b85c2397356c543b13be4894bf06900a41a13450,tests/python/frontend/tensorflow/test_forward.py,,_test_lstm_cell,#Any#Any#Any#Any#Any#,2047

Before Change

    tf.reset_default_graph()
    input_size = num_hidden
    input_data = np.full((batch_size, input_size), 1., dtype=dtype)
    in_state_c = np.full(
        (num_layers, batch_size, num_hidden), 0.1, dtype=dtype)
    in_state_h = np.full(
        (num_layers, batch_size, num_hidden), 0.1, dtype=dtype)

    def _get_tensorflow_output():
        with tf.Session() as sess:
            with variable_scope.variable_scope(
                    "root", initializer=init_ops.constant_initializer(0.5)):
                m0 = array_ops.zeros([batch_size, num_hidden])
                m1 = array_ops.zeros([batch_size, num_hidden])
                x = tf.placeholder(shape=(batch_size, input_size), dtype=dtype)
                g, ((out_m0, out_m1)) = \
                    tensorflow.contrib.rnn.LSTMBlockCell(num_hidden,
                                                         forget_bias=forget_bias)(x, (m0, m1))
                sess.run([variables.global_variables_initializer()])
                res = sess.run([g, out_m0, out_m1], {
                    x.name: np.array([[1., 1.]]),
                    m0.name: 0.1 * np.ones([batch_size, num_hidden]),
                    m1.name: 0.1 * np.ones([batch_size, num_hidden]),
                })
            graph_def = sess.graph.as_graph_def(add_shapes=True)
            final_graph_def = graph_util.convert_variables_to_constants(
                sess,
                graph_def,
                ["root/lstm_cell/LSTMBlockCell"])
            return final_graph_def, res

    graph_def, tf_out = _get_tensorflow_output()
    tvm_output = run_tvm_graph(graph_def, [input_data, in_state_c, in_state_h],
                               ["root/Placeholder", "root/lstm_cell/LSTMBlockCell_c",
                                "root/lstm_cell/LSTMBlockCell_h"], num_output=2)
    assert isinstance(tvm_output, list)

    out = tvm_output[0]
    out_state = tvm_output[1]
    out_state_tup = np.split(out_state, indices_or_sections=2, axis=1)
    out_state_c = np.reshape(out_state_tup[0], (batch_size, num_hidden))
    out_state_h = np.reshape(out_state_tup[1], (batch_size, num_hidden))
    tvm_out = [out, out_state_c, out_state_h]
    tvm.testing.assert_allclose(tf_out[0], tvm_out[0], rtol=1e-3, atol=1e-3)


def test_forward_lstm():

After Change

    graph_def, tf_out = _get_tensorflow_output()
    tvm_output = run_tvm_graph(graph_def, [input_data, in_state_c, in_state_h],
                               ["root/input", "root/m0", "root/m1"], num_output=7)
    assert isinstance(tvm_output, list)

    tvm.testing.assert_allclose(tf_out[0], tvm_output[6], rtol=1e-3, atol=1e-3)

Instances