Python tvm.placeholder函数代码示例

def verify_conv2d_nchw(batch, in_channel, in_size, num_filter, kernel, stride, padding, dilation=1, add_bias=False, add_relu=False):
    print("Workload: (%d, %d, %d, %d, %d, %d, %d, %d)" % (batch, in_channel, in_size, num_filter, kernel, stride, padding, dilation))

    in_height = in_width = in_size

    A = tvm.placeholder((batch, in_channel, in_height, in_width), name='A')
    W = tvm.placeholder((num_filter, in_channel, kernel, kernel), name='W')
    bias = tvm.placeholder((num_filter, 1, 1), name='bias')

    a_shape = get_const_tuple(A.shape)
    w_shape = get_const_tuple(W.shape)
    bias_shape = get_const_tuple(bias.shape)
    dtype = A.dtype

    @memoize("topi.tests.test_topi_conv2d_nchw.verify_conv2d_nchw")
    def get_ref_data():
        a_np = np.random.uniform(size=a_shape).astype(dtype)
        w_np = np.random.uniform(size=w_shape).astype(dtype)
        b_np = np.random.uniform(size=bias_shape).astype(dtype)
        dw_np = topi.testing.dilate_python(w_np, (1, 1, dilation, dilation))
        c_np = topi.testing.conv2d_nchw_python(a_np, dw_np, stride, padding)
        if add_bias:
            b_np = np.random.uniform(size=bias_shape).astype(dtype)
            c_np += b_np
        if add_relu:
            c_np = np.maximum(c_np, 0)
        return a_np, w_np, b_np, c_np

    a_np, w_np, b_np, c_np = get_ref_data()

    def check_device(device):
        ctx = tvm.context(device, 0)
        if not ctx.exist:
            print("Skip because %s is not enabled" % device)
            return
        print("Running on target: %s" % device)
        with tvm.target.create(device):
            C = topi.nn.conv2d(A, W, (stride, stride), (padding, padding),
                               (dilation, dilation), layout='NCHW', out_dtype=dtype)
            if add_bias:
                C = topi.add(C, bias)
            if add_relu:
                C = topi.nn.relu(C)
            s = topi.generic.schedule_conv2d_nchw([C])

        a = tvm.nd.array(a_np, ctx)
        w = tvm.nd.array(w_np, ctx)
        b = tvm.nd.array(b_np, ctx)
        c = tvm.nd.array(np.zeros(get_const_tuple(C.shape), dtype=C.dtype), ctx)
        if add_bias:
            func = tvm.build(s, [A, W, bias, C], device, name="relu_%d_%d_%d_%d_%d_%d_%d_%d" % (batch, in_channel, in_size, num_filter, kernel, stride, padding, dilation))
            func(a, w, b, c)
        else:
            func = tvm.build(s, [A, W, C], device, name="relu_%d_%d_%d_%d_%d_%d_%d_%d" % (batch, in_channel, in_size, num_filter, kernel, stride, padding, dilation))
            func(a, w, c)
        tvm.testing.assert_allclose(c.asnumpy(), c_np, rtol=1e-4)

    for device in get_all_backend():
        with autotvm.tophub.context(device):  # load tophub pre-tuned parameters
            check_device(device)

def test_conv_tiling():
    HSTR = WSTR = 1
    in_channel = 128
    kernel_height = kernel_width = 3
    out_channel = 64
    batch_size = 1
    in_height = in_width = 64
    out_height = out_width = in_height - kernel_height + 1
    data = tvm.placeholder((batch_size, in_channel, in_height, in_width), name='data')
    kernel = tvm.placeholder((kernel_height, kernel_width, in_channel,
        out_channel), name='kernel')
    ic = tvm.reduce_axis((0, in_channel), name='ic')
    kh = tvm.reduce_axis((0, kernel_height), name='kh')
    kw = tvm.reduce_axis((0, kernel_width), name='kw')
    conv = tvm.compute((batch_size, out_channel, out_height, out_width),
                       lambda n, oc, oh, ow: tvm.sum(data[n, ic, oh*HSTR + kh, ow*WSTR + kw] *
                                                     kernel[kh, kw, ic, oc],
                                                     axis=[ic, kh, kw]),
                       name="conv2d")
    s = tvm.create_schedule(conv.op)

    n, oc, oh, ow = conv.op.axis
    oho, owo, ohi, owi = s[conv].tile(oh, ow, 16, 16)
    bounds = tvm.schedule.InferBound(s)
    stmt = tvm.schedule.ScheduleOps(s, bounds)
    stmt = tvm.ir_pass.LoopPartition(stmt, True)
    stmt = tvm.ir_pass.Simplify(stmt)
    assert(not any(collect_visit(stmt, lambda x: isinstance(x, tvm.stmt.IfThenElse))))

def test_matmul_add():
    n = 1024
    l = 128
    m = 235
    A = tvm.placeholder((n, l), name='A')
    B = tvm.placeholder((l, m), name='B')
    C = rocblas.matmul(A, B)
    s = tvm.create_schedule(C.op)

    def verify(target="rocm"):
        if not tvm.module.enabled(target):
            print("skip because %s is not enabled..." % target)
            return
        if not tvm.get_global_func("tvm.contrib.rocblas.matmul", True):
            print("skip because extern function is not available")
            return
        ctx = tvm.rocm(0)
        f = tvm.build(s, [A, B, C], target)
        a = tvm.nd.array(np.random.uniform(size=(n, l)).astype(A.dtype), ctx)
        b = tvm.nd.array(np.random.uniform(size=(l, m)).astype(B.dtype), ctx)
        c = tvm.nd.array(np.zeros((n, m), dtype=C.dtype), ctx)
        f(a, b, c)
        tvm.testing.assert_allclose(
            c.asnumpy(), np.dot(a.asnumpy(), b.asnumpy()), rtol=1e-5)
    verify()

def test_lstm_cell_inline():
    num_step = 128
    num_input = 256
    num_hidden = 1152
    batch_size = 4
    # Global transition matrix
    X = tvm.placeholder((num_step - 1, batch_size, num_input), name="X")
    Wi2h = tvm.placeholder((4, num_hidden, num_input), name="Wi2h")
    Wh2h = tvm.placeholder((4, num_hidden, num_hidden), name="Wh2h")
    # h: output hidden state, c: cell state.
    s_state_h = tvm.placeholder((num_step, batch_size, num_hidden))
    s_state_c = tvm.placeholder((num_step, batch_size, num_hidden))
    s_init_c = tvm.compute((1, batch_size, num_hidden),
                           lambda *i: 0.0, name="init_c")
    s_init_h = tvm.compute((1, batch_size, num_hidden),
                           lambda *i: 0.0, name="init_h")
    # LSTM transition
    k = tvm.reduce_axis((0, num_input), name="ki2h")
    s_i2h = tvm.compute(
        (num_step, 4, batch_size, num_hidden),
        lambda t, x, i, j: tvm.sum(X[t - 1, i, k] * Wi2h[x, j, k], axis=k),
        name="s_i2h")
    k = tvm.reduce_axis((0, num_hidden), name="ki2h")
    s_h2h = tvm.compute(
        (num_step, 4, batch_size, num_hidden),
        lambda t, x, i, j: tvm.sum(s_state_h[t - 1, i, k] * Wh2h[x, j, k], axis=k),
        name="s_h2h")
    # Gate rules
    gates = tvm.compute(s_i2h.shape, lambda *i:
                        s_i2h(*i) + s_h2h(*i), name="gates")
    gshape = (num_step, batch_size, num_hidden)
    in_gate = tvm.compute(gshape, lambda t, i, j: tvm.sigmoid(gates[t, 0, i, j]), name="in_gate")
    in_transform = tvm.compute(gshape, lambda t, i, j: tvm.tanh(gates[t, 1, i, j]), name="in_transform")
    forget_gate = tvm.compute(gshape, lambda t, i, j: tvm.sigmoid(gates[t, 2, i, j]), name="forget_gate")
    out_gate = tvm.compute(gshape, lambda t, i, j: tvm.sigmoid(gates[t, 3, i, j]), name="out_gate")
    next_c = tvm.compute(gshape,
                         lambda t, i, j:
                         forget_gate[t, i, j] * s_state_c[t - 1, i, j] +
                         in_gate[t, i, j] * in_transform[t, i, j], name="next_c")
    next_h = tvm.compute(gshape,
                         lambda t, i, j: out_gate[t, i, j] * tvm.tanh(next_c[t, i, j]), name="next_h")
    update_c = tvm.compute(gshape, lambda *i: next_c(*i), name="update_c")
    update_h = tvm.compute(gshape, lambda *i: next_h(*i), name="update_h")
    # schedule
    scan_h, scan_c = tvm.scan(
        [s_init_h, s_init_c],
        [update_h, update_c],
        [s_state_h, s_state_c],
        inputs=[X],
        name="lstm_scan")
    # schedule
    s = tvm.create_schedule(scan_h.op)
    # Inline gate computations
    s[gates].compute_inline()
    s[in_gate].compute_inline()
    s[in_transform].compute_inline()
    s[forget_gate].compute_inline()
    s[out_gate].compute_inline()
    # verify we can lower correctly
    tvm.lower(s, [X, Wi2h, Wh2h, scan_h, scan_c])

def intrin_gemv(m, n):
    w = tvm.placeholder((m, n), name='w')
    x = tvm.placeholder((n,), name='x')
    k = tvm.reduce_axis((0, n), name='k')
    z = tvm.compute((m,), lambda i:
                    tvm.sum(w[i, k] * x[k], axis=k), name='z')
    Wb = tvm.decl_buffer(w.shape, w.dtype,
                         name="W",
                         offset_factor=16,
                         strides=[tvm.var('ldw'), 1])
    def intrin_func(ins, outs):
        ww, xx = ins
        zz = outs[0]
        ww_ptr = ww.access_ptr("r")
        xx_ptr = xx.access_ptr("r")
        zz_ptr = zz.access_ptr("w")
        body = tvm.call_packed(
            "gemm", ww_ptr, xx_ptr, zz_ptr, n, ww.strides[0])
        reset = tvm.call_packed(
            "fill_zero", zz_ptr, n)
        update = tvm.call_packed(
            "gemv_add", ww_ptr, xx_ptr, zz_ptr, n, ww.strides[0])
        return body, reset, update

    with tvm.build_config(data_alignment=16,
                          offset_factor=16):
        return tvm.decl_tensor_intrin(z.op, intrin_func,
                                      binds={w: Wb})

def test_schedule_create():
    m = tvm.var('m')
    n = tvm.var('n')
    l = tvm.var('l')
    A = tvm.placeholder((m, l), name='A')
    B = tvm.placeholder((n, l), name='B')
    AA = tvm.compute((m, l), lambda i, j: A[i, j])
    T = tvm.compute((m, n, l), lambda i, j, k: AA(i, k) * B(j, k))
    s = tvm.create_schedule(T.op)
    s[AA].set_scope("shared")
    xo, xi = s[T].split(T.op.axis[0], factor=10)
    xi1, xi2 = s[T].split(xi, factor=2)
    s[AA].compute_at(s[T], xi1)
    xo, xi = s[AA].split(AA.op.axis[0], factor=10)
    s[T].reorder(xi2, xi1)
    assert T.op.axis[1] in s[T].leaf_iter_vars

    # save load json
    json_str = tvm.save_json(s)
    s_loaded = tvm.load_json(json_str)
    assert isinstance(s_loaded, tvm.schedule.Schedule)
    assert(str(s_loaded.outputs[0].body) == str(s.outputs[0].body))

    # pickle unpickle
    dump = pkl.dumps(s)
    s_loaded = pkl.loads(dump)
    assert isinstance(s_loaded, tvm.schedule.Schedule)
    assert(str(s_loaded.outputs[0].body) == str(s.outputs[0].body))

def test_sort():
    n = 2
    l = 5
    m = 3
    data = tvm.placeholder((n, l, m), name='data')
    sort_num = tvm.placeholder((n, m), name="sort_num", dtype="int32")
    axis = 1
    is_descend = True
    out = tvm.extern(data.shape, [data, sort_num],
                     lambda ins, outs: tvm.call_packed(
                         "tvm.contrib.sort.argsort", ins[0],
                         ins[1], outs[0], axis, is_descend),
                     dtype='int32', name="sort_tensor")
    input = [[[1, 2, 3], [2, 4.5, 3.5], [1.1, 0.5, 1], [3.2, -5, 0.5], [1.5, 0, 0]],
             [[1, 2, 3], [4, 5, 6], [7, 8, 9], [10, 11, 12], [13, 14, 15]]]
    sort_num_input = [[1, 2, 3], [4, 5, 5]]
    sorted_index = [[[0, 1, 1], [1, 0, 0], [2, 2, 2], [3, 3, 3], [4, 4, 4]],
                    [[3, 4, 4], [2, 3, 3], [1, 2, 2], [0, 1, 1], [4, 0, 0]]]

    ctx = tvm.cpu(0)
    target = "llvm"
    s = tvm.create_schedule(out.op)
    f = tvm.build(s, [data, sort_num, out], target)
    a = tvm.nd.array(np.array(input).astype(data.dtype), ctx)
    b = tvm.nd.array(np.array(sort_num_input).astype(sort_num.dtype), ctx)
    c = tvm.nd.array(np.zeros(a.shape, dtype=out.dtype), ctx)
    f(a, b, c)
    tvm.testing.assert_allclose(c.asnumpy(), np.array(sorted_index).astype(out.dtype), rtol=1e-5)

 def check_cuda(dtype, n, lanes):
     if not tvm.gpu(0).exist or not tvm.module.enabled("cuda"):
         print("skip because cuda is not enabled..")
         return
     if dtype == "int8" and not have_int8(tvm.gpu(0).compute_version):
         print("skip because gpu does not support int8")
         return
     A = tvm.placeholder((n,), name='A', dtype="%sx%d" % (dtype, lanes))
     B = tvm.placeholder((n,), name='B', dtype="%sx%d" % (dtype, lanes))
     C = tvm.placeholder((n,), name='C', dtype="int32")
     D = tvm.compute((n,),
                     lambda i: tvm.call_pure_extern("int32", "__dp4a", A[i], B[i], C[i]), name='D')
     s = tvm.create_schedule(D.op)
     xo, xi = s[D].split(D.op.axis[0], factor=num_thread)
     s[D].bind(xo, tvm.thread_axis("blockIdx.x"))
     s[D].bind(xi, tvm.thread_axis("threadIdx.x"))
     fun = tvm.build(s, [A, B, C, D], "cuda")
     np_a = np.random.randint(low=-128, high=127, size=(n,lanes))
     np_b = np.random.randint(low=-128, high=127, size=(n,lanes))
     np_c = np.random.randint(low=0, high=127, size=(n,))
     np_d = [sum(x * y) + z for x, y, z in zip(np_a, np_b, np_c)]
     ctx = tvm.gpu(0)
     a = tvm.nd.empty((n,), A.dtype, ctx).copyfrom(np_a)
     b = tvm.nd.empty((n,), B.dtype, ctx).copyfrom(np_b)
     c = tvm.nd.empty((n,), C.dtype, ctx).copyfrom(np_c)
     d = tvm.nd.empty((n,), D.dtype, ctx)
     fun(a, b, c, d)
     tvm.testing.assert_allclose(d.asnumpy(), np_d)

def verify_bitserial_dense(batch, in_dim, out_dim, activation_bits, weight_bits, unipolar):
    input_dtype = 'uint32'
    out_dtype = 'int16'

    with tvm.target.create('llvm'):
        A = tvm.placeholder((batch, in_dim), dtype=input_dtype, name='A')
        B = tvm.placeholder((out_dim, in_dim), dtype=input_dtype, name='B')
        C = topi.nn.bitserial_dense(A, B, activation_bits, weight_bits, out_dtype=out_dtype,
                                    unipolar=unipolar)
        s = topi.generic.schedule_bitserial_dense([C])

    a_shape = get_const_tuple(A.shape)
    b_shape = get_const_tuple(B.shape)

    @memoize("topi.tests.test_topi_bitseral_dense")
    def get_ref_data():
        a_np = generate_quantized_np(get_const_tuple(a_shape), activation_bits, input_dtype)
        b_np = generate_quantized_np(get_const_tuple(b_shape), weight_bits, input_dtype)
        if unipolar:
            b_ = np.copy(b_np).astype(out_dtype)
            for x in np.nditer(b_, op_flags=['readwrite']):
                x[...] = 1 if x == 1 else -1
            c_np = np.dot(a_np, b_.T)
        else:
            c_np = np.dot(a_np, b_np.T)
        return a_np, b_np, c_np
    a_np, b_np, c_np = get_ref_data()

    ctx = tvm.cpu(0)
    a = tvm.nd.array(a_np, ctx)
    b = tvm.nd.array(b_np, ctx)
    c = tvm.nd.array(np.zeros(get_const_tuple(C.shape), dtype=C.dtype), ctx)
    func = tvm.build(s, [A, B, C], "llvm")
    func(a, b, c)
    tvm.testing.assert_allclose(c.asnumpy(), c_np, rtol=1e-5)

def test_matmul_add():
    n = 1024
    l = 128
    m = 235
    bias = tvm.var('bias', dtype=tvm.float32)
    A = tvm.placeholder((n, l), name='A')
    B = tvm.placeholder((l, m), name='B')
    C = cblas.matmul(A, B)
    D = tvm.compute(C.shape, lambda i, j: C[i,j] + bias, name="D")
    s = tvm.create_schedule(D.op)

    def verify(target="llvm"):
        if not tvm.module.enabled(target):
            print("skip because %s is not enabled..." % target)
            return
        if not tvm.get_global_func("tvm.contrib.cblas.matmul", True):
            print("skip because extern function is not available")
            return
        ctx = tvm.cpu(0)
        f = tvm.build(s, [A, B, D, bias], target)
        a = tvm.nd.array(np.random.uniform(size=(n, l)).astype(A.dtype), ctx)
        b = tvm.nd.array(np.random.uniform(size=(l, m)).astype(B.dtype), ctx)
        d = tvm.nd.array(np.zeros((n, m), dtype=D.dtype), ctx)
        bb = 10.0
        f(a, b, d, bb)
        tvm.testing.assert_allclose(
            d.asnumpy(), np.dot(a.asnumpy(), b.asnumpy()) + bb, rtol=1e-5)
    verify()

def verify_conv2d(batch, in_size, in_channel, num_filter, kernel, stride, padding):
    in_height = in_width = in_size

    with tvm.target.rasp():
        A = tvm.placeholder((batch, in_channel, in_height, in_width), name='A')
        W = tvm.placeholder((num_filter, in_channel, kernel, kernel), name='W')
        B = topi.nn.conv2d(A, W, stride, padding)
        s = topi.generic.schedule_conv2d_nchw([B])

    a_shape = get_const_tuple(A.shape)
    w_shape = get_const_tuple(W.shape)
    dtype = A.dtype

    @memoize("topi.tests.test_topi_conv2d.verify_conv2d")
    def get_ref_data():
        a_np = np.random.uniform(size=a_shape).astype(dtype)
        w_np = np.random.uniform(size=w_shape).astype(dtype)
        b_np = topi.testing.conv2d_nchw_python(a_np, w_np, stride, padding)
        return a_np, w_np, b_np

    a_np, w_np, b_np = get_ref_data()

    ctx = tvm.cpu(0)
    a = tvm.nd.array(a_np, ctx)
    w = tvm.nd.array(w_np, ctx)
    b = tvm.nd.array(np.zeros(get_const_tuple(B.shape), dtype=B.dtype), ctx)
    func = tvm.build(s, [A, W, B], "llvm")
    func(a, w, b)
    np.testing.assert_allclose(b.asnumpy(), b_np, rtol=1e-5)

def _topi_nn_depthwise_conv2d_NCHWc(*args, **kwargs):
    assert not kwargs, "Do not support kwargs in template function call"
    data, kernel, strides, padding, dilation, dtype = deserialize_args(args)

    batch, in_channel, height, width = get_const_tuple(data.shape)
    filter_channel, channel_multiplier, kh, kw = get_const_tuple(kernel.shape)
    ph, pw = padding if isinstance(padding, (tuple, list)) else (padding, padding)
    sh, sw = strides if isinstance(strides, (tuple, list)) else (strides, strides)
    out_height = (height - kh + 2 * ph) // sh + 1
    out_width = (width - kw + 2 * pw) // sw + 1
    out_channel = filter_channel * channel_multiplier

    # get config here
    cfg = get_config()
    cfg.define_split("tile_ic", in_channel, num_outputs=2)
    cfg.define_split("tile_oc", out_channel, num_outputs=2)
    cfg.define_split("tile_ow", out_width, num_outputs=2, filter=lambda y: y.size[-1] <= 64)

    # change shape with the value in config
    ic_bn, oc_bn = cfg["tile_ic"].size[-1], cfg["tile_oc"].size[-1]
    new_data_shape = (batch, in_channel // ic_bn, height, width, ic_bn)
    new_kernel_shape = (out_channel // oc_bn, kh, kw, oc_bn)
    new_data = tvm.placeholder(new_data_shape, data.dtype)
    new_kernel = tvm.placeholder(new_kernel_shape, kernel.dtype)

    data_layout = "NCHW%dc" % ic_bn
    out_layout = "NCHW%dc" % oc_bn

    C = _depthwise_conv2d_NCHWc_cpu(cfg, new_data, new_kernel, strides, padding, dilation,
                                    data_layout, out_layout, dtype)
    s = schedule_depthwise_conv2d_NCHWc(cfg, [C])
    return s, [new_data, new_kernel, C]

def verify_gather_nd(src_shape, indices_src, indices_dtype):
    src_dtype = "float32"
    indices_src = np.array(indices_src, dtype=indices_dtype)
    A = tvm.placeholder(shape=src_shape, dtype=src_dtype, name="A")
    indices = tvm.placeholder(shape=indices_src.shape, dtype=indices_dtype, name="indices")
    out_tensor = topi.gather_nd(a=A, indices=indices)

    def check_device(device):
        ctx = tvm.context(device, 0)
        if not ctx.exist:
            print("Skip because %s is not enabled" % device)
            return
        print("Running on target: %s" % device)
        with tvm.target.create(device):
            s = topi.generic.schedule_injective(out_tensor)

        func = tvm.build(s, [A, indices, out_tensor] , device, name="take")
        shape_size = 1
        for i in range(len(src_shape)):
            shape_size = shape_size * src_shape[i]
        data_npy = np.arange(shape_size, dtype=src_dtype).reshape((src_shape))
        out_npys = topi.testing.gather_nd_python(data_npy, indices_src)

        data_nd = tvm.nd.array(data_npy, ctx)
        indices_nd = tvm.nd.array(indices_src, ctx)
        out_nd = tvm.nd.empty(out_npys.shape, ctx=ctx, dtype=src_dtype)
        func(data_nd, indices_nd, out_nd)
        tvm.testing.assert_allclose(out_nd.asnumpy(), out_npys)

    for device in get_all_backend():
        check_device(device)

def verify_expand_like(in_shape, out_shape, axis):
    A = tvm.placeholder(shape=in_shape, name="A")
    B = tvm.placeholder(shape=out_shape, name="B")
    C = topi.expand_like(A, B, axis)
    s = tvm.create_schedule([C.op])

    def check_device(device):
        if not tvm.module.enabled(device):
            print("Skip because %s is not enabled" % device)
            return
        print("Running on target: %s" % device)

        ctx = tvm.context(device, 0)
        f = tvm.build(s, [A, B, C], device, name="expand_like")
        input = np.random.uniform(size=in_shape).astype(A.dtype)
        tvm_input = tvm.nd.array(input, ctx)

        odim = len(out_shape)
        real_axis = [x if x >= 0 else x + odim for x in axis]
        real_axis = sorted(real_axis)
        for x in real_axis:
            input = np.expand_dims(input, x).astype(A.dtype)
        for x in real_axis:
            input = np.concatenate([input]*out_shape[x], axis=x).astype(A.dtype)
        assert input.shape == out_shape

        tvm_shape_like = tvm.nd.array(np.zeros(out_shape).astype(B.dtype), ctx)
        out = tvm.nd.array(np.zeros(out_shape).astype(A.dtype), ctx)
        f(tvm_input, tvm_shape_like, out)
        tvm.testing.assert_allclose(out.asnumpy(), input)

    for device in ["llvm"]:
        check_device(device)

def test_compile_cache():
    x = sym.Variable("x")
    y = sym.Variable("y")
    z = sym.exp(y + x)
    shape = (10, 1)
    dtype = tvm.float32
    shape_dict = {"x": shape, "y": shape}
    def verify(graph, lib):
        m = graph_runtime.create(graph, lib, tvm.cpu(0))
        # get member functions
        na = tvm.nd.array(np.random.uniform(size=shape).astype(dtype))
        nb = tvm.nd.array(np.random.uniform(size=shape).astype(dtype))
        m.run(x=na, y=nb)
        # get outputs
        out = m.get_output(0, tvm.nd.empty(shape, dtype))
        tvm.testing.assert_allclose(
            out.asnumpy(), np.exp(na.asnumpy() + nb.asnumpy()))

    engine = nnvm.compiler.engine
    graph, lib, _ = nnvm.compiler.build(z, "llvm", shape_dict)
    inputs = [tvm.placeholder((10,)), tvm.placeholder((10,))]

    gkey = nnvm.compiler.graph_key(nnvm.graph.create(z), inputs, "llvm")
    gkey2 = nnvm.compiler.graph_key(nnvm.graph.create(z), inputs + inputs, "llvm")
    gf = engine[gkey]
    assert gf is not None
    assert engine[gkey2] is None
    graph, lib, _ = nnvm.compiler.build(z, "llvm", shape_dict)
    assert graph.index.num_nodes == 3
    verify(graph, lib)
    # Test various set external cache
    engine.clear_cache()
    engine[gkey] = gf