[Language] Support sequence comparisons (#872)

* Update submodule 'tvm' to latest commit 7a71ee34

* lint fix

tvm @ 7a71ee34

-Subproject commit 0524f7601d77df47c56253c9a675a6807f737d79
+Subproject commit 7a71ee3411e49c3e05b1f1a910cf7f73adc7a5b2

testing/python/language/test_tilelang_language_if_range.py

+import tilelang
+import tilelang.language as T
+import torch
+import tilelang.testing
+
+
+@tilelang.jit(out_idx=[1],)
+def tilelang_if_range(M, N, block_M, block_N, dtype="float16"):
+
+    @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):
+            for i, j in T.Parallel(block_M, block_N):
+                row_idx = by * block_M + i
+                col_idx = bx * block_N + j
+                # Test condition: ca < i < cb where ca=16, cb=96
+                if 16 < row_idx < 96:
+                    B[row_idx, col_idx] = A[row_idx, col_idx] * 2.0
+                else:
+                    B[row_idx, col_idx] = A[row_idx, col_idx] * 0.5
+
+    return main
+
+
+def run_tilelang_if_range(M=128, N=128, block_M=32, block_N=32, dtype="float16"):
+    kernel = tilelang_if_range(M, N, block_M, block_N, dtype)
+    a = torch.randn(M, N, device="cuda", dtype=getattr(torch, dtype))
+    b = kernel(a)
+
+    # Reference computation
+    ref_b = torch.zeros_like(a)
+    for i in range(M):
+        for j in range(N):
+            # ca < i < cb where ca=16, cb=96
+            if 16 < i < 96:
+                ref_b[i, j] = a[i, j] * 2.0
+            else:
+                ref_b[i, j] = a[i, j] * 0.5
+
+    torch.testing.assert_close(b, ref_b, rtol=1e-2, atol=1e-2)
+
+
+def test_tilelang_if_range():
+    run_tilelang_if_range(M=128, N=128, block_M=32, block_N=32)
+
+
+if __name__ == "__main__":
+    tilelang.testing.main()