Method update_actor

verl/workers/fsdp_workers.py:400–442

(self, data: DataProto)

    @register(dispatch_mode=Dispatch.DP_COMPUTE_PROTO)
    def update_actor(self, data: DataProto):
        data = data.to('cuda')

        assert self._is_actor
        if self._is_offload_param:
            load_fsdp_param_and_grad(module=self.actor_module_fsdp,
                                     device_id=torch.cuda.current_device(),
                                     load_grad=self._is_offload_grad)
        if self._is_offload_optimizer:
            load_fsdp_optimizer(optimizer=self.actor_optimizer, device_id=torch.cuda.current_device())

        data.batch = data.batch.cuda()

        log_gpu_memory_usage('Before update policy', logger=logger)

        with self.ulysses_sharding_manager:
            data = self.ulysses_sharding_manager.preprocess_data(data=data)
            # perform training
            with Timer(name='update_policy', logger=None) as timer:
                metrics = self.actor.update_policy(data=data)
            delta_time = timer.last
            global_num_tokens = data.meta_info['global_token_num']
            estimated_flops, promised_flops = self.flops_counter.estimate_flops(global_num_tokens, delta_time)
            metrics['mfu/actor'] = estimated_flops * self.config.actor.ppo_epochs / promised_flops / self.world_size

            self.actor_lr_scheduler.step()
            lr = self.actor_lr_scheduler.get_last_lr()[0]
            metrics['actor/lr'] = lr

            log_gpu_memory_usage('After update policy', logger=logger)

            # TODO: here, we should return all metrics
            output = DataProto(meta_info={'metrics': metrics})

            output = self.ulysses_sharding_manager.postprocess_data(data=output)
            output = output.to('cpu')

        if self._is_offload_param:
            offload_fsdp_param_and_grad(module=self.actor_module_fsdp, offload_grad=self._is_offload_grad)
        if self._is_offload_optimizer:
            offload_fsdp_optimizer(optimizer=self.actor_optimizer)
        torch.cuda.empty_cache()
        return output

    @register(dispatch_mode=Dispatch.DP_COMPUTE_PROTO)
    def generate_sequences(self, prompts: DataProto):