.py b/tests/python/unittest/test_tir_schedule_compute_inline.py
index f9049f6da732b..617c75b75cd91 100644
--- a/tests/python/unittest/test_tir_schedule_compute_inline.py
+++ b/tests/python/unittest/test_tir_schedule_compute_inline.py
@@ -31,10 +31,14 @@ def elementwise(a: T.handle, c: T.handle) -> None:
     A = T.match_buffer(a, (128, 128))
     B = T.alloc_buffer((128, 128))
     C = T.match_buffer(c, (128, 128))
-    with T.block([128, 128], "B") as [vi, vj]:
-        B[vi, vj] = A[vi, vj] * 2.0
-    with T.block([128, 128], "C") as [vi, vj]:
-        C[vi, vj] = B[vi, vj] + 1.0
+    for i, j in T.grid(128, 128):
+        with T.block("B"):
+            vi, vj = T.axis.remap("SS", [i, j])
+            B[vi, vj] = A[vi, vj] * 2.0
+    for i, j in T.grid(128, 128):
+        with T.block("C"):
+            vi, vj = T.axis.remap("SS", [i, j])
+            C[vi, vj] = B[vi, vj] + 1.0
 
 
 @T.prim_func
@@ -43,12 +47,18 @@ def elementwise_multi_producer_consumer(a: T.handle, c: T.handle, d: T.handle) -
     B = T.alloc_buffer((128, 128))
     C = T.match_buffer(c, (128, 128))
     D = T.match_buffer(d, (128, 128))
-    with T.block([128, 128], "B") as [vi, vj]:
-        B[vi, vj] = A[vi, vj] * 2.0  # B has two consumers
-    with T.block([128, 128], "C") as [vi, vj]:
-        C[vi, vj] = B[vi, vj] + 1.0
-    with T.block([128, 128], "D") as [vi, vj]:
-        D[vi, vj] = B[vi, vj] + 2.0 + C[vi, vj]  # D has two producers
+    for i, j in T.grid(128, 128):
+        with T.block("B"):
+            vi, vj = T.axis.remap("SS", [i, j])
+            B[vi, vj] = A[vi, vj] * 2.0  # B has two consumers
+    for i, j in T.grid(128, 128):
+        with T.block("C"):
+            vi, vj = T.axis.remap("SS", [i, j])
+            C[vi, vj] = B[vi, vj] + 1.0
+    for i, j in T.grid(128, 128):
+        with T.block("D"):
+            vi, vj = T.axis.remap("SS", [i, j])
+            D[vi, vj] = B[vi, vj] + 2.0 + C[vi, vj]  # D has two producers
 
 
 @T.prim_func
@@ -56,10 +66,14 @@ def elementwise_multi_consumer_inlined(a: T.handle, c: T.handle, d: T.handle) ->
     A = T.match_buffer(a, (128, 128))
     C = T.match_buffer(c, (128, 128))
     D = T.match_buffer(d, (128, 128))
-    with T.block([128, 128], "C") as [vi, vj]:
-        C[vi, vj] = A[vi, vj] * 2.0 + 1.0
-    with T.block([128, 128], "D") as [vi, vj]:
-        D[vi, vj] = A[vi, vj] * 2.0 + 2.0 + C[vi, vj]
+    for i, j in T.grid(128, 128):
+        with T.block("C"):
+            vi, vj = T.axis.remap("SS", [i, j])
+            C[vi, vj] = A[vi, vj] * 2.0 + 1.0
+    for i, j in T.grid(128, 128):
+        with T.block("D"):
+            vi, vj = T.axis.remap("SS", [i, j])
+            D[vi, vj] = A[vi, vj] * 2.0 + 2.0 + C[vi, vj]
 
 
 @T.prim_func
@@ -67,18 +81,24 @@ def elementwise_standalone(a: T.handle, c: T.handle) -> None:
     A = T.match_buffer(a, (128, 128))
     B = T.alloc_buffer((128, 128))
     C = T.match_buffer(c, (128, 128))
-    with T.block([128, 128], "B") as [vi, vj]:
-        B[vi, vj] = A[vi, vj] * 2.0
-    with T.block([128, 128], "C") as [vi, vj]:
-        C[vi, vj] = A[vi, vj] + 1.0
+    for i, j in T.grid(128, 128):
+        with T.block("B"):
+            vi, vj = T.axis.remap("SS", [i, j])
+            B[vi, vj] = A[vi, vj] * 2.0
+    for i, j in T.grid(128, 128):
+        with T.block("C"):
+            vi, vj = T.axis.remap("SS", [i, j])
+            C[vi, vj] = A[vi, vj] + 1.0
 
 
 @T.prim_func
 def elementwise_standalone_dce(a: T.handle, c: T.handle) -> None:
     A = T.match_buffer(a, (128, 128))
     C = T.match_buffer(c, (128, 128))
-    with T.block([128, 128], "C") as [vi, vj]:
-        C[vi, vj] = A[vi, vj] + 1.0
+    for i, j in T.grid(128, 128):
+        with T.block("C"):
+            vi, vj = T.axis.remap("SS", [i, j])
+            C[vi, vj] = A[vi, vj] + 1.0
 
 
 @T.prim_func
@@ -88,14 +108,12 @@ def elementwise_under_loop(a: T.handle, c: T.handle) -> None:
     B = T.alloc_buffer((128, 128))
     for i in T.serial(0, 128):
         for j in T.serial(0, 128):
-            with T.block([128, 128], "B") as [vi, vj]:
-                T.bind(vi, i)
-                T.bind(vj, j)
+            with T.block("B"):
+                vi, vj = T.axis.remap("SS", [i, j])
                 B[vi, vj] = A[vi, vj] * 2.0
         for j in T.serial(0, 128):
-            with T.block([128, 128], "C") as [vi, vj]:
-                T.bind(vi, i)
-                T.bind(vj, j)
+            with T.block("C"):
+                vi, vj = T.axis.remap("SS", [i, j])
                 C[vi, vj] = B[vi, vj] + 1.0
 
 
@@ -103,8 +121,10 @@ def elementwise_under_loop(a: T.handle, c: T.handle) -> None:
 def elementwise_inlined(a: T.handle, c: T.handle) -> None:
     A = T.match_buffer(a, (128, 128))
     C = T.match_buffer(c, (128, 128))
-    with T.block([128, 128], "C") as [vi, vj]:
-        C[vi, vj] = A[vi, vj] * 2.0 + 1.0
+    for i, j in T.grid(128, 128):
+        with T.block("C"):
+            vi, vj = T.axis.remap("SS", [i, j])
+            C[vi, vj] = A[vi, vj] * 2.0 + 1.0
 
 
 @T.prim_func
@@ -113,11 +133,15 @@ def fail_multi_reader_writer(a: T.handle, d: T.handle) -> None:
     B = T.alloc_buffer((128, 128))
     C = T.alloc_buffer((128, 128))
     D = T.match_buffer(d, (128, 128))
-    with T.block([128, 128], "B") as [vi, vj]:
-        B[vi, vj] = A[vi, vj] * 2.0
-        C[vi, vj] = A[vi, vj] + 2.0
-    with T.block([128, 128], "C") as [vi, vj]:
-        D[vi, vj] = B[vi, vj] + C[vi, vj]
+    for i, j in T.grid(128, 128):
+        with T.block("B"):
+            vi, vj = T.axis.remap("SS", [i, j])
+            B[vi, vj] = A[vi, vj] * 2.0
+            C[vi, vj] = A[vi, vj] + 2.0
+    for i, j in T.grid(128, 128):
+        with T.block("C"):
+            vi, vj = T.axis.remap("SS", [i, j])
+            D[vi, vj] = B[vi, vj] + C[vi, vj]
 
 
 @T.prim_func
@@ -125,18 +149,24 @@ def elementwise_multi_reverse_loads(a: T.handle, c: T.handle) -> None:
     A = T.match_buffer(a, (128, 128))
     B = T.alloc_buffer((128, 128))
     C = T.match_buffer(c, (128, 128))
-    with T.block([128, 128], "B") as [vi, vj]:
-        B[vi, vj] = A[vi, vj] * 2.0
-    with T.block([128, 128], "C") as [vi, vj]:
-        C[vi, vj] = (B[vi, vj] + 1.0) * (B[vi, vj] * 2.0) + 3.0
+    for i, j in T.grid(128, 128):
+        with T.block("B"):
+            vi, vj = T.axis.remap("SS", [i, j])
+            B[vi, vj] = A[vi, vj] * 2.0
+    for i, j in T.grid(128, 128):
+        with T.block("C"):
+            vi, vj = T.axis.remap("SS", [i, j])
+            C[vi, vj] = (B[vi, vj] + 1.0) * (B[vi, vj] * 2.0) + 3.0
 
 
 @T.prim_func
 def elementwise_multi_reverse_loads_inlined(a: T.handle, c: T.handle) -> None:
     A = T.match_buffer(a, (128, 128))
     C = T.match_buffer(c, (128, 128))
-    with T.block([128, 128], "B") as [vi, vj]:
-        C[vi, vj] = (A[vi, vj] * 2.0 + 1.0) * (A[vi, vj] * 2.0 * 2.0) + 3.0
+    for i, j in T.grid(128, 128):
+        with T.block("B"):
+            vi, vj = T.axis.remap("SS", [i, j])
+            C[vi, vj] = (A[vi, vj] * 2.0 + 1.0) * (A[vi, vj] * 2.0 * 2.0) + 3.0
 
 
 @T.prim_func
@@ -144,12 +174,16 @@ def opaque_access_load(a: T.handle, c: T.handle) -> None:
     A = T.match_buffer(a, (128, 128))
     B = T.alloc_buffer((128, 128))
     C = T.match_buffer(c, (128, 128))
-    with T.block([128, 128], "B") as [vi, vj]:
-        B[vi, vj] = A[vi, vj] * 2.0
-    with T.block([128, 128], "C") as [vi, vj]:
-        T.reads(B[0:128, 0:128])
-        T.writes(C[0:128, 0:128])
-        C[vi, vj] = T.load("float32", B.data, vi * 128 + vj) + 1.0
+    for i, j in T.grid(128, 128):
+        with T.block("B"):
+            vi, vj = T.axis.remap("SS", [i, j])
+            B[vi, vj] = A[vi, vj] * 2.0
+    for i, j in T.grid(128, 128):
+        with T.block("C"):
+            vi, vj = T.axis.remap("SS", [i, j])
+            T.reads(B[0:128, 0:128])
+            T.writes(C[0:128, 0:128])
+            C[vi, vj] = T.load("float32", B.data, vi * 128 + vj) + 1.0
 
 
 @T.prim_func
@@ -157,13 +191,17 @@ def opaque_access_store(a: T.handle, c: T.handle) -> None:
     A = T.match_buffer(a, (128, 128))
     B = T.alloc_buffer((128, 128))
     C = T.match_buffer(c, (128, 128))
-    with T.block([128, 128], "B") as [vi, vj]:
-        B[vi, vj] = A[vi, vj] * 2.0
-    with T.block([128, 128], "C") as [vi, vj]:
-        T.reads(B[0:128, 0:128])
-        T.writes(C[0:128, 0:128])
-        T.store(C.data, vi * 128 + vj, B[vi, vj] + 1.0)
-        C[vi, vj] = T.load("float32", B.data, vi * 16 + vj) + 1.0
+    for i, j in T.grid(128, 128):
+        with T.block("B"):
+            vi, vj = T.axis.remap("SS", [i, j])
+            B[vi, vj] = A[vi, vj] * 2.0
+    for i, j in T.grid(128, 128):
+        with T.block("C"):
+            vi, vj = T.axis.remap("SS", [i, j])
+            T.reads(B[0:128, 0:128])
+            T.writes(C[0:128, 0:128])
+            T.store(C.data, vi * 128 + vj, B[vi, vj] + 1.0)
+            C[vi, vj] = T.load("float32", B.data, vi * 16 + vj) + 1.0
 
 
 @T.prim_func
@@ -171,11 +209,15 @@ def buffer_matched(a: T.handle, c: T.handle) -> None:
     A = T.match_buffer(a, (128, 128))
     B = T.alloc_buffer((128, 128))
     C = T.match_buffer(c, (128, 128))
-    with T.block([128, 128], "B") as [vi, vj]:
-        B[vi, vj] = A[vi, vj] * 2.0
-    with T.block([128, 128], "C") as [vi, vj]:
-        Bb = T.match_buffer(B[vi : vi + 1, vj], (1, 1))
-        C[vi, vj] = Bb[0, 0] + 1.0
+    for i, j in T.grid(128, 128):
+        with T.block("B"):
+            vi, vj = T.axis.remap("SS", [i, j])
+            B[vi, vj] = A[vi, vj] * 2.0
+    for i, j in T.grid(128, 128):
+        with T.block("C"):
+            vi, vj = T.axis.remap("SS", [i, j])
+            Bb = T.match_buffer(B[vi : vi + 1, vj], (1, 1))
+            C[vi, vj] = Bb[0, 0] + 1.0
 
 
 @T.prim_func
@@ -183,10 +225,13 @@ def elementwise_predicate(a: T.handle, c: T.handle) -> None:
     A = T.match_buffer(a, (128, 128))
     B = T.alloc_buffer((128, 128))
     C = T.match_buffer(c, (128, 128))
-    with T.block([128, 128], "B") as [vi, vj]:
-        B[vi, vj] = A[vi, vj] * 2.0
     for i, j in T.grid(128, 128):
-        with T.block([128, 128], "C") as [vi, vj]:
+        with T.block("B"):
+            vi, vj = T.axis.remap("SS", [i, j])
+            B[vi, vj] = A[vi, vj] * 2.0
+    for i, j in T.grid(128, 128):
+        with T.block("C"):
+            vi, vj = T.axis.remap("SS", [i, j])
             T.where(B[i, j] < 10.0)
             C[vi, vj] = B[vi, vj] + 1.0
 
@@ -196,7 +241,8 @@ def elementwise_predicate_inlined(a: T.handle, c: T.handle) -> None:
     A = T.match_buffer(a, (128, 128))
     C = T.match_buffer(c, (128, 128))
     for i, j in T.grid(128, 128):
-        with T.block([128, 128], "C") as [vi, vj]:
+        with T.block("C"):
+            vi, vj = T.axis.remap("SS", [i, j])
             T.where(A[i, j] * 2.0 < 10.0)
             C[vi, vj] = A[vi, vj] * 2.0 + 1.0
 
@@ -206,18 +252,24 @@ def elementwise_multi_loads(a: T.handle, c: T.handle) -> None:
     A = T.match_buffer(a, (128, 128))
     B = T.alloc_buffer((128, 128))
     C = T.match_buffer(c, (128, 128))
-    with T.block([128, 128], "B") as [vi, vj]:
-        B[vi, vj] = A[vi, vj] * 2.0
-    with T.block([128, 126], "C") as [vi, vj]:
-        C[vi, vj] = B[vi, vj] + B[vi, vj + 1] + B[vi, vj + 2]
+    for i, j in T.grid(128, 128):
+        with T.block("B"):
+            vi, vj = T.axis.remap("SS", [i, j])
+            B[vi, vj] = A[vi, vj] * 2.0
+    for i, j in T.grid(128, 128):
+        with T.block("C"):
+            vi, vj = T.axis.remap("SS", [i, j])
+            C[vi, vj] = B[vi, vj] + B[vi, vj + 1] + B[vi, vj + 2]
 
 
 @T.prim_func
 def elementwise_multi_loads_inlined(a: T.handle, c: T.handle) -> None:
     A = T.match_buffer(a, (128, 128))
     C = T.match_buffer(c, (128, 128))
-    with T.block([128, 126], "C") as [vi, vj]:
-        C[vi, vj] = A[vi, vj] * 2.0 + A[vi, vj + 1] * 2.0 + A[vi, vj + 2] * 2.0
+    for i, j in T.grid(128, 128):
+        with T.block("C"):
+            vi, vj = T.axis.remap("SS", [i, j])
+            C[vi, vj] = A[vi, vj] * 2.0 + A[vi, vj + 1] * 2.0 + A[vi, vj + 2] * 2.0
 
 
 # pylint: enable=no-member,invalid-name,unused-variable
diff --git a/tests/python/unittest/test_tir_schedule_error.py b/tests/python/unittest/test_tir_schedule_error.py
index 7a9c8e01d3554..ad6a1931bb0b4 100644
--- a/tests/python/unittest/test_tir_schedule_error.py
+++ b/tests/python/unittest/test_tir_schedule_error.py
@@ -31,10 +31,12 @@ def matmul(a: T.handle, b: T.handle, c: T.handle) -> None:
     B = T.match_buffer(b, [128, 128])
     C = T.match_buffer(c, [128, 128])
     for i, j in T.grid(128, 128):
-        with T.block([128, 128], "init") as [vi, vj]:
+        with T.block("init"):
+            vi, vj = T.axis.remap("SS", [i, j])
             C[vi, vj] = T.float32(0)
-        for k in range(0, 128):
-            with T.block([128, 128, T.reduce_axis(0, 128)], "update") as [vi, vj, vk]:
+        for k in range(128):
+            with T.block("update"):
+                vi, vj, vk = T.axis.remap("SSR", [i, j, k])
                 C[vi, vj] = C[vi, vj] + A[vi, vk] * B[vj, vk]
 
 
diff --git a/tests/python/unittest/test_tir_schedule_for_kind.py b/tests/python/unittest/test_tir_schedule_for_kind.py
index 60269ac01c14d..9075e93b9d45c 100644
--- a/tests/python/unittest/test_tir_schedule_for_kind.py
+++ b/tests/python/unittest/test_tir_schedule_for_kind.py
@@ -31,9 +31,10 @@
 def element_wise(a: T.handle, b: T.handle) -> None:
     A = T.match_buffer(a, (128, 128))
     B = T.match_buffer(b, (128, 128))
-
-    with T.block([128, 128], "B") as [vi, vj]:
-        B[vi, vj] = A[vi, vj] * 2.0
+    for i, j in T.grid(128, 128):
+        with T.block("B"):
+            vi, vj = T.axis.remap("SS", [i, j])
+            B[vi, vj] = A[vi, vj] * 2.0
 
 
 @T.prim_func
@@ -42,9 +43,8 @@ def element_wise_parallelized(a: T.handle, b: T.handle) -> None:
     B = T.match_buffer(b, (128, 128))
     for i0 in T.parallel(0, 128):
         for i1 in T.serial(0, 128):
-            with T.block([128, 128], "B") as [vi, vj]:
-                T.bind(vi, i0)
-                T.bind(vj, i1)
+            with T.block("B"):
+                vi, vj = T.axis.remap("SS", [i0, i1])
                 B[vi, vj] = A[vi, vj] * 2.0
 
 
@@ -54,9 +54,8 @@ def element_wise_i_bound(a: T.handle, b: T.handle) -> None:
     B = T.match_buffer(b, (128, 128))
     for i0 in T.thread_binding(0, 128, thread="threadIdx.x"):
         for i1 in T.serial(0, 128):
-            with T.block([128, 128], "B") as [vi, vj]:
-                T.bind(vi, i0)
-                T.bind(vj, i1)
+            with T.block("B"):
+                vi, vj = T.axis.remap("SS", [i0, i1])
                 B[vi, vj] = A[vi, vj] * 2.0
 
 
@@ -67,14 +66,13 @@ def element_wise_compute_at_split(a: T.handle, c: T.handle) -> None:
     B = T.alloc_buffer((128, 128))
     for i in T.serial(0, 128):
         for j0 in T.serial(0, 128):
-            with T.block([128, 128], "B") as [vi, vj]:
-                T.bind(vi, i)
-                T.bind(vj, j0)
+            with T.block("B"):
+                vi, vj = T.axis.remap("SS", [i, j0])
                 B[vi, vj] = A[vi, vj] * 2.0
         for j1o, j1i in T.grid(32, 4):
-            with T.block([128, 128], "C") as [vi, vj]:
-                T.bind(vi, i)
-                T.bind(vj, j1o * 4 + j1i)
+            with T.block("C"):
+                vi = T.axis.S(128, i)
+                vj = T.axis.S(128, j1o * 4 + j1i)
                 C[vi, vj] = B[vi, vj] + 1.0
 
 
@@ -85,15 +83,14 @@ def element_wise_compute_at_split_vectorized(a: T.handle, c: T.handle) -> None:
     B = T.alloc_buffer((128, 128))
     for i in T.serial(0, 128):
         for j0 in T.serial(0, 128):
-            with T.block([128, 128], "B") as [vi, vj]:
-                T.bind(vi, i)
-                T.bind(vj, j0)
+            with T.block("B"):
+                vi, vj = T.axis.remap("SS", [i, j0])
                 B[vi, vj] = A[vi, vj] * 2.0
         for j1o in T.serial(0, 32):
             for j1i in T.vectorized(0, 4):
-                with T.block([128, 128], "C") as [vi, vj]:
-                    T.bind(vi, i)
-                    T.bind(vj, j1o * 4 + j1i)
+                with T.block("C"):
+                    vi = T.axis.S(128, i)
+                    vj = T.axis.S(128, j1o * 4 + j1i)
                     C[vi, vj] = B[vi, vj] + 1.0
 
 
@@ -102,10 +99,10 @@ def element_wise_split_predicate(a: T.handle, b: T.handle) -> None:
     A = T.match_buffer(a, [128, 128])
     B = T.match_buffer(b, [128, 128])
     for i, j_0, j_1 in T.grid(128, 13, 10):
-        with T.block([128, 128], "B") as [vi, vj]:
+        with T.block("B"):
             T.where(j_0 * 10 + j_1 < 128)
-            T.bind(vi, i)
-            T.bind(vj, j_0 * 10 + j_1)
+            vi = T.axis.S(128, i)
+            vj = T.axis.S(128, j_0 * 10 + j_1)
             B[vi, vj] = A[vi, vj] * 2.0
 
 
@@ -116,10 +113,10 @@ def element_wise_split_predicate_parallelized(a: T.handle, b: T.handle) -> None:
     for i in T.serial(0, 128):
         for j_0 in T.parallel(0, 13):
             for j_1 in T.serial(0, 10):
-                with T.block([128, 128], "B") as [vi, vj]:
+                with T.block("B"):
                     T.where(j_0 * 10 + j_1 < 128)
-                    T.bind(vi, i)
-                    T.bind(vj, j_0 * 10 + j_1)
+                    vi = T.axis.S(128, i)
+                    vj = T.axis.S(128, j_0 * 10 + j_1)
                     B[vi, vj] = A[vi, vj] * 2.0
 
 
@@ -129,10 +126,10 @@ def element_wise_split_predicate_vectorized(a: T.handle, b: T.handle) -> None:
     B = T.match_buffer(b, [128, 128])
     for i in T.vectorized(0, 128):
         for j_0, j_1 in T.grid(13, 10):
-            with T.block([128, 128], "B") as [vi, vj]:
+            with T.block("B"):
                 T.where(j_0 * 10 + j_1 < 128)
-                T.bind(vi, i)
-                T.bind(vj, j_0 * 10 + j_1)
+                vi = T.axis.S(128, i)
+                vj = T.axis.S(128, j_0 * 10 + j_1)
                 B[vi, vj] = A[vi, vj] * 2.0
 
 
@@ -143,15 +140,14 @@ def element_wise_compute_at_split_j0_j1o_bound(a: T.handle, c: T.handle) -> None
     B = T.alloc_buffer((128, 128))
     for i in T.serial(0, 128):
         for j0 in T.thread_binding(0, 128, thread="threadIdx.x"):
-            with T.block([128, 128], "B") as [vi, vj]:
-                T.bind(vi, i)
-                T.bind(vj, j0)
+            with T.block("B"):
+                vi, vj = T.axis.remap("SS", [i, j0])
                 B[vi, vj] = A[vi, vj] * 2.0
         for j1o in T.thread_binding(0, 32, thread="threadIdx.x"):
             for j1i in T.serial(0, 4):
-                with T.block([128, 128], "C") as [vi, vj]:
-                    T.bind(vi, i)
-                    T.bind(vj, j1o * 4 + j1i)
+                with T.block("C"):
+                    vi = T.axis.S(128, i)
+                    vj = T.axis.S(128, j1o * 4 + j1i)
                     C[vi, vj] = B[vi, vj] + 1.0
 
 
@@ -161,10 +157,12 @@ def matmul(a: T.handle, b: T.handle, c: T.handle) -> None:
     B = T.match_buffer(b, (128, 128))
     C = T.match_buffer(c, (128, 128))
 
-    with T.block([128, 128, T.reduce_axis(0, 128)], "C") as [vi, vj, vk]:
-        with T.init():
-            C[vi, vj] = 0.0
-        C[vi, vj] = C[vi, vj] + A[vi, vk] * B[vj, vk]
+    for i, j, k in T.grid(128, 128, 128):
+        with T.block("C"):
+            vi, vj, vk = T.axis.remap("SSR", [i, j, k])
+            with T.init():
+                C[vi, vj] = 0.0
+            C[vi, vj] = C[vi, vj] + A[vi, vk] * B[vj, vk]
 
 
 @T.prim_func
@@ -172,10 +170,12 @@ def rowsum(a: T.handle, b: T.handle) -> None:
     A = T.match_buffer(a, (128, 128))
     B = T.match_buffer(b, (128,))
 
-    with T.block([128, T.reduce_axis(0, 128)], "B") as [vi, vk]:
-        with T.init():
-            B[vi] = 0.0
-        B[vi] = B[vi] + A[vi, vk]
+    for i, k in T.grid(128, 128):
+        with T.block("B"):
+            vi, vk = T.axis.remap("SR", [i, k])
+            with T.init():
+                B[vi] = 0.0
+            B[vi] = B[vi] + A[vi, vk]
 
 
 @T.prim_func
@@ -184,9 +184,8 @@ def rowsum_unrolled(a: T.handle, b: T.handle) -> None:
     B = T.match_buffer(b, (128,))
     for i0 in T.unroll(0, 128):
         for i1 in T.serial(0, 128):
-            with T.block([128, T.reduce_axis(0, 128)], "B") as [vi, vk]:
-                T.bind(vi, i0)
-                T.bind(vk, i1)
+            with T.block("B"):
+                vi, vk = T.axis.remap("SR", [i0, i1])
                 with T.init():
                     B[vi] = 0.0
                 B[vi] = B[vi] + A[vi, vk]
@@ -198,9 +197,9 @@ def rowsum_not_quasi_affine(a: T.handle, b: T.handle) -> None:
     B = T.match_buffer(b, (128,))
 
     for i, k in T.grid(128, 16):
-        with T.block([128, T.reduce_axis(0, 128)], "B") as [vi, vk]:
-            T.bind(vi, i)
-            T.bind(vk, T.floordiv(k * k, 2))
+        with T.block("B"):
+            vi = T.axis.S(128, i)
+            vk = T.axis.R(128, T.floordiv(k * k, 2))
             with T.init():
                 B[vi] = 0.0
             B[vi] = B[vi] + A[vi, vk]
@@ -211,10 +210,12 @@ def rowsum_not_compact_data_flow(a: T.handle, b: T.handle) -> None:
     A = T.match_buffer(a, (128, 128))
     B = T.match_buffer(b, (128,))
 
-    with T.block([128, T.reduce_axis(0, 128)], "B") as [vi, vk]:
-        with T.init():
-            B[vk] = 0.0
-        B[vk] = B[vk] + A[vi, vk]
+    for i, k in T.grid(128, 16):
+        with T.block("B"):
+            vi, vk = T.axis.remap("SR", [i, k])
+            with T.init():
+                B[vk] = 0.0
+            B[vk] = B[vk] + A[vi, vk]
 
 
 @T.prim_func
@@ -223,9 +224,8 @@ def rowsum_cross_thread_reduction(a: T.handle, b: T.handle) -> None:
     B = T.match_buffer(b, (128,))
     for i0 in T.serial(0, 128):
         for i1 in T.thread_binding(0, 128, thread="threadIdx.x"):
-            with T.block([128, T.reduce_axis(0, 128)], "B") as [vi, vk]:
-                T.bind(vi, i0)
-                T.bind(vk, i1)
+            with T.block("B"):
+                vi, vk = T.axis.remap("SR", [i0, i1])
                 with T.init():
                     B[vi] = 0.0
                 B[vi] = B[vi] + A[vi, vk]
@@ -235,7 +235,7 @@ def rowsum_cross_thread_reduction(a: T.handle, b: T.handle) -> None:
 def opaque_block(a: T.handle) -> None:
     A = T.match_buffer(a, (16,))
     for i in T.serial(0, 15):
-        with T.block([], "opaque"):
+        with T.block("opaque"):
             A[i + 1] = A[i + 1] + A[i]
 
 
diff --git a/tests/python/unittest/test_tir_schedule_reduction.py b/tests/python/unittest/test_tir_schedule_reduction.py
index 8460b5cf3e66f..e158f6a026e13 100644
--- a/tests/python/unittest/test_tir_schedule_reduction.py
+++ b/tests/python/unittest/test_tir_schedule_reduction.py
@@ -32,18 +32,17 @@ def rowsum_blockized(a: T.handle, b: T.handle) -> None:
     B = T.match_buffer(b, [32, 4])
     A = T.match_buffer(a, [32, 4, 128])
     for i0, i2_0 in T.grid(32, 16):
-        with T.block([32, T.reduce_axis(0, 16)], "blockized_B") as [io, ko]:
-            T.bind(io, i0)
-            T.bind(ko, i2_0)
+        with T.block("blockized_B"):
+            io, ko = T.axis.remap("SR", [i0, i2_0])
             with T.init():
                 for i1 in T.serial(0, 4):
-                    with T.block([4], "B_init") as [ii_init]:
-                        T.bind(ii_init, i1)
+                    with T.block("B_init"):
+                        ii_init = T.axis.S(4, i1)
                         B[io, ii_init] = 0.0
             for i1_1, i2_1 in T.grid(4, 8):
-                with T.block([4, T.reduce_axis(0, 128)], "B") as [ii, k]:
-                    T.bind(ii, i1_1)
-                    T.bind(k, ko * 8 + i2_1)
+                with T.block("B"):
+                    ii = T.axis.S(4, i1_1)
+                    k = T.axis.R(128, ko * 8 + i2_1)
                     B[io, ii] = B[io, ii] + A[io, ii, k]
 
 
@@ -52,11 +51,12 @@ def matmul(a: T.handle, b: T.handle, c: T.handle) -> None:
     A = T.match_buffer(a, [128, 128])
     B = T.match_buffer(b, [128, 128])
     C = T.match_buffer(c, [128, 128])
-
-    with T.block([128, 128, T.reduce_axis(0, 128)], "update") as [vi, vj, vk]:
-        with T.init():
-            C[vi, vj] = 0.0
-        C[vi, vj] = C[vi, vj] + A[vi, vk] * B[vj, vk]
+    for i, j, k in T.grid(128, 128, 128):
+        with T.block("update"):
+            vi, vj, vk = T.axis.remap("SSR", [i, j, k])
+            with T.init():
+                C[vi, vj] = 0.0
+            C[vi, vj] = C[vi, vj] + A[vi, vk] * B[vj, vk]
 
 
 @T.prim_func
@@ -65,11 +65,15 @@ def matmul_decompose0(a: T.handle, b: T.handle, c: T.handle) -> None:
     B = T.match_buffer(b, [128, 128])
     C = T.match_buffer(c, [128, 128])
 
-    with T.block([128, 128], "init") as [vi, vj]:
-        C[vi, vj] = 0.0
+    for i, j in T.grid(128, 128):
+        with T.block("init"):
+            vi, vj = T.axis.remap("SS", [i, j])
+            C[vi, vj] = 0.0
 
-    with T.block([128, 128, T.reduce_axis(0, 128)], "update") as [vi, vj, vk]:
-        C[vi, vj] = C[vi, vj] + A[vi, vk] * B[vj, vk]
+    for i, j, k in T.grid(128, 128, 128):
+        with T.block("update"):
+            vi, vj, vk = T.axis.remap("SSR", [i, j, k])
+            C[vi, vj] = C[vi, vj] + A[vi, vk] * B[vj, vk]
 
 
 @T.prim_func
@@ -78,16 +82,19 @@ def matmul_decompose1(a: T.handle, b: T.handle) -> None:
     B = T.match_buffer(b, [32, 4], elem_offset=0, align=128, offset_factor=1)
 
     for i0 in T.serial(0, 32):
-        with T.block([32], "blockized_B_init") as [io]:
+        with T.block("blockized_B_init"):
+            io = T.axis.S(32, i0)
             for i1 in T.serial(0, 4):
-                with T.block([4], "B_init") as [ii]:
+                with T.block("B_init"):
+                    ii = T.axis.S(4, i1)
                     B[io, ii] = T.float32(0)
     for i0, i2_o in T.grid(32, 16):
-        with T.block([32, T.reduce_axis(0, 16)], "blockized_B_update") as [io, ko]:
+        with T.block("blockized_B_update"):
+            io, ko = T.axis.remap("SR", [i0, i2_o])
             for i1, i2_i in T.grid(4, 8):
-                with T.block([4, T.reduce_axis(0, 128)], "B") as [ii, k]:
-                    T.bind(ii, i1)
-                    T.bind(k, ((ko * 8) + i2_i))
+                with T.block("B"):
+                    ii = T.axis.S(4, i1)
+                    k = T.axis.R(128, ko * 8 + i2_i)
                     B[io, ii] = B[io, ii] + A[io, ii, k]
 
 
@@ -98,10 +105,12 @@ def matmul_decompose2(a: T.handle, b: T.handle, c: T.handle) -> None:
     A = T.match_buffer(a, [128, 128], elem_offset=0, align=128, offset_factor=1)
 
     for i0, i1 in T.grid(128, 128):
-        with T.block([128, 128], "update_init") as [vi_init, vj_init]:
+        with T.block("update_init"):
+            vi_init, vj_init = T.axis.remap("SS", [i0, i1])
             C[vi_init, vj_init] = T.float32(0)
         for i2 in T.serial(0, 128):
-            with T.block([128, 128, T.reduce_axis(0, 128)], "update_update") as [vi, vj, vk]:
+            with T.block("update_update"):
+                vi, vj, vk = T.axis.remap("SSR", [i0, i1, i2])
                 C[vi, vj] = C[vi, vj] + (A[vi, vk] * B[vj, vk])
 
 
@@ -112,12 +121,10 @@ def matmul_decompose_fail3(a: T.handle, b: T.handle, c: T.handle) -> None:
     C = T.match_buffer(c, [128, 128])
 
     for i, k, j in T.grid(128, 128, 128):
-        with T.block([128, 128, T.reduce_axis(0, 128)], "update") as [vi, vj, vk]:
+        with T.block("update"):
+            vi, vj, vk = T.axis.remap("SSR", [i, j, k])
             with T.init():
                 C[vi, vj] = 0.0
-            T.bind(vi, i)
-            T.bind(vj, j)
-            T.bind(vk, k)
             C[vi, vj] = C[vi, vj] + A[vi, vk] * B[vj, vk]
 
 
@@ -127,25 +134,21 @@ def matmul_decompose4(a: T.handle, b: T.handle, c: T.handle) -> None:
     B = T.match_buffer(b, [128, 128], elem_offset=0, align=128, offset_factor=1)
     A = T.match_buffer(a, [128, 128], elem_offset=0, align=128, offset_factor=1)
     # body
-    with T.block([], "root"):
+    with T.block("root"):
         T.reads([])
         T.writes([])
         for i0_0 in T.serial(0, 16):
             for i0_1_init, i1_init in T.grid(8, 128):
-                with T.block([128, 128], "update_init") as [vi_init, vj_init]:
-                    T.bind(vi_init, ((i0_0 * 8) + i0_1_init))
-                    T.bind(vj_init, i1_init)
+                with T.block("update_init"):
+                    vi_init = T.axis.S(128, i0_0 * 8 + i0_1_init)
+                    vj_init = T.axis.S(128, i1_init)
                     C[vi_init, vj_init] = T.float32(0)
             for i0_1, i1, i2_0, i2_1 in T.grid(8, 128, 19, 7):
-                with T.block([128, 128, T.reduce_axis(0, 128)], "update_update") as [
-                    vi,
-                    vj,
-                    vk,
-                ]:
+                with T.block("update_update"):
                     T.where((((i2_0 * 7) + i2_1) < 128))
-                    T.bind(vi, ((i0_0 * 8) + i0_1))
-                    T.bind(vj, i1)
-                    T.bind(vk, ((i2_0 * 7) + i2_1))
+                    vi = T.axis.S(128, i0_0 * 8 + i0_1)
+                    vj = T.axis.S(128, i1)
+                    vk = T.axis.R(128, i2_0 * 7 + i2_1)
                     C[vi, vj] = C[vi, vj] + (A[vi, vk] * B[vj, vk])
 
 
diff --git a/tests/python/unittest/test_tir_schedule_reorder.py b/tests/python/unittest/test_tir_schedule_reorder.py
index a60ab8dca9725..8267a369cf5de 100644
--- a/tests/python/unittest/test_tir_schedule_reorder.py
+++ b/tests/python/unittest/test_tir_schedule_reorder.py
@@ -30,8 +30,10 @@
 def elementwise(a: T.handle, b: T.handle) -> None:
     A = T.match_buffer(a, (128, 128, 128, 128))
     B = T.match_buffer(b, (128, 128, 128, 128))
-    with T.block([128, 128, 128, 128], "B") as [vi, vj, vk, vl]:
-        B[vi, vj, vk, vl] = A[vi, vj, vk, vl] * 2.0
+    for i, j, k, l in T.grid(128, 128, 128, 128):
+        with T.block("B"):
+            vi, vj, vk, vl = T.axis.remap("SSSS", [i, j, k, l])
+            B[vi, vj, vk, vl] = A[vi, vj, vk, vl] * 2.0
 
 
 @T.prim_func
@@ -39,11 +41,9 @@ def elementwise_not_affine(a: T.handle, b: T.handle) -> None:
     A = T.match_buffer(a, (128, 128, 128, 128))
     B = T.match_buffer(b, (128, 128, 128, 128))
     for i, j, k, l in T.grid(128, 128, 128, 8):
-        with T.block([128, 128, 128, 128], "B") as [vi, vj, vk, vl]:
-            T.bind(vi, i)
-            T.bind(vj, j)
-            T.bind(vk, k)
-            T.bind(vl, l * 16)
+        with T.block("B"):
+            vi, vj, vk = T.axis.remap("SSS", [i, j, k])
+            vl = T.axis.S(128, l * 16)
             B[vi, vj, vk, vl] = A[vi, vj, vk, vl] * 2.0
 
 
@@ -53,7 +53,8 @@ def elementwise_dependent_loop(a: T.handle, b: T.handle) -> None:
     B = T.match_buffer(b, (128, 128, 128, 128))
     for i in T.serial(0, 128):
         for j, k, l in T.grid(128, i, 128):
-            with T.block([128, 128, i, 128], "B") as [vi, vj, vk, vl]:
+            with T.block("B"):
+                vi, vj, vk, vl = T.axis.remap("SSSS", [i, j, k, l])
                 B[vi, vj, vk, vl] = A[vi, vj, vk, vl] * 2.0
 
 
@@ -62,8 +63,9 @@ def elementwise_predicate(a: T.handle, b: T.handle) -> None:
     A = T.match_buffer(a, (128, 128, 128, 128))
     B = T.match_buffer(b, (128, 128, 128, 128))
     for i, j, k, l in T.grid(128, 128, 128, 128):
-        with T.block([128, 128, 128, 128], "B") as [vi, vj, vk, vl]:
+        with T.block("B"):
             T.where(i * 2097152 + j * 16384 + k * 128 + l < 100)
+            vi, vj, vk, vl = T.axis.remap("SSSS", [i, j, k, l])
             B[vi, vj, vk, vl] = A[vi, vj, vk, vl] * 2.0
 
 
@@ -74,16 +76,12 @@ def elementwise_non_single_branch(a: T.handle, b: T.handle) -> None:
     B = T.match_buffer(b, (128, 128, 128))
     for i, j in T.grid(128, 128):
         for k in T.serial(0, 128):
-            with T.block([128, 128, 128], "C") as [vi, vj, vk]:
-                T.bind(vi, i)
-                T.bind(vj, j)
-                T.bind(vk, k)
+            with T.block("C"):
+                vi, vj, vk = T.axis.remap("SSS", [i, j, k])
                 C[vi, vj, vk] = A[vi, vj, vk] * 2.0
         for k in T.serial(0, 128):
-            with T.block([128, 128, 128], "B") as [vi, vj, vk]:
-                T.bind(vi, i)
-                T.bind(vj, j)
-                T.bind(vk, k)
+            with T.block("B"):
+                vi, vj, vk = T.axis.remap("SSS", [i, j, k])
                 B[vi, vj, vk] = C[vi, vj, vk] * 2.0
 
 
@@ -92,12 +90,11 @@ def elementwise_with_loops_not_same_scope(a: T.handle, b: T.handle) -> None:
     A = T.match_buffer(a, (128, 128, 128))
     B = T.match_buffer(b, (128, 128, 128))
     for i, j in T.grid(128, 128):
-        with T.block([128, 128], "A") as [vi, vj]:
-            T.bind(vi, i)
-            T.bind(vj, j)
+        with T.block("A"):
+            vi, vj = T.axis.remap("SS", [i, j])
             for k in T.serial(0, 128):
-                with T.block([128], "B") as [vk]:
-                    T.bind(vk, k)
+                with T.block("B"):
+                    vk = T.axis.S(128, k)
                     T.reads([A[vi, vj, vk]])
                     T.writes([B[vi, vj, vk]])
                     B[vi, vj, vk] = A[vi, vj, vk] * 2.0
@@ -108,10 +105,9 @@ def elementwise_with_wrong_block_var_type(a: T.handle, b: T.handle) -> None:
     A = T.match_buffer(a, (128, 128, 128))
     B = T.match_buffer(b, (128, 128, 128))
     for i, j, k in T.grid(128, 128, 128):
-        with T.block([128, 128, T.scan_axis(0, 128)], "B") as [vi, vj, vk]:
-            T.bind(vi, i)
-            T.bind(vj, j)
-            T.bind(vk, k)
+        with T.block("B"):
+            vi, vj = T.axis.remap("SS", [i, j])
+            vk = T.axis.scan(128, k)
             T.reads([A[vi, vj, vk]])
             T.writes([B[vi, vj, vk]])
             B[vi, vj, vk] = A[vi, vj, vk] * 2.0
@@ -122,11 +118,8 @@ def elementwise_reordered(a: T.handle, b: T.handle) -> None:
     A = T.match_buffer(a, (128, 128, 128, 128))
     B = T.match_buffer(b, (128, 128, 128, 128))
     for l, j, k, i in T.grid(128, 128, 128, 128):
-        with T.block([128, 128, 128, 128], "B") as [vi, vj, vk, vl]:
-            T.bind(vi, i)
-            T.bind(vj, j)
-            T.bind(vk, k)
-            T.bind(vl, l)
+        with T.block("B"):
+            vi, vj, vk, vl = T.axis.remap("SSSS", [i, j, k, l])
             B[vi, vj, vk, vl] = A[vi, vj, vk, vl] * 2.0
 
 
@@ -135,11 +128,8 @@ def elementwise_reordered2(a: T.handle, b: T.handle) -> None:
     A = T.match_buffer(a, (128, 128, 128, 128))
     B = T.match_buffer(b, (128, 128, 128, 128))
     for k, j, i, l in T.grid(128, 128, 128, 128):
-        with T.block([128, 128, 128, 128], "B") as [vi, vj, vk, vl]:
-            T.bind(vi, i)
-            T.bind(vj, j)
-            T.bind(vk, k)
-            T.bind(vl, l)
+        with T.block("B"):
+            vi, vj, vk, vl = T.axis.remap("SSSS", [i, j, k, l])
             B[vi, vj, vk, vl] = A[vi, vj, vk, vl] * 2.0
 
 
@@ -148,12 +138,9 @@ def elementwise_reordered_with_predicate(a: T.handle, b: T.handle) -> None:
     A = T.match_buffer(a, (128, 128, 128, 128))
     B = T.match_buffer(b, (128, 128, 128, 128))
     for l, j, k, i in T.grid(128, 128, 128, 128):
-        with T.block([128, 128, 128, 128], "B") as [vi, vj, vk, vl]:
+        with T.block("B"):
             T.where(i * 2097152 + j * 16384 + k * 128 + l < 100)
-            T.bind(vi, i)
-            T.bind(vj, j)
-            T.bind(vk, k)
-            T.bind(vl, l)
+            vi, vj, vk, vl = T.axis.remap("SSSS", [i, j, k, l])
             B[vi, vj, vk, vl] = A[vi, vj, vk, vl] * 2.0
 
 
@@ -161,14 +148,18 @@ def elementwise_reordered_with_predicate(a: T.handle, b: T.handle) -> None:
 def opaque_access(a: T.handle, b: T.handle) -> None:
     A = T.match_buffer(a, [16, 16], "float32")
     B = T.match_buffer(b, [16, 16], "float32")
-    with T.block([16, 16], "A") as [vi, vj]:
-        T.reads([])
-        T.writes([A[0:16, 0:16]])
-        T.store(A.data, vi * 16 + vj, 1)
-    with T.block([16, 16], "B") as [vi, vj]:
-        T.reads([])
-        T.writes([B[0:16, 0:16]])
-        T.evaluate(T.tvm_fill_fragment(B.data, 16, 16, 16, 0, vi * 16 + vj, dtype="handle"))
+    for i, j in T.grid(16, 16):
+        with T.block("A"):
+            vi, vj = T.axis.remap("SS", [i, j])
+            T.reads([])
+            T.writes([A[0:16, 0:16]])
+            T.store(A.data, vi * 16 + vj, 1)
+    for i, j in T.grid(16, 16):
+        with T.block("B"):
+            vi, vj = T.axis.remap("SS", [i, j])
+            T.reads([])
+            T.writes([B[0:16, 0:16]])
+            T.evaluate(T.tvm_fill_fragment(B.data, 16, 16, 16, 0, vi * 16 + vj, dtype="handle"))
 
 
 @T.prim_func
@@ -176,16 +167,14 @@ def opaque_access_reorder(a: T.handle, b: T.handle) -> None:
     A = T.match_buffer(a, [16, 16], "float32")
     B = T.match_buffer(b, [16, 16], "float32")
     for j, i in T.grid(16, 16):
-        with T.block([16, 16], "A") as [vi, vj]:
-            T.bind(vi, i)
-            T.bind(vj, j)
+        with T.block("A"):
+            vi, vj = T.axis.remap("SS", [i, j])
             T.reads([])
             T.writes([A[0:16, 0:16]])
             T.store(A.data, vi * 16 + vj, 1)
     for j, i in T.grid(16, 16):
-        with T.block([16, 16], "B") as [vi, vj]:
-            T.bind(vi, i)
-            T.bind(vj, j)
+        with T.block("B"):
+            vi, vj = T.axis.remap("SS", [i, j])
             T.reads([])
             T.writes([B[0:16, 0:16]])
             T.evaluate(T.tvm_fill_fragment(B.data, 16, 16, 16, 0, vi * 16 + vj, dtype="handle"))
diff --git a/tests/python/unittest/test_tir_schedule_rfactor.py b/tests/python/unittest/test_tir_schedule_rfactor.py
index 78b6a4696baa0..bd474ed342954 100644
--- a/tests/python/unittest/test_tir_schedule_rfactor.py
+++ b/tests/python/unittest/test_tir_schedule_rfactor.py
@@ -34,10 +34,9 @@ def transformed_matmul(a: T.handle, b: T.handle, c: T.handle) -> None:
     C = T.match_buffer(c, [128, 128])
 
     for i0, i1, i2_outer, i2_inner_outer, i2_inner_inner in T.grid(128, 128, 4, 8, 4):
-        with T.block([128, 128, T.reduce_axis(0, 128)], "update") as [vi, vj, vk]:
-            T.bind(vi, i0)
-            T.bind(vj, i1)
-            T.bind(vk, (((i2_outer * 32) + (i2_inner_outer * 4)) + i2_inner_inner))
+        with T.block("update"):
+            vi, vj = T.axis.remap("SS", [i0, i1])
+            vk = T.axis.R(128, i2_outer * 32 + i2_inner_outer * 4 + i2_inner_inner)
             T.reads([C[vi, vj], A[vi, vk], B[vj, vk]])
             T.writes([C[vi, vj]])
             with T.init():
@@ -53,18 +52,12 @@ def matmul_rfactor(a: T.handle, b: T.handle, c: T.handle) -> None:
     C_rf = T.alloc_buffer([4, 128, 128])
 
     for i0, i1, i2_outer, i2_inner_outer, i2_inner_inner in T.grid(128, 128, 4, 8, 4):
-        with T.block([4, 128, 128, T.reduce_axis(0, 4), T.reduce_axis(0, 8)], "update_rf") as [
-            vi2_inner_inner,
-            vi,
-            vj,
-            vi2_outer,
-            vi2_inner_outer,
-        ]:
-            T.bind(vi2_inner_inner, i2_inner_inner)
-            T.bind(vi, i0)
-            T.bind(vj, i1)
-            T.bind(vi2_outer, i2_outer)
-            T.bind(vi2_inner_outer, i2_inner_outer)
+        with T.block("update_rf"):
+            vi2_inner_inner = T.axis.S(4, i2_inner_inner)
+            vi = T.axis.S(128, i0)
+            vj = T.axis.S(128, i1)
+            vi2_outer = T.axis.R(4, i2_outer)
+            vi2_inner_outer = T.axis.R(8, i2_inner_outer)
             with T.init():
                 C_rf[vi2_inner_inner, vi, vj] = 0.0
             C_rf[vi2_inner_inner, vi, vj] = C_rf[vi2_inner_inner, vi, vj] + (
@@ -73,14 +66,8 @@ def matmul_rfactor(a: T.handle, b: T.handle, c: T.handle) -> None:
             )
 
     for i0_1, i1_1, i2_inner_inner_1 in T.grid(128, 128, 4):
-        with T.block([T.reduce_axis(0, 4), 128, 128], "update") as [
-            vi2_inner_inner_1,
-            vi_1,
-            vj_1,
-        ]:
-            T.bind(vi2_inner_inner_1, i2_inner_inner_1)
-            T.bind(vi_1, i0_1)
-            T.bind(vj_1, i1_1)
+        with T.block("update"):
+            vi2_inner_inner_1, vi_1, vj_1 = T.axis.remap("RSS", [i2_inner_inner_1, i0_1, i1_1])
             with T.init():
                 C[vi_1, vj_1] = 0.0
             C[vi_1, vj_1] = C[vi_1, vj_1] + C_rf[vi2_inner_inner_1, vi_1, vj_1]
@@ -93,13 +80,17 @@ def matmul_not_stage_pipeline(a: T.handle, b: T.handle, d: T.handle) -> None:
     D = T.match_buffer(d, [256, 256])
     C = T.alloc_buffer([256, 256])
 
-    with T.block([128, 128, T.reduce_axis(0, 128)], "C") as [vi, vj, vk]:
-        with T.init():
-            C[vi, vj] = 0.0
-        C[vi, vj] = C[vi, vj] + A[vi, vk] * B[vk, vj]
+    for i, j, k in T.grid(128, 128, 128):
+        with T.block("C"):
+            vi, vj, vk = T.axis.remap("SSR", [i, j, k])
+            with T.init():
+                C[vi, vj] = 0.0
+            C[vi, vj] = C[vi, vj] + A[vi, vk] * B[vk, vj]
 
-    with T.block([256, 256], "D") as [vi, vj]:
-        D[vi, vj] = C[vi, vj]
+    for i, j in T.grid(256, 256):
+        with T.block("D"):
+            vi, vj = T.axis.remap("SS", [i, j])
+            D[vi, vj] = C[vi, vj]
 
 
 @T.prim_func
@@ -108,10 +99,12 @@ def matmul_not_same_buffer_access(a: T.handle, b: T.handle, c: T.handle) -> None
     B = T.match_buffer(b, (128, 128))
     C = T.match_buffer(c, (128, 128))
 
-    with T.block([128, 128, T.reduce_axis(0, 128)], "C") as [vi, vj, vk]:
-        with T.init():
-            C[vi, vj] = 0.0
-        C[vj, vi] = C[vj, vi] + A[vi, vk] * B[vk, vj]
+    for i, j, k in T.grid(128, 128, 128):
+        with T.block("C"):
+            vi, vj, vk = T.axis.remap("SSR", [i, j, k])
+            with T.init():
+                C[vi, vj] = 0.0
+            C[vj, vi] = C[vj, vi] + A[vi, vk] * B[vk, vj]
 
 
 @T.prim_func
@@ -122,17 +115,13 @@ def matmul_loop_multiple_children(a: T.handle, b: T.handle, c: T.handle, d: T.ha
     D = T.match_buffer(d, [128, 128])
 
     for k, i, j in T.grid(128, 128, 128):
-        with T.block([T.reduce_axis(0, 128), 128, 128], "C") as [ck, ci, cj]:
-            T.bind(ck, k)
-            T.bind(ci, i)
-            T.bind(cj, j)
+        with T.block("C"):
+            ck, ci, cj = T.axis.remap("RSS", [k, i, j])
             with T.init():
                 C[ci, cj] = 0.0
             C[ci, cj] = C[ci, cj] + A[ci, ck] * B[ck, cj]
-        with T.block([T.reduce_axis(0, 128), 128, 128], "D") as [dk, di, dj]:
-            T.bind(dk, k)
-            T.bind(di, i)
-            T.bind(dj, j)
+        with T.block("D"):
+            dk, di, dj = T.axis.remap("RSS", [k, i, j])
             with T.init():
                 D[di, dj] = 0.0
             D[di, dj] = D[di, dj] + B[di, dk] * A[dk, dj]
@@ -143,10 +132,12 @@ def square_sum(a: T.handle, c: T.handle) -> None:
     A = T.match_buffer(a, [16, 256, 256])
     C = T.match_buffer(c, [16])
 
-    with T.block([16, T.reduce_axis(0, 256), T.reduce_axis(0, 256)], "C") as [b, i, j]:
-        with T.init():
-            C[b] = 0.0
-        C[b] = C[b] + A[b, i, j] * A[b, i, j]
+    for b0, i0, j0 in T.grid(16, 256, 256):
+        with T.block("C"):
+            b, i, j = T.axis.remap("SRR", [b0, i0, j0])
+            with T.init():
+                C[b] = 0.0
+            C[b] = C[b] + A[b, i, j] * A[b, i, j]
 
 
 @T.prim_func
@@ -156,18 +147,15 @@ def square_sum_rfactor(a: T.handle, c: T.handle) -> None:
     C_rf = T.alloc_buffer([16, 256])
 
     for i0, i1, i2 in T.grid(16, 256, 256):
-        with T.block([256, 16, T.reduce_axis(0, 256)], "C_rf") as [vi2, b, i]:
-            T.bind(vi2, i2)
-            T.bind(b, i0)
-            T.bind(i, i1)
+        with T.block("C_rf"):
+            vi2, b, i = T.axis.remap("SSR", [i2, i0, i1])
             with T.init():
                 C_rf[b, vi2] = 0.0
             C_rf[b, vi2] = C_rf[b, vi2] + (A[b, i, vi2] * A[b, i, vi2])
 
     for i0_1, i2_1 in T.grid(16, 256):
-        with T.block([T.reduce_axis(0, 256), 16], "C") as [vi2_1, b_1]:
-            T.bind(vi2_1, i2_1)
-            T.bind(b_1, i0_1)
+        with T.block("C"):
+            vi2_1, b_1 = T.axis.remap("RS", [i2_1, i0_1])
             with T.init():
                 C[b_1] = 0.0
             C[b_1] = C[b_1] + C_rf[b_1, vi2_1]
@@ -180,18 +168,18 @@ def transformed_square_sum_square_root(a: T.handle, d: T.handle) -> None:
     C = T.alloc_buffer([16])
 
     for i0, i1_i2_fused_outer, i1_i2_fused_inner in T.grid(16, 65536, 1):
-        with T.block([16, T.reduce_axis(0, 256), T.reduce_axis(0, 256)], "C") as [b, i, j]:
-            T.bind(b, i0)
-            T.bind(i, T.floordiv(i1_i2_fused_outer, 256))
-            T.bind(j, T.floormod(i1_i2_fused_outer, 256))
+        with T.block("C"):
+            b = T.axis.S(16, i0)
+            i = T.axis.R(256, T.floordiv(i1_i2_fused_outer, 256))
+            j = T.axis.R(256, T.floormod(i1_i2_fused_outer, 256))
             T.reads([C[b], A[b, i, j]])
             T.writes([C[b]])
             with T.init():
                 C[b] = 0.0
             C[b] = C[b] + (A[b, i, j] * A[b, i, j])
     for i0_1 in T.serial(0, 16):
-        with T.block([16], "D") as [b_1]:
-            T.bind(b_1, i0_1)
+        with T.block("D"):
+            b_1 = T.axis.S(16, i0_1)
             T.reads([C[b_1]])
             T.writes([D[b_1]])
             D[b_1] = T.sqrt(C[b_1], dtype="float32")
@@ -205,31 +193,24 @@ def square_sum_square_root_rfactor(a: T.handle, d: T.handle) -> None:
     C_rf = T.alloc_buffer([1, 16])
 
     for i0, i1_i2_fused_outer, i1_i2_fused_inner in T.grid(16, 65536, 1):
-        with T.block([1, 16, T.reduce_axis(0, 256), T.reduce_axis(0, 256)], "C_rf") as [
-            vi1_i2_fused_inner,
-            b,
-            i,
-            j,
-        ]:
-            T.bind(vi1_i2_fused_inner, i1_i2_fused_inner)
-            T.bind(b, i0)
-            T.bind(i, T.floordiv(i1_i2_fused_outer, 256))
-            T.bind(j, T.floormod(i1_i2_fused_outer, 256))
+        with T.block("C_rf"):
+            vi1_i2_fused_inner, b = T.axis.remap("SS", [i1_i2_fused_inner, i0])
+            i = T.axis.R(256, T.floordiv(i1_i2_fused_outer, 256))
+            j = T.axis.R(256, T.floormod(i1_i2_fused_outer, 256))
             with T.init():
                 C_rf[vi1_i2_fused_inner, b] = 0.0
             C_rf[vi1_i2_fused_inner, b] = C_rf[vi1_i2_fused_inner, b] + (A[b, i, j] * A[b, i, j])
 
     for i0_1, i1_i2_fused_inner_1 in T.grid(16, 1):
-        with T.block([T.reduce_axis(0, 1), 16], "C") as [vi1_i2_fused_inner_1, b_1]:
-            T.bind(vi1_i2_fused_inner_1, i1_i2_fused_inner_1)
-            T.bind(b_1, i0_1)
+        with T.block("C"):
+            vi1_i2_fused_inner_1, b_1 = T.axis.remap("RS", [i1_i2_fused_inner_1, i0_1])
             with T.init():
                 C[b_1] = 0.0
             C[b_1] = C[b_1] + C_rf[vi1_i2_fused_inner_1, b_1]
 
     for i0_2 in T.serial(0, 16):
-        with T.block([16], "D") as [b_2]:
-            T.bind(b_2, i0_2)
+        with T.block("D"):
+            b_2 = T.axis.S(16, i0_2)
             D[b_2] = T.sqrt(C[b_2], dtype="float32")
 
 
@@ -238,8 +219,10 @@ def element_wise(a: T.handle, b: T.handle) -> None:
     A = T.match_buffer(a, (128, 128))
     B = T.match_buffer(b, (128, 128))
 
-    with T.block([128, 128], "B") as [vi, vj]:
-        B[vi, vj] = A[vi, vj] * 2.0
+    for i, j in T.grid(128, 128):
+        with T.block("B"):
+            vi, vj = T.axis.remap("SS", [i, j])
+            B[vi, vj] = A[vi, vj] * 2.0
 
 
 @T.prim_func
@@ -247,10 +230,12 @@ def rowsum(a: T.handle, b: T.handle) -> None:
     A = T.match_buffer(a, (128, 128))
     B = T.match_buffer(b, (128,))
 
-    with T.block([128, T.reduce_axis(0, 128)], "B") as [vi, vk]:
-        with T.init():
-            B[vi] = 0.0
-        B[vi] = B[vi] + A[vi, vk]
+    for i, k in T.grid(128, 128):
+        with T.block("B"):
+            vi, vk = T.axis.remap("SR", [i, k])
+            with T.init():
+                B[vi] = 0.0
+            B[vi] = B[vi] + A[vi, vk]
 
 
 @T.prim_func
@@ -259,9 +244,9 @@ def rowsum_not_quasi_affine(a: T.handle, b: T.handle) -> None:
     B = T.match_buffer(b, (128,))
 
     for i, k in T.grid(128, 16):
-        with T.block([128, T.reduce_axis(0, 128)], "B") as [vi, vk]:
-            T.bind(vi, i)
-            T.bind(vk, T.floordiv(k * k, 2))
+        with T.block("B"):
+            vi = T.axis.S(128, i)
+            vk = T.axis.R(128, T.floordiv(k * k, 2))
             with T.init():
                 B[vi] = 0.0
             B[vi] = B[vi] + A[vi, vk]
@@ -272,10 +257,12 @@ def rowsum_not_dominant(a: T.handle, b: T.handle) -> None:
     A = T.match_buffer(a, (128, 128))
     B = T.match_buffer(b, (128, 128))
 
-    with T.block([128, T.reduce_axis(0, 128)], "B") as [vi, vk]:
-        with T.init():
-            B[vi, vk] = 0.0
-        B[vi, vk] = B[vi, vk] + A[vi, vk]
+    for i, k in T.grid(128, 128):
+        with T.block("B"):
+            vi, vk = T.axis.remap("SR", [i, k])
+            with T.init():
+                B[vi, vk] = 0.0
+            B[vi, vk] = B[vi, vk] + A[vi, vk]
 
 
 @T.prim_func
@@ -285,9 +272,8 @@ def rowsum_not_serial(a: T.handle, b: T.handle) -> None:
 
     for i in T.serial(0, 128):
         for k in T.parallel(0, 128):
-            with T.block([128, T.reduce_axis(0, 128)], "B") as [vi, vk]:
-                T.bind(vi, i)
-                T.bind(vk, k)
+            with T.block("B"):
+                vi, vk = T.axis.remap("SR", [i, k])
                 with T.init():
                     B[vi] = 0.0
                 B[vi] = B[vi] + A[vi, vk]
@@ -298,10 +284,12 @@ def rowsum_wrong_reduce_pattern1(a: T.handle, b: T.handle) -> None:
     A = T.match_buffer(a, (128, 128))
     B = T.match_buffer(b, (128,))
 
-    with T.block([128, T.reduce_axis(0, 128)], "B") as [vi, vk]:
-        with T.init():
-            B[vi] = 1.0
-        B[vi] = B[vi] + A[vi, vk]
+    for i, k in T.grid(128, 128):
+        with T.block("B"):
+            vi, vk = T.axis.remap("SR", [i, k])
+            with T.init():
+                B[vi] = 1.0
+            B[vi] = B[vi] + A[vi, vk]
 
 
 @T.prim_func
@@ -309,10 +297,12 @@ def rowsum_wrong_reduce_pattern2(a: T.handle, b: T.handle) -> None:
     A = T.match_buffer(a, (128, 128))
     B = T.match_buffer(b, (128,))
 
-    with T.block([128, T.reduce_axis(0, 128)], "B") as [vi, vk]:
-        with T.init():
-            B[vi] = 0.0
-        B[vi] = B[vi] - A[vi, vk]
+    for i, k in T.grid(128, 128):
+        with T.block("B"):
+            vi, vk = T.axis.remap("SR", [i, k])
+            with T.init():
+                B[vi] = 0.0
+            B[vi] = B[vi] - A[vi, vk]
 
 
 @T.prim_func
@@ -321,9 +311,9 @@ def rowsum_transformed(a: T.handle, b: T.handle) -> None:
     B = T.match_buffer(b, (128,))
 
     for io, ii_ko_fused, ki in T.grid(32, 128, 4):
-        with T.block([128, T.reduce_axis(0, 128)], "B") as [vi, vk]:
-            T.bind(vi, io * 4 + T.floordiv(ii_ko_fused, 32))
-            T.bind(vk, T.floormod(ii_ko_fused, 32) * 4 + ki)
+        with T.block("B"):
+            vi = T.axis.S(128, io * 4 + T.floordiv(ii_ko_fused, 32))
+            vk = T.axis.R(128, T.floormod(ii_ko_fused, 32) * 4 + ki)
             with T.init():
                 B[vi] = 0.0
             B[vi] = B[vi] + A[vi, vk]
@@ -334,10 +324,12 @@ def rowsum_zero_dim(a: T.handle, b: T.handle) -> None:
     A = T.match_buffer(a, [128])
     B = T.match_buffer(b, [])
 
-    with T.block([T.reduce_axis(0, 128)], "B") as [k]:
-        with T.init():
-            B[()] = 0.0
-        B[()] = B[()] + A[k]
+    for k0 in range(128):
+        with T.block("B"):
+            k = T.axis.R(128, k0)
+            with T.init():
+                B[()] = 0.0
+            B[()] = B[()] + A[k]
 
 
 @T.prim_func
@@ -346,15 +338,19 @@ def rowsum_zero_dim_rfactor(a: T.handle, b: T.handle) -> None:
     B = T.match_buffer(b, [])
     B_rf = T.alloc_buffer([128])
 
-    with T.block([128], "B_rf") as [vi0]:
-        with T.init():
-            B_rf[vi0] = 0.0
-        B_rf[vi0] = B_rf[vi0] + A[vi0]
+    for i in range(128):
+        with T.block("B_rf"):
+            vi0 = T.axis.S(128, i)
+            with T.init():
+                B_rf[vi0] = 0.0
+            B_rf[vi0] = B_rf[vi0] + A[vi0]
 
-    with T.block([T.reduce_axis(0, 128)], "B") as [vi0_1]:
-        with T.init():
-            B[()] = 0.0
-        B[()] = B[()] + B_rf[vi0_1]
+    for i in range(128):
+        with T.block("B"):
+            vi0_1 = T.axis.R(128, i)
+            with T.init():
+                B[()] = 0.0
+            B[()] = B[()] + B_rf[vi0_1]
 
 
 @T.prim_func
@@ -362,10 +358,10 @@ def rowsum_predicate(a: T.handle, b: T.handle) -> None:
     A = T.match_buffer(a, [128, 128], dtype="float32")
     B = T.match_buffer(b, [128], dtype="float32")
     for i, k_0, k_1 in T.grid(128, 13, 10):
-        with T.block([128, T.reduce_axis(0, 128)], "B") as [vi, vk]:
+        with T.block("B"):
             T.where(k_0 * 10 + k_1 < 128)
-            T.bind(vi, i)
-            T.bind(vk, k_0 * 10 + k_1)
+            vi = T.axis.S(128, i)
+            vk = T.axis.R(128, k_0 * 10 + k_1)
             with T.init():
                 B[vi] = 0.0
             B[vi] = B[vi] + A[vi, vk]
@@ -377,18 +373,15 @@ def rowsum_predicate_rfactor(a: T.handle, b: T.handle) -> None:
     B = T.match_buffer(b, [128], dtype="float32")
     B_rf = T.alloc_buffer([128, 13], dtype="float32")
     for i, k_0, k_1 in T.grid(128, 13, 10):
-        with T.block([13, 128, T.reduce_axis(0, 10)], "B_rf") as [vk_0, vi, vk_1]:
+        with T.block("B_rf"):
+            vk_0, vi, vk_1 = T.axis.remap("SSR", [k_0, i, k_1])
             T.where(k_0 * 10 + k_1 < 128)
-            T.bind(vk_0, k_0)
-            T.bind(vi, i)
-            T.bind(vk_1, k_1)
             with T.init():
                 B_rf[vi, vk_0] = T.float32(0)
             B_rf[vi, vk_0] = B_rf[vi, vk_0] + A[vi, vk_0 * 10 + vk_1]
     for i, k_0 in T.grid(128, 13):
-        with T.block([T.reduce_axis(0, 13), 128], "B") as [vk_0, vi]:
-            T.bind(vk_0, k_0)
-            T.bind(vi, i)
+        with T.block("B"):
+            vk_0, vi = T.axis.remap("RS", [k_0, i])
             with T.init():
                 B[vi] = T.float32(0)
             B[vi] = B[vi] + B_rf[vi, vk_0]
@@ -405,35 +398,31 @@ def multiple_reduction_blocks(a: T.handle, f: T.handle) -> None:
     for i in T.serial(0, 16):
         for j1 in T.serial(0, 16):
             for k1o, k1i in T.grid(4, 4):
-                with T.block([16, 16, T.reduce_axis(0, 16)], "C") as [ci, cj, ck]:
-                    T.bind(ci, i)
-                    T.bind(cj, j1)
-                    T.bind(ck, k1o * 4 + k1i)
+                with T.block("C"):
+                    ci, cj = T.axis.remap("SS", [i, j1])
+                    ck = T.axis.R(16, k1o * 4 + k1i)
                     with T.init():
                         C[ci, cj] = 0.0
                     C[ci, cj] = C[ci, cj] + A[ci, cj, ck]
             for k2o, k2i in T.grid(4, 4):
-                with T.block([16, 16, T.reduce_axis(0, 16)], "D") as [di, dj, dk]:
-                    T.bind(di, i)
-                    T.bind(dj, j1)
-                    T.bind(dk, k2o * 4 + k2i)
+                with T.block("D"):
+                    di, dj = T.axis.remap("SS", [i, j1])
+                    dk = T.axis.R(16, k2o * 4 + k2i)
                     with T.init():
                         D[di, dj] = 0.0
                     D[di, dj] = D[di, dj] + A[di, dj, dk] + C[di, dj]
         for j2 in T.serial(0, 16):
             for k3o, k3i in T.grid(4, 4):
-                with T.block([16, 16, T.reduce_axis(0, 16)], "E") as [ei, ej, ek]:
-                    T.bind(ei, i)
-                    T.bind(ej, j2)
-                    T.bind(ek, k3o * 4 + k3i)
+                with T.block("E"):
+                    ei, ej = T.axis.remap("SS", [i, j2])
+                    ek = T.axis.R(16, k3o * 4 + k3i)
                     with T.init():
                         E[ei, ej] = 0.0
                     E[ei, ej] = E[ei, ej] + A[ei, ej, ek] + D[ei, ej]
             for k4o, k4i in T.grid(4, 4):
-                with T.block([16, 16, T.reduce_axis(0, 16)], "F") as [fi, fj, fk]:
-                    T.bind(fi, i)
-                    T.bind(fj, j2)
-                    T.bind(fk, k4o * 4 + k4i)
+                with T.block("F"):
+                    fi, fj = T.axis.remap("SS", [i, j2])
+                    fk = T.axis.R(16, k4o * 4 + k4i)
                     with T.init():
                         F[fi, fj] = 0.0
                     F[fi, fj] = F[fi, fj] + A[fi, fj, fk] + E[fi, fj]
@@ -449,46 +438,38 @@ def multiple_reduction_blocks_rfactor(a: T.handle, f: T.handle) -> None:
     C_rf = T.alloc_buffer([16, 16, 4])
 
     for i, j1, k1o, k1i in T.grid(16, 16, 4, 4):
-        with T.block([4, 16, 16, T.reduce_axis(0, 4)], "C_rf") as [vk1o, ci, cj, vk1i]:
-            T.bind(vk1o, k1o)
-            T.bind(ci, i)
-            T.bind(cj, j1)
-            T.bind(vk1i, k1i)
+        with T.block("C_rf"):
+            vk1o, ci, cj, vk1i = T.axis.remap("SSSR", [k1o, i, j1, k1i])
             with T.init():
                 C_rf[ci, cj, vk1o] = 0.0
             C_rf[ci, cj, vk1o] = C_rf[ci, cj, vk1o] + A[ci, cj, ((vk1o * 4) + vk1i)]
     for i_1 in T.serial(0, 16):
         for j1_1 in T.serial(0, 16):
             for k1o_1 in T.serial(0, 4):
-                with T.block([T.reduce_axis(0, 4), 16, 16], "C") as [vk1o_1, ci_1, cj_1]:
-                    T.bind(vk1o_1, k1o_1)
-                    T.bind(ci_1, i_1)
-                    T.bind(cj_1, j1_1)
+                with T.block("C"):
+                    vk1o_1, ci_1, cj_1 = T.axis.remap("RSS", [k1o_1, i_1, j1_1])
                     with T.init():
                         C[ci_1, cj_1] = 0.0
                     C[ci_1, cj_1] = C[ci_1, cj_1] + C_rf[ci_1, cj_1, vk1o_1]
             for k2o, k2i in T.grid(4, 4):
-                with T.block([16, 16, T.reduce_axis(0, 16)], "D") as [di, dj, dk]:
-                    T.bind(di, i_1)
-                    T.bind(dj, j1_1)
-                    T.bind(dk, (k2o * 4) + k2i)
+                with T.block("D"):
+                    di, dj = T.axis.remap("SS", [i_1, j1_1])
+                    dk = T.axis.R(16, k2o * 4 + k2i)
                     with T.init():
                         D[di, dj] = 0.0
                     D[di, dj] = (D[di, dj] + A[di, dj, dk]) + C[di, dj]
         for j2 in T.serial(0, 16):
             for k3o, k3i in T.grid(4, 4):
-                with T.block([16, 16, T.reduce_axis(0, 16)], "E") as [ei, ej, ek]:
-                    T.bind(ei, i_1)
-                    T.bind(ej, j2)
-                    T.bind(ek, (k3o * 4) + k3i)
+                with T.block("E"):
+                    ei, ej = T.axis.remap("SS", [i_1, j2])
+                    ek = T.axis.R(16, k3o * 4 + k3i)
                     with T.init():
                         E[ei, ej] = 0.0
                     E[ei, ej] = (E[ei, ej] + A[ei, ej, ek]) + D[ei, ej]
             for k4o, k4i in T.grid(4, 4):
-                with T.block([16, 16, T.reduce_axis(0, 16)], "F") as [fi, fj, fk]:
-                    T.bind(fi, i_1)
-                    T.bind(fj, j2)
-                    T.bind(fk, (k4o * 4) + k4i)
+                with T.block("F"):
+                    fi, fj = T.axis.remap("SS", [i_1, j2])
+                    fk = T.axis.R(16, k4o * 4 + k4i)
                     with T.init():
                         F[fi, fj] = 0.0
                     F[fi, fj] = (F[fi, fj] + A[fi, fj, fk]) + E[fi, fj]
diff --git a/tests/python/unittest/test_tir_schedule_sampling.py b/tests/python/unittest/test_tir_schedule_sampling.py
index c93c7ca63aa88..fbf0a6a5bd78d 100644
--- a/tests/python/unittest/test_tir_schedule_sampling.py
+++ b/tests/python/unittest/test_tir_schedule_sampling.py
@@ -32,8 +32,10 @@
 def elementwise(a: T.handle, b: T.handle) -> None:
     A = T.match_buffer(a, (128, 128, 128))
     B = T.match_buffer(b, (128, 128, 128))
-    with T.block([128, 128, 128], "B") as [vi, vj, vk]:
-        B[vi, vj, vk] = A[vi, vj, vk] * 2.0
+    for i, j, k in T.grid(128, 128, 128):
+        with T.block("B"):
+            vi, vj, vk = T.axis.remap("SSS", [i, j, k])
+            B[vi, vj, vk] = A[vi, vj, vk] * 2.0
 
 
 # pylint: enable=no-member,invalid-name,unused-variable
diff --git a/tests/python/unittest/test_tir_schedule_split_fuse.py b/tests/python/unittest/test_tir_schedule_split_fuse.py
index 29cfe8cadfb35..d2365c39c9cb2 100644
--- a/tests/python/unittest/test_tir_schedule_split_fuse.py
+++ b/tests/python/unittest/test_tir_schedule_split_fuse.py
@@ -30,8 +30,10 @@
 def elementwise(a: T.handle, b: T.handle) -> None:
     A = T.match_buffer(a, (128, 128, 128))
     B = T.match_buffer(b, (128, 128, 128))
-    with T.block([128, 128, 128], "B") as [vi, vj, vk]:
-        B[vi, vj, vk] = A[vi, vj, vk] * 2.0
+    for i, j, k in T.grid(128, 128, 128):
+        with T.block("B"):
+            vi, vj, vk = T.axis.remap("SSS", [i, j, k])
+            B[vi, vj, vk] = A[vi, vj, vk] * 2.0
 
 
 @T.prim_func
@@ -40,7 +42,10 @@ def elementwise_dependent_loops(a: T.handle, b: T.handle) -> None:
     B = T.match_buffer(b, (128, 128, 128))
     for i in T.serial(0, 128):
         for j, k in T.grid(i, 128):
-            with T.block([128, i, 128], "B") as [vi, vj, vk]:
+            with T.block("B"):
+                vi = T.axis.S(128, i)
+                vj = T.axis.S(i, j)
+                vk = T.axis.S(128, k)
                 B[vi, vj, vk] = A[vi, vj, vk] * 2.0
 
 
@@ -49,7 +54,8 @@ def elementwise_symbolic(a: T.handle, b: T.handle, n: T.int32) -> None:
     A = T.match_buffer(a, (128, 128, n))
     B = T.match_buffer(b, (128, 128, n))
     for i, j, k in T.grid(128, 128, n):
-        with T.block([128, 128, n], "B") as [vi, vj, vk]:
+        with T.block("B"):
+            vi, vj, vk = T.axis.remap("SSS", [i, j, k])
             B[vi, vj, vk] = A[vi, vj, vk] * 2.0
 
 
@@ -58,10 +64,10 @@ def elementwise_symbolic_fused(a: T.handle, b: T.handle, n: T.int32) -> None:
     A = T.match_buffer(a, (128, 128, n))
     B = T.match_buffer(b, (128, 128, n))
     for i_j_k_fused in T.serial(0, (n * 16384)):
-        with T.block([128, 128, n], "B") as [vi, vj, vk]:
-            T.bind(vi, T.floordiv(i_j_k_fused, (n * 128)))
-            T.bind(vj, T.floormod(T.floordiv(i_j_k_fused, n), 128))
-            T.bind(vk, T.floormod(i_j_k_fused, n))
+        with T.block("B"):
+            vi = T.axis.S(128, T.floordiv(i_j_k_fused, n * 128))
+            vj = T.axis.S(128, T.floormod(T.floordiv(i_j_k_fused, n), 128))
+            vk = T.axis.S(n, T.floormod(i_j_k_fused, n))
             T.reads([A[vi, vj, vk]])
             T.writes([B[vi, vj, vk]])
             B[vi, vj, vk] = A[vi, vj, vk] * 2.0
@@ -72,11 +78,10 @@ def elementwise_symbolic_split(a: T.handle, b: T.handle, n: T.int32) -> None:
     A = T.match_buffer(a, (128, 128, n))
     B = T.match_buffer(b, (128, 128, n))
     for i, j, k0, k1 in T.grid(128, 128, 10, T.floordiv((n + 9), 10)):
-        with T.block([128, 128, n], "B") as [vi, vj, vk]:
+        with T.block("B"):
             T.where((((k0 * T.floordiv((n + 9), 10)) + k1) < n))
-            T.bind(vi, i)
-            T.bind(vj, j)
-            T.bind(vk, ((k0 * T.floordiv((n + 9), 10)) + k1))
+            vi, vj = T.axis.remap("SS", [i, j])
+            vk = T.axis.S(n, k0 * T.floordiv(n + 9, 10) + k1)
             T.reads([A[vi, vj, vk]])
             T.writes([B[vi, vj, vk]])
             B[vi, vj, vk] = A[vi, vj, vk] * 2.0
@@ -89,10 +94,12 @@ def elementwise_with_seq(a: T.handle, b: T.handle) -> None:
     C = T.alloc_buffer((128, 128, 128))
     for i, j in T.grid(128, 128):
         for k in T.serial(0, 128):
-            with T.block([128, 128, 128], "C") as [vi, vj, vk]:
+            with T.block("C"):
+                vi, vj, vk = T.axis.remap("SSS", [i, j, k])
                 C[vi, vj, vk] = A[vi, vj, vk] * 2.0
         for k in T.serial(0, 128):
-            with T.block([128, 128, 128], "B") as [vi, vj, vk]:
+            with T.block("B"):
+                vi, vj, vk = T.axis.remap("SSS", [i, j, k])
                 B[vi, vj, vk] = C[vi, vj, vk] * 2.0
 
 
@@ -102,10 +109,8 @@ def elementwise_with_anno(a: T.handle, b: T.handle) -> None:
     B = T.match_buffer(b, (128, 128, 128))
     for i, j in T.grid(128, 128):
         for k in T.serial(0, 128, annotations={"useless_annotation": True}):
-            with T.block([128, 128, 128], "B") as [vi, vj, vk]:
-                T.bind(vi, i)
-                T.bind(vj, j)
-                T.bind(vk, k)
+            with T.block("B"):
+                vi, vj, vk = T.axis.remap("SSS", [i, j, k])
                 T.reads([A[vi, vj, vk]])
                 T.writes([B[vi, vj, vk]])
                 B[vi, vj, vk] = A[vi, vj, vk] * 2.0
@@ -117,10 +122,8 @@ def elementwise_with_thread_binding(a: T.handle, b: T.handle) -> None:
     B = T.match_buffer(b, (128, 128, 128))
     for i, j in T.grid(128, 128):
         for k in T.thread_binding(0, 128, thread="threadIdx.x"):
-            with T.block([128, 128, 128], "B") as [vi, vj, vk]:
-                T.bind(vi, i)
-                T.bind(vj, j)
-                T.bind(vk, k)
+            with T.block("B"):
+                vi, vj, vk = T.axis.remap("SSS", [i, j, k])
                 T.reads([A[vi, vj, vk]])
                 T.writes([B[vi, vj, vk]])
                 B[vi, vj, vk] = A[vi, vj, vk] * 2.0
@@ -132,10 +135,8 @@ def elementwise_with_starting_point(a: T.handle, b: T.handle) -> None:
     B = T.match_buffer(b, (128, 128, 128))
     for i, j in T.grid(128, 128):
         for k in T.serial(10, 128):
-            with T.block([128, 128, 128], "B") as [vi, vj, vk]:
-                T.bind(vi, i)
-                T.bind(vj, j)
-                T.bind(vk, k)
+            with T.block("B"):
+                vi, vj, vk = T.axis.remap("SSS", [i, j, k])
                 T.reads([A[vi, vj, vk]])
                 T.writes([B[vi, vj, vk]])
                 B[vi, vj, vk] = A[vi, vj, vk] * 2.0
@@ -146,13 +147,11 @@ def elementwise_with_opaque_block(a: T.handle, b: T.handle) -> None:
     A = T.match_buffer(a, (128, 128, 128))
     B = T.match_buffer(b, (128, 128, 128))
     for i, j, k in T.grid(128, 128, 128):
-        with T.block([], "opaque"):
+        with T.block("opaque"):
             T.reads([A[i, j, k]])
             T.writes([B[i, j, k]])
-            with T.block([128, 128, 128], "B") as [vi, vj, vk]:
-                T.bind(vi, i)
-                T.bind(vj, j)
-                T.bind(vk, k)
+            with T.block("B"):
+                vi, vj, vk = T.axis.remap("SSS", [i, j, k])
                 T.reads([A[vi, vj, vk]])
                 T.writes([B[vi, vj, vk]])
                 B[vi, vj, vk] = A[vi, vj, vk] * 2.0
@@ -163,10 +162,10 @@ def elementwise_fused(a: T.handle, b: T.handle) -> None:
     A = T.match_buffer(a, (128, 128, 128))
     B = T.match_buffer(b, (128, 128, 128))
     for fused in T.serial(0, 2097152):
-        with T.block([128, 128, 128], "B") as [vi, vj, vk]:
-            T.bind(vi, T.floordiv(fused, 16384))
-            T.bind(vj, T.floormod(T.floordiv(fused, 128), 128))
-            T.bind(vk, T.floormod(fused, 128))
+        with T.block("B"):
+            vi = T.axis.S(128, T.floordiv(fused, 16384))
+            vj = T.axis.S(128, T.floormod(T.floordiv(fused, 128), 128))
+            vk = T.axis.S(128, T.floormod(fused, 128))
             T.reads([A[vi, vj, vk]])
             T.writes([B[vi, vj, vk]])
             B[vi, vj, vk] = A[vi, vj, vk] * 2.0
@@ -177,10 +176,10 @@ def elementwise_split_case0(a: T.handle, b: T.handle) -> None:
     A = T.match_buffer(a, [128, 128, 128])
     B = T.match_buffer(b, [128, 128, 128])
     for i1, i2, i3, j1, j2, k1, k2 in T.grid(2, 1, 64, 4, 32, 16, 8):
-        with T.block([128, 128, 128], "B") as [vi, vj, vk]:
-            T.bind(vi, ((i1 * 64) + i3))
-            T.bind(vj, ((j1 * 32) + j2))
-            T.bind(vk, ((k1 * 8) + k2))
+        with T.block("B"):
+            vi = T.axis.S(128, i1 * 64 + i3)
+            vj = T.axis.S(128, j1 * 32 + j2)
+            vk = T.axis.S(128, k1 * 8 + k2)
             T.reads([A[vi, vj, vk]])
             T.writes([B[vi, vj, vk]])
             B[vi, vj, vk] = A[vi, vj, vk] * 2.0
@@ -191,10 +190,10 @@ def elementwise_split_case1(a: T.handle, b: T.handle) -> None:
     A = T.match_buffer(a, [128, 128, 128])
     B = T.match_buffer(b, [128, 128, 128])
     for i1, i2, i3, j1, j2, j3, k1, k2, k3 in T.grid(2, 1, 64, 2, 1, 64, 2, 1, 64):
-        with T.block([128, 128, 128], "B") as [vi, vj, vk]:
-            T.bind(vi, i1 * 64 + i3)
-            T.bind(vj, j1 * 64 + j3)
-            T.bind(vk, k1 * 64 + k3)
+        with T.block("B"):
+            vi = T.axis.S(128, i1 * 64 + i3)
+            vj = T.axis.S(128, j1 * 64 + j3)
+            vk = T.axis.S(128, k1 * 64 + k3)
             T.reads([A[vi, vj, vk]])
             T.writes([B[vi, vj, vk]])
             B[vi, vj, vk] = A[vi, vj, vk] * 2.0
@@ -205,16 +204,11 @@ def elementwise_split_with_predicate(a: T.handle, b: T.handle) -> None:
     B = T.match_buffer(b, [128, 128, 128])
     A = T.match_buffer(a, [128, 128, 128])
     for i0, i1, i2, j0, j1, k0, k1 in T.grid(1000, 2, 3, 1, 129, 3, 43):
-        with T.block([128, 128, 128], "B") as [vi, vj, vk]:
-            T.where(
-                (
-                    ((((((i0 * 2) + i1) * 3) + i2) < 128) and (((j0 * 129) + j1) < 128))
-                    and (((k0 * 43) + k1) < 128)
-                )
-            )
-            T.bind(vi, (((i0 * 6) + (i1 * 3)) + i2))
-            T.bind(vj, j1)
-            T.bind(vk, ((k0 * 43) + k1))
+        with T.block("B"):
+            T.where((i0 * 2 + i1) * 3 + i2 < 128 and j0 * 129 + j1 < 128 and k0 * 43 + k1 < 128)
+            vi = T.axis.S(128, i0 * 6 + i1 * 3 + i2)
+            vj = T.axis.S(128, j1)
+            vk = T.axis.S(128, k0 * 43 + k1)
             T.reads([A[vi, vj, vk]])
             T.writes([B[vi, vj, vk]])
             B[vi, vj, vk] = A[vi, vj, vk] * 2.0
@@ -225,7 +219,7 @@ def elementwise_fuse_with_opaque_block(a: T.handle, b: T.handle) -> None:
     B = T.match_buffer(b, [128, 128, 128])
     A = T.match_buffer(a, [128, 128, 128])
     for i_j_k_fused in T.serial(0, 2097152):
-        with T.block([], "opaque"):
+        with T.block("opaque"):
             T.reads(
                 [
                     A[
@@ -244,10 +238,10 @@ def elementwise_fuse_with_opaque_block(a: T.handle, b: T.handle) -> None:
                     ]
                 ]
             )
-            with T.block([128, 128, 128], "B") as [vi, vj, vk]:
-                T.bind(vi, T.floordiv(i_j_k_fused, 16384))
-                T.bind(vj, T.floormod(T.floordiv(i_j_k_fused, 128), 128))
-                T.bind(vk, T.floormod(i_j_k_fused, 128))
+            with T.block("B"):
+                vi = T.axis.S(128, T.floordiv(i_j_k_fused, 16384))
+                vj = T.axis.S(128, T.floormod(T.floordiv(i_j_k_fused, 128), 128))
+                vk = T.axis.S(128, T.floormod(i_j_k_fused, 128))
                 T.reads([A[vi, vj, vk]])
                 T.writes([B[vi, vj, vk]])
                 B[vi, vj, vk] = A[vi, vj, vk] * 2.0
@@ -259,13 +253,12 @@ def elementwise_split_with_opaque_block(a: T.handle, b: T.handle) -> None:
     A = T.match_buffer(a, [128, 128, 128])
 
     for i0, i1, j, k in T.grid(8, 16, 128, 128):
-        with T.block([], "opaque"):
+        with T.block("opaque"):
             T.reads([A[i0 * 16 + i1, j, k]])
             T.writes([B[i0 * 16 + i1, j, k]])
-            with T.block([128, 128, 128], "B") as [vi, vj, vk]:
-                T.bind(vi, i0 * 16 + i1)
-                T.bind(vj, j)
-                T.bind(vk, k)
+            with T.block("B"):
+                vi = T.axis.S(128, i0 * 16 + i1)
+                vj, vk = T.axis.remap("SS", [j, k])
                 T.reads([A[vi, vj, vk]])
                 T.writes([B[vi, vj, vk]])
                 B[vi, vj, vk] = A[vi, vj, vk] * 2.0
@@ -275,14 +268,18 @@ def elementwise_split_with_opaque_block(a: T.handle, b: T.handle) -> None:
 def opaque_access(a: T.handle, b: T.handle) -> None:
     A = T.match_buffer(a, [16, 16], "float32")
     B = T.match_buffer(b, [16, 16], "float32")
-    with T.block([16, 16], "A") as [vi, vj]:
-        T.reads([])
-        T.writes([A[0:16, 0:16]])
-        T.store(A.data, vi * 16 + vj, 1)
-    with T.block([16, 16], "B") as [vi, vj]:
-        T.reads([])
-        T.writes([B[0:16, 0:16]])
-        T.evaluate(T.tvm_fill_fragment(B.data, 16, 16, 16, 0, vi * 16 + vj, dtype="handle"))
+    for i, j in T.grid(16, 16):
+        with T.block("A"):
+            vi, vj = T.axis.remap("SS", [i, j])
+            T.reads([])
+            T.writes([A[0:16, 0:16]])
+            T.store(A.data, vi * 16 + vj, 1)
+    for i, j in T.grid(16, 16):
+        with T.block("B"):
+            vi, vj = T.axis.remap("SS", [i, j])
+            T.reads([])
+            T.writes([B[0:16, 0:16]])
+            T.evaluate(T.tvm_fill_fragment(B.data, 16, 16, 16, 0, vi * 16 + vj, dtype="handle"))
 
 
 @T.prim_func
@@ -290,16 +287,16 @@ def opaque_access_fused(a: T.handle, b: T.handle) -> None:
     A = T.match_buffer(a, [16, 16])
     B = T.match_buffer(b, [16, 16])
     for i_j_fused in T.serial(0, 256):
-        with T.block([16, 16], "A") as [vi, vj]:
-            T.bind(vi, T.floordiv(i_j_fused, 16))
-            T.bind(vj, T.floormod(i_j_fused, 16))
+        with T.block("A"):
+            vi = T.axis.S(16, T.floordiv(i_j_fused, 16))
+            vj = T.axis.S(16, T.floormod(i_j_fused, 16))
             T.reads([])
             T.writes([A[0:16, 0:16]])
             T.store(A.data, ((vi * 16) + vj), 1, 1)
     for i_j_fused in T.serial(0, 256):
-        with T.block([16, 16], "B") as [vi, vj]:
-            T.bind(vi, T.floordiv(i_j_fused, 16))
-            T.bind(vj, T.floormod(i_j_fused, 16))
+        with T.block("B"):
+            vi = T.axis.S(16, T.floordiv(i_j_fused, 16))
+            vj = T.axis.S(16, T.floormod(i_j_fused, 16))
             T.reads([])
             T.writes([B[0:16, 0:16]])
             T.evaluate(T.tvm_fill_fragment(B.data, 16, 16, 16, 0, ((vi * 16) + vj), dtype="handle"))
@@ -310,16 +307,16 @@ def opaque_access_split(a: T.handle, b: T.handle) -> None:
     A = T.match_buffer(a, (16, 16))
     B = T.match_buffer(b, (16, 16))
     for i, j0, j1 in T.grid(16, 4, 4):
-        with T.block([16, 16], "A") as [vi, vj]:
-            T.bind(vi, i)
-            T.bind(vj, ((j0 * 4) + j1))
+        with T.block("A"):
+            vi = T.axis.S(16, i)
+            vj = T.axis.S(16, j0 * 4 + j1)
             T.reads([])
             T.writes([A[0:16, 0:16]])
             T.store(A.data, ((vi * 16) + vj), 1, 1)
     for i, j0, j1 in T.grid(16, 4, 4):
-        with T.block([16, 16], "B") as [vi, vj]:
-            T.bind(vi, i)
-            T.bind(vj, ((j0 * 4) + j1))
+        with T.block("B"):
+            vi = T.axis.S(16, i)
+            vj = T.axis.S(16, j0 * 4 + j1)
             T.reads([])
             T.writes([B[0:16, 0:16]])
             T.evaluate(T.tvm_fill_fragment(B.data, 16, 16, 16, 0, ((vi * 16) + vj), dtype="handle"))
@@ -331,9 +328,9 @@ def elementwise_not_affine(a: T.handle, b: T.handle) -> None:
     B = T.match_buffer(b, (127, 128))
     for i in T.serial(0, 4):
         for j, k in T.grid(T.min(31, 126 - i * 32) + 1, 128):
-            with T.block([127, 128], "B") as [vi, vj]:
-                T.bind(vi, i * 32 + j)
-                T.bind(vj, k)
+            with T.block("B"):
+                vi = T.axis.S(127, i * 32 + j)
+                vj = T.axis.S(128, k)
                 B[vi, vj] = A[vi, vj]
 
 
@@ -343,12 +340,12 @@ def elementwise_not_affine_fused(a: T.handle, b: T.handle) -> None:
     B = T.match_buffer(b, [127, 128])
     for i in T.grid(4):
         for j_k_fused in T.serial(0, T.min(31, 126 - i * 32) * 128 + 128):
-            with T.block([127, 128], "B") as [vi, vj]:
-                T.bind(
-                    vi,
+            with T.block("B"):
+                vi = T.axis.S(
+                    127,
                     i * 32 + T.floormod(T.floordiv(j_k_fused, 128), T.min(31, 126 - i * 32) + 1),
                 )
-                T.bind(vj, T.floormod(j_k_fused, 128))
+                vj = T.axis.S(128, T.floormod(j_k_fused, 128))
                 T.reads([A[vi, vj]])
                 T.writes([B[vi, vj]])
                 B[vi, vj] = A[vi, vj]
diff --git a/tests/python/unittest/test_tir_schedule_state.py b/tests/python/unittest/test_tir_schedule_state.py
index 94e1b4a6b3959..bc62fa1ba950d 100644
--- a/tests/python/unittest/test_tir_schedule_state.py
+++ b/tests/python/unittest/test_tir_schedule_state.py
@@ -32,10 +32,14 @@ def elementwise(a: T.handle, c: T.handle) -> None:
     A = T.match_buffer(a, (128, 128), "float32")
     C = T.match_buffer(c, (128, 128), "float32")
     B = T.alloc_buffer((128, 128), "float32")
-    with T.block([128, 128], "B") as [vi, vj]:
-        B[vi, vj] = A[vi, vj] * 2.0
-    with T.block([128, 128], "C") as [vi, vj]:
-        C[vi, vj] = B[vi, vj] + 1.0
+    for i, j in T.grid(128, 128):
+        with T.block("B"):
+            vi, vj = T.axis.remap("SS", [i, j])
+            B[vi, vj] = A[vi, vj] * 2.0
+    for i, j in T.grid(128, 128):
+        with T.block("C"):
+            vi, vj = T.axis.remap("SS", [i, j])
+            C[vi, vj] = B[vi, vj] + 1.0
 
 
 @T.prim_func
@@ -44,10 +48,12 @@ def matmul(a: T.handle, b: T.handle, c: T.handle) -> None:
     B = T.match_buffer(b, [128, 128])
     C = T.match_buffer(c, [128, 128])
     for i, j in T.grid(128, 128):
-        with T.block([128, 128], "init") as [vi, vj]:
+        with T.block("init"):
+            vi, vj = T.axis.remap("SS", [i, j])
             C[vi, vj] = T.float32(0)
         for k in range(0, 128):
-            with T.block([128, 128, T.reduce_axis(0, 128)], "update") as [vi, vj, vk]:
+            with T.block("update"):
+                vi, vj, vk = T.axis.remap("SSR", [i, j, k])
                 C[vi, vj] = C[vi, vj] + A[vi, vk] * B[vj, vk]
 
 
@@ -55,22 +61,28 @@ def matmul(a: T.handle, b: T.handle, c: T.handle) -> None:
 def block_in_opaque_block(a: T.handle, b: T.handle) -> None:
     A = T.match_buffer(a, (128, 128), "float32")
     B = T.match_buffer(b, (128, 128), "float32")
-    with T.block([128], "B") as vi:
-        T.reads([A[0:128, 0:128]])
-        T.writes([B[0:128, 0:128]])
-        B[vi, 0] = A[vi, 0]
-        if A[vi, 0] == 0.0:
-            with T.block([], "C"):
-                T.reads([A[0:128, 0:128]])
-                T.writes([B[0:128, 0:128]])
-                with T.block([128], "D") as vj:
-                    B[vi, vj] = A[vi, vj] * 3.0
-        else:
-            with T.block([], "E"):
-                T.reads([A[0:128, 0:128]])
-                T.writes([B[0:128, 0:128]])
-                with T.block([128], "F") as vj:
-                    B[vi, vj] = A[vi, vj] * 2.0
+    for i in range(128):
+        with T.block("B"):
+            vi = T.axis.S(128, i)
+            T.reads([A[0:128, 0:128]])
+            T.writes([B[0:128, 0:128]])
+            B[vi, 0] = A[vi, 0]
+            if A[vi, 0] == 0.0:
+                with T.block("C"):
+                    T.reads([A[0:128, 0:128]])
+                    T.writes([B[0:128, 0:128]])
+                    for j in range(128):
+                        with T.block("D"):
+                            vj = T.axis.S(128, j)
+                            B[vi, vj] = A[vi, vj] * 3.0
+            else:
+                with T.block("E"):
+                    T.reads([A[0:128, 0:128]])
+                    T.writes([B[0:128, 0:128]])
+                    for j in range(128):
+                        with T.block("F"):
+                            vj = T.axis.S(128, j)
+                            B[vi, vj] = A[vi, vj] * 2.0
 
 
 # pylint: enable=no-member,invalid-name,unused-variable
diff --git a/tests/python/unittest/test_tir_schedule_state_cached_flags.py b/tests/python/unittest/test_tir_schedule_state_cached_flags.py
index e2b39ce7c2895..e3bd000c2e705 100644
--- a/tests/python/unittest/test_tir_schedule_state_cached_flags.py
+++ b/tests/python/unittest/test_tir_schedule_state_cached_flags.py
@@ -32,10 +32,14 @@ def elementwise(a: T.handle, c: T.handle) -> None:
     A = T.match_buffer(a, (128, 128), "float32")
     C = T.match_buffer(c, (128, 128), "float32")
     B = T.alloc_buffer((128, 128), "float32")
-    with T.block([128, 128], "B") as [vi, vj]:
-        B[vi, vj] = A[vi, vj] * 2.0
-    with T.block([128, 128], "C") as [vi, vj]:
-        C[vi, vj] = B[vi, vj] + 1.0
+    for i, j in T.grid(128, 128):
+        with T.block("B"):
+            vi, vj = T.axis.remap("SS", [i, j])
+            B[vi, vj] = A[vi, vj] * 2.0
+    for i, j in T.grid(128, 128):
+        with T.block("C"):
+            vi, vj = T.axis.remap("SS", [i, j])
+            C[vi, vj] = B[vi, vj] + 1.0
 
 
 @T.prim_func
@@ -44,10 +48,12 @@ def matmul(a: T.handle, b: T.handle, c: T.handle) -> None:
     B = T.match_buffer(b, [128, 128])
     C = T.match_buffer(c, [128, 128])
     for i, j in T.grid(128, 128):
-        with T.block([128, 128], "init") as [vi, vj]:
+        with T.block("init"):
+            vi, vj = T.axis.remap("SS", [i, j])
             C[vi, vj] = 0.0
         for k in range(0, 128):
-            with T.block([128, 128, T.reduce_axis(0, 128)], "update") as [vi, vj, vk]:
+            with T.block("update"):
+                vi, vj, vk = T.axis.remap("SSR", [i, j, k])
                 C[vi, vj] = C[vi, vj] + A[vi, vk] * B[vj, vk]
 
 
@@ -55,22 +61,28 @@ def matmul(a: T.handle, b: T.handle, c: T.handle) -> None:
 def block_in_opaque_block(a: T.handle, b: T.handle) -> None:
     A = T.match_buffer(a, (128, 128), "float32")
     B = T.match_buffer(b, (128, 128), "float32")
-    with T.block([128], "B") as vi:
-        T.reads([A[0:128, 0:128]])
-        T.writes([B[0:128, 0:128]])
-        B[vi, 0] = A[vi, 0]
-        if A[vi, 0] == 0.0:
-            with T.block([], "C"):
-                T.reads([A[0:128, 0:128]])
-                T.writes([B[0:128, 0:128]])
-                with T.block([128], "D") as vj:
-                    B[vi, vj] = A[vi, vj] * 3.0
-        else:
-            with T.block([], "E"):
-                T.reads([A[0:128, 0:128]])
-                T.writes([B[0:128, 0:128]])
-                with T.block([128], "F") as vj:
-                    B[vi, vj] = A[vi, vj] * 2.0
+    for i in range(128):
+        with T.block("B"):
+            vi = T.axis.S(128, i)
+            T.reads([A[0:128, 0:128]])
+            T.writes([B[0:128, 0:128]])
+            B[vi, 0] = A[vi, 0]
+            if A[vi, 0] == 0.0:
+                with T.block("C"):
+                    T.reads([A[0:128, 0:128]])
+                    T.writes([B[0:128, 0:128]])
+                    for j in range(128):
+                        with T.block("D"):
+                            vj = T.axis.S(128, j)
+                            B[vi, vj] = A[vi, vj] * 3.0
+            else:
+                with T.block("E"):
+                    T.reads([A[0:128, 0:128]])
+                    T.writes([B[0:128, 0:128]])
+                    for j in range(128):
+                        with T.block("F"):
+                            vj = T.axis.S(128, j)
+                            B[vi, vj] = A[vi, vj] * 2.0
 
 
 @T.prim_func
@@ -78,10 +90,14 @@ def write_after_read(a: T.handle, b: T.handle, c: T.handle) -> None:
     A = T.match_buffer(a, (128, 128))
     B = T.match_buffer(b, (128, 128))
     C = T.match_buffer(c, (128, 128))
-    with T.block([128, 128], "C") as [vi, vj]:
-        C[vi, vj] = B[vi, vj] + 1.0
-    with T.block([128, 128], "B") as [vi, vj]:
-        B[vi, vj] = A[vi, vj] * 2.0
+    for i, j in T.grid(128, 128):
+        with T.block("C"):
+            vi, vj = T.axis.remap("SS", [i, j])
+            C[vi, vj] = B[vi, vj] + 1.0
+    for i, j in T.grid(128, 128):
+        with T.block("B"):
+            vi, vj = T.axis.remap("SS", [i, j])
+            B[vi, vj] = A[vi, vj] * 2.0
 
 
 @T.prim_func
@@ -90,9 +106,11 @@ def loop_carried_dependency(a: T.handle, b: T.handle, c: T.handle) -> None:
     B = T.match_buffer(b, (128,))
     C = T.match_buffer(c, (128,))
     for i in range(0, 128):
-        with T.block([128], "B") as vi:
+        with T.block("B"):
+            vi = T.axis.S(128, i)
             B[vi] = A[vi] * 2.0
-        with T.block([128], "C") as vi:
+        with T.block("C"):
+            vi = T.axis.S(128, i)
             C[vi] = T.if_then_else(vi >= 1, B[vi - 1] + 1.0, 0.0, dtype="float32")
 
 
@@ -101,14 +119,17 @@ def concatenate_multi_producer(a: T.handle, b: T.handle) -> None:
     A = T.match_buffer(a, (128,))
     B = T.match_buffer(b, (128,))
     for i in range(0, 64):
-        with T.block([64], "A_0") as vi:
+        with T.block("A_0"):
+            vi = T.axis.S(64, i)
             A[vi] = vi + 1
     for i in range(0, 64):
-        with T.block([64], "A_1") as vi:
-            T.bind(vi, i + 64)
+        with T.block("A_1"):
+            vi = T.axis.S(64, i + 64)
             A[vi] = vi + 2
-    with T.block([128], "B") as vi:
-        B[vi] = A[vi] * 2.0
+    for i in range(0, 128):
+        with T.block("B"):
+            vi = T.axis.S(128, i)
+            B[vi] = A[vi] * 2.0
 
 
 @T.prim_func
@@ -116,14 +137,17 @@ def concatenate_multi_producer_uncovered(a: T.handle, b: T.handle) -> None:
     A = T.match_buffer(a, (128,))
     B = T.match_buffer(b, (128,))
     for i in range(0, 63):
-        with T.block([63], "A_0") as vi:
+        with T.block("A_0"):
+            vi = T.axis.S(63, i)
             A[vi] = vi + 1
     for i in range(0, 64):
-        with T.block([64], "A_1") as vi:
-            T.bind(vi, i + 64)
+        with T.block("A_1"):
+            vi = T.axis.S(64, i + 64)
             A[vi] = vi + 2
-    with T.block([128], "B") as vi:
-        B[vi] = A[vi] * 2.0
+    for i in range(0, 128):
+        with T.block("B"):
+            vi = T.axis.S(128, i)
+            B[vi] = A[vi] * 2.0
 
 
 @T.prim_func
@@ -132,9 +156,11 @@ def lca_at_loop(a: T.handle, b: T.handle, c: T.handle) -> None:
     B = T.match_buffer(b, (128,))
     C = T.match_buffer(c, (128,))
     for i in range(0, 128):
-        with T.block([128], "B") as vi:
+        with T.block("B"):
+            vi = T.axis.S(128, i)
             B[vi] = A[vi] * 2.0
-        with T.block([128], "C") as vi:
+        with T.block("C"):
+            vi = T.axis.S(128, i)
             C[vi] = B[vi] + 1.0
 
 
@@ -143,18 +169,20 @@ def multi_producer_consumer(a: T.handle, b: T.handle) -> None:
     A = T.match_buffer(a, (128,))
     B = T.match_buffer(b, (128,))
     for i in range(0, 64):
-        with T.block([64], "A_0") as vi:
+        with T.block("A_0"):
+            vi = T.axis.S(64, i)
             A[vi] = vi + 1
     for i in range(0, 64):
-        with T.block([64], "A_1") as vi:
-            T.bind(vi, i + 64)
+        with T.block("A_1"):
+            vi = T.axis.S(64, i + 64)
             A[vi] = vi + 2
     for i in range(0, 64):
-        with T.block([64], "B_0") as vi:
+        with T.block("B_0"):
+            vi = T.axis.S(64, i)
             B[vi] = A[vi] + 2.0
     for i in range(0, 64):
-        with T.block([64], "B_1") as vi:
-            T.bind(vi, i + 64)
+        with T.block("B_1"):
+            vi = T.axis.S(64, i + 64)
             B[vi] = A[vi] + 3.0
 
 
@@ -164,12 +192,14 @@ def elementwise_affine_producer(a: T.handle, c: T.handle) -> None:
     C = T.match_buffer(c, (128, 128), "float32")
     B = T.alloc_buffer((128, 128), "float32")
     for i, j, k, l in T.grid(16, 2, 32, 16):
-        with T.block([128, 128], "B") as [vi, vj]:
-            T.bind(vi, i * 8 + j * 4 + k // 8)
-            T.bind(vj, k % 8 * 16 + l)
+        with T.block("B"):
+            vi = T.axis.S(128, i * 8 + j * 4 + k // 8)
+            vj = T.axis.S(128, k % 8 * 16 + l)
             B[vi, vj] = A[vi, vj] * 2.0
-    with T.block([128, 128], "C") as [vi, vj]:
-        C[vi, vj] = B[vi, vj] + 1.0
+    for i, j in T.grid(128, 128):
+        with T.block("C"):
+            vi, vj = T.axis.remap("SS", [i, j])
+            C[vi, vj] = B[vi, vj] + 1.0
 
 
 @T.prim_func
@@ -177,13 +207,19 @@ def elementwise_subblock(a: T.handle, c: T.handle) -> None:
     A = T.match_buffer(a, (128, 128), "float32")
     C = T.match_buffer(c, (128, 128), "float32")
     B = T.alloc_buffer((128, 128), "float32")
-    with T.block([32, 32], "B") as [vi, vj]:
-        T.reads([A[vi * 4 : vi * 4 + 4, vj * 4 : vj * 4 + 4]])
-        T.writes([B[vi * 4 : vi * 4 + 4, vj * 4 : vj * 4 + 4]])
-        with T.block([4, 4], "B_sub") as [vi_i, vj_i]:
-            B[vi * 4 + vi_i, vj * 4 + vj_i] = A[vi * 4 + vi_i, vj * 4 + vj_i] * 2.0
-    with T.block([128, 128], "C") as [vi, vj]:
-        C[vi, vj] = B[vi, vj] + 1.0
+    for i, j in T.grid(32, 32):
+        with T.block("B"):
+            vi, vj = T.axis.remap("SS", [i, j])
+            T.reads([A[vi * 4 : vi * 4 + 4, vj * 4 : vj * 4 + 4]])
+            T.writes([B[vi * 4 : vi * 4 + 4, vj * 4 : vj * 4 + 4]])
+            for ii, jj in T.grid(4, 4):
+                with T.block("B_sub"):
+                    vi_i, vj_i = T.axis.remap("SS", [ii, jj])
+                    B[vi * 4 + vi_i, vj * 4 + vj_i] = A[vi * 4 + vi_i, vj * 4 + vj_i] * 2.0
+    for i, j in T.grid(128, 128):
+        with T.block("C"):
+            vi, vj = T.axis.remap("SS", [i, j])
+            C[vi, vj] = B[vi, vj] + 1.0
 
 
 @T.prim_func
@@ -191,13 +227,19 @@ def elementwise_subblock_uncovered(a: T.handle, c: T.handle) -> None:
     A = T.match_buffer(a, (128, 128), "float32")
     C = T.match_buffer(c, (128, 128), "float32")
     B = T.alloc_buffer((128, 128), "float32")
-    with T.block([32, 32], "B") as [vi, vj]:
-        T.reads([A[vi * 4 : vi * 4 + 2, vj * 4 : vj * 4 + 2]])
-        T.writes([B[vi * 4 : vi * 4 + 2, vj * 4 : vj * 4 + 2]])
-        with T.block([2, 2], "B_sub") as [vi_i, vj_i]:
-            B[vi * 4 + vi_i, vj * 4 + vj_i] = A[vi * 4 + vi_i, vj * 4 + vj_i] * 2.0
-    with T.block([128, 128], "C") as [vi, vj]:
-        C[vi, vj] = B[vi, vj] + 1.0
+    for i, j in T.grid(32, 32):
+        with T.block("B"):
+            vi, vj = T.axis.remap("SS", [i, j])
+            T.reads([A[vi * 4 : vi * 4 + 2, vj * 4 : vj * 4 + 2]])
+            T.writes([B[vi * 4 : vi * 4 + 2, vj * 4 : vj * 4 + 2]])
+            for ii, jj in T.grid(2, 2):
+                with T.block("B_sub"):
+                    vi_i, vj_i = T.axis.remap("SS", [ii, jj])
+                    B[vi * 4 + vi_i, vj * 4 + vj_i] = A[vi * 4 + vi_i, vj * 4 + vj_i] * 2.0
+    for i, j in T.grid(128, 128):
+        with T.block("C"):
+            vi, vj = T.axis.remap("SS", [i, j])
+            C[vi, vj] = B[vi, vj] + 1.0
 
 
 @T.prim_func
@@ -207,10 +249,12 @@ def bound_to_thread(a: T.handle, c: T.handle) -> None:
     B = T.alloc_buffer([128, 128], scope="shared")
     for i in T.thread_binding(0, 128, thread="threadIdx.x"):
         for j in T.serial(0, 128):
-            with T.block([128, 128], "B") as [vi, vj]:
+            with T.block("B"):
+                vi, vj = T.axis.remap("SS", [i, j])
                 B[vi, vj] = A[vi, vj] * 2.0
         for j in T.serial(0, 128):
-            with T.block([128, 128], "C") as [vi, vj]:
+            with T.block("C"):
+                vi, vj = T.axis.remap("SS", [i, j])
                 C[vj, vi] = B[vj, vi] + 1.0
 
 
@@ -222,14 +266,14 @@ def equal_ranked_threads(a: T.handle, c: T.handle) -> None:
     for i_o in T.thread_binding(0, 16, thread="threadIdx.x"):
         for i_i in T.thread_binding(0, 8, thread="threadIdx.y"):
             for j in T.serial(0, 128):
-                with T.block([128, 128], "B") as [vi, vj]:
-                    T.bind(vi, i_o * 8 + i_i)
-                    T.bind(vj, j)
+                with T.block("B"):
+                    vi = T.axis.S(128, i_o * 8 + i_i)
+                    vj = T.axis.S(128, j)
                     B[vi, vj] = A[vi, vj] * 2.0
             for j in T.serial(0, 128):
-                with T.block([128, 128], "C") as [vi, vj]:
-                    T.bind(vi, i_o * 8 + i_i)
-                    T.bind(vj, j)
+                with T.block("C"):
+                    vi = T.axis.S(128, i_o * 8 + i_i)
+                    vj = T.axis.S(128, j)
                     C[vj, vi] = B[vj, vi] + 1.0
 
 
@@ -241,10 +285,12 @@ def warp_memory(a: T.handle, c: T.handle) -> None:
     for i_o in T.thread_binding(0, 4, thread="threadIdx.y"):
         for i_i in T.thread_binding(0, 32, thread="threadIdx.x"):
             for j in T.serial(0, 128):
-                with T.block([4, 32, 128], "B") as [warp_id, lane_id, vj]:
+                with T.block("B"):
+                    warp_id, lane_id, vj = T.axis.remap("SSS", [i_o, i_i, j])
                     B[vj, warp_id, lane_id] = A[warp_id * 32 + lane_id, vj] * 2.0
             for j in T.serial(0, 128):
-                with T.block([4, 32, 128], "C") as [warp_id, lane_id, vj]:
+                with T.block("C"):
+                    warp_id, lane_id, vj = T.axis.remap("SSS", [i_o, i_i, j])
                     C[warp_id * 32 + lane_id, vj] = B[vj, warp_id, lane_id] + 1.0
 
 
@@ -256,11 +302,15 @@ def warp_memory_negative(a: T.handle, c: T.handle) -> None:
     for i_o in T.thread_binding(0, 4, thread="threadIdx.y"):
         for i_i in T.thread_binding(0, 32, thread="threadIdx.x"):
             for j in T.serial(0, 128):
-                with T.block([4, 32, 128], "B") as [warp_id, lane_id, vj]:
+                with T.block("B"):
+                    warp_id, lane_id, vj = T.axis.remap("SSS", [i_o, i_i, j])
                     B[vj, warp_id, lane_id] = A[warp_id * 32 + lane_id, vj] * 2.0
             for i_o_prime in T.thread_binding(0, 4, thread="threadIdx.y"):
                 for j in T.serial(0, 128):
-                    with T.block([4, 32, 4, 128], "C") as [_warp_id, lane_id, warp_id, vj]:
+                    with T.block("C"):
+                        _warp_id, warp_id, lane_id, vj = T.axis.remap(
+                            "SSSS", [i_o, i_i, i_o_prime, j]
+                        )
                         C[warp_id * 32 + lane_id, vj] = B[vj, warp_id, lane_id] + 1.0
 
 
diff --git a/tests/python/unittest/test_tir_schedule_storage_align.py b/tests/python/unittest/test_tir_schedule_storage_align.py
index 7d0e91f70e609..3b699fd8f1b2d 100644
--- a/tests/python/unittest/test_tir_schedule_storage_align.py
+++ b/tests/python/unittest/test_tir_schedule_storage_align.py
@@ -29,22 +29,20 @@ def element_wise(a: T.handle, c: T.handle) -> None:
     C = T.match_buffer(c, [128, 128], elem_offset=0, align=128, offset_factor=1)
     A = T.match_buffer(a, [128, 128], elem_offset=0, align=128, offset_factor=1)
     # body
-    with T.block([], "root"):
+    with T.block("root"):
         T.reads([])
         T.writes([])
         B = T.alloc_buffer([128, 128], elem_offset=0, align=128, offset_factor=1)
         for i0 in T.serial(0, 128):
             for ax1 in T.serial(0, 128):
-                with T.block([128, 128], "B") as [vi, vj]:
-                    T.bind(vi, i0)
-                    T.bind(vj, ax1)
+                with T.block("B"):
+                    vi, vj = T.axis.remap("SS", [i0, ax1])
                     T.reads([A[vi, vj]])
                     T.writes([B[vi, vj]])
                     B[vi, vj] = (A[vi, vj]*T.float32(2))
             for i1 in T.serial(0, 128):
-                with T.block([128, 128], "C") as [vi_1, vj_1]:
-                    T.bind(vi_1, i0)
-                    T.bind(vj_1, i1)
+                with T.block("C"):
+                    vi_1, vj_1 = T.axis.remap("SS", [i0, i1])
                     T.reads([B[vi_1, vj_1]])
                     T.writes([C[vi_1, vj_1]])
                     C[vi_1, vj_1] = (B[vi_1, vj_1] + T.float32(1))
@@ -55,23 +53,21 @@ def element_wise_storage_align(a: T.handle, c: T.handle) -> None:
     C = T.match_buffer(c, [128, 128], elem_offset=0, align=128, offset_factor=1)
     A = T.match_buffer(a, [128, 128], elem_offset=0, align=128, offset_factor=1)
     # body
-    with T.block([], "root"):
+    with T.block("root"):
         T.reads([])
         T.writes([])
         B = T.alloc_buffer([128, 128], elem_offset=0, align=128, offset_factor=1)
         for i0 in T.serial(0, 128):
             for ax1 in T.serial(0, 128):
-                with T.block([128, 128], "B") as [vi, vj]:
-                    T.bind(vi, i0)
-                    T.bind(vj, ax1)
+                with T.block("B"):
+                    vi, vj = T.axis.remap("SS", [i0, ax1])
                     T.reads([A[vi, vj]])
                     T.writes([B[vi, vj]])
                     T.block_attr({"buffer_dim_align":[[0, 0, 128, 127]]})
                     B[vi, vj] = (A[vi, vj]*T.float32(2))
             for i1 in T.serial(0, 128):
-                with T.block([128, 128], "C") as [vi_1, vj_1]:
-                    T.bind(vi_1, i0)
-                    T.bind(vj_1, i1)
+                with T.block("C"):
+                    vi_1, vj_1 = T.axis.remap("SS", [i0, i1])
                     T.reads([B[vi_1, vj_1]])
                     T.writes([C[vi_1, vj_1]])
                     C[vi_1, vj_1] = (B[vi_1, vj_1] + T.float32(1))
@@ -82,23 +78,21 @@ def element_wise_invalid_annotation(a: T.handle, c: T.handle) -> None:
     C = T.match_buffer(c, [128, 128], elem_offset=0, align=128, offset_factor=1)
     A = T.match_buffer(a, [128, 128], elem_offset=0, align=128, offset_factor=1)
     # body
-    with T.block([], "root"):
+    with T.block("root"):
         T.reads([])
         T.writes([])
         B = T.alloc_buffer([128, 128], elem_offset=0, align=128, offset_factor=1)
         for i0 in T.serial(0, 128):
             for ax1 in T.serial(0, 128):
-                with T.block([128, 128], "B") as [vi, vj]:
+                with T.block("B"):
                     T.block_attr({"buffer_dim_align": [0]})
-                    T.bind(vi, i0)
-                    T.bind(vj, ax1)
+                    vi, vj = T.axis.remap("SS", [i0, ax1])
                     T.reads([A[vi, vj]])
                     T.writes([B[vi, vj]])
                     B[vi, vj] = (A[vi, vj]*T.float32(2))
             for i1 in T.serial(0, 128):
-                with T.block([128, 128], "C") as [vi_1, vj_1]:
-                    T.bind(vi_1, i0)
-                    T.bind(vj_1, i1)
+                with T.block("C"):
+                    vi_1, vj_1 = T.axis.remap("SS", [i0, i1])
                     T.reads([B[vi_1, vj_1]])
                     T.writes([C[vi_1, vj_1]])
                     C[vi_1, vj_1] = (B[vi_1, vj_1] + T.float32(1))
diff --git a/tests/python/unittest/test_tir_schedule_trace.py b/tests/python/unittest/test_tir_schedule_trace.py
index 36e05c6b51701..f1c97c57b2ff0 100644
--- a/tests/python/unittest/test_tir_schedule_trace.py
+++ b/tests/python/unittest/test_tir_schedule_trace.py
@@ -32,18 +32,24 @@ def elementwise(a: T.handle, c: T.handle) -> None:
     A = T.match_buffer(a, (128, 128))
     B = T.alloc_buffer((128, 128))
     C = T.match_buffer(c, (128, 128))
-    with T.block([128, 128], "B") as [vi, vj]:
-        B[vi, vj] = A[vi, vj] * 2.0
-    with T.block([128, 128], "C") as [vi, vj]:
-        C[vi, vj] = B[vi, vj] + 1.0
+    for i, j in T.grid(128, 128):
+        with T.block("B"):
+            vi, vj = T.axis.remap("SS", [i, j])
+            B[vi, vj] = A[vi, vj] * 2.0
+    for i, j in T.grid(128, 128):
+        with T.block("C"):
+            vi, vj = T.axis.remap("SS", [i, j])
+            C[vi, vj] = B[vi, vj] + 1.0
 
 
 @T.prim_func
 def elementwise_inlined(a: T.handle, c: T.handle) -> None:
     A = T.match_buffer(a, (128, 128))
     C = T.match_buffer(c, (128, 128))
-    with T.block([128, 128], "C") as [vi, vj]:
-        C[vi, vj] = A[vi, vj] * 2.0 + 1.0
+    for i, j in T.grid(128, 128):
+        with T.block("C"):
+            vi, vj = T.axis.remap("SS", [i, j])
+            C[vi, vj] = A[vi, vj] * 2.0 + 1.0
 
 
 # pylint: enable=no-member,invalid-name,unused-variable
diff --git a/tests/python/unittest/test_tir_schedule_utilities.py b/tests/python/unittest/test_tir_schedule_utilities.py
index 185d229b44e14..440d0ab67a50a 100644
--- a/tests/python/unittest/test_tir_schedule_utilities.py
+++ b/tests/python/unittest/test_tir_schedule_utilities.py
@@ -34,10 +34,12 @@ def matmul(a: T.handle, b: T.handle, c: T.handle) -> None:
     B = T.match_buffer(b, [128, 128])
     C = T.match_buffer(c, [128, 128])
     for i, j in T.grid(128, 128):
-        with T.block([128, 128], "init") as [vi, vj]:
+        with T.block("init"):
+            vi, vj = T.axis.remap("SS", [i, j])
             C[vi, vj] = T.float32(0)
         for k in range(0, 128):
-            with T.block([128, 128, T.reduce_axis(0, 128)], "update") as [vi, vj, vk]:
+            with T.block("update"):
+                vi, vj, vk = T.axis.remap("SSR", [i, j, k])
                 C[vi, vj] = C[vi, vj] + A[vi, vk] * B[vj, vk]
 
 
diff --git a/tests/python/unittest/test_tir_specialize.py b/tests/python/unittest/test_tir_specialize.py
index 86dc5dffed9f1..72666a89ebcb0 100644
--- a/tests/python/unittest/test_tir_specialize.py
+++ b/tests/python/unittest/test_tir_specialize.py
@@ -27,10 +27,12 @@ def matmul(a: T.handle, b: T.handle, c: T.handle, n: T.int32) -> None:
     B = T.match_buffer(b, [m, n])
     C = T.match_buffer(c, [m, m])
 
-    with T.block([m, m, T.reduce_axis(0, n)], "update") as [vi, vj, vk]:
-        with T.init():
-            C[vi, vj] = 0.0
-        C[vi, vj] = C[vi, vj] + A[vi, vk] * B[vj, vk]
+    for i, j, k in T.grid(m, m, n):
+        with T.block("update"):
+            vi, vj, vk = T.axis.remap("SSR", [i, j, k])
+            with T.init():
+                C[vi, vj] = 0.0
+            C[vi, vj] = C[vi, vj] + A[vi, vk] * B[vj, vk]
 
 
 @T.prim_func
@@ -39,10 +41,12 @@ def matmul_128(a: T.handle, b: T.handle, c: T.handle) -> None:
     B = T.match_buffer(b, [128, 128])
     C = T.match_buffer(c, [128, 128])
 
-    with T.block([128, 128, T.reduce_axis(0, 128)], "update") as [vi, vj, vk]:
-        with T.init():
-            C[vi, vj] = 0.0
-        C[vi, vj] = C[vi, vj] + A[vi, vk] * B[vj, vk]
+    for i, j, k in T.grid(128, 128, 128):
+        with T.block("update"):
+            vi, vj, vk = T.axis.remap("SSR", [i, j, k])
+            with T.init():
+                C[vi, vj] = 0.0
+            C[vi, vj] = C[vi, vj] + A[vi, vk] * B[vj, vk]
 
 
 @T.prim_func
@@ -52,10 +56,12 @@ def matmul_m_128(a: T.handle, b: T.handle, c: T.handle) -> None:
     B = T.match_buffer(b, [m, 128])
     C = T.match_buffer(c, [m, m])
 
-    with T.block([m, m, T.reduce_axis(0, 128)], "update") as [vi, vj, vk]:
-        with T.init():
-            C[vi, vj] = 0.0
-        C[vi, vj] = C[vi, vj] + A[vi, vk] * B[vj, vk]
+    for i, j, k in T.grid(m, m, 128):
+        with T.block("update"):
+            vi, vj, vk = T.axis.remap("SSR", [i, j, k])
+            with T.init():
+                C[vi, vj] = 0.0
+            C[vi, vj] = C[vi, vj] + A[vi, vk] * B[vj, vk]
 
 
 @T.prim_func
@@ -66,10 +72,12 @@ def matmul_m_8x(a: T.handle, b: T.handle, c: T.handle) -> None:
     B = T.match_buffer(b, [m, x * 8])
     C = T.match_buffer(c, [m, m])
 
-    with T.block([m, m, T.reduce_axis(0, x * 8)], "update") as [vi, vj, vk]:
-        with T.init():
-            C[vi, vj] = 0.0
-        C[vi, vj] = C[vi, vj] + A[vi, vk] * B[vj, vk]
+    for i, j, k in T.grid(m, m, x * 8):
+        with T.block("update"):
+            vi, vj, vk = T.axis.remap("SSR", [i, j, k])
+            with T.init():
+                C[vi, vj] = 0.0
+            C[vi, vj] = C[vi, vj] + A[vi, vk] * B[vj, vk]
 
 
 @T.prim_func
@@ -81,11 +89,15 @@ def element_wise(a: T.handle, c: T.handle) -> None:
 
     B = T.alloc_buffer((m, n), "float32")
 
-    with T.block([m, n], "B") as [vi, vj]:
-        B[vi, vj] = A[vi, vj] * 2.0
+    for i, j in T.grid(m, n):
+        with T.block("B"):
+            vi, vj = T.axis.remap("SS", [i, j])
+            B[vi, vj] = A[vi, vj] * 2.0
 
-    with T.block([m, n], "C") as [vi, vj]:
-        C[vi, vj] = B[vi, vj] + 1.0
+    for i, j in T.grid(m, n):
+        with T.block("C"):
+            vi, vj = T.axis.remap("SS", [i, j])
+            C[vi, vj] = B[vi, vj] + 1.0
 
 
 @T.prim_func
@@ -94,11 +106,15 @@ def element_wise_128_64(a: T.handle, c: T.handle) -> None:
     C = T.match_buffer(c, (128, 64), "float32")
     B = T.alloc_buffer((128, 64), "float32")
 
-    with T.block([128, 64], "B") as [vi, vj]:
-        B[vi, vj] = A[vi, vj] * 2.0
+    for i, j in T.grid(128, 64):
+        with T.block("B"):
+            vi, vj = T.axis.remap("SS", [i, j])
+            B[vi, vj] = A[vi, vj] * 2.0
 
-    with T.block([128, 64], "C") as [vi, vj]:
-        C[vi, vj] = B[vi, vj] + 1.0
+    for i, j in T.grid(128, 64):
+        with T.block("C"):
+            vi, vj = T.axis.remap("SS", [i, j])
+            C[vi, vj] = B[vi, vj] + 1.0
 
 
 @T.prim_func
@@ -108,11 +124,15 @@ def element_wise_128_n(a: T.handle, c: T.handle) -> None:
     C = T.match_buffer(c, (128, n), "float32")
     B = T.alloc_buffer((128, n), "float32")
 
-    with T.block([128, n], "B") as [vi, vj]:
-        B[vi, vj] = A[vi, vj] * 2.0
+    for i, j in T.grid(128, n):
+        with T.block("B"):
+            vi, vj = T.axis.remap("SS", [i, j])
+            B[vi, vj] = A[vi, vj] * 2.0
 
-    with T.block([128, n], "C") as [vi, vj]:
-        C[vi, vj] = B[vi, vj] + 1.0
+    for i, j in T.grid(128, n):
+        with T.block("C"):
+            vi, vj = T.axis.remap("SS", [i, j])
+            C[vi, vj] = B[vi, vj] + 1.0
 
 
 @T.prim_func
@@ -120,8 +140,10 @@ def mem_copy(a: T.handle, b: T.handle, m: T.int32, n: T.int32, p: T.int32, q: T.
     A = T.match_buffer(a, (m, n), "float32", strides=[p, 1], elem_offset=q)
     B = T.match_buffer(b, (m, n), "float32", strides=[p, 1], elem_offset=q)
 
-    with T.block([m, n], "") as [vi, vj]:
-        B[vi, vj] = A[vi, vj]
+    for i, j in T.grid(m, n):
+        with T.block():
+            vi, vj = T.axis.remap("SS", [i, j])
+            B[vi, vj] = A[vi, vj]
 
 
 @T.prim_func
@@ -129,8 +151,10 @@ def mem_copy_16_16_8_4(a: T.handle, b: T.handle) -> None:
     A = T.match_buffer(a, (16, 16), "float32", strides=[8, 1], elem_offset=4)
     B = T.match_buffer(b, (16, 16), "float32", strides=[8, 1], elem_offset=4)
 
-    with T.block([16, 16], "") as [vi, vj]:
-        B[vi, vj] = A[vi, vj]
+    for i, j in T.grid(16, 16):
+        with T.block():
+            vi, vj = T.axis.remap("SS", [i, j])
+            B[vi, vj] = A[vi, vj]
 
 
 @T.prim_func
@@ -138,8 +162,10 @@ def mem_copy_m_n_p_n(a: T.handle, b: T.handle, m: T.int32, n: T.int32, p: T.int3
     A = T.match_buffer(a, (m, n), "float32", strides=[p, 1], elem_offset=n)
     B = T.match_buffer(b, (m, n), "float32", strides=[p, 1], elem_offset=n)
 
-    with T.block([m, n], "") as [vi, vj]:
-        B[vi, vj] = A[vi, vj]
+    for i, j in T.grid(m, n):
+        with T.block():
+            vi, vj = T.axis.remap("SS", [i, j])
+            B[vi, vj] = A[vi, vj]
 
 
 @T.prim_func
@@ -147,8 +173,10 @@ def param_in_arith_exprs(a: T.handle, b: T.handle) -> None:
     n = T.var("int32")
     A = T.match_buffer(a, [n // 8, 8], "int32")
     B = T.match_buffer(b, [n], "int32")
-    with T.block([n - 1], "") as [vi]:
-        B[vi] = A[vi // 8, vi % 8] + (n + 1) * 42
+    for i in range(n - 1):
+        with T.block():
+            vi = T.axis.S(n - 1, i)
+            B[vi] = A[vi // 8, vi % 8] + (n + 1) * 42
 
 
 @T.prim_func
@@ -156,8 +184,10 @@ def param_in_arith_exprs_n_16(a: T.handle, b: T.handle) -> None:
     n = T.var("int32")
     A = T.match_buffer(a, [2, 8], "int32")
     B = T.match_buffer(b, [16], "int32")
-    with T.block([15], "") as [vi]:
-        B[vi] = A[vi // 8, vi % 8] + 714
+    for i in range(15):
+        with T.block():
+            vi = T.axis.S(15, i)
+            B[vi] = A[vi // 8, vi % 8] + 714
 
 
 def test_specialize_nothing():
diff --git a/tests/python/unittest/test_tir_transform_compact_buffer_region.py b/tests/python/unittest/test_tir_transform_compact_buffer_region.py
index 0cfc724e41de2..7d3115428f5ad 100644
--- a/tests/python/unittest/test_tir_transform_compact_buffer_region.py
+++ b/tests/python/unittest/test_tir_transform_compact_buffer_region.py
@@ -32,17 +32,17 @@ def elementwise_func(a: T.handle, c: T.handle) -> None:
     A = T.match_buffer(a, (16, 16), "float32")
     C = T.match_buffer(c, (16, 16), "float32")
     for i in range(0, 16):
-        with T.block([]):
+        with T.block():
             T.reads(A[i, 0:16])
             T.writes(C[i, 0:16])
             B = T.alloc_buffer((16, 16), "float32")
             for j in range(0, 16):
-                with T.block([]) as []:
+                with T.block() as []:
                     T.reads(A[i, j])
                     T.writes(B[i, j])
                     B[i, j] = A[i, j] + 1.0
             for j in range(0, 16):
-                with T.block([]) as []:
+                with T.block() as []:
                     T.reads(B[i, j])
                     T.writes(C[i, j])
                     C[i, j] = B[i, j] * 2.0
@@ -53,7 +53,7 @@ def compacted_elementwise_func(a: T.handle, c: T.handle) -> None:
     A = T.match_buffer(a, (16, 16), "float32")
     C = T.match_buffer(c, (16, 16), "float32")
     for i in range(0, 16):
-        with T.block([]):
+        with T.block():
             T.reads(A[i, 0:16])
             T.writes(C[i, 0:16])
             B = T.alloc_buffer((1, 16), "float32")
@@ -74,7 +74,7 @@ def unschedulable_func(a: T.handle, c: T.handle) -> None:
     A = T.match_buffer(a, (16, 16), "float32")
     C = T.match_buffer(c, (16, 16), "float32")
     for i in range(0, 16):
-        with T.block([]):
+        with T.block():
             T.reads(A[i, 0:16])
             T.writes(C[i, 0:16])
             B = T.alloc_buffer((16, 16), "float32")
@@ -89,11 +89,11 @@ def param_buffer_access_func(a: T.handle, c: T.handle) -> None:
     A = T.match_buffer(a, (20, 20), "float32")
     B = T.match_buffer(c, (20, 20), "float32")
     for i in range(0, 16):
-        with T.block([]):
+        with T.block():
             T.reads(A[i, 0:16])
             T.writes(B[i, 0:16])
             for j in range(0, 16):
-                with T.block([]) as []:
+                with T.block() as []:
                     T.reads(A[i, j])
                     T.writes(B[i, j])
                     B[i, j] = A[i, j] + 1.0
@@ -106,17 +106,17 @@ def shared_mem_func(a: T.handle, c: T.handle) -> None:
     for i0 in T.thread_binding(0, 2, thread="blockIdx.x"):
         for i1 in T.thread_binding(0, 2, thread="vthread"):
             for i2 in T.thread_binding(0, 4, thread="threadIdx.x"):
-                with T.block([]):
+                with T.block():
                     T.reads(A[i0 * 8 + i1 * 4 + i2, 0:16])
                     T.writes(C[i0 * 8 + i1 * 4 + i2, 0:16])
                     B = T.alloc_buffer((16, 16), "float32", scope="shared")
                     for j in range(0, 16):
-                        with T.block([]) as []:
+                        with T.block() as []:
                             T.reads(A[i0 * 8 + i1 * 4 + i2, j])
                             T.writes(B[i0 * 8 + i1 * 4 + i2, j])
                             B[i0 * 8 + i1 * 4 + i2, j] = A[i0 * 8 + i1 * 4 + i2, j] + 1.0
                     for j in range(0, 16):
-                        with T.block([]) as []:
+                        with T.block() as []:
                             T.reads(B[i0 * 8 + i1 * 4 + i2, j])
                             T.writes(C[i0 * 8 + i1 * 4 + i2, j])
                             C[i0 * 8 + i1 * 4 + i2, j] = B[i0 * 8 + i1 * 4 + i2, j] * 2.0
@@ -129,17 +129,17 @@ def compacted_shared_mem_func(a: T.handle, c: T.handle) -> None:
     for i0 in T.thread_binding(0, 2, thread="blockIdx.x"):
         for i1 in T.thread_binding(0, 2, thread="vthread"):
             for i2 in T.thread_binding(0, 4, thread="threadIdx.x"):
-                with T.block([]):
+                with T.block():
                     T.reads(A[i0 * 8 + i1 * 4 + i2, 0:16])
                     T.writes(C[i0 * 8 + i1 * 4 + i2, 0:16])
                     B = T.alloc_buffer((8, 16), "float32", scope="shared")
                     for j in range(0, 16):
-                        with T.block([]) as []:
+                        with T.block() as []:
                             T.reads(A[i0 * 8 + i1 * 4 + i2, j])
                             T.writes(B[i1 * 4 + i2, j])
                             B[i1 * 4 + i2, j] = A[i0 * 8 + i1 * 4 + i2, j] + 1.0
                     for j in range(0, 16):
-                        with T.block([]) as []:
+                        with T.block() as []:
                             T.reads(B[i1 * 4 + i2, j])
                             T.writes(C[i0 * 8 + i1 * 4 + i2, j])
                             C[i0 * 8 + i1 * 4 + i2, j] = B[i1 * 4 + i2, j] * 2.0
@@ -152,17 +152,17 @@ def warp_mem_func(a: T.handle, c: T.handle) -> None:
     for i0 in T.thread_binding(0, 2, thread="blockIdx.x"):
         for i1 in T.thread_binding(0, 2, thread="vthread"):
             for i2 in T.thread_binding(0, 4, thread="threadIdx.x"):
-                with T.block([]):
+                with T.block():
                     T.reads(A[i0 * 8 + i1 * 4 + i2, 0:16])
                     T.writes(C[i0 * 8 + i1 * 4 + i2, 0:16])
                     B = T.alloc_buffer((16, 16), "float32", scope="warp")
                     for j in range(0, 16):
-                        with T.block([]) as []:
+                        with T.block() as []:
                             T.reads(A[i0 * 8 + i1 * 4 + i2, j])
                             T.writes(B[i0 * 8 + i1 * 4 + i2, j])
                             B[i0 * 8 + i1 * 4 + i2, j] = A[i0 * 8 + i1 * 4 + i2, j] + 1.0
                     for j in range(0, 16):
-                        with T.block([]) as []:
+                        with T.block() as []:
                             T.reads(B[i0 * 8 + i1 * 4 + i2, j])
                             T.writes(C[i0 * 8 + i1 * 4 + i2, j])
                             C[i0 * 8 + i1 * 4 + i2, j] = B[i0 * 8 + i1 * 4 + i2, j] * 2.0
@@ -175,17 +175,17 @@ def compacted_warp_mem_func(a: T.handle, c: T.handle) -> None:
     for i0 in T.thread_binding(0, 2, thread="blockIdx.x"):
         for i1 in T.thread_binding(0, 2, thread="vthread"):
             for i2 in T.thread_binding(0, 4, thread="threadIdx.x"):
-                with T.block([]):
+                with T.block():
                     T.reads(A[i0 * 8 + i1 * 4 + i2, 0:16])
                     T.writes(C[i0 * 8 + i1 * 4 + i2, 0:16])
                     B = T.alloc_buffer((4, 16), "float32", scope="warp")
                     for j in range(0, 16):
-                        with T.block([]) as []:
+                        with T.block() as []:
                             T.reads(A[i0 * 8 + i1 * 4 + i2, j])
                             T.writes(B[i2, j])
                             B[i2, j] = A[i0 * 8 + i1 * 4 + i2, j] + 1.0
                     for j in range(0, 16):
-                        with T.block([]) as []:
+                        with T.block() as []:
                             T.reads(B[i2, j])
                             T.writes(C[i0 * 8 + i1 * 4 + i2, j])
                             C[i0 * 8 + i1 * 4 + i2, j] = B[i2, j] * 2.0
@@ -196,17 +196,17 @@ def symbolic_func(a: T.handle, c: T.handle, n: T.int32) -> None:
     A = T.match_buffer(a, (n * 8,), "float32")
     C = T.match_buffer(c, (n * 8,), "float32")
     for i in range(0, n):
-        with T.block([]):
+        with T.block():
             T.reads(A[i * 8 : i * 8 + 8])
             T.writes(C[i * 8 : i * 8 + 8])
             B = T.alloc_buffer((n * 8,), "float32")
             for j in range(0, 8):
-                with T.block([]) as []:
+                with T.block() as []:
                     T.reads(A[i * 8 + j])
                     T.writes(B[i * 8 + j])
                     B[i * 8 + j] = A[i * 8 + j] + 1.0
             for j in range(0, 8):
-                with T.block([]) as []:
+                with T.block() as []:
                     T.reads(B[i * 8 + j])
                     T.writes(C[i * 8 + j])
                     C[i * 8 + j] = B[i * 8 + j] * 2.0
@@ -217,17 +217,17 @@ def compacted_symbolic_func(a: T.handle, c: T.handle, n: T.int32) -> None:
     A = T.match_buffer(a, (n * 8,), "float32")
     C = T.match_buffer(c, (n * 8,), "float32")
     for i in range(0, n):
-        with T.block([]):
+        with T.block():
             T.reads(A[i * 8 : i * 8 + 8])
             T.writes(C[i * 8 : i * 8 + 8])
             B = T.alloc_buffer((8,), "float32")
             for j in range(0, 8):
-                with T.block([]) as []:
+                with T.block() as []:
                     T.reads(A[i * 8 + j])
                     T.writes(B[j])
                     B[j] = A[i * 8 + j] + 1.0
             for j in range(0, 8):
-                with T.block([]) as []:
+                with T.block() as []:
                     T.reads(B[j])
                     T.writes(C[i * 8 + j])
                     C[i * 8 + j] = B[j] * 2.0
@@ -238,12 +238,12 @@ def complex_func(a: T.handle, c: T.handle, n: T.int32) -> None:
     A = T.match_buffer(a, (8, 8), "float32")
     C = T.match_buffer(c, (8, 8), "float32")
     for i in range(0, 8):
-        with T.block([]):
+        with T.block():
             T.reads(A[0, 8])
             T.writes(C[0, 8])
             B = T.alloc_buffer((8, 8), "float32")
             for j in range(0, 4):
-                with T.block([]) as []:
+                with T.block() as []:
                     D = T.alloc_buffer((8, 8), "float32")
                     T.reads(A[i, j])
                     T.writes(B[i, j])
@@ -252,12 +252,12 @@ def complex_func(a: T.handle, c: T.handle, n: T.int32) -> None:
                     for k in range(2, 4):
                         T.store(B.data, j, A[i, j] + D[k, j])
             for j in range(3, 5):
-                with T.block([]) as []:
+                with T.block() as []:
                     T.reads(B[i, j])
                     T.writes(C[i, j])
                     C[i, j] = B[i, j]
             for j in range(6, 8):
-                with T.block([]) as []:
+                with T.block() as []:
                     T.reads(B[i, j])
                     T.writes(C[i, j])
                     C[i, j] = B[i, j]
@@ -268,12 +268,12 @@ def compacted_complex_func(a: T.handle, c: T.handle, n: T.int32) -> None:
     A = T.match_buffer(a, (8, 8), "float32")
     C = T.match_buffer(c, (8, 8), "float32")
     for i in range(0, 8):
-        with T.block([]):
+        with T.block():
             T.reads(A[0, 8])
             T.writes(C[0, 8])
             B = T.alloc_buffer((1, 8), "float32")
             for j in range(0, 4):
-                with T.block([]) as []:
+                with T.block() as []:
                     D = T.alloc_buffer((6, 1), "float32")
                     T.reads(A[i, j])
                     T.writes(B[0, j])
@@ -282,12 +282,12 @@ def compacted_complex_func(a: T.handle, c: T.handle, n: T.int32) -> None:
                     for k in range(2, 4):
                         T.store(B.data, j, A[i, j] + D[k - 2, 0])
             for j in range(3, 5):
-                with T.block([]) as []:
+                with T.block() as []:
                     T.reads(B[0, j])
                     T.writes(C[i, j])
                     C[i, j] = B[0, j]
             for j in range(6, 8):
-                with T.block([]) as []:
+                with T.block() as []:
                     T.reads(B[0, j])
                     T.writes(C[i, j])
                     C[i, j] = B[0, j]
@@ -298,19 +298,19 @@ def match_buffer_func(a: T.handle, c: T.handle) -> None:
     A = T.match_buffer(a, (16, 16))
     C = T.match_buffer(c, (16, 16))
     for i in range(0, 16):
-        with T.block([]):
+        with T.block():
             A0 = T.match_buffer(A[i, 0:16], (16))
             C0 = T.match_buffer(C[i, 0:16], (16))
             B = T.alloc_buffer((16, 16))
-            with T.block([]):
+            with T.block():
                 B0 = T.match_buffer(B[i, 0:16], (16))
                 for j in range(0, 16):
-                    with T.block([]) as []:
+                    with T.block() as []:
                         A1 = T.match_buffer(A0[j], ())
                         B1 = T.match_buffer(B0[j], ())
                         B1[()] = A1[()] + 1.0
             for j in range(0, 16):
-                with T.block([]) as []:
+                with T.block() as []:
                     C1 = T.match_buffer(C0[j], ())
                     B2 = T.match_buffer(B[i, j], ())
                     C1[()] = B2[()] * 2.0
@@ -321,19 +321,19 @@ def compacted_match_buffer_func(a: T.handle, c: T.handle) -> None:
     A = T.match_buffer(a, (16, 16))
     C = T.match_buffer(c, (16, 16))
     for i in range(0, 16):
-        with T.block([]):
+        with T.block():
             A0 = T.match_buffer(A[i, 0:16], (16))
             C0 = T.match_buffer(C[i, 0:16], (16))
             B = T.alloc_buffer((1, 16))
-            with T.block([]):
+            with T.block():
                 B0 = T.match_buffer(B[0, 0:16], (16))
                 for j in range(0, 16):
-                    with T.block([]) as []:
+                    with T.block() as []:
                         A1 = T.match_buffer(A0[j], ())
                         B1 = T.match_buffer(B0[j], ())
                         B1[()] = A1[()] + 1.0
             for j in range(0, 16):
-                with T.block([]) as []:
+                with T.block() as []:
                     C1 = T.match_buffer(C0[j], ())
                     B2 = T.match_buffer(B[0, j], ())
                     C1[()] = B2[()] * 2.0
@@ -344,18 +344,18 @@ def storage_align_func(a: T.handle, c: T.handle) -> None:
     A = T.match_buffer(a, (16, 16), "float32")
     C = T.match_buffer(c, (16, 16), "float32")
     for i in range(0, 16):
-        with T.block([]):
+        with T.block():
             T.reads(A[i, 0:16])
             T.writes(C[i, 0:16])
             B = T.alloc_buffer((16, 16), "float32")
             for j in range(0, 16):
-                with T.block([]) as []:
+                with T.block() as []:
                     T.reads(A[i, j])
                     T.writes(B[i, j])
                     T.block_attr({"buffer_dim_align": [[0, 0, 16, 15]]})
                     B[i, j] = A[i, j] + 1.0
             for j in range(0, 16):
-                with T.block([]) as []:
+                with T.block() as []:
                     T.reads(B[i, j])
                     T.writes(C[i, j])
                     C[i, j] = B[i, j] * 2.0
@@ -366,7 +366,7 @@ def compacted_storage_align_func(a: T.handle, c: T.handle) -> None:
     A = T.match_buffer(a, (16, 16), "float32")
     C = T.match_buffer(c, (16, 16), "float32")
     for i in range(0, 16):
-        with T.block([]):
+        with T.block():
             T.reads(A[i, 0:16])
             T.writes(C[i, 0:16])
             B = T.alloc_buffer((1, 16), strides=(31, 1), dtypes="float32")
diff --git a/tests/python/unittest/test_tir_transform_convert_blocks_to_opaque.py b/tests/python/unittest/test_tir_transform_convert_blocks_to_opaque.py
index 287a30916520c..ee323a64c50f8 100644
--- a/tests/python/unittest/test_tir_transform_convert_blocks_to_opaque.py
+++ b/tests/python/unittest/test_tir_transform_convert_blocks_to_opaque.py
@@ -32,19 +32,19 @@ def elementwise_func(a: T.handle, c: T.handle) -> None:
     A = T.match_buffer(a, (16, 16), "float32")
     C = T.match_buffer(c, (16, 16), "float32")
     for i in range(0, 16):
-        with T.block([]):
+        with T.block():
             T.reads(A[i, 0:16])
             T.writes(C[i, 0:16])
             B = T.alloc_buffer((16, 16), "float32")
             for j in range(0, 16):
-                with T.block([16, 16]) as [vi, vj]:
-                    T.bind(vi, i)
-                    T.bind(vj, j)
+                with T.block():
+                    vi = T.axis.S(16, i)
+                    vj = T.axis.S(16, j)
                     B[vi, vj] = A[vi, vj] + 1.0
             for j in range(0, 16):
-                with T.block([16, 16]) as [vi, vj]:
-                    T.bind(vi, i)
-                    T.bind(vj, j)
+                with T.block():
+                    vi = T.axis.S(16, i)
+                    vj = T.axis.S(16, j)
                     C[vi, vj] = B[vi, vj] * 2.0
 
 
@@ -53,7 +53,7 @@ def substituted_elementwise_func(a: T.handle, c: T.handle) -> None:
     A = T.match_buffer(a, (16, 16), "float32")
     C = T.match_buffer(c, (16, 16), "float32")
     for i in range(0, 16):
-        with T.block([]):
+        with T.block():
             T.reads(A[i, 0:16])
             T.writes(C[i, 0:16])
             B = T.alloc_buffer([16, 16], "float32")
diff --git a/tests/python/unittest/test_tir_transform_flatten_buffer.py b/tests/python/unittest/test_tir_transform_flatten_buffer.py
index 21c896c7bb7ef..eed82ebb91187 100644
--- a/tests/python/unittest/test_tir_transform_flatten_buffer.py
+++ b/tests/python/unittest/test_tir_transform_flatten_buffer.py
@@ -32,7 +32,7 @@ def compacted_elementwise_func(a: T.handle, c: T.handle) -> None:
     A = T.match_buffer(a, (16, 16), "float32")
     C = T.match_buffer(c, (16, 16), "float32")
     for i in range(0, 16):
-        with T.block([]):
+        with T.block():
             T.reads(A[i, 0:16])
             T.writes(C[i, 0:16])
             B = T.alloc_buffer([1, 16], "float32", scope="global")
@@ -67,7 +67,7 @@ def compacted_gpu_func(a: T.handle, c: T.handle) -> None:
     for i0 in T.thread_binding(0, 4, thread="blockIdx.x"):
         for i1 in T.thread_binding(0, 2, thread="threadIdx.x"):
             for i2 in T.thread_binding(0, 2, thread="vthread"):
-                with T.block([]):
+                with T.block():
                     T.reads(A[i0 * 4 + i1 * 2 + i2, 0:16])
                     T.writes(C[i0 * 4 + i1 * 2 + i2, 0:16])
                     B = T.alloc_buffer([1, 16], "float32", scope="local")
@@ -108,17 +108,17 @@ def compacted_symbolic_func(a: T.handle, c: T.handle, n: T.int32, m: T.int32) ->
     C = T.match_buffer(c, (n, m), "float32")
 
     for i in range(0, n):
-        with T.block([]):
+        with T.block():
             T.reads(A[i, m])
             T.writes(C[i, m])
             B = T.alloc_buffer((m,), "float32", scope="global")
             for j in range(0, m):
-                with T.block([]) as []:
+                with T.block() as []:
                     T.reads(A[i, j])
                     T.writes(B[j])
                     B[j] = A[i, j] + 1.0
             for j in range(0, m):
-                with T.block([]) as []:
+                with T.block() as []:
                     T.reads(B[j])
                     T.writes(C[i, j])
                     C[i, j] = B[j] * 2.0
@@ -143,7 +143,7 @@ def compacted_predicate_func(a: T.handle, c: T.handle) -> None:
     C = T.match_buffer(c, (32), "float32")
 
     for i, j in T.grid(5, 7):
-        with T.block([]) as []:
+        with T.block() as []:
             T.reads(A[i * 7 + j])
             T.writes(C[i * 7 + j])
             T.where(i * 7 + j < 32)
@@ -166,7 +166,7 @@ def compacted_unit_loop_func(a: T.handle, c: T.handle) -> None:
     C = T.match_buffer(c, (32), "float32")
 
     for x, y, z in T.grid(4, 1, 8):
-        with T.block([]) as []:
+        with T.block() as []:
             T.reads(A[x * 8 + y * 8 + z])
             T.writes(C[x * 8 + y * 8 + z])
             C[x * 8 + y * 8 + z] = A[x * 8 + y * 8 + z] + 1.0
@@ -187,7 +187,7 @@ def compacted_multi_alloc_func(a: T.handle, d: T.handle) -> None:
     D = T.match_buffer(d, (32), "float32")
 
     for i in range(0, 32):
-        with T.block([]) as []:
+        with T.block() as []:
             T.reads(A[i])
             T.writes(D[i])
             B = T.alloc_buffer((32,), scope="global")
@@ -215,7 +215,7 @@ def compacted_strided_buffer_func(a: T.handle, c: T.handle) -> None:
     A = T.match_buffer(a, (16, 16), "float32")
     C = T.match_buffer(c, (16, 16), "float32")
     for i0 in range(0, 4):
-        with T.block([]):
+        with T.block():
             T.reads(A[i0 * 4 : i0 * 4 + 4, 0:16])
             T.writes(C[i0 * 4 : i0 * 4 + 4, 0:16])
             B = T.alloc_buffer([4, 16], "float32", strides=[17, 1], scope="global")
diff --git a/tests/python/unittest/test_tir_transform_lower_init_block.py b/tests/python/unittest/test_tir_transform_lower_init_block.py
index c1c4fb3d2e8fa..a4fd9404eee4f 100644
--- a/tests/python/unittest/test_tir_transform_lower_init_block.py
+++ b/tests/python/unittest/test_tir_transform_lower_init_block.py
@@ -15,7 +15,7 @@
 # specific language governing permissions and limitations
 # under the License.
 import tvm
-from tvm import tir, te
+from tvm import te
 from tvm.script import tir as T
 
 # pylint: disable=no-self-argument
@@ -28,10 +28,13 @@ def main(a: T.handle, b: T.handle) -> None:
         A = T.match_buffer(a, [64, 64, 64])
         B = T.match_buffer(b, [64])
 
-        with T.block([64, T.reduce_axis(0, 64), T.reduce_axis(32, 64)]) as [i, j, k]:
-            with T.init():
-                B[i] = T.float32(0)
-            B[i] += A[i, j, k]
+        for i0, j0 in T.grid(64, 64):
+            for k0 in T.serial(32, 64):
+                with T.block():
+                    i, j, k = T.axis.remap("SRR", [i0, j0, k0])
+                    with T.init():
+                        B[i] = T.float32(0)
+                    B[i] += A[i, j, k]
 
 
 @tvm.script.ir_module
@@ -41,10 +44,13 @@ def main(a: T.handle, b: T.handle) -> None:
         A = T.match_buffer(a, [64, 64, 64])
         B = T.match_buffer(b, [64])
 
-        with T.block([64, T.reduce_axis(0, 64), T.reduce_axis(32, 64)]) as [i, j, k]:
-            if (j == 0) and (k == 32):
-                B[i] = T.float32(0)
-            B[i] += A[i, j, k]
+        for i0, j0 in T.grid(64, 64):
+            for k0 in T.serial(32, 64):
+                with T.block():
+                    i, j, k = T.axis.remap("SRR", [i0, j0, k0])
+                    if (j == 0) and (k == 32):
+                        B[i] = T.float32(0)
+                    B[i] += A[i, j, k]
 
 
 @tvm.script.ir_module
@@ -54,12 +60,15 @@ def main(a: T.handle, b: T.handle) -> None:
         A = T.match_buffer(a, [64, 64, 64])
         B = T.match_buffer(b, [64])
 
-        with T.block([64, T.reduce_axis(0, 64), T.reduce_axis(32, 64)]) as [i, j, k]:
-            BB = T.match_buffer(B[i], ())
-            AA = T.match_buffer(A[i, 0:64, 0:64], (64, 64))
-            with T.init():
-                BB[()] = T.float32(0)
-            BB[()] += AA[j, k]
+        for i0, j0 in T.grid(64, 64):
+            for k0 in T.serial(32, 64):
+                with T.block():
+                    i, j, k = T.axis.remap("SRR", [i0, j0, k0])
+                    BB = T.match_buffer(B[i], ())
+                    AA = T.match_buffer(A[i, 0:64, 0:64], (64, 64))
+                    with T.init():
+                        BB[()] = T.float32(0)
+                    BB[()] += AA[j, k]
 
 
 @tvm.script.ir_module
@@ -69,17 +78,21 @@ def main(a: T.handle, b: T.handle) -> None:
         A = T.match_buffer(a, [64, 64, 64])
         B = T.match_buffer(b, [64])
 
-        with T.block([64, T.reduce_axis(0, 64), T.reduce_axis(32, 64)]) as [i, j, k]:
-            BB = T.match_buffer(B[i], ())
-            AA = T.match_buffer(A[i, 0:64, 0:64], (64, 64))
-            if (j == 0) and (k == 32):
-                BB[()] = T.float32(0)
-            BB[()] += AA[j, k]
+        for i0, j0 in T.grid(64, 64):
+            for k0 in T.serial(32, 64):
+                with T.block():
+                    i, j, k = T.axis.remap("SRR", [i0, j0, k0])
+                    BB = T.match_buffer(B[i], ())
+                    AA = T.match_buffer(A[i, 0:64, 0:64], (64, 64))
+                    if (j == 0) and (k == 32):
+                        BB[()] = T.float32(0)
+                    BB[()] += AA[j, k]
 
 
 def test_lower_reduction():
     origin_mod = WithInit
     mod = tvm.tir.transform.LowerInitBlock()(origin_mod)
+    print(mod.script())
     tvm.ir.assert_structural_equal(mod, WithBranch, True)
 
 
diff --git a/tests/python/unittest/test_tir_transform_plan_update_buffer_allocation_location.py b/tests/python/unittest/test_tir_transform_plan_update_buffer_allocation_location.py
index e55555305a09c..c22f5f82ee10f 100644
--- a/tests/python/unittest/test_tir_transform_plan_update_buffer_allocation_location.py
+++ b/tests/python/unittest/test_tir_transform_plan_update_buffer_allocation_location.py
@@ -15,7 +15,7 @@
 # specific language governing permissions and limitations
 # under the License.
 import tvm
-from tvm import tir, te
+from tvm import te
 from tvm.script import tir as T
 
 
@@ -31,12 +31,14 @@ def element_func(a: T.handle, c: T.handle) -> None:
     A = T.match_buffer(a, (16, 16))
     C = T.match_buffer(c, (16, 16))
     B = T.alloc_buffer((16, 16))
-    for i_0 in range(0, 16):
-        for j_0 in range(0, 16):
-            with T.block([16, 16]) as [i, j]:
+    for i0 in range(0, 16):
+        for j0 in range(0, 16):
+            with T.block():
+                i, j = T.axis.remap("SS", [i0, j0])
                 B[i, j] = A[i, j] + 1.0
-        for j_0 in range(0, 16):
-            with T.block([16, 16]) as [i, j]:
+        for j0 in range(0, 16):
+            with T.block():
+                i, j = T.axis.remap("SS", [i0, j0])
                 C[i, j] = B[i, j] * 2.0
 
 
@@ -46,95 +48,112 @@ def transformed_element_func(a: T.handle, c: T.handle) -> None:
     C = T.match_buffer(c, [16, 16])
 
     for i_0 in range(0, 16):
-        with T.block([]):
+        with T.block():
             T.reads([A[i_0, 0:16]])
             T.writes([C[i_0, 0:16]])
             B = T.alloc_buffer([16, 16])
             for j_0 in T.serial(0, 16):
-                with T.block([16, 16], "") as [i, j]:
-                    T.bind(i, i_0)
-                    T.bind(j, j_0)
+                with T.block():
+                    i, j = T.axis.remap("SS", [i_0, j_0])
                     B[i, j] = A[i, j] + 1.0
             for j_0 in T.serial(0, 16):
-                with T.block([16, 16], "") as [i, j]:
-                    T.bind(i, i_0)
-                    T.bind(j, j_0)
+                with T.block():
+                    i, j = T.axis.remap("SS", [i_0, j_0])
                     C[i, j] = B[i, j] * 2.0
 
 
 @T.prim_func
 def original_func() -> None:
     A = T.alloc_buffer((128, 128), "float32")
-    with T.block([128, 128]) as [i, j]:
-        A[i, j] = T.float32(0)
-    with T.block([32, 32, T.reduce_axis(0, 32)]) as [i, j, k]:
-        B = T.alloc_buffer((128, 128), "float32")
-        C = T.alloc_buffer((128, 128), "float32")
-        D = T.alloc_buffer((128, 128), "float32")
-        if k == 0:
+    for i0, j0 in T.grid(128, 128):
+        with T.block():
+            i, j = T.axis.remap("SS", [i0, j0])
+            A[i, j] = T.float32(0)
+    for i0, j0, k0 in T.grid(32, 32, 32):
+        with T.block():
+            i, j, k = T.axis.remap("SSR", [i0, j0, k0])
+            B = T.alloc_buffer((128, 128), "float32")
+            C = T.alloc_buffer((128, 128), "float32")
+            D = T.alloc_buffer((128, 128), "float32")
+            if k == 0:
+                for ii, jj in T.grid(4, 4):
+                    B[i * 4 + ii, j * 4 + jj] = A[i * 4 + ii, j * 4 + jj]
             for ii, jj in T.grid(4, 4):
-                B[i * 4 + ii, j * 4 + jj] = A[i * 4 + ii, j * 4 + jj]
-        for ii, jj in T.grid(4, 4):
-            for kk in range(0, 4):
-                B[i * 4 + ii, j * 4 + jj] += C[i * 4 + ii, k * 4 + kk]
-            for kk in range(0, 4):
-                B[i * 4 + ii, j * 4 + jj] += D[j * 4 + jj, k * 4 + kk] * C[i * 4 + ii, k * 4 + kk]
+                for kk in range(0, 4):
+                    B[i * 4 + ii, j * 4 + jj] += C[i * 4 + ii, k * 4 + kk]
+                for kk in range(0, 4):
+                    B[i * 4 + ii, j * 4 + jj] += (
+                        D[j * 4 + jj, k * 4 + kk] * C[i * 4 + ii, k * 4 + kk]
+                    )
 
 
 @T.prim_func
 def transformed_func() -> None:
     A = T.alloc_buffer([128, 128])
-    with T.block([128, 128], "") as [i, j]:
-        A[i, j] = T.float32(0)
-    with T.block([32, 32, T.reduce_axis(0, 32)], "") as [i, j, k]:
-        B = T.alloc_buffer([128, 128])
-        if k == 0:
+    for i0, j0 in T.grid(128, 128):
+        with T.block():
+            i, j = T.axis.remap("SS", [i0, j0])
+            A[i, j] = T.float32(0)
+    for i0, j0, k0 in T.grid(32, 32, 32):
+        with T.block():
+            i, j, k = T.axis.remap("SSR", [i0, j0, k0])
+            B = T.alloc_buffer([128, 128])
+            if k == 0:
+                for ii, jj in T.grid(4, 4):
+                    B[i * 4 + ii, j * 4 + jj] = A[i * 4 + ii, j * 4 + jj]
             for ii, jj in T.grid(4, 4):
-                B[i * 4 + ii, j * 4 + jj] = A[i * 4 + ii, j * 4 + jj]
-        for ii, jj in T.grid(4, 4):
-            with T.block([], ""):
-                T.reads([B[((i * 4) + ii), ((j * 4) + jj)]])
-                T.writes([B[((i * 4) + ii), ((j * 4) + jj)]])
-                C = T.alloc_buffer([128, 128])
-                for kk in T.serial(0, 4):
-                    B[((i * 4) + ii), ((j * 4) + jj)] = (
-                        B[((i * 4) + ii), ((j * 4) + jj)] + C[((i * 4) + ii), ((k * 4) + kk)]
-                    )
-                for kk in T.serial(0, 4):
-                    with T.block([], ""):
-                        T.reads(
-                            [
-                                B[((i * 4) + ii), ((j * 4) + jj)],
-                                C[((i * 4) + ii), ((k * 4) + kk)],
-                            ]
-                        )
-                        T.writes([B[((i * 4) + ii), ((j * 4) + jj)]])
-                        D = T.alloc_buffer([128, 128])
-                        B[((i * 4) + ii), ((j * 4) + jj)] = B[((i * 4) + ii), ((j * 4) + jj)] + (
-                            D[((j * 4) + jj), ((k * 4) + kk)] * C[((i * 4) + ii), ((k * 4) + kk)]
+                with T.block(""):
+                    T.reads([B[((i * 4) + ii), ((j * 4) + jj)]])
+                    T.writes([B[((i * 4) + ii), ((j * 4) + jj)]])
+                    C = T.alloc_buffer([128, 128])
+                    for kk in T.serial(0, 4):
+                        B[((i * 4) + ii), ((j * 4) + jj)] = (
+                            B[((i * 4) + ii), ((j * 4) + jj)] + C[((i * 4) + ii), ((k * 4) + kk)]
                         )
+                    for kk in T.serial(0, 4):
+                        with T.block(""):
+                            T.reads(
+                                [
+                                    B[((i * 4) + ii), ((j * 4) + jj)],
+                                    C[((i * 4) + ii), ((k * 4) + kk)],
+                                ]
+                            )
+                            T.writes([B[((i * 4) + ii), ((j * 4) + jj)]])
+                            D = T.alloc_buffer([128, 128])
+                            B[((i * 4) + ii), ((j * 4) + jj)] = B[
+                                ((i * 4) + ii), ((j * 4) + jj)
+                            ] + (
+                                D[((j * 4) + jj), ((k * 4) + kk)]
+                                * C[((i * 4) + ii), ((k * 4) + kk)]
+                            )
 
 
 @T.prim_func
 def match_buffer_func() -> None:
     C = T.alloc_buffer((128, 128))
-    with T.block([128]) as [vi]:
-        C0 = T.match_buffer(C[vi, 0:128], (128))
-        with T.block([128]) as [jj]:
-            C1 = T.match_buffer(C0[jj], ())
-            C1[()] = 0
+    for i in range(128):
+        with T.block():
+            vi = T.axis.S(128, i)
+            C0 = T.match_buffer(C[vi, 0:128], (128))
+            for j in range(128):
+                with T.block():
+                    jj = T.axis.S(128, j)
+                    C1 = T.match_buffer(C0[jj], ())
+                    C1[()] = 0
 
 
 @T.prim_func
 def transformed_match_buffer_func() -> None:
     for i in range(0, 128):
-        with T.block([128]) as [vi]:
-            T.bind(vi, i)
+        with T.block():
+            vi = T.axis.S(128, i)
             C = T.alloc_buffer((128, 128))
             C0 = T.match_buffer(C[vi, 0:128], (128))
-            with T.block([128]) as [jj]:
-                C1 = T.match_buffer(C0[jj], ())
-                C1[()] = 0
+            for j in range(128):
+                with T.block():
+                    jj = T.axis.S(128, j)
+                    C1 = T.match_buffer(C0[jj], ())
+                    C1[()] = 0
 
 
 @T.prim_func
@@ -143,9 +162,10 @@ def opaque_access(a: T.handle, b: T.handle) -> None:
     B = T.match_buffer(b, [1024])
     A_cache = T.alloc_buffer([1024])
     for i in T.serial(0, 8):
-        with T.block([8]) as [vi]:
-            with T.block([8]) as [v]:
-                T.bind(v, vi)
+        with T.block():
+            vi = T.axis.S(8, i)
+            with T.block():
+                v = T.axis.S(8, vi)
                 T.reads([A[(v * 128) : ((v * 128) + 128)]])
                 T.writes([A_cache[(v * 128) : ((v * 128) + 128)]])
                 T.evaluate(
@@ -161,8 +181,8 @@ def opaque_access(a: T.handle, b: T.handle) -> None:
                     )
                 )
             for j in T.serial(0, 128):
-                with T.block([1024]) as [v]:
-                    T.bind(v, ((vi * 128) + j))
+                with T.block():
+                    v = T.axis.S(1024, vi * 128 + j)
                     T.reads([A_cache[v]])
                     T.writes([B[v]])
                     B[v] = A_cache[v]
@@ -173,12 +193,13 @@ def transformed_opaque_access(a: T.handle, b: T.handle) -> None:
     A = T.match_buffer(a, [1024])
     B = T.match_buffer(b, [1024])
     for i in T.serial(0, 8):
-        with T.block([8]) as [vi]:
+        with T.block():
+            vi = T.axis.S(8, i)
             T.reads(A[vi * 128 : vi * 128 + 128])
             T.writes(B[vi * 128 : vi * 128 + 128])
             A_cache = T.alloc_buffer([1024])
-            with T.block([8]) as [v]:
-                T.bind(v, vi)
+            with T.block():
+                v = T.axis.S(8, vi)
                 T.reads([A[v * 128 : v * 128 + 128]])
                 T.writes([A_cache[v * 128 : v * 128 + 128]])
                 T.evaluate(
@@ -187,8 +208,8 @@ def transformed_opaque_access(a: T.handle, b: T.handle) -> None:
                     )
                 )
             for j in T.serial(0, 128):
-                with T.block([1024]) as [v]:
-                    T.bind(v, ((vi * 128) + j))
+                with T.block():
+                    v = T.axis.S(1024, vi * 128 + j)
                     T.reads([A_cache[v]])
                     T.writes([B[v]])
                     B[v] = A_cache[v]
diff --git a/tests/python/unittest/test_tvmscript_complete.py b/tests/python/unittest/test_tvmscript_complete.py
index 7c521db21bb84..d3b4b23acdb66 100644
--- a/tests/python/unittest/test_tvmscript_complete.py
+++ b/tests/python/unittest/test_tvmscript_complete.py
@@ -26,10 +26,12 @@ def matmul(a: T.handle, b: T.handle, c: T.handle) -> None:
     B = T.match_buffer(b, [128, 128])
     C = T.match_buffer(c, [128, 128])
 
-    with T.block([128, 128, T.reduce_axis(0, 128)], "update") as [vi, vj, vk]:
-        with T.init():
-            C[vi, vj] = T.float32(0)
-        C[vi, vj] = C[vi, vj] + A[vi, vk] * B[vj, vk]
+    for i, j, k in T.grid(128, 128, 128):
+        with T.block("update"):
+            vi, vj, vk = T.axis.remap("SSR", [i, j, k])
+            with T.init():
+                C[vi, vj] = T.float32(0)
+            C[vi, vj] = C[vi, vj] + A[vi, vk] * B[vj, vk]
 
 
 @T.prim_func
@@ -39,12 +41,14 @@ def matmul_original(a: T.handle, b: T.handle, c: T.handle) -> None:
     C = T.match_buffer(c, [128, 128])
 
     for i, j in T.grid(32, 32):
-        with T.block([32, 32], "init") as [vi, vj]:
+        with T.block("init"):
+            vi, vj = T.axis.remap("SS", [i, j])
             for ii, jj in T.grid(4, 4):
                 C[vi * 4 + ii, vj * 4 + jj] = T.float32(0)
 
         for k in range(0, 32):
-            with T.block([128, 128, T.reduce_axis(0, 128)], "update") as [vi, vj, vk]:
+            with T.block("update"):
+                vi, vj, vk = T.axis.remap("SSR", [i, j, k])
                 for ii, jj, kk in T.grid(4, 4, 4):
                     C[vi * 4 + ii, vj * 4 + jj] = (
                         C[vi * 4 + ii, vj * 4 + jj]
@@ -58,12 +62,15 @@ def elementwise_with_root(a: T.handle, b: T.handle, c: T.handle) -> None:
     B = T.match_buffer(b, [128, 128])
     C = T.match_buffer(c, [128, 128])
 
-    with T.block([]) as []:
-        with T.block([128, 128]) as [vi, vj]:
-            B[vi, vj] = A[vi, vj] + T.float32(1)
-
-        with T.block([128, 128]) as [vi, vj]:
-            C[vi, vj] = B[vi, vj] + T.float32(1)
+    with T.block() as []:
+        for i, j in T.grid(128, 128):
+            with T.block():
+                vi, vj = T.axis.remap("SS", [i, j])
+                B[vi, vj] = A[vi, vj] + T.float32(1)
+        for i, j in T.grid(128, 128):
+            with T.block():
+                vi, vj = T.axis.remap("SS", [i, j])
+                C[vi, vj] = B[vi, vj] + T.float32(1)
 
 
 def func_with_opaque_block(a: T.handle, b: T.handle, c: T.handle) -> None:
@@ -71,12 +78,13 @@ def func_with_opaque_block(a: T.handle, b: T.handle, c: T.handle) -> None:
     B = T.match_buffer(b, [128, 128])
     C = T.match_buffer(c, [128, 128])
 
-    with T.block([]) as []:
-        with T.block([]) as []:
+    with T.block() as []:
+        with T.block() as []:
             B[0, 0] = A[0, 0] + T.float32(1)
-
-        with T.block([128, 128]) as [vi, vj]:
-            C[vi, vj] = B[vi, vj] + T.float32(1)
+        for i, j in T.grid(128, 128):
+            with T.block():
+                vi, vj = T.axis.remap("SS", [i, j])
+                C[vi, vj] = B[vi, vj] + T.float32(1)
 
 
 @T.prim_func
@@ -85,14 +93,18 @@ def func_with_part_access_region(a: T.handle, b: T.handle, c: T.handle) -> None:
     B = T.match_buffer(b, [128, 128])
     C = T.match_buffer(c, [128, 128])
 
-    with T.block([]) as []:
-        with T.block([128, 128]) as [vi, vj]:
-            T.reads(A[vi, vj])
-            B[vi, vj] = A[vi, vj] + T.float32(1)
+    with T.block() as []:
+        for i, j in T.grid(128, 128):
+            with T.block():
+                vi, vj = T.axis.remap("SS", [i, j])
+                T.reads(A[vi, vj])
+                B[vi, vj] = A[vi, vj] + T.float32(1)
 
-        with T.block([128, 128]) as [vi, vj]:
-            T.writes(C[vi, vj])
-            C[vi, vj] = B[vi, vj] + T.float32(1)
+        for i, j in T.grid(128, 128):
+            with T.block():
+                vi, vj = T.axis.remap("SS", [i, j])
+                T.writes(C[vi, vj])
+                C[vi, vj] = B[vi, vj] + T.float32(1)
 
 
 def test_complete_matmul():
@@ -181,22 +193,23 @@ def func_with_bufferslice_indices(data: T.handle, index: T.handle) -> None:
     index_buf = T.match_buffer(index, (1,), "int32")
     out_buf = T.alloc_buffer((16, 16), "float32")
 
-    with T.block([16, 16]) as [vi, vj]:
-        out_buf[vi, vj] = data_buf[vi, index_buf[0]]
+    for i, j in T.grid(16, 16):
+        with T.block():
+            vi, vj = T.axis.remap("SS", [i, j])
+            out_buf[vi, vj] = data_buf[vi, index_buf[0]]
 
 
 @T.prim_func
 def expected_bufferslice_indices(data: T.handle, index: T.handle) -> None:
     index_buf = T.match_buffer(index, [1], dtype="int32", elem_offset=0, align=128, offset_factor=1)
     data_buf = T.match_buffer(data, [16, 16], elem_offset=0, align=128, offset_factor=1)
-    with T.block([], "root"):
+    with T.block("root"):
         T.reads([])
         T.writes([])
         out_buf = T.alloc_buffer([16, 16], elem_offset=0, align=128, offset_factor=1)
         for i0, i1 in T.grid(16, 16):
-            with T.block([16, 16], "") as [vi, vj]:
-                T.bind(vi, i0)
-                T.bind(vj, i1)
+            with T.block():
+                vi, vj = T.axis.remap("SS", [i0, i1])
                 T.reads([data_buf[vi, 0:16], index_buf[0]])
                 T.writes([out_buf[vi, vj]])
                 out_buf[vi, vj] = data_buf[vi, index_buf[0]]
@@ -208,22 +221,23 @@ def func_with_recursive_bufferslice_indices(data: T.handle, index: T.handle) ->
     index_buf = T.match_buffer(index, (1,), "int32")
     out_buf = T.alloc_buffer((16, 16), "float32")
 
-    with T.block([16, 16]) as [vi, vj]:
-        out_buf[vi, vj] = data_buf[index_buf[index_buf[0]], index_buf[0]]
+    for i, j in T.grid(16, 16):
+        with T.block():
+            vi, vj = T.axis.remap("SS", [i, j])
+            out_buf[vi, vj] = data_buf[index_buf[index_buf[0]], index_buf[0]]
 
 
 @T.prim_func
 def expected_recursive_bufferslice_indices(data: T.handle, index: T.handle) -> None:
     index_buf = T.match_buffer(index, [1], dtype="int32", elem_offset=0, align=128, offset_factor=1)
     data_buf = T.match_buffer(data, [16, 16], elem_offset=0, align=128, offset_factor=1)
-    with T.block([], "root"):
+    with T.block("root"):
         T.reads([])
         T.writes([])
         out_buf = T.alloc_buffer([16, 16], elem_offset=0, align=128, offset_factor=1)
         for i0, i1 in T.grid(16, 16):
-            with T.block([16, 16], "") as [vi, vj]:
-                T.bind(vi, i0)
-                T.bind(vj, i1)
+            with T.block():
+                vi, vj = T.axis.remap("SS", [i0, i1])
                 T.reads([data_buf[0:16, 0:16], index_buf[0]])
                 T.writes([out_buf[vi, vj]])
                 out_buf[vi, vj] = data_buf[index_buf[index_buf[0]], index_buf[0]]
@@ -240,11 +254,11 @@ def test_complete_buffer_indices():
 def match_buffer_func(a: T.handle) -> None:
     A = T.match_buffer(a, (16, 16))
     for i in range(0, 16):
-        with T.block([]):
+        with T.block():
             A0 = T.match_buffer(A[i, 0:16], (16))
-            with T.block([]):
+            with T.block():
                 for j in range(0, 16):
-                    with T.block([]) as []:
+                    with T.block() as []:
                         A1 = T.match_buffer(A0[j], ())
                         A1[()] = 1.0
 
@@ -253,15 +267,15 @@ def match_buffer_func(a: T.handle) -> None:
 def expected_match_buffer_func(a: T.handle) -> None:
     A = T.match_buffer(a, (16, 16))
     for i in range(0, 16):
-        with T.block([]):
+        with T.block():
             T.reads([])
             T.writes(A[i, 0:16])
             A0 = T.match_buffer(A[i, 0:16], (16))
-            with T.block([]):
+            with T.block():
                 T.reads([])
                 T.writes(A0[0:16])
                 for j in range(0, 16):
-                    with T.block([]) as []:
+                    with T.block() as []:
                         T.reads([])
                         T.writes(A0[j])
                         A1 = T.match_buffer(A0[j], ())
diff --git a/tests/python/unittest/test_tvmscript_error_report.py b/tests/python/unittest/test_tvmscript_error_report.py
index 99a22636b9272..80c37229f5193 100644
--- a/tests/python/unittest/test_tvmscript_error_report.py
+++ b/tests/python/unittest/test_tvmscript_error_report.py
@@ -155,33 +155,83 @@ def test_allocate_with_buffers():
     check_error(allocate_with_buffers, 2)
 
 
-def inconsistent_binding() -> None:
-    with T.block([128, 128]) as [vi]:  # error
+def inconsistent_binding_value() -> None:
+    for i, j in T.grid(16, 16):
+        vi, vj = T.axis.remap("SS", [i])  # error
+        T.evaluate(1.0)
+
+
+def inconsistent_binding_type() -> None:
+    for i, j in T.grid(16, 16):
+        vi, vj = T.axis.remap("S", [i, j])  # error
         T.evaluate(1.0)
 
 
 def test_inconsistent_binding():
-    check_error(inconsistent_binding, 2)
+    check_error(inconsistent_binding_value, 3)
+    check_error(inconsistent_binding_type, 3)
+
+
+def error_remap_type() -> None:
+    for i, j in T.grid(16, 16):
+        with T.block():
+            vi, vj = T.axis.remap("TT", [i, j])  # error
+            T.evaluate(1.0)
+
+
+def error_remap_value() -> None:
+    for i, j in T.grid(16, 16):
+        with T.block():
+            vi, vj = T.axis.remap("SS", [i + j, j])  # error
+            T.evaluate(1.0)
+
+
+def test_error_remap_args():
+    check_error(error_remap_type, 4)
+    check_error(error_remap_value, 4)
 
 
 def invalid_block_axes(a: T.handle) -> None:
     A = T.match_buffer(a, (16, 16), "float32")
-    with T.block([A]) as [vi]:  # error
-        T.evaluate(1.0)
+    for i, j in T.grid(16, 16):
+        with T.block():
+            vi = T.axis.S(i, A)  # error
+            T.evaluate(1.0)
 
 
 def test_invalid_block_axes():
-    check_error(invalid_block_axes, 3)
+    check_error(invalid_block_axes, 5)
 
 
-def miss_block_bind() -> None:
-    with T.block([16, 16]) as [vi, vj]:  # error
-        T.bind(vi, 1)
-        T.evaluate(1.0)
+def duplicate_block_axes() -> None:
+    for i, j in T.grid(16, 16):
+        with T.block():
+            vi = T.axis.S(16, i)
+            vi = T.axis.S(16, j)  # error
+            T.evaluate(1.0)
+
+
+def duplicate_block_axes_remap() -> None:
+    for i, j in T.grid(16, 16):
+        with T.block():
+            vi, vi = T.axis.remap("SS", [i, j])  # error
+            T.evaluate(1.0)
+
+
+def test_duplicate_block_axes():
+    check_error(duplicate_block_axes, 5)
+    check_error(duplicate_block_axes_remap, 4)
+
+
+def miss_block_bind_value() -> None:
+    for i, j in T.grid(128, 128):
+        with T.block():
+            vi = T.axis.S(i)  # error
+            T.evaluate(1.0)
 
 
 def test_miss_block_bind():
-    check_error(miss_block_bind, 2)
+    check_error(miss_block_bind_value, 4)
 
 
 def invalid_loop_var() -> None:
@@ -203,74 +253,99 @@ def test_inconsistent_grid():
 
 
 def invalid_match_buffer_region() -> None:
-    with T.block([16, 16]) as [vi, vj]:
-        A = T.match_buffer(vi)  # error
-        T.evaluate(1.0)
+    for i, j in T.grid(128, 128):
+        with T.block():
+            vi, vj = T.axis.remap("SS", [i, j])
+            A = T.match_buffer(vi)  # error
+            T.evaluate(1.0)
 
 
 def test_invalid_match_buffer_region():
-    check_error(invalid_match_buffer_region, 3)
+    check_error(invalid_match_buffer_region, 5)
 
 
 def duplicate_buffer() -> None:
     A = T.alloc_buffer((128, 128), "float32")
-    with T.block([16, 16]) as [vi, vj]:
-        A = T.alloc_buffer((128, 128), "float32")  # error
-        T.evaluate(1.0)
+    for i, j in T.grid(128, 128):
+        with T.block():
+            vi, vj = T.axis.remap("SS", [i, j])
+            A = T.alloc_buffer((128, 128), "float32")  # error
+            T.evaluate(1.0)
 
 
 def test_duplicate_buffer():
-    check_error(duplicate_buffer, 4)
+    check_error(duplicate_buffer, 6)
 
 
 def duplicate_reads() -> None:
     A = T.alloc_buffer((128, 128), "float32")
-    with T.block([16, 16]) as [vi, vj]:
-        T.reads(A[0:8, 0:8])
-        T.reads(A[0:16, 0:16])  # error
-        T.evaluate(1.0)
+    for i, j in T.grid(128, 128):
+        with T.block():
+            vi, vj = T.axis.remap("SS", [i, j])
+            T.reads(A[0:8, 0:8])
+            T.reads(A[0:16, 0:16])  # error
+            T.evaluate(1.0)
 
 
 def duplicate_writes() -> None:
     A = T.alloc_buffer((128, 128), "float32")
-    with T.block([16, 16]) as [vi, vj]:
-        T.writes(A[0:8, 0:8])
-        T.writes(A[0:16, 0:16])  # error
-        T.evaluate(1.0)
+    for i, j in T.grid(128, 128):
+        with T.block():
+            vi, vj = T.axis.remap("SS", [i, j])
+            T.writes(A[0:8, 0:8])
+            T.writes(A[0:16, 0:16])  # error
+            T.evaluate(1.0)
 
 
 def duplicate_predicate() -> None:
-    with T.block([16, 16]) as [vi, vj]:
-        T.where(1)
-        T.where(0)  # error
+    for i, j in T.grid(16, 16):
+        with T.block():
+            vi, vj = T.axis.remap("SS", [i, j])
+            T.where(1)
+            T.where(0)  # error
 
 
 def duplicate_annotations() -> None:
-    with T.block([16, 16]) as [vi, vj]:
-        T.block_attr({})
-        T.block_attr({})  # error
+    for i, j in T.grid(16, 16):
+        with T.block():
+            vi, vj = T.axis.remap("SS", [i, j])
+            T.block_attr({})
+            T.block_attr({})  # error
 
 
 def duplicate_init() -> None:
-    with T.block([16, 16]) as [vi, vj]:
-        with T.init():
-            T.evaluate(1.0)
-        with T.init():  # error
+    for i, j in T.grid(16, 16):
+        with T.block():
+            vi, vj = T.axis.remap("SS", [i, j])
+            with T.init():
+                T.evaluate(1.0)
+            with T.init():  # error
+                T.evaluate(1.0)
+
+
+def duplicate_axes() -> None:
+    for i, j in T.grid(16, 16):
+        with T.block():
+            vi, vj = T.axis.remap("SS", [i, j])
+            vi = T.axis.S(i, 16)  # error
             T.evaluate(1.0)
 
 
 def test_duplicate_block_signature():
-    check_error(duplicate_reads, 5)
-    check_error(duplicate_writes, 5)
-    check_error(duplicate_predicate, 4)
-    check_error(duplicate_annotations, 4)
-    check_error(duplicate_init, 5)
+    check_error(duplicate_reads, 7)
+    check_error(duplicate_writes, 7)
+    check_error(duplicate_predicate, 6)
+    check_error(duplicate_annotations, 6)
+    check_error(duplicate_init, 7)
+    check_error(duplicate_axes, 5)
 
 
 def opaque_access_during_complete(a: T.handle) -> None:  # error
     A = T.match_buffer(a, (16, 16), "float32")
-    with T.block([16, 16]) as [vi, vj]:
-        T.evaluate(T.load("float32", A.data, vi * 16 + vj))
+    for i, j in T.grid(16, 16):
+        with T.block():
+            vi, vj = T.axis.remap("SS", [i, j])
+            T.evaluate(T.load("float32", A.data, vi * 16 + vj))
 
 
 def test_opaque_access_during_complete():
@@ -279,55 +354,65 @@ def test_opaque_access_during_complete():
 
 def convert_slice_to_bufferload() -> None:
     A = T.alloc_buffer((128, 128), "float32")
-    with T.block([16, 16]) as [vi, vj]:
-        A[vi, vj] = A[vi : vi + 2, vj] + 1  # error
+    for i, j in T.grid(128, 128):
+        with T.block():
+            vi, vj = T.axis.remap("SS", [i, j])
+            A[vi, vj] = A[vi : vi + 2, vj] + 1  # error
 
 
 def test_convert_slice_to_bufferload():
-    check_error(convert_slice_to_bufferload, 4)
+    check_error(convert_slice_to_bufferload, 6)
 
 
 def error_index_type() -> None:
     A = T.alloc_buffer((128, 128), "float32")
-    with T.block([16, 16]) as [vi, vj]:
-        A[vi, vj] = A[vi, 0.0] + 1  # error
+    for i, j in T.grid(16, 16):
+        with T.block():
+            vi, vj = T.axis.remap("SS", [i, j])
+            A[vi, vj] = A[vi, 0.0] + 1  # error
 
 
 def error_bufferslice_index_type() -> None:
     A = T.alloc_buffer((1,), "float32")
     B = T.alloc_buffer((16, 16), "float32")
     C = T.alloc_buffer((16, 16), "float32")
-    with T.block([16, 16]) as [vi, vj]:
-        C[vi, vj] = B[vi, A[0]]  # error
+    for i, j in T.grid(16, 16):
+        with T.block():
+            vi, vj = T.axis.remap("SS", [i, j])
+            C[vi, vj] = B[vi, A[0]]  # error
 
 
 def test_error_index_type():
-    check_error(error_index_type, 4)
-    check_error(error_bufferslice_index_type, 6)
+    check_error(error_index_type, 6)
+    check_error(error_bufferslice_index_type, 8)
 
 
 def error_index_with_stop() -> None:
     A = T.alloc_buffer((128, 128), "float32")
-    with T.block([16, 16]) as [vi, vj]:
-        A[vi, vj] = A[vi, 1:10] + 1  # error
+    for i, j in T.grid(128, 128):
+        with T.block():
+            vi, vj = T.axis.remap("SS", [i, j])
+            A[vi, vj] = A[vi, 1:10] + 1  # error
 
 
 def error_bufferslice_index_with_stop() -> None:
     A = T.alloc_buffer((1,), "int32")
     B = T.alloc_buffer((16, 16), "float32")
     C = T.alloc_buffer((16, 16), "float32")
-    with T.block([16, 16]) as [vi, vj]:
-        C[vi, vj] = B[vi, A[0:1]]  # error
+    for i, j in T.grid(16, 16):
+        with T.block():
+            vi, vj = T.axis.remap("SS", [i, j])
+            C[vi, vj] = B[vi, A[0:1]]  # error
 
 
 def test_error_index_with_stop_slice():
-    check_error(error_index_with_stop, 4)
-    check_error(error_bufferslice_index_with_stop, 6)
+    check_error(error_index_with_stop, 6)
+    check_error(error_bufferslice_index_with_stop, 8)
 
 
 def mismatch_args() -> None:
     A = T.alloc_buffer((128, 128), "float32")
-    with T.block([16, 16]) as [vi, vj]:
+    with T.block():
         T.reads(A[0, 0], A[1, 1])  # error
         T.evaluate(1.0)
 
@@ -338,8 +423,7 @@ def test_mismatch_args():
 
 def special_stmt_except() -> None:
     A = T.alloc_buffer("(128, 128)", "float32")  # error
-    with T.block([16, 16]) as [vi, vj]:
-        T.evaluate(1.0)
+    T.evaluate(1.0)
 
 
 def scope_handler_except() -> None:
@@ -368,7 +452,7 @@ def test_tvm_exception_catch():
 def buffer_shape_mismatch(a: T.handle) -> None:
     A = T.match_buffer(a, (8, 8))
     for i, j in T.grid(8, 2):
-        with T.block([]):
+        with T.block():
             T.reads([])
             T.writes([A[i, j * 4 : j * 4 + 4]])
             sub_A = T.match_buffer(
@@ -383,7 +467,7 @@ def test_match_buffer_shape_mismatch():
 
 
 def high_dim_store() -> None:
-    with T.block([], "root"):
+    with T.block("root"):
         B = T.allocate([256], "float32", "global")
         for i, j in T.grid(16, 16):
             B[i, j] = 1.0  # error: Store is only allowed with one index
@@ -393,6 +477,15 @@ def test_high_dim_store():
     check_error(high_dim_store, 5)
 
 
+def block_has_option_vars() -> None:
+    with T.block("root") as x:  # error: block does not support option_vars
+        T.evaluate(0.0)
+
+
+def test_block_has_option_vars():
+    check_error(block_has_option_vars, 2)
+
+
 def check_error(func, rel_lineno):
     # Override the default renderer to accumulate errors
     errors = []
@@ -416,5 +509,7 @@ def render(e):
         ), f"Expected error to be on line {rel_lineno}, but it was on {d.span.line - 1}"
 
 
+# TODO(Siyuan): block iter errors.
+
 if __name__ == "__main__":
     sys.exit(pytest.main([__file__] + sys.argv[1:]))
diff --git a/tests/python/unittest/test_tvmscript_ops.py b/tests/python/unittest/test_tvmscript_ops.py
index c55fd7b692823..6203277c42e0d 100644
--- a/tests/python/unittest/test_tvmscript_ops.py
+++ b/tests/python/unittest/test_tvmscript_ops.py
@@ -37,22 +37,25 @@ def get_valid_counts(
     out_buf = T.match_buffer(out, (1, 2500, 6), "float32")
     out_indices_buf = T.match_buffer(out_indices, (1, 2500), "int32")
 
-    with T.block([1], "init") as [vi]:
+    with T.block("init"):
+        vi = T.axis.S(1, 0)
         valid_count_buf[vi] = T.int32(0)
-        with T.block([2500], "update") as [vj]:
-            T.reads([data_buf[vi, vj, 6]])
-            T.writes([valid_count_buf[vi], out_indices_buf[vi, vj], out_buf[vi, vj, 6]])
-            if (data_buf[vi, vj, score_index] > score_threshold) and (
-                (id_index < 0) or (data_buf[vi, vj, id_index] >= T.float32(0))
-            ):
-                for k in T.serial(0, 6):
-                    out_buf[vi, valid_count_buf[vi], k] = data_buf[vi, vj, k]
-                out_indices_buf[vi, valid_count_buf[vi]] = vj
-                valid_count_buf[vi] = valid_count_buf[vi] + 1
-            if vj >= valid_count_buf[vi]:
-                for k in T.serial(0, 6):
-                    out_buf[vi, vj, k] = T.float32(-1)
-                out_indices_buf[vi, vj] = T.int32(-1)
+        for j in range(2500):
+            with T.block("update"):
+                vj = T.axis.S(2500, j)
+                T.reads([data_buf[vi, vj, 6]])
+                T.writes([valid_count_buf[vi], out_indices_buf[vi, vj], out_buf[vi, vj, 6]])
+                if (data_buf[vi, vj, score_index] > score_threshold) and (
+                    (id_index < 0) or (data_buf[vi, vj, id_index] >= T.float32(0))
+                ):
+                    for k in T.serial(0, 6):
+                        out_buf[vi, valid_count_buf[vi], k] = data_buf[vi, vj, k]
+                    out_indices_buf[vi, valid_count_buf[vi]] = vj
+                    valid_count_buf[vi] = valid_count_buf[vi] + 1
+                if vj >= valid_count_buf[vi]:
+                    for k in T.serial(0, 6):
+                        out_buf[vi, vj, k] = T.float32(-1)
+                    out_indices_buf[vi, vj] = T.int32(-1)
 
 
 def _check_get_valid_counts_with_numpy(f, dshape, score_threshold, id_index, score_index):
diff --git a/tests/python/unittest/test_tvmscript_roundtrip.py b/tests/python/unittest/test_tvmscript_roundtrip.py
index 8058b96b024d1..7c54cdc85f821 100644
--- a/tests/python/unittest/test_tvmscript_roundtrip.py
+++ b/tests/python/unittest/test_tvmscript_roundtrip.py
@@ -2672,10 +2672,12 @@ def matmul(a: T.handle, b: T.handle, c: T.handle) -> None:
     B = T.match_buffer(b, [128, 128])
     C = T.match_buffer(c, [128, 128])
 
-    with T.block([128, 128, T.reduce_axis(0, 128)], "update") as [vi, vj, vk]:
-        with T.init():
-            C[vi, vj] = T.float32(0)
-        C[vi, vj] = C[vi, vj] + A[vi, vk] * B[vj, vk]
+    for i, j, k in T.grid(128, 128, 128):
+        with T.block("update"):
+            vi, vj, vk = T.axis.remap("SSR", [i, j, k])
+            with T.init():
+                C[vi, vj] = T.float32(0)
+            C[vi, vj] = C[vi, vj] + A[vi, vk] * B[vj, vk]
 
 
 @T.prim_func
@@ -2685,11 +2687,13 @@ def matmul_original(a: T.handle, b: T.handle, c: T.handle) -> None:
     C = T.match_buffer(c, [128, 128])
 
     for i, j in T.grid(128, 128):
-        with T.block([128, 128], "init") as [vi, vj]:
+        with T.block("init"):
+            vi, vj = T.axis.remap("SS", [i, j])
             C[vi, vj] = T.float32(0)
 
         for k in range(128):
-            with T.block([128, 128, T.reduce_axis(0, 128)], "update") as [vi, vj, vk]:
+            with T.block("update"):
+                vi, vj, vk = T.axis.remap("SSR", [i, j, k])
                 C[vi, vj] = C[vi, vj] + A[vi, vk] * B[vj, vk]
 
 
@@ -2699,11 +2703,14 @@ def element_wise(a: T.handle, c: T.handle) -> None:
     C = T.match_buffer(c, (128, 128), "float32")
     B = T.alloc_buffer((128, 128), "float32")
 
-    with T.block([128, 128], "B") as [vi, vj]:
-        B[vi, vj] = A[vi, vj] * T.float32(2)
-
-    with T.block([128, 128], "C") as [vi, vj]:
-        C[vi, vj] = B[vi, vj] + T.float32(1)
+    for i, j in T.grid(128, 128):
+        with T.block("B"):
+            vi, vj = T.axis.remap("SS", [i, j])
+            B[vi, vj] = A[vi, vj] * T.float32(2)
+    for i, j in T.grid(128, 128):
+        with T.block("C"):
+            vi, vj = T.axis.remap("SS", [i, j])
+            C[vi, vj] = B[vi, vj] + T.float32(1)
 
 
 @T.prim_func
@@ -2712,9 +2719,9 @@ def predicate(b: T.handle, c: T.handle) -> None:
     C = T.match_buffer(c, (16, 16), "float32")
 
     for i, jo, ji in T.grid(16, 4, 5):
-        with T.block([16, 16], "update") as [vi, vj]:
-            T.bind(vi, i)
-            T.bind(vj, jo * 4 + ji)
+        with T.block("update"):
+            vi = T.axis.S(16, i)
+            vj = T.axis.S(16, jo * 4 + ji)
             T.where(jo * 4 + ji < 16)
             C[vi, vj] = B[vi, vj] + T.float32(1)
 
@@ -2807,12 +2814,16 @@ def match_buffer_region(a: T.handle, b: T.handle) -> None:
     A = T.match_buffer(a, (16, 16, 16), "float32")
     B = T.match_buffer(b, (1), "float32")
 
-    with T.block([16, 4]) as [vi, vj]:
-        C = T.match_buffer(A[0:16, vi, vj * 4 : vj * 4 + 4], (16, 1, 4))
-        with T.block([4]) as [vii]:
-            D = T.match_buffer(C[vii * 4 : vii * 4 + 4, 0, 0:4], (4, 1, 4))
-            for i, j in T.grid(4, 4):
-                B[0] += D[i, 0, j]
+    for i, j in T.grid(16, 4):
+        with T.block():
+            vi, vj = T.axis.remap("SS", [i, j])
+            C = T.match_buffer(A[0:16, vi, vj * 4 : vj * 4 + 4], (16, 1, 4))
+            for ii in range(4):
+                with T.block():
+                    vii = T.axis.S(4, ii)
+                    D = T.match_buffer(C[vii * 4 : vii * 4 + 4, 0, 0:4], (4, 1, 4))
+                    for i, j in T.grid(4, 4):
+                        B[0] += D[i, 0, j]
 
 
 def test_match_buffer_region():
@@ -2844,8 +2855,8 @@ def block_elements(a: T.handle, b: T.handle) -> None:
     A = T.match_buffer(a, (16, 16), "float32")
     B = T.match_buffer(b, (1, 1), "float32")
 
-    with T.block([1], "update") as [vi]:
-        T.bind(vi, 0)
+    with T.block("update"):
+        vi = T.axis.S(1, 0)
         T.where(True)
         T.reads(A[0:16, 0:16])
         T.writes(B[0, 0])
@@ -2879,11 +2890,11 @@ def opaque_block(a: T.handle, b: T.handle) -> None:
 
     for i in range(16):
         for j in range(16):
-            with T.block([]):
+            with T.block():
                 T.reads([])
                 T.writes(A[i, j])
                 A[i, j] = T.float32(0)
-        with T.block([]):
+        with T.block():
             T.reads([A[i, 0:16]])
             T.writes([B[i, 0:16]])
             for j in range(16):
@@ -2927,7 +2938,7 @@ def rank0_block(a: T.handle) -> None:
     B = T.alloc_buffer((), "float32")
     T.store(B.data, 0, T.load("float32", A.data, 0))
 
-    with T.block([], "update") as []:
+    with T.block("update") as []:
         T.reads([A[()]])
         T.writes([B[()]])
         for i in range(1):
@@ -2969,8 +2980,10 @@ def test_minmax():
 def abs(a: T.handle) -> None:
     A = T.match_buffer(a, (128, 128), "float32")
 
-    with T.block([128, 128], "A") as [vi, vj]:
-        A[vi, vj] = T.abs(A[vi, vj])
+    for i, j in T.grid(128, 128):
+        with T.block("A"):
+            vi, vj = T.axis.remap("SS", [i, j])
+            A[vi, vj] = T.abs(A[vi, vj])
 
 
 def test_abs():
@@ -3011,15 +3024,13 @@ def test_simplify_bracket():
 @T.prim_func
 def var_with_same_name(a: T.handle) -> None:
     A = T.match_buffer(a, (16, 16), "float32")
-    with T.block([16, 16]) as [vi, vj]:
-        A[vi, vj] = 0
-    with T.block([16, 16]) as [vi, vj]:
-        A[vi, vj] = 0
     for i, j in T.grid(16, 16):
-        with T.block([16, 16]) as [vi, vj]:
+        with T.block():
+            vi, vj = T.axis.remap("SS", [i, j])
             A[vi, vj] = 0
     for i, j in T.grid(16, 16):
-        with T.block([16, 16]) as [vi, vj]:
+        with T.block():
+            vi, vj = T.axis.remap("SS", [i, j])
             A[vi, vj] = 0
 
 
@@ -3029,14 +3040,10 @@ def test_same_name_var():
     rt_func = tvm.script.from_source(out_str)
     tvm.ir.assert_structural_equal(func, rt_func)
 
-    assert out_str.count("with T.block([16, 16]) as [vi, vj]") == 4
+    assert out_str.count('vi, vj = T.axis.remap("SS", [i, j])') == 2
     assert out_str.find("vi_") == -1
     assert out_str.find("vj_") == -1
 
-    assert out_str.count("for i0, i1 in T.grid(16, 16)") == 2
-    assert out_str.find("i0_") == -1
-    assert out_str.find("i1_") == -1
-
     assert out_str.count("for i, j in T.grid(16, 16)") == 2
     assert out_str.find("i_") == -1
     assert out_str.find("i_") == -1
@@ -3047,11 +3054,13 @@ def while_loop(a: T.handle, b: T.handle) -> None:
     A = T.match_buffer(a, (16,), "float32")
     B = T.match_buffer(b, (16,), "float32")
     i = T.alloc_buffer((), "int32", scope="local")
-    with T.block([16]) as [vi]:
-        B[vi] = 0
-    while i[()] < 10:
-        for j in range(16):
-            B[j] += A[j]
+    for ii in range(16):
+        with T.block():
+            vi = T.axis.S(16, ii)
+            B[vi] = 0
+        while i[()] < 10:
+            for j in range(16):
+                B[j] += A[j]
 
 
 def test_while_loop():
diff --git a/tests/scripts/task_ci_setup.sh b/tests/scripts/task_ci_setup.sh
index 7138effe395a4..dfd2a32165f1c 100755
--- a/tests/scripts/task_ci_setup.sh
+++ b/tests/scripts/task_ci_setup.sh
@@ -30,7 +30,7 @@ set -o pipefail
 #
 echo "Addtiional setup in" ${CI_IMAGE_NAME}
 
-python3 -m pip install --user tlcpack-sphinx-addon==0.2.1 synr==0.4.1
+python3 -m pip install --user tlcpack-sphinx-addon==0.2.1 synr==0.5.0
 
 # Rebuild standalone_crt in build/ tree. This file is not currently archived by pack_lib() in
 # Jenkinsfile. We expect config.cmake to be present from pack_lib().