[Feature] Support None type as input for `T.ptr` and `T.Tensor` (#1114)

* [Feature] Support None type as input for T.ptr and T.Tensor

* lint

* lint

* lint

* lint fix

testing/python/jit/test_tilelang_jit_nullptr.py

+import torch
+from tilelang import tvm as tvm
+import tilelang.testing
+import tilelang as tl
+import tilelang.language as T
+from tilelang.utils import map_torch_type
+
+
+@tl.jit
+def ptr_null_test(M, N, K, block_M, block_N, block_K, dtype="float16", accum_dtype="float"):
+
+    @T.prim_func
+    def main(
+        a_ptr: T.ptr,
+        b_ptr: T.ptr,
+        c_ptr: T.ptr,
+        bias_ptr: T.ptr,
+        m: T.int32,
+        n: T.int32,
+        k: T.int32,
+        with_bias: T.bool,
+    ):
+        A = T.make_tensor(a_ptr, (m, k), dtype)
+        B = T.make_tensor(b_ptr, (k, n), dtype)
+        C = T.make_tensor(c_ptr, (m, n), accum_dtype)
+        Bias = T.make_tensor(bias_ptr, (n), accum_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_K), dtype)
+            B_shared = T.alloc_shared((block_N, block_K), dtype)
+            C_local = T.alloc_fragment((block_M, block_N), accum_dtype)
+
+            T.clear(C_local)
+
+            for ko in T.Pipelined(T.ceildiv(k, block_K), num_stages=3):
+                # Copy tile of A
+                T.copy(A[by * block_M, ko * block_K], A_shared)
+                T.copy(B[bx * block_N, ko * block_K], B_shared)
+                T.gemm(A_shared, B_shared, C_local, transpose_B=True)
+
+            if with_bias:
+                for i, j in T.Parallel(block_M, block_N):
+                    C_local[i, j] += Bias[bx * block_N + j]
+
+            T.copy(C_local, C[by * block_M, bx * block_N])
+
+    return main
+
+
+@tl.jit
+def tensor_null_test(M, N, K, block_M, block_N, block_K, dtype="float16", accum_dtype="float"):
+
+    @T.prim_func
+    def main(
+            A: T.Tensor((M, K), dtype),
+            B: T.Tensor((K, N), dtype),
+            C: T.Tensor((M, N), accum_dtype),
+            Bias: T.Tensor((N), accum_dtype),
+            with_bias: T.bool,
+    ):
+        # 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_K), dtype)
+            B_shared = T.alloc_shared((block_N, block_K), dtype)
+            C_local = T.alloc_fragment((block_M, block_N), accum_dtype)
+
+            T.clear(C_local)
+
+            for ko in T.Pipelined(T.ceildiv(K, block_K), num_stages=3):
+                # Copy tile of A
+                T.copy(A[by * block_M, ko * block_K], A_shared)
+                T.copy(B[bx * block_N, ko * block_K], B_shared)
+                T.gemm(A_shared, B_shared, C_local, transpose_B=True)
+
+            if with_bias:
+                for i, j in T.Parallel(block_M, block_N):
+                    C_local[i, j] += Bias[bx * block_N + j]
+
+            T.copy(C_local, C[by * block_M, bx * block_N])
+
+    return main
+
+
+def run_test(M, N, K, block_M, block_N, block_K, dtype="float16", accum_dtype="float"):
+    func = ptr_null_test(M, N, K, block_M, block_N, block_K, dtype, accum_dtype)
+
+    a = torch.randn(M, K, device="cuda", dtype=map_torch_type(dtype))
+    b = torch.randn(N, K, device="cuda", dtype=map_torch_type(dtype))
+    c = torch.zeros(M, N, device="cuda", dtype=map_torch_type(accum_dtype))
+    d = torch.randn(N, device="cuda", dtype=map_torch_type(accum_dtype))
+
+    func(a, b, c, None, M, N, K, False)
+
+    ref_no_bias = (a @ b.T).to(map_torch_type(accum_dtype))
+    ref_with_bias = ref_no_bias + d
+
+    torch.testing.assert_close(c, ref_no_bias, atol=1e-2, rtol=1e-2)
+
+    func(a, b, c, d, M, N, K, True)
+
+    torch.testing.assert_close(c, ref_with_bias, atol=1e-2, rtol=1e-2)
+
+    func = tensor_null_test(M, N, K, block_M, block_N, block_K, dtype, accum_dtype)
+    func(a, b, c, None, False)
+    torch.testing.assert_close(c, ref_no_bias, atol=1e-2, rtol=1e-2)
+    func(a, b, c, d, True)
+    torch.testing.assert_close(c, ref_with_bias, atol=1e-2, rtol=1e-2)
+
+
+def test_nullptr():
+    run_test(1024, 1024, 1024, 128, 128, 32)
+
+
+if __name__ == "__main__":
+    tilelang.testing.main()

tilelang/jit/adapter/cython/cython_wrapper.pyx

@@ -251,6 +251,8 @@ cdef class CythonKernelWrapper:
                     if dtype not in dtype_to_ctype:
                         raise ValueError(f"Unsupported tensor dtype: {dtype}")
                     call_args.append(dtype_to_ctype[dtype](tensor))
+            elif tensor is None:
+                call_args.append(ctypes.c_void_p(0))
             else:
                 raise ValueError(f"Unsupported tensor type: {type(tensor)}")
 

tilelang/language/allocate.py

@@ -14,11 +14,11 @@ Each function takes shape and dtype parameters and returns a TVM buffer object
 with the appropriate memory scope.
 """
 
+from __future__ import annotations
 from tilelang import tvm as tvm
 from tvm.script import tir as T
 from tvm.tir import PrimExpr
 from tvm.script.parser.tir import block_attr
-from typing import Union
 
 
 def alloc_shared(shape, dtype, scope="shared.dyn"):
@@ -67,7 +67,7 @@ def alloc_fragment(shape, dtype, scope="local.fragment"):
     return T.alloc_buffer(shape, dtype, scope=scope)
 
 
-def alloc_var(dtype, *args, scope="local.var", init: Union[PrimExpr] = None):
+def alloc_var(dtype, *args, scope="local.var", init: PrimExpr | None = None):
     """Allocate a single-element variable buffer.
 
     Args: