[Enhancement] Remove constraint requiring last dimension stride to be 1

testing/python/language/test_tilelang_language_vectorize.py

@@ -0,0 +1,63 @@
+import torch
+import tilelang.testing
+import tilelang.language as T
+
+
+@tilelang.jit(pass_configs={tilelang.PassConfigKey.TL_DISABLE_VECTORIZE_256: True})
+def vectorize_test(N, M, stride_A, stride_B):
+    assert N % 128 == 0 and M % 128 == 0
+
+    @T.prim_func
+    def main(
+            A: T.StridedTensor[(N, M), (1, stride_A), "float32"],  # noqa: F821
+            B: T.StridedTensor[(N, M), (1, stride_B), "float32"],  # noqa: F821
+    ):
+        with T.Kernel(M // 128, threads=128) as (bx):
+            tx = T.get_thread_binding(0)
+            col = bx * 128 + tx
+
+            for row in T.vectorized(N):
+                B[row, col] = A[row, col]
+
+    return main
+
+
+def run_vectorize(N, M, stride_A, stride_B):
+    assert stride_A >= N and stride_B >= N
+
+    jit_kernel = vectorize_test(N, M, stride_A, stride_B)
+
+    base_a = torch.randn(stride_A, M, device="cuda", dtype=torch.float32)
+    base_b = torch.zeros(stride_B, M, device="cuda", dtype=torch.float32)
+    a = torch.as_strided(base_a, size=(N, M), stride=(1, stride_A))
+    b = torch.as_strided(base_b, size=(N, M), stride=(1, stride_B))
+
+    jit_kernel(a, b)
+
+    torch.testing.assert_close(a, b, atol=1e-8, rtol=1e-8)
+
+    code = jit_kernel.get_kernel_source()
+
+    vectorize_size = 1
+    while vectorize_size <= 2 and \
+          stride_A % (vectorize_size * 2) == 0 and \
+          stride_B % (vectorize_size * 2) == 0:
+        vectorize_size *= 2
+
+    if vectorize_size == 4:
+        assert "float4" in code
+    elif vectorize_size == 2:
+        assert "float2" in code
+
+
+def test_vectorize():
+    N, M = 512, 256
+
+    run_vectorize(N, M, N, N)
+    run_vectorize(N, M, N + 2, N + 4)
+    run_vectorize(N, M, N + 4, N + 8)
+    run_vectorize(N, M, N + 8, N + 16)
+
+
+if __name__ == "__main__":
+    tilelang.testing.main()

tilelang/language/proxy.py

@@ -178,9 +178,6 @@ def __call__(self,
                  scope=None) -> tir.Buffer:
         if len(shape) != len(strides):
             raise ValueError("Invalid shape/strides' dimensions")
-        if not bool(strides[-1] == 1):
-            # TODO(chenggang): shall we support non-contiguous even for the last dimension?
-            raise ValueError("The stride of the last dimension must be 1 (contiguous)")
         return super().__call__(shape, dtype=dtype, strides=strides, scope=scope)