cec704bd54458bb5b86ca7db4061a5c597fac85e,python/tf_cnn_benchmarks/variable_mgr.py,VariableMgrIndependent,get_gradients_to_apply,#VariableMgrIndependent#Any#Any#,224

Before Change

    device_grads = gradient_state
    // Note that each grad_and_vars looks like the following:
    //   ((grad0_gpu0, var0_gpu0), ... , (grad0_gpuN, var0_gpuN))
    return [grad_and_vars[device_num] for grad_and_vars in zip(*device_grads)]

  def get_devices(self):
    return self.benchmark_cnn.raw_devices

After Change

      // Since we don"t aggregate variables in --independent mode, we cannot tell
      // if there are NaNs on all GPUs. So we arbitrarily choose to only check
      // NaNs on the first GPU.
      has_inf_nan_list = []
      for grad, _ in tower_grad:
        has_inf_nan_list.append(tf.reduce_all(tf.is_finite(grad)))
      self.grad_has_inf_nan = tf.logical_not(tf.reduce_all(has_inf_nan_list))

    return tower_grad

  def get_devices(self):

Instances