from tvm.script import ir as I
from tvm.script import tir as T

# fmt: off
# from tvm.script import ir as I
# from tvm.script import tir as T

@I.ir_module
class Module:
    @T.prim_func
    def NT_matmul1(var_A: T.handle, B: T.Buffer((T.int64(4096), T.int64(4096)), "float16"), var_NT_matmul: T.handle):
        T.func_attr({"op_pattern": 4, "tir.noalias": T.bool(True)})
        n = T.int64()
        A = T.match_buffer(var_A, (T.int64(1), n, T.int64(4096)), "float16")
        NT_matmul = T.match_buffer(var_NT_matmul, (T.int64(1), n, T.int64(4096)), "float16")
        # with T.block("root"):
        for i0, i1, i2, k in T.grid(T.int64(1), n, T.int64(4096), T.int64(4096)):
            with T.block("NT_matmul"):
                v_i0, v_i1, v_i2, v_k = T.axis.remap("SSSR", [i0, i1, i2, k])
                T.reads(A[v_i0, v_i1, v_k], B[v_i2, v_k])
                T.writes(NT_matmul[v_i0, v_i1, v_i2])
                with T.init():
                    NT_matmul[v_i0, v_i1, v_i2] = T.float16(0)
                NT_matmul[v_i0, v_i1, v_i2] = NT_matmul[v_i0, v_i1, v_i2] + A[v_i0, v_i1, v_k] * B[v_i2, v_k]

    @T.prim_func
    def extend_te(var_A: T.handle, var_concat_te: T.handle):
        T.func_attr({"op_pattern": 8, "tir.noalias": T.bool(True)})
        n = T.int64()
        A = T.match_buffer(var_A, (T.int64(1), T.int64(1), n, n), "float16")
        m = T.int64()
        concat_te = T.match_buffer(var_concat_te, (T.int64(1), T.int64(1), n, m), "float16")
        # with T.block("root"):
        for b, _, i, j in T.grid(T.int64(1), T.int64(1), n, m):
            with T.block("concat_te"):
                v_b, v__, v_i, v_j = T.axis.remap("SSSS", [b, _, i, j])
                T.reads(A[v_b, v__, v_i, v_j + n - m])
                T.writes(concat_te[v_b, v__, v_i, v_j])
                concat_te[v_b, v__, v_i, v_j] = T.if_then_else(v_j < m - n, T.float16(65504), A[v_b, v__, v_i, v_j + n - m])

    @T.prim_func
    def full(var_T_full: T.handle):
        T.func_attr({"op_pattern": 0, "tir.noalias": T.bool(True)})
        n = T.int64()
        T_full = T.match_buffer(var_T_full, (T.int64(1), T.int64(1), T.int64(1), n), "float16")
        # with T.block("root"):
        for ax0, ax1, ax2, ax3 in T.grid(T.int64(1), T.int64(1), T.int64(1), n):
            with T.block("T_full"):
                v_ax0, v_ax1, v_ax2, v_ax3 = T.axis.remap("SSSS", [ax0, ax1, ax2, ax3])
                T.reads()
                T.writes(T_full[v_ax0, v_ax1, v_ax2, v_ax3])
                T_full[v_ax0, v_ax1, v_ax2, v_ax3] = T.float16(65504)

    @T.prim_func
    def fused_NT_matmul1_add1(p_lv41: T.handle, lv1386: T.Buffer((T.int64(4096), T.int64(4096)), "float16"), p_lv2: T.handle, p_output0: T.handle):
        T.func_attr({"tir.noalias": T.bool(True)})
        n = T.int64()
        lv41 = T.match_buffer(p_lv41, (T.int64(1), n, T.int64(4096)), "float16")
        lv2 = T.match_buffer(p_lv2, (T.int64(1), n, T.int64(4096)), "float16")
        var_T_add_intermediate = T.match_buffer(p_output0, (T.int64(1), n, T.int64(4096)), "float16")
        # with T.block("root"):
        var_NT_matmul_intermediate = T.alloc_buffer((T.int64(1), n, T.int64(4096)), "float16")
        for i0, i1, i2, k in T.grid(T.int64(1), n, T.int64(4096), T.int64(4096)):
            with T.block("NT_matmul"):
                v_i0, v_i1, v_i2, v_k = T.axis.remap("SSSR", [i0, i1, i2, k])
                T.reads(lv41[v_i0, v_i1, v_k], lv1386[v_i2, v_k])
                T.writes(var_NT_matmul_intermediate[v_i0, v_i1, v_i2])
                with T.init():
                    var_NT_matmul_intermediate[v_i0, v_i1, v_i2] = T.float16(0)
                var_NT_matmul_intermediate[v_i0, v_i1, v_i2] = var_NT_matmul_intermediate[v_i0, v_i1, v_i2] + lv41[v_i0, v_i1, v_k] * lv1386[v_i2, v_k]
        for ax0, ax1, ax2 in T.grid(T.int64(1), n, T.int64(4096)):
            with T.block("T_add"):
                v_ax0, v_ax1, v_ax2 = T.axis.remap("SSS", [ax0, ax1, ax2])
                T.reads(lv2[v_ax0, v_ax1, v_ax2], var_NT_matmul_intermediate[v_ax0, v_ax1, v_ax2])
                T.writes(var_T_add_intermediate[v_ax0, v_ax1, v_ax2])
                var_T_add_intermediate[v_ax0, v_ax1, v_ax2] = lv2[v_ax0, v_ax1, v_ax2] + var_NT_matmul_intermediate[v_ax0, v_ax1, v_ax2]

    @T.prim_func
    def fused_NT_matmul2_divide2_maximum1_minimum1_cast3(p_lv28: T.handle, p_lv29: T.handle, p_lv5: T.handle, p_output0: T.handle):
        T.func_attr({"tir.noalias": T.bool(True)})
        n = T.int64()
        lv28 = T.match_buffer(p_lv28, (T.int64(1), T.int64(32), n, T.int64(128)), "float16")
        m = T.int64()
        lv29 = T.match_buffer(p_lv29, (T.int64(1), T.int64(32), m, T.int64(128)), "float16")
        lv5 = T.match_buffer(p_lv5, (T.int64(1), T.int64(1), n, m), "float16")
        var_compute_intermediate = T.match_buffer(p_output0, (T.int64(1), T.int64(32), n, m))
        # with T.block("root"):
        var_NT_matmul_intermediate = T.alloc_buffer((T.int64(1), T.int64(32), n, m), "float16")
        var_T_divide_intermediate = T.alloc_buffer((T.int64(1), T.int64(32), n, m), "float16")
        var_T_maximum_intermediate = T.alloc_buffer((T.int64(1), T.int64(32), n, m), "float16")
        var_T_minimum_intermediate = T.alloc_buffer((T.int64(1), T.int64(32), n, m), "float16")
        for i0, i1, i2, i3, k in T.grid(T.int64(1), T.int64(32), n, m, T.int64(128)):
            with T.block("NT_matmul"):
                v_i0, v_i1, v_i2, v_i3, v_k = T.axis.remap("SSSSR", [i0, i1, i2, i3, k])
                T.reads(lv28[v_i0, v_i1, v_i2, v_k], lv29[v_i0, v_i1, v_i3, v_k])
                T.writes(var_NT_matmul_intermediate[v_i0, v_i1, v_i2, v_i3])
                with T.init():
                    var_NT_matmul_intermediate[v_i0, v_i1, v_i2, v_i3] = T.float16(0)
                var_NT_matmul_intermediate[v_i0, v_i1, v_i2, v_i3] = var_NT_matmul_intermediate[v_i0, v_i1, v_i2, v_i3] + lv28[v_i0, v_i1, v_i2, v_k] * lv29[v_i0, v_i1, v_i3, v_k]
        for ax0, ax1, ax2, ax3 in T.grid(T.int64(1), T.int64(32), n, m):
            with T.block("T_divide"):
                v_ax0, v_ax1, v_ax2, v_ax3 = T.axis.remap("SSSS", [ax0, ax1, ax2, ax3])
                T.reads(var_NT_matmul_intermediate[v_ax0, v_ax1, v_ax2, v_ax3])
                T.writes(var_T_divide_intermediate[v_ax0, v_ax1, v_ax2, v_ax3])
                var_T_divide_intermediate[v_ax0, v_ax1, v_ax2, v_ax3] = var_NT_matmul_intermediate[v_ax0, v_ax1, v_ax2, v_ax3] * T.float16(0.088397790055248615)
        for ax0, ax1, ax2, ax3 in T.grid(T.int64(1), T.int64(32), n, m):
            with T.block("T_maximum"):
                v_ax0, v_ax1, v_ax2, v_ax3 = T.axis.remap("SSSS", [ax0, ax1, ax2, ax3])
                T.reads(var_T_divide_intermediate[v_ax0, v_ax1, v_ax2, v_ax3])
                T.writes(var_T_maximum_intermediate[v_ax0, v_ax1, v_ax2, v_ax3])
                var_T_maximum_intermediate[v_ax0, v_ax1, v_ax2, v_ax3] = T.max(var_T_divide_intermediate[v_ax0, v_ax1, v_ax2, v_ax3], T.float16(-65504))
        for ax0, ax1, ax2, ax3 in T.grid(T.int64(1), T.int64(32), n, m):
            with T.block("T_minimum"):
                v_ax0, v_ax1, v_ax2, v_ax3 = T.axis.remap("SSSS", [ax0, ax1, ax2, ax3])
                T.reads(var_T_maximum_intermediate[v_ax0, v_ax1, v_ax2, v_ax3], lv5[v_ax0, T.int64(0), v_ax2, v_ax3])
                T.writes(var_T_minimum_intermediate[v_ax0, v_ax1, v_ax2, v_ax3])
                var_T_minimum_intermediate[v_ax0, v_ax1, v_ax2, v_ax3] = T.min(var_T_maximum_intermediate[v_ax0, v_ax1, v_ax2, v_ax3], lv5[v_ax0, T.int64(0), v_ax2, v_ax3])
        for i0, i1, i2, i3 in T.grid(T.int64(1), T.int64(32), n, m):
            with T.block("compute"):
                v_i0, v_i1, v_i2, v_i3 = T.axis.remap("SSSS", [i0, i1, i2, i3])
                T.reads(var_T_minimum_intermediate[v_i0, v_i1, v_i2, v_i3])
                T.writes(var_compute_intermediate[v_i0, v_i1, v_i2, v_i3])
                var_compute_intermediate[v_i0, v_i1, v_i2, v_i3] = T.Cast("float32", var_T_minimum_intermediate[v_i0, v_i1, v_i2, v_i3])

    @T.prim_func
    def fused_NT_matmul3_multiply1(p_lv45: T.handle, lv1400: T.Buffer((T.int64(11008), T.int64(4096)), "float16"), p_lv50: T.handle, p_output0: T.handle):
        T.func_attr({"tir.noalias": T.bool(True)})
        n = T.int64()
        lv45 = T.match_buffer(p_lv45, (T.int64(1), n, T.int64(4096)), "float16")
        lv50 = T.match_buffer(p_lv50, (T.int64(1), n, T.int64(11008)), "float16")
        var_T_multiply_intermediate = T.match_buffer(p_output0, (T.int64(1), n, T.int64(11008)), "float16")
        # with T.block("root"):
        var_NT_matmul_intermediate = T.alloc_buffer((T.int64(1), n, T.int64(11008)), "float16")
        for i0, i1, i2, k in T.grid(T.int64(1), n, T.int64(11008), T.int64(4096)):
            with T.block("NT_matmul"):
                v_i0, v_i1, v_i2, v_k = T.axis.remap("SSSR", [i0, i1, i2, k])
                T.reads(lv45[v_i0, v_i1, v_k], lv1400[v_i2, v_k])
                T.writes(var_NT_matmul_intermediate[v_i0, v_i1, v_i2])
                with T.init():
                    var_NT_matmul_intermediate[v_i0, v_i1, v_i2] = T.float16(0)
                var_NT_matmul_intermediate[v_i0, v_i1, v_i2] = var_NT_matmul_intermediate[v_i0, v_i1, v_i2] + lv45[v_i0, v_i1, v_k] * lv1400[v_i2, v_k]
        for ax0, ax1, ax2 in T.grid(T.int64(1), n, T.int64(11008)):
            with T.block("T_multiply"):
                v_ax0, v_ax1, v_ax2 = T.axis.remap("SSS", [ax0, ax1, ax2])
                T.reads(lv50[v_ax0, v_ax1, v_ax2], var_NT_matmul_intermediate[v_ax0, v_ax1, v_ax2])
                T.writes(var_T_multiply_intermediate[v_ax0, v_ax1, v_ax2])
                var_T_multiply_intermediate[v_ax0, v_ax1, v_ax2] = lv50[v_ax0, v_ax1, v_ax2] * var_NT_matmul_intermediate[v_ax0, v_ax1, v_ax2]

    @T.prim_func
    def fused_NT_matmul3_silu1(p_lv45: T.handle, lv1393: T.Buffer((T.int64(11008), T.int64(4096)), "float16"), p_output0: T.handle):
        T.func_attr({"tir.noalias": T.bool(True)})
        n = T.int64()
        lv45 = T.match_buffer(p_lv45, (T.int64(1), n, T.int64(4096)), "float16")
        var_T_multiply_intermediate = T.match_buffer(p_output0, (T.int64(1), n, T.int64(11008)), "float16")
        # with T.block("root"):
        var_NT_matmul_intermediate = T.alloc_buffer((T.int64(1), n, T.int64(11008)), "float16")
        compute = T.alloc_buffer((T.int64(1), n, T.int64(11008)), "float16")
        for i0, i1, i2, k in T.grid(T.int64(1), n, T.int64(11008), T.int64(4096)):
            with T.block("NT_matmul"):
                v_i0, v_i1, v_i2, v_k = T.axis.remap("SSSR", [i0, i1, i2, k])
                T.reads(lv45[v_i0, v_i1, v_k], lv1393[v_i2, v_k])
                T.writes(var_NT_matmul_intermediate[v_i0, v_i1, v_i2])
                with T.init():
                    var_NT_matmul_intermediate[v_i0, v_i1, v_i2] = T.float16(0)
                var_NT_matmul_intermediate[v_i0, v_i1, v_i2] = var_NT_matmul_intermediate[v_i0, v_i1, v_i2] + lv45[v_i0, v_i1, v_k] * lv1393[v_i2, v_k]
        for i0, i1, i2 in T.grid(T.int64(1), n, T.int64(11008)):
            with T.block("compute"):
                v_i0, v_i1, v_i2 = T.axis.remap("SSS", [i0, i1, i2])
                T.reads(var_NT_matmul_intermediate[v_i0, v_i1, v_i2])
                T.writes(compute[v_i0, v_i1, v_i2])
                compute[v_i0, v_i1, v_i2] = T.sigmoid(var_NT_matmul_intermediate[v_i0, v_i1, v_i2])
        for ax0, ax1, ax2 in T.grid(T.int64(1), n, T.int64(11008)):
            with T.block("T_multiply"):
                v_ax0, v_ax1, v_ax2 = T.axis.remap("SSS", [ax0, ax1, ax2])
                T.reads(var_NT_matmul_intermediate[v_ax0, v_ax1, v_ax2], compute[v_ax0, v_ax1, v_ax2])
                T.writes(var_T_multiply_intermediate[v_ax0, v_ax1, v_ax2])
                var_T_multiply_intermediate[v_ax0, v_ax1, v_ax2] = var_NT_matmul_intermediate[v_ax0, v_ax1, v_ax2] * compute[v_ax0, v_ax1, v_ax2]

    @T.prim_func
    def fused_NT_matmul4_add1(p_lv51: T.handle, lv1407: T.Buffer((T.int64(4096), T.int64(11008)), "float16"), p_lv44: T.handle, p_output0: T.handle):
        T.func_attr({"tir.noalias": T.bool(True)})
        n = T.int64()
        lv51 = T.match_buffer(p_lv51, (T.int64(1), n, T.int64(11008)), "float16")
        lv44 = T.match_buffer(p_lv44, (T.int64(1), n, T.int64(4096)), "float16")
        var_T_add_intermediate = T.match_buffer(p_output0, (T.int64(1), n, T.int64(4096)), "float16")
        # with T.block("root"):
        var_NT_matmul_intermediate = T.alloc_buffer((T.int64(1), n, T.int64(4096)), "float16")
        for i0, i1, i2, k in T.grid(T.int64(1), n, T.int64(4096), T.int64(11008)):
            with T.block("NT_matmul"):
                v_i0, v_i1, v_i2, v_k = T.axis.remap("SSSR", [i0, i1, i2, k])
                T.reads(lv51[v_i0, v_i1, v_k], lv1407[v_i2, v_k])
                T.writes(var_NT_matmul_intermediate[v_i0, v_i1, v_i2])
                with T.init():
                    var_NT_matmul_intermediate[v_i0, v_i1, v_i2] = T.float16(0)
                var_NT_matmul_intermediate[v_i0, v_i1, v_i2] = var_NT_matmul_intermediate[v_i0, v_i1, v_i2] + lv51[v_i0, v_i1, v_k] * lv1407[v_i2, v_k]
        for ax0, ax1, ax2 in T.grid(T.int64(1), n, T.int64(4096)):
            with T.block("T_add"):
                v_ax0, v_ax1, v_ax2 = T.axis.remap("SSS", [ax0, ax1, ax2])
                T.reads(lv44[v_ax0, v_ax1, v_ax2], var_NT_matmul_intermediate[v_ax0, v_ax1, v_ax2])
                T.writes(var_T_add_intermediate[v_ax0, v_ax1, v_ax2])
                var_T_add_intermediate[v_ax0, v_ax1, v_ax2] = lv44[v_ax0, v_ax1, v_ax2] + var_NT_matmul_intermediate[v_ax0, v_ax1, v_ax2]

    @T.prim_func
    def fused_NT_matmul_divide1_maximum_minimum_cast(lv1605: T.Buffer((T.int64(1), T.int64(32), T.int64(1), T.int64(128)), "float16"), p_lv1606: T.handle, p_lv1582: T.handle, p_output0: T.handle):
        T.func_attr({"tir.noalias": T.bool(True)})
        n = T.int64()
        lv1606 = T.match_buffer(p_lv1606, (T.int64(1), T.int64(32), n, T.int64(128)), "float16")
        lv1582 = T.match_buffer(p_lv1582, (T.int64(1), T.int64(1), T.int64(1), n), "float16")
        var_compute_intermediate = T.match_buffer(p_output0, (T.int64(1), T.int64(32), T.int64(1), n))
        # with T.block("root"):
        var_NT_matmul_intermediate = T.alloc_buffer((T.int64(1), T.int64(32), T.int64(1), n), "float16")
        var_T_divide_intermediate = T.alloc_buffer((T.int64(1), T.int64(32), T.int64(1), n), "float16")
        var_T_maximum_intermediate = T.alloc_buffer((T.int64(1), T.int64(32), T.int64(1), n), "float16")
        var_T_minimum_intermediate = T.alloc_buffer((T.int64(1), T.int64(32), T.int64(1), n), "float16")
        for i0, i1, i2, i3, k in T.grid(T.int64(1), T.int64(32), T.int64(1), n, T.int64(128)):
            with T.block("NT_matmul"):
                v_i0, v_i1, v_i2, v_i3, v_k = T.axis.remap("SSSSR", [i0, i1, i2, i3, k])
                T.reads(lv1605[v_i0, v_i1, v_i2, v_k], lv1606[v_i0, v_i1, v_i3, v_k])
                T.writes(var_NT_matmul_intermediate[v_i0, v_i1, v_i2, v_i3])
                with T.init():
                    var_NT_matmul_intermediate[v_i0, v_i1, v_i2, v_i3] = T.float16(0)
                var_NT_matmul_intermediate[v_i0, v_i1, v_i2, v_i3] = var_NT_matmul_intermediate[v_i0, v_i1, v_i2, v_i3] + lv1605[v_i0, v_i1, v_i2, v_k] * lv1606[v_i0, v_i1, v_i3, v_k]
        for ax0, ax1, ax2, ax3 in T.grid(T.int64(1), T.int64(32), T.int64(1), n):
            with T.block("T_divide"):
                v_ax0, v_ax1, v_ax2, v_ax3 = T.axis.remap("SSSS", [ax0, ax1, ax2, ax3])
                T.reads(var_NT_matmul_intermediate[v_ax0, v_ax1, v_ax2, v_ax3])
                T.writes(var_T_divide_intermediate[v_ax0, v_ax1, v_ax2, v_ax3])
                var_T_divide_intermediate[v_ax0, v_ax1, v_ax2, v_ax3] = var_NT_matmul_intermediate[v_ax0, v_ax1, v_ax2, v_ax3] * T.float16(0.088397790055248615)
        for ax0, ax1, ax2, ax3 in T.grid(T.int64(1), T.int64(32), T.int64(1), n):
            with T.block("T_maximum"):
                v_ax0, v_ax1, v_ax2, v_ax3 = T.axis.remap("SSSS", [ax0, ax1, ax2, ax3])
                T.reads(var_T_divide_intermediate[v_ax0, v_ax1, v_ax2, v_ax3])
                T.writes(var_T_maximum_intermediate[v_ax0, v_ax1, v_ax2, v_ax3])
                var_T_maximum_intermediate[v_ax0, v_ax1, v_ax2, v_ax3] = T.max(var_T_divide_intermediate[v_ax0, v_ax1, v_ax2, v_ax3], T.float16(-65504))
        for ax0, ax1, ax2, ax3 in T.grid(T.int64(1), T.int64(32), T.int64(1), n):
            with T.block("T_minimum"):
                v_ax0, v_ax1, v_ax2, v_ax3 = T.axis.remap("SSSS", [ax0, ax1, ax2, ax3])
                T.reads(var_T_maximum_intermediate[v_ax0, v_ax1, v_ax2, v_ax3], lv1582[v_ax0, T.int64(0), v_ax2, v_ax3])
                T.writes(var_T_minimum_intermediate[v_ax0, v_ax1, v_ax2, v_ax3])
                var_T_minimum_intermediate[v_ax0, v_ax1, v_ax2, v_ax3] = T.min(var_T_maximum_intermediate[v_ax0, v_ax1, v_ax2, v_ax3], lv1582[v_ax0, T.int64(0), v_ax2, v_ax3])
        for i0, i1, i2, i3 in T.grid(T.int64(1), T.int64(32), T.int64(1), n):
            with T.block("compute"):
                v_i0, v_i1, v_i2, v_i3 = T.axis.remap("SSSS", [i0, i1, i2, i3])
                T.reads(var_T_minimum_intermediate[v_i0, v_i1, v_i2, v_i3])
                T.writes(var_compute_intermediate[v_i0, v_i1, v_i2, v_i3])
                var_compute_intermediate[v_i0, v_i1, v_i2, v_i3] = T.Cast("float32", var_T_minimum_intermediate[v_i0, v_i1, v_i2, v_i3])

    @T.prim_func
    def fused_min_max_triu_te_broadcast_to(p_output0: T.handle, n: T.int64):
        T.func_attr({"tir.noalias": T.bool(True)})
        var_T_broadcast_to_intermediate = T.match_buffer(p_output0, (T.int64(1), T.int64(1), n, n), "float16")
        # with T.block("root"):
        var_make_diag_mask_te_intermediate = T.alloc_buffer((n, n), "float16")
        for i, j in T.grid(n, n):
            with T.block("make_diag_mask_te"):
                v_i, v_j = T.axis.remap("SS", [i, j])
                T.reads()
                T.writes(var_make_diag_mask_te_intermediate[v_i, v_j])
                var_make_diag_mask_te_intermediate[v_i, v_j] = T.Select(v_i < v_j, T.float16(-65504), T.float16(65504))
        for ax0, ax1, ax2, ax3 in T.grid(T.int64(1), T.int64(1), n, n):
            with T.block("T_broadcast_to"):
                v_ax0, v_ax1, v_ax2, v_ax3 = T.axis.remap("SSSS", [ax0, ax1, ax2, ax3])
                T.reads(var_make_diag_mask_te_intermediate[v_ax2, v_ax3])
                T.writes(var_T_broadcast_to_intermediate[v_ax0, v_ax1, v_ax2, v_ax3])
                var_T_broadcast_to_intermediate[v_ax0, v_ax1, v_ax2, v_ax3] = var_make_diag_mask_te_intermediate[v_ax2, v_ax3]

    @T.prim_func
    def fused_softmax1_cast1(p_lv1613: T.handle, p_output0: T.handle):
        T.func_attr({"tir.noalias": T.bool(True)})
        n = T.int64()
        lv1613 = T.match_buffer(p_lv1613, (T.int64(1), T.int64(32), T.int64(1), n))
        var_compute_intermediate = T.match_buffer(p_output0, (T.int64(1), T.int64(32), T.int64(1), n), "float16")
        # with T.block("root"):
        T_softmax_maxelem = T.alloc_buffer((T.int64(1), T.int64(32), T.int64(1)))
        T_softmax_exp = T.alloc_buffer((T.int64(1), T.int64(32), T.int64(1), n))
        T_softmax_expsum = T.alloc_buffer((T.int64(1), T.int64(32), T.int64(1)))
        var_T_softmax_norm_intermediate = T.alloc_buffer((T.int64(1), T.int64(32), T.int64(1), n))
        for i0, i1, i2, k in T.grid(T.int64(1), T.int64(32), T.int64(1), n):
            with T.block("T_softmax_maxelem"):
                v_i0, v_i1, v_i2, v_k = T.axis.remap("SSSR", [i0, i1, i2, k])
                T.reads(lv1613[v_i0, v_i1, v_i2, v_k])
                T.writes(T_softmax_maxelem[v_i0, v_i1, v_i2])
                with T.init():
                    T_softmax_maxelem[v_i0, v_i1, v_i2] = T.float32(-3.4028234663852886e+38)
                T_softmax_maxelem[v_i0, v_i1, v_i2] = T.max(T_softmax_maxelem[v_i0, v_i1, v_i2], lv1613[v_i0, v_i1, v_i2, v_k])
        for i0, i1, i2, i3 in T.grid(T.int64(1), T.int64(32), T.int64(1), n):
            with T.block("T_softmax_exp"):
                v_i0, v_i1, v_i2, v_i3 = T.axis.remap("SSSS", [i0, i1, i2, i3])
                T.reads(lv1613[v_i0, v_i1, v_i2, v_i3], T_softmax_maxelem[v_i0, v_i1, v_i2])
                T.writes(T_softmax_exp[v_i0, v_i1, v_i2, v_i3])
                T_softmax_exp[v_i0, v_i1, v_i2, v_i3] = T.exp(lv1613[v_i0, v_i1, v_i2, v_i3] - T_softmax_maxelem[v_i0, v_i1, v_i2])
        for i0, i1, i2, k in T.grid(T.int64(1), T.int64(32), T.int64(1), n):
            with T.block("T_softmax_expsum"):
                v_i0, v_i1, v_i2, v_k = T.axis.remap("SSSR", [i0, i1, i2, k])
                T.reads(T_softmax_exp[v_i0, v_i1, v_i2, v_k])
                T.writes(T_softmax_expsum[v_i0, v_i1, v_i2])
                with T.init():
                    T_softmax_expsum[v_i0, v_i1, v_i2] = T.float32(0)
                T_softmax_expsum[v_i0, v_i1, v_i2] = T_softmax_expsum[v_i0, v_i1, v_i2] + T_softmax_exp[v_i0, v_i1, v_i2, v_k]
        for i0, i1, i2, i3 in T.grid(T.int64(1), T.int64(32), T.int64(1), n):
            with T.block("T_softmax_norm"):
                v_i0, v_i1, v_i2, v_i3 = T.axis.remap("SSSS", [i0, i1, i2, i3])
                T.reads(T_softmax_exp[v_i0, v_i1, v_i2, v_i3], T_softmax_expsum[v_i0, v_i1, v_i2])
                T.writes(var_T_softmax_norm_intermediate[v_i0, v_i1, v_i2, v_i3])
                T.block_attr({"axis": 3})
                var_T_softmax_norm_intermediate[v_i0, v_i1, v_i2, v_i3] = T_softmax_exp[v_i0, v_i1, v_i2, v_i3] / T_softmax_expsum[v_i0, v_i1, v_i2]
        for i0, i1, i2, i3 in T.grid(T.int64(1), T.int64(32), T.int64(1), n):
            with T.block("compute"):
                v_i0, v_i1, v_i2, v_i3 = T.axis.remap("SSSS", [i0, i1, i2, i3])
                T.reads(var_T_softmax_norm_intermediate[v_i0, v_i1, v_i2, v_i3])
                T.writes(var_compute_intermediate[v_i0, v_i1, v_i2, v_i3])
                var_compute_intermediate[v_i0, v_i1, v_i2, v_i3] = T.Cast("float16", var_T_softmax_norm_intermediate[v_i0, v_i1, v_i2, v_i3])

    @T.prim_func
    def fused_softmax2_cast4(p_lv36: T.handle, p_output0: T.handle):
        T.func_attr({"tir.noalias": T.bool(True)})
        n, m = T.int64(), T.int64()
        lv36 = T.match_buffer(p_lv36, (T.int64(1), T.int64(32), n, m))
        var_compute_intermediate = T.match_buffer(p_output0, (T.int64(1), T.int64(32), n, m), "float16")
        # with T.block("root"):
        T_softmax_maxelem = T.alloc_buffer((T.int64(1), T.int64(32), n))
        T_softmax_exp = T.alloc_buffer((T.int64(1), T.int64(32), n, m))
        T_softmax_expsum = T.alloc_buffer((T.int64(1), T.int64(32), n))
        var_T_softmax_norm_intermediate = T.alloc_buffer((T.int64(1), T.int64(32), n, m))
        for i0, i1, i2, k in T.grid(T.int64(1), T.int64(32), n, m):
            with T.block("T_softmax_maxelem"):
                v_i0, v_i1, v_i2, v_k = T.axis.remap("SSSR", [i0, i1, i2, k])
                T.reads(lv36[v_i0, v_i1, v_i2, v_k])
                T.writes(T_softmax_maxelem[v_i0, v_i1, v_i2])
                with T.init():
                    T_softmax_maxelem[v_i0, v_i1, v_i2] = T.float32(-3.4028234663852886e+38)
                T_softmax_maxelem[v_i0, v_i1, v_i2] = T.max(T_softmax_maxelem[v_i0, v_i1, v_i2], lv36[v_i0, v_i1, v_i2, v_k])
        for i0, i1, i2, i3 in T.grid(T.int64(1), T.int64(32), n, m):
            with T.block("T_softmax_exp"):
                v_i0, v_i1, v_i2, v_i3 = T.axis.remap("SSSS", [i0, i1, i2, i3])
                T.reads(lv36[v_i0, v_i1, v_i2, v_i3], T_softmax_maxelem[v_i0, v_i1, v_i2])
                T.writes(T_softmax_exp[v_i0, v_i1, v_i2, v_i3])
                T_softmax_exp[v_i0, v_i1, v_i2, v_i3] = T.exp(lv36[v_i0, v_i1, v_i2, v_i3] - T_softmax_maxelem[v_i0, v_i1, v_i2])
        for i0, i1, i2, k in T.grid(T.int64(1), T.int64(32), n, m):
            with T.block("T_softmax_expsum"):
                v_i0, v_i1, v_i2, v_k = T.axis.remap("SSSR", [i0, i1, i2, k])
                T.reads(T_softmax_exp[v_i0, v_i1, v_i2, v_k])
                T.writes(T_softmax_expsum[v_i0, v_i1, v_i2])
                with T.init():
                    T_softmax_expsum[v_i0, v_i1, v_i2] = T.float32(0)
                T_softmax_expsum[v_i0, v_i1, v_i2] = T_softmax_expsum[v_i0, v_i1, v_i2] + T_softmax_exp[v_i0, v_i1, v_i2, v_k]
        for i0, i1, i2, i3 in T.grid(T.int64(1), T.int64(32), n, m):
            with T.block("T_softmax_norm"):
                v_i0, v_i1, v_i2, v_i3 = T.axis.remap("SSSS", [i0, i1, i2, i3])
                T.reads(T_softmax_exp[v_i0, v_i1, v_i2, v_i3], T_softmax_expsum[v_i0, v_i1, v_i2])
                T.writes(var_T_softmax_norm_intermediate[v_i0, v_i1, v_i2, v_i3])
                T.block_attr({"axis": 3})
                var_T_softmax_norm_intermediate[v_i0, v_i1, v_i2, v_i3] = T_softmax_exp[v_i0, v_i1, v_i2, v_i3] / T_softmax_expsum[v_i0, v_i1, v_i2]
        for i0, i1, i2, i3 in T.grid(T.int64(1), T.int64(32), n, m):
            with T.block("compute"):
                v_i0, v_i1, v_i2, v_i3 = T.axis.remap("SSSS", [i0, i1, i2, i3])
                T.reads(var_T_softmax_norm_intermediate[v_i0, v_i1, v_i2, v_i3])
                T.writes(var_compute_intermediate[v_i0, v_i1, v_i2, v_i3])
                var_compute_intermediate[v_i0, v_i1, v_i2, v_i3] = T.Cast("float16", var_T_softmax_norm_intermediate[v_i0, v_i1, v_i2, v_i3])

    @T.prim_func
    def matmul10(var_A: T.handle, var_B: T.handle, var_matmul: T.handle):
        T.func_attr({"op_pattern": 4, "tir.noalias": T.bool(True)})
        n, m = T.int64(), T.int64()
        A = T.match_buffer(var_A, (T.int64(1), T.int64(32), n, m), "float16")
        B = T.match_buffer(var_B, (T.int64(1), T.int64(32), m, T.int64(128)), "float16")
        matmul = T.match_buffer(var_matmul, (T.int64(1), T.int64(32), n, T.int64(128)), "float16")
        # with T.block("root"):
        for i0, i1, i2, i3, k in T.grid(T.int64(1), T.int64(32), n, T.int64(128), m):
            with T.block("matmul"):
                v_i0, v_i1, v_i2, v_i3, v_k = T.axis.remap("SSSSR", [i0, i1, i2, i3, k])
                T.reads(A[v_i0, v_i1, v_i2, v_k], B[v_i0, v_i1, v_k, v_i3])
                T.writes(matmul[v_i0, v_i1, v_i2, v_i3])
                with T.init():
                    matmul[v_i0, v_i1, v_i2, v_i3] = T.float16(0)
                matmul[v_i0, v_i1, v_i2, v_i3] = matmul[v_i0, v_i1, v_i2, v_i3] + A[v_i0, v_i1, v_i2, v_k] * B[v_i0, v_i1, v_k, v_i3]

    @T.prim_func
    def matmul5(var_A: T.handle, var_B: T.handle, matmul: T.Buffer((T.int64(1), T.int64(32), T.int64(1), T.int64(128)), "float16")):
        T.func_attr({"op_pattern": 4, "tir.noalias": T.bool(True)})
        n = T.int64()
        A = T.match_buffer(var_A, (T.int64(1), T.int64(32), T.int64(1), n), "float16")
        B = T.match_buffer(var_B, (T.int64(1), T.int64(32), n, T.int64(128)), "float16")
        # with T.block("root"):
        for i0, i1, i2, i3, k in T.grid(T.int64(1), T.int64(32), T.int64(1), T.int64(128), n):
            with T.block("matmul"):
                v_i0, v_i1, v_i2, v_i3, v_k = T.axis.remap("SSSSR", [i0, i1, i2, i3, k])
                T.reads(A[v_i0, v_i1, v_i2, v_k], B[v_i0, v_i1, v_k, v_i3])
                T.writes(matmul[v_i0, v_i1, v_i2, v_i3])
                with T.init():
                    matmul[v_i0, v_i1, v_i2, v_i3] = T.float16(0)
                matmul[v_i0, v_i1, v_i2, v_i3] = matmul[v_i0, v_i1, v_i2, v_i3] + A[v_i0, v_i1, v_i2, v_k] * B[v_i0, v_i1, v_k, v_i3]

    @T.prim_func
    def reshape3(var_A: T.handle, var_T_reshape: T.handle):
        T.func_attr({"op_pattern": 8, "tir.noalias": T.bool(True)})
        n = T.int64()
        A = T.match_buffer(var_A, (n, T.int64(32), T.int64(128)), "float16")
        T_reshape = T.match_buffer(var_T_reshape, (T.int64(1), n, T.int64(32), T.int64(128)), "float16")
        # with T.block("root"):
        for ax0, ax1, ax2, ax3 in T.grid(T.int64(1), n, T.int64(32), T.int64(128)):
            with T.block("T_reshape"):
                v_ax0, v_ax1, v_ax2, v_ax3 = T.axis.remap("SSSS", [ax0, ax1, ax2, ax3])
                T.reads(A[((v_ax3 // T.int64(128) + v_ax2) // T.int64(32) + v_ax0 * n + v_ax1) % n, (v_ax3 // T.int64(128) + v_ax2) % T.int64(32), v_ax3 % T.int64(128)])
                T.writes(T_reshape[v_ax0, v_ax1, v_ax2, v_ax3])
                T_reshape[v_ax0, v_ax1, v_ax2, v_ax3] = A[((v_ax3 // T.int64(128) + v_ax2) // T.int64(32) + v_ax0 * n + v_ax1) % n, (v_ax3 // T.int64(128) + v_ax2) % T.int64(32), v_ax3 % T.int64(128)]

    @T.prim_func
    def reshape5(var_A: T.handle, var_T_reshape: T.handle):
        T.func_attr({"op_pattern": 8, "tir.noalias": T.bool(True)})
        n = T.int64()
        A = T.match_buffer(var_A, (T.int64(1), n), "int32")
        T_reshape = T.match_buffer(var_T_reshape, (n,), "int32")
        # with T.block("root"):
        for ax0 in range(n):
            with T.block("T_reshape"):
                v_ax0 = T.axis.spatial(n, ax0)
                T.reads(A[T.int64(0), v_ax0 % n])
                T.writes(T_reshape[v_ax0])
                T_reshape[v_ax0] = A[T.int64(0), v_ax0 % n]

    @T.prim_func
    def reshape6(var_A: T.handle, var_T_reshape: T.handle):
        T.func_attr({"op_pattern": 8, "tir.noalias": T.bool(True)})
        n = T.int64()
        A = T.match_buffer(var_A, (n, T.int64(4096)), "float16")
        T_reshape = T.match_buffer(var_T_reshape, (T.int64(1), n, T.int64(4096)), "float16")
        # with T.block("root"):
        for ax0, ax1, ax2 in T.grid(T.int64(1), n, T.int64(4096)):
            with T.block("T_reshape"):
                v_ax0, v_ax1, v_ax2 = T.axis.remap("SSS", [ax0, ax1, ax2])
                T.reads(A[(v_ax2 // T.int64(4096) + v_ax0 * n + v_ax1) % n, v_ax2 % T.int64(4096)])
                T.writes(T_reshape[v_ax0, v_ax1, v_ax2])
                T_reshape[v_ax0, v_ax1, v_ax2] = A[(v_ax2 // T.int64(4096) + v_ax0 * n + v_ax1) % n, v_ax2 % T.int64(4096)]

    @T.prim_func
    def reshape7(var_A: T.handle, var_T_reshape: T.handle):
        T.func_attr({"op_pattern": 8, "tir.noalias": T.bool(True)})
        n = T.int64()
        A = T.match_buffer(var_A, (T.int64(1), n, T.int64(4096)), "float16")
        T_reshape = T.match_buffer(var_T_reshape, (T.int64(1), n, T.int64(32), T.int64(128)), "float16")
        # with T.block("root"):
        for ax0, ax1, ax2, ax3 in T.grid(T.int64(1), n, T.int64(32), T.int64(128)):
            with T.block("T_reshape"):
                v_ax0, v_ax1, v_ax2, v_ax3 = T.axis.remap("SSSS", [ax0, ax1, ax2, ax3])
                T.reads(A[T.int64(0), ((v_ax2 * T.int64(128) + v_ax3) // T.int64(4096) + v_ax0 * n + v_ax1) % n, (v_ax2 * T.int64(128) + v_ax3) % T.int64(4096)])
                T.writes(T_reshape[v_ax0, v_ax1, v_ax2, v_ax3])
                T_reshape[v_ax0, v_ax1, v_ax2, v_ax3] = A[T.int64(0), ((v_ax2 * T.int64(128) + v_ax3) // T.int64(4096) + v_ax0 * n + v_ax1) % n, (v_ax2 * T.int64(128) + v_ax3) % T.int64(4096)]

    @T.prim_func
    def reshape8(var_A: T.handle, var_T_reshape: T.handle):
        T.func_attr({"op_pattern": 8, "tir.noalias": T.bool(True)})
        n = T.int64()
        A = T.match_buffer(var_A, (T.int64(1), n, T.int64(32), T.int64(128)), "float16")
        T_reshape = T.match_buffer(var_T_reshape, (T.int64(1), n, T.int64(4096)), "float16")
        # with T.block("root"):
        for ax0, ax1, ax2 in T.grid(T.int64(1), n, T.int64(4096)):
            with T.block("T_reshape"):
                v_ax0, v_ax1, v_ax2 = T.axis.remap("SSS", [ax0, ax1, ax2])
                T.reads(A[T.int64(0), (v_ax2 // T.int64(4096) + v_ax0 * n + v_ax1) % n, v_ax2 % T.int64(4096) // T.int64(128), v_ax2 % T.int64(128)])
                T.writes(T_reshape[v_ax0, v_ax1, v_ax2])
                T_reshape[v_ax0, v_ax1, v_ax2] = A[T.int64(0), (v_ax2 // T.int64(4096) + v_ax0 * n + v_ax1) % n, v_ax2 % T.int64(4096) // T.int64(128), v_ax2 % T.int64(128)]

    @T.prim_func
    def rms_norm(var_A: T.handle, B: T.Buffer((T.int64(4096),), "float16"), var_rms_norm: T.handle):
        T.func_attr({"op_pattern": 4, "tir.noalias": T.bool(True)})
        n = T.int64()
        A = T.match_buffer(var_A, (T.int64(1), n, T.int64(4096)), "float16")
        rms_norm_1 = T.match_buffer(var_rms_norm, (T.int64(1), n, T.int64(4096)), "float16")
        # with T.block("root"):
        Ared_temp = T.alloc_buffer((T.int64(1), n))
        for bsz, i, k in T.grid(T.int64(1), n, T.int64(4096)):
            with T.block("Ared_temp"):
                v_bsz, v_i, v_k = T.axis.remap("SSR", [bsz, i, k])
                T.reads(A[v_bsz, v_i, v_k])
                T.writes(Ared_temp[v_bsz, v_i])
                with T.init():
                    Ared_temp[v_bsz, v_i] = T.float32(0)
                Ared_temp[v_bsz, v_i] = Ared_temp[v_bsz, v_i] + T.Cast("float32", A[v_bsz, v_i, v_k]) * T.Cast("float32", A[v_bsz, v_i, v_k])
        for bsz, i, k in T.grid(T.int64(1), n, T.int64(4096)):
            with T.block("rms_norm"):
                v_bsz, v_i, v_k = T.axis.remap("SSS", [bsz, i, k])
                T.reads(B[v_k], A[v_bsz, v_i, v_k], Ared_temp[v_bsz, v_i])
                T.writes(rms_norm_1[v_bsz, v_i, v_k])
                rms_norm_1[v_bsz, v_i, v_k] = T.Cast("float16", T.Cast("float32", B[v_k]) * (T.Cast("float32", A[v_bsz, v_i, v_k]) / T.sqrt(Ared_temp[v_bsz, v_i] * T.float32(0.000244140625) + T.float32(9.9999999999999995e-07))))

    @T.prim_func
    def rotary_embedding(var_A: T.handle, B: T.Buffer((T.int64(2048), T.int64(128)), "float16"), C: T.Buffer((T.int64(2048), T.int64(128)), "float16"), var_rotary: T.handle, m: T.int64):
        T.func_attr({"op_pattern": 8, "tir.noalias": T.bool(True)})
        n = T.int64()
        A = T.match_buffer(var_A, (T.int64(1), n, T.int64(32), T.int64(128)), "float16")
        rotary = T.match_buffer(var_rotary, (T.int64(1), n, T.int64(32), T.int64(128)), "float16")
        # with T.block("root"):
        for i0, i1, i2, i3 in T.grid(T.int64(1), n, T.int64(32), T.int64(128)):
            with T.block("rotary"):
                v_i0, v_i1, v_i2, v_i3 = T.axis.remap("SSSS", [i0, i1, i2, i3])
                T.reads(B[m + v_i1 - n, v_i3], A[v_i0, v_i1, v_i2, v_i3 - T.int64(64):v_i3 - T.int64(64) + T.int64(129)], C[m + v_i1 - n, v_i3])
                T.writes(rotary[v_i0, v_i1, v_i2, v_i3])
                rotary[v_i0, v_i1, v_i2, v_i3] = B[m + v_i1 - n, v_i3] * A[v_i0, v_i1, v_i2, v_i3] + C[m + v_i1 - n, v_i3] * T.Select(T.int64(64) <= v_i3, A[v_i0, v_i1, v_i2, v_i3 - T.int64(64)], A[v_i0, v_i1, v_i2, v_i3 + T.int64(64)] * T.float16(-1))

    @T.prim_func
    def slice(var_A: T.handle, slice_1: T.Buffer((T.int64(1), T.int64(1), T.int64(4096)), "float16")):
        T.func_attr({"op_pattern": 8, "tir.noalias": T.bool(True)})
        n = T.int64()
        A = T.match_buffer(var_A, (T.int64(1), n, T.int64(4096)), "float16")
        # with T.block("root"):
        for i, j, k in T.grid(T.int64(1), T.int64(1), T.int64(4096)):
            with T.block("slice"):
                v_i, v_j, v_k = T.axis.remap("SSS", [i, j, k])
                T.reads(A[v_i, n - T.int64(1), v_k])
                T.writes(slice_1[v_i, v_j, v_k])
                slice_1[v_i, v_j, v_k] = A[v_i, n - T.int64(1), v_k]

    @T.prim_func
    def squeeze1(var_A: T.handle, var_T_squeeze: T.handle):
        T.func_attr({"op_pattern": 1, "tir.noalias": T.bool(True)})
        n = T.int64()
        A = T.match_buffer(var_A, (T.int64(1), n, T.int64(32), T.int64(128)), "float16")
        T_squeeze = T.match_buffer(var_T_squeeze, (n, T.int64(32), T.int64(128)), "float16")
        # with T.block("root"):
        for ax0, ax1, ax2 in T.grid(n, T.int64(32), T.int64(128)):
            with T.block("T_squeeze"):
                v_ax0, v_ax1, v_ax2 = T.axis.remap("SSS", [ax0, ax1, ax2])
                T.reads(A[T.int64(0), v_ax0, v_ax1, v_ax2])
                T.writes(T_squeeze[v_ax0, v_ax1, v_ax2])
                T_squeeze[v_ax0, v_ax1, v_ax2] = A[T.int64(0), v_ax0, v_ax1, v_ax2]

    @T.prim_func
    def take_decode1(A: T.Buffer((T.int64(32000), T.int64(824)), "uint16"), B: T.Buffer((T.int64(32000), T.int64(103)), "float16"), var_C: T.handle, var_take_decode: T.handle):
        T.func_attr({"op_pattern": 8, "tir.noalias": T.bool(True)})
        n = T.int64()
        C = T.match_buffer(var_C, (n,), "int32")
        take_decode = T.match_buffer(var_take_decode, (n, T.int64(4096)), "float16")
        # with T.block("root"):
        for i, j in T.grid(n, T.int64(4096)):
            with T.block("take_decode"):
                v_i, v_j = T.axis.remap("SS", [i, j])
                T.reads(A[C[v_i], v_j // T.int64(5)], C[v_i], B[C[v_i], v_j // T.int64(40)])
                T.writes(take_decode[v_i, v_j])
                take_decode[v_i, v_j] = (T.Cast("float16", T.bitwise_and(T.shift_right(T.Cast("uint32", A[C[v_i], v_j // T.int64(5)]), T.Cast("uint32", v_j % T.int64(5)) * T.uint32(3)), T.uint32(7))) - T.float16(3)) * B[C[v_i], v_j // T.int64(40)]

    @T.prim_func
    def transpose4(var_A: T.handle, var_T_transpose: T.handle):
        T.func_attr({"op_pattern": 2, "tir.noalias": T.bool(True)})
        n = T.int64()
        A = T.match_buffer(var_A, (T.int64(1), n, T.int64(32), T.int64(128)), "float16")
        T_transpose = T.match_buffer(var_T_transpose, (T.int64(1), T.int64(32), n, T.int64(128)), "float16")
        # with T.block("root"):
        for ax0, ax1, ax2, ax3 in T.grid(T.int64(1), T.int64(32), n, T.int64(128)):
            with T.block("T_transpose"):
                v_ax0, v_ax1, v_ax2, v_ax3 = T.axis.remap("SSSS", [ax0, ax1, ax2, ax3])
                T.reads(A[v_ax0, v_ax2, v_ax1, v_ax3])
                T.writes(T_transpose[v_ax0, v_ax1, v_ax2, v_ax3])
                T_transpose[v_ax0, v_ax1, v_ax2, v_ax3] = A[v_ax0, v_ax2, v_ax1, v_ax3]

    @T.prim_func
    def transpose7(var_A: T.handle, var_T_transpose: T.handle):
        T.func_attr({"op_pattern": 2, "tir.noalias": T.bool(True)})
        n = T.int64()
        A = T.match_buffer(var_A, (T.int64(1), T.int64(32), n, T.int64(128)), "float16")
        T_transpose = T.match_buffer(var_T_transpose, (T.int64(1), n, T.int64(32), T.int64(128)), "float16")
        # with T.block("root"):
        for ax0, ax1, ax2, ax3 in T.grid(T.int64(1), n, T.int64(32), T.int64(128)):
            with T.block("T_transpose"):
                v_ax0, v_ax1, v_ax2, v_ax3 = T.axis.remap("SSSS", [ax0, ax1, ax2, ax3])
                T.reads(A[v_ax0, v_ax2, v_ax1, v_ax3])
                T.writes(T_transpose[v_ax0, v_ax1, v_ax2, v_ax3])
                T_transpose[v_ax0, v_ax1, v_ax2, v_ax3] = A[v_ax0, v_ax2, v_ax1, v_ax3]
# fmt: on