[Language] Recommend using `T.dynamic` instead of `T.symbolic` (#1076)

* recommend using T.dynamic instead of T.symbolic

* lint fix

* lint fix

README.md

@@ -178,7 +178,7 @@ def matmul(M, N, K, block_M, block_N, block_K, dtype="float16", accum_dtype="flo
     return matmul_relu_kernel
 
 
-M = 1024  # M = T.symbolic("m") if you want to use dynamic shape
+M = 1024  # M = T.dynamic("m") if you want to use dynamic shape
 N = 1024
 K = 1024
 block_M = 128

docs/deeplearning_operators/elementwise.md

@@ -89,7 +89,7 @@ def elementwise_add(
 In the compilation process above, a fixed shape was used. However, in practical usage, we often want the kernel to support dynamic shapes. So, how can we compile a kernel in TileLang to handle dynamic shapes? In TileLang, we can replace the target size with a dynamic symbolic value, making the dimension dynamic. The following example illustrates this:
 
 ```python
-program = elementwise_add(T.symbolic("N"), threads=256, dtype="bfloat16")
+program = elementwise_add(T.dynamic("N"), threads=256, dtype="bfloat16")
 kernel = tilelang.compile(program, out_idx=-1, target="cuda", execution_backend="cython")
 ```
 

examples/blocksparse_attention/example_tilelang_sparse_gqa_decode_paged.py

@@ -223,12 +223,12 @@ class SparseFlashAttn(torch.nn.Module):
             block_N=block_N,
             block_H=self.block_H,
             page_block_size=page_block_size,
-            num_split=T.symbolic("num_split"),
+            num_split=T.dynamic("num_split"),
             num_stages=2,
             threads=128,
             num_pages=num_pages,
-            max_num_blocks_per_seq=T.symbolic("max_num_blocks_per_seq"),
-            max_selected_blocks=T.symbolic("max_selected_blocks"),
+            max_num_blocks_per_seq=T.dynamic("max_num_blocks_per_seq"),
+            max_selected_blocks=T.dynamic("max_selected_blocks"),
         )
 
         props = torch.cuda.get_device_properties(torch.device("cuda:0"))

examples/blocksparse_attention/example_tilelang_sparse_gqa_decode_varlen_indice.py

@@ -206,11 +206,11 @@ class SparseFlashAttn(torch.nn.Module):
         self.kernel = flashattn(batch, heads, heads_kv, dim, dim_v)(
             block_N=block_size,
             block_H=self.block_H,
-            num_split=T.symbolic("num_split"),
+            num_split=T.dynamic("num_split"),
             num_stages=2,
             threads=128,
-            max_cache_seqlen=T.symbolic("max_cache_seqlen"),
-            max_selected_blocks=T.symbolic("max_selected_blocks"))
+            max_cache_seqlen=T.dynamic("max_cache_seqlen"),
+            max_selected_blocks=T.dynamic("max_selected_blocks"))
 
         props = torch.cuda.get_device_properties(torch.device("cuda:0"))
         self.num_sm = props.multi_processor_count
@@ -301,11 +301,11 @@ def sparse_gqa_decode_varlen_indice(query, key, value, block_indices, cache_seql
     kernel = flashattn(batch, heads, heads_kv, dim, dim_v)(
         block_N=block_size,
         block_H=block_H,
-        num_split=T.symbolic("num_split"),
+        num_split=T.dynamic("num_split"),
         num_stages=2,
         threads=128,
-        max_cache_seqlen=T.symbolic("max_cache_seqlen"),
-        max_selected_blocks=T.symbolic("max_selected_blocks"))
+        max_cache_seqlen=T.dynamic("max_cache_seqlen"),
+        max_selected_blocks=T.dynamic("max_selected_blocks"))
 
     output = kernel(query, key, value, block_indices, cache_seqlens, glse, Output_partial)
     return output

examples/blocksparse_attention/example_tilelang_sparse_gqa_decode_varlen_mask.py

@@ -193,11 +193,11 @@ class SparseFlashAttn(torch.nn.Module):
         self.kernel = flashattn(batch, heads, heads_kv, dim, dim_v)(
             block_N=block_size,
             block_H=self.block_H,
-            num_split=T.symbolic("num_split"),
+            num_split=T.dynamic("num_split"),
             num_stages=2,
             threads=128,
-            max_cache_seqlen=T.symbolic("max_cache_seqlen"),
-            num_blocks=T.symbolic("num_blocks"))
+            max_cache_seqlen=T.dynamic("max_cache_seqlen"),
+            num_blocks=T.dynamic("num_blocks"))
 
         props = torch.cuda.get_device_properties(torch.device("cuda:0"))
         self.num_sm = props.multi_processor_count
@@ -282,11 +282,11 @@ def sparse_gqa_decode_varlen_mask(query, key, value, block_mask, cache_seqlens,
     kernel = flashattn(batch, heads, heads_kv, dim, dim_v)(
         block_N=block_size,
         block_H=block_H,
-        num_split=T.symbolic("num_split"),
+        num_split=T.dynamic("num_split"),
         num_stages=2,
         threads=128,
-        max_cache_seqlen=T.symbolic("max_cache_seqlen"),
-        num_blocks=T.symbolic("num_blocks"))
+        max_cache_seqlen=T.dynamic("max_cache_seqlen"),
+        num_blocks=T.dynamic("num_blocks"))
     glse = torch.empty((batch, heads, num_split), dtype=torch.float32, device='cuda')
     Output_partial = torch.empty((batch, heads, num_split, dim_v),
                                  dtype=torch.float32,

examples/deepseek_v32/fp8_lighting_indexer.py

@@ -103,8 +103,8 @@ def mqa_attn_return_logits(
     accum_dtype = "float"
     index_dtype = "int32"
 
-    seq_len = T.symbolic("seq_len")
-    seq_len_kv = T.symbolic("seq_len_kv")
+    seq_len = T.dynamic("seq_len")
+    seq_len_kv = T.dynamic("seq_len_kv")
 
     index_q_shape = [seq_len * heads, index_dim]
     index_k_shape = [seq_len_kv, index_dim]
@@ -182,8 +182,8 @@ def clean_logits_(
     threads: int = 512,
     block_K: int = 4096,
 ):
-    seq_len = T.symbolic("seq_len")
-    seq_len_kv = T.symbolic("seq_len_kv")
+    seq_len = T.dynamic("seq_len")
+    seq_len_kv = T.dynamic("seq_len_kv")
 
     dtype = "float"
     indices_dtype = "int32"

examples/deepseek_v32/inference/kernel.py

@@ -34,7 +34,7 @@ def fast_round_scale(amax, fp8_max_inv):
 
 @tilelang.jit(pass_configs=pass_configs)
 def act_quant_kernel(N, in_dtype=BF16, out_dtype=FP8, scale_dtype=FP32, round_scale=False):
-    M = T.symbolic("M")
+    M = T.dynamic("M")
     fp8_min = -448.0
     fp8_max = 448.0
     fp8_max_inv = 1 / fp8_max
@@ -110,7 +110,7 @@ def act_quant(x: torch.Tensor,
 def fp8_gemm_kernel(N, K, out_dtype=BF16, accum_dtype="float32"):
     assert out_dtype in [BF16, "float32"]
 
-    M = T.symbolic("M")
+    M = T.dynamic("M")
     group_size = 128
     block_M = 32
     block_N = 128
@@ -192,9 +192,9 @@ def fp8_gemm(a: torch.Tensor, a_s: torch.Tensor, b: torch.Tensor,
 
 @tilelang.jit(out_idx=[4], pass_configs=pass_configs)
 def fp8_index_kernel(h: int, d: int):
-    b = T.symbolic("b")
-    m = T.symbolic("m")
-    n = T.symbolic("n")
+    b = T.dynamic("b")
+    m = T.dynamic("m")
+    n = T.dynamic("n")
 
     blk_n1 = 512
     blk_n2 = 128

examples/deepseek_v32/sparse_mla_fwd.py

@@ -37,9 +37,9 @@ def sparse_mla_fwd(
     else:
         sm_scale = sm_scale * 1.44269504  # log2(e)
 
-    batch = T.symbolic("batch")
-    seq_len = T.symbolic("seq_len")
-    seq_len_kv = T.symbolic("seq_len_kv")
+    batch = T.dynamic("batch")
+    seq_len = T.dynamic("seq_len")
+    seq_len_kv = T.dynamic("seq_len_kv")
 
     head_kv = heads // kv_group
     q_shape = [batch, seq_len, heads, dim + tail_dim]

examples/deepseek_v32/topk_selector.py

@@ -26,8 +26,8 @@ def convert_to_uint32(x):
 
 @tilelang.jit(pass_configs=pass_configs)
 def tl_topk_impl(topk, in_dtype="float32", out_dtype="int32"):
-    batch = T.symbolic("batch")
-    seq_len = T.symbolic("seq_len")
+    batch = T.dynamic("batch")
+    seq_len = T.dynamic("seq_len")
     RADIX = 1 << 8
     BLOCK_SIZE = 1024
     SMEM_INPUT_SIZE = 4096  # assume the threshold bucket size after first pass is less than 4K

examples/gemm_sm100/gemm_mma.py

@@ -41,7 +41,7 @@ def matmul(M, N, K, block_M, block_N, block_K, dtype="float16", accum_dtype="flo
     return main
 
 
-M = 128  # M = T.symbolic("m") if you want to use dynamic shape
+M = 128  # M = T.dynamic("m") if you want to use dynamic shape
 N = 128
 K = 32
 block_M = 128

examples/quickstart.py

@@ -48,7 +48,7 @@ def matmul(M, N, K, block_M, block_N, block_K, dtype="float16", accum_dtype="flo
     return matmul_relu_kernel
 
 
-M = 1024  # M = T.symbolic("m") if you want to use dynamic shape
+M = 1024  # M = T.dynamic("m") if you want to use dynamic shape
 N = 1024
 K = 1024
 block_M = 128

testing/python/issue/test_tilelang_issue_830.py

@@ -24,7 +24,7 @@ def test_empty_kernel_lowering():
 
 @tilelang.jit
 def _empty_with_dead_code_kernel():
-    num_tokens = T.symbolic("num_tokens")
+    num_tokens = T.dynamic("num_tokens")
 
     @T.prim_func
     def buggy_kernel(x: T.Tensor[(num_tokens,), "float32"]):

testing/python/jit/test_tilelang_jit_gemm_ctypes.py

@@ -395,14 +395,14 @@ def run_ctypes_dynamic_shape(M,
 
 def test_ctypes_dynamic_shape():
     run_ctypes_dynamic_shape(
-        T.symbolic("m"), 1024, 768, False, False, "float16", "float16", "float16", 128, 256, 32, 2)
+        T.dynamic("m"), 1024, 768, False, False, "float16", "float16", "float16", 128, 256, 32, 2)
 
     run_ctypes_dynamic_shape(
-        T.symbolic("m"), T.symbolic("n"), 768, False, False, "float16", "float16", "float16", 128,
+        T.dynamic("m"), T.dynamic("n"), 768, False, False, "float16", "float16", "float16", 128,
         256, 32, 2)
 
     run_ctypes_dynamic_shape(
-        T.symbolic("m"), T.symbolic("n"), T.symbolic("k"), False, False, "float16", "float16",
+        T.dynamic("m"), T.dynamic("n"), T.dynamic("k"), False, False, "float16", "float16",
         "float16", 128, 256, 32, 2)
 
 

testing/python/jit/test_tilelang_jit_gemm_cython.py

@@ -404,14 +404,14 @@ def run_cython_dynamic_shape(M,
 
 def test_cython_dynamic_shape():
     run_cython_dynamic_shape(
-        T.symbolic("m"), 1024, 768, False, False, "float16", "float16", "float16", 128, 256, 32, 2)
+        T.dynamic("m"), 1024, 768, False, False, "float16", "float16", "float16", 128, 256, 32, 2)
 
     run_cython_dynamic_shape(
-        T.symbolic("m"), T.symbolic("n"), 768, False, False, "float16", "float16", "float16", 128,
+        T.dynamic("m"), T.dynamic("n"), 768, False, False, "float16", "float16", "float16", 128,
         256, 32, 2)
 
     run_cython_dynamic_shape(
-        T.symbolic("m"), T.symbolic("n"), T.symbolic("k"), False, False, "float16", "float16",
+        T.dynamic("m"), T.dynamic("n"), T.dynamic("k"), False, False, "float16", "float16",
         "float16", 128, 256, 32, 2)
 
 
@@ -473,7 +473,7 @@ def run_cython_dynamic_shape_with_out_idx(M,
 
 def test_cython_dynamic_shape_with_out_idx():
     run_cython_dynamic_shape_with_out_idx(
-        T.symbolic("m"), 1024, 768, False, False, "float16", "float16", "float16", 128, 256, 32, 2)
+        T.dynamic("m"), 1024, 768, False, False, "float16", "float16", "float16", 128, 256, 32, 2)
 
 
 def matmul_int_variable(

testing/python/language/test_tilelang_language_copy.py

@@ -83,7 +83,7 @@ def run_tilelang_copy_with_stride(M=1024,
 
 def test_tilelang_copy_with_stride():
     run_tilelang_copy_with_stride(M=1024, N=1024, NN=2048, block_M=128, block_N=128)
-    run_tilelang_copy_with_stride(M=1024, N=1024, NN=T.symbolic("NN"), block_M=128, block_N=128)
+    run_tilelang_copy_with_stride(M=1024, N=1024, NN=T.dynamic("NN"), block_M=128, block_N=128)
 
 
 def tilelang_copy_bufferload(num_tokens, dtype="float16"):

testing/python/transform/test_tilelang_transform_legalize_safe_memory_access.py

@@ -41,7 +41,7 @@ def assert_vectorize_access(M: int = 64, N: int = 64):
 def issue_1013_buggy_kernel():
     # NOTE: This kernel is mainly to test some corner cases in boundary check
 
-    num_tokens = T.symbolic('num_tokens')
+    num_tokens = T.dynamic('num_tokens')
     num_threads = 128
 
     @T.prim_func

tilelang/language/__init__.py

 """The language interface for tl programs."""
 
-from typing import Optional, Callable, Dict
+from typing import Optional
 # from .parser import *
 # now is fully compatible with the upstream
 # tir script
@@ -84,124 +84,10 @@ from .builtin import *  # noqa: F401
 
 from .utils import index_to_coordinates  # noqa: F401
 
-
-def symbolic(name: str, dtype: str = "int32"):
-    """
-    Create a TIR symbolic variable.
-
-    Parameters:
-        name (str): Identifier for the variable in generated TIR.
-        dtype (str): Data type string for the variable (e.g., "int32"). Defaults to "int32".
-
-    Returns:
-        tir.Var: A TIR variable with the given name and dtype for use in TIR/TensorIR kernels.
-    """
-    return tir.Var(name, dtype)
-
-
-def use_swizzle(panel_size: int, order: str = "row", enable: bool = True):
-    # If order is row, use rasterization2DRow, otherwise use rasterization2DColumn
-    # The panel size is the number of threads in a warp
-    # Use to improve the L2 Cache Locality
-    device_func = ("rasterization2DRow" if order == "row" else "rasterization2DColumn")
-    return attr(None, "threadblock_swizzle_pattern",
-                f"tl::{device_func}<{panel_size}>") if enable else None
-
-
-def annotate_layout(layout_map: Dict):
-    """Annotate the layout of the buffer
-
-    Args:
-        layout_map (Dict): a dictionary of buffer to layout
-
-    Returns:
-        block_attr: a block attribute
-
-    Example:
-        @T.prim_func
-        def main(
-                A: T.Tensor((M, N), dtype),
-                B: T.Tensor((M, N), dtype),
-        ):
-            # Initialize Kernel Context
-            with T.Kernel(T.ceildiv(N, block_N), T.ceildiv(M, block_M), threads=128) as (bx, by):
-                A_shared = T.alloc_shared((block_M, block_N), dtype)
-
-                T.annotate_layout({A_shared: layout})
-                for i, j in T.Parallel(block_M, block_N):
-                    A_shared[i, j] = A[by * block_M + i, bx * block_N + j]
-
-                for i, j in T.Parallel(block_M, block_N):
-                    B[by * block_M + i, bx * block_N + j] = A_shared[i, j]
-
-        return main
-    """
-    # layout_map is a dictionary of buffer to layout
-    _layout_map = {}
-    for buffer, layout in layout_map.items():
-        if isinstance(layout, Layout):
-            _layout_map[buffer.data] = layout
-        elif isinstance(layout, Callable):
-            _layout_map[buffer.data] = Layout(buffer.shape, layout)
-        else:
-            raise ValueError(f"Invalid layout: {layout}")
-
-    return block_attr({"layout_map": _layout_map})
-
-
-def annotate_safe_value(safe_value_map: Dict):
-    """Annotate the safe value of the buffer.
-
-    A safe value of a buffer is the value that will be used when the
-    buffer is accessed out of bounds.
-
-    Args:
-        safe_value_map (dict): a dictionary of buffer to safe value
-
-    Returns:
-        block_attr: a block attribute
-
-    Example:
-        @T.prim_func
-        def main(
-                A: T.Tensor((M, N), dtype),
-                B: T.Tensor((M, N), dtype),
-        ):
-            # Initialize Kernel Context
-            with T.Kernel(T.ceildiv(N, block_N), T.ceildiv(M, block_M), threads=128) as (bx, by):
-                A_shared = T.alloc_shared((block_M, block_N), dtype)
-
-                T.annotate_safe_value({A: safe_value})
-                for i, j in T.Parallel(block_M, block_N):
-                    A_shared[i, j] = A[by * block_M + i - 10, bx * block_N + j]
-
-                for i, j in T.Parallel(block_M, block_N):
-                    B[by * block_M + i, bx * block_N + j] = A_shared[i, j]
-
-        return main
-    """
-    # safe_value_map is a dictionary of buffer to safe value
-    _safe_value_map = {}
-    for buffer, safe_value in safe_value_map.items():
-        _safe_value_map[buffer.data] = safe_value
-    return block_attr({"safe_value_map": _safe_value_map})
-
-
-def annotate_l2_hit_ratio(l2_hit_ratio_map: Dict):
-    """Annotate the L2 hit ratio of the buffer, detailed explanation please refer to:
-    https://docs.nvidia.com/cuda/cuda-c-programming-guide/#l2-policy-for-persisting-accesses
-
-    Args:
-        l2_hit_ratio_map (dict): a dictionary of buffer to L2 hit ratio value
-    Example:
-        # 0.5 is the hit ratio
-        T.annotate_l2_hit_ratio({A: 0.5})
-    """
-    _l2_hit_ratio_map = {}
-    for buffer, hit_ratio in l2_hit_ratio_map.items():
-        assert buffer.scope() == "global", "persistent L2 can only be applied to global buffers"
-        _l2_hit_ratio_map[buffer.data] = float(hit_ratio)
-    return block_attr({"l2_hit_ratio_map": _l2_hit_ratio_map})
+from .symbolics import dynamic, symbolic  # noqa: F401
+from .annotations import (  # noqa: F401
+    use_swizzle, annotate_layout, annotate_safe_value, annotate_l2_hit_ratio,
+)
 
 
 def import_source(source: Optional[str] = None):

tilelang/language/annotations.py

+"""Annotation helpers exposed on the TileLang language surface."""
+
+from typing import Callable, Dict
+
+from tilelang.layout import Layout
+from tvm.script.parser.tir import attr, block_attr
+
+__all__ = [
+    "use_swizzle",
+    "annotate_layout",
+    "annotate_safe_value",
+    "annotate_l2_hit_ratio",
+]
+
+
+def use_swizzle(panel_size: int, order: str = "row", enable: bool = True):
+    """Annotate a kernel to use a specific threadblock swizzle pattern."""
+    device_func = "rasterization2DRow" if order == "row" else "rasterization2DColumn"
+    if not enable:
+        return None
+    return attr(None, "threadblock_swizzle_pattern", f"tl::{device_func}<{panel_size}>")
+
+
+def annotate_layout(layout_map: Dict):
+    """Annotate the layout of the buffer."""
+    _layout_map = {}
+    for buffer, layout in layout_map.items():
+        if isinstance(layout, Layout):
+            _layout_map[buffer.data] = layout
+        elif isinstance(layout, Callable):
+            _layout_map[buffer.data] = Layout(buffer.shape, layout)
+        else:
+            raise ValueError(f"Invalid layout: {layout}")
+
+    return block_attr({"layout_map": _layout_map})
+
+
+def annotate_safe_value(safe_value_map: Dict):
+    """Annotate the safe value of the buffer."""
+    _safe_value_map = {}
+    for buffer, safe_value in safe_value_map.items():
+        _safe_value_map[buffer.data] = safe_value
+    return block_attr({"safe_value_map": _safe_value_map})
+
+
+def annotate_l2_hit_ratio(l2_hit_ratio_map: Dict):
+    """Annotate the L2 hit ratio of the buffer."""
+    _l2_hit_ratio_map = {}
+    for buffer, hit_ratio in l2_hit_ratio_map.items():
+        assert buffer.scope() == "global", "persistent L2 can only be applied to global buffers"
+        _l2_hit_ratio_map[buffer.data] = float(hit_ratio)
+    return block_attr({"l2_hit_ratio_map": _l2_hit_ratio_map})

tilelang/language/symbolics.py

+"""Symbolic variable helpers exposed on the TileLang language surface."""
+
+from tvm import tir
+
+from tilelang.utils import deprecated
+
+__all__ = ["dynamic", "symbolic"]
+
+
+@deprecated("T.dynamic(...)", "tir.Var(...)", "v0.1.9")
+def dynamic(name: str, dtype: str = "int32"):
+    """
+    Create a TIR dynamic symbolic variable.
+
+    Parameters:
+        name (str): Identifier for the variable in generated TIR.
+        dtype (str): Data type string for the variable (e.g., "int32"). Defaults to "int32".
+
+    Returns:
+        tir.Var: A TIR variable with the given name and dtype for use in TIR/TensorIR kernels.
+    """
+    return tir.Var(name, dtype)
+
+
+@deprecated("T.symbolic(...)", "T.dynamic(...)")
+def symbolic(name: str, dtype: str = "int32"):
+    """Deprecated alias for `T.dynamic`."""
+    return tir.Var(name, dtype)